54251 WORLD DEVELOPMENT INDICATORS 10 The world by income Low ($975 or less) Classified according to Lower middle ($976­$3,855) World Bank estimates of 2008 GNI per capita Upper middle ($3,856­$11,905) High ($11,906 or more) No data Greenland (Den) Iceland Faeroe Norway Islands (Den) Sweden Finland Russian Federation The Netherlands Estonia Isle of Man (UK) Russian Latvia Canada Denmark Fed. Lithuania United Ireland Kingdom Germany Poland Belarus Channel Islands (UK) Belgium Ukraine Luxembourg Moldova Kazakhstan Mongolia Liechtenstein France Italy Romania Switzerland Bulgaria Georgia Uzbekistan Kyrgyz Andorra Armenia Azer- Rep. Dem.People's United States Spain baijan Turkmenistan Rep.of Korea Portugal Turkey Tajikistan Monaco Greece Japan Cyprus Syrian Rep.of Gibraltar (UK) Arab Islamic Rep. Korea Bermuda Malta Lebanon China Tunisia Rep. of Iran Afghanistan (UK) Israel Iraq Morocco Kuwait West Bank and Gaza Jordan Algeria Bahrain Pakistan Bhutan Libya Arab Rep. Qatar Nepal The Bahamas Former of Egypt Spanish Saudi Sahara Arabia Bangladesh Cayman Is.(UK) United Arab Emirates India Mexico Cuba Myanmar Mauritania Oman Lao Haiti Cape Verde P.D.R. Mali N. Mariana Islands (US) Belize Jamaica Niger Chad Eritrea Rep. of Yemen Thailand Guatemala Honduras Senegal Sudan Vietnam Guam (US) El Salvador Nicaragua The Gambia Burkina Cambodia Guinea-Bissau Faso Djibouti Philippines Guinea Federated States of Micronesia Costa Rica Benin Marshall Islands Panama Nigeria Central Ethiopia Sri R.B. de Guyana Sierra Leone Côte Ghana Lanka Venezuela d'Ivoire African Brunei Darussalam Suriname Republic Liberia Palau French Guiana (Fr) Cameroon Malaysia Colombia Togo Somalia Equatorial Guinea Maldives Uganda São Tomé and Príncipe Kenya Nauru Kiribati Congo Singapore Ecuador Gabon Rwanda Kiribati Dem.Rep.of Burundi Seychelles Congo Solomon Tanzania Papua New Guinea Islands Comoros Indonesia Tuvalu Peru Brazil Timor-Leste Samoa French Polynesia (Fr) Angola Malawi Zambia Mayotte American (Fr) Vanuatu Fiji Samoa (US) Bolivia Mozambique Fiji Zimbabwe Madagascar Tonga Mauritius Namibia Botswana New Paraguay Réunion (Fr) Caledonia Australia (Fr) Swaziland Dominican Germany South Republic Puerto Poland Lesotho Rico (US) Africa Czech Republic Ukraine Uruguay Slovak Republic Antigua and Barbuda Chile U.S. Virgin Argentina Islands (US) Guadeloupe (Fr) Austria St. Kitts Hungary New and Nevis Zealand Dominica Slovenia Romania Netherlands Croatia Antilles (Neth) Martinique (Fr) Bosnia and St. Lucia Herzegovina Serbia Aruba St. Vincent and (Neth) Barbados San the Grenadines Marino Kosovo Bulgaria Grenada Italy Montenegro FYR Macedonia Trinidad Vatican Albania and Tobago City Greece R.B. de Venezuela Antarctica IBRD 37654 MARCH 2010 Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2010 WORLD DEVELOPMENT INDICATORS Copyright 2010 by the International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street NW, Washington, D.C. 20433 USA All rights reserved Manufactured in the United States of America First printing April 2010 This volume is a product of the staff of the Development Data Group of the World Bank's Development Economics Vice Presidency, and the judgments herein do not necessarily reflect the views of the World Bank's Board of Execu- tive Directors or the countries they represent. 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Permission to photocopy portions for classroom use is granted through the Copyright Center, Inc., Suite 910, 222 Rosewood Drive, Danvers, MA 01923 USA. Photo credits: Front cover, Joerg Boethling/Peter Arnold, Inc.; page xxiv, Curt Carnemark/World Bank; page 52, Curt Carnemark/World Bank; page 148, Scott Wallace/World Bank; page 216, Curt Carnemark/World Bank; page 286, Scott Wallace/World Bank; page 344, Curt Carnemark/World Bank. If you have questions or comments about this product, please contact: Development Data Group The World Bank 1818 H Street NW, Room MC2-812, Washington, D.C. 20433 USA Hotline: 800 590 1906 or 202 473 7824; fax 202 522 1498 Email: data@worldbank.org Web site: www.worldbank.org or www.worldbank.org/data ISBN 978-0-8213-8232-5 ECO -AUDIT Environmental Benefits Statement The World Bank is committed to preserving endangered forests and natural resources. The Office of the Publisher has chosen to print World Development Indicators 2010 on recycled paper with 50 percent post-consumer fiber in accordance with the recommended standards for paper usage set by the Green Press Initiative, a nonprofit program supporting publishers in using fiber that is not sourced from endangered forests. For more information, visit www. greenpressinitiative.org. Saved: 116 trees 37 million Btu of total energy 11,069 pounds of net greenhouse gases 53,312 gallons of waste water 3,237 pounds of solid waste 2010 WORLD DEVELOPMENT INDICATORS PREFACE The 1998 edition of World Development Indicators initiated a series of annual reports on progress toward the International Development Goals. In the foreword then­World Bank President James D. Wolfensohn recognized that "by reporting regularly and systematically on progress toward the targets the international community has set for itself, we will focus attention on the task ahead and make those responsible for advancing the development agenda accountable for results." The same vision inspired world leaders to commit themselves to the Millennium Development Goals. On this, the 10th anniversary of the Millennium Declaration, World Development Indicators 2010 focuses on progress toward the Millennium Development Goals and the challenges of meeting them. There has been remarkable progress. Despite the global financial crisis, poverty rates in developing countries continue to fall, with every likelihood of reaching and then exceeding the Millennium Development Goals target in most regions of the world. Since the turn of the century, 37 million more children have enrolled in primary school. Measles immunization rates have risen to 81 percent, with similar progress in other vaccination programs and health-related services. Since 2000 the number of children dying before age 5 has fallen from more than 10 million a year to 8.8 million. So, much progress. But we still have far to go. Global and regional averages cannot disguise the large differences between countries. Average annual incomes range from $280 to more than $60,000 per person. Life expectancy ranges from 44 years to 83 years. And differences within countries can be even greater. But we should not be discouraged. Nor should we conclude that the effort has failed just because some countries will fall short of the targets. The Millennium Development Goals have helped to focus development efforts where they will do the most good and have created new demand for good statistics. Responding to the demand for statistics to monitor progress on the Millennium Development Goals, developing countries and donor agencies have invested in statistical systems, conducted more frequent surveys, and improved methodologies. And the results are beginning to show in the pages of World Development Indicators. But here too our success makes us keenly aware of the need to do more to enrich the quality of development statistics. And we are just as committed to making them more widely available. With the release of the 2010 edition of World Development Indicators, the World Bank is redesigning its Web sites and making its development databases freely and fully accessible. As always, we invite your ideas and innovations in putting statistics in service to people. Shaida Badiee Director Development Economics Data Group 2010 World Development Indicators v ACKNOWLEDGMENTS This book and its companion volumes, The Little Data Book and The Little Green Data Book, are prepared by a team led by Soong Sup Lee under the supervision of Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, David Cieslikowski, Loveena Dookhony, Richard Fix, Shota Hatakeyama, Masako Hiraga, Kiyomi Horiuchi, Bala Bhaskar Naidu Kalimili, Buyant Erdene Khaltarkhuu, Alison Kwong, K. Sarwar Lateef, Ibrahim Levent, Raymond Muhula, Changqing Sun, K.M. Vijayalakshmi, and Estela Zamora, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The electronic products were prepared with contributions from Azita Amjadi, Ramvel Chandrasekaran, Ying Chi, Jean-Pierre Djomalieu, Ramgopal Erabelly, Reza Farivari, Shelley Fu, Gytis Kanchas, Buyant Erdene Khaltarkhuu, Ugendran Makhachkala, Vilas Mandlekar, Nacer Megherbi, Parastoo Oloumi, Abarna Panchapakesan, William Prince, Sujay Ramasamy, Malarvizhi Veerappan, and Vera Wen. The work was carried out under the management of Shaida Badiee. Valuable advice was provided by Shahrokh Fardoust. The choice of indicators and text content was shaped through close consultation with and substantial contributions from staff in the World Bank's four thematic networks--Sustainable Development, Human Development, Poverty Reduction and Economic Management, and Financial and Private Sector Development--and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substan- tial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book's content, please see Credits. For a listing of our key partners, see Partners. Communications Development Incorporated provided overall design direction, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher Trott. Elaine Wilson created the cover and graphics and typeset the book. Joseph Caponio provided production assistance. Communications Development's London part- ner, Peter Grundy of Peter Grundy Art & Design, designed the report. Staff from External Affairs oversaw printing and dissemination of the book. 2010 World Development Indicators vii TABLE OF CONTENTS FRONT Preface v 2.7 Poverty rates at national poverty lines 86 Acknowledgments vii 2.8 Poverty rates at international poverty lines 89 Partners xii 2.9 Distribution of income or consumption 94 Users guide xxii 2.10 Assessing vulnerability and security 98 2.11 Education inputs 102 2.12 Participation in education 106 1. WORLD VIEW Introduction 1 2.13 2.14 2.15 2.16 Education efficiency Education completion and outcomes Education gaps by income and gender Health services 110 114 118 120 2.17 Health information 124 Tables 2.18 Disease prevention coverage and quality 128 1.1 Size of the economy 32 2.19 Reproductive health 132 1.2 Millennium Development Goals: eradicating poverty and saving lives 2.20 Nutrition 136 36 2.21 Health risk factors and future challenges 140 1.3 Millennium Development Goals: protecting our common 2.22 Mortality 144 environment 40 1.4 Millennium Development Goals: overcoming obstacles 44 Text figures, tables, and boxes 1.5 Women in development 46 2a Child mortality is higher among the poorest children . . . 53 1.6 Key indicators for other economies 50 2b . . . as is child malnutrition 53 2c The poorest women have the least access to prenatal care 54 Text figures, tables, and boxes 2d Poor and rural children are less likely to complete primary 1a Progress toward the Millennium Development Goals, by country 2 school . . . 54 1b Progress toward the Millennium Development Goals, by population 2 2e . . . and more likely to be out of school 54 1c Progress toward the Millennium Development Goals among 2f Poorer children are more likely to die before age 5 . . . 54 low-income countries 3 2g . . . and to be out of school 54 1d Progress toward the Millennium Development Goals among 2h First-line health facilities in many countries lack electricity lower middle-income countries 3 and clean water 55 1e Progress toward the Millennium Development Goals among 2i Fewer health facilities in Guinea had electricity in 2001 than in upper middle-income countries 3 1998, but more had running water 55 1f Inequalities for school completion rates persist for men 2j Availability of child health services is weak in Egypt and Rwanda 55 and women 24 2k Wealthy people have better access to child health services 56 1g Large disparities in child survival 24 2l Absenteeism among health workers reduces access to health care 56 1h Brazil improves income distribution 25 2m Distribution of health workers in Zambia , 2004 56 1i Child mortality rates rise when adjusted for equity 25 2n Many schools lack electricity, blackboards, seating, and libraries 57 1j How governance contributes to social outcomes 27 2o Absenteeism is high among teachers in some poor countries, 1k Under-five mortality rates vary considerably among core 2002­03 57 fragile states 27 2p The cost of education 57 1l Status of national strategies for the development of 2q Public expenditures on primary education, by region, 2004 58 statistics, 2009 28 2r Available data on human development indicators vary by region 58 1m Statistical capacity indicators by region and areas of performance 29 2s In many regions fewer than half of births are reported to the 1n Statistical capacity has improved . . . 29 United Nations Statistics Division . . 59 1o . . . but data are still missing for key indicators 29 2t . . . and even fewer child deaths are reported 59 1.2a Location of indicators for Millennium Development Goals 1­4 39 2u Out of school children are difficult to measure 59 1.3a Location of indicators for Millennium Development Goals 5­7 43 2v Out-of-pocket health care costs are too high for many people 1.4a Location of indicators for Millennium Development Goal 8 45 to afford 60 2w Informal payments to health care providers are common 60 2x Primary school enrollment and attendance, 2003­08 61 2y Instructional time for children varies considerably by country, 2. PEOPLE 2004­06 61 2.6a Brazil has rapidly reduced children's employment and raised school attendance 85 Introduction 53 2.8a While the number of people living on less than $1.25 a day has fallen, the number living on $1.25­$2.00 a day has increased 91 Tables 2.8b Poverty rates have begun to fall 91 2.1 Population dynamics 62 2.8c Regional poverty estimates 92 2.2 Labor force structure 66 2.9a The Gini coefficient and ratio of income or consumption of the 2.3 Employment by economic activity 70 richest quintile to the poorest quintiles are closely correlated 97 2.4 Decent work and productive employment 74 2.12a The situations of out of school children vary widely 109 2.5 Unemployment 78 2.15a Gender disparities in net primary school attendance are 2.6 Children at work 82 largest in poor and rural households 119 viii 2010 World Development Indicators 3. ENVIRONMENT Introduction 149 3.2a Nearly 40 percent of land globally is devoted to agriculture 161 Tables 3.2b Developing regions lag in agricultural machinery, which reduces their agricultural productivity 161 3.1 Rural population and land use 154 3.3a Cereal yield in low-income economies is less than 3.2 Agricultural inputs 158 40 percent of the yield in high-income countries 165 3.3 Agricultural output and productivity 162 3.3b Sub-Saharan Africa has the lowest yield, while East Asia 3.4 Deforestation and biodiversity 166 and Pacific is closing the gap with high-income economies 165 3.5 Freshwater 170 3.5a Agriculture is still the largest user of water, accounting for 3.6 Water pollution 174 some 70 percent of global withdrawals in 2007 . . . 173 3.7 Energy production and use 178 3.5b . . . and approaching 90 percent in some developing regions 3.8 Energy dependency and efficiency and carbon dioxide emissions 182 in 2007 173 3.9 Trends in greenhouse gas emissions 186 3.6a Emissions of organic water pollutants declined in most 3.10 Sources of electricity 190 economies from 1990 to 2006, even in some of the top emitters 177 3.11 Urbanization 194 3.7a A person in a high-income economy uses more than 12 times as 3.12 Urban housing conditions 198 much energy on average as a person in a low-income economy 181 3.13 Traffic and congestion 202 3.8a High-income economies depend on imported energy . . . 185 3.14 Air pollution 206 3.8b . . . mostly from middle-income economies in the Middle East 3.15 Government commitment 208 and North Africa and Latin America and the Caribbean 185 3.16 Toward a broader measure of savings 212 3.9a The 10 largest contributors to methane emissions account Text figures, tables, and boxes for about 62 percent of emissions 189 3a Carbon dioxide is the most common greenhouse gas 149 3.9b The 10 largest contributors to nitrous oxide emissions 3b Carbon dioxide emissions have surged since the 1950s 150 account for about 56 percent of emissions 189 3c Carbon dioxide emissions are growing, 1990­2006 150 3.10a Sources of electricity generation have shifted since 1990 . . . 193 3d A few rapidly developing and high-income countries produce 3.10b . . . with developing economies relying more on coal 193 70 percent of carbon dioxide emissions 150 3.11a Urban population nearly doubled in low- and lower 3e Trends in fossil fuel use and energy intensity 151 middle-income economies between 1990 and 2008 197 3f Emission reductions by 2030 151 3.11b Latin America and the Caribbean had the same share of 3g Future energy use under the IEA-450 scenario 151 urban population as high-income economies in 2008 197 3h People affected by natural disasters and projected changes in 3.12a Selected housing indicators for smaller economies 201 rainfall and agricultural production 152 3.13a Particulate matter concentration has fallen in all income groups, 3i Potential contributions of the water sector to attaining the and the higher the income, the lower the concentration 205 Millennium Development Goals 153 3.1a What is rural? Urban? 157 2010 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 217 Introduction 287 Tables Tables 4.a Recent economic performance of selected developing countries 224 5.1 Private sector in the economy 292 4.1 Growth of output 226 5.2 Business environment: enterprise surveys 296 4.2 Structure of output 230 5.3 Business environment: Doing Business indicators 300 4.3 Structure of manufacturing 234 5.4 Stock markets 304 4.4 Structure of merchandise exports 238 5.5 Financial access, stability, and efficiency 308 4.5 Structure of merchandise imports 242 5.6 Tax policies 312 4.6 Structure of service exports 246 5.7 Military expenditures and arms transfers 316 4.7 Structure of service imports 250 5.8 Fragile situations 320 4.8 Structure of demand 254 5.9 Public policies and institutions 324 4.9 Growth of consumption and investment 258 5.10 Transport services 328 4.10 Central government finances 262 5.11 Power and communications 332 4.11 Central government expenses 266 5.12 The information age 336 4.12 Central government revenues 270 5.13 Science and technology 340 4.13 Monetary indicators 274 Text figures, tables, and boxes 4.14 Exchange rates and prices 278 5a Pakistani women without access to an all-weather road have 4.15 Balance of payments current account 282 fewer prenatal consultations and fewer births attended by Text figures, tables, and boxes skilled health staff, 2001­02 288 4a As incomes rise, poverty rates fall 217 5b Private investment in water and sanitation 4b Income per capita is highly correlated with many development is only about 2­3 percent of the total, 2005­08 289 indicators 217 5c More than half of firms in South Asia and Sub-Saharan Africa say 4c After years of record economic growth the global economy that lack of reliable electricity is a major constraint to business 290 experienced a recession in 2009 218 5d Regional collaboration in infrastructure--the Greater Mekong 4d Trade contracted in almost every region 218 Subregion program 290 4e Private capital flows began to slow in 2008 218 5e In 2008 investment in infrastructure with private 4f Some developing country regions maintained growth 218 participation grew in all but two developing country regions 290 4g Current account surpluses and deficits both decreased 219 5f Five countries accounted for almost half of investment 4h Economies with large government deficits 219 in infrastructure with private participation, 1990­2008 291 4i Economies with large government debts 219 5g Investment rose in energy, telecommunications, and 4j Economies with increasing default risk 219 transport, but remained flat in water and sanitation, 2005­08 291 4m­4r Growth in GDP, selected major developing economies 220 5h Investment in water and sanitation with private participation 4s­4x Growth in industrial production, selected major developing accounted for only 4.4 percent of the total, 1990­2008 291 economies 220 4y­4dd Lending and inflation rates, selected major developing economies 220 4ee­4jj Central government debt, selected major developing economies 220 4kk­4pp Merchandise trade, selected major developing economies 222 4qq­4vv Equity price indexes, selected major developing economies 222 4ww­4bbb Bond spreads, selected major developing economies 222 4ccc­4hhh Financing through international capital markets, selected major developing economies 222 4.3a Manufacturing continues to show strong growth in East Asia through 2008 237 4.4a Developing economies' share of world merchandise exports continues to expand 241 4.5a Top 10 developing economy exporters of merchandise goods in 2008 245 4.6a Top 10 developing economy exporters of commercial services in 2008 249 4.7a The mix of commercial service imports by developing economies is changing 253 4.9a GDP per capita is still lagging in some regions 261 4.10a Twenty developing economies had a government expenditure to GDP ratio of 30 percent or higher 265 4.11a Interest payments are a large part of government expenses for some developing economies 269 4.12a Rich economies rely more on direct taxes 273 4.15a Top 15 economies with the largest reserves in 2008 285 x 2010 World Development Indicators 6. GLOBAL LINKS Introduction 345 6i Nontariff barriers on imports may be higher than tariff barriers 349 Tables 6j Agricultural exports from low-income economies face the highest overall restrictions 349 6.1 Integration with the global economy 354 6k Some OECD members apply very high tariffs selectively 350 6.2 Growth of merchandise trade 358 6l Growth of trade in services peaked in the last three years 350 6.3 Direction and growth of merchandise trade 362 6m Developing economies expanded their share in the world 6.4 High-income economy trade with low- and middle-income tourism industry 351 economies 365 6.5 Direction of trade of developing economies 368 6n Remittances have become an important source of external financing for low- and middle-income economies 351 6.6 Primary commodity prices 371 6o Logistics performance is lowest for low-income economies 352 6.7 Regional trade blocs 374 6p Challenges for landlocked economies 353 6.8 Tariff barriers 378 6q Lead time to import and export is 6.9 Trade facilitation 382 longest for low-income economies 353 6.10 External debt 386 6.1a Services trade has not grown as rapidly as merchandise trade 357 6.11 Ratios for external debt 390 6.3a Trade among developing economies has grown faster than 6.12 Global private financial flows 394 trade among high-income economies 364 6.13 Net official financial flows 398 6.4a High-income economies export mostly manufactured goods 6.14 Financial flows from Development Assistance Committee to low- and middle-income economies 367 members 402 6.15 Allocation of bilateral aid from Development Assistance 6.5a Developing economies are increasingly trading with other developing economies in the same region 370 Committee members 404 6.16 Aid dependency 406 6.6a Primary commodity prices have been volatile over the past two years 373 6.17 Distribution of net aid by Development Assistance Committee 6.7a The number of trade agreements has increased rapidly members 410 since 1990, especially agreements between high-income 6.18 Movement of people 414 economies and developing economies and agreements 6.19 Travel and tourism 418 among developing economies 377 Text figures, tables, and boxes 6.10a Debt flows from private creditors to low- and middle-income 6a Growth of exports and growth of GDP go hand in hand 346 economies fell sharply in 2008 389 6b Export revenues are increasingly larger portions of low-income 6.11a The burden of external debt service declined over 2000­08 393 economies' GDP 347 6.12a Most global foreign direct investment is directed to high- 6c Developing economies' share in world exports has income economies and a few large middle-income economies 397 increased, especially for large middle-income economies 347 6.13a Net lending from the International Bank for Reconstruction 6d Low-income economies specialize in labor-intensive exports 347 and Development declined as countries paid off loans, and 6e Labor-intensive products face higher tariffs than other concessional lending from the International Development commodities 348 Association increased 401 6f Low-income economies have a small share in the global 6.16a Official development assistance from non-DAC donors, 2004­08 409 agricultural market 348 6.17a Destination of aid varies by donor 413 6g Developing economies are trading more with other developing 6.19a High-income economies remain the main recipients of economies 348 increased international tourism expenditure, but the share 6h For some developing economies only five products make up of developing economies' receipts has risen 421 more than 90 percent of total merchandise exports 349 BACK Primary data documentation 423 Statistical methods 434 Credits 436 Bibliography 438 Index of indicators 448 2010 World Development Indicators xi PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organizations. The indicators presented in World Development Indicators are the fruit of decades of work at many levels, from the field workers who administer censuses and household surveys to the committees and working parties of the national and international statistical agencies that develop the nomenclature, classifications, and standards fundamental to an international statistical system. Nongovernmental organiza- tions and the private sector have also made important contributions, both in gathering primary data and in organizing and publishing their results. And academic researchers have played a crucial role in developing statistical methods and carrying on a continuing dialogue about the quality and interpretation of statistical indicators. All these contributors have a strong belief that available, accurate data will improve the quality of public and private decisionmaking. The organizations listed here have made World Development Indicators possible by sharing their data and their expertise with us. More important, their collaboration contributes to the World Bank's efforts, and to those of many others, to improve the quality of life of the world's people. We acknowledge our debt and gratitude to all who have helped to build a base of comprehensive, quantitative information about the world and its people. For easy reference, Web addresses are included for each listed organization. The addresses shown were active on March 1, 2010. Information about the World Bank is also provided. International and government agencies Carbon Dioxide Information Analysis Center The Carbon Dioxide Information Analysis Center (CDIAC) is the primary global climate change data and infor- mation analysis center of the U.S. Department of Energy. The CDIAC's scope includes anything that would potentially be of value to those concerned with the greenhouse effect and global climate change, including concentrations of carbon dioxide and other radiatively active gases in the atmosphere, the role of the ter- restrial biosphere and the oceans in the biogeochemical cycles of greenhouse gases, emissions of carbon dioxide to the atmosphere, long-term climate trends, the effects of elevated carbon dioxide on vegetation, and the vulnerability of coastal areas to rising sea levels. For more information, see http://cdiac.esd.ornl.gov/. Deutsche Gesellschaft für Technische Zusammenarbeit The Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ) GmbH is a German government-owned corporation for international cooperation with worldwide operations. GTZ's aim is to positively shape politi- cal, economic, ecological, and social development in partner countries, thereby improving people's living conditions and prospects. For more information, see www.gtz.de/. Food and Agriculture Organization The Food and Agriculture Organization, a specialized agency of the United Nations, was founded in October 1945 with a mandate to raise nutrition levels and living standards, to increase agricultural productivity, and to better the condition of rural populations. The organization provides direct development assistance; xii 2010 World Development Indicators collects, analyzes, and disseminates information; offers policy and planning advice to governments; and serves as an international forum for debate on food and agricultural issues. For more information, see www.fao.org/. Internal Displacement Monitoring Centre The Internal Displacement Monitoring Centre was established in 1998 by the Norwegian Refugee Council and is the leading international body monitoring conflict-induced internal displacement worldwide. The center contributes to improving national and international capacities to protect and assist the millions of people around the globe who have been displaced within their own country as a result of conflicts or human rights violations. For more information, see www.internal-displacement.org/. International Civil Aviation Organization The International Civil Aviation Organization (ICAO), a specialized agency of the United Nations, is respon- sible for establishing international standards and recommended practices and procedures for the technical, economic, and legal aspects of international civil aviation operations. ICAO's strategic objectives include enhancing global aviation safety and security and the efficiency of aviation operations, minimizing the adverse effect of global civil aviation on the environment, maintaining the continuity of aviation operations, and strengthening laws governing international civil aviation. For more information, see www.icao.int/. International Labour Organization The International Labour Organization (ILO), a specialized agency of the United Nations, seeks the promotion of social justice and internationally recognized human and labor rights. ILO helps advance the creation of decent jobs and the kinds of economic and working conditions that give working people and business people a stake in lasting peace, prosperity, and progress. As part of its mandate, the ILO maintains an extensive statistical publication program. For more information, see www.ilo.org/. International Monetary Fund The International Monetary Fund (IMF) is an international organization of 186 member countries established to promote international monetary cooperation, a stable system of exchange rates, and the balanced expan- sion of international trade and to foster economic growth and high levels of employment. The IMF reviews national, regional, and global economic and financial developments; provides policy advice to member countries; and serves as a forum where they can discuss the national, regional, and global consequences of their policies. The IMF also makes financing temporarily available to member countries to help them address balance of payments problems. Among the IMF's core missions are the collection and dissemination of high-quality macroeconomic and financial statistics as an essential prerequisite for formulating appropriate policies. The 2010 World Development Indicators xiii PARTNERS IMF provides technical assistance and training to member countries in areas of its core expertise, including the development of economic and financial data in accordance with international standards. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and communica- tion technologies. ITU's mission is to enable the growth and sustained development of telecommunications and information networks and to facilitate universal access so that people everywhere can participate in, and benefit from, the emerging information society and global economy. A key priority lies in bridging the so- called Digital Divide by building information and communication infrastructure, promoting adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster prevention and mitigation. For more information, see www.itu.int/. KPMG KPMG operates as an international network of member firms in more than 140 countries offering audit, tax, and advisory services. It works closely with clients, helping them to mitigate risks and perform in the dynamic and challenging environment in which they do business. For more information, see www.kpmg.com/Global. National Science Foundation The National Science Foundation (NSF) is an independent U.S. government agency whose mission is to promote the progress of science; to advance the national health, prosperity, and welfare; and to secure the national defense. NSF's goals--discovery, learning, research infrastructure, and stewardship--provide an integrated strategy to advance the frontiers of knowledge, cultivate a world-class, broadly inclusive science and engineering workforce, expand the scientific literacy of all citizens, build the nation's research capabil- ity through investments in advanced instrumentation and facilities, and support excellence in science and engineering research and education through a capable and responsive organization. For more information, see www.nsf.gov/. Organisation for Economic Co-operation and Development The Organisation for Economic Co-operation and Development (OECD) includes 30 member countries sharing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other countries' economic development, and contribute to growth in world trade. With active relationships with some 100 other countries, it has a global reach. It is best known for its publications and statistics, which cover economic and social issues from macroeconomics to trade, education, development, and science and innovation. The Development Assistance Committee (DAC, www.oecd.org/dac/) is one of the principal bodies through which the OECD deals with issues related to cooperation with developing countries. The DAC is a key forum xiv 2010 World Development Indicators of major bilateral donors, who work together to increase the effectiveness of their common efforts to sup- port sustainable development. The DAC concentrates on two key areas: the contribution of international development to the capacity of developing countries to participate in the global economy and the capacity of people to overcome poverty and participate fully in their societies. For more information, see www.oecd.org/. Stockholm International Peace Research Institute The Stockholm International Peace Research Institute (SIPRI) conducts research on questions of conflict and cooperation of importance for international peace and security, with the aim of contributing to an under- standing of the conditions for peaceful solutions to international conflicts and for a stable peace. SIPRI's main publication, SIPRI Yearbook, is an authoritive and independent source on armaments and arms control and other conflict and security issues. For more information, see www.sipri.org/. Understanding Children's Work As part of broader efforts to develop effective and long-term solutions to child labor, the International Labour Organization, the United Nations Children's Fund (UNICEF), and the World Bank initiated the joint interagency research program "Understanding Children's Work and Its Impact" in December 2000. The Understanding Children's Work (UCW) project was located at UNICEF's Innocenti Research Centre in Florence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. The UCW project addresses the crucial need for more and better data on child labor. UCW's online data- base contains data by country on child labor and the status of children. For more information, see www.ucw-project.org/. United Nations The United Nations currently has 192 member states. The purposes of the United Nations, as set forth in its charter, are to maintain international peace and security; to develop friendly relations among nations; to cooperate in solving international economic, social, cultural, and humanitarian problems and in promot- ing respect for human rights and fundamental freedoms; and to be a center for harmonizing the actions of nations in attaining these ends. For more information, see www.un.org/. United Nations Centre for Human Settlements, Global Urban Observatory The Urban Indicators Programme of the United Nations Human Settlements Programme was established to address the urgent global need to improve the urban knowledge base by helping countries and cities design, collect, and apply policy-oriented indicators related to development at the city level. With the Urban Indicators and Best Practices programs, the Global Urban Observatory is establishing a worldwide information, assessment, and capacity-building network to help governments, local authorities, the private sector, and nongovernmental and other civil society organizations. For more information, see www.unhabitat.org/. 2010 World Development Indicators xv PARTNERS United Nations Children's Fund The United Nations Children's Fund (UNICEF) works with other UN bodies and with governments and non- governmental organizations to improve children's lives in more than 190 countries through various programs in education and health. UNICEF focuses primarily on five areas: child survival and development, basic education and gender equality (including girls' education), child protection, HIV/AIDS, and policy advocacy and partnerships. For more information, see www.unicef.org/. United Nations Conference on Trade and Development The United Nations Conference on Trade and Development (UNCTAD) is the principal organ of the United Nations General Assembly in the field of trade and development. Its mandate is to accelerate economic growth and development, particularly in developing countries. UNCTAD discharges its mandate through policy analysis; intergovernmental deliberations, consensus building, and negotiation; monitoring, implementation, and follow-up; and technical cooperation. For more information, see www.unctad.org/. United Nations Department of Peacekeeping Operations The United Nations Department of Peacekeeping Operations contributes to the most important function of the United Nations--maintaining international peace and security. The department helps countries torn by conflict to create the conditions for lasting peace. The first peacekeeping mission was established in 1948 and has evolved to meet the demands of different conflicts and a changing political landscape. Today's peacekeepers undertake a wide variety of complex tasks, from helping build sustainable institutions of gov- ernance, to monitoring human rights, to assisting in security sector reform, to disarmaming, demobilizing, and reintegrating former combatants. For more information, see www.un.org/en/peacekeeping/. United Nations Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization (UNESCO) is a specialized agency of the United Nations that promotes international cooperation among member states and associate members in education, science, culture, and communications. The UNESCO Institute for Statistics is the organization's statistical branch, established in July 1999 to meet the growing needs of UNESCO member states and the international community for a wider range of policy-relevant, timely, and reliable statistics on these topics. For more information, see www.uis.unesco.org/. United Nations Environment Programme The mandate of the United Nations Environment Programme is to provide leadership and encourage partner- ship in caring for the environment by inspiring, informing, and enabling nations and people to improve their quality of life without compromising that of future generations. For more information, see www.unep.org/. xvi 2010 World Development Indicators United Nations Industrial Development Organization The United Nations Industrial Development Organization was established to act as the central coordinating body for industrial activities and to promote industrial development and cooperation at the global, regional, national, and sectoral levels. Its mandate is to help develop scientific and technological plans and programs for industrialization in the public, cooperative, and private sectors. For more information, see www.unido.org/. United Nations Office on Drugs and Crime The United Nations Office on Drugs and Crime was established in 1977 and is a global leader in the fight against illicit drugs and international crime. The office assists member states in their struggle against illicit drugs, crime, and terrorism by helping build capacity, conducting research and analytical work, and assist- ing in the ratification and implementation of relevant international treaties and domestic legislation related to drugs, crime, and terrorism. For more information, see www.unodc.org/. The UN Refugee Agency The UN Refugee Agency (UNHCR) is mandated to lead and coordinate international action to protect refugees and resolve refugee problems worldwide. Its primary purpose is to safeguard the rights and well-being of refugees. UNHCR also collects and disseminates statistics on refugees. For more information, see www.unhcr.org Upsalla Conflict Data Program The Upsalla Conflict Data Program has collected information on armed violence since 1946 and is one of the most accurate and well used data sources on global armed conflicts. Its definition of armed conflict is becoming a standard in how conflicts are systematically defined and studied. In addition to data collection on armed violence, its researchers conduct theoretically and empirically based analyses of the causes, escalation, spread, prevention, and resolution of armed conflict. For more information, see www.pcr.uu.se/research/UCDP/. World Bank The World Bank is a vital source of financial and technical assistance for developing countries. The World Bank is made up of two unique development institutions owned by 186 member countries--the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). These institutions play different but collaborative roles to advance the vision of an inclusive and sustainable globalization. The IBRD focuses on middle-income and creditworthy poor countries, while IDA focuses on the poorest countries. Together they provide low-interest loans, interest-free credits, and grants to developing countries for a wide array of purposes, including investments in education, health, public administration, infrastructure, financial and private sector development, agriculture, and environmental and natural resource management. The World Bank's work focuses on achieving the Millennium Development Goals by working with partners to alleviate poverty. For more information, see www.worldbank.org/data/. 2010 World Development Indicators xvii PARTNERS World Health Organization The objective of the World Health Organization (WHO), a specialized agency of the United Nations, is the attainment by all people of the highest possible level of health. It is responsible for providing leadership on global health matters, shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends. For more information, see www.who.int/. World Intellectual Property Organization The World Intellectual Property Organization (WIPO) is a specialized agency of the United Nations dedicated to developing a balanced and accessible international intellectual property (IP) system, which rewards creativ- ity, stimulates innovation, and contributes to economic development while safeguarding the public interest. WIPO carries out a wide variety of tasks related to the protection of IP rights. These include developing international IP laws and standards, delivering global IP protection services, encouraging the use of IP for economic development, promoting better understanding of IP, and providing a forum for debate. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promot- ing and developing tourism. It serves as a global forum for tourism policy issues and a source of tourism know-how. For more information, see www.unwto.org/. World Trade Organization The World Trade Organization (WTO) is the only international organization dealing with the global rules of trade between nations. Its main function is to ensure that trade flows as smoothly, predictably, and freely as pos- sible. It does this by administering trade agreements, acting as a forum for trade negotiations, settling trade disputes, reviewing national trade policies, assisting developing countries in trade policy issues--through technical assistance and training programs--and cooperating with other international organizations. At the heart of the system--known as the multilateral trading system--are the WTO's agreements, negotiated and signed by a large majority of the world's trading nations and ratified by their parliaments. For more information, see www.wto.org/. xviii 2010 World Development Indicators Private and nongovernmental organizations Containerisation International Containerisation International Yearbook is one of the most authoritative reference books on the container industry. The information can be accessed on the Containerisation International Web site, which also provides a comprehensive online daily business news and information service for the container industry. For more information, see www.ci-online.co.uk/. DHL DHL provides shipping and customized transportation solutions for customers in more than 220 countries and territories. It offers expertise in express, air, and ocean freight; overland transport; contract logistics solutions; and international mail services. For more information, see www.dhl.com/. International Institute for Strategic Studies The International Institute for Strategic Studies (IISS) provides information and analysis on strategic trends and facilitates contacts between government leaders, business people, and analysts that could lead to better public policy in international security and international relations. The IISS is a primary source of accurate, objective information on international strategic issues. For more information, see www.iiss.org/. International Road Federation The International Road Federation (IRF) is a nongovernmental, not-for-profit organization whose mission is to encourage and promote development and maintenance of better, safer, and more sustainable roads and road networks. Working together with its members and associates, the IRF promotes social and economic benefits that flow from well planned and environmentally sound road transport networks. It helps put in place technological solutions and management practices that provide maximum economic and social returns from national road investments. The IRF works in all aspects of road policy and development worldwide with governments and financial institutions, members, and the community of road professionals. For more information, see www.irfnet.org/. Netcraft Netcraft provides Internet security services such as antifraud and antiphishing services, application testing, code reviews, and automated penetration testing. Netcraft also provides research data and analysis on many aspects of the Internet and is a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic commerce, script- ing languages, and content technologies on the Internet. For more information, see http://news.netcraft.com/. 2010 World Development Indicators xix PARTNERS PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused services in the fields of assurance, tax, human resources, transactions, performance improvement, and crisis management services to help address client and stake- holder issues. For more information, see www.pwc.com/. Standard & Poor's Standard & Poor's is the world's foremost provider of independent credit ratings, indexes, risk evaluation, investment research, and data. S&P's Global Stock Markets Factbook draws on data from S&P's Emerging Markets Database (EMDB) and other sources covering data on more than 100 markets with comprehensive market profiles for 82 countries. Drawing a sample of stocks in each EMDB market, Standard & Poor's calculates indexes to serve as benchmarks that are consistent across national boundaries. For more information, see www.standardandpoors.com/. World Conservation Monitoring Centre The World Conservation Monitoring Centre provides information on the conservation and sustainable use of the world's living resources and helps others to develop information systems of their own. It works in close collaboration with a wide range of people and organizations to increase access to the information needed for wise management of the world's living resources. For more information, see www.unep-wcmc.org/. World Economic Forum The World Economic Forum (WEF) is an independent international organization committed to improving the state of the world by engaging leaders in partnerships to shape global, regional, and industry agendas. Economic research at the WEF--led by the Global Competitiveness Programme--focuses on identifying the impediments to growth so that strategies to achieve sustainable economic progress, reduce poverty, and increase prosperity can be developed. The WEF's competitiveness reports range from global coverage, such as Global Competitiveness Report, to regional and topical coverage, such as Africa Competitiveness Report, The Lisbon Review, and Global Information Technology Report. For more information, see: www.weforum.org/. World Information Technology and Services Alliance The World Information Technology and Services Alliance (WITSA) is a consortium of more than 60 informa- tion technology (IT) industry associations from economies around the world. WITSA members represent over 90 percent of the world IT market. As the global voice of the IT industry, WITSA has an active role in international public policy issues affecting the creation of a robust global information infrastructure, includ- ing advocating policies that advance the industry's growth and development, facilitating international trade and investment in IT products and services, increasing competition through open markets and regulatory reform, strengthening national industry associations through the sharing of knowledge, protecting intel- lectual property, encouraging cross-industry and government cooperation to enhance information security, xx 2010 World Development Indicators bridging the education and skills gap, and safeguarding the viability and continued growth of the Internet and electronic commerce. For more information, see www.witsa.org/. World Resources Institute The World Resources Institute is an independent center for policy research and technical assistance on global environmental and development issues. The institute provides--and helps other institutions provide-- objective information and practical proposals for policy and institutional change that will foster environmen- tally sound, socially equitable development. The institute's current areas of work include trade, forests, energy, economics, technology, biodiversity, human health, climate change, sustainable agriculture, resource and environmental information, and national strategies for environmental and resource management. For more information, see www.wri.org/. 2010 World Development Indicators xxi USERS GUIDE Tables simple totals, where they do not), median values (m), not be complete because of special circumstances The tables are numbered by section and display weighted averages (w), or simple (unweighted) aver- affecting the collection and reporting of data, such the identifying icon of the section. Countries and ages (u). Gap filling of amounts not allocated to coun- as problems stemming from conflicts. economies are listed alphabetically (except for Hong tries may result in discrepancies between subgroup For these reasons, although data are drawn from Kong SAR, China, which appears after China). Data aggregates and overall totals. For further discussion the sources thought to be most authoritative, they are shown for 155 economies with populations of of aggregation methods, see Statistical methods. should be construed only as indicating trends and more than 1 million, as well as for Taiwan, China, in characterizing major differences among economies selected tables. Table 1.6 presents selected indi- Aggregate measures for regions rather than as offering precise quantitative mea- cators for 55 other economies--small economies The aggregate measures for regions cover only low- and sures of those differences. Discrepancies in data with populations between 30,000 and 1 million and middle-income economies, including economies with presented in different editions of World Development smaller economies if they are members of the Interna- populations of less than 1 million listed in table 1.6. Indicators reflect updates by countries as well as tional Bank for Reconstruction and Development or, The country composition of regions is based on revisions to historical series and changes in meth- as it is commonly known, the World Bank. A complete the World Bank's analytical regions and may differ odology. Thus readers are advised not to compare set of indicators for these economies is available from common geographic usage. For regional clas- data series between editions of World Development on the World Development Indicators CD-ROM and in sifications, see the map on the inside back cover and Indicators or between different World Bank publica- WDI Online. The term country, used interchangeably the list on the back cover flap. For further discussion tions. Consistent time-series data for 1960­2008 with economy, does not imply political independence, of aggregation methods, see Statistical methods. are available on the World Development Indicators but refers to any territory for which authorities report CD-ROM and in WDI Online. separate social or economic statistics. When avail- Statistics Except where otherwise noted, growth rates are able, aggregate measures for income and regional Data are shown for economies as they were con- in real terms. (See Statistical methods for information groups appear at the end of each table. stituted in 2008, and historical data are revised to on the methods used to calculate growth rates.) Data Indicators are shown for the most recent year reflect current political arrangements. Exceptions are for some economic indicators for some economies or period for which data are available and, in most noted throughout the tables. are presented in fiscal years rather than calendar tables, for an earlier year or period (usually 1990 or Additional information about the data is provided years; see Primary data documentation. All dollar fig- 1995 in this edition). Time-series data for all 210 in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. economies are available on the World Development marizes national and international efforts to improve The methods used for converting national currencies Indicators CD-ROM and in WDI Online. basic data collection and gives country-level informa- are described in Statistical methods. Known deviations from standard definitions or tion on primary sources, census years, fiscal years, breaks in comparability over time or across countries statistical methods and concepts used, and other Country notes are either footnoted in the tables or noted in About background information. Statistical methods provides · Unless otherwise noted, data for China do not the data. When available data are deemed to be technical information on some of the general calcula- include data for Hong Kong SAR, China; Macao too weak to provide reliable measures of levels and tions and formulas used throughout the book. SAR, China; or Taiwan, China. trends or do not adequately adhere to international · Data for Indonesia include Timor-Leste through standards, the data are not shown. Data consistency, reliability, and comparability 1999 unless otherwise noted Considerable effort has been made to standardize · Montenegro declared independence from Serbia Aggregate measures for income groups the data, but full comparability cannot be assured, and Montenegro on June 3, 2006. When available, The aggregate measures for income groups include and care must be taken in interpreting the indicators. data for each country are shown separately. How- 210 economies (the economies listed in the main Many factors affect data availability, comparability, ever, some indicators for Serbia continue to include tables plus those in table 1.6) whenever data are and reliability: statistical systems in many develop- data for Montenegro through 2005; these data are available. To maintain consistency in the aggregate ing economies are still weak; statistical methods, footnoted in the tables. Moreover, data for most measures over time and between tables, missing coverage, practices, and definitions differ widely; and indicators from 1999 onward for Serbia exclude data are imputed where possible. The aggregates cross-country and intertemporal comparisons involve data for Kosovo, which in 1999 became a terri- are totals (designated by a t if the aggregates include complex technical and conceptual problems that can- tory under international administration pursuant gap-filled estimates for missing data and by an s, for not be resolved unequivocally. Data coverage may to UN Security Council Resolution 1244 (1999); xxii 2010 World Development Indicators any exceptions are noted. Kosovo became a World Symbols Bank member on June 29, 2009, and its data are .. shown in the tables when available. means that data are not available or that aggregates cannot be calculated because of missing data in the Classification of economies years shown. For operational and analytical purposes the World Bank's main criterion for classifying economies is 0 or 0.0 gross national income (GNI) per capita (calculated means zero or small enough that the number would by the World Bank Atlas method). Every economy round to zero at the displayed number of decimal is classified as low income, middle income (subdi- places. vided into lower middle and upper middle), or high income. For income classifications see the map on / the inside front cover and the list on the front cover in dates, as in 2003/04, means that the period of flap. Low- and middle-income economies are some- time, usually 12 months, straddles two calendar times referred to as developing economies. The term years and refers to a crop year, a survey year, or a is used for convenience; it is not intended to imply fiscal year. that all economies in the group are experiencing similar development or that other economies have $ reached a preferred or final stage of development. means current U.S. dollars unless otherwise noted. Note that classification by income does not neces- sarily reflect development status. Because GNI per > capita changes over time, the country composition of means more than. income groups may change from one edition of World Development Indicators to the next. Once the classifi - < cation is fixed for an edition, based on GNI per capita means less than. in the most recent year for which data are available (2008 in this edition), all historical data presented Data presentation conventions are based on the same country grouping. · A blank means not applicable or, for an aggre- Low-income economies are those with a GNI per gate, not analytically meaningful. capita of $975 or less in 2008. Middle-income econo- · A billion is 1,000 million. mies are those with a GNI per capita of more than · A trillion is 1,000 billion. $975 but less than $11,906. Lower middle-income · Figures in italics refer to years or periods other and upper middle-income economies are separated than those specified or to growth rates calculated at a GNI per capita of $3,855. High-income econo- for less than the full period specified. mies are those with a GNI per capita of $11,906 or · Data for years that are more than three years more. The 16 participating member countries of the from the range shown are footnoted. euro area are presented as a subgroup under high- income economies. The cutoff date for data is February 1, 2010. 2010 World Development Indicators xxiii WORLD VIEW Introduction T 1 he Millennium Declaration, adopted unanimously by world leaders at the United Nations in September 2000, was not the first effort to mobilize global action to end poverty. The First United Nations Development Decade, proclaimed in 1961, drew attention to the great differences among development outcomes and called for accelerating growth. Subsequent Development Decades formulated new development strategies. But not until the 1990s did a consensus emerge that eliminating poverty, broadly defined, should be at the center of development efforts. Analytical work at the World Bank (World Bank 1990) and the United Nations Development Programme (UNDP 1990) shaped a view of poverty as multifaceted, not just about income or con- sumption, and envisioned development as a means to empower the poor by increasing their access to employment and to health, education, and other social services. This consensus was reflected in a series of UN summits in the early 1990s that culmi- nated in the 1995 World Summit on Social Development, which endorsed the goal of eradicating poverty. The Millennium Development increasing aid for the poorest and most isolated coun- Goals: countdown to 2015 tries, and improving access to new technologies. Inspired by the lofty goals announced at these sum- A decade has passed since the Millennium Dec- mits, a high-level meeting of the Development Assis- laration, and the countdown to 2015 has begun. tance Committee in 1996 endorsed seven Internation- World Development Indicators 2010 takes a compre- al Development Goals and accompanying indicators hensive look at the issues facing developing coun- for assessing aid efforts (OECD DAC 1996). Despite tries as they attempt to meet the targets set for the goals' origins in UN summits and conferences, 2015. This section looks at progress through 2008 some viewed them with suspicion because they had and examines such cross-cutting issues as inequal- been promulgated by rich donors. Nevertheless, the ity in outcomes; tension between quantitative tar- goals helped focus attention on the need to measure gets and quality outcomes; impact of the quality of development outcomes. And most were incorporated governance on implementation of the MDGs, par- into the Millennium Declaration, which was adopted ticularly in fragile states; and progress in data avail- unanimously at the United Nations Millennium Sum- ability and quality. mit in 2000. A year later the UN Secretary-General's Road Map towards the Implementation of the Millen- An overview of progress on the nium Declaration (UN 2001) formally unveiled eight Millennium Development Goals goals, supported by 18 quantified and time-bound tar- Opinion is divided on whether the MDGs were in- gets and 48 indicators, which became the Millennium tended as global targets and whether each country Development Goals (MDGs). was intended to adopt or adapt them. The Millen- Like the International Development Goals, the nium Declaration enunciated global targets, but the MDGs took 1990 as their benchmark and 2015 as UN Secretary- General's road map saw the targets the completion date. An important difference is the as operational goals for member states. For coun- inclusion in the MDGs of an eighth goal defining a tries already making strong development progress, global partnership for development between rich the targets were relatively easy. Economic growth is countries and developing countries. The partnership closely associated with progress on the MDGs (see is intended to achieve the seven other goals by creat- section 4). Where economic growth was rapid, pov- ing a fair and rule-based financial and trading system, erty reduction and social indicators improved; where 2010 World Development Indicators 1 growth was slow and institutional deficits large, Progress on the human development indica- the going was more difficult. What worked in one tors is mixed. In absolute terms progress has setting did not always work in others. Respons- been impressive. Since 2000 some 37 million es to policy initiatives depend on constraints more children have been able to attend and imposed by local culture, the resources at soci- complete primary school. More than 14 million ety's disposal, and the local environment. children have been vaccinated against mea- Progress has been considerable. Despite sles, with similar progress in other vaccination the current crisis, the target to reduce by half programs and health-related services. Since the proportion of people living in extreme pov- 2000 the number of children dying before age erty is within reach at a global level. Rapid 5 has fallen from more than 10 million a year to growth in East Asia and Pacific and falling pov- 8.8 million. erty rates in South Asia, the two regions with The greatest progress has been toward the the most people living on less than $1.25 a day, targets for primary school attendance, gender account for this remarkable achievement. equality in primary and secondary school, and But progress has been uneven at the coun- access to safe drinking water: try level (figure 1a). Only 49 of 87 countries with · Seven of ten people in developing countries data are on track to achieve the poverty target. live in countries that have already attained Some 47 percent of the people in low- and mid- universal primary school completion or are dle-income countries live in countries that have on track to do so. But only two in five devel- already attained the target or are on track to do oping countries will have done so, while so, while 41 percent live in countries that are more than one in three countries is off track off track or seriously off track (figure 1b). And or seriously off track. 12 percent live in the 60 countries for which · Four of five people in developing countries there are insufficient data to assess progress. live in countries that have attained or are likely to attain gender equality in primary Progress toward the Millennium Development Goals, by country 1a and secondary education. Some 81 of Share of countries making progress toward Reached target On track Off track 144 countries have attained this goal, and Millennium Development Goals (percent) Seriously off track Insufficient data 100 another 10 are on track to do so. · Seven of ten people in developing coun- 50 tries live in countries that have halved the proportion of people without sustainable 0 access to improved water, though more than half of developing countries have not 50 achieved the target. Progress in sanitation 100 has been much slower, among the worst for Goal 1 Goal 2 Goal 3 Goal 3 Goal 4 Goal 5 Goal 7 Goal 7 Extreme Primary Gender Gender Child Attended Access to Access to the MDGs. Only 16 percent of the popula- poverty education parity in parity in mortality births safe water sanitation completion primary secondary tion in developing countries live in countries education education Source: World Bank staff estimates. that have managed to halve the proportion of people with sustainable access to basic sanitation, and only one in five countries Progress toward the Millennium Development Goals, by population 1b has succeeded in doing so. Nearly 7 of 10 Share of population in countries making progress toward Reached target On track Off track countries are off track or seriously off track Millennium Development Goals (percent) Seriously off track Insufficient data 100 on this goal. Progress has been slowest in reducing child 50 malnutrition and child mortality. · Standards for measuring malnutrition 0 (weight for age) among children have been revised. Under the new methodology 25 of 50 the 55 countries with data have met or are 100 on track to meet this goal, while 30 are not. Goal 1 Goal 2 Goal 3 Goal 3 Goal 4 Goal 5 Goal 7 Goal 7 Extreme Primary Gender Gender Child Attended Access to Access to · Some 45 percent of people in developing poverty education parity in parity in mortality births safe water sanitation completion primary secondary countries live in countries that have reduced education education Source: World Bank staff estimates. or are on track to reduce the under-fi ve mortality rate by two-thirds, while some 56 2 2010 World Development Indicators WORLD VIEW percent live in the 102 of 144 countries that Progress toward the Millennium Development Goals among low-income countries 1c are unlikely to attain this goal. Share of low-income countries making progress toward Reached target On track Off track Millennium Development Goals (percent) Seriously off track Insufficient data 100 Progress by income group The first edition of World Development Indica- 50 tors (in 1997) reported a developing country population in 1995 of 4.8 billion, two-thirds in 0 low-income countries. China and India together 50 had 2.1 billion people. This edition reports a de- veloping country population in 2008 of 5.6 bil- 100 Goal 1 Goal 2 Goal 3 Goal 3 Goal 4 Goal 5 Goal 7 Goal 7 lion, two-thirds of whom live in lower middle- Extreme Primary Gender Gender Child Attended Access to Access to poverty education parity in parity in mortality births safe water sanitation income countries. This massive shift reflects completion primary secondary education education the advance of China and India from low-income Source: World Bank staff estimates. to lower middle-income status. Today, 43 low- income countries account for just under 1 billion people, and 46 upper middle-income countries Progress toward the Millennium Development Goals among lower middle-income countries 1d account for about 950 million. Some 3.7 billion Share of lower middle-income countries making progress Reached target On track Off track toward Millennium Development Goals (percent) Seriously off track Insufficient data people live in 55 lower middle-income coun- 100 tries, two-thirds of them in China and India. Progress on the MDGs among low-income 50 countries has generally been poor (figure 1c). This is not surprising considering the domi- 0 nation of this group by states in fragile situa- 50 tions. With the exception of gender equality in primary schools (61 percent of low-income 100 Goal 1 Goal 2 Goal 3 Goal 3 Goal 4 Goal 5 Goal 7 Goal 7 countries expect to ensure gender equality in Extreme Primary Gender Gender Child Attended Access to Access to poverty education parity in parity in mortality births safe water sanitation primary schools but only 30 percent in second- completion primary secondary education education ary schools) and access to water (35 percent Source: World Bank staff estimates. of countries expect to reach this goal), no more than one in five countries has reached or is on track to reach the goals. Progress toward the Millennium Development Goals among upper middle-income countries 1e Middle-income countries generally do much Share of upper middle-income countries making progress Reached target On track Off track toward Millennium Development Goals (percent) Seriously off track Insufficient data better (figure 1d). Progress for upper middle- 100 income countries is more difficult when the goal involves a large reduction from already 50 advanced levels attained (figure 1e). Thus, for example, child mortality rates in upper middle- 0 income countries averaged 47 per 1,000 in 50 1990 (four times the average for high-income countries) and have fallen to 24 (compared 100 Goal 1 Goal 2 Goal 3 Goal 3 Goal 4 Goal 5 Goal 7 Goal 7 with 7 for high-income countries). A two-thirds Extreme Primary Gender Gender Child Attended Access to Access to poverty education parity in parity in mortality births safe water sanitation reduction would require the rate to fall to 16. completion primary secondary education education Still, a majority of these countries are expected Source: World Bank staff estimates. to attain most of the goals. Lower middle-income countries also do much better than low-income countries, And 43 percent expect to attain access to the though they still face serious challenges in water goal. The two areas where lower middle- meeting human development­ related goals. income countries do poorly are child mortality A third expect to reach the poverty reduction and access to sanitation, with 7 of 10 coun- goal, and 38 percent have already attained tries not expected to attain the child mortality the primary school completion goal and 7 per- reduction goal and 2 of 3 countries the sanita- cent are on track to do so. Two of three coun- tion goal. Many of these countries have large tries in this group have attained or expect to concentrations of poverty reflecting high levels attain gender equality in secondary schools. of income inequality. 2010 World Development Indicators 3 Goal 1 Eradicate poverty and hunger almost 90 percent from 1990 to 2015. South We will spare no effort to free our fellow men, women and children from the Asia, which made slower progress through the abject and dehumanizing conditions of extreme poverty . . . We resolve further early part of the 21st century, will also reach to halve, by the year 2015, the proportion of the world's people whose income is the target if growth continues, as will the Middle less than one dollar a day. East and North Africa and Latin America and --United Nations Millennium Declaration (2000) the Caribbean. Sub-Saharan Africa will be the only region with a sizable number of people in extreme poverty that fails to reach the target. Target 1A Uneven progress Halve, between 1990 and Global and regional averages disguise large 2015, the proportion of differences among countries. Since 2000, 49 people whose income is countries have attained the rate of poverty re- less than $1.25 a day duction needed to cut 1990 poverty rates by half and achieve the target. Thirty-eight remain off track and unlikely to reach the target. And 57 Defined as average daily consumption of $1.25 countries--22 of them in Sub-Saharan Africa-- or less, extreme poverty means living on the lack sufficient survey data to measure progress edge of subsistence. The number of people liv- since 1990. ing in extreme poverty has been falling since 1990, slowly at first and more rapidly since the Living below the poverty line turn of the century. The largest reduction has Poverty lines in poor countries are usually set occurred in East Asia and Pacific, where China at the level needed to obtain a basic supply of has made great strides. In South Asia acceler- food and the bare necessities of life. Many poor ated growth in India could lift millions more out people subsist on far less than that. The aver- of poverty. Sub-Saharan Africa, which stagnat- age daily expenditure of the poor is derived from ed through most of the 1990s, has begun to re- the poverty gap ratio--the average shortfall of duce the number of people in extreme poverty. the total population from the poverty line as a Although the decline was slowed by the global percentage of the poverty line. But averages are financial crisis, the number of people living in only that: many more live on even less. To over- extreme poverty is expected to fall to around come extreme poverty, everyone must first get 900 million by 2015, even as the population liv- to the poverty line. ing in developing countries rises to 5.8 billion. Still, an additional 1.1 billion people will live on Monitoring inequality less than $2 a day. The share of income or consumption received by the poorest 20 percent of the population was Most regions on track incorporated in the Millennium Development The international poverty line was revalued from Goals as a basic measure of equity. In a typical $1.08 a day (in 1993 prices) to $1.25 (in 2005 developing country the poorest 20 percent of prices), using new estimates of the cost of liv- the population accounts for just 6 percent of ing derived from the 2005 International Com- total income or consumption. Since 1990 that parison Program. Although the new estimates share has increased most in low-income coun- increased the number and proportion of people tries and has tended to shrink in upper middle- living in extreme poverty, the reduction in pov- income countries. Many factors affect the dis- erty rates remained the same. East Asia and tribution of income or consumption, and there Pacific will exceed the target set by the Millenni- is no clear link between economic growth and um Declaration, reducing extreme poverty rates changes in income distribution. 4 2010 World Development Indicators WORLD VIEW All regions but Sub-Saharan Africa are on The number of people living in extreme poverty has been falling since 1990 track to reach the poverty reduction target Number of people in developing countries East Asia & Pacific South Asia above and below $1.25 poverty line (billions) Sub-Saharan Africa Other regions People living on less than $1.25 a day (percent) 6 60 Sub-Saharan Africa 5 People above the $2 a day poverty line 4 40 South Asia 3 East Asia & Pacific 2 People between the $1.25 and 20 $2 a day poverty lines Middle East & Latin America & Caribbean North Africa People below $1.25 a day poverty line 1 Europe & Central Asia 0 0 1981 1985 1990 1995 2000 2005 2010 2015 1990 1999 2005 2015 Source: World Bank staff calculations. Source: World Bank staff calculations. Progress in reducing poverty More gainers than losers Share of countries in region Reached target On track making progress toward reducing Off track Seriously Income or consumption share of poorest quintile, Low income Lower middle income extreme poverty (percent) Insufficient data off track average for 2000­07 (percent) Upper middle income Line of equality 100 10 8 50 Median of all countries, 2000­07 6 0 4 50 2 Median of all countries, 1990­99 100 0 East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan 0 2 4 6 8 10 Pacific Asia Caribbean North Africa Income or consumption share of poorest quintile, average for 1990­99 (percent) Africa Source: World Bank staff estimates. Source: World Bank staff calculations. Living below the poverty line--poorer than poor Average daily expenditure of the poor, 2005 ($) 1.00 0.75 0.50 0.25 0.00 East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff calculations. 2010 World Development Indicators 5 Goal 1 Eradicate poverty and hunger very low, roughly at the level in East Asia and We strongly support fair globalization and resolve to make the goals of full and Pacific in 1999. productive employment and decent work for all, including for women and young people . . . part of our efforts to achieve the Millennium Development Goals. Workers at risk --United Nations World Summit Outcome (2005) Vulnerable employment--own-account and unpaid family workers, who are least likely to be protected by labor laws and social safety nets--accounted for just over half of world Target 1B employment in 2007 and remains high in East Achieve full and productive Asia and Pacific, South Asia, and Sub-Saharan employment and decent Africa. Women are more likely than men to be in work for all, including vulnerable employment. women and young people Target 1C Recognizing the importance of productive em- Halve, between 1990 ployment for creating the means for poverty and 2015, the proportion reduction, the UN General Assembly adopted of people who suffer a new target at its 2005 high-level review of from hunger the Millennium Development Goals. Because employment patterns change as economies develop, the target does not specify values to A calorie shortfall be achieved. But time trends and differences Undernourishment measures the availability of between regions provide evidence of structural food to meet people's basic energy needs. Rising change--and progress toward the Millennium agricultural production has kept ahead of popu- Development Goals. lation growth in most regions, but rising prices and the diversion of food crops to fuel production Full employment have reversed the declining rate of undernourish- Labor time lost can never be recovered, so main- ment since 2004­06. The Food and Agriculture taining full employment is important for sustain- Organization of the United Nations estimates that ing growth and income generation. But over the the number of people worldwide who receive less long run employment to population ratios tend than 2,100 calories a day rose from 873 million to fall as economies become wealthier, young in 2004­06 to 915 million in 2006­08 and could people stay in school longer, and people live rise further in the next two years (FAO 2009b). longer past their working years. Underweight children Raising productivity A shortfall in food calories is only one cause Increasing productivity is the key to raising of malnutrition. The distribution of food within incomes and reducing poverty. Over the past families, a person's health, and the availability two decades output per worker has grown of micronutrients (minerals and vitamins) also faster in Asia and Eastern Europe than in affect nutritional outcomes. Women and chil- high-income economies. East Asia and Pacific, dren are the most vulnerable. Even before the starting from a low level, has made the larg- recent food crisis, about a quarter of children in est gains but has still not caught up with the Sub-Saharan Africa and two-fifths in South Asia middle-income economies of Europe and Cen- were underweight. And children in the poorest tral Asia, Latin America and the Caribbean, households in developing countries are more and the Middle East and North Africa. Average than twice as likely to be underweight as those productivity in Sub-Saharan Africa remains in the richest households. 6 2010 World Development Indicators WORLD VIEW Labor productivity has increased . . . . . . but large differences remain Average annual growth in output per Output per employed person (2005 purchasing power parity $, thousands) 1991 2000 2008 employed person, 1991­2008 (percent) 80 8 60 6 4 40 2 20 0 0 East Europe & Latin Middle South Sub- High East Asia Europe & Latin America Middle East & South Sub-Saharan High income Asia & Central America East & Asia Saharan income & Pacific Central Asia & Caribbean North Africa Asia Africa Pacific Asia & Carib. N. Africa Africa Source: International Labour Organization and World Bank staff estimates. Source: World Bank staff estimates. Employment ratios tend to fall as income increases Workers in vulnerable employment lack safety nets Employment to population ratio, 2008 (percent) Vulnerable employment as share of total employment (percent) 2000 2008 80 80 60 60 40 40 20 20 0 0 Low Lower Upper High East Asia Europe & Latin America Middle East & South Sub-Saharan income middle income middle income income & Pacific Central Asia & Caribbean North Africa Asia Africa Source: International Labour Organization, Key Indicators of the Labour Market database. Source: International Labour Organization, Key Indicators of the Labour Market database. People with insufficient daily nourishment Child malnutrition rates remain high in South Asia and Sub-Saharan Africa Prevalance of undernourishment (percent of population) 1990­92 2004­06 Share of children under age 5 under weight for age (percent) 2000 2008 40 50 40 30 30 20 20 10 10 0 0 East Europe & Latin Middle South Sub- East Asia Europe & Latin America Middle East & South Sub-Saharan Asia & Central America & East & Asia Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa Pacific Asia Caribbean North Africa Africa Source: Food and Agriculture Organization. Source: United Nations Children's Fund and World Health Organization. 2010 World Development Indicators 7 Goal 2 Achieve universal primary education value of education. Many things discourage Every person--child, youth and adult--shall be able to benefit from educational children and their parents: absent or indifferent opportunities designed to meet their basic learning needs. teachers, inadequate or dangerous facilities, --World Declaration on Education for All, Jomtien, Thailand (1990) and demand for children's labor at home or in the market. Enrolling all children and keeping them in school will require continuing reforms and increased investment. Target 2C Ensure that by 2015 children Progress toward education for all everywhere, boys and girls Based on available data, 50 developing coun- alike, will be able to complete a tries have achieved universal primary education, full course of primary schooling and 7 more are on track to do so. Countries in Europe and Central Asia and Latin America and the Caribbean have been most successful in The goal of educating every child at least reaching the target. Thirty-eight countries, most through primary school was announced in 1990 of them in Sub-Saharan Africa, are seriously off by the Jomtien Conference on Education for All. track and unlikely to reach the target. Progress in the least developed countries, slow through the 1990s, has accelerated since 2000. The literacy challenge Countries in three regions--East Asia and Pacif- Literacy comes closest to a general measure of ic, Europe and Central Asia, and Latin America the quality of education outcomes. Throughout and the Caribbean--are close to enrolling all developing countries youth literacy rates are their primary-school-age children. The sharp in- higher than adult literacy rates--a result of ex- crease in enrollment rates in Sub-Saharan Africa panded access to formal schooling. despite population growth is also encouraging. The United Nations Educational, Scientific, But as of 2006 an estimated 72 million children and Cultural Organization Institute of Statistics worldwide were not in school--and about half of defines literacy as the ability to read and write them will have no contact with formal education. with understanding a short, simple sentence Within countries, poor children are less likely to about everyday life. In many countries national be enrolled in school, but large proportions of assessment tests are enabling ministries of children in wealthier households in the poorest education to monitor progress. But practices developing countries are also not enrolled. differ, and in some places literacy is assessed simply by school attendance. Keeping children in school Dramatic improvements have occurred in For all children to complete a course of primary the Middle East and North Africa and South education, they must be enrolled in school. Asia. But in every region except Latin America Although enrollments in grade 1 have been and the Caribbean boys are more literate than increasing, many children drop out of primary girls, a difference seen most starkly in South school because their families do not see the Asia and Sub-Saharan Africa. 8 2010 World Development Indicators WORLD VIEW Progress toward universal primary education To reach the goal of universal primary education, children must remain in school Share of countries in region Reached target On track making progress toward universal Off track Seriously Share of students starting grade 1 who reach the last grade of primary education (percent) primary education (percent) Insufficient data off track 110 100 East Asia and Pacific Latin America and Caribbean 100 50 Europe and Central Asia 90 Middle East and North Africa 0 80 South Asia 70 50 Sub-Saharan Africa 60 100 50 East Europe & Latin Middle South Sub- 1999 2000 2001 2002 2003 2004 2005 2006 2007 Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. Youth literacy is on the rise . . . Primary school enrollments are rising Youth literacy rate (percent) 1990 2008 Net primary enrollment rate (percent) 2000 2007 100 100 75 75 50 50 25 25 0 East Europe & Latin Middle South Sub- 0 Asia & Central America & East & Asia Saharan Girls Boys Girls Boys Girls Boys Girls Boys Girls Boys Girls Boys Pacific Asia Caribbean North Africa Africa East Asia Europe & Latin America Middle East & South Sub-Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. . . . but in most regions girls lag behind boys Youth literacy rate, 2008 (percent) Female Male 100 75 50 25 0 East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. 2010 World Development Indicators 9 Goal 3 Promote gender equality and empower women has been faster in secondary schools than in We also resolve . . . to promote gender equality and the empowerment of women primary schools. In Latin America and the Carib- as effective ways to combat poverty, hunger and disease and to stimulate bean, for example, four of five countries have development that is truly sustainable. reached the target at the secondary level, while --United Nations Millennium Declaration (2000) only slightly more than half have reached the target or are on track to do so at the primary level. These patterns imply that boys are leav- ing secondary school in disproportionate num- Target 3A bers--not a good solution to achieving gender Eliminate gender disparity parity. in primary and secondary Data for tertiary education are not widely education, preferably by reported. Most countries with data have made 2005, and in all levels of progress toward gender parity, but countries in education no later than 2015 South Asia and Sub-Saharan Africa lag behind. Where and how women work Education opportunities for girls have expanded. Women's share in paid employment in the Patterns of enrollment in upper middle- income nonagricultural sector has risen marginally in countries now resemble those in high-income some regions but remains less than 20 percent countries, while those in lower middle-income in South Asia and Sub-Saharan Africa. There countries are nearing equity. But gender gaps are more men than women in wage and sala- remain large in low-income countries, especially ried employment in all regions but Europe and at the primary and secondary levels. Girls born Central Asia and Latin America and the Carib- in poor households and living in rural commu- bean. In Sub-Saharan Africa there are almost nities are least likely to be enrolled in school. twice as many men as women in salaried and Cultural attitudes and practices that promote wage employment. Women are also clearly seg- early marriage, the seclusion of girls, and the regated in sectors that are generally known to education of boys over girls continue to present be lower paid. And in the sectors where women formidable barriers to gender parity. dominate, such as health care, women rarely hold upper-level management jobs. Progress toward gender parity in education Developing countries continue to make progress Women in government toward gender parity in primary and secondary The proportion of parliamentary seats held education. Sixty-four countries, many of them in by women has increased steadily since the Europe and Central Asia and Latin America and 1990s. The most impressive gains have come the Caribbean, have achieved gender parity in in Latin America and the Caribbean, the Middle enrollment, and another twenty are on track to East and North Africa, and South Asia, where do so by 2015. But 22 countries are seriously women's representation rose 30­50 percent off track, the majority of them in Sub-Saharan over 1990­2009. But while countries in the Africa. Middle East and North Africa made substantial Patterns of progress at the secondary level gains, women still hold less than 10 percent are similar to those at the primary level: 73 coun- of parliamentary seats, the lowest among all tries have achieved gender parity, and another regions. Latin America and the Caribbean is 14 are on track. Latin America and the Carib- out in front, with women holding 23 percent of bean and Europe and Central Asia have made the seats. But Rwanda leads the way, making the most progress. However, 29 countries, history in 2008 when it elected 56 percent of more than two-thirds of them in Sub-Saharan women to its parliament. Worldwide, women are Africa, are seriously off track and are unlikely entering more political leadership positions. In to achieve parity if current trends continue. In March 2009, 15 women were heads of state, most regions, progress toward gender parity up from 9 in 2000. 10 2010 World Development Indicators WORLD VIEW Progress toward gender parity As income rises, so does female enrollment in primary education Share of countries in region making Reached target On track Ratio of female to male enrollment rates, 2007 (percent) Primary Secondary Tertiary progress toward gender parity in Off track Seriously primary education (percent) Insufficient data off track 125 100 100 50 75 0 50 50 25 0 100 Low Lower Upper High East Europe & Latin Middle South Sub- income middle income middle income income Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. Progress toward gender parity Women's share in nonagricultural work has barely changed in secondary education Share of countries in region making Reached target On track Share of women employed in nonagricultural sector (percent) progress toward gender parity in Off track Seriously secondary education (percent) Insufficient data off track 50 100 Europe and Central Asia 40 Latin America and Caribbean 50 East Asia and Pacific 30 0 Middle East and North Africa 20 South Asia 50 10 0 100 1990 1995 2000 2005 2007 East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: International Labour Organization. Progress toward gender parity Women's political representation is growing in tertiary education Share of countries in region Reached target On track Share of seats held by women in national parliaments (percent) making progress toward gender Off track Seriously parity in tertiary education (percent) Insufficient data off track 25 100 High income 20 East Asia and Pacific 50 15 Sub-Saharan Africa Latin America and Caribbean 0 South Asia 10 Europe and Central Asia Middle East and North Africa 50 5 0 100 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: Inter-Parliamentary Union. 2010 World Development Indicators 11 Goal 4 Reduce child mortality half the population of low- and middle-income As leaders we have a duty therefore to all the world's people, especially the most vul- economies. nerable and, in particular, the children of the world, to whom the future belongs. --United Nations Millennium Declaration (2000) Preventing child deaths Immunizations for measles continue to ex- pand worldwide. In all regions coverage is now more than 70 percent, resulting in marked im- Target 4A provements in child survival. However, severe Reduce by two-thirds, disparities remain within countries. Only 40 between 1990 and 2015, the percent of poor children are immunized, com- under-five mortality rate pared with more than 60 percent of children from wealthier households. In some coun- tries, however, the poor have shared in these Deaths of children under age 5 have been de- health improvements. In Mozambique immu- clining since 1990. In 2006, for the first time, nization coverage increased from 58 percent the number of children who died before their in 1997 to 77 percent in 2003. The poorest fifth birthday fell below 10 million. In develop- 40 percent of households were the beneficia- ing countries child mortality declined about 25 ries of most of this increase. Despite all these percent, from 101 per 1,000 in 1990 to 73 in improvements, measles remains one of the 2008. Still, many countries in Sub-Saharan Af- leading causes of vaccine-preventable child rica have made little progress--there, one child mortality. in seven dies before the fifth birthday. The odds are slightly better in South Asia, where one child Life expectancy begins at birth in thirteen dies before the fifth birthday. These Infant mortality--child deaths before age 1--is two regions remain overriding priorities for child the primary contributor to child mortality. Im- survival interventions such as immunizations, provements in infant and child mortality are the exclusive breastfeeding, and insecticide-treated major contributors to increasing life expectancy nets. in developing countries. Success in reducing infant mortality may be viewed as a general in- Measuring progress dicator of progress toward the human develop- Thirty-nine countries have achieved or are now ment outcomes in the Millennium Development on track to achieve the target of a two-thirds Goals: access to medicines, health facilities, reduction in under-five mortality rates. Two of water, and sanitation; fertility patterns; mater- the poorest countries in Sub-Saharan Africa, nal health; maternal and infant nutrition; ma- Eritrea and Malawi, have made remarkable ternal and infant disease exposure; and female progress. Successful countries now account for literacy (Mishra and Newhouse 2007). 12 2010 World Development Indicators WORLD VIEW Progress toward reducing child mortality Child mortality rates have fallen by as much as 57 percent since 1990 Share of countries in region Reached target On track making progress toward Off track Seriously Under-five mortality rate (per 1,000) reducing child mortality (percent) Insufficient data off track 200 100 Sub-Saharan Africa 150 50 South Asia 0 100 Middle East and North Africa East Asia and Pacific 50 50 Europe and Central Asia Latin America and Caribbean High income 0 100 East Europe & Latin Middle South Sub- 1990 1995 2000 2005 2008 Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: Inter-agency Group for Child Mortality Estimation. Progress toward measles immunization Measles is the leading cause of vaccine-preventable deaths in children Share of countries in region Reached target On track making progress toward Off track Seriously Measles immunization rate (percent) measles immunization (percent) Insufficient data off track 100 Europe and Central Asia 100 High income East Asia and Pacific 50 Middle East and North Africa 80 Latin America and Caribbean 0 South Asia 60 50 Sub-Saharan Africa 100 40 East Europe & Latin Middle South Sub- 1990 1995 2000 2005 2008 Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: United Nations Children's Fund and World Health Organization. Infant mortality rates are falling Reduction of infant mortality rate, 1990­2008 (percent) 0 ­20 ­40 ­60 East Asia Europe & Latin America Middle East & South Sub-Saharan High income & Pacific Central Asia & Caribbean North Africa Asia Africa Source: World Bank staff estimates. 2010 World Development Indicators 13 Goal 5 Improve maternal health women are more than twice as likely as the [W]e resolve to promote gender equality and eliminate pervasive gender discrimi- poorest women to have access to skilled health nation by . . . ensuring equal access to reproductive health. staff at childbirth. --United Nations World Summit Outcome (2005) Many health problems among pregnant women are preventable and treatable through visits with trained health workers before child- birth. At least four visits would enable women Target 5A to receive important services such as tetanus Reduce by three-quarters, vaccinations, treatment of infections, and treat- between 1990 and 2015, the ment for life-threatening complications. The maternal mortality ratio proportion of pregnant women who had at least one antenatal visit rose from about 64 percent in 1990 to 79 percent in 2008. But the pro- Every year more than 500,000 women die from portion who had four or more visits is still less complications of pregnancy or childbirth, almost than 50 percent in South Asia and Sub-Saharan all of them (99 percent) in developing countries. Africa, where the majority of maternal deaths For each woman who dies, 30­50 women suffer occur. The provision of reproductive health ser- injury, infection, or disease. Pregnancy-related vices is advancing very slowly in these regions. complications are among the leading causes of death and disability for women ages 15­49 in developing countries. Target 5B Achieve, by 2015, universal Dangerous for mothers access to reproductive health About half of maternal deaths occur in Sub- Saharan Africa, and about a third in South Asia. Together the two regions accounted for 85 per- High risks for young mothers and their children cent of maternal deaths in 2005. The causes of In developing countries women continue to die maternal death vary. Hemorrhage is the leading because they lack access to contraception. cause in South Asia and Sub-Saharan Africa, And early pregnancy multiplies the chance of while hypertensive disorders during pregnancy dying in childbirth. Contraceptive use has in- and labor are more common in Latin America creased in most developing countries for which and the Caribbean. data are available, generally accompanied by reductions in fertility. In almost all regions Providing care to mothers more than half of women who are married or Skilled attendance at delivery is critical for re- in union use some method of birth control. The ducing maternal mortality. Since 1990 every re- exception is Sub-Saharan Africa, where contra- gion has made some progress in improving the ceptive prevalence has remained at a little over availability of skilled health personnel at child- 20 percent. birth. In developing countries births attended by More than 200 million women want to skilled health staff rose from about 50 percent delay or cease childbearing--roughly one in in 1990 to 66 percent in 2008. Countries in six women of reproductive age. Substantial Europe and Central Asia have made the most proportions of women in every country--more progress in ensuring safe deliveries. Most have than half in some--say that their last birth was achieved universal coverage, and the rest are unwanted or mistimed. More than a quarter of on track to achieve it by 2015. But the over- these pregnancies, about 52 million annually, all picture remains sobering. In South Asia and end in abortion. About 13 percent of maternal Sub-Saharan Africa more than half of births deaths are attributed to unsafe abortions, and are not attended by skilled staff. And wealthy young women are especially vulnerable. 14 2010 World Development Indicators WORLD VIEW Skilled care at birth can prevent Most deaths from complications of childbirth are in Sub-Saharan Africa and South Asia complications from becoming fatalities Births attended by Maternal mortality ratio (per 100,000 live births) 1990 2005 skilled health staff (percent) 2000 2008 1,000 100 750 75 500 50 250 25 0 0 East Asia Europe & Latin America Middle East & South Sub-Saharan East Europe & Latin Middle South Sub- & Pacific Central Asia & Caribbean North Africa Asia Africa Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: Estimates developed by World Health Organization, United Nations Children's Fund, United Nations Source: United Nations Children's Fund. Population Fund, and World Bank. Contraceptive use has increased, but many women The risks are higher for both mother and child when are still unable to get family planning services births occur frequently and at young ages Contraceptive prevalence rate (percent of women ages 15­49) 2000 2008 Adolescent fertility rate (births per 1,000 women ages 15­19) 1998 2008 100 150 75 100 50 50 25 0 0 East Europe & Latin Middle South Sub- East Asia Europe & Latin America Middle East & South Sub-Saharan Asia & Central America & East & Asia Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa Pacific Asia Caribbean North Africa Africa Source: United Nations Children's Fund. Source: United Nations Population Division. Progress in providing care to mothers Care before delivery reduces risks for mothers and children Share of countries in region Reached target On track making progress toward births Off track Seriously Pregnant women receiving prenatal care at least four times (percent) 2000 2008 attended by skilled staff (percent) Insufficient data off track 100 100 75 50 50 0 25 50 0 100 East Europe & Latin Middle South Sub- East Asia Europe & Latin America Middle East & South Sub-Saharan Asia & Central America & East & Asia Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: United Nations Children's Fund. 2010 World Development Indicators 15 Goal 6 Combat HIV/AIDS, malaria, and other diseases We recognize that HIV/AIDS, malaria, tuberculosis and other infectious diseases Target 6B pose severe risks for the entire world and serious challenges to the achievement Achieve by 2010 universal of development goals. access to treatment for HIV/ --United Nations World Summit Outcome (2005) AIDS for all those who need it Treating HIV/AIDS Target 6A Wider access to antiretroviral treatment has Have halted by 2015 and contributed to the first decline in AIDS deaths begun to reverse the since the epidemic began. Coverage has im- spread of HIV/AIDS proved substantially in Sub-Saharan Africa, but more than 60 percent of the population in need still do not have access to treatment. Living with HIV/AIDS Worldwide, some 33.4 million people--two-thirds of them in Sub-Saharan Africa and most of Target 6C them women--are living with HIV/AIDS, but the Have halted by 2015 and begun prevalence rate has remained constant since to reverse the incidence of 2000. There were 2.7 million new HIV infections malaria and other diseases in 2008, a 17 percent decline over eight years. In 14 of 17 African countries with adequate survey data the proportion of pregnant women Curbing the toll of malaria ages 15­24 living with HIV/AIDS has declined The World Health Organization estimates that in since 2000­01. Some of the most worrisome 2006 there were 190­330 million malaria epi- increases in new infections are now occurring sodes, leading to nearly 1 million malaria-related in populous countries in other regions, such as deaths. While malaria is endemic in most tropi- Indonesia, the Russian Federation, and some cal and subtropical regions, 90 percent of ma- high-income countries. Even more worrisome, laria deaths occur in Sub-Saharan Africa, and an estimated 370,000 children younger than most are among children under age 5. age 15 became infected with HIV in 2007. Glob- Children who survive malaria do not escape ally, the number rose from 1.6 million in 2001 unharmed. Repeated episodes of fever and to 2.0 million in 2007. Most of these children anemia take a toll on their mental and physical (90 percent) live in Sub-Saharan Africa. development. Much progress has been made across Sub-Saharan Africa in scaling up insecti- Orphaned and vulnerable cide-treated net use among children, which rose Worldwide in 2008 some 17.5 million children from 2 percent in 2000 to 20 percent in 2006. had lost one or both parents to AIDS, including In countries with trend data, 19 of 22 coun- nearly 14.1 million children in Sub-Saharan Afri- tries showed at least a threefold increase over ca. A key indicator of progress in HIV/AIDS treat- 2000­06, and 17 showed a fivefold increase. ment and the situation of children affected by AIDS is school attendance by orphans. Orphans Tuberculosis rates falling, but not fast enough and vulnerable children are at higher risk of The number of new tuberculosis cases globally missing out on schooling, live in households peaked in 2004 and is leveling off. Tuberculosis with less food security, and are in greater dan- prevalence (cases per 100,000 people) has fall- ger of exposure to HIV. The disparity in school en, but the target of halving 1990 prevalence and attendance between orphans and nonorphans death rates by 2015 is unlikely to be met in all re- appears to be shrinking in many countries. gions. Prevalence is still high in Sub-Saharan Afri- ca, and South Asian countries appear to have just returned to 1990 prevalence levels in 2007. In 2007 there were 13.7 million cases globally, down only slightly from the 13.9 million in 2006, when 1.3 million infected people died. An estimated half million people who died were also HIV positive. 16 2010 World Development Indicators WORLD VIEW Tuberculosis prevalence and mortality are Two-thirds of young people living with HIV/AIDS are in falling, but resistant strains remain a challenge Sub-Saharan Africa, most of them women Deaths from tuberculosis (per 100,000 people) HIV prevalence among people ages 15­24, 2007 (percent) Male Female 125 4 Sub-Saharan Africa 100 3 75 2 50 South Asia East Asia & Pacific 1 25 Latin America Middle East & & Caribbean North Africa Europe & Central Asia 0 High income 0 1991 1995 2000 2007 East Asia Europe & Latin America Middle East & South Sub-Saharan High & Pacific Central Asia & Caribbean North Africa Asia Africa income Source: World Health Organization. Source: Joint United Nations Programme on HIV/AIDS and World Health Organization. Use of insecticide-treated nets by children is rising Orphaned children are less likely to attend school 1999­2005 2005­08 2001 Chad Rwanda 2007 Gambia, The 1997 Zambia 2003 São Tomé and Príncipe Zambia 2000 Tanzania 2005 Guinea-Bissau 2000 Togo Namibia 2005 Ethiopia 2000 Senegal Central African Rep. 2005 Ghana 1998 Sierra Leone Lesotho 2003 Tanzania 1999 Zimbabwe Malawi 2005­06 Benin 2000 Kenya 2004 Central African Rep. Cameroon 2000 Burundi 2006 Burkina Faso 2000 Uganda Senegal 2006 Burundi 1999 Niger Sierra Leone 2004­05 Nigeria 1996 Congo, Dem. Rep. Mozambique 2005 Côte d'Ivoire 1996­97 Congo, Dem. Rep. Swaziland 2004 0 20 40 60 0.00 0.25 0.50 0.75 1.00 1.25 Children sleeping under insecticide-treated nets Ratio of school attendance of orphans to school attendance of nonorphans, 2000­07 (percent of children under age 5) Source: United Nations Children's Fund. Source: United Nations Children's Fund. 2010 World Development Indicators 17 Goal 7 Ensure environmental sustainability specific targets for reducing emissions of green- We must spare no effort to free all of humanity, and above all our children and house gases. And despite the commitments grandchildren, from the threat of living on a planet irredeemably spoilt by human made by the 37 industrial countries that are activities, and whose resources would no longer be sufficient for their needs. parties to the 1997 Kyoto Protocol, emissions of --United Nations Millennium Declaration (2000) greenhouse gases have continued to rise. As economies develop, their use of energy derived from fossil fuels increases. Even with improved energy efficiency, which has lowered Target 7A carbon dioxide emissions per unit of GDP, aver- Integrate the principles of age emissions per person continue to rise. With- sustainable development out agreed and enforceable targets for reduced into country policies and emissions, little progress will be made in reduc- programs and reverse the loss ing the threat of global climate change. of environmental resources Greater demands on water resources Most water is used for agriculture and industry, International concern for the loss of environ- with only a small part going to domestic con- mental resources and the impact on human sumption. Growing economies and populations welfare was first expressed in 1972 at the UN are putting greater demands on the world's Conference on the Human Environment. The freshwater resources. In 2007 there were 62 1992 "Earth Summit" in Rio de Janeiro ad- economies with less than 1,700 cubic meters opted Agenda 21, a comprehensive blueprint of freshwater resources per person, a level as- of actions to be taken globally, nationally, and sociated with water stress. Of these, 41 were locally in every area in which humans directly in water scarcity, with less than 1,000 cubic affect the environment. Agenda 21 was incorpo- meters per person. Water pollution and waste- rated into the Millennium Declaration along with ful practices further reduce available water. In a commitment to embark on the reductions in some economies, especially in the Middle East greenhouse gas emissions required under the and North Africa, withdrawals exceed available Kyoto Protocol and to implement the conven- resources, and the difference is made up by de- tions on biodiversity and desertification. salinization of sea water. Forests lost Loss of forests is one of the most tangible mea- Target 7B sures of environmental destruction. Many of the Reduce biodiversity loss, world's poor people depend on forests. The loss achieving, by 2010, a significant of forests threatens their livelihoods, destroys reduction in the rate of loss habitat that harbors biodiversity, and eliminates an important carbon sink that helps to moder- ate the climate. Net losses since 1990 have The loss of habitat for animal and plant species been substantial, especially in Latin America has led to widespread extinctions. Developing and the Caribbean and Sub-Saharan Africa, but economies, especially those near the equator, recent data show a slowing in the global rate of contain some of the most important regions of deforestation. biodiversity. Preservation of habitat through the desig- Rising greenhouse gas emissions nation of protected areas is an important prac- The United Nations Framework Convention on Cli- tical step to ensure sustainable development. mate Change (UNFCCC) was agreed at the 1972 The number and size of protected areas have Earth Summit. Neither the UNFCCC nor the Mil- increased, but there is no direct evidence that lennium Development Goals commit countries to the rate of biodiversity loss has slowed. 18 2010 World Development Indicators WORLD VIEW Demands on the world's freshwater The world has lost more than 1.4 million square kilometers of forest since 1990 resources are rising Renewable internal freshwater Total water resources resources (trillion cubic meters) Freshwater withdrawals East Asia & Pacific 15 Europe & Central Asia Latin America & Caribbean 10 Middle East & North Africa South Asia 5 Sub-Saharan Africa High income 0 East Europe & Latin Middle South Sub- ­800 ­600 ­400 ­200 0 +200 Asia & Central America & East & Asia Saharan Forest loss (­) or gain (+) (thousand square kilometers) Pacific Asia Caribbean North Africa Africa Source: Food and Agriculture Organization. Source: Food and Agriculture Organization. Some 18 million square kilometers Lowering the carbon footprint of GDP of land have been protected . . . Terrestrial protected areas, 2008 (percent of surface area) Carbon dioxide emissions (kilograms per 2005 purchasing power parity $ of GDP) 25 1.5 20 Europe and Central Asia 1.0 15 East Asia and Pacific 10 South Asia Sub-Saharan Africa Middle East and North Africa 0.5 5 High income Latin America and Caribbean 0 East Europe & Latin Middle South Sub- High 0.0 Asia & Central America East & Asia Saharan income 1990 1992 1994 1996 1998 2000 2002 2004 2006 Pacific Asia & Carib. N. Africa Africa Source: United Nations Environment Programme, World Conservation Monitoring Centre. Source: Carbon Dioxide Information Analysis Center and World Bank staff calculations. . . . but only 3 million square kilometers Carbon dioxide emissions continue to rise of marine areas are protected Marine protected areas, 2008 (percent of surface area) Total carbon dioxide emissions from fossil fuels (million metric tons) 5 15,000 High income 4 3 10,000 East Asia and Pacific 2 5,000 1 Europe and Central Asia Sub-Saharan Africa Middle East and North Africa Latin America and Caribbean South Asia 0 East Europe & Latin Middle South Sub- High 0 Asia & Central America East & Asia Saharan income 1990 1992 1994 1996 1998 2000 2002 2004 2006 Pacific Asia & Carib. N. Africa Africa Source: United Nations Environment Programme, World Conservation Monitoring Centre. Source: Carbon Dioxide Information Analysis Center. 2010 World Development Indicators 19 Goal 7 Ensure environmental sustainability sanitation, a number that has barely changed We will put into place policies to ensure adequate investment in a sustainable since 1990. In developing countries the pro- manner in health, clean water and sanitation . . . portion of the population without access to im- --United Nations World Summit Outcome (2005) proved sanitation fell from 55 percent in 1990 to 45 percent in 2006. To reach the target in 2015, more than 1.1 billion more people will have to gain access to an improved facility. . . . recognizing the urgent need for the provision of increased resources for af- Progress has been slowest in South Asia and fordable housing and housing-related infrastructure . . . Sub-Saharan Africa. --United Nations World Summit Outcome (2005) Even as countries try to improve their sani- tation systems, 18 percent of the world's popu- lation lack any form of sanitation. They prac- tice open defecation, at great risk to their own Target 7C health and to that of others around them. Halve by 2015 the proportion of people without sustainable access to safe drinking Target 7D water and basic sanitation Achieve by 2020 a significant improvement in the lives of at least 100 million slum dwellers More people have access to an improved water source In 1990 almost 1 billion people in developing A growing need for urban housing countries lacked convenient access to an ad- The Millennium Declaration adopted the goal of equate daily supply of water from an improved the "Cities without Slums" initiative, to improve source. The numbers lacking access have been the lives of 100 million slum dwellers, although declining. At least 65 developing countries are at the time there was no standard definition of on track to reduce by half the proportion of peo- a slum. Since then work by the United Nations ple lacking access to an improved water source, Human Settlements Programme (UN-HABITAT) and others could still reach the target by 2015. has helped quantify the number of people liv- But an "improved source" does not always ing in urban slums. That evidence suggests mean a source of safe water. Water from that more than 200 million urban dwellers have improved sources, such as public taps or hand enjoyed improved living conditions, but the pumps and tubewells, may not meet standards number of people moving into urban areas has of water quality set by the World Health Orga- grown even faster. UN-HABITAT estimates that nization. Such sources may also require much more than 825 million people are now living fetching and carrying of water: many people, in dwellings that lack access to an improved especially in rural areas, still do not have the drinking water source, improved sanitation fa- convenience of piped water in their homes. cilities, sufficient living area, durable structure, or security of tenure. In Sub-Saharan Africa Access to improved sanitation more than half the urban population lives in has proved more difficult slum conditions. More than 1.5 billion people lack access to toilets, latrines, and other forms of improved 20 2010 World Development Indicators WORLD VIEW Progress on access to an improved water source Most regions will achieve the 2015 target for access to an improved water source Share of countries in region Reached target On track making progress toward Off track Seriously water access (percent) Insufficient data off track Population without access to improved drinking water source (millions) 100 1,000 50 750 Middle East & North Africa Latin America & Caribbean 0 500 South Asia 50 250 Sub-Saharan Africa Europe & Central Asia East Asia & Pacific 100 0 East Europe & Latin Middle South Sub- 1990 1992 1994 1996 1998 2000 2002 2004 2006 Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: World Health Organization­United Nations Children's Fund Joint Monitoring Programme. Progress on access to improved sanitation More than 1.5 billion people still lack sanitation facilities Share of countries in region Reached target On track making progress toward Off track Seriously sanitation access (percent) Insufficient data off track Population without access to improved sanitation (millions) 100 1,600 Europe & Central Asia 50 1,200 South Asia Sub-Saharan Africa 0 800 Middle East & North Africa East Asia & Pacific 50 400 Latin America & Caribbean 100 0 East Europe & Latin Middle South Sub- 1990 1992 1994 1996 1998 2000 2002 2004 2006 Asia & Central America & East & Asia Saharan Pacific Asia Caribbean North Africa Africa Source: World Bank staff estimates. Source: World Health Organization­United Nations Children's Fund Joint Monitoring Programme. The original housing target has been met, but millions still live in slum conditions Share of urban population living in slums (percent) 75 Sub-Saharan Africa 50 South Asia East Asia and Pacific Middle East and North Africa 25 Latin America and Caribbean Europe and Central Asia 0 1990 1995 2000 2005 2007 Source: United Nations Human Settlements Programme. 2010 World Development Indicators 21 Goal 8 Develop a global partnership for development disguise high peak tariffs applied selectively to Success in meeting these objectives depends, inter alia, on good governance certain goods. Arcane rules of origin may also within each country. It also depends on good governance at the international prevent countries from qualifying for duty-free level and on transparency in the financial, monetary and trading systems. We are access. And subsidies paid by rich countries to committed to an open, equitable, rule-based, predictable and nondiscriminatory agricultural producers make it hard for develop- multilateral trading and financial system. ing countries to compete. Though falling, subsi- --United Nations Millennium Declaration (2000) dies are still much higher than the level of aid provided by the same counties. Official development assistance Debt sustainability Following the Millennium Summit, world leaders Better debt management, trade expansion, meeting at Monterrey, Mexico, in 2002 agreed and, for the poorest countries, substantial on the need to provide financing for develop- debt relief have reduced the burden of debt ment through a coherent process that recog- service. The slowdown in the global economy nized the need for domestic as well as interna- since 2007 is likely to reverse these trends in tional resources. These leaders called on rich the near term and increase the difficulties of countries to increase aid levels to 0.7 percent servicing debt or borrowing to finance balance of their gross national income (GNI), but only a of payments deficits, especially for countries few have. Three years later, the leaders of the with above average debt levels. Debt relief un- Group of Eight industrialized countries meeting der the Heavily Indebted Poor Countries Initia- in Gleneagles, Scotland, made specific commit- tive has reduced future debt payments by $57 ments to increase aid flows to Africa. Aid flows billion (in end-2008 net present value terms) have increased substantially since 2000--from for 35 countries that have reached their de- $69 billion in 2000 to $122 billion in 2008 (in cision point. And 28 countries that have also 2007 dollars). While aid flows to Africa have in- reached the completion point have received ad- creased, at $38 billion in 2008 they have fallen ditional assistance of $25 billion (in end-2008 well short of the Gleneagles commitments. net present value terms) under the Multilateral Debt Relief Initiative. Market access The world economy is bound together by trade Access to technology and investment. To improve the opportunities If trade and investment provide the economic for developing countries, the Millennium Decla- sinews that bind the world together, communi- ration calls for rich countries to permit tariff- and cations is the nerve tissue, relaying messages duty-free access of the exports of developing from the most remote parts of the planet. While countries and draws attention to the need for the growth of fixed-line systems has peaked in assistance to improve countries' capacity to high-income economies and slowed apprecia- export. bly in developing countries, mobile cellular sub- Average tariffs levied by rich countries scriptions continue to grow at a rapid pace, and have been falling, but many obstacles remain Internet use, barely under way in 2000, is now for developing country exporters. The averages spreading through many developing countries. 22 2010 World Development Indicators WORLD VIEW The burden of debt service has been Aid efforts by DAC donors have increased, but most fall short of their commitments falling for developing countries Debt service as share of exports (percent) Official development assistance as share of GNI (percent) 25 1.25 Heavily indebted poor countries Sweden 20 1.00 Netherlands Upper middle-income 15 economies 0.75 United Kingdom 10 0.50 Lower middle-income All DAC donors economies 5 0.25 Japan United States 0 0.00 1990 1995 2000 2005 2008 1990 1995 2000 2005 2008 Source: World Bank staff estimates. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. Developing countries have easier Growth of fixed telephone lines has slowed, but cellular phone use is rising rapidly access to OECD markets . . . Share of goods (excluding arms) admitted Telephone subscriptions (per 100 people) free of tariffs from developing countries (percent) 125 100 Mobile cellular, high-income economies 100 75 United States Japan 75 Fixed lines, high-income economies 50 50 European Union Mobile cellular, low- and middle-income economies 25 25 Fixed lines, low- and middle-income economies 0 0 1990 1995 2000 2005 2008 1996 2000 2005 2007 Source: World Trade Organization. Source: International Telecommunication Union. . . . but agricultural subsidies by OECD members Internet use in developing countries is beginning to take off exceed their net official development assistance Internet users (per 100 people) Percent 80 2.5 High-income economies 2.0 60 OECD countries: agricultural support as a share of GDP 1.5 40 1.0 20 0.5 DAC donors: net aid as a share of GNI Low- and middle-income economies 0.0 0 1990 1995 2000 2005 2008 1990 1995 2000 2005 2008 Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. Source: International Telecommunication Union. 2010 World Development Indicators 23 Millennium Development differences between urban and rural areas, Goals and inequality among religions and ethnic groups, between the The first cross-cutting challenge for quality of sexes (figure 1f), and almost always between life indicators is to detail the inequalities in in- the rich and the poor. The degree of inequality dividual conditions in the various dimensions of varies from country to country, but within each life, rather than just the average conditions in country it is usually the poor who fare the worst each country. To some extent, the failure to ac- (World Bank and IMF 2007). count for these inequalities explains the "growing Child death rates have fallen in low- and gap" . . . between the aggregate statistics that middle-income countries from 100 per 1,000 dominate policy discussions and people's senti- live births in 1990 to 75 in 2008, but they are ments about their own condition. falling less rapidly for the very poor (figure 1g). Report by the Commission on the Poor people have less access to health ser- Measurement of Economic Performance vices. They are more exposed to health risks and Social Progress (Stiglitz, Sen, and because of malnutrition, high treatment costs, Fitoussi 2009), September 14, 2009 and long distances to health clinics. Some programs can be targeted to reach the most Although goal 1 includes a measure of income needy--oral rehydration therapy has been suc- inequality, the MDGs do not directly address cessful in reaching poor children and Brazil has differences in outcome associated with socio- improved income distribution (box 1h)--but economic status, race, religion, or ethnic iden- large disparities remain. More midwives attend tity, although goal 3 addresses gender inequal- births in richer than in poorer areas of coun- ity. For nearly all the goals, the indicators used tries (Watkins 2008). Similarly, while primary to monitor them may conceal large disparities school completion rates rose between 1991 within populations. Country averages obscure and 2008 in low- and middle- income countries from an average of 78 percent to 86 percent Inequalities for school completion rates persist for men and women 1f (see table 1.2), in 36 countries primary comple- tion rates were 20 or more percentage points School completion rate (percent) Female Male higher for the richest quintile than the poorest 100 (see table 2.15).The probability that a poor Tan- zanian child will complete primary school is less 75 than one in three, whereas almost all rich Tan- 50 zanian children complete primary school. Large gaps persist between the rich and the poor in 25 the gross intake rate in grade 1, average years of schooling, and out of school children (see 0 Poorest 2nd 3rd 4th Richest table 2.15). In Mali nearly 7 of 10 children ages quintile quintile quintile quintile quintile 6­11 in the poorest 20 percent of the popula- Source: Gwatkin and others 2007. tion are out of school, compared with 2 of 10 in the richest 20 percent. Large disparities in child survival 1g An equity-based measure of the Millennium Development Goals Deaths per 1,000 live births Under age 1 Under age 5 In a world of great inequality progress on the 150 MDG indicators does not guarantee that poor people will be better off. Some countries could 100 attain the MDG targets largely by improving out- comes for the richest 60 percent of the popula- 50 tion, without improving conditions for the poor. Until the late 1990s difficulties measuring income and consumption had inhibited assess- 0 Poorest 2nd 3rd 4th Richest ment of economic inequalities. Then researchers quintile quintile quintile quintile quintile found that information about readily observable Source: Gwatkin and others 2007. household characteristics such as the type of roof or possessions such as radios and bicycles 24 2010 World Development Indicators WORLD VIEW Brazil improves income distribution 1h While income inequalities have worsened in most middle-income coun- tries, Brazil has seen dramatic improvements in both poverty and income Annual growth rate of GNI per capita, by income decile, 2001­06 (percent) distribution. Brazil's poverty rate fell from 41 percent in the early 1990s 10 to 33­34 percent in 1995, where it stayed until 2003, when a steady decline began that lowered the poverty rate to 25.6 percent by 2006. Extreme poverty rates followed a similar path, falling from 14.5 percent in 8 2003 to 9.1 percent in 2006. Using the World Bank's global definition of poverty of living on less than $1 a day, the poverty headcount ratio dropped steadily from 14 percent in 1990 to 8 percent in 2004. Reductions in the 6 number of people living in poverty have been accompanied by a decline in income inequality as measured by the Gini index, from 0.60 in 2002 to 0.54 in 2006. Income growth for the poorest 10 percent outstripped 4 income growth for the richest 10 percent (see figure). Lower poverty and more equitable income distribution have been fueled National average by low inflation, a targeted transfer program (including Bolsa Famillia, a conditional cash transfer program), improvements in labor productiv- 2 ity following gains in schooling, and better integration of labor markets across Brazil. Inequality remains among the highest in the world, but recent improvements show that inequality does not inevitably accompany devel- 0 opment. Social indicators have improved as well. Child mortality rates Poorest Second Third Fourth Fifth Sixth Seventh Eighth Ninth Richest 10% decile decile decile decile decile decile decile decile 10% fell sharply, from 56 per 1,000 in 1990 to 22 in 2008, in part reflecting better immunization rates. Births attended by skilled health staff rose to 97 percent. Primary completion rates rose 90 percent in 1990 to universal coverage. Source: World Bank 2008a. could provide a reliable measure of household Child mortality rates rise when adjusted for equity 1i wealth and a reasonable proxy for household income and consumption data. An index con- Under-five mortality rate (per 1,000) Unadjusted rate Equity-adjusted rate structed from these physical indicators is now 125 widely used to analyze data from Demographic 100 and Health Surveys and Multiple Indicator Clus- 75 ter Surveys wealth quintiles. Jan Vandermoortele (2009) has suggested adjusting national aver- 50 ages by assigning greater weight to the scores 25 of the poor. Giving a score of 30 instead of 20 to 0 the poorest quintile and 10 instead of 20 to the India Bolivia South Africa Peru Indonesia Egypt, Dominican Vietnam 1998/99 2003 1998 2000 2002/03 Arab Rep. Republic 2002 richest quintile, with intermediate quintile scores 2005 2002 of 25, 20, and 15, enables the weighted aver- Source: Vandermoortele 2009. age to better measure progress by the poorer quintiles. Figure 1i shows how average child mortality rates would be adjusted for selected and developing countries. The duration and cur- countries. The adjusted measure would credit riculum of primary schooling reflect the need for countries making more rapid progress in improv- children to acquire competencies in basic knowl- ing the outcomes of the poor with making faster edge, skills, values, and behavior. Thus the im- progress toward the MDG targets. This or other portance of ensuring that all children complete methods of adjusting for distributional patterns at least the primary stage of schooling. are worth exploring for future use. Yet the evidence suggests that merely attaining 100 percent primary completion Progress in service quality rates, which many developing countries are on Inequality in access to health and education ser- track to do, will not ensure that children acquire vices is mirrored by unevenness in the quality of the necessary competencies. The expansion services delivered. Education quality has lagged of education systems, including the building of behind the substantial quantitative progress schools and hiring and training of teachers, is in access to schools. Quality as measured by a popular and appropriate strategy for meeting differences in cognitive skills is not just a rich- the MDG target. But promoting learning and the poor issue in developing countries: it is also a acquisition of competencies is a much more source of huge disparities between developed diffi cult challenge. Reading comprehension 2010 World Development Indicators 25 tests organized under the Programme for Inter- Governance and fragility national Student Assessment show that on a Reaching poor people and ensuring that the ser- scale of 1 (low) to 5 (high), some 15 percent of vices they receive improve human outcomes de- students in rich countries scored below level 1, pend on governments discharging their responsi- while 45 percent of students in Asian countries bility to their citizens. This is more than a matter and 54 percent in Latin American and Carib- of ensuring economic growth, although growth bean countries did (Watkins 2008). In a recent and per capita incomes are closely associated survey of students in grades 6­8 in government with improved education and health outcomes. schools in India, 31 percent of students who And it is more than a matter of public spending, had completed primary education could not although financing is a critical input. It is also a read a simple story and 29 percent could not matter of ensuring that the money is well spent do two-digit subtraction --competencies they and achieves the intended objectives. And that should have acquired by grade 2. Similar results requires strong governance and public account- emerge from other testing. ability between citizens and their elected govern- Low education quality refl ects resource ments, between governments and service provid- constraints and weak management. These ers, and between service providers and citizens. lead to poor infrastructure, high pupil­teacher To meet the MDGs, governments need to provide ratios, and poorly trained, paid, and motivated the physical and human infrastructure to improve teachers, resulting in teacher absenteeism and access to schools and public health facilities, but low-quality teaching. This erodes the implicit they also need to provide incentives for the effi- MDG target of achieving universal competen- cient delivery of services and to be responsive to cies. Low-quality schooling also leads to high public complaints about service provision. levels of repetition and drop-out and weakens Countries that underperform on health the demand for education from parents. Poor and education outcomes often have poor gov- people pay the greatest price for low quality ernance. Cameroon, for example, is a lower because their children are the most likely to middle-income country whose pattern of health attend the worst schools. This is not to sug- outcomes matches that of countries that are far gest that the focus should shift from quantity poorer. Its under-five mortality rate of 131 per to quality. The two need to go together. More 1,000 is above the average for low-income coun- effort is also needed to monitor student learn- tries and almost double the average for lower ing through national and international assess- middle-income countries, while the under-five ments and benchmarking to create stronger mortality rate for the poorest 20 percent (189 per incentives for better performance. 1,000) was more than twice the average for the Quality is of equal concern in health care, richest (88) in 2006. The maternal mortality ratio though data are still hard to collect. There is is also high (669 per 100,000) despite improving considerable evidence of widespread misdiag- indicators for prenatal care and births attended nosis of ailments and failure to adhere to recom- by skilled health staff. These poor health out- mended protocols or checklists for treatment of comes reflect in part low public spending: less major diseases. High maternal mortality ratios than 1 percent of GDP in 2007, compared with through much of South Asia and Sub-Saha- an average of 2.4 percent for Sub-Saharan Africa ran Africa reflect poor-quality care that is not and 1.8 percent for lower middle-income coun- directly measured in the MDGs. The number of tries (see table 2.16). Resources are also poorly antenatal visits is measured but not their qual- allocated, directed at centralized administration ity. The proportions of births attended by skilled rather than front-line workers. But poor gover- attendants are monitored, but not the practices nance drives many of these outcomes as well. followed. This poor state of health information As in many developing countries citizens needing systems has resulted in a dependence on sur- health care must pay high "informal payments." veys, which take place at long intervals and Drugs and procurement are major sources of ille- do not capture moment of care data (Shankar gally diverted funds. and others 2008). An upgrade in locally based Measuring governance is not easy, involv- health information systems is needed to pro- ing many institutions and formal and informal vide real-time data on delivery practices, care, rules that guide their operation. Formal rules are and outcome to improve practices and reduce easier to observe and measure. Informal rules, maternal and infant mortality. steeped in a country's culture and history, are 26 2010 World Development Indicators WORLD VIEW less easy to observe but may have a greater How governance contributes to social outcomes 1j impact. Efforts are being made to measure Country Policy and Institutional Assessment governance. The World Bank's Country Policy ratings for public sector management and institutions, 2008 5 and Institutional Assessment measure (see table 5.9) provides two sets of indicators of the relationship between governance and social 4 indicators. One set captures dimensions of gov- ernance that influence social outcomes, such as the quality of budgetary and financial manage- 3 ment, the efficiency of revenue mobilization, the quality of public administration, and corruption. Another set captures policies for social inclusion 2 and equity directed at such outcomes as gender equality, equity of public resource use, human capacity building, social protection and labor, 1 and policies for environmental sustainability. The relationship between the two indicator sets is close (figure 1j). While some countries 0 appear to have strong social policies and out- 0 1 2 3 4 5 Country Policy and Institutional Assessment ratings for social inclusion and equity, 2008 comes despite poor governance (Bangladesh is Source: World Bank staff estimates. an example), in most cases better governance goes with better policies and outcomes. Countries facing particularly severe develop- Under-five mortality rates vary considerably among core fragile states 1k ment challenges--weak institutional capacity, poor governance, political instability, and con- Under-five mortality rate (per 1,000) 1990 2008 300 flict--are sometimes referred to as fragile states. They are least likely to achieve the MDGs. The 200 2010 list of core fragile states accounts for only Low-income country average 6 percent of the population of developing coun- tries but for more than 12 percent of the extreme 100 poor. They account for 21 percent of child deaths in developing countries and 20 percent of chil- 0 dren who did not complete primary school on n la u ea ria ad m a Bu . nt imo ndi ca te p. d' i ire go ea ya n r Gu . Zi nea e it ep ep Pa Con ma bw ta sa i da go al Ha Re s To in Ch itr be Ivo ru .R l A r-Le ,R is o, om is Su i m ba An Gu Er an Li n -B time. Fragile states make up a majority of the go m S ea gh w De M te fri Ne Af in Cô T Gu low-income countries that will not achieve the a ng ra pu Co Ce goal of gender parity in primary and secondary schools. This lack of progress reflects weak gov- Source: Inter-agency Group for Child Mortality Estimation. ernance, low institutional capacity, and frequent internal and sometimes external conflicts. This is not to suggest that fragile states pressure to produce better data and monitor have a uniformly poor record on the MDGs. The development outcomes more systematically. performance of 20 large (more than 1 million Increasingly, the need for improved statistical population) fragile states on under-five mortality capacity building began to emerge from poverty shows that child mortality increased in 3 coun- reduction strategies supported by grants and tries and showed little or no progress in 9, but concessional funding from the International improved in 8 to a level below the average for Monetary Fund and the World Bank, and United low-income countries (figure 1k). Nations development assistance frameworks. In response, the Partnership in Statistics for Building capacity for better data Development in the 21st Century (PARIS21) The second half of the 1990s saw a marked was created in November 1999 to bring togeth- increase in demand for reliable data to design er donors and developing countries to promote poverty reduction programs and demonstrate statistical capacity- building programs. their effectiveness. Developing country gov- Adoption of the MDGs gave new momen- ernments faced both domestic and external tum to the demand for better data. A series of 2010 World Development Indicators 27 Managing for Development Results conferences To ensure that the increased efforts and produced a Marrakech Action Plan calling for: donor financing for statistical capacity devel- · All low-income countries to develop and opment are producing results, the World Bank implement national strategies for the devel- launched a statistical capacity indicator in opment of statistics. 2004 based on information that is easily col- · All low-income countries to participate in lected and publicly available in most countries. censuses in 2010. The indicator combines three measures of sta- · Greater domestic and foreign financing for tistical capacity: statistical capacity building. · Practice, a measure of a country's capacity · Establishment of an International Household to meet international standards, methods, Survey Network to help countries learn from and data reporting practices in economic each other and benchmark their progress. and social statistics. · Major improvements in MDG monitoring. · Collection, a measure of a country's ability · Increased accountability of the international to collect data at recommended intervals. statistical system. · Availability, a measure of a country's capac- Progress in designing and implementing ity to make data available and accessible to national strategies for statistics has been users in international data sources such as impressive (fi gure 1l). Some 42 percent of World Development Indicators. the 78 International Development Association Over 1999­2009 the statistical capacity (IDA)­eligible countries are already implement- index for 117 World Bank borrower countries ing a strategy, and 32 percent are designing a rose from 52 to 65. Progress was faster in non- strategy or awaiting its adoption. Sub-Saharan IDA countries (up 20 percentage points) than in Sub-Saharan IDA countries (up Measuring progress in statistical 6 percentage points). Of 42 Sub-Saharan Afri- capacity building can countries with statistical capacity data for Developing a strategy is only the beginning. both 1999 and 2009, 12 saw a decline and 8 Implementation calls for increased investment barely improved. However, several Sub-Saharan to address structural and capacity constraints. countries recorded substantial improvements. Strategies must be effectively linked to govern- The predominance of core fragile states in Sub- ment budgets and action plans. Progress in Saharan Africa contributed to the poor scores. improving institutional capacity through better Some 16 core fragile states saw a modest practices and better data collection was mod- improvement over the decade (up 6 percent- est over the decade (table 1m). The major area age points). A few exceptions--Afghanistan, of improvement was in the availability of data. Burundi, Republic of Congo, and Liberia--saw Countries in Europe and Central Asia and Latin substantial increases in their statistical capac- America and the Caribbean also greatly im- ity index, albeit from a low base (figure 1n). proved their scores on practice and collection and South Asia on collection. Data availability improves Data availability has improved considerably. The Status of national strategies for the development of statistics, 2009 1l United Nations Educational, Scientific, and Cul- tural Organization has reported a substantial in- 6 countries without a strategy crease in the availability of enrollment data. The and not planning one number of countries conducting health-related surveys at least every three years has doubled. 14 countries with expired strategy or without strategy Thus, MDG-related data are becoming more now planning one 33 countries available. In 2003 only 4 countries had two currently implementing data points for 16 or more of the 22 MDG indi- a strategy cators. Today, some 118 countries (72 percent) 25 countries currently designing have two data points for the 16­22 indicators a strategy or awaiting adoption grouping (figure 1o). Improvements in statistical capacity have been accompanied by improve- ments in reporting to international agencies and Source: PARIS21 2009. increased access and understanding in these agencies of national sources (PARIS21 2009). 28 2010 World Development Indicators WORLD VIEW Statistical capacity indicators by region and areas of performance 1m Europe and Latin America and Middle East and All countries East Asia Central Asia the Caribbean North Africa South Asia Sub-Saharan Africa Statistical capacity index component 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 1999 2009 Overall 52 65 55 68 55 79 62 75 49 59 50 68 47 54 Practice 45 56 50 63 51 76 53 68 46 55 43 58 36 38 Collection 53 63 56 65 63 81 65 75 45 55 50 72 45 48 Availability 59 77 59 76 50 80 68 84 55 68 56 73 61 75 Source: World Bank staff estimates. The effort to get low-income countries to par- Statistical capacity has improved . . . 1n ticipate in 2010 censuses promises to greatly improve global coverage. Only nine countries Statistical capacity index, 1999­2009 (1, low, to 100, high) 1990 2008 have not yet scheduled a census (seven also 100 did not participate in 2000). PARIS21 (2009) 75 estimates that some 140 million people will not be covered by the 2010 censuses, well down 50 from the 550 million in the 2000 censuses. 25 Data quality remains uncertain 0 Despite this impressive progress, data quality Kazakhstan Ukraine Sri Guatemala Tajikistan Cameroon Serbia Bosnia and Burundi Congo, Lanka Herzegovina Rep. remains a concern. "Far too much of the grow- ing amount of data cited in high-level reports Source: World Bank staff estimates. is still based on poor quality information, ex- trapolation, and guesswork" (Manning 2009, p. 38). Strengthening national statistical systems . . . but data are still missing for key indicators 1o must go beyond producing a few high-profile statistics to improving the underlying process- Share of countries lacking Low-income economies Lower middle-income economies two or more data points (percent) Upper middle-income economies es. This includes the sectoral agencies respon- 50 sible for delivering services and collecting data 40 on the population served. A heavy reliance on household surveys--which can produce 30 high-quality data but at greater cost and lower 20 frequency than national statistical systems-- 10 may be necessary to compensate for a lack of vital registration systems and reliable ad- 0 Poverty Primary school Gender Gender Child Child births ministrative systems. But if the main source completion (primary) (secondary) mortality attended of information is surveys that take place only Source: PARIS21 2009. every three to five years, it is difficult to re- ward good performance or correct poor per- formance. There is a "growing mismatch value. And someone--usually the government-- between the multiple demands for monitor- must be willing to pay for them. Statistics are ing and the ability of local systems to gener- the classic example of a public good, which can ate credible data" (Manning 2009, p. 38). be shared by many without loss to any--but they The best assurance of high-quality data is are still costly to produce. The MDGs have helped an independent and well managed statistical to raise the profile of statistics and the agencies system that adheres to recognized standards, that produce them, but the achievements of the uses a variety of instruments (surveys, cen- last two decades are far from secure. Without suses, and administrative records), documents continuous improvement and a strong commit- its processes, and publishes its results. High- ment to producing useful, high-quality data, sta- quality systems do not exist in a vacuum. Users tistical systems will languish. Public and private must demand reliable data and recognize their sectors will be the poorer for it. 2010 World Development Indicators 29 Millennium Development Goals Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 1 Eradicate extreme poverty and hunger Target 1.A Halve, between 1990 and 2015, the proportion of 1.1 Proportion of population below $1 purchasing power people whose income is less than $1 a day parity (PPP) a day1 1.2 Poverty gap ratio [incidence × depth of poverty] 1.3 Share of poorest quintile in national consumption Target 1.B Achieve full and productive employment and decent 1.4 Growth rate of GDP per person employed work for all, including women and young people 1.5 Employment to population ratio 1.6 Proportion of employed people living below $1 (PPP) a day 1.7 Proportion of own-account and contributing family workers in total employment Target 1.C Halve, between 1990 and 2015, the proportion of 1.8 Prevalence of underweight children under five years of age people who suffer from hunger 1.9 Proportion of population below minimum level of dietary energy consumption Goal 2 Achieve universal primary education Target 2.A Ensure that by 2015 children everywhere, boys and 2.1 Net enrollment ratio in primary education girls alike, will be able to complete a full course of 2.2 Proportion of pupils starting grade 1 who reach last primary schooling grade of primary education 2.3 Literacy rate of 15- to 24-year-olds, women and men Goal 3 Promote gender equality and empower women Target 3.A Eliminate gender disparity in primary and secondary 3.1 Ratios of girls to boys in primary, secondary, and tertiary education, preferably by 2005, and in all levels of education education no later than 2015 3.2 Share of women in wage employment in the nonagricultural sector 3.3 Proportion of seats held by women in national parliament Goal 4 Reduce child mortality Target 4.A Reduce by two-thirds, between 1990 and 2015, the 4.1 Under-five mortality rate under-five mortality rate 4.2 Infant mortality rate 4.3 Proportion of one-year-old children immunized against measles Goal 5 Improve maternal health Target 5.A Reduce by three-quarters, between 1990 and 2015, 5.1 Maternal mortality ratio the maternal mortality ratio 5.2 Proportion of births attended by skilled health personnel Target 5.B Achieve by 2015 universal access to reproductive 5.3 Contraceptive prevalence rate health 5.4 Adolescent birth rate 5.5 Antenatal care coverage (at least one visit and at least four visits) 5.6 Unmet need for family planning Goal 6 Combat HIV/AIDS, malaria, and other diseases Target 6.A Have halted by 2015 and begun to reverse the 6.1 HIV prevalence among population ages 15­24 years spread of HIV/AIDS 6.2 Condom use at last high-risk sex 6.3 Proportion of population ages 15­24 years with comprehensive, correct knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of nonorphans ages 10­14 years Target 6.B Achieve by 2010 universal access to treatment for 6.5 Proportion of population with advanced HIV infection with HIV/AIDS for all those who need it access to antiretroviral drugs Target 6.C Have halted by 2015 and begun to reverse the 6.6 Incidence and death rates associated with malaria incidence of malaria and other major diseases 6.7 Proportion of children under age five sleeping under insecticide-treated bednets 6.8 Proportion of children under age five with fever who are treated with appropriate antimalarial drugs 6.9 Incidence, prevalence, and death rates associated with tuberculosis 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course The Millennium Development Goals and targets come from the Millennium Declaration, signed by 189 countries, including 147 heads of state and government, in September 2000 (www. un.org/millennium/declaration/ares552e.htm) as updated by the 60th UN General Assembly in September 2005. The revised Millennium Development Goal (MDG) monitoring framework shown here, including new targets and indicators, was presented to the 62nd General Assembly, with new numbering as recommended by the Inter-agency and Expert Group on MDG Indicators at its 12th meeting on 14 November 2007. The goals and targets are interrelated and should be seen as a whole. They represent a partnership between the developed countries and the developing countries "to create an environment--at the national and global levels alike--which is conducive to development and the elimination of poverty." All indicators should be disaggregated by sex and urban-rural location as far as possible. 30 2010 World Development Indicators Goals and targets from the Millennium Declaration Indicators for monitoring progress Goal 7 Ensure environmental sustainability Target 7.A Integrate the principles of sustainable development 7.1 Proportion of land area covered by forest into country policies and programs and reverse the 7.2 Carbon dioxide emissions, total, per capita and loss of environmental resources per $1 GDP (PPP) 7.3 Consumption of ozone-depleting substances Target 7.B Reduce biodiversity loss, achieving, by 2010, a 7.4 Proportion of fish stocks within safe biological limits significant reduction in the rate of loss 7.5 Proportion of total water resources used 7.6 Proportion of terrestrial and marine areas protected 7.7 Proportion of species threatened with extinction Target 7.C Halve by 2015 the proportion of people without 7.8 Proportion of population using an improved drinking water sustainable access to safe drinking water and basic source sanitation 7.9 Proportion of population using an improved sanitation facility Target 7.D Achieve by 2020 a significant improvement in the 7.10 Proportion of urban population living in slums2 lives of at least 100 million slum dwellers Goal 8 Develop a global partnership for development Target 8.A Develop further an open, rule-based, predictable, Some of the indicators listed below are monitored separately nondiscriminatory trading and financial system for the least developed countries (LDCs), Africa, landlocked developing countries, and small island developing states. (Includes a commitment to good governance, development, and poverty reduction--both Official development assistance (ODA) nationally and internationally.) 8.1 Net ODA, total and to the least developed countries, as percentage of OECD/DAC donors' gross national income 8.2 Proportion of total bilateral, sector-allocable ODA of OECD/DAC donors to basic social services (basic Target 8.B Address the special needs of the least developed education, primary health care, nutrition, safe water, and countries sanitation) 8.3 Proportion of bilateral official development assistance of (Includes tariff and quota-free access for the least OECD/DAC donors that is untied developed countries' exports; enhanced program of 8.4 ODA received in landlocked developing countries as a debt relief for heavily indebted poor countries (HIPC) proportion of their gross national incomes and cancellation of official bilateral debt; and more 8.5 ODA received in small island developing states as a generous ODA for countries committed to poverty proportion of their gross national incomes reduction.) Market access Target 8.C Address the special needs of landlocked 8.6 Proportion of total developed country imports (by value developing countries and small island developing and excluding arms) from developing countries and least states (through the Programme of Action for developed countries, admitted free of duty the Sustainable Development of Small Island 8.7 Average tariffs imposed by developed countries on Developing States and the outcome of the 22nd agricultural products and textiles and clothing from special session of the General Assembly) developing countries 8.8 Agricultural support estimate for OECD countries as a percentage of their GDP 8.9 Proportion of ODA provided to help build trade capacity Target 8.D Deal comprehensively with the debt problems of developing countries through national and Debt sustainability international measures in order to make debt 8.10 Total number of countries that have reached their HIPC sustainable in the long term decision points and number that have reached their HIPC completion points (cumulative) 8.11 Debt relief committed under HIPC Initiative and Multilateral Debt Relief Initiative (MDRI) 8.12 Debt service as a percentage of exports of goods and services Target 8.E In cooperation with pharmaceutical companies, 8.13 Proportion of population with access to affordable provide access to affordable essential drugs in essential drugs on a sustainable basis developing countries Target 8.F In cooperation with the private sector, make 8.14 Telephone lines per 100 population available the benefits of new technologies, 8.15 Cellular subscribers per 100 population especially information and communications 8.16 Internet users per 100 population 1. Where available, indicators based on national poverty lines should be used for monitoring country poverty trends. 2. The proportion of people living in slums is measured by a proxy, represented by the urban population living in households with at least one of these characteristics: lack of access to improved water supply, lack of access to improved sanitation, overcrowding (3 or more persons per room), and dwellings made of nondurable material. 2010 World Development Indicators 31 1.1 Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2007­08 2007­08 Afghanistan 29 652 44 10.6 121 370 198 32.0a 1,100a 192 2.3 ­0.4 Albania 3 29 115 12.1 114 3,840 113 23.6 7,520 112 6.0 5.6 Algeria 34 2,382 14 144.2 50 4,190 109 270.9a 7,880a 107 3.0 1.5 Angola 18 1,247 14 60.2 63 3,340 124 86.9 4,820 130 13.2 10.2 Argentina 40 2,780 15 286.6 28 7,190 85 557.9 13,990 76 6.8b 5.7b Armenia 3 30 109 10.3 122 3,350 123 19.4 6,310 119 6.8 6.6 Australia 21 7,741 3 862.5 15 40,240 29 798.3 37,250 26 3.7 0.6 Austria 8 84 101 382.7 25 45,900 19 311.5 37,360 25 1.8 1.3 Azerbaijan 9 87 105 33.2 80 3,830 114 67.4 7,770 109 10.8 9.5 Bangladesh 160 144 1,229 83.4 58 520 186 232.4 1,450 179 6.2 4.7 Belarus 10 208 48 51.9 66 5,360 98 117.2 12,110 88 10.0 10.3 Belgium 11 31 354 477.3 18 44,570 21 378.9 35,380 31 1.1 0.3 Benin 9 113 78 6.1 140 700 178 12.7 1,470 178 5.1 1.8 Bolivia 10 1,099 9 14.1 106 1,460 152 40.1 4,140 141 6.1 4.3 Bosnia and Herzegovina 4 51 74 17.1 102 4,520 106 31.5 8,360 104 5.4 5.6 Botswana 2 582 3 12.8 112 6,640 90 25.6 13,300 81 2.9 1.4 Brazil 192 8,515 23 1,401.3 10 7,300 83 1,933.0 10,070 95 5.1 4.1 Bulgaria 8 111 70 41.8 73 5,490 96 86.7 11,370 91 6.0 6.5 Burkina Faso 15 274 56 7.3 135 480 187 17.6 1,160 188 4.5 1.0 Burundi 8 28 314 1.1 188 140 210 3.1 380 208 4.5 1.4 Cambodia 15 181 82 9.3 125 640 179 27.2 1,860 172 6.7 5.0 Cameroon 19 475 40 21.9 92 1,150 156 41.4 2,170 163 3.9 1.6 Canada 33 9,985 4 1,453.8 9 43,640 22 1,289.5 38,710 21 0.4 ­0.6 Central African Republic 4 623 7 1.8 175 410 192 3.2 730 202 2.2 0.3 Chad 11 1,284 9 5.9 141 540 185 11.7 1,070 194 ­0.2 ­2.9 Chile 17 756 23 157.5 46 9,370 76 222.4 13,240 82 3.2 2.1 China 1,325 9,598 142 3,888.1 3 2,940 127 7,960.7 6,010 122 9.0 8.4 Hong Kong SAR, China 7 1 6,696 219.3 36 31,420 37 306.8 43,960 15 2.4 1.6 Colombia 45 1,142 41 207.9 37 4,620 104 379.4 8,430 102 2.5 1.0 Congo, Dem. Rep. 64 2,345 28 9.8 124 150 209 18.0 280 210 6.2 3.3 Congo, Rep. 4 342 11 6.5 137 1,790 147 10.1 2,800 155 5.6 3.7 Costa Rica 5 51 89 27.4 85 6,060 92 49.5a 10,950a 93 2.6 1.2 Côte d'Ivoire 21 322 65 20.3 97 980 166 32.6 1,580 176 2.2 ­0.1 Croatia 4 57 82 60.2 64 13,580 63 75.6 17,050 68 2.4 2.4 Cuba 11 111 102 .. ..c .. .. .. .. Czech Republic 10 79 135 173.6 43 16,650 55 238.6 22,890 55 2.5 1.6 Denmark 5 43 129 323.0 26 58,800 7 206.2 37,530 24 ­1.1 ­1.7 Dominican Republic 10 49 206 43.1 71 4,330 107 77.6a 7,800a 108 5.3 3.8 Ecuador 13 284 49 49.8 67 3,690 116 104.8 7,770 109 6.5 5.4 Egypt, Arab Rep. 82 1,001 82 146.8 49 1,800 146 445.7 5,470 125 7.2 5.2 El Salvador 6 21 296 21.2 95 3,460 122 40.7a 6,630a 118 2.5 2.1 Eritrea 5 118 49 1.5 179 300 202 3.1a 640a 205 2.0 ­1.0 Estonia 1 45 32 19.5 100 14,570 62 25.9 19,320 65 ­3.6 ­3.5 Ethiopia 81 1,104 81 22.4 89 280 204 70.2 870 197 11.3 8.5 Finland 5 338 17 252.9 32 47,600 16 191.0 35,940 29 0.9 0.5 France 62d 549d 114 d 2,695.6 6 42,000 25 2,135.8 33,280 36 0.4 ­0.1 Gabon 1 268 6 10.6 120 7,320 82 17.9 12,390 87 2.3 0.5 Gambia, The 2 11 166 0.7 194 400 194 2.1 1,280 183 5.9 3.0 Georgia 4 70 62 10.8 118 2,500 135 21.2 4,920 129 2.0 3.2 Germany 82 357 235 3,506.9 4 42,710 23 2,951.8 35,950 28 1.3 1.5 Ghana 23 239 103 14.7 104 630 180 30.9 1,320 182 7.3 5.1 Greece 11 132 87 319.2 27 28,400 40 318.0 28,300 42 2.9 2.5 Guatemala 14 109 128 36.6 76 2,680 132 64.2a 4,690a 132 4.0 1.5 Guinea 10 246 40 3.5 159 350 199 9.5 970 196 4.7 2.4 Guinea-Bissau 2 36 56 0.4 203 250 206 0.8 520 206 3.3 1.0 Haiti 10 28 358 .. ..e .. .. 1.3 ­0.3 Honduras 7 112 65 12.7 113 1,740 149 28.0a 3,830a 144 4.0 1.9 32 2010 World Development Indicators 1.1 WORLD VIEW Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2007­08 2007­08 Hungary 10 93 112 128.6 52 12,810 66 182.8 18,210 66 0.6 0.8 India 1,140 3,287 383 1,186.7 12 1,040 162 3,339.3 2,930 153 6.1 4.7 Indonesia 227 1,905 125 426.8 22 1,880 145 816.9 3,590 147 6.1 4.8 Iran, Islamic Rep. 72 1,745 44 251.5 30 3,540 117 769.7 10,840 92 7.8 6.4 Iraq 31 438 70 .. ..f .. .. .. .. Ireland 4 70 64 220.3 34 49,770 12 158.0 35,710 30 ­3.0 ­4.5 Israel 7 22 338 180.6 40 24,720 45 200.6 27,450 44 4.0 2.2 Italy 60 301 203 2,121.6 7 35,460 32 1,843.0 30,800 39 ­1.0 ­1.8 Jamaica 3 11 248 12.9 110 4,800 101 19.8a 7,360a 114 ­1.3 ­1.7 Japan 128 378 350 4,869.1 2 38,130 31 4,493.7 35,190 33 ­0.7 ­0.6 Jordan 6 89 67 20.5 96 3,470 121 33.8 5,710 124 7.9 4.5 Kazakhstan 16 2,725 6 96.6 55 6,160 91 152.2 9,710 97 3.2 1.9 Kenya 39 580 68 28.4 83 730 177 60.3 1,550 177 1.7 ­1.0 Korea, Dem. Rep. 24 121 198 .. ..e .. .. .. .. Korea, Rep. 49 100 502 1,046.3 14 21,530 49 1,353.2 27,840 43 2.2 1.9 Kosovo 2 11 165 .. ..f .. .. .. .. Kuwait 3 18 153 117.0 51 43,930 14 142.3 53,430 4 4.4 1.9 Kyrgyz Republic 5 200 28 4.1 155 780 174 11.4 2,150 164 7.6 6.7 Lao PDR 6 237 27 4.7 147 760 175 12.7 2,050 167 7.5 5.5 Latvia 2 65 36 26.9 86 11,860 69 36.3 16,010 71 ­4.6 ­4.2 Lebanon 4 10 410 28.4 82 6,780 86 49.2 11,740 90 8.5 7.7 Lesotho 2 30 68 2.2 173 1,060 160 4.0 1,970 170 3.9 3.0 Liberia 4 111 39 0.7 195 170 208 1.2 310 209 7.1 2.4 Libya 6 1,760 4 77.9 60 12,380g 67 102.4a 16,260a 70 3.8 1.7 Lithuania 3 65 54 39.9 75 11,870 68 57.7 17,170 67 3.0 3.6 Macedonia, FYR 2 26 80 8.4 129 4,130 110 18.9 9,250 99 5.0 4.9 Madagascar 19 587 33 7.9 131 420 190 20.0 1,050 195 7.3 4.5 Malawi 15 118 158 4.2 152 280 204 12.1 810 199 9.7 6.7 Malaysia 27 330 82 196.0 39 7,250 84 370.8 13,730 77 4.6 2.9 Mali 13 1,240 10 7.4 134 580 184 13.9 1,090 193 5.0 2.5 Mauritania 3 1,031 3 2.6 164 840 169 6.3 1,990 166 1.9 ­0.6 Mauritius 1 2 625 8.5 127 6,700 87 16.0 12,570 85 4.5 3.9 Mexico 106 1,964 55 1,062.4 13 9,990 74 1,525.4 14,340 75 1.8 0.7 Moldova 4 34 110 5.3h 144 1,500h 151 11.7h 3,270h 149 7.2h 7.4h Mongolia 3 1,564 2 4.4 149 1,670 150 9.2 3,470 148 8.9 7.6 Morocco 32 447 71 80.8i 59 2,520i 134 134.3i 4,180i 140 5.6i 4.3i Mozambique 22 799 28 8.4 130 380 197 17.2 770 200 6.8 4.3 Myanmar 50 677 76 .. ..e .. .. 12.7 11.8 Namibia 2 824 3 9.0 126 4,210 108 13.3 6,240 120 2.9 0.9 Nepal 29 147 201 11.5 117 400 194 32.1 1,110 190 5.3 3.4 Netherlands 16 42 487 811.4 16 49,340 13 667.9 40,620 17 2.1 1.7 New Zealand 4 268 16 118.8 53 27,830 41 107.6 25,200 49 ­1.1 ­2.0 Nicaragua 6 130 47 6.1 138 1,080 159 14.9a 2,620a 158 3.5 2.2 Niger 15 1,267 12 4.8 146 330 200 10.0 680 204 9.5 5.3 Nigeria 151 924 166 177.4 42 1,170 155 298.8 1,980 169 6.0 3.6 Norway 5 324 16 416.4 24 87,340 2 282.5 59,250 3 2.1 0.9 Oman 3 310 9 39.1 72 14,330 59 60.4 22,150 54 7.7 5.5 Pakistan 166 796 215 157.3 47 950j 169 429.9 2,590 159 2.0 ­0.2 Panama 3 75 46 22.7 88 6,690 88 42.9a 12,620a 84 9.2 7.4 Papua New Guinea 7 463 15 6.8 136 1,040 162 13.3a 2,030a 168 6.6 4.1 Paraguay 6 407 16 13.1 108 2,110 141 29.0 4,660 134 5.8 3.9 Peru 29 1,285 23 115.1 54 3,990 112 229.1 7,940 106 9.8 8.5 Philippines 90 300 303 170.4 44 1,890 144 352.4 3,900 143 3.8 2.0 Poland 38 313 125 447.1 20 11,730 70 637.0 16,710 69 4.9 4.9 Portugal 11 92 116 219.6 35 20,680 50 237.2 22,330 56 0.0 ­0.2 Puerto Rico 4 9 446 .. ..k .. .. .. .. Qatar 1 12 111 .. ..k .. .. 12.2 ­0.7 2010 World Development Indicators 33 1.1 Size of the economy Population Surface Population Gross national Gross national Purchasing power parity Gross domestic area density income, income per capita, gross national income product Atlas method Atlas method thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2007­08 2007­08 Romania 22 238 94 178.1 41 8,280 80 287.9 13,380 80 9.4 9.6 Russian Federation 142 17,098 9 1,371.2 11 9,660 75 2,192.2 15,440 73 5.6 5.7 Rwanda 10 26 394 4.3 150 440 188 10.8 1,110 190 11.2 8.2 Saudi Arabia 25 2,000l 12 440.5 21 17,870 54 603.5 24,490 52 4.4 2.4 Senegal 12 197 63 11.9 116 980 m 166 21.7 1,780 175 3.3 0.6 Serbia 7 88 83 41.1 74 5,590 95 76.3 10,380 94 1.2 1.7 Sierra Leone 6 72 78 1.8 176 320 201 4.3 770 200 5.5 2.9 Singapore 5 1 6,943 168.2 45 34,760 33 232.0 47,940 12 1.1 ­4.1 Slovak Republic 5 49 112 89.7 56 16,590 56 116.0 21,460 60 6.2 6.0 Slovenia 2 20 100 49.0 68 24,230 46 54.9 27,160 45 3.5 3.4 Somalia 9 638 14 .. ..e .. .. .. .. South Africa 49 1,219 40 283.2 29 5,820 94 476.2 9,780 96 3.1 1.3 Spain 46 505 91 1,454.8 8 31,930 36 1,404.4 30,830 38 1.2 ­0.3 Sri Lanka 20 66 312 35.8 78 1,780 148 89.9 4,460 136 6.0 5.2 Sudan 41 2,506 17 45.7 69 1,100 158 79.4 1,920 171 8.3 5.9 Swaziland 1 17 68 3.0 165 2,600 133 5.8 5,000 128 2.4 1.0 Sweden 9 450 22 469.4 19 50,910 10 348.3 37,780 23 ­0.2 ­0.9 Switzerland 8 41 191 424.5 23 55,510 8 299.8 39,210 20 1.8 0.5 Syrian Arab Republic 21 185 112 44.4 70 2,160 140 92.4 4,490 135 5.2 2.7 Tajikistan 7 143 49 4.1 156 600 183 12.7 1,860 172 7.9 6.2 Tanzania 42 947 48 18.4n 101 440n 188 52.0n 1,260n 184 7.5n 4.4n Thailand 67 513 132 247.2 33 3,670 118 523.1 7,760 111 2.5 1.8 Timor-Leste 1 15 74 2.7 166 2,460 136 5.2a 4,690a 132 13.2 9.6 Togo 6 57 119 2.6 167 410 192 5.3 830 198 1.1 ­1.4 Trinidad and Tobago 1 5 260 22.1 90 16,590 56 32.3a 24,230a 53 3.5 3.1 Tunisia 10 164 66 36.0 77 3,480 120 77.0 7,450 113 4.5 3.5 Turkey 74 784 96 666.6 17 9,020 78 991.7 13,420 78 0.9 ­0.3 Turkmenistan 5 488 11 14.4 105 2,840 128 30.9a 6,120a 121 9.8 8.4 Uganda 32 241 161 13.3 107 420 190 36.1 1,140 189 9.5 6.0 Ukraine 46 604 80 148.6 48 3,210 126 333.5 7,210 116 2.1 2.7 United Arab Emirates 4 84 54 .. ..k .. .. 6.3 3.1 United Kingdom 61 244 254 2,827.3 5 46,040 18 2,225.5 36,240 27 0.7 0.0 United States 304 9,632 33 14,572.9 1 47,930 15 14,724.7 48,430 11 0.4 ­0.5 Uruguay 3 176 19 27.5 84 8,260 81 41.8 12,540 86 8.9 8.6 Uzbekistan 27 447 64 24.7 87 910 172 72.6a 2,660a 157 9.0 7.2 Venezuela, RB 28 912 32 257.9 31 9,230 77 358.6 12,840 83 4.8 3.1 Vietnam 86 331 278 76.8 61 890 173 232.2 2,690 156 6.2 4.9 West Bank and Gaza 4 6 654 .. ..f .. .. .. .. Yemen, Rep. 23 528 43 21.9 91 960 168 50.9 2,220 162 3.9 1.0 Zambia 13 753 17 12.0 115 950 169 15.5 1,230 185 6.0 3.4 Zimbabwe 12 391 32 .. ..e .. .. .. .. World 6,697 s 134,097 s 52 w 57,960.4 t 8,654 w 69,749.6 t 10,415 w 1.7 w 0.5 w Low income 976 19,313 52 510.5 523 1,321.9 1,354 6.3 4.1 Middle income 4,652 79,485 60 15,123.0 3,251 28,533.4 6,133 5.8 4.7 Lower middle income 3,703 32,309 119 7,674.5 2,073 16,994.4 4,589 7.4 6.1 Upper middle income 949 47,176 21 7,454.1 7,852 11,589.1 12,208 4.2 3.3 Low & middle income 5,629 98,797 59 15,648.9 2,780 29,847.2 5,303 5.8 4.5 East Asia & Pacific 1,930 16,299 122 5,102.0 2,644 10,461.1 5,421 8.0 7.2 Europe & Central Asia 443 23,926 19 3,258.0 7,350 5,298.2 11,953 4.1 3.8 Latin America & Carib. 566 20,421 28 3,831.0 6,768 5,837.8 10,312 4.3 3.2 Middle East & N. Africa 325 8,778 38 1,052.6 3,237 2,345.5 7,343 5.5 3.7 South Asia 1,545 5,131 324 1,487.5 963 4,163.4 2,695 5.6 4.1 Sub-Saharan Africa 819 24,242 35 882.6 1,077 1,596.5 1,949 5.1 2.5 High income 1,069 35,299 32 42,415.0 39,687 40,253.8 37,665 0.5 ­0.2 Euro area 326 2,583 130 12,663.5 38,839 10,822.7 33,193 0.7 0.2 a. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. b. Private analysts estimate that GDP volume growth has been significantly lower than official reports have shown since the last quarter of 2008. c. Estimated to be upper middle income ($3,856­$11,905). d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Estimated to be low income ($975 or less). f. Estimated to be lower middle income ($976­$3,855). g. Included in the aggregates for upper middle-income economies based on earlier data. h. Excludes Transnistria. i. Includes Former Spanish Sahara. j. Included in the aggregates for lower middle-income economies based on earlier data. k. Estimated to be high income ($11,906 or more). l. Provisional estimate. m. Included in the aggregates for low-income economies based on earlier data. n. Covers mainland Tanzania only. 34 2010 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data Definitions Population, land area, income, and output are basic conventional price indexes allow comparison of real · Population is based on the de facto definition of measures of the size of an economy. They also values over time. population, which counts all residents regardless of provide a broad indication of actual and potential PPP rates are calculated by simultaneously compar- legal status or citizenship--except for refugees not resources. Population, land area, income (as mea- ing the prices of similar goods and services among a permanently settled in the country of asylum, who sured by gross national income, GNI), and output large number of countries. In the most recent round are generally considered part of the population of (as measured by gross domestic product, GDP) are of price surveys conducted by the International Com- their country of origin. The values shown are midyear therefore used throughout World Development Indica- parison Program (ICP), 146 countries and territories estimates. See also table 2.1. · Surface area is tors to normalize other indicators. participated in the data collection, including China a country's total area, including areas under inland Population estimates are generally based on for the first time, India for the first time since 1985, bodies of water and some coastal waterways. · Pop- extrapolations from the most recent national cen- and almost all African countries. The PPP conver- ulation density is midyear population divided by land sus. For further discussion of the measurement of sion factors presented in the table come from three area in square kilometers. · Gross national income population and population growth, see About the data sources. For 45 high- and upper middle-income (GNI) is the sum of value added by all resident pro- for table 2.1. countries conversion factors are provided by Euro- ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland stat and the Organisation for Economic Co-operation included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- and Development (OECD), with PPP estimates for of primary income (compensation of employees and face area thus differs from land area, which excludes 34 European countries incorporating new price data property income) from abroad. Data are in current bodies of water, and from gross area, which may collected since 2005. For the remaining 2005 ICP U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- countries the PPP estimates are extrapolated from method (see Statistical methods). · GNI per capita is ticularly important for understanding an economy's the 2005 ICP benchmark results, which account for GNI divided by midyear population. GNI per capita in agricultural capacity and the environmental effects relative price changes between each economy and U.S. dollars is converted using the World Bank Atlas of human activity. (For measures of land area and the United States. For countries that did not partici- method. · Purchasing power parity (PPP) GNI is GNI data on rural population density, land use, and agri- pate in the 2005 ICP round, the PPP estimates are converted to international dollars using PPP rates. An cultural productivity, see tables 3.1­3.3.) Innova- imputed using a statistical model. international dollar has the same purchasing power tions in satellite mapping and computer databases More information on the results of the 2005 ICP over GNI that a U.S. dollar has in the United States. have resulted in more precise measurements of land is available at www.worldbank.org/data/icp. · Gross domestic product (GDP) is the sum of value and water areas. All 210 economies shown in World Development added by all resident producers plus any product GNI measures total domestic and foreign value Indicators are ranked by size, including those that taxes (less subsidies) not included in the valuation added claimed by residents. GNI comprises GDP appear in table 1.6. The ranks are shown only in of output. Growth is calculated from constant price plus net receipts of primary income (compensation table 1.1. No rank is shown for economies for which GDP data in local currency. · GDP per capita is GDP of employees and property income) from nonresident numerical estimates of GNI per capita are not pub- divided by midyear population. sources. The World Bank uses GNI per capita in U.S. lished. Economies with missing data are included in dollars to classify countries for analytical purposes the ranking at their approximate level, so that the rel- and to determine borrowing eligibility. For definitions ative order of other economies remains consistent. of the income groups in World Development Indica- tors, see Users guide. For discussion of the useful- ness of national income and output as measures of productivity or welfare, see About the data for tables Data sources 4.1 and 4.2. When calculating GNI in U.S. dollars from GNI Population estimates are prepared by World Bank reported in national currencies, the World Bank fol- staff from a variety of sources (see Data sources lows the World Bank Atlas conversion method, using for table 2.1). Data on surface and land area are a three-year average of exchange rates to smooth from the Food and Agriculture Organization (see the effects of transitory fluctuations in exchange Data sources for table 3.1). GNI, GNI per capita, rates. (For further discussion of the World Bank Atlas GDP growth, and GDP per capita growth are esti- method, see Statistical methods.) mated by World Bank staff based on national Because exchange rates do not always refl ect accounts data collected by World Bank staff during differences in price levels between countries, economic missions or reported by national statis- the table also converts GNI and GNI per capita tical offices to other international organizations estimates into international dollars using purchas- such as the OECD. PPP conversion factors are ing power parity (PPP) rates. PPP rates provide estimates by Eurostat/OECD and by World Bank a standard measure allowing comparison of real staff based on data collected by the ICP. levels of expenditure between countries, just as 2010 World Development Indicators 35 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary school mortality rate % 1995­ % of total employment under age 5 % % per 1,000 2008a,b 1990 2008 1990 2000­08a 1991 2008c 1991 2008c 1990 2008 Afghanistan .. .. .. .. 32.9 .. .. 54 58 260 257 Albania 7.8 .. .. .. 6.6 .. .. 96 .. 46 14 Algeria 6.9 .. .. .. 11.1 80 114 83 .. 64 41 Angola 2.0 .. .. .. 27.5 34 .. .. .. 260 220 Argentina 3.6d .. 20e,f .. 2.3 .. 100 .. 104 29 16 Armenia 8.6 .. .. .. 4.2 .. 98 .. 104 56 23 Australia .. 10 9 .. .. .. .. 101 98 9 6 Austria 8.6 .. 9 .. .. .. 102 95 97 9 4 Azerbaijan 13.3 .. 53 .. 8.4 .. 121 100 98 98 36 Bangladesh 9.4 .. .. 64.3 41.3 .. 58 .. 106 149 54 Belarus 8.8 .. .. .. 1.3 94 96 .. 101 24 13 Belgium 8.5 16 10 .. .. 79 86 101 98 10 5 Benin 6.9 .. .. .. 20.2 22 65 50 .. 184 121 Bolivia 2.7 40e .. 8.9 5.9 71 98 .. 99 122 54 Bosnia and Herzegovina 6.7 .. .. .. 1.6 .. .. .. 100 23 15 Botswana 3.1 .. .. .. 10.7 90 99 109 100 50 31 Brazil 3.0 29e 27 .. 2.2 .. .. .. 102 56 22 Bulgaria 8.7 .. 9 .. 1.6 101 98 99 97 18 11 Burkina Faso 7.0 .. .. .. 37.4 20 38 62 85f 201 169 Burundi 9.0 .. .. .. 38.9 46 45 82 91 189 168 Cambodia 6.5 .. .. .. 28.8 .. 79 73 90 117 90 Cameroon 5.6 .. .. 18.0 16.6 53 73 83 84 149 131 Canada 7.2 .. 10e .. .. .. 96 99 99 8 6 Central African Republic 5.2 .. .. .. 21.8 28 33 61 .. 178 173 Chad 6.3 .. .. .. 33.9 18 31 42 64 201 209 Chile 4.1 .. 24 .. 0.5 .. 96 100 99 22 9 China 5.7 .. .. .. 6.8 107 99 86 103 46 21 Hong Kong SAR, China 5.3 .. 7 .. .. 102 .. 103 100 .. .. Colombia 2.3 28e 46 .. 5.1 73 110 108 104 35 20 Congo, Dem. Rep. 5.5 .. .. .. 28.2 48 53 .. 76 199 199 Congo, Rep. 5.0 .. .. .. 11.8 54 73 86 .. 104 127 Costa Rica 4.4 25 20 .. .. 79 93 101 102 22 11 Côte d'Ivoire 5.0 .. .. .. 16.7 42 48 65 .. 150 114 Croatia 8.8 .. 16e .. .. .. 102 .. 102 13 6 Cuba .. .. .. .. .. 99 90 106 99 14 6 Czech Republic 10.2 .. 13 .. 2.1 .. 94 98 101 12 4 Denmark 8.3 7 5 .. .. 98 101 101 102 9 4 Dominican Republic 4.4 39 42 8.4 3.4 .. 91 .. 103 62 33 Ecuador 3.4 36e 34e .. 6.2 .. 106 .. 100 53 25 Egypt, Arab Rep. 9.0 .. 25 11.6 6.8 .. 95 81 .. 90 23 El Salvador 4.3 .. 36 11.1 6.1 65 89 101 98 62 18 Eritrea .. .. .. .. 34.5 .. 47 .. 77 150 58 Estonia 6.8 2 6 .. .. .. 100 103 101 18 6 Ethiopia 9.3 .. 52e .. 34.6 .. 52 68 85 210 109 Finland 9.6 .. 9 .. .. 97 98 109 102 7 3 France 7.2 11 6 .. .. 106 .. 102 100 9 4 Gabon 6.1 .. .. .. 8.8 .. .. .. .. 92 77 Gambia, The 4.8 .. .. .. 15.8 .. 79 64 102 153 106 Georgia 5.4 .. 62 .. 2.3 .. 100 98 96 47 30 Germany 8.5 .. 7 .. 1.1 .. 105 99 98 9 4 Ghana 5.2 .. .. 24.1 13.9 64 79 79 96 118 76 Greece 6.7 .. 27 .. .. .. 101 99 97 11 4 Guatemala 3.4 .. .. .. 17.7 .. 80 .. 94 77 35 Guinea 5.8 .. .. .. 22.5 17 55 45 77 231 146 Guinea-Bissau 7.2 .. .. .. 17.4 .. .. .. .. 240 195 Haiti 2.5 .. .. .. 18.9 27 .. 94 .. 151 72 Honduras 2.5 49e .. .. 8.6 64 90 106 107 55 31 36 2010 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary school mortality rate % 1995­ % of total employment under age 5 % % per 1,000 2008a,b 1990 2008 1990 2000­08a 1991 2008c 1991 2008c 1990 2008 Hungary 8.6 7 7 2.3 .. 94 95 100 99 17 7 India 8.1 .. .. .. 43.5 63 94 70 92 116 69 Indonesia 7.4 .. 63 31.0 19.6 93 108 93 98 86 41 Iran, Islamic Rep. 6.4 .. 43 .. .. 88 117 85 116 73 32 Iraq .. .. .. .. 7.1 .. .. 78 .. 53 44 Ireland 7.4 20 12 .. .. .. 97 104 103 9 4 Israel 5.7 .. 7 .. .. .. 102 105 101 11 5 Italy 6.5 27 19 .. .. 98 101 100 99 10 4 Jamaica 5.2 42 35 .. 2.2 94 89 101 100 33 31 Japan .. 19 11 .. .. 102 .. 101 100 6 4 Jordan 7.2 .. .. 4.8 3.6 95 99 101 103 38 20 Kazakhstan 8.7 .. .. .. 4.9 .. 105f 102 98f 60 30 Kenya 4.7 .. .. .. 16.5 .. 80 94 96 105 128 Korea, Dem. Rep. .. .. .. .. 17.8 .. .. .. .. 55 55 Korea, Rep. 7.9 .. 25 .. .. 98 99 99 97 9 5 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. 98 97 100 15 11 Kyrgyz Republic 8.8 .. 47 .. 2.7 .. 92 .. 100 75 38 Lao PDR 8.5 .. .. .. 31.6 45 75 76 87 157 61 Latvia 6.7 .. 7 .. .. .. 95 101 100 17 9 Lebanon .. .. .. .. 4.2 .. 87 .. 103 40 13 Lesotho 3.0 .. .. .. 16.6 59 73 124 105 101 79 Liberia 6.4 .. .. .. 20.4 .. 58 .. 86 219 145 Libya .. .. .. .. 5.6 .. .. .. 105 38 17 Lithuania 6.8 .. 9 .. .. .. 96 .. 100 16 7 Macedonia, FYR 5.2 .. 22 .. 1.8 .. 92 .. 98 36 11 Madagascar 6.2 .. .. 35.5 36.8 36 71 98 97 167 106 Malawi 7.0 .. .. 24.4 15.5 28 54 81 99 225 100 Malaysia 6.4 29 22 .. .. 91 96 101 .. 18 6 Mali 6.5 .. .. .. 27.9 12 57 58 78 250 194 Mauritania 6.2 .. .. .. 23.2 33 64 71 104 129 118 Mauritius .. 12 17 .. .. 115 90 102 100 24 17 Mexico 3.8 26 30 13.9 3.4 88 104 97 101 45 17 Moldova 6.7 .. 32 .. 3.2 .. 84 105 102 37 17 Mongolia 7.1 .. .. .. 5.3 .. 93 109 104 98 41 Morocco 6.5 .. 51 8.1 9.9 48 81 70 88 88 36 Mozambique 5.4 .. .. .. 21.2 26 59 71 87 249 130 Myanmar .. .. .. .. 29.6 .. 97 95 99 120 98 Namibia .. .. .. 21.5 17.5 .. 81 106 104 72 42 Nepal 6.1 .. .. .. 38.8 50 76 59 93 142 51 Netherlands 7.6 8 9 .. .. .. .. 97 98 8 5 New Zealand 6.4 13 12 .. .. 103 .. 100 102 11 6 Nicaragua 3.8 .. 45 .. 4.3 42 75 109 102 68 27 Niger 5.9 .. .. 41.0 39.9 17 40 f 53 74 305 167 Nigeria 5.1 .. .. 35.1 27.2 .. .. 78 85 230 186 Norway 9.6 .. 6 .. .. 100 96 102 99 9 4 Oman .. .. .. .. .. 65 80 89 99 31 12 Pakistan 9.1 .. 62 39.0 31.3 .. 60 .. 80 130 89 Panama 2.5 34 28 .. .. .. 102 .. 101 31 23 Papua New Guinea 4.5 .. .. .. 18.1 46 .. 80 .. 91 69 Paraguay 3.4 23e 47 2.8 .. 68 95 98 99 42 28 Peru 3.6 36e 40e 8.8 5.4 .. 103 96 101 81 24 Philippines 5.6 .. 45 .. 26.2 88 92 100 102 61 32 Poland 7.3 .. 19 .. .. 98 96 100 99 17 7 Portugal 5.8 25 19 .. .. 95 .. 103 101 15 4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar 3.9 .. .. .. .. 71 115 98 120 20 10 2010 World Development Indicators 37 1.2 Millennium Development Goals: eradicating poverty and saving lives Eradicate extreme poverty and hunger Achieve universal Promote gender Reduce primary education equality child mortality Share of poorest quintile Vulnerable Prevalence of in national employment malnutrition Ratio of girls to boys consumption Unpaid family workers and Underweight Primary enrollments in primary Under-fi ve or income own-account workers % of children completion rate and secondary school mortality rate % 1995­ % of total employment under age 5 % % per 1,000 2008a,b 1990 2008 1990 2000­08a 1991 2008c 1991 2008c 1990 2008 Romania 7.9 27 31 .. 3.5 100 120 99 99 32 14 Russian Federation 5.6 1 6 .. .. .. 94 104 98 27 13 Rwanda 5.4 .. .. 24.3 18.0 35 54 95 100 174 112 Saudi Arabia .. .. .. .. 5.3 55 95 84 91 43 21 Senegal 6.2 83 .. .. 14.5 43 56 68 96 149 108 Serbia 9.1 .. 23 .. 1.8 .. 104 .. 102 29 7 Sierra Leone 6.1 .. .. .. 28.3 .. 88 64 84 278 194 Singapore 5.0 8 10 .. 3.3 .. .. .. .. 7 3 Slovak Republic 8.8 .. 11 .. .. .. 94 .. 100 15 8 Slovenia 8.2 .. 11 .. .. .. .. .. 99 10 4 Somalia .. .. .. .. 32.8 .. .. .. .. 200 200 South Africa 3.1 .. 3 .. .. 76 86 104 100 56 67 Spain 7.0 22 12 .. .. 103 98 104 103 9 4 Sri Lanka 6.8 .. 41e .. 21.1 101 105 102 .. 29 15 Sudan .. .. .. .. 31.7 40 57f 78 89 f 124 109 Swaziland 4.5 .. .. .. 6.1 61 72 98 92 84 83 Sweden 9.1 .. 7 .. .. 96 95 102 99 7 3 Switzerland 7.6 9 10 .. .. 53 93 97 97 8 5 Syrian Arab Republic .. .. .. .. 10.0 89 114 85 97 37 16 Tajikistan 7.8 .. .. .. 14.9 .. 98 .. 91 117 64 Tanzania 7.3 .. 88e 25.1 16.7 63 83 97 .. 157 104 Thailand 6.1 70 53 .. 7.0 .. .. .. .. 32 14 Timor-Leste 8.9 .. .. .. 40.6 .. 80 .. .. 184 93 Togo 5.4 .. .. 21.2 22.3 35 61 59 75 150 98 Trinidad and Tobago .. 22 .. .. 4.4 102 92 101 101 34 35 Tunisia 5.9 .. .. 8.5 3.3 74 102 86 103 50 21 Turkey 5.4 .. 35 .. 3.5 90 99 81 89 84 22 Turkmenistan 6.0 .. .. .. .. .. .. .. .. 99 48 Uganda 6.1 .. .. 19.7 16.4 .. 56 82 99 186 135 Ukraine 9.4 .. .. .. 4.1 94 99 .. 99 21 16 United Arab Emirates .. .. .. .. .. 103 105 104 101 17 8 United Kingdom 6.1 10 11 .. .. .. .. 102 102 9 6 United States 5.4 .. .. .. 1.3 .. 96 100 100 11 8 Uruguay 4.3 .. 25 .. 6.0 94 104 .. 98 24 14 Uzbekistan 7.1 .. .. .. 4.4 .. 96 94 98 74 38 Venezuela, RB 4.9 .. 30 .. .. 79 95 105 102 32 18 Vietnam 7.1 .. .. .. 20.2 .. .. .. .. 56 14 West Bank and Gaza .. .. 36 .. 2.2 .. 83 .. 104 38 27 Yemen, Rep. 7.2 .. .. .. 43.1 .. 61 .. .. 127 69 Zambia 3.6 65 .. 21.2 14.9 .. 93 .. 95 172 148 Zimbabwe 4.6 .. .. 8.0 14.0 97 .. 92 97 79 96 World .. w .. w 22.4 w 79 w 89 w 87 w 96 w 92 w 67 w Low income .. .. .. 27.5 44 66 80 91 160 118 Middle income .. .. .. 22.2 83 94 85 97 85 57 Lower middle income .. .. .. 25.1 82 92 81 96 93 64 Upper middle income .. 24 .. 3.8 88 100 98 100 47 23 Low & middle income .. .. .. 23.5 78 88 84 96 101 73 East Asia & Pacific .. .. .. 11.9 101 100 89 102 55 29 Europe & Central Asia .. 19 .. .. 93 98 98 97 50 22 Latin America & Carib. .. 31 .. 4.5 84 101 99 102 53 23 Middle East & N. Africa .. 37 .. 12.2 .. 94 80 96 76 34 South Asia .. .. .. 41.1 62 79 69 91 125 76 Sub-Saharan Africa .. .. .. 25.3 51 62 82 88 185 144 High income .. .. .. .. .. .. 100 99 12 7 Euro area .. 12 .. .. 101 .. .. .. 9 4 a. Data are for the most recent year available. b. See table 2.9 for survey year and whether share is based on income or consumption expenditure. c. Provisional data. d. Urban data. e. Limited coverage. f. Data are for 2009. 38 2010 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives About the data Definitions Tables 1.2­1.4 present indicators for 17 of the 21 nutrients, and undernourished mothers who give · Share of poorest quintile in national consump- targets specified by the Millennium Development birth to underweight children. tion or income is the share of the poorest 20 per- Goals. Each of the eight goals includes one or more Progress toward universal primary education is cent of the population in consumption or, in some targets, and each target has several associated measured by the primary completion rate. Because cases, income. · Vulnerable employment is the sum indicators for monitoring progress toward the target. many school systems do not record school comple- of unpaid family workers and own-account workers Most of the targets are set as a value of a specific tion on a consistent basis, it is estimated from the as a percentage of total employment. · Prevalence indicator to be attained by a certain date. In some gross enrollment rate in the final grade of primary of malnutrition is the percentage of children under cases the target value is set relative to a level in school, adjusted for repetition. Official enrollments age 5 whose weight for age is more than two standard 1990. In others it is set at an absolute level. Some sometimes differ significantly from attendance, and deviations below the median for the international ref- of the targets for goals 7 and 8 have not yet been even school systems with high average enrollment erence population ages 0­59 months. The data are quantified. ratios may have poor completion rates. based on the new international child growth stan- The indicators in this table relate to goals 1­4. Eliminating gender disparities in education would dards for infants and young children, called the Child Goal 1 has three targets between 1990 and 2015: help increase the status and capabilities of women. Growth Standards, released in 2006 by the World to halve the proportion of people whose income is The ratio of female to male enrollments in primary Health Organization. · Primary completion rate is less than $1.25 a day, to achieve full and productive and secondary school provides an imperfect measure the percentage of students completing the last year employment and decent work for all, and to halve the of the relative accessibility of schooling for girls. of primary school. It is calculated as the total num- proportion of people who suffer from hunger. Esti- The targets for reducing under-five mortality rates ber of students in the last grade of primary school, mates of poverty rates are in tables 2.7 and 2.8. are among the most challenging. Under-five mortal- minus the number of repeaters in that grade, divided The indicator shown here, the share of the poorest ity rates are harmonized estimates produced by a by the total number of children of official graduation quintile in national consumption or income, is a dis- weighted least squares regression model and are age. · Ratio of girls to boys enrollments in primary tributional measure. Countries with more unequal available at regular intervals for most countries. and secondary school is the ratio of the female to distributions of consumption (or income) have a Most of the 60 indicators relating to the Millennium male gross enrollment rate in primary and secondary higher rate of poverty for a given average income. Development Goals can be found in World Develop- school. · Under-five mortality rate is the probability Vulnerable employment measures the portion of the ment Indicators. Table 1.2a shows where to find the that a newborn baby will die before reaching age 5, labor force that receives the lowest wages and least indicators for the first four goals. For more informa- if subject to current age-specific mortality rates. The security in employment. No single indicator captures tion about data collection methods and limitations, probability is expressed as a rate per 1,000. the concept of suffering from hunger. Child malnutri- see About the data for the tables listed there. For tion is a symptom of inadequate food supply, lack information about the indicators for goals 5­8, see of essential nutrients, illnesses that deplete these About the data for tables 1.3 and 1.4. Location of indicators for Millennium Development Goals 1­4 1.2a Goal 1. Eradicate extreme poverty and hunger Table 1.1 Proportion of population below $1.25 a day 2.8 1.2 Poverty gap ratio 2.7, 2.8 1.3 Share of poorest quintile in national consumption 1.2, 2.9 1.4 Growth rate of GDP per person employed 2.4 1.5 Employment to population ratio 2.4 1.6 Proportion of employed people living below $1 per day -- 1.7 Proportion of own-account and unpaid family workers in total employment 1.2, 2.4 1.8 Prevalence of underweight in children under age five 1.2, 2.20 1.9 Proportion of population below minimum level of dietary energy consumption 2.20 Goal 2. Achieve universal primary education Data sources 2.1 Net enrollment ratio in primary education 2.12 The indicators here and throughout this book have 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.13 2.3 Literacy rate of 15- to 24-year-olds 2.14 been compiled by World Bank staff from primary Goal 3. Promote gender equality and empower women and secondary sources. Efforts have been made 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.12* to harmonize the data series used to compile this 3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3* table with those published on the United Nations 3.3 Proportion of seats held by women in national parliament 1.5 Millennium Development Goals Web site (www. Goal 4. Reduce child mortality 4.1 Under-five mortality rate 1.2, 2.22 un.org/millenniumgoals), but some differences in 4.2 Infant mortality rate 2.22 timing, sources, and definitions remain. For more 4.3 Proportion of one-year-old children immunized against measles 2.18 information see the data sources for the indica- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in table 1.2a. 2010 World Development Indicators 39 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15­49 ages 15­49 people metric tons % % of population peoplea 2005 1990 2003­08b 2007 2008 1990 2006 2008 1990 2006 2008 Afghanistan 1,800 .. 15 .. 189 0.1 0.0 0.7 .. 30 1.7 Albania 92 .. 60 .. 16 2.3 1.4 1.5 .. 97 23.9 Algeria 180 47 61 0.1 58 3.1 4.0 2.1 88 94 11.9 Angola 1,400 .. .. 2.1 292 0.4 0.6 1.4 26 50 3.1 Argentina 77 .. .. 0.5 30 3.5 4.4 1.9 81 91 28.1 Armenia 76 .. 53 0.1 73 1.1 1.4 0.9 .. 91 6.2 Australia 4 .. .. 0.2 7 17.2 18.0 4.7 100 100 70.8 Austria 4 .. .. 0.2 0 7.9 8.7 1.9 100 100 71.2 Azerbaijan 82 .. 51 0.2 110 6.0 4.1 0.8 .. 80 28.2 Bangladesh 570 40 56 .. 225 0.1 0.3 1.9 26 36 0.3 Belarus 18 .. 73 0.2 43 9.6 7.1 0.7 .. 93 32.1 Belgium 8 78 .. 0.2 9 10.8 10.2 1.3 .. .. 68.1 Benin 840 .. 17 1.2 92 0.1 0.4 1.5 12 30 1.8 Bolivia 290 30 61 0.2 144 0.8 1.2 0.8 33 43 10.8 Bosnia and Herzegovina 3 .. 36 <0.1 51 1.2 7.3 13.1 .. 95 34.7 Botswana 380 33 .. 23.9 712 1.6 2.6 0.5 38 47 6.2 Brazil 110 59 81 0.6 46 1.4 1.9 1.3 71 77 37.5 Bulgaria 11 .. .. .. 43 8.8 6.2 1.1 99 99 34.7 Burkina Faso 700 .. 17 1.6 220 0.1 0.1 1.0 5 13 0.9 Burundi 1,100 .. 9 2.0 357 0.1 0.0 1.5 44 41 0.8 Cambodia 540 .. 40 0.8 490 0.0 0.3 29.8 8 28 0.5 Cameroon 1,000 16 29 5.1 187 0.1 0.2 5.4 39 51 3.8 Canada 7 .. .. 0.4 5 16.2 16.7 1.8 100 100 75.3 Central African Republic 980 .. 19 6.3 336 0.1 0.1 0.6 11 31 0.4 Chad 1,500 .. 3 3.5 291 0.0 0.0 1.0 5 9 1.2 Chile 16 56 58 0.3 11 2.7 3.7 2.4 84 94 32.5 China 45 85 85 0.1 97 2.1 4.7 2.4 48 65 22.5 Hong Kong SAR, China .. 86 .. .. 91 4.8 5.7 13.2 .. .. 67.0 Colombia 130 66 78 0.6 36 1.7 1.5 1.2 68 78 38.5 Congo, Dem. Rep. 1,100 8 21 .. 382 0.1 0.0 2.5 15 31 .. Congo, Rep. 740 .. 44 3.5 393 0.5 0.4 1.0 .. 20 4.3 Costa Rica 30 .. 96 0.4 11 1.0 1.8 1.9 94 96 32.3 Côte d'Ivoire 810 .. 13 3.9 410 0.5 0.3 3.9 20 24 3.2 Croatia 7 .. .. <0.1 25 3.8 5.3 1.8 99 99 50.5 Cuba 45 .. 77 0.1 6 3.1 2.6 4.2 98 98 12.9 Czech Republic 4 78 .. .. 9 12.7 11.2 1.5 100 99 57.8 Denmark 3 78 .. 0.2 7 9.8 9.9 1.6 100 100 83.3 Dominican Republic 150 56 73 1.1 73 1.3 2.1 2.1 68 79 21.6 Ecuador 210 53 73 0.3 72 1.6 2.4 10.4 71 84 28.8 Egypt, Arab Rep. 130 47 60 .. 20 1.3 2.1 4.1 50 66 16.6 El Salvador 170 47 73 0.8 32 0.5 1.1 1.8 73 86 10.6 Eritrea 450 .. .. 1.3 97 .. 0.1 15.0 3 5 4.1 Estonia 25 .. .. 1.3 34 16.3 13.0 0.6 95 95 66.2 Ethiopia 720 4 15 2.1 368 0.1 0.1 1.3 4 11 0.4 Finland 7 77 .. 0.1 7 10.2 12.7 1.3 100 100 82.5 France 8 81 .. 0.4 6 7.0 6.2 2.5 .. .. 67.9 Gabon 520 .. .. 5.9 452 6.6 1.5 2.1 .. 36 6.2 Gambia, The 690 12 .. 0.9 263 0.2 0.2 2.2 .. 52 6.9 Georgia 66 .. 47 0.1 107 2.9 1.2 1.0 94 93 23.8 Germany 4 75 .. 0.1 5 12.0 9.8 2.2 100 100 75.5 Ghana 560 13 24 1.9 202 0.3 0.4 3.7 6 10 4.3 Greece 3 .. .. 0.2 6 7.2 8.6 2.1 97 98 43.1 Guatemala 290 .. .. 0.8 63 0.6 0.9 2.4 70 84 14.3 Guinea 910 .. 9 1.6 302 0.2 0.1 2.2 13 19 0.9 Guinea-Bissau 1,100 .. 10 1.8 224 0.2 0.2 2.4 .. 33 2.4 Haiti 670 10 32 2.2 246 0.1 0.2 2.3 29 19 10.1 Honduras 280 47 65 0.7 64 0.5 1.0 3.5 45 66 13.1 40 2010 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15­49 ages 15­49 people metric tons % % of population peoplea 2005 1990 2003­08b 2007 2008 1990 2006 2008 1990 2006 2008 Hungary 6 .. .. 0.1 16 6.0 5.7 1.8 100 100 58.5 India 450 43 56 0.3 168 0.8 1.4 3.3 14 28 4.5 Indonesia 420 50 61 0.2 189 0.8 1.5 3.4 51 52 7.9 Iran, Islamic Rep. 140 49 79 0.2 20 4.2 6.7 1.0 83 .. 32.0 Iraq 300 14 50 .. 64 2.8 3.2 11.0 .. 76 1.0 Ireland 1 60 .. 0.2 9 8.8 10.3 1.8 .. .. 62.7 Israel 4 68 .. 0.1 6 7.2 10.0 4.3 .. .. 47.9 Italy 3 .. .. 0.4 7 7.5 8.0 2.2 .. .. 41.8 Jamaica 170 55 .. 1.6 7 3.3 4.6 7.7 83 83 57.3 Japan 6 58 .. .. 22 9.5 10.1 4.9 100 100 75.2 Jordan 62 40 57 .. 6 3.3 3.7 3.4 .. 85 27.0 Kazakhstan 140 .. 51 0.1 175 15.9 12.6 1.1 97 97 10.9 Kenya 560 27 39 .. 328 0.2 0.3 3.9 39 42 8.7 Korea, Dem. Rep. 370 62 .. .. 344 12.1 3.6 1.3 .. .. 0.0 Korea, Rep. 14 79 .. <0.1 88 5.6 9.8 1.7 .. .. 75.8 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 4 .. .. .. 34 19.2 33.3 6.3 .. .. 36.7 Kyrgyz Republic 150 .. 48 0.1 159 2.4 1.1 0.8 .. 93 16.1 Lao PDR 660 .. 38 0.2 150 0.1 0.2 1.2 .. 48 8.5 Latvia 10 .. .. 0.8 50 5.1 3.3 1.4 .. 78 60.4 Lebanon 150 .. 58 0.1 14 3.1 3.8 1.2 .. .. 22.5 Lesotho 960 23 37 23.2 635 .. .. 0.6 .. 36 3.6 Liberia 1,200 .. 11 1.7 283 0.2 0.2 3.8 40 32 0.5 Libya 97 .. .. .. 17 9.2 9.2 1.6 97 97 5.1 Lithuania 11 .. .. 0.1 71 6.0 4.2 0.9 .. .. 54.4 Macedonia, FYR 10 .. 14 <0.1 24 5.6 5.3 0.9 .. 89 41.5 Madagascar 510 17 27 0.1 256 0.1 0.2 6.4 8 12 1.7 Malawi 1,100 13 41 11.9 324 0.1 0.1 3.3 46 60 2.1 Malaysia 62 50 .. 0.5 102 3.1 7.2 6.9 .. 94 55.8 Mali 970 .. 8 1.5 322 0.0 0.0 1.0 35 45 1.6 Mauritania 820 3 9 0.8 324 1.3 0.5 2.9 20 24 1.9 Mauritius 15 75 .. 1.7 22 1.4 3.1 24.3 94 94 22.2 Mexico 60 .. 71 0.3 19 4.6 4.2 3.2 56 81 22.2 Moldova 22 .. 68 0.4 175 4.8 2.1 1.3 .. 79 23.4 Mongolia 46 .. 66 0.1 205 4.5 3.7 1.1 .. 50 12.5 Morocco 240 42 63 0.1 116 0.9 1.5 1.9 52 72 33.0 Mozambique 520 .. 16 12.5 420 0.1 0.1 2.9 20 31 1.6 Myanmar 380 17 34 0.7 404 0.1 0.2 2.7 23 82 0.2 Namibia 210 29 55 15.3 747 0.0 1.4 2.1 26 35 5.3 Nepal 830 23 48 0.5 163 0.0 0.1 1.1 9 27 1.7 Netherlands 6 76 .. 0.2 7 11.2 10.3 1.3 100 100 87.0 New Zealand 9 .. .. 0.1 8 6.6 7.3 5.1 .. .. 71.4 Nicaragua 170 .. 72 0.2 46 0.6 0.8 1.3 42 48 3.3 Niger 1,800 4 11 0.8 178 0.1 0.1 1.0 3 7 0.5 Nigeria 1,100 6 15 3.1 303 0.5 0.7 4.3 26 30 15.9 Norway 7 74 .. 0.1 6 7.4 8.6 1.5 .. .. 82.5 Oman 64 9 .. .. 14 5.6 15.5 4.2 85 .. 20.0 Pakistan 320 15 30 0.1 231 0.6 0.9 1.7 33 58 11.1 Panama 130 .. .. 1.0 47 1.3 2.0 2.9 .. 74 27.5 Papua New Guinea 470 .. 32 1.5 250 0.5 0.7 3.6 44 45 1.8 Paraguay 150 48 79 0.6 47 0.5 0.7 0.5 60 70 14.3 Peru 240 59 71 0.5 119 1.0 1.4 2.8 55 72 24.7 Philippines 230 36 51 .. 285 0.7 0.8 6.6 58 78 6.2 Poland 8 49 .. 0.1 25 9.1 8.3 1.2 .. .. 49.0 Portugal 11 .. .. 0.5 30 4.5 5.7 2.8 92 99 42.1 Puerto Rico 18 .. .. .. 3 .. .. 3.6 .. .. 25.3 Qatar 12 .. .. .. 55 25.2 46.1 .. 100 100 34.0 2010 World Development Indicators 41 1.3 Millennium Development Goals: protecting our common environment Improve maternal Combat HIV/AIDS Ensure environmental Develop health and other diseases sustainability a global partnership for development Maternal Proportion mortality ratio Contraceptive HIV of species Modeled prevalence prevalence Incidence threatened estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved Internet users per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities per 100 live births ages 15­49 ages 15­49 people metric tons % % of population peoplea 2005 1990 2003­08b 2007 2008 1990 2006 2008 1990 2006 2008 Romania 24 .. 70 0.1 134 6.8 4.6 1.6 72 72 28.8 Russian Federation 28 34 .. 1.1 107 13.9 11.0 1.3 87 87 31.9 Rwanda 1,300 21 36 2.8 387 0.1 0.1 1.6 29 23 3.1 Saudi Arabia 18 .. .. .. 19 13.2 16.1 3.8 91 99 31.5 Senegal 980 .. 12 1.0 277 0.4 0.4 2.2 26 28 8.4 Serbia 14 c .. 41 0.1 18 .. .. .. .. 92 44.9 Sierra Leone 2,100 .. 8 1.7 608 0.1 0.2 3.2 .. 11 0.3 Singapore 14 65 .. 0.2 39 15.4 12.8 9.7 100 100 69.6 Slovak Republic 6 74 .. <0.1 12 8.4 6.9 1.1 100 100 66.0 Slovenia 6 .. .. <0.1 12 6.2 7.6 2.1 .. .. 55.7 Somalia 1,400 1 15 0.5 388 0.0 0.0 3.2 .. 23 1.1 South Africa 400 57 60 18.1 960 9.5 8.7 1.6 55 59 8.6 Spain 4 .. .. 0.5 17 5.9 8.0 3.8 100 100 55.4 Sri Lanka 58 .. 68 .. 66 0.2 0.6 14.0 71 86 5.8 Sudan 450 9 8 1.4 119 0.2 0.3 2.4 33 35 10.2 Swaziland 390 20 51 26.1 1,227 0.5 0.9 0.8 .. 50 6.9 Sweden 3 .. .. 0.1 6 6.0 5.6 1.4 100 100 87.7 Switzerland 5 .. .. 0.6 5 6.4 5.6 1.4 100 100 75.9 Syrian Arab Republic 130 .. 58 .. 22 2.9 3.5 2.0 81 92 17.3 Tajikistan 170 .. 37 0.3 199 3.9 1.0 0.8 .. 92 8.8 Tanzania 950 10 26 6.2 190 0.1 0.1 5.1 35 33 1.2 Thailand 110 .. 77 1.4 137 1.7 4.1 3.4 78 96 23.9 Timor-Leste 380 .. 20 .. 498 .. 0.2 .. .. 41 .. Togo 510 34 17 3.3 438 0.2 0.2 1.2 13 12 5.4 Trinidad and Tobago 45 .. 43 1.5 24 13.9 25.3 1.7 93 92 17.0 Tunisia 100 50 60 0.1 24 1.6 2.3 2.1 74 85 27.1 Turkey 44 63 73 .. 30 2.6 3.7 1.4 85 88 34.4 Turkmenistan 130 .. 48 <0.1 68 7.2 9.0 10.7 .. .. 1.5 Uganda 550 5 24 5.4 311 0.0 0.1 2.5 29 33 7.9 Ukraine 18 .. 67 1.6 102 11.7 6.8 1.1 96 93 10.5 United Arab Emirates 37 .. .. .. 6 29.3 32.8 14.1 97 97 65.2 United Kingdom 8 .. .. 0.2 12 10.0 9.4 2.8 .. .. 76.0 United States 11 71 .. 0.6 5 19.5 19.3 5.7 100 100 75.9 Uruguay 20 .. .. 0.6 22 1.3 2.1 2.6 100 100 40.2 Uzbekistan 24 .. 65 0.1 128 5.3 4.4 1.0 93 96 9.0 Venezuela, RB 57 .. .. .. 33 6.2 6.3 1.1 83 .. 25.7 Vietnam 150 53 76 0.5 200 0.3 1.3 3.5 29 65 24.2 West Bank and Gaza .. .. 50 .. 19 .. 0.8 .. .. 80 9.0 Yemen, Rep. 430 10 28 .. 88 .. 1.0 12.6 28 46 1.6 Zambia 830 15 41 15.2 468 0.3 0.2 0.7 42 52 5.5 Zimbabwe 880 43 60 15.3 762 1.6 0.9 0.9 44 46 11.4 World 400 w 57 w 61 w 0.8 w 139 w 4.3d w 4.4d w 51 w 60 w 23.9 w Low income 790 26 38 2.3 282 0.6 0.5 25 38 4.6 Middle income 320 58 66 0.6 137 1.8 3.3 47 58 17.3 Lower middle income 370 60 65 0.4 145 1.4 2.8 39 52 13.9 Upper middle income 110 52 72 1.5 106 3.8 5.2 76 82 30.6 Low & middle income 440 54 61 0.9 162 1.6 2.8 43 55 15.3 East Asia & Pacific 150 75 77 0.2 138 1.9 3.8 48 66 19.4 Europe & Central Asia 45 .. .. 0.6 87 9.4 7.3 88 89 28.6 Latin America & Carib. 130 56 75 0.5 47 2.4 2.6 68 78 28.9 Middle East & N. Africa 200 42 62 0.1 44 2.5 3.5 67 74 18.9 South Asia 500 40 53 0.3 180 0.7 1.1 18 33 4.7 Sub-Saharan Africa 900 15 23 5.0 352 0.9 0.8 26 31 6.5 High income 10 72 .. 0.3 14 12.1 12.7 99 100 69.1 Euro area 5 .. .. 0.3 8 7.5 8.4 .. .. 62.6 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. b. Data are for the most recent year available. c. Includes Montenegro. d. Includes emissions not allocated to specific countries. 42 2010 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data Definitions The Millennium Development Goals address con- between contraction of the virus and the appearance · Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- of symptoms, or malaria, which has periods of dor- who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- mancy, can be particularly difficult. The table shows nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose the estimated prevalence of HIV among adults ages are from various years and adjusted to a common a threat to people everywhere. And environmental 15­49. Prevalence among older populations can be 2005 base year. The values are modeled estimates damage in one location may affect the well-being of affected by life-prolonging treatment. The incidence of (see About the data for table 2.19). · Contraceptive plants, animals, and humans far away. The indicators tuberculosis is based on case notifications and esti- prevalence rate is the percentage of women ages in the table relate to goals 5, 6, and 7 and the targets mates of cases detected in the population. 15­49 married or in union who are practicing, or of goal 8 that address access to new technologies. Carbon dioxide emissions are the primary source whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. of greenhouse gases, which contribute to global contraception. · HIV prevalence is the percentage The target of achieving universal access to repro- warming, threatening human and natural habitats. of people ages 15­49 who are infected with HIV. ductive health has been added to goal 5 to address In recognition of the vulnerability of animal and plant · Incidence of tuberculosis is the estimated number the importance of family planning and health services species, a new target of reducing biodiversity loss of new tuberculosis cases (pulmonary, smear posi- in improving maternal health and preventing maternal has been added to goal 7. tive, and extrapulmonary). · Carbon dioxide emis- death. Women with multiple pregnancies are more Access to reliable supplies of safe drinking water and sions are those stemming from the burning of fossil likely to die in childbirth. Access to contraception is sanitary disposal of excreta are two of the most impor- an important way to limit and space births. tant means of improving human health and protecting fuels and the manufacture of cement. They include Measuring disease prevalence or incidence can be the environment. Improved sanitation facilities prevent emissions produced during consumption of solid, difficult. Most developing economies lack reporting human, animal, and insect contact with excreta. liquid, and gas fuels and gas flaring (see table 3.8). systems for monitoring diseases. Estimates are often Internet use includes narrowband and broadband · Proportion of species threatened with extinction derived from survey data and report data from sentinel Internet. Narrowband is often limited to basic applica- is the total number of threatened mammal (exclud- sites, extrapolated to the general population. Tracking tions; broadband is essential to promote e-business, ing whales and porpoises), bird, and higher native, diseases such as HIV/AIDS, which has a long latency e-learning, e-government, and e-health. vascular plant species as a percentage of the total number of known species of the same categories. Location of indicators for Millennium Development Goals 5­7 1.3a · Access to improved sanitation facilities is the percentage of the population with at least adequate Goal 5. Improve maternal health Table access to excreta disposal facilities (private or 5.1 Maternal mortality ratio 1.3, 2.19 shared, but not public) that can effectively prevent 5.2 Proportion of births attended by skilled health personnel 2.19 human, animal, and insect contact with excreta 5.3 Contraceptive prevalence rate 1.3, 2.19 (facilities do not have to include treatment to ren- 5.4 Adolescent fertility rate 2.19 5.5 Antenatal care coverage 1.5, 2.19 der sewage outflows innocuous). Improved facilities 5.6 Unmet need for family planning 2.19 range from simple but protected pit latrines to flush Goal 6. Combat HIV/AIDS, malaria, and other diseases toilets with a sewerage connection. To be effective, 6.1 HIV prevalence among population ages 15­24 1.3*, 2.21* facilities must be correctly constructed and properly 6.2 Condom use at last high-risk sex 2.21* maintained. · Internet users are people with access 6.3 Proportion of population ages 15­24 with comprehensive, correct -- to the worldwide network. knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of -- nonorphans ages 10­14 6.5 Proportion of population with advanced HIV infection with access -- to antiretroviral drugs 6.6 Incidence and death rates associated with malaria -- 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets 2.18 6.8 Proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 2.18 6.9 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.21 6.10 Proportion of tuberculosis cases detected and cured under directly observed treatment short course 2.18 Data sources Goal 7. Ensure environmental sustainability 7.1 Proportion of land area covered by forest 3.1 The indicators here and throughout this book have 7.2 Carbon dioxide emissions, total, per capita, and per $1 purchasing power been compiled by World Bank staff from primary parity GDP 3.8 7.3 Consumption of ozone-depleting substances 3.9* and secondary sources. Efforts have been made 7.4 Proportion of fish stocks within safe biological limits -- to harmonize the data series used to compile this 7.5 Proportion of total water resources used 3.5 table with those published on the United Nations 7.6 Proportion of terrestrial and marine areas protected -- Millennium Development Goals Web site (www. 7.7 Proportion of species threatened with extinction 1.3 7.8 Proportion of population using an improved drinking water source 1.3, 2.18, 3.5 un.org/millenniumgoals), but some differences in 7.9 Proportion of population using an improved sanitation facility 1.3, 2.18, 3.11 timing, sources, and definitions remain. For more 7.10 Proportion of urban population living in slums -- information see the data sources for the indica- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. tors listed in table 1.3a. 2010 World Development Indicators 43 1.4 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Net official development Least developed countries' access Support to assistance (ODA) to high-income markets agriculture by donor For basic Goods social servicesa (excluding arms) Average tariff on exports of % of % of total admitted free of tariffs least developed countries donor sector-allocable % of exports from least % GNI ODA developed countries Agricultural products Textiles Clothing % of GDP 2008 2008 2001 2007 2001 2007 2001 2007 2001 2007 2008b Australia 0.34 15.6 94.5 100.0 0.2 0.0 5.0 0.0 19.6 0.0 0.29 Canada 0.32 16.2 48.3 99.9 0.3 0.1 5.8 0.2 18.8 1.7 0.55 European Union 99.8 98.2 1.7 1.5 0.0 0.1 0.0 1.2 0.91 Austria 0.42 4.7 Belgium 0.47 17.6 Denmark 0.82 12.6 Finland 0.43 10.9 France 0.39 10.2 Germany 0.38 7.7 Greece 0.20 3.7 Ireland 0.58 27.6 Italy 0.20 8.8 Luxembourg 0.92 34.4 Netherlands 0.80 20.9 Portugal 0.27 3.0 Spain 0.43 20.5 Sweden 0.98 11.7 United Kingdom 0.43 20.9 Japan 0.18 2.4 82.2 99.6 4.9 1.3 0.2 2.6 0.0 0.1 1.06 New Zealandc 0.30 22.7 64.2 99.2 0.0 0.0 9.3 0.0 12.9 0.0 0.23 Norway 0.88 13.1 97.6 99.8 3.1 0.2 4.5 0.0 1.4 1.0 0.95 Switzerland 0.41 9.4 93.3 95.0 6.0 2.8 0.0 0.0 0.0 0.0 1.24 United States 0.18 32.1 46.2 76.8 6.3 6.0 6.8 5.6 13.9 11.3 0.67 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Initiative assistance decision completion Initiative assistance pointd pointd assistance pointd pointd assistance end-2008 end-2008 net present value net present value $ millions $ millions Afghanistan Jul. 2007 Jan. 2010 600 38 e Honduras Jul. 2000 Apr. 2005 822 1,599 Benin Jul. 2000 Mar. 2003 388 633 Liberia Mar. 2008 Floating 2,988 .. Boliviaf Feb. 2000 Jun. 2001 1,967 1,655 Madagascar Dec. 2000 Oct. 2004 1,236 1,351 Burkina Fasof,g Jul. 2000 Apr. 2002 818 638 Malawig Dec. 2000 Aug. 2006 1,388 733 Burundi Aug. 2005 Jan. 2009 964 70 h Malif Sep. 2000 Mar. 2003 797 1,097 Cameroon Oct. 2000 Apr. 2006 1,874 778 Mauritania Feb. 2000 Jun. 2002 920 465 Central African Republic Sep. 2007 Jun. 2009 638 146 Mozambiquef Apr. 2000 Sep. 2001 3,169 1,107 Chad May 2001 Floating 240 .. Nicaragua Dec. 2000 Jan. 2004 4,894 985 Congo, Dem. Rep. Jul. 2003 Floating 8,061 .. Niger g Dec. 2000 Apr.2004 953 542 Congo, Rep. Mar. 2006 Jan. 2010 1,945 201e Rwandag Dec. 2000 Apr. 2005 963 234 Côte d'Ivoire Mar. 2009 Floating 3,005 .. São Tomé & Príncipeg Dec. 2000 Mar. 2007 173 27 Ethiopiag Nov. 2001 Apr. 2004 2,726 1,512 Senegal Jun. 2000 Apr. 2004 722 1,435 Gambia, The Dec. 2000 Dec. 2007 99 191 Sierra Leone Mar. 2002 Dec. 2006 906 368 Ghana Feb. 2002 Jul. 2004 3,080 2,181 Tanzania Apr. 2000 Nov. 2001 2,997 2,124 Guinea Dec. 2000 Floating 807 .. Togo Nov. 2008 Floating 270 .. Guinea-Bissau Dec. 2000 Floating 615 .. Ugandaf Feb. 2000 May 2000 1,520 1,879 Guyanaf Nov. 2000 Dec. 2003 903 416 Zambia Dec. 2000 Apr. 2005 3,697 1,701 Haiti Nov. 2006 Jun. 2009 155 557 a. Includes primary education, basic life skills for youth, adult and early childhood education, basic health care, basic health infrastructure, basic nutrition, infectious disease control, health education, health personnel development, population policy and administrative management, reproductive health care, family planning, sexually transmitted disease control including HIV/AIDS, personnel development for population and reproductive health, basic drinking water supply and basic sanitation, and multisector aid for basic social services. b. Provisional data. c. Calculated by World Bank staff using the World Integrated Trade Solution based on the United Nations Conference on Trade and Development's Trade Analysis and Information Systems database. d. Refers to the Enhanced HIPC Initiative. e. Data are in nominal terms because data in end-2008 net present value terms are unavailable. f. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. g. Assistance includes topping up at completion point. h. Includes $15 million (in nominal terms) of committed debt relief by the International Monetary Fund, converted to end-2008 net present value terms. 44 2010 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals lines with "international peaks"). The averages in the · Net official development assistance (ODA) is grants requires an open, rule-based global economy in table include ad valorem duties and equivalents. and loans (net of repayments of principal) that meet which all countries, rich and poor, participate. Many Subsidies to agricultural producers and exporters the DAC definition of ODA and are made to countries poor countries, lacking the resources to fi nance in OECD countries are another barrier to developing on the DAC list of recipients. · ODA for basic social development, burdened by unsustainable debt, and economies' exports. Agricultural subsidies in OECD services is aid reported by DAC donors for basic edu- cation, primary health care, nutrition, population poli- unable to compete globally, need assistance from economies are estimated at $376 billion in 2008. cies and programs, reproductive health, and water and rich countries. For goal 8--develop a global part- The Debt Initiative for Heavily Indebted Poor Countries sanitation services. · Goods admitted free of tariffs nership for development--many indicators therefore (HIPCs), an important step in placing debt relief within are exports of goods (excluding arms) from least devel- monitor the actions of members of the Organisa- the framework of poverty reduction, is the first com- oped countries admitted without tariff. · Average tariff tion for Economic Co-operation and Development's prehensive approach to reducing the external debt of is the unweighted average of the effectively applied (OECD) Development Assistance Committee (DAC). the world's poorest, most heavily indebted countries. A rates for all products subject to tariffs. · Agricultural Official development assistance (ODA) has risen 1999 review led to an enhancement of the framework. products are plant and animal products, including tree in recent years as a share of donor countries' gross In 2005, to further reduce the debt of HIPCs and pro- crops but excluding timber and fish products. · Tex- national income (GNI), but the poorest economies vide resources for meeting the Millennium Development tiles and clothing are natural and synthetic fibers and need additional assistance to achieve the Millennium Goals, the Multilateral Debt Relief Initiative (MDRI), pro- fabrics and articles of clothing made from them. · Sup- Development Goals. Net ODA disbursements from posed by the Group of Eight countries, was launched. port to agriculture is the value of gross transfers from DAC donors reached $120 billion in 2008--the high- Under the MDRI four multilateral institutions--the taxpayers and consumers arising from policy mea- est level ever--representing a 16 percent increase International Development Association (IDA), Inter- sures, net of associated budgetary receipts, regard- in nominal terms from the 2007 level. national Monetary Fund (IMF), African Development less of their objectives and impacts on farm production One important action that high-income economies can Fund (AfDF), and Inter-American Development Bank and income or consumption of farm products. · HIPC decision point is the date when a heavily indebted take is to reduce barriers to exports from low- and mid- (IDB)--provide 100 percent debt relief on eligible poor country with an established track record of good dle-income economies. The European Union has begun debts due to them from countries having completed performance under adjustment programs supported to eliminate tariffs on developing economy exports of the HIPC Initiative process. Data in the table refer by the IMF and the World Bank commits to additional "everything but arms," and the United States offers to status as of February 2010 and might not show reforms and a poverty reduction strategy and starts special concessions to Sub-Saharan African exports. countries that have since reached the decision or receiving debt relief. · HIPC completion point is the However, these programs still have many restrictions. completion point. Debt relief under the HIPC Initiative date when a country successfully completes the key Average tariffs in the table refl ect high-income has reduced future debt payments by $57 billion (in structural reforms agreed on at the decision point, OECD member tariff schedules for exports of coun- end-2008 net present value terms) for 35 countries including implementing a poverty reduction strategy. tries designated least developed countries by the that have reached the decision point. And 28 coun- The country then receives full debt relief under the United Nations. Although average tariffs have been tries that have reached the completion point have HIPC Initiative without further policy conditions. · HIPC falling, averages may disguise high tariffs on specific received additional assistance of $25 billion (in end- Initiative assistance is the debt relief committed as of goods (see table 6.8 for each country's share of tariff 2008 net present value terms) under the MDRI. the decision point (assuming full participation of credi- tors). Topping-up assistance and assistance provided Location of indicators for Millennium Development Goal 8 1.4a under the original HIPC Initiative were committed in net present value terms as of the decision point and Goal8. Develop a global partnership for development Table are converted to end-2008 terms. · MDRI assistance 8.1 Net ODA as a percentage of DAC donors' gross national income 1.4, 6.14 8.2 Proportion of ODA for basic social services 1.4 is 100 percent debt relief on eligible debt from IDA, 8.3 Proportion of ODA that is untied 6.15b IMF, AfDF, and IDB, delivered in full to countries having 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI -- reached the HIPC completion point. 8.5 Proportion of ODA received in small island developing states as a percentage of GNI -- 8.6 Proportion of total developed country imports (by value, excluding arms) from least Data sources developed countries admitted free of duty 1.4 Data on ODA are from the OECD. Data on goods 8.7 Average tariffs imposed by developed countries on agricultural products and admitted free of tariffs and average tariffs are textiles and clothing from least developed countries 1.4, 6.8* 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 from the World Trade Organization, in collabora- 8.9 Proportion of ODA provided to help build trade capacity -- tion with the United Nations Conference on Trade 8.10 Number of countries reaching HIPC decision and completion points 1.4 and Development and the International Trade Cen- 8.11 Debt relief committed under new HIPC initiative 1.4 tre. These data are available at www.mdg-trade. 8.12 Debt services as a percentage of exports of goods and services 6.11* 8.13 Proportion of population with access to affordable, essential drugs on a org. Data on subsidies to agriculture are from sustainable basis -- the OECD's Producer and Consumer Support Esti- 8.14 Telephone lines per 100 people 1.3*, 5.11 mates, OECD Database 1986­2008. Data on the 8.15 Cellular subscribers per 100 people 1.3*, 5.11 HIPC Initiative and MDRI are from the World Bank's 8.16 Internet users per 100 people 5.12 Economic Policy and Debt Department. -- No data are available in the World Development Indicators database. * Table shows information on related indicators. 2010 World Development Indicators 45 1.5 Women in development Female Life Pregnant Teenage Women in wage Unpaid family Women in population expectancy women mothers employment in workers parliaments at birth receiving nonagricultural sector prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2008 2008 2008 2003­08a 2003­08a 2007 2003­08a 2003­08a 1990 2009 Afghanistan 48.2 44 44 36 .. .. .. .. 4 28 Albania 50.6 74 80 97 .. .. .. .. 29 16 Algeria 49.5 71 74 89 .. .. 7.1 13.6 2 8 Angola 50.7 45 49 80 29 .. .. .. 15 37 Argentina 51.0 72 79 99 .. 45 0.7b 1.6b 6 42 Armenia 53.4 70 77 93 5 46 .. .. 36 8 Australia 50.3 79 84 .. .. 47 0.2 0.4 6 27 Austria 51.3 78 83 .. .. 46 2.0 2.7 12 28 Azerbaijan 51.2 68 73 77 6 50 0.0 0.0 .. 11 Bangladesh 49.4 65 67 51 33 20 9.7 60.1 10 19 Belarus 53.5 65 77 99 .. 56 .. .. .. 32 Belgium 51.0 77 83 .. .. 46 0.4 2.2 9 35 Benin 49.6 60 63 84 21 .. .. .. 3 11 Bolivia 50.1 64 68 77 16 .. .. .. 9 17 Bosnia and Herzegovina 51.9 73 78 99 .. 35 2.0 8.9 .. 12 Botswana 50.1 54 54 .. .. 42 2.2 2.2 5 11 Brazil 50.7 69 76 98 .. .. 4.6 8.1 5 9 Bulgaria 51.6 70 77 .. .. 52 0.6 1.5 21 21 Burkina Faso 50.1 52 54 85 23 .. .. .. .. 15 Burundi 51.0 49 52 92 .. .. .. .. .. 31 Cambodia 51.1 59 63 69 8 .. .. .. .. 16 Cameroon 50.0 51 52 82 28 .. .. .. 14 14 Canada 50.5 79 83 .. .. 50 0.1b 0.2b 13 22 Central African Republic 50.9 45 49 69 .. .. .. .. 4 11 Chad 50.3 47 50 39 37 .. .. .. .. 5 Chile 50.5 76 82 .. .. 37 0.9 2.8 .. 15 China 48.1c 71c 75c 91 .. .. .. .. 21 21 Hong Kong SAR, China 52.5 79 86 .. .. 48 0.1 1.1 .. .. Colombia 50.8 69 77 94 21 49 2.7 6.3 5 8 Congo, Dem. Rep. 50.5 46 49 85 24 .. .. .. 5 8 Congo, Rep. 50.1 53 55 86 27 .. .. .. 14 7 Costa Rica 49.2 77 81 90 .. 41 1.3 2.8 11 37 Côte d'Ivoire 49.0 56 59 85 .. .. .. .. 6 9 Croatia 51.8 72 80 100 4 46 1.1b 3.7b .. 21 Cuba 49.9 77 81 100 .. 44 .. .. 34 43 Czech Republic 51.0 74 81 .. .. 46 0.3 1.0 .. 16 Denmark 50.5 77 81 .. .. 49 0.3 0.5 31 38 Dominican Republic 49.7 70 75 99 21 39 2.9 3.4 8 20 Ecuador 49.9 72 78 84 .. 37 4.4b 11.1b 5 32 Egypt, Arab Rep. 49.7 68 72 74 9 18 8.6 32.6 4 2 El Salvador 52.7 67 76 94 .. 49 8.8 9.9 12 19 Eritrea 50.9 57 62 .. .. .. .. .. .. 22 Estonia 53.9 69 80 .. .. 52 0.0 0.0 .. 21 Ethiopia 50.3 54 57 28 17 47 7.8b 12.7b .. 22 Finland 51.0 76 83 .. .. 51 0.6 0.4 32 42 France 51.4 78 85 .. .. 49 0.3 0.9 7 18 Gabon 50.1 59 62 .. .. .. .. .. 13 17 Gambia, The 50.4 54 58 98 .. .. .. .. 8 9 Georgia 52.9 68 75 94 .. 49 19.0 39.0 .. 5 Germany 51.0 78 83 .. .. 47 0.4 1.5 .. 33 Ghana 49.3 56 58 95 13 .. .. .. .. 8 Greece 50.4 78 82 .. .. 42 3.4 9.8 7 15 Guatemala 51.3 67 74 .. .. 43 .. .. 7 12 Guinea 49.5 56 60 88 32 .. .. .. .. 19 Guinea-Bissau 50.5 46 49 78 .. .. .. .. 20 10 Haiti 50.6 59 63 85 14 .. .. .. .. 4 Honduras 50.1 70 75 92 22 33 12.1b 8.3b 10 23 46 2010 World Development Indicators 1.5 WORLD VIEW Women in development Female Life Pregnant Teenage Women in wage Unpaid family Women in population expectancy women mothers employment in workers parliaments at birth receiving nonagricultural sector prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2008 2008 2008 2003­08a 2003­08a 2007 2003­08a 2003­08a 1990 2009 Hungary 52.5 70 78 .. .. 48 0.3 0.5 21 11 India 48.3 62 65 74 16 18 .. .. 5 11 Indonesia 50.1 69 73 93 9 31 7.8 33.6 12 18 Iran, Islamic Rep. 49.1 70 73 98 .. 16 5.4 32.7 2 3 Iraq 49.4 64 72 84 .. .. .. .. 11 26 Ireland 49.9 78 82 .. .. 48 0.6 0.8 8 13 Israel 50.4 79 83 .. .. 49 0.1 0.4 7 18 Italy 51.4 79 85 .. .. 43 1.2 2.5 13 21 Jamaica 51.1 69 75 91 .. 46 0.5 2.2 5 13 Japan 51.3 79 86 .. .. 42 1.1 7.3 1 11 Jordan 48.7 71 75 99 4 26 .. .. 0 6 Kazakhstan 52.3 61 72 100 7 .. 1.0 1.3 .. 16 Kenya 50.0 54 55 88 23 .. .. .. 1 10 Korea, Dem. Rep. 50.6 65 69 .. .. .. .. .. 21 16 Korea, Rep. 50.5 77 83 .. .. 42 1.2 12.7 2 14 Kosovo .. 67 72 .. .. .. .. .. .. .. Kuwait 40.3 76 80 .. .. .. .. .. .. 8 Kyrgyz Republic 50.7 63 72 97 .. 51 8.8 19.3 .. 26 Lao PDR 50.1 64 66 35 .. 50 .. .. 6 25 Latvia 53.9 67 78 .. .. 52 1.4 1.2 .. 20 Lebanon 51.0 70 74 96 .. .. .. .. 0 3 Lesotho 52.9 44 46 90 20 .. .. .. .. 25 Liberia 50.3 57 60 79 32 .. .. .. .. 13 Libya 48.3 72 77 .. .. .. .. .. .. 8 Lithuania 53.3 66 78 .. .. 53 1.0 2.0 .. 18 Macedonia, FYR 50.1 72 77 94 .. 42 7.0 14.9 .. 28 Madagascar 50.2 59 62 80 34 38 32.1 73.0 7 8 Malawi 50.3 52 54 92 34 .. .. .. 10 21 Malaysia 49.2 72 77 79 .. 39 2.7 8.8 5 11 Mali 50.6 48 49 70 36 .. 18.4 10.2 .. 10 Mauritania 49.3 55 59 75 .. .. .. .. .. 22 Mauritius 50.4 69 76 .. .. 37 0.9 4.7 7 17 Mexico 50.7 73 78 94 .. 39 4.9 10.0 12 28 Moldova 52.5 65 72 98 6 55 1.3 3.4 .. 26 Mongolia 50.5 63 70 89 .. 53 18.4 31.7 25 4 Morocco 50.9 69 74 68 7 28 16.5 51.8 0 11 Mozambique 51.4 47 49 89 41 .. .. .. 16 35 Myanmar 51.1 59 64 .. .. .. .. .. .. .. Namibia 50.7 60 62 95 15 .. 3.2 5.8 7 27 Nepal 50.3 66 67 44 19 .. .. .. 6 33 Netherlands 50.5 78 82 .. .. 47 0.2 0.8 21 41 New Zealand 50.6 78 82 .. .. 49 0.8 1.5 14 34 Nicaragua 50.5 70 76 90 .. 39 12.2 9.1 15 19 Niger 49.9 51 52 46 39 .. .. .. 5 12 Nigeria 49.9 47 48 58 25 21 .. .. .. 7 Norway 50.3 78 83 .. .. 49 0.2 0.4 36 36 Oman 43.5 74 78 .. .. .. .. .. .. 0 Pakistan 48.5 66 67 61 9 13 18.6 61.9 10 23 Panama 49.6 73 78 .. .. 43 2.3 4.0 8 9 Papua New Guinea 49.2 59 63 79 .. .. .. .. 0 1 Paraguay 49.5 70 74 96 .. 40 10.8 8.9 6 13 Peru 49.9 71 76 91 26 43 4.7b 9.9b 6 28 Philippines 49.6 70 74 91 8 42 9.0 18.0 9 21 Poland 51.7 71 80 .. .. 47 2.7 5.9 14 20 Portugal 51.6 76 82 .. .. 48 0.7 1.2 8 28 Puerto Rico 52.0 75 83 .. .. 42 0.0 0.0 .. .. Qatar 24.8 75 77 .. .. .. 0.0 0.0 .. 0 2010 World Development Indicators 47 1.5 Women in development Female Life Pregnant Teenage Women in wage Unpaid family Women in population expectancy women mothers employment in workers parliaments at birth receiving nonagricultural sector prenatal care Male Female years % of women % of nonagricultural % of male % of female % of total Male Female % ages 15­19 wage employment employment employment % of total seats 2008 2008 2008 2003­08a 2003­08a 2007 2003­08a 2003­08a 1990 2009 Romania 51.4 70 77 94 .. 46 6.0 18.9 34 11 Russian Federation 53.8 62 74 .. .. 51 0.1 0.1 .. 14 Rwanda 51.6 48 52 96 4 .. .. .. 17 56 Saudi Arabia 45.1 71 75 .. .. 15 .. .. .. 0 Senegal 50.4 54 57 87 19 .. .. .. 13 22 Serbia 50.5 71 76 98 .. 14 d 3.1 11.9 .. 22 Sierra Leone 51.3 46 49 81 .. .. 14.8 21.6 .. 13 Singapore 49.7 78 83 .. .. 45 0.4 1.3 5 25 Slovak Republic 51.5 71 79 .. .. 50 0.1 0.2 .. 19 Slovenia 51.2 76 83 .. .. 47 3.2 5.4 .. 13 Somalia 50.4 48 51 26 .. .. .. .. 4 6 South Africa 50.7 50 53 92 .. 44 0.3 0.6 3 45 Spain 50.7 78 84 .. .. 44 0.8 1.4 15 36 Sri Lanka 50.7 70 78 99 .. 31 4.4b 21.7b 5 6 Sudan 49.7 57 60 64 .. .. .. .. .. 18 Swaziland 51.2 46 45 85 23 .. .. .. 4 14 Sweden 50.4 79 83 .. .. 50 0.2 0.3 38 47 Switzerland 51.1 80 85 .. .. 47 1.7 3.2 14 29 Syrian Arab Republic 49.5 72 76 84 .. .. .. .. 9 12 Tajikistan 50.6 64 69 80 .. 37 .. .. .. 18 Tanzania 50.2 55 56 76 26 31 9.7b 13.0 b .. 30 Thailand 50.8 66 72 98 .. 45 14.0 29.9 3 12 Timor-Leste 49.1 60 62 61 .. .. .. .. .. 29 Togo 50.5 61 64 84 .. .. .. .. 5 11 Trinidad and Tobago 51.4 66 73 96 .. 44 0.3 1.7 17 27 Tunisia 49.7 72 76 96 .. .. .. .. 4 23 Turkey 49.8 70 74 54 .. 21 5.3 37.7 1 9 Turkmenistan 50.7 61 69 99 .. .. .. .. 26 17 Uganda 49.9 52 53 94 25 .. 10.3b 40.5b 12 31 Ukraine 53.9 63 74 99 4 55 0.4 0.3 .. 8 United Arab Emirates 32.5 77 79 .. .. 14 0.0 0.0 0 23 United Kingdom 51.0 78 82 .. .. 52 0.2 0.5 6 20 United States 50.7 76 81 .. .. 47 0.1 0.1 7 17 Uruguay 51.8 72 80 97 .. 46 0.9b 3.0 b 6 12 Uzbekistan 50.3 65 71 99 .. .. .. .. .. 18 Venezuela, RB 49.8 71 77 .. .. 41 0.6 1.6 10 19 Vietnam 50.7 72 76 91 .. .. 18.9 47.2 18 26 West Bank and Gaza 49.1 72 75 99 .. 17 6.6 31.5 .. .. Yemen, Rep. 49.4 61 65 47 .. .. .. .. 4 0e Zambia 50.1 45 46 94 28 .. .. .. 7 15 Zimbabwe 51.7 44 45 94 21 .. .. .. 11 15 World 49.6 w 67 w 71 w 82 w .. w .. w .. w 13 w 19 w Low income 50.2 58 60 69 .. .. .. .. 19 Middle income 49.2 67 71 84 .. .. .. 13 17 Lower middle income 48.8 66 70 83 .. .. .. 14 16 Upper middle income 51.1 68 75 90 45 3.4 7.3 12 20 Low & middle income 49.4 65 69 82 .. .. .. 13 18 East Asia & Pacific 48.8 70 74 91 .. .. .. 17 18 Europe & Central Asia 52.2 66 75 .. 48 2.0 5.4 .. 15 Latin America & Carib. 50.6 70 77 95 .. 4.0 7.5 12 23 Middle East & N. Africa 49.6 69 73 83 .. .. .. 4 9 South Asia 48.5 63 65 69 18 .. .. 6 20 Sub-Saharan Africa 50.2 51 53 72 .. .. .. .. 18 High income 50.6 77 83 .. 46 0.5 2.3 12 22 Euro area 51.1 78 84 .. 46 0.8 1.8 12 25 a. Data are for the most recent year available. b. Limited coverage. c. Includes Taiwan, China. d. Data are for 2008. e. Less than 0.5. 48 2010 World Development Indicators 1.5 WORLD VIEW Women in development About the data Definitions Despite much progress in recent decades, gender Women's wage work is important for economic · Female population is the percentage of the popu- inequalities remain pervasive in many dimensions of growth and the well-being of families. But women lation that is female. · Life expectancy at birth is life--worldwide. But while disparities exist through- often face such obstacles as restricted access to the number of years a newborn infant would live if out the world, they are most prevalent in developing education and vocational training, heavy workloads prevailing patterns of mortality at the time of its birth countries. Gender inequalities in the allocation of at home and in unpaid domestic and market activi- were to stay the same throughout its life. · Pregnant such resources as education, health care, nutrition, ties, and labor market discrimination. These obsta- women receiving prenatal care are the percentage and political voice matter because of the strong cles force women to limit their participation in paid of women attended at least once during pregnancy association with well-being, productivity, and eco- economic activities. And even when women work, by skilled health personnel for reasons related to nomic growth. These patterns of inequality begin at these obstacles cause women to be less productive pregnancy. · Teenage mothers are the percentage of an early age, with boys routinely receiving a larger and to receive lower wages. When women are in paid women ages 15­19 who already have children or are share of education and health spending than do girls, employment, they tend to be concentrated in the currently pregnant. · Women in wage employment for example. nonagricultural sector. However, in many develop- in nonagricultural sector are female wage employ- Because of biological differences girls are ing countries women are a large part of agricultural ees in the nonagricultural sector as a percentage expected to experience lower infant and child mor- employment, often as unpaid family workers. Among of total nonagricultural wage employment. · Unpaid tality rates and to have a longer life expectancy people who are unsalaried, women are more likely family workers are those who work without pay in a than boys. This biological advantage may be over- than men to be unpaid family workers, while men market-oriented establishment or activity operated shadowed, however, by gender inequalities in nutri- are more likely than women to be self-employed or by a related person living in the same household. tion and medical interventions and by inadequate employers. There are several reasons for this. · Women in parliaments are the percentage of par- care during pregnancy and delivery, so that female Few women have access to credit markets, capital, liamentary seats in a single or lower chamber held rates of illness and death sometimes exceed male land, training, and education, which may be required by women. rates, particularly during early childhood and the to start a business. Cultural norms may prevent reproductive years. In high-income countries women women from working on their own or from super- tend to outlive men by four to eight years on aver- vising other workers. Also, women may face time age, while in low-income countries the difference is constraints due to their traditional family respon- narrower--about two to three years. The difference sibilities. Because of biases and misclassification in child mortality rates (table 2.22) is another good substantial numbers of employed women may be Data sources indicator of female social disadvantage because underestimated or reported as unpaid family workers nutrition and medical interventions are particularly even when they work in association or equally with Data on female population are from the United important for the 1­4 age group. Female child mor- their husbands in the family enterprise. Nations Population Division's World Population tality rates that are as high as or higher than male Women are vastly underrepresented in decision- Prospects: The 2008 Revision, and data on life child mortality rates may indicate discrimination making positions in government, although there is expectancy for more than half the countries in the against girls. some evidence of recent improvement. Gender parity table (most of them developing countries) are from Having a child during the teenage years limits in parliamentary representation is still far from being its World Population Prospects: The 2008 Revision, girls' opportunities for better education, jobs, and realized. In 2009 women accounted for 19 percent with additional data from census reports, other income. Pregnancy is more likely to be unintended of parliamentarians worldwide, compared with 9 per- statistical publications from national statistical during the teenage years, and births are more likely cent in 1987. Without representation at this level, it offi ces, Eurostat's Demographic Statistics, the to be premature and are associated with greater is difficult for women to influence policy. Secretariat of the Pacific Community's Statistics risks of complications during delivery and of death. For information on other aspects of gender, see and Demography Programme, and the U.S. Bureau In many countries maternal mortality (tables 1.3 and tables 1.2 (Millennium Development Goals: eradicat- of the Census International Data Base. Data on 2.19) is a leading cause of death among women of ing poverty and saving lives), 1.3 (Millennium Devel- pregnant women receiving prenatal care are from reproductive age. Most maternal deaths result from opment Goals: protecting our common environment), household surveys, including Demographic and preventable causes--hemorrhage, infection, and 2.3 (Employment by economic activity), 2.4 (Decent Health Surveys by Macro International and Multi- complications from unsafe abortions. Prenatal care work and productive employment), 2.5 (Unemploy- ple Indicator Cluster Surveys by the United Nations is essential for recognizing, diagnosing, and promptly ment), 2.6 (Children at work), 2.10 (Assessing vulner- Children's Fund (UNICEF), and UNICEF's The State treating complications that arise during pregnancy. ability and security), 2.13 (Education efficiency), 2.14 of the World's Children 2010. Data on teenage In high-income countries most women have access (Education completion and outcomes), 2.15 (Educa- mothers are from Demographic and Health Sur- to health care during pregnancy, but in developing tion gaps by income and gender), 2.19 (Reproductive veys by Macro International. Data on labor force countries many women suffer pregnancy-related health), 2.21 (Health risk factors and future chal- and employment are from the International Labour complications, and over half a million die every year lenges), and 2.22 (Mortality). Organization's Key Indicators of the Labour Market, (Glasier and others 2006). This is reflected in the 6th edition. Data on women in parliaments are differences in maternal mortality ratios between from the Inter-Parliamentary Union. high- and low-income countries. 2010 World Development Indicators 49 1.6 Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at birth rate emissions Purchasing Atlas method power parity thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2008 2008 2008 2008 2008 2008 2008 2007­08 2007­08 2008 2008 2006 American Samoa 66 0.2 331 .. ..a .. .. .. .. .. .. .. Andorra 84 0.5 178 3,038 36,970 .. .. 1.4 ­1.4 .. .. .. Antigua and Barbuda 87 0.4 197 1,143 13,200 1,702b 19,650 b 2.5 1.3 .. .. 410 Aruba 105 0.2 586 .. ..c .. .. .. .. 75 98 2,308 Bahamas, The 338 13.9 34 7,136 21,390 .. .. 2.8 1.5 73 .. 2,107 Bahrain 776 0.7 1,092 19,713 25,420 25,906 33,400 6.3 4.1 76 91 19,668 Barbados 255 0.4 593 .. ..c .. .. .. .. 77 .. 1,315 Belize 322 23.0 14 1,205 3,740 1,913b 5,940 b 3.8 0.4 76 .. 817 Bermuda 64 0.1 1,284 .. ..c .. .. 4.6 4.3 79 .. 564 Bhutan 687 38.4 18 1,307 1,900 3,310 4,820 13.8 12.0 66 .. 392 Brunei Darussalam 392 5.8 74 10,211 27,050 19,540 50,770 0.6 ­1.3 77 95 5,903 Cape Verde 499 4.0 124 1,399 2,800 1,537 3,080 2.8 1.4 71 84 297 Cayman Islands 54 0.3 209 .. ..c .. .. .. .. .. 99 502 Channel Islands 150 0.2 787 10,242 68,610 .. .. 5.9 5.7 79 .. .. Comoros 644 1.9 346 483 750 754 1,170 1.0 ­1.4 65 74 88 Cyprus 862 9.3 93 21,367d 26,940 d 19,811d 24,980 d 3.6d 2.4 d 80 98 7,497 Djibouti 849 23.2 37 957 1,130 1,972 2,320 3.9 2.0 55 .. 473 Dominica 73 0.8 98 348 4,750 607b 8,290 b 4.3 3.7 .. .. 114 Equatorial Guinea 659 28.1 24 9,875 14,980 14,305 21,700 11.3 8.4 50 93 4,338 Faeroe Islands 49 1.4 35 .. ..c .. .. .. .. 79 .. 678 Fiji 844 18.3 46 3,382 4,010 3,647 4,320 0.2 ­0.4 69 .. 1,663 French Polynesia 266 4.0 73 .. ..c .. .. .. .. 74 .. 854 Greenland 56 410.5 0e 1,682 29,740 .. .. 0.7 1.1 68 .. 557 Grenada 104 0.3 305 609 5,880 873b 8,430 b 2.1 1.7 75 .. 234 Guam 176 0.5 325 .. ..c .. .. .. .. 76 .. .. Guyana 763 215.0 4 1,107 1,450 2,308 b 3,020 b 3.0 3.1 67 .. 1,491 Iceland 317 103.0 3 12,839 40,450 8,031 25,300 0.3 ­1.5 82 .. 2,184 Isle of Man 81 0.6 141 3,972 49,310 .. .. 7.5 7.4 .. .. .. About the data Definitions The table shows data for 55 economies with popula- · Population is based on the de facto definition of included in the valuation of output plus net receipts tions between 30,000 and 1 million and for smaller population, which counts all residents regardless of of primary income (compensation of employees and economies if they are members of the World Bank. legal status or citizenship--except for refugees not property income) from abroad. Data are in current Where data on gross national income (GNI) per capita permanently settled in the country of asylum, who U.S. dollars converted using the World Bank Atlas are not available, the estimated range is given. For are generally considered part of the population of method (see Statistical methods). · Purchasing more information on the calculation of GNI and pur- their country of origin. The values shown are midyear power parity (PPP) GNI is GNI converted to interna- chasing power parity (PPP) conversion factors, see estimates. For more information, see About the data tional dollars using PPP rates. An international dollar About the data for table 1.1. Additional data for the for table 2.1. · Surface area is a country's total has the same purchasing power over GNI that a U.S. economies in the table are available on the World area, including areas under inland bodies of water dollar has in the United States. · GNI per capita is Development Indicators CD-ROM or in WDI Online. and some coastal waterways. · Population density GNI divided by midyear population. · Gross domes- is midyear population divided by land area in square tic product (GDP) is the sum of value added by all kilometers. · Gross national income (GNI), Atlas resident producers plus any product taxes (less sub- method, is the sum of value added by all resident sidies) not included in the valuation of output. Growth producers plus any product taxes (less subsidies) not is calculated from constant price GDP data in local 50 2010 World Development Indicators 1.6 WORLD VIEW Key indicators for other economies Population Surface Population Gross national income Gross domestic Life Adult Carbon area density product expectancy literacy dioxide at birth rate emissions Purchasing Atlas method power parity thousand people per Per capita Per capita Per capita % ages 15 thousand thousands sq. km sq. km $ millions $ $ millions $ % growth % growth years and older metric tons 2008 2008 2008 2008 2008 2008 2008 2007­08 2007­08 2008 2008 2006 Kiribati 97 0.8 119 197 2,040 349b 3,610 b 3.0 1.4 .. .. 26 Liechtenstein 36 0.2 223 3,463 97,990 .. .. 3.1 2.2 83 .. .. Luxembourg 489 2.6 189 33,960 69,390 25,785 52,770 ­0.9 ­2.7 81 .. 11,318 Macau SAR, China 526 0.0 18,659 18,142 35,360 26,811 52,260 13.2 10.4 81 93 2,308 Maldives 305 0.3 1,017 1,110 3,640 1,613 5,290 5.2 3.7 72 98 678 Malta 412 0.3 1,287 6,825 16,690 8,418 20,580 3.8 3.1 80 .. 2,587 Marshall Islands 60 0.2 331 195 3,270 .. .. 1.5 ­0.8 .. .. 84 Mayotte 191 0.4 511 .. ..a .. .. .. .. 76 .. .. Micronesia, Fed. Sts. 110 0.7 158 272 2,460 361b 3,270 b ­2.9 ­3.1 69 .. .. Monaco 33 0.0 16,358 .. ..c .. .. .. .. .. .. .. Montenegro 622 13.8 46 4,146 6,660 8,350 13,420 8.1 7.9 74 .. .. Netherlands Antilles 195 0.8 244 .. ..c .. .. .. .. 76 96 3,752 New Caledonia 247 18.6 13 .. ..c .. .. .. .. 76 96 2,799 Northern Mariana Islands 85 0.5 186 .. ..c .. .. .. .. .. .. .. Palau 20 0.5 44 175 8,630 .. .. ­1.0 ­1.6 .. .. 117 Samoa 179 2.8 63 504 2,820 789b 4,410 b ­3.4 ­3.4 72 99 158 San Marino 31 0.1 517 1,430 46,770 .. .. 4.5 3.1 82 .. .. São Tomé and Príncipe 160 1.0 167 164 1,030 286 1,790 5.8 4.1 66 88 103 Seychelles 87 0.5 189 889 10,220 1,707b 19,630 b 2.8 0.5 73 .. 696 Solomon Islands 511 28.9 18 518 1,010 1,090 b 2,130 b 6.9 4.3 66 .. 180 St. Kitts and Nevis 49 0.3 189 535 10,870 761b 15,480 b 8.2 7.4 .. .. 136 St. Lucia 170 0.6 279 921 5,410 1,535b 9,020 b 0.5 ­0.6 .. .. 374 St. Vincent & Grenadines 109 0.4 280 551 5,050 934b 8,560 b ­1.1 ­1.2 72 .. 194 Suriname 515 163.8 3 2,454 4,760 3,439b 6,680 b 5.1 4.2 69 91 2,378 Tonga 104 0.8 144 279 2,690 412b 3,980 b 0.8 0.4 72 99 132 Vanuatu 234 12.2 19 442 1,940 793b 3,480 b 6.6 3.9 70 81 88 Virgin Islands (U.S.) 110 0.4 314 .. ..c .. .. .. .. 79 .. .. a. Estimated to be upper middle income ($3,856­$11,905). b. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. c. Estimated to be high income ($11,906 or more). d. Data are for the area controlled by the government of the Republic of Cyprus. e. Less than 0.5. currency. · GDP per capita is GDP divided by midyear population. · Life expectancy at birth is the number of years a newborn infant would live if prevailing pat- terns of mortality at the time of its birth were to stay the same throughout its life. · Adult literacy rate is Data sources the percentage of adults ages 15 and older who can, with understanding, read and write a short, simple The indicators here and throughout the book statement about their everyday life. · Carbon dioxide are compiled by World Bank staff from primary emissions are those stemming from the burning of and secondary sources. More information about fossil fuels and the manufacture of cement. They the indicators and their sources can be found in include carbon dioxide produced during consumption the About the data, Definitions, and Data sources of solid, liquid, and gas fuels and gas flaring. entries that accompany each table in subsequent sections. 2010 World Development Indicators 51 Text figures, tables, and boxes PEOPLE Introduction A 2 chieving the Millennium Development Goals (MDGs) promises a better life for mil- lions: lives saved; women empowered; illiteracy, hunger, and malnutrition reduced or eliminated; and children ensured access to high-quality education and health services. Because the MDGs are so important, countries have been striving to effec- tively monitor progress toward achieving them. Though the world overall has made progress, many countries remain off track--particularly fragile states and countries emerging from conflict. The global financial crisis could push an estimated 50 million people into poverty, with serious consequences for human development. Poor fami- lies cut short their children's schooling, prolonging poverty into the next generation because dropouts earn less as adults. Families are also likely to have to cut back on consumption as recent increases in food prices put pressures on budgets, damaging children's nutrition and health. But the story is not all bleak. Monitoring the Millennium of malaria--a major killer of children--and higher Development Goals: what do immunization rates are making advances against the available data tell us? measles (United Nations 2009a). Many countries and regions have made remarkable Healthy births continue to be a privilege of the progress. Deaths of children under age 5 have de- rich. Developed countries report 10 maternal deaths clined steadily in developing countries, falling from per 100,000 live births, compared with 440 in devel- 101 per 1,000 live births in 1990 to 73 in 2008, oping countries--and 14 developing countries have despite population growth. But many countries have maternal mortality ratios of 1,000 or higher (United made little progress, especially in Sub-Saharan Afri- Nations 2009a). Interventions to prevent maternal ca, and large disparities persist between the richest deaths, such as prenatal care, have improved in and poorest children in countries across all regions all regions, but poor women in the world's poorest (figure 2a). countries have the least access to them (figure 2c). Interventions that could yield breakthroughs for Access to contraception is increasing in all regions, children show mixed success. One child in four in but unmet need remains high at 11 percent (United developing countries is underweight--even more Nations 2009a). in low-income countries (figure 2b). But distribu- Major accomplishments have also been made tion of insecticide-treated nets has reduced the toll in education. Enrollment in primary school reached Child mortality is higher . . . as is among the poorest children . . . 2a child malnutrition 2b Poorest quintile Prevalence of child malnutrition Poorest quintile Under-five mortality rate (per 1,000) Richest quintile (percent of children under age 5) Richest quintile 150 60 100 40 50 20 0 0 Bangladesh Haiti Liberia Cambodia Nepal Rwanda (2007) (2006) (2007) (2005) (2006) (2005) Source: Demographic and Health Surveys. Source: Demographic and Health Surveys. 2010 World Development Indicators 53 The poorest women have the Poorer children are more least access to prenatal care 2c likely to die before age 5 . . . 2f Poorest quintile Under-five mortality rate Poorest quintile Pregnant women receiving prenatal care (percent) Richest quintile (per 1,000) Richest quintile 100 250 200 75 150 50 100 25 50 0 0 Bangladesh Congo, Dem. Rep. Vietnam Brazil Côte d'Ivoire Turkey (2007) (2007) (2006) (1996) (2006) (1998) Source: Demographic and Health Surveys and Multiple Indicators Cluster Surveys. Source: Demographic and Health Surveys. Poor and rural children are less likely to complete primary school . . . 2d . . . and to be out of school 2g Urban Rural Primary completion rate (percent of relevant age group) Poorest quintile Richest quintile Children out of school Poorest quintile (percent of children ages 6­11) Richest quintile 125 15 100 75 10 50 5 25 0 0 Bangladesh Ghana Yemen, Rep. Georgia Namibia Peru (2006) (2003) (2006) (2006) (2006) (2004) Source: Demographic and Health Surveys. Source: Demographic and Health Surveys. . . . and more likely to be out of school 2e improvement was not uniformly distributed: chil- dren from poorer households and rural areas Poorest quintile are less likely to complete their primary educa- Children out of school (percent of children ages 6­11) Richest quintile 80 tion (figure 2d). In many countries and regions universal 60 primary education (Millennium Development Goal 2) is jeopardized by the number of children 40 who are out of school. In 2007 an estimated 72 20 million children were out of school, almost half of them in Sub- Saharan Africa and 18 million 0 of them in South Asia. Nearly half of children Cambodia Ethiopia Kenya (2005) (2005) (2003) out of school have had no contact with formal Source: Demographic and Health Surveys. education. Another 23 percent were previ- ously enrolled but dropped out (United Nations 2009a). Inequalities within countries mean that 86 percent in developing countries overall in the poor are most likely to lose out: children 2007, up from 81 percent in 2000. Most of from poor households are two to three times as the progress was in regions lagging furthest likely to be out of school as their richest coun- behind--Sub-Saharan Africa (up 14 percent- terparts (figure 2e). age points) and South Asia (up 10 percentage And although some of the MDG targets and points). Primary school completion rates also indicators may be less relevant for many upper improved, from 80 percent in 2000 to 88 per- middle-income countries, they continue to mat- cent in 2007, with the greatest improvements ter for others. Wide disparities in well-being in the Middle East and North Africa. But the and achievement between the poorest and the 54 2010 World Development Indicators PEOPLE wealthiest populations persist, impeding coun- First-line health facilities in many tries' ability to meet their targets (figures 2f and countries lack electricity and clean water 2h 2g). Under-five mortality for richer children is Percent Regular water Electricity 80 less than half that for poorer children. Beyond data: what more do 60 countries need to do? The MDGs continue to provide a focus for coun- 40 try efforts, along with the need to strengthen data collection and analysis to assess prog- ress. At the same time governments need to 20 concentrate on interventions that improve ac- cess to quality health and education services 0 that can produce favorable outcomes for the Health center Clinic Health center Clinic Health center Clinic, health post, dispensary MDGs. Ghana Kenya Rwanda (2002) (2004) (2007) Source: Ghana Service Provision Assessment Survey 2002; Kenya HIV/AIDS Service Provision Assessment Health Survey 2004; Rwanda Service Provision Assessment Survey 2007. Slow progress on meeting the health MDGs has been associated with disappointing advances in access to health care. Many health systems Fewer health facilities in Guinea had electricity in 2001 than in 1998, but more had running water 2i are not equipped to provide health care for all, reflecting the inability of governments and so- Percent Electricity Running water cieties to mobilize the requisite resources and 60 institutions. In particular, countries need to im- prove three areas of service delivery that focus on people's needs: infrastructure, available 40 staff to deliver services, and adequate and ef- fective funding (Gauthier and others 2009). Infrastructure. The quality of health services de- 20 pends on the availability of basic infrastructure services such as electricity and water. Electricity- --limited in many poor countries--is necessary 0 1998 2001 for operating medical equipment and facilities. Because unclean water is an important vector Source: Guinea Health Facility Survey 2001. of sickness, clean running water is fundamental to service quality. Yet data indicate that first- line facilities in many countries lack these ba- Availability of child health services sic services (figure 2h). And in some countries is weak in Egypt and Rwanda 2j infrastructure is deteriorating (figure 2i). Share of facilities providing service (percent) Egypt (2004) Rwanda (2007) The accessibility and quality of health care 100 services are often constrained by the unavail- ability of basic medical services and equipment (figure 2j). Poor countries often have fewer than 75 1.1 doctors and 0.9 nurse per 1,000 people, with access unevenly distributed across income 50 groups. Wealthy people are better able to get to well staffed facilities and can afford to be seen 25 by doctors (figure 2k). Staffing. In many developing countries staff 0 Outpatient care Growth Childhood All basic child absenteeism is high, reflecting inadequate in- for sick children monitoring immunization health services centives and weak local accountability. For ex- Source: Egypt Service Provision Assessment Surveys 2004; Rwanda Service Provision Assessment Survey 2007. ample, on an average day 40 percent of primary 2010 World Development Indicators 55 Wealthy people have better times more common in urban areas--followed access to child health services 2k by pharmacists. Children with fever treated at a private facility (percent) Poorest quintile Richest quintile 50 Funding. As a country's income grows, total spending on health care rises. In 2007 high- 40 income economies spent an average of 11 per- 30 cent of GDP on health services and develop- 20 ing economies 5 percent. Funding for front-line service providers is low in many developing 10 countries because of leakages and allocation 0 rules that favor other purposes. Industrialized Philippines Bangladesh Tanzania (2003) (2004) (2004) countries have shown that the disproportion- Source: Demographic and Health Surveys. ate focus on hospitals and tertiary care, which dominated practice worldwide for much of the last two decades of the 20th century, has been Absenteeism among health workers a major source of inefficiency and inequality. reduces access to health care 2l For example, less than 20 percent of doctors in Thailand were specialists 30 years ago; by Absentee rate (percent) 2003, 70 percent were (World Health Organiza- 40 tion, World Health Report 2008). 30 Education 20 In 2002 the World Bank and development part- ners launched the Education for All Fast Track 10 Initiative, a global partnership to help low-in- 0 come countries meet the MDG target of univer- India Honduras Uganda Bangladesh Mozambique (2003) (2001) (2004) (2004) (2003) sal primary education and the Education for All goal that all children complete a full cycle of pri- Source: Lewis 2006. mary education by 2015. The initiative encour- ages countries to design sound education plans Distribution of health workers in Zambia, 2004 and provides additional indicators for tracking (per 100,000 people) 2m progress toward the education MDG, including indicators to monitor infrastructure and capac- Ratio of urban ity at the primary school level, availability and Classification National Rural Urban to rural presence in the classroom of qualified teach- Doctors 6.4 5.2 36.1 6.9 ers, and expenditures on primary education. Nurses 41.6 17.7 44.7 2.5 Pharmacists/technicians 1.2 1.1 5.5 5.0 At the school. Many schools lack the most basic Laboratory technicians 2.7 2.5 7.9 3.2 infrastructure elements that are taken for grant- All health workers 119.8 115.0 234.6 2.0 ed in developed countries (figure 2n). For exam- Source: Zambian Ministry of Health and WHO 2005. ple, a 2008 survey of primary schools in Asia, Latin America, and North Africa found that more than one student in five in Paraguay, the Philip- health care workers in India are not at work (fig- pines, and Sri Lanka was in a school that lacked ure 2l). Because most people have to travel far running water (UNESCO Institute for Statistics to get to a health center, a high probability that 2008b). No country in the survey had a library in the clinic will not be staffed may discourage pa- every school. In India, Paraguay, Peru, the Philip- tients from seeking care. pines, Sri Lanka, and Tunisia, less than half the Uneven distribution of health workers is a students were in schools with a telephone. major problem in several countries, especially The survey also showed major gaps in in rural and poorer areas. For example, Zam- resources between urban and rural schools. In bia has twice as many health workers in urban four states of India 27 percent of village schools areas as in rural areas (table 2m). Distribution have electricity while 76 percent of schools in of doctors is most uneven--they are seven towns and cities do. Only about half these rural 56 2010 World Development Indicators PEOPLE schools have enough toilets for girls, and less Many schools lack electricity, blackboards, seating, and libraries 2n than 4 percent have a telephone. In Peru less than half of village schools have electricity, a Share of students in schools Electricity Blackboard with the resource, 2005­06 (percent) Sufficient seating Library library, or toilets for boys or girls; nearly all urban 100 schools have electricity, 65 percent have enough lavatories, and 74 percent have libraries. 75 50 Teachers. To achieve Education for All goals, few inputs are more essential than having a teacher 25 in the classroom. It is obvious that teacher ab- sence will affect education quality. But teacher 0 Indiaa Philippines Peru absence can also affect education access and school completion rates because poor quality a. Data cover four states only: Assam, Madhya Pradesh, Rajasthan, and Tamil Nadu. Source: UNESCO Institute for Statistics 2008b. discourages parents from making the sacrific- es necessary to send their children to school. More important, high rates of teacher absence Absenteeism is high among teachers often signal deeper problems of accountability in some poor countries, 2002­03 2o and governance that are themselves barriers to Absentee rate (percent) educational progress. 30 How prevalent is the problem of teacher absence? One difficulty in studying teacher 20 absence is that administrative records of teach- ers' attendance may not be accurate. In coun- 10 tries with the highest absence rates, adminis- trative records may be an especially poor guide to teacher attendance. If poor governance and 0 Uganda India Indonesia Zambia Bangladesh Papua Ecuador Peru low levels of accountability undermine teach- New Guinea ers' incentives to attend school, those same Source: Abadzi 2007. factors are likely to reduce the accuracy of offi - cial attendance records. A study that measured attendance through direct observation of teach- The cost of education 2p ers during surprise visits to primary schools in six poor countries in 2002­03 found that teach- The allocation of public budgets is ultimately the result of competing demands for limited ers were absent about 19 percent of the time resources. Countries with rising demand for education and limited funding need to keep costs per student low. In 2005 in Sub-Saharan Africa average spending per primary school on average (Abadzi 2007; figure 2o). On an aver- student was almost 13 percent of per capita GDP, though spending ranged from 4 percent age day 27 percent of the teachers in Uganda in the Republic of Congo to 35 percent in Burkina Faso. In East Asia and Pacific average were not at work compared with 5 percent in spending per primary school student was 15 percent, yet two countries reported the lowest spending levels in the world (Indonesia and Myanmar, at just 3 percent). By contrast, coun- New York State. tries in North America and Western Europe tend to spend an average of about 22 percent Few teachers face serious threats of being and those in Central and Eastern Europe around 17 percent. fired for excessive absences. In a survey of Source: UNESCO Institute for Statistics, Global Education Digest 2007. 3,000 Indian government schools only one teacher was fired for poor attendance (Abdul Latif Jameel Poverty Action Lab 2009). Even Countries with higher primary gross enrollment in private schools, where teachers are less ratios and primary completion rates tend to de- protected and schools have financial incen- vote a greater share of national income or gov- tives to provide better service, only 35 of 600 ernment budgets to public primary education. schools reported a teacher being fired for poor Governments struggle to fund free basic attendance. education for all (box 2p). Almost a third of education funding worldwide is allocated to Funding. Adequate resources are critical for the primary level ($741 billion, or 1.3 per- ensuring good quality outcomes in education. cent of global GDP in purchasing power parity Studies have repeatedly stressed the need to terms; table 2q). Sub-Saharan Africa invests ensure adequate and stable funding for educa- the greatest share (2.1 percent of GDP), fol- tion (Bruns, Mingat, and Rakotomalala 2003). lowed by the Arab States (1.8 percent) and 2010 World Development Indicators 57 Latin America and the Caribbean (1.6 per- large school-age populations--or they may cent). In Burkina Faso, Cambodia, Cameroon, indicate that few students pursue higher lev- Dominican Republic, and Kenya the share of els of education. education funding going to primary education exceeded 60 percent in 2005. These large Achievements and gaps investments in primary education may reflect in data availability efforts to provide basic education to relatively Attention to the MDGs has been accompanied by substantial increases in the availability of Public expenditures on health and education statistics. But data avail- primary education, by region, 2004 2q ability remains inadequate in some countries, and some countries still lack sufficient informa- Expenditure on primary Total expenditure Expenditure on education as share of tion (two or more data points) to assess prog- on education primary education total expenditure on Region (percent of GDP) (percent of GDP) education (percent) ress (figure 2r). Efforts to expand and improve Arab states 4.9 1.8 37 statistical output have benefited from programs to strengthen institutions and individual skills Central Asia 2.8 0.6 21 within national statistical systems. Where ad- Central and Eastern Europe 4.2 1.1 26 ministrative sources are weak, progress in East Asia and Pacific 2.8 1.0 36 closing data gaps has been made by mount- Latin America ing household surveys to supplement available and Caribbean 4.4 1.6 36 data. North America and One of the most serious gaps is the lack Western Europe 5.6 1.5 27 of reliable information on births and deaths South and West Asia 3.6 1.2 33 in poor countries that lack vital registration Sub-Saharan Africa 4.5 2.1 47 systems. On average, only half the births in World 4.3 1.3 30 developing countries were reported from civil Note: Data are classified by United Nations Education, Scientific, and Cultural Organization regions. registration systems to the United Nations Sta- Source: UNESCO Institute for Statistics, Global Education Digest 2007. tistics Division during 2000­07, with coverage especially low in South Asia and Sub-Saharan Available data on human development indicators vary by region 2r Percent Two data points One data point No data points 100 75 50 25 0 All developing regions East Asia & Pacific Europe & Central Asia Latin America Middle East & & Caribbean North Africa South Asia Sub-Saharan Africa All developing Middle East & Middle East & North Africa regions East Asia & Pacific Europe & Central Asia Latin America & Caribbean North Africa South Asia Sub-Saharan Africa Sub-Saharan All developing regions East Asia Europe & & Caribbean South Central Asia Latin America Asia Africa East Asia Europe & Latin America Middle East & & Pacific All developing regions & Pacific Central Asia & Caribbean North Africa South Asia Sub-Saharan Africa Child malnutrition Births attended by skilled health staff Net primary enrollment ratio Youth literacy rate Source: World Development Indicators data files. 58 2010 World Development Indicators PEOPLE Africa (figure 2s). Reporting of infant deaths is In many regions fewer than half of births are even lower: fewer than a third of infant deaths reported to the United Nations Statistics Division . . 2s in developing countries were reported, with low Births reported to the United Nations Statistics Division, 2000­07 (percent) coverage in all regions except Europe and Cen- 100 tral Asia and Latin America and the Caribbean (figure 2t). 75 For countries lacking vital registration sys- tems, household surveys and censuses are 50 important sources of fertility and mortality data. The 2010 round of censuses, covering 25 1996­2014, promises to be far more success- ful than the previous round. More than 500 mil- 0 East Asia Europe & Latin America Middle East & South Sub-Saharan All developing lion people in 27 countries and areas were not & Pacific Central Asia & Caribbean North Africa Asia Africa regions included in the 2000 census round. For 2010 Source: World Bank staff calculations, based on data from United Nations Statistics Division's Population and Vital Statistics Report and United Nations Population Division (2009a). only nine countries have not yet scheduled a census, reducing the number of people not enu- merated to about 140 million, a drop of 75 per- . . . and even fewer cent from the previous census round. child deaths are reported 2t In education, despite improved reporting of school enrollment and completion rates, diffi - Infant deaths reported to the United Nations Statistics Division, 2000­07 (percent) 100 culties remain in measuring dropouts and out of school children. Many children have had some 75 contact with schooling (figure 2u), but there is still a lack of conceptual clarity in the defini- 50 tions of the school-age population and school participation. Data from school censuses may 25 overestimate enrollment rates because reg- istered children may not show up or may drop 0 out during the school year. Or the data may East Asia Europe & Latin America Middle East & South Sub-Saharan All developing & Pacific Central Asia & Caribbean North Africa Asia Africa regions undercount students because some students Source: World Bank staff calculations, based on data from United Nations Statistics Division's Population and who did not register or officially enroll did attend Vital Statistics Report and United Nations Population Division (2009a). school. Likewise, household surveys may not use consistent definitions of school attendance or may fail to correct for seasonal variation in Out of school children attendance. are difficult to measure 2u Another problem is inaccuracies in data Expected to drop out Expected to enter late for school-age populations (the denominator Percent of children out of school, 2006 Expected never to enroll in calculations of enrollment rates). Differ- 80 ent compilers may use different estimates of 60 population size, or ministries of educations may use outdated population estimates. For 40 example, some population estimates or enroll- ment numbers may include migrants while oth- 20 ers exclude them. If the school enrollment data 0 include migrant children while the population Arab States (5.7 million) Central Asia (0.3 million) Central & Eastern Europe (1.6 million) East Asia & Pacific (9.5 million) Latin America & Caribbean (2.6 million) North America & Western Europe (2.0 million) South & West Asia (18.4 million) Sub-Saharan Africa (35.2 million) World (75.3 million) estimates exclude them, the resulting enroll- ment rates will not be accurate. Most critical is the problem of going from sample statistics to estimates for the universe if the sample frame or sample design is not accurate. Cen- suses, which will become available for virtually all developing countries, remain an important Note: Numbers in parentheses are total number of children out of school. Data are classified by United Nations source of information on the age-sex structure Education, Scientific, and Cultural Organization regions. Source: UNESCO Institute for Statistics 2008a. of populations. 2010 World Development Indicators 59 Beyond the Millennium The increase in noncommunicable dis- Development Goals: monitoring eases, accompanied by a shift in the distri- emerging challenges bution of death and disease from younger to On the whole, people are healthier and bet- older people as the population ages, will affect ter educated than they were 30 years ago, service delivery and the allocation of health but progress has been deeply unequal. And budgets. Among the economically and socially the nature of some problems is changing at deprived populations of poor countries, these a rate that is wholly unexpected. Thirty years changes are most likely to affect children and ago about 38 percent of the world's popula- young adults, especially women. And this rise in tion lived in cities. By 2008 more than 50 per- chronic, noncommunicable diseases comes on cent (3.3 billion people) did. A third of urban top of an unfinished agenda on communicable dwellers (more than 1 billion people) live in diseases and maternal and child health. slum areas that lack basic social services. By In addition to the shifting epidemiological 2030, 60 percent of the world's population burden of diseases, developing countries, espe- (almost 5 billion people) are projected to live cially low-income countries, continue to struggle in urban areas, and most of this growth will with low access to health services. For people in be concentrated in smaller cities in developing these countries, out-of-pocket expenses make countries and in megacities of unprecedent- up more than half of health care costs, depriv- ed size in Southern and Eastern Asia (World ing many families of needed care because they Health Organization, World Health Report cannot afford it (figure 2v). Also, more than 100 2008). While health and education outcomes million people worldwide are pushed into pov- are better in urban areas on average, econom- erty each year because of catastrophic health ic and social stratification perpetuates inequi- care expenditures (World Health Organization, ties. These and other emerging challenges will raise demand for types of data different from Out-of-pocket health care costs those routinely collected today by statistical are too high for many people to afford 2v offi ces--and will call for increased national accountability. Public health expenditure Out-of-pocket health expenditure Experience shows that targeted interven- Percent Other private health expenditure 100 tions and funding have succeeded in expanding programs to deliver services to those most in 75 need. But achieving the MDGs will also require stronger accountability and a clearer focus 50 on data and statistics, analytic methods for 25 new data collection, improved use of data by national policymakers and planners, and regu- 0 Low income Middle income High income lar evaluations of programs and new initiatives. Achieving the MDGs will also require targeting Source: WHO's National Health Account database. areas and population groups that have been left behind--rural communities and the poor- est households. Informal payments to health care providers are common 2w Health Share of patients who made informal payments, Urbanization, aging populations, and a global- selected countries, 1999 (percent) 100 ized lifestyle combine to make chronic and non- communicable diseases, including diabetes, 75 cancers, cardiovascular diseases, and injuries, increasingly important causes of mortality and 50 morbidity in developing countries (World Health 25 Organization, World Health Report 2008). In re- sponse, countries need to collect and strength- 0 en statistics on cause of death and move away Armenia Moldova Tajikistan Slovak Republic from fragmented attention to the needs of Source: World Bank 2000a. single- disease programs such as HIV/AIDS. 60 2010 World Development Indicators PEOPLE World Health Report 2008). Compounding prob- Primary school enrollment lems of access and equity is weak governance and attendance, 2003­08 2x in the delivery of health services. In developing Primary school net Primary school net and transition economies, informal payments to enrollment rate attendance rate (percent) (percent) health care providers are high (figure 2w). Economy or group Male Female Male Female Reliable mortality statistics, the corner- stone of national health information systems, Africa 79 74 69 66 are necessary for assessing population health, Sub-Saharan Africa 76 70 65 63 planning health policies and health services, Eastern and Southern Africa 83 82 69 70 and evaluating epidemiological and other health West and Central Africa 68 58 63 58 system programs. And the data are essential for Middle East and North Africa 92 88 85 81 monitoring progress toward the health-related Asia 92 89 84 81a MDGs of reducing maternal and child mortality South Asia 87 82 83 79 and mortality from HIV/AIDS, tuberculosis, and East Asia and Pacific 98 97 88 88a malaria. Yet in 2007 only 61 percent of devel- Latin America and Caribbean 95 95 92 93 oping countries had complete registration sys- tems, and among these only a handful had reli- CEE/CIS 92 90 94 92 able cause of death statistics. Few efforts have Developed economies 94 95 .. .. been made to systematically build or strengthen Developing economies 89 86 80 78a country capacities to collect and use data. The Least developed countries 81 76 67 65 pace needs to quicken. World 90 87 81 78a Note: CEE/CIS is Central and Eastern Europe and Commonwealth of Independent States. Education a. Excludes China. Achieving universal primary education (Goal 2) is Source: UNICEF, The State of the World's Children 2010. vital to meeting all the other MDGs. The steady increase in primary school enrollment in nearly all regions is an encouraging sign. But countries Instructional time for children varies still need to translate these enrollment rates considerably by country, 2004­06 2y into opportunities for learning. Instructional time, 2004­06 (percent) Enrollment is not a sufficient measure of 80 learning. Because school attendance is a better predictor of learning outcomes (Abadzi 2007), 60 monitoring of student attendance needs to be 40 strengthened. Many students enrolled on the first day of school do not actually attend during 20 some or part of the year (table 2x). Retaining all enrolled children in school is a challenge for 0 Tunisia Morocco Pernambuco, Ghana most countries, requiring varied strategies, con- Brazil certed effort, and investment. And school reten- Source: Abadzi 2007. tion should translate into instructional time for children, so that they can develop cognitive skills and knowledge. Instructional time varies in regional or international assessments. Coun- greatly by country (figure 2y), and few countries tries must shift their focus from access to systematically monitor learning outcomes by achievement, making learning outcomes a cen- assessing student achievement or participating tral part of the education agenda. 2010 World Development Indicators 61 Tables 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2008 2015 1990­2008 2008­15 2008 2008 2008 2008 2008 2008 2008 Afghanistan 18.6 29.0 35.0 2.5 2.7 46 51 2 90 4 20 47 Albania 3.3 3.1 3.3 ­0.3 0.5 24 66 9 36 14 6 15 Algeria 25.3 34.4 38.1 1.7 1.5 28 68 5 41 7 5 21 Angola 10.7 18.0 21.7 2.9 2.6 45 52 2 87 5 17 43 Argentina 32.5 39.9 42.4 1.1 0.9 25 64 11 40 16 8 17 Armenia 3.5 3.1 3.1 ­0.8 0.2 21 68 12 30 17 9 15 Australia 17.1 21.4 23.4 1.3 1.3 19 67 13 28 20 7 14 Austria 7.7 8.3 8.4 0.4 0.2 15 68 17 22 25 9 9 Azerbaijan 7.2 8.7 9.4 1.1 1.1 25 69 7 36 10 6 18 Bangladesh 115.6 160.0 176.3 1.8 1.4 32 64 4 50 6 7 21 Belarus 10.2 9.7 9.4 ­0.3 ­0.4 15 71 14 21 19 14 11 Belgium 10 10.7 11.0 0.4 0.4 17 66 17 26 26 9 12 Benin 4.8 8.7 10.6 3.3 2.9 43 54 3 81 6 9 39 Bolivia 6.7 9.7 10.8 2.1 1.6 37 59 5 63 8 8 27 Bosnia and Herzegovina 4.3 3.8 3.7 ­0.7 ­0.2 16 71 14 22 20 10 9 Botswana 1.4 1.9 2.1 2.0 1.3 34 63 4 54 6 12 25 Brazil 149.6 192.0 202.4 1.4 0.8 26 67 7 39 10 6 16 Bulgaria 8.7 7.6 7.3 ­0.7 ­0.6 13 69 17 19 25 14 10 Burkina Faso 8.8 15.2 19.0 3.0 3.2 46 52 2 89 4 13 47 Burundi 5.7 8.1 9.4 2.0 2.2 39 58 3 67 5 14 34 Cambodia 9.7 14.6 16.4 2.3 1.7 34 62 3 55 5 8 25 Cameroon 12.2 19.1 22.2 2.5 2.1 41 55 4 74 6 14 37 Canada 27.8 33.3 35.7 1.0 1.0 17 70 14 24 20 7 11 Central African Republic 2.9 4.3 4.9 2.2 1.8 41 55 4 74 7 17 35 Chad 6.1 10.9 13.1 3.2 2.6 46 51 3 89 6 17 46 Chile 13.2 16.8 17.9 1.3 0.9 23 68 9 34 13 5 15 China 1,135.2 1,324.7 1,377.7 0.9 0.6 21a 72a 8a 29a 11a 7 12 Hong Kong SAR, China 5.7 7.0 7.3 1.1 0.7 13 75 13 17 17 6 11 Colombia 33.2 45.0 49.3 1.7 1.3 30 65 5 45 8 6 20 Congo, Dem. Rep. 37 64.3 77.4 3.1 2.7 47 50 3 93 5 17 45 Congo, Rep. 2.4 3.6 4.2 2.2 2.2 41 56 4 73 7 13 35 Costa Rica 3.1 4.5 4.9 2.1 1.3 26 67 6 39 9 4 17 Côte d'Ivoire 12.6 20.6 24.2 2.7 2.3 41 55 4 74 7 11 35 Croatia 4.8 4.4 4.4 ­0.4 ­0.2 15 68 17 23 25 12 10 Cuba 10.6 11.2 11.2 0.3 0.0 18 70 12 26 16 7 10 Czech Republic 10.4 10.4 10.6 0.0 0.3 14 71 15 20 21 10 11 Denmark 5.1 5.5 5.6 0.4 0.3 18 66 16 28 24 10 12 Dominican Republic 7.4 10.0 10.8 1.7 1.1 32 62 6 51 9 6 23 Ecuador 10.3 13.5 14.6 1.5 1.1 31 62 6 51 10 5 21 Egypt, Arab Rep. 57.8 81.5 91.7 1.9 1.7 32 63 5 52 7 6 25 El Salvador 5.3 6.1 6.4 0.8 0.6 33 60 7 55 12 7 20 Eritrea 3.2 4.9 6.0 2.5 2.8 42 56 2 74 4 8 37 Estonia 1.6 1.3 1.3 ­0.9 ­0.1 15 68 17 22 25 12 12 Ethiopia 48.3 80.7 96.2 2.9 2.5 44 53 3 83 6 12 38 Finland 5.0 5.3 5.4 0.4 0.3 17 67 17 25 25 9 11 Franceb 56.7 62.3 63.9 0.5 0.4 18 65 17 28 26 9 13 Gabon 0.9 1.4 1.6 2.5 1.8 37 59 4 62 7 10 27 Gambia, The 0.9 1.7 2.0 3.4 2.5 42 55 3 78 5 11 37 Georgia 5.5 4.3 4.1 ­1.3 ­0.8 17 68 14 25 21 12 12 Germany 79.4 82.1 80.6 0.2 ­0.3 14 66 20 21 30 10 8 Ghana 15.0 23.4 26.6 2.5 1.9 39 58 4 67 6 11 32 Greece 10.2 11.2 11.4 0.6 0.2 14 68 18 21 27 10 10 Guatemala 8.9 13.7 16.2 2.4 2.4 42 53 4 79 8 6 33 Guinea 6.1 9.8 11.8 2.6 2.6 43 54 3 80 6 11 40 Guinea-Bissau 1.0 1.6 1.8 2.4 2.3 43 54 3 79 6 17 41 Haiti 7.1 9.9 10.7 1.8 1.1 37 59 4 62 7 9 28 Honduras 4.9 7.3 8.4 2.2 1.9 38 58 4 66 7 5 27 62 2010 World Development Indicators 2.1 PEOPLE Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2008 2015 1990­2008 2008­15 2008 2008 2008 2008 2008 2008 2008 Hungary 10.4 10.0 9.9 ­0.2 ­0.2 15 69 16 22 23 13 10 India 849.5 1,140.0 1,246.9 1.6 1.3 32 63 5 50 8 7 23 Indonesia 177.4 227.3 247.5 1.4 1.2 27 67 6 41 9 6 19 Iran, Islamic Rep. 54.4 72.0 78.6 1.6 1.3 24 71 5 35 7 6 19 Iraq 18.9 30.7 36.3 2.7 2.4 41 55 3 75 6 6 31 Ireland 3.5 4.4 4.8 1.3 1.0 21 68 11 30 16 6 17 Israel 4.7 7.3 8.2 2.5 1.7 28 62 10 45 16 5 22 Italy 56.7 59.8 60.8 0.3 0.2 14 66 20 22 31 10 10 Jamaica 2.4 2.7 2.8 0.7 0.4 30 62 8 48 12 6 17 Japan 123.5 127.7 125.3 0.2 ­0.3 13 65 21 21 33 9 9 Jordan 3.2 5.9 6.8 3.5 2.0 35 61 4 57 6 4 26 Kazakhstan 16.3 15.7 16.9 ­0.2 1.0 24 69 7 34 11 10 23 Kenya 23.4 38.8 46.4 2.8 2.6 43 55 3 78 5 12 39 Korea, Dem. Rep. 20.1 23.8 24.4 0.9 0.3 22 68 9 32 14 10 14 Korea, Rep. 42.9 48.6 49.3 0.7 0.2 17 72 10 24 14 5 9 Kosovo 1.9 1.8 1.9 ­0.2 0.6 .. .. .. .. .. 7 19 Kuwait 2.1 2.7 3.2 1.4 2.1 23 74 2 31 3 2 18 Kyrgyz Republic 4.4 5.3 5.7 1.0 1.2 30 65 5 46 8 7 24 Lao PDR 4.2 6.2 7.0 2.2 1.8 38 58 4 66 6 7 27 Latvia 2.7 2.3 2.2 ­0.9 ­0.5 14 69 17 20 25 14 11 Lebanon 3.0 4.2 4.4 1.9 0.8 26 67 7 39 11 7 16 Lesotho 1.6 2.0 2.2 1.4 0.8 39 56 5 70 8 17 29 Liberia 2.2 3.8 4.8 3.1 3.3 43 54 3 80 6 10 38 Libya 4.4 6.3 7.2 2.0 1.8 30 66 4 46 6 4 23 Lithuania 3.7 3.4 3.2 ­0.5 ­0.7 15 69 16 22 23 13 10 Macedonia, FYR 1.9 2.0 2.0 0.4 0.0 18 70 12 26 17 9 11 Madagascar 11.3 19.1 22.8 2.9 2.5 43 54 3 81 6 9 36 Malawi 9.5 14.8 18.0 2.5 2.7 46 50 3 92 6 12 40 Malaysia 18.1 27.0 30.0 2.2 1.5 30 65 5 46 7 4 20 Mali 8.7 12.7 15.4 2.1 2.7 44 53 2 83 4 16 43 Mauritania 2.0 3.2 3.7 2.7 2.1 40 58 3 69 5 10 34 Mauritius 1.1 1.3 1.3 1.0 0.4 23 70 7 33 10 7 13 Mexico 83.2 106.4 113.1 1.4 0.9 29 65 6 45 10 5 18 Moldova 4.4 3.6 3.5 ­1.0 ­0.7 17 72 11 24 16 13 12 Mongolia 2.2 2.6 2.9 1.0 1.1 27 70 4 38 6 7 19 Morocco 24.8 31.6 34.3 1.3 1.2 29 66 5 44 8 6 20 Mozambique 13.5 22.4 25.9 2.8 2.1 44 53 3 84 6 16 39 Myanmar 40.8 49.6 53.0 1.1 1.0 27 67 5 40 8 10 21 Namibia 1.4 2.1 2.4 2.3 1.8 37 59 4 63 6 9 28 Nepal 19.1 28.8 32.5 2.3 1.7 37 59 4 63 7 6 25 Netherlands 15.0 16.4 16.8 0.5 0.3 18 67 15 27 22 8 11 New Zealand 3.4 4.3 4.6 1.2 1.0 21 67 13 31 19 7 15 Nicaragua 4.1 5.7 6.3 1.7 1.4 36 60 4 60 7 5 25 Niger 7.9 14.7 19.1 3.4 3.8 50 48 2 103 4 15 54 Nigeria 97.3 151.2 178.7 2.4 2.4 43 54 3 79 6 16 40 Norway 4.2 4.8 5.1 0.7 0.9 19 66 15 29 22 9 13 Oman 1.8 2.8 3.2 2.3 2.0 32 65 3 49 4 3 22 Pakistan 108.0 166.1 193.5 2.4 2.2 37 59 4 63 7 7 30 Panama 2.4 3.4 3.8 1.9 1.5 30 64 6 46 10 5 21 Papua New Guinea 4.1 6.6 7.7 2.6 2.2 40 57 2 70 4 8 31 Paraguay 4.2 6.2 7.0 2.1 1.6 34 61 5 57 8 6 25 Peru 21.8 28.8 31.2 1.6 1.1 31 64 6 48 9 5 21 Philippines 62.4 90.3 102.7 2.1 1.8 34 62 4 56 7 5 25 Poland 38.1 38.1 38.0 0.0 ­0.1 15 71 13 21 19 10 11 Portugal 9.9 10.6 10.7 0.4 0.0 15 67 18 23 26 10 10 Puerto Rico 3.5 4.0 4.0 0.6 0.3 21 66 13 31 20 8 12 Qatar 0.5 1.3 1.6 5.6c 3.4 16 83 1 20 1 2 12 2010 World Development Indicators 63 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate % % of working-age Ages Ages Ages population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2008 2015 1990­2008 2008­15 2008 2008 2008 2008 2008 2008 2008 Romania 23.2 21.5 21.0 ­0.4 ­0.4 15 70 15 22 21 12 10 Russian Federation 148.3 142.0 139.0 ­0.2 ­0.3 15 72 13 20 18 15 12 Rwanda 7.2 9.7 11.7 1.7 2.7 42 55 3 76 5 14 41 Saudi Arabia 16.3 24.6 28.6 2.3 2.1 33 64 3 51 4 4 23 Senegal 7.5 12.2 14.5 2.7 2.5 44 54 2 81 4 11 38 Serbia 7.6 7.4 7.2 ­0.2 ­0.3 18d 68d 15d 26d 21d 14 9 Sierra Leone 4.1 5.6 6.6 1.7 2.4 43 55 2 79 3 16 40 Singapore 3.0 4.8 5.4 2.6 1.5 17 73 9 23 13 4 10 Slovak Republic 5.3 5.4 5.4 0.1 0.1 16 72 12 22 17 10 11 Slovenia 2.0 2.0 2.1 0.1 0.4 14 70 16 20 23 9 10 Somalia 6.6 8.9 10.7 1.7 2.6 45 52 3 86 5 16 44 South Africa 35.2 48.7 51.1 1.8 0.7 31 65 4 47 7 15 22 Spain 38.8 45.6 47.9 0.9 0.7 15 68 17 22 25 9 11 Sri Lanka 17.1 20.2 21.2 0.9 0.7 24 68 7 35 11 6 19 Sudan 27.1 41.3 47.7 2.3 2.0 40 57 4 69 6 10 31 Swaziland 0.9 1.2 1.3 1.7 1.4 40 57 3 70 6 16 30 Sweden 8.6 9.2 9.6 0.4 0.6 17 66 18 25 27 10 12 Switzerland 6.7 7.6 7.9 0.7 0.4 16 68 17 23 25 8 10 Syrian Arab Republic 12.7 20.6 24.1 2.7 2.2 35 61 3 58 5 3 28 Tajikistan 5.3 6.8 7.8 1.4 1.8 38 59 4 64 6 6 28 Tanzania 25.5 42.5 52.1 2.8 2.9 45 52 3 85 6 11 42 Thailand 56.7 67.4 69.9 1.0 0.5 22 71 7 31 11 9 15 Timor-Leste 0.7 1.1 1.4 2.2 3.3 45 52 3 87 6 9 40 Togo 3.9 6.5 7.6 2.8 2.3 40 56 3 72 6 8 33 Trinidad and Tobago 1.2 1.3 1.4 0.5 0.3 21 73 7 29 9 8 15 Tunisia 8.2 10.3 11.1 1.3 1.1 24 70 7 34 10 6 18 Turkey 56.1 73.9 79.9 1.5 1.1 27 67 6 41 9 6 18 Turkmenistan 3.7 5.0 5.5 1.8 1.3 30 66 4 46 7 8 22 Uganda 17.7 31.7 39.7 3.2 3.2 49 48 3 101 5 13 46 Ukraine 51.9 46.3 44.4 ­0.6 ­0.6 14 70 16 20 23 16 11 United Arab Emirates 1.9 4.5 5.2 4.9 2.1 19 80 1 24 1 2 14 United Kingdom 57.2 61.4 63.8 0.4 0.5 18 66 16 27 25 9 13 United States 249.6 304.1 323.5 1.1 0.9 20 67 13 31 19 8 14 Uruguay 3.1 3.3 3.4 0.4 0.3 23 63 14 36 22 9 15 Uzbekistan 20.5 27.3 30.2 1.6 1.5 30 65 5 46 7 5 22 Venezuela, RB 19.8 27.9 31.0 1.9 1.5 30 65 5 47 8 5 21 Vietnam 66.2 86.2 92.8 1.5 1.1 27 67 6 39 9 5 17 West Bank and Gaza 2.0 3.9 4.8 3.8 2.8 45 52 3 87 6 4 36 Yemen, Rep. 12.3 22.9 27.8 3.5 2.8 44 53 2 83 4 7 37 Zambia 7.9 12.6 15.0 2.6 2.4 46 51 3 91 6 17 43 Zimbabwe 10.5 12.5 14.0 1.0 1.7 40 56 4 72 7 16 30 World 5,278.9 s 6,697.3 s 7,241.2 s 1.3 w 1.1 w 27 w 65 w 7w 42 w 11 w 8w 20 w Low income 653.6 976.2 1,127.4 2.2 2.1 38 58 4 66 6 11 32 Middle income 3,685.7 4,652.3 5,006.3 1.3 1.0 27 66 6 41 10 8 19 Lower middle income 2,889.5 3,703.0 4,011.2 1.4 1.1 28 66 6 42 9 8 20 Upper middle income 796.2 949.3 995.1 1.0 0.7 25 67 8 36 12 8 17 Low & middle income 4,339.3 5,628.5 6,133.7 1.4 1.2 29 65 6 45 9 8 21 East Asia & Pacific 1,599.6 1,929.6 2,035.6 1.0 0.8 23 70 7 33 10 7 14 Europe & Central Asia 433.2 443.3 449.2 0.1 0.2 19 70 11 28 16 11 14 Latin America & Carib. 435.5 566.1 606.8 1.5 1.0 29 65 7 44 10 6 19 Middle East & N. Africa 227.4 325.2 366.1 2.0 1.7 31 64 4 49 7 6 24 South Asia 1,128.7 1,545.1 1,706.5 1.7 1.4 33 63 5 52 7 7 24 Sub-Saharan Africa 514.9 819.3 969.5 2.6 2.4 43 54 3 79 6 14 38 High income 939.6 1,068.7 1,107.4 0.7 0.5 18 67 15 26 23 8 12 Euro area 301.6 326.1 331.0 0.4 0.2 15 67 18 23 27 9 10 a. Includes Taiwan, China. b. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. c. Increase is due to a surge in the number of migrants since 2004. d. Includes Kosovo. 64 2010 World Development Indicators 2.1 PEOPLE Population dynamics About the data Definitions Population estimates are usually based on national population is shrinking. Previously high fertility rates · Population is based on the de facto definition of population censuses, but the frequency and quality and declining mortality rates are now reflected in the population, which counts all residents regardless of vary by country. Most countries conduct a complete larger share of the working-age population. legal status or citizenship--except for refugees not enumeration no more than once a decade. Estimates Dependency ratios capture variations in the pro- permanently settled in the country of asylum, who for the years before and after the census are inter- portions of children, elderly people, and working-age are generally considered part of the population of polations or extrapolations based on demographic people in the population that imply the dependency bur- their country of origin. The values shown are mid- models. Errors and undercounting occur even in high- den that the working-age population bears in relation to year estimates for 1990 and 2008 and projections income countries; in developing countries errors may children and the elderly. But dependency ratios show for 2015. · Average annual population growth is be substantial because of limits in the transport, only the age composition of a population, not economic the exponential change for the period indicated. See communications, and other resources required to dependency. Some children and elderly people are part Statistical methods for more information. · Popula- conduct and analyze a full census. of the labor force, and many working-age people are not. tion age composition is the percentage of the total The quality and reliability of official demographic Vital rates are based on data from birth and death population that is in specific age groups. · Depen- data are also affected by public trust in the govern- registration systems, censuses, and sample surveys dency ratio is the ratio of dependents--people ment, government commitment to full and accurate by national statistical offices and other organiza- younger than 15 or older than 64--to the working- enumeration, confidentiality and protection against tions, or on demographic analysis. Data for 2008 age population--those ages 15­64. · Crude death misuse of census data, and census agencies' indepen- for most high-income countries are provisional esti- rate and crude birth rate are the number of deaths dence from political influence. Moreover, comparability mates based on vital registers. The estimates for and the number of live births occurring during the of population indicators is limited by differences in the many countries are projections based on extrapola- year, per 1,000 people, estimated at midyear. Sub- concepts, definitions, collection procedures, and esti- tions of levels and trends from earlier years or inter- tracting the crude death rate from the crude birth mation methods used by national statistical agencies polations of population estimates and projections rate provides the rate of natural increase, which is and other organizations that collect the data. from the United Nations Population Division. equal to the population growth rate in the absence Of the 155 economies in the table and the 55 econo- Vital registers are the preferred source for these of migration. mies in table 1.6, 180 (about 86 percent) conducted a data, but in many developing countries systems for census during the 2000 census round (1995­2004). registering births and deaths are absent or incomplete As of March 2010, 61 countries have completed a because of deficiencies in the coverage of events or census for the 2010 census round (2005­14). The geographic areas. Many developing countries carry out currentness of a census and the availability of comple- special household surveys that ask respondents about mentary data from surveys or registration systems recent births and deaths. Estimates derived in this way are objective ways to judge demographic data quality. are subject to sampling errors and recall errors. Some European countries' registration systems offer The United Nations Statistics Division monitors complete information on population in the absence of the completeness of vital registration systems. The Data sources a census. See table 2.17 and Primary data documenta- share of countries with at least 90 percent complete tion for the most recent census or survey year and for vital registration rose from 45 percent in 1988 to The World Bank's population estimates are com- the completeness of registration. 61 percent in 2007. Still, some of the most populous piled and produced by its Development Data Current population estimates for developing countries developing countries--China, India, Indonesia, Bra- Group in consultation with its Human Develop- that lack recent census data and pre- and post-census zil, Pakistan, Bangladesh, Nigeria--lack complete ment Network, operational staff, and country estimates for countries with census data are provided vital registration systems. From 2000 to 2007, on offices. The United Nations Population Division's by the United Nations Population Division and other average 64 percent of births, 62 percent of deaths, World Population Prospects: The 2008 Revision agencies. The cohort component method--a standard and 45 percent of infant deaths were registered and is a source of the demographic data for more estimation method for estimating and projecting pop- reported to the United Nations Statistics Division. than half the countries, most of them developing ulation--requires fertility, mortality, and net migration International migration is the only other factor countries, and the source of data on age com- data, often collected from sample surveys, which can besides birth and death rates that directly determines position and dependency ratios for all countries. be small or limited in coverage. Population estimates a country's population growth. From 1990 to 2005 the Other important sources are census reports and are from demographic modeling and so are susceptible number of migrants in high-income countries rose 40 other statistical publications from national sta- to biases and errors from shortcomings in the model million. About 195 million people (3 percent of the world tistical offices; household surveys conducted by and in the data. Because the five-year age group is the population) live outside their home country. Estimat- national agencies, Macro International, and the cohort unit and five-year period data are used, interpo- ing migration is difficult. At any time many people are U.S. Centers for Disease Control and Prevention; lations to obtain annual data or single age structure located outside their home country as tourists, work- Eurostat's Demographic Statistics; Secretariat of may not reflect actual events or age composition. ers, or refugees or for other reasons. Standards for the the Pacific Community, Statistics and Demogra- The growth rate of the total population conceals duration and purpose of international moves that qualify phy Programme; and U.S. Bureau of the Census, age-group differences in growth rates. In many devel- as migration vary, and estimates require information on International Data Base. oping countries the once rapidly growing under-15 flows into and out of countries that is difficult to collect. 2010 World Development Indicators 65 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2008 1990 2008 1990 2008 1990­2008 1990 2008 Afghanistan 87 89 32 33 5.9 9.3 2.5 26.2 26.6 Albania 84 70 51 49 1.4 1.4 0.1 39.9 42.3 Algeria 75 77 23 37 7.0 14.5 4.0 23.4 31.2 Angola 90 89 74 74 4.6 8.0 3.1 46.3 46.8 Argentina 78 75 43 51 13.5 19.1 1.9 36.9 41.1 Armenia 78 67 61 59 1.7 1.6 ­0.3 46.3 49.6 Australia 74 70 52 58 8.5 11.3 1.6 41.3 45.3 Austria 68 65 43 53 3.5 4.3 1.1 40.9 45.5 Azerbaijan 78 71 59 61 3.1 4.1 1.6 46.8 50.2 Bangladesh 89 84 61 58 49.5 76.8 2.4 39.9 40.9 Belarus 74 65 60 55 5.3 4.9 ­0.4 48.9 49.5 Belgium 59 58 36 47 3.9 4.8 1.1 39.0 44.9 Benin 88 86 57 67 1.9 3.6 3.5 41.1 45.7 Bolivia 85 83 59 62 2.8 4.4 2.6 43.1 43.9 Bosnia and Herzegovina 82 66 53 55 2.0 1.9 0.0a 45.2 47.1 Botswana 78 64 64 72 0.5 1.0 3.3 45.5 47.5 Brazil 84 81 45 60 62.6 99.9 2.6 35.1 43.5 Bulgaria 63 55 55 49 4.1 3.7 ­0.6 47.9 46.3 Burkina Faso 91 90 77 78 3.9 6.9 3.2 48.0 47.0 Burundi 90 91 91 91 2.8 4.4 2.5 52.5 52.7 Cambodia 85 87 78 73 4.3 7.5 3.1 52.8 48.8 Cameroon 79 75 48 53 4.4 7.5 3.0 37.5 39.8 Canada 75 71 58 62 14.7 18.7 1.3 44.1 46.9 Central African Republic 88 88 69 71 1.3 2.0 2.4 45.6 46.6 Chad 84 77 65 63 2.4 4.2 3.1 45.6 45.3 Chile 77 70 32 44 5.0 7.7 2.4 30.5 37.5 China 85 78 73 68 643.9 776.9 1.0 44.8 44.6 Hong Kong SAR, China 79 67 47 53 2.9 3.7 1.5 36.3 45.8 Colombia 76 78 29 41 11.2 18.6 2.8 28.2 35.7 Congo, Dem. Rep. 86 89 53 56 13.4 24.0 3.2 39.9 40.6 Congo, Rep. 83 82 59 63 1.0 1.6 2.6 42.1 43.5 Costa Rica 84 78 33 45 1.2 2.1 3.3 27.4 35.2 Côte d'Ivoire 89 85 43 51 4.7 8.1 3.1 30.1 36.7 Croatia 74 59 47 46 2.2 2.0 ­0.5 42.7 45.5 Cuba 72 68 36 42 4.4 5.1 0.8 33.0 38.0 Czech Republic 79 66 52 49 4.9 5.2 0.3 44.4 43.4 Denmark 74 68 62 61 2.9 3.0 0.1 46.1 46.9 Dominican Republic 82 72 43 51 2.9 4.4 2.3 33.2 38.9 Ecuador 78 78 33 47 3.5 5.7 2.8 29.5 37.9 Egypt, Arab Rep. 74 71 27 23 16.8 26.3 2.5 26.6 23.9 El Salvador 80 75 41 47 1.9 2.5 1.5 35.2 42.2 Eritrea 88 86 55 60 1.2 2.1 3.2 41.4 43.6 Estonia 71 64 63 55 0.8 0.7 ­1.0 49.5 49.2 Ethiopia 89 91 72 78 21.5 38.2 3.2 45.1 47.1 Finland 70 63 59 58 2.6 2.7 0.2 47.1 48.1 France 63 59 46 51 25.0 28.6 0.7 43.3 47.0 Gabon 83 79 63 69 0.4 0.7 3.0 44.2 46.4 Gambia, The 86 83 71 71 0.4 0.7 3.4 46.2 46.2 Georgia 82 74 60 55 2.8 2.3 ­1.2 46.9 47.0 Germany 71 64 45 53 38.8 42.4 0.5 40.7 45.4 Ghana 74 73 70 74 6.0 10.6 3.2 48.9 49.2 Greece 65 63 36 43 4.2 5.2 1.2 36.2 40.4 Guatemala 89 84 39 48 3.1 5.3 3.0 31.0 37.8 Guinea 90 89 79 79 2.9 4.7 2.7 46.8 46.8 Guinea-Bissau 86 90 59 60 0.4 0.6 2.4 43.0 42.4 Haiti 81 83 57 58 2.8 4.4 2.5 43.0 42.7 Honduras 87 81 41 42 1.7 2.8 2.7 32.3 34.0 66 2010 World Development Indicators 2.2 PEOPLE Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2008 1990 2008 1990 2008 1990­2008 1990 2008 Hungary 64 57 46 43 4.5 4.3 ­0.3 44.5 45.4 India 85 81 34 33 317.8 449.9 1.9 27.1 27.8 Indonesia 81 86 50 52 74.9 112.8 2.3 38.4 38.4 Iran, Islamic Rep. 80 75 22 31 15.5 27.8 3.2 20.1 30.1 Iraq 73 69 11 13 4.3 7.5 3.0 13.1 16.1 Ireland 68 72 35 54 1.3 2.2 2.8 33.9 42.8 Israel 61 59 42 54 1.7 3.1 3.5 40.6 46.0 Italy 64 58 35 38 23.7 25.2 0.3 36.5 40.4 Jamaica 80 73 65 57 1.1 1.2 0.5 46.6 45.1 Japan 77 69 50 49 63.9 66.9 0.3 40.7 41.5 Jordan 68 70 15 23 0.7 1.9 5.2 16.2 22.8 Kazakhstan 78 75 62 66 7.8 8.5 0.4 47.0 50.0 Kenya 90 87 75 76 9.8 18.2 3.4 46.0 46.5 Korea, Dem. Rep. 79 78 55 55 10.0 12.2 1.1 42.6 42.6 Korea, Rep. 73 72 47 50 19.2 24.4 1.3 39.7 41.9 Kosovo .. .. .. .. .. .. .. .. .. Kuwait 81 81 36 44 0.9 1.4 2.8 22.4 24.3 Kyrgyz Republic 74 75 58 56 1.8 2.5 1.8 46.1 42.6 Lao PDR 83 80 80 78 1.9 3.0 2.4 49.8 50.6 Latvia 76 69 63 56 1.4 1.2 ­1.0 49.6 48.9 Lebanon 83 77 20 22 0.9 1.4 2.8 23.3 24.9 Lesotho 85 75 68 70 0.7 0.9 1.9 51.7 52.4 Liberia 86 85 65 67 0.8 1.5 3.3 46.7 47.6 Libya 78 77 15 24 1.2 2.3 3.7 14.8 21.9 Lithuania 73 60 59 51 1.9 1.6 ­0.9 48.1 48.9 Macedonia, FYR 73 65 46 43 0.8 0.9 0.6 40.7 39.7 Madagascar 85 89 83 84 5.4 9.4 3.1 48.4 49.2 Malawi 80 80 76 75 3.9 6.1 2.5 50.7 49.9 Malaysia 80 80 43 44 7.0 11.7 2.9 34.5 35.2 Mali 70 66 37 37 2.5 3.7 2.1 36.1 36.8 Mauritania 84 80 53 59 0.7 1.4 3.4 39.8 41.7 Mauritius 82 75 38 42 0.4 0.6 1.4 32.1 36.4 Mexico 84 79 34 43 29.9 46.7 2.5 30.0 36.0 Moldova 73 48 61 47 2.1 1.5 ­2.0 48.7 50.7 Mongolia 65 61 63 67 0.9 1.4 2.5 45.6 47.4 Morocco 82 79 25 27 7.8 11.8 2.3 23.7 26.1 Mozambique 84 77 85 85 6.3 10.8 3.0 53.2 52.1 Myanmar 87 86 71 64 20.7 26.8 1.4 45.3 44.5 Namibia 64 60 48 52 0.4 0.8 3.0 44.9 46.7 Nepal 80 76 52 63 7.5 12.9 3.0 38.0 45.4 Netherlands 69 69 43 59 6.9 8.9 1.4 38.8 45.5 New Zealand 73 73 54 62 1.7 2.3 1.7 43.0 46.2 Nicaragua 85 87 39 46 1.4 2.3 2.8 32.3 37.8 Niger 87 88 27 38 2.3 4.6 3.8 24.7 30.8 Nigeria 75 71 36 39 29.4 48.6 2.8 33.0 34.9 Norway 71 69 57 64 2.2 2.6 1.0 44.7 47.6 Oman 81 77 19 25 0.6 1.1 3.4 13.7 18.3 Pakistan 85 85 14 21 31.0 55.8 3.3 12.7 19.2 Panama 81 79 39 49 0.9 1.6 3.0 32.4 36.9 Papua New Guinea 75 73 71 71 1.8 2.9 2.7 46.9 48.9 Paraguay 82 84 47 56 1.7 2.9 3.1 34.9 38.7 Peru 75 82 49 57 8.3 13.3 2.6 39.7 43.3 Philippines 83 80 48 49 24.1 37.9 2.5 36.5 38.2 Poland 71 60 55 47 18.1 17.7 ­0.1 45.4 44.8 Portugal 72 68 49 56 4.7 5.6 0.9 42.4 46.8 Puerto Rico 59 56 31 37 1.2 1.5 1.3 35.8 41.7 Qatar 93 90 40 48 0.3 0.9 6.7 13.5 11.6 2010 World Development Indicators 67 2.2 Labor force structure Labor force participation rate Labor force Ages 15 and older % ages 15 and older Total average annual Female Male Female millions % growth % of labor force 1990 2008 1990 2008 1990 2008 1990­2008 1990 2008 Romania 66 58 60 47 11.8 10.0 ­0.9 46.3 44.5 Russian Federation 75 69 60 57 76.8 76.0 ­0.1 48.6 49.7 Rwanda 88 80 87 86 3.2 4.8 2.3 52.1 52.8 Saudi Arabia 80 80 15 21 5.0 9.0 3.2 11.5 16.3 Senegal 90 87 62 65 3.0 5.2 3.0 40.8 43.1 Serbia .. .. .. .. .. .. .. .. .. Sierra Leone 65 67 66 66 1.6 2.1 1.6 50.9 51.4 Singapore 79 75 51 54 1.6 2.6 2.9 39.1 41.7 Slovak Republic 78 68 59 51 2.6 2.7 0.3 46.8 44.7 Slovenia 75 64 47 54 0.8 1.0 1.2 46.8 46.5 Somalia 89 88 58 57 2.6 3.5 1.6 41.8 40.9 South Africa 64 60 36 47 10.4 18.6 3.2 37.5 43.7 Spain 68 66 34 49 15.6 22.8 2.1 34.8 43.0 Sri Lanka 79 74 37 35 6.8 8.3 1.1 31.8 32.7 Sudan 78 72 27 31 8.0 13.1 2.7 26.0 29.5 Swaziland 79 68 45 53 0.3 0.4 2.7 41.2 43.4 Sweden 70 67 63 61 4.7 5.0 0.3 47.7 47.4 Switzerland 77 72 57 61 3.8 4.4 0.8 42.9 46.5 Syrian Arab Republic 81 78 18 21 3.3 6.7 4.0 18.3 20.7 Tajikistan 83 68 59 56 2.1 2.8 1.7 43.3 43.6 Tanzania 93 90 87 86 12.3 20.8 2.9 49.8 49.4 Thailand 87 80 75 66 32.1 38.5 1.0 47.0 46.2 Timor-Leste 81 83 58 59 0.3 0.4 1.7 40.4 40.9 Togo 88 87 56 63 1.5 2.9 3.5 40.1 43.3 Trinidad and Tobago 76 78 39 54 0.5 0.7 2.3 35.0 43.0 Tunisia 75 70 21 26 2.4 3.8 2.5 21.6 26.6 Turkey 81 70 34 25 20.7 25.8 1.2 29.7 26.2 Turkmenistan 74 70 58 61 1.4 2.4 2.8 46.1 46.7 Uganda 91 90 81 78 7.9 13.6 3.0 47.7 46.6 Ukraine 71 64 56 52 25.5 23.1 ­0.6 49.2 48.9 United Arab Emirates 92 94 25 42 1.0 2.8 6.0 9.8 15.5 United Kingdom 73 67 52 55 29.0 31.5 0.5 43.2 45.7 United States 75 70 57 59 129.2 158.2 1.1 44.4 46.1 Uruguay 71 73 48 53 1.4 1.6 0.9 40.8 43.7 Uzbekistan 85 70 53 58 7.3 12.3 2.9 45.5 45.9 Venezuela, RB 82 81 36 51 7.2 12.7 3.2 30.5 39.1 Vietnam 81 75 74 68 31.1 45.6 2.1 50.7 48.7 West Bank and Gaza 66 67 11 16 0.4 0.9 4.6 13.8 18.0 Yemen, Rep. 70 66 16 20 2.6 6.0 4.5 18.0 20.8 Zambia 81 81 61 60 3.0 4.7 2.5 44.3 43.8 Zimbabwe 80 78 67 60 4.1 4.9 1.0 46.3 47.8 World 80 w 77 w 52 w 52 w 2,322.0 t 3,102.8 t 1.6 w 39.2 w 40.4 w Low income 85 83 65 65 276.8 441.4 2.6 44.1 44.5 Middle income 82 78 51 49 1,612.4 2,159.8 1.6 37.8 38.8 Lower middle income 83 79 53 49 1,290.3 1,727.9 1.6 37.7 38.1 Upper middle income 78 73 46 50 322.0 432.0 1.6 38.0 42.0 Low & middle income 83 79 53 52 1,889.1 2,601.3 1.8 38.7 39.8 East Asia & Pacific 84 79 69 64 847.3 1,079.8 1.3 44.1 44.6 Europe & Central Asia 75 67 56 50 199.9 200.0 0.0a 45.8 45.2 Latin America & Carib. 81 79 40 52 163.3 264.5 2.7 32.1 41.1 Middle East & N. Africa 77 73 22 26 62.4 111.1 3.2 21.4 26.5 South Asia 85 82 35 35 421.5 618.6 2.1 28.1 29.4 Sub-Saharan Africa 82 80 57 60 194.7 327.2 2.9 42.2 43.6 High income 72 68 49 53 432.9 501.5 0.8 41.1 43.3 Euro area 67 62 42 49 130.4 147.6 0.7 39.4 43.7 a. Less than 0.05. 68 2010 World Development Indicators 2.2 PEOPLE Labor force structure About the data Definitions The labor force is the supply of labor available for pro- further information on source, reference period, or · Labor force participation rate is the proportion ducing goods and services in an economy. It includes definition, consult the original source. of the population ages 15 and older that is eco- people who are currently employed and people who The labor force participation rates in the table are nomically active: all people who supply labor for the are unemployed but seeking work as well as first-time from the ILO's Key Indicators of the Labour Market, production of goods and services during a specified job-seekers. Not everyone who works is included, 6th edition, database. These harmonized estimates period. · Total labor force is people ages 15 and however. Unpaid workers, family workers, and stu- use strict data selection criteria and enhanced older who meet the ILO definition of the economi- dents are often omitted, and some countries do not methods to ensure comparability across countries cally active population. It includes both the employed count members of the armed forces. Labor force size and over time, including collection and tabulation and the unemployed. · Average annual percentage tends to vary during the year as seasonal workers methodologies and methods applied to such country- growth of the labor force is calculated using the enter and leave. specifi c factors as military service requirements. exponential endpoint method (see Statistical meth- Data on the labor force are compiled by the Inter- Estimates are based mainly on labor force surveys, ods for more information). · Female labor force as as national Labour Organization (ILO) from labor force with other sources (population censuses and nation- a percentage of the labor force shows the extent to surveys, censuses, establishment censuses and ally reported estimates) used only when no survey which women are active in the labor force. surveys, and administrative records such as employ- data are available. ment exchange registers and unemployment insur- Participation rates indicate the relative size of ance schemes. For some countries a combination the labor supply. Beginning in the 2008 edition of of these sources is used. Labor force surveys are World Development Indicators, the indicator covers the most comprehensive source for internationally the population ages 15 and older, to include peo- comparable labor force data. They can cover all ple who continue working past age 65. In previous noninstitutionalized civilians, all branches and sec- editions the indicator was for the population ages tors of the economy, and all categories of workers, 15­64, so participation rates are not comparable including people holding multiple jobs. By contrast, across editions. labor force data from population censuses are often The labor force estimates in the table were calcu- based on a limited number of questions on the eco- lated by applying labor force participation rates from nomic characteristics of individuals, with little scope the ILO database to World Bank population estimates to probe. The resulting data often differ from labor to create a series consistent with these population force survey data and vary considerably by country, estimates. This procedure sometimes results in depending on the census scope and coverage. Estab- labor force estimates that differ slightly from those lishment censuses and surveys provide data only on in the ILO's Yearbook of Labour Statistics and its data- the employed population, not unemployed workers, base Key Indicators of the Labour Market. workers in small establishments, or workers in the Estimates of women in the labor force and employ- informal sector (ILO, Key Indicators of the Labour ment are generally lower than those of men and are Market 2001­2002). not comparable internationally, reflecting that demo- The reference period of a census or survey is graphic, social, legal, and cultural trends and norms another important source of differences: in some determine whether women's activities are regarded countries data refer to people's status on the day as economic. In many countries many women work of the census or survey or during a specific period on farms or in other family enterprises without pay, before the inquiry date, while in others data are and others work in or near their homes, mixing work recorded without reference to any period. In devel- and family activities during the day. oping countries, where the household is often the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working. Data sources Differing definitions of employment age also affect comparability. For most countries the working age is Data on labor force participation rates are from 15 and older, but in some countries children younger the ILO's Key Indicators of the Labour Market, 6th than 15 work full- or part-time and are included in edition, database. Labor force numbers were cal- the estimates. Similarly, some countries have an culated by World Bank staff, applying labor force upper age limit. As a result, calculations may sys- participation rates from the ILO database to popu- tematically over- or underestimate actual rates. For lation estimates. 2010 World Development Indicators 69 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 20 .. 22 .. 26 .. 28 .. 54 .. 49 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b,c 3c 0 b,c 0 b,c 40 c 34 c 18 c 11c 59c 63c 81c 88 c Armenia .. 46 .. 46 .. 21 .. 10 .. 33 .. 45 Australia 6 4 4 2 32 31 12 9 61 64 84 89 Austria 6 6 8 6 47 37 20 12 46 57 72 82 Azerbaijan .. 40 .. 38 .. 17 .. 9 .. 44 .. 53 Bangladesh 54 42 85 68 16 15 9 13 25 43 2 19 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 3 2 2 1 41 36 16 11 56 61 81 88 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c .. 1c .. 42c .. 17c .. 55c .. 82c .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 35 .. 24 .. 19 .. 11 .. 46 .. 65 Brazil 31c 23 25c 15 27c 28 10 c 13 43c 50 65c 72 Bulgaria .. 9 .. 6 .. 42 .. 29 .. 49 .. 65 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 6c 3c 2c 2c 31c 32c 11c 11c 64 c 65c 87c 88 c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 16 6 6 32 31 15 11 45 53 79 84 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 1 0b 0b 0b 37 21 27 6 63 78 73 94 Colombia 2c 27 1c 6 35c 22 25c 16 63c 51 74 c 78 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 18 5 5 27 28 25 13 41 54 69 82 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 12 .. 14 .. 40 .. 18 .. 48 .. 67 Cuba .. 25 .. 9 .. 22 .. 12 .. 54 .. 79 Czech Republic .. 4 .. 2 .. 51 .. 27 .. 45 .. 71 Denmark 7 4 3 1 37 32 16 12 56 64 82 86 Dominican Republic 26 21 3 2 23 26 21 14 52 53 76 84 Ecuador 10 c 11c 2c 4c 29c 28 c 17c 13c 62c 61c 81c 83c Egypt, Arab Rep. 35 28 52 43 25 26 10 6 41 46 37 51 El Salvador 48 29 15 5 23 26 23 19 29 45 63 76 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 5 13 2 42 48 30 23 36 46 57 75 Ethiopia .. 12c .. 6c .. 27c .. 17c .. 61c .. 77c Finland 11 6 6 3 38 39 15 11 51 54 78 86 France 7 4 5 2 39 34 17 11 54 61 78 86 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 51 .. 57 .. 17 .. 4 .. 33 .. 39 Germany 4 3 4 2 50 41 24 16 46 56 73 83 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 8 26 9 29 22 17c 7 51 44 57c 59 Guatemala 19c 44 3c 16 36c 24 27c 21 45c 32 70 c 63 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 76 .. 50 .. 9 .. 9 .. 13 .. 38 .. Honduras 53c 51c 6c 13c 18 c 20 c 25c 23c 29c 29c 69c 63c 70 2010 World Development Indicators 2.3 PEOPLE Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a Hungary 19 6 13 2 43 42 29 21 38 52 58 77 India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 41 57 41 15 21 13 15 31 38 31 44 Iran, Islamic Rep. .. 21 .. 33 .. 33 .. 29 .. 47 .. 38 Iraq .. 14 .. 33 .. 20 .. 7 .. 66 .. 60 Ireland 19 9 3 2 33 38 18 10 48 53 78 88 Israel 5 3 2 1 38 32 15 11 57 65 83 88 Italy 8 5 9 3 41 39 23 16 52 57 68 81 Jamaica 36 26 16 8 25 27 12 5 39 47 72 87 Japan 6 4 7 4 40 35 27 17 54 59 65 77 Jordan .. .. .. .. .. .. .. .. .. .. .. .. Kazakhstan .. 35 .. 32 .. 24 .. 10 .. 41 .. 58 Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 7 18 8 40 33 28 16 46 60 54 76 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 37 .. 35 .. 26 .. 11 .. 37 .. 54 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 10 .. 6 .. 40 .. 17 .. 49 .. 77 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 10 .. 6 .. 41 .. 19 .. 49 .. 75 Macedonia, FYR .. 19 .. 17 .. 33 .. 29 .. 48 .. 54 Madagascar .. 82 .. 83 .. 5 .. 2 .. 13 .. 16 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 18 20 10 31 32 32 23 46 51 48 67 Mali .. 50 .. 30 .. 18 .. 15 .. 32 .. 55 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 10 13 8 36 36 48 26 48 54 39 66 Mexico 34 19 11 4 25 31 19 18 41 50 70 77 Moldova .. 36 .. 30 .. 25 .. 12 .. 39 .. 58 Mongolia .. 41 .. 35 .. 21 .. 15 .. 39 .. 50 Morocco 4c 37 3c 61 33c 22 46c 15 63c 41 51c 24 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 34 52 25 21 19 8 9 34 47 40 65 Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 3 2 2 33 27 10 8 60 63 81 85 New Zealand 13c 9 8c 5 31c 32 13c 10 56c 58 79c 85 Nicaragua .. 42 .. 8 .. 20 .. 18 .. 38 .. 73 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 4 3 1 34 33 10 8 58 63 86 90 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 36 69 72 20 23 15 13 35 41 16 15 Panama 35 21 3 3 20 25 11 10 45 54 85 87 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay .. 31 .. 19 .. 24 .. 10 .. 45 .. 71 Peru 1c 12c 0 b,c 6c 30 c 41c 13c 43c 69c 46c 87c 51c Philippines 53 44 32 24 17 18 14 11 29 39 55 65 Poland .. 15 .. 14 .. 41 .. 18 .. 44 .. 68 Portugal 10 11 13 12 39 40 24 17 51 49 63 71 Puerto Rico 5 2 0b 0b 27 26 19 10 67 72 80 89 Qatar .. 4 .. 0 .. 48 .. 4 .. 48 .. 96 2010 World Development Indicators 71 2.3 Employment by economic activity Agriculture Industry Services Male Female Male Female Male Female % of male % of female % of male % of female % of male % of female employment employment employment employment employment employment 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a 1990­92a 2004­08a Romania 29 27 38 30 44 38 30 24 28 35 33 46 Russian Federation .. 11 .. 7 .. 38 .. 20 .. 51 .. 73 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 0b .. 23 .. 1 .. 72 .. 99 Senegal .. 34 .. 33 .. 20 .. 5 .. 33 .. 42 Serbia .. 24 .. 26 .. 34 .. 16 .. 42 .. 58 Sierra Leone .. 66 .. 71 .. 10 .. 3 .. 23 .. 26 Singapore 1 2 0b 1 36 26 32 18 63 72 68 82 Slovak Republic .. 6 .. 2 .. 52 .. 24 .. 43 .. 74 Slovenia .. 10 .. 10 .. 44 .. 23 .. 45 .. 65 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 11 .. 7 .. 35 .. 14 .. 54 .. 80 Spain 11 6 8 3 41 40 17 11 49 55 75 86 Sri Lanka .. 28 c .. 37c .. 26c .. 27 .. 41 .. 34 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5 3 2 1 40 33 12 9 55 64 86 90 Switzerland 5 5 4 3 39 34 15 12 57 62 81 86 Syrian Arab Republic 23 .. 54 .. 28 .. 8 .. 49 .. 38 .. Tajikistan .. 42 .. 75 .. 27 .. 5 .. 31 .. 20 Tanzania .. 71 .. 78 .. 7 .. 3 .. 22 .. 19 Thailand 59 43 62 40 17 22 13 19 24 35 25 41 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 6 6 2 34 41 14 16 51 52 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 19 72 46 26 30 11 15 41 51 17 39 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 6 .. 0b .. 45 .. 6 .. 49 .. 92 United Kingdom 3 2 1 1 41 32 16 9 55 66 82 90 United States 4 2 1 1 34 30 14 9 62 68 85 90 Uruguay 7c 16 1c 5 36c 29 21c 13 57c 56 78 c 83 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 13 2 2 32 30 16 12 52 56 82 86 Vietnam .. 56 .. 60 .. 21 .. 14 .. 23 .. 26 West Bank and Gaza .. 11 .. 36 .. 27 .. 10 .. 61 .. 53 Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 .. Zambia 47 .. 56 .. 15 .. 3 .. 22 .. 18 .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 16 .. 9 .. 33 .. 19 .. 51 .. 72 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 16 .. 16 .. 35 .. 19 .. 48 .. 65 Latin America & Carib. 21 20 13 9 30 29 14 16 49 51 72 75 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 2 38 34 19 12 55 62 76 85 Euro area 7 5 6 3 42 38 20 13 50 56 73 83 Note: Data across sectors may not sum to 100 percent because of workers not classified by sector. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. 72 2010 World Development Indicators 2.3 PEOPLE Employment by economic activity About the data Definitions The International Labour Organization (ILO) classi- aggregated into three broad groups: agriculture, · Agriculture corresponds to division 1 (ISIC revi- fies economic activity using the International Stan- industry, and services. Such broad classification may sion 2) or tabulation categories A and B (ISIC revi- dard Industrial Classification (ISIC) of All Economic obscure fundamental shifts within countries' indus- sion 3) and includes hunting, forestry, and fishing. Activities, revision 2 (1968) and revision 3 (1990). trial patterns. A slight majority of countries report · Industry corresponds to divisions 2­5 (ISIC revi- Because this classification is based on where work economic activity according to the ISIC revision 2 sion 2) or tabulation categories C­F (ISIC revision is performed (industry) rather than type of work per- instead of revision 3. The use of one classification or 3) and includes mining and quarrying (including oil formed (occupation), all of an enterprise's employees the other should not have a significant impact on the production), manufacturing, construction, and public are classified under the same industry, regardless information for the three broad sectors presented utilities (electricity, gas, and water). · Services corre- of their trade or occupation. The categories should in the table. spond to divisions 6­9 (ISIC revision 2) or tabulation sum to 100 percent. Where they do not, the differ- The distribution of economic wealth in the world categories G­P (ISIC revision 3) and include whole- ences are due to workers who cannot be classified remains strongly correlated with employment by sale and retail trade and restaurants and hotels; by economic activity. economic activity. The wealthier economies are transport, storage, and communications; financing, Data on employment are drawn from labor force those with the largest share of total employment in insurance, real estate, and business services; and surveys, household surveys, official estimates, cen- services, whereas the poorer economies are largely community, social, and personal services. suses and administrative records of social insurance agriculture based. schemes, and establishment surveys when no other The distribution of economic activity by gender information is available. The concept of employment reveals some clear patterns. Men still make up the generally refers to people above a certain age who majority of people employed in all three sectors, but worked, or who held a job, during a reference period. the gender gap is biggest in industry. Employment in Employment data include both full-time and part-time agriculture is also male-dominated, although not as workers. much as industry. Segregating one sex in a narrow There are many differences in how countries define range of occupations significantly reduces economic and measure employment status, particularly mem- efficiency by reducing labor market flexibility and thus bers of the armed forces, self-employed workers, and the economy's ability to adapt to change. This seg- unpaid family workers. Where members of the armed regation is particularly harmful for women, who have forces are included, they are allocated to the service a much narrower range of labor market choices and sector, causing that sector to be somewhat over- lower levels of pay than men. But it is also detri- stated relative to the service sector in economies mental to men when job losses are concentrated where they are excluded. Where data are obtained in industries dominated by men and job growth is from establishment surveys, data cover only employ- centered in service occupations, where women have ees; thus self-employed and unpaid family workers better chances, as has been the recent experience are excluded. In such cases the employment share in many countries. of the agricultural sector is severely underreported. There are several explanations for the rising impor- Caution should be also used where the data refer tance of service jobs for women. Many service jobs-- only to urban areas, which record little or no agricul- such as nursing and social and clerical work--are tural work. Moreover, the age group and area covered considered "feminine" because of a perceived simi- could differ by country or change over time within a larity to women's traditional roles. Women often do country. For detailed information on breaks in series, not receive the training needed to take advantage of consult the original source. changing employment opportunities. And the greater Countries also take different approaches to the availability of part-time work in service industries may treatment of unemployed people. In most countries lure more women, although it is unclear whether this unemployed people with previous job experience are is a cause or an effect. classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribu- tion of employment by economic activity may not be fully comparable across countries. Data sources The ILO's Yearbook of Labour Statistics and its data- base Key Indicators of the Labour Market report data Data on employment are from the ILO's Key Indica- by major divisions of the ISIC revision 2 or revision 3. tors of the Labour Market, 6th edition, database. In the table the reported divisions or categories are 2010 World Development Indicators 73 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15­24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2008a 1990 2008 1990 2008 1990­92 2003­05 Afghanistan 54 55 45 47 16 29 .. .. .. .. .. .. Albania 49 46 37 36 88 .. .. .. .. .. ­17.5 5.4 Algeria 39 49 25 31 60 .. .. .. .. .. ­4.0 1.3 Angola 77 76 71 69 10 .. .. .. .. .. ­5.0 12.0 Argentina 53 57 42 36 74 85 .. 22b .. 17b 9.0 2.1 Armenia 38 38 24 25 .. 88 .. .. .. .. ­24.8 12.7 Australia 56 59 58 64 82 148 12 11 9 7 3.3 0.3 Austria 52 55 61 53 102 100 .. 9 .. 9 0.7 2.4 Azerbaijan 57 60 38 39 88 106 .. 41 .. 66 ­12.6 24.8 Bangladesh 74 68 66 56 20 44 .. .. .. .. 1.9 4.2 Belarus 58 52 40 35 93 95 .. .. .. .. ­4.0 10.3 Belgium 44 47 31 27 101 110 17 11 15 9 1.6 1.4 Benin 70 72 64 59 11 .. .. .. .. .. .. .. Bolivia 61 71 48 49 44 82 32b .. 50 b .. 2.6 1.0 Bosnia and Herzegovina 42 42 17 18 .. 89 .. .. .. .. ­14.8 4.6 Botswana 47 46 34 27 49 80 .. .. .. .. .. .. Brazil 56 64 54 53 61 100 29b 30 30 b 24 ­0.3 0.4 Bulgaria 45 46 27 27 86 105 .. 10 .. 8 3.1 3.7 Burkina Faso 82 82 77 74 7 20 c .. .. .. .. 1.3 2.1 Burundi 85 84 74 73 5 18 .. .. .. .. .. .. Cambodia 77 75 66 68 25 40 .. .. .. .. 4.0 6.5 Cameroon 59 59 37 33 26 37 .. .. .. .. ­6.7 1.1 Canada 58 61 57 61 101 101 .. 12b .. 9b 0.8 1.4 Central African Republic 73 73 59 58 12 .. .. .. .. .. .. .. Chad 67 70 51 50 7 19 .. .. .. .. .. .. Chile 51 50 34 24 73 94 .. 25 .. 21 6.6 2.9 China 75 71 71 55 41 74 .. .. .. .. 6.8 9.2 Hong Kong SAR, China 62 57 54 38 80 83 .. 10 .. 4 5.3 5.5 Colombia 52 62 38 43 53 91 30 b 48 26 b 46 ­0.7 2.2 Congo, Dem. Rep. 68 67 60 62 21 35 .. .. .. .. ­12.9 4.2 Congo, Rep. 66 65 49 46 46 .. .. .. .. .. .. .. Costa Rica 56 57 48 43 45 89 26 20 21 20 2.4 1.3 Côte d'Ivoire 63 60 52 45 20 .. .. .. .. .. ­3.6 ­0.3 Croatia 50 46 27 29 .. 94 .. 15b .. 17b ­7.7 3.0 Cuba 52 54 40 32 94 91 .. .. .. .. .. .. Czech Republic 58 54 48 29 91 95 .. 15 .. 9 ­5.2 4.7 Denmark 59 60 65 61 109 119 7 7 6 3 2.5 2.2 Dominican Republic 44 53 28 34 .. 75 42 49 30 30 0.7 2.2 Ecuador 52 61 39 40 55 70 33b 29 b 41b 41b ­0.1 1.9 Egypt, Arab Rep. 43 43 22 23 69 .. .. 20 .. 44 2.1 1.6 El Salvador 59 54 42 39 38 64 .. 29 .. 44 .. .. Eritrea 66 66 60 54 .. 30 .. .. .. .. .. .. Estonia 61 55 43 29 100 100 2 8 3 4 ­9.4 7.4 Ethiopia 71 81 64 74 14 33 .. 48b .. 56 b ­8.4 7.3 Finland 57 55 45 44 116 111 .. 11 .. 7 1.4 2.3 France 47 48 28 29 100 113 11 7 10 5 1.4 1.8 Gabon 58 58 37 33 39 .. .. .. .. .. .. .. Gambia, The 73 72 59 55 19 51 .. .. .. .. .. .. Georgia 57 54 28 22 95 90 .. .. .. .. ­25.3 9.8 Germany 54 52 58 44 98 101 .. 7 .. 6 3.7 0.8 Ghana 68 65 40 40 35 54 .. .. .. .. 2.8 3.0 Greece 44 48 31 28 94 102 .. 27 .. 27 2.4 2.8 Guatemala 55 62 50 52 23 57 .. .. .. .. 1.0 2.1 Guinea 82 81 75 73 10 36 .. .. .. .. .. .. Guinea-Bissau 66 67 57 63 6 36 .. .. .. .. .. .. Haiti 56 55 37 47 21 .. .. .. .. .. .. .. Honduras 59 56 49 43 33 65 48b .. 50 b .. .. .. 74 2010 World Development Indicators 2.4 PEOPLE Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15­24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2008a 1990 2008 1990 2008 1990­92 2003­05 Hungary 48 45 37 20 86 97 8 8 7 6 0.3 4.6 India 58 56 46 40 42 57 .. .. .. .. 1.0 5.8 Indonesia 63 62 46 41 46 76 .. 60 .. 68 6.2 4.7 Iran, Islamic Rep. 46 49 33 36 53 80 .. 40 .. 56 6.5 0.4 Iraq 37 37 27 23 44 .. .. .. .. .. ­33.6 17.5 Ireland 44 58 38 44 100 113 25 17 9 5 2.4 1.6 Israel 45 50 25 27 92 91 .. 9 .. 5 0.0 3.1 Italy 43 44 30 25 79 100 29 21 24 15 0.6 0.6 Jamaica 61 56 40 29 66 90 46 38 37 31 0.7 ­0.4 Japan 61 54 43 40 97 101 15 10 26 12 0.7 2.0 Jordan 36 38 25 20 82 86 .. .. .. .. ­5.5 3.9 Kazakhstan 63 64 46 42 100 95c .. .. .. .. ­15.1 7.5 Kenya 73 73 62 59 46 58 .. .. .. .. ­3.9 2.1 Korea, Dem. Rep. 62 64 46 39 .. .. .. .. .. .. .. .. Korea, Rep. 59 58 36 28 90 97 .. 23 .. 28 5.0 2.8 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 62 65 29 30 43 91 .. .. .. .. ­0.2 1.4 Kyrgyz Republic 58 58 41 40 100 85 .. 47 .. 47 ­13.1 ­0.4 Lao PDR 80 78 74 64 23 44 .. .. .. .. .. .. Latvia 58 55 43 35 92 115 .. 8 .. 6 ­19.6 8.1 Lebanon 44 46 31 29 62 82 .. .. .. .. .. .. Lesotho 48 54 40 40 24 40 .. .. .. .. .. .. Liberia 66 66 57 57 .. 32 .. .. .. .. .. .. Libya 45 49 28 27 .. 93 .. .. .. .. .. .. Lithuania 54 50 36 18 92 99 .. 11 .. 8 ­13.9 6.3 Macedonia, FYR 37 35 17 13 .. 84 .. 24 .. 20 ­5.6 4.1 Madagascar 79 83 65 71 18 30 .. .. .. .. ­5.9 1.5 Malawi 72 72 48 49 8 29 .. .. .. .. ­1.9 1.9 Malaysia 60 61 47 45 57 .. 31 23 25 21 6.0 5.1 Mali 49 47 40 35 7 35 .. .. .. .. 0.4 1.0 Mauritania 67 47 54 23 13 23 .. .. .. .. .. .. Mauritius 56 54 45 37 55 88 13 18 7 15 .. .. Mexico 57 57 50 42 54 87 29 28 15 32 1.0 1.6 Moldova 58 45 39 17 80 83 .. 35 .. 30 ­22.0 9.0 Mongolia 50 52 39 35 82 95 .. .. .. .. .. .. Morocco 46 46 40 35 36 56 .. 46 .. 65 ­1.7 1.7 Mozambique 80 78 67 66 7 21 .. .. .. .. ­3.0 6.2 Myanmar 74 74 62 53 23 49 .. .. .. .. 2.0 8.9 Namibia 45 43 24 14 43 66 .. .. .. .. .. .. Nepal 60 62 52 46 33 43 .. .. .. .. .. .. Netherlands 51 59 55 67 120 120 7 10 10 8 0.4 2.3 New Zealand 55 63 55 56 92 120 15 14 10 10 0.5 0.3 Nicaragua 57 58 46 48 43 68 .. 45 .. 46 .. .. Niger 59 60 50 52 7 11 .. .. .. .. ­5.7 0.2 Nigeria 53 52 29 24 23 30 .. .. .. .. ­2.9 4.6 Norway 58 62 49 56 103 113 .. 8 .. 3 3.9 2.4 Oman 53 51 30 29 45 88 .. .. .. .. 0.2 3.7 Pakistan 48 52 38 44 23 33 .. 58 .. 75 6.5 4.4 Panama 50 59 33 40 62 71 44 30 19 24 .. .. Papua New Guinea 70 70 57 54 12 .. .. .. .. .. .. .. Paraguay 61 73 51 58 31 66 17b 45 31b 50 .. .. Peru 53 69 34 53 67 98 30 b 33b 46b 47b ­0.8 2.7 Philippines 59 60 42 39 70 81 .. 44 .. 47 ­3.3 2.5 Poland 53 48 31 27 87 100 .. 20 .. 18 2.8 2.7 Portugal 58 56 53 35 66 101 22 18 30 19 2.2 1.4 Puerto Rico 37 41 21 29 .. .. .. .. .. .. .. .. Qatar 73 77 35 47 84 93 .. .. .. .. 0.1 7.4 2010 World Development Indicators 75 2.4 Decent work and productive employment Employment to Gross enrollment Vulnerable Labor population ratio ratio, secondary employment productivity Unpaid family workers and own-account workers GDP per person Total Youth Male Female employed % ages 15 and older % ages 15­24 % of relevant age group % of male employment % of female employment % growth 1991 2008 1991 2008 1991 2008a 1990 2008 1990 2008 1990­92 2003­05 Romania 56 48 42 24 92 87 21 31 33 32 ­9.3 8.0 Russian Federation 57 57 34 33 93 84 1 6 1 6 ­7.9 6.1 Rwanda 87 80 79 64 9 22 .. .. .. .. .. .. Saudi Arabia 50 51 26 25 44 95 .. .. .. .. 4.9 1.8 Senegal 67 66 60 55 15 31 77 .. 91 .. ­1.0 3.4 Serbia 49d 47d 28d 30 d .. 90 .. 20 .. 14 .. .. Sierra Leone 64 65 38 42 16 35 .. .. .. .. .. .. Singapore 64 62 56 38 .. .. 10 12 6 7 1.5 6.7 Slovak Republic 55 53 43 30 .. 93 .. 14 .. 6 ­0.8 5.2 Slovenia 55 54 38 32 89 94 .. 12 .. 10 ­2.3 4.2 Somalia 66 67 59 58 .. .. .. .. .. .. .. .. South Africa 39 41 19 15 69 95 .. 2 .. 3 ­4.5 3.9 Spain 41 49 36 37 105 119 20 13 24 10 2.4 ­1.2 Sri Lanka 51 55 31 36 72 .. .. 39 b .. 44b 5.5 2.2 Sudan 46 47 29 23 20 38 c .. .. .. .. ­1.3 ­0.2 Swaziland 54 50 34 26 43 53 .. .. .. .. .. .. Sweden 62 58 59 45 90 103 .. 9 .. 4 1.9 3.9 Switzerland 65 61 69 63 98 96 8 10 11 11 ­0.6 2.0 Syrian Arab Republic 47 45 38 32 48 74 .. .. .. .. 6.5 0.6 Tajikistan 54 55 36 38 102 84 .. .. .. .. ­20.4 2.5 Tanzania 87 78 79 70 5 .. .. 82b .. 93b ­2.4 4.8 Thailand 77 72 70 46 30 .. 67 51 74 56 6.8 3.3 Timor-Leste 64 67 51 58 .. .. .. .. .. .. .. .. Togo 66 65 58 53 20 41 .. .. .. .. .. .. Trinidad and Tobago 45 61 33 46 82 89 22 .. 21 .. ­3.5 4.5 Tunisia 41 41 29 22 45 90 .. .. .. .. 2.6 2.1 Turkey 53 42 48 31 48 82 .. 30 .. 49 1.0 6.7 Turkmenistan 56 58 35 34 .. .. .. .. .. .. ­13.0 6.0 Uganda 82 83 73 75 11 25 .. .. .. .. ­1.1 3.3 Ukraine 57 54 37 34 94 94 .. .. .. .. ­7.9 6.0 United Arab Emirates 71 76 43 46 68 94 .. .. .. .. ­3.9 2.2 United Kingdom 56 56 66 56 87 97 13 14 6 7 2.0 1.7 United States 59 59 56 51 92 94 .. .. .. .. 1.7 1.9 Uruguay 53 56 42 39 84 92 .. 26 .. 24 5.2 5.1 Uzbekistan 54 58 36 39 99 102 .. .. .. .. ­7.8 4.1 Venezuela, RB 51 61 35 40 53 81 .. 28 .. 33 4.5 15.0 Vietnam 75 69 75 51 32 .. .. .. .. .. 4.6 5.7 West Bank and Gaza 30 30 19 15 .. 92 .. 34 .. 44 .. .. Yemen, Rep. 38 39 23 22 .. .. .. .. .. .. 0.9 0.6 Zambia 57 61 40 46 23 52 56 .. 81 .. ­2.5 3.2 Zimbabwe 70 65 48 50 49 41 .. .. .. .. ­4.7 ­5.6 World 62 w 60 w 52 w 45 w 50 w 66 w .. w .. w .. w .. w 0.7 w 3.3 w Low income 70 69 60 56 26 41 .. .. .. .. ­2.8 4.5 Middle income 62 60 52 42 47 67 .. .. .. .. 1.3 5.9 Lower middle income 65 62 55 43 42 62 .. .. .. .. 3.2 6.7 Upper middle income 54 56 41 38 67 90 .. 25 .. 24 ­2.1 3.9 Low & middle income 63 62 53 45 44 62 .. .. .. .. 1.1 5.8 East Asia & Pacific 73 69 67 51 41 73 .. .. .. .. 6.5 7.8 Europe & Central Asia 55 52 38 32 85 89 .. 19 .. 18 ­8.0 6.3 Latin America & Carib. 55 61 46 45 57 88 .. 32 .. 32 1.8 2.5 Middle East & N. Africa 43 45 29 29 54 72 .. 33 .. 52 1.5 0.9 South Asia 59 57 48 42 37 52 .. .. .. .. 3.1 5.5 Sub-Saharan Africa 64 64 50 49 22 33 .. .. .. .. ­5.3 3.7 High income 55 55 47 43 91 100 .. .. .. .. 2.3 1.8 Euro area 48 50 41 37 .. .. .. 12 .. 9 2.4 1.0 a. Provisional data. b. Limited coverage. c. Data are for 2009. d. Includes Montenegro. 76 2010 World Development Indicators 2.4 PEOPLE Decent work and productive employment About the data Definitions Four targets were added to the UN Millennium Dec- small group of countries. The labor force survey is · Employment to population ratio is the proportion laration at the 2005 World Summit High-Level Ple- the most comprehensive source for internationally of a country's population that is employed. People nary Meeting of the 60th Session of the UN General comparable employment, but there are still some ages 15 and older are generally considered the work- Assembly. One was full and productive employment limitations for comparing data across countries and ing-age population. People ages 15­24 are generally and decent work for all, which is seen as the main over time even within a country. Information from considered the youth population. · Gross enrollment route for people to escape poverty. The four indi- labor force surveys is not always consistent in what ratio, secondary, is the ratio of total enrollment in cators for this target have an economic focus, and is included in employment. For example, informa- secondary education, regardless of age, to the popu- three of them are presented in the table. tion provided by the Organisation for Economic Co- lation of the age group that officially corresponds to The employment to population ratio indicates operation and Development relates only to civilian secondary education. · Vulnerable employment is how efficiently an economy provides jobs for people employment, which can result in an underestimation unpaid family workers and own-account workers as a who want to work. A high ratio means that a large of "employees" and "workers not classified by sta- percentage of total employment. · Labor productiv- propor tion of the population is employed. But a tus," especially in countries with large armed forces. ity is the growth rate of gross domestic product lower employment to population ratio can be seen While the categories of unpaid family workers and (GDP) divided by total employment in the economy. as a positive sign, especially for young people, if it self-employed workers, which include own-account is caused by an increase in their education. This workers, would not be affected, their relative shares indicator has a gender bias because women who do would be. Geographic coverage is another factor that not consider their work employment or who are not can limit cross-country comparisons. The employment perceived as working tend to be undercounted. This by status data for most Latin American countries cov- bias has different effects across countries. ers urban areas only. Similarly, in some countries Comparability of employment ratios across coun- in Sub-Saharan Africa, where limited information is tries is also affected by variations in definitions of available anyway, the members of producer coopera- employment and population (see About the data for tives are usually excluded from the self-employed table 2.3). The biggest difference results from the category. For detailed information on definitions and age range used to define labor force activity. The coverage, consult the original source. population base for employment ratios can also vary Labor productivity is used to assess a country's (see table 2.1). Most countries use the resident, economic ability to create and sustain decent employ- noninstitutionalized population of working age living ment opportunities with fair and equitable remunera- in private households, which excludes members of tion. Productivity increases obtained through invest- the armed forces and individuals residing in men- ment, trade, technological progress, or changes in tal, penal, or other types of institutions. But some work organization can increase social protection countries include members of the armed forces in and reduce poverty, which in turn reduce vulner- the population base of their employment ratio while able employment and working poverty. Productivity excluding them from employment data (International increases do not guarantee these improvements, Labour Organization, Key Indicators of the Labour but without them--and the economic growth they Market, 6th edition). bring--improvements are highly unlikely. For compa- The proportion of unpaid family workers and own- rability of individual sectors labor productivity is esti- account workers in total employment is derived from mated according to national accounts conventions. information on status in employment. Each status However, there are still significant limitations on the group faces different economic risks, and unpaid availability of reliable data. Information on consis- family workers and own-account workers are the tent series of output in both national currencies and most vulnerable--and therefore the most likely to purchasing power parity dollars is not easily avail- fall into poverty. They are the least likely to have for- able, especially in developing countries, because the mal work arrangements, are the least likely to have definition, coverage, and methodology are not always Data sources social protection and safety nets to guard against consistent across countries. For example, countries economic shocks, and often are incapable of gen- employ different methodologies for estimating the Data on employment to population ratio, vulner- erating sufficient savings to offset these shocks. A missing values for the nonmarket service sectors able employment, and labor productivity are from high proportion of unpaid family workers in a country and use different definitions of the informal sector. the International Labour Organization's Key Indi- indicates weak development, little job growth, and cators of the Labour Market, 6th edition, data- often a large rural economy. base. Data on gross enrollment ratios are from Data on employment by status are drawn from the United Nations Educational, Scientific, and labor force surveys and household surveys, supple- Cultural Organization Institute for Statistics. mented by offi cial estimates and censuses for a 2010 World Development Indicators 77 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2005­08a 1990­92a 2005­08a 1990­92a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a Afghanistan .. 8.5 .. 7.6 .. 9.5 .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria 23.0 13.8 24.2 12.9 20.3 18.4 .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.7b 7.3b 6.4b 6.0 b 7.0 b 8.9b .. .. .. 48.1b 36.7b 15.3b Armenia .. .. .. .. .. .. .. .. .. 5.2 83.0 11.9 Australia 10.8 4.2 11.4 4.0 10.0 4.6 14.9 b 15.7b 13.9 b 48.0 34.1 17.9 Austria 3.6 3.8 3.5 3.6 3.8 4.1 24.2 25.8 22.6 37.9 52.1 10.0 Azerbaijan .. 6.5 .. 7.8 .. 5.3 .. .. .. 6.3 78.9 14.9 Bangladesh .. 4.3 .. 3.4 .. 7.0 .. .. .. 33.0 24.4 15.9 Belarus .. .. .. .. .. .. .. .. .. 10.0 39.0 51.0 Belgium 6.7 7.0 4.8 6.5 9.5 7.6 52.6 49.9 55.7 42.1 38.2 19.7 Benin 1.5 .. 2.2 .. 0.6 .. .. .. .. .. .. .. Bolivia 5.5b .. 5.5b .. 5.6b .. .. .. .. .. .. .. Bosnia and Herzegovina 17.6 29.0 15.5 26.7 21.6 33.0 .. .. .. 95.7 .. 4.0 Botswana .. 17.6 .. 15.3 .. 19.9 .. .. .. .. .. .. Brazil 6.4b 7.9b 5.4b 6.1b 7.9b 10.0 b .. .. .. 51.6 33.6 3.6 Bulgaria .. 5.7 .. 5.5 .. 5.8 51.7 50.1 53.5 41.8 49.7 8.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 0.5 .. 0.7 .. 0.3 .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 11.2b 6.1 12.0 b 6.6 10.2b 5.7 7.1b 7.9b 6.1b 27.7b 41.1b 31.2b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 7.8 3.9 6.8 5.3 9.5 .. .. .. 17.8 58.5 23.5 China 2.3b 4.2b .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China 2.0 3.5 2.0 4.5 1.9 3.4 .. .. .. 40.8 41.4 16.6 Colombia 9.5b 11.7 6.8 b 8.9 13.0 b 14.5 .. .. .. 76.6 .. 20.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.1 4.6 3.5 3.3 5.4 6.8 .. .. .. 65.2 27.3 6.4 Côte d'Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia .. 8.4 .. 7.0 .. 10.0 61.5 57.2 65.3 20.4 67.8 11.8 Cuba .. 1.8 .. 1.7 .. 1.9 .. .. .. 43.0 52.4 4.6 Czech Republic .. 4.4 .. 3.5 .. 5.6 50.2 50.4 50.0 26.8 68.8 4.3 Denmark 9.0 3.3 8.3 3.0 9.9 3.7 16.1 19.0 13.9 35.9 35.1 23.0 Dominican Republic 20.7 15.6 12.0 9.3 35.2 25.4 .. .. .. 35.0 44.5 16.4 Ecuador 8.9b 6.9 6.0 b 5.6b 13.2b 10.9b .. .. .. 74.0 b .. 23.6b Egypt, Arab Rep. 9.0 8.7 6.4 5.9 17.0 19.3 .. .. .. .. .. .. El Salvador 7.9b 6.6 8.4b 8.5 7.2b 3.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7 5.5 3.9 5.8 3.5 5.2 .. .. .. 23.1 57.8 16.6 Ethiopia .. 17.0 b .. 11.7b .. 22.6b .. .. .. 35.9 13.3 3.2 Finland 11.6 6.4 13.3 6.1 9.6 6.7 18.2 20.1 16.2 35.5 45.9 18.6 France 10.2 7.4 8.1 6.9 12.8 7.9 37.9 39.3 36.5 39.9 39.6 19.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 13.3 .. 13.9 .. 12.6 .. .. .. 5.1 52.5 42.3 Germany 6.3 7.5 4.9 7.4 8.2 7.5 53.4 54.0 52.7 33.1 56.3 10.6 Ghana 4.7 .. 3.7 .. 5.5 .. .. .. .. .. .. .. Greece 7.8 7.7 4.9 5.1 12.9 11.4 49.6 42.8 53.8 29.3 48.4 21.8 Guatemala .. 1.8 .. 1.5 .. 2.4 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.7 .. 11.9 .. 13.8 .. .. .. .. .. .. .. Honduras 3.2b 3.1b 3.3b 2.5b 3.0 b 4.2b .. .. .. .. .. .. 78 2010 World Development Indicators 2.5 PEOPLE Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2005­08a 1990­92a 2005­08a 1990­92a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a Hungary 9.9 7.8 11.0 7.6 8.7 8.1 47.6 48.8 46.3 33.1 58.7 8.1 India .. .. .. .. .. .. .. .. .. 29.0 37.7 33.3 Indonesia 2.8 8.4 2.7 8.1 3.0 10.8 .. .. .. 44.4 40.7 9.6 Iran, Islamic Rep. 11.1 10.5 9.5 9.3 24.4 15.7 .. .. .. 41.8 34.7 19.6 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 15.0 6.0 14.9 7.0 15.3 4.6 29.4 33.2 21.7 39.8 37.2 18.2 Israel 11.2 6.2b 9.2 5.7b 13.9 7.0 b .. .. .. 12.2 12.8 72.5 Italy 9.3 6.7 6.7 5.5 13.9 8.5 47.5 44.9 49.9 46.5 40.6 11.3 Jamaica 15.4 10.6 9.4 7.3 22.2 14.6 .. .. .. 9.7 4.3 8.4 Japan 2.2 4.0 2.1 4.1 2.2 3.8 33.3 39.9 23.8 67.2 .. 32.8 Jordan .. 12.7 .. 10.1 .. 24.3 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5 3.2 2.8 3.6 2.1 2.6 2.7 3.7 0.4 15.2 49.7 35.2 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. 19.4 41.4 9.6 Kyrgyz Republic .. 8.3 .. 7.7 .. 9.0 .. .. .. 13.3 77.1 9.6 Lao PDR .. 1.4 .. 1.3 .. 1.4 .. .. .. .. .. .. Latvia .. 7.5 .. 8.0 .. 6.9 25.7 44.6 39.7 24.3 59.9 14.6 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. 5.6 .. 6.8 .. 4.2 .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 5.8 .. 6.1 .. 5.6 52.4 54.1 50.8 14.2 70.4 15.4 Macedonia, FYR .. 33.8 .. 33.5 .. 34.2 .. .. .. .. .. .. Madagascar .. 2.6 .. 1.7 .. 3.5 .. .. .. 43.9 23.8 9.3 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 3.7 3.2 .. 3.1 .. 3.4 .. .. .. 13.3 61.6 25.1 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 7.3 .. 4.1 .. 12.8 .. .. .. 44.2 48.5 6.4 Mexico 3.1 4.0 2.7 3.9 4.0 4.2 1.7 1.6 1.8 50.7 24.5 22.9 Moldova .. 4.0 .. 4.6 .. 3.4 .. .. .. .. .. .. Mongolia .. 2.8 .. 2.3 .. 3.2 .. .. .. .. .. .. Morocco 16.0 b 9.6 13.0 b 9.6 25.3b 9.8 .. .. .. 51.1b 22.4b 21.6b Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. .. Namibia 19.0 .. 20.0 .. 19.0 .. .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5.5 2.8 4.3 2.5 7.3 3.0 36.3 38.3 34.4 41.3 39.7 17.0 New Zealand 10.4b 4.1 11.0 b 4.0 9.6b 4.2 4.4b 5.5b 3.2b 30.6 38.8 26.9 Nicaragua 14.4 5.2 11.3 5.4 19.5 4.9 .. .. .. 72.8 2.1 18.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 2.6 6.6 2.7 5.1 2.4 6.0 6.0 6.0 25.4 49.2 20.6 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 5.1 3.8 4.2 14.0 8.6 .. .. .. 14.3 11.4 26.0 Panama 14.7 6.8 10.8 5.3 22.3 9.3 .. .. .. 36.0 39.6 24.0 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.3b 5.7 6.4b 4.6 3.8b 7.4 .. .. .. 49.9 38.0 9.9 Peru 9.4b 7.0 b 7.5b 5.9b 12.5b 8.2b .. .. .. 30.0 b 31.9b 37.6b Philippines 8.6 7.4 7.9 7.6 9.9 7.1 .. .. .. 13.6 46.2 39.4 Poland 13.3 7.1 12.2 6.4 14.7 8.0 29.0 27.3 30.8 16.4 73.2 10.4 Portugal 4.1b 7.6 3.5b 6.5 5.0 b 8.8 48.3 49.9 46.9 68.1 15.4 13.2 Puerto Rico 16.9 11.6 19.1 12.0 13.3 9.5 .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 79 2.5 Unemployment Unemployment Long-term Unemployment by unemployment educational attainment Total Male Female % of total % of total % of total % of male % of female unemployment unemployment labor force labor force labor force Total Male Female Primary Secondary Tertiary 1990­92a 2005­08a 1990­92a 2005­08a 1990­92a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a 2005­08a Romania .. 5.8 .. 6.7 .. 4.7 41.3 43.0 38.4 25.8 66.3 6.1 Russian Federation 5.3 6.2 5.4 6.4 5.2 5.8 .. .. .. 13.7 54.2 32.1 Rwanda 0.3 .. 0.6 .. 0.2 .. .. .. .. .. .. .. Saudi Arabia .. 5.6 .. 4.2 .. 13.2 .. .. .. 26.2 44.6 28.7 Senegal .. 11.1 .. 7.9 .. 13.6 .. .. .. 40.2 6.9 2.5 Serbia .. 13.6 .. 11.9 .. 15.8 71.1 70.1 72.1 20.3 68.4 11.2 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 3.2 2.7 3.0 2.6 3.5 .. .. .. 31.0 25.6 43.2 Slovak Republic .. 9.5 .. 8.4 .. 10.9 66.1 65.6 66.6 29.2 65.3 5.3 Slovenia .. 4.4 .. 4.0 .. 4.9 42.2 38.5 40.0 25.0 60.4 12.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 22.9 .. 20.0 .. 26.3 .. .. .. 36.2 56.3 4.5 Spain 18.1 11.3 13.9 10.1 25.8 13.0 23.8 18.8 28.9 54.8 23.6 20.4 Sri Lanka 14.6b 5.2b 10.6b 3.6b 21.0 b 8.0 b .. .. .. 45.4b 22.0 b 32.6b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. 28.2 .. .. .. .. .. .. .. .. .. .. Sweden 5.7 6.2 6.7 5.9 4.6 6.6 12.4 13.5 11.3 32.2 46.0 17.1 Switzerland 2.8 3.4 2.3 2.8 3.5 4.0 34.3 27.3 39.9 28.8 53.2 17.9 Syrian Arab Republic 6.8 .. 5.2 .. 14.0 .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. 66.5 28.8 4.6 Tanzania 3.6b 4.3 2.8b 2.8 4.3b 5.8 .. .. .. .. .. .. Thailand 1.4 1.4 1.3 1.5 1.5 1.3 .. .. .. 40.5 45.5 0.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 6.5 17.0 4.4 23.9 9.6 .. .. .. .. .. .. Tunisia .. 14.2 .. 13.1 .. 17.3 .. .. .. 41.4 37.7 13.6 Turkey 8.5 9.4 8.8 9.4 7.8 9.4 26.9 24.0 34.4 52.3 28.2 12.7 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine .. 6.4 .. 6.7 .. 6.0 .. .. .. 8.5 52.2 39.3 United Arab Emirates .. 3.1 .. 2.5 .. 7.1 .. .. .. 24.3 36.0 21.6 United Kingdom 9.8 5.6 11.6 6.1 7.4 5.1 25.5 30.5 18.4 37.3 47.7 14.3 United States 7.5b 5.8 7.9b 6.0 7.0 b 5.4 10.6b 10.9b 10.3b 18.7 35.5 45.7 Uruguay 9.0 b 7.6 6.8b 5.4 11.8b 10.1 .. .. .. 59.1 27.0 13.8 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7.7 7.4 8.2 7.1 6.8 7.8 .. .. .. .. .. .. Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. 26.0 .. 26.4 .. 23.8 .. .. .. 54.3 14.2 23.5 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income 7.2 8.0 6.7 7.3 8.0 10.0 .. .. .. 37.3 43.2 17.9 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.7 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 6.9 .. 7.9 .. 7.2 .. .. .. 25.7 52.4 22.8 Latin America & Carib. 6.6 7.3 5.4 5.9 8.3 9.0 .. .. .. 51.6 34.5 12.1 Middle East & N. Africa 12.7 10.6 10.8 9.0 21.6 16.2 .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.2 5.9 6.9 5.8 7.7 6.0 25.2 26.4 23.2 35.3 41.5 26.6 Euro area 9.0 7.5 7.1 6.8 11.9 8.3 42.4 41.6 42.8 41.4 42.9 14.9 a. Data are for the most recent year available. b. Limited coverage. 80 2010 World Development Indicators 2.5 PEOPLE Unemployment About the data Definitions Unemployment and total employment are the broad- generate statistics that are more comparable inter- · Unemployment is the share of the labor force with- est indicators of economic activity as reflected by nationally. But the age group, geographic coverage, out work but available for and seeking employment. the labor market. The International Labour Organiza- and collection methods could differ by country or Definitions of labor force and unemployment may tion (ILO) defines the unemployed as members of the change over time within a country. For detailed infor- differ by country (see About the data). · Long-term economically active population who are without work mation, consult the original source. unemployment is the number of people with continu- but available for and seeking work, including people Women tend to be excluded from the unemploy- ous periods of unemployment extending for a year or who have lost their jobs or who have voluntarily left ment count for various reasons. Women suffer more longer, expressed as a percentage of the total unem- work. Some unemployment is unavoidable. At any from discrimination and from structural, social, and ployed. · Unemployment by educational attainment time some workers are temporarily unemployed-- cultural barriers that impede them from seeking is the unemployed by level of educational attainment between jobs as employers look for the right workers work. Also, women are often responsible for the as a percentage of the total unemployed. The levels and workers search for better jobs. Such unemploy- care of children and the elderly and for household of educational attainment accord with the ISCED97 ment, often called frictional unemployment, results affairs. They may not be available for work during of the United Nations Educational, Scientific, and from the normal operation of labor markets. the short reference period, as they need to make Cultural Organization. Changes in unemployment over time may reflect arrangements before starting work. Furthermore, changes in the demand for and supply of labor; they women are considered to be employed when they may also refl ect changes in reporting practices. are working part-time or in temporary jobs, despite Paradoxically, low unemployment rates can disguise the instability of these jobs or their active search for substantial poverty in a country, while high unemploy- more secure employment. ment rates can occur in countries with a high level of Long-term unemployment is measured by the economic development and low rates of poverty. In length of time that an unemployed person has been countries without unemployment or welfare benefits without work and looking for a job. The data in the people eke out a living in vulnerable employment. In table are from labor force surveys. The underlying countries with well developed safety nets workers assumption is that shorter periods of joblessness can afford to wait for suitable or desirable jobs. But are of less concern, especially when the unem- high and sustained unemployment indicates serious ployed are covered by unemployment benefi ts or inefficiencies in resource allocation. similar forms of support. The length of time that a The ILO definition of unemployment notwithstand- person has been unemployed is difficult to measure, ing, reference periods, the criteria for people consid- because the ability to recall that time diminishes as ered to be seeking work, and the treatment of people the period of joblessness extends. Women's long- temporarily laid off or seeking work for the first time term unemployment is likely to be lower in countries vary across countries. In many developing countries where women constitute a large share of the unpaid it is especially difficult to measure employment and family workforce. unemployment in agriculture. The timing of a survey, Unemployment by level of educational attainment for example, can maximize the effects of seasonal provides insights into the relation between the edu- unemployment in agriculture. And informal sector cational attainment of workers and unemployment employment is difficult to quantify where informal and may be used to draw inferences about changes activities are not tracked. in employment demand. Information on educational Data on unemployment are drawn from labor force attainment is the best available indicator of skill sample surveys and general household sample levels of the labor force. Besides the limitations to surveys, censuses, and offi cial estimates, which comparability raised for measuring unemployment, are generally based on information from different the different ways of classifying the education level sources and can be combined in many ways. Admin- may also cause inconsistency. Education level is istrative records, such as social insurance statistics supposed to be classifi ed according to Interna- and employment office statistics, are not included tional Standard Classifi cation of Education 1997 in the table because of their limitations in cover- (ISCED97). For more information on ISCED97, see age. Labor force surveys generally yield the most About the data for table 2.11. comprehensive data because they include groups not covered in other unemployment statistics, par- Data sources ticularly people seeking work for the first time. These surveys generally use a definition of unemployment Data on unemployment are from the ILO's Key Indi- that follows the international recommendations more cators of the Labour Market, 6th edition, database. closely than that used by other sources and therefore 2010 World Development Indicators 81 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7­14 % of children ages 7­14 % of children ages 7­14 % of children in employment in employment in employment ages 7­14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2000 36.6 41.1 31.8 43.1 56.9 .. .. .. .. 1.4 93.1 Algeria .. .. .. .. .. .. .. .. .. .. .. Angolab 2001 30.1 30.0 30.1 26.6 73.4 .. .. .. .. 6.2 80.1 Argentina 2004 12.9 15.7 9.8 4.8 95.2 .. .. .. 34.2 8.1 56.2 Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2005 5.2 5.8 4.5 6.3 93.7 91.7 0.7 7.4 4.1 3.8 92.1 Bangladesh 2006 16.2 25.7 6.4 37.8 62.2 .. .. .. ­ 17.0 77.8 Belarus 2005 11.7 12.1 11.2 0.0 100.0 .. .. .. .. 9.2 78.8 Belgium .. .. .. .. .. .. .. .. .. .. .. Benin 2006 74.4 72.8 76.1 36.1 63.9 .. .. .. .. .. .. Bolivia 2008 32.1 33.0 31.1 5.0 95.0 73.2 6.1 19.2 1.4 8.7 89.9 Bosnia and Herzegovina 2006 10.6 11.7 9.5 0.1 99.9 .. .. .. .. 1.6 92.1 Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2007 6.1 8.1 4.0 6.6 93.4 55.5 8.7 33.5 6.8 23.1 70.1 Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 2006 42.1 49.0 34.5 67.7 32.3 70.9 1.4 24.9 1.9 2.2 95.8 Burundi 2005 11.7 12.5 11.0 38.9 61.1 .. .. 25.9 68.6 .. .. Cambodiac 2003­04 48.9 49.6 48.1 13.8 86.2 82.3 4.2 12.9 6.0 4.1 89.4 Cameroon 2007 43.6 43.7 43.5 21.9 78.1 88.8 3.2 8.0 2.5 0.8 96.1d Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. 2.0 56.4 Chad 2004 60.4 64.4 56.2 49.1 50.9 .. .. .. .. 1.8 77.2 Chile 2003 4.1 5.1 3.1 3.2 96.8 24.1 6.9 66.9 .. .. .. China .. .. .. .. .. .. .. .. .. .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2007 3.9 5.3 2.3 24.8 75.2 41.2 10.8 46.1 22.7 29.1 45.6 Congo, Dem. Rep.c 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. 6.6 76.7 Congo, Rep 2005 30.1 29.9 30.2 9.9 90.1 .. .. .. .. 4.2 84.5 Costa Ricac 2004 5.7 8.1 3.5 44.6 55.4 40.3 9.5 49.0 15.8 57.7 26.6 Côte d'Ivoire 2006 45.7 47.7 43.6 46.8 53.2 .. .. .. .. 2.4 88.0 Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republicc 2005 5.8 9.0 2.7 6.2 93.8 18.5 9.8 57.5 23.8 19.5 56.2d Ecuador 2006 14.3 16.9 11.6 21.0 79.0 69.3 6.3 22.8 3.6 15.2 81.2 Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. 11.4 87.4 .. El Salvador 2007 7.1 10.1 3.8 24.9 75.1 50.1 13.3 35.2 2.2 23.6 74.2 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2005 56.0 64.3 47.1 69.4 30.6 94.6 1.5 3.7 1.7 2.4 95.8 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2005 43.5 33.9 52.3 32.1 67.9 .. .. .. .. 1.1 87.3 Georgia .. .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2006 48.9 49.9 48.0 18.7 81.3 .. .. .. .. 6.1 76.2 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2006 18.2 24.5 11.7 30.5 69.5 63.7 9.7 24.7 2.0 18.8 79.2 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2006 50.5 52.8 48.1 36.4 63.6 .. .. .. .. 4.0 87.7 Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. 1.8 79.4 Honduras 2004 6.8 10.4 3.2 48.6 51.4 63.4 8.3 24.7 2.7 19.9 73.8 82 2010 World Development Indicators 2.6 PEOPLE Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7­14 % of children ages 7­14 % of children ages 7­14 % of children in employment in employment in employment ages 7­14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Hungary .. .. .. .. .. .. .. .. .. .. .. India 2004­05 4.2 4.2 4.2 84.9 15.2 69.4 16.0 12.4 7.1 6.8 59.3 Indonesia 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. 17.8 75.8d Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq 2006 14.7 17.9 11.3 32.4 67.6 .. .. .. .. 7.0 85.3 Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 9.8 11.3 8.3 2.5 97.5 .. .. .. .. 16.3 74.9 Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 2006 3.6 4.4 2.8 1.6 98.4 .. .. .. ­ 4.0 75.0 Kenya 2000 37.7 40.1 35.2 14.1 85.9 .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2006 5.2 5.8 4.6 7.9 92.1 .. .. .. ­ 3.7 81.9 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2002 2.6 4.0 1.3 74.4 25.6 58.0 0.0 10.4 3.7 36.6 59.7e Liberia 2007 37.4 37.8 37.1 45.0 55.0 .. .. .. .. 1.7 79.3 Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 2005 11.8 14.8 8.6 2.8 97.2 .. .. .. .. 3.9 89.5 Madagascar 2007 26.1 28.0 24.1 40.6 59.4 86.9 2.5 8.6 0.0 10.4 89.6 Malawi 2006 40.3 41.3 39.4 10.5 89.5 .. .. .. .. 6.7 75.5 Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2006 49.5 55.0 44.1 59.5 40.5 .. .. .. .. 1.6 80.4 Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexico 2007 8.3 10.9 5.6 17.2 82.8 36.7 10.8 48.5 3.4 33.7 62.9 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. 2.9 82.0 Mongolia 2006­07 10.1 11.4 8.6 16.4 83.6 91.3 0.3 6.3 5.4 0.2 94.5 Morocco 1998­99 13.2 13.5 12.8 93.2 6.8 60.6 8.3 10.1 2.1 10.0 81.7 Mozambiquec 1996 1.8 1.9 1.7 100.0 0.0 .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 0.4 8.0 0.1 4.5 95.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 87.0 1.4 11.1 4.2 3.3 92.4 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2005 10.1 16.2 3.9 30.8 69.2 70.5 9.7 19.3 1.2 13.8 85.0e Niger 2006 47.1 49.2 45.0 66.5 33.5 .. .. .. 4.8 74.5 .. Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panama 2008 8.9 12.1 5.4 14.6 85.4 73.3 2.9 22.9 12.6 11.3 76.1d Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguayc 2005 15.3 22.6 7.7 24.2 75.7 60.8 6.2 32.1 9.3 24.8 65.8 Peru 2007 42.2 44.8 39.4 4.0 96.0 62.6 5.0 31.2 3.9 6.1 90.0 Philippines 2001 13.3 16.3 10.0 14.8 85.2 64.3 4.1 30.6 4.1 22.8 73.1 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 48.5 11.2 33.3 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 83 2.6 Children at work Survey Children in employment Employment by Status in year economic activitya employmenta % of children ages 7­14 % of children ages 7­14 % of children ages 7­14 % of children in employment in employment in employment ages 7­14 Work Study Self- Unpaid Total Male Female only and work Agriculture Manufacturing Services employed Wage family Romania 2000 1.4 1.7 1.1 20.7 79.3 97.1 0.0 2.3 4.5 92.9d .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2008 7.5 8.0 7.0 18.5 81.5 80.8 0.6 9.4 14.9 12.8 72.4 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2005 18.5 24.4 12.6 61.9 38.1 79.1 5.0 14.0 6.3 4.4 84.1 Serbia 2005 6.9 7.2 6.6 2.1 97.9 .. .. .. .. 5.2 89.4 Sierra Leone 2005 62.7 63.6 61.8 29.9 70.1 .. .. .. .. 1.0 71.1 Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia 2006 43.5 45.5 41.5 53.5 46.5 .. .. .. .. 1.6 94.8 South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. 7.1 7.1 85.8 Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 1999 17.0 20.4 13.4 5.4 94.6 71.2 13.1 15.0 2.9 8.3 88.0 Sudanf 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. 7.3 81.3 Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. 10.4 85.9 Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2006 6.6 8.8 4.3 34.6 65.4 .. .. .. .. 21.5 68.8 Tajikistan 2005 8.9 8.7 9.1 9.0 91.0 .. .. .. .. 24.2 71.3 Tanzania 2006 31.1 35.0 27.1 28.2 71.8 85.3 0.7 14.0 56.3g 0.9 42.8 Thailand 2005 15.1 15.7 14.4 4.2 95.8 .. .. .. .. 13.5 80.0 Timor-Lestec 2001 7.6 7.1 8.1 26.8 73.2 91.8 0.0 8.2 28.0 0.0 72.0 Togo 2006 38.7 39.8 37.4 29.8 70.2 82.9 1.3 15.1 5.0 1.6 93.4 Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. 29.8 64.9 Tunisia .. .. .. .. .. .. .. .. .. .. .. Turkey h 2006 2.6 3.3 1.8 38.8 61.2 57.1 14.3 20.9 2.1 34.1 63.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2005­06 38.2 39.8 36.5 7.7 92.3 95.5 1.4 3.0 1.4 1.5 97.1 Ukraine 2005 17.3 18.0 16.6 0.1 99.9 .. .. .. .. 3.1 79.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBc 2006 5.1 6.9 3.3 19.8 80.2 32.3 7.2 55.7 31.6 33.1 35.3 Vietnam 2006 21.3 21.0 21.6 11.9 88.1 .. .. .. .. 5.9 91.2 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2006 18.3 20.7 15.9 30.9 69.1 .. .. .. .. 6.1 86.1 Zambia 2005 47.9 48.9 46.8 25.9 74.1 95.9 0.6 3.5 2.6 0.7 96.5 Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. 3.4 28.4 68.2 a. Shares may not sum to 100 percent because of a residual category not included in the table. b. Covers only Angola-secured territory. c. Covers children ages 10­14. d. Refers to family workers, regardless of whether they are paid. e. Refers to unpaid workers, regardless of whether they are family workers. f. Covers northern Sudan only. g. Includes workers who are self- employed in the nonagricultural sector and workers who are working on their own or family farm or shamba. h. Covers children ages 6­14. 84 2010 World Development Indicators 2.6 PEOPLE Children at work About the data Definitions The data in the table refer to children's work in the data in the table have been recalculated to present · Survey year is the year in which the underlying sense of "economic activity"--that is, children in statistics for children ages 7­14. data were collected. · Children in employment are employment, a broader concept than child labor Although efforts are made to harmonize the defini- children involved in any economic activity for at least (see ILO 2009a for details on this distinction). tion of employment and the questions on employ- one hour in the reference week of the survey. · Work In line with the defi nition of economic activity ment in survey questionnaires, significant differences only refers to children who are employed and not adopted by the 13th International Conference of remain in the survey instruments that collect data on attending school. · Study and work refer to chil- Labour Statisticians, the threshold for classifying children in employment and in the sampling design dren attending school in combination with employ- a person as employed is to have been engaged at underlying the surveys. Differences exist not only ment. · Employment by economic activity is the least one hour in any activity during the reference across different household surveys in the same coun- distribution of children in employment by the major period relating to the production of goods and try but also across the same type of survey carried industrial categories (ISIC revision 2 or revision 3). services set by the 1993 UN System of National out in different countries, so estimates of working · Agriculture corresponds to division 1 (ISIC revi- Accounts. Children seeking work are thus excluded. children are not fully comparable across countries. sion 2) or categories A and B (ISIC revision 3) and Economic activity covers all market production and The table aggregates the distribution of children includes agriculture and hunting, forestry and log- certain nonmarket production, including production in employment by the industrial categories of the ging, and fishing. · Manufacturing corresponds to of goods for own use. It excludes unpaid household International Standard Industrial Classification division 3 (ISIC revision 2) or category D (ISIC revi- services (commonly called "household chores")-- (ISIC): agriculture, manufacturing, and services. sion 3). · Services correspond to divisions 6­9 (ISIC that is, the production of domestic and personal A residual category--which includes mining and revision 2) or categories G­P (ISIC revision 3) and services by household members for own-household quarrying; electricity, gas, and water; construction; include wholesale and retail trade, hotels and restau- consumption. extraterritorial organization; and other inadequately rants, transport, financial intermediation, real estate, Data are from household surveys conducted by defined activities--is not presented. Both ISIC revi- public administration, education, health and social the International Labor Organization (ILO), the United sion 2 and revision 3 are used, depending on the work, other community services, and private house- Nations Children's Fund (UNICEF), the World Bank, country's codification for describing economic activ- hold activity. · Self-employed workers are people and national statistical offi ces. The surveys yield ity. This does not affect the definition of the groups whose remuneration depends directly on the profits data on education, employment, health, expendi- in the table. derived from the goods and services they produce, ture, and consumption indicators related to chil- The table also aggregates the distribution of with or without other employees, and include employ- dren's work. children in employment by status in employment, ers, own-account workers, and members of produc- Household survey data generally include informa- based on the International Classification of Status ers cooperatives. · Wage workers (also known as tion on work type--for example, whether a child is in Employment (1993), which shows the distribu- employees) are people who hold explicit (written or working for payment in cash or in kind or is involved tion in employment by three major categories: self- oral) or implicit employment contracts that provide in unpaid work, working for someone who is not a employed workers, wage workers (also known as basic remuneration that does not depend directly on member of the household, or involved in any type of employees), and unpaid family workers. A residual the revenue of the unit for which they work. · Unpaid family work (on the farm or in a business). Country category--which includes those not classifiable by family workers are people who work without pay in a surveys define the ages for child labor as 5­17. The status--is not presented. market-oriented establishment operated by a related person living in the same household. Brazil has rapidly reduced children's employment and raised school attendance 2.6a Data sources Children's employment Children's school attendance Percent of children ages 7­15 Percent of children ages 7­15 Data on children at work are estimates produced 50 100 by the Understanding Children's Work project 2008 1992 based on household survey data sets made avail- 40 90 able by the ILO's International Programme on the 1997 Elimination of Child Labour under its Statistical 30 80 1997 Monitoring Programme on Child Labour, UNICEF under its Multiple Indicator Cluster Survey pro- 20 70 2008 1992 gram, the World Bank under its Living Standards Measurement Study program, and national sta- 10 60 tistical offices. Information on how the data were collected and some indication of their reliability 0 50 7 8 9 10 11 12 13 14 15 7 8 9 10 11 12 13 14 15 can be found at www.ilo.org/public/english/ Age Age standards/ipec/simpoc/, www.childinfo.org, and www.worldbank.org/lsms. Detailed country statis- Source: Understanding Children's Work project calculations based on Brazilian Pesquisa Nacional por Amostra de Domicílios Surveys. tics can be found at www.ucw-project.org. 2010 World Development Indicators 85 2.7 Poverty rates at national poverty lines Population below national poverty line Poverty gap at national poverty line Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year % % % year % % % year % % % Afghanistan 2007 45.0 27.0 42.0 .. .. .. .. .. .. Albania 2002 29.6 19.5 25.4 2005 24.2 11.2 18.5 2005 5.3 2.3 4.0 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995 4.5 1.8 3.2 Argentinaa 2001 .. 35.9 .. .. .. .. .. .. .. Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2001 .. .. 15.1 Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.6 2001 .. .. 15.5 Bangladesh 2000 52.3 35.1 48.9 2005 43.8 28.4 40.0 2005 9.8 6.5 9.0 Belarus 2002 .. .. 30.5 2004 .. .. 17.4 .. .. .. Benin 1999 33.0 23.3 29.0 2003 46.0 29.0 39.0 2003 14.0 8.0 12.0 Bolivia 2000 75.0 27.9 45.2 2007 63.9 23.7 37.7 .. .. .. Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. 2001­02 4.9 2.8 4.6 Brazil 1998 51.4 14.7 22.0 2002­03 41.0 17.5 21.5 2002­03 28.4 17.8 19.6 Bulgaria 1997 .. .. 36.0 2001 .. .. 12.8 2001 .. .. 4.2 Burkina Faso 1998 61.1 22.4 54.6 2003 52.4 19.2 46.4 2003 17.6 5.1 15.3 Burundi 1998 64.6 66.5 68.0 .. .. .. .. .. .. Cambodia 2004 39.2 34.7 2007 34.7 30.1 2007 8.3 .. 7.2 Cameroona 2001 52.1 17.9 40.2 2007 55.0 12.2 39.9 2007 17.5 2.8 13.2 Chad 1995­96 48.6 .. 43.4 .. .. .. 1995­96 26.3 .. 27.5 Chilea 2003 .. .. 18.7 2006 .. .. 13.7 .. .. .. Chinaa 2000 3.5 .. .. 2005 2.5 .. .. .. .. .. Colombia 2002 70.1 50.4 55.7 2006 62.1 39.1 45.1 .. .. .. Congo, Dem. Rep. 2004­05 75.7 61.5 71.3 .. .. .. 2004­05 34.9 26.2 32.2 Congo, Rep. 2005 49.2 42.3 .. .. .. .. .. .. Costa Rica 1989 35.8 26.2 31.7 2004 28.3 20.8 23.9 2004 10.8 7.0 8.6 Croatia 2002 .. .. 11.2 2004 .. .. 11.1 .. .. .. Dominican Republica 2000 50.8 28.9 36.5 2007 54.1 45.4 48.5 .. .. .. Ecuador a 1999 75.1 36.4 52.2 2006 61.5 24.9 38.3 .. .. .. Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­2000 .. .. 16.7 1999­2000 .. .. 3.0 El Salvador a 2000 53.7b 29.9b 38.8b 2006 36.0 b 27.8b 30.7b .. .. .. Eritrea 1993­94 .. .. 53.0 .. .. .. .. .. .. Estonia 1995 14.7 6.8 8.9 .. .. .. 1995 6.6 1.8 3.1 Ethiopia 1995­96 47.0 33.3 45.5 1999­2000 45.0 37.0 44.2 1999­2000 12.0 10.0 12.0 Gambia, The 1998 61.0 48.0 57.6 2003 63.0 57.0 61.3 2003 .. .. 25.9 Georgia 2002 55.4 48.5 52.1 2003 52.7 56.2 54.5 .. .. .. Ghana 1998­99 49.6 19.4 39.5 2005­06 39.2 10.8 28.5 2005­06 13.5 3.1 9.6 Guatemala 2000 .. .. 56.2 2006 72.0 28.0 51.0 .. .. .. Guinea 1994 .. .. 40.0 .. .. .. .. .. .. Guinea-Bissau 2002 .. 52.6 65.7 .. .. .. 2000 .. 17.5 25.7 Haiti 1987 .. .. 65.0 1995 66.0 .. .. .. .. .. Honduras 1998­99 71.2 28.6 52.5 2004 70.4 29.5 50.7 2004 34.5 9.1 22.3 Hungary 1993 .. .. 14.5 1997 .. .. 17.3 1997 4.1 .. .. India 1993­94 37.3 32.4 36.0 1999­2000 30.2 24.7 28.6 1999­2000 5.6 6.9 .. Indonesia 1996 19.8 13.6 17.6 2004 20.1 12.1 16.7 2004 .. .. 2.9 Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 .. .. .. Jordan 1997 27.0 19.7 21.3 2002 18.7 12.9 14.2 2002 4.7 2.9 3.3 Kazakhstan 2001 .. .. 17.6 2002 .. .. 15.4 2002 4.5 2.0 3.1 Kenya 1997 53.0 49.0 52.0 2005/06 49.7 34.4 46.6 2005/06 14.1 2.5 16.6 Kosovo 2003­04 44.2 42.1 43.5 2005­06 49.2 37.4 45.1 2005­06 .. .. 13.3 Kyrgyz Republic 2003 57.5 35.7 49.9 2005 50.8 29.8 43.1 2005 12.0 7.0 10.0 Lao PDR 1997­98 41.0 26.9 38.6 2002­03 .. .. 33.5 2002­03 .. .. 8.0 Latvia 2002 11.6 .. 7.5 2004 12.7 .. 5.9 2004 .. .. 1.2 86 2010 World Development Indicators 2.7 PEOPLE Poverty rates at national poverty lines Population below national poverty line Poverty gap at national poverty line Survey Rural Urban National Survey Rural Urban National Survey Rural Urban National year % % % year % % % year % % % Lesothoa 1994/95 68.9 36.7 66.6 2002/03 60.5 41.5 56.3 .. .. .. Macedonia, FYR 2002 25.3 .. 21.4 2003 22.3 .. 21.7 2003 6.5 .. 6.7 Madagascara 1999 76.7 52.1 71.3 2005 53.5 52.0 68.7 2005 28.9 19.3 26.8 Malawi 1997­98 66.5 54.9 65.3 2004­05 55.9 25.4 52.4 2004­05 8.6 2.8 8.0 Malaysia 1989 .. .. 15.5 .. .. .. .. .. .. Mali 1998 75.9 30.1 63.8 .. .. .. .. .. .. Mauritania 1996 65.5 30.1 50.0 2000 61.2 25.4 46.3 .. .. .. Mauritius 1992 .. .. 10.6 .. .. .. .. .. .. Mexico 2002 65.4 41.5 50.6 2004 56.9 41.0 47.0 .. .. .. Moldova 2001 64.1 58.0 62.4 2002 67.2 42.6 48.5 2002 .. .. 16.5 Mongolia 1998 32.6 39.4 35.6 2002 43.4 30.3 36.1 2002 13.2 9.2 11.0 Morocco 1990­91 18.0 7.6 13.1 1998­99 27.2 12.0 19.0 1998­99 6.7 2.5 4.4 Mozambique 1996­97 71.3 62.0 69.4 2002­03 54.1 51.6 55.2 2002­03 19.9 18.9 20.4 Myanmar 2004­05 36.0 22.0 32.0 .. .. .. 2004­05 7.0 4.0 7.0 Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04 8.5 2.2 7.5 Nicaragua 1998 68.5 30.5 47.9 2001 64.3 28.7 45.8 2001 25.9 8.7 17.0 Niger 1989­93 66.0 52.0 63.0 .. .. .. .. .. .. Nigeria 1985 49.5 31.7 43.0 1992­93 36.4 30.4 34.1 .. .. .. Pakistan 1993 33.4 17.2 28.6 1998­99 35.9 24.2 32.6 1998­99 7.9 5.0 7.0 Panama 1997 64.9 15.3 37.3 2003 .. .. 36.8 1997 32.1 3.9 16.4 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. 1996 13.8 4.3 12.4 Paraguayc 1990 28.5 19.7 20.5 .. .. .. 1990 10.5 5.6 6.0 Peru 2003 75.7 39.5 52.2 2004 72.5 40.3 51.6 2004 28.3 12.4 18.0 Philippines 1994 45.4 18.6 32.1 1997 36.9 11.9 25.1 1997 10.0 2.6 6.4 Poland 1996 .. .. 14.6 2001 .. .. 14.8 .. .. .. Romania 1995 .. .. 25.4 2002 .. .. 28.9 2002 .. .. 7.6 Russian Federation 1998 .. .. 31.4 2002 .. .. 19.6 2002 .. .. 5.1 Rwandaa 1999­2000 65.7 14.3 60.3 2005­06 62.5 56.9 .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 1992 16.4 3.1 13.9 Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 2003­04 34.0 .. 29.0 Slovak Republic 2004 .. .. 16.8 .. .. .. 2004 .. .. 5.5 South Africaa 2000 .. .. 38.0 2008 .. .. 22.0 2008 .. .. 6.0 Sri Lanka 1995­96 27.0 15.0 25.0 2002 7.9 24.7 22.7 2002 5.6 1.7 5.1 Swaziland 2000­01 75.0 49.0 69.2 .. .. .. 2000­01 .. .. 32.9 Tajikistan 2003 73.8 68.8 72.4 2007 55.0 49.4 53.5 2003 12.4 12.5 12.4 Tanzania 1991 40.8 31.2 38.6 2000­01 38.7 29.5 35.7 .. .. .. Thailand 1994 .. .. 9.8 1998 .. .. 13.6 1998 .. .. 3.0 Timor-Leste 2001 .. .. 39.7 .. .. .. 2001 .. .. 11.9 Togo 1987­89 .. .. 32.3 .. .. .. 1987­89 .. .. 10.0 Trinidad and Tobago 1992 20.0 24.0 21.0 .. .. .. 1992 6.2 7.4 7.3 Tunisia 1990 13.1 3.5 7.4 1995 13.9 3.6 7.6 1990 3.3 0.9 1.7 Turkey 1994 .. .. 28.3 2002 34.5 22.0 27.0 2002 .. .. 0.3 Ugandaa 2002­03 42.7 14.4 38.8 2005­06 34.2 13.7 31.1 2005­06 9.7 3.5 8.7 Ukraine 2000 34.9 .. 31.5 2003 28.4 .. 19.5 .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 1998 .. 8.6 .. Uzbekistan 2000­01 33.6 27.8 31.5 2003 29.8 22.6 27.2 .. .. .. Venezuela, RB 1989 .. .. 31.3 1997­99 .. .. 52.0 1997­99 .. .. 24.0 Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 2002 8.7 1.3 6.9 Yemen, Rep. 1998 45.0 30.8 41.8 .. .. .. 1998 14.7 8.2 13.2 Zambia 1998 83.1 56.0 72.9 2004 78.0 53.0 68.0 2004 44.0 22.0 36.0 Zimbabwe 1990­91 35.8 3.4 25.8 1995­96 48.0 7.9 34.9 .. .. .. a. Data are from national sources. b. Data refer to share of households rather than share of population. c. Covers Asunción metropolitan area only. 2010 World Development Indicators 87 2.7 Poverty rates at national poverty lines About the data Definitions The World Bank periodically prepares poverty made about every three years. A complete overview · Survey year is the year in which the underlying data assessments of countries in which it has an active of data availability by year and country is available at were collected. · Rural population below national program, in close collaboration with national institu- http://iresearch.worldbank.org/povcalnet/. poverty line is the percentage of the rural population tions, other development agencies, and civil society living below the national rural poverty line. · Urban groups, including poor people's organizations. Pov- Data quality population below national poverty line is the per- erty assessments report the extent and causes of Poverty assessments are based on surveys fielded to centage of the urban population living below the poverty and propose strategies to reduce it. Since collect, among other things, information on income national urban poverty line. · National population 1992 the World Bank has conducted about 200 pov- or consumption from a sample of households. To be below national poverty line is the percentage of the erty assessments, which are the main source of the useful for poverty estimates, surveys must be nation- country's population living below the national poverty poverty estimates presented in the table. Countries ally representative and include sufficient information line. National estimates are based on population- report similar assessments as part of their Poverty to compute a comprehensive estimate of total house- weighted subgroup estimates from household sur- Reduction Strategies. hold consumption or income (including consumption veys. · Poverty gap at national poverty line is the The poverty assessments are the best available or income from own production), from which it is pos- mean shortfall from the poverty line (counting the source of information on poverty estimates using sible to construct a correctly weighted distribution nonpoor as having zero shortfall) as a percentage of national poverty lines. They often include separate of consumption or income per person. There remain the poverty line. This measure reflects the depth of assessments of urban and rural poverty. Data are many potential problems with household survey data, poverty as well as its incidence. derived from nationally representative household including selective nonresponse and differences in surveys conducted by national statistical offices or the menu of consumption items presented and the by private agencies under the supervision of govern- length of the period over which respondents must ment or international agencies and obtained from recall their expenditures. These issues are dis- government statistical offices and World Bank Group cussed in About the data for table 2.8. country departments. Some poverty assessments analyze the current National poverty lines poverty status of a country using the latest available National poverty lines are used to make estimates household survey data, while others use survey data of poverty consistent with the country's specific eco- for several years to analyze poverty trends. Thus, nomic and social circumstances and are not intended poverty estimates for more than one year might be for international comparisons of poverty rates. The derived from a single poverty assessment. A poverty setting of national poverty lines reflects local percep- assessment might not use all available household tions of the level of consumption or income needed surveys, or survey data might become available at not to be poor. The perceived boundary between a later date even though data were collected before poor and not poor rises with the average income of the poverty assessment date. Thus poverty assess- a country and so does not provide a uniform measure ments may not fully represent all household survey for comparing poverty rates across countries. Never- data. theless, national poverty estimates are clearly the Many developing countries, particularly middle- appropriate measure for setting national policies for income countries, have their own poverty monitor- poverty reduction and for monitoring their results. ing programs with well documented estimation meth- Almost all the national poverty lines use a food Data sources odologies. The programs regularly publish what the bundle based on prevailing diets that attains pre- countries consider official poverty estimates. Such determined nutritional requirements for good health The poverty measures are prepared by the World estimates are reviewed by World Bank researchers and normal activity levels, plus an allowance for non- Bank's Development Research Group, based on and included in the table. food spending. The rise in poverty lines with average data from World Bank's country poverty assess- income is driven more by the gradient in the non- ments and country Poverty Reduction Strategies. Data availability food component of the poverty lines than in the food Summaries of poverty assessments are available The number of data sets within two years of any given component, although there is still an appreciable at www.worldbank.org/povertynet, by selecting year rose dramatically, from 13 between 1978 and share attributable to the gradient in food poverty "Poverty assessments" from the left side bar. 1982 to 158 between 2001 and 2006. Data cover- lines. While nutritional requirements tend to be fairly Poverty assessment documents are available at age is improving in all regions, but the Middle East similar even across countries at different levels of www-wds.worldbank.org, under "By topic," "Pov- and North Africa and Sub-Saharan Africa continue to economic development, richer countries tend to use erty reduction," "Poverty assessment." Further lag. The database, maintained by a team in the World a more expensive food bundle--more meat and veg- discussion of how national poverty lines vary Bank's Development Research Group, is updated etables, less starchy staples, and more processed across countries can be found in Ravallion, Chen, annually as new survey data become available, and foods generally--for attaining the same nutritional and Sangraula's "Dollar a Day Revisited" (2008). a major reassessment of progress against poverty is needs. 88 2010 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty International poverty line line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year % % % % year % % % % Albania 75.51 120.82 2002a <2 <0.5 8.7 1.4 2005a <2 <0.5 7.8 1.4 Algeria 48.42b 77.48b 1988a 6.6 1.8 23.8 6.6 1995a 6.8 1.4 23.6 6.4 Angola 88.13 141.01 2000a 54.3 29.9 70.2 42.3 .. .. .. .. Argentina 1.69 2.71 2005c,d 4.5 1.0 11.3 3.6 2006c,d 3.4 1.2 7.3 2.7 Armenia 245.24 392.38 2003a 10.6 1.9 43.4 11.3 2007a 3.7 0.7 21.0 4.6 Azerbaijan 2,170.94 3,473.51 2001a 6.3 1.1 27.1 6.8 2005a <2 <0.5 <2 <0.5 Bangladesh 31.87 50.99 2000a 57.8e 17.3e 85.4 e 38.7e 2005a 49.6e 13.1e 81.3e 33.8e Belarus 949.53 1,519.25 2005a <2 <0.5 <2 <0.5 2007a <2 <0.5 <2 <0.5 Belize 1.83 2.93 1995a 13.4 5.4 23.1 10.3 .. .. .. .. Benin 343.99 550.38 2003a 47.3 15.7 75.3 33.5 .. .. .. .. Bhutan 23.08 36.93 2003a 26.2 7.0 49.5 18.8 .. .. .. .. Bolivia 3.21 5.14 2005b 19.6 9.7 30.3 15.5 2007b 11.9 5.6 21.9 9.5 Bosnia and Herzegovina 1.09 1.74 2004 a <2 <0.5 <2 <0.5 2007a <2 <0.5 <2 <0.5 Botswana 4.23 6.77 1985­86a 35.6 13.8 54.7 25.8 1993­94 a 31.2 11.0 49.4 22.3 Brazil 1.96 3.14 2005d 7.8 1.6 18.3 5.9 2007d 5.2 1.3 12.7 4.1 Bulgaria 0.92 1.47 2001a 2.6 <0.5 7.8 2.2 2003a <2 <0.5 <2 0.9 Burkina Faso 303.02 484.83 1998a 70.0 30.2 87.6 49.1 2003a 56.5 20.3 81.2 39.2 Burundi 558.79 894.07 1998a 86.4 47.3 95.4 64.1 2006a 81.3 36.4 93.4 56.0 Cambodia 2,019.12 3,230.60 2004a 40.2 11.3 68.2 28.0 2007a 25.8 6.1 57.8 20.1 Cameroon 368.12 588.99 1996a 51.5 18.9 74.4 36.0 2001a 32.8 10.2 57.7 23.6 Cape Verde 97.72 156.35 2001a 20.6 5.9 40.2 14.9 .. .. .. .. Central African Republic 384.33 614.93 1993a 82.8 57.0 90.7 68.4 2003a 62.4 28.3 81.9 45.3 Chad 409.46 655.14 2002­03a 61.9 25.6 83.3 43.9 .. .. .. .. Chile 484.20 774.72 2003d <2 <0.5 5.3 1.3 2006d <2 <0.5 2.4 0.39 China 5.11f 8.17f 2002a 28.4g 8.7g 51.1g 20.6g 2005a 15.9g 4.0 g 36.3g 12.2g Colombia 1,489.68 2,383.48 2003d 15.4 6.1 26.3 10.9 2006d 16.0 5.7 27.9 11.9 Comoros 368.01 588.82 2004 a 46.1 20.8 65.0 34.2 .. .. .. .. Congo, Dem. Rep. 395.29 632.46 2005­06a 59.2 25.3 79.5 42.4 .. .. .. .. Congo, Rep. 469.46 751.14 2005a 54.1 22.8 74.4 38.8 .. .. .. .. Costa Rica 348.70 b 557.92b 2005d 2.4 <0.5 8.6 2.3 2007d <2 <0.5 4.3 1.3 Croatia 5.58 8.92 2001a <2 <0.5 <2 <0.5 2005a <2 <0.5 <2 <0.5 Czech Republic 19.00 30.39 1993d <2 <0.5 <2 <0.5 1996d <2 <0.5 <2 <0.5 Côte d'Ivoire 407.26 651.62 1998 a 24.1 6.7 49.1 18.1 2002a 23.3 6.8 46.8 17.6 Djibouti 134.76 215.61 1996a 4.8 1.6 15.1 4.5 2002a 18.8 5.3 41.2 14.6 Dominican Republic 25.50 b 40.79b 2005d 5.0 0.9 15.1 4.3 2007d 4.4 1.3 12.3 3.9 Ecuador 0.63 1.00 2005d 9.8 3.2 20.4 7.6 2007d 4.7 1.2 12.8 4.0 Egypt, Arab Rep. 2.53 4.04 1999­00a <2 <0.5 19.3 3.5 2004­05a <2 <0.5 18.4 3.5 El Salvador 6.02b 9.62b 2005d 11.0 4.8 20.5 8.9 2007d 6.4 2.7 13.2 5.3 Estonia 11.04 17.66 2002a <2 <0.5 2.5 0.6 2004 a <2 <0.5 <2 <0.5 Ethiopia 3.44 5.50 1999­00a 55.6 16.2 86.4 37.9 2005a 39.0 9.6 77.5 28.8 Gabon 554.69 887.50 2005a 4.8 0.9 19.6 5.0 .. .. .. .. Gambia, The 12.93 20.69 1998a 66.7 34.7 82.0 50.0 2003a 34.3 12.1 56.7 24.9 Georgia 0.98 1.57 2002a 15.1 4.7 34.2 12.2 2005a 13.4 4.4 30.4 10.9 Ghana 5,594.78 8,951.64 1998­99a 39.1 14.4 63.3 28.5 2006a 30.0 10.5 53.6 22.3 Guatemala 5.68b 9.08b 2002d 16.9 6.5 29.8 12.9 2006d 11.7 3.5 24.3 8.9 Guinea-Bissau 355.34 568.55 1993a 52.1 20.6 75.7 37.4 2002a 48.8 16.5 77.9 34.8 Guinea 1,849.46 2,959.13 1994 a 36.8 11.5 63.8 26.4 2003a 70.1 32.2 87.2 50.2 Guyana 131.47b 210.35b 1993d 5.8 2.6 15.0 5.4 1998 d 7.7 3.9 16.8 6.9 Haiti 24.21b 38.73b 2001d 54.9 28.2 72.1 41.8 .. .. .. .. Honduras 12.08b 19.32b 2005d 22.2 10.2 34.8 16.7 2006d 18.2 8.2 29.7 14.2 Hungary 171.90 275.03 2002a <2 <0.5 <2 <0.5 2004 a <2 <0.5 <2 <0.5 India 19.50h 31.20h 1993­94 a 49.4g 14.4g 81.7g 35.3g 2004­05a 41.6g 10.8g 75.6g 30.4g Indonesia 5,241.03h 8,385.65h 2005a 21.4g 4.6g 53.8g 17.3g 2007a 29.4 7.1 60.0 21.8 Iran, Islamic Rep. 3,393.53 5,429.65 1998a <2 <0.5 8.3 1.8 2005a <2 <0.5 8.0 1.8 Jamaica 54.20 b 86.72b 2002a <2 <0.5 8.7 1.6 2004 a <2 <0.5 5.8 0.9 Jordan 0.62 0.99 2002­03a <2 <0.5 11.0 2.1 2006a <2 <0.5 3.5 0.6 Kazakhstan 81.21 129.93 2003a 3.1 <0.5 17.2 3.9 2007a <2 <0.5 <2 <0.5 2010 World Development Indicators 89 2.8 Poverty rates at international poverty lines International poverty International poverty line line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year % % % % year % % % % Kenya 40.85 65.37 1997a 19.6 4.6 42.7 14.7 2005­06a 19.7 6.1 39.9 15.1 Kyrgyz Republic 16.25 26.00 2004a 21.8 4.4 51.9 16.8 2007a 3.4 <0.5 27.5 5.2 Lao PDR 4,677.02 7,483.24 1997­98 49.3e 14.9e 79.9e 34.4 e 2002­03a 44.0e 12.1e 76.8e 31.0e Latvia 0.43 0.69 2004 a <2 <0.5 <2 <0.5 2007a <2 <0.5 <2 <0.5 Lesotho 4.28 6.85 1995a 47.6 26.7 61.1 37.3 2002­03a 43.4 20.8 62.2 33.0 Liberia 0.64 1.02 2007a 83.7 40.8 94.8 59.5 .. .. .. .. Lithuania 2.08 3.32 2002a <2 <0.5 <2 <0.5 2004 a <2 <0.5 <2 <0.5 Macedonia, FYR 29.47 47.16 2003a <2 <0.5 3.2 0.7 2006a <2 <0.5 5.3 1.3 Madagascar 945.48 1,512.76 2001a 76.3 41.4 88.7 57.2 2005a 67.8 26.5 89.6 46.9 Malawi 71.15 113.84 1997­98d 83.1 46.0 93.5 62.3 2004­05a,i 73.9 32.3 90.4 51.8 Malaysia 2.64 4.23 1997d <2 <0.5 6.8 1.3 2004 d <2 <0.5 7.8 1.4 Mali 362.10 579.36 2001a 61.2 25.8 82.0 43.6 2006a 51.4 18.8 77.1 36.5 Mauritania 157.08 251.33 1995­96a 23.4 7.1 48.3 17.8 2000a 21.2 5.7 44.1 15.9 Mexico 9.56 15.30 2006a <2 <0.5 4.8 1.0 2008d 4.0 1.8 8.2 3.3 Moldova 6.03 9.65 2004 a 8.1 1.7 28.9 7.9 2007a 2.4 0.5 11.5 2.7 Mongolia 653.12 1,044.99 2002a 15.5 3.6 38.8 12.3 2007­08a 2.2 0.4 13.6 2.9 Montenegro 0.62 1.00 2005a <2 <0.5 5.7 1.1 2007a <2 <0.5 <2 <0.5 Morocco 6.89 11.02 2000a 6.3 0.9 24.3 6.3 2007a 2.5 0.5 14.0 3.1 Mozambique 14,532.12 23,251.39 1996­97a 81.3 42.0 92.9 59.4 2002­03a 74.7 35.4 90.0 53.5 Namibia 6.33 10.13 1993d 49.1 24.6 62.2 36.5 .. .. .. .. Nepal 33.08 52.93 1995­96a 68.4 26.7 88.1 46.8 2003­04 a 55.1 19.7 77.6 37.8 Nicaragua 9.12b 14.59b 2001d 19.4 6.7 37.5 14.4 2005d 15.8 5.2 31.8 12.3 Niger 334.16 534.66 1994 a 78.2 38.6 91.5 56.5 2005a 65.9 28.1 85.6 46.6 Nigeria 98.23 157.17 1996­97a 68.5 32.1 86.4 49.7 2003­04 a 64.4 29.6 83.9 46.9 Pakistan 25.89 41.42 2001­02a 35.9 7.9 73.9 26.4 2004­05a 22.6 4.4 60.3 18.7 Panama 0.76b 1.22b 2004 d 9.2 2.7 18.0 6.8 2006d 9.5 3.1 17.8 7.1 Papua New Guinea 2.11b 3.37b 1996a 35.8 12.3 57.4 25.5 .. .. .. .. Paraguay 2,659.74 4,255.59 2005d 9.3 3.4 18.4 7.3 2007d 6.5 2.7 14.2 5.5 Peru 2.07 3.31 2005d 8.2 2.0 19.4 6.3 2007d 7.7 2.3 17.8 6.2 Philippines 30.22 48.36 2003a 22.0 5.5 43.8 16.0 2006a 22.6 5.5 45.0 16.3 Poland 2.69 4.31 2002a <2 <0.5 <2 <0.5 2005a <2 <0.5 <2 <0.5 Romania 2.15 3.44 2002a 2.9 0.8 13.0 3.2 2007a <2 <0.5 4.1 0.1 Russian Federation 16.74 26.78 2002a <2 <0.5 3.7 0.6 2007a <2 <0.5 <2 <0.5 Rwanda 295.93 473.49 1984­85a 63.3 19.7 88.4 41.8 2000a 76.6 38.2 90.3 55.7 São Tomé and Príncipe 7,949.55 12,725.55 2000­01a 28.4 8.4 56.6 21.6 .. .. .. .. Senegal 372.81 596.49 2001a 44.2 14.3 71.3 31.2 2005a 33.5 10.8 60.3 24.6 Serbia 42.86 68.62 2003a <2 <0.5 <2 <0.5 2008a <2 <0.5 <2 <0.5 Seychelles 6.53 10.46 1999­00a <2 <0.5 <2 <0.5 2006­07a <2 <0.5 <2 <0.5 Sierra Leone 1,745.26 2,792.42 1989­90a 62.8 44.8 75.0 54.0 2003a 53.4 20.3 76.1 37.5 Slovak Republic 23.53 37.66 1992d <2 <0.5 <2 <0.5 1996d <2 <0.5 <2 <0.5 Slovenia 198.25 317.20 2002a <2 <0.5 <2 <0.5 2004 a <2 <0.5 <2 <0.5 South Africa 5.71 9.14 1995a 21.4 5.2 39.9 15.0 2000a 26.2 8.2 42.9 18.3 Sri Lanka 50.05 80.08 1995­96a 16.3 3.0 46.7 13.7 2002a 14.0 2.6 39.7 11.8 St. Lucia 2.37b 3.80 b 1995d 20.9 7.2 40.6 15.5 .. .. .. .. Suriname 2.29b 3.67b 1999d 15.5 5.9 27.2 11.7 .. .. .. .. Swaziland 4.66 7.45 1994­95a 78.6 47.7 89.3 61.6 2000­01a 62.9 29.4 81.0 45.8 Tajikistan 1.16 1.85 2003a 36.3 10.3 68.8 26.7 2004 a 21.5 5.1 50.8 16.8 Tanzania 603.06 964.90 1991­92a 72.6 29.7 91.3 50.1 2000­01a 88.5 46.8 96.6 64.4 Thailand 21.83 34.93 2002a <2 <0.5 15.1 2.8 2004 a <2 <0.5 11.5 2.0 Timor-Leste 0.61b 0.98b 2001a 52.9 19.1 77.5 37.0 2007a 37.2 8.7 72.8 27.0 Togo 352.82 564.51 2006a 38.7 11.4 69.3 27.9 .. .. .. .. Trinidad and Tobago 5.77b 9.23b 1988d <2 <0.5 8.6 1.9 1992d 4.2 1.1 13.5 3.9 Tunisia 0.87 1.39 1995a 6.5 1.3 20.4 5.8 2000a 2.6 <0.5 12.8 3.0 Turkey 1.25 2.00 2002a 2.0 <0.5 9.6 2.3 2006a 2.6 <0.5 8.2 2.4 Turkmenistan 5,961.06b 9,537.69b 1993d 63.5 25.8 85.7 44.8 1998a 24.8 7.0 49.6 18.4 Uganda 930.77 1,489.24 2002a 57.4 22.7 79.8 40.6 2005a 51.5 19.1 75.6 36.4 90 2010 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines International poverty International poverty line line in local currency Population Poverty Population Poverty below gap at Population Poverty below gap at Population Poverty $1.25 $2 $1.25 $1.25 below gap at $1.25 $1.25 below gap at a day a day Survey a day a day $2 a day $2 a day Survey a day a day $2 a day $2 a day 2005 2005 year % % % % year % % % % Ukraine 2.14 3.42 2005a <2 <0.5 <2 <0.5 2008a <2 <0.5 <2 <0.5 Uruguay 19.14 30.62 2005c,d <2 <0.5 4.5 0.7 2007d <2 <0.5 4.3 1.0 Uzbekistan 470.09b 752.14b .. .. .. .. .. .. .. .. Venezuela, RB 1,563.90 2,502.24 2003d 18.4 8.8 31.7 14.6 2006d 3.5 1.2 10.2 3.2 Vietnam 7,399.87 11,839.79 2004 a 24.2 5.1 52.5 17.9 2006a 21.5 4.6 48.4 16.2 Yemen, Rep. 113.83 182.12 1998a 12.9 3.0 36.3 11.1 2005a 17.5 4.2 46.6 14.8 Zambia 3,537.91 5,660.65 2002­03a 64.6 27.1 85.1 45.8 2004­05a 64.3 32.8 81.5 48.3 a. Expenditure based. b. In purchasing power parity (PPP) dollars imputed using regression. c. Covers urban areas only. d. Income based. e. Adjusted by spatial consumer price index information. f. PPP conversion factor based on urban prices. g. Weighted average of urban and rural estimates. h. Weighted average of urban and rural poverty lines. i. Due to change in survey design, the most recent survey is not strictly comparable with the previous one. Regional poverty estimates and progress toward 84 percent to 16 percent, leaving 620 million fewer $1.25 a day poverty line than had been expected the Millennium Development Goals people in poverty. before the crisis. Global poverty measured at the $1.25 a day poverty Over the same period the poverty rate in South Most of the people who have escaped extreme line has been decreasing since the 1980s. The share Asia fell from 59 percent to 40 percent (table 2.8c). poverty remain very poor by the standards of mid- of population living on less than $1.25 a day fell In contrast, the poverty rate fell only slightly in Sub- dle-income economies. The median poverty line for 10 percentage points, to 42 percent, in 1990 and Saharan Africa--from less than 54 percent in 1981 developing countries in 2005 was $2.00 a day. The then fell nearly 17 percentage points between 1990 to more than 58 percent in 1999 then down to poverty rate for all developing countries measured and 2005. The number of people living in extreme 51 percent in 2005. But the number of people living at this line fell from nearly 70 percent in 1981 to poverty fell from 1.9 billion in 1981 to 1.8 billion below the poverty line has nearly doubled. 47 percent in 2005, but the number of people liv- in 1990 to about 1.4 billion in 2005 (figure 2.8a). Only East Asia and Pacific is consistently on track ing on less than $2.00 a day has remained nearly This substantial reduction in extreme poverty over to meet the Millennium Development Goal target of constant at 2.5 billion. The largest decrease, both the past quarter century, however, disguises large reducing 1990 poverty rates by half by 2015. A slight in number and proportion, occurred in East Asia regional differences. acceleration over historical growth rates could lift and Pacifi c, led by China. Elsewhere, the number of The greatest reduction in poverty occurred in East Latin America and the Caribbean and South Asia people living on less than $2.00 a day increased, Asia and Pacific, where the poverty rate declined to the target. However, the recent slowdown in the and the number of people living between $1.25 from 78 percent in 1981 to 17 percent in 2005 and global economy may leave these regions and many and $2.00 a day nearly doubled, to 1.2 billion. the number of people living on less than $1.25 a day countries short of the target. Preliminary estimates In 2009 the global growth deceleration will likely dropped more than 750 million (figure 2.8b). Much for 2009 suggest that lower economic growth rates leave 57 million more people below the $2 a day of this decline was in China, where poverty fell from will likely leave 50 million more people below the poverty line. While the number of people living on less than $1.25 a day has Poverty rates fallen, the number living on $1.25­$2.00 a day has increased 2.8a have begun to fall 2.8b People living in poverty (billions) Share of population living on less than $1.25 a day, by region (percent) 3.0 80 2.5 People living on more than $1.25 and less than $2.00 Sub-Saharan Africa People living on less than a day, all developing regions 60 2.0 $1.25 a day, other developing regions 1.5 40 People living on less than South Asia $1.25 a day, East Asia & Pacific 1.0 East Asia Europe & Central Asia & Pacific 20 People living on less than Middle East & North Africa 0.5 Latin America & Caribbean $1.25 a day, South Asia People living on less than $1.25 a day, Sub-Saharan Africa 0 0 1981 1984 1987 1990 1993 1996 1999 2002 2005 1981 1984 1987 1990 1993 1996 1999 2002 2005 Source: PovcalNet, World Bank. Source: PovcalNet, World Bank. 2010 World Development Indicators 91 2.8 Poverty rates at international poverty lines Regional poverty estimates 2.8c Region or country 1981 1984 1987 1990 1993 1996 1999 2002 2005 People living on less than 2005 PPP $1.25 a day (millions) East Asia & Pacific 1,072 947 822 873 845 622 635 507 316 China 835 720 586 683 633 443 447 363 208 Europe & Central Asia 7 6 5 9 20 22 24 22 17 Latin America & Caribbean 47 59 57 50 47 53 55 57 45 Middle East & North Africa 14 12 12 10 10 11 12 10 11 South Asia 548 548 569 579 559 594 589 616 596 India 420 416 428 436 444 442 447 460 456 Sub-Saharan Africa 211 242 258 297 317 356 383 390 388 Total 1,900 1,814 1,723 1,818 1,799 1,658 1,698 1,601 1,374 Share of people living on less than 2005 PPP $1.25 a day (percent) East Asia & Pacific 77.7 65.5 54.2 54.7 50.8 36.0 35.5 27.6 16.8 China 84.0 69.4 54.0 60.2 53.7 36.4 35.6 28.4 15.9 Europe & Central Asia 1.7 1.3 1.1 2.0 4.3 4.6 5.1 4.6 3.7 Latin America & Caribbean 12.9 15.3 13.7 11.3 10.1 10.9 10.9 10.7 8.2 Middle East & North Africa 7.9 6.1 5.7 4.3 4.1 4.1 4.2 3.6 3.6 South Asia 59.4 55.6 54.2 51.7 46.9 47.1 44.1 43.8 40.3 India 59.8 55.5 53.6 51.3 49.4 46.6 44.8 43.9 41.6 Sub-Saharan Africa 53.4 55.8 54.5 57.6 56.9 58.8 58.4 55.0 50.9 Total 51.9 46.7 41.9 41.7 39.2 34.5 33.7 30.5 25.2 People living on less than 2005 PPP $2.00 a day (millions) East Asia & Pacific 1,278 1,280 1,238 1,274 1,262 1,108 1,105 954 729 China 972 963 907 961 926 792 770 655 474 Europe & Central Asia 35 28 25 32 49 56 68 57 42 Latin America & Caribbean 90 110 103 96 96 107 111 114 94 Middle East & North Africa 46 44 47 44 48 52 52 51 51 South Asia 799 836 881 926 950 1,009 1,031 1,084 1,092 India 609 635 669 702 735 757 783 813 828 Sub-Saharan Africa 294 328 351 393 423 471 509 536 556 Total 2,542 2,625 2,646 2,765 2,828 2,803 2,875 2,795 2,564 Share of people living on less than 2005 PPP $2.00 a day (percent) East Asia & Pacific 92.6 88.5 81.6 79.8 75.8 64.1 61.8 51.9 38.7 China 97.8 92.9 83.7 84.6 78.6 65.1 61.4 51.2 36.3 Europe & Central Asia 8.3 6.5 5.6 6.9 10.3 11.9 14.3 12.0 8.9 Latin America & Caribbean 24.6 28.1 24.9 21.9 20.7 22.0 21.8 21.6 17.1 Middle East & North Africa 26.7 23.1 22.7 19.7 19.8 20.2 19.0 17.6 16.9 South Asia 86.5 84.8 83.9 82.7 79.7 79.9 77.2 77.1 73.9 India 86.6 84.8 83.8 82.6 81.7 79.8 78.4 77.6 75.6 Sub-Saharan Africa 73.8 75.5 74.0 76.1 75.9 77.9 77.6 75.6 72.9 Total 69.4 67.7 64.3 63.4 61.6 58.3 57.1 53.3 47.0 Source: World Bank PovcalNet. 92 2010 World Development Indicators 2.8 PEOPLE Poverty rates at international poverty lines About the data The World Bank produced its first global poverty esti- The statistics reported here are based on con- PPP exchange rates are used to estimate global mates for developing countries for World Development sumption data or, when unavailable, on income poverty, because they take into account the local Report 1990: Poverty using household survey data for surveys. Analysis of some 20 countries for which prices of goods and services not traded internation- 22 countries (Ravallion, Datt, and van de Walle 1991). income and consumption expenditure data were ally. But PPP rates were designed for comparing Since then there has been considerable expansion in both available from the same surveys found income aggregates from national accounts, not for mak- the number of countries that field household income to yield a higher mean than consumption but also ing international poverty comparisons. As a result, and expenditure surveys. The World Bank's poverty higher inequality. When poverty measures based on there is no certainty that an international poverty line monitoring database now includes more than 600 consumption and income were compared, the two measures the same degree of need or deprivation surveys representing 115 developing countries. More effects roughly cancelled each other out: there was across countries. So-called poverty PPPs, designed than 1.2 million randomly sampled households were no significant statistical difference. to compare the consumption of the poorest people interviewed in these surveys, representing 96 per- in the world, might provide a better basis for com- cent of the population of developing countries. International poverty lines parison of poverty across countries. Work on these International comparisons of poverty estimates entail measures is ongoing. Data availability both conceptual and practical problems. Countries Definitions The number of data sets within two years of any given have different definitions of poverty, and consistent year rose dramatically, from 13 between 1978 and comparisons across countries can be difficult. Local · International poverty line in local currency is the 1982 to 158 between 2001 and 2006. Data cover- poverty lines tend to have higher purchasing power in international poverty lines of $1.25 and $2.00 a day age is improving in all regions, but the Middle East rich countries, where more generous standards are in 2005 prices, converted to local currency using and North Africa and Sub-Saharan Africa continue to used, than in poor countries. the PPP conversion factors estimated by the Interna- lag. The database, maintained by a team in the World Poverty measures based on an international pov- tional Comparison Program. · Survey year is the year Bank's Development Research Group, is updated erty line attempt to hold the real value of the poverty in which the underlying data were collected. · Popu- annually as new survey data become available, and line constant across countries, as is done when mak- lation below $1.25 a day and population below $2 a major reassessment of progress against poverty is ing comparisons over time. Since World Development a day are the percentages of the population living made about every three years. A complete overview Report 1990 the World Bank has aimed to apply a on less than $1.25 a day and $2.00 a day at 2005 of data availability by year and country is available at common standard in measuring extreme poverty, international prices. As a result of revisions in PPP http://iresearch.worldbank.org/povcalnet/. anchored to what poverty means in the world's poor- exchange rates, poverty rates for individual countries est countries. The welfare of people living in different cannot be compared with poverty rates reported in Data quality countries can be measured on a common scale by earlier editions. · Poverty gap is the mean shortfall Besides the frequency and timeliness of survey data, adjusting for differences in the purchasing power of from the poverty line (counting the nonpoor as having other data quality issues arise in measuring house- currencies. The commonly used $1 a day standard, zero shortfall), expressed as a percentage of the pov- hold living standards. The surveys ask detailed ques- measured in 1985 international prices and adjusted erty line. This measure reflects the depth of poverty tions on sources of income and how it was spent, to local currency using purchasing power parities as well as its incidence. which must be carefully recorded by trained person- (PPPs), was chosen for World Development Report nel. Income is generally more difficult to measure 1990 because it was typical of the poverty lines in accurately, and consumption comes closer to the low-income countries at the time. Data sources notion of living standards. And income can vary over Early editions of World Development Indicators time even if living standards do not. But consumption used PPPs from the Penn World Tables to convert The poverty measures are prepared by the World data are not always available: the latest estimates values in local currency to equivalent purchasing Bank's Development Research Group. The interna- reported here use consumption for about two-thirds power measured in U.S dollars. Later editions used tional poverty lines are based on nationally repre- of countries. 1993 consumption PPP estimates produced by the sentative primary household surveys conducted by However, even similar surveys may not be strictly World Bank. International poverty lines were recently national statistical offices or by private agencies comparable because of differences in timing or in the revised using the new data on PPPs compiled in under the supervision of government or interna- quality and training of enumerators. Comparisons the 2005 round of the International Comparison tional agencies and obtained from government of countries at different levels of development also Program, along with data from an expanded set of statistical offices and World Bank Group country pose a potential problem because of differences household income and expenditure surveys. The new departments. The World Bank Group has prepared in the relative importance of the consumption of extreme poverty line is set at $1.25 a day in 2005 an annual review of its poverty work since 1993. nonmarket goods. The local market value of all con- PPP terms, which represents the mean of the poverty For details on data sources and methods used in sumption in kind (including own production, particu- lines found in the poorest 15 countries ranked by per deriving the World Bank's latest estimates, and fur- larly important in underdeveloped rural economies) capita consumption. The new poverty line maintains ther discussion of the results, see Shaohua Chen should be included in total consumption expenditure, the same standard for extreme poverty--the poverty and Martin Ravallion's "The Developing World Is but may not be. Most survey data now include valu- line typical of the poorest countries in the world--but Poorer Than We Thought, but No Less Successful ations for consumption or income from own produc- updates it using the latest information on the cost of in the Fight against Poverty?" (2008). tion, but valuation methods vary. living in developing countries. 2010 World Development Indicators 93 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Afghanistan .. .. .. .. .. .. .. .. Albania 2005b 33.0 3.2 7.8 12.2 16.6 22.6 40.9 25.9 Algeria 1995b 35.3 2.8 6.9 11.5 16.3 22.8 42.4 26.9 Angola 2000 b 58.6 0.6 2.0 5.7 10.8 19.7 61.9 44.7 Argentinac 2006d 48.8 1.2 3.6 8.2 13.4 21.7 53.0 36.1 Armenia 2007b 30.2 3.6 8.6 13.0 17.1 22.1 39.2 24.5 Australia 1994 d 35.2 2.0 5.9 12.0 17.2 23.6 41.3 25.4 Austria 2000 d 29.1 3.3 8.6 13.3 17.4 22.9 37.8 23.0 Azerbaijan 2005b 16.8 6.1 13.3 16.2 18.7 21.7 30.2 17.5 Bangladesh 2005b 31.0 4.3 9.4 12.6 16.1 21.1 40.8 26.6 Belarus 2007b 28.8 3.6 8.8 13.4 17.5 22.6 37.7 23.0 Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Belize 1995b 59.6 0.6 2.1 5.4 10.4 19.2 62.9 45.8 Benin 2003b 38.6 2.9 6.9 10.9 15.1 21.2 45.9 31.0 Bhutan 2003b 46.7 2.2 5.4 8.8 12.9 20.0 53.0 37.5 Bolivia 2007b 57.2 0.7 2.7 6.5 11.0 18.6 61.2 45.3 Bosnia and Herzegovina 2007b 36.3 2.6 6.7 11.4 16.0 22.9 43.1 27.1 Botswana 1993­94b 61.0 1.3 3.1 5.8 9.6 16.4 65.0 51.2 Brazil 2007d 55.0 1.1 3.0 6.9 11.8 19.6 58.7 43.0 Bulgaria 2003b 29.2 3.5 8.7 13.5 17.4 22.3 38.1 23.8 Burkina Faso 2003b 39.6 3.0 7.0 10.6 14.7 20.6 47.1 32.4 Burundi 2006b 33.3 4.1 9.0 11.9 15.4 21.0 42.8 28.0 Cambodia 2007b 44.2 2.7 6.5 9.7 12.9 18.9 52.0 36.9 Cameroon 2001b 44.6 2.4 5.6 9.3 13.7 20.5 50.9 35.5 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Cape Verde 2001b 50.4 1.7 4.5 8.1 12.2 19.1 56.1 40.5 Central African Republic 2003b 43.6 2.1 5.2 9.4 14.3 21.7 49.4 33.0 Chad 2002­03b 39.8 2.6 6.3 10.4 15.0 21.8 46.6 30.8 Chile 2006d 52.0 1.6 4.1 7.7 12.2 19.3 56.8 41.7 China 2005d 41.5 2.4 5.7 9.8 14.7 22.0 47.8 31.4 Hong Kong SAR, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2006d 58.5 0.8 2.3 6.0 11.0 19.1 61.6 45.9 Comoros 2004b 64.3 0.9 2.6 5.4 8.9 15.1 68.1 55.0 Congo, Dem. Rep. 2005­06b 44.4 2.3 5.5 9.2 13.8 20.9 50.6 34.7 Congo, Rep. 2005b 47.3 2.1 5.0 8.4 13.0 20.5 53.1 37.1 Costa Rica 2007d 48.9 1.6 4.4 8.5 12.7 19.7 54.6 38.6 Côte d'Ivoire 2002b 48.4 2.0 5.0 8.7 12.9 19.3 54.1 39.6 Croatia 2005b 29.0 3.6 8.8 13.3 17.3 22.7 37.9 23.1 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996d 25.8 4.3 10.2 14.3 17.5 21.7 36.2 22.7 Denmark 1997d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Djibouti 2002b 39.9 2.3 6.0 10.6 15.1 21.8 46.5 30.8 Dominican Republic 2007d 48.4 1.6 4.4 8.5 13.1 20.2 53.8 37.7 Ecuador 2007d 54.4 1.2 3.4 7.2 11.8 19.2 58.5 43.3 Egypt, Arab Rep. 2004­05b 32.1 3.9 9.0 12.6 16.1 20.9 41.5 27.6 El Salvador 2007d 46.9 1.3 4.3 9.2 13.7 20.8 52.0 36.1 Eritrea .. .. .. .. .. .. .. .. Estonia 2004b 36.0 2.7 6.8 11.6 16.2 22.5 43.0 27.7 Ethiopia 2005b 29.8 4.1 9.3 13.2 16.8 21.4 39.4 25.6 Finland 2000 d 26.9 4.0 9.6 14.1 17.5 22.1 36.7 22.6 France 1995d 32.7 2.8 7.2 12.6 17.2 22.8 40.2 25.1 Gabon 2005b 41.5 2.5 6.1 10.1 14.6 21.2 47.9 32.7 Gambia, The 2003b 47.3 2.0 4.8 8.6 13.2 20.6 52.8 36.9 Georgia 2005b 40.8 1.9 5.4 10.5 15.3 22.2 46.7 30.6 Germany 2000 d 28.3 3.2 8.5 13.7 17.8 23.1 36.9 22.1 Ghana 2006b 42.8 1.9 5.2 9.8 14.8 21.9 48.3 32.5 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 94 2010 World Development Indicators 2.9 PEOPLE Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Guatemala 2006d 53.7 1.3 3.4 7.2 12.0 19.5 57.8 42.4 Guinea 2003b 43.3 2.4 5.8 9.6 14.1 20.8 49.7 34.4 Guinea-Bissau 2002b 35.5 2.9 7.2 11.6 16.0 22.1 43.0 28.0 Guyana 1998 d 43.2 1.1 4.3 9.8 14.5 21.3 50.1 34.4 Haiti 2001d 59.5 0.9 2.5 5.9 10.5 18.1 63.0 47.8 Honduras 2006d 55.3 0.7 2.5 6.7 12.1 20.4 58.4 42.2 Hungary 2004b 30.0 3.5 8.6 13.1 17.1 22.5 38.7 24.1 India 2004­05b 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2007b 37.6 3.1 7.4 11.0 14.9 21.3 45.5 30.1 Iran, Islamic Rep. 2005b 38.3 2.6 6.4 10.9 15.6 22.2 45.0 29.6 Iraq .. .. .. .. .. .. .. .. Ireland 2000 d 34.3 2.9 7.4 12.3 16.3 21.9 42.0 27.2 Israel 2001d 39.2 2.1 5.7 10.5 15.9 23.0 44.9 28.8 Italy 2000 d 36.0 2.3 6.5 12.0 16.8 22.8 42.0 26.8 Jamaica 2004b 45.5 2.1 5.2 9.0 13.8 20.9 51.2 35.6 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2006b 37.7 3.0 7.2 11.1 15.2 21.1 45.4 30.7 Kazakhstan 2007b 30.9 3.6 8.7 12.8 16.6 22.0 39.9 25.1 Kenya 2005­06b 47.7 1.8 4.7 8.8 13.3 20.3 53.0 37.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kosovo .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2007b 33.5 3.9 8.8 11.9 15.1 21.6 42.6 27.6 Lao PDR 2002­03b 32.6 3.7 8.5 12.3 16.2 21.6 41.4 27.0 Latvia 2007b 36.3 2.6 6.7 11.5 15.9 22.6 43.3 27.8 Lebanon .. .. .. .. .. .. .. .. Lesotho 2002­03b 52.5 1.0 3.0 7.2 12.5 21.0 56.4 39.4 Liberia 2007b 52.6 2.4 6.4 11.4 15.7 21.6 45.0 30.1 Libya .. .. .. .. .. .. .. .. Lithuania 2004b 35.8 2.7 6.8 11.5 16.3 22.7 42.8 27.4 Macedonia, FYR 2006b 42.8 1.9 5.2 10.0 14.5 21.5 48.8 32.3 Madagascar 2005b 47.2 2.6 6.2 9.6 13.1 17.7 53.5 41.5 Malawi 2004­05b 39.0 2.9 7.0 10.8 14.9 20.9 46.4 31.7 Malaysia 2004 d 37.9 2.6 6.4 10.8 15.8 22.8 44.4 28.5 Maldives 2004b 37.4 2.6 6.5 10.9 15.6 22.6 44.3 27.9 Mali 2006b 39.0 2.7 6.5 10.7 15.2 21.6 46.0 30.5 Mauritania 2000 b 39.0 2.5 6.2 10.5 15.4 22.3 45.7 29.6 Mauritius .. .. .. .. .. .. .. .. Mexico 2008d 51.6 1.2 3.8 8.1 12.4 19.2 56.4 41.3 Micronesia 2000 b 0.5 1.6 5.1 10.2 19.0 64.0 47.1 Moldova 2007b 37.4 2.7 6.7 11.1 15.6 22.0 44.6 28.9 Mongolia 2007­08b 36.6 2.9 7.1 11.2 15.6 22.1 44.0 28.3 Montenegro 2007b 36.9 2.6 6.5 11.4 16.1 22.2 43.7 28.6 Morocco 2007b 40.9 2.7 6.5 10.5 14.5 20.6 47.9 33.2 Mozambique 2002­03b 47.1 2.1 5.4 9.2 13.1 19.0 53.3 39.2 Myanmar .. .. .. .. .. .. .. .. Namibia 1993d 74.3 0.6 1.5 2.8 5.5 12.0 78.3 65.0 Nepal 2003­04b 47.3 2.7 6.1 8.9 12.5 18.4 54.2 40.4 Netherlands 1999d 30.9 2.5 7.6 13.2 17.2 23.3 38.7 22.9 New Zealand 1997d 36.2 2.2 6.4 11.4 15.8 22.6 43.8 27.8 Nicaragua 2005d 52.3 1.4 3.8 7.7 12.3 19.4 56.9 41.8 Niger 2005b 43.9 2.3 5.9 9.8 13.9 20.1 50.3 35.7 Nigeria 2003­04b 42.9 2.0 5.1 9.7 14.7 21.9 48.6 32.4 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2004­05b 31.2 3.9 9.1 12.8 16.3 21.3 40.5 26.5 2010 World Development Indicators 95 2.9 Distribution of income or consumption Survey Gini Percentage share of year index income or consumptiona Lowest 10% Lowest 20% Second 20% Third 20% Fourth 20% Highest 20% Highest 10% Panama 2006d 54.9 0.8 2.5 6.6 12.1 20.8 58.0 41.4 Papua New Guinea 1996b 50.9 1.9 4.5 7.7 12.1 19.3 56.4 40.9 Paraguay 2007d 53.2 1.1 3.4 7.6 12.2 19.4 57.4 42.3 Peru 2007d 50.5 1.3 3.6 7.8 13.0 20.8 54.8 38.4 Philippines 2006b 44.0 2.4 5.6 9.1 13.7 21.2 50.4 33.9 Poland 2005b 34.9 3.0 7.3 11.7 16.2 22.4 42.4 27.2 Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. Qatar 2006­07b 41.1 1.3 3.9 .. .. .. 52.0 35.9 Romania 2007b 32.1 3.2 7.9 12.7 16.8 22.3 40.3 25.6 Russian Federation 2007b 43.7 2.2 5.6 9.6 13.9 20.7 50.2 34.3 Rwanda 2000 b 46.7 2.3 5.4 9.0 13.2 19.6 52.8 38.2 São Tomé and Príncipe 2000­01b 50.6 2.1 5.2 8.7 12.1 17.6 56.5 43.6 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2005b 39.2 2.5 6.2 10.6 15.3 22.0 45.9 30.1 Serbia 2008b 28.2 3.8 9.1 13.6 17.4 22.5 37.5 22.7 Seychelles 2006­07b 1.6 3.7 5.7 8.4 12.4 69.8 60.0 Sierra Leone 2003b 42.5 2.6 6.1 9.7 14.0 20.9 49.3 33.6 Singapore 1998d 42.5 1.9 5.0 9.4 14.6 22.0 49.0 32.8 Slovak Republic 1996d 25.8 3.1 8.8 14.9 18.6 22.9 34.8 20.8 Slovenia 2004b 31.2 3.4 8.2 12.8 17.0 22.6 39.4 24.6 Somalia .. .. .. .. .. .. .. .. South Africa 2000 b 57.8 1.3 3.1 5.6 9.9 18.8 62.7 44.9 Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2002b 41.1 2.9 6.8 10.4 14.4 20.5 48.0 33.3 St. Lucia 1995d 42.6 1.7 5.1 10.3 14.4 21.4 48.8 31.6 Sudan .. .. .. .. .. .. .. .. Suriname 1999d 52.8 1.0 3.1 7.5 12.2 19.9 57.4 40.0 Swaziland 2000­01b 50.7 1.8 4.5 8.0 12.3 19.4 55.9 40.8 Sweden 2000 d 25.0 3.6 9.1 14.0 17.6 22.7 36.6 22.2 Switzerland 2000 d 33.7 2.9 7.6 12.2 16.3 22.6 41.3 25.9 Syrian Arab Republic .. .. .. .. .. .. .. .. Tajikistan 2004b 33.6 3.2 7.8 12.0 16.4 21.9 41.9 26.6 Tanzania 2000­01b 34.6 3.1 7.3 11.8 16.3 22.3 42.3 27.0 Thailand 2004b 42.5 2.6 6.1 9.8 14.2 21.0 49.0 33.7 Timor-Leste 2007b 31.9 3.9 8.9 12.5 16.0 21.2 41.3 27.0 Togo 2006b 34.4 2.0 5.4 10.3 15.2 22.0 47.1 31.3 Trinidad and Tobago 1992d 40.3 2.1 5.5 10.3 15.5 22.7 45.9 29.9 Tunisia 2000 b 40.81 2.4 5.9 10.2 14.9 21.8 47.2 31.6 Turkey 2006b 41.2 2.0 5.4 10.3 15.2 22.0 47.1 31.3 Turkmenistan 1998b 40.8 2.5 6.0 10.2 14.9 21.7 47.2 31.8 Uganda 2005b 42.6 2.6 6.1 9.8 14.1 20.7 49.3 34.1 Ukraine 2008b 27.6 3.9 9.4 13.6 17.4 22.6 37.0 22.5 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 1999d 36.0 2.1 6.1 11.4 16.0 22.5 44.0 28.5 United States 2000d 40.8 1.9 5.4 10.7 15.7 22.4 45.8 29.9 Uruguay 2007d 47.1 1.6 4.3 8.6 13.6 21.4 52.1 35.5 Uzbekistan 2003b 36.7 2.9 7.1 11.5 15.7 21.5 44.2 29.5 Venezuela, RB 2006d 43.4 1.7 4.9 9.6 14.8 22.1 48.6 32.7 Vietnam 2006b 37.8 3.1 7.1 10.8 15.2 21.6 45.4 29.8 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 2005b 37.7 2.9 7.2 11.3 15.3 21.0 45.3 30.8 Zambia 2004­05b 50.7 1.3 3.6 7.8 12.8 20.6 55.2 38.9 Zimbabwe 1995b 50.1 1.8 4.6 8.1 12.2 19.3 55.7 40.3 a. Percentage shares by quintile may not sum to 100 percent because of rounding. b. Refers to expenditure shares by percentiles of population, ranked by per capita expenditure. c. Urban data. d. Refers to income shares by percentiles of population, ranked by per capita income. 96 2010 World Development Indicators 2.9 PEOPLE Distribution of income or consumption About the data Definitions Inequality in the distribution of income is refl ected improve and become more standardized, but achiev- · Survey year is the year in which the underlying in the percentage shares of income or consumption ing strict comparability is still impossible (see About data were collected. · Gini index measures the accruing to portions of the population ranked by the data for tables 2.7 and 2.8). extent to which the distribution of income (or con- income or consumption levels. The portions ranked Two sources of noncomparability should be noted sumption expenditure) among individuals or house- lowest by personal income receive the smallest in particular. First, the surveys can differ in many holds within an economy deviates from a perfectly shares of total income. The Gini index provides respects, including whether they use income or con- equal distribution. A Lorenz curve plots the cumula- a convenient summary measure of the degree of sumption expenditure as the living standard indi- tive percentages of total income received against the inequality. Data on the distribution of income or cator. The distribution of income is typically more cumulative number of recipients, starting with the consumption come from nationally representa- unequal than the distribution of consumption. In poorest individual. The Gini index measures the area tive household surveys. Where the original data addition, the definitions of income used differ more between the Lorenz curve and a hypothetical line from the household survey were available, they often among surveys. Consumption is usually a of absolute equality, expressed as a percentage of have been used to directly calculate the income much better welfare indicator, particularly in devel- the maximum area under the line. Thus a Gini index or consumption shares by quintile. Otherwise, oping countries. Second, households differ in size of 0 represents perfect equality, while an index of shares have been estimated from the best avail- (number of members) and in the extent of income 100 implies perfect inequality. · Percentage share able grouped data. sharing among members. And individuals differ in of income or consumption is the share of total The distribution data have been adjusted for age and consumption needs. Differences among income or consumption that accrues to subgroups of household size, providing a more consistent mea- countries in these respects may bias comparisons population indicated by deciles or quintiles. sure of per capita income or consumption. No adjust- of distribution. ment has been made for spatial differences in cost World Bank staff have made an effort to ensure of living within countries, because the data needed that the data are as comparable as possible. Wher- for such calculations are generally unavailable. For ever possible, consumption has been used rather further details on the estimation method for low- and than income. Income distribution and Gini indexes for middle-income economies, see Ravallion and Chen high-income economies are calculated directly from (1996). the Luxembourg Income Study database, using an Because the underlying household surveys differ in estimation method consistent with that applied for method and type of data collected, the distribution developing countries. data are not strictly comparable across countries. These problems are diminishing as survey methods The Gini coefficient and ratio of income or consumption of the richest quintile to the poorest quintiles are closely correlated 2.9a Gini coefficient (percent) 80 70 60 50 40 30 20 10 0 0 10 20 30 40 50 60 Ratio of income or consumption of richest quintile to poorest quintile (percent) Data sources There are many ways to measure income or consumption inequality. The Gini coefficient shows inequal- Data on distribution are compiled by the World ity over the entire population; the ratio of income or consumption of the richest quintile to the poorest Bank's Development Research Group using pri- quintiles shows differences only at the tails of the population distribution. Both measures are closely mary household survey data obtained from govern- correlated and provide similar information. At low levels of inequality the Gini coefficient is a more sensi- ment statistical agencies and World Bank country tive measure, but above a Gini value of 45­55 percent the inequality ratio rises faster. departments. Data for high-income economies are Source: World Development Indicators data files. from the Luxembourg Income Study database. 2010 World Development Indicators 97 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15­24 ages 15­24 total % of labor age % of average 2005­08a 2005­08a 2005­08a Year force population Year GDP Year wage Afghanistan .. .. .. 2005 .. 2.2 2005 0.5 .. Albania .. .. .. 2007 49.8 34.1 2007 5.9 .. Algeria .. .. .. 2002 36.7 22.1 2002 3.2 .. Angola .. .. 25 .. .. .. .. Argentina 16b 24b 34 2007 42.5 30.8 2007 8.0 2000 43.8 Armenia .. .. 36 2007 88.0 65.7 2006 3.2 2007 20.3 Australia 9b 9b .. 2005 92.6 69.6 2005 3.5c .. Austria 8 8 .. 2005 96.4 68.7 2005 12.6c .. Azerbaijan 18 10 25 2007 36.8 30.2 2006 3.7 2006 24.3 Bangladesh 8 14 13 2004 2.8 2.1 2001 0.5 .. Belarus .. .. 54 2008 94.7 67.0 2008 10.2 2002 41.6 Belgium 17 19 .. 2005 94.2 61.6 2005 9.0 c .. Benin .. .. 23 1996 4.8 .. 2006 1.5 .. Bolivia .. .. .. 2007 11.5 9.2 2000 4.5 .. Bosnia and Herzegovina 55 62 .. 2005 35.5 25.5 2005 7.7 .. Botswana .. .. .. .. .. .. .. Brazil .. .. .. 2007 51.0 39.2 2004 12.6 .. Bulgaria 14 11 .. 2007 83.5 48.3 2007 9.8 2004 42.9 Burkina Faso .. .. .. 1993 3.1 3.0 1992 0.3 .. Burundi .. .. .. 1993 3.3 3.0 1991 0.2 .. Cambodia .. .. 24 .. .. .. .. Cameroon .. .. .. 1993 13.7 11.5 2001 0.8 .. Canada 12b 10 b .. 2005 90.5 71.4 2005 4.1c .. Central African Republic .. .. .. 2004 1.5 1.3 2004 0.8 .. Chad .. .. .. 1990 1.1 1.0 1997 0.1 .. Chile 17 22 .. 2007 57.3 35.5 2001 2.9 2006 53.5 China .. .. .. 2005 20.5 17.2 1996 2.7 .. Hong Kong SAR, China 11 7 .. 2008 77.0 55.6 .. .. Colombia 16 28 19 2007 24.9 19.0 2006 2.7 .. Congo, Dem. Rep. .. .. 21 .. .. .. .. Congo, Rep. .. .. 23 1992 5.8 5.6 2004 0.9 .. Costa Rica 8 15 .. 2004 55.3 37.6 2006 2.4 .. Côte d'Ivoire .. .. .. 1997 9.3 9.1 1997 0.3 .. Croatia 19 27 .. 2007 75.2 51.0 2007 11.3 2005 32.4 Cuba .. .. 46 .. .. 1992 12.6 .. Czech Republic 10 10 .. 2008 93.0 66.0 2008 8.1 2005 40.7 Denmark 7 8 .. 2007 94.4 86.9 2005 5.4 c .. Dominican Republic 21 45 35 2007 21.4 15.1 2000 0.8 .. Ecuador 12b 23b .. 2004 27.0 20.8 2002 2.5 .. Egypt, Arab Rep. 23 62 12 2004 55.5 27.7 2004 4.1 .. El Salvador 14 10 .. 2007 24.0 16.6 2006 1.9 .. Eritrea .. .. .. .. .. 2001 0.3 .. Estonia 12 12 .. 2004 95.2 68.6 2003 6.0 2007 35.4 Ethiopia 20 b 29b 23 .. .. 2007 0.3 .. Finland 17 16 .. 2005 88.7 67.2 2005 8.4 c .. France 18 18 .. 2005 89.9 61.4 2005 12.4 c .. Gabon .. .. .. 1995 15.0 14.0 .. .. Gambia, The .. .. .. 2003 3.8 2.9 .. .. Georgia 28 37 .. 2004 29.9 22.7 2004 3.0 2003 13.0 Germany 11 10 .. 2005 88.2 65.5 2005 11.4 c .. Ghana .. .. 34 2004 9.1 7.1 2002 1.3 .. Greece 17 29 .. 2005 85.2 58.5 2005 11.5c .. Guatemala .. .. .. 2005 24.0 18.0 2005 1.0 .. Guinea .. .. 17 1993 1.5 1.8 .. .. Guinea-Bissau .. .. .. 2004 1.9 1.5 2005 2.1 .. Haiti .. .. 44 .. .. .. .. Honduras 5b 11b 26 2006 16.1 12.4 1994 0.6 .. 98 2010 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15­24 ages 15­24 total % of labor age % of average 2005­08a 2005­08a 2005­08a Year force population Year GDP Year wage Hungary 19 21 .. 2008 92.0 56.0 2008 10.5 2005 39.8 India .. .. 14 2004 9.0 5.7 2007 2.0 .. Indonesia 24 27 13 2002 15.5 11.3 .. .. Iran, Islamic Rep. 20 30 .. 2001 35.1 20.0 2000 1.1 .. Iraq .. .. 11 .. .. .. .. Ireland 15 10 .. 2005 88.0 63.9 2005 3.4 c .. Israel 15 17 .. 1992 82.0 63.0 1996 5.9 .. Italy 19 25 .. 2005 92.4 58.4 2005 14.0 c .. Jamaica .. .. .. 2004 17.4 12.6 1996 .. .. Japan 8 7 .. 2005 95.3 75.0 2005 8.7c .. Jordan .. .. 41 2004 32.2 18.6 2001 2.2 .. Kazakhstan .. .. .. 2004 33.8 26.4 2004 4.9 2003 24.9 Kenya .. .. .. 2005 8.0 6.7 2003 1.1 .. Korea, Dem. Rep. .. .. .. .. .. .. .. Korea, Rep. 11 7 .. 2005 78.0 55.0 2005 1.6c .. Kosovo .. .. .. 2005 23.0 .. 2005 3.4 .. Kuwait .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 14 16 25 2006 42.2 28.9 2006 4.8 2003 27.5 Lao PDR .. .. .. .. .. .. .. Latvia 13 13 .. 2003 92.4 66.5 2002 7.5 2005 33.1 Lebanon .. .. .. 2003 33.1 19.9 2003 2.1 .. Lesotho .. .. .. 2005 5.7 3.6 .. .. Liberia 6 4 31 .. .. .. .. Libya .. .. .. 2004 65.5 38.1 2001 2.1 .. Lithuania 13 15 .. 2007 .. 68.7 2007 6.3 2005 30.9 Macedonia, FYR 57 58 8 2008 48.4 30.4 2008 9.4 2006 55.0 Madagascar 2 3 .. 1993 5.4 4.8 1990 0.2 .. Malawi .. .. .. .. .. .. .. Malaysia 11 12 .. 2008 46.9 32.5 1999 6.5 .. Mali .. .. 12 1990 2.5 2.0 1991 0.4 .. Mauritania .. .. .. 1995 5.0 4.0 1992 0.2 .. Mauritius 20 31 .. 2000 51.4 33.6 1999 4.4 .. Mexico 6 8 .. 2006 36.2 24.3 2005 1.3c .. Moldova 15 14 34 2007 42.0 77.8 2007 7.2 2003 20.9 Mongolia .. .. 29 2005 33.6 21.4 2007 6.5d .. Morocco 18 16 .. 2003 22.4 12.8 2003 1.9 .. Mozambique .. .. .. 1995 2.0 2.1 1996 0.0 .. Myanmar .. .. .. .. .. .. .. Namibia .. .. 44 .. .. .. .. Nepal .. .. 23 2006 3.5 2.5 2003 0.3 .. Netherlands 7 8 .. 2005 90.3 70.4 2005 5.0 c .. New Zealand 10 b 10 b .. 2003 92.7 72.2 2005 4.4 c .. Nicaragua 8 10 .. 2005 17.9 11.5 1996 2.5 .. Niger .. .. 19 2006 1.3 1.2 2006 0.7 .. Nigeria .. .. .. 2005 1.7 1.2 1991 0.1 .. Norway 8 7 .. 2005 90.8 75.7 2005 4.8 c .. Oman .. .. .. .. .. .. .. Pakistan 7 9 10 2004 6.4 4.0 1993 0.9 .. Panama 13 24 .. 2008 .. 42.0 1996 4.3 .. Papua New Guinea .. .. .. .. .. .. .. Paraguay 9 18 .. 2004 11.6 9.1 2001 1.2 .. Peru 14b 15b 22 2007 16.5 13.1 2000 2.6 .. Philippines 14 17 19 2007 20.8 15.5 1993 1.0 .. Poland 15 20 .. 2005 84.9 54.5 2005 11.4 c 2007 47.1 Portugal 13 20 .. 2005 91.4 71.9 2005 10.2c .. Puerto Rico 24 19 .. .. .. .. .. Qatar .. .. .. .. .. .. .. 2010 World Development Indicators 99 2.10 Assessing vulnerability and security Youth Female-headed Pension Public expenditure unemployment households contributors on pensions Male Female Average % of male % of female % of pension labor force labor force % of working- % of ages 15­24 ages 15­24 total % of labor age % of average 2005­08a 2005­08a 2005­08a Year force population Year GDP Year wage Romania 19 18 .. 2007 53.4 36.3 2007 5.7 2005 41.5 Russian Federation 14 15 .. .. .. 2007 4.7 2003 29.2 Rwanda .. .. 34 2004 4.8 4.1 .. .. Saudi Arabia .. .. .. .. .. 1998 0.2 .. Senegal .. .. 23 2003 5.3 3.9 2003 1.3 .. Serbia 41 48 29 2003 46.0e 32.2e 2007 13.3e .. Sierra Leone .. .. .. 2004 4.6 3.6 .. .. Singapore 7 11 .. 2008 62.0 45.3 1996 1.4 .. Slovak Republic 19 20 .. 2005 85.5 55.3 2005 6.2c 2005 44.7 Slovenia 10 11 .. 2007 87.3 62.7 2007 11.8 2005 44.3 Somalia .. .. .. .. .. .. .. South Africa 43 52 .. .. .. .. .. Spain 24 26 .. 2005 91.0 63.2 2005 8.1c 2006 58.6 Sri Lanka 17b 28b .. 2004 35.6 22.2 2002 2.0 .. Sudan .. .. 19 1995 12.1 12.0 .. .. Swaziland .. .. 48 .. .. .. .. Sweden 20 21 .. 2005 91.0 72.3 2005 7.7c .. Switzerland 7 7 .. 2005 100.0 79.1 2005 6.8 c 2000 40.0 Syrian Arab Republic .. .. .. 2004 17.4 11.4 2004 1.3 .. Tajikistan .. .. .. .. .. 2005 2.4 2003 25.7 Tanzania 7 10 25 2006 4.3 4.1 2006 0.9 .. Thailand 5 4 30 2005 27.2 21.8 .. .. Timor-Leste .. .. .. .. .. .. .. Togo .. .. .. 1997 15.9 15.0 1997 0.6 .. Trinidad and Tobago 13 22 .. 2004 55.6 .. 1996 0.6 .. Tunisia 31 29 .. 2004 45.3 25.4 2003 4.3 .. Turkey 18 18 .. 2007 55.0 30.5 2007 9.6 2007 61.3 Turkmenistan .. .. .. .. .. 1996 2.3 .. Uganda .. .. 30 2004 10.7 9.3 2003 0.3 .. Ukraine 15 14 49 2007 68.2 47.4 2007 15.5 2007 48.3 United Arab Emirates 7 13 .. .. .. .. .. United Kingdom 17 13 .. 2005 92.7 71.4 2005 5.7c .. United States 12b 9b .. 2005 92.5 72.5 2005 6.0 c 2006 29.2 Uruguay 20 30 .. 2004 55.0 44.3 2007 10.0 .. Uzbekistan .. .. 18 2005 86.0 57.0 2005 6.5 2005 40.0 Venezuela, RB 13 17 .. 2004 31.8 23.8 2001 2.7 .. Vietnam .. .. .. 2005 13.2 10.8 1998 1.6 .. West Bank and Gaza 34 43 .. 2008 17.0 7.8 2008 4.0 .. Yemen, Rep. .. .. .. 2005 10.0 5.5 1999 0.9 .. Zambia .. .. 24 2006 10.9 8.0 2006 1.0 .. Zimbabwe .. .. 38 1995 12.0 10.0 2002 2.3 .. World .. w .. w Low income .. Middle income .. .. Lower middle income .. .. Upper middle income 17 21 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia 18 18 Latin America & Carib. .. .. Middle East & N. Africa .. .. South Asia .. .. Sub-Saharan Africa .. .. High income 13 12 Euro area 16 17 a. Data are for the most recent year available. b. Limited coverage. c. Includes expenditure on old-age and survivors benefi ts only. d. Includes old-age, survivors, disability, military, and work accident or disease pensions. e. Includes Montenegro. 100 2010 World Development Indicators 2.10 PEOPLE Assessing vulnerability and security About the data Definitions As traditionally measured, poverty is a static con- residency, or income status. In contribution-related · Youth unemployment is the share of the labor force cept, and vulnerability a dynamic one. Vulnerabil- schemes, however, eligibility is usually restricted ages 15­24 without work but available for and seek- ity reflects a household's resilience in the face of to individuals who have contributed for a minimum ing employment. · Female-headed households are shocks and the likelihood that a shock will lead to a number of years. Definitional issues--relating to the the percentage of households with a female head. decline in well-being. Thus, it depends primarily on labor force, for example--may arise in comparing · Pension contributors are the share of the labor the household's assets and insurance mechanisms. coverage by contribution-related schemes over time force or working-age population (here defined as Because poor people have fewer assets and less and across countries (for country-specific informa- ages 15 and older) covered by a pension scheme. diversified sources of income than do the better-off, tion, see Hinz and others forthcoming). The share · Public expenditure on pensions is all government fluctuations in income affect them more. of the labor force covered by a pension scheme may expenditures on cash transfers to the elderly, the Enhancing security for poor people means reduc- be overstated in countries that do not try to count disabled, and survivors and the administrative costs ing their vulnerability to such risks as ill health, pro- informal sector workers as part of the labor force. of these programs. · Average pension is the aver- viding them the means to manage risk themselves, Public interventions and institutions can provide age pension payment of all pensioners of the main and strengthening market or public institutions for services directly to poor people, although whether pension schemes (including old-age, survivors, dis- managing risk. Tools include microfinance programs, these interventions and institutions work well for the ability, military, and work accident or disease pen- public provision of education and basic health care, poor is debated. State action is often ineffective, sions) divided by the average wage of all formal sec- and old age assistance (see tables 2.11 and 2.16). in part because governments can influence only a tor workers. Poor households face many risks, and vulnerability few of the many sources of well-being and in part is thus multidimensional. The indicators in the table because of difficulties in delivering goods and ser- focus on individual risks--youth unemployment, vices. The effectiveness of public provision is further female-headed households, income insecurity in constrained by the fiscal resources at governments' old age--and the extent to which publicly provided disposal and the fact that state institutions may not services may be capable of mitigating some of these be responsive to the needs of poor people. risks. Poor people face labor market risks, often hav- The data on public pension spending cover the ing to take up precarious, low-quality jobs and to pension programs of the social insurance schemes increase their household's labor market participa- for which contributions had previously been made. tion by sending their children to work (see tables In many cases noncontributory pensions or social 2.4 and 2.6). Income security is a prime concern assistance targeted to the elderly and disabled are for the elderly. also included. A country's pattern of spending is cor- Youth unemployment is an important policy issue related with its demographic structure--spending for many economies. Experiencing unemployment increases as the population ages. may permanently impair a young person's produc- tive potential and future employment opportunities. The table presents unemployment among youth ages 15­24, but the lower age limit for young people in a country could be determined by the minimum age for leaving school, so age groups could dif- fer across countries. Also, since this age group is likely to include school leavers, the level of youth unemployment varies considerably over the year as a result of different school opening and closing dates. The youth unemployment rate shares similar limita- tions on comparability as the general unemployment Data sources rate. For further information, see About the data for table 2.5 and the original source. Data on youth unemployment are from the ILO's The definition of female-headed household differs Key Indicators of the Labour Market, 6th edition, greatly across countries, making cross-country com- database. Data on female-headed household are parison difficult. In some cases it is assumed that a from Demographic and Health Surveys by Macro woman cannot be the head of any household with an International. Data on pension contributors and adult male, because of sex-biased stereotype. Cau- pension spending are from Hinz and others' tion should be used in interpreting the data. "International Patterns of Pension Provision II" Pension scheme coverage may be broad or even uni- (forthcoming). versal where eligibility is determined by citizenship, 2010 World Development Indicators 101 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil­teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2008a 1999 2008a 1999 2008a 2008a 2008a 2008a 2008a Afghanistan .. .. .. .. .. .. .. .. .. 43 Albania .. .. .. .. .. .. .. .. .. .. Algeria 12.0 .. .. .. .. .. .. .. 98.9 23 Angola .. .. .. .. .. 80.8 2.6 .. .. .. Argentina 12.9 13.2 18.2 20.3 17.7 14.2 5.5 15.0 .. 16 Armenia .. .. .. .. .. .. 3.0 15.0 .. 19 Australia 16.9 18.2 15.4 16.2 27.2 24.7 5.2 14.0 .. .. Austria 24.9 .. 29.9 .. 51.6 .. .. .. .. 12 Azerbaijan 6.9 5.2 17.0 8.0 19.1 9.2 1.9 11.9 99.9 11 Bangladesh .. 10.5 13.6 14.3 50.7 39.8 2.4 14.0 54.4 44 Belarus .. .. .. .. .. 18.1 5.2 9.3 99.9 15 Belgium 18.2 20.5 23.7 .. 38.3 35.5 6.0 12.4 .. 11 Benin 11.9 12.4 24.2 .. 157.0 153.4 3.6 15.9 71.8 45 Bolivia 14.2 13.7 11.7 14.5 44.1 .. 6.3 .. .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 12.6 .. 38.3 .. .. 8.1 21.0 94.3 25 Brazil 10.8 .. 9.5 .. 57.1 .. 5.0 16.2 .. 24 Bulgaria 15.5 23.6 18.8 22.0 17.9 23.2 4.2 11.6 .. 16 Burkina Faso .. 29.1 .. 30.3 .. 308.3 4.6 15.4 87.7 49b Burundi 14.7 18.8 .. 58.2 1,051.5 563.9 7.2 22.3 87.4 52 Cambodia 5.9 .. 11.5 .. 43.7 .. 1.6 12.4 98.2 49 Cameroon .. 7.6 .. 39.1 .. 126.1 3.9 17.0 61.8 46 Canada .. .. .. .. 47.1 .. .. .. .. .. Central African Republic .. 5.5 .. .. .. 305.2 1.3 12.0 .. 90 Chad 9.2 .. 28.3 .. .. .. .. .. 35.5 62 Chile 14.4 11.9 14.8 13.4 19.4 11.5 3.4 18.2 .. 27 China .. .. 11.6 .. 90.1 .. .. .. .. 18 Hong Kong SAR, China 12.4 12.7 17.7 15.6 .. 47.3 3.3 23.0 95.1 17 Colombia 15.2 12.4 16.1 14.8 37.8 26.0 3.9 14.9 100.0 29 Congo, Dem. Rep. .. .. .. .. .. .. .. .. 93.3 39 Congo, Rep. 15.2 .. .. .. 362.2 .. .. .. 89.0 52 Costa Rica 16.0 .. 23.2 .. 55.0 .. 5.0 22.8 86.0 19 Côte d'Ivoire 17.9 .. 56.1 .. 218.9 .. 4.6 24.6 100.0 42 Croatia .. .. .. .. 35.8 .. .. .. .. 17 Cuba 27.9 51.1 41.4 60.1 86.6 43.5 13.3 18.5 100.0 10 Czech Republic 11.2 13.6 21.7 23.1 33.7 37.4 4.6 10.5 .. 19 Denmark 24.6 24.5 38.1 34.4 65.9 53.4 7.9 15.5 .. .. Dominican Republic 7.1 7.4 .. 6.5 .. .. 2.2 11.0 89.2 20 Ecuador 4.5 .. 9.7 .. .. .. .. .. 71.6 23 Egypt, Arab Rep. .. .. .. .. .. .. 3.7 12.1 .. 27 El Salvador 8.6 8.5 7.5 9.1 8.9 31.5 3.6 13.1 93.2 33 Eritrea 15.1 8.2 37.6 8.1 433.2 .. 2.0 .. 89.3 47 Estonia 21.0 .. 27.3 .. 32.0 .. .. .. .. 13 Ethiopia .. 12.4 .. 8.9 .. 642.7 5.5 23.3 89.7 59 Finland 17.4 17.9 25.8 31.5 40.3 33.1 6.1 12.6 .. 15 France 17.3 17.1 28.5 26.6 29.7 33.5 5.6 10.6 .. 19 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. 34 Georgia .. 14.7 .. 15.4 .. 11.4 2.9 7.2 95.0 9 Germany 14.8 16.1 20.5 20.7 .. .. 4.4 9.7 .. 14 Ghana .. 17.9 .. 28.3 .. .. .. .. 49.1 31 Greece 11.7 .. 15.5 .. 26.2 .. .. .. .. 10 Guatemala 6.7 10.3 4.3 5.9 .. 19.0 3.0 .. .. 29 Guinea 11.4 5.0 .. 4.4 .. 71.5 1.7 19.2 82.1 44 Guinea-Bissau .. .. .. .. .. .. .. .. .. 62 Haiti .. .. .. .. .. .. .. .. .. .. Honduras .. 1.1 .. 1.1 .. .. .. .. 36.4 33 102 2010 World Development Indicators 2.11 PEOPLE Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil­teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2008a 1999 2008a 1999 2008a 2008a 2008a 2008a 2008a Hungary 18.0 25.6 19.1 23.2 34.2 23.8 5.4 10.4 .. 10 India 11.9 8.9 24.7 16.2 90.8 55.0 3.2 .. .. .. Indonesia .. .. .. .. .. .. 3.5 17.5 .. 19 Iran, Islamic Rep. 9.1 13.5 9.9 20.3 34.8 20.7 4.8 20.0 .. 20 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 11.0 15.0 16.8 22.8 28.5 26.4 4.8 14.0 .. 16 Israel 20.6 20.2 22.0 20.5 31.1 23.1 6.2 .. .. 13 Italy 24.0 25.1 27.7 28.6 27.6 23.4 4.7 9.7 .. 10 Jamaica 13.4 17.3 21.0 19.9 70.4 .. 5.5 .. .. .. Japan 21.1 21.9 20.9 22.4 15.1 19.1 3.5 9.5 .. 18 Jordan 13.7 13.0 15.8 16.5 .. .. .. .. .. .. Kazakhstan .. .. .. .. .. 7.9 2.8 .. .. 16b Kenya 22.5 22.3 15.1 22.0 207.8 .. 6.6 20.2 98.4 47 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 18.4 17.2 15.7 22.2 8.4 9.5 4.2 .. .. 26 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 19.2 11.1 .. 14.6 .. 82.8 3.8 12.9 100.0 9 Kyrgyz Republic .. .. .. .. 24.3 22.8 6.6 25.6 64.4 24 Lao PDR 2.2 .. 4.5 .. 68.6 .. 2.3 12.2 96.9 30 Latvia 19.5 37.3 23.7 19.3 27.9 15.9 5.1 13.4 .. 12 Lebanon .. .. .. .. 14.2 12.5 2.0 8.1 12.8 14 Lesotho 34.4 22.3 76.6 50.2 1,385.2 1,182.4 12.4 23.7 71.4 37 Liberia .. 5.7 .. 8.4 .. .. 2.7 12.1 40.2 24 Libya .. .. .. .. 23.9 .. .. .. .. .. Lithuania .. 16.4 .. 20.3 34.2 17.0 4.8 14.4 .. 13 Macedonia, FYR .. .. .. .. .. .. 4.7 13.3 .. 18 Madagascar 8.9 7.4 .. 13.0 171.7 137.2 2.9 13.4 52.1 47 Malawi .. .. .. .. .. .. .. .. .. 93 Malaysia 12.5 10.8 21.7 .. 81.1 59.7 4.7 .. .. 16 Mali 13.5 10.4 53.0 34.5 227.7 114.8 3.8 19.5 50.1 51 Mauritania 11.2 12.8 35.3 36.7 77.8 .. 4.4 15.6 100.0 37 Mauritius 9.7 10.3 15.3 17.4 40.4 29.8 3.9 12.7 100.0 22 Mexico 11.7 13.4 14.2 13.8 47.8 35.4 4.8 .. .. 28 Moldova .. 34.3 .. 32.4 .. 38.9 8.2 19.8 .. 16 Mongolia .. 14.7 .. 14.7 .. .. 5.1 .. 99.0 30 b Morocco 17.0 16.3 44.5 38.3 94.9 72.1 5.5 26.1 100.0 27 Mozambique .. 14.5 .. 83.7 .. .. 5.0 21.0 67.0 64 Myanmar .. .. 6.8 .. 27.5 .. .. .. 99.0 29 Namibia 22.1 15.7 36.2 16.0 156.9 117.8 6.5 22.4 95.0 29 Nepal 9.1 15.1 13.1 11.2 141.6 .. 3.8 .. 66.4 38 Netherlands 15.2 17.8 22.2 25.4 47.4 43.9 5.5 12.0 .. .. New Zealand 20.1 17.6 24.3 19.8 41.6 29.2 6.2 19.7 .. 16 Nicaragua .. 9.8 .. 4.5 .. .. .. .. 72.7 29 Niger 20.2 27.1 60.9 49.6 .. 398.0 3.7 15.5 98.0 b 39b Nigeria .. .. .. .. .. .. .. .. 51.2 46 Norway 19.8 18.2 26.8 .. 45.8 44.8 6.5 16.2 .. .. Oman 11.2 .. 21.8 .. .. .. 4.0 31.1 100.0 12 Pakistan .. .. .. .. .. .. 2.9 11.2 85.1 41 Panama 13.7 7.5 19.1 10.0 33.6 .. 3.8 18.0 91.3 24 Papua New Guinea .. .. .. .. .. .. .. .. .. 36 Paraguay 13.6 .. 18.4 .. 58.9 .. .. .. .. .. Peru 7.6 7.3 10.8 8.9 21.2 10.9 2.5 16.4 .. 22 Philippines 12.8 .. 11.0 .. 15.4 .. .. .. .. 34 Poland .. 27.0 16.5 24.9 21.1 18.4 5.7 12.0 .. 11 Portugal 19.5 22.4 27.5 34.0 28.1 28.8 5.3 11.3 .. 12 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 52.3 13 2010 World Development Indicators 103 2.11 Education inputs Public expenditure Public expenditure Trained Primary per student on education teachers school in primary pupil­teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1999 2008a 1999 2008a 1999 2008a 2008a 2008a 2008a 2008a Romania 8.5 .. 16.0 .. 32.6 .. .. .. .. 17 Russian Federation .. .. .. .. .. 16.0 4.0 11.8 .. 17 Rwanda 7.7 8.2 29.4 34.3 680.7 222.8 4.1 20.4 94.2 68 Saudi Arabia .. 18.4 .. 18.3 .. .. .. .. 91.5 11 Senegal 14.1 17.0 .. 31.3 .. 207.7 4.8 26.3 .. 36 Serbia .. .. .. .. .. .. .. .. 100.0 17 Sierra Leone .. .. .. .. .. .. .. .. 49.4 44 Singapore .. 11.2b .. 16.6b .. 26.9b 3.2b 11.6b 97.1 19 Slovak Republic 10.2 15.3 18.4 .. 32.9 .. 3.8 10.2 .. 15 Slovenia 26.3 .. 25.7 .. 27.9 21.6 5.7 12.9 .. 16 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 14.2 13.7 20.0 16.0 60.7 .. 5.1 16.2 .. 31 Spain 18.0 19.4 24.4 24.0 19.6 23.5 4.3 11.1 .. 13 Sri Lanka .. .. .. .. .. .. .. .. .. 24 Sudan .. .. .. .. .. .. .. .. 59.7b 38b Swaziland 8.4 16.3 23.7 41.1 351.5 347.5 7.9 21.6 94.0 32 Sweden 22.5 24.7 26.2 32.0 52.1 39.5 6.9 12.6 .. 10 Switzerland 22.7 23.3 27.3 26.5 53.8 53.5 5.5 16.3 .. .. Syrian Arab Republic 11.2 18.4 21.7 14.0 .. .. 4.9 16.7 .. 18 Tajikistan .. .. .. .. .. 21.8 3.5 18.7 88.3 23 Tanzania .. .. .. .. .. .. .. .. 100.0 52 Thailand 17.8 .. 15.9 .. 36.0 30.5 4.0 20.9 .. 16 Timor-Leste .. 27.6 .. .. .. .. 7.1 7.3 .. 41 Togo 8.5 9.4 30.3 19.1 .. 155.2 3.7 17.2 14.6 39 Trinidad and Tobago 11.6 .. 12.3 .. 149.3 .. .. .. 86.6 17 Tunisia 15.6 .. 27.1 .. 89.4 54.0 7.1 20.5 .. 18 Turkey 8.2 .. 10.4 .. 33.5 28.1 .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 7.5b .. 20.3b .. 121.1 3.3b 15.6b 89.4 50 Ukraine .. .. .. .. 36.5 25.1 5.3 20.2 99.8 16 United Arab Emirates 8.7 4.9 11.6 6.9 41.5 .. .. .. 100.0 17 United Kingdom 14.1 22.1 24.2 27.3 26.0 29.2 5.6 11.9 .. 17 United States 17.9 22.2 22.5 24.6 27.0 25.4 5.7 14.8 .. 14 Uruguay 7.2 8.5 9.9 10.4 .. 18.1 3.9 14.4 .. 15 Uzbekistan .. .. .. .. .. .. .. .. 100.0 18 Venezuela, RB .. 9.1 .. 8.1 .. .. 3.7 .. 83.5 16 Vietnam .. 19.7 .. 17.3 .. 61.7 5.3 .. 98.6 20 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 30 Yemen, Rep. .. .. .. .. .. .. 5.2 16.0 .. .. Zambia 7.2 .. 19.4 .. 164.6 .. 1.4 .. .. 61 Zimbabwe 12.7 .. 19.3 .. 193.0 .. .. .. .. 38 World .. m .. m .. m .. m .. m .. m 4.6 m .. m 24 w Low income .. .. .. .. .. .. .. .. 45 Middle income .. .. .. .. .. .. 4.5 .. 23 Lower middle income .. .. .. .. .. .. 4.0 .. .. Upper middle income 13.5 .. 18.1 .. 34.2 18.4 4.6 14.0 22 Low & middle income .. .. .. .. .. .. 4.0 .. 27 East Asia & Pacific .. .. .. .. 38.2 .. .. .. 19 Europe & Central Asia .. .. .. .. .. 18.4 4.5 14.4 16 Latin America & Carib. 12.7 11.0 13.7 10.7 44.0 .. 3.6 .. 25 Middle East & N. Africa .. .. .. .. .. .. 5.2 18.5 24 South Asia .. .. 13.6 .. 90.8 .. 2.9 .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. 49 High income 17.9 18.2 22.4 23.2 32.9 29.0 5.4 12.6 15 Euro area 17.3 17.8 24.4 26.0 29.1 28.8 5.3 11.3 14 a. Provisional data. b. Data are for 2009. 104 2010 World Development Indicators 2.11 PEOPLE Education inputs About the data Definitions Data on education are compiled by the United private spending adds to the complexity of collecting · Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- accurate data on public spending. and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official The share of trained teachers in primary educa- number of students by level as a percentage of gross responses to surveys and from reports provided by tion measures the quality of the teaching staff. It domestic product (GDP) per capita. · Public expen- education authorities in each country. The data are does not take account of competencies acquired by diture on education is current and capital public used for monitoring, policymaking, and resource teachers through their professional experience or expenditure on education as a percentage of GDP allocation. However, coverage and data collection self-instruction or of such factors as work experi- and as a percentage of total government expendi- methods vary across countries and over time within ence, teaching methods and materials, or classroom ture. · Trained teachers in primary education are countries, so comparisons should be made with conditions, which may affect the quality of teaching. the percentage of primary school teachers who have caution. Since the training teachers receive varies greatly received the minimum organized teacher training For most countries the data on education spending (pre-service or in-service), care should be taken in (pre-service or in-service) required for teaching in in the table refer to public spending--government making comparisons across countries. their country. · Primary school pupil­teacher ratio spending on public education plus subsidies for pri- The primary school pupil­teacher ratio refl ects is the number of pupils enrolled in primary school vate education--and generally exclude foreign aid for the average number of pupils per teacher. It differs divided by the number of primary school teachers education. They may also exclude spending by reli- from the average class size because of the differ- (regardless of their teaching assignment). gious schools, which play a significant role in many ent practices countries employ, such as part-time developing countries. Data for some countries and teachers, school shifts, and multigrade classes. The some years refer to ministry of education spending comparability of pupil­teacher ratios across coun- only and exclude education expenditures by other tries is affected by the definition of teachers and by ministries and local authorities. differences in class size by grade and in the number Many developing countries seek to supplement of hours taught, as well as the different practices public funds for education, some with tuition fees mentioned above. Moreover, the underlying enroll- to recover part of the cost of providing education ment levels are subject to a variety of reporting errors services or to encourage development of private (for further discussion of enrollment data, see About schools. Fees raise diffi cult questions of equity, the data for table 2.12). While the pupil­teacher ratio efficiency, access, and taxation, however, and some is often used to compare the quality of schooling governments have used scholarships, vouchers, and across countries, it is often weakly related to the other public finance methods to counter criticism. value added of schooling systems. For greater detail, consult the country- and indicator- In 1998 UNESCO introduced the new International specific notes in the original source. Standard Classification of Education 1997 (ISCED The share of public expenditure devoted to edu- 1997). Consistent historical time series with reclas- cation allows an assessment of the priority a gov- sification of the pre­ISCED 1997 series were pro- ernment assigns to education relative to other duced for a selection of indicators in 2008. The full public investments, as well as a government's set of the historical series is forthcoming. commitment to investing in human capital develop- In 2006 the UNESCO Institute for Statistics also ment. It also reflects the development status of a changed its convention for citing the reference year country's education system relative to that of oth- of education data and indicators to the calendar year ers. However, returns on investment to education, in which the academic or financial year ends. Data especially primary and lower secondary education, that used to be listed for 2006, for example, are cannot be understood simply by comparing current now listed for 2007. This change was implemented education indicators with national income. It takes to present the most recent data available and to a long time before currently enrolled children can align the data reporting with that of other interna- productively contribute to the national economy tional organizations (in particular the Organisation (Hanushek 2002). for Economic Co-operation and Development and Data on education finance are generally of poor Eurostat). Data sources quality. This is partly because ministries of education, from which the UNESCO Institute for Statistics col- Data on education inputs are from the UNESCO lects data, may not be the best source for education Institute for Statistics, which compiles inter- finance data. Other agencies, particularly ministries national data on education in cooperation with of finance, need to be consulted, but coordination is national commissions and national statistical not easy. It is also difficult to track actual spending services. from the central government to local institutions. And 2010 World Development Indicators 105 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio ratio enrollment school ratio, primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2008a 2008a 2008a 2008a 1991 2008a 1999 2008a 2008a 2008a 2008a 2008a Afghanistan .. 106 29 .. 25 .. .. 27 .. .. .. .. Albania .. .. .. .. .. .. 70 .. .. .. .. .. Algeria 23 108 .. 24 89 95 .. .. 96 95 68 88 Angola .. .. .. 3 49 .. .. .. .. .. .. .. Argentina 67 115 85 68 95 .. 76 79 .. .. .. .. Armenia 33 80 88 34 .. 74 86 86 73 75 22 18 Australia 101 105 148 75 98 97 90 88 97 98 32 22 Austria 92 101 100 50 88 98 .. .. 97 98 5 3 Azerbaijan 26 116 106 16 89 96 75 98 97 95 7 9 Bangladesh .. 94 44 7 70 88 40 41 88 94 1,028 518 Belarus 102 99 95 73 85 94 82 87 94 96 12 7 Belgium 121 102 110 62 96 98 .. 87 98 98 8 6 Benin 13 117 .. 6 46 93 18 .. 99 86 7 91 Bolivia 49 108 82 38 .. 94 68 70 95 95 39 32 Bosnia and Herzegovina 11 111 89 34 79 .. .. .. .. .. .. .. Botswana 16 110 80 .. 89 86 60 .. .. .. 10 2 Brazil 62 130 100 30 .. 93 66 77 93 94 465 440 Bulgaria 82 101 105 50 .. 95 85 88 97 96 5 5 Burkina Faso 3 79b 20 b 3 27 63b 9 15b 68b 60 b 392b 473b Burundi 3 136 18 3 53 99 .. .. 91 89 55 67 Cambodia 13 116 40 7 72 89 15 34 90 87 99 131 Cameroon 25 111 37 8 69 88 .. .. 94 82 83 255 Canada 70 99 101 .. 98 .. 95 .. .. .. .. .. Central African Republic 3 77 .. 2 53 59 .. .. 68 50 107 168 Chad .. 83 19 2 34 .. 7 .. .. .. .. .. Chile 56 105 94 50 89 94 .. 85 95 94 41 46 China 42 112 74 22 97 .. .. .. .. .. .. .. Hong Kong SAR, China .. .. 83 34 92 .. 74 75 .. .. .. .. Colombia 49 120 91 35 71 90 56 71 93 94 147 138 Congo, Dem. Rep. 3 90 35 5 56 .. .. .. .. .. .. .. Congo, Rep. 12 114 .. .. 81 59 .. .. 66 62 91 102 Costa Rica 69 110 89 .. 87 .. .. .. .. .. .. .. Côte d'Ivoire 3 74 .. 8 46 .. 18 .. .. .. .. .. Croatia 51 99 94 47 .. 90 81 88 98 100 2 0c Cuba 111 102 91 122 94 99 73 84 100 99 2 2 Czech Republic 114 102 95 54 87 92 .. .. 91 94 22 14 Denmark 96 99 119 80 98 96 88 90 95 97 10 6 Dominican Republic 35 104 75 .. .. 80 38 58 82 83 117 103 Ecuador 100 118 70 35 98 97 46 59 .. .. .. .. Egypt, Arab Rep. 16 100 .. .. 81 94 71 .. 97 93 137 324 El Salvador 60 115 64 25 .. 94 47 55 95 96 23 15 Eritrea 13 52 30 2b 15 39 17 26 43 37 173 187 Estonia 95 99 100 65 88 94 84 90 96 97 1 1 Ethiopia 4 98 33 4 24 78 12 25 82 76 1,180 1,552 Finland 64 98 111 94 98 96 95 97 96 97 7 6 France 113 110 113 55 100 99 94 98 99 99 16 13 Gabon .. .. .. .. 91 .. .. .. .. .. .. .. Gambia, The 22 86 51 .. 50 69 26 42 69 74 40 33 Georgia 63 107 90 34 97 99 76 81 96 93 6 10 Germany 108 106 101 .. 84 98 .. .. .. .. .. .. Ghana 67 102 54 6 56 74 33 46 74 75 460 422 Greece 69 101 102 91 95 99 82 91 99 100 2 0c Guatemala 29 114 57 18 64 95 24 40 98 95 23 55 Guinea 11 90 36 9 27 71 12 28 77 67 175 245 Guinea-Bissau .. 120 36 3 40 .. 10 .. .. .. .. .. Haiti .. .. .. .. 21 .. .. .. .. .. .. .. Honduras 40 116 65 19 88 97 .. .. 96 98 22 9 106 2010 World Development Indicators 2.12 PEOPLE Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio ratio enrollment school ratio, primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2008a 2008a 2008a 2008a 1991 2008a 1999 2008a 2008a 2008a 2008a 2008a Hungary 89 98 97 67 .. 89 82 90 95 95 10 10 India 47 113 57 13 .. 90 .. .. 97 94 1,782 3,781 Indonesia 45 121 76 18 98 95 50 70 .. .. .. .. Iran, Islamic Rep. 52 128 80 36 91 .. .. 75 .. .. .. .. Iraq .. .. .. .. 92 .. 30 .. .. .. .. .. Ireland .. 105 113 61 90 97 84 88 96 97 8 6 Israel 98 111 91 60 .. 97 86 88 97 98 13 8 Italy 101 104 100 67 98 99 88 92 100 99 5 14 Jamaica 89 90 90 .. 97 85 83 77 86 85 24 26 Japan 88 102 101 58 100 100 99 98 .. .. .. .. Jordan 33 96 86 38 .. 89 79 84 93 94 32 23 Kazakhstan 39b 109b 95b 41b 88 89b 87 87b 99b 100 b 4b 2b Kenya 48 112 58 4b .. 82 33 49 82 83 563 524 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 105 104 97 96 99 99 97 96 100 98 4 41 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 76 95 91 18 49 88 89 80 94 93 6 8 Kyrgyz Republic 17 95 85 52 92 84 .. 80 91 91 19 19 Lao PDR 15 112 44 13 60 82 26 36 84 81 65 76 Latvia 90 97 115 69 94 .. .. .. .. .. .. .. Lebanon 72 101 82 52 66 88 .. 75 90 89 25 25 Lesotho .. 108 40 4 72 73 17 25 71 75 54 47 Liberia 84 91 32 .. .. .. 20 .. .. .. .. .. Libya 9 110 93 .. .. .. .. .. .. .. .. .. Lithuania 69 96 99 76 .. 91 90 92 94 94 4 4 Macedonia, FYR 38 93 84 36 .. 87 79 .. 91 92 5 4 Madagascar 9 152 30 3 72 98 12 24 99 100 16 3 Malawi .. 120 29 0 48 91 29 25 88 94 155 80 Malaysia 57 98 .. 30 93 97 65 .. 98 97 39 41 Mali 4 91 35 5 23 72 .. 29 81 68 190 316 Mauritania .. 98 23 4 35 80 14 16 78 83 54 40 Mauritius 98 99 88 16b 93 93 67 .. 93 94 4 4 Mexico 113 113 87 26 98 98 56 71 99 100 52 28 Moldova 72 89 83 40 89 83 80 79 86 85 12 12 Mongolia 57 102 95 50 90 89 58 82 99 99 1 1 Morocco 57 107 56 12 56 89 30 .. 92 88 148 217 Mozambique .. 114 21 .. 42 80 3 6 82 77 378 486 Myanmar 6 115 49 11 96 .. 31 46 .. .. .. .. Namibia 31 112 66 9 84 89 39 54 88 93 22 12 Nepal 35 124 43 .. 62 .. .. .. .. .. .. .. Netherlands 101 107 120 60 95 99 91 89 99 98 5 11 New Zealand 93 101 120 79 100 99 90 .. 99 100 2 1 Nicaragua 56 117 68 .. 70 92 35 45 93 94 29 24 Niger 3b 62b 11 1b 23 54b 6 9 60 b 48b 510 b 636b Nigeria 16 93 30 .. 54 61 .. 26 66 60 4,023 4,626 Norway 92 98 113 76 100 98 96 97 98 98 4 3 Oman 34 75 88 26 69 68 65 78 71 73 54 48 Pakistan .. 85 33 5 33 66 22 33 72 60 3,060 4,201 Panama 69 111 71 45 92 98 59 66 99 98 2 3 Papua New Guinea .. 55 .. .. 66 .. .. .. .. .. .. .. Paraguay 34 108 66 .. 94 92 .. 58 93 93 31 27 Peru 68 113 98 34 87 97 62 76 .. .. .. .. Philippines 47 108 81 28 96 90 50 60 90 92 635 481 Poland 60 97 100 67 .. 96 90 94 95 96 60 49 Portugal 80 115 101 57 98 99 82 88 99 99 2 4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 51 109 93 11 89 .. 74 79 .. .. .. .. 2010 World Development Indicators 107 2.12 Participation in education Gross enrollment Net enrollment Adjusted net Children out of ratio ratio enrollment school ratio, primary thousand % of primary-school- primary-school- % of relevant age group % of relevant age group age children age children Preprimary Primary Secondary Tertiary Primary Secondary Male Female Male Female 2008a 2008a 2008a 2008a 1991 2008a 1999 2008a 2008a 2008a 2008a 2008a Romania 72 105 87 58 81 94 75 73 96 97 16 14 Russian Federation 89 97 84 75 99 .. .. .. .. .. .. .. Rwanda .. 151 22 4 67 96 .. .. 95 97 38 22 Saudi Arabia 11 98 95 30 59 85 .. 73 85 84 244 259 Senegal 11 84 31 8 45 73 .. 25 75 76 248 233 Serbia 57 101 90 49 .. 97 .. 90 98 98 3 3 Sierra Leone 5 158 35 .. 40 .. .. 25 .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 94 102 93 50 .. .. .. .. .. .. .. .. Slovenia 80 103 94 85 96 96 90 89 97 96 2 2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 51 105 95 .. 90 87 63 72 92 94 284 219 Spain 123 105 119 68 100 100 88 94 100 100 1 5 Sri Lanka .. 105 .. .. 83 100 .. .. .. .. .. .. Sudan 28b 74b 38b .. .. .. .. .. .. .. .. .. Swaziland .. 108 53 4 74 83 32 29 82 84 19 18 Sweden 101 94 103 75 100 94 87 99 94 94 20 20 Switzerland 101 102 96 47 84 93 84 85 98 99 5 3 Syrian Arab Republic 10 124 74 .. 91 .. 36 68 .. .. .. .. Tajikistan 9 102 84 20 77 97 63 83 99 96 2 15 Tanzania 34 110 .. 1 51 99 5 .. 96 95 138 179 Thailand .. .. .. .. .. .. .. .. .. .. .. .. Timor-Leste .. 107 .. 15b .. 76 23 31 79 76 20 22 Togo 4 105 41 5 65 83 20 .. 91 80 44 99 Trinidad and Tobago 82 103 89 .. 90 92 70 74 96 95 3 3 Tunisia .. 108 90 32 94 98 69 .. 99 100 6 0c Turkey 16 98 82 37 89 94 .. 71 95 92 194 313 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 19 120 25 4 51 97 8 19 96 99 134 49 Ukraine 98 98 94 79 81 89 91 85 89 90 89 81 United Arab Emirates 87 108 94 25 99 92 69 84 99 99 1 1 United Kingdom 73 104 97 59 98 97 95 91 98 99 42 24 United States 61 98 94 82 97 91 88 88 92 93 1,018 797 Uruguay 81 114 92 64 91 98 .. 68 98 98 4 3 Uzbekistan 27 94 102 10 78 90 .. 92 94 91 74 98 Venezuela, RB 69 103 81 78 .. 90 47 69 92 92 141 123 Vietnam .. .. .. .. 89 .. 59 .. .. .. .. .. West Bank and Gaza 30 80 92 47 .. 73 77 89 77 78 56 52 Yemen, Rep. .. 85 .. 10 .. 73 32 .. 80 66 395 641 Zambia .. 119 52 .. 80 95 17 49 96 98 47 29 Zimbabwe .. 104 41 .. 84 90 40 38 90 91 121 103 World 45 w 106 w 66 w 26 w .. w 87 w .. w .. w 90 w 88 w Low income .. 101 41 6 .. 80 .. .. 83 79 Middle income 46 109 67 23 .. 88 .. .. 92 90 Lower middle income 41 108 62 18 .. 87 .. .. 91 89 Upper middle income 64 110 90 43 .. 94 .. .. 95 95 Low & middle income 41 107 62 20 .. 86 .. .. 90 87 East Asia & Pacific 42 112 73 .. 96 .. .. .. .. .. Europe & Central Asia 54 98 89 55 90 92 .. .. 94 93 Latin America & Carib. 66 117 88 35 .. 94 60 71 94 95 Middle East & N. Africa 29 106 72 26 .. 91 60 .. 94 90 South Asia 47 108 52 11 68 86 .. .. 92 86 Sub-Saharan Africa 16 97 33 6 .. 73 .. .. 76 71 High income 79 102 100 69 95 95 .. .. 94 95 Euro area 106 .. .. .. .. .. .. .. .. .. a. Provisional data. b. Data are for 2009. c. Less than 0.5. 108 2010 World Development Indicators 2.12 PEOPLE Participation in education About the data Definitions School enrollment data are reported to the United at other than the official age. Age at enrollment may · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organi- be inaccurately estimated or misstated, especially ment, regardless of age, to the population of the age zation (UNESCO) Institute for Statistics by national in communities where registration of births is not group that officially corresponds to the level of educa- education authorities and statistical offices. Enroll- strictly enforced. tion shown. · Preprimary education refers to the ini- ment ratios help monitor whether a country is on Other problems of cross-country comparison stem tial stage of organized instruction, designed primarily track to achieve the Millennium Development Goal from errors in school-age population estimates. Age- to introduce very young children to a school-type envi- of universal primary education by 2015, and whether sex structures drawn from censuses or vital registra- ronment. · Primary education provides children with an education system has the capacity to meet the tions, the primary data sources on school-age popu- basic reading, writing, and mathematics skills along needs of universal primary education, as indicated lation, commonly underenumerate (especially young with an elementary understanding of such subjects in part by gross enrollment ratios. children) to circumvent laws or regulations. Errors are as history, geography, natural science, social sci- Enrollment ratios, while a useful measure of participa- also introduced when parents round children's ages. ence, art, and music. · Secondary education com- tion in education, have limitations. They are based on While census data are often adjusted for age bias, pletes the provision of basic education that began annual school surveys, which are typically conducted adjustments are rarely made for inadequate vital at the primary level and aims at laying the founda- at the beginning of the school year and do not reflect registration systems. Compounding these problems, tions for lifelong learning and human development actual attendance or dropout rates during the year. And pre- and postcensus estimates of school-age children by offering more subject- or skill-oriented instruction school administrators may exaggerate enrollments, are model interpolations or projections that may miss using more specialized teachers. · Tertiary educa- especially if there is a financial incentive to do so. important demographic events (see discussion of tion refers to a wide range of post-secondary educa- Also, the gross and net primary enrollment ratios have demographic data in About the data for table 2.1). tion institutions, including technical and vocational an inherent weakness: the length of primary education Gross enrollment ratios indicate the capacity of education, colleges, and universities, whether or not differs across countries, although the International each level of the education system, but a high ratio leading to an advanced research qualification, that Standard Classification of Education tries to minimize may reflect a substantial number of overage children normally require as a minimum condition of admis- the difference. A shorter duration for primary education enrolled in each grade because of repetition rather sion the successful completion of education at the tends to increase the ratio; a longer one to decrease than a successful education system. The net enroll- secondary level. · Net enrollment ratio is the ratio it (in part because more older children drop out). ment ratio excludes overage and underage students of total enrollment of children of official school age Overage or underage enrollments are frequent, par- to capture more accurately the system's coverage and based on the International Standard Classification of ticularly when parents prefer children to start school internal efficiency but does not account for children Education 1997 to the population of the age group who fall outside the official school age because of that officially corresponds to the level of education The situations of out of school late or early entry rather than grade repetition. Differ- shown. · Adjusted net enrollment ratio, primary, children vary widely 2.12a ences between gross and net enrollment ratios show is the ratio of total enrollment of children of official the incidence of overage and underage enrollments. school age for primary education who are enrolled in Percent Not expected to enroll Adjusted net primary enrollment (called total net primary or secondary education to the total primary- Expected to enroll Drop out 100 primary enrollment in the 2008 edition), recently school-age population. · Children out of school added as a Millennium Development Goal indica- are the number of primary-school-age children not tor, captures primary-school-age children who have enrolled in primary or secondary school. 75 progressed to secondary education, which the tradi- tional net enrollment ratio excludes. 50 Data on children out of school (primary-school-age children not enrolled in school--dropouts, children never enrolled, and children of primary age enrolled 25 in preprimary education) are compiled from adminis- trative data. Large numbers of children out of school 0 create pressure to enroll children and provide class- 2003 2006 rooms, teachers, and educational materials, a task Some children who are out of school can be made difficult in many countries by limited education expected to enter school late, some have already budgets. However, getting children into school is a had some contact with schooling but will drop high priority for countries and crucial for achieving out, and others will never enter school. For coun- the Millennium Development Goal of universal pri- tries to reach the goal of education for all, poli- Data sources mary education. cies that address all three situations will need In 2006 the UNESCO Institute for Statistics Data on gross and net enrollment ratios and out to be implemented. changed its convention for citing the reference of school children are from the UNESCO Institute Source: UNESCO Institute for Statistics 2008b. year. For more information, see About the data for for Statistics. table 2.11. 2010 World Development Indicators 109 2.13 Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2008a 2008a 1991 2007a 1991 2007a 2007a 2007a 2008a 2008a 2007a 2007a Afghanistan 119 82 .. .. .. .. .. .. 0 0 .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria 104 102 95 95 94 97 91 95 10 6 90 92 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 112 111 .. 95 .. 97 93 96 8 5 93 95 Armenia 90 93 .. .. .. .. 29 27 0b 0b 100 98 Australia .. .. 98 .. 99 .. .. .. .. .. .. .. Austria 106 102 .. .. .. .. 97 99 .. .. 100 99 Azerbaijan 115 114 .. .. .. .. 100 98 0b 0b 100 99 Bangladesh 97 99 .. 52 .. 58 52 58 13 13 95 100 Belarus 97 102 .. .. .. .. 99 100 0b 0b 100 100 Belgium 99 101 90 95 92 97 92 95 3 3 100 99 Benin 171 157 54 .. 56 .. .. .. 14 14 72 70 Bolivia 121 120 .. 83 .. 83 81 80 3 2 90 90 Bosnia and Herzegovina .. .. .. .. .. .. .. .. 1 0b .. .. Botswana 116 113 81 94 87 95 90 94 6 4 98 98 Brazil .. .. .. .. .. .. .. .. .. .. .. .. Bulgaria 109 110 .. .. .. .. 94 94 3 2 94 95 Burkina Faso 90 c 83c 71 78 68 81 68 71 11 10 54 50 Burundi 151 142 65 59 58 65 51 57 33 34 37 31 Cambodia 129 122 .. 60 .. 65 52 57 12 10 80 79 Cameroon 127 110 .. 63 .. 63 57 56 17 16 46 50 Canada 98 98 95 .. 98 .. .. .. 0 0 .. .. Central African Republic 93 70 24 61 22 57 53 47 26 27 44 51 Chad 114 84 56 41 41 34 33 25 21 23 64 65 Chile 103 102 94 96 91 97 .. .. 5 3 .. .. China 92 95 58 .. 78 .. .. .. 0b 0b .. .. Hong Kong SAR, China .. .. .. 100 .. 100 100 100 1 1 100 100 Colombia 127 124 .. 85 .. 93 85 93 4 3 98 99 Congo, Dem. Rep. 105 128 58 80 50 79 82 76 15 16 64 54 Congo, Rep. 107 98 56 76 65 80 70 71 23 22 63 63 Costa Rica 95 95 83 95 85 98 93 96 8 6 100 94 Côte d'Ivoire 81 69 75 83 70 73 83 66 18 18 50 43 Croatia 94 94 .. .. .. .. 100 100 0b 0b 99 100 Cuba 96 98 .. 96 .. 97 95 97 1 0b 98 99 Czech Republic 112 110 .. 98 .. 99 98 99 1 0b 99 99 Denmark 98 99 94 100 94 100 .. .. 0 0 97 96 Dominican Republic 110 98 .. 70 .. 77 64 74 4 3 90 94 Ecuador 141 139 .. 46 .. 39 38 32 2 1 72 67 Egypt, Arab Rep. 98 96 .. 96 .. 97 94 96 4 2 .. .. El Salvador 123 119 .. 78 .. 82 74 78 7 5 92 92 Eritrea 44 37 .. 77 .. 69 77 69 16 15 84 81 Estonia 100 99 .. 98 .. 98 99 98 0 0 .. .. Ethiopia 162 144 16 46 23 49 39 42 5 5 88 89 Finland 99 99 100 100 100 100 100 100 1 0b 100 100 France .. .. 69 .. 95 .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 91 96 .. 71 .. 72 68 72 6 5 84 84 Georgia 114 118 .. 94 .. 97 94 97 0b 0b 99 100 Germany 104 102 .. .. .. .. 98 99 1 1 99 98 Ghana 109 112 81 .. 79 .. .. .. 7 6 90 96 Greece 102 103 100 99 100 98 98 98 1 1 100 99 Guatemala 123 121 .. 71 .. 70 65 64 13 11 93 90 Guinea 97 87 64 74 48 65 60 49 15 16 34 26 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 126 122 .. 75 .. 80 74 79 6 5 68 74 110 2010 World Development Indicators 2.13 PEOPLE Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2008a 2008a 1991 2007a 1991 2007a 2007a 2007a 2008a 2008a 2007a 2007a Hungary 99 98 .. .. .. .. 98 98 2 2 100 95 India 132 124 .. 66 .. 65 66 65 3 3 86 84 Indonesia 131 125 34 92 78 94 78 81 4 3 99 98 Iran, Islamic Rep. 118 159 91 .. 89 .. .. .. 3 1 84 74 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 100 102 99 97 100 100 .. .. 1 1 .. .. Israel 100 103 .. 100 .. 99 100 99 2 1 71 71 Italy 106 105 .. 99 .. 100 99 100 0b 0b 100 99 Jamaica 90 86 .. .. .. .. .. .. 3 2 .. .. Japan 102 101 100 .. 100 .. .. .. .. .. .. .. Jordan 94 96 .. .. .. .. .. .. 1 1 98 97 Kazakhstan 108 c 108 c .. .. .. .. 99d 99d 0 b,c 0 b,c 100 d 100 d Kenya .. .. .. 71 .. 74 .. .. .. .. 61d 59d Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 113 110 99 98 100 98 97 97 0b 0b 99 98 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 95 93 .. 100 .. 99 100 99 1 1 100 100 Kyrgyz Republic 97 96 .. .. .. .. 98 98 0b 0b 100 100 Lao PDR 124 115 .. 66 .. 68 66 68 18 16 80 77 Latvia 100 101 .. .. .. .. 97 97 4 2 97 97 Lebanon 97 95 .. 91 .. 95 86 93 10 7 83 89 Lesotho 101 94 58 55 73 69 37 56 24 18 68 66 Liberia 117 107 .. .. .. .. .. .. 6 7 .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 100 99 .. .. .. .. 98 98 1 1 99 99 Macedonia, FYR 92 93 .. .. .. .. 98 97 0b 0b 100 99 Madagascar 188 185 22 42 21 43 42 43 21 19 61 59 Malawi 137 144 71 44 57 43 37 35 21 20 79 75 Malaysia 96 96 97 92 97 92 89 90 .. .. 100 98 Mali 104 91 71 87 67 81 79 72 14 14 68 64 Mauritania 117 125 76 48 75 51 40 42 2 2 45 39 Mauritius 100 102 97 100 98 98 100 97 5 3 63 74 Mexico 118 118 35 94 71 95 91 94 5 3 95 94 Moldova 95 91 .. .. .. .. 94 97 0b 0b 99 98 Mongolia 134 133 .. 94 .. 95 94 95 0b 0b 96 98 Morocco 107 105 75 83 76 82 77 76 14 10 80 78 Mozambique 165 155 36 63 32 58 46 42 6 5 56 60 Myanmar 138 132 .. .. .. .. .. .. 1 0b 75 70 Namibia 101 101 60 97 65 99 87 87 22 14 76 79 Nepal .. .. 51 60 51 64 60 64 17 17 81 81 Netherlands 103 102 .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 158 148 11 48 37 55 45 52 13 9 .. .. Niger 97c 83c 61 72d 65 66d 69d 64 d 5c 5c 49d 44 d Nigeria .. .. .. .. .. .. .. .. 3 3 .. .. Norway 101 100 99 100 100 99 100 99 .. .. 100 99 Oman 73 73 97 99 96 100 99 100 1 1 97 97 Pakistan 114 98 .. .. .. .. .. .. 5 4 73 71 Panama 109 106 .. 87 .. 88 85 86 6 4 98 99 Papua New Guinea 33 29 70 .. 68 .. .. .. .. .. .. .. Paraguay 107 103 73 82 75 82 75 78 3 4 .. .. Peru 108 111 .. 93 .. 93 90 90 8 8 99 96 Philippines 134 126 .. 73 .. 81 69 78 3 2 98 97 Poland 97 98 .. .. .. .. .. .. 1 0b .. .. Portugal 113 110 .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 106 107 63 93 65 100 94 100 1 1 97 100 2010 World Development Indicators 111 2.13 Education efficiency Gross intake rate Cohort Repeaters in Transition to in grade 1 survival rate primary school secondary school % of grade 1 students Reaching Reaching last grade of % of relevant % of grade 5 primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2008a 2008a 1991 2007a 1991 2007a 2007a 2007a 2008a 2008a 2007a 2007a Romania 98 98 .. .. .. .. 95 95 2 1 99 98 Russian Federation 102 101 .. .. .. .. .. .. 1 1 .. .. Rwanda 213 207 61 .. 59 .. .. .. 18 18 .. .. Saudi Arabia 100 101 82 100 84 94 100 93 3 3 92 97 Senegal 97 102 .. 70 .. 72 57 60 8 8 65 58 Serbia 101 100 .. .. .. .. 98 99 1 1 99 99 Sierra Leone 201 182 .. .. .. .. .. .. 10 10 .. .. Singapore .. .. .. .. .. .. .. .. 0b 0b 88 95 Slovak Republic 102 102 .. .. .. .. 98 98 3 2 97 98 Slovenia 100 100 .. .. .. .. .. .. 1 0b .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 112 104 .. .. .. .. .. .. 8 8 93 94 Spain 107 106 .. 100 .. 100 100 100 3 2 .. .. Sri Lanka 104 105 92 98 93 99 98 99 1 1 98 99 Sudan 86 76 90 89 99 100 88 100 4c 4c 90 98 Swaziland 105 101 74 76 80 88 71 76 21 15 90 87 Sweden 100 100 100 100 100 100 100 100 0 0 100 100 Switzerland 93 96 .. .. .. .. .. .. 2 1 99 100 Syrian Arab Republic 118 116 97 .. 95 .. 96 97 8 6 95 96 Tajikistan 106 101 .. .. .. .. 100 97 0b 0b 98 98 Tanzania 107 105 81 85 82 89 81 85 4 4 47 45 Thailand .. .. .. .. .. .. .. .. 12 6 85 89 Timor-Leste 144 134 .. .. .. .. .. .. 13 12 100 100 Togo 106 99 52 58 42 50 49 39 23 24 56 49 Trinidad and Tobago 97 96 .. .. .. .. .. .. 8 5 88 92 Tunisia 104 105 94 96 77 96 94 94 9 6 86 90 Turkey 100 96 98 100 97 94 .. .. 3 3 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 158 160 .. 59 .. 59 34 31 11 11 63 60 Ukraine 100 100 .. .. .. .. 96 98 0b 0b 100 100 United Arab Emirates 110 109 80 100 80 100 100 100 2 2 98 99 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 100 106 .. 96 .. 98 .. .. 0 0 .. .. Uruguay 104 103 96 93 98 96 92 95 8 6 71 83 Uzbekistan 94 91 .. .. .. .. 99 99 0b 0b 100 100 Venezuela, RB 103 101 .. 82 .. 87 78 83 4 3 95 96 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 80 79 .. .. .. .. 99 99 1 1 97 98 Yemen, Rep. 110 98 .. .. .. .. .. .. 6 5 .. .. Zambia 122 127 .. 92 .. 88 82 75 6 6 69 72 Zimbabwe .. .. 72 .. 81 .. .. .. .. .. .. .. World 114 w 110 w .. w .. w .. w .. w .. w .. w 4w 4w .. w .. w Low income 120 113 .. .. .. .. .. .. .. .. .. .. Middle income 115 110 .. .. .. .. .. .. 4 3 .. .. Lower middle income 115 111 .. .. .. .. .. .. 3 3 .. .. Upper middle income 106 104 .. .. .. .. .. .. 6 4 .. .. Low & middle income 116 111 .. .. .. .. .. .. 5 4 .. .. East Asia & Pacific 103 104 55 .. 78 .. .. .. 2 1 .. .. Europe & Central Asia 99 98 .. .. .. .. .. .. 1 1 .. .. Latin America & Carib. .. .. .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 105 112 .. .. .. .. .. .. 7 4 .. .. South Asia 126 117 .. 65 .. 65 65 65 4 4 85 83 Sub-Saharan Africa 121 113 .. .. .. .. .. .. 10 10 .. .. High income 102 104 .. .. .. .. .. .. 1 1 .. .. Euro area 105 104 .. .. .. .. 98 99 1 1 .. .. a. Provisional data. b. Less than 0.5. c. Data are for 2009. d. Data are for 2008. 112 2010 World Development Indicators 2.13 PEOPLE Education efficiency About the data Definitions The United Nations Educational, Scientific, and Cul- The transition rate from primary to secondary · Gross intake rate in grade 1 is the number of tural Organization (UNESCO) Institute for Statistics school conveys the degree of access or transition new entrants in the first grade of primary education estimates indicators of students' progress through between the two levels. As completing primary edu- regardless of age as a percentage of the population school. These indicators measure an education sys- cation is a prerequisite for participating in lower of the official primary school entrance age. · Cohort tem's success in reaching all students, efficiently secondary school, growing numbers of primary survival rate is the percentage of children enrolled moving students from one grade to the next, and completers will inevitably create pressure for more in the first grade of primary school who eventually imparting a particular level of education. available places at the secondary level. A low transi- reach grade 5 or the last grade of primary educa- The gross intake rate indicates the level of access to tion rate can signal such problems as an inadequate tion. The estimate is based on the reconstructed primary education and the education system's capac- examination and promotion system or insufficient cohort method (see About the data). · Repeaters in ity to provide access to primary education. Low gross secondary school capacity. The quality of data on primary school are the number of students enrolled intake rates in grade 1 reflect the fact that many chil- the transition rate is affected when new entrants and in the same grade as in the previous year as a per- dren do not enter primary school even though school repeaters are not correctly distinguished in the first centage of all students enrolled in primary school. attendance, at least through the primary level, is grade of secondary school. Students who interrupt · Transition to secondary school is the number of mandatory in all countries. Because the gross intake their studies after completing primary school could new entrants to the first grade of secondary school rate includes all new entrants regardless of age, it also affect data quality. in a given year as a percentage of the number of can exceed 100 percent in some situations, such as In 2006 the UNESCO Institute for Statistics students enrolled in the final grade of primary school immediately after fees have been abolished or when changed its convention for citing the reference in the previous year. the number of reenrolled children is large. The quality year. For more information, see About the data for of data is reduced when new entrants and repeaters table 2.11. are not correctly distinguished in grade 1. The cohort survival rate is the estimated proportion of an entering cohort of grade 1 students that even- tually reaches grade 5 or the last grade of primary education. It measures an education system's hold- ing power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Cohort survival rates are typically estimated from data on enrollment and repetition by grade for two consecutive years. This procedure, called the reconstructed cohort method, makes three simplify- ing assumptions: dropouts never return to school; promotion, repetition, and dropout rates remain con- stant over the period in which the cohort is enrolled in school; and the same rates apply to all pupils enrolled in a grade, regardless of whether they previ- ously repeated a grade (Fredricksen 1993). Cross- country comparisons should thus be made with cau- tion, because other flows--caused by new entrants, reentrants, grade skipping, migration, or transfers during the school year--are not considered. Data on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources. Country policies on repetition and promo- tion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because Data sources of automatic promotion--suggesting a system that is highly efficient but that may not be endowing stu- Data on education efficiency are from the UNESCO dents with enough cognitive skills. Care should be Institute for Statistics. taken in interpreting this indicator. 2010 World Development Indicators 113 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2008a 1991 2008a 1991 2008a 1990 2005­08b 1990 2005­08b 2005­08b 2005­08b Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. 99 .. 100 99 99 Algeria 80 114 86 119 73 108 .. 94 .. 89 81 64 Angola 34 .. .. .. .. .. .. 81 .. 65 83 57 Argentina .. 100 .. 98 .. 102 98 99 99 99 98 98 Armenia .. 98 .. 97 .. 98 100 100 100 100 100 99 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. 102 .. 102 .. 102 .. .. .. .. .. .. Azerbaijan .. 121 .. 123 .. 119 .. 100 .. 100 100 99 Bangladesh .. 58 .. 56 .. 60 52 73 38 76 60 50 Belarus 94 96 .. 93 .. 92 100 100 100 100 100 100 Belgium 79 86 76 84 82 88 .. .. .. .. .. Benin 22 65 30 75 14 55 55 64 27 42 54 28 Bolivia 71 98 78 98 64 98 96 100 92 99 96 86 Bosnia and Herzegovina .. .. .. .. .. .. .. 100 .. 99 99 96 Botswana 90 99 83 96 98 102 86 94 92 96 83 84 Brazil .. .. .. .. .. .. .. 97 .. 99 90 90 Bulgaria 101 98 101 99 101 98 .. 97 .. 97 99 98 Burkina Faso 20 38 25 42 15 34 27 47 14 33 37 22 Burundi 46 45 49 48 43 42 59 77 48 75 72 60 Cambodia .. 79 .. 80 .. 79 .. 90 .. 84 86 69 Cameroon 53 73 57 79 49 67 .. 88 .. 84 84 68 Canada .. 96 .. 96 .. 96 .. .. .. .. .. .. Central African Republic 28 33 37 41 20 25 63 72 35 56 69 41 Chad 18 31 29 40 7 22 .. 54 .. 37 44 22 Chile .. 96 .. 94 .. 96 98 99 99 99 99 99 China 107 99 .. 98 .. 102 97 99 91 99 97 91 Hong Kong SAR, China 102 .. .. .. .. .. .. .. .. .. .. .. Colombia 73 110 70 109 76 112 .. 98 .. 98 93 93 Congo, Dem. Rep. 48 53 61 63 36 44 .. 69 .. 62 78 56 Congo, Rep. 54 73 59 75 49 71 .. .. .. .. .. .. Costa Rica 79 93 77 91 81 95 .. 98 .. 99 96 96 Côte d'Ivoire 42 48 53 57 32 39 60 72 38 60 64 44 Croatia .. 102 .. 102 .. 101 100 100 100 100 100 98 Cuba 99 90 .. 90 .. 90 .. 100 .. 100 100 100 Czech Republic .. 94 .. 95 .. 94 .. .. .. .. .. .. Denmark 98 101 98 100 98 101 .. .. .. .. .. .. Dominican Republic .. 91 .. 89 .. 92 .. 95 .. 97 88 88 Ecuador .. 106 .. 105 .. 107 97 95 96 96 87 82 Egypt, Arab Rep. .. 95 .. 97 .. 93 .. 88 .. 82 75 58 El Salvador 65 89 64 88 66 91 85 95 85 96 87 81 Eritrea .. 47 .. 52 .. 42 .. 91 .. 84 77 55 Estonia .. 100 .. 101 .. 100 100 100 100 100 100 100 Ethiopia .. 52 .. 56 .. 48 .. .. .. .. .. .. Finland 97 98 98 98 97 98 .. .. .. .. .. .. France 106 .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. 98 .. 96 91 83 Gambia, The .. 79 .. 76 .. 83 .. 70 .. 58 57 34 Georgia .. 100 .. 103 .. 97 .. 100 .. 100 100 100 Germany .. 105 .. 104 .. 105 .. .. .. .. .. .. Ghana 64 79 71 81 56 77 .. 81 .. 78 72 59 Greece .. 101 .. 102 .. 101 99 99 99 99 98 96 Guatemala .. 80 .. 83 .. 77 .. 89 .. 84 80 69 Guinea 17 55 24 62 9 47 .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. 78 .. 62 66 37 Haiti 27 .. 29 .. 26 .. .. .. .. .. .. .. Honduras 64 90 67 87 61 93 .. 93 .. 95 84 83 114 2010 World Development Indicators 2.14 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2008a 1991 2008a 1991 2008a 1990 2005­08b 1990 2005­08b 2005­08b 2005­08b Hungary 94 95 93 95 95 94 .. 98 .. 99 99 99 India 63 94 75 95 51 92 74c 88 49c 74 75 51 Indonesia 93 108 .. 109 .. 107 97 97 95 96 95 89 Iran, Islamic Rep. 88 117 93 108 82 126 92 97 81 96 87 77 Iraq .. .. .. .. .. .. .. 85 .. 80 86 69 Ireland .. 97 .. 96 .. 98 .. .. .. .. .. .. Israel .. 102 .. 101 .. 104 .. .. .. .. .. .. Italy 98 101 98 101 97 100 .. 100 .. 100 99 99 Jamaica 94 89 90 88 98 90 .. 92 .. 98 81 91 Japan 102 .. 102 .. 102 .. .. .. .. .. .. .. Jordan 95 99 94 98 95 100 .. 99 .. 99 95 89 Kazakhstan .. 105d .. 105d .. 105d 100 100 100 100 100 100 Kenya .. 80 .. 85 .. 75 .. 92 .. 93 90 83 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 99 97 101 98 97 .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. 98 .. 98 .. 98 .. 98 .. 99 95 93 Kyrgyz Republic .. 92 .. 93 .. 92 .. 100 .. 100 100 99 Lao PDR 45 75 .. 78 .. 71 .. 89 .. 79 82 63 Latvia .. 95 .. 97 .. 94 100 100 100 100 100 100 Lebanon .. 87 .. 84 .. 89 .. 98 .. 99 93 86 Lesotho 59 73 42 62 76 84 .. 86 .. 98 83 95 Liberia .. 58 .. .. .. 53 .. 70 .. 80 63 53 Libya .. .. .. .. .. .. .. 100 .. 100 95 81 Lithuania .. 96 .. 96 .. 96 100 100 100 100 100 100 Macedonia, FYR .. 92 .. 92 .. 92 .. 99 .. 99 99 95 Madagascar 36 71 35 71 37 71 .. .. .. .. .. .. Malawi 28 54 36 54 21 54 .. 87 .. 85 80 66 Malaysia 91 96 91 97 91 96 96 98 95 99 94 90 Mali 12 57 15 65 9 48 .. 47 .. 31 35 18 Mauritania 33 64 39 63 26 66 .. 71 .. 63 64 50 Mauritius 115 90 115 90 115 91 91 95 92 97 90 85 Mexico 88 104 .. 103 .. 105 96 98 95 98 95 91 Moldova .. 84 .. 85 .. 84 100 99 100 100 99 98 Mongolia .. 93 .. 94 .. 92 .. 93 .. 97 97 98 Morocco 48 81 57 85 39 78 .. 85 .. 68 69 44 Mozambique 26 59 32 67 21 52 .. 78 .. 62 70 40 Myanmar .. 97 .. 94 .. 100 .. 96 .. 95 95 89 Namibia .. 81 .. 76 .. 86 86 91 90 95 89 88 Nepal 50 76 .. 79 .. 72 68 86 33 75 71 45 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand 103 .. 104 .. 102 .. .. .. .. .. .. .. Nicaragua 42 75 .. 71 .. 78 .. 85 .. 89 78 78 Niger 17 40 d 21 47d 13 34 d .. 52 .. 23 43 15 Nigeria .. .. .. .. .. .. 81 78 62 65 72 49 Norway 100 96 100 96 100 97 .. .. .. .. .. .. Oman 65 80 67 80 62 81 .. 98 .. 98 90 81 Pakistan .. 60 .. 67 .. 53 .. 79 .. 59 67 40 Panama .. 102 .. 102 .. 102 95 97 95 96 94 93 Papua New Guinea 46 .. 51 .. 42 .. .. 65 .. 69 64 56 Paraguay 68 95 68 95 69 95 96 99 95 99 96 93 Peru .. 103 .. 103 .. 102 .. 98 .. 97 95 85 Philippines 88 92 .. 90 .. 95 96 94 97 96 93 94 Poland 98 96 .. .. .. .. .. 100 .. 100 100 99 Portugal 95 .. 94 .. 95 .. 99 100 99 100 97 93 Puerto Rico .. .. .. .. .. .. 92 86 94 85 90 90 Qatar 71 115 71 119 72 112 .. 99 .. 99 94 90 2010 World Development Indicators 115 2.14 Education completion and outcomes Primary completion Youth literacy Adult literacy rate rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2008a 1991 2008a 1991 2008a 1990 2005­08b 1990 2005­08b 2005­08b 2005­08b Romania 100 120 100 120 100 121 99 97 99 98 98 97 Russian Federation .. 94 .. .. .. .. 100 100 100 100 100 99 Rwanda 35 54 39 52 32 56 75 77 75 77 75 66 Saudi Arabia 55 95 60 99 51 92 94 98 81 96 90 80 Senegal 43 56 52 57 33 56 49 58 28 45 52 33 Serbia .. 104 .. 104 .. 105 99e .. 98e .. .. .. Sierra Leone .. 88 .. 101 .. 75 .. 66 .. 46 52 29 Singapore .. .. .. .. .. .. 99 100 99 100 97 92 Slovak Republic .. 94 .. 94 .. 94 .. .. .. .. .. .. Slovenia .. .. .. .. .. .. 100 100 100 100 100 100 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 86 72 86 80 86 .. 96 .. 98 90 88 Spain 103 98 104 99 103 98 100 100 100 100 98 97 Sri Lanka 101 105 101 105 101 105 .. 97 .. 99 92 89 Sudan 40 57d 45 53 36 47 .. 89 .. 82 79 60 Swaziland 61 72 57 75 64 69 .. 92 .. 95 87 86 Sweden 96 95 96 94 96 95 .. .. .. .. .. .. Switzerland 53 93 53 92 54 94 .. .. .. .. .. .. Syrian Arab Republic 89 114 94 114 84 113 .. 96 .. 93 90 77 Tajikistan .. 98 .. 97 .. 93 100 100 100 100 100 100 Tanzania 63 83 62 85 64 81 86 79 78 76 79 66 Thailand .. .. .. .. .. .. .. 98 .. 98 96 92 Timor-Leste .. 80 .. 80 .. 79 .. .. .. .. .. .. Togo 35 61 48 71 22 51 .. 87 .. 80 77 54 Trinidad and Tobago 102 92 99 92 105 92 99 100 99 100 99 98 Tunisia 74 102 79 103 70 102 .. 97 .. 95 86 70 Turkey 90 99 93 104 86 94 97 99 88 94 96 81 Turkmenistan .. .. .. .. .. .. .. 100 .. 100 100 99 Uganda .. 56 .. 57 .. 55 77 89 63 86 82 67 Ukraine 94 99 .. 98 .. 99 .. 100 .. 100 100 100 United Arab Emirates 103 105 104 103 103 107 .. 94 .. 97 89 91 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. 96 .. 95 .. 97 .. .. .. .. .. .. Uruguay 94 104 91 102 96 105 .. 99 .. 99 98 98 Uzbekistan .. 96 .. 97 .. 95 .. 100 .. 100 100 99 Venezuela, RB 79 95 .. 94 .. 97 95 98 96 99 95 95 Vietnam .. .. .. .. .. .. 94 97 93 96 95 90 West Bank and Gaza .. 83 .. 83 .. 83 .. 99 .. 99 97 91 Yemen, Rep. .. 61 .. 72 .. 49 .. 95 .. 70 79 43 Zambia .. 93 .. 98 .. 88 67 82 66 68 81 61 Zimbabwe 97 .. 99 .. 96 .. 97 98 94 99 94 89 World .. w 89 w .. w 90 w .. w 88 w 87 w 92 w 76 w 86 w 87 w 76 w Low income .. 66 .. 69 .. 62 .. 81 .. 77 76 63 Middle income .. 94 .. 95 .. 93 89 93 79 88 88 77 Lower middle income .. 92 .. 93 .. 91 88 92 76 85 87 73 Upper middle income .. 100 .. .. .. .. 96 98 94 98 95 92 Low & middle income .. 88 .. 90 .. 86 87 92 76 86 87 76 East Asia & Pacific .. 100 .. 99 .. 101 97 98 92 98 96 90 Europe & Central Asia .. 98 .. .. .. .. 99 99 97 99 99 97 Latin America & Carib. .. 101 .. 102 .. 103 .. 97 .. 98 92 91 Middle East & N. Africa .. 94 .. 95 .. 92 82 92 62 86 82 65 South Asia .. 79 .. 82 .. 76 71 86 48 73 73 50 Sub-Saharan Africa .. 62 .. 67 .. 57 .. 79 .. 71 74 57 High income .. .. .. .. .. .. .. .. .. .. .. .. Euro area 101 .. 100 .. 100 .. .. .. .. .. .. .. a. Provisional data. b. Data are for the most recent year available. c. Includes the Indian-held part of Jammu and Kashmir. d. Data are for 2009. e. Includes Montenegro. 116 2010 World Development Indicators 2.14 PEOPLE Education completion and outcomes About the data Definitions Many governments publish statistics that indi- Institute for Statistics has established literacy as · Primary completion rate is the percentage of stu- cate how their education systems are working and an outcome indicator based on an internationally dents completing the last year of primary school. It is developing--statistics on enrollment and such effi - agreed definition. calculated by taking the total number of students in ciency indicators as repetition rates, pupil­teacher The literacy rate is the percentage of people who the last grade of primary school, minus the number of ratios, and cohort progression. The World Bank and can, with understanding, both read and write a repeaters in that grade, divided by the total number the United Nations Educational, Scientific, and Cul- short, simple statement about their everyday life. In of children of official completing age. · Youth literacy tural Organization (UNESCO) Institute for Statistics practice, literacy is difficult to measure. To estimate rate is the percentage of people ages 15­24 that jointly developed the primary completion rate indica- literacy using such a definition requires census or can, with understanding, both read and write a short, tor. Increasingly used as a core indicator of an educa- survey measurements under controlled conditions. simple statement about their everyday life. · Adult tion system's performance, it reflects an education Many countries estimate the number of literate literacy rate is the literacy rate among people ages system's coverage and the educational attainment people from self-reported data. Some use educa- 15 and older. of students. The indicator is a key measure of edu- tional attainment data as a proxy but apply different cation outcome at the primary level and of progress lengths of school attendance or levels of completion. toward the Millennium Development Goals and the Because definitions and methodologies of data col- Education for All initiative. However, because curri- lection differ across countries, data should be used cula and standards for school completion vary across cautiously. countries, a high primary completion rate does not The reported literacy data are compiled by the necessarily mean high levels of student learning. UNESCO Institute for Statistics based on national The primary completion rate reflects the primary censuses and household surveys during 1985­2007. cycle as defined by the International Standard Clas- For countries that have not reported national esti- sification of Education, ranging from three or four mates, the UNESCO Institute for Statistics derived years of primary education (in a very small number the modeled estimates. For detailed information on of countries) to five or six years (in most countries) sources, definitions, and methodology, consult the and seven (in a small number of countries). original source. The table shows the proxy primary completion rate, Literacy statistics for most countries cover the pop- calculated by subtracting the number of repeaters ulation ages 15 and older, but some include younger in the last grade of primary school from the total ages or are confined to age ranges that tend to inflate number of students in that grade and dividing by the literacy rates. The literacy data in the narrower age total number of children of official graduation age. range of 15­24 better captures the ability of partici- Data limitations preclude adjusting for students who pants in the formal education system and reflects drop out during the final year of primary school. Thus recent progress in education. The youth literacy rate proxy rates should be taken as an upper estimate of reported in the table measures the accumulated out- the actual primary completion rate. comes of primary education over the previous 10 There are many reasons why the primary comple- years or so by indicating the proportion of people who tion rate can exceed 100 percent. The numerator have passed through the primary education system may include late entrants and overage children who and acquired basic literacy and numeracy skills. have repeated one or more grades of primary school as well as children who entered school early, while the denominator is the number of children of official completing age. Other data limitations contribute to completion rates exceeding 100 percent, such as the use of population estimates of varying reliability, the conduct of school and population surveys at dif- ferent times of year, and other discrepancies in the numbers used in the calculation. Basic student outcomes include achievements in reading and mathematics judged against established standards. In many countries national assessments Data sources are enabling the ministry of education to monitor Data on primary completion rates and lit- progress in these outcomes. Internationally compa- eracy rates are from the UNESCO Institute for rable assessments are not yet available, except for Statistics. a few, mostly industrialized, countries. The UNESCO 2010 World Development Indicators 117 2.15 Education gaps by income and gender Survey Gross intake Gross primary Average years Primary Children year rate in grade 1 participation rate of schooling completion rate out of school % of relevant % of relevant % of relevant % of relevant age group age group Ages 15­19 age group age group Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile Male Female quintile quintile Armenia 2005 93 80 106 102 9 10 119 116 113 112 2 1 Azerbaijan 2006 92 118 100 108 9 11 94 109 103 105 20 11 Bangladesh 2006 144 147 96 105 8 13 65 97 83 86 12 6 Belize 2006 80 89 106 113 8 11 59 130 107 72 5 7 Benin 2006 67 107 61 114 6 8 31 95 67 52 57 12 Bolivia 2003 92 95 108 129 6 9 76 98 90 81 22 5 Burundi 2005 201 191 91 144 4 7 20 70 44 39 5 3 Cambodia 2005 208 151 113 134 5 8 42 121 88 85 37 13 Cameroon 2006 108 75 93 116 6 14 43 111 90 74 3 2 Colombia 2005 161 84 127 99 6 10 94 109 100 103 11 2 Côte d'Ivoire 2006 51 77 57 110 5 8 47 127 88 71 4 3 Dominican Republic 2007 130 112 113 107 7 11 69 109 88 106 12 4 Egypt, Arab Rep. 2005 107 97 95 99 9 12 84 92 92 88 12 1 Ethiopia 2005 86 124 47 112 3 6 14 90 46 33 74 30 Georgia 2006 90 104 101 103 15 14 102 102 106 104 2 1 Ghana 2006 107 121 81 117 5 8 62 88 93 86 22 12 Guatemala 2000 176 124 81 114 4 8 15 80 34 36 7 3 Guinea 2005 55 119 52 121 5 7 32 93 76 48 60 16 Guinea-Bissau 2006 135 184 94 166 4 7 34 125 80 54 12 11 Guyana 2006 74 76 105 101 10 10 109 118 91 112 2 1 Haiti 2005 177 188 87 159 4 7 31 136 73 82 69 24 Kazakhstan 2006 118 101 106 103 9 9 102 115 102 97 0a 1 Kenya 2003 134 125 92 106 6 9 40 76 71 72 38 11 Kosovo 2000 104 119 95 104 9 11 82 94 98 83 1 4 Lesotho 2004 169 111 116 124 5 8 36 122 69 85 18 3 Macedonia, FYR 2005 102 190 89 97 8 10 120 119 133 78 0a 0a Madagascar 2003­04 250 153 118 145 3 8 42 141 77 77 33 3 Malawi 2006 234 207 133 169 5 7 30 80 49 52 0a 0a Mali 2006 41 98 46 110 5 8 36 79 55 41 67 20 Mauritania 2007 67 96 62 116 5 9 17 89 48 52 2 2 Moldova 2005 96 84 99 95 9 12 97 100 96 98 2 1 Mozambique 2003 128 143 75 143 3 6 13 100 57 43 46 7 Namibia 2006 112 104 118 109 7 10 81 109 94 90 11 2 Nepal 2001 184 141 109 139 5 8 49 96 69 62 33 6 Nicaragua 2001 149 106 85 105 4 9 34 124 78 83 40 4 Niger 2006 50 90 35 89 4 7 31 71 60 30 74 28 Nigeria 2003 78 101 70 108 7 10 48 71 70 54 52 6 Panama 2003 125 116 108 102 7 11 100 94 105 88 1 1 Peru 2004 121 90 118 96 7 11 106 99 100 97 6 1 Rwanda 2005 274 195 131 151 3 5 31 88 48 42 13 8 Serbia 2005 90 98 98 100 9 10 86 96 94 89 1 0a Somalia 2005 13 44 8 93 8 10 2 58 26 20 87 46 Swaziland 2006 147 117 117 114 6 9 69 110 85 98 17 4 Syrian Arab Republic 2006 110 149 102 107 7 8 92 93 93 92 0a 0a Tanzania 2004 123 123 82 119 5 7 32 108 58 60 44 15 Togo 2006 115 148 99 128 6 7 40 82 67 56 1 1 Turkey 2003 108 111 97 97 6 7 95 85 100 81 20 5 Uganda 2006 180 144 107 124 5 8 27 68 50 42 25 7 Vietnam 2006 99 100 108 100 13 18 99 104 96 103 3 2 Yemen, Rep. 2006 66 109 50 101 7 10 25 103 84 31 2 2 Zambia 2007 135 123 105 112 5 9 50 101 88 73 22 3 Zimbabwe 1999 106 111 144 144 7 10 36 80 51 57 22 8 a. Less than 0.5. 118 2010 World Development Indicators 2.15 PEOPLE Education gaps by income and gender About the data Definitions The data in the table describe basic information on Socioeconomic status as displayed in the table · Survey year is the year in which the underlying data school participation and educational attainment by is based on a household's assets, including owner- were collected. · Gross intake rate in grade 1 is individuals in different socioeconomic groups within ship of consumer items, features of the household's the number of students in the first grade of primary countries. The data are from Demographic and Health dwelling, and other characteristics related to wealth. education regardless of age as a percentage of the Surveys conducted by Macro International with the Each household asset on which information was population of the official primary school entrance support of the U.S. Agency for International Develop- collected was assigned a weight generated through age. These data may differ from those in table 2.13. ment, Multiple Indicator Cluster Surveys conducted principal-component analysis, which is used to cre- · Gross primary participation rate is the ratio of by the United Nations Children's Fund (UNICEF), ate break-points defining wealth quintiles, expressed total students attending primary school regardless and Living Standards Measurement Studies con- as quintiles of individuals in the population. of age to the population of the age group that offi - ducted by the World Bank's Development Econom- The selection of the asset index for defining socio- cially corresponds to primary education. · Average ics Research Group. These large-scale household economic status was based on pragmatic rather than years of schooling are the years of formal school- sample surveys, conducted periodically in developing conceptual considerations: Demographic and Health ing received, on average, by youths and adults ages countries, collect information on a large number of Surveys do not collect income or consumption data 15­19. · Primary completion rate is the number of health, nutrition, and population measures as well as but do have detailed information on households' own- students in the last year of primary school minus on respondents' social, demographic, and economic ership of consumer goods and access to a variety the number of repeaters in that grade, divided by the characteristics using a standard set of question- of goods and services. Like income or consumption, number of students of official graduation age. These naires. The data presented here draw on responses the asset index defines disparities primarily in eco- data differ from those in table 2.14 because the defi - to individual and household questionnaires. nomic terms. It therefore excludes other possibilities nition and methodology are different. · Children out Typically, the surveys collect basic information on of disparities among groups, such as those based of school are the percentage of children of official educational attainment and enrollment levels from on gender, education, ethnic background, or other primary school age who are not attending primary every household member ages 5 or 6 and older as facets of social exclusion. To that extent the index or secondary education. Children of official primary part of household socioeconomic characteristics. provides only a partial view of the multidimensional school age who are attending preprimary education The surveys are not intended for the collection of concepts of poverty, inequality, and inequity. are considered out of school. These data differ from detailed education data; thus the education sec- Creating one index that includes all asset indica- those in table 2.12 because the definition and meth- tion of the surveys is not as detailed as the Demo- tors limits the types of analysis that can be per- odology are different. graphic and Health Surveys health section and the formed. In particular, the use of a unified index does data obtained from them do not replace other data not permit a disaggregated analysis to examine on education flows. Still, the education data provide which asset indicators have a more or less important micro-level information on education that cannot be association with education status. In addition, some obtained from administrative data, such as informa- asset indicators may reflect household wealth better tion on children not attending school. in some countries than in others--or reflect differ- ent degrees of wealth in different countries. Taking such information into account and creating country- Gender disparities in net primary school attendance are largest in specific asset indexes with country-specific choices poor and rural households 2.15a of asset indicators might produce a more effective and accurate index for each country. The asset index Primary school net attendance ratio of boys and girls, used in the table does not have this flexibility. by background characteristics, 2000­06 (percent) 100 The analysis was carried out for about 80 coun- Boys Girls tries. The table shows the most recent estimates for the poorest and richest quintiles and by gender 75 Data sources only; the full set of estimates for all other subgroups, including by urban and rural location and for other Data on education gaps by income and gender 50 years, is available in the country reports (see Data are from an analysis by the World Bank's Human sources). The data in the table differ from data for Development Network Education Group of Demo- similar indicators in preceding tables either because graphic and Health Surveys conducted by Macro 25 the indicator refers to a period a few years preceding International, Multiple Indicator Cluster Surveys the survey date or because the indicator definition conducted by UNICEF, and Living Standards Mea- 0 or methodology is different. Findings should be used surement Studies conducted by the World Bank's Urban Rural Poorest Middle Richest 20 20 20 with caution because of measurement error inherent Development Economics Research Group and the percent percent percent in the use of survey data. World Bank. Country reports are available at www. Source: UNICEF 2007. worldbank.org/education/edstats/. 2010 World Development Indicators 119 2.16 Health services Health Health workers Hospital Outpatient expenditure beds visits per 1,000 people Total Public Out of pocket Per capita Nurses and per 1,000 % of GDP % of total % of private $ PPP $ Physicians midwives people per capita 2007 2007 2007 2007 2007 2003­08a 2003­08a 2003­08a 2000­08a Afghanistan 7.6 23.6 98.9 42 126 2.0 b 0.5 0.4 .. Albania 7.0 41.2 93.9 244 505 1.1 4.0 2.9 1.5 Algeria 4.4 81.6 94.7 173 338 1.2 1.9 1.7 .. Angola 2.5c 80.3c 100.0 c 86c 131c 0.1 1.4 0.8 .. Argentina 10.0 50.8 42.9 663 1,322 3.2 0.5 4.0 .. Armenia 4.4 47.3 91.4 133 246 3.7 4.9 4.1 2.8 Australia 8.9 67.5 55.5 3,986 3,261 1.0 10.9 4.0 6.2 Austria 10.1 76.4 65.2 4,523 3,763 3.8 6.6 7.8 6.7 Azerbaijan 3.7 26.8 87.8 140 279 3.8 8.4 7.9 4.6 Bangladesh 3.4 33.6 97.4 15 42 0.3 0.3 0.4 .. Belarus 6.5 74.9 69.4 302 704 4.9 12.6 11.2 13.2 Belgium 9.4 74.1 76.4 4,056 3,323 4.2 0.5 5.3 7.0 Benin 4.8 51.8 94.9 32 70 0.1 0.8 0.5 .. Bolivia 5.0 69.2 79.4 69 219 .. .. 1.1 .. Bosnia and Herzegovina 9.8 56.8 100.0 397 766 1.4 4.7 3.0 3.3 Botswana 5.7 74.6 27.3 372 762 0.4 2.7 1.8 .. Brazil 8.4 41.6 58.8 606 799 1.7 2.9 2.4 .. Bulgaria 7.3 57.2 86.4 384 800 3.7 4.7 6.4 .. Burkina Faso 6.1 56.1 91.3 29 67 0.1 0.7 0.9 .. Burundi 13.9c 37.7c 60.5c 17c 51c 0.0 d 0.2 0.7 .. Cambodia 5.9 29.0 84.7 36 108 .. .. 0.1 .. Cameroon 4.9c 25.9c 94.5c 54 c 104 c 0.2 1.6 1.5 .. Canada 10.1 70.0 49.6 4,409 3,899 1.9 10.1 3.4 6.3 Central African Republic 4.1 34.7 95.0 16 30 0.1 0.4 1.2 .. Chad 4.8 56.3 96.2 32 72 0.0 d 0.3 0.4 .. Chile 6.2 58.7 53.2 615 768 1.3 0.6 2.3 .. China 4.3 44.7 92.0 108 233 1.5 1.0 2.2 .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. Colombia 6.1 84.2 48.7 284 516 1.4 .. 1.0 .. Congo, Dem. Rep. 5.8 20.8 51.7 9 18 0.1 0.5 0.8 .. Congo, Rep. 2.4 70.4 100.0 52 90 0.1 0.8 1.6 .. Costa Rica 8.1 72.9 84.6 488 878 .. .. 1.3 .. Côte d'Ivoire 4.2 24.0 88.7 41 67 0.1 0.5 0.4 .. Croatia 7.6 87.0 91.9 1,009 1,398 2.6 5.6 5.3 6.4 Cuba 10.4 95.5 91.3 585 1,001 6.4 8.6 6.0 .. Czech Republic 6.8 85.2 89.0 1,141 1,626 3.6 9.0 8.1 15.0 Denmark 9.8 84.5 89.0 5,551 3,558 3.2 9.8 3.5 4.1 Dominican Republic 5.4 35.9 65.3 224 411 .. .. 1.0 .. Ecuador 5.8 39.1 75.2 200 434 .. .. 0.6 .. Egypt, Arab Rep. 6.3 38.1 95.1 101 310 2.4 3.4 2.1 .. El Salvador 6.2 58.9 89.0 24 402 1.5 .. 0.8 .. Eritrea 3.3c 45.3c 100.0 c 9c 20 c 0.1 0.6 1.2 .. Estonia 5.4 76.5 94.1 837 1,106 3.3 7.0 5.6 6.9 Ethiopia 3.8 58.1 80.6 9 30 0.0 d 0.2 0.2 .. Finland 8.2 74.6 74.3 3,809 2,840 3.3 8.9 6.8 4.3 France 11.0 79.0 32.5 4,627 3,709 3.7 8.1 7.2 6.9 Gabon 4.6c 64.5c 100.0 c 373c 650 c 0.3 5.0 1.3 .. Gambia, The 5.5 47.9 48.4 22 90 0.0 d 0.6 1.1b .. Georgia 8.2 18.4 86.8 191 384 4.5 3.9 3.3 2.2 Germany 10.4 76.9 56.6 4,209 3,588 3.5 8.0 8.3 7.0 Ghana 8.3 51.6 79.3 54 113 0.1 1.0 0.9b .. Greece 9.6 60.3 94.5 2,679 2,727 5.4 3.5 4.8 .. Guatemala 7.3 29.3 92.6 186 336 .. .. 0.6 .. Guinea 5.6 11.0 99.5 26 62 0.1 0.0 d 0.3 .. Guinea-Bissau 6.1c 25.9c 55.7c 16c 33c 0.0 d 0.6 1.0 b .. Haiti 5.3 23.3 57.4 35 58 .. .. 1.3 .. Honduras 6.2 65.7 96.0 107 260 .. .. 0.7 .. 120 2010 World Development Indicators 2.16 PEOPLE Health services Health Health workers Hospital Outpatient expenditure beds visits per 1,000 people Total Public Out of pocket Per capita Nurses and per 1,000 % of GDP % of total % of private $ PPP $ Physicians midwives people per capita 2007 2007 2007 2007 2007 2003­08a 2003­08a 2003­08a 2000­08a Hungary 7.4 70.6 84.7 1,019 1,388 2.8 9.2 7.1 12.9 India 4.1 26.2 89.9 40 109 0.6 1.3 0.9 .. Indonesia 2.2 54.5 66.2 42 81 0.1 0.8 .. .. Iran, Islamic Rep. 6.4 46.8 95.4 253 689 0.9 1.6 1.4 .. Iraq 2.5e 75.0e 100.0e 62e 121e 0.5 1.0 1.3 .. Ireland 7.6 80.7 51.2 4,556 3,424 3.1 15.8 5.3 .. Israel 8.0 55.9 74.4 1,893 2,181 3.6 6.1 5.8 7.1 Italy 8.7 76.5 85.9 3,136 2,686 3.7 6.9 3.9 6.1 Jamaica 4.7 50.3 71.0 224 357 0.9 1.7 1.7 .. Japan 8.0 81.3 80.8 2,751 2,696 2.1 9.5 14.0 14.4 Jordan 8.9 f 60.6f 88.3f 248f 434f 2.6 3.2 1.8 .. Kazakhstan 3.7 66.1 98.4 253 405 3.9 7.8 7.7 6.6 Kenya 4.7 42.0 77.2 34 72 0.1 .. 1.4 .. Korea, Dem. Rep. 3.6 83.7 100.0 22 .. 3.3 4.1 .. .. Korea, Rep. 6.3 54.9 79.2 1,362 1,688 1.7 4.4 8.6 .. Kosovo .. .. .. .. .. .. .. .. .. Kuwait 2.2 77.5 91.6 901 911 1.8 3.7 1.8 .. Kyrgyz Republic 6.5 54.0 91.9 46 111 2.3 5.7 5.1 3.6 Lao PDR 4.0 18.9 76.1 27 84 0.4 1.0 1.2 .. Latvia 6.2 57.9 97.1 784 1,071 3.0 5.7 7.6 5.5 Lebanon 8.8 44.7 77.6 525 921 3.3 1.3 3.4 .. Lesotho 6.2 58.3 68.9 51 92 0.1 0.6 1.3 .. Liberia 10.6c 26.2c 52.2c 22c 39c 0.0 d 0.3 0.7b .. Libya 2.7c 71.8 c 100.0 c 299c 453c 1.3 4.8 3.7 .. Lithuania 6.2 73.0 98.3 717 1,109 4.0 7.6 8.1 6.6 Macedonia, FYR 7.1 65.6 100.0 277 698 2.6 4.3 4.6 6.0 Madagascar 4.1 66.2 67.9 16 32 0.2 0.3 0.3 0.5 Malawi 9.9 59.7 28.4 17 50 0.0 d 0.3 1.1 .. Malaysia 4.4 44.4 73.2 307 604 .. .. 1.8 .. Mali 5.7 51.4 99.5 34 67 0.1 0.2 0.6 .. Mauritania 2.4 c 65.3c 100.0 c 22c 47c 0.1b 0.7b 0.4 .. Mauritius 4.2 49.0 81.5 247 502 1.1 3.7 3.3 .. Mexico 5.9 45.4 93.1 564 823 2.9 4.0 1.7 2.5 Moldova 10.3g 50.8g 97.6g 127g 281g 2.7 6.6 6.1 6.0 Mongolia 4.3 81.7 84.4 64 176 .. .. 6.1 .. Morocco 5.0 33.8 86.3 120 215 0.6 0.8 1.1 .. Mozambique 4.9 71.8 42.1 18 38 0.0 d 0.3 0.8 .. Myanmar 1.9 11.7 95.1 7 26 0.4 1.0 0.6 .. Namibia 7.6 42.1 5.8 319 467 0.3 3.1 2.7b .. Nepal 5.1 39.7 90.8 20 55 0.2 0.5 5.0 .. Netherlands 8.9 82.0 33.5 4,243 3,621 3.9 15.1 4.8 5.4 New Zealand 9.0 78.9 71.7 2,790 2,497 2.2 8.9 .. 4.4 Nicaragua 8.3 54.9 93.0 92 258 0.4 1.1 0.9 .. Niger 5.3 52.8 96.4 16 34 0.0d 0.1 0.3 .. Nigeria 6.6 25.3 95.9 74 131 0.4 1.6 0.5 .. Norway 8.9 84.1 95.1 7,354 4,774 3.9 16.3 3.9 .. Oman 2.4 78.7 61.3 375 513 1.8 3.9 2.0 .. Pakistan 2.7 30.0 82.1 23 64 0.8 0.4 0.6 .. Panama 6.7 64.6 82.7 396 773 .. .. 2.2 .. Papua New Guinea 3.2 81.3 41.3 31 65 .. .. .. .. Paraguay 5.7 42.4 97.0 114 253 .. .. 1.3 .. Peru 4.3 58.4 75.3 160 327 .. .. 1.5 .. Philippines 3.9 34.7 83.7 63 130 .. .. 1.1 .. Poland 6.4 70.9 83.2 716 1,035 2.0 5.2 5.2 6.1 Portugal 10.0 70.6 77.5 2,108 2,284 3.4 4.8 3.5 3.9 Puerto Rico .. .. .. .. .. .. .. .. .. Qatar 3.8 75.6 88.2 2,403 2,571 2.8 7.4 2.5 .. 2010 World Development Indicators 121 2.16 Health services Health Health workers Hospital Outpatient expenditure beds visits per 1,000 people Total Public Out of pocket Per capita Nurses and per 1,000 % of GDP % of total % of private $ PPP $ Physicians midwives people per capita 2007 2007 2007 2007 2007 2003­08a 2003­08a 2003­08a 2000­08a Romania 4.7 80.3 98.8 369 592 1.9 4.2 6.5 5.6 Russian Federation 5.4 64.2 83.0 493 797 4.3 8.5 9.7 9.0 Rwanda 10.3 47.0 44.4 37 90 0.0 d 0.4 1.6 .. Saudi Arabia 3.4 79.5 32.2 531 768 1.6 3.6 2.2 .. Senegal 5.7 56.0 78.5 54 101 0.1 0.4 0.3 .. Serbia 9.9h 61.8h 91.7h 408h 784h 2.0 4.4 5.4 .. Sierra Leone 4.4 c 31.3c 58.8 c 14 c 32c 0.0 d 0.2 0.4 .. Singapore 3.1 32.6 93.9 1,148 1,643 1.5 4.4 3.2 .. Slovak Republic 7.7 66.8 79.1 1,077 1,555 3.1 6.6 6.8 12.5 Slovenia 7.8 71.5 48.6 1,836 2,099 2.4 7.8 4.7 6.6 Somalia .. .. .. .. .. 0.0 d 0.1 .. .. South Africa 8.6 41.4 29.7 497 819 0.8 4.1 2.8 .. Spain 8.5 71.8 74.6 2,712 2,671 3.8 7.6 3.4 9.5 Sri Lanka 4.2 47.5 86.7 68 179 0.6 1.7 3.1 .. Sudan 3.5c 36.8 c 100.0 c 40 c 71c 0.3 0.9 0.7 .. Swaziland 6.0 62.5 42.3 151 287 0.2 6.3 2.1 .. Sweden 9.1 81.7 87.0 4,495 3,323 3.6 11.6 .. 2.8 Switzerland 10.8 59.3 75.0 6,108 4,417 4.0 .. 5.5 .. Syrian Arab Republic 3.6 45.9 100.0 68 154 0.5 1.4 1.5 .. Tajikistan 5.3 21.5 94.4 29 93 2.0 5.0 5.4 8.3 Tanzania 5.3 65.8 75.0 22 63 0.0d 0.2 1.1 .. Thailand 3.7 73.2 71.7 136 286 .. .. .. .. Timor-Leste 13.6 84.6 37.2 58 116 0.1 2.2 .. .. Togo 6.1 24.9 84.2 33 68 0.1 0.3 0.9 .. Trinidad and Tobago 4.8 56.1 89.7 785 1,178 1.2 3.6 2.7 .. Tunisia 6.0 50.5 84.3 211 463 1.3 2.9 2.0 .. Turkey 5.0 69.0 71.8 465 677 1.5 1.9 2.8 4.6 Turkmenistan 2.6c 52.1c 100.0 c 139c 1c 2.4 4.5 4.1 3.7 Uganda 6.3 26.2 51.0 28 74 0.1 1.3 0.4b .. Ukraine 6.9 57.6 92.4 210 470 3.1 8.5 8.7 10.8 United Arab Emirates 2.7 70.5 64.9 1,253 1,414 1.5 4.6 1.9 .. United Kingdom 8.4 81.7 62.7 3,867 2,992 2.2 0.6 3.9 4.9 United States 15.7 45.5 22.6 7,285 7,290 2.7 9.8 3.1 9.0 Uruguay 8.0 74.0 50.3 582 994 4.2 .. 2.9 .. Uzbekistan 5.0 46.1 98.0 41 114 2.6 10.8 4.8 8.7 Venezuela, RB 5.8 46.5 88.1 477 641 .. .. 1.3 .. Vietnam 7.1 39.3 90.2 58 183 .. 0.7 2.7 .. West Bank and Gaza .. .. .. .. .. .. .. .. .. Yemen, Rep. 3.9 39.6 97.8 43 104 0.3 0.7 0.7 .. Zambia 6.2 57.7 67.6 57 79 0.1 0.7 1.9 .. Zimbabwe 8.9c 46.3c 50.4 c 79c 1c 0.2 0.7 3.0 .. World 9.7 w 59.6 w 43.9 w 806 w 871 w .. w .. w .. w .. w Low income 5.4 42.7 83.2 27 69 .. .. .. .. Middle income 5.4 50.2 78.8 164 299 1.3 .. 2.2 .. Lower middle income 4.3 42.4 90.5 80 182 1.1 .. 1.7 .. Upper middle income 6.4 55.2 69.0 488 753 .. .. 4.8 .. Low & middle income 5.4 49.9 78.9 140 261 .. .. .. .. East Asia & Pacific 4.1 46.3 89.1 95 208 1.5 1.0 2.1 .. Europe & Central Asia 5.6 65.7 83.8 396 647 3.1 6.6 7.1 7.6 Latin America & Carib. 7.1 48.5 68.2 473 715 .. .. .. .. Middle East & N. Africa 5.5 50.8 93.1 151 364 .. .. 1.6 .. South Asia 4.0 27.5 89.8 36 98 0.6 1.3 0.9 .. Sub-Saharan Africa 6.4 41.1 60.2 69 124 .. .. .. .. High income 11.2 61.3 36.1 4,406 4,182 .. .. 6.2 8.6 Euro area 9.7 76.4 60.8 3,695 3,189 3.6 7.9 6.0 6.8 a. Data are for the most recent year available. b. Data are for 2009. c. Derived from incomplete data. d. Less than 0.05. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees. g. Excludes Transnistria. h. Excludes Metohija. 122 2010 World Development Indicators 2.16 PEOPLE Health services About the data Definitions Health systems--the combined arrangements of funded by donors to obtain health system data. Data · Total health expenditure is the sum of public and institutions and actions whose primary purpose on health worker (physicians, nurses, and midwives) private health expenditure. It covers the provision is to promote, restore, or maintain health (World density shows the availability of medical personnel. of health services (preventive and curative), family Health Organization, World Health Report 2000)--are The WHO estimates that at least 2.5 physicians, planning and nutrition activities, and emergency aid increasingly being recognized as key to combating nurses, and midwives per 1,000 people are needed for health but excludes provision of water and sani- disease and improving the health status of popu- to provide adequate coverage with primary care tation. · Public health expenditure is recurrent and lations. The World Bank's (2007a) Healthy Develop- interventions associated with achieving the Millen- capital spending from central and local governments, ment: Strategy for Health, Nutrition, and Population nium Development Goals (WHO, World Health Report external borrowing and grants (including donations Results emphasizes the need to strengthen health 2006). The WHO compiles data from household and from international agencies and nongovernmental systems, which are weak in many countries, in order labor force surveys, censuses, and administrative organizations), and social (or compulsory) health to increase the effectiveness of programs aimed at records. Data comparability is limited by differences insurance funds. · Out-of-pocket health expendi- reducing specific diseases and further reduce mor- in definitions and training of medical personnel var- ture, part of private health expenditure, is direct bidity and mortality (World Bank 2007a). To evaluate ies. In addition, human resources tend to be concen- household outlays, including gratuities and in-kind health systems, the World Health Organization (WHO) trated in urban areas, so that average densities do payments, for health practitioners and pharmaceu- has recommended that key components--such as not provide a full picture of health personnel avail- tical suppliers, therapeutic appliances, and other financing, service delivery, workforce, governance, able to the entire population. goods and services whose primary intent is to restore and information--be monitored using several key Availability and use of health services, shown by or enhance health. · Health expenditure per capita indicators (WHO 2008b). The data in the table are hospital beds per 1,000 people and outpatient visits is total health expenditure divided by population in a subset of the first four indicators. Monitoring per capita, reflect both demand- and supply-side fac- U.S. dollars and in international dollars converted health systems allows the effectiveness, efficiency, tors. In the absence of a consistent definition these using 2005 purchasing power parity (PPP) rates. and equity of different health system models to be are crude indicators of the extent of physical, finan- · Physicians include generalist and specialist medi- compared. Health system data also help identify cial, and other barriers to health care. cal practitioners.· Nurses and midwives include pro- weaknesses and strengths and areas that need fessional nurses and midwives, auxiliary nurses and investment, such as additional health facilities, midwives, enrolled nurses and midwives, and other better health information systems, or better trained personnel, such as dental nurses and primary care human resources. nurses. · Hospital beds are inpatient beds for both Health expenditure data are broken down into acute and chronic care available in public, private, public and private expenditures, with private expen- general, and specialized hospitals and rehabilitation diture further broken down into out-of-pocket expen- centers. · Outpatient visits per capita are the num- diture (direct payments by households to providers), ber of visits to health care facilities per capita, includ- which make up the largest proportion of private ing repeat visits. expenditures. In general, low-income economies have a higher share of private health expenditure than do middle- and high-income countries. High out-of-pocket expenditures may discourage people from accessing preventive or curative care and can impoverish households that cannot afford needed care. Health financing data are collected through national health accounts, which systematically, comprehensively, and consistently monitoring health system resource flows. To establish a national health account, countries must define the boundaries of the Data sources health system and classify health expenditure infor- mation along several dimensions, including sources Data on health expenditures are from the WHO's of financing, providers of health services, functional National Health Account database (www.who.int/ use of health expenditures, and benefi ciaries of nha/en), supplemented by country data. Data on expenditures. The accounting system can then pro- physicians, nurses and midwives, hospital beds, vide an accurate picture of resource envelopes and and outpatient visits are from the WHO, Organi- financial flows and allow analysis of the equity and sation for Economic Co-operation and Develop- efficiency of financing to inform policy. ment, and TransMONEE, supplemented by country Many low-income countries use Demographic and data. Health Surveys or Multiple Indicator Cluster Surveys 2010 World Development Indicators 123 2.17 Health information Year last national Number of Year of last Year of last Completeness health account national health health survey census completed accounts completed % Birth Infant death Total death registration reporting reporting 1995­2008 2000­10 2000­08a 2003­08a 2003­08a Afghanistan 0 2003 6 .. .. Albania 2005 3 2005 2001 98 28 76 Algeria 2001 2 2006 2008 99 .. 89 Angola 0 2001 29 .. .. Argentina 1999 5 2001 91 100 100 Armenia 2008 5 2005 2001 96 38 100 Australia 2007 13 2006 .. 95 97 Austria 2007 13 2001 .. 89 97 Azerbaijan 0 2006 2009 94 24 100 Bangladesh 2007 12 2007 2001 10 .. .. Belarus 0 2005 2009 .. 55 94 Belgium 2007 5 2001 .. 100 97 Benin 2006 3 2006 2002 60 .. .. Bolivia 2007 13 2008 2001 74 .. 30 Bosnia and Herzegovina 2006 3 2006 100 54 92 Botswana 2003 3 2000 2001 58 .. .. Brazil 2006 7 1996 2000 89 47 86 Bulgaria 2006 5 2001 .. 79 100 Burkina Faso 2006 4 2006 2006 64 29 61 Burundi 2007 1 2005 2008 60 .. .. Cambodia 0 2005 2008 66 0 100 Cameroon 1995 1 2006 2005 70 .. 0 Canada 2008 14 2006 .. 100 98 Central African Republic 0 2006 2003 49 .. .. Chad 0 2004 9 .. .. Chile 2007 13 2002 96 100 100 China 2006 12 2000 .. .. 99 Hong Kong SAR, China 0 2006 .. 66 97 Colombia 2003 9 2005 2005 90 57 76 Congo, Dem. Rep. 0 2007 31 .. .. Congo, Rep. 2005 1 2005 2007 81 .. .. Costa Rica 2003 2 1993 2000 .. 91 97 Côte d'Ivoire 0 2006 55 .. .. Croatia 0 2001 .. 75 100 Cuba 0 2006 2002 100 97 100 Czech Republic 2007 13 1993 2001 .. 84 94 Denmark 2007 13 2001 .. 97 97 Dominican Republic 2002 2 2007 2002 78 1 54 Ecuador 2005 5 2004 2001 85 59 85 Egypt, Arab Rep. 2002 2 2008 2006 99 49 96 El Salvador 2008 13 2008 2007 .. 36 75 Eritrea 0 2002 .. .. 0 Estonia 2007 5 2000 .. 68 96 Ethiopia 2005 3 2005 2007 7 .. .. Finland 2007 13 2000 .. 84 98 France 2007 13 2006 .. 95 100 Gabon 0 2000 2003 89 .. .. Gambia, The 2004 3 2005/06 2003 55 .. .. Georgia 2008 8 2005 2002 92 54 83 Germany 2007 13 2001 .. 96 99 Ghana 2002 1 2008 2000 51 .. .. Greece 0 2001 .. 78 95 Guatemala 2007 13 2002 2002 .. 62 93 Guinea 0 2005 2009 43 .. .. Guinea-Bissau 0 2006 2009 39 .. .. Haiti 2006 1 2005/06 2003 81 .. 9 Honduras 2005 3 2005/06 2001 94 100 99 124 2010 World Development Indicators 2.17 PEOPLE Health information Year last national Number of Year of last Year of last Completeness health account national health health survey census completed accounts completed % Birth Infant death Total death registration reporting reporting 1995­2008 2000­10 2000­08a 2003­08a 2003­08a Hungary 2007 13 2001 .. 84 97 India 2004 2 2005/06 2001 41 .. .. Indonesia 2008 8 2007 2000 55 .. .. Iran, Islamic Rep. 2001 3 2000 2006 .. .. 99 Iraq 0 2006 95 100 100 Ireland 2007 13 2006 .. 75 99 Israel 0 2008 .. 90 100 Italy 0 2001 .. 99 98 Jamaica 2007 10 2005 2001 89 76 85 Japan 2006 12 2005 .. 88 98 Jordan 2007 4 2007 2004 .. .. 83 Kazakhstan 2007 1 2006 99 95 88 Kenya 2006 2 2004 48 37 39 Korea, Dem. Rep. 0 2000 2008 99 .. .. Korea, Rep. 2008 14 2005 .. 85 94 Kosovo 0 .. .. .. Kuwait 0 1996 2005 .. 97 100 Kyrgyz Republic 2008 4 2005/06 2009 94 86 97 Lao PDR 0 2006 2005 72 .. .. Latvia 2005 3 2000 .. 79 99 Lebanon 2005 4 2000 .. .. 72 Lesotho 0 2004 2006 26 .. .. Liberia 2008 1 2007 2008 4 .. .. Libya 0 2000 2006 .. .. .. Lithuania 2006 5 2001 .. 64 100 Macedonia, FYR 0 2005 2002 94 94 100 Madagascar 2007 2 2003/04 75 .. .. Malawi 2006 5 2006 2008 .. .. 100 Malaysia 2006 10 2000 .. 62 100 Mali 2004 6 2006 2009 53 .. .. Mauritania 0 2007 2000 56 .. .. Mauritius 2004 2 2000 .. 99 94 Mexico 2007 13 1995 2005 .. 87 100 Moldova 0 2005 2004 98 43 88 Mongolia 2003 5 2005 2000 98 48 88 Morocco 2006 3 2006 2004 85 .. .. Mozambique 2006 4 2003 2007 31 .. .. Myanmar 2001 4 2000 65 49 50 Namibia 2006 9 2006/07 2001 67 .. 100 Nepal 2005 5 2006 2001 35 .. .. Netherlands 2007 13 2001 .. 84 97 New Zealand 2006 12 2006 .. 100 97 Nicaragua 2004 9 2006/07 2005 81 64 65 Niger 2006 4 2006 2001 32 .. .. Nigeria 2005 8 2008 2006 30 .. .. Norway 2008 12 2001 .. 82 100 Oman 1998 1 1995 2003 .. 49 88 Pakistan 2006 1 2006/07 .. 84 .. Panama 2003 1 2003 2000 .. 77 88 Papua New Guinea 2000 3 1996 2000 .. 19 14 Paraguay 2007 2 2004 2002 .. 11 58 Peru 2005 11 2008 2007 93 80 54 Philippines 2007 13 2007/08 2007 83 39 100 Poland 2007 13 2002 .. 95 100 Portugal 2007 8 2001 .. 79 96 Puerto Rico 0 1996 2000 .. 100 95 Qatar 0 2004 .. 95 77 2010 World Development Indicators 125 2.17 Health information Year last national Number of Year of last Year of last Completeness health account national health health survey census completed accounts completed % Birth Infant death Total death registration reporting reporting 1995­2008 2000­10 2000­08a 2003­08a 2003­08a Romania 2006 9 1999 2002 .. 79 96 Russian Federation 2007 13 1996 2002 .. 79 96 Rwanda 2006 5 2007/08 2002 82 .. .. Saudi Arabia 0 2007 2004 .. 94 100 Senegal 2005 2 2005 2002 55 .. .. Serbia 2008 6 2005/06 2002 99 35 89 Sierra Leone 0 2008 2004 48 .. .. Singapore 0 2005 2000 .. 84 75 Slovak Republic 2007 11 2001 .. 90 99 Slovenia 2006 5 2002 .. 72 95 Somalia 0 2006 3 .. .. South Africa 1998 3 1998 2001 78 81 87 Spain 2007 13 2001 .. 99 100 Sri Lanka 2006 12 1987 2001 .. .. 92 Sudan 0 2006 2008 33 .. .. Swaziland 0 2006/07 2007 30 .. .. Sweden 2007 7 .. 83 99 Switzerland 2007 13 2000 .. 100 98 Syrian Arab Republic 0 2006 2004 95 .. 100 Tajikistan 0 2005 2000 88 19 69 Tanzania 2006 3 2004/05 2002 8 .. .. Thailand 2007 13 2005/06 2000 99 84 66 Timor-Leste 0 2003 2004 53 .. .. Togo 2002 1 2006 78 .. .. Trinidad and Tobago 2000 1 2006 2000 96 50 94 Tunisia 2005 5 2006 2004 .. .. 93 Turkey 2005 8 2003 2000 84 56 .. Turkmenistan 0 2006 96 .. .. Uganda 2006 6 2006 2002 21 .. .. Ukraine 2004 2 2007 2001 100 90 100 United Arab Emirates 0 2005 .. 75 100 United Kingdom 2007 11 2001 .. 100 94 United States 2007 13 2009 2000 .. 100 100 Uruguay 2008 13 2004 .. 86 100 Uzbekistan 0 2006 100 .. .. Venezuela, RB 0 2000 2001 92 62 84 Vietnam 2007 10 2006 2009 88 72 83 West Bank and Gaza 0 2006 2007 96 .. .. Yemen, Rep. 2006 3 2006 2004 22 .. 15 Zambia 2006 11 2007 2000 10 .. .. Zimbabwe 2001 3 2005/06 2002 74 .. .. a. Data are for the most recent year available. 126 2010 World Development Indicators 2.17 PEOPLE Health information About the data Definitions According to the World Health Organization (WHO), · Year last national health account completed is the health information systems are crucial for moni- latest year for which the health expenditure data are toring and evaluating health systems, which are available using the national health account approach. increasingly recognized as important for combating · Number of national health accounts completed is disease and improving health status. Health informa- the number of national health accounts completed tion systems underpin decisionmaking through four between 1995 and 2008. · Year of last health survey data functions: generation, compilation, analysis and is the latest year the national survey that collects synthesis, and communication and use. The health health information was conducted. · Year of last cen- information system collects data from the health sec- sus is the latest year a census was conducted in the tor and other relevant sectors; analyzes the data and last 10 years. · Completeness of birth registration is ensures their overall quality, relevance, and timeli- the percentage of children under age 5 whose births ness; and converts data into information for health- were registered at the time of the survey. The numer- related decisionmaking (WHO 2008b). ator of completeness of birth registration includes Numerous indicators have been proposed to children whose birth certificate was seen by the inter- assess a country's health information system. viewer or whose mother or caretaker says the birth They can be grouped into two broad types: indica- has been registered. · Completeness of infant death tors related to data generation using core sources reporting is the number of infant deaths reported by and methods (health surveys, civil registration, cen- national statistical authorities to the United Nations suses, facility reporting, health system resource Statistics Division's Demographic Yearbook divided tracking) and indicators related to capacity for data by the number of infant deaths estimated by the synthesis, analysis, and validation. Indicators related United Nations Population Division. · Complete- to data generation reflect a country's capacity to col- ness of total death reporting is the number of total lect relevant data at suitable intervals using the most deaths reported by national statistical authorities to appropriate data sources. Benchmarks include peri- the United Nations Statistics Division's Demographic odicity, timeliness, contents, and availability. Indi- Yearbook divided by the number of total deaths esti- cators related to capacity for synthesis, analysis, mated by the United Nations Population Division. and validation measure the dimensions of the insti- Data sources tutional frameworks needed to ensure data quality, including independence, transparency, and access. Data on year last national health account com- Benchmarks include the availability of independent pleted and number of national health accounts coordination mechanisms and micro- and meta-data completed were compiled by staff in the World (WHO 2008a). Bank's Health, Nutrition, and Population Unit using The indicators in the table are all related to data data on the health expenditures reported by the generation, including the years the last national WHO and OECD and consultation with colleagues health account, last health survey, and latest popu- from countries and other international organiza- lation census were completed. Frequency of data col- tions. Data on year of last health survey are from lection, a benchmark of data generation, is shown Macro International and the United Nations Chil- as the number of years for which a national health dren's Fund (UNICEF). Data on year of last cen- account was completed between 1995 and 2008. sus are from United Nations Statistics Division's National health account data may be collected 2010 World Population and Housing Census Pro- using different approaches such as Organisation for gram (http://unstats.un.org/unsd/demographic/ Economic Co-operation and Development (OECD) sources/census/2010_PHC/default.htm). Data System of Health Accounts, WHO National Health on completeness of birth registration are compiled Account producers guide approach, local national by UNICEF in State of the World's Children 2010 health accounting methods, or Pan American based mostly on household surveys and ministry Health Organization/WHO satellite health accounts of health data. Data used to calculate complete- approach. ness of infant death reporting and total death Indicators related to data generation include com- reporting are from the United Nations Statistics pleteness of birth registration, infant death report- Division's Population and Vital Statistics Report ing, and total death reporting. and the United Nations Population Division's World Population Prospects: The 2008 Revision. 2010 World Development Indicators 127 2.18 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2006 1990 2006 2008 2008 2003­08c 2003­08c 2003­08c 2003­08c 2007 2008 Afghanistan .. 22 .. 30 75 85 28 48 .. .. 87 55 Albania .. 97 .. 97 98 99 45 50 .. .. 85 87 Algeria 94 85 88 94 88 93 53 24 .. .. 90 103 Angola 39 51 26 50 79 81 .. .. 17.7 29.3 74 85 Argentina 94 96 81 91 99 96 .. .. .. .. 62 78 Armenia .. 98 .. 91 94 89 36 59 .. .. 70 74 Australia 100 100 100 100 94 92 .. .. .. .. 85 87 Austria 100 100 100 100 83 83 .. .. .. .. 71 87 Azerbaijan 68 78 .. 80 66 70 33 45 .. .. 58 67 Bangladesh 78 80 26 36 89 95 28 68 .. .. 92 42 Belarus 100 100 .. 93 99 97 90 54 .. .. 74 82 Belgium .. .. .. .. 93 99 .. .. .. .. 73 87 Benin 63 65 12 30 61 67 36 42 20.1 54.0 87 49 Bolivia 72 86 33 43 86 83 51 54 .. .. 85 65 Bosnia and Herzegovina 97 99 .. 95 84 91 91 53 .. .. 97 90 Botswana 93 96 38 47 94 96 .. .. .. .. 73 63 Brazil 83 91 71 77 99 97 50 .. .. .. 73 82 Bulgaria 99 99 99 99 96 95 .. .. .. .. 80 91 Burkina Faso 34 72 5 13 75 79 39 42 9.6 48.0 72 13 Burundi 70 71 44 41 84 92 38 23 8.3 30.0 86 24 Cambodia 19 65 8 28 89 91 48 50 4.2 0.2 94 55 Cameroon 49 70 39 51 80 84 35 22 13.1 57.8 74 69 Canada 100 100 100 100 94 94 .. .. .. .. 64 87 Central African Republic 58 66 11 31 62 54 32 47 15.1 57.0 67 47 Chad .. 48 5 9 23 20 12 27 .. 44.0 54 22 Chile 91 95 84 94 92 96 .. .. .. .. 85 126 China 67 88 48 65 94 97 .. .. .. .. 94 75 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. 66 87 Colombia 89 93 68 78 92 92 62 39 .. .. 77 70 Congo, Dem. Rep. 43 46 15 31 67 69 42 42 5.8 29.8 87 43 Congo, Rep. .. 71 .. 20 79 89 48 39 6.1 48.0 53 63 Costa Rica .. 98 94 96 91 90 .. .. .. .. 88 104 Côte d'Ivoire 67 81 20 24 63 74 35 45 3.0 36.0 73 28 Croatia 99 99 99 99 96 96 .. .. .. .. 30 87 Cuba .. 91 98 98 99 99 .. .. .. .. 90 123 Czech Republic 100 100 100 99 97 99 .. .. .. .. 69 87 Denmark 100 100 100 100 89 75 .. .. .. .. 77 87 Dominican Republic 84 95 68 79 79 77 70 55 .. 0.6 78 59 Ecuador 73 95 71 84 66 75 .. .. .. .. 75 50 Egypt, Arab Rep. 94 98 50 66 92 97 73 19 .. .. 89 57 El Salvador 69 84 73 86 95 94 62 .. .. .. 91 88 Eritrea 43 60 3 5 95 97 .. .. .. .. 88 62 Estonia 100 100 95 95 95 95 .. .. .. .. 68 88 Ethiopia 13 42 4 11 74 81 19 15 33.1 9.5 84 47 Finland 100 100 100 100 97 99 .. .. .. .. .. 87 France .. 100 .. .. 87 98 .. .. .. .. .. 87 Gabon .. 87 .. 36 55 38 .. .. .. .. 36 69 Gambia, The .. 86 .. 52 91 96 69 38 49.0 62.6 84 48 Georgia 76 99 94 93 96 92 74 37 .. .. 75 96 Germany 100 100 100 100 95 90 .. .. .. .. 40 87 Ghana 56 80 6 10 86 87 51 29 28.2 43.0 84 30 Greece 96 100 97 98 99 99 .. .. .. .. .. 87 Guatemala 79 96 70 84 96 85 .. .. .. .. 47 38 Guinea 45 70 13 19 64 66 42 38 1.4 43.5 79 34 Guinea-Bissau .. 57 .. 33 76 63 57 25 39.0 45.7 71 68 Haiti 52 58 29 19 58 53 31 43 .. 5.1 82 60 Honduras 72 84 45 66 95 93 56 49 .. 0.5 85 60 128 2010 World Development Indicators 2.18 PEOPLE Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2006 1990 2006 2008 2008 2003­08c 2003­08c 2003­08c 2003­08c 2007 2008 Hungary 96 100 100 100 99 99 .. .. .. .. 46 87 India 71 89 14 28 70 66 69 33 .. 8.2 87 67 Indonesia 72 80 51 52 83 77 66 54 3.3 .. 91 69 Iran, Islamic Rep. 92 .. 83 .. 98 99 .. .. .. .. 83 65 Iraq 83 77 .. 76 69 62 82 64 .. .. 86 47 Ireland .. .. .. .. 89 93 .. .. .. .. 66 87 Israel 100 100 .. .. 84 93 .. .. .. .. 74 87 Italy .. .. .. .. 91 96 .. .. .. .. 0 87 Jamaica 92 93 83 83 88 87 75 39 .. .. 56 59 Japan 100 100 100 100 97 98 .. .. .. .. 46 87 Jordan 97 98 .. 85 95 97 75 32 .. .. 71 91 Kazakhstan 96 96 97 97 99 99 71 48 .. .. 69 85 Kenya 41 57 39 42 90 85 49 33 4.6 26.5 85 79 Korea, Dem. Rep. .. 100 .. .. 98 92 93 .. .. .. 87 88 Korea, Rep. .. .. .. .. 92 94 .. .. .. .. 81 87 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. 99 99 .. .. .. .. 79 87 Kyrgyz Republic .. 89 .. 93 99 95 62 22 .. .. 85 77 Lao PDR .. 60 .. 48 52 61 32 49 40.5 .. 92 44 Latvia 99 99 .. 78 97 97 .. .. .. .. 73 93 Lebanon 100 100 .. .. 53 74 .. .. .. .. 90 91 Lesotho .. 78 .. 36 85 83 59 53 .. .. 67 92 Liberia 57 64 40 32 64 64 62 47 .. 58.8 71 46 Libya 71 .. 97 97 98 98 .. .. .. .. 67 192 Lithuania .. .. .. .. 97 96 .. .. .. .. 74 89 Macedonia, FYR .. 100 .. 89 98 95 93 45 .. .. 87 91 Madagascar 39 47 8 12 81 82 42d 47 45.8d 19.7d 80 45 Malawi 41 76 46 60 88 91 52 27 24.7 24.9 85 50 Malaysia 98 99 .. 94 95 90 .. .. .. .. 72 62 Mali 33 60 35 45 68 68 38 38 27.1 31.7 78 15 Mauritania 37 60 20 24 65 74 45 32 2.1 20.7 66 26 Mauritius 100 100 94 94 98 99 .. .. .. .. 85 38 Mexico 88 95 56 81 96 98 .. .. .. .. 84 93 Moldova .. 90 .. 79 94 95 60 48 .. .. 62 70 Mongolia 64 72 .. 50 97 96 63 47 .. .. 89 83 Morocco 75 83 52 72 96 99 38 46 .. .. 86 73 Mozambique 36 42 20 31 77 72 65 47 22.8 14.9 79 42 Myanmar 57 80 23 82 82 85 66 65 .. .. 85 62 Namibia 57 93 26 35 73 83 72 48 10.5 9.8 82 84 Nepal 72 89 9 27 79 82 43 37 .. 0.1 88 70 Netherlands 100 100 100 100 96 97 .. .. .. .. 84 87 New Zealand 97 .. .. .. 86 89 .. .. .. .. 86 87 Nicaragua 70 79 42 48 99 96 .. .. .. .. 86 89 Niger 41 42 3 7 80 66 47 34 7.4 33.0 79 35 Nigeria 50 47 26 30 62 54 32 28 5.5 33.9 82 19 Norway 100 100 .. .. 93 94 .. .. .. .. 93 87 Oman 81 .. 85 .. 99 92 .. .. .. .. 91 87 Pakistan 86 90 33 58 85 73 69 37 .. 3.3 91 60 Panama .. 92 .. 74 85 82 .. .. .. .. 79 95 Papua New Guinea 39 40 44 45 54 52 .. .. .. .. 39 85 Paraguay 52 77 60 70 77 76 .. .. .. .. 82 75 Peru 75 84 55 72 90 99 67 60 .. .. 92 94 Philippines 83 93 58 78 92 91 50 76 .. 0.2 89 54 Poland .. .. .. .. 98 99 .. .. .. .. 75 79 Portugal 96 99 92 99 97 97 .. .. .. .. 87 87 Puerto Rico .. .. .. .. .. .. .. .. .. .. 80 87 Qatar 100 100 100 100 92 94 .. .. .. .. 69 81 2010 World Development Indicators 129 2.18 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis an improved improved immunization with acute diarrhea who sleeping with fever water source sanitation rate respiratory received oral under receiving facilities infection rehydration treated antimalarial taken to and continuous netsa drugs Treatment Case health feeding success detection provider rate rate % of children ages % of children % of children % of % of children % of new % of new % of % of 12­23 monthsb under age 5 under age 5 children under age 5 registered estimated population population Measles DTP3 with ARI with diarrhea under age 5 with fever cases cases 1990 2006 1990 2006 2008 2008 2003­08c 2003­08c 2003­08c 2003­08c 2007 2008 Romania 76 88 72 72 .. .. .. .. .. .. 83 76 Russian Federation 94 97 87 87 99 98 .. .. .. .. 58 85 Rwanda 65 65 29 23 92 97 28 24 55.7 12.3 86 20 Saudi Arabia 94 96 91 99 97 98 .. .. .. .. 67 86 Senegal 67 77 26 28 77 88 47 43 29.2d 22.0 77 33 Serbia .. 99e .. 92e 92 95 93 71 .. .. 84 95 Sierra Leone .. 53 .. 11 60 60 46 31 25.9 51.9 89 32 Singapore 100 100 100 100 95 97 .. .. .. .. 84 87 Slovak Republic 100 100 100 100 99 99 .. .. .. .. 81 87 Slovenia .. .. .. .. 96 97 .. .. .. .. 92 87 Somalia .. 29 .. 23 24 31 13 7 11.4 7.9 86 36 South Africa 81 93 55 59 62 67 65 .. .. .. 74 72 Spain 100 100 100 100 98 97 .. .. .. .. .. 87 Sri Lanka 67 82 71 86 98 98 58 .. 2.9 0.3 86 70 Sudan 64 70 33 35 79 86 90 56 27.6 54.2 78 49 Swaziland .. 60 .. 50 95 95 73 22 0.6 0.6 58 61 Sweden 100 100 100 100 96 98 .. .. .. .. 63 87 Switzerland 100 100 100 100 87 95 .. .. .. .. .. 87 Syrian Arab Republic 83 89 81 92 81 82 77 34 .. .. 88 79 Tajikistan .. 67 .. 92 86 86 64 22 1.3 1.2 83 47 Tanzania 49 55 35 33 88 84 59 53 25.7 58.2 88 75 Thailand 95 98 78 96 98 99 84 46 .. .. 83 60 Timor-Leste .. 62 .. 41 .. .. 24 .. .. .. 84 60 Togo 49 59 13 12 77 89 23 22 38.4 47.7 76 10 Trinidad and Tobago 88 94 93 92 91 90 74 32 .. .. 65 87 Tunisia 82 94 74 85 98 99 59 62 .. .. 89 94 Turkey 85 97 85 88 97 96 41 .. .. .. 91 79 Turkmenistan .. .. .. .. 99 96 83 25 .. .. 84 110 Uganda 43 64 29 33 68 64 73 39 9.7 61.3 75 43 Ukraine .. 97 96 93 94 90 .. .. .. .. 59 81 United Arab Emirates 100 100 97 97 92 92 .. .. .. .. 64 37 United Kingdom 100 100 .. .. 86 92 .. .. .. .. 72 87 United States 99 99 100 100 92 96 .. .. .. .. 85 87 Uruguay 100 100 100 100 95 94 .. .. .. .. 87 93 Uzbekistan 90 88 93 96 98 98 68 28 .. .. 79 49 Venezuela, RB 89 .. 83 .. 82 47 .. .. .. .. 82 68 Vietnam 52 92 29 65 92 93 83 65 5.0 2.6 92 56 West Bank and Gaza .. 89 .. 80 .. .. .. .. .. .. 93 5 Yemen, Rep. .. 66 28 46 62 69 47 48 .. .. 84 41 Zambia 50 58 42 52 85 80 68 56 41.1 38.4 85 74 Zimbabwe 78 81 44 46 66 62 25 47 2.9 4.7 78 39 World 76 w 86 w 51 w 60 w 83 w 82 w .. w .. w .. w .. w 85 w 61 w Low income 54 67 25 38 78 w 80 45 .. .. 28.3 85 48 Middle income 74 88 47 58 83 81 .. .. .. .. 85 66 Lower middle income 71 86 39 52 81 79 .. .. .. .. 87 64 Upper middle income 88 94 76 82 93 92 .. .. .. .. 73 78 Low & middle income 72 84 43 55 82 81 .. .. .. .. 85 61 East Asia & Pacific 68 87 48 66 91 92 .. .. .. .. 91 69 Europe & Central Asia 90 95 88 89 96 96 .. .. .. .. 70 78 Latin America & Carib. 84 91 68 78 93 91 .. .. .. .. 76 77 Middle East & N. Africa 89 88 67 74 86 89 62 .. .. .. 86 70 South Asia 73 87 18 33 75 71 .. .. .. 7.2 87 63 Sub-Saharan Africa 49 58 26 31 72 72 44 .. 15.9 34.4 76 46 High income 99 100 99 100 93 95 .. .. .. .. 67 87 Euro area .. 100 .. .. 93 95 .. .. .. .. .. 87 a. For malaria prevention only. b. Refers to children who were immunized before age 12 months or in some cases at any time before the survey (12­23 months). c. Data are for the most recent year available. d. Data are for 2009. e. Includes Kosovo. 130 2010 World Development Indicators 2.18 PEOPLE Disease prevention coverage and quality About the data Definitions People's health is influenced by the environment rehydration salts at home. However, recommenda- · Access to an improved water source refers to in which they live. Lack of clean water and basic tions for the use of oral rehydration therapy have people with access to at least 20 liters of water sanitation is the main reason diseases transmitted changed over time based on scientific progress, so a person a day from an improved source, such as by feces are so common in developing countries. it is difficult to accurately compare use rates across piped water into a dwelling, public tap, tubewell, Access to drinking water from an improved source countries. Until the current recommended method protected dug well, and rainwater collection, within and access to improved sanitation do not ensure for home management of diarrhea is adopted and 1 kilometer of the dwelling. · Access to improved safety or adequacy, as these characteristics are applied in all countries, the data should be used sanitation facilities refers to people with at least not tested at the time of the surveys. But improved with caution. Also, the prevalence of diarrhea may adequate access to excreta disposal facilities that drinking water technologies and improved sanitation vary by season. Since country surveys are adminis- can effectively prevent human, animal, and insect facilities are more likely than those characterized tered at different times, data comparability is further contact with excreta. Improved facilities range from as unimproved to provide safe drinking water and to affected. protected pit latrines to flush toilets. · Child immu- prevent contact with human excreta. The data are Malaria is endemic to the poorest countries in the nization rate refers to children ages 12­23 months derived by the Joint Monitoring Programme (JMP) world, mainly in tropical and subtropical regions of who, before 12 months or at any time before the of the World Health Organization (WHO) and United Africa, Asia, and the Americas. Insecticide-treated survey, had received one dose of measles vaccine Nations Children's Fund (UNICEF) based on national nets, properly used and maintained, are one of the and three doses of diphtheria, pertussis (whooping censuses and nationally representative household most important malaria-preventive strategies to limit cough), and tetanus (DTP3) vaccine. · Children with surveys. The coverage rates for water and sanita- human-mosquito contact. Studies have emphasized acute respiratory infection (ARI) taken to health tion are based on information from service users that mortality rates could be reduced by about provider are children under age 5 with ARI in the on the facilities their households actually use rather 25­30 percent if every child under age 5 in malaria- two weeks before the survey who were taken to an than on information from service providers, which risk areas such as Africa slept under a treated net appropriate health provider. · Children with diarrhea may include nonfunctioning systems. While the esti- every night. who received oral rehydration and continuous feed- mates are based on use, the JMP reports use as Prompt and effective treatment of malaria is a criti- ing are children under age 5 with diarrhea in the two access, because access is the term used in the Mil- cal element of malaria control. It is vital that suffer- weeks before the survey who received either oral lennium Development Goal target for drinking water ers, especially children under age 5, start treatment rehydration therapy or increased fluids, with con- and sanitation. within 24 hours of the onset of symptoms, to pre- tinuous feeding. · Children sleeping under treated Governments in developing countries usually vent progression--often rapid--to severe malaria nets are children under age 5 who slept under an finance immunization against measles and diphthe- and death. insecticide-treated net to prevent malaria the night ria, pertussis (whooping cough), and tetanus (DTP) Data on the success rate of tuberculosis treatment before the survey. · Children with fever receiving as part of the basic public health package. In many are provided for countries that have submitted data antimalarial drugs are children under age 5 who were developing countries lack of precise information on to the WHO. The treatment success rate for tuber- ill with fever in the two weeks before the survey and the size of the cohort of one-year-old children makes culosis provides a useful indicator of the quality of received any appropriate (locally defined) antimalarial immunization coverage diffi cult to estimate from health services. A low rate suggests that infectious drugs. · Tuberculosis treatment success rate is new program statistics. The data shown here are based patients may not be receiving adequate treatment. registered infectious tuberculosis cases that were on an assessment of national immunization cover- An important complement to the tuberculosis treat- cured or that completed a full course of treatment as age rates by the WHO and UNICEF. The assessment ment success rate is the case detection rate, which a percentage of smear-positive cases registered for considered both administrative data from service indicates whether there is adequate coverage by treatment outcome evaluation. · Tuberculosis case providers and household survey data on children's the recommended case detection and treatment detection rate is newly identified tuberculosis cases immunization histories. Based on the data available, strategy. (including relapses) as a percentage of estimated consideration of potential biases, and contributions Previous editions included the tuberculosis detec- incident cases (case detection, all forms). of local experts, the most likely true level of immuni- tion rates by DOTS, the internationally recommended Data sources zation coverage was determined for each year. strategy for tuberculosis control. This year's edition Data on access to water and sanitation are from Acute respiratory infection continues to be a lead- shows the tuberculosis detection rate for all detec- the WHO and UNICEF's Progress on Drinking Water ing cause of death among young children, killing tion methods, so data on the case detection rate and Sanitation (2008). Data on immunization are about 2 million children under age 5 in developing cannot be compared with data in previous editions. from WHO and UNICEF estimates (www.who.int/ countries each year. Data are drawn mostly from For indicators that are from household surveys, the immunization_monitoring). Data on children with household health surveys in which mothers report year in the table refers to the survey year. For more ARI, with diarrhea, sleeping under treated nets, and on number of episodes and treatment for acute respi- information, consult the original sources. receiving antimalarial drugs are from UNICEF's State ratory infection. of the World's Children 2009, Childinfo, and Demo- Since 1990 diarrhea-related deaths among chil- graphic and Health Surveys by Macro International. dren have declined tremendously. Most diarrhea- Data on tuberculosis are from the WHO's Global Tuber- related deaths are due to dehydration, and many of culosis Control: A Short Update to the 2009 Report. these deaths can be prevented with the use of oral 2010 World Development Indicators 131 2.19 Reproductive health Total fertility Wanted Adolescent Unmet Contraceptive Pregnant Births attended Maternal rate fertility rate fertility rate need for prevalence women by skilled mortality contraception rate receiving health staff ratio prenatal care births per % of married % of married per 100,000 live births births per births per 1,000 women women ages women ages National Modeled woman woman ages 15­19 15­49 15­49 % % of total estimates estimates 1990 2008 2003­08a 2008 2003­08a 2003­08a 2003­08a 1990 2003­08a 2000­08a 2005 Afghanistan 8.0 6.6 .. 120 .. 15 36 .. 24 1,600 1,800 Albania 2.9 1.9 .. 14 .. 60 97 .. 100 20 92 Algeria 4.7 2.4 .. 7 .. 61 89 77 95 .. 180 Angola 7.2 5.8 .. 123 .. .. 80 .. 47 .. 1,400 Argentina 3.0 2.2 .. 57 .. .. 99 96 99 44 77 Armenia 2.5 1.7 1.6 36 13 53 93 .. 100 15 76 Australia 1.9 2.0 .. 15 .. .. .. 100 100 .. 4 Austria 1.5 1.4 .. 13 .. .. .. .. .. .. 4 Azerbaijan 2.7 2.3 1.8 34 23 51 77 .. 88 26 82 Bangladesh 4.4 2.3 1.9 70 17 56 51 .. 18 351 570 Belarus 1.9 1.4 .. 21 .. 73 99 .. 100 12 18 Belgium 1.6 1.8 .. 8 .. .. .. .. .. .. 8 Benin 6.7 5.4 4.8 111 30 17 84 .. 74 397 840 Bolivia 4.9 3.5 2.1 78 23 61 77 43 66 229 290 Bosnia and Herzegovina 1.7 1.2 .. 16 23 36 99 97 100 3 3 Botswana 4.7 2.9 .. 51 .. .. .. 77 .. .. 380 Brazil 2.8 1.9 .. 75 .. 81 98 72 97 53 110 Bulgaria 1.8 1.5 .. 42 .. .. .. .. 99 7 11 Burkina Faso 6.8 5.9 5.1 129 29 17 85 .. 54 .. 700 Burundi 6.6 4.6 .. 19 .. 9 92 .. 34 615 1,100 Cambodia 5.8 2.9 2.8 39 25 40 69 .. 44 472 540 Cameroon 5.9 4.6 4.5 126 20 29 82 58 63 669 1,000 Canada 1.8 1.6 .. 13 .. .. .. .. 100 .. 7 Central African Republic 5.8 4.8 .. 104 .. 19 69 .. 53 543 980 Chad 6.7 6.2 6.1 162 21 3 39 .. 14 1,099 1,500 Chile 2.6 1.9 .. 59 .. 58 .. .. 100 20 16 China 2.3b 1.8b .. 10 b .. 85 91 50 98 37 45 Hong Kong SAR, China 1.3 1.0 .. 6 .. .. .. .. 100 .. .. Colombia 3.1 2.4 1.7 74 6 78 94 82 96 75 130 Congo, Dem. Rep. 7.1 6.0 5.6 198 24 21 85 .. 74 549 1,100 Congo, Rep. 5.4 4.4 4.4 111 16 44 86 .. 83 781 740 Costa Rica 3.2 2.0 .. 67 .. 96 90 98 99 33 30 Côte d'Ivoire 6.3 4.6 .. 128 29 13 85 .. 57 543 810 Croatia 1.6 1.5 .. 14 .. .. 100 100 100 10 7 Cuba 1.8 1.5 .. 45 8 77 100 .. 100 29 45 Czech Republic 1.9 1.5 .. 11 .. .. .. .. 100 8 4 Denmark 1.7 1.9 .. 6 .. .. .. .. .. .. 3 Dominican Republic 3.5 2.6 1.9 108 11 73 99 93 98 159 150 Ecuador 3.7 2.6 .. 83 .. 73 84 .. 75 60 210 Egypt, Arab Rep. 4.6 2.9 2.3 38 10 60 74 37 79 84 130 El Salvador 4.0 2.3 .. 82 .. 73 94 52 92 59 170 Eritrea 6.2 4.6 .. 66 .. .. .. .. .. .. 450 Estonia 2.0 1.7 .. 21 .. .. .. .. 100 7 25 Ethiopia 7.1 5.3 4.0 102 34 15 28 .. 6 673 720 Finland 1.8 1.8 .. 11 .. .. .. .. 100 .. 7 France 1.8 2.0 .. 7 .. .. .. .. .. .. 8 Gabon 5.2 3.3 .. 89 .. .. .. .. .. 519 520 Gambia, The 6.1 5.1 .. 88 .. .. 98 44 57 730 690 Georgia 2.2 1.6 .. 44 .. 47 94 .. 98 23 66 Germany 1.5 1.4 .. 8 .. .. .. .. 100 .. 4 Ghana 5.6 4.0 3.7 63 34 24 95 40 59 451 560 Greece 1.4 1.5 .. 9 .. .. .. .. .. .. 3 Guatemala 5.6 4.1 .. 106 .. .. .. .. .. 133 290 Guinea 6.7 5.4 5.1 151 21 9 88 31 46 980 910 Guinea-Bissau 5.9 5.7 .. 128 .. 10 78 .. 39 405 1,100 Haiti 5.4 3.5 2.4 46 38 32 85 23 26 630 670 Honduras 5.1 3.3 2.3 92 17 65 92 45 67 .. 280 132 2010 World Development Indicators 2.19 PEOPLE Reproductive health Total fertility Wanted Adolescent Unmet Contraceptive Pregnant Births attended Maternal rate fertility rate fertility rate need for prevalence women by skilled mortality contraception rate receiving health staff ratio prenatal care births per % of married % of married per 100,000 live births births per births per 1,000 women women ages women ages National Modeled woman woman ages 15­19 15­49 15­49 % % of total estimates estimates 1990 2008 2003­08a 2008 2003­08a 2003­08a 2003­08a 1990 2003­08a 2000­08a 2005 Hungary 1.8 1.4 .. 20 .. .. .. .. 100 8 6 India 4.0 2.7 1.9 67 13 56 74 .. 47 301 450 Indonesia 3.1 2.2 2.2 39 9 61 93 32 79 228 420 Iran, Islamic Rep. 4.8 1.8 .. 18 .. 79 98 .. 97 25 140 Iraq 6.0 4.1 .. 84 .. 50 84 54 80 84 300 Ireland 2.1 2.1 .. 16 .. .. .. .. 100 .. 1 Israel 2.8 3.0 .. 14 .. .. .. .. .. .. 4 Italy 1.3 1.4 .. 5 .. .. .. .. 99 .. 3 Jamaica 2.9 2.4 .. 77 .. .. 91 79 95 95 170 Japan 1.5 1.3 .. 5 .. .. .. 100 100 .. 6 Jordan 5.5 3.5 2.8 24 12 57 99 87 99 .. 62 Kazakhstan 2.7 2.6 .. 30 .. 51 100 .. 100 31 140 Kenya 6.0 4.9 3.6 103 25 39 88 50 42 414 560 Korea, Dem. Rep. 2.4 1.9 .. 0 .. .. .. .. 97 .. 370 Korea, Rep. 1.6 1.2 .. 6 .. .. .. 98 100 .. 14 Kosovo 3.9 2.4 .. .. .. .. .. .. .. .. .. Kuwait 3.5 2.2 .. 13 .. .. .. .. .. .. 4 Kyrgyz Republic 3.7 2.7 .. 32 1 48 97 .. 98 104 150 Lao PDR 6.0 3.5 .. 37 .. 38 35 .. 20 405 660 Latvia 2.0 1.5 .. 15 .. .. .. .. 100 9 10 Lebanon 3.1 1.8 .. 16 .. 58 96 .. 98 .. 150 Lesotho 4.9 3.3 2.5 72 31 37 90 .. 55 762 960 Liberia 6.5 5.9 4.6 140 36 11 79 .. 46 994 1,200 Libya 4.8 2.7 .. 3 .. .. .. .. .. .. 97 Lithuania 2.0 1.5 .. 21 .. .. .. .. 100 13 11 Macedonia, FYR 2.1 1.4 .. 21 34 14 94 .. 99 4 10 Madagascar 6.3 4.7 4.6 131 24 40 c 86c 57 44 c 469 510 Malawi 7.0 5.5 4.9 133 28 41 92 55 54 807 1,100 Malaysia 3.7 2.6 .. 13 .. .. 79 .. 98 30 62 Mali 6.7 6.5 6.0 161 31 8 70 .. 49 464 970 Mauritania 5.9 4.5 .. 88 .. 9 75 40 61 686 820 Mauritius 2.3 1.6 .. 40 .. .. .. 91 99 22 15 Mexico 3.4 2.1 .. 64 .. 71 94 .. 93 56 60 Moldova 2.4 1.5 .. 33 7 68 98 .. 100 16 22 Mongolia 4.2 2.0 .. 16 14 66 89 .. 100 49 46 Morocco 4.0 2.4 1.8 19 10 63 68 31 63 227 240 Mozambique 6.2 5.1 4.9 146 18 16 89 .. 55 408 520 Myanmar 3.4 2.3 .. 18 .. 34 .. .. 68 316 380 Namibia 5.2 3.4 2.7 72 7 55 95 68 81 449 210 Nepal 5.2 2.9 2.0 99 25 48 44 7 19 281 830 Netherlands 1.6 1.8 .. 4 .. .. .. .. 100 .. 6 New Zealand 2.2 2.2 .. 22 .. .. .. .. .. .. 9 Nicaragua 4.8 2.7 .. 112 8 72 90 .. 74 87 170 Niger 7.9 7.1 6.8 156 16 11 46 15 33 648 1,800 Nigeria 6.6 5.7 5.3 124 17 15 58 33 39 .. 1,100 Norway 1.9 2.0 .. 8 .. .. .. 100 .. .. 7 Oman 6.6 3.0 .. 10 .. .. .. .. 99 23 64 Pakistan 6.1 4.0 3.1 45 25 30 61 19 39 276 320 Panama 3.0 2.5 .. 82 .. .. .. .. 92 60 130 Papua New Guinea 4.8 4.1 .. 54 .. 32 79 .. 53 .. 470 Paraguay 4.5 3.0 .. 72 .. 79 96 66 82 121 150 Peru 3.8 2.6 .. 54 8 71 91 80 71 185 240 Philippines 4.3 3.1 2.5 44 22 51 91 .. 62 162 230 Poland 2.0 1.4 .. 14 .. .. .. .. 100 3 8 Portugal 1.4 1.4 .. 16 .. .. .. 98 .. .. 11 Puerto Rico 2.2 1.8 .. 53 .. .. .. .. 100 .. 18 Qatar 4.4 2.4 .. 16 .. .. .. .. .. .. 12 2010 World Development Indicators 133 2.19 Reproductive health Total fertility Wanted Adolescent Unmet Contraceptive Pregnant Births attended Maternal rate fertility rate fertility rate need for prevalence women by skilled mortality contraception rate receiving health staff ratio prenatal care births per % of married % of married per 100,000 live births births per births per 1,000 women women ages women ages National Modeled woman woman ages 15­19 15­49 15­49 % % of total estimates estimates 1990 2008 2003­08a 2008 2003­08a 2003­08a 2003­08a 1990 2003­08a 2000­08a 2005 Romania 1.8 1.4 .. 31 .. 70 94 .. 98 15 24 Russian Federation 1.9 1.5 .. 25 .. .. .. .. 100 22 28 Rwanda 6.8 5.4 4.6 36 38 36 96 26 52 750 1,300 Saudi Arabia 5.8 3.1 .. 26 .. .. .. .. 96 10 18 Senegal 6.7 4.8 4.5 102 32 12 87 .. 52 401 980 Serbia 1.8 1.4 .. 22 29 41 98 .. 99 13 14 d Sierra Leone 5.5 5.2 .. 125 .. 8 81 .. 43 857 2,100 Singapore 1.9 1.3 .. 4 .. .. .. .. 100 .. 14 Slovak Republic 2.1 1.3 .. 20 .. .. .. .. 100 4 6 Slovenia 1.5 1.5 .. 5 .. .. .. 100 100 17 6 Somalia 6.6 6.4 .. 70 .. 15 26 .. 33 1,044 1,400 South Africa 3.7 2.5 .. 58 .. 60 92 .. 91 166 400 Spain 1.3 1.5 .. 12 .. .. .. .. .. .. 4 Sri Lanka 2.5 2.3 .. 29 .. 68 99 .. 99 44 58 Sudan 6.0 4.2 .. 56 6 8 64 69 49 1,107 450 Swaziland 5.7 3.5 2.1 82 24 51 85 .. 69 589 390 Sweden 2.1 1.9 .. 8 .. .. .. .. .. .. 3 Switzerland 1.6 1.5 .. 5 .. .. .. .. 100 .. 5 Syrian Arab Republic 5.5 3.2 .. 59 .. 58 84 .. 93 65 130 Tajikistan 5.2 3.4 .. 28 .. 37 80 .. 88 97 170 Tanzania 6.2 5.6 4.9 130 22 26 76 53 43 578 950 Thailand 2.1 1.8 .. 37 .. 77 98 .. 97 12 110 Timor-Leste 5.3 6.5 .. 53 .. 20 61 .. 18 .. 380 Togo 6.3 4.3 .. 64 .. 17 84 31 62 .. 510 Trinidad and Tobago 2.4 1.6 .. 34 .. 43 96 .. 98 .. 45 Tunisia 3.5 2.1 .. 7 .. 60 96 69 95 .. 100 Turkey 3.1 2.1 .. 38 .. 73 54 .. 91 29 44 Turkmenistan 4.3 2.5 .. 19 .. 48 99 .. 100 14 130 Uganda 7.1 6.3 5.1 148 41 24 94 38 42 435 550 Ukraine 1.8 1.4 1.1 28 10 67 99 .. 99 24 18 United Arab Emirates 4.4 1.9 .. 16 .. .. .. .. 100 .. 37 United Kingdom 1.8 1.9 .. 24 .. .. .. .. .. .. 8 United States 2.1 2.1 .. 35 .. .. .. 99 99 .. 11 Uruguay 2.5 2.0 .. 61 .. .. 97 .. 99 18 20 Uzbekistan 4.1 2.6 .. 13 8 65 99 .. 100 28 24 Venezuela, RB 3.4 2.5 .. 90 .. .. .. .. 95 61 57 Vietnam 3.7 2.1 .. 17 .. 76 91 .. 88 162 150 West Bank and Gaza 6.4 5.0 .. 77 .. 50 99 .. 99 .. .. Yemen, Rep. 8.1 5.2 .. 67 .. 28 47 16 36 365 430 Zambia 6.5 5.8 5.2 139 27 41 94 51 47 591 830 Zimbabwe 5.2 3.4 3.3 64 13 60 94 70 69 555 880 World 3.3 w 2.5 w .. w 51 w .. w 61 w 82 w 50 w 66 w 400 w Low income 5.4 4.0 .. 90 .. 38 69 .. 44 790 Middle income 3.3 2.4 .. 47 .. 66 84 46 70 320 Lower middle income 3.4 2.5 .. 46 .. 65 83 41 65 370 Upper middle income 2.8 2.0 .. 51 .. 72 90 .. 95 110 Low & middle income 3.6 2.7 .. 55 .. 61 82 46 63 440 East Asia & Pacific 2.6 1.9 .. 17 .. 77 91 48 89 150 Europe & Central Asia 2.3 1.8 .. 27 .. .. .. .. 97 45 Latin America & Carib. 3.2 2.2 .. 72 .. 75 95 72 90 130 Middle East & N. Africa 4.9 2.7 .. 35 .. 62 83 47 80 200 South Asia 4.3 2.9 1.9 66 13 53 69 32 42 500 Sub-Saharan Africa 6.3 5.1 .. 116 .. 23 72 .. 46 900 High income 1.8 1.8 .. 19 .. .. .. .. 99 10 Euro area 1.5 1.6 .. 8 .. .. .. .. .. 5 a. Data are for most recent year available. b. Includes Taiwan, China. c. Data are for 2009. d. Includes Montenegro. 134 2010 World Development Indicators 2.19 PEOPLE Reproductive health About the data Definitions Reproductive health is a state of physical and men- Good prenatal and postnatal care improve mater- · Total fertility rate is the number of children that would tal well-being in relation to the reproductive system nal health and reduce maternal and infant mortality. be born to a woman if she were to live to the end of her and its functions and processes. Means of achieving But data may not reflect such improvements because childbearing years and bear children in accordance with reproductive health include education and services health information systems are often weak, mater- current age-specific fertility rates. · Wanted fertility during pregnancy and childbirth, safe and effec- nal deaths are underreported, and rates of maternal rate is the estimated total fertility rate if all unwanted tive contraception, and prevention and treatment mortality are difficult to measure. births were avoided. · Adolescent fertility rate is of sexually transmitted diseases. Complications of The share of births attended by skilled health staff the number of births per 1,000 women ages 15­19. pregnancy and childbirth are the leading cause of is an indicator of a health system's ability to provide · Unmet need for contraception is the percentage of death and disability among women of reproductive adequate care for pregnant women. Maternal mor- fertile, married women of reproductive age who do not age in developing countries. tality ratios are generally of unknown reliability, as want to become pregnant and are not using contracep- Total and adolescent fertility rates are based on are many other cause-specific mortality indicators. tion. · Contraceptive prevalence rate is the percent- data on registered live births from vital registration Household surveys such as Demographic and Health age of women married or in union ages 15­49 who are systems or, in the absence of such systems, from Surveys attempt to measure maternal mortality by practicing, or whose sexual partners are practicing, any censuses or sample surveys. The estimated rates asking respondents about survivorship of sisters. form of contraception. · Pregnant women receiving are generally considered reliable measures of fertility The main disadvantage of this method is that the prenatal care are the percentage of women attended at in the recent past. Where no empirical information estimates of maternal mortality that it produces least once during pregnancy by skilled health personnel on age- specific fertility rates is available, a model is pertain to 12 years or so before the survey, making for reasons related to pregnancy. · Births attended used to estimate the share of births to adolescents. them unsuitable for monitoring recent changes or by skilled health staff are the percentage of deliveries For countries without vital registration systems fertil- observing the impact of interventions. In addition, attended by personnel trained to give the necessary ity rates are generally based on extrapolations from measurement of maternal mortality is subject to care to women during pregnancy, labor, and post- trends observed in censuses or surveys from earlier many types of errors. Even in high-income countries partum; to conduct deliveries on their own; and to care years. with vital registration systems, misclassification of for newborns. · Maternal mortality ratio is the number Unwanted fertility--actual fertility minus desired maternal deaths has been found to lead to serious of women who die from pregnancy-related causes dur- fertility--can been avoided when couples use underestimation. ing pregnancy and childbirth per 100,000 live births. effective contraception. One approach to measur- The national estimates of maternal mortality Data sources ing unwanted fertility is to calculate what the total ratios in the table are based on national surveys, fertility rate would be if all unwanted births were vital registration records, and surveillance data or Data on total fertility are compiled from the United avoided--the wanted fertility rate. It is calculated are derived from community and hospital records. Nations Population Division's World Population Pros- in the same manner as the total fertility rate (from a The modeled estimates are based on an exercise by pects: The 2008 Revision, census reports and other household survey), but unwanted births are excluded the World Health Organization (WHO), United Nations statistical publications from national statistical from the numerator. Unwanted births are defined as Children's Fund (UNICEF), United Nations Population offices, household surveys conducted by national those that exceed the number considered ideal by Fund (UNFPA), and World Bank. For countries with agencies, Macro International, and the U.S. Cen- the same respondent in the survey. complete vital registration systems with good attribu- ters for Disease Control and Prevention, Eurostat's More couples in developing countries want to limit tion of cause of death, the data are used as reported. Demographic Statistics, and the U.S. Bureau of the or postpone childbearing but are not using effec- For countries with national data either from complete Census International Data Base. Data on wanted tive contraception. These couples have an unmet vital registration systems with uncertain or poor attri- fertility are from Demographic and Health Surveys by need for contraception. Common reasons are lack bution of cause of death or from household surveys Macro International. Data on adolescent fertility are of knowledge about contraceptive methods and reported maternal mortality was adjusted, usually by from World Population Prospects: The 2008 Revision, concerns about possible side effects. This indica- a factor of underenumeration and misclassification. with annual data linearly interpolated by the Develop- tor excludes women not exposed to the risk of unin- For countries with no empirical national data (about ment Data Group. Data on women with unmet need tended pregnancy because of menopause, infertility, 35 percent of countries), maternal mortality was esti- for contraception and contraceptive prevalence are from household surveys, including Demographic and or postpartum anovulation. mated with a regression model using socioeconomic Health Surveys by Macro International and Multiple Contraceptive prevalence reflects all methods-- information, including fertility, birth attendants, and Indicator Cluster Surveys by UNICEF. Data on preg- ineffective traditional methods as well as highly GDP. Neither set of ratios can be assumed to provide nant women receiving prenatal care, births attended effective modern methods. Contraceptive prevalence an exact estimate of maternal mortality for any of the by skilled health staff, and national estimates of rates are obtained mainly from household surveys, countries in the table. maternal mortality ratios are from UNICEF's State including Demographic and Health Surveys, Multiple For the indicators that are from household surveys, of the World's Children 2010 and Childinfo and Demo- Indicator Cluster Surveys, and contraceptive preva- the year in the table refers to the survey year. For graphic and Health Surveys by Macro International. lence surveys (see Primary data documentation for more information, consult the original sources. Modeled estimates of maternal mortality ratios are the most recent survey year). Unmarried women are from WHO, UNICEF, UNFPA and the World Bank's often excluded from such surveys, which may bias Maternal Mortality in 2005 (2007). the estimates. 2010 World Development Indicators 135 2.20 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months under age 5 women 1990­92 2004­06 2000­08a 2000­08a 2000­08a 2003­08a 2003­08a 2003­08a 2008 2000­06 a 2000­06a Afghanistan .. .. 32.9 59.3 4.6 .. 83 28 96 38 61 Albania <5 <5 6.6 27.0 25.2 7 40 60 .. 31 34 Algeria <5 <5 11.1 23.3 14.7 6 7 61 .. 43 43 Angola 66 44 27.5 50.8 5.3 .. .. 45 82 .. 57 Argentina <5 <5 2.3 8.2 6.5 7 .. .. .. 18 25 Armenia 46 23 4.2 18.2 11.7 7 33 97 .. 37 12 Australia <5 <5 .. .. .. .. .. .. .. 8 12 Austria <5 <5 .. .. .. .. .. .. .. 11 15 Azerbaijan 27 11 8.4 26.8 13.9 10 12 54 90 b 32 38 Bangladesh 36 26 41.3 43.2 1.1 22 43 84 97 47 47 Belarus <5 <5 1.3 4.5 9.7 4 9 55 .. 27 26 Belgium <5 <5 .. .. .. .. .. .. .. 9 13 Benin 28 19 20.2 44.7 11.4 15 43 55 52 78 75 Bolivia 24 23 5.9 32.5 9.2 7 60 88 45 52 37 Bosnia and Herzegovina <5 <5 1.6 11.8 25.6 5 18 62 .. 27 35 Botswana 20 26 10.7 29.1 10.4 .. .. .. .. .. 21 Brazil 10 6 2.2 7.1 7.3 8 40 96 .. 55 29 Bulgaria <5 <5 1.6 8.8 13.6 9 .. 100 .. 27 30 Burkina Faso 14 9 37.4 44.5 7.7 16 7 34 100 92 68 Burundi 44 63 38.9 63.1 1.4 11 45 98 80 56 47 Cambodia 38 25 28.8 39.5 2.0 14 60 73 88 62 66 Cameroon 34 23 16.6 36.4 9.6 11 21 49 .. 68 51 Canada <5 <5 .. .. .. .. .. .. .. 8 12 Central African Republic 47 41 21.8 44.6 10.8 13 23 62 68 .. .. Chad 59 38 33.9 44.8 4.4 22 2 56 0 71 60 Chile 7 <5 0.5 2.0 9.5 6 85 .. .. 24 28 China 15c 10 c 6.8 21.8 9.2 2 51 95 .. 20 29 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. Colombia 15 10 5.1 16.2 4.2 6 47 .. .. 28 31 Congo, Dem. Rep. 29 75 28.2 45.8 6.8 8 36 79 85 71 67 Congo, Rep. 40 21 11.8 31.2 8.5 13 19 82 10 66 55 Costa Rica <5 <5 .. .. .. 7 15 .. .. .. .. Côte d'Ivoire 15 14 16.7 40.1 9.0 17 4 84 90 69 55 Croatia <5 <5 .. .. .. 5 .. .. .. 23 28 Cuba 5 <5 .. .. .. 5 26 88 .. 27 39 Czech Republic <5 <5 2.1 2.6 4.4 .. .. .. .. 18 22 Denmark <5 <5 .. .. .. .. .. .. .. 9 12 Dominican Republic 27 21 3.4 10.1 8.3 11 9 19 .. 35 40 Ecuador 24 13 6.2 29.0 5.1 10 40 .. .. 38 38 Egypt, Arab Rep. <5 <5 6.8 30.7 20.5 13 53 79 68b 49 34 El Salvador 9 10 6.1 24.6 5.8 7 31 .. 20 18 .. Eritrea 67 66 34.5 43.7 1.6 .. .. .. 49 70 55 Estonia <5 <5 .. .. .. .. .. .. .. 23 23 Ethiopia 71 44 34.6 50.7 5.1 20 49 20 88 75 63 Finland <5 <5 .. .. .. .. .. .. .. 11 15 France <5 <5 .. .. .. .. .. .. .. 8 11 Gabon 5 <5 8.8 26.3 5.6 .. .. .. 0 44 46 Gambia, The 20 29 15.8 27.6 2.7 20 41 7 28 .. .. Georgia 47 12 2.3 14.7 21.0 5 11 87 .. 41 42 Germany <5 <5 1.1 1.3 3.5 .. .. .. .. 8 12 Ghana 34 8 13.9 28.1 2.6 9 63 32 24 76 65 Greece <5 <5 .. .. .. .. .. .. .. 12 19 Guatemala 14 16 17.7 54.3 5.6 .. .. 76 20 38 22 Guinea 19 16 22.5 39.3 5.1 12 48 41 94 76 63 Guinea-Bissau 20 31 17.4 47.7 17.0 24 16 1 66 75 58 Haiti 63 58 18.9 29.7 3.9 25 41 3 42 65 50 Honduras 19 12 8.6 29.9 5.8 10 30 .. 40 30 21 136 2010 World Development Indicators 2.20 PEOPLE Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months under age 5 women 1990­92 2004­06 2000­08a 2000­08a 2000­08a 2003­08a 2003­08a 2003­08a 2008 2000­06 a 2000­06a Hungary <5 <5 .. .. .. .. .. .. .. 19 21 India 24 22 43.5 47.9 1.9 28 46 51 53 74 50 Indonesia 19 16 19.6 40.1 11.2 9 32 62 86 44 44 Iran, Islamic Rep. <5 <5 .. .. .. 7 23 99 .. 35 21 Iraq .. .. 7.1 27.5 15.0 15 25 28 1 56 38 Ireland <5 <5 .. .. .. .. .. .. .. 10 15 Israel <5 <5 .. .. .. .. .. .. .. 12 17 Italy <5 <5 .. .. .. .. .. .. .. 11 15 Jamaica 11 5 2.2 3.7 7.5 14 15 .. .. .. .. Japan <5 <5 .. .. .. .. .. .. .. 11 15 Jordan <5 <5 3.6 12.0 4.7 13 22 .. .. 28 39 Kazakhstan <5 <5 4.9 17.5 14.8 6 17 92 .. .. 26 Kenya 33 30 16.5 35.8 5.8 10 13 .. 27 .. .. Korea, Dem. Rep. 21 32 17.8 44.7 0.9 .. 65 40 98 .. .. Korea, Rep. <5 <5 .. .. .. .. .. .. .. .. 23 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 20 <5 .. .. .. .. .. .. .. 32 31 Kyrgyz Republic 17 <5 2.7 18.1 10.7 5 32 76 99 .. 34 Lao PDR 27 19 31.6 47.6 1.3 11 26 84 83 48 56 Latvia <5 <5 .. .. .. .. .. .. .. 27 25 Lebanon <5 <5 4.2 16.5 16.7 .. .. 92 .. .. 32 Lesotho 15 15 16.6 45.2 6.8 13 36 91 85 49 25 Liberia 30 38 20.4 39.4 4.2 14 29 .. 85 .. .. Libya <5 <5 5.6 21.0 22.4 .. .. .. .. 34 34 Lithuania <5 <5 .. .. .. .. .. .. .. 24 24 Macedonia, FYR <5 <5 1.8 11.5 16.2 6 16 94 .. .. 32 Madagascar 32 35 36.8 52.8 6.2 17 67 75 97 68 50 Malawi 45 29 15.5 53.2 11.3 13 57 50 95 73 47 Malaysia <5 <5 .. .. .. .. .. .. .. 32 38 Mali 14 10 27.9 38.5 4.7 19 38 79 97 83 73 Mauritania 10 8 23.2 28.9 2.3 34 16 2 87 68 53 Mauritius 7 6 .. .. .. 14 .. .. .. .. .. Mexico <5 <5 3.4 15.5 7.6 8 .. 91 68 24 21 Moldova <5 <5 3.2 11.3 9.1 6 46 60 .. 41 36 Mongolia 30 29 5.3 27.5 14.2 6 57 83 95 21 37 Morocco 5 <5 9.9 23.1 13.3 15 31 21 .. 32 37 Mozambique 59 37 21.2 47.0 6.3 15 37 25 83 75 52 Myanmar 44 17 29.6 40.6 2.4 .. 15 93 94 63 50 Namibia 29 19 17.5 29.6 4.6 16 24 .. 68 41 31 Nepal 21 16 38.8 49.3 0.6 21 53 .. 93 48 42 Netherlands <5 <5 .. .. .. .. .. .. .. 9 13 New Zealand <5 <5 .. .. .. .. .. .. .. 11 18 Nicaragua 52 21 4.3 18.8 5.2 8 31 97 95 17 33 Niger 38 28 39.9 54.8 3.5 27 4 46 92 81 61 Nigeria 15 8 27.2 43.0 6.2 14 13 97 74 .. .. Norway <5 <5 .. .. .. .. .. .. .. 6 9 Oman .. .. .. .. .. 9 .. .. .. 42 43 Pakistan 22 23 31.3 41.5 4.8 32 37 .. 97 51 39 Panama 18 17 .. .. .. 10 .. .. 4 .. .. Papua New Guinea .. .. 18.1 43.9 3.4 10 56 92 7 60 55 Paraguay 16 12 .. .. .. 9 22 94 .. 30 39 Peru 28 13 5.4 29.8 9.1 8 69 91 .. 50 43 Philippines 21 15 26.2 27.9 2.0 20 34 81 86 36 44 Poland <5 <5 .. .. .. .. .. .. .. 23 25 Portugal <5 <5 .. .. .. .. .. .. .. 13 17 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 29 2010 World Development Indicators 137 2.20 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A Prevalence undernourishment malnutrition of overweight birthweight breast- of iodized supplemen- of anemia children babies feeding salt tation % % of children under age 5 % of children % of children % of % of children Children Pregnant % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months under age 5 women 1990­92 2004­06 2000­08a 2000­08a 2000­08a 2003­08a 2003­08a 2003­08a 2008 2000­06 a 2000­06a Romania <5 <5 3.5 12.8 8.3 8 16 74 .. 40 30 Russian Federation <5 <5 .. .. .. 6 .. .. .. 27 21 Rwanda 45 40 18.0 51.7 6.7 6 88 88 89 56 .. Saudi Arabia <5 <5 5.3 9.3 6.1 .. .. .. .. 33 32 Senegal 28 25 14.5 20.1 2.4 19 34 41 90 70 58 Serbia <5d <5d 1.8 8.1 19.3 8 15 .. .. .. .. Sierra Leone 45 46 28.3 46.9 5.9 24 11 45 12 83 60 Singapore .. .. 3.3 4.4 2.6 .. .. .. .. 19 24 Slovak Republic <5 <5 .. .. .. .. .. .. .. 23 25 Slovenia <5 <5 .. .. .. .. .. .. .. 14 19 Somalia .. .. 32.8 42.1 4.7 11 9 1 100 .. .. South Africa <5 <5 .. .. .. .. 8 .. 39 .. 22 Spain <5 <5 .. .. .. .. .. .. .. 13 18 Sri Lanka 27 21 21.1 17.3 1.6 18 76 94 64 30 29 Sudan 31 20 31.7 37.9 5.3 .. 34 11 67 85 58 Swaziland 12 18 6.1 29.5 11.4 9 32 80 44 47 24 Sweden <5 <5 .. .. .. .. .. .. .. 9 13 Switzerland <5 <5 .. .. .. .. .. .. .. 6 .. Syrian Arab Republic <5 <5 10.0 28.6 18.7 9 29 79 .. 41 39 Tajikistan 34 26 14.9 33.1 6.7 10 25 49 87 38 45 Tanzania 28 35 16.7 44.4 4.9 10 41 43 93 72 58 Thailand 29 17 7.0 15.7 8.0 9 5 47 .. .. .. Timor-Leste 18 23 40.6 55.7 5.7 12 31 60 57 32 23 Togo 45 37 22.3 27.8 4.7 12 48 25 64 52 50 Trinidad and Tobago 11 10 4.4 5.3 4.9 19 13 28 .. 30 30 Tunisia <5 <5 3.3 9.0 8.8 5 6 .. .. .. .. Turkey <5 <5 3.5 15.6 9.1 .. 40 69 .. 33 40 Turkmenistan 9 6 .. .. .. 4 11 87 .. 36 30 Uganda 19 15 16.4 38.7 4.9 14 60 96 67 73 64 Ukraine <5 <5 4.1 22.9 26.5 4 18 18 .. 22 27 United Arab Emirates <5 <5 .. .. .. .. .. .. .. 28 28 United Kingdom <5 <5 .. .. .. .. .. .. .. .. 15 United States <5 <5 1.3 3.9 8.0 .. .. .. .. 3 6 Uruguay 5 <5 6.0 13.9 9.4 9 57 .. .. 19 27 Uzbekistan 5 13 4.4 19.6 12.8 5 26 53 38 38 .. Venezuela, RB 10 12 .. .. .. 9 .. .. .. 33 40 Vietnam 28 13 20.2 35.8 2.5 7 17 93 98b 34 32 West Bank and Gaza 8 15 2.2 11.8 11.4 7 27 86 .. .. .. Yemen, Rep. 30 32 43.1 57.7 5.0 .. 12 30 47b 68 58 Zambia 40 45 14.9 45.8 8.4 11 61 .. 96 53 .. Zimbabwe 40 39 14.0 35.8 9.1 11 22 91 20 58 47 World 17 w 14 w 22.4 w 34.6 w 6.3 w 15 w 39 w 71 w .. w .. w .. w Low income 35 30 27.5 43.6 4.7 15 37 62 81 .. .. Middle income 16 13 22.2 33.6 6.7 16 40 73 .. .. .. Lower middle income 19 15 25.1 36.8 6.4 17 40 72 .. .. .. Upper middle income 8 6 3.8 13.5 8.8 7 .. 73 .. 38 30 Low & middle income 19 16 23.5 36.1 6.2 15 39 71 .. .. .. East Asia & Pacific 18 12 11.9 27.4 8.1 6 42 86 .. 20 29 Europe & Central Asia 7 6 .. .. .. 6 .. 50 .. 30 30 Latin America & Carib. 12 9 4.5 15.9 7.2 9 .. 89 .. .. .. Middle East & N. Africa 7 7 12.2 30.0 15.3 11 29 67 .. 48 .. South Asia 25 22 41.1 46.6 2.2 27 45 55 65 74 50 Sub-Saharan Africa 31 28 25.3 43.3 6.0 14 31 60 73 .. .. High income 5 5 .. .. .. .. .. .. .. .. 13 Euro area 5 5 .. .. .. .. .. .. .. 10 14 a. Data are for the most recent year available. b. Country's vitamin A supplementation programs do not target children all the way up to 59 months of age. c. Includes Hong Kong SAR, China; Macau SAR, China; and Taiwan, China. d. Includes Montenegro. 138 2010 World Development Indicators 2.20 PEOPLE Nutrition About the data Definitions Data on undernourishment are from the Food and Low birthweight, which is associated with maternal · Prevalence of undernourishment is the percent- Agriculture Organization (FAO) of the United Nations malnutrition, raises the risk of infant mortality and age of the population whose dietary energy consump- and measure food deprivation based on average food stunts growth in infancy and childhood. There is also tion is continuously below a minimum requirement available for human consumption per person, the emerging evidence that low-birthweight babies are for maintaining a healthy life and carrying out light level of inequality in access to food, and the mini- more prone to noncommunicable diseases such as physical activity with an acceptable minimum weight mum calories required for an average person. diabetes and cardiovascular diseases. Estimates of for height. · Prevalence of child malnutrition is the From a policy and program standpoint, however, low-birthweight infants are drawn mostly from hos- percentage of children under age 5 whose weight for this measure has its limits. First, food insecurity pital records and household surveys. Many births age (underweight) or height for age (stunting) is more exists even where food availability is not a problem in developing countries take place at home and are than two standard deviations below the median for because of inadequate access of poor households seldom recorded. A hospital birth may indicate higher the international reference population ages 0­59 to food. Second, food insecurity is an individual income and therefore better nutrition, or it could indi- months. Height is measured by recumbent length or household phenomenon, and the average food cate a higher risk birth, possibly skewing the data on for children up to two years old and by stature while available to each person, even corrected for possible birthweights downward. The data should therefore be standing for older children. Data are for the WHO child effects of low income, is not a good predictor of food used with caution. growth standards released in 2006. · Prevalence insecurity among the population. And third, nutrition Improved breastfeeding can save an estimated 1.3 of over weight children is the percentage of children security is determined not only by food security but million children a year. Breast milk alone contains under age 5 whose weight for height is more than two also by the quality of care of mothers and children all the nutrients, antibodies, hormones, and antioxi- standard deviations above the median for the interna- and the quality of the household's health environ- dants an infant needs to thrive. It protects babies tional reference population of the corresponding age ment (Smith and Haddad 2000). from diarrhea and acute respiratory infections, stimu- as established by the WHO child growth standards Estimates of child malnutrition, based on weight for lates their immune systems and response to vaccina- released in 2006. · Low-birthweight babies are age (underweight) and height for age (stunting), are tion, and may confer cognitive benefits. The data on the percentage of newborns weighing less than 2.5 from national survey data. The proportion of under- breastfeeding are derived from national surveys. kilograms within the first hours of life, before signifi - weight children is the most common malnutrition Iodine defi ciency is the single most important cant postnatal weight loss has occurred. · Exclusive indicator. Being even mildly underweight increases cause of preventable mental retardation, and it breastfeeding is the percentage of children less than the risk of death and inhibits cognitive development contributes significantly to the risk of stillbirth and six months old who were fed breast milk alone (no in children. And it perpetuates the problem across miscarriage. Widely used and inexpensive, iodized other liquids) in the past 24 hours. · Consumption of generations, as malnourished women are more salt is the best source of iodine, and a global cam- iodized salt is the percentage of households that use likely to have low-birthweight babies. Height for age paign to iodize edible salt is significantly reducing the edible salt fortified with iodine. · Vitamin A supple- reflects linear growth achieved pre- and postnatally; risks (www.childinfo.org). The data on iodized salt are mentation is the percentage of children ages 6­59 a deficit indicates long-term, cumulative effects of derived from household surveys. months old who received at least one dose of vitamin inadequate health, diet, or care. Stunting is often Vitamin A is essential for immune system function- A in the previous six months, as reported by mothers. used as a proxy for multifaceted deprivation and as ing. Vitamin A deficiency, a leading cause of blind- · Prevalence of anemia, children under age 5, is the an indicator of long-term changes in malnutrition. ness, also causes a 23 percent greater risk of dying percentage of children under age 5 whose hemoglo- Estimates of overweight children are also from from a range of childhood ailments such as measles, bin level is less than 110 grams per liter at sea level. national survey data. Overweight children have malaria, and diarrhea. Giving vitamin A to new breast- · Prevalence of anemia, pregnant women, is the per- become a growing concern in developing countries. feeding mothers helps protect their children during centage of pregnant women whose hemoglobin level Research shows an association between childhood the first months of life. Food fortification with vitamin is less than 110 grams per liter at sea level. obesity and a high prevalence of diabetes, respiratory A is being introduced in many developing countries. disease, high blood pressure, and psychosocial and Data on anemia are compiled by the WHO based Data sources orthopedic disorders (de Onis and Blössner 2000). mainly on nationally representative surveys between New international growth reference standards for 1993 and 2005, which measured hemoglobin in the Data on undernourishment are from www.fao. infants and young children were released in 2006 by blood. WHO's hemoglobin thresholds were then used org/faostat/foodsecurity/index_en.htm. Data the World Health Organization (WHO) to monitor chil- to determine anemia status based on age, sex, and on malnutrition and overweight children are from dren's nutritional status. They are also key in moni- physiological status. Children under age 5 and preg- the WHO's Global Database on Child Growth and toring health targets for the Millennium Development nant women have the highest risk for anemia. Data Malnutrition (www.who.int/nutgrowthdb). Data on Goals. Differences in growth to age 5 are influenced should be used with caution because surveys dif- low-birthweight babies, breastfeeding, iodized salt more by nutrition, feeding practices, environment, fer in quality, coverage, age group interviewed, and consumption, and vitamin A supplementation are and healthcare than by genetics or ethnicity. The treatment of missing values across countries and from the United Nations Children's Fund's State of previously reported data were based on the U.S. over time. the World's Children 2010 and Childinfo. Data on National Center for Health Statistics­WHO growth For indicators from household surveys, the year in anemia are from the WHO's Worldwide Prevalence reference. Because of the change in standards, the the table refers to the survey year. For more informa- of Anemia 1993­2005 (2008) and Integrated WHO data in this edition should not be compared with data tion, consult the original sources. Nutrition Global Databases. in editions prior to 2008. 2010 World Development Indicators 139 2.21 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIV Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15­24 ages 15­24 Male Female people ages 20­79 ages 15­49 with HIV Male Female Male Female 2006 2006 2008 2010 1990 2007 2001 2007 2007 2007 2000­08a 2000­08a Afghanistan .. .. 189 8.6 .. .. .. .. .. .. .. .. Albania 43 4 16 4.5 .. .. .. .. .. .. .. .. Algeria 26 0b 58 8.5 .. 0.1 25.0 28.6 0.1 0.1 .. .. Angola .. .. 292 3.5 0.3 2.1 60.9 61.1 0.2 0.3 .. .. Argentina 34 24 30 5.7 0.2 0.5 25.0 26.7 0.6 0.3 .. .. Armenia 61 3 73 7.8 .. 0.1 <27.8 <41.7 0.2 0.1 32 7 Australia 22 19 7 5.7 0.1 0.2 <7.1 6.7 0.2 <0.1 .. .. Austria 47 41 0 8.9 <0.1 0.2 27.3 29.6 0.2 0.1 .. .. Azerbaijan .. .. 110 7.5 .. 0.2 .. 16.7 0.3 0.1 25 1 Bangladesh 43 1 225 6.6 .. .. <1.3 16.7 .. .. .. .. Belarus 64 22 43 7.6 .. 0.2 27.5 30.0 0.3 0.1 .. .. Belgium 30 24 9 5.3 0.1 0.2 26.2 27.3 0.2 0.1 .. .. Benin 13 1 92 4.6 0.1 1.2 63.3 62.7 0.3 0.9 39 10 Bolivia 34 26 144 6.0 0.1 0.2 24.6 27.8 0.2 0.1 29 10 Bosnia and Herzegovina 49 35 51 7.1 .. <0.1 .. .. .. .. .. .. Botswana .. .. 712 5.4 4.7 23.9 59.3 60.7 5.1 15.3 .. .. Brazil 19 12 46 6.4 0.4 0.6 34.4 33.8 1.0 0.6 .. .. Bulgaria 49 38 43 6.5 .. .. .. .. .. .. .. .. Burkina Faso 13 1 220 3.8 1.9 1.6 45.4 50.8 0.5 0.9 54 17 Burundi .. .. 357 1.8 1.7 2.0 59.2 58.9 0.4 1.3 .. .. Cambodia 46 6 490 5.2 0.7 0.8 25.8 28.6 0.8 0.3 31 3 Cameroon 9 1 187 3.9 0.8 5.1 61.2 60.0 1.2 4.3 52 24 Canada 21 18 5 9.2 0.2 0.4 26.5 27.4 0.4 0.2 .. .. Central African Republic .. .. 336 4.5 1.8 6.3 66.7 65.0 1.1 5.5 .. .. Chad 12 1 291 3.7 0.7 3.5 60.7 61.1 2.0 2.8 18 7 Chile 42 31 11 5.7 <0.1 0.3 26.0 28.1 0.3 0.2 .. .. China 59 4 97 4.2 .. 0.1c 25.5c 29.0 c 0.1c 0.1c .. .. Hong Kong SAR, China .. .. 91 8.5 .. .. .. .. .. .. .. .. Colombia .. .. 36 5.2 0.1 0.6 26.9 29.4 0.7 0.3 .. 23 Congo, Dem. Rep. 10 1 382 3.2 .. .. .. .. .. .. 16 26 Congo, Rep. 9 0b 393 5.1 5.1 3.5 58.4 58.9 0.8 2.3 36 16 Costa Rica 26 7 11 9.3 0.1 0.4 27.5 28.1 0.4 0.2 .. .. Côte d'Ivoire 11 1 410 4.7 2.2 3.9 58.2 59.5 0.8 2.4 .. .. Croatia 39 29 25 6.9 .. <0.1 .. .. .. .. .. .. Cuba 36 28 6 9.5 .. 0.1 <43.5 29.0 0.1 0.1 .. .. Czech Republic 35 27 9 6.4 .. .. <38.5 <33.3 <0.1 .. .. .. Denmark 35 30 7 5.6 0.1 0.2 .. 22.9 0.2 0.1 .. .. Dominican Republic 15 11 73 11.2 0.6 1.1 54.0 50.8 0.3 0.6 58 19 Ecuador 23 5 72 5.9 0.1 0.3 25.8 28.4 0.4 0.2 .. .. Egypt, Arab Rep. 24 1 20 11.4 .. .. 26.8 28.9 .. .. .. .. El Salvador .. .. 32 9.0 0.1 0.8 25.7 28.5 0.9 0.5 .. .. Eritrea 15 1 97 2.5 0.1 1.3 60.0 60.0 0.3 0.9 .. 2 Estonia 48 25 34 7.6 .. 1.3 <28.6 24.2 1.6 0.7 .. .. Ethiopia 8 1 368 2.5 0.7 2.1 59.5 59.6 0.5 1.5 18 2 Finland 33 23 7 5.7 .. 0.1 <50.0 <41.7 0.1 <0.1 .. .. France 36 27 6 6.7 0.1 0.4 25.0 27.1 0.4 0.2 .. .. Gabon .. .. 452 5.0 0.9 5.9 58.3 58.7 1.3 3.9 .. .. Gambia, The 17 1 263 4.3 .. 0.9 59.0 60.0 0.2 0.6 .. .. Georgia 57 6 107 7.5 .. 0.1 20.0 37.0 0.1 0.1 .. .. Germany 37 26 5 8.9 <0.1 0.1 27.3 28.8 0.1 0.1 .. .. Ghana 7 1 202 4.3 0.1 1.9 58.3 60.0 0.4 1.3 45 19 Greece 63 39 6 6.0 0.1 0.2 26.5 27.3 0.2 0.1 .. .. Guatemala 24 4 63 8.6 <0.1 0.8 97.9 98.1 .. 1.5 .. .. Guinea .. .. 302 4.3 0.2 1.6 59.6 59.3 0.4 1.2 35 10 Guinea-Bissau .. .. 224 3.9 0.2 1.8 59.2 58.0 0.4 1.2 .. .. Haiti .. .. 246 7.2 1.2 2.2 45.7 52.7 0.6 1.4 42 37 Honduras .. .. 64 9.1 1.3 0.7 25.7 28.5 0.7 0.4 .. 7 140 2010 World Development Indicators 2.21 PEOPLE Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIV Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15­24 ages 15­24 Male Female people ages 20­79 ages 15­49 with HIV Male Female Male Female 2006 2006 2008 2010 1990 2007 2001 2007 2007 2007 2000­08a 2000­08a Hungary 45 35 16 6.4 .. 0.1 <35.7 <30.3 0.1 <0.1 .. .. India 28 1 168 7.8 0.1 0.3 38.5 38.3 0.3 0.3 37 18 Indonesia 58 4 189 4.8 .. 0.2 10.8 20.0 0.3 0.1 .. 1 Iran, Islamic Rep. 24 2 20 8.0 .. 0.2 26.7 28.2 0.2 0.1 .. .. Iraq 29 3 64 10.2 .. .. .. .. .. .. .. .. Ireland 34 28 9 5.2 .. 0.2 26.1 27.3 0.2 0.1 .. .. Israel 31 18 6 6.5 <0.1 0.1 60.0 59.2 <0.1 0.1 .. .. Italy 34 19 7 5.9 0.4 0.4 25.7 27.3 0.4 0.2 .. .. Jamaica 18 8 7 10.6 0.3 1.6 26.4 29.2 1.7 0.9 74 66 Japan 42 13 22 5.0 .. .. 22.2 24.0 .. .. .. .. Jordan 59 10 6 10.1 .. .. .. .. .. .. .. 4 Kazakhstan 43 9 175 5.8 .. 0.1 <29.4 27.5 0.2 0.1 .. .. Kenya 23 1 328 3.5 .. .. .. .. .. .. 39 9 Korea, Dem. Rep. 58 .. 344 5.3 .. .. .. .. .. .. .. .. Korea, Rep. 53 6 88 7.9 .. <0.1 26.5 27.7 <0.1 <0.1 .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 36 4 34 14.6 .. .. .. .. .. .. .. .. Kyrgyz Republic 46 2 159 5.2 .. 0.1 <50 26.2 0.2 0.1 .. .. Lao PDR 60 13 150 5.6 .. 0.2 <45.5 24.1 0.2 0.1 .. .. Latvia 53 24 50 7.6 .. 0.8 <23.8 27.0 0.9 0.5 .. .. Lebanon 31 7 14 7.8 <0.1 0.1 <45.5 <33.3 0.1 0.1 .. .. Lesotho .. .. 635 3.9 0.8 23.2 58.3 57.7 5.9 14.9 44 26 Liberia 10 .. 283 4.7 0.4 1.7 59.1 59.4 0.4 1.3 19 9 Libya .. .. 17 9.0 .. .. .. .. .. .. .. .. Lithuania 50 22 71 7.6 .. 0.1 <35.7 <45.5 0.1 0.1 .. .. Macedonia, FYR .. .. 24 6.9 .. <0.1 .. .. .. .. .. .. Madagascar .. .. 256 3.2 .. 0.1 23.8 26.2 0.2 0.1 8 2 Malawi 17 2 324 2.3 2.1 11.9 56.4 58.3 2.4 8.4 32 9 Malaysia 49 2 102 11.6 0.1 0.5 23.3 26.6 0.6 0.3 .. .. Mali 13 1 322 4.2 0.2 1.5 60.5 60.2 0.4 1.1 29 4 Mauritania 24 1 324 4.8 <0.1 0.8 25.8 27.9 0.9 0.5 .. .. Mauritius 34 1 22 16.2 <0.1 1.7 <27.8 29.2 1.8 1.0 .. .. Mexico 36 12 19 10.8 0.2 0.3 27.1 28.5 0.3 0.2 .. .. Moldova 45 5 175 7.6 .. 0.4 <50.0 29.5 0.4 0.2 55 22 Mongolia 46 6 205 1.6 .. 0.1 .. <20.0 0.1 .. .. .. Morocco 27 0b 116 8.3 .. 0.1 27.5 28.1 0.1 0.1 .. .. Mozambique 19 1 420 4.0 1.4 12.5 59.4 57.9 2.9 8.5 27 12 Myanmar 40 13 404 3.2 0.4 0.7 33.4 41.7 0.7 0.6 .. .. Namibia 22 8 747 4.4 1.2 15.3 60.7 61.1 3.4 10.3 81 64 Nepal 30 28 163 3.9 <0.1 0.5 21.8 25.0 0.5 0.3 24 8 Netherlands 33 28 7 5.3 0.1 0.2 25.6 27.2 0.2 0.1 .. .. New Zealand 22 20 8 5.2 0.1 0.1 <16.7 <35.7 0.1 .. .. .. Nicaragua .. .. 46 10.0 <0.1 0.2 25.6 28.0 0.3 0.1 .. 7 Niger .. .. 178 3.9 0.1 0.8 29.3 30.4 0.9 0.5 .. .. Nigeria 8 0b 303 4.7 0.7 3.1 60.0 58.3 0.8 2.3 38 8 Norway 30 30 6 3.6 <0.1 0.1 <41.7 <33.3 0.1 0.1 .. .. Oman 20 0b 14 13.4 .. .. .. .. .. .. .. .. Pakistan 30 3 231 9.1 .. 0.1 26.0 28.7 0.1 0.1 .. .. Panama .. .. 47 9.6 0.4 1.0 26.9 28.9 1.1 0.6 .. .. Papua New Guinea .. .. 250 3.0 .. 1.5 34.7 39.6 0.6 0.7 .. .. Paraguay 33 14 47 4.9 <0.1 0.6 26.4 29.0 0.7 0.3 .. .. Peru .. .. 119 6.2 0.1 0.5 26.8 28.4 0.5 0.3 .. 9 Philippines 50 11 285 7.7 .. .. <50 26.8 .. .. 13 3 Poland 30 38 25 7.6 .. 0.1 26.0 28.9 0.1 0.1 .. .. Portugal 34 15 30 9.7 0.2 0.5 26.6 27.6 0.5 0.3 .. .. Puerto Rico .. .. 3 10.6 .. .. .. .. .. .. .. .. Qatar .. .. 55 15.4 .. .. .. .. .. .. .. .. 2010 World Development Indicators 141 2.21 Health risk factors and future challenges Prevalence Incidence of Prevalence Prevalence of HIV Condom use of smoking tuberculosis of diabetes Female Youth per % of Total % of total % of population % of population % of adults 100,000 population % of population population ages 15­24 ages 15­24 Male Female people ages 20­79 ages 15­49 with HIV Male Female Male Female 2006 2006 2008 2010 1990 2007 2001 2007 2007 2007 2000­08a 2000­08a Romania 46 24 134 6.9 .. 0.1 50.7 50.0 0.2 0.2 .. .. Russian Federation 70 28 107 7.6 .. 1.1 22.1 25.5 1.3 0.6 .. .. Rwanda .. .. 387 1.6 9.2 2.8 60.6 60.0 0.5 1.4 19 5 Saudi Arabia 22 3 19 16.8 .. .. .. .. .. .. .. .. Senegal 13 1 277 4.7 0.1 1.0 60.9 59.4 0.3 0.8 48 5 Serbia 40 27 18 6.9d <0.1 0.1 25.5 28.1 0.1 0.1 .. .. Sierra Leone .. .. 608 4.4 0.2 1.7 59.4 58.8 0.4 1.3 .. .. Singapore 34 5 39 10.2 .. 0.2 <34.5 29.3 0.2 0.1 .. .. Slovak Republic 41 20 12 6.4 .. <0.1 .. .. .. .. .. .. Slovenia 32 21 12 7.7 .. <0.1 .. .. .. .. .. .. Somalia .. .. 388 3.0 <0.1 0.5 26.5 27.9 0.6 0.3 .. .. South Africa 27 8 960 4.5 0.8 18.1 58.7 59.3 4.0 12.7 57 46 Spain 37 27 17 6.6 0.4 0.5 20.8 20.0 0.6 0.2 .. .. Sri Lanka 27 0b 66 10.9 .. .. <33.3 37.8 <0.1 .. .. .. Sudan 25 2 119 4.2 0.8 1.4 56.0 58.6 0.3 1.0 .. .. Swaziland 21 2 1,227 4.2 0.9 26.1 60.7 58.8 5.8 22.6 66 44 Sweden 17 23 6 5.2 0.1 0.1 43.4 46.8 0.1 0.1 .. .. Switzerland 32 23 5 8.9 0.4 0.6 33.2 36.8 0.4 0.5 .. .. Syrian Arab Republic 40 .. 22 10.8 .. .. .. .. .. .. .. .. Tajikistan .. .. 199 5.0 .. 0.3 <20.8 21.0 0.4 0.1 .. .. Tanzania 20 2 190 3.2 4.8 6.2 61.7 58.5 0.5 0.9 36 13 Thailand 40 2 137 7.1 1.0 1.4 36.9 41.7 1.2 1.2 .. .. Timor-Leste .. .. 498 3.5 .. .. .. .. .. .. .. .. Togo .. .. 438 4.3 0.7 3.3 61.0 57.5 0.8 2.4 .. .. Trinidad and Tobago .. .. 24 11.7 0.2 1.5 57.5 59.2 0.3 1.0 .. .. Tunisia 53 6 24 9.3 .. 0.1 <45.5 27.8 0.1 <0.1 .. .. Turkey 51 20 30 8.0 .. .. .. .. .. .. .. .. Turkmenistan .. .. 68 5.3 .. <0.1 .. .. .. .. .. 1 Uganda 17 2 311 2.2 13.7 5.4 58.9 59.3 1.3 3.9 56 39 Ukraine 65 24 102 7.6 .. 1.6 35.7 44.2 1.5 1.5 69 73 United Arab Emirates 24 2 6 18.7 .. .. .. .. .. .. .. .. United Kingdom 26 24 12 3.6 <0.1 0.2 .. .. .. .. .. .. United States 25 19 5 10.3 0.5 0.6 18.0 20.9 0.7 0.3 .. .. Uruguay 39 29 22 5.7 0.1 0.6 25.4 28.0 0.6 0.3 .. .. Uzbekistan 23 3 128 5.2 .. 0.1 <35.7 28.8 0.1 0.1 18 2 Venezuela, RB 32 27 33 6.5 .. .. .. .. .. .. .. .. Vietnam 41 2 200 3.5 0.1 0.5 24.7 27.1 0.6 0.3 16 8 West Bank and Gaza .. .. 19 8.6 .. .. .. .. .. .. .. .. Yemen, Rep. 28 6 88 3.0 .. .. .. .. .. .. .. .. Zambia 17 2 468 4.0 8.9 15.2 54.7 57.1 3.6 11.3 47 39 Zimbabwe 28 2 762 4.1 14.2 15.3 58.8 56.7 2.9 7.7 52 9 World 39 w 8w 139 w 6.4 w 0.3 w 0.8 w 30.8 w 32.9 w 0.5 w 0.7 w .. .. Low income 29 3 282 4.3 2.1 2.3 35.0 39.2 .. .. .. .. Middle income 42 6 137 6.4 0.1 0.6 31.6 33.4 0.4 0.6 .. .. Lower middle income 43 3 145 6.2 0.1 0.4 31.8 33.7 0.3 0.4 .. .. Upper middle income 39 18 106 7.5 .. 1.5 30.8 32.0 0.9 1.3 .. .. Low & middle income 40 6 162 6.1 0.4 0.9 32.1 34.2 0.5 0.7 .. .. East Asia & Pacific 56 4 138 4.6 0.1 0.2 25.5 28.5 0.2 0.2 .. .. Europe & Central Asia 55 24 87 7.3 .. 0.6 28.6 30.5 0.8 0.5 .. .. Latin America & Carib. 27 15 47 7.4 0.3 0.5 32.1 32.8 0.7 0.4 .. .. Middle East & N. Africa 28 2 44 9.1 .. 0.1 27.9 28.6 .. .. .. .. South Asia 30 2 180 7.8 0.1 0.3 32.8 34.6 0.3 0.3 36 17 Sub-Saharan Africa 14 2 352 3.8 2.1 5.0 57.1 56.9 1.1 3.3 36 15 High income 33 20 14 7.9 0.3 0.3 23.3 24.9 0.5 0.2 .. .. Euro area 37 25 8 7.1 0.2 0.3 25.8 26.9 0.3 0.2 .. .. a. Data are for the most recent year available. b. Less than 0.5. c. Includes Hong Kong SAR, China. 142 2010 World Development Indicators 2.21 PEOPLE Health risk factors and future challenges About the data Definitions The limited availability of data on health status is a The Joint United Nations Programme on HIV/AIDS · Prevalence of smoking is the adjusted and age- major constraint in assessing the health situation in (UNAIDS) and the WHO estimate HIV prevalence from standardized prevalence estimate of smoking among developing countries. Surveillance data are lacking sentinel surveillance, population-based surveys, adults. The age range varies but in most countries is for many major public health concerns. Estimates and special studies. Since the 2009 edition the 18 and older or 15 and older. · Incidence of tuber- of prevalence and incidence are available for some estimates in the table have been more reliable than culosis is the estimated number of new tuberculosis diseases but are often unreliable and incomplete. previous estimates because of expanded sentinel cases (pulmonary, smear positive, extrapulmonary). National health authorities differ widely in capacity surveillance and improved data quality. Findings from · Prevalence of diabetes refers to the percentage of and willingness to collect or report information. To population-based HIV surveys, which are geographi- people ages 20­79 who have type 1 or type 2 diabe- compensate for this and improve reliability and inter- cally more representative than sentinel surveillance tes. · Prevalence of HIV is the percentage of people national comparability, the World Health Organization and include both men and women, influenced a down- who are infected with HIV. Total and youth rates are (WHO) prepares estimates in accordance with epide- ward adjustment to prevalence rates based on senti- percentages of the relevant age group. Female rate miological models and statistical standards. nel surveillance. And assumptions about the average is as a percentage of the total population with HIV. Smoking is the most common form of tobacco use time people living with HIV survive without antiret- · Condom use is the percentage of the population and the prevalence of smoking is therefore a good roviral treatment were improved in the most recent ages 15­24 who used a condom at last intercourse measure of the tobacco epidemic (Corrao and others model. Thus, estimates in this edition should not be in the last 12 months. 2000). Tobacco use causes heart and other vascular compared with estimates in previous editions. diseases and cancers of the lung and other organs. Estimates from recent Demographic and Health Given the long delay between starting to smoke and Surveys that have collected data on HIV/AIDS dif- the onset of disease, the health impact of smoking fer somewhat from those of UNAIDS and the WHO, in developing countries will increase rapidly only in which are based on surveillance systems that focus the next few decades. Because the data present a on pregnant women who attend sentinel antenatal one-time estimate, with no information on intensity clinics. Caution should be used in comparing the or duration of smoking, and because the definition of two sets of estimates. Demographic and Health adult varies, the data should be used with caution. Surveys are household surveys that use a represen- Tuberculosis is one of the main causes of adult tative sample from the whole population, whereas deaths from a single infectious agent in develop- surveillance data from antenatal clinics are limited ing countries. In developed countries tuberculosis to pregnant women. Household surveys also fre- has reemerged largely as a result of cases among quently provide better coverage of rural populations. immigrants. Since tuberculosis incidence cannot be However, respondents who refuse to participate or directly measured, estimates are obtained by elicit- are absent from the household add considerable ing expert opinion or are derived from measurements uncertainty to survey-based HIV estimates, because of prevalence or mortality. These estimates include the possible association of absence or refusal with uncertainty intervals, which are not shown in the table. higher HIV prevalence is unknown. UNAIDS and the Diabetes, an important cause of ill health and a WHO estimate HIV prevalence for the adult popu- risk factor for other diseases in developed countries, lation (ages 15­49) by assuming that prevalence is spreading rapidly in developing countries. Highest among pregnant women is a good approximation of among the elderly, prevalence rates are rising among prevalence among men and women. However, this younger and productive populations in developing assumption might not apply to all countries or over countries. Economic development has led to the time. Other potential biases are associated with the Data sources spread of Western lifestyles and diet to develop- use of antenatal clinic data, such as differences ing countries, resulting in a substantial increase in among women who attend antenatal clinics and Data on smoking are from the WHO's Report on diabetes. Without effective prevention and control those who do not. the Global Tobacco Epidemic 2009: Implementing programs, diabetes will likely continue to increase. Data on condom use are from household surveys Smoke-Free Environments. Data on tuberculosis Data are estimated based on sample surveys. and refer to condom use at last intercourse. How- are from the WHO's Global Tuberculosis Control Adult HIV prevalence rates reflect the rate of HIV ever, condoms are not as effective at preventing the Report 2009. Data on diabetes are from the infection in each country's population. Low national transmission of HIV unless used consistently. Some International Diabetes Federation's Diabetes prevalence rates can be misleading, however. They surveys have asked directly about consistent use, Atlas, 3rd edition. Data on prevalence of HIV are often disguise epidemics that are initially concentrated but the question is subject to recall and other biases. from UNAIDS and the WHO's 2008 Report on the in certain localities or population groups and threaten Caution should be used in interpreting the data. Global AIDS Epidemic. Data on condom use are to spill over into the wider population. In many devel- For indicators from household surveys, the year in from Demographic and Health Surveys by Macro oping countries most new infections occur in young the table refers to the survey year. For more informa- International. adults, with young women especially vulnerable. tion, consult the original sources. 2010 World Development Indicators 143 2.22 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2008 1990 2008 1990 2008 2003­08a,b 2003­08a,b 2005­08a 2005­08a 2008 2008 Afghanistan 41 44 168 165 260 257 .. .. 439 412 34 36 Albania 72 77 37 13 46 14 3 1 100 52 82 90 Algeria 67 72 52 36 64 41 .. .. 120 101 78 82 Angola 42 47 154 130 260 220 .. .. 409 353 37 44 Argentina 72 75 25 15 29 16 .. .. 165 77 74 87 Armenia 68 74 48 21 56 23 8 3 165 80 72 85 Australia 77 81 8 5 9 6 .. .. 82 47 88 93 Austria 76 80 8 3 9 4 .. .. 111 55 85 93 Azerbaijan 65 70 78 32 98 36 9 5 181 110 69 79 Bangladesh 54 66 103 43 149 54 16 20 209 176 65 70 Belarus 71 71 20 11 24 13 .. .. 330 115 53 83 Belgium 76 80 9 4 10 5 .. .. 111 61 85 92 Benin 54 61 111 76 184 121 64 65 211 174 61 67 Bolivia 59 66 88 46 122 54 18 20 235 175 63 71 Bosnia and Herzegovina 67 75 21 13 23 15 .. .. 135 62 78 89 Botswana 64 54 39 26 50 31 .. .. 487 497 42 44 Brazil 66 72 46 18 56 22 .. .. 230 120 67 80 Bulgaria 72 73 15 9 18 11 .. .. 213 91 71 87 Burkina Faso 47 53 110 92 201 169 110 113 335 280 45 51 Burundi 46 50 113 102 189 168 65 65 387 353 41 46 Cambodia 55 61 85 69 117 90 20 20 294 223 55 63 Cameroon 55 51 92 82 149 131 73 72 406 402 42 45 Canada 77 81 7 6 8 6 .. .. 92 56 86 92 Central African Republic 49 47 116 115 178 173 74 82 456 428 35 40 Chad 51 49 120 124 201 209 96 101 361 319 41 47 Chile 74 79 18 7 22 9 .. .. 129 64 80 90 China 68 c 73c 37 18 46 21 .. .. 149c 89c 76c 83c Hong Kong SAR, China 77 82 .. .. .. .. .. .. 76 33 87 94 Colombia 68 73 28 16 35 20 4 3 200 93 71 84 Congo, Dem. Rep. 48 48 126 126 199 199 70 64 400 350 38 44 Congo, Rep. 59 54 67 80 104 127 49 43 377 354 45 49 Costa Rica 76 79 19 10 22 11 .. .. 113 59 82 90 Côte d'Ivoire 58 57 104 81 150 114 .. .. 313 278 52 58 Croatia 72 76 11 5 13 6 .. .. 147 58 77 90 Cuba 75 79 11 5 14 6 .. .. 109 68 83 89 Czech Republic 71 77 10 3 12 4 .. .. 143 65 79 90 Denmark 75 79 7 4 9 4 .. .. 116 69 83 89 Dominican Republic 68 73 48 27 62 33 6 4 206 136 70 79 Ecuador 69 75 41 21 53 25 5 5 166 87 76 85 Egypt, Arab Rep. 63 70 66 20 90 23 5 5 163 107 72 80 El Salvador 66 71 48 16 62 18 .. .. 288 123 63 80 Eritrea 48 59 92 41 150 58 .. .. 381 286 46 57 Estonia 69 74 14 4 18 6 .. .. 283 92 64 87 Ethiopia 47 55 124 69 210 109 56 56 339 297 48 54 Finland 75 80 6 3 7 3 .. .. 133 57 83 93 Franced 77 82 7 3 9 4 .. .. 121 55 85 93 Gabon 61 60 67 57 92 77 .. .. 323 278 55 61 Gambia, The 51 56 104 80 153 106 46 39 329 269 47 55 Georgia 70 72 41 26 47 30 5 4 198 78 69 84 Germany 75 80 7 4 9 4 .. .. 107 56 85 92 Ghana 57 57 75 51 118 76 38 28 327 289 51 55 Greece 77 80 9 3 11 4 .. .. 93 38 85 93 Guatemala 62 70 58 29 77 35 .. .. 234 128 67 79 Guinea 48 58 137 90 231 146 89 86 256 198 54 63 Guinea-Bissau 44 48 142 117 240 195 110 88 403 350 38 44 Haiti 55 61 105 54 151 72 33 36 287 225 56 64 Honduras 66 72 43 26 55 31 8 9 172 120 73 80 144 2010 World Development Indicators 2.22 PEOPLE Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2008 1990 2008 1990 2008 2003­08a,b 2003­08a,b 2005­08a 2005­08a 2008 2008 Hungary 69 74 15 5 17 7 .. .. 250 104 67 86 India 58 64 83 52 116 69 9 12 261 174 58 68 Indonesia 62 71 56 31 86 41 13 12 166 116 72 80 Iran, Islamic Rep. 65 71 55 27 73 32 .. .. 144 99 75 81 Iraq 65 68 42 36 53 44 6 7 226 107 64 81 Ireland 75 80 8 3 9 4 .. .. 88 56 87 92 Israel 77 81 10 4 11 5 .. .. 86 48 87 93 Italy 77 82 9 3 10 4 .. .. 82 43 86 94 Jamaica 71 72 28 26 33 31 5 6 225 117 69 81 Japan 79 83 5 3 6 4 .. .. 87 43 87 94 Jordan 67 73 31 17 38 20 2 3 162 112 73 81 Kazakhstan 68 66 51 27 60 30 5 4 405 153 46 75 Kenya 60 54 68 81 105 128 42 39 402 412 46 47 Korea, Dem. Rep. 70 67 42 42 55 55 .. .. 172 120 66 76 Korea, Rep. 71 80 8 5 9 5 .. .. 106 42 83 93 Kosovo 68 69 .. .. .. .. .. .. .. .. .. .. Kuwait 75 78 13 9 15 11 .. .. 85 52 85 90 Kyrgyz Republic 68 67 63 33 75 38 8 4 262 125 61 77 Lao PDR 54 65 108 48 157 61 .. .. 226 184 63 69 Latvia 69 72 13 8 17 9 .. .. 311 114 63 85 Lebanon 69 72 33 12 40 13 .. .. 152 100 74 82 Lesotho 59 45 80 63 101 79 22 19 674 630 24 30 Liberia 49 58 146 100 219 145 62 64 255 209 56 62 Libya 68 74 33 15 38 17 .. .. 147 91 75 84 Lithuania 71 72 12 6 16 7 .. .. 346 116 59 86 Macedonia, FYR 71 74 32 10 36 11 2 1 134 80 77 85 Madagascar 51 60 101 68 167 106 45 45 270 220 57 63 Malawi 49 53 133 65 225 100 52 54 448 403 42 48 Malaysia 70 74 16 6 18 6 .. .. 150 86 76 85 Mali 43 48 139 103 250 194 117 114 389 358 38 42 Mauritania 56 57 81 75 129 118 53 44 308 241 49 58 Mauritius 69 73 21 15 24 17 .. .. 228 113 67 81 Mexico 71 75 36 15 45 17 .. .. 139 77 78 86 Moldova 67 68 30 15 37 17 7 4 283 127 59 78 Mongolia 61 67 71 34 98 41 11 10 291 184 57 71 Morocco 64 71 68 32 88 36 9 11 147 97 74 82 Mozambique 43 48 166 90 249 130 61 64 489 462 36 40 Myanmar 59 62 85 71 120 98 .. .. 257 195 57 65 Namibia 62 61 49 31 72 42 24 19 346 327 54 59 Nepal 54 67 99 41 142 51 21 18 199 175 67 71 Netherlands 77 80 7 4 8 5 .. .. 81 59 87 92 New Zealand 75 80 9 5 11 6 .. .. 92 59 87 91 Nicaragua 64 73 51 23 68 27 .. .. 205 116 71 81 Niger 42 51 144 79 305 167 138 135 351 302 43 48 Nigeria 45 48 120 96 230 186 91 93 406 382 39 42 Norway 77 81 7 3 9 4 .. .. 81 53 87 92 Oman 70 76 23 10 31 12 .. .. 98 73 82 87 Pakistan 61 67 101 72 130 89 14 22 165 133 68 71 Panama 72 76 24 19 31 23 .. .. 137 73 79 87 Papua New Guinea 55 61 67 53 91 69 .. .. 348 255 49 60 Paraguay 68 72 34 24 42 28 .. .. 172 125 73 79 Peru 66 73 64 22 81 24 13 4 164 101 73 83 Philippines 65 72 42 26 61 32 10 9 156 102 73 82 Poland 71 76 15 6 17 7 .. .. 209 80 72 89 Portugal 74 79 11 3 15 4 .. .. 128 53 82 92 Puerto Rico 75 79 .. .. .. .. .. .. 133 53 80 91 Qatar 70 76 17 9 20 10 .. .. 111 102 81 83 2010 World Development Indicators 145 2.22 Mortality Life expectancy Infant mortality Under-five Child mortality Adult mortality Survival to at birth rate mortality rate rate rate age 65 per 1,000 per 1,000 % of cohort years per 1,000 live births per 1,000 Male Female Male Female Male Female 1990 2008 1990 2008 1990 2008 2003­08a,b 2003­08a,b 2005­08a 2005­08a 2008 2008 Romania 70 73 25 12 32 14 .. .. 196 83 70 85 Russian Federation 69 68 23 12 27 13 .. .. 429 158 46 78 Rwanda 33 50 106 72 174 112 69 55 403 357 40 46 Saudi Arabia 68 73 35 18 43 21 3 4 139 89 76 84 Senegal 52 56 72 57 149 108 43 39 329 271 47 54 Serbia 71 74 25 6 29 7 4 3 155e 83e 74 e 85e Sierra Leone 40 48 163 123 278 194 134 124 503 470 29 33 Singapore 74 81 6 2 7 3 .. .. 81 41 86 93 Slovak Republic 71 75 13 7 15 8 .. .. 196 76 72 88 Slovenia 73 79 9 3 10 4 .. .. 149 57 81 92 Somalia 45 50 119 119 200 200 53 54 371 318 41 47 South Africa 61 51 44 48 56 67 13 9 577 511 31 41 Spain 77 81 8 4 9 4 .. .. 106 44 85 94 Sri Lanka 70 74 23 13 29 15 .. .. 196 77 71 86 Sudan 53 58 78 70 124 109 38 30 306 261 53 59 Swaziland 60 46 62 59 84 83 32 30 615 639 29 29 Sweden 78 81 6 2 7 3 .. .. 78 48 88 93 Switzerland 77 82 7 4 8 5 .. .. 78 46 88 93 Syrian Arab Republic 68 74 30 14 37 16 5 3 122 83 78 85 Tajikistan 63 67 91 54 117 64 18 13 210 139 63 73 Tanzania 51 56 97 67 157 104 56 52 377 362 48 51 Thailand 69 69 26 13 32 14 .. .. 297 172 62 77 Timor-Leste 46 61 138 75 184 93 .. .. 264 229 57 62 Togo 58 63 89 64 150 98 55 43 242 199 61 68 Trinidad and Tobago 69 69 30 31 34 35 5 8 239 141 63 77 Tunisia 70 74 40 18 50 21 .. .. 123 72 78 86 Turkey 65 72 69 20 84 22 9 9 151 84 74 84 Turkmenistan 63 65 81 43 99 48 .. .. 303 154 54 73 Uganda 48 53 114 85 186 135 75 62 412 411 43 45 Ukraine 70 68 18 14 21 16 4 1 385 142 53 80 United Arab Emirates 73 78 15 7 17 8 .. .. 77 64 86 88 United Kingdom 76 80 8 5 9 6 .. .. 100 61 85 91 United States 75 78 9 7 11 8 .. .. 141 81 83 89 Uruguay 73 76 21 12 24 14 .. .. 141 64 77 89 Uzbekistan 67 68 61 34 74 38 11 7 240 137 62 75 Venezuela, RB 71 74 27 16 32 18 .. .. 177 93 74 84 Vietnam 65 74 39 12 56 14 5 4 136 90 78 85 West Bank and Gaza 68 73 33 24 38 27 3 3 128 92 78 84 Yemen, Rep. 54 63 90 53 127 69 10 11 251 202 59 66 Zambia 51 45 105 92 172 148 66 55 542 530 31 34 Zimbabwe 61 44 51 62 79 96 21 21 718 681 21 26 World 65 w 69 w 64 w 46 w 92 w 67 w 216f w 153f w 68 w 77 w Low income 54 59 102 76 160 118 295 254 55 61 Middle income 64 69 60 41 85 57 205 136 67 76 Lower middle income 63 68 65 45 93 64 204 138 67 75 Upper middle income 68 71 38 19 47 23 210 127 66 81 Low & middle income 63 67 69 50 101 73 219 156 65 74 East Asia & Pacific 67 72 42 23 55 29 161 101 74 81 Europe & Central Asia 69 70 41 19 50 22 305g 126g 59 81 Latin America & Carib. 68 73 42 20 53 23 192 104 72 83 Middle East & N. Africa 64 71 58 29 76 34 158 106 73 81 South Asia 58 64 89 58 125 76 246 173 60 68 Sub-Saharan Africa 50 52 109 86 185 144 395 362 43 48 High income 76 80 10 6 12 7 116g 62g 84 91 Euro area 76 81 8 3 9 4 107g 52g 85 93 a. Data are for the most recent year available. b. Refers to a survey year. Values were estimated directly from surveys and cover the 5 or 10 years preceding the survey. c. Includes Taiwan, China. d. Excludes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. e. Includes Kosovo. f. These world aggregates for 2008 do not include data for many lower mortality countries because recent estimates are unavailable. The world aggregates for 2006 are 213 for men and 143 for women. g. Data are for 2006. 146 2010 World Development Indicators 2.22 PEOPLE Mortality About the data Definitions Mortality rates for different age groups (infants, chil- weighted least squares method to fit a regression · Life expectancy at birth is the number of years dren, and adults) and overall mortality indicators (life line to the relationship between mortality rates and a newborn infant would live if prevailing patterns of expectancy at birth or survival to a given age) are their reference dates and then extrapolate the trend mortality at the time of its birth were to stay the important indicators of health status in a country. to the present. (For further discussion of childhood same throughout its life. · Infant mortality rate is Because data on the incidence and prevalence of mortality estimates, see UNICEF, WHO, World Bank, the number of infants dying before reaching one year diseases are frequently unavailable, mortality rates and United Nations Population Division 2007; for a of age, per 1,000 live births in a given year. · Under- are often used to identify vulnerable populations. graphic presentation and detailed background data, five mortality rate is the probability per 1,000 that a And they are among the indicators most frequently see www.childmortality.org). newborn baby will die before reaching age 5, if sub- used to compare socioeconomic development across Infant and child mortality rates are higher for boys ject to current age-specific mortality rates. · Child countries. than for girls in countries in which parental gender mortality rate is the probability per 1,000 of dying The main sources of mortality data are vital reg- preferences are insignificant. Child mortality cap- between ages 1 and 5--that is, the probability of a istration systems and direct or indirect estimates tures the effect of gender discrimination better than 1-year-old dying before reaching age 5--if subject to based on sample surveys or censuses. A "complete" infant mortality does, as malnutrition and medical current age-specific mortality rates. · Adult mortal- vital registration system--covering at least 90 per- interventions are more important in this age group. ity rate is the probability per 1,000 of dying between cent of vital events in the population--is the best Where female child mortality is higher, as in some the ages of 15 and 60--that is, the probability of a source of age-specific mortality data. Where reliable countries in South Asia, girls probably have unequal 15-year-old dying before reaching age 60--if subject age-specific mortality data are available, life expec- access to resources. Child mortality rates in the to current age-specific mortality rates between those tancy at birth is directly estimated from the life table table are not compatible with infant mortality and ages. · Survival to age 65 refers to the percent- constructed from age- specific mortality data. under-five mortality rates because of differences in age of a hypothetical cohort of newborn infants that But complete vital registration systems are fairly methodology and reference year. Child mortality data would survive to age 65, if subject to current age- uncommon in developing countries. Thus estimates were estimated directly from surveys and cover the specific mortality rates. must be obtained from sample surveys or derived 10 years preceding the survey. In addition to esti- by applying indirect estimation techniques to reg- mates from Demographic Health Surveys, estimates Data sources istration, census, or survey data (see table 2.17 derived from Multiple Indicator Cluster Surveys have and Primary data documentation). Survey data are been added to the table; they cover the 5 years pre- Data on infant and under-five mortality are esti- subject to recall error, and surveys estimating infant ceding the survey. mates by the Inter-agency Group for Child Mortality deaths require large samples because households Rates for adult mortality and survival to age 65 Estimation based mainly on household surveys, in which a birth has occurred during a given year come from life tables. Adult mortality rates increased censuses, and vital registration data, supple- cannot ordinarily be preselected for sampling. Indi- notably in a dozen countries in Sub-Saharan Africa mented by the World Bank's Human Development rect estimates rely on model life tables that may be between 1995­2000 and 2000­05 and in several Network estimates based on vital registration and inappropriate for the population concerned. Because countries in Europe and Central Asia during the first sample registration data. Data on child mortal- life expectancy at birth is estimated using infant mor- half of the 1990s. In Sub-Saharan Africa the increase ity are from Demographic and Health Surveys by tality data and model life tables for many develop- stems from AIDS-related mortality and affects both Macro International (Measure DHS) and World ing countries, similar reliability issues arise for this sexes, though women are more affected. In Europe Bank calculations based on infant and under-five indicator. Extrapolations based on outdated surveys and Central Asia the causes are more diverse (high mortality from Multiple Indicator Cluster Surveys may not be reliable for monitoring changes in health prevalence of smoking, high-fat diet, excessive alco- by UNICEF. Data on survival to age 65 and most status or for comparative analytical work. hol use, stressful conditions related to the economic data on adult mortality are linear interpolations Estimates of infant and under-five mortality tend transition) and affect men more. of five-year data from World Population Prospects: to vary by source and method for a given time and The percentage of a hypothetical cohort surviv- The 2008 Revision. Remaining data on adult mor- place. Years for available estimates also vary by ing to age 65 reflects both child and adult mortality tality are from the Human Mortality Database by country, making comparison across countries and rates. Like life expectancy, it is a synthetic mea- the University of California, Berkeley, and the Max over time diffi cult. To make infant and under-fi ve sure based on current age-specific mortality rates. Planck Institute for Demographic Research (www. mortality estimates comparable and to ensure It shows that even in countries where mortality is mortality.org). Data on life expectancy at birth are consistency across estimates by different agen- high, a certain share of the current birth cohort will World Bank calculations based on male and female cies, the United Nations Children's Fund (UNICEF) live well beyond the life expectancy at birth, while in data from World Population Prospects: The 2008 and the World Bank (now working together with low-mortality countries close to 90 percent will reach Revision (for more than half of countries, most of the World Health Organization (WHO), the United at least age 65. them developing countries), census reports and Nations Population Division, and other universities Annual data series from the United Nations are other statistical publications from national sta- and research institutes as the Inter-agency Group for interpolated based on five-year estimates and thus tistical offices, Eurostat's Demographic Statistics, Child Mortality Estimation) developed and adopted may not reflect actual events. and the U.S. Bureau of the Census International a statistical method that uses all available informa- Data Base. tion to reconcile differences. The method uses the 2010 World Development Indicators 147 Text figures, tables, and boxes ENVIRONMENT Introduction G 3 lobal climate change presents a significant challenge to achieving the Millennium Development Goals (MDGs). The expected extreme changes in weather--such as shifts in the intensity and pattern of rainfall and variations in temperature-- may lower agricultural productivity and damage infrastructure, leading to slower eco- nomic growth, threatening food security, and increasing poverty. Projected floods and droughts could cause many people to lose their livelihoods, be displaced, or migrate, while rising temperatures could increase the incidence of vector-borne diseases and lead to heat-related deaths and water scarcity. The poorest countries and regions face the greatest danger. Africa--with the most rainfed agricultural land of any continent, half its population without access to improved water sources, and about 70 percent without access to improved sanitation facilities-- is particularly vulnerable to climate change. International action on greenhouse gas Greenhouse gas emissions have emissions and developing countries been rising at increasing rates Economic growth--necessary for reducing poverty, Carbon dioxide is the most common of the Kyoto improving people's lives, and achieving the MDGs-- Protocol greenhouse gases, which also include entails significant energy use. Generating this en- methane, nitrous oxide, and other artificial gases. ergy will affect greenhouse gas emissions. There It constitutes more than 75 percent of greenhouse is now consensus that greenhouse gas emissions gas emissions (figure 3a). About 80 percent of car- need to peak by 2015 to curb emissions to about 50 bon dioxide is generated by the energy sector. percent of their 1990 levels by 2050, to keep global Carbon dioxide emissions, on the rise since the warming below 2°C, and to avoid more dangerous beginning of the industrial revolution 150 years ago, and catastrophic climate change (United Nations began to surge in the second half of the 20th cen- 2009b; World Bank 2009k). To meet this target tury (figure 3b), reaching more than 30 petagrams and achieve the MDGs, sustainable energy systems (billion metric tons) a year in 2006 (see table 3.8). need to be part of long-term economic planning for developed and developing countries. Carbon dioxide is the most The 2009 United Nations Climate Change Con- common greenhouse gas 3a ference in Copenhagen did not reach a binding Global greenhouse gas emissions (share of total carbon dioxide equivalent), 2005 agreement on targets and timetables for reducing Other gases 2% greenhouse gas emissions. The Copenhagen Accord recognized the critical importance of keeping global Nitrous oxide 7% warming below 2°C and affirmed that the first prior- ity of developing countries is to eradicate poverty Methane 16% and promote socioeconomic development--but that a low-emission development strategy is indispens- Carbon dioxide 76% able to sustainable development. On the principle of "differentiated responsibilities and respective capa- bilities," the accord urged developed countries to help developing countries in their mitigation efforts and their adaptation to the adverse effects of cli- Source: International Energy Agency. mate change (United Nations 2009c). 2010 World Development Indicators 149 High-income Organisation for Economic Co-oper- unit of gross domestic product--decreased 2 ation and Development (OECD) countries, which percent a year, indicating greater economic pro- have produced more than 55 percent of total ductivity and energy efficiency. cumulative emissions since the beginning of The world's top five carbon dioxide emitters - industrialization, stabilized their emissions --China, the United States, the Russian growth at about 0.9 percent a year between Federation, India, and Japan (fi gure 3d)--all 1990 and 2006. decreased their carbon income intensity in Carbon dioxide emissions per capita from 1990­2006. Only China and India increased developing economies were less than a fourth their carbon energy intensity, because of a those of developed economies in 2006, but higher share of fossil fuel in their energy con- their total emissions rate grew about twice as sumption. Energy use has been increasing in fast during 1990­2006 (table 3c). Over the China and India, both as a share of the global same period carbon dioxide emissions grew total and per capita. Among the five economies 5.1 percent a year in China and 4.8 percent a with the highest energy consumption, India year in India. China became the largest emitter uses fossil fuels the least--but its depen- of carbon dioxide in 2006 (see tables 3.8 and dence on fossil fuels is growing the most, at 3.9). During 1990­2006 developing economies' about 1.3 percent annually during 1990­2007 carbon energy intensity--the ratio of carbon (table 3e). dioxide emissions per unit of energy used-- World energy consumption has increased remained unchanged. But their carbon income about 2 percent a year since 1970 but intensity--the carbon dioxide emitted for each decreased in 2009 because of the economic crisis. According to the International Energy Agency (IEA), energy demand could increase Carbon dioxide emissions have surged since the 1950s 3b 40 percent by 2030 under business as usual conditions. Fossil fuels would remain the main Industrial carbon dioxide emissions (petagrams of carbon dioxide equivalent) 30 energy source, accounting for 77 percent of increased demand during 2007­30 (IEA 2009). The IEA estimates that using fossil fuels at this 20 rate will increase carbon dioxide emissions to about 40 petagrams a year by 2030, result- 10 ing in a long-term atmospheric concentration of 1,000 parts per million. This increase will 0 be environmentally, socially, and economically 1751 1800 1850 1900 1950 2006 unsustainable. Source: Carbon Dioxide Information Analysis Center and World Development Indicators data files. In Copenhagen the IEA proposed an emis- sion reduction scenario to limit the concentration Carbon dioxide emissions are growing, 1990­2006 (percent) 3c A few rapidly developing and high-income countries produce 70 percent of Carbon intensity carbon dioxide emissions 3d (average annual Carbon dioxide growth) Carbon dioxide emissions, 2006 Average annual Per capita Country or group growth growth Energy Income China 5.1 4.1 0.8 ­4.3 Other United States 1.2 0.1 ­0.1 ­1.9 countries 30% High income Russian Federation ­2.4 ­2.2 ­0.9 ­2.8 OECD 40% India 4.8 3.1 1.3 ­1.2 Japan 0.6 0.3 ­0.5 ­0.5 Developing economiesa 2.1 0.6 0.0 ­2.0 India 5% China 20% High-income OECD 0.9 0.2 ­0.4 ­1.7 Russian Federation 5% a. Emissions from oil-producing economies constitute 8 percent (excluding the Russian Federation). Source: World Development Indicators data files; International Energy Agency; Carbon Dioxide Informa- Source: Carbon Dioxide Information Analysis Center and World tion Analysis Center. Bank. 150 2010 World Development Indicators ENVIRONMENT of greenhouse gases in the atmosphere to 450 to reduce their power generation emissions parts per million of carbon dioxide equivalent, from 484 grams of carbon dioxide per kilowatt which would reduce energy-related carbon diox- hour of energy produced to 145, a 70 percent ide emissions from 28.8 petagrams in 2007 to reduction. According to this scenario, high- 26.4 petagrams in 2030. Under this scenario income OECD economies should also reduce carbon dioxide emissions from the power sec- their carbon energy intensity by about 38 per- tor are projected to be reduced the most (figure cent, and other economies by less than half 3f). High-income OECD countries are projected that (table 3g). Trends in fossil fuel use and energy intensity (percent) 3e Energy intensity Energy use Fossil fuel of GDP Net importsa Share of Average Average Average Share of world energy annual growth, Share of total, annual growth, annual growth, energy use, Country or group use, 2007 1990­2007 2007 1990­2007 1990­2007 2007 China 16.8 4.5 86.9 0.8 ­4.9 7.2 United States 20.1 1.2 85.6 0.0 ­1.8 28.8 Russian Federation 5.8 ­1.3 89.3 ­0.3 ­2.1 ­83.1 India 5.1 3.5 70.0 1.3 ­2.6 24.2 Japan 4.4 0.9 83.2 ­0.1 ­0.2 82.4 Developing economies 52.1 2.2 79.8 0.2 ­2.1 ­20.1 High-income OECD 43.9 1.3 81.6 ­0.1 ­1.3 31.9 a. A negative value indicates that the economy is a net energy exporter. Source: World Development Indicators data files and International Energy Agency. Emission reductions by 2030 3f Power generation Transport Industry Share of global energy-related carbon dioxide emissions (percent) Buildings Other 2007 2030 (450 scenario) 0 25 50 75 100 Note: Based on International Energy Agency 450 scenario. Source: IEA 2009. Future energy use under the IEA-450 scenario (percentage change, 2007­30) 3g Energy use Carbon dioxide emissions Carbon dioxide intensity Power Group Total Per capita Total Per capita Income Energy intensity World 17.6 ­5.6 ­8.3 ­26.4 ­55.0 ­22.1 ­53.1 European Union ­3.8 ­5.7 ­41.0 ­42.2 ­58.2 ­38.7 ­72.9 OECDa ­5.2 ­13.3 ­41.2 ­46.3 ­60.7 ­38.0 ­70.0 Other major economiesb 36.3 21.1 14.4 1.7 ­62.1 ­16.0 ­48.3 Other economies 55.9 14.3 28.0 ­6.2 ­51.5 ­17.9 ­49.9 a. OECD economies and European Union. b. Other major economies are those in the Middle East and North Africa and Brazil, China, and South Africa. Source: IEA 2009. 2010 World Development Indicators 151 People affected by natural disasters and projected changes in rainfall and agricultural production (percent) 3h Average share of Projected change in precipitation Projected change in agricultural population affected by outcome, 2000­50 outcome, 2000­50 droughts, fl oods, and Country storms, 1971­2008 Total Intensity Output Yield Bangladesh 9.1 1.4 5.4 ­21.7 8.9 China 5.2 4.5 5.4 ­7.2 8.4 Ethiopia 6.6 2.4 5.0 ­31.3 0.5 India 7.2 1.9 2.7 ­38.1 ­12.2 Malawi 12.3 ­0.1 2.4 ­31.3 ­3.0 Mozambique 13.8 ­2.7 1.4 ­21.7 ­10.7 Niger 13.2 5.6 2.5 ­34.1 ­1.7 Senegal 11.3 ­1.9 3.1 ­51.9 ­19.3 Swaziland 18.3 .. .. .. .. Zimbabwe 10.7 ­3.7 4.8 ­37.9 ­10.6 Source: World Bank 2009k. Climate change will affect Bates and others 2008) (table 3i). Some 1 bil- food and water security lion people lack access to safe water, and more During the last century rising atmospheric con- than 2.5 billion need access to improved sanita- centrations of carbon dioxide led to a 0.74°C tion facilities. The world's population is growing increase in average global temperature. Even if by about 80 million people a year, demanding greenhouse gas emissions stop growing, glob- an additional 64 billion cubic meters of fresh- al warming is expected to continue because water a year (UNESCO 2009). And about 90 per- changes in temperature lag behind changes cent of population growth by 2050 is projected in concentrations, which lag behind changes to occur in developing countries, where many in emissions (World Bank 2009k). According people still lack access to safe water and im- to the Intergovernmental Panel on Climate proved sanitation. By 2025, 1.8 billion people Change, during the coming decades global will be living in countries or regions with abso- warming will cause droughts, floods, changes lute water scarcity, and two-thirds of the world's in rainfall patterns, severe freshwater short- population could be living under conditions of ages, and shifts in crop growing seasons--es- water stress (FAO 2007, 2010). pecially in developing countries (FAO 2008a). The effects of climate change on fresh- The agriculture and water sectors will be af- water availability will depend on temperature fected most by climate change, and adaptive increases, droughts, floods, regional variation measures are needed to mitigate expected in precipitation, and rising sea levels (UNESCO adverse outcomes; otherwise, areas such as 2009; Bates and others 2008; Kundzewicz and Southern Africa will suffer severe drops in ag- Mata 2007; FAO 2008b). Precipitation is the ricultural yields by 2030 (World Bank 2009a) most important source of freshwater; 80 per- (table 3h). Developing countries already suffer- cent of the world's cultivated land and about 60 ing from hunger and water supply problems, percent of crops depend on rainwater (UNESCO especially those in Southeast Asia and Sub- 2009). Climate change models predict more Saharan Africa, will be hardest hit without aid precipitation in high latitudes and the tropics for adaptation. but less precipitation in subtropical regions such as the northern Sahara (UNESCO 2009; Demand for water will Kundzewicz and Mata 2007; IPCC 2007). In increase, making better addition, non-climate-related water stresses-- water management crucial such as industrial water pollution, extensive Properly using and managing water resources irrigation, construction of dams, and draining are important components of sustainable of wetlands--have already raised concerns development--and essential for achieving the about future freshwater shortages (Bates and MDGs (World Bank and IMF 2008b; FAO 2010; others 2008). 152 2010 World Development Indicators ENVIRONMENT Potential contributions of the water sector to attaining the Millennium Development Goals 3i Goal Relation to water 1 Eradicate extreme Water is a factor in many production activities (agriculture, animal husbandry, poverty and hunger cottage industries). 3 Promote gender equity More gender-sensitive water management programs can reduce time wasted and and empower women health burdens through improved water service, leading to more time for income earning and more-balanced gender roles. 4 Reduce child mortality Improved access to more and better quality drinking water and improved sanitation can reduce the main factors contributing to illness and death among young children. 6 Combat HIV/AIDS, malaria, Improved access to water and sanitation supports HIV/AIDS-affected households and other diseases and may improve the impact of health care programs. Better water management reduces mosquito habitats and the risk of malaria transmission. 7 Ensure environmental Improved water management reduces water consumption and allows recycling of sustainability nutrients and organics. Action could ensure improved water supply and sanitation services for poor communities, and reduced wastewater discharge and improved environmental health in slum areas. Source: Bates and others 2008. In response to higher freshwater demand adaptation and MDG efforts into their sustain- and geographic changes in water supply caused able development policies. Research and devel- by climate change and other factors, coun- opment in sustainable agriculture could signifi - tries must improve water storage, use water cantly affect agricultural resource conservation, more efficiently, reuse freshwater (especially promoting synergy among human needs. in agriculture), and use technology to antici- The agriculture sector also causes green- pate regional, local, and seasonal variation in house gas emissions--primarily nitrous oxide water availability and water use (UNESCO 2009; and methane. Climate change mitigation in Bates and others 2008; Faurèsa, Hoogeveena, agriculture will require more efficient use of fer- and Bruinsmab 2004; FAO 2009a). tilizer, soil conservation, and better production management. Inefficient use of fertilizers has Sustainable agriculture can undesirable environmental impacts, such as help developing countries increased nitrogen loss into the atmosphere. adapt to climate change Under current fertilization practices, crop plant Sustainable agriculture is essential for uptake of nitrogen as a nutrient is about 50 development--and for achieving the MDG to percent, with losses and emissions to the eradicate poverty and hunger (World Bank and atmosphere through runoff and leaching from IFPRI 2006). Today's challenges for sustainable soil erosion (Takle and Hofstrand 2008; FAO agricultural development are to respond to in- 2001). Use of fossil fuels in agricultural pro- creasing demand for food, adjust to rapid cli- duction causes 7 percent of agricultural emis- mate changes caused by global warming, and sions, primarily from combustion of gasoline reduce agricultural greenhouse gas emissions and diesel fuel (Takle and Hofstrand 2008). (FAO 2008a). Capturing and using methane from livestock Adaptation strategies for agriculture will production as an energy source can reduce require balancing many environmental variables emissions and improve profitability by reducing and socioeconomic factors--and their interac- the need to buy fossil fuel energy (Takle and tions. Countries may integrate climate change Hofstrand 2008). 2010 World Development Indicators 153 Tables 3.1 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2008 1990­2008 2008 1990 2007 1990 2007 1990 2007 1990­92 2005­07 Afghanistan .. .. .. 652.2 2.0 1.2 0.2 0.2 12.1 13.1 .. .. Albania 64 53 ­1.2 27.4 28.8 29.3 4.6 4.4 21.1 21.1 18.8 18.2 Algeria 48 35 ­0.1 2,381.7 0.8 1.0 0.2 0.4 3.0 3.1 24.5 22.4 Angola 63 43 0.8 1,246.7 48.9 47.2 0.4 0.2 2.3 2.6 20.6 19.3 Argentina 13 8 ­1.6 2,736.7 12.9 12.0 0.4 0.4 9.6 11.9 75.1 80.5 Armenia 33 36 ­0.2 28.2 12.0 9.7 2.1 1.9 15.0 14.4 14.4 a 13.4 Australia 15 11 ­0.2 7,682.3 21.9 21.3 0.0 0.0 6.2 5.8 248.9 227.5 Austria 34 33 0.2 82.5 45.8 47.0 1.0 0.8 17.3 16.8 17.3 16.7 Azerbaijan 46 48 1.3 82.6 11.2 11.3 3.7 2.7 20.5 22.4 22.6a 21.8 Bangladesh 80 73 1.3 130.2 6.8 6.7 2.3 3.7 70.2 61.2 5.6 5.1 Belarus 34 27 ­1.7 202.9 36.8 39.0 0.9 0.6 30.0 27.3 58.6a 56.9 Belgium 4 3 ­1.3 30.3 22.3b 22.0 0.5b 0.8 0.6b 27.7 8.2 8.0 Benin 66 59 2.7 110.6 30.0 20.1 0.9 2.4 14.6 24.4 35.7 33.4 Bolivia 44 34 0.7 1,083.3 58.0 53.7 0.1 0.2 1.9 3.3 35.7 38.9 Bosnia and Herzegovina 61 53 ­1.5 51.2 43.1 42.7 2.9 1.9 16.6 20.0 26.8a 27.1 Botswana 58 40 ­0.1 566.7 24.2 20.7 0.0 0.0 0.7 0.4 15.2 13.0 Brazil 25 14 ­1.7 8,459.4 61.5 55.7 0.8 0.8 6.0 7.0 33.2 31.6 Bulgaria 34 29 ­1.6 108.6 30.1 34.3 2.7 1.8 34.9 28.4 43.4 40.5 Burkina Faso 86 80 2.7 273.6 26.1 24.7 0.2 0.2 12.9 19.0 36.5 35.3 Burundi 94 90 1.7 25.7 11.3 5.2 14.0 13.6 36.2 38.7 14.7 13.0 Cambodia 87 78 1.7 176.5 73.3 56.7 0.6 0.9 20.9 21.5 28.5 26.7 Cameroon 59 43 0.7 472.7 51.9 44.0 2.6 2.5 12.6 12.6 36.7 32.7 Canada 23 20 0.0 9,093.5 34.1 34.1 0.7 0.8 5.0 5.0 147.4 138.3 Central African Republic 63 61 2.0 623.0 37.2 36.4 0.1 0.1 3.1 3.1 50.6 46.0 Chad 79 73 2.8 1,259.2 10.4 9.3 0.0 0.0 2.6 3.4 41.0 41.3 Chile 17 12 ­0.7 743.8 20.5 21.8 0.3 0.6 3.8 1.7 11.0 8.3 China 73 57 ­0.5 9,327.5 16.8 22.0 0.8 1.3 13.3 15.1 10.4 10.5 Hong Kong SAR, China 1 0 .. 1.0 .. .. .. .. .. .. .. .. Colombia 32 26 0.5 1,109.5 55.4 54.6 1.5 1.4 3.0 1.8 6.2 4.5 Congo, Dem. Rep. 72 66 2.6 2,267.1 62.0 58.7 0.5 0.4 2.9 3.0 12.8 11.0 Congo, Rep. 46 39 1.2 341.5 66.5 65.7 0.1 0.1 1.4 1.4 15.8 14.2 Costa Rica 49 37 0.5 51.1 50.2 46.9 4.9 5.9 5.1 3.9 5.1 4.6 Côte d'Ivoire 60 51 1.8 318.0 32.1 32.8 11.0 13.2 7.6 8.8 15.8 14.2 Croatia 46 43 ­0.8 53.9 37.9 39.6 2.0 1.5 21.7 15.8 25.2a 19.4 Cuba 27 24 ­0.2 109.8 18.7 25.7 4.1 3.8 30.9 32.5 32.5 32.4 Czech Republic 25 27 0.4 77.3 34.1 34.3 3.1 3.1 41.1 39.2 30.1 29.6 Denmark 15 13 ­0.4 42.4 10.5 11.9 0.2 0.2 60.4 54.3 42.6 42.8 Dominican Republic 45 31 ­0.4 48.3 28.5 28.5 9.3 10.3 18.6 17.0 9.1 8.5 Ecuador 45 34 0.0 276.8 49.9 37.8 4.8 4.4 5.8 4.3 12.0 9.4 Egypt, Arab Rep. 57 57 2.0 995.5 0.0 0.1 0.4 0.5 2.3 3.0 4.0 3.8 El Salvador 51 39 ­0.6 20.7 18.1 13.9 12.5 11.4 26.5 32.9 11.2 11.6 Eritrea 84 79 2.1 101.0 15.9 15.3 0.0 0.0 4.9 6.3 14.8 13.8 Estonia 29 31 ­0.6 42.4 51.4 54.3 0.3 0.2 26.3 14.1 52.1a 43.3 Ethiopia 87 83 2.6 1,000.0 14.7 12.7 0.5 1.0 10.0 14.0 15.2 17.5 Finland 39 37 0.1 304.1 72.9 74.0 0.0 0.0 7.4 7.4 42.2 42.7 France 26 23 ­0.2 547.7 26.5 28.5 2.2 2.0 32.9 33.7 31.1 30.1 Gabon 31 15 ­1.5 257.7 85.1 84.4 0.6 0.7 1.1 1.3 25.8 23.3 Gambia, The 62 44 1.5 10.0 44.2 47.5 0.5 0.6 18.2 34.8 22.4 21.7 Georgia 45 47 ­1.0 69.5 39.7 39.7 4.8 1.6 11.4 6.7 17.0a 10.5 Germany 27 26 0.1 348.8 30.8 31.8 1.3 0.6 34.3 34.1 14.3 14.4 Ghana 64 50 1.1 227.5 32.7 23.2 6.6 10.5 11.9 18.0 20.3 18.2 Greece 41 39 0.3 128.9 25.6 29.6 8.3 8.8 22.5 19.8 24.9 23.1 Guatemala 59 51 1.6 107.2 44.3 35.7 4.5 8.8 12.1 14.7 12.2 11.5 Guinea 72 66 2.1 245.7 30.1 27.1 2.0 2.7 3.3 9.0 16.8 21.9 Guinea-Bissau 72 70 2.3 28.1 78.8 73.0 4.2 8.9 10.7 10.7 22.5 19.9 Haiti 72 53 0.2 27.6 4.2 3.8 11.6 10.9 28.3 32.7 10.2 9.4 Honduras 60 52 1.5 111.9 66.0 38.7 3.2 3.2 13.1 9.5 16.8 15.2 154 2010 World Development Indicators 3.1 ENVIRONMENT Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2008 1990­2008 2008 1990 2007 1990 2007 1990 2007 1990­92 2005­07 Hungary 34 33 ­0.5 89.6 20.0 22.4 2.6 2.2 56.2 51.2 45.2 45.6 India 75 71 1.3 2,973.2 21.5 22.8 2.2 3.6 54.8 53.4 15.6 14.3 Indonesia 69 49 ­0.6 1,811.6 64.3 46.8 6.5 8.6 11.2 12.1 9.7 9.9 Iran, Islamic Rep. 44 32 ­0.3 1,628.6 6.8 6.8 0.8 1.0 9.3 10.4 24.0 23.8 Iraq 30 .. .. 437.4 1.8 1.9 0.7 0.6 13.3 11.9 20.3 .. Ireland 43 39 0.7 68.9 6.4 10.1 0.0 0.0 15.1 15.4 29.7 26.6 Israel 10 8 1.7 21.6 7.1 8.0 4.1 3.2 15.9 14.2 5.3 4.4 Italy 33 32 0.1 294.1 28.5 34.6 10.1 8.6 30.6 24.4 14.7 12.6 Jamaica 51 47 0.2 10.8 31.9 31.2 9.2 10.2 11.0 16.1 6.7 6.5 Japan 37 34 ­0.3 364.5 68.4 68.2 1.3 0.9 13.1 11.9 3.5 3.4 Jordan 28 22 2.0 88.2 0.9 0.9 0.8 0.9 2.0 1.6 3.9 3.1 Kazakhstan 44 42 ­0.4 2,699.7 1.3 1.2 0.1 0.0 13.0 8.4 148.7a 148.1 Kenya 82 78 2.6 569.1 6.5 6.1 0.8 0.9 8.8 9.1 15.6 14.3 Korea, Dem. Rep. 42 37 0.3 120.4 68.1 49.3 1.5 1.7 19.0 23.3 11.4 11.8 Korea, Rep. 26 19 ­1.2 96.9 64.5 64.5 1.6 1.9 19.8 16.5 3.6 3.4 Kosovo .. .. .. 10.9c .. 41.3c .. .. .. 27.6c .. 16.8 c Kuwait 2 2 0.3 17.8 0.2 0.3 0.1 0.2 0.2 0.8 0.6 0.6 Kyrgyz Republic 62 64 1.1 191.8 4.4 4.6 0.4 0.4 6.9 6.7 27.2a 24.7 Lao PDR 85 69 1.0 230.8 75.0 69.3 0.3 0.4 3.5 5.1 16.4 18.5 Latvia 31 32 ­0.7 62.3 45.1 47.6 0.4 0.2 27.2 19.1 41.0a 50.8 Lebanon 17 13 0.5 10.2 11.8 13.6 11.9 14.0 17.9 14.1 3.3 3.5 Lesotho 86 75 0.6 30.4 0.2 0.3 0.1 0.1 10.4 9.9 16.7 15.4 Liberia 55 40 1.4 96.3 42.1 31.5 1.6 2.2 3.6 4.0 12.9 11.0 Libya 24 23 1.6 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 33.3 29.0 Lithuania 32 33 ­0.4 62.7 31.3 34.0 0.7 0.5 46.0 29.3 58.8a 55.2 Macedonia, FYR 42 33 ­1.0 25.4 35.6 35.6 2.2 1.4 23.8 16.9 26.8a 21.8 Madagascar 76 71 2.5 581.5 23.5 21.9 1.0 1.0 4.7 5.1 18.7 16.3 Malawi 88 81 2.0 94.1 41.4 35.5 1.4 1.3 23.9 31.9 23.1 21.4 Malaysia 50 30 ­0.7 328.6 68.1 62.7 16.0 17.6 5.2 5.5 7.6 6.9 Mali 77 68 1.4 1,220.2 11.5 10.1 0.1 0.1 1.7 4.0 43.1 39.1 Mauritania 60 59 2.5 1,030.7 0.4 0.2 0.0 0.0 0.4 0.4 16.7 15.5 Mauritius 56 58 1.2 2.0 19.2 18.0 3.0 2.0 49.3 44.3 8.2 7.3 Mexico 29 23 0.1 1,944.0 35.5 32.8 1.0 1.2 12.5 12.6 25.4 23.7 Moldova 53 58 ­0.5 32.9 9.7 10.0 14.2 9.2 52.8 55.3 45.7a 49.3 Mongolia 43 43 1.0 1,553.6 7.4 6.5 0.0 0.0 0.9 0.5 42.4 32.5 Morocco 52 44 0.5 446.3 9.6 9.8 1.6 2.0 19.5 18.1 29.7 26.5 Mozambique 79 63 1.6 786.4 25.4 24.4 0.3 0.4 4.4 5.7 21.9 21.2 Myanmar 75 67 0.5 653.5 60.0 47.9 0.8 1.7 14.6 16.2 21.1 21.2 Namibia 72 63 1.5 823.3 10.6 9.1 0.0 0.0 0.8 1.0 43.8 39.6 Nepal 91 83 1.7 143.4 33.7 24.6 0.5 0.8 16.0 16.4 9.4 8.5 Netherlands 31 18 ­2.5 33.8 10.2 10.9 0.9 1.0 26.0 31.4 5.7 6.2 New Zealand 15 13 0.5 267.7 28.8 31.2 0.2 0.2 9.9 3.2 33.2 22.1 Nicaragua 48 43 1.2 120.0 54.5 41.5 1.6 2.0 10.8 16.3 37.6 36.0 Niger 85 84 3.4 1,266.7 1.5 1.0 0.0 0.0 8.7 11.6 122.6 106.2 Nigeria 65 52 1.2 910.8 18.9 11.3 2.8 3.3 32.4 40.1 24.0 24.8 Norway 28 23 ­0.6 304.3 30.0 31.0 0.0 0.0 2.8 2.8 19.5 18.4 Oman 34 28 1.3 309.5 0.0 0.0 0.1 0.1 0.1 0.2 1.6 2.3 Pakistan 69 64 1.9 770.9 3.3 2.4 0.6 1.0 26.6 27.9 15.2 13.4 Panama 46 27 ­1.1 74.3 58.9 57.7 2.1 2.0 6.7 7.4 18.2 16.7 Papua New Guinea 85 88 2.7 452.9 69.6 64.4 1.2 1.3 0.4 0.6 3.8 3.9 Paraguay 51 40 0.7 397.3 53.3 45.6 0.2 0.3 5.3 10.8 56.9 68.7 Peru 31 29 1.1 1,280.0 54.8 53.6 0.3 0.7 2.7 2.9 13.9 13.0 Philippines 51 35 0.0 298.2 35.5 23.0 14.8 16.4 18.4 17.1 6.3 5.8 Poland 39 39 0.0 304.3 29.2 30.4 1.1 1.3 47.3 41.1 35.3 32.3 Portugal 52 41 ­1.0 91.5 33.9 42.2 8.5 6.4 25.6 11.8 15.4 11.0 Puerto Rico 28 2 ­15.0 8.9 45.5 46.0 5.6 4.2 7.3 7.0 1.7 1.6 Qatar 8 4 2.4 11.6 .. .. 0.1 0.3 0.9 1.6 2.8 1.8 2010 World Development Indicators 155 3.1 Rural population and land use Rural population Land area Land use average % of land area Arable land annual thousand hectares per % of total % growth sq. km Forest area Permanent cropland Arable land 100 people 1990 2008 1990­2008 2008 1990 2007 1990 2007 1990 2007 1990­92 2005­07 Romania 47 46 ­0.5 229.9 27.8 27.7 2.6 2.0 41.2 37.2 42.4 40.9 Russian Federation 27 27 ­0.1 16,377.7 49.3 49.4 0.1 0.1 0.0 7.4 84.9a 85.3 Rwanda 95 82 0.9 24.7 12.9 21.7 12.4 11.1 35.7 48.6 12.1 12.7 Saudi Arabia 23 18 0.7 2,000.0 d 1.4 1.4 0.0 0.1 1.7 1.7 17.0 14.6 Senegal 61 58 2.4 192.5 48.6 44.6 0.2 0.3 16.1 15.5 30.4 26.3 Serbia 50 48 ­0.4 88.4 .. 23.6 .. 3.4 .. 37.3 .. 44.7 Sierra Leone 67 62 1.3 71.6 42.5 37.9 0.8 1.1 6.8 12.6 11.6 16.4 Singapore 0 0 .. 0.7 3.4 3.3 1.5 0.3 1.5 0.9 0.0 0.0 Slovak Republic 44 43 0.1 48.1 40.0 40.2 0.5 0.5 32.5 28.6 27.1 25.6 Slovenia 50 51 0.3 20.1 59.5 63.3 1.8 1.3 9.9 8.8 8.6a 8.9 Somalia 70 64 1.1 627.3 13.2 11.1 0.0 0.0 1.6 1.6 14.4 13.7 South Africa 48 39 0.7 1,214.5 7.6 7.6 0.7 0.8 11.1 11.9 33.0 30.7 Spain 25 23 0.5 499.0 27.0 37.1 9.7 9.7 30.7 25.5 32.2 29.0 Sri Lanka 83 85 1.0 64.6 36.4 29.0 15.5 14.7 13.9 15.0 4.9 5.0 Sudan 73 57 0.9 2,376.0 32.1 27.9 0.0 0.1 5.4 8.1 45.9 48.9 Swaziland 77 75 1.5 17.2 27.4 32.0 0.7 0.8 10.5 10.3 16.3 15.6 Sweden 17 16 ­0.1 410.3 66.7 67.1 0.0 0.0 6.9 6.4 30.2 29.3 Switzerland 27 27 0.7 40.0 28.9 30.7 0.5 0.6 9.8 10.2 5.7 5.4 Syrian Arab Republic 51 46 2.1 183.6 2.0 2.6 4.0 5.2 26.6 25.8 27.1 24.0 Tajikistan 68 74 1.8 140.0 2.9 2.9 0.9 0.7 6.1 5.1 12.5a 11.2 Tanzania 81 75 2.4 885.8 46.8 38.9 1.1 1.4 10.2 10.2 25.5 23.3 Thailand 71 67 0.6 510.9 31.2 28.2 6.1 7.3 34.2 29.8 24.7 22.9 Timor-Leste 79 73 1.7 14.9 65.0 52.2 3.9 4.6 7.4 11.4 16.3 16.5 Togo 70 58 1.7 54.4 12.6 6.4 1.7 3.1 38.6 45.2 46.5 40.2 Trinidad and Tobago 92 87 0.2 5.1 45.8 43.9 6.8 4.3 7.0 4.9 2.4 1.9 Tunisia 42 34 0.0 155.4 4.1 7.0 12.5 14.0 18.7 17.7 29.0 27.2 Turkey 41 31 0.1 769.6 12.6 13.3 3.9 3.8 32.0 28.5 35.4 31.8 Turkmenistan 55 51 1.4 469.9 8.8 8.8 0.1 0.1 2.9 3.9 37.8a 37.9 Uganda 89 87 3.1 197.1 25.0 17.5 9.4 11.2 25.4 27.9 20.2 18.3 Ukraine 33 32 ­0.8 579.3 16.1 16.6 1.9 1.6 57.6 56.0 66.9a 69.3 United Arab Emirates 21 22 5.2 83.6 2.9 3.7 0.2 2.6 0.4 0.8 2.0 1.6 United Kingdom 11 10 ­0.3 241.9 10.8 11.8 0.3 0.2 27.4 25.2 9.8 9.8 United States 25 18 ­0.6 9,161.9 32.6 33.1 0.2 0.3 20.3 18.6 61.6 57.4 Uruguay 11 8 ­1.6 175.0 5.2 8.8 0.3 0.2 7.2 7.7 40.8 40.1 Uzbekistan 60 63 1.9 425.4 7.2 7.8 0.9 0.8 10.5 10.1 18.0a 16.4 Venezuela, RB 16 7 ­2.8 882.1 59.0 53.4 0.9 0.8 3.2 3.0 10.4 9.8 Vietnam 80 72 0.9 310.1 28.8 43.3 3.2 9.9 16.4 20.5 8.2 7.6 West Bank and Gaza 32 28 3.1 6.0 .. 1.5 .. 18.9 .. 18.1 3.4 2.9 Yemen, Rep. 79 69 2.7 528.0 1.0 1.0 0.2 0.5 2.9 2.6 7.9 6.2 Zambia 61 65 2.9 743.4 66.1 55.9 0.0 0.0 7.1 7.1 49.1 43.8 Zimbabwe 71 63 0.3 386.9 57.5 43.7 0.3 0.3 7.5 8.3 25.9 25.9 World 57 w 50 w 0.6 w 129,611.3 s 31.4 w 30.3 w 0.9 w 1.1 w 9.1 w 10.9 w 22.8 w 21.7 w Low income 77 71 1.8 18,731.9 27.9 24.7 0.7 1.0 6.8 8.7 18.1 17.3 Middle income 61 52 0.4 77,325.4 33.5 32.3 1.1 1.3 8.6 11.6 20.4 19.6 Lower middle income 69 59 0.5 31,182.2 25.7 24.8 1.7 2.3 14.8 17.0 15.0 14.5 Upper middle income 32 25 ­0.3 46,143.3 38.7 37.3 0.6 0.7 4.3 7.9 40.9 39.0 Low & middle income 64 55 0.7 96,057.3 32.4 30.8 1.0 1.2 8.2 11.0 20.0 19.2 East Asia & Pacific 71 56 ­0.3 15,853.6 28.9 28.5 2.2 3.0 12.1 13.3 11.0 10.9 Europe & Central Asia 37 36 0.0 23,054.0 38.2 38.4 0.4 0.4 2.7 10.8 58.5 57.1 Latin America & Carib. 29 21 ­0.3 20,147.6 48.8 44.9 0.9 1.0 6.6 7.4 27.5 26.7 Middle East & N. Africa 48 43 1.3 8,643.6 2.3 2.5 0.8 1.0 5.9 6.0 17.9 16.4 South Asia 75 71 1.4 4,773.1 16.5 16.7 1.8 2.8 42.6 41.9 14.5 13.3 Sub-Saharan Africa 72 64 1.9 23,585.4 29.2 26.1 0.8 1.0 6.3 8.3 25.9 25.0 High income 27 22 ­0.3 33,554.0 28.6 28.9 0.7 0.7 11.5 10.7 37.2 35.0 Euro area 29 27 ­0.1 2,509.8 33.6 37.7 4.6 4.2 26.7 24.8 20.6 19.4 a. Data are not available for all three years. b. Includes Luxembourg. c. Data are from national sources. d. Provisional estimate. 156 2010 World Development Indicators 3.1 ENVIRONMENT Rural population and land use About the data Definitions With more than 3 billion people, including 70 percent Satellite images show land use that differs from · Rural population is calculated as the difference of the world's poor people, living in rural areas, ade- that of ground-based measures in area under cultiva- between the total population and the urban popula- quate indicators to monitor progress in rural areas tion and type of land use. Moreover, land use data tion (see Definitions for tables 2.1 and 3.11). · Land are essential. However, few indicators are disaggre- in some countries (India is an example) are based area is a country's total area, excluding area under gated between rural and urban areas (for some that on reporting systems designed for collecting tax rev- inland water bodies and national claims to the con- are, see tables 2.7, 3.5, and 3.11). The table shows enue. With land taxes no longer a major source of tinental shelf and to exclusive economic zones. In indicators of rural population and land use. Rural government revenue, the quality and coverage of land most cases the definition of inland water bodies population is approximated as the midyear nonurban use data have declined. Data on forest area may be includes major rivers and lakes. (See table 1.1 for population. While a practical means of identifying the particularly unreliable because of irregular surveys the total surface area of countries.) Variations from rural population, it is not precise (see box 3.1a for and differences in definitions (see About the data year to year may be due to updated or revised data further discussion). for table 3.4). FAO's Global Forest Resources Assess- rather than to change in area. · Land use can be The data in the table show that land use patterns ment 2005 is an important background document for broken into several categories, three of which are are changing. They also indicate major differences the data. Conducted during 2003­05, it covers 229 presented in the table (not shown are land used as in resource endowments and uses among countries. countries and is the most comprehensive assess- permanent pasture and land under urban develop- True comparability of the data is limited, however, ment of forests, forestry, and the benefits of forest ments). · Forest area is land under natural or planted by variations in definitions, statistical methods, and resources in both scope and number of countries and stands of trees of at least 5 meters in height in situ, quality of data. Countries use different definitions of people involved. It examines status and trends for whether productive or not, and excludes tree stands rural and urban population and land use. The Food about 40 variables on the extent, condition, uses, in agricultural production systems (for example, in and Agriculture Organization of the United Nations and values of forests and other wooded land. fruit plantations and agroforestry systems) and trees (FAO), the primary compiler of the data, occasion- in urban parks and gardens. · Permanent cropland ally adjusts its definitions of land use categories is land cultivated with crops that occupy the land for and revises earlier data. Because the data reflect long periods and need not be replanted after each changes in reporting procedures as well as actual harvest, such as cocoa, coffee, and rubber. Land changes in land use, apparent trends should be inter- under flowering shrubs, fruit trees, nut trees, and preted cautiously. vines is included, but land under trees grown for wood or timber is not. · Arable land is land defined by the What is rural? Urban? 3.1a FAO as under temporary crops (double-cropped areas The rural population identified in table 3.1 is approximated as the difference between total population and are counted once), temporary meadows for mowing urban population, calculated using the urban share reported by the United Nations Population Division. or pasture, land under market or kitchen gardens, There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is and land temporarily fallow. Land abandoned as a an oversimplification (see About the data for table 3.11). The two distinct images--isolated farm, thriving result of shifting cultivation is excluded. metropolis--represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income. Because rurality has many dimensions, for policy purposes the rural-urban dichotomy presented in tables 3.1, 3.5, and 3.11 is inadequate. A 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the Data sources unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and Data on urban population shares used to estimate it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population rural population are from the United Nations Popu- density and remoteness together define a set of rural areas that face special development challenges. lation Division's World Urbanization Prospects: The Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors' estimates of 2007 Revision, and data on total population are the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their World Bank estimates. Data on land area and land estimates range from 13 percent of the population, based on a population density of less than 20 people use are from the FAO's electronic files. The FAO per square kilometer, to 64 percent, based on a population density of more than 500 people per square gathers these data from national agencies through kilometer. Taking remoteness into account, the estimated rural population would be 13­52 percent. The annual questionnaires and by analyzing the results estimate for Latin America and the Caribbean in table 3.1 is 21 percent. of national agricultural censuses. 2010 World Development Indicators 157 3.2 Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare Tractors % of % thousand fertilizer of arable % of total per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990­92 2005­07 2005­07 2008 1990­92 2006­08 2005­07 2005­07 1990­92 2005­07 1990­92 2005­07 Afghanistan 58 59 5.8 327 2,283.3 2,913.0 171.2 3.7 .. .. 0.1 0.6 Albania 41 40 .. 1,485 242.6 136.4 .. 81.9 .. 58.3 177.3 143.0 Algeria 16 17 2.0 89 3,104.9 2,831.5 86.1 12.7 .. .. 128.5 136.9 Angola 46 46 .. 1,010 892.6 1,487.5 .. 2.9 5.1 .. 30.5 27.3 Argentina 47 48 1.1 591 8,509.6 9,584.1 231.8 43.0 0.4 1.0 98.8 80.1 Armenia 41b 56 .. 562 162.8b 178.4 523.6 27.5 .. 46.2 345.5b 353.1 Australia 60 57 0.6 534 12,813.8 19,153.0 231.5 46.3 5.5 3.5 67.4 67.0 Austria 42 39 1.1 1,110 903.2 813.6 80.3 173.0 7.5 5.6 2,367.1 2,392.0 Azerbaijan 53b 58 30.0 447 627.0b 795.1 .. 11.2 32.5b 39.0 194.8b 83.7 Bangladesh 73 70 55.7 2,666 10,985.4 11,616.4 149.1 185.6 66.4 48.1 2.4 3.2 Belarus 46b 44 1.3 618 2,603.0b 2,368.2 17.8 172.2 .. .. 206.9b 94.3 Belgium 44 c 46 1.6 847 354.3c 333.2 .. .. 2.8 1.9 .. 1,128.0 Benin 21 32 .. 1,039 659.9 902.6 .. 2.9 .. .. 1.0 0.7 Bolivia 33 34 .. 1,146 642.4 926.6 .. 4.8 1.7 .. 24.9 16.5 Bosnia and Herzegovina 43b 42 .. 1,028 304.1b 308.0 .. 44.9 .. .. 235.3b 283.0 Botswana 46 46 0.0 416 140.1 81.7 .. .. .. 29.9 142.9 117.4 Brazil 29 31 .. 1,782 19,632.5 19,592.8 293.6 156.8 25.6 19.9 144.0 131.9 Bulgaria 56 48 1.3 608 2,179.3 1,598.5 71.4 97.0 19.7 8.2 127.8 132.2 Burkina Faso 35 40 .. 748 2,724.5 3,529.1 .. 8.5 .. .. 2.9 16.9 Burundi 83 89 .. 1,274 218.8 221.7 .. 1.8 .. .. 1.8 1.7 Cambodia 25 31 .. 1,904 1,800.8 2,702.1 .. 2.5 .. .. 3.2 11.0 Cameroon 19 19 .. 1,604 816.1 1,106.6 .. 8.1 .. .. 0.8 0.8 Canada 7 7 .. 537 20,864.4 16,235.7 24.3 69.7 4.2 2.6 162.0 162.4 Central African Republic 8 8 .. 1,343 104.0 204.7 .. .. .. .. 0.2 0.2 Chad 38 39 .. 322 1,241.9 2,541.1 .. .. .. .. 0.5 0.4 Chile 21 21 6.1 1,522 741.6 542.3 101.6 428.2 18.8 12.8 143.7 396.6 China 57 59 .. .. 93,430.3 86,057.9 103.4 327.9 53.5 .. 64.4 124.3 Hong Kong, China .. .. .. .. .. .. .. .. 0.8 0.2 .. .. Colombia 41 38 .. 2,612 1,598.1 997.6 827.6 344.1 1.4 20.1 97.8 106.3 Congo, Dem. Rep. 10 10 .. 1,543 1,867.6 1,976.1 .. 0.2 .. .. 3.6 3.6 Congo, Rep. 31 31 .. 1,646 9.1 27.4 .. 0.5 .. .. 14.7 14.1 Costa Rica 54 54 .. 2,926 83.1 62.8 .. 799.9 25.2 14.1 259.4 350.0 Côte d'Ivoire 60 63 .. 1,348 1,434.0 808.6 .. 25.7 .. .. 19.7 33.4 Croatia 43b 22 0.6 1,113 592.7b 559.7 58.9 238.9 .. 14.8 35.2b 2,203.3 Cuba 62 60 .. 1,335 235.0 277.4 449.4 26.6 25.1 19.6 221.1 205.4 Czech Republic .. 55 1.5 677 .. 1,561.9 137.6 146.4 .. 3.8 .. 281.0 Denmark 65 63 8.5 703 1,581.3 1,484.3 228.7 129.9 5.4 3.0 624.9 481.0 Dominican Republic 53 52 .. 1,410 134.2 170.5 .. .. 19.5 14.7 25.5 22.8 Ecuador 29 27 9.7 2,087 861.0 810.0 .. 579.5 7.0 8.3 54.1 118.6 Egypt, Arab Rep. 3 4 .. 51 2,410.2 2,956.4 86.2 570.5 36.2 31.1 250.7 333.1 El Salvador 69 76 1.9 1,724 452.6 361.9 .. 84.9 23.1 19.5 60.3 48.6 Eritrea .. 75 .. 384 345.6 419.2 .. 2.3 .. .. .. 7.3 Estonia 32b 19 .. 626 453.6b 293.9 227.5 118.1 19.5b 5.0 455.3b 573.2 Ethiopia .. 34 0.4 848 .. 8,589.5 .. 8.3 .. 44.4 .. 2.2 Finland 8 8 2.8 536 1,050.5 1,158.6 88.0 138.2 8.8 4.6 899.9 779.8 France 56 54 5.8 867 9,211.6 9,260.9 219.8 205.2 5.6 3.6 784.1 624.7 Gabon 20 20 .. 1,831 14.4 20.2 .. 5.9 .. .. 28.5 29.0 Gambia, The 63 81 .. 836 89.5 212.7 .. 4.9 .. .. 1.9 2.6 Georgia 46b 36 4.0 1,026 248.5b 195.1 19.5 37.1 .. 54.3 295.6b 468.4 Germany 50 49 .. 700 6,673.0 6,770.8 54.2 212.8 3.9 2.2 1,253.3 673.0 Ghana 56 65 .. 1,187 1,077.6 1,409.4 .. 9.5 62.0 .. 14.7 8.9 Greece 71 64 15.8 652 1,455.2 1,196.0 236.4 141.1 22.7 12.0 773.6 1,008.1 Guatemala 40 41 .. 1,996 768.2 854.2 .. 119.6 13.3 33.2 32.6 28.9 Guinea 49 55 .. 1,651 774.2 1,822.9 .. 1.6 .. .. 43.4 27.0 Guinea-Bissau 53 58 .. 1,577 112.4 142.6 .. .. .. .. 0.6 0.7 Haiti 58 61 .. 1,440 406.5 445.5 .. .. 65.6 .. 2.4 1.7 Honduras 30 28 .. 1,976 502.3 409.3 .. 117.8 42.1 39.2 31.1 49.6 158 2010 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare Tractors % of % thousand fertilizer of arable % of total per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990­92 2005­07 2005­07 2008 1990­92 2006­08 2005­07 2005­07 1990­92 2005­07 1990­92 2005­07 Hungary 71 65 2.2 589 2,803.5 2,899.4 215.4 118.0 15.2 4.9 157.8 264.2 India 61 61 30.4 1,083 100,759.8 99,791.3 133.0 121.3 .. .. 65.4 186.9 Indonesia 24 27 15.4 2,702 13,861.2 15,740.9 117.3 158.8 54.9 42.4 2.7 2.3 Iran, Islamic Rep. 39 29 15.1 228 9,611.9 7,534.3 179.5 92.7 .. 23.9 135.9 178.4 Iraq 23 22 .. 216 3,506.1 3,334.8 132.7 22.0 .. .. 64.9 139.3 Ireland 70 62 .. 1,118 298.0 291.0 285.3 525.2 14.1 5.6 1,666.7 1,548.4 Israel 27 23 31.2 435 107.8 90.4 18.9 1,443.9 3.7 1.8 763.0 796.4 Italy 55 49 18.0 832 4,346.9 3,933.6 365.3 173.2 8.4 4.2 1,619.3 2,539.4 Jamaica 44 47 .. 2,051 2.6 1.5 .. 54.3 27.3 18.2 158.0 128.4 Japan 16 13 35.7 1,668 2,438.6 2,002.4 145.4 347.2 6.8 4.3 .. .. Jordan 12 11 7.6 111 111.9 56.4 10.7 911.4 .. .. 351.9 323.9 Kazakhstan 82b 77 .. 250 22,152.4b 14,857.6 119.0 5.9 .. .. 62.0 b 18.8 Kenya 47 47 0.1 630 1,765.9 2,149.1 .. 35.0 .. .. 20.0 26.2 Korea, Dem. Rep. 21 25 .. 1,054 1,569.0 1,268.7 .. .. .. .. 297.1 229.3 Korea, Rep. 22 19 52.7 1,274 1,367.8 1,030.5 150.8 453.6 16.7 7.7 274.6 1,458.2 Kosovo .. 52 .. .. .. .. .. .. .. .. Kuwait 8 9 .. 121 0.4 1.4 4.4 1,022.2 .. .. 215.0 70.0 Kyrgyz Republic 53b 56 9.4 533 578.0b 586.3 .. 20.5 35.5b 37.4 189.4b 178.6 Lao PDR 7 9 .. 1,834 625.3 951.8 .. .. .. .. 11.4 9.8 Latvia 41b 29 .. 641 696.7b 526.4 .. 59.5 .. 11.0 363.7b 498.3 Lebanon 59 66 19.9 661 41.5 70.4 31.8 273.5 .. .. 187.6 576.6 Lesotho 77 76 .. 788 177.6 222.9 .. .. .. .. 57.1 64.6 Liberia 26 27 .. 2,391 135.0 160.0 .. .. .. .. 9.4 8.5 Libya 9 9 .. 56 355.0 342.9 25.0 53.5 .. .. 187.2 227.1 Lithuania 54b 44 .. 656 1,134.0b 996.1 26.6 172.2 .. 12.3 256.0 b 650.6 Macedonia, FYR 51b 46 2.7 619 235.2b 179.2 .. 50.2 .. 19.3 730.2b 1,208.9 Madagascar 63 70 2.2 1,513 1,321.0 1,580.1 .. 2.9 .. 82.0 4.6 1.9 Malawi 45 53 .. 1,181 1,442.6 1,701.1 .. 36.4 .. .. 6.1 4.8 Malaysia 23 24 .. 2,875 699.3 683.2 229.6 821.3 24.4 14.7 .. .. Mali 26 32 .. 282 2,392.7 3,424.1 .. 0.0 .. .. 10.5 2.3 Mauritania 38 39 .. 92 132.9 235.0 .. .. .. .. 8.2 8.2 Mauritius 56 51 20.4 2,041 0.5 0.1 507.5 278.7 15.5 9.6 36.2 60.0 Mexico 54 55 3.5 752 10,075.0 10,233.1 702.5 64.0 24.7 14.2 126.7 98.8 Moldova 78b 76 9.7 450 675.6b 923.1 .. 12.9 38.5b 35.7 310.1b 208.5 Mongolia 81 75 .. 241 620.0 134.0 .. 6.0 .. 38.8 73.2 46.1 Morocco 68 67 5.2 346 5,373.9 5,253.5 29.0 49.1 3.8 44.4 46.0 53.5 Mozambique 61 62 .. 1,032 1,508.6 2,037.6 .. 4.3 .. .. 14.3 14.5 Myanmar 16 18 23.8 2,091 5,282.9 8,860.7 1,444.8 6.4 69.4 .. 11.5 6.8 Namibia 47 47 .. 285 206.4 289.1 .. 2.6 48.2 .. 30.3 24.7 Nepal 29 29 27.7 1,500 2,957.2 3,360.7 .. 23.4 81.2 .. 26.4 123.0 Netherlands 59 57 .. 778 185.0 222.2 49.4 892.4 4.2 3.1 2,056.1 1,433.6 New Zealand 60 46 3.0 1,732 153.5 122.4 309.1 1,054.7 10.7 7.1 323.1 829.8 Nicaragua 34 44 .. 2,391 299.3 458.9 .. 29.9 38.7 29.0 20.3 20.1 Niger 27 34 .. 151 7,010.6 9,313.6 .. 0.4 .. .. 0.1 0.1 Nigeria 79 85 .. 1,150 16,416.7 19,152.0 974.8 5.0 .. .. 4.9 6.7 Norway 3 3 4.2 1,414 361.4 336.8 28.0 237.0 5.9 3.2 1,731.8 1,544.2 Oman 3 6 .. 125 2.4 4.8 3.6 285.7 .. .. 42.0 35.2 Pakistan 34 35 64.9 494 11,776.8 13,145.5 132.6 160.8 48.9 43.3 133.3 207.8 Panama 29 30 .. 2,692 182.4 149.0 .. 39.9 26.2 15.4 103.3 147.8 Papua New Guinea 2 2 .. 3,142 1.9 3.2 .. 139.3 .. .. 59.4 50.0 Paraguay 43 51 .. 1,130 454.7 969.4 .. 63.2 .. 29.5 72.4 40.0 Peru 17 17 .. 1,738 682.5 1,170.9 75,752.0 91.2 1.0 10.8 35.9 36.0 Philippines 37 38 .. 2,348 6,957.4 6,924.3 265.1 150.6 45.3 36.6 72.1 124.4 Poland 62 53 0.5 600 8,522.7 8,444.3 102.8 170.8 25.2 16.0 820.7 1,211.8 Portugal 43 39 12.2 854 780.1 348.2 175.1 199.3 15.6 11.7 569.5 1,522.1 Puerto Rico 48 22 8.0 2,054 0.5 0.3 .. .. 3.5 1.5 478.2 504.1 Qatar 64 61 .. 74 5,842.3 5,006.4 30.6 30.7 146.1 197.1 2010 World Development Indicators 159 3.2 Agricultural inputs Agricultural Average Land under Fertilizer Agricultural Agricultural landa annual cereal production consumption employment machinery precipitation kilograms % of per hectare Tractors % of % thousand fertilizer of arable % of total per 100 sq. km land area irrigated millimeters hectares production land employment of arable land 1990­92 2005­07 2005­07 2008 1990­92 2006­08 2005­07 2005­07 1990­92 2005­07 1990­92 2005­07 Romania 14 13 2.1 637 59,541.3 41,825.3 42.6 42.2 14.5 9.7 97.8 36.3 Russian Federation 76b 77 2.1 460 258.2b 328.0 12.1 12.4 ..b .. 1.0 b 0.5 Rwanda 5 6 .. 1,212 1.2 2.0 .. 2.6 .. 3.0 75.9 41.3 Saudi Arabia .. .. .. 59 1,061.8 596.7 22.5 99.0 .. 4.4 20.3 28.8 Senegal 46 45 0.7 686 1,153.8 1,230.8 78.3 9.0 .. 33.7 1.7 3.0 Serbia .. 57 0.5 .. .. 1,886.8 332.9 38.8 .. .. .. 19.8 Sierra Leone 38 44 .. 2,526 451.7 1,037.2 .. .. .. .. 3.3 1.1 Singapore 2 1 .. 2,497 .. .. .. 13,528.1 0.3 1.2 636.7 1,083.3 Slovak Republic .. 40 2.7 824 .. 772.2 50.9 84.3 .. 4.4 .. 158.6 Slovenia 28b 25 0.5 1,162 112.5b 101.5 38,791.4 354.9 .. 9.5 .. .. Somalia 70 70 .. 282 531.4 536.0 .. .. .. .. 15.5 12.0 South Africa 80 82 .. 495 5,735.9 3,408.5 170.2 48.7 .. 8.3 101.1 43.3 Spain 61 58 12.1 636 7,588.5 6,381.8 117.0 155.5 10.5 4.9 494.2 782.0 Sri Lanka 36 37 .. 1,712 834.3 955.1 2,497.4 289.5 44.3 31.3 175.0 213.2 Sudan 52 58 1.1 416 6,266.9 11,122.4 .. 3.4 .. .. 7.8 31.3 Swaziland 76 78 .. 788 69.1 48.5 .. .. .. .. 251.4 86.0 Sweden 8 8 .. 624 1,184.3 1,009.4 316.9 100.3 3.3 2.1 604.4 596.7 Switzerland 47 39 .. 1,537 207.3 159.8 .. 214.0 4.2 3.8 2,870.2 2,624.7 Syrian Arab Republic 74 76 10.1 252 3,811.9 3,108.4 149.0 77.9 28.2 .. 136.7 229.0 Tajikistan 32b 33 .. 691 266.5b 403.3 369.3 22.0 45.8b .. 415.4b 299.0 Tanzania 38 39 .. 1,071 3,003.3 5,013.0 .. 6.0 .. 74.6 8.2 23.1 Thailand 42 39 .. 1,622 10,593.6 11,520.2 1,148.7 123.3 61.1 42.1 38.8 529.6 Timor-Leste 22 26 .. .. 83.7 101.7 .. .. .. .. 8.0 5.2 Togo 59 67 .. 1,168 610.2 797.5 .. .. .. .. 0.5 0.3 Trinidad and Tobago 16 11 12.7 2,200 6.4 2.0 3.5 406.8 11.8 4.3 .. .. Tunisia 58 63 3.6 207 1,524.7 1,311.0 9.1 39.3 .. .. 88.3 142.5 Turkey 52 52 12.8 593 13,759.9 12,183.5 218.9 102.5 46.5 27.7 286.7 447.0 Turkmenistan 69b 69 .. 161 331.3b 1,000.5 .. .. .. .. 464.7b 268.8 Uganda 61 65 .. 1,180 1,097.6 1,724.7 .. 1.5 .. .. 9.2 8.7 Ukraine 72b 71 5.3 565 12,542.3b 14,012.9 28.3 21.8 20.0 b 17.9 153.3b 106.2 United Arab Emirates 4 7 .. 78 1.4 0.0 14.4 615.8 .. 4.9 49.8 56.0 United Kingdom 75 72 .. 1,220 3,548.5 3,006.3 126.3 289.3 2.2 1.3 761.2 744.2 United States 47 45 .. 715 64,547.3 58,581.6 134.1 149.9 2.9 1.5 235.8 258.9 Uruguay 85 84 1.2 1,265 509.4 711.4 1,040.8 133.4 1.5 8.9 259.5 274.4 Uzbekistan 65b 63 .. 206 1,225.3b 1,562.9 .. .. .. .. 402.3b 390.8 Venezuela, RB 25 24 .. 1,875 798.7 1,138.7 61.7 167.1 12.6 8.9 176.1 184.9 Vietnam 21 32 .. 1,821 6,726.1 8,390.6 441.2 374.3 .. .. 60.4 256.6 West Bank and Gaza .. 62 4.3 402 .. 32.8 .. .. .. 15.4 .. 694.2 Yemen, Rep. 45 45 2.8 167 738.2 879.7 .. 10.0 52.6 .. 40.4 48.4 Zambia 31 34 .. 1,020 813.4 896.0 .. 16.3 49.8 .. 11.3 11.4 Zimbabwe 34 40 .. 657 1,430.8 2,139.5 161.6 35.5 .. .. 61.4 74.3 World 38 w 38 w 1.8 w 707,271.9 s 697,843.7 s 99.5 w 117.7 w .. w .. w 189.6 w 198.7 w Low income 36 38 1.5 73,977.3 100,232.3 256.2 35.0 .. .. 33.5 33.7 Middle income 38 38 2.3 483,693.9 456,809.5 101.5 120.2 .. .. 131.7 152.1 Lower middle income 49 50 3.6 310,393.3 314,214.7 109.1 155.1 .. .. 72.3 140.7 Upper middle income 30 30 1.0 173,300.6 142,594.7 83.1 70.5 20.9 15.7 252.6 175.2 Low & middle income 37 38 2.2 557,671.3 557,041.8 104.2 108.5 .. .. 117.0 133.1 East Asia & Pacific 48 50 0.9 142,265.1 143,348.3 114.0 271.0 53.5 .. 55.1 137.7 Europe & Central Asia 28 28 2.0 136,657.9 109,979.2 35.2 37.7 23.4 17.6 187.1 175.3 Latin America & Carib. 34 36 0.5 47,722.2 50,046.2 298.3 111.9 18.7 16.4 121.7 109.0 Middle East & N. Africa 24 23 5.9 30,590.3 27,701.1 58.0 89.9 .. .. 114.2 161.4 South Asia 55 55 16.1 129,690.1 131,869.1 135.4 122.8 .. .. 67.1 173.2 Sub-Saharan Africa 43 44 0.2 70,745.7 94,098.0 343.8 10.8 .. .. 17.7 14.9 High income 38 38 0.8 149,600.7 140,801.9 90.8 143.8 5.6 3.2 360.2 380.7 Euro area 50 47 4.2 33,854.7 31,664.6 107.3 200.8 6.9 4.2 989.0 1,013.0 a. Includes permanent pastures, arable land, and land under permanent crops. b. Data are not available for all three years. c. Includes Luxembourg. 160 2010 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs About the data Definitions Agriculture is still a major sector in many economies, appropriate levels and application rates vary by coun- · Agricultural land is the share of land area that and agricultural activities provide developing coun- try and over time and depend on the type of crops, the is permanent pastures, arable, or under permanent tries with food and revenue. But agricultural activi- climate and soils, and the production process used. crops. Permanent pasture is land used for fi ve or ties also can degrade natural resources. Poor farming The agriculture sector is the most water-intensive more years for forage, including natural and culti- practices can cause soil erosion and loss of soil fertil- sector, and water delivery in agriculture is increas- vated crops. Arable land includes land defined by the ity. Efforts to increase productivity by using chemical ingly important. The table shows irrigated agricultural FAO as land under temporary crops (double-cropped fertilizers, pesticides, and intensive irrigation have land as share of total agricultural land area and data areas are counted once), temporary meadows for environmental costs and health impacts. Excessive on average precipitation to illustrate how countries mowing or for pasture, land under market or kitchen use of chemical fertilizers can alter the chemistry of obtain water for agricultural use. gardens, and land temporarily fallow. Land aban- soil. Pesticide poisoning is common in developing The data shown here and in table 3.3 are collected doned as a result of shifting cultivation is excluded. countries. And salinization of irrigated land dimin- by the Food and Agriculture Organization of the United Land under permanent crops is land cultivated with ishes soil fertility. Thus, inappropriate use of inputs Nations (FAO) through annual questionnaires. The FAO crops that occupy the land for long periods and need for agricultural production has far-reaching effects. tries to impose standard definitions and reporting not be replanted after each harvest, such as cocoa, The table provides indicators of major inputs to methods, but complete consistency across countries coffee, and rubber. Land under flowering shrubs, fruit agricultural production: land, fertilizer, labor, and and over time is not possible. Thus, data on agricul- trees, nut trees, and vines is included, but land under machinery. There is no single correct mix of inputs: tural land in different climates may not be comparable. trees grown for wood or timber is not. · Irrigated For example, permanent pastures are quite different land refers to areas purposely provided with water, Nearly 40 percent of land globally in nature and intensity in African countries and dry including land irrigated by controlled flooding. · Aver- is devoted to agriculture 3.2a Middle Eastern countries. Data on agricultural employ- age annual precipitation is the long-term average in ment, in particular, should be used with caution. In depth (over space and time) of annual precipitation Total land area in 2007: 130 million sq. km many countries much agricultural employment is in the country. Precipitation is defined as any kind informal and unrecorded, including substantial work of water that falls from clouds as a liquid or a solid. Permanent performed by women and children. To address some · Land under cereal production refers to harvested Others pastures 31.8% 26.0% of these concerns, this indicator is heavily footnoted areas, although some countries report only sown or in the database in sources, definition, and coverage. cultivated area. · Fertilizer consumption is the quan- Arable land Fertilizer consumption measures the quantity of tity of plant nutrients used per unit of arable land. 10.8% plant nutrients. Consumption is calculated as pro- Fertilizer products cover nitrogen, potash, and phos- Forests 30.3% duction plus imports minus exports. Because some phate fertilizers (including ground rock phosphate). Permanent chemical compounds used for fertilizers have other Traditional nutrients--animal and plant manures-- crops 1.1% industrial applications, the consumption data may are not included. · Fertilizer production is fertilizer Note: Agricultural land includes permanent pastures, overstate the quantity available for crops. Fertil- consumption, exports, and nonfertilizer use of fertil- arable land, and land under permanent crops. Source: Tables 3.1 and 3.2. izer consumption as a share of production shows izer products minus fertilizer imports. · Agricultural the agriculture sector's vulnerability to import and employment is employment in agriculture, forestry, Developing regions lag in agricultural energy price fluctuation. The FAO recently revised hunting, and fishing (see table 2.3). · Agricultural machinery, which reduces their the time series for fertilizer consumption and irriga- machinery refers to wheel and crawler tractors agricultural productivity 3.2b tion for 2002 onward, but recent data are not avail- (excluding garden tractors) in use in agriculture at Tractors per 100 square able for all countries. FAO collects fertilizer statistics the end of the calendar year specified or during the kilometers of arable land 1990­92 2005­07 for production, imports, exports, and consumption first quarter of the following year. 400 through the new FAO fertilizer resources question- naire. In the previous release, the data were based 300 on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fer- tilizers. Some countries compile fertilizer data on a 200 calendar year basis, while others do so on a crop year basis (July­June). Previous editions of World 100 Development Indicators reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used 0 Data sources East Europe Latin Middle South Sub- High when comparing data over time. Asia & & America East & Asia Saharan income Pacific Central & North Africa Asia Caribbean Africa To smooth annual fluctuations in agricultural activity, Data on agricultural inputs are from electronic files Source: Table 3.2. all the indicators in the table (except average annual that the FAO makes available to the World Bank. precipitation) have been averaged over three years. 2010 World Development Indicators 161 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2005­07 1990­92 2005­07 1990­92 2005­07 1990­92 2006­08 1990­92 2005­07 Afghanistan 148.0 124.0 119.7 94.3 100.0 71.0 1,153 1,603 .. .. Albania 81.0 112.3 69.7 112.0 62.0 110.3 2,372 3,717 837 1,663 Algeria 99.7 144.3 95.7 126.3 94.7 107.0 915 1,384 1,823 2,239 Angola 76.7 147.3 82.3 126.0 96.3 83.3 378 490 176 222 Argentina 74.3 120.0 81.7 113.3 99.0 102.0 2,652 3,991 6,919 11,191 Armenia 95.0a 180.3 101.0a 164.3 106.0a 131.7 1,843a 1,992 1,607a 4,508 Australia 66.7 73.3 104.0 77.3 155.0 84.3 1,739 1,292 20,676 30,830 Austria 96.3 99.3 93.3 91.7 95.7 91.0 5,400 6,128 12,060 21,440 Azerbaijan 147.0a 137.7 112.0a 132.0 103.0a 128.0 2,113a 2,669 1,067a 1,222 Bangladesh 90.7 103.0 88.7 104.3 87.3 114.3 2,567 3,896 255 387 Belarus 107.0a 146.3 132.0a 138.0 142.0a 129.0 2,741a 3,000 2,042a 4,266 Belgium 77.6b 103.0 87.8b 62.3 93.8b 50.7 6,122.0 b 8,223 .. 38,337 Benin 76.7 88.3 83.3 94.0 118.3 98.7 880 1,247 429 661 Bolivia 78.0 107.7 86.3 101.7 93.3 101.7 1,373 1,933 703 732 Bosnia and Herzegovina 101.0a 113.3 113.0a 119.0 114.0a 139.3 3,553a 3,977 .. 10,352 Botswana 117.7 103.0 137.3 103.7 141.3 103.7 312 487 766 452 Brazil 87.3 122.0 79.7 118.0 74.3 113.3 1,916 3,531 1,611 3,315 Bulgaria 137.7 89.3 125.3 82.0 133.7 68.0 3,633 3,252 2,686 8,015 Burkina Faso 95.7 115.3 94.7 103.7 88.0 103.3 783 1,118 126 182 Burundi 128.7 86.7 128.3 86.0 153.0 74.7 1,370 1,307 117 70 Cambodia 82.7 145.3 82.7 139.0 82.3 104.0 1,356 2,672 .. 376 Cameroon 89.0 98.3 93.0 98.7 106.0 90.0 1,166 1,343 409 703 Canada 95.3 101.0 91.0 103.0 83.7 104.7 2,559 3,133 28,541 46,138 Central African Republic 92.7 89.7 86.7 98.3 84.3 101.3 883 1,115 322 409 Chad 92.0 92.3 97.0 95.0 113.3 90.3 636 775 209 246 Chile 89.3 111.7 84.0 110.0 77.3 109.0 3,949 5,960 3,618 6,103 China 75.7 116.0 66.3 117.7 54.7 116.3 4,307 5,388 269 459 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 106.3 92.7 93.3 95.3 94.0 101.3 2,492 4,046 3,342 3,001 Congo, Dem. Rep. 160.3 82.7 156.3 82.7 130.0 80.3 794 772 209 162 Congo, Rep. 102.0 98.3 100.7 103.7 96.7 128.3 688 776 .. .. Costa Rica 89.7 98.3 90.3 103.7 99.0 101.3 3,188 3,433 3,158 5,132 Côte d'Ivoire 92.7 95.3 95.3 101.0 117.3 100.0 863 1,713 652 875 Croatia 78.0a 84.0 98.0a 92.3 124.0a 113.0 3,975a 5,535 5,553a 14,823 Cuba 117.0 83.7 116.0 84.3 135.0 83.7 2,092 2,787 .. .. Czech Republic .. 94.3 .. 96.0 .. 89.7 .. 4,679 .. 5,871 Denmark 106.0 94.7 100.3 100.0 91.7 102.0 5,448 5,825 15,190 43,201 Dominican Republic 137.3 108.3 120.3 122.3 92.7 133.3 4,078 4,292 2,055 3,829 Ecuador 92.3 96.0 83.3 103.3 75.0 105.7 1,724 2,995 1,801 1,872 Egypt, Arab Rep. 81.3 104.7 79.3 105.3 77.0 104.7 5,738 7,537 1,826 2,758 El Salvador 120.7 88.7 106.3 100.0 92.3 113.7 1,871 2,957 1,774 2,404 Eritrea .. 83.7 .. 81.3 .. 78.7 .. 456 .. 118 Estonia 108.0a 113.3 162.0a 122.7 173.0a 108.7 1,304 a 2,679 .. 4,550 Ethiopia .. 115.7 .. 116.0 .. 113.3 .. 1,489 .. 187 Finland 100.0 107.0 106.7 101.7 109.7 100.0 3,246 3,497 19,011 35,653 France 97.0 90.0 100.7 91.7 100.7 92.7 6,370 6,880 22,254 47,418 Gabon 108.7 91.0 111.0 91.0 108.0 91.0 1,712 1,656 1,246 1,741 Gambia, The 77.3 68.3 83.0 69.3 136.0 87.3 1,114 935 262 269 Georgia 108.0a 87.3 93.0a 95.0 71.0a 96.0 1,998a 1,954 2,359a 1,871 Germany 86.0 90.3 101.0 94.0 110.0 99.7 5,578 6,596 13,863 26,745 Ghana 72.3 111.3 75.0 110.3 114.3 93.7 1,084 1,330 352 378 Greece 91.3 85.7 99.7 89.3 111.7 95.7 3,589 4,069 7,669 8,656 Guatemala 95.7 109.3 93.0 112.0 94.3 91.3 1,882 1,582 2,304 2,719 Guinea 98.7 107.3 99.0 107.7 78.3 121.0 1,423 1,501 156 208 Guinea-Bissau 92.3 95.0 95.0 95.0 105.3 95.0 1,529 1,464 236 315 Haiti 127.7 88.0 117.7 92.7 82.0 102.0 997 885 .. .. Honduras 113.7 128.0 107.0 125.3 84.7 117.0 1,371 1,662 1,227 1,858 162 2010 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2005­07 1990­92 2005­07 1990­92 2005­07 1990­92 2006­08 1990­92 2005­07 Hungary 111.7 108.3 115.3 101.7 124.7 86.7 4,551 5,226 4,289 8,136 India 95.0 100.3 91.0 101.7 83.3 112.3 1,947 2,574 359 460 Indonesia 93.7 120.3 95.3 121.3 99.3 132.3 3,826 4,508 519 657 Iran, Islamic Rep. 85.7 117.3 83.3 118.7 77.7 119.7 1,523 2,574 2,042 2,931 Iraq 120.3 96.7 117.7 92.7 117.0 93.0 872 1,377 .. .. Ireland 99.3 80.0 102.0 84.7 101.0 85.7 6,653 7,417 .. 14,217 Israel 127.0 100.7 108.0 93.3 94.7 93.7 3,132 2,741 .. .. Italy 98.7 94.3 98.0 94.0 96.3 95.0 4,340 5,282 11,714 26,784 Jamaica 91.0 88.0 82.0 92.7 70.7 104.0 1,298 1,227 2,366 2,400 Japan 115.0 93.3 110.7 96.7 109.0 98.7 5,713 5,977 20,350 39,368 Jordan 139.0 127.3 122.7 118.3 97.3 98.7 1,167 891 2,348 2,232 Kazakhstan 149.0a 121.7 149.0a 121.0 163.0a 124.7 1,338a 1,169 1,776a 1,730 Kenya 108.3 101.0 111.3 108.7 114.3 117.0 1,645 1,621 379 367 Korea, Dem. Rep. 140.7 106.0 129.0 109.7 131.7 128.7 5,073 3,607 .. .. Korea, Rep. 94.7 90.7 85.7 92.3 73.3 98.0 5,885 6,525 5,804 14,501 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 35.7 96.0 27.7 101.3 29.0 99.3 3,112 2,623 .. .. Kyrgyz Republic 76.0a 93.7 81.0a 96.7 118.0a 97.3 2,772a 2,481 684 a 1,017 Lao PDR 77.0 117.7 73.0 115.7 74.3 109.3 2,355 3,612 382 495 Latvia 117.0a 139.3 203.0a 128.7 249.0a 114.3 1,641a 2,767 1,896a 3,260 Lebanon 136.3 88.0 124.3 96.7 80.3 115.0 2,001 2,351 .. 30,573 Lesotho 77.3 68.0 91.3 78.3 110.3 87.0 703 569 259 193 Liberia 90.0 90.3 116.3 95.7 132.0 99.0 951 1,421 .. .. Libya 94.3 91.0 93.0 90.3 92.7 89.0 706 619 .. .. Lithuania 75.0a 99.3 149.0a 125.3 175.0a 125.0 1,938a 2,762 .. 4,636 Macedonia, FYR 112.0a 101.0 116.0 105.7 119.0 118.0 2,652 3,135 2,413 4,395 Madagascar 122.3 103.7 121.0 100.7 130.7 89.7 1,935 2,418 210 182 Malawi 66.3 98.3 56.3 99.3 97.3 107.0 871 1,837 86 126 Malaysia 92.7 116.7 88.0 114.7 100.3 114.3 2,827 3,422 398 583 Mali 93.0 101.3 103.3 113.0 114.7 109.0 840 1,133 405 515 Mauritania 80.3 85.3 111.3 95.3 116.3 96.3 802 760 671 414 Mauritius 122.0 90.0 111.3 98.3 77.0 129.7 4,117 8,381 3,747 5,222 Mexico 94.0 105.7 89.0 110.0 83.3 110.0 2,520 3,341 2,274 3,022 Moldova 127.0a 102.7 146.0a 116.3 183.0a 116.7 2,928a 2,236 1,349a 1,278 Mongolia 270.0 115.0 110.0 72.7 104.3 70.7 967 1,141 1,150 1,511 Morocco 115.0 129.7 107.3 121.7 93.3 101.3 1,094 1,057 1,788 2,306 Mozambique 81.0 105.7 87.0 92.3 112.3 104.3 330 787 117 173 Myanmar 68.7 133.3 71.0 139.7 66.0 180.3 2,739 3,670 .. .. Namibia 90.3 117.0 133.3 93.0 141.7 86.3 388 434 1,307 1,917 Nepal 92.3 104.3 93.7 103.0 99.3 102.0 1,831 2,286 245 241 Netherlands 98.3 93.7 110.3 89.7 110.3 89.0 7,142 7,813 24,752 39,910 New Zealand 87.3 100.0 86.7 110.3 89.7 109.7 5,257 7,439 19,150 26,105 Nicaragua 91.7 112.3 76.0 119.3 68.7 125.0 1,543 1,866 .. 2,334 Niger 96.7 116.3 90.0 112.3 81.3 106.0 323 460 242 .. Nigeria 86.7 108.0 86.7 106.3 90.3 99.0 1,135 1,502 .. .. Norway 125.0 99.0 108.7 94.3 103.0 91.7 3,744 3,690 19,077 39,206 Oman 78.0 87.7 74.7 104.0 81.7 139.3 2,206 3,265 1,012 .. Pakistan 99.7 102.7 87.3 106.0 83.3 108.7 1,818 2,656 765 888 Panama 130.7 100.3 107.0 97.0 82.7 95.0 1,862 2,195 2,341 4,011 Papua New Guinea 99.0 91.0 100.7 95.7 102.0 100.3 2,504 3,700 555 639 Paraguay 105.0 126.0 96.3 116.0 113.7 91.7 1,905 3,092 1,648 2,136 Peru 57.0 120.3 63.3 121.3 77.7 121.0 2,463 3,657 879 1,390 Philippines 103.7 109.7 95.3 108.0 74.7 105.3 2,070 3,278 905 1,148 Poland 109.3 88.0 110.0 104.0 115.0 106.0 2,958 3,022 1,605 2,901 Portugal 109.0 89.7 103.0 94.3 87.3 96.7 1,939 3,418 4,642 6,387 Puerto Rico 176.7 98.7 136.3 89.0 127.0 86.7 1,100 1,882 .. .. Qatar 83.3 73.0 98.0 51.7 109.7 34.3 2,941 3,585 .. .. 2010 World Development Indicators 163 3.3 Agricultural output and productivity Crop Food Livestock Cereal Agricultural production index production index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2005­07 1990­92 2005­07 1990­92 2005­07 1990­92 2006­08 1990­92 2005­07 Romania 87.3 97.3 94.7 104.3 116.3 112.7 2,777 2,664 2,129 6,179 Russian Federation 125.0a 134.7 130.0a 122.3 149.0a 111.0 1,743a 2,092 1,917a 2,914 Rwanda 129.0 102.3 125.0 103.3 93.3 105.3 1,088 1,110 193 226 Saudi Arabia 149.7 112.0 130.7 99.3 84.0 95.0 4,212 5,099 8,476 17,365 Senegal 90.0 67.3 92.3 72.7 110.3 96.7 803 892 251 224 Serbia 101.0a,c 123.0a,c 113.0a,c 109.0a,c 107.0a,c 97.0a,c 2,926a,c 4,087 .. 1,890a,c Sierra Leone 136.7 148.7 131.0 146.0 117.0 107.7 1,223 1,016 .. .. Singapore 204.3 343.7 467.3 122.0 526.0 100.7 .. .. 22,695 50,828 Slovak Republic .. 99.0 .. 93.7 .. 79.3 .. 4,244 .. 4,995 Slovenia 84.0a 102.7 78.0a 100.7 78.0a 100.0 3,279a 5,310 13,217a 50,960 Somalia 110.3 87.0 90.7 87.0 88.0 87.0 622 408 .. .. South Africa 95.3 92.7 101.3 104.0 114.3 116.3 1,602 3,244 2,149 3,077 Spain 90.7 90.7 89.3 90.3 81.3 95.0 2,310 3,493 9,583 17,894 Sri Lanka 93.0 104.0 96.0 106.0 101.7 114.7 2,950 3,700 697 823 Sudan 83.3 100.3 78.0 107.3 77.0 113.7 596 600 526 844 Swaziland 126.3 101.0 128.7 106.7 152.3 110.7 1,299 845 993 1,108 Sweden 104.7 91.7 100.0 96.7 97.3 93.3 4,272 4,781 22,319 39,578 Switzerland 117.3 92.3 110.0 98.0 110.0 99.3 6,102 6,361 19,369 22,653 Syrian Arab Republic 92.0 103.0 94.0 107.3 95.0 116.7 947 1,749 2,778 4,479 Tajikistan 138.0a 141.7 151.0a 147.7 214.0a 168.0 1,020a 2,246 370a 517 Tanzania 118.0 122.3 111.0 109.7 100.3 92.3 1,276 1,209 261 324 Thailand 89.7 110.0 92.0 109.0 94.7 103.0 2,186 3,007 480 653 Timor-Leste 103.7 81.0 113.3 86.7 109.0 102.0 1,694 1,184 .. .. Togo 93.7 85.7 95.3 98.0 112.7 98.7 791 1,130 345 394 Trinidad and Tobago 122.7 65.0 93.0 106.3 77.0 140.3 3,159 2,656 1,818 1,317 Tunisia 119.7 118.3 103.7 109.3 68.7 95.0 1,401 1,278 2,975 3,424 Turkey 102.3 100.0 104.0 99.3 107.0 94.3 2,192 2,548 2,204 3,229 Turkmenistan 114.0a 119.0 69.0a 122.3 75.0a 118.3 2,210a 3,079 1,321a .. Uganda 103.3 85.0 105.3 87.3 109.3 93.3 1,487 1,528 175 191 Ukraine 124.0a 133.3 139.0a 119.0 161.0a 108.3 2,834 a 2,707 1,232a 2,010 United Arab Emirates 35.0 38.7 39.7 41.7 94.7 90.3 2,042 2,200 10,414 29,465 United Kingdom 105.7 91.7 109.3 93.0 108.3 95.0 6,321 7,110 21,817 28,065 United States 97.0 99.7 92.7 99.7 91.3 98.0 4,875 6,578 20,353 45,015 Uruguay 73.7 137.7 80.3 124.0 88.0 116.7 2,445 4,185 6,278 9,370 Uzbekistan 124.0a 124.7 107.0a 121.7 113.0a 112.3 1,777a 4,287 1,427a 2,231 Venezuela, RB 95.7 96.0 89.7 95.7 89.7 93.7 2,561 3,533 4,584 7,386 Vietnam 69.7 116.0 71.3 114.3 60.0 113.3 3,097 4,883 229 335 West Bank and Gaza .. 91.0 .. 92.7 .. 91.3 .. 1,863 .. .. Yemen, Rep. 102.7 97.7 99.0 102.7 93.3 110.7 906 963 412 .. Zambia 95.3 116.3 105.3 103.7 106.3 96.0 1,251 1,803 189 232 Zimbabwe 81.0 56.3 90.7 81.0 105.0 94.7 1,125 592 271 239 World 82.0 w 114.7 w 78.8 w 114.3 w 83.7 w 112.2 w 2,847 w 3,397 w 801 w 959 w Low income 77.3 122.8 76.0 123.2 82.5 122.9 1,710 2,190 249 307 Middle income 79.5 119.3 73.2 119.7 77.5 119.0 2,537 3,122 501 741 Lower middle income 76.0 119.3 69.6 120.3 64.9 121.2 2,672 3,324 383 570 Upper middle income 89.8 119.5 82.7 118.4 99.8 114.9 1,961 2,676 2,154 3,286 Low & middle income 79.3 119.7 73.4 120.1 77.9 119.2 2,419 2,954 465 663 East Asia & Pacific 71.6 122.6 65.0 124.0 53.9 122.2 3,816 4,767 307 491 Europe & Central Asia 114.2 115.8 116.7 113.9 150.7 109.0 1,935 2,335 2,009 2,842 Latin America & Carib. 77.2 124.0 73.5 121.7 73.0 117.9 2,234 3,487 2,213 3,273 Middle East & N. Africa 79.1 123.7 76.6 123.1 71.3 120.6 1,544 2,308 1,846 2,823 South Asia 80.0 112.1 75.8 114.0 69.9 122.8 1,977 2,678 372 480 Sub-Saharan Africa 74.9 118.0 76.4 119.5 82.6 120.1 987 1,205 305 318 High income 90.3 99.9 91.4 100.4 93.1 101.0 4,260 5,147 14,601 27,557 Euro area 91.8 95.1 96.0 94.8 97.8 96.0 4,631 5,597 12,696 22,921 a. Data are not available for all three years. b. Includes Luxembourg. c. Includes Montenegro. 164 2010 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity About the data Definitions The agricultural production indexes in the table are single enterprise, estimates of the amounts retained · Crop production index is agricultural production prepared by the Food and Agriculture Organization of for seed and feed are subtracted from the produc- for each period relative to the base period 1999­ the United Nations (FAO). The FAO obtains data from tion data to avoid double counting. The resulting 2001. It includes all crops except fodder crops. The official and semiofficial reports of crop yields, area aggregate represents production available for any regional and income group aggregates for the FAO's under production, and livestock numbers. If data are use except as seed and feed. The FAO's indexes production indexes are calculated from the under- unavailable, the FAO makes estimates. The indexes may differ from those from other sources because lying values in international dollars, normalized to the are calculated using the Laspeyres formula: produc- of differences in coverage, weights, concepts, time base period 1999­2001. · Food production index tion quantities of each commodity are weighted by periods, calculation methods, and use of interna- covers food crops that are considered edible and average international commodity prices in the base tional prices. that contain nutrients. Coffee and tea are excluded period and summed for each year. Because the FAO's To facilitate cross-country comparisons, the FAO because, although edible, they have no nutritive indexes are based on the concept of agriculture as a uses international commodity prices to value produc- value. · Livestock production index includes meat tion. These prices, expressed in international dollars and milk from all sources, dairy products such as (equivalent in purchasing power to the U.S. dollar), cheese, and eggs, honey, raw silk, wool, and hides Cereal yield in low-income economies are derived using a Geary-Khamis formula applied to and skins. · Cereal yield, measured in kilograms is less than 40 percent of the yield in high-income countries 3.3a agricultural outputs (see United Nations System of per hectare of harvested land, includes wheat, rice, National Accounts 1993, sections 16.93­96). This maize, barley, oats, rye, millet, sorghum, buckwheat, Kilograms per hectare method assigns a single price to each commodity so and mixed grains. Production data on cereals refer (thousands) 1990­92 2006­08 6 that, for example, one metric ton of wheat has the to crops harvested for dry grain only. Cereal crops same price regardless of where it was produced. The harvested for hay or harvested green for food, feed, 5 use of international prices eliminates fluctuations in or silage, and those used for grazing, are excluded. the value of output due to transitory movements of The FAO allocates production data to the calendar 4 nominal exchange rates unrelated to the purchasing year in which the bulk of the harvest took place. But 3 power of the domestic currency. most of a crop harvested near the end of a year will Data on cereal yield may be affected by a variety of be used in the following year. · Agricultural produc- 2 reporting and timing differences. Millet and sorghum, tivity is the ratio of agricultural value added, mea- which are grown as feed for livestock and poultry in sured in 2000 U.S. dollars, to the number of workers 1 Europe and North America, are used as food in Africa, in agriculture. Agricultural productivity is measured 0 Asia, and countries of the former Soviet Union. So by value added per unit of input. (For further discus- World Low Lower Upper High Euro some cereal crops are excluded from the data for sion of the calculation of value added in national income middle middle income area income income some countries and included elsewhere, depending accounts, see About the data for tables 4.1 and 4.2.) Source: Table 3.3. on their use. To smooth annual fluctuations in agri- Agricultural value added includes that from forestry cultural activity, the indicators in the table have been and fishing. Thus interpretations of land productivity Sub-Saharan Africa has the averaged over three years. should be made with caution. lowest yield, while East Asia and Pacific is closing the gap with high-income economies 3.3b Kilograms per hectare (thousands) 1990­92 2006­08 6 5 4 3 Data sources 2 Data on agricultural production indexes, cereal yield, and agricultural employment are from elec- 1 tronic files that the FAO makes available to the 0 World Bank. The files may contain more recent East Europe Latin Middle South Sub- High Asia & & America East & Asia Saharan income information than published versions. Data on agri- Pacific Central & North Africa Asia Caribbean Africa cultural value added are from the World Bank's Source: Table 3.3. national accounts files. 2010 World Development Indicators 165 3.4 Deforestation and biodiversity Forest Average annual Threatened GEF benefits Nationally area deforestationa species index for protected areas biodiversity 0­100 (no Terrestrial Marine biodiversity % of % of thousand Higher to maximum surface Number surface Number sq. km % Mammals Birds Fish plantsb biodiversity) area of areas area of areas 1990 2007 1990­2000 2000­07 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan 13 8 2.5 3.2 11 13 3 2 .. 0.2 7 0.0 0 Albania 8 8 0.3 ­0.6 3 6 33 0 0.2 8.0 80 1.1 7 Algeria 18 23 ­1.8 ­1.2 14 11 23 3 2.9 5.0 23 0.3 6 Angola 610 589 0.2 0.2 14 18 22 26 8.3 8.3 15 0.2 4 Argentina 353 327 0.4 0.4 35 49 31 44 17.7 6.5 307 0.6 36 Armenia 3 3 1.0 1.5 9 12 4 1 0.2 8.2 10 0.0 0 Australia 1,679 1,633 0.2 0.1 57 49 84 55 87.7 0.1 5,485 70.6 384 Austria 38 39 ­0.2 ­0.1 4 9 9 4 0.3 28.0 1,087 0.0 0 Azerbaijan 9 9 0.0 0.0 7 15 9 0 0.8 7.3 42 0.0 0 Bangladesh 9 9 0.0 0.3 34 28 12 12 1.4 2.2 20 0.5 7 Belarus 75 79 ­0.5 ­0.1 4 4 1 0 0.0 6.5 440 0.0 0 Belgium .. 7 .. 0.0 3 2 9 1 0.0 3.2 502 0.1 2 Benin 33 22 2.1 2.6 10 4 15 14 0.2 23.2 49 0.0 0 Bolivia 628 582 0.4 0.5 19 29 0 71 12.5 21.2 53 0.0 0 Bosnia and Herzegovina 22 22 0.1 0.0 4 6 27 1 0.4 0.8 32 0.0 0 Botswana 137 117 0.9 1.0 6 7 2 0 1.4 30.1 60 0.0 0 Brazil 5,200 4,715 0.5 0.6 82 122 64 382 100.0 29.6 1,444 4.8 58 Bulgaria 33 37 ­0.1 ­1.4 7 12 17 0 0.8 10.1 905 0.0 1 Burkina Faso 72 67 0.3 0.4 8 5 0 2 0.3 14.4 72 0.0 0 Burundi 3 1 3.7 5.5 9 8 18 2 0.3 5.6 15 0.0 0 Cambodia 129 100 1.1 2.0 37 25 18 31 3.5 24.0 30 0.4 2 Cameroon 245 208 0.9 1.0 41 15 43 355 12.5 10.1 39 0.1 2 Canada 3,101 3,101 0.0 0.0 12 16 26 2 21.5 8.2 5,122 1.1 563 Central African Republic 232 227 0.1 0.1 7 5 0 15 1.5 18.2 32 0.0 0 Chad 131 118 0.6 0.7 12 7 0 2 2.2 9.0 9 0.0 0 Chile 153 162 ­0.4 ­0.4 21 32 18 40 15.3 18.8 102 0.3 9 China 1,571 2,054 ­1.2 ­2.1 74 85 70 446 66.6 15.1 1,981 0.3 36 Hong Kong SAR, China .. .. .. .. 2 16 13 6 .. 44.1 98 0.0 22 Colombia 614 606 0.1 0.1 52 86 31 223 51.5 26.2 263 84.2 15 Congo, Dem. Rep. 1,405 1,330 0.4 0.2 29 31 25 65 19.9 12.2 66 1.8 1 Congo, Rep. 227 224 0.1 0.1 11 3 15 35 3.6 10.3 14 0.0 0 Costa Rica 26 24 0.8 ­0.1 8 17 19 111 9.7 31.0 165 9.8 35 Côte d'Ivoire 102 104 ­0.1 ­0.1 24 14 19 105 3.4 21.1 240 0.0 3 Croatia 21 21 ­0.1 ­0.1 7 11 46 1 0.6 7.5 177 4.4 19 Cuba 21 28 ­1.7 ­2.1 14 17 28 163 12.5 18.8 71 12.6 42 Czech Republic 26 27 0.0 ­0.1 2 6 5 4 0.1 15.8 1,765 0.0 0 Denmark 4 5 ­0.9 ­0.6 2 2 13 3 0.2 5.7 3,847 2.7 52 Dominican Republic 14 14 0.0 0.0 6 14 15 30 6.0 28.5 59 0.0 15 Ecuador 138 105 1.5 1.8 43 69 15 1,839 29.3 25.4 104 12.4 3 Egypt, Arab Rep. 0c 1 ­3.0 ­2.5 17 10 24 2 2.9 7.7 26 9.9 8 El Salvador 4 3 1.5 1.7 5 3 7 26 0.9 1.3 77 0.0 1 Eritrea 16 15 0.2 0.2 9 9 14 3 0.8 4.3 3 0.0 0 Estonia 22 23 ­0.3 ­0.4 1 3 4 0 0.1 46.8 9,617 2.5 3 Ethiopia 147 127 0.7 1.1 31 22 2 22 8.4 17.5 42 0.0 0 Finland 222 225 ­0.1 0.0 1 4 5 1 0.2 9.3 6,046 3.4 15 France 145 156 ­0.5 ­0.3 9 6 31 8 5.3 15.4 1,541 3.2 64 Gabon 219 218 0.0 0.0 13 5 21 108 3.0 16.5 22 4.9 5 Gambia, The 4 5 ­0.4 ­0.4 9 5 16 4 0.1 2.0 6 1.5 6 Georgia 28 28 0.0 0.0 10 10 12 0 0.6 3.9 33 0.0 2 Germany 107 111 ­0.3 0.0 6 6 20 12 0.6 56.2 14,388 26.7 21 Ghana 74 53 2.0 2.0 17 8 17 117 1.9 16.6 302 0.0 0 Greece 33 38 ­0.9 ­0.8 10 11 62 11 2.8 3.4 111 2.4 12 Guatemala 47 38 1.2 1.3 16 11 16 83 8.0 32.7 163 4.7 7 Guinea 74 67 0.7 0.5 22 12 19 22 2.3 6.6 102 0.0 0 Guinea-Bissau 22 21 0.4 0.5 11 2 18 4 0.6 18.2 9 54.4 4 Haiti 1 1 0.6 0.8 5 13 15 29 5.2 0.3 8 0.0 0 Honduras 74 43 3.0 3.2 6 7 19 110 7.2 21.0 77 2.8 22 166 2010 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity Forest Average annual Threatened GEF benefits Nationally area deforestationa species index for protected areas biodiversity 0­100 (no Terrestrial Marine biodiversity % of % of thousand Higher to maximum surface Number surface Number sq. km % Mammals Birds Fish plantsb biodiversity) area of areas area of areas 1990 2007 1990­2000 2000­07 2008 2008 2008 2008 2008 2008 2008 2008 2008 Hungary 18 20 ­0.6 ­0.7 2 9 9 1 0.2 5.6 136 0.0 0 India 639 678 ­0.6 0.0 96 76 40 246 39.9 4.8 556 1.5 117 Indonesia 1,166 848 1.7 2.0 183 115 111 386 81.0 15.7 469 1.8 139 Iran, Islamic Rep. 111 111 0.0 0.0 16 20 21 1 7.3 7.0 145 3.5 12 Iraq 8 8 ­0.2 ­0.1 13 18 6 0 1.6 0.0 8 0.0 0 Ireland 4 7 ­3.3 ­1.9 5 1 16 1 0.6 1.1 85 0.1 12 Israel 2 2 ­0.6 ­0.8 15 13 31 0 0.8 34.5 222 0.5 13 Italy 84 102 ­1.2 ­1.1 7 8 33 19 3.8 7.1 456 3.1 58 Jamaica 3 3 0.1 0.1 5 10 15 209 4.4 20.9 71 3.6 12 Japan 250 249 0.0 0.0 27 40 40 12 36.0 14.1 216 5.2 135 Jordan 1 1 0.0 0.0 13 8 14 0 0.4 10.5 12 21.6 1 Kazakhstan 34 33 0.1 0.2 16 21 13 16 5.1 2.8 77 0.0 0 Kenya 37 35 0.3 0.3 27 27 71 103 8.8 12.3 284 5.8 11 Korea, Dem. Rep. 82 59 1.8 2.0 9 20 8 3 0.7 2.6 31 0.0 0 Korea, Rep. 64 63 0.1 0.1 9 30 14 0 1.7 4.3 32 3.2 6 Kosovo .. 5d .. .. 0 0 .. 0 .. .. Kuwait 0c 0c ­3.4 ­2.4 6 8 10 0 0.1 0.8 5 1.8 5 Kyrgyz Republic 8 9 ­0.2 ­0.3 6 12 3 14 1.1 3.1 29 0.0 0 Lao PDR 173 160 0.5 0.5 46 23 6 21 5.0 15.9 25 0.0 0 Latvia 28 30 ­0.3 ­0.4 1 4 6 0 0.0 16.4 540 0.0 1 Lebanon 1 1 ­0.8 ­0.8 10 6 15 0 0.2 0.4 11 0.0 1 Lesotho 0c 0c ­3.4 ­2.6 2 5 1 1 0.3 0.2 1 0.0 0 Liberia 41 30 1.6 1.8 20 11 19 46 2.6 15.0 16 0.0 1 Libya 2 2 0.0 0.0 12 4 14 1 1.6 0.1 8 1.0 4 Lithuania 20 21 ­0.3 ­0.8 3 4 6 0 0.0 6.0 250 7.9 3 Macedonia, FYR 9 9 0.0 0.0 5 10 14 0 0.2 0.0 61 0.0 0 Madagascar 137 128 0.5 0.3 62 35 75 281 29.2 3.1 53 0.1 8 Malawi 39 33 0.9 1.0 6 12 101 14 3.5 15.5 96 0.0 0 Malaysia 224 206 0.4 0.7 70 42 49 686 13.9 20.3 684 4.6 147 Mali 141 124 0.7 0.8 11 6 1 6 1.5 2.1 10 0.0 0 Mauritania 4 2 2.7 3.5 14 8 23 0 1.3 0.9 3 31.3 3 Mauritius 0c 0c 0.3 0.5 6 11 11 88 3.3 5.5 23 0.3 18 Mexico 690 637 0.5 0.4 100 54 114 261 68.7 8.0 182 14.0 38 Moldova 3 3 ­0.2 ­0.2 4 9 9 0 0.0 1.4 63 0.0 0 Mongolia 115 101 0.7 0.8 11 21 1 0 4.2 13.9 51 0.0 0 Morocco 43 44 ­0.1 ­0.2 18 10 31 2 3.5 1.2 31 1.6 11 Mozambique 200 192 0.3 0.3 11 21 45 46 7.2 15.7 46 4.0 3 Myanmar 392 313 1.3 1.4 45 41 17 38 10.0 6.7 49 0.5 6 Namibia 88 75 0.9 1.0 11 21 21 24 5.2 15.0 31 0.2 4 Nepal 48 35 2.1 1.4 32 32 0 7 2.1 16.6 19 0.0 0 Netherlands 3 4 ­0.4 ­0.3 4 2 11 0 0.2 19.8 1,948 3.1 6 New Zealand 77 83 ­0.6 ­0.2 8 69 14 21 20.2 29.5 3,878 7.1 87 Nicaragua 65 50 1.6 1.5 5 9 21 39 3.3 16.9 74 10.3 5 Niger 19 12 3.7 1.0 11 5 2 2 0.9 6.6 6 0.0 0 Nigeria 172 103 2.7 3.5 27 12 21 171 6.0 16.0 972 0.0 0 Norway 91 94 ­0.2 ­0.2 7 2 14 2 1.3 5.2 1,795 0.5 17 Oman 0c 0c 0.0 0.0 9 9 20 6 3.7 9.4 6 1.2 3 Pakistan 25 18 1.8 2.2 23 27 22 2 4.9 9.0 151 1.1 5 Panama 44 43 0.2 0.1 14 17 19 194 10.9 28.1 53 8.6 21 Papua New Guinea 315 292 0.5 0.5 41 36 38 142 25.4 9.7 67 0.5 24 Paraguay 212 181 0.9 0.9 8 27 0 10 2.8 6.0 33 0.0 0 Peru 702 686 0.1 0.1 53 93 10 275 33.4 13.8 61 2.9 2 Philippines 106 68 2.8 2.1 39 67 60 216 32.3 17.2 204 0.7 212 Poland 89 92 ­0.2 ­0.3 5 6 6 4 0.5 24.3 1,605 2.5 3 Portugal 31 39 ­1.5 ­1.1 11 8 38 16 5.5 6.6 59 1.1 27 Puerto Rico 4 4 ­0.1 0.0 3 8 13 53 4.0 6.8 50 11.5 19 Qatar .. .. 2 4 7 0 0.1 0.0 0 .. .. 2010 World Development Indicators 167 3.4 Deforestation and biodiversity Forest Average annual Threatened GEF benefits Nationally area deforestationa species index for protected areas biodiversity 0­100 (no Terrestrial Marine biodiversity % of % of thousand Higher to maximum surface Number surface Number sq. km % Mammals Birds Fish plantsb biodiversity) area of areas area of areas 1990 2007 1990­2000 2000­07 2008 2008 2008 2008 2008 2008 2008 2008 2008 Romania 64 64 0.0 0.0 7 12 16 1 0.7 10.7 923 37.9 10 Russian Federation 8,090 8,086 0.0 0.0 33 51 32 7 34.1 9.0 11,181 6.3 27 Rwanda 3 5 ­0.8 ­6.5 19 10 9 3 0.9 7.6 5 0.0 0 Saudi Arabia 27 27 0.0 0.0 9 14 16 3 3.2 38.4 30 1.1 3 Senegal 93 86 0.5 0.5 15 8 28 7 1.0 25.0 109 13.0 11 Serbia .. 21 .. .. 6 11 8 1 0.2 2.7 68 0.0 0 Sierra Leone 30 27 0.7 0.7 16 10 16 47 1.3 4.1 39 0.0 0 Singapore 0c 0c 0.0 0.0 12 14 22 54 0.1 5.2 7 0.8 3 Slovak Republic 19 19 0.0 ­0.1 3 7 7 2 0.1 19.6 1,126 0.0 0 Slovenia 12 13 ­0.4 ­0.4 4 4 24 0 0.2 6.6 30 0.4 3 Somalia 83 70 1.0 1.1 14 12 26 17 6.1 0.6 7 0.2 2 South Africa 92 92 0.0 0.0 23 35 65 74 20.7 6.0 931 6.2 30 Spain 135 185 ­2.0 ­1.7 16 15 52 49 6.8 9.5 468 5.3 47 Sri Lanka 24 19 1.2 1.5 30 13 31 280 7.9 20.6 234 1.0 14 Sudan 764 664 0.8 0.9 14 13 13 17 5.1 4.6 20 0.0 1 Swaziland 5 6 ­0.9 ­0.9 4 7 3 11 0.1 3.1 7 0.0 0 Sweden 274 275 0.0 0.0 1 3 12 3 0.3 10.4 4,622 4.9 477 Switzerland 12 12 ­0.4 ­0.4 2 2 11 3 0.2 28.6 2,146 0.0 0 Syrian Arab Republic 4 5 ­1.5 ­1.3 16 13 27 0 0.9 0.7 9 1.3 4 Tajikistan 4 4 0.0 0.0 8 9 8 14 0.7 13.7 15 0.0 0 Tanzania 414 344 1.0 1.1 34 40 138 240 14.8 38.8 537 12.5 17 Thailand 160 144 0.7 0.4 57 44 50 86 8.0 20.4 206 3.9 19 Timor-Leste 10 8 1.2 1.4 4 5 5 0 0.6 14.6 6 0.0 0 Togo 7 3 3.4 4.7 10 2 16 10 0.3 11.1 90 0.2 1 Trinidad and Tobago 2 2 0.3 0.2 2 2 19 1 2.2 35.0 64 0.3 13 Tunisia 6 11 ­4.1 ­1.9 14 8 20 0 0.5 1.5 36 0.2 4 Turkey 97 102 ­0.4 ­0.2 17 15 60 3 6.2 1.9 236 2.8 13 Turkmenistan 41 41 0.0 0.0 9 15 12 3 1.8 2.6 18 0.0 0 Uganda 49 35 1.9 2.3 21 18 54 38 2.8 26.1 732 0.0 0 Ukraine 93 96 ­0.2 ­0.1 11 12 20 1 0.5 3.4 5,197 4.3 15 United Arab Emirates 2 3 ­2.4 ­0.1 7 8 9 0 0.2 0.3 10 0.1 3 United Kingdom 26 29 ­0.7 ­0.4 5 2 34 14 3.5 22.3 778 4.6 149 United States 2,986 3,034 ­0.1 ­0.1 37 74 164 244 94.2 27.1 6,770 67.6 787 Uruguay 9 15 ­4.5 ­1.3 10 24 28 1 1.2 0.4 20 0.1 4 Uzbekistan 31 33 ­0.4 ­0.5 11 15 8 15 1.1 1.9 13 0.0 0 Venezuela, RB 520 471 0.6 0.6 32 26 29 69 25.3 71.3 231 10.9 19 Vietnam 94 134 ­2.3 ­1.9 54 39 33 147 12.1 5.6 116 1.4 36 West Bank and Gaza .. 0c .. 0.0 3 7 1 0 .. 0.0 0 0.0 0 Yemen, Rep. 5 5 0.0 0.0 9 13 18 159 3.2 0.3 3 2.7 1 Zambia 491 416 0.9 1.0 8 12 10 8 3.8 41.1 625 0.0 0 Zimbabwe 222 169 1.5 1.7 8 11 3 17 1.9 15.8 240 0.0 0 World 40,678 s 39,280 s 0.2 w 0.2 w 1,141 1,222 1,275 8,457 14.4 w 112,355 s 1.7 w 4,949 s Low income 5,221 4,635 0.7 0.7 11.9 3,970 0.2 121 Middle income 25,888 24,955 0.2 0.2 12.9 33,010 0.9 1,484 Lower middle income 8,016 7,725 0.3 0.1 11.2 11,729 1.3 791 Upper middle income 17,872 17,230 0.2 0.2 14.0 21,281 0.6 693 Low & middle income 31,109 29,591 0.3 0.3 12.7 36,980 0.8 1,605 East Asia & Pacific 4,580 4,525 0.3 ­0.2 14.7 4,044 1.8 754 Europe & Central Asia 8,812 8,837 0.0 0.0 7.8 21,825 0.4 84 Latin America & Carib. 9,834 9,052 0.5 0.5 22.8 3,801 1.6 422 Middle East & N. Africa 200 212 ­0.4 ­0.3 3.8 313 0.1 53 South Asia 789 799 ­0.2 0.1 5.5 996 0.1 143 Sub-Saharan Africa 6,894 6,165 0.7 0.6 12.4 6,001 0.1 149 High income 9,569 9,689 ­0.1 ­0.1 19.1 75,375 4.3 3,344 Euro area 843 947 ­0.7 ­0.6 17.1 28,025 1.0 277 a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Less than 0.5. d. Data are from national sources. 168 2010 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity About the data Definitions Biological diversity is defined in terms of variability in information on individual species range maps avail- · Forest area is land under natural or planted stands genes, species, and ecosystems. A 2008 comprehen- able from the IUCN for virtually all mammals (5,487), of trees, whether productive or not. · Average annual sive assessment of world species shows that at least amphibians (5,915), and endangered birds (1,098); deforestation is the permanent conversion of natu- 1,141 of 5,487 known mammals are threatened with country data from the World Resources Institute ral forest area to other uses, including agriculture, extinction. As threats to biodiversity mount, the inter- for reptiles and vascular plants; country data from ranching, settlements, and infrastructure. It does not national community is increasingly focusing on con- FishBase for 31,190 fish species; and the ecological include areas logged but intended for regeneration or serving diversity. Deforestation is a major cause of characteristics of 867 world terrestrial ecoregions areas degraded by fuelwood gathering, acid precipita- loss of biodiversity, and habitat conservation is vital from WWF International. For each country the bio- tion, or forest fires. · Threatened species are species for stemming this loss. Conservation efforts have diversity indicator incorporates the best available classified by the IUCN as endangered, vulnerable, rare, focused on protecting areas of high biodiversity. and comparable information in four relevant dimen- indeterminate, out of danger, or insufficiently known. The Food and Agriculture Organization of the United sions: represented species, threatened species, Mammals exclude whales and porpoises. Birds are Nations (FAO) Global Forest Resources Assessment represented ecoregions, and threatened ecoregions. listed for the country where their breeding or winter- 2005 provides detailed information on forest cover in To combine these dimensions into one measure, the ing ranges are located. Fish are cold-blooded aquatic 2005 and adjusted estimates of forest cover in 1990 indicator uses dimensional weights that reflect the vertebrates of the superclass Pisces. Higher plants are and 2000. The current survey uses a uniform defini- consensus of conservation scientists at the GEF, native vascular plant species. · GEF benefits index for tion of forest. Because of space limitations, the table IUCN, WWF International, and other nongovernmen- biodiversity is a composite index of relative biodivers- does not break down forest cover between natural for- tal organizations. ity potential based on the species in each country and est and plantation, a breakdown the FAO provides for The World Conservation Monitoring Centre (WCMC) their threat status and diversity of habitat types. The developing countries. Thus the deforestation data in compiles data on protected areas, numbers of cer- index is normalized from 0 (no biodiversity potential) the table may underestimate the rate at which natural tain species, and numbers of those species under to 100 (maximum biodiversity potential). · Nationally forest is disappearing in some countries. threat from various sources. Because of differences protected areas are totally or partially protected areas The number of threatened species is also an in definitions, reporting practices, and reporting peri- of at least 1,000 hectares that are designated as sci- important measure of the immediate need for con- ods, cross-country comparability is limited. entific reserves with limited public access, national servation in an area. Global analyses of the status Nationally protected areas are defined using the parks, natural monuments, nature reserves or wildlife of threatened species have been carried out for few six IUCN management categories for areas of at sanctuaries, and protected landscapes. Terrestrial groups of organisms. Only for mammals, birds, and least 1,000 hectares: scientific reserves and strict protected areas exclude marine areas, unclassified amphibians has the status of virtually all known spe- nature reserves with limited public access; national areas, littoral (intertidal) areas, and sites protected cies been assessed. Threatened species are defined parks of national or international significance and not under local or provincial law. Marine protected areas using the World Conservation Union's (IUCN) clas- materially affected by human activity; natural monu- are areas of intertidal or subtidal terrain--and over- sification: endangered (in danger of extinction and ments and natural landscapes with unique aspects; lying water and associated flora and fauna and histori- unlikely to survive if causal factors continue operat- managed nature reserves and wildlife sanctuaries; cal and cultural features--that have been reserved to ing) and vulnerable (likely to move into the endan- protected landscapes (which may include cultural protect part of or the entire enclosed environment. gered category in the near future if causal factors landscapes); and areas managed mainly for the sus- Data sources continue operating). tainable use of natural systems to ensure long-term Unlike mammals, birds, and fish, it is difficult to protection and maintenance of biological diversity. Data on forest area are from the FAO's electronic accurately count plants. The number of plant species The data in the table cover these six categories files. The FAO gathers these data from national is highly debated. The 2008 IUCN Red List of Threat- as well as terrestrial protected areas that are not agencies through annual questionnaires and ened Species, the result of more than 20 years' work assigned to a category by the IUCN. Designating an country official publications and websites and by by botanists worldwide, is the most comprehensive area as protected does not mean that protection analyzing national agricultural censuses. Data list of threatened species on a global scale. Only 5 is in force. And for small countries that have only on species are from the electronic files of the percent of plant species have been evaluated, and protected areas smaller than 1,000 hectares, the United Nations Environment Programme (UNEP) 70 percent of these are threatened with extinction. size limit in the definition leads to an underestimate and WCMC, the 2008 IUCN Red List of Threatened Plant species data may not be comparable across of protected areas. Species, and Froese and Pauly's (2008) FishBase countries because of differences in taxonomic con- Due to variations in consistency and methods of database. The GEF benefits index for biodiversity cepts and coverage and so should be used with cau- collection, data quality is highly variable across coun- is from Pandey and others' "Biodiversity Conser- tion. However, the data identify countries that are tries. Some countries update their information more vation Indicators: New Tools for Priority Setting at major sources of global biodiversity and that show frequently than others, some have more accurate the Global Environment Facility" (2006a). Data on national commitments to habitat protection. data on extent of coverage, and many underreport protected areas are from the UNEP and WCMC, as The Global Environment Facility's (GEF) benefits the number or extent of protected areas. compiled by the World Resources Institute, based index for biodiversity is a comprehensive indicator on data from national authorities and national leg- of national biodiversity status and is used to guide islation and international agreements. its biodiversity priorities. The indicator incorporates 2010 World Development Indicators 169 3.5 Freshwater Internal renewable Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007 2007 2007 2007 2007 2007 2006 2006 Afghanistan 55 .. 23.3 42.3 98 0 2 .. .. .. Albania 27 8,588 1.7 6.4 62 11 27 36.2 97 97 Algeria 11 332 6.1 54.0 65 13 22 9.0 87 81 Angola 148 8,431 0.4 0.2 60 17 23 26.1 62 39 Argentina 276 6,989 29.2 10.6 74 9 17 9.7 98 80 Armenia 9 2,952 3.0 32.5 66 4 30 0.6 99 96 Australia 492 23,348 23.9 4.9 75 10 15 16.9 100 100 Austria 55 6,626 2.1 3.8 1 64 35 91.9 100 100 Azerbaijan 8 946 12.2 150.5 76 19 4 0.8 95 59 Bangladesh 105 666 79.4 75.6 96 1 3 0.6 85 78 Belarus 37 3,834 2.8 7.5 30 47 23 4.6 100 99 Belgium 12 1,129 .. .. .. .. .. .. 100 .. Benin 10 1,227 0.1 1.3 45 23 32 18.2 78 57 Bolivia 304 31,868 1.4 0.5 81 7 13 5.8 96 69 Bosnia and Herzegovina 36 9,395 .. .. .. .. .. .. 100 98 Botswana 2 1,268 0.2 8.1 41 18 41 31.8 100 90 Brazil 5,418 28,498 59.3 1.1 62 18 20 10.9 97 58 Bulgaria 21 2,742 10.5 50.0 19 78 3 1.2 100 97 Burkina Faso 13 849 0.8 6.4 86 1 13 3.3 97 66 Burundi 10 1,283 0.3 2.9 77 6 17 2.5 84 70 Cambodia 121 8,417 4.1 3.4 98 0 1 0.9 80 61 Cameroon 273 14,630 1.0 0.4 74 8 18 10.2 88 47 Canada 2,850 86,426 46.0 1.6 12 69 20 15.8 100 99 Central African Republic 141 33,119 0.0 0.0 4 16 80 38.4 90 51 Chad 15 1,412 0.2 1.5 83 0 17 6.0 71 40 Chile 884 53,137 12.6 1.4 64 25 11 6.0 98 72 China 2,812 2,134 630.3 22.4 68 26 7 1.9 98 81 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 47,611 10.7 0.5 46 4 50 8.8 99 77 Congo, Dem. Rep. 900 14,395 0.4 0.0 31 17 53 12.0 82 29 Congo, Rep. 222 62,516 0.0 0.0 9 22 70 76.0 95 35 Costa Rica 112b 25,209b 2.7 2.4 53 17 29 6.0 99 96 Côte d'Ivoire 77 3,819 0.9 1.2 65 12 24 11.2 98 66 Croatia 38 8,493 .. .. .. .. .. .. 100 98 Cuba 38 3,402 8.2 21.5 69 12 19 .. 95 78 Czech Republic 13 1,272 2.6 19.6 2 57 41 22.0 100 100 Denmark 6 1,099 1.3 21.2 43 25 32 126.0 100 100 Dominican Republic 21 2,139 3.4 16.1 66 2 32 7.1 97 91 Ecuador 432 32,379 17.0 3.9 82 5 12 0.9 98 91 Egypt, Arab Rep. 2 22 68.3 3,794.4 86 6 8 1.5 99 98 El Salvador 18 2,907 1.3 7.2 59 16 25 10.3 94 68 Eritrea 3b 586b 0.6 20.8 95 0 5 1.2 74 57 Estonia 13 9,475 0.2 1.2 5 38 57 35.6 100 99 Ethiopia 122b 1,551b 5.6 4.6 94 0 6 1.6 96 31 Finland 107 20,232 2.5 2.3 3 84 14 49.2 100 100 France 179 2,882 40.0 22.4 10 74 16 33.2 100 100 Gabon 164 115,340 0.1 0.1 42 8 50 42.2 95 47 Gambia, The 3 1,857 0.0 1.0 65 12 23 13.8 91 81 Georgia 58 13,339 1.6 2.8 65 13 22 2.7 100 97 Germany 107 1,301 47.1 44.0 20 68 12 40.4 100 100 Ghana 30 1,325 1.0 3.2 66 10 24 5.1 90 71 Greece 58 5,182 7.8 13.4 80 3 16 16.2 100 99 Guatemala 109 8,177 2.0 1.8 80 13 6 9.6 99 94 Guinea 226 23,505 1.5 0.7 90 2 8 2.1 91 59 Guinea-Bissau 16 10,383 0.2 1.1 82 5 13 1.2 82 47 Haiti 13 1,338 1.0 7.6 94 1 5 3.7 70 51 Honduras 96 13,372 0.9 0.9 80 12 8 8.3 95 74 170 2010 World Development Indicators 3.5 ENVIRONMENT Freshwater Internal renewable Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007 2007 2007 2007 2007 2007 2006 2006 Hungary 6 597 7.6 127.3 32 59 9 6.3 100 100 India 1,261 1,121 645.8 51.2 86 5 8 0.7 96 86 Indonesia 2,838 12,578 82.8 2.9 91 1 8 2.0 89 71 Iran, Islamic Rep. 129 1,809 93.3 72.6 92 1 7 1.4 99 84 Iraq 35 .. 66.0 187.5 79 15 7 0.4 .. .. Ireland 49 11,246 1.1 2.3 0 77 23 85.3 100 .. Israel 1 104 2.0 260.5 58 6 36 67.6 100 100 Italy 183 3,074 44.4 24.3 45 37 18 24.7 100 .. Jamaica 9 3,514 0.4 4.4 49 17 34 22.0 97 88 Japan 430 3,365 88.4 20.6 62 18 20 52.8 100 100 Jordan 1 119 0.9 138.0 65 4 31 12.2 99 91 Kazakhstan 75 4,871 35.0 46.4 82 17 2 0.5 99 91 Kenya 21 548 2.7 13.2 79 4 17 5.0 85 49 Korea, Dem. Rep. 67 2,824 9.0 13.5 55 25 20 .. 100 100 Korea, Rep. 65 1,338 18.6 28.7 48 16 36 28.7 97 71 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait .. .. 0.9 .. 54 2 44 42.9 .. .. Kyrgyz Republic 46 8,873 10.1 21.7 94 3 3 0.1 99 83 Lao PDR 190 31,256 3.0 1.6 90 6 4 0.6 86 53 Latvia 17 7,355 0.3 1.8 13 33 53 26.1 100 96 Lebanon 5 1,153 1.3 27.3 60 11 29 15.9 100 100 Lesotho 5 2,574 0.1 1.0 20 40 40 15.7 93 74 Liberia 200 b 55,138b 0.1 0.1 55 18 27 5.1 72 52 Libya 1 97 4.3 721.0 83 3 14 7.8 72 68 Lithuania 16 4,610 0.3 1.7 7 15 78 42.3 .. .. Macedonia, FYR 5 2,647 .. .. .. .. .. .. 100 99 Madagascar 337 18,114 15.0 4.4 96 2 3 0.3 76 36 Malawi 16b 1,118b 1.0 6.3 80 5 15 1.7 96 72 Malaysia 580 21,841 9.0 1.6 62 21 17 10.4 100 96 Mali 60 4,835 6.5 10.9 90 1 9 0.4 86 48 Mauritania 0c 127 1.7 425.0 88 3 9 0.6 70 54 Mauritius 3 2,182 0.7 26.4 68 3 30 6.9 100 100 Mexico 409 3,885 78.2 19.1 77 5 17 7.4 98 85 Moldova 1 273 2.3 231.0 33 58 10 0.6 96 85 Mongolia 35 13,326 0.4 1.3 52 27 20 2.5 90 48 Morocco 29 940 12.6 43.4 87 3 10 2.9 100 58 Mozambique 100 4,586 0.6 0.6 87 2 11 6.7 71 26 Myanmar 881 17,924 33.2 3.8 98 1 1 .. 80 80 Namibia 6 2,949 0.3 4.9 71 5 24 13.0 99 90 Nepal 198 7,007 10.2 5.1 96 1 3 0.5 94 88 Netherlands 11 671 7.9 72.2 34 60 6 48.5 100 100 New Zealand 327 77,336 2.1 0.6 42 9 48 24.1 100 .. Nicaragua 190 33,912 1.3 0.7 83 2 15 3.0 90 63 Niger 4 248 2.2 62.3 95 0 4 0.8 91 32 Nigeria 221 1,496 8.0 3.6 69 10 21 5.7 65 30 Norway 382 81,119 2.2 0.6 11 67 23 76.8 100 100 Oman 1 514 1.3 94.4 88 1 10 16.6 85 73 Pakistan 55b 338b 169.4 308.0 96 2 2 0.4 95 87 Panama 147 44,094 0.8 0.6 28 5 67 14.2 96 81 Papua New Guinea 801 124,716 0.1 0.0 1 42 56 49.6 88 32 Paraguay 94 15,343 0.5 0.5 71 8 20 14.4 94 52 Peru 1,616 56,685 20.1 1.2 82 10 8 2.6 92 63 Philippines 479 5,399 28.5 6.0 74 9 17 2.7 96 88 Poland 54 1,406 16.2 30.2 8 79 13 10.6 100 .. Portugal 38 3,582 11.3 29.6 78 12 10 10.0 99 100 Puerto Rico 7 1,802 .. .. .. .. .. .. .. .. Qatar 0.1 45 0.4 870.6 59 2 39 58.7 100 100 2010 World Development Indicators 171 3.5 Freshwater Internal renewable Annual freshwater Water Access to an improved freshwater resourcesa withdrawals productivity water source GDP/water use Flows Per capita billion % of internal % for % for % for 2000 $ per % of urban % of rural billion cu. m cu. m cu. m resources agriculture industry domestic cu. m population population 2007 2007 2007 2007 2007 2007 2007 2007 2006 2006 Romania 42 1,963 23.2 54.8 57 34 9 1.6 99 76 Russian Federation 4,313 30,350 76.7 1.8 18 63 19 3.4 100 88 Rwanda 10 b 1,005b 0.2 1.6 68 8 24 11.6 82 61 Saudi Arabia 2 99 23.7 986.1 88 3 9 9.9 97 .. Senegal 26b 2,169b 2.2 8.6 93 3 4 2.2 93 65 Serbia 44 d 5,419d .. .. .. .. .. .. 99e .. Sierra Leone 160 b 29,518b 0.4 0.2 92 3 5 1.7 83 32 Singapore 1 131 .. .. .. .. .. .. 100 .. Slovak Republic 13 2,334 .. .. .. .. .. .. 100 100 Slovenia 19 9,251 .. .. .. .. .. .. .. .. Somalia 6 687 3.3 55.0 99 0 0 .. 63 10 South Africa 45 936 12.5 27.9 63 6 31 10.6 100 82 Spain 111 2,478 35.6 32.0 68 19 13 16.3 100 100 Sri Lanka 50 2,499 12.6 25.2 95 2 2 1.3 98 79 Sudan 30 742 37.3 124.4 97 1 3 0.3 78 64 Swaziland 3 2,293 1.0 39.5 97 1 2 1.4 87 51 Sweden 171 18,692 3.0 1.7 9 54 37 83.0 100 100 Switzerland 40 5,350 2.6 6.4 2 74 24 97.2 100 100 Syrian Arab Republic 7 349 16.7 238.4 88 4 9 1.3 95 83 Tajikistan 66 9,855 12.0 18.0 92 5 4 0.1 93 58 Tanzania 84 2,035 5.2 6.2 89 0 10 2.0 81 46 Thailand 210 3,135 87.1 41.5 95 2 2 1.4 99 97 Togo 12 1,825 0.2 1.5 45 2 53 8.2 86 40 Trinidad and Tobago 4 2,891 0.3 8.1 6 26 68 26.3 97 93 Tunisia 4 410 2.6 62.9 82 4 14 7.4 99 84 Turkey 227 3,109 40.1 17.7 74 11 15 7.0 98 95 Turkmenistan 1 273 24.7 1,812.5 98 1 2 0.1 .. .. Uganda 39 1,273 .. .. .. .. .. .. 90 60 Ukraine 53 1,142 37.5 70.7 52 35 12 0.8 97 97 United Arab Emirates 0 34 4.0 2,665.3 83 2 15 24.5 100 100 United Kingdom 145 2,377 9.5 6.6 3 75 22 152.1 100 100 United States 2,800 9,293 479.3 17.1 41 46 13 20.4 100 94 Uruguay 59 17,750 3.2 5.3 96 1 3 7.2 100 100 Uzbekistan 16 608 58.3 357.0 93 2 5 0.2 98 82 Venezuela, RB 722 26,287 8.4 1.2 47 7 46 14.0 .. .. Vietnam 367 4,304 71.4 19.5 68 24 8 0.4 98 90 West Bank and Gaza .. .. .. .. .. .. .. .. 90 88 Yemen, Rep. 2 94 3.4 161.9 90 2 8 2.8 68 65 Zambia 80 6,513 1.7 2.2 76 7 17 1.9 90 41 Zimbabwe 12 985 4.2 34.3 79 7 14 1.6 98 72 World 43,464 s 6,616 w 3,765.3 s 9.0 w 70 w 20 w 10 w 8.3 w 96 w 77 w Low income 4,784 5,004 357.3 7.9 88 6 5 1.2 86 60 Middle income 29,126 6,350 2,518.2 8.8 77 14 9 2.9 95 81 Lower middle income 11,525 3,154 2,039.5 18.1 81 12 7 1.7 94 81 Upper middle income 17,601 18,876 478.7 2.7 58 25 17 15.9 98 82 Low & middle income 33,910 6,118 2,875.5 8.7 78 13 8 2.9 94 76 East Asia & Pacific 9,454 4,938 959.0 10.2 74 20 7 1.9 96 81 Europe & Central Asia 5,129 11,867 356.5 7.2 60 30 10 1.0 99 88 Latin America & Carib. 13,425 24,004 264.9 2.0 71 10 19 7.8 97 73 Middle East & N. Africa 225 715 253.2 122.3 86 6 8 14.4 95 81 South Asia 1,819 1,194 941.1 51.7 90 4 6 0.7 94 84 Sub-Saharan Africa 3,858 4,829 100.8 3.2 87 3 10 1.2 81 46 High income 9,554 9,305 .. 10.4 43 42 15 27.9 100 98 Euro area 942 2,905 200.0 22.3 38 48 15 29.8 100 100 a. Excludes river flows from other countries because of data unreliability. b. Food and Agriculture Organization estimates. c. Less than 0.5. d. Includes Montenegro. e. Includes Kosovo and Metohija. 172 2010 World Development Indicators 3.5 ENVIRONMENT Freshwater About the data Definitions The data on freshwater resources are based on to variations in collection and estimation methods. · Internal renewable freshwater resources are estimates of runoff into rivers and recharge of In addition, inflows and outflows are estimated at the average annual flows of rivers and groundwater groundwater. These estimates are based on differ- different times and at different levels of quality and from rainfall) in the country. Natural incoming flows ent sources and refer to different years, so cross- precision, requiring caution in interpreting the data, originating outside a country's borders are excluded. country comparisons should be made with caution. particularly for water-short countries, notably in the Overlapping water resources between surface run- Because the data are collected intermittently, they Middle East and North Africa. off and groundwater recharge are also deducted. may hide significant variations in total renewable Water productivity is an indication only of the · Renewable internal freshwater resources per water resources from year to year. The data also effi ciency by which each country uses its water capita are calculated using the World Bank's popu- fail to distinguish between seasonal and geographic resources. Given the different economic structure lation estimates (see table 2.1). · Annual freshwater variations in water availability within countries. Data of each country, these indicators should be used withdrawals are total water withdrawals, not count- for small countries and countries in arid and semiarid carefully, taking into account the countries' sectoral ing evaporation losses from storage basins. With- zones are less reliable than those for larger countries activities and natural resource endowments. drawals also include water from desalination plants and countries with greater rainfall. The data on access to an improved water source in countries where they are a significant source. With- Caution should also be used in comparing data measure the percentage of the population with ready drawals can exceed 100 percent of total renewable on annual freshwater withdrawals, which are subject access to water for domestic purposes. The data resources where extraction from nonrenewable aqui- are based on surveys and estimates provided by fers or desalination plants is considerable or where Agriculture is still the largest user of governments to the Joint Monitoring Programme of water reuse is significant. Withdrawals for agriculture water, accounting for some 70 percent of global withdrawals in 2007 . . . 3.5a the World Health Organization (WHO) and the United and industry are total withdrawals for irrigation and Nations Children's Fund (UNICEF). The coverage livestock production and for direct industrial use Percent Industry Domestic Agriculture rates are based on information from service users (including for cooling thermoelectric plants). With- 100 on actual household use rather than on information drawals for domestic uses include drinking water, from service providers, which may include nonfunc- municipal use or supply, and use for public services, 80 tioning systems. Access to drinking water from an commercial establishments, and homes. · Water improved source does not ensure that the water productivity is calculated as GDP in constant prices 60 is safe or adequate, as these characteristics are divided by annual total water withdrawal. · Access not tested at the time of survey. While information to an improved water source is the percentage of the 40 on access to an improved water source is widely population with reasonable access to an adequate used, it is extremely subjective, and such terms as amount of water from an improved source, such as 20 safe, improved, adequate, and reasonable may have piped water into a dwelling, plot, or yard; public tap different meaning in different countries despite offi - or standpipe; tubewell or borehole; protected dug 0 Low Lower Upper High World cial WHO definitions (see Definitions). Even in high- well or spring; and rainwater collection. Unimproved income middle middle income income income income countries treated water may not always be sources include unprotected dug wells or springs, Source: Table 3.5. safe to drink. Access to an improved water source is carts with small tank or drum, bottled water, and equated with connection to a supply system; it does tanker trucks. Reasonable access is defined as the . . . and approaching 90 percent not take into account variations in the quality and availability of at least 20 liters a person a day from in some developing regions in 2007 3.5b cost (broadly defined) of the service. a source within 1 kilometer of the dwelling. Percent Industry Domestic Agriculture 100 80 60 Data sources 40 Data on freshwater resources and withdrawals are from the Food and Agriculture Organization 20 of the United Nations AQUASTAT data. The GDP estimates used to calculate water productivity 0 are from the World Bank national accounts data- East Europe Latin Middle South Sub- Asia & America East & Asia Saharan base. Data on access to water are from WHO and & Central & North Africa Pacific Asia Caribbean Africa UNICEF's Progress on Drinking Water and Sanita- Source: Table 3.5. tion (2008). 2010 World Development Indicators 173 3.6 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2006a 1990 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a Afghanistan 5.9 0.2 0.16 0.21 .. 19.7 27.9 14.1 11.7 23.3 .. 3.1 Albania 2.4 3.6 0.25 0.25 0.0 0.0 0.0 39.8 0.0 60.2 0.0 0.0 Algeria 107.0 .. 0.25 .. .. .. .. .. .. .. .. .. Angola 4.5 .. 0.19 .. .. .. .. .. .. .. .. .. Argentina 181.4 155.5 0.21 0.23 3.8 8.4 15.8 30.5 3.5 14.3 2.1 21.6 Armenia 37.9 7.1 0.11 0.28 .. .. .. 77.6 .. 22.4 .. .. Australia 186.1 111.7 0.18 0.18 12.4 22.8 6.7 43.5 0.2 5.3 2.8 6.3 Austria 90.5 84.8 0.15 0.14 5.7 7.1 9.2 12.5 5.9 4.5 5.9 49.0 Azerbaijan 41.3 18.8 0.15 0.18 9.7 2.5 18.7 19.0 6.5 13.6 1.4 28.5 Bangladesh 250.8 303.0 0.15 0.14 0.7 2.3 3.0 7.6 2.6 79.3 0.5 4.2 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 107.8 97.9 0.17 0.17 6.4 7.8 17.3 15.7 5.5 6.9 2.2 38.3 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 11.3 11.5 0.24 0.25 0.9 9.8 13.1 35.4 7.7 18.4 5.3 9.5 Bosnia and Herzegovina 50.7 .. 0.14 .. .. .. .. .. .. .. .. .. Botswana 2.5 5.0 0.30 0.28 0.0 2.4 0.0 56.7 0.6 3.4 0.0 36.9 Brazil 780.4 .. 0.19 .. .. .. .. .. .. .. .. .. Bulgaria 124.3 101.2 0.17 0.17 3.8 4.3 7.6 18.0 4.6 28.0 3.0 30.6 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 1.6 .. 0.24 .. .. .. .. .. .. .. .. .. Cambodia 3.6 .. 0.21 .. .. .. .. .. .. .. .. .. Cameroon 14.0 10.0 0.28 0.19 0.4 5.2 36.1 48.8 0.0 3.8 5.0 0.8 Canada 300.9 310.3 0.17 0.16 4.4 9.1 10.6 13.9 2.8 7.9 6.7 44.6 Central African Republic 1.0 .. 0.18 .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 92.5 .. 0.25 7.6 6.3 13.7 35.1 3.6 9.1 6.9 17.7 China 7,038.1 6,088.7 0.14 0.14 20.4 10.9 14.8 28.1 0.5 15.5 0.9 8.8 Hong Kong SAR, China 86.1 34.3 0.12 0.20 1.2 43.5 3.9 30.5 0.1 16.2 0.2 4.6 Colombia .. 87.0 .. 0.20 2.3 8.9 17.3 21.3 5.3 24.1 0.9 19.9 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 2.5 .. 0.32 .. .. .. .. .. .. .. .. .. Costa Rica 27.2 31.2 0.20 0.22 1.6 10.0 8.2 65.7 0.1 10.2 1.3 2.9 Côte d'Ivoire 7.9 .. 0.22 .. .. .. .. .. .. .. .. .. Croatia 48.5 41.8 0.17 0.17 3.2 7.2 9.5 18.0 5.9 15.3 4.8 36.0 Cuba 173.0 .. 0.25 .. .. .. .. .. .. .. .. .. Czech Republic 176.8 146.5 0.15 0.13 5.4 4.8 10.9 10.9 6.4 7.4 4.4 49.8 Denmark 84.5 60.5 0.18 0.16 1.4 11.3 12.4 16.2 4.4 2.2 4.0 48.1 Dominican Republic 88.6 88.6 0.18 0.18 0.1 1.3 2.3 18.6 1.4 73.1 0.1 3.1 Ecuador 28.6 44.7 0.24 0.28 1.8 7.8 12.8 46.4 4.4 12.3 2.2 12.3 Egypt, Arab Rep. 206.5 206.5 0.19 0.19 5.8 4.0 13.9 20.0 8.2 31.1 0.6 16.4 El Salvador 5.5 .. 0.22 .. .. .. .. .. .. .. .. .. Eritrea 2.4 2.8 0.19 0.20 0.2 4.1 9.5 30.0 13.2 25.1 0.0 17.8 Estonia 21.7 16.4 0.15 0.15 0.4 7.3 8.4 15.1 5.1 8.8 17.0 37.9 Ethiopia 18.5 26.8 0.23 0.23 1.8 6.8 10.6 30.7 8.5 28.8 1.5 11.3 Finland 72.5 61.6 0.19 0.16 4.8 15.6 8.6 8.8 4.0 2.8 6.7 48.7 France 326.5 578.2 0.11 0.16 3.3 7.4 15.0 16.2 3.8 5.1 2.3 46.9 Gabon 2.0 .. 0.25 .. .. .. .. .. .. .. .. .. Gambia, The 0.8 .. 0.34 .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 1,020.9 954.2 0.14 0.14 3.8 7.2 12.0 11.8 3.4 2.5 2.0 57.4 Ghana .. 15.4 .. 0.17 3.1 2.8 15.0 19.2 4.2 10.0 34.3 11.4 Greece 50.9 58.6 0.19 0.20 4.4 9.0 10.3 23.1 6.7 15.3 2.7 28.6 Guatemala 21.6 .. 0.23 .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.1 0.0 0.01 0.01 0.0 2.0 0.0 0.0 0.0 0.0 0.0 98.0 Honduras 17.8 .. 0.23 .. .. .. .. .. .. .. .. .. 174 2010 World Development Indicators 3.6 ENVIRONMENT Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2006a 1990 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a Hungary 122.1 115.1 0.18 0.15 2.7 6.4 10.5 15.8 3.8 10.5 3.4 46.9 India 1,410.6 1,519.8 0.20 0.20 12.2 7.6 9.2 53.7 0.3 12.7 0.3 3.9 Indonesia 721.8 764.0 0.18 0.18 1.3 4.0 13.0 21.5 3.9 29.0 7.4 19.8 Iran, Islamic Rep. 131.6 160.8 0.16 0.15 7.1 2.8 12.8 16.1 13.8 11.2 0.7 35.5 Iraq 7.7 7.7 0.27 0.27 13.1 25.6 29.9 16.9 5.4 9.1 .. .. Ireland 36.1 34.1 0.19 0.18 1.3 10.1 17.2 21.6 5.8 1.9 3.5 38.6 Israel 43.9 42.8 0.18 0.18 2.2 8.5 15.0 19.7 0.0 9.1 1.5 43.9 Italy 378.3 475.8 0.13 0.12 3.5 5.2 10.5 9.0 5.5 14.2 2.9 49.2 Jamaica 18.7 .. 0.29 .. .. .. .. .. .. .. .. .. Japan 1,451.4 1,122.7 0.14 0.15 3.2 7.1 11.2 15.1 3.6 5.3 2.0 52.6 Jordan 15.0 27.2 0.18 0.18 2.5 6.1 14.7 21.6 11.6 16.8 2.6 24.2 Kazakhstan 1.3 1.7 0.40 0.41 0.0 50.0 0.0 47.6 0.0 0.0 0.0 2.4 Kenya 42.6 56.1 0.23 0.24 .. 11.5 5.4 66.8 0.1 12.8 1.7 1.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 366.9 319.6 0.12 0.11 4.2 5.4 12.1 6.3 3.0 9.3 0.9 58.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 9.1 11.9 0.16 0.17 2.1 16.6 11.1 50.2 0.4 11.6 2.8 5.2 Kyrgyz Republic 28.9 11.8 0.14 0.20 8.6 6.0 8.4 24.8 14.9 11.8 1.8 23.7 Lao PDR 0.5 0.5 0.44 0.44 0.0 26.3 0.0 73.7 0.0 0.0 0.0 0.0 Latvia 39.8 29.3 0.12 0.18 2.6 6.8 5.6 21.9 3.7 12.6 19.7 27.2 Lebanon 14.7 14.7 0.19 0.19 0.5 7.5 6.0 25.5 12.9 16.7 4.5 26.3 Lesotho .. 15.3 .. 0.13 0.9 0.5 1.2 3.6 1.2 90.7 .. 1.9 Liberia 0.6 .. 0.30 .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 54.0 42.6 0.15 0.17 0.8 5.2 7.6 20.0 4.4 19.3 11.5 31.2 Macedonia, FYR 32.4 .. 0.18 .. .. .. .. .. .. .. .. .. Madagascar .. 92.8 .. 0.14 0.3 1.6 12.4 7.6 2.8 58.9 6.3 10.0 Malawi 37.2 32.7 0.40 0.39 .. 1.4 3.7 82.1 0.6 7.5 1.1 3.6 Malaysia .. 208.4 .. 0.13 2.9 5.2 16.2 9.5 3.9 6.8 7.9 47.5 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 0.3 0.4 0.05 0.06 0.0 13.7 0.0 0.0 .. 0.0 0.0 86.3 Mexico 370.8 .. 0.19 .. .. .. .. .. .. .. .. .. Moldova 29.2 21.1 0.44 0.45 0.0 3.3 0.0 95.7 0.0 0.0 .. 1.0 Mongolia 10.2 .. 0.18 .. .. .. .. .. .. .. .. .. Morocco .. 80.4 .. 0.16 1.0 2.8 8.7 17.6 9.4 42.0 1.9 16.6 Mozambique 20.4 10.2 0.27 0.31 1.1 7.1 2.7 81.2 0.1 5.8 1.4 0.7 Myanmar 7.7 6.2 0.17 0.18 56.5 4.6 13.2 14.9 0.4 2.9 1.7 5.8 Namibia 7.4 .. 0.35 .. .. .. .. .. .. .. .. .. Nepal 26.4 26.8 0.14 0.16 1.6 3.9 7.2 19.2 29.9 29.4 2.0 6.8 Netherlands 142.3 122.1 0.20 0.18 1.2 13.8 14.8 18.4 4.1 2.6 2.5 42.5 New Zealand 46.7 62.5 0.24 0.23 2.1 12.7 8.6 30.6 3.2 6.1 7.8 28.9 Nicaragua 10.5 .. 0.27 .. .. .. .. .. .. .. .. .. Niger .. 0.4 .. 0.32 .. 17.0 4.4 76.9 0.3 .. 0.8 0.6 Nigeria 70.8 .. 0.22 .. .. .. .. .. .. .. .. .. Norway 51.8 50.5 0.20 0.20 5.1 14.3 7.5 20.9 4.0 2.1 5.6 40.5 Oman 3.8 6.6 0.15 0.17 4.3 5.1 16.3 21.6 23.7 5.2 2.1 21.6 Pakistan 104.1 .. 0.18 .. .. .. .. .. .. .. .. .. Panama 10.3 13.7 0.30 0.32 0.9 11.7 7.0 55.7 4.0 4.8 1.7 14.2 Papua New Guinea 5.7 .. 0.25 .. .. .. .. .. .. .. .. .. Paraguay 15.3 10.8 0.20 0.28 3.1 9.3 16.7 42.6 5.9 11.0 4.5 6.9 Peru 56.1 .. 0.20 .. .. .. .. .. .. .. .. .. Philippines 118.4 97.9 0.26 0.23 5.8 6.3 13.2 33.1 6.2 3.1 0.0 32.4 Poland 446.7 364.5 0.16 0.16 3.1 5.2 11.1 18.8 5.4 11.0 4.8 40.6 Portugal 140.6 105.0 0.14 0.15 1.7 7.2 6.6 15.1 5.0 19.1 6.8 38.5 Puerto Rico 19.0 9.2 0.15 0.18 1.9 14.9 21.9 34.4 0.2 15.5 1.4 9.7 Qatar .. 3.7 .. 0.12 5.6 1.3 17.2 10.7 29.7 2.2 20.4 12.8 2010 World Development Indicators 175 3.6 Water pollution Emissions of organic Industry shares of emissions water pollutants of organic water pollutants % of total thousand kilograms Stone, kilograms per day Primary Paper and Food and ceramics, per day per worker metals pulp Chemicals beverages and glass Textiles Wood Other 1990 2006a 1990 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a Romania 411.2 228.1 0.12 0.15 4.6 3.4 6.7 13.4 3.9 27.4 5.1 35.4 Russian Federation 1,521.4 1,388.1 0.16 0.17 9.0 5.0 11.9 17.8 8.0 6.6 4.2 37.7 Rwanda 7.1 7.1 0.44 0.44 .. .. 0.0 97.0 0.0 0.0 0.0 3.0 Saudi Arabia .. 6.8 .. 0.39 0.0 96.9 0.0 0.5 0.0 0.0 0.0 2.6 Senegal 6.1 6.6 0.30 0.29 4.9 6.3 23.8 44.6 3.9 10.5 0.8 5.3 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 4.2 .. 0.32 .. .. .. .. .. .. .. .. .. Singapore 32.3 35.3 0.09 0.09 0.0 5.8 11.4 5.3 1.4 2.4 0.4 73.3 Slovak Republic 72.8 51.4 0.13 0.14 7.6 4.8 8.8 10.7 5.9 11.5 3.9 46.8 Slovenia 28.1 28.2 0.13 0.13 4.5 6.4 11.9 8.1 3.5 11.4 4.9 49.3 Somalia 6.2 .. 0.38 .. .. .. .. .. .. .. .. .. South Africa 260.5 191.6 0.17 0.16 5.8 7.0 11.4 14.7 5.2 11.9 4.3 39.6 Spain 348.0 379.7 0.16 0.15 3.1 7.9 10.8 15.2 7.9 9.0 3.7 42.4 Sri Lanka .. 266.1 .. 0.19 2.6 4.3 9.0 22.4 6.3 43.6 2.5 9.3 Sudan .. 38.6 .. 0.29 0.6 1.9 7.0 57.5 14.2 8.0 1.7 9.1 Swaziland 146.0 .. 0.16 .. .. .. .. .. .. .. .. .. Sweden 116.8 97.6 0.15 0.14 5.4 12.2 9.9 8.7 2.5 1.4 5.4 54.4 Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 6.6 4.5 0.45 0.45 0.0 6.2 0.0 93.8 0.0 0.0 0.0 0.0 Tajikistan 29.1 16.1 0.17 0.23 21.9 1.4 5.1 20.2 7.6 37.5 0.4 5.9 Tanzania 31.1 35.2 0.24 0.25 1.5 9.4 2.7 69.3 0.1 14.0 1.5 1.4 Thailand 369.4 333.8 0.15 0.16 1.8 4.1 13.2 16.5 3.4 22.5 2.4 36.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 7.0 7.6 0.23 0.29 0.0 18.1 21.4 39.1 0.4 7.6 8.5 4.9 Tunisia 44.6 55.8 0.18 0.14 2.5 6.1 5.5 35.8 0.4 43.3 1.9 4.6 Turkey 174.9 177.7 0.18 0.16 5.2 3.0 9.8 15.2 6.2 35.7 1.0 24.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 3.3 17.5 0.29 0.26 .. 4.6 7.9 44.5 0.0 14.4 16.8 11.7 Ukraine .. 537.4 .. 0.20 14.5 4.1 10.3 20.7 6.5 6.1 2.1 35.8 United Arab Emirates 5.6 .. 0.14 .. .. .. .. .. .. .. .. .. United Kingdom 599.9 521.7 0.16 0.17 2.7 12.5 13.5 14.9 3.6 4.3 2.5 46.1 United States 2,307.0 1,889.4 0.14 0.14 3.4 8.3 13.1 12.0 3.7 4.7 4.1 50.6 Uruguay 38.7 15.8 0.23 0.28 1.2 3.7 6.6 79.2 0.1 7.4 0.6 1.2 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 96.5 .. 0.21 .. .. .. .. .. .. .. .. .. Vietnam 141.0 500.5 0.16 0.15 1.4 3.5 6.8 13.3 6.7 40.3 3.3 24.7 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.5 1.6 0.43 0.41 .. 67.4 0.0 32.6 0.0 0.0 0.0 0.0 Zambia 15.9 .. 0.23 .. .. .. .. .. .. .. .. .. Zimbabwe 29.3 29.3 0.20 0.20 8.0 4.7 11.0 21.5 6.3 25.2 1.7 21.5 a. Data are derived using the United Nations Industrial Development Organization's (UNIDO) industry database four-digit International Standard Industrial Classification (ISIC). Data in italics are for the most recent year available and are derived using UNIDO's industry database at the three-digit ISIC. 176 2010 World Development Indicators 3.6 ENVIRONMENT Water pollution About the data Definitions Emissions of organic pollutants from industrial emissions of organic water pollutants. Such data · Emissions of organic water pollutants are mea- activities are a major cause of degradation of water are fairly reliable because sampling techniques for sured as biochemical oxygen demand, or the amount quality. Water quality and pollution levels are gener- measuring water pollution are more widely under- of oxygen that bacteria in water will consume in ally measured as concentration or load--the rate of stood and much less expensive than those for air breaking down waste, a standard water treatment occurrence of a substance in an aqueous solution. pollution. test for the presence of organic pollutants. Emis- Polluting substances include organic matter, metals, Hettige, Mani, and Wheeler (1998) used plant- and sions per worker are total emissions divided by the minerals, sediment, bacteria, and toxic chemicals. sector-level information on emissions and employ- number of industrial workers. · Industry shares of The table focuses on organic water pollution result- ment from 13 national environmental protection emissions of organic water pollutants are emissions ing from industrial activities. Because water pollu- agencies and sector-level information on output from manufacturing activities as defined by two-digit tion tends to be sensitive to local conditions, the and employment from the United Nations Industrial divisions of the International Standard Industrial national-level data in the table may not reflect the Development Organization (UNIDO). Their economet- Classification revision 3. quality of water in specific locations. ric analysis found that the ratio of BOD to employ- The data in the table come from an international ment in each industrial sector is about the same study of industrial emissions that may have been across countries. This finding allowed the authors to the first to include data from developing countries estimate BOD loads across countries and over time. (Hettige, Mani, and Wheeler 1998). These data were The estimated BOD intensities per unit of employ- updated through 2006 by the World Bank's Develop- ment were multiplied by sectoral employment num- ment Research Group. Unlike estimates from earlier bers from UNIDO's industry database for 1980­98. studies based on engineering or economic models, These estimates of sectoral emissions were then these estimates are based on actual measurements used to calculate kilograms of emissions of organic of plant-level water pollution. The focus is on organic water pollutants per day for each country and year. water pollution caused by organic waste, measured in The data in the table were derived by updating these terms of biochemical oxygen demand (BOD), because estimates through 2006. the data for this indicator are the most plentiful and reliable for cross-country comparisons of emissions. BOD measures the strength of an organic waste by the amount of oxygen consumed in breaking it down. A sewage overload in natural waters exhausts the water's dissolved oxygen content. Wastewater treat- ment, by contrast, reduces BOD. Data on water pollution are more readily available than are other emissions data because most indus- trial pollution control programs start by regulating Emissions of organic water pollutants declined in most economies from 1990 to 2006, even in some of the top emitters 3.6a Kilograms per day (millions) 1990­98 2000­06 8 6 Data sources 4 Data on water pollutants are from Hettige, Mani, and Wheeler, "Industrial Pollution in Economic 2 Development: Kuznets Revisited" (1998). The data were updated through 2006 by the World 0 Bank's Development Research Group using the China United States India Russian Japan Germany Indonesia Federation same methodology as the initial study. Data on industrial sectoral employment are from UNIDO's Note: Data are for the most recent year available during the period specified. Source: Table 3.6. industry database. 2010 World Development Indicators 177 3.7 Energy production and use Energy Energy Alternative and production use nuclear energy production Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2007 1990 2007 1990­2007 1990 2007 1990 2007 1990 2007 1990 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 2.4 1.1 2.7 2.2 2.0 809 694 76.5 67.8 13.6 9.9 9.2 11.1 Algeria 100.1 164.3 22.2 36.9 2.7 878 1,089 99.9 99.7 0.1 0.2 0.1 0.1 Angola 28.7 95.0 5.9 10.6 3.4 552 606 25.5 34.0 73.5 63.4 1.1 2.6 Argentina 48.4 81.9 46.1 73.1 2.1 1,418 1,850 88.7 89.5 3.7 3.5 7.5 6.2 Armenia 0.1 0.8 7.7 2.8 ­3.7 2,171 926 97.2 70.7 0.1 0.0 1.7 29.0 Australia 157.5 289.2 86.2 124.1 2.3 5,053 5,888 93.9 94.4 4.6 4.3 1.5 1.3 Austria 8.1 10.9 24.8 33.2 2.0 3,214 3,997 79.2 72.6 10.0 15.4 11.0 10.3 Azerbaijan 21.3 52.1 25.8 11.9 ­3.2 3,609 1,388 .. 98.4 0.0 0.0 0.2 1.7 Bangladesh 10.8 21.3 12.7 25.8 4.5 110 163 45.5 66.2 53.9 33.3 0.6 0.5 Belarus 3.3 4.0 42.3 28.0 ­1.9 4,155 2,891 95.4 91.5 0.5 5.2 0.0 0.0 Belgium 13.1 14.4 48.2 57.0 1.1 4,840 5,366 76.0 73.1 1.6 3.6 23.1 22.2 Benin 1.8 1.8 1.7 2.9 3.1 346 343 4.8 36.8 94.2 61.5 0.0 0.0 Bolivia 4.9 15.1 2.8 5.4 3.4 416 571 69.1 81.8 27.2 14.5 3.6 3.7 Bosnia and Herzegovina 4.6 3.9 7.0 5.6 2.2 1,627 1,483 93.9 91.5 2.3 3.3 3.8 6.1 Botswana 0.9 1.1 1.3 2.0 2.6 933 1,068 66.1 69.4 33.4 23.1 0.1 0.0 Brazil 103.7 215.6 139.5 235.6 3.1 933 1,239 51.1 52.6 34.1 30.7 13.2 15.1 Bulgaria 9.6 10.0 28.6 20.2 ­1.3 3,277 2,641 84.3 77.8 0.6 3.7 13.9 20.4 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 3.6 .. 5.1 3.5 .. 358 .. 29.1 .. 70.5 .. 0.1 Cameroon 11.0 10.2 5.0 7.3 2.3 407 391 18.7 27.4 76.7 68.1 4.6 4.5 Canada 273.8 413.2 208.7 269.4 1.6 7,509 8,169 74.5 75.6 4.0 4.3 21.5 20.9 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. .. Chile 7.4 8.5 13.8 30.8 5.0 1,048 1,851 75.1 77.7 19.4 15.4 5.5 6.5 China 886.3 1,814.0 863.1 1,955.8 4.5 760 1,484 75.5 86.9 23.2 9.9 1.3 3.2 Hong Kong SAR, China 0.0 0.0 8.8 13.7 2.4 1,539 1,985 99.4 95.3 0.6 0.4 0.0 0.0 Colombia 48.2 87.6 24.2 29.5 0.5 730 655 67.4 71.5 22.8 15.5 9.8 13.2 Congo, Dem. Rep. 12.0 18.4 11.8 18.1 2.5 319 289 11.2 4.2 84.7 92.7 4.1 3.9 Congo, Rep. 8.7 12.5 0.8 1.3 2.8 326 357 35.0 38.7 59.5 55.9 5.3 2.3 Costa Rica 1.0 2.5 2.0 4.8 5.1 643 1,070 47.2 47.1 37.4 17.7 14.7 35.0 Côte d'Ivoire 3.4 11.2 4.3 10.0 5.0 343 496 23.3 22.7 73.5 76.4 2.6 1.6 Croatia 5.1 4.1 9.0 9.3 1.4 1,884 2,099 86.5 86.7 3.5 3.5 3.6 4.0 Cuba 6.6 5.2 16.5 9.9 ­1.7 1,558 884 64.3 86.8 35.6 13.1 0.1 0.1 Czech Republic 40.1 33.7 48.8 45.8 0.2 4,705 4,428 93.2 83.0 0.0 4.6 6.9 15.4 Denmark 10.1 27.0 17.3 19.6 0.2 3,374 3,598 89.6 82.3 6.6 14.8 0.3 3.3 Dominican Republic 1.0 1.5 4.1 7.9 4.0 556 804 74.8 80.5 24.4 18.0 0.7 1.5 Ecuador 16.5 28.9 6.0 11.8 3.9 583 885 79.1 86.6 13.8 6.2 7.2 6.6 Egypt, Arab Rep. 54.9 82.3 31.8 67.2 4.7 551 840 94.0 95.8 3.3 2.2 2.7 2.1 El Salvador 1.7 2.8 2.5 4.9 3.8 463 800 31.4 41.9 48.2 30.7 20.3 27.4 Eritrea 0.7 0.5 0.9 0.7 ­2.2 276 151 19.3 26.5 80.7 73.5 0.0 0.0 Estonia 5.1 4.4 9.6 5.6 ­2.2 6,099 4,198 99.8 91.3 2.0 10.5 0.0 0.2 Ethiopia 14.1 20.9 14.9 22.8 2.6 308 290 5.5 8.5 93.9 90.2 0.6 1.3 Finland 12.1 15.9 28.4 36.5 1.8 5,692 6,895 55.5 50.0 16.1 20.1 20.9 20.1 France 112.5 135.5 224.5 263.7 1.1 3,957 4,258 58.0 51.2 5.2 5.1 38.6 45.6 Gabon 14.6 12.0 1.2 1.8 2.3 1,275 1,300 32.0 39.6 62.9 56.6 5.2 3.7 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. .. Georgia 1.8 1.1 12.1 3.3 ­7.3 2,217 767 88.6 70.7 3.8 11.8 5.4 18.0 Germany 186.2 137.0 351.4 331.3 ­0.1 4,424 4,027 86.8 80.8 1.4 6.8 11.8 12.8 Ghana 4.4 6.5 5.3 9.5 3.4 353 415 18.2 31.8 73.7 64.7 9.3 3.4 Greece 9.2 12.1 21.4 32.2 2.6 2,110 2,875 94.6 93.4 4.2 3.7 1.0 1.7 Guatemala 3.4 5.3 4.4 8.3 4.0 498 620 28.1 46.0 68.5 50.4 3.4 3.8 Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1.3 2.0 1.6 2.8 3.6 219 286 19.7 27.8 77.8 71.7 2.5 0.5 Honduras 1.7 2.1 2.4 4.7 3.6 486 661 30.0 55.3 62.9 40.7 8.2 4.0 178 2010 World Development Indicators 3.7 ENVIRONMENT Energy production and use Energy Energy Alternative and production use nuclear energy production Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2007 1990 2007 1990­2007 1990 2007 1990 2007 1990 2007 1990 2007 Hungary 14.6 10.2 28.7 26.7 0.0 2,762 2,658 81.5 79.0 2.3 5.0 12.8 14.8 India 291.1 450.9 318.2 594.9 3.5 375 529 55.6 70.0 41.9 27.2 2.4 2.7 Indonesia 170.0 331.1 102.5 190.6 3.5 575 845 54.6 68.8 43.9 27.5 1.5 3.7 Iran, Islamic Rep. 179.8 323.1 68.3 184.9 5.7 1,256 2,604 98.2 98.7 1.0 0.5 0.8 0.8 Iraq 104.9 104.8 18.1 33.1 3.9 1,000 .. 98.6 99.4 0.1 0.1 1.2 0.1 Ireland 3.5 1.4 10.0 15.1 2.9 2,843 3,457 84.8 90.9 1.1 1.6 0.6 1.5 Israel 0.4 2.7 11.6 22.0 3.6 2,486 3,059 97.2 97.4 0.0 0.0 3.1 3.4 Italy 25.3 26.4 146.7 178.2 1.4 2,586 3,001 93.4 90.5 0.6 2.6 3.9 4.6 Jamaica 0.5 0.5 2.8 5.0 2.8 1,167 1,852 82.6 89.9 17.1 9.8 0.3 0.4 Japan 75.1 90.5 438.1 513.5 0.9 3,546 4,019 84.5 83.2 1.1 1.4 14.4 15.3 Jordan 0.2 0.3 3.3 7.2 4.4 1,028 1,259 98.2 98.4 0.1 0.1 1.8 1.5 Kazakhstan 90.5 136.0 72.7 66.5 ­1.5 4,450 4,292 96.9 98.9 0.2 0.1 0.9 1.1 Kenya 9.0 14.7 11.2 18.3 3.0 479 485 19.5 19.6 75.9 74.0 4.4 6.4 Korea, Dem. Rep. 28.9 19.7 33.2 18.4 ­2.3 1,649 774 93.1 88.1 2.9 5.7 4.0 6.2 Korea, Rep. 22.6 42.5 93.1 222.2 5.0 2,171 4,586 83.8 81.9 0.8 1.2 15.4 16.9 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 50.4 146.6 7.8 25.2 7.5 3,681 9,463 99.9 100.0 0.1 0.0 0.0 0.0 Kyrgyz Republic 2.5 1.4 7.6 2.9 ­4.3 1,713 556 93.6 65.7 0.1 0.1 11.3 41.2 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1.1 1.8 7.8 4.7 ­2.6 2,913 2,052 81.6 64.2 8.5 25.1 5.0 5.1 Lebanon 0.1 0.2 2.2 4.0 3.8 755 959 93.5 92.7 4.6 3.5 1.9 1.7 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. .. Libya 73.2 101.6 11.3 17.8 2.3 2,596 2,889 98.9 99.1 1.1 0.9 0.0 0.0 Lithuania 4.9 3.8 16.1 9.3 ­2.4 4,357 2,740 75.8 61.9 1.8 8.3 28.2 28.7 Macedonia, FYR 1.3 1.5 2.5 3.0 0.8 1,298 1,482 98.0 85.0 0.0 4.8 1.7 3.2 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 50.3 94.4 22.7 72.6 6.2 1,252 2,733 89.1 95.5 9.4 4.0 1.5 0.8 Mali .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. .. Mexico 193.4 251.1 121.2 184.3 2.3 1,456 1,750 88.1 89.3 6.1 4.5 5.9 6.3 Moldova 0.1 0.1 9.9 3.3 ­5.6 2,261 910 99.6 90.0 0.4 2.3 0.2 0.1 Mongolia 2.7 3.6 3.4 3.1 ­1.3 1,541 1,182 97.0 96.1 2.5 3.3 0.0 0.0 Morocco 0.8 0.7 6.9 14.4 4.0 287 465 93.8 93.8 4.6 3.1 1.5 1.0 Mozambique 5.6 11.0 5.9 9.2 2.8 437 418 5.5 8.0 93.9 80.3 0.4 15.1 Myanmar 10.7 23.9 10.7 15.6 2.4 261 319 14.4 31.7 84.7 66.3 1.0 1.9 Namibia 0.2 0.3 0.7 1.6 5.1 446 745 62.0 68.0 16.0 12.3 17.5 8.7 Nepal 5.5 8.5 5.8 9.6 3.1 303 338 5.1 10.7 93.7 86.7 1.3 2.5 Netherlands 60.5 61.5 65.7 80.4 1.0 4,392 4,909 96.0 92.9 1.4 3.5 1.4 1.8 New Zealand 12.0 14.0 13.3 16.8 1.3 3,859 3,966 64.2 67.4 4.1 6.6 31.7 25.9 Nicaragua 1.5 2.1 2.1 3.5 3.2 506 621 28.3 40.6 53.9 52.4 17.5 6.8 Niger .. .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150.5 231.7 70.6 106.7 2.4 725 722 19.3 19.3 80.2 80.2 0.5 0.5 Norway 119.1 213.9 21.0 26.9 1.7 4,951 5,704 51.9 54.8 4.9 5.1 49.6 43.2 Oman 38.3 59.3 4.2 15.5 7.0 2,304 5,678 100.0 100.0 0.0 0.0 0.0 0.0 Pakistan 34.2 63.6 42.9 83.3 3.7 397 512 52.7 62.1 43.8 33.9 3.6 3.9 Panama 0.6 0.7 1.5 2.8 3.5 618 845 58.4 75.7 28.3 13.5 12.7 11.2 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4.6 7.1 3.1 4.2 1.5 723 686 21.3 29.4 72.5 53.0 76.0 109.9 Peru 10.6 12.2 9.7 14.1 2.2 447 494 63.3 69.8 27.5 18.2 9.2 12.0 Philippines 15.7 22.4 27.5 40.0 2.3 440 451 45.8 57.0 35.2 19.2 19.0 23.8 Poland 103.9 72.6 103.1 97.1 ­0.6 2,705 2,547 97.8 94.8 2.2 5.4 0.1 0.3 Portugal 3.4 4.6 16.7 25.1 2.9 1,691 2,363 80.4 79.1 14.8 12.6 4.8 5.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. Qatar 26.6 103.0 6.9 22.2 6.4 14,732 19,504 99.9 100.0 0.1 0.0 0.0 0.0 2010 World Development Indicators 179 3.7 Energy production and use Energy Energy Alternative and production use nuclear energy production Total Total % of total million million average Per capita metric tons of metric tons of annual kilograms of Combustible % of total oil equivalent oil equivalent % growth oil equivalent Fossil fuel renewables and waste energy use 1990 2007 1990 2007 1990­2007 1990 2007 1990 2007 1990 2007 1990 2007 Romania 40.8 27.6 62.3 38.9 ­2.1 2,683 1,806 96.1 82.8 1.0 8.9 1.6 8.7 Russian Federation 1,280.3 1,230.6 870.0 672.1 ­1.3 5,867 4,730 93.3 89.3 1.4 1.0 5.2 8.6 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 370.8 551.3 59.3 150.3 4.8 3,618 6,223 100.0 100.0 0.0 0.0 0.0 0.0 Senegal 1.0 1.3 1.7 2.7 3.5 224 225 43.2 53.1 56.8 45.9 0.0 0.7 Serbia 13.4 9.8 19.3 15.8 .. 2,550 2,141 90.6 89.2 6.0 5.1 4.2 5.7 Serbia 25.2 .. 43.8 .. .. 4,182 .. 90.6 .. 2.1 .. 7.4 .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. .. Singapore 0.0 0.0 11.5 26.8 3.8 3,760 5,831 100.0 100.0 0.0 0.0 0.0 0.0 Slovak Republic 5.3 6.0 21.3 17.8 ­0.2 4,037 3,307 81.6 70.8 0.8 3.5 15.5 24.9 Slovenia 3.1 3.5 5.7 7.3 2.0 2,835 3,632 71.1 69.2 4.7 6.5 25.8 24.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114.5 159.6 90.9 134.3 2.2 2,581 2,807 86.1 87.7 11.5 10.2 2.5 2.3 Spain 34.6 30.3 90.1 144.0 3.2 2,320 3,208 77.4 83.2 4.5 3.7 18.1 13.3 Sri Lanka 4.2 5.1 5.5 9.3 3.5 322 464 24.1 45.5 71.0 50.8 4.9 3.7 Sudan 8.8 34.6 10.6 14.7 2.6 392 363 17.5 26.3 81.8 72.8 0.8 0.9 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. .. Sweden 29.7 33.6 47.2 50.4 0.5 5,514 5,512 37.3 32.9 11.7 19.6 50.9 46.2 Switzerland 9.7 12.6 23.8 25.7 0.6 3,545 3,406 59.9 51.6 3.8 8.2 37.0 40.9 Syrian Arab Republic 22.3 24.4 11.4 19.6 2.9 895 958 97.9 98.4 0.0 0.0 2.1 1.5 Tajikistan 2.0 1.6 5.6 3.9 ­1.9 1,051 580 72.7 62.0 0.0 0.0 25.5 37.7 Tanzania 9.1 16.9 9.7 18.3 4.1 382 443 6.9 10.3 91.7 88.6 1.4 1.2 Thailand 26.5 59.4 42.0 104.0 5.2 742 1,553 63.9 81.2 34.9 17.8 1.0 0.7 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. .. Togo 1.1 2.1 1.3 2.5 4.5 322 390 15.0 12.8 82.8 85.1 0.6 0.3 Trinidad and Tobago 12.6 37.0 6.0 15.3 6.1 4,899 11,506 99.2 99.9 0.8 0.1 0.0 0.0 Tunisia 5.7 7.9 4.9 8.8 3.7 607 864 87.0 86.3 12.9 13.6 0.1 0.1 Turkey 25.8 27.3 52.8 100.0 3.6 941 1,370 81.8 90.5 13.7 5.1 4.6 4.6 Turkmenistan 74.9 66.1 19.6 18.1 1.2 5,352 3,631 100.0 100.0 0.0 0.0 0.3 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 135.8 81.6 251.8 137.3 ­3.2 4,851 2,953 91.8 81.7 0.1 0.6 8.2 18.2 United Arab Emirates 110.2 178.4 19.9 51.6 5.2 10,645 11,833 100.0 100.0 0.0 0.0 0.0 0.0 United Kingdom 208.0 176.2 207.2 211.3 0.2 3,619 3,464 90.7 89.6 0.3 1.9 8.5 8.2 United States 1,649.4 1,665.2 1,913.2 2,339.9 1.2 7,664 7,766 86.4 85.6 3.3 3.5 10.3 10.8 Uruguay 1.1 1.2 2.3 3.2 1.3 725 953 58.7 62.3 24.3 16.4 26.8 21.9 Uzbekistan 38.6 60.1 46.4 48.7 0.6 2,261 1,812 99.2 98.9 0.0 0.0 1.2 1.1 Venezuela, RB 148.9 183.8 43.6 63.7 1.6 2,206 2,319 91.5 87.8 1.2 0.8 7.3 11.2 Vietnam 24.7 73.9 24.3 55.8 5.1 367 655 20.4 51.4 77.7 44.0 1.9 4.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.4 16.5 2.5 7.2 6.2 204 324 97.0 98.9 3.1 1.1 0.0 0.0 Zambia 4.9 6.8 5.4 7.4 1.8 683 604 15.6 10.7 74.3 78.3 12.7 11.3 Zimbabwe 8.6 8.7 9.3 9.4 ­0.2 889 759 44.8 27.9 50.9 65.0 4.0 4.7 World 8,823.2 t 11,926.4 t 8,555.5 t 11,664.3 t 1.8 w 1,666 w 1,819 w 81.0 w 81.3 w 10.2 w 9.6 w 8.8 w 9.0 w Low income 249.0 407.6 277.3 378.3 2.1 449 423 50.6 46.7 46.2 49.3 3.4 4.1 Middle income 4,811.7 6,906.3 3,884.6 5,715.4 2.2 1,054 1,242 79.5 81.6 16.4 13.2 4.1 5.1 Lower middle income 2,296.4 3,981.5 2,013.2 3,713.4 3.5 696 1,013 71.4 80.0 25.9 16.3 2.8 3.8 Upper middle income 2,515.6 2,926.8 1,871.9 2,004.7 0.5 2,354 2,130 88.1 84.7 6.2 7.3 5.4 7.4 Low & middle income 5,055.3 7,298.0 4,145.7 6,074.4 2.2 980 1,127 77.9 79.8 18.1 15.1 4.0 5.0 East Asia & Pacific 1,225.4 2,460.3 1,138.8 2,475.5 4.4 715 1,295 71.5 83.8 26.6 12.8 1.8 3.4 Europe & Central Asia 1,861.3 1,796.8 1,675.5 1,297.3 ­1.3 3,885 2,948 93.2 89.2 1.6 2.1 5.1 8.1 Latin America & Carib. 608.4 919.8 453.0 711.2 2.5 1,042 1,273 71.2 72.8 19.7 16.3 9.2 10.8 Middle East & N. Africa 558.6 836.8 185.5 406.5 4.5 819 1,276 97.2 97.9 1.7 1.1 1.1 0.9 South Asia 348.7 554.1 388.3 728.9 3.6 347 484 53.7 67.9 43.7 29.3 2.5 2.8 Sub-Saharan Africa 475.6 779.9 310.8 474.1 2.5 676 662 41.3 41.8 56.5 55.8 2.2 2.5 High income 3,785.4 4,654.1 4,433.0 5,625.0 1.5 4,733 5,321 83.9 82.9 2.8 3.7 13.1 13.3 Euro area 477.1 459.9 1,060.4 1,229.2 1.1 3,516 3,789 79.8 75.7 3.2 5.6 16.7 18.2 180 2010 World Development Indicators 3.7 ENVIRONMENT Energy production and use About the data Definitions In developing economies growth in energy use is assumed for converting nuclear electricity into oil · Energy production refers to forms of primary closely related to growth in the modern sectors-- equivalents and 100 percent efficiency for converting energy--petroleum (crude oil, natural gas liquids, industry, motorized transport, and urban areas-- hydroelectric power. and oil from nonconventional sources), natural gas, but energy use also reflects climatic, geographic, The IEA makes these estimates in consultation solid fuels (coal, lignite, and other derived fuels), and economic factors (such as the relative price with national statistical offices, oil companies, elec- and combustible renewables and waste--and pri- of energy). Energy use has been growing rapidly in tric utilities, and national energy experts. The IEA mary electricity, all converted into oil equivalents low- and middle-income economies, but high-income occasionally revises its time series to reflect politi- (see About the data). · Energy use refers to the use economies still use almost five times as much energy cal changes, and energy statistics undergo contin- of primary energy before transformation to other on a per capita basis. ual changes in coverage or methodology as more end-use fuels, which is equal to indigenous produc- Energy data are compiled by the International detailed energy accounts become available. Breaks tion plus imports and stock changes, minus exports Energy Agency (IEA). IEA data for economies that in series are therefore unavoidable. and fuels supplied to ships and aircraft engaged in are not members of the Organisation for Economic international transport (see About the data). · Fos- Co-operation and Development (OECD) are based sil fuel comprises coal, oil, petroleum, and natural on national energy data adjusted to conform to gas products. · Combustible renewables and waste annual questionnaires completed by OECD member comprise solid biomass, liquid biomass, biogas, governments. industrial waste, and municipal waste. · Alternative Total energy use refers to the use of primary energy and nuclear energy production is noncarbohydrate before transformation to other end-use fuels (such energy that does not produce carbon dioxide when as electricity and refined petroleum products). It generated. It includes hydropower and nuclear, geo- includes energy from combustible renewables and thermal, and solar power, among others. waste--solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Data for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences (see Data sources). All forms of energy--primary energy and primary electricity--are converted into oil equiva- lents. A notional thermal efficiency of 33 percent is A person in a high-income economy uses more than 12 times as much energy on average as a person in a low-income economy 3.7a Energy use per capita (thousands of kilograms of oil equivalent) 1990 2007 6 4 Data sources 2 Data on energy production and use are from IEA electronic files and are published in IEA's annual publications, Energy Statistics and Bal- 0 ances of Non-OECD Countries, Energy Statistics Low income Lower Upper High income World middle income middle income of OECD Countries, and Energy Balances of OECD Source: Table 3.7. Countries. 2010 World Development Indicators 181 3.8 Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2007 1990 2007 1990 2006 1990 2006 1990 2006 1990 2006 Afghanistan .. .. .. .. 2.7 0.7 .. .. 0.2 0.0 .. 0.0 Albania 8 51 4.5 9.2 7.5 4.3 2.8 2.0 2.3 1.4 0.6 0.2 Algeria ­351 ­346 7.1 6.7 78.8 132.6 3.6 3.8 3.1 4.0 0.5 0.6 Angola ­387 ­793 5.8 8.1 4.4 10.6 0.8 1.1 0.4 0.6 0.1 0.1 Argentina ­5 ­12 5.3 6.8 112.5 173.4 2.4 2.5 3.5 4.4 0.5 0.4 Armenia 98 71 1.4 5.7 3.7 4.4 0.9 1.7 1.1 1.4 0.7 0.3 Australia ­83 ­133 4.8 6.0 292.9 371.7 3.4 3.0 17.2 18.0 0.7 0.5 Austria 67 67 8.0 8.9 60.7 71.8 2.4 2.1 7.9 8.7 0.3 0.3 Azerbaijan 17 ­337 1.3 5.3 44.1 35.0 2.7 2.6 6.0 4.1 1.7 0.7 Bangladesh 16 17 6.2 7.2 15.5 41.6 1.2 1.7 0.1 0.3 0.2 0.2 Belarus 92 86 1.5 3.6 98.5 68.8 2.6 2.4 9.6 7.1 1.7 0.7 Belgium 73 75 5.2 6.2 107.5 107.1 2.2 1.8 10.8 10.2 0.4 0.3 Benin ­7 39 3.2 3.9 0.7 3.1 0.4 1.1 0.1 0.4 0.1 0.3 Bolivia ­77 ­177 7.0 6.6 5.5 11.4 2.0 2.4 0.8 1.2 0.3 0.3 Bosnia and Herzegovina 34 30 .. 4.5 4.7 27.4 1.1 5.1 1.2 7.3 .. 1.2 Botswana 28 45 7.5 11.6 2.2 4.8 1.7 2.4 1.6 2.6 0.2 0.2 Brazil 26 8 7.7 7.4 208.7 352.3 1.5 1.6 1.4 1.9 0.2 0.2 Bulgaria 66 51 2.2 3.8 76.6 48.0 2.7 2.4 8.8 6.2 1.2 0.7 Burkina Faso .. .. .. .. 0.6 0.8 .. .. 0.1 0.1 0.1 0.1 Burundi .. .. .. .. 0.3 0.2 .. .. 0.1 0.0 0.1 0.1 Cambodia .. 29 .. 4.8 0.5 4.1 .. 0.8 0.0 0.3 .. 0.2 Cameroon ­120 ­39 5.1 5.1 1.7 3.6 0.3 0.5 0.1 0.2 0.1 0.1 Canada ­31 ­53 3.6 4.4 449.7 544.3 2.2 2.0 16.2 16.7 0.6 0.5 Central African Republic .. .. .. .. 0.2 0.2 .. .. 0.1 0.1 0.1 0.1 Chad .. .. .. .. 0.1 0.4 .. .. 0.0 0.0 0.0 0.0 Chile 46 73 6.3 7.1 35.5 60.1 2.6 2.0 2.7 3.6 0.4 0.3 China ­3 7 1.4 3.4 2,412.9 6,099.1 2.8 3.3 2.1 4.7 1.9 1.0 Hong Kong SAR, China 100 100 15.4 20.1 27.6 39.0 3.1 2.9 4.8 5.7 0.2 0.2 Colombia ­99 ­202 8.2 12.3 57.3 63.4 2.4 2.1 1.7 1.5 0.3 0.2 Congo, Dem. Rep. ­2 ­2 1.9 1.0 4.1 2.2 0.3 0.1 0.1 0.0 0.2 0.1 Congo, Rep. ­997 ­891 10.7 9.9 1.2 1.5 1.5 1.2 0.5 0.4 0.1 0.1 Costa Rica 48 47 9.7 9.6 3.0 7.8 1.5 1.8 1.0 1.8 0.2 0.2 Côte d'Ivoire 22 ­13 5.5 3.1 5.8 6.9 1.3 0.7 0.5 0.3 0.2 0.2 Croatia 43 57 6.6 7.5 16.9 23.7 2.5 2.6 3.8 5.3 0.4 0.4 Cuba 60 48 .. .. 33.3 29.6 2.0 3.0 3.1 2.6 .. .. Czech Republic 18 26 3.5 5.2 131.0 114.8 3.0 2.5 12.7 11.2 0.9 0.5 Denmark 42 ­38 7.5 9.6 50.4 53.9 2.9 2.7 9.8 9.9 0.4 0.3 Dominican Republic 75 80 6.7 9.0 9.6 20.3 2.3 2.6 1.3 2.1 0.3 0.3 Ecuador ­175 ­145 9.4 7.9 16.8 31.3 2.8 2.9 1.6 2.4 0.3 0.3 Egypt, Arab Rep. ­72 ­22 5.8 5.7 75.9 166.7 2.4 2.6 1.3 2.1 0.4 0.5 El Salvador 31 42 8.0 7.7 2.6 6.5 1.1 1.4 0.5 1.1 0.1 0.2 Eritrea 19 26 1.9 4.0 .. 0.6 .. 0.8 .. 0.1 .. 0.2 Estonia 47 22 1.7 4.7 25.0 17.5 4.0 3.5 16.3 13.0 2.2 0.7 Ethiopia 5 9 1.8 2.6 3.0 6.0 0.2 0.3 0.1 0.1 0.1 0.1 Finland 57 56 4.1 4.8 51.0 66.6 1.8 1.8 10.2 12.7 0.4 0.4 France 50 49 6.3 7.4 397.8 382.9 1.8 1.4 7.0 6.2 0.3 0.2 Gabon ­1,139 ­549 11.8 10.3 6.1 2.1 5.2 1.2 6.6 1.5 0.4 0.1 Gambia, The .. .. .. .. 0.2 0.3 .. .. 0.2 0.2 0.2 0.2 Georgia 85 68 2.4 5.8 15.3 5.5 1.8 1.8 2.9 1.3 1.2 0.3 Germany 47 59 5.8 8.2 962.7 804.5 2.8 2.4 12.0 9.8 0.4 0.3 Ghana 17 32 2.5 3.1 3.9 9.2 0.7 1.0 0.3 0.4 0.3 0.3 Greece 57 62 8.3 9.4 72.7 96.3 3.4 3.2 7.2 8.6 0.4 0.3 Guatemala 24 36 6.7 7.0 5.1 11.8 1.1 1.4 0.6 0.9 0.2 0.2 Guinea .. .. .. .. 1.1 1.4 .. .. 0.2 0.1 0.2 0.2 Guinea-Bissau .. .. .. .. 0.3 0.3 .. .. 0.2 0.2 0.4 0.4 Haiti 20 28 6.4 3.6 1.0 1.8 0.6 0.7 0.1 0.2 0.1 0.2 Honduras 29 55 5.5 5.4 2.6 7.2 1.1 1.8 0.5 1.0 0.2 0.3 182 2010 World Development Indicators 3.8 ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2007 1990 2007 1990 2006 1990 2006 1990 2006 1990 2006 Hungary 49 62 4.5 6.7 61.9 57.6 2.2 2.1 6.0 5.7 0.5 0.3 India 9 24 3.2 4.9 690.1 1,509.3 2.2 2.7 0.8 1.4 0.7 0.6 Indonesia ­66 ­74 3.6 4.1 150.3 333.2 1.5 1.8 0.8 1.5 0.4 0.4 Iran, Islamic Rep. ­163 ­75 5.0 4.0 227.0 466.6 3.3 2.7 4.2 6.7 0.7 0.7 Iraq ­480 ­217 .. .. 52.5 92.5 2.9 2.7 2.8 3.2 .. .. Ireland 65 91 6.2 11.9 30.9 43.8 3.1 3.0 8.8 10.3 0.5 0.3 Israel 96 88 7.2 8.2 33.5 70.4 2.9 3.3 7.2 10.0 0.4 0.4 Italy 83 85 9.2 9.6 424.7 473.8 2.9 2.6 7.5 8.0 0.3 0.3 Jamaica 83 90 5.1 3.9 8.0 12.1 2.9 2.8 3.3 4.6 0.6 0.6 Japan 83 82 7.3 7.9 1,171.4 1,292.5 2.7 2.5 9.5 10.1 0.4 0.3 Jordan 95 96 3.2 3.8 10.4 20.7 3.2 3.0 3.3 3.7 1.0 0.8 Kazakhstan ­24 ­105 1.6 2.4 261.1 193.4 3.3 3.0 15.9 12.6 2.7 1.3 Kenya 20 20 3.0 3.0 5.8 12.1 0.5 0.7 0.2 0.3 0.2 0.2 Korea, Dem. Rep. 13 ­7 .. .. 244.6 84.7 7.4 3.9 12.1 3.6 .. .. Korea, Rep. 76 81 5.2 5.5 241.5 474.9 2.6 2.2 5.6 9.8 0.5 0.4 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait ­544 ­482 2.8 4.8 40.7 86.5 5.2 3.5 19.2 33.3 0.6 0.7 Kyrgyz Republic 67 51 1.5 3.4 11.0 5.6 2.2 2.0 2.4 1.1 1.3 0.6 Lao PDR .. .. .. .. 0.2 1.4 .. .. 0.1 0.2 0.1 0.1 Latvia 86 61 3.2 7.4 13.3 7.5 2.2 1.6 5.1 3.3 0.9 0.2 Lebanon 94 95 7.5 10.5 9.1 15.3 4.0 3.3 3.1 3.7 0.5 0.4 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. 0.5 0.8 .. .. 0.2 0.2 0.5 0.7 Libya ­546 ­470 .. 5.1 40.3 55.5 3.6 3.1 9.2 9.2 .. 0.6 Lithuania 69 59 2.7 5.8 22.1 14.2 2.0 1.7 6.0 4.2 0.7 0.3 Macedonia, FYR 49 50 6.0 5.3 10.8 10.9 4.0 3.7 5.6 5.3 0.8 0.7 Madagascar .. .. .. .. 1.0 2.8 .. .. 0.1 0.2 0.1 0.2 Malawi .. .. .. .. 0.6 1.0 .. .. 0.1 0.1 0.1 0.1 Malaysia ­122 ­30 5.3 4.7 56.5 187.7 2.5 2.8 3.1 7.2 0.5 0.6 Mali .. .. .. .. 0.4 0.6 .. .. 0.0 0.0 0.1 0.0 Mauritania .. .. .. .. 2.7 1.7 .. .. 1.3 0.5 0.9 0.3 Mauritius .. .. .. .. 1.5 3.8 .. .. 1.4 3.1 0.2 0.3 Mexico ­60 ­36 6.9 7.6 384.4 435.8 3.2 2.5 4.6 4.2 0.5 0.3 Moldova 99 97 1.7 2.7 21.0 7.8 3.1 2.3 4.8 2.1 2.1 0.9 Mongolia 20 ­15 1.4 2.6 10.0 9.4 2.9 3.3 4.5 3.7 2.0 1.3 Morocco 89 95 9.7 8.3 23.5 45.3 3.4 3.4 0.9 1.5 0.4 0.4 Mozambique 5 ­20 0.9 1.8 1.0 2.0 0.2 0.2 0.1 0.1 0.2 0.1 Myanmar 0 ­53 .. .. 4.3 10.0 0.4 0.6 0.1 0.2 .. .. Namibia 67 79 9.4 7.9 0.0 2.8 0.0 1.9 0.0 1.4 0.0 0.2 Nepal 5 11 2.3 2.9 0.6 3.2 0.1 0.3 0.0 0.1 0.0 0.1 Netherlands 8 24 6.0 7.6 167.3 168.4 2.5 2.2 11.2 10.3 0.4 0.3 New Zealand 10 16 4.8 6.4 22.7 30.5 1.7 1.8 6.6 7.3 0.4 0.3 Nicaragua 29 41 3.7 3.9 2.6 4.3 1.3 1.3 0.6 0.8 0.3 0.3 Niger .. .. .. .. 1.1 0.9 .. .. 0.1 0.1 0.2 0.1 Nigeria ­113 ­117 2.0 2.6 45.3 97.2 0.6 0.9 0.5 0.7 0.3 0.4 Norway ­467 ­696 6.5 8.6 31.3 40.2 1.5 1.4 7.4 8.6 0.2 0.2 Oman ­802 ­283 6.4 3.8 10.3 41.3 2.4 2.8 5.6 15.5 0.4 0.8 Pakistan 20 24 4.2 4.6 68.5 142.6 1.6 1.8 0.6 0.9 0.4 0.4 Panama 59 75 9.8 12.7 3.1 6.4 2.1 2.2 1.3 2.0 0.2 0.2 Papua New Guinea .. .. .. .. 2.1 4.6 .. .. 0.5 0.7 0.3 0.4 Paraguay ­49 ­70 5.5 6.1 2.3 4.0 0.7 1.0 0.5 0.7 0.1 0.2 Peru ­9 13 10.0 14.7 21.1 38.6 2.2 2.9 1.0 1.4 0.2 0.2 Philippines 43 44 5.4 7.1 44.5 68.3 1.6 1.7 0.7 0.8 0.3 0.3 Poland ­1 25 3.0 6.1 347.6 318.0 3.4 3.3 9.1 8.3 1.1 0.6 Portugal 80 82 9.4 9.0 44.3 60.0 2.6 2.4 4.5 5.7 0.3 0.3 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar ­286 ­364 .. 3.4 11.8 46.2 1.7 2.5 25.2 46.1 .. 0.7 2010 World Development Indicators 183 3.8 Energy dependency and efficiency and carbon dioxide emissions Net energy GDP per unit of Carbon dioxide importsa energy use emissions Carbon intensity 2005 PPP $ kilograms per kilograms per per kilogram Total kilogram of oil Per capita 2005 PPP $ % of energy use of oil equivalent million metric tons equivalent energy use metric tons of GDP 1990 2007 1990 2007 1990 2006 1990 2006 1990 2006 1990 2006 Romania 34 29 2.7 5.6 158.7 98.4 2.5 2.5 6.8 4.6 0.9 0.5 Russian Federation ­47 ­83 2.2 2.9 2,073.5 1,563.5 2.7 2.3 13.9 11.0 1.4 0.9 Rwanda .. .. .. .. 0.7 0.8 .. .. 0.1 0.1 0.1 0.1 Saudi Arabia ­526 ­267 5.3 3.5 214.9 381.3 3.6 2.6 13.2 16.1 0.7 0.8 Senegal 43 53 6.3 7.3 3.2 4.3 1.9 1.5 0.4 0.4 0.3 0.2 Serbia 31 38 4.8 4.4 .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. 0.4 1.0 .. .. 0.1 0.2 0.1 0.3 Singapore 100 100 6.3 8.1 46.9 56.2 4.1 2.1 15.4 12.8 0.6 0.3 Slovak Republic 75 67 3.1 5.9 44.3 37.4 2.4 2.0 8.4 6.9 0.8 0.4 Slovenia 46 53 5.8 7.2 12.3 15.2 2.4 2.1 6.2 7.6 0.4 0.3 Somalia .. .. .. .. 0.0 0.2 .. .. 0.0 0.0 .. .. South Africa ­26 ­19 3.0 3.3 333.3 414.3 3.7 3.2 9.5 8.7 1.2 1.0 Spain 62 79 8.5 8.9 229.0 352.0 2.5 2.5 5.9 8.0 0.3 0.3 Sri Lanka 24 45 6.3 8.6 3.8 11.9 0.7 1.3 0.2 0.6 0.1 0.2 Sudan 17 ­136 2.5 5.2 5.6 10.8 0.5 0.7 0.2 0.3 0.2 0.2 Swaziland .. .. .. .. 0.4 1.0 .. .. 0.5 0.9 0.1 0.2 Sweden 37 33 4.5 6.2 51.2 50.8 1.1 1.0 6.0 5.6 0.2 0.2 Switzerland 59 51 9.4 11.0 42.9 41.8 1.8 1.5 6.4 5.6 0.2 0.2 Syrian Arab Republic ­96 ­24 3.3 4.2 37.4 68.4 3.3 3.7 2.9 3.5 1.0 0.9 Tajikistan 64 59 2.9 2.9 21.3 6.4 4.4 1.7 3.9 1.0 2.0 0.6 Tanzania 7 8 2.3 2.5 2.4 5.4 0.2 0.3 0.1 0.1 0.1 0.1 Thailand 37 43 5.3 4.7 95.8 272.3 2.3 2.7 1.7 4.1 0.4 0.6 Timor-Leste .. .. .. .. .. 0.2 .. .. .. 0.2 .. 0.3 Togo 17 15 2.7 2.0 0.8 1.2 0.6 0.5 0.2 0.2 0.2 0.3 Trinidad and Tobago ­111 ­142 2.1 2.0 16.9 33.6 2.8 2.4 13.9 25.4 1.4 1.2 Tunisia ­16 11 6.6 8.2 13.3 23.1 2.7 2.7 1.6 2.3 0.4 0.3 Turkey 51 73 8.3 8.7 146.5 269.3 2.8 2.9 2.6 3.7 0.3 0.3 Turkmenistan ­281 ­266 0.7 1.6 28.0 44.1 2.5 2.7 7.2 9.0 2.3 1.7 Uganda .. .. .. .. 0.8 2.7 .. .. 0.0 0.1 0.1 0.1 Ukraine 46 41 1.7 2.2 611.0 318.9 2.8 2.3 11.7 6.8 1.8 1.1 United Arab Emirates ­454 ­245 4.8 4.5 54.8 139.5 2.8 3.1 29.3 32.9 0.6 0.6 United Kingdom 0 17 6.6 9.9 573.3 568.1 2.8 2.6 10.0 9.4 0.4 0.3 United States 14 29 4.2 5.5 4,861.1 5,748.1 2.5 2.5 19.5 19.3 0.6 0.5 Uruguay 49 62 10.1 11.4 4.0 6.9 1.8 2.2 1.3 2.1 0.2 0.2 Uzbekistan 17 ­23 0.9 1.3 113.9 115.6 2.5 2.4 5.3 4.4 3.1 2.1 Venezuela, RB ­242 ­188 4.3 4.9 122.1 171.5 2.8 2.8 6.2 6.3 0.6 0.6 Vietnam ­2 ­33 2.5 3.7 21.4 106.1 0.9 2.0 0.3 1.3 0.4 0.6 West Bank and Gaza .. .. .. .. .. 3.0 .. .. .. 0.8 .. .. Yemen, Rep. ­273 ­129 8.7 6.8 .. 21.2 .. 3.1 .. 1.0 .. 0.4 Zambia 9 8 1.8 2.0 2.4 2.5 0.5 0.3 0.3 0.2 0.2 0.2 Zimbabwe 8 8 0.3 0.2 16.6 11.1 1.8 1.2 1.6 0.9 6.0 5.0 World ­3b w ­2b w 4.2 w 5.4 w 22,511.6c t 30,154.7c t 2.5c w 2.5c w 4.3c w 4.4c w 0.6c w 0.5c w Low income 10 ­8 2.2 3.2 508.5 478.2 1.9 1.5 0.6 0.5 0.2 0.4 Middle income ­24 ­21 3.0 4.4 9,936.6 14,821.4 2.6 2.7 1.8 3.3 0.7 0.6 Lower middle income ­14 ­7 2.5 3.9 4,849.7 9,976.8 2.4 2.8 1.4 2.8 0.9 0.8 Upper middle income ­34 ­46 3.6 5.2 5,086.1 4,837.2 2.8 2.5 3.8 5.2 0.5 0.5 Low & middle income ­22 ­20 3.0 4.3 10,445.0 15,299.3 2.5 2.7 1.6 2.8 0.7 0.6 East Asia & Pacific ­8 1 2.0 3.6 3,046.8 7,188.2 2.7 3.1 1.9 3.8 1.3 0.9 Europe & Central Asia ­11 ­39 2.3 3.7 4,566.0 3,195.3 3.2 2.5 9.4 7.3 1.3 0.7 Latin America & Carib. ­34 ­29 6.9 7.5 1,044.8 1,462.3 2.3 2.2 2.4 2.6 0.3 0.3 Middle East & N. Africa ­201 ­106 5.6 5.0 565.9 1,111.4 3.1 2.9 2.5 3.5 0.5 0.6 South Asia 10 24 3.5 5.0 781.5 1,710.4 2.0 2.5 0.7 1.1 0.6 0.5 Sub-Saharan Africa ­54 ­64 2.6 3.2 466.4 640.8 1.7 1.5 0.9 0.8 0.6 0.5 High income 15 18 5.3 6.5 11,332.7 13,377.9 2.5 2.4 12.1 12.7 0.5 0.4 Euro area 55 63 6.7 8.2 2,602.0 2,701.4 2.5 2.2 7.5 8.4 0.3 0.3 a. Negative values indicate that a country is a net exporter. b. Deviation from zero is due to statistical errors and changes in stock. c. Includes emissions not allocated to specific countries. 184 2010 World Development Indicators 3.8 ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions About the data Because commercial energy is widely traded, its pro- combustion different fossil fuels release different estimated from a consistent time series tend to be duction and use need to be distinguished. Net energy amounts of carbon dioxide for the same level of more accurate than individual values. Each year imports show the extent to which an economy's use energy use: oil releases about 50 percent more car- the CDIAC recalculates the entire time series since exceeds its production. High-income economies are bon dioxide than natural gas, and coal releases about 1949, incorporating recent findings and corrections. net energy importers; middle-income economies are twice as much. Cement manufacturing releases Estimates exclude fuels supplied to ships and aircraft their main suppliers. about half a metric ton of carbon dioxide for each in international transport because of the difficulty of The ratio of gross domestic product (GDP) to energy metric ton of cement produced. apportioning the fuels among benefiting countries. use indicates energy efficiency. To produce compa- The U.S. Department of Energy's Carbon Diox- The ratio of carbon dioxide per unit of energy shows rable and consistent estimates of real GDP across ide Information Analysis Center (CDIAC) calculates carbon intensity, which is the amount of carbon diox- economies relative to physical inputs to GDP--that annual anthropogenic emissions from data on fossil ide emitted as a result of using one unit of energy in is, units of energy use--GDP is converted to 2005 fuel consumption (from the United Nations Statistics the process of production. The proportion of carbon international dollars using purchasing power parity Division's World Energy Data Set) and world cement dioxide per unit of GDP indicates how clean produc- (PPP) rates. Differences in this ratio over time and manufacturing (from the U.S. Bureau of Mines's tion processes are. across economies reflect structural changes in an Cement Manufacturing Data Set). Carbon dioxide Definitions economy, changes in sectoral energy efficiency, and emissions, often calculated and reported as elemen- differences in fuel mixes. tal carbon, were converted to actual carbon dioxide · Net energy imports are estimated as energy use Carbon dioxide emissions, largely by-products of mass by multiplying them by 3.664 (the ratio of the less production, both measured in oil equivalents. energy production and use (see table 3.7), account mass of carbon to that of carbon dioxide). Although · GDP per unit of energy use is the ratio of gross for the largest share of greenhouse gases, which estimates of global carbon dioxide emissions are domestic product (GDP) per kilogram of oil equiv- are associated with global warming. Anthropogenic probably accurate within 10 percent (as calculated alent of energy use, with GDP converted to 2005 carbon dioxide emissions result primarily from fos- from global average fuel chemistry and use), coun- international dollars using purchasing power parity sil fuel combustion and cement manufacturing. In try estimates may have larger error bounds. Trends (PPP) rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has High-income economies depend on imported energy . . . 3.8a in the United States. Energy use refers to the use of primary energy before transformation to other Net energy imports (% of energy use) 1990 2007 end-use fuel, which is equal to indigenous produc- Low income tion plus imports and stock changes minus exports and fuel supplied to ships and aircraft engaged in Lower middle income international transport (see About the data for table Upper middle income 3.7). · Carbon dioxide emissions are emissions from the burning of fossil fuels and the manufacture of High income cement and include carbon dioxide produced dur- Euro area ing consumption of solid, liquid, and gas fuels and gas flaring. ­60 ­40 ­20 0 20 40 60 80 Note: Negative values indicate that the income group is a net energy exporter. Source: Table 3.8. . . . mostly from middle-income economies in the Middle East and North Africa and Latin America and the Caribbean 3.8b Net energy imports (% of energy use) 1990 2007 East Asia & Pacific Europe & Central Asia Latin America & Caribbean Middle East & North Africa Data sources South Asia Data on energy use are from the electronic files Sub-Saharan Africa of the International Energy Agency. Data on car- bon dioxide emissions are from the CDIAC, Envi- ­250 ­200 ­150 ­100 ­50 0 50 100 ronmental Sciences Division, Oak Ridge National Note: Negative values indicate that the region is a net energy exporter. Source: Table 3.8. Laboratory, Tennessee, United States. 2010 World Development Indicators 185 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons of carbon % of total of carbon % of total of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb From energy dioxide % changeb equivalent processes Agricultural equivalent processes Agricultural equivalent 1990­ 1990­ 1990­ 1990­ 1990­ 2006 2006 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Afghanistan ­8.6 ­74.0 .. .. .. .. .. .. .. .. .. .. Albania 2.3 ­42.6 2,300 0.0 16.1 73.9 970 ­21.1 5.2 82.5 60 .. Algeria 3.7 68.2 53,720 33.0 83.0 8.3 4,640 28.5 10.1 61.4 490 48.5 Angola 5.8 138.9 44,680 ­9.6 14.3 28.4 68,590 4.9 0.2 21.7 20 .. Argentina 2.2 54.1 101,180 ­8.7 18.4 71.0 35,890 5.8 1.8 89.8 790 ­65.7 Armenia 0.6 5.2 2,960 10.9 50.7 36.8 570 ­21.9 1.8 82.5 340 .. Australia 1.3 26.9 119,560 8.3 28.6 58.3 57,910 ­0.7 5.3 84.5 6,510 33.4 Austria 1.1 18.3 8,210 ­14.2 18.9 50.4 3,640 ­19.8 14.3 63.2 2,330 45.6 Azerbaijan ­2.4 ­29.7 36,600 111.3 82.0 13.6 2,460 ­1.6 6.5 82.5 90 ­50.0 Bangladesh 6.6 168.0 93,200 6.6 10.8 69.9 21,260 40.4 6.5 83.0 0 .. Belarus ­3 ­38.1 11,110 ­33.2 4.4 73.4 11,370 ­26.4 3.3 74.4 460 .. Belgium ­0.1 ­0.4 11,760 ­18.0 8.6 48.5 5,940 ­28.0 10.3 49.0 2,110 603.3 Benin 8.3 334.9 3,940 ­17.4 12.7 49.5 4,320 ­15.6 2.8 40.0 0 .. Bolivia 2.8 107.2 30,400 30.9 25.7 34.1 25,400 17.1 0.5 19.5 0 .. Bosnia and Herzegovina 14.7 292.5 2,550 ­53.6 42.7 45.5 1,100 ­42.7 27.3 61.8 570 ­8.1 Botswana 4.1 119.8 4,460 ­22.8 7.8 84.8 2,930 ­43.0 1.4 96.6 0 .. Brazil 3.5 68.8 482,860 57.4 6.1 62.2 255,970 36.3 1.5 54.7 11,810 40.4 Bulgaria ­2.5 ­37.3 7,160 ­33.5 14.9 28.8 3,770 ­56.7 6.9 53.8 380 .. Burkina Faso 1.9 34.4 .. .. .. .. .. .. .. .. .. .. Burundi ­4.6 ­34.9 .. .. .. .. .. .. .. .. .. .. Cambodia 16.7 803.2 20,350 35.1 5.6 75.6 6,010 63.3 3.3 62.4 0 .. Cameroon 3.4 109.7 18,460 38.0 38.9 42.5 11,470 ­10.4 2.1 59.5 420 ­54.8 Canada 1.5 21.0 72,860 27.5 39.0 35.9 33,380 ­12.5 16.3 64.4 21,940 69.7 Central African Republic 1 25.9 .. .. .. .. .. .. .. .. .. .. Chad 10.3 169.9 .. .. .. .. .. .. .. .. .. .. Chile 3.7 69.4 17,800 49.7 23.8 40.2 7,650 61.1 6.4 77.8 10 ­50.0 China 5.1 152.8 1,287,860 32.6 44.1 40.2 414,800 43.3 8.7 82.0 137,120 1,085.1 Hong Kong SAR, China 2.3 41.1 2,610 97.7 28.7 0.0 200 25.0 95.0 0.0 120 ­68.4 Colombia ­0.4 10.6 57,720 13.1 19.3 68.5 22,710 2.8 2.2 80.4 80 100.0 Congo, Dem. Rep. ­4.7 ­45.9 56,230 ­41.5 9.9 23.2 108,260 ­20.9 1.1 15.7 0 .. Congo, Rep. ­1.4 23.1 5,460 ­11.8 30.6 32.6 5,890 ­8.7 0.7 31.2 0 .. Costa Rica 5.4 165.8 2,570 ­31.3 9.3 67.3 1,250 ­28.2 3.2 90.4 60 .. Côte d'Ivoire 1.9 18.7 10,420 ­6.0 12.4 18.3 14,010 6.9 1.5 15.3 0 .. Croatia 1.7 ­5.4 3,750 ­61.0 55.7 34.4 2,550 ­26.5 5.1 56.9 60 ­93.3 Cuba ­1.3 ­11.1 9,470 ­23.1 11.4 62.3 6,010 ­30.6 3.0 82.5 130 .. Czech Republic ­1.5 ­29.4 9,250 ­39.8 37.2 41.7 8,370 ­4.1 41.0 38.8 1,130 .. Denmark ­1.1 7.0 11,990 ­7.5 9.9 43.1 5,780 ­21.3 5.9 79.8 1,420 468.0 Dominican Republic 5.1 112.7 5,940 2.4 5.6 65.3 1,980 3.7 8.1 86.9 0 .. Ecuador 3.3 86.1 17,120 31.8 31.3 57.8 4,280 44.6 4.0 89.7 60 .. Egypt, Arab Rep. 5.4 119.6 46,160 69.9 49.8 32.2 17,650 59.0 5.0 85.0 3,180 54.4 El Salvador 5 146.8 3,150 18.4 12.7 52.7 1,230 1.7 8.9 82.9 80 .. Eritrea 8.9 .. 2,390 26.5 7.9 75.3 1,160 16.0 4.3 92.2 0 .. Estonia ­2.6 ­37.9 1,990 ­37.8 38.7 32.2 810 ­51.8 19.8 69.1 30 .. Ethiopia 4.5 99.0 52,320 32.9 14.5 72.4 29,160 19.0 5.5 91.5 10 .. Finland 1.3 30.8 8,660 ­2.3 6.5 23.3 5,050 ­16.9 12.7 58.8 830 730.0 France ­0.3 ­3.8 79,540 6.3 41.3 46.4 45,560 ­30.7 4.8 71.0 15,540 57.1 Gabon ­7 ­66.2 8,210 1.4 90.4 1.1 660 40.4 7.6 16.7 10 .. Gambia, The 4 75.0 .. .. .. .. .. .. .. .. .. .. Georgia ­7.4 ­68.1 4,130 ­14.0 31.7 54.2 1,970 ­25.7 2.5 57.9 10 .. Germany ­1.1 ­16.4 57,030 ­46.0 26.8 51.9 52,590 ­23.1 7.5 56.0 30,930 6.8 Ghana 4.8 135.1 8,520 22.6 19.1 41.7 5,060 ­6.1 7.9 68.2 20 ­96.7 Greece 2.3 32.5 5,770 ­2.0 31.5 63.1 4,810 ­21.7 10.6 71.9 1,840 ­21.0 Guatemala 6 131.4 8,280 75.1 12.2 48.9 7,000 182.3 3.9 42.7 480 .. Guinea 1.6 28.8 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 0.1 10.2 .. .. .. .. .. .. .. .. .. .. Haiti 6.6 82.3 3,860 39.4 8.8 58.3 1,380 62.4 6.5 86.2 0 .. Honduras 7.4 177.5 5,180 31.5 6.9 78.6 2,620 21.3 4.2 92.4 0 .. 186 2010 World Development Indicators 3.9 ENVIRONMENT Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons of carbon % of total of carbon % of total of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb From energy dioxide % changeb equivalent processes Agricultural equivalent processes Agricultural equivalent 1990­ 1990­ 1990­ 1990­ 1990­ 2006 2006 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Hungary ­0.4 ­6.9 7,510 ­20.0 26.6 34.8 6,640 ­29.8 4.4 62.5 1,550 121.4 India 4.8 118.7 589,630 10.5 16.8 63.8 196,110 30.1 12.3 77.1 8,430 ­11.8 Indonesia 3.9 121.7 208,910 18.7 25.5 46.4 165,370 48.6 1.8 53.1 1,030 ­40.5 Iran, Islamic Rep. 4.5 105.6 114,180 32.3 70.6 18.3 23,230 41.9 6.5 85.3 2,570 ­2.7 Iraq 3.2 76.2 15,910 ­45.8 58.3 18.6 2,540 ­23.7 13.0 84.6 90 ­64.0 Ireland 2.6 41.7 13,540 12.8 12.0 86.9 7,150 ­9.3 2.7 94.7 1,150 3,733.3 Israel 4.4 110.1 3,510 84.7 18.2 31.3 1,410 43.9 20.6 66.7 1,980 90.4 Italy 0.7 11.5 40,190 ­13.9 13.4 40.4 25,810 ­1.5 9.1 47.5 13,580 213.6 Jamaica 2.1 52.6 1,230 12.8 6.5 53.7 440 10.0 11.4 79.5 50 .. Japan 0.6 10.3 39,300 ­32.9 4.5 77.5 22,790 ­24.0 28.8 36.0 52,740 105.6 Jordan 4.3 99.2 1,770 110.7 23.7 22.0 530 43.2 13.2 69.8 110 .. Kazakhstan ­3.1 ­34.4 46,120 ­28.5 65.4 25.8 15,950 ­45.4 10.9 68.8 340 .. Kenya 4.8 108.7 20,100 19.5 8.6 72.1 10,200 15.4 5.5 90.2 0 .. Korea, Dem. Rep. ­9.6 ­65.4 17,090 ­14.8 55.9 25.0 2,730 ­63.8 16.5 69.2 2,790 .. Korea, Rep. 4.2 96.7 146,330 296.3 3.8 8.5 10,960 41.8 24.5 43.8 10,220 66.2 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 9.6 112.5 14,350 119.1 93.4 1.0 390 129.4 59.0 28.2 940 261.5 Kyrgyz Republic ­4.8 ­55.3 3,590 ­37.9 6.7 72.4 1,460 ­57.3 11.6 74.0 20 .. Lao PDR 14.5 507.8 .. .. .. .. .. .. .. .. .. .. Latvia ­5 ­50.4 2,760 ­45.8 49.3 31.2 1,180 ­57.2 11.9 82.2 890 .. Lebanon 3.7 68.5 990 45.6 9.1 26.3 550 77.4 14.5 70.9 0 .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia 5.2 62.1 .. .. .. .. .. .. .. .. .. .. Libya 2 37.7 14,630 ­34.8 86.5 5.7 920 ­6.1 17.4 71.7 280 0.0 Lithuania ­3.7 ­43.2 5,330 ­34.3 29.6 34.9 2,360 ­44.1 5.1 88.6 660 .. Macedonia, FYR ­0.4 ­31.8 1,350 ­36.9 29.6 48.1 530 ­32.9 17.0 71.7 120 .. Madagascar 7.7 187.4 .. .. .. .. .. .. .. .. .. .. Malawi 4 71.3 .. .. .. .. .. .. .. .. .. .. Malaysia 6.7 232.0 46,130 65.0 69.1 12.5 18,570 8.3 4.2 52.8 1,000 66.7 Mali 2 34.8 .. .. .. .. .. .. .. .. .. .. Mauritania ­5.5 ­37.5 .. .. .. .. .. .. .. .. .. .. Mauritius 6.3 163.2 .. .. .. .. .. .. .. .. .. .. Mexico 0.6 13.4 127,490 25.8 40.0 42.5 41,030 12.8 6.8 76.5 4,560 53.0 Moldova ­7.3 ­66.9 3,330 ­14.4 44.4 29.7 780 ­51.6 5.1 74.4 10 .. Mongolia ­1.2 ­6.0 5,990 ­25.0 1.3 93.2 3,410 ­28.4 2.1 95.3 0 .. Morocco 3.9 92.5 10,490 15.4 7.3 52.1 5,460 10.8 3.1 86.4 0 .. Mozambique 5 103.7 12,570 17.5 21.0 45.2 10,020 ­5.1 3.2 66.9 290 .. Myanmar 5.6 134.5 77,410 ­7.0 12.9 68.8 64,000 ­15.9 1.3 19.6 0 .. Namibia 47.2 38,647.9 5,070 47.4 0.4 94.7 3,620 48.4 0.6 98.9 0 .. Nepal 8.5 411.0 22,370 9.8 6.8 82.0 4,310 24.9 12.8 78.2 0 .. Netherlands 0 0.7 21,070 ­30.5 22.9 43.8 13,840 ­10.3 4.8 41.6 3,740 ­41.1 New Zealand 2.1 34.1 27,570 3.6 3.4 90.4 12,700 24.0 3.0 95.8 970 3.2 Nicaragua 4.7 63.9 6,010 26.5 6.3 74.9 3,150 7.1 3.2 94.6 0 .. Niger ­1.2 ­11.2 .. .. .. .. .. .. .. .. .. .. Nigeria 6.1 114.4 129,790 11.7 68.8 19.9 20,550 11.4 9.9 78.0 670 179.2 Norway 3.3 28.4 16,580 55.4 75.3 12.8 4,370 ­0.5 5.0 42.3 5,200 ­39.4 Oman 8.5 299.8 17,850 195.0 94.1 3.0 540 92.9 27.8 70.4 180 .. Pakistan 4.5 108.1 138,400 50.5 24.2 63.0 25,710 46.5 12.6 76.5 820 ­18.8 Panama 4.3 105.0 3,230 16.6 4.3 78.9 1,100 12.2 4.5 90.9 0 .. Papua New Guinea 5.9 115.7 .. .. .. .. .. .. .. .. .. .. Paraguay 3.2 76.2 15,320 2.1 3.5 84.5 9,210 ­6.9 1.6 68.9 0 .. Peru 3.6 82.6 17,010 22.8 13.2 62.0 8,000 27.6 2.8 76.6 330 .. Philippines 3.4 53.5 51,340 28.8 8.4 64.4 11,660 37.8 9.0 81.0 370 131.3 Poland ­1 ­8.5 60,660 ­41.6 56.8 25.3 27,770 5.5 10.6 62.5 2,450 362.3 Portugal 2.4 35.2 7,720 22.3 19.8 55.8 5,160 24.6 8.3 50.4 780 609.1 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 4.7 292.3 15,700 387.6 96.4 0.4 200 122.2 75.0 25.0 0 .. 2010 World Development Indicators 187 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total Total thousand thousand thousand average metric tons metric tons metric tons of carbon % of total of carbon % of total of carbon annual % growtha % changeb dioxide % changeb From energy dioxide % changeb From energy dioxide % changeb equivalent processes Agricultural equivalent processes Agricultural equivalent 1990­ 1990­ 1990­ 1990­ 1990­ 2006 2006 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Romania ­3.1 ­38.0 23,270 ­36.9 40.0 37.6 10,860 ­44.3 5.7 58.7 740 ­63.2 Russian Federation ­2.4 ­33.2 557,200 ­17.1 79.1 9.2 68,900 ­48.6 10.4 48.4 58,600 130.4 Rwanda 1.2 16.7 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 2.3 77.4 47,790 66.9 83.9 4.0 4,680 14.7 26.7 63.9 2,190 ­10.6 Senegal 2.8 33.9 6,900 38.8 6.8 70.6 3,870 37.2 3.1 93.0 0 .. Serbiac 0.3 ­20.6 6,720 ­47.7 .. 59.2 4,700 ­48.2 .. 81.5 840 147.1 Sierra Leone 5.4 155.6 .. .. .. .. .. .. .. .. .. .. Singapore 0.4 19.8 2,190 138.0 58.9 1.4 960 500.0 15.6 3.1 2,540 408.0 Slovak Republic ­1.8 ­32.0 3,800 ­39.2 15.8 40.3 3,140 ­36.2 13.4 39.8 390 457.1 Slovenia 0.7 ­16.9 3,380 0.3 28.4 33.1 1,010 ­17.2 9.9 80.2 470 ­39.0 Somalia 31 841.0 .. .. .. .. .. .. .. .. .. .. South Africa 1.2 24.3 61,610 23.8 43.7 32.5 20,530 10.6 10.8 69.5 2,170 45.6 Spain 3 53.7 37,510 16.5 9.1 55.1 23,170 4.3 8.4 71.3 9,080 47.6 Sri Lanka 7.9 214.8 10,220 ­11.5 5.4 65.2 1,830 10.2 13.1 72.7 0 .. Sudan 6.2 94.5 65,270 55.2 4.0 88.1 46,880 36.4 1.3 97.5 0 .. Swaziland 10.9 138.8 .. .. .. .. .. .. .. .. .. .. Sweden ­0.3 ­0.7 11,150 1.5 8.6 28.5 5,050 ­13.2 13.5 69.7 2,080 136.4 Switzerland ­0.2 ­2.7 4,780 ­16.0 17.4 67.2 2,000 ­14.2 10.0 71.5 2,110 97.2 Syrian Arab Republic 3.6 82.8 12,530 ­10.4 54.1 27.9 5,010 35.4 4.2 84.0 0 .. Tajikistan ­8.2 ­73.4 3,920 ­5.3 13.3 68.1 1,350 ­0.7 1.5 88.1 380 ­86.5 Tanzania 4.4 126.4 30,240 19.1 7.4 67.0 23,420 5.7 2.6 72.2 0 .. Thailand 5.9 184.4 80,540 6.8 14.1 68.2 20,210 13.2 17.4 71.4 1,100 ­23.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 3.9 57.8 2,660 2.3 16.9 43.2 1,980 ­17.2 5.1 58.1 0 .. Trinidad and Tobago 3.6 98.1 9,940 30.3 84.9 0.7 210 23.5 23.8 66.7 0 .. Tunisia 3.3 74.3 8,000 107.3 54.8 26.0 2,150 16.8 5.6 71.6 0 .. Turkey 3.5 83.8 49,970 26.9 17.5 43.2 29,790 11.7 9.0 71.6 5,070 96.5 Turkmenistan 2.7 39.3 27,950 ­4.7 75.2 21.6 4,280 98.1 3.0 78.0 70 .. Uganda 7.8 230.9 .. .. .. .. .. .. .. .. .. .. Ukraine ­4.8 ­53.7 66,990 ­42.4 60.2 24.5 24,160 ­51.3 4.4 47.9 690 213.6 United Arab Emirates 6.6 154.6 23,250 57.9 93.1 2.6 1,080 151.2 43.5 47.2 1,080 27.1 United Kingdom ­0.3 ­0.9 42,290 ­43.2 34.6 59.4 27,750 ­44.0 8.5 65.1 10,400 96.6 United States 1.2 18.2 610,910 ­3.4 32.6 31.2 257,060 ­2.4 24.9 59.1 238,510 158.7 Uruguay 2 71.9 19,570 24.0 1.5 94.4 6,750 13.4 0.6 98.4 60 .. Uzbekistan 0.1 ­10.1 39,530 25.3 57.2 33.8 9,630 6.9 3.4 87.4 610 .. Venezuela, RB 2.4 40.5 61,170 5.8 47.4 40.1 16,760 25.6 4.7 66.9 2,470 ­24.0 Vietnam 11.8 395.8 83,660 39.9 23.3 63.4 21,660 96.6 6.3 87.3 0 .. West Bank and Gaza 24.1 .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 4.5 ­806.8 6,650 73.6 16.7 55.0 2,710 39.0 4.1 86.3 0 .. Zambia ­0.7 1.0 18,600 ­30.0 3.2 61.6 30,500 ­22.9 0.7 58.9 0 .. Zimbabwe ­3.2 ­33.5 9,500 ­5.4 10.9 73.7 5,490 ­14.5 3.6 93.8 0 .. World 1.7 w 34.0 w 7,138,440 s 9.9 w 35.4 w 42.5 w 2,827,550 s 4.8 w 8.2 w 63.9 w 715,400 s 123.7 w Low income ­1.4 ­6.0 595,600 2.6 17.0 60.0 369,940 ­7.3 3.0 49.5 4,120 20.8 Middle income 2.2 49.2 4,962,640 12.9 38.7 42.8 1,786,860 15.7 6.2 67.7 256,890 206.4 Lower middle income 4.1 105.7 3,085,730 20.5 36.1 45.9 1,151,140 28.2 7.0 70.1 157,820 393.5 Upper middle income ­0.3 ­4.9 1,876,910 2.2 42.8 37.7 635,720 ­1.8 4.8 63.3 99,070 91.0 Low & middle income 2.1 46.5 5,558,240 11.7 36.4 44.7 2,156,800 11.0 5.7 64.6 261,010 199.2 East Asia & Pacific 4.5 135.9 1,879,280 27.2 37.7 44.7 728,420 33.1 6.5 68.9 .. .. Europe & Central Asia ­2.2 ­30.0 966,150 ­19.6 67.4 18.5 225,390 ­35.3 8.5 60.9 77,050 117.8 Latin America & Carib. 2 40.0 996,560 30.3 16.5 59.3 459,810 24.8 2.4 62.4 20,970 23.4 Middle East & N. Africa 4.2 96.4 285,030 19.8 64.4 20.8 65,390 34.9 6.3 82.7 6,720 20.9 South Asia 4.8 118.9 853,820 14.7 16.9 64.8 249,220 32.2 11.8 77.5 9,250 ­12.5 Sub-Saharan Africa 2 37.4 577,400 4.7 29.0 44.9 428,570 ­3.2 2.6 51.1 .. .. High income 1.1 18.0 1,580,200 4.3 32.1 35.1 670,750 ­11.0 16.2 61.7 454,390 95.4 Euro area 0.2 3.8 300,030 ­16.1 23.8 49.3 197,530 ­18.3 7.4 60.7 83,170 36.4 a. Calculated using the least squares method, which accounts for ups and downs of all data points in the period (see Statistical methods). b. Calculated as the change in emission since 1990, which is the baseline for Kyoto Protocol requirements. c. Includes Kosovo and Montenegro. 188 2010 World Development Indicators 3.9 ENVIRONMENT Trends in greenhouse gas emissions About the data Definitions Greenhouse gases--which include carbon dioxide, compared. A kilogram of methane is 21 times as · Carbon dioxide emissions are emissions from methane, nitrous oxide, hydrofluorocarbons, per- effective at trapping heat in the earth's atmosphere the burning of fossil fuels and the manufacture of fluorocarbons, and sulfur hexafluoride--contribute as a kilogram of carbon dioxide within 100 years. cement and include carbon dioxide produced during to climate change. Nitrous oxide emissions are mainly from fossil fuel consumption of solid, liquid, and gas fuels and gas Carbon dioxide emissions, largely a byproduct of combustion, fertilizers, rainforest fires, and animal flaring. · Methane emissions are emissions from energy production and use (see table 3.7), account waste. Nitrous oxide is a powerful greenhouse gas, human activities such as agriculture and from indus- for the largest share of greenhouse gases. Anthro- with an estimated atmospheric lifetime of 114 years, trial methane production. · Methane emissions from pogenic carbon dioxide emissions result primarily compared with 12 years for methane. The per kilo- energy processes are emissions from the produc- from fossil fuel combustion and cement manufactur- gram global warming potential of nitrous oxide is tion, handling, transmission, and combustion of fos- ing. Burning oil releases more carbon dioxide than nearly 310 times that of carbon dioxide within 100 sil fuels and biofuels. · Agricultural methane emis- burning natural gas, and burning coal releases even years. sions are emissions from animals, animal waste, rice more for the same level of energy use. Cement manu- Other greenhouse gases covered under the Kyoto production, agricultural waste burning (nonenergy, facturing releases about half a metric ton of carbon Protocol are hydrofluorocarbons, perfluorocarbons, on-site), and savannah burning. · Nitrous oxide dioxide for each metric ton of cement produced. and sulfur hexafluoride. Although emissions of these emissions are emissions from agricultural biomass Methane emissions result largely from agricultural artificial gases are small, they are more powerful burning, industrial activities, and livestock manage- activities, industrial production landfills and waste- greenhouse gases than carbon dioxide, with much ment. · Nitrous oxide emissions from energy pro- water treatment, and other sources such as tropi- higher atmospheric lifetimes and high global warm- cesses are emissions produced by the combustion cal forest and other vegetation fires. The emissions ing potential. of fossil fuels and biofuels. · Agricultural nitrous are usually expressed in carbon dioxide equivalents For a discussion of carbon dioxide sources and oxide emissions are emissions produced through using the global warming potential, which allows the methodology behind emissions calculation, see fertilizer use (synthetic and animal manure), ani- the effective contributions of different gases to be About the data for table 3.8. mal waste management, agricultural waste burning (nonenergy, on-site), and savannah burning. · Other The 10 largest contributors to methane emissions greenhouse gas emissions are byproduct emissions account for about 62 percent of emissions 3.9a of hydrofl uorocarbons (byproduct emissions of fluoroform from chlorodifluoromethane manufacture Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) and use of hydrofluorocarbons), perfluoro carbons 1,000 (byproduct emissions of tetrafluoromethane and 750 hexafluoroethane from primary aluminum produc- tion and use of perfluoro carbons, in particular for 500 semiconductor manufacturing), and sulfur hexa- fluoride (various sources, the largest being the use 250 and manufacture of gas insulated switchgear used in electricity distribution networks). 0 China United India Russian Brazil Indonesia Mexico Australia Pakistan Canada States Federation Source: Table 3.9. The 10 largest contributors to nitrous oxide emissions account for about 56 percent of emissions 3.9b Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) 600 400 Data sources Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Center, Envi- 200 ronmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United States. Data on 0 methane, nitrous oxide, and other greenhouse China United India Brazil Australia Argentina Pakistan France Mexico Indonesia States gases emissions are compiled by the International Source: Table 3.9. Energy Agency. 2010 World Development Indicators 189 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.2 2.9 0.0 0.0 0.0 0.0 10.9 2.5 89.1 97.5 0.0 0.0 Algeria 16.1 37.2 0.0 0.0 93.7 97.3 5.4 2.1 0.8 0.6 0.0 0.0 Angola 0.8 3.8 0.0 0.0 0.0 0.0 13.8 15.5 86.2 84.5 0.0 0.0 Argentina 50.7 115.1 1.3 2.2 39.2 54.3 9.8 9.4 35.2 26.5 14.3 6.3 Armenia 10.4 5.9 0.0 0.0 16.4 25.2 68.6 0.0 15.0 31.4 0.0 43.3 Australia 154.3 254.6 77.1 76.3 10.6 15.4 2.7 0.9 9.2 5.7 0.0 0.0 Austria 49.3 60.9 14.2 12.5 15.7 16.2 3.8 2.1 63.9 59.1 0.0 0.0 Azerbaijan 23.2 24.2 0.0 0.0 0.0 74.5 97.0 15.7 3.0 9.8 0.0 0.0 Bangladesh 7.7 24.4 0.0 0.0 84.3 87.6 4.3 6.7 11.4 5.7 0.0 0.0 Belarus 39.5 31.8 0.0 0.0 58.1 99.0 41.8 0.5 0.1 0.1 0.0 0.0 Belgium 70.3 87.5 28.2 9.5 7.7 29.0 1.9 0.9 0.4 0.4 60.8 55.1 Benin 0.0 0.1 0.0 0.0 0.0 0.0 100.0 99.2 0.0 0.8 0.0 0.0 Bolivia 2.1 5.7 0.0 0.0 37.6 42.3 5.3 14.3 55.3 40.4 0.0 0.0 Bosnia and Herzegovina 14.6 11.8 71.8 64.8 0.0 0.0 7.3 1.3 20.9 33.8 0.0 0.0 Botswana 0.9 1.1 88.1 99.5 0.0 0.0 11.9 0.5 0.0 0.0 0.0 0.0 Brazil 222.8 445.1 2.1 2.3 0.0 3.5 2.2 3.1 92.8 84.0 1.0 2.8 Bulgaria 42.1 42.9 50.3 52.3 7.6 5.4 2.9 1.3 4.5 6.7 34.8 34.1 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 1.3 .. 0.0 .. 0.0 .. 95.9 .. 3.7 .. 0.0 Cameroon 2.7 5.8 0.0 0.0 0.0 7.6 1.5 25.5 98.5 66.9 0.0 0.0 Canada 482.0 639.7 17.1 18.1 2.0 6.4 3.4 1.5 61.6 57.6 15.1 14.6 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 58.5 38.3 22.7 2.1 7.9 9.2 24.6 48.5 39.5 0.0 0.0 China 621.2 3,279.2 71.3 81.0 0.4 0.9 7.9 1.0 20.4 14.8 0.0 1.9 Hong Kong SAR, China 28.9 39.0 98.3 73.3 0.0 26.5 1.7 0.2 0.0 0.0 0.0 0.0 Colombia 36.4 55.3 10.1 6.3 12.4 11.9 1.0 0.3 75.6 80.4 0.0 0.0 Congo, Dem. Rep. 5.7 8.3 0.0 0.0 0.0 0.0 0.4 0.3 99.6 99.7 0.0 0.0 Congo, Rep. 0.5 0.4 0.0 0.0 0.0 17.7 0.6 0.0 99.4 82.3 0.0 0.0 Costa Rica 3.5 9.1 0.0 0.0 0.0 0.0 2.5 8.0 97.5 74.8 0.0 0.0 Côte d'Ivoire 2.0 5.6 0.0 0.0 0.0 65.7 33.3 0.3 66.7 31.9 0.0 0.0 Croatia 9.2 12.1 6.8 20.1 20.2 25.4 31.6 19.2 41.3 35.1 0.0 0.0 Cuba 15.0 17.6 0.0 0.0 0.2 0.0 91.4 97.4 0.8 0.7 0.0 0.0 Czech Republic 62.3 87.8 76.4 62.5 0.6 3.6 0.9 0.1 1.9 2.4 20.2 29.8 Denmark 26.0 39.2 90.7 50.8 2.7 17.7 3.4 3.3 0.1 0.1 0.0 0.0 Dominican Republic 3.7 14.8 1.2 13.2 0.0 11.5 88.6 65.6 9.4 9.4 0.0 0.0 Ecuador 6.3 17.3 0.0 0.0 0.0 6.8 21.5 41.0 78.5 52.1 0.0 0.0 Egypt, Arab Rep. 42.3 125.1 0.0 0.0 39.6 68.4 36.9 18.6 23.5 12.4 0.0 0.0 El Salvador 2.2 5.8 0.0 0.0 0.0 0.0 6.9 45.7 73.5 30.0 0.0 0.0 Eritrea 0.1 0.3 0.0 0.0 0.0 0.0 100.0 99.3 0.0 0.0 0.0 0.0 Estonia 17.4 12.2 85.8 93.5 5.9 4.8 8.3 0.3 0.0 0.2 0.0 0.0 Ethiopia 1.2 3.5 0.0 0.0 0.0 0.0 11.6 3.8 88.4 96.2 0.0 0.0 Finland 54.4 81.2 18.5 17.9 8.6 13.0 3.1 0.6 20.0 17.4 35.3 28.8 France 417.2 564.4 8.5 5.0 0.7 3.9 2.1 1.1 12.9 10.3 75.3 77.9 Gabon 1.0 1.8 0.0 0.0 16.4 16.0 11.2 40.2 72.1 43.4 0.0 0.0 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 13.7 8.3 0.0 0.0 15.6 17.9 29.2 0.3 55.2 81.8 0.0 0.0 Germany 547.7 629.5 58.7 49.3 7.4 11.6 1.9 1.8 3.2 3.3 27.8 22.3 Ghana 5.7 7.0 0.0 0.0 0.0 0.0 0.0 46.6 100.0 53.4 0.0 0.0 Greece 34.8 62.7 72.4 55.3 0.3 22.0 22.3 15.4 5.1 4.1 0.0 0.0 Guatemala 2.3 8.8 0.0 12.8 0.0 0.0 9.0 30.1 76.0 41.5 0.0 0.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.5 0.0 0.0 0.0 0.0 20.6 67.2 76.5 32.8 0.0 0.0 Honduras 2.3 6.3 0.0 0.0 0.0 0.0 1.7 62.3 98.3 35.1 0.0 0.0 190 2010 World Development Indicators 3.10 ENVIRONMENT Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 Hungary 28.4 40.0 30.5 18.7 15.7 38.1 4.8 1.3 0.6 0.5 48.3 36.7 India 289.4 803.4 66.2 68.4 3.4 8.3 3.5 4.1 24.8 15.4 2.1 2.1 Indonesia 33.3 142.2 31.5 44.9 2.3 15.7 42.7 26.5 20.2 7.9 0.0 0.0 Iran, Islamic Rep. 59.1 204.0 0.0 0.0 52.5 78.6 37.3 12.5 10.3 8.8 0.0 0.0 Iraq 24.0 33.2 0.0 0.0 0.0 0.0 89.2 98.5 10.8 1.5 0.0 0.0 Ireland 14.2 27.9 41.6 19.7 27.7 55.5 10.0 7.1 4.9 2.4 0.0 0.0 Israel 20.9 53.8 50.1 69.5 0.0 19.7 49.9 10.8 0.0 0.0 0.0 0.0 Italy 213.1 308.2 16.8 16.1 18.6 56.0 48.2 11.5 14.8 10.6 0.0 0.0 Jamaica 2.5 7.8 0.0 0.0 0.0 0.0 92.4 95.9 3.6 2.1 0.0 0.0 Japan 835.5 1,123.5 14.0 27.7 20.0 25.8 18.5 9.8 10.7 6.6 24.2 23.5 Jordan 3.6 13.0 0.0 0.0 11.9 76.4 87.8 23.0 0.3 0.5 0.0 0.0 Kazakhstan 87.4 76.6 71.1 70.3 10.5 10.7 10.0 8.3 8.4 10.7 0.0 0.0 Kenya 3.2 6.8 0.0 0.0 0.0 0.0 7.1 28.8 76.6 51.4 0.0 0.0 Korea, Dem. Rep. 27.7 21.5 40.1 34.8 0.0 0.0 3.6 3.5 56.3 61.7 0.0 0.0 Korea, Rep. 105.4 425.9 16.8 40.1 9.1 19.3 17.9 5.9 6.0 0.9 50.2 33.6 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 18.5 48.8 0.0 0.0 45.7 27.7 54.3 72.3 0.0 0.0 0.0 0.0 Kyrgyz Republic 15.7 16.2 13.1 3.3 23.5 10.8 0.0 0.0 63.5 85.9 0.0 0.0 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 6.6 4.8 0.0 0.0 26.1 40.3 5.4 0.4 67.6 57.3 0.0 0.0 Lebanon 1.5 9.6 0.0 0.0 0.0 0.0 66.7 93.9 33.3 6.1 0.0 0.0 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 10.2 25.7 0.0 0.0 0.0 44.9 100.0 55.1 0.0 0.0 0.0 0.0 Lithuania 28.4 13.5 0.0 0.0 23.8 17.9 14.6 2.1 1.5 3.1 60.0 73.0 Macedonia, FYR 5.8 6.7 89.7 77.9 0.0 0.0 1.8 7.1 8.5 15.0 0.0 0.0 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 101.3 12.3 29.5 22.0 62.0 48.4 2.0 17.3 6.4 0.0 0.0 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 124.1 257.5 6.3 12.3 11.6 48.8 56.7 20.3 18.9 10.6 2.4 4.0 Moldova 16.2 3.8 30.8 0.0 42.3 98.2 25.4 0.0 1.6 0.9 0.0 0.0 Mongolia 3.5 3.8 92.4 96.1 0.0 0.0 7.6 3.9 0.0 0.0 0.0 0.0 Morocco 9.6 22.9 23.0 57.1 0.0 13.6 64.4 22.3 12.7 5.8 0.0 0.0 Mozambique 0.5 16.1 13.9 0.0 0.0 0.1 23.6 0.0 62.6 99.9 0.0 0.0 Myanmar 2.5 6.5 1.6 0.0 39.3 41.6 10.9 4.5 48.1 53.9 0.0 0.0 Namibia 1.4 1.7 1.5 7.1 0.0 0.0 3.3 0.5 95.2 92.3 0.0 0.0 Nepal 0.9 2.8 0.0 0.0 0.0 0.0 0.1 0.4 99.9 99.6 0.0 0.0 Netherlands 71.9 103.2 38.3 27.6 50.9 57.2 4.3 2.1 0.1 0.1 4.9 4.1 New Zealand 32.3 43.8 1.9 7.1 17.6 27.3 0.0 0.0 72.3 53.6 0.0 0.0 Nicaragua 1.4 3.2 0.0 0.0 0.0 0.0 39.8 71.1 28.8 9.5 0.0 0.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 13.5 23.0 0.1 0.0 53.7 67.2 13.7 4.9 32.6 27.9 0.0 0.0 Norway 121.6 136.4 0.1 0.1 0.0 0.5 0.0 0.0 99.6 98.2 0.0 0.0 Oman 4.5 14.4 0.0 0.0 81.6 82.0 18.4 18.0 0.0 0.0 0.0 0.0 Pakistan 37.7 95.7 0.1 0.1 33.6 34.4 20.6 32.2 44.9 30.0 0.8 3.2 Panama 2.7 6.5 0.0 0.0 0.0 0.0 14.7 43.1 83.2 56.6 0.0 0.0 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 53.7 0.0 0.0 0.0 0.0 0.0 0.0 99.9 100.0 0.0 0.0 Peru 13.8 29.9 0.0 2.8 1.7 24.3 21.5 6.0 75.8 65.3 0.0 0.0 Philippines 27.4 59.6 7.0 28.2 0.0 32.6 45.3 7.5 22.1 14.4 0.0 0.0 Poland 134.4 158.8 97.5 93.0 0.1 1.9 1.2 1.5 1.1 1.5 0.0 0.0 Portugal 28.4 46.9 32.1 26.4 0.0 28.0 33.1 10.4 32.3 21.5 0.0 0.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 4.8 16.1 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 2010 World Development Indicators 191 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 Romania 64.3 61.7 28.8 41.0 35.1 18.7 18.4 1.8 17.7 25.9 0.0 12.5 Russian Federation 1,082.2 1,013.4 14.3 16.7 47.3 48.0 11.9 1.7 15.3 17.5 10.9 15.8 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 69.2 189.1 0.0 0.0 48.1 44.8 51.9 55.2 0.0 0.0 0.0 0.0 Senegal 0.9 2.0 0.0 0.0 2.3 2.0 93.0 83.1 0.0 10.8 0.0 0.0 Serbia 40.9b 36.5 69.1b 70.2 3.2b 1.1 4.6b 1.3 23.1b 27.5 0.0 b 0.0 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 41.1 0.0 0.0 0.0 78.7 100.0 21.3 0.0 0.0 0.0 0.0 Slovak Republic 25.5 27.9 31.9 18.7 7.1 5.8 6.4 2.5 7.4 16.0 47.2 55.0 Slovenia 12.4 15.0 31.3 36.5 0.0 3.0 7.9 0.2 23.7 21.7 37.1 37.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 260.5 94.3 94.7 0.0 0.0 0.0 0.4 0.6 0.4 5.1 4.3 Spain 151.2 300.2 40.1 24.8 1.0 30.8 5.7 6.2 16.8 9.2 35.9 18.4 Sri Lanka 3.2 9.9 0.0 0.0 0.0 0.0 0.2 59.9 99.8 39.9 0.0 0.0 Sudan 1.5 4.5 0.0 0.0 0.0 0.0 36.8 68.0 63.2 32.0 0.0 0.0 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 148.8 1.1 0.9 0.3 0.6 0.9 0.7 49.7 44.5 46.7 45.0 Switzerland 55.0 66.5 0.1 0.0 0.6 1.1 0.7 0.3 54.2 53.0 43.0 42.0 Syrian Arab Republic 11.6 38.6 0.0 0.0 20.5 31.2 56.0 59.7 23.5 9.1 0.0 0.0 Tajikistan 18.1 17.5 0.0 0.0 9.1 2.2 0.0 0.0 90.9 97.8 0.0 0.0 Tanzania 1.6 4.2 0.0 2.7 0.0 36.2 4.9 0.9 95.1 60.1 0.0 0.0 Thailand 44.2 143.4 25.0 21.4 40.2 67.3 23.5 2.7 11.3 5.7 0.0 0.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 0.2 0.2 0.0 0.0 0.0 0.0 39.9 48.0 60.1 46.9 0.0 0.0 Trinidad and Tobago 3.6 7.7 0.0 0.0 99.0 99.6 0.1 0.2 0.0 0.0 0.0 0.0 Tunisia 5.8 14.7 0.0 0.0 63.7 83.1 35.5 16.2 0.8 0.3 0.0 0.0 Turkey 57.5 191.6 35.1 27.9 17.7 49.6 6.9 3.4 40.2 18.7 0.0 0.0 Turkmenistan 14.6 14.9 0.0 0.0 95.2 100.0 0.0 0.0 4.8 0.0 0.0 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 298.6 196.1 38.2 34.2 16.7 13.0 16.1 0.4 3.5 5.2 25.5 47.2 United Arab Emirates 17.1 76.1 0.0 0.0 96.3 98.1 3.7 1.9 0.0 0.0 0.0 0.0 United Kingdom 317.8 392.3 65.0 35.3 1.6 41.9 10.9 1.2 1.6 1.3 20.7 16.1 United States 3,202.8 4,322.9 53.1 49.0 11.9 21.2 4.1 1.8 8.5 5.8 19.1 19.4 Uruguay 7.4 9.4 0.0 0.0 0.0 0.0 5.1 13.0 94.2 85.6 0.0 0.0 Uzbekistan 56.3 49.0 7.4 5.0 76.4 70.6 4.4 11.3 11.8 13.1 0.0 0.0 Venezuela, RB 59.3 114.9 0.0 0.0 26.2 16.3 11.5 11.4 62.3 72.3 0.0 0.0 Vietnam 8.7 69.5 23.1 21.4 0.1 32.1 15.0 3.5 61.8 43.0 0.0 0.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 6.0 0.0 0.0 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 Zambia 8.0 9.9 0.5 0.2 0.0 0.0 0.3 0.4 99.2 99.4 0.0 0.0 Zimbabwe 9.4 9.2 53.3 43.0 0.0 0.0 0.0 0.3 46.7 56.8 0.0 0.0 World 11,847.9 t19,818.9 t 37.3 w 41.5 w 14.6 w 20.8 w 10.3 w 5.3 w 18.0 w 15.5 w 17.0 w 13.7 w Low income 210.5 336.8 11.6 8.7 26.5 25.1 4.2 7.7 41.2 41.9 0.0 0.0 Middle income 4,066.0 8,665.5 34.9 49.1 20.8 18.9 14.5 5.8 22.5 19.8 6.2 4.7 Lower middle income 1,672.0 5,425.0 46.8 62.7 10.8 11.8 16.3 5.4 19.9 15.2 4.9 3.3 Upper middle income 2,393.6 3,244.0 26.5 26.3 27.8 30.7 13.2 6.4 24.3 27.5 7.1 7.2 Low & middle income 4,271.6 9,008.7 33.8 47.5 21.1 19.1 14.0 5.9 23.4 20.6 5.9 4.6 East Asia & Pacific 796.3 3,851.0 61.0 73.3 3.4 6.7 12.6 2.3 21.4 14.7 0.0 1.6 Europe & Central Asia 2,076.4 1,991.2 27.8 29.2 34.3 37.4 12.9 2.3 13.8 16.2 10.9 14.4 Latin America & Carib. 606.2 1,245.6 3.9 5.2 9.2 19.8 18.9 13.3 63.5 55.8 2.1 2.4 Middle East & N. Africa 187.9 536.9 1.2 2.4 36.9 61.6 48.3 27.0 12.4 7.4 0.0 0.0 South Asia 341.7 944.1 56.1 58.2 8.5 12.8 5.3 7.6 27.4 17.0 1.9 2.1 Sub-Saharan Africa 260.2 432.3 62.2 58.3 2.8 5.0 1.9 3.7 15.9 16.9 3.2 2.6 High income 7,595.3 10,858.4 39.2 36.2 10.9 22.2 8.3 4.7 14.9 11.1 23.2 21.3 Euro area 1,694.1 2,326.1 33.7 25.2 8.6 22.0 9.6 4.3 11.1 9.1 35.6 31.5 a. Shares may not sum to 100 percent because some sources of generated electricity (such as wind, solar, and geothermal) are not shown. b. Includes Kosovo and Montenegro. 192 2010 World Development Indicators 3.10 ENVIRONMENT Sources of electricity About the data Definitions Use of energy is important in improving people's adjustments are made to compensate for differences · Electricity production is measured at the termi- standard of living. But electricity generation also in definitions. The IEA makes these estimates in con- nals of all alternator sets in a station. In addition to can damage the environment. Whether such damage sultation with national statistical offices, oil compa- hydropower, coal, oil, gas, and nuclear power gen- occurs depends largely on how electricity is gener- nies, electric utilities, and national energy experts. It eration, it covers generation by geothermal, solar, ated. For example, burning coal releases twice as occasionally revises its time series to reflect political wind, and tide and wave energy as well as that from much carbon dioxide--a major contributor to global changes. For example, the IEA has constructed his- combustible renewables and waste. Production warming--as does burning an equivalent amount torical energy statistics for countries of the former includes the output of electric plants designed to of natural gas (see About the data for table 3.8). Soviet Union. In addition, energy statistics for other produce electricity only, as well as that of combined Nuclear energy does not generate carbon dioxide countries have undergone continuous changes in heat and power plants. · Sources of electricity are emissions, but it produces other dangerous waste coverage or methodology in recent years as more the inputs used to generate electricity: coal, gas, oil, products. The table provides information on electric- detailed energy accounts have become available. hydropower, and nuclear power. · Coal is all coal and ity production by source. Breaks in series are therefore unavoidable. brown coal, both primary (including hard coal and The International Energy Agency (IEA) compiles lignite-brown coal) and derived fuels (including pat- data on energy inputs used to generate electricity. ent fuel, coke oven coke, gas coke, coke oven gas, IEA data for countries that are not members of the and blast furnace gas). Peat is also included in this Organisation for Economic Co-operation and Devel- category. · Gas is natural gas but not natural gas opment (OECD) are based on national energy data liquids. · Oil is crude oil and petroleum products. adjusted to conform to annual questionnaires com- · Hydropower is electricity produced by hydroelectric pleted by OECD member governments. In addition, power plants. · Nuclear power is electricity produced estimates are sometimes made to complete major by nuclear power plants. aggregates from which key data are missing, and Sources of electricity generation have shifted since 1990 . . . 3.10a World 1990 2007 Other 3% Other 3% Nuclear Nuclear power power 17% 14% Coal 37% Coal Hydropower 41% Hydropower 16% 18% Oil Oil Gas 5% 10% 15% Gas 21% Source: Table 3.10. . . . with developing economies relying more on coal 3.10b Developing economies 1990 2007 Nuclear power 6% Other 2% Nuclear power 5% Other 2% Coal Hydropower Hydropower 34% 23% 21% Coal Data sources 47% Oil Data on electricity production are from the IEA's 6% Oil electronic files and its annual publications Energy 14% Gas Gas 21% 19% Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Balances of OECD Countries. Source: Table 3.10. 2010 World Development Indicators 193 3.11 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2008 1990 2008 1990­2008 1990 2007 1990 2007 1990 2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 1.2 1.5 36 47 1.1 .. .. .. .. 97 98 .. 97 Algeria 13.2 22.4 52 65 3.0 8 10 14 15 99 98 77 87 Angola 4.0 10.2 37 57 5.3 15 23 40 41 55 79 9 16 Argentina 28.3 36.7 87 92 1.4 39 39 37 35 86 92 45 83 Armenia 2.4 2.0 68 64 ­1.1 33 36 49 56 94 96 .. 81 Australia 14.6 19.0 85 89 1.5 60 61 25 23 100 100 100 100 Austria 5.1 5.6 66 67 0.5 27 28 41 41 100 100 100 100 Azerbaijan 3.8 4.5 54 52 0.9 24 22 45 43 .. 90 .. 70 Bangladesh 22.9 43.4 20 27 3.6 8 12 29 32 56 48 18 32 Belarus 6.7 7.1 66 73 0.3 16 19 24 26 .. 91 .. 97 Belgium 9.6 10.4 96 97 0.5 10 17 10 17 .. .. .. .. Benin 1.7 3.6 35 41 4.3 .. .. .. .. 32 59 2 11 Bolivia 3.7 6.4 56 66 3.0 25 32 29 26 47 54 15 22 Bosnia and Herzegovina 1.7 1.8 39 47 0.3 .. .. .. .. 99 99 .. 92 Botswana 0.6 1.1 42 60 3.9 .. .. .. .. 60 60 22 30 Brazil 111.9 164.3 75 86 2.1 34 39 13 12 82 84 37 37 Bulgaria 5.8 5.4 66 71 ­0.4 14 16 21 22 100 100 96 96 Burkina Faso 1.2 3.0 14 20 5.0 .. 8 49 41 23 41 2 6 Burundi 0.4 0.8 6 10 4.7 .. .. .. .. 41 44 44 41 Cambodia 1.2 3.1 13 22 5.2 6 10 49 49 .. 62 2 19 Cameroon 5.0 10.8 41 57 4.3 14 19 19 18 47 58 34 42 Canada 21.3 26.8 77 80 1.3 40 44 18 20 100 100 99 99 Central African Republic 1.1 1.7 37 39 2.4 .. .. .. .. 21 40 5 25 Chad 1.3 2.9 21 27 4.6 .. .. 38 35 19 23 1 4 Chile 11.0 14.9 83 88 1.7 35 34 42 39 91 97 48 74 China 311.0 570.9 27 43 3.4 13 18 3 3 61 74 43 59 Hong Kong SAR, China 5.7 7.0 100 100 1.1 100 100 100 100 .. .. .. .. Colombia 22.7 33.5 68 75 2.2 32 35 22 23 81 85 39 58 Congo, Dem. Rep. 10.3 21.8 28 34 4.2 15 17 35 37 53 42 1 25 Congo, Rep. 1.3 2.2 54 61 2.8 29 38 53 63 .. 19 .. 21 Costa Rica 1.6 2.9 51 63 3.4 24 29 47 46 96 96 92 95 Côte d'Ivoire 5.0 10.0 40 49 3.9 17 19 42 39 39 38 8 12 Croatia 2.6 2.5 54 57 ­0.1 .. .. .. .. 99 99 98 98 Cuba 7.8 8.5 73 76 0.5 20 19 27 26 99 99 95 95 Czech Republic 7.8 7.7 75 74 ­0.1 12 11 16 16 100 100 98 98 Denmark 4.4 4.8 85 87 0.5 26 20 31 23 100 100 100 100 Dominican Republic 4.1 6.9 55 69 2.9 21 22 37 32 77 81 57 74 Ecuador 5.7 8.8 55 66 2.5 26 32 28 29 88 91 50 72 Egypt, Arab Rep. 25.1 34.8 44 43 1.8 21 20 36 35 68 85 37 52 El Salvador 2.6 3.7 49 61 1.9 18 23 37 39 88 90 59 80 Eritrea 0.5 1.0 16 21 4.0 .. .. .. .. 20 14 0 3 Estonia 1.1 0.9 71 69 ­1.0 .. .. .. .. 96 96 94 94 Ethiopia 6.1 13.7 13 17 4.5 4 4 29 22 19 27 2 8 Finland 3.1 3.4 61 63 0.5 17 21 28 34 100 100 100 100 France 42.0 48.2 74 77 0.8 23 22 22 21 .. .. .. .. Gabon 0.6 1.2 69 85 3.6 .. .. .. .. .. 37 .. 30 Gambia, The 0.3 0.9 38 56 5.6 .. .. .. .. .. 50 .. 55 Georgia 3.0 2.3 55 53 ­1.6 22 25 41 48 96 94 91 92 Germany 58.1 60.5 73 74 0.2 8 9 6 6 100 100 100 100 Ghana 5.4 11.7 36 50 4.2 13 16 22 19 11 15 3 6 Greece 6.0 6.9 59 61 0.8 30 29 51 48 100 99 93 97 Guatemala 3.7 6.6 41 49 3.3 .. 8 22 16 87 90 58 79 Guinea 1.7 3.4 28 34 3.8 15 16 52 46 19 33 10 12 Guinea-Bissau 0.3 0.5 28 30 2.7 .. .. .. .. .. 48 .. 26 Haiti 2.0 4.6 29 47 4.6 16 21 56 45 49 29 20 12 Honduras 2.0 3.5 40 48 3.2 .. .. 29 29 68 78 29 55 194 2010 World Development Indicators 3.11 ENVIRONMENT Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2008 1990 2008 1990­2008 1990 2007 1990 2007 1990 2006 1990 2006 Hungary 6.8 6.8 66 68 0.0 19 17 29 25 100 100 100 100 India 216.6 336.7 26 30 2.5 10 11 6 6 44 52 4 18 Indonesia 54.3 117.0 31 51 4.3 9 9 14 8 73 67 42 37 Iran, Islamic Rep. 30.6 49.3 56 68 2.6 23 23 21 16 86 .. 78 .. Iraq 13.2 .. 70 .. .. 26 .. 31 .. 75 .. .. .. Ireland 2.0 2.7 57 61 1.7 26 25 46 40 .. .. .. .. Israel 4.2 6.7 90 92 2.6 43 60 48 49 100 100 .. .. Italy 37.8 40.7 67 68 0.4 19 17 9 8 .. .. .. .. Jamaica 1.2 1.4 49 53 1.1 .. .. .. .. 82 82 83 84 Japan 78.0 84.9 63 66 0.5 46 48 42 42 100 100 100 100 Jordan 2.3 4.6 72 78 3.9 27 18 37 30 .. 88 .. 71 Kazakhstan 9.2 9.1 56 58 ­0.1 7 8 12 14 97 97 96 98 Kenya 4.3 8.4 18 22 3.7 6 8 32 37 18 19 44 48 Korea, Dem. Rep. 11.8 14.9 58 63 1.3 15 19 21 22 .. .. .. .. Korea, Rep. 31.6 39.6 74 81 1.2 51 48 33 25 .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 2.1 2.7 98 98 1.4 65 72 67 74 .. .. .. .. Kyrgyz Republic 1.7 1.9 38 36 0.8 .. .. 38 43 .. 94 .. 93 Lao PDR 0.6 1.9 15 31 6.0 .. .. .. .. .. 87 .. 38 Latvia 1.9 1.5 69 68 ­1.0 .. .. .. .. .. 82 .. 71 Lebanon 2.5 3.6 83 87 2.2 43 44 52 51 100 100 .. .. Lesotho 0.2 0.5 14 25 4.7 .. .. .. .. .. 43 30 34 Liberia 1.0 2.3 45 60 4.7 .. 29 54 48 59 49 24 7 Libya 3.3 4.9 76 78 2.2 48 55 45 46 97 97 96 96 Lithuania 2.5 2.2 68 67 ­0.6 .. .. .. .. .. .. .. .. Macedonia, FYR 1.1 1.4 58 67 1.2 .. .. .. .. .. 92 .. 81 Madagascar 2.7 5.6 24 30 4.2 8 9 36 31 15 18 6 10 Malawi 1.1 2.8 12 19 5.2 .. .. .. .. 50 51 46 62 Malaysia 9.0 19.0 50 70 4.1 6 5 12 8 95 95 .. 93 Mali 2.0 4.1 23 32 3.9 9 12 37 38 53 59 30 39 Mauritania 0.8 1.3 40 41 2.9 .. .. .. .. 33 44 11 10 Mauritius 0.5 0.5 44 42 0.8 .. .. .. .. 95 95 94 94 Mexico 59.4 82.1 71 77 1.8 32 34 26 23 74 91 8 48 Moldova 2.0 1.5 47 42 ­1.7 .. .. .. .. .. 85 .. 73 Mongolia 1.3 1.5 57 57 1.0 .. .. 45 60 .. 64 .. 31 Morocco 12.0 17.7 48 56 2.2 16 19 22 18 80 85 25 54 Mozambique 2.9 8.2 21 37 5.9 6 7 27 18 .. 53 12 19 Myanmar 10.2 16.1 25 33 2.6 7 8 28 26 47 85 15 81 Namibia 0.4 0.8 28 37 3.8 .. .. .. .. 73 66 8 18 Nepal 1.7 5.0 9 17 6.0 .. .. 23 19 36 45 6 24 Netherlands 10.3 13.5 69 82 1.5 14 12 10 8 100 100 100 100 New Zealand 2.9 3.7 85 87 1.3 25 30 30 34 .. .. 88 .. Nicaragua 2.2 3.2 52 57 2.2 18 21 34 38 59 57 23 34 Niger 1.2 2.4 15 17 3.8 .. .. 35 40 16 27 1 3 Nigeria 34.4 73.1 35 48 4.2 11 14 14 13 33 35 22 25 Norway 3.1 3.7 72 77 1.1 .. .. 22 22 .. .. .. .. Oman 1.2 2.0 66 72 2.7 .. .. .. .. 97 97 61 .. Pakistan 33.0 60.1 31 36 3.3 16 18 22 21 76 90 14 40 Panama 1.3 2.5 54 73 3.6 35 38 65 53 .. 78 .. 63 Papua New Guinea 0.6 0.8 15 13 1.6 .. .. .. .. 67 67 41 41 Paraguay 2.1 3.8 49 60 3.3 22 30 45 51 88 89 34 42 Peru 15.0 20.6 69 71 1.8 27 28 39 39 73 85 15 36 Philippines 30.5 58.7 49 65 3.6 14 14 26 19 71 81 46 72 Poland 23.4 23.4 61 61 0.0 4 4 7 7 .. .. .. .. Portugal 4.7 6.3 48 59 1.6 37 39 54 45 97 99 88 98 Puerto Rico 2.6 3.9 72 98 2.3 44 67 60 69 .. .. .. .. Qatar 0.4 1.2 92 96 5.8 .. .. .. .. 100 100 100 100 2010 World Development Indicators 195 3.11 Urbanization Urban Population Population in Access to improved population in urban largest city sanitation facilities agglomerations of more than 1 million average % of total annual % of total % of urban % of urban % of rural millions population % growth population population population population 1990 2008 1990 2008 1990­2008 1990 2007 1990 2007 1990 2006 1990 2006 Romania 12.3 11.7 53 54 ­0.3 8 9 14 17 88 88 52 54 Russian Federation 108.8 103.4 73 73 ­0.3 18 18 8 10 93 93 70 70 Rwanda 0.4 1.8 5 18 8.5 .. .. 57 49 31 34 29 20 Saudi Arabia 12.5 20.3 77 82 2.7 30 40 19 22 100 100 .. .. Senegal 2.9 5.2 39 42 3.1 18 22 47 52 52 54 9 9 Serbia 3.8 3.8 50 52 0.0 .. 11 .. 21 .. 96a .. 88a Sierra Leone 1.3 2.1 33 38 2.5 .. .. 40 43 .. 20 .. 5 Singapore 3.0 4.8 100 100 2.6 99 100 99 100 100 100 .. .. Slovak Republic 3.0 3.1 57 57 0.1 .. .. .. .. 100 100 99 99 Slovenia 1.0 1.0 50 49 ­0.1 .. .. .. .. .. .. .. .. Somalia 2.0 3.3 30 37 2.8 14 13 48 35 .. 51 .. 7 South Africa 18.3 29.6 52 61 2.7 25 33 10 12 64 66 45 49 Spain 29.3 35.1 75 77 1.0 22 24 15 16 100 100 100 100 Sri Lanka 2.9 3.0 17 15 0.2 .. .. .. .. 85 89 68 86 Sudan 7.2 18.0 27 43 5.1 9 12 33 28 53 50 26 24 Swaziland 0.2 0.3 23 25 2.1 .. .. .. .. .. 64 .. 46 Sweden 7.1 7.8 83 85 0.5 17 14 21 16 100 100 100 100 Switzerland 4.9 5.6 73 73 0.7 14 15 19 20 100 100 100 100 Syrian Arab Republic 6.2 11.2 49 54 3.2 26 31 25 25 94 96 69 88 Tajikistan 1.7 1.8 32 26 0.4 .. .. .. .. .. 95 .. 91 Tanzania 4.8 10.8 19 26 4.5 5 7 27 28 29 31 36 34 Thailand 16.7 22.5 29 33 1.7 10 10 35 30 92 95 72 96 Timor-Leste 0.2 0.3 21 27 3.7 .. .. .. .. .. 64 .. 32 Togo 1.2 2.7 30 42 4.6 16 23 53 56 25 24 8 3 Trinidad and Tobago 0.1 0.2 9 13 3.0 .. .. .. .. 93 92 93 92 Tunisia 4.7 6.9 58 67 2.1 .. .. .. .. 95 96 44 64 Turkey 33.2 50.8 59 69 2.4 22 27 20 20 96 96 69 72 Turkmenistan 1.7 2.5 45 49 2.2 .. .. .. .. .. .. .. .. Uganda 2.0 4.1 11 13 4.1 4 5 38 36 27 29 29 34 Ukraine 34.7 31.4 67 68 ­0.5 12 11 7 9 98 97 93 83 United Arab Emirates 1.5 3.5 79 78 4.8 25 31 32 40 98 98 95 95 United Kingdom 50.8 55.2 89 90 0.5 26 26 15 16 .. .. .. .. United States 188.0 248.4 75 82 1.5 41 43 9 8 100 100 99 99 Uruguay 2.8 3.1 89 92 0.6 41 45 46 49 100 100 99 99 Uzbekistan 8.2 10.1 40 37 1.1 10 8 25 22 97 97 91 95 Venezuela, RB 16.6 26.1 84 93 2.5 34 32 17 12 90 .. 47 .. Vietnam 13.4 24.0 20 28 3.2 13 13 30 22 62 88 21 56 West Bank and Gaza 1.3 2.8 68 72 4.1 .. .. .. .. .. 84 .. 69 Yemen, Rep. 2.6 7.0 21 31 5.6 5 9 25 30 79 88 14 30 Zambia 3.1 4.5 39 35 2.0 10 11 24 31 49 55 38 51 Zimbabwe 3.0 4.7 29 37 2.4 10 13 35 34 65 63 35 37 World 2,257.4 s 3,330.6 s 43 w 50 w 2.2 w 18 w 20 w 17 w 16 w 76 w 78 w 34 w 44 w Low income 148.4 280.4 23 29 3.5 9 11 31 31 48 52 19 33 Middle income 1,435.2 2,238.0 39 48 2.5 15 18 14 12 71 75 32 43 Lower middle income 891.8 1,528.3 31 41 3.0 .. .. 11 10 62 69 30 41 Upper middle income 543.4 709.7 68 75 1.5 24 27 18 18 86 89 52 63 Low & middle income 1,583.6 2,518.4 37 45 2.6 14 17 16 14 69 73 30 41 East Asia & Pacific 461.3 851.6 29 44 3.4 .. .. 9 7 65 75 42 59 Europe & Central Asia 271.1 281.4 63 64 0.2 15 16 13 14 95 94 77 79 Latin America & Carib. 308.2 445.0 71 79 2.0 32 34 24 22 81 86 35 51 Middle East & N. Africa 117.5 186.4 52 57 2.6 20 20 27 24 83 89 50 59 South Asia 280.7 455.6 25 29 2.7 10 12 10 11 49 57 8 23 Sub-Saharan Africa 144.9 298.4 28 36 4.0 .. 13 26 25 41 42 20 24 High income 673.7 812.1 73 78 1.0 .. .. 20 19 100 100 99 99 Euro area 213.0 238.7 71 73 0.6 18 18 15 15 .. .. .. .. a. Includes Kosovo. 196 2010 World Development Indicators 3.11 ENVIRONMENT Urbanization About the data Definitions There is no consistent and universally accepted populous nations were to change their definition of · Urban population is the midyear population of standard for distinguishing urban from rural areas, in urban centers. According to China's State Statis- areas defined as urban in each country and reported part because of the wide variety of situations across tical Bureau, by the end of 1996 urban residents to the United Nations (see About the data). · Popula- countries (see About the data for table 3.1). Most accounted for about 43 percent of China's popula- tion in urban agglomerations of more than 1 million countries use an urban classification related to the tion, more than double the 20 percent considered is the percentage of a country's population living in size or characteristics of settlements. Some define urban in 1994. In addition to the continuous migra- metropolitan areas that in 2005 had a population of urban areas based on the presence of certain infra- tion of people from rural to urban areas, one of the more than 1 million. · Population in largest city is structure and services. And other countries designate main reasons for this shift was the rapid growth in the percentage of a country's urban population living urban areas based on administrative arrangements. the hundreds of towns reclassified as cities in recent in that country's largest metropolitan area. · Access The population of a city or metropolitan area years. to improved sanitation facilities is the percentage depends on the boundaries chosen. For example, in Because the estimates in the table are based on of the urban or rural population with access to at 1990 Beijing, China, contained 2.3 million people in national definitions of what constitutes a city or met- least adequate excreta disposal facilities (private or 87 square kilometers of "inner city" and 5.4 million ropolitan area, cross-country comparisons should be shared but not public) that can effectively prevent in 158 square kilometers of "core city." The popula- made with caution. To estimate urban populations, human, animal, and insect contact with excreta. tion of "inner city and inner suburban districts" was UN ratios of urban to total population were applied Improved facilities range from simple but protected 6.3 million and that of "inner city, inner and outer to the World Bank's estimates of total population pit latrines to flush toilets with a sewerage connec- suburban districts, and inner and outer counties" (see table 2.1). tion. To be effective, facilities must be correctly con- was 10.8 million. (Most countries use the last defini- The table shows access to improved sanitation structed and properly maintained. tion.) For further discussion of urban-rural issues see facilities for both urban and rural populations to box 3.1a in About the data for table 3.1. allow comparison of access. Definitions of access Estimates of the world's urban population would and urban areas vary, however, so comparisons change significantly if China, India, and a few other between countries can be misleading. Urban population nearly doubled in low- and lower middle-income economies between 1990 and 2008 3.11a Urban population (millions) 1990 2008 1,600 1,200 800 400 0 Low income Lower middle income Upper middle income High income Source: Table 3.11. Latin America and the Caribbean had the same share of urban population as high-income economies in 2008 3.11b Percent Urban Rural 100 Data sources 75 Data on urban population and the population in urban agglomerations and in the largest city are 50 from the United Nations Population Division's World Urbanization Prospects: The 2007 Revi- 25 sion. Data on total population are World Bank estimates. Data on access to sanitation are from 0 the World Health Organization and United Nations East Asia Europe & Latin America Middle East & South Sub-Saharan High income & Pacific Central Asia & Caribbean North Africa Asia Africa Children's Fund's Progress on Drinking Water and Source: Tables 3.1 and 3.11. Sanitation (2008). 2010 World Development Indicators 197 3.12 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2001 4.2 3.9 .. .. .. .. 65b 30 b .. .. 12 13 Algeria 1998 4.9 .. .. .. .. .. 67 .. .. .. 19 .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2001 3.6 .. 19 .. 97 .. .. .. 4 .. 16b .. Armenia 2001 4.1 4.0 4 6 93 93 95 90 1 1 .. .. Australia 2001 3.8 .. 1 .. .. .. .. .. .. .. .. .. Austria 2001 2.4 .. 2 .. .. .. 48 .. .. .. .. .. Azerbaijan 1999 4.7 4.4 .. .. .. .. 74 62 4 5 .. .. Bangladesh 2001 4.8 4.8 .. .. 21b 42b 88b 61b .. .. .. .. Belarus 1999 .. .. .. .. .. .. .. .. .. .. .. .. Belgium 2001 2.6 .. 0b .. .. .. 67 .. 32b .. .. .. Benin 1992 5.9 .. .. .. 26 .. 59 .. .. .. .. .. Bolivia 2001 4.2 4.3 40 .. 43 58 70 59 3b 5b 6 4 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 2001 4.2 3.9 27 47 88 90 b 61 47 1 .. .. .. Brazil 2000 3.8 3.7 .. .. .. .. 74 75 .. .. .. .. Bulgaria 2001 2.7 2.7 .. .. 79 89 98 98 .. .. 23 17 Burkina Faso 1996 6.2 5.8 30 53 .. .. .. .. .. .. .. .. Burundi 1990 4.7 .. .. .. .. .. .. .. .. .. .. .. Cambodia 2005 5.0 4.9 35 32 79 88 58 57 27 32 .. .. Cameroon 1987 5.2 5.1 67 77 77 .. 73 48 27 42 .. .. Canada 2001 2.6 .. .. .. .. .. 64 .. 32 .. 8 .. Central African Republic 2003 5.2 5.8 32 36b 78 92 85 74 .. .. .. .. Chad 1993 5.1 5.1 .. .. .. .. .. .. .. .. .. .. Chile 2002 3.4 3.5 .. .. 91 92 66 65 13 15 11 10 China 2000 3.4 3.2 .. .. 82 .. 88 74 .. .. 1 .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 1993 4.8 .. 27b .. 83b .. 68b .. 13 .. 10 b .. Congo Dem Rep 1984 5.4 .. 55 .. .. .. .. .. .. .. .. .. Congo Rep 1984 10.5 .. .. .. .. .. 76 .. .. .. .. .. Costa Rica 2000 4.0 .. 22 .. 88 .. 72 .. 2 3 9 6 Côte D'Ivoire 1998 5.4 .. .. .. .. .. .. .. .. .. .. .. Croatia 2001 3.0 .. .. .. .. .. .. .. .. .. 12 .. Cuba 1981 4.2 4.2 .. .. .. .. .. .. 15 21 0 0 Czech Republic 2001 2.4 .. .. .. .. .. 52 .. 49 .. 12 .. Denmark 2001 2.2 .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2002 3.9 .. .. .. 97 .. .. .. 8 .. 11 .. Ecuador 2001 3.5 3.7 30 .. 81 88 68 b 58b 9 14 12 7 Egypt 1996 4.7 .. .. .. .. .. .. .. 75 .. .. .. El Salvador 1992 .. .. 63 .. 67 83 70 68 3 6 11 11 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2000 2.4 2.3 3 .. .. .. .. .. 72 .. 13 .. Ethiopia 1994 4.8 4.7 .. .. .. 23 .. 54 .. .. .. .. Finland 2000 2.2 .. .. .. .. .. 64 .. 44 .. .. .. France 1999 2.5 .. .. .. .. .. 55 .. .. .. 7 .. Gabon 2003 5.2 .. .. .. .. .. .. .. .. .. .. .. Gambia 1993 8.9 .. .. .. 18 .. 68 .. .. .. .. .. Georgia 2002 3.5 3.5 .. .. .. .. .. .. .. .. .. .. Germany 2001 2.3 .. .. .. .. .. 43 .. .. .. 7 .. Ghana 2000 5.1 5.1 .. .. 45 .. 57 .. 53 .. 5 .. Greece 2001 3.0 .. 1 .. .. .. .. .. .. .. .. .. Guatemala 2002 4.4 4.7 .. .. 67 80 81 74 2 4 13 11 Guinea 1996 6.7 .. 63 .. .. .. 76 .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1982 4.2 .. 26 .. .. .. 92 68 .. .. 9 19 Honduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 .. 198 2010 World Development Indicators 3.12 ENVIRONMENT Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Hungary 2001 2.6 .. 2 .. .. .. .. .. .. .. 4 .. India 2001 5.3 5.3 77 71 83 81 87 67 .. .. 6 9 Indonesia 2000 4.0 .. .. .. .. .. .. .. .. .. .. .. Iran, Islamic Rep. 1996 4.8 4.6 33b 26b 72 76 73 67 .. .. .. .. Iraq 1997 7.7 7.2 .. .. 88 96 70 66 4 5 13 15 Ireland 2002 3.0 .. .. .. .. .. .. .. 8b .. .. .. Israel 1995 3.5 .. .. .. .. .. .. .. .. .. .. .. Italy 2001 2.8 .. .. .. .. .. .. .. .. .. 21 .. Jamaica 2001 3.5 .. .. .. 98b .. 58b .. 2b .. .. .. Japan 2000 2.7 .. .. .. .. .. 61 .. 37 .. .. .. Jordan 2004 5.3 5.1 35 34 .. .. 64 60 72 80 .. .. Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. Kenya 1999 4.6 3.4 .. .. 35 72 72 25 .. .. 39 17 Korea, Dem Rep 2000 3.8 .. 23 .. .. .. 50 .. 15 .. .. .. Korea, Rep. 1993 4.4 .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 1995 6.4 .. .. .. .. .. .. .. 9b .. 11 .. Kyrgyz Republic 1999 4.4 3.6 .. .. .. .. .. .. .. .. .. .. Laos 1995 6.1 6.1 .. .. 49 77 96 86 .. .. .. .. Latvia 2000 3.0 2.6 4 .. 88 .. 58 .. 74 .. 0 .. Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho 2001 5.0 .. 10 b .. .. .. 84 .. 0 .. .. .. Liberia 1974 4.8 .. 31 .. 20 .. 1 .. .. .. .. .. Libya 6.4 .. .. .. .. .. .. .. .. .. 7 .. Lithuania 2001 2.6 .. 7 .. .. .. .. .. .. .. .. .. Macedonia, FYR 2002 3.6 3.6b 8b .. 95b 95b 48b .. .. .. 7b 3b Madagascar 1993 4.9 4.8 64 57 .. .. 81 59 .. .. .. .. Malawi 1998 4.4 4.4 30 .. 48 84 86 47 .. .. .. .. Malaysia 2000 4.5 4.4 .. .. .. .. .. .. 10 b 16b .. .. Mali 1998 5.6 .. .. .. .. .. .. .. .. .. .. .. Mauritania 1988 .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 2000 3.9 3.8 6 7 91 94 87 81 .. .. 7 6 Mexico 2005 4.0 3.9 24 20 .. .. .. .. .. .. 3 2 Moldova 2003 .. .. .. .. .. .. .. .. .. .. .. .. Mongolia 2000 4.4 4.5 .. .. .. .. .. .. 48 56 .. .. Morocco 1982 5.9 5.3 .. .. .. .. .. .. .. .. .. .. Mozambique 1997 4.4 4.9 37 28 7 20 92 83 1 1 0 .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2001 5.3 .. .. .. .. .. .. .. .. .. .. .. Nepal 2001 5.4 4.9 .. .. .. .. 88 .. .. .. 0 .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand 2001 2.8 .. 1b .. .. .. 65 .. 17 .. 10 .. Nicaragua 1995 5.3 .. .. .. 79 87 84 86 0 0 8 .. Niger 2001 6.4 6.0 .. .. .. .. 77 40 .. .. .. .. Nigeria 1991 5.0 4.7 .. .. .. .. .. .. .. .. .. .. Norway 1980 2.7 .. 1 .. .. .. 67 .. 38 .. .. .. Oman 2003 7.1 .. .. .. .. .. .. .. .. .. .. .. Pakistan 1998 6.8 6.8 .. .. 58 86 81 .. .. .. .. .. Panama 2000 4.1 .. 28b .. 88 98b 80 66b 10 b 10 b 14 .. Papua New Guinea 1990 4.5b 6.5 .. .. .. .. .. 44 .. 8 .. .. Paraguay 2002 4.6 4.5 38b ..b 95b 98b 79 75 1b 2b 6b 6b Peru 1993 .. .. .. .. 49 64 .. .. .. .. 7 3 Philippines 2000 4.9 .. .. .. .. .. 71 .. 12 Poland 1988 3.2 .. .. .. .. .. .. .. .. .. 1 .. Portugal 2001 2.8 .. .. .. .. .. 76 .. 86 .. .. .. Puerto Rico 1990 3.3 .. .. .. .. .. 72 .. .. .. 11 .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 199 3.12 Urban housing conditions Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate units Households living in overcrowded Buildings with Privately owned Unoccupied number of dwellingsa durable structure dwellings dwellings people % of total % of total % of total % of total % of total National Urban National Urban National Urban National Urban National Urban National Urban Romania 2002 2.9 2.8 20 20 .. .. 84 72 .. .. .. .. Russia 2002 2.8 2.7 7 5 .. .. .. .. 73 86 .. .. Rwanda 2002 4.4 3.7 43 36 13 31 79 41 36 60 .. .. Saudi Arabia 2004 5.5 .. .. .. 92b .. 43 .. .. .. .. .. Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 2001 2.9 2.2 .. .. .. .. .. .. .. .. .. .. Sierra Leone 1985 6.8 .. .. .. 34 .. 68 .. .. .. .. .. Singapore 2000 4.4 .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia 2002 2.8 2.7 14 17 .. .. 91 87 33 56 .. .. Somalia 1975 .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2007 3.0 2.8 16 15 .. .. 43 40 .. .. .. .. Spain 2001 2.9 .. 1 .. .. .. 82 .. .. .. .. .. Sri Lanka 2001 3.8 .. .. .. 93b 92b 70 b 58b 1 14b 13 1b Sudan 1993 5.8 6.0 .. .. .. .. 86b 58b 0b 1b .. .. Swaziland 1997 5.4 3.7 .. .. .. .. .. .. .. .. .. .. Sweden 1990 2.0 .. .. .. .. .. .. .. 54 .. 1 .. Switzerland 1990 2.4 2.1 .. .. .. .. 31 24 28 32 11 7 Syrian Arab Republic 1981 6.3 6.0 .. .. .. .. .. .. .. .. .. .. Tajikistan 2000 .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 2002 4.9 4.5b 33b 7b .. .. 82b 43b .. .. .. .. Thailand 2000 3.8 .. .. .. 93 93 81 62 3 .. 3 .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 2000 3.7 .. 9b .. 98b .. 74b .. 17b .. .. .. Tunisia 1994 8.0 .. .. .. 99 .. 71 89b 6 10 b 15 12b Turkey 1990 5.0 .. .. .. .. .. 70 .. .. .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2002 4.7 3.9 .. .. 19 61 76 28 37 71 .. .. Ukraine 2003 .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 2001 .. 2.4 .. .. .. .. .. 69 .. 19 .. .. United States 2005 2.5 .. 0 .. .. .. 74 .. 26 .. .. .. Uruguay 1996 3.3 3.4b 22b .. .. .. 57b 57b .. .. 13b 13b Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela. RB 2001 4.4 .. .. .. .. .. 78 .. 14 .. 16 .. Vietnam 1999 4.6 4.5 .. .. 77 89 95 86 .. .. .. .. West Bank and Gaza 1997 7.1 .. .. .. .. .. 78 .. 45 .. .. .. Yemen 1994 6.7 6.8 54b 6b .. .. 88b 68b 3b 11b .. .. Zambia 2000 5.3 5.9 .. .. .. .. 94 30 .. .. .. .. Zimbabwe 1992 4.8 4.2 .. .. .. .. 94 30 6 .. .. .. a. More than two people per room. b. Data are from a previous census. 200 2010 World Development Indicators 3.12 ENVIRONMENT Urban housing conditions About the data Definitions Urbanization can yield important social benefi ts, There is a strong demand for quantitative indi- · Census year is the year in which the underlying improving access to public services and the job mar- cators that can measure housing conditions on a data were collected. · Household size is the average ket. It also leads to significant demands for services. regular basis to monitor progress. However, data number of people within a household, calculated by Inadequate living quarters and demand for housing deficiencies and lack of rigorous quantitative analy- dividing total population by the number of households and shelter are major concerns for policymakers. sis hamper informed decisionmaking on desirable in the country and in urban areas. · Overcrowding The unmet demand for affordable housing, along policies to improve housing conditions. The data refers to the number of households living in dwell- with urban poverty, has led to the emergence of in the table are from housing and population cen- ings with two or more people per room as a percent- slums in many poor countries. Improving the shel- suses, collected using similar definitions. The table age of total households in the country and in urban ter situation requires a better understanding of the will incorporate household survey data in future edi- areas. · Durable dwelling units are the number of mechanisms governing housing markets and the pro- tions. The table focuses attention on urban areas, housing units in structures made of durable building cesses governing housing availability. That requires where housing conditions are typically most severe. materials (concrete, stone, cement, brick, asbestos, good data and adequate policy-oriented analysis so Not all the compiled indicators are presented in the zinc, and stucco) expected to maintain their stability that housing policy can be formulated in a global table because of space limitations. for 20 years or longer under local conditions with comparative perspective and drawn from lessons normal maintenance and repair, taking into account learned in other countries. Housing policies and location and environmental hazards such as floods, outcomes affect such broad socioeconomic condi- mudslides, and earthquakes, as a percentage of tions as the infant mortality rate, performance in total dwellings. · Home ownership refers to the school, household saving, productivity levels, capital number of privately owned dwellings as a percent- formation, and government budget deficits. A good age of total dwellings. When the number of private understanding of housing conditions thus requires dwellings is not available from the census data, the an extensive set of indicators within a reasonable share of households that own their housing unit is framework. used. Privately owned and owner-occupied units are included, depending on the definition used in the Selected housing indicators for smaller economies 3.12a census data. State- and community-owned units and rented, squatted, and rent-free units are excluded. Census Household Overcrowding Durable Home Multiunit Vacancy year size dwelling ownership dwellings rate · Multiunit dwellings are the number of multiunit units dwellings, such as apartments, flats, condominiums, Households living in Buildings Privately barracks, boardinghouses, orphanages, retirement overcrowded with durable owned Unoccupied houses, hostels, hotels, and collective dwellings, number of dwellingsa structure dwellings dwellings people % of total % of total % of total % of total % of total as a percentage of total dwellings. · Vacancy rate Antigua and Barbuda 2001 3.0 .. 99b 65b 3b 22 is the percentage of completed dwelling units that Bahamas 1990 3.8 12 99 55 13 14 are currently unoccupied. It includes all vacant Bahrain 2001 5.9 .. 94b 51 28 6 units, whether on the market or not (such as second Barbados 1990 3.5 3 100 76 9 9 Belize 2000 4.6 .. 93 63 4 .. homes). Cape Verde 1990 5.1 28 78 72 2 .. Cayman Islands 1999 3.1 .. 100 53 38 19 Equatorial Guinea 1993 7.5 14 56b 75 14 .. Fiji 1996 5.4 .. 60 65 7 .. Guam 2000 4.0 2b 93 48 29 19 Isle of Man 2001 2.4 0 .. 68 16 .. Maldives 2000 6.6 .. 93 .. 1 15 Marshall Islands 1999 7.8 .. 95 72 12 8 Netherlands Antilles 2001 2.9 24b 99 60 16 12 New Caledonia 1989 4.1 .. 77 53 9 13 Northern Mariana Islands 1995 4.9 9b 99 33 27 17 Palau 2000 5.7 8 76 79 11 3 Seychelles 1997 4.2 15b 97 78 .. 0 Solomon Islands 1999 6.3 51 23 85 1 .. St. Vincent & Grenadines 1991 3.9 .. 98 71 7 .. Turks and Caicos 1990 3.3 4 96 66 11 .. Virgin Islands (UK) 1991 3.0 2 99 40 46 .. Data sources Western Samoa 1991 7.3 .. 42 90 47 30 Data on urban housing conditions are from a. More than two people per room. b. Data are from a previous census. Source: National population and housing censuses. national population and housing censuses. 2010 World Development Indicators 201 3.13 Traffic and congestion Motor Passenger Road Road sector Fuel Particulate matter vehicles cars density energy consumption price concentration km. of road per Urban-population- per per per 100 sq. kilograms of oil equivalent $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total Per Super grade micrograms per people of road people land area consumption capita Diesel fuel Gasoline fuel gasoline Diesel cubic meter 2007 2007 2007 2007 2007 2007 2007 2007 2008 2008 1990 2006 Afghanistan 23 9 15 6 .. .. .. .. 1.05 0.96 78 41 Albania 102 15 75 63 29 198 151 42 1.36 1.31 92 44 Algeria 91 27 58 5 16 170 91 61 0.34 0.20 115 71 Angola 40 .. 8 .. 11 68 41 24 0.53 0.39 142 66 Argentina 314 .. .. 8 19 343 172 102 0.78 0.58 105 73 Armenia 105 42 96 25 6 58 0 55 1.08 1.11 453 59 Australia 653 17 545 11 19 1,103 315 664 0.74 0.94 23 15 Austria 556 43 511 128 23 933 646 233 1.37 1.43 38 33 Azerbaijan 61 10 57 68 10 134 30 92 0.74 0.56 226 60 Bangladesh 2 .. 1 166 5 8 5 2 1.17 0.70 231 135 Belarus 282 .. 240 46 5 147 86 50 1.33 1.06 23 6 Belgium 539 37 471 499 14 762 599 131 1.50 1.34 30 22 Benin 21 .. 17 17 22 77 27 46 1.03 1.03 75 46 Bolivia 68 7 18 6 26 149 67 53 0.68 0.53 120 94 Bosnia and Herzegovina 170 .. 152 43 15 230 143 80 1.13 1.18 36 19 Botswana 113 7 56 4 26 283 96 172 0.88 1.02 95 67 Brazil 198 18 158 20 23 281 143 72 1.26 1.03 40 23 Bulgaria 295 63 257 37 12 315 175 78 1.28 1.37 111 57 Burkina Faso 11 .. 7 34 .. .. .. .. 1.38 1.33 151 84 Burundi 6 .. 2 48 .. .. .. .. 1.39 1.23 56 29 Cambodia .. .. .. 22 8 27 15 11 0.94 0.89 86 46 Cameroon .. .. 11 11 9 37 14 20 1.14 1.04 116 62 Canada 597 14 372 14 16 1,341 328 914 0.76 0.90 25 17 Central African Republic 0 .. 0 .. .. .. .. .. 1.44 1.44 62 44 Chad 6 2 .. 3 .. .. .. .. 1.30 1.32 217 109 Chile 164 .. 103 .. 18 338 187 134 0.95 0.95 88 48 China 32 12 22 36 5 72 29 40 0.99 1.01 114 73 Hong Kong SAR, China 72 247 54 184 11 209 152 48 1.95 1.16 .. .. Colombia 66 16 38 15 24 159 79 65 1.04 0.73 39 22 Congo, Dem. Rep. 5 .. .. .. 1 3 0 3 1.23 1.21 73 47 Congo, Rep. 26 .. 15 5 23 80 51 26 0.81 0.57 135 64 Costa Rica 152 18 118 72 30 322 160 145 1.24 1.10 45 36 Côte d'Ivoire .. .. 7 25 4 18 11 6 1.33 1.20 94 36 Croatia 377 58 336 51 21 445 260 160 1.27 1.37 44 30 Cuba 38 .. 21 .. 3 26 19 5 1.67 1.51 44 17 Czech Republic 470 38 414 163 13 576 344 203 1.37 1.45 67 21 Denmark 466 35 370 168 22 799 446 326 1.54 1.54 30 19 Dominican Republic 123 .. 62 .. 20 161 57 96 1.04 0.94 44 20 Ecuador 63 19 38 15 33 288 134 139 0.51 0.27 38 25 Egypt, Arab Rep. .. .. 29 9 16 138 80 48 0.49 0.20 223 119 El Salvador 84 .. 41 .. 19 151 76 67 0.78 0.81 46 33 Eritrea 11 .. 6 .. 5 8 8 1 2.53 1.07 118 56 Estonia 444 10 390 128 13 559 302 240 1.18 1.30 45 13 Ethiopia 3 4 1 3 5 15 12 2 0.92 0.89 112 68 Finland 559 37 483 23 11 782 417 340 1.57 1.39 23 18 France 600 39 498 172 16 691 501 147 1.52 1.45 18 13 Gabon .. .. .. 3 9 117 87 25 1.14 0.90 10 8 Gambia, The 7 3 5 33 .. .. .. .. 0.79 0.75 144 86 Georgia 116 16 95 29 20 150 48 93 1.09 1.16 208 47 Germany 623 80 566 181 15 623 304 250 1.56 1.56 27 19 Ghana 33 9 21 25 13 52 23 27 0.90 0.90 39 34 Greece 112 47 429 89 21 597 198 367 1.23 1.41 67 36 Guatemala 117 .. .. .. 24 150 76 66 0.86 0.82 63 62 Guinea .. .. .. 10 .. .. .. .. 1.02 1.02 108 70 Guinea-Bissau 33 1 27 12 .. .. .. .. 0.00 0.00 119 72 Haiti .. .. .. .. 9 25 0 23 1.16 0.89 70 37 Honduras 97 .. 69 .. 22 149 85 57 0.80 0.80 45 43 202 2010 World Development Indicators 3.13 ENVIRONMENT Traffic and congestion Motor Passenger Road Road sector Fuel Particulate matter vehicles cars density energy consumption price concentration km. of road per Urban-population- per per per 100 sq. kilograms of oil equivalent $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total Per Super grade micrograms per people of road people land area consumption capita Diesel fuel Gasoline fuel gasoline Diesel cubic meter 2007 2007 2007 2007 2007 2007 2007 2007 2008 2008 1990 2006 Hungary 384 20 300 210 16 423 252 152 1.27 1.38 36 19 India 12 3 8 1,001 6 33 21 9 1.09 0.70 112 65 Indonesia 76 62 42 20 12 99 32 62 0.60 0.46 137 83 Iran, Islamic Rep. 16 .. 13 10 19 497 214 242 0.53a 0.03 86 51 Iraq .. .. .. .. 30 332 186 131 0.03 0.01 146 115 Ireland 537 20 437 132 31 1,064 610 417 1.56 1.64 25 16 Israel 305 122 251 81 16 504 163 314 1.47 1.27 71 31 Italy 677 81 601 162 22 659 415 199 1.57 1.63 42 27 Jamaica 188 24 138 201 11 198 0 185 0.74 0.84 59 43 Japan 595 64 325 316 14 572 195 340 1.74 1.54 43 30 Jordan 137 101 94 9 23 295 125 162 0.61 0.61 110 45 Kazakhstan 170 28 141 3 5 234 22 200 0.83 0.72 43 19 Kenya 21 10 15 11 6 29 16 11 1.20 1.14 67 36 Korea, Dem. Rep. .. .. .. 21 2 17 9 7 0.76 0.95 165 68 Korea, Rep. 338 161 248 103 13 573 297 151 1.65 1.33 51 35 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 502 181 282 32 13 1,232 302 860 0.24 0.20 75 97 Kyrgyz Republic 59 9 44 .. 9 51 0 49 0.80 0.88 75 22 Lao PDR 21 10 2 13 .. .. .. .. 0.92 0.76 91 49 Latvia 459 15 398 108 25 511 305 178 1.12 1.23 38 16 Lebanon .. .. .. 67 26 252 2 234 0.76 0.76 43 36 Lesotho .. .. .. .. .. .. .. .. 0.79 0.93 86 41 Liberia 3 .. 2 .. .. .. .. .. 0.74 1.03 61 40 Libya 291 .. 225 .. 19 536 313 198 0.14 0.12 106 88 Lithuania 479 0 470 124 18 485 267 123 1.13 1.22 53 19 Macedonia, FYR 136 20 122 54 13 190 106 56 1.15 1.12 46 21 Madagascar .. .. .. .. .. .. .. .. 1.55 1.43 78 34 Malawi 9 .. 4 16 .. .. .. .. 1.78 1.67 75 33 Malaysia 272 72 225 28 18 505 180 306 0.53 0.53 37 23 Mali 9 .. 7 1 .. .. .. .. 1.30 1.10 274 152 Mauritania .. .. .. 1 .. .. .. .. 1.49 1.06 147 86 Mauritius 150 93 115 99 .. .. .. .. 0.74 0.56 23 18 Mexico 244 71 167 18 26 455 119 299 0.74 0.54 69 36 Moldova 120 36 89 38 9 78 50 22 1.20 1.04 97 36 Mongolia 61 2 42 3 13 150 7 133 1.38 1.42 198 110 Morocco 71 38 53 13 23 105 88 13 1.29 0.83 34 21 Mozambique 10 .. 7 .. 5 20 14 5 1.71 1.37 111 28 Myanmar 7 .. 6 4 8 26 17 8 0.43 0.52 107 58 Namibia 109 4 52 5 37 278 78 173 0.78 0.88 74 47 Nepal 5 .. 3 12 3 10 7 2 1.13 0.82 67 34 Netherlands 503 62 441 372 14 711 394 255 1.68 1.45 46 34 New Zealand 729 33 615 35 27 1,062 450 558 1.09 0.85 16 14 Nicaragua 48 13 18 14 15 92 53 36 0.87 0.82 48 28 Niger 5 4 4 1 .. .. .. .. 0.99 0.97 220 132 Nigeria 31 .. 31 21 7 50 5 41 0.59 1.13 175 45 Norway 572 29 458 29 14 773 449 293 1.63 1.63 24 15 Oman 225 12 174 16 10 582 53 492 0.31 0.38 148 108 Pakistan 11 8 9 34 13 66 45 9 0.84 0.77 224 120 Panama 188 .. 131 .. 16 136 0 127 0.67 0.68 58 35 Papua New Guinea 9 .. 6 .. .. .. .. .. 0.94 0.90 34 21 Paraguay 82 .. 39 .. 27 185 147 29 1.17 0.96 106 77 Peru 52 16 33 6 25 124 87 26 1.42 0.99 98 54 Philippines 32 14 11 67 20 90 56 29 0.91 0.81 55 23 Poland 451 66 383 83 14 365 197 106 1.43 1.40 59 37 Portugal 507 67 471 90 24 578 398 150 1.61 1.47 51 23 Puerto Rico 642 .. 614 289 .. .. .. .. 0.65 0.78 27 21 Qatar 724 .. 335 68 9 2 1 1 0.22 0.19 57 51 2010 World Development Indicators 203 3.13 Traffic and congestion Motor Passenger Road Road sector Fuel Particulate matter vehicles cars density energy consumption price concentration km. of road per Urban-population- per per per 100 sq. kilograms of oil equivalent $ per liter weighted PM10 1,000 kilometer 1,000 km. of % of total Per Super grade micrograms per people of road people land area consumption capita Diesel fuel Gasoline fuel gasoline Diesel cubic meter 2007 2007 2007 2007 2007 2007 2007 2007 2008 2008 1990 2006 Romania 180 20 156 .. 10 188 111 67 1.11 1.22 36 14 Russian Federation 245 35 206 5 6 291 69 202 0.89 0.86 41 18 Rwanda 4 .. 2 57 .. .. .. .. 1.37 1.37 49 26 Saudi Arabia .. 20 415 10 20 1,230 553 615 0.16 0.09 161 113 Senegal 20 .. 15 7 19 42 35 6 1.35 1.26 97 95 Serbia 244 46 204 44 .. .. .. .. 1.11 1.29 33b 15b Sierra Leone 5 2 3 16 .. .. .. .. 0.91 0.91 92 50 Singapore 149 207 113 472 9 527 325 179 1.07 0.90 106 41 Slovak Republic 282 35 272 89 11 354 214 114 1.57 1.68 41 15 Slovenia 547 29 505 191 23 838 502 305 1.18 1.26 40 30 Somalia .. .. .. .. .. .. .. .. 1.12 1.15 78 31 South Africa 159 .. 108 .. 11 303 119 172 0.87 0.95 34 21 Spain 601 35 485 132 23 749 573 149 1.23 1.28 42 32 Sri Lanka 58 11 18 148 21 96 65 25 1.43 0.75 94 82 Sudan 28 .. 20 .. 14 51 33 16 0.65 0.45 296 165 Swaziland 89 25 46 21 .. .. .. .. 0.86 0.93 56 33 Sweden 523 11 465 95 15 807 354 394 1.38 1.52 15 12 Switzerland 569 60 524 173 22 746 259 457 1.30 1.52 37 26 Syrian Arab Republic 52 26 22 21 21 198 115 74 0.85 0.53 159 75 Tajikistan 38 .. 29 .. 39 224 0 214 1.03 1.00 103 50 Tanzania 12 .. 2 8 5 24 18 6 1.11 1.30 57 25 Thailand .. .. 54 35 17 269 172 78 0.87 0.64 88 71 Timor-Leste .. .. .. .. .. .. .. .. 1.22 1.35 .. .. Togo 2 .. 2 .. 9 34 15 17 0.89 0.88 57 35 Trinidad and Tobago 351 .. .. .. 5 546 203 314 0.36 0.24 142 101 Tunisia 103 49 73 12 17 151 101 41 0.96 0.84 74 30 Turkey 131 20 88 55 14 193 125 33 1.87 1.63 68 40 Turkmenistan 106 .. 81 .. 5 179 0 170 0.22 0.20 177 55 Uganda 7 .. 3 17 .. .. .. .. 1.30 1.22 28 12 Ukraine 140 39 128 28 6 173 52 112 0.88 0.96 72 21 United Arab Emirates 313 .. 293 5 16 1,867 958 819 0.37 0.52 266 127 United Kingdom 527 76 463 172 19 662 345 288 1.44 1.65 25 15 United States 814c 31 461c,d 68 23 1,785 422 1,218 0.56 0.78 30 21 Uruguay 176 .. 151 102 26 250 164 71 1.18 1.17 237 175 Uzbekistan .. .. .. .. 3 58 9 43 1.35 0.75 85 55 Venezuela, RB 147 .. 107 .. 24 553 81 416 0.02 0.01 22 11 Vietnam 13 7 13 49 13 86 48 35 0.80 0.77 123 55 West Bank and Gaza 16 18 16 .. .. .. .. .. 1.34 1.25 .. .. Yemen, Rep. 35 .. .. 14 26 83 15 59 0.30 0.17 .. .. Zambia 18 .. 11 .. 4 25 11 14 1.70 1.61 96 40 Zimbabwe 106 .. 91 25 4 29 17 11 1.30 1.05 35 27 World 183 w .. w 132 w .. w 14 w 262 w 103 w 138 w 1.11 m 1.03 m 80 w 50 w Low income 12 .. 8 .. 7 31 15 15 1.13 1.03 120 65 Middle income 85 .. 61 89 10 125 55 59 0.91 0.90 96 57 Lower middle income 18 10 14 240 8 81 39 37 0.87 0.82 121 69 Upper middle income 206 .. 155 .. 14 296 118 142 1.11 1.01 55 32 Low & middle income 70 .. 51 .. 10 112 49 52 1.03 0.95 98 58 East Asia & Pacific 36 12 23 36 7 87 38 45 0.92 0.85 112 69 Europe & Central Asia 219 30 182 9 8 232 88 121 1.13 1.12 63 27 Latin America & Carib. 175 .. 119 18 23 292 117 133 0.87 0.83 59 35 Middle East & N. Africa .. .. 32 .. 20 250 125 107 0.61 0.53 125 73 South Asia 12 3 8 1,001 7 34 22 8 1.09 0.76 134 78 Sub-Saharan Africa 30 .. 24 .. 8 56 23 31 1.14 1.06 114 53 High income 621 41 434 76 19 1,019 372 568 1.28 1.37 37 26 Euro area 588 66 418e 123 18 686 432 207 1.54 1.44 33 23 a. $1.12 for consumption below 120 liters a month. b. Includes Montenegro. c. Data are from the U.S. Federal Highway Administration. d. Excludes personal passenger vans, passenger minivans, and utility-type vehicles, which are all treated as trucks. e. Data are from the European Commission and the European Road Federation. 204 2010 World Development Indicators 3.13 ENVIRONMENT Traffic and congestion About the data Definitions Traffic congestion in urban areas constrains economic Road sector energy consumption includes energy · Motor vehicles include cars, buses, and freight productivity, damages people's health, and degrades from petroleum products, natural gas, renewable and vehicles but not two-wheelers. Population fi gures the quality of life. In recent years ownership of pas- combustible waste, and electricity. Biodiesel and bio- are midyear population in the year for which data senger cars has increased, and the expansion of eco- gasoline, forms of renewable energy, are biodegrad- are available. Roads refer to motorways (a road nomic activity has led to more goods and services able and emit less sulfur and carbon monoxide than designed and built for motor traffic that separates being transported by road over greater distances petroleum-derived fuels. They can be produced from the traffic flowing in opposite directions), highways, (see table 5.10). These developments have increased vegetable oils, such as soybean, corn, palm, peanut, main or national roads, and secondary or regional demand for roads and vehicles, adding to urban con- or sunflower oil, and can be used directly only in a roads. · Passenger cars are road motor vehicles, gestion, air pollution, health hazards, and traffic acci- modified internal combustion engine. other than two-wheelers, intended for the carriage dents and injuries. Congestion, the most visible cost Data on fuel prices are compiled by the German of passengers and designed to seat no more than of expanding vehicle ownership, is reflected in the Agency for Technical Cooperation (GTZ), from its nine people (including the driver). · Road density indicators in the table. Other relevant indicators-- global network and other sources, including the is the ratio of the length of the country's total road such as average vehicle speed and the economic cost Allgemeiner Deutscher Automobile Club (for Europe) network to the country's land area. It includes all of traffic congestion--are not included because data and the Latin American Energy Organization (for Latin roads in the country-- motorways, highways, main are incomplete or difficult to compare. America). Local prices are converted to U.S. dollars or national roads, secondary or regional roads, and The data in the table--except those on fuel prices using the exchange rate in the Financial Times inter- other urban and rural roads. · Road sector energy and particulate matter--are compiled by the Interna- national monetary table on the survey date. When consumption is the total energy used in the road sec- tional Road Federation (IRF) through questionnaires multiple exchange rates exist, the market, parallel, tor from all sources, including energy from petroleum sent to national organizations. Primary sources are or black market rate is used. Prices were compiled products, natural gas, combustible and renewable national road associations. If they lack data or do not in mid-November 2008, when crude oil prices had waste, and electricity (see table 3.7). · Gasoline respond, other agencies are contacted, including road dropped to $48 a barrel Brent (from a high of $148 is light hydrocarbon oil use in internal combustion directorates, ministries of transport or public works, in July). engines such as motor vehicles, excluding aircraft. and central statistical offices. As a result, data qual- Considerable uncertainty surrounds estimates · Diesel is heavy oils used as a fuel for internal com- ity is uneven. Coverage of each indicator may differ of particulate matter concentrations, and caution bustion in diesel engines and heating installations. across countries because of different definitions. The should be used in interpreting them. They allow for · Fuel price is the pump price of super grade gaso- IRF is taking steps to improve the quality of the data cross-country comparisons of the relative risk of line (usually 95 octane) and diesel fuel, converted in its World Road Statistics 2009. Because this effort particulate matter pollution facing urban residents. from the local currency to U.S. dollars (see About covers only 2002­07, time series data may not be Major sources of urban outdoor particulate matter the data). · Particulate matter concentration is fine comparable. Another reason is coverage. For exam- pollution are traffic and industrial emissions, but suspended particulates of less than 10 microns in ple, the 2005 estimate for U.S. passenger cars from nonanthropogenic sources such as dust storms may diameter (PM10) that are capable of penetrating the U.S. Federal Highway Administration excludes be a substantial contributor for some cities. Country deep into the respiratory tract and causing severe personal passenger vans, passenger minivans, and technology and pollution controls are important deter- health damage. Data are urban-population-weighted utility-type vehicles. Road density is a rough indicator minants of particulate matter. Data on particulate PM10 levels in residential areas of cities with more of accessibility and does not capture road width, type, matter for selected cities are in table 3.14. Estimates than 100,000 residents. The estimates represent or condition. Thus comparisons over time and across of economic damages from death and illness due to the average annual exposure level of the average countries require caution. particulate matter pollution are in table 3.16. urban resident to outdoor particulate matter. Particulate matter concentration has fallen in all income groups, and the higher the income, the lower the concentration 3.13a Data sources Urban-population-weighted particulate matter (PM10, micrograms per cubic meter) 1990 2006 Data on vehicles and road density are from the 150 IRF's electronic files and its annual World Road Statistics, except where noted. Data on road sector 100 energy consumption are from the IRF and the Inter- national Energy Agency. Data on fuel prices are from the GTZ's electronic files. Data on particulate 50 matter concentrations are from Pandey and oth- ers' "Ambient Particulate Matter Concentrations in 0 Residential and Pollution Hotspot Areas of World Low income Lower Upper High income Euro area Cities: New Estimates Based on the Global Model middle income middle income Source: Table 3.13. of Ambient Particulates (GMAPS)" (2006b). 2010 World Development Indicators 205 3.14 Air pollution City City Particulate Sulfur Nitrogen About the data population matter dioxide dioxide concentration Urban- Indoor and outdoor air pollution place a major burden population- on world health. More than half the world's people weighted PM10 rely on dung, wood, crop waste, or coal to meet basic micrograms per micrograms per micrograms per thousands cubic meter cubic meter cubic meter energy needs. Cooking and heating with these fuels on 2007 2006 2001 a 2001 a open fires or stoves without chimneys lead to indoor air Argentina Córdoba 1,452 55 .. 97 pollution, which is responsible for 1.6 million deaths a Australia Melbourne 3,728 12 .. 30 year--one every 20 seconds. In many urban areas air Perth 1,532 12 5 19 pollution exposure is the main environmental threat to Sydney 4,327 19 28 81 health. Long-term exposure to high levels of soot and Austria Vienna 2,315 39 14 42 small particles contributes to a range of health effects, Belgium Brussels 1,743 25 20 48 including respiratory diseases, lung cancer, and heart Brazil Rio de Janeiro 11,748 29 129 .. São Paulo 18,845 34 43 83 disease. Particulate pollution, alone or with sulfur diox- Bulgaria Sofia 1,185 63 39 122 ide, creates an enormous burden of ill health. Canada Montréal 3,678 17 10 42 Sulfur dioxide and nitrogen dioxide emissions Toronto 5,213 20 17 43 lead to deposition of acid rain and other acidic com- Vancouver 2,146 12 14 37 pounds over long distances, which can lead to the Chile Santiago 5,720 54 29 81 leaching of trace minerals and nutrients critical to China Anshan 1,639 83 115 88 trees and plants. Sulfur dioxide emissions can dam- Beijing 11,106 90 90 122 Changchun 3,183 75 21 64 age human health, particularly that of the young and Chengdu 4,123 87 77 74 old. Nitrogen dioxide is emitted by bacteria, motor Chongqing 6,461 124 340 70 vehicles, industrial activities, nitrogen fertilizers, fuel Dalian 3,167 50 61 100 and biomass combustion, and aerobic decomposi- Guangzhou 8,829 64 57 136 tion of organic matter in soils and oceans. Guiyang 3,662 71 424 53 Where coal is the primary fuel for power plants Harbin 3,621 77 23 30 Jinan 2,798 95 132 45 without effective dust controls, steel mills, industrial Kunming 2,931 71 19 33 boilers, and domestic heating, high levels of urban Lanzhou 2,561 92 102 104 air pollution are common-- especially particulates Liupanshui 1,221 60 102 .. and sulfur dioxide. Elsewhere the worst emissions Nanchang 2,350 79 69 29 are from petroleum product combustion. Pingxiang 905 67 75 .. Sulfur dioxide and nitrogen dioxide concentration Quingdao 2,817 62 190 64 data are based on average observed concentrations Shanghai 14,987 74 53 73 Shenyang 4,787 102 99 73 at urban monitoring sites, which not all cities have. Taiyuan 2,794 89 211 55 The data on particulate matter are estimated aver- Tianjin 7,180 126 82 50 age annual concentrations in residential areas away Wulumqi 2,025 57 60 70 from air pollution "hotspots," such as industrial Wuhan 7,243 80 40 43 districts and transport corridors. The data are from Zhengzhou 2,636 98 63 95 the World Bank's Development Research Group and Zibo 3,061 75 198 43 Environment Department estimates of annual ambi- Colombia Bogotá 7,772 30 .. .. Croatia Zagreb 908 32 31 .. ent concentrations of particulate matter in cities Cuba Havana 2,174 20 1 5 with populations exceeding 100,000 (Pandey and Czech Republic Prague 1,162 21 14 33 others 2006b). A country's technology and pollution Denmark Copenhagen 1,085 19 7 54 controls are important determinants of particulate Ecuador Guayaquil 2,514 23 15 .. matter concentrations. Quito 1,701 30 22 .. Pollutant concentrations are sensitive to local con- Egypt, Arab Rep. Cairo 11,893 149 69 .. Finland Helsinki 1,115 19 4 35 ditions, and even monitoring sites in the same city France Paris 9,904 11 14 57 may register different levels. Thus these data should Germany Berlin 3,406 21 18 26 be considered only a general indication of air qual- Frankfurt 668 18 11 45 ity, and comparisons should be made with caution. Munich 1,275 19 8 53 Current World Health Organization (WHO) air quality Ghana Accra 2,121 33 .. .. guidelines are annual mean concentrations of 20 Greece Athens 3,242 38 34 64 micrograms per cubic meter for particulate matter Hungary Budapest 1,679 20 39 51 Iceland Reykjavik 164 18 5 42 less than 10 microns in diameter and 40 micrograms India Ahmadabad 5,375 76 30 21 for nitrogen dioxide and daily mean concentrations of Bengaluru 6,787 41 .. .. 20 micrograms per cubic meter for sulfur dioxide. 206 2010 World Development Indicators 3.14 ENVIRONMENT Air pollution City City Particulate Sulfur Nitrogen Definitions population matter dioxide dioxide concentration Urban- · City population is the number of residents of population- the city or metropolitan area as defined by national weighted PM10 micrograms per micrograms per micrograms per authorities and reported to the United Nations. · Par- thousands cubic meter cubic meter cubic meter ticulate matter concentration is fine suspended par- 2007 2006 2001 a 2001 a ticulates of less than 10 microns in diameter (PM10) India Chennai 7,163 34 15 17 that are capable of penetrating deep into the respi- Delhi 15,926 136 24 41 ratory tract and causing significant health damage. Hyderabad 6,376 37 12 17 Data are urban- population-weighted PM10 levels in Kanpur 3,162 99 15 14 Kolkata 14,787 116 49 34 residential areas of cities with more than 100,000 Lucknow 2,695 99 26 25 residents. The estimates represent the average Mumbai 18,978 57 33 39 annual exposure level of the average urban resident Nagpur 2,454 50 6 13 to outdoor particulate matter. · Sulfur dioxide is an Pune 4,672 42 .. .. air pollutant produced when fossil fuels containing Indonesia Jakarta 9,125 84 .. .. Iran, Islamic Rep. Tehran 7,873 50 209 .. sulfur are burned. · Nitrogen dioxide is a poisonous, Ireland Dublin 1,059 16 20 .. pungent gas formed when nitric oxide combines with Italy Milan 2,945 30 31 248 hydrocarbons and sunlight, producing a photochemi- Rome 3,339 29 .. .. cal reaction. These conditions occur in both natural Turin 1,652 43 .. .. and anthropogenic activities. Japan Osaka-Kobe 11,294 33 19 63 Tokyo 35,676 38 18 68 Yokohama 3,366 29 100 13 Kenya Nairobi 3,010 40 .. .. Korea, Rep. Pusan 3,480 35 60 51 Seoul 9,796 37 44 60 Taegu 2,460 40 81 62 Malaysia Kuala Lumpur 1,448 23 24 .. Mexico Mexico City 19,028 48 74 130 Netherlands Amsterdam 1,031 34 10 58 New Zealand Auckland 1,245 13 3 20 Norway Oslo 802 18 8 43 Philippines Manila 11,100 28 33 .. Poland Katowice 2,914 39 83 79 Lódz 776 38 21 43 Warsaw 1,707 42 16 32 Portugal Lisbon 2,812 21 8 52 Romania Bucharest 1,942 16 10 71 Russian Federation Moscow 10,452 19 109 .. Omsk 1,135 19 20 34 Singapore Singapore 4,436 41 20 30 Slovak Republic Bratislava 456 15 21 27 South Africa Cape Town 3,215 13 21 72 Durban 2,729 25 31 .. Johannesburg 3,435 26 19 31 Spain Barcelona 4,920 33 11 43 Data sources Madrid 5,567 29 24 66 Sweden Stockholm 1,264 11 3 20 Data on city population are from the United Switzerland Zurich 1,108 24 11 39 Nations Population Division. Data on particulate Thailand Bangkok 6,704 76 11 23 matter concentrations are from Pandey and oth- Turkey Ankara 3,716 39 55 46 Istanbul 10,061 46 120 .. ers' "Ambient Particulate Matter Concentration in Ukraine Kiev 2,709 26 14 51 Residential and Pollution Hotspot Areas of World United Kingdom Birmingham 2,285 14 9 45 Cities: New Estimates Based on the Global Model London 8,567 19 25 77 of Ambient Particulates (GMAPS)" (2006b). Data Manchester 2,230 15 26 49 on sulfur dioxide and nitrogen dioxide concen- United States Chicago 8,990 23 14 57 Los Angeles 12,500 32 9 74 trations are from the WHO's Healthy Cities Air New York-Newark 19,040 20 26 79 Management Information System and the World Venezuela, RB Caracas 2,985 16 33 57 Resources Institute. a. Data are for the most recent year available. 2010 World Development Indicators 207 3.15 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Afghanistan 2002 2004d 2004 d 2002 1985d 1995d Albania 1993 1995 1999d 1999d 2003d 1994 d 2005d 2003d 2000 d 2004 Algeria 2001 1994 1992d 1992d 1996 1995 2005d 1983d 1996 2006 Angola 2000 2000 d 2000 d 1994 1998 2007 1997 2006 Argentina 1992 1994 1990 1990 1995 1994 2001 1981 1997 2005 Armenia 1994 1999d 1999d 2002d 1993e 2008e 1997 2003 Australia 1992 1994 1994 1987d 1989 1994 1993 1976 2000 2004 Austria 1994 1987 1989 1995 1994 2002 1982d 1997d 2002 Azerbaijan 1998 1995 1996d 1996d 2000 f 2000 d 1998d 1998d 2004 d Bangladesh 1991 1990 1994 1990 d 1990 d 2001 1994 2001d 1981 1996 2007 Belarus 2000 1986e 1988e 2006d 1993 2007e 1995d 2001d 2004 d Belgium 1996 1988 1988 1998 1996 2002 1983 1997d 2006 Benin 1993 1994 1993d 1993d 1997 1994 2002d 1984 d 1996 2004 Bolivia 1994 1988 1995 1994 d 1994 d 1995 1994 1999 1979 1996 2003 Bosnia and Herzegovina 2000 1992g 1992g 1994g 2002d 2007 2002 2002d Botswana 1990 1991 1994 1991d 1991d 1994 1995 2003d 1977d 1996 2002d Brazil 1988 1994 1990 d 1990 d 1994 1994 2002 1975 1997 2004 Bulgaria 1994 1995 1990 d 1990 d 1996 1996 2002 1991d 2001d 2004 Burkina Faso 1993 1994 1989 1989 2005 1993 2005d 1989d 1996 2004 Burundi 1994 1989 1997 1997d 1997d 1997 2001d 1988d 1997 2005 Cambodia 1999 1996 2001d 2001d 1995d 2002d 1997 1997 2006 Cameroon 1989 1995 1989d 1989d 1994 1994 2002d 1981d 1997 Canada 1990 1994 1994 1986 1988 2003 1992 2002 1975 1995 2001 Central African Republic 1995 1993d 1993d 1995 2008 1980 d 1996 Chad 1990 1994 1989d 1994 1994 1989d 1996 2004 Chile 1993 1995 1990 1990 1997 1994 2002 1975 1997 2005 China 1994 1994 1994 1989 d 1991d 1996 1993 2002f 1981d 1997 2004 Hong Kong SAR, China Colombia 1998 1988 1995 1990 d 1993d 1994 2001d 1981 1999 Congo, Dem. Rep. 1990 1995 1994 d 1994 d 1995 1996 2005d 1976d 1997 2005d Congo, Rep. 1990 1997 1994 d 1994 d 2008 1994 2007 1983d 1999 2007 Costa Rica 1990 1992 1994 1991d 1991d 1994 1994 2002 1975 1998 2007 Côte d'Ivoire 1994 1991 1995 1993d 1993d 1994 1994 2007 1994 d 1997 2004 Croatia 2001 2000 1996 1991e 1991e 1994g 1996 2000 d 2000e 2007 Cuba 1994 1992d 1992d 1994 1994 2002 1990 d 1997 2007 Czech Republic 1994 1994 1993e 1993e 1996 1993f 2007e 993g 2000 d 2002 Denmark 1994 1994 1988 1988 2004 1993 2002 1977 1995d 2003 Dominican Republic 1995 1999 1993d 1993d 1996 2002d 1986d 1997d 2007 Ecuador 1993 1995 1994 1990d 1990 d 1993 2000 1975 1995 2004 Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 2005d 1978 1995 2003 El Salvador 1994 1988 1996 1992 1992 1994 1998 1987d 1997d Eritrea 1995 1995 2005d 2005d 1996d 2005d 1994 d 1996 2005d Estonia 1998 1994 1996d 1996d 2005d 1994 2002 1992d Ethiopia 1994 1991 1994 1994 d 1994 d 1994 2005d 1989d 1997 2003 Finland 1995 1994 1986 1988 1996 1994 e 2002 1976d 1995e 2002e France 1990 1994 1987f 1988f 1996 1994 2002f 978 1997 2004f Gabon 1990 1998 1994 d 1994 d 1998 1997 1989d 1996d 2007 Gambia, The 1992 1989 1994 1990d 1990 d 1994 1994 2001d 1977d 1996 2006 Georgia 1998 1994 1996d 1996d 1996d 1994 d 1999d 1996d 1999 2006 Germany 1994 1988 1988 1994 d 1993 2002 1976 1996 2002 Ghana 1992 1988 1995 1989d 1989 1994 1994 2003d 1975 1996 2003 Greece 1994 1988 1988 1995 1994 2002 1992d 1997 2006 Guatemala 1994 1988 1996 1987d 1989d 1997 1995 1999 1979 1998d Guinea 1994 1988 1994 1992d 1992d 1994 1993 2000 d 1981d 1997 Guinea-Bissau 1993 1991 1996 2002d 2002d 1994 1995 1990 d 1995 2008 Haiti 1999 1996 2000d 2000 d 1996 1996 2005d 1996 Honduras 1993 1996 1993d 1993d 1994 1995 2000 1985d 1997 2005 208 2010 World Development Indicators 3.15 ENVIRONMENT Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Hungary 1995 1994 1988d 1989d 2002 1994 2002d 1985d 1999d 2008 India 1993 1994 1994 1991d 1992d 1995 1994 2008e 1976 1996 2006 Indonesia 1993 1993 1994 1992d 1992 1994 1994 2004 1978d 1998 Iran, Islamic Rep. 1996 1990 d 1990 d 1996 2005d 1976 1997 2006 Iraq 1994 Ireland 1994 1988d 1988 1996 1996 2002 2002 1997 Israel 1996 1992d 1992 1995 2004 1979 1996 Italy 1994 1988 1988 1995 1994 2002 1979 1997 Jamaica 1994 1995 1993d 1993d 1994 1995 1999d 1997d 1997d 2007 Japan 1994 1988 d 1988 1996 1993e 2002e 1980 1998e 2002d Jordan 1991 1994 1989d 1989d 1995d 1993 2003d 1978d 1996 2004 Kazakhstan 1995 1998d 1998d 1994 2000 d 1997 Kenya 1994 1992 1994 1988d 1988 1994 1994 2005d 1978 1997 2004 Korea, Dem. Rep. 1995 1995d 1995d 1994f 2005d 2003d 2002d Korea, Rep. 1994 1992 1992 1996 1994 2002 1993d 1999 2007 Kosovo Kuwait 1995 1992d 1992d 1994 2002 2005d 2002 1997 2006 Kyrgyz Republic 1995 2000 2000 d 2000 d 1996f 2003d 1997d 2006 Lao PDR 1995 1995 1998d 1998d 1998 1996f 2003d 2004 d 1996e 2006 Latvia 1995 1995d 1995d 2004 d 1995 2002 1997d 2002d 2004 Lebanon 1995 1993d 1993d 1995 1994 2006 1996 2003 Lesotho 1989 1995 1994 d 1994 d 2007 1995 2000d 2003 1995 2002 Liberia 2003 1996d 1996d 2008 2000 2002d 2005d 1998d 2002d Libya 1999 1990 d 1990 d 2001 2006 2003d 1996 2005d Lithuania 1995 1995d 1995d 2003d 1996 2003 2001d 2003d 2006 Macedonia, FYR 1998 1994g 1994g 1994g 1997d 2004 d 2000 d 2002d 2004 Madagascar 1988 1991 1999 1996d 1996d 2001 1996 2003d 1975 1997 Malawi 1994 1994 1991d 1991d 1994 2001d 1982d 1996 Malaysia 1991 1988 1994 1989d 1989d 1996 1994 2002 1977d 1997 Mali 1989 1995 1994 d 1994 d 1994 1995 2002 1994 d 1995 2003 Mauritania 1988 1994 1994 d 1994 d 1996 1996 2005d 1998d 1996 2005 Mauritius 1990 1994 1992d 1992d 1994 1992 2001d 1975 1996 2004 Mexico 1988 1994 1987 1988 1994 1993 2000 1991d 1995 2003 Moldova 2002 1995 1996d 1996d 2007 1995 2008e 2001d 1999d 2004 Mongolia 1995 1994 1996d 1996d 1996 1993 1999d 1996d 1996 2004 Morocco 1988 1996 1995 1995 2007 1995 2002d 1975 1996 2004 Mozambique 1994 1995 1994 d 1994 d 1997 1995 2005d 1981d 1997 2005 Myanmar 1989 1995 1993d 1993d 1996 1995 2003d 1997d 1997d 2004 d Namibia 1992 1995 1993d 1993d 1994 1997 2003d 1990 d 1997 2005d Nepal 1993 1994 1994d 1994 d 1998 1993 2005d 1975d 1996 2007 Netherlands 1994 1994 1988d 1988e 1996 1994 e 2002d 1984 1995e 2002e New Zealand 1994 1994 1987 1988 1996 1993 2002 1989d 2000 d 2004 Nicaragua 1994 1996 1993d 1993d 2000 1995 1999 1977d 1998 Niger 1991 1995 1992d 1992d 1995 2004 1975 1996 2006 Nigeria 1990 1992 1994 1988d 1988d 1994 1994 2004 d 1974 1997 2004 Norway 1994 1994 1986 1988 1996 1993 2008e 1976 1996 2002 Oman 1995 1999d 1999d 1994 1995 2005d 1996d 2005 Pakistan 1994 1991 1994 1992d 1992d 1997 1994 2005d 1976d 1997 Panama 1990 1995 1989d 1989 1996 1995 1999 1978 1996 2003 Papua New Guinea 1992 1993 1994 1992d 1992d 1997 1993 2002 1975d 2000 d 2003 Paraguay 1994 1992d 1992d 1994 1994 1999 1976 1997 2004 Peru 1988 1994 1989 1993d 1993 2002 1975 1995 2005 Philippines 1989 1989 1994 1991d 1991 1994 1993 2003 1981 2000 2004 Poland 1993 1991 1994 1990d 1990 d 1998 1996 2002 1989 2001d 2008 Portugal 1995 1994 1988d 1988 1997 1993 2002f 1980 1996 2004 e Puerto Rico Qatar 2010 World Development Indicators 209 3.15 Government commitment Environ- Biodiversity Participation mental assessments, in treatiesa strategies strategies, or or action action plans plans Climate Ozone CFC Law of Biological Kyoto Stockholm changeb layer control the Seac diversity b Protocol CITES CCD Convention 1992 1985 1987 1982 1992 1997 1973 1994 2001 Romania 1995 1994 1993d 1993d 1996 1994 2001 1994 d 1998d 2004 Russian Federation 1999 1994 1995 1986e 1988e 1997 1995 2008e 1992 2003d Rwanda 1991 1998 2001d 2001d 1996 2004 d 1980 d 1998 2002d Saudi Arabia 1995 1993d 1993d 1996 2001f 2005d 1996d 1997d Senegal 1984 1991 1995 1993d 1993 1994 1994 2001d 1977d 1995 2003 Serbia 2001h 2001g,h 2001g,h 2001g,h 2002h 2007 2002h 2002h Sierra Leone 1994 1995 2001d 2001d 1994 1994f 2006d 1994 d 1997 2003d Singapore 1993 1995 1997 1989d 1989d 1994 1995 2006d 1986d 1999d 2005 Slovak Republic 1994 1993g 1993g 1996 1994f 2002 1993 2002d 2002 Slovenia 1994 1996 1992g 1992g 1995g 1996 2002 2000d 2001d 2004 Somalia 2001d 2001d 1994 1985d 2002d South Africa 1993 1997 1990 d 1990 d 1997 1995 2002d 1975 1997 2002 Spain 1994 1988d 1988 1997 1995 2002 1986d 1996 2004 Sri Lanka 1994 1991 1994 1989d 1989d 1994 1994 2002d 1979d 1998d Sudan 1994 1993d 1993d 1994 1995 2004 d 1982 1995 2006 Swaziland 1997 1992d 1992d 1994 1997d 1996 2006 Sweden 1994 1986 1988 1996 1993 2002 1974 1995 2002 Switzerland 1994 1987 1988 1994 2006d 1974 1996 2003 Syrian Arab Republic 1999 1996 1989d 1989d 1996 2006d 2003d 1997 2005 Tajikistan 1998 1996d 1998d 1997f 1997d 2007 Tanzania 1994 1988 1996 1993d 1993d 1994 1996 2002d 1979 1997 2004 Thailand 1995 1989d 1989 2004 2002 1983 2001d 2005 Togo 1991 1995 1991d 1991 1994 1995e 2004 d 1978 1995e 2004 Trinidad and Tobago 1994 1989d 1989d 1994 1996 1999 1984 d 2000 d 2002d Tunisia 1994 1988 1994 1989d 1989d 1994 1993 2003d 1974 1995 2004 Turkey 1998 2004 1991d 1991d 1997 1996d 1998 Turkmenistan 1995 1993d 1993d 1996f 2008e 1996 Uganda 1994 1988 1994 1988d 1988 1994 1993 2002d 1991d 1997 2004 d Ukraine 1999 1997 1986e 1988e 1999 1995 2004 1999d 2002d United Arab Emirates 1996 1989d 1989d 2000 2005d 1990 d 1998d 2002 United Kingdom 1995 1994 1994 1987 1988 1997d 1994 2002 1976 1996 2005 United States 1995 1995 1994 1986 1988 1974 2000 Uruguay 1994 1989d 1991d 1994 1993 2001 1975 1999d 2004 Uzbekistan 1994 1993d 1993d 1995f 2007e 1997d 1995 Venezuela 1995 1988d 1989 1994 1977 1998d 2005 Vietnam 1993 1995 1994 d 1994 d 2006d 1994 2008e 1994 d 1998d 2002 West Bank and Gaza Yemen, Rep. 1996 1992 1996 1996d 1996d 1994 1996 2004 d 1997d 1997d 2004 Zambia 1994 1994 1990 d 1990 d 1994 1993 2006d 1980 d 1996 2006 Zimbabwe 1987 1994 1992d 1992d 1994 1994 1981d 1997 a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Accession. e. Acceptance. f. Approval. g. Succession. h. Signed by Serbia and Montenegro as a unified country before Montenegro declared its independence. 210 2010 World Development Indicators 3.15 ENVIRONMENT Government commitment About the data Definitions National environmental strategies and participation Environment and Development (the Earth Summit) in · Environmental strategies or action plans pro- in international treaties on environmental issues pro- Rio de Janeiro, which produced Agenda 21--an array vide a comprehensive analysis of conservation and vide some evidence of government commitment to of actions to address environmental challenges: resource management issues that integrate envi- sound environmental management. But the signing · The Framework Convention on Climate Change ronmental concerns with development. They include of these treaties does not always imply ratification, aims to stabilize atmospheric concentrations of national conservation strategies, environmental nor does it guarantee that governments will comply greenhouse gases at levels that will prevent human action plans, environmental management strategies, with treaty obligations. activities from interfering dangerously with the and sustainable development strategies. The date In many countries efforts to halt environmental global climate. is the year a country adopted a strategy or action degradation have failed, primarily because govern- · The Vienna Convention for the Protection of the plan. · Biodiversity assessments, strategies, or ments have neglected to make this issue a priority, a Ozone Layer aims to protect human health and the action plans include biodiversity profiles (see About reflection of competing claims on scarce resources. environment by promoting research on the effects the data). · Participation in treaties covers nine To address this problem, many countries are prepar- of changes in the ozone layer and on alternative international treaties (see About the data). · Climate ing national environmental strategies--some focus- substances (such as substitutes for chlorofluoro- change refers to the Framework Convention on Cli- ing narrowly on environmental issues, and others carbon) and technologies, monitoring the ozone mate Change (signed in 1992). · Ozone layer refers integrating environmental, economic, and social layer, and taking measures to control the activities to the Vienna Convention for the Protection of the concerns. Among such initiatives are conservation that produce adverse effects. Ozone Layer (signed in 1985). · CFC control refers to strategies and environmental action plans. Some · The Montreal Protocol for Chlorofl uorocarbon the Protocol on Substances That Deplete the Ozone countries have also prepared country environmental Control requires that countries help protect the Layer (the Montreal Protocol for Chlorofluorocarbon profiles and biodiversity strategies and profiles. earth from excessive ultraviolet radiation by cut- Control) (signed in 1987). · Law of the Sea refers National conservation strategies--promoted by ting chlorofluorocarbon consumption by 20 per- to the United Nations Convention on the Law of the the World Conservation Union (IUCN)--provide a cent over their 1986 level by 1994 and by 50 Sea (signed in 1982). · Biological diversity refers comprehensive, cross-sectoral analysis of conser- percent over their 1986 level by 1999, with allow- to the Convention on Biological Diversity (signed at vation and resource management issues to help inte- ances for increases in consumption by developing the Earth Summit in 1992). · Kyoto Protocol refers grate environmental concerns with the development countries. to the protocol on climate change adopted at the process. Such strategies discuss current and future · The United Nations Convention on the Law of the third conference of the parties to the United Nations needs, institutional capabilities, prevailing technical Sea, which became effective in November 1994, Framework Convention on Climate Change in Decem- conditions, and the status of natural resources in establishes a comprehensive legal regime for seas ber 1997. · CITES is the Convention on International a country. and oceans, establishes rules for environmental Trade in Endangered Species of Wild Fauna and Flora, National environmental action plans, supported by standards and enforcement provisions, and devel- an agreement among governments to ensure that the World Bank and other development agencies, ops international rules and national legislation to the survival of wild animals and plants is not threat- describe a country's main environmental concerns, prevent and control marine pollution. ened by uncontrolled exploitation. Adopted in 1973, identify the principal causes of environmental prob- · The Convention on Biological Diversity promotes it entered into force in 1975. · CCD is the United lems, and formulate policies and actions to deal with conservation of biodiversity through scientifi c Nations Convention to Combat Desertification, an them. These plans are a continuing process in which and technological cooperation among countries, international convention addressing the problems governments develop comprehensive environmental access to financial and genetic resources, and of land degradation in the world's drylands. Adopted policies, recommend specific actions, and outline the transfer of ecologically sound technologies. in 1994, it entered into force in 1996. · Stockholm investment strategies, legislation, and institutional But 10 years after the Earth Summit in Rio de Convention is an international legally binding instru- arrangements required to implement them. Janeiro the World Summit on Sustainable Develop- ment to protect human health and the environment Biodiversity profiles--prepared by the World Con- ment in Johannesburg recognized that many of the from persistent organic pollutants. Adopted in 2001, servation Monitoring Centre and the IUCN--provide proposed actions had yet to materialize. To help it entered into force in 2004. basic background on species diversity, protected developing countries comply with their obligations areas, major ecosystems and habitat types, and under these agreements, the Global Environment Data sources legislative and administrative support. In an effort Facility (GEF) was created to focus on global improve- to establish a scientific baseline for measuring prog- ment in biodiversity, climate change, international Data on environmental strategies and participation ress in biodiversity conservation, the United Nations waters, and ozone layer depletion. The UNEP, United in international environmental treaties are from Environment Programme (UNEP) coordinates global Nations Development Programme, and World Bank the Secretariat of the United Nations Framework biodiversity assessments. manage the GEF according to the policies of its gov- Convention on Climate Change, the Ozone Secre- To address global issues, many governments have erning body of country representatives. The World tariat of the UNEP, the World Resources Institute, also signed international treaties and agreements Bank is responsible for the GEF Trust Fund and chairs the UNEP, the Center for International Earth Sci- launched in the wake of the 1972 United Nations the GEF. ence Information Network, and the United Nations Conference on the Human Environment in Stock- Treaty Series. holm and the 1992 United Nations Conference on 2010 World Development Indicators 211 3.16 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed national expenditure depletion depletion forest dioxide emission net capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan .. 7.0 ­7.0 .. 0.0 0.0 3.4 0.1 0.2 .. Albania 18.0 10.1 7.9 2.8 1.7 0.0 0.0 0.3 0.2 8.5 Algeria 58.8 10.9 47.9 4.5 29.9 0.2 0.1 0.6 0.2 21.4 Angola 24.1 12.9 11.2 2.3 54.6 0.0 0.0 0.2 1.3 ­42.6 Argentina 25.5 11.8 13.8 4.5 8.6 0.4 0.0 0.5 1.1 7.7 Armenia 28.1 10.0 18.1 2.2 0.0 0.8 0.0 0.3 1.2 18.1 Australia 32.9 14.7 18.1 5.1 4.1 3.8 0.0 0.3 0.0 15.0 Austria 27.2 14.3 12.9 5.3 0.2 0.0 0.0 0.1 0.1 17.6a Azerbaijan 63.0 12.3 50.7 2.0 51.4 0.0 0.0 1.2 0.3 ­0.1 Bangladesh 33.9 6.8 27.1 2.0 4.0 0.0 0.6 0.4 0.4 23.7 Belarus 28.4 11.2 17.2 4.9 1.3 0.0 0.0 1.1 0.0 19.8 Belgium .. 13.9 .. 5.8 0.0 0.0 0.0 0.2 0.1 .. Benin .. 8.1 .. 3.3 0.0 0.0 1.0 0.3 0.3 .. Bolivia 29.9 9.5 20.4 4.7 27.6 0.8 0.0 0.5 0.9 ­4.7 Bosnia and Herzegovina 41.0 10.4 30.6 .. 2.0 0.0 .. 1.2 0.1 .. Botswana 46.3 11.5 34.8 6.6 0.5 3.2 0.0 0.3 0.2 37.2b Brazil 17.5 11.8 5.8 4.8 2.7 2.3 0.0 0.2 0.1 5.2 Bulgaria 14.1 11.6 2.5 4.1 1.1 0.8 0.0 0.9 0.9 2.9 Burkina Faso .. 7.5 .. 3.3 0.0 0.0 1.2 0.1 0.6 .. Burundi .. 5.6 .. 5.1 0.0 0.6 10.9 0.1 0.1 .. Cambodia .. 8.3 .. 1.7 0.0 0.0 0.2 0.4 0.3 .. Cameroon .. 8.8 .. 2.6 7.8 0.0 0.0 0.1 0.4 .. Canada 23.4 14.0 9.4 4.8 5.5 0.6 0.0 0.3 0.1 7.6 Central African Republic 1.8 7.4 ­5.6 1.3 0.0 0.0 0.0 0.1 0.2 ­4.6 Chad 3.7 10.0 ­6.4 1.2 43.7 0.0 0.0 0.0 1.0 ­49.9 Chile 24.2 12.9 11.4 3.6 0.3 14.3 0.0 0.3 0.4 ­0.4 China 53.9 10.1 43.8 1.8 6.7 1.7 0.0 1.3 0.8 35.1 Hong Kong SAR, China 29.7 13.4 16.3 3.0 0.0 0.0 0.0 0.2 .. 19.1c Colombia 20.2 11.4 8.8 3.6 10.0 0.6 0.0 0.2 0.1 1.5 Congo, Dem. Rep. 9.4 6.7 2.7 0.9 3.1 2.3 0.0 0.2 0.6 ­2.5 Congo, Rep. 26.7 14.1 12.6 2.3 71.2 0.0 0.0 0.2 0.6 ­57.1 Costa Rica 15.9 11.5 4.5 5.0 0.0 0.0 0.1 0.2 0.1 9.1 Côte d'Ivoire 12.7 9.0 3.8 4.7 6.2 0.0 0.0 0.2 0.3 1.7 Croatia 21.8 12.9 8.9 4.3 1.3 0.0 0.2 0.3 0.2 11.3 Cuba .. .. .. 13.2 .. .. .. .. 0.1 .. Czech Republic 24.2 13.8 10.4 4.4 0.7 0.0 0.0 0.5 0.0 13.4 Denmark 23.6 14.2 9.4 7.4 3.0 0.0 0.0 0.1 0.0 13.7 Dominican Republic 9.0 11.1 ­2.1 3.5 0.0 1.3 0.0 0.4 0.0 ­0.3 Ecuador 31.8 10.8 21.0 1.4 21.1 0.4 0.0 0.5 0.1 0.4 Egypt, Arab Rep. 23.5 9.3 14.2 4.4 14.5 0.5 0.2 0.9 0.5 2.1 El Salvador 7.9 10.5 ­2.6 3.3 0.0 0.0 0.4 0.2 0.1 ­0.1 Eritrea .. 6.9 .. 1.9 0.0 0.0 0.8 0.3 0.3 .. Estonia 20.1 13.5 6.6 4.6 1.5 0.0 0.0 0.7 0.0 9.0 Ethiopia 17.3 6.7 10.6 3.7 0.0 0.3 4.7 0.2 0.2 8.9 Finland 24.8 14.1 10.7 5.6 0.0 0.1 0.0 0.2 0.0 16.0a France 18.7 13.9 4.9 5.1 0.0 0.0 0.0 0.1 0.0 9.8 Gabon 48.8 13.9 34.9 3.1 34.3 0.0 0.0 0.1 0.0 3.6 Gambia, The 11.1 7.9 3.2 2.0 0.0 0.0 0.6 0.4 0.4 3.9 Georgia 8.3 10.1 ­1.8 2.8 0.2 0.0 0.0 0.3 0.7 ­0.3 Germany .. 13.8 .. 4.3 0.3 0.0 0.0 0.2 0.0 .. Ghana 7.3 8.8 ­1.5 4.7 0.0 6.5 2.8 0.5 0.1 ­6.5 Greece 7.4 13.9 ­6.5 2.8 0.3 0.1 0.0 0.2 0.3 ­4.8 Guatemala 14.4 10.1 4.3 2.9 0.8 0.0 0.7 0.3 0.1 5.3 Guinea 2.9 7.7 ­4.8 2.0 0.0 5.2 2.6 0.3 0.5 ­11.3 Guinea-Bissau 22.4 6.7 15.7 2.3 0.0 0.0 0.0 0.5 0.8 16.6 Haiti .. .. .. 1.5 .. .. .. .. 0.4 .. Honduras 21.2 9.5 11.7 3.5 0.0 1.4 0.0 0.5 0.2 13.1 212 2010 World Development Indicators 3.16 ENVIRONMENT Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed national expenditure depletion depletion forest dioxide emission net capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Hungary 15.9 15.1 0.8 5.3 0.8 0.0 0.0 0.3 0.0 5.0 India 38.2 8.5 29.7 3.2 4.9 1.4 0.8 1.2 0.5 24.2 Indonesia 22.2 10.7 11.6 1.1 12.6 1.4 0.0 0.6 0.5 ­2.4 Iran, Islamic Rep. .. .. .. 4.2 .. .. .. .. 0.4 .. Iraq .. .. .. .. .. .. .. .. 2.7 .. Ireland 19.7 17.1 2.5 5.2 0.0 0.0 0.0 0.1 0.0 7.5a Israel 19.8 13.5 6.3 5.9 0.2 0.3 0.0 0.3 0.1 11.3 Italy 18.5 14.0 4.5 4.5 0.2 0.0 0.0 0.2 0.1 8.5 Jamaica .. 11.4 .. 5.3 0.0 1.3 0.0 0.6 0.2 .. Japan 25.9 13.3 12.6 3.2 0.0 0.0 0.0 0.2 0.3 15.3a Jordan 13.7 9.8 3.8 5.6 0.2 4.5 0.0 0.8 0.2 3.6 Kazakhstan 46.2 13.5 32.8 4.4 31.3 1.8 0.0 1.4 0.1 2.5 Kenya 13.1 8.0 5.0 6.6 0.0 0.1 1.0 0.3 0.1 10.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. 0.8 .. Korea, Rep. 30.5 12.6 17.9 3.9 0.0 0.0 0.0 0.4 0.3 21.1 Kosovo .. .. .. .. .. .. .. .. .. Kuwait 58.7 13.3 45.3 3.0 38.0 0.0 0.0 0.4 0.3 9.7 Kyrgyz Republic 14.9 8.5 6.4 5.8 0.7 0.0 0.0 1.0 0.2 10.4 Lao PDR 25.2 8.6 16.6 1.2 0.0 0.0 0.0 0.2 0.5 17.1 Latvia 22.3 12.6 9.6 5.6 0.0 0.0 0.2 0.2 0.0 14.8 Lebanon 10.2 11.3 ­1.1 1.8 0.0 0.0 0.0 0.5 0.1 0.1 Lesotho 17.8 6.4 11.4 9.4 0.0 0.0 1.3 0.0 0.1 19.4 Liberia ­2.7 7.8 ­10.5 .. 0.0 0.0 7.7 0.9 0.3 .. Libya 66.8 12.3 54.5 .. 38.8 0.0 0.0 0.5 1.0 .. Lithuania 15.2 12.7 2.5 4.6 0.1 0.0 0.1 0.3 0.1 6.6 Macedonia, FYR 16.1 10.8 5.3 4.9 0.0 0.0 0.1 1.0 0.1 9.0 Madagascar 14.7 7.4 7.2 2.6 0.0 0.0 2.5 0.3 0.1 7.0 Malawi 29.3 6.5 22.8 3.5 0.0 0.0 0.9 0.2 0.1 25.1 Malaysia .. 11.9 .. 4.0 13.1 0.1 0.0 0.7 0.0 .. Mali .. 8.1 .. 3.6 0.0 0.0 0.0 0.1 1.1 .. Mauritania .. .. .. 2.8 .. .. .. .. 0.5 .. Mauritius 16.5 11.1 5.4 3.4 0.0 0.0 0.0 0.3 0.0 8.5 Mexico 25.3 12.0 13.3 4.8 8.2 0.3 0.0 0.3 0.3 9.0 Moldova 20.8 8.3 12.5 6.5 0.0 0.0 0.1 1.0 0.5 17.3 Mongolia 26.5 9.7 16.8 4.6 5.9 9.2 0.0 1.7 1.6 3.0 Morocco 31.4 10.1 21.3 5.2 0.0 6.1 0.0 0.4 0.1 19.8 Mozambique 7.4 7.9 ­0.5 3.8 7.0 0.0 0.5 0.2 0.1 ­4.6 Myanmar .. .. .. 0.8 .. .. .. .. 0.4 .. Namibia 17.1 12.1 5.0 7.3 0.0 2.1 0.0 0.3 0.0 9.9 Nepal 37.5 7.1 30.4 3.4 0.0 0.0 3.1 0.2 0.0 30.5 Netherlands 10.3 13.9 ­3.6 4.8 2.0 0.0 0.0 0.2 0.2 ­1.2 New Zealand .. 14.5 .. 6.6 2.3 0.2 0.0 0.2 0.0 .. Nicaragua .. 8.9 .. 3.0 0.0 0.6 0.0 0.6 0.0 .. Niger .. 2.6 .. 2.6 0.0 0.0 2.3 0.2 1.1 .. Nigeria .. 1.2 .. 0.9 23.8 0.0 0.2 0.5 0.5 .. Norway 41.2 15.0 26.2 6.0 15.9 0.0 0.0 0.1 0.0 16.2 Oman .. .. .. 3.9 .. .. 0.0 .. 0.0 .. Pakistan 19.3 8.2 11.1 2.1 4.9 0.0 0.7 0.7 0.8 6.1 Panama 25.9 11.1 14.8 4.4 0.0 0.0 0.0 0.3 0.1 18.8 Papua New Guinea 30.8 9.4 21.4 6.3 0.0 24.1 0.0 0.5 0.0 3.1 Paraguay 16.1 9.9 6.2 3.9 0.0 0.0 0.0 0.2 0.8 9.0 Peru 24.1 11.4 12.7 2.5 1.4 6.2 0.0 0.3 0.3 7.0 Philippines 30.3 8.4 21.9 2.2 0.5 0.8 0.1 0.3 0.1 22.3 Poland 19.1 12.7 6.4 5.4 1.5 0.3 0.1 0.5 0.2 9.2 Portugal 12.6 13.6 ­1.0 5.3 0.0 0.1 0.0 0.2 0.0 4.1 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 0.1 .. 2010 World Development Indicators 213 3.16 Toward a broader measure of savings Gross Consumption Net Education Energy Mineral Net Carbon Particulate Adjusted savings of fixed national expenditure depletion depletion forest dioxide emission net capital savings depletion damage damage savings % of % of % of % of % of % of % of % of % of % of GNI GNI GNI GNI GNI GNI GNI GNI GNI GNI 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Romania 25.0 11.7 13.3 3.4 2.4 0.1 0.0 0.4 0.0 13.7 Russian Federation 32.8 12.4 20.4 3.5 20.5 1.0 0.0 0.9 0.1 1.5 Rwanda 25.4 6.7 18.7 4.6 0.0 0.0 3.0 0.2 0.1 20.1 Saudi Arabia 48.3 12.5 35.9 7.2 43.5 0.0 0.0 0.6 0.7 ­1.8 Senegal 18.0 8.6 9.4 4.5 0.0 0.9 0.0 0.3 0.5 12.2 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone 5.5 7.0 ­1.6 3.9 0.0 0.5 1.5 0.4 0.8 ­1.0 Singapore 47.0 14.1 32.9 2.7 0.0 0.0 0.0 0.3 0.6 34.7 Slovak Republic ­70.9 13.1 ­83.9 3.7 0.1 0.0 0.4 0.4 0.0 ­81.1 Slovenia 27.0 13.6 13.4 5.3 0.1 0.0 0.2 0.2 0.1 18.1 Somalia .. .. .. .. .. .. .. .. 0.5 .. South Africa 16.1 13.9 2.2 5.1 6.4 2.6 0.5 1.3 0.1 ­3.4 Spain 20.6 14.0 6.6 3.9 0.0 0.0 0.0 0.2 0.2 10.1 Sri Lanka 18.4 9.7 8.8 2.6 0.0 0.0 0.4 0.3 0.2 10.4 Sudan 15.9 9.9 6.0 0.9 19.1 0.1 0.0 0.2 0.5 ­13.1 Swaziland 10.7 9.6 1.1 6.4 0.0 0.0 0.0 0.3 0.0 7.1 Sweden 27.1 12.5 14.6 6.4 0.0 0.4 0.0 0.1 0.0 20.5 Switzerland .. 13.3 .. 4.7 0.0 0.0 0.0 0.1 0.1 .. Syrian Arab Republic 12.6 10.1 2.6 2.6 17.6 1.1 0.0 1.1 0.7 ­15.2 Tajikistan 25.5 8.2 17.3 3.2 0.4 0.0 0.0 1.1 0.3 18.8 Tanzania .. 7.6 .. 2.4 0.7 5.0 0.0 0.2 0.1 .. Thailand 30.7 10.9 19.8 4.8 5.3 0.0 0.2 0.8 0.2 18.0 Timor-Leste .. 1.2 .. 0.9 0.0 0.0 .. 0.1 .. .. Togo .. 7.3 .. 3.7 0.0 5.2 2.5 0.4 0.1 .. Trinidad and Tobago 41.8 13.1 28.7 4.0 50.5 0.0 0.0 1.2 0.2 ­19.2 Tunisia 22.6 11.1 11.5 6.7 5.8 4.7 0.1 0.5 0.1 7.0 Turkey 17.7 11.8 5.9 3.7 0.3 0.1 0.0 0.3 0.6 8.3 Turkmenistan 32.1 10.9 21.2 .. 133.3 0.0 .. 3.1 0.6 .. Uganda 12.6 7.4 5.2 3.3 0.0 0.0 5.1 0.1 0.0 3.3 Ukraine 20.2 10.5 9.7 5.9 5.3 0.0 0.0 1.6 0.2 8.5 United Arab Emirates .. .. .. .. .. .. .. .. 0.6 .. United Kingdom 14.8 13.7 1.2 5.1 2.1 0.0 0.0 0.2 0.0 3.9 United States 12.6 14.0 ­1.4 4.8 1.9 0.1 0.0 0.3 0.1 0.9 Uruguay 18.2 11.9 6.3 2.6 0.0 0.0 0.4 0.2 1.1 7.2 Uzbekistan 40.5 8.5 32.0 9.4 51.1 0.0 0.0 4.0 0.4 ­14.1 Venezuela, RB 34.6 11.9 22.7 3.5 18.6 0.6 0.0 0.5 0.0 6.5 Vietnam 30.4 8.8 21.6 2.8 12.9 0.3 0.2 1.0 0.3 9.7 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 9.4 .. .. 22.3 0.0 0.0 0.7 .. .. Zambia 21.4 9.5 11.9 1.3 0.1 13.4 0.0 0.2 0.3 ­0.7 Zimbabwe .. .. .. 6.9 .. .. .. .. 0.1 .. World 20.9 w 13.0 w 7.9 w 4.2 w 3.9 w 0.5 w 0.0 w 0.4 w 0.2 w 7.2 w Low income 25.3 7.9 17.4 3.4 7.8 1.0 1.0 0.7 0.3 10.1 Middle income 31.6 10.9 20.7 3.3 8.8 1.3 0.1 0.8 0.4 12.6 Lower middle income 41.1 9.6 31.4 2.3 8.1 1.4 0.2 1.1 0.6 22.4 Upper middle income 23.8 12.1 11.8 4.2 9.4 1.3 0.0 0.5 0.2 4.6 Low & middle income 31.4 10.8 20.6 3.3 8.7 1.3 0.1 0.8 0.4 12.5 East Asia & Pacific 47.3 10.1 37.1 2.0 7.2 1.5 0.0 1.1 0.7 28.6 Europe & Central Asia 24.8 12.1 12.7 4.1 12.1 0.6 0.0 0.8 0.2 3.2 Latin America & Carib. 22.4 11.8 10.6 4.4 6.3 1.8 0.0 0.3 0.3 6.3 Middle East & N. Africa .. 10.5 .. 4.4 18.6 1.5 0.1 0.7 0.4 South Asia 35.0 8.4 26.6 3.0 4.6 1.1 0.8 1.0 0.5 21.6 Sub-Saharan Africa 16.5 9.0 7.6 3.3 14.2 1.3 0.6 0.6 0.4 ­6.2 High income 18.5 13.8 4.7 4.6 2.0 0.2 0.0 0.2 0.1 6.7 Euro area .. 14.0 .. 4.6 0.3 0.0 0.0 0.2 0.1 a. World Bank staff estimate. b. Likely to be overestimated because mineral depletion excludes diamonds. c. Excludes particulate emissions damage. 214 2010 World Development Indicators 3.16 ENVIRONMENT Toward a broader measure of savings About the data Definitions Adjusted net savings measure the change in value of future rents from resource extractions. An economic · Gross savings are the difference between gross a specified set of assets, excluding capital gains. If rent represents an excess return to a given factor national income and public and private consumption, a country's net savings are positive and the account- of production. Natural resources give rise to rents plus net current transfers. · Consumption of fixed ing includes a sufficiently broad range of assets, because they are not produced; in contrast, for pro- capital is the replacement value of capital used up in economic theory suggests that the present value duced goods and services competitive forces will production. · Net national savings are gross savings of social welfare is increasing. Conversely, persis- expand supply until economic profits are driven to minus consumption of fixed capital. · Education expen- tently negative adjusted net savings indicate that an zero. For each type of resource and each country, unit diture is public current operating expenditures in edu- economy is on an unsustainable path. resource rents are derived by taking the difference cation, including wages and salaries and excluding capi- The table provides a check on the extent to which between world prices (to reflect the social oppor- tal investments in buildings and equipment. · Energy today's rents from a number of natural resources tunity cost of resource extraction) and the average depletion is the ratio of the value of the stock of energy and changes in human capital are balanced by unit extraction or harvest costs (including a "normal" resources to the remaining reserve lifetime (capped at net savings, or this generation's bequest to future return on capital). Unit rents are then multiplied by 25 years). It covers coal, crude oil, and natural gas. generations. the physical quantity extracted or harvested to arrive · Mineral depletion is the ratio of the value of the stock Adjusted net savings are derived from standard at total rent. To estimate the value of the resource, of mineral resources to the remaining reserve lifetime national accounting measures of gross savings by rents are assumed to be constant over the life of the (capped at 25 years). It covers tin, gold, lead, zinc, iron, making four adjustments. First, estimates of capital resource (the El Serafy approach), and the present copper, nickel, silver, bauxite, and phosphate. · Net for- consumption of produced assets are deducted to value of the rent flow is calculated using a 4 percent est depletion is unit resource rents times the excess of obtain net savings. Second, current public expen- social discount rate. For details on the estimation of roundwood harvest over natural growth. · Carbon diox- ditures on education are added to net savings (in natural wealth see World Bank (2006). ide damage is estimated at $20 per ton of carbon (the standard national accounting these expenditures A positive net depletion figure for forest resources unit damage in 1995 U.S. dollars) times tons of carbon are treated as consumption). Third, estimates of implies that the harvest rate exceeds the rate of emitted. · Particulate emission damage is the will- the depletion of a variety of natural resources are natural growth; this is not the same as deforesta- ingness to pay to avoid illness and death attributable deducted to reflect the decline in asset values asso- tion, which represents a change in land use (see to particulate emissions.· Adjusted net savings are ciated with their extraction and harvest. And fourth, Definitions for table 3.4). In principle, there should net savings plus education expenditure minus energy deductions are made for damages from carbon diox- be an addition to savings in countries where growth depletion, mineral depletion, net forest depletion, and ide and particulate emissions. exceeds harvest, but empirical estimates suggest carbon dioxide and particulate emissions damage. The exercise treats public education expenditures that most of this net growth is in forested areas that Data sources as an addition to savings. However, because of the cannot currently be exploited economically. Because wide variability in the effectiveness of public edu- the depletion estimates reflect only timber values, Data on gross savings are from World Bank cation expenditures, these figures cannot be con- they ignore all the external and nontimber benefits national accounts data files (see table 4.8). strued as the value of investments in human capital. associated with standing forests. Data on consumption of fi xed capital are from A current expenditure of $1 on education does not Pollution damage from emissions of carbon dioxide the United Nations Statistics Division's National necessarily yield $1 of human capital. The calcula- is calculated as the marginal social cost per unit mul- Accounts Statistics: Main Aggregates and Detailed tion should also consider private education expen- tiplied by the increase in the stock of carbon dioxide. Tables, 1997, extrapolated to 2008. Data on edu- diture, but data are not available for a large number The unit damage figure represents the present value cation expenditure are from the United Nations of countries. of global damage to economic assets and to human Statistics Division's Statistical Yearbook 1997 and While extensive, the accounting of natural resource welfare over the time the unit of pollution remains from the United Nations Educational, Scientific, and Cultural Organization Institute for Statistics depletion and pollution costs still has some gaps. in the atmosphere. online database. Missing data are estimated by Key estimates missing on the resource side include Pollution damage from particulate emissions is World Bank staff. Data on energy, mineral, and the value of fossil water extracted from aquifers, net estimated by valuing the human health effects from forest depletion are estimates based on sources depletion of fish stocks, and depletion and degrada- exposure to particulate matter pollution in urban and methods in Kunte and others' "Estimat- tion of soils. Important pollutants affecting human areas. The estimates are calculated as willingness ing National Wealth: Methodology and Results" health and economic assets are excluded because to pay to avoid illness and death from cardiopulmo- (1998). Data on carbon dioxide damage are from no internationally comparable data are widely avail- nary disease and lung cancer in adults and acute Fankhauser's Valuing Climate Change: The Econom- able on damage from ground-level ozone or sulfur respiratory infections in children that is attributable ics of the Greenhouse (1995). Data on particulate oxides. to particulate emissions. emission damage are from Pandey and others' Estimates of resource depletion are based on the For a detailed note on methodology, see www. "The Human Costs of Air Pollution: New Estimates "change in real wealth" method described in Hamil- worldbank.org/data. for Developing Countries" (2006). The conceptual ton and Ruta (2008), which estimates depletion as underpinnings of the savings measure appear in the ratio between the total value of the resource Hamilton and Clemens' "Genuine Savings Rates and the remaining reserve lifetime. The total value in Developing Countries" (1999). of the resource is the present value of current and 2010 World Development Indicators 215 Text figures, tables, and boxes ECONOMY Introduction E 4 conomic growth is not explicitly targeted in the Millennium Development Goals (MDGs), yet income per capita measures are highly correlated with widely used indicators of poverty, health, and education. As countries become richer, poverty rates generally fall (figure 4a). During 2000­08 low- and middle-income countries aver- aged economic growth of 6.2 percent a year, and during 1999­2005 the number of people living on less than $1.25 a day fell by 325 million. Economic growth is clearly necessary for achieving the MDG targets. The 2008 financial crisis and ensuing global recession have substantially increased the challenge of meeting the MDG targets. In contrast to the record growth in 2000­07, the global economy grew only 1.9 percent in 2008 and declined an estimated 2.2 per- cent in 2009. Some 64 million more people will be living in extreme poverty by 2010 because of the crisis. The effects on human welfare may be costly and long-lasting. Relationship between economic nearly $1 trillion in 2007--dropped to $765 billion growth and development outcomes in 2008 and are estimated to have been much lower Income per capita is highly correlated with many devel- in 2009 (figure 4e). Workers' remittances were more opment indicators, such as secondary school enroll- ment, access to water and sanitation, births attended As incomes rise, poverty rates fall 4a by skilled staff, total fertility rate, children immunized PPP GNI per capita (international $) Poverty headcount ratio at PPP $1.25 a day (percent) against measles, malnutrition prevalence, and infant 6,000 60 mortality. The correlation coefficients--measuring 5,000 50 the degree of relationship--between gross national 4,000 40 income (GNI) per capita and selected nonmonetary 3,000 30 measures of welfare are generally high using either the World Bank Atlas method for calculating GNI or 2,000 20 purchasing power parity­converted GNI (figure 4b). 1,000 10 The highest correlation is between GNI per capita and 0 0 1980 1985 1990 1995 2000 2005 2008 the poverty headcount ratio ($2 a day). Note: PPP is purchasing power parity. Source: World Development Indicators data files. The global economy in 2009 The 2008 financial crisis led to a global economic Income per capita is highly correlated with many development indicators 4b recession in 2009, the most severe in 50 years. Poverty headcount ratio at PPP $2 a day (percent) GDP fell 3.2 percent in high-income economies and 100 grew only 1.2 percent in developing economies (fig- ure 4c). The effects of the crisis were transmitted 75 from high-income economies to developing econo- 50 mies as exports, private capital flows, commodity prices, and workers' remittances declined. 25 Global trade, whose growth had slowed to 3 percent in 2008, declined an estimated 12 percent 0 0 5,000 10,000 15,000 20,000 in 2009 (figure 4d). Developing economies' trade PPP GNI per capita (international $) shrank an estimated 9 percent in 2009. Private cap- Note: PPP is purchasing power parity. Source: World Development Indicators data files. ital flows to developing economies--after peaking at 2010 World Development Indicators 217 After years of record economic growth the resilient--falling 6.1 percent to $317 billion in global economy experienced a recession in 2009 4c 2009--but varied by country. Among developing country regions Europe GDP growth (percent) 10 and Central Asia fared the worst, as GDP fell Developing economies 6.2 percent (figure 4f). Severe economic adjust- ments were necessary as private capital flows, 5 which had financed large current account defi - cits, were cut from $97 billion in 2007 to $50 0 billion in 2008. Latin America and the Caribbean World economies contracted 2.6 percent, with Mex- ­5 ico--relying almost solely on the U.S. market 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 for its exports--the worst off. China and India Source: World Bank 2010 and World Development Indicators data files. managed to continue growing at nearly the same rate as before the crisis, but other economies Trade contracted in almost every region 4d in Asia did not do as well. Growth in the Middle East and North Africa dropped to 2.3 percent on Average annual growth (percent) 2008 2009 20 lower oil prices and exports to Europe. Sub-Saharan Africa barely grew, hurt by fall- 10 ing export commodity prices, falling remittances, 0 lower tourism revenues, and declining private capital flows. Home to 30 of the 43 low-income ­10 economies, Sub-Saharan Africa has been sub- ­20 ject to the most severe consequences of the East Europe & Latin Middle South Sub- East Europe & Latin Middle South Sub- Asia & Central America East & Asia Saharan Asia & Central America East & Asia Saharan crisis. Low-income households, at risk of being Pacific Asia & Carib. N. Africa Africa Pacific Asia & Carib. N. Africa Africa pushed into poverty, have suffered from deterio- Exports Imports Source: World Development Indicators data files. rating health and lost education opportunities. Global imbalances are easing Private capital flows began to slow in 2008 4e The structural imbalances in the global econo- Foreign direct investment, net inflows Equity my predating the crisis eased as the current ac- Private capital flows, net ($ millions) Bonds Commercial banks and other creditors count balances of the largest surplus and defi - 1,000 cit economies moderated (figure 4g). The crisis 800 has given impetus to rebalancing the economies 600 of China and the United States. China focused 400 on domestic sources of growth in its 11th five- 200 year plan, and in the United States the 2010 0 Economic Report of the President proposed a ­200 transition from consumption-driven growth to 2000 2001 2002 2003 2004 2005 2006 2007 2008 an emphasis on investment and exports. Source: World Development Indicators data files. Consumers in high-income economies have reduced spending, and imports have declined Some developing country regions maintained growth 4f faster than exports. In 2008 and 2009 pri- vate consumption expenditures declined in the GDP growth (percent) United States. 12 Middle East & North Africa In China imports outpaced exports, driven East Asia & Pacific 8 by domestic demand as the government South Asia 4 increased spending on infrastructure, social Sub-Saharan Africa programs, and environmental protection. The 0 Latin America & Caribbean result: China's current account surplus dropped ­4 High income from its peak of 11.0 percent of GDP in 2007 Europe & Central Asia ­8 to 6.6 percent in the first half of 2009. And 2007 2008 2009 the U.S. current account deficit was more than Source: World Development Indicators data files. halved, from ­6.0 percent in 2006 to ­2.8 per- cent in the second quarter of 2009. 218 2010 World Development Indicators ECONOMY New risks have emerged Current account surpluses and deficits both decreased 4g If household consumption in high-income econo- Current account balance as a share of GDP (percent) mies continues to decline, new drivers of global 12 economic growth will be crucial. China and In- China 8 dia might become new drivers, but large differ- Germany ences between the scale and structure of their 4 economies and of the U.S. economy will delay 0 United Kingdom their replacing the U.S. role in the global econo- ­4 my. For example, U.S. household consumption United States was more than $10 trillion in 2008, four times ­8 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 that of China and India combined. Developing second quarter economies have growth potential because they Source: Principal Global Indicators, Haver Analytics, and World Development Indicators data files. have room for productivity gains from increased investment. High-income economies face over- capacity that could limit recovery, but they Economies with large government deficits 4h are investing in transforming their economies Central government cash deficit as a share of GDP (percent) 2007 2008 through technological innovations to protect the 0 environment and combat global warming. ­2 Although the world avoided the most cata- ­4 strophic potential effects of the crisis, the result- ing conditions require careful navigation and ­6 eventual resolution. Fiscal deficits and public ­8 debt have increased substantially in many high- ­10 income economies (figures 4h and 4i). In some ­12 Lebanon Pakistan Egypt, United Jamaica United Romania Burkina Kenya Hungary cases high deficits and debt levels raised percep- Arab Rep. States Kingdom Faso tions of sovereign default risk, indicated by the Source: World Development Indicators data files. mounting cost of credit default swaps (figure 4j). Rising public deficits and debt are accom- panied by increased uncertainty in measuring Economies with large government debts 4i risk when debt includes derivatives. Private cor- Ratio of public debt to GDP (percent) 2007 2008 porations took on high levels of debt in the run- 180 up to the financial crisis. They believed--as did 150 creditors, rating agencies, and regulators--that 120 complex financial instruments, or derivatives, 90 provided a hedge against default. Derivatives 60 also play a role in public debt. For example, gov- ernments can use interest and currency swaps 30 to raise capital in return for increased future 0 Japan Jamaica Greece Singapore Italy Belgium Portugal Hungary Austria Brazil payments. But such derivatives are not included Source: Organisation for Economic Co-operation and Development, Japan Ministry of Finance, and World Devel- opment Indicators data files. in traditional measures of indebtedness. Governments must maintain reasonable budget balances and debt levels to keep the Economies with increasing default risk 4j confidence of taxpayers and creditors. Without fiscal credibility, creditors will refuse to continue Five-year sovereign credit default swap spreads (basis points) 400 lending. To reduce deficits, governments must Greece raise revenues or reduce spending. Economic 300 expansion can boost revenues through higher tax receipts, but if expansion is too slow, govern- 200 Portugal ments must resort to the unpopular alternatives Spain Italy of increasing tax rates and cutting spending-- 100 United Kingdom as in the United Kingdom and the United States, Japan United States 0 where buoyant revenues created by structural Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 imbalances cannot be restored by returning to Source: Thomson Reuters Datastream. the unsustainable conditions of 2007. 2010 World Development Indicators 219 Growth in GDP Brazil 4m China 4n Quarterly data for selected major economies in each devel- Year on year change in GDP (percent) Year on year change in GDP (percent) oping country region show economic contraction in Brazil, 15 15 the Russian Federation, and South Africa and slowing out- put in China, Egypt, and India. The contractions and slow- 10 10 downs bottom out around the first quarter of 2009. 5 5 0 0 ­5 ­5 ­10 ­10 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Source: Haver Analytics. Source: Haver Analytics. Brazil 4s China 4t Growth in industrial production The industrial sector shrank in all the large developing Year on year change in industrial Year on year change in industrial production (percent) production (percent) countries shown here except China. The low point at the 30 30 end of 2008 was followed by improvements throughout 2009. 15 15 0 0 ­15 ­15 ­30 ­30 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Source: Haver Analytics. Source: Haver Analytics. Brazil 4y China 4z Lending and inflation rates Inflation accelerated in 2008 as food and fuel prices rose Lending and inflation rates (percent) Lending and inflation rates (percent) 50 50 but fell in 2009 with the slowdown in output. India was the exception, as food prices remained high because of 40 40 Lending rate drought. 30 30 20 20 10 10 Lending rate Consumer price index 0 0 Consumer price index ­10 ­10 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Source: Haver Analytics. Source: Haver Analytics. Central government debt Brazil 4ee China 4ff These countries have increased public spending without Central government Domestic debt Central government Domestic debt debt (percent of GDP) Foreign debt debt (percent of GDP) Foreign debt substantially increasing debt levels. 120 120 90 90 60 60 30 30 0 0 ­30 ­30 Q1-07 Q1-08 Q1-09 Q4-09 2000 2002 2004 2006 2008 Source: Haver Analytics. Source: Haver Analytics. 220 2010 World Development Indicators ECONOMY Arab Republic of Egypt 4o India 4p Russian Federation 4q South Africa 4r Year on year change in GDP (percent) Year on year change in GDP (percent) Year on year change in GDP (percent) Year on year change in GDP (percent) 15 15 15 15 10 10 10 10 5 5 5 5 0 0 0 0 ­5 ­5 ­5 ­5 ­10 ­10 ­10 ­10 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4u India 4v Russian Federation 4w South Africa 4x Year on year change in industrial Year on year change in industrial Year on year change in industrial Year on year change in industrial production (percent) production (percent) production (percent) production (percent) 30 30 30 30 15 15 15 15 0 0 0 0 ­15 ­15 ­15 ­15 ­30 ­30 ­30 ­30 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4aa India 4bb Russian Federation 4cc South Africa 4dd Lending and inflation rates (percent) Lending and inflation rates (percent) Lending and inflation rates (percent) Lending and inflation rates (percent) 50 50 50 50 40 40 40 40 30 30 30 30 Consumer price index Lending rate Lending rate 20 20 20 20 Lending rate Lending rate 10 10 10 10 Consumer price index Consumer price index Consumer price index 0 0 0 0 ­10 ­10 ­10 ­10 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Jan-07 Jan-08 Jan-09 Dec-09 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4gg India 4hh Russian Federation 4ii South Africa 4jj Central government Domestic debt Central government Domestic debt Central government Domestic debt Central government Domestic debt debt (percent of GDP) Foreign debt debt (percent of GDP) Foreign debt debt (percent of GDP) Foreign debt debt (percent of GDP) Foreign debt 120 120 120 120 90 90 90 90 60 60 60 60 30 30 30 30 0 0 0 0 ­30 ­30 ­30 ­30 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. 2010 World Development Indicators 221 Merchandise trade Brazil 4kk China 4ll China's imports have declined less than exports, resulting Percent of GDP Percent of GDP in a smaller trade surplus. For most countries trade has 50 50 declined in absolute terms as well as relative to GDP. 40 40 Merchandise exports 30 30 20 20 Merchandise exports Merchandise imports 10 10 Merchandise imports 0 0 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Source: Haver Analytics. Source: Haver Analytics. Brazil 4qq China 4rr Equity price indexes Equity prices in large developing countries have rebounded MSCI equity price index MSCI equity price index (January 1, 2008 = 100) (January 1, 2008 = 100) from their lows in late 2008 as investors regained confi - 150 150 dence on growing signs of economic recovery. 100 100 50 50 0 0 Jan-08 Jan-09 Nov-09 Jan-08 Jan-09 Nov-09 Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Reuters Datastream. Reuters Datastream. Brazil 4ww China 4xx Bond spreads Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes The cost of borrowing for large developing countries has (basis points) (basis points) declined after rising in reaction to the financial crisis but 2,500 2,500 remains above precrisis levels. 2,000 2,000 1,500 1,500 Corporate--CEMBI 1,000 1,000 Corporate--CEMBI 500 500 Sovereign EMBI-Global 0 0 Sovereign EMBI-Global Jan-08 Jan-09 Jan-10 Jan-08 Jan-09 Jan-10 Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, and Thomson Reuters Datastream. and Thomson Reuters Datastream. Financing through international Brazil 4ccc China 4ddd capital markets Gross capital inflows over previous Gross capital inflows over previous 12 months ($ billions) 12 months ($ billions) Capital flows to large developing countries rebounded 160 160 somewhat in 2008 but remain below their peak levels of 2007. 120 120 80 80 40 40 0 0 Jan-08 Jan-09 Dec-09 Jan-08 Jan-09 Dec-09 Source: Dealogic. Source: Dealogic. 222 2010 World Development Indicators ECONOMY Arab Republic of Egypt 4mm India 4nn Russian Federation 4oo South Africa 4pp Percent of GDP Percent of GDP Percent of GDP Percent of GDP 50 50 50 50 40 40 40 40 Merchandise Merchandise imports Merchandise exports imports 30 30 30 30 Merchandise imports 20 20 20 20 Merchandise exports Merchandise imports 10 10 10 10 Merchandise exports Merchandise exports 0 0 0 0 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Q1-07 Q1-08 Q1-09 Q4-09 Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Source: Haver Analytics. Arab Republic of Egypt 4ss India 4tt Russian Federation 4uu South Africa 4vv MSCI equity price index MSCI equity price index MSCI equity price index MSCI equity price index (January 1, 2008 = 100) (January 1, 2008 = 100) (January 1, 2008 = 100) (January 1, 2008 = 100) 150 150 150 150 100 100 100 100 50 50 50 50 0 0 0 0 Jan-08 Jan-09 Nov-09 Jan-08 Jan-09 Nov-09 Jan-08 Jan-09 Nov-09 Jan-08 Jan-09 Nov-09 Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Source: MSCI Barra and Thomson Reuters Datastream. Reuters Datastream. Reuters Datastream. Reuters Datastream. Arab Republic of Egypt 4yy India 4zz Russian Federation 4aaa South Africa 4bbb Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes Spreads over U.S. Treasury notes (basis points) (basis points) (basis points) (basis points) 2,500 2,500 2,500 2,500 Corporate--CEMBI 2,000 2,000 2,000 2,000 Corporate--CEMBI 1,500 1,500 1,500 1,500 Corporate--CEMBI 1,000 1,000 1,000 1,000 Sovereign EMBI-Global 500 500 500 500 Sovereign EMBI-Global Sovereign EMBI-Global 0 0 0 0 Jan-08 Jan-09 Jan-10 Jan-08 Jan-09 Jan-10 Jan-08 Jan-09 Jan-10 Jan-08 Jan-09 Jan-10 Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, Source: JP Morgan Chase, Bloomberg, and Thomson Reuters Datastream. and Thomson Reuters Datastream. and Thomson Reuters Datastream. and Thomson Reuters Datastream. Arab Republic of Egypt 4eee India 4fff Russian Federation 4ggg South Africa 4hhh Gross capital inflows over previous Gross capital inflows over previous Gross capital inflows over previous Gross capital inflows over previous 12 months ($ billions) 12 months ($ billions) 12 months ($ billions) 12 months ($ billions) 160 160 160 160 120 120 120 120 80 80 80 80 40 40 40 40 0 0 0 0 Jan-08 Jan-09 Dec-09 Jan-08 Jan-09 Dec-09 Jan-08 Jan-09 Dec-09 Jan-08 Jan-09 Dec-09 Source: Dealogic. Source: Dealogic. Source: Dealogic. Source: Dealogic. 2010 World Development Indicators 223 Tables 4.a Recent economic performance of selected developing countries Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2008 2009a 2008 2009a 2008 2009a 2008 2009a 2008 2009a 2009a 2009a Algeria 3.0 2.1 1.6 ­3.0 6.6 .. 10.8 .. .. .. 149,347 34.4 Angola 13.2 0.2 .. .. .. .. 23.9 .. 7.5 .. 13,349 5.6 Argentinab 6.8 ­1.5 1.2 .. 14.1 .. 19.1 13.1 2.2 .. 46,190 .. Armenia 6.8 ­15.6 ­13.1 ­32.8 7.3 ­21.0 8.4 1.5 ­11.6 ­12.5 2,003 7.6 Azerbaijan 10.8 2.1 10.4 2.8 13.2 ­5.3 20.9 ­17.8 35.7 22.9 5,364 6.2 Bangladesh 6.2 5.9 7.0 12.2 ­2.1 15.2 8.8 6.4 1.3 2.8 10,225 5.2 Belarus 10.0 ­1.0 1.7 ­6.1 14.6 ­8.7 20.5 9.8 ­8.6 1.8 4,872 1.2 Bolivia 6.1 0.9 2.2 ­5.9 9.4 3.1 10.4 ­5.6 12.1 ­0.1 7,634 15.6 Bosnia and Herzegovina 5.4 ­4.0 4.2 ­3.5 ­1.9 ­4.5 7.8 1.6 ­14.9 ­9.4 6,269 5.6 Botswana 2.9 ­1.8 2.5 0.4 11.8 6.5 17.0 ­10.6 3.7 ­7.6 12,438 19.5 Brazil 5.1 ­0.5 ­0.6 11.4 18.5 8.1 5.9 4.2 ­1.8 ­1.0 237,424 13.4 Bulgaria 6.0 ­6.3 2.9 ­9.2 4.9 ­12.8 11.4 2.7 ­25.2 ­11.6 17,198 7.1 Cameroon 3.9 4.1 4.7 6.1 5.1 3.4 1.7 ­2.6 ­2.2 ­4.0 4,590 7.2 Chile 3.2 ­1.6 3.1 ­4.7 12.9 ­13.8 0.2 ­0.8 ­2.0 1.5 25,282 5.6 China 9.0 8.7 ­9.6 ­12.1 ­13.4 ­5.7 7.2 0.3 9.8 7.4 2,544,706 21.6 Colombia 2.5 ­0.2 7.0 6.6 9.8 9.5 8.3 3.5 ­2.8 ­2.9 24,760 7.3 Congo, Dem. Rep. 6.2 2.7 ­3.9 ­32.9 15.7 ­17.4 19.4 32.7 .. ­21.0 .. .. Costa Rica 2.6 ­1.5 ­1.8 0.6 4.3 ­12.4 12.1 8.9 ­9.2 ­3.6 4,066 3.6 Côte d'Ivoire 2.2 3.6 ­8.1 9.3 ­5.4 11.0 8.1 1.3 2.1 1.6 .. .. Croatia 2.4 ­5.8 1.7 ­4.0 3.6 ­5.9 6.4 3.5 ­9.0 ­7.4 14,895 5.4 Dominican Republic 5.3 0.5 ­1.8 ­21.8 0.0 ­35.9 9.8 3.9 ­9.7 ­6.1 2,886 2.9 Ecuador 6.5 ­1.0 3.3 ­3.8 10.2 3.3 12.1 2.7 2.0 ­1.5 2,920 2.0 Egypt, Arab Rep. 7.2 4.7 28.8 ­12.8 26.3 ­17.9 11.8 10.8 ­0.9 ­2.3 45,757 6.3 El Salvador 2.5 ­2.5 6.8 ­14.6 4.8 ­14.8 5.9 2.8 ­7.2 ­1.8 2,882 4.1 Gabon 2.3 ­1.0 0.3 ­4.9 3.1 ­2.8 14.7 ­19.0 .. 2.8 .. .. Ghana 7.3 4.5 2.0 4.4 13.3 1.7 16.9 17.2 ­21.3 ­6.9 3,050 2.8 Guatemala 4.0 0.6 3.0 ­4.2 ­3.4 .. 8.5 2.4 ­4.8 ­1.7 4,976 4.8 Honduras 4.0 ­2.0 2.6 ­6.7 8.8 ­7.5 9.8 5.3 ­14.8 ­9.2 3,004 2.9 India 6.1 6.8 12.8 ­15.8 17.9 ­17.2 6.2 3.6 ­3.1 ­2.7 266,166 9.0 Indonesia 6.1 4.5 9.5 ­9.7 10.0 ­15.0 18.3 8.4 0.0 2.0 63,692 8.7 Jordan 7.9 3.2 ­11.3 ­8.0 3.3 ­14.1 15.7 4.1 ­11.3 ­10.1 11,132 8.6 Kazakhstan 3.2 1.2 1.0 6.9 8.9 9.1 21.1 ­4.0 4.9 ­3.1 20,844 6.1 Kenya 1.7 3.0 3.6 1.6 5.3 4.5 13.1 17.0 ­6.5 ­6.6 3,127 3.0 Latvia ­4.6 ­18.4 ­1.3 .. ­13.6 .. 15.2 .. ­13.3 .. 6,645 .. Lebanon 8.5 6.0 14.8 10.0 28.4 3.8 7.7 4.0 ­10.4 ­13.9 29,609 14.8 Lesotho 3.9 2.1 ­22.0 ­17.1 7.5 ­5.9 9.6 8.7 15.1 ­8.5 1,323 8.0 Lithuania 3.0 ­15.0 .. ­29.3 .. ­15.5 10.3 .. ­11.9 .. 6,463 .. Macedonia, FYR 5.0 ­1.3 ­9.2 ­12.8 ­1.0 ­10.6 7.2 1.1 ­12.7 ­9.4 2,063 3.9 224 2010 World Development Indicators ECONOMY Gross domestic Exports of goods Imports of goods GDP deflator Current account Gross international product and services and services balance reserves months average annual average annual average annual average annual of import % growth % growth % growth % growth % of GDP $ millions coverage 2008 2009a 2008 2009a 2008 2009a 2008 2009a 2008 2009a 2009a 2009a Malawi 9.7 6.9 ­5.4 ­5.4 ­4.6 ­4.6 8.9 10.0 .. .. .. .. Malaysia 4.6 ­1.7 .. ­10.1 .. ­12.5 10.3 ­7.1 17.5 15.3 95,496 6.4 Mauritius 4.5 2.0 2.6 ­12.6 2.0 ­5.4 7.6 3.7 ­10.5 ­8.8 2,186 5.0 Mexico 1.8 ­6.5 1.0 ­15.9 4.1 ­21.0 6.5 5.4 ­1.5 ­0.6 99,604 4.8 Moldova 7.2 ­9.0 ­11.4 ­22.5 ­6.1 ­36.4 9.7 5.4 ­16.3 ­21.2 1,480 2.5 Morocco 5.6 5.0 ­1.1 ­9.4 10.9 ­3.2 5.9 2.5 ­5.1 ­6.0 22,836 7.3 Montenegro 7.7 5.1 7.7 5.1 7.7 5.1 10.4 ­5.1 ­33.1 ­20.3 573 2.9 Nicaragua 3.5 4.0 .. .. .. .. 16.8 11.2 ­22.9 ­22.7 1,573 3.2 Nigeria 6.0 2.9 .. .. .. .. 11.0 ­2.0 19.0 7.1 102,614 12.1 Pakistan 2.0 3.7 ­5.3 9.0 3.6 ­9.2 16.3 22.7 ­9.4 ­5.1 11,434 3.6 Panama 9.2 1.5 9.2 ­3.1 9.2 11.9 8.5 5.3 ­11.6 ­9.4 2,492 1.3 Papua New Guinea 6.6 3.9 .. .. .. .. 11.6 ­3.3 .. ­6.7 2,620 4.9 Paraguay 5.8 ­3.8 11.6 ­14.9 18.0 ­11.5 7.1 3.0 ­2.2 ­0.6 3,840 6.5 Peru 9.8 1.0 8.2 0.2 19.9 ­8.9 2.3 3.0 ­3.2 ­3.0 32,074 14.3 Philippines 3.8 0.9 ­1.9 ­14.2 2.4 ­5.8 7.5 2.3 2.3 3.4 38,152 5.1 Poland 4.9 1.7 7.2 ­10.5 8.2 ­13.9 3.0 3.5 ­5.1 ­2.0 76,105 4.5 Romania 9.4 ­8.5 19.4 ­11.8 17.5 ­24.6 11.6 7.0 ­11.9 ­5.3 42,353 5.9 Russian Federation 5.6 ­7.9 0.2 ­4.9 17.7 ­26.6 19.2 10.0 6.1 3.8 417,773 19.8 Senegal 3.3 1.5 6.2 ­11.2 6.9 ­10.5 6.0 2.2 .. .. 2,227 3.8 Serbia 1.2 ­3.4 11.6 ­25.3 11.4 ­33.1 12.7 ­1.1 ­17.7 ­27.4 14,792 5.6 Slovak Republic 6.2 ­4.7 3.2 .. 3.3 .. 2.9 0.0 ­6.5 ­0.9 .. .. South Africa 3.1 ­1.8 1.7 ­9.2 2.2 .. 10.8 8.0 ­7.3 ­5.3 35,458 4.8 Sri Lanka 6.0 3.5 .. .. .. .. 16.3 4.0 ­9.3 ­1.4 5,578 4.2 Sudan 8.3 9.7 23.0 .. 0.3 .. 15.8 4.2 ­2.3 ­7.5 .. .. Swaziland 2.4 0.4 ­12.3 2.9 1.4 3.5 10.1 8.0 .. ­6.9 660 2.7 Syrian Arab Republic 5.2 5.7 ­2.4 5.6 2.5 6.4 20.5 ­14.4 .. ­1.1 6,512 3.7 Thailand 2.5 ­2.3 5.1 ­12.7 8.5 ­21.8 3.8 2.0 0.0 7.7 135,631 6.4 Tunisia 4.5 3.3 3.5 ­1.6 8.3 6.7 5.9 3.5 ­4.2 ­3.5 11,069 5.5 Turkey 0.9 ­6.0 2.3 .. ­3.8 .. 11.7 6.0 ­5.6 ­1.8 71,078 6.2 Uganda 9.5 2.1 7.3 9.3 28.1 12.3 6.3 2.5 ­5.9 .. 2,664 5.5 Ukraine 2.1 ­15.0 2.5 ­16.0 12.5 ­32.8 29.1 13.0 ­7.1 ­1.7 25,605 3.2 Uruguay 8.9 1.5 10.5 3.8 19.9 1.1 8.8 8.5 ­3.8 0.1 8,029 11.2 Uzbekistan 9.0 7.0 15.8 13.6 20.0 13.4 19.9 19.9 .. 12.8 2,747 3.1 Venezuela, RB 4.8 ­3.5 ­2.8 ­4.4 3.8 ­19.1 31.3 24.0 11.9 3.0 22,339 5.8 Vietnam 6.2 5.3 5.0 ­11.6 7.6 ­10.9 21.7 5.8 ­11.8 ­5.1 .. .. Zambia 6.0 4.0 20.7 21.5 15.3 15.6 10.8 7.9 ­7.3 .. 2,562 2.4 a. Data are preliminary estimates. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007­09 and believe that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. Source: World Development Indicators data files. 2010 World Development Indicators 225 4.1 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Afghanistan .. 11.8 .. 4.4 .. 17.5 .. .. .. 15.4 Albania 3.8 5.4 4.3 1.4 ­0.5 4.0 .. ­0.2 6.9 8.3 Algeria 1.9 4.3 3.6 5.3 1.8 3.5 ­2.1 2.4 1.8 5.3 Angolaa 1.6 13.5 ­1.4 13.6 4.4 13.9 ­0.3 20.7 ­2.2 12.4 Argentina 4.3 5.3b 3.5 3.7 3.8 6.4 2.7 6.0 4.5 4.4 Armenia ­1.9 12.4 0.5 7.3 ­7.8 15.1 ­4.3 5.7 6.4 13.4 Australia 3.6 3.3 3.1 0.0 2.7 2.6 1.8 1.3 4.2 3.7 Austria 2.4 2.2 ­0.1 0.9 2.5 3.1 2.5 3.3 2.5 2.1 Azerbaijan ­6.3 18.1 ­1.7 5.4 ­2.1 24.2 ­15.7 10.8 ­2.7 10.6 Bangladesh 4.8 5.8 2.9 3.2 7.3 7.9 7.2 7.9 4.5 6.0 Belarus ­1.6 8.6 ­4.0 5.5 ­1.8 12.6 ­0.7 11.6 ­0.4 6.0 Belgium 2.1 2.0 2.7 ­2.7 1.8 1.4 3.1 1.1 1.9 2.2 Benina 4.8 3.9 5.8 4.6 4.1 3.8 5.8 2.7 4.2 3.2 Bolivia 4.0 4.1 2.9 3.2 4.1 5.1 3.8 4.3 4.3 3.2 Bosnia and Herzegovina .. 5.4 .. 5.2 .. 7.4 .. 8.3 .. 4.6 Botswana 6.0 4.5 ­1.2 ­1.0 5.8 4.2 4.4 3.5 7.8 5.2 Brazil 2.7 3.6 3.6 4.2 2.4 3.2 2.0 3.1 3.8 3.8 Bulgaria ­1.8 5.8 3.0 ­3.8 ­5.0 6.1 .. 6.7 ­5.2 6.4 Burkina Faso 5.5 5.6 5.9 6.2 5.9 7.3 5.9 6.3 3.9 5.5 Burundi ­2.9 2.9 ­1.9 ­1.5 ­4.3 ­6.2 ­8.7 .. ­2.8 10.4 Cambodia 7.0 9.8 3.7 5.6 14.3 13.3 18.6 12.9 7.1 10.2 Cameroon 1.7 3.5 5.4 3.4 ­0.9 ­0.4 1.4 5.8 0.2 6.2 Canada 3.1 2.5 1.1 2.3 3.2 1.5 .. 0.1 .. .. Central African Republic 2.0 0.5 3.8 0.3 0.7 ­0.4 ­0.2 ­0.1 0.2 ­2.5 Chad 2.2 11.9 4.9 2.2 0.6 50.7 .. .. 0.8 9.1 Chile 6.6 4.4 2.2 5.6 5.6 3.2 4.4 3.8 6.9 4.9 Chinaa 10.6 10.4 4.1 4.4 13.7 11.7 12.9 11.6 11.0 10.7 Hong Kong SAR, China 3.6 5.2 .. ­3.3 .. ­2.6 .. ­3.1 .. 5.3 Colombia 2.8 4.9 ­2.6 3.0 1.5 4.9 ­2.5 5.3 4.1 4.8 Congo, Dem. Rep. ­4.9 5.2 1.4 1.5 ­8.0 9.5 ­8.7 6.3 ­13.0 11.5 Congo, Rep.a 1.0 3.9 0.7 .. 1.7 .. ­2.4 .. ­0.7 .. Costa Rica 5.3 5.4 4.1 4.0 6.2 5.8 6.8 5.5 4.7 5.7 Côte d'Ivoirea 3.2 0.5 3.5 1.3 6.3 ­0.7 5.5 ­2.3 2.0 0.7 Croatia 0.5 4.5 ­2.1 1.7 ­2.3 5.1 ­3.5 3.7 1.9 4.7 Cubaa 4.2 .. .. .. .. .. .. .. .. .. Czech Republic 1.1 4.6 0.0 0.1 0.2 6.6 4.3 8.3 1.2 4.3 Denmark 2.7 1.6 4.6 ­3.5 2.5 0.4 2.2 0.4 2.7 1.7 Dominican Republica 6.3 5.4 1.9 2.9 7.1 2.6 7.0 3.0 5.9 7.0 Ecuador 1.9 5.0 ­1.7 4.9 2.6 4.9 1.5 5.5 2.4 3.1 Egypt, Arab Rep. 4.4 4.7 3.1 3.3 5.1 5.3 6.3 4.5 4.1 5.2 El Salvador 4.8 2.9 1.2 3.9 5.1 2.3 5.2 2.4 4.0 3.0 Eritrea 5.7 1.3 1.5 9.3 15.0 0.8 10.6 ­4.9 5.7 0.1 Estonia 0.5 7.4 .. ­2.9 ­14.6 8.6 7.7 8.9 .. 7.0 Ethiopia 3.8 8.2 2.6 6.8 4.1 9.2 3.9 6.7 5.2 9.5 Finland 2.7 3.0 ­1.1 1.2 4.1 4.7 6.4 5.4 2.5 1.8 France 1.9 1.8 2.0 ­0.1 1.1 1.0 .. 0.8 2.2 2.1 Gabona 2.3 2.2 2.0 1.5 1.6 1.1 3.0 3.3 3.1 3.1 Gambia, The 3.0 5.1 3.3 2.8 1.0 7.4 0.9 4.2 3.7 6.1 Georgia ­7.1 8.1 ­11.0 2.3 ­8.1 11.3 .. 10.8 ­0.3 9.7 Germany 1.8 1.2 0.1 0.2 ­0.1 1.9 0.2 2.8 2.9 1.2 Ghanaa 4.3 5.6 3.4 3.5 2.7 7.4 .. .. 5.6 6.7 Greece 2.2 4.2 0.5 ­4.3 1.0 4.5 .. 5.2 2.6 4.8 Guatemalaa 4.2 3.9 2.8 3.1 4.3 3.0 2.8 2.9 4.7 4.4 Guinea 4.4 3.2 4.3 9.9 4.9 4.0 4.0 3.1 3.6 ­4.2 Guinea-Bissau 1.2 0.6 3.9 4.5 ­3.1 3.7 ­2.0 3.7 ­0.6 1.0 Haiti 0.5 0.5 .. ­0.6 .. 0.9 .. 0.6 .. 0.8 Honduras 3.2 5.3 2.2 3.8 3.6 4.6 4.0 5.4 3.8 6.4 226 2010 World Development Indicators 4.1 ECONOMY Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Hungary 1.5 3.6 ­2.4 5.3 3.6 3.5 8.0 5.0 1.3 3.4 India 5.9 7.9 3.2 3.2 6.1 8.4 6.7 7.8 7.7 9.5 Indonesiaa 4.2 5.2 2.0 3.3 5.2 4.2 6.7 4.9 4.0 7.1 Iran, Islamic Rep. 3.1 5.9 3.2 5.9 2.6 6.9 5.1 9.9 3.8 5.3 Iraq .. ­11.4 .. .. .. .. .. .. .. .. Ireland 7.4 5.0 1.1 ­3.4 12.7 5.2 .. .. 7.9 6.0 Israela 5.5 3.5 .. .. .. .. .. .. .. .. Italy 1.5 1.0 2.1 0.0 1.0 0.4 1.6 ­0.4 1.6 1.3 Jamaica 1.6 1.8 ­0.6 ­1.1 ­0.8 1.4 ­1.8 ­1.3 3.8 2.2 Japan 1.1 1.6 ­1.3 ­1.1 ­0.3 1.9 .. 1.9 2.0 1.6 Jordan 5.0 7.2 ­3.0 8.5 5.2 8.8 5.6 10.1 5.0 6.4 Kazakhstan ­4.1 9.5 ­8.0 5.3 0.6 10.6 2.7 8.2 0.3 10.6 Kenya 2.2 4.5 1.9 2.7 1.2 4.9 1.3 4.4 3.2 4.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5.8 4.5 1.6 1.7 6.0 5.9 7.3 6.9 5.6 3.9 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait a 4.9 8.4 1.0 .. 0.3 .. ­0.1 .. 3.5 .. Kyrgyz Republic ­4.1 4.4 1.5 1.8 ­10.3 0.8 ­7.5 ­1.2 ­5.2 7.9 Lao PDR 6.4 6.9 4.8 3.3 11.1 11.9 11.7 ­1.9 6.6 7.6 Latvia ­1.5 8.2 ­5.2 2.7 ­8.3 7.6 ­7.3 5.2 2.7 8.7 Lebanon 5.3 4.0 2.9 0.7 ­0.2 3.6 1.9 2.5 1.5 3.6 Lesotho 3.8 3.9 0.9 ­3.6 4.0 5.8 7.7 9.5 5.0 3.0 Liberia 4.1 ­1.1 .. .. .. .. .. .. .. .. Libya .. 5.6 .. .. .. .. .. .. .. .. Lithuania ­2.7 7.7 ­3.3 1.7 3.3 0.3 7.0 10.0 5.8 6.0 Macedonia, FYR ­0.8 3.2 0.2 1.7 ­2.3 2.9 ­5.3 2.3 0.5 3.4 Madagascar 2.0 3.8 1.8 2.0 2.4 3.5 2.0 4.3 2.3 4.4 Malawi 3.7 4.2 8.6 1.1 2.0 5.1 0.5 3.6 1.6 4.3 Malaysiaa 7.0 5.5 0.3 3.8 8.6 4.6 9.5 5.8 7.3 6.6 Mali 4.1 5.2 2.6 4.8 6.4 4.5 ­1.4 5.1 3.0 6.5 Mauritania 2.9 5.1 ­0.2 0.6 3.4 4.2 5.8 ­1.4 4.9 6.9 Mauritius 5.2 3.7 0.0 ­1.2 5.4 1.4 5.3 0.2 6.3 5.9 Mexico 3.1 2.7 1.5 2.1 3.8 1.8 4.3 1.8 2.9 3.1 Moldova ­9.6 6.3 ­11.2 ­1.6 ­13.6 0.6 ­7.1 4.1 0.7 11.3 Mongolia 1.0 7.8 2.5 5.6 ­2.5 7.4 ­9.7 8.2 0.7 8.8 Morocco 2.4 5.0 ­0.4 4.9 3.2 4.4 2.6 3.2 3.1 5.2 Mozambique 6.1 8.0 5.2 7.8 12.3 10.1 10.2 9.4 5.0 7.2 Myanmar a 7.0 .. 5.7 .. .. .. 7.9 .. 7.2 .. Namibia 4.0 5.6 3.8 1.3 2.4 7.4 7.4 5.4 4.2 5.4 Nepal 4.9 3.5 2.5 3.2 7.1 2.8 8.9 1.0 6.2 3.8 Netherlands 3.2 1.9 1.8 1.0 1.7 1.1 2.6 1.4 3.6 2.3 New Zealand 3.3 3.1 2.9 2.0 2.5 2.7 2.2 2.6 .. .. Nicaragua 3.7 3.5 4.7 3.0 5.5 4.4 5.3 5.5 5.0 3.5 Niger a 2.4 4.4 3.0 .. 2.0 .. 2.6 .. 1.9 .. Nigeria 2.5 6.6 .. 7.0 .. 3.8 .. .. .. 14.4 Norway 3.9 2.4 2.6 3.4 3.8 0.1 1.5 3.3 3.8 3.2 Omana 4.5 4.0 5.0 2.2 3.9 ­0.5 6.0 9.3 5.0 5.9 Pakistan 3.8 5.4 4.4 3.4 4.1 7.6 3.8 9.6 4.4 6.2 Panama 4.7 6.6 3.1 4.1 6.0 5.2 2.7 1.2 4.5 7.1 Papua New Guinea 3.8 2.9 4.5 1.9 5.4 3.8 4.6 3.7 ­0.6 3.5 Paraguaya 2.2 3.7 3.3 5.8 0.6 1.8 1.4 1.3 2.5 3.4 Peru 4.7 6.0 5.5 4.0 5.4 6.8 3.8 6.7 4.0 5.9 Philippinesa 3.3 5.1 1.7 3.8 3.5 4.2 3.0 4.4 4.0 6.4 Poland 4.7 4.4 0.5 1.3 7.1 5.7 9.9 8.5 5.1 3.7 Portugal 2.8 0.9 ­0.4 ­0.2 3.2 ­0.3 2.6 ­0.2 2.4 1.7 Puerto Ricoa 4.2 .. .. .. .. .. .. .. .. .. Qatar .. 9.0 .. .. .. .. .. .. .. .. 2010 World Development Indicators 227 4.1 Growth of output Gross domestic product Agriculture Industry Manufacturing Services average annual average annual average annual average annual average annual % growth % growth % growth % growth % growth 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Romania ­0.6 6.4 ­1.9 7.5 ­1.2 6.2 .. 5.7 0.9 5.0 Russian Federation ­4.7 6.7 ­4.9 4.0 ­7.1 5.6 .. .. ­1.7 7.4 Rwandaa ­0.2 6.7 2.5 3.5 ­3.8 8.7 ­5.8 5.4 ­0.9 8.9 Saudi Arabiaa 2.1 4.1 1.6 1.5 2.2 4.4 5.6 6.0 2.2 4.2 Senegal 3.0 4.5 2.4 1.3 3.8 3.5 3.1 1.4 3.0 6.5 Serbia ­4.7 5.4 .. .. .. .. .. .. .. .. Sierra Leone ­5.0 10.3 ­13.0 .. ­4.5 .. 6.1 .. ­2.9 .. Singapore 7.6 5.8 ­2.4 2.3 7.8 5.4 7.0 6.5 7.8 6.2 Slovak Republic 2.2 6.3 0.4 0.6 3.8 7.7 9.3 10.7 5.3 5.6 Slovenia 2.7 4.4 0.4 ­1.8 1.6 5.3 1.8 5.3 3.3 4.2 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 4.3 1.0 1.7 1.1 3.3 1.6 3.2 2.7 4.9 Spain 2.7 3.3 3.1 ­1.3 2.3 2.3 5.2 1.2 2.7 3.8 Sri Lankaa 5.3 5.5 1.8 2.4 6.9 5.4 8.1 4.3 5.7 6.4 Sudan 5.5 7.4 7.4 2.1 8.5 10.6 7.5 3.8 1.9 10.5 Swaziland 3.4 2.6 0.9 1.3 3.2 1.7 2.8 1.8 3.9 4.1 Sweden 2.1 2.8 ­0.8 3.9 4.3 3.9 8.7 5.4 1.8 2.4 Switzerland 1.0 1.9 ­0.9 ­0.8 0.3 2.1 .. 2.1 1.2 1.7 Syrian Arab Republic 5.1 4.4 6.0 3.6 9.2 2.7 .. 15.5 1.5 7.9 Tajikistan ­10.4 8.6 ­6.8 8.3 ­11.4 8.8 ­12.6 9.0 ­10.8 8.3 Tanzaniac 2.9 6.8 3.2 4.9 3.1 9.6 2.7 8.0 2.7 6.2 Thailanda 4.2 5.2 1.0 2.5 5.7 6.3 6.9 6.6 3.7 4.5 Timor-Lestea .. 1.9 .. .. .. .. .. .. .. .. Togoa 3.5 2.4 4.0 2.8 1.8 8.1 1.8 7.5 3.9 ­0.7 Trinidad and Tobago 3.2 8.4 2.7 ­9.2 3.2 11.4 4.9 10.4 3.2 6.2 Tunisiaa 4.7 4.9 2.3 2.7 2.3 2.7 4.6 3.5 5.5 3.4 Turkey 3.9 5.7 1.3 1.4 4.7 6.5 4.7 6.3 4.0 5.9 Turkmenistan ­4.9 14.5 4.9 ­13.3 ­2.7 27.3 .. .. ­4.7 17.1 Uganda 7.1 7.5 3.7 1.8 12.1 10.2 14.1 6.7 8.2 10.0 Ukraine ­9.3 7.2 ­5.6 3.1 ­12.6 6.6 ­11.2 10.5 ­8.1 7.0 United Arab Emirates 4.8 7.8 13.2 3.6 3.0 6.0 11.9 8.1 7.2 9.6 United Kingdom 2.8 2.5 ­0.2 1.1 1.5 0.2 1.3 ­0.4 3.4 3.2 United States 3.5 2.4 3.7 2.8 3.7 1.2 .. 2.5 3.4 2.9 Uruguay 3.4 3.8 2.8 4.4 1.1 4.4 ­0.1 6.3 1.3 2.9 Uzbekistan ­0.2 6.6 0.5 6.6 ­3.4 4.6 0.7 2.1 0.4 7.8 Venezuela, RB 1.6 5.2 1.2 3.9 1.2 3.2 4.5 3.4 ­0.1 6.5 Vietnama 7.9 7.7 4.3 3.9 11.9 10.0 11.2 11.9 7.5 7.5 West Bank and Gaza 7.3 ­0.9 .. .. .. .. .. .. .. .. Yemen, Rep.a 6.0 3.9 5.6 .. 8.2 .. 5.7 .. 5.0 .. Zambia 0.5 5.3 4.2 1.3 ­4.2 9.0 0.8 5.3 2.5 7.3 Zimbabwe 2.1 ­5.7 4.3 ­8.5 0.4 ­10.0 0.4 ­12.0 2.9 ­10.0 World 2.9 w 3.2 w 2.0 w 2.5 w 2.4 w 3.0 w .. w 3.2 w 3.1 w 3.2 w Low income 3.5 5.8 2.9 3.8 4.6 7.5 5.0 7.6 3.4 6.3 Middle income 3.9 6.4 2.4 3.6 4.7 7.3 6.3 7.7 4.3 6.4 Lower middle income 6.3 8.3 3.1 3.8 8.4 9.5 9.0 9.8 6.7 8.8 Upper middle income 2.3 4.6 0.8 3.2 1.7 4.3 3.5 4.4 3.1 4.7 Low & middle income 3.9 6.4 2.5 3.7 4.7 7.3 6.3 7.7 4.3 6.4 East Asia & Pacific 8.5 9.1 3.5 4.1 11.0 10.2 10.9 10.3 8.5 9.4 Europe & Central Asia ­0.8 6.2 ­1.7 3.0 ­2.6 6.7 .. .. 0.9 6.2 Latin America & Carib. 3.2 3.9 2.1 3.6 3.1 3.5 2.9 3.4 3.5 4.0 Middle East & N. Africa 3.8 4.8 2.9 4.2 4.2 3.6 4.3 5.4 3.3 5.6 South Asia 5.5 7.3 3.3 3.2 6.0 8.1 6.4 7.9 6.9 8.7 Sub-Saharan Africa 2.5 5.2 3.2 3.2 2.0 5.1 2.2 3.3 2.5 5.3 High income 2.7 2.3 1.3 0.7 1.9 1.7 .. 2.1 2.9 2.6 Euro area 2.1 1.8 1.6 ­0.6 1.1 1.6 2.2 1.1 2.5 2.0 a. Components are at producer prices. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007­09 and believe that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. c. Covers mainland Tanzania only. 228 2010 World Development Indicators 4.1 ECONOMY Growth of output About the data Definitions An economy's growth is measured by the change in Rebasing national accounts · Gross domestic product (GDP) at purchaser prices the volume of its output or in the real incomes of When countries rebase their national accounts, they is the sum of gross value added by all resident pro- its residents. The 1993 United Nations System of update the weights assigned to various components ducers in the economy plus any product taxes (less National Accounts (1993 SNA) offers three plausible to better reflect current patterns of production or subsidies) not included in the valuation of output. It indicators for calculating growth: the volume of gross uses of output. The new base year should represent is calculated without deducting for depreciation of domestic product (GDP), real gross domestic income, normal operation of the economy--it should be a fabricated capital assets or for depletion and degra- and real gross national income. The volume of GDP year without major shocks or distortions. Some dation of natural resources. Value added is the net is the sum of value added, measured at constant developing countries have not rebased their national output of an industry after adding up all outputs and prices, by households, government, and industries accounts for many years. Using an old base year subtracting intermediate inputs. The industrial origin operating in the economy. can be misleading because implicit price and vol- of value added is determined by the International Each industry's contribution to growth in the econ- ume weights become progressively less relevant Standard Industrial Classifi cation (ISIC) revision omy's output is measured by growth in the industry's and useful. 3. · Agriculture is the sum of gross output less value added. In principle, value added in constant To obtain comparable series of constant price data, the value of intermediate input used in production prices can be estimated by measuring the quantity the World Bank rescales GDP and value added by for industries classified in ISIC divisions 1­5 and of goods and services produced in a period, valu- industrial origin to a common reference year. This includes forestry and fishing. · Industry is the sum ing them at an agreed set of base year prices, and year's World Development Indicators continues to of gross output less the value of intermediate input subtracting the cost of intermediate inputs, also in use 2000 as the reference year. Because rescaling used in production for industries classified in ISIC constant prices. This double-deflation method, rec- changes the implicit weights used in forming regional divisions 10­45, which cover mining, manufactur- ommended by the 1993 SNA and its predecessors, and income group aggregates, aggregate growth rates ing (also reported separately), construction, electric- requires detailed information on the structure of in this year's edition are not comparable with those ity, water, and gas. · Manufacturing is the sum of prices of inputs and outputs. from earlier editions with different base years. gross output less the value of intermediate input In many industries, however, value added is Rescaling may result in a discrepancy between used in production for industries classified in ISIC extrapolated from the base year using single volume the rescaled GDP and the sum of the rescaled com- divisions 15­37. · Services correspond to ISIC divi- indexes of outputs or, less commonly, inputs. Par- ponents. Because allocating the discrepancy would sions 50­99. This sector is derived as a residual ticularly in the services industries, including most of cause distortions in the growth rates, the discrep- (from GDP less agriculture and industry) and may not government, value added in constant prices is often ancy is left unallocated. As a result, the weighted properly reflect the sum of services output, including imputed from labor inputs, such as real wages or average of the growth rates of the components gen- banking and financial services. For some countries number of employees. In the absence of well defined erally will not equal the GDP growth rate. it includes product taxes (minus subsidies) and may measures of output, measuring the growth of ser- also include statistical discrepancies. vices remains difficult. Computing growth rates Moreover, technical progress can lead to improve- Growth rates of GDP and its components are calcu- ments in production processes and in the quality of lated using the least squares method and constant goods and services that, if not properly accounted price data in the local currency. Constant price U.S. for, can distort measures of value added and thus dollar series are used to calculate regional and of growth. When inputs are used to estimate output, income group growth rates. Local currency series are Data sources as for nonmarket services, unmeasured technical converted to constant U.S. dollars using an exchange progress leads to underestimates of the volume of rate in the common reference year. The growth rates Data on national accounts for most developing output. Similarly, unmeasured improvements in qual- in the table are average annual compound growth countries are collected from national statistical ity lead to underestimates of the value of output and rates. Methods of computing growth are described organizations and central banks by visiting and value added. The result can be underestimates of in Statistical methods. resident World Bank missions. Data for high- growth and productivity improvement and overesti- income economies are from Organisation for mates of inflation. Changes in the System of National Accounts Economic Co-operation and Development (OECD) Informal economic activities pose a particular World Development Indicators adopted the termi- data files. The World Bank rescales constant price measurement problem, especially in developing nology of the 1993 SNA in 2001. Although many data to a common reference year. The complete countries, where much economic activity is unre- countries continue to compile their national accounts national accounts time series is available on the corded. A complete picture of the economy requires according to the SNA version 3 (referred to as the World Development Indicators 2010 CD-ROM. estimating household outputs produced for home 1968 SNA), more and more are adopting the 1993 The United Nations Statistics Division publishes use, sales in informal markets, barter exchanges, SNA. Some low-income countries still use concepts detailed national accounts for UN member coun- and illicit or deliberately unreported activities. The from the even older 1953 SNA guidelines, including tries in National Accounts Statistics: Main Aggre- consistency and completeness of such estimates valuations such as factor cost, in describing major gates and Detailed Tables and publishes updates depend on the skill and methods of the compiling economic aggregates. Countries that use the 1993 in the Monthly Bulletin of Statistics. statisticians. SNA are identified in Primary data documentation. 2010 World Development Indicators 229 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. 10,624 .. 32 .. 26 .. 16 .. 42 Albania 2,424 12,295 56 21 22 20 14 20 22 60 Algeria 41,764 166,545 10 7 50 62 11 5 39 31 Angolaa 5,040 84,945 7 7 66 68 4 5 26 26 Argentina 258,032 328,465 6 10 28 32 18 21 66 58 Armenia 1,468 11,917 42 18 32 45 25 15 26 37 Australia 361,306 1,015,217 3 3 29 29 15 10 68 68 Austria 238,314 413,503 3 2 31 31 20 20 67 67 Azerbaijan 3,052 46,135 27 6 34 70 13 4 39 24 Bangladesh 37,940 79,554 26 19 25 29 15 18 49 52 Belarus 13,973 60,313 17 10 37 44 31 33 46 46 Belgium 284,321 504,206 2 1 28 23 20 16 70 76 Benina 2,009 6,680 34 .. 15 .. 9 .. 51 .. Bolivia 6,715 16,674 17 13 33 38 19 14 50 48 Bosnia and Herzegovina 1,867 18,512 .. .. .. .. .. .. .. .. Botswana 4,774 13,414 4 2 51 53 5 4 45 45 Brazil 768,951 1,575,151 6 7 28 28 19 16 67 65 Bulgaria 13,107 49,900 14 7 35 31 24 15 50 62 Burkina Faso 2,380 7,948 35 33 21 22 15 14 43 44 Burundi 1,000 1,163 48 .. 19 .. 9 .. 33 .. Cambodia 3,441 10,354 50 35 15 24 10 16 36 41 Cameroon 8,733 23,396 24 19 31 31 22 17 45 50 Canada 590,517 1,501,329 3 .. 31 .. 18 .. 66 .. Central African Republic 1,122 1,988 46 53 21 14 10 8 33 33 Chad 1,446 8,400 36 14 14 49 11 7 51 38 Chile 71,349 169,458 9 4 35 44 18 13 55 52 Chinaa 728,007 4,326,996 20 11 47 49 34 34 33 40 Hong Kong SAR, China 144,230 215,355 0 0 15 8 8 3 85 92 Colombia 92,503 243,765 15 9 32 36 16 16 53 55 Congo, Dem. Rep. 5,643 11,668 57 40 17 28 9 6 26 32 Congo, Rep.a 2,116 10,723 10 4 45 75 8 4 45 21 Costa Rica 11,722 29,664 14 7 30 29 22 21 57 64 Côte d'Ivoirea 11,000 23,414 25 25 21 26 15 18 55 49 Croatia 22,122 69,332 10 6 31 28 22 17 59 65 Cubaa .. .. 6 .. 45 .. 38 .. 49 .. Czech Republic 55,257 215,500 5 3 38 38 24 25 57 60 Denmark 181,984 341,255 3 1 25 26 17 15 71 73 Dominican Republica 16,358 45,541 10 7 36 33 26 24 54 60 Ecuador 20,206 54,686 .. 7 .. 41 .. 10 .. 53 Egypt, Arab Rep. 60,159 162,283 17 13 32 38 17 16 51 49 El Salvador 9,500 22,115 14 13 30 28 23 22 56 58 Eritrea 578 1,654 21 24 17 19 9 5 62 56 Estonia 4,353 23,401 6 3 33 29 21 17 61 68 Ethiopia 7,606 25,585 57 44 10 13 5 5 33 42 Finland 130,599 272,700 4 3 33 32 25 24 63 65 France 1,569,983 2,856,556 3 2 25 20 .. 12 72 78 Gabona 4,959 14,535 8 4 52 64 5 3 40 32 Gambia, The 382 811 30 29 13 15 6 5 57 56 Georgia 2,694 12,791 52 10 16 21 17 12 32 69 Germany 2,522,792 3,649,494 1 1 32 30 23 24 67 69 Ghanaa 6,457 16,653 39 33 24 25 9 6 37 41 Greece 131,718 355,876 9 3 21 20 .. 10 70 77 Guatemalaa 14,657 38,983 24 12 20 30 14 20 56 58 Guinea 3,694 3,799 19 25 29 46 4 4 52 29 Guinea-Bissau 254 430 55 55 12 13 8 10 33 32 Haiti 2,696 7,205 .. .. .. .. .. .. .. .. Honduras 3,911 13,343 22 14 31 31 18 22 48 55 230 2010 World Development Indicators 4.2 ECONOMY Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 44,656 154,668 7 4 32 29 24 22 61 66 India 356,299 1,159,171 26 17 28 29 18 16 46 54 Indonesiaa 202,132 510,730 17 14 42 48 24 28 41 37 Iran, Islamic Rep. 90,829 286,058 18 10 34 44 12 11 47 45 Iraq 10,114 .. 9 .. 75 .. 1 .. 16 .. Ireland 67,036 267,576 7 2 38 34 30 22 55 64 Israela 96,065 202,101 .. .. .. .. .. .. .. .. Italy 1,126,041 2,303,079 3 2 30 27 22 18 66 71 Jamaica 5,813 14,614 9 5 37 25 16 9 54 69 Japan 5,247,610 4,910,840 2 1 34 29 23 21 64 69 Jordan 6,727 21,238 4 3 29 34 15 20 67 63 Kazakhstan 20,374 133,442 13 6 31 43 15 13 56 51 Kenya 9,046 30,355 31 27 16 19 10 12 53 54 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 517,118 929,121 6 3 42 37 28 28 52 60 Kosovo .. 5,448 .. 12 .. 20 .. 16 .. 68 Kuwait a 27,192 148,024 0 .. 55 .. 4 .. 45 .. Kyrgyz Republic 1,661 5,059 44 30 20 20 9 13 37 51 Lao PDR 1,764 5,543 56 35 19 28 14 9 25 37 Latvia 5,236 33,784 9 3 30 23 21 11 61 74 Lebanon 11,719 29,264 8 5 25 21 14 10 68 73 Lesotho 890 1,622 17 7 39 35 16 16 44 58 Liberia 135 843 82 61 5 17 3 13 13 22 Libya 25,541 93,168 .. 2 .. 78 .. 4 .. 20 Lithuania 7,621 47,341 11 4 33 33 20 19 56 63 Macedonia, FYR 4,449 9,521 13 11 30 34 23 22 57 55 Madagascar 3,160 9,463 27 25 9 17 8 15 64 57 Malawi 1,397 4,269 30 34 20 21 16 14 50 45 Malaysiaa 88,832 221,773 13 10 41 48 26 28 46 42 Mali 2,466 8,740 50 37 19 24 8 3 32 39 Mauritania 1,415 2,858 37 13 25 47 8 .. 37 41 Mauritius 4,040 9,320 10 4 32 29 23 20 58 67 Mexico 286,698 1,088,128 6 4 28 37 21 19 66 59 Moldova 1,753 6,047 33 11 32 15 26 14 35 74 Mongolia 1,227 5,258 41 21 29 40 12 4 30 39 Morocco 32,986 88,883 15 15 34 30 19 14 51 55 Mozambique 2,247 9,846 35 29 15 24 8 14 51 47 Myanmar a .. .. 60 .. 10 .. 7 .. 30 .. Namibia 3,503 8,837 12 9 28 37 13 14 60 53 Nepal 4,401 12,615 42 34 23 17 10 7 35 50 Netherlands 418,969 871,004 3 2 27 25 17 14 69 73 New Zealand 62,049 129,940 7 .. 27 .. 19 .. 66 .. Nicaragua 3,191 6,592 23 19 27 30 19 19 49 51 Niger a 1,881 5,354 40 .. 17 .. 6 .. 43 .. Nigeria 28,109 207,118 .. 33 .. 41 .. 3 .. 27 Norway 148,920 451,830 3 1 34 46 13 9 63 53 Omana 13,803 41,638 3 .. 46 .. 5 .. 51 .. Pakistan 60,636 164,539 26 20 24 27 16 20 50 53 Panama 7,906 23,088 8 6 18 17 9 7 74 76 Papua New Guinea 4,636 8,239 35 34 34 48 8 6 31 18 Paraguaya 8,066 15,977 21 20 23 18 16 13 56 61 Peru 53,674 129,109 9 7 31 36 17 16 60 57 Philippinesa 74,120 166,909 22 15 32 32 23 22 46 53 Poland 139,062 527,866 8 5 35 31 21 17 57 65 Portugal 112,960 243,497 6 2 28 24 18 14 66 74 Puerto Ricoa 42,647 .. 1 .. 44 .. 42 .. 55 .. Qatar 8,138 71,041 .. .. .. .. .. .. .. .. 2010 World Development Indicators 231 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 35,477 200,071 21 7 43 25 29 21 36 68 Russian Federation 395,528 1,679,484 7 5 37 37 .. 18 56 58 Rwandaa 1,293 4,457 44 37 16 14 10 4 40 48 Saudi Arabiaa 142,458 468,800 6 2 49 70 10 8 45 27 Senegal 4,879 13,273 21 16 24 22 17 13 55 63 Serbia 19,681 50,061 .. .. .. .. .. .. .. .. Sierra Leone 871 1,954 43 50 39 23 9 .. 18 26 Singapore 84,291 181,948 0 0 35 28 27 21 65 72 Slovak Republic 19,579 94,957 6 4 38 41 27 22 56 55 Slovenia 20,814 54,613 4 2 35 34 26 23 60 63 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 151,113 276,445 4 3 35 34 21 19 61 63 Spain 596,751 1,604,235 5 3 29 29 18 15 66 68 Sri Lankaa 13,030 40,565 23 13 27 29 16 18 50 57 Sudan 13,830 55,927 39 26 11 34 5 6 51 40 Swaziland 1,699 2,837 12 7 45 49 39 44 43 43 Sweden 253,705 478,961 3 2 31 28 23 20 67 70 Switzerland 315,940 491,950 2 1 30 28 20 20 68 71 Syrian Arab Republic 11,397 55,204 32 20 20 35 15 13 48 45 Tajikistan 1,232 5,134 38 18 39 23 28 16 22 59 Tanzaniab 5,255 20,490 47 45 14 17 7 7 38 37 Thailanda 168,019 272,429 10 12 41 44 30 35 50 44 Timor-Lestea .. 498 .. .. .. .. .. .. .. .. Togoa 1,309 2,898 38 .. 22 .. 10 .. 40 .. Trinidad and Tobago 5,329 24,145 2 0 47 62 9 5 51 37 Tunisiaa 18,031 40,309 11 10 29 33 19 18 59 58 Turkey 169,708 734,853 16 9 33 28 23 18 50 64 Turkmenistan 2,482 15,327 17 12 63 54 40 50 20 34 Uganda 5,756 14,326 49 23 14 26 7 8 36 52 Ukraine 48,214 180,355 15 8 43 37 35 23 42 55 United Arab Emirates 42,807 198,693 3 2 52 61 10 12 45 38 United Kingdom 1,157,119 2,674,057 2 1 31 24 21 .. 67 76 United States 7,342,300 14,591,381 2 1 26 22 19 14 72 77 Uruguay 19,298 32,186 9 11 29 27 20 18 62 63 Uzbekistan 13,350 27,934 32 21 28 31 12 12 40 48 Venezuela, RB 74,889 314,150 6 .. 41 .. 15 .. 53 .. Vietnama 20,736 90,645 27 22 29 40 15 21 44 38 West Bank and Gaza 3,220 .. .. .. .. .. .. .. .. .. Yemen, Rep.a 4,236 26,576 20 .. 32 .. 14 .. 48 .. Zambia 3,478 14,314 18 21 36 46 11 12 46 33 Zimbabwe 7,111 .. 15 .. 29 .. 22 .. 56 .. World 29,604,170 t 60,521,123 t 4w 3w 31 w 28 w 20 w 18 w 65 w 69 w Low income 195,611 564,572 35 25 22 28 12 14 43 47 Middle income 4,894,312 16,722,126 14 9 35 37 23 22 51 53 Lower middle income 2,044,366 8,277,781 21 13 39 41 26 27 41 46 Upper middle income 2,851,464 8,442,445 8 6 32 34 20 17 60 60 Low & middle income 5,091,618 17,299,923 15 10 34 37 22 21 51 53 East Asia & Pacific 1,312,702 5,695,585 19 12 44 47 31 33 36 41 Europe & Central Asia 904,254 3,872,528 13 7 36 32 22 18 52 61 Latin America & Carib. 1,755,662 4,216,075 7 7 29 33 19 18 64 61 Middle East & N. Africa 315,651 1,074,015 16 11 34 43 15 12 50 46 South Asia 476,175 1,469,613 26 18 27 28 17 16 46 53 Sub-Saharan Africa 327,684 978,062 18 12 29 33 16 15 53 55 High income 24,508,224 43,273,506 2 1 30 26 20 17 68 73 Euro area 7,274,360 13,566,882 3 2 29 27 22 18 68 72 a. Components are at producer prices. b. Covers mainland Tanzania only. 232 2010 World Development Indicators 4.2 ECONOMY Structure of output About the data Definitions An economy's gross domestic product (GDP) rep- Ideally, industrial output should be measured · Gross domestic product (GDP) at purchaser prices resents the sum of value added by all its produc- through regular censuses and surveys of fi rms. is the sum of gross value added by all resident pro- ers. Value added is the value of the gross output of But in most developing countries such surveys are ducers in the economy plus any product taxes (less producers less the value of intermediate goods and infrequent, so earlier survey results must be extrapo- subsidies) not included in the valuation of output. services consumed in production, before accounting lated using an appropriate indicator. The choice of It is calculated without deducting for depreciation for consumption of fixed capital in production. The sampling unit, which may be the enterprise (where of fabricated assets or for depletion and degrada- United Nations System of National Accounts calls responses may be based on financial records) or tion of natural resources. Value added is the net for value added to be valued at either basic prices the establishment (where production units may be output of an industry after adding up all outputs and (excluding net taxes on products) or producer prices recorded separately), also affects the quality of subtracting intermediate inputs. The industrial origin (including net taxes on products paid by producers the data. Moreover, much industrial production is of value added is determined by the International but excluding sales or value added taxes). Both valu- organized in unincorporated or owner-operated ven- Standard Industrial Classifi cation (ISIC) revision ations exclude transport charges that are invoiced tures that are not captured by surveys aimed at the 3. · Agriculture is the sum of gross output less separately by producers. Total GDP shown in the formal sector. Even in large industries, where regu- the value of intermediate input used in production table and elsewhere in this volume is measured at lar surveys are more likely, evasion of excise and for industries classified in ISIC divisions 1­5 and purchaser prices. Value added by industry is normally other taxes and nondisclosure of income lower the includes forestry and fishing. · Industry is the sum measured at basic prices. When value added is mea- estimates of value added. Such problems become of gross output less the value of intermediate input sured at producer prices, this is noted in Primary data more acute as countries move from state control of used in production for industries classified in ISIC documentation and footnoted in the table. industry to private enterprise, because new firms and divisions 10­45, which cover mining, manufactur- While GDP estimates based on the production growing numbers of established firms fail to report. ing (also reported separately), construction, electric- approach are generally more reliable than estimates In accordance with the System of National Accounts, ity, water, and gas. · Manufacturing is the sum of compiled from the income or expenditure side, dif- output should include all such unreported activity as gross output less the value of intermediate input ferent countries use different definitions, methods, well as the value of illegal activities and other unre- used in production for industries classified in ISIC and reporting standards. World Bank staff review the corded, informal, or small-scale operations. Data divisions 15­37. · Services correspond to ISIC divi- quality of national accounts data and sometimes on these activities need to be collected using tech- sions 50­99. This sector is derived as a residual make adjustments to improve consistency with niques other than conventional surveys of firms. (from GDP less agriculture and industry) and may not international guidelines. Nevertheless, significant In industries dominated by large organizations properly reflect the sum of services output, including discrepancies remain between international stan- and enterprises, such as public utilities, data on banking and financial services. For some countries dards and actual practice. Many statistical offices, output, employment, and wages are usually read- it includes product taxes (minus subsidies) and may especially those in developing countries, face severe ily available and reasonably reliable. But in the also include statistical discrepancies. limitations in the resources, time, training, and bud- services industry the many self-employed workers gets required to produce reliable and comprehensive and one-person businesses are sometimes difficult series of national accounts statistics. to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Data problems in measuring output Compounding these problems are the many forms Among the difficulties faced by compilers of national of economic activity that go unrecorded, including accounts is the extent of unreported economic activ- the work that women and children do for little or no ity in the informal or secondary economy. In develop- pay. For further discussion of the problems of using Data sources ing countries a large share of agricultural output is national accounts data, see Srinivasan (1994) and either not exchanged (because it is consumed within Heston (1994). Data on national accounts for most developing the household) or not exchanged for money. countries are collected from national statistical Agricultural production often must be estimated Dollar conversion organizations and central banks by visiting and indirectly, using a combination of methods involv- To produce national accounts aggregates that are resident World Bank missions. Data for high- ing estimates of inputs, yields, and area under cul- measured in the same standard monetary units, income economies are from Organisation for Eco- tivation. This approach sometimes leads to crude the value of output must be converted to a single nomic Co-operation and Development (OECD) data approximations that can differ from the true values common currency. The World Bank conventionally files. The complete national accounts time series over time and across crops for reasons other than uses the U.S. dollar and applies the average official is available on the World Development Indicators climate conditions or farming techniques. Similarly, exchange rate reported by the International Monetary 2010 CD-ROM. The United Nations Statistics Divi- agricultural inputs that cannot easily be allocated to Fund for the year shown. An alternative conversion sion publishes detailed national accounts for UN specific outputs are frequently "netted out" using factor is applied if the official exchange rate is judged member countries in National Accounts Statistics: equally crude and ad hoc approximations. For further to diverge by an exceptionally large margin from the Main Aggregates and Detailed Tables and publishes discussion of the measurement of agricultural pro- rate effectively applied to transactions in foreign cur- updates in the Monthly Bulletin of Statistics. duction, see About the data for table 3.3. rencies and traded products. 2010 World Development Indicators 233 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Afghanistan .. 1,663 .. .. .. .. .. .. .. .. .. .. Albania 405 2,047 .. 17 .. 22 .. 3 .. 17 .. 41 Algeria 4,366 7,471 .. .. .. .. .. .. .. .. .. .. Angola 202 4,040 .. .. .. .. .. .. .. .. .. .. Argentina 44,502 63,983 29 .. 7 .. 13 .. 4 .. 46 .. Armenia 356 1,558 .. .. .. .. .. .. .. .. .. .. Australia 50,044 97,613 .. 17 .. 1 .. 5 .. 7 .. 69 Austria 42,134 67,615 10 9 5 3 27 31 2 6 56 51 Azerbaijan 352 1,922 .. 15 .. 1 .. 13 .. 4 .. 67 Bangladesh 5,586 13,672 28 .. 44 .. 4 .. 11 .. 13 .. Belarus 3,909 16,966 .. .. .. .. .. .. .. .. .. .. Belgium 51,721 66,902 13 13 6 4 23 21 8 22 50 41 Benin 174 .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,123 1,862 31 .. 4 .. 1 .. 4 .. 60 .. Bosnia and Herzegovina 213 2,130 .. .. .. .. .. .. .. .. .. .. Botswana 242 458 25 22 8 5 15 .. 5 .. 66 73 Brazil 124,976 212,923 21 19 8 6 23 21 13 11 35 43 Bulgaria 2,015 6,199 23 16 12 13 20 18 15 7 30 45 Burkina Faso 336 775 .. .. .. .. .. .. .. .. .. .. Burundi 83 .. .. .. .. .. .. .. .. .. .. .. Cambodia 315 1,589 20 .. 22 .. 0 .. 0 .. 57 .. Cameroon 1,758 3,328 .. .. .. .. .. .. .. .. .. .. Canada 100,393 .. 14 .. 4 .. 23 .. 10 .. 49 .. Central African Republic 108 106 .. .. .. .. .. .. .. .. .. .. Chad 159 383 .. .. .. .. .. .. .. .. .. .. Chile 10,594 21,660 .. 14 .. 2 .. 2 .. 14 .. 68 China 245,002 1,487,812 4 4 2 2 2 3 .. .. 93 93 Hong Kong SAR, China 10,524 5,040 .. .. .. .. .. .. .. .. .. .. Colombia 13,506 35,885 .. 27 .. 9 .. 7 .. 13 .. 44 Congo, Dem. Rep. 510 630 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 172 411 .. .. .. .. .. .. .. .. .. .. Costa Rica 2,339 5,505 .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 1,655 4,219 .. .. .. .. .. .. .. .. .. .. Croatia 4,121 10,137 .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 12,124 47,842 12 9 7 3 23 37 4 3 54 48 Denmark 26,925 39,213 20 14 2 2 23 17 1 2 53 65 Dominican Republic 3,824 9,785 .. .. .. .. .. .. .. .. .. .. Ecuador 2,830 5,004 26 30 6 4 4 3 7 5 56 58 Egypt, Arab Rep. 9,829 24,461 19 .. 13 .. 12 .. 18 .. 38 .. El Salvador 2,026 4,452 .. .. .. .. .. .. .. .. .. .. Eritrea 47 72 63 35 9 16 1 4 13 10 15 34 Estonia 804 3,472 .. 12 .. 5 .. 10 .. 4 .. 69 Ethiopia 344 1,149 52 47 18 9 2 2 4 4 23 38 Finland 28,814 50,717 10 7 3 2 30 36 6 3 51 51 France .. 306,281 13 13 5 4 28 29 12 12 41 42 Gabon 224 503 .. .. .. .. .. .. .. .. .. .. Gambia, The 20 35 65 .. 8 .. 1 .. 9 .. 17 .. Georgia 523 1,362 .. 36 .. 2 .. 5 .. 15 .. 42 Germany 516,542 711,089 8 8 3 2 43 42 10 10 37 38 Ghana 602 1,055 .. .. .. .. .. .. .. .. .. .. Greece .. 28,544 25 23 14 9 13 13 10 6 38 50 Guatemala 2,069 7,312 .. .. .. .. .. .. .. .. .. .. Guinea 142 155 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 41 .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 607 2,593 .. .. .. .. .. .. .. .. .. .. 234 2010 World Development Indicators 4.3 ECONOMY Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Hungary 8,839 28,619 18 12 3 3 14 42 11 10 54 33 India 57,917 169,986 13 9 12 9 20 20 22 16 34 46 Indonesia 48,781 142,345 21 25 18 12 16 23 10 11 35 30 Iran, Islamic Rep. 10,918 29,832 13 10 10 4 18 27 16 13 44 46 Iraq 67 .. 23 .. 26 .. 4 .. 8 .. 39 .. Ireland 18,096 50,926 15 14 1 0 21 10 18 26 45 50 Israel .. .. 13 11 6 4 25 23 6 10 50 53 Italy 225,513 344,676 9 9 14 10 27 27 8 7 43 46 Jamaica 865 983 .. .. .. .. .. .. .. .. .. .. Japan 1,077,348 923,108 10 11 4 2 38 41 10 11 38 35 Jordan 866 3,834 31 24 6 11 4 6 15 15 43 44 Kazakhstan 2,976 15,711 .. .. .. .. .. .. .. .. .. .. Kenya 757 3,229 .. 30 .. 6 .. 4 .. 5 .. 55 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 128,839 234,688 9 6 10 5 40 50 8 8 33 31 Kosovo .. 732 .. .. .. .. .. .. .. .. .. .. Kuwait 1,032 .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 142 570 .. 14 .. 5 .. 1 .. 1 .. 78 Lao PDR 245 484 .. .. .. .. .. .. .. .. .. .. Latvia 965 3,200 39 20 11 7 15 11 4 4 31 58 Lebanon 1,465 2,448 26 .. 10 .. 5 .. 6 .. 53 .. Lesotho 129 234 .. .. .. .. .. .. .. .. .. .. Liberia 4 105 .. .. .. .. .. .. .. .. .. .. Libya .. 3,879 .. .. .. .. .. .. .. .. .. .. Lithuania 1,351 6,615 .. 22 .. 11 .. 13 .. 6 .. 49 Macedonia, FYR 873 1,780 35 .. 17 .. 9 .. 8 .. 31 .. Madagascar 233 1,345 .. 46 .. 31 .. 1 .. 2 .. 20 Malawi 195 504 .. .. .. .. .. .. .. .. .. .. Malaysia 23,432 52,224 .. 9 .. 3 .. 34 .. 13 .. 42 Mali 174 195 .. .. .. .. .. .. .. .. .. .. Mauritania 107 .. .. .. .. .. .. .. .. .. .. .. Mauritius 822 1,648 25 30 52 42 2 3 .. .. 21 25 Mexico 54,546 199,410 26 .. 4 .. 24 .. 14 .. 31 .. Moldova 400 702 .. 50 .. 13 .. 5 .. .. .. 31 Mongolia 143 210 46 .. 36 .. 3 .. 2 .. 12 .. Morocco 6,056 11,225 .. 37 .. 14 .. 8 .. 13 .. 28 Mozambique 166 1,298 .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 403 1,108 .. .. .. .. .. .. .. .. .. .. Nepal 393 862 35 .. 34 .. 2 .. .. .. 29 .. Netherlands 65,999 94,324 19 15 3 1 16 19 14 18 48 47 New Zealand 10,645 .. 29 25 .. .. .. .. .. .. 71 75 Nicaragua 533 946 .. .. .. .. .. .. .. .. .. .. Niger 120 .. .. .. .. .. .. .. .. .. .. .. Nigeria .. 3,760 .. .. .. .. .. .. .. .. .. .. Norway 17,018 38,595 17 23 2 1 25 23 9 9 48 43 Oman 643 .. 15 6 6 0 4 2 8 11 66 80 Pakistan 8,864 31,196 .. .. .. .. .. .. .. .. .. .. Panama 694 1,454 54 .. 7 .. .. .. 7 .. 32 .. Papua New Guinea 372 446 .. .. .. .. .. .. .. .. .. .. Paraguay 1,280 2,022 .. .. .. .. .. .. .. .. .. .. Peru 8,105 18,770 26 30 10 13 6 3 9 11 49 44 Philippines 17,043 37,247 29 24 7 6 20 30 11 8 34 33 Poland 25,885 80,227 23 23 8 4 22 20 3 7 44 46 Portugal 18,249 23,939 13 14 22 9 18 15 6 2 41 61 Puerto Rico 17,867 .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. 1 .. 2 .. 0 .. 17 .. 80 2010 World Development Indicators 235 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicals Other value added beverages, clothing and transport manufacturinga and tobacco equipment $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2005 1995 2005 1995 2005 1995 2005 1995 2005 Romania 9,387 37,959 31 15 12 14 18 18 6 5 33 47 Russian Federation .. 256,618 .. 15 .. 2 .. 12 .. 8 .. 63 Rwanda 132 200 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 13,714 38,737 .. 19 .. 5 .. 13 .. 27 .. 35 Senegal 730 1,548 44 .. 3 .. 1 .. 29 .. 23 .. Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 75 .. .. .. .. .. .. .. .. .. .. .. Singapore 20,799 35,535 4 2 1 1 60 49 9 29 26 19 Slovak Republic 6,064 21,332 13 7 2 4 19 24 8 3 57 62 Slovenia 4,573 9,677 10 7 12 6 21 26 2 2 56 60 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 29,274 46,692 15 18 8 4 19 17 10 7 47 55 Spain 101,524 195,804 16 15 7 5 25 21 9 8 42 51 Sri Lanka 1,836 7,283 .. 29 .. 29 .. 4 .. 14 .. 24 Sudan 640 3,028 .. .. .. .. .. .. .. .. .. .. Swaziland 557 1,054 .. .. .. .. .. .. .. .. .. .. Sweden 49,767 79,279 8 7 1 1 35 29 2 12 53 51 Switzerland 50,562 72,675 .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 1,574 6,092 .. .. .. .. .. .. .. .. .. .. Tajikistan 331 745 .. .. .. .. .. .. .. .. .. .. Tanzaniab 349 819 .. .. .. .. .. .. .. .. .. .. Thailand 50,231 95,146 21 .. 9 .. 29 .. 6 .. 35 .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 130 .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 439 1,263 30 12 1 1 2 1 26 38 41 48 Tunisia 3,419 7,209 .. .. .. .. .. .. .. .. .. .. Turkey 38,296 118,702 16 .. 17 .. 16 .. 10 .. 41 .. Turkmenistan 948 9,158 .. .. .. .. .. .. .. .. .. .. Uganda 359 1,000 50 .. 3 .. 6 .. .. .. 41 .. Ukraine 14,922 37,161 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 4,452 24,643 .. .. .. .. .. .. .. .. .. .. United Kingdom 219,282 .. 14 15 5 3 29 26 11 11 41 45 United States 1,289,100 1,755,600 12 14 4 2 34 28 12 15 38 40 Uruguay 3,801 4,996 36 42 9 9 4 2 8 9 43 38 Uzbekistan 1,376 3,061 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 10,668 .. .. .. .. .. .. .. .. .. .. .. Vietnam 3,109 19,129 30 .. 22 .. 12 .. 7 .. 29 .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 599 .. 45 60 5 9 0 0 2 4 48 27 Zambia 344 1,421 .. .. .. .. .. .. .. .. .. .. Zimbabwe 1,370 .. 30 .. 7 .. 29 .. 6 .. 29 .. World 5,484,300 t 9,054,590 t .. .. .. .. .. .. .. .. .. .. Low income 22,364 68,035 .. .. .. .. .. .. .. .. .. .. Middle income 990,706 3,514,162 .. .. .. .. .. .. .. .. .. .. Lower middle income 496,790 2,159,122 .. .. .. .. .. .. .. .. .. .. Upper middle income 503,477 1,319,723 .. .. .. .. .. .. .. .. .. .. Low & middle income 1,013,160 3,584,044 .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 390,767 1,859,969 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 292,587 640,171 .. .. .. .. .. .. .. .. .. .. Middle East & N. Africa 39,269 106,905 .. .. .. .. .. .. .. .. .. .. South Asia 75,041 224,444 .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa 46,018 89,446 .. .. .. .. .. .. .. .. .. .. High income 4,490,475 6,039,774 .. .. .. .. .. .. .. .. .. .. Euro area 1,325,850 1,958,265 .. .. .. .. .. .. .. .. .. .. a. Includes unallocated data. b. Covers mainland Tanzania only. 236 2010 World Development Indicators 4.3 ECONOMY Structure of manufacturing About the data Definitions The data on the distribution of manufacturing value revision 3. Concordances matching ISIC categories · Manufacturing value added is the sum of gross added by industry are provided by the United Nations to national classifi cation systems and to related output less the value of intermediate inputs used in Industrial Development Organization (UNIDO). UNIDO systems such as the Standard International Trade production for industries classified in ISIC major divi- obtains the data from a variety of national and inter- Classification are available. sion 3. · Food, beverages, and tobacco correspond national sources, including the United Nations Sta- In establishing classifi cations systems compil- to ISIC divisions 15 and 16. · Textiles and clothing tistics Division, the World Bank, the Organisation for ers must define both the types of activities to be correspond to ISIC divisions 17­19. · Machinery and Economic Co-operation and Development, and the described and the units whose activities are to transport equipment correspond to ISIC divisions International Monetary Fund. To improve comparabil- be reported. There are many possibilities, and the 29, 30, 32, 34, and 35. · Chemicals correspond to ity over time and across countries, UNIDO supple- choices affect how the statistics can be interpreted ISIC division 24. · Other manufacturing, a residual, ments these data with information from industrial and how useful they are in analyzing economic covers wood and related products (ISIC division 20), censuses, statistics from national and international behavior. The ISIC emphasizes commonalities in the paper and related products (ISIC divisions 21 and organizations, unpublished data that it collects in the production process and is explicitly not intended to 22), petroleum and related products (ISIC division field, and estimates by the UNIDO Secretariat. Nev- measure outputs (for which there is a newly devel- 23), basic metals and mineral products (ISIC divi- ertheless, coverage may be incomplete, particularly oped Central Product Classification). Nevertheless, sion 27), fabricated metal products and professional for the informal sector. When direct information on the ISIC views an activity as defined by "a process goods (ISIC division 28), and other industries (ISIC inputs and outputs is not available, estimates may resulting in a homogeneous set of products" (United divisions 25, 26, 31, 33, 36, and 37). be used, which may result in errors in industry totals. Nations 1990 [ISIC, series M, no. 4, rev. 3], p. 9). Moreover, countries use different reference periods Firms typically use multiple processes to produce (calendar or fiscal year) and valuation methods (basic a product. For example, an automobile manufac- or producer prices) to estimate value added. (See turer engages in forging, welding, and painting as About the data for table 4.2.) well as advertising, accounting, and other service The data on manufacturing value added in U.S. dol- activities. Collecting data at such a detailed level lars are from the World Bank's national accounts files is not practical, nor is it useful to record produc- and may differ from those UNIDO uses to calculate tion data at the highest level of a large, multiplant, shares of value added by industry, in part because multiproduct firm. The ISIC has therefore adopted as of differences in exchange rates. Thus value added the definition of an establishment "an enterprise or in a particular industry estimated by applying the part of an enterprise which independently engages in shares to total manufacturing value added will not one, or predominantly one, kind of economic activity match those from UNIDO sources. Classification of at or from one location . . . for which data are avail- manufacturing industries in the table accords with able . . ." (United Nations 1990, p. 25). By design, the United Nations International Standard Industrial this definition matches the reporting unit required Classifi cation (ISIC) revision 3. Editions of World for the production accounts of the United Nations Development Indicators prior to 2008 used revision System of National Accounts. The ISIC system is 2, first published in 1948. Revision 3 was completed described in the United Nations' International Stan- in 1989, and many countries now use it. But revi- dard Industrial Classification of All Economic Activi- sion 2 is still widely used for compiling cross-country ties, Third Revision (1990). The discussion of the ISIC data. UNIDO has converted these data to accord with draws on Ryten (1998). Manufacturing continues to show strong growth in East Asia through 2008 4.3a Value added in manufacturing (index, 1990 = 100) 600 East Asia & Pacific 500 400 South Asia 300 Middle East & North Africa Latin America & Caribbean Data sources 200 Data on manufacturing value added are from the 100 Sub-Saharan Africa World Bank's national accounts files. Data used 0 to calculate shares of industry value added are 1990 1995 2000 2005 2008 provided to the World Bank in electronic files Manufacturing continues to be the dominant sector in East Asia and Pacific, growing an average of about by UNIDO. The most recent published source is 10.5 percent a year between 1990 and 2008. UNIDO's International Yearbook of Industrial Sta- Source: World Development Indicators data files. tistics 2010. 2010 World Development Indicators 237 4.4 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan 166 680 .. 5 .. 0 .. .. .. .. .. 41 Albania 202 1,353 11 4 9 8 3 22 12 33 65 33 Algeria 10,258 78,233 1 0 0 0 95 98 1 1 4 2 Angola 3,642 66,300 .. .. .. .. .. .. .. .. .. .. Argentina 20,967 70,588 50 53 4 1 10 10 2 3 34 31 Armenia 271 1,069 11 19 5 2 1 0 26 29 54 51 Australia 53,111 187,428 22 12 8 2 19 34 18 27 30 20 Austria 57,738 182,158 4 7 3 2 1 3 3 3 88 81 Azerbaijan 635 31,500 4 1 8 0 66 97 1 0 20 1 Bangladesh 3,501 15,369 10 7 3 3 0 2 0 0 85 88 Belarus 4,803 32,902 .. 7 .. 1 .. 37 .. 1 .. 52 Belgium 178,265a 476,953 10a 9 1a 1 3a 9 3a 3 78a 75 Benin 420 1,050 14 41 75 44 5 0 0 1 6 14 Bolivia 1,100 6,370 21 14 10 1 15 52 35 27 19 6 Bosnia and Herzegovina 152 5,064 .. 6 .. 7 .. 10 .. 13 .. 64 Botswana 2,142 5,040 .. 3 .. 0 .. 0 .. 20 .. 77 Brazil 46,506 197,942 29 28 5 4 1 9 10 12 54 45 Bulgaria 5,355 23,124 18 12 3 1 7 16 10 17 60 51 Burkina Faso 276 620 25 .. 69 .. 0 .. 0 .. 6 .. Burundi 105 56 91 65 4 6 0 1 1 9 3 18 Cambodia 855 4,290 .. .. .. .. .. .. .. .. .. .. Cameroon 1,651 4,350 27 12 28 16 29 62 8 5 8 3 Canada 192,197 456,420 8 9 9 5 9 29 7 8 63 47 Central African Republic 171 185 4 .. 20 .. 1 .. 30 .. 45 .. Chad 243 4,800 .. .. .. .. .. .. .. .. .. .. Chile 16,024 67,788 24 16 12 6 0 1 48 61 13 12 China 148,780 1,428,488 8 3 2 0 4 2 2 2 84 93 Hong Kong SAR, Chinab 173,871 370,242 3 4 0 2 0 3 1 8 94 83 Colombia 10,056 37,626 31 15 5 4 28 47 1 2 35 32 Congo, Dem. Rep. 1,563 3,950 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 1,172 9,050 1 .. 8 .. 88 .. 0 .. 3 .. Costa Rica 3,453 9,675 63 32 5 3 1 1 1 1 25 63 Côte d'Ivoire 3,806 10,100 63 41 20 9 10 37 0 1 7 12 Croatia 4,517 14,112 11 10 5 3 9 13 2 4 74 70 Cuba 1,600 3,500 .. 10 .. 0 .. 0 .. 2 .. 24 Czech Republic 21,335 146,934 6 4 4 1 4 3 3 2 82 87 Denmark 50,906 117,174 24 17 3 2 3 11 1 2 60 66 Dominican Republic 3,780 6,910 19 21 0 1 0 0 0 3 78 75 Ecuador 4,307 18,511 53 25 3 4 36 62 0 1 8 9 Egypt, Arab Rep. 3,450 25,483 10 10 6 2 37 44 6 7 40 37 El Salvador 1,652 4,549 57 20 1 1 0 3 3 2 39 74 Eritrea 86 20 .. .. .. .. .. .. .. .. .. .. Estonia 1,840 12,343 16 9 10 4 6 12 3 4 65 66 Ethiopia 422 1,500 73 75 13 14 3 0 0 1 11 9 Finland 40,490 96,714 2 2 8 4 2 7 3 4 83 81 France 301,162 608,684 14 12 1 1 2 5 3 3 79 78 Gabon 2,713 8,350 0 1 13 7 83 86 2 3 2 4 Gambia, The 16 14 60 60 1 4 0 0 1 15 36 21 Georgia 151 1,498 29 18 3 2 19 3 8 22 41 55 Germany 523,461 1,465,215 5 5 1 1 1 3 3 3 87 82 Ghana 1,724 5,650 58 63 15 9 5 2 9 6 13 19 Greece 11,054 25,311 30 21 4 2 7 11 7 9 50 54 Guatemala 2,155 7,765 65 38 4 4 2 7 0 4 28 47 Guinea 702 1,300 8 2 1 5 0 2 67 59 24 32 Guinea-Bissau 24 98 89 .. 11 .. 0 .. 0 .. 0 .. Haiti 110 490 37 .. 0 .. 0 .. 0 .. 62 .. Honduras 1,769 6,130 87 53 3 2 0 6 0 8 9 29 Data for Taiwan, China 113,047 255,629 3 1 2 1 1 7 1 2 93 88 238 2010 World Development Indicators 4.4 ECONOMY Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 12,865 107,904 21 7 2 1 3 3 5 2 68 80 India 30,630 179,073 19 10 1 2 2 18 3 6 74 63 Indonesia 45,417 139,281 11 18 7 6 25 29 6 8 51 39 Iran, Islamic Rep. 18,360 116,350 4 4 1 0 86 83 1 2 9 10 Iraq 496 59,800 .. 0 .. 0 .. 34 .. 0 .. 0 Ireland 44,705 124,158 19 10 1 0 0 1 1 1 72 85 Israel 19,046 60,825 5 3 2 1 0 1 1 1 89 92 Italy 233,766 539,727 7 7 1 1 1 5 1 2 89 83 Jamaica 1,427 2,400 22 15 0 0 1 18 6 6 71 61 Japan 443,116 782,337 0 1 1 1 1 2 1 3 95 89 Jordan 1,769 7,790 25 14 2 0 0 0 24 11 49 75 Kazakhstan 5,250 71,184 10 4 3 0 25 69 24 12 38 15 Kenya 1,878 4,972 56 44 7 14 6 2 3 3 28 37 Korea, Dem. Rep. 959 1,950 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 125,058 422,007 2 1 1 1 2 7 1 3 93 89 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 12,785 93,180 0 0 0 0 95 96 0 0 5 3 Kyrgyz Republic 409 1,642 23 23 13 7 11 16 13 5 40 47 Lao PDR 311 1,080 .. .. .. .. .. .. .. .. .. .. Latvia 1,305 10,081 14 16 23 9 2 3 1 4 58 63 Lebanon 816 4,454 20 11 2 1 0 0 8 11 70 34 Lesotho 160 900 .. .. .. .. .. .. .. .. .. .. Liberia 820 262 .. .. .. .. .. .. .. .. .. .. Libya 8,975 63,050 0 .. 0 .. 95 .. 0 .. 5 .. Lithuania 2,705 23,728 18 15 8 2 11 25 5 2 58 55 Macedonia, FYR 1,204 3,978 18 14 5 1 0 5 18 5 58 76 Madagascar 507 1,345 69 21 6 3 1 6 7 3 14 67 Malawi 405 790 90 86 2 4 0 0 0 0 7 10 Malaysia 73,914 199,516 10 12 6 2 7 18 1 2 75 54 Mali 441 1,650 23 28 75 42 0 6 0 1 2 22 Mauritania 488 1,750 57 12 0 0 1 22 42 60 0 0 Mauritius 1,538 2,351 29 27 1 1 0 0 0 1 70 57 Mexico 79,542 291,807 8 6 1 0 10 17 3 3 78 74 Moldova 745 1,597 72 59 2 1 1 0 3 8 23 32 Mongolia 473 2,539 2 2 28 12 0 10 60 70 10 6 Morocco 6,881 20,065 31 19 3 2 2 2 12 10 51 67 Mozambique 168 2,600 66 15 16 4 2 11 2 57 13 6 Myanmar 860 6,900 .. .. .. .. .. .. .. .. .. .. Namibia 1,409 2,960 .. 23 .. 0 .. 0 .. 31 .. 45 Nepal 345 1,100 8 .. 1 .. 0 .. 0 .. 84 .. Netherlands 203,171 633,974 20 13 4 3 7 11 3 2 63 55 New Zealand 13,645 30,586 45 53 19 9 2 7 5 5 29 23 Nicaragua 466 1,489 75 85 3 1 1 1 1 2 21 10 Niger 288 820 17 18 1 4 0 2 80 69 1 7 Nigeria 12,342 81,900 2 1 2 1 96 92 0 0 1 5 Norway 41,992 167,941 8 5 2 0 47 68 9 6 27 17 Oman 6,068 37,670 5 2 0 0 79 86 2 1 14 7 Pakistan 8,029 20,375 12 18 4 1 1 6 0 1 83 73 Panama 625 1,180 75 84 0 1 3 1 1 5 20 9 Papua New Guinea 2,654 5,700 13 .. 20 .. 38 .. 25 .. 4 .. Paraguay 919 4,434 44 88 36 3 0 0 0 1 19 8 Peru 5,575 31,529 31 19 3 1 5 11 46 52 15 16 Philippines 17,502 49,025 13 7 1 1 2 3 4 5 42 83 Poland 22,895 167,944 10 9 3 1 8 4 7 4 71 80 Portugal 22,783 55,861 7 10 5 2 3 6 2 3 83 72 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 3,651 63,830 0 0 0 0 82 94 0 0 17 5 2010 World Development Indicators 239 4.4 Structure of merchandise exports Merchandise Food Agricultural Fuels Ores and Manufactures exports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 7,910 49,546 7 6 3 2 8 9 3 5 78 77 Russian Federation 81,095 471,763 2 2 3 2 43 66 10 6 26 17 Rwanda 54 250 57 66 16 1 0 0 12 28 14 4 Saudi Arabia 50,040 328,930 1 1 0 0 88 90 1 0 10 9 Senegal 993 2,390 9 21 7 2 22 34 12 4 48 39 Serbia .. 10,973 28 19 4 2 2 3 15 10 49 66 Sierra Leone 42 220 .. .. .. .. .. .. .. .. .. .. Singaporeb 118,268 338,176 4 2 1 0 7 18 2 1 84 70 Slovak Republic 8,580 70,967 6 4 4 1 4 5 4 2 82 86 Slovenia 8,316 34,199 4 4 2 2 1 3 3 4 90 87 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 27,853c 80,781 8c 7 4c 2 9c 9 8c 29 44 c 52 Spain 97,849 268,108 15 13 2 1 2 5 2 3 78 76 Sri Lanka 3,798 8,370 21 25 4 3 0 0 1 2 73 67 Sudan 555 12,450 44 3 47 1 0 94 0 1 6 0 Swaziland 866 1,790 .. 21 .. 7 .. 1 .. 1 .. 70 Sweden 80,440 183,975 2 4 6 4 2 7 3 4 79 75 Switzerland 81,641 200,387 3 3 1 0 0 3 3 4 94 89 Syrian Arab Republic 3,563 14,300 12 21 7 1 63 41 1 1 17 35 Tajikistan 750 1,406 .. .. .. .. .. .. .. .. .. .. Tanzania 682 2,870 65 49 23 9 0 1 0 18 10 23 Thailand 56,439 177,844 19 13 5 5 1 6 1 1 73 74 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 378 790 19 16 42 9 0 0 32 13 7 62 Trinidad and Tobago 2,455 17,800 8 2 0 0 48 70 0 3 43 25 Tunisia 5,475 19,319 10 9 1 0 8 17 2 2 79 72 Turkey 21,637 131,975 20 8 1 0 1 6 3 3 74 81 Turkmenistan 1,880 10,780 1 .. 13 .. 77 .. 1 .. 8 .. Uganda 460 2,180 90 63 5 6 0 1 1 2 4 27 Ukraine 13,128 67,049 19 16 1 1 4 6 7 6 68 70 United Arab Emirates 28,364 231,550 8 1 0 0 9 65 55 1 28 4 United Kingdom 237,953 457,983 8 6 1 1 6 13 3 5 81 70 United States 584,743 1,300,532 11 10 4 2 2 7 3 4 77 74 Uruguay 2,106 5,949 44 59 15 8 1 3 1 0 39 29 Uzbekistan 3,430 10,360 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 18,457 93,542 3 0 0 0 77 94 6 2 14 4 Vietnam 5,449 62,906 30 20 3 3 18 21 0 1 44 55 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,945 9,270 3 5 1 0 95 92 1 0 1 2 Zambia 1,040 5,093 3 6 1 1 3 1 87 85 7 7 Zimbabwe 2,118 2,150 43 17 7 12 1 1 12 20 37 50 World 5,172,492 t 16,129,607 t 9w 8w 3w 2w 7w 12 w 3w 4w 76 w 70 w Low income 35,717 167,308 27 21 7 5 19 20 6 7 40 46 Middle income 906,854 4,905,095 14 10 3 2 11 21 5 6 64 59 Lower middle income 408,391 2,627,173 14 8 3 2 7 13 3 3 70 71 Upper middle income 498,548 2,276,454 15 11 4 2 15 27 6 8 59 49 Low & middle income 942,571 5,072,412 15 10 4 2 11 21 5 6 63 59 East Asia & Pacific 354,784 2,081,208 11 8 4 2 6 9 2 3 74 76 Europe & Central Asia 179,048 1,141,248 9 6 3 2 25 39 8 5 47 43 Latin America & Carib. 223,927 873,299 20 16 3 2 15 22 7 8 55 51 Middle East & N. Africa 62,002 418,183 6 5 1 0 73 75 3 2 17 16 South Asia 46,657 225,882 17 13 2 2 1 14 3 5 76 65 Sub-Saharan Africa 76,554 336,637 18 12 7 3 36 36 8 16 28 32 High income 4,229,538 11,060,159 8 7 3 2 6 10 3 4 79 73 Euro area 1,742,200 4,612,227 11 8 2 1 2 5 2 3 81 77 Note: Components may not sum to 100 percent because of unclassified trade. Exports of gold are excluded. a. Includes Luxembourg. b. Includes re-exports. c. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 240 2010 World Development Indicators 4.4 ECONOMY Structure of merchandise exports About the data Definitions Data on merchandise trade are from customs are classified as re-exports. Because of differences · Merchandise exports are the f.o.b. value of goods reports of goods moving into or out of an economy in reporting practices, data on exports may not be provided to the rest of the world. · Food corresponds or from reports of financial transactions related to fully comparable across economies. to the commodities in SITC sections 0 (food and live merchandise trade recorded in the balance of pay- The data on total exports of goods (merchandise) animals), 1 (beverages and tobacco), and 4 (animal ments. Because of differences in timing and defi - are from the World Trade Organization (WTO), which and vegetable oils and fats) and SITC division 22 nitions, trade flow estimates from customs reports obtains data from national statistical offices and the (oil seeds, oil nuts, and oil kernels). · Agricultural and balance of payments may differ. Several inter- IMF's International Financial Statistics, supplemented raw materials correspond to SITC section 2 (crude national agencies process trade data, each correct- by the Comtrade database and publications or data- materials except fuels), excluding divisions 22, 27 ing unreported or misreported data, leading to other bases of regional organizations, specialized agen- (crude fertilizers and minerals excluding coal, petro- differences. cies, economic groups, and private sources (such as leum, and precious stones), and 28 (metalliferous The most detailed source of data on international Eurostat, the Food and Agriculture Organization, and ores and scrap). · Fuels correspond to SITC section trade in goods is the United Nations Statistics Divi- country reports of the Economist Intelligence Unit). 3 (mineral fuels). · Ores and metals correspond to sion's Commodity Trade (Comtrade) database. The Country websites and email contact have improved the commodities in SITC divisions 27, 28, and 68 International Monetary Fund (IMF) also collects collection of up-to-date statistics, reducing the pro- (nonferrous metals). · Manufactures correspond to customs-based data on trade in goods. Exports are portion of estimates. The WTO database now covers the commodities in SITC sections 5 (chemicals), 6 recorded as the cost of the goods delivered to the most major traders in Africa, Asia, and Latin America, (basic manufactures), 7 (machinery and transport frontier of the exporting country for shipment--the which together with high-income countries account equipment), and 8 (miscellaneous manufactured free on board (f.o.b.) value. Many countries report for nearly 95 percent of world trade. Reliability of goods), excluding division 68. trade data in U.S. dollars. When countries report in data for countries in Europe and Central Asia has local currency, the United Nations Statistics Division also improved. applies the average official exchange rate to the U.S. Export shares by major commodity group are from dollar for the period shown. Comtrade. The values of total exports reported Countries may report trade according to the gen- here have not been fully reconciled with the esti- eral or special system of trade. Under the general mates from the national accounts or the balance system exports comprise outward-moving goods that of payments. are (a) goods wholly or partly produced in the country; The classification of commodity groups is based (b) foreign goods, neither transformed nor declared on the Standard International Trade Classification for domestic consumption in the country, that move (SITC) revision 3. Previous editions contained data outward from customs storage; and (c) goods previ- based on the SITC revision 1. Data for earlier years in ously included as imports for domestic consumption previous editions may differ because of this change but subsequently exported without transformation. in methodology. Concordance tables are available to Under the special system exports comprise catego- convert data reported in one system to another. ries a and c. In some compilations categories b and c Developing economies' share of world merchandise exports continues to expand 4.4a 1995 2008 Data sources ($5.2 billion) ($16 billion) East Asia & Pacific 13% Data on merchandise exports are from the WTO. East Asia & Pacific 7% Europe & Data on shares of exports by major commodity Central Asia 7% Latin America & Caribbean 4% group are from Comtrade. The WTO publishes Europe & Central Asia 3% Middle East & N. Africa 1% Latin America data on world trade in its Annual Report. The IMF South Asia 1% & Caribbean 5% Sub-Saharan Africa 1% publishes estimates of total exports of goods in Middle East & N. Africa 3% High income High income its International Financial Statistics and Direction 83% 69% South Asia 2% Sub-Saharan Africa 1% of Trade Statistics, as does the United Nations Statistics Division in its Monthly Bulletin of Sta- tistics. And the United Nations Conference on Trade and Development publishes data on the Developing economies' share of world merchandise exports increased 13 percentage points from 1995 structure of exports in its Handbook of Statistics. to 2008. East Asia and Pacific was the biggest gainer, capturing an additional 6 percentage points. Every Tariff line records of exports are compiled in the region increased its share in world trade. United Nations Statistics Division's Comtrade Source: World Development Indicators data files and World Trade Organization. database. 2010 World Development Indicators 241 4.5 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan 387 3,350 .. 7 .. .. .. 8 .. .. .. 12 Albania 714 5,230 34 16 1 1 2 16 1 3 61 63 Algeria 10,100 39,156 29 20 3 2 1 1 2 2 65 75 Angola 1,468 21,100 .. .. .. .. .. .. .. .. .. .. Argentina 20,122 57,413 5 5 2 1 4 7 2 3 86 83 Armenia 674 4,412 31 19 0 2 27 16 0 3 39 61 Australia 61,283 200,272 5 5 2 1 5 16 1 2 86 75 Austria 66,237 184,247 6 7 3 2 4 12 4 5 82 73 Azerbaijan 668 7,200 39 16 1 1 4 2 2 2 53 79 Bangladesh 6,694 23,860 17 22 3 8 8 11 2 3 69 54 Belarus 5,564 39,483 .. 7 .. 1 .. 35 .. 4 .. 49 Belgium 164,934a 469,889 11a 8 2a 1 7a 15 5a 4 70a 70 Benin 746 1,990 27 31 3 5 9 22 1 1 59 42 Bolivia 1,424 4,987 10 9 2 1 5 11 3 1 82 77 Bosnia and Herzegovina 1,082 12,282 .. 16 .. 1 .. 16 .. 3 .. 63 Botswana 1,911 5,180 .. 12 .. 1 .. 17 .. 2 .. 67 Brazil 54,137 182,810 11 4 3 1 12 20 3 4 71 70 Bulgaria 5,660 38,256 8 7 3 1 34 14 4 8 48 66 Burkina Faso 455 1,800 21 .. 2 .. 14 .. 1 .. 62 .. Burundi 234 403 21 11 2 2 11 3 1 1 64 83 Cambodia 1,187 6,510 .. .. .. .. .. .. .. .. .. .. Cameroon 1,199 4,360 17 18 3 2 3 31 2 1 76 48 Canada 168,426 418,336 6 6 2 1 4 12 3 3 83 76 Central African Republic 175 310 16 .. 10 .. 9 .. 2 .. 64 .. Chad 365 1,700 24 .. 1 .. 18 .. 1 .. 56 .. Chile 15,900 61,901 7 7 2 1 9 29 2 3 79 60 China 132,084 1,133,040 7 5 5 4 4 16 4 13 79 62 Hong Kong SAR, China 196,072 392,962 5 4 2 1 2 4 2 2 88 90 Colombia 13,853 39,669 9 10 3 1 3 5 2 3 78 80 Congo, Dem. Rep. 871 4,100 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 670 2,850 21 .. 1 .. 20 .. 1 .. 58 .. Costa Rica 4,036 15,374 10 9 1 1 9 14 2 2 78 74 Côte d'Ivoire 2,931 7,150 21 20 1 1 19 36 1 1 57 42 Croatia 7,352 30,728 12 8 2 1 12 18 3 3 67 70 Cuba 2,825 14,500 .. 12 .. 0 .. 0 .. 1 .. 50 Czech Republic 25,085 141,882 7 5 3 1 8 10 4 4 77 78 Denmark 45,939 112,296 12 12 3 2 3 8 2 2 73 75 Dominican Republic 5,170 16,400 .. 12 .. 1 .. 26 .. 1 .. 60 Ecuador 4,152 18,686 8 9 3 1 6 14 2 1 82 75 Egypt, Arab Rep. 11,760 48,382 28 17 7 3 1 12 3 9 61 59 El Salvador 3,329 9,755 15 15 2 2 9 19 2 1 72 63 Eritrea 454 530 .. .. .. .. .. .. .. .. .. .. Estonia 2,546 15,990 14 10 3 2 11 16 1 2 71 66 Ethiopia 1,145 7,600 14 14 2 1 11 23 1 1 72 60 Finland 29,470 91,045 6 5 4 3 9 18 6 7 74 64 France 289,391 707,720 11 8 3 1 7 17 4 3 76 70 Gabon 882 2,550 19 17 1 0 4 4 1 1 75 77 Gambia, The 182 329 36 30 1 2 14 20 0 1 46 47 Georgia 392 6,058 36 15 0 1 39 18 0 2 24 64 Germany 463,872 1,206,213 10 7 3 1 6 14 4 5 73 65 Ghana 1,906 10,400 8 15 1 1 6 14 0 1 77 69 Greece 25,898 77,970 16 11 2 1 7 20 3 4 71 64 Guatemala 3,292 14,545 12 13 2 1 12 20 1 1 73 65 Guinea 819 1,600 31 13 1 0 19 33 1 0 47 53 Guinea-Bissau 133 160 44 .. 0 .. 16 .. 0 .. 40 .. Haiti 653 2,148 .. .. .. .. .. .. .. .. .. .. Honduras 1,879 9,990 13 15 1 1 12 20 1 1 74 64 Data for Taiwan, China 103,558 240,448 6 4 4 1 7 26 6 8 75 60 242 2010 World Development Indicators 4.5 ECONOMY Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 15,465 107,864 6 4 3 1 12 9 4 2 75 73 India 34,707 291,598 4 2 4 2 24 39 7 6 54 47 Indonesia 40,630 126,177 9 7 6 3 8 24 4 4 73 62 Iran, Islamic Rep. 13,882 57,230 21 2 2 1 2 4 3 0 71 16 Iraq 665 31,200 .. .. .. .. .. .. .. .. .. .. Ireland 32,340 82,774 8 10 1 1 3 12 2 2 76 70 Israel 29,578 67,410 7 7 2 1 6 20 2 2 82 70 Italy 205,990 556,311 12 8 6 2 7 14 5 5 68 62 Jamaica 2,818 7,880 14 12 2 1 13 41 1 0 68 45 Japan 335,882 761,984 16 9 6 2 16 35 7 8 54 45 Jordan 3,697 16,888 21 17 2 1 13 22 3 3 61 56 Kazakhstan 3,807 37,889 10 8 2 1 25 14 5 2 59 74 Kenya 2,991 11,074 10 12 2 1 15 27 2 2 71 58 Korea, Dem. Rep. 1,380 3,950 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 135,119 435,275 6 4 6 2 14 27 6 9 68 58 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 7,790 25,125 16 13 1 1 1 1 2 3 81 83 Kyrgyz Republic 522 4,058 18 15 3 2 36 31 3 2 40 50 Lao PDR 589 1,390 .. .. .. .. .. .. .. .. .. .. Latvia 1,815 16,007 10 13 2 2 21 15 1 2 66 65 Lebanon 7,278 16,754 21 16 2 1 9 22 2 2 66 35 Lesotho 1,107 2,030 .. .. .. .. .. .. .. .. .. .. Liberia 510 865 .. .. .. .. .. .. .. .. .. .. Libya 5,392 11,500 23 .. 1 .. 0 .. 1 .. 75 .. Lithuania 3,650 30,811 13 11 4 1 19 28 4 2 58 56 Macedonia, FYR 1,719 6,852 17 11 3 1 12 21 3 5 64 62 Madagascar 628 4,040 16 11 2 1 14 13 1 0 65 75 Malawi 475 1,700 14 12 1 1 11 10 1 1 73 77 Malaysia 77,691 156,896 5 7 1 2 2 11 3 5 86 66 Mali 772 2,550 20 12 1 0 16 21 1 1 62 65 Mauritania 431 1,750 24 28 1 1 22 35 0 0 53 36 Mauritius 1,976 4,646 17 21 3 3 7 21 1 1 72 54 Mexico 75,858 323,151 6 7 2 1 2 10 2 3 80 78 Moldova 840 4,899 8 12 3 1 46 23 2 1 42 61 Mongolia 415 3,616 14 12 1 0 19 27 1 1 65 60 Morocco 10,023 41,699 20 12 6 3 14 20 4 4 56 61 Mozambique 704 4,100 22 14 3 1 10 20 1 0 62 47 Myanmar 1,348 4,290 .. .. .. .. .. .. .. .. .. .. Namibia 1,616 4,520 .. 14 .. 1 .. 14 .. 1 .. 70 Nepal 1,333 3,570 12 .. 3 .. 12 .. 3 .. 46 .. Netherlands 185,232 573,924 14 10 2 1 8 15 3 3 72 58 New Zealand 13,957 34,366 7 9 1 1 5 18 3 3 83 69 Nicaragua 975 4,287 18 16 1 0 18 23 1 0 63 60 Niger 374 1,450 32 25 1 5 13 17 3 2 51 52 Nigeria 8,222 41,700 18 10 1 1 1 2 2 2 77 85 Norway 32,968 89,070 7 7 3 1 3 5 6 7 81 79 Oman 4,379 23,095 20 11 1 1 2 3 2 4 70 80 Pakistan 11,515 42,326 18 12 6 5 16 33 3 3 57 47 Panama 2,510 9,050 11 11 1 0 14 21 1 1 73 67 Papua New Guinea 1,452 3,550 .. .. .. .. .. .. .. .. .. .. Paraguay 3,144 10,180 19 7 0 0 7 16 1 1 74 77 Peru 7,584 29,981 14 4 2 5 9 0 1 0 75 77 Philippines 28,341 59,170 8 11 2 1 9 21 3 2 58 65 Poland 29,050 203,924 10 7 3 2 9 11 3 3 74 73 Portugal 32,610 89,753 14 12 4 1 8 17 2 3 72 61 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 3,398 26,850 9 6 1 0 1 1 2 3 87 90 2010 World Development Indicators 243 4.5 Structure of merchandise imports Merchandise Food Agricultural Fuels Ores and Manufactures imports raw materials metals $ millions % of total % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 10,278 82,707 8 7 2 1 21 13 4 3 63 74 Russian Federation 60,945 291,971 18 12 1 1 3 2 2 2 45 79 Rwanda 236 1,110 19 10 3 2 12 7 3 3 64 78 Saudi Arabia 28,091 111,870 17 13 1 1 0 0 4 5 76 81 Senegal 1,412 5,702 25 26 2 1 30 28 1 2 42 42 Serbia .. 22,999 14 6 4 2 14 17 7 6 60 69 Sierra Leone 133 560 .. .. .. .. .. .. .. .. .. .. Singapore 124,507 319,780 5 3 1 0 8 27 2 2 83 64 Slovak Republic 8,770 73,321 9 6 3 1 13 13 6 3 70 77 Slovenia 9,492 36,993 8 7 5 3 7 13 4 5 74 72 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 30,546b 99,480 7b 5 2b 1 8b 22 2b 3 78b 62 Spain 113,537 402,302 14 9 3 1 8 15 4 4 71 70 Sri Lanka 5,306 14,008 16 14 2 1 6 23 1 2 75 60 Sudan 1,218 9,200 24 7 2 0 14 0 0 0 59 68 Swaziland 1,008 2,200 .. 21 .. 1 .. 14 .. 1 .. 63 Sweden 65,036 166,971 7 8 2 1 6 15 4 4 80 69 Switzerland 80,152 183,491 6 6 2 1 3 9 3 5 85 80 Syrian Arab Republic 4,709 18,320 17 13 3 2 1 33 1 4 76 48 Tajikistan 810 3,270 .. .. .. .. .. .. .. .. .. .. Tanzania 1,675 6,954 10 12 1 1 1 30 4 1 84 55 Thailand 70,786 178,655 4 5 4 2 7 21 3 5 81 66 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 594 1,540 18 15 2 1 30 27 1 2 49 55 Trinidad and Tobago 1,714 9,900 16 8 1 0 1 35 6 6 77 50 Tunisia 7,902 24,612 13 10 4 2 7 17 3 6 73 65 Turkey 35,709 201,960 7 4 6 2 13 17 6 9 68 60 Turkmenistan 1,365 4,680 24 .. 0 .. 3 .. 2 .. 71 .. Uganda 1,056 4,800 16 13 3 1 2 19 2 1 78 66 Ukraine 15,484 84,032 8 7 2 1 48 27 3 4 38 60 United Arab Emirates 23,778 158,900 15 7 0 0 4 1 6 5 75 73 United Kingdom 267,250 631,913 10 9 2 1 4 13 3 4 80 68 United States 770,852 2,165,982 5 4 2 1 8 23 3 3 79 66 Uruguay 2,867 8,933 10 8 4 2 10 30 1 1 74 59 Uzbekistan 2,750 5,260 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 12,649 49,635 14 16 4 1 1 1 4 2 77 79 Vietnam 8,155 80,416 5 6 2 3 10 14 2 4 76 72 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,582 9,300 29 25 2 1 8 29 1 1 59 45 Zambia 700 5,070 10 6 2 0 13 16 2 13 72 64 Zimbabwe 2,660 2,900 6 11 2 1 9 17 2 6 78 57 World 5,228,953 t 16,300,527 w 9w 7w 3w 1w 7w 18 w 4w 4w 75 w 67 w Low income 50,461 239,464 13 13 3 3 13 16 2 3 67 63 Middle income 965,308 4,547,215 8 7 4 2 7 16 3 6 75 67 Lower middle income 436,271 2,376,905 8 7 5 3 8 21 4 8 72 61 Upper middle income 528,947 2,164,216 8 7 3 1 7 13 3 4 77 71 Low & middle income 1,015,776 4,786,667 8 7 3 2 7 16 3 6 75 67 East Asia & Pacific 366,057 1,762,013 6 6 4 3 5 17 4 9 78 64 Europe & Central Asia 193,383 1,146,612 11 8 3 1 14 14 3 4 61 68 Latin America & Carib. 241,363 896,683 8 8 2 1 5 13 2 3 78 74 Middle East & N. Africa 77,167 315,621 22 12 4 2 6 14 3 2 66 50 South Asia 60,322 380,660 8 5 4 2 21 36 6 5 56 48 Sub-Saharan Africa 78,377 296,944 12 10 2 1 10 19 2 2 73 64 High income 4,212,901 11,522,679 9 7 3 1 7 18 4 4 76 67 Euro area 1,644,739 4,599,680 11 8 3 1 7 15 4 4 73 66 Note: Components may not sum to 100 percent because of unclassified trade. a. Includes Luxembourg. b. Refers to the South African Customs Union (Botswana, Lesotho, Namibia, South Africa, and Swaziland). 244 2010 World Development Indicators 4.5 ECONOMY Structure of merchandise imports About the data Definitions Data on imports of goods are derived from the and free trade zones. Goods transported through a · Merchandise imports are the c.i.f. value of goods same sources as data on exports. In principle, world country en route to another are excluded. purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, The data on total imports of goods (merchandise) dollars. · Food corresponds to the commodities in exports from an economy should equal the sum of in the table come from the World Trade Organization SITC sections 0 (food and live animals), 1 (beverages imports by the rest of the world from that economy. (WTO). For further discussion of the WTO's sources and tobacco), and 4 (animal and vegetable oils and But differences in timing and definitions result in dis- and methodology, see About the data for table 4.4. fats) and SITC division 22 (oil seeds, oil nuts, and oil crepancies in reported values at all levels. For further The import shares by major commodity group are kernels). · Agricultural raw materials correspond to discussion of indicators of merchandise trade, see from the United Nations Statistics Division's Com- SITC section 2 (crude materials except fuels), exclud- About the data for tables 4.4 and 6.2. modity Trade (Comtrade) database. The values of ing divisions 22, 27 (crude fertilizers and minerals The value of imports is generally recorded as the total imports reported here have not been fully rec- excluding coal, petroleum, and precious stones), and cost of the goods when purchased by the importer onciled with the estimates of imports of goods and 28 (metalliferous ores and scrap). · Fuels correspond plus the cost of transport and insurance to the fron- services from the national accounts (shown in table to SITC section 3 (mineral fuels). · Ores and met- tier of the importing country--the cost, insurance, 4.8) or those from the balance of payments (table als correspond to the commodities in SITC divisions and freight (c.i.f.) value, corresponding to the landed 4.15). 27, 28, and 68 (nonferrous metals). · Manufactures cost at the point of entry of foreign goods into the The classification of commodity groups is based correspond to the commodities in SITC sections 5 country. A few countries, including Australia, Canada, on the Standard International Trade Classification (chemicals), 6 (basic manufactures), 7 (machinery and the United States, collect import data on a free (SITC) revision 3. Previous editions contained data and transport equipment), and 8 (miscellaneous on board (f.o.b.) basis and adjust them for freight and based on the SITC revision 1. Data for earlier years in manufactured goods), excluding division 68. insurance costs. Many countries report trade data in previous editions may differ because of this change U.S. dollars. When countries report in local currency, in methodology. Concordance tables are available to the United Nations Statistics Division applies the convert data reported in one system to another. average official exchange rate to the U.S. dollar for the period shown. Countries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic con- sumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses Top 10 developing economy exporters of merchandise goods in 2008 4.5a Merchandise exports ($ billions) 1995 2008 1,500 Data sources 1,200 Data on merchandise imports are from the WTO. Data on shares of imports by major commodity group are from Comtrade. The WTO publishes data 900 on world trade in its Annual Report. The Interna- tional Monetary Fund publishes estimates of total 600 imports of goods in its International Financial Sta- tistics and Direction of Trade Statistics, as does the 300 United Nations Statistics Division in its Monthly Bulletin of Statistics. And the United Nations Con- 0 China Russian Mexico Malaysia Brazil India Thailand Poland Indonesia Turkey ference on Trade and Development publishes Federation data on the structure of imports in its Handbook China continues to dominate merchandise exports among developing economies. Even when developed of Statistics. Tariff line records of imports are com- economies are included, China ranks as the second leading merchandise exporter. piled in the United Nations Statistics Division's Source: World Development Indicators data files and World Trade Organization. Comtrade database. 2010 World Development Indicators 245 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 94 2,419 19.1 8.8 69.3 70.8 1.4 3.1 10.2 17.3 Algeria .. .. .. .. .. .. .. .. .. .. Angola 113 329 31.8 4.4 0.7 86.5 9.2 .. 59.0 9.1 Argentina 3,676 11,929 27.4 15.6 60.5 39.0 0.2 0.1 11.9 45.3 Armenia 27 636 53.4 21.7 5.2 52.0 6.7 3.0 41.3 23.3 Australia 16,076 44,513 29.3 17.8 50.6 56.3 5.4 3.4 14.8 22.5 Austria 31,692 61,447 11.8 21.9 42.4 35.2 3.9 4.6 41.9 38.2 Azerbaijan 166 1,454 45.9 54.6 42.3 13.1 0.1 0.3 11.7 32.0 Bangladesh 469 891 15.0 12.5 5.3 10.2 0.1 4.6 79.6 72.6 Belarus 466 4,221 64.8 70.9 5.0 8.6 0.5 0.4 29.7 20.1 Belgium 33,619a 84,065 29.4 a 32.9 17.4 a 14.0 14.8a 6.3 38.4 a 46.7 Benin 159 281 25.8 4.5 53.2 73.5 6.9 2.2 14.1 19.8 Bolivia 174 482 44.8 13.1 31.5 57.0 9.8 12.9 13.9 17.1 Bosnia and Herzegovina 457 1,658 3.8 19.9 54.1 49.8 2.6 0.9 39.5 29.4 Botswana 236 878 16.2 9.5 68.5 58.4 7.8 3.8 7.5 28.3 Brazil 6,005 28,822 43.3 18.8 16.2 20.1 16.9 7.2 23.6 54.0 Bulgaria 1,431 8,000 34.5 28.9 33.0 47.5 7.6 1.2 32.5 22.4 Burkina Faso 38 .. 17.3 .. 47.8 .. .. .. 34.8 .. Burundi 4 3 46.2 27.3 32.4 40.8 0.5 7.6 21.0 24.4 Cambodia 103 1,615 30.5 14.8 51.7 75.6 .. 0.4 17.7 9.2 Cameroon 242 1,384 48.3 46.4 14.8 11.1 7.2 3.6 29.7 38.8 Canada 25,425 64,795 20.7 18.4 31.1 23.6 11.4 10.4 36.8 47.7 Central African Republic 0 .. 34.1 .. 33.9 .. 19.6 .. 12.5 .. Chad 23 .. 4.5 .. 49.8 .. 1.7 .. 43.9 .. Chile 3,249 10,645 36.8 59.9 28.0 16.5 7.4 2.9 27.8 20.6 China 18,430 146,446 18.2 26.2 47.4 27.9 10.1 1.2 24.4 44.7 Hong Kong SAR, China 33,790 92,318 32.5 30.2 16.8 16.3 9.2 15.2 41.5 38.3 Colombia 1,641 3,967 34.4 31.2 40.0 46.5 6.5 1.8 19.1 20.5 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 61 303 52.2 4.0 22.4 18.0 0.0 31.4 25.4 46.6 Costa Rica 957 4,055 14.0 9.1 71.2 56.1 ­0.2 0.3 14.9 34.4 Côte d'Ivoire 426 845 28.9 28.2 20.9 13.5 12.3 12.8 37.9 58.3 Croatia 2,223 15,160 31.8 11.8 60.7 74.4 1.3 0.5 6.2 13.3 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 6,638 22,179 22.0 28.1 43.4 34.8 1.1 1.3 33.5 35.8 Denmark 15,171 72,468 44.6 .. 24.3 .. .. .. 31.0 .. Dominican Republic 1,894 4,866 2.2 7.8 82.9 85.8 0.1 0.9 14.9 5.5 Ecuador 687 1,223 46.8 29.9 37.1 60.7 0.0 0.0 16.0 9.4 Egypt, Arab Rep. 8,262 24,668 38.8 33.1 32.5 44.5 1.0 2.0 27.8 20.4 El Salvador 342 1,483 28.3 23.7 25.0 60.2 7.8 2.1 39.0 14.0 Eritrea 49 .. 70.4 .. 3.1 .. 1.0 .. 26.5 .. Estonia 868 5,129 43.0 39.3 41.1 23.6 0.4 2.1 15.5 35.0 Ethiopia 310 1,775 76.9 59.0 5.3 21.2 1.5 1.3 16.4 18.4 Finland 7,334 31,784 28.1 11.5 22.4 10.1 2.0 2.6 47.5 75.8 France 83,108 163,573 24.6 25.1 33.2 34.4 5.3 1.6 36.9 38.9 Gabon 191 120 46.4 22.0 9.0 7.7 3.3 24.1 41.3 46.2 Gambia, The 38 123 21.7 16.8 73.4 67.6 0.3 0.4 4.7 15.1 Georgia 188 1,157 48.2 53.1 25.0 38.6 .. 2.1 26.9 6.2 Germany 73,576 241,590 27.0 24.4 24.5 16.6 5.0 7.3 43.5 51.8 Ghana 139 1,559 58.7 15.4 7.9 58.9 3.0 0.9 30.3 24.8 Greece 9,528 50,377 3.9 56.2 43.4 34.6 0.3 1.2 52.4 8.0 Guatemala 628 1,649 8.6 11.4 33.9 64.8 4.0 1.7 53.6 22.2 Guinea 17 99 75.3 11.7 5.1 1.5 1.4 4.9 18.2 81.9 Guinea-Bissau 2 .. 18.2 .. 14.0 .. .. .. 81.8 .. Haiti 98 288 5.1 .. 91.9 96.7 0.6 .. 2.4 3.3 Honduras 221 903 25.6 5.8 36.3 68.8 2.0 7.7 36.1 17.8 246 2010 World Development Indicators 4.6 ECONOMY Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 5,086 19,910 8.0 20.0 57.6 30.3 3.2 1.5 31.3 48.2 India 6,763 102,562 28.0 11.0 38.2 11.5 2.5 5.5 31.4 72.0 Indonesia 5,342 14,731 1.1 19.0 97.9 50.1 .. 2.2 2.1 28.7 Iran, Islamic Rep. 533 .. 25.9 .. 12.6 .. 8.8 .. 52.7 .. Iraq .. 839 .. 30.7 .. 61.5 .. 2.3 .. 5.6 Ireland 4,799 101,580 22.2 4.4 46.1 6.2 17.9 22.3 31.7 67.0 Israel 7,906 24,061 25.5 21.5 37.9 16.9 0.2 0.1 36.5 61.6 Italy 61,173 118,398 17.7 15.5 47.0 39.0 6.6 5.2 28.8 40.3 Jamaica 1,568 2,762 16.0 17.0 68.2 71.5 1.1 1.9 14.7 9.6 Japan 63,966 146,440 35.2 32.0 5.0 7.4 0.9 4.4 58.8 56.3 Jordan 1,689 4,291 24.8 19.5 39.1 68.6 0.2 .. 36.1 12.0 Kazakhstan 535 3,936 65.7 56.9 22.7 25.7 0.0 5.0 11.6 12.4 Kenya 1,183 2,520 59.4 51.0 35.7 29.9 1.4 0.4 3.4 18.7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 22,133 74,107 41.9 58.8 23.3 12.2 0.4 5.6 34.5 23.4 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 1,124 10,301 83.6 37.4 10.7 2.5 5.7 1.2 0.0 58.9 Kyrgyz Republic 39 884 39.6 16.6 11.9 58.2 0.6 2.5 48.4 22.8 Lao PDR 68 278 22.8 .. 76.0 .. 0.6 .. 0.6 .. Latvia 718 4,496 91.9 51.2 2.8 17.9 2.4 6.8 3.0 24.1 Lebanon .. 18,928 .. 2.6 .. 38.0 .. 2.0 .. 57.4 Lesotho 30 60 7.0 1.1 90.9 56.4 1.4 0.5 0.7 42.0 Liberia .. 182 .. 10.6 .. 86.9 .. .. .. 2.5 Libya 20 208 62.7 56.7 12.0 35.7 .. 2.2 25.3 5.3 Lithuania 482 4,767 59.6 59.6 16.0 28.2 0.9 1.2 23.5 11.0 Macedonia, FYR 151 992 32.0 33.0 13.6 23.0 3.6 1.3 50.7 42.6 Madagascar 219 420 29.8 28.2 26.3 43.7 2.2 0.1 41.6 28.1 Malawi 24 .. 27.6 .. 72.4 .. 0.3 .. 0.0 .. Malaysia 11,438 30,283 21.6 22.3 34.7 50.5 0.1 1.5 43.7 25.6 Mali 68 359 32.5 7.2 37.3 61.5 5.1 1.3 25.2 30.0 Mauritania 19 .. 9.1 .. 57.9 .. .. .. 33.0 .. Mauritius 773 2,530 25.8 17.6 55.6 57.5 0.0 2.5 18.5 22.4 Mexico 9,585 18,474 12.1 12.5 64.5 71.9 6.7 10.9 16.7 4.7 Moldova 143 817 29.5 43.7 39.8 25.9 11.6 0.8 19.1 29.6 Mongolia 47 483 31.7 44.4 43.6 46.6 5.3 2.0 19.5 7.0 Morocco 2,020 12,840 20.3 19.5 64.2 56.2 1.4 1.2 14.2 23.1 Mozambique 242 488 24.8 32.3 .. 38.9 .. 0.9 75.2 27.9 Myanmar 353 256 6.5 50.8 42.7 18.1 0.0 .. 50.9 31.2 Namibia 301 538 .. 21.4 92.4 71.1 1.5 3.5 6.2 4.0 Nepal 592 494 9.3 5.5 30.0 67.7 .. 0.1 60.7 26.6 Netherlands 44,646 102,710 40.4 30.2 14.7 13.0 1.2 2.1 43.7 54.7 New Zealand 4,401 8,997 34.7 21.9 52.7 57.0 0.1 1.3 12.6 19.8 Nicaragua 94 357 17.7 12.6 52.5 77.3 2.5 1.2 27.4 8.9 Niger 12 79 3.3 14.8 57.8 52.0 0.0 6.9 38.9 26.2 Nigeria 608 1,421 16.4 80.7 2.8 15.5 0.6 1.1 80.2 2.7 Norway 13,458 45,595 63.3 47.4 16.6 10.2 3.7 3.3 16.4 39.1 Oman 13 1,974 .. 23.7 .. 40.7 .. 0.8 .. 34.8 Pakistan 1,432 2,393 58.0 49.6 7.7 10.2 1.0 5.3 33.4 34.9 Panama 1,298 5,756 60.4 53.8 23.8 24.5 6.1 10.0 9.6 11.7 Papua New Guinea 321 285 10.8 10.9 7.8 1.3 1.2 5.4 80.2 82.4 Paraguay 566 999 13.3 20.4 24.3 11.0 5.0 2.3 57.4 66.4 Peru 1,042 3,502 32.5 23.4 41.1 56.8 7.2 7.8 19.3 11.9 Philippines 9,323 10,195 2.9 13.4 12.2 43.0 0.7 0.8 84.2 42.8 Poland 10,637 35,428 28.6 30.9 21.7 33.2 8.3 2.0 41.4 33.8 Portugal 8,161 26,135 18.6 26.9 59.2 42.0 4.5 1.9 17.7 29.2 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 247 4.6 Structure of service exports Commercial Transport Travel Insurance and Computer, information, service exports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 1,476 12,818 31.9 30.8 40.0 15.5 5.4 4.1 22.7 49.6 Russian Federation 10,567 50,694 35.8 29.6 40.8 23.6 0.6 3.6 22.8 43.2 Rwanda 11 326 60.6 17.2 21.9 62.0 1.1 1.8 17.6 19.0 Saudi Arabia 3,475 9,383 .. 26.3 .. 63.0 .. 6.1 .. 4.6 Senegal 364 1,097 15.4 12.3 46.1 48.4 0.6 1.9 37.9 37.4 Serbia .. 3,985 .. 24.0 .. 23.6 .. 1.7 .. 50.7 Sierra Leone 71 60 13.7 33.5 80.5 56.2 0.3 1.7 5.6 8.6 Singapore 25,404 83,049 32.7 34.8 30.0 12.7 8.5 10.2 28.9 42.3 Slovak Republic 2,378 8,435 25.9 34.5 26.2 30.7 4.9 2.9 43.0 31.8 Slovenia 2,016 7,417 25.1 28.5 53.8 38.5 0.6 2.1 20.6 30.9 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4,414 12,394 24.2 12.6 48.2 64.2 9.9 8.5 17.7 14.7 Spain 40,019 142,612 15.8 17.0 63.4 43.5 3.9 5.1 16.9 34.5 Sri Lanka 800 1,982 41.9 50.4 28.2 17.3 3.4 3.5 26.5 28.9 Sudan 82 457 0.9 3.8 9.7 72.4 3.7 15.4 85.8 8.4 Swaziland 150 447 18.2 2.0 32.2 7.1 0.0 6.9 49.6 84.0 Sweden 15,336 71,592 32.2 17.7 22.6 17.6 2.4 3.9 42.7 60.8 Switzerland 25,179 76,349 15.1 8.5 37.6 18.9 27.8 32.7 19.5 39.8 Syrian Arab Republic 1,632 3,562 14.5 6.3 77.1 81.0 .. 2.9 8.4 9.8 Tajikistan .. 134 .. 35.4 .. 3.1 .. 12.9 .. 48.5 Tanzania 566 2,136 0.3 17.1 88.6 63.4 0.0 1.1 11.1 18.4 Thailand 14,652 33,392 16.8 21.8 54.8 52.8 0.7 1.3 27.7 24.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 64 197 33.9 53.5 19.9 17.3 1.8 4.8 44.3 24.4 Trinidad and Tobago 331 910 58.6 25.2 23.4 50.9 9.2 15.4 8.8 8.5 Tunisia 2,401 5,831 24.9 32.5 63.7 50.7 1.5 2.2 9.8 14.6 Turkey 14,475 34,519 11.8 22.5 34.2 63.6 1.5 4.6 52.4 9.3 Turkmenistan 79 .. 79.9 .. 9.3 .. 0.9 .. 10.0 .. Uganda 104 696 17.9 7.4 75.1 71.6 .. 3.8 7.0 17.1 Ukraine 2,846 17,302 75.6 44.1 6.7 33.3 2.7 3.2 15.0 19.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 77,549 285,123 20.7 14.0 26.4 12.8 17.5 28.8 35.4 44.5 United States 198,501 518,319 22.7 17.5 37.7 26.0 4.2 13.7 35.5 42.8 Uruguay 1,309 2,192 30.5 29.7 46.7 48.1 1.5 4.0 21.3 18.3 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,529 2,003 38.2 36.8 55.5 45.8 0.1 0.1 6.1 17.3 Vietnam 2,243 7,096 .. .. .. .. .. .. .. .. West Bank and Gaza 265 261 0.3 2.1 96.2 81.3 .. .. 3.5 16.5 Yemen, Rep. 141 1,049 21.9 4.3 35.3 84.5 0.0 0.0 42.8 11.2 Zambia 112 297 64.3 35.3 25.9 49.2 .. 6.4 9.8 9.1 Zimbabwe 353 .. 26.4 .. 50.6 .. 0.3 .. 22.7 .. World 1,211,384 t 3,799,197 t 26.9 w 24.3 w 32.5 w 26.3 w 6.0 w 7.8 w 35.3 w 41.7 w Low income 9,383 32,263 .. .. .. .. .. .. .. .. Middle income 183,323 753,498 24.8 24.8 44.1 41.2 5.8 3.7 27.7 30.3 Lower middle income 85,495 424,953 21.6 25.6 44.9 35.0 6.1 1.9 30.9 37.6 Upper middle income 97,818 330,409 27.4 24.2 43.3 46.3 5.6 5.1 24.8 24.4 Low & middle income 192,169 785,087 24.9 24.7 43.8 41.3 5.7 3.7 28.0 30.4 East Asia & Pacific 62,745 247,458 17.4 23.1 49.2 39.4 7.1 1.3 30.6 36.2 Europe & Central Asia 46,721 195,032 37.3 33.1 32.0 30.4 2.4 3.3 28.5 33.2 Latin America & Carib. 37,663 108,606 24.0 19.8 51.3 53.8 6.9 7.0 17.9 19.5 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. South Asia 10,333 109,513 31.8 19.9 29.7 12.5 2.1 5.1 36.4 62.5 Sub-Saharan Africa 12,142 37,475 26.2 32.7 31.3 48.0 5.8 5.2 40.1 14.9 High income 1,016,999 3,012,629 27.5 24.1 29.3 22.1 6.1 8.9 37.5 44.9 Euro area 422,580 1,225,741 25.6 23.6 31.5 24.1 5.6 5.4 37.6 46.9 a. Includes Luxembourg. 248 2010 World Development Indicators 4.6 ECONOMY Structure of service exports About the data Definitions Balance of payments statistics, the main source of payments statistics is establishment trade--sales · Commercial service exports are total service information on international trade in services, have in the host country by foreign affiliates. By contrast, exports minus exports of government services not many weaknesses. Disaggregation of important cross-border intrafirm transactions in merchandise included elsewhere. · Transport covers all transport components may be limited and varies considerably may be reported as exports or imports in the balance services (sea, air, land, internal waterway, space, across countries. There are inconsistencies in the of payments. and pipeline) performed by residents of one economy methods used to report items. And the recording of The data on exports of services in the table and for those of another and involving the carriage of major flows as net items is common (for example, on imports of services in table 4.7, unlike those in passengers, movement of goods (freight), rental of insurance transactions are often recorded as premi- editions before 2000, include only commercial ser- carriers with crew, and related support and auxiliary ums less claims). These factors contribute to a down- vices and exclude the category "government services services. Excluded are freight insurance, which is ward bias in the value of the service trade reported not included elsewhere." The data are compiled by included in insurance services; goods procured in in the balance of payments. the IMF based on returns from national sources. ports by nonresident carriers and repairs of trans- Efforts are being made to improve the coverage, Data on total trade in goods and services from the port equipment, which are included in goods; repairs quality, and consistency of these data. Eurostat and IMF's Balance of Payments database are shown in of harbors, railway facilities, and airfield facilities, the Organisation for Economic Co-operation and table 4.15. which are included in construction services; and Development, for example, are working together International transactions in services are defined rental of carriers without crew, which is included to improve the collection of statistics on trade in by the IMF's Balance of Payments Manual (1993) as in other services. · Travel covers goods and ser- services in member countries. In addition, the Inter- the economic output of intangible commodities that vices acquired from an economy by travelers in that national Monetary Fund (IMF) has implemented may be produced, transferred, and consumed at the economy for their own use during visits of less than the new classifi cation of trade in services intro- same time. Definitions may vary among reporting one year for business or personal purposes. · Insur- duced in the fifth edition of its Balance of Payments economies. Travel services include the goods and ance and financial services cover freight insurance Manual (1993). services consumed by travelers, such as meals, on goods exported and other direct insurance such Still, difficulties in capturing all the dimensions of lodging, and transport (within the economy visited), as life insurance; financial intermediation services international trade in services mean that the record including car rental. such as commissions, foreign exchange transac- is likely to remain incomplete. Cross-border intrafirm tions, and brokerage services; and auxiliary services service transactions, which are usually not captured such as financial market operational and regulatory in the balance of payments, have increased in recent services. · Computer, information, communica- years. An example is transnational corporations' use tions, and other commercial services cover such of mainframe computers around the clock for data activities as international telecommunications and processing, exploiting time zone differences between postal and courier services; computer data; news- their home country and the host countries of their related service transactions between residents and affiliates. Another important dimension of service nonresidents; construction services; royalties and trade not captured by conventional balance of license fees; miscellaneous business, professional, and technical services; and personal, cultural, and Top 10 developing economy exporters of commercial services in 2008 4.6a recreational services. Commercial service exports ($ billions) 1995 2008 150 120 90 60 30 0 Data sources China India Russian Poland Turkey Thailand Malaysia Brazil Egypt, Mexico Federation Arab. Rep Data on exports of commercial services are from The top 10 developing economy exporters of commercial services accounted for almost 64 percent of the IMF, which publishes balance of payments developing economy commercial service exports and 13 percent of world commercial service exports. data in its International Financial Statistics and Source: International Monetary Fund balance of payments data files. Balance of Payments Statistics Yearbook. 2010 World Development Indicators 249 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 98 2,361 61 15 7 66 22 6 10 13 Algeria .. .. .. .. .. .. .. .. .. .. Angola 1,665 20,020 18 19 5 1 3 10 75 70 Argentina 6,992 12,664 30 31 47 36 7 4 16 29 Armenia 52 952 83 49 6 34 10 8 1 8 Australia 16,979 47,613 37 31 30 39 7 3 26 27 Austria 27,552 42,738 12 32 40 27 6 4 43 37 Azerbaijan 297 3,826 31 18 49 9 1 2 19 72 Bangladesh 1,192 3,684 65 87 20 5 6 1 10 6 Belarus 276 2,614 36 50 32 26 4 3 29 22 Belgium 32,511a 81,978 24 a 28 28a 24 10a 6 38a 42 Benin 235 491 59 60 15 15 10 10 16 15 Bolivia 321 1,018 66 42 15 28 9 13 10 18 Bosnia and Herzegovina 262 632 51 43 31 33 10 7 8 17 Botswana 440 1,348 43 34 33 36 8 4 16 26 Brazil 13,161 44,396 44 23 26 25 10 6 21 46 Bulgaria 1,278 6,696 42 33 15 36 9 4 43 28 Burkina Faso 116 .. 56 .. 20 .. 5 .. 20 .. Burundi 62 173 49 39 41 52 6 4 4 5 Cambodia 181 959 46 64 5 11 4 5 45 20 Cameroon 485 2,859 35 31 22 12 7 6 36 51 Canada 32,985 86,644 24 23 31 32 11 11 34 34 Central African Republic 114 .. 44 .. 38 .. 8 .. 10 .. Chad 174 .. 55 .. 15 .. 2 .. 29 .. Chile 3,524 11,143 54 60 20 12 4 9 22 19 China 24,635 158,004 39 32 15 23 17 8 29 37 Hong Kong SAR, China 24,962 45,849 22 33 54 35 6 8 18 23 Colombia 2,813 7,108 42 42 31 24 12 8 15 25 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 690 3,523 19 15 8 5 7 5 67 75 Costa Rica 895 1,878 41 36 36 32 5 9 18 23 Côte d'Ivoire 1,235 2,444 50 59 15 16 11 10 23 25 Croatia 1,373 4,517 28 23 31 25 3 4 38 49 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 4,860 17,256 16 26 34 27 5 6 45 42 Denmark 13,945 62,432 45 .. 31 .. .. .. 24 .. Dominican Republic 957 1,757 61 67 18 18 10 8 11 7 Ecuador 1,141 2,885 42 57 21 19 6 6 31 18 Egypt, Arab Rep. 4,511 16,335 35 45 28 18 5 10 32 27 El Salvador 488 1,972 55 48 15 32 11 10 19 10 Eritrea 45 .. 2 .. 7 .. 0 .. 93 .. Estonia 420 3,374 53 39 22 24 5 2 21 35 Ethiopia 337 2,379 63 68 8 7 7 4 22 22 Finland 9,418 29,257 23 23 24 15 5 1 48 61 France 64,523 141,704 33 30 25 31 6 3 36 37 Gabon 832 1,020 18 31 17 27 9 7 57 35 Gambia, The 47 88 60 46 30 9 6 8 4 36 Georgia 249 1,154 27 56 63 18 8 14 2 12 Germany 128,865 283,196 18 23 47 32 2 4 33 41 Ghana 331 2,038 61 54 6 27 6 5 26 14 Greece 4,003 24,392 30 56 33 16 5 7 33 20 Guatemala 672 2,125 41 54 21 29 9 10 29 7 Guinea 252 398 58 67 8 2 7 4 26 27 Guinea-Bissau 27 .. 53 .. 14 .. 5 .. 28 .. Haiti 236 753 78 63 15 8 2 1 6 27 Honduras 326 1,204 60 60 18 29 2 4 20 7 250 2010 World Development Indicators 4.7 ECONOMY Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 3,765 18,491 13 20 40 22 5 3 43 56 India 10,062 56,053 57 24 10 17 6 8 28 50 Indonesia 13,230 27,994 37 50 16 20 3 4 43 27 Iran, Islamic Rep. 2,192 .. 43 .. 11 .. 10 .. 36 .. Iraq .. 4,741 .. 48 .. 13 .. 24 .. 14 Ireland 11,252 109,290 16 3 18 10 1 15 65 73 Israel 8,131 19,629 45 34 26 18 3 2 26 46 Italy 54,613 127,861 24 23 27 24 10 4 39 49 Jamaica 1,073 2,304 46 48 14 12 9 10 31 30 Japan 121,547 167,443 30 32 30 17 2 5 38 46 Jordan 1,385 3,926 52 57 31 26 6 9 11 9 Kazakhstan 776 10,794 38 22 36 9 2 5 25 63 Kenya 900 1,663 46 52 21 16 10 7 22 25 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 25,394 91,768 38 40 25 19 2 2 36 39 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 3,826 12,149 39 35 59 62 2 2 0 1 Kyrgyz Republic 193 988 27 49 3 31 4 2 65 18 Lao PDR 119 76 43 .. 25 .. 4 .. 28 .. Latvia 225 3,163 68 26 11 36 7 5 14 33 Lebanon .. 13,392 .. 14 .. 27 .. 2 .. 57 Lesotho 58 85 75 79 23 16 0 .. 2 5 Liberia .. 323 .. 65 .. 9 .. 3 .. 23 Libya 510 3,572 60 42 15 36 .. 7 25 16 Lithuania 457 4,133 64 48 23 36 1 2 12 13 Macedonia, FYR 300 970 50 42 9 14 21 3 21 41 Madagascar 277 462 56 48 21 16 4 1 20 35 Malawi 151 .. 67 .. 26 .. 0 .. 7 .. Malaysia 14,821 30,060 38 38 16 22 .. 3 47 36 Mali 412 774 60 59 12 18 1 4 27 19 Mauritania 197 .. 62 .. 12 .. 1 .. 25 .. Mauritius 630 1,911 40 34 25 24 5 6 30 37 Mexico 9,021 24,701 38 14 35 35 12 50 14 1 Moldova 193 779 52 42 29 35 9 3 10 20 Mongolia 87 514 70 50 22 37 2 4 8 10 Morocco 1,350 5,628 48 47 22 19 4 3 26 31 Mozambique 350 901 33 40 .. 23 2 1 65 35 Myanmar 233 547 11 46 8 7 1 .. 81 47 Namibia 538 559 37 43 17 16 9 4 37 37 Nepal 305 840 36 40 45 45 3 4 16 12 Netherlands 43,618 91,918 29 25 27 24 3 3 41 48 New Zealand 4,571 9,553 41 33 28 31 5 3 26 32 Nicaragua 207 572 39 54 19 25 3 10 38 11 Niger 120 369 74 74 11 8 3 3 12 15 Nigeria 4,398 12,320 22 30 21 29 3 2 54 39 Norway 13,052 43,928 38 33 32 36 6 2 24 28 Oman 985 6,122 42 42 5 14 5 7 49 37 Pakistan 2,431 9,079 67 44 18 17 4 4 10 35 Panama 1,049 2,550 71 61 12 14 9 16 9 9 Papua New Guinea 642 1,151 25 24 9 5 3 10 63 61 Paraguay 676 563 66 66 20 22 12 9 1 3 Peru 1,781 5,425 51 46 17 20 10 9 22 25 Philippines 6,906 8,570 30 50 6 26 2 4 63 20 Poland 7,008 30,035 25 24 6 33 14 5 55 39 Portugal 6,339 16,497 27 31 33 26 9 4 31 39 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 251 4.7 Structure of service imports Commercial Transport Travel Insurance and Computer, information, service imports financial services communications, and other commercial services $ millions % of total % of total % of total % of total 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 1,801 11,776 34 34 39 18 5 4 22 43 Russian Federation 20,205 74,572 16 17 57 33 0 4 26 45 Rwanda 58 504 73 56 17 14 1 0 10 29 Saudi Arabia 8,670 48,926 25 32 .. 31 3 7 72 30 Senegal 405 1,205 57 50 18 21 7 9 18 21 Serbia .. 4,223 .. 30 .. 30 .. 3 .. 37 Sierra Leone 79 117 17 47 63 21 4 10 16 22 Singapore 20,728 78,967 45 38 22 18 10 6 23 39 Slovak Republic 1,800 9,084 17 27 18 24 5 11 60 38 Slovenia 1,429 4,944 31 25 40 27 2 3 27 45 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 5,756 16,515 40 46 32 27 14 4 14 23 Spain 22,354 104,263 31 25 20 20 7 7 41 49 Sri Lanka 1,169 2,967 58 66 16 14 5 6 21 13 Sudan 150 2,552 27 51 29 47 0 1 44 2 Swaziland 206 494 16 12 21 10 4 12 59 66 Sweden 17,112 54,280 28 17 32 28 1 2 38 52 Switzerland 14,899 36,277 35 23 50 30 1 8 14 38 Syrian Arab Republic 1,358 2,917 57 58 37 22 6 9 6 11 Tajikistan .. 453 .. 40 .. 2 .. 11 .. 47 Tanzania 729 1,576 30 42 49 46 3 4 18 8 Thailand 18,629 46,314 42 50 23 11 5 5 30 34 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 148 303 71 71 12 6 4 11 12 12 Trinidad and Tobago 223 320 42 54 31 29 8 0 19 17 Tunisia 1,245 3,226 45 58 20 14 6 8 28 20 Turkey 4,654 16,228 30 46 20 22 8 15 42 17 Turkmenistan 403 .. 40 .. 18 .. 7 .. 35 .. Uganda 563 1,219 38 72 14 13 4 8 43 8 Ukraine 1,334 15,777 34 42 16 25 7 10 43 22 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 62,524 196,896 27 18 40 35 4 8 29 38 United States 129,227 364,928 32 29 36 23 6 17 26 31 Uruguay 814 1,365 46 51 29 26 5 4 20 19 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 4,654 10,073 31 48 37 18 3 6 30 28 Vietnam 2,304 7,931 .. .. .. .. .. .. .. .. West Bank and Gaza 349 638 28 12 46 57 3 2 25 29 Yemen, Rep. 604 2,289 36 48 12 8 7 9 45 34 Zambia 282 881 79 58 9 7 0 11 12 24 Zimbabwe 645 .. 56 .. 19 .. 3 .. 23 .. World 1,219,124 t 3,440,367 t 31 w 29 w 31 w 25 w 6w 9w 32 w 37 w Low income 13,458 43,441 .. .. .. .. .. .. .. .. Middle income 222,345 807,544 39 34 23 25 10 13 29 28 Lower middle income 108,492 441,066 42 39 16 21 10 7 32 33 Upper middle income 113,681 366,143 37 30 28 28 9 18 27 24 Low & middle income 235,415 850,688 39 35 23 25 10 13 29 28 East Asia & Pacific 82,593 285,803 38 38 16 21 12 6 37 34 Europe & Central Asia 42,554 202,447 29 33 26 28 7 7 38 32 Latin America & Carib. 52,171 139,670 41 27 31 29 10 28 17 15 Middle East & N. Africa 19,565 65,951 45 47 21 20 .. 11 28 22 South Asia 15,377 73,655 59 38 13 16 5 7 23 39 Sub-Saharan Africa 24,584 91,672 40 45 24 24 9 5 28 27 High income 983,235 2,596,070 29 27 33 26 5 8 33 39 Euro area 421,722 1,114,691 25 26 32 26 5 4 38 43 a. Includes Luxembourg. 252 2010 World Development Indicators 4.7 ECONOMY Structure of service imports About the data Definitions Trade in services differs from trade in goods because · Commercial service imports are total service services are produced and consumed at the same imports minus imports of government services not time. Thus services to a traveler may be consumed included elsewhere. · Transport covers all transport in the producing country (for example, use of a hotel services (sea, air, land, internal waterway, space, room) but are classified as imports of the traveler's and pipeline) performed by residents of one economy country. In other cases services may be supplied for those of another and involving the carriage of from a remote location; for example, insurance passengers, movement of goods (freight), rental of services may be supplied from one location and carriers with crew, and related support and auxiliary consumed in another. For further discussion of the services. Excluded are freight insurance, which is problems of measuring trade in services, see About included in insurance services; goods procured in the data for table 4.6. ports by nonresident carriers and repairs of trans- The data on imports of services in the table and on port equipment, which are included in goods; repairs exports of services in table 4.6, unlike those in edi- of harbors, railway facilities, and airfield facilities, tions before 2000, include only commercial services which are included in construction services; and and exclude the category "government services not rental of carriers without crew, which is included included elsewhere." The data are compiled by the in other services. · Travel covers goods and ser- International Monetary Fund (IMF) based on returns vices acquired from an economy by travelers in that from national sources. economy for their own use during visits of less than International transactions in services are defined one year for business or personal purposes. · Insur- by the IMF's Balance of Payments Manual (1993) as ance and financial services cover freight insurance the economic output of intangible commodities that on goods imported and other direct insurance such may be produced, transferred, and consumed at the as life insurance; financial intermediation services same time. Definitions may vary among reporting such as commissions, foreign exchange transac- economies. tions, and brokerage services; and auxiliary services Travel services include the goods and services such as financial market operational and regulatory consumed by travelers, such as meals, lodging, and services. · Computer, information, communica- transport (within the economy visited), including car tions, and other commercial services cover such rental. activities as international telecommunications, and postal and courier services; computer data; news- related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and The mix of commercial service imports by developing economies is changing 4.7a recreational services. 1995 2008 ($235 million) ($850 million) Other 28% Transport 39% Other 28% Transport Travel 35% Insurance and financial 10% 23% Insurance and financial 13% Travel 25% Data sources Between 1995 and 2008 developing economies' commercial service imports more than tripled. Insur- Data on imports of commercial services are from ance and financial services and travel services are displacing transport and other services as the most the IMF, which publishes balance of payments important services imported. data in its International Financial Statistics and Source: International Monetary Fund balance of payments data files. Balance of Payments Statistics Yearbook. 2010 World Development Indicators 253 4.8 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. 98 .. 10 .. 28 .. 17 .. 53 .. .. Albania 87 86 14 9 21 32 12 31 35 59 20 19 Algeria 55 29 17 13 31 34 26 48 29 24 .. .. Angola .. .. .. .. 35 12 82 76 68 51 78 20 Argentina 69 59 13 13 18 23 10 24 10 21 16 25 Armenia 109 73 11 12 18 41 24 15 62 40 ­9 29 Australia 59 55 18 18 24 29 18 21 20 23 23 29 Austria 56 53 20 18 25 23 35 59 36 54 22 27 Azerbaijan 77 24 13 11 24 20 28 69 42 25 13 56 Bangladesh 83 79 5 5 19 24 11 20 17 29 22 37 Belarus 59 54 21 17 25 36 50 62 54 69 21 28 Belgium 54 54 22 23 20 24 68 92 63 93 24 22 Benin 82 .. 11 .. 20 21 20 15 33 29 11 9 Bolivia 76 62 14 13 15 18 23 45 27 38 11 29 Bosnia and Herzegovina .. 87 .. 21 20 24 20 37 71 69 .. 42 Botswana 34 40 29 20 25 32 51 46 38 39 36 45 Brazil 62 61 21 20 18 19 7 14 9 14 16 17 Bulgaria 71 68 15 16 16 38 45 60 46 83 12 14 Burkina Faso 63 75 25 22 24 18 14 12 27 27 29 .. Burundi 89 91 19 29 6 16 13 11 27 47 6 4 Cambodia 95 83 6 3 15 21 31 65 47 73 5 16 Cameroon 72 72 9 9 13 18 24 30 18 29 14 20 Canada 57 55 21 19 19 23 37 35 34 33 18 24 Central African Republic 79 94 15 7 14 12 20 11 28 23 11 .. Chad 91 68 7 12 13 15 22 54 34 50 12 .. Chile 61 59 10 12 26 25 29 45 27 41 25 22 China 42 34 14 14 42 44 23 37 21 28 43 54 Hong Kong SAR, China 62 60 8 8 34 20 143 212 148 202 .. 30 Colombia 65 63 15 16 26 25 15 18 21 22 19 19 Congo, Dem. Rep. 81 80 5 11 9 24 28 23 24 39 .. .. Congo, Rep. 49 39 13 13 37 21 65 79 64 51 ­2 14 Costa Rica 71 70 14 14 18 26 38 46 40 55 15 16 Côte d'Ivoire 66 74 11 9 16 10 42 47 34 39 12 12 Croatia 66 59 25 19 16 31 33 42 41 50 11 21 Cuba 71 .. 24 .. 7 .. 13 .. 16 .. .. .. Czech Republic 51 50 21 20 33 25 51 77 55 73 29 22 Denmark 51 49 25 27 20 22 38 55 33 52 22 25 Dominican Republic 81 87 5 8 18 18 36 26 39 39 16 9 Ecuador 68 61 13 11 22 28 26 38 28 38 17 31 Egypt, Arab Rep. 74 72 11 11 20 22 23 33 28 39 21 24 El Salvador 87 98 9 9 20 15 22 28 38 50 18 8 Eritrea 94 86 44 31 23 11 22 6 83 34 19 .. Estonia 54 55 26 19 28 30 68 76 76 80 24 20 Ethiopia 80 90 8 10 18 20 10 12 16 31 21 17 Finland 52 53 23 22 18 21 36 44 29 40 22 24 France 57 57 24 23 19 22 23 26 22 29 19 19 Gabon 41 33 12 8 23 24 59 67 36 32 33 .. Gambia, The 90 78 14 16 20 25 49 30 73 49 8 10 Georgia 102 85 11 14 4 30 26 29 42 58 1 8 Germany 58 56 20 18 22 19 24 47 23 41 20 26 Ghana 76 77 12 20 20 36 24 42 33 75 18 7 Greece 75 71 15 17 19 21 17 23 27 32 18 10 Guatemala 86 88 6 9 15 18 19 25 25 40 11 14 Guinea 74 81 8 9 21 15 21 33 25 38 21 3 Guinea-Bissau 95 81 6 14 22 25 12 30 35 50 10 .. Haiti .. .. .. .. 26 26 9 11 29 37 .. .. Honduras 64 83 9 16 32 34 44 49 48 82 27 21 254 2010 World Development Indicators 4.8 ECONOMY Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 66 67 11 9 23 22 45 81 45 80 19 15 India 64 54 11 12 27 40 11 23 12 28 27 38 Indonesia 62 63 8 8 32 28 26 30 28 29 28 20 Iran, Islamic Rep. 46 45 16 11 29 33 22 32 13 22 37 .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 54 47 16 16 18 26 76 79 65 69 23 22 Israel 56 58 28 25 25 18 29 40 37 42 13 19 Italy 58 59 18 20 20 21 26 29 22 29 22 18 Jamaica 70 82 11 14 29 .. 51 .. 61 .. 25 14 Japan 55 56 15 18 28 24 9 18 8 16 30 29 Jordan 65 81 24 25 33 26 52 58 73 91 29 14 Kazakhstan 68 35 14 10 23 34 39 57 44 37 18 40 Kenya 70 78 15 17 22 19 33 27 39 41 23 13 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 55 11 15 38 31 29 53 30 54 36 31 Kosovo .. 97 .. 18 .. 27 .. 14 .. 57 .. .. Kuwait 43 28 32 13 15 19 52 66 42 26 38 63 Kyrgyz Republic 75 104 20 9 18 24 29 57 42 94 8 15 Lao PDR .. 66 .. 8 .. 37 23 33 37 44 .. 22 Latvia 63 58 24 20 14 35 43 42 45 55 14 22 Lebanon 103 84 12 14 36 31 11 27 62 57 .. 10 Lesotho 105 108 27 27 63 28 22 47 117 111 30 22 Liberia .. 202 .. 19 .. 20 9 31 72 173 .. ­2 Libya 59 23 22 9 12 28 29 67 22 27 .. 67 Lithuania 67 66 22 18 22 27 49 59 60 71 12 15 Macedonia, FYR 70 79 19 19 21 28 33 53 43 79 13 16 Madagascar 90 85 7 5 11 36 24 27 32 52 2 .. Malawi 79 63 21 13 17 27 30 23 48 26 8 .. Malaysia 48 46 12 12 44 22 94 110 98 90 34 38 Mali 83 76 10 11 23 23 21 27 36 37 15 28 Mauritania 77 61 11 20 20 26 37 58 45 65 14 .. Mauritius 63 74 14 13 26 27 59 53 61 68 25 17 Mexico 67 66 10 10 20 26 30 28 28 30 19 25 Moldova 57 93 27 21 25 37 49 41 58 92 18 23 Mongolia 56 61 13 15 32 39 48 57 49 72 35 46 Morocco 68 60 17 17 21 36 27 37 34 50 17 31 Mozambique 90 82 8 12 27 19 16 33 41 46 9 7 Myanmar .. .. .. .. 14 .. 1 .. 2 .. .. .. Namibia 54 74 30 20 22 26 49 42 56 61 32 17 Nepal 75 79 9 10 25 32 25 12 35 33 21 38 Netherlands 49 46 24 25 21 21 59 77 54 69 27 26 New Zealand 58 58 17 19 23 24 29 29 28 30 18 16 Nicaragua 83 90 11 12 22 32 19 33 35 67 ­1 14 Niger 86 .. 14 .. 7 .. 17 .. 24 .. ­1 .. Nigeria .. .. .. .. .. .. 44 42 42 25 .. .. Norway 50 39 22 19 22 23 38 48 32 29 26 42 Oman 51 35 25 18 15 31 44 56 36 40 10 36 Pakistan 72 77 12 12 19 22 17 13 19 24 21 20 Panama 52 65 15 11 30 23 101 75 98 74 30 26 Papua New Guinea 44 58 17 10 22 19 61 72 44 60 35 .. Paraguay 76 75 10 11 26 20 59 53 71 59 18 16 Peru 71 64 10 9 25 26 13 27 18 26 16 22 Philippines 74 77 11 10 22 15 36 37 44 39 19 34 Poland 60 60 20 19 19 24 23 40 21 43 20 18 Portugal 65 67 18 21 23 22 29 33 35 42 23 10 Puerto Rico .. .. .. .. .. .. 72 .. 97 .. .. .. Qatar 32 21 32 21 35 32 44 64 43 38 .. .. 2010 World Development Indicators 255 4.8 Structure of demand Household General Gross Exports Imports Gross final consumption government capital of goods and of goods and savings expenditure final consumption formation services services expenditure % of GDP % of GDP % of GDP % of GDP % of GDP % of GDP 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 68 64 14 16 24 31 28 30 33 40 19 25 Russian Federation 52 48 19 17 25 26 29 31 26 22 28 32 Rwanda 97 82 10 10 13 24 5 15 26 31 20 27 Saudi Arabia 47 27 24 20 20 21 38 69 28 38 20 49 Senegal 80 82 13 10 14 30 31 25 37 47 8 19 Serbia 73 78 23 21 12 23 17 30 24 52 .. 7 Sierra Leone 88 86 14 12 6 15 19 16 26 29 ­3 5 Singapore 41 39 8 11 34 31 .. 234 .. 215 52 46 Slovak Republic 52 56 22 17 24 29 58 83 56 85 41 ­16 Slovenia 60 52 19 18 24 31 50 70 52 71 23 27 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 63 60 18 20 18 23 23 35 22 38 17 16 Spain 60 57 18 19 22 30 22 26 22 32 22 20 Sri Lanka 73 70 11 16 26 27 36 25 46 38 20 18 Sudan 85 59 5 16 14 24 5 24 10 23 3 14 Swaziland 82 74 15 21 16 17 60 69 74 81 16 19 Sweden 50 46 27 26 17 20 40 54 33 47 20 28 Switzerland 60 58 12 11 23 22 36 56 31 47 30 36 Syrian Arab Republic 66 75 13 12 27 14 31 31 38 32 27 20 Tajikistan 62 114 16 8 29 20 66 17 72 58 .. 25 Tanzaniaa 86 73 12 16 20 17 24 22 42 27 7 13 Thailand 55 56 10 12 42 29 42 77 49 74 34 29 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 77 .. 12 9 16 .. 32 42 37 62 17 .. Trinidad and Tobago 53 45 12 11 21 13 54 73 39 42 27 38 Tunisia 63 63 16 15 25 27 45 61 49 65 20 21 Turkey 68 70 11 13 25 22 20 24 24 28 22 18 Turkmenistan 44 55 12 8 49 6 84 81 84 51 50 .. Uganda 85 82 11 12 12 24 12 16 21 33 13 12 Ukraine 55 64 21 17 27 25 47 42 50 48 23 20 United Arab Emirates 48 44 16 10 30 21 69 91 63 67 .. .. United Kingdom 63 64 20 22 17 17 28 29 28 32 15 15 United States 68 71 15 16 18 18 11 12 12 17 15 14 Uruguay 73 69 12 12 15 23 19 28 19 32 14 18 Uzbekistan 51 49 22 18 27 23 28 42 28 32 .. .. Venezuela, RB 69 54 7 11 18 25 27 30 22 20 21 35 Vietnam 74 69 8 6 27 41 33 78 42 95 20 29 West Bank and Gaza 98 .. 18 .. 35 .. 16 .. 68 .. 12 .. Yemen, Rep. 71 .. 14 .. 22 .. 51 .. 58 .. 26 .. Zambia 72 66 15 9 16 22 36 37 40 34 9 19 Zimbabwe 65 .. 18 .. 20 .. 38 .. 41 .. 18 .. World 61 w 61 w 17 w 17 w 22 w 22 w 21 w 28 w 21 w 28 w 20 w 21 w Low income 77 76 11 10 20 27 22 34 30 47 17 .. Middle income 60 55 14 14 27 30 23 31 24 30 26 31 Lower middle income 54 49 13 13 34 37 24 35 24 34 33 41 Upper middle income 64 60 15 15 21 24 23 28 24 27 20 23 Low & middle income 60 56 14 14 27 30 23 31 24 31 26 31 East Asia & Pacific 47 42 13 13 40 40 29 40 29 35 39 48 Europe & Central Asia 61 59 17 16 23 25 28 34 29 35 23 24 Latin America & Carib. 66 63 15 15 19 23 18 24 19 24 18 22 Middle East & N. Africa 63 55 15 13 25 28 26 38 29 33 .. .. South Asia 67 60 10 11 25 36 12 21 15 28 25 35 Sub-Saharan Africa 69 66 15 17 18 23 28 36 30 39 16 16 High income 61 62 17 18 21 21 21 27 20 28 19 19 Euro area 57 57 20 20 21 22 29 41 28 39 15 16 a. Covers mainland Tanzania only. 256 2010 World Development Indicators 4.8 ECONOMY Structure of demand About the data Definitions Gross domestic product (GDP) from the expenditure capital outlays on defense establishments that may · Household final consumption expenditure is the side is made up of household final consumption be used by the general public, such as schools, air- market value of all goods and services, including expenditure, general government final consumption fields, and hospitals, and intangibles such as com- durable products (such as cars and computers), expenditure, gross capital formation (private and puter software and mineral exploration outlays. Data purchased by households. It excludes purchases public investment in fixed assets, changes in inven- on capital formation may be estimated from direct of dwellings but includes imputed rent for owner- tories, and net acquisitions of valuables), and net surveys of enterprises and administrative records occupied dwellings. It also includes government fees exports (exports minus imports) of goods and ser- or based on the commodity flow method using data for permits and licenses. Expenditures of nonprofit vices. Such expenditures are recorded in purchaser from production, trade, and construction activities. institutions serving households are included, even prices and include net taxes on products. The quality of data on government fixed capital forma- when reported separately. Household consumption Because policymakers have tended to focus on tion depends on the quality of government account- expenditure may include any statistical discrepancy fostering the growth of output, and because data on ing systems (which tend to be weak in developing in the use of resources relative to the supply of production are easier to collect than data on spend- countries). Measures of fixed capital formation by resources. · General government fi nal consump- ing, many countries generate their primary estimate households and corporations--particularly capital tion expenditure is all government current expendi- of GDP using the production approach. Moreover, outlays by small, unincorporated enterprises--are tures for purchases of goods and services (including many countries do not estimate all the components usually unreliable. compensation of employees). It also includes most of national expenditures but instead derive some Estimates of changes in inventories are rarely expenditures on national defense and security but of the main aggregates indirectly using GDP (based complete but usually include the most important excludes military expenditures with potentially wider on the production approach) as the control total. activities or commodities. In some countries these public use that are part of government capital forma- Household final consumption expenditure (private estimates are derived as a composite residual along tion. · Gross capital formation is outlays on addi- consumption in the 1968 United Nations System of with household fi nal consumption expenditure. tions to fixed assets of the economy, net changes in National Accounts, or SNA) is often estimated as According to national accounts conventions, adjust- inventories, and net acquisitions of valuables. Fixed a residual, by subtracting all other known expendi- ments should be made for appreciation of the value assets include land improvements (fences, ditches, tures from GDP. The resulting aggregate may incor- of inventory holdings due to price changes, but this drains); plant, machinery, and equipment purchases; porate fairly large discrepancies. When household is not always done. In highly inflationary economies and construction (roads, railways, schools, buildings, consumption is calculated separately, many of the this element can be substantial. and so on). Inventories are goods held to meet tem- estimates are based on household surveys, which Data on exports and imports are compiled from porary or unexpected fluctuations in production or tend to be one-year studies with limited coverage. customs reports and balance of payments data. sales, and "work in progress." · Exports and imports Thus the estimates quickly become outdated and Although the data from the payments side provide of goods and services are the value of all goods must be supplemented by estimates using price- and reasonably reliable records of cross-border transac- and other market services provided to or received quantity-based statistical procedures. Complicating tions, they may not adhere strictly to the appropriate from the rest of the world. They include the value of the issue, in many developing countries the distinc- definitions of valuation and timing used in the bal- merchandise, freight, insurance, transport, travel, tion between cash outlays for personal business ance of payments or correspond to the change-of- royalties, license fees, and other services (com- and those for household use may be blurred. World ownership criterion. This issue has assumed greater munication, construction, financial, information, Development Indicators includes in household con- significance with the increasing globalization of inter- business, personal, government services, and so sumption the expenditures of nonprofit institutions national business. Neither customs nor balance of on). They exclude compensation of employees and serving households. payments data usually capture the illegal transac- investment income (factor services in the 1968 SNA) General government final consumption expenditure tions that occur in many countries. Goods carried and transfer payments. · Gross savings are gross (general government consumption in the 1968 SNA) by travelers across borders in legal but unreported national income less total consumption, plus net includes expenditures on goods and services for shuttle trade may further distort trade statistics. transfers. individual consumption as well as those on services Gross savings represent the difference between for collective consumption. Defense expenditures, disposable income and consumption and replace including those on capital outlays (with certain excep- gross domestic savings, a concept used by the World tions), are treated as current spending. Bank and included in World Development Indicators Data sources Gross capital formation (gross domestic investment editions before 2006. The change was made to con- in the 1968 SNA) consists of outlays on additions form to SNA concepts and definitions. For further Data on national accounts indicators for most to the economy's fixed assets plus net changes in discussion of the problems in compiling national developing countries are collected from national the level of inventories. It is generally obtained from accounts, see Srinivasan (1994), Heston (1994), statistical organizations and central banks by vis- industry reports of acquisitions and distinguishes only and Ruggles (1994). For an analysis of the reliability iting and resident World Bank missions. Data for the broad categories of capital formation. The 1993 of foreign trade and national income statistics, see high-income economies are from Organisation for SNA recognizes a third category of capital forma- Morgenstern (1963). Economic Co-operation and Development (OECD) tion: net acquisitions of valuables. Included in gross data files. capital formation under the 1993 SNA guidelines are 2010 World Development Indicators 257 4.9 Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 1.3 5.3 2.2 4.9 14.5 8.0 25.8 6.4 18.9 10.4 15.7 15.1 Algeria ­0.1 5.2 ­1.9 3.7 3.6 5.3 ­0.6 8.4 3.2 2.9 ­1.0 7.5 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.8 4.6 1.5 3.6 2.2 3.0 7.4 12.0 8.7 7.2 15.6 10.0 Armenia ­0.5 8.8 1.1 8.7 ­1.5 11.3 ­1.9 23.4 ­18.4 9.6 ­12.7 10.8 Australia 3.2 3.9 2.0 2.4 2.9 3.2 5.1 7.6 7.7 2.2 7.6 9.2 Austria 1.7 1.7 1.3 1.1 2.7 1.3 2.3 1.8 5.8 6.1 4.8 5.4 Azerbaijan 2.0 14.5 1.0 13.4 ­4.8 24.2 41.6 23.6 5.7 23.8 14.1 22.2 Bangladesh 2.6 4.3 0.6 2.6 4.7 9.4 9.2 8.2 13.1 12.0 9.7 9.5 Belarus ­0.5 11.5 ­0.3 12.0 ­1.9 0.1 ­7.5 19.2 ­4.8 6.9 ­8.7 11.9 Belgium 1.8 1.3 1.5 0.8 1.4 1.6 2.3 4.4 4.7 3.3 4.5 3.4 Benin 2.6 2.3 ­0.7 ­1.1 4.4 8.3 12.2 7.7 1.8 2.7 2.1 1.8 Bolivia 3.6 3.4 1.4 1.5 3.6 3.4 8.5 2.6 4.5 9.3 6.0 6.5 Bosnia and Herzegovina .. 1.9 .. .. .. 7.0 .. 8.9 .. 9.8 .. 8.0 Botswana 2.5 7.7 0.1 6.3 6.5 3.6 6.7 ­2.6 4.7 4.0 3.8 4.6 Brazil 3.7 3.3 2.2 2.0 1.0 3.3 4.2 4.0 5.9 8.6 11.6 8.0 Bulgaria ­3.7 6.3 ­3.0 7.0 ­8.4 3.1 ­5.0 17.2 3.9 8.8 2.7 12.1 Burkina Faso 5.7 4.5 2.8 1.1 2.9 8.7 3.1 9.0 4.4 10.9 1.9 7.2 Burundi ­4.9 .. .. .. ­2.6 .. ­0.5 .. ­1.2 .. ­1.6 .. Cambodia 6.0 8.9 3.4 7.1 7.2 1.9 10.3 13.5 21.7 16.9 14.8 15.4 Cameroon 3.1 4.5 0.5 2.1 0.7 2.8 0.4 3.9 3.2 0.4 5.1 4.2 Canada 2.6 3.5 1.6 2.5 0.3 2.7 4.6 6.6 8.7 0.9 7.1 4.4 Central African Republic .. ­0.9 .. ­2.7 .. ­1.3 .. ­0.1 .. ­3.6 .. ­3.9 Chad 1.5 9.2 ­1.7 5.4 ­8.3 5.8 4.0 19.7 2.3 52.0 ­1.8 27.0 Chile 7.3 5.7 5.6 4.5 3.7 5.1 9.3 9.6 9.4 6.4 11.7 12.0 China 8.9 7.1 7.8 6.5 9.7 8.2 11.7 12.1 12.9 18.9 14.3 13.7 Hong Kong SAR, China 3.8 3.4 2.0 2.9 3.7 1.0 4.8 2.2 7.8 9.7 8.4 8.6 Colombia 2.2 4.5 0.4 2.9 10.5 4.3 2.0 13.4 5.3 5.8 9.0 11.2 Congo, Dem. Rep. ­4.5 .. ­3.8 .. ­17.4 .. ­0.7 .. ­0.5 7.0 ­2.4 18.9 Congo, Rep. ­1.8 .. .. .. ­4.4 .. 10.4 .. 3.0 .. 2.0 .. Costa Rica 5.1 4.1 2.5 2.3 2.0 1.6 5.1 8.6 10.9 7.5 9.2 6.6 Côte d'Ivoire 4.1 .. 0.9 .. 0.8 3.1 8.1 1.0 1.9 3.0 8.2 3.9 Croatia 2.3 4.8 3.0 4.8 1.5 1.7 4.9 10.7 6.3 5.5 4.9 7.5 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 3.0 3.8 3.0 3.6 ­0.9 2.2 4.6 4.5 8.7 11.8 12.0 10.4 Denmark 2.2 2.7 1.8 2.3 2.4 1.5 5.7 3.2 5.0 4.1 6.0 6.4 Dominican Republic 6.1 6.6 4.2 5.0 7.0 5.0 11.7 1.7 8.3 2.0 9.9 2.9 Ecuador 2.1 5.7 0.3 4.5 ­1.5 3.9 ­0.6 8.1 5.3 7.1 2.8 9.8 Egypt, Arab Rep. 3.7 4.2 1.7 2.2 4.4 2.6 5.8 7.4 3.5 18.1 3.0 15.5 El Salvador 5.3 3.8 4.1 3.4 2.8 1.4 7.1 2.3 13.4 5.1 11.6 5.3 Eritrea ­5.0 1.6 ­6.6 ­2.2 22.6 1.2 19.1 ­1.0 ­2.5 ­6.3 7.5 ­3.7 Estonia 0.7 8.8 2.2 9.1 5.6 2.1 0.5 12.0 11.0 8.3 12.0 10.9 Ethiopia 3.6 9.7 0.4 6.9 9.0 2.2 6.5 10.0 7.1 11.2 5.8 16.1 Finland 1.7 3.3 1.4 3.0 0.6 1.6 2.2 4.2 10.3 5.3 6.5 5.9 Francea 1.6 2.3 1.2 1.6 1.4 1.6 1.8 2.8 6.9 2.3 5.7 4.2 Gabon ­0.3 5.1 ­3.1 3.0 3.7 0.7 3.0 5.9 2.1 ­2.0 0.1 3.9 Gambia, The 3.6 1.8 ­0.2 ­1.5 ­2.2 4.2 1.9 .. 0.1 0.6 0.1 1.1 Georgia 6.1 9.5 7.6 10.9 12.0 8.8 ­12.5 17.2 12.2 6.9 11.2 9.4 Germany 1.9 0.2 1.6 0.2 1.9 0.7 1.1 0.6 6.0 7.2 5.8 5.6 Ghana 4.7 .. .. .. 4.8 ­6.0 4.3 19.3 10.1 5.9 10.4 8.5 Greece 2.1 3.9 1.4 3.6 2.1 2.5 4.1 4.1 7.6 4.5 7.4 3.1 Guatemala 4.2 3.9 1.8 1.4 5.1 1.6 6.1 3.2 6.1 2.5 9.2 3.1 Guinea 5.2 4.0 2.0 1.9 ­0.5 ­0.3 0.1 ­3.7 0.3 1.9 ­1.1 ­0.8 Guinea-Bissau 2.6 3.9 0.2 1.4 1.9 ­1.9 ­6.5 ­0.1 15.4 2.6 ­0.4 ­0.6 Haiti .. .. .. .. .. .. 9.0 1.1 10.1 3.7 19.4 1.5 Honduras 3.0 6.1 0.6 4.0 2.0 6.1 6.9 7.2 1.6 6.5 3.8 7.9 258 2010 World Development Indicators 4.9 ECONOMY Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Hungary ­0.1 3.8 0.1 4.1 0.9 1.3 9.6 1.3 9.9 11.2 11.4 10.0 India 4.8 5.9 2.9 4.4 6.6 5.0 6.9 15.0 12.3 15.2 14.4 19.5 Indonesia 6.6 4.2 5.0 2.8 0.1 7.8 ­0.6 6.0 5.9 8.7 5.7 10.0 Iran, Islamic Rep. 3.2 7.4 1.6 5.8 1.6 3.6 ­0.1 8.3 1.2 5.0 ­6.8 13.2 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 5.5 5.0 4.7 3.0 4.0 4.5 10.0 6.2 15.7 5.2 14.5 4.9 Israel 5.0 3.4 2.5 1.5 2.7 1.4 2.0 2.3 10.9 5.9 7.6 3.8 Italy 1.5 0.8 1.5 0.1 ­0.2 1.7 1.6 1.4 5.9 1.7 4.4 2.4 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.5 1.2 1.3 1.1 2.9 1.9 ­0.8 0.4 4.1 8.0 4.2 4.2 Jordan 4.9 6.8 1.1 4.1 4.7 6.6 0.3 11.0 2.6 7.0 1.5 8.0 Kazakhstan ­8.1 10.4 ­7.0 9.6 ­7.1 8.1 ­18.3 21.0 ­2.6 6.9 ­11.2 8.6 Kenya 3.6 4.2 0.6 1.5 6.9 2.6 6.1 8.5 1.0 7.0 9.4 8.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 3.5 3.9 3.1 4.7 4.8 3.4 3.4 16.0 11.4 10.0 9.4 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 4.5 5.9 0.6 2.9 ­2.4 6.6 1.0 13.7 ­1.6 5.4 0.8 9.4 Kyrgyz Republic ­5.0 11.8 ­5.9 10.8 ­7.2 3.4 ­1.1 2.9 ­0.3 14.2 ­2.0 20.6 Lao PDR .. ­7.8 .. ­9.4 .. 9.7 .. 15.2 .. ­7.6 .. ­7.2 Latvia ­3.9 10.0 ­2.7 10.6 1.8 2.8 ­3.7 16.4 4.3 8.8 7.6 12.1 Lebanon ­0.2 2.5 ­1.9 1.1 10.9 2.4 ­5.8 8.5 18.6 11.1 ­1.1 6.3 Lesotho 1.8 13.7 0.2 12.6 7.2 1.4 ­1.5 ­2.1 9.7 11.5 2.1 13.7 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 5.2 10.0 5.9 10.6 1.9 4.1 11.1 14.2 4.9 11.7 7.5 14.5 Macedonia, FYR 2.2 5.1 1.7 4.9 ­0.4 ­0.2 3.6 4.8 4.2 3.3 7.5 4.6 Madagascar 2.2 3.8 ­0.8 1.0 0.0 8.3 3.3 19.3 3.8 5.3 4.1 8.3 Malawi 5.4 3.6 3.2 0.7 ­4.4 5.6 ­8.4 24.5 4.0 ­9.6 ­1.1 1.0 Malaysia 5.3 7.5 2.6 5.6 4.8 8.4 5.3 2.7 12.0 6.8 10.3 7.8 Mali 3.0 0.9 1.0 ­1.5 3.2 .. 0.4 6.2 9.9 6.3 3.5 3.9 Mauritania .. 7.4 .. 4.5 .. 3.1 .. 23.8 ­1.3 11.5 0.6 14.1 Mauritius 5.1 5.7 3.9 4.8 3.6 3.9 4.8 6.1 5.6 2.2 5.1 2.6 Mexico 3.9 3.7 2.2 2.7 1.8 0.4 4.7 1.4 14.6 5.7 12.3 6.3 Moldovab 9.9 9.4 11.7 9.7 ­12.4 6.7 ­15.5 11.9 1.0 11.2 5.9 13.8 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Moroccoc 1.8 4.9 0.3 3.7 3.9 3.3 2.5 9.1 5.9 7.1 5.1 8.8 Mozambique 5.8 7.6 2.6 4.9 3.2 ­7.0 8.6 3.3 13.1 16.5 7.6 6.7 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 4.8 5.8 2.3 3.8 3.3 3.3 7.3 8.8 3.8 7.6 5.4 8.6 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 3.1 0.9 2.4 0.5 2.0 3.0 4.4 1.5 7.3 4.8 7.6 4.5 New Zealand 3.3 4.5 2.0 3.1 2.4 4.0 6.1 6.5 5.2 3.1 6.2 7.2 Nicaragua 6.1 3.6 3.9 2.3 ­1.5 2.7 11.3 2.2 9.3 8.8 12.2 5.5 Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. ­2.1 .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 3.5 4.0 2.9 3.3 2.7 2.2 6.0 6.7 5.5 1.0 5.8 5.7 Oman 5.4 1.3 2.6 ­0.4 2.4 6.1 4.0 17.0 6.2 7.0 5.9 12.8 Pakistan 4.9 4.6 2.3 2.2 0.7 10.2 1.8 6.7 1.7 8.6 2.5 9.0 Panama 6.4 7.1 4.2 5.2 1.7 4.4 10.4 8.3 ­0.4 6.8 1.2 7.2 Papua New Guinea 2.5 0.4 ­0.2 ­2.2 2.5 1.1 1.9 ­1.1 5.1 6.3 3.4 6.3 Paraguay 2.6 3.0 0.3 1.0 2.5 3.0 0.7 3.5 3.1 8.0 2.9 6.6 Peru 4.0 5.1 2.2 3.7 5.2 4.6 7.4 10.9 8.5 8.6 9.0 10.1 Philippines 3.7 5.1 1.5 3.1 3.8 2.6 4.1 1.3 7.8 6.6 7.8 3.7 Poland 5.2 3.7 5.1 3.8 3.7 4.2 10.6 6.4 11.3 10.2 16.7 9.4 Portugal 3.0 1.6 2.7 1.1 2.9 1.3 5.8 ­1.3 5.3 4.0 7.3 3.2 Puerto Rico .. .. .. .. .. .. .. .. 1.6 .. 4.5 .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 259 4.9 Growth of consumption and investment Household final General government Gross capital Goods and consumption final consumption formation services expenditure expenditure average annual average annual % growth average annual average annual % growth Total Per capita % growth % growth Exports Imports 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Romania 1.3 6.7 1.7 7.2 0.8 5.0 ­5.1 12.3 8.1 10.8 6.0 15.8 Russian Federation ­0.9 10.5 ­0.7 11.0 ­2.2 2.1 ­19.1 12.3 0.8 8.2 ­6.1 19.7 Rwanda 0.4 .. .. .. ­2.6 .. 0.4 .. ­6.4 .. 6.1 .. Saudi Arabia .. 5.5 .. 3.1 .. 7.6 .. 11.4 .. 6.9 .. 16.9 Senegal 2.6 5.3 ­0.2 2.6 0.9 0.5 3.5 9.9 4.1 4.0 2.0 7.9 Serbia .. 5.4 .. 5.7 .. 2.7 .. 15.8 .. 12.1 .. 12.8 Sierra Leone ­4.4 .. .. .. 10.4 .. ­5.6 .. ­11.2 .. ­0.2 .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 6.0 5.2 5.8 5.1 1.8 3.2 7.7 7.5 9.6 11.2 12.4 9.8 Slovenia 3.9 3.0 4.0 2.8 2.2 3.2 10.4 7.2 1.7 9.2 5.2 8.8 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 5.5 0.6 4.3 0.3 5.2 5.0 9.3 5.6 4.0 7.1 10.0 Spain 2.4 3.4 2.0 1.8 2.7 5.1 3.2 4.9 10.5 3.8 9.4 6.6 Sri Lanka .. .. .. .. 10.5 .. 6.9 .. 7.5 .. 8.6 .. Sudan 3.7 5.9 1.1 3.7 5.5 8.4 22.0 12.5 11.6 14.3 8.4 12.0 Swaziland 7.3 2.2 4.9 1.3 7.1 3.2 ­4.7 ­1.1 6.4 7.0 6.2 5.7 Sweden 1.4 2.2 1.0 1.7 0.6 0.8 1.8 4.8 8.5 5.8 6.3 5.1 Switzerland 1.1 1.4 0.5 0.7 0.5 1.0 0.7 1.1 4.1 4.9 4.3 4.2 Syrian Arab Republic 3.0 7.6 0.3 4.7 2.0 8.0 3.3 0.4 12.0 6.6 4.4 12.1 Tajikistan ­11.8 11.9 ­13.1 10.5 ­15.7 1.5 ­17.6 8.6 ­5.3 8.8 ­6.0 10.0 Tanzaniad 4.9 2.8 1.9 0.1 ­7.0 16.9 ­1.6 7.3 9.3 12.0 3.9 5.7 Thailand 3.7 4.5 2.7 3.5 5.1 5.2 ­4.0 7.0 9.5 7.0 4.5 7.7 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 5.0 0.5 2.0 ­2.1 0.0 1.3 ­0.1 5.9 1.2 6.0 1.1 3.1 Trinidad and Tobago 0.7 13.3 0.1 12.9 0.3 4.3 12.5 4.2 6.9 5.8 9.9 9.5 Tunisia 4.3 5.0 2.6 4.1 4.1 4.3 3.6 2.1 5.1 4.3 3.8 3.0 Turkey 3.8 6.0 2.1 4.6 4.6 3.7 4.7 10.1 11.1 7.2 10.8 11.1 Turkmenistan .. .. .. .. .. .. .. ­16.9 ­2.4 21.5 7.2 13.3 Uganda 6.7 7.9 3.3 4.5 7.1 3.9 8.9 12.1 14.7 12.4 10.0 11.4 Ukraine ­6.9 13.8 ­6.4 14.7 ­4.1 2.9 ­18.5 9.6 ­3.6 3.2 ­6.6 8.0 United Arab Emirates 7.1 12.9 1.2 7.5 6.8 0.8 5.5 5.5 5.5 12.2 6.4 13.6 United Kingdom 3.0 2.6 2.7 2.1 1.0 2.6 4.7 3.3 6.5 3.8 6.8 4.7 United States 3.7 3.0 2.4 2.0 0.7 2.3 7.5 2.1 7.3 4.4 9.8 5.2 Uruguay 5.0 2.7 4.3 2.6 2.3 0.3 6.1 5.6 6.0 7.9 9.9 6.3 Uzbekistan .. .. .. .. .. .. ­2.5 8.3 2.5 8.2 ­0.4 10.4 Venezuela, RB 0.6 9.0 ­1.5 7.1 3.7 7.0 11.0 12.4 1.0 ­1.1 8.2 15.3 Vietnam 5.4 7.9 3.9 6.5 3.2 7.6 19.8 12.7 19.2 12.1 19.5 14.4 West Bank and Gaza 5.3 ­1.5 1.1 ­4.9 12.7 1.3 9.2 ­3.0 8.7 ­3.1 7.5 ­2.3 Yemen, Rep. 3.2 .. ­0.7 .. 1.7 .. 11.4 .. 16.6 .. 8.3 .. Zambia 2.4 0.2 ­0.5 ­2.1 ­8.1 25.2 3.9 6.8 6.7 21.7 15.5 15.6 Zimbabwe 0.0 ­3.8 ­1.7 ­3.8 ­2.2 ­3.0 ­2.5 ­10.6 10.5 ­7.5 9.4 ­3.3 World 3.0 w 3.0 w 1.6 w 1.8 w 1.7 w 2.6 w 3.3 w 4.1 w 6.9 w 6.8 w 7.0 w 6.6 w Low income 3.7 4.7 1.3 2.5 0.2 5.8 6.5 9.9 9.4 10.3 8.2 10.9 Middle income 4.1 5.6 2.6 4.4 3.5 5.0 2.9 9.7 7.3 10.4 6.6 11.0 Lower middle income 5.6 6.2 4.0 4.9 6.4 6.9 6.1 11.3 8.1 14.0 6.9 12.2 Upper middle income 3.0 5.1 1.9 4.3 1.7 3.4 ­0.1 7.4 6.8 6.8 6.4 10.1 Low & middle income 4.0 5.6 2.4 4.2 3.4 5.0 3.0 9.7 7.4 10.4 6.6 11.0 East Asia & Pacific 7.4 6.5 6.1 5.7 8.1 7.8 8.3 11.0 10.9 13.8 10.2 11.0 Europe & Central Asia 1.3 7.5 1.1 7.4 0.2 3.5 ­8.5 10.9 2.7 8.6 0.1 13.6 Latin America & Carib. 3.6 4.2 2.0 2.9 2.1 3.0 5.4 5.7 8.5 6.0 10.8 7.9 Middle East & N. Africa 2.8 5.3 0.7 3.4 3.5 3.6 1.2 7.5 4.0 7.7 0.0 10.6 South Asia 4.6 5.5 2.6 3.9 5.9 5.7 6.5 13.8 10.0 14.0 11.2 17.5 Sub-Saharan Africa 3.1 5.3 0.4 2.7 0.5 5.0 4.5 8.5 5.0 4.3 6.0 8.7 High income 2.8 2.4 2.0 1.7 1.5 2.2 3.3 2.3 6.8 5.1 7.1 5.3 Euro area 1.9 1.5 1.6 0.9 1.5 1.8 2.1 2.3 6.8 4.7 6.2 4.7 a. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. b. Excludes Transnistria. c. Includes Former Spanish Sahara. d. Covers mainland Tanzania only. 260 2010 World Development Indicators 4.9 ECONOMY Growth of consumption and investment About the data Definitions Measures of growth in consumption and capital for- the change in government employment. Neither · Household final consumption expenditure is the mation are subject to two kinds of inaccuracy. The technique captures improvements in productivity market value of all goods and services, including first stems from the difficulty of measuring expendi- or changes in the quality of government services. durable products (such as cars and computers), tures at current price levels, as described in About Deflators for household consumption are usually cal- purchased by households. It excludes purchases of the data for table 4.8. The second arises in deflat- culated on the basis of the consumer price index. dwellings but includes imputed rent for owner-occu- ing current price data to measure volume growth, Many countries estimate household consumption pied dwellings. It also includes government fees for where results depend on the relevance and reliability as a residual that includes statistical discrepancies permits and licenses. Expenditures of nonprofit insti- of the price indexes and weights used. Measuring associated with the estimation of other expenditure tutions serving households are included, even when price changes is more difficult for investment goods items, including changes in inventories; thus these reported separately. Household consumption expen- than for consumption goods because of the one-time estimates lack detailed breakdowns of household diture may include any statistical discrepancy in the nature of many investments and because the rate consumption expenditures. use of resources relative to the supply of resources. of technological progress in capital goods makes · Household fi nal consumption expenditure per capturing change in quality diffi cult. (An example capita is household final consumption expenditure is computers--prices have fallen as quality has divided by midyear population. · General government improved.) Several countries estimate capital forma- final consumption expenditure is all government cur- tion from the supply side, identifying capital goods rent expenditures for goods and services (including entering an economy directly from detailed produc- compensation of employees). It also includes most tion and international trade statistics. This means expenditures on national defense and security but that the price indexes used in deflating production excludes military expenditures with potentially wider and international trade, reflecting delivered or offered public use that are part of government capital forma- prices, will determine the deflator for capital forma- tion. · Gross capital formation is outlays on addi- tion expenditures on the demand side. tions to fixed assets of the economy, net changes in Growth rates of household final consumption expen- inventories, and net acquisitions of valuables. Fixed diture, household final consumption expenditure per assets include land improvements (fences, ditches, capita, general government final consumption expen- drains); plant, machinery, and equipment purchases; diture, gross capital formation, and exports and and construction (roads, railways, schools, buildings, imports of goods and services are estimated using and so on). Inventories are goods held to meet tem- constant price data. (Consumption, capital forma- porary or unexpected fluctuations in production or tion, and exports and imports of goods and services sales, and "work in progress." · Exports and imports as shares of GDP are shown in table 4.8.) of goods and services are the value of all goods To obtain government consumption in constant and other market services provided to or received prices, countries may defl ate current values by from the rest of the world. They include the value of applying a wage (price) index or extrapolate from merchandise, freight, insurance, transport, travel, royalties, license fees, and other services (commu- GDP per capita is still lagging in some regions 4.9a nication, construction, financial, information, busi- ness, personal, government services, and so on). GDP per capita (2000 $) 5,000 They exclude compensation of employees and invest- Latin America & Caribbean ment income (factor services in the 1968 System of 4,000 National Accounts) and transfer payments. Europe & Central Asia 3,000 2,000 Middle East & North Africa East Asia & Pacific Data sources 1,000 South Asia Data on national accounts indicators for most Sub-Saharan Africa 0 developing countries are collected from national 1990 1995 2000 2005 2008 statistical organizations and central banks by vis- Although GDP per capita more than tripled in East Asia and Pacific between 1990 and 2008, it is still iting and resident World Bank missions. Data for less than GDP per capita in Latin America and Caribbean, Europe and Central Asia, and Middle East high-income economies are from Organisation for and North Africa. Economic Co-operation and Development (OECD) Source: World Development Indicators data files. data files. 2010 World Development Indicators 261 4.10 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 2008 2008 Afghanistanb .. 7.6 .. 23.0 .. ­2.2 .. 0.2 .. 1.9 9.6 0.1 Albaniab 21.2 .. 25.6 .. ­8.9 .. 7.4 .. 2.1 .. .. .. Algeriab .. 48.5 .. 23.9 .. 9.5 .. 1.2 .. 0.0 .. 1.2 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. .. .. Armeniab .. 22.4 .. 20.7 .. ­0.5 .. 0.7 .. 1.2 .. 1.3 Australia .. 25.4 .. 23.6 .. 1.5 .. .. .. .. 19.4 3.5 Austria 36.6 37.4 42.5 38.4 ­5.5 ­0.7 .. .. .. .. 64.5 6.5 Azerbaijanb 18.0 27.3 19.8 15.5 ­3.1 0.4 .. 0.0 .. 0.2 .. 0.3 Bangladeshb .. 11.0 .. 10.9 .. ­1.0 .. 4.1 .. 1.1 .. 21.8 Belarusb 30.0 39.2 28.7 34.2 ­2.7 2.4 2.2 1.3 0.4 2.3 10.7 1.4 Belgium 41.5 41.2 45.6 42.5 ­3.9 ­1.1 2.5 1.0 ­0.5 6.5 88.0 8.5 Beninb .. 18.6 .. 14.9 .. ­0.3 .. ­2.6 .. 2.4 .. 1.7 Bolivia .. 23.3 .. 21.8 .. 1.2 .. ­0.2 .. ­0.1 .. 8.0 Bosnia and Herzegovina .. 39.1 .. 38.9 .. ­1.5 .. 1.0 .. 0.6 .. 1.2 Botswanab 40.5 .. 30.3 .. 4.9 .. 0.2 .. ­0.4 .. .. .. Brazilb .. 24.7 .. 25.0 .. ­1.3 .. 4.6 .. ­0.2 60.9 15.2 Bulgariab 35.5 36.4 39.4 30.9 ­5.1 3.2 7.4 ­0.5 ­0.8 ­1.4 .. 2.3 Burkina Faso .. 13.6 .. 12.8 .. ­4.2 .. 0.5 .. 2.8 .. 2.0 Burundib 19.3 .. 23.6 .. ­4.7 .. 3.1 .. 4.0 .. .. .. Cambodia .. 9.8 .. 8.6 .. ­1.7 .. ­0.3 .. 2.1 .. 1.5 Cameroonb 11.8 .. 10.6 .. 0.2 .. ­0.3 .. 0.3 .. .. .. Canadab 20.3 19.6 24.2 17.8 ­4.3 1.6 4.9 ­0.9 0.0 0.2 45.2 6.1 Central African Republicb .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 26.0 .. 19.7 .. 4.8 .. ­0.3 .. ­0.4 .. 1.9 Chinab 5.4 10.3 .. 11.4 .. ­1.4 1.6 1.2 .. ­0.1 .. 4.3 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 23.5 .. 23.7 .. ­2.1 .. ­2.0 .. 1.5 54.3 29.4 Congo, Dem. Rep.b 5.3 .. 8.2 .. 0.0 .. 0.0 .. 0.2 .. .. .. Congo, Rep. .. 39.9 .. 24.8 .. 9.6 .. .. .. .. .. 6.5 Costa Ricab .. 25.3 .. 22.5 .. ­0.8 .. .. ­0.8 .. .. 8.6 Côte d'Ivoireb .. 18.9 .. 17.9 .. ­0.3 .. ­0.1 .. .. .. 8.4 Croatiab 36.7 35.9 36.1 34.7 ­1.1 ­1.1 ­2.3 0.6 0.7 ­0.5 .. 4.8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republicb 33.2 31.4 32.6 34.1 ­0.9 ­1.5 ­0.5 1.5 ­0.4 0.8 26.6 3.6 Denmark 39.1 40.6 38.2 36.5 1.5 4.8 .. .. .. .. 24.1 4.5 Dominican Republicb .. 17.6 .. 14.8 .. 0.3 .. ­0.4 .. 0.6 .. 6.8 Ecuador b 30.9 .. 26.3 .. 0.1 .. .. .. .. .. .. .. Egypt, Arab Rep.b 34.8 27.7 28.1 30.4 3.4 ­6.4 .. 8.5 .. 1.3 .. 16.5 El Salvador .. 19.9 .. 18.5 .. 0.3 .. ­0.7 .. ­0.8 39.4 9.8 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 31.9 .. 26.8 .. 3.1 .. .. .. .. 4.1 0.2 Ethiopiab .. .. .. .. .. .. .. .. .. .. .. .. Finland 40.6 38.7 49.9 33.8 ­7.5 5.5 8.9 ­0.4 0.2 ­0.8 37.3 3.2 France 43.3 41.8 47.6 44.4 ­4.1 ­2.3 .. .. .. .. 66.6 5.9 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Theb 23.7 .. .. .. .. .. .. .. .. .. .. .. Georgiab 12.2 25.7 15.4 29.1 ­4.3 ­1.9 2.2 ­0.3 2.4 5.3 27.0 2.2 Germany 29.9 28.5 38.6 29.0 ­8.3 ­0.4 .. 0.2 .. 0.1 40.8 6.0 Ghanab 17.0 25.8 .. 29.5 .. ­7.7 .. 5.1 .. 2.3 .. 9.6 Greece 35.1 39.0 42.6 41.8 ­9.1 ­3.7 .. .. .. .. 114.1 11.1 Guatemalab 8.4 11.9 7.6 11.7 ­0.5 ­1.6 .. 0.6 0.4 0.3 20.1 11.2 Guineab 11.2 .. 12.1 .. ­4.3 .. ­0.1 .. 4.5 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras .. 22.3 .. 21.6 .. ­0.2 .. ­1.2 .. 2.6 .. 2.5 262 2010 World Development Indicators 4.10 ECONOMY Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 2008 2008 Hungary 43.0 40.7 53.2 45.0 ­9.1 ­3.9 17.0 2.3 0.2 6.1 73.8 9.7 Indiab 12.3 15.0 14.4 16.2 ­2.2 ­1.6 5.1 2.1 0.0 0.1 57.6 23.3 Indonesiab 17.7 .. 9.7 .. 3.0 .. ­0.6 .. ­0.4 .. .. .. Iran, Islamic Rep.b 24.2 34.8 15.8 20.6 1.1 7.9 .. 1.4 0.1 0.0 .. 0.8 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 33.6 33.1 37.5 32.0 ­2.2 0.4 .. .. .. .. 27.2 2.8 Israel .. 36.8 .. 40.7 .. ­1.9 .. .. .. .. .. 9.0 Italy 40.4 37.5 48.0 40.1 ­7.5 ­2.5 .. .. .. .. 106.3 12.8 Jamaicab .. 29.1 .. 33.2 .. ­5.1 .. ­0.8 .. 6.3 112.9 39.1 Japan 20.7 .. .. .. .. .. 1.5 .. .. .. .. .. Jordanb .. 32.9 .. 36.6 .. ­1.1 .. 13.5 .. ­11.6 115.1 6.7 Kazakhstanb 14.0 13.4 18.7 14.8 ­1.8 4.3 0.8 1.9 2.8 0.0 6.3 1.9 Kenyab 21.6 19.5 25.8 21.5 ­5.1 ­4.1 3.9 ­0.7 ­1.3 0.1 .. 11.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 17.8 24.6 14.3 18.6 2.4 4.3 ­0.3 ­2.4 ­0.1 ­0.1 .. 5.6 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 36.8 47.4 46.4 24.0 ­13.6 23.4 .. .. .. .. .. 0.0 Kyrgyz Republicb 16.7 20.5 25.6 17.0 ­10.8 0.0 .. 0.1 .. 0.3 .. 3.3 Lao PDR .. 13.0 .. 10.3 .. ­2.9 .. 0.1 .. 3.6 .. 3.1 Latviab 25.8 26.0 28.3 29.4 ­2.7 ­2.6 2.4 5.0 1.5 4.7 22.8 1.3 Lebanon .. 21.5 .. 30.4 .. ­10.0 .. 17.1 .. ­0.1 .. 50.1 Lesothob 52.2 65.3 36.0 51.2 5.3 5.7 0.0 ­0.4 6.5 1.5 .. 1.3 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 28.7 .. 31.4 .. ­3.1 .. 1.1 .. ­0.1 18.4 2.0 Macedonia, FYR .. 34.0 .. 31.3 .. ­0.8 .. ­0.6 .. 0.2 .. 1.9 Madagascar .. 11.9 .. 11.2 .. ­2.7 .. 0.7 .. 2.2 .. 7.0 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 24.4 .. 17.2 .. 2.4 .. .. .. ­0.8 .. .. .. Mali .. 16.2 .. 15.2 .. ­5.6 .. ­1.0 .. 3.5 .. 1.7 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusb 20.5 21.7 18.9 19.3 ­1.2 0.6 2.9 2.0 ­0.5 ­0.1 36.1 13.7 Mexicob 15.3 .. 15.0 .. ­0.6 .. .. .. 5.5 .. .. .. Moldovab 28.4 34.4 38.4 32.8 ­6.3 ­0.4 3.0 ­0.5 2.7 0.0 18.5 3.2 Mongolia 19.0 32.1 13.8 26.3 2.9 ­3.5 1.6 ­0.4 1.3 0.7 46.9 1.0 Moroccob .. 36.0 .. 30.1 .. 2.9 .. ­0.7 .. 0.3 .. 3.6 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.4 .. .. .. .. .. .. .. .. .. .. .. Namibiab 31.7 29.1 35.7 24.0 ­5.0 2.0 .. ­0.8 .. ­0.1 .. 6.3 Nepalb 10.5 12.3 .. 15.1 .. ­1.0 0.6 1.5 2.5 0.1 43.7 5.3 Netherlands 41.5 40.8 50.8 40.3 ­9.2 0.3 .. .. .. .. 43.4 4.4 New Zealand .. 37.1 .. 32.9 .. 3.2 .. ­1.7 .. 2.8 38.9 3.4 Nicaraguab 12.8 18.4 14.2 19.6 0.6 ­1.1 .. .. 3.4 .. .. 5.4 Niger .. 13.6 .. 11.8 .. ­0.9 .. ­1.9 .. 2.4 .. 1.8 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 51.2 .. 30.7 .. 19.9 .. 1.9 .. ­10.7 44.9 1.6 Omanb 27.8 .. 32.4 .. ­8.9 .. ­0.1 .. 0.0 .. .. .. Pakistanb 17.2 13.4 19.1 18.6 ­5.3 ­7.4 .. .. .. .. .. 34.8 Panamab 26.1 .. 22.0 .. 1.5 .. .. .. .. .. .. .. Papua New Guineab 22.7 .. 24.5 .. ­0.5 .. 1.5 .. ­0.7 .. .. .. Paraguay b .. 21.3 .. 16.7 .. 3.4 .. ­0.6 .. ­0.5 .. 2.9 Perub 17.4 19.6 17.4 16.5 ­1.3 2.0 .. 0.1 3.9 ­1.0 24.3 7.7 Philippinesb 17.7 15.8 15.9 17.0 ­0.8 ­1.3 ­0.5 1.5 ­0.7 0.2 .. 24.1 Poland .. 32.0 .. 35.3 .. ­3.7 .. 5.0 .. ­0.9 44.8 7.0 Portugal 33.3 39.2 38.9 42.9 ­5.1 ­2.7 ­1.4 ­0.9 4.3 4.6 76.0 7.2 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. 45.5 .. 17.8 .. 12.5 .. .. .. .. .. 1.5 2010 World Development Indicators 263 4.10 Central government finances Revenuea Expense Cash surplus Net incurrence Debt and interest or deficit of liabilities payments Interest % of GDP Total debt % of % of GDP % of GDP % of GDP Domestic Foreign % of GDP revenue 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 2008 2008 Romania .. 30.9 .. 33.8 .. ­4.6 .. 2.4 .. 0.9 .. 2.0 Russian Federation .. 33.4 .. 21.3 .. 5.6 .. 0.2 .. 0.2 6.4 1.1 Rwandab 10.6 .. 15.0 .. ­5.6 .. 2.9 .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 15.2 .. .. .. .. .. .. .. .. .. .. .. Serbiab .. 37.6 .. 37.4 .. ­1.6 .. ­0.8 .. ­0.1 .. 1.4 Sierra Leoneb 9.4 .. .. .. .. .. 0.3 .. .. .. .. .. Singaporeb 26.7 21.7 12.4 15.4 19.8 8.1 10.3 7.9 0.0 .. 102.6 0.1 Slovak Republic .. 28.9 .. 30.8 .. ­2.2 .. 1.2 .. 0.0 36.5 4.1 Sloveniab 35.8 38.1 34.3 37.4 ­0.1 ­0.2 ­0.4 ­0.5 0.3 ­0.1 .. 2.8 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 30.7 .. 30.9 .. ­0.4 .. 1.6 .. ­0.1 .. 7.8 Spain 32.0 24.5 37.1 26.3 ­5.8 ­2.0 .. .. .. .. 33.8 4.8 Sri Lankab 20.4 15.8 26.0 20.0 ­7.6 ­6.5 5.2 4.2 3.2 2.8 85.0 30.7 Sudanb 7.2 .. 6.8 .. ­0.4 .. 0.3 .. .. .. .. .. Swazilandb .. .. .. .. .. .. .. .. .. .. .. .. Sweden 35.0 .. 44.1 .. ­9.3 .. .. .. ­1.2 .. 47.3 .. Switzerlandb 22.6 18.3 25.7 17.6 ­0.6 1.1 ­0.5 ­1.1 .. .. 23.6 4.4 Syrian Arab Republicb 22.9 .. .. .. .. .. .. .. .. .. .. .. Tajikistanb 9.3 .. 11.4 .. ­3.3 .. 0.1 .. 2.3 .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 20.1 .. 18.2 .. 0.5 .. 1.1 .. ­0.5 24.0 4.9 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togob .. 17.0 .. 15.1 .. 0.3 .. 1.8 .. ­0.2 .. 4.3 Trinidad and Tobagob 27.2 30.3 25.3 24.4 ­0.1 0.7 2.8 ­0.8 2.6 0.5 .. 6.5 Tunisiab 30.0 32.5 28.4 30.4 ­2.4 ­0.7 0.9 ­1.3 2.9 0.3 48.2 7.0 Turkey b .. 22.6 .. 22.8 .. ­1.9 .. 1.7 .. 0.4 44.5 24.2 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandab 10.6 13.2 .. 15.2 .. ­1.5 .. 2.1 .. 1.7 28.9 7.9 Ukraineb .. 35.7 .. 37.2 .. ­1.5 .. 3.1 .. 0.4 .. 1.3 United Arab Emiratesb 10.1 .. 9.3 .. 0.5 .. .. .. .. .. .. .. United Kingdom 35.2 38.4 40.4 42.8 ­5.5 ­4.7 ­0.3 .. 0.0 .. 57.5 5.8 United States .. 17.3 .. 22.7 .. ­5.4 .. 4.1 .. 5.0 53.8 11.6 Uruguay b 27.6 25.0 27.1 24.2 ­1.2 ­0.9 7.9 1.4 1.1 ­1.3 54.0 10.9 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 16.9 28.3 18.5 25.1 ­2.3 2.2 1.1 1.2 0.1 3.3 .. 10.4 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.b 17.3 .. 19.1 .. ­3.9 .. .. .. .. .. .. .. Zambiab 20.0 17.6 21.4 22.9 ­3.1 ­0.8 28.0 .. 16.2 .. .. 7.2 Zimbabweb 26.7 .. 32.1 .. ­5.4 .. ­1.4 .. 1.6 .. .. .. World .. w 27.4 w .. w 28.1 w .. w ­0.9 w .. m .. m .. m .. m .. m 5.6 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. 20.2 .. 19.7 .. ­0.6 .. 0.9 .. 0.2 .. 3.6 Lower middle income 11.4 15.2 .. 15.6 .. ­1.5 .. .. .. .. .. 4.3 Upper middle income .. .. .. .. .. .. .. 1.2 .. 0.0 .. 2.3 Low & middle income .. 20.0 .. 19.6 .. ­0.6 .. .. .. .. .. 5.8 East Asia & Pacific 8.4 11.6 .. 12.2 .. ­1.1 .. .. .. .. .. .. Europe & Central Asia .. 29.6 .. 26.7 .. 0.3 .. 0.9 .. 0.3 .. 1.9 Latin America & Carib. .. .. .. .. .. .. .. 0.9 .. ­0.2 .. 9.2 Middle East & N. Africa .. 32.2 .. 24.9 .. 1.7 .. 4.8 .. 0.1 .. 6.7 South Asia 13.1 14.4 15.3 16.1 ­2.7 ­2.2 3.8 1.8 1.1 0.6 61.3 21.8 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income .. 27.9 .. 28.9 .. ­1.0 .. .. .. .. 43.4 4.8 Euro area 34.8 38.1 42.3 39.0 ­7.4 ­0.9 .. .. .. .. 51.7 6.5 a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 264 2010 World Development Indicators 4.10 ECONOMY Central government finances About the data Definitions Tables 4.10­4.12 present an overview of the size borrowing for temporary periods can also be used. · Revenue is cash receipts from taxes, social con- and role of central governments relative to national Government excludes public corporations and quasi tributions, and other revenues such as fines, fees, economies. The tables are based on the concepts corporations (such as the central bank). rent, and income from property or sales. Grants, usu- and recommendations of the second edition of the Units of government at many levels meet this defini- ally considered revenue, are excluded. · Expense is International Monetary Fund's (IMF) Government tion, from local administrative units to the national cash payments for government operating activities in Finance Statistics Manual 2001. Before 2005 World government, but inadequate statistical coverage pre- providing goods and services. It includes compensa- Development Indicators reported data derived on the cludes presenting subnational data. Although data tion of employees, interest and subsidies, grants, basis of the 1986 manual's cash-based method. The for general government under the 2001 manual are social benefi ts, and other expenses such as rent 2001 manual, harmonized with the 1993 United available for a few countries, only data for the cen- and dividends. · Cash surplus or deficit is revenue Nations System of National Accounts, recommends tral government are shown to minimize disparities. (including grants) minus expense, minus net acquisi- an accrual accounting method, focusing on all eco- Still, different accounting concepts of central govern- tion of nonfinancial assets. In editions before 2005 nomic events affecting assets, liabilities, revenues, ment make cross-country comparisons potentially nonfinancial assets were included under revenue and expenses, not only those represented by cash misleading. and expenditure in gross terms. This cash surplus transactions. It takes all stocks into account, so Central government can refer to consolidated or bud- or deficit is close to the earlier overall budget balance that stock data at the end of an accounting period getary accounting. For most countries central govern- (still missing is lending minus repayments, which are equal stock data at the beginning of the period plus ment finance data have been consolidated into one included as a financing item under net acquisition flows over the period. The 1986 manual considered account, but for others only budgetary central gov- of financial assets). · Net incurrence of liabilities only the debt stock data. Further, the new manual no ernment accounts are available. Countries reporting is domestic financing (obtained from residents) and longer distinguishes between current and capital rev- budgetary data are noted in Primary data documenta- foreign financing (obtained from nonresidents), or enue or expenditures, and it introduces the concepts tion. Because budgetary accounts may not include the means by which a government provides financial of nonfinancial and financial assets. Most countries all central government units (such as social security resources to cover a budget deficit or allocates finan- still follow the 1986 manual, however. The IMF has funds), they usually provide an incomplete picture. cial resources arising from a budget surplus. The net reclassified historical Government Finance Statistics Data on government revenue and expense are col- incurrence of liabilities should be offset by the net Yearbook data to conform to the 2001 manual's for- lected by the IMF through questionnaires to member acquisition of financial assets (a third financing item). mat. Because of reporting differences, the reclassi- countries and by the Organisation for Economic Co- The difference between the cash surplus or deficit fied data understate both revenue and expense. operation and Development. Despite IMF efforts to stan- and the three financing items is the net change in The 2001 manual describes government's eco- dardize data collection, statistics are often incomplete, the stock of cash. · Total debt is the entire stock of nomic functions as the provision of goods and ser- untimely, and not comparable across countries. direct government fixed-term contractual obligations vices on a nonmarket basis for collective or individual Government finance statistics are reported in local to others outstanding on a particular date. It includes consumption, and the redistribution of income and currency. The indicators here are shown as percent- domestic and foreign liabilities such as currency and wealth through transfer payments. Government ages of GDP. Many countries report government money deposits, securities other than shares, and activities are financed mainly by taxation and other finance data by fiscal year; see Primary data documen- loans. It is the gross amount of government liabili- income transfers, though other financing such as tation for information on fiscal year end by country. ties reduced by the amount of equity and financial derivatives held by the government. Because debt Twenty developing economies had a government expenditure is a stock rather than a flow, it is measured as of to GDP ratio of 30 percent or higher 4.10a a given date, usually the last day of the fiscal year. Central government expense, 2008 (percent of GDP) · Interest payments are interest payments on gov- 75 ernment debt--including long-term bonds, long-term loans, and other debt instruments --to domestic and foreign residents. 50 25 Data sources Data on central government finances are from the IMF's Government Finance Statistics Yearbook 0 2008 and data files. Each country's accounts o s a Uk a e an Be d s yc a Ja s ca va ia YR Eg uth ria ab a Le p. n M ia co ve ru lle th in i in n Se ani c no rb an s Re do ai t, Afri oc rd la So lga ,F ni ov ra so la di he ba Se m Po m ac ithu Jo or ol are reported using the system of common defi - Tu ia al eg Bu Le Ro M on M rz Ar L He ed nitions and classifications in the IMF's Govern- yp d an M a ment Finance Statistics Manual 2001. See these s ni Bo sources for complete and authoritative explana- Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2010 World Development Indicators 265 4.11 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistana .. 42 .. 39 .. 0 .. 16 .. 4 Albaniaa 18 .. 14 .. 9 .. 59 .. 0 .. Algeriaa .. 12 .. 31 .. 3 .. 49 .. 6 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. Armeniaa .. 12 .. 21 .. 2 .. 43 .. 22 Australia .. 11 .. 11 .. 4 .. 70 .. 6 Austria 5 6 14 13 9 6 68 70 6 6 Azerbaijana .. 9 .. 12 .. 1 .. 18 .. 61 Bangladesha .. 12 .. 21 .. 23 .. 32 .. 13 Belarusa 39 12 5 10 1 2 55 69 0 7 Belgium 3 3 7 7 18 8 71 81 2 2 Benina .. 23 .. 40 .. 2 .. 33 .. 2 Bolivia .. 14 .. 22 .. 10 .. 47 .. 7 Bosnia and Herzegovina .. 23 .. 28 .. 1 .. 45 .. 3 Botswanaa 32 .. 31 .. 2 .. 36 .. 2 .. Brazila .. 13 .. 18 .. 15 .. 52 .. 2 Bulgariaa 18 13 7 18 37 3 38 60 2 6 Burkina Faso .. 21 .. 44 .. 3 .. 10 .. 23 Burundia 20 .. 30 .. 6 .. 14 .. 10 .. Cambodia .. 41 .. 33 .. 2 .. 19 .. 5 Cameroona 17 .. 40 .. 26 .. 14 .. .. .. Canadaa 8 8 10 12 18 7 64 67 .. 6 Central African Republica .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. Chile .. 10 .. 20 .. 3 .. 59 .. 13 Chinaa .. 27 .. 5 .. 4 .. 60 .. 4 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. Colombia .. 8 .. 15 .. 29 .. 41 .. 7 Congo, Dem. Rep.a 37 .. 58 .. 1 .. 2 .. .. .. Congo, Rep. .. 18 .. 18 .. 11 .. 53 .. 0 Costa Ricaa .. 12 .. 44 .. 10 .. 14 .. 20 Côte d'Ivoirea .. 32 .. 38 .. 10 .. 13 .. 7 Croatiaa 35 10 27 26 3 5 32 53 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republica 7 6 9 8 3 4 75 70 5 12 Denmark 8 9 13 13 13 5 64 70 5 4 Dominican Republica .. 19 .. 34 .. 8 .. 29 .. 8 Ecuador a 6 .. 49 .. 26 .. .. .. .. .. Egypt, Arab Rep.a 18 7 22 23 26 15 6 46 .. 9 El Salvador .. 17 .. 37 .. 11 .. 22 .. 15 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. 14 .. 22 .. 0 .. 43 .. 4 Ethiopiaa .. .. .. .. .. .. .. .. .. .. Finland 8 10 9 10 8 4 68 72 11 8 France 8 6 23 22 7 6 59 62 6 6 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. Georgiaa 52 27 11 16 10 2 26 45 .. 9 Germany 4 5 5 5 6 6 67 82 20 3 Ghanaa .. 15 .. 38 .. 11 .. 37 .. 0 Greece 10 11 22 24 27 10 36 45 5 4 Guatemalaa 15 15 50 27 12 12 18 37 6 10 Guineaa 17 .. 34 .. 28 .. 9 .. 1 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. Honduras .. 16 .. 50 .. 3 .. 13 .. 18 266 2010 World Development Indicators 4.11 ECONOMY Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 8 9 10 13 17 9 56 63 13 9 Indiaa 15 11 10 7 27 22 33 54 0 7 Indonesiaa 21 .. 20 .. 16 .. 41 .. 2 .. Iran, Islamic Rep.a 21 10 56 38 0 1 .. 37 .. 13 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5 12 15 24 14 3 33 37 1 1 Israel .. 28 .. 25 .. 9 .. 31 .. 9 Italy 4 4 14 15 24 12 54 65 6 5 Jamaicaa .. 8 .. 17 .. 35 .. 6 .. 33 Japan .. .. .. .. .. .. .. .. .. .. Jordana .. 8 .. 45 .. 7 .. 36 .. 5 Kazakhstana .. 20 .. 7 3 3 58 68 .. 2 Kenyaa 15 20 28 38 46 11 .. 31 2 0 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 16 7 15 12 3 7 63 58 3 15 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 33 19 31 29 5 0 24 29 7 23 Kyrgyz Republica 32 29 37 28 5 4 27 36 .. 3 Lao PDR .. 37 .. 38 .. 5 .. 18 .. 3 Latviaa 20 11 20 19 3 1 56 65 0 4 Lebanon .. 3 .. 27 .. 37 .. 29 .. 3 Lesothoa 32 42 45 35 5 2 8 14 3 7 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 13 .. 18 .. 2 .. 65 .. 6 Macedonia, FYR .. 28 .. 17 .. 2 .. 49 .. 4 Madagascar .. 14 .. 46 .. 10 .. 14 .. 16 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 23 .. 34 .. 17 .. 27 .. 1 .. Mali .. 38 .. 33 .. 2 .. 16 .. 11 Mauritania .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 11 45 34 13 16 28 34 2 6 Mexicoa 9 .. 19 .. 19 .. .. .. .. .. Moldovaa 10 20 8 14 11 4 71 57 1 6 Mongolia 30 27 12 34 2 1 56 38 0 1 Moroccoa .. 9 .. 43 .. 5 .. 36 .. 9 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibiaa 29 20 53 45 1 8 .. 13 4 14 Nepala .. .. .. .. .. 7 .. .. .. .. Netherlands 5 8 8 8 9 5 77 79 3 3 New Zealand .. 30 .. 25 .. 4 .. 38 .. 7 Nicaraguaa 14 16 25 36 17 6 29 36 14 7 Niger .. 30 .. 30 .. 3 .. 9 .. 28 Nigeria .. .. .. .. .. .. .. .. .. .. Norway .. 11 .. 17 .. 3 .. 67 .. 6 Omana 55 .. 30 .. 7 .. 8 .. 0 .. Pakistana .. 21 .. 4 28 26 2 27 .. 23 Panamaa 16 .. 45 .. 8 .. 30 .. 1 .. Papua New Guineaa 19 .. 36 .. 20 .. 26 .. 1 .. Paraguaya .. 10 .. 55 .. 4 .. 23 .. 8 Perua 20 18 19 18 19 9 33 49 8 7 Philippinesa 15 25 34 30 33 22 15 19 .. 4 Poland .. 8 .. 12 .. 6 .. 69 .. 7 Portugal 8 7 29 26 14 7 42 50 9 2 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. 29 .. 33 .. 4 .. 17 .. 18 2010 World Development Indicators 267 4.11 Central government expenses Goods and Compensation Interest Subsidies and Other services of employees payments other transfers expense % of expense % of expense % of expense % of expense % of expense 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania .. 13 .. 19 .. 2 .. 60 .. 8 Russian Federation .. 14 .. 18 .. 2 .. 64 .. 7 Rwandaa 52 .. 36 .. 12 .. 5 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala .. .. .. .. .. .. .. .. .. .. Serbia .. 13 .. 27 .. 2 .. 57 .. 1 Sierra Leonea .. .. .. .. .. .. .. .. .. .. Singaporea 38 40 39 27 8 0 15 0 .. .. Slovak Republic .. 11 .. 14 .. 4 .. 67 .. 5 Sloveniaa 19 13 21 19 3 3 55 62 3 3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 12 .. 13 .. 8 .. 61 .. 6 Spain 5 4 14 9 12 5 42 79 2 6 Sri Lankaa 23 11 20 30 22 26 24 23 10 10 Sudana 44 .. 38 .. 8 .. 10 .. .. .. Swazilanda .. .. .. .. .. .. .. .. .. .. Sweden 10 .. 5 .. 13 .. 71 .. 1 .. Switzerlanda 24 8 6 7 4 5 66 76 1 4 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 47 .. 8 .. 12 .. 33 .. .. .. Tanzania .. .. .. .. .. .. .. .. .. .. Thailand .. 27 .. 35 .. 5 .. 31 .. 4 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togoa .. 21 .. 35 .. 6 .. 19 .. 19 Trinidad and Tobagoa 20 17 36 26 20 8 24 40 1 8 Tunisiaa 7 6 37 35 13 8 36 41 7 11 Turkeya .. 8 .. 26 .. 24 .. 41 .. 4 Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa .. 28 .. 13 .. 8 .. 51 .. 0 Ukrainea .. 12 .. 13 .. 1 .. 71 .. 4 United Arab Emiratesa 50 .. 37 .. .. .. .. .. .. .. United Kingdom 14 18 15 14 9 5 57 51 8 13 United States .. 16 .. 12 .. 9 .. 60 .. 5 Uruguaya 13 15 17 24 6 11 64 50 0 .. Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 6 6 22 16 27 12 61 64 2 3 Vietnam .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 8 .. 67 .. 16 .. 8 .. 0 .. Zambiaa 32 32 35 30 16 7 19 24 0 7 Zimbabwea 16 .. 34 .. 31 .. 19 .. .. .. World .. m 14 m .. m 24 m .. m 6m .. m 43 m .. m 7m Low income .. .. .. .. .. .. .. .. .. .. Middle income .. 13 .. 23 .. 5 .. 43 .. 7 Lower middle income .. 16 .. 34 .. 5 .. 36 .. 9 Upper middle income .. 12 .. 18 .. 3 .. 52 .. 6 Low & middle income .. 15 .. 26 .. 6 .. 36 .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 13 .. 18 .. 2 .. 59 .. 6 Latin America & Carib. .. 14 .. 25 .. 10 .. 36 .. 10 Middle East & N. Africa .. 8 .. 35 .. 7 .. 37 .. 9 South Asia .. 17 .. 14 27 22 24 29 .. 10 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 8 10 14 15 9 5 56 62 4 6 Euro area 5 6 14 14 12 6 55 67 5 5 Note: Components may not sum to 100 percent because of rounding or missing data. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 268 2010 World Development Indicators 4.11 ECONOMY Central government expenses About the data Definitions The term expense has replaced expenditure in the transfers, and other expenses. The economic clas- · Goods and services are all government payments table since the 2005 edition of World Development sification can be problematic. For example, the dis- in exchange for goods and services used for the Indicators in accordance with use in the International tinction between current and capital expense may production of market and nonmarket goods and ser- Monetary Fund's (IMF) Government Finance Statis- be arbitrary, and subsidies to public corporations or vices. Own-account capital formation is excluded. tics Manual 2001. Government expenses include all banks may be disguised as capital financing. Subsi- · Compensation of employees is all payments in nonrepayable payments, whether current or capital, dies may also be hidden in special contractual pric- cash, as well as in kind (such as food and hous- requited or unrequited. The concept of total central ing for goods and services. For further discussion of ing), to employees in return for services rendered, government expense as presented in the IMF's Gov- government finance statistics, see About the data for and government contributions to social insurance ernment Finance Statistics Yearbook is comparable to tables 4.10 and 4.12. schemes such as social security and pensions that the concept used in the 1993 United Nations System provide benefits to employees. · Interest payments of National Accounts. are payments made to nonresidents, to residents, Expenses can be measured either by function and to other general government units for the use of (health, defense, education) or by economic type borrowed money. (Repayment of principal is shown (interest payments, wages and salaries, purchases as a financing item, and commission charges are of goods and services). Functional data are often shown as purchases of services.) · Subsidies and incomplete, and coverage varies by country because other transfers include all unrequited, nonrepayable functional responsibilities stretch across levels of transfers on current account to private and public government for which no data are available. Defense enterprises; grants to foreign governments, inter- expenses, usually the central government's respon- national organizations, and other government units; sibility, are shown in table 5.7. For more information and social security, social assistance benefits, and on education expenses, see table 2.11; for more on employer social benefits in cash and in kind. · Other health expenses, see table 2.16. expense is spending on dividends, rent, and other The classification of expenses by economic type in miscellaneous expenses, including provision for con- the table shows whether the government produces sumption of fixed capital. goods and services and distributes them, purchases the goods and services from a third party and dis- tributes them, or transfers cash to households to make the purchases directly. When the government produces and provides goods and services, the cost is reflected in compensation of employees, use of goods and services, and consumption of fixed capi- tal. Purchases from a third party and cash transfers to households are shown as subsidies and other Interest payments are a large part of government expenses for some developing economies 4.11a Central government interest payments as a share of total expense, 2008 (percent) 40 30 20 Data sources 10 Data on central government expenses are from the IMF's Government Finance Statistics Yearbook 0 2008 and data files. Each country's accounts n ca a an sh es a s s p. il a a y y ua ke az lle iu ny no bi di al Re ai de in st rit m In em Br r Ke ug he are reported using the system of common defi - ba Tu m pp ki lo la au ab Ur Ja yc Pa at Le Co ng ili M Ar Se Gu Ph Ba nitions and classifications in the IMF's Govern- t, yp Eg ment Finance Statistics Manual 2001. See these Interest payments accounted for more than 11 percent of total expenses in 2008 for 15 countries. sources for complete and authoritative explana- Source: International Monetary Fund, Government Finance Statistics data files. tions of concepts, definitions, and data sources. 2010 World Development Indicators 269 4.12 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistana .. 5 .. 5 .. 9 .. 0 .. 1 .. 80 Albaniaa 8 .. 39 .. 14 .. 1 .. 15 .. 23 .. Algeriaa .. 58 .. 34 .. 3 .. 1 .. .. .. 4 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. .. .. .. .. .. .. .. .. .. .. .. Armeniaa .. 17 .. 43 .. 5 .. 10 .. 13 .. 13 Australia .. 66 .. 23 .. 2 .. 0 .. .. .. 9 Austria 21 26 22 23 0 0 5 5 43 40 9 6 Azerbaijana .. 33 .. 23 .. 4 .. 1 .. .. .. 39 Bangladesha .. 19 .. 28 .. 27 .. 4 .. .. .. 23 Belarusa 16 7 33 30 6 21 11 7 31 29 3 6 Belgium 36 37 23 24 .. .. 2 1 36 35 3 3 Benina .. 18 .. 38 .. 20 .. 10 .. 2 .. 13 Bolivia .. 10 .. 43 .. 3 .. 9 .. 7 .. 28 Bosnia and Herzegovina .. 3 .. 46 .. 0 .. 4 .. 37 .. 10 Botswanaa 21 .. 4 .. 15 .. 0 .. .. .. 59 .. Brazila .. 31 .. 23 .. 2 .. 10 .. 23 .. 11 Bulgariaa 17 17 28 47 8 1 4 0 21 22 23 14 Burkina Faso .. 12 .. 39 .. 13 .. 7 .. .. .. 30 Burundia 14 .. 30 .. 20 .. 1 .. 5 .. 31 .. Cambodia .. 10 .. 40 .. 22 .. 0 .. .. .. 28 Cameroona 17 .. 25 .. 28 .. 3 .. 2 .. 25 .. Canadaa 50 55 17 16 2 1 .. .. 22 21 10 7 Central African Republica .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 29 .. 39 .. 1 .. 7 .. 6 .. 18 Chinaa 9 25 61 57 8 5 0 1 .. .. 22 12 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 16 .. 25 .. 5 .. 8 .. 4 .. 42 Congo, Dem. Rep.a 21 .. 12 .. 21 .. 5 .. 1 .. 41 .. Congo, Rep. .. 5 .. 6 .. 3 .. 1 .. 1 .. 84 Costa Ricaa .. 17 .. 37 .. 5 .. 3 .. 31 .. 7 Côte d'Ivoirea .. 23 .. 15 .. 35 .. 3 .. 7 .. 18 Croatiaa 11 9 42 45 9 1 1 1 33 33 4 11 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republica 15 18 32 26 4 0 1 1 40 45 8 10 Denmark 34 44 40 40 .. .. 7 2 5 3 14 10 Dominican Republica .. 22 .. 52 .. 10 .. 4 .. 1 .. 11 Ecuador a 50 .. 26 .. 11 .. 1 .. .. .. 12 .. Egypt, Arab Rep.a 17 27 13 20 10 6 10 3 10 .. 41 45 El Salvador .. 24 .. 41 .. 4 .. 1 .. 10 .. 21 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 11 .. 41 .. .. .. .. .. 34 .. .. Ethiopiaa .. .. .. .. .. .. .. .. .. .. .. .. Finland 16 21 31 32 0 0 1 2 34 31 17 14 France 17 25 26 23 0 0 3 4 47 43 8 6 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea 14 .. 32 .. 42 .. 0 .. 0 .. 7 .. Georgiaa 7 33 48 47 10 1 .. 2 13 17 22 18 Germany 16 18 20 23 .. .. 0 .. 58 55 6 4 Ghanaa 15 19 31 34 24 18 .. .. .. .. 9 30 Greece 18 19 32 29 0 0 3 3 31 36 16 14 Guatemalaa 19 27 46 58 23 8 3 1 2 2 6 4 Guineaa 8 .. 5 .. 62 .. 2 .. 1 .. 23 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras .. 20 .. 39 .. 5 .. 1 .. 11 .. 24 270 2010 World Development Indicators 4.12 ECONOMY Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 16 25 28 31 10 0 1 2 35 34 9 9 Indiaa 23 44 28 27 24 15 0 0 0 0 25 14 Indonesiaa 46 .. 33 .. 4 .. 1 .. 6 .. 9 .. Iran, Islamic Rep.a 12 16 6 2 9 6 1 1 7 14 66 62 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 37 37 .. 34 0 0 2 6 17 18 9 4 Israel .. 29 .. 30 .. 1 .. 5 .. 17 .. 18 Italy 32 35 21 20 .. .. 6 5 35 36 6 4 Jamaicaa .. 37 .. 31 .. 7 .. 10 .. 2 .. 13 Japan 35 .. 14 .. 1 .. 6 .. 26 .. 18 .. Jordana .. 11 .. 30 .. 5 .. 3 .. 0 .. 51 Kazakhstana 11 28 28 21 3 13 5 0 48 .. 6 38 Kenyaa 35 37 40 41 14 11 1 1 0 0 10 10 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 32 31 32 25 7 3 10 8 8 15 12 18 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 1 1 0 .. 2 1 0 0 .. .. 97 98 Kyrgyz Republica 26 11 56 53 5 11 1 .. .. .. 11 25 Lao PDR .. 19 .. 36 .. 9 .. 1 .. .. .. 36 Latviaa 7 15 41 36 3 1 0 0 35 30 14 18 Lebanon .. 13 .. 39 .. 7 .. 14 .. 1 .. 26 Lesothoa 15 17 12 12 49 57 1 3 .. .. 24 11 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 21 .. 37 .. .. .. 0 .. 32 .. 10 Macedonia, FYR .. 13 .. 40 .. 5 .. 0 .. 29 .. 14 Madagascar .. 9 .. 18 .. 35 .. 9 .. .. .. 29 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 37 .. 26 .. 12 .. 5 .. 1 .. 19 .. Mali .. 18 .. 38 .. 9 .. 8 .. .. .. 27 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 18 25 46 34 11 6 8 6 4 17 13 Mexicoa 27 .. 54 .. 4 .. 2 .. 14 .. 16 .. Moldovaa 6 1 38 50 5 5 1 0 38 29 2 14 Mongolia 31 38 18 26 9 8 0 1 15 11 27 17 Moroccoa .. 31 .. 32 .. 6 .. 5 .. 10 .. 16 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 20 25 26 31 12 2 .. .. .. .. 42 42 Namibiaa 27 28 32 19 28 44 2 2 .. 0 11 7 Nepala 10 14 33 35 26 18 4 5 .. .. 27 29 Netherlands 26 27 24 28 .. 1 2 3 40 34 8 8 New Zealand .. 57 .. 26 .. 3 .. 0 .. 0 .. 15 Nicaraguaa 9 26 52 50 7 4 0 0 11 19 31 20 Niger .. 12 .. 18 .. 26 .. 3 .. .. .. 41 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 33 .. 21 .. 0 .. 1 .. 17 .. 28 Omana 21 .. 1 .. 3 .. 2 .. .. .. 74 .. Pakistana 18 27 27 33 24 11 7 1 .. .. 24 28 Panamaa 20 .. 17 .. 11 .. 3 .. 16 .. 34 .. Papua New Guineaa 40 .. 8 .. 27 .. 2 .. 1 .. 23 .. Paraguaya .. 10 .. 39 .. 7 .. 1 .. 17 .. 26 Perua 15 33 46 37 10 3 8 6 11 8 11 13 Philippinesa 33 41 26 26 29 22 4 6 .. .. 8 11 Poland .. 16 .. 39 .. 0 .. 1 .. 35 .. 9 Portugal 22 23 32 32 0 0 2 2 30 33 14 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. 45 .. .. .. 3 .. .. .. .. .. 53 2010 World Development Indicators 271 4.12 Central government revenues Taxes on income, Taxes on Taxes on Other Social Grants and profits, and goods and international taxes contributions other revenue capital gains services trade % of revenue % of revenue % of revenue % of revenue % of revenue % of revenue 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania .. 22 .. 35 .. 0 .. 0 .. 33 .. 10 Russian Federation .. 5 .. 16 .. 25 .. 0 .. 16 .. 38 Rwandaa 11 .. 25 .. 23 .. 3 .. 2 .. 36 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 17 .. 19 .. 36 .. 2 .. .. .. 26 .. Serbiaa .. 10 .. 42 .. 6 .. 0 .. 35 .. 7 Sierra Leonea 15 .. 34 .. 39 .. 0 .. .. .. 12 .. Singaporea 26 34 20 22 1 0 15 11 .. .. 38 33 Slovak Republic .. 13 .. 33 .. 0 .. 0 .. 41 .. 14 Sloveniaa 13 17 33 32 9 0 0 2 42 38 3 11 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 54 .. 30 .. 3 .. 3 .. 2 .. 8 Spain 28 28 21 15 0 .. 0 0 40 52 .. 5 Sri Lankaa 12 18 50 48 17 14 4 5 1 2 18 13 Sudana 17 .. 41 .. 27 .. 1 .. .. .. 14 .. Swazilanda .. .. .. .. .. .. .. .. .. .. .. .. Sweden 12 .. 31 .. 1 .. 7 .. 37 .. 13 .. Switzerlanda 11 19 21 32 1 1 2 2 49 36 17 10 Syrian Arab Republica 23 .. 37 .. 13 .. 8 .. 0 .. 19 .. Tajikistana 6 .. 63 .. 12 .. 0 .. 14 .. 5 .. Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 39 .. 37 .. 5 .. 1 .. 5 .. 14 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togoa .. 17 .. 43 .. 21 .. 3 .. .. .. 16 Trinidad and Tobagoa 50 57 26 15 6 5 1 9 2 4 15 11 Tunisiaa 16 28 20 31 28 6 4 4 15 17 17 13 Turkeya .. 26 .. 49 .. 1 .. 6 .. .. .. 18 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 22 45 49 7 9 2 0 .. .. 37 19 Ukrainea .. 14 .. 31 .. 4 .. 0 .. 36 .. 16 United Arab Emiratesa .. .. 15 .. .. .. .. .. 1 .. 84 .. United Kingdom 37 37 32 27 .. .. 6 10 20 21 6 5 United States .. 53 .. 3 .. 1 .. 1 .. 39 .. 4 Uruguaya 10 20 32 48 4 4 10 ­3 31 22 8 9 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 38 22 33 25 9 5 0 4 4 2 20 43 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 17 .. 10 .. 18 .. 3 .. .. .. 51 .. Zambiaa 27 33 22 36 36 8 0 0 0 .. 15 23 Zimbabwea 36 .. 23 .. 17 .. 3 .. 3 .. 19 .. World .. m 21 m .. m 33 m .. m 5m .. m 2m .. m .. m .. m 14 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. 22 .. 36 .. 5 .. 1 .. 13 .. 14 Lower middle income .. 26 .. 32 .. 6 .. 1 .. .. .. 17 Upper middle income .. 18 .. 37 .. 4 .. 3 .. 22 .. 13 Low & middle income .. 18 .. 36 .. 7 .. 2 .. .. .. 16 East Asia & Pacific 33 .. 26 .. 12 .. 2 .. .. .. 22 .. Europe & Central Asia .. 16 .. 40 .. 4 .. 0 .. 30 .. 14 Latin America & Carib. .. 25 .. 39 .. 4 .. 2 .. 10 .. 16 Middle East & N. Africa .. 27 .. 31 .. 6 .. 3 .. .. .. 26 South Asia 15 19 31 28 24 15 4 1 .. 0 25 28 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 21 29 25 27 .. 1 2 2 35 34 10 10 Euro area 22 26 24 24 0 0 2 2 36 38 8 6 Note: Components may not sum to 100 percent because of missing data or adjustment to tax revenue. a. Data were reported on a cash basis and have been adjusted to the accrual framework. 272 2010 World Development Indicators 4.12 ECONOMY Central government revenues About the data Definitions The International Monetary Fund (IMF) classifi es Direct taxes tend to be progressive, whereas indirect · Taxes on income, profits, and capital gains are government revenues as taxes, grants, and property taxes are proportional. levied on the actual or presumptive net income income. Taxes are classified by the base on which Social security taxes do not reflect compulsory pay- of individuals, on the profi ts of corporations and the tax is levied, grants by the source, and property ments made by employers to provident funds or other enterprises, and on capital gains, whether real- income by type (for example, interest, dividends, agencies with a like purpose. Similarly, expenditures ized or not, on land, securities, and other assets. or rent). The most important source of revenue is from such funds are not reflected in government Intragovernmental payments are eliminated in con- taxes. Grants are unrequited, nonrepayable, non- expenses (see table 4.11). For further discussion of solidation. · Taxes on goods and services include compulsory receipts from other government units taxes and tax policies, see About the data for table general sales and turnover or value added taxes, and foreign governments or from international orga- 5.6. For further discussion of government revenues selective excises on goods, selective taxes on ser- nizations. Transactions are generally recorded on an and expenditures, see About the data for tables 4.10 vices, taxes on the use of goods or property, taxes accrual basis. and 4.11. on extraction and production of minerals, and prof- The IMF's Government Finance Statistics Manual its of fiscal monopolies. · Taxes on international 2001 describes taxes as compulsory, unrequited trade include import duties, export duties, profi ts payments made to governments by individuals, busi- of export or import monopolies, exchange profi ts, nesses, or institutions. Taxes are classified in six and exchange taxes. · Other taxes include employer major groups by the base on which the tax is levied: payroll or labor taxes, taxes on property, and taxes income, profits, and capital gains; payroll and work- not allocable to other categories, such as penalties force; property; goods and services; international for late payment or nonpayment of taxes. · Social trade and transactions; and other. However, the dis- contributions include social security contributions by tinctions are not always clear. Taxes levied on the employees, employers, and self-employed individu- income and profits of individuals and corporations als, and other contributions whose source cannot are classified as direct taxes, and taxes and duties be determined. They also include actual or imputed levied on goods and services are classified as indi- contributions to social insurance schemes operated rect taxes. This distinction may be a useful simplifica- by governments. · Grants and other revenue include tion, but it has no particular analytical significance grants from other foreign governments, international except with respect to the capacity to fix tax rates. organizations, and other government units; interest; dividends; rent; requited, nonrepayable receipts for Rich economies rely more on direct taxes 4.12a public purposes (such as fines, administrative fees, and entrepreneurial income from government owner- Taxes on income and capital gains as a share of central government revenue, 2008 (percent) ship of property); and voluntary, unrequited, nonre- 70 payable receipts other than grants. 60 South Africa 50 United States India 40 30 Norway Data sources 20 Data on central government revenues are from 10 the IMF's Government Finance Statistics Yearbook 2008 and data files. Each country's accounts 0 are reported using the system of common defini- 100 1,000 10,000 100,000 tions and classifications in the IMF's Government GNI per capita ($, log scale) Finance Statistics Manual 2001. The IMF receives Low income Middle income High income additional information from the Organisation for High-income economies tend to tax income and property, whereas low-income economies tend to rely on Economic Co-operation and Development on the indirect taxes on international trade and goods and services. But there are exceptions in all groups. tax revenues of some of its members. See the IMF sources for complete and authoritative explana- Note: Data are for the most recent year for 2005­08. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2010 World Development Indicators 273 4.13 Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. 27.8 .. 12.8 .. 1.2 .. .. .. 14.9 .. 9.6 Albania 51.8 7.7 1.8 12.3 ­8.3 2.1 15.3 6.8 19.7 13.0 13.3 10.3 Algeria 9.6 15.7 1.0 3.6 ­10.0 ­26.9 16.6 1.8 18.4 8.0 ­7.9 ­2.5 Angola 4,105.6 66.2 471.4 36.9 119.5 18.0 125.9 6.2 206.3 12.5 ­84.7 ­9.2 Argentina ­2.8 8.1 ­1.1 9.1 7.8 ­1.2 11.9 11.0 17.9 19.5 14.2 0.3 Armenia 64.3 2.4 70.3 29.9 7.2 2.2 63.2 6.6 111.9 17.1 ­18.9 8.0 Australia 8.5 14.2 12.5 15.0 0.4 3.3 6.1 5.2 10.7 8.9 9.1 4.3 Austriaa .. .. .. .. .. .. 2.2 .. 6.4 .. 6.1 .. Azerbaijan 25.4 44.3 6.1 37.2 ­32.7 ­15.2 .. 12.2 .. 19.8 .. ­1.0 Bangladesh 12.1 16.3 25.0 13.5 4.8 3.4 6.0 9.7 14.0 16.4 6.2 7.0 Belarus 158.4 28.3 61.4 55.8 44.7 ­12.5 100.8 8.5 175.0 8.6 ­63.9 ­9.9 Belgiuma .. .. .. .. .. .. 4.0 .. 8.4 8.6 7.1 6.8 Benin ­1.8 26.6 2.2 12.0 6.0 12.2 3.5 3.5 16.8 .. 13.0 .. Bolivia 7.7 22.7 13.7 4.5 1.1 ­2.1 18.9 4.7 51.0 13.9 35.5 3.2 Bosnia and Herzegovina 22.0 ­0.1 23.9 17.3 ­0.4 0.6 51.9 3.5 73.5 7.0 76.3 ­0.7 Botswana 12.3 21.1 ­1.7 12.6 10.0 ­5.9 9.8 8.7 14.4 16.5 5.2 ­0.4 Brazil 44.3 17.3 40.5 20.6 14.6 7.6 52.2 11.7 78.2 47.3 65.5 39.1 Bulgaria 40.5 8.8 22.1 27.5 ­7.2 ­2.0 35.9 4.4 79.4 10.9 10.1 ­0.5 Burkina Faso 22.3 12.3 2.9 15.3 ­7.3 4.1 3.5 3.5 16.8 .. 16.5 .. Burundi ­8.0 42.4 ­7.1 11.3 0.2 4.0 .. .. 15.3 16.5 ­0.7 ­6.4 Cambodia 43.6 5.4 12.5 30.7 1.2 ­10.5 8.7 1.9 18.7 16.4 6.4 11.2 Cameroon ­6.2 13.7 0.3 9.3 ­2.2 ­9.1 5.5 3.8 16.0 15.0 6.0 12.7 Canada 4.8 15.1 3.8 6.1 0.2 5.0 5.3 1.5 8.7 4.7 6.2 0.5 Central African Republic 4.3 16.5 3.9 6.0 ­7.9 10.2 5.5 3.8 16.0 15.0 5.2 12.2 Chad 48.8 13.6 6.4 10.1 ­18.6 ­46.5 5.5 3.8 16.0 15.0 6.6 9.4 Chile 24.3 15.6 34.9 24.6 ­2.0 ­3.6 13.7 7.5 18.2 13.3 7.0 13.0 China 29.5 17.8 22.5 9.9 0.8 0.3 11.0 2.3 12.1 5.3 ­1.5 ­1.8 Hong Kong SAR, China 10.6 4.2 9.8 2.8 ­2.4 ­1.5 5.6 0.4 8.8 5.0 4.4 3.5 Colombia 28.2 8.5 34.3 23.1 2.9 2.5 32.3 9.7 42.7 17.2 20.1 8.2 Congo, Dem. Rep. 357.6 55.7 59.6 42.7 ­7.9 5.3 60.0 .. 293.9 .. ­30.5 .. Congo, Rep. ­0.1 37.1 6.3 10.6 2.0 ­83.4 5.5 3.8 16.0 15.0 12.2 24.9 Costa Rica 4.7 11.2 ­1.4 51.7 5.6 0.4 23.9 4.2 36.7 15.8 11.9 3.3 Côte d'Ivoire 18.1 5.7 13.3 6.1 0.3 ­1.1 3.5 3.5 16.8 .. 16.8 .. Croatia 40.4 4.4 30.5 11.0 ­2.4 2.9 5.5 2.8 20.2 10.1 ­2.9 3.5 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 29.3 8.6 15.8 9.7 2.1 0.4 7.0 1.6 12.8 6.3 ­3.6 4.3 Denmark 6.2 7.8 2.6 30.7 ­1.5 ­14.0 3.9 .. 10.3 .. 9.0 .. Dominican Republic 16.6 1.3 14.4 10.5 ­1.7 12.3 14.9 10.3 30.7 20.0 19.4 9.2 Ecuador 6.8 23.6 15.1 21.6 ­74.8 ­12.8 43.3 4.9 55.7 12.1 45.7 4.8 Egypt, Arab Rep. 9.9 10.5 12.1 6.2 0.6 4.6 10.9 6.6 16.5 12.3 4.6 0.4 El Salvador 13.5 ­0.4 22.6 4.5 ­0.9 0.7 14.4 .. 19.1 .. 7.8 .. Eritrea 21.0 15.9 27.8 1.9 20.5 14.0 .. .. .. .. .. .. Estonia 27.5 6.0 28.9 12.5 ­9.3 1.8 8.7 5.7 19.0 8.6 ­11.4 1.8 Ethiopia 9.0 23.4 13.4 17.7 ­3.5 2.5 11.5 3.6 15.1 8.0 2.1 ­15.9 Finlanda .. .. .. .. .. .. 3.2 .. 7.8 .. 2.9 .. Francea .. .. .. .. .. .. 4.5 3.7 8.1 .. 6.7 .. Gabon 10.1 9.1 11.9 ­10.6 5.8 30.3 5.5 3.8 16.0 15.0 14.5 9.3 Gambia, The 14.2 18.4 ­5.0 6.8 15.2 21.4 12.5 12.9 25.0 27.0 20.3 21.0 Georgia 40.2 6.9 ­11.1 37.9 73.8 ­13.6 31.0 10.4 58.2 21.2 10.6 10.2 Germanya .. .. .. .. .. .. 3.9 .. 10.9 .. 8.9 .. Ghana 43.2 42.8 10.2 20.1 28.1 10.9 28.7 8.9 .. .. .. .. Greecea .. .. .. .. .. .. 15.8 2.2 23.1 .. 12.1 .. Guatemala 15.6 8.9 36.1 6.2 ­7.1 0.1 7.9 5.1 21.2 13.4 11.5 4.5 Guinea 11.3 33.4 12.1 19.8 8.4 18.1 17.5 14.4 21.5 .. 14.7 .. Guinea-Bissau 43.0 29.5 ­6.7 11.8 ­20.4 ­1.6 3.5 3.5 32.9 .. ­8.2 .. Haiti 27.1 11.1 15.7 2.7 0.1 ­3.6 32.5 2.1 15.1 17.8 ­0.8 ­2.8 Honduras 28.9 5.4 18.0 10.8 ­7.5 2.9 12.0 9.5 27.0 17.9 1.7 7.4 274 2010 World Development Indicators 4.13 ECONOMY Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 20.9 9.4 4.9 21.0 20.2 ­2.3 24.4 9.9 32.6 10.2 4.6 6.1 India 11.0 20.5 6.0 14.1 3.4 7.9 .. .. 15.5 13.3 5.9 6.7 Indonesia 27.5 15.0 25.9 18.7 ­2.3 ­7.0 16.7 8.5 18.9 13.6 8.3 ­4.0 Iran, Islamic Rep. 30.1 7.9 9.8 8.4 17.3 ­4.7 .. 11.6 .. 12.0 .. ­7.0 Iraq .. 35.2 .. 5.3 .. ­54.7 .. 11.4 .. 19.7 .. .. Irelanda .. .. .. .. .. .. 0.4 0.0 6.6 2.7 3.4 0.1 Israel 21.7 ­3.4 18.3 6.9 ­0.5 3.6 14.1 3.3 20.2 6.1 ­0.3 4.4 Italya .. .. .. .. .. .. 6.4 .. 13.2 6.8 7.9 3.8 Jamaica 28.0 5.7 18.0 14.2 6.1 11.3 23.2 7.6 43.6 16.8 17.5 ­3.6 Japan 4.1 0.8 1.3 0.5 2.5 2.7 0.9 0.6 3.5 1.9 4.0 3.0 Jordan 5.7 21.1 9.6 9.5 ­3.8 12.0 7.7 5.5 10.7 9.0 8.6 ­5.8 Kazakhstan 108.2 35.4 ­72.5 8.6 24.7 ­9.6 .. .. .. .. .. .. Kenya 29.0 15.6 26.7 17.3 6.6 3.3 13.6 5.3 28.8 14.0 15.8 0.8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 15.6 15.9 21.6 24.5 ­1.2 2.5 8.8 5.9 9.0 7.2 1.5 4.3 Kosovo .. 23.6 .. 25.8 .. ­1.6 .. .. .. 13.8 .. .. Kuwait 9.4 15.6 10.9 20.3 ­2.0 ­0.8 6.5 4.8 8.4 7.6 3.4 2.5 Kyrgyz Republic 14.8 33.2 0.1 29.2 62.6 ­8.8 36.7 4.0 65.0 19.9 21.9 ­1.1 Lao PDR 16.4 18.3 18.1 19.4 ­9.7 ­3.4 14.0 5.0 25.7 24.0 5.0 14.6 Latvia ­21.4 ­4.0 ­23.8 24.2 6.5 ­4.7 14.8 6.3 34.6 11.9 5.5 ­2.9 Lebanon 16.4 14.8 13.1 6.4 5.1 2.1 16.3 7.7 24.7 10.0 12.8 2.1 Lesotho 9.8 19.7 ­2.3 6.8 ­18.7 ­15.9 13.3 7.6 16.4 16.2 6.0 6.0 Liberia 29.5 42.6 ­6.0 17.5 37.2 21.6 6.4 3.8 15.6 14.4 8.5 3.6 Libya 9.6 49.2 3.1 9.5 3.6 ­37.4 5.5 2.5 7.0 6.0 .. ­15.5 Lithuania 28.9 ­0.7 12.7 24.3 ­2.4 3.9 20.1 7.7 27.1 8.4 ­14.5 ­1.7 Macedonia, FYR 1.8 10.9 ­138.9 24.7 ­229.7 2.2 24.1 5.9 46.0 9.7 24.6 2.4 Madagascar 16.2 12.8 9.4 12.8 ­10.3 ­8.5 18.5 11.5 37.5 45.0 ­5.3 32.5 Malawi 56.2 62.6 2.8 39.8 ­10.4 74.3 37.3 6.0 47.3 25.3 ­16.9 15.0 Malaysia 18.5 11.3 29.2 8.8 ­0.7 4.1 5.9 3.1 8.7 6.1 4.9 ­3.8 Mali 7.3 ­1.5 18.9 5.1 ­11.6 ­2.5 3.5 3.5 16.8 .. 14.5 .. Mauritania ­5.1 .. ­42.5 .. ­28.9 .. 9.0 8.0 20.3 23.5 17.0 26.7 Mauritius 18.6 14.7 8.7 20.8 3.0 0.6 12.2 10.1 20.8 21.5 14.6 12.9 Mexico 31.9 9.9 ­2.9 1.3 27.6 2.1 39.8 3.0 59.4 8.7 15.6 2.1 Moldova 65.3 15.9 34.6 11.9 19.1 ­2.5 25.4 17.9 36.7 21.1 7.7 10.3 Mongolia 32.6 ­6.7 14.4 24.4 ­31.8 5.8 74.6 11.2 134.4 20.6 46.9 ­1.5 Morocco 7.0 10.8 6.9 16.0 5.1 ­0.1 7.3 3.9 11.3 .. 3.1 .. Mozambique 47.7 23.9 21.8 24.5 ­12.5 ­1.8 38.8 11.0 24.4 18.3 18.0 9.8 Myanmar 36.5 14.8 13.4 2.6 19.7 13.2 9.8 12.0 16.5 17.0 ­2.6 .. Namibia 22.6 17.9 30.5 9.2 1.7 ­10.0 10.8 8.4 18.5 13.7 12.1 0.0 Nepal 15.4 23.3 18.1 15.9 3.3 0.5 9.6 2.3 12.9 8.0 4.7 0.3 Netherlandsa .. .. .. .. .. .. 4.4 4.4 7.2 4.6 5.0 1.7 New Zealand 9.4 10.3 16.2 14.0 ­4.1 4.5 8.5 7.6 11.3 12.2 9.1 7.9 Nicaragua 35.1 7.3 30.3 11.9 ­21.5 4.4 11.1 6.6 19.9 13.2 5.7 ­3.1 Niger 3.8 11.9 ­22.8 19.9 10.2 ­18.3 3.5 3.5 16.8 .. 15.5 .. Nigeria 19.4 52.5 22.3 51.9 ­9.1 ­23.5 13.5 12.0 20.2 15.5 ­22.9 4.1 Norway 3.8 .. 9.5 .. ­1.9 .. 5.0 5.5 7.6 7.3 4.4 ­1.8 Oman 7.7 23.3 9.3 40.5 ­2.3 ­20.4 6.5 4.5 9.4 7.1 7.5 2.1 Pakistan 13.8 19.5 10.8 10.0 8.7 7.1 .. 6.9 .. 12.9 .. ­2.9 Panama 8.4 14.6 14.5 16.4 ­4.3 ­3.1 7.2 3.5 11.1 8.2 10.6 ­0.3 Papua New Guinea 13.7 11.2 0.2 17.4 5.0 ­2.8 7.3 1.3 13.1 9.3 ­2.3 ­2.1 Paraguay 0.5 12.2 4.9 29.4 0.1 ­8.8 21.2 3.1 33.9 25.8 17.9 17.4 Peru 29.3 23.2 31.1 22.0 ­8.1 ­7.2 9.6 3.5 36.2 23.7 20.5 20.9 Philippines 23.9 5.4 27.9 2.1 3.0 0.1 8.4 4.5 14.7 8.8 6.6 1.1 Poland 35.6 19.1 19.1 29.9 3.1 9.0 26.8 2.2 33.5 5.5 ­5.2 3.9 Portugala .. .. .. .. .. .. 8.4 .. 13.8 .. 10.0 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 1.1 19.7 3.9 32.9 ­3.8 ­1.3 6.2 3.0 8.9 6.8 .. ­10.0 2010 World Development Indicators 275 4.13 Monetary indicators Money and Claims on Claims on Interest rate quasi money private sector governments and other public entities Annual growth Annual growth % annual % growth % of M2 % of M2 Deposit Lending Real 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 69.6 17.5 23.1 33.2 11.6 7.9 44.7 9.5 50.7 15.0 11.4 3.1 Russian Federation 112.6 14.6 46.2 31.2 73.6 ­17.2 102.0 5.8 320.3 12.2 72.3 ­5.8 Rwanda 69.5 18.0 32.7 14.5 ­41.0 ­13.8 11.1 6.8 18.5 16.5 6.9 ­0.8 Saudi Arabia 3.4 18.0 3.4 19.7 1.4 ­59.6 6.2 2.9 .. .. .. .. Senegal 7.4 1.8 1.2 10.6 1.0 ­3.3 3.5 3.5 16.8 .. 17.8 .. Serbia 33.0 9.8 88.5 29.8 34.1 6.8 19.1 7.3 78.0 18.1 23.0 0.5 Sierra Leone 19.6 22.5 1.6 13.9 ­101.6 19.9 7.0 9.7 28.8 24.5 ­3.6 12.0 Singapore 8.5 12.0 19.7 11.9 ­8.1 ­4.9 3.5 0.4 6.4 5.4 4.0 4.2 Slovak Republica .. .. .. .. .. .. 9.0 3.7 16.9 8.0 7.1 6.8 Sloveniaa .. .. .. .. .. .. 15.4 4.1 23.4 6.7 ­4.0 2.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 16.0 15.2 18.9 12.1 ­4.1 0.4 13.5 11.6 17.9 15.1 6.9 3.9 Spaina .. .. .. .. .. .. 7.7 .. 10.1 .. 4.9 .. Sri Lanka 35.8 8.4 75.4 6.2 5.4 13.3 12.1 10.9 18.0 18.9 8.0 2.2 Sudan 72.7 16.3 10.6 4.8 389.1 0.7 .. .. .. .. .. .. Swaziland 3.9 15.4 1.3 6.5 ­14.8 ­26.2 9.4 8.2 17.1 14.8 ­1.5 4.3 Sweden .. 10.1 .. 17.2 .. ­2.8 6.2 0.8 11.1 3.3 7.2 2.4 Switzerland 4.6 3.0 4.0 ­1.2 0.2 2.7 1.3 0.2 5.5 3.3 4.7 1.1 Syrian Arab Republic 9.2 25.2 3.9 7.3 6.1 ­2.9 4.0 8.3 9.0 10.2 2.2 ­3.2 Tajikistan .. ­3.6 .. 145.1 .. ­9.8 23.9 8.4 75.5 23.7 6.2 ­3.1 Tanzania 33.0 19.8 ­3.9 13.9 16.3 ­0.2 24.6 8.0 42.8 15.0 12.6 5.6 Thailand 17.7 8.7 40.3 8.0 ­4.2 0.9 11.6 2.5 13.3 7.0 7.3 3.1 Timor-Leste .. 34.1 .. 1.4 .. ­7.6 .. 0.8 .. 13.1 .. 2.2 Togo 22.3 18.2 17.6 ­2.6 14.9 15.7 3.5 3.5 17.5 .. 13.8 .. Trinidad and Tobago 4.0 17.1 9.0 7.9 0.6 ­17.4 6.9 7.4 15.2 12.4 10.7 5.1 Tunisia 6.6 14.8 10.4 14.7 ­1.2 ­0.3 .. .. .. .. .. .. Turkey 104.2 24.9 66.9 16.5 30.1 5.8 76.0 22.9 .. .. .. .. Turkmenistan 449.5 .. 76.3 .. ­573.1 .. .. .. .. .. .. .. Uganda 13.9 30.8 9.6 28.2 ­41.2 12.0 7.6 9.3 20.2 20.5 9.9 13.3 Ukraine 115.5 31.0 7.7 71.9 95.4 7.0 70.3 9.9 122.7 17.5 ­56.8 ­9.0 United Arab Emirates 10.2 19.2 10.7 43.6 ­4.3 ­1.3 .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. 4.1 .. 6.7 4.6 3.9 2.2 United States 6.9 8.0 6.0 1.7 0.2 ­3.1 .. .. 8.8 5.1 6.7 2.9 Uruguay 36.9 31.2 35.2 22.4 1.0 16.4 57.7 3.2 93.1 12.5 36.9 3.4 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 36.6 26.1 15.3 18.6 32.8 ­1.9 24.7 16.2 39.7 22.4 ­7.9 ­6.8 Vietnam 25.8 20.7 18.9 21.7 0.7 2.6 8.5 12.7 20.1 15.8 10.5 ­4.9 West Bank and Gaza .. 5.6 .. 2.9 .. 2.4 .. 3.0 .. 7.7 .. 2.3 Yemen, Rep. 50.7 13.2 6.0 4.1 13.3 1.6 23.8 13.0 31.5 18.0 ­3.2 ­0.5 Zambia 55.5 23.2 34.2 26.5 185.8 2.0 30.2 6.6 45.5 19.1 5.4 7.5 Zimbabwe 25.5 60,376.3 25.5 67,582.1 ­0.3 11,566.1 25.9 121.5 34.7 579.0 23.0 ­0.7 a. As members of the European Monetary Union, these countries share a single currency, the euro. 276 2010 World Development Indicators 4.13 ECONOMY Monetary indicators About the data Definitions Money and the financial accounts that record the reporting period. The valuation of financial deriva- · Money and quasi money are the sum of currency supply of money lie at the heart of a country's tives and the net liabilities of the banking system outside banks, demand deposits other than those of financial system. There are several commonly used can also be difficult. The quality of commercial bank the central government, and the time, savings, and defi nitions of the money supply. The narrowest, reporting also may be adversely affected by delays in foreign currency deposits of resident sectors other M1, encompasses currency held by the public and reports from bank branches, especially in countries than the central government. This definition of the demand deposits with banks. M2 includes M1 plus where branch accounts are not computerized. Thus money supply, often called M2, corresponds to lines time and savings deposits with banks that require the data in the balance sheets of commercial banks 34 and 35 in the IMF's International Financial Statis- prior notice for withdrawal. M3 includes M2 as well may be based on preliminary estimates subject to tics (IFS). The change in money supply is measured as various money market instruments, such as cer- constant revision. This problem is likely to be even as the difference in end-of-year totals relative to M2 tificates of deposit issued by banks, bank deposits more serious for nonbank financial intermediaries. in the preceding year. · Claims on private sector denominated in foreign currency, and deposits with Many interest rates coexist in an economy, reflect- (IFS line 32 d) include gross credit from the financial fi nancial institutions other than banks. However ing competitive conditions, the terms governing loans system to individuals, enterprises, nonfinancial pub- defined, money is a liability of the banking system, and deposits, and differences in the position and lic entities not included under net domestic credit, distinguished from other bank liabilities by the spe- status of creditors and debtors. In some economies and financial institutions not included elsewhere. cial role it plays as a medium of exchange, a unit of interest rates are set by regulation or administra- · Claims on governments and other public enti- account, and a store of value. tive fiat. In economies with imperfect markets, or ties (IFS line 32 an + 32 b + 32 bx + 32 c) usually The banking system's assets include its net for- where reported nominal rates are not indicative of comprise direct credit for specific purposes, such eign assets and net domestic credit. Net domestic effective rates, it may be difficult to obtain data on as financing the government budget deficit; loans credit includes credit extended to the private sector interest rates that reflect actual market transactions. to state enterprises; advances against future credit and general government and credit extended to the Deposit and lending rates are collected by the Inter- authorizations; and purchases of treasury bills and nonfinancial public sector in the form of investments national Monetary Fund (IMF) as representative inter- bonds, net of deposits by the public sector. Public in short- and long-term government securities and est rates offered by banks to resident customers. sector deposits with the banking system also include loans to state enterprises; liabilities to the public The terms and conditions attached to these rates sinking funds for the service of debt and temporary and private sectors in the form of deposits with the differ by country, however, limiting their comparabil- deposits of government revenues. · Deposit interest banking system are netted out. Net domestic credit ity. Real interest rates are calculated by adjusting rate is the rate paid by commercial or similar banks also includes credit to banking and nonbank financial nominal rates by an estimate of the inflation rate in for demand, time, or savings deposits. · Lending institutions. the economy. A negative real interest rate indicates interest rate is the rate charged by banks on loans to Domestic credit is the main vehicle through which a loss in the purchasing power of the principal. The prime customers. · Real interest rate is the lending changes in the money supply are regulated, with cen- real interest rates in the table are calculated as interest rate adjusted for inflation as measured by tral bank lending to the government often playing the (i ­ P) / (1 + P), where i is the nominal lending inter- the GDP deflator. most important role. The central bank can regulate est rate and P is the inflation rate (as measured by lending to the private sector in several ways--for the GDP deflator). example, by adjusting the cost of the refinancing facilities it provides to banks, by changing market interest rates through open market operations, or by Data sources controlling the availability of credit through changes in the reserve requirements imposed on banks and Data on monetary and financial statistics are ceilings on the credit provided by banks to the pri- published by the IMF in its monthly International vate sector. Financial Statistics and annual International Finan- Monetary accounts are derived from the balance cial Statistics Yearbook. The IMF collects data on sheets of financial institutions--the central bank, the financial systems of its member countries. The commercial banks, and nonbank financial interme- World Bank receives data from the IMF in elec- diaries. Although these balance sheets are usually tronic files that may contain more recent revisions reliable, they are subject to errors of classification, than the published sources. The discussion of valuation, and timing and to differences in account- monetary indicators draws from an IMF publication ing practices. For example, whether interest income by Marcello Caiola, A Manual for Country Econo- is recorded on an accrual or a cash basis can make mists (1995). Also see the IMF's Monetary and a substantial difference, as can the treatment of non- Financial Statistics Manual (2000) for guidelines performing assets. Valuation errors typically arise for the presentation of monetary and financial sta- for foreign exchange transactions, particularly in tistics. Data on real interest rates are derived from countries with flexible exchange rates or in countries World Bank data on the GDP deflator. that have undergone currency devaluation during the 2010 World Development Indicators 277 4.14 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2008 2009a 1995 2008 2008 2008 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Afghanistan 50.25 49.04 .. 17.0 0.3 .. .. 6.9 .. 12.9 .. .. Albania 83.89 93.98 26.0 46.4 0.5 .. 37.7 3.5 27.8 2.9 .. 4.8 Algeria 64.58 72.93 15.4 39.0 0.6 101.9 18.5 9.2 17.3 2.8 .. 3.5 Angola 75.03 88.75 0.0 60.8 0.8 .. 739.4 48.4 711.0 47.0 .. .. Argentina 3.14 3.79 1.0 1.8 0.6 .. 5.2 12.8b 8.9 10.4b 0.1 16.9 Armenia 305.97 380.47 116.6 195.2 0.6 140.4 212.5 4.6 70.5 3.8 .. 1.0 Australia 1.19 1.11 1.3 1.5 1.3 105.6 1.5 3.8 2.1 3.0 1.1 3.9 Austriac 0.68 0.68 0.9 0.9 1.3 101.8 1.6 1.8 2.2 2.0 0.3 2.8 Azerbaijan 0.82 0.80 0.2 0.5 0.6 .. 203.0 10.9 170.9 10.0 .. .. Bangladesh 68.60 69.16 19.2 25.6 0.4 .. 4.1 4.9 5.5 6.7 .. .. Belarus 2,136.40 2,838.98 3.5 1,084.7 0.5 .. 355.1 25.5 271.3 20.2 267.8 24.3 Belgiumc 0.68 0.68 0.9 0.9 1.3 104.9 1.8 2.1 1.9 2.2 1.2 2.8 Benin 447.81 449.31 187.5 234.6 0.5 .. 8.7 3.4 8.7 3.1 .. .. Bolivia 7.24 7.02 1.7 2.9 0.4 117.6 8.6 7.0 8.7 4.9 .. .. Bosnia and Herzegovina 1.34 1.34 0.6 0.8 0.6 .. 3.7 4.2 .. .. .. .. Botswana 6.83 6.54 1.4 3.5 0.5 .. 9.7 9.2 10.4 8.7 .. .. Brazil 1.83 1.75 0.7 1.5 0.8 .. 211.8 8.1 199.5 7.3 204.9 11.0 Bulgaria 1.34 1.34 0.0 0.7 0.6 121.6 103.3 5.6 117.5 6.3 85.7 6.7 Burkina Faso 447.81 449.31 189.6 201.5 0.5 .. 3.7 2.4 5.5 2.9 .. .. Burundi 1,185.73 1,230.50 126.6 446.2 0.4 99.5 13.4 9.6 16.1 8.5 .. .. Cambodia 4,054.17 4,160.00 1,142.8 1,478.8 0.4 94.3 4.4 4.6 6.3 5.6 .. .. Cameroon 447.81 449.31 241.2 250.3 0.6 108.7 6.3 2.2 6.5 2.3 .. .. Canada 1.07 1.05 1.2 1.2 1.2 108.8 1.5 2.8 1.7 2.2 2.7 1.5 Central African Republic 447.81 449.31 272.0 277.1 0.6 113.6 4.5 2.4 5.3 3.0 6.0 4.4 Chad 447.81 449.31 163.2 256.9 0.6 126.7 7.1 6.5 6.9 2.2 .. .. Chile 522.46 501.45 263.8 365.3 0.7 106.0 7.9 6.6 8.9 3.2 7.0 6.5 China 6.95 6.83 3.4 3.8 0.6 116.2 7.9 4.3 8.6 2.2 .. .. Hong Kong SAR, China 7.79 7.75 7.9 5.5 0.7 .. 4.5 ­1.7 5.9 0.0 0.6 1.1 Colombia 1,967.71 2,016.70 423.8 1,212.2 0.6 114.3 22.3 7.0 20.2 5.9 16.4 5.2 Congo, Dem. Rep. 559.29 904.31 0.0 323.8 0.6 107.4 964.9 28.5 930.2 26.9 .. .. Congo, Rep. 447.81 449.31 149.1 336.6 0.8 .. 9.0 7.2 9.3 3.1 .. .. Costa Rica 526.24 567.99 103.1 307.8 0.6 109.2 15.9 10.2 15.6 11.3 14.1 13.0 Côte d'Ivoire 447.81 449.31 261.9 308.4 0.7 106.0 9.2 3.4 7.2 3.0 .. .. Croatia 4.94 4.98 3.1 4.4 0.9 107.1 90.1 3.8 86.3 2.8 69.8 2.9 Cuba .. .. .. .. .. .. 3.0 2.6 .. .. .. .. Czech Republic 17.07 17.84 11.1 14.4 0.8 125.9 12.8 2.2 7.8 2.5 8.2 2.5 Denmark 5.10 5.09 8.5 8.6 1.7 103.6 1.6 2.4 2.1 2.0 1.1 2.7 Dominican Republic 34.62 36.21 7.3 19.5 0.6 97.3 9.8 15.1 8.7 16.0 .. .. Ecuador 1.00 1.00 0.4 0.5 0.5 93.1 4.4 9.8 37.1 7.0 .. 9.3 Egypt, Arab Rep. 5.43 5.60 1.2 2.0 0.4 .. 8.7 7.8 8.8 7.2 6.1 10.2 El Salvador 1.00 1.00 0.4 0.5 0.5 .. 6.2 3.7 8.5 3.9 .. 5.2 Eritrea 15.38 15.38 1.9 8.1 0.5 .. 7.9 18.0 .. .. .. .. Estonia 10.69 10.70 4.8 9.1 0.9 .. 53.6 5.6 21.6 4.3 8.1 3.3 Ethiopia 9.60 12.58 2.1 3.5 0.4 99.6 6.5 8.7 5.5 11.1 .. .. Finlandc 0.68 0.68 1.0 1.0 1.4 104.2 2.0 1.2 1.5 1.5 1.0 2.4 Francec 0.68 0.68 1.0 0.9 1.4 103.0 1.3 2.1 1.6 1.9 .. 2.1 Gabon 447.81 449.31 188.0 308.6 0.7 105.9 7.0 6.0 4.6 1.5 .. .. Gambia, The 22.19 26.90 3.9 8.0 0.4 125.6 4.2 10.8 4.0 8.1 .. .. Georgia 1.49 1.68 0.4 0.9 0.6 127.4 356.7 7.3 24.7 7.1 .. 7.4 Germanyc 0.68 0.68 1.0 0.9 1.3 103.0 1.7 1.1 2.1 1.7 0.4 2.7 Ghana 1.06 1.43 0.1 0.5 0.5 101.7 26.7 18.6 28.4 16.4 .. .. Greecec 0.68 0.68 0.6 0.7 1.1 105.6 9.2 3.3 9.0 3.4 3.6 4.7 Guatemala 7.56 8.33 2.9 4.5 0.6 .. 10.4 5.2 10.1 7.5 .. .. Guinea 5,500.00 .. 747.7 2,013.9 0.4 .. 5.5 16.5 .. .. .. .. Guinea-Bissau 447.81 449.31 114.6 243.1 0.5 .. 32.5 3.3 34.0 2.3 .. .. Haiti 39.11 41.88 5.8 25.4 0.7 .. 18.1 17.5 21.9 18.0 .. .. Honduras 18.90 18.90 3.0 9.3 0.5 .. 19.9 6.5 18.8 7.9 .. .. 278 2010 World Development Indicators 4.14 ECONOMY Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2008 2009a 1995 2008 2008 2008 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Hungary 172.11 186.76 61.7 134.0 0.8 112.9 19.6 5.0 20.3 5.5 16.8 3.3 India 43.51 46.63 11.1 15.9 0.4 .. 8.1 4.5 9.1 4.8 7.4 5.2 Indonesia 9,698.96 9,457.75 1,031.8 5,460.4 0.6 .. 15.8 10.9 13.7 9.3 15.4 11.0 Iran, Islamic Rep. 9,428.53 9,969.95 567.5 3,412.4 0.4 126.7 27.7 17.4 26.0 15.1 28.4 10.8 Iraq 1,193.08 1,170.00 .. .. .. .. .. .. .. .. .. .. Irelandc 0.68 0.68 0.8 1.0 1.4 113.8 3.6 2.6 2.6 3.6 1.6 ­1.9 Israel 3.59 3.79 3.1 3.6 1.0 114.3 11.0 1.2 9.7 1.7 8.1 4.9 Italyc 0.68 0.68 0.8 0.8 1.2 103.5 3.8 2.6 3.7 2.3 2.9 3.0 Jamaica 72.76 88.92 14.6 51.3 0.7 .. 24.8 11.5 23.5 11.4 .. .. Japan 103.36 89.56 174.6 116.5 1.1 91.5 0.1 ­1.2 0.8 ­0.1 ­1.0 0.8 Jordan 0.71 0.71 0.4 0.5 0.7 .. 3.2 4.3 3.5 4.2 .. 13.6 Kazakhstan 120.30 148.69 17.5 90.5 0.8 .. 204.7 15.1 67.8 8.3 16.3 14.9 Kenya 69.18 74.74 15.8 35.0 0.5 .. 16.6 5.7 15.6 10.7 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 1,102.05 1,166.13 691.1 761.7 0.7 .. 5.9 2.2 5.1 3.1 3.7 2.5 Kosovo .. .. .. .. .. .. .. .. .. 1.3 .. .. Kuwait 0.27 0.29 0.2 0.3 0.9 .. 1.5 9.8 2.0 3.0 1.4 2.5 Kyrgyz Republic 36.57 43.99 3.5 16.0 0.4 .. 110.6 7.8 23.3 6.1 35.6 9.4 Lao PDR 8,744.06 8,486.32 327.7 3,634.2 0.4 .. 27.2 9.6 28.3 9.0 .. .. Latvia 0.48 0.48 0.2 0.4 0.9 .. 48.0 8.7 29.2 6.1 12.0 7.6 Lebanon 1,507.50 1,507.50 775.4 893.9 0.6 .. 19.0 2.0 .. .. .. .. Lesotho 8.26 7.48 2.1 4.2 0.5 87.7 9.5 7.2 5.9 7.8 .. .. Liberia 63.21 67.81 0.6 36.2 0.6 .. 51.8 10.3 .. .. .. .. Libya 1.22 1.21 .. 1.1 0.9 .. .. 21.9 5.6 ­0.5 .. .. Lithuania 2.36 2.36 1.2 1.9 0.8 .. 75.2 4.0 32.6 2.5 24.8 5.5 Macedonia, FYR 41.87 41.81 17.7 20.9 0.5 104.7 79.3 3.5 10.6 2.3 8.5 0.7 Madagascar 1,708.37 1,951.98 287.6 803.1 0.5 .. 19.1 11.5 18.7 10.8 .. .. Malawi 140.52 142.73 3.9 50.2 0.4 102.7 33.6 19.3 33.8 12.7 .. .. Malaysia 3.34 3.41 1.4 1.9 0.6 108.4 4.1 4.4 3.6 2.3 3.4 5.0 Mali 447.81 449.31 226.8 273.1 0.6 .. 7.0 4.2 5.2 2.2 .. .. Mauritania 258.59 .. 62.4 118.1 0.4 .. 8.7 11.3 6.1 7.5 .. .. Mauritius 28.45 29.29 10.5 16.9 0.6 .. 6.3 6.3 6.9 6.3 .. .. Mexico 11.13 12.85 2.9 7.8 0.7 .. 19.0 8.2 19.5 4.5 18.4 6.1 Moldova 10.39 11.93 1.2 5.9 0.6 134.8 119.6 11.6 21.4 11.3 .. .. Mongolia 1,165.74 1,446.52 158.7 653.0 0.6 .. 57.8 15.0 35.7 8.1 .. .. Morocco 7.75 7.76 4.9 5.0 0.7 102.0 4.0 1.8 3.9 1.9 2.9 .. Mozambique 24.30 27.15 4.0 12.8 0.5 .. 34.1 8.2 31.8 11.5 .. .. Myanmar 5.39 5.38 .. .. .. .. 25.3 23.5 25.9 23.7 .. .. Namibia 8.26 7.48 2.2 5.4 0.7 .. 11.1 6.7 .. 5.4 .. .. Nepal 69.76 74.54 15.4 25.7 0.4 .. 8.0 6.2 8.7 5.6 .. .. Netherlandsc 0.68 0.68 0.9 0.9 1.3 102.5 2.1 2.3 2.4 2.0 1.3 3.0 New Zealand 1.42 1.40 1.5 1.6 1.1 93.7 1.7 2.7 1.8 2.7 1.4 3.2 Nicaragua 19.37 20.80 3.5 8.4 0.4 107.8 42.4 8.5 .. 8.6 .. .. Niger 447.81 449.31 209.8 239.0 0.5 .. 6.0 2.6 6.1 2.4 .. .. Nigeria 118.55 149.36 15.5 77.4 0.7 116.6 29.5 16.8 32.5 12.9 .. .. Norway 5.64 5.75 9.2 9.1 1.6 102.0 2.7 4.8 2.2 1.7 1.6 7.8 Oman 0.38 0.38 0.2 0.3 0.7 .. 0.1 7.9 .. 2.3 .. .. Pakistan 70.41 84.12 10.1 24.4 0.4 100.1 11.1 7.4 9.7 7.1 10.4 8.3 Panama 1.00 1.00 0.5 0.5 0.5 .. 3.6 2.2 1.1 2.1 1.0 3.8 Papua New Guinea 2.70 2.70 0.7 1.6 0.6 108.7 7.6 7.4 9.3 5.9 .. .. Paraguay 4,363.24 4,654.00 949.3 2,377.3 0.5 146.0 11.5 10.5 13.1 8.7 .. 11.2 Peru 2.92 2.88 1.2 1.5 0.5 .. 26.7 3.6 27.3 2.3 23.7 2.8 Philippines 44.32 46.42 14.1 23.4 0.5 128.6 8.4 5.2 7.7 5.5 5.6 7.7 Poland 2.41 2.84 1.2 1.9 0.8 116.8 24.7 2.6 25.3 2.4 19.8 2.7 Portugalc 0.68 0.68 0.7 0.7 1.0 103.7 5.2 2.9 4.5 2.9 .. 2.9 Puerto Rico 1.00 1.00 .. .. .. .. 3.0 .. .. .. .. .. Qatar 3.64 3.64 .. 3.2 0.9 .. .. 12.6 2.8 7.3 .. .. 2010 World Development Indicators 279 4.14 Exchange rates and prices Official Purchasing Ratio of PPP Real GDP implicit Consumer price Wholesale price exchange rate power parity conversion effective deflator index index (PPP) factor to exchange conversion market rate factor exchange rate local currency local currency units Index average annual average annual average annual units to $ to international $ 2000 = 100 % growth % growth % growth 2008 2009a 1995 2008 2008 2008 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 Romania 2.52 2.90 0.1 1.7 0.7 112.4 98.0 17.1 100.5 12.5 93.8 16.6 Russian Federation 24.85 29.94 1.5 18.5 0.7 123.4 161.5 16.8 99.1 12.7 99.8 17.1 Rwanda 546.85 568.80 128.7 244.3 0.5 .. 14.3 10.0 16.2 8.5 .. .. Saudi Arabia 3.75 3.75 1.8 3.0 0.8 97.8 1.6 8.9 1.0 1.7 1.3 2.5 Senegal 447.81 449.31 252.0 271.6 0.6 .. 6.0 2.8 5.4 2.2 .. .. Serbia 55.72 65.73 2.8 36.0 0.7 .. .. 17.6 50.2 16.6 .. .. Sierra Leone 2,981.51 3,280.15 379.6 1,340.7 0.5 109.5 31.9 9.4 .. .. .. .. Singapore 1.41 1.40 1.3 1.1 0.8 110.1 1.3 1.5 1.7 1.3 ­1.0 3.7 Slovak Republicc 0.68 0.68 0.4 0.6 0.0 127.8 11.1 3.7 8.4 5.2 9.5 5.2 Sloveniac 0.68 0.68 0.4 0.7 1.0 .. 29.3 4.2 12.0 4.4 9.1 4.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 8.26 7.48 2.3 4.6 0.6 81.8 9.9 7.1 8.7 4.3 7.7 6.7 Spainc 0.68 0.68 0.7 0.8 1.1 106.9 3.9 4.0 3.8 3.3 2.4 3.4 Sri Lanka 108.33 114.35 18.3 48.0 0.4 .. 9.1 10.6 9.9 11.0 8.1 12.6 Sudan 2.09 2.20 0.3 1.3 0.6 .. 65.5 9.9 72.0 8.2 .. .. Swaziland 8.26 7.48 2.2 4.1 0.5 .. 10.5 7.8 9.5 6.9 .. .. Sweden 6.59 7.13 9.4 9.3 1.4 100.6 2.2 1.6 1.9 1.5 2.0 2.9 Switzerland 1.08 1.03 2.0 1.6 1.5 99.2 1.1 1.0 1.6 1.0 ­0.4 1.1 Syrian Arab Republic 11.23 .. 12.8 27.2 0.6 .. 7.9 8.4 6.4 5.9 4.7 2.2 Tajikistan 3.43 4.37 0.0 1.4 0.4 .. 235.0 21.0 .. 13.0 .. .. Tanzania 1,196.31 1,327.00 154.8 456.9 0.4 .. 21.6 9.4 20.9 6.0 .. .. Thailand 33.31 33.18 15.1 16.7 0.5 .. 4.2 3.2 4.9 3.0 3.8 5.8 Timor-Leste 1.00 1.00 .. 0.6 0.6 .. .. 3.6 .. 5.2 .. .. Togo 447.81 449.31 238.6 242.3 0.5 107.1 7.0 1.3 8.5 2.7 .. .. Trinidad and Tobago 6.29 6.35 2.9 4.5 0.7 113.9 5.4 6.9 5.7 6.1 2.8 2.0 Tunisia 1.23 1.29 0.5 0.6 0.5 95.8 4.4 3.0 4.4 3.2 3.6 4.3 Turkey 1.30 1.45 0.0 1.0 0.7 .. 81.7 16.8 79.9 18.6 75.2 19.0 Turkmenistan .. .. 0.0 1.3 0.5 .. 408.2 11.5 .. .. .. .. Uganda 1,720.44 1,873.78 472.1 668.5 0.4 104.0 12.0 5.1 8.3 6.0 .. .. Ukraine 5.27 7.98 0.3 2.8 0.5 115.8 271.0 15.7 155.7 9.8 161.6 14.1 United Arab Emirates 3.67 3.67 1.7 3.0 0.8 .. 2.2 8.9 .. .. .. .. United Kingdom 0.54 0.62 0.6 0.7 1.2 93.8 2.8 2.6 2.9 3.0 2.4 1.8 United States 1.00 1.00 1.0 1.0 1.0 92.5 2.0 2.9 2.7 2.8 1.2 4.7 Uruguay 20.95 19.70 5.5 15.9 0.8 116.2 33.2 8.2 33.9 9.5 27.2 14.6 Uzbekistan .. .. 11.2 507.8 0.4 .. 245.8 25.5 .. .. .. .. Venezuela, RB 2.14 2.14 0.1 1.9 0.9 144.9 45.3 26.3 49.0 20.6 44.1 26.4 Vietnam 16,302.25 16,968.00 3,170.2 6,154.4 0.4 .. 15.2 7.7 4.1 7.1 .. .. West Bank and Gaza .. .. .. .. .. .. 5.7 3.4 .. 3.8 .. .. Yemen, Rep. 199.76 205.04 22.0 95.9 0.5 .. 22.4 13.6 26.3 11.7 .. .. Zambia 3,745.66 4,682.22 404.2 3,133.5 0.8 145.2 52.1 17.1 57.0 16.6 101.4 .. Zimbabwe 6,715,424,238.75 .. 25.7 .. .. .. 26.7 232.0 29.0 497.7 25.9 .. Note: The differences in the growth rates of the GDP deflator and consumer and wholesale price indexes are due mainly to differences in data availability for each of the indexes during the period. a. Average for December or latest monthly data available. b. Private analysts estimate that consumer price index inflation was considerably higher for 2007­09 and believe that GDP volume growth has been significantly lower than official reports indicate since the last quarter of 2008. c. As members of the euro area, these countries share a single currency, the euro. 280 2010 World Development Indicators 4.14 ECONOMY Exchange rates and prices About the data Definitions In a market-based economy, household, producer, for currencies of selected countries and the euro · Official exchange rate is the exchange rate deter- and government choices about resource allocation area. For most high-income countries weights are mined by national authorities or the rate determined are influenced by relative prices, including the real derived from industrial country trade in manufac- in the legally sanctioned exchange market. It is cal- exchange rate, real wages, real interest rates, and tured goods. Data are compiled from the nominal culated as an annual average based on monthly other prices in the economy. Relative prices also effective exchange rate index and a cost indicator averages (local currency units relative to the U.S. largely reflect these agents' choices. Thus relative of relative normalized unit labor costs in manufactur- dollar). · Purchasing power parity (PPP) conversion prices convey vital information about the interaction ing. For selected other countries the nominal effec- factor is the number of units of a country's currency of economic agents in an economy and with the rest tive exchange rate index is based on manufactured required to buy the same amount of goods and ser- of the world. goods and primary products trade with partner or vices in the domestic market that a U.S. dollar would The exchange rate is the price of one currency competitor countries. For these countries the real buy in the United States. · Ratio of PPP conver- in terms of another. Offi cial exchange rates and effective exchange rate index is the nominal index sion factor to market exchange rate is the result exchange rate arrangements are established by adjusted for relative changes in consumer prices; an obtained by dividing the PPP conversion factor by the governments. Other exchange rates recognized by increase represents an appreciation of the local cur- market exchange rate. · Real effective exchange governments include market rates, which are deter- rency. Because of conceptual and data limitations, rate is the nominal effective exchange rate (a mea- mined largely by legal market forces, and for coun- changes in real effective exchange rates should be sure of the value of a currency against a weighted tries with multiple exchange arrangements, principal interpreted with caution. average of several foreign currencies) divided by rates, secondary rates, and tertiary rates. (Also see Inflation is measured by the rate of increase in a a price deflator or index of costs. · GDP implicit Statistical methods for alternative conversion factors price index, but actual price change can be nega- deflator measures the average annual rate of price in the World Bank Atlas method of calculating gross tive. The index used depends on the prices being change in the economy as a whole for the periods national income [GNI] per capita in U.S. dollars.) examined. The GDP deflator reflects price changes shown. · Consumer price index reflects changes Official or market exchange rates are often used for total GDP. The most general measure of the over- in the cost to the average consumer of acquiring a to convert economic statistics in local currencies to all price level, it accounts for changes in government basket of goods and services that may be fixed or a common currency in order to make comparisons consumption, capital formation (including inventory may change at specified intervals, such as yearly. across countries. Since market rates reflect at best appreciation), international trade, and the main com- The Laspeyres formula is generally used. · Whole- the relative prices of tradable goods, the volume of ponent, household final consumption expenditure. sale price index refers to a mix of agricultural and goods and services that a U.S. dollar buys in the The GDP deflator is usually derived implicitly as the industrial goods at various stages of production and United States may not correspond to what a U.S. ratio of current to constant price GDP--or a Paasche distribution, including import duties. The Laspeyres dollar converted to another country's currency at index. It is defective as a general measure of inflation formula is generally used. the official exchange rate would buy in that country, for policy use because of long lags in deriving esti- particularly when nontradable goods and services mates and because it is often an annual measure. account for a significant share of a country's output. Consumer price indexes are produced more fre- An alternative exchange rate--the purchasing power quently and so are more current. They are also con- parity (PPP) conversion factor--is preferred because structed explicitly, based on surveys of the cost of it reflects differences in price levels for both tradable a defined basket of consumer goods and services. and nontradable goods and services and therefore Nevertheless, consumer price indexes should be provides a more meaningful comparison of real out- interpreted with caution. The definition of a house- put. See table 1.1 for further discussion. hold, the basket of goods, and the geographic (urban The ratio of the PPP conversion factor to the official or rural) and income group coverage of consumer exchange rate--the national price level or compara- price surveys can vary widely by country. In addi- tive price level--measures differences in the price tion, weights are derived from household expendi- level at the gross domestic product (GDP) level. The ture surveys, which, for budgetary reasons, tend to price level index tends to be lower in poorer coun- be conducted infrequently in developing countries, tries and to rise with income. The market exchange impairing comparability over time. Although useful for rate (or alternative conversion factor) is the official measuring consumer price inflation within a country, exchange rate adjusted for some countries by World consumer price indexes are of less value in compar- Bank staff to reflect actual price changes. National ing countries. price levels vary systematically, rising with GNI per Wholesale price indexes are based on the prices at Data sources capita. The real effective exchange rate is a nominal the first commercial transaction of commodities that effective exchange rate index adjusted for relative are important in a country's output or consumption. Data on official and real effective exchange rates movements in national price or cost indicators of Prices are farm-gate for agricultural commodities and and consumer and wholesale price indexes are the home country, selected countries, and the euro ex-factory for industrial goods. Preference is given to from the International Monetary Fund's Interna- area. A nominal effective exchange rate index is the indexes with the broadest coverage of the economy. tional Financial Statistics. PPP conversion factors ratio (expressed on the base 2000 = 100) of an The least squares method is used to calculate and GDP deflators are from the World Bank's data index of a currency's period-average exchange rate growth rates of the GDP implicit deflator, consumer files. to a weighted geometric average of exchange rates price index, and wholesale price index. 2010 World Development Indicators 281 4.15 Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 304 3,833 836 7,287 44 150 477 1,379 ­12 ­1,924 265 2,364 Algeria .. .. .. .. .. .. .. .. .. .. 4,164 148,099 Angola 3,836 64,243 3,519 43,122 ­767 ­14,504 156 ­210 ­295 6,408 213 17,869 Argentina 24,987 82,101 26,066 67,588 ­4,636 ­7,550 597 115 ­5,118 7,078 15,979 46,385 Armenia 300 1,757 726 4,749 40 471 168 1,138 ­218 ­1,383 111 1,407 Australia 69,710 234,298 74,841 242,311 ­14,036 ­39,399 ­109 ­374 ­19,277 ­47,786 14,952 32,924 Austria 89,906 241,307 92,055 222,639 ­1,597 ­2,867 ­1,702 ­2,647 ­5,448 13,154 23,369 16,741 Azerbaijan 785 32,133 1,290 11,464 ­6 ­5,266 111 1,050 ­401 16,454 121 6,467 Bangladesh 4,431 17,372 7,589 25,344 68 ­771 2,265 9,774 ­824 1,032 2,376 5,787 Belarus 5,269 37,063 5,752 41,676 ­51 ­788 76 192 ­458 ­5,209 377 3,063 Belgium 190,686b 459,890 178,798b 470,702 6,808b 6,966 ­4,463b ­8,255 14,232b ­12,101 24,120 15,681 Benin 614 1,348 895 2,102 ­8 ­50 121 268 ­167 ­535 198 1,263 Bolivia 1,234 6,947 1,574 5,680 ­207 ­536 244 1,284 ­303 2,015 1,005 7,720 Bosnia and Herzegovina .. 6,856 .. 12,935 .. 603 .. 2,712 .. ­2,764 80 3,516 Botswana 2,421 5,585 2,050 5,837 ­32 ­256 ­39 1,010 300 502 4,695 9,119 Brazil 52,641 228,393 63,293 220,247 ­11,105 ­40,562 3,621 4,224 ­18,136 ­28,192 51,477 193,783 Bulgaria 6,776 30,589 6,502 42,158 ­432 ­1,798 132 791 ­26 ­12,577 1,635 17,930 Burkina Faso 272 .. 483 .. ­29 .. 255 .. 15 .. 347 928 Burundi 129 136 259 529 ­13 ­4 153 186 10 ­212 216 267 Cambodia 969 6,356 1,375 7,594 ­57 ­409 277 594 ­186 ­1,053 192 2,639 Cameroon 2,040 7,454 1,608 8,349 ­412 ­205 69 590 90 ­510 15 3,112 Canada 219,501 529,160 200,991 486,728 ­22,721 ­14,065 ­117 ­1,085 ­4,328 27,281 16,369 43,872 Central African Republic 179 .. 244 .. ­23 .. 63 .. ­25 .. 238 131 Chad 190 .. 411 .. ­7 .. 191 .. ­38 .. 147 1,355 Chile 19,358 77,210 18,301 69,010 ­2,714 ­14,563 307 2,924 ­1,350 ­3,440 14,860 23,079 China 147,240 1,581,713 135,282 1,232,843 ­11,774 31,438 1,435 45,799 1,618 426,107 80,288 1,966,037 Hong Kong SAR, China .. 457,554 .. 434,202 .. 10,457 .. ­3,277 .. 30,532 55,424 182,527 Colombia 12,294 42,579 16,012 44,743 ­1,596 ­10,063 799 5,514 ­4,516 ­6,713 8,452 23,671 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 157 78 Congo, Rep. 1,374 6,127 1,346 6,386 ­695 ­1,885 42 ­38 ­625 ­2,181 64 3,881 Costa Rica 4,451 13,651 4,717 16,433 ­226 ­389 134 442 ­358 ­2,729 1,060 3,801 Côte d'Ivoire 4,337 11,103 3,806 9,377 ­787 ­894 ­237 ­345 ­492 488 529 2,253 Croatia 6,972 29,623 9,152 35,007 ­53 ­2,406 802 1,524 ­1,431 ­6,267 1,896 12,957 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 28,202 167,927 30,044 156,708 ­104 ­17,276 572 ­575 ­1,374 ­6,631 14,613 37,009 Denmark 65,655 187,208 57,860 177,818 ­4,549 3,892 ­1,391 ­5,734 1,855 7,549 11,652 42,327 Dominican Republic 5,731 11,888 6,137 17,941 ­769 ­1,815 992 3,432 ­183 ­4,437 373 2,288 Ecuador 5,196 20,460 5,708 20,730 ­930 ­1,598 442 2,989 ­1,000 1,120 1,788 4,473 Egypt, Arab Rep. 13,260 54,761 17,140 67,223 ­405 1,289 4,031 9,758 ­254 ­1,415 17,122 34,331 El Salvador 2,040 6,121 3,623 11,012 ­67 ­536 1,389 3,832 ­262 ­1,596 940 2,646 Eritrea 135 .. 498 .. 8 .. 324 .. ­31 .. 40 58 Estonia 2,573 17,750 2,860 18,757 3 ­1,512 126 273 ­158 ­2,245 583 3,972 Ethiopia 768 3,514 1,446 9,617 ­19 2 736 4,295 39 ­1,806 815 871 Finland 47,973 128,904 37,705 117,521 ­4,440 ­1,093 ­597 ­2,335 5,231 7,955 10,657 8,354 France 362,717 770,104 333,746 835,249 ­8,964 36,057 ­9,167 ­35,141 10,840 ­64,229 58,510 103,306 Gabon 2,945 .. 1,723 .. ­665 .. ­42 .. 515 .. 153 1,935 Gambia, The 177 271 232 371 ­5 ­27 52 84 ­8 ­43 106 117 Georgia 575 3,688 1,413 7,499 127 ­166 197 1,061 ­514 ­2,915 199 1,480 Germany 600,347 1,744,963 586,662 1,516,863 ­2,814 64,513 ­38,768 ­48,738 ­27,897 243,875 121,816 138,564 Ghana 1,582 7,071 2,120 12,567 ­129 ­259 523 2,212 ­144 ­3,543 804 2,269 Greece 15,523 79,635 24,711 119,112 ­1,684 ­16,015 8,008 4,180 ­2,864 ­51,313 16,119 3,490 Guatemala 2,823 9,637 3,728 15,581 ­159 ­930 491 5,011 ­572 ­1,863 783 4,654 Guinea 700 1,449 1,011 1,810 ­85 ­91 179 18 ­216 ­434 87 .. Guinea-Bissau 30 .. 89 .. ­21 .. 46 .. ­35 .. 20 124 Haiti 192 833 802 2,871 ­31 6 553 1,876 ­87 ­156 199 543 Honduras 1,635 6,956 1,852 11,603 ­226 ­350 243 3,021 ­201 ­1,977 270 2,492 Data for Taiwan, China 128,369 288,756 124,171 271,117 4,188 9,978 ­2,912 ­2,979 5,474 24,638 95,559 303,553 282 2010 World Development Indicators 4.15 ECONOMY Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 19,765 127,274 19,916 126,041 ­1,701 ­11,190 203 ­982 ­1,650 ­10,939 12,017 33,874 India 38,013 290,861 48,225 371,616 ­3,734 ­3,539 8,382 48,206 ­5,563 ­36,088 22,865 257,423 Indonesia 52,923 154,852 54,461 144,935 ­5,874 ­15,155 981 5,364 ­6,431 125 14,908 51,641 Iran, Islamic Rep. 18,953 .. 15,113 .. ­478 .. ­4 .. 3,358 .. .. .. Iraq .. 40,455 .. 21,488 .. ­3,067 .. ­381 .. 15,519 8,347 50,207 Ireland 49,439 221,383 42,169 194,363 ­7,325 ­39,430 1,776 ­1,812 1,721 ­14,222 8,770 1,024 Israel 27,478 81,245 35,287 84,309 ­2,654 ­3,298 5,673 8,482 ­4,790 2,120 8,123 42,513 Italy 295,618 666,484 250,319 677,886 ­15,644 ­43,548 ­4,579 ­23,194 25,076 ­78,144 60,690 105,649 Jamaica 3,394 5,294 3,729 9,914 ­371 ­568 607 2,150 ­99 ­3,038 681 1,773 Japan 493,991 895,228 419,556 877,887 44,285 152,336 ­7,676 ­13,043 111,044 156,634 192,620 1,030,763 Jordan 3,479 12,353 4,903 19,228 ­279 951 1,444 3,532 ­259 ­2,393 2,279 8,918 Kazakhstan 5,975 76,354 6,102 49,451 ­146 ­19,323 59 ­985 ­213 6,596 1,660 19,883 Kenya 3,526 8,291 5,922 12,559 ­219 ­45 1,037 2,336 ­1,578 ­1,978 384 2,879 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 147,761 509,417 155,104 520,157 ­1,303 5,107 ­19 ­773 ­8,665 ­6,406 32,804 201,545 Kosovo .. .. .. .. .. .. .. .. .. .. .. 892 Kuwait 14,215 98,335 12,615 37,948 4,881 10,119 ­1,465 ­5,765 5,016 64,742 4,543 19,321 Kyrgyz Republic 448 2,743 726 4,747 ­35 ­103 79 1,477 ­235 ­631 134 1,225 Lao PDR 408 1,201 748 1,141 ­6 ­50 110 98 ­237 107 99 875 Latvia 2,088 14,172 2,193 18,838 19 ­596 71 769 ­16 ­4,492 602 5,244 Lebanon .. 24,041 .. 29,718 .. ­77 .. 2,698 .. ­3,056 8,100 28,265 Lesotho 199 950 1,046 1,728 314 507 210 515 ­323 244 457 658 Liberia .. 635 .. 2,344 .. ­653 .. 1,175 .. ­1,187 28 161 Libya 7,513 62,158 5,755 26,003 133 586 ­220 ­1,040 1,672 35,702 7,415 96,335 Lithuania 3,191 28,594 3,902 33,759 ­13 ­1,539 109 1,077 ­614 ­5,627 829 6,442 Macedonia, FYR 1,302 4,982 1,773 7,532 ­30 ­108 213 1,448 ­288 ­1,210 275 2,110 Madagascar 749 .. 987 .. ­167 .. 129 .. ­276 .. 109 982 Malawi 470 .. 660 .. ­44 .. 157 .. ­78 .. 115 254 Malaysia 83,369 230,054 86,851 178,741 ­4,144 ­7,137 ­1,017 ­5,262 ­8,644 38,914 24,699 92,166 Mali 529 1,933 991 2,623 ­41 ­291 219 400 ­284 ­581 323 1,072 Mauritania 504 .. 510 .. ­48 .. 76 .. 22 .. 90 207 Mauritius 2,349 4,944 2,454 6,320 ­19 178 101 224 ­22 ­974 887 1,796 Mexico 89,321 309,822 82,168 333,838 ­12,689 ­17,250 3,960 25,461 ­1,576 ­15,805 17,046 95,300 Moldova 884 2,483 1,006 5,691 ­18 598 56 1,623 ­85 ­987 257 1,672 Mongolia 508 2,031 521 1,880 ­25 ­145 77 215 39 222 158 1,396 Morocco 9,044 33,746 11,243 46,521 ­1,318 ­522 2,330 8,768 ­1,186 ­4,528 3,874 22,720 Mozambique 411 3,208 1,055 4,406 ­140 ­631 339 854 ­445 ­975 195 1,661 Myanmar 1,307 4,834 2,020 2,906 ­110 ­1,248 562 122 ­261 802 651 1,383 Namibia 1,734 3,671 2,100 4,400 139 ­40 403 1,127 176 358 221 1,293 Nepal 1,029 1,710 1,624 4,371 9 151 230 3,243 ­356 733 646 .. Netherlands 241,517 638,348 216,558 568,373 7,247 ­14,582 ­6,434 ­12,822 25,773 42,571 47,162 28,603 New Zealand 17,883 40,320 17,248 42,482 ­3,955 ­9,831 255 756 ­3,065 ­11,237 4,410 11,052 Nicaragua 662 2,937 1,150 5,357 ­372 ­161 138 1,068 ­722 ­1,513 142 1,141 Niger 321 748 457 1,284 ­47 0 31 185 ­152 ­351 95 705 Nigeria 12,342 80,160 12,841 47,592 ­2,878 ­11,180 799 17,969 ­2,578 39,357 1,709 53,599 Norway 56,058 219,417 46,848 130,667 ­1,919 2,915 ­2,059 ­3,323 5,233 88,341 22,976 50,950 Oman 6,078 39,693 5,035 26,830 ­374 ­2,214 ­1,469 ­5,181 ­801 5,469 1,943 11,582 Pakistan 10,214 25,454 14,185 47,586 ­1,939 ­4,294 2,562 11,024 ­3,349 ­15,402 2,528 9,024 Panama 7,610 16,149 7,768 17,490 ­466 ­1,574 153 238 ­471 ­2,677 781 1,935 Papua New Guinea 2,992 .. 1,905 .. ­488 .. 75 .. 674 .. 267 2,008 Paraguay 4,802 8,831 5,200 9,393 110 ­151 195 369 ­92 ­345 1,106 2,863 Peru 6,622 35,166 9,597 34,005 ­2,482 ­8,144 832 2,803 ­4,625 ­4,180 8,653 31,241 Philippines 26,795 58,448 33,317 69,917 3,662 140 880 15,226 ­1,980 3,897 7,781 37,498 Poland 35,716 214,004 33,825 234,960 ­1,995 ­14,210 958 8,257 854 ­26,909 14,957 62,184 Portugal 32,260 82,807 39,545 104,560 21 ­11,495 7,132 3,649 ­132 ­29,599 22,063 12,006 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. 848 9,997 2010 World Development Indicators 283 4.15 Balance of payments current account Goods and Net Net current Current account Total services income transfers balance reservesa $ millions Exports Imports $ millions $ millions $ millions $ millions 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 9,404 62,616 11,306 89,847 ­241 ­5,372 369 8,884 ­1,774 ­23,719 2,624 39,768 Russian Federation 92,987 522,909 82,809 368,217 ­3,372 ­49,196 157 ­3,096 6,963 102,401 18,024 427,077 Rwanda 75 665 374 1,401 7 ­34 350 518 57 ­252 99 596 Saudi Arabia 53,450 323,071 44,874 176,040 2,800 10,027 ­16,694 ­23,012 ­5,318 134,046 10,399 34,340 Senegal 1,506 2,875 1,821 5,402 ­124 ­74 195 1,290 ­244 ­1,311 272 1,602 Serbia .. 14,986 .. 26,631 .. ­1,348 .. 4,138 .. ­8,855 .. 11,478 Sierra Leone 128 334 260 597 ­30 ­75 43 111 ­118 ­227 35 220 Singapore 157,658 427,595 144,520 392,690 2,133 ­4,969 ­894 ­2,756 14,377 27,181 68,816 174,193 Slovak Republic 10,969 78,765 10,658 80,348 ­14 ­3,344 93 ­1,257 390 ­6,185 3,863 18,836 Slovenia 10,377 37,008 10,749 38,505 201 ­1,533 95 ­299 ­75 ­3,329 1,821 957 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 34,402 98,923 33,375 107,542 ­2,875 ­9,132 ­645 ­2,333 ­2,493 ­20,084 4,464 34,070 Spain 133,910 429,488 135,000 520,200 ­5,402 ­49,585 4,525 ­13,832 ­1,967 ­154,129 40,531 20,288 Sri Lanka 4,617 10,140 5,982 15,609 ­137 ­972 732 2,666 ­770 ­3,775 2,112 2,617 Sudan 681 12,163 1,238 10,849 ­3 ­3,013 60 385 ­500 ­1,314 163 1,399 Swaziland 1,020 2,199 1,274 2,523 81 64 144 194 ­30 ­66 298 752 Sweden 95,525 258,075 81,142 222,243 ­6,473 10,814 ­2,970 ­6,330 4,940 40,317 25,870 29,727 Switzerland 123,320 319,253 108,916 264,149 10,708 ­37,311 ­4,409 ­12,699 20,703 5,094 68,620 74,146 Syrian Arab Republic 5,757 15,617 5,541 15,289 ­560 ­689 607 821 263 459 .. .. Tajikistan .. 1,756 .. 4,155 .. ­52 .. 2,498 .. 48 39 204 Tanzania 1,265 5,206 2,139 8,038 ­110 ­92 395 617 ­590 ­2,307 270 2,863 Thailand 70,292 208,998 82,246 203,874 ­2,114 ­10,003 487 4,766 ­13,582 ­113 36,939 111,009 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. 210 Togo 465 913 671 1,377 ­34 ­30 118 279 ­122 ­216 130 582 Trinidad and Tobago 2,799 14,315 2,110 8,047 ­390 ­964 ­4 60 294 5,364 379 9,496 Tunisia 7,979 25,197 8,811 26,564 ­716 ­2,267 774 1,922 ­774 ­1,711 1,689 9,039 Turkey 36,581 175,978 40,113 211,309 ­3,204 ­7,964 4,398 2,006 ­2,338 ­41,289 13,891 73,675 Turkmenistan 1,774 .. 1,796 .. 17 .. 5 .. 0 .. 1,168 .. Uganda 664 3,426 1,490 5,224 ­96 ­288 639 1,240 ­281 ­845 459 2,301 Ukraine 17,090 85,612 18,280 99,962 ­434 ­1,540 472 3,127 ­1,152 ­12,763 1,069 31,543 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 7,778 31,694 United Kingdom 322,114 756,476 327,000 845,303 3,393 75,372 ­11,943 ­26,449 ­13,436 ­39,904 49,144 53,024 United States 794,397 1,826,595 890,784 2,522,531 20,899 118,233 ­38,073 ­128,363 ­113,561 ­706,066 175,996 294,046 Uruguay 3,507 9,334 3,568 10,083 ­227 ­627 76 150 ­213 ­1,225 1,813 6,360 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 20,753 97,300 16,905 59,998 ­1,943 698 109 ­608 2,014 37,392 10,715 43,065 Vietnam 9,498 69,781 12,334 83,398 ­384 ­4,400 1,200 7,311 ­2,020 ­10,706 1,324 23,890 West Bank and Gaza 764 926 2,789 4,430 607 734 435 2,361 ­984 ­408 .. .. Yemen, Rep. 2,160 10,182 2,471 11,681 ­561 ­1,915 1,056 2,163 184 ­1,251 638 8,155 Zambia 1,222 5,254 1,338 5,466 ­249 ­1,398 182 565 ­182 ­1,046 223 1,096 Zimbabwe 2,344 .. 2,515 .. ­294 .. 40 .. ­425 .. 888 .. World 6,393,697 t 19,557,280 t 6,247,179 t 19,164,685 t Low income 45,519 207,241 64,928 282,482 Middle income 1,110,795 5,731,256 1,154,326 5,223,924 Lower middle income 497,910 3,110,947 531,746 2,758,700 Upper middle income 612,966 2,636,805 622,049 2,469,508 Low & middle income 1,152,982 5,938,347 1,217,464 5,502,978 East Asia & Pacific 397,583 2,334,814 413,802 1,947,840 Europe & Central Asia 231,676 1,366,736 239,492 1,352,255 Latin America & Carib. 272,861 1,001,523 288,143 1,002,903 Middle East & N. Africa .. .. 106,333 374,714 South Asia 58,893 348,172 78,652 470,488 Sub-Saharan Africa 89,262 378,970 99,763 371,927 High income 5,235,576 13,709,918 5,028,511 13,740,945 Euro area 2,097,732 5,619,649 1,974,159 5,491,628 a. International reserves including gold valued at London gold price. b. Includes Luxembourg. 284 2010 World Development Indicators 4.15 ECONOMY Balance of payments current account About the data Definitions The balance of payments records an economy's trans- system, external debt records, information provided · Exports and imports of goods and services are all actions with the rest of the world. Balance of payments by enterprises, surveys to estimate service transac- transactions between residents of an economy and accounts are divided into two groups: the current tions, and foreign exchange records. Differences in the rest of the world involving a change in ownership account, which records transactions in goods, ser- collection methods--such as in timing, definitions of general merchandise, goods sent for processing vices, income, and current transfers, and the capital of residence and ownership, and the exchange rate and repairs, nonmonetary gold, and services. · Net and financial account, which records capital transfers, used to value transactions--contribute to net errors income is receipts and payments of employee com- acquisition or disposal of nonproduced, nonfinancial and omissions. In addition, smuggling and other ille- pensation for nonresident workers, and investment assets, and transactions in financial assets and liabili- gal or quasi-legal transactions may be unrecorded or income (receipts and payments on direct investment, ties. The table presents data from the current account misrecorded. For further discussion of issues relat- portfolio investment, and other investments and plus gross international reserves. ing to the recording of data on trade in goods and receipts on reserve assets). Income derived from The balance of payments is a double-entry account- services, see About the data for tables 4.4­4.7. the use of intangible assets is recorded under busi- ing system that shows all flows of goods and services The concepts and definitions underlying the data in ness services. · Net current transfers are recorded into and out of an economy; all transfers that are the the table are based on the fifth edition of the Inter- in the balance of payments whenever an economy counterpart of real resources or financial claims pro- national Monetary Fund's (IMF) Balance of Payments provides or receives goods, services, income, or vided to or by the rest of the world without a quid pro Manual (1993). That edition redefined as capital trans- financial items without a quid pro quo. All transfers quo, such as donations and grants; and all changes fers some transactions previously included in the cur- not considered to be capital are current. · Current in residents' claims on and liabilities to nonresidents rent account, such as debt forgiveness, migrants' cap- account balance is the sum of net exports of goods that arise from economic transactions. All transac- ital transfers, and foreign aid to acquire capital goods. and services, net income, and net current transfers. tions are recorded twice--once as a credit and once Thus the current account balance now reflects more · Total reserves are holdings of monetary gold, as a debit. In principle the net balance should be accurately net current transfer receipts in addition to special drawing rights, reserves of IMF members zero, but in practice the accounts often do not bal- transactions in goods, services (previously nonfac- held by the IMF, and holdings of foreign exchange ance, requiring inclusion of a balancing item, net tor services), and income (previously factor income). under the control of monetary authorities. The gold errors and omissions. Many countries maintain their data collection systems component of these reserves is valued at year-end Discrepancies may arise in the balance of pay- according to the fourth edition of the Balance of Pay- (December 31) London prices ($386.75 an ounce in ments because there is no single source for balance ments Manual (1977). Where necessary, the IMF con- 1995 and $871.70 an ounce in 2008). of payments data and therefore no way to ensure verts such reported data to conform to the fifth edition that the data are fully consistent. Sources include (see Primary data documentation). Values are in U.S. customs data, monetary accounts of the banking dollars converted at market exchange rates. Top 15 economies with the largest reserves in 2008 4.15a Total reserves ($ billions) Share of world Annual Months of total (%) change (%) imports 2007 2008 2008 2007­08 2008 China 1,546 1,966 25.8 27.1 18.2 Japan 973 1,031 13.5 5.9 13.2 Russian Federation 479 427 5.6 ­10.8 10.8 Data sources United States 278 294 3.9 5.9 1.1 Data on the balance of payments are published in India 277 257 3.4 ­6.9 7.9 the IMF's Balance of Payments Statistics Yearbook Korea, Rep. 263 202 2.6 ­23.2 4.5 and International Financial Statistics. The World Brazil 180 194 2.5 7.5 8.5 Bank exchanges data with the IMF through elec- tronic files that in most cases are more timely and Hong Kong, China 153 183 2.4 19.5 4.0 cover a longer period than the published sources. Singapore 163 174 2.3 6.9 4.6 More information about the design and compila- Algeria 115 148 1.9 28.8 .. tion of the balance of payments can be found in Germany 136 139 1.8 1.9 0.9 the IMF's Balance of Payments Manual, fifth edition Thailand 87 111 1.5 26.9 6.0 (1993), Balance of Payments Textbook (1996), and Italy 94 106 1.4 12.3 1.5 Balance of Payments Compilation Guide (1995). France 115 103 1.4 ­10.5 1.2 The IMF's International Financial Statistics and Libya 83 96 1.3 15.7 38.7 Balance of Payments databases are available on Source: International Monetary Fund, International Financial Statistics data files. CD-ROM. 2006 World Development Indicators 285 STATES AND MARKETS Introduction I 5 nfrastructure is the missing link of the Millennium Development Goals (MDGs). Infrastructure--the basic framework for delivering energy, transport, water and sanitation, and information and communication technology services to people-- directly or indirectly affects people's lives everywhere. That relationship is reflected in the MDGs. Yet only two MDG targets touch on infrastructure services: water and sanitation (target 7.C) and telephones and the Internet (target 8.F); energy and trans- port are missing entirely. And no goal or target addresses the comprehensive role of infrastructure in achieving the MDGs. Although income influences performance on the for fuel, enabling them to attend school. A house- MDGs, it has long been recognized that growth in hold energy and universal access project in Mali is productivity and incomes and improvements in extending electricity to semi-urban and rural areas, health and education outcomes require investment improving the quality and efficiency of health and in infrastructure. The MDGs are designed to make education services and helping sustainably manage economic growth more inclusive. Since a large share forest resources and biomass energy. The project of people live in rural areas, often far from employ- has connected 40,000 homes, 1,080 enterprises, ment opportunities, policies to reduce poverty re- 1,025 rural schools, and 107 health clinics. quire investment in infrastructure and transport. By Clean cooking fuels and efficient, ventilated improving productivity, investments in infrastructure stoves improve indoor air quality by reducing particu- reduce poverty. Access to clean water and sanita- late matter, a risk factor for acute respiratory infec- tion reduces infant mortality. Electricity powers hos- tions and other health problems. The World Health pitals and refrigerators for vaccines. Roads in rural Organization estimates that the indoor air pollution areas boost school attendance and use of medical created by the more than 3 billion people who use clinics. And information and communication tech- wood, dung, coal, and other traditional fuels inside nologies can improve teacher training and promote their homes for cooking and heating is responsible for better health practices. 1.5 million deaths a year. A rural energy project in Viet- nam connected 976 communes with 555,327 house- How infrastructure affects the holds and 2.7 million people to the national power Millennium Development Goals grid, providing some of the poorest rural areas with The results of infrastructure investments are reflect- reliable electricity. However, some energy production, ed in progress toward the MDGs. Infrastructure that transformation, and transportation has detrimental reaches poor people raises their income and welfare effects on people and the environment; these can be by increasing the value of their assets or lowering mitigated by using cleaner, more efficient fuels. the costs of inputs and providing better access to markets for their products. Improved transport can boost income and improve health and education outcomes. Transport infra- Access to energy improves health and raises house- structure and services--the roads, bridges, rails, hold and business productivity. Modern, clean, effi - cient fuels and electricity power lights that extend livelihood activities beyond daylight hours and power Improved infrastructure not only improves the living conditions of the poor, but also reduces the costs of business and further encourages business to invest in manufacturing equipment that lowers unit costs and productive assets. It enlarges markets. It is not surprising that the poor of Africa increases labor productivity. Modern energy also re- perceive the isolation associated with the lack of infrastructure to be the cause of duces the cost of home cooking, heating, and light- their poverty and marginalization. Too far from markets, too far from arable land, too far from hospitals and clinics. ing, freeing resources for other essential needs, and --Trevor Manuel, Finance Minister, South Africa relieves girls of the need to collect water and wood 2010 World Development Indicators 287 waterways, ports, and equipment and services transport has added benefits for development-- they provide--can eliminate growth-constrain- more energy savings, less local air pollution, ing bottlenecks and shortages, increase agri- greater energy security, more employment in cultural productivity, improve poor rural farm- local industry, and greater competitiveness from ers' incomes and nutrition, and expand nonfarm higher productivity. Many countries are setting employment. Lower transportation costs en- targets and policies for clean energy technolo- able farmers to use fertilizers, mechanized gies. China has achieved a vehicle fuel economy equipment, and new seed varieties, boosting standard of 35 miles per gallon of gas and plans yields and lowering costs. A rural road proj- to be the world leader in electric vehicles. ect in Bangladesh reduced transport costs by 36­38 percent, lowered fertilizer prices, and in- Water and sanitation services promote health, in- creased output. Extreme poverty fell 5 percent. crease productivity, and raise school enrollment. In Vietnam rehabilitating rural roads increased Water and sanitation are crucial for promoting the availability of food, boosted wages for ag- health, enabling people to work productively, ricultural workers, raised completion rates of and contributing to human dignity and social primary school students, and lifted more than development. Worldwide, more than 1.1 billion 200,000 people out of poverty (Calderon and people lack clean water, and about 2.6 billion Servin 2008). lack access to basic sanitation. About 90 per- Timely and affordable delivery of basic ser- cent of diarrhea cases are attributable to inad- vices for health, education, water, and sanita- equate sanitation, hygiene, and water supply, tion depends on transportation systems. There causing 1.7 million deaths a year, mostly in chil- is a clear association between infant, child, and dren under age 5. maternal mortality rates and distance to health Water and sanitation services at home and care services. Some 40­60 percent of people in schools increase learning capacity because Pakistani women without in developing countries live more than 8 kilome- students are healthier. School attendance also access to an all-weather ters from a health care facility. In Morocco the rises for girls if they can spend less time fetch- road have fewer prenatal number of visits to primary health care facilities ing water and if separate sanitation facilities consultations and fewer doubled in areas with an expanded rural road net- are provided in schools. In a village in Morocco births attended by skilled work compared with a control area. In Pakistan girls' primary school attendance more than health staff, 2001­02 5a women in villages without an all-weather road doubled a year after a new water supply system Women who had prenatal consultations have less access to health services (figure 5a). began operating, with separate sanitation facili- (percent) 30 Improved and affordable transportation sys- ties for girls. An improved water quality project tems and safer roads raise school attendance for Uganda's small towns reduced water-borne by reducing travel time from home to school. diseases and benefited women and children by 20 Better accessibility also makes it easier to hire saving time associated with collecting water teachers who commute between rural and urban (World Bank 2007b). 10 areas. In the Philippines school enrollment rose The number of countries that are off track 10 percent and dropout rates fell 55 percent to meet the sanitation MDG target is second 0 after rural roads were built. A similar project in only to the number off track in reducing child Villages with Villages without access to access to Morocco raised girls' enrollments from 28 per- mortality. Investment in water and sanitation all-weather roads all-weather roads cent to 68 percent in less than 10 years. with private participation remains low compared Transportation services contribute strongly with other infrastructure sectors, accounting Births assisted by a skilled attendant (percent) to growth and poverty reduction, but emissions for about 2­3 percent of investment in infra- 60 from the transport sector have a deleterious structure (figure 5b). impact on the health and environmental MDG 40 targets. The transport sector generates about Information and communication technologies 13 percent of global greenhouse gas emis- reach into all sectors to improve living conditions. 20 sions. Transport policy measures can reduce Information and communication technologies emissions through greater use of railways and (ICTs) are enabling tools used in all sectors, inland waterways for freight, better urban public ranging from telecommunication infrastructure 0 Villages with Villages without transport services, management of urban road (voice, data, and media services) to informa- access to access to all-weather roads all-weather roads traffic demand to reduce congestion, support tion applications in banking and finance, land Source: Babinard and Roberts 2006. of nonmotorized transport, and management of management, education, health, and elec- vehicle emissions. Improving energy efficiency in tronic government services. ICT use has grown 288 2010 World Development Indicators STATES AND MARKETS dramatically since 2000--mobile cellular sub- Private investment in water and sanitation is scriptions in developing countries increased only about 2­3 percent of the total, 2005­08 5b from 220 million to about 2.9 billion by the end Telecommunications Energy of 2008. Worldwide, more than 1.5 billion peo- Share of total (percent) Transport Water and sanitation ple have access to the Internet. But even within 100 the same region, access is uneven. In 2008 mo- 75 bile phone penetration was about 60 percent in Equatorial Guinea, the Gambia, and Mauritania 50 but just 4 percent in Eritrea and Ethiopia. There is a strong association between ICT 25 adoption (such as mobile phones and Internet 0 access) and GDP growth. Mobile communica- 2005 2006 2007 2008 tions have a particularly important impact in Source: World Bank Private Participation in Infrastructure database and World Development Indicators table 5.1. rural areas, home to half the world's population and 75 percent of poor people. The mobility, ease of use, and relatively low and declining roll- and monitoring environmental risks. Telecom- out costs of wireless technologies enable them muting and attending virtual meetings through to reach rural populations with low incomes and video conferencing can reduce travel and energy literacy rates. Farmers with mobile phones are use, helping lower greenhouse gases. Smart more likely to have better information on mar- grids and building construction using information ket prices and therefore to get better prices and communication technologies lower energy from traders. For farmers in rural areas of the consumption and greenhouse gases, leading to Philippines, having a mobile phone increased a more sustainable environment and way of life. income 11­17 percent during 2003­06. India's e-Choupal program, which expanded broadband Bridging the infrastructure to millions of small farmers through centers gap through public and private set up by a large agricultural exporter, enabled financing and better management farmers to access information on local weather, of infrastructure services crop prices, and farming techniques, increasing Meeting the world's infrastructure needs in- productivity and incomes (World Bank 2009l). volves enormous challenges. More than 1.1 bil- Information and communication technolo- lion people do not have safe water to drink and gies also contribute to women's economic 2.6 billion lack access to adequate sanitation opportunities. In the Philippines 65 percent services. Some 1.6 billion have no electricity of professional and technical workers in ICT- in their homes. And 1 billion rural residents live enabled services are women. Information more than 2 kilometers from an all-weather and communication technologies can improve road. World Bank Enterprise Surveys, completed health outcomes and combat diseases. In in more than 100 countries, find that the three rural Niger the number of emergency evacua- main deterrents to private investment are the tions from outlying health centers to the district regulatory environment, access to finance, and hospital increased from 10 to 197 following infrastructure. In many developing countries in- the introduction of a radio-ambulance system. adequate infrastructure constrains businesses Good communications and information sharing as much as crime, red tape, corruption, and un- help to deliver diagnostic information and drugs derdeveloped financial markets. In South Asia and to spread information on reproductive and Sub-Saharan Africa more than half of firms health and communicable diseases. Through report that lack of reliable electricity is a major the Global Media AIDS Initiative more than 50 constraint to doing business (figure 5c). broadcast networks are promoting AIDS preven- Job creation and economic growth in the tion messages. In the fight against malaria sat- private sector require a supportive investment ellite monitoring identifies and targets mosquito climate. Developing countries need about $900 breeding areas for control. billion (7­9 percent of GDP) to maintain existing Information and communication technologies infrastructure and to build new infrastructure, are also used for early disaster warning and for but only half that amount is available. The mitigation and relief following natural disasters. global financial and economic crisis is expected Remote sensing is used in managing resources to severely curtail infrastructure services as 2010 World Development Indicators 289 governments face shrinking budgets and declin- countercyclical stimulus by increasing demand ing private financial flows. Capital market financ- and employment while supporting longer term ing for developing country infrastructure has growth. Over time, inadequate infrastructure contracted from $200 billion in 2007 to $135 slows economic development and poverty reduc- billion in 2008, with a further decline expected tion. During a crisis countries under financial for 2009 (World Bank 2009j). stress often cut infrastructure more than other As budgets shrink, priorities are to protect government spending. In Latin America and poor and vulnerable groups by strengthening the Caribbean during the fiscal austerity of the social safety nets and supporting economic 1980s and 1990s, half the fiscal adjustment growth. But there is also a need to invest in came from cuts in public infrastructure, reduc- infrastructure, which can create jobs and lay the ing long-term growth by 1­3 percent (Schwartz, groundwork for productivity and growth. Infra- Andres, and Dragoiu 2009). structure spending can provide an important The World Bank has focused its response to the crisis on protecting the most vulnerable More than half of firms in South Asia and Sub-Saharan Africa say that groups, maintaining long-term infrastructure lack of reliable electricity is a major constraint to business 5c investment, and supporting private sector­led economic growth, microfinance, and employ- Share of firms (percent) Transportation is a major constraint Electricity is a major constraint 60 ment creation, especially among small and medium-size enterprises. The World Bank Infra- structure Recovery and Assets platform mobi- 40 lizes finance to support infrastructure spending critical for growth. The International Finance Cor- 20 poration's Infrastructure Crisis Facility supports and refinances public-private partnerships at 0 risk; it is investing about $300 million to mobi- East Asia Europe & Latin America Middle East & South Sub-Saharan & Pacific Central Asia & Caribbean North Africa Asia Africa lize $1.5­$10 billion from other sources. Source: World Bank Enterprise Surveys. Governments can leverage the benefits of private investment in infrastructure by intro- ducing competition. Regional collaboration Regional collaboration in infrastructure-- on infrastructure projects, by sharing scarce the Greater Mekong Subregion program 5d resources such as energy, capital, knowledge, In East Asia and Pacific, where countries are increasingly interconnected through land, sea, and services, can lower unit costs, improve and air transportation networks, national economic development plans are often supple- international competitiveness, and increase mented by regional and subregional programs. With support from the Asian Development connectivity (box 5d). Private companies can Bank since 1992 and the World Bank since 2007, Greater Mekong Subregion countries (Cambodia, China, Lao PDR, Myanmar, Thailand, and Vietnam) have established priority better manage infrastructure services by oper- transport corridors, laid the groundwork for power interconnection and trade, and devel- ating efficiently (improving bill collection, reduc- oped an information superhighway network. These projects have improved market access, ing corruption and red tape, improving labor increased trade and investment, and enabled businesses to take advantage of regional and global production chains. productivity, reducing transmission losses) and getting infrastructure prices right (prices should also cover basic operations and maintenance). In 2008 investment in infrastructure with private participation If countries in Sub-Saharan Africa addressed grew in all but two developing country regions 5e these inefficiencies, the funding required to close the infrastructure gap might be halved. $ billions 50 Despite the financial crisis, private sector Europe & Central Asia Latin America & Caribbean investment in infrastructure remains strong. 40 South Asia Commitments to infrastructure projects with 30 private participation fell in 2008, but they were 20 East Asia & Pacific still at the second highest level since 1990. In Sub-Saharan Africa 48 low- and middle-income economies, 216 proj- 10 ects reached financial or contractual closure. Middle East & North Africa 0 Infrastructure investments, including new com- 2005 2006 2007 2008 mitments for projects implemented in previous Source: World Bank Private Participation in Infrastructure database and World Development Indicators table 5.1. years, totaled $154.4 billion in 2008. Invest- ment grew in all developing country regions 290 2010 World Development Indicators STATES AND MARKETS except East Asia and Pacific and the Middle East life (by farmers to receive crop price information and North Africa. But there were large dispari- and health workers to increase the effectiveness ties. Five countries accounted for almost half the and reach of health programs), increased almost investment in infrastructure with private partici- 20-fold on a per capita subscription basis over pation over 1990­2008-- Brazil, India, China, 2000­08. In 2008 almost a third of Sub-Saharan Mexico, and the Russian Federation (figures 5e Africa's people had mobile phone subscriptions. and 5f). Despite this impressive expansion, a "digital Telecommunications was the only infra- divide" remains: Eritrea has 2 subscriptions per structure sector with increasing investment 100 people while South Africa has more than 90. in 2008, up 1 percent over 2007. Investment was down 7 percent in energy, 10 percent in Five countries accounted for almost half of investment in transport, and 27 percent in water and sani- infrastructure with private participation, 1990­2008 5f tation. Over 1990­2008 water and sanitation had the lowest share of infrastructure invest- Brazil ment with private participation, attracting only 16% 4.4 percent (World Bank Private Participation in India Infrastructure Project Database 2008) (figures All other 9% developing 5g and 5h). countries China 54% 8% Mexico Special focus on Africa's 7% infrastructure With 15 landlocked countries, transport costs Russian Federation 6% in Sub-Saharan Africa are high, hampering Source: World Bank Private Participation in Infrastructure database and World Development Indicators table 5.1. trade and slowing growth. Sub-Saharan Africa also has the lowest population density and the second lowest urbanization rate of all develop- Investment rose in energy, telecommunications, and transport, ing country regions, raising the cost of infra- but remained flat in water and sanitation, 2005­08 5g structure investments. Not surprisingly, Africa $ billions has a major infrastructure deficit--unreliable 100 power supplies, only about 12 percent of roads Telecommunications paved, and the lowest rates of access to wa- 75 ter and sanitation among developing country Energy 50 regions. Sub-Saharan Africa is farthest behind in achieving the MDGs and is expected to fall Transport 25 short of meeting most targets related to poverty, Water and sanitation health, education, and water and sanitation-- 0 2005 2006 2007 2008 which all depend on infrastructure services. Inadequate infrastructure in Sub-Saharan Source: World Bank Private Participation in Infrastructure database and World Development Indicators table 5.1. Africa also contributes to the region's poor economic performance and competitiveness. Business managers report that the main infra- Investment in water and sanitation with private participation structure deficiencies hampering business accounted for only 4.4 percent of the total, 1990­2008 5h activity are electricity (48 percent), transpor- Water and sanitation 4.4% tation (25 percent), and telecommunications (22 percent). Improvements in infrastructure Transport sectors, such as rural roads in Guinea, have 17.1% raised incomes and increased food supplies for farming families, reducing poverty and hunger. Energy 48.8% Expanding information and communications Telecommunications 29.8% services in Sub-Saharan Africa is connecting the region to the rest of the world and is a key factor in fostering long-term growth. Many governments are beginning to provide affordable ICT services Source: World Bank Private Participation in Infrastructure database and World Development Indicators table 5.1. more broadly. Mobile phones, used in all walks of 2010 World Development Indicators 291 Tables 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Telecommunications Energy Transport sanitation % of GDP New Total 2000­05 2006­08 2000­05 2006­08 2000­05 2006­08 2000­05 2006­08 2008 2007 2007 Afghanistan 466.1 980.4 1.6 .. .. .. .. .. 8.9 .. .. Albania 569.2 331.0 790.6 .. 308.0 .. 8.0 0.0 36.0 2,176 16,110 Algeria 3,422.5 1,527.0 962.0 2,320.0 120.9 161.0 510.0 1,104.0 13.5 10,662 105,128 Angola 278.7 775.0 45.0 9.4 .. 53.0 .. .. 12.5 .. .. Argentina 5,836.8 3,714.2 3,826.9 3,126.5 203.6 1,396.7 791.6 .. 13.7 16,400 218,700 Armenia 317.1 214.6 74.0 57.0 63.0 585.0 0.0 0.0 17.4 3,822 56,461 Australia .. .. .. .. .. .. .. .. 121.5 89,960 641,538 Austria .. .. .. .. .. .. .. .. 119.1 3,484 76,374 Azerbaijan 355.6 1,075.6 375.2 .. .. .. 0.0 .. 16.5 4,945 69,309 Bangladesh 1,294.3 3,357.8 501.5 48.8 0.0 0.0 .. .. 39.2 5,328 67,459 Belarus 735.4 2,011.3 .. .. .. 4.0 .. .. 28.8 .. .. Belgium .. .. .. .. .. .. .. .. 94.5 28,016 354,489 Benin 116.9 272.7 590.0 .. .. .. .. .. 20.9 .. .. Bolivia 520.5 247.3 884.4 137.3 16.6 .. .. .. 34.7 1,625 24,649 Bosnia and Herzegovina 0.0 916.0 .. 800.0 .. .. .. .. 57.8 314 23,634 Botswana 104.0 97.9 .. .. .. .. .. .. 21.1 7,301 .. Brazil 41,053.8 21,365.2 25,625.6 17,490.8 4,469.3 10,625.9 1,215.3 1,507.5 55.7 490,542 5,668,003 Bulgaria 2,179.1 1,526.8 3,062.1 1,547.8 2.1 531.6 152.0 .. 74.5 49,328 315,037 Burkina Faso 41.9 487.6 .. .. .. .. .. .. 18.6 639 .. Burundi 53.6 0.0 .. .. .. .. .. .. 21.5 .. .. Cambodia 136.1 205.9 82.1 695.8 125.3 200.0 .. .. 23.5 .. .. Cameroon 394.4 423.4 91.8 440.0 0.0 .. .. 0.0 10.4 .. .. Canada .. .. .. .. .. .. .. .. 128.4 207,000 2,500,000 Central African Republic 0.0 14.8 .. .. .. .. .. .. 7.0 .. .. Chad 11.0 178.4 0.0 .. .. .. .. .. 3.7 .. .. Chile 3,561.6 3,060.2 1,590.5 1,370.8 4,821.2 830.1 1,495.2 3.1 97.7 25,124 223,345 China 8,548.0 0.0 10,970.9 4,075.5 15,350.1 13,320.1 3,505.2 3,904.8 108.3 .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. 142.8 80,935 524,445 Colombia 1,570.9 4,395.0 351.6 709.7 1,005.4 2,344.4 314.3 305.0 34.2 28,801 497,778 Congo, Dem. Rep. 473.4 729.0 .. .. .. .. .. .. 7.1 .. .. Congo, Rep. 61.8 220.7 .. .. .. 735.0 0.0 .. 3.5 237 .. Costa Rica .. .. 80.0 80.0 465.2 373.0 .. .. 50.8 10,567 102,311 Côte d'Ivoire 134.9 567.4 0.0 0.0 176.4 .. .. 0.0 16.3 .. .. Croatia 1,205.7 3,035.0 7.1 85.0 451.0 492.0 298.7 .. 64.9 11,055 200,955 Cuba 60.0 0.0 116.0 60.0 0.0 .. 600.0 .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. 52.8 16,395 244,417 Denmark .. .. .. .. .. .. .. .. 218.0 28,811 200,060 Dominican Republic 393.0 110.1 1,306.6 0.0 898.9 250.0 .. .. 20.9 .. 20,808 Ecuador 357.8 1,505.6 302.0 129.0 685.0 1,166.0 500.0 .. 26.1 3,196 37,434 Egypt, Arab Rep. 3,471.9 7,073.0 678.0 469.0 821.5 1,370.0 .. .. 42.9 9,595 367,559 El Salvador 1,110.6 822.1 85.0 0.0 .. .. .. .. 41.3 1,802 .. Eritrea 40.0 0.0 .. .. .. .. .. .. 18.4 .. .. Estonia .. .. .. .. .. .. .. .. 97.4 .. .. Ethiopia .. .. .. .. .. .. .. .. 18.0 .. .. Finland .. .. .. .. .. .. .. .. 85.8 10,424 120,294 France .. .. .. .. .. .. .. .. 107.8 137,481 1,267,419 Gabon 26.6 187.8 0.0 0.0 177.4 3.9 .. .. 8.5 .. .. Gambia, The 6.6 35.0 .. 0.0 .. .. .. .. 17.6 .. .. Georgia 173.8 564.7 40.0 607.3 .. 573.0 .. 435.0 33.3 5,260 59,641 Germany .. .. .. .. .. .. .. .. 107.8 66,747 573,985 Ghana 156.5 2,069.0 590.0 100.0 10.0 .. 0.0 .. 17.8 .. .. Greece .. .. .. .. .. .. .. .. 93.5 .. .. Guatemala 560.1 1,305.1 110.0 263.8 .. .. .. 6.7 27.2 4,251 .. Guinea 50.6 155.2 .. .. .. .. .. .. .. .. .. Guinea-Bissau 21.9 68.4 .. .. .. .. .. .. 9.1 .. .. Haiti 18.0 306.0 5.5 .. .. .. .. .. 12.1 9 300 Honduras 135.0 653.9 358.8 .. 120.0 .. 207.9 .. 51.4 .. .. 292 2010 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Telecommunications Energy Transport sanitation % of GDP New Total 2000­05 2006­08 2000­05 2006­08 2000­05 2006­08 2000­05 2006­08 2008 2007 2007 Hungary 5,172.8 1,523.3 851.6 1,707.0 3,297.5 1,588.0 0.0 0.0 69.6 28,153 273,549 India 20,642.0 24,702.2 8,369.2 28,507.7 4,281.3 19,009.2 112.9 218.2 51.4 20,000 732,000 Indonesia 6,557.2 8,068.7 1,828.5 3,779.3 159.2 1,433.9 44.8 20.2 26.5 18,960 271,255 Iran, Islamic Rep. 695.0 1,023.0 650.0 .. .. .. .. .. 49.2 .. .. Iraq 984.0 4,074.0 .. 590.0 .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. 217.0 18,704 180,891 Israel .. .. .. .. .. .. .. .. 90.1 18,814 162,910 Italy .. .. .. .. .. .. .. .. 105.1 77,587 638,987 Jamaica 700.3 241.2 201.0 78.0 565.0 .. .. .. 28.3 2,023 54,116 Japan .. .. .. .. .. .. .. .. 163.5 145,151 2,572,088 Jordan 1,589.0 484.6 .. 524.0 0.0 1,380.0 169.0 .. 83.8 2,361 .. Kazakhstan 1,153.7 2,619.8 300.0 0.0 231.0 31.0 .. .. 49.6 .. .. Kenya 1,434.0 2,695.8 .. 306.7 .. 404.0 .. .. 30.0 7,371 125,102 Korea, Dem. Rep. .. 400.0 .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 109.1 .. .. Kosovo .. .. .. .. .. .. .. .. 34.3 .. .. Kuwait .. .. .. .. .. .. .. .. 66.4 .. .. Kyrgyz Republic 11.5 115.9 .. .. .. .. 0.0 .. 15.1 3,987 .. Lao PDR 87.7 10.0 1,250.0 1,465.0 0.0 .. .. .. 9.5 .. .. Latvia 700.0 428.1 71.1 .. .. 135.0 .. .. 90.2 12,017 .. Lebanon 138.1 0.0 .. .. 153.0 .. 0.0 .. 75.9 1,030 .. Lesotho 88.4 19.6 0.0 .. .. .. .. .. 10.9 .. .. Liberia 70.3 49.8 .. .. .. .. .. .. 12.5 .. .. Libya .. .. .. .. .. .. .. .. 6.8 .. .. Lithuania 993.0 489.2 514.3 350.6 .. .. .. .. 62.7 6,578 67,095 Macedonia, FYR 706.6 256.6 .. 391.0 .. 295.0 .. .. 43.8 .. .. Madagascar 12.6 221.8 0.0 .. 61.0 17.5 .. .. 11.1 1,234 19,305 Malawi 36.3 124.7 0.0 .. .. .. .. .. 11.4 420 5,595 Malaysia 3,777.0 1,494.0 6,637.6 203.0 4,263.0 1,379.0 6,502.2 0.0 100.6 43,279 .. Mali 82.6 154.0 365.9 .. 55.4 .. .. .. 17.1 .. .. Mauritania 92.1 90.1 .. .. .. .. .. .. .. .. .. Mauritius 413.0 67.1 0.0 .. .. .. .. 0.0 87.7 .. .. Mexico 18,191.4 9,278.7 6,749.3 1,883.0 2,970.4 10,113.5 523.7 303.8 21.1 306,400 4,290,000 Moldova 46.1 278.3 127.2 434.0 0.0 60.0 .. .. 36.5 6,806 73,532 Mongolia 22.1 0.0 .. .. .. .. .. .. 43.6 .. .. Morocco 6,139.5 2,309.6 1,049.0 .. 200.0 200.0 .. .. 77.4 24,811 .. Mozambique 123.0 104.2 1,205.8 .. 334.6 0.0 .. .. 18.4 .. .. Myanmar .. .. .. 556.1 .. .. .. .. .. .. .. Namibia 35.0 8.5 1.0 .. .. .. 0.0 .. 45.6 .. .. Nepal 109.3 26.0 15.1 .. .. .. .. .. 40.8 .. .. Netherlands .. .. .. .. .. .. .. .. 193.2 116,000 1,030,000 New Zealand .. .. .. .. .. .. .. .. 150.4 74,247 474,212 Nicaragua 218.5 327.2 126.3 95.0 104.0 .. .. .. 36.4 2,070 .. Niger 85.5 164.7 .. .. .. .. 3.4 .. 11.0 .. .. Nigeria 6,949.7 8,291.1 1,920.0 280.0 2,355.4 644.1 .. .. 33.9 .. .. Norway .. .. .. .. .. .. .. .. .. 18,082 132,788 Oman .. .. .. .. .. .. .. .. 35.9 6,362 38,864 Pakistan 6,595.1 7,868.4 375.4 2,578.7 153.8 923.7 .. .. 29.5 4,840 .. Panama 211.4 1,141.5 429.8 495.7 51.4 .. .. .. 89.7 .. .. Papua New Guinea .. 150.0 .. .. .. .. .. .. 24.8 .. .. Paraguay 199.0 498.5 .. .. .. .. .. .. 24.5 .. .. Peru 2,241.4 1,959.4 2,498.9 743.8 522.5 2,329.8 152.0 .. 24.8 .. .. Philippines 4,616.4 3,353.0 3,428.4 5,076.8 943.5 515.3 0.0 503.9 28.8 18,189 .. Poland 16,800.1 6,158.7 2,555.5 1,410.4 1,672.0 1,439.3 64.3 0.8 49.8 26,388 523,584 Portugal .. .. .. .. .. .. .. .. 179.7 30,934 423,719 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 46.7 .. .. 2010 World Development Indicators 293 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses projects with private participationa credit to registered private sector $ millions Water and Telecommunications Energy Transport sanitation % of GDP New Total 2000­05 2006­08 2000­05 2006­08 2000­05 2006­08 2000­05 2006­08 2008 2007 2007 Romania 3,793.9 4,921.5 1,240.8 4,090.5 .. 116.8 116.0 41.0 38.5 103,733 870,195 Russian Federation 22,049.4 20,675.1 1,726.0 25,376.2 109.4 191.0 904.7 1,212.3 41.0 489,955 3,267,325 Rwanda 72.3 168.0 1.6 .. .. .. .. .. .. .. 455 Saudi Arabia .. .. .. .. .. .. .. .. 55.4 .. .. Senegal 593.1 1,077.0 93.3 .. 55.4 134.0 0.0 0.0 24.2 23 1,000 Serbia 563.5 3,107.4 .. .. .. .. 0.0 .. 38.4 10,876 83,499 Sierra Leone 48.8 88.2 .. 1.2 .. .. .. .. 7.1 .. .. Singapore .. .. .. .. .. .. .. .. 107.9 25,904 133,235 Slovak Republic .. .. .. .. .. .. .. .. 44.7 16,025 135,330 Slovenia .. .. .. .. .. .. .. .. 85.6 4,957 47,312 Somalia 13.4 0.0 .. .. .. .. .. .. .. .. .. South Africa 10,519.5 5,327.0 1,251.3 9.9 504.7 3,483.0 31.3 0.0 145.2 41,356 553,425 Spain .. .. .. .. .. .. .. .. 201.4 145,593 2,435,689 Sri Lanka 766.1 1,024.4 270.8 .. .. .. .. .. 28.9 4,529 .. Sudan 747.7 1,391.3 .. .. .. 30.0 .. 120.7 10.9 .. .. Swaziland 27.7 23.3 .. .. .. .. .. .. 23.6 .. .. Sweden .. .. .. .. .. .. .. .. 129.6 27,994 326,052 Switzerland .. .. .. .. .. .. .. .. 168.2 18,284 162,326 Syrian Arab Republic 583.0 199.7 .. .. .. 37.0 .. .. 15.6 216 2,268 Tajikistan 8.5 64.0 16.0 .. .. .. .. .. 29.0 794 .. Tanzania 515.3 962.5 348.0 28.4 27.7 134.0 8.5 .. 16.3 3,933 59,163 Thailand 5,602.7 2,567.0 4,693.3 2,341.0 939.0 .. 522.7 18.8 113.1 25,184 297,084 Timor-Leste 0.0 0.0 .. .. .. .. .. .. 20.6 .. .. Togo 0.0 0.0 657.7 190.0 .. .. .. .. 18.7 .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. 28.9 .. .. Tunisia 751.0 2,518.0 30.0 .. .. 840.0 .. .. 66.6 6,675 63,584 Turkey 12,788.6 8,160.7 6,754.8 3,762.7 3,118.6 4,441.0 .. .. 32.6 93,634 764,240 Turkmenistan 20.0 106.1 .. .. .. .. .. .. .. .. .. Uganda 387.6 1,180.0 113.9 964.6 .. 404.0 0.0 .. 13.9 8,906 89,503 Ukraine 3,162.9 3,574.8 160.0 .. .. .. 100.0 .. 73.7 41,809 528,864 United Arab Emirates .. .. .. .. .. .. .. .. 72.7 .. .. United Kingdom .. .. .. .. .. .. .. .. 211.1 449,700 2,546,200 United States .. .. .. .. .. .. .. .. 187.1 676,830 5,156,000 Uruguay 114.2 113.8 330.0 .. 251.1 .. 368.0 .. 26.3 .. .. Uzbekistan 285.6 680.9 .. .. .. 25.0 0.0 .. .. 10,264 56,465 Venezuela, RB 3,337.0 2,074.0 39.5 .. 34.0 .. 15.0 .. 21.5 .. .. Vietnam 430.0 1,326.7 2,360.6 287.0 20.0 765.0 174.0 .. 90.6 .. 52,506 West Bank and Gaza 279.8 0.0 150.0 .. .. .. .. .. .. .. .. Yemen, Rep. 376.8 342.2 .. 15.8 .. 220.0 .. .. 7.8 50 .. Zambia 208.3 510.0 3.0 .. 15.6 .. 0.0 .. 15.3 5,300 .. Zimbabwe 72.0 143.0 .. .. .. .. .. .. .. .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 129.7 w Low income 8,043.3 19,559.7 8,201.6 4,659.3 705.4 2,303.5 185.9 0.0 36.5 Middle income 243,326.2 196,510.7 106,361.7 116,921.3 53,504.4 83,650.3 18,975.5 9,705.6 62.6 Lower middle income 84,659.7 88,720.8 38,070.8 50,571.3 26,894.4 43,175.4 5,220.1 5,228.2 82.6 Upper middle income 158,666.6 107,789.9 68,290.9 66,350.1 26,610.0 40,474.9 13,755.5 4,477.4 44.6 Low & middle income 251,369.6 216,070.4 114,563.3 121,580.7 54,209.9 85,953.8 19,161.4 9,705.6 61.9 East Asia & Pacific 29,862.2 17,755.8 31,258.4 18,479.5 21,800.1 17,613.3 10,748.9 4,447.7 99.8 Europe & Central Asia 67,661.7 58,420.5 17,807.6 38,827.4 5,504.1 8,427.7 1,345.0 1,689.1 43.0 Latin America & Carib. 80,834.5 53,217.6 45,116.5 26,889.7 17,221.2 29,429.3 6,232.5 2,126.1 38.8 Middle East & N. Africa 18,430.6 19,551.1 3,519.0 3,918.8 1,475.4 4,508.0 679.0 1,104.0 35.8 South Asia 29,926.2 37,976.7 9,533.6 31,135.2 4,435.1 19,932.9 112.9 218.2 49.5 Sub-Saharan Africa 24,654.4 29,148.6 7,328.3 2,330.2 3,774.1 6,042.5 43.2 120.7 58.5 High income .. .. .. .. .. .. .. .. 155.9 Euro area .. .. .. .. .. .. .. .. 126.4 a. Data refer to total for the period shown. Includes infrastructure projects with private sector participation that reached financial closure in 1990­2008. 294 2010 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy About the data Definitions Private sector development and investment--tapping and small-scale operators--may be omitted because · Investment commitments in infrastructure private sector initiative and investment for socially they are not publicly reported. The database is a joint projects with private participation refers to infra- useful purposes--are critical for poverty reduction. product of the World Bank's Finance, Economics, and structure projects in telecommunications, energy In parallel with public sector efforts, private invest- Urban Development Department and the Public- (electricity and natural gas transmission and dis- ment, especially in competitive markets, has tre- Private Infrastructure Advisory Facility. Geographic tribution), transport, and water and sanitation mendous potential to contribute to growth. Private and income aggregates are calculated by the World that have reached financial closure and directly or markets are the engine of productivity growth, creat- Bank's Development Data Group. For more informa- indirectly serve the public. Incinerators, movable ing productive jobs and higher incomes. And with gov- tion, see http://ppi.worldbank.org/. assets, standalone solid waste projects, and small ernment playing a complementary role of regulation, Credit is an important link in money transmission; projects such as windmills are excluded. Included funding, and service provision, private initiative and it finances production, consumption, and capital for- are operation and management contracts, opera- investment can help provide the basic services and mation, which in turn affect economic activity. The tion and management contracts with major capital conditions that empower poor people--by improving data on domestic credit to the private sector are expenditure, greenfield projects (new facilities built health, education, and infrastructure. taken from the banking survey of the International and operated by a private entity or a public-private Investment in infrastructure projects with private Monetary Fund's (IMF) International Financial Statis- joint venture), and divestitures. Investment commit- participation has made important contributions to tics or, when unavailable, from its monetary survey. ments are the sum of investments in facilities and easing fiscal constraints, improving the efficiency The monetary survey includes monetary authorities investments in government assets. Investments in of infrastructure services, and extending delivery (the central bank), deposit money banks, and other facilities are resources the project company com- to poor people. Developing countries have been in banking institutions, such as finance companies, mits to invest during the contract period in new the forefront, pioneering better approaches to infra- development banks, and savings and loan institu- facilities or in expansion and modernization of exist- structure services and reaping the benefits of greater tions. Credit to the private sector may sometimes ing facilities. Investments in government assets competition and customer focus. include credit to state-owned or partially state-owned are the resources the project company spends on The data on investment in infrastructure projects enterprises. acquiring government assets such as state-owned with private participation refer to all investment (pub- Entrepreneurship is essential to the dynamism of enterprises, rights to provide services in a specific lic and private) in projects in which a private com- the modern market economy, and a greater entry rate area, or use of specific radio spectrums. · Domestic pany assumes operating risk during the operating of new businesses can foster competition and eco- credit to private sector is financial resources pro- period or development and operating risk during the nomic growth. The table includes data on business vided to the private sector--such as through loans, contract period. Investment refers to commitments registrations from the 2008 World Bank Group Entre- purchases of nonequity securities, and trade cred- not disbursements. Foreign state-owned companies preneurship Survey, which includes entrepreneurial its and other accounts receivable--that establish are considered private entities for the purposes of activity in more than 100 countries for 2000­08. a claim for repayment. For some countries these this measure. Survey data are used to analyze firm creation, its claims include credit to public enterprises. · New Investments are classified into two types: invest- relationship to economic growth and poverty reduc- businesses registered are the number of limited ments in physical assets--the resources a com- tion, and the impact of regulatory and institutional liability firms registered in the calendar year. · Total pany commits to invest in expanding and modern- reforms. The 2008 survey improves on earlier sur- businesses registered are the year-end stock of total izing facilities--and payments to the government to veys' methodology and country coverage for better registered limited liability firms. acquire state-owned enterprises or rights to provide cross-country comparability. Data on total and newly services in a specific area or to use part of the radio registered businesses were collected directly from spectrum. national registrars of companies. For cross-country The data are from the World Bank's Private Partici- comparability, only limited liability corporations pation in Infrastructure (PPI) Project database, which that operate in the formal sector are included. For tracks infrastructure projects with private participa- additional information on sources, methodology, tion in developing countries. It provides information calculation of entrepreneurship rates, and data limi- Data sources on more than 4,300 infrastructure projects in 137 tations see http://econ.worldbank.org/research/ developing economies from 1984 to 2008. The data- entrepreneurship. Data on investment commitments in infra- base contains more than 30 fields per project record, structure projects with private participation are including country, financial closure year, infrastructure from the World Bank's PPI Project database services provided, type of private participation, (http://ppi.worldbank.org). Data on domestic investment, technology, capacity, project location, credit are from the IMF's International Financial contract duration, private sponsors, bidding process, Statistics. Data on business registration and are and development bank support. Data on the projects from the World Bank's Entrepreneurship Survey are compiled from publicly available information. The and database (http://econ.worldbank.org/ database aims to be as comprehensive as possible, research/entrepreneurship). but some projects--particularly those involving local 2010 World Development Indicators 295 5.2 Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Firms Inter- Average Time Losses due formally nationally time to dealing with Time required Informal to theft, registered Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, when female banks to Value lost due quality exports offering number operating to public vandalism, operations participation finance to electrical certification through formal % of of times license officials and arson started in ownership investment outages ownership customs training management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Afghanistan 2008 6.8 1.2 13.8 41.5 1.5 88.0 2.8 1.4 6.5 8.5 14.6 14.6 Albania 2007 18.7 3.9 21.2 57.7 0.5 89.4 10.8 12.4 13.7 24.6 1.9 19.9 Algeria 2007 25.1 2.3 19.3 64.7 0.9 98.3 15.0 8.9 4.0 5.0 14.1 17.3 Angola 2006 7.1 3.3 24.1 46.8 0.4 .. 23.4 2.1 3.7 5.1 16.5 19.4 Argentina 2006 13.8 2.5 116.0 18.7 1.3 93.8 30.3 6.9 1.4 26.9 5.5 52.2 Armenia 2009 10.3 2.1 20.0 11.6 0.6 96.2 31.8 31.9 1.8 26.9 3.3 30.4 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2009 3.0 2.1 15.8 32.0 0.3 85.1 10.8 19.0 1.8 18.2 1.9 10.5 Bangladesh 2007 3.2 1.3 6.0 85.1 0.1 .. 16.1 24.7 10.6 7.8 8.4 16.2 Belarus 2008 13.6 1.1 38.2 13.5 0.4 98.5 52.9 35.8 0.8 13.9 2.6 44.4 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 2009 20.7 1.2 64.3 54.5 1.9 87.9 43.9 4.2 7.5 7.3 9.6 32.4 Bolivia 2006 13.5 1.7 26.0 32.4 0.9 90.5 41.1 22.2 4.4 13.8 15.3 53.9 Bosnia and Herzegovina 2009 11.2 1.0 21.4 8.1 0.2 98.6 32.8 59.7 1.9 30.1 1.4 66.5 Botswana 2006 5.0 0.9 13.7 27.6 1.3 .. 40.9 11.3 1.4 12.7 1.4 37.7 Brazil 2009 18.7 1.2 83.5 9.7 1.7 95.8 59.3 48.4 3.0 25.7 15.9 52.9 Bulgaria 2009 10.6 2.2 20.8 8.5 0.5 98.5 33.9 34.7 1.6 19.9 4.2 30.7 Burkina Faso 2009 22.2 1.5 35.8 8.5 0.3 77.7 19.2 25.6 5.8 14.4 7.4 24.8 Burundi 2006 5.7 1.8 27.3 56.5 1.1 .. 34.8 12.3 10.7 7.1 .. 22.1 Cambodia 2007 5.6 1.0 .. 61.2 1.6 87.5 .. 11.3 2.4 2.8 1.5 48.4 Cameroon 2009 7.0 4.4 30.0 50.8 1.7 82.1 15.7 31.4 4.9 20.4 15.1 25.5 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 2009 20.8 3.4 24.3 41.8 2.5 77.1 40.1 4.2 3.3 43.3 11.9 43.4 Chile 2006 9.0 3.0 67.7 8.2 0.6 97.8 27.8 29.1 1.8 22.0 5.8 46.9 China 2003 18.3 14.4 11.6 72.6 0.1 .. .. 28.8 1.3 35.9 6.6 84.8 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2006 14.3 0.6 28.2 8.2 0.7 85.6 43.0 30.6 2.3 5.9 7.0 39.5 Congo, Dem. Rep. 2006 6.3 8.4 17.8 83.8 2.0 .. 21.2 3.3 5.6 4.3 3.6 11.4 Congo, Rep. 2009 6.0 2.7 .. 49.2 3.3 84.3 31.8 7.7 16.4 19.6 .. 37.5 Costa Ricaa 2005 9.6 0.5 .. 33.8 0.4 .. 65.3 14.9 1.9 10.5 3.5 46.4 Côte d'Ivoire 2009 1.8 3.6 14.5 30.6 3.4 56.4 61.9 13.9 5.0 4.3 16.6 19.1 Croatia 2007 10.9 0.7 26.5 14.5 0.2 98.1 33.5 60.0 0.8 16.5 1.3 28.0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 2009 10.4 1.5 19.9 8.7 0.4 98.0 25.0 33.4 0.6 43.5 5.7 70.7 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republica 2005 8.8 0.5 .. 26.3 0.7 .. .. 12.5 15.2 9.6 11.4 53.3 Ecuador 2006 17.3 0.6 19.9 21.5 0.9 91.1 32.7 24.0 2.7 18.2 7.0 61.6 Egypt, Arab Rep. 2008 8.8 3.4 90.6 98.3 3.0 14.3 34.0 5.6 3.4 21.1 6.2 21.7 El Salvador 2006 9.2 2.7 35.4 34.3 2.6 79.5 39.6 17.3 2.9 11.0 2.5 49.6 Eritrea 2009 0.5 0.2 .. 0.0 .. 100.0 4.2 11.9 0.2 15.1 9.6 26.1 Estonia 2009 5.5 0.4 8.3 1.6 0.9 97.4 36.3 41.5 0.5 21.2 1.8 69.3 Ethiopia 2006 3.8 1.1 11.4 12.4 1.4 .. 30.9 11.0 0.9 4.2 4.3 38.2 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 2009 2.8 15.2 12.1 26.1 0.4 63.7 33.1 6.3 1.7 18.6 3.8 30.9 Gambia, The 2006 7.3 2.5 8.4 52.4 2.7 .. 21.3 7.6 11.8 22.2 5.0 25.6 Georgia 2008 2.1 0.6 11.8 4.1 0.7 99.6 40.8 38.2 1.4 16.0 3.8 14.5 Germany 2005 1.2 1.3 .. .. 0.5 .. 20.3 45.0 .. .. 4.7 35.4 Ghana 2007 4.0 4.1 6.4 38.8 0.9 66.4 44.0 16.0 6.0 6.8 7.8 33.0 Greece 2005 1.8 1.7 .. 21.6 0.0 .. 24.4 25.9 .. 11.7 5.5 20.0 Guatemala 2006 9.2 2.1 75.4 15.7 1.5 91.3 28.4 12.8 4.5 8.0 4.5 28.1 Guinea 2006 2.7 2.8 13.0 84.8 2.0 .. 25.4 0.9 14.0 5.2 4.3 21.1 Guinea-Bissau 2006 2.9 3.4 30.4 62.7 1.1 .. 19.9 0.7 5.3 8.4 5.6 12.4 Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 2006 4.6 1.5 31.6 16.7 2.2 89.4 39.9 8.5 3.8 16.5 6.0 33.3 296 2010 World Development Indicators 5.2 STATES AND MARKETS Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Firms Inter- Average Time Losses due formally nationally time to dealing with Time required Informal to theft, registered Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, when female banks to Value lost due quality exports offering number operating to public vandalism, operations participation finance to electrical certification through formal % of of times license officials and arson started in ownership investment outages ownership customs training management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Hungary 2009 13.5 0.8 35.6 4.0 0.1 100.0 42.4 48.7 0.9 39.4 4.3 14.8 India 2006 6.7 2.6 .. 47.5 0.1 .. 9.1 46.7 6.6 22.5 15.1 15.9 Indonesiaa 2003 4.0 1.0 .. 44.2 0.2 .. .. 34.0 3.3 22.1 3.7 23.8 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2005 2.3 1.3 .. 8.3 0.3 .. 41.6 37.4 1.5 17.2 2.6 73.2 Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaicaa 2005 6.3 1.8 .. 17.7 1.1 .. 32.2 37.0 11.8 16.4 4.3 53.5 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 2006 6.7 1.7 6.4 18.1 0.1 .. 13.1 8.6 1.7 15.5 3.8 23.9 Kazakhstan 2009 4.7 2.6 30.8 23.3 1.0 97.4 34.4 31.0 3.7 10.8 8.5 40.9 Kenya 2007 5.1 6.7 23.4 79.2 3.9 .. 37.1 22.9 6.4 9.8 5.6 40.7 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 2005 0.1 2.2 .. 14.1 0.0 .. 19.1 39.9 .. 17.6 7.2 39.5 Kosovo 2009 9.8 4.5 18.8 2.2 0.3 89.2 10.9 25.3 17.1 7.9 1.7 24.6 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 2009 4.9 2.1 18.0 37.5 0.3 95.9 60.4 17.9 10.5 16.2 15.8 29.7 Lao PDR 2009 1.6 4.4 13.6 39.8 0.1 93.5 39.4 0.0 0.0 7.2 7.5 11.1 Latvia 2009 9.7 1.5 11.5 11.3 0.3 98.5 46.3 37.3 1.1 18.2 1.9 43.4 Lebanon 2006 12.0 3.2 .. 51.2 0.5 .. 27.9 53.5 6.0 20.9 6.7 67.8 Lesotho 2009 5.6 1.8 16.4 14.0 2.9 86.8 18.4 32.7 6.7 24.7 5.4 42.5 Liberia 2009 7.5 6.5 16.0 55.2 2.8 73.8 53.0 10.1 2.9 2.4 0.0 17.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 2009 9.3 0.8 65.5 8.5 0.4 97.1 38.7 47.4 0.7 15.6 2.4 46.0 Macedonia, FYR 2009 14.5 3.0 33.8 11.5 0.7 99.2 36.4 47.0 5.9 21.5 2.5 19.0 Madagascar 2009 17.1 0.9 41.3 19.2 1.2 97.5 50.0 12.2 7.7 8.7 14.2 27.0 Malawi 2009 3.5 2.7 15.0 10.8 5.7 78.6 23.9 20.6 17.0 17.9 4.9 48.4 Malaysiaa 2007 7.8 2.6 22.4 .. 1.0 53.0 13.1 48.6 3.0 54.1 2.7 50.1 Mali 2007 2.4 1.6 41.0 28.9 0.6 85.4 18.4 7.0 1.8 8.6 4.8 22.5 Mauritania 2006 5.8 1.8 10.7 82.1 0.6 .. 17.3 3.2 1.6 5.9 3.9 25.5 Mauritius 2009 9.4 0.5 19.1 1.6 1.4 84.2 16.9 37.5 2.2 11.1 10.3 25.6 Mexico 2006 20.5 0.6 11.2 22.6 0.7 94.1 24.8 2.6 2.4 20.3 5.2 24.6 Moldova 2009 7.0 1.9 13.9 25.4 0.4 97.9 53.1 30.8 2.0 9.1 2.4 33.1 Mongolia 2009 12.1 2.0 43.5 30.4 0.6 90.1 52.0 26.5 0.8 16.7 18.6 61.2 Morocco 2007 11.4 0.9 3.4 13.4 0.0 86.0 13.1 12.3 1.3 17.3 1.8 24.7 Mozambique 2007 3.3 1.9 35.2 14.8 1.8 85.9 24.4 10.5 2.4 18.7 10.1 22.1 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 2006 2.9 0.3 9.6 11.4 1.3 .. 33.4 8.1 0.7 17.6 1.4 44.5 Nepal 2009 6.5 1.3 14.5 15.2 0.9 94.0 27.4 17.5 27.0 3.1 5.6 8.8 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2006 9.3 1.3 19.7 17.2 0.9 85.4 41.4 13.0 8.7 18.7 5.0 28.9 Niger 2009 21.1 1.6 39.7 35.2 0.9 90.5 17.6 9.3 1.9 4.6 2.6 32.1 Nigeria 2007 6.1 3.0 12.1 40.9 4.1 .. 20.0 2.7 8.9 8.5 7.5 25.7 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. 4.4 11.8 33.2 .. .. .. 31.0 4.2 10.8 3.4 20.9 Pakistana 2007 2.2 1.6 16.4 27.2 0.5 .. 6.7 9.7 9.9 9.6 4.8 6.7 Panama 2006 10.3 1.4 41.2 25.4 0.5 98.0 37.1 19.2 2.4 14.7 5.7 43.9 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 2006 7.9 0.7 37.8 84.8 0.9 94.0 44.8 8.2 2.5 7.1 5.5 46.9 Peru 2006 13.5 1.4 81.1 11.3 0.4 99.2 32.8 30.9 3.2 14.6 5.4 57.7 Philippinesa 2003 6.9 3.2 18.8 44.7 0.9 .. .. 21.8 5.9 15.8 6.6 21.7 Poland 2009 12.8 0.6 14.6 5.0 0.5 99.3 47.9 40.7 1.9 17.3 6.0 60.9 Portugal 2005 1.1 1.6 .. 14.5 0.2 .. 50.8 24.4 .. 12.7 7.2 31.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 297 5.2 Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Firms Inter- Average Time Losses due formally nationally time to dealing with Time required Informal to theft, registered Firms with Firms using recognized clear direct Firms Average officials to obtain payments robbery, when female banks to Value lost due quality exports offering number operating to public vandalism, operations participation finance to electrical certification through formal % of of times license officials and arson started in ownership investment outages ownership customs training management meeting with time tax officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Romania 2009 9.2 2.3 23.7 9.8 0.3 98.7 47.9 37.3 2.2 26.1 2.0 24.9 Russian Federation 2009 19.9 1.6 57.4 29.4 0.8 94.7 33.1 30.6 1.2 11.7 4.6 52.2 Rwanda 2006 5.9 3.3 6.5 20.0 1.3 .. 41.0 15.9 8.7 10.8 6.7 27.6 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2007 2.9 1.3 21.4 18.1 0.5 78.9 26.3 19.8 5.0 6.1 7.4 16.3 Serbia 2009 12.2 1.4 28.0 18.0 0.6 95.0 28.8 42.8 1.3 21.8 1.6 36.5 Sierra Leone 2009 7.4 1.9 12.6 18.8 0.8 89.2 7.9 6.9 6.6 13.8 0.0 18.6 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 2009 6.7 0.9 36.2 11.6 0.6 100.0 30.9 31.5 0.5 27.0 2.3 31.3 Slovenia 2009 7.3 0.3 56.1 5.4 0.4 99.9 42.2 52.2 0.5 28.0 2.2 47.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2007 6.0 0.8 36.2 15.1 1.0 91.0 22.6 34.8 1.6 26.4 4.5 36.8 Spain 2005 0.8 1.5 .. 4.4 0.2 .. 34.1 32.6 3.0 21.3 4.9 51.3 Sri Lankaa 2004 3.5 4.9 49.5 16.3 0.5 .. .. 26.2 .. .. 7.6 32.6 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 2006 4.4 1.4 24.0 40.6 1.3 .. 28.6 7.7 2.5 22.1 2.1 51.0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republica 2003 10.3 4.4 .. .. .. .. .. 7.6 8.6 7.4 5.9 21.0 Tajikistan 2008 11.7 1.4 22.6 40.5 0.3 92.7 34.4 21.4 15.1 16.7 20.4 21.1 Tanzania 2006 4.0 2.7 15.9 49.5 1.2 .. 30.9 6.8 9.6 14.7 5.7 36.5 Thailanda 2006 0.4 1.0 32.1 .. 0.1 .. .. 74.4 1.5 39.0 1.3 75.3 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 2009 2.7 1.2 56.4 16.7 2.4 75.8 31.8 16.9 10.5 6.6 6.7 31.0 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 2008 27.1 1.3 36.0 17.7 0.4 94.1 40.7 51.9 2.8 30.0 5.2 28.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2006 5.2 2.4 9.3 51.7 1.0 .. 34.7 7.7 10.2 15.5 3.2 35.0 Ukraine 2008 11.3 2.1 31.0 22.9 0.6 95.8 47.1 32.1 4.4 13.0 3.4 24.8 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 2006 7.0 0.7 133.8 7.3 0.7 97.8 41.6 6.9 0.9 6.8 2.5 24.6 Uzbekistan 2008 11.1 0.7 9.1 56.2 0.7 100.0 39.8 8.2 5.4 1.3 5.1 9.6 Venezuela, RB 2006 33.6 2.9 41.6 .. 1.4 97.3 .. 35.7 4.4 12.5 14.1 42.3 Vietnama 2005 0.8 1.9 .. 67.2 0.1 .. 27.4 29.4 .. 11.4 4.9 44.0 West Bank and Gaza 2006 5.7 1.7 21.3 13.3 1.2 .. 18.0 4.2 4.6 18.2 6.0 26.5 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 2007 4.6 1.9 48.3 14.3 1.0 96.2 37.2 10.2 3.7 17.2 2.3 26.0 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. Note: Enterprise surveys are updated several times a year; see www.enterprisesurveys.org for the most recent updates. a. The sample was drawn from the manufacturing sector only. 298 2010 World Development Indicators 5.2 STATES AND MARKETS Business environment: enterprise surveys About the data Definitions The World Bank Group's Enterprise Survey gath- distortions limit access to credit and thus restrain · Survey year is the year in which the underlying data ers firm-level data on the business environment growth. were collected. · Time dealing with officials is the to assess constraints to private sector growth and The reliability and availability of infrastructure average percentage of senior management's time enterprise performance. Standardized surveys are benefit households and support development. Firms that is spent in a typical week dealing with require- conducted all over the world, and data are available with access to modern and efficient infrastructure-- ments imposed by government regulations. · Aver- on more than 100,000 firms in 118 countries. The telecommunications, electricity, and transport--can age number of times meeting with tax officials is survey covers 11 dimensions of the business envi- be more productive. Firm-level innovation and use of the average number of visits or required meetings ronment, including regulation, corruption, crime, modern technology may help firms compete. with tax officials. · Time required to obtain operat- informality, finance, infrastructure, and trade. For Delays in clearing customs can be costly, deterring ing license is the average wait to obtain an operating some countries, firm-level panel data are available, firms from engaging in trade or making them uncom- license from the day applied for to the day granted. making it possible to track changes in the business petitive globally. Ill-considered labor regulations dis- · Informal payments to public offi cials are the environment over time. courage firms from creating jobs, and while employed percentage of firms that answered positively to the Firms evaluating investment options, governments workers may benefit, unemployed, low-skilled, and question "Was a gift or informal payment expected interested in improving business conditions, and informally employed workers will not. A trained labor or requested during a meeting with tax officials?" economists seeking to explain economic perfor- force enables firms to thrive, compete, innovate, and · Losses due to theft, robbery, vandalism, and mance have all grappled with defining and measur- adopt new technology. arson are the estimated losses from those causes ing the business environment. The firm-level data The data in the table are from Enterprise Surveys that occurred on establishments' premises as a from Enterprise Surveys provide a useful tool for implemented by the World Bank's Financial and Pri- percentage of annual sales. · Firms formally regis- benchmarking economies across a large number of vate Sector Development Enterprise Analysis Unit. All tered when operations started are the percentage indicators measured at the firm level. economies in East Asia and Pacific, Europe and Cen- of firms formally registered when they started opera- Most countries can improve regulation and taxa- tral Asia, Latin America and the Caribbean, Middle tions in the country. Firms not formally registered (the tion without compromising broader social interests. East and North Africa, and Sub-Saharan Africa (for residual) are in the informal sector of the economy. Excessive regulation may harm business perfor- 2009) and Afghanistan, Bangladesh, and India draw · Firms with female participation in ownership are mance and growth. For example, time spent with a sample of registered nonagricultural businesses, the percentage of firms with a woman among the own- tax officials is a burden firms may face in paying excluding those in the financial and public sectors. ers. · Firms using banks to finance investment are taxes. The business environment suffers when gov- Samples for other economies are drawn only from the percentage of firms that invested in fixed assets ernments increase uncertainty and risks or impose the manufacturing sector and are footnoted in the during the last fiscal year that used banks to finance unnecessary costs and unsound regulation and taxa- table. Typical Enterprise Survey sample sizes range fixed assets. · Value lost due to electrical outages tion. Time to obtain licenses and permits and the from 150 to 1,800, depending on the size of the is losses that resulted from power outages as a per- associated red tape constrain firm operations. economy. In each country samples are selected centage of annual sales. · Internationally recognized In some countries doing business requires informal by stratifi ed random sampling, unless otherwise quality certification ownership is the percentage of payments to "get things done" in customs, taxes, noted. Stratified random sampling allows indicators firms that have an internationally recognized quality licenses, regulations, services, and the like. Such to be computed by sector, firm size, and region and certification, such as International Organization for corruption harms the business environment by dis- increases the precision of economywide indicators Standardization 9000, 9002, or 14000. · Average torting policymaking, undermining government cred- compared with alternative simple random sampling. time to clear direct exports through customs is ibility, and diverting public resources. Crime, theft, Stratification by sector of activity divides the econ- the average number of days to clear direct exports and disorder also impose costs on businesses and omy into manufacturing and retail and other services through customs. · Firms offering formal training society. sectors. For medium-size and large economies the are the percentage of firms offering formal training In many developing countries informal businesses manufacturing sector is further stratified by industry. programs for their permanent, full-time employees. operate without formal registration. These firms have Firm size is stratified into small (5­19 employees), less access to financial and public services and can medium-size (20­100 employees), and large (more engage in fewer types of contracts and investments, than 100 employees). Geographic stratifi cation constraining growth. divides the national economy into the main centers Equal opportunities for men and women contrib- of economic activity. ute to development. Female participation in firm ownership is a measure of women's integration as decisionmakers. Data sources Financial markets connect firms to lenders and investors, allowing firms to grow their businesses: Data on the business environment are from the creditworthy firms can obtain credit from financial World Bank Group's Enterprise Surveys website intermediaries at competitive prices. But too often (www.enterprisesurveys.org). market imperfections and government-induced 2010 World Development Indicators 299 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property construction workers contracts investors business permits Time Rigidity of Disclosure Cost Number of required employment index Time to Time % of per Time procedures to build a index Time 0­10 (least resolve Number of required capita Number of required to build a warehouse 0­100 (least Number of required to most insolvency procedures days income procedures days warehouse days to most rigid) procedures days disclosure) years June June June June June June June June June June June June 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Afghanistan 4 7 30.2 9 250 13 340 20 47 1,642 0 .. Albania 5 5 17.0 6 42 24 331 25 39 390 8 .. Algeria 14 24 12.1 11 47 22 240 41 46 630 6 2.5 Angola 8 68 151.1 7 184 12 328 66 46 1,011 5 6.2 Argentina 15 27 11.0 6 52 28 338 21 36 590 6 2.8 Armenia 6 15 2.6 3 4 20 137 21 48 285 5 1.9 Australia 2 2 0.8 5 5 16 221 0 28 395 8 1.0 Austria 8 28 5.1 3 32 14 194 24 25 397 3 1.1 Azerbaijan 6 10 2.9 4 11 31 207 10 39 237 7 2.7 Bangladesh 7 44 36.2 8 245 14 231 28 41 1,442 6 4.0 Belarus 5 6 1.7 3 18 15 161 11 28 225 5 5.8 Belgium 3 4 5.3 7 79 14 169 17 25 505 8 0.9 Benin 7 31 155.5 4 120 15 410 40 42 825 6 4.0 Bolivia 15 50 99.2 7 92 17 249 77 40 591 1 1.8 Bosnia and Herzegovina 12 60 15.8 7 84 16 255 33 38 595 3 3.3 Botswana 10 61 2.1 5 16 24 167 13 29 687 7 1.7 Brazil 16 120 6.9 14 42 18 411 46 45 616 6 4.0 Bulgaria 4 18 1.7 8 15 24 139 19 39 564 10 3.3 Burkina Faso 4 14 50.3 4 59 15 132 21 37 446 6 4.0 Burundi 11 32 151.6 5 94 22 212 28 44 832 4 .. Cambodia 9 85 138.4 7 56 23 709 36 44 401 5 .. Cameroon 12 34 121.1 5 93 15 426 39 43 800 6 3.2 Canada 1 5 0.4 6 17 14 75 4 36 570 8 0.8 Central African Republic 8 22 244.9 5 75 21 239 50 43 660 6 4.8 Chad 19 75 176.7 6 44 9 181 33 41 743 6 .. Chile 9 27 6.9 6 31 18 155 18 36 480 7 4.5 China 14 37 4.9 4 29 37 336 31 34 406 10 1.7 Hong Kong SAR, China 3 6 1.8 5 45 7 67 0 24 280 10 1.1 Colombia 9 20 12.8 7 20 11 51 10 34 1,346 8 3.0 Congo, Dem. Rep. 13 149 391.0 8 57 14 322 63 43 625 3 5.2 Congo, Rep. 10 37 86.5 7 116 14 169 63 44 560 6 3.0 Costa Rica 12 60 20.0 6 21 23 191 39 40 852 2 3.5 Côte d'Ivoire 10 40 133.3 6 62 22 629 33 33 770 6 2.2 Croatia 7 22 8.4 5 104 14 420 50 38 561 1 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 8 15 9.2 4 78 36 150 11 27 611 2 6.5 Denmark 4 6 0.0 6 42 6 69 7 34 380 7 1.1 Dominican Republic 8 19 17.3 7 60 17 214 21 34 460 5 3.5 Ecuador 13 64 37.7 9 16 19 155 38 39 588 1 5.3 Egypt, Arab Rep. 6 7 16.1 7 72 25 218 27 41 1,010 8 4.2 El Salvador 8 17 38.7 5 31 34 155 24 30 786 5 4.0 Eritrea 13 84 76.5 12 101 .. .. 20 39 405 4 .. Estonia 5 7 1.7 3 18 14 118 51 36 425 8 3.0 Ethiopia 5 9 18.9 10 41 12 128 28 37 620 4 3.0 Finland 3 14 0.9 3 14 18 38 41 32 375 6 0.9 France 5 7 0.9 8 98 13 137 52 29 331 10 1.9 Gabon 9 58 17.8 7 39 16 210 52 38 1,070 6 5.0 Gambia, The 8 27 215.1 5 371 17 146 27 32 434 2 3.0 Georgia 3 3 3.7 2 3 10 98 7 36 285 8 3.3 Germany 9 18 4.7 4 40 12 100 42 30 394 5 1.2 Ghana 8 33 26.4 5 34 18 220 27 36 487 7 1.9 Greece 15 19 10.9 11 22 15 169 50 39 819 1 2.0 Guatemala 11 29 45.4 4 27 22 178 28 31 1,459 3 3.0 Guinea 13 41 139.2 6 104 32 255 24 50 276 6 3.8 Guinea-Bissau 16 213 323.0 9 211 15 167 54 41 1,140 6 .. Haiti 13 195 227.9 5 405 11 1,179 10 35 508 2 5.7 Honduras 13 14 47.3 7 23 17 106 57 45 900 0 3.8 300 2010 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property construction workers contracts investors business permits Time Rigidity of Disclosure Cost Number of required employment index Time to Time % of per Time procedures to build a index Time 0­10 (least resolve Number of required capita Number of required to build a warehouse 0­100 (least Number of required to most insolvency procedures days income procedures days warehouse days to most rigid) procedures days disclosure) years June June June June June June June June June June June June 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Hungary 4 4 8.0 4 17 31 204 22 33 395 2 2.0 India 13 30 66.1 5 44 37 195 30 46 1,420 7 7.0 Indonesia 9 60 26.0 6 22 14 160 40 39 570 10 5.5 Iran, Islamic Rep. 7 9 3.9 9 36 17 322 29 39 520 5 4.5 Iraq 11 77 75.9 5 8 14 215 24 51 520 4 .. Ireland 4 13 0.3 5 38 11 185 10 20 515 10 0.4 Israel 5 34 4.2 7 144 20 235 17 35 890 7 4.0 Italy 6 10 17.9 8 27 14 257 38 40 1,210 7 1.8 Jamaica 6 8 5.3 6 55 10 156 4 35 655 4 1.1 Japan 8 23 7.5 6 14 15 187 16 30 360 7 0.6 Jordan 8 13 49.5 7 21 19 87 24 38 689 5 4.3 Kazakhstan 7 20 4.8 5 40 37 211 17 38 390 7 1.5 Kenya 12 34 36.5 8 64 11 120 17 40 465 3 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 8 14 14.7 7 11 13 34 38 35 230 7 1.5 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 13 35 1.0 8 55 25 104 0 50 566 7 4.2 Kyrgyz Republic 3 11 5.2 4 5 12 137 18 39 260 8 4.0 Lao PDR 7 100 12.3 9 135 24 172 20 42 443 0 .. Latvia 5 16 2.1 6 45 25 187 43 27 309 5 3.0 Lebanon 5 9 78.2 8 25 20 211 25 37 721 9 4.0 Lesotho 7 40 27.0 6 101 15 601 14 41 695 2 2.6 Liberia 5 20 52.9 10 50 24 77 27 41 1,280 4 3.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 7 26 2.4 2 3 17 162 38 30 275 5 1.5 Macedonia, FYR 4 4 2.5 5 58 21 146 14 37 370 9 2.9 Madagascar 2 7 7.1 7 74 16 178 56 38 871 5 .. Malawi 10 39 108.0 6 88 21 213 21 42 432 4 2.6 Malaysia 9 11 11.9 5 144 25 261 10 30 585 10 2.3 Mali 7 15 89.2 5 29 14 185 31 36 626 6 3.6 Mauritania 9 19 34.7 4 49 25 201 39 46 370 5 8.0 Mauritius 5 6 4.1 4 26 18 107 18 36 720 6 1.7 Mexico 8 13 11.7 5 74 12 138 41 38 415 8 1.8 Moldova 8 10 7.0 5 5 30 292 41 31 365 7 2.8 Mongolia 7 13 3.0 5 11 21 215 17 32 314 5 4.0 Morocco 6 12 16.1 8 47 19 163 60 40 615 6 1.8 Mozambique 10 26 19.3 8 42 17 381 40 30 730 5 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 10 66 20.4 9 23 12 139 13 33 270 5 1.5 Nepal 7 31 53.6 3 5 15 424 46 39 735 6 5.0 Netherlands 6 10 5.6 2 5 18 230 42 25 514 4 1.1 New Zealand 1 1 0.4 2 2 7 65 7 30 216 10 1.3 Nicaragua 6 39 111.7 8 124 17 219 27 35 540 4 2.2 Niger 9 17 118.7 4 35 17 265 68 39 545 6 5.0 Nigeria 8 31 76.7 13 82 18 350 7 39 457 5 2.0 Norway 5 7 1.9 1 3 14 252 44 33 280 7 0.9 Oman 5 12 2.2 2 16 16 242 13 51 598 8 4.0 Pakistan 10 20 5.8 6 50 12 223 43 47 976 6 2.8 Panama 6 12 10.3 7 32 20 116 66 31 686 1 2.5 Papua New Guinea 8 56 20.5 4 72 24 217 4 42 591 5 3.0 Paraguay 7 35 56.7 6 46 13 291 56 38 591 6 3.9 Peru 9 41 24.5 4 14 21 205 39 41 428 8 3.1 Philippines 15 52 28.2 8 33 24 203 29 37 842 2 5.7 Poland 6 32 17.9 6 197 30 308 25 38 830 7 3.0 Portugal 6 6 6.4 5 12 19 287 43 31 547 6 2.0 Puerto Rico 7 7 0.7 8 194 22 209 14 39 620 7 3.8 Qatar 19 76 0.6 10 16 19 76 13 43 570 5 2.8 2010 World Development Indicators 301 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property construction workers contracts investors business permits Time Rigidity of Disclosure Cost Number of required employment index Time to Time % of per Time procedures to build a index Time 0­10 (least resolve Number of required capita Number of required to build a warehouse 0­100 (least Number of required to most insolvency procedures days income procedures days warehouse days to most rigid) procedures days disclosure) years June June June June June June June June June June June June 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 Romania 6 10 2.9 8 48 17 243 46 31 512 9 3.3 Russian Federation 9 30 2.7 6 43 54 704 38 37 281 6 3.8 Rwanda 2 3 10.1 4 60 14 210 7 24 260 7 .. Saudi Arabia 4 5 7.7 2 2 17 94 13 43 635 9 1.5 Senegal 4 8 63.7 6 124 16 220 59 44 780 6 3.0 Serbia 7 13 7.1 6 111 20 279 35 36 635 7 2.7 Sierra Leone 6 12 118.8 7 236 25 283 41 40 515 6 2.6 Singapore 3 3 0.7 3 5 11 25 0 21 150 10 0.8 Slovak Republic 6 16 2.0 3 17 13 287 22 30 565 3 4.0 Slovenia 3 6 0.0 6 391 14 197 54 32 1,290 3 2.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6 22 5.9 6 24 17 174 35 30 600 8 2.0 Spain 10 47 15.0 4 18 11 233 49 39 515 5 1.0 Sri Lanka 4 38 5.9 8 83 22 214 20 40 1,318 4 1.7 Sudan 10 36 36.0 6 9 19 271 36 53 810 0 .. Swaziland 13 61 33.9 11 46 13 93 10 40 972 0 2.0 Sweden 3 15 0.6 2 15 8 116 38 30 508 6 2.0 Switzerland 6 20 2.0 4 16 14 154 7 31 417 0 3.0 Syrian Arab Republic 7 17 27.8 4 19 26 128 20 55 872 6 4.1 Tajikistan 12 25 24.3 6 37 32 250 49 34 430 6 3.0 Tanzania 12 29 36.8 9 73 22 328 54 38 462 3 3.0 Thailand 7 32 6.3 2 2 11 156 11 35 479 10 2.7 Timor-Leste 10 83 4.1 .. .. 22 208 32 51 1,435 3 .. Togo 7 75 205.0 5 295 15 277 54 41 588 6 3.0 Trinidad and Tobago 9 43 0.7 8 162 20 261 7 42 1,340 4 .. Tunisia 10 11 5.7 4 39 20 84 40 39 565 5 1.3 Turkey 6 6 14.2 6 6 25 188 35 35 420 9 3.3 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 18 25 84.4 13 77 16 143 0 38 510 2 2.2 Ukraine 10 27 5.8 10 93 30 476 31 30 345 5 2.9 United Arab Emirates 8 15 6.2 1 2 17 64 7 49 537 4 5.1 United Kingdom 6 13 0.7 2 8 11 95 10 30 399 10 1.0 United States 6 6 0.7 4 12 19 40 0 32 300 7 1.5 Uruguay 11 65 40.0 9 66 30 234 18 40 720 3 2.1 Uzbekistan 7 15 11.2 12 78 26 260 32 42 195 4 4.0 Venezuela, RB 16 141 24.0 8 47 11 395 69 29 510 3 4.0 Vietnam 11 50 13.3 4 57 13 194 21 34 295 6 5.0 West Bank and Gaza 11 49 55.0 7 47 21 199 31 44 600 6 .. Yemen, Rep. 6 12 83.0 6 19 15 107 24 36 520 6 3.0 Zambia 6 18 28.4 6 39 17 254 21 35 471 3 2.7 Zimbabwe 10 96 499.5 5 31 19 1,426 33 38 410 8 3.3 World 8u 36 u 41.5 u 6u 65 u 18 u 216 u 27 u 38 u 607 u 5u 3.0 u Low income 9 44 107.5 7 100 18 291 33 39 605 5 3.8 Middle income 8 40 31.0 6 61 19 209 27 39 649 5 3.1 Lower middle income 9 34 42.7 6 69 19 209 28 40 678 5 3.3 Upper middle income 8 47 16.0 6 52 20 209 26 37 612 6 2.9 Low & middle income 9 41 53.5 6 73 19 232 29 39 636 5 3.3 East Asia & Pacific 8 42 30.1 5 112 18 182 17 37 572 5 3.1 Europe & Central Asia 7 19 8.8 6 46 24 249 27 37 398 6 3.0 Latin America & Carib. 10 67 41.2 7 65 17 228 29 39 710 4 3.2 Middle East & N. Africa 9 23 51.5 7 35 19 181 33 42 707 6 3.5 South Asia 7 28 27.0 6 106 18 241 27 44 1,053 4 4.5 Sub-Saharan Africa 9 44 99.7 7 82 17 262 35 39 646 5 3.4 High income 6 19 6.7 5 43 16 169 24 35 526 6 2.1 Euro area 6 15 6.0 5 57 14 225 38 31 602 5 1.6 302 2010 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators About the data Definitions The economic health of a country is measured not only The Doing Business project encompasses two · Number of procedures for starting a business is the in macroeconomic terms but also by other factors that types of data: data from readings of laws and regu- number of procedures required to start a business, shape daily economic activity such as laws, regula- lations and data on time and motion indicators that including interactions to obtain necessary permits and tions, and institutional arrangements. The Doing Busi- measure efficiency in achieving a regulatory goal. licenses and to complete all inscriptions, verifications, ness indicators measure business regulation, gauge Within the time and motion indicators cost estimates and notifications to start operations for businesses regulatory outcomes, and measure the extent of legal are recorded from official fee schedules where appli- with specific characteristics of ownership, size, and protection of property, the flexibility of employment cable. The data from surveys are subjected to numer- type of production. · Time required for starting a busi- regulation, and the tax burden on businesses. ous tests for robustness, which lead to revision or ness is the number of calendar days to complete the The table presents a subset of Doing Business expansion of the information collected. procedures for legally operating a business using the indicators covering 7 of the 10 sets of indicators: The Doing Business methodology has limitations fastest procedure, independent of cost. · Cost for starting a business, registering property, dealing with that should be considered when interpreting the starting a business is normalized as a percentage of construction permits, employing workers, enforcing data. First, the data collected refer to businesses gross national income (GNI) per capita. It includes all contracts, protecting investors, and closing a busi- in the economy's largest city and may not represent official fees for professional or legal services if they are ness. Table 5.5 includes Doing Business measures regulations in other locations of the economy. To required by law. · Number of procedures for register- of getting credit, and table 5.6 presents data on address this limitation, subnational indicators are ing property is the number of procedures required for paying taxes. being collected for selected economies. These sub- a business to legally transfer property. · Time required The fundamental premise of the Doing Business national studies point to significant differences in for registering property is the number of calendar days project is that economic activity requires good rules the speed of reform and the ease of doing business for a business to legally transfer property. · Number of and regulations that are efficient, accessible to all across cities in the same economy. Second, the data procedures for dealing with licenses to build a ware- who need to use them, and simple to implement. often focus on a specific business form--generally house is the number of interactions of a company's Thus some Doing Business indicators give a higher a limited liability company of a specified size--and employees or managers with external parties, includ- score for more regulation, such as stricter disclosure may not represent regulation for other types of busi- ing government staff, public inspectors, notaries, land requirements in related-party transactions, and oth- nesses such as sole proprietorships. Third, transac- registry and cadastre staff, and technical experts apart ers give a higher score for simplified regulations, tions described in a standardized business case refer from architects and engineers. · Time required for such as a one-stop shop for completing business to a specific set of issues and may not represent the dealing with construction permits to build a ware- startup formalities. full set of issues a business encounters. Fourth, the house is the number of calendar days to complete the In constructing the indicators, it is assumed that time measures involve an element of judgment by the required procedures for building a warehouse using the entrepreneurs know about all regulations and comply expert respondents. When sources indicate different fastest procedure, independent of cost. · Rigidity of with them; in practice, entrepreneurs may not be estimates, the Doing Business time indicators repre- employment index, a measure of employment regula- aware of all required procedures or may avoid legally sent the median values of several responses given tion, is the average of three subindexes: a difficulty of required procedures altogether. But where regula- under the assumptions of the standardized case. hiring index, a rigidity of hours index, and a difficulty of tion is particularly onerous, levels of informality are Fifth, the methodology assumes that a business has firing index. Higher values indicate more rigid regula- higher, which comes at a cost: firms in the informal full information on what is required and does not tions. · Number of procedures for enforcing contracts sector usually grow more slowly, have less access waste time when completing procedures. is the number of independent actions, mandated to credit, and employ fewer workers--and those by law or court regulation, that demand interaction workers remain outside the protections of labor law. between the parties to a contract or between them and The indicators in the table can help policymakers the judge or court officer. · Time required for enforc- understand the business environment in a country ing contracts is the number of calendar days from and--along with information from other sources such the time of the filing of a lawsuit in court to the final as the World Bank's Enterprise Surveys--provide determination and payment. · Extent of disclosure insights into potential areas of reform. index measures the degree to which investors are pro- Doing Business data are collected with a stan- tected through disclosure of ownership and financial dardized survey that uses a simple business case information. Higher values indicate more disclosure. to ensure comparability across economies and over · Time to resolve insolvency is the number of years time--with assumptions about the legal form of the from time of filing for insolvency in court until resolu- business, its size, its location, and nature of its oper- tion of distressed assets and payment of creditors. ation. Surveys in 183 countries are administered Data sources through more than 8,000 local experts, including lawyers, business consultants, accountants, freight Data on the business environment are from forwarders, government officials, and other profes- the World Bank's Doing Business project (www. sionals who routinely administer or advise on legal doingbusiness.org). and regulatory requirements. 2010 World Development Indicators 303 5.4 Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2009 2000 2008 2000 2008 2000 2009 2000 2009 2008 2009 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 166,068 48,033 58.4 15.9 2.1 4.1 4.8 5.4 127 107 ­56.2a 97.8a Armenia 2 176 0.1 1.5 0.0 0.0 4.6 0.6 105 26 .. .. Australia 372,794 675,619 92.0 66.5 55.9 99.9 56.5 103.1 1,330 1,924 .. .. Austria 29,935 72,300 15.7 17.5 4.9 25.3 29.8 69.0 97 85 .. .. Azerbaijan 3 .. 0.1 .. .. .. .. .. 2 .. .. .. Bangladesh 1,186 7,068 2.5 8.4 1.6 11.6 74.4 212.6 221 295 4.3a 38.6a Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 182,481 167,447 78.7 33.2 16.4 41.9 20.7 76.1 174 167 .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,742 2,672 20.7 16.0 0.8 0.0 0.1 .. 26 37 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 978 4,283 15.8 26.5 0.8 1.1 4.8 2.6 16 20 ­38.4 a 24.3a Brazil 226,152 1,337,723 35.1 37.4 15.7 46.2 43.5 67.4 459 425 ­57.2 125.1 Bulgaria 617 7,330 4.9 17.8 0.5 3.3 9.2 4.9 503 337 ­70.2a 17.2a Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 841,385 1,002,215 116.1 66.8 87.6 117.3 77.3 123.7 1,418 3,755 .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 60,401 230,732 80.3 78.1 8.1 21.6 9.4 20.7 258 232 ­41.2 84.0 China 580,991 5,010,656 48.5 64.6 60.2 126.4 158.3 229.5 1,086 1,700 ­52.7 66.3 Hong Kong SAR, China 623,398 468,595 368.6 217.6 223.4 288.7 61.3 81.8 779 1,017 .. .. Colombia 9,560 140,520 10.2 35.7 0.4 5.1 3.8 11.4 126 96 .. 75.7a Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2,924 1,887 18.3 6.4 0.7 0.2 12.0 3.0 21 11 .. .. Côte d'Ivoire 1,185 6,141 11.4 30.2 0.3 1.3 2.6 2.0 41 38 ­16.9a ­10.7a Croatia 2,742 26,619 12.8 38.6 0.9 5.0 7.4 5.3 64 368 ­59.3a 31.1a Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11,002 54,477 19.4 22.7 11.6 20.0 60.3 39.9 131 25 ­45.9 23.0 Denmark 107,666 131,526 67.3 38.5 57.2 62.1 86.0 104.8 225 216 .. .. Dominican Republic .. .. .. .. .. .. .. .. .. .. .. .. Ecuador 704 4,248 4.4 8.3 0.1 0.3 5.5 30.7 30 39 ­8.8a ­13.1a Egypt, Arab Rep. 28,741 91,091 28.8 52.9 11.1 42.9 34.7 59.7 1,076 306 ­55.8 35.6 El Salvador 2,041 4,656 15.5 21.1 0.2 0.9 1.3 .. 40 51 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 1,846 170 32.5 8.3 5.7 3.3 18.9 0.8 23 16 ­65.5a 32.9a Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 293,635 154,367 241.1 56.6 169.7 143.2 64.3 155.1 154 126 .. .. France 1,446,634 1,492,327 108.9 52.2 81.6 114.0 74.1 152.4 808 966 .. 22.0 b Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 24 327 0.8 2.6 0.1 0.1 .. 4.4 269 161 .. .. Germany 1,270,243 1,107,957 66.8 30.4 56.3 84.8 79.1 191.5 1,022 638 .. 18.0 c Ghana 502 2,507 10.1 20.4 0.2 0.9 1.5 2.0 22 35 ­10.4a ­42.7a Greece 110,839 90,396 88.3 25.4 75.7 29.7 63.7 59.2 329 280 .. .. Guatemala 172 .. 0.9 .. 0.1 .. 0.0 .. 7 .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. Honduras 458 .. 8.8 .. .. .. .. .. 94 .. .. .. 304 2010 World Development Indicators 5.4 STATES AND MARKETS Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2009 2000 2008 2000 2008 2000 2009 2000 2009 2008 2009 Hungary 12,021 30,332 25.1 12.0 25.4 19.9 90.7 106.1 60 45 ­62.5 73.0 India 148,064 1,226,676 32.2 55.7 110.8 90.6 133.6 116.3 5,937 4,946 ­64.1 94.1 Indonesia 26,834 196,661 16.3 19.3 8.7 21.7 32.9 78.1 290 401 ­61.1 130.1 Iran, Islamic Rep. 7,350 49,040 7.3 15.9 1.1 2.9 12.7 33.6 304 356 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 81,882 49,401 84.8 18.5 14.9 30.8 19.2 85.0 76 58 .. .. Israel 64,081 188,734 51.4 66.5 18.8 54.0 36.3 54.6 654 622 ­33.1 56.8 Italy 768,364 520,855 70.0 22.6 70.9 64.2 104.0 284.2 291 294 .. .. Jamaica 3,582 6,127 39.8 51.4 0.8 2.5 2.5 1.8 46 38 ­38.2a ­15.8a Japan 3,157,222 3,220,485 67.6 65.6 57.7 119.5 69.9 153.2 2,561 3,299 ­40.0 d 6.0 d Jordan 4,943 31,891 58.4 168.8 4.9 131.9 7.7 40.3 163 272 .. ­13.9 Kazakhstan 1,342 57,273 7.3 23.3 0.5 2.6 25.1 9.1 23 72 ­47.0a 1.5a Kenya 1,283 10,967 10.1 36.0 0.4 4.7 3.6 4.5 57 53 ­40.3a 0.6a Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 171,587 836,462 32.2 53.2 200.2 157.8 233.2 238.2 1,308 1,798 ­55.6 67.2 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 20,772 96,317 55.1 72.4 11.2 82.9 21.3 23.7 77 207 .. ­10.4a Kyrgyz Republic 4 94 0.3 1.9 1.7 2.2 .. 131.2 80 8 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 563 1,872 7.2 4.8 2.9 0.1 48.6 0.0 64 34 ­58.7a 2.2a Lebanon 1,583 12,885 9.2 32.9 0.7 2.4 6.7 14.4 12 11 ­25.3a 43.4 a Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,588 4,619 13.9 7.7 1.8 1.0 14.8 4.3 54 40 ­73.0a 36.7a Macedonia, FYR 7 823 0.2 8.6 3.3 1.6 6.6 8.9 1 38 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. 1,771 .. 41.5 .. 1.4 13.8 3.9 .. 14 .. .. Malaysia 116,935 263,362 124.7 84.4 62.4 38.4 44.6 54.7 795 957 ­43.7 46.7 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 1,331 4,982 29.0 36.9 1.6 4.3 5.0 0.3 40 40 ­49.2a 44.2a Mexico 125,204 352,045 21.5 21.4 7.8 9.9 32.3 43.4 179 125 ­45.1 55.8 Moldova 392 .. 30.4 .. 1.9 2.6 5.8 .. 36 .. .. .. Mongolia 37 407 3.4 7.7 0.7 1.0 7.3 14.7 410 420 .. .. Morocco 10,899 64,479 29.4 74.0 3.0 24.7 9.2 12.0 53 78 ­17.0 ­1.7 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 311 968 8.0 7.0 0.6 0.2 4.5 0.1 13 7 ­9.9a 22.6a Nepal 790 4,894 14.4 38.8 0.6 2.9 6.9 7.5 110 149 .. .. Netherlands 640,456 387,906 166.3 44.5 175.9 130.9 101.4 169.2 234 110 .. .. New Zealand 18,866 24,166 37.1 18.6 21.2 12.7 45.9 46.1 142 149 .. .. Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 4,237 33,374 9.2 24.0 0.6 9.6 7.3 26.9 195 216 .. ­35.4 Norway 65,034 125,920 38.6 27.9 35.7 81.4 93.4 152.2 191 209 .. .. Oman 3,463 17,304 17.4 55.4 2.8 13.0 14.2 17.4 131 125 .. 22.0a Pakistan 6,581 32,206 8.9 14.3 44.6 33.0 475.5 99.9 762 650 .. 56.7 Panama 2,794 8,048 24.0 28.4 1.3 1.1 1.7 0.3 29 30 ­15.7a 15.4 a Papua New Guinea 1,520 .. 49.3 118.3 0.0 0.4 .. .. 7 15 .. .. Paraguay 224 .. 3.5 4.4 0.1 0.0 3.5 .. 56 50 .. .. Peru 10,562 69,753 19.8 43.1 2.9 4.0 12.6 8.2 230 201 ­41.1 79.3 Philippines 25,957 82,546 34.2 31.2 10.8 10.3 15.8 24.9 228 245 ­53.7 71.5 Poland 31,279 147,178 18.3 17.1 8.5 12.9 49.9 56.0 225 354 ­57.8 41.9 Portugal 60,681 68,713 53.9 28.2 48.3 33.9 85.5 81.8 109 49 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar 5,152 87,843 29.0 134.4 1.3 42.1 4.5 28.7 22 44 .. 5.1a 2010 World Development Indicators 305 5.4 Stock markets Market Market Turnover Listed domestic S&P/Global capitalization liquidity ratio companies Equity Indices Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2009 2000 2008 2000 2008 2000 2009 2000 2009 2008 2009 Romania 1,069 31,318 2.9 10.0 0.6 1.8 23.1 3.5 5,555 1,571 ­72.2a 26.1a Russian Federation 38,922 861,424 15.0 78.7 7.8 33.5 36.9 154.9 249 333 ­73.4 106.6 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 67,171 318,737 35.6 52.5 9.2 111.9 27.1 41.1 75 135 .. 28.5 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 734 12,165 4.6 24.3 0.1 2.5 0.0 14.6 6 1,771 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 152,827 180,021 164.8 98.9 98.7 148.9 52.1 101.3 418 455 .. .. Slovak Republic 1,217 4,672 4.2 5.2 3.1 0.0 129.8 0.1 493 111 ­36.0a ­23.1a Slovenia 2,547 12,141 12.8 21.6 2.3 2.6 20.7 11.9 38 80 ­66.9a 16.1a Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 204,952 805,169 154.2 177.7 58.3 145.2 33.9 83.8 616 411 ­41.7 53.7 Spain 504,219 946,113 86.8 59.0 169.8 152.1 210.7 177.6 1,019 3,536 .. .. Sri Lanka 1,074 8,172 6.6 10.7 0.9 2.5 11.0 14.2 239 232 .. 118.0a Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 73 203 4.9 6.9 0.0 0.0 9.8 .. 6 7 .. .. Sweden 328,339 252,542 133.7 52.7 158.8 133.4 111.2 157.0 292 341 .. .. Switzerland 792,316 862,663 317.0 175.4 243.7 307.1 82.0 145.6 252 253 .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 233 1,293 2.6 6.3 0.4 0.1 2.4 .. 4 7 .. .. Thailand 29,489 142,247 24.0 37.7 19.0 42.9 53.2 110.2 381 497 ­50.5 72.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 4,330 11,145 53.1 50.4 1.7 1.5 3.1 2.0 27 37 ­9.9a ­10.2a Tunisia 2,828 9,309 14.5 15.8 3.2 3.7 23.3 16.0 44 50 ­3.1a 40.6a Turkey 69,659 234,004 26.1 16.0 67.1 32.6 206.2 138.4 315 315 ­62.4 99.6 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 35 .. 0.6 1.2 0.0 0.1 .. .. 2 6 .. .. Ukraine 1,881 16,859 6.0 13.5 0.9 1.4 19.6 2.9 139 149 ­82.2a 31.1a United Arab Emirates 5,727 109,613 8.1 113.1 0.2 75.7 3.9 63.3 54 101 .. 24.6a United Kingdom 2,576,992 1,851,954 174.4 69.3 124.2 242.5 66.6 226.9 1,904 2,415 ­40.0e 22.0e United States 15,104,037 11,737,646 154.7 80.4 326.3 249.9 200.8 232.3 7,524 5,603 ­39.0 f 23.0 f Uruguay 161 159 0.7 0.7 0.0 0.1 0.5 12.0 16 8 .. .. Uzbekistan 32 .. 0.2 4.2 0.1 0.3 .. .. 5 114 .. .. Venezuela, RB 8,128 .. 6.9 4.5 0.6 0.4 8.9 1.3 85 60 .. .. Vietnam .. 21,529 .. 10.6 .. 4.6 .. 42.7 .. 162 ­68.2a 46.9a West Bank and Gaza 765 2,123 18.6 .. 4.6 .. 10.0 31.3 24 35 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 236 2,346 7.3 20.6 0.2 0.6 20.8 4.1 9 15 .. 16.7a Zimbabwe 2,432 5,333 32.9 .. 3.8 .. 10.8 5.1 69 81 World 32,187,516 s ..g s 102.2 w 59.2 w 152.2 w 136.9 w 122.3 w ..g w 47,787 s ..g s Low income .. .. .. .. .. .. .. .. .. .. Middle income 1,973,751 11,586,208 36.1 49.5 33.8 61.3 83.5 213.8 21,842 15,575 Lower middle income 886,833 6,956,558 35.3 53.5 52.7 93.4 128.1 228.5 11,779 9,819 Upper middle income 1,086,918 4,629,650 36.7 45.5 17.5 29.9 46.7 59.3 10,063 5,756 Low & middle income 1,980,449 11,628,278 35.5 48.9 33.2 60.4 83.1 213.8 22,419 16,120 East Asia & Pacific 780,487 5,717,001 47.1 58.0 49.8 103.7 125.0 229.5 3,190 3,962 Europe & Central Asia 147,380 1,635,890 17.6 44.4 25.4 23.8 94.4 68.0 7,524 3,610 Latin America & Carib. 620,023 1,178,104 31.8 31.9 8.5 24.9 27.2 46.1 1,672 1,471 Middle East & N. Africa 57,110 209,656 19.9 55.9 5.1 18.8 12.4 28.7 1,676 717 South Asia 157,695 1,274,122 26.1 47.0 90.2 76.5 167.9 88.9 7,269 6,123 Sub-Saharan Africa 217,754 868,391 89.7 148.5 32.3 101.3 22.1 76.5 1,088 820 High income 30,207,068 27,380,501 116.6 62.9 177.7 164.0 130.4 187.1 25,368 31,198 Euro area 5,433,547 5,152,619 86.9 37.9 80.3 91.3 90.7 176.8 5,028 6,700 a. Refers to the S&P Frontier BMI index. b. Refers to the CAC 40 index. c. Refers to the DAX index. d. Refers to the Nikkei 225 index. e. Refers to the FT 100 index. f. Refers to the S&P 500 index. g. Aggregates not preserved because data for high-income economies are not available for 2008. 306 2010 World Development Indicators 5.4 STATES AND MARKETS Stock markets About the data Definitions The development of an economy's financial markets countries. Market capitalization shows the overall · Market capitalization (also known as market is closely related to its overall development. Well size of the stock market in U.S. dollars and as a value) is the share price times the number of shares functioning financial systems provide good and eas- percentage of GDP. The number of listed domestic outstanding. · Market liquidity is the total value ily accessible information. That lowers transaction companies is another measure of market size. Mar- of shares traded during the period divided by gross costs, which in turn improves resource allocation and ket size is positively correlated with the ability to domestic product (GDP). This indicator complements boosts economic growth. Both banking systems and mobilize capital and diversify risk. the market capitalization ratio by showing whether stock markets enhance growth, the main factor in Market liquidity, the ability to easily buy and sell market size is matched by trading. · Turnover ratio poverty reduction. At low levels of economic develop- securities, is measured by dividing the total value is the total value of shares traded during the period ment commercial banks tend to dominate the finan- of shares traded by GDP. The turnover ratio--the divided by the average market capitalization for the cial system, while at higher levels domestic stock value of shares traded as a percentage of market period. Average market capitalization is calculated as markets tend to become more active and efficient capitalization--is also a measure of liquidity as well the average of the end-of-period values for the cur- relative to domestic banks. as of transaction costs. (High turnover indicates low rent period and the previous period. · Listed domes- Open economies with sound macroeconomic poli- transaction costs.) The turnover ratio complements tic companies are the domestically incorporated cies, good legal systems, and shareholder protection the ratio of value traded to GDP, because the turn- companies listed on the country's stock exchanges attract capital and therefore have larger financial mar- over ratio is related to the size of the market and the at the end of the year. This indicator does not include kets. Recent research on stock market development value traded ratio to the size of the economy. A small, investment companies, mutual funds, or other col- shows that modern communications technology and liquid market will have a high turnover ratio but a low lective investment vehicles. · S&P/Global Equity increased financial integration have resulted in more value of shares traded ratio. Liquidity is an impor- Indices measure the U.S. dollar price change in the cross-border capital flows, a stronger presence of tant attribute of stock markets because, in theory, stock markets. financial firms around the world, and the migration of liquid markets improve the allocation of capital and stock exchange activities to international exchanges. enhance prospects for long-term economic growth. Many firms in emerging markets now cross-list on inter- A more comprehensive measure of liquidity would national exchanges, which provides them with lower include trading costs and the time and uncertainty cost capital and more liquidity-traded shares. However, in finding a counterpart in settling trades. this also means that exchanges in emerging markets Standard & Poor's Index Services, the source for may not have enough financial activity to sustain them, all the data in the table, provides regular updates on putting pressure on them to rethink their operations. 22 emerging stock markets and 36 frontier markets. The indicators in the table are from Standard & Standard & Poor's maintains a series of indexes for Poor's Emerging Markets Data Base. They include investors interested in investing in stock markets in measures of size (market capitalization, number of developing countries. The S&P/IFCI index, Standard listed domestic companies) and liquidity (value of & Poor's leading emerging markets index, is designed shares traded as a percentage of gross domestic to be sufficiently investable to support index tracking product, value of shares traded as a percentage of portfolios in emerging market stocks that are legally market capitalization). The comparability of such indi- and practically open to foreign portfolio investment. cators across countries may be limited by concep- The S&P/Frontier BMI measures the performance of tual and statistical weaknesses, such as inaccurate 36 smaller and less liquid markets. The individual reporting and differences in accounting standards. country indexes include all publicly listed equities The percentage change in stock market prices in U.S. representing an aggregate of at least 80 percent or dollars for developing economies is from Standard & more of market capitalization in each market. These Poor's Global Equity Indices (S&P IFCI) and Standard & indexes are widely used benchmarks for international Poor's Frontier Broad Market Index (BMI). The percent- portfolio management. See www.standardandpoors. Data sources age change for France, Germany, Japan, the United com for further information on the indexes. Kingdom, and the United States is from local stock Because markets included in Standard & Poor's Data on stock markets are from Standard & Poor's market prices. The indicator is an important measure emerging markets category vary widely in level of Global Stock Markets Factbook 2009, which draws of overall performance. Regulatory and institutional development, it is best to look at the entire category on the Emerging Markets Data Base, supple- factors that can affect investor confidence, such as to identify the most significant market trends. And it mented by other data from Standard & Poor's. entry and exit restrictions, the existence of a securi- is useful to remember that stock market trends may The firm collects data through an annual survey ties and exchange commission, and the quality of laws be distorted by currency conversions, especially when of the world's stock exchanges, supplemented by to protect investors, may influence the functioning of a currency has registered a significant devaluation. information provided by its network of correspon- stock markets but are not included in the table. About the data is based on Demirgüç-Kunt and dents and by Reuters. Data on GDP are from the Stock market size can be measured in various Levine (1996), Beck and Levine (2001), and Claes- World Bank's national accounts data files. ways, and each may produce a different ranking of sens, Klingebiel, and Schmukler (2002). 2010 World Development Indicators 307 5.5 Financial access, stability, and efficiency Getting Bank Ratio of bank Domestic Interest Risk premium credit capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Strength of Depth of Lending Prime lending legal rights credit % of adult population rate minus rate minus index information Public Private deposit rate treasury bill rate 0­10 (weak index credit registry credit bureau percentage percentage to strong) 0­6 (low to high) coverage coverage % % % of GDP points points June 2009 June 2009 June 2009 June 2009 2008 2008 2008 2008 2008 Afghanistan 6 0 0.0 0.0 .. .. 3.5 .. .. Albania 9 4 9.9 0.0 6.7 6.6 67.6 6.2 6.8 Algeria 3 2 0.2 0.0 .. .. ­13.0 6.3 7.7 Angola 4 4 2.5 0.0 .. .. 9.3 6.3 .. Argentina 4 6 34.3 100.0 12.9 2.7 24.4 8.4 .. Armenia 6 5 4.4 34.5 23.0 4.4 16.7 10.4 9.4 Australia 9 5 0.0 100.0 4.2 0.5 137.8 3.7 .. Austria 7 6 1.4 39.2 6.3 2.0 129.7 .. .. Azerbaijan 8 5 6.9 0.0 .. .. 17.1 7.5 8.5 Bangladesh 7 2 0.9 0.0 6.5 11.2 59.4 6.7 .. Belarus 2 5 23.4 0.0 17.4 0.6 31.5 0.0 .. Belgium 7 4 56.5 0.0 3.3 1.7 113.8 .. 4.8 Benin 3 1 10.9 0.0 .. .. 14.8 .. .. Bolivia 1 6 11.6 33.9 9.3 4.3 48.4 9.2 5.6 Bosnia and Herzegovina 5 5 23.2 64.3 13.1 3.1 58.5 3.5 .. Botswana 7 4 0.0 51.9 .. .. ­11.2 7.9 .. Brazil 3 5 23.7 59.2 9.1 3.1 101.7 35.6 33.6 Bulgaria 8 6 34.8 6.2 8.5 2.4 66.7 6.4 6.2 Burkina Faso 3 1 1.9 0.0 .. .. 15.5 .. .. Burundi 2 1 0.2 0.0 .. .. 34.9 .. 8.2 Cambodia 8 0 0.0 0.0 .. .. 16.2 14.6 .. Cameroon 3 2 1.8 0.0 .. .. 5.8 10.8 .. Canada 6 6 0.0 100.0 5.1 1.1 177.8 3.2 2.3 Central African Republic 3 2 2.1 0.0 .. .. 18.0 10.8 .. Chad 3 1 0.2 0.0 .. .. ­2.7 10.8 .. Chile 4 5 32.9 33.9 6.9 1.0 98.3 5.8 .. China 6 4 62.1 0.0 6.1 2.4 126.2 3.1 .. Hong Kong SAR, China 10 4 0.0 71.9 12.0 0.9 124.6 4.6 5.0 Colombia 5 5 0.0 60.5 12.2 4.0 43.1 7.4 .. Congo, Dem. Rep. 3 0 0.0 0.0 .. .. 9.1 .. .. Congo, Rep. 3 2 3.0 0.0 .. .. ­18.5 10.8 .. Costa Rica 5 5 24.3 56.0 13.3 1.5 53.9 11.7 .. Côte d'Ivoire 3 1 2.7 0.0 .. .. 20.1 .. .. Croatia 6 4 0.0 77.0 13.5 4.9 75.1 7.2 .. Cuba .. .. .. .. .. .. .. .. .. Czech Republic 6 5 4.9 73.1 5.7 3.3 58.0 4.6 2.6 Denmark 9 4 0.0 5.2 5.7 0.3 211.2 .. .. Dominican Republic 3 6 29.7 46.1 9.7 3.5 39.1 9.6 .. Ecuador 3 5 37.2 46.0 8.8 2.5 17.3 7.1 .. Egypt, Arab Rep. 3 6 2.5 8.2 5.3 14.8 78.0 5.7 1.0 El Salvador 5 6 21.0 94.6 12.7 2.8 44.9 .. .. Eritrea 2 0 0.0 0.0 .. .. 112.7 .. .. Estonia 6 5 0.0 20.6 9.3 1.9 97.3 2.8 .. Ethiopia 4 2 0.1 0.0 .. .. 37.6 3.4 7.3 Finland 7 5 0.0 14.7 7.4 0.4 87.7 .. .. France 7 4 32.5 0.0 4.2 2.8 126.1 .. .. Gabon 3 2 3.9 0.0 10.7 8.5 6.1 10.8 .. Gambia, The 5 0 0.0 0.0 .. .. 34.3 15.0 .. Georgia 6 6 0.0 12.2 17.1 4.1 32.9 10.9 .. Germany 7 6 0.8 98.3 4.5 2.7 125.7 .. .. Ghana 7 0 0.0 0.0 12.8 7.7 32.9 .. .. Greece 3 5 0.0 46.9 4.5 5.0 109.0 .. .. Guatemala 8 6 16.9 28.4 10.3 2.4 36.8 8.3 .. Guinea 3 0 0.0 0.0 .. .. .. .. .. Guinea-Bissau 3 1 1.1 0.0 .. .. 13.5 .. .. Haiti 3 2 0.7 0.0 .. .. 24.6 15.7 .. Honduras 6 6 21.7 58.7 .. .. 50.4 8.4 .. 308 2010 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency Getting Bank Ratio of bank Domestic Interest Risk premium credit capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Strength of Depth of Lending Prime lending legal rights credit % of adult population rate minus rate minus index information Public Private deposit rate treasury bill rate 0­10 (weak index credit registry credit bureau percentage percentage to strong) 0­6 (low to high) coverage coverage % % % of GDP points points June 2009 June 2009 June 2009 June 2009 2008 2008 2008 2008 2008 Hungary 7 5 0.0 10.3 8.0 3.0 80.7 0.3 1.3 India 8 4 0.0 10.2 6.4 2.3 71.6 .. .. Indonesia 3 4 22.0 0.0 9.2 3.2 36.7 5.1 .. Iran, Islamic Rep. 4 3 31.3 0.0 .. .. 50.5 0.4 .. Iraq 3 0 0.0 0.0 .. .. .. 8.4 ­1.3 Ireland 8 5 0.0 100.0 4.7 2.6 204.3 .. .. Israel 9 5 0.0 89.8 5.7 1.5 79.9 2.8 2.2 Italy 3 5 12.2 77.5 6.6 4.9 132.4 .. 3.1 Jamaica 8 0 0.0 0.0 .. .. 54.1 9.3 0.9 Japan 7 6 0.0 76.2 3.6 1.7 293.0 1.3 1.6 Jordan 4 2 1.0 0.0 10.4 4.2 114.9 3.6 .. Kazakhstan 5 6 0.0 29.5 12.2 5.1 33.5 .. .. Kenya 10 4 0.0 2.3 11.4 9.0 40.1 8.7 6.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. 7 6 0.0 93.8 8.8 1.1 112.6 1.3 .. Kosovo 8 3 18.9 0.0 .. 3.7 10.9 .. .. Kuwait 4 4 0.0 30.4 11.6 3.1 68.1 2.8 .. Kyrgyz Republic 10 3 0.0 5.9 .. .. 14.0 15.9 6.7 Lao PDR 4 0 0.0 0.0 .. .. 10.5 23.5 11.5 Latvia 9 5 46.5 0.0 7.3 3.6 89.1 5.5 4.9 Lebanon 3 5 8.3 0.0 7.8 7.5 172.9 2.3 4.8 Lesotho 7 0 0.0 0.0 7.9 3.5 ­18.4 8.5 6.4 Liberia 4 1 0.3 0.0 .. .. 144.5 11.3 .. Libya .. .. .. .. .. .. ­50.0 3.5 .. Lithuania 5 6 12.1 18.4 7.6 4.6 64.2 0.8 2.6 Macedonia, FYR 7 4 28.1 0.0 .. 6.8 42.7 3.8 .. Madagascar 2 1 0.1 0.0 .. .. 9.3 33.5 36.2 Malawi 8 0 0.0 0.0 .. .. 26.2 21.7 14.0 Malaysia 10 6 48.5 82.0 8.0 4.8 115.2 3.0 2.7 Mali 3 1 4.0 0.0 .. .. 13.2 .. .. Mauritania 3 1 0.2 0.0 .. .. .. 15.5 13.1 Mauritius 5 3 36.8 0.0 .. .. 111.7 11.4 .. Mexico 4 6 0.0 77.5 9.6 3.2 37.5 5.7 1.0 Moldova 8 0 0.0 0.0 17.0 5.2 39.8 3.1 3.0 Mongolia 6 3 22.2 0.0 .. .. 34.4 9.4 20.4 Morocco 3 5 0.0 14.0 7.3 6.0 95.5 .. .. Mozambique 2 4 2.3 0.0 6.7 2.8 14.2 7.3 4.6 Myanmar .. .. .. .. .. .. .. 5.0 .. Namibia 8 5 0.0 57.7 8.0 3.1 43.8 5.4 4.1 Nepal 5 2 0.0 0.3 .. .. 52.7 5.8 4.4 Netherlands 6 5 0.0 83.5 3.2 0.8 196.0 0.2 .. New Zealand 9 5 0.0 100.0 .. .. 156.3 4.7 5.2 Nicaragua 3 5 16.0 28.4 .. .. 66.2 6.6 .. Niger 3 1 0.9 0.0 .. .. 6.2 .. .. Nigeria 8 0 0.0 0.0 18.0 6.3 26.7 3.5 7.3 Norway 7 4 0.0 100.0 4.2 0.8 .. 1.8 .. Oman 4 2 17.0 0.0 15.5 2.4 32.9 2.6 .. Pakistan 6 4 5.6 1.5 10.4 9.1 45.9 6.0 1.6 Panama 6 6 0.0 45.9 13.4 1.7 85.8 4.6 .. Papua New Guinea 5 0 0.0 0.0 .. .. 24.9 8.0 3.1 Paraguay 3 6 10.9 47.4 11.2 1.2 22.0 22.7 .. Peru 7 6 23.0 31.8 8.3 2.2 18.5 20.2 .. Philippines 3 3 0.0 6.1 11.1 4.5 46.0 4.3 5.3 Poland 9 4 0.0 68.3 7.9 4.4 60.1 3.3 1.3 Portugal 3 5 81.3 16.4 6.1 2.0 183.8 .. .. Puerto Rico 7 5 0.0 73.8 .. .. .. .. .. Qatar 3 2 0.0 0.0 .. .. 56.9 3.9 .. 2010 World Development Indicators 309 5.5 Financial access, stability, and efficiency Getting Bank Ratio of bank Domestic Interest Risk premium credit capital to nonperforming credit rate spread on lending asset ratio loans to total provided by gross loans banking sector Strength of Depth of Lending Prime lending legal rights credit % of adult population rate minus rate minus index information Public Private deposit rate treasury bill rate 0­10 (weak index credit registry credit bureau percentage percentage to strong) 0­6 (low to high) coverage coverage % % % of GDP points points June 2009 June 2009 June 2009 June 2009 2008 2008 2008 2008 2008 Romania 8 5 5.7 30.2 7.0 13.8 40.9 5.5 4.6 Russian Federation 3 5 0.0 14.3 13.6 3.8 25.9 6.5 .. Rwanda 8 2 0.4 0.0 12.3 12.6 .. 9.3 8.9 Saudi Arabia 4 6 0.0 17.9 10.0 1.4 9.5 .. .. Senegal 3 1 4.4 0.0 9.1 19.1 24.7 .. .. Serbia 8 6 0.0 94.2 20.5 5.3 38.4 10.8 8.5 Sierra Leone 6 0 0.0 0.0 18.7 23.3 7.4 14.8 9.0 Singapore 10 4 0.0 40.3 8.5 1.4 84.1 5.0 4.5 Slovak Republic 9 4 1.4 44.0 9.8 3.2 53.8 4.3 .. Slovenia 6 2 2.7 0.0 8.4 1.6 87.5 2.6 2.8 Somalia .. .. .. .. .. .. .. .. .. South Africa 9 6 0.0 54.7 7.9 3.9 172.2 3.5 4.3 Spain 6 5 45.3 7.6 6.4 3.4 212.9 .. .. Sri Lanka 4 5 0.0 14.3 .. .. 42.8 8.0 0.0 Sudan 5 0 0.0 0.0 .. .. 17.1 .. .. Swaziland 6 5 0.0 42.3 20.7 8.4 2.0 6.7 4.1 Sweden 5 4 0.0 100.0 4.7 1.0 135.8 .. .. Switzerland 8 5 0.0 22.5 4.6 0.5 183.6 3.2 2.0 Syrian Arab Republic 1 0 0.0 0.0 .. .. 36.9 1.8 .. Tajikistan 3 0 0.0 0.0 .. .. 27.5 14.4 .. Tanzania 8 0 0.0 0.0 .. .. 17.2 6.9 6.9 Thailand 4 5 0.0 32.9 9.5 5.7 130.6 4.6 3.9 Timor-Leste 1 0 0.0 0.0 .. .. ­25.5 12.3 .. Togo 3 1 2.7 0.0 .. .. 24.8 .. .. Trinidad and Tobago 8 4 0.0 41.7 .. .. 12.8 5.1 5.4 Tunisia 3 5 19.9 0.0 .. 15.5 73.0 .. .. Turkey 4 5 15.9 42.9 11.7 3.6 52.6 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. Uganda 7 0 0.0 0.0 13.8 2.2 11.4 9.8 11.9 Ukraine 9 3 0.0 3.0 14.0 17.4 81.9 7.5 .. United Arab Emirates 4 5 7.3 12.6 10.6 2.5 78.1 .. .. United Kingdom 9 6 0.0 100.0 4.4 1.6 212.3 .. 0.3 United States 8 6 0.0 100.0 9.3 3.0 216.1 .. 3.6 Uruguay 5 6 17.8 97.2 8.9 1.0 32.5 9.2 1.8 Uzbekistan 2 3 2.6 2.1 .. .. .. .. .. Venezuela, RB 2 0 0.0 0.0 9.4 1.9 20.3 6.2 .. Vietnam 8 4 19.0 0.0 .. .. 95.0 3.1 7.0 West Bank and Gaza 0 3 6.5 0.0 .. .. .. 4.8 .. Yemen, Rep. 2 2 0.2 0.0 .. .. 11.4 5.0 2.8 Zambia 9 3 0.0 0.4 .. .. 19.3 12.5 5.6 Zimbabwe 7 0 0.0 0.0 .. .. .. 457.5 330.2 World 5.4 u 2.9 u 6.6 u 21.8 u 9.0 m 3.2 m 156.8 w 6.0 m Low income 4.7 1.2 1.4 0.3 .. .. 43.7 10.8 Middle income 5.2 3.1 9.2 18.6 9.7 3.9 76.1 6.4 Lower middle income 4.7 2.6 7.1 9.6 10.2 4.4 98.7 6.6 Upper middle income 5.8 3.7 11.9 30.3 9.4 3.6 55.7 6.2 Low & middle income 5.0 2.5 6.9 13.2 .. 4.1 75.2 6.5 East Asia & Pacific 5.3 1.7 8.7 8.5 .. .. 116.4 5.1 Europe & Central Asia 6.6 4.2 11.3 18.5 12.2 4.4 41.5 6.2 Latin America & Carib. 5.2 3.5 11.5 33.8 9.7 2.5 62.0 7.9 Middle East & N. Africa 2.5 2.8 5.8 1.9 .. .. 39.7 4.3 South Asia 5.3 2.1 0.8 3.3 6.4 9.1 69.3 6.6 Sub-Saharan Africa 4.7 1.4 2.3 4.7 .. .. 65.5 10.0 High income 6.6 4.1 5.7 46.5 6.3 1.9 188.4 .. Euro area 6.3 4.1 15.6 35.2 6.1 2.6 142.7 .. 310 2010 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency About the data Definitions Financial sector development has positive impacts financial activity and impose widespread costs on · Strength of legal rights index measures the degree on economic growth and poverty. The size of the the economy. The ratio of bank capital to assets, to which collateral and bankruptcy laws protect the sector determines the resources mobilized for invest- a measure of bank solvency and resiliency, shows rights of borrowers and lenders and thus facilitate ment. Access to finance can expand opportunities for the extent to which banks can deal with unexpected lending. Higher values indicate that the laws are bet- all with higher levels of access and use of banking losses. Capital includes tier 1 capital (paid-up shares ter designed to expand access to credit. · Depth of services associated with lower financing obstacles and common stock), a common feature in all coun- credit information index measures rules affecting the for people and businesses. A stable financial sys- tries' banking systems, and total regulatory capital, scope, accessibility, and quality of information avail- tem that promotes efficient savings and investment which includes several types of subordinated debt able through public or private credit registries. Higher is also crucial for a thriving democracy and market instruments that need not be repaid if the funds values indicate the availability of more credit informa- economy. The banking system is the largest sector are required to maintain minimum capital levels tion. · Public credit registry coverage is the number in the financial system in most countries, so most (tier 2 and tier 3 capital). Total assets include all of individuals and firms listed in a public credit reg- indicators in the table cover the banking system. nonfinancial and financial assets. Data are from istry with current information on repayment history, There are several aspects of access to financial internally consistent financial statements. unpaid debts, or credit outstanding as a percentage services: availability, cost, and quality of services. The ratio of bank nonperforming loans to total of the adult population. · Private credit bureau cov- The development and growth of credit markets gross loans, a measure of bank health and efficiency, erage is the number of individuals or firms listed by depend on access to timely, reliable, and accurate helps to identify problems with asset quality in the a private credit bureau with current information on data on borrowers' credit experiences. For secured loan portfolio. A high ratio may signal deterioration repayment history, unpaid debts, or credit outstand- transactions, such as mortgages or vehicle loans, of the credit portfolio. International guidelines rec- ing as a percentage of the adult population. · Bank rapid access to information in property registries is ommend that loans be classified as nonperforming capital to asset ratio is the ratio of bank capital and also vital, and for small business loans corporate when payments of principal and interest are 90 reserves to total assets. Capital and reserves include registry data are needed. Access to credit can be days or more past due or when future payments are funds contributed by owners, retained earnings, gen- improved by increasing information about potential not expected to be received in full. See the Interna- eral and special reserves, provisions, and valuation borrowers' creditworthiness and making it easy to tional Monetary Fund's (IMF) Global Financial Stability adjustments. · Ratio of bank nonperforming loans to create and enforce collateral agreements. Lenders Report for details. total gross loans is the value of nonperforming loans look at a borrower's credit history and collateral. Domestic credit by the banking sector as a share divided by the total value of the loan portfolio (includ- Where credit registries and effective collateral laws of GDP is a measure of banking sector depth and ing nonperforming loans before the deduction of loan are absent--as in many developing countries-- financial sector development in terms of size. In a loss provisions). The amount recorded as nonperform- banks make fewer loans. Indicators that cover finan- few countries governments may hold international ing should be the gross value of the loan as recorded cial access, or getting credit, include the strength reserves as deposits in the banking system rather on the balance sheet, not just the amount overdue. of legal rights index (ranges from 0, weak, to 10, than in the central bank. Since the claims on the · Domestic credit provided by banking sector is all strong), depth of credit information index (ranges central government are a net item (claims on the credit to various sectors on a gross basis, except to from 0, low, to 6, high), public registry coverage, and central government minus central government depos- the central government, which is net. The banking private bureau coverage. its), this net figure may be negative, resulting in a sector includes monetary authorities, deposit money The strength of legal rights index is based on eight negative figure of domestic credit provided by the banks, and other banking institutions for which data aspects related to legal rights in collateral law and banking sector. are available. · Interest rate spread is the interest two aspects in bankruptcy law. It is based on a stan- The interest rate spread--the margin between rate charged by banks on loans to prime customers dardized case scenario and measures the degree the cost of mobilizing liabilities and the earnings on minus the interest rate paid by commercial or similar to which collateral and bankruptcy laws protect the assets--is a measure of financial sector efficiency in banks for demand, time, or savings deposits. · Risk rights of borrowers and lenders and thus facilitate intermediation. A narrow interest rate spread means premium on lending is the interest rate charged by lending. The indicator focuses on revolving movable low transaction costs, which lowers the cost of funds banks on loans to prime private sector customers collateral, such as accounts receivable and inven- for investment, crucial to economic growth. minus the "risk-free" treasury bill interest rate at tory, rather than tangible movable collateral, such The risk premium on lending is the spread between which short-term government securities are issued as equipment. The depth of credit information index the lending rate to the private sector and the "risk- or traded in the market. assesses six features of the public registry or the free" government rate. Spreads are expressed as Data sources private credit bureau (or both). For more information annual averages. A small spread indicates that the on these indexes, see www.doingbusiness.org/ market considers its best corporate customers to be Data on getting credit are from the World Bank's MethodologySurveys/. low risk. A negative rate indicates that the market Doing Business project (www.doingbusiness.org). The size and mobility of international capital considers its best corporate clients to be lower risk Data on bank capital and nonperforming loans are flows make it increasingly important to monitor the than the government. from the IMF's Global Financial Stability Report. strength of financial systems. Robust financial sys- Data on credit and interest rates are from the tems can increase economic activity and welfare, IMF's International Financial Statistics. but instability in the financial system can disrupt 2010 World Development Indicators 311 5.6 Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Individual Number file, and pay taxes Total tax rate On income Corporate % of GDP of payments hours % of profi t % over $ % 2000 2008 June 2009 June 2009 June 2009 2009 2009 2009 Afghanistanb .. 5.8 8 275 36.4 .. .. 20 Albaniab 16.1 .. 44 244 44.9 .. .. 10 Algeriab .. 46.5 34 451 72.0 .. .. .. Angola .. .. 31 272 53.2 .. .. 35 Argentina 9.8 .. 9 453 108.1 35 32,434 35 Armeniab .. 17.0 50 958 36.2 20 2,577 20 Australia 22.1 23.1 12 107 48.0 45 133,560 30 Austria 19.9 20.1 22 170 55.5 50 80,268 25 Azerbaijanb .. 0.0 22 376 40.9 .. .. .. Bangladeshb 7.6 8.8 21 302 35.0 .. .. 28 Belarusb 16.6 25.5 107 900 99.7 .. .. 24 Belgium 27.4 25.6 11 156 57.3 50 45,926 34 Beninb 15.5 17.3 55 270 73.3 .. .. .. Bolivia 13.2 17.0 42 1,080 80.0 .. .. 25 Bosnia and Herzegovina .. 21.0 51 422 27.1 .. .. 10 Botswanab .. .. 19 140 17.1 25c 17,647c 25c Brazilb .. 16.4 10 2,600 69.2 28 20,218 34 Bulgariab 18.3 24.2 17 616 31.4 10 .. 10 Burkina Faso .. 12.5 46 270 44.9 .. .. .. Burundib 13.6 .. 32 140 278.6 .. .. .. Cambodia 8.2 8.2 39 173 22.7 .. .. .. Cameroonb 11.2 .. 41 1,400 50.5 .. .. .. Canadab 15.0 14.2 9 119 43.6 29 107,542 33 Central African Republicb .. .. 54 504 203.8 .. .. .. Chad .. .. 54 122 60.9 .. .. .. Chile 16.7 19.8 10 316 25.3 40 109,484 17 Chinab 6.8 9.4 7 504 78.5 45 175,716 25 Hong Kong SAR, China .. .. 4 80 24.2 15 .. 17 Colombia 11.7 12.6 20 208 78.7 33 43,467 33 Congo, Dem. Rep.b 3.5 .. 32 308 322.0 50 c 14,304 c 38 c Congo, Rep. 9.2 .. 61 606 65.5 .. .. .. Costa Ricab .. 15.8 42 282 54.8 15 1,526 30 Côte d'Ivoireb .. 15.6 66 270 44.7 10 c 5,360 c 25c Croatiab 22.6 20.4 17 196 32.5 45 54,696 20 Cuba .. .. .. .. .. .. .. .. Czech Republicb 15.4 14.8 12 613 47.2 15 .. 20 Denmark 31.0 35.6 9 135 29.2 62 62,286 25 Dominican Republicb .. 15.9 9 324 39.0 .. .. 25 Ecuador b .. .. 8 600 34.9 35 62,000 25 Egypt, Arab Rep.b 13.4 15.4 29 480 43.0 20 7,146 20 El Salvador 10.7 13.9 53 320 35.0 .. .. .. Eritrea .. .. 18 216 84.5 .. .. .. Estonia 15.8 16.8 10 81 49.1 21 .. 21 Ethiopiab 10.2 .. 19 198 31.1 35 .. 30 Finland 24.6 21.7 8 243 47.7 31 86,288 26 France 23.2 21.8 7 132 65.8 40 92,983 33 Gabon .. .. 26 272 44.7 .. .. .. Gambia, Theb .. .. 50 376 292.4 .. .. .. Georgiab 7.7 23.8 18 387 15.3 .. .. .. Germany 11.9 11.8 16 196 44.9 45 334,448 29 Ghanab 17.2 22.9 33 224 32.7 25c 10,213c 25c Greece 23.3 19.9 10 224 47.4 40 100,334 25 Guatemalab 10.1 11.3 24 344 40.9 31 37,389 31 Guineab 11.1 .. 56 416 49.9 .. .. .. Guinea-Bissau .. .. 46 208 45.9 .. .. .. Haiti .. .. 42 160 40.1 .. .. .. Honduras .. 15.8 47 224 48.3 .. .. 30 312 2010 World Development Indicators 5.6 STATES AND MARKETS Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Individual Number file, and pay taxes Total tax rate On income Corporate % of GDP of payments hours % of profi t % over $ % 2000 2008 June 2009 June 2009 June 2009 2009 2009 2009 Hungary 21.9 23.6 14 330 57.5 36 8,014 16 Indiab 9.0 12.9 59 271 64.7 30 10,115 34 Indonesiab 11.6 .. 51 266 37.6 30 47,500 28 Iran, Islamic Rep.b 6.3 7.3 22 344 44.2 .. .. 25 Iraq .. .. 13 312 28.4 .. .. .. Ireland 26.0 25.4 9 76 26.5 46 48,697 13 Israel 28.7 25.3 33 230 32.6 46 110,230 26 Italy 23.2 22.6 15 334 68.4 43 100,334 31 Jamaicab .. 25.4 72 414 51.3 25 .. 33 Japan .. .. 13 355 55.7 50 182,062 41 Jordanb .. 18.3 26 101 31.1 .. .. 25 Kazakhstanb 10.2 12.7 9 271 35.9 10 .. 20 Kenyab 16.8 18.9 41 417 49.7 .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep.b 15.4 16.6 14 250 31.9 35 69,379 24 Kosovo .. 25.5 33 163 28.3 .. .. .. Kuwait 1.3 0.9 15 118 15.5 0 .. 15 Kyrgyz Republicb 11.7 16.8 75 202 59.4 .. .. .. Lao PDR .. 10.1 34 362 33.7 .. .. .. Latviab 14.2 15.0 7 279 33.0 23 .. 15 Lebanon 11.9 16.3 19 180 30.2 .. .. .. Lesothob 35.6 58.9 21 324 18.5 .. .. .. Liberia .. .. 32 158 43.7 .. .. .. Libya .. .. .. .. .. .. .. 40 Lithuania 14.6 17.4 12 166 42.7 15 .. 20 Macedonia, FYRb .. 19.7 40 75 16.4 .. .. 10 Madagascar 11.3 11.4 23 201 39.2 .. .. .. Malawi .. .. 19 157 25.8 .. .. .. Malaysiab 13.8 .. 12 145 34.2 27 28,470 25 Mali 13.2 15.6 58 270 52.1 .. .. .. Mauritania .. .. 38 696 86.1 .. .. .. Mauritiusb 17.3 18.2 7 161 22.9 15c .. 15c Mexicob 11.7 .. 6 517 51.0 28 29,591 28 Moldovab 14.7 20.5 48 228 31.1 .. .. .. Mongolia 14.5 23.2 43 192 22.8 .. .. .. Moroccob 19.9 27.5 28 358 41.7 .. .. .. Mozambique .. .. 37 230 34.3 32c 58,514 c 32c Myanmar 3.0 .. .. .. .. .. .. .. Namibiab 27.5 27.2 37 375 9.6 37c 90,361c 35c Nepalb 8.7 10.4 34 338 38.8 .. .. .. Netherlands 22.3 23.6 9 164 39.3 52 73,279 26 New Zealand 29.5 31.7 8 70 32.8 38 40,498 30 Nicaraguab 13.8 17.0 64 240 63.2 .. .. .. Niger .. 11.5 41 270 46.5 .. .. .. Nigeria .. .. 35 938 32.2 .. .. 30 Norway 27.4 28.1 4 87 41.6 40 110,229 28 Omanb 7.2 .. 14 62 21.6 0 .. 12 Pakistanb 10.1 9.8 47 560 31.6 20 107,838 35 Panamab 10.2 .. 59 482 50.1 27 29,294 30 Papua New Guineab 19.0 .. 33 194 42.3 42 92,996 30 Paraguay b .. 12.5 35 328 35.0 10 31,600 10 Perub 12.2 15.4 9 380 40.3 30 63,033 30 Philippinesb 13.7 14.1 47 195 49.4 32 10,474 30 Poland 16.0 18.4 40 395 42.5 32 26,116 19 Portugal 21.3 22.2 8 328 42.9 42 85,766 25 Puerto Rico .. .. 16 218 64.7 .. .. .. Qatar .. 23.1 1 36 11.3 0 .. 35 2010 World Development Indicators 313 5.6 Tax policies Tax revenue collected Taxes payable Highest marginal by central government by businesses tax ratea Time to prepare, Individual Number file, and pay taxes Total tax rate On income Corporate % of GDP of payments hours % of profi t % over $ % 2000 2008 June 2009 June 2009 June 2009 2009 2009 2009 Romania 11.7 17.9 113 202 44.6 16 .. 16 Russian Federation 13.6 15.7 11 320 48.3 13 .. 20 Rwandab .. .. 34 160 31.3 .. .. .. Saudi Arabia .. .. 14 79 14.5 0 .. 20 Senegalb 16.1 .. 59 666 46.0 .. .. .. Serbiab .. 22.0 66 279 34.0 15 46,146 10 Sierra Leoneb 10.2 .. 29 357 235.6 .. .. .. Singaporeb 15.4 14.6 5 84 27.8 20 217,317 18 Slovak Republic .. 13.4 31 257 48.6 19 .. 19 Sloveniab 20.6 83.5 22 260 37.5 41 19,827 21 Somalia .. .. .. .. .. .. .. .. South Africa 24.0 27.7 9 200 30.2 40 c 63,253c 35c Spain 16.2 10.6 8 213 56.9 43 71,447 30 Sri Lankab 14.5 14.2 62 256 63.7 35 13,346 35 Sudanb 6.4 .. 42 180 36.1 .. .. 35 Swazilandb .. .. 33 104 36.6 33c 12,048 c 30 c Sweden 20.6 .. 2 122 54.6 57 66,419 26 Switzerlandb 11.1 10.2 24 63 29.7 40 630,312 21 Syrian Arab Republicb 17.4 .. 20 336 42.9 .. .. 28 Tajikistanb 7.7 .. 54 224 85.9 .. .. .. Tanzania .. .. 48 172 45.2 30 c 7,222c 30 c Thailand .. 16.5 23 264 37.2 37 113,147 30 Timor-Leste .. .. 6 276 0.2 .. .. .. Togob .. 16.3 53 270 52.7 .. .. .. Trinidad and Tobagob 22.1 25.9 40 114 33.1 .. .. .. Tunisiab 21.3 22.8 22 228 62.8 .. .. 30 Turkey b .. 18.6 15 223 44.5 35 28,564 20 Turkmenistan .. .. .. .. .. .. .. .. Ugandab 10.4 12.8 32 161 35.7 30 c 2,855c 45c Ukraineb 14.1 17.8 147 736 57.2 15 .. 25 United Arab Emiratesb 1.7 .. 14 12 14.1 0 .. 55 United Kingdom 28.4 28.6 8 110 35.9 40 66,047 28 United States 12.7 9.9 10 187 46.3 35 372,950 40 Uruguay b 14.7 17.2 53 336 46.7 25 96,076 25 Uzbekistan .. .. 106 356 94.9 .. .. .. Venezuela, RBb 13.3 .. 71 864 61.1 34 156,000 34 Vietnam .. .. 32 1,050 40.1 35 4,447 25 West Bank and Gaza .. .. 27 154 16.8 .. .. 16 Yemen, Rep.b 9.4 .. 44 248 47.8 .. .. 35 Zambiab 18.6 17.1 37 132 16.1 .. .. 35 Zimbabweb .. .. 51 270 39.4 .. .. 31 World 15.6 w 17.5 w 31 u 286 u 48.3 u .. .. .. Low income .. .. 41 291 74.4 Middle income 11.2 14.2 34 341 42.0 Lower middle income 8.1 10.9 36 332 40.1 Upper middle income .. 18.1 32 352 44.3 Low & middle income 11.2 14.1 36 326 51.5 East Asia & Pacific 7.7 10.1 27 243 38.0 Europe & Central Asia 14.5 17.0 51 365 44.5 Latin America & Carib. .. .. 33 419 48.5 Middle East & N. Africa 11.8 28.4 27 276 41.6 South Asia 9.3 12.3 31 285 40.0 Sub-Saharan Africa .. .. 38 306 67.6 High income 16.4 17.8 16 170 39.2 Euro area 19.1 21.4 15 197 45.9 a. Data are from KPMG's Individual Income and Corporate Tax Rate Surveys 2009, unless otherwise noted. b. Data on central government taxes were reported on a cash basis and have been adjusted to the accrual framework of the International Monetary Fund's Government Finance Statistics Manual 2001. c. Data are from PriceWaterhouseCooper's Worldwide Tax Summaries online. 314 2010 World Development Indicators 5.6 STATES AND MARKETS Tax policies About the data Definitions Taxes are the main source of revenue for most they have certain levels of start-up capital, employ- · Tax revenue collected by central government is governments. The sources of tax revenue and their ees, and turnover. For details about the assump- compulsory transfers to the central government for relative contributions are determined by government tions, see the World Bank's Doing Business 2010. public purposes. Certain compulsory transfers such policy choices about where and how to impose taxes A potentially important influence on both domestic as fines, penalties, and most social security contribu- and by changes in the structure of the economy. Tax and international investors is a tax system's progres- tions are excluded. Refunds and corrections of erro- policy may refl ect concerns about distributional sivity, as reflected in the highest marginal tax rate neously collected tax revenue are treated as negative effects, economic efficiency (including corrections levied at the national level on individual and corpo- revenue. The analytic framework of the International for externalities), and the practical problems of rate income. Data for individual marginal tax rates Monetary Fund's (IMF) Government Finance Statis- administering a tax system. There is no ideal level generally refer to employment income. In some coun- tics Manual 2001 (GFSM 2001) is based on accrual of taxation. But taxes influence incentives and thus tries the highest marginal tax rate is also the basic accounting and balance sheets. For countries still the behavior of economic actors and the economy's or flat rate, and other surtaxes, deductions, and the reporting government finance data on a cash basis, competitiveness. like may apply. And in many countries several differ- the IMF adjusts reported data to the GFSM 2001 The level of taxation is typically measured by tax ent corporate tax rates may be levied, depending on accrual framework. These countries are footnoted in revenue as a share of gross domestic product (GDP). the type of business (mining, banking, insurance, the table. · Number of tax payments by businesses Comparing levels of taxation across countries pro- agriculture, manufacturing), ownership (domestic or is the total number of taxes paid by businesses dur- vides a quick overview of the fiscal obligations and foreign), volume of sales, and whether surtaxes or ing one year. When electronic filing is available, the incentives facing the private sector. The table shows exemptions are included. The corporate tax rates tax is counted as paid once a year even if payments only central government data, which may significantly in the table are general headline rates applied to are more frequent. · Time to prepare, file, and pay understate the total tax burden, particularly in coun- domestic companies. For more detailed information, taxes is the time, in hours per year, it takes to pre- tries where provincial and municipal governments are see the country's laws, regulations, and tax treaties; pare, file, and pay (or withhold) three major types of large or have considerable tax authority. KPMG's Corporate and Indirect Tax Rate Survey 2009 taxes: the corporate income tax, the value-added or Low ratios of tax revenue to GDP may reflect weak and Individual Income Tax and Social Security Rate sales tax, and labor taxes, including payroll taxes administration and large-scale tax avoidance or eva- Survey 2009 (www.kpmg.com); and Pricewaterhouse- and social security contributions. · Total tax rate is sion. Low ratios may also reflect a sizable parallel Coopers's Worldwide Tax Summaries Online (www. the total amount of taxes payable by a standard busi- economy with unrecorded and undisclosed incomes. pwc.com). ness in the second year of operation after account- Tax revenue ratios tend to rise with income, with ing for deductions and exemptions as a percentage higher income countries relying on taxes to finance of profit. Taxes withheld (such as personal income a much broader range of social services and social tax) or collected by the company and remitted to security than lower income countries are able to. tax authorities but not borne by the company (such The indicators covering taxes payable by busi- as value added tax, sales tax on goods, and taxes nesses measure all taxes and contributions that on services) are excluded. For further details on the are government mandated (at any level--federal, method used for assessing the total tax payable, see state, or local), apply to standardized businesses, the World Bank's Doing Business 2010. · Highest and have an impact in their income statements. The marginal tax rate is the highest rate shown on the taxes covered go beyond the definition of a tax for national schedule of tax rates applied to the annual government national accounts (compulsory, unre- taxable income of individuals and corporations. Also quited payments to general government) and also presented are the income levels for individuals above measure any imposts that affect business accounts. which the highest marginal tax rates levied at the The main differences are in labor contributions national level apply. and value added taxes. The indicators account for Data sources government-mandated contributions paid by the employer to a requited private pension fund or work- Data on central government tax revenue are from ers insurance fund but exclude value added taxes print and electronic editions of the IMF's Govern- because they do not affect the accounting profits of ment Finance Statistics Yearbook. Data on taxes the business--that is, they are not reflected in the payable by businesses are from Doing Business income statement. 2010 (www.doingbusiness.org). Data on individual To make the data comparable across countries, and corporate tax rates are from KPMG's Corporate several assumptions are made about businesses. and Indirect Tax Rate Survey 2009 and Individual The main assumptions are that they are limited liabil- Income Tax and Social Security Rate Survey 2009 ity companies, they operate in the country's most (www.kpmg.com), and PricewaterhouseCoopers's populous city, they are domestically owned, they per- Worldwide Tax Summaries Online (www.pwc.com). form general industrial or commercial activities, and 2010 World Development Indicators 315 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Afghanistan .. 2.2 .. 9.7 400 53 5.4 0.6 .. .. 33 134 Albania 1.2 2.0 5.4 .. 68 15 5.2 1.0 .. .. .. 13 Algeria 3.4 3.1 .. 13.0 305 334 2.7 2.3 .. .. 418 1,590 Angola 6.4 2.9 .. .. 118 117 1.9 1.5 1 .. 180 37 Argentina 1.3 0.8 6.2 .. 102 107 0.6 0.6 2 .. 228 32 Armenia 3.6 3.2 .. 15.4 42 42 2.9 2.6 .. .. 2 1 Australia 1.8 1.8 7.8 7.7 52 55 0.5 0.5 43 6 364 344 Austria 1.0 0.9 2.5 2.2 41 35 1.0 0.8 21 30 25 434 Azerbaijan 2.3 2.7 .. 17.4 87 82 2.5 2.0 .. .. 3 1 Bangladesh 1.4 1.1 14.9 10.4 137 221 0.2 0.3 .. .. 205 10 Belarus 1.3 1.4 5.3 4.0 91 183 1.9 3.7 295 72 41 254 Belgium 1.4 1.1 3.2 2.6 39 39 0.9 0.8 24 408 39 171 Benin 0.6 1.0 4.7 6.8 7 8 0.3 0.2 .. .. 6 2 Bolivia 1.9 1.5 7.6 8.0 70 83 2.0 1.9 .. .. 19 5 Bosnia and Herzegovina 3.6 1.4 .. 3.6 76 9 4.1 0.5 4 .. 25 .. Botswana 3.0 3.4 .. .. 10 11 1.3 1.1 .. .. 52 .. Brazil 1.8 1.5 .. 5.9 673 721 0.8 0.7 26 48 124 156 Bulgaria 2.8 2.2 8.6 7.1 114 75 3.2 2.0 2 5 7 127 Burkina Faso 1.2 1.8 .. 14.0 11 11 0.2 0.2 .. .. .. 4 Burundi 6.0 3.8 30.3 .. 46 51 1.4 1.2 .. .. 1 .. Cambodia 2.2 1.1 16.8 12.8 360 191 6.1 2.5 .. .. .. 40 Cameroon 1.3 1.5 12.0 .. 22 23 0.4 0.3 .. .. 1 0 Canada 1.1 1.3 6.0 6.9 69 64 0.4 0.3 110 215 557 434 Central African Republic 1.0 1.6 .. .. 5 3 0.3 0.1 .. .. .. 9 Chad 1.9 1.0 .. .. 35 35 1.1 0.8 .. .. 15 36 Chile 3.7 3.5 17.7 17.9 117 103 1.9 1.3 1 133 176 543 China 1.8a 2.0a 19.8a 18.0a 3,910 2,885 0.5 0.4 268 428 1,960 1,241 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 3.0 3.7 15.6 15.7 247 411 1.6 2.2 .. .. 62 131 Congo, Dem. Rep. 1.0 1.4 11.4 .. 93 151 0.5 0.6 .. .. 41 17 Congo, Rep. 1.4 1.3 5.9 .. 15 12 1.2 0.8 .. .. 0 0 Costa Rica .. .. .. .. 15 10 1.0 0.5 .. 0 .. .. Côte d'Ivoire .. 1.5 .. 8.2 15 19 0.2 0.2 .. .. 32 .. Croatia 3.1 1.8 7.8 4.7 101 22 5.1 1.1 2 .. 70 99 Cuba .. .. .. .. 85 76 1.8 1.5 .. .. .. .. Czech Republic 2.0 1.5 6.1 4.3 63 27 1.2 0.5 78 20 16 17 Denmark 1.5 1.3 4.2 3.7 22 30 0.8 1.0 20 12 64 90 Dominican Republic 0.7 0.6 .. 3.8 40 65 1.1 1.5 .. .. 13 .. Ecuador 1.7 2.8 .. .. 58 58 1.2 1.0 .. .. 12 133 Egypt, Arab Rep. 3.2 2.3 12.3 7.6 679 866 3.1 3.3 38 .. 810 119 El Salvador 0.9 0.5 4.3 2.9 29 33 1.3 1.3 .. .. 16 4 Eritrea 36.4 .. .. .. 200 202 14.5 9.8 0 .. 4 10 Estonia 1.4 2.2 4.7 8.0 8 7 1.2 1.0 .. .. 27 50 Ethiopia 7.6 1.5 18.0 .. 353 138 1.2 0.4 .. .. 125 .. Finland 1.3 1.3 3.7 3.6 35 32 1.3 1.2 9 76 516 142 France 2.5 2.3 5.7 5.3 389 353 1.5 1.2 1,052 1,585 106 68 Gabon 1.8 1.1 .. .. 7 7 1.2 1.0 .. .. .. 21 Gambia, The 0.8 0.7 .. .. 1 1 0.1 0.1 .. .. .. .. Georgia 0.6 8.1 5.3 27.9 33 33 1.4 1.5 22 .. 6 63 Germany 1.5 1.3 4.7 4.4 221 244 0.5 0.6 .. .. 135 104 Ghana 1.0 0.7 3.3 2.9 8 14 0.1 0.1 .. .. 1 13 Greece 4.3 3.5 9.8 7.9 163 161 3.3 3.1 2 23 710 518 Guatemala 0.8 0.5 7.5 4.1 53 35 1.3 0.7 .. .. 1 12 Guinea 1.5 .. 11.8 .. 19 19 0.5 0.4 .. .. 19 .. Guinea-Bissau 4.4 .. .. .. 9 6 1.7 0.9 .. .. .. .. Haiti .. .. .. .. 5 0 0.1 0.0 .. .. .. 1 Honduras 0.5 0.7 .. 3.1 14 20 0.6 0.7 .. .. .. 0 316 2010 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Hungary 1.7 1.2 4.1 2.7 58 37 1.4 0.9 34 6 14 5 India 3.1 2.5 19.5 15.2 2,372 2,582 0.6 0.6 16 21 822 1,847 Indonesia 1.0 1.0 5.7 .. 492 582 0.5 0.5 16 8 171 290 Iran, Islamic Rep. 3.8 2.9 22.5 12.6 753 563 3.4 2.0 0 2 411 87 Iraq .. .. .. .. 479 577 8.0 7.7 .. .. .. 351 Ireland 0.7 0.6 2.6 1.6 12 10 0.7 0.5 .. .. 0 16 Israel 7.8 8.0 17.6 19.7 181 185 7.2 5.9 354 410 350 524 Italy 2.0 1.8 5.2 4.4 503 436 2.2 1.7 176 484 241 270 Jamaica 0.5 0.5 .. 1.7 3 3 0.3 0.2 .. .. 5 2 Japan 1.0 0.9 .. .. 249 242 0.4 0.4 .. .. 431 578 Jordan 6.2 5.9 .. 16.1 149 111 10.4 5.8 .. 20 130 336 Kazakhstan 0.8 1.0 5.7 6.7 99 81 1.3 1.0 16 12 147 3 Kenya 1.3 2.0 7.8 9.1 27 29 0.2 0.2 .. .. 9 8 Korea, Dem. Rep. .. .. .. .. 1,244 1,295 11.2 10.6 13 .. 19 5 Korea, Rep. 2.4 2.6 14.4 13.1 688 692 3.0 2.8 8 141 1,262 1,898 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 7.1 3.2 18.9 13.2 20 23 1.8 1.6 99 .. 238 276 Kyrgyz Republic 2.9 2.4 18.0 14.2 14 21 0.7 0.8 .. 16 .. 2 Lao PDR 0.8 0.3 .. 3.3 129 129 5.2 4.3 .. .. 7 .. Latvia 0.9 1.9 3.2 6.4 9 16 0.8 1.3 .. .. 3 44 Lebanon 5.4 4.4 17.7 14.6 77 76 6.5 5.4 45 .. 4 3 Lesotho 3.9 2.6 7.8 5.0 2 2 0.2 0.2 .. .. 6 1 Liberia .. 0.5 .. .. 15 2 1.3 0.1 .. .. 8 .. Libya 3.2 1.2 .. .. 77 76 4.2 3.3 11 9 145 3 Lithuania 1.7 1.6 6.5 5.0 17 24 1.0 1.5 3 .. 5 26 Macedonia, FYR 1.9 2.0 .. 6.5 24 19 2.8 2.1 .. .. 11 0 Madagascar 1.2 1.1 11.5 10.0 29 22 0.4 0.2 .. .. .. .. Malawi 0.7 1.2 .. .. 6 7 0.1 0.1 1 .. .. .. Malaysia 1.6 2.0 10.5 .. 116 134 1.2 1.1 8 .. 30 529 Mali 2.4 2.0 20.7 15.1 15 12 0.5 0.3 .. .. 7 8 Mauritania 3.5 3.8 .. .. 21 21 2.0 1.6 .. .. 31 .. Mauritius 0.2 0.2 1.0 0.8 2 2 0.3 0.3 .. .. .. 4 Mexico 0.5 0.4 3.4 .. 208 286 0.5 0.6 .. .. 227 11 Moldova 0.4 0.4 1.4 1.4 13 8 0.7 0.5 6 37 .. .. Mongolia 2.2 1.4 9.5 5.8 16 17 1.4 1.2 .. .. .. 14 Morocco 2.3 3.3 12.0 11.0 241 246 2.4 2.1 .. .. 123 32 Mozambique 1.3 0.9 .. .. 6 11 0.1 0.1 .. .. 0 .. Myanmar 2.3 .. .. .. 429 513 1.7 1.9 .. .. 3 1 Namibia 2.4 3.0 8.3 10.7 9 15 1.5 2.0 .. .. 18 66 Nepal 1.0 1.5 .. .. 90 131 0.9 1.0 .. .. 11 .. Netherlands 1.6 1.4 4.0 3.7 57 47 0.7 0.5 258 554 141 152 New Zealand 1.2 1.1 3.5 3.1 9 9 0.5 0.4 1 .. 45 4 Nicaragua 0.8 0.6 4.7 3.2 16 12 0.9 0.5 .. .. .. .. Niger 0.0 .. .. .. 11 10 0.3 0.2 .. .. .. 7 Nigeria 0.0 0.0 .. .. 107 162 0.3 0.3 .. .. 39 17 Norway 1.7 1.3 5.3 4.2 27 19 1.1 0.7 3 14 263 590 Oman 10.6 10.4 40.4 .. 48 47 5.4 4.5 .. .. 120 66 Pakistan 4.0 3.3 23.4 17.6 900 921 2.2 1.6 3 .. 158 1,094 Panama 1.0 .. 4.6 .. 12 12 0.9 0.8 .. .. 0 .. Papua New Guinea 0.9 0.4 2.9 .. 4 3 0.2 0.1 .. .. .. .. Paraguay 1.1 0.8 .. 5.0 35 26 1.5 0.9 .. .. 6 .. Peru 1.7 1.2 9.7 7.5 193 191 1.7 1.4 10 .. 24 172 Philippines 1.1 0.8 6.2 4.8 149 147 0.5 0.4 .. 4 9 11 Poland 1.8 2.0 5.4 5.8 239 143 1.4 0.8 45 96 159 611 Portugal 2.0 2.0 5.1 4.6 91 91 1.7 1.6 .. 87 2 183 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. 12 12 3.6 1.3 9 6 11 .. 2010 World Development Indicators 317 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers % of central government % of 1990 $ millions % of GDP expenditure thousands labor force Exports Imports 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Romania 2.5 1.5 8.9 4.4 283 153 2.4 1.5 3 32 23 37 Russian Federation 3.7 3.5 19.3 16.4 1,427 1,476 2.0 1.9 4,302 5,953 .. 100 Rwanda 3.5 1.5 .. .. 76 35 2.0 0.7 .. .. 14 15 Saudi Arabia 10.6 8.2 .. .. 217 238 3.1 2.6 .. .. 80 56 Senegal 1.3 1.6 10.4 .. 15 19 0.4 0.4 .. .. .. 19 Serbia 5.4 2.3 .. 6.1 136 24 .. .. .. 6 .. .. Sierra Leone 3.7 2.3 12.8 .. 4 11 0.2 0.5 .. .. 13 10 Singapore 4.7 4.1 28.7 26.7 169 167 8.2 6.4 10 1 612 1,014 Slovak Republic 1.7 1.6 .. 5.0 41 17 1.6 0.6 92 3 2 1 Slovenia 1.1 1.6 2.9 4.4 14 12 1.4 1.2 .. .. 1 8 Somalia .. .. .. .. 50 .. 1.7 .. .. .. 1 .. South Africa 1.6 1.4 5.6 4.4 72 62 0.5 0.3 18 95 16 312 Spain 1.2 1.2 3.9 4.6 242 223 1.3 1.0 46 623 334 363 Sri Lanka 4.5 3.0 19.7 14.2 204 213 2.6 2.6 .. .. 274 75 Sudan 4.7 4.2 53.0 .. 120 127 1.1 1.0 .. .. 146 94 Swaziland 1.6 2.1 .. .. 3 .. 0.8 .. .. .. 1 .. Sweden 2.0 1.3 5.5 .. 88 18 2.0 0.4 306 380 210 21 Switzerland 1.1 0.8 4.2 4.6 28 23 0.7 0.5 111 378 14 32 Syrian Arab Republic 5.3 3.4 .. .. 425 401 8.6 6.0 .. 3 439 81 Tajikistan 1.2 .. 13.4 .. 7 17 0.4 0.6 .. .. .. 13 Tanzania 1.5 0.9 .. .. 35 28 0.2 0.1 .. .. .. 0 Thailand 1.4 1.5 .. 8.3 417 421 1.2 1.1 .. .. 90 12 Timor-Leste .. .. .. .. .. 1 .. 0.2 .. .. .. .. Togo .. 2.0 .. 13.0 8 10 0.4 0.3 .. .. .. .. Trinidad and Tobago .. .. .. .. 8 4 1.3 0.6 .. .. 10 .. Tunisia 1.7 1.3 6.2 4.3 47 48 1.5 1.3 .. .. 11 7 Turkey 3.7 2.2 .. 9.5 828 613 3.6 2.4 15 29 1,148 723 Turkmenistan 2.9 .. .. .. 15 22 0.8 0.9 .. .. .. .. Uganda 2.5 2.3 16.0 13.7 51 47 0.5 0.3 .. .. 6 3 Ukraine 3.6 2.7 13.5 7.2 420 215 1.8 0.9 288 233 .. .. United Arab Emirates 3.4 .. 45.7 .. 66 51 3.5 1.8 .. 3 310 671 United Kingdom 2.4 2.4 6.6 5.7 213 160 0.7 0.5 1,474 1,075 824 590 United States 3.1 4.2 15.6 18.4 1,455 1,540 1.0 1.0 7,526 6,159 301 904 Uruguay 1.3 1.2 5.0 5.1 25 26 1.6 1.6 1 .. 4 63 Uzbekistan 0.8 .. .. .. 79 87 0.9 0.7 .. .. 6 .. Venezuela, RB 1.5 1.1 7.1 .. 79 115 0.8 0.9 .. 1 85 733 Vietnam .. 2.0 .. .. 524 495 1.4 1.1 .. 14 5 250 West Bank and Gaza .. .. .. .. .. 56 .. 5.9 .. .. .. 2 Yemen, Rep. 5.0 4.5 23.9 .. 136 138 3.2 2.3 .. .. 158 44 Zambia 1.8 1.8 10.3 5.7 23 16 0.6 0.3 .. .. 27 3 Zimbabwe 4.7 .. .. .. 62 51 1.2 1.0 3 .. 2 20 World 2.3 w 2.4 w 10.2 w 10.2 w 29,353 s 27,469 s 1.1 w 0.9 w .. s .. s 18,556 s 22,681 s Low income 2.3 1.7 .. .. 4,795 4,261 1.3 1.0 .. .. 691 480 Middle income 2.1 2.0 15.6 12.9 18,417 17,484 1.0 0.8 .. .. 8,598 11,720 Lower middle income 2.2 2.0 17.8 16.1 12,599 11,804 0.8 0.7 1,282 1,231 5,639 5,971 Upper middle income 2.0 2.0 .. 9.7 5,817 5,680 1.5 1.3 .. .. 2,959 6,157 Low & middle income 2.1 2.0 .. 12.9 23,212 21,745 1.0 0.8 .. .. 9,289 12,224 East Asia & Pacific 1.7 1.8 18.6 17.0 7,794 6,817 0.8 0.6 367 428 2,285 2,352 Europe & Central Asia 3.1 2.7 12.8 11.4 4,119 3,373 2.0 1.6 4,994 6,441 .. 2,565 Latin America & Carib. 1.4 1.3 .. .. 2,084 2,399 0.9 0.9 .. .. 963 2,067 Middle East & N. Africa 3.5 2.8 12.3 10.8 3,379 3,505 3.8 3.2 .. .. 2,494 2,647 South Asia 3.1 2.5 19.9 15.3 4,114 4,121 0.8 0.7 19 21 1,459 3,160 Sub-Saharan Africa 1.9 1.3 .. .. 1,724 1,530 0.7 0.5 .. .. 583 608 High income 2.3 2.6 10.1 10.2 6,141 5,724 1.2 1.0 11,681 .. 9,267 10,457 Euro area 1.8 1.6 4.8 4.5 1,862 1,714 1.3 1.1 .. .. 2,254 2,429 Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2009). a. Estimates differ from official statistics of the government of China, which has published the following estimates: military expenditure as 1.2 percent of GDP in 2000 and 1.4 percent in 2007 and 7.6 percent of central government expenditure in 2000 and 7.1 percent in 2007 (see National Bureau of Statistics of China, www.stats.gov.cn). 318 2010 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers About the data Definitions Although national defense is an important function of completeness of data, data on military expenditures · Military expenditures are SIPRI data derived from government and security from external threats that are not strictly comparable across countries. More the NATO definition, which includes all current and contributes to economic development, high levels of information on SIPRI's military expenditure project capital expenditures on the armed forces, including military expenditures for defense or civil conflicts bur- can be found at www.sipri.org/contents/milap/. peacekeeping forces; defense ministries and other den the economy and may impede growth. Data on Data on armed forces refer to military personnel on government agencies engaged in defense projects; military expenditures as a share of gross domestic active duty, including paramilitary forces. Because paramilitary forces, if judged to be trained and product (GDP) are a rough indicator of the portion of data exclude personnel not on active duty, they equipped for military operations; and military space national resources used for military activities and of underestimate the share of the labor force working activities. Such expenditures include military and civil the burden on the national economy. As an "input" for the defense establishment. Governments rarely personnel, including retirement pensions and social measure military expenditures are not directly related report the size of their armed forces, so such data services for military personnel; operation and main- to the "output" of military activities, capabilities, or typically come from intelligence sources. tenance; procurement; military research and develop- security. Comparisons of military spending between SIPRI's Arms Transfers Project collects data on ment; and military aid (in the military expenditures countries should take into account the many fac- arms transfers from open sources. Since publicly of the donor country). Excluded are civil defense and tors that influence perceptions of vulnerability and available information is inadequate for tracking all current expenditures for previous military activities, risk, including historical and cultural traditions, the weapons and other military equipment, SIPRI covers such as for veterans benefits, demobilization, and length of borders that need defending, the quality of only what it terms major conventional weapons. Data weapons conversion and destruction. This definition relations with neighbors, and the role of the armed cover the supply of weapons through sales, aid, gifts, cannot be applied for all countries, however, since forces in the body politic. and manufacturing licenses; therefore the term arms that would require more detailed information than is Data on military spending reported by governments transfers rather than arms trade is used. SIPRI data available about military budgets and off-budget mili- are not compiled using standard definitions. They are also cover weapons supplied to or from rebel forces tary expenditures (for example, whether military bud- often incomplete and unreliable. Even in countries in an armed conflict as well as arms deliveries for gets cover civil defense, reserves and auxiliary forces, where the parliament vigilantly reviews budgets and which neither the supplier nor the recipient can be police and paramilitary forces, and military pensions). spending, military expenditures and arms transfers identified with acceptable certainty; these data are · Armed forces personnel are active duty military per- rarely receive close scrutiny or full, public disclosure available in SIPRI's database. sonnel, including paramilitary forces if the training, (see Ball 1984 and Happe and Wakeman-Linn 1994). SIPRI's estimates of arms transfers are designed organization, equipment, and control suggest they Therefore, SIPRI has adopted a definition of military as a trend-measuring device in which similar weap- may be used to support or replace regular military expenditure derived from the North Atlantic Treaty ons have similar values, reflecting both the value and forces. Reserve forces, which are not fully staffed or Organization (NATO) definition (see Definitions). The quality of weapons transferred. SIPRI cautions that operational in peace time, are not included. The data data on military expenditures as a share of GDP and the estimated values do not reflect financial value also exclude civilians in the defense establishment as a share of central government expenditure are (payments for weapons transferred) because reliable and so are not consistent with the data on military estimated by the Stockholm International Peace data on the value of the transfer are not available, expenditures on personnel. · Arms transfers cover Research Institute (SIPRI). Central government and even when values are known, the transfer usually the supply of military weapons through sales, aid, expenditures are from the International Monetary includes more than the actual conventional weapons, gifts, and manufacturing licenses. Weapons must be Fund (IMF). Therefore the data in the table may such as spares, support systems, and training, and transferred voluntarily by the supplier, have a military differ from comparable data published by national details of the financial arrangements (such as credit purpose, and be destined for the armed forces, para- governments. and loan conditions and discounts) are usually not military forces, or intelligence agencies of another SIPRI's primary source of military expenditure data known. country. The trends shown in the table are based on is official data provided by national governments. Given these measurement issues, SIPRI's method actual deliveries only. Data cover major conventional These data are derived from national budget docu- of estimating the transfer of military resources weapons such as aircraft, armored vehicles, artil- ments, defense white papers, and other public docu- includes an evaluation of the technical parameters lery, radar systems, missiles, and ships designed for ments from official government agencies, including of the weapons. Weapons for which a price is not military use. Excluded are transfers of other military governments' responses to questionnaires sent by known are compared with the same weapons for equipment such as small arms and light weapons, SIPRI, the United Nations, or the Organization for which actual acquisition prices are available (core trucks, small artillery, ammunition, support equip- Security and Co-operation in Europe. Secondary weapons) or for the closest match. These weapons ment, technology transfers, and other services. sources include international statistics, such as are assigned a value in an index that reflects their Data sources those of NATO and the IMF's Government Finance military resource value in relation to the core weap- Statistics Yearbook. Other secondary sources include ons. These matches are based on such characteris- Data on military expenditures are from SIPRI's country reports of the Economist Intelligence Unit, tics as size, performance, and type of electronics, Yearbook 2009: Armaments, Disarmament, and country reports by IMF staff, and specialist journals and adjustments are made for secondhand weapons. International Security. Data on armed forces per- and newspapers. More information on SIPRI's Arms Transfers Project sonnel are from the International Institute for Stra- In the many cases where SIPRI cannot make is available at www.sipri.org/contents/armstrad/. tegic Studies' The Military Balance 2010. Data on independent estimates, it uses the national data arms transfers are from SIPRI's Arms Transfer provided. Because of the differences in defi ni- Project (www.sipri.org/contents/armstrad/). tions and the difficulty in verifying the accuracy and 2010 World Development Indicators 319 5.8 Fragile situations International Peacebuilding and Battle- Intentional Military Business environment Development peacekeeping related homicides expenditures Association deaths Resource Troops, police, per 100,000 people Allocation Losses due to Firms formally and military Index observers CTS and theft, robbery, registered when Operation 1­6 national vandalism, operations namea number (low to high) number WHO sources % of GDP and arson started December December Survey 2008 2009 2009 2000­08b 2004 2004 2008 year % of sales % of firms Afghanistan 2.6 UNAMA 20 26,589 3.4 .. 2.2 2008 1.5 88.0 Angola 2.7 .. 3,534 36.0 5.2e 2.9 2006 0.4 .. Bosnia and Herzegovina 3.7 .. 0 1.9 1.8f 1.4 2009 0.2 98.6 Burundi 3.0 BINUB 15 4,937 35.4 .. 3.8 2006 1.1 .. Cameroon 3.2 .. 0 16.1 5.8g 1.5 2009 1.3 82.1 Central African Republic 2.5 MINURCATh 2,777 350 29.1 .. 1.6 .. .. Chad 2.5 MINURCAT .. 4,328 19.0 .. 1.0 2009 2.5 77.1 Comoros 2.3 .. 0 9.3 .. .. .. .. Congo, Rep. 2.7 .. 116 18.8 .. 1.3 2009 3.3 84.3 Côte d'Ivoire 2.7 UNOCI 8,536 1,265 45.7 .. 1.5 2009 3.4 56.4 Congo, Dem. Rep. 2.7 MONUC 20,509 75,118 35.2 .. 1.4 2006 2.0 .. Djibouti 3.1 .. 0 3.5 .. 4.1 .. .. Eritrea 2.3 .. 57 15.9 .. .. 2009 .. 100.0 Gambia, The 3.2 .. 0 13.5 .. 0.7 2006 2.7 .. Georgia 4.4 .. 648 3.7 6.2i 8.1 2008 0.7 99.6 Guinea 3.0 .. 1,174 17.3 .. .. 2006 2.0 .. Guinea-Bissau 2.6 .. 0 16.3 .. .. 2006 1.1 .. Haiti 2.9 MINUSTAH 9,057 244 5.3 33.9j .. .. .. Kiribati 3.0 .. 0 6.5 .. .. .. .. Kosovo .. UNMIK 17 0 .. .. .. 2009 0.3 89.2 Liberia .. UNMIL 10,947 2,487 16.8 .. 0.5 2009 2.8 73.8 Myanmar .. .. 2,833 15.7 .. .. .. .. Nepal 3.3 UNMIN 72 11,520 8.0 2.1f 1.5 2009 0.9 94.0 Papua New Guinea 3.3 .. 0 15.2 .. 0.4 .. .. São Tomé and Príncipe 3.0 .. 0 5.4 .. .. .. .. Sierra Leone 3.1 .. 212 34.0 2.1k 2.3 2009 0.8 89.2 Solomon Islands 2.8 RAMSIl 572 0 1.5 .. .. .. .. Somalia .. .. 3,983 3.3 .. .. .. .. Sudan 2.5 UNMISm 10,262 12,363 28.6 .. 4.2 .. .. Tajikistan 3.2 .. 0 2.2 2.4i .. 2008 0.3 92.7 Timor-Leste 2.8 UNMIT 1,552 0 11.7 .. .. .. .. Togo 2.7 .. 0 13.7 .. 2.0 2009 2.4 75.8 Tonga 3.2 .. 0 1.0 .. 1.5 .. .. West Bank and Gaza .. .. 0 .. 4.0 .. 2006 1.2 .. Western Saharan .. MINURSO 232 .. .. .. .. .. .. Yemen, Rep. 3.2 .. 0 2.5 3.2o 4.5 .. .. Zimbabwe 1.4 .. 0 32.9 8.4i .. .. .. Fragile situations .. 151,759 s .. .. 3.0 w Low income .. 147,275 .. .. 1.7 Note: The countries with fragile situations in the table are International Development Association­eligible countries with a 3.2 or lower harmonized average of the World Bank's Country Policy and Institutional Assessment (CPIA) rating and the corresponding rating by a regional development bank or that have had a UN or regional peacebuilding mission (for example, by the African Union, European Union, or Organization of American States) or peacekeeping mission (for example, by the African Union, European Union, North Atlantic Treaty Organization, or Organization of American States) during the last three years. Because fragility is an evolving concept, this definition will be updated as understanding changes. a. UNAMA is United Nations Assistance Mission in Afghanistan, BINUB is Bureau Intégré des Nations Unies au Burundi (United Nations Integrated Office in Burundi), MINURCAT is United Nations Mission in the Central African Republic and Chad, UNOCI is United Nations Operation in Côte d'Ivoire, MONUC is United Nations Organization Mission in DR Congo, MINUSTAH is United Nations Stabilization Mission in Haiti, UNMIK is Interim Administration Mission in Kosovo, UNMIL is United Nations Mission in Liberia, UNMIN is United Nations Mission in Nepal, RAMSI is Regional Assistance Mission to Solomon Islands, UNMIS is United Nations Missions in Sudan, UNMIT is United Nations Integrated Mission in Timor-Leste, and MINURSO is United Nations Mission for the Referendum in Western Sahara. b. Total over the period. c. Data are for the most recent year available. d. Average over the period. e. Data are from Interpol. f. Data are from UNODC's 10th UN Survey of Crime Trends and are for 2005. g. National Statistical Office of Cameroon. h. Includes peacekeepers in Chad. i. Data are from UNODC's 9th UN Survey of Crime Trends. j. Data are for 2001. k. National Statistical Office of Sierra Leone. l. Data are for 2007. m. Does not include 19,949 troops, police, and military observers from the African Union-UN Hybrid Operation in Darfur. n. The designation Western Sahara is used instead of Former Spanish Sahara (the designation used on the maps on the front and back cover flaps) because it is the designation used by the UN operation established there by Security Council resolution 690/1991. Neither designation expresses any World Bank view on the status of the territory so-identified. o. National Statistical Office of Yemen. 320 2010 World Development Indicators 5.8 STATES AND MARKETS Fragile situations Children in Refugees Internally Access to Access to Maternal mortality Under-five Depth of Primary employment displaced an improved improved ratio mortality hunger gross persons water sanitation rate enrollment source facilities ratio per 100,000 live births kilocalories By country By country % of % of National Modeled per person % of relevant % of of origin of asylum number population population estimates estimates per 1,000 per day age group Survey children year ages 7­14 2008 2008 2008 2006 2006 2000­08c 2005 2008 2004­06d 2008 Afghanistan .. 37 2,833,128 230,670 22 30 1,600 1,800 257 .. 106 Angola 2001 30.1 12,710 171,393 .. 51 50 .. 1,400 220 290 .. Bosnia and Herzegovina 2005 10.6 7,257 74,366 124,529 99 95 23 3 15 140 111 Burundi 2000 37.0 21,093 281,592 100,000 71 41 615 1,100 168 360 136 Cameroon 2001 15.9 81,037 13,870 .. 70 51 669 1,000 131 160 111 Central African Republic 2000 67.0 7,429 125,106 197,000 66 31 543 980 173 280 77 Chad 2004 60.4 330,510 55,105 166,718 48 9 1,099 1,500 209 290 83 Comoros .. 1 378 .. 85 35 380 400 105 340 .. Congo, Rep. 2005 30.1 24,779 19,925 3,492 71 20 781 740 127 250 114 Côte d'Ivoire 2006 45.7 24,811 22,227 683,956 81 24 543 810 114 190 74 Congo, Dem. Rep. 2000 39.8 155,162 367,995 1,460,102 46 31 1,838 1,100 199 430 90 Djibouti .. 9,228 650 .. 92 67 546 650 95 220 55 Eritrea .. 4,862 186,398 .. 60 5 .. 450 58 350 52 Gambia, The 2005 43.5 14,836 1,352 .. 86 52 730 690 106 240 86 Georgia .. 996 12,598 293,048 99 93 98 66 30 180 107 Guinea .. 21,488 9,495 .. 70 19 980 910 146 130 90 Guinea-Bissau 2000 67.5 7,884 1,065 .. 57 33 405 1,100 195 240 120 Haiti 2005 33.4 3 23,066 .. 58 19 1,150 670 72 430 .. Kiribati .. .. 38 .. 65 33 56 .. 48 140 .. Kosovo .. .. .. .. .. .. .. .. .. .. .. Liberia 2007 37.4 10,224 75,213 .. 64 32 994 1,200 145 310 91 Myanmar .. .. 184,413 67,290 80 82 316 380 98 300 115 Nepal 1999 47.2 124,832 4,189 50,000 89 27 281 830 51 190 124 Papua New Guinea .. 10,006 46 .. 40 45 .. 470 69 .. 55 São Tomé and Príncipe .. .. 35 .. 86 24 248 .. 98 110 130 Sierra Leone 2005 62.7 7,826 32,536 .. 53 11 2,657 2,100 194 390 158 Solomon Islands .. .. 52 .. 70 32 .. 220 36 130 107 Somalia 2006 43.5 1,842 561,155 1,277,200 29 23 1,044 1,400 200 .. .. Sudan .. 181,605 419,248 1,201,040 70 35 1,107 450 109 240 69 Tajikistan 2005 8.9 1,799 544 .. 67 92 179 170 64 190 102 Timor-Leste .. 1 7 15,860 62 41 .. 380 93 230 107 Togo 2006 39.6 9,377 16,750 .. 59 12 .. 510 98 280 105 Tonga .. .. 7 .. 100 96 214 .. 19 .. 112 West Bank and Gaza .. 1,836,123 340,026 .. 89 80 .. .. 27 180 80 Western Saharan .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. 140,169 1,777 100,000 66 46 365 430 69 270 85 Zimbabwe .. 3,468 16,841 .. 81 46 1,655 880 96 310 104 Fragile situations 3,051,394 s 5,852,585 s .. 63 w 41 w .. 958 w 148 w 285 w .. w Low income 2,024,889 5,386,084 .. 67 38 .. 790 118 277 101 About the data The table focuses on countries with fragile situa- make slower progress toward the Millennium Devel- and is linked to building peace and creating the condi- tions and highlights the links among weak institu- opment Goals. tions that lead to sustained poverty reduction. The tions, poor development outcomes, fragility, and According to the Geneva Declaration on Armed Vio- World Bank and other international development risk of conflict. These countries often have weak lence and Development, more than 740,000 people agencies can help, but countries with fragile situa- institutions that are ill-equipped to handle eco- die each year because of the violence associated with tions have to build their own institutions tailored to nomic shocks, natural disasters, and illegal trade armed conflict and large- and small-scale criminal- their own needs. Peacekeeping operations in post- or to resist conflict, which increasingly spills across ity. Recovery and rebuilding can take years, and the conflict situations have been effective in reducing borders. Organized violence, including violent crime, challenges are numerous: infrastructure to be rebuilt, the risks of reversion to conflict. interrupts economic and social development through persistently high crime, widespread health problems, The countries with fragile situations in the table are lost human and social capital, disrupted services, education systems in disrepair, and landmines to be International Development Association­eligible coun- displaced populations and reduced confidence for cleared. Most countries emerging from conflict lack tries with a 3.2 or lower harmonized average of the future investment. As a result, countries with fragile the capacity to rebuild the economy. Thus, capacity World Bank's Country Policy and Institutional Assess- situations achieve lower development outcomes and building is one of the first tasks for restoring growth ment (CPIA) rating and the corresponding rating by a 2010 World Development Indicators 321 5.8 Fragile situations About the data (continued) regional development bank or that have had a UN impose costs on businesses and society. And in Department of Field Support. The UN Charter gives or regional peacebuilding mission (for example, by many developing countries informal businesses oper- the Security Council primary responsibility for main- the African Union, European Union, or Organization ate without licenses. These firms have less access taining international peace and security, including of American States) or peacekeeping mission (for to financial and public services and can engage in the establishment of a UN peacekeeping operation. example, by the African Union, European Union, fewer types of contracts and investments, constrain- · Troops, police, and military observers in peace- North Atlantic Treaty Organization, or Organization of ing growth. The table presents data on the loss of building and peacekeeping refer to people active in American States) during the last three years. Peace- sales due to theft, robbery, vandalism, and arson and peacebuilding and peacekeeping as part of an official building and peacekeeping involve many elements-- on the percentage of firms operating informally. For operation. Peacekeepers deploy to war-torn regions military, police, and civilian--working together to lay further information on enterprise surveys, see About where no one else is willing or able to go to prevent the foundations for sustainable peace. Because the data for table 5.2. conflict from returning or escalating. · Battle-related fragility is an evolving concept, this definition will be As the table shows, the human toll of armed vio- deaths are deaths of members of warring parties in updated as understanding changes. lence across various contexts is severe. Additionally, battle-related confl icts. Typically, battle-related An armed conflict is a contested incompatibility that in countries with fragile situations weak institutional deaths occur in warfare involving the armed forces concerns a government or territory where the use capacity often results in poor performance and fail- of the warring parties (battlefield fighting, guerrilla of armed force between two parties (one of them ure to meet expectations of effective service deliv- activities, and all kinds of bombardments of military the government) results in at least 25 battle-related ery. Failure to deliver water, health, and education units, cities, and villages). The targets are usually deaths in a calendar year. There were 36 active armed services can weaken struggling governments. The the military and its installations or state institutions conflicts in 26 locations in 2008. Separate measures table includes several indicators related to living con- and state representatives, but there is often sub- are presented for intentional homicides--unlawful ditions in fragile situations: children in employment, stantial collateral damage of civilians killed in cross- deaths purposefully inflicted on a person by another refugees, internally displaced persons, access to fire, indiscriminate bombings, and other military person--which exclude deaths arising from armed water and sanitation, maternal and under-five mortal- activities. All deaths--civilian as well as military-- conflict. One measure draws from public health data ity, depth of hunger, and primary school enrollment. incurred in such situations are counted as battle- sources, while the other draws from estimates by For more detailed information on these indicators, related deaths. · Intentional homicides are esti- the United Nations Office on Drugs and Crime, which see About the data for table 2.6 (children in employ- mates of unlawful homicides purposely inflicted as obtains data from criminal justice sources. Data from ment), table 6.18 (refugees), table 2.18 (access to a result of domestic disputes, interpersonal violence, these two sources measure different phenomena and improved water and sanitation), table 2.19 (maternal violent conflicts over land resources, intergang vio- are therefore unlikely to provide identical numbers. mortality), table 2.22 (under-five mortality), and table lence over turf or control, and predatory violence and Data on military expenditures reported by govern- 2.12 (primary school enrollment). killing by armed groups. Intentional homicide does ments are not compiled using standard definitions not include all intentional killing; the difference is Definitions and are often incomplete and unreliable. Even in usually in the organization of the killing. Individuals countries where the parliament vigilantly reviews · International Development Association Resource or small groups usually commit homicide, whereas budgets and spending, military expenditures and Allocation Index is from the Country Policy and Insti- killing in armed conflict is usually committed by fairly arms transfers rarely receive close scrutiny or full tutional Assessment rating, which is the average cohesive groups of up to several hundred members public disclosure. Data in the table are from the score of four clusters of indicators designed to mea- and is thus usually excluded. Data from the World Stockholm International Peace Research Institute sure macroeconomic, governance, social, and struc- Health Organization (WHO) are from public health (SIPRI), which has adopted a definition of military tural dimensions of development: economic manage- sources; data from the United Nations Survey of expenditure derived from the North Atlantic Treaty ment, structural policies, policies for social inclusion Crime Trends and Operations of Criminal Justice Sys- Organization (NATO) defi nition (see Definitions). and equity, and public sector management and insti- tems (CTS) and national sources are based on crimi- Therefore, the data in the table may differ from com- tutions (see table 5.9). Countries are rated on a nal justice sources. · Military expenditures are parable data published by national governments. For scale of 1 (low) to 6 (high). · Peacebuilding and SIPRI data derived from the NATO definition, which a more detailed discussion of military expenditures, peacekeeping refer to operations that engage in includes all current and capital expenditures on the see About the data for table 5.7. peacebuilding (reducing the risk of lapsing or relaps- armed forces, including peacekeeping forces; Along with public sector efforts, private sector ing into conflict by strengthening national capacities defense ministries and other government agencies development and investment, especially in competi- for conflict management and laying the foundation engaged in defense projects; paramilitary forces, if tive markets, has tremendous potential to contribute for sustainable peace and development) or peace- judged to be trained and equipped for military opera- to growth and poverty reduction. The World Bank's keeping (providing essential security to preserve the tions; and military space activities. Such expendi- Enterprise Surveys review the business environment, peace where fighting has been halted and to assist tures include military and civil personnel, including assessing constraints to private sector growth and in implementing agreements achieved by the peace- retirement pensions and social services for military enterprise performance. In some countries doing makers). UN peacekeeping operations are authorized personnel; operation and maintenance; procure- business requires informal payments to "get things by the UN Secretary-General and planned, managed, ment; military research and development; and mili- done" in customs, taxes, licenses, regulations, ser- directed, and supported by the United Nations tary aid (in the military expenditures of the donor vices, and the like. Crime, theft, and disorder also Department of Peacekeeping Operations and the country). Excluded are civil defense and current 322 2010 World Development Indicators 5.8 STATES AND MARKETS Fragile situations expenditures for previous military activities, such as improved source, such as piped water into a dwelling, from the Uppsala Conflict Data Program (www. for veterans benefits, demobilization, and weapons public tap, tubewell, protected dug well, and rainwa- pcr.uu.se/research/UCDP/index.htm). Data on conversion and destruction. This definition cannot ter collection. Reasonable access is the availability intentional homicides are from the UN Office on be applied to all countries, however, since the neces- of at least 20 liters a person a day from a source Drugs and Crime's International Homicide Statis- sary detailed information is missing in some cases within 1 kilometer of the dwelling. · Access to tics database. Data on military expenditures are for military budgets and off-budget military expendi- improved sanitation facilities refers to people with from SIPRI's Yearbook 2009: Armaments, Disar- tures (for example, whether military budgets cover at least adequate access to excreta disposal facili- mament, and International Security and database civil defense, reserves and auxiliary forces, police ties that can effectively prevent human, animal, and (www.sipri.org/databases/milex). Data on the and paramilitary forces, and military pensions). insect contact with excreta. Improved facilities range business environment are from the World Bank's · Survey year is the year in which the underlying data from protected pit latrines to flush toilets. · Mater- Enterprise Surveys (www.enterprisesurveys.org). were collected. · Losses due to theft, robbery, van- nal mortality ratio is the number of women who die Data on children in employment are estimates pro- dalism, and arson are the estimated losses from from pregnancy-related causes during pregnancy and duced by the Understanding Children's Work proj- those causes that occurred on business establish- childbirth per 100,000 live births. National estimates ect based on household survey data sets made ment premises calculated as a percentage of annual are based on national surveys, vital registration available by the International Labour Organiza- sales. · Firms formally registered when operations records, and surveillance data or are derived from tion's International Programme on the Elimination started are the percentage of firms formally regis- community and hospital records. Modeled estimates of Child Labour under its Statistical Monitoring tered when they started operations in the country. are based on an exercise by the WHO, United Nations Programme on Child Labour, UNICEF under its Mul- · Children in employment are children involved in Children's Fund (UNICEF), United Nations Population tiple Indicator Cluster Survey program, the World any economic activity for at least one hour in the Fund (UNFPA), and the World Bank. See About the Bank under its Living Standards Measurement reference week of the survey. · Refugees are people data for table 2.19 for further details. · Under-five Study program, and national statistical offi ces who are recognized as refugees under the 1951 Con- mortality rate is the probability per 1,000 that a (see table 2.6). Data on refugees and internally vention Relating to the Status of Refugees or its newborn baby will die before reaching age 5, if sub- displaced persons are from the UNHCR's Statisti- 1967 Protocol, the 1969 Organization of African ject to current age-specific mortality rates · Depth cal Yearbook 2008, complemented by statistics Unity Convention Governing the Specific Aspects of of hunger, or the intensity of food deprivation, indi- on Palestinian refugees under the mandate of the Refugee Problems in Africa, people recognized as cates how much people who are food-deprived fall United Nations Relief and Works Agency for Pales- refugees in accordance with the UN Refugee Agency short of minimum food needs in terms of dietary tine Refugees in the Near East as published on its (UNHCR) statute, people granted refugee-like human- energy. It is measured by comparing the average website (www.unrwa.org). Data on access to water itarian status, and people provided temporary protec- amount of dietary energy that undernourished people and sanitation are from the WHO and UNICEF's tion. Asylum seekers--people who have applied for get from the foods they eat with the minimum amount Progress on Drinking Water and Sanitation (2008). asylum or refugee status and who have not yet of dietary energy they need to maintain body weight National estimates of maternal mortality are from received a decision, or who are registered as asylum and undertake light activity. Depth of hunger is low UNICEF's The State of the World's Children 2009 seekers --are excluded. Palestinian refugees are when it is less than 200 kilocalories per person per and Childinfo and Demographic and Health Sur- people (and their descendants) whose residence was day and high when it is above 300. · Primary gross veys by Macro International. Modeled estimates Palestine between June 1946 and May 1948 and enrollment ratio is the ratio of total enrollment, for maternal mortality are from WHO, UNICEF, who lost their homes and means of livelihood as a regardless of age, to the population of the age group UNFPA, and the World Bank's Maternal Mortality result of the 1948 Arab-Israeli conflict. · Country of that officially corresponds to the primary level of edu- in 2005 (2007). Data on under-five mortality esti- origin refers to the nationality or country of citizen- cation. Primary education provides children with mates by the Inter-agency Group for Child Mortality ship of a claimant. · Country of asylum is the country basic reading, writing, and mathematics skills along Estimation (which comprises UNICEF, WHO, the where an asylum claim was filed and granted. · Inter- with an elementary understanding of such subjects World Bank, United Nations Population Division, nally displaced persons are people or groups of as history, geography, natural science, social sci- and other universities and research institutes) people who have been forced to leave their homes ence, art, and music. and are based mainly on household surveys, cen- or places of habitual residence, in particular as a suses, and vital registration data, supplemented result of armed conflict, or to avoid the effects of by the World Bank's Human Development Network Data sources armed conflict, situations of generalized violence, estimates based on vital registration and sample violations of human rights, or natural or human-made Data on the International Development Associa- registration data (see table 2.22). Data on depth disasters and who have not crossed an international tion Resource Allocation Index are from the World of hunger are from the Food and Agriculture Orga- border. UNHCR statistics for this population include Bank Group's International Development Asso- nization's Food Security Statistics (www.fao.org/ conflict-generated internally displaced persons to ciation database (www.worldbank.org/ida). Data economic/ess/food-security-statistics/en/). Data whom UNHCR extends protection or assistance and on peacebuilding and peacekeeping operations on primary gross enrollment are from the United people in an internally displaced person­like situa- are from the UN Department of Peacekeeping Nations Educational, Scientific, and Cultural Orga- tion. · Access to an improved water source refers Operations. Data on battle-related deaths are nization's Institute for Statistics. to people with reasonable access to water from an 2010 World Development Indicators 323 5.9 Public policies and institutions International Economic management Structural policies Development 1­6 (low to high) 1­6 (low to high) Association Resource Allocation Index 1­6 Business (low to high) Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan 2.6 3.5 3.0 3.0 3.2 2.5 2.5 2.5 2.5 Angola 2.7 3.0 3.0 3.0 3.0 4.0 2.5 2.0 2.8 Armenia 4.4 5.5 5.0 6.0 5.5 4.5 4.0 4.0 4.2 Azerbaijan 3.8 4.0 4.5 5.0 4.5 4.0 3.5 4.0 3.8 Bangladesh 3.5 4.0 4.0 4.5 4.2 3.5 3.0 3.5 3.3 Benin 3.6 4.5 4.0 3.5 4.0 4.0 3.5 3.5 3.7 Bhutan 3.9 4.5 4.5 4.5 4.5 3.0 3.0 3.5 3.2 Bolivia 3.8 4.0 4.0 4.5 4.2 5.0 4.0 2.5 3.8 Bosnia and Herzegovina 3.7 4.0 3.5 4.0 3.8 4.0 4.0 4.0 4.0 Burkina Faso 3.7 4.5 4.5 4.0 4.3 4.0 3.0 3.5 3.5 Burundi 3.0 3.5 3.5 3.0 3.3 3.5 2.5 2.5 2.8 Cambodia 3.3 4.5 3.5 3.5 3.8 4.0 2.5 3.5 3.3 Cameroon 3.2 4.0 4.0 3.0 3.7 3.5 3.0 3.0 3.2 Cape Verde 4.2 4.5 4.5 4.5 4.5 4.0 4.0 3.5 3.8 Central African Republic 2.5 3.5 3.0 2.0 2.8 3.5 2.5 2.0 2.7 Chad 2.5 2.5 2.5 3.0 2.7 3.0 3.0 2.5 2.8 Comoros 2.3 2.5 1.5 2.0 2.0 3.0 2.5 2.5 2.7 Congo, Dem. Rep. 2.7 3.5 3.5 2.5 3.2 4.0 2.0 2.0 2.7 Congo, Rep. 2.7 3.5 2.5 2.5 2.8 3.5 2.5 2.5 2.8 Côte d'Ivoire 2.7 3.0 2.5 2.0 2.5 4.0 3.0 3.0 3.3 Djibouti 3.1 3.5 3.0 2.5 3.0 4.0 3.5 3.5 3.7 Dominica 3.9 4.0 4.5 3.0 3.8 4.0 4.0 4.5 4.2 Eritrea 2.3 2.0 2.0 2.5 2.2 1.5 1.0 2.0 1.5 Ethiopia 3.4 2.5 4.0 3.5 3.3 3.0 3.0 3.5 3.2 Gambia, The 3.2 4.0 3.5 3.0 3.5 3.5 3.0 3.5 3.3 Georgia 4.4 4.5 4.5 5.0 4.7 6.0 3.5 5.5 5.0 Ghana 3.9 3.5 3.5 4.0 3.7 4.0 4.0 4.0 4.0 Grenada 3.7 3.5 2.5 3.0 3.0 4.5 4.0 4.0 4.2 Guinea 3.0 3.0 3.5 2.5 3.0 4.0 3.0 3.0 3.3 Guinea-Bissau 2.6 2.0 2.5 1.0 1.8 4.0 3.0 2.5 3.2 Guyana 3.4 3.5 3.5 4.0 3.7 4.0 3.5 3.0 3.5 Haiti 2.9 3.5 3.5 2.5 3.2 4.0 3.0 2.5 3.2 Honduras 3.7 3.5 3.5 4.0 3.7 4.5 3.0 4.0 3.8 India 3.8 4.5 3.5 4.5 4.2 3.5 4.0 3.5 3.7 Kenya 3.6 4.0 4.0 4.0 4.0 4.0 3.5 4.0 3.8 Kiribati 3.0 2.5 2.0 5.0 3.2 3.0 3.0 3.0 3.0 Kyrgyz Republic 3.7 4.5 4.0 4.0 4.2 5.0 3.5 4.0 4.2 Lao PDR 3.3 4.5 4.0 3.5 4.0 3.5 2.0 3.0 2.8 About the data The International Development Association (IDA) is the IDA eligible. Country assessments have been car- higher IDA allocation in per capita terms. The IRAI is a part of the World Bank Group that helps the poorest ried out annually since the mid-1970s by World Bank key element in the country performance rating. countries reduce poverty by providing concessional loans staff. Over time the criteria have been revised from a The CPIA exercise is intended to capture the quality and grants for programs aimed at boosting economic largely macroeconomic focus to include governance of a country's policies and institutional arrangements, growth and improving living conditions. IDA funding helps aspects and a broader coverage of social and struc- focusing on key elements that are within the country's these countries deal with the complex challenges they tural dimensions. Country performance is assessed control, rather than on outcomes (such as economic face in meeting the Millennium Development Goals. against a set of 16 criteria grouped into four clus- growth rates) that are influenced by events beyond The World Bank's IDA Resource Allocation Index ters: economic management, structural policies, the country's control. More specifically, the CPIA (IRAI), presented in the table, is based on the results policies for social inclusion and equity, and public measures the extent to which a country's policy and of the annual Country Policy and Institutional Assess- sector management and institutions. IDA resources institutional framework supports sustainable growth ment (CPIA) exercise, which covers the IDA-eligible are allocated to a country on per capita terms based and poverty reduction and, consequently, the effective countries. The table does not include Kosovo, Libe- on its IDA country performance rating and, to a lim- use of development assistance. ria, Myanmar, and Somalia because they were not ited extent, based on its per capita gross national All criteria within each cluster receive equal weight, rated in the 2008 exercise even though they are income. This ensures that good performers receive a and each cluster has a 25 percent weight in the over- 324 2010 World Development Indicators 5.9 STATES AND MARKETS Public policies and institutions International Economic management Structural policies Development 1­6 (low to high) 1­6 (low to high) Association Resource Allocation Index 1­6 Business (low to high) Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2008 2008 2008 2008 2008 2008 2008 2008 2008 Lesotho 3.5 4.0 4.0 4.0 4.0 3.5 3.5 3.0 3.3 Madagascar 3.7 4.0 3.5 4.0 3.8 4.0 3.0 3.5 3.5 Malawi 3.4 3.5 3.5 3.0 3.3 4.0 3.0 3.5 3.5 Maldives 3.4 2.5 2.0 3.0 2.5 4.0 3.5 4.0 3.8 Mali 3.7 4.5 4.0 4.5 4.3 4.0 3.0 3.5 3.5 Mauritania 3.3 3.5 3.0 4.0 3.5 4.0 2.5 3.5 3.3 Moldova 3.8 4.0 4.0 4.0 4.0 4.5 3.5 3.5 3.8 Mongolia 3.3 3.0 2.5 3.0 2.8 4.5 2.5 3.5 3.5 Mozambique 3.7 4.5 4.0 4.5 4.3 4.5 3.5 3.0 3.7 Nepal 3.3 4.0 3.5 3.5 3.7 3.5 3.0 3.0 3.2 Nicaragua 3.8 4.0 4.0 4.5 4.2 4.5 3.5 3.5 3.8 Niger 3.3 4.0 3.5 3.5 3.7 4.0 3.0 3.0 3.3 Nigeria 3.4 4.0 4.5 4.5 4.3 3.5 3.5 3.0 3.3 Pakistan 3.3 2.5 2.5 4.0 3.0 4.0 4.0 4.0 4.0 Papua New Guinea 3.3 4.0 3.5 4.5 4.0 4.5 3.0 3.0 3.5 Rwanda 3.7 4.0 4.0 3.5 3.8 3.5 3.5 3.5 3.5 Samoa 4.0 4.0 4.0 4.5 4.2 4.5 4.0 3.5 4.0 São Tomé and Príncipe 3.0 3.0 3.0 2.5 2.8 4.0 2.5 3.0 3.2 Senegal 3.6 4.0 3.5 4.0 3.8 4.0 3.5 4.0 3.8 Sierra Leone 3.1 4.0 3.5 3.5 3.7 3.5 3.0 3.0 3.2 Solomon Islands 2.8 3.5 2.5 2.5 2.8 3.0 3.0 3.0 3.0 Sri Lanka 3.4 2.5 3.0 3.5 3.0 3.5 3.5 4.0 3.7 St. Lucia 3.9 4.0 3.5 3.5 3.7 4.0 4.0 4.5 4.2 St. Vincent & Grenadines 3.8 4.0 3.5 3.5 3.7 4.0 4.0 4.5 4.2 Sudan 2.5 3.5 3.0 1.5 2.7 2.5 2.5 3.0 2.7 Tajikistan 3.2 3.5 3.5 3.5 3.5 4.0 2.5 3.0 3.2 Tanzania 3.8 4.5 4.5 4.0 4.3 4.0 4.0 3.5 3.8 Timor-Leste 2.8 2.5 3.0 3.5 3.0 4.5 2.5 1.5 2.8 Togo 2.7 3.0 3.0 2.0 2.7 4.0 2.5 3.0 3.2 Tonga 3.2 3.0 3.0 3.0 3.0 3.5 3.0 3.0 3.2 Uganda 3.9 4.5 4.5 4.5 4.5 4.0 3.5 4.0 3.8 Uzbekistan 3.3 4.0 4.0 4.0 4.0 2.5 3.0 3.0 2.8 Vanuatu 3.3 4.0 3.5 4.0 3.8 3.5 3.0 3.0 3.2 Vietnam 3.8 4.5 4.5 4.0 4.3 3.5 3.0 3.5 3.3 Yemen, Rep. 3.2 3.5 3.0 4.0 3.5 4.0 2.0 3.5 3.2 Zambia 3.5 4.0 3.5 3.5 3.7 4.0 3.5 3.5 3.7 Zimbabwe 1.4 1.0 1.0 1.0 1.0 2.0 1.0 1.5 1.5 all score, which is obtained by averaging the aver- should be noted that the criteria are designed in a consistent across countries, the process involves age scores of the four clusters. For each of the 16 developmentally neutral manner. Accordingly, higher two key phases. In the benchmarking phase a small criteria countries are rated on a scale of 1 (low) to scores can be attained by a country that, given its representative sample of countries drawn from all 6 (high). The scores depend on the level of perfor- stage of develop ment, has a policy and institutional regions is rated. Country teams prepare proposals mance in a given year assessed against the criteria, framework that more strongly fosters growth and that are reviewed first at the regional level and then rather than on changes in performance compared poverty reduction. in a Bankwide review process. A similar process is with the previous year. All 16 CPIA criteria contain a The country teams that prepare the ratings are very followed to assess the performance of the remaining detailed description of each rating level. In assess- familiar with the country, and their assessments are countries, using the benchmark countries' scores as ing country performance, World Bank staff evaluate based on country diagnostic studies prepared by the guideposts. The final ratings are determined following the country's performance on each of the criteria World Bank or other development organizations and a Bankwide review. The overall numerical IRAI score and assign a rating. The ratings reflect a variety of on their own professional judgment. An early con- and the separate criteria scores were first publicly indicators, observations, and judgments based on sultation is conducted with country authorities to disclosed in June 2006. country knowledge and on relevant publicly available make sure that the assessments are informed by See IDA's website at www.worldbank.org/ida for indicators. In interpreting the assessment scores, it up-to-date information. To ensure that scores are more information. 2010 World Development Indicators 325 5.9 Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1­6 (low to high) 1­6 (low to high) Quality of budgetary Transparency, Equity Policies and Property and accountability, of public Building Social institutions for rights and financial Efficiency Quality and corruption Gender resource human protection environmental rule-based manage- of revenue of public in the public equality use resources and labor sustainability Average governance ment mobilization administration sector Average 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan 2.0 2.5 3.0 2.5 2.5 2.5 1.5 3.0 2.5 2.0 2.0 2.2 Angola 3.0 2.5 2.5 2.5 3.0 2.7 2.0 2.5 2.5 2.5 2.5 2.4 Armenia 4.5 4.5 4.0 4.5 3.0 4.1 3.5 4.5 3.5 4.0 3.0 3.7 Azerbaijan 4.0 4.0 4.0 4.0 3.0 3.8 3.0 4.0 3.5 3.0 2.5 3.2 Bangladesh 4.0 3.5 4.0 3.5 3.0 3.6 3.0 3.0 3.0 3.0 3.0 3.0 Benin 3.5 3.0 3.5 3.0 3.5 3.3 3.0 3.5 3.5 3.0 3.5 3.3 Bhutan 4.0 4.0 4.0 3.5 4.5 4.0 3.5 3.5 4.0 4.0 4.0 3.8 Bolivia 4.0 4.0 4.0 3.5 3.5 3.8 2.5 3.5 4.0 3.0 3.5 3.3 Bosnia and Herzegovina 4.5 3.0 3.5 3.5 3.5 3.6 3.0 3.5 4.0 3.0 3.0 3.3 Burkina Faso 3.5 4.0 3.5 3.5 3.5 3.6 3.5 4.0 3.5 3.5 3.0 3.5 Burundi 4.0 3.5 3.0 3.0 3.0 3.3 2.5 3.0 3.0 2.5 2.0 2.6 Cambodia 4.0 3.0 3.5 3.0 3.0 3.3 2.5 3.0 3.0 2.5 2.5 2.7 Cameroon 3.0 3.0 3.5 3.0 3.0 3.1 2.5 3.0 3.5 3.0 2.5 2.9 Cape Verde 4.5 4.5 4.5 4.5 3.5 4.3 4.0 4.0 3.5 4.0 4.5 4.0 Central African Republic 2.5 2.0 2.0 2.0 2.5 2.2 2.0 2.0 2.5 2.5 2.5 2.3 Chad 2.5 2.5 2.5 2.5 2.0 2.4 2.0 2.0 2.5 2.5 2.0 2.2 Comoros 3.0 2.5 2.5 2.5 2.0 2.5 2.5 1.5 2.5 2.0 2.5 2.2 Congo, Dem. Rep. 3.0 3.0 3.0 3.0 2.5 2.9 2.0 2.5 2.5 2.0 2.0 2.2 Congo, Rep. 3.0 2.5 3.0 2.5 2.5 2.7 2.5 2.5 3.0 2.5 2.5 2.6 Côte d'Ivoire 2.5 1.5 2.5 2.5 2.5 2.3 2.0 2.0 4.0 2.0 2.5 2.5 Djibouti 2.5 3.0 3.5 3.0 3.0 3.0 2.5 3.0 3.5 2.5 2.5 2.8 Dominica 3.5 3.5 4.0 3.5 3.5 3.6 4.0 3.5 4.0 3.5 4.0 3.8 Eritrea 3.5 3.0 3.5 3.0 2.0 3.0 2.5 2.5 3.5 3.0 2.0 2.7 Ethiopia 3.0 4.5 4.0 3.5 3.0 3.6 3.0 4.0 4.0 3.0 2.5 3.3 Gambia, The 3.5 3.0 3.5 2.5 3.5 3.2 3.0 3.0 3.5 3.0 2.0 2.9 Georgia 4.5 4.5 4.5 4.5 3.0 4.2 3.5 4.0 4.5 4.0 3.0 3.8 Ghana 4.0 4.0 4.5 4.0 3.5 4.0 3.5 4.0 4.5 3.5 4.0 3.9 Grenada 5.0 3.5 4.0 3.5 4.0 4.0 3.5 4.0 3.5 3.5 4.0 3.7 Guinea 3.5 3.0 3.0 3.0 2.5 3.0 2.0 3.0 3.0 3.0 2.0 2.6 Guinea-Bissau 2.5 3.0 2.5 2.5 2.5 2.6 2.5 2.5 3.0 2.5 2.5 2.6 Guyana 4.0 3.5 4.0 3.0 3.0 3.5 3.0 3.5 3.5 2.5 3.0 3.1 Haiti 3.0 3.0 2.5 2.5 2.5 2.7 2.0 3.0 2.5 2.5 2.0 2.4 Honduras 4.0 4.0 4.0 3.5 3.5 3.8 3.0 4.0 4.0 3.0 3.0 3.4 India 3.5 4.0 4.0 3.5 3.5 3.7 3.5 4.0 4.0 3.5 3.5 3.7 Kenya 3.0 3.0 3.5 3.0 3.5 3.2 2.5 3.5 4.0 3.5 3.0 3.3 Kiribati 2.5 3.0 2.5 3.0 3.0 2.8 3.5 3.0 3.0 3.0 3.0 3.1 Kyrgyz Republic 4.5 3.5 3.5 3.5 3.0 3.6 2.5 3.5 3.5 3.0 2.5 3.0 Lao PDR 3.5 4.0 3.0 2.5 4.0 3.4 3.0 3.5 3.0 3.0 2.0 2.9 Definitions · International Development Association Resource long-term debt sustainability. · Structural policies protection under law. · Equity of public resource use Allocation Index is obtained by calculating the aver- cluster: Trade assesses how the policy framework assesses the extent to which the pattern of public age score for each cluster and then by averaging fosters trade in goods. · Financial sector assesses expenditures and revenue collection affects the poor those scores. For each of 16 criteria countries are the structure of the financial sector and the poli- and is consistent with national poverty reduction rated on a scale of 1 (low) to 6 (high) · Economic cies and regulations that affect it. · Business regu- priorities. · Building human resources assesses management cluster: Macro economic manage- latory environment assesses the extent to which the national policies and public and private sec- ment assesses the monetary, exchange rate, and the legal, regulatory, and policy environments help tor service delivery that affect the access to and aggregate demand policy framework. · Fiscal policy or hinder private businesses in investing, creating quality of health and education services, including assesses the short- and medium-term sustainability jobs, and becoming more productive. · Policies for prevention and treatment of HIV/AIDS, tuberculosis, of fiscal policy (taking into account monetary and social inclusion and equity cluster: Gender equal- and malaria. · Social protection and labor assess exchange rate policy and the sustainability of the ity assesses the extent to which the country has government policies in social protection and labor public debt) and its impact on growth. · Debt policy installed institutions and programs to enforce laws market regulations that reduce the risk of becoming assesses whether the debt management strategy is and policies that promote equal access for men poor, assist those who are poor to better manage conducive to minimizing budgetary risks and ensuring and women in education, health, the economy, and further risks, and ensure a minimal level of welfare 326 2010 World Development Indicators 5.9 STATES AND MARKETS Public policies and institutions Policies for social inclusion and equity Public sector management and institutions 1­6 (low to high) 1­6 (low to high) Quality of budgetary Transparency, Equity Policies and Property and accountability, of public Building Social institutions for rights and financial Efficiency Quality and corruption Gender resource human protection environmental rule-based manage- of revenue of public in the public equality use resources and labor sustainability Average governance ment mobilization administration sector Average 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Lesotho 4.0 3.0 3.5 3.0 3.0 3.3 3.5 3.0 4.0 3.0 3.5 3.4 Madagascar 3.5 4.0 3.5 3.5 4.0 3.7 3.5 3.5 4.0 3.5 3.5 3.6 Malawi 3.5 3.5 3.0 3.5 3.5 3.4 3.5 3.0 4.0 3.5 3.0 3.4 Maldives 4.0 4.0 4.0 3.5 4.0 3.9 4.0 3.0 4.0 4.0 2.5 3.5 Mali 3.5 3.5 3.5 3.5 3.0 3.4 3.5 3.5 3.5 3.0 3.5 3.4 Mauritania 4.0 3.5 3.5 3.0 3.5 3.5 3.0 3.0 3.5 3.0 2.5 3.0 Moldova 5.0 3.5 4.0 3.5 3.5 3.9 3.5 4.0 3.5 3.5 3.0 3.5 Mongolia 3.5 3.5 4.0 3.5 3.0 3.5 3.0 4.0 3.0 3.5 3.0 3.3 Mozambique 3.5 3.5 4.0 3.0 3.0 3.4 3.0 3.5 4.0 3.0 3.0 3.3 Nepal 3.5 3.5 4.0 3.0 3.0 3.4 2.5 3.0 3.5 3.0 3.0 3.0 Nicaragua 3.5 4.0 3.5 3.5 3.5 3.6 3.0 4.0 4.0 3.0 3.0 3.4 Niger 2.5 3.5 3.0 3.0 3.0 3.0 3.0 3.5 3.5 3.0 3.0 3.2 Nigeria 3.0 3.5 3.0 3.5 3.0 3.2 2.5 3.0 3.0 3.0 3.0 2.9 Pakistan 2.0 3.5 3.5 3.0 3.0 3.0 2.5 3.5 3.0 3.5 2.5 3.0 Papua New Guinea 2.5 3.0 2.5 3.0 2.0 2.6 2.0 3.5 3.5 2.5 3.0 2.9 Rwanda 3.5 4.5 4.5 3.5 3.5 3.9 3.0 4.0 3.5 3.5 3.5 3.5 Samoa 3.5 4.0 4.0 4.0 4.0 3.9 4.0 3.5 4.0 4.0 4.0 3.9 São Tomé and Príncipe 3.0 3.0 3.0 2.5 2.5 2.8 2.5 3.0 3.5 3.0 3.5 3.1 Senegal 3.5 3.5 3.5 3.0 3.5 3.4 3.5 3.0 4.0 3.5 3.0 3.4 Sierra Leone 3.0 3.0 3.5 3.0 2.0 2.9 2.5 3.5 2.5 2.5 2.5 2.7 Solomon Islands 3.0 2.5 3.0 2.5 2.0 2.6 3.0 2.5 2.5 2.0 3.0 2.6 Sri Lanka 4.0 3.5 4.5 3.5 3.5 3.8 3.0 4.0 3.5 3.0 3.0 3.3 St. Lucia 3.5 4.0 4.0 4.0 3.5 3.8 4.0 3.5 4.0 3.5 4.5 3.9 St. Vincent & Grenadines 4.0 3.5 4.0 3.5 3.5 3.7 4.0 3.5 4.0 3.5 4.0 3.8 Sudan 2.0 2.5 2.5 2.5 2.0 2.3 2.0 2.0 3.0 2.5 2.0 2.3 Tajikistan 4.0 3.5 3.0 3.5 3.0 3.4 2.5 3.0 3.0 2.5 2.0 2.6 Tanzania 3.5 4.0 4.0 3.5 3.5 3.7 3.5 3.5 4.0 3.5 3.0 3.5 Timor-Leste 3.5 3.0 2.5 2.0 2.5 2.7 2.0 3.0 3.0 2.5 3.0 2.7 Togo 3.0 2.0 3.0 3.0 2.5 2.7 2.5 2.0 2.5 2.0 2.0 2.2 Tonga 3.0 3.5 4.0 3.0 3.0 3.3 3.5 3.0 3.0 3.5 3.5 3.3 Uganda 3.5 4.0 4.0 3.5 4.0 3.8 3.5 4.0 3.5 3.0 3.0 3.4 Uzbekistan 4.0 3.5 4.0 3.5 3.5 3.7 2.5 3.0 3.5 3.0 1.5 2.7 Vanuatu 3.5 3.5 2.5 2.0 3.0 2.9 3.5 3.5 3.5 3.0 3.0 3.3 Vietnam 4.5 4.5 4.0 3.5 3.5 4.0 3.5 4.0 4.0 3.5 3.0 3.6 Yemen, Rep. 2.0 3.5 3.0 3.5 3.5 3.1 2.5 3.5 3.0 3.0 3.0 3.0 Zambia 3.5 3.5 4.0 3.0 3.5 3.5 3.0 3.5 3.5 3.0 3.0 3.2 Zimbabwe 2.5 1.0 1.0 1.0 2.0 1.5 1.0 1.5 3.5 1.0 1.0 1.6 to all people. · Policies and institutions for envi- and timely and accurate accounting and fiscal report- extent to which public employees within the executive ronmental sustainability assess the extent to which ing, including timely and audited public accounts. are required to account for administrative decisions, environmental policies foster the protection and sus- · Efficiency of revenue mobilization assesses the use of resources, and results obtained. The three tainable use of natural resources and the manage- overall pat tern of revenue mobilization--not only main dimensions assessed are the accountability ment of pollution. · Public sector management and the de facto tax structure, but also revenue from of the executive to oversight institutions and of pub- institutions cluster: Property rights and rule-based all sources as actually collected. · Quality of public lic employees for their performance, access of civil governance assess the extent to which private eco- administration assesses the extent to which civilian society to information on public affairs, and state nomic activity is facilitated by an effective legal sys- central government staff is structured to design and capture by narrow vested interests. tem and rule-based governance structure in which implement government policy and deliver services property and contract rights are reliably respected effectively. · Transparency, accountability, and cor- Data sources and enforced. · Quality of budgetary and financial ruption in the public sector assess the extent to Data on public policies and institutions are from management assesses the extent to which there is which the executive can be held accountable for its the World Bank Group's CPIA database available a comprehensive and credible budget linked to policy use of funds and for the results of its actions by the at www.worldbank.org/ida. priorities, effective financial management systems, elector ate, the legislature, and the judiciary and the 2010 World Development Indicators 327 5.10 Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000­07a 2000­07a 2000­07a 2000­07a 2000­08a 2000­08a 2000­08a 2008 2008 2008 2008 Afghanistan 42,150 29.3 .. .. .. .. .. .. .. .. .. Albania 18,000 39.0 197 2,200 423 51 53 .. .. .. 0 Algeria 108,302 70.2 .. .. 3,572 937 1,562 311 31 2,885 17 Angola 51,429 10.4 166,045 4,709 .. .. .. .. 3 284 71 Argentina 231,374 30.0 .. .. 35,753 .. 12,871 1,997 75 6,147 132 Armenia 7,515 89.8 2,693 434 845 27 354 .. .. .. 7 Australia 815,074 .. 301,550 173,000 9,661 1,526 61,019 6,143 393 51,488 2,212 Austria 107,206 100.0 69,000 26,411 5,755 10,275 18,710 .. 151 9,141 421 Azerbaijan 59,141 49.4 11,786 8,222 2,099 1,047 10,021 .. 12 756 12 Bangladesh 239,226 9.5 .. .. 2,835 5,609 870 1,070 11 1,224 84 Belarus 94,797 88.6 9,353 15,779 5,491 8,188 47,933 .. 6 500 66 Belgium 153,070 78.2 130,868 51,572 3,513 10,403 7,882 10,938 179 5,879 982 Benin 19,000 9.5 .. .. 758 .. 36 .. .. .. .. Bolivia 62,479 7.0 .. .. 2,866 313 1,060 .. 21 1,718 9 Bosnia and Herzegovina 21,846 52.3 .. 300 1,016 78 1,237 .. .. .. .. Botswana 25,798 32.6 .. .. 888 94 674 .. 6 236 0 Brazil 1,751,868 5.5 .. .. 29,817 .. 267,700 6,879 648 58,763 1,807 Bulgaria 40,231 98.4 13,688 11,843 4,159 2,335 4,673 .. 16 1,073 2 Burkina Faso 92,495 4.2 .. .. 622 .. .. .. 1 81 0 Burundi 12,322 10.4 .. .. .. .. .. .. .. .. .. Cambodia 38,257 6.3 201 3 .. .. .. .. 4 211 1 Cameroon 51,346 8.4 .. .. 977 379 978 .. 9 471 26 Canada 1,409,000 39.9 493,814 184,774 57,216 3,056 358,154 4,721 1,200 53,719 1,389 Central African Republic 24,307 .. .. .. .. .. .. .. .. .. .. Chad 40,000 0.8 .. .. .. .. .. .. .. .. .. Chile 79,814 20.2 .. .. 5,898 759 4,296 3,123 109 8,022 1,308 China 3,583,715 70.7 1,150,677 975,420 60,809 772,834 2,511,804 115,061 1,853 191,001 11,386 Hong Kong SAR, China 2,009 100.0 .. .. .. .. .. 24,494 .. .. 8,326 Colombia 164,278 .. 157 39,726 1,663 .. 9,049 1,955 187 12,339 1,100 Congo, Dem. Rep. 153,497 1.8 .. .. 4,007 95 352 .. .. .. .. Congo, Rep. 17,289 5.0 .. .. 795 211 234 .. .. .. .. Costa Rica 36,654 25.5 27 .. .. .. .. 1,005 37 1,024 11 Côte d'Ivoire 80,000 8.1 .. .. 639 .. 675 710 .. .. .. Croatia 29,038 89.1 3,277 10,175 2,722 1,810 3,312 113 25 1,753 2 Cuba 60,856 49.0 5,266 2,133 5,076 1,285 1,351 .. 12 861 32 Czech Republic 128,511 100.0 90,055 46,600 9,487 6,759 15,961 .. 78 4,975 27 Denmark 72,412 100.0 70,635 11,495 2,133 5,843 .. 680 .. .. 1 Dominican Republic 12,600 49.4 .. .. .. .. .. 1,092 .. .. .. Ecuador 43,670 14.8 11,819 5,453 .. .. .. 671 49 2,927 5 Egypt, Arab Rep. 92,370 81.0 .. .. 5,063 40,830 4,188 6,115 58 6,689 195 El Salvador 10,029 19.8 .. .. .. .. .. .. 21 2,280 18 Eritrea 4,010 21.8 .. .. .. .. .. .. .. .. .. Estonia 58,034 28.8 3,190 7,641 816 274 5,683 .. 11 686 1 Ethiopia 42,429 12.8 219,113 2,456 .. .. .. .. 40 2,715 228 Finland 78,889 65.4 71,300 26,400 5,919 4,052 10,777 1,605 114 7,916 543 France 951,125 100.0 775,000 313,000 29,901 88,283 41,530 4,619 828 61,215 6,188 Gabon 9,170 10.2 .. .. 810 99 2,502 .. 6 546 68 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,329 38.6 5,269 586 1,513 774 6,928 .. .. .. 3 Germany 644,471 100.0 966,692 461,900 33,862 76,997 91,178 17,177 1,154 107,942 8,353 Ghana 57,614 14.9 .. .. 953 85 181 .. .. .. .. Greece 117,533 91.8 .. 18,360 2,552 2,003 786 1,769 128 9,443 78 Guatemala 14,095 34.5 .. .. .. .. .. 910 .. .. .. Guinea 44,348 9.8 .. .. .. .. .. .. .. .. .. Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. Honduras 13,600 20.4 .. .. .. .. .. 553 .. .. .. 328 2010 World Development Indicators 5.10 STATES AND MARKETS Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000­07a 2000­07a 2000­07a 2000­07a 2000­08a 2000­08a 2000­08a 2008 2008 2008 2008 Hungary 195,719 37.7 11,784 30,495 7,942 5,927 7,786 .. .. .. 20 India 3,316,452 47.4 .. .. 63,327 769,956 521,371 6,623 592 49,878 1,234 Indonesia 391,009 55.4 .. .. 3,370 14,344 4,390 6,788 345 29,766 395 Iran, Islamic Rep. 172,927 72.8 .. .. 7,335 13,900 21,829 2,000 122 12,029 97 Iraq 45,550 84.3 .. .. 2,032 61 640 .. .. .. .. Ireland 96,602 100.0 .. 15,900 1,919 1,976 103 1,044 .. .. 131 Israel 17,870 100.0 .. .. 1,005 1,968 1,055 2,090 45 4,563 902 Italy 487,700 100.0 97,560 192,700 16,862 46,998 19,918 10,520 383 30,672 1,279 Jamaica 22,121 73.3 .. .. .. .. .. 1,916 .. .. 15 Japan 1,196,999 79.3 947,562 327,632 20,048 255,865 23,032 18,795 655 97,022 8,173 Jordan 7,768 100.0 .. .. 251 .. 789 .. 31 2,355 141 Kazakhstan 93,123 90.3 103,381 53,816 14,205 14,450 214,907 .. 19 1,276 16 Kenya 63,265 14.1 .. 22 1,917 250 1,399 .. 32 2,881 295 Korea, Dem. Rep. 25,554 2.8 .. .. .. .. .. .. .. .. 2 Korea, Rep. 102,061 77.6 97,854 12,545 3,381 32,025 11,566 17,774 250 36,078 8,727 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 5,749 85.0 .. .. .. .. .. 750 .. .. 239 Kyrgyz Republic 34,000 91.1 6,468 819 417 60 849 .. 4 205 2 Lao PDR 29,811 13.4 .. .. .. .. .. .. 10 323 3 Latvia 69,687 100.0 2,664 2,729 2,263 951 17,704 .. .. .. 13 Lebanon 6,970 .. .. .. .. .. .. 945 .. .. 74 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. .. .. .. .. .. .. .. Libya 83,200 57.2 .. .. .. .. .. .. 10 1,214 0 Lithuania 80,715 28.6 42,739 18,134 1,765 398 14,748 .. 12 610 1 Macedonia, FYR 13,840 .. 1,027 8,299 699 148 743 .. .. .. 0 Madagascar 49,827 11.6 .. .. 854 10 1 .. 12 559 12 Malawi 15,451 45.0 .. .. 797 44 33 .. 4 160 2 Malaysia 93,109 79.8 .. .. 1,665 2,268 1,350 15,742 177 22,421 2,444 Mali 18,709 18.0 .. .. .. .. .. .. .. .. .. Mauritania 11,066 26.8 .. .. 728 47 7,622 .. 1 154 0 Mauritius 2,028 98.0 .. .. .. .. .. 381 12 1,257 191 Mexico 360,075 38.2 449,917 209,392 26,677 84 71,136 3,161 266 18,826 483 Moldova 12,755 85.7 1,640 1,577 1,156 485 3,092 .. .. .. 1 Mongolia 49,250 3.5 557 242 1,810 1,400 8,261 .. 6 364 6 Morocco 57,799 62.0 .. 1,212 1,989 3,836 4,959 561 61 4,927 55 Mozambique 30,400 18.7 .. .. 3,116 114 695 .. 11 461 7 Myanmar 27,000 11.9 .. .. .. 4,163 885 .. 30 1,638 3 Namibia 42,237 12.8 47 591 .. .. .. .. 5 452 0 Nepal 17,280 56.9 .. .. .. .. .. .. 7 520 7 Netherlands 126,100 90.0 .. 77,100 2,896 15,313 .. 11,362 263 29,601 4,903 New Zealand 93,748 65.4 .. .. .. .. .. 2,296 220 12,951 921 Nicaragua 18,669 11.4 .. .. .. .. .. .. .. .. .. Niger 18,951 20.7 .. .. .. .. .. .. .. .. .. Nigeria 193,200 15.0 .. .. 3,528 174 77 513 18 1,461 10 Norway 92,920 79.6 60,597 14,966 4,114 2,705 .. .. .. .. .. Oman 48,874 41.3 .. .. .. .. .. 3,428 .. .. .. Pakistan 260,420 65.4 263,788 129,249 7,791 24,731 6,187 1,938 52 5,606 320 Panama 11,643 34.6 .. .. .. .. .. 5,127 .. .. 36 Papua New Guinea 19,600 3.5 .. .. .. .. .. .. 22 905 22 Paraguay 29,500 50.8 .. .. .. .. .. .. 11 466 0 Peru 78,986 13.9 .. .. 2,020 55 627 1,396 68 6,184 230 Philippines 200,037 9.9 .. .. 479 83 .. 4,466 72 9,508 277 Poland 258,910 90.3 27,359 136,490 19,627 17,958 39,200 859 90 4,635 79 Portugal 82,900 86.0 .. 45,032 2,842 3,814 2,550 1,238 159 11,171 347 Puerto Rico 25,645 95.0 .. 10 .. .. .. 1,685 .. .. .. Qatar 7,790 90.0 .. .. .. .. .. .. .. .. 888 2010 World Development Indicators 329 5.10 Transport services Roads Railways Ports Air Passengers Passengers Port Registered carried Goods carried Goods container carrier Total road Paved million hauled Rail lines million hauled traffic departures Passengers Air freight network roads passenger- million total route- passenger- million thousand worldwide carried million km % km ton-km km km ton-km TEU thousands thousands ton-km 2000­07a 2000­07a 2000­07a 2000­07a 2000­08a 2000­08a 2000­08a 2008 2008 2008 2008 Romania 198,817 30.2 7,985 51,531 10,784 6,880 12,861 1,381 55 3,253 6 Russian Federation 933,000 80.9 78,000 199,000 84,158 175,800 2,400,000 3,303 523 37,940 2,400 Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. .. Saudi Arabia 221,372 21.5 .. .. 2,758 337 1,748 4,652 148 16,708 1,383 Senegal 13,576 29.3 .. .. .. 129 384 .. 0 567 0 Serbia 39,184 62.7 3,865 452 4,058 749 4,214 .. .. .. 4 Sierra Leone 11,300 8.0 .. .. .. .. .. .. .. .. 10 Singapore 3,297 100.0 .. .. .. .. .. 29,918 .. .. 7,981 Slovak Republic 43,761 87.0 7,816 22,114 3,592 2,279 9,004 .. 24 2,690 46 Slovenia 38,708 100.0 817 12,112 1,228 834 3,520 .. .. .. 2 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. South Africa 362,099 17.3 .. 434 24,487 13,865 106,014 3,797 157 13,135 761 Spain 666,292 99.0 397,117 132,868 15,046 23,344 10,224 13,248 617 55,214 1,306 Sri Lanka 97,286 81.0 21,067 .. 1,463 4,767 135 3,687 .. .. 325 Sudan 11,900 36.3 .. .. 4,578 34 766 .. 7 618 47 Swaziland 3,594 30.0 .. .. 300 0 2 .. .. .. .. Sweden 427,045 31.7 109,300 40,123 9,830 7,156 11,500 1,312 .. .. .. Switzerland 71,354 100.0 94,250 16,337 3,499 18,367 16,227 .. 159 14,353 1,182 Syrian Arab Republic 40,032 100.0 589 .. 2,139 1,120 2,370 .. 19 1,358 14 Tajikistan 27,767 .. 150 14,572 616 53 1,274 .. 8 683 5 Tanzania 78,891 8.6 .. .. 2,600 b 475b 728b .. 5 203 1 Thailand 180,053 98.5 .. .. 4,429 8,037 3,161 6,586 126 19,993 2,289 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 7,520 31.6 .. .. .. .. .. .. .. .. .. Trinidad and Tobago 8,320 51.1 .. .. .. .. .. 440 15 1,103 49 Tunisia 19,232 65.8 .. 16,611 2,218 1,487 2,197 .. .. .. 19 Turkey 426,951 .. 209,115 177,399 8,699 5,097 10,104 5,218 215 25,505 481 Turkmenistan 24,000 81.2 .. .. 3,181 1,570 10,973 .. 16 1,823 11 Uganda 70,746 23.0 .. .. .. .. .. .. .. .. 34 Ukraine 169,422 97.8 55,446 26,625 21,676 53,056 257,006 1,112 53 3,456 63 United Arab Emirates 4,030 100.0 .. .. .. .. .. 12,254 .. .. .. United Kingdom 420,009 100.0 736,000 166,728 16,321 51,759 12,512 7,081 1,056 104,714 6,284 United States 6,544,257 65.3 7,940,003 1,889,923 227,058 9,935 2,788,230c 40,345 9,054 d 701,780 d 39,314 d Uruguay 77,732 10.0 2,032 .. 2,993 15 284 302 .. .. 4 Uzbekistan 81,600 87.3 .. 1,200 4,230 2,264 21,594 .. 22 2,034 72 Venezuela, RB 96,155 33.6 .. .. 336 .. 81 1,325 138 5,767 2 Vietnam 160,089 47.6 49,372 20,537 3,147 4,659 3,910 4,394 75 9,991 296 West Bank and Gaza 5,147 100.0 .. .. .. .. .. .. .. .. .. Yemen, Rep. 71,300 8.7 .. .. .. .. .. 377 14 1,065 33 Zambia 66,781 22.0 .. .. .. .. .. .. 4 62 0 Zimbabwe 97,267 19.0 .. .. 2,583 .. 1,580 .. 6 264 7 World 37.6 m .. m .. m .. s 2,003 m 4,343 m 486,792 s 24,225 s 2,049,275 s 124,557 s Low income 12.1 .. .. .. .. .. .. 302 26,002 1,057 Middle income 37.6 .. .. .. 1,120 4,296 215,508 6,565 588,155 28,431 Lower middle income 37.6 .. .. .. 1,400 3,127 155,286 3,622 351,921 16,708 Upper middle income 41.8 .. .. .. 944 9,049 60,222 2,942 236,233 11,723 Low & middle income 24.3 .. .. .. 937 3,127 220,972 6,867 614,156 29,488 East Asia & Pacific 34.3 .. .. .. 4,248 3,447 153,036 2,795 287,490 17,220 Europe & Central Asia .. 11,786 15,779 193,080 999 10,063 11,874 1,051 83,250 3,151 Latin America & Carib. 22.0 .. .. .. .. .. 29,887 1,646 125,654 5,163 Middle East & N. Africa 75.6 .. .. .. 1,487 2,284 .. 346 32,523 554 South Asia 56.9 .. .. .. 15,170 3,529 13,319 663 57,228 1,645 Sub-Saharan Africa 11.9 .. .. .. .. .. .. 366 28,012 1,754 High income 87.0 .. 41,824 .. 6,343 10,777 265,820 17,358 1,435,119 95,069 Euro area 100.0 97,840 45,032 126,162 10,275 9,614 74,159 4,000 330,883 24,446 a. Data are for the latest year available in the period shown. b. Includes Tazara railway. c. Refers to class 1 railways only. d. Covers only carriers designated by the U.S. Department of Transportation as major and national air carriers. 330 2010 World Development Indicators 5.10 STATES AND MARKETS Transport services About the data Definitions Transport infrastructure--highways, railways, ports some indication of economic growth in a country. · Total road network covers motorways, highways, and waterways, and airports and air traffic control But when traffic is merely transshipment, much of main or national roads, secondary or regional roads, systems--and the services that fl ow from it are the economic benefit goes to the terminal operator and all other roads in a country. · Paved roads are crucial to the activities of households, producers, and ancillary services for ships and containers rather roads surfaced with crushed stone (macadam) and and governments. Because performance indicators than to the country more broadly. In transshipment hydrocarbon binder or bituminized agents, with con- vary widely by transport mode and focus (whether centers empty containers may account for as much crete, or with cobblestones. · Passengers carried physical infrastructure or the services flowing from as 40 percent of traffic. by road are the number of passengers transported that infrastructure), highly specialized and carefully The air transport data represent the total (interna- by road times kilometers traveled. · Goods hauled specified indicators are required. The table provides tional and domestic) scheduled traffic carried by the by road are the volume of goods transported by road selected indicators of the size, extent, and produc- air carriers registered in a country. Countries submit vehicles, measured in millions of metric tons times tivity of roads, railways, and air transport systems air transport data to ICAO on the basis of standard kilometers traveled. · Rail lines are the length of rail- and of the volume of traffic in these modes as well instructions and definitions issued by ICAO. In many way route available for train service, irrespective of as in ports. cases, however, the data include estimates by ICAO the number of parallel tracks. · Passengers carried Data for transport sectors are not always inter- for nonreporting carriers. Where possible, these esti- by railway are the number of passengers transported nationally comparable. Unlike for demographic sta- mates are based on previous submissions supple- by rail times kilometers traveled. · Goods hauled tistics, national income accounts, and international mented by information published by the air carriers, by railway are the volume of goods transported by trade data, the collection of infrastructure data has such as flight schedules. railway, measured in metric tons times kilometers not been "internationalized." But data on roads are The data cover the air traffic carried on scheduled traveled. · Port container traffic measures the flow collected by the International Road Federation (IRF), services, but changes in air transport regulations of containers from land to sea transport modes and and data on air transport by the International Civil in Europe have made it more diffi cult to classify vice versa in twenty-foot-equivalent units (TEUs), a Aviation Organization (ICAO). traffic as scheduled or nonscheduled. Thus recent standard-size container. Data cover coastal shipping National road associations are the primary source increases shown for some European countries may as well as international journeys. Transshipment traf- of IRF data. In countries where a national road asso- be due to changes in the classification of air traffic fic is counted as two lifts at the intermediate port ciation is lacking or does not respond, other agencies rather than actual growth. For countries with few air (once to off-load and again as an outbound lift) and are contacted, such as road directorates, ministries carriers or only one, the addition or discontinuation includes empty units. · Registered carrier depar- of transport or public works, or central statistical of a home-based air carrier may cause significant tures worldwide are domestic takeoffs and takeoffs offices. As a result, definitions and data collection changes in air traffic. abroad of air carriers registered in the country. · Pas- methods and quality differ, and the compiled data sengers carried by air include both domestic and are of uneven quality. Moreover, the quality of trans- international passengers of air carriers registered port service (reliability, transit time, and condition of in the country. · Air freight is the volume of freight, goods delivered) is rarely measured, though it may be express, and diplomatic bags carried on each flight as important as quantity in assessing an economy's stage (operation of an aircraft from takeoff to its next transport system. landing), measured in metric tons times kilometers Unlike the road sector, where numerous qualified traveled. motor vehicle operators can operate anywhere on the road network, railways are a restricted transport system with vehicles confined to a fixed guideway. Considering the cost and service characteristics, railways generally are best suited to carry--and can Data sources effectively compete for--bulk commodities and con- tainerized freight for distances of 500­5,000 kilo- Data on roads are from the IRF's World Road meters, and passengers for distances of 50­1,000 Statistics, supplemented by World Bank staff kilometers. Below these limits road transport estimates. Data on railways are from a database tends to be more competitive, while above these maintained by the World Bank's Transport and limits air transport for passengers and freight and Urban Development Department, Transport Divi- sea transport for freight tend to be more competi- sion, based on data from the International Union tive. The railways indicators in the table focus on of Railways. Data on port container traffic are from scale and output measures: total route-kilometers, Containerisation International's Containerisation passenger- kilometers, and goods (freight) hauled in International Yearbook. Data on air transport are ton-kilometers. from the ICAO's Civil Aviation Statistics of the World Measures of port container traffi c, much of it and ICAO staff estimates. commodities of medium to high value added, give 2010 World Development Indicators 331 5.11 Power and communications Electric power Telephones Affordability and efficiency Access and use Quality Population Transmission Inter national covered by $ per month Telecom- Mobile cellular Consumption and distribution per 100 people voice traffic a mobile cellular Residential Mobile cellular munications and fi xed-line per capita losses Fixed Mobile cellular minutes per networka fi xed-line prepaid revenuea subscribers kWh % of output linesa subscriptionsa person % tariff a tariff a % of GDP per employeea 2007 2007 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan .. .. 0 27 1 75 .. .. .. 58 Albania 1,186 69 11 100 127 99 4.3 22.7 6.0 871 Algeria 902 18 10 93 18 82 4.6 8.2 2.7 285 Angola 185 14 1 38 .. 40 20.2 11.8 .. .. Argentina 2,659 16 24 117 42 94 4.8 12.5 3.1 1,929 Armenia 1,692 13 20 100 .. 88 5.1 8.4 .. .. Australia 11,249 7 44 103 .. 99 27.5 26.5 3.3 346 Austria 8,033 6 39 130 .. 99 28.7 24.3 1.7 843 Azerbaijan 2,394 14 15 75 .. 99 2.4 15.2 2.4 484 Bangladesh 144 7 1 28 6 90 1.3 1.3 .. .. Belarus 3,345 12 38 84 .. 99 .. .. 2.1 .. Belgium 8,614 5 42 110 .. 100 36.4 21.9 2.8 732 Benin 72 87 2 40 12 80 7.5 15.5 1.0 1,539 Bolivia 515 14 7 50 80 46 22.7 5.9 6.8 376 Bosnia and Herzegovina 2,381 19 27 84 109 99 9.5 9.9 5.5 567 Botswana 1,435 15 7 77 115 99 16.9 8.3 3.0 1,018 Brazil 2,171 16 21 78 .. 91 29.1 37.0 4.6 358 Bulgaria 4,456 11 29 138 27 100 9.2 18.6 5.3 565 Burkina Faso .. .. 1 17 11 61 10.3 16.9 4.0 .. Burundi .. .. 0 6 .. 80 .. .. 3.1 492 Cambodia 94 12 0 29 .. 87 8.0 5.0 .. 1,712 Cameroon 265 14 1 32 4 58 14.8 17.8 3.1 1,050 Canada 16,995 8 55 66 .. 98 32.8 19.2 2.5 .. Central African Republic .. .. 0 4 .. 19 10.6 12.6 .. 293 Chad .. .. 0 17 .. 24 .. .. .. .. Chile 3,318 8 21 88 35 100 27.0 13.7 .. 592 China 2,332 6 26 48 9 97 3.7 3.6 2.9 1,310 Hong Kong SAR, China 5,899 13 59 166 1,435 100 11.3 2.6 3.6 980 Colombia 977 20 18 92 142 83 7.6 9.6 3.7 .. Congo, Dem. Rep. 97 4 .. .. .. .. .. .. .. .. Congo, Rep. 135 93 1 50 .. 53 .. .. .. .. Costa Rica 1,863 10 32 42 120 69 4.6 4.5 1.8 497 Côte d'Ivoire 178 23 2 51 .. 59 22.8 14.8 5.5 1,445 Croatia 3,738 17 42 133 229 100 16.4 18.7 4.6 892 Cuba 1,309 17 10 3 .. 77 13.2 22.7 .. .. Czech Republic 6,496 6 22 132 136 100 30.9 18.6 3.8 812 Denmark 6,670 5 45 125 210 114 28.5 5.8 2.4 543 Dominican Republic 1,378 9 10 72 .. .. 14.4 9.1 .. .. Ecuador 788 44 14 86 3 84 1.1 9.0 4.1 513 Egypt, Arab Rep. 1,384 11 15 51 27 95 3.0 4.7 3.7 856 El Salvador 939 2 18 113 578 95 10.4 10.5 4.8 2,275 Eritrea .. .. 1 2 17 80 .. .. 3.0 117 Estonia 6,273 11 37 188 .. 100 13.7 13.6 4.5 742 Ethiopia 40 9 1 2 2 10 1.5 3.1 1.3 233 Finland 17,162 4 31 129 .. 100 19.3 14.1 2.3 708 France 7,772 6 56 93 242 99 30.9 35.7 2.0 695 Gabon 1,066 18 2 90 .. 79 .. .. 2.0 .. Gambia, The .. .. 3 70 .. 85 4.0 6.0 .. 466 Georgia 1,620 13 14 64 44 98 7.3 8.5 6.9 355 Germany 7,184 5 63 129 .. 99 28.8 10.1 2.5 789 Ghana 259 18 1 50 6 73 4.7 5.9 .. 1,780 Greece 5,628 8 53 123 .. 100 26.7 25.1 3.7 813 Guatemala 558 14 11 109 .. 76 8.7 4.5 .. .. Guinea .. .. 0 39 .. 80 3.4 3.5 .. .. Guinea-Bissau .. .. 0 32 .. 65 .. .. .. .. Haiti 30 37 1 32 .. .. .. .. .. .. Honduras 692 22 11 85 39 90 .. .. 7.1 391 332 2010 World Development Indicators 5.11 STATES AND MARKETS Power and communications Electric power Telephones Affordability and efficiency Access and use Quality Population Transmission Inter national covered by $ per month Telecom- Mobile cellular Consumption and distribution per 100 people voice traffic a mobile cellular Residential Mobile cellular munications and fi xed-line per capita losses Fixed Mobile cellular minutes per networka fi xed-line prepaid revenuea subscribers kWh % of output linesa subscriptionsa person % tariff a tariff a % of GDP per employeea 2007 2007 2008 2008 2008 2008 2008 2008 2008 2008 Hungary 3,977 10 31 122 120 99 30.2 16.1 3.8 1,127 India 542 25 3 30 .. 61 3.5 1.6 2.0 .. Indonesia 566 11 13 62 .. 90 4.5 5.3 .. .. Iran, Islamic Rep. 2,325 19 34 60 .. 95 0.2 3.8 .. 913 Iraq 1,080 7 4 57 0 72 .. .. .. 1,098 Ireland 6,263 8 50 121 .. 99 42.2 18.7 2.5 .. Israel 7,002 3 44 123 413 100 .. .. 1.1 .. Italy 5,713 7 36 151 .. 100 27.4 17.1 2.9 1,657 Jamaica 2,542 13 12 101 39 101 10.8 7.0 1.4 .. Japan 8,474 5 38 86 .. 100 18.3 32.2 3.0 12 Jordan 1,956 14 9 90 66 99 8.3 4.5 6.7 1,105 Kazakhstan 4,448 10 22 95 47 94 .. .. 2.9 253 Kenya 151 15 1 42 3 83 11.6 13.4 6.3 2,298 Korea, Dem. Rep. 764 16 5 0 .. 0 .. .. .. .. Korea, Rep. 8,502 4 44 94 33 94 6.4 14.6 4.7 657 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait 16,198 12 20 107 .. 100 9.3 7.9 .. .. Kyrgyz Republic 1,772 28 9 64 .. 24 .. .. 4.8 311 Lao PDR .. .. 2 33 .. .. 3.9 3.0 .. 748 Latvia 3,064 17 28 99 .. 99 11.9 7.3 4.0 697 Lebanon 2,154 17 18 34 .. 100 10.9 22.2 .. .. Lesotho .. .. 3 28 .. 55 12.5 12.6 .. .. Liberia .. .. 0 19 .. .. .. .. 8.2 .. Libya 3,871 7 16 77 65 71 .. .. .. 1,717 Lithuania 3,414 8 23 150 57 100 15.0 8.7 2.8 402 Macedonia, FYR 3,780 22 22 123 159 100 8.7 13.2 6.3 1,065 Madagascar .. .. 1 25 1 23 18.3 12.4 3.9 2,427 Malawi .. .. 1 12 .. 93 3.3 12.0 3.4 .. Malaysia 3,667 2 16 103 .. 92 5.1 5.9 .. .. Mali .. .. 1 27 2 22 9.9 10.0 4.3 2,059 Mauritania .. .. 2 65 4 62 12.9 9.9 7.7 2,842 Mauritius .. .. 29 81 100 99 5.5 4.4 3.6 .. Mexico 2,036 16 19 71 174 100 22.3 15.0 2.7 840 Moldova 1,319 50 31 67 155 98 3.1 8.9 10.1 294 Mongolia 1,369 12 8 67 5 66 .. .. 6.0 393 Morocco 707 19 9 72 21 98 27.4 22.2 5.1 .. Mozambique 472 14 0 20 .. 44 17.7 10.1 1.2 .. Myanmar 94 29 2 1 .. 10 .. .. .. 90 Namibia 1,541 28 7 49 .. 95 14.5 11.5 .. .. Nepal 80 22 3 15 .. 10 3.4 2.9 1.0 565 Netherlands 7,097 4 44 125 .. 98 31.2 17.7 0.7 .. New Zealand 9,622 7 41 108 310 97 34.4 23.1 2.9 605 Nicaragua 446 24 6 55 39 .. 5.1 13.8 .. .. Niger .. .. 0 13 .. 45 13.6 13.8 .. .. Nigeria 137 12 1 42 1 83 10.3 12.1 3.4 .. Norway 24,980 7 40 110 .. .. 37.6 9.7 1.2 .. Oman 4,484 15 10 116 30 96 32.6 5.5 3.4 967 Pakistan 474 19 3 53 .. 90 3.6 1.9 2.7 50 Panama 1,592 16 15 115 61 83 9.1 5.1 3.2 380 Papua New Guinea .. .. 1 9 .. .. 4.0 12.8 .. .. Paraguay 958 5 8 95 35 .. 7.2 5.7 4.8 799 Peru 961 8 10 73 .. 95 15.4 8.0 3.1 624 Philippines 586 13 5 75 .. 99 14.2 5.7 .. .. Poland 3,662 9 25 115 .. 99 28.0 12.5 3.9 396 Portugal 4,860 7 39 140 .. 99 25.7 26.4 4.6 1,534 Puerto Rico .. .. 26 .. .. .. .. .. .. .. Qatar 12,915 9 21 131 .. 100 .. .. 1.8 597 2010 World Development Indicators 333 5.11 Power and communications Electric power Telephones Affordability and efficiency Access and use Quality Population Transmission Inter national covered by $ per month Telecom- Mobile cellular Consumption and distribution per 100 people voice traffic a mobile cellular Residential Mobile cellular munications and fi xed-line per capita losses Fixed Mobile cellular minutes per networka fi xed-line prepaid revenuea subscribers kWh % of output linesa subscriptionsa person % tariff a tariff a % of GDP per employeea 2007 2007 2008 2008 2008 2008 2008 2008 2008 2008 Romania 2,452 11 23 114 41 98 12.2 11.9 3.4 561 Russian Federation 6,317 10 32 141 .. 95 11.7 8.6 2.6 .. Rwanda .. .. 0 14 11 92 7.3 10.0 3.1 1,952 Saudi Arabia 7,247 7 17 146 .. 98 9.2 8.8 2.7 1,618 Senegal 128 25 2 44 27 85 17.4 8.4 9.8 1,859 Serbia 4,155 16 42 131 142 93 4.9 4.9 5.3 872 Sierra Leone .. .. 1 18 .. 70 .. .. .. .. Singapore 8,514 5 38 132 1,531 100 7.1 4.0 2.8 .. Slovak Republic 5,250 5 20 102 123 100 24.5 16.1 3.3 665 Slovenia 7,138 6 50 102 96 100 20.5 12.4 3.3 644 Somalia .. .. 1 7 .. .. .. .. .. .. South Africa 4,986 8 9 92 .. 100 22.4 12.3 7.4 .. Spain 6,296 5 44 109 .. 99 30.8 33.3 4.0 855 Sri Lanka 417 16 17 55 34 95 4.8 2.4 .. 919 Sudan 90 20 1 29 6 66 4.4 4.8 3.3 2,168 Swaziland .. .. 4 46 .. 91 4.8 12.1 4.5 1,118 Sweden 15,238 7 58 118 .. 98 22.8 7.5 2.7 894 Switzerland 8,164 6 63 116 .. 100 29.0 35.5 3.3 601 Syrian Arab Republic 1,469 24 18 34 78 96 1.2 9.1 3.0 409 Tajikistan 2,176 17 4 54 .. .. .. .. .. .. Tanzania 82 19 0 31 0 65 10.9 11.1 .. .. Thailand 2,055 6 10 92 .. 38 5.8 3.9 4.0 1,957 Timor-Leste .. .. .. .. .. .. .. .. 7.9 .. Togo 96 53 2 24 6 85 13.1 18.0 7.4 1,059 Trinidad and Tobago 5,642 2 23 113 .. 100 19.7 7.9 2.5 .. Tunisia 1,248 13 12 83 79 100 3.0 7.2 4.3 1,004 Turkey 2,238 14 24 89 39 100 .. .. 2.3 2,145 Turkmenistan 2,279 14 9 23 .. 14 .. .. .. .. Uganda .. .. 1 27 7 100 12.6 10.4 .. .. Ukraine 3,529 12 28 120 0 100 4.2 8.2 5.7 .. United Arab Emirates 16,165 7 34 209 .. 100 5.0 4.1 3.1 924 United Kingdom 6,120 7 54 126 .. 100 27.3 20.5 4.3 .. United States 13,652 6 51 89 .. 100 17.2 15.3 .. .. Uruguay 2,197 20 29 105 0 100 13.0 13.8 3.1 692 Uzbekistan 1,658 9 7 47 .. 93 .. .. 2.5 758 Venezuela, RB 3,077 27 23 97 .. 90 7.0 24.7 3.5 914 Vietnam 728 11 34 81 .. 70 2.3 4.2 .. .. West Bank and Gaza .. .. 9 29 .. 95 .. .. .. 880 Yemen, Rep. 202 25 5 16 .. 68 0.8 4.9 .. .. Zambia 720 7 1 28 .. 50 27.7 12.3 2.6 .. Zimbabwe 898 7 3 13 22 75 .. .. .. 711 World 2,846 w 8w 19 w 61 w .. w 80 w 10.9 m 10.1 m .. w 651 m Low income 324 13 5 28 .. 56 9.0 10.0 .. .. Middle income 1,666 11 15 57 .. 80 8.5 9.0 3.2 595 Lower middle income 1,310 11 14 47 .. 77 4.8 8.4 3.0 685 Upper middle income 3,052 13 22 95 .. 94 11.7 9.9 3.3 559 Low & middle income 1,478 11 13 52 .. 76 8.5 9.1 3.2 559 East Asia & Pacific 1,883 6 22 53 9 93 4.5 5.0 3.0 .. Europe & Central Asia 3,958 11 26 110 .. 92 8.7 8.9 2.8 462 Latin America & Carib. 1,866 17 19 80 .. 92 10.4 9.6 3.8 550 Middle East & N. Africa 1,435 16 16 58 27 93 3.0 7.2 .. 880 South Asia 482 24 3 33 .. 61 3.5 1.9 2.1 565 Sub-Saharan Africa 550 10 2 33 .. 56 11.6 11.8 .. .. High income 9,753 6 47 106 .. 99 27.0 16.1 .. 801 Euro area 6,963 5 49 122 .. 99 28.7 18.7 2.6 789 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. 334 2010 World Development Indicators 5.11 STATES AND MARKETS Power and communications About the data Definitions The quality of an economy's infrastructure, includ- Access to telephone services rose on an unprec- · Electric power consumption per capita measures ing power and communications, is an important ele- edented scale over the past 15 years. This growth the production of power plants and combined heat ment in investment decisions for both domestic and was driven primarily by wireless technologies and and power plants less transmission, distribution, foreign investors. Government effort alone is not liberalization of telecommunications markets, which and transformation losses and own use by heat and enough to meet the need for investments in modern have enabled faster and less costly network rollout. power plants divided by midyear population. · Elec- infrastructure; public-private partnerships, especially In 2002 the number of mobile phones in the world tric power transmission and distribution losses are those involving local providers and financiers, are surpassed the number of fixed telephones; by the losses in transmission between sources of supply critical for lowering costs and delivering value for end of 2008 there were an estimated 4 billion mobile and points of distribution and in distribution to con- money. In telecommunications, competition in the phones globally. No technology has ever spread sumers, including pilferage. · Fixed telephone lines marketplace, along with sound regulation, is lower- faster around the world. Mobile communications are telephone lines connecting a subscriber to the ing costs, improving quality, and easing access to have had a particularly important impact in rural telephone exchange equipment. · Mobile cellular services around the globe. areas. The mobility, ease of use, flexible deployment, telephone subscriptions are subscriptions to a pub- An economy's production and consumption of elec- and relatively low and declining rollout costs of wire- lic mobile telephone service using cellular technol- tricity are basic indicators of its size and level of less technologies enable them to reach rural popu- ogy, which provide access to the public switched development. Although a few countries export elec- lations with low levels of income and literacy. The telephone network. Post-paid and prepaid subscrip- tric power, most production is for domestic consump- next billion mobile subscribers will consist mainly tions are included. · International voice traffic is tion. Expanding the supply of electricity to meet the of the rural poor. the sum of international incoming and outgoing tele- growing demand of increasingly urbanized and indus- Access is the key to delivering telecommunications phone traffic (in minutes) divided by total population. trialized economies without incurring unacceptable services to people. If the service is not affordable · Population covered by mobile cellular network is social, economic, and environmental costs is one of to most people, goals of universal usage will not be the percentage of people that live in areas served by the great challenges facing developing countries. met. Two indicators of telecommunications afford- a mobile cellular signal regardless of whether they Data on electric power production and consump- ability are presented in the table: fixed-line telephone use it. · Residential fixed-line tariff is the monthly tion are collected from national energy agencies by service tariff and prepaid mobile cellular service tar- subscription charge plus the cost of 30 three-minute the International Energy Agency (IEA) and adjusted iff. Telecommunications efficiency is measured by local calls (15 peak and 15 off-peak). · Mobile cel- by the IEA to meet international definitions (for data total telecommunications revenue divided by GDP lular prepaid tariff is based on the Organisation for on electricity production, see table 3.10). Electricity and by mobile cellular and fixed-line telephone sub- Economic Co-operation and Development's low-user consumption is equivalent to production less power scribers per employee. definition, which includes the cost of monthly mobile plants' own use and transmission, distribution, and Operators have traditionally been the main source use for 25 outgoing calls per month spread over the transformation losses less exports plus imports. It of telecommunications data, so information on sub- same mobile network, other mobile networks, and includes consumption by auxiliary stations, losses scribers has been widely available for most coun- mobile to fixed-line calls and during peak, off-peak, in transformers that are considered integral parts tries. This gives a general idea of access, but a and weekend times as well as 30 text messages of those stations, and electricity produced by pump- more precise measure is the penetration rate--the per month. · Telecommunications revenue is the ing installations. Where data are available, it covers share of households with access to telecommunica- revenue from the provision of telecommunications electricity generated by primary sources of energy-- tions. During the past few years more information services such as fixed-line, mobile, and data divided coal, oil, gas, nuclear, hydro, geothermal, wind, tide on information and communication technology use by GDP. · Mobile cellular and fixed-line subscribers and wave, and combustible renewables. Neither pro- has become available from household and business per employee are telephone subscribers (fixed-line duction nor consumption data capture the reliability surveys. Also important are data on actual use of plus mobile) divided by the total number of telecom- of supplies, including breakdowns, load factors, and telecommunications equipment. Ideally, statistics munications employees. frequency of outages. on telecommunications (and other information and Over the past decade new financing and technol- communications technologies) should be compiled ogy, along with privatization and liberalization, have for all three measures: subscription and possession, Data sources spurred dramatic growth in telecommunications access, and use. The quality of data varies among in many countries. With the rapid development of reporting countries as a result of differences in regu- Data on electricity consumption and losses are mobile telephony and the global expansion of the lations covering data provision and availability. from the IEA's Energy Statistics and Balances of Internet, information and communication technolo- Non-OECD Countries 2009, the IEA's Energy Sta- gies are increasingly recognized as essential tools of tistics of OECD Countries 2009, and the United development, contributing to global integration and Nations Statistics Division's Energy Statistics enhancing public sector effectiveness, efficiency, Yearbook. Data on telecommunications are from and transparency. The table presents telecommuni- the International Telecommunication Union's cations indicators covering access and use, quality, World Telecommunication Development Report and affordability and efficiency. database and World Bank estimates. 2010 World Development Indicators 335 5.12 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona Access and use Quality Affordability Application technology trade Fixed International Fixed Information broadband Internet broadband Secure and com- Goods Services Internet bandwidth a Internet Internet munications Exports Imports Exports per 100 people subscribersa bits per access servers technology % of total % of total % of total per 1,000 Personal Internet per 100 second per tariff a per million expenditures goods goods service people % computersa usersa people capita $ per month people % of GDP exports imports exports 2000­07b 2007 2008 2008 2008 2008 2008 December 2009 2008 2008 2008 2008 Afghanistan .. .. 0.4 1.7 0.00 1 .. 0 .. .. 0.4 .. Albania 24 .. 4.6 23.9 2.04 220 31 7 .. 0.8 4.0 6.8 Algeria .. .. .. 11.9 1.41 .. 17 1 2.3 0.0 5.8 .. Angola 2 34 0.6 3.1 0.09 17 164 2 .. .. .. .. Argentina 36 .. .. 28.1 7.99 2,320 38 20 4.8 0.5 9.4 9.1 Armenia 8 85 .. 6.2 0.16 .. 39 7 .. 1.3 5.9 17.4 Australia 155 .. .. 70.8 23.98 5,457 28 1,212 4.9 1.5 10.0 4.9 Austria 311 97 .. 71.2 20.74 20,323 61 553 5.5 5.8 6.9 6.3 Azerbaijan 16 99 8.0 28.2 0.69 1,180 85 2 .. 0.0 5.0 3.6 Bangladesh .. .. 2.3 0.3 0.03 4 54 0 9.0 0.6 5.7 6.2 Belarus 81 93 .. 32.1 4.94 748 .. 3 .. 0.6 2.7 7.2 Belgium 165 99 .. 68.1 27.66 24,945 31 310 5.2 2.9 4.0 8.7 Benin 0 23 0.7 1.8 0.03 18 105 0 .. 0.1 3.5 0.8 Bolivia .. 63 .. 10.8 0.68 225 34 4 4.9 0.0 4.2 12.4 Bosnia and Herzegovina .. .. 6.4 34.7 4.99 529 15 8 .. 0.5 4.1 .. Botswana 41 .. 6.2 6.2 0.46 220 30 4 .. 0.2 4.3 3.1 Brazil 36 97 .. 37.5 5.26 2,108 47 26 5.3 1.8 10.9 2.2 Bulgaria 79 98 11.0 34.7 11.07 37,657 16 35 6.3 2.6 6.1 5.5 Burkina Faso .. .. 0.6 0.9 0.03 15 1,861 0 .. .. .. .. Burundi .. .. 0.9 0.8 0.00 2 .. 0 .. 0.8 8.3 0.0 Cambodia .. 63 0.4 0.5 0.11 19 91 1 .. .. .. 2.8 Cameroon .. .. .. 3.8 0.00 8 184 1 4.6 0.0 3.2 6.4 Canada 175 99 94.3 75.3 29.55 16,193 20 984 6.6 3.8 8.8 10.4 Central African Republic .. .. .. 0.4 0.00 .. 1,396 0 .. .. .. .. Chad .. .. .. 1.2 0.00 1 .. .. .. .. .. .. Chile 51 100 .. 32.5 8.49 4,076 53 39 5.1 0.2 6.4 2.5 China 74 .. 5.7 22.5 6.29 483 19 1 6.0 27.5 23.2 5.3 Hong Kong SAR, China 222 99 69.3 67.0 28.13 548,318 25 350 9.2 42.6 40.8 1.3 Colombia 23 85 11.2 38.5 4.23 2,233 36 12 4.7 0.2 11.2 7.3 Congo, Dem. Rep. .. .. .. .. .. .. .. 0 .. .. .. .. Congo, Rep. .. 25 .. 4.3 0.00 0 .. 1 .. .. .. .. Costa Rica 65 94 .. 32.3 2.38 857 17 98 6.2 23.8 19.0 17.9 Côte d'Ivoire .. 38 .. 3.2 0.05 40 47 1 .. 0.3 3.9 11.0 Croatia .. .. .. 50.5 11.83 15,892 21 117 .. 5.0 6.1 3.1 Cuba 65 88 5.6 12.9 0.02 27 1,630 0 .. 1.9 2.9 .. Czech Republic 183 .. .. 57.8 16.88 7,075 29 185 7.6 15.2 15.2 8.6 Denmark 353 98 54.9 83.3 36.88 34,506 30 1,167 5.0 5.0 8.1 .. Dominican Republic 39 77 .. 21.6 2.27 1,407 28 14 .. 6.0 5.2 3.5 Ecuador 99 90 13.0 28.8 0.26 443 40 12 5.3 0.2 8.2 8.1 Egypt, Arab Rep. .. 97 3.9 16.6 0.94 332 8 1 5.7 1.8 4.4 7.3 El Salvador 38 .. .. 10.6 2.01 33 18 12 .. 2.5 5.3 10.2 Eritrea .. .. 1.0 4.1 0.00 5 .. .. .. .. .. .. Estonia 191 98 25.5 66.2 23.71 126,802 39 315 .. 6.5 7.2 7.1 Ethiopia 5 5 0.7 0.4 0.00 3 644 0 .. 0.5 7.9 3.9 Finland 431 93 .. 82.5 30.45 17,221 38 802 6.5 16.5 12.0 27.4 France 164 97 65.2 67.9 28.41 29,356 38 210 5.2 5.4 7.2 3.7 Gabon .. .. 3.4 6.2 0.15 141 .. 7 .. 0.1 6.6 .. Gambia, The .. .. 3.5 6.9 0.02 38 384 3 .. 2.9 3.8 10.4 Georgia 4 .. 27.2 23.8 2.23 752 48 9 .. 0.4 7.8 2.2 Germany 267 95 65.6 75.5 27.52 25,654 38 641 5.4 6.9 8.8 8.3 Ghana .. .. 1.1 4.3 0.10 86 64 1 .. 0.1 7.3 0.0 Greece .. 100 9.4 43.1 13.41 4,537 25 79 4.5 3.2 5.6 1.7 Guatemala .. .. .. 14.3 0.58 186 34 9 .. 0.5 6.3 16.1 Guinea .. 11 .. 0.9 0.00 0 800 0 .. 0.0 5.8 11.2 Guinea-Bissau .. .. .. 2.4 0.00 1 .. .. .. .. .. .. Haiti .. 25 5.1 10.1 0.00 16 .. 1 .. .. .. 5.1 Honduras .. 64 2.5 13.1 0.00 241 .. 7 8.6 0.2 6.4 14.4 336 2010 World Development Indicators 5.12 STATES AND MARKETS The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona Access and use Quality Affordability Application technology trade Fixed International Fixed Information broadband Internet broadband Secure and com- Goods Services Internet bandwidth a Internet Internet munications Exports Imports Exports per 100 people subscribersa bits per access servers technology % of total % of total % of total per 1,000 Personal Internet per 100 second per tariff a per million expenditures goods goods service people % computersa usersa people capita $ per month people % of GDP exports imports exports 2000­07b 2007 2008 2008 2008 2008 2008 December 2009 2008 2008 2008 2008 Hungary 217 99 25.6 58.5 17.43 5,977 25 113 8.9 24.6 18.8 8.3 India 71 46 3.3 4.5 0.46 32 6 2 4.5 1.3 5.0 50.3 Indonesia .. .. 2.0 7.9 0.18 120 22 1 3.3 4.6 9.8 8.4 Iran, Islamic Rep. .. .. 10.6 32.0 0.42 151 43 0 3.5 0.1 1.9 .. Iraq .. .. .. 1.0 0.00 1 .. 0 .. .. .. 3.3 Ireland 182 98 58.2 62.7 20.14 15,261 38 737 4.6 16.3 17.5 34.4 Israel .. .. .. 47.9 23.04 2,003 .. 291 5.4 13.5 9.1 29.6 Italy 137 94 .. 41.8 18.86 12,989 26 109 4.9 2.8 5.7 3.0 Jamaica .. .. .. 57.3 3.62 744 30 36 3.3 0.3 3.9 5.9 Japan 551 .. .. 75.2 23.58 5,770 32 519 6.7 14.3 10.3 1.1 Jordan .. .. 7.5 27.0 2.32 781 31 12 7.3 5.5 7.2 0.0 Kazakhstan .. .. .. 10.9 4.22 702 .. 3 .. 0.1 3.3 2.4 Kenya .. .. .. 8.7 0.01 21 168 1 5.8 1.3 6.2 13.5 Korea, Dem. Rep. .. .. .. 0.0 0.00 0 .. 0 .. .. .. .. Korea, Rep. .. .. 57.6 75.8 31.84 4,528 20 927 9.1 26.2 15.2 1.3 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. 36.7 1.47 871 46 85 3.2 0.3 6.0 45.9 Kyrgyz Republic 1 .. .. 16.1 0.09 113 .. 1 .. 0.8 5.1 2.0 Lao PDR 3 .. .. 8.5 0.10 129 268 0 .. .. .. .. Latvia 154 99 32.7 60.4 8.83 3,537 26 114 .. 5.1 6.6 5.2 Lebanon 54 .. 10.2 22.5 5.03 223 23 15 .. .. .. 1.9 Lesotho .. .. .. 3.6 0.01 5 49 0 .. .. .. .. Liberia .. 7 .. 0.5 .. .. .. 0 .. .. .. .. Libya .. .. .. 5.1 0.16 50 .. 1 .. .. .. 2.5 Lithuania 108 98 24.2 54.4 17.57 9,751 16 121 .. 3.2 5.1 3.1 Macedonia, FYR 89 99 36.8 41.5 8.87 17 15 17 .. 0.4 5.0 12.9 Madagascar .. .. .. 1.7 0.02 8 120 0 .. 0.3 4.2 .. Malawi .. .. .. 2.1 0.02 5 900 0 .. 0.2 3.4 .. Malaysia 109 .. 23.1 55.8 4.93 2,374 20 34 9.7 26.2 25.3 5.4 Mali .. 22 0.8 1.6 0.04 51 58 1 .. 0.2 3.6 .. Mauritania .. .. 4.5 1.9 0.18 76 62 2 .. .. 1.6 .. Mauritius 77 96 17.6 22.2 7.23 364 51 62 .. 4.0 5.9 3.6 Mexico 93 93 14.4 22.2 7.14 285 37 17 4.6 20.9 17.2 2.3 Moldova .. .. 11.4 23.4 3.17 966 23 10 .. 6.8 3.4 16.8 Mongolia 20 86 24.6 12.5 1.37 947 .. 8 .. 0.1 5.1 3.7 Morocco 12 77 5.7 33.0 1.53 795 20 2 12.5 5.7 6.7 5.9 Mozambique 3 .. .. 1.6 0.05 3 100 0 .. 0.2 3.9 6.1 Myanmar .. .. 0.9 0.2 0.02 20 .. 0 .. .. .. .. Namibia 28 .. 23.9 5.3 0.02 27 46 9 .. 0.6 4.9 2.3 Nepal .. 28 .. 1.7 0.03 5 23 1 .. .. .. .. Netherlands 307 98 91.2 87.0 35.31 78,156 38 1,416 6.3 11.8 12.6 10.6 New Zealand 182 99 52.6 71.4 21.43 4,544 31 1,059 5.5 1.8 8.4 4.9 Nicaragua .. .. .. 3.3 0.64 144 30 6 .. 0.2 6.2 8.2 Niger 0 6 .. 0.5 0.00 11 58 0 .. 0.7 3.6 32.8 Nigeria .. .. .. 15.9 0.04 5 690 1 3.1 0.0 10.2 .. Norway 516 95 62.9 82.5 33.26 26,904 57 1,011 3.7 2.0 8.2 5.7 Oman .. .. 16.9 20.0 1.15 894 31 11 .. 1.6 3.2 .. Pakistan 50 56 .. 11.1 0.10 43 18 1 4.4 0.5 5.7 6.7 Panama 65 83 2.8 27.5 5.76 15,964 15 86 5.5 0.0 6.9 4.5 Papua New Guinea 9 .. .. 1.8 0.00 2 144 1 .. .. .. .. Paraguay .. 79 .. 14.3 1.43 481 35 6 .. 0.2 21.3 1.3 Peru .. 69 .. 24.7 2.52 2,646 36 10 3.4 0.1 5.3 3.9 Philippines 79 .. 7.2 6.2 1.16 113 23 5 6.1 54.1 34.7 7.9 Poland 114 98 16.9 49.0 12.57 2,748 27 123 5.5 7.5 8.9 4.5 Portugal .. 99 18.2 42.1 15.39 4,790 30 136 6.0 7.4 7.9 5.0 Puerto Rico .. .. .. 25.3 5.42 .. .. 61 .. .. .. .. Qatar .. .. 15.7 34.0 8.07 2,044 .. 64 .. 0.0 8.2 .. 2010 World Development Indicators 337 5.12 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisiona Access and use Quality Affordability Application technology trade Fixed International Fixed Information broadband Internet broadband Secure and com- Goods Services Internet bandwidth a Internet Internet munications Exports Imports Exports per 100 people subscribersa bits per access servers technology % of total % of total % of total per 1,000 Personal Internet per 100 second per tariff a per million expenditures goods goods service people % computersa usersa people capita $ per month people % of GDP exports imports exports 2000­07b 2007 2008 2008 2008 2008 2008 December 2009 2008 2008 2008 2008 Romania 70 97 19.2 28.8 11.65 9,111 23 21 4.9 5.3 7.5 15.8 Russian Federation 92 .. 13.3 31.9 6.54 573 14 11 3.5 0.4 8.9 6.1 Rwanda .. 2 0.3 3.1 0.04 27 92 1 .. 0.5 12.5 1.9 Saudi Arabia .. .. 69.8 31.5 4.25 1,224 40 11 5.2 0.4 8.0 .. Senegal 9 43 .. 8.4 0.39 237 29 1 10.8 0.6 3.4 15.5 Serbia .. .. 25.8 44.9 6.14 4,506 9 2 .. 2.2 5.4 6.7 Sierra Leone .. .. .. 0.3 .. .. .. 0 .. .. .. 0.2 Singapore 361 .. 74.3 69.6 20.73 22,783 22 421 7.1 35.9 28.2 3.5 Slovak Republic 126 99 58.1 66.0 11.18 5,555 28 79 6.4 17.5 14.7 7.2 Slovenia 173 99 42.5 55.7 21.11 6,720 27 210 4.7 3.5 5.1 6.7 Somalia .. .. .. 1.1 0.00 .. .. 0 .. .. .. .. South Africa 30 .. .. 8.6 0.87 71 26 40 10.1 1.6 8.8 3.2 Spain 144 100 39.3 55.4 19.75 11,008 29 192 4.8 3.2 7.9 5.8 Sri Lanka 26 .. .. 5.8 0.51 190 21 4 4.3 1.8 4.6 15.5 Sudan .. 16 10.7 10.2 0.11 322 29 0 .. 0.0 2.3 1.2 Swaziland 24 35 3.7 6.9 0.07 31 1,877 4 .. 0.1 3.6 1.4 Sweden 481 94 88.1 87.7 41.12 49,828 32 858 5.7 9.5 10.1 13.6 Switzerland 420 .. 96.2 75.9 33.68 29,413 32 1,118 7.2 3.5 6.6 .. Syrian Arab Republic .. .. 9.0 17.3 0.05 102 51 0 .. 0.6 2.0 4.5 Tajikistan .. .. .. 8.8 0.05 37 .. 0 .. .. .. 21.6 Tanzania 2 6 .. 1.2 0.02 2 68 0 .. 0.4 6.2 2.0 Thailand .. .. .. 23.9 1.41 818 18 10 6.2 19.4 15.4 .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 2 .. .. 5.4 0.03 8 106 2 .. 0.1 4.2 .. Trinidad and Tobago 149 .. 13.2 17.0 4.58 678 13 46 .. 0.1 3.4 .. Tunisia 23 .. 9.7 27.1 2.20 1,115 13 12 5.4 5.0 5.6 2.4 Turkey .. 98 6.1 34.4 7.78 2,794 .. 66 4.1 2.1 4.7 2.1 Turkmenistan 9 .. .. 1.5 0.05 48 .. 0 .. .. .. .. Uganda .. 6 1.7 7.9 0.02 12 170 0 .. 4.9 9.3 7.2 Ukraine 131 .. 4.5 10.5 3.46 206 21 6 5.9 1.3 2.6 3.3 United Arab Emirates .. .. 33.1 65.2 12.43 8,686 22 165 4.9 2.0 5.3 .. United Kingdom 290 99 80.2 76.0 28.13 39,648 29 905 6.3 7.7 10.1 8.0 United States 193 98 80.6 75.9 24.05 11,289 15 1,234 7.1 12.8 12.5 4.0 Uruguay .. 91 .. 40.2 7.33 903 24 36 4.3 0.1 6.2 9.0 Uzbekistan .. .. 3.1 9.0 0.24 30 .. 0 .. .. .. .. Venezuela, RB 93 92 .. 25.7 4.76 628 31 7 3.5 0.0 11.6 7.4 Vietnam .. .. 9.6 24.2 2.38 581 17 2 4.9 5.6 8.2 .. West Bank and Gaza 10 95 .. 9.0 2.54 313 .. 2 .. .. .. 7.6 Yemen, Rep. 4 .. 2.8 1.6 0.00 28 226 0 .. 0.3 1.8 18.9 Zambia 5 .. .. 5.5 0.04 8 91 1 .. 0.1 3.4 8.1 Zimbabwe .. 31 7.6 11.4 0.14 10 .. 1 .. 0.3 2.1 .. World 105 w .. m 15.3 w 23.9 w 6.21 w 3,546 w 31.4 m 114 w 6.0 w 12.2 w 12.5 w 8.3 w Low income .. .. .. 4.6 0.26 24 102.4 1 .. 2.5 6.3 .. Middle income 70 .. 5.6 17.3 3.26 377 29.4 7 5.1 14.4 14.6 12.6 Lower middle income 72 .. 4.5 13.9 2.59 153 31.4 2 5.5 19.7 17.0 18.6 Upper middle income 61 .. .. 30.6 5.88 1,281 26.3 28 4.8 10.1 12.7 5.2 Low & middle income 59 .. 5.2 15.3 2.78 320 36.4 6 5.2 14.3 14.4 12.4 East Asia & Pacific 74 .. 5.6 19.4 4.63 470 21.7 2 5.9 25.5 22.4 5.6 Europe & Central Asia 94 .. 10.7 28.6 6.34 1,244 22.7 30 4.2 2.1 6.5 5.4 Latin America & Carib. 64 88 .. 28.9 4.88 1,391 34.0 20 4.8 10.9 13.5 4.8 Middle East & N. Africa .. .. 5.7 18.9 0.84 323 23.0 2 5.8 .. .. .. South Asia 68 46 3.3 4.7 0.36 31 21.0 1 4.7 1.2 5.1 47.3 Sub-Saharan Africa .. .. .. 6.5 0.11 34 100.1 3 8.5 0.9 7.5 .. High income 261 98 67.8 69.1 24.05 20,143 29.8 715 6.3 11.7 12.0 7.3 Euro area 201 98 56.1 62.6 23.96 32,540 30.5 380 5.3 7.0 8.2 9.1 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. Data are for the most recent year available. 338 2010 World Development Indicators 5.12 STATES AND MARKETS The information age About the data The digital and information revolution has changed the particularly in developing countries, where many com- minicomputers intended primarily for shared use and way the world learns, communicates, does business, mercial subscribers rent out computers connected devices such as smart phones and personal digital and treats illnesses. New information and communi- to the Internet or prepaid cards are used to access assistants. · Internet users are people with access cations technologies (ICT) offer vast opportunities for the Internet. to the worldwide network. · Fixed broadband Internet progress in all walks of life in all countries--opportu- Broadband refers to technologies that provide subscribers are the number of broadband subscrib- nities for economic growth, improved health, better Internet speeds of at least 256 kilobits a second of ers with a digital subscriber line, cable modem, or service delivery, learning through distance education, upstream and downstream capacity and includes digi- other high-speed technology. · International Internet and social and cultural advances. tal subscriber lines, cable modems, satellite broad- bandwidth is the contracted capacity of international Comparable statistics on access, use, quality, band Internet, fiber-to-home Internet access, ethernet connections between countries for transmitting Inter- and affordability of ICT are needed to formulate local access networks, and wireless area networks. net traffic. · Fixed broadband Internet access tariff growth-enabling policies for the sector and to moni- Bandwidth refers to the range of frequencies available is the lowest sampled cost per 100 kilobits a second tor and evaluate the sector's impact on development. for signals. The higher the bandwidth, the more infor- per month and are calculated from low- and high-speed Although basic access data are available for many mation that can be transmitted at one time. Reporting monthly service charges. Monthly charges do not countries, in most developing countries little is known countries may have different definitions of broadband, include installation fees or modem rentals. · Secure about who uses ICT; what they are used for (school, so data are not strictly comparable. Internet servers are servers using encryption tech- work, business, research, government); and how they The number of secure Internet servers, from the nology in Internet transactions. · Information and affect people and businesses. The global Partner- Netcraft Secure Server Survey, indicates how many communications technology expenditures include ship on Measuring ICT for Development is helping to companies conduct encrypted transactions over the computer hardware (computers, storage devices, set standards, harmonize information and commu- Internet. The survey examines the use of encrypted printers, and other peripherals); computer software nications technology statistics, and build statistical transactions through extensive automated explora- (operating systems, programming tools, utilities, appli- capacity in developing countries. For more informa- tion, tallying the number of Web sites using a secure cations, and internal software development); computer tion see www.itu.int/ITU-D/ict/partnership/. socket layer (SSL). The country of origin of about a services (information technology consulting, computer Data on daily newspapers in circulation are from third of the 1.2 million distinct valid third-party cer- and network systems integration, Web hosting, data United Nations Educational, Scientific, and Cultural tifi cates is unknown. Some countries, such as the processing services, and other services); and com- Organization (UNESCO) Institute for Statistics surveys Republic of Korea, use application layers to establish munications services (voice and data communications on circulation, online newspapers, journalists, com- the encryption channel, which is SSL equivalent. services) and wired and wireless communications munity newspapers, and news agencies. According to the World Information Technology equipment. · Information and communication tech- Estimates of households with television are derived and Services Alliance's (WITSA) Digital Planet 2009, nology goods exports and imports include telecom- from household surveys. Some countries report only the global marketplace for information and commu- munications, audio and video, computer and related the number of households with a color television set, nications technologies was estimated to be about equipment; electronic components; and other informa- and so the true number may be higher than reported. $3.4 trillion in 2009 and to rise to about $3.6 trillion tion and communication technology goods. Software is Estimates of personal computers are from an in 2010. The data on information and communica- excluded. · Information and communication technol- annual International Telecommunication Union (ITU) tions technology expenditures cover the world's 75 ogy service exports include computer and communica- questionnaire sent to member states, supplemented largest buyers among countries and regions. tions services (telecommunications and postal and by other sources. Many governments lack the capac- Information and communication technology goods courier services) and information services (computer ity to survey all places where personal computers exports and imports are defi ned by the Working data and news-related service transactions). are used (homes, schools, businesses, government Party on Indicators for the Information Society and offices, libraries, Internet cafes) so most estimates are reported in the Organisation for Economic Co- Data sources are derived from the number of personal computers operation and Development's Guide to Measuring the Data on newspapers are compiled by the UNESCO sold each year. Annual shipment data can also be mul- Information Society (2005). Information and commu- Institute for Statistics. Data on televisions, per- tiplied by an estimated average useful lifespan before nication technology service exports data are based sonal computers, Internet users, Internet broad- replacement to approximate the number of personal on the International Monetary Fund's (IMF) Balance of band users and cost, and Internet bandwidth are computers. There is no precise method for determin- Payments Statistics Yearbook classification. from the ITU's World Telecommunication Develop- ing replacement rates, but in general personal com- ment Report database. Data on secure Internet Definitions puters are replaced every three to five years. servers are from Netcraft (www.netcraft.com/) Data on Internet users and related indicators · Daily newspapers are newspapers issued at least and official government sources. Data on informa- (broadband and bandwidth) are based on nationally four times a week that report mainly on events in the tion and communication technology goods trade reported data to the ITU. Some countries derive these 24-hour period before going to press. The indicator is are from the United Nations Statistics Division's data from surveys, but since survey questions and average circulation (or copies printed) per 1,000 peo- Commodity Trade (Comtrade) database. Data on definitions differ, the estimates may not be strictly ple. · Households with television are the percentage information and communication technology expen- comparable. Countries without surveys generally of households with a television set. · Personal com- ditures are from WITSA's Digital Planet 2009 and derive their estimates by multiplying subscriber puters are self-contained computers designed for use Global Insight, Inc. Data on information and com- counts reported by Internet service providers by a by a single individual, including laptops and notebooks munication technology service exports are from the IMF's Balance of Payments Statistics database. multiplier. This method may undercount actual users, and excluding terminals connected to mainframe and 2010 World Development Indicators 339 5.13 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda,c journal articles % of manu- per million per million factured $ millions Non- people people % of GDP $ millions exports Receipts Payments Residents residents Total 2000­07d 2000­07d 2005 2000­07d 2008 2008 2008 2008 2008 2008 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. 7 4 39 12 .. .. 4,596 Algeria 170 35 350 0.07 7 1 .. .. 84 765 2,489 Angola .. .. .. .. .. .. 12 0 .. .. .. Argentina 980 196 3,058 0.51 1,949 9 94 1,274 .. .. 73,717 Armenia .. .. 180 0.21 11 2 .. .. 226 4 4,735 Australia 4,231 993 15,957 2.17 4,154 12 703 3,026 2,718 24,122 59,370 Austria 3,774 1,792 4,566 2.52 15,230 11 905 1,598 2,298 329 5,216 Azerbaijan .. .. 116 0.18 6 1 0 5 222 5 5,609 Bangladesh .. .. 193 .. 97 1 0 19 29 270 8,232 Belarus .. .. 490 0.97 405 2 5 75 1,188 337 11,454 Belgium 3,413 1,445 6,841 1.91 29,163 8 1,188 2,133 575 133 28,897e Benin .. .. .. .. 0 0 0 2 .. .. .. Bolivia 120 .. .. 0.28 17 4 2 18 .. .. 6,081 Bosnia and Herzegovina 197 71 .. 0.03 125 4 5 11 59 12 5,538 Botswana .. .. .. 0.38 21 1 1 13 .. .. 920 Brazil 629 .. 9,889 1.02 10,572 12 465 2,697 3,810 20,264 119,841 Bulgaria 1,466 500 764 0.48 755 7 11 95 249 22 10,853 Burkina Faso .. .. .. 0.11 .. .. .. .. .. .. .. Burundi .. .. .. .. 1 8 0 .. .. .. .. Cambodia 17 13 .. 0.05 .. .. 1 6 .. .. 2,866 Cameroon .. .. 131 .. 3 3 0 17 .. .. .. Canada 4,157 1,595 25,836 2.03 29,388 14 3,432 8,766 5,061 37,028 45,619 Central African Republic .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. Chile 833 302 1,559 0.67 515 6 64 526 291 2,924 44,320 China 1,071 .. 41,596 1.49 381,345 29 571 10,319 194,579 95,259 669,088 Hong Kong SAR, China 2,650 459 .. 0.81 2,164 22 358 1,504 173 13,489 24,230 Colombia 151 .. 400 0.18 445 4 30 263 121 1,860 23,994 Congo, Dem. Rep. .. .. .. 0.48 .. .. .. .. .. .. .. Congo, Rep. 34 37 .. .. .. .. .. .. .. .. .. Costa Rica 122 .. 105 0.37 2,378 39 1 62 .. .. 11,754 Côte d'Ivoire 66 .. .. .. 180 16 0 22 .. .. .. Croatia 1,384 643 953 0.93 898 9 44 257 330 71 10,324 Cuba .. .. 261 0.44 248 35 .. .. 69 189 3,041 Czech Republic 2,715 1,503 3,169 1.59 18,200 14 55 726 712 142 13,106 Denmark 5,431 2,006 5,040 2.57 11,850 16 .. .. 1,634 195 8,015 Dominican Republic .. .. .. .. 315 8 0 33 .. .. 5,208 Ecuador 69 20 .. 0.15 71 5 0 47 .. 794 12,605 Egypt, Arab Rep. 617 378 1,658 0.23 85 1 122 322 516 1,589 3,340 El Salvador 49 .. .. .. 146 4 1 34 .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia 2,748 599 439 1.12 950 10 27 50 62 10 4,652 Ethiopia 21 12 88 0.17 8 6 0 2 12 25 719 Finland 7,382 .. 4,811 3.47 16,664 21 1,495 2,047 1,799 147 7,328 France 3,440 1,768 30,309 2.10 93,209 20 10,269 4,916 14,743 1,962 79,206 Gabon .. .. .. .. 71 32 .. .. .. .. .. Gambia, The .. .. .. .. 0 14 .. .. 0 0 327 Georgia .. .. 145 0.18 21 3 6 8 221 26 5,441 Germany 3,453 1,200 44,145 2.55 162,421 14 8,792 11,958 49,240 13,177 80,865 Ghana .. .. 81 .. 6 1 0 .. .. .. 61 Greece 1,873 764 4,291 0.50 1,380 10 44 713 803 3,675 10,598 Guatemala 25 12 .. 0.05 150 4 12 80 5 306 11,003 Guinea .. .. .. .. 0 0 0 0 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. 6 Haiti .. .. .. .. .. .. .. 0 .. .. .. Honduras .. .. .. 0.04 8 1 .. 18 .. .. 7,403 340 2010 World Development Indicators 5.13 STATES AND MARKETS Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda,c journal articles % of manu- per million per million factured $ millions Non- people people % of GDP $ millions exports Receipts Payments Residents residents Total 2000­07d 2000­07d 2005 2000­07d 2008 2008 2008 2008 2008 2008 2008 Hungary 1,733 512 2,614 0.97 20,990 24 803 2,007 683 89 7,903 India 137 86 14,608 0.80 6,497 6 148 1,578 5,314 23,626 103,419 Indonesia 205 .. 205 0.05 5,625 11 27 1,328 282 4,324 52,649 Iran, Islamic Rep. 706 .. 2,635 0.67 375 6 .. .. .. .. 3,468 Iraq .. .. .. .. 0 0 0 204 .. .. .. Ireland 2,849 734 2,120 1.34 28,606 26 1,321 30,082 931 76 5,183 Israel .. .. 6,309 4.74 9,239 16 804 1,107 1,528 6,214 13,801 Italy 1,499 .. 24,645 1.14 29,814 7 864 1,811 9,255 870 6,181 Jamaica .. .. .. 0.07 6 0 17 48 21 132 1,708 Japan 5,573 589 55,471 3.45 123,733 18 25,701 18,312 330,110 60,892 119,448 Jordan .. .. 275 0.34 41 1 0 0 59 507 .. Kazakhstan .. .. 96 0.21 2,250 22 .. 87 11 162 8,407 Kenya .. .. 226 .. 78 5 33 28 38 33 1,729 Korea, Dem. Rep. .. .. .. .. .. .. .. .. 6,846 76 2,007 Korea, Rep. 4,627 720 16,396 3.47 110,633 33 2,403 5,543 127,114 43,518 137,461 Kosovo .. .. .. .. .. .. .. .. .. .. .. Kuwait 166 33 233 0.09 9 0 0 0 .. .. .. Kyrgyz Republic .. .. .. 0.25 8 2 3 15 135 3 3,966 Lao PDR 16 .. .. 0.04 .. .. .. .. .. .. .. Latvia 1,861 496 134 0.63 419 7 13 36 114 37 5,101 Lebanon .. .. 234 .. 4 0 .. .. .. .. .. Lesotho 10 11 .. 0.06 .. .. 20 .. .. .. 910 Liberia .. .. .. .. .. .. .. .. .. .. 781 Libya .. .. .. .. .. .. .. 0 .. .. .. Lithuania 2,529 530 406 0.83 1,493 11 1 34 87 18 6,332 Macedonia, FYR 521 75 .. 0.21 21 1 6 25 34 406 4,890 Madagascar 50 15 .. 0.14 7 1 .. .. 14 63 1,318 Malawi .. .. .. .. 2 2 .. .. .. .. 804 Malaysia 372 44 615 0.64 42,764 40 199 1,268 818 4,485 26,027 Mali 42 13 .. .. 3 3 0 1 .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. 0.38 99 7 0 6 .. .. .. Mexico 460 257 3,902 0.50 41,201 19 440 503 685 15,896 84,287 Moldova 724 116 89 0.55 13 4 4 15 273 22 6,643 Mongolia .. .. .. 0.23 7 8 .. .. 103 110 1,936 Morocco 647 48 443 0.64 858 9 0 15 177 834 4,367 Mozambique .. .. .. 0.50 6 4 0 2 18 22 1,240 Myanmar 18 137 .. 0.16 .. .. .. .. .. .. .. Namibia .. .. .. .. 21 1 .. 15 .. .. 1,139 Nepal 59 137 .. .. .. .. .. .. .. .. 1,132 Netherlands 2,680 1,677 13,885 1.75 67,056 22 4,870 3,529 2,421 311 .. New Zealand 4,365 894 2,983 1.26 616 9 178 573 1,256 4,468 17,582 Nicaragua .. .. .. 0.05 5 4 0 .. .. .. 5,975 Niger 8 10 .. .. 2 8 0 0 .. .. .. Nigeria .. .. 362 .. 15 0 .. 178 .. .. .. Norway 5,247 .. 3,644 1.67 5,729 20 642 712 1,223 5,431 16,324 Oman .. .. 111 .. 18 1 .. .. .. .. 1,847 Pakistan 152 64 492 0.67 275 2 38 117 91 1,647 14,872 Panama 62 20 .. 0.25 0 0 0 38 .. 371 10,716 Papua New Guinea .. .. .. .. .. .. .. .. 1 45 612 Paraguay 71 .. .. 0.09 32 9 282 2 .. .. .. Peru .. .. 133 0.15 92 2 3 140 31 1,504 24,825 Philippines 81 10 178 0.12 26,875 66 5 382 216 3,095 15,834 Poland 1,610 226 6,844 0.57 7,172 5 204 1,770 2,488 290 20,609 Portugal 2,630 389 2,910 1.19 3,355 8 80 496 381 24 20,325 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. 0 0 .. .. .. .. .. 2010 World Development Indicators 341 5.13 Science and technology Researchers Technicians Scientific Expenditures High-technology Royalty and Patent Trademark in R&D in R&D and for R&D exports license fees applications applications technical fileda,b fileda,c journal articles % of manu- per million per million factured $ millions Non- people people % of GDP $ millions exports Receipts Payments Residents residents Total 2000­07d 2000­07d 2005 2000­07d 2008 2008 2008 2008 2008 2008 2008 Romania 877 203 887 0.54 2,744 7 240 346 995 36 15,578 Russian Federation 3,305 516 14,412 1.12 5,107 7 453 4,595 27,712 14,137 57,165 Rwanda .. .. .. .. 1 7 62 1 .. .. 238 Saudi Arabia .. .. 575 0.05 121 1 0 0 128 642 .. Senegal 276 .. 83 0.09 46 5 1 8 .. .. .. Serbia 1,190 298 849 0.34 .. .. 27 192 386 237 9,479 Sierra Leone .. .. .. .. .. .. .. 1 .. .. 1,017 Singapore 6,088 529 3,609 2.61 120,345 51 839 9,148 793 8,899 18,263 Slovak Republic 2,290 415 919 0.46 3,171 5 164 182 167 75 7,267 Slovenia 3,109 1,537 1,035 1.48 1,558 6 41 250 301 6 5,192 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 382 130 2,392 0.96 2,011 5 54 1,676 .. 5,781 29,833 Spain 2,784 1,029 18,336 1.28 9,916 5 801 3,251 3,632 252 55,586 Sri Lanka 93 65 136 0.17 101 2 0 0 201 264 5,916 Sudan .. .. .. 0.29 0 0 .. 0 3 13 1,075 Swaziland .. .. .. .. 0 0 0 121 .. .. 1,004 Sweden 5,215 .. 10,012 3.68 21,778 16 4,938 2,005 2,527 398 14,998 Switzerland 3,436 2,317 8,749 2.93 41,111 23 .. .. 1,594 439 31,514 Syrian Arab Republic .. .. 77 .. 51 1 0 25 124 133 2,757 Tajikistan .. .. .. 0.06 .. .. 1 0 26 .. 2,284 Tanzania .. .. 107 .. 5 1 0 0 .. .. 556 Thailand 311 160 1,249 0.25 32,370 25 101 2,559 802 5,939 35,422 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 34 17 .. .. 0 0 0 5 .. .. .. Trinidad and Tobago .. .. .. 0.10 36 1 .. .. .. 551 .. Tunisia 1,588 43 571 1.02 674 5 32 12 .. .. .. Turkey 680 102 7,815 0.71 1,807 2 .. 729 2,221 176 76,333 Turkmenistan .. .. .. .. .. .. .. .. .. .. 2,819 Uganda .. .. 93 0.41 5 1 3 2 6 1 .. Ukraine 1,458 325 2,105 0.87 1,519 3 72 754 2,825 2,872 33,019 United Arab Emirates .. .. 229 .. 207 3 .. .. .. .. .. United Kingdom 2,881 879 45,572 1.84 61,767 19 13,904 10,615 16,523 6,856 35,705 United States 4,663 .. 205,320 2.67 231,126 27 91,600 26,615 231,588 224,733 294,070 Uruguay 373 .. 204 0.36 72 4 0 8 33 706 11,501 Uzbekistan .. .. 157 .. .. .. .. .. 262 186 5,007 Venezuela, RB .. .. 534 .. 122 4 0 349 .. .. .. Vietnam 115 .. 221 0.19 2,376 9 .. .. .. .. 4,971 West Bank and Gaza .. .. .. .. .. .. 0 1 .. .. .. Yemen, Rep. .. .. .. .. 0 0 9 ­5 11 24 4,375 Zambia .. .. .. 0.03 8 2 0 1 .. .. 1,159 Zimbabwe .. .. .. .. 48 3 .. .. .. .. .. World 1,270 w .. w 708,086 s 2.21 w 1,856,930 s 17 w 181,285 s 187,563 s 988,514 s 633,066 s 2,963,306 s Low income .. .. .. .. .. 6 111 73 .. .. .. Middle income 613 .. 123,584 0.96 540,759 16 3,751 34,266 175,013 192,993 1,750,931 Lower middle income 479 .. 67,251 1.23 339,779 22 1,420 17,845 134,138 130,987 1,096,947 Upper middle income 1,252 .. 56,333 0.81 124,869 9 2,331 16,421 40,875 62,006 558,047 Low & middle income 595 .. 124,833 0.96 467,925 16 3,862 34,339 181,806 193,656 1,815,399 East Asia & Pacific 1,071 .. 44,064 1.49 .. 28 898 15,864 196,416 108,823 755,285 Europe & Central Asia 2,013 336 35,489 0.83 23,854 6 1,090 8,805 38,406 18,614 317,547 Latin America & Carib. 495 .. 20,045 0.66 58,093 12 1,453 5,669 .. .. 440,687 Middle East & N. Africa .. .. 6,243 0.48 1,528 4 41 345 .. .. 16,421 South Asia 129 86 15,429 0.79 .. 5 196 1,714 5,580 25,831 132,894 Sub-Saharan Africa .. .. .. .. 3,260 3 184 1,942 .. .. .. High income 3,948 .. 583,253 2.47 1,313,457 18 177,423 153,224 796,585 427,519 1,223,790 Euro area 2,872 1,313 158,985 2.04 454,048 14 31,349 63,603 77,364 20,234 316,397 a. Original information was provided by the World Intellectual Property Organization (WIPO). The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. b. Excludes applications filed under the auspices of the African Regional Intellectual Property Organization (11 by residents, 424 by nonresidents), the European Patent Office (146,150 by nonresidents), and the Eurasian Patent Organization (3,066 by nonresidents). c. Excludes applications filed under the auspices of the Office for Harmonization in the Internal Market (87,640). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands. 342 2010 World Development Indicators 5.13 STATES AND MARKETS Science and technology About the data Definitions Science and technology is too broad and complex to A patent is an exclusive right granted for a specified · Researchers in R&D are professionals engaged in quantify with a single set of indicators, but those in the period (generally 20 years) for a new way of doing conceiving of or creating new knowledge, products, table shed light on countries' technology base. Tech- something or a new technical solution to a problem-- processes, methods, and systems and in managing the nological innovation, often fueled by government-led an invention. The invention must be of practical use projects concerned. Postgraduate doctoral students research and development (R&D), has been the driving and display a characteristic unknown in the existing (ISCED97 level 6) engaged in R&D are considered force for industrial growth. The best opportunities to body of knowledge in its field. researchers. · Technicians in R&D and equivalent improve living standards come from science and tech- Most countries have systems to protect patentable staff are people whose main tasks require technical nology. Countries able to access, generate, and apply inventions. The Patent Cooperation Treaty provides a knowledge and experience in engineering, physical and scientific knowledge have a competitive edge. And two-phase system for filing patent applications. An appli- life sciences (technicians), and social sciences and high-quality scientific input improves public policy. cant files an international application and designates humanities (equivalent staff). They engage in R&D by The United Nations Educational, Scientific, and Cul- the countries in which protection is sought (all eligible performing scientific and technical tasks involving the tural Organization (UNESCO) Institute for Statistics col- countries are automatically designated in every applica- application of concepts and operational methods, nor- lects data on researchers, technicians, and expenditure tion under the treaty). The application is searched and mally under researcher supervision. · Scientific and on R&D through surveys and from other international published, and, optionally, an international preliminary technical journal articles are published articles in sources. R&D covers basic research, applied research, examination is conducted. In the national (or regional) physics, biology, chemistry, mathematics, clinical med- and experimental development. Data on researchers phase the applicant requests national processing of the icine, biomedical research, engineering and technol- and technicians are calculated as full-time equivalents. application and initiates the national search and granting ogy, and earth and space sciences. · Expenditures for Scientific and technical article counts are from jour- procedure. International applications under the treaty R&D are current and capital expenditures on creative nals classified by the Institute for Scientific Informa- provide for a national patent grant only--there is no inter- work undertaken to increase the stock of knowledge, tion's Science Citation Index (SCI) and Social Sciences national patent. The national filing represents the appli- including on humanity, culture, and society, and the Citation Index (SSCI). Counts are based on fractional cant's seeking of patent protection for a given territory, use of knowledge to devise new applications. · High- assignments; articles with authors from different whereas international filings, while representing a legal technology exports are products with high R&D inten- countries are allocated proportionally to each country right, do not accurately reflect where patent protection sity, such as in aerospace, computers, pharmaceuti- (see Definitions for fields covered). The SCI and SSCI is sought. Resident filings are those from residents of cals, scientific instruments, and electrical machinery. databases cover the core set of scientific journals but the country or region concerned. Nonresident filings are · Royalty and license fees are payments and receipts may exclude some of local importance and may reflect from outside applicants. For regional offices such as the between residents and nonresidents for authorized some bias toward English-language journals. European Patent Office, applications from residents of use of intangible, nonproduced, nonfinancial assets R&D expenditures include all expenditures for R&D any member state of the regional patent convention are and proprietary rights (such as patents, copyrights, performed within a country, including capital costs considered a resident filing. Some offices (notably the trademarks, and industrial processes) and for the use, and current costs (wages and associated costs of U.S. Patent and Trademark Office) use the residence of through licensing, of produced originals of prototypes researchers, technicians, and supporting staff and the inventor rather than the applicant to classify filings. (such as films and manuscripts). · Patent applications other current costs, including noncapital purchases A trademark is a distinctive sign identifying goods or filed are worldwide patent applications filed through of materials, supplies, and R&D equipment such as services as produced or provided by a specific person or the Patent Cooperation Treaty procedure or with a utilities, reference materials, subscriptions to librar- enterprise. A trademark protects the owner of the mark national patent office. · Trademark applications filed ies and scientific societies, and lab materials). by ensuring exclusive right to use it to identify goods or are annual applications to register a trademark with a The method for determining high-technology exports services or to authorize another to use it. The period national or regional IP office. was developed by the Organisation for Economic Co- of protection varies, but a trademark can be renewed Data sources operation and Development in collaboration with Euro- indefinitely for an additional fee. Detailed components stat. It takes a "product approach" (as distinguished of trademark filings, available on the World Develop- Data on R&D are provided by the UNESCO Insti- from a "sectoral approach") based on R&D intensity ment Indicators CD-ROM and WDI Online, include appli- tute for Statistics. Data on scientific and technical (R&D expenditure divided by total sales) for groups of cations filed by direct residents (domestic applicants journal articles are from the U.S. National Science products from Germany, Italy, Japan, the Netherlands, filing directly at a given national intellectual property Board's Science and Engineering Indicators 2008. Sweden, and the United States. Because industrial [IP] office); direct nonresident (foreign applicants filing Data on high-technology exports are from the sectors specializing in a few high-technology prod- directly at a given national IP office); aggregate direct United Nations Statistics Division's Commodity ucts may also produce low-technology products, the (applicants not identified as direct resident or direct Trade (Comtrade) database. Data on royalty and product approach is more appropriate for analyzing nonresident by the national office); and Madrid (interna- license fees are from the International Monetary international trade. This method takes only R&D inten- tional applications filed via the World Intellectual Prop- Fund's Balance of Payments Statistics Yearbook. sity into account, but other characteristics of high erty Organization (WIPO)­administered Madrid System Data on patents and trademarks are from the technology are also important, such as know-how, to the national or regional IP office). Data are based on World Intellectual Property Organization's WIPO Pat- scientific personnel, and technology embodied in pat- information supplied to WIPO by IP offices in annual sur- ent Report: Statistics on Worldwide Patent Activity ents. Considering these characteristics would yield a veys, supplemented by data in national IP office reports. (2009) and www.wipo.int. different list (see Hatzichronoglou 1997). Data may be missing for some offices or periods. 2010 World Development Indicators 343 Text figures, tables, and boxes GLOBAL LINKS Introduction T 6 he Millennium Development Goals (MDGs) recognize that expanding international trade can help developing economies achieve the MDGs by fostering economic growth and increasing job opportunities. At the 2000 Millennium Summit devel- oped countries agreed to increase market access for developing countries by lowering tariffs and granting tariff-free access to all goods (except weapons). They also agreed to increase aid for promoting trade and to decrease domestic agricultural subsidies that harm imports from developing economies. The world today is a more integrated place than in 1990--the MDGs benchmark year. World exports of goods and services nearly tripled between 1990 and 2007--a 7 percent annual average growth rate--and foreign direct investment increased ninefold between 1990 and 2008. More people are moving abroad (temporarily or permanently), more investors are buying foreign stocks, and more companies are expanding to over- seas markets. And developing economies' trade has expanded from 17.3 percent of world exports and 17.0 percent of world imports in 1990 to 28.1 percent of exports and 25.9 percent of imports in 2007. Though all economies may benefit from international Four MDG indicators track developed econo- integration, the benefits may not be shared equally mies' commitments to increase market access for among them. Successful integration depends partly developing economies and support their programs on geography and natural resources: economies with to promote trade: the proportion of total developed substantial coastal areas or located near large eco- country imports (by value and excluding arms) admit- nomic centers may increase their share of the global ted free of duty from developing economies and market much faster than landlocked or isolated econ- least developed countries; average tariffs imposed omies. And economies with abundant natural resourc- by developed economies on agricultural products, es and cheap labor may attract foreign investors and textiles, and clothing from developing economies; grow faster than economies with fewer resources. agricultural support in Organisation for Economic The question remains: can trade expansion and Co-operation and Development (OECD) economies economic integration promote human development? as a percentage of their gross domestic product Trade expansion provides developing economies with (GDP); and the share of official development assis- a larger market in which to sell goods and services, tance provided to build trade capacity. boosting production. But trade liberalization can also But these indicators are not enough to describe harm domestic industries by exposing them to fierce the many instruments of trade policy, the changing international competition. Trade expansion can pro- patterns of trade, and their impact on human devel- mote rapid economic growth, potentially furthering opment. This introduction looks beyond the MDG human development. But greater engagement with indicators to the characteristics of economies and international markets is sometimes accompanied by their trade policies that may ultimately affect their increased income inequality. Developing economies success in achieving the MDGs. must consider how integration affects the most vul- nerable segments of the population. Increased trade Trade expansion and development can accelerate progress toward the MDGs only if it How does trade expansion affect human develop- both fosters economic growth and improves living ment and poverty? In theory, trade expansion should standards for the poorest and most vulnerable. contribute directly to poverty reduction by increasing 2010 World Development Indicators 345 the returns on the most abundant factor of pro- of successful economic development (OECD duction, which in developing economies tends 2009b; Commission on Growth and Develop- to be low-skilled labor. But empirical studies ment 2008). disagree on the causal relationship between Economies benefit from increased interna- trade expansion and poverty reduction. Some tional trade because it allows them to produce studies find an increase in inequality after trade commodities for which they have a comparative liberalization (World Bank and others 2005; advantage, sell those goods in a larger world UNDP 2005; Kremer and Maskin 2006). Others market, and import goods and services that find that trade has a beneficial effect on poverty are more costly to produce domestically. Export reduction--but may not be the most important expansion increases output, generates jobs, factor (Billmeier and Nannicini 2007). and raises household income, which may in turn Despite the lack of agreement on the effects improve health and other living conditions. Trade of trade expansion on poverty, economic the- expansion can also raise education standards by ory and empirical evidence offer no reason to giving people greater incentives to improve their restrict trade. An increase in trade, especially skills. Foreign direct investors that are initially exports, is associated with economic growth attracted by developing economies' cheap and (figure 6a). This simple association between abundant labor supply may also introduce new GDP growth and export growth overlooks differ- technology and know-how, and foreign competi- ences between countries and other factors that tion may spur productivity and efficiency gains. affect economic growth. For example, small Exports generate the foreign exchange needed island economies may have to be more open to finance critical imports and may increase gov- in order to generate economic growth, while ernment revenue through taxes that can be used economies with sufficient domestic markets to finance social protection programs. Econo- may require less export expansion to achieve mies with small domestic markets likely have economic growth. Analysis in World Develop- lower welfare and growth rates if they isolate ment Indicators 2007 showed that for countries themselves from the international movement of starting from similar positions, countries that goods, factors, people, and ideas. opened their economy (as measured by the But trade liberalization brings additional ratio of imports and exports of goods and ser- challenges for developing economies in man- vices to GDP) less rapidly recorded much lower aging their external accounts. Economies that per capita GDP growth. import more than they export are vulnerable In recent years many economies, especially to trade imbalance. Eighty of 111 developing in East Asia, experienced rapid growth in GDP economies for which data are available had a and exports. But did export expansion trigger negative trade balance in 2008. For 67 of them this economic growth or are increasing exports the trade balance to GDP ratio has deteriorated an outcome of growth? Although economists since 1990. The ratio has worsened by more do not agree on causality, most empirical evi- than 5 percentage points for 44 economies and dence indicates that greater openness to trade by more than 10 percentage points for 26 econ- is an important element explaining growth omies. Dependence on imports may put poor performance--and has been a central feature countries at risk for currency crises, especially if they have limited access to foreign capital. Growth of exports and growth of GDP go hand in hand 6a Many developing economies are sensitive to opening their economies to trade because they Average annual growth of GDP, 2000­08 (percent) 20 worry that liberalization might merely increase cheap imports and harm local businesses instead of creating new export enterprises. 10 Developing economies have 0 increased their share of world trade Developing economies have become more ­10 open, as measured by the ratio of trade (im- ­10 0 10 20 30 Average annual growth of exports of goods and services, 2000­08 (percent) ports plus exports) to GDP, which rose from Source: World Development Indicators data files. 34 percent in 1990 to 62 percent in 2008. Export revenues, constituting 30 percent of 346 2010 World Development Indicators GLOBAL LINKS developing economies' outputs in 2008 (up economies cannot match. Under the MDG from 18 percent in 1990), are especially im- framework, OECD members promised to lower portant for low-income economies (figure 6b). subsidies to agricultural producers, exporters, Developing economies increased their share of and consumers. Total agricultural supports as world trade from 16 percent of merchandise ex- a share of GDP have fallen for most OECD mem- ports in 1990 to 33 percent in 2008 and from bers, but in nominal dollar terms support actu- 12 percent of services exports in 1990 to 21 ally increased 3.2 percent between 2007 and percent in 2008 (figure 6c). But the benefits 2008, to $376 billion. were not shared equally. Low-income econo- In the last decade some developing econo- mies accounted for only 1 percent of world mer- mies, especially the least developed countries, chandise exports and less than 1 percent of became net food importers. In the mid-1970s world service exports in 2008. Export revenues are increasingly larger portions of low-income economies' GDP 6b Low-income exporters specialize Share of GDP (percent) in labor-intensive goods 50 Export-led growth can improve human devel- Low-income economies 40 opment outcomes and reduce poverty if it Middle-income economies China (excluding China and India) fosters employment in labor-intensive sectors 30 High-income economies where the poor have a stake. As the share of 20 India agriculture and labor-intensive manufacturing- 10 --such as textiles, clothing, and footwear--in world exports has fallen, both developing and 0 1990 1995 2000 2005 2008 high-income economies have adjusted by mov- ing to capital-intensive manufacturing. But low- Source: World Development Indicators data files. income economies, following their comparative advantage, still specialize in labor-intensive ex- Developing economies' share in world exports has increased, ports (figure 6d), which face higher tariffs than especially for large middle-income economies 6c do other products (figure 6e). Low-income economies China India Transitioning from labor-intensive exports to Share of world exports (percent) Middle-income economies (excluding China and India) 40 capital-intensive exports may be difficult. Ide- ally, increased trade should mean more jobs, 30 lower unemployment, and higher wages. But trade liberalization has often failed to improve 20 employment because new export industries 10 have been capital- intensive manufactures, unable to create suffi cient employment to 0 absorb all of the workers transitioning from the 1990 2000 2005 2008 1990 2000 2005 2008 Exports of goods Exports of services agriculture sector. Source: World Bank staff estimates, based on data from International Monetary Fund's Balance of Payments database. The poorest of the world's population live in rural areas and work in agriculture or fisheries. Boosting agricultural exports could increase Low-income economies specialize in labor-intensive exports 6d agricultural employment and wages, thereby Agricultural products Clothing reducing poverty. Share of group nonoil merchandise exports (percent) Footwear and leather Textiles 40 Most world trade in agriculture occurs be- tween high-income economies, with low-income 60 economies providing only about 2 percent of global agricultural exports (figure 6f). Devel- 40 oping economies have limited representation in global agricultural markets partly because 20 their exports face higher tariffs from both high- 0 income and developing economy partners. 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 High-income economies also provide subsidies Least developed Low income Lower middle income Upper middle income High income to their farmers, enabling them to sell agricul- Source: World Bank staff estimates, based on data from the United Nations Statistics Division's Comtrade database. tural products at very low prices that developing 2010 World Development Indicators 347 Labor-intensive products face higher tariffs than other commodities 6e 18 of 28 least developed countries were net food exporters; by the mid-1990s 7 of them Simple applied tariff rates (percent) 1998 2008 had become net food importers; and by the 10 mid-2000s 10 of them had become net food 8 importers. Vulnerable to increasing food prices, 6 net food importers can suffer food insecurity 4 and malnutrition. 2 Trade diversification has 0 improved--but unevenly Food Agricultural Fuels Ores and Machinery Textiles Food Agricultural Fuels Ores and Machinery Textiles raw materials nonferrous and and clothing raw materials nonferrous and and clothing Trade diversifi cation--in both partners and metals transport metals transport equipment equipment products--affects developing economies' ability On imports from low-income economies On imports from middle-income economies to cope with external shocks such as commod- Source: World Bank staff estimates, based on data from the United Nations Statistics Division's Comtrade database and the United Nations Conference on Trade and Development's Trade Analysis and Information System database. ity price changes and demand fluctuations. Compared with two decades ago, devel- oping economies are trading more with other Low-income economies have a small share in the global agricultural market 6f developing economies, especially with econo- mies in the same region (figure 6g). Developing From low-income economies From lower middle-income economies Share of world exports (percent) From upper middle-income economies economies' exports to other developing econo- 75 mies increased from 16 percent of merchan- dise exports in 1990 to 31 percent in 2008. 50 Expansion of East Asia and Pacific and Sub- Saharan African economies' trade with other 25 developing economies has been remarkable. East Asia and Pacific's exports to other devel- 0 oping economies rose from 13 percent of the 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 1990 2007 region's total merchandise exports in 1990 Agricultural products Manufactures Textiles Fuels Clothing Footwear and leather to 29 percent in 2008. Sub-Saharan Africa's Source: World Bank staff estimates, based on data from the United Nations Statistics Division's Comtrade database. exports to other developing economies rose from 12 percent in 1990 to 37 percent in 2008. Still, more than 60 percent of developing Developing economies are trading more with other developing economies 6g economies' merchandise exports in 2008 were Low-income economies directed to high-income economies. The eco- Share of exports (percent) nomic crisis that began in 2008 lowered devel- 100 oped economies' demand for imports, hurting Exports to high-income economies 75 the export revenues of developing economies that depended on high-income markets. Some 50 economies, especially the poorest, depend on Exports to developing economies within region just a few partner economies. For example, 25 more than 95 percent of the merchandise 0 Exports to developing economies outside region exports from Chad, Guinea-Bissau, and Niger 1990 1995 2000 2005 2008 in 2008 were directed to their five largest trad- Middle-income economies ing partners. Many developing economies have improved 100 their product diversification, but some remain Exports to high-income economies 75 dependent on only a few products. The top-five export commodities (which differ by country) 50 made up around 75 percent of Sub-Saharan African economies' merchandise exports. And 25 Exports to developing economies within region for some developing economies the top-fi ve 0 Exports to developing economies outside region share exceeds 90 percent of total merchandise 1990 1995 2000 2005 2008 exports (figure 6h). On average, the share of Source: World Bank staff estimates, based on data from the International Monetary Fund Direction of Trade database. the top-five export commodities in total exports tends to be higher for low-income economies 348 2010 World Development Indicators GLOBAL LINKS (71 percent) than for middle-income (59 percent) For some developing economies only five products and high-income (50 percent) economies. make up more than 90 percent of total merchandise exports 6h Nigeria Developed economies have lowered Comoros trade barriers, but not enough Maldives Algeria Trade barriers encompass tariffs, quotas, anti- Azerbaijan dumping duties, export subsidies, monopolistic Bhutan Sudan measures, and technical regulations. Measur- São Tomé and Príncipe ing overall trade restrictiveness involves aggre- Tonga Mali gating these different forms of trade barriers Venezuela, RB across goods with different economic impor- Seychelles Yemen, Rep. tance. The World Bank's Tariff Trade Restric- Belize tiveness Index (TTRI) and Overall Trade Restric- Botswana tiveness Index (OTRI) measure the impact of a Jamaica Rwanda country's trade policy on its imports. The TTRI Niger is the estimated uniform tariff equivalent to the St. Kitts and Nevis Zambia effectively applied tariffs currently imposed on various import products. The OTRI is the uni- Sub-Saharan Africa average Low-income economy average form tariff equivalent to current tariff and non- Middle-income economy average tariff barriers to imports. A comparison of the High-income economy average TTRI and the OTRI implies that nontariff barri- 40 50 60 70 80 90 100 Share of top-five export products in total merchandise exports (percent) ers are much higher than tariffs (figure 6i). On Source: World Bank's World Trade Indicators 2009/10 database. average, low-income economies impose higher tariffs and nontariff barriers than do other in- come groups to protect domestic production Nontariff barriers on imports may be higher than tariff barriers 6i and raise revenue through taxes on imports. Percent All imports Agricultural imports Nonagricultural imports Although the average tariff and nontariff barri- 40 ers imposed by high-income economies are low, their restrictions on agricultural products tend 30 to be high. Because most of the world's poor 20 earn their living through agriculture and other labor-intensive activities, substantial trade re- 10 strictions on these commodities block market access by the poor. Because high-income econ- 0 Low Lower Upper High Low Lower Upper High omies have the largest consumer markets, their income middle middle income income middle middle income income income income income trade policies have the most impact on develop- Tariff Trade Restrictiveness Index, 2007 Overall Trade Restrictiveness Index, 2007 ing economies' exports. Exports from most economies face tariff Source: World Bank and IMF 2009b. and nontariff barriers in other economies. But low-income economies tend to face higher over- Agricultural exports from low-income economies face the highest overall restrictions 6j all restrictions, especially for agricultural prod- ucts (figure 6j). Market access by developing Percent All exports Agricultural exports Nonagricultural exports economies may also be affected by strict rules 40 of origin that restrict preferential treatment of 30 commodities not wholly produced in the export- ing country. 20 As tariffs are the most widely known trade barrier, the MDG framework monitors the aver- 10 age tariffs imposed by OECD members on 0 imports from developing economies. When Low Lower Upper High Low Lower Upper High income middle middle income income middle middle income examined in isolation, this indicator appears income income income income to show that developed economies have sig- Tariff Trade Restrictiveness Index, 2007 Overall Trade Restrictiveness Index, 2007 nificantly lowered tariff barriers. But the actual Source: World Bank and IMF 2009b. situation is more complex. Averaging tariff rates 2010 World Development Indicators 349 across thousands of products can mask high tar- States, or shoes entering the European Union, iffs on certain commodities that are particularly antidumping initiations have a chilling effect on important to developing economies. For some imports--even when they do not result in impo- OECD members the maximum applied tariff rate sition of antidumping duties. Only about half of can be as high as 887 percent (table 6k). antidumping initiations are later imposed. Among the most commonly used nontariff barriers are antidumping actions. Many high- Trade in services has grown rapidly, income economies--and recently develop- but total value remains small ing economies as well--initiate antidumping Growth in developing economies' trade in ser- investigations. Whether for shiitake mush- vices averaged 21 percent a year between rooms entering Japan, steel entering the United 2005 and 2008, surpassing their previous performances and those of high-income econo- Some OECD members apply very high tariffs selectively (percent) 6k mies (figure 6l). Europe and Central Asia expe- rienced the highest growth, while Sub-Saharan Simple Weighted Share of tariff average average Maximum lines with rate of Africa lagged behind. But trade in services still Year tariff rate tariff rate tariff rate 15 percent or more made up less than 20 percent of world trade Australia 2008 4 2 18 5 in 2008. Canada 2008 4 1 95 7 The World Trade Organization recognizes Iceland 2008 2 1 76 6 four modes of trade in services: cross-border Japan 2008 3 1 50 7 exchange of services (such as the purchase of services from a foreign supplier and outsourc- Korea, Rep. 2007 8 7 887 5 ing), consumption abroad (such as tourism, New Zealand 2008 3 2 13 0 education, and health services), commercial Norway 2008 1 0 555 1 presence of foreign companies in a country United States 2008 3 1 350 4 (involving foreign direct investments), and move- European Union 2008 2 1 75 2 ment of people. Trade in services usually faces Note: Based on effectively applied tariffs across all imports. tighter regulation and higher barriers than trade Source: World Bank staff estimates, based on data from the United Nations Conference on Trade and in merchandise. Development's Trade Analysis and Information System database. Large middle-income economies recently increased their market shares in the outsourc- ing of services. For example, China is a key prod- Growth of trade in services peaked in the last three years 6l uct development center for General Electric, Intel, Microsoft, Philips, and other large elec- tronic firms focused on hardware and software Europe & Central Asia design. India is the largest offshore provider of information technology services, technical South help desks, and web support. But few develop- Asia ing economies have benefited from the recent expansion of outsourcing. Poorer countries tend East Asia & Pacific to lack the necessary infrastructure--such as robust telecommunication networks and a reli- Sub-Saharan able power supply--and skilled and educated Africa workers. Outsourcing companies also require Latin America & strong legal systems that ensure data security Caribbean and privacy, which many developing economies lack. Middle East & North Africa a Tourism is one of the largest segments of trade in services, generating employment, High providing valuable foreign currency exchange, income 1990­2000 2000­05 and increasing government revenues through 2005­08 0 10 20 30 taxation. The tourism industry employs peo- Average annual nominal growth in trade in services (percent) ple with various skill sets, including cleaners, a. Data for 2000­05 and 2005­08 are unavailable. Source: World Development Indicators data files. drivers, beauticians, managers, and chefs. Developing economies' receipts from tourism 350 2010 World Development Indicators GLOBAL LINKS have increased considerably, from $92 bil- Developing economies expanded their lion (19 percent of the world total) in 1995 to share in the world tourism industry 6m $324 billion (28 percent) in 2008 (figure 6m). Share of world tourism East Asia & Pacific Europe & Central Asia Latin America & Caribbean East Asia and Pacific and Europe and Central receipts (percent) Middle East & North Africa South Asia Sub-Saharan Africa Asia are the biggest benefiters. Residents of 30 developing economies are also increasing their spending on tourism to other countries. 20 Human migration brings many benefi ts, such as remittances, improved skills and 10 experience, and the transfer of technology dur- ing return migration. Workers' remittances-- including employee compensation and migrant 0 1995 2000 2005 2008 transfers--have become a large source of for- eign exchange for many developing economies, increasing consumption and investment as well Share of world tourism expenditures (percent) as the income of recipient families (figure 6n). 30 For low-income economies with a negative trade balance, remittances provide an impor- 20 tant source of external financing. Yet migration may have negative effects, siphoning off skilled workers and increasing inequality between 10 remittance recipients and other families. Some economies have increased restrictions on labor 0 services, including restrictions on the tempo- 1995 2000 2005 2008 rary cross-border movement of construction workers. Source: World Development Indicators data files. Trade facilitation is improving Remittances have become an important source of slowly--but lower income external financing for low- and middle-income economies 6n economies lag behind Trade facilitation may boost trade as effectively Low-income economies Share of GDP (percent) as tariff reduction (Hertel, Walmsley, and Ita- 15 kura 2001; Wilson, Mann, and Otsuki 2004). 10 The MDG framework recognizes the importance Net aid received Remittances received of trade facilitation by including an indicator to 5 monitor aid for building trade capacity. 0 Foreign direct investment net inflows Aid for trade aims to help developing ­5 Net exports of goods and services economies--especially low-income ones-- ­10 overcome structural and capacity limitations ­15 that undermine their ability to produce, com- 1990 1995 2000 2005 2008 pete, and fully benefit from global integration. One of many ways aid for trade can help devel- Middle-income economies oping economies is by identifying infrastructure bottlenecks, improving logistics efficiency, and 15 smoothing the supply chain. But how should the 10 impact of aid for trade on the performance of 5 Foreign direct investment net inflows Net exports of goods and services developing economies be measured? Outcome 0 indicators such as value and growth of exports Remittances received Net aid received and imports are important; so are indicators ­5 that measure trade logistics performance and ­10 trade-related infrastructure. Better logistics ­15 1990 1995 2000 2005 2008 performance is associated with trade expan- sion, export diversification, and the ability to Source: World Development Indicators data files. attract foreign direct investment. 2010 World Development Indicators 351 Logistics performance is lowest for low-income economies 6o Overall Logistics Performance Index (1 = low, 5 = high), 2007 and 2009 surveys 2007 2009 5 4 3 2 1 East Europe & Latin Middle South Sub- Low Lower Upper High Asia & Central America & East & Asia Saharan income middle middle income Pacific Asia Caribbean North Africa income income Africa Source: Arvis and others 2007 and 2010. Until recently, data on trade facilitation and export competitiveness. For example, a 2001 logistics performance have been scarce. But study found that a 1 percent reduction in the in 2007 and 2009 the World Bank surveyed cost of maritime and air transport services logistics professionals and created a Logistics could increase Asian GDP by $3.3 billion Performance Index that summarizes a country's (UNCTAD 2001). Transport costs are asymmet- performance in six areas of trade logistics: effi - ric worldwide and are especially high for land- ciency of customs clearance processes, quality locked developing economies. In Central Asia of trade- and transport-related infrastructure, the cost of transporting a 40 ton container by ease of arranging competitively priced ship- road between Central Asia and Europe varies ments, competence and quality of logistics ser- depending on the direction traveled: moving vices, ability to track and trace consignments, goods west to east costs $6,000 but moving and frequency with which shipments reach the them east to west costs only $4,000 (Arvis, Ra- consignee within the scheduled or expected balland, and Marteau 2007). time. The surveys suggest that transportation Comprehensive data on transport costs for costs, the time to import and export, and cus- all developing economies are not available. One toms efficiency are all key but that the most proxy indicator for transport costs is the ship- important determinant of export competitive- ping rates of companies that operate globally ness and volume is the overall reliability and in the international freight moving business. predictability of the supply chain (Arvis and For example, the median DHL rate for sending others 2007, 2010). In essence, traders need a 1 kilogram package to the United States was to be able to move goods and services across 1.6 times higher from low-income economies borders quickly and cheaply. And in economies than from high-income economies. with poor logistics performance, importers and Transport costs depend on a mixture of exporters incur additional expenses to mitigate geographic and economic circumstances. the effects of unreliable supply chains. Freight costs tend to be higher for low-income High-income economies dominate the top economies. Landlocked countries or countries ratings for logistics performance, while the 10 without access to large economic centers face lowest performing economies are all low- and much higher transportation costs than do lower middle-income economies, mostly in coastal countries and countries located near Africa. Between 2006 and 2009 the overall business centers (box 6p). These economies logistics performance of economies improved, tend to have poor infrastructure and thin traf- but low-income economies tend to perform fi c densities, further impeding their export worse than middle- and high-income economies competitiveness. (figure 6o). Improving trade infrastructure Higher transport costs impede to facilitate trade trade in developing economies Infrastructure, especially transport services The costs of international transport services are infrastructure, is vital for trade facilitation. a crucial determinant of developing economies' Port quality and accessibility, road quality, 352 2010 World Development Indicators GLOBAL LINKS and access to global shipping and air freight Challenges for landlocked economies 6p networks influence the overall logistics perfor- Millennium Development Goal 8 focuses on landlocked countries. Like other small, low- mance of international traders. For example, a income economies, landlocked developing economies have unpredictable supply chains, 2006 study concluded that investment in up- reflecting uncertainty in shipment delivery time, low demand levels, greater inventory costs, grading and maintaining a trans-African highway and low private sector capacities. Rent-seeking activities are higher when shipments transit through other economies and international corridors, contributing to higher trading costs network linking 83 major African cities could in- (Arvis, Raballand, and Marteau 2007). On average, landlocked economies trade 30 percent crease intra-African trade from $10 billion a year less than coastal economies (Limao and Venables 2001). to $30 billion (Buys, Deichmann, and Wheeler Access to global shipping and freight networks is an important determinant of a country's export competitiveness. Because landlocked economies lack direct access to liner ship- 2006). Similarly, improving road networks in 27 ping networks, access to air cargo networks is especially important to them. Though faster European and Central Asian economies could and more reliable than road transport, air freight typically costs 4­5 times more than road increase their trade by as much as 50 percent transport and 12­16 times more than sea transport (World Bank 2009a). Consequently, demand for air freight is limited in landlocked developing economies that ship small volumes (Shepherd and Wilson 2007). of low-value-per-unit goods. Establishing and improving the efficiency of international trade Infrastructure quality may be just as impor- corridors could significantly benefit landlocked economies. tant as its availability. According to a survey of Source: World Bank staff. logistics professionals, poor infrastructure qual- ity is a widespread constraint on the logistics performance of developing economies. More- Lead time to import and export is longest for low-income economies 6q over, satisfaction with infrastructure quality was much higher for economies ranked highest Average lead time, 2009 (days) To export To import in overall logistics performance (Arvis and oth- 10 ers 2010). Port infrastructure is important for 8 economies that rely heavily on sea transport. 6 According to a survey of business executives, 62 of 69 economies ranked below average in 4 port quality were developing economies, 22 of 2 them low-income (World Economic Forum 2009). 0 East Europe & Latin Middle South Sub- Low Middle High Asia & Central America & East & Asia Saharan income income income Time delays add to trading costs Pacific Asia Caribbean North Africa Africa Traders also face indirect transport costs: the time required to import and export goods, bor- Source: Arvis and others 2010. der inefficiency, and the risk of freight loss or damage. Indirect costs can be higher than di- rect costs. For example, World Bank research Survey, for 50 percent of shipments, import suggests that one additional day of ship- and export lead times are three times longer ping delays cuts trade by at least 1 percent for low- income economies than for high-income (Djankov, Freund, and Pham 2010). In Europe economies (figure 6q). And lead time to export and Central Asia and Sub-Saharan Africa tariff from or import to Sub-Saharan African econo- equivalents of time to export were more than mies averages 7­8 days--much longer than for twice the average applied tariff (USAID 2007). other developing regions such as Europe and According to the 2009 Logistics Performance Central Asia and South Asia. 2010 World Development Indicators 353 Tables 6.1 Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers' people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2008 2008 2008 2008 2008 2008 2000­05 2005 2000 2008 2008 Afghanistan 37.9 .. 0.0 2.8 .. .. 805 0.3 22.6 1 1 Albania 53.5 39.5 0.0 7.6 0.8 12.2 ­100 2.7 17.4 127 220 Algeria 70.5 .. 1.0 1.6 .. 1.3b ­140 0.7 9.4 18 .. Angola 102.9 26.5 4.6 2.0 3.0 0.1 175 0.3 3.6 .. 17 Argentina 39.0 7.6 0.5 3.0 0.4 0.2 ­100 3.9 2.8 42 2,320 Armenia 46.0 13.6 0.0 7.8 0.1 8.9 ­100 16.1 8.9 .. .. Australia 38.2 9.2 .. 4.7 3.8 0.5 641 21.3 2.7 .. 5,457 Austria 88.0 25.2 .. 3.5 7.4 0.8 220 14.0 13.5 .. 20,323 Azerbaijan 83.9 11.8 2.8 0.0 1.2 3.4 ­100 3.0 1.8 .. 1,180 Bangladesh 49.3 7.2 0.1 1.2 .. 11.3 ­700 0.7 4.4 6 4 Belarus 120.0 11.4 0.5 3.6 0.0 0.7 20 11.3 3.2 .. 748 Belgium 190.3 33.9 .. 19.8 23.6 2.1 196 8.4 5.5 .. 24,945 Benin 45.5 14.5 0.0 1.8 ­0.1 4.1b 99 2.4 8.6 12 18 Bolivia 68.1 9.2 0.0 3.1 0.0 6.9 ­100 1.2 5.8 80 225 Bosnia and Herzegovina 93.7 12.5 0.0 5.7 0.1 14.8 62 0.9 20.3 109 529 Botswana 76.2 16.6 0.0 0.8 0.0 0.9 20 4.4 5.1 115 220 Brazil 24.2 4.9 2.1 2.9 1.3 0.3 ­229 0.4 2.0 .. 2,108 Bulgaria 123.0 29.5 2.5 18.4 1.5 5.3 ­41 1.3 9.6 27 37,657 Burkina Faso 30.4 .. 0.0 1.7 .. 0.6b 100 5.6 2.5 11 15 Burundi 39.5 22.1 0.0 0.3 0.0 0.3 192 1.1 7.3 .. 2 Cambodia 104.3 26.2 0.3 7.9 0.2 3.1 10 2.2 21.4 .. 19 Cameroon 37.2 19.1 0.0 0.2 ­0.2 0.6 ­12 1.2 17.1 4 8 Canada 62.5 11.0 .. 3.0 5.3 .. 1,089 19.5 4.7 .. 16,193 Central African Republic 24.9 .. 0.1 6.1 .. .. ­45 1.8 7.2 .. .. Chad 77.4 .. 0.0 9.9 .. .. 219 3.6 9.0 .. 1 Chile 76.5 13.1 4.0 9.9 4.1 0.0 30 1.4 6.0 35 4,076 China 59.2 7.1 0.7 3.4 1.2 1.1b ­2,058 c 0.0 3.8 9 483 Hong Kong SAR, China 354.4 64.2 .. 29.3 27.8 0.2 113 39.9 29.6 1,435 548,318 Colombia 31.7 4.6 0.6 4.3 0.9 2.0 ­120 0.3 10.4 142 2,233 Congo, Dem. Rep. 69.0 .. 0.0 8.6 .. .. ­237 0.8 9.0 .. .. Congo, Rep. 111.0 50.3 0.0 24.5 .. 0.1b 4 3.8 22.9 .. 0 Costa Rica 84.4 20.1 1.7 6.8 0.0 2.0 84 10.2 7.1 120 857 Côte d'Ivoire 73.7 15.5 0.2 1.7 .. 0.8 ­339 12.3 6.1 .. 40 Croatia 64.7 28.5 .. 6.9 0.3 2.3 ­13 14.9 24.6 229 15,892 Cuba .. .. .. .. .. .. ­163 0.1 28.8 .. 27 Czech Republic 134.0 18.4 .. 5.0 0.9 0.7 67 4.4 8.5 136 7,075 Denmark 67.0 39.4 .. 0.9 4.4 0.3 46 7.8 7.8 210 34,506 Dominican Republic 51.2 14.9 1.3 6.3 0.0 7.8 ­148 4.1 22.4 .. 1,407 Ecuador 68.0 7.8 0.0 1.8 0.0 5.2 ­400 0.9 9.5 3 443 Egypt, Arab Rep. 45.5 26.2 5.1 5.9 1.2 5.4 ­291 0.3 4.7 27 332 El Salvador 64.7 15.9 0.0 3.5 0.3 17.2 ­340 0.6 31.7 578 33 Eritrea 33.3 .. 0.0 2.2 .. .. 229 0.3 35.2 17 5 Estonia 121.1 36.8 .. 8.3 4.6 1.7 1 15.0 9.9 .. 126,802 Ethiopia 35.6 17.1 0.0 0.4 0.0 1.5 ­340 0.7 9.8 2 3 Finland 69.2 22.6 .. ­2.8 1.1 0.3 33 3.3 7.2 .. 17,221 France 46.1 10.8 .. 3.5 7.2 0.6 761 10.6 3.4 242 29,356 Gabon 75.0 .. 4.1 0.1 .. 0.1b 10 17.9 14.4 .. 141 Gambia, The 42.3 26.0 0.0 8.9 .. 8.2 31 15.2 67.8 .. 38 Georgia 59.1 19.5 4.7 12.2 0.3 5.7 ­309 4.3 2.8 44 752 Germany 73.1 14.6 .. 0.6 4.3 0.3 930 12.9 5.7 .. 25,654 Ghana 96.4 24.6 8.0 12.7 0.0 0.8 12 7.6 44.6 6 86 Greece 28.9 21.1 .. 1.5 0.8 0.8 154 8.8 12.1 .. 4,537 Guatemala 57.2 10.1 0.0 2.1 0.0 11.4 ­300 0.4 23.9 .. 186 Guinea 76.3 14.5 3.3 10.1 3.3 1.9 ­425 4.4 4.6 .. 0 Guinea-Bissau 60.1 .. 0.0 3.5 .. 7.0 b 1 1.3 27.7 .. 1 Haiti 36.6 15.4 0.0 0.4 .. 19.6 ­140 0.3 83.4 .. 16 Honduras 120.8 15.9 1.6 6.6 0.0 21.5 ­150 0.4 24.8 39 241 354 2010 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers' people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2008 2008 2008 2008 2008 2008 2000­05 2005 2000 2008 2008 Hungary 139.5 25.1 .. 40.6 39.5 1.7 70 3.3 12.8 120 5,977 India 40.6 13.8 2.7 3.6 1.6 4.3 ­1,540 0.5 4.3 .. 32 Indonesia 52.0 8.5 4.1 1.8 1.2 1.3 ­1,000 0.1 2.9 .. 120 Iran, Islamic Rep. 46.7 .. 0.0 0.6 .. 0.4b ­993 3.0 14.3 .. 151 Iraq .. .. .. .. .. .. ­224 0.4 10.9 0 1 Ireland 73.4 74.9 .. ­7.4 5.0 0.2 230 14.8 33.7 .. 15,261 Israel 63.5 21.8 .. 4.8 3.9 0.7 115 38.4 7.8 413 2,003 Italy 47.8 10.9 .. 0.7 1.9 0.1 1,750 5.2 9.6 .. 12,989 Jamaica 70.3 35.3 3.1 9.8 0.5 14.9 ­76 1.0 84.7 39 744 Japan 31.5 6.5 .. 0.5 2.7 0.0 82 1.6 1.2 .. 5,770 Jordan 116.2 40.2 8.2 9.3 0.1 17.9 104 43.3 7.4 66 781 Kazakhstan 81.7 11.5 15.3 11.0 2.9 0.1 ­200 19.6 1.2 47 702 Kenya 52.9 16.9 0.8 0.3 0.1 5.6b 25 2.2 38.5 3 21 Korea, Dem. Rep. .. .. .. .. .. .. 0 0.2 .. .. 0 Korea, Rep. 92.3 18.2 .. 0.2 1.4 0.3 ­65 1.1 7.5 33 4,528 Kosovo .. .. 0.0 .. .. .. .. .. .. .. .. Kuwait 79.9 17.8 .. 0.0 5.9 .. 264 73.7 7.1 .. 871 Kyrgyz Republic 112.7 37.4 0.0 4.6 0.0 24.4 ­75 5.6 0.9 .. 113 Lao PDR 44.6 8.2 10.9 4.1 .. 0.0 b ­115 0.3 37.2 .. 129 Latvia 77.2 22.9 4.2 4.0 0.8 1.8 ­20 16.5 8.5 .. 3,537 Lebanon 72.5 110.6 5.2 12.3 3.4 24.5 100 17.7 43.8 .. 223 Lesotho 180.6 10.9 0.0 13.4 .. 27.0 ­36 0.3 4.1 .. 5 Liberia 133.8 209.9 117.5 17.1 0.0 6.9 62 2.9 44.3 .. .. Libya 80.0 4.9 0.0 4.4 6.3 0.0 b 14 10.4 4.3 65 50 Lithuania 115.2 19.2 0.0 3.7 0.8 3.1 ­36 4.8 8.3 57 9,751 Macedonia, FYR 113.7 21.2 0.0 6.3 ­0.1 4.3 ­10 5.9 29.4 159 17 Madagascar 56.9 .. 0.0 15.6 .. 0.1b ­5 0.2 7.7 1 8 Malawi 58.3 .. 0.0 0.9 .. 0.0 b ­30 2.0 20.9 .. 5 Malaysia 160.7 27.3 3.2 3.3 6.9 0.9b 150 7.9 10.5 .. 2,374 Mali 48.1 16.8 1.3 1.5 0.1 3.9b ­134 1.4 14.7 2 51 Mauritania 122.5 .. 0.0 3.6 .. 0.1b 30 2.2 8.5 4 76 Mauritius 75.1 47.9 0.1 4.1 0.6 2.3 0 3.3 55.8 100 364 Mexico 56.5 4.0 1.7 2.1 0.1 2.4 ­2,702 0.6 15.5 174 285 Moldova 107.4 27.5 0.0 11.7 0.3 31.4 ­320 11.7 4.1 155 966 Mongolia 117.1 32.2 0.0 13.0 .. 3.8b 17 0.4 7.4 5 947 Morocco 69.5 22.6 1.7 2.8 0.4 7.8 ­550 0.2 18.0 21 795 Mozambique 68.1 15.3 0.8 6.0 0.0 1.2 ­20 1.9 22.5 .. 3 Myanmar .. .. .. .. .. .. ­1,000 0.2 3.9 .. 20 Namibia 84.6 12.7 0.0 6.1 0.1 0.2 ­1 6.6 3.4 .. 27 Nepal 37.0 12.5 0.0 0.0 .. 21.6 ­100 3.0 4.0 .. 5 Netherlands 140.4 23.0 .. ­0.3 2.2 0.4 110 10.6 9.5 .. 78,156 New Zealand 49.7 14.4 .. 4.2 0.2 0.5 103 20.7 21.8 310 4,544 Nicaragua 87.6 15.3 0.0 9.5 0.0 12.4 ­206 0.6 30.2 39 144 Niger 42.4 10.7 0.0 2.7 0.2 1.5b ­28 1.4 5.4 .. 11 Nigeria 59.7 7.3 1.1 1.8 0.2 4.8b ­170 0.7 10.5 1 5 Norway 57.1 20.1 .. ­0.3 5.9 0.2 84 8.0 6.2 .. 26,904 Oman 97.8 15.6 .. 7.5 0.6 0.1 ­50 25.5 0.4 30 894 Pakistan 38.1 8.3 0.4 3.3 0.0 4.3 ­1,239 2.3 12.7 .. 43 Panama 44.3 36.6 11.0 10.4 0.0 0.9 8 3.2 16.7 61 15,964 Papua New Guinea 112.3 .. 5.3 ­0.4 .. 0.2b 0 0.4 27.8 .. 2 Paraguay 91.5 10.3 0.0 2.0 0.1 3.1 ­45 2.8 3.8 35 481 Peru 47.6 7.1 2.2 3.2 .. 1.9 ­525 0.1 5.8 .. 2,646 Philippines 64.8 11.4 1.7 0.8 0.2 11.2 ­900 0.4 13.5 .. 113 Poland 70.4 12.5 1.7 2.8 0.6 2.0 ­200 2.2 14.2 .. 2,748 Portugal 60.0 17.8 .. 1.5 0.9 1.7 291 7.2 18.9 .. 4,790 Puerto Rico .. .. .. .. .. .. ­27 9.0 .. .. .. Qatar 90.1 .. .. .. .. .. 219 80.5 2.1 .. 2,044 2010 World Development Indicators 355 6.1 Integration with the global economy Trade International finance Movement of people Communication % of GDP Financing Emigration of through Workers' people with tertiary International international remittances education to International Internet capital Foreign direct and International OECD countries voice bandwidtha markets investment compensation migrant stock % of population age traffic a bits per % of GDP Gross Net Net of employees Net migration % of total 25 and older with minutes second Merchandise Services inflows inflows outflows received thousands population tertiary education per person per capita 2008 2008 2008 2008 2008 2008 2000­05 2005 2000 2008 2008 Romania 66.1 12.4 1.2 6.9 0.1 4.7 ­270 0.6 11.2 41 9,111 Russian Federation 45.5 7.6 5.2 4.3 3.1 0.4 964 8.4 1.4 .. 573 Rwanda 30.5 20.9 0.0 2.3 ­0.4 1.5 6 4.8 26.3 11 27 Saudi Arabia 94.0 18.0 .. 4.8 0.7 0.0 285 27.4 0.9 .. 1,224 Senegal 61.0 21.6 0.0 5.3 0.2 9.7b ­100 2.0 17.1 27 237 Serbia 67.9 16.6 0.8 6.0 0.6 11.1b,d ­339 9.1 .. 142 4,506 Sierra Leone 39.9 9.5 0.0 ­0.2 .. 7.7b 336 3.0 49.2 .. .. Singapore 361.6 89.3 .. 12.5 4.9 .. 139 35.0 14.5 1,531 22,783 Slovak Republic 152.0 18.6 .. 3.3 0.3 2.0 10 2.3 14.3 123 5,555 Slovenia 130.4 22.8 .. 3.5 2.6 0.6 23 8.4 10.9 96 6,720 Somalia .. .. .. .. .. .. ­200 0.3 34.5 .. .. South Africa 65.2 10.8 2.5 3.5 ­0.8 0.3 700 2.7 7.4 .. 71 Spain 41.8 15.5 .. 4.4 5.0 0.7 2,504 10.6 4.2 .. 11,008 Sri Lanka 55.2 12.3 1.0 1.9 0.2 7.3 ­442 1.9 28.2 34 190 Sudan 38.7 5.6 0.0 4.6 0.2 5.5 ­532 1.7 6.8 6 322 Swaziland 140.6 32.6 0.0 0.4 0.8 3.5b ­46 3.4 5.3 .. 31 Sweden 73.1 26.4 .. 8.7 8.4 0.2 186 12.3 4.5 .. 49,828 Switzerland 78.6 23.5 .. 1.3 10.2 0.4 200 22.3 9.5 .. 29,413 Syrian Arab Republic 59.1 17.0 0.7 3.1 0.0 1.5b 300 6.9 6.1 78 102 Tajikistan 91.1 12.4 0.0 7.3 0.0 49.6 ­345 4.7 0.6 .. 37 Tanzania 47.9 18.4 4.1 3.6 .. 0.1 ­345 2.0 12.1 0 2 Thailand 130.9 29.5 1.5 3.6 1.0 0.7 1,411 1.5 2.2 .. 818 Timor-Leste .. .. 0.0 .. .. .. 41 1.2 16.5 .. .. Togo 80.4 21.7 0.0 2.3 0.0 9.8b ­4 3.1 16.3 6 8 Trinidad and Tobago 114.7 6.0 .. 3.8 2.0 0.5b ­20 2.9 78.9 .. 678 Tunisia 109.0 23.3 0.4 6.5 0.1 4.9 ­81 0.3 12.4 79 1,115 Turkey 45.4 7.1 2.9 2.5 0.3 0.2 ­71 1.9 5.8 39 2,794 Turkmenistan 100.9 .. 0.0 5.3 .. .. ­25 4.6 0.4 .. 48 Uganda 48.7 13.7 0.0 5.5 0.0 5.1 ­5 2.3 36.0 7 12 Ukraine 83.8 18.9 2.9 6.1 0.6 3.2 ­173 11.4 4.3 0 206 United Arab Emirates 157.7 .. .. .. .. .. 577 70.0 0.7 .. 8,686 United Kingdom 41.2 18.6 .. 3.5 6.1 0.3 948 9.7 17.1 .. 39,648 United States 24.4 6.7 .. 2.2 2.3 0.0 5,676 13.3 0.5 .. 11,289 Uruguay 46.2 11.3 10.9 6.9 0.0 0.3 ­104 2.5 9.0 0 903 Uzbekistan 55.9 .. 0.0 3.3 .. 0.0 ­400 4.8 0.8 .. 30 Venezuela, RB 45.6 4.0 1.9 0.1 0.4 0.0 40 3.8 3.8 .. 628 Vietnam 158.1 16.6 3.1 10.6 0.3 7.9b ­200 0.1 26.9 .. 581 West Bank and Gaza .. .. .. .. .. .. 11 46.5 12.0 .. 313 Yemen, Rep. 69.9 13.4 10.5 5.8 0.0 5.3 ­100 2.2 6.0 .. 28 Zambia 71.0 8.4 1.9 6.6 0.0 0.5 ­82 2.4 16.4 .. 8 Zimbabwe .. .. .. .. .. .. ­700 3.1 13.1 22 10 World 53.0 w 12.3 w .. w 3.0 w 3.5 w 0.8 s ..e s 3.0 w 5.4 w .. 3,546 w Low income 74.9 15.2 2.0 5.1 .. 7.1 ­3,728 1.6 13.2 .. 24 Middle income 56.2 9.6 2.2 3.5 1.3 1.9 ­14,512 1.4 6.7 .. 377 Lower middle income 60.0 10.6 1.5 3.4 1.1 2.6 ­11,119 0.9 6.4 .. 153 Upper middle income 52.5 8.6 2.9 3.6 1.5 1.2 ­3,393 3.3 7.1 .. 1,281 Low & middle income 56.7 9.7 2.2 3.6 1.3 2.0 ­18,240 1.4 7.2 .. 320 East Asia & Pacific 68.0 9.4 1.2 3.3 1.4 1.5 ­3,722 0.3 7.0 9 470 Europe & Central Asia 58.1 10.2 3.9 4.4 1.7 1.5 ­2,138 6.6 4.2 .. 1,244 Latin America & Carib. 41.9 6.1 1.9 3.0 0.8 1.5 ­5,738 1.1 10.6 .. 1,391 Middle East & N. Africa 68.6 26.3 2.7 4.6 .. 4.8 ­1,850 3.2 10.4 27 323 South Asia 41.3 12.8 2.2 3.3 1.4 4.9 ­3,181 0.8 5.3 .. 31 Sub-Saharan Africa 64.9 13.3 1.8 3.5 0.1 2.3 ­1,611 2.1 12.3 .. 34 High income 51.5 13.3 .. 2.8 4.4 0.3 18,091 11.4 4.0 .. 20,143 Euro area 67.9 17.4 .. 3.1 6.1 0.5 7,269 9.9 7.0 .. 32,540 a. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. World Bank estimates. c. Includes Taiwan, China. d. Includes Montenegro. e. World totals computed by the United Nations sum to zero, but because the aggregates shown here refer to World Bank definitions, regional and income group totals do not equal zero. 356 2010 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy About the data Definitions Globalization--the integration of the world investment, FDI establishes a lasting interest in or · Trade in merchandise is the sum of merchandise economy--has been a persistent theme of the past effective management control over an enterprise in exports and imports. · Trade in services is the 25 years. Growth of cross-border economic activity another country. FDI may be understated in develop- sum of services exports and imports. · Financ- has changed countries' economic structure and polit- ing countries because some fail to report reinvested ing through international capital markets is the sum ical and social organization. Not all effects of glo- earnings and because the definition of long-term of the absolute values of new bond issuance, syndi- balization can be measured directly. But the scope loans differs by country. However, data quality and cated bank lending, and new equity placements. · For- and pace of change can be monitored along four key coverage are improving as a result of continuous eign direct investment net inflows and outflows are dimensions: trade in goods and services, financial efforts by international and national statistics agen- net inflows and outflows of FDI (equity capital, rein- flows, movement of people, and communication. cies (see About the data for table 6.12). vestment of earnings, and other short- and long-term Trade data are based on gross flows that capture Workers' remittances are current private transfers capital). · Workers' remittances and compensation the two-way flow of goods and services. In conven- from migrant workers resident in the host country for of employees received are current transfers by migrant tional balance of payments accounting, exports are more than a year, irrespective of their immigration workers and wages and salaries of nonresident work- recorded as a credit and imports as a debit. See status, to recipients in their country of origin. Com- ers. · Net migration is the number of immigrants tables 4.4 and 4.5 for data on the main trade com- pensation of employees is the income of migrants minus the number of emigrants, including citizens and ponents of merchandise trade and tables 4.6 and resident in the host country for less than a year. noncitizens, for the five-year period. · International 4.7 for the same data on services trade. Migration has increased in importance, accounting migrant stock is the number of people born in a coun- Financing through international capital markets for a substantial part of global integration. The esti- try other than that in which they live, including refu- includes gross bond issuance, bank lending, and mates of the international migrant stock are derived gees. · Emigration of people with tertiary education new equity placement as reported by Dealogic, a from data on people who reside in one country but to OECD countries is adults ages 25 and older, resid- company specializing in the investment banking were born in another, mainly from population cen- ing in an OECD country other than that in which they industry. In financial accounting inward investment suses (see About the data and Definitions for table were born, with at least one year of tertiary education. is a credit and outward investment a debit. Gross 6.18). One negative effect of migration is "brain · International voice traffic is the sum of international fl ow is a better measure of integration than net drain"--emigration of highly educated people. The incoming and outgoing telephone traffic divided by fl ow because gross fl ow shows the total value of table shows data on emigration of people with ter- total population. · International Internet bandwidth financial transactions over a period, while net flow tiary education, drawn from Docquier, Marfouk, and is the contracted capacity of international connections is the sum of credits and debits and represents a Lowell (2007), who analyzed skilled migration using between countries for transmitting Internet traffic. balance in which many transactions are canceled data from censuses and registers of Organisation Data sources out. Components of financing through international for Economic Development and Co-operation (OECD) capital markets are reported in U.S. dollars by mar- countries and provide data disaggregated by gender Data on merchandise trade are from the World ket sources. for 1990 and 2000. Trade Organization's Annual Report. Data on trade in Foreign direct investment (FDI) includes equity Well developed communications infrastructure services are from the International Monetary Fund's investment, reinvested earnings, and short- and long- attracts investments and allows investors to capital- (IMF) Balance of Payments database. Data on inter- term loans between parent firms and foreign affili- ize on benefits of the digital age. See About the data national capital market financing are based on data ates. Distinguished from other kinds of international for tables 5.11 and 5.12 for more information. from Dealogic. Data on FDI are based on balance of payments data from the IMF, supplemented by staff estimates using data from the United Nations Services trade has not grown as rapidly as merchandise trade 6.1a Conference on Trade and Development and official Trade as a share of GDP (percent) Merchandise trade Services trade national sources. Data on workers' remittances are 80 Low income World Bank staff estimates based on IMF balance of 70 payments data. Data on net migration are from the 60 Middle income United Nations Population Division's World Popula- 50 High income tion Prospects: The 2008 Revision. Data on interna- 40 30 tional migrant stock are from the United Nations 20 High income Middle income Low income Population Division's Trends in Total Migrant Stock: 10 The 2008 Revision. Data on emigration of people 0 with tertiary education are from Docquier, Marfouk, 1990 1995 2000 2005 2008 and Lowell's "A Gendered Assessment of the Brain Merchandise trade in low-income economies grew from 34 percent of GDP in 1990 to 75 percent in Drain" (2007). Data on international voice traffic 2008 and in middle-income economies from 32 percent to 56 percent. The shares of services trade in and international Internet bandwidth are from the GDP also rose but not as much. International Telecommunication Union's Interna- Source: World Development Indicators data files. tional Development Report database. 2010 World Development Indicators 357 6.2 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1995 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria 2.8 1.8 ­0.8 12.2 2.1 21.8 ­1.3 18.6 57.9 238.8 Angola 6.2 13.4 7.1 20.4 6.1 34.9 7.8 25.0 80.8 253.9 Argentina 8.4 6.6 17.7 12.6 10.1 13.6 17.0 16.3 91.6 132.7 Armenia .. .. .. .. .. .. .. .. .. .. Australiaa 7.3 7.7 9.2 8.1 5.7 21.1 8.7 13.4 99.4 174.6 Austriaa 6.2 6.6 5.6 5.6 .. .. .. .. .. .. Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh 12.9 11.8 5.9 5.0 15.7 12.8 10.4 13.6 111.8 57.7 Belarus .. .. .. .. .. .. .. .. .. .. Belgiuma 6.0 4.1 5.7 4.7 4.8 13.3 5.3 14.1 104.3 98.3 Benin 1.0 9.8 8.2 5.2 3.3 15.2 9.7 14.8 106.6 68.6 Bolivia 2.8 11.3 9.1 7.2 4.3 24.4 9.7 13.2 89.4 144.0 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 4.8 4.6 4.0 5.0 4.7 10.2 2.7 11.5 89.3 90.0 Brazil 5.1 9.5 16.7 7.7 5.9 18.7 12.5 15.3 110.4 110.4 Bulgaria .. .. .. .. .. .. .. .. .. .. Burkina Faso 13.2 12.7 3.6 6.6 12.9 17.4 3.6 15.6 131.0 71.5 Burundi 8.6 ­5.9 4.0 10.8 ­4.3 5.2 ­6.9 17.0 163.6 140.3 Cambodia .. 14.1 .. 10.5 26.8 16.6 25.2 17.0 .. 78.5 Cameroon 0.3 ­1.9 5.0 3.8 ­3.6 13.0 2.1 13.7 90.4 138.2 Canadaa 9.1 0.6 9.0 4.6 9.4 7.8 8.9 9.5 103.2 120.7 Central African Republic 20.0 0.5 4.3 5.3 3.5 4.1 0.2 13.2 193.0 72.9 Chad .. .. .. .. .. .. .. .. .. .. Chile 11.1 6.2 10.7 13.1 9.4 21.9 10.3 18.1 135.6 164.9 China 13.8 25.0 12.8 17.4 14.5 26.9 13.0 24.2 101.9 73.8 Hong Kong SAR, China 8.4 8.9 8.9 8.3 8.3 9.3 8.8 9.5 99.1 96.4 Colombia 4.5 6.1 8.5 12.6 7.3 15.8 9.7 17.4 86.8 138.1 Congo, Dem. Rep. ­1.8 8.4 4.6 17.1 ­7.2 20.8 ­0.5 24.6 79.8 147.2 Congo, Rep. 6.6 1.4 4.9 19.2 7.5 20.1 8.7 26.1 52.0 212.3 Costa Rica 14.0 8.5 14.9 9.0 17.0 8.5 13.9 11.9 104.6 81.7 Côte d'Ivoire 5.0 0.7 ­0.3 7.6 6.0 13.1 2.4 17.2 122.0 138.8 Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. 1.9 .. 6.6 ­1.7 13.2 2.5 15.6 .. 111.1 Czech Republic .. .. .. .. .. .. .. .. .. .. Denmarka 5.4 3.3 5.8 4.8 4.1 11.6 4.9 13.1 102.1 100.2 Dominican Republic 3.9 0.5 11.6 3.1 4.2 3.9 12.0 7.5 98.1 93.6 Ecuador 6.3 9.3 5.9 13.7 6.8 19.9 7.9 20.0 80.6 124.0 Egypt, Arab Rep. ­0.2 9.7 1.8 7.2 0.7 25.6 4.7 15.9 116.3 144.4 El Salvador 2.9 3.2 7.6 5.5 8.9 5.6 10.9 9.2 121.1 91.0 Eritrea ­28.3 ­11.3 ­3.2 ­4.9 ­31.1 ­5.7 ­0.2 1.7 101.7 90.1 Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 10.5 7.7 7.3 18.0 10.7 18.2 7.3 26.4 151.0 111.5 Finland .. .. .. .. .. .. .. .. 110.6 81.0 Francea 8.3 4.9 6.6 6.4 4.9 10.5 3.7 12.2 106.4 99.8 Gabon 5.2 ­0.6 2.5 8.5 0.8 16.8 2.2 14.0 125.4 215.3 Gambia, The ­11.6 ­4.6 0.1 2.6 ­12.3 0.7 0.2 10.7 100.0 83.4 Georgia .. .. .. .. .. .. .. .. .. .. Germanya .. .. .. .. .. .. .. .. 107.5 100.1 Ghana 7.7 4.7 8.6 11.4 9.0 15.7 8.3 18.3 106.7 151.3 Greecea 8.9 .. 9.3 .. 8.2 .. 8.2 .. 89.6 94.9 Guatemala 8.5 10.3 10.0 8.0 10.1 14.7 10.4 14.1 117.9 87.1 Guinea 5.0 ­8.2 ­1.4 4.3 0.6 8.2 ­2.6 11.9 89.6 168.9 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 12.6 5.8 13.3 2.5 12.2 8.6 14.4 9.9 113.2 62.4 Honduras 2.5 8.0 12.7 9.6 7.2 10.8 13.8 15.2 96.3 80.9 Data for Taiwan, China 3.1 7.4 4.8 3.6 7.2 9.9 8.5 10.3 89.9 73.8 358 2010 World Development Indicators 6.2 GLOBAL LINKS Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1995 2008 Hungarya 10.1 12.3 11.6 9.7 10.1 19.1 11.8 17.4 104.3 93.8 India 6.9 11.6 9.0 18.5 5.3 21.4 7.9 26.8 108.0 91.5 Indonesia 10.0 .. 2.9 .. 7.8 .. 0.1 .. 90.4 .. Iran, Islamic Rep. .. 3.2 .. 12.1 1.2 21.5 ­4.8 20.9 .. 175.3 Iraq .. .. .. .. .. .. .. .. .. .. Irelanda 15.2 2.5 11.4 2.5 14.3 6.1 10.9 7.8 98.9 87.9 Israela 9.7 4.9 8.9 2.7 10.0 10.1 8.2 8.9 92.1 92.9 Italya 4.8 1.6 4.2 1.6 4.6 11.6 3.2 12.6 96.6 94.8 Jamaica 2.2 2.2 .. 2.1 2.2 9.7 6.9 11.2 .. 83.7 Japana 2.6 4.5 5.3 3.0 2.1 5.2 5.2 10.2 114.9 61.7 Jordan 4.7 6.3 3.8 7.6 6.6 18.1 5.1 19.1 115.6 118.0 Kazakhstan .. .. .. .. .. .. .. .. .. .. Kenya 3.9 6.1 7.4 8.9 6.3 13.7 6.0 18.8 103.9 83.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 15.8 13.2 10.0 7.9 10.1 14.4 7.1 15.4 138.5 62.3 Kosovo .. .. .. .. .. .. .. .. .. .. Kuwait .. 10.6 .. 13.0 16.5 25.0 5.5 18.1 .. 165.7 Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. Lao PDR .. 9.5 .. 7.6 15.4 19.3 12.7 15.0 .. 112.9 Latviaa 7.2 .. .. .. 11.8 .. .. .. .. .. Lebanon .. 14.8 .. 1.8 4.1 23.7 8.7 10.9 .. 91.8 Lesotho 13.3 18.7 3.1 8.6 12.8 18.8 1.9 13.5 100.0 71.9 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. 5.8 0.0 19.9 ­2.6 26.0 ­1.4 27.4 .. 205.3 Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar 4.1 5.7 4.5 10.5 9.0 8.9 6.3 18.1 79.6 71.2 Malawi 2.7 6.8 ­2.4 9.9 0.9 11.0 ­0.6 17.7 105.7 76.2 Malaysia 13.6 7.4 10.6 7.2 12.2 11.5 9.5 10.8 108.5 104.4 Mali 10.3 2.5 6.4 8.3 6.3 15.7 4.7 17.4 109.6 140.1 Mauritania 1.9 7.8 4.2 13.3 ­1.9 25.5 ­1.6 20.6 102.2 190.9 Mauritius 2.7 4.3 3.4 8.2 2.2 4.6 3.3 11.1 88.5 81.8 Mexico 15.5 3.7 13.2 4.4 16.1 8.7 14.2 8.3 92.5 105.9 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. 5.3 .. 13.9 0.7 23.8 0.5 23.4 .. 180.5 Morocco 7.5 3.6 7.2 8.8 7.2 12.1 5.5 17.8 89.1 98.4 Mozambique 15.2 15.9 1.0 9.6 10.2 27.6 1.1 17.4 151.1 107.7 Myanmar 15.5 5.6 13.8 ­2.0 14.4 18.0 22.6 4.8 214.3 144.4 Namibia 2.4 6.7 7.7 10.0 0.9 15.4 3.9 14.8 82.6 120.6 Nepal .. ­1.4 .. ­6.0 10.7 4.9 9.3 3.4 .. 78.5 Netherlandsa 8.0 5.4 8.4 5.4 5.7 13.5 5.5 13.1 97.6 103.1 New Zealanda 4.6 3.0 5.9 7.6 3.9 11.2 5.7 13.5 102.0 126.2 Nicaragua 10.4 9.3 9.3 6.1 10.3 12.4 11.6 12.3 128.9 75.2 Niger 3.1 ­7.1 ­2.1 9.1 0.0 15.7 0.8 18.3 121.4 232.9 Nigeria 3.3 0.8 2.5 13.6 1.1 19.9 3.1 21.1 55.6 209.8 Norwaya 6.6 0.4 7.8 6.8 5.7 15.4 4.4 14.8 60.3 156.7 Oman 4.0 ­3.2 .. 8.5 5.7 16.1 6.1 19.3 .. 155.6 Pakistan 2.5 8.5 2.4 9.8 4.3 11.6 3.1 21.2 119.2 57.6 Panama 6.0 2.5 7.8 9.5 9.4 4.4 8.7 13.8 100.0 85.9 Papua New Guinea ­7.7 ­2.5 .. 6.9 3.7 16.4 ­0.8 16.1 .. 177.1 Paraguay ­0.2 15.7 5.4 18.9 1.7 20.8 7.0 23.7 118.3 107.3 Peru 9.4 9.1 10.6 9.6 9.0 24.3 10.8 17.3 123.4 136.9 Philippines 16.0 4.1 11.3 1.9 18.8 4.7 12.5 7.6 80.2 66.5 Polanda 9.8 13.4 19.0 10.9 9.5 24.9 17.0 21.0 102.4 106.9 Portugala 0.3 .. 0.5 .. ­3.0 .. ­2.6 .. 104.7 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar .. 5.5 .. 26.7 10.1 25.0 7.4 32.2 .. 249.4 2010 World Development Indicators 359 6.2 Growth of merchandise trade Export Import Export Import Net barter volume volume value value terms of trade index average annual average annual average annual average annual % growth % growth % growth % growth 2000 = 100 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1990­2000 2000­08 1995 2008 Romania .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda ­8.0 4.7 0.8 14.0 ­4.0 19.4 ­1.7 21.6 110.1 169.5 Saudi Arabia 2.9 2.2 .. 13.7 3.1 22.1 0.8 19.4 .. 236.2 Senegal 10.6 0.7 4.9 6.7 4.0 9.7 3.6 16.8 156.3 94.0 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore 11.7 12.7 8.3 9.2 9.9 14.8 7.8 13.9 104.3 83.0 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4.5 1.7 7.6 8.8 2.5 15.0 5.8 19.3 106.0 130.0 Spaina 11.4 4.0 9.3 6.5 8.6 12.8 6.2 15.1 104.3 102.8 Sri Lanka 7.4 3.9 8.0 2.7 11.3 7.2 8.9 11.1 99.0 68.6 Sudan 12.6 9.1 8.4 21.5 14.0 28.8 9.8 27.2 100.0 232.2 Swaziland 4.0 10.1 3.1 7.5 5.9 16.0 5.0 14.4 100.0 92.5 Swedena 8.9 3.9 6.4 2.1 7.4 10.2 5.4 10.3 110.2 86.6 Switzerlanda 3.7 4.9 4.2 3.2 4.4 6.6 3.6 5.5 96.4 99.4 Syrian Arab Republic 2.2 0.3 .. 13.8 0.9 15.0 3.6 22.7 .. 145.3 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania 6.0 5.9 ­2.0 12.3 6.4 16.8 0.1 21.9 98.0 108.9 Thailand 9.6 8.8 2.6 10.0 10.5 14.0 5.0 15.3 116.0 94.3 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 9.1 16.8 6.0 ­7.3 6.6 10.1 5.5 16.6 99.1 21.4 Trinidad and Tobago .. 5.8 .. 3.8 6.8 23.8 12.1 14.3 .. 157.5 Tunisia 5.7 9.3 4.3 5.8 6.0 15.3 5.2 13.1 95.8 95.0 Turkey 10.7 13.1 11.1 11.9 9.2 22.3 10.3 22.1 105.7 91.2 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 17.8 15.3 22.4 8.6 15.4 25.4 21.0 16.6 197.2 106.2 Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. 8.5 .. 16.6 6.5 22.8 10.7 23.0 .. 148.1 United Kingdoma 6.2 2.1 6.5 4.5 6.2 8.5 6.5 10.4 100.1 105.0 United Statesa 6.6 4.9 9.1 4.5 7.2 7.7 9.5 8.7 103.3 91.8 Uruguay 6.1 9.1 10.5 6.3 5.2 15.0 10.1 14.3 116.2 92.3 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 5.2 ­2.4 4.8 14.2 5.4 16.2 5.3 18.3 63.4 249.5 Vietnam .. 13.2 .. 14.3 22.7 21.3 22.7 23.4 .. 92.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. ­4.5 4.4 11.1 20.6 12.8 0.6 21.0 .. 165.4 Zambia 6.1 8.4 2.9 17.1 ­4.6 28.0 1.3 24.8 189.7 170.6 Zimbabwe 8.8 ­5.8 8.0 ­5.6 3.4 3.5 1.9 4.7 96.8 91.5 a. Data are from the International Monetary Fund's International Financial Statistics database. 360 2010 World Development Indicators 6.2 GLOBAL LINKS Growth of merchandise trade About the data Definitions Data on international trade in goods are available from national and international sources such as the · Export and import volumes are indexes of the from each country's balance of payments and IMF's International Financial Statistics database, the quantity of goods traded. They are derived from customs records. While the balance of payments United Nations Economic Commission for Latin Amer- UNCTAD's volume index series and are the ratio of focuses on the financial transactions that accom- ica and the Caribbean, the United Nations Statistics the export or import value indexes to the correspond- pany trade, customs data record the direction of Division's Monthly Bulletin of Statistics database, ing unit value indexes. Unit value indexes are based trade and the physical quantities and value of goods the World Bank Africa Database, the U.S. Bureau on data reported by countries that demonstrate entering or leaving the customs area. Customs data of Labor Statistics, Japan Customs, and UNCTAD's consistency under UNCTAD quality controls, supple- may differ from data recorded in the balance of pay- Commodity Price Statistics. The IMF also compiles mented by UNCTAD's estimates using the previous ments because of differences in valuation and time data on trade prices and volumes in its International year's trade values at the Standard International of recording. The 1993 United Nations System of Financial Statistics (IFS) database. Trade Classification three-digit level as weights. For National Accounts and the fifth edition of the Inter- Unless otherwise noted, the growth rates and economies for which UNCTAD does not publish data, national Monetary Fund's (IMF) Balance of Payments terms of trade in the table were calculated from the export and import volume indexes (lines 72 and Manual (1993) attempted to reconcile definitions and index numbers compiled by UNCTAD. The growth 73) in the IMF's International Financial Statistics are reporting standards for international trade statistics, rates and terms of trade for selected economies used to calculate the average annual growth rates. but differences in sources, timing, and national prac- were calculated from index numbers compiled in · Export and import values are the current value of tices limit comparability. Real growth rates derived the IMF's International Financial Statistics. In some exports (free on board, f.o.b.) or imports (cost, insur- from trade volume indexes and terms of trade based cases price and volume indexes from different ance, and freight, c.i.f.), converted to U.S. dollars on unit price indexes may therefore differ from those sources vary significantly as a result of differences and expressed as a percentage of the average for derived from national accounts aggregates. in estimation procedures. Because the IMF does not the base period (2000). UNCTAD's export or import Trade in goods, or merchandise trade, includes all publish trade value indexes, for selected economies value indexes are reported for most economies. For goods that add to or subtract from an economy's the trade value indexes were derived from the vol- selected economies for which UNCTAD does not pub- material resources. Trade data are collected on the ume and price indexes. All indexes are rescaled to lish data, the value indexes are derived from export basis of a country's customs area, which in most a 2000 base year. or import volume indexes (lines 72 and 73) and cor- cases is the same as its geographic area. Goods The terms of trade measures the relative prices of responding unit value indexes of exports or imports provided as part of foreign aid are included, but a country's exports and imports. There are several (lines 74 and 75) in the IMF's International Financial goods destined for extraterritorial agencies (such ways to calculate it. The most common is the net Statistics. · Net barter terms of trade index is calcu- as embassies) are not. barter (or commodity) terms of trade index, or the lated as the percentage ratio of the export unit value Collecting and tabulating trade statistics are dif- ratio of the export price index to the import price indexes to the import unit value indexes, measured ficult. Some developing countries lack the capacity index. When a country's net barter terms of trade relative to the base year 2000. to report timely data, especially landlocked coun- index increases, its exports become more valuable tries and countries whose territorial boundaries are or its imports cheaper. porous. Their trade has to be estimated from the data reported by their partners. (For further discussion of the use of partner country reports, see About the data for table 6.3.) Countries that belong to common customs unions may need to collect data through direct inquiry of companies. Economic or political concerns may lead some national authorities to sup- press or misrepresent data on certain trade flows, such as oil, military equipment, or the exports of a dominant producer. In other cases reported trade data may be distorted by deliberate under- or over- invoicing to affect capital transfers or avoid taxes. And in some regions smuggling and black market trading result in unreported trade flows. By international agreement customs data are Data sources reported to the United Nations Statistics Division, which maintains the Commodity Trade (Comtrade) Data on trade indexes are from UNCTAD's annual and Monthly Bulletin of Statistics databases. The Handbook of Statistics for most economies and United Nations Conference on Trade and Develop- from the IMF's International Financial Statistics for ment (UNCTAD) compiles international trade sta- selected economies. tistics, including price, value, and volume indexes, 2010 World Development Indicators 361 6.3 Direction and growth of merchandise trade Direction of trade High-income importers % of world trade, 2008 European United Other high- Source of exports Union Japan States income Total High-income economies 27.9 2.5 7.1 11.6 49.2 European Union 22.1 0.4 2.3 3.4 28.2 Japan 0.7 .. 0.9 1.5 3.0 United States 1.7 0.4 .. 3.0 5.1 Other high-income economies 3.5 1.7 4.0 3.6 12.9 Low- and middle-income economies 8.2 1.7 5.6 5.5 21.0 East Asia & Pacific 2.2 1.3 2.1 3.8 9.3 China 1.7 0.7 1.6 2.7 6.7 Europe & Central Asia 3.4 0.1 0.2 0.5 4.2 Russian Federation 1.4 0.1 0.1 0.2 1.7 Latin America & Caribbean 0.8 0.1 2.3 0.5 3.7 Brazil 0.3 0.0 0.2 0.1 0.6 Middle East & N. Africa 1.0 0.1 0.3 0.3 1.7 Algeria 0.3 0.0 0.1 0.0 0.4 South Asia 0.3 0.0 0.2 0.4 0.9 India 0.2 0.0 0.1 0.3 0.7 Sub-Saharan Africa 0.5 0.1 0.5 0.1 1.2 South Africa 0.1 0.1 0.1 0.1 0.3 World 36.2 4.3 12.7 17.1 70.2 Low- and middle-income importers % of world trade, 2008 Europe Latin Middle East Asia & Central America East & South Sub-Saharan Source of exports & Pacific Asia & Caribbean N. Africa Asia Africa Total High-income economies 7.3 4.1 3.3 1.2 1.3 1.0 18.4 European Union 1.0 3.3 0.7 0.7 0.3 0.5 6.6 Japan 1.3 0.2 0.2 0.0 0.1 0.1 1.8 United States 0.7 0.2 1.7 0.1 0.1 0.1 3.0 Other high-income economies 4.3 0.4 0.6 0.4 0.8 0.3 6.9 Low- and middle-income economies 2.3 2.9 1.9 0.8 0.9 0.7 9.8 East Asia & Pacific 1.4 0.6 0.5 0.2 0.4 0.3 3.5 China 0.5 0.6 0.4 0.2 0.3 0.2 2.2 Europe & Central Asia 0.2 2.0 0.1 0.2 0.1 0.0 2.7 Russian Federation 0.2 0.8 0.0 0.1 0.0 0.0 1.1 Latin America & Caribbean 0.3 0.1 1.1 0.1 0.1 0.1 1.7 Brazil 0.1 0.0 0.3 0.0 0.0 0.0 0.6 Middle East & N. Africa 0.2 0.1 0.1 0.2 0.2 0.1 0.8 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.1 South Asia 0.1 0.0 0.0 0.1 0.1 0.1 0.4 India 0.1 0.0 0.0 0.0 0.1 0.1 0.4 Sub-Saharan Africa 0.1 0.0 0.1 0.0 0.1 0.2 0.7 South Africa 0.0 0.0 0.0 0.0 0.0 0.1 0.2 World 10.2 6.9 5.1 2.0 2.3 1.7 28.2 362 2010 World Development Indicators 6.3 GLOBAL LINKS Direction and growth of merchandise trade Nominal growth of trade High-income importers average annual % growth, 1998­2008 European United Other high- Source of exports Union Japan States income Total High-income economies 9.7 7.9 5.8 9.0 8.8 European Union 10.1 5.9 7.6 9.7 9.8 Japan 4.1 .. 1.4 8.1 4.8 United States 5.4 0.7 .. 6.0 5.3 Other high-income economies 10.8 11.7 6.1 12.3 9.5 Low- and middle-income economies 18.4 13.1 12.9 18.2 16.1 East Asia & Pacific 19.4 12.4 16.3 18.0 17.0 China 27.7 15.1 23.5 23.7 23.2 Europe & Central Asia 22.0 16.5 10.1 20.8 20.9 Russian Federation 23.9 15.8 5.9 20.0 21.5 Latin America & Caribbean 13.2 11.8 9.3 17.1 10.8 Brazil 12.7 10.1 11.1 20.7 13.3 Middle East & N. Africa 16.6 19.0 26.7 20.5 18.7 Algeria 18.1 16.8 30.7 29.8 22.0 South Asia 14.1 6.1 9.7 20.0 14.5 India 15.9 7.5 12.1 22.4 17.0 Sub-Saharan Africa 12.3 23.2 21.4 14.1 16.1 South Africaa 12.6 25.0 17.4 14.6 15.2 World 11.1 9.7 8.4 11.2 10.5 Low- and middle-income importers average annual % growth, 1998­2008 Europe Latin Middle East Asia & Central America East & South Sub-Saharan Source of exports & Pacific Asia & Caribbean N. Africa Asia Africa Total High-income economies 16.4 18.8 7.6 12.9 18.2 12.6 14.4 European Union 15.9 18.8 8.0 11.2 16.1 11.5 14.9 Japan 14.2 28.0 7.4 10.5 11.2 10.7 13.3 United States 12.1 12.6 6.5 10.6 18.5 11.6 8.6 Other high-income economies 18.2 20.1 11.9 19.0 20.3 15.9 17.8 Low- and middle-income economies 23.9 23.9 17.1 23.0 24.3 21.3 22.3 East Asia & Pacific 22.4 37.6 25.7 24.6 26.1 26.1 25.5 China 28.4 41.1 30.8 30.1 35.0 30.7 32.5 Europe & Central Asia 20.8 21.9 20.2 22.3 23.2 20.6 21.9 Russian Federation 21.2 21.9 21.8 23.7 21.7 14.7 21.8 Latin America & Caribbean 31.8 20.8 13.9 16.3 22.7 25.1 17.1 Brazil 31.2 22.2 15.9 19.3 18.0 27.0 19.4 Middle East & N. Africa 31.2 22.1 18.8 27.3 31.8 27.5 26.9 Algeria 64.2 14.1 12.1 27.7 86.5 8.3 19.9 South Asia 27.5 15.5 23.0 24.1 20.8 24.0 23.1 India 29.8 14.7 26.0 27.9 21.1 25.1 24.9 Sub-Saharan Africa 22.8 23.2 25.0 13.6 14.0 15.2 21.5 South Africaa 28.5 20.0 10.9 19.5 17.9 12.4 16.0 World 18.1 20.6 10.1 15.8 20.3 15.5 16.5 a. Data for 1998 are based on imports from South Africa reported by other economies because data on exports for South Africa were not available. 2010 World Development Indicators 363 6.3 Direction and growth of merchandise trade About the data Definitions The table provides estimates of the flow of trade in using the IMF's published period average exchange · Merchandise trade includes all trade in goods; goods between groups of economies. The data are rate (series rf or rh, monthly averages of the market trade in services is excluded. · High-income econo- from the International Monetary Fund's (IMF) Direc- or official rates) for the reporting country or, if unavail- mies are those classified as such by the World Bank tion of Trade database. All high-income economies able, monthly average rates in New York. Because (see inside front cover). · European Union is defined and major developing economies report trade on imports are reported at cost, insurance, and freight as all high-income EU members: Austria, Belgium, a timely basis, covering about 85 percent of trade (c.i.f.) valuations, and exports at free on board (f.o.b.) Cyprus, Czech Republic, Denmark, Estonia, Finland, for recent years. Trade by less timely reporters and valuations, the IMF adjusts country reports of import France, Germany, Greece, Hungary, Ireland, Italy, Lux- by countries that do not report is estimated using values by dividing them by 1.10 to estimate equiva- embourg, Malta, the Netherlands, Portugal, Slovak reports of trading partner countries. Because the lent export values. The accuracy of this approxima- Republic, Slovenia, Spain, Sweden, and the United largest exporting and importing countries are reli- tion depends on the set of partners and the items Kingdom. · Other high-income economies include able reporters, a large portion of the missing trade traded. Other factors affecting the accuracy of trade all high-income economies (both Organisation for flows can be estimated from partner reports. Part- data include lags in reporting, recording differences Economic Co-operation and Development members ner country data may introduce discrepancies due to across countries, and whether the country reports and others) except the high-income European Union, smuggling, confidentiality, different exchange rates, trade according to the general or special system of Japan, and the United States. · Low- and middle- overreporting of transit trade, inclusion or exclusion trade. (For further discussion of the measurement of income regional groupings are based on World Bank of freight rates, and different points of valuation and exports and imports, see About the data for tables classifications (see inside back cover for regional times of recording. 4.4 and 4.5.) groupings) and may differ from those used by other In addition, estimates of trade within the European The regional trade flows in the table are calculated organizations. Union (EU) have been significantly affected by changes from current price values. The growth rates are in in reporting methods following the creation of a cus- nominal terms; that is, they include the effects of toms union. The current system for collecting data on changes in both volumes and prices. trade between EU members--Intrastat, introduced in 1993--has less exhaustive coverage than the previ- ous customs-based system and has resulted in some problems of asymmetry (estimated imports are about 5 percent less than exports). Despite these issues, only a small portion of world trade is estimated to be omitted from the IMF's Direction of Trade Statistics Yearbook and Direction of Trade database. Most countries report their trade data in national currencies, which are converted into U.S. dollars Trade among developing economies has grown faster than trade among high-income economies 6.3a Annual average growth of merchandise trade, 1998­2008 (percent) High-income importers Low- and middle-income importers 30 20 10 0 High income East Asia Europe & Latin America Middle East South Sub-Saharan & Pacific Central Asia & Caribbean & N. Africa Asia Africa Source of exports Data sources Low- and middle-income economies increased their imports from other low- and middle-income econo- Data on the direction and growth of merchandise mies. High-income economies are also increasingly importing from low- and middle-income economies. trade were calculated using the IMF's Direction of Source: World Bank staff calculations based on data from the International Monetary Fund's Direction of Trade database. Trade database. 364 2010 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies Exports to low-income economies High-income economies European Union Japan United States 1998 2008 1998 2008 1998 2008 1998 2008 Total ($ billions) 47.0 146.3 21.7 56.2 5.2 15.4 4.5 15.9 % of total exports Food 12.2 8.9 14.4 10.2 1.0 0.9 23.9 21.4 Cereals 4.3 3.0 3.6 2.3 0.5 0.1 17.0 12.9 Agricultural raw materials 2.1 2.1 1.5 1.5 1.2 1.6 5.1 6.2 Ores and nonferrous metals 1.1 1.9 0.9 1.3 0.6 1.4 0.6 1.9 Fuels 3.7 15.9 2.2 14.8 1.1 2.4 1.0 6.7 Crude petroleum 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.5 15.0 2.0 14.5 1.0 2.3 0.8 6.3 Manufactured goods 78.9 65.9 79.0 69.2 92.6 90.2 65.2 59.4 Chemical products 12.3 10.3 14.2 11.1 5.1 5.3 11.6 6.3 Iron and steel 3.0 3.8 2.4 2.3 7.7 11.4 1.7 1.4 Machinery and transport equipment 45.4 38.6 46.2 42.0 66.4 61.3 40.1 43.6 Furniture 0.3 0.2 0.5 0.4 0.1 0.1 0.3 0.2 Textiles 6.2 3.7 2.3 1.8 5.0 3.3 4.0 0.7 Footwear 0.4 0.2 0.3 0.2 0.0 0.0 0.5 0.3 Other 11.2 9.0 13.2 11.4 8.3 8.7 7.1 6.9 Miscellaneous goods 1.7 4.8 1.2 2.9 3.6 3.5 4.2 4.4 Imports from low-income economies Total ($ billions) 49.6 188.4 25.4 73.7 4.0 14.8 11.7 71.4 % of total imports Food 23.5 11.9 30.4 18.5 32.1 11.8 10.6 4.3 Cereals 0.3 0.6 0.2 0.4 0.1 0.2 0.1 0.1 Agricultural raw materials 6.6 2.3 8.5 3.7 6.1 2.4 1.0 0.6 Ores and nonferrous metals 4.6 4.0 4.9 5.9 9.8 7.0 2.4 0.4 Fuels 21.0 43.7 10.9 31.2 14.3 37.8 41.1 62.1 Crude petroleum 19.6 36.9 10.4 23.1 11.7 22.5 37.6 58.3 Petroleum products 1.1 1.9 0.3 0.4 0.6 2.2 3.1 3.2 Manufactured goods 42.7 35.9 43.8 40.0 35.5 38.2 44.2 32.2 Chemical products 0.9 1.1 1.1 1.0 0.4 1.5 0.3 0.7 Iron and steel 0.5 0.3 0.4 0.3 1.4 0.5 0.3 0.2 Machinery and transport equipment 3.5 3.6 2.6 2.3 3.7 16.4 0.2 1.4 Furniture 0.6 2.0 0.6 1.7 1.9 2.1 0.2 2.6 Textiles 26.1 20.4 23.1 23.3 21.8 9.2 38.0 22.9 Footwear 3.4 3.8 5.2 6.5 1.5 2.7 1.1 1.8 Other 7.8 4.5 10.8 5.1 4.8 5.8 4.2 2.6 Miscellaneous goods 1.6 2.3 1.2 0.7 2.3 2.8 0.7 0.5 Simple applied tariff rates on imports from low-income economies (%)a Average 5.5 3.9 1.7 0.9 3.3 1.9 5.3 4.2 Food 6.8 4.2 5.3 1.0 9.6 3.8 3.4 1.5 Cereals 8.9 2.2 30.0 0.0 4.4 7.4 1.7 1.0 Agricultural raw materials 2.5 1.5 0.2 0.1 1.5 0.2 0.3 0.2 Ores and nonferrous metals 1.6 1.1 0.3 0.3 0.8 0.0 0.2 0.6 Fuels 3.1 1.1 0.0 0.0 2.6 0.2 0.5 0.7 Crude petroleum 1.3 0.7 0.0 0.0 1.2 0.0 0.4 0.0 Petroleum products 5.1 1.5 0.0 0.0 6.0 0.6 0.9 1.2 Manufactured goods 5.6 4.1 1.2 1.0 2.3 1.8 6.2 4.8 Chemical products 3.8 2.6 1.2 0.4 3.3 0.3 0.7 1.1 Iron and steel 5.1 2.1 0.3 0.2 0.6 0.0 1.3 0.8 Machinery and transport equipment 2.5 1.7 0.3 0.2 0.1 0.0 0.5 0.6 Furniture 4.3 3.5 0.2 0.1 0.0 0.0 0.9 1.2 Textiles 9.0 6.8 3.1 2.8 4.3 3.4 11.2 9.5 Footwear 8.6 6.4 3.1 2.0 7.7 7.1 13.4 8.4 Other 3.3 2.4 0.5 0.2 0.6 0.7 1.6 1.2 Miscellaneous goods 0.9 0.8 0.0 0.0 0.0 0.0 0.0 0.0 2010 World Development Indicators 365 6.4 High-income economy trade with low- and middle-income economies Exports to middle-income economies High-income economies European Union Japan United States 1998 2008 1998 2008 1998 2008 1998 2008 Total ($ billions) 691.6 2,371.8 280.7 1,004.8 81.8 280.1 191.8 425.5 % of total exports Food 7.3 5.9 8.1 5.7 0.6 0.3 8.8 12.0 Cereals 1.6 1.3 1.1 0.9 0.2 0.0 2.8 3.8 Agricultural raw materials 1.8 1.8 1.2 1.4 1.1 0.9 2.5 3.5 Ores and nonferrous metals 1.8 4.1 1.5 2.6 1.7 3.4 1.6 4.3 Fuels 2.4 7.0 1.4 3.3 0.5 2.6 2.0 7.7 Crude petroleum 0.4 1.0 0.1 0.1 0.0 0.0 0.0 0.0 Petroleum products 1.4 4.9 1.1 2.8 0.4 2.5 1.3 6.0 Manufactured goods 84.2 77.2 85.7 83.4 94.0 88.5 81.4 69.2 Chemical products 11.6 13.0 12.9 13.3 7.6 8.8 10.5 14.1 Iron and steel 2.7 3.8 2.8 4.0 6.1 7.5 1.0 1.7 Machinery and transport equipment 49.3 44.5 46.7 46.9 66.6 60.6 50.5 39.8 Furniture 0.6 0.4 0.8 0.7 0.1 0.2 0.7 0.3 Textiles 6.0 2.5 5.7 3.4 3.1 1.4 5.3 2.0 Footwear 0.4 0.2 0.6 0.5 0.0 0.0 0.2 0.1 Other 13.7 12.7 16.0 14.5 10.6 10.0 13.1 11.2 Miscellaneous goods 2.2 3.5 1.6 3.1 2.1 4.2 3.8 3.3 Imports from middle-income economies Total ($ billions) 914.3 3,595.8 285.2 1,364.7 90.5 315.7 320.0 1,023.2 % of total imports Food 10.8 6.5 14.8 8.4 17.2 7.3 7.2 4.7 Cereals 0.5 0.4 0.3 0.6 0.6 0.2 0.2 0.2 Agricultural raw materials 2.6 1.3 3.7 1.6 4.6 2.2 1.3 0.9 Ores and nonferrous metals 5.3 4.8 7.0 4.9 9.5 11.0 2.9 2.4 Fuels 11.2 24.0 15.3 27.7 13.1 23.4 9.8 26.1 Crude petroleum 7.2 16.2 9.8 19.1 5.5 10.7 7.5 21.8 Petroleum products 1.5 4.1 1.9 4.1 0.8 3.1 1.9 3.7 Manufactured goods 68.1 61.3 56.9 55.2 54.2 54.5 76.1 63.8 Chemical products 3.2 3.9 4.1 3.9 3.0 4.4 2.1 3.2 Iron and steel 2.5 3.7 2.7 3.7 1.3 2.1 2.2 2.5 Machinery and transport equipment 27.9 29.4 17.7 23.9 20.2 25.1 35.9 32.1 Furniture 1.6 1.8 1.7 1.8 1.3 1.3 2.0 2.4 Textiles 14.5 7.9 15.7 8.8 14.4 8.7 13.5 7.6 Footwear 2.8 1.4 1.9 1.4 1.7 1.1 3.6 1.7 Other 15.6 13.4 13.1 11.9 12.3 11.8 16.8 14.3 Miscellaneous goods 2.0 1.8 1.9 1.3 1.4 1.5 2.6 2.1 Simple applied tariff rates on imports from middle-income economies (%)a Average 5.9 4.2 3.5 1.1 2.7 2.5 3.8 2.6 Food 9.6 6.0 13.0 2.8 12.2 7.0 3.9 2.9 Cereals 11.3 6.4 32.3 0.5 15.7 11.2 1.2 0.7 Agricultural raw materials 2.6 2.0 0.9 0.4 1.3 0.6 0.6 0.4 Ores and nonferrous metals 2.1 1.3 1.3 0.5 0.1 0.1 0.6 0.4 Fuels 3.1 1.4 0.1 0.0 1.4 0.3 0.5 1.1 Crude petroleum 5.5 0.6 0.0 0.0 1.2 0.0 0.5 0.0 Petroleum products 6.1 2.4 0.3 0.1 6.0 1.0 1.5 2.9 Manufactured goods 5.6 4.1 2.7 0.9 1.5 2.1 4.0 2.7 Chemical products 3.7 2.5 2.0 0.6 0.7 0.3 1.7 1.1 Iron and steel 3.4 2.0 1.2 0.1 0.1 0.2 2.8 0.3 Machinery and transport equipment 3.7 2.6 1.1 0.2 0.0 0.0 0.6 0.5 Furniture 5.8 4.6 0.6 0.0 0.0 0.1 0.6 0.4 Textiles 9.9 7.5 6.8 3.1 4.4 5.7 11.1 7.6 Footwear 9.6 7.2 6.5 2.8 12.8 16.7 11.6 6.9 Other 5.3 5.1 1.4 0.3 0.4 0.8 1.2 0.8 Miscellaneous goods 0.8 0.4 0.0 0.0 0.0 0.0 0.9 0.2 a. Includes ad valorem equivalents of specific rates. 366 2010 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies About the data Definitions Developing economies are becoming increasingly manufactures as a share of goods imports from both The product groups in the table are defined in accor- important in the global trading system. Since the low- and middle-income economies have grown. And dance with SITC revision 3: food (0, 1, 22, and 4) and early 1990s trade between high-income economies trade between developing economies has grown cereals (04); agricultural raw materials (2 exclud- and low- and middle-income economies has grown substantially over the past decade, a result of their ing 22, 27, and 28); ores and nonferrous metals faster than trade among high-income economies. increasing share of world output and liberalization of (27, 28, and 68); fuels (3), crude petroleum (crude The increased trade benefi ts consumers and pro- trade, among other influences. petroleum oils and oils obtained from bituminous ducers. But as was apparent at the World Trade Orga- Yet trade barriers remain high. The table includes minerals; 333), and petroleum products (noncrude nization's (WTO) Ministerial Conferences in Doha, information about tariff rates by selected product petroleum and preparations; 334); manufactured Qatar, in October 2001; Cancun, Mexico, in Septem- groups. Applied tariff rates are the tariffs in effect goods (5­8 excluding 68), chemical products (5), ber 2003; and Hong Kong SAR, China, in December for partners in preferential trade agreements such iron and steel (67), machinery and transport equip- 2005, achieving a more pro-development outcome as the North American Free Trade Agreement. When ment (7), furniture (82), textiles (65 and 84), foot- from trade remains a challenge. Doing so will require these rates are unavailable, most favored nation wear (85), and other manufactured goods (6 and 8 strengthening international consultation. After the rates are used. The difference between most favored excluding 65, 67, 68, 82, 84, and 85); and miscel- Doha meetings negotiations were launched on ser- nation and applied rates can be substantial. Simple laneous goods (9). · Exports are all merchandise vices, agriculture, manufactures, WTO rules, the averages of applied rates are shown because they exports by high-income economies to low-income environment, dispute settlement, intellectual prop- are generally a better indicator of tariff protection and middle-income economies as recorded in the erty rights protection, and disciplines on regional than weighted average rates are. United Nations Statistics Division's Comtrade data- integration. At the most recent negotiations in Hong The data are from the United Nations Conference base. Exports are recorded free on board (f.o.b.). Kong SAR, China, trade ministers agreed to eliminate on Trade and Development (UNCTAD). Partner coun- · Imports are all merchandise imports by high- subsidies of agricultural exports by 2013; to abolish try reports by high-income economies were used for income economies from low-income and middle- cotton export subsidies and grant unlimited export both exports and imports. Because of differences in income economies as recorded in the United Nations access to selected cotton-growing countries in Sub- sources of data, timing, and treatment of missing data, Statistics Division's Commodity Trade (Comtrade) Saharan Africa; to cut more domestic farm supports the numbers in the table may not be fully comparable database. Imports include insurance and freight in the European Union, Japan, and the United States; with those used to calculate the direction of trade charges (c.i.f.). · High-, middle-, and low-income and to offer more aid to developing countries to help statistics in table 6.3 or the aggregate flows in tables economies are those classified as such by the World them compete in global trade. 4.4, 4.5, and 6.2. Tariff line data were matched to Bank (see inside front cover). · European Union Trade flows between high-income and low- and Standard International Trade Classification (SITC) revi- is defined as all high-income EU members: Austria, middle-income economies reflect the changing mix of sion 3 codes to define commodity groups. For further Belgium, Cyprus, Czech Republic, Denmark, Estonia, exports to and imports from developing economies. discussion of merchandise trade statistics, see About Finland, France, Germany, Greece, Hungary, Ireland, While food and primary commodities have continued the data for tables 4.4, 4.5, 6.2, 6.3, and 6.5, and for Italy, Luxembourg, Malta, the Netherlands, Portugal, to fall as a share of high-income economies' imports, information about tariff barriers, see table 6.8. Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom. High-income economies export mostly manufactured goods to low- and middle-income economies 6.4a Share of total exports (%) 1998 2008 100 80 60 40 20 0 Food Agricultural Fuels Manu- Machinery Textiles Food Agricultural Fuels Manu- Machinery Textiles Data sources raw factured and raw factured and materials goods transport materials goods transport equipment equipment Data on trade values are from United Nations Exports to low-income economies Exports to middle-income economies Statistics Division's Comtrade database. Data Some 65­75 percent of high-income economy exports to low-income and middle-income economies in on tariffs are from UNCTAD's Trade Analysis and 2008 were manufactured goods. Machinery and equipment accounted for nearly 60 percent of manu- Information System database and are calculated factured goods exports from high-income economies. by World Bank staff using the World Integrated Source: World Bank staff calculations based on data from the United Nations Statistics Division's Comtrade database. Trade Solution system. 2010 World Development Indicators 367 6.5 Direction of trade of developing economies Exports Imports % of total merchandise exports % of total merchandise imports To developing economies To high-income From developing economies From high-income Within region Outside region economies Within region Outside region economies 1998 2008 1998 2008 1998 2008 1998 2008 1998 2008 1998 2008 East Asia & Pacific 7.7 w 10.8 w 7.9 w 16.3 w 83.1 w 72.0 w 7.6 w 12.6 w 7.9 w 16.3 w 81.3 w 63.9 w Cambodia .. .. 0.8 1.8 66.1 90.4 .. 65.6 1.8 1.4 60.4 32.9 China 4.1 6.0 9.4 18.9 86.4 75.0 6.2 8.8 8.1 18.4 83.6 64.6 Indonesia 11.2 19.1 8.0 13.7 80.7 67.3 10.8 24.8 6.8 9.4 81.9 65.7 Kiribati .. .. .. .. 67.5 50.9 .. .. .. .. .. .. Korea, Dem. Rep. .. .. 43.6 52.3 48.6 11.1 .. .. 17.7 55.1 46.5 7.8 Lao PDR .. .. 0.5 1.0 39.4 19.0 .. .. .. 1.2 16.1 12.2 Malaysia 9.9 20.5 8.1 11.5 82.0 67.9 12.4 26.1 3.0 6.1 83.3 66.7 Mongolia .. .. 12.3 4.2 58.4 31.2 .. .. .. 43.6 52.5 26.7 Myanmar 11.6 65.5 18.0 15.6 55.4 13.6 .. .. 2.0 4.2 50.9 33.0 Papua New Guinea 8.8 9.0 0.9 2.3 58.6 51.0 8.3 21.8 1.3 1.2 89.5 75.3 Philippines 7.9 20.4 1.9 2.4 90.0 76.8 13.4 22.7 3.7 4.2 82.3 73.1 Thailand 11.8 26.2 5.5 12.2 76.5 61.0 13.3 24.6 5.7 8.5 75.2 65.5 Tonga 6.1 18.0 .. .. 74.3 79.9 7.4 47.6 .. .. 84.6 48.5 Vietnam 18.6 19.3 6.5 4.1 74.9 68.9 17.1 32.0 3.9 5.6 78.9 49.0 Europe & Central Asia 28.6 w 28.1 w 8.8 w 10.3 w 60.5 w 60.2 w 28.8 w 28.3 w 8.8 w 10.3 w 65.1 w 55.8 w Albania 3.1 11.1 0.3 7.8 96.5 81.1 9.9 22.6 1.1 7.4 88.8 70.0 Armenia 37.5 37.5 .. 4.5 43.1 57.3 34.5 41.6 .. 19.3 .. 39.0 Azerbaijan 63.7 5.7 .. 13.2 28.1 81.2 60.0 45.6 .. 13.4 33.1 41.0 Belarus 81.3 59.3 6.6 10.1 12.0 30.6 72.2 70.7 2.9 5.9 24.8 21.8 Bosnia and Herzegovina 4.8 5.9 .. .. 89.9 92.0 5.2 13.6 .. .. .. .. Bulgaria 29.4 31.3 6.5 7.8 62.5 58.1 31.7 38.9 8.9 7.7 58.9 52.9 Georgia 69.5 63.3 5.8 6.5 25.8 30.1 50.3 51.5 3.4 9.4 45.2 39.0 Kazakhstan 42.4 31.7 10.9 15.4 45.2 45.3 54.1 47.0 4.6 26.3 40.7 26.7 Kyrgyz Republic 49.6 53.1 .. .. 45.1 40.4 58.1 56.8 .. 19.5 29.7 23.8 Latvia 28.1 39.3 2.3 4.3 69.5 56.2 26.5 40.2 1.5 3.2 72.0 56.6 Lithuania 50.8 44.2 1.1 3.8 47.9 51.7 34.2 49.9 3.2 4.1 61.3 46.0 Macedonia, FYR 28.1 37.9 1.2 0.8 70.6 61.2 34.1 35.5 5.3 3.5 60.6 61.0 Moldova 79.5 35.7 1.7 49.8 18.4 14.4 57.9 69.1 1.7 3.3 35.8 27.4 Poland 14.6 16.2 3.0 3.1 82.1 80.1 7.6 14.5 7.0 6.9 85.3 77.8 Romania 11.7 21.8 9.3 6.6 78.5 71.2 16.1 23.2 6.1 7.2 76.7 69.6 Russian Federation 29.1 29.2 9.6 10.8 58.9 59.9 31.7 20.1 10.5 20.3 57.7 59.5 Serbia .. 40.9 .. 1.6 .. 56.2 .. 25.3 .. 4.3 .. 62.8 Tajikistan 34.5 46.2 .. .. 61.9 39.6 65.2 66.4 .. .. 32.5 13.8 Turkey 14.3 17.9 12.0 16.8 69.5 60.7 9.3 24.9 11.2 22.9 77.9 51.5 Turkmenistan 45.3 73.3 .. .. 24.6 19.8 63.5 45.0 .. .. 28.4 29.3 Ukraine 46.3 51.1 19.3 17.0 34.0 30.5 61.2 49.3 4.1 12.3 34.7 38.4 Uzbekistan 52.1 66.1 .. .. 36.9 20.3 44.0 49.7 .. .. 51.5 33.5 Latin America & Carib. 18.3 w 18.9 w 4.6 w 11.4 w 73.1 w 64.5 w 19.9 w 21.4 w 4.6 w 11.4 w 76.0 w 59.7 w Argentina 48.3 39.3 14.3 26.6 33.4 32.1 30.4 40.9 7.6 19.1 58.6 34.5 Bolivia 44.3 72.9 0.4 3.1 54.1 23.6 35.2 68.8 1.5 6.5 63.3 24.5 Brazil 27.2 24.3 11.4 21.1 60.0 49.8 21.6 16.3 8.6 29.8 69.7 53.7 Chile 21.9 18.5 5.7 20.3 62.1 56.1 24.2 32.6 7.9 19.7 54.1 45.8 Colombia 28.6 34.0 1.2 2.5 68.7 60.2 24.2 26.2 4.4 16.4 68.9 53.9 Costa Rica 14.6 20.6 1.7 17.3 39.9 62.1 19.1 29.5 3.4 6.9 46.1 63.0 Cuba 5.9 15.3 40.6 34.8 53.5 49.9 23.4 41.1 14.5 18.3 62.1 40.5 Dominican Republic 2.5 15.2 0.5 3.1 96.7 73.3 16.5 27.4 2.3 6.0 81.0 63.5 Ecuador 26.2 35.8 5.2 6.2 67.9 57.5 34.2 44.7 4.5 15.4 60.6 38.9 El Salvador 55.4 42.6 2.8 0.6 41.7 56.7 37.9 42.1 3.8 7.6 57.0 48.7 Guatemala 22.6 45.7 2.9 1.9 72.6 51.3 29.2 33.5 3.7 9.3 66.2 56.1 Haiti 0.7 10.7 .. .. 98.6 83.4 12.9 33.7 5.0 10.7 81.7 55.6 Honduras 15.4 20.8 0.0 2.0 65.2 77.1 21.0 29.6 0.0 6.5 62.5 63.6 Jamaica 2.5 2.6 8.4 8.3 88.7 88.5 10.2 21.8 3.6 5.9 83.2 71.5 Mexico 4.3 6.9 0.3 1.7 94.8 90.4 2.3 4.6 3.5 15.7 93.8 78.8 Nicaragua 26.0 43.4 .. 0.9 67.9 55.0 49.5 53.3 0.5 13.1 45.2 32.9 Panama 22.2 15.6 1.0 5.5 74.7 77.0 21.2 21.5 1.2 7.0 64.2 46.7 Paraguay 60.1 68.4 0.8 9.8 35.2 17.5 52.5 53.1 3.4 11.7 44.0 32.6 Peru 17.8 21.9 8.1 18.5 74.0 59.6 28.0 36.2 3.1 18.5 68.8 45.3 Uruguay 62.8 42.7 6.1 19.6 30.6 34.9 48.9 47.8 7.7 21.9 42.9 30.2 Venezuela, RB 22.2 10.7 0.9 7.8 59.0 58.8 18.9 37.9 1.7 11.6 67.7 47.1 368 2010 World Development Indicators 6.5 GLOBAL LINKS Direction of trade of developing economies Exports Imports % of total merchandise exports % of total merchandise imports To developing economies To high-income From developing economies From high-income Within region Outside region economies Within region Outside region economies 1998 2008 1998 2008 1998 2008 1998 2008 1998 2008 1998 2008 Middle East & N. Africa 4.9 w 6.1 w 14.3 w 22.7 w 76.6 w 66.1 w 5.2 w 6.7 w 14.3 w 22.7 w 71.4 w 60.0 w Algeria 1.2 2.9 14.3 12.6 84.5 84.5 2.2 2.1 15.7 29.5 82.1 68.4 Djibouti 22.4 4.1 .. .. 17.5 13.6 2.9 0.9 .. .. 67.6 45.8 Egypt, Arab Rep. 8.7 11.8 10.6 19.3 70.5 59.8 1.1 3.6 23.4 32.0 65.8 58.4 Iran, Islamic Rep. 0.0 2.0 14.4 38.0 78.1 45.7 0.0 0.5 21.8 36.4 67.4 61.9 Iraq 5.9 2.0 17.1 19.4 77.0 78.6 13.9 35.3 31.5 36.4 54.6 28.4 Jordan 26.1 27.2 26.3 25.0 43.6 38.5 12.8 8.3 20.8 29.6 63.8 61.9 Lebanon 17.3 37.2 11.6 14.2 69.2 47.9 5.7 14.2 18.0 23.5 75.7 60.4 Libya 7.3 2.8 9.9 10.2 82.9 87.0 8.2 9.9 9.4 25.6 82.4 64.4 Morocco 3.9 2.3 14.1 25.2 71.2 71.5 2.4 6.3 12.4 24.1 71.4 69.6 Syrian Arab Republic 15.6 50.6 14.2 6.8 65.2 42.6 4.9 20.1 25.5 33.5 47.9 46.5 Tunisia 6.3 9.7 7.2 10.2 83.3 77.6 4.2 9.7 9.7 19.3 85.2 70.3 Yemen, Rep. 3.4 1.4 51.6 81.5 44.3 16.5 3.6 3.2 23.1 37.6 70.7 58.3 South Asia 5.0 w 5.9 w 15.8 w 25.5 w 78.2 w 65.9 w 6.2 w 6.8 w 15.8 w 25.5 w 69.6 w 58.0 w Afghanistan 31.6 41.5 14.4 25.9 54.0 32.6 15.0 43.4 40.3 25.3 44.7 31.4 Bangladesh 2.9 3.1 6.4 7.1 90.2 75.9 17.3 16.4 18.7 29.5 48.4 47.6 India 5.0 4.9 18.0 28.1 76.5 65.0 1.0 0.7 24.1 39.0 74.8 59.8 Nepal 36.2 64.4 .. .. 61.7 29.0 31.7 55.6 .. .. 56.2 15.1 Pakistan 5.3 12.2 13.6 20.6 78.9 66.1 2.7 4.3 24.9 30.8 70.5 61.5 Sri Lanka 2.4 6.1 13.1 15.3 81.0 73.6 10.4 20.6 17.5 31.2 63.0 47.6 Sub-Saharan Africa 12.6 w 11.1 w 10.0 w 26.1 w 64.7 w 61.1 w 12.6 w 11.9 w 10.0 w 26.1 w 71.8 w 53.7 w Angola 0.3 4.6 6.4 42.1 93.3 53.3 11.8 5.3 12.2 32.3 76.0 62.3 Benin 14.5 26.0 58.0 45.4 27.4 28.6 14.6 7.0 17.2 57.2 67.9 35.7 Burkina Faso 8.7 17.2 .. .. 52.1 45.7 26.6 38.5 25.6 15.7 44.7 38.8 Burundi 3.0 11.9 0.3 18.2 61.5 53.7 21.2 25.0 .. 10.6 63.3 56.7 Cameroon 8.2 9.8 6.2 11.8 84.9 76.3 14.5 19.8 9.9 27.0 71.3 52.7 Central African Republic 1.8 8.6 8.3 43.9 90.0 47.4 17.4 12.7 10.6 8.2 57.5 56.0 Chad 4.5 0.3 .. .. 83.4 98.1 33.2 20.7 .. .. 63.2 54.9 Congo, Dem. Rep. 1.3 7.5 2.0 52.5 96.4 39.8 42.4 55.4 10.4 10.6 45.8 33.8 Congo, Rep. 1.5 1.0 6.4 37.5 89.5 61.3 9.8 5.5 12.0 35.3 68.9 57.6 Côte d'Ivoire 25.1 28.3 8.9 13.6 58.9 57.1 13.3 32.5 15.7 24.8 61.8 41.9 Ethiopia 0.9 5.9 13.3 19.2 81.1 74.1 1.6 2.4 16.3 38.4 74.6 41.2 Gabon 1.5 2.3 7.6 28.5 83.3 58.6 15.3 10.0 4.0 14.7 79.7 73.7 Gambia, The 10.8 7.9 6.6 58.4 82.6 33.7 11.4 21.3 31.8 49.5 56.7 29.2 Ghana 7.6 9.0 9.4 28.3 77.2 51.3 26.0 23.2 12.7 35.5 60.7 40.5 Guinea 4.8 1.9 0.9 37.7 90.9 44.0 11.6 5.8 15.2 20.9 73.0 36.5 Guinea-Bissau 3.4 31.1 .. .. 25.9 2.5 11.5 20.8 .. .. 59.6 39.3 Kenya 39.0 33.5 18.8 16.7 40.9 41.8 8.5 9.1 17.7 33.4 73.3 56.6 Liberia 1.2 5.5 3.3 39.9 95.5 54.6 0.7 1.3 1.9 16.2 97.4 82.6 Madagascar 9.8 4.9 6.2 5.9 75.7 81.2 8.1 9.0 25.3 35.2 58.1 40.6 Malawi 21.8 28.8 12.6 28.0 65.2 42.6 68.0 57.1 3.9 19.0 27.1 23.2 Mali 8.2 7.9 34.7 56.5 55.5 25.8 24.7 30.7 5.4 11.4 39.8 28.2 Mauritania 9.6 10.3 6.9 44.4 82.5 44.2 5.2 5.2 17.1 29.6 69.7 55.2 Mauritius 6.3 10.7 0.9 4.2 92.7 85.0 14.5 12.6 24.3 43.7 61.1 43.4 Mozambique 44.4 13.8 11.6 3.4 44.0 61.7 43.7 30.0 11.2 16.6 36.9 37.0 Niger 31.6 55.0 0.4 1.9 68.0 43.0 26.8 17.9 16.6 32.8 54.2 49.3 Nigeria 10.4 8.1 17.9 21.1 71.1 69.8 4.2 4.6 23.1 26.4 72.5 54.9 Rwanda 2.5 4.2 14.7 26.2 63.7 24.5 32.4 35.3 6.2 11.1 44.7 33.6 Senegal 26.2 43.5 20.2 12.9 45.5 27.3 11.0 8.8 17.9 25.4 69.1 65.7 Sierra Leone 0.0 3.3 0.0 12.9 72.1 80.3 13.1 11.9 9.6 40.5 72.8 42.5 Somalia 0.5 4.5 29.9 26.3 69.6 69.2 13.3 10.7 60.8 53.6 14.3 23.2 South Africa 13.1 16.5 7.1 16.8 51.7 66.7 2.1 7.4 15.3 32.0 81.2 60.6 Sudan 0.8 1.1 24.9 59.2 74.2 39.6 4.1 5.9 37.9 43.3 58.0 47.5 Tanzania 12.7 19.7 26.8 24.0 59.2 43.9 18.6 17.0 22.6 36.5 57.9 42.6 Togo 17.8 47.0 29.0 26.6 52.4 25.4 18.8 7.1 9.3 56.8 70.0 35.2 Uganda 1.7 46.0 8.5 3.7 89.8 47.4 45.1 20.5 .. 28.9 43.5 50.6 Zambia 22.7 26.9 13.3 32.8 59.3 40.2 52.6 64.0 4.0 11.7 43.3 24.2 Zimbabwe 34.7 57.5 9.1 13.4 55.9 29.0 42.4 72.9 6.4 8.2 46.0 14.6 Note: Bilateral trade data are not available for Timor-Leste, Kosovo, West Bank and Gaza, Botswana, Eritrea, Lesotho, Namibia, and Swaziland. Components may not sum to 100 percent because of trade with unspecified partners or with economies not covered by World Bank classification. 2010 World Development Indicators 369 6.5 Direction of trade of developing economies About the data Definitions Developing economies are an increasingly impor- and commodity. Larger shares of exports from oil- · Exports to developing economies within region tant part of the global trading system. Their share and resource-rich economies are to high-income are the sum of merchandise exports from the report- of world merchandise exports rose from 15 percent economies. ing economy to other developing economies in the in 1990 to 31 percent in 2008. And trade between The relative importance of intraregional trade same World Bank region as a percentage of total high-income economies and low- and middle-income is higher for both landlocked countries and small merchandise exports by the economy. · Exports to economies has grown faster than trade between countries with close trade links to the largest developing economies outside region are the sum high-income economies. This increased trade ben- regional economy. For most developing economies-- of merchandise exports from the reporting economy efits both producers and consumers in developing especially smaller ones--there is a "geographic to other developing economies in other World Bank and high-income economies. bias" favoring intraregional trade. Despite the broad regions as a percentage of total merchandise exports The table shows trade in goods between devel- trend toward globalization and the reduction of trade by the economy. · Exports to high-income econo- oping economies in the same region and other barriers, the relative share of intraregional trade mies are the sum of merchandise exports from the regions and between developing economies and increased for most economies between 1998 and reporting economy to high-income economies as a high-income economies. Data on exports and 2008. This is due partly to trade-related advantages, percentage of total merchandise exports by the econ- imports are from the International Monetary Fund's such as proximity, lower transport costs, increased omy. · Imports from developing economies within (IMF) Direction of Trade database and should be knowledge from repeated interaction, and cultural region are the sum of merchandise imports by the broadly consistent with data from other sources, and historical affinity. The direction of trade is also reporting economy from other developing economies such as the United Nations Statistics Division's influenced by preferential trade agreements that a in the same World Bank region as a percentage of Commodity Trade (Comtrade) database. Generally, country has made with other economies. Though total merchandise imports by the economy. · Imports data on trade between developing and high-income formal agreements on trade liberalization do not from developing economies outside region are the economies are complete. But trade fl ows between automatically increase trade, they nevertheless sum of merchandise imports by the reporting econ- many developing economies--particularly those in affect the direction of trade between the participat- omy from other developing economies in other World Sub-Saharan Africa--are not well recorded, and ing economies. Table 6.7 illustrates the size of exist- Bank regions as a percentage of total merchandise the value of trade among developing economies ing regional trade blocs that have formal preferential imports by the economy. · Imports from high-income may be understated. The table does not include trade agreements. economies are the sum of merchandise imports by some developing economies because data on their Although global integration has increased, develop- the reporting economy from high-income economies bilateral trade fl ows are not available. Data on the ing economies still face trade barriers when access- as a percentage of total merchandise imports by the direction of trade between selected high-income ing other markets (see table 6.8). economy. economies are presented and discussed in tables 6.3 and 6.4. At the regional level most exports from develop- ing economies are to high-income economies, but the share of intraregional trade is increasing. Geo- graphic patterns of trade vary widely by country Developing economies are increasingly trading with other developing economies in the same region 6.5a Share of within-region trade in total merchandise trade (percent) 30 Europe & Central Asia 20 Middle East & North Africa Latin America & Caribbean Data sources Sub-Saharan Africa 10 East Asia & Pacific Data on merchandise trade flows are published in the IMF's Direction of Trade Statistics Yearbook and South Asia 0 Direction of Trade Statistics Quarterly; the data in 1970 1975 1980 1985 1990 1995 2000 2005 2008 the table were calculated using the IMF's Direction Within-region trade (merchandise exports plus merchandise imports) has increased in all regions. In of Trade database. Regional and income group 2008 nearly 30 percent of merchandise trade in Europe and Central Asia and 20 percent in East Asia classifications are according to the World Bank and Pacific was with other economies in the region. classification of economies as of July 1, 2009, Source: World Bank staff calculations based on data from International Monetary Fund's Direction of Trade database. and are as shown on the cover flaps. 370 2010 World Development Indicators 6.6 GLOBAL LINKS Primary commodity prices 1970 1980 1990 1995 2000 2003 2004 2005 2006 2007 2008 2009 World Bank commodity price index (2000 = 100) Energy 19 153 79 53 100 101 123 171 197 207 273 180 Nonenergy commodities 183 177 115 117 100 108 121 135 172 190 217 179 Agriculture 188 195 113 122 100 114 118 121 134 153 183 166 Beverages 230 273 117 136 100 117 109 125 130 144 168 185 Food 201 199 116 117 100 117 123 121 131 156 198 172 Fats and oils 237 196 105 126 100 129 134 120 123 177 222 182 Grains 204 199 121 124 100 112 115 115 134 160 225 181 Other food 151 205 124 101 100 105 117 129 140 126 142 153 Raw materials 136 143 105 125 100 107 109 119 143 148 156 142 Timber 97 92 88 105 100 91 90 100 113 116 120 117 Other raw materials 179 198 124 146 100 124 129 140 177 184 196 169 Fertilizers 82 177 98 110 100 110 125 148 151 203 453 246 Metals and minerals 185 141 122 106 100 96 126 162 251 266 260 198 Steel productsa 0 134 131 118 100 100 153 170 162 154 231 191 Commodity prices (2000 prices) Energy Coal, Australian ($/mt) .. 49 39 33 26 25 48 43 44 56 102 60 Natural gas, Europe ($/mmBtu) .. 5 2 2 4 4 4 6 8 7 11 7 Natural gas, U.S. ($/mmBtu) 1 2 2 1 4 5 5 8 6 6 7 3 Natural gas, liquefied, Japan ($/mmBtu) .. 7 4 3 5 5 5 5 6 7 10 7 Petroleum, avg, spot ($/bbl) 4 45 22 14 28 28 34 48 57 60 78 52 Beverages (cents/kg) Cocoa 233 321 123 119 91 170 141 140 142 165 206 243 Coffee, Arabica 397 427 192 277 192 137 161 230 225 231 246 267 Coffee, robusta 321 400 115 230 91 79 72 101 133 162 186 138 Tea, avg., 3 auctions 289 205 200 124 188 147 153 150 168 172 193 229 Tea, Colombo auctions 217 137 182 118 179 150 162 167 171 214 223 264 Tea, Kolkata auctions 343 253 273 145 181 142 156 147 157 163 180 211 Tea, Mombasa auctions 307 224 144 108 203 150 141 134 175 141 177 212 Food Fats and oils ($/mt) Coconut oil 1,376 831 327 556 450 454 600 560 542 778 978 610 Copraa 779 558 224 364 305 291 409 376 360 514 652 403 Groundnut oil 1,312 1,059 937 823 714 1,207 1,054 963 867 1,145 1,704 995 Palm oil 901 719 282 521 310 430 428 383 427 661 758 574 Palm kernel oila .. .. .. .. 444 445 588 569 519 753 903 589 Soybeans 405 365 240 215 212 256 278 249 240 325 418 367 Soybean meal 357 323 195 164 189 205 219 195 187 261 339 343 Soybean oil 992 737 435 519 338 538 559 495 535 747 1,006 714 Grains ($/mt) Barley .. 96 78 86 77 102 90 86 104 146 160 108 Maize 202 154 106 103 89 102 102 90 109 139 178 139 Rice, Thailand, 5% 438 506 263 266 202 192 216 260 272 277 520 467 Sorghuma 179 159 101 99 88 103 100 87 110 138 166 127 Wheat, Canadaa 218 235 152 172 147 172 169 179 194 254 363 253 Wheat, U.S., hard red winter 190 213 132 147 114 142 142 138 172 216 261 188 Wheat, U.S., soft red winter a 197 208 125 139 99 134 131 123 142 202 217 156 2010 World Development Indicators 371 6.6 Primary commodity prices 1970 1980 1990 1995 2000 2003 2004 2005 2006 2007 2008 2009 Commodity prices (continued) (2000 prices) Food (continued) Other food Bananas, U.S. ($/mt) 573 467 526 369 424 364 476 547 605 572 675 712 Beef (cents/kg) 452 340 249 158 193 192 228 238 228 220 251 222 Chicken meat (cents/kg) .. 85 96 92 119 129 138 135 124 133 136 144 Fishmeal ($/mt)a 682 621 401 411 413 593 589 664 1,040 997 906 1,034 Oranges ($/mt) 582 482 516 441 363 661 780 794 741 810 885 764 Shrimp, Mexico (cents/kg) .. 1,420 1,039 1,253 1,515 1,110 928 939 915 855 854 795 Sugar, EU domestic (cents/kg) 39 60 57 57 56 58 61 60 58 58 56 44 Sugar, U.S. domestic (cents/kg) 57 82 50 42 43 46 41 43 44 39 37 46 Sugar, world (cents/kg) 29 78 27 24 18 15 14 20 29 19 23 34 Agricultural raw materials Cotton A index (cents/kg) 219 252 177 177 130 136 124 110 113 118 126 116 Logs, Cameroon ($/cu. m)a 149 310 334 282 275 271 301 304 285 323 421 354 Logs, Malaysian ($/cu. m) 149 241 172 212 190 182 179 184 214 227 234 241 Rubber, Singapore (cents/kg) 141 176 84 131 67 105 116 135 186 192 207 161 Plywood (cents/sheet)a 357 338 345 485 448 419 422 462 532 543 516 475 Sawnwood, Malaysian ($/cu. m) 608 489 518 614 595 535 528 599 670 683 711 677 Tobacco ($/mt)a 3,727 2,806 3,297 2,194 2,976 2,568 2,488 2,533 2,653 2,808 2,869 3,523 Woodpulp ($/mt)a 615 661 792 708 664 510 582 577 624 650 656 517 Fertilizers ($/mt) Diammonium phosphate 187 274 167 180 154 174 201 224 233 366 773 272 Phosphate rock 38 58 39 29 44 37 37 38 40 60 276 102 Potassium chloride 109 143 95 98 123 110 113 144 156 170 456 530 Triple superphosphate 147 222 128 124 138 145 169 183 180 287 703 216 Urea .. .. 116 155 101 135 159 199 199 262 394 210 Metals and minerals Aluminum ($/mt) 1,926 1,795 1,593 1,499 1,549 1,389 1,558 1,724 2,297 2,235 2,057 1,400 Copper ($/mt) 4,895 2,690 2,586 2,437 1,813 1,727 2,602 3,340 6,007 6,030 5,560 4,330 Gold ($/toz)a 125 750 373 319 279 353 372 404 540 590 697 818 Iron ore (cents/dmtu) 34 35 32 24 29 31 34 59 69 72 112 85 Lead (cents/kg) 105 112 79 52 45 50 80 89 115 219 167 145 Nickel ($/mt) 9,860 8,037 8,614 6,830 8,638 9,346 12,551 13,387 21,675 31,537 16,875 12,322 Silver (cents/toz)a 614 2,544 475 431 500 477 607 666 1,034 1,136 1,199 1,235 Tin (cents/kg) 1,273 2,068 591 516 544 475 773 670 785 1,231 1,480 1,141 Zinc (cents/kg) 102 94 147 86 113 80 95 125 293 275 150 139 MUV G-5 index (2000 = 100) 29 81 103 120 100 103 110 110 112 118 125 119 Note: bbl = barrel, cu. m = cubic meter, dmtu = dry metric ton unit, kg = kilogram, mmBtu = million British thermal units, mt = metric ton, toz = troy ounce. a. Series not included in the nonenergy index. 372 2010 World Development Indicators 6.6 GLOBAL LINKS Primary commodity prices About the data Definitions Primary commodities--raw or partially processed commodity price index contains 41 price series for · Energy price index is the composite price index for materials that will be transformed into fi nished 34 nonenergy commodities. coal, petroleum, and natural gas, weighted by exports goods--are often developing countries' most impor- Separate indexes are compiled for energy and steel of each commodity from low- and middle-income tant exports, and commodity revenues can affect liv- products, which are not included in the nonenergy countries. · Nonenergy commodity price index cov- ing standards. Price data are collected from various commodity price index. ers the 34 nonenergy primary commodities that make sources, including international commodity study The MUV index is a composite index of prices up the agriculture, fertilizer, and metals and miner- groups, government agencies, industry trade jour- for manufactured exports from the five major (G-5) als indexes. · Agriculture includes beverages, food, nals, and Bloomberg and Datastream. Prices are industrial economies (France, Germany, Japan, the and agricultural raw materials. · Beverages include compiled in U.S. dollars or converted to U.S. dollars United Kingdom, and the United States) to low- and cocoa, coffee, and tea. · Food includes fats and oils, when quoted in local currencies. middle-income economies, valued in U.S. dollars. grains, and other food items. Fats and oils include The table is based on frequently updated price The index covers products in groups 5­8 of SITC coconut oil, groundnut oil, palm oil, soybeans, soy- reports. Prices are those received by exporters when revision 1. For the MUV G-5 index, unit value indexes bean oil, and soybean meal. Grains include barley, available, or the prices paid by importers or trade in local currency for each country are converted to maize, rice, and wheat. Other food items include unit values. Annual price series are generally simple U.S. dollars using market exchange rates and are bananas, beef, chicken meat, oranges, shrimp, and averages based on higher frequency data. The con- combined using weights determined by each coun- sugar. · Agricultural raw materials include timber stant price series in the table are deflated by the try's export share in the base year (1995). The export and other raw materials. Timber includes tropical manufactures unit value (MUV) index for the Group shares were 8.2 percent for France, 17.4 percent hard logs and sawnwood. Other raw materials include of Five (G-5) countries (see below). for Germany, 35.6 percent for Japan, 6.6 percent cotton, natural rubber, and tobacco. · Fertilizers Commodity price indexes are calculated as for the United Kingdom, and 32.2 percent for the include phosphate, phosphate rock, potassium, and Laspeyres index numbers; the fixed weights are the United States. nitrogenous products. · Metals and minerals include 2002­04 average export values for low- and middle- aluminum, copper, iron ore, lead, nickel, tin, and zinc. income economies (based on 2001 gross national · Steel products price index is the composite price income) rebased to 2000. Data for exports are from index for eight steel products based on quotations the United Nations Statistics Division's Commodity free on board (f.o.b.) Japan excluding shipments to Trade Statistics (Comtrade) database Standard Inter- the United States for all years and to China prior national Trade Classification (SITC) revision 3, the to 2001, weighted by product shares of apparent Food Agriculture Organization's FAOSTAT database, combined consumption (volume of deliveries) for Ger- the International Energy Agency database, BP's Sta- many, Japan, and the United States. · Commodity tistical Review of World Energy, the World Bureau of prices--for definitions and sources, see "Commod- Metal Statistics, World Bank staff estimates, and ity price data" (also known as the "Pink Sheet") at other sources. the World Bank Prospects for Development website Each index in the table represents a fixed basket of (www.worldbank.org/prospects, click on Products). primary commodity exports over time. The nonenergy · MUV G-5 index is the manufactures unit value index for G-5 country exports to low- and middle- income economies. Primary commodity prices have been volatile over the past two years 6.6a World Bank commodity price index, current prices (2000 = 100) 500 Energy index 400 Nonenergy commodities 300 Food 200 Raw Agriculture materials Data sources 100 2004 2005 2006 2007 2008 2009 2010 Data on commodity prices and the MUV G-5 index Commodity prices rose rapidly in early 2008 before collapsing in the second half of the year. But prices are compiled by the World Bank's Development rose again in 2009. Between January 2009 and January 2010 the average price of energy commodities Prospects Group. Monthly updates of commod- increased 57 percent and the average price of nonenergy commodities increased 30 percent. ity prices are available at www.worldbank.org/ Source: World Bank commodity price data. prospects. 2010 World Development Indicators 373 6.7 Regional trade blocs Merchandise exports within bloc Year of entry into Type force of the of most $ millions Year of most recent recent creation agreement agreementa 1990 1995 2000 2005 2006 2007 2008 High-income and low- and middle-income economies APECb 1989 None 901,560 1,688,708 2,261,791 3,310,523 3,775,795 4,193,036 4,607,766 EEA 1994 1994 EIA 1,079,711 1,463,232 1,714,018 2,863,903 3,237,586 3,805,786 4,190,268 EFTA 1960 2002 EIA 782 925 831 1,252 1,524 2,196 2,910 European Union 1957 1958 EIA, CU 1,032,397 1,404,255 1,641,609 2,732,159 3,089,257 3,627,406 3,977,321 NAFTA 1994 1994 FTA 226,273 394,472 676,141 824,658 902,193 951,551 1,013,245 SPARTECA 1981 1981 PTA 5,299 9,135 8,579 15,201 15,562 18,617 20,263 Trans-Pacific SEP 2006 2006 EIA, FTA 1,110 2,614 1,438 2,345 2,927 3,290 4,278 East Asia and Pacific and South Asia APTA 1975 1976 PTA 2,429 21,728 37,895 127,340 154,380 193,951 233,606 ASEAN 1967 1992 FTA 27,365 79,544 98,060 165,458 191,392 216,727 251,367 MSG 1993 1994 PTA 5 18 22 51 63 78 89 PICTA 2001 2003 FTA 6 53 81 158 195 242 277 SAARC 1985 2006 FTA 863 2,024 2,680 7,301 8,053 10,720 10,665 Europe, Central Asia, and Middle East CEFTA 1992 1994 FTA .. 619 1,047 2,452 4,801 7,029 8,266 CIS 1991 1994 FTA .. 31,529 28,753 59,441 67,926 100,540 126,005 EAEC 1997 2000 CU .. 10,919 13,936 24,818 24,711 45,714 51,186 ECO 1985 2003 PTA 1,243 4,746 4,518 12,579 17,365 22,064 26,739 GCC 1981 2003c CU 6,906 6,832 8,029 15,408 19,257 23,988 32,699 PAFTA (GAFTA) 1997 1998 FTA 13,204 12,948 16,188 43,393 52,733 63,563 83,484 UMA 1989 1994 c NNA 958 1,109 1,041 1,885 2,402 2,695 4,570 Latin America and the Caribbean Andean Community 1969 1988 CU 544 1,788 2,046 4,572 5,011 5,875 6,757 CACM 1961 1961 CU 667 1,594 2,586 4,342 4,808 5,677 6,708 CARICOM 1973 1997 EIA 456 877 1,078 2,090 2,429 3,112 3,808 LAIA (ALADI) 1980 1981 PTA 13,350 35,986 44,253 71,720 90,358 110,421 136,896 MERCOSUR 1991 2005 EIA 4,127 14,199 17,829 21,128 25,775 33,038 42,733 OECS 1981 1981c NNA 29 39 38 68 84 104 118 Sub-Saharan Africa CEMAC 1994 1999 CU 139 120 96 201 247 305 355 COMESA 1994 1994 FTA 1,146 1,367 1,443 2,962 3,363 4,501 5,296 EAC 1996 2000 CU 335 628 689 1,075 1,062 1,385 1,616 ECCAS 1983 2004 c NNA 160 157 182 255 313 385 449 ECOWAS 1975 1993 PTA 1,532 1,875 2,715 5,497 5,956 6,676 8,251 Indian Ocean Commission 1984 2005c NNA 63 113 106 162 182 214 190 SADC 1992 2000 FTA 1,655 3,615 4,427 7,799 8,701 11,912 15,468 UEMOA 1994 2000 CU 621 560 741 1,390 1,544 1,835 2,096 Note: Regional bloc memberships are as follows: Andean Community, Bolivia, Colombia, Ecuador, and Peru; Arab Maghreb Union (UMA), Algeria, Libyan Arab Repub- lic, Mauritania, Morocco, and Tunisia; Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong SAR, China, Indo- nesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federation, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; Asia-Pacific Trade Agreement (APTA; formerly Bangkok Agreement), Bangladesh, China, India, the Republic of Korea, the Lao People's Democratic Republic, and Sri Lanka; Association of South East Asian Nations (ASEAN), Brunei Darussalam, Cambodia, Indonesia, the Lao People's Democratic Republic, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam; Caribbean Community and Common Market (CARICOM), Antigua and Barbuda, the Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Suriname, and Trinidad and Tobago; Central American Common Market (CACM), Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua; Central European Free Trade Area (CEFTA), Albania, Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Moldova, Montenegro, and Serbia; Common Market for Eastern and South- ern Africa (COMESA), Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Libyan Arab Republic, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, and Zimbabwe; Commonwealth of Independent States (CIS), Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, the Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Com- munity (EAC), Burundi, Kenya, Rwanda, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Central African Customs and Economic Union [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Cen- tral African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Economic Cooperation Organization (ECO), Afghanistan, Azerbaijan, the Islamic Republic of Iran, Kazakhstan, the Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, the Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechten- 374 2010 World Development Indicators 6.7 GLOBAL LINKS Regional trade blocs Merchandise exports within bloc Year of entry into Type force of the of most % of total bloc exports Year of most recent recent creation agreement agreementa 1990 1995 2000 2005 2006 2007 2008 High-income and low- and middle-income economies APECb 1989 None 68.3 71.7 73.0 70.8 69.4 67.4 65.3 EEA 1994 1994 EIA 68.8 67.9 69.0 68.8 69.0 69.4 68.8 EFTA 1960 2002 EIA 0.8 0.7 0.6 0.5 0.6 0.7 0.8 European Union 1957 1958 EIA, CU 67.3 66.5 67.7 67.4 67.6 67.9 67.3 NAFTA 1994 1994 FTA 41.4 46.2 55.7 55.7 53.9 51.3 49.5 SPARTECA 1981 1981 PTA 10.5 12.9 10.7 11.4 10.2 10.5 8.9 Trans-Pacific SEP 2006 2006 EIA, FTA 1.5 1.7 0.8 0.8 0.8 0.8 1.0 East Asia and Pacific and South Asia APTA 1975 1976 PTA 1.6 6.8 8.0 11.0 10.7 11.0 11.4 ASEAN 1967 1992 FTA 18.9 24.4 23.0 25.3 24.9 25.2 25.6 MSG 1993 1994 PTA 0.3 0.4 0.6 0.8 0.8 0.8 0.8 PICTA 2001 2003 FTA 0.3 1.3 2.1 2.3 2.4 2.6 2.4 SAARC 1985 2006 FTA 3.2 4.4 4.2 5.6 5.1 5.5 4.8 Europe, Central Asia, and Middle East CEFTA 1992 1994 FTA .. 9.0 13.4 14.1 19.8 21.9 22.6 CIS 1991 1994 FTA .. 28.6 20.0 18.0 16.9 20.5 18.4 EAEC 1997 2000 CU .. 12.3 11.5 8.9 7.2 10.9 9.3 ECO 1985 2003 PTA 3.2 7.9 5.6 6.9 7.6 8.0 6.8 GCC 1981 2003c CU 8.0 6.8 4.9 4.5 4.5 4.9 4.7 PAFTA (GAFTA) 1997 1998 FTA 10.2 9.8 7.2 9.6 9.2 9.7 9.0 UMA 1989 1994 c NNA 2.9 3.8 2.2 1.9 2.0 2.0 2.5 Latin America and the Caribbean Andean Community 1969 1988 CU 4.0 8.6 7.7 9.0 7.8 7.9 7.5 CACM 1961 1961 CU 15.3 21.8 19.1 20.1 16.3 17.4 18.7 CARICOM 1973 1997 EIA 8.0 12.0 14.4 11.5 11.2 13.1 12.9 LAIA (ALADI) 1980 1981 PTA 11.6 17.3 13.2 13.6 14.3 15.2 16.0 MERCOSUR 1991 2005 EIA 8.9 20.3 20.0 12.9 13.5 14.9 15.0 OECS 1981 1981c NNA 8.1 12.6 10.0 11.4 8.0 12.0 12.0 Sub-Saharan Africa CEMAC 1994 1999 CU 2.3 2.1 1.0 0.9 0.9 1.0 0.8 COMESA 1994 1994 FTA 4.7 6.1 4.6 4.7 4.0 4.5 4.1 EAC 1996 2000 CU 17.7 19.5 22.6 17.7 15.9 17.5 17.6 ECCAS 1983 2004 c NNA 1.4 1.5 1.0 0.6 0.5 0.6 0.4 ECOWAS 1975 1993 PTA 8.0 9.0 7.6 9.3 7.9 7.7 7.6 Indian Ocean Commission 1984 2005c NNA 3.9 5.9 4.4 4.9 5.0 5.8 5.1 SADC 1992 2000 FTA 6.6 10.2 9.5 9.3 9.1 10.0 10.1 UEMOA 1994 2000 CU 13.0 10.3 13.1 13.4 13.1 14.8 14.5 stein, and Norway; European Free Trade Association (EFTA), Iceland, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Co- operation Council (GCC), Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Latin American Integration Association (LAIA; formerly Latin American Free Trade Area), Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Bolivarian Republic of Venezuela; Melanesian Spearhead Group (MSG), Fiji, Papua New Guinea, Solomon Islands, and Vanuatu; North American Free Trade Agreement (NAFTA), Canada, Mexico, and the United States; Organization of Eastern Caribbean States (OECS), Anguilla, Antigua and Barbuda, British Virgin Islands, Dominica, Grenada, Montserrat, St. Kitts and Nevis, St. Lucia, and St. Vincent and the Grenadines; Pacific Island Coun- tries Trade Agreement (PICTA), Cook Islands, Fiji, Kiribati, Nauru, Niue, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, and Vanuatu; Pan-Arab Free Trade Area (PAFTA; also known as Greater Arab Trade Area [GAFTA]), Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, the United Arab Emirates, and Yemen; South Asian Association for Regional Cooperation (SAARC), Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka; South Pacific Regional Trade and Economic Cooperation Agreement (SPARTECA), Australia, Cook Islands, Fiji, Kiribati, Marshall Islands, Federated States of Micronesia, Nauru, New Zealand, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Western Samoa; Southern African Development Community (SADC), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Southern Common Market (MERCOSUR), Argentina, Brazil, Paraguay, Uruguay, and Bolivarian Republic of Venezuela; Trans-Pacific Strategic Economic Partnership (Trans-Pacific SEP), Brunei Darussalam, Chile, New Zealand, and Singapore; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. 2010 World Development Indicators 375 6.7 Regional trade blocs Merchandise exports by bloc Year of entry into Type force of the of most % of world exports Year of most recent recent creation agreement agreementa 1990 1995 2000 2005 2006 2007 2008 High-income and low- and middle-income economies APECb 1989 None 39.0 46.4 48.5 45.1 45.3 44.7 44.0 EEA 1994 1994 EIA 46.4 42.4 38.9 40.1 39.1 39.4 38.0 EFTA 1960 2002 EIA 2.9 2.4 2.2 2.3 2.3 2.3 2.3 European Union 1957 1958 EIA, CU 45.3 41.5 38.0 39.1 38.1 38.4 36.9 NAFTA 1994 1994 FTA 16.2 16.8 19.0 14.3 14.0 13.3 12.8 SPARTECA 1981 1981 PTA 1.5 1.4 1.3 1.3 1.3 1.3 1.4 Trans-Pacific SEP 2006 2006 EIA, FTA 2.2 3.0 2.7 2.9 3.0 2.9 2.8 East Asia and Pacific and South Asia APTA 1975 1976 PTA 4.5 6.3 7.5 11.2 12.0 12.7 12.8 ASEAN 1967 1992 FTA 4.3 6.4 6.7 6.3 6.4 6.2 6.1 MSG 1993 1994 PTA 0.1 0.1 0.1 0.1 0.1 0.1 0.1 PICTA 2001 2003 FTA 0.1 0.1 0.1 0.1 0.1 0.1 0.1 SAARC 1985 2006 FTA 0.8 0.9 1.0 1.3 1.3 1.4 1.4 Europe, Central Asia, and Middle East CEFTA 1992 1994 FTA .. 0.1 0.1 0.2 0.2 0.2 0.2 CIS 1991 1994 FTA .. 2.2 2.2 3.2 3.4 3.5 4.3 EAEC 1997 2000 CU .. 1.7 1.9 2.7 2.9 3.0 3.4 ECO 1985 2003 PTA 1.1 1.2 1.3 1.8 1.9 2.0 2.4 GCC 1981 2003c CU 2.6 2.0 2.6 3.3 3.6 3.5 4.3 PAFTA (GAFTA) 1997 1998 FTA 3.8 2.6 3.5 4.3 4.8 4.7 5.8 UMA 1989 1994 c NNA 1.0 0.6 0.8 0.9 1.0 1.0 1.1 Latin America and the Caribbean Andean Community 1969 1988 CU 0.4 0.4 0.4 0.5 0.5 0.5 0.6 CACM 1961 1961 CU 0.1 0.1 0.2 0.2 0.2 0.2 0.2 CARICOM 1973 1997 EIA 0.2 0.1 0.1 0.2 0.2 0.2 0.2 LAIA (ALADI) 1980 1981 PTA 3.4 4.1 5.3 5.1 5.2 5.2 5.4 MERCOSUR 1991 2005 EIA 1.4 1.4 1.4 1.6 1.6 1.6 1.8 OECS 1981 1981c NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sub-Saharan Africa CEMAC 1994 1999 CU 0.2 0.1 0.1 0.2 0.2 0.2 0.3 COMESA 1994 1994 FTA 0.7 0.4 0.5 0.6 0.7 0.7 0.8 EAC 1996 2000 CU 0.1 0.1 0.0 0.1 0.1 0.1 0.1 ECCAS 1983 2004 c NNA 0.3 0.2 0.3 0.4 0.5 0.5 0.7 ECOWAS 1975 1993 PTA 0.6 0.4 0.6 0.6 0.6 0.6 0.7 Indian Ocean Commission 1984 2005c NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 SADC 1992 2000 FTA 0.7 0.7 0.7 0.8 0.8 0.9 1.0 UEMOA 1994 2000 CU 0.1 0.1 0.1 0.1 0.1 0.1 0.1 a. CU is customs union; EIA is economic integration agreement; FTA is free trade agreement; PTA is preferential trade agreement; and NNA is not notified agreement, which refers to preferential trade arrangements established among member countries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of the other agreements). b. No preferential trade agreement c. Years of the most recent agreement are collected from the official website of the trade bloc. 376 2010 World Development Indicators 6.7 GLOBAL LINKS Regional trade blocs About the data Definitions Trade blocs are groups of countries with preferential to each bloc's exports of goods and the share of the · Merchandise exports within bloc are the sum of arrangements governing trade between members. bloc's exports in world exports. Although the Asia merchandise exports by members of a trade bloc to Although the preferences--such as lower tariff duties Pacific Economic Cooperation (APEC) has no prefer- other members of the bloc. They are shown both in or exemptions from quantitative restrictions--may ential arrangements, it is included because of the U.S. dollars and as a percentage of total merchan- be no greater than those available to other trading volume of trade between its members. dise exports by the bloc. · Merchandise exports partners, such arrangements are intended to encour- The data on country exports are from the International by bloc as a share of world exports are the bloc's age exports by bloc members to one another--some- Monetary Fund's (IMF) Direction of Trade database and total merchandise exports (within the bloc and to the times called intratrade. should be broadly consistent with those from sources rest of the world) as a share of total merchandise Most countries are members of a regional trade bloc, such as the United Nations Statistics Division's Com- exports by all economies in the world. · Type of most and more than a third of world trade takes place within modity Trade (Comtrade) database. However, trade recent agreement includes customs union, under such arrangements. While trade blocs vary in structure, flows between many developing countries, particularly which members substantially eliminate all tariff and they have the same objective: to reduce trade barriers in Sub-Saharan Africa, are not well recorded, so the nontariff barriers among themselves and establish between members. But effective integration requires value of intratrade for certain groups may be under- a common external tariff for nonmembers; economic more than reducing tariffs and quotas. Economic gains stated. Data on trade between developing and high- integration agreement, which liberalizes trade in ser- from competition and scale may not be achieved unless income countries are generally complete. vices among members and covers a substantial num- other barriers that divide markets and impede the free Unless otherwise noted, the type of agreement and ber of sectors, affects a sufficient volume of trade, flow of goods, services, and investments are lifted. date of enforcement are based on the World Trade includes substantial modes of supply, and is non- For example, many regional trade blocs retain contin- Organization's (WTO) list of regional trade agree- discriminatory (in the sense that similarly situated gent protections on intratrade, including antidumping, ments. Other types of preferential trade agreements service suppliers are treated the same); free trade countervailing duties, and "emergency protection" to may have entered into force earlier than those shown agreement, under which members substantially address balance of payments problems or protect an in the table and may still be effective. eliminate all tariff and nontariff barriers but set tar- industry from import surges. Other barriers include Although bloc exports have been calculated back iffs on imports from nonmembers; preferential trade differing product standards, discrimination in public to 1990 based on current membership, several blocs agreement, which is an agreement notified to the procurement, and cumbersome border formalities. came into existence after that and membership may WTO that is not a free trade agreement, a customs Trade bloc membership may reduce the frictional have changed over time. For this reason, and because union, or an economic integration agreement; and costs of trade, increase the credibility of reform initia- systems of preferences also change over time, intra- not notified agreement, which is a preferential trade tives, and strengthen security among partners. But trade in earlier years may not have been affected by arrangement established among member countries making it work effectively is challenging. All economic the same preferences as in recent years. In addition, that is not notified to the World Trade Organization sectors may be affected, and some may expand while some countries belong to more than one trade bloc, so (the agreement may be functionally equivalent to any others contract, so it is important to weigh the poten- shares of world exports exceed 100 percent. Exports of the other agreements). tial costs and benefits of membership. include all commodity trade, which may include items The table shows the value of merchandise intra- not specified in trade bloc agreements. Differences trade (service exports are excluded) for important from previously published estimates may be due to regional trade blocs and the size of intratrade relative changes in membership or revisions in underlying data. The number of trade agreements has increased rapidly since 1990, especially agreements between high-income economies and developing economies and agreements among developing economies 6.7a Low and middle income­low and middle income Cumulative agreements High income­low and middle income High income­high income 200 Data sources 150 Data on merchandise trade flows are published in the IMF's Direction of Trade Statistics Yearbook and 100 Direction of Trade Statistics Quarterly; the data in the table were calculated using the IMF's Direc- 50 tion of Trade database. Data on trade bloc mem- 0 bership are from the World Bank Policy Research 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2009 Report Trade Blocs (2000b), UNCTAD's Trade and Note: Data are cumulative number of trade agreements notified to the General Agreement on Tariffs and Trade/World Development Report 2007, WTO's Regional Trade Trade Organization (GATT/WTO) at the time they entered into force. Includes only agreements that are currently in Agreements Information System, and the World force. Excludes agreements on services and accessions of new members to an existing agreement. Source: World Bank staff calculations based on the World Trade Organization's Regional Trade Agreements Information System. Bank's International Trade Unit. 2010 World Development Indicators 377 6.8 Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Afghanistan 2008 .. .. 6.2 6.5 4.2 0.0 7.0 6.7 6.1 6.3 Albania 2008 100.0 7.0 2.4 2.1 10.4 0.0 4.8 3.2 2.1 1.4 Algeria 2008 .. .. 16.3 9.7 60.6 0.0 17.0 8.8 16.2 9.8 Angola 2008 100.0 59.2 7.5 7.7 23.0 0.0 11.6 14.0 6.8 6.2 Antigua and Barbuda 2008 97.9 58.7 11.6 13.6 48.3 0.0 13.8 13.1 11.2 13.7 Argentina 2008 100.0 31.9 9.8 5.3 21.9 0.0 7.8 1.3 10.1 5.9 Armenia 2008 100.0 8.5 3.7 2.3 0.0 0.0 5.5 2.2 3.5 2.4 Australia 2008 97.1 9.9 3.9 2.5 5.2 0.0 1.4 0.4 4.4 3.3 Azerbaijan 2008 .. .. 8.4 3.9 47.4 0.0 9.8 3.4 8.2 4.1 Bahamas, The 2006 .. .. 28.5 23.9 77.4 0.0 24.4 15.1 29.3 29.7 Bahrain 2008 73.4 34.4 4.3 3.6 0.2 0.0 6.8 6.9 4.0 3.1 Bangladesh 2007 15.5 169.3 14.5 11.0 41.1 0.0 15.1 7.3 14.5 13.1 Barbados 2007 97.9 78.1 15.1 14.8 44.9 0.6 26.3 21.9 13.4 12.3 Belarus 2008 .. .. 8.0 2.3 27.3 0.0 7.0 0.6 8.1 3.9 Belize 2008 97.9 58.2 11.6 9.3 43.3 0.0 15.5 6.5 11.1 11.0 Benin 2008 39.0 28.6 13.3 15.5 50.6 0.0 15.1 11.3 13.0 17.5 Bermuda 2008 .. .. 18.1 29.5 66.7 0.0 10.0 16.1 19.6 31.3 Bhutan 2007 .. .. 17.7 16.5 49.3 0.0 43.5 44.9 15.5 16.0 Bolivia 2008 100.0 40.0 6.2 4.1 0.0 0.0 6.1 3.3 6.2 4.1 Bosnia and Herzegovina 2008 .. .. 6.6 4.7 10.9 0.0 3.2 1.9 7.0 6.2 Botswana 2008 96.3 18.9 8.0 8.7 30.8 0.0 4.5 1.1 8.5 9.9 Brazil 2008 100.0 31.4 13.1 6.7 26.4 0.0 7.9 1.1 13.7 9.3 Brunei Darussalam 2007 95.4 24.3 3.1 6.1 21.6 0.1 0.9 13.2 3.4 4.6 Burkina Faso 2008 39.2 41.9 11.5 6.9 40.9 0.0 11.0 6.8 11.5 6.6 Burundi 2008 21.8 68.3 12.8 10.7 34.1 0.0 11.7 7.9 12.9 11.3 Cambodia 2007 .. .. 12.5 10.0 49.2 0.0 14.7 10.5 12.1 9.9 Cameroon 2007 13.3 79.9 18.6 12.7 52.4 0.0 21.9 10.8 18.2 14.4 Canada 2008 99.7 5.1 3.6 1.0 6.7 0.0 1.9 0.3 4.1 1.2 Cape Verde 2008 .. .. 15.3 12.2 46.8 0.0 15.9 12.6 15.0 11.8 Central African Republic 2007 .. .. 17.5 13.5 46.8 0.0 18.9 13.9 17.3 13.2 Chad 2007 .. .. 16.9 13.3 44.3 0.0 20.6 18.3 16.5 12.7 Chile 2008 100.0 25.1 1.4 1.0 0.0 0.0 1.4 1.4 1.4 0.8 China 2008 100.0 10.0 8.6 3.9 13.3 0.0 8.8 2.4 8.7 5.8 Hong Kong SAR, China 2008 46.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Macau SAR, China 2008 28.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Colombia 2008 100.0 42.8 10.7 8.7 41.0 0.0 9.7 7.7 10.8 9.4 Congo, Dem. Rep. 2008 .. .. 12.8 11.0 42.1 0.0 14.1 10.8 12.6 11.1 Congo, Rep. 2007 16.1 27.3 18.6 14.5 52.3 0.0 21.9 18.6 18.2 14.1 Costa Rica 2007 100.0 42.9 6.0 3.8 0.3 0.0 8.5 5.1 5.8 3.8 Côte d'Ivoire 2008 33.1 11.2 13.2 6.6 48.6 0.0 15.3 4.1 12.9 9.8 Croatia 2008 100.0 6.0 2.5 1.1 3.9 0.0 4.4 1.9 2.3 0.9 Cuba 2008 31.6 21.0 11.0 9.1 33.6 0.0 10.6 8.1 11.1 9.4 Djibouti 2006 100.0 41.0 30.2 29.1 87.9 0.0 23.1 23.2 31.3 31.0 Dominica 2007 94.8 58.7 11.9 7.9 43.3 0.0 19.3 5.7 10.6 9.3 Dominican Republic 2008 100.0 34.9 9.0 5.1 29.2 0.0 11.6 4.5 8.7 5.2 Ecuador 2008 100.0 21.8 9.7 5.4 32.1 0.0 7.7 4.2 9.9 5.5 Egypt, Arab Rep. 2008 99.3 36.8 12.3 8.0 18.1 0.0 36.2 6.3 9.5 9.8 El Salvador 2008 100.0 36.6 3.9 3.1 15.7 0.0 5.2 2.4 3.8 3.9 Eritrea 2006 .. .. 9.6 5.4 22.4 0.0 9.2 3.5 9.6 7.2 Ethiopia 2008 .. .. 18.2 10.1 56.0 0.0 19.4 6.6 18.0 12.8 European Union 2008 100.0 4.2 1.6 1.7 1.4 0.0 2.3 0.4 1.5 1.2 Fiji 2008 51.3 40.1 11.0 8.9 38.2 0.0 11.9 7.3 10.8 10.5 Gabon 2008 100.0 21.2 18.6 14.4 52.1 0.0 21.0 15.2 18.3 14.2 Gambia, The 2008 13.7 101.8 18.7 14.7 90.9 0.0 17.0 12.2 19.2 17.4 Georgia 2008 100.0 7.2 0.6 0.5 0.0 0.0 4.3 1.2 0.1 0.1 Ghana 2008 14.3 92.5 13.0 9.8 40.8 0.0 16.8 14.4 12.5 8.8 Grenada 2008 100.0 56.8 10.6 8.8 43.3 0.0 14.1 9.9 10.0 8.4 Guatemala 2008 100.0 42.2 4.4 3.0 18.9 0.0 5.1 2.4 4.3 3.5 Guinea 2008 38.6 20.3 13.9 12.5 57.7 0.0 15.4 14.0 13.7 11.2 Data for Taiwan, China 2008 100.0 5.9 5.3 1.9 6.8 0.0 8.0 2.0 4.8 1.9 378 2010 World Development Indicators 6.8 GLOBAL LINKS Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Guinea-Bissau 2008 .. .. 12.9 10.7 50.3 0.0 14.9 10.9 12.6 10.4 Guyana 2008 100.0 56.7 10.8 6.9 41.9 0.0 17.7 5.9 9.7 7.3 Haiti 2008 .. .. 3.0 4.8 5.0 0.0 5.6 3.9 2.4 5.6 Honduras 2008 100.0 32.4 4.6 3.2 20.6 0.0 5.6 3.5 4.4 3.1 Iceland 2008 95.0 13.5 2.4 1.1 6.5 0.0 2.7 1.7 2.4 0.9 India 2008 73.8 49.6 9.7 6.1 7.3 0.0 19.5 7.3 8.4 5.9 Indonesia 2007 96.6 37.1 5.8 3.6 12.6 0.0 6.6 2.6 5.8 4.4 Iran, Islamic Rep. 2008 .. .. 24.8 20.1 56.6 0.0 21.5 12.5 25.1 21.2 Iraq .. .. .. .. .. .. .. .. .. .. Israel 2008 75.0 21.5 2.2 1.1 0.9 0.0 3.5 1.2 2.1 1.1 Jamaica 2006 100.0 49.6 9.2 8.9 35.8 0.0 15.8 9.4 8.3 8.5 Japan 2008 99.7 2.9 2.6 1.3 6.9 0.0 4.9 1.2 2.3 1.6 Jordan 2008 99.9 16.2 10.7 5.6 33.2 0.0 14.4 3.8 10.1 7.3 Kazakhstan 2008 .. .. 3.9 2.1 6.7 0.0 5.8 0.8 3.7 2.6 Kenya 2008 14.8 95.4 12.1 6.3 36.5 0.0 15.2 5.6 11.7 6.9 Kosovo .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2008 94.6 15.8 8.3 7.1 4.6 0.0 20.7 11.6 6.6 4.8 Kuwait 2008 99.9 100.0 4.1 4.0 0.0 0.0 3.3 3.1 4.3 4.4 Kyrgyz Republic 2008 99.9 7.4 3.5 8.5 0.9 0.0 4.3 1.2 3.4 9.4 Lao PDR 2007 .. .. 5.8 8.3 15.1 0.0 9.9 8.3 5.3 8.3 Lebanon 2007 .. .. 5.7 4.8 11.6 0.0 8.2 5.0 5.2 5.1 Lesotho 2008 .. .. 9.2 14.4 37.8 0.0 7.7 1.1 9.6 17.2 Liberia .. .. .. .. .. .. .. .. .. .. Libya 2006 .. .. 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Macedonia, FYR 2008 100.0 6.9 4.9 3.3 15.7 0.0 7.9 4.8 4.7 2.6 Madagascar 2008 30.0 27.3 12.1 8.4 41.6 0.0 13.9 4.2 11.9 10.4 Malawi 2008 31.6 75.4 12.1 6.0 43.8 0.0 12.8 5.7 11.8 6.0 Malaysia 2007 83.7 14.5 5.9 3.1 24.8 0.0 2.9 2.3 6.5 3.4 Maldives 2008 97.1 36.9 21.5 20.3 87.0 0.0 17.5 18.4 22.8 22.6 Mali 2008 40.2 28.5 12.9 8.4 48.4 0.0 12.8 7.9 12.8 8.7 Mauritania 2007 39.3 19.6 12.6 10.1 49.0 0.0 11.2 9.2 12.8 11.0 Mauritius 2008 17.8 94.4 4.2 2.1 16.8 0.0 6.2 1.5 4.0 2.6 Mayotte 2008 .. .. 5.3 1.9 2.3 0.0 3.8 1.3 5.6 2.1 Mexico 2008 100.0 35.0 6.4 1.9 11.5 0.0 7.3 0.9 6.4 2.2 Moldova 2008 .. .. 4.1 2.4 6.7 0.0 6.5 2.1 3.8 2.7 Mongolia 2008 100.0 17.5 4.9 5.1 0.4 0.0 5.2 5.4 4.9 4.9 Morocco 2008 100.0 41.3 11.7 9.4 41.1 0.0 19.3 11.4 10.9 8.2 Mozambique 2007 .. .. 11.0 7.7 36.7 0.0 13.9 8.0 10.5 7.5 Myanmar 2007 17.4 83.6 4.1 3.9 8.1 0.0 5.8 4.5 3.9 3.6 Namibia 2008 96.3 19.2 6.3 1.1 25.3 0.0 3.6 0.6 6.9 1.3 Nepal 2007 .. .. 12.4 13.1 40.9 0.0 12.5 9.7 12.6 15.8 New Zealand 2008 99.9 10.0 2.8 2.0 0.0 0.0 1.5 0.4 3.0 2.7 Nicaragua 2007 100.0 41.7 5.4 3.6 0.4 0.0 7.7 3.9 5.1 3.4 Niger 2008 96.7 44.7 13.0 9.2 49.3 0.0 14.0 10.7 12.8 7.6 Nigeria 2008 19.3 118.4 10.7 8.9 33.5 0.0 12.3 9.6 10.5 8.1 Norway 2008 100.0 3.0 0.6 0.4 0.6 0.0 1.9 1.1 0.4 0.2 Oman 2008 100.0 13.8 3.8 3.3 0.2 0.0 4.6 3.0 3.7 3.4 Pakistan 2008 98.7 60.0 14.0 9.0 51.1 0.0 12.8 6.3 14.3 12.3 Panama 2008 99.9 23.4 7.2 7.1 33.6 0.0 11.1 7.9 6.8 6.8 Papua New Guinea 2008 100.0 31.7 4.5 2.3 23.3 0.0 12.6 2.7 3.4 2.2 Paraguay 2008 100.0 33.5 8.3 3.3 18.0 0.0 6.4 1.1 8.6 3.9 Peru 2008 100.0 30.1 3.8 2.1 6.3 0.0 5.3 1.7 3.8 2.3 Philippines 2007 67.0 25.7 5.0 3.6 15.8 0.0 6.0 5.2 4.8 2.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. Qatar 2008 100.0 15.9 4.3 3.7 0.2 0.0 5.4 4.0 4.2 3.8 Russian Federation 2008 .. .. 8.2 5.8 25.1 0.0 7.7 4.8 8.2 5.9 Rwanda 2008 100.0 89.5 18.6 12.0 52.9 0.0 15.8 9.1 19.0 13.7 Saudi Arabia 2008 .. .. 4.0 3.8 0.0 0.0 3.3 2.7 4.1 4.2 2010 World Development Indicators 379 6.8 Tariff barriers All Primary Manufactured products products products % Share of tariff Share of Most Simple Simple Weighted lines with tariff lines % % recent Binding mean mean mean international with specific Simple Weighted Simple Weighted year coverage bound rate tariff tariff peaks rates mean tariff mean tariff mean tariff mean tariff Senegal 2008 100.0 30.0 13.4 8.5 50.6 0.0 14.1 7.0 13.3 10.4 Serbiaa 2005 .. .. 8.1 6.0 17.8 0.0 10.9 4.5 7.3 6.8 Seychelles 2007 .. .. 6.5 28.3 12.8 0.0 14.0 50.5 4.8 6.4 Sierra Leone 2004 100.0 47.4 .. .. .. .. .. .. .. .. Singapore 2008 69.7 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Solomon Islands 2008 100.0 78.7 9.2 13.8 1.9 0.0 10.3 17.5 9.1 8.6 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2008 96.0 19.2 7.7 4.5 25.4 0.0 5.2 1.7 8.2 6.1 Sri Lanka 2006 38.1 29.8 11.3 7.1 23.5 0.8 17.6 9.0 10.6 6.4 St. Kitts and Nevis 2008 97.9 75.9 12.3 12.3 44.3 0.0 12.7 11.5 12.1 12.6 St. Lucia 2007 99.6 61.9 9.6 9.0 39.9 0.0 12.6 4.9 9.1 12.2 St. Vincent & Grenadines 2007 .. .. 11.3 8.4 44.4 0.2 15.1 7.8 10.6 8.6 Sudan 2008 .. .. 14.3 11.4 34.9 0.0 18.0 11.6 13.7 11.3 Suriname 2007 .. .. 11.5 11.8 39.4 0.0 17.8 15.9 10.6 10.9 Swaziland 2008 96.3 19.2 9.5 5.2 34.2 0.0 9.2 1.5 10.0 7.3 Switzerland 2008 99.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Syrian Arab Republic 2002 .. .. 14.7 15.5 23.3 0.0 14.4 11.7 14.7 17.1 Tajikistan 2006 .. .. 4.9 3.8 0.1 0.7 5.4 2.6 4.8 4.4 Tanzania 2008 13.4 120.0 11.7 10.2 35.4 0.0 15.8 12.8 11.2 10.1 Thailand 2006 75.0 25.7 10.8 4.6 22.9 0.9 13.5 2.1 10.4 5.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 2008 14.0 80.0 13.1 13.9 48.9 0.0 14.7 10.4 12.9 15.9 Trinidad and Tobago 2008 100.0 55.7 8.2 4.2 43.6 0.0 13.1 2.7 7.5 5.6 Tunisia 2006 57.9 57.9 23.0 18.3 55.5 0.0 32.2 13.9 22.2 20.0 Turkey 2008 50.6 28.5 2.4 1.8 4.4 0.0 12.4 2.8 1.4 1.5 Turkmenistan 2002 .. .. 5.4 2.9 14.8 2.8 14.8 12.6 3.8 1.1 Uganda 2008 15.7 73.4 12.0 7.4 37.3 0.0 14.4 7.4 11.7 8.0 Ukraine 2008 .. .. 4.9 3.7 4.7 0.0 4.8 0.8 5.0 5.6 United Arab Emirates 2008 100.0 14.7 4.2 3.6 0.2 0.0 4.5 2.6 4.2 4.4 United States 2008 100.0 3.6 3.0 1.5 3.6 0.0 2.5 1.0 3.1 1.9 Uruguay 2008 100.0 31.6 9.5 3.6 26.6 0.0 5.7 1.1 9.9 4.9 Uzbekistan 2008 .. .. 12.1 7.3 21.6 0.0 12.4 3.6 11.9 7.1 Vanuatu 2008 .. .. 16.8 15.0 65.0 0.0 19.5 16.9 16.2 14.3 Venezuela, RB 2008 100.0 36.5 11.9 11.4 44.0 0.0 11.4 10.0 12.0 11.6 Vietnam 2007 .. .. 11.7 10.6 32.2 0.0 14.5 10.2 11.3 11.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2006 .. .. 6.7 6.9 1.8 0.0 9.6 8.6 6.3 5.6 Zambia 2008 16.8 106.0 10.8 5.0 49.7 0.0 8.0 5.7 10.8 4.4 Zimbabwe 2007 22.4 89.8 16.7 17.3 38.8 0.0 19.5 19.8 16.3 15.3 World 79.6 w 31.9 w 7.1 w 2.8 w 16.6 w 0.1 w 8.7 w 2.2 w 6.9 w 3.3 w Low income 40.7 52.4 12.1 9.2 35.3 0.0 13.5 8.0 11.8 9.9 Middle income 87.4 34.0 8.2 4.6 21.8 0.3 10.5 3.3 7.9 5.5 Lower middle income 84.2 35.7 9.7 5.2 25.6 0.1 12.7 3.7 9.3 6.6 Upper middle income 90.4 32.3 7.2 4.0 19.5 0.1 8.8 2.8 7.0 4.5 Low & middle income 75.4 36.5 8.8 4.8 23.4 0.1 11.0 3.6 8.6 5.7 East Asia & Pacific 79.1 32.5 8.4 4.1 21.7 0.1 9.2 2.8 8.3 5.5 Europe & Central Asia 94.7 11.6 4.8 3.6 12.6 0.2 8.2 3.0 4.4 3.8 Latin America & Carib. 97.0 40.9 8.0 4.3 21.7 0.0 8.9 2.1 8.0 5.2 Middle East & N. Africa 92.9 32.8 12.8 10.8 37.7 0.0 17.8 8.3 12.3 11.9 South Asia 64.7 52.5 13.3 7.0 37.8 0.0 15.8 7.0 13.0 7.1 Sub-Saharan Africa 47.9 43.2 11.7 7.5 37.9 0.0 13.1 6.4 11.5 8.1 High income 92.2 21.5 4.2 1.8 6.0 0.2 5.4 1.6 4.1 2.1 OECD 98.6 7.2 4.0 1.8 4.9 0.0 4.5 1.6 4.1 2.1 Non-OECD 87.8 31.7 4.4 1.5 7.7 0.4 6.7 1.6 4.1 1.9 a. Includes Montenegro. 380 2010 World Development Indicators 6.8 GLOBAL LINKS Tariff barriers About the data Definitions Poor people in developing countries work primarily in nation or applied rates are calculated using all traded · Binding coverage is the percentage of product agriculture and labor-intensive manufactures, sectors items. Weighted mean tariffs are weighted by the lines with an agreed bound rate. · Simple mean that confront the greatest trade barriers. Removing value of the country's trade with each trading part- bound rate is the unweighted average of all the lines barriers to merchandise trade could increase growth ner. Simple averages are often a better indicator of in the tariff schedule in which bound rates have been in these countries--even more if trade in services tariff protection than weighted averages, which are set. · Simple mean tariff is the unweighted average (retailing, business, financial, and telecommunica- biased downward because higher tariffs discourage of effectively applied rates or most favored nation tions services) were also liberalized. trade and reduce the weights applied to these tariffs. rates for all products subject to tariffs calculated In general, tariffs in high-income countries on Bound rates result from trade negotiations incorpo- for all traded goods. · Weighted mean tariff is the imports from developing countries, though low, are rated into a country's schedule of concessions and average of effectively applied rates or most favored twice those collected from other high-income coun- are thus enforceable. nation rates weighted by the product import shares tries. But protection is also an issue for developing Some countries set fairly uniform tariff rates across corresponding to each partner country. · Share of countries, which maintain high tariffs on agricultural all imports. Others are selective, setting high tariffs tariff lines with international peaks is the share commodities, labor-intensive manufactures, and to protect favored domestic industries. The share of lines in the tariff schedule with tariff rates that other products and services. In some developing of tariff lines with international peaks provides an exceed 15 percent. · Share of tariff lines with spe- country regions new trade policies could make the indication of how selectively tariffs are applied. The cific rates is the share of lines in the tariff schedule difference between achieving important Millennium effective rate of protection--the degree to which the that are set on a per unit basis or that combine ad Development Goals--reducing poverty, lowering value added in an industry is protected--may exceed valorem and per unit rates. · Primary products are maternal and child mortality rates, improving edu- the nominal rate if the tariff system systematically commodities classified in SITC revision 3 sections cational attainment--and falling far short. differentiates among imports of raw materials, inter- 0­4 plus division 68 (nonferrous metals). · Manu- Countries use a combination of tariff and nontariff mediate products, and finished goods. factured products are commodities classified in measures to regulate imports. The most common The share of tariff lines with specific rates shows SITC revision 3 sections 5­8 excluding division 68. form of tariff is an ad valorem duty, based on the the extent to which countries use tariffs based on value of the import, but tariffs may also be levied physical quantities or other, non­ad valorem mea- on a specific, or per unit, basis or may combine ad sures. Some countries such as Switzerland apply valorem and specific rates. Tariffs may be used to mainly specific duties. To the extent possible, these raise fiscal revenues or to protect domestic indus- specifi c rates have been converted to their ad tries from foreign competition--or both. Nontariff valorem equivalent rates and have been included in barriers, which limit the quantity of imports of a par- the calculation of simple and weighted tariffs. ticular good, include quotas, prohibitions, licensing Data are classified using the Harmonized System schemes, export restraint arrangements, and health of trade at the six- or eight-digit level. Tariff line data and quarantine measures. Because of the difficulty were matched to Standard International Trade Clas- of combining nontariff barriers into an aggregate indi- sification (SITC) revision 3 codes to define commodity cator, they are not included in the table. groups and import weights. Import weights were cal- Unless specified as most favored nation rates, the culated using the United Nations Statistics Division's tariff rates used in calculating the indicators in the Commodity Trade (Comtrade) database. The table table are effectively applied rates. Effectively applied shows tariff rates for three commodity groups: all rates are those in effect for partners in preferen- products, primary products, and manufactured prod- tial trade arrangements such as the North Ameri- ucts. Effectively applied tariff rates at the six- and can Free Trade Agreement. The difference between eight-digit product level are averaged for products in most favored nation and applied rates can be sub- each commodity group. When the effectively applied stantial. As more countries report their free trade rate is unavailable, the most favored nation rate is agreements, suspensions of tariffs, or other spe- used instead. cial preferences, World Development Indicators will Data are shown only for the last year for which com- Data sources include their effectively applied rates. All estimates plete data are available and for all economies with are calculated using the most recent information, populations of 1 million or more and for countries All indicators in the table were calculated by World which is not necessarily revised every year. As a with populations of less than 1 million when avail- Bank staff using the World Integrated Trade Solu- result, data for the same year may differ from data able. EU member countries apply a common tariff tion system. Data on tariffs were provided by the in last year's edition. schedule that is listed under European Union and United Nations Conference on Trade and Develop- Three measures of average tariffs are shown: sim- are thus not listed separately. ment and the World Trade Organization. Data on ple bound rates and the simple and the weighted global imports are from the United Nations Statis- tariffs. Bound rates are based on all products in a tics Division's Comtrade database. country's tariff schedule, while the most favored 2010 World Development Indicators 381 6.9 Trade facilitation Logistics Burden of Lead time Documents Liner Quality of Freight Performance customs Shipping port costs to the Index procedures Connectivity infrastructure United Index States days number 1 kilogram DHL 1­5 1­7 0­100 1­7 air package (worst to best) (worst to best) To export To import To export To import (low to high) (worst to best) $ 2009 2008­09a 2009 2009 June 2009 June 2009 2009 2008­09 2010 Afghanistan 2.24 .. 2.0 4.0 12 11 .. .. 143.10 Albania 2.46 3.6 1.7 2.0 7 9 2.3 3.2 150.40 Algeria 2.36 2.7 4.6 7.1 8 9 8.4 2.9 154.40 Angola 2.25 .. 6.0 8.0 11 8 11.3 .. 154.40 Argentina 3.10 2.8 3.7 3.8 9 7 26.0 3.6 88.55 Armenia 2.52 2.7 .. .. 5 7 .. 2.9b 143.10 Australia 3.84 4.9 2.6 2.8 6 5 28.8 4.6 90.75 Austria 3.76 5.3 2.0 3.7 4 5 .. 5.0 b 113.80 Azerbaijan 2.64 3.9 7.0 3.0 9 14 .. 4.2b 150.40 Bangladesh 2.74 2.8 1.4 1.4 6 8 7.9 3.0 90.75 Belarus 2.53 .. .. .. 8 8 .. .. 150.40 Belgium 3.94 4.6 1.7 1.6 4 5 82.8 6.3 103.00 Benin 2.79 3.4 3.0 7.0 7 7 13.5 3.3 154.40 Bolivia 2.51 2.8 15.0 28.3 8 7 .. 3.0 b 88.55 Bosnia and Herzegovina 2.66 3.5 2.0 2.0 6 7 .. 1.5 150.40 Botswana 2.32 4.4 .. .. 6 9 .. 3.7b 154.40 Brazil 3.20 2.9 2.8 3.9 8 7 31.1 2.6 88.55 Bulgaria 2.83 3.6 2.0 3.9 5 7 5.8 3.6 150.40 Burkina Faso 2.23 3.8 4.0 14.0 11 11 .. 4.0 b 154.40 Burundi 2.29 3.0 .. .. 9 10 .. 3.1b 154.40 Cambodia 2.37 3.3 1.3 4.0 11 11 4.7 3.5 88.50 Cameroon 2.55 3.2 3.4 8.9 10 11 11.6 2.7 154.40 Canada 3.87 4.7 2.8 3.7 3 4 41.3 5.6 68.20 Central African Republic .. .. .. .. 9 17 .. .. 154.40 Chad 2.49 2.3 74.0 35.0 6 10 .. 2.7b 154.40 Chile 3.09 5.8 3.5 3.0 6 7 18.8 5.4 88.55 China 3.49 4.6 2.8 2.6 7 5 132.5 4.3 78.25 Hong Kong SAR, China 3.88 6.1 1.7 1.6 4 4 104.5 6.8 82.10 Colombia 2.77 3.8 7.0 7.0 6 8 23.2 3.2 88.55 Congo, Dem. Rep. 2.68 .. 2.0 3.0 8 9 3.8 .. 154.40 Congo, Rep. 2.48 .. .. .. 11 12 11.4 .. 154.40 Costa Rica 2.91 3.9 2.0 2.0 6 7 14.6 2.6 88.55 Côte d'Ivoire 2.53 3.3 1.0 1.0 10 9 19.4 5.0 154.40 Croatia 2.77 3.8 1.0 1.0 7 8 8.5 3.8 150.40 Cuba 2.07 .. .. .. .. .. 5.9 .. 72.85 Czech Republic 3.51 4.6 2.5 3.5 4 7 0.4 4.2b 150.40 Denmark 3.85 5.8 1.0 1.0 4 3 27.7 6.2 113.80 Dominican Republic 2.82 4.5 2.2 3.5 6 7 21.6 4.3 72.85 Ecuador 2.77 3.1 2.1 3.4 9 7 17.1 3.3 88.55 Egypt, Arab Rep. 2.61 4.0 1.3 3.1 6 6 52.0 4.3 143.10 El Salvador 2.67 4.1 2.0 2.0 8 8 10.3 4.2 88.55 Eritrea 1.70 .. 3.0 3.0 9 13 3.3 .. 154.40 Estonia 3.16 5.5 4.0 4.0 3 4 5.7 5.6 150.40 Ethiopia 2.41 3.3 5.0 6.0 8 8 .. 3.8b 154.40 Finland 3.89 5.7 1.6 1.8 4 5 10.2 6.5 113.80 France 3.84 4.8 3.2 4.5 2 2 67.0 5.9 103.00 Gabon 2.41 .. 4.3 13.0 7 8 9.2 .. 154.40 Gambia, The 2.49 5.1 4.6 3.5 6 8 7.5 4.7 154.40 Georgia 2.61 4.6 .. .. 4 4 3.8 4.0 150.40 Germany 4.11 5.1 3.6 2.4 4 5 84.3 6.4 103.00 Ghana 2.47 3.4 2.9 6.8 6 7 19.3 4.0 154.40 Greece 2.96 4.1 3.0 3.5 5 6 41.9 4.1 113.80 Guatemala 2.63 4.2 2.6 3.4 10 10 14.7 4.3 88.55 Guinea 2.60 .. 3.5 3.9 7 9 8.3 .. 154.40 Guinea-Bissau 2.10 .. .. .. 6 6 3.5 .. 154.40 Haiti 2.59 .. 4.2 5.3 8 10 4.4 .. 72.85 Honduras 2.78 4.0 2.4 3.2 7 10 10.7 5.1 88.55 382 2010 World Development Indicators 6.9 GLOBAL LINKS Trade facilitation Logistics Burden of Lead time Documents Liner Quality of Freight Performance customs Shipping port costs to the Index procedures Connectivity infrastructure United Index States days number 1 kilogram DHL 1­5 1­7 0­100 1­7 air package (worst to best) (worst to best) To export To import To export To import (low to high) (worst to best) $ 2009 2008­09a 2009 2009 June 2009 June 2009 2009 2008­09 2010 Hungary 2.99 4.3 3.5 5.0 5 7 .. 3.9b 150.40 India 3.12 3.9 2.3 5.3 8 9 41.0 3.5 90.75 Indonesia 2.76 3.7 2.1 5.4 5 6 25.7 3.4 90.75 Iran, Islamic Rep. 2.57 .. 2.6 28.3 7 8 28.9 .. 143.10 Iraq 2.11 .. .. .. 10 10 5.1 .. 143.10 Ireland 3.89 5.1 1.0 1.0 4 4 7.6 4.4 103.00 Israel 3.41 4.0 2.0 2.0 5 4 18.7 4.6 143.10 Italy 3.64 4.0 2.6 3.0 4 4 70.0 3.7 103.00 Jamaica 2.53 3.4 10.0 10.0 6 6 19.6 5.3 72.85 Japan 3.97 4.4 1.0 1.0 4 5 66.3 5.2 113.80 Jordan 2.74 4.6 3.2 4.6 7 7 23.7 4.5 143.10 Kazakhstan 2.83 3.3 2.8 11.5 11 13 .. 3.0 b 150.40 Kenya 2.59 3.3 3.0 5.9 9 8 12.8 3.6 154.40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. 88.50 Korea, Rep. 3.64 4.6 1.6 2.0 3 3 86.7 5.1 90.75 Kosovo .. .. .. .. 8 8 .. .. .. Kuwait 3.28 3.5 2.0 3.0 8 10 6.5 4.1 143.10 Kyrgyz Republic 2.62 2.8 2.0 .. 7 7 .. 1.6b 150.40 Lao PDR 2.46 .. .. .. 9 10 .. .. 88.50 Latvia 3.25 4.1 1.3 1.6 6 6 5.2 4.4 150.40 Lebanon 3.34 .. 3.4 2.2 5 7 29.6 .. 143.10 Lesotho 2.30 3.8 .. .. 6 8 .. 3.0 b 154.40 Liberia 2.38 .. 4.0 5.0 10 9 5.5 .. 154.40 Libya 2.33 3.5 3.2 10.0 .. .. 9.4 3.3 154.40 Lithuania 3.13 4.8 2.0 2.3 6 6 8.1 4.7 150.40 Macedonia, FYR 2.77 4.0 .. .. 6 6 .. 3.4b 150.40 Madagascar 2.66 3.6 .. .. 4 9 8.6 3.0 154.40 Malawi 2.42 3.7 4.2 3.7 11 10 .. 3.5b 154.40 Malaysia 3.44 4.8 2.6 2.8 7 7 81.2 5.5 90.75 Mali 2.27 3.7 5.0 4.0 7 10 .. 3.8b 154.40 Mauritania 2.63 4.0 2.0 3.0 11 11 7.5 3.5 154.40 Mauritius 2.72 4.6 3.0 2.4 5 6 14.8 4.3 154.40 Mexico 3.05 3.7 2.1 2.5 5 5 31.9 3.7 58.80 Moldova 2.57 .. .. .. 6 7 .. .. 150.40 Mongolia 2.25 3.1 14.0 12.0 8 8 .. 2.9 b 88.50 Morocco 2.38 4.1 2.0 3.2 7 10 38.4 4.2 154.40 Mozambique 2.29 3.1 .. .. 7 10 9.4 3.2 154.40 Myanmar 2.33 .. 4.6 8.4 .. .. 3.8 .. 88.50 Namibia 2.02 4.2 3.0 3.0 11 9 13.6 5.4 154.40 Nepal 2.20 3.1 1.8 6.3 9 10 .. 2.8b 88.50 Netherlands 4.07 5.2 1.8 1.9 4 5 88.7 6.6 103.00 New Zealand 3.65 5.9 1.3 1.6 7 5 10.6 5.5 90.75 Nicaragua 2.54 3.8 3.2 3.2 5 5 10.6 2.7 88.55 Niger 2.54 .. .. .. 8 10 .. .. 154.40 Nigeria 2.59 3.1 2.5 4.1 10 9 19.9 2.8 154.40 Norway 3.93 5.2 1.0 2.0 4 4 7.9 5.8 113.80 Oman 2.84 5.1 .. .. 10 10 45.3 5.2 143.10 Pakistan 2.53 3.6 2.3 1.6 9 8 26.6 4.0 143.10 Panama 3.02 4.3 1.4 1.4 3 4 32.7 5.5 88.55 Papua New Guinea 2.41 .. .. .. 7 9 6.6 .. 88.50 Paraguay 2.75 3.6 1.0 4.0 8 10 0.0 3.5b 88.55 Peru 2.80 3.8 2.0 3.8 7 8 17.0 2.7 88.55 Philippines 3.14 3.0 1.8 5.0 8 8 15.9 3.0 90.75 Poland 3.44 3.9 3.0 3.6 5 5 9.2 2.8 150.40 Portugal 3.34 4.9 2.5 5.0 4 5 33.0 4.7 113.80 Puerto Rico .. 4.7 .. .. 7 10 10.9 5.4 .. Qatar 2.95 4.5 3.8 2.3 5 7 2.1 5.0 143.10 2010 World Development Indicators 383 6.9 Trade facilitation Logistics Burden of Lead time Documents Liner Quality of Freight Performance customs Shipping port costs to the Index procedures Connectivity infrastructure United Index States days number 1 kilogram DHL 1­5 1­7 0­100 1­7 air package (worst to best) (worst to best) To export To import To export To import (low to high) (worst to best) $ 2009 2008­09a 2009 2009 June 2009 June 2009 2009 2008­09 2010 Romania 2.84 4.1 2.0 2.0 5 6 23.3 3.3 150.40 Russian Federation 2.61 2.7 4.0 2.9 8 13 20.6 3.5 150.40 Rwanda 2.04 .. .. .. 9 9 .. .. 154.40 Saudi Arabia 3.22 4.8 2.3 6.3 5 5 47.3 4.7 143.10 Senegal 2.86 4.4 1.4 2.7 6 5 15.0 4.4 154.40 Serbia 2.69c 3.3 2.0 c 3.0 c 6 6 .. 3.3c 150.40 Sierra Leone 1.97 .. 2.0 32.0 7 7 5.6 .. 154.40 Singapore 4.09 6.4 2.2 1.8 4 4 99.5 6.8 82.10 Slovak Republic 3.24 4.7 3.0 5.0 6 8 .. 4.1b 150.40 Slovenia 2.87 5.4 1.0 2.0 6 8 19.8 5.2 150.40 Somalia 1.34 .. .. .. .. .. 2.8 .. 154.40 South Africa 3.46 4.3 2.3 3.3 8 9 32.1 4.7 154.40 Spain 3.63 4.4 4.0 7.1 6 8 70.2 5.2 113.80 Sri Lanka 2.29 3.7 1.3 2.5 8 6 34.7 4.8 90.75 Sudan 2.21 .. 39.0 5.0 6 6 9.3 .. 154.40 Swaziland .. .. .. .. 9 11 .. .. 154.40 Sweden 4.08 5.8 1.0 2.6 4 3 31.3 5.9 113.80 Switzerland 3.97 5.1 2.6 2.6 4 5 2.7 5.4b 113.80 Syrian Arab Republic 2.74 2.9 2.5 3.2 8 9 11.0 3.3 143.10 Tajikistan 2.35 3.2 7.0 .. 10 10 .. 1.9b 150.40 Tanzania 2.60 3.0 3.2 7.1 5 7 9.5 2.8 154.40 Thailand 3.29 4.1 1.6 2.6 4 3 36.8 4.7 90.75 Timor-Leste 1.71 3.0 .. .. 6 7 .. 2.3 88.50 Togo 2.60 .. .. .. 6 8 14.4 .. 154.40 Trinidad and Tobago .. 2.8 .. .. 5 6 15.9 4.0 72.85 Tunisia 2.84 4.2 1.7 7.0 5 7 6.5 4.9 154.40 Turkey 3.22 3.4 2.2 3.8 7 8 32.0 3.7 143.10 Turkmenistan 2.49 .. 3.0 .. .. .. .. .. 150.40 Uganda 2.82 3.4 5.5 14.0 6 7 .. 3.4b 154.40 Ukraine 2.57 3.0 1.7 7.0 6 10 22.8 3.7 150.40 United Arab Emirates 3.63 5.9 2.5 2.0 4 5 60.5 6.2 143.10 United Kingdom 3.95 4.6 3.3 1.9 4 4 84.8 5.2 103.00 United States 3.86 4.6 2.8 4.0 4 5 82.4 5.7 .. Uruguay 2.75 3.8 3.0 3.0 10 10 22.3 4.9 88.55 Uzbekistan 2.79 .. 1.4 2.0 7 11 .. .. 150.40 Venezuela, RB 2.68 1.8 9.4 12.1 8 9 20.4 2.4 88.55 Vietnam 2.96 3.6 1.4 1.7 6 8 26.4 3.3 90.75 West Bank and Gaza .. .. .. .. 6 6 .. .. .. Yemen, Rep. 2.58 .. 3.1 3.6 6 9 14.6 .. 143.10 Zambia 2.28 3.8 9.2 4.0 6 9 .. 3.7b 154.40 Zimbabwe 2.29 3.0 25.0 18.0 7 9 .. 4.4b 154.40 World 2.87d u 4.1d u 3.8d u 4.6d u 7u 7u .. 4.2 u .. Low income 2.43d 3.4 d 6.0 d 6.4 d 8 9 .. 3.4 .. Middle income 2.69d 3.7d 3.8d 5.1d 7 8 .. 3.7 .. Lower middle income 2.59d 3.6d 4.7d 6.1d 7 8 .. 3.7 .. Upper middle income 2.80 d 3.8d 2.9d 4.0 d 7 8 .. 3.7 .. Low & middle income 2.61d 3.6d 4.5d 5.5d 7 8 .. 3.6 .. East Asia & Pacific 2.73d 3.7d 3.6d 4.9d 7 7 .. 3.7 .. Europe & Central Asia 2.74 d 3.6d 2.8d 3.0 d 7 8 .. 3.3 .. Latin America & Carib. 2.74 d 3.6d 3.9d 5.5d 7 7 .. 3.8 .. Middle East & N. Africa 2.60 d 3.7d 2.7d 7.2d 7 8 .. 3.9 .. South Asia 2.49d 3.4 d 1.9d 3.3d 9 9 .. 3.6 .. Sub-Saharan Africa 2.42d 3.6d 8.1d 7.0 d 8 9 .. 3.7 .. High income 3.55d 4.9d 2.1d 2.7d 5 5 .. 5.3 .. Euro area 3.57d 4.9d 2.2d 2.9d 4 5 .. 5.3 .. a. Average of the 2008 and 2009 survey ratings. b. Landlocked country. c. Includes Montenegro. d. Aggregates are computed according to the World Bank classification of economies as of July 1, 2009, and may differ from data published in the original source. 384 2010 World Development Indicators 6.9 GLOBAL LINKS Trade facilitation About the data Definitions Broadly defi ned, trade facilitation encompasses The direct costs of cross-border trade include · Logistics Performance Index reflects perceptions customs efficiency and other physical and regulatory freight, customs, and storage fees. Indirect costs of a country's logistics based on efficiency of customs environments where trade takes place, harmoniza- include the value of time to import or export and the clearance process, quality of trade- and transport- tion of standards and conformance to international risk of delay or loss of shipments. Long lead times related infrastructure, ease of arranging competitively regulations, and the logistics of moving goods and and burdensome regulatory procedures may lower priced shipments, quality of logistics services, abil- associated documentation through countries and competitiveness. Data on lead time are from the LPI ity to track and trace consignments, and frequency ports. Though collection of trade facilitation data survey. Respondents provided separate values for with which shipments reach the consignee within has improved over the last decade, data that allow the best case (10 percent of shipments) and the the scheduled time. The index ranges from 1 to 5, meaningful evaluation, especially for developing median case (50 percent of shipments). The data with a higher score representing better performance. economies, are lacking. Data on trade facilitation are exponentiated averages of the logarithm of sin- · Burden of customs procedure measures business are drawn from research by private and international gle value responses and of midpoint values of range executives' perceptions of their country's efficiency of agencies. Most data are perception-based evalua- responses for the median case. customs procedures. Values range from 1 to 7, with a tions by business executives and professionals. Data on the number of documents needed to export higher rating indicating greater efficiency. · Lead time Because of different backgrounds, values, and per- or import are from the World Bank's Doing Business to export is the median time (the value for 50 percent sonalities, those surveyed may evaluate the same surveys, which compile procedural requirements for of shipments) from shipment point to port of loading. situation quite differently. Perception-based indica- exporting and importing a standardized cargo of goods · Lead time to import is the median time (the value tors are thus subject to bias and require caution when by ocean transport from local freight forwarders, ship- for 50 percent of shipments) from port of discharge to interpreting the results. Nevertheless, they convey ping lines, customs brokers, port officials, and banks. arrival at the consignee. · Documents to export and much needed information on trade facilitation. To make the data comparable across economies, sev- documents to import are all documents required per The table presents data from Logistics Perfor- eral assumptions about the business and the traded shipment by government ministries, customs authori- mance Surveys conducted by the World Bank in part- goods are used (see www.doingbusiness.org). ties, port and container terminals, health and techni- nership with academic and international institutions Access to global shipping and air freight networks cal control agencies, and banks to export or import and private companies and individuals engaged in and the quality and accessibility of ports and roads goods. Documents renewed annually and not requiring international logistics. The Logistics Performance affect logistics performance. The table shows two renewal per shipment are excluded. · Liner Shipping Index assesses logistics performance across six indicators related to trade and transport service infra- Connectivity Index indicates how well countries are aspects of the logistics environment (see Defini- structure: the Liner Shipping Connectivity Index and connected to global shipping networks based on the tions), based on more than 5,000 country assess- the quality of port infrastructure rating. The Liner Ship- status of their maritime transport sector. The highest ments by nearly 1,000 international freight forward- ping Connectivity Index captures how well countries value in 2004 is 100. · Quality of port infrastructure ers. Respondents evaluate eight markets on six core are connected to global shipping networks. It is com- measures business executives' perceptions of their dimensions on a scale from 1 (worst) to 5 (best). puted by the United Nations Conference on Trade and country's port facilities. Values range from 1 to 7, with The markets are chosen based on the most impor- Development (UNCTAD) based on five components of a higher rating indicating better development of port tant export and import markets of the respondent's the maritime transport sector: number of ships, their infrastructure. · Freight costs to the United States country, random selection, and, for landlocked coun- container-carrying capacity, maximum vessel size, is the DHL international U.S. inbound worldwide pri- tries, neighboring countries that connect them with number of services, and number of companies that international markets. Scores for the six areas are deploy container ships in a country's ports. For each ority express rate for a 1 kilogram air package. Any averaged across all respondents and aggregated to component a country's value is divided by the maxi- surcharges are excluded. a single score. Details of the survey methodology mum value of each component in 2004, the five com- Data sources and index construction methodology are in Arvis and ponents are averaged for each country, and the aver- others (2010). age is divided by the maximum average for 2004 and Data on the Logistics Performance Index and lead Data on the burden of customs procedures are multiplied by 100. The index generates a value of 100 time to export and import are from Arvis and oth- from the World Economic Forum's Executive Opinion for the country with the highest average index in 2004. ers' Connecting to Compete: Trade Logistics in the Survey. The 2009 round included more than 13,000 The quality of port infrastructure measures busi- Global Economy 2010. Data on the burden of cus- respondents from 133 countries. Sampling follows ness executives' perception of their country's port toms procedure and quality of port infrastructure a dual stratifi cation based on company size and facilities. Values range from 1 (port infrastructure ratings are from the World Economic Forum's the sector of activity. Data are collected online or considered extremely underdeveloped) to 7 (port Global Competitiveness Report 2009­2010. Data through in-person interviews. Responses are aggre- infrastructure considered efficient by international on number of documents to export and import are gated using sector-weighted averaging. The data for standards). Respondents in landlocked countries from the World Bank's Doing Business project the latest year are combined with the data for the were asked: "How accessible are port facilities (1 = (www.doingbusiness.org). Data on the Liner Ship- previous year to create a two-year moving average. extremely inaccessible; 7 = extremely accessible.)" ping Connectivity Index are from UNCTAD's Trans- Respondents evaluated the efficiency of customs The costs of transport services are a crucial deter- port Newsletter, No. 43 (2009). Freight costs to the procedures in their country. The lowest value (1) rates minant of export competitiveness. The proxy indica- United States are based on DHL's "DHL Express the customs procedure as extremely inefficient, and tor in the table is the shipping rates to the United Standard Rate Guideline 2010" (2010). the highest score (7) as extremely efficient. States of an international freight moving business. 2010 World Development Indicators 385 6.10 External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. 2,200 .. 2,096 .. 444 .. 0 .. 17 .. 87 Albania 456 3,188 330 2,222 109 835 0 106 62 779 65 80 Algeria 33,042 5,476 31,303 3,011 2,049 11 0 1,161 261 1,304 1,478 0 Angola 11,500 15,130 9,543 12,711 81 369 0 0 1,958 2,419 0 0 Argentina 98,465 128,285 54,913 66,410 4,913 5,069 16,066 24,352 21,355 37,523 6,131 0 Armenia 371 3,418 298 1,446 96 1,030 0 1,373 2 465 70 135 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 321 4,309 206 2,734 30 775 0 327 14 1,169 101 79 Bangladesh 15,941 23,644 15,121 20,973 5,692 10,613 0 0 199 1,986 622 686 Belarus 1,694 12,299 1,301 3,752 116 42 0 1,589 110 6,959 283 0 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,614 986 1,483 926 498 255 0 0 47 38 84 22 Bolivia 5,272 5,537 4,459 2,403 865 282 239 2,969 307 166 268 0 Bosnia and Herzegovina .. 8,316 .. 3,006 472 1,520 .. 4,398 .. 912 48 0 Botswana 717 438 707 395 108 5 0 0 10 43 0 0 Brazil 160,469 255,614 98,260 73,623 6,038 10,671 30,830 145,339 31,238 36,652 142 0 Bulgaria 10,379 38,045 8,808 4,663 444 1,207 342 14,889 512 18,493 717 0 Burkina Faso 1,271 1,681 1,140 1,517 608 626 0 0 56 110 75 54 Burundi 1,162 1,445 1,099 1,308 591 819 0 0 15 19 48 117 Cambodia 2,284 4,215 2,110 3,892 65 545 0 0 102 323 72 0 Cameroon 10,942 2,794 9,612 2,129 1,082 260 288 636 991 5 51 24 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 946 949 854 815 414 390 0 0 57 71 35 63 Chad 843 1,749 777 1,705 379 905 0 0 17 4 49 41 Chile 22,038 64,277 7,178 8,818 1,383 202 11,429 40,549 3,431 14,910 0 0 China 118,090 378,245 94,674 89,283 14,248 22,250 1,090 101,774 22,325 187,188 0 0 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 25,044 46,887 13,946 29,390 2,559 5,439 5,553 11,812 5,545 5,684 0 0 Congo, Dem. Rep. 13,239 12,199 9,636 10,872 1,413 2,437 0 0 3,118 673 485 654 Congo, Rep. 5,893 5,485 4,872 5,084 279 299 0 0 1,002 363 19 38 Costa Rica 3,774 8,812 3,106 3,043 303 41 214 1,904 430 3,864 24 0 Côte d'Ivoire 18,899 12,561 11,902 10,615 2,386 1,914 2,660 414 3,910 1,344 427 188 Croatia .. .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,447 10,484 3,653 7,146 300 458 19 843 616 2,003 160 492 Ecuador 13,877 16,851 11,951 9,595 1,108 624 440 5,592 1,312 1,664 173 0 Egypt, Arab Rep. 33,475 32,616 30,687 28,518 2,356 2,700 313 1,579 2,372 2,519 103 0 El Salvador 2,509 10,110 1,979 5,742 327 409 5 3,316 525 1,052 0 0 Eritrea 37 962 37 957 24 473 0 0 0 5 0 0 Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 10,322 2,882 9,788 2,826 1,470 859 0 0 460 56 73 0 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 4,361 2,367 3,977 2,247 110 20 0 0 287 120 97 0 Gambia, The 426 453 385 420 162 62 0 0 15 20 26 12 Georgia 1,240 3,380 1,039 2,222 84 989 0 341 85 357 116 460 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 5,495 4,970 4,200 3,412 2,434 1,330 27 39 620 1,356 648 162 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 3,282 15,889 2,328 4,374 158 806 142 9,364 811 2,151 0 0 Guinea 3,248 3,092 2,991 2,830 847 1,288 0 0 164 192 94 71 Guinea-Bissau 895 1,157 794 1,004 210 309 0 0 95 144 6 9 Haiti 821 1,935 766 1,830 389 507 0 0 27 0 29 105 Honduras 4,851 3,430 4,247 2,291 828 449 123 590 382 518 99 31 386 2010 World Development Indicators 6.10 GLOBAL LINKS External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 95,174 230,611 81,091 78,733 27,348 32,848 6,618 106,632 5,049 45,246 2,416 0 Indonesia 124,413 150,851 65,323 76,904 13,259 8,974 33,123 47,383 25,966 26,565 0 0 Iran, Islamic Rep. 21,565 13,937 15,116 8,902 316 761 0 0 6,449 5,035 0 0 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,581 10,034 3,721 6,598 595 327 128 2,164 492 1,271 240 0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 7,661 6,577 6,624 5,123 806 872 0 0 785 1,426 251 28 Kazakhstan 3,750 107,595 2,834 1,915 295 463 103 95,043 381 10,637 432 0 Kenya 7,309 7,441 5,857 6,268 2,412 3,050 445 0 634 921 374 252 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 609 2,464 472 1,963 141 655 0 307 13 30 124 165 Lao PDR 2,155 4,944 2,091 2,710 285 685 0 2,213 0 0 64 21 Latvia 463 42,108 271 2,258 55 61 0 24,934 31 14,091 160 825 Lebanon 2,966 24,395 1,550 20,561 113 368 50 470 1,365 3,246 0 117 Lesotho 684 682 642 653 207 306 0 0 4 0 38 30 Liberia 2,478 3,484 1,164 1,237 269 72 0 0 978 1,389 336 858 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 769 31,719 430 5,329 62 27 29 18,222 49 8,169 262 0 Macedonia, FYR 1,277 4,678 788 1,538 181 591 289 1,412 143 1,729 57 0 Madagascar 4,302 2,086 3,687 1,722 1,121 1,066 0 6 542 258 73 99 Malawi 2,238 963 2,078 838 1,306 188 0 0 44 0 116 125 Malaysia 34,343 66,182 16,023 21,464 1,059 85 11,046 21,918 7,274 22,800 0 0 Mali 2,958 2,190 2,739 2,150 863 534 0 0 72 0 147 40 Mauritania 2,396 1,960 2,127 1,643 347 243 0 0 169 301 100 16 Mauritius 1,416 626 1,148 577 157 111 267 49 1 0 0 0 Mexico 165,379 203,984 93,902 113,955 13,823 5,867 18,348 65,602 37,300 24,427 15,828 0 Moldova 695 3,787 450 792 152 440 9 1,516 6 1,314 230 166 Mongolia 520 1,721 472 1,653 59 338 0 48 0 0 47 20 Morocco 23,771 20,825 23,190 16,538 3,999 2,555 331 2,656 198 1,631 52 0 Mozambique 7,458 3,432 5,209 2,788 890 1,149 1,769 0 279 629 202 15 Myanmar 5,771 7,210 5,378 5,413 777 770 0 0 393 1,797 0 0 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 2,410 3,685 2,339 3,551 1,023 1,507 0 0 23 57 48 77 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,396 3,558 8,572 2,259 341 347 0 468 1,785 720 39 111 Niger 1,608 966 1,351 883 598 248 133 13 72 19 52 51 Nigeria 34,092 11,221 28,140 3,590 3,489 2,455 301 175 5,651 7,456 0 0 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 30,229 49,337 23,788 39,359 6,403 10,999 1,593 4,232 3,235 1,395 1,613 4,352 Panama 6,098 10,722 3,781 9,661 175 271 0 1,061 2,207 0 111 0 Papua New Guinea 2,506 1,418 1,668 1,064 407 229 711 345 78 9 50 0 Paraguay 2,574 4,163 1,453 2,265 189 230 338 751 784 1,146 0 0 Peru 30,833 28,555 18,931 19,330 1,729 2,712 1,288 3,078 9,659 6,147 955 0 Philippines 39,379 64,856 28,525 39,058 5,185 2,720 4,847 18,797 5,279 7,001 728 0 Poland 44,080 218,022 40,890 43,426 2,067 1,776 1,012 109,692 2,178 64,904 0 0 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 387 6.10 External debt Total external Long-term Short-term Use of IMF debt debt debt credit $ millions Public and publicly guaranteed IBRD loans Private $ millions Total and IDA credits nonguaranteed $ millions $ millions 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 6,832 104,943 3,957 14,988 844 2,572 534 58,839 1,303 31,116 1,038 0 Russian Federation 121,401 402,453 101,582 103,246 1,524 3,851 0 244,552 10,201 54,655 9,617 0 Rwanda 1,029 679 971 645 512 242 0 0 32 23 26 11 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,916 2,861 3,266 2,419 1,160 791 44 180 260 197 347 64 Serbia 10,785a 30,918 6,788a 8,475 1,252a 2,931 1,773a 18,320 2,139a 4,123 84 a 0 Sierra Leone 1,220 389 1,028 327 234 108 0 0 27 9 165 53 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,678 2,949 1,961 1,983 432 446 0 0 551 793 166 173 South Africa 25,358 41,943 9,837 13,173 0 26 4,935 10,833 9,673 17,937 913 0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 8,395 15,154 7,175 12,624 1,512 2,381 90 275 535 2,087 595 169 Sudan 17,603 19,633 9,779 12,599 1,279 1,300 496 0 6,368 6,628 960 406 Swaziland 249 362 238 348 25 17 0 0 11 15 0 0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan 634 1,466 590 1,357 0 365 0 53 43 41 0 15 Tanzania 7,364 5,938 6,203 3,710 2,269 1,971 0 889 963 1,322 197 17 Thailand 100,039 64,798 16,826 12,167 1,906 128 39,117 28,421 44,095 24,210 0 0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 1,476 1,573 1,286 1,433 541 604 0 0 85 92 105 48 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 10,818 20,776 9,215 16,449 1,766 1,375 0 0 1,310 4,327 293 0 Turkey 73,781 277,277 50,317 77,945 5,069 8,100 7,079 140,094 15,701 50,714 685 8,524 Turkmenistan 402 638 385 587 1 14 0 1 17 51 0 0 Uganda 3,609 2,249 3,089 1,781 1,792 1,004 0 0 103 458 417 9 Ukraine 8,429 92,479 6,581 10,726 491 3,022 84 56,648 223 20,397 1,542 4,709 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 5,318 11,049 3,833 10,044 513 713 127 187 1,336 817 21 0 Uzbekistan 1,799 3,995 1,415 3,156 157 368 15 629 212 211 157 0 Venezuela, RB 35,744 50,229 28,428 29,925 1,639 0 2,013 3,310 3,063 16,994 2,239 0 Vietnam 25,428 26,158 21,778 21,618 231 5,074 0 0 3,272 4,419 377 121 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,251 6,258 5,562 5,679 827 2,113 0 0 689 483 0 95 Zambia 6,958 2,986 5,291 1,167 1,434 371 13 1,049 415 676 1,239 96 Zimbabwe 4,989 ..b 3,462 .. 896 .. 381 .. 685 .. 461 .. World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 167,801 168,325 141,776 137,779 35,778 46,592 2,827 5,390 15,555 20,533 7,642 4,623 Middle income 1,704,407 3,550,214 1,133,675 1,241,882 143,754 163,171 206,482 1,463,386 312,058 823,829 52,239 21,117 Lower middle income 805,205 1,324,547 545,642 547,976 95,455 109,824 94,294 399,839 153,797 365,611 11,472 11,122 Upper middle income 899,202 2,225,666 588,033 693,906 48,299 53,347 112,188 1,063,547 158,262 458,218 40,767 9,996 Low & middle income 1,872,207 3,718,539 1,275,451 1,379,661 179,531 209,763 209,310 1,468,776 327,614 844,362 59,881 25,740 East Asia & Pacific 455,541 771,628 255,407 276,172 37,604 41,959 89,982 220,924 108,814 274,370 1,337 162 Europe & Central Asia 290,169 1,398,989 229,733 298,622 13,644 31,975 11,268 793,291 33,428 291,917 15,788 15,158 Latin America & Carib. 598,197 894,367 361,873 411,812 38,485 35,635 87,303 323,261 122,389 158,470 26,632 824 Middle East & N. Africa 139,821 131,545 123,516 105,449 12,279 10,907 694 5,866 13,434 19,972 2,177 258 South Asia 152,409 326,311 129,770 158,527 42,036 58,965 8,301 111,139 9,045 51,271 5,293 5,374 Sub-Saharan Africa 236,070 195,699 175,152 129,079 35,483 30,324 11,760 14,295 40,504 48,361 8,654 3,963 High income Euro area a. Includes Montenegro. b. Data are likely to be revised after being reconciled with creditor data. Total external debt for 2008 was $5.199 billion, according to debtor reports published in Global Development Finance. 388 2010 World Development Indicators 6.10 GLOBAL LINKS External debt About the data Definitions External indebtedness affects a country's creditworthi- Debt data, normally reported in the currency of · Total external debt is debt owed to nonresidents ness and investor perceptions. Data on external debt repayment, are converted into U.S. dollars to pro- repayable in foreign currency, goods, or services. It are gathered through the World Bank's Debtor Report- duce summary tables. Stock fi gures (amount of is the sum of public, publicly guaranteed, and private ing System. Indebtedness is calculated using loan-by- debt outstanding) are converted using end-of-period nonguaranteed long-term debt, short-term debt, and loan reports submitted by countries on long-term pub- exchange rates, as published in the IMF's Interna- use of IMF credit. · Long-term debt is debt that has lic and publicly guaranteed borrowing and information tional Financial Statistics (line ae). Flow figures are an original or extended maturity of more than one on short-term debt collected by the countries or from converted at annual average exchange rates (line year. It has three components: public, publicly guar- creditors through the reporting systems of the Bank rf). Projected debt service is converted using end- anteed, and private nonguaranteed debt. · Public for International Settlements. These data are supple- of-period exchange rates. Debt repayable in multiple and publicly guaranteed debt comprises the long- mented by information from major multilateral banks currencies, goods, or services and debt with a provi- term external obligations of public debtors, including and official lending agencies in major creditor coun- sion for maintenance of the value of the currency of the national government and political subdivisions tries and by estimates by World Bank and International repayment are shown at book value. (or an agency of either) and autonomous public bod- Monetary Fund (IMF) staff. The table includes data on Because flow data are converted at annual aver- ies, and the external obligations of private debtors long-term private nonguaranteed debt reported to the age exchange rates and stock data at end-of-period that are guaranteed for repayment by a public entity. World Bank or estimated by its staff. exchange rates, year-to-year changes in debt outstand- · IBRD loans and IDA credits are extended by the Data coverage, quality, and timeliness vary by coun- ing and disbursed are sometimes not equal to net flows World Bank. The International Bank for Reconstruc- try. Coverage varies for debt instruments and borrow- (disbursements less principal repayments); similarly, tion and Development (IBRD) lends at market rates. ers. The widening spectrum of debt instruments and changes in debt outstanding, including undisbursed The International Development Association (IDA) pro- investors alongside the expansion of private nonguar- debt, differ from commitments less repayments. Dis- vides credits at concessional rates. · Private non- anteed borrowing makes comprehensive coverage crepancies are particularly notable when exchange guaranteed debt consists of the long-term external of external debt more complex. Reporting countries rates have moved sharply during the year. Cancella- obligations of private debtors that are not guaranteed differ in their capacity to monitor debt, especially tions and reschedulings of other liabilities into long- for repayment by a public entity. · Short-term debt is private nonguaranteed debt. Even data on public term public debt also contribute to the differences. debt owed to nonresidents having an original matu- and publicly guaranteed debt are affected by cover- Variations in reporting rescheduled debt also affect rity of one year or less and interest in arrears on long- age and reporting accuracy--because of monitoring cross-country comparability. For example, reschedul- term debt. · Use of IMF credit denotes members' capacity and sometimes because of unwillingness to ing of official Paris Club creditors may be subject to drawings on the IMF other than those drawn against provide information. A key part often underreported lags between completion of the general rescheduling the country's reserve tranche position and includes is military debt. Currently, 128 developing countries agreement and completion of the specific bilateral purchases and drawings under Stand-By, Extended, report to the Debtor Reporting System. Nonreporting agreements that define the terms of the rescheduled Structural Adjustment, Enhanced Structural Adjust- countries might have outstanding debt with the World debt. Other areas of inconsistency include country ment, and Systemic Transformation Facility Arrange- Bank, other international financial institutions, and treatment of arrears and of nonresident national ments, together with Trust Fund loans. private creditors. deposits denominated in foreign currency. Debt flows from private creditors to low- and middle-income economies fell sharply in 2008 6.10a Low-income economies Middle-income economies Net inflows Net inflows ($ billions) ($ billions) Data sources 8 600 Official creditors Private creditors Data on external debt are mainly from reports to 500 6 the World Bank through its Debtor Reporting Sys- 400 tem from member countries that have received 4 300 IBRD loans or IDA credits, with additional infor- 200 mation from the files of the World Bank, the IMF, 2 the African Development Bank and African Devel- 100 0 opment Fund, the Asian Development Bank and 0 Asian Development Fund, and the Inter-American Private creditors Official creditors ­2 ­100 Development Bank. Summary tables of the exter- 1990 1995 2000 2005 2008 1990 1995 2000 2005 2008 nal debt of developing countries are published In 2008 debt flows from private creditors to low- and middle-income economies fell 61 percent, a decline annually in the World Bank's Global Develop- only partially offset by an increase in net flows from official creditors. ment Finance, on its Global Development Finance Source: Global Development Finance data files. CD-ROM, and on GDF Online. 2010 World Development Indicators 389 6.11 Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNI and incomea 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 2008b 2008b Afghanistan .. .. .. .. .. 96.7 .. 0.8 .. .. 4 21 Albania 18.4 25.2 1.4 3.0 11.4 50.2 13.7 24.4 23.5 33.0 21 51 Algeria 83.5 3.2 .. .. 17.7 13.6 0.8 23.8 6.3 0.9 3 6 Angola 311.9 21.3 12.0 2.5 0.6 0.7 17.0 16.0 919.7 13.5 24 27 Argentina 38.9 39.9 30.1 10.7 21.6 74.6 21.7 29.2 133.6 80.9 48 171 Armenia 25.3 27.6 3.1 12.7 69.8 83.6 0.6 13.6 1.9 33.1 27 97 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 10.6 10.5 1.3 0.9 21.8 23.8 4.4 27.1 11.6 18.1 12 14 Bangladesh 40.8 27.7 13.2 3.9 27.1 61.3 1.2 8.4 8.4 34.3 20 67 Belarus 12.2 20.6 3.4 3.1 55.4 6.8 6.5 56.6 29.2 227.2 24 38 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 82.1 14.8 6.8 .. 54.8 38.1 2.9 3.8 23.7 3.0 10 c 35c Bolivia 81.2 34.3 29.4 11.3 75.5 92.6 5.8 3.0 30.5 2.2 14 c 29c Bosnia and Herzegovina .. 43.9 .. 4.4 .. 73.3 .. 11.0 .. 25.9 44 81 Botswana 15.1 3.4 3.1 .. 76.0 47.3 1.4 9.8 0.2 0.5 3 5 Brazil 21.2 16.2 36.6 22.7 18.5 8.9 19.5 14.3 60.7 18.9 19 121 Bulgaria 81.8 79.0 16.5 14.7 10.5 76.8 4.9 48.6 31.3 103.1 91 128 Burkina Faso 53.6 21.2 .. .. 76.7 52.6 4.4 6.5 16.1 11.9 14 c 110 c Burundi 117.6 124.7 27.6 28.1 70.6 94.1 1.3 1.3 6.9 7.2 80 c 705c Cambodia 71.8 46.0 0.7 0.6 11.9 75.5 4.5 7.7 53.1 12.2 42 57 Cameroon 133.3 12.1 20.9 .. 61.0 39.8 9.1 0.2 6,444.5 0.2 4c 15c Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 85.9 48.7 .. .. 100.0 85.1 6.0 7.5 24.0 54.2 41c 267c Chad 58.5 26.1 .. .. 86.1 92.5 2.0 0.2 11.6 0.3 19c 32c Chile 32.1 41.3 24.5 18.2 76.2 13.5 15.6 23.2 23.1 64.6 41 74 China 16.5 8.7 9.9 2.0 7.6 24.1 18.9 49.5 27.8 9.5 10 25 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 27.5 20.2 31.5 16.2 32.7 32.3 22.1 12.1 65.6 24.0 23 108 Congo, Dem. Rep. 271.4 118.2 .. .. .. 45.9 23.6 5.5 1,980.9 865.3 100 c 316c Congo, Rep. 479.7 65.6 13.2 .. 21.8 24.5 17.0 6.6 1,575.1 9.3 74 c 70 c Costa Rica 32.8 30.3 13.8 10.5 50.5 44.0 11.4 43.9 40.5 101.7 33 61 Côte d'Ivoire 188.7 56.0 23.1 9.2 59.3 99.8 20.7 10.7 739.1 59.7 76c 144 c Croatia .. .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 28.5 23.8 6.1 .. 39.8 28.5 13.8 19.1 165.3 87.6 24 61 Ecuador 72.0 33.1 24.8 .. 32.0 62.3 9.5 9.9 73.4 37.2 34 75 Egypt, Arab Rep. 55.8 19.9 13.2 4.7 26.3 23.7 7.1 7.7 13.9 7.3 20 49 El Salvador 26.7 46.6 8.9 9.9 55.1 59.3 20.9 10.4 55.9 39.8 47 98 Eritrea 6.3 58.6 0.1 .. 100.0 66.8 0.0 0.5 0.0 8.0 38c 697c Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 136.8 10.9 18.5 2.8 41.9 45.0 4.5 1.9 56.5 6.4 8c 49c Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 101.6 19.4 15.3 .. 17.9 13.1 6.6 5.1 187.8 6.2 23 27 Gambia, The 113.0 61.5 15.5 .. 49.1 56.0 3.5 4.5 14.0 17.5 29c 63c Georgia 48.2 26.6 .. 4.2 0.4 37.4 6.9 10.6 43.0 24.1 24 65 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 86.9 31.3 24.0 3.2 48.4 20.7 11.3 27.3 77.1 .. 20c 46c Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 22.6 41.3 11.1 12.2 47.5 74.3 24.7 13.5 103.6 46.2 42 109 Guinea 90.0 73.2 24.8 9.6 30.5 66.7 5.0 6.2 188.9 .. 49c 149c Guinea-Bissau 379.4 274.1 52.4 .. 86.3 100.0 10.6 12.5 469.2 116.0 214 c 496c Haiti 28.1 27.8 51.0 1.9 92.2 79.2 3.2 0.0 13.4 0.0 17c 51c Honduras 132.9 25.0 32.3 .. 55.9 66.7 7.9 15.1 141.7 20.8 12c 15c 390 2010 World Development Indicators 6.11 GLOBAL LINKS Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNI and incomea 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 2008b 2008b Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 27.0 19.0 29.8 8.7 24.2 14.3 5.3 19.6 22.1 17.6 18 70 Indonesia 63.4 30.4 29.9 13.4 28.4 30.6 20.9 17.6 174.2 51.4 35 102 Iran, Islamic Rep. 23.9 .. 29.7 .. 1.3 3.4 29.9 36.1 .. .. 4 12 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 82.3 69.7 16.2 14.2 40.6 23.8 10.7 12.7 72.2 71.7 87 148 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 118.8 31.4 12.4 16.0 33.5 10.4 10.2 21.7 34.4 16.0 32 41 Kazakhstan 18.5 95.0 3.9 41.8 7.8 47.3 10.2 9.9 23.0 53.5 106 164 Kenya 83.8 21.7 24.7 4.5 32.5 36.9 8.7 12.4 164.9 32.0 19 68 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 37.5 56.9 13.2 8.2 59.0 79.7 2.1 1.2 9.7 2.4 42c 53c Lao PDR 122.6 99.5 6.1 .. 37.4 87.6 0.0 0.0 0.0 0.0 83 261 Latvia 8.8 127.3 1.6 37.7 60.3 16.8 6.7 33.5 5.2 268.7 147 301 Lebanon 24.3 90.6 .. 14.0 13.2 5.1 46.0 13.3 16.9 11.5 95 89 Lesotho 55.8 33.4 6.1 2.5 60.3 71.3 0.6 0.0 0.9 .. 18 27 Liberia .. 515.4 .. 131.3 .. 100.0 39.5 39.9 3,481.0 863.2 340 c 306c Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 10.1 69.3 1.3 30.6 31.8 6.0 6.4 25.8 6.0 126.8 78 120 Macedonia, FYR 29.0 49.6 .. 8.7 99.9 64.1 11.2 37.0 51.9 81.9 55 96 Madagascar 143.3 23.4 7.6 .. 74.3 74.8 12.6 12.4 497.1 26.3 20c 68 c Malawi 165.8 22.7 24.9 .. 51.4 35.2 1.9 0.0 37.8 0.0 9c 39c Malaysia 40.6 35.1 7.0 .. 15.5 4.8 21.2 34.5 29.5 24.7 35 30 Mali 122.3 25.8 13.4 .. 45.5 54.2 2.4 0.0 22.2 0.0 11c 33c Mauritania 175.3 .. 22.9 .. 49.6 65.5 7.1 15.4 187.9 .. 41c 65c Mauritius 37.3 7.0 8.7 2.8 34.5 27.6 0.1 0.0 0.1 0.0 7 10 Mexico 60.5 19.1 27.0 12.1 19.5 7.7 22.6 12.0 218.8 25.6 20 62 Moldova 40.3 57.0 7.9 11.3 79.1 56.9 0.9 34.7 2.3 78.5 67 96 Mongolia 43.3 33.6 10.1 .. 2.8 37.6 0.1 0.0 0.3 .. 28 42 Morocco 75.1 24.4 33.4 10.3 30.3 39.0 0.8 7.8 5.1 7.2 24 51 Mozambique 360.6 39.4 34.5 1.2 17.4 59.0 3.7 18.3 142.8 37.9 15c 36c Myanmar .. .. 17.8 .. 15.0 0.6 6.8 24.9 60.4 .. 35 84 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 54.7 28.9 7.5 3.6 54.2 76.3 0.9 1.5 3.5 .. 21 63 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 368.6 55.3 38.7 7.3 30.3 51.3 17.2 20.2 1,256.8 63.1 32c 53c Niger 87.9 18.1 16.7 .. 95.5 89.7 4.5 2.0 75.6 2.7 13c 63c Nigeria 131.7 5.7 13.8 .. 45.4 80.0 16.6 66.4 330.7 13.9 6 12 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 49.5 28.7 26.5 8.7 43.2 66.3 10.7 2.8 128.0 15.5 24 120 Panama 80.9 49.8 3.4 9.2 52.7 13.0 36.2 0.0 282.4 .. 54 62 Papua New Guinea 57.3 19.2 20.8 .. 31.7 41.1 3.1 0.6 29.1 0.4 21 22 Paraguay 31.5 25.5 5.6 4.8 48.0 59.0 30.4 27.5 70.8 40.0 29 50 Peru 60.3 23.9 15.9 12.5 49.9 35.9 31.3 21.5 111.6 19.7 28 81 Philippines 51.7 35.0 16.1 15.5 29.2 19.9 13.4 10.8 67.8 18.7 37 77 Poland 32.2 42.1 11.0 25.0 13.5 4.9 4.9 29.8 14.6 104.4 46 103 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 391 6.11 Ratios for external debt Total Total debt Multilateral Short-term Present value external debt service debt service debt of debt % of exports % of exports of % of public and of goods, goods and services publicly guaranteed services, % of GNI and incomea debt service % of total debt % of total reserves % of GNI and incomea 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 2008b 2008b Romania 19.4 54.7 10.5 25.3 21.3 28.3 19.1 29.7 49.7 78.2 57 149 Russian Federation 31.0 25.8 6.3 11.5 9.7 4.0 8.4 13.6 56.6 12.8 30 81 Rwanda 79.2 15.4 20.5 .. 99.0 69.7 3.1 3.3 32.3 3.8 8c 74 c Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal .. 21.8 16.8 .. 62.2 55.7 6.6 6.9 95.6 12.3 16c 50 c Serbia .. 63.5 .. 13.9 100.0d 60.4 19.8d 13.3 .. 35.9 70 111 Sierra Leone 149.0 20.3 53.7 .. 8.4 63.5 2.2 2.3 77.8 4.1 10 c 36c Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. 20.6 26.9 .. .. .. .. South Africa 17.1 15.7 9.5 4.4 0.0 1.8 38.1 42.8 216.7 52.6 16 46 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 65.3 38.1 8.0 9.3 14.0 21.0 6.4 13.8 25.3 79.7 35 96 Sudan 136.3 37.5 6.7 2.5 100.0 20.6 36.2 33.8 3,898.2 473.7 78 c 296c Swaziland 14.0 13.6 1.5 .. 64.0 73.4 4.5 4.1 3.7 2.0 12 14 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan 53.6 29.2 .. 3.1 .. 27.0 6.8 2.8 .. .. 23 26 Tanzania 143.5 29.9 17.4 1.2 66.7 99.9 13.1 22.3 356.6 46.2 14 c 54 c Thailand 60.6 32.0 11.6 7.7 20.9 1.4 44.1 37.4 119.4 21.8 31 32 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 116.7 56.1 6.0 .. 75.5 99.3 5.8 5.9 65.1 15.9 51c 106c Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 63.0 58.5 16.9 .. 43.8 48.2 12.1 20.8 77.6 47.9 58 85 Turkey 44.3 35.3 27.7 29.5 20.7 10.6 21.3 18.3 113.0 68.8 40 170 Turkmenistan 16.1 3.7 .. .. 1.9 2.4 4.3 8.0 1.5 .. 5 6 Uganda 63.3 15.8 19.8 1.7 69.7 56.7 2.8 20.4 22.4 19.9 10c 37c Ukraine 17.8 51.7 6.6 19.4 13.6 29.7 2.6 22.1 20.9 64.7 63 124 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 28.0 34.9 22.1 14.6 27.3 26.7 25.1 7.4 73.7 12.8 40 122 Uzbekistan 13.5 14.3 .. .. 1.9 19.2 11.8 5.3 .. .. 15 31 Venezuela, RB 49.0 16.0 22.9 5.6 11.6 11.2 8.6 33.8 28.6 39.5 21 58 Vietnam 124.0 29.7 .. 1.9 2.9 13.2 12.9 16.9 247.2 18.5 29 36 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 169.9 25.6 3.1 2.4 78.3 57.1 11.0 7.7 107.9 5.9 18 39 Zambia 215.1 23.0 .. 3.2 50.6 50.4 6.0 22.6 186.2 61.7 6c 14 c Zimbabwe 73.5 .. .. .. 33.6 .. 13.7 .. 77.2 .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 89.2 30.8 18.0 3.5 37.8 45.0 9.3 12.2 111.8 22.0 .. .. Middle income 36.0 21.9 17.0 9.7 21.5 17.7 18.3 23.2 69.0 19.8 .. .. Lower middle income 39.5 16.0 16.7 5.2 23.5 26.6 19.1 27.6 66.8 13.3 .. .. Upper middle income 33.4 28.0 17.3 15.2 19.6 12.7 17.6 20.6 70.9 31.8 .. .. Low & middle income 38.1 22.1 17.1 9.5 22.1 18.5 17.5 22.7 70.2 19.9 .. .. East Asia & Pacific 35.5 13.7 12.7 3.9 18.2 21.7 23.9 35.6 64.8 11.9 .. .. Europe & Central Asia 32.5 37.3 10.6 18.6 16.3 10.7 11.5 20.9 53.3 40.6 .. .. Latin America & Carib. 35.2 21.8 25.4 14.0 24.2 20.2 20.5 17.7 88.6 31.8 .. .. Middle East & N. Africa 53.5 15.1 19.7 5.3 19.3 18.9 9.6 15.2 18.4 .. .. .. South Asia 32.2 21.3 25.6 8.4 27.4 23.1 5.9 15.7 29.5 18.6 .. .. Sub-Saharan Africa 76.2 21.2 15.9 3.3 35.0 32.8 17.2 24.7 193.5 29.1 .. .. High income Euro area a. Includes workers' remittances. b. The numerator refers to 2008, whereas the denominator is a three year average of 2006­08 data. c. Data are from debt sustainability analyses for low-income countries. Present value estimates for these countries are for public and publicly guaranteed debt only. d. Includes Montenegro. 392 2010 World Development Indicators 6.11 GLOBAL LINKS Ratios for external debt About the data Definitions A country's external debt burden, both debt outstand- providing coverage for such obligations. The present · Total external debt is debt owed to nonresidents ing and debt service, affects its creditworthiness and value of external debt provides a measure of future and comprises public, publicly guaranteed, and pri- vulnerability. The table shows total external debt rela- debt service obligations. vate nonguaranteed long-term debt, short-term debt, tive to a country's size--gross national income (GNI). The present value of external debt is calculated by and use of IMF credit. It is presented as a share of Total debt service is contrasted with countries' ability discounting the debt service (interest plus amortiza- GNI. · Total debt service is the sum of principal to obtain foreign exchange through exports of goods, tion) due on long-term external debt over the life of repayments and interest actually paid in foreign cur- services, income, and workers' remittances. existing loans. Short-term debt is included at face rency, goods, or services on long-term debt; inter- Multilateral debt service (shown as a share of the value. The data on debt are in U.S. dollars converted est paid on short-term debt; and repayments (repur- country's total public and publicly guaranteed debt at official exchange rates (see About the data for chases and charges) to the IMF. · Exports of goods, service) are obligations to international fi nancial table 6.10). The discount rate on long-term debt services, and income refer to international trans- institutions, such as the World Bank, the Interna- depends on the currency of repayment and is based actions involving a change in ownership of general tional Monetary Fund (IMF), and regional develop- on commercial interest reference rates established merchandise, goods sent for processing and repairs, ment banks. Multilateral debt service takes priority by the Organisation for Economic Co-operation and nonmonetary gold, services, receipts of employee over private and bilateral debt service, and bor- Development. Loans from the International Bank compensation for nonresident workers, investment rowers must stay current with multilateral debts for Reconstruction and Development (IBRD), cred- income, and workers' remittances. · Multilateral to remain creditworthy. While bilateral and private its from the International Development Association debt service is the repayment of principal and inter- creditors often write off debts, international financial (IDA), and obligations to the IMF are discounted using est to the World Bank, regional development banks, institution bylaws prohibit granting debt relief or can- a special drawing rights reference rate. When the and other multilateral and intergovernmental agen- celing debts directly. However, the recent decrease discount rate is greater than the loan interest rate, cies. · Short-term debt includes all debt having an in multilateral debt service ratios for some countries the present value is less than the nominal sum of original maturity of one year or less and interest in reflects debt relief from special programs, such as future debt service obligations. arrears on long-term debt. · Total reserves comprise the Heavily Indebted Poor Countries (HIPC) Debt Debt ratios are used to assess the sustainability of holdings of monetary gold, special drawing rights, Initiative and the Multilateral Debt Relief Initiative a country's debt service obligations, but no absolute reserves of IMF members held by the IMF, and hold- (MDRI) (see table 1.4.) Other countries have accel- rules determine what values are too high. Empirical ings of foreign exchange under the control of mon- erated repayment of debt outstanding. Indebted analysis of developing countries' experience and etary authorities. · Present value of debt is the sum countries may also apply to the Paris and London debt service performance shows that debt service of short-term external debt plus the discounted sum Clubs to renegotiate obligations to public and private difficulties become increasingly likely when the pres- of total debt service payments due on public, publicly creditors. ent value of debt reaches 200 percent of exports. guaranteed, and private nonguaranteed long-term Because short-term debt poses an immediate Still, what constitutes a sustainable debt burden var- external debt over the life of existing loans. burden and is particularly important for monitoring ies by country. Countries with fast-growing econo- vulnerability, it is compared with the total debt and mies and exports are likely to be able to sustain foreign exchange reserves that are instrumental in higher debt levels. Data sources The burden of external debt service declined over 2000­08 6.11a Data on external debt are mainly from reports to the World Bank through its Debtor Reporting Sys- Share of exports of goods, services, and income (percent) 30 tem from member countries that have received IBRD loans or IDA credits, with additional infor- mation from the files of the World Bank, the IMF, 20 the African Development Bank and African Devel- opment Fund, the Asian Development Bank and Total debt service Asian Development Fund, and the Inter- American 10 Development Bank. Data on GNI, exports of goods and services, and total reserves are from Interest payments the World Bank's national accounts files and the 0 IMF's Balance of Payments and International 1981 1985 1990 1995 2000 2005 2008 Financial Statistics databases. Summary tables The total external debt service of low- and middle-income economies fell from 20 percent of export of the external debt of developing countries are revenues in 2000 to under 10 percent in 2008. About 26 percent of the total debt service in 2008 was published annually in the World Bank's Global interest payments on outstanding debt compared with 32 percent in 2000. Development Finance, on its Global Development Source: Global Development Finance data files. Finance CD-ROM, and on GDF Online. 2010 World Development Indicators 393 6.12 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. 300 .. .. .. 0 .. 0 Albania 70 937 0 .. 0 0 0 396 Algeria 0 2,646 .. .. ­278 0 788 ­474 Angola 472 1,679 0 .. 0 0 123 2,667 Argentina 5,609 9,753 1,552 ­531 3,705 14 754 1,889 Armenia 25 935 .. ­1 0 0 0 374 Australia 12,026 47,281 2,585 19,408 .. .. .. .. Austria 1,901 14,440 1,262 ­6,945 .. .. .. .. Azerbaijan 330 15 .. 0 0 0 0 350 Bangladesh 2 973 ­15 10 0 0 ­21 112 Belarus 15 2,158 .. 1 0 0 103 385 Belgium 10,689a 99,732 6,505a 8,818 .. .. .. .. Benin 13 120 0 .. 0 0 0 0 Bolivia 393 512 0 0 0 0 41 343 Bosnia and Herzegovina 0 1,056 .. .. .. 0 .. 254 Botswana 70 109 6 ­37 0 0 ­6 ­1 Brazil 4,859 45,058 2,775 ­7,565 2,636 1,637 8,283 25,551 Bulgaria 90 9,205 0 ­106 ­6 ­287 ­93 4,379 Burkina Faso 10 137 .. .. 0 0 0 ­3 Burundi 2 4 0 .. 0 0 ­1 0 Cambodia 151 815 .. 0 0 0 13 0 Cameroon 7 38 0 ­1 0 0 ­65 ­106 Canada 9,319 45,364 ­3,077 3,109 .. .. .. .. Central African Republic 6 121 .. .. 0 0 0 0 Chad 33 834 .. .. 0 0 0 0 Chile 2,957 16,787 ­249 1,823 489 ­1,688 1,773 5,053 China 35,849 147,791 0 8,721 317 ­2,096 4,696 14,238 Hong Kong SAR, China .. 63,005 .. 19,477 .. .. .. .. Colombia 968 10,583 165 ­86 1,008 47 1,250 486 Congo, Dem. Rep. 122 1,000 .. .. 0 0 0 ­7 Congo, Rep. 125 2,622 0 .. 0 0 ­50 ­6 Costa Rica 337 2,021 0 0 ­4 ­240 ­20 258 Côte d'Ivoire 211 402 1 79 0 0 14 ­177 Croatia 108 4,798 4 ­115 .. .. .. .. Cuba .. .. .. .. .. .. .. .. Czech Republic 2,568 10,864 1,236 ­1,124 .. .. .. .. Denmark 4,139 3,111 .. 2,792 .. .. .. .. Dominican Republic 414 2,885 .. 0 0 ­20 ­31 ­89 Ecuador 452 993 13 1 0 0 59 592 Egypt, Arab Rep. 598 9,495 0 ­674 0 0 ­311 ­235 El Salvador 38 784 0 0 0 0 ­31 298 Eritrea .. 36 .. .. 0 0 0 1 Estonia 201 1,947 10 ­308 .. .. .. .. Ethiopia 14 109 .. 0 0 0 ­48 ­33 Finland 1,044 ­7,765 2,027 ­1,782 .. .. .. .. France 23,736 100,372 6,823 ­16,145 .. .. .. .. Gabon ­315 20 .. .. 0 ­50 ­75 ­3 Gambia, The 8 72 .. .. 0 0 0 11 Georgia 6 1,564 .. 118 0 500 0 123 Germany 11,985 21,248 ­1,513 ­85,366 .. .. .. .. Ghana 107 2,112 0 0 0 0 38 68 Greece 1,053 5,304 0 ­5,260 .. .. .. .. Guatemala 75 838 .. 0 44 5 ­34 1,007 Guinea 1 382 .. .. 0 0 ­15 4 Guinea-Bissau 0 15 .. .. 0 0 0 0 Haiti 7 30 .. 0 0 0 0 50 Honduras 50 877 0 0 ­13 0 38 ­5 394 2010 World Development Indicators 6.12 GLOBAL LINKS Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 4,804 62,786 ­62 ­197 .. .. .. .. India 2,144 41,169 1,590 ­15,030 285 1,754 955 10,028 Indonesia 4,346 9,318 1,493 322 2,248 3,519 60 3,573 Iran, Islamic Rep. 17 1,492 0 .. 0 0 ­37 ­1,197 Iraq .. .. .. .. .. .. .. .. Ireland 1,447 ­19,886 0 931 .. .. .. .. Israel 1,350 9,638 991 994 .. .. .. .. Italy 4,842 15,442 5,358 ­29,022 .. .. .. .. Jamaica 147 1,437 0 0 13 250 15 12 Japan 39 24,552 50,597 ­69,692 .. .. .. .. Jordan 13 1,966 0 500 0 ­2 ­201 ­65 Kazakhstan 964 14,648 .. ­1,280 0 ­310 240 12,174 Kenya 32 96 5 5 0 0 ­163 ­8 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1,776 2,200 4,219 ­41,247 .. .. .. .. Kosovo .. .. .. .. .. .. .. .. Kuwait 7 57 0 0 .. .. .. .. Kyrgyz Republic 96 233 .. 6 0 0 0 ­74 Lao PDR 95 228 0 .. 0 0 0 366 Latvia 180 1,357 0 ­50 43 154 3 5,020 Lebanon 35 3,606 .. 466 350 ­233 333 ­80 Lesotho 275 218 .. .. 0 0 12 ­3 Liberia 5 144 .. 0 0 0 0 0 Libya ­88 4,111 .. 0 .. .. .. .. Lithuania 73 1,770 6 113 0 ­184 55 2,942 Macedonia, FYR 9 598 .. ­49 0 0 0 460 Madagascar 10 1,477 .. .. 0 0 ­4 3 Malawi 6 37 .. .. 0 0 ­23 0 Malaysia 4,178 7,376 0 ­10,716 2,440 ­250 1,231 ­106 Mali 111 127 .. .. 0 0 0 ­1 Mauritania 7 103 0 .. 0 0 0 ­6 Mauritius 19 378 22 34 150 0 126 ­29 Mexico 9,526 22,481 519 ­3,503 3,758 ­4,540 1,401 16,603 Moldova 26 708 ­1 11 0 ­6 24 386 Mongolia 10 683 0 .. 0 0 ­14 44 Morocco 92 2,466 20 148 0 ­589 158 ­67 Mozambique 45 587 0 0 0 0 24 ­1 Myanmar 280 283 .. .. 0 0 36 0 Namibia 153 535 46 4 .. .. .. .. Nepal .. 1 0 .. 0 0 ­5 ­1 Netherlands 12,206 ­2,389 ­743 ­12,565 .. .. .. .. New Zealand 3,316 5,466 .. 170 .. .. .. .. Nicaragua 89 626 0 0 0 0 ­81 77 Niger 7 147 .. .. 0 0 ­24 ­7 Nigeria 1,079 3,636 0 ­4,684 0 0 ­448 ­37 Norway 2,393 ­1,543 636 ­11,888 .. .. .. .. Oman 46 2,928 0 ­809 .. .. .. .. Pakistan 723 5,438 10 ­270 0 0 317 652 Panama 223 2,402 0 0 0 ­507 ­12 157 Papua New Guinea 455 ­30 .. .. ­32 0 ­311 149 Paraguay 103 320 0 0 0 0 ­16 91 Peru 2,557 4,079 171 180 0 ­1,488 43 ­83 Philippines 1,478 1,403 0 ­1,289 1,110 ­839 ­215 ­1,351 Poland 3,659 14,849 219 564 250 2,811 228 26,111 Portugal 685 3,575 ­179 6,776 .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. 2010 World Development Indicators 395 6.12 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2008 1995 2008 1995 2008 1995 2008 Romania 419 13,883 0 23 0 221 413 17,036 Russian Federation 2,065 72,885 47 ­15,005 ­810 15,402 444 23,060 Rwanda 2 103 0 0 0 0 0 0 Saudi Arabia ­1,875 22,486 0 .. .. .. .. .. Senegal 32 706 4 .. 0 0 ­25 ­37 Serbia 45b 2,992 .. ­57 0b 0 0b 3,400 Sierra Leone 7 ­3 0 0 0 0 ­28 0 Singapore 11,535 22,724 ­159 ­2,209 .. .. .. .. Slovak Republic 236 3,231 ­16 103 .. .. .. .. Slovenia 150 1,917 .. ­291 .. .. .. .. Somalia 1 87 .. .. 0 0 0 0 South Africa 1,248 9,645 2,914 ­4,707 731 ­698 748 ­805 Spain 8,086 71,207 4,216 ­446 .. .. .. .. Sri Lanka 56 752 .. ­488 0 ­65 103 155 Sudan 12 2,601 0 0 0 0 0 0 Swaziland 52 10 1 .. 0 0 0 0 Sweden 14,939 41,908 1,853 ­1,494 .. .. .. .. Switzerland 4,158 6,549 5,851 24,352 .. .. .. .. Syrian Arab Republic 100 .. 0 .. .. .. .. .. Tajikistan 10 376 .. 0 0 0 0 17 Tanzania 120 744 0 3 0 0 18 ­9 Thailand 2,068 9,835 2,253 ­4,594 2,123 ­778 3,702 ­554 Timor-Leste .. .. .. .. .. .. .. .. Togo 26 68 0 .. 0 0 0 0 Trinidad and Tobago 299 .. 17 .. .. .. .. .. Tunisia 264 2,638 12 ­39 588 0 ­96 29 Turkey 885 18,299 195 716 627 248 174 21,760 Turkmenistan 233 820 .. .. 0 0 20 ­36 Uganda 121 788 0 ­32 0 0 ­9 ­1 Ukraine 267 10,913 .. 388 ­200 780 ­19 16,521 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 21,731 93,506 8,070 72,710 .. .. .. .. United States 57,800 319,737 16,523 110,447 .. .. .. .. Uruguay 157 2,205 0 2 144 ­534 39 32 Uzbekistan ­24 918 .. .. 0 0 201 ­146 Venezuela, RB 985 349 270 3 ­468 3,051 ­216 ­434 Vietnam 1,780 9,579 .. ­578 0 ­26 356 ­51 West Bank and Gaza 123 .. 0 .. .. .. .. .. Yemen, Rep. ­218 1,555 .. 0 0 0 ­2 ­1 Zambia 97 939 .. ­6 0 0 ­37 71 Zimbabwe 118 52 .. .. ­30 0 140 11 World 328,496 s 1,823,282 s 127,074 s ­207,952 s .. s .. s .. s .. s Low income 3,243 26,440 ­6 ­591 ­30 ­26 420 329 Middle income 95,596 571,567 14,050 ­56,548 21,247 14,984 26,959 213,213 Lower middle income 52,899 267,487 5,393 ­16,777 6,470 692 8,429 48,183 Upper middle income 42,698 304,080 8,657 ­39,771 14,777 14,291 18,529 165,030 Low & middle income 98,839 598,007 14,043 ­57,139 21,216 14,958 27,379 213,542 East Asia & Pacific 50,798 187,724 3,746 ­8,139 8,206 ­470 9,554 16,310 Europe & Central Asia 9,443 172,056 467 ­14,608 ­96 19,329 1,794 134,897 Latin America & Carib. 30,181 125,669 5,216 ­9,674 11,311 ­4,015 13,833 51,851 Middle East & N. Africa 940 30,229 32 402 660 ­824 633 ­2,090 South Asia 2,931 48,678 1,585 ­15,778 285 1,689 1,349 10,978 Sub-Saharan Africa 4,546 33,651 2,998 ­9,342 851 ­750 217 1,597 High income 229,657 1,225,275 113,030 ­150,812 .. .. .. .. Euro area 78,432 426,921 23,737 ­273,433 .. .. .. .. a. Includes Luxembourg. b. Includes Montenegro. 396 2010 World Development Indicators 6.12 GLOBAL LINKS Global private financial flows About the data Definitions Private financial flows--equity and debt--account for Statistics on bonds, bank lending, and supplier · Foreign direct investment is net inflows of invest- the bulk of development finance. Equity flows com- credits are produced by aggregating transactions ment to acquire a lasting interest in or management prise foreign direct investment (FDI) and portfolio of public and publicly guaranteed debt and private control over an enterprise operating in an economy equity. Debt flows are financing raised through bond nonguaranteed debt. Data on public and publicly other than that of the investor. It is the sum of equity issuance, bank lending, and supplier credits. Data on guaranteed debt are reported through the Debtor capital, reinvested earnings, other long-term capital, equity flows are based on balance of payments data Reporting System by World Bank member econo- and short-term capital, as shown in the balance of reported by the International Monetary Fund (IMF). mies that have received loans from the International payments. · Portfolio equity includes net inflows FDI data are supplemented by staff estimates using Bank for Reconstruction and Development or cred- from equity securities other than those recorded data from the United Nations Conference on Trade its from the International Development Association. as direct investment and including shares, stocks, and Development and official national sources. The reports are cross-checked with data from market depository receipts, and direct purchases of shares The internationally accepted definition of FDI (from sources that include transactions data. Information in local stock markets by foreign investors · Bonds the fifth edition of the IMF's Balance of Payments on private nonguaranteed bonds and bank lending are securities issued with a fixed rate of interest for a Manual [1993]), includes three components: equity is collected from market sources, because official period of more than one year. They include net flows investment, reinvested earnings, and short- and national sources reporting to the Debtor Reporting through cross-border public and publicly guaranteed long-term loans between parent firms and foreign System are not asked for a breakdown of private and private nonguaranteed bond issues. · Commer- affiliates. Distinguished from other kinds of interna- nonguaranteed bonds and loans. cial bank and other lending includes net commercial tional investment, FDI is made to establish a lasting Data on equity flows are shown for all countries bank lending (public and publicly guaranteed and pri- interest in or effective management control over an for which data are available. Debt flows are shown vate nonguaranteed) and other private credits. enterprise in another country. The IMF suggests that only for 128 developing countries that report to the investments should account for at least 10 percent Debtor Reporting System; nonreporting countries of voting stock to be counted as FDI. In practice many may also receive debt flows. countries set a higher threshold. Many countries fail The volume of global private fi nancial fl ows to report reinvested earnings, and the definition of reported by the World Bank generally differs from long-term loans differs among countries. that reported by other sources because of differ- FDI data do not give a complete picture of inter- ences in sources, classification of economies, and national investment in an economy. Balance of method used to adjust and disaggregate reported payments data on FDI do not include capital raised information. In addition, particularly for debt financ- locally, an important source of investment financing ing, differences may also reflect how some install- in some developing countries. In addition, FDI data ments of the transactions and certain offshore issu- omit nonequity cross-border transactions such as ances are treated. intrafirm flows of goods and services. For a detailed discussion of the data issues, see the World Bank's World Debt Tables 1993­94 (vol. 1, chap. 3). Most global foreign direct investment is directed to high-income economies and a few large middle-income economies 6.12a FDI net inflows To low-income economies To other middle-income economies (percent of world total) To Brazil, China, India, the Russian Federation, and South Africa 40 30 20 Data sources Data on equity and debt flows are compiled from a 10 variety of public and private sources, including the 0 World Bank's Debtor Reporting System, the IMF's 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 International Financial Statistics and Balance of The share of FDI net inflows to developing economies increased 10 percentage points between 2007 and Payments databases, and Dealogic. These data 2008 because of decreasing inflows to high-income economies. Brazil, China, India, the Russian Federa- are also published annually in the World Bank's tion, and South Africa received more than half the FDI net inflows to all developing economies. Global Development Finance, on its Global Develop- Source: World Development Indicators data files. ment Finance CD-ROM, and on GDF Online. 2010 World Development Indicators 397 6.13 Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b,c IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan 8.5 190.5 41.3 0.0 35.7 0.0 50.9 0.0 3.2 35.7 0.0 1.0 22.7 Albania 7.8 140.9 19.9 11.7 ­11.4 3.8 0.0 26.5 85.4 0.8 0.0 0.4 3.8 Algeria ­162.9 ­106.2 0.0 ­102.2 0.0 0.0 0.0 0.0 ­13.9 1.1 0.0 0.9 7.9 Angola 816.6 34.8 6.1 0.0 0.0 0.0 2.1 ­0.4 0.1 16.3 0.0 0.8 9.8 Argentina ­70.4 ­401.3 0.0 ­604.6 0.0 0.0 0.0 ­60.6 260.5 0.7 0.0 1.0 1.7 Armenia 69.7 69.6 68.8 ­0.9 ­19.6 0.0 8.0 0.0 3.6 0.6 0.0 1.6 7.5 Australia Austria Azerbaijan 29.1 174.0 42.2 56.3 ­15.4 ­6.2 8.8 48.8 28.1 1.0 0.0 0.6 9.8 Bangladesh 57.5 1,274.0 607.0 0.0 198.7 0.0 298.5 121.7 ­2.0 20.8 0.0 0.8 28.5 Belarus 1,501.4 0.9 0.0 ­0.9 0.0 0.0 0.0 ­2.1 0.0 0.6 0.0 0.5 2.8 Belgium Benin ­31.9 154.8 84.1 0.0 18.9 0.0 29.5 0.0 3.6 5.4 0.0 0.8 12.5 Bolivia 46.4 119.6 24.3 0.0 0.0 0.0 36.9 ­43.4 92.0 1.1 0.0 0.6 8.1 Bosnia and Herzegovina 24.1 63.6 16.5 ­24.6 0.0 ­2.4 0.0 56.9 8.9 0.8 0.0 0.8 6.7 Botswana ­19.4 23.0 ­0.5 0.0 0.0 0.0 ­2.3 ­4.7 25.5 0.7 0.0 0.4 3.9 Brazil 581.5 943.4 0.0 914.2 0.0 0.0 3.0 ­592.7 603.9 1.8 0.0 1.5 11.7 Bulgaria 71.9 ­432.1 0.0 ­406.5 0.0 0.0 0.0 ­2.6 ­23.0 .. .. .. .. Burkina Faso 40.5 299.1 159.2 0.0 18.2 0.0 38.8 0.0 46.0 15.8 0.0 1.1 20.0 Burundi ­0.8 43.7 ­6.2 0.0 21.7 0.0 2.2 0.0 ­1.7 9.2 0.0 0.6 17.9 Cambodia 235.8 156.8 14.1 0.0 0.0 0.0 97.3 0.0 16.7 6.4 0.0 0.8 21.5 Cameroon 60.5 94.7 28.7 ­5.4 8.4 0.0 45.3 ­20.9 18.5 6.1 0.0 1.0 13.0 Canada Central African Republic ­2.3 17.9 ­10.5 0.0 15.3 0.0 ­3.7 0.0 ­2.1 5.6 0.0 0.5 12.8 Chad 53.4 ­46.5 ­57.0 ­25.7 ­14.0 0.0 5.2 0.0 15.9 11.1 0.0 0.5 17.5 Chile 138.1 ­101.8 ­0.7 ­154.3 0.0 0.0 0.0 50.9 0.0 0.4 0.0 0.9 1.0 China ­447.0 1,394.2 ­299.8 632.9 0.0 0.0 0.0 1,001.7 15.7 12.0 0.0 2.2 29.5 Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. .. Colombia ­102.6 1,946.8 ­0.7 689.0 0.0 0.0 ­5.5 1,349.8 ­97.4 2.0 0.0 0.8 8.8 Congo, Dem. Rep. ­108.4 ­16.7 50.7 0.0 ­137.0 0.0 1.2 ­33.7 ­15.4 57.9 0.0 1.3 58.3 Congo, Rep. ­18.1 10.1 ­2.4 0.0 1.9 0.0 ­0.4 ­8.7 ­0.6 2.8 0.0 0.2 17.3 Costa Rica 22.2 ­228.8 ­0.2 ­4.2 0.0 0.0 ­9.7 ­261.5 42.6 0.6 0.0 0.7 2.9 Côte d'Ivoire 1.7 ­503.4 ­103.6 ­377.4 ­44.5 64.3 ­0.6 ­59.6 ­13.1 7.7 0.0 1.1 22.3 Croatia .. .. .. 143.0 .. .. .. .. .. 0.4 0.0 0.6 3.7 Cuba .. .. .. .. .. .. .. .. .. 0.6 0.0 1.4 2.0 Czech Republic .. .. 0.0 0.0 .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 696.7 ­41.0 ­0.7 ­22.9 0.0 ­42.6 ­21.3 ­9.3 52.4 0.6 0.0 0.8 2.0 Ecuador ­121.8 ­434.7 ­1.1 ­71.3 0.0 0.0 ­26.4 ­17.8 ­323.3 1.1 0.0 0.8 3.3 Egypt, Arab Rep. ­960.1 118.6 ­39.2 65.4 0.0 0.0 8.5 125.5 ­60.2 3.0 0.0 1.4 14.2 El Salvador ­2.2 264.6 ­0.8 ­1.0 0.0 0.0 ­14.1 201.0 74.9 0.6 0.0 0.7 3.3 Eritrea 71.9 40.0 19.5 0.0 0.0 0.0 4.8 0.0 1.7 2.6 0.0 1.1 10.3 Estonia .. .. 0.0 ­7.2 .. .. .. .. .. .. .. .. .. Ethiopia 86.6 354.7 156.5 0.0 0.0 0.0 72.2 ­6.4 31.8 45.9 0.0 1.1 53.6 Finland France Gabon ­194.9 ­56.9 0.0 9.2 0.0 ­24.7 ­0.2 ­25.1 ­19.8 0.7 0.0 0.4 2.6 Gambia, The 5.3 54.0 2.3 0.0 6.3 0.0 13.5 0.0 22.6 1.2 0.0 0.3 7.8 Georgia 1.8 417.8 110.2 0.0 ­35.0 255.6 69.9 0.3 2.8 1.3 0.0 0.8 11.9 Germany Ghana 25.9 371.4 256.5 0.0 0.0 0.0 85.9 ­4.1 1.5 9.4 0.0 0.9 21.3 Greece .. .. 0.0 0.0 .. .. .. .. .. .. .. .. .. Guatemala ­24.5 167.6 0.0 66.1 0.0 0.0 ­6.4 58.5 40.6 1.6 0.0 0.6 6.6 Guinea ­7.1 15.5 ­8.4 0.0 7.9 0.0 11.6 ­5.6 ­16.8 6.0 0.0 0.5 20.3 Guinea-Bissau 0.0 8.3 ­3.9 0.0 ­2.0 5.6 ­1.2 0.0 0.0 2.2 0.0 0.2 7.4 Haiti 162.3 183.5 ­3.9 0.0 50.5 0.0 100.5 0.0 6.7 4.7 0.0 0.8 24.2 Honduras 219.1 175.0 51.1 0.0 0.0 0.0 100.7 ­18.4 28.7 0.9 0.0 1.1 10.9 398 2010 World Development Indicators 6.13 GLOBAL LINKS Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b,c IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Hungary .. .. 0.0 ­29.8 .. .. .. .. .. .. .. .. .. India 551.1 2,359.6 192.2 731.6 0.0 0.0 0.0 1,306.4 57.6 36.6 0.0 0.3 34.9 Indonesia ­2,040.2 942.5 466.6 146.7 0.0 0.0 24.6 283.7 0.0 5.2 0.0 1.1 14.6 Iran, Islamic Rep. ­89.6 84.7 0.0 77.2 0.0 0.0 0.0 0.0 0.0 1.4 0.0 0.6 5.5 Iraq .. .. .. .. .. .. .. .. .. 2.1 0.0 0.4 7.3 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica ­82.3 62.2 0.0 ­34.0 0.0 0.0 ­5.3 73.8 25.5 0.6 0.0 0.3 1.3 Japan Jordan ­2,086.4 51.8 ­2.5 ­44.4 0.0 ­59.3 0.0 0.0 19.9 0.6 130.8 0.9 5.8 Kazakhstan ­11.0 40.9 0.0 36.4 0.0 0.0 ­0.2 ­1.1 1.5 1.1 0.0 0.3 2.9 Kenya ­83.9 182.4 103.6 0.0 ­10.6 0.0 28.9 ­3.6 ­4.4 15.1 0.0 1.9 51.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. 2.3 0.0 1.4 5.5 Korea, Rep. .. .. ­3.5 ­458.0 .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic ­5.2 58.3 9.3 0.0 19.0 0.0 17.3 ­5.0 ­0.7 1.0 0.0 1.3 16.1 Lao PDR 86.5 74.7 3.8 0.0 ­4.3 0.0 24.8 0.7 15.0 2.5 0.0 0.7 31.5 Latvia ­0.4 1,066.0 0.0 ­23.8 0.0 846.2 0.0 ­2.0 245.6 .. .. .. .. Lebanon 115.2 128.3 0.0 ­75.7 0.0 40.1 0.0 0.0 38.0 0.6 122.5 1.0 1.8 Lesotho 5.1 13.8 10.9 ­1.0 ­5.0 0.0 2.2 0.0 ­0.8 1.1 0.0 0.6 5.8 Liberia 0.0 527.7 ­4.3 0.0 302.5 226.0 ­26.8 ­6.4 0.0 5.6 0.0 0.4 30.7 Libya .. .. .. .. .. .. .. .. .. 0.0 0.0 0.4 1.1 Lithuania ­2.4 ­19.7 0.0 3.0 0.0 0.0 0.0 ­7.7 ­15.0 .. .. .. .. Macedonia, FYR 30.5 32.7 ­5.9 37.0 0.0 0.0 0.0 6.1 ­8.9 0.7 0.0 1.0 2.7 Madagascar 27.8 390.3 210.2 0.0 59.0 0.0 75.5 0.0 8.2 17.1 0.0 1.3 19.0 Malawi ­6.5 185.5 11.7 0.0 97.0 0.0 43.5 ­2.2 1.9 9.2 0.0 1.0 23.4 Malaysia ­634.9 ­82.1 0.0 ­49.8 0.0 0.0 0.0 ­34.5 ­2.5 0.4 0.0 0.6 3.7 Mali ­5.3 263.5 87.1 0.0 28.4 0.0 56.8 0.0 65.6 11.2 0.0 0.7 13.7 Mauritania 128.3 157.7 42.6 0.0 3.1 0.0 19.2 ­7.6 80.1 2.6 0.0 0.7 17.0 Mauritius ­16.4 23.9 ­0.6 20.5 0.0 0.0 ­0.1 10.5 ­7.9 0.0 0.0 0.6 0.9 Mexico ­192.0 1,503.0 0.0 1,343.3 0.0 0.0 0.0 153.1 0.0 0.8 0.0 1.0 4.8 Moldova ­12.9 50.2 22.2 ­16.8 10.9 0.0 0.0 ­3.3 18.9 0.7 0.0 1.5 16.1 Mongolia 6.2 33.0 9.9 0.0 ­5.0 0.0 15.2 0.0 3.6 1.2 0.0 1.2 6.9 Morocco 816.9 952.5 ­1.3 ­20.0 0.0 0.0 ­1.1 398.9 564.5 1.3 0.0 0.9 9.3 Mozambique ­4.3 403.3 254.4 0.0 0.0 0.0 65.3 0.0 36.1 15.7 0.0 0.8 31.0 Myanmar ­150.4 38.0 0.0 0.0 0.0 0.0 0.0 0.0 ­0.7 13.9 0.0 1.1 23.7 Namibia .. .. .. .. .. .. .. .. .. 1.3 0.0 0.7 5.2 Nepal ­31.1 42.9 ­7.9 0.0 0.0 0.0 13.7 0.0 2.6 6.0 0.0 1.1 27.4 Netherlands New Zealand Nicaragua 21.5 154.4 28.3 0.0 29.1 0.0 79.9 ­4.5 9.6 0.7 0.0 1.4 9.9 Niger 17.3 123.4 15.0 0.0 11.9 0.0 19.7 0.0 30.9 19.5 0.0 0.7 25.7 Nigeria ­27.3 167.4 333.0 ­188.8 0.0 0.0 27.9 ­81.9 0.0 43.3 0.0 1.0 32.9 Norway Oman .. .. 0.0 0.0 .. .. .. .. .. 0.2 0.0 0.1 0.4 Pakistan 195.5 4,210.7 37.9 ­243.2 ­162.7 3,183.5 435.6 1,065.8 ­163.5 21.0 0.0 1.9 34.4 Panama ­4.2 147.1 0.0 53.8 0.0 ­5.3 ­6.9 94.3 7.2 0.4 0.0 0.5 3.1 Papua New Guinea ­97.0 ­27.6 ­1.7 ­25.7 0.0 0.0 ­7.6 4.0 ­3.5 1.3 0.0 0.0 5.6 Paraguay 3.3 ­22.1 ­1.5 ­20.7 0.0 0.0 ­6.9 ­0.2 2.4 1.2 0.0 0.5 3.1 Peru ­122.0 21.8 0.0 63.2 0.0 0.0 ­7.0 103.6 ­147.7 0.9 0.0 0.8 8.0 Philippines ­761.0 ­94.1 ­7.1 ­279.6 0.0 0.0 ­33.0 205.7 1.0 3.0 0.0 0.8 15.1 Poland ­3,201.7 ­73.6 0.0 ­73.6 0.0 0.0 0.0 0.0 0.0 .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 399 6.13 Net official financial flows Total International financial institutions United Nationsb,c $ millions $ millions Regional From IMF development banksb $ millions From bilateral multilateral World Banka Conces- Non- Conces- Non- Other sources sourcesa,b,c IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA UNTA Others 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Romania 17.8 811.6 0.0 ­48.5 0.0 0.0 0.7 64.5 794.9 .. .. .. .. Russian Federation ­539.3 ­679.2 0.0 ­485.2 0.0 0.0 0.0 ­193.8 ­0.2 .. .. .. .. Rwanda 3.4 156.7 40.5 0.0 3.6 0.0 31.3 0.0 25.2 8.3 0.0 0.8 47.0 Saudi Arabia .. .. .. .. .. .. .. .. .. 0.0 0.0 0.7 .. Senegal 233.6 261.3 133.9 0.0 38.4 0.0 68.5 ­10.9 6.7 5.4 0.0 1.3 18.0 Serbia ­48.7 439.0 34.5 ­22.1 0.0 0.0 0.0 301.8 107.4 2.3 0.0 1.0 14.1 Sierra Leone 12.2 92.4 25.6 0.0 18.0 0.0 15.5 0.0 6.6 8.3 0.0 1.1 17.3 Singapore .. .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. 0.0 ­50.2 .. .. .. .. .. .. .. .. .. Slovenia .. .. 0.0 ­14.3 .. .. .. .. .. .. .. .. .. Somalia 0.0 31.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.2 0.0 0.0 19.6 South Africa 0.0 ­14.2 0.0 ­0.3 0.0 0.0 0.0 ­22.4 0.0 2.8 0.0 0.5 5.2 Spain Sri Lanka 34.0 167.0 40.0 0.0 ­6.1 ­72.2 74.1 84.8 28.9 1.1 0.0 1.2 15.2 Sudan 274.2 157.2 ­1.2 0.0 0.0 ­65.5 0.0 0.0 149.5 17.7 0.0 0.8 55.9 Swaziland 10.1 22.8 ­0.2 ­5.9 0.0 0.0 1.9 ­8.2 30.3 1.4 0.0 0.6 2.9 Sweden Switzerland Syrian Arab Republic .. .. ­1.5 0.0 .. .. .. .. .. 0.8 57.1 1.3 6.2 Tajikistan 234.5 54.0 7.4 0.0 ­31.0 0.0 48.9 ­1.3 2.7 2.2 0.0 0.8 24.3 Tanzania 0.0 567.9 392.9 0.0 0.0 0.0 98.8 ­1.0 19.8 17.9 0.0 1.1 38.4 Thailand ­219.7 ­12.9 ­3.4 1.6 0.0 0.0 ­4.2 ­4.0 ­12.7 1.0 0.0 1.2 7.6 Timor-Leste .. .. .. .. .. .. .. .. .. 1.1 0.0 0.5 6.1 Togo ­0.7 ­76.3 ­117.0 0.0 47.7 0.0 ­14.9 0.0 ­6.3 4.3 0.0 0.5 9.4 Trinidad and Tobago .. .. 0.0 ­11.1 .. .. .. .. .. 0.0 0.0 0.1 0.6 Tunisia ­30.6 ­76.1 ­2.1 ­203.1 0.0 0.0 0.0 ­19.2 139.8 0.7 0.0 0.8 7.0 Turkey 434.9 3,424.6 ­5.9 570.0 0.0 1,587.7 0.0 0.0 1,259.3 1.6 0.0 0.6 11.3 Turkmenistan ­76.0 0.8 0.0 ­1.2 0.0 0.0 0.0 0.0 ­1.5 0.9 0.0 0.0 2.6 Uganda ­16.1 295.2 171.8 0.0 0.0 0.0 67.6 ­0.9 0.9 22.4 0.0 1.1 32.3 Ukraine ­220.7 5,070.5 0.0 686.6 0.0 4,401.4 0.0 ­34.5 9.1 1.2 0.0 1.8 4.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay ­19.8 408.6 0.0 61.4 0.0 0.0 ­2.4 200.2 146.4 0.5 0.0 0.6 1.9 Uzbekistan ­1.1 52.6 12.7 ­13.8 0.0 0.0 2.7 12.2 29.6 3.0 0.0 0.4 5.8 Venezuela, RB ­56.9 251.7 0.0 0.0 0.0 0.0 0.0 159.1 87.2 1.0 0.0 0.5 3.9 Vietnam 410.7 770.3 555.1 0.0 ­39.3 0.0 194.5 17.8 16.6 4.1 0.0 1.5 20.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. 3.7 496.6 0.1 9.3 Yemen, Rep. ­9.7 118.9 69.9 0.0 ­61.6 ­9.9 0.0 0.0 93.2 10.3 0.0 0.9 16.1 Zambia ­16.3 125.6 50.7 0.0 11.0 0.0 45.6 ­5.0 ­15.8 8.8 0.0 1.6 28.7 Zimbabwe 13.0 10.6 0.0 0.0 ­1.7 0.0 ­0.3 ­0.1 ­2.2 4.6 0.0 0.5 9.8 World .. s .. s .. s .. s .. s .. s .. s .. s .. s 984.1 s 807.1 s 645.3 s 1,941.2 s Low income 1,433.9 8,021.8 3,369.2 ­39.4 716.6 221.8 1,697.9 58.8 523.4 473.9 0.0 37.3 962.3 Middle income ­5,743.3 26,241.4 1,121.8 2,619.3 ­243.5 10,112.2 828.3 5,893.3 4,262.5 238.0 807.1 107.4 495.0 Lower middle income ­4,014.2 17,133.6 1,062.3 964.7 ­252.0 7,703.0 879.5 4,589.2 738.4 212.0 684.5 36.8 515.2 Upper middle income ­1,729.1 9,213.7 59.5 1,654.6 8.5 2,409.2 ­51.2 1,304.2 3,524.1 26.0 122.5 23.1 133.2 Low & middle income ­4,309.4 35,140.6 4,491.0 2,579.9 473.1 10,334.0 2,526.2 5,952.1 4,785.9 982.9 807.1 644.0 1,564.4 East Asia & Pacific ­3,563.6 3,304.2 738.5 424.8 ­48.6 0.0 306.7 1,480.7 48.0 59.8 0.0 98.3 196.0 Europe & Central Asia ­1,682.4 10,789.2 347.3 272.6 ­82.5 7,086.2 156.2 263.2 2,566.1 20.1 0.0 13.7 146.3 Latin America & Carib. 1,151.3 5,245.4 102.2 2,271.8 88.1 ­47.5 251.0 1,434.9 916.4 27.9 0.0 67.5 133.1 Middle East & N. Africa ­2,391.2 1,945.5 30.3 ­302.8 ­59.8 ­29.1 13.6 505.2 784.9 27.2 807.1 71.9 97.0 South Asia 823.5 8,284.8 921.1 488.3 65.7 3,108.9 876.1 2,578.8 ­52.9 122.5 0.0 6.4 169.9 Sub-Saharan Africa 1,353.0 5,140.7 2,351.5 ­574.9 510.2 215.4 922.6 ­310.7 523.4 466.6 0.0 156.0 880.6 High income .. .. .. .. .. .. .. .. 1.2 0.0 1.3 7.2 Euro area .. .. .. .. .. .. .. .. .. .. .. .. a. Aggregates include amounts for economies that do not report to the World Bank's Debtor Reporting System and may differ from aggregates published in Global Development Finance 2010. b. Aggregates include amounts for economies not specified elsewhere. c. World and income group aggregates include flows not allocated by country or region. 400 2010 World Development Indicators 6.13 GLOBAL LINKS Net official financial flows About the data Definitions The table shows concessional and nonconcessional creditworthy for International Bank for Reconstruction · Total net official financial flows are disbursements financial flows from official bilateral sources, the and Development (IBRD) lending. Exceptions are also of public or publicly guaranteed loans and credits, major international financial institutions, and UN made for small island economies. The IBRD lends to less repayments of principal. · IDA is the Interna- agencies. The international financial institutions fund creditworthy countries at a variable base rate of six- tional Development Association, the concessional nonconcessional lending operations primarily by sell- month LIBOR plus a spread, either variable or fixed, for loan window of the World Bank Group. · IBRD is the ing low-interest, highly rated bonds backed by prudent the life of the loan. The rate is reset every six months International Bank for Reconstruction and Develop- lending and financial policies and the strong financial and applies to the interest period beginning on that ment, the founding and largest member of the World support of their members. Funds are then on-lent to date. Although some outstanding IBRD loans have a Bank Group. · IMF is the International Monetary developing countries at slightly higher interest rates low enough interest rate to be classified as conces- Fund, which provides concessional lending through with 15- to 20-year maturities. Lending terms vary sional under the DAC definition, all IBRD loans in the the Poverty Reduction and Growth Facility and the with market conditions and institutional policies. table are classified as nonconcessional. Lending by the IMF Trust Fund and nonconcessional lending through Concessional flows from international financial International Finance Corporation is not included. credit to its members, mainly for balance of payments institutions are credits provided through concessional The International Monetary Fund makes conces- needs. · Regional development banks are the African lending facilities. Subsidies from donors or other sional funds available through its Poverty Reduction Development Bank, which serves all of Africa, includ- resources reduce the cost of these loans. Grants and Growth Facility and the IMF Trust Fund. Eligibility ing North Africa; the Asian Development Bank, which are not included in net flows. The Organisation for is based principally on a country's per capita income serves South and Central Asia and East Asia and Economic Co-operation and Development's (OECD) and eligibility under IDA. Pacific; the European Bank for Reconstruction and Development Assistance Committee (DAC) defines Regional development banks also maintain conces- Development, which serves Europe and Central Asia; concessional flows from bilateral donors as flows with sional windows. Their loans are recorded in the table and the Inter-American Development Bank, which a grant element of at least 25 percent, evaluated according to each institution's classification and not serves the Americas. · Concessional financial flows assuming a 10 percent nominal discount rate. according to the DAC definition. are disbursements made through concessional lend- World Bank concessional lending is done by the Data for flows from international financial institutions ing facilities. · Nonconcessional financial flows are International Development Association (IDA) based are available for 128 countries that report to the World all disbursements that are not concessional. · Other on gross national income (GNI) per capita and perfor- Bank's Debtor Reporting System. World Bank flows for institutions, a residual category in the World Bank's mance standards assessed by World Bank staff. Cut- nonreporting countries were collected from its opera- Debtor Reporting System, includes other multilateral off for IDA eligibility, set at the beginning of the World tional records. Nonreporting countries may have net institutions such as the Caribbean Development Fund, Bank's fiscal year, has been $1,135 since July 1, 2009, flows from other international financial institutions. Council of Europe, European Development Fund, measured in 2008 U.S. dollars using the Atlas method Official flows from the United Nations are mainly con- Islamic Development Bank, and Nordic Development (see Users guide). In exceptional circumstances IDA cessional flows classified as official development assis- Fund. · United Nations includes the United Nations extends temporary eligibility to countries above the cut- tance but may include nonconcessional flows classified Children's Fund (UNICEF), United Nations Relief and off that are undertaking major adjustments but are not as other official flows in OECD-DAC databases. Works Agency for Palestine Refugees in the Near East (UNRWA), United Nations Regular Programme for Technical Assistance (UNTA), and other UN agen- Net lending from the International Bank for Reconstruction and Development declined as countries paid off loans, and concessional lending from the cies, such as the International Fund for Agricultural International Development Association increased 6.13a Development, Joint United Nations Programme on HIV/AIDS, United Nations Development Programme, From IDA From IBRD Net inflows ($ billions) 1990 2008 Net inflows ($ billions) 1990 2008 United Nations Population Fund, United Nations Refu- 3,000 3,000 gee Agency, and World Food Programme. 2,000 2,000 Data sources Data on net fi nancial fl ows from international 1,000 1,000 financial institutions are from the World Bank's Debtor Reporting System and published annually 0 0 in the World Bank's Global Development Finance, on its Global Development Finance CD-ROM, and ­1,000 ­1,000 East Europe & Latin Middle East South Sub-Saharan on GDF Online. Data on official flows from UN agen- East Europe & Latin Middle East South Sub-Saharan Asia & Central America & & North Asia Africa Asia & Central America & & North Asia Africa cies are from the OECD DAC annual Development Pacific Asia Caribbean Africa Pacific Asia Caribbean Africa Co-operation Report and are available electroni- All regions except Middle East and North Africa and Sub-Saharan Africa received positive net disburse- cally on the OECD DAC's International Develop- ments from the IBRD. The world's poorest countries in Sub-Saharan Africa continue to receive conces- ment Statistics CD-ROM and at www.oecd.org/ sional lending from the International Development Association. dac/stats/idsonline. Source: Global Development Finance data files. 2010 World Development Indicators 401 6.14 Financial flows from Development Assistance Committee members Net disbursements Total net Official Other Private Net flowsa development assistancea official flowsa grants by flowsa NGOsa Contributions Foreign Bilateral Multilateral Private Bilateral Bilateral to multilateral direct portfolio portfolio export Total grants loans institutions Total investment investment investment credits 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 $ millions Australia 3,997 2,954 2,600 53 301 59 314 1,673 ­1,223 0 ­136 670 Austria 11,302 1,714 1,275 ­42 480 103 9,348 7,532 0 0 1,817 137 Belgium 4,425 2,386 1,404 ­28 1,010 ­138 1,816 1,617 0 0 199 361 Canada 24,068 4,785 3,396 ­39 1,428 1,608 16,184 14,872 988 0 324 1,491 Denmark 5,150 2,803 1,853 ­25 975 ­84 2,303 2,303 0 0 0 129 Finland ­222 1,166 681 13 473 22 ­1,422 ­32 ­1,390 0 0 13 France 40,641 10,908 5,980 481 4,446 ­229 29,962 24,609 6,098 0 ­745 0 Germany 33,395 13,981 9,392 ­329 4,918 ­462 18,251 9,598 5,218 ­275 3,708 1,626 Greece 1,166 703 312 0 391 1 460 460 0 0 0 2 Ireland 6,101 1,328 931 0 397 0 4,500 0 4,500 0 0 273 Italy 5,581 4,861 1,919 ­81 3,022 408 207 1,544 ­1,339 0 2 105 Japan 31,783 9,579 7,764 ­940 2,756 ­1,986 23,738 25,710 3,952 ­1,046 ­4,878 452 Luxembourg 426 415 279 0 136 0 0 0 0 0 0 11 Netherlands ­14,022 6,993 5,312 ­112 1,793 0 ­21,345 ­24,523 3,365 ­169 ­18 330 New Zealand 433 348 278 0 70 8 29 29 0 0 0 48 Norway 3,963 3,963 2,941 95 928 0 0 0 0 0 0 0 Portugal 1,528 620 238 136 247 0 906 341 ­95 0 660 1 Spain 30,087 6,867 4,776 25 2,065 0 23,220 23,334 0 0 ­114 0 Sweden 5,896 4,732 3,086 57 1,589 31 1,108 ­314 0 0 1,422 25 Switzerland 12,923 2,038 1,536 14 487 0 10,487 11,432 0 ­274 ­671 398 United Kingdom 41,878 11,500 7,064 303 4,133 ­22 29,938 23,783 2,223 0 3,932 462 United States 14,084 26,842 24,825 ­965 2,982 ­1,100 ­28,781 54,172 ­75,801 ­8,220 1,068 17,122 Total 264,581 121,483 87,839 ­1,384 35,029 ­1,782 121,224 178,140 ­53,504 ­9,983 6,572 23,655 Official development assistance Commitmentsb Gross Net disbursementsb disbursements % of general Per capitab government $ millions $ millions $ millionsb $ % of GNIa disbursementsa 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Australia 2,141 4,698 1,845 2,834 1,845 2,834 96 133 0.27 0.32 0.73 0.88 Austria 956 1,693 738 1,631 734 1,585 90 190 0.23 0.43 0.46 0.86 Belgium 1,451 2,949 1,451 2,319 1,413 2,219 138 208 0.36 0.48 0.74 0.96 Canada 3,282 5,343 2,908 4,673 2,867 4,635 93 139 0.25 0.32 0.60 0.81 Denmark 2,732 2,298 2,914 2,631 2,883 2,573 540 467 1.06 0.82 1.96 1.62 Finland 574 1,221 614 1,073 603 1,072 116 201 0.31 0.44 0.64 0.89 France 8,066 14,861 8,601 11,637 7,062 10,122 120 163 0.30 0.39 0.62 0.74 Germany 9,183 16,864 9,321 14,910 8,076 13,060 98 159 0.27 0.38 0.57 0.89 Greece 415 645 415 645 415 645 38 57 0.20 0.21 0.42 0.42 Ireland 430 1,272 430 1,272 430 1,272 114 293 0.29 0.59 0.87 1.20 Italy 2,869 5,158 2,838 4,655 2,443 4,440 43 75 0.13 0.22 0.28 0.44 Japan 14,388 18,425 13,704 15,491 11,357 8,502 89 67 0.28 0.19 0.86 0.53 Luxembourg 227 388 227 388 227 388 515 792 0.70 0.97 1.76 1.94 Netherlands 6,072 9,010 5,694 6,792 5,532 6,522 347 396 0.84 0.80 1.86 1.77 New Zealand 235 456 221 357 221 357 58 84 0.25 0.30 0.57 0.68 Norway 2,189 4,489 2,471 3,635 2,459 3,635 548 757 0.76 0.88 1.83 2.22 Portugal 764 582 764 582 497 576 48 56 0.26 0.27 0.60 0.56 Spain 2,717 6,015 2,717 6,864 2,339 6,304 59 137 0.22 0.45 0.57 1.07 Sweden 2,189 4,018 2,739 4,513 2,738 4,510 309 487 0.80 0.98 1.35 1.93 Switzerland 1,359 1,891 1,339 1,824 1,335 1,813 186 235 0.34 0.42 1.11 1.29 United Kingdom 7,224 12,825 7,224 12,825 7,144 12,315 122 202 0.32 0.43 0.80 0.92 United States 15,108 33,919 13,015 27,210 11,928 26,254 43 86 0.10 0.19 0.31 0.48 Total 84,571 149,021 82,188 128,762 74,548 115,632 88 129 0.22 0.31 0.60 0.73 Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2007 prices and exchange rates. 402 2010 World Development Indicators 6.14 GLOBAL LINKS Financial flows from Development Assistance Committee members About the data The flows of official and private financial resources dropped. ODA recipients now comprise all low- and are transactions by the official sector whose main from the members of the Development Assistance middle-income countries except those that are mem- objective is other than development or whose grant Committee (DAC) of the Organisation for Economic bers of the Group of Eight or the European Union element is less than 25 percent. · Private flows are Co-operation and Development (OECD) to developing (including countries with a firm date for EU acces- flows at market terms financed from private sector economies are compiled by DAC, based principally on sion). The content and structure of tables 6.14­6.17 resources in donor countries. They include changes reporting by DAC members using standard question- were revised to reflect this change. Because official in holdings of private long-term assets by reporting naires issued by the DAC Secretariat. aid flows are quite small relative to ODA, the net country residents. · Foreign direct investment is The table shows data reported by DAC member effect of these changes is believed to be minor. investment by residents of DAC member countries economies and does not include aid provided by the Flows are transfers of resources, either in cash or to acquire a lasting management interest (at least European Commission--a multilateral member of in the form of commodities or services measured on 10 percent of voting stock) in an enterprise operating DAC. a cash basis. Short-term capital transactions (with in the recipient country. The data reflect changes in DAC exists to help its members coordinate their one year or less maturity) are not counted. Repay- the net worth of subsidiaries in recipient countries development assistance and to encourage the ments of the principal (but not interest) of ODA loans whose parent company is in the DAC source coun- expansion and improve the effectiveness of the are recorded as negative flows. Proceeds from offi - try. · Bilateral portfolio investment is bank lending aggregate resources flowing to recipient economies. cial equity investments in a developing country are and the purchase of bonds, shares, and real estate In this capacity DAC monitors the flow of all financial reported as ODA, while proceeds from their later sale by residents of DAC member countries in recipi- resources, but its main concern is official develop- are recorded as negative flows. ent countries. · Multilateral portfolio investment ment assistance (ODA). Grants or loans to countries The table is based on donor country reports and is transactions of private banks and nonbanks in and territories on the DAC list of aid recipients have does not provide a complete picture of the resources DAC member countries in the securities issued by to meet three criteria to be counted as ODA. They received by developing economies for two reasons. multilateral institutions. · Private export credits are are undertaken by the official sector. They promote First, flows from DAC members are only part of the loans extended to recipient countries by the private economic development and welfare as the main aggregate resource flows to these economies. Sec- sector in DAC member countries to promote trade; objective. And they are provided on concessional ond, the data that record contributions to multilateral they may be supported by an official guarantee. · Net financial terms (loans must have a grant element of institutions measure the flow of resources made grants by nongovernmental organizations (NGOs) at least 25 percent, calculated at a discount rate of available to those institutions by DAC members, not are private grants by NGOs, net of subsidies from 10 percent). The DAC Statistical Reporting Directives the flow of resources from those institutions to devel- the official sector. · Commitments are obligations, provide the most detailed explanation of this defini- oping and transition economies. expressed in writing and backed by funds, under- tion and all ODA-related rules. Aid as a share of gross national income (GNI), aid taken by an official donor to provide specified assis- This definition excludes nonconcessional fl ows per capita, and ODA as a share of the general gov- tance to a recipient country or multilateral organiza- from official creditors, which are classified as "other ernment disbursements of the donor are calculated tion. · Gross disbursements are the international official flows," and aid for military purposes. Trans- by the OECD. The denominators used in calculating transfer of financial resources, goods, and services, fer payments to private individuals, such as pen- these ratios may differ from corresponding values valued at the cost to the donor. sions, reparations, and insurance payouts, are in elsewhere in this book because of differences in tim- general not counted. In addition to financial flows, ing or definitions. ODA includes technical cooperation, most expen- Definitions ditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral · Net disbursements are gross disbursements of institutions such as the United Nations and its spe- grants and loans minus repayments of principal on cialized agencies, and concessional funding to multi- earlier loans. · Total net flows are ODA or official lateral development banks. aid flows, other official flows, private flows, and net A DAC revision of the list of countries and terri- grants by nongovernmental organizations. · Official tories counted as aid recipients has governed aid development assistance refers to flows that meet Data sources reporting for the three years starting in 2005. In the the DAC definition of ODA and are made to coun- past DAC distinguished aid going to Part I and Part II tries and territories on the DAC list of aid recipients. Data on financial flows are compiled by OECD DAC countries. Part I countries, the recipients of ODA, · Bilateral grants are transfers of money or in kind and published in its annual statistical report, Geo- comprised many of the countries classified by the for which no repayment is required. · Bilateral loans graphical Distribution of Financial Flows to Devel- World Bank as low- and middle-income economies. are loans extended by governments or official agen- oping Countries, and its annual Development Part II countries, whose assistance was designated cies with a grant element of at least 25 percent (at a Co-operation Report. Data are available electroni- official aid, included the more advanced countries 10 percent discount rate). · Contributions to multi- cally on the OECD-DAC's International Develop- of Central and Eastern Europe, countries of the for- lateral institutions are concessional funding received ment Statistics CD-ROM and at www.oecd.org/ mer Soviet Union, and certain advanced developing by multilateral institutions from DAC members as dac/stats/idsonline. countries and territories. This distinction has been grants or capital subscriptions. · Other official flows 2010 World Development Indicators 403 6.15 Allocation of bilateral aid from Development Assistance Committee members 6.15a Aid by purpose Net disbursements Share of bilateral ODA net disbursements % Development projects, programs, and other Technical Debt-related Humanitarian Administrative $ millionsa resource provisions cooperationb aid assistance costs 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Australia 758 2,653 27.8 41.2 55.1 34.0 1.1 9.7 9.7 11.3 6.2 3.8 Austria 273 1,234 28.7 12.2 41.8 21.5 20.4 59.4 2.7 3.6 6.4 3.3 Belgium 477 1,376 33.6 25.5 46.9 52.9 6.6 7.3 5.4 9.2 7.5 5.1 Canada 1,160 3,357 39.6 28.5 43.0 49.3 1.1 4.0 5.0 10.7 11.4 7.5 Denmark 1,024 1,828 65.8 67.9 25.3 10.0 1.0 5.3 0.0 9.2 8.0 7.6 Finland 217 693 40.8 33.7 41.4 42.3 0.0 0.3 10.5 12.9 7.2 10.8 France 2,829 6,461 25.4 33.2 50.6 45.0 17.0 15.1 0.4 0.4 6.7 6.4 Germany 2,687 9,063 16.8 17.9 63.8 47.2 6.6 28.3 4.1 3.3 8.7 3.3 Greece 99 312 69.6 16.4 23.8 70.6 0.0 0.0 6.4 5.5 0.2 7.5 Ireland 154 931 79.1 71.1 0.4 4.4 0.0 0.0 15.5 19.1 5.1 5.4 Italy 377 1,838 10.2 32.3 8.1 9.2 57.5 48.4 18.3 6.5 5.9 3.6 Japan 9,768 6,823 60.4 28.8 24.9 28.7 4.2 25.0 0.9 3.8 9.5 13.8 Luxembourg 99 279 84.4 76.8 3.2 3.6 0.8 0.0 10.4 12.1 1.2 7.5 Netherlands 2,243 5,200 41.1 71.1 33.7 12.6 6.8 2.4 9.1 7.7 9.4 6.2 New Zealand 85 278 39.7 56.4 48.1 25.7 0.0 0.0 3.4 9.4 8.8 8.4 Norway 934 3,036 57.9 56.0 23.0 23.8 1.0 1.4 11.3 11.9 6.9 6.9 Portugal 179 373 30.4 53.0 50.4 42.3 14.6 0.1 1.9 0.3 2.7 4.3 Spain 720 4,802 69.3 54.3 17.9 25.3 2.3 7.1 3.7 9.0 6.8 4.3 Sweden 1,242 3,142 60.9 62.5 13.6 18.7 3.1 0.0 14.6 11.6 7.7 7.2 Switzerland 627 1,550 58.6 46.3 19.4 27.6 0.9 6.4 20.2 10.7 0.9 9.1 United Kingdom 2,710 7,367 47.7 61.5 25.5 15.7 5.7 7.5 12.7 9.1 8.4 6.3 United States 7,405 23,860 14.6 70.0 64.4 5.4 1.7 0.9 9.6 18.4 9.7 5.3 Total 36,064 86,455 40.5 50.4 39.4 23.0 5.4 10.2 6.1 10.2 8.6 6.2 a. At current exchange rates and prices. b. Includes aid for promoting development awareness and aid provided to refugees in donor economies. About the data Aid can be used in many ways. The sector to which provide debt relief on liabilities that recipient coun- human resources from donors or action directed to aid goes, the form it takes, and the procurement tries have difficulty servicing. Thus, this type of aid human resources (such as training or advice). Also restrictions attached to it are important influences may not provide a full value of new resource flows included are aid for promoting development aware- on aid effectiveness. The data on allocation of offi - for development, in particular for heavily indebted ness and aid provided to refugees in the donor econ- cial development assistance (ODA) in the table are poor countries. Humanitarian assistance provides omy. Assistance specifically to facilitate a capital based principally on reporting by members of the relief following sudden disasters and supports food project is not included. · Debt-related aid groups Organisation for Economic Co-operation and Devel- programs in emergency situations. This type of aid all actions relating to debt, including forgiveness, opment (OECD) Development Assistance Committee does not generally contribute to financing long-term swaps, buybacks, rescheduling, and refinancing. (DAC). For more detailed explanation of ODA, see development. · Humanitarian assistance is emergency and dis- About the data for table 6.14. tress relief (including aid to refugees and assistance Definitions The form in which an ODA contribution reaches for disaster preparedness). · Administrative costs the benefiting sector or the economy is important. A · Net disbursements are gross disbursements of are the total current budget outlays of institutions distinction is made between resource provision and grants and loans minus repayments of principal on responsible for the formulation and implementation technical cooperation. Resource provision involves earlier loans · Development projects, programs, of donor's aid programs and other administrative mainly cash or in-kind transfers and financing of and other resource provisions are aid provided as costs incurred by donors in aid delivery. capital projects, with the deliverables being finan- cash transfers, aid in kind, development food aid, cial support and the provision of commodities and and the financing of capital projects, intended to Data sources supplies. Technical cooperation includes grants to increase or improve the recipient's stock of physical nationals of aid-recipient countries receiving educa- capital and to support recipient's development plans Data on aid flows are published by OECD DAC in its tion or training at home or abroad, and payments and other activities with finance and commodity annual statistical report, Geographical Distribution to consultants, advisers, and similar personnel and supply. · Technical cooperation is the provision of of Financial Flows to Developing Countries, and its to teachers and administrators serving in recipient resources whose main aim is to augment the stock of annual Development Co-operation Report. Data are countries. Technical cooperation is spent mostly in human intellectual capital, such as the level of knowl- available electronically on the OECD DAC's Interna- the donor economy. edge, skills, and technical know-how in the recipient tional Development Statistics CD-ROM and at www. Two other types of aid are presented because they country (including the cost of associated equipment). oecd.org/dac/stats/idsonline. serve distinctive purposes. Debt-related aid aims to Contributions take the form mainly of the supply of 404 2010 World Development Indicators 6.15 GLOBAL LINKS Allocation of bilateral aid from Development Assistance Committee members 6.15b Aid by sector Total Social infrastructure and services Economic infrastructure, Multi- Untied sector- services, and production sector sector or aida allocable Water Government Transport cross- aid supply and and civil and com- cutting Share of bilateral Total Education Health Population sanitation society Total munication Agriculture ODA commitments (%) 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Australia 70.9 45.3 10.4 5.5 2.3 0.5 23.1 11.3 5.2 3.9 14.3 96.7 Austria 33.0 24.9 12.6 3.4 0.4 2.8 4.9 5.6 0.3 0.9 2.5 82.3 Belgium 71.7 45.5 12.2 11.4 0.6 5.8 11.8 17.9 3.1 6.8 8.3 91.9 Canada 63.1 41.9 7.5 9.7 1.7 1.2 20.3 14.1 2.9 7.1 7.1 90.8 Denmark 64.9 34.9 3.9 1.2 1.7 1.2 23.3 15.9 1.3 3.6 14.0 98.5 Finland 73.9 37.2 7.9 3.8 1.4 5.6 12.9 21.7 1.3 9.6 15.0 92.3 France 66.2 29.7 18.6 1.9 0.1 3.8 1.6 25.8 15.1 5.6 10.6 81.9 Germany 66.2 35.6 13.7 2.4 1.4 7.2 9.6 23.3 1.8 1.9 7.3 98.2 Greece 71.8 63.3 27.7 2.5 1.9 0.2 19.8 4.9 2.0 1.3 3.7 37.9 b Ireland 65.4 53.1 12.8 13.3 3.6 3.0 16.3 8.2 0.4 6.0 4.1 100.0 b Italy 42.5 24.1 3.5 5.3 0.4 7.0 6.2 11.7 1.7 3.2 6.7 78.0 Japan 68.8 17.4 4.4 1.3 0.2 9.3 1.5 48.7 25.3 5.8 2.7 96.5 Luxembourg 70.4 46.7 10.2 13.3 7.1 6.8 4.0 14.5 1.8 6.2 9.2 100.0 b Netherlands 74.6 58.5 13.2 5.1 4.9 5.7 27.2 10.2 1.6 1.7 5.9 94.5 New Zealand 54.5 41.8 17.5 5.3 1.6 1.2 14.7 9.4 1.8 3.1 3.3 92.7 Norway 67.2 42.1 8.7 6.0 2.3 1.5 20.4 14.0 0.4 4.1 11.1 100.0 Portugal 65.8 48.9 19.1 2.1 0.1 0.1 21.3 14.0 12.9 0.8 2.9 29.1b Spain 65.0 43.4 9.4 5.2 2.1 10.7 9.9 15.0 4.2 3.7 6.6 69.1 Sweden 51.3 30.4 3.9 4.8 2.0 2.4 14.8 11.7 1.5 3.0 9.2 99.9 Switzerland 45.9 21.8 3.2 3.1 0.2 3.2 11.3 13.9 1.3 5.0 10.3 97.3 United Kingdom 62.7 42.4 7.4 7.0 5.5 2.0 17.3 16.4 1.5 1.2 3.9 100.0b United States 74.9 51.8 3.5 3.9 19.4 2.7 15.4 20.3 5.8 5.1 2.8 75.0 Total 67.8 39.2 8.0 4.0 6.6 4.8 12.2 22.9 7.8 4.3 5.7 87.3 a. Excludes technical cooperation and administrative costs. b. Gross disbursements. About the data Definitions The Development Assistance Committee (DAC) · Bilateral official development assistance (ODA) and planning and activities promoting good gover- records the sector classifi cation of aid using a commitments are firm obligations, expressed in nance and civil society. · Economic infrastructure, three-level hierarchy. The top level is grouped by writing and backed by the necessary funds, under- services, and production sector group assistance themes, such as social infrastructure and services; taken by official bilateral donors to provide specified for networks, utilities, services that facilitate eco- economic infrastructure, services, and production; assistance to a recipient country or a multilateral nomic activity, and contributions to all directly pro- and multisector or cross-cutting areas. The second organization. Bilateral commitments are recorded ductive sectors. · Transport and communication level is more specifi c. Education and health and in the full amount of expected transfer, irrespective refer to road, rail, water, and air transport; post transport and storage are examples. The third level of the time required for completing disbursements. and telecommunications; and television and print comprises subsectors such as basic education and · Total sector-allocable aid is the sum of aid that media. · Agriculture refers to sector policy, devel- basic health. Some contributions are reported as can be assigned to specific sectors or multisector opment, and inputs; crop and livestock production; non-sector-allocable aid. activities. · Social infrastructure and services refer and agricultural credit, cooperatives, and research. Reporting on the sectoral destination and the to efforts to develop the human resources poten- · Multisector or cross-cutting refers to support for form of aid by donors may not be complete. Also, tial of aid recipients. · Education refers to general projects that straddle several sectors. · Untied aid measures of aid allocation may differ from the per- teaching and instruction at all levels, as well as con- is ODA not subject to restrictions by donors on pro- spectives of donors and recipients because of dif- struction to improve or adapt educational establish- curement sources. ference in classification, available information, and ments. Training in a particular field is reported for the Data sources recording time. sector concerned. · Health refers to assistance to The proportion of untied aid is reported because hospitals, clinics, other medical and dental services, Data on aid flows are published annually by the tying arrangements may prevent recipients from public health administration, and medical insur- Organisation for Economic Co-operation and obtaining the best value for their money. Tying ance programs. · Population refers to all activities Development (OECD) DAC in Geographical Distri- requires recipients to purchase goods and services related to family planning and research into popula- bution of Financial Flows to Developing Countries from the donor country or from a specified group of tion problems. · Water supply and sanitation refer and Development Co-operation Report. Data are countries. Such arrangements prevent a recipient to assistance for water supply and use, sanitation, available electronically on the OECD DAC's Inter- from misappropriating or mismanaging aid receipts, and water resources development (including rivers). national Development Statistics CD-ROM and at but they may also be motivated by a desire to benefit · Government and civil society refer to assistance www.oecd.org/dac/stats/idsonline. donor country suppliers. to strengthen government administrative apparatus 2010 World Development Indicators 405 6.16 Aid dependency Net official Aid dependency development assistance (ODA) ratios Net ODA as Net ODA as Net ODA as Total Per capita Net ODA as % of gross capital % of imports of goods, % of central government $ millions $ % of GNI formation services, and income expense 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Afghanistan 136 4,865 6 168 .. 45.8 .. 165.8 .. .. .. 196.1 Albania 317 386 103 123 8.4 3.0 34.8 9.7 21.0 5.1 .. .. Algeria 200 316 7 9 0.4 0.2 1.5 0.6 .. .. .. 0.8 Angola 302 369 21 20 4.1 0.5 22.0 3.5 4.1 0.6 .. .. Argentina 52 131 1 3 0.0 0.0 0.1 0.2 0.1 0.2 .. .. Armenia 216 303 70 98 11.0 2.4 60.6 6.2 21.2 5.7 .. 12.3 Australia Austria Azerbaijan 139 235 17 27 2.8 0.6 12.8 2.5 5.8 1.4 .. 3.3 Bangladesh 1,172 2,061 8 13 2.4 2.4 10.8 10.7 11.7 7.8 .. 23.9 Belarus .. 110 .. 11 .. 0.2 .. 0.5 .. 0.3 1.3 0.5 Belgium Benin 241 641 36 74 10.7 9.6 56.4 46.3 32.4 .. .. 64.3 Bolivia 482 628 58 65 5.9 3.9 31.6 21.5 19.7 9.6 .. .. Bosnia and Herzegovina 737 482 199 128 12.4 2.5 65.1 10.7 17.4 3.6 .. 6.7 Botswana 31 716 18 373 0.5 5.4 1.4 16.5 1.0 10.9 .. .. Brazil 231 460 1 2 0.0 0.0 0.2 0.2 0.2 0.2 .. 0.1 Bulgaria .. .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 338 998 29 66 13.0 12.6 77.2 .. 48.9 .. .. 98.2 Burundi 93 509 14 63 12.9 43.9 213.8 .. 56.5 93.4 .. .. Cambodia 396 743 31 51 11.2 7.5 61.8 .. 16.1 9.2 .. .. Cameroon 377 525 24 27 4.0 2.3 22.4 .. 12.7 6.0 .. .. Canada Central African Republic 75 256 20 59 8.0 13.0 82.4 111.2 .. .. .. .. Chad 130 416 15 38 9.5 6.2 40.4 32.8 .. .. .. .. Chile 49 73 3 4 0.1 0.0 0.3 0.2 0.2 0.1 0.3 0.2 China 1,712 1,489 1 1 0.1 0.0 0.4 0.1 0.6 0.1 .. .. Hong Kong SAR, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 186 972 5 22 0.2 0.4 1.3 1.6 1.0 1.7 .. 1.7 Congo, Dem. Rep. 177 1,610 3 25 4.5 15.5 119.1 57.8 .. .. 15.2 .. Congo, Rep. 32 505 11 140 1.4 6.6 4.4 22.6 1.6 .. .. .. Costa Rica 10 66 2 15 0.1 0.2 0.4 0.9 0.1 0.4 .. 1.0 Côte d'Ivoire 351 617 20 30 3.6 2.7 31.2 26.0 7.9 5.9 .. 14.7 Croatia 66 397 15 90 0.3 0.6 1.6 1.9 0.6 1.0 0.8 .. Cuba 44 127 4 11 .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 56 153 6 15 0.2 0.3 1.0 1.8 0.5 0.7 .. .. Ecuador 146 231 12 17 1.0 0.4 4.6 1.5 2.3 1.0 .. .. Egypt, Arab Rep. 1,327 1,348 19 17 1.3 0.8 6.8 3.7 5.6 2.0 .. 2.7 El Salvador 180 233 30 38 1.4 1.1 8.1 7.1 3.0 2.0 .. 49.9 Eritrea 176 143 48 29 27.7 8.7 116.6 .. 34.4 .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 686 3,327 10 41 8.4 13.0 41.4 64.7 41.0 34.5 .. .. Finland France Gabon 12 55 9 38 0.3 0.4 1.1 1.5 0.5 .. .. .. Gambia, The 50 94 38 57 12.4 12.3 67.8 46.2 .. 22.8 .. .. Georgia 169 888 36 206 5.3 7.0 20.8 22.9 13.6 10.9 47.9 23.8 Germany Ghana 598 1,293 31 55 12.4 8.6 50.0 21.6 17.2 10.0 .. .. Greece Guatemala 263 536 23 39 1.4 1.4 7.6 7.8 4.4 3.1 12.5 11.7 Guinea 153 319 18 32 5.0 9.1 24.9 54.2 15.7 16.7 .. .. Guinea-Bissau 80 132 62 84 39.5 31.6 329.8 123.3 .. .. .. .. Haiti 208 912 24 92 .. .. 20.8 49.3 15.1 31.5 .. .. Honduras 448 564 72 77 6.4 4.3 22.3 12.6 8.9 4.7 .. 18.4 406 2010 World Development Indicators 6.16 GLOBAL LINKS Aid dependency Net official Aid dependency development assistance (ODA) ratios Net ODA as Net ODA as Net ODA as Total Per capita Net ODA as % of gross capital % of imports of goods, % of central government $ millions $ % of GNI formation services, and income expense 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Hungary .. .. .. .. .. .. .. .. .. .. .. .. India 1,457 2,108 1 2 0.3 0.2 1.3 0.5 1.8 0.5 2.0 1.1 Indonesia 1,651 1,225 8 5 1.1 0.3 4.5 0.9 2.5 0.7 .. .. Iran, Islamic Rep. 130 98 2 1 0.1 .. 0.4 .. 0.7 .. 0.2 0.1 Iraq 100 9,870 4 321 .. .. .. .. .. .. .. .. Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica 9 79 3 30 0.1 0.6 .. .. 0.2 0.7 .. 1.6 Japan Jordan 552 742 115 126 6.4 3.3 29.2 13.7 8.7 3.8 .. 9.6 Kazakhstan 189 333 13 21 1.1 0.3 5.7 0.7 1.8 0.5 7.5 1.7 Kenya 509 1,360 16 35 4.1 4.5 23.0 23.4 12.9 10.6 23.9 20.9 Korea, Dem. Rep. 73 218 3 9 .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 215 360 44 68 16.7 7.2 78.3 29.3 28.5 7.4 99.2 41.8 Lao PDR 281 496 52 80 16.9 9.3 57.2 24.1 44.0 .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. .. Lebanon 199 1,076 53 257 1.1 3.7 5.7 12.0 .. 3.3 3.8 12.1 Lesotho 37 143 19 70 3.6 7.0 11.1 31.4 4.4 8.2 .. 17.2 Liberia 67 1,250 24 330 17.4 185.8 .. 742.0 .. 41.4 .. .. Libya .. 60 .. 10 .. 0.1 .. 0.2 .. 0.2 .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR 251 221 125 108 7.1 2.3 31.5 8.4 10.5 2.8 .. 7.4 Madagascar 320 841 21 44 8.4 8.9 54.9 25.0 20.2 .. 77.8 .. Malawi 446 913 38 61 26.1 21.2 188.6 80.6 65.6 .. .. .. Malaysia 45 158 2 6 0.1 0.1 0.2 .. 0.0 0.1 0.3 .. Mali 359 964 34 76 15.0 11.4 60.4 .. 34.4 .. 127.8 .. Mauritania 216 311 83 97 19.8 .. 103.3 .. .. .. .. .. Mauritius 20 110 17 86 0.4 1.2 1.7 4.3 0.7 1.6 2.2 6.1 Mexico ­58 149 ­1 1 0.0 0.0 0.0 0.1 0.0 0.0 ­0.1 .. Moldova 123 299 30 82 9.4 4.5 39.7 13.4 11.2 5.0 32.9 15.1 Mongolia 217 246 91 93 20.0 4.8 68.6 12.1 27.4 .. 85.2 17.8 Morocco 419 1,217 15 39 1.2 1.4 4.4 3.8 3.1 2.5 .. 4.6 Mozambique 903 1,994 49 89 22.5 22.0 68.7 109.4 51.2 38.3 .. .. Myanmar 106 534 2 11 .. .. .. .. 4.0 .. .. .. Namibia 152 207 84 97 3.9 2.4 22.8 9.1 8.2 4.4 13.7 .. Nepal 387 716 16 25 7.0 5.6 29.0 17.9 21.2 16.1 .. .. Netherlands New Zealand Nicaragua 560 741 110 131 15.0 11.5 47.2 .. 23.5 13.4 86.4 57.3 Niger 208 605 19 41 11.7 11.3 101.4 .. 43.0 .. .. .. Nigeria 174 1,290 1 9 0.4 0.7 .. .. 1.1 2.1 .. .. Norway Oman 45 32 19 11 0.2 .. 1.9 .. 0.6 0.1 0.9 .. Pakistan 700 1,539 5 9 1.0 0.9 5.5 4.3 4.8 2.9 5.7 5.7 Panama 15 29 5 8 0.1 0.1 0.5 0.5 0.1 0.1 0.6 .. Papua New Guinea 275 304 51 46 8.3 4.0 35.7 19.0 13.7 .. 26.2 .. Paraguay 82 134 15 21 1.1 0.8 6.1 4.1 2.3 1.3 .. 5.0 Peru 397 466 15 16 0.8 0.4 3.7 1.4 3.4 1.1 4.2 2.2 Philippines 572 61 7 1 0.7 0.0 3.6 0.2 1.1 0.1 4.3 0.2 Poland .. .. .. .. .. .. .. .. .. .. .. .. Portugal Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. Qatar .. .. .. .. .. .. .. .. .. .. .. .. 2010 World Development Indicators 407 6.16 Aid dependency Net official Aid dependency development assistance (ODA) ratios Net ODA as Net ODA as Net ODA as Total Per capita Net ODA as % of gross capital % of imports of goods, % of central government $ millions $ % of GNI formation services, and income expense 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 2000 2008 Romania .. .. .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 321 931 40 96 18.7 19.3 101.2 86.7 71.2 63.6 .. .. Saudi Arabia 22 .. 1 .. 0.0 .. 0.1 .. 0.0 .. .. .. Senegal 424 1,058 43 87 .. 8.0 44.2 26.4 22.0 .. 71.1 .. Serbia 1,134 a 1,047 151a 142 12.6a 2.1 150.1a 8.9 .. 3.6 .. 5.6 Sierra Leone 181 367 43 66 29.3 19.2 413.2 127.5 68.8 53.3 98.8 .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. .. Slovenia 61 .. 31 .. 0.3 .. 1.1 .. 0.5 .. 0.8 .. Somalia 101 758 14 85 .. .. .. .. .. .. .. .. South Africa 486 1,125 11 23 0.4 0.4 2.3 1.8 1.3 0.9 1.3 1.3 Spain Sri Lanka 275 730 15 36 1.7 1.8 6.0 6.6 3.2 4.3 7.3 .. Sudan 220 2,384 6 58 1.9 4.8 9.7 18.0 8.5 17.1 .. .. Swaziland 13 67 12 58 0.9 2.3 5.1 14.4 0.9 .. .. .. Sweden Switzerland Syrian Arab Republic 158 136 10 7 0.9 0.3 4.7 1.8 2.4 .. .. .. Tajikistan 124 291 20 43 15.8 5.8 125.1 28.2 .. 6.9 160.3 .. Tanzania 1,035 2,331 30 55 11.6 11.7 64.7 .. 46.4 28.2 .. .. Thailand 697 ­621 11 ­9 0.6 ­0.2 2.5 ­0.8 0.9 ­0.3 .. ­1.2 Timor-Leste 231 278 284 253 71.6 9.5 285.9 .. .. .. .. .. Togo 70 330 13 51 5.4 11.4 29.4 .. 10.5 .. .. 75.3 Trinidad and Tobago ­2 12 ­1 9 0.0 0.1 ­0.1 0.4 0.0 .. .. .. Tunisia 222 479 23 46 1.2 1.3 4.2 4.4 2.1 1.6 4.1 3.9 Turkey 327 2,024 5 27 0.1 0.3 0.6 1.3 0.5 0.9 .. 1.2 Turkmenistan 31 18 7 4 1.2 0.1 3.1 1.8 .. .. .. .. Uganda 844 1,657 35 52 13.9 11.8 70.0 49.1 53.6 29.4 95.5 76.3 Ukraine .. 618 .. 13 .. 0.3 .. 1.4 .. 0.6 .. 0.9 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay 17 33 5 10 0.1 0.1 0.5 0.5 0.3 0.3 0.3 0.4 Uzbekistan 186 187 8 7 1.4 0.7 8.3 2.9 .. .. .. .. Venezuela, RB 76 59 3 2 0.1 0.0 0.3 0.1 0.3 0.1 0.3 .. Vietnam 1,681 2,552 22 30 5.5 2.9 18.2 6.8 9.3 2.9 .. .. West Bank and Gaza 637 2,593 212 659 13.3 .. 47.4 .. 18.0 .. .. .. Yemen, Rep. 263 305 14 13 3.0 1.3 14.3 .. 6.2 2.2 .. .. Zambia 795 1,086 76 86 25.8 8.4 140.8 34.1 53.1 15.8 .. .. Zimbabwe 176 611 14 49 2.5 .. 17.5 .. .. .. .. .. World 49,791 s 128,609 s 8w 19 w 0.2 w 0.2 w 0.7 w .. w 0.5 w 0.6 w .. w .. w Low income 15,018 41,380 18 42 6.5 7.4 30.3 27.3 18.4 13.6 .. .. Middle income 22,711 50,070 5 11 0.4 0.3 1.6 1.0 1.3 0.9 .. .. Lower middle income 16,814 37,094 5 10 0.6 0.4 2.2 1.2 2.1 1.3 .. .. Upper middle income 5,182 11,825 6 12 0.2 0.1 0.8 0.6 0.5 0.4 .. .. Low & middle income 49,488 128,113 10 23 0.8 0.7 3.4 2.4 2.7 2.1 .. .. East Asia & Pacific 8,563 9,118 5 5 0.5 0.2 1.6 0.4 1.4 0.4 .. .. Europe & Central Asia 4,462 8,241 10 19 0.5 0.2 2.4 0.8 1.4 0.5 .. .. Latin America & Carib. 4,838 9,299 9 16 0.2 0.2 1.2 1.0 0.9 0.8 .. .. Middle East & N. Africa 4,472 23,624 16 73 1.0 1.9 4.0 .. 3.3 6.0 .. .. South Asia 4,199 12,318 3 8 0.7 0.8 3.0 2.3 3.6 2.5 .. .. Sub-Saharan Africa 13,245 40,090 20 49 4.1 4.3 23.1 21.9 11.0 9.2 .. .. High income 304 496 0 0 0.0 0.0 0.0 .. 0.0 0.0 .. .. Euro area .. .. .. .. .. .. .. .. .. .. .. .. Note: Regional aggregates include data for economies not listed in the table. World and income group totals include aid not allocated by country or region--including administrative costs, research on development issues, and aid to nongovernmental organizations. Thus regional and income group totals do not sum to the world total. a. Includes Montenegro. 408 2010 World Development Indicators 6.16 GLOBAL LINKS Aid dependency About the data Unless otherwise noted, aid includes official devel- provide measures of recipient country dependency on The nominal values used here may overstate the opment assistance (ODA; see About the data for aid. But care must be taken in drawing policy conclu- real value of aid to recipients. Changes in interna- table 6.14). The data cover loans and grants from sions. For foreign policy reasons some countries have tional prices and exchange rates can reduce the pur- Development Assistance Committee (DAC) member traditionally received large amounts of aid. Thus aid chasing power of aid. Tying aid, still prevalent though countries, multilateral organizations, and non-DAC dependency ratios may reveal as much about a donor's declining in importance, also tends to reduce its pur- donors. They do not reflect aid given by recipient interests as about a recipient's needs. Ratios are gen- chasing power (see About the data for table 6.15). countries to other developing countries. As a result, erally much higher in Sub-Saharan Africa than in other The aggregates refer to World Bank definitions. some countries that are net donors (such as Saudi regions, and they increased in the 1980s. High ratios Therefore the ratios shown may differ from those Arabia) are shown in the table as aid recipients (see are due only in part to aid flows. Many African countries of the Organisation for Economic Co-operation and table 6.16a). Aid given before 2005 to countries that saw severe erosion in their terms of trade in the 1980s, Development (OECD). were Part II recipients (see About the data for table which, along with weak policies, contributed to falling Definitions 6.14 for more information) is defined as official aid. incomes, imports, and investment. Thus the increase The table does not distinguish types of aid (pro- in aid dependency ratios reflects events affecting both · Net official development assistance is flows (net of gram, project, or food aid; emergency assistance; the numerator (aid) and the denominator (GNI). repayment of principal) that meet the DAC definition postconflict peacekeeping assistance; or technical Because the table relies on information from of ODA and are made to countries and territories on cooperation), which may have different effects on the donors, it is not necessarily consistent with infor- the DAC list of aid recipients. See About the data for economy. Expenditures on technical cooperation do mation recorded by recipients in the balance of pay- table 6.14. · Net official development assistance not always directly benefit the economy to the extent ments, which often excludes all or some technical per capita is net ODA divided by midyear population. that they defray costs incurred outside the country on assistance--particularly payments to expatriates · Aid dependency ratios are calculated using values salaries and benefits of technical experts and over- made directly by the donor. Similarly, grant commod- in U.S. dollars converted at official exchange rates. head costs of firms supplying technical services. ity aid may not always be recorded in trade data or in Imports of goods, services, and income refer to inter- Ratios of aid to gross national income (GNI), gross the balance of payments. Moreover, DAC statistics national transactions involving a change in ownership capital formation, imports, and government spending exclude purely military aid. of general merchandise, goods sent for processing and repairs, nonmonetary gold, services, receipts Official development assistance from non-DAC donors, 2004­08 6.16a of employee compensation for nonresident workers, and investment income. For definitions of GNI, gross Net disbursements ($ millions) capital formation, and central government expense, 2004 2005 2006 2007 2008 see Definitions for tables 1.1, 4.8, and 4.10. OECD members (non-DAC) Czech Republic 108 135 161 179 249 Hungary 70 100 149 103 107 Iceland 21 27 41 48 48 Korea, Rep.a 423 752 455 696 802 Poland 118 205 297 363 372 Slovak Republic 28 56 55 67 92 Turkey 339 601 714 602 780 Arab countries Kuwait 161 218 158 110 283 Saudi Arabia 1,734 1,005 2,095 2,079 5,564 Data sources United Arab Emirates 181 141 219 429 88 Data on financial flows are compiled by OECD DAC Other donors and published in its annual statistical report, Geo- Israelb 84 95 90 111 138 graphical Distribution of Financial Flows to Devel- Taiwan, China 421 483 513 514 435 oping Countries, and in its annual Development Thailand .. .. 74 67 178 Co- operation Report. Data are available electroni- Others 22 86 121 188 343 cally on the OECD DAC's International Development Total 3,712 3,905 5,142 5,558 9,481 Statistics CD-ROM and at www.oecd.org/dac/ Note: The table does not reflect aid provided by several major emerging non­Organisation for Economic Co-operation stats/idsonline. Data on population, GNI, gross and Development donors because information on their aid has not been disclosed. capital formation, imports of goods and services, a. The Republic of Korea became a DAC member in November 2009. Its disbursements will be reflected in DAC data beginning with 2010 flows. b. Includes $47.9 million in 2004, $49.2 million in 2005, $45.5 million in 2006, $42.9 million in and central government expense used in comput- 2007, and $43.0 million in 2008 for first-year sustenance expenses for people arriving from developing countries (many of ing the ratios are from World Bank and Interna- which are experiencing civil war or severe unrest) or people who have left their country for humanitarian or political reasons. Source: Organisation for Economic Co-operation and Development. tional Monetary Fund databases. 2010 World Development Indicators 409 6.17 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Germany Commission Kingdom France Japan Netherlands Spain Sweden Canada $ millions 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Afghanistan 4,300.1 2,111.6 294.0 349.3 322.3 19.9 208.0 112.0 71.8 73.9 207.9 529.4 Albania 352.2 35.9 44.7 84.6 2.8 4.4 ­2.5 18.3 16.9 11.3 0.0 135.8 Algeria 325.9 9.1 12.5 84.7 2.1 121.8 4.0 0.0 64.2 2.1 2.9 22.6 Angola 233.3 42.7 11.7 49.4 9.6 2.9 17.8 ­2.7 13.6 5.0 0.4 83.1 Argentina 103.8 7.2 22.1 16.6 1.0 12.8 5.9 0.3 29.7 0.2 1.9 6.3 Armenia 225.1 93.8 27.9 16.3 6.6 5.5 57.7 0.2 0.7 2.7 0.3 13.4 Australia Austria Azerbaijan 129.2 42.0 26.4 13.0 1.9 28.2 ­2.8 0.0 0.4 1.0 0.2 19.0 Bangladesh 1,009.0 93.2 65.9 194.5 252.5 ­3.7 41.1 84.7 9.4 38.1 82.1 151.1 Belarus 75.3 8.9 21.3 17.4 1.1 1.5 0.4 0.0 0.1 14.8 0.0 9.6 Belgium Benin 429.6 34.6 46.6 127.1 0.0 66.4 27.2 35.3 2.0 0.5 7.0 83.0 Bolivia 539.1 123.8 52.7 43.8 1.0 13.9 35.5 41.4 93.0 27.6 21.5 84.9 Bosnia and Herzegovina 426.7 26.4 46.9 105.2 9.3 6.3 10.6 31.3 42.4 28.7 5.0 114.6 Botswana 709.4 231.9 439.0 26.7 1.1 2.4 ­2.1 0.0 0.0 4.3 1.4 4.8 Brazil 427.0 12.3 126.7 48.6 13.5 41.0 93.3 0.5 36.8 3.2 11.4 39.7 Bulgaria Burkina Faso 620.6 19.4 44.9 145.5 0.2 142.0 21.0 88.9 2.9 23.0 29.9 102.9 Burundi 339.7 30.2 23.1 84.6 14.2 17.4 23.3 32.3 1.9 7.0 4.2 101.4 Cambodia 462.5 69.8 33.8 37.5 30.4 35.2 114.8 1.9 11.8 16.1 11.5 99.8 Cameroon 357.6 16.1 110.0 59.9 2.9 113.2 15.6 0.6 12.6 0.7 11.9 14.0 Canada Central African Republic 170.4 34.2 6.8 41.9 5.7 26.4 12.2 2.9 2.2 6.4 2.9 29.1 Chad 423.0 80.7 32.6 145.6 11.5 39.5 14.4 7.5 10.9 10.1 6.3 64.0 Chile 58.3 1.0 20.1 6.5 0.5 9.1 6.6 0.2 7.1 0.6 2.5 4.2 China 1,477.7 65.2 411.9 124.7 174.9 207.5 278.3 16.7 43.0 14.8 54.3 86.3 Hong Kong SAR, China Colombia 955.2 636.1 42.1 57.0 3.3 22.7 ­6.9 32.6 85.0 26.3 14.4 42.8 Congo, Dem. Rep. 1,168.7 196.6 61.2 224.3 192.9 30.5 51.2 47.8 0.7 68.0 22.9 272.7 Congo, Rep. 449.3 0.3 ­0.3 28.5 0.0 368.0 10.6 0.0 39.3 1.6 0.8 0.6 Costa Rica 66.9 ­0.1 29.7 5.9 ­0.2 6.6 ­1.2 4.7 15.5 1.0 2.9 2.1 Côte d'Ivoire 337.2 88.8 17.5 144.3 0.3 39.5 19.5 0.1 5.2 0.0 3.4 18.6 Croatia 387.3 7.4 21.2 337.0 1.4 4.3 0.0 0.1 0.9 2.7 0.2 12.0 Cuba 94.4 12.0 2.6 2.6 0.2 3.0 4.0 0.1 45.8 0.9 8.3 14.9 Czech Republic Denmark Dominican Republic 136.6 24.8 8.1 57.7 1.5 9.9 1.6 0.0 32.1 0.7 1.6 ­1.4 Ecuador 232.8 46.4 24.7 40.4 ­0.6 ­0.8 ­5.7 3.3 87.9 0.7 4.5 32.1 Egypt, Arab Rep. 1,167.5 470.8 170.3 207.7 8.8 142.0 11.6 19.7 15.6 2.2 14.5 104.4 El Salvador 232.3 42.4 13.4 28.4 0.0 3.4 30.6 0.3 83.6 3.6 3.6 23.0 Eritrea 69.4 3.4 1.3 16.9 5.6 0.8 17.7 3.9 1.8 1.9 0.1 16.1 Estonia Ethiopia 2,299.8 811.4 98.3 460.8 253.7 18.7 47.1 113.6 60.5 46.9 152.6 236.2 Finland France Gabon 44.3 0.5 ­3.0 6.7 0.0 37.4 1.8 0.0 0.5 0.0 0.7 ­0.2 Gambia, The 37.7 12.0 0.8 9.9 3.8 0.5 1.1 3.9 2.2 0.9 0.8 1.9 Georgia 691.9 402.1 70.7 113.4 12.8 5.4 2.4 8.9 2.7 27.3 3.8 42.5 Germany Ghana 839.1 79.5 71.7 115.9 150.8 43.0 54.0 120.2 16.1 1.3 74.0 112.5 Greece Guatemala 504.7 70.4 18.8 39.1 0.7 2.6 10.6 27.4 255.9 27.3 15.0 37.0 Guinea 243.9 43.3 23.7 35.0 1.2 73.0 16.9 0.0 2.9 0.7 6.5 40.7 Guinea-Bissau 101.0 0.7 0.6 48.4 0.1 5.6 5.8 0.0 16.4 0.1 0.5 22.9 Haiti 673.3 259.1 5.7 117.2 0.0 38.4 11.7 4.6 45.5 9.1 147.6 34.5 Honduras 369.7 96.3 32.2 23.8 0.0 1.4 40.9 1.2 117.6 17.5 14.2 24.8 410 2010 World Development Indicators 6.17 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Germany Commission Kingdom France Japan Netherlands Spain Sweden Canada $ millions 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Hungary India 1,669.2 52.1 147.7 122.3 613.1 ­27.2 599.8 4.9 14.0 12.3 13.3 116.8 Indonesia 628.9 115.1 29.7 54.5 100.7 103.5 ­284.9 75.5 9.5 12.2 82.4 330.7 Iran, Islamic Rep. 64.8 2.7 42.7 1.8 1.7 15.6 ­16.7 4.5 0.7 0.0 0.2 11.6 Iraq 9,780.9 2,742.0 1,854.3 38.0 639.0 315.0 1,755.2 81.8 129.0 26.7 142.0 2,057.8 Ireland Israel Italy Jamaica 68.8 ­0.9 ­7.8 74.3 6.0 ­0.8 ­3.7 ­5.3 2.0 0.1 3.4 1.5 Japan Jordan 539.0 384.1 21.7 122.5 4.5 3.3 ­50.1 0.3 12.7 0.1 7.7 32.3 Kazakhstan 245.8 157.6 18.4 16.2 5.4 3.4 37.9 0.0 0.1 0.3 0.2 6.3 Kenya 1,042.7 439.4 85.3 91.3 91.4 55.7 8.8 15.9 38.6 65.9 26.2 124.3 Korea, Dem. Rep. 207.8 152.4 5.5 18.2 0.3 1.2 0.0 1.0 0.4 5.8 2.4 20.6 Korea, Rep. Kosovo Kuwait Kyrgyz Republic 175.2 63.6 21.3 33.4 13.7 1.0 12.4 0.1 3.3 8.7 0.3 17.4 Lao PDR 232.4 3.2 28.8 18.7 0.3 25.4 66.3 0.0 0.2 20.8 1.7 67.0 Latvia Lebanon 865.7 209.6 36.4 121.6 1.0 305.8 13.8 2.5 51.8 1.8 16.2 105.1 Lesotho 91.5 13.7 7.4 25.6 7.9 ­1.8 13.2 0.0 1.5 0.2 0.7 23.3 Liberia 858.4 276.0 316.6 49.5 32.4 26.8 14.0 20.0 24.3 26.3 2.0 70.6 Libya 56.0 14.3 3.4 4.3 1.1 29.0 0.2 0.0 0.0 0.0 0.0 3.7 Lithuania Macedonia, FYR 202.6 32.1 24.8 61.6 2.1 3.8 21.4 20.2 4.1 11.3 0.0 21.3 Madagascar 414.0 83.9 17.7 139.9 2.4 88.4 20.4 4.2 14.6 0.6 3.1 39.0 Malawi 562.2 87.7 29.6 130.5 146.9 0.9 30.8 0.1 2.9 14.5 16.3 102.1 Malaysia 152.4 5.6 10.8 0.3 18.9 ­9.2 117.5 0.0 0.0 0.4 0.3 7.8 Mali 680.1 53.3 39.4 149.0 0.0 81.9 34.5 79.6 31.2 29.3 99.1 82.9 Mauritania 179.5 25.6 17.4 40.4 0.0 29.4 14.5 0.2 34.1 1.1 1.5 15.2 Mauritius 111.1 0.2 0.8 95.0 0.6 15.8 0.4 0.0 0.0 0.0 0.3 ­2.0 Mexico 126.1 102.5 39.2 21.7 6.8 10.8 ­54.7 ­0.3 ­15.1 0.0 5.9 9.4 Moldova 197.2 35.9 10.7 83.1 6.1 7.3 9.6 6.9 1.8 13.5 0.2 22.1 Mongolia 171.6 35.4 32.4 10.6 1.2 0.9 60.7 6.6 4.1 1.2 1.8 16.9 Morocco 1,095.5 5.7 90.6 483.8 6.7 163.2 105.8 0.6 117.4 0.0 10.3 111.5 Mozambique 1,501.7 226.7 74.9 161.4 197.9 12.3 23.7 105.7 78.5 119.6 77.2 423.8 Myanmar 475.9 71.6 14.3 58.4 82.4 5.8 42.5 15.8 9.2 21.6 22.3 131.9 Namibia 173.2 71.0 22.2 23.3 1.0 1.9 9.7 0.3 15.1 2.3 1.1 25.5 Nepal 497.2 77.7 62.3 46.2 98.6 ­2.8 33.9 2.4 2.9 2.5 9.4 164.2 Netherlands New Zealand Nicaragua 566.1 103.5 26.1 34.8 10.7 1.4 43.8 37.0 125.4 33.5 16.8 133.3 Niger 420.6 45.9 21.0 151.5 7.4 67.8 16.9 0.1 24.0 1.6 15.3 69.2 Nigeria 727.5 363.9 27.5 91.1 47.2 11.9 29.0 1.7 25.4 1.0 26.7 102.2 Norway Oman 3.8 1.4 0.5 0.0 0.6 0.7 0.5 0.1 0.0 0.0 0.0 0.0 Pakistan 979.4 350.6 89.0 62.7 260.3 9.4 34.2 31.1 3.3 9.0 41.6 88.2 Panama 30.2 13.7 1.2 2.8 0.0 0.2 4.1 0.0 7.4 0.2 0.6 ­0.1 Papua New Guinea 288.8 2.0 0.1 25.4 1.1 ­0.1 ­82.6 0.0 0.0 0.2 0.7 342.0 Paraguay 115.2 29.7 7.0 17.5 0.1 0.9 30.9 0.0 23.0 1.9 1.0 3.3 Peru 437.1 94.0 93.6 52.4 ­11.6 9.3 ­17.9 ­1.4 131.5 3.0 15.6 68.6 Philippines 32.2 71.3 31.6 58.2 1.3 ­5.6 ­284.4 1.2 35.3 6.2 15.5 101.6 Poland Portugal Puerto Rico Qatar 2010 World Development Indicators 411 6.17 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Germany Commission Kingdom France Japan Netherlands Spain Sweden Canada $ millions 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 Romania Russian Federation Rwanda 553.4 117.4 24.5 103.2 99.9 4.0 17.8 38.8 24.9 14.4 14.3 94.4 Saudi Arabia Senegal 678.8 71.6 27.8 134.7 1.0 189.0 25.1 37.9 59.1 0.3 73.3 59.0 Serbia 932.8 94.5 107.8 392.6 12.1 17.8 5.9 6.1 16.3 39.4 4.5 235.9 Sierra Leone 219.0 15.8 14.9 44.2 92.6 1.3 14.1 0.0 3.4 2.3 4.2 26.4 Singapore Slovak Republic Slovenia Somalia 704.7 242.7 10.1 139.3 76.1 8.5 23.3 18.7 14.6 25.0 21.8 124.7 South Africa 1,045.3 378.7 150.1 164.0 113.5 32.9 3.7 36.3 0.6 12.6 14.7 138.3 Spain Sri Lanka 497.4 51.8 11.1 111.6 1.8 19.6 96.7 19.8 26.0 15.5 42.1 101.5 Sudan 2,096.1 848.2 47.3 277.8 199.2 11.2 109.6 157.6 37.7 65.0 83.9 258.6 Swaziland 28.8 8.7 ­1.0 11.2 2.5 0.3 3.2 0.0 0.0 0.0 0.6 3.3 Sweden Switzerland Syrian Arab Republic 106.2 16.2 27.0 51.6 2.3 26.7 ­56.7 2.9 7.5 0.0 1.9 26.8 Tajikistan 174.3 59.9 22.2 31.1 7.7 5.9 8.1 0.8 2.5 12.5 5.7 17.9 Tanzania 1,551.6 247.0 87.4 185.9 254.2 4.8 71.0 114.9 3.4 125.5 44.7 412.9 Thailand ­673.4 39.6 ­19.2 27.2 2.2 ­2.9 ­748.5 1.1 0.1 7.7 1.2 18.1 Timor-Leste 255.4 32.7 6.7 27.4 0.2 0.5 26.5 0.0 14.0 6.0 1.8 139.7 Togo 215.0 3.0 8.4 39.0 9.0 127.7 0.3 13.2 3.5 0.8 2.6 7.5 Trinidad and Tobago 11.6 0.3 0.3 7.7 0.9 1.7 0.0 0.0 0.0 0.0 0.7 0.1 Tunisia 478.6 ­8.1 27.4 230.2 1.5 160.5 54.0 ­1.3 16.2 0.1 2.5 ­4.4 Turkey 1,991.4 ­5.4 ­50.1 1,342.5 4.6 293.8 285.9 ­0.6 92.1 7.3 ­2.3 23.7 Turkmenistan 2.4 ­3.4 1.8 3.5 0.4 0.3 ­1.5 0.0 0.0 0.0 0.0 1.2 Uganda 1,280.1 352.9 37.8 275.1 65.7 17.4 57.0 82.9 11.5 64.1 21.2 294.6 Ukraine 527.2 98.9 77.1 242.3 3.2 25.0 8.4 0.0 0.3 21.5 18.7 31.7 United Arab Emirates United Kingdom United States Uruguay 24.2 1.0 ­0.5 11.3 0.1 1.4 1.0 0.0 9.4 0.2 1.1 ­0.7 Uzbekistan 119.9 18.0 29.5 10.6 1.0 3.0 48.6 0.0 0.4 0.1 0.0 8.8 Venezuela, RB 53.4 9.6 8.2 6.8 0.5 6.6 2.8 0.2 15.5 0.1 0.6 2.6 Vietnam 1,664.2 62.6 115.0 68.0 125.9 165.6 619.0 40.6 49.5 37.6 37.6 342.8 West Bank and Gaza 2,043.6 490.6 77.4 661.3 102.6 74.2 30.3 75.1 103.2 71.8 44.3 312.9 Yemen, Rep. 224.4 25.5 67.2 17.7 33.2 4.7 12.0 37.8 1.9 1.2 1.3 22.0 Zambia 812.8 226.5 45.5 109.7 61.6 1.2 37.1 85.1 1.0 51.5 14.3 179.5 Zimbabwe 592.2 222.9 24.9 62.0 89.2 7.4 10.0 29.8 4.8 25.7 21.1 94.5 World 100,882.5 s 23,859.6 s 9,062.7 s 14,427.7 s 7,366.8 s 6,461.3 s 6,823.3 s 5,199.6 s 4,801.6 s 3,142.3 s 3,356.7 s 16,381.0 s Low income 29,256.8 7,152.3 2,060.2 4,472.9 2,835.3 1,507.0 1,929.7 1,405.5 703.2 983.2 1,295.7 4,911.8 Middle income 42,807.3 9,982.7 5,122.7 7,431.6 2,554.4 3,615.3 2,700.6 916.8 2,278.5 623.5 872.6 6,708.7 Lower middle income 30,905.9 7,734.7 3,708.6 4,054.3 2,301.8 2,055.2 2,115.1 663.3 1,517.1 449.8 713.3 5,592.8 Upper middle income 10,837.8 2,184.5 1,242.5 2,938.9 229.9 1,482.4 584.9 195.1 690.1 165.9 118.4 1,005.2 Low & middle income 100,440.2 23,850.3 9,040.7 14,069.3 7,363.8 6,450.3 6,822.0 5,199.3 4,786.7 3,134.1 3,355.3 16,368.5 East Asia & Pacific 6,695.7 943.2 725.9 599.6 545.0 691.8 86.7 171.7 189.3 165.7 280.1 2,296.7 Europe & Central Asia 6,867.0 1,353.2 547.6 2,636.0 91.5 426.0 505.4 94.4 185.2 205.9 37.2 784.7 Latin America & Carib. 8,059.3 1,870.7 818.1 1,102.2 167.0 211.4 268.7 230.2 1,975.6 200.4 480.6 734.5 Middle East & N. Africa 17,614.1 4,701.8 2,499.9 2,174.0 843.2 1,449.9 1,874.7 224.6 596.3 129.6 272.1 2,848.1 South Asia 9,186.2 2,813.1 676.3 912.8 1,552.7 16.0 1,044.6 297.5 128.0 156.1 400.7 1,188.4 Sub-Saharan Africa 29,656.6 6,691.1 2,353.5 4,849.5 2,507.0 2,681.5 1,391.9 1,496.6 846.6 1,015.6 1,302.5 4,520.9 High income 442.4 9.3 22.0 358.4 3.1 11.0 1.3 0.3 14.9 8.3 1.3 12.5 Euro area .. .. .. .. .. .. .. .. .. .. .. .. Note: Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. 412 2010 World Development Indicators 6.17 GLOBAL LINKS Distribution of net aid by Development Assistance Committee members About the data Definitions The table shows net bilateral aid to low- and middle- and payment of experts hired by donor countries. · Net aid refers to net bilateral official development income economies from members of the Develop- Moreover, a full accounting would include donor assistance that meets the DAC definition of official ment Assistance Committee (DAC) of the Organisa- country contributions to multilateral institutions, development assistance and is made to countries tion for Economic Co-operation and Development the flow of resources from multilateral institutions and territories on the DAC list of aid recipients. See (OECD). The data include aid to some countries and to recipient countries, and fl ows from countries About the data for table 6.14. · Other DAC donors territories not shown in the table and aid to unspeci- that are not members of DAC. Previous editions of are Australia, Austria, Belgium, Denmark, Finland, fied economies recorded only at the regional or global the table included only DAC member economies. Greece, Ireland, Italy, Luxembourg, New Zealand, level. Aid to countries and territories not shown in the The table also includes net aid from the European Norway, Portugal, and Switzerland. table has been assigned to regional totals based Commission--a multilateral member of DAC. on the World Bank's regional classification system. The expenditures that countries report as official Aid to unspecified economies is included in regional development assistance (ODA) have changed. For totals and, when possible, income group totals. Aid example, some DAC members have reported as not allocated by country or region--including admin- ODA the aid provided to refugees during the first 12 istrative costs, research on development, and aid to months of their stay within the donor's borders. nongovernmental organizations--is included in the Some of the aid recipients shown in the table are world total. Thus regional and income group totals also aid donors. See table 6.16a for a summary of do not sum to the world total. ODA from non-DAC countries. The table is based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development- oriented research, stipends and tuition costs for aid-financed students in donor countries, Destination of aid varies by donor 6.17a Share of bilateral official development assistance East Asia & Pacific Europe & Central Asia Latin America & Caribbean net disbursements, 2008 Middle East & North Africa South Asia Sub-Saharan Africa United States Germany European Commission 5% 9% 5% 7% 7% 31% 21% 10% 37% 40% 11% 9% 26% 9% 15% 33% 7% 18% United Kingdom France Japan 2% 2% 9% 3% 10% 13% 27% 5% 8% 44% 15% 4% 49% Data sources 36% 26% 20% 27% Data on financial flows are compiled by OECD DAC and published in its annual statistical report, Geo- 0% graphical Distribution of Financial Flows to Devel- In 2008 Sub-Saharan Africa received 38 percent of total bilateral net official development assistance oping Countries, and its annual Development disbursements from Development Assistance Committee (DAC) donors, and the Middle East and North Co-operation Report. Data are available electroni- Africa received 22 percent. However, destinations of aid vary by donor. cally on the OECD DAC's International Develop- Note: Data are the distribution of bilateral aid from the top six DAC donors in 2008 and exclude aid to high-income ment Statistics CD-ROM and at www.oecd.org/ economies (less than 1 percent of bilateral aid) and aid unallocated by region. Source: Organisation for Economic Co-operation and Development, Development Assistance Committee. dac/stats/idsonline. 2010 World Development Indicators 413 6.18 Movement of people Net migration International Refugees Workers' remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1995 2005 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan 3,266 805 70 86 2,679.1 2,833.1 19.6 0.0 .. .. .. .. Albania ­423 ­100 71 83 5.8 15.0 4.7 0.1 427 1,495 .. 16 Algeria ­50 ­140 299 242 1.5 9.1 192.5 94.1 1,120a 2,202a .. .. Angola 143 175 38 56 246.7 171.4 10.9 12.7 5 82 210 669 Argentina 120 ­100 1,588 1,494 0.3 1.0 10.3 2.8 64 694 195 596 Armenia ­500 ­100 682 493 201.4 16.3 219.0 4.0 65 1,062 17 185 Australia 371 641 3,854 4,336 0.0 0.0 62.1 20.9 1,651 4,713 700 3,049 Austria 234 220 989 1,156 0.0 0.0 34.4 37.6 1,012 3,239 346 3,446 Azerbaijan ­116 ­100 525 255 200.5 16.3 233.7 2.1 3 1,554 9 593 Bangladesh ­500 ­700 1,006 1,032 57.0 10.1 51.1 28.4 1,202 8,995 1 15 Belarus 0 20 1,185 1,107 0.1 5.4 29.0 0.6 29 443 12 141 Belgium 85 196 916 882 0.0 0.1 31.7 17.0 4,937 10,425 3,252 4,240 Benin 105 99 146 188 0.1 0.3 23.8 6.9 100 271a 26 115a Bolivia ­100 ­100 70 114 0.2 0.5 0.7 0.7 7 1,144 9 106 Bosnia and Herzegovina ­1,025 62 73 35 769.8 74.4 40.0 7.3 .. 2,735 .. 70 Botswana 14 20 39 80 0.0 0.0 0.3 3.0 59 114 200 145 Brazil ­184 ­229 731 686 0.1 1.4 2.1 3.9 3,315 5,089 347 1,191 Bulgaria ­349 ­41 47 104 4.2 3.0 1.3 5.1 42 2,634 34 74 Burkina Faso ­128 100 464 773 0.1 0.7 29.8 0.6 80a 50a 51 44 a Burundi ­250 192 295 82 350.6 281.6 173.0 21.1 .. 4 5 0 Cambodia 150 10 116 304 61.2 17.3 0.0 0.2 12 325 52 187 Cameroon ­5 ­12 246 212 2.0 13.9 45.8 81.0 11 145 22 42 Canada 643 1,089 5,047 6,304 0.0 0.1 152.1 173.7 .. .. .. .. Central African Republic 37 ­45 67 76 0.2 125.1 33.9 7.4 0 .. 27 .. Chad ­10 219 78 358 59.7 55.1 0.1 330.5 1 .. 15 .. Chile 90 30 136 231 14.3 1.0 0.3 1.6 .. 3 7a 6 China ­829b ­2,058b 437b 590 b 124.7c 195.3c 288.3 301.0 878a 48,524 a 19 5,737 Hong Kong SAR, China 300 113 2,431 2,721 0.2 0.0 1.5 0.1 .. 355 .. 393 Colombia ­250 ­120 109 110 1.9 373.5 0.2 0.2 815 4,884 150 88 Congo, Dem. Rep. 1,208 ­237 1,919 480 89.7 368.0 1,433.8 155.2 .. .. .. .. Congo, Rep. ­14 4 131 129 0.2 19.9 19.4 24.8 4 15a 27 102a Costa Rica 62 84 228 443 0.2 0.4 24.2 18.1 123 605 36 269 Côte d'Ivoire 375 ­339 1,985 2,371 0.2 22.2 297.9 24.8 151 195 457 21 Croatia 153 ­13 721 661 245.6 97.0 198.6 1.6 544 1,602 16 116 Cuba ­120 ­163 25 15 24.9 7.9 1.8 0.5 .. .. .. .. Czech Republic 8 67 454 453 2.0 1.4 2.7 2.1 191 1,415 101 3,826 Denmark 58 46 297 421 0.0 0.0 64.8 23.4 523 890 209 3,222 Dominican Republic ­129 ­148 322 393 0.0 0.3 1.0 .. 839 3,556 7 29 Ecuador ­50 ­400 88 124 0.2 1.1 0.2 101.4 386 2,828 4 66 Egypt, Arab Rep. ­498 ­291 174 247 0.9 6.8 5.4 97.9 3,226 8,694 223 241 El Salvador ­249 ­340 28 36 23.5 5.2 0.2 0.0 1,064 3,804 1 19 Eritrea ­359 229 12 15 286.7 186.4 1.1 4.9 .. .. .. .. Estonia ­108 1 309 202 0.4 0.2 .. 0.0 1 398 3 105 Ethiopia 768 ­340 795 554 101.0 63.9 393.5 83.6 27 387 0 21 Finland 43 33 103 171 0.0 0.0 10.2 6.6 74 828 54 457 France 239 761 6,085 6,479 0.0 0.1 155.2 171.2 4,640 15,908 4,935 6,247 Gabon 20 10 164 245 0.0 0.1 0.8 9.0 4 11a 99 186a Gambia, The 45 31 148 232 0.2 1.4 6.6 14.8 19a 67 .. 3 Georgia ­544 ­309 250 191 0.3 12.6 0.1 1.0 284 732 12 47 Germany 2,649 930 8,992 10,598 0.4 0.2 1,267.9 582.7 4,523 11,064 11,348 14,976 Ghana 40 12 1,038 1,669 13.6 13.2 83.2 18.2 17 126 5 6a Greece 470 154 549 975 0.2 0.1 4.4 2.2 3,286 2,687 300 1,912 Guatemala ­360 ­300 46 53 42.9 5.9 1.5 0.1 358 4,460 8 26 Guinea 350 ­425 814 401 0.4 9.5 672.3 21.5 1 72 10 56 Guinea-Bissau 20 1 32 19 0.8 1.1 15.4 7.9 2 30a 3 ..a Haiti ­133 ­140 22 30 13.9 23.1 .. 0.0 109a 1,410 .. 117 Honduras ­120 ­150 31 26 1.2 1.1 0.1 0.0 124 2,869 8 5 414 2010 World Development Indicators 6.18 GLOBAL LINKS Movement of people Net migration International Refugees Workers' remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1995 2005 1995 2008 1995 2008 1995 2008 1995 2008 Hungary 104 70 293 333 2.3 1.6 11.4 7.8 152 2,631 146 1,562 India ­960 ­1,540 7,022 5,887 5.0 19.6 227.5 184.5 6,223 49,941 419 3,815a Indonesia ­725 ­1,000 219 136 9.8 19.3 0.0 0.4 651 6,794 .. 1,971 Iran, Islamic Rep. ­1,164 ­993 3,016 2,062 112.4 69.1 2,072.0 980.1 1,600a 1,115a .. .. Iraq ­154 ­224 134 128 718.7 1,903.5 116.7 39.5 .. 3a .. 17a Ireland ­1 230 264 618 0.0 0.0 0.4 9.7 347 646 173 2,829 Israel 484 115 1,919 2,661 0.9 1.5 .. 9.1 701 1,422 1,407 3,550 Italy 294 1,750 1,723 3,068 0.1 0.1 74.3 47.1 2,364 3,139 1,824 12,716 Jamaica ­113 ­76 22 27 0.0 0.8 0.0 .. 653 2,180 74 419 Japan 474 82 1,363 1,999 0.0 0.2 5.4 2.0 1,151 1,929 1,820 4,743 Jordan 509 104 1,608 2,345 0.5 1.9 1,288.9d 2,452.0 d 1,441 3,794 107 472 Kazakhstan ­1,509 ­200 3,295 2,974 0.1 4.8 15.6 4.4 116 192 503 3,559 Kenya 222 25 528 790 9.3 9.7 234.7 320.6 298 a 1,692a 4 65 Korea, Dem. Rep. 0 0 35 37 0.0 0.9 .. .. .. .. .. .. Korea, Rep. ­627 ­65 584 551 0.0 1.1 0.0 0.2 1,080 3,062 634 1,973 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait ­598 264 1,090 1,870 0.8 0.9 3.3 38.2 .. .. 1,354 5,559 Kyrgyz Republic ­273 ­75 482 288 0.0 2.5 13.4 0.4 1 1,232 41 196 Lao PDR ­30 ­115 23 20 58.2 8.6 .. .. 22 1a 9 1a Latvia ­134 ­20 527 380 0.2 0.8 .. 0.0 41 601 1 58 Lebanon 230 100 656 721 13.5 13.0 348.0 d 472.6d 1,225a 7,180 .. 4,028 Lesotho ­84 ­36 6 6 0.0 0.0 0.1 .. 411 439 75 13 Liberia ­523 62 199 97 744.6 75.2 120.1 10.2 .. 58 .. 0 Libya 10 14 506 618 0.6 2.1 4.0 6.7 .. 16a 222 964 Lithuania ­99 ­36 272 165 0.1 0.5 0.0 0.8 1 1,460 1 615 Macedonia, FYR ­27 ­10 115 120 12.9 7.5 9.0 1.7 68 407 1 33 Madagascar ­7 ­5 44 40 0.1 0.3 0.1 .. 14 11a 11 21a Malawi ­920 ­30 325 279 0.0 0.1 1.0 4.2 1 1a 1a 1a Malaysia 287 150 1,193 2,029 0.1 0.6 5.3 36.7 716a 1,920a 1,329 6,385 Mali ­260 ­134 174 165 77.2 1.8 17.9 9.6 112 344 a 42 83a Mauritania ­15 30 118 66 84.3 45.6 34.4 27.0 5 2a 14 .. Mauritius ­7 0 18 41 0.0 0.0 .. .. 132 215 1 14 Mexico ­1,364 ­2,702 458 605 0.4 6.2 38.7 1.1 4,368 26,304 .. .. Moldova ­121 ­320 473 440 0.5 5.6 .. 0.1 1 1,897 1 115 Mongolia ­173 17 7 9 0.0 1.3 .. 0.0 .. 200a .. 77a Morocco ­450 ­550 55 51 0.3 3.5 0.1 0.8 1,970 6,895 20 58 Mozambique 650 ­20 246 406 125.6 0.2 0.1 3.2 59 116 21 57 Myanmar ­126 ­1,000 114 93 152.3 184.4 .. .. 81 150a .. 32a Namibia ­13 ­1 118 132 0.0 1.0 1.7 6.8 16 14 11 43 Nepal ­101 ­100 625 819 0.0 4.2 124.8 124.8 57 2,727 9 5 Netherlands 191 110 1,387 1,735 0.1 0.0 80.0 77.6 1,359 3,299 2,802 8,280 New Zealand 143 103 594 858 .. 0.0 3.8 2.7 1,652 626 427 1,202 Nicaragua ­114 ­206 27 35 23.9 1.5 0.6 0.1 75 818 .. .. Niger ­3 ­28 171 183 10.3 0.8 27.6 0.3 8 79a 29 18a Nigeria ­96 ­170 582 972 1.9 14.2 8.1 10.1 804 a 9,980a 5 103a Norway 42 84 237 371 0.0 0.0 47.6 36.1 239 685 603 4,776 Oman 23 ­50 582 666 0.0 0.1 .. 0.0 39 39 1,537 5,181 Pakistan ­2,611 ­1,239 4,077 3,554 5.3 32.4 1,202.5 1,780.9 1,712 7,039 4 2a Panama 8 8 73 102 0.2 0.1 0.9 16.9 112 196 20 198 Papua New Guinea 0 0 31 25 2.0 0.0 9.6 10.0 16 13a 16 135a Paraguay ­30 ­45 183 168 0.1 0.1 0.1 0.1 287 503 .. .. Peru ­300 ­525 51 42 5.9 7.3 0.6 1.1 599 2,437 34 133 Philippines ­900 ­900 210 375 0.5 1.4 0.8 0.1 5,360 18,643 151 44 Poland ­77 ­200 964 825 19.7 2.4 0.6 12.8 724 10,447 262 1,717 Portugal 0 291 528 764 0.0 0.0 0.2 0.4 3,953 4,057 527 1,410 Puerto Rico ­4 ­27 339 352 0.0 .. .. .. .. .. .. .. Qatar 14 219 406 713 0.0 0.1 .. 0.0 .. .. .. .. 2010 World Development Indicators 415 6.18 Movement of people Net migration International Refugees Workers' remittances and migrant stock compensation of employees thousands $ millions thousands thousands By country of origin By country of asylum Received Paid 1990­95 2000­05 1995 2005 1995 2008 1995 2008 1995 2008 1995 2008 Romania ­529 ­270 135 133 17.0 4.8 0.2 1.6 9 9,381 2 664 Russian Federation 2,220 964 11,707 12,080 207.0 103.1 246.7 3.5 2,502 6,033 3,938 26,145 Rwanda ­1,681 6 337 436 1,819.4 72.5 7.8 55.1 21 68 1 70 Saudi Arabia ­500 285 4,611 6,337 0.3 0.7 13.2 240.6 .. 216 16,594 21,696 Senegal ­100 ­100 291 220 17.6 16.0 66.8 33.2 146 1,288a 76 143a Serbia 451 ­339 874 675 86.1e 185.9 650.7e 96.7 1,295a,e 5,538a,e .. 138 Sierra Leone ­450 336 101 152 379.5 32.5 4.7 7.8 24 150a .. 3 Singapore 250 139 992 1,494 0.0 0.1 0.1 0.0 .. .. .. .. Slovak Republic ­3 10 114 124 0.0 0.3 2.3 0.3 26 1,973 3 144 Slovenia 38 23 200 167 12.9 0.1 22.3 0.3 272 343 31 353 Somalia ­893 ­200 19 21 638.7 561.2 0.6 1.8 .. .. .. .. South Africa 900 700 1,098 1,249 0.5 0.5 101.4 43.5 105 823 629 1,133 Spain 324 2,504 1,041 4,608 0.0 0.0 5.9 4.7 3,237 11,776 868 14,659 Sri Lanka ­256 ­442 426 366 107.6 137.8 0.0 0.3 809 2,947 16 385 Sudan ­168 ­532 1,111 640 445.3 419.2 674.1 181.6 346 3,100 1 2a Swaziland ­38 ­46 35 39 0.0 0.0 0.7 0.8 83 100a 4 8a Sweden 151 186 906 1,113 0.0 0.0 199.2 77.0 288 822 336 854 Switzerland 227 200 1,471 1,660 0.0 0.0 82.9 46.1 1,473 2,200 10,114 19,022 Syrian Arab Republic ­70 300 817 1,326 8.0 15.2 373.5d 1,567.6d 339 850a 15 252a Tajikistan ­296 ­345 305 306 59.0 0.5 0.6 1.8 .. 2,544 .. 199 Tanzania 591 ­345 1,134 798 0.1 1.3 829.7 321.9 1 19 1 54 Thailand ­39 1,411 549 982 0.2 1.8 106.6 112.9 1,695 1,898 .. .. Timor-Leste 0 41 10 12 .. 0.0 .. 0.0 .. .. .. .. Togo ­122 ­4 169 183 93.2 16.8 10.9 9.4 15 284 a 47a Trinidad and Tobago ­24 ­20 46 38 0.0 0.2 .. 0.0 32 109a 14 .. Tunisia ­43 ­81 38 35 0.3 2.3 0.2 0.1 680 1,977 36 16 Turkey ­70 ­71 1,212 1,334 44.9 214.4 12.8 11.1 3,327 1,360 .. 111 Turkmenistan 50 ­25 260 224 0.0 0.7 23.3 0.1 4 .. 7 .. Uganda 120 ­5 661 652 24.2 7.5 229.4 162.1 .. 724 .. 364 Ukraine 100 ­173 6,172 5,391 1.7 28.4 5.2 7.2 6 5,769 1 54 United Arab Emirates 340 577 1,716 2,863 0.0 0.3 0.4 0.2 .. .. .. .. United Kingdom 167 948 4,191 5,838 0.1 0.2 90.9 292.1 2,469 7,861 2,581 4,633 United States 6,565 5,676 28,522 39,266 0.2 2.1 623.3 279.5 2,179 3,045 22,181 48,187 Uruguay ­20 ­104 93 84 0.3 0.2 0.1 0.1 .. 108 .. 5 Uzbekistan ­340 ­400 1,474 1,268 0.1 6.3 2.6 0.8 .. .. .. .. Venezuela, RB 40 40 1,019 1,011 0.5 5.8 1.6 201.2 2 137 203 860 Vietnam ­840 ­200 39 55 543.5 328.2 34.4 2.4 .. 7,200a .. .. West Bank and Gaza 1 11 1,201 1,661 72.8 340.0 1,201.0d 1,836.1d 582 630a 19 18a Yemen, Rep. 650 ­100 378 455 0.4 1.8 53.5 140.2 1,081 1,411 61 337 Zambia ­11 ­82 271 287 0.0 0.2 130.0 83.5 .. 68 59 139 Zimbabwe ­192 ­700 433 391 0.0 16.8 0.5 3.5 44 .. 7 .. World ..f s ..f s165,659g s 194,797g s 18,068.7d,h s15,161.6d,h s 18,068.7d s 15,161.6d s 101,963 s 443,392 s 100,821 s 288,361 s Low income ­344 ­3,728 15,731 14,820 8,552.0 5,386.1 4,882.4 2,024.9 3,525 31,917 431 2,400 Middle income ­12,982 ­14,512 62,754 62,860 3,719.2 4,672.9 9,932.2 10,916.7 53,106 303,872 10,282 65,441 Lower middle income ­11,033 ­11,119 33,284 32,088 2,490.7 3,629.0 8,477.2 9,826.5 31,986 203,769 1,959 15,361 Upper middle income ­1,949 ­3,393 29,471 30,772 1,228.5 1,043.9 1,455.1 1,090.2 21,121 100,103 8,323 50,080 Low & middle income ­13,325 ­18,240 78,485 77,680 12,271.2 10,059.0 14,814.7 12,941.6 56,631 335,789 10,714 67,841 East Asia & Pacific ­3,285 ­3,722 3,047 4,739 952.9 761.1 447.0 463.6 9,525 86,060 1,617 14,551 Europe & Central Asia ­3,597 ­2,138 31,097 28,924 1,631.5 712.5 1,221.9 187.7 7,206 57,516 4,770 34,731 Latin America & Carib. ­3,388 ­5,738 5,440 5,951 155.7 446.6 93.9 350.2 13,427 64,438 1,123 4,258 Middle East & N. Africa ­1,044 ­1,850 8,985 10,002 948.0 2,368.9 5,683.0 7,696.9 13,275 34,798 722 6,156 South Asia ­1,262 ­3,181 13,257 11,785 2,958.7 3,142.1 1,625.5 2,119.0 10,005 71,652 476 4,352 Sub-Saharan Africa ­749 ­1,611 16,659 16,279 5,624.4 2,627.7 5,743.4 2,124.1 3,193 21,324 2,006 3,794 High income 13,308 18,091 87,174 117,117 267.3 109.4 3,254.1 2,220.0 45,332 107,603 90,107 220,520 Euro area 4,604 7,269 23,080 31,629 13.9 1.0 1,690.4 966.3 30,826 71,436 28,737 83,142 a. World Bank estimates. b. Includes Taiwan, China. c. Includes Tibetans, who are listed separately by the UN Refugee Agency (UNHCR). d. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), who are not included in data from the UNHCR. e. Includes Montenegro. f. World totals computed by the United Nations sum to zero, but because the aggregates refer to World Bank definitions, regional and income group totals do not. g. World totals are computed by the World Bank and include only economies covered by World Development Indicators, so data may differ from what is published by the United Nations Population Division. h. Includes refugees without specified country of origin and Palestinian refugees under the mandate of the UNRWA, so regional and income group totals do not sum to the world total. 416 2010 World Development Indicators 6.18 GLOBAL LINKS Movement of people About the data Definitions Movement of people, most often through migration, registrations varies greatly. Many refugees may not · Net migration is the net total of migrants during the is a significant part of global integration. Migrants be aware of the need to register or may choose not period. It is the total number of immigrants less the contribute to the economies of both their host coun- to do so. And administrative records tend to over- total number of emigrants, including both citizens and try and their country of origin. Yet reliable statistics estimate the number of refugees because it is eas- noncitizens. Data are five-year estimates. · Interna- on migration are difficult to collect and are often ier to register than to de-register. The UN Refugee tional migrant stock is the number of people born in incomplete, making international comparisons a Agency (UNHCR) collects and maintains data on a country other than that in which they live. It includes challenge. refugees, except for Palestinian refugees residing refugees. · Refugees are people who are recognized The United Nations Population Division provides in areas under the mandate of the United Nations as refugees under the 1951 Convention Relating data on net migration and migrant stock. To derive Relief and Works Agency for Palestine Refugees in to the Status of Refugees or its 1967 Protocol, the estimates of net migration, the organization takes the Near East (UNRWA). The UNRWA provides ser- 1969 Organization of African Unity Convention Gov- into account the past migration history of a country vices to Palestinian refugees who live in certain erning the Specific Aspects of Refugee Problems in or area, the migration policy of a country, and the areas and who register with the agency. Registra- Africa, people recognized as refugees in accordance influx of refugees in recent periods. The data to cal- tion is voluntary, and estimates by the UNRWA are with the UNHCR statute, people granted refugee-like culate these official estimates come from a variety not an accurate count of the Palestinian refugee humanitarian status, and people provided temporary of sources, including border statistics, administra- population. The table shows estimates of refugees protection. Asylum seekers--people who have applied tive records, surveys, and censuses. When no offi - collected by the UNHCR, complemented by estimates for asylum or refugee status and who have not yet cial estimates can be made because of insufficient of Palestinian refugees under the UNRWA mandate. received a decision or who are registered as asylum data, net migration is derived through the balance Thus, the aggregates differ from those published by seekers--are excluded. Palestinian refugees are equation, which is the difference between overall the UNHCR. people (and their descendants) whose residence was population growth and the natural increase during Workers' remittances and compensation of employ- Palestine between June 1946 and May 1948 and who the 1990­2000 intercensal period. ees are World Bank staff estimates based on data lost their homes and means of livelihood as a result The data used to estimate the international migrant from the International Monetary Fund's (IMF) Balance of the 1948 Arab-Israeli conflict. · Country of origin stock at a particular time are obtained mainly from of Payments Statistics Yearbook. The IMF data are refers to the nationality or country of citizenship of a population censuses. The estimates are derived supplemented by World Bank staff estimates for claimant. · Country of asylum is the country where an from the data on foreign-born population--people missing data for countries where workers' remit- asylum claim was filed and granted. · Workers' remit- who have residence in one country but were born in tances are important. The data reported here are the tances and compensation of employees received and another country. When data on the foreign-born popu- sum of three items defined in the fifth edition of the paid comprise current transfers by migrant workers lation are not available, data on foreign population-- IMF's Balance of Payments Manual: workers' remit- and wages and salaries earned by nonresident work- that is, people who are citizens of a country other tances, compensation of employees, and migrants' ers. Remittances are classified as current private than the country in which they reside--are used as transfers. transfers from migrant workers resident in the host estimates. The distinction among these three items is not country for more than a year, irrespective of their After the breakup of the Soviet Union in 1991 peo- always consistent in the data reported by countries immigration status, to recipients in their country of ple living in one of the newly independent countries to the IMF. In some cases countries compile data on origin. Migrants' transfers are defined as the net worth who were born in another were classified as interna- the basis of the citizenship of migrant workers rather of migrants who are expected to remain in the host tional migrants. Estimates of migrant stock in the than their residency status. Some countries also country for more than one year that is transferred to newly independent states from 1990 on are based report remittances entirely as workers' remittances another country at the time of migration. Compensa- on the 1989 census of the Soviet Union. or compensation of employees. Following the fifth tion of employees is the income of migrants who have For countries with information on the interna- edition of the Balance of Payments Manual in 1993, lived in the host country for less than a year. tional migrant stock for at least two points in time, migrants' transfers are considered a capital transac- Data sources interpolation or extrapolation was used to estimate tion, but previous editions regarded them as current the international migrant stock on July 1 of the refer- transfers. For these reasons the figures presented in Data on net migration are from the United Nations ence years. For countries with only one observation, the table take all three items into account. Population Division's World Population Prospects: estimates for the reference years were derived using The 2008 Revision. Data on migration stock are rates of change in the migrant stock in the years pre- from the United Nations Population Division's ceding or following the single observation available. Trends in Total Migrant Stock: The 2008 Revision. A model was used to estimate migrants for countries Data on refugees are from the UNHCR's Statisti- that had no data. cal Yearbook 2008, complemented by statistics Registrations, together with other sources-- on Palestinian refugees under the mandate of including estimates and surveys--are the main the UNRWA as published on its website. Data on sources of refugee data. But there are difficulties remittances are World Bank staff estimates based in collecting accurate statistics. Although refugees on IMF balance of payments data. are often registered individually, the accuracy of 2010 World Development Indicators 417 6.19 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 304a 2,675a 12 3,716 70 1,849 23.2 48.2 19 1,644 2.3 22.6 Algeria 520a,b 1,772a,b 1,090 1,539 32c 325c .. .. 186c 469c .. .. Angola 9 294 3 .. 27 293 0.7 0.5 113 447 3.2 1.0 Argentina 2,289 4,665 3,815 4,611 2,550 5,308 10.2 6.5 4,013 5,971 15.4 8.8 Armenia 12 558 .. 516 14 377 4.7 21.5 12 383 1.7 8.1 Australia 3,726a 5,586a 2,519 5,808 11,915 28,470 17.1 12.2 7,260 24,903 9.7 10.3 Austria 17,173d 21,935d 3,713 9,876 14,529 24,343 16.2 10.1 11,686 13,988 12.7 6.3 Azerbaijan .. 1,409 432 2,162 87 381 11.1 1.2 165 454 12.8 4.0 Bangladesh 156 467 830 875 25e 91e 0.6 0.5 234e 918 3.1 3.6 Belarus 161 91 626 380 28 585 0.5 1.6 101 812 1.8 1.9 Belgium 5,560d 7,165d 5,645 8,887 4,548e 13,063 2.4 2.8 8,115e 20,883 4.5 4.4 Benin 138 186 .. .. 85e 206 13.8 15.3 48 107 5.4 5.1 Bolivia 284 594 249 589 92 302 7.5 4.3 72 381 4.6 6.7 Bosnia and Herzegovina 115d 322d .. .. 257 920 22.9 13.4 97 274 2.4 2.1 Botswana 521 1,500 .. .. 176 515e 7.3 9.2 153 490e 7.5 8.4 Brazil 1,991 5,050 2,600 4,936 1,085 6,109 2.1 2.7 3,982 13,269 6.3 6.0 Bulgaria 3,466 5,780 3,524 5,727 662 4,831 9.8 15.8 312 3,380 4.8 8.0 Burkina Faso 124f 226f .. .. .. 57 .. .. .. 91 .. .. Burundi 34b 201b 36 .. 2 2 1.9 1.2 25e 98 9.7 18.5 Cambodia .. 2,001 31 786 71 1,300 7.3 20.5 22 191 1.6 2.5 Cameroon 100f 185f .. .. 75 165 3.7 2.2 140 502 8.7 6.0 Canada 16,932 17,142 18,206 27,037 9,176 17,771 4.2 3.4 12,658 34,007 6.3 7.0 Central African Republic 26g 14g .. 11 4c .. .. .. 43c .. .. .. Chad 19f 25f .. .. 43c .. .. .. 38c .. .. .. Chile 1,540 2,699 1,070 3,061 1,186 2,632 6.1 3.4 934 1,788 5.1 2.6 China 20,034 53,049 4,520 45,844 8,730e 44,130 5.9 2.8 3,688e 40,987 2.7 3.3 Hong Kong SAR, China 7,137 17,319 .. 81,911 9,604c,e 20,413c 3.5 4.5 10,497c,e 15,888c,e 6.5 3.7 Colombia 1,433a 1,222a 1,057 2,041 887 2,499 7.2 5.9 1,162 2,337 7.3 5.2 Congo, Dem. Rep. 35g 47g 50 .. .. .. .. .. .. .. .. .. Congo, Rep. 37f 43f .. .. 15 54 1.1 0.9 69 168 5.1 2.6 Costa Rica 785 2,089 273 519 763 2,526 17.1 18.5 336 718 7.1 4.4 Côte d'Ivoire 188 .. .. .. 103 114e 2.4 1.0 312 380e 8.2 4.1 Croatia 1,485d 9,415d .. .. 1,349e 11,668 19.3 39.4 422e 1,152 4.6 3.3 Cuba 742g 2,316g 72 202 1,100c 2,548 .. .. .. .. .. .. Czech Republic 3,381d 6,649d .. .. 2,880e 8,728 10.2 5.2 1,635e 4,731 5.4 3.0 Denmark 2,124d 4,503d 5,035 6,347 3,691e 6,686e 5.6 3.6 4,288e 9,678e 7.4 5.4 Dominican Republic 1,776b,g 3,980b,g 168 413 1,571e 4,176e 27.4 35.1 267 522e 4.4 2.9 Ecuador 440a,h 1,005a,h 271 815 315 745 6.1 3.6 331 790 5.8 3.8 Egypt, Arab Rep. 2,871 12,296 2,683 4,531 2,954 12,104 22.3 22.1 1,371 3,390 8.0 5.0 El Salvador 235 1,385 348 1,012 152 1,180 7.5 19.3 99 709 2.7 6.4 Eritrea 315a,b 81a,b .. .. 58c 60 43.1 .. .. .. .. .. Estonia 530 1,970 1,764 .. 452 1,662 17.6 9.4 121 938 4.2 5.0 Ethiopia 103g 330b 120 .. 177 1,184 23.1 33.7 30 156e 2.1 1.6 Finland 2,644 3,583 5,147 5,854 2,383 4,861 5.0 3.8 2,853 5,534 7.6 4.7 France 60,033 78,449 18,686 23,347 31,295 66,821 8.6 8.7 20,699 52,135 6.2 6.2 Gabon 125g .. 203 .. 94 13 3.2 0.2 182 346 10.6 14.4 Gambia, The 45 147 .. 307 28e 83 15.8 30.8 16 8 6.9 2.2 Georgia 85a 1,290a 228 .. 75 505 13.1 13.7 171 338 12.1 4.5 Germany 14,847d 24,884d 55,800 73,000 24,052 51,225 4.0 2.9 66,527 103,386 11.3 6.8 Ghana 286b 587b .. .. 30 970 1.9 13.7 74 870 3.5 6.9 Greece 10,130 15,939 .. .. 4,182 17,586 26.9 22.1 1,495 3,946 6.0 3.3 Guatemala 563a 1,715a 333 1,277 216 1,068e 7.7 11.1 167 750 4.5 4.8 Guinea 12g 46g .. .. 1 2 0.1 0.2 29 30 2.9 1.7 Guinea-Bissau .. 30 .. .. 3 3 5.3 .. 6 16 6.7 .. Haiti 145 304 .. .. 90e 279 46.8 33.5 35e 383 4.4 13.3 Honduras 271 899 149 387 85 622 5.2 8.9 99 421 5.3 3.6 418 2010 World Development Indicators 6.19 GLOBAL LINKS Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Hungary .. 8,814 13,083 18,471 2,938 7,112 14.9 5.6 1,501 4,637 7.5 3.7 India 2,124h 5,367h 3,056 10,647 2,582e 12,461 6.8 4.3 996e 12,081 2.1 3.3 Indonesia 4,324 6,234 1,782 5,486 5,229e 8,150 9.9 5.3 2,172e 8,547 4.0 5.9 Iran, Islamic Rep. 489 2,034 1,000 .. 205 2,196 1.1 .. 247 9,482 1.6 .. Iraq 61a .. .. .. 18e 555 .. 1.4 117e 705 .. 3.3 Ireland 4,818 8,026 2,547 7,713 2,698 9,953 5.5 4.5 2,034e 10,551 4.8 5.4 Israel 2,215h 2,572h 2,259 4,207 3,491 4,807 12.7 5.9 2,626 4,445 7.4 5.3 Italy 31,052 42,734 18,173 28,284 30,426 48,793 10.3 7.3 17,219 37,728 6.9 5.6 Jamaica 1,147b,g 1,767b,g .. .. 1,199 2,222 35.3 42.0 173 312 4.6 3.1 Japan 3,345a,h 8,351a,h 15,298 15,987 4,894 13,781 1.0 1.5 46,966 38,976 11.2 4.4 Jordan 1,075h 3,729b 1,128 2,288 973 3,539 28.0 28.6 719 1,140 14.7 5.9 Kazakhstan .. 3,447 523 5,243 155 1,255 2.6 1.6 296 1,305 4.9 2.6 Kenya 896 1,644 .. .. 590 1,398 16.7 16.9 183 266e 3.1 2.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,753a,b 6,891a,b 3,819 11,996 6,670 12,783 4.5 2.5 6,947 19,512 4.5 3.8 Kosovo .. .. .. .. .. .. .. .. .. .. .. .. Kuwait 72f 293f 878 2,649 307 610 2.2 0.6 2,514 8,341 19.9 22.0 Kyrgyz Republic 36 2,435 42 1,521 5e 569 1.1 20.7 7e 451 1.0 9.5 Lao PDR 60 1,295 .. .. 52 276e 12.8 19.4 34 .. 4.5 .. Latvia 539 1,684 1,812 3,782 37 1,134 1.8 8.0 62 1,250 2.8 6.6 Lebanon 450 1,333 .. .. 710 7,690 .. 32.0 .. 4,297 .. 14.5 Lesotho 87 285 .. .. 29 34e 14.6 3.6 17 19 1.6 1.1 Liberia .. .. .. .. .. 158e .. 24.9 .. 58 .. 2.5 Libya 56 .. 484 .. 4 99 0.1 0.2 493 1,339 8.6 5.1 Lithuania 650 1,611 1,925 2,847 102 1,406 3.2 4.9 107 1,533 2.7 4.5 Macedonia, FYR 147d 255d .. .. 19e 262 2.7 5.3 27e 190 1.7 2.5 Madagascar 75g 375g 39 .. 106 620 14.2 21.8 79 143e 8.0 3.9 Malawi 192 742 .. .. 22 48 4.7 .. 53 84 8.0 .. Malaysia 7,469 22,052 20,642 .. 5,044 18,553 6.1 8.1 2,722 7,724 3.1 4.3 Mali 42f,g 190f,g .. .. 26 227 4.9 11.7 74 201 7.5 7.7 Mauritania .. .. .. .. 11e .. 2.2 .. 30 .. 5.9 .. Mauritius 422 930 107 226 616 1,823 26.2 36.9 184 489 7.5 7.7 Mexico 20,241b 22,637b 8,450 14,450 6,847 14,647 7.7 4.7 3,587 10,185 4.4 3.1 Moldova 32 7 71 85 71 289 8.0 11.6 73 345 7.3 6.1 Mongolia 108 446 .. .. 33 261 6.5 12.9 22 212 4.2 11.3 Morocco 2,602b 7,879b 1,317 3,058 1,469 8,885 16.2 26.3 356 1,910 3.2 4.1 Mozambique .. 771 .. .. 49 213 10.2 6.6 68 241 6.6 5.5 Myanmar 117 193 .. .. 169 59 12.9 1.2 18e 40 0.9 1.4 Namibia 272 929 .. .. 278e 382 16.0 10.4 90e 92 4.3 2.1 Nepal 363 500 100 561 232 353 22.5 20.6 167 545 10.3 12.5 Netherlands 6,574d 10,104d 12,313 18,458 10,611 20,526 4.4 3.2 13,151 22,212 6.1 3.9 New Zealand 1,475 2,411 920 1,965 2,318e 5,030e 13.0 12.5 1,259e 2,991e 7.3 7.0 Nicaragua 281 858 255 1,100 51 276e 7.7 9.4 56 218 4.9 4.1 Niger 35 48 10 .. 7e 45 2.2 5.9 26 49 5.7 3.7 Nigeria 656 1,111 .. .. 47 586 0.4 0.7 939 4,774 7.3 10.0 Norway 2,880 4,440 590 3,395 2,730 5,559 4.9 2.5 4,481 15,932e 9.6 12.2 Oman 279f 1,273f .. .. 193 1,111 2.5 2.8 349e 1,199 6.3 4.5 Pakistan 378 823 .. .. 582 915 5.7 3.6 654 2,035 4.6 4.3 Panama 345 1,293 185 369 372 2,223 4.9 13.8 181 560 2.3 3.2 Papua New Guinea 42 114 51 .. 25e 4 0.8 0.1 58e 56 3.0 2.1 Paraguay 438h 428h 427 278 162 128 3.4 1.4 173 210 3.3 2.2 Peru 479 2,058 508 1,971 521 2,396 7.9 6.8 428 1,353 4.5 4.0 Philippines 1,760b 3,139b 1,615 2,745 1,141 4,990 4.3 8.5 551 2,778 1.7 4.0 Poland 19,215 12,960 36,387 47,561 6,927 12,841 19.4 6.0 5,865 10,381 17.3 4.4 Portugal 9,511h 12,321b .. 20,989 5,646 14,047 17.5 17.0 2,540 5,283 6.4 5.1 Puerto Rico 3,131g 3,894g 1,237 1,493 1,828c 3,645c .. .. 1,155c 1,834c .. .. Qatar 309f 1,405f .. .. .. 874c .. .. .. 3,751c .. .. 2010 World Development Indicators 419 6.19 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 1995 2008 Romania 5,445a 8,862a 5,737 13,072 689 2,627 7.3 4.2 749 2,411 6.6 2.7 Russian Federation 10,290a 23,676a 21,329 36,538 4,312e 15,923 4.6 3.0 11,599e 28,122 14.0 7.6 Rwanda .. 981 .. .. 4 202 5.4 30.4 13 104 3.5 7.4 Saudi Arabia 3,325 14,757 .. 4,087 .. 7,227c .. 2.2 .. 16,666c .. 9.5 Senegal .. 875 .. .. 168 622 11.2 21.6 154 352 8.5 6.5 Serbia .. 646 .. .. .. 1,113 .. 7.4 .. 1,435 .. 5.4 Sierra Leone 38g 36g 6 73 57e 34e 44.4 10.2 51 24 19.4 4.1 Singapore 6,070 7,778 2,867 6,828 7,611e 10,583e 4.8 2.5 4,663e 14,189e 3.2 3.6 Slovak Republic 903d 1,767d 218 23,837 630 3,004 5.7 3.8 338 2,596 3.2 3.2 Slovenia 732d 1,771d .. 2,459 1,128 3,115 10.9 8.4 606 1,567 5.6 4.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4,488 9,592 2,520 4,429 2,655 8,861 7.7 9.0 2,414 6,792 7.2 6.3 Spain 34,920 57,316 3,648 11,229 27,369 70,234 20.4 16.4 5,826 26,829 4.3 5.2 Sri Lanka 403h 438h 504 966 367 803 7.9 7.9 279 777 4.7 5.0 Sudan 29 436 195 .. 8e 331e 1.2 2.7 43e 1,188e 3.5 11.0 Swaziland 300i 754f .. 1,177 54 32 5.3 1.5 45 63 3.5 2.5 Sweden 2,310d 3,434d 10,127 12,681 4,390 14,399 4.6 5.6 6,816 17,310 8.4 7.8 Switzerland 6,946f 8,608f 11,148 .. 11,354 17,573 9.2 5.5 9,478 13,407 8.7 5.1 Syrian Arab Republic 815d 5,430d 1,746 5,253 1,258e 2,972 21.9 19.0 498e 710 9.0 4.6 Tajikistan .. .. .. .. .. 24 .. 1.4 .. 11e .. 0.3 Tanzania 285 750 157 .. 502e 1,358 39.7 26.1 360e 746 16.8 9.3 Thailand 6,952b 14,536 1,820 4,018 9,257 21,980 13.2 10.5 4,791 6,963 5.8 3.4 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 53f 74f .. .. 13e 38 2.8 4.2 40 59 6.0 4.3 Trinidad and Tobago 260g 433g 261 .. 232 615 8.3 4.3 91 204 4.3 1.9 Tunisia 4,120h 7,049h 1,778 3,118 1,838 3,909 23.0 15.5 294 555 3.3 2.1 Turkey 7,083 24,994 3,981 9,873 4,957e 25,019 13.6 14.2 911e 4,031 2.3 1.9 Turkmenistan 218 8 21 38 13 .. 0.7 .. 74 .. 4.1 .. Uganda 160 844 148 337 78e 531 11.7 15.5 80e 314 5.4 6.0 Ukraine 3,716 25,449 6,552 15,499 191e 6,722 1.1 7.9 210e 4,585 1.1 4.6 United Arab Emirates 2,315b,i 7,126b,i .. .. 632 7,162c .. .. .. 13,288c .. .. United Kingdom 21,719 30,142 41,345 69,011 27,577 45,345 8.6 6.0 30,749 84,218 9.4 10.0 United States 43,490 57,938 51,285 63,549 93,700 166,530 11.8 9.1 60,924 117,969 6.8 4.7 Uruguay 2,022 1,938 562 734 725 1,180 20.7 12.6 332 487 9.3 4.8 Uzbekistan 92 1,069 246 1,150 15 64c .. .. .. .. .. .. Venezuela, RB 700 745 534 1,745 995 984 4.8 1.0 1,852 2,566 11.0 4.3 Vietnam 1,351a 4,254a .. .. .. 3,926c .. 5.6 .. .. .. .. West Bank and Gaza 220f 387f .. .. 255e 212 33.4 22.9 162e 376 5.8 8.5 Yemen, Rep. 61f 404f .. .. 50e 886e 2.3 8.7 76e 246 3.1 2.1 Zambia 163 812 .. .. 29 146 2.4 2.8 83 107 6.2 2.0 Zimbabwe 1,416a 2,508a 256 .. 145 365c .. .. 106c .. .. .. World 536,909 t 927,848 t 576,560 t 1,027,062 t 486,780 t 1,139,379 t 7.6 w 5.8 w 458,837 t 1,028,751 t 7.4 w 5.3 w Low income 8,001 23,743 .. .. .. .. 10.6 9.1 .. .. 5.0 .. Middle income 157,253 345,869 175,694 363,133 88,430 305,501 8.1 5.5 65,704 227,180 5.8 4.3 Lower middle income 57,366 166,594 36,710 142,650 39,463 146,807 8.2 4.9 20,472 111,854 3.9 3.9 Upper middle income 100,145 179,371 124,078 208,648 48,963 158,652 7.9 6.0 45,510 115,055 7.4 4.9 Low & middle income 167,692 375,532 195,002 413,087 92,494 323,111 8.1 5.6 69,333 239,061 5.8 4.3 East Asia & Pacific 43,653 109,209 36,055 .. 31,197 104,753 7.8 4.5 14,769 70,557 3.5 3.7 Europe & Central Asia 54,490 123,198 86,619 149,215 19,155 81,028 8.4 5.9 22,399 68,075 9.5 4.9 Latin America & Carib. 38,965 60,922 21,780 41,578 21,591 55,179 7.5 5.3 18,751 44,731 6.5 4.5 Middle East & N. Africa 13,349 42,820 13,407 25,352 9,771 43,878 13.0 16.7 4,844 19,782 5.7 5.7 South Asia 3,819 8,472 5,151 15,005 4,016 15,342 6.8 4.4 2,393 16,660 3.0 3.5 Sub-Saharan Africa 12,954 29,153 .. .. 6,729 22,956 7.6 6.0 6,761 22,119 6.7 5.9 High income 364,532 546,528 337,054 531,446 394,244 816,408 7.5 5.8 388,810 790,377 7.8 5.7 Euro area 202,533 290,386 140,127 232,712 164,023 356,496 7.8 6.3 154,993 312,836 7.8 5.6 Note: Aggregates are based on World Bank country classifications and differ from those of the World Tourism Organization. Regional and income group totals include countries not shown in the table for which data are available. a. Arrivals of nonresident visitors at national borders. b. Includes nationals residing abroad. c. Country estimates. d. Arrivals in all types of accommodation establishments. e. Expenditure of travel related items only; excludes passenger transport items. f. Arrivals in hotels and similar establishments. g. Arrivals by air only. h. Excludes nationals residing abroad. i. Arrivals in hotels only. 420 2010 World Development Indicators 6.19 GLOBAL LINKS Travel and tourism About the data Definitions Tourism is defined as the activities of people trav- are unavailable or incomplete, the table shows the · International inbound tourists (overnight visitors) eling to and staying in places outside their usual arrivals of international visitors, which include tour- are the number of tourists who travel to a country environment for no more than one year for leisure, ists, same-day visitors, cruise passengers, and crew other than that in which they usually reside, and out- business, and other purposes not related to an activ- members. side their usual environment, for a period not exceed- ity remunerated from within the place visited. The Sources and collection methods for arrivals differ ing 12 months and whose main purpose in visiting social and economic phenomenon of tourism has across countries. In some cases data are from bor- is other than an activity remunerated from within the grown substantially over the past quarter century. der statistics (police, immigration, and the like) and country visited. When data on number of tourists are Statistical information on tourism is based mainly supplemented by border surveys. In other cases data not available, the number of visitors, which includes on data on arrivals and overnight stays along with are from tourism accommodation establishments. tourists, same-day visitors, cruise passengers, and balance of payments information. These data do For some countries number of arrivals is limited to crew members, is shown instead. · International out- not completely capture the economic phenomenon arrivals by air and for others to arrivals staying in bound tourists are the number of departures that of tourism or provide the information needed for hotels. Some countries include arrivals of nation- people make from their country of usual residence effective public policies and effi cient business als residing abroad while others do not. Caution to any other country for any purpose other than an operations. Data are needed on the scale and sig- should thus be used in comparing arrivals across activity remunerated in the country visited. · Inbound nifi cance of tourism. Information on the role of tour- countries. tourism expenditure is expenditures by international ism in national economies is particularly deficient. The World Tourism Organization is improving its inbound visitors, including payments to national carri- Although the World Tourism Organization reports coverage of tourism expenditure data, using balance ers for international transport. These receipts include progress in harmonizing definitions and measure- of payments data from the International Monetary any other prepayment made for goods or services ment, differences in national practices still prevent Fund (IMF) supplemented by data from individual received in the destination country. They may include full comparability. countries. These data, shown in the table, include receipts from same-day visitors, except when these The data in the table are from the World Tourism travel and passenger transport items as defined in are important enough to justify separate classifica- Organization, a United Nations agency. The data on the IMF's (1993) Balance of Payments Manual. When tion. For some countries they do not include receipts inbound and outbound tourists refer to the number of the IMF does not report data on passenger transport for passenger transport items. Their share in exports arrivals and departures, not to the number of people items, expenditure data for travel items are shown. is calculated as a ratio to exports of goods and ser- traveling. Thus a person who makes several trips to The aggregates are calculated using the World vices (all transactions between residents of a coun- a country during a given period is counted each time Bank's weighted aggregation methodology (see Sta- try and the rest of the world involving a change of as a new arrival. Unless otherwise indicated in the tistical methods) and differ from the World Tourism ownership from residents to nonresidents of general footnotes, the data on inbound tourism show the Organization's aggregates. merchandise, goods sent for processing and repairs, arrivals of nonresident tourists (overnight visitors) at nonmonetary gold, and services). · Outbound tour- national borders. When data on international tourists ism expenditure is expenditures of international out- bound visitors in other countries, including payments High-income economies remain the main recipients of increased international to foreign carriers for international transport. These tourism expenditure, but the share of developing economies' receipts has risen 6.19a expenditures may include those by residents travel- ing abroad as same-day visitors, except when these 1995 2008 Total receipts from international tourism: $487 billion Total receipts from international tourism: $1,139 billion are important enough to justify separate classifica- Middle East & South Asia 1% Middle East & South Asia 1% tion. For some countries they do not include expen- North Africa 2% North Africa 4% Sub-Saharan Africa 1% Sub-Saharan Africa 2% ditures for passenger transport items. Their share in Latin America & Caribbean 5% Europe & Latin America & imports is calculated as a ratio to imports of goods Central Asia 4% Caribbean 5% East Asia & Pacific 6% and services (all transactions between residents of a Europe & Central Asia 7% country and the rest of the world involving a change of ownership from nonresidents to residents of general High income East Asia merchandise, goods sent for processing and repairs, 81% & Pacific nonmonetary gold, and services). 9% High income Data sources 72% Data on visitors and tourism expenditure are from the World Tourism Organization's Yearbook of Tourism Statistics and Compendium of Tourism Statistics 2010. Data in the table are updated Although more than 70 percent of international tourism expenditures went to high-income economies in from electronic files provided by the World Tour- 2008, the share of developing economies' receipts has increased since 1995. The share of receipts by ism Organization. Data on exports and imports East Asia and Pacific and Europe and Central Asia increased the most--about 3 percentage points. are from the IMF's Balance of Payments Statistics Source: World Bank staff calculations based on World Tourism Organization data. Yearbook and data files. 2010 World Development Indicators 421 Text figures, tables, and boxes PRIMARY DATA DOCUMENTATION As a major user of socioeconomic data, the World Bank recognizes the impor- tance of data documentation to inform users of differences in the methods and conventions used by primary data collectors--usually national statistical agen- cies, central banks, and customs services--and by international organizations, which compile the statistics that appear in the World Development Indicators database. These differences may give rise to significant discrepancies over time both within countries and across them. Delays in reporting data and the use of old surveys as the base for current estimates may further compromise the qual- ity of data reported here. The tables in this section provide information on sources, methods, and reporting standards of the principal demographic, economic, and environmental indicators in World Development Indicators. Additional documentation is avail- able from the World Bank's Country Statistical Information Database at www. worldbank.org/data. The demand for good-quality statistical data is increasing. Timely and reliable statistics are key to the broad development strategy often referred to as "manag- ing for results." Monitoring and reporting on publicly agreed indicators are central to implementing poverty reduction strategies and lie at the heart of the Millen- nium Development Goals and the new Results Measurement System adopted for the 14th replenishment of the International Development Association. A global action plan to improve national and international statistics was agreed on during the Second Roundtable on Managing for Development Results in February 2004 in Marrakech, Morocco. The plan, now referred to as the Mar- rakech Action Plan for Statistics, or MAPS, has been widely endorsed and forms the overarching framework for statistical capacity building. The third roundtable conference, held in February 2007 in Hanoi, Vietnam, reaffirmed MAPS as the guiding strategy for improving the capacity of the national and international sta- tistical systems. See www.mfdr.org/RT3 for reports from the conference. 2010 World Development Indicators 423 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Afghanistan Afghan afghani 2002/03 VAB Actual G C G Albania Albanian lek a 1996 b VAB 2005 BPM5 Actual G C G Algeria Algerian dinar 1980 VAB BPM5 Actual S B G American Samoa U.S. dollar Andorra Euro G Angola Angolan kwanza 1997 VAP 1991­96 2005 BPM5 Actual S G Antigua and Barbuda East Caribbean dollar 1990 VAB BPM5 G G Argentina Argentine peso 1993 b VAB 1971­84 2005 BPM5 Actual S C S Armenia Armenian dram a 1996 b VAB 1990­95 2005 BPM5 Actual S C S Aruba Aruban florin 1995 S Australia Australian dollar a 2007 b VAB 2005 BPM5 G C S Austria Euro 2000 b VAB 2005 BPM5 S C S Azerbaijan New Azeri manat a 2003 b VAB 1992­95 2005 BPM5 Actual G C G Bahamas, The Bahamian dollar 2006 b VAB BPM5 G B G Bahrain Bahraini dinar 1985 VAP 2005 BPM5 G C G Bangladesh Bangladeshi taka 1995/96 b VAB 2005 BPM5 Preliminary G C G Barbados Barbados dollar 1974 VAB BPM5 G C G Belarus Belarusian rubel a 2000 b VAB 1990­95 2005 BPM5 Actual G C S Belgium Euro 2000 b VAB 2005 BPM5 S C S Belize Belize dollar 2000 b VAB BPM5 Actual G B G Benin CFA franc 1985 VAP 1992 2005 BPM5 Preliminary S B G Bermuda Bermuda dollar 1996 VAB Bhutan Bhutanese ngultrum 2000 b VAB 2005 Actual C Bolivia Bolivian Boliviano 1990 b VAB 1960­85 2005 BPM5 Actual S C G Bosnia and Herzegovina Bosnia and Herzegovina a 1996 b VAB 2005 BPM5 Actual G C convertible mark Botswana Botswana pula 1993/94 b VAB 2005 BPM5 Preliminary G B G Brazil Brazilian real 2000 b VAB 2005 BPM5 Actual S C S Brunei Darussalam Brunei dollar 2000 VAP 2005 G G Bulgaria Bulgarian lev a 2002 b VAB 1978­89, 2005 BPM5 Actual G C S 1991­92 Burkina Faso CFA franc 1999 VAB 1992­93 2005 BPM4 Actual G B G Burundi Burundi franc 1980 VAB 2005 BPM5 Actual S C Cambodia Cambodian riel 2000 VAB 2005 BPM5 Actual G C G Cameroon CFA franc 2000 b VAB 2005 BPM5 Actual S C G Canada Canadian dollar 2000 b VAB 2005 BPM5 G C S Cape Verde Cape Verde escudo 1980 VAP 2005 BPM5 Actual S G Cayman Islands Cayman Islands dollar Central African Republic CFA franc 2000 VAB 2005 BPM4 Preliminary S B G Chad CFA franc 1995 b VAB 2005 BPM5 Actual S G Channel Islands Jersey pound and 2007, 2007 b VAB Guernsey pound 2003 Chile Chilean peso 2003 b VAB 2005 BPM5 Actual S C S China Chinese yuan 2000 b VAP 1978­93 2005 BPM5 Preliminary S B G Hong Kong SAR, China Hong Kong dollar 2006 b VAB 2005 BPM5 G C S Colombia Colombian peso 2000 b VAB 1992­94 2005 BPM5 Actual S B S Comoros Comorian franc 1990 VAP 2005 Preliminary Congo, Dem. Rep. Congolese franc 1987 b VAB 1999­2001 2005 BPM5 Estimate S C G Congo, Rep. CFA franc 1978 VAP 1993 2005 BPM5 Preliminary S C G Costa Rica Costa Rican colon 1991 b VAB BPM5 Actual S C S Côte d'Ivoire CFA franc 1996 VAP 2005 BPM5 Actual S C G Croatia Croatian kuna a 1997 b VAB 2005 BPM5 G C S Cuba Cuban peso 1984 VAP G Cyprus Euro a 2000 VAB 2005 BPM5 G C S Czech Republic Czech koruna 2000 1995 b VAB 2005 BPM5 G C S Denmark Danish krone 2000 b VAB 2005 BPM5 G C S Djibouti Djibouti franc 1990 VAB 2005 Actual 424 2010 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Afghanistan 1979 MICS, 2003 2008 2000 Albania 2001 MICS, 2005 LSMS, 2005 Yes 1998 2005 2008 2000 Algeria 2008 MICS, 2006 IHS, 1995 2001 2007 2000 American Samoa 2000 Yes Andorra c Yes 2006 Angola 1970 MICS, 2001; MIS, 2006/07 IHS, 2000 1964­65 1991 2000 Antigua and Barbuda 2001 Yes 2007 1990 Argentina 2001 IHS, 2006 Yes 2002 2001 2008 2000 Armenia 2001 DHS, 2005 IHS, 2007 Yes 2008 2000 Aruba 2000 2008 Australia 2006 ES/BS, 1994 Yes 2001 2004 2008 2000 Austria 2001 IS, 2000 Yes 1999­2000 2004 2008 2000 Azerbaijan 2009 DHS, 2006 ES/BS, 2005 Yes 2005 2008 2005 Bahamas, The 2000 1997 2008 Bahrain 2001 Yes 2007 2003 Bangladesh 2001 DHS, 2007 IHS, 2005 2005 1997 2007 2000 Barbados 2000 Yes 2008 2000 Belarus 1999 MICS, 2005 ES/BS, 2007 Yes 1994 2008 2000 Belgium 2001 IHS, 2000 Yes 1999­2000d 2004 2008 Belize 2000 MICS, 2006 ES/BS, 1995 2008 2000 Benin 2002 DHS, 2006 CWIQ, 2003 1992 2005 2001 Bermuda 2000 Yes 2008 Bhutan 2005 IHS, 2003 2000 2008 2000 Bolivia 2001 DHS, 2008 IHS, 2007 1984­88 2000 2008 2000 Bosnia and Herzegovina 1991 MICS, 2006 LSMS, 2007 Yes 2008 Botswana 2001 MICS, 2000 ES/BS, 1993/94 1993 2005 2008 2000 Brazil 2000 DHS, 1996 LFS, 2007 1996 2004 2008 2000 Brunei Darussalam 2001 Yes 2006 Bulgaria 2001 ES/BS, 2003 Yes 2005 2008 2000 Burkina Faso 2006 MICS, 2006 CWIQ, 2003 1993 2005 2000 Burundi 1990 MICS, 2005 CWIQ, 2006 2008 2000 Cambodia 2008 DHS, 2005 IHS, 2007 1999 2004 2000 Cameroon 1987 MICS, 2006 PS, 2001 1984 2006 2000 Canada 2006 LFS, 2000 Yes 1996/2001 2001 2008 2000 Cape Verde 2000 ES/BS, 2001 Yes 2004 2008 Cayman Islands 1999 Yes Central African Republic 2003 MICS, 2006 PS, 2003 1985 2005 2000 Chad 1993 DHS, 2004 PS, 2002­03 1996 2000 Channel Islands 2001 Yes Chile 2002 IHS, 2006 Yes 1997 2005 2008 2000 China 2000 NSS, 2007 IHS, 2005 1997 2005 2008 2000 Hong Kong SAR, China 2006 Yes 2008 Colombia 2005 DHS, 2005 IHS, 2006 2001 2004 2008 2000 Comoros 2003 MICS, 2000 IHS, 2004 2007 Congo, Dem. Rep. 1984 DHS, 2007 1-2-3, 2005­06 1990 1986 2000 Congo, Rep. 1996 DHS, 2005 CWIQ/PS, 2005 1985­86 1995 2002 Costa Rica 2000 RHS, 1993 LFS, 2007 Yes 1973 2008 2000 Côte d'Ivoire 1998 MICS, 2006 IHS, 2002 2001 2008 Croatia 2001 ES/BS, 2005 Yes 2003 2008 Cuba 2002 MICS, 2006 Yes 2006 2000 Cyprus 2001 Yes 2005 2008 2000 Czech Republic 2001 RHS, 1993 IS, 1996 Yes 2000 2005 2008 2000 Denmark 2001 ITR, 1997 Yes 1999­2000 2004 2008 2000 Djibouti 2009 MICS, 2006 PS, 2002 1998 2000 2010 World Development Indicators 425 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Dominica East Caribbean dollar 1990 b VAB BPM5 Actual G G Dominican Republic Dominican peso 1991 VAB BPM5 Actual G C G Ecuador U.S. dollar 2000 b VAB 2005 BPM5 Actual S B S Egypt, Arab Rep. Egyptian pound 1991/92 VAB 2005 BPM5 Actual S B S El Salvador U.S. dollar 1990 VAB BPM5 Actual S C S Equatorial Guinea CFA franc 2000 VAB 1965­84 2005 Eritrea Eritrean nakfa 1992 VAB BPM4 Actual G Estonia Estonian kroon 2000 b VAB 1987­95 2005 BPM5 G C S Ethiopia Ethiopian birr 1999/2000 b VAB 2005 BPM5 Actual G C G Faeroe Islands Danish krone VAB BPM5 G Fiji Fijian dollar 1995 VAB 2005 BPM4 Actual G B G Finland Euro 2000 b VAB 2005 BPM5 G C S France Euro a 2000 b VAB 2005 BPM5 S C S French Polynesia CFP franc G Gabon CFA franc 1991 VAP 1993 2005 BPM5 Preliminary S G Gambia, The Gambian dalasi 1987 VAB 2005 BPM5 Estimate G C G Georgia Georgian lari a 1996 b VAB 1990­95 2005 BPM5 Actual G C G Germany Euro 2000 b VAB 2005 BPM5 S C S Ghana New Ghanaian cedi 1975 VAP 1973­87 2005 BPM5 Actual G B G Greece Euro a 2000 VAB 2005 BPM5 S C S Greenland Danish krone G Grenada East Caribbean dollar 1990 VAB BPM5 Actual G B G Guam U.S. dollar Guatemala Guatemalan quetzal 2001 b VAB BPM5 Actual S B G Guinea Guinean franc 1996 VAB 2005 BPM5 Estimate S C G Guinea-Bissau CFA franc 1986 VAB 2005 BPM5 Actual G G Guyana Guyana dollar 1988 VAB BPM5 Actual S Haiti Haitian gourde 1986/87 VAB 1991 BPM5 Preliminary G Honduras Honduran lempira 2000 b VAB 1988­89 BPM5 Actual S B G Hungary Hungarian forint a 2000 b VAB 2005 BPM5 S C S Iceland Iceland krona 2000 VAB 2005 BPM5 G C S India Indian rupee 1999/2000 b VAB 2005 BPM5 Actual G C S Indonesia Indonesian rupiah 2000 VAP 2005 BPM5 Actual S C S Iran, Islamic Rep. Iranian rial 1997/98 VAB 1980­2002 2005 BPM5 Actual G C Iraq Iraqi dinar 1997 VAB 1997, 2004 2005 BPM5 S G Ireland Euro 2000 b VAB 2005 BPM5 G C S Isle of Man Manx pound 2005 2003 Israel Israeli new shekel 2005 b VAP 2005 BPM5 S C S Italy Euro 2000 b VAB 2005 BPM5 S C S Jamaica Jamaican dollar 2003 VAB BPM5 Actual G C G Japan Japanese yen 2000 VAB 2005 BPM5 G C S Jordan Jordanian dinar 1994 VAB 2005 BPM5 Actual G B S Kazakhstan Kazakh tenge a 1995 b VAB 1987­95 2005 BPM5 Actual G C S Kenya Kenyan shilling 2001 b VAB 2005 BPM5 Actual G B G Kiribati Australian dollar 1991 VAB G G Korea, Dem. Rep. Democratic People's BPM5 Republic of Korean won Korea, Rep. Korean won 2000 b VAB 2005 BPM5 S C S Kosovo Euro Kuwait Kuwaiti dinar 1995 VAP 2005 BPM5 S C G Kyrgyz Republic Kyrgyz som a 1995 b VAB 1990­95 2005 BPM5 Actual G C S Lao PDR Lao kip 1990 VAB 2005 BPM5 Preliminary G Latvia Latvian lats 2000 b VAB 1987­95 2005 BPM5 Actual S C S Lebanon Lebanese pound 1997 VAB 2005 BPM5 Actual G B G Lesotho Lesotho loti 1995 b VAB 2005 BPM5 Actual G C G Liberia Liberian dollar 1992 VAP 2005 BPM5 Estimate G 426 2010 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Dominica 2001 Yes 2008 Dominican Republic 2002 DHS, 2007 IHS, 2005 1971 2008 2000 Ecuador 2001 RHS, 2004 LFS, 2005 1999­2000 2004 2008 2000 Egypt, Arab Rep. 2006 DHS, 2008 ES/BS, 2004­05 Yes 1999­2000 2001 2008 2000 El Salvador 2007 RHS, 2008 IHS, 2007 Yes 1970­71 2008 2000 Equatorial Guinea 2002 2000 Eritrea 1984 DHS, 2002 2005 2003 2004 Estonia 2000 ES/BS, 2004 Yes 2001 2005 2008 2000 Ethiopia 2007 DHS, 2005 ES/BS, 2005 2001­02 2005 2008 2002 Faeroe Islands c 2005 Fiji 2007 Yes 2003 2007 2000 Finland 2000 IS, 2000 Yes 1999­2000 2004 2008 2000 France 2006e ES/BS, 1994/95 Yes 1999­2000 2004 2008 2000 French Polynesia 2007 Yes 2008 Gabon 2003 DHS, 2000 CWIQ/IHS, 2005 1974­75 2006 2000 Gambia, The 2003 MICS, 2005/06 IHS, 2003 2001­02 2008 2000 Georgia 2002 MICS, 2005; RHS, 2005 IHS, 2007 Yes 2004 2005 2008 2005 Germany 2001 IHS, 2000 Yes 1999­2000 2004 2008 2000 Ghana 2000 DHS, 2008 LSMS, 2006 1984 2002 2008 2000 Greece 2001 IHS, 2000 Yes 1999­2000 2003 2008 2000 Greenland c Yes 2007 Grenada 2001 2008 Guam 2000 Yes Guatemala 2002 RHS, 2002 LSMS, 2006 Yes 2003 2008 2000 Guinea 1996 DHS, 2005 CWIQ, 2003 2000­01 2008 2000 Guinea-Bissau 2009 MICS, 2006 CWIQ, 2002 1988 2005 2000 Guyana 2002 MICS, 2006 IHS, 1998 2008 2000 Haiti 2003 DHS, 2005/06 IHS, 2001 1971 1997 2000 Honduras 2001 DHS, 2005/06 IHS, 2006 1993 2007 2000 Hungary 2001 ES/BS, 2004 Yes 2000 2004 2008 2000 Iceland c Yes 2004 2008 2000 India 2001 DHS, 2005/06 IHS, 2004/05 1995­96/ 2003 2008 2000 2000­01 Indonesia 2000 DHS, 2007 IHS, 2007 2003 2004 2008 2000 Iran, Islamic Rep. 2006 DHS, 2000 ES/BS, 2005 Yes 2003 2004 2006 2004 Iraq 1997 MICS, 2006 1981 1996 2008 2000 Ireland 2006 IHS, 2000 Yes 2000 2004 2008 2000 Isle of Man 2006 Yes Israel 2008 ES/BS, 2001 Yes 1981 2004 2008 2004 Italy 2001 ES/BS, 2000 Yes 2000 2004 2008 2000 Jamaica 2001 MICS 2005 LSMS, 2004 1996 2008 2000 Japan 2005 IS, 1993 Yes 2000 2004 2008 2000 Jordan 2004 DHS, 2007 ES/BS, 2006 1997 2005 2008 2005 Kazakhstan 1999 MICS, 2006 ES/BS, 2007 Yes 2008 2000 Kenya 1999 DHS, 2003; SPA, 2004 IHS, 2005­06 1977­79 2005 2008 2003 Kiribati 2005 2005 Korea, Dem. Rep. 2008 MICS, 2000 2000 Korea, Rep. 2005 ES/BS, 1998 Yes 2000 2005 2008 2000 Kosovo 1981 Kuwait 2005 FHS, 1996 Yes 1970 2007 2002 Kyrgyz Republic 2009 MICS 2005/06 ES/BS, 2007 Yes 2002 2004 2007 2000 Lao PDR 2005 MICS, 2006 ES/BS, 2002­03 1998­99 1998 1975 2000 Latvia 2000 IHS, 2007 Yes 2001 2005 2008 2000 Lebanon 1970 MICS, 2000 1998­99 1997 2008 2005 Lesotho 2006 DHS, 2004 ES/BS, 2002­03 1999­2000 2004 2000 Liberia 2008 DHS, 2007; MIS, 2008/09 CWIQ, 2007 1985 2000 2010 World Development Indicators 427 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept Libya Libyan dinar 1999 VAB 1986 BPM5 G G Liechtenstein Swiss franc VAB S Lithuania Lithuanian litas 2000 b VAB 1990­95 2005 BPM5 Actual G C S Luxembourg Euro 2000 VAB 2005 BPM5 S C S Macau SAR, China Macao pataca 2002 VAB 2005 BPM5 G C G Macedonia, FYR Macedonian denar 1997 1995 b VAB 2005 BPM5 Actual G G Madagascar Malagasy ariary 1984 VAB 2005 BPM5 Actual S C G Malawi Malawi kwacha 1994 VAB 2005 BPM5 Actual G G Malaysia Malaysian ringgit 2000 VAP 2005 BPM5 Estimate G C S Maldives Maldivian rufiyaa 1995 VAB 2005 BPM5 Actual G C Mali CFA franc 1987 VAB 2005 BPM4 Actual G B G Malta Euro (data reported 1973 VAB 2005 BPM5 G C S in Maltese liri) Marshall Islands U.S. dollar 1991 VAB Mauritania Mauritanian ouguiya 1998 VAB 2005 BPM4 Actual G G Mauritius Mauritian rupee 2006 VAB 2005 BPM5 Actual G C G Mayotte Euro Mexico Mexican peso 2003 b VAB 2005 BPM5 Actual G C S Micronesia, Fed. Sts. U.S. dollar 1998 VAB Moldova Moldovan leu a 1996 b VAB 1990­95 2005 BPM5 Actual G C S Monaco Euro Mongolia Mongolian tugrik 2005 b VAB 2005 BPM5 Estimate S C G Montenegro Euro 2000 b VAB 2005 Actual Morocco Moroccan dirham 1998 VAB 2005 BPM5 Actual S C S Mozambique New Mozambican 2003 VAB 1992­95 2005 BPM5 Actual S G metical Myanmar Myanmar kyat 1985/86 VAP BPM5 Estimate G C Namibia Namibian dollar 2004/05 b VAB 2005 BPM5 G B G Nepal Nepalese rupee 2000/01 VAB 2005 BPM5 Actual S C G Netherlands Antilles Netherlands BPM5 S Antilles guilder Netherlands Euro a 2000 b VAB 2005 BPM5 S C S New Caledonia CFP franc S New Zealand New Zealand dollar 2000/01 VAB 2005 BPM5 G C Nicaragua Nicaraguan gold 1994 b VAB 1965­95 BPM5 Actual S B G cordoba Niger CFA franc 1987 VAP 1993 2005 BPM5 Preliminary S G Nigeria Nigerian naira 2002 VAB 1971­98 2005 BPM5 Preliminary G G Northern Mariana Islands U.S. dollar Norway Norwegian krone a 2000 b VAB 2005 BPM5 G C S Oman Rial Omani 1988 VAP 2005 BPM5 G B G Pakistan Pakistani rupee 1999/2000 b VAB 2005 BPM5 Actual G C G Palau U.S. dollar 1995 VAB Panama Panamanian balboa 1996 b VAB BPM5 Actual S C G Papua New Guinea Papua New Guinea kina 1998 VAB 1989 BPM5 Actual G B Paraguay Paraguayan guarani 1994 VAP 2005 BPM5 Actual S C G Peru Peruvian new sol 1994 VAB 1985­90 2005 BPM5 Actual S C S Philippines Philippine peso 1985 VAP 2005 BPM5 Actual G B S Poland Polish zloty a 2002 b VAB 2005 BPM5 Actual S C S Portugal Euro 2000 b VAB 2005 BPM5 S C S Puerto Rico U.S. dollar 1954 VAP G Qatar Qatari riyal 2001 VAP 2005 G B G Romania New Romanian leu a 2005 b VAB 1987­89, 2005 BPM5 Actual S C S 1992 Russian Federation Russian ruble 2000 b VAB 1987­95 2005 BPM5 Preliminary G C S Rwanda Rwandan franc 1995 VAP 1994 2005 BPM5 Estimate G C G Samoa Samoan tala 2002 VAB BPM5 Preliminary G 428 2010 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data Libya 1995 MICS, 2000 2001 2004 2000 Liechtenstein 2000 Yes Lithuania 2001 ES/BS, 2004 Yes 2003 2005 2008 2000 Luxembourg 2001 Yes 1999­2000d 2004 2008 Macau SAR, China 2006 Yes 2008 Macedonia, FYR 2002 MICS, 2005 ES/BS, 2006 Yes 1994 2000 2008 Madagascar 1993 DHS, 2003/04 PS, 2005 2004 2005 2008 2000 Malawi 2008 MICS 2006 LSMS, 2004­05 1993 2000 2008 2000 Malaysia 2000 ES/BS, 2004 Yes 2004 2008 2000 Maldives 2006 MICS, 2001 Yes 2008 Mali 1998 DHS, 2006 IHS, 2006 1984 2008 2000 Malta 2005 Yes 2001 2004 2008 2000 Marshall Islands 1999 Mauritania 2000 MICS, 2007 IHS, 2000 1984­85 2008 2000 Mauritius 2000 Yes 2003 2008 2003 Mayotte 2007 2007 Mexico 2005 ENPF, 1995 LFS, 2008 1991 1999 2008 2000 Micronesia, Fed. Sts. 2000 Moldova 2004 DHS, 2005 ES/BS, 2007 Yes 2004 2008 2000 Monaco 2008 Mongolia 2000 MICS, 2005 LSMS, 2006­08 Yes 1999 2007 2000 Montenegro 2003 MICS, 2005/06 ES/BS, 2007 Yes Morocco 2004 MICS, 2006 ES/BS, 2007 1996 2005 2008 2000 Mozambique 2007 DHS, 2003 ES/BS, 2002/03 1999­2000 2008 2000 Myanmar 1983 MICS, 2000 2003 2001 2000 Namibia 2001 DHS, 2006/07 ES/BS, 1993/94 1996­97 2008 2000 Nepal 2001 DHS, 2006 LSMS, 2003/04 2002 2001 2002 2000 Netherlands Antilles 2001 Yes 2008 2000 Netherlands 2001 IHS, 1999 Yes 1999­2000d 2004 2008 New Caledonia 2009 Yes 2008 New Zealand 2006 IS, 1997 Yes 2002 2003 2008 2000 Nicaragua 2005 RHS, 2006/07 LSMS, 2005 2001 2007 2000 Niger 2001 DHS/MICS, 2006 QWIC/PS, 2005 1980 2008 2000 Nigeria 2006 DHS, 2008 IHS, 2003­04 1960 2008 2000 Northern Mariana Islands 2000 Norway 2001 IS, 2000 Yes 1999 2003 2008 2000 Oman 2003 FHS, 1995 1978­79 2005 2008 2003 Pakistan 1998 DHS, 2006/07 LSMS, 2004/05 2000 2008 2000 Palau 2005 Yes Panama 2000 LSMS, 2003 LFS, 2006 2001 2000 2008 2000 Papua New Guinea 2000 DHS, 1996 IHS, 1996 2004 2000 Paraguay 2002 RHS, 2004 IHS, 2007 1991 2008 2000 Peru 2007 DHS, 2007/08 LSMS, 2007 1994 2006 2008 2000 Philippines 2007 DHS, 2008 ES/BS, 2006 Yes 2002 2004 2008 2000 Poland 2002 ES/BS, 2005 Yes 1996/2002 2004 2008 2000 Portugal 2001 IS, 1997 Yes 1999 2004 2008 2000 Puerto Rico 2000 RHS, 1995/96 Yes 1997/2002 Qatar 2004 Yes 2000­01 2005 2008 2005 Romania 2002 RHS, 1999 LFS, 2007 Yes 2002 2005 2008 2000 Russian Federation 2002 RHS, 1996 IHS, 2007 Yes 1994­95 2005 2008 2000 Rwanda 2002 DHS, 2007/08 IHS, 2000 1984 1998 2008 2000 Samoa 2006 1999 2008 2010 World Development Indicators 429 PRIMARY DATA DOCUMENTATION Currency National Balance of payments Government IMF data accounts and trade finance dissem- ination standard Balance of System of SNA Alternative PPP Payments Base Reference National price conversion survey Manual External System Accounting year year Accounts valuation factor year in use debt of trade concept San Marino Euro 1995 2000 b VAB S C G São Tomé and Príncipe São Tomé and 2001 VAP 2005 Preliminary S G Principe dobra Saudi Arabia Saudi Arabian riyal 1999 VAP 2005 BPM4 G G Senegal CFA franc 1999 1987 b VAB 2005 BPM5 Actual S B G Serbia Serbian dinar a 2002 b VAB 2005 Actual S C G Seychelles Seychelles rupee 1986 VAP BPM5 Actual G C S Sierra Leone Sierra Leonean leone 1990 b VAB 2005 BPM5 Preliminary S B G Singapore Singapore dollar 2000 b VAB 2005 BPM5 G C S Slovak Republic Euro 2000 1995 b VAB 2005 BPM5 G C S Slovenia Euro a 2000 b VAB 2005 BPM5 S C S Solomon Islands Solomon Islands dollar 2004 VAB BPM5 Actual Somalia Somali shilling 1985 VAB 1977­90 Estimate South Africa South African rand 2000 b VAB 2005 BPM5 Preliminary G C S Spain Euro 2000 b VAB 2005 BPM5 S C S Sri Lanka Sri Lankan rupee 2002 VAP 2005 BPM5 Actual G B G St. Kitts and Nevis East Caribbean dollar 1990 b VAB BPM5 Preliminary G C G St. Lucia East Caribbean dollar 1990 VAB BPM5 Actual G G St. Vincent & Grenadines East Caribbean dollar 1990 VAB BPM5 Preliminary G C G Sudan Sudanese pound 1981/82f 1996 VAB 2005 BPM5 Actual G B G Suriname Suriname dollar 1990 b VAB BPM5 G G Swaziland Swaziland lilangeni 2000 VAB 2005 Preliminary G C G Sweden Swedish krona a 2000 VAB 2005 BPM5 G C S Switzerland Swiss franc 2000 VAB 2005 BPM5 S C S Syrian Arab Republic Syrian pound 2000 VAB 1970­2008 2005 BPM5 S C G Tajikistan Tajik somoni a 2000 b VAB 1990­95 2005 BPM5 Preliminary G C G Tanzania Tanzanian shilling 1992 VAB 2005 BPM5 Actual S G Thailand Thai baht 1988 VAP 2005 BPM5 Estimate G C S Timor-Leste U.S. dollar 2000 VAP Togo CFA franc 1978 VAP 2005 BPM5 Actual S B G Tonga Tongan pa'anga 2000/01 VAB BPM5 Actual G Trinidad and Tobago Trinidad and 2000 b VAB BPM5 S C G Tobago dollar Tunisia Tunisian dinar 1990 VAP 2005 BPM5 Actual G C S Turkey New Turkish lira 1998 VAB 2005 BPM5 Actual S B S Turkmenistan New Turkmen manat a 2007 b VAB 1987­95, BPM5 Estimate G 1997­2007 Uganda Ugandan shilling 2001/02 VAB 2005 BPM5 Actual G B G Ukraine Ukrainian hryvnia a 2003 b VAB 1987­95 2005 BPM5 Actual G C S United Arab Emirates U.A.E. dirham 1995 VAB BPM4 G C G United Kingdom Pound sterling 2000 b VAB 2005 BPM5 G C S United States U.S. dollar a 2000 VAB 2005 BPM5 G C S Uruguay Uruguayan peso 2005 VAB 2005 BPM5 Actual S C S Uzbekistan Uzbek sum a 1997 b VAB 1990­95 BPM5 Actual G Vanuatu Vanuatu vatu 1983 VAP BPM5 Estimate C G Venezuela, RB Venezuelan 1997 VAB 2005 BPM5 Actual G C G bolivar fuerte Vietnam Vietnamese dong 1994 b VAP 1991 2005 BPM4 Estimate G C G Virgin Islands (U.S.) U.S. dollar 1982 G West Bank and Gaza Israeli new shekel 1997 VAB B G Yemen, Rep. Yemeni rial 1990 VAP 1990­96 2005 BPM5 Actual G B G Zambia Zambian kwacha 1994 VAB 1990­92 2005 BPM5 Preliminary G B G Zimbabwe Zimbabwe dollar 1990 VAB 1991, 1998 2005 BPM5 Actual G C G 430 2010 World Development Indicators PRIMARY DATA DOCUMENTATION Latest Latest demographic, Source of most Vital Latest Latest Latest Latest population education, or health recent income registration agricultural industrial trade water census household survey and expenditure data complete census data data withdrawal data San Marino c Yes São Tomé and Príncipe 2001 PS, 2000­01 2008 Saudi Arabia 2004 Demographic survey, 2007 1999 2005 2007 2006 Senegal 2002 DHS, 2005; MIS, 2008­09 PS, 2005 1998­99 2001 2008 2002 Serbia 2002 MICS, 2005­06 Yes 2008 Seychelles 2002 Yes 1998 2008 2003 Sierra Leone 2004 DHS, 2008 IHS, 2003 1984­85 2002 2000 Singapore 2000 General household, 2005 Yes 2005 2008 Slovak Republic 2001 IS, 1996 Yes 2001 2004 2008 Slovenia 2002 ES/BS, 2004 Yes 2000 2005 2008 Solomon Islands 1999 2007 Somalia 1987 MICS, 2006 1982 2003 South Africa 2001 DHS, 2003 ES/BS, 2000 2000 2005 2008 2000 Spain 2001 IHS, 2000 Yes 1999 2004 2008 2000 Sri Lanka 2001 DHS, 1987 ES/BS, 2002 Yes 2002 2005 2008 2000 St. Kitts and Nevis 2001 2007 St. Lucia 2001 IHS, 1995 Yes 2008 St. Vincent & Grenadines 2001 Yes 2008 Sudan 2008 MICS-PAPFAM, 2006 2000 2008 2000 Suriname 2004 MICS, 2000 ES/BS, 1999 Yes 2008 2000 Swaziland 2007 DHS, 2006/07 ES/BS, 2000/01 2003 2007 2000 Sweden c IS, 2000 Yes 1999­2000 2004 2008 2000 Switzerland 2000 ES/BS, 2000 Yes 2000 2008 2000 Syrian Arab Republic 2004 MICS, 2006 1981 2007 2003 Tajikistan 2000 MICS, 2005 LSMS, 2004 1994 2000 2000 Tanzania 2002 DHS, 2004/05; ES/BS, 2000/01 2002­03 2007 2002 AIS, 2007/08 Thailand 2000 MICS, 2005/06 IHS, 2004 2003 1999 2008 2000 Timor-Leste 2004 DGHS, 2003 LSMS, 2007 2005 Togo 1981 MICS, 2006 CWIQ, 2006 1996 2007 2002 Tonga 2006 Yes 2001 2007 Trinidad and Tobago 2000 MICS, 2006 IHS, 1992 Yes 2004 2005 2008 2000 Tunisia 2004 MICS, 2006 IHS, 2000 2004 2008 2000 Turkey 2000 DHS, 2003 LFS, 2006 2001 2000 2008 2003 Turkmenistan 1995 MICS,2006 LSMS, 1998 Yes 2000 2000 Uganda 2002 DHS, 2006; SPA, 2007 PS, 2005 1991 2001 2008 Ukraine 2001 DHS, 2007 ES/BS, 2008 Yes 2008 2000 United Arab Emirates 2005 1998 2008 2005 United Kingdom 2001 IS, 1999 Yes 1999­2000d 2004 2008 2000 United States 2000 CPS (monthly) LFS, 2000 Yes 1997/2002 2004 2008 2000 Uruguay 2004 IHS, 2007 Yes 2000 2004 2008 2000 Uzbekistan 1989 MICS, 2006 ES/BS, 2003 Yes 2000 Vanuatu 2009 2007 Venezuela, RB 2001 MICS, 2000 IHS, 2003 Yes 1997 2008 Vietnam 2009 MICS, 2006 IHS, 2006 2001 1999 2008 2000 Virgin Islands (U.S.) 2000 Yes West Bank and Gaza 2007 PAPFAM, 2006 1971 Yemen, Rep. 2004 MICS, 2006 ES/BS, 2005 2002 2005 2008 2000 Zambia 2000 DHS, 2007 IHS, 2004­05 1990 2008 2000 Zimbabwe 2002 DHS, 2005/06 1960 1995 2008 2002 Note: For explanation of the abbreviations used in the table see notes following the table. a. Original chained constant price data are rescaled. b. Country uses the 1993 System of National Accounts methodology. c. Register based. d. Conducted annually. e. Rolling. f. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. 2010 World Development Indicators 431 Primary data documentation notes · Base year is the base or pricing period used for element of staff estimation, and estimate that data The preliminary results from the very recent censuses constant price calculations in the country's national are World Bank staff estimates. · System of trade could be reflected in timely revisions if basic data are accounts. Price indexes derived from national refers to the United Nations general trade system (G) available, such as population by age and sex, as well accounts aggregates, such as the implicit deflator for or special trade system (S). Under the general trade as the detailed definition of counting, coverage, and gross domestic product (GDP), express the price level system goods entering directly for domestic con- completeness. Countries that hold register-based relative to base year prices. · Reference year is the sumption and goods entered into customs storage censuses produce similar census tables every 5 or year in which the local currency, constant price series are recorded as imports at arrival. Under the special 10 years. Germany's 2001 census is a register-based of a country is valued. The reference year is usually trade system goods are recorded as imports when test census using a sample of 1.2 percent of the the same as the base year used to report the con- declared for domestic consumption whether at time population. A rare case, France has been conducting stant price series. However, when the constant price of entry or on withdrawal from customs storage. a rolling census every year since 2004; the 1999 data are chain linked, the base year is changed annu- Exports under the general system comprise outward- general population census was the last to cover the ally, so the data are rescaled to a specific reference moving goods: (a) national goods wholly or partly entire population simultaneously (www.insee.fr/en/ year to provide a consistent time series. When the produced in the country; (b) foreign goods, neither recensement/page_accueil_rp.htm). · Latest demo- country has not rescaled following a change in base transformed nor declared for domestic consumption graphic, education, or health household survey indi- year, World Bank staff rescale the data to maintain a in the country, that move outward from customs stor- cates the household surveys used to compile the longer historical series. To allow for cross-country age; and (c) nationalized goods that have been demographic, education, and health data in sec- comparison and data aggregation, constant price declared for domestic consumption and move out- tion 2. AIS is HIV/AIDS Indicator Survey, CPS is Cur- data reported in World Development Indicators are ward without being transformed. Under the special rent Population Survey, DGHS is Demographic and rescaled to a common reference year (2000) and cur- system of trade, exports are categories a and c. In General Health Survey, DHS is Demographic and rency (U.S. dollars). · System of National Accounts some compilations categories b and c are classified Health Survey, ENPF is National Family Planning Sur- identifies countries that use the 1993 System of as re-exports. Direct transit trade--goods entering vey (Encuesta Nacional de Planificacion Familiar), National Accounts (1993 SNA), the terminology or leaving for transport only--is excluded from both FHS is Family Health Survey, LSMS is Living Stan- applied in World Development Indicators since 2001, import and export statistics. See About the data for dards Measurement Survey, MICS is Multiple Indica- to compile national accounts. Although more coun- tables 4.4, 4.5, and 6.2 for further discussion. · Gov- tor Cluster Survey, MIS is Malaria Indicator Survey, tries are adopting the 1993 SNA, many still follow the ernment finance accounting concept is the account- NSS is National Sample Survey on Population Change, 1968 SNA, and some low-income countries use con- ing basis for reporting central government financial PAPFAM is Pan Arab Project for Family Health, RHS is cepts from the 1953 SNA. · SNA price valuation data. For most countries government finance data Reproductive Health Survey, and SPA is Service Provi- shows whether value added in the national accounts have been consolidated (C) into one set of accounts sion Assessments. Detailed information for AIS, is reported at basic prices (VAB) or producer prices capturing all central government fiscal activities. Bud- DHS, MIS, and SPA are available at www.measuredhs. (VAP). Producer prices include taxes paid by produc- getary central government accounts (B) exclude some com/aboutsurveys; for MICS at www.childinfo.org; ers and thus tend to overstate the actual value added central government units. See About the data for and for RHS at www.cdc.gov/reproductivehealth/sur- in production. However, VAB can be higher than VAP tables 4.10, 4.11, and 4.12 for further details. · IMF veys. · Source of most recent income and expendi- in countries with high agricultural subsidies. See data dissemination standard shows the countries ture data shows household surveys that collect About the data for tables 4.1 and 4.2 for further dis- that subscribe to the IMF's Special Data Dissemina- income and expenditure data. Names and detailed cussion of national accounts valuation. · Alternative tion Standard (SDDS) or General Data Dissemination information on household surveys can be found on conversion factor identifies the countries and years System (GDDS). S refers to countries that subscribe the website of the International Household Survey for which a World Bank­estimated conversion factor to the SDDS and have posted data on the Dissemina- Network (www.surveynetwork.org). Core Welfare Indi- has been used in place of the official exchange rate tion Standards Bulletin Board at http://dsbb.imf.org. cator Questionnaire Surveys (CWIQ), developed by (line rf in the International Monetary Fund's [IMF] G refers to countries that subscribe to the GDDS. The the World Bank, measure changes in key social indi- International Financial Statistics). See Statistical meth- SDDS was established for member countries that cators for different population groups--specifically ods for further discussion of alternative conversion have or might seek access to international capital indicators of access, utilization, and satisfaction with factors. · Purchasing power parity (PPP) survey year markets to guide them in providing their economic core social and economic services. Expenditure sur- is the latest available survey year for the International and financial data to the public. The GDDS helps vey/budget surveys (ES/BS) collect detailed informa- Comparison Program's estimates of PPPs. See About countries disseminate comprehensive, timely, acces- tion on household consumption as well as on general the data for table 1.1 for a more detailed description sible, and reliable economic, financial, and socio- demographic, social, and economic characteristics. of PPPs. · Balance of Payments Manual in use refers demographic statistics. IMF member countries elect Integrated household surveys (IHS) collect detailed to the classification system used to compile and to participate in either the SDDS or the GDDS. Both information on a wide variety of topics, including report data on balance of payments items in table standards enhance the availability of timely and com- health, education, economic activities, housing, and 4.15. BPM4 refers to the 4th edition of the IMF's prehensive data and therefore contribute to the pur- utilities. Income surveys (IS) collect information on Balance of Payments Manual (1977), and BPM5 to the suit of sound macroeconomic policies. The SDDS is the income and wealth of households as well as vari- 5th edition (1993). · External debt shows debt also expected to improve the functioning of financial ous social and economic characteristics. Labor force reporting status for 2008 data. Actual indicates that markets. · Latest population census shows the most surveys (LFS) collect information on employment, data are as reported, preliminary that data are based recent year in which a census was conducted and in unemployment, hours of work, income, and wages. on reported or collected information but include an which at least preliminary results have been released. Living Standards Measurement Studies (LSMS), 432 2010 World Development Indicators Primary data documentation notes developed by the World Bank, provide a comprehen- national accounts and balance of payments data Revisions to national accounts data sive picture of household welfare and the factors that using calendar years, but some use fiscal years. In National accounts data are revised by national sta- affect it; they typically incorporate data collection at World Development Indicators fiscal year data are tistical offices when methodologies change or data the individual, household, and community levels. Pri- assigned to the calendar year that contains the larger sources improve. National accounts data in World ority surveys (PS) are a light monitoring survey, share of the fiscal year. If a country's fiscal year ends Development Indicators are also revised when data designed by the World Bank, for collecting data from before June 30, data are shown in the first year of sources change. The following notes, while not com- a large number of households cost-effectively and the fiscal period; if the fiscal year ends on or after prehensive, provide information on revisions from quickly. Income tax registers (ITR) provide information June 30, data are shown in the second year of the previous data. on a population's income and allowance, such as period. Balance of payments data are reported in · Antigua and Barbuda. The government has gross income, taxable income, and taxes by socio- World Development Indicators by calendar year and revised national accounts data for 1998­2008. economic group. 1-2-3 surveys (1-2-3) are imple- so are not comparable to the national accounts data · Bahamas. The government has revised national mented in three phases and collect sociodemographic of the countries that report their national accounts accounts data for 1997­2007. The new base year and employment data, data on the informal sector, on a fiscal year basis. is 2006. · Belize. The government has revised and information on living conditions and household national accounts data for 1991­2008. · Ber- consumption. · Vital registration complete identifies Economies with exceptional reporting periods muda. The Statistical Offi ce has revised national countries judged to have at least 90 percent com- Reporting period accounts data for 1996­2007. · Croatia. The Sta- Fiscal for national plete registries of vital (birth and death) statistics by Economy year end accounts data tistical Bureau has revised main GDP aggregates the United Nations Statistics Division and reported Afghanistan Mar. 20 FY for 1995­2005. · Guatemala. The government in Population and Vital Statistics Reports. Countries has revised national accounts data to conform to Australia Jun. 30 FY with complete vital statistics registries may have the 1993 SNA methodology. The new base year is Bangladesh Jun. 30 FY more accurate and more timely demographic indica- 2001. · Haiti. The government has revised national Botswana Jun. 30 FY tors than other countries. · Latest agricultural cen- accounts data following changes in the methodol- Canada Mar. 31 CY sus shows the most recent year in which an agricul- ogy. Current price series since 1991 and constant Egypt, Arab Rep. Jun. 30 FY tural census was conducted and reported to the Food price series since 1996 have been revised. The new Ethiopia Jul. 7 FY and Agriculture Organization of the United Nations. base year is 1986/87. · Kiribati. The government · Latest industrial data show the most recent year Gambia, The Jun. 30 CY statistical office has revised national accounts data for which manufacturing value added data at the Haiti Sep. 30 FY for 1970­2008. · Lebanon. The government has three-digit level of the International Standard Indus- India Mar. 31 FY revised national accounts data for 1997­2007. trial Classification (ISIC, revision 2 or 3) are available Indonesia Mar. 31 CY The new base year is 1997. · Maldives. National in the United Nations Industrial Development Organi- Iran, Islamic Rep. Mar. 20 FY accounts data for 2001­08 have been revised to zation database. · Latest trade data show the most Japan Mar. 31 CY refl ect a change in source from the Asian Develop- recent year for which structure of merchandise trade Kenya Jun. 30 CY ment Bank to the Maldives Planning Department. data from the United Nations Statistics Division's Kuwait Jun. 30 CY · Mauritius. National accounts now refl ect fiscal Commodity Trade (Comtrade) database are available. Lesotho Mar. 31 CY year data rather than calendar year data. The new · Latest water withdrawal data show the most base year is 2006. · Micronesia, Fed. Sts. The Malawi Mar. 31 CY recent year for which data on freshwater withdrawals government statistical offi ce has revised national Myanmar Mar. 31 FY have been compiled from a variety of sources. See accounts data for 1995­2008. · Namibia. The gov- Namibia Mar. 31 CY About the data for table 3.5 for more information. ernment has revised national accounts data since Nepal Jul. 14 FY 2000. The new base year is 2004/05. · Serbia. The New Zealand Mar. 31 FY Exceptional reporting periods Statistical Bureau has revised current and constant Pakistan Jun. 30 FY In most economies the fiscal year is concurrent with GDP for 1997­2006. · St. Lucia. The government the calendar year. Exceptions are shown in the table Puerto Rico Jun. 30 FY has revised national accounts data for 1998­2008. at right. The ending date reported here is for the fis- Sierra Leone Jun. 30 CY · Uruguay. The government has revised national cal year of the central government. Fiscal years for Singapore Mar. 31 CY accounts data for 1997­2008. The new base year other levels of government and reporting years for South Africa Mar. 31 CY is 2005. statistical surveys may differ. And some countries Swaziland Mar. 31 CY that follow a fiscal year report their national accounts Sweden Jun. 30 CY Changes to national currencies data on a calendar year basis as shown in the report- Thailand Sep. 30 CY · Slovak Republic. On January 1, 2009, the euro ing period column. Uganda Jun. 30 FY replaced the Slovak koruna as the Slovak Repub- The reporting period for national accounts data United States Sep. 30 CY lic's currency. · Turkmenistan. On January 1, 2009, is designated as either calendar year basis (CY) or the Turkmen manat was redenominated (1 new Zimbabwe Jun. 30 CY fiscal year basis (FY). Most economies report their manat = 5,000 old manats). 2010 World Development Indicators 433 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing World indicator as a weight) and denoted by a u when calculated as unweighted Development Indicators. It covers the methods employed for calculating regional averages. The aggregate ratios are based on available data, including data and income group aggregates and for calculating growth rates, and it describes the for economies not shown in the main tables. Missing values are assumed World Bank Atlas method for deriving the conversion factor used to estimate gross to have the same average value as the available data. No aggregate is cal- national income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures culated if missing data account for more than a third of the value of weights and calculations are described in the About the data sections following each table. in the benchmark year. In a few cases the aggregate ratio may be computed as the ratio of group totals after imputing values for missing data according Aggregation rules to the above rules for computing totals. Aggregates based on the World Bank's regional and income classifications of econo- · Aggregate growth rates are denoted by a w when calculated as a weighted mies appear at the end of most tables. The countries included in these classifica- average of growth rates. In a few cases growth rates may be computed from tions are shown on the flaps on the front and back covers of the book. Most tables time series of group totals. Growth rates are not calculated if more than half also include the aggregate euro area. This aggregate includes the member states of the observations in a period are missing. For further discussion of methods the Economic and Monetary Union (EMU) of the European Union that have adopted of computing growth rates see below. the euro as their currency: Austria, Belgium, Cyprus, Finland, France, Germany, · Aggregates denoted by an m are medians of the values shown in the table. Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovak Republic, No value is shown if more than half the observations for countries with a Slovenia, and Spain. Other classifications, such as the European Union and regional population of more than 1 million are missing. trade blocs, are documented in About the data for the tables in which they appear. Exceptions to the rules occur throughout the book. Depending on the judg- Because of missing data, aggregates for groups of economies should be ment of World Bank analysts, the aggregates may be based on as little as 50 treated as approximations of unknown totals or average values. Regional and percent of the available data. In other cases, where missing or excluded values income group aggregates are based on the largest available set of data, including are judged to be small or irrelevant, aggregates are based only on the data values for the 154 economies shown in the main tables, other economies shown shown in the tables. in table 1.6, and Taiwan, China. The aggregation rules are intended to yield esti- mates for a consistent set of economies from one period to the next and for all Growth rates indicators. Small differences between sums of subgroup aggregates and overall Growth rates are calculated as annual averages and represented as percentages. totals and averages may occur because of the approximations used. In addition, Except where noted, growth rates of values are computed from constant price compilation errors and data reporting practices may cause discrepancies in theo- series. Three principal methods are used to calculate growth rates: least squares, retically identical aggregates such as world exports and world imports. exponential endpoint, and geometric endpoint. Rates of change from one period Five methods of aggregation are used in World Development Indicators: to the next are calculated as proportional changes from the earlier period. · For group and world totals denoted in the tables by a t, missing data are imputed based on the relationship of the sum of available data to the total Least squares growth rate. Least squares growth rates are used wherever in the year of the previous estimate. The imputation process works forward there is a sufficiently long time series to permit a reliable calculation. No growth and backward from 2000. Missing values in 2000 are imputed using one of rate is calculated if more than half the observations in a period are missing. several proxy variables for which complete data are available in that year. The The least squares growth rate, r, is estimated by fitting a linear regression trend imputed value is calculated so that it (or its proxy) bears the same relation- line to the logarithmic annual values of the variable in the relevant period. The ship to the total of available data. Imputed values are usually not calculated regression equation takes the form if missing data account for more than a third of the total in the benchmark year. The variables used as proxies are GNI in U.S. dollars, total population, ln Xt = a + bt exports and imports of goods and services in U.S. dollars, and value added in agriculture, industry, manufacturing, and services in U.S. dollars. which is the logarithmic transformation of the compound growth equation, · Aggregates marked by an s are sums of available data. Missing values are Xt = Xo (1 + r ) t. not imputed. Sums are not computed if more than a third of the observations in the series or a proxy for the series are missing in a given year. In this equation X is the variable, t is time, and a = ln Xo and b = ln (1 + r) are · Aggregates of ratios are denoted by a w when calculated as weighted averages parameters to be estimated. If b* is the least-squares estimate of b, then the of the ratios (using the value of the denominator or, in some cases, another average annual growth rate, r, is obtained as [exp(b*) ­ 1] and is multiplied by 100 434 2010 World Development Indicators for expression as a percentage. The calculated growth rate is an average rate that The inflation rate for Japan, the United Kingdom, the United States, and the is representative of the available observations over the entire period. It does not euro area, representing international inflation, is measured by the change in the necessarily match the actual growth rate between any two periods. "SDR deflator." (Special drawing rights, or SDRs, are the International Monetary Fund's unit of account.) The SDR deflator is calculated as a weighted average of Exponential growth rate. The growth rate between two points in time for cer- these countries' GDP deflators in SDR terms, the weights being the amount of tain demographic indicators, notably labor force and population, is calculated each country's currency in one SDR unit. Weights vary over time because both from the equation the composition of the SDR and the relative exchange rates for each currency change. The SDR deflator is calculated in SDR terms first and then converted r = ln(pn/p 0)/n to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conver- sion factor is then applied to a country's GNI. The resulting GNI in U.S. dollars is where pn and p 0 are the last and first observations in the period, n is the number divided by the midyear population to derive GNI per capita. of years in the period, and ln is the natural logarithm operator. This growth rate is When official exchange rates are deemed to be unreliable or unrepresenta- based on a model of continuous, exponential growth between two points in time. tive of the effective exchange rate during a period, an alternative estimate of the It does not take into account the intermediate values of the series. Nor does it exchange rate is used in the Atlas formula (see below). correspond to the annual rate of change measured at a one-year interval, which The following formulas describe the calculation of the Atlas conversion fac- is given by (pn ­ pn­1)/pn­1. tor for year t: Geometric growth rate. The geometric growth rate is applicable to compound growth over discrete periods, such as the payment and reinvestment of interest or dividends. Although continuous growth, as modeled by the exponential growth rate, may be more realistic, most economic phenomena are measured only at intervals, in which case the compound growth model is appropriate. The average and the calculation of GNI per capita in U.S. dollars for year t: growth rate over n periods is calculated as Yt$ = (Yt/Nt)/et* r = exp[ln(pn/p 0)/n] ­ 1. where et* is the Atlas conversion factor (national currency to the U.S. dollar) for Like the exponential growth rate, it does not take into account intermediate year t, et is the average annual exchange rate (national currency to the U.S. dollar) values of the series. for year t, pt is the GDP deflator for year t, ptS$ is the SDR deflator in U.S. dollar terms for year t, Yt$ is the Atlas GNI per capita in U.S. dollars in year t, Yt is current World Bank Atlas method GNI (local currency) for year t, and Nt is the midyear population for year t. In calculating GNI and GNI per capita in U.S. dollars for certain operational purposes, the World Bank uses the Atlas conversion factor. The purpose of the Alternative conversion factors Atlas conversion factor is to reduce the impact of exchange rate fluctuations in The World Bank systematically assesses the appropriateness of official exchange the cross-country comparison of national incomes. rates as conversion factors. An alternative conversion factor is used when the The Atlas conversion factor for any year is the average of a country's exchange official exchange rate is judged to diverge by an exceptionally large margin from rate (or alternative conversion factor) for that year and its exchange rates for the rate effectively applied to domestic transactions of foreign currencies and the two preceding years, adjusted for the difference between the rate of infla- traded products. This applies to only a small number of countries, as shown tion in the country and that in Japan, the United Kingdom, the United States, in Primary data documentation. Alternative conversion factors are used in the and the euro area. A country's inflation rate is measured by the change in its Atlas methodology and elsewhere in World Development Indicators as single-year GDP deflator. conversion factors. 2010 World Development Indicators 435 CREDITS 1. World view Nations; Ricardo Quercioli of the International Energy Agency; Amy Cassara, Chris- Section 1 was prepared by a team led by Eric Swanson. Sarwar Lateef and Eric tian Layke, Daniel Prager, and Robin White of the World Resources Institute; Laura Swanson wrote the introduction with input from Sulekha Patel, Uranbileg Batjargal, Battlebury of the World Conservation Monitoring Centre; and Gerhard Metchies of and Masako Hiraga. Bhaskar Naidu Kalimili coordinated tables 1.1 and 1.6. Shota German Technical Cooperation (GTZ). The World Bank's Environment Department Hatakeyama, Mehdi Akhlagi, Raymond Muhula, and Masako Hiraga prepared devoted substantial staff resources to the book, for which the team is very grateful. tables 1.2, 1.3, and 1.5. Uranbileg Batjargal prepared table 1.4, with valuable Mehdi Akhlaghi wrote the introduction with valuable comments from Sarwar Lateef, assistance from Azita Amjadi. Yuri Dikhanov and the International Comparison Bruce Ross-Larson, and Eric Swanson. Other contributions were made by Susmita Program team provided the new estimates of purchasing power parities (PPP), and Dasgupta, Kirk Hamilton, Craig Meisner, Brian Blankespoor, Olivier Dupriz, Akiko Sup Lee prepared the special PPP table. Changqing Sun prepared the estimates of Saesaka, Kiran Pandey, Giovanni Ruta, and Lopamudra Chakraborti. gross national income in PPP terms. Luca Bandiera of the World Bank's Economic Policy and Debt Department provided the estimates of debt relief for the Heavily 4. Economy Indebted Poor Countries Debt Initiative and Multilateral Debt Relief Initiative. Section 4 was prepared by Bala Bhaskar Naidu Kalimili and Soong Sup Lee in close collaboration with the Sustainable Development and Economic Data Team 2. People of the World Bank's Development Data Group, led by Soong Sup Lee. Soong Sup Section 2 was prepared by Sulekha Patel and Shota Hatakeyama in partner- Lee wrote the introduction with valuable suggestions from Sarwar Lateef and ship with the World Bank's Human Development Network and the Development Eric Swanson, and with assistance from Uranbileg Batjargal and Olga Akcadag. Research Group in the Development Economics Vice Presidency. Masako Hiraga Contributions to the section were provided by Azita Amjadi (trade). The national and William Prince provided invaluable assistance in data and table preparation, accounts data for low- and middle-income economies were gathered by the World and Kiyomi Horiuchi prepared the demographic estimates and projections. The Bank's regional staff through the annual Unified Survey. Maja Bresslauer, Mah- introduction was written by Sulekha Patel with valuable inputs and comments yar Eshragh-Tabary, Victor Gabor, Bala Bhaskar Naidu Kalimili, and Raymond from Albert Motivans of the United Nations Educational, Scientific, and Cultural Muhula worked on updating, estimating, and validating the databases for national Organization Institute for Statistics. Carla AbouZahr from the World Health Organi- accounts. The team is grateful to the International Monetary Fund, Organisation for zation provided comments during initial discussions, and Sarwar Lateef provided Economic Co-operation and Development, United Nations Industrial Development comments on the first draft. The poverty estimates were prepared by Shaohua Organization, and World Trade Organization for access to their databases. Chen and and Prem Sangraula of the World Bank's Poverty Monitoring Group and Changquin Sun. The data on children at work were prepared by Lorenzo 5. States and markets Guarcello and Furio Rosati from the Understanding Children's Work project. Other Section 5 was prepared by David Cieslikowski and Raymond Muhula, in partner- contributions were provided by Eduard Bos, Charu Garg, and Emi Suzuki (popula- ship with the World Bank's Financial and Private Sector Development Network, tion, health, and nutrition); Montserrat Pallares-Miralles and Carolina Romero Poverty Reduction and Economic Management Network, Sustainable Develop- Robayo (vulnerability and security); Lawrence Jeffrey Johnson and Sara Elder of ment Network, the International Finance Corporation, and external partners. the International Labour Organization (labor force); Juan Cruz Perusia and Olivier David Cieslikowski wrote the introduction with input from Eric Swanson. Gary Labe of the United Nations Educational, Scientific, and Cultural Organization Milante and Nadia F. Pittaretti gave valuable advice on the development of the Institute for Statistics (education and literacy); the World Health Organization's fragile situations table. Other contributors include Ada Karina Izaguirre (privatiza- Chandika Indikadahena (health expenditure), Monika Bloessner and Mercedes tion and infrastructure projects); Leora Klapper (business registration); Federica de Onis (malnutrition and overweight), Neeru Gupta and Teena Kunjument (health Saliola (Enterprise Surveys); Sylvia Solf (Doing Business); Alka Banerjee, Isilay workers), Jessica Ho (hospital beds), Rifat Hossain (water and sanitation) and Cabuk, and Nabeel Gadit (Standard & Poor's global stock market indexes); Jeff Philippe Glaziou (tuberculosis); Delice Gan of International Diabetes Federation Wagland of KPMG (tax rates); Satish Mannan (public policies and institutions); (diabetes); and Nyein Nyein Lwin of the United Nations Children's Fund (health). Nigel Adderley of the International Institute for Strategic Studies (military person- Eric Swanson provided valuable comments and suggestions on the introduction nel); Bjorn Hagelin and Sam Perlo-Freeman of the Stockholm International Peace and at all stages of production. Research Institute (military expenditures and arms transfers); Kacem Iaych of the International Road Federation, Ananthanaryan Sainarayan of the Internataional 3. Environment Civil Aviation Organization, and Helene Stephan (transport); Jane Degerlund of Section 3 was prepared by Mehdi Akhlaghi in partnership with the World Bank's Containerisation International (ports); Vanessa Grey and Esperanza Magpan- Sustainable Development Network. Important contributions were made by Carola tay of the International Telecommunication Union; Ernesto Fernandez Polcuch Fabi and Edward Gillin of the Food and Agriculture Organization of the United and Georges Boade of the United Nations Educational, Scientific, and Cultural 436 2010 World Development Indicators Organization Institute for Statistics (research and development, researchers, and Joseph Caponio provided production assistance. Communications Development's technicians); Anders Halvorsen of the World Information Technology and Services London partner, Peter Grundy of Peter Grundy Art & Design, designed the report. Alliance (information and communication technology expenditures); and Ryan Staff from External Affairs oversaw printing and dissemination of the book. Lamb of the World Intellectual Property Organization (patents and trademarks). Client services 6. Global links The Development Data Group's Client Services and Communications Team (Azita Section 6 was prepared by Uranbileg Batjargal in partnership with the Financial Amjadi, Richard Fix, Buyant Erdene Khaltarkhuu, Alison Kwong, Beatriz Prieto- Data Team of the World Bank's Development Data Group, Development Research Oramas, and Vera Wen) contributed to the design and planning and helped coor- Group (trade), Development Prospects Group (commodity prices and remittances), dinate work with the Office of the Publisher. International Trade Department (trade facilitation), and external partners. Uranbi- leg Batjargal wrote the introduction, with valuable comments from Eric Swanson. Administrative assistance, office technology, and systems support Substantial input for the data and tables came from Azita Amjadi (trade and tariffs) Awatif Abuzeid and Estela Zamora provided administrative assistance. Jean-Pierre and Yasue Sakuramoto (external debt and financial data). Eric Swanson provided Djomalieu, Gytis Kanchas, and Nacer Megherbi provided information technology guidance on table contents and organization. Other contributors include Frederic support. Ramvel Chandrasekaran, Ugendran Makhachkala, and Malarvizhi Veerap- Docquier (emigration rates); Flavine Creppy and Yumiko Mochizuki of the United pan provided systems support on the Development Data Platform application. Nations Conference on Trade and Development, and Francis Ng (trade); Betty Dow (commodity prices); Ciara Browne and Thierry Geiger of the World Economic Publishing and dissemination Forum, Jean François Arvis, Monica Alina Mustra, Philip Schuler, and Vera Wen The Office of the Publisher, under the direction of Carlos Rossel, provided valu- (trade facilitation); Christine Nashick, Jeff Reynolds, and Joe Siegel of DHL (freight able assistance throughout the production process. Denise Bergeron, Stephen costs); Yasmin Ahmad, Elena Bernaldo, and Aimee Nichols of the Organisation for McGroarty, and Nora Ridolfi coordinated printing and supervised marketing and Economic Co-operation and Development (aid); Akane Hanai and Ibrahim Levent distribution. Merrell Tuck-Primdahl of the Development Economics Vice Presi- (external debt); Henrik Pilgaard of the United Nations Refugee Agency (refugees); dent's Office managed the communications strategy. Costanza Giovannelli and Bela Hovy of the United Nations Population Division (migration); Sanket Mohapatra and Ani Rudra Silwal (remittances); and Teresa World Development Indicators CD-ROM Ciller of the World Tourism Organization (tourism). Ramgopal Erabelly, Shelley Lai Programming was carried out under the management of Vilas Mandlekar by Abarna Fu, and William Prince provided valuable technical assistance. Panchapakesan and Sujay Ramasamy. System testing was carried out under the guidance of Azita Amjadi and Vilas Mandlekar and included Buyant Erdene Khal- Other parts of the book tarkhuu, Parastoo Oloumi, William Prince, and Vera Wen. Systems development Jeff Lecksell of the World Bank's Map Design Unit coordinated preparation of was undertaken in the Data and Information Systems Team lead by Reza Farivari. the maps on the inside covers. David Cieslikowski prepared Users guide. Eric Masako Hiraga produced the social indicators tables. Kiyomi Horiuchi produced Swanson wrote Statistical methods. Maja Bresslauer, Buyant Erdene Khaltarkhuu, the population projection tables. William Prince coordinated user interface design and William Prince prepared Primary data documentation. Richard Fix and Alison and overall production and provided quality assurance, with assistance from Jomo Kwong prepared Partners and Index of indicators. Tariku. Photo credits belong to the World Bank photo library. Database management WDI Online Mehdi Akhlaghi and William Prince coordinated management of the integrated Design, programming, and testing were carried out by Reza Farivari and his team: World Development Indicators database. Operation of the database management Azita Amjadi, Ying Chi, Ramgopal Erabelly, Shelley Fu, and Buyant Erdene Khal- system was made possible by Ramgopal Erabelly, Shelley Fu, and Shahin Outadi in tarkhuu. William Prince coordinated production and provided quality assurance. the Data and Information Systems Team under the leadership of Reza Farivari. Malika Khek and Devika Levy of the Office of the Publisher were responsible for implementation of WDI Online and management of the subscription service. Design, production, and editing Richard Fix and Alison Kwong coordinated all stages of production with Communica- Client feedback tions Development Incorporated, which provided overall design direction, editing, The team is grateful to the many people who have taken the time to provide and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, and Christopher assistance on its publications. Their feedback and suggestions have helped Trott. Elaine Wilson created the cover and graphics and typeset the book. improve this year's edition. 2010 World Development Indicators 437 BIBLIOGRAPHY Abadzi, Helen. 2007. "Absenteeism and Beyond: Instructional Time Loss and Conse- Ball, Nicole. 1984. "Measuring Third World Security Expenditure: A Research Note." quences." Policy Research Working Paper 4376. World Bank, Washington, D.C. World Development 12 (2): 157­64. Abdul Latif Jameel Poverty Action Lab. 2009. "Showing Up Is the First Step: Barnes, Douglas F. 2009. 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Aid A Agriculture by recipient aid dependency ratios per capita 6.16 6.16 agricultural raw materials total 6.16 commodity prices 6.6 net concessional flows exports from international financial institutions 6.13 as share of total exports 4.4 from UN agencies 6.13 from high-income economies as share of total exports 6.4 official development assistance by DAC members imports administrative costs, as share of net bilateral ODA as share of total imports 4.4 disbursements 6.15a by high-income economies as share of total exports 6.4 bilateral aid 6.15a, 6.15b, 6.17 tariff rates applied by high-income countries 6.4 by purpose 6.15a cereal by sector 6.15b area under production 3.2 commitments 6.14, 6.15b exports from high-income economies as share of total exports 6.4 debt-related aid, as share of net bilateral ODA disbursements 6.15a imports, by high-income economies as share of total imports 6.4 development projects, programs, and other resource provisions, tariff rates applied by high-income countries 6.4 as share of net bilateral ODA disbursements 6.15a yield 3.3 for basic social services, as share of sector-allocable bilateral employment, as share of total 3.2 ODA commitments 1.4 fertilizer gross disbursements 6.14 commodity prices 6.6 humanitarian assistance, as share of net bilateral ODA consumption, per hectare of arable land 3.2 disbursements 6.15a food net disbursements beverages and tobacco 4.3 as share of general government disbursements 6.14 commodity prices 6.6 as share of GNI of donor country 1.4, 6.14 exports from high-income economies as share of total exports 4.4, 6.4 from major donors, by recipient 6.17 imports by high-income economies as share of total imports 4.5, 6.4 per capita of donor country 6.14 tariff rates applied by high-income countries 6.4 total 6.14, 6.15a freshwater withdrawals for, as share of total 3.5 technical cooperation, as share of net bilateral ODA land disbursements 6.15a agricultural, as share of land area 3.2 total sector allocable, as share of bilateral ODA commitments 6.15b arable, as share of land area 3.1 untied aid 6.15b arable, per 100 people 3.1 official development assistance by non-DAC members 6.15a area under cereal production 3.2 permanent cropland, as share of land area 3.1 AIDS--see HIV, prevalence machinery tractors per 100 square kilometers of arable land 3.2 Air pollution--see Pollution production indexes crop 3.3 Air transport food 3.3 air freight 5.10 livestock 3.3 passengers carried 5.10 value added registered carrier departures worldwide 5.10 annual growth 4.1 as share of GDP 4.2 Asylum seekers--see Migration; Refugees per worker 3.3 448 2010 World Development Indicators B Balance of payments corruption informal payments to public officials crime 5.2 current account balance 4.15 losses due to theft, robbery, vandalism, and arson 5.2, 5.8 exports and imports of goods and services 4.15 customs net current transfers 4.15 average time to clear exports 5.2 net income 4.15 dealing with construction permits to build a warehouse total reserves 4.15 number of procedures 5.3 See also Exports; Imports; Investment; Private financial flows; Trade time required 5.3 employing workers Battle-related deaths 5.8 rigidity of employment index 5.3 enforcing contracts Beverages number of procedures 5.3 commodity prices 6.6 time required 5.3 finance Biodiversity--see Biological diversity firms using banks to finance investment 5.2 gender Biological diversity female participation in ownership 5.2 assessment, date prepared, by country 3.15 informality GEF benefits index 3.4 firms formally registered when operations started 5.2 threatened species 3.4 infrastructure animal 3.4 value lost due to electrical outages 5.2 higher plants 3.4 innovation treaty 3.15 ISO certification ownership 5.2 permits and licenses Birth rate, crude 2.1 time required to obtain operating license 5.2 See also Fertility rate protecting investors disclosure, index 5.3 registering property Births attended by skilled health staff 2.19 number of procedures 5.3 time to register 5.3 Birthweight, low 2.19 regulation and tax average number of times firms spend meeting with tax officials 5.2 Bonds--see Debt flows; Private financial flows time dealing with officials 5.2 starting a business Brain drain--see Emigration of people with tertiary education to OECD cost to start a business 5.3 countries number of start-up procedures 5.3 time to start a business 5.3 Breastfeeding, exclusive 2.19, 2.21 workforce, firms offering formal training 5.2 Business environment businesses registered new 5.1 C Carbon dioxide total 5.1 damage 3.16 closing a business emissions time to resolve insolvency 5.3 per 2005 PPP dollar of GDP 3.8 per capita 1.3, 3.8 2010 World Development Indicators 449 INDEX OF INDICATORS total 1.6, 3.8 Contraceptives intensity 3.8 prevalence rate 1.3, 2.19 unmet need for 2.19 Children at work by economic activity 2.6 Contract enforcement male and female 2.6 number of procedures 5.3 study and work 2.6 time required for 5.3 status in employment 2.6 total 2.6, 5.8 Corruption, informal payments to public officials 5.2 work only 2.6 Country Policy and Institutional Assessment (CPIA)--see Economic Cities management; Social inclusion and equity policies; Public sector management air pollution 3.14 and institutions; Structural policies population in largest city 3.11 Credit in selected cities 3.14 getting credit in urban agglomerations of more than 1 million 3.11 depth of credit information index 5.5 urban population 3.11 strength of legal rights index 5.5 See also Urban environment private credit registry coverage 5.5 public credit registry coverage 5.5 Closing a business--see Business environment provided by banking sector 5.5 to private sector 5.1 Commercial bank and other lending 6.12 See also Debt flows; Private financial flows Crime intentional homicide rate 5.8 Commodity prices and price indexes 6.6 losses due to 5.2 Communications--see Internet; Newspapers, daily; Telephones; Television, Current account balance 4.15 households with See also Balance of payments Compensation of government employees 4.11 Customs average time to clear 5.2 Computers (personal) per 100 people 5.12 burden of procedures 6.9 Consumption distribution--see Income distribution fixed capital 3.16 D government, general DAC (Development Assistance Committee)--see Aid annual growth 4.9 as share of GDP 4.8 Death rate, crude 2.1 household See also Mortality rate average annual growth 4.9 per capita 4.9 Debt, external as share of GDP 4.8 as share of GNI 6.11 See also Purchasing power parity (PPP) debt ratios 6.11 450 2010 World Development Indicators debt service gross, by level 2.12 multilateral, as share of public and publicly guaranteed debt net, by level 2.12 service 6.11 adjusted net, primary 2.12 total, as share of exports of goods and services and income 6.11 gross intake rate, grade 1 2.13, 2.15 IMF credit, use of 6.10 gross primary participation rate 2.15 long-term out of school children, male and female 2.12, 2.15 private nonguaranteed 6.10 primary completion rate 1.2, 2.14, 2.15 public and publicly guaranteed male and female 2.14, 2.15 IBRD loans and IDA credits 6.10 progression total 6.10 share of cohort reaching grade 5, male and female 2.13 present value share of cohort reaching last grade of primary, male and female 2.13 as share of GNI 6.11 public expenditure on as share of exports of goods and services and income 6.11 as share of GDP 2.11 short-term as share of total government expenditure 2.11 as share of total debt 6.11 per student, as share of GDP per capita, by level 2.11 as share of total reserves 6.11 pupil-teacher ratio, primary level 2.11 total 6.10 repeaters, primary level, male and female 2.13 total 6.10 teachers, primary, trained 2.11 transition to secondary school, male and female 2.13 Debt flows unemployment by level of educational attainment 2.5 bonds 6.12 years of schooling, average 2.15 commercial banks and other lending 6.12 See also Private financial flows Electricity consumption 5.11 Deforestation, average annual 3.4 production share of total 3.10 Density--see Population, density sources 3.10 transmission and distribution losses 5.11 Dependency ratio--See Population value lost due to outages 5.2 Development assistance--see Aid Emigration of people with tertiary education to OECD countries 6.1 Disease--see Health risks Emissions carbon dioxide Distribution of income or consumption--see Income distribution average annual growth 3.9 intensity 3.8 E Economic management (Country Policy and Institutional Assessment) per capita total methane 3.8 3.8 debt policy 5.9 agricultural as share of total 3.9 economic management cluster average 5.9 industrial as share of total 3.9 fiscal policy 5.9 total 3.9 macroeconomic management 5.9 nitrous oxide agricultural as share of total 3.9 Education industrial as share of total 3.9 enrollment ratio total 3.9 girls to boys enrollment in primary and secondary schools 1.2 other greenhouse gases 3.9 2010 World Development Indicators 451 INDEX OF INDICATORS Employment Exchange rates children in employment 2.6 official, local currency units to U.S. dollar 4.14 in agriculture ratio of PPP conversion factor to official exchange rate 4.14 as share of total employment 3.2 real effective 4.14 male and female 2.3 See also Purchasing power parity (PPP) in industry, male and female 2.3 in services, male and female 2.3 Export credits rigidity index 5.3 private, from DAC members 6.14 to population ratio 2.4 vulnerable 2.4 Exports See also Labor force; Unemployment arms 5.7 documents required for 6.9 Employing workers goods and services rigidity of employment index 5.3 as share of GDP 4.8 average annual growth 4.9 Endangered species--see Animal species; Biological diversity; Plants, higher total 4.15 high-technology Energy share of manufactured exports 5.13 commodity prices 6.6 total 5.13 depletion, as share of GNI 3.16 lead time 6.9 emissions--see Pollution merchandise imports, net 3.8 annual growth 6.2, 6.3 production 3.7 by high-income countries, by product 6.4 use by developing countries, by partner 6.5 2005 PPP dollar of GDP per unit 3.8 by regional trade blocs 6.7 average annual growth 3.8 direction of trade 6.3 combustible renewables and waste as share of total 3.7 structure 4.4 fossil fuel consumption as share of total 3.7 total 4.4 total 3.7 value, average annual growth 6.2 See also Electricity; Fuels volume, average annual growth 6.2 services Enforcing contracts--see Business environment structure 4.6 total 4.6 Enrollment--see Education transport 4.6 travel 4.6, 6.19 Entry regulations for business--see Business environment See also Trade Environmental strategy, year adopted Equity flows 3.15 F Female-headed households 2.10 foreign direct investment, net inflows 6.12 portfolio equity 6.12 Fertility rate See also Private financial flows adolescent 2.19 crude birth rate 2.1 European Commission desired 2.19 distribution of net aid from 6.17 total 2.18, 2.21 452 2010 World Development Indicators Finance, firms using banks to finance investment 5.2 Fuels consumption Financial access, stability, and efficiency road sector 3.13 bank capital to asset ratio 5.5 transportation sector 3.13 bank nonperforming loans 5.5 exports as share of total exports 4.5 Financial flows, net crude petroleum, from high-income economies, as share of total from DAC members 6.14 exports 6.4 official from high-income economies, as share of total exports 6.4 from bilateral sources 6.13 petroleum products, from high-income economies, as share of from international financial institutions 6.13 total exports 6.4 from multilateral sources 6.13 imports from UN agencies 6.13 as share of total imports 4.4 total 6.13 crude petroleum, by high-income economies, as share of total official development assistance and official aid imports 6.4 grants from NGOs 6.14 by high-income economies, as share of total imports 6.4 other official flows 6.14 petroleum products, by high-income economies, as share of total private 6.14 imports 6.4 total 6.14 prices 3.13 See also Aid tariff rates applied by high-income countries 6.4 Financing through international capital markets See also Private financial flows 6.1 G GEF benefits index for biodiversity 3.4 Food--see Agriculture, production indexes; Commodity prices and price indexes Gender, female participation in ownership 5.2 Foreign direct investment, net--see Investment; Private financial flows Gender differences in children in employment 2.6, 5.8 Forest in education 1.2, 2.12, 2.13, 2.14 area, as share of total land area 3.1 in employment 2.3 deforestation, average annual 3.4 in HIV prevalence 2.21 net depletion 3.16 in labor force participation 2.2 in life expectancy at birth 1.5 Freshwater in literacy annual withdrawals adult 2.14 amount 3.5 youth 2.14 as share of internal resources 3.5 in mortality for agriculture 3.5 adult 2.22 for domestic use 3.5 child 2.22 for industry 3.5 in smoking 2.21 renewable internal resources in survival to age 65 2.22 flows 3.5 in youth unemployment 2.10 per capita 3.5 unpaid family workers 1.5 See also Water, access to improved source of women in nonagricultural sector 1.5 women in parliaments 1.5 2010 World Development Indicators 453 INDEX OF INDICATORS Gini index 2.9 Gross savings as share of GDP 4.8 Government, central as share of GNI 3.16 cash surplus or deficit 4.10 debt as share of GDP interest, as share of revenue 4.10 4.10 H Health care interest, as share of total expenses 4.11 children sleeping under treated bednets 2.18 expense children with acute respiratory infection taken to health provider 2.18 as share of GDP 4.10 children with diarrhea who received oral rehydration and continued by economic type 4.11 feeding 2.18 net incurrence of liabilities, as share of GDP children with fever receiving antimalarial drugs 2.18 domestic 4.10 hospital beds per 1,000 people 2.16 foreign 4.10 immunization 2.18 revenues, current physicians, nurses, and midwives 2.16 as share of GDP 4.10 outpatient visits per capita 2.16 grants and other 4.12 physicians per 1,000 people 2.16 social contributions 4.12 reproductive tax, as share of GDP 5.6 anemia, prevalence of, pregnant women 2.20 tax, by source 4.12 births attended by skilled health staff 2.19 contraceptive prevalence rate 1.3, 2.19 Greenhouse gases--see Emissions fertility rate adolescent 2.19 Gross capital formation total 2.19 annual growth 4.9 low-birthweight babies 2.20 as share of GDP 4.8 maternal mortality ratio 1.3, 2.19, 5.8 unmet need for contraception 2.19 Gross domestic product (GDP) tuberculosis annual growth 1.1, 1.6, 4.1 incidence 1.3, 2.20 implicit deflator--see Prices treatment success rate 2.18 per capita, annual growth 1.1, 1.6 total 4.2 Health expenditure as share of GDP 2.16 Gross enrollment--see Education out of pocket 2.16 per capita 2.16 Gross national income (GNI) public 2.16 per capita total 2.16 PPP dollars 1.1, 1.6 rank 1.1 Health information U.S. dollars 1.1, 1.6 census, year last completed 2.17 rank completeness of vital registration PPP dollars 1.1 birth registration 2.17 U.S. dollars 1.1 infant death 2.17 total total death 2.17 PPP dollars 1.1, 1.6 health survey, year last completed 2.17 U.S. dollars 1.1, 1.6 national health account number completed 2.17 454 2010 World Development Indicators year last completed 2.17 Immunization rate, child DPT, share of children ages 12­23 months 2.18 Health risks measles, share of children ages 12­23 months 2.18 anemia, prevalence of children ages under 5 2.20 Imports pregnant women 2.20 arms 5.7 child malnutrition, prevalence 1.2, 2.20 documents required for 6.9 condom use 2.21 energy, net, as share of total energy use 3.8 diabetes, prevalence 2.21 goods and services HIV prevalence 1.3, 2.21 as share of GDP 4.8 overweight children, prevalence 2.20 average annual growth 4.9 smoking, prevalence 2.21 total 4.15 tuberculosis, incidence 1.3, 2.21 lead time 6.9 undernourishment, prevalence 2.20 merchandise annual growth 6.3 Heavily indebted poor countries (HIPCs) by high-income countries, by product 6.4 assistance 1.4 by developing countries, by partner 6.5 completion point 1.4 structure 4.5 decision point 1.4 tariffs 6.4, 6.8 Multilateral Debt Relief Initiative (MDRI) assistance 1.4 total 4.5 value, average annual growth 6.2 HIV volume, average annual growth 6.2 prevalence 1.3, 2.21 services female 2.21 structure 4.7 population ages 15­24, male and female 2.21 total 4.7 total 2.21 transport 4.7 prevention travel 4.7, 6.18 condom use, male and female 2.21 See also Trade Homicide rate, intentional 5.8 Income distribution Gini index 2.9 Hospital beds--see Health care percentage of 1.2, 2.9 Housing conditions, national and urban Industry durable dwelling units 3.12 annual growth 4.1 home ownership 3.12 as share of GDP 4.2 household size 3.12 employment, male and female 2.3 multiunit dwellings 3.12 overcrowding 3.12 Inflation--see Prices vacancy rate 3.12 Informal economy, firms formally registered when operations started 5.2 Hunger, depth 5.8 Information and communications technology expenditures I IDA Resource Allocation Index (IRAI) 5.9 as share of GDP Innovation, ISO certification ownership 5.11 5.2 2010 World Development Indicators 455 INDEX OF INDICATORS Integration, global economic, indicators 6.1 transport 5.1 water and sanitation 5.1 Interest payments--see Government, central, debt See also Gross capital formation; Private financial flows Interest rates Iodized salt, consumption of 2.20 deposit 4.13 lending real risk premium on lending 4.13 4.13 5.5 L Labor force spread 5.5 annual growth 2.2 armed forces 5.7 Internally displaced persons 5.8 children at work 2.6 female 2.2 International Bank for Reconstruction and Development (IBRD) participation of population ages 15 and older, male and female 2.2 IBRD loans and IDA credits 6.10 total 2.2 net financial flows from 6.13 See also Employment; Migration; Unemployment International Development Association (IDA) Land area IBRD loans and IDA credits 6.10 arable--see Agriculture, land; Land use net concessional flows from 6.13 See also Protected areas; Surface area International migrant stock Land use as share of total population 6.1 arable land, as share of total land 3.1 total 6.18 area under cereal production 3.2 See also Migration by type 3.1 forest area, as share of total land 3.1 International Monetary Fund (IMF) irrigated land 3.2 net financial flows from 6.13 permanent cropland, as share of total land 3.1 use of IMF credit 6.10 total area 3.1 Internet Life expectancy at birth broadband subscribers 5.12 male and female 1.5 fixed broadband access tariff 5.12 total 1.6, 2.22 secure servers 5.12 users 5.12 Literacy international Internet bandwidth 5.12, 6.1 adult, male and female 1.6, 2.14 youth, male and female 1.6, 2.14 Investment foreign direct, net inflows Logistics Performance Index 6.9 as share of GDP 6.1 from DAC members total foreign direct, net outflows 6.14 6.12 M Malnutrition, in children under age 5 1.2, 2.20 as share of GDP 6.1 infrastructure, private participation in Malaria energy 5.1 children sleeping under treated bednets 2.18 telecommunications 5.1 children with fever receiving antimalarial drugs 2.18 456 2010 World Development Indicators Management time dealing with officials 5.2 volume, average annual growth 6.2 within regional trade blocs 6.7 Manufacturing imports chemicals 4.3 agricultural raw materials 4.5 exports 4.4, 6.4 by developing countries, by partner 6.5 food 4.3 cereals 6.4 imports 4.5, 6.4 chemicals 6.4 machinery 4.3 crude petroleum 6.4 structure 4.3 food 4.5 textile 4.3 footwear 6.4 value added fuels 4.5 annual growth 4.1 furniture 6.4 as share of GDP 4.2 information and communications technology goods 5.12 total 4.3 iron and steel 6.4 See also Merchandise machinery and transport equipment 6.4 manufactures 4.5 Market access to high-income countries ores and metals 4.5 goods admitted free of tariffs 1.4 ores and nonferrous materials 6.4 support to agriculture 1.4 petroleum products 6.4 tariffs on exports from least developed countries textiles 6.4 agricultural products 1.4 total 4.5 clothing 1.4 value, average annual growth 6.2 textiles 1.4 volume, average annual growth 6.2 trade Merchandise as share of GDP 6.1 exports by developing countries, by partner 6.5 agricultural raw materials 4.4, 6.4 direction 6.3 by regional trade blocs 6.7 growth 6.3 by developing countries, by partner 6.5 regional trade blocs 6.7 cereals 6.4 chemicals 6.4 Metals and minerals crude petroleum 6.4 commodity prices and price index 6.6 food 4.4, 6.4 footwear 6.4 Methane emissions fuels 4.4 agricultural as share of total 3.9 furniture 6.4 industrial as share of total 3.9 information and communications technology goods 5.12 total 3.9 information and communications technology services 5.12 iron and steel 6.4 Migration machinery and transport equipment 6.4 emigration of people with tertiary education to OECD countries 6.1 manufactures 4.4 international migrant stock ores and metals 4.4 as share of total population 6.1 ores and nonferrous materials 6.4 total 6.18 petroleum products 6.4 net 6.1, 6.18 textiles 6.4 See also Refugees; Remittances total 4.4 value, average annual growth 6.2 2010 World Development Indicators 457 INDEX OF INDICATORS Military official development assistance armed forces personnel for basic social services as share of total sector allocable as share of labor force 5.7 ODA commitments 1.4 total 5.7 net disbursements as share of GNI of donor country 1.4, 6.14 arms transfers untied commitments 6.15b exports 5.7 poverty gap 2.7, 2.8 imports 5.7 pregnant women receiving prenatal care 1.5, 2.19 military expenditure share of cohort reaching last grade of primary 2.13 as share of central government expenditure 5.7 support to agriculture 1.4 as share of GDP 5.7, 5.8 telephone lines, fixed, per 100 people 5.11 tuberculosis Millennium Development Goals, indicators for case detection rate 2.18 access to improved sanitation facilities 1.3, 2.18, 5.8 incidence 1.3, 2.21 access to improved water source 2.18, 3.5, 5.8 treatment success rate 2.18 average tariff imposed by developed countries on exports of under-five mortality rate 1.2, 2.22, 5.8 least developed countries 1.4 undernourishment, prevalence 2.20 births attended by skilled health staff 2.19 unmet need for contraception 2.19 carbon dioxide emissions per capita 1.3, 3.8 vulnerable employment 1.2, 2.4 cellular subscribers per 100 people 5.11 women in wage employment in the nonagricultural sector 1.5 children sleeping under treated bednets 2.18 contraceptive prevalence rate 1.3, 2.19 Minerals, depletion of 3.16 employment to population ratio 2.4 enrollment ratio, net, primary 2.12 Monetary indicators female to male enrollments, primary and secondary 1.2 claims on governments and other public entities 4.13 fertility rate, adolescent 2.19 claims on private sector 4.13 goods admitted free of tariffs from least developed countries 1.4 heavily indebted poor countries (HIPCs) Money and quasi money, annual growth 4.13 completion point 1.4 decision point 1.4 Mortality rate nominal debt service relief committed 1.4 adult, male and female 2.22 immunization child, male and female 2.22 DPT 2.18 children under age 5 1.2, 2.22, 5.8 measles 2.18 crude death rate 2.1 income or consumption, national share of poorest quintile 1.2, 2.9 infant 2.22 infant mortality rate 2.22 maternal 1.3, 2.19, 5.8 Internet users per 100 people 5.12 labor productivity, GDP per person employed 2.4 Motor vehicles literacy rate of 15- to 24-year-olds 2.14 passenger cars 3.13 malnutrition, prevalence 1.2, 2.19 per 1,000 people 3.13 malaria per kilometer of road 3.13 children under age 5 sleeping under insecticide treated bednets 2.18 road density 3.13 children under age 5 with fever who are treated with See also Roads; Traffic appropriate antimalarial drugs 2.18 maternal mortality ratio 1.3, 2.18 MUV G-5 index 6.6 national parliament seats held by women 1.5 458 2010 World Development Indicators N Net enrollment--see Education Peacebuilding and peacekeeping operations mission name troops, police, and military observers 5.8 5.8 Net national savings 3.16 Pension average, as share of per capita income 2.10 Newspapers, daily 5.12 contributors as share of labor force 2.10 Nitrous oxide emissions as share of working age population 2.10 agricultural as share of total 3.9 public expenditure on, as share of GDP 2.10 industrial as share of total 3.9 total 3.9 Permits and licenses, time required to obtain operating license 5.2 Nutrition Physicians--see Health care anemia, prevalence of children ages under 5 2.20 Plants, higher pregnant women 2.20 species 3.4 breastfeeding 2.20 threatened species 3.4 iodized salt consumption 2.20 malnutrition, child 1.2, 2.11, 2.20 Pollution overweight children, prevalence 2.20 carbon dioxide undernourishment, prevalence 2.20 damage, as share of GNI 3.16 vitamin A supplementation 2.20 emissions per 2005 PPP dollar of GDP 3.8 O Official development assistance--see Aid per capita total methane emissions 3.8 3.8 agricultural as share of total 3.9 Official flows industrial as share of total 3.9 net total 3.9 from bilateral sources 6.13 nitrogen dioxide, selected cities 3.14 from international financial institutions 6.13 nitrous oxide emissions from multilateral sources 6.13 agricultural as share of total 3.9 from United Nations 6.13 industrial as share of total 3.9 other 6.14 total 3.9 organic water pollutants, emissions P Passenger cars per 1,000 people 3.13 by industry per day per worker 3.6 3.6 3.6 particulate matter, selected cities 3.14 Particulate matter sulfur dioxide, selected cities 3.14 emission damage 3.16 urban-population-weighted PM10 3.13 selected cities 3.14 urban-population-weighted PM10 3.13 Population age dependency ratio, young and old 2.1 Patent applications filed 5.13 average annual growth 2.1 2010 World Development Indicators 459 INDEX OF INDICATORS by age group, as share of total wholesale, annual growth 4.14 0­14 2.11 5­64 2.1 Primary education--see Education 65 and older 2.1 density 1.1, 1.6 Private financial flows female, as share of total 1.5 debt flows rural bonds 6.12 annual growth 3.1 commercial bank and other lending 6.12 as share of total 3.1 equity flows total 1.1, 1.6, 2.1 foreign direct investment, net inflows 6.12 urban portfolio equity 6.12 as share of total 3.11 financing through international capital markets, as share of GDP 6.1 average annual growth 3.11 from DAC members 6.14 in largest city 3.11 See also Investment in selected cities 3.14 in urban agglomerations 3.11 Productivity total 3.11 in agriculture See also Migration value added per worker 3.3 labor productivity, GDP per person employed 2.4 Portfolio--see Equity flows; Private financial flows water productivity, total 3.5 Ports Protected areas container traffic in 5.9 marine quality of infrastructure 6.9 as share of total surface area 3.4 total 3.4 Poverty international poverty line Protecting investors disclosure index 5.3 local currency 2.8 population living below Public sector management and institutions (Country Policy and Institutional $1.25 a day 2.8 Assessment) $2 a day 2.8 efficiency of revenue mobilization 5.9 national poverty line property rights and rule-based governance 5.9 population living below 2.7 public sector management and institutions cluster average 5.9 national 2.7 quality of budgetary and financial management 5.9 rural 2.7 quality of public administration 5.9 urban 2.7 transparency, accountability, and corruption in the public sector 5.9 Power--see Electricity, production Purchasing power parity (PPP) conversion factor 4.14 Prenatal care, pregnant women receiving 1.5, 2.19 gross national income 1.1, 1.6 Prices commodity prices and price indexes consumer, annual growth 6.6 4.14 R Railways fuel 3.8 goods hauled by 5.10 GDP implicit deflator, annual growth 4.14 lines, total 5.10 terms of trade 6.2 passengers carried 5.10 460 2010 World Development Indicators Refugees passengers carried 5.10 by country of asylum 5.8, 6.18 paved, as share of total 5.10 by country of origin 5.8, 6.18 total network 5.10 traffic 3.13 Regional development banks, net financial flows from 6.13 Royalty and license fees Regional trade agreements--see Trade blocs, regional payments 5.13 receipts 5.13 Registering property number of procedures 5.3 Rural environment time to register 5.3 access to improved sanitation facilities 3.11, 5.8 population Regulation and tax administration annual growth 3.1 management time dealing with officials 5.2 as share of total 3.1 meeting with tax officials, number of times 5.2 Relative prices (PPP)--see Purchasing power parity (PPP) S S&P/EMDB Indexes 5.4 Remittances workers' remittances and compensation of employees Sanitation, access to improved facilities, population with as share of GDP 6.1 rural 3.11 paid 6.18 total 1.3, 2.18 received 6.18 urban 3.11 Reproductive health Savings anemia, prevalence of, pregnant women 2.20 gross, as share of GDP 4.8 births attended by skilled health staff 2.19 gross, as share of GNI 3.16 contraception net 3.16 prevalence rate 1.3, 2.19 unmet need for 2.19 Schooling--see Education fertility rate adolescent 2.19 Science and technology desired 2.19 scientific and technical journal articles 5.13 total 2.19 See also Research and development low-birthweight babies 2.20 maternal mortality ratio 1.3, 2.19, 5.8 Secondary education--see Education pregnant women receiving prenatal care 1.5, 2.19, Services Research and development employment, male and female 2.3 expenditures 5.13 exports researchers 5.13 structure 4.6 technicians 5.13 total 4.6 imports Reserves, gross international--see Balance of payments structure 4.7 total 4.7 Roads trade, as share of GDP 6.1 goods hauled by 5.10 2010 World Development Indicators 461 INDEX OF INDICATORS value added simple mean bound rate 6.8 annual growth 4.1 simple mean tariff 6.8 as share of GDP 4.2 weighted mean tariff 6.8 applied rates on imports from low- and middle-income economies 6.4 Smoking, prevalence, male and female 2.21 manufactured products simple mean tariff 6.8 Social inclusion and equity policies (Country Policy and Institutional Assessment) weighted mean tariff 6.8 building human resources 5.9 on exports of least developed countries 1.4 equity of public resource use 5.9 primary products gender equity 5.9 simple mean tariff 6.8 policy and institutions for environmental sustainability 5.9 weighted mean tariff 6.8 social inclusion and equity cluster average 5.9 social protection and labor 5.9 Taxes and tax policies business taxes Starting a business--see Business environment average number of times firms spent meeting tax officials 5.2 number of payments 5.6 Stock markets time to prepare, file, and pay 5.6 listed domestic companies 5.4 total tax rate, percent profit 5.6 market capitalization goods and services taxes, domestic 4.12 as share of GDP 5.4 highest marginal tax rate total 5.4 corporate 5.6 market liquidity 5.4 individual 5.6 S&P/EMDB Indices 5.4 income, profit, and capital gains taxes 4.12 turnover ratio 5.4 international trade taxes 4.12 other taxes 4.12 Steel products, commodity prices and price index 6.6 social contributions 4.12 tax revenue, as share of GDP 5.6 Structural policies (Country Policy and Institutional Assessment) business regulating environment 5.9 Technology--see Computers; Exports, high-technology; Internet; Research and financial sector 5.9 development; Science and technology structural policies cluster average 5.9 trade 5.9 Telephones fixed line Sulfur dioxide emissions--see Pollution per 100 people 5.11 residential tariff 5.11 Surface area 1.1, 1.6 international voice traffic 5.11, 6.1 See also Land use per 100 people 5.11 mobile cellular Survival to age 65, male and female 2.22 per 100 people 1.3, 5.11 population covered 5.11 Suspended particulate matter--see Pollution prepaid tariff 5.11 mobile cellular and fixed-line subscribers per employee 5.11 T Tariffs total revenue Television, households with 5.11 5.12 all products binding coverage 6.8 Terms of trade index, net barter 6.2 462 2010 World Development Indicators Tertiary education--see Education total exports, by bloc 6.7 type of agreement 6.7 Threatened species--see Animal species; Biological diversity; Plants, higher year of creation 6.7 year of entry into force of the most recent agreement 6.7 Tourism, international expenditures in the country Trademark applications filed 5.13 as share of exports 6.19 total 6.19 Trade policies--see Tariffs expenditures in other countries as share of imports 6.19 Traffic total 6.19 road traffic 3.13 inbound tourists, by country 6.19 road traffic injury and mortality 2.18 outbound tourists, by country 6.19 See also Roads Trade Transport--see Air transport; Railways; Roads; Traffic; Urban environment arms 5.7 facilitation Travel--see Tourism, international burden of customs procedures 6.9 documents Treaties, participation in to export 6.9 biological diversity 3.15 to import 6.9 CFC control 3.15 freight costs to the United States 6.9 climate change 3.15 lead time Convention on International Trade on Endangered Species (CITES) 3.15 to export 6.9 Convention to Combat Desertification (CCD) 3.15 to import 6.9 Kyoto Protocol 3.15 liner shipping connectivity index 6.9 Law of the Sea 3.15 logistics performance index 6.9 ozone layer 3.15 quality of port infrastructure 6.9 Stockholm Convention on Persistent Organic Pollutants 3.15 merchandise as share of GDP 6.1 Tuberculosis, incidence 1.3, 2.20 direction of, by developing countries 6.5 direction of, by region high-income economy with low- and middle-income economies, by product 6.3 6.4 U UN agencies, net official financial flows from 6.13 nominal growth, by region 6.3 regional trading blocs 6.7 Undernourishment, prevalence of 2.20 services as share of GDP 6.1 Unemployment computer, information, communications, and other 4.6, 4.7 incidence of long-term, total, male, and female 2.5 insurance and financial 4.6, 4.7 by level of educational attainment, primary, secondary, tertiary 2.5 transport 4.6, 4.7 total, male, and female 2.5 travel 4.6, 4.7 youth, male, and female 1.3, 2.10 See also Balance of payments; Exports; Imports; Manufacturing; Merchandise; Terms of trade; Trade blocs UNHCR, refugees under the mandate of 6.18 Trade blocs, regional UNICEF, net official financial flows from 6.13 exports within bloc 6.7 2010 World Development Indicators 463 INDEX OF INDICATORS UNTA, net official financial flows from 6.13 per worker in agriculture 3.3 UNRWA total, in manufacturing 4.3 net official financial flows from 6.13 refugees under the mandate of 6.18 Vulnerable employment 1.2, 2.4 Urban environment access to sanitation employment, informal sector 3.11, 5.8 2.8 W Water population access to improved source of, population with 1.3, 2.18, 5.8 as share of total 3.11 pollution--see Pollution, organic water pollutants average annual growth 3.11 productivity 3.5 in largest city 3.11 in urban agglomerations 3.11 Women in development total 3.11 female-headed households 2.10 selected cities female population, as share of total 1.5 nitrogen dioxide 3.14 life expectancy at birth 1.5 particulate matter 3.14 pregnant women receiving prenatal care 1.5, 2.19 population 3.14 teenage mothers 1.5 sulfur dioxide 3.14 unpaid family workers 1.5 See also Pollution; Population; Sanitation; Water vulnerable employment 2.4 women in nonagricultural sector 1.5 V Value added women in parliaments Workforce, firms offering formal training 1.5 5.2 as share of GDP in agriculture 4.2 World Bank commodity price index in industry 4.2 energy 6.6 in manufacturing 4.2 nonenergy commodities 6.6 in services 4.2 steel products 6.6 growth in agriculture 4.1 World Bank, net financial flows from 6.13 in industry 4.1 See also International Bank for Reconstruction and Development; in manufacturing 4.1 International Development Association in services 4.1 464 2010 World Development Indicators The world by region Classified according to Low- and middle-income economies World Bank analytical East Asia and Pacific Middle East and North Africa High-income economies grouping Europe and Central Asia South Asia OECD Latin America and the Caribbean Sub-Saharan Africa Other No data Greenland (Den) Iceland Faeroe Norway Islands (Den) Sweden Finland Russian Federation The Netherlands Estonia Isle of Man (UK) Russian Latvia Canada Denmark Fed. Lithuania United Ireland Kingdom Germany Poland Belarus Channel Islands (UK) Belgium Ukraine Luxembourg Moldova Kazakhstan Mongolia Liechtenstein France Italy Romania Switzerland Bulgaria Georgia Uzbekistan Kyrgyz Andorra Armenia Azer- Rep. Dem.People's United States Spain baijan Turkmenistan Rep.of Korea Portugal Turkey Tajikistan Monaco Greece Japan Cyprus Syrian Rep.of Gibraltar (UK) Arab Islamic Rep. Korea Bermuda Malta Lebanon China Tunisia Rep. of Iran Afghanistan (UK) Israel Iraq Morocco Kuwait West Bank and Gaza Jordan Algeria Bahrain Pakistan Bhutan Libya Arab Rep. Qatar Nepal The Bahamas Former of Egypt Spanish Saudi Sahara Arabia Bangladesh Cayman Is.(UK) United Arab Emirates India Mexico Cuba Myanmar Mauritania Oman Lao Haiti Cape Verde P.D.R. Mali N. Mariana Islands (US) Belize Jamaica Niger Chad Eritrea Rep. of Yemen Thailand Guatemala Honduras Senegal Sudan Vietnam Guam (US) El Salvador Nicaragua The Gambia Burkina Cambodia Guinea-Bissau Faso Djibouti Philippines Guinea Federated States of Micronesia Costa Rica Benin Marshall Islands Panama Nigeria Central Ethiopia Sri R.B. de Guyana Sierra Leone Côte Ghana Lanka Venezuela d'Ivoire African Brunei Darussalam Suriname Republic Liberia Palau French Guiana (Fr) Cameroon Malaysia Colombia Togo Somalia Equatorial Guinea Maldives Uganda São Tomé and Príncipe Kenya Nauru Kiribati Congo Singapore Ecuador Gabon Rwanda Kiribati Dem.Rep.of Burundi Seychelles Congo Solomon Tanzania Papua New Guinea Islands Comoros Indonesia Tuvalu Peru Brazil Timor-Leste Samoa French Polynesia (Fr) Angola Malawi Zambia Mayotte American (Fr) Vanuatu Fiji Samoa (US) Bolivia Mozambique Fiji Zimbabwe Madagascar Tonga Mauritius Namibia Botswana New Paraguay Réunion (Fr) Caledonia Australia (Fr) Swaziland Dominican Germany South Republic Puerto Poland Lesotho Rico (US) Africa Czech Republic Ukraine Uruguay Slovak Republic Antigua and Barbuda Chile U.S. Virgin Argentina Islands (US) Guadeloupe (Fr) Austria St. Kitts Hungary New and Nevis Zealand Dominica Slovenia Romania Netherlands Croatia Antilles (Neth) Martinique (Fr) Bosnia and St. Lucia Herzegovina Serbia Aruba St. Vincent and (Neth) Barbados San the Grenadines Marino Kosovo Bulgaria Grenada Italy Montenegro FYR Macedonia Trinidad Vatican Albania and Tobago City Greece R.B. de Venezuela Antarctica IBRD 37655 MARCH 2010 The World Bank 1818 H Street N.W. ISBN 978-0-8213-8232-5 Washington, D.C. 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org SKU 18232 Email: feedback@worldbank.org The World Development Indicators · Includes more than 800 indicators for 155 economies · Provides definitions, sources, and other information about the data · Organizes the data into six thematic areas WORLD VIEW PEOPLE ENVIRONMENT Living standards Natural resources and development Gender, health, and and environmental progress employment changes ECONOMY STATES & MARKETS GLOBAL LINKS New opportunities Elements of a good Evidence on for growth investment climate globalization Saved: 116 trees 37 million Btu of total energy 11,069 pounds of net greenhouse gases 53,312 gallons of waste water 3,237 pounds of solid waste