54167 08 WORLD DEVELOPMENT INDICATORS WORLD VIEW ENVIRONMENT PEOPLE STATES & MARKETS GLOBAL LINKS ECONOMY INCOME MAP The world by income Low income Azerbaijan Costa Rica Greece Afghanistan Belarus Croatia Greenland Bangladesh Bhutan Dominica Guam Benin Bolivia Equatorial Guinea Hong Kong, China Burkina Faso Bosnia and Herzegovina Gabon Iceland Burundi Cameroon Grenada Ireland Cambodia Cape Verde Hungary Isle of Man Central African Republic China Kazakhstan Israel Chad Colombia Latvia Italy Comoros Congo, Rep. Lebanon Japan Congo, Dem. Rep. Cuba Libya Korea, Rep. Côte d'Ivoire Djibouti Lithuania Kuwait Eritrea Dominican Republic Malaysia Liechtenstein Ethiopia Ecuador Mauritius Luxembourg Gambia, The Egypt, Arab Rep. Mayotte Macao, China Ghana El Salvador Mexico Malta Guinea Fiji Montenegro Monaco Guinea-Bissau Georgia Northern Mariana Islands Netherlands Haiti Guatemala Oman Netherlands Antilles India Guyana Palau New Caledonia Kenya Honduras Panama New Zealand Korea, Dem. Rep. Indonesia Poland Norway Kyrgyz Republic Iran, Islamic Rep. Romania Portugal Lao PDR Iraq Russian Federation Puerto Rico Liberia Jamaica Serbia Qatar Madagascar Jordan Seychelles San Marino Malawi Kiribati Slovak Republic Saudi Arabia Mali Lesotho South Africa Singapore Mauritania Macedonia, FYR St. Kitts and Nevis Slovenia Mongolia Maldives St. Lucia Spain Mozambique Marshall Islands St. Vincent and the Sweden Myanmar Micronesia, Fed. Sts. Grenadines Switzerland Nepal Moldova Turkey Trinidad and Tobago Niger Morocco Uruguay United Arab Emirates Nigeria Namibia Venezuela, RB United Kingdom Pakistan Nicaragua United States Papua New Guinea Paraguay High income Virgin Islands (U.S.) Rwanda Peru Andorra São Tomé and Principe Philippines Antigua and Barbuda Senegal Samoa Aruba Sierra Leone Sri Lanka Australia Solomon Islands Suriname Austria Somalia Swaziland Bahamas, The Sudan Syrian Arab Republic Bahrain Tajikistan Thailand Barbados Tanzania Tonga Belgium Timor-Leste Tunisia Bermuda Togo Turkmenistan Brunei Darussalam Uganda Ukraine Canada Uzbekistan Vanuatu Cayman Islands Vietnam West Bank and Gaza Channel Islands Yemen, Rep. Cyprus Zambia Upper middle income Czech Republic Zimbabwe American Samoa Denmark Argentina Estonia Lower middle income Belize Faeroe Islands Albania Botswana Finland Algeria Brazil France Angola Bulgaria French Polynesia Armenia Chile Germany The world by income Low ($905 or less) Classified according to Lower middle ($906­$3,595) World Bank estimates of 2006 GNI per capita Upper middle ($3,596­$11,115) High ($11,116 or more) No data Designed, edited, and produced by Communications Development Incorporated, Washington, D.C., with Peter Grundy Art & Design, London 2008 WORLD DEVELOPMENT INDICATORS Copyright 2008 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 2008 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. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsi- bility whatsoever for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this volume do not imply on the part of the World Bank any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. This publication uses the Robinson projection for maps, which represents both area and shape reasonably well for most of the earth's surface. Nevertheless, some distortions of area, shape, distance, and direction remain. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address in the copyright notice above. The World Bank encourages dissemina- tion of its work and will normally give permission promptly and, when reproduction is for noncommercial purposes, without asking a fee. 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, clockwise from top left, Roobon/The Hunger Project, Curt Carnemark/World Bank, Curt Carnemark/World Bank, and Digital Vision. 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 978-0-8213-7386-6 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 2008 on recycled paper with 30 percent post-consumer waste, 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: 70 trees 3,290 pounds of solid waste 25,621 gallons of waste water 6,172 pounds of net greenhouse gases 49 million BTUs of total energy 2008 WORLD DEVELOPMENT INDICATORS PREFACE Release of the final report of the International Comparison Program (ICP) and publication of new estimates of purchas- ing power parities (PPPs) in World Development Indicators 2008 are an important statistical milestone. The estimates offer a consistent and comprehensive set of data on the cost of living in developed and developing countries, the first since 1997, when the results of the previous ICP data collection were published in World Development Indicators. The 2005 data cover 146 countries and territories, 29 more than the last round in 1993--and many for the first time. Collecting data on thousands of products sold through a multitude of outlets, the 2005 ICP is the largest international statistical program ever undertaken. New methods were used to describe the products being priced, record the data, and analyze the results. Countries in Africa took the opportunity to review their national accounts and adopt new stan- dards and methods. In all regions regional coordinators worked closely with national statistical offices to collect and validate the data. The result is a genuine global effort, with an extensive capacity building component. More work will follow from the ICP. First is the revision of the international ($1 a day) poverty line and estimation of the corresponding poverty rates, certain to change our view of the absolute level of poverty in the world. PPPs have many applications in economic analysis. They are used to determine the relative size of countries and their obligations to international institutions. The publication of new estimates will inspire a new wave of academic studies. And as all of this work goes on, planning for the next round of the ICP will be getting under way. There is much of interest in this year's World Development Indicators besides the ICP results. The Millennium Develop- ment Goal targets have been expanded to include new ones for reproductive health, protection of biodiversity, access to treatment for HIV/AIDS, and full and productive employment and decent work for all. Measuring the associated indicators consistently and reporting on progress pose new challenges for statisticians. The World Development Indi- cators database includes as many of these indicators as possible. The introduction to the People section looks at the importance of reproductive health for the well-being of women and children. The Environment section considers today's great environmental challenge: climate change. Governance--the performance of public officials and the quality of government institutions--has long been recognized as an important determinant of development success. But to understand how governance, good or bad, affects devel- opment, it must be measured. And to provide guidance for improved performance, it must be measured in ways that are sensible to politicians, citizens, and others responsible for improving governance. The States and Markets section discusses how to measure governance and the problems frequently encountered in doing so. The tables provide a selection of governance indicators and other measures of the interaction of states and markets. World Development Indicators remains a rich source of information on the world's people, their economies, and the environment. To make it more useful, we have expanded the Primary data documentation section. As always, we could not bring it to you without the help of our many partners and the work of hundreds of thousands of statisticians and others in developed and developing countries who gather the primary data on which these statistics are based. Shaida Badiee Director Development Data Group 2008 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 David Cieslikowski under the supervision of Eric Swanson and comprising Awatif Abuzeid, Mehdi Akhlaghi, Azita Amjadi, Uranbileg Batjargal, Sebastien Dessus, Richard Fix, Masako Hiraga, Kiyomi Horiuchi, Soong Sup Lee, Ibrahim Levent, Raymond Muhula, Kyoko Okamoto, M.H. Saeed Ordoubadi, Sulekha Patel, Beatriz Prieto-Oramas, Changqing Sun, and K.M. Vijayalakshmi, working closely with other teams in the Development Economics Vice Presidency's Development Data Group. The CD-ROM development team included Azita Amjadi, Ramgopal Erabelly, Reza Farivari, Buyant Erdene Khaltarkhuu, and William Prince. The work was carried out under the management of Shaida Badiee. 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--Financial and Private Sector Development, Human Development, Poverty Reduction and Economic Management, and Sustainable Development--and staff of the International Finance Corporation and the Multilateral Investment Guarantee Agency. Most important, the team received substantial help, guidance, and data from external partners. For individual acknowledgments of contributions to the book's content, please see Credits. For a listing of 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 graphics and typeset the book. Amye Kenall and Joseph Caponio provided proofreading and production assistance. Communications Development's London partner, Peter Grundy of Peter Grundy Art & Design, provided art direction and design. Staff from External Affairs oversaw printing and dissemination of the book. 2008 World Development Indicators vii TABLE OF CONTENTS FRONT 2. PEOPLE Introduction 35 Preface v Acknowledgments vii Tables Partners xii 2.1 Population dynamics 40 Users guide xx 2.2 Labor force structure 44 2.3 Employment by economic activity 48 2.4 Decent work and productive employment 52 1. WORLD VIEW Introduction 1 2.5 2.6 2.7 2.8 Unemployment Children at work Poverty Distribution of income or consumption 56 60 64 68 Tables 2.9 Assessing vulnerability and security 72 2.10 Education inputs 76 1.a New purchasing power parity estimates from the 2005 2.11 Participation in education 80 International Comparison Program 8 2.12 Education efficiency 84 1.1 Size of the economy 14 2.13 Education completion and outcomes 88 1.2 Millennium Development Goals: eradicating poverty and 2.14 Education gaps by income and gender 92 saving lives 18 2.15 Health expenditure, services, and use 94 1.3 Millennium Development Goals: protecting our common 2.16 Disease prevention coverage and quality 98 environment 22 2.17 Reproductive health 102 1.4 Millennium Development Goals: overcoming obstacles 26 2.18 Nutrition 106 1.5 Women in development 28 2.19 Health risk factors and public health challenges 110 1.6 Key indicators for other economies 32 2.20 Health gaps by income and gender 114 Text figures, tables, and boxes 2.21 Mortality 118 1a Participation in the International Comparison Program has been Text figures, tables, and boxes growing 2 2a Most maternal deaths occur in developing countries . . . 35 1b The 2005 International Comparison Program's population 2b . . . especially in Sub-Saharan Africa and South Asia 35 coverage was above 85 percent in every region 2 2c Women in developing countries are more likely to die of 1c Nontradable goods and services show wider variation in prices 2 pregnancy-related causes than women in high-income countries 36 1d Purchasing power parities transform the size of developing 2d The lifetime risk of dying from pregnancy-related causes is economies' GDP in 2005 . . . 3 unacceptably high in Sub-Saharan Africa and South Asia 36 1e . . . and their shares of world GDP 3 2e East Asia and Pacific leads in contraceptive use among 1f China and India's economies, revised downward, remain large 3 married women ages 15­49 36 1g Income disparities remain wide . . . 4 2f Women from the richest households are more likely to use 1h . . . and regional rankings change under purchasing power parities 4 contraception--but contraceptive prevalence rates remain low 36 1i Half the people in the world consumed less than 2g Meeting family planning needs remains a challenge--despite PPP $1,300 a year in 2005 4 benefits such as reduced fertility 37 1j The global distribution of consumption is highly uneven 4 2h Many women in developing countries have an unmet need for 1k Latin America and the Caribbean and Sub-Saharan Africa contraception 37 have the most unequal income distributions 5 2i High adolescent fertility rates mean young women and their 1l Inequality within countries is greatest in Latin America and children are at higher risk of death and disability 37 the Caribbean and lowest in Sub-Saharan Africa 5 2j Age-specific fertility for girls ages 15­17 37 1m For similar investment efforts poor countries grew faster 2k All regions have made progress in providing prenatal care to between 1996 and 2006 . . . 5 women at least once during their pregnancy 38 1n . . . but investment efforts in low-income countries were 2l In South Asia rich women are three times more likely to insufficient to match the growth of richer countries 5 receive prenatal care than are poor women 38 1o Regional differences in food consumption are less than 2m The proportion of births attended by skilled health staff differences in income 6 remains low in South Asia and Sub-Saharan Africa 38 1p For similar levels of food consumption, malnutrition is 2n Nearly all women in Europe and Central Asia have births attended particularly high in South Asia 6 by skilled health staff--but even there poor women lag behind 38 1q Health spending has less impact on life expectancy in 2o The importance of emergency obstetric care 39 Sub-Saharan Africa 6 2p Most unsafe abortions take place in developing countries, 1r For similar education spending youth literacy rates are much especially in Latin America and the Caribbean and Africa 39 lower in West Africa 6 2.6a In developing countries the majority of child workers ages 1s Fragile states spend more on collective goods 7 5­14 are involved in unpaid family work 63 1t The world economy is becoming more energy efficient, but too 2.8a The Gini coefficient and ratio of income or consumptionof the slowly to stabilize energy consumption 7 richest quintile to the poorest quintiles are closely correlated 71 1u Workers' remittances play a sizable role in the Middle East 2.11a In some countries close to 10 percent of primary-school-age and North Africa and Latin America and the Caribbean 7 children are enrolled in secondary school 83 1v Sub-Saharan Africa is the main recipient of programmable aid 7 2.12a In Lesotho more girls who enroll in primary school stay in and 1.2a Location of indicators for Millennium Development Goals 1­4 21 complete school than boys do 87 1.3a Location of indicators for Millennium Development Goals 5­7 25 2.13a In 2005 more than 770 million people were illiterate-- 1.4a Location of indicators for Millennium Development Goal 8 27 64 percent of them women, a share unchanged since 1990 91 viii 2008 World Development Indicators 3. ENVIRONMENT Introduction 123 3o Forested areas are shrinking in Latin America and Sub-Saharan Africa--recovering in East Asia 128 Tables 3.1 Rural population and land use 130 3p The vast majority of people without access to electricity in 2004 lived in developing countries 128 3.2 Agricultural inputs 134 3q China and India generate more than two-thirds of their 3.3 Agricultural output and productivity 138 electricity from coal 128 3.4 Deforestation and biodiversity 142 3r Greater coal efficiency can reduce carbon dioxide emissions 128 3.5 Freshwater 146 3s Social insurance spending is lower in developing countries, where 3.6 Water pollution 150 people are exposed to higher risk of climate change impact 129 3.7 Energy production and use 154 3t The climate information gap makes adaptation more difficult 129 3.8 Energy dependency and efficiency and carbon 3u Adaptation is expensive, and funding for developing dioxide emissions 158 countries is inadequate 129 3.9 Trends in greenhouse gas emissions 162 3.1a What is rural? Urban? 133 3.10 Sources of electricity 166 3.2a Nearly 40 percent of land globally is devoted to agriculture 137 3.11 Urbanization 170 3.2b Developing regions lag in agricultural machinery, which 3.12 Urban housing conditions 174 reduces their agricultural productivity 137 3.13 Traffic and congestion 178 3.3a Cereal yield in low-income countries was only 40 percent of 3.14 Air pollution 182 the yield in high-income countries 141 3.15 Government commitment 184 3.3b Sub-Saharan Africa had the lowest yield, while East Asia 3.16 Toward a broader measure of savings 188 and Pacific is closing the gap with high-income countries 141 Text figures, tables, and boxes 3.5a Agriculture is still the largest user of water, accounting for 3a Greenhouse gas emissions by sector and by activity 123 some 70 percent of global withdrawals 149 3b Use of ozone-depleting substances has dropped 3.5b The share of withdrawals for agriculture approaches substantially since 1990 124 90 percent in some developing regions 149 3c The United States and China lead the world in carbon 3.6a Emissions of organic water pollutants declined in most countries dioxide emissions 124 from 1990 to 2004, even in some of the top emitters 153 3d High-income countries produce far more carbon dioxide 3.7a A person in a high-income economy uses an average of emissions per capita than low- or middle-income countries 124 more than 11 times as much energy as a person in a 3e High-income economies emitted half the global carbon low-income economy 157 dioxide emissions in 2005 124 3.8a High-income economies depend on imported energy . . . 161 3f Power generation and land use change were the two largest 3.8b . . . mostly from middle-income countries in the Middle East sources of greenhouse gas emissions in 2000 125 and North Africa and Latin America and the Caribbean 161 3g Fossil fuels accounted for three-quarters of the fuel used in 3.9a The 10 largest contributors to methane emissions account the power sector in 2002 125 for about 62 percent of emissions 165 3h Coal was responsible for the majority of emissions from the 3.9b The 10 largest contributors to nitrous oxide emissions power sector in 2002 125 account for about 56 percent of emissions 165 3i Road transport accounted for more than three-quarters of 3.10a Sources of electricity generation have shifted since 1990 . . . 169 total transport carbon dioxide emissions in 2000 125 3.10b . . . with low-income countries relying more on coal 169 3j Climate change would hurt developing countries' 3.11a Developing economies had the largest increase in urban agricultural output 126 population between 1990 and 2006 173 3k Less rain is falling in the Sahel, with dire consequences 126 3.11b Latin America and the Caribbean had the same share of 3l The rise in global mean surface temperature is accelerating 127 urban population as high-income economies in 2006 173 3m Climate disasters are affecting more and more people, 3.12a Selected housing indicators for smaller economies 177 mostly in developing countries 127 3.13a Particulate matter concentration has fallen in all 3n Developing countries are exposed to higher risk of income groups, and the higher the income, the lower natural disaster 127 the concentration 181 2008 World Development Indicators ix TABLE OF CONTENTS 4. ECONOMY 5. STATES AND MARKETS Introduction 193 Introduction 259 Tables Tables 4.1 Growth of output 198 5.1 Private sector in the economy 268 4.2 Structure of output 202 5.2 Business environment: enterprise surveys 272 4.3 Structure of manufacturing 206 5.3 Business environment: Doing Business indicators 276 4.4 Structure of merchandise exports 210 5.4 Stock markets 280 4.5 Structure of merchandise imports 214 5.5 Financial access, stability, and efficiency 284 4.6 Structure of service exports 218 5.6 Tax policies 288 4.7 Structure of service imports 222 5.7 Military expenditures and arms transfers 292 4.8 Structure of demand 226 5.8 Public policies and institutions 296 4.9 Growth of consumption and investment 230 5.9 Transport services 300 4.10 Central government finances 234 5.10 Power and communications 304 4.11 Central government expenses 238 5.11 The information age 308 4.12 Central government revenues 242 5.12 Science and technology 312 4.13 Monetary indicators 246 Text figures, tables, and boxes 4.14 Exchange rates and prices 250 5a Governance and growth go together 259 4.15 Balance of payments current account 254 5b Who uses governance indicators? 260 Text figures, tables, and boxes 5c Not producing the desired results 261 4a Developing economies increased their share of world output 193 5d Governance in theory and in practice 261 4b Low- and lower middle-income economies have had the 5e Examples of governance outcome indicators 262 strongest growth 194 5f Selected actionable governance indicators 263 4c Patterns of regional growth vary widely 194 5g Drilling down: the Worldwide Governance Indicators 263 4d Inflation is now less than 9 percent in all developing regions 194 5h Experts generally agree on governance assessments at the 4e Real interest rates have fallen in many developing economies 194 aggregate level . . . 264 4f Oil, metal, and mineral prices have increased since 1990 195 5i . . . but experts can still disagree, even using a very specific 4g Oil-exporting economies have experienced gains 195 assessment protocol 264 4h Terms of trade, gross domestic product, and gross domestic 5j Comparing governance scores in the light of uncertainty 265 income growth for selected economies 195 5k The World Bank and governance indicators 267 4.3a Manufacturing continues to show strong growth in East Asia 209 4.4a Developing economies' share of world merchandise exports continues to expand 213 4.5a Top 10 developing country exporters of merchandise goods in 2006 217 4.6a Top 10 developing country exporters of commercial services in 2006 221 4.7a The mix of commercial service imports by developing countries is changing 225 4.9a Investment is rising rapidly in Asia 233 4.10a Fifteen developing economies had a total debt to GDP ratio of 50 percent or higher 237 4.11a Interest payments are a large part of government expenses for some developing countries 241 4.12a Rich countries rely more on direct taxes 245 4.15a Top 15 economies with the largest current account surplus--and top 15 economies with the largest current account deficit in 2006 257 x 2008 World Development Indicators 6. GLOBAL LINKS BACK Introduction 317 Primary data documentation 381 Statistical methods 390 Tables Credits 392 6.1 Integration with the global economy 320 Bibliography 394 6.2 Growth of merchandise trade 324 Index of indicators 403 6.3 Direction and growth of merchandise trade 328 6.5 Primary commodity prices 334 6.6 Regional trade blocs 336 6.7 Tariff barriers 340 6.8 External debt 344 6.9 Ratios for external debt 348 6.10 Global private financial flows 352 6.11 Net official financial flows 356 6.12 Financial flows from Development Assistance Committee members 360 6.13 Allocation of bilateral aid fromDevelopment Assistance Committee members 362 6.14 Aid dependency 364 6.15 Distribution of net aid by Development Assistance Committee members 368 6.16 Movement of people 372 6.17 Travel and tourism 376 Text figures, tables, and boxes 6a Developing countries' share of global trade is rising 318 6b Manufactured goods dominate the exports of developing countries 318 6c Rising reserves and falling debt make developing countries less vulnerable to crises 318 6d Private financing has long exceeded official development assistance to developing countries 318 6e More migrants in high-income economies . . . 319 6f . . . are sending more remittances to developing countries 319 6g Europe and Central Asia and Latin America and the Caribbean lead other developing regions in access to the Internet . . . 319 6h . . . and in international bandwith per capita 319 6.1a Trade and international finance are leading globalization 323 6.3a More than half of the world's merchandise trade takes place between high-income economies. But integration of low- and middle-income economies in global merchandise trade increased substantially during 1996­2006 330 6.4a The composition of high-income economies' imports from low- and middle-income economies has changed over the last decade 333 6.6a The number of trade agreements has increased rapidly since 1990, especially free trade agreements 339 6.8a Financial integration has complemented growth 347 6.9a Developing countries have reduced financial vulnerability 351 6.10a Financial integration of low-income economies remains marginal 355 6.11a While net financial flows to middle-income economies are falling, low-income economies are still borrowing from international financial institutions 359 6.14a Official development assistance from non-DAC donors, 2002­06 367 6.15a Debt relief and political interests have shaped the allocation of official development assistance 371 6.17a Developing countries are spending more on tourism in other countries 379 2008 World Development Indicators xi PARTNERS Defining, gathering, and disseminating international statistics is a collective effort of many people and organiza- tions. 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 stan- dards fundamental to an international statistical system. Nongovernmental organizations 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, 2008. 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 corpora- tion for international cooperation with worldwide operations. GTZ's aim is to positively shape political, economic, eco- logical, 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; 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/. xii 2008 World Development Indicators 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 promo- tion of social justice and internationally recognized human and labor rights. 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 the world's central organization for international monetary coop- eration. Its 184 member countries work together to promote sustainable economic growth and rising living standards by ensuring the stability of the international monetary system--the system of exchange rates and international payments that enables countries (and their citizens) to buy goods and services from each other. The IMF reviews national, regional, and global economic and financial developments, provides finan- cial 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 and provides technical assistance and training to help countries build the expertise and institutions they need for economic stability and growth. For more information, see www.imf.org/. International Telecommunication Union The International Telecommunication Union (ITU) is the leading UN agency for information and com- munication technologies. ITU's mission is to enable the growth and sustained development of telecom- munications 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, promot- ing adequate capacity building, and developing confidence in the use of cyberspace through enhanced online security. ITU also concentrates on strengthening emergency communications for disaster preven- tion and mitigation. For more information, see www.itu.int/. 2008 World Development Indicators xiii PARTNERS 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 shar- ing a commitment to democratic government and the market economy to support sustainable economic growth, boost employment, raise living standards, maintain financial stability, assist other coun- tries' 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 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 Labor 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 Flor- ence, Italy, until June 2004, when it moved to the Centre for International Studies on Economic Growth in Rome. xiv 2008 World Development Indicators 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 the 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/. United Nations Children's Fund The United Nations Children's Fund (UNICEF) works with other UN bodies and with governments and nongovern- mental organizations to improve children's lives in more than 190 countries through various programs in educa- tion 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 Educational, Scientific, and Cultural Organization, Institute for Statistics The United Nations Educational, Scientific, and Cultural Organization is a specialized agency of the United Nations that promotes "collaboration among nations through education, science, and culture in order to 2008 World Development Indicators xv PARTNERS further universal respect for justice, for the rule of law, and for the human rights and fundamental free- doms . . . for the peoples of the world, without distinction of race, sex, language, or religion." 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/. 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/. 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 World Bank Group The World Bank is one of the world's largest sources of funding and knowledge for developing countries. Its main focus is on helping the poorest people and the poorest countries. It uses its financial resources, staff, and extensive experience to help developing countries reduce poverty, increase economic growth, and improve their quality of life. The Bank brings a mix of money and knowledge to encourage economic and social develop- ment and help countries achieve the internationally agreed Millennium Development Goals. The World Bank supports projects that help countries to invest in many different areas: health and education, fighting corruption, boosting agricultural production, building roads and ports, and protecting the environment. Since resources are scarce, assessing the effect of projects the Bank supports is essential in developing countries and is part of its focus on actual results for poor people. The World Bank Group has 185 member countries. For more information, see www.worldbank.org/data/. 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. The WHO carries out a wide range of func- tions, including coordinating international health work; helping governments strengthen health services; xvi 2008 World Development Indicators providing technical assistance and emergency aid; working for the prevention and control of disease; pro- moting improved nutrition, housing, sanitation, recreation, and economic and working conditions; promoting and coordinating biomedical and health services research; promoting improved standards of teaching and training in health and medical professions; establishing international standards for biological, pharmaceuti- cal, and similar products; and standardizing diagnostic procedures. 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 assisting gov- ernments and organizations to develop the policies, structures and skills needed to harness the potential of IP for economic development; working with member states to develop international IP law; administering treaties; running global registration systems for trademarks, industrial designs, and appellations of origin and a filing system for patents; delivering dispute resolution services; and providing a forum for informed debate and for the exchange of expertise. For more information, see www.wipo.int/. World Tourism Organization The World Tourism Organization is an intergovernmental body entrusted by the United Nations with promoting 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/. 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/. 2008 World Development Indicators xvii PARTNERS 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 unique global platform that brings together public and private entities committed to road development. Working together with its members and associates, the IRF pro- motes social and economic benefits that flow from well planned and environmentally sound transportation networks. The IRF serves as a catalyst for public and private partnership to organize, promote, and develop international road programs. The main objectives include promoting the understanding of the social, eco- nomic, and environmental benefits derived from developing modern road networks, road transport systems, and road traffic control; improving road safety; planning and executing economically and environmentally sound programs for the improvement and extension of road networks; conducting educational and training programs relating to the development and maintenance of road and road transport systems; facilitating the exchange of experience with national, regional, and international institutions; and harmonizing standards, research, and dissemination of road related information. For more information, see www.irfnet.org/. Netcraft Netcraft is an Internet services company and a respected authority on the market share of web servers, operating systems, hosting providers, Internet service providers, encrypted transactions, electronic com- merce, scripting languages, and content technologies on the Internet. Netcraft provides Internet security services, including antifraud and antiphishing services, application testing, code reviews, and automated penetration testing as well as research data and analysis on many aspects of the Internet. For more information, see www.netcraft.com/. PricewaterhouseCoopers PricewaterhouseCoopers provides industry-focused assurance, tax, human resources, transactions, perfor- mance improvement, and crisis management services to help address client and stakeholder 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 draw 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 indices to serve as benchmarks that are consistent across national boundaries. Standard & xviii 2008 World Development Indicators Poor's calculates one index, the S&P/IFCG (Global) index, that reflects the perspective of local investors and those interested in broad trends in emerging markets and another, the S&P/IFCI (Investable) index, that provides a broad, neutral, and historically consistent benchmark for the growing emerging market invest- ment community. 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 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, 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--objec- tive information and practical proposals for policy and institutional change that will foster environmentally 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/. 2008 World Development Indicators xix USERS GUIDE Tables Gap filling of amounts not allocated to countries may affecting the collection and reporting of data, such The tables are numbered by section and display the result in discrepancies between subgroup aggregates as problems stemming from conflicts. identifying icon of the section. Countries and econo- and overall totals. For further discussion of aggrega- For these reasons, although data are drawn from mies are listed alphabetically (except for Hong Kong, tion methods, see Statistical methods. the sources thought to be most authoritative, they China, which appears after China). Data are shown should be construed only as indicating trends and for 153 economies with populations of more than Aggregate measures for regions characterizing major differences among economies 1 million, as well as for Taiwan, China, in selected The aggregate measures for regions cover only low- rather than as offering precise quantitative mea- tables. Table 1.6 presents selected indicators for and middle-income economies, including econo- sures of those differences. Discrepancies in data 56 other economies--small economies with popu- mies with populations of less than 1 million listed presented in different editions of World Development lations between 30,000 and 1 million and smaller in table 1.6. Indicators reflect updates by countries as well as economies if they are members of the International The country composition of regions is based on revisions to historical series and changes in meth- Bank for Reconstruction and Development (IBRD) or, the World Bank's analytical regions and may differ odology. Thus readers are advised not to compare as it is commonly known, the World Bank. The term from common geographic usage. For regional clas- data series between editions of World Development country, used interchangeably with economy, does sifications, see the map on the inside back cover and Indicators or between different World Bank publica- not imply political independence, but refers to any the list on the back cover flap. For further discussion tions. Consistent time-series data for 1960­2006 territory for which authorities report separate social of aggregation methods, see Statistical methods. are available on the World Development Indicators or economic statistics. When available, aggregate CD-ROM and in WDI Online. measures for income and regional groups appear at Statistics Except where otherwise noted, growth rates are the end of each table. Data are shown for economies as they were con- in real terms. (See Statistical methods for information Indicators are shown for the most recent year stituted in 2006, and historical data are revised to on the methods used to calculate growth rates.) Data or period for which data are available and, in most reflect current political arrangements. Exceptions are for some economic indicators for some economies tables, for an earlier year or period (usually 1990 or noted throughout the tables. are presented in fiscal years rather than calendar 1995 in this edition). Time-series data are available Additional information about the data is provided years; see Primary data documentation. All dollar fig- on the World Development Indicators CD-ROM and in Primary data documentation. That section sum- ures are current U.S. dollars unless otherwise stated. in WDI Online. marizes national and international efforts to improve The methods used for converting national currencies Known deviations from standard definitions or basic data collection and gives country-level informa- are described in Statistical methods. breaks in comparability over time or across countries tion on primary sources, census years, fiscal years, are either footnoted in the tables or noted in About statistical methods and concepts used, and other Country notes the data. When available data are deemed to be background information. Statistical methods provides · Unless otherwise noted, data for China do not too weak to provide reliable measures of levels and technical information on some of the general calcula- include data for Hong Kong, China; Macao, China; trends or do not adequately adhere to international tions and formulas used throughout the book. or Taiwan, China. standards, the data are not shown. · Data for Indonesia include Timor-Leste through Data consistency, reliability, and comparability 1999 unless otherwise noted Aggregate measures for income groups Considerable effort has been made to standardize · Montenegro declared independence from Serbia The aggregate measures for income groups include the data, but full comparability cannot be assured, and Montenegro on June 3, 2006. When avail- 209 economies (the economies listed in the main and care must be taken in interpreting the indicators. able, data for each country are shown separately. tables plus those in table 1.6) whenever data are Many factors affect data availability, comparability, However, some indicators for Serbia continue to available. To maintain consistency in the aggregate and reliability: statistical systems in many develop- include data for Montenegro through 2005; these measures over time and between tables, missing ing economies are still weak; statistical methods, data are footnoted in the tables. Moreover, data data are imputed where possible. The aggregates coverage, practices, and definitions differ widely; and for most indicators from 1999 onward for Serbia are totals (designated by a t if the aggregates include cross-country and intertemporal comparisons involve exclude data for Kosovo, a territory within Serbia gap-filled estimates for missing data and by an s, complex technical and conceptual problems that can- that is currently under international administra- for simple totals, where they do not), median values not be resolved unequivocally. Data coverage may tion pursuant to UN Security Council Resolution (m), weighted averages (w), or simple averages (u). not be complete because of special circumstances 1244 (1999); any exceptions are noted. xx 2008 World Development Indicators Classification of economies Symbols For operational and analytical purposes the World .. Bank's main criterion for classifying economies is means that data are not available or that aggregates gross national income (GNI) per capita (calculated cannot be calculated because of missing data in the by the World Bank Atlas method). Every economy is years shown. classified as low income, middle income (subdivided into lower middle and upper middle), or high income. 0 or 0.0 For income classifications see the map on the inside means zero or small enough that the number would front cover and the list on the front cover fl ap. Low- round to zero at the displayed number of decimal and middle-income economies are sometimes places. referred to as developing economies. The term is used for convenience; it is not intended to imply / that all economies in the group are experiencing in dates, as in 2003/04, means that the period of time, similar development or that other economies have usually 12 months, straddles two calendar years and reached a preferred or final stage of development. refers to a crop year, a survey year, or a fiscal year. Note that classifi cation by income does not neces- sarily refl ect development status. Because GNI per $ capita changes over time, the country composition means current U.S. dollars unless otherwise noted. of income groups may change from one edition of World Development Indicators to the next. Once the > classifi cation is fi xed for an edition, based on GNI means more than. per capita in the most recent year for which data are available (2006 in this edition), all historical < data presented are based on the same country means less than. grouping. Low-income economies are those with a GNI Data presentation conventions per capita of $905 or less in 2006. Middle-income · A blank means not applicable or, for an aggre- economies are those with a GNI per capita of more gate, not analytically meaningful. than $905 but less than $11,116. Lower middle- · A billion is 1,000 million. income and upper middle-income economies are · A trillion is 1,000 billion. separated at a GNI per capita of $3,595. High- · Figures in italics refer to years or periods other income economies are those with a GNI per capita than those specified or to growth rates calculated of $11,116 or more. The 15 participating mem- for less than the full period specified. ber countries of the euro area are presented as a · Data for years that are more than three years subgroup under high-income economies. Note that from the range shown are footnoted. Cyprus and Malta joined the euro area on January 1, 2008. The cutoff date for data is February 1, 2008. 2008 World Development Indicators xxi WORLD VIEW 1 Introduction V iewing the world at purchasing power parity Comparable measures of economic activity and living standards are useful for many purposes. Foreign investors, traders, and potential immigrants want to know an economy's market size, productivity, and prices. The globalization of markets for goods, services, finance, labor, and ideas reinforces the interdependence of economies and the need to measure them on a common scale. Countries cannot share responsibilities for global public goods--the environment, security, development assistance, and global governance--without meaningful assessments of the real size of their economies and the well-being of their people. But comparing the real size of economies is not easy. Even in an integrated global economy large differences in the costs of goods and services persist. Exchange rates can be used to convert values in one currency to another, but since they do not fully reflect differences in price levels they cannot measure the real volume of output. Exchange rates are determined by the demand for and supply of currencies used in international transactions, ignoring domestic economic sectors where prices are set in relative isolation from the rest of the world. Thus the familiar experience of international travelers, who discover that they can buy more, or less, of the same goods in different countries when converting their money using the prevailing exchange rates. To measure the real size of the world's economy and to compare costs of living across coun- tries, we need to adjust for differences in purchasing power. Finding a way to adjust for those differences has given rise to the efforts to measure purchasing power parties (PPPs), which convert local currencies to a common currency, such as the U.S. dollar. Since 1970 the International Comparison Program (ICP) has conducted eight rounds of PPP estimates for the major components of countries' gross domestic product (GDP)--the most recent for 2005. The PPP process calls for the systematic collection of price data on hundreds of representative and carefully defined products and services consumed in each country, requir- ing the full cooperation of national statistical agencies and international organizations. High-income countries regularly take part in such programs, but 2005 was the first time since 1993 that comprehensive price surveys were carried out in developing economies. An unprecedented number, 101, took part. These new PPPs provide a better and more complete view of the world economy. They show that in 2005 developing country economies were on average 2.2 times larger when measured by PPPs than by exchange rates. They also reveal that past estimates of the real size of the economies of developing countries based on the 1993 ICP round were often too large. This section reports the major findings of the 2005 ICP round and explores some of the implications. In doing so, it aims to provide a better picture of today's important issues, highlighting the diversity--and the commonality--of development patterns and outcomes. 2008 World Development Indicators 1 Country participation and population coverage Measuring price differences The eighth round of the ICP included 146 economies--101 of Purchasing power parities are needed because similar goods them classified by the World Bank as low and middle income and services have widely varying prices across countries when based on gross national income per capita at market exchange converted to a common currency using market exchange rates. rates--covering more than 95 percent of the world's people Differences are greatest in sectors not commonly traded in- (figure 1a). This was the first global price collection since 1993, ternationally, such as housing, construction, and health and although some European economies have carried out regular education services (figure 1c). Price differences are smaller for price comparisons, the last in 2002. Some large economies, widely traded products, such as machinery and equipment, af- such as China, and many smaller ones in Africa, took part for ter allowing for taxes, distributor margins, and transport costs. the first time. India took part for the first time since 1985. PPPs include the prices of tradable and nontradable goods, us- Noteworthy is that the two poorest developing regions, ing weights that reflect their relative importance in total GDP. South Asia and Sub-Saharan Africa, have the best population Comparing prices across economies is complicated by ten- coverage--more than 98 percent (figure 1b). Latin America and sion between comparability and representativeness. Goods and the Caribbean and the Middle East and North Africa recorded services should have similar characteristics (comparable) and less coverage, both below 87 percent. Caribbean countries and be consumed everywhere (representative). To compensate for Algeria, Libya, and West Bank and Gaza did not participate in noncomparability of representative products, the ICP conducted the 2005 round. Many fragile and conflict-beset states were parallel programs: selecting items at the regional level, where underrepresented (with coverage around 50 percent), with weak consumption patterns are broadly similar across countries, and statistical capacity and conditions inimical to data collection. selecting items for global comparison among a few countries The new ICP round, with its expanded coverage, provides from each region. The results of the second program were used a more complete view of the world economy and, not surpris- to link the results of the first into a single set of global PPPs. For ingly, a different picture of its size and structure. details see the ICP Global Report (World Bank 2008). Participation in the International Nontradable goods and services Comparison Program has been growing 1a show wider variation in prices 1c Number of economies participating in the International Comparison Program Cross-country variations in price level indexes, by product groups (coefficient of variation) 150 Machinery and equipment Food 100 Household equipment Recreation and culture 50 Other household goods Clothing and footwear 0 Transportation 1970 1973 1975 1980 1985 1990 1993 2005 Source: World Bank 2008. Communications The 2005 International Comparison Program's population Restaurants and hotels coverage was above 85 percent in every region 1b Other goods and services Percent 100 Alcohol and tobacco Construction 75 Housing and utilities 50 Health Other government services 25 Education 0 East Asia Europe Latin Middle East South Sub-Saharan High- ­1.0 ­0.5 0.0 0.5 1.0 & Pacific & Central America & & North Asia Africa income Asia Caribbean Africa Source: World Bank staff estimates. Source: World Bank staff estimates. 2 2008 World Development Indicators The size of the global economy What has changed since the 1993 round? Converting GDP and its components to a common currency The PPPs previously published in World Development Indica- using PPPs leads to dramatic revisions in size and structure tors and used to estimate international poverty rates were of world economies. Generally, the poorer an economy, the extrapolated from the benchmark results of the 1993 ICP. greater the upward revision of estimates based on market Data for economies participating in the more recent price col- exchange rates. The GDPs of low-income economies are on lection by Eurostat were updated through 2002 and then ex- average revised upward 160 percent and those of middle- trapolated forward and backward. The extrapolation method income economies 120 percent (figure 1d). The GDPs of high- assumes that an economy's PPP conversion factor adjusts income economies are revised upward only 10 percent. But according to the different rates of inflation for its economy the results are not uniform. Within each group, particularly and the base economy, the United States. A good approxima- low-income economies, the diversity of patterns is great. tion in the short run, but over a longer period changes in the Viewed through PPPs, low-income economies produced relative prices of goods and services and in the structure of 7 percent of global GDP in 2005, compared with 3 percent at economies--what they produce and consume--distort this market exchange rates. Middle-income economies produced 33 relationship, and new measurements must be made. New percent, compared with 19 percent at market exchange rates. methods of data collection, differences in country participa- High-income economies produced 60 percent of world GDP at tion, and changes in analytical methods all add to the differ- PPPs, compared with 78 percent at market exchange rates. ences between new PPPs and old. East Asia and Pacific has the largest upward revision--from Under the new PPPs the aggregate GDP of developing 7 percent of world GDP to 13 percent (figure 1e). But South Asia economies in 2005 is 21 percent smaller than previously esti- and the Middle East and North Africa have the largest relative mated, corresponding to a 7 percentage point reduction in their increases. Sub-Saharan Africa produced 2 percent of world GDP share of world GDP--from 47 percent to 40 percent. at PPPs in 2005, twice that at market exchange rates. The largest revisions are for developing economies. Among the 20 economies with the largest revisions are 14 Sub-Saha- Purchasing power parities transform the size of developing economies' GDP in 2005 . . . 1d ran African countries, 10 fragile states, and 10 economies Unweighted average and standard deviation of GDP correction that did not participate in the 1993 ICP. In absolute terms from market exchange rates to purchasing power parities (%) 250 the largest changes were for China and India, which did not 200 participate in the 1993 ICP. China's estimated GDP in 2005 was revised downward 40 percent and India's 36 percent, 150 accounting for a large part of the net decrease in develop- 100 ing economy GDP (figure 1f). The smaller share of world GDP 50 attributed to developing economies increases high-income 0 economies' shares. The United States--as the base country, ­50 Low-income Middle-income High-income unaffected by any revision--increased its share from 20.6 Source: World Bank staff estimates. percent to 22.1 percent. . . . and their shares China and India's economies, of world GDP 1e revised downward, remain large 1f Shares of world GDP in purchasing power parities (and market exchange rates), 2005 Old estimate (1993) Sub-Saharan Africa 2% (1%) GDP, 2005 (PPP $ billions) New estimate (2005) Middle East & North Africa 3% (1%) South Asia 5% (2%) United States Europe & Central Asia China 7% (5%) Japan Latin America Germany & Caribbean India 8% (6%) High-income United Kingdom 60% (78%) France East Asia & Pacific Russian Federation 13% (7%) Italy Brazil 0 5,000 10,000 15,000 Source: World Development Indicators data files. Source: World Bank staff estimates. 2008 World Development Indicators 3 Combining inequalities within The global distribution of income and between countries From a global perspective income inequality has two sources: Inequality within countries is measured using household sur- inequalities within countries and inequalities between coun- vey data on income or consumption per capita. Common in- tries. PPPs provide a clearer picture of both. equality measures include the Gini coefficient and the ratio The distribution of income between economies can be of income or consumption of the richest 20 percent of the measured by differences in their average GDP per capita. population to that of the poorest 20 percent (table 2.7). At the Because PPPs tend to increase the value of output from low end of the inequality range the Gini may be 25­30 and the poorer economies, inequality between economies is less ratio of the richest to poorest less than 4 (many countries in when measured in PPPs. Eastern Europe). At the high end the Gini may be as high as In 2005 PPP GDP per capita in high-income economies 60 and the ratio of the richest to poorest more than 15 (many was more than five times higher than that in middle-income countries in Latin America and parts of Africa). economies and more than 19 times higher than that in low- Under PPPs both sources of inequalities--between and income economies (figure 1g). At market exchange rates the within countries--can be combined. PPPs are used to compare inequalities would have been greater. incomes of individuals from different countries and create a The use of PPPs also leads to a reordering of regions by global income distribution curve. Including inequalities within GDP per capita. South Asia, the poorest region at market countries widens already highly unequal income distribution exchange rates, surpasses Sub-Saharan Africa at PPPs (figure between countries. Based on countries with data (90 percent of 1h). Average incomes in Europe and Central Asia are higher the world's population), half the world's people consumed less than those in Latin America and the Caribbean at PPPs, and than PPP $1,300 a year and the bottom quarter less than PPP the gap between the Middle East and North Africa and East $660 in 2005 (figure 1i). The richest 20 percent of the world's Asia and Pacific widens under PPPs compared with the gap population spent more than 75 percent of the world total, while under market exchange rates. the poorest 20 percent spent less than 2 percent (figure 1j). Income disparities Half the people in the world consumed remain wide . . . 1g less than PPP $1,300 a year in 2005 1i GDP per capita, population-weighted Market exchange rates Share of world population (%) average, 2005 ($ thousands) Purchasing power parities 100 40 75 30 50 20 25 10 0 0 660 1,300 21,500 Low-income Middle-income High-income Private consumption per capita, 2005 (PPP $, log scale) Source: World Development Indicators data files. Source: World Bank staff estimates. . . . and regional rankings change The global distribution of under purchasing power parities 1h consumption is highly uneven 1j GDP per capita, population-weighted Market exchange rates Share of world private consumption (%) average, 2005 ($ thousands) Purchasing power parities 60 59.0 10 8 40 6 4 20 17.6 2 8.1 3.3 4.8 1.0 1.4 1.9 2.4 0 0.5 0 East Asia Europe Latin Middle East South Sub-Saharan 1 2 3 4 5 6 7 8 9 10 & Pacific & Central America & & North Asia Africa Asia Caribbean Africa World population decile Source: World Development Indicators data files. Source: World Bank staff estimates. 4 2008 World Development Indicators Regional inequalities Convergence in incomes? Inequalities between individuals are high in Latin America Have income inequalities across countries declined? Although and the Caribbean and Sub-Saharan Africa, where the income developing economies have grown faster than high-income share of the richest 20 percent of the population is at least economies, PPP data show that economies starting from a lower 18 times that of the poorest 20 percent, and lower in South GDP per capita did not systematically grow more rapidly between Asia and Europe and Central Asia, where the ratio falls below 7 1996 and 2006. The reason: large, high-performing economies, (figure 1k). East Asia and Pacific and the Middle East and North such as China and India, raise their group averages. Africa stand in between, but the estimate for the Middle East But after controlling for investment in 1996 (PPP per and North Africa is less reliable because many countries have capita expenditure in education and gross fi xed capital no household surveys for estimating income distribution. formation), initial GDP per capita had a substantial effect Half of Sub-Saharan Africa's inequalities can be attrib- on future growth: for the same investment poorer countries uted to differences in average incomes between countries, grew faster than richer ones over the decade (figure 1m). This reflecting the region's low economic integration. Its average emphasizes the importance of improving the investment cli- per capita private consumption is the lowest of all regions, mate in developing economies; an effectively invested dollar but there are large differences across countries. By contrast, generates much higher growth in poor countries. less than 20 percent of inequality in South Asia, East Asia Yet low-income countries did not systematically catch up and Pacific, and Latin American and the Caribbean can be with richer ones, as their investments in human and physical attributed to different country patterns (figure 1l). There are capital were on average much smaller. From 1996 to 2006 different reasons for similar patterns. South Asia and East the average yield of these expenditures is about 2 percent- Asia and Pacific are each dominated by one large economy. In age points of annual per capita GDP growth in low-income contrast, Latin America and the Caribbean has more equally countries, compared with more than 3 percentage points in sized economies with similar consumption per capita. middle-income countries (figure 1n). Latin America and the Caribbean and Sub-Saharan For similar investment efforts poor countries Africa have the most unequal income distributions 1k grew faster between 1996 and 2006 . . . 1m Share of income (%) Poorest 20% Richest 20% Per capita GDP growth conditional on investment effort, 1996­2006 (%) 75 10 64.5 8 57.1 48.5 49.8 6 50 45.6 41.1 41.7 4 2 25 0 ­2 8.7 5.2 6.6 5.1 5.9 50 400 3,000 22,000 50,000 2.9 3.6 0 GDP per capita, 1996 (2005 $, log scale) East Asia Europe Latin Middle East South Sub-Saharan High- & Pacific & Central America & & North Asia Africa income Note: In line with Mankiw, Romer, and Weil (1992), per capita GDP growth rates are Asia Caribbean Africa regressed on the logarithms of initial per capita GDP, initial per capita investment expenditure, initial per capita education expenditure, and population growth rate. Source: World Bank staff estimates. Source: World Bank staff estimates. Inequality within countries is greatest in Latin America . . . but investment efforts in low-income countries were and the Caribbean and lowest in Sub-Saharan Africa 1l insufficient to match the growth of richer countries 1n Share of inequality (%) Between-country Within-country Estimated contribution of investment efforts 100 to countries' annual per capita GDP growth, 1996­2006 (%) 3.5 3.0 75 2.5 50 2.0 1.5 25 1.0 0.5 0 East Asia Europe Latin Middle East South Sub-Saharan High- 0.0 & Pacific & Central America & & North Asia Africa income Low-income Lower Upper High-income Asia Caribbean Africa middle-income middle-income Source: World Bank staff estimates. Source: World Bank staff estimates. 2008 World Development Indicators 5 Comparing standards of living Health and education The 2005 ICP estimated PPPs for subcomponents of GDP, Similar cross-country comparisons can be made for the rela- including expenditures on food, health, and education. As tive impact of health and education expenditures on selected has long been observed, differences in spending on food are outcomes, such as life expectancy at birth and the youth lit- smaller than differences in income or overall consumption. eracy rate. Both public and private expenditures contribute to South Asia's GDP per capita is one-sixteenth that of high-in- the improvement of these and of many other indicators. And come economies; per capita food consumption, only one-fifth. many factors other than spending affect life expectancy and And despite wide differences in income per capita, food ex- literacy outcomes. But it is still interesting to observe that penditures in South Asia and East Asia and Pacific are almost among countries with similar expenditures per capita, there the same (figure 1o). These two regions also have the small- is a large range of outcomes. est range between maximum and minimum average food. Among developing economies with similar per capita Within developing countries per capita food consumption health spending, Southern African countries have much lower is strongly correlated with malnutrition, accounting for more life expectancy, which must to some extent be the conse- than half the differences across countries. But even at similar quence of high HIV/AIDS prevalence (figure 1q). In contrast, average food per capita consumption, differences in malnutri- most developing regions have some countries that record tion rates remain significant. Average expenditures conceal above-average life expectancies. inequalities in the food consumption measure, specific diets, Compared with developing countries at similar per capita geographic conditions, and the absence of complementary education expenditures, West African countries record par- factors that can prevent malnutrition (micronutrients, health ticularly low literacy rates for youth ages 15­24 (figure 1r). care, education). In South Asia five of seven countries have Again, while worst performers are concentrated geographi- malnutrition rates much above the average of developing cally, best performers are from diverse regions, including economies at similar food consumption levels. Sub-Saharan Africa. Regional differences in food consumption Health spending has less impact on are less than differences in income 1o life expectancy in Sub-Saharan Africa 1q Per capita food consumption, unweighted average, Actual Developing country average maximum, and minimum, 2005 (PPP $ per day) Life expectancy at birth, 2005 (years) for similar health spending 6 80 5 4 40 3 2 0 da aa oa ea ia a ea a aa aa a ia la law on fric th bi an n mb go uin ila mi so Le tsw Ma An hA 1 az Za Le lG Na ra Bo Sw ut ria er So Si to 0 ua Eq East Asia Europe Latin Middle East South Sub-Saharan & Pacific & Central America & & North Asia Africa Note: Calculations based on countries that took part in the 2005 International Asia Caribbean Africa Comparison Program. The relationship between life expectancy and health spending is estimated for a sample of 105 developing countries with data. Source: World Bank staff estimates. a. Economy deviates significantly from the sample average. Source: World Bank staff estimates. For similar levels of food consumption, For similar education spending youth malnutrition is particularly high in South Asia 1p literacy rates are much lower in West Africa 1r Underweight children Actual Developing country average Youth literacy rate Actual Developing country average under age 5 (%) at similar food consumption levels (% ages 15­24) for similar education spending 50 100 75 25 50 25 0 a al a oa a a na la ra 0 an e ad i ne ea qu ga ge s ni aa a ha t a a M Fa Ch o in is l es n ka R ne bi Be Ni pa n an n Le ija PD k Gu di da es ta am an in a Pa Se Ne Om ba In in is ra ad pp Su iL o oz k rk er er La gl ili Pa Sr M Bu Az Si Ph n Ba Note: Calculations based on countries that took part in the 2005 International Note: Calculations based on countries that took part in the 2005 International Com- Comparison Program. The relationship between malnutrition and food consumption parison Program. The relationship between youth literacy and education spending is is estimated for a sample of 77 developing countries with data. estimated for a sample of 86 developing countries with data. a. Economy deviates significantly from the sample average. a. Economy deviates significantly from the sample average. Source: World Bank staff estimates. Source: World Bank staff estimates. 6 2008 World Development Indicators Public goods Foreign resources Governments finance the provision of services destined to Developing economies receive large financial flows from of- individuals, such as public health and education, and the ficial development assistance (ODA) and the remittances of provision of public goods, such as security, justice, and the workers abroad. Because prices in developing economies are environment. Countries at similar levels of development de- lower, the purchasing power of aid or remittances spent in vote different amounts to collective consumption, most to the local economy is greater than the purchasing power of the financing public institutions through recurrent administrative same amount spent in the sending country. Adjusting ODA expenditures. While fragile states spend relatively more on and remittances by the PPP price level index provides better collective goods than do nonfragile states at similar levels measures of their relative impact. of development (figure 1s), interpreting this result is difficult. In 2006 developing countries received PPP $15 per It might reflect a response to the poor quality and prior un- capita in net programmable assistance (net ODA excluding derfunding of general administration, poor governance that debt relief, humanitarian assistance, and technical cooper- yields less value for money, or the diversion of resources into ation). Low-income countries received PPP $25 per capita, conflict-related expenditures, such as security and defense. and middle-income countries received PPP $7. Fragile states Energy consumption has a strong impact on the local and received PPP $50. global environment. Regions differ in energy efficiency (PPP Developing countries received 2006 PPP $62 per cap- GDP per unit of energy consumed), but all increased energy ita in net workers' remittances. Middle-income countries efficiency between 1995 and 2005, except the Middle East received PPP $67, low-income countries PPP $55, and fragile and North Africa (figure 1t). In 2005 $1 of GDP was produced states PPP $16. The Middle East and North Africa is the main with 13 percent less energy than in 1995. But the world's recipient of remittances. At the other end Sub-Saharan Africa GDP grew 42 percent in that same period, for a net increase received PPP $22 in remittances in 2006 (figure 1u), half what of 24 percent in global energy consumption. it received in programmable aid (figure 1v). Fragile states spend Workers' remittances play a sizable role in the Middle East more on collective goods 1s and North Africa and Latin America and the Caribbean 1u Individual public consumption Net workers' remittances per capita, 2006 (PPP $) Per capita public consumption (PPP$, 2005) Collective public consumption 150 1,500 100 1,000 500 50 0 0 Other low-income Low-income Other middle-income Middle-income East Asia Europe Latin Middle East South Sub-Saharan countries fragile states countries fragile states & Pacific & Central America & & North Asia Africa Asia Caribbean Africa Source: World Bank staff estimates. Source: World Bank staff estimates. The world economy is becoming more energy efficient, Sub-Saharan Africa is the main but too slowly to stabilize energy consumption 1t recipient of programmable aid 1v GDP per unit of energy use, weighted average, 2005 Programmable aid per capita, 2006 (PPP $) (PPP $ per kilogram of oil equivalent) 1995 2005 50 8 40 6 30 4 20 2 10 0 East Asia Europe Latin Middle East South Sub-Saharan High- 0 & Pacific & Central America & & North Asia Africa income East Asia Europe Latin Middle East South Sub-Saharan Asia Caribbean Africa & Pacific & Central America & & North Asia Africa Asia Caribbean Africa Source: World Development Indicators data files. Source: World Bank staff estimates. 2008 World Development Indicators 7 Tables 1.a New purchasing power parity estimates from the 2005 International Comparison Program Purchasing Market Ratio Gross domestic Fixed Collective Consumption expenditure power exchange of PPP product capital government parity (PPP) rate conversion formation consumption conversion factor to factor market per capita exchange PPP $ local currency Individual units to rate Individual international local currency per capita per capita by household Actual $ units to $ PPP $ billions PPP $ PPP $ Final individual Food Education Health 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Albania 48.56 99.87 0.49 17.2 5,465 1,374 639 3,241 4,280 650 681 855 Angola 44.49 87.16 0.51 60.0 3,729 850 712 541 692 132 122 75 Argentina 1.269 2.904 0.44 419.0 10,815 1,775 1,120 6,226 7,463 1,192 779 1,641 Armenia 178.6 457.7 0.39 12.6 4,162 750 423 2,855 3,925 1,380 1,237 510 Australia 1.388 1.309 1.06 695.8 34,106 8,133 3,297 17,487 21,915 1,613 3,421 3,449 Austria 0.8736 0.8041 1.09 280.6 34,075 6,254 2,424 18,163 23,443 1,813 2,568 3,499 Azerbaijana 0.3263 0.9454 0.35 38.4 4,573 1,073 334 1,795 2,669 903 1,127 385 Bahrain 0.2488 0.376 0.66 24.2 33,451 6,926 2,441 10,170 12,822 2,268 2,632 2,376 Bangladesh 22.64 61.75 0.37 163.7 1,068 254 71 764 903 290 238 112 Belarus 779.3 2154 0.36 83.5 8,541 1,351 829 4,438 6,733 1,422 2,435 1,453 Belgium 0.8988 0.8041 1.12 332.2 31,699 6,512 2,427 16,077 21,647 1,958 2,759 3,957 Benin 219.6 527.5 0.42 10.3 1,213 184 232 758 948 197 168 73 Bhutan 15.74 44.1 0.36 2.3 3,649 1,715 868 1,277 1,924 417 446 906 Bolivia 2.232 8.066 0.28 34.1 3,715 298 557 2,151 2,972 481 1,129 519 Bosnia and Herzegovina 0.7268 1.573 0.46 23.3 5,949 1,157 923 4,859 6,320 1,163 1,075 963 Botswana 2.421 5.110 0.47 22.0 12,010 1,981 3,491 2,228 2,895 352 1,428 307 Brazil 1.357 2.434 0.56 1,583.2 8,474 1,218 1,640 4,416 5,639 712 851 1,306 Brunei Darussalam 0.9031 1.664 0.54 17.6 46,991 4,825 14,595 9,283 12,672 1,489 6,086 1,653 Bulgaria 0.5928 1.574 0.38 72.2 9,328 1,418 1,563 5,234 7,285 925 1,822 1,306 Burkina Faso 200.2 527.5 0.38 14.8 1,061 136 414 624 778 170 135 51 Burundi 343.0 1082 0.32 2.5 319 .. .. .. .. .. .. .. Cambodia 1,279 4097 0.31 20.1 1,440 146 202 926 1,197 324 594 430 Cameroon 251.0 527.5 0.48 35.5 1,993 210 268 1,211 1,499 335 233 72 Canada 1.214 1.212 1.00 1,130.0 34,972 7,265 2,695 18,233 23,526 1,465 2,743 3,269 Cape Verde 69.36 88.67 0.78 1.3 2,521 936 421 1,964 2,449 480 766 239 Central African Republic 263.7 527.5 0.50 2.7 654 36 85 496 607 168 96 22 Chad 208.0 527.5 0.39 14.9 1,471 166 576 548 781 169 469 62 Chile 333.7 560.1 0.60 199.6 12,248 2,372 995 6,143 7,430 917 1,084 1,323 Chinab 3.448 8.194 0.42 5,333.2 4,088 1,581 823 1,310 1,751 265 582 549 Hong Kong, China 5.688 7.777 0.73 243.2 35,690 8,326 3,078 16,320 19,622 1,266 2,923 3,632 Macao, China 5.270 7.987 0.66 17.4 36,869 8,520 2,735 8,266 10,525 963 2,181 2,164 Taiwan, China 19.34 32.18 0.60 592.3 26,057 5,303 4,257 13,645 16,836 1,407 4,727 4,803 Colombia 1,082 2135 0.51 263.7 5,867 962 1,002 3,266 4,098 610 678 914 Comoros 226.2 395.6 0.57 0.7 1,127 98 406 762 918 330 171 39 Congo, Dem. Rep. 214.3 473.9 0.45 15.7 267 52 77 125 151 45 20 16 Congo, Rep. 268.8 527.5 0.51 11.7 3,246 252 549 679 943 166 478 135 Côte d'Ivoire 287.5 527.5 0.55 30.0 1,614 63 279 991 1,216 271 118 90 Croatia 3.935 5.949 0.66 58.8 13,231 3,161 1,695 6,641 9,076 1,423 1,740 1,805 Cyprus 0.424 0.4636 0.91 18.6 24,534 4,647 2,601 14,709 17,859 2,213 2,420 1,725 Czech Republic 14.40 23.96 0.60 207.6 20,280 3,770 2,897 9,278 13,145 1,322 2,145 2,756 Denmark 8.517 5.997 1.42 182.2 33,645 6,955 2,960 15,082 21,490 1,583 2,895 3,283 Djibouti 84.69 177.7 0.48 1.5 1,850 240 762 864 1,135 187 366 104 Ecuador 0.4226 1 0.42 88.0 6,737 1,329 690 3,680 4,577 781 781 785 Egypt, Arab Rep. 1.616 6.004 0.27 333.2 4,574 570 887 2,835 3,662 856 1,230 665 Equatorial Guineac 287.4 527.5 0.54 13.8 13,610 2,019 860 2,359 2,912 558 731 612 Estonia 7.813 12.59 0.62 22.2 16,456 3,694 2,008 7,811 11,291 1,306 2,605 1,731 Ethiopia 2.254 8.652 0.26 43.7 581 70 121 373 457 139 .. 29 Fiji 1.430 1.691 0.85 3.5 4,282 1,116 731 2,996 3,768 750 1,016 691 Finland 0.9834 0.8041 1.22 159.8 30,462 5,969 2,475 13,761 19,501 1,672 2,473 3,234 France 0.9225 0.8041 1.15 1,862.2 30,591 5,654 2,260 16,724 23,027 2,263 2,567 4,059 Gabon 256.2 527.5 0.49 17.8 13,821 2,428 2,304 2,641 3,620 594 1,691 595 Gambia, The 7.560 28.58 0.26 1.7 1,078 62 409 405 550 75 .. 121 Georgia 0.7380 1.812 0.41 15.7 3,520 650 366 2,200 3,063 564 820 836 Germany 0.8926 0.8041 1.11 2,510.7 30,445 4,963 2,325 17,278 21,742 1,780 1,436 4,123 Ghana 3,721 9073 0.41 26.1 1,160 254 118 745 912 189 241 140 Greece 0.7022 0.8041 0.87 324.9 29,261 5,523 3,313 15,481 18,545 2,168 2,170 2,557 8 2008 World Development Indicators 1.a WORLD VIEW New purchasing power parity estimates from the 2005 International Comparison Program Purchasing Market Ratio Gross domestic Fixed Collective Consumption expenditure power exchange of PPP product capital government parity (PPP) rate conversion formation consumption conversion factor to factor market per capita exchange PPP $ local currency Individual units to rate Individual international local currency per capita per capita by household Actual $ units to $ PPP $ billions PPP $ PPP $ Final individual Food Education Health 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Guinea 1,219 3640 0.33 9.9 1,105 167 95 548 682 123 241 143 Guinea-Bissau 217.3 527.5 0.41 0.7 458 57 266 295 361 96 49 25 Hungary 128.5 199.6 0.64 171.6 17,014 2,804 2,129 8,481 12,365 1,242 2,189 2,434 Iceland 97.06 62.98 1.54 10.5 35,465 12,207 3,245 19,100 26,816 1,808 4,118 4,394 India 14.67 44.27 0.33 2,431.9 2,222 504 233 1,183 1,464 317 391 485 Indonesia 3,934 9705 0.41 707.9 3,209 615 248 1,934 2,326 607 658 144 Iran, Islamic Rep. 2,675 8964 0.30 643.5 9,314 1,646 1,489 5,275 6,645 655 1,257 2,119 Iraq 558.7 .. .. .. .. 269 1,643 1,297 1,862 394 543 877 Ireland 1.023 0.8041 1.27 157.6 37,886 8,864 2,183 15,560 20,997 867 3,177 2,998 Israel 3.717 4.488 0.83 156.7 22,627 3,775 3,602 11,096 15,278 1,681 3,385 2,248 Italy 0.8750 0.8041 1.09 1,626.3 27,750 6,016 2,165 15,678 19,667 2,032 1,865 2,914 Japan 129.6 110.2 1.18 3,870.3 30,290 6,656 2,615 15,342 20,438 1,348 1,767 4,653 Jordan 0.3805 0.709 0.54 23.5 4,342 1,552 875 2,947 3,843 898 1,202 724 Kazakhstan 57.61 132.9 0.43 131.8 8,699 1,632 811 3,746 5,426 735 2,768 1,728 Kenya 29.52 75.55 0.39 49.0 1,375 145 177 948 1,196 221 351 259 Korea, Rep. 788.9 1024 0.77 1,027.4 21,273 6,376 2,046 9,829 12,157 874 2,124 2,240 Kuwait 0.2136 0.292 0.73 110.4 43,551 9,288 5,292 10,978 13,683 2,316 2,437 1,365 Kyrgyz Republic 11.35 41.02 0.28 8.9 1,728 138 251 1,249 1,901 403 841 282 Lao PDR 2,988 10636 0.28 10.3 1,814 476 678 859 1,109 268 575 165 Latvia 0.2980 0.5647 0.53 30.4 13,215 2,663 2,007 6,985 9,745 1,277 2,464 1,498 Lebanon 847.5 1508 0.56 38.3 9,545 2,814 1,715 6,265 7,639 1,842 3,260 1,390 Lesotho 3.490 6.359 0.55 2.6 1,311 274 219 1,319 1,686 309 738 446 Liberiad 0.4926 1 0.49 1.1 312 59 60 200 248 31 216 37 Lithuania 1.484 2.776 0.53 48.1 14,084 2,030 1,551 8,169 11,402 1,888 2,478 1,944 Luxembourg 0.9225 0.8041 1.15 31.9 69,776 14,390 3,898 27,061 34,295 1,849 2,853 4,345 Macedonia, FYR 19.06 49.29 0.39 15.0 7,394 905 1,276 4,623 6,123 1,181 991 1,007 Madagascar 649.6 2003 0.32 15.5 834 119 249 557 702 189 383 66 Malawi 39.46 118.4 0.33 8.6 648 121 124 400 482 53 161 139 Malaysia 1.734 3.8 0.46 299.6 11,678 2,483 1,642 4,302 5,669 649 1,728 779 Maldives 8.134 12.8 0.64 1.2 3,995 1,965 1,497 1,496 2,190 355 2,095 932 Mali 240.1 527.5 0.46 11.7 1,004 98 290 616 772 180 176 76 Malta 0.2474 0.346 0.71 8.3 20,483 3,462 2,471 11,778 15,662 1,887 2,164 2,457 Mauritania 98.84 268.6 0.37 5.0 1,684 647 556 906 1,150 336 222 124 Mauritius 14.68 28.94 0.51 12.4 9,975 1,524 1,768 5,837 7,621 1,158 1,778 889 Mexico 7.127 10.90 0.65 1,173.9 11,387 1,631 798 7,189 8,924 1,658 2,007 910 Moldova 4.434 12.60 0.35 8.5 2,190 305 237 1,854 2,688 374 1,345 364 Mongolia 417.2 1205 0.35 6.7 2,609 714 402 1,159 1,618 353 1,137 421 Montenegro 0.3659 0.8027 0.46 4.5 7,450 980 3,144 4,201 5,739 1,112 885 975 Morocco 4.8782 8.865 0.55 107.1 3,554 851 540 1,801 2,254 494 372 191 Mozambique 10,909 23061 0.47 13.9 677 104 108 455 574 180 117 53 Namibia 4.265 6.359 0.67 9.3 4,599 979 1,233 2,068 2,769 483 1,046 589 Nepal 22.65 72.06 0.31 26.0 960 179 98 706 850 277 183 303 Netherlands 0.8983 0.8041 1.12 562.9 34,492 5,711 3,468 16,477 22,587 1,974 2,515 3,680 New Zealand 1.535 1.420 1.08 101.6 24,566 4,842 2,114 13,620 17,750 1,670 2,180 2,698 Niger 226.7 527.5 0.43 8.0 602 80 164 370 453 103 51 43 Nigeria 60.23 131.3 0.46 214.8 1,520 150 207 937 1,172 269 280 97 Norway 8.840 6.443 1.37 219.8 47,538 8,600 3,358 17,357 24,603 1,885 2,832 4,502 Oman 0.2324 0.3845 0.60 51.0 20,350 4,800 4,385 5,814 7,402 1,515 1,446 723 Pakistan 19.10 59.36 0.32 340.3 2,184 329 266 1,663 2,026 525 491 511 Paraguay 2,007 6178 0.32 22.6 3,824 480 353 2,763 3,350 761 505 348 Peru 1.487 3.296 0.45 176.0 6,452 1,070 536 3,834 4,564 854 799 559 Philippines 21.75 55.09 0.39 250.0 2,956 382 308 1,845 2,218 612 811 175 Poland 1.898 3.235 0.59 516.6 13,535 1,945 1,504 7,421 10,271 1,423 1,985 1,858 Portugal 0.7074 0.8041 0.88 210.5 19,956 4,337 1,940 11,920 15,288 1,851 1,681 2,778 Qatar 2.745 3.64 0.75 56.3 70,716 29,906 7,576 9,476 12,893 2,072 3,756 2,503 Romania 1.421 2.914 0.49 202.7 9,368 1,499 1,483 5,280 7,311 1,165 1,350 1,438 2008 World Development Indicators 9 1.a New purchasing power parity estimates from the 2005 International Comparison Program Purchasing Market Ratio Gross domestic Fixed Collective Consumption expenditure power exchange of PPP product capital government parity (PPP) rate conversion formation consumption conversion factor to factor market per capita exchange PPP $ local currency Individual units to rate Individual international local currency per capita per capita by household Actual $ units to $ PPP $ billions PPP $ PPP $ Final individual Food Education Health 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 Russian Federation 12.736 28.28 0.45 1,697.5 11,858 1,377 1,333 5,545 7,916 1,298 1,723 1,394 Rwanda 186.2 557.8 0.33 6.4 696 109 243 464 592 148 202 59 São Tomé and Principe 5,558 10558 0.53 0.2 1,401 199 418 1,167 1,446 388 300 176 Saudi Arabia 2.410 3.747 0.64 490.6 21,220 4,657 3,376 5,037 6,976 1,108 1,924 1,229 Senegal 251.7 527.5 0.48 18.1 1,541 262 250 988 1,239 300 181 144 Serbia 27.21 66.71 0.41 64.3 8,644 1,139 1,050 4,726 6,712 1,015 1,109 1,209 Sierra Leone 1,074 2890 0.37 3.3 584 62 254 523 667 118 240 278 Singapore 1.079 1.665 0.65 180.1 41,479 10,352 5,534 12,636 15,564 929 3,159 3,043 Slovak Republic 17.20 31.02 0.55 85.6 15,881 2,856 2,561 8,181 11,077 1,227 1,916 1,990 Slovenia 147.0 192.7 0.76 45.0 22,506 5,638 2,094 11,305 14,970 1,457 2,075 2,628 South Africa 3.872 6.359 0.61 397.5 8,478 1,214 1,587 4,582 5,886 764 1,228 1,062 Spain 0.7676 0.8041 0.95 1,179.6 27,180 7,020 2,265 14,826 19,232 2,117 2,156 3,280 Sri Lanka 35.17 100.5 0.35 67.3 3,420 658 499 2,126 2,735 568 393 341 Sudan 107.7 243.6 0.44 63.1 1,711 257 234 1,493 1,799 489 77 69 Swaziland 3.293 6.359 0.52 5.0 4,461 678 752 2,537 3,157 746 625 1,057 Sweden 9.243 7.473 1.24 288.9 32,016 4,784 2,752 14,381 21,833 1,631 3,339 3,635 Switzerland 1.741 1.245 1.40 261.7 35,182 7,609 1,779 19,472 23,235 1,871 2,413 4,294 Syrian Arab Republic 19.72 52.86 0.37 75.6 4,002 909 542 2,210 2,881 861 878 664 Tajikistan 0.7444 3.117 0.24 9.7 1,478 67 209 948 1,560 363 1,161 236 Tanzania 395.6 1129 0.35 35.9 933 132 126 618 750 261 .. 40 Thailand 15.93 40.22 0.40 444.9 7,061 1,908 747 3,638 4,616 448 1,451 1,072 Togo 240.4 527.5 0.46 4.6 742 75 170 618 767 174 168 41 Tunisia 0.5813 1.297 0.45 64.0 6,382 1,149 894 3,463 4,371 697 553 519 Turkey 0.8683 1.341 0.65 561.1 7,786 1,192 1,057 4,612 5,715 888 913 346 Uganda 619.6 1737 0.36 24.5 848 115 181 583 748 155 .. 98 Ukraine 1.678 5.125 0.33 263.0 5,583 732 512 3,138 4,657 953 2,081 922 United Kingdom 0.6489 0.5493 1.18 1,889.4 31,371 4,937 2,841 19,187 25,155 1,586 1,955 3,665 United States 1 1 1.00 12,397.9 41,813 8,018 3,962 29,368 32,045 1,998 2,725 5,853 Uruguay 13.28 24.48 0.54 30.6 9,266 1,111 933 5,886 7,074 1,071 716 1,506 Venezuela, RB 1,153 2090 0.55 262.5 9,877 1,287 985 4,290 5,364 844 1,026 866 Vietnam 4,713 15804 0.30 178.1 2,143 634 367 990 1,310 238 1,009 466 Yemen, Rep. 69.49 191.5 0.36 46.2 2,188 472 386 1,073 1,405 376 454 190 Zambia 2,415 4464 0.54 13.4 1,171 211 275 672 894 59 .. 233 Zimbabwe 33,068 22364 1.48 2.3 176 45 169 284 381 90 159 9 a. Original data collected in old manat are converted to new manat at 1 new manat = 5,000 old manat. b. Results for China were based on national average prices extrapolated by the World Bank and Asian Development Bank using price data for 11 cities submitted by the National Bureau of Statistics for China. The data for China do not include Hong Kong, China; Macao, China; and Taiwan, China. c. Per capita figures derived using population from the International Comparison Program. d. Data in U.S. dollars. 10 2008 World Development Indicators 1.a WORLD VIEW New purchasing power parity estimates from the 2005 International Comparison Program About the data The International Comparison Program (ICP) is a methodology, such as how basic heading PPPs were compensation of employees). Data are converted to worldwide statistical initiative to collect comparative computed and aggregated. Annex F of the 2005 ICP U.S. dollars using PPP rates and divided by midyear price data and estimate purchasing power parities report (available at www.worldbank.org/data/ICP) population. · PPP individual by household final con- (PPPs) of the world's economies. Using PPPs instead provides a review of the methods used. sumption expenditure per capita is the market value of market exchange rates to convert currencies For the 2005 ICP GDP data were compiled using of all goods and services, including durable products, allows the output of economies and the welfare of the expenditure approach, with its components purchased by households. It excludes purchases of their inhabitants to be compared in real terms--that allocated to 155 basic headings for the year 2005. dwellings but includes imputed rent for owner-occupied is, controlling for differences in price levels. PPPs The detailed breakdown of GDP expenditure used dwellings. Data are converted to U.S. dollars using are the preferred means of converting gross domes- by the ICP may differ from other national accounts PPP rates and divided by midyear population. · PPP tic product (GDP) and its components to a common data presented in World Development Indicators actual individual consumption expenditure per cap- currency. They enable cross-country comparison of 2008 because of the timing of data collection and ita is household final consumption expenditure plus the size of economies, average consumption levels, differences in methodology. In table 1.a gross fixed the individual component of government consumption poverty rates, productivity, and use of resources. capital formation and consumption data are from the expenditure and the final consumption expenditure The ratio of the PPP conversion factor to the market ICP, and GDP data are collected by World Bank staff by nonprofit institutions serving households. The exchange rate (also referred to as the price level from national and international sources and in some individual component of government consumption index) allows the cost of the goods and services that cases differ from ICP data. All per capita figures are expenditure relates to services provided to specific make up GDP to be compared across countries. estimated using the World Bank's population data, individuals, such as health and education. Data are The new estimates of PPP, published for the first except where otherwise noted. converted to U.S. dollars using PPP rates and divided time in World Development Indicators, are the result by midyear population. · PPP individual consumption Definitions of a global program of price surveys carried out using expenditure on food per capita is expenditure on food similar methods in 146 countries. New methods of · Purchasing power parity (PPP) conversion factor is products and nonalcoholic beverages purchased for data collection and analysis were used to overcome the number of units of a country's currency required consumption at home. It excludes food products and problems encountered in previous rounds of the to buy the same amount of goods and services in beverages sold for immediate consumption away from ICP. Teams in each region identified characteristic the domestic market as a U.S. dollar would buy in home, cooked dishes prepared by restaurants and goods and services to be priced. Surveys conducted the United States. · Market exchange rate is the catering contractors, and products sold as pet foods. by each country in 2005 and 2006 yielded prices for exchange rate determined by national authorities or Data are converted to U.S. dollars using PPP rates more than 1,000 goods and services. Many coun- the rate determined in the legally sanctioned exchange and divided by midyear population. · PPP individual tries participated for the first time, including China. market. When the official exchange rate diverges by consumption expenditure on education per capita is (Previous estimates of China's PPPs came from a an exceptionally large margin from the rate effectively expenditures by households on pre-primary, primary, research study using data for 1986.) India partici- applied to domestic transactions of foreign currencies secondary, post-secondary, and tertiary education. pated for the first time since 1985. and traded products, the market exchange rate is an Data are converted to U.S. dollars using PPP rates The ICP Global Office within the World Bank coordi- estimated alternative conversion factor. It is calcu- and divided by midyear population. · PPP individual nated the collection of data and calculation of PPPs lated as an annual average based on monthly aver- consumption expenditure on health per capita is in more than 100 (mostly developing) economies. ages (local currency units relative to the U.S. dollar). expenditures by households on medical products, The program was organized in five geographic areas: · Ratio of PPP conversion factor to market exchange appliances and equipment, outpatient services, and Africa, Asia-Pacific, Commonwealth of Independent rate, also known as the price level index, is obtained hospital services. Data are converted to U.S. dollars States, South America, and Western Asia. Regional by dividing the PPP conversion factor by the market using PPP rates and divided by midyear population. agencies coordinated the work in the five regions. In exchange rate. · PPP gross domestic product (GDP) parallel the Statistical Office of the European Commu- is GDP converted to U.S. dollars using PPP rates. GDP Data sources nities (Eurostat) and the Organisation for Economic is the sum of value added by all resident producers PPP conversion factors are estimates by World Co-operation and Development (OECD) conducted its plus any product taxes (less subsidies) not included Bank staff based on data collected by the Interna- 2005 PPP program, which included 46 countries. in the valuation of output. · PPP GDP per capita is tional Comparison Program (www.worldbank.org/ Each region and the Eurostat-OECD group differ in PPP GDP divided by midyear population. Population is data/ICP). Data on GDP are estimated by World the size and structure of their economies and their based on the de facto definition of population, which Bank staff based on national accounts data col- statistical capacity. To ensure the most consistent counts all residents regardless of legal status or citi- lected by World Bank staff during economic mis- comparisons of countries within regions, different zenship, except refugees not permanently settled in sions or reported to other international organiza- methods were used in each region. Three methods the country of asylum, who are generally considered tions such as the OECD. Population estimates were used to compute housing PPPs. Asia and Africa part of the population of their country of origin. · PPP are prepared by World Bank staff from a variety used reference volumes, Eurostat and West Asia gross fixed capital formation per capita is outlays on of sources (see Data sources for table 2.1). Data used a combination of rentals and quantities, and additions to the fixed assets of an economy converted on gross fixed capital formation, government con- the CIS and Latin America used the quantity method. to U.S. dollars using PPP rates and divided by midyear sumption, and household consumption expendi- In Africa, Asia-Pacific, and Western Asia government population. · PPP collective government consump- tures are based on data collected by the Interna- expenditures were adjusted to account for produc- tion per capita is all government current expendi- tional Comparison Program. tivity differences. There were other differences in tures for purchases of goods and services (including 2008 World Development Indicators 11 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 enrolment 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. 12 2008 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. 2008 World Development Indicators 13 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2006 2006 2006 2006b 2005 2006b 2006 2006 2006 2006 2005­06 2005­06 Afghanistan .. 652 .. 8.1 117 ..c .. 23.9d ..d .. 5.3 .. Albania 3 29 116 9.3 109 2,930 116 19.0 6,000 118 5.0 4.4 Algeria 33 2,382 14 101.2 49 3,030 111 198.0 d 5,940 d 119 3.0 1.5 Angola 17 1,247 13 32.7 69 1,970 131 64.5 3,890 139 18.6 15.3 Argentina 39 2,780 14 201.4 31 5,150 88 456.8 11,670 78 8.5 7.4 Armenia 3 30 107 5.8 132 1,920 133 14.9 4,950 127 13.3 13.6 Australia 21 7,741 3 742.3 15 35,860 25 702.5 33,940 26 2.5 1.0 Austria 8 84 100 329.2 22 39,750 18 298.4 36,040 18 3.1 2.5 Azerbaijan 8 87 103 15.6 95 1,840 134 46.1 5,430 123 34.5 33.0 Bangladesh 156 144 1,198 70.5 55 450 182 191.9 1,230 180 6.6 4.8 Belarus 10 208 47 33.8 66 3,470 105 94.4 9,700 88 9.9 10.4 Belgium 11 31 349 405.4 18 38,460 20 356.9 33,860 27 3.2 2.6 Benin 9 113 79 4.7 138 530 176 10.9 1,250 178 4.1 0.9 Bolivia 9 1,099 9 10.3 105 1,100 149 35.6 3,810 142 4.6 2.7 Bosnia and Herzegovina 4 51 77 12.7 102 3,230 106 26.6 6,780 109 6.0 5.7 Botswana 2 582 3 10.4 104 5,570 81 21.8 11,730 77 2.1 0.9 Brazil 189 8,515 22 892.6 11 4,710 93 1,647.5 8,700 96 3.7 2.4 Bulgaria 8 111 71 30.7 71 3,990 98 79.0 10,270 84 6.1 6.7 Burkina Faso 14 274 52 6.3 129 440 184 16.2 1,130 184 6.4 3.2 Burundi 8 28 318 0.8 189 100 209 2.6 320 206 5.1 1.1 Cambodia 14 181 80 7.0 123 490 180 22.1 1,550 174 10.8 9.0 Cameroon 18 475 39 18.1 87 990 154 37.4 2,060 163 3.8 1.6 Canada 33 9,985 4 1,196.6 9 36,650 22 1,184.4 36,280 16 2.8 1.7 Central African Republic 4 623 7 1.5 173 350 188 2.9 690 196 4.1 2.3 Chad 10 1,284 8 4.7 137 450 182 12.3 1,170 181 0.5 ­2.6 Chile 16 757 22 111.9 46 6,810 76 185.6 11,300 80 4.0 3.1 China 1,312 9,635e 141 2,621.0 4 2,000 130 6,119.1 4,660 133 10.7 10.1 Hong Kong, China 7 1 6,581 199.1 32 29,040 31 268.8 39,200 12 6.8 6.1 Colombia 46 1,142 41 142.0 39 3,120 108 279.2 6,130 114 6.8 5.3 Congo, Dem. Rep. 61 2,345 27 7.7 119 130 207 16.2 270 207 5.1 1.8 Congo, Rep. 4 342 11 3.8 .. 1,050 .. 8.7 2,420 .. 6.4 4.1 Costa Rica 4 51 86 21.9 82 4,980 90 40.6d 9,220 d 91 8.2 6.4 Côte d'Ivoire 19 322 59 16.6 91 880 158 29.8 1,580 171 0.9 ­0.9 Croatia 4 57 79 41.4 62 9,310 65 61.5 13,850 72 4.8 4.8 Cuba 11 111 103 .. .. ..f .. .. .. .. 5.4 5.2 Czech Republic 10 79 133 131.4 40 12,790 56 214.9 20,920 55 6.1 5.7 Denmark 5 43 128 283.3 27 52,110 7 196.7 36,190 17 3.2 2.8 Dominican Republic 10 49 199 28.0 77 2,910 118 53.3d 5,550 d 121 10.7 9.0 Ecuador 13 284 48 38.5 63 2,910 118 89.9 6,810 108 3.9 2.8 Egypt, Arab Rep. 74 1,001 75 100.9 50 1,360 143 366.5 4,940 128 6.8 4.9 El Salvador 7 21 326 18.1 86 2,680 121 37.9 d 5,610 d 120 4.2 2.7 Eritrea 5 118 46 0.9 183 190 202 3.2d 680 d 198 ­1.0 ­4.5 Estonia 1 45 32 15.3 96 11,400 60 24.3 18,090 58 11.4 11.7 Ethiopia 77 1,104 77 12.9 101 170 204 49.0 630 200 9.0 6.2 Finland 5 338 17 217.8 29 41,360 16 174.7 33,170 30 5.5 5.1 France 61 552 111 2,306.7g 6 36,560 g 24 1,974.9 32,240 34 2.0 1.4 Gabon 1 268 5 7.0 121 5,360 85 14.7 11,180 81 1.2 ­0.4 Gambia, The 2 11 166 0.5 194 290 196 1.8 1,110 186 4.5 1.6 Georgia 4 70 64 7.0 122 1,580 137 17.2 3,880 140 9.4 10.4 Germany 82 357 236 3,032.6 3 36,810 21 2,692.3 32,680 32 2.8 2.9 Ghana 23 239 101 11.8 103 510 177 28.4 1,240 179 6.2 4.0 Greece 11 132 86 305.3 26 27,390 34 344.1 30,870 36 4.3 3.9 Guatemala 13 109 120 33.7 67 2,590 123 66.7d 5,120 d 124 4.5 1.9 Guinea 9 246 37 3.7 147 400 186 10.4 1,130 184 2.8 0.8 Guinea-Bissau 2 36 59 0.3 203 190 202 0.8 460 205 4.2 1.1 Haiti 9 28 343 4.0 144 430 185 10.1d 1,070 d 187 2.3 0.7 14 2008 World Development Indicators 1.1 WORLD VIEW Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2006 2006 2006 2006b 2005 2006b 2006 2006 2006 2006 2005­06 2005­06 Honduras 7 112 62 8.8 113 1,270 146 23.9d 3,420 d 147 6.0 4.0 Hungary 10 93 112 109.5 47 10,870 62 170.8 16,970 61 3.9 4.1 India 1,110 3,287 373 909.1 10 820 161 2,726.3 2,460 155 9.2 7.7 Indonesia 223 1,905 123 315.9 24 1,420 140 737.2 3,310 149 5.5 4.3 Iran, Islamic Rep. 70 1,745 43 205.0 30 2,930 116 686.9 9,800 87 4.6 3.1 Iraq .. 438 .. .. .. ..f .. .. .. .. 46.5 .. Ireland 4 70 62 191.3 34 44,830 10 148.2 34,730 19 5.7 3.0 Israel 7 22 326 142.2 38 20,170 44 168.1 23,840 49 5.1 3.2 Italy 59 301 200 1,882.5 7 31,990 28 1,704.9 28,970 38 1.9 1.5 Jamaica 3 11 246 9.5 107 3,560 104 18.8d 7,050 d 107 2.5 2.0 Japan 128 378 350 4,934.7 2 38,630 19 4,195.9 32,840 31 2.2 2.2 Jordan 6 89 63 14.7 99 2,650 122 26.7 4,820 129 5.7 3.3 Kazakhstan 15 2,725 6 59.2 57 3,870 99 133.2 8,700 96 10.7 9.5 Kenya 37 580 64 21.3 83 580 175 53.8 1,470 176 6.1 3.3 Korea, Dem. Rep. 24 121 197 .. .. ..c .. .. .. .. .. .. Korea, Rep. 48 99 490 856.6 12 17,690 51 1,113.0 22,990 50 5.0 4.7 Kuwait 3 18 146 77.7 .. 30,630 .. 122.5 48,310 .. 8.5 5.3 Kyrgyz Republic 5 200 27 2.6 157 500 178 9.3 1,790 167 2.7 1.7 Lao PDR 6 237 25 2.9 155 500 178 10.0 1,740 169 7.6 5.8 Latvia 2 65 37 18.5 85 8,100 71 33.9 14,840 67 11.9 12.6 Lebanon 4 10 396 22.6 81 5,580 80 38.9 9,600 89 0.0 ­1.1 Lesotho 2 30 66 2.0 167 980 155 3.6 1,810 166 7.2 6.4 Liberia 4 111 37 0.5 195 130 207 0.9 260 208 7.8 3.7 Libya 6 1,760 3 44.0 61 7,290 75 70.2d 11,630 d 79 5.6 3.5 Lithuania 3 65 54 26.9 78 7,930 73 49.4 14,550 68 7.7 8.3 Macedonia, FYR 2 26 80 6.3 128 3,070 109 16.0 7,850 102 3.0 2.9 Madagascar 19 587 33 5.3 134 280 197 16.6 870 193 4.9 2.1 Malawi 14 118 144 3.1 152 230 201 9.4 690 196 7.4 4.7 Malaysia 26 330 79 146.8 37 5,620 79 317.4 12,160 75 5.9 4.0 Mali 12 1,240 10 5.6 133 460 181 11.9 1,000 189 5.3 2.2 Mauritania 3 1,031 3 2.3 163 760 165 6.0 1,970 164 11.7 8.7 Mauritius 1 2 617 6.8 124 5,430 82 13.3 10,640 83 3.5 2.7 Mexico 104 1,964 54 815.7 14 7,830 74 1,249.2 11,990 76 4.8 3.6 Moldova 4 34 117 3.7h 149 1,080h 151 10.2 2,660 152 4.0 5.2 Mongolia 3 1,567 2 2.6 158 1,000i 153 7.3 2,810 150 8.6 7.3 Morocco 30 447 68 65.8 56 2,160 128 117.7 3,860 141 8.0 6.7 Mozambique 21 799 27 6.5 126 310 193 13.9 660 199 8.0 5.7 Myanmar 48 677 74 .. .. ..c .. .. .. .. 5.0 4.1 Namibia 2 824 2 6.6 125 3,210 107 9.8 4,770 130 2.9 1.6 Nepal 28 147 193 8.8 114 320 192 27.8 1,010 188 2.8 0.8 Netherlands 16 42 482 703.5 16 43,050 13 620.0 37,940 15 2.9 2.7 New Zealand 4 268 16 112.0 45 26,750 37 107.7 25,750 44 1.9 0.7 Nicaragua 6 130 46 5.2 135 930 156 15.1d 2,720 d 151 3.7 2.4 Niger 14 1,267 11 3.7 148 270 198 8.6 630 200 4.8 1.2 Nigeria 145 924 159 90.0 52 620 173 203.7 1,410 177 5.2 2.8 Norway 5 324 15 318.9 23 68,440 2 233.3 50,070 4 2.9 2.1 Oman 3 310 8 27.9 .. 11,120 j .. 49.5 19,740 .. 5.8 4.6 Pakistan 159 796 206 126.7 42 800 162 382.8 2,410 156 6.9 4.7 Panama 3 76 44 16.4 93 5,000 89 28.6d 8,690 d 98 8.1 6.3 Papua New Guinea 6 463 14 4.6 141 740 168 10.1d 1,630 d 170 2.6 0.4 Paraguay 6 407 15 8.5 115 1,410 141 24.3 4,040 137 4.3 2.2 Peru 28 1,285 22 82.2 54 2,980 113 179.2 6,490 110 7.7 6.5 Philippines 86 300 289 120.2 44 1,390 142 296.2 3,430 146 5.4 3.4 Poland 38 313 124 313.0 25 8,210 70 543.4 14,250 71 6.1 6.2 Portugal 11 92 116 189.0 35 17,850 50 211.3 19,960 57 1.3 0.9 Puerto Rico 4 9 443 .. .. ..k .. .. .. .. .. .. 2008 World Development Indicators 15 1.1 Size of the economy Population Surface Population Gross national Gross national PPP gross national Gross domestic area density income income per capita incomea product thousand people Per capita Per capita millions sq. km per sq. km $ billions Rank $ Rank $ billions $ Rank % growth % growth 2006 2006 2006 2006b 2005 2006b 2006 2006 2006 2006 2005­06 2005­06 Romania 22 238 94 104.4 48 4,830 91 219.2 10,150 85 7.7 7.9 Russian Federation 143 17,098 9 822.3 13 5,770 78 1,814.9 12,740 74 6.7 7.2 Rwanda 9 26 384 2.3 162 250 199 6.9 730 195 5.3 2.7 Saudi Arabia 24 2,000l 12 331.0 21 13,980 55 528.0 22,300 52 4.3 1.8 Senegal 12 197 63 9.1 110 760 165 18.8 1,560 172 2.3 ­0.3 Serbia 7m 88 96m 30.0m 75 4,030m 97 69.3 9,320 90 5.7 5.8 Sierra Leone 6 72 80 1.4 175 240 200 3.5 610 202 7.4 4.4 Singapore 4 1 6,508 128.8 41 28,730 33 194.1 43,300 9 7.9 4.5 Slovak Republic 5 49 112 51.8 60 9,610 64 91.9 17,060 60 8.3 8.2 Slovenia 2 20 100 37.4 64 18,660 49 48.1 23,970 48 5.2 4.9 Somalia 8 638 13 .. .. ..c .. .. .. .. .. .. South Africa 47 1,219 39 255.4 28 5,390 84 421.7 8,900 94 5.0 3.9 Spain 44 505 88 1,206.2 8 27,340 35 1,244.2 28,200 39 3.9 2.2 Sri Lanka 20 66 308 26.0 79 1,310 144 74.2 3,730 143 7.4 6.2 Sudan 38 2,506 16 30.1 74 800 162 67.2 1,780 168 11.8 9.4 Swaziland 1 17 66 2.7 156 2,400 124 5.3 4,700 132 2.1 1.5 Sweden 9 450 22 395.4 19 43,530 12 311.7 34,310 20 4.2 3.5 Switzerland 7 41 187 434.8 17 58,050 6 305.9 40,840 11 3.2 2.5 Syrian Arab Republic 19 185 106 30.3 72 1,560 138 79.7 4,110 136 5.1 2.3 Tajikistan 7 143 47 2.6 159 390 187 10.3 1,560 172 7.0 5.6 Tanzania 39 947 45 13.4n 100 350n 188 38.8 980 190 5.9 3.3 Thailand 63 513 124 193.7 33 3,050 110 472.2 7,440 104 5.0 4.3 Timor-Leste 1 15 69 0.9 185 840 160 5.2d 5,100 d 125 ­1.6 ­6.7 Togo 6 57 118 2.3 165 350 188 4.9 770 194 4.1 1.3 Trinidad and Tobago 1 5 259 16.6 90 12,500 57 22.3d 16,800 d 62 12.0 11.6 Tunisia 10 164 65 30.1 73 2,970 115 65.7 6,490 110 5.2 4.2 Turkey 73 784 95 393.9 20 5,400 83 613.7 8,410 99 6.1 4.8 Turkmenistan 5 488 10 .. .. ..f .. 19.3d 3,990d .. .. .. Uganda 30 241 152 9.0 112 300 195 26.3 880 192 5.4 2.1 Ukraine 47 604 81 90.7 51 1,940 132 286.0 6,110 115 7.1 7.8 United Arab Emirates 4 84 51 103.5 .. 26,210 .. 123.1d 31,190d .. 8.5 4.3 United Kingdom 61 244 250 2,455.7 5 40,560 17 2,037.2 33,650 29 2.8 2.2 United States 299 9,632 33 13,386.9 1 44,710 11 13,195.7 44,070 8 2.9 1.9 Uruguay 3 176 19 17.6 89 5,310 86 32.9 9,940 86 7.0 6.7 Uzbekistan 27 447 62 16.2 94 610 174 58.1d 2,190 d 159 7.3 5.8 Venezuela, RB 27 912 31 164.0 36 6,070 77 296.4 10,970 82 10.3 8.5 Vietnam 84 329 271 58.5 58 700 169 194.4 2,310 157 8.2 6.9 West Bank and Gaza 4 6 627 4.5 .. 1,230 .. 14.0 d 3,720 d 144 1.4 ­2.6 Yemen, Rep. 22 528 41 16.4 92 760 165 45.5 2,090 162 3.3 0.3 Zambia 12 753 16 7.4 120 630 172 13.4 1,140 182 6.2 4.2 Zimbabwe 13 391 34 4.5 .. 340 .. 2.2 170 .. ­5.3 ­6.0 World 6,538 s 133,946 s 50 w 48,694.1 t 7,448 w 60,210 t 9,209 w 3.8 w 2.6 w Low income 2,420 29,220 86 1,570.8 649 4,501 1,860 8.0 6.1 Middle income 3,088 70,112 45 9,426.9 3,053 19,920 6,451 7.2 6.3 Lower middle income 2,276 28,646 81 4,639.8 2,038 11,152 4,899 8.8 7.9 Upper middle income 811 41,466 20 4,797.3 5,913 8,826 10,879 5.7 4.9 Low & middle income 5,507 99,332 57 10,997.7 1,997 24,430 4,436 7.3 6.0 East Asia & Pacific 1,899 16,300 120 3,524.7 1,856 8,277 4,359 9.4 8.6 Europe & Central Asia 461 24,114 20 2,217.1 4,815 4,509 9,791 6.8 6.7 Latin America & Carib. 556 20,421 28 2,661.2 4,785 4,828 8,682 5.5 4.2 Middle East & N. Africa 311 9,087 35 778.8 2,507 2,084 6,710 5.1 3.3 South Asia 1,499 5,140 314 1,151.3 768 3,432 2,289 8.7 7.0 Sub-Saharan Africa 782 24,270 33 647.9 829 1,314 1,681 5.6 3.0 High income 1,031 34,614 31 37,731.7 36,608 36,005 34,933 2.9 2.2 Euro area 317 2,536 128 10,864.1 34,307 9,874 31,181 2.7 2.2 a. PPP is purchasing power parity; see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be low income ($905 or less). d. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. e. Includes Taiwan, China; Macao, China; and Hong Kong, China. f. Estimated to be lower middle income ($906­$3,595). g. Includes the French overseas departments of French Guiana, Guadeloupe, Martinique, and Réunion. h. Excludes Transnistria. i. Included in the aggregates for low-income economies based on earlier data. j. Included in the aggregates for upper middle-income economies based on earlier data. k. Estimated to be high income ($11,116 or more). l. Provisional estimate. m. Excludes Kosovo and Metohija. n. Covers mainland Tanzania only. 16 2008 World Development Indicators 1.1 WORLD VIEW Size of the economy About the data Definitions Population, land area, income, output, and growth in allowing comparison of real levels of expenditure · Population is based on the de facto definition of output are basic measures of the size of an economy. between countries, just as conventional price population, which counts all residents regardless of They also provide a broad indication of actual and indexes allow comparison of real values over time. legal status or citizenship--except for refugees not potential resources. Population, land area, income The PPP conversion factors used are derived from the permanently settled in the country of asylum, who (as measured by gross national income, GNI) and out- 2005 round of price surveys covering 146 economies are generally considered part of the population of put (as measured by gross domestic product, GDP) conducted by the International Comparison Program. their country of origin. The values shown are midyear are therefore used throughout World Development For Organisation for Economic Co-operation and estimates. See also table 2.1. · Surface area is Indicators to normalize other indicators. Development (OECD) countries data come from the a country's total area, including areas under inland Population estimates are generally based on most recent round of surveys, completed in 2005. bodies of water and some coastal waterways. · Pop- extrapolations from the most recent national cen- Estimates for economies not included in the surveys ulation density is midyear population divided by land sus. For further discussion of the measurement of are derived from statistical models using available area in square kilometers. · Gross national income population and population growth, see About the data data. (GNI) is the sum of value added by all resident pro- for table 2.1 and Statistical methods. For more information on the results of the 2005 ducers plus any product taxes (less subsidies) not The surface area of an economy includes inland International Comparison Program, see the introduc- included in the valuation of output plus net receipts bodies of water and some coastal waterways. Sur- tion to World View. The final report of the program is of primary income (compensation of employees and face area thus differs from land area, which excludes available at www.worldbank.org/data/icp. property income) from abroad. Data are in current bodies of water, and from gross area, which may All 209 economies shown in World Development U.S. dollars converted using the World Bank Atlas include offshore territorial waters. Land area is par- Indicators are ranked by size, including those that method (see Statistical methods). · GNI per capita is ticularly important for understanding an economy's appear in table 1.6. The ranks are shown only in GNI divided by midyear population. GNI per capita in agricultural capacity and the environmental effects table 1.1. No rank is shown for economies for which U.S. dollars is converted using the World Bank Atlas of human activity. (For measures of land area and numerical estimates of GNI per capita are not pub- method. · Purchasing power parity (PPP) GNI is GNI data on rural population density, land use, and agri- lished. Economies with missing data are included in converted to international dollars using PPP rates. An cultural productivity, see tables 3.1­3.3.) Innova- the ranking at their approximate level, so that the rel- international dollar has the same purchasing power tions in satellite mapping and computer databases ative order of other economies remains consistent. over GNI that a U.S. dollar has in the United States. have resulted in more precise measurements of land · Gross domestic product (GDP) is the sum of value and water areas. added by all resident producers plus any product GNI measures total domestic and foreign value taxes (less subsidies) not included in the valuation added claimed by residents. GNI comprises GDP of output. Growth is calculated from constant price plus net receipts of primary income (compensation GDP data in local currency. · GDP per capita is GDP of employees and property income) from nonresident divided by midyear population. sources. The World Bank uses GNI per capita in U.S. dollars to classify countries for analytical purposes and to determine borrowing eligibility. For definitions 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 Data sources productivity or welfare, see About the data for tables 4.1 and 4.2. Population estimates are prepared by World Bank When calculating GNI in U.S. dollars from GNI staff from a variety of sources (see Data sources reported in national currencies, the World Bank fol- for table 2.1). Data on surface and land area lows the World Bank Atlas conversion method, using are from the Food and Agriculture Organization a three-year average of exchange rates to smooth (see Data sources for table 3.1). GNI, GNI per the effects of transitory fluctuations in exchange capita, GDP growth, and GDP per capita growth rates. (For further discussion of the World Bank Atlas are estimated by World Bank staff based on method, see Statistical methods.) GDP and GDP per national accounts data collected by World Bank capita growth rates are calculated from data in con- staff during economic missions or reported by stant prices and national currency units. national statistical offices to other international Because exchange rates do not always reflect dif- organizations such as the OECD. PPP conversion ferences in price levels between countries, the table factors are estimates by World Bank staff based also converts GNI and GNI per capita estimates into on data collected by the International Comparison international dollars using purchasing power parity Program. (PPP) rates. PPP rates provide a standard measure 2008 World Development Indicators 17 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 Prevalence of in national Vulnerable malnutrition Ratio of girls to boys consumption employment Underweight Primary enrollments in primary Under-fi ve or income Unpaid family workers % of children completion ratea and secondary schoola mortality rate % 1992­ % of total employment under age 5 % % per 1,000 2005b,c 1990 2005 1990 2000­06b 1991 2006d 1991 2006d 1990 2006 Afghanistan .. .. .. .. .. .. .. 54 56 .. .. Albania 8.2 .. .. .. 17.0 .. 96 96 97 45 17 Algeria 7.0 .. 29 .. 10.2 80 85 83 99 69 38 Angola .. .. .. .. 27.5 35 .. .. .. 260 260 Argentina 3.1e .. 21 .. 2.3 .. 99 .. 104 29 16 Armenia 8.5 .. .. .. 4.2 90 91 .. 104 56 24 Australia 5.9 10 10 .. .. .. .. 101 97 10 6 Austria 8.6 .. 9 .. .. .. 103 95 97 10 5 Azerbaijan 7.4 .. .. .. 14.0 .. 92 100 96 105 88 Bangladesh 8.8 .. 63 .. 39.2 49 72 .. 103 149 69 Belarus 8.8 .. .. .. .. 94 95 .. 101 24 13 Belgium 8.5 .. 11 .. .. 79 .. 101 98 10 4 Benin 7.4 .. .. .. 21.5 21 65 49 73 185 148 Bolivia 1.5 40 62 8.9 5.9 .. 101 .. 98 125 61 Bosnia and Herzegovina 7.0 .. .. .. 4.2 .. .. .. .. 22 15 Botswana 3.2 .. 12 .. 10.7 89 95 109 100 58 124 Brazil 2.9 29 29 .. 3.7 93 105 .. 102 57 20 Bulgaria 8.7 .. 10 .. 1.6 84 99 99 97 19 14 Burkina Faso 6.9 .. .. .. 35.2 20 31 62 80 206 204 Burundi 5.1 .. .. .. 38.9 46 36 82 89 190 181 Cambodia 6.8 .. 87 .. 28.4 .. 87 73 89 116 82 Cameroon 5.6 .. .. .. 15.1 53 58 83 84 139 149 Canada 7.2 .. .. .. .. .. .. 99 98 8 6 Central African Republic 2.0 .. .. .. 21.8 27 24 60 .. 173 175 Chad .. 94 .. .. 33.9 18 31 42 61 201 209 Chile 3.8 .. 27 .. .. .. 123 100 98 21 9 China 4.3 .. .. .. 6.8 105 .. 87 100 45 24 Hong Kong, China 5.3 5 8 .. .. 102 .. 103 .. .. .. Colombia 2.9 28 44 .. 5.1 70 105 108 104 35 21 Congo, Dem. Rep. .. .. .. .. 33.6 46 38 .. 73 205 205 Congo, Rep. .. .. .. .. 11.8 54 73 85 90 103 126 Costa Rica 4.1 25 21 .. .. 79 89 101 102 18 12 Côte d'Ivoire 5.2 .. .. .. .. 43 43 65 .. 153 127 Croatia 8.8 .. 19 .. .. 85 92 102 101 12 6 Cuba .. .. .. .. .. 99 92 106 100 13 7 Czech Republic 10.3 7 12 .. 2.1 .. 102 98 101 13 4 Denmark 8.3 .. .. .. .. 98 99 101 102 9 5 Dominican Republic 4.1 39 43 8.4 4.2 61 83 .. 104 65 29 Ecuador 3.3 36 33 .. 6.2 91 106 .. 100 57 24 Egypt, Arab Rep. 8.9 28 26 .. 5.4 .. 98 81 93 91 35 El Salvador 2.7 35 36 7.2 6.1 41 88 102 99 60 25 Eritrea .. .. .. .. 34.5 19 48 .. 72 147 74 Estonia 6.8 2 5 .. .. 93 106 103 100 16 7 Ethiopia 9.1 .. 91 .. 34.6 26 49 68 81 204 123 Finland 9.6 .. .. .. .. 97 100 109 102 7 4 France 7.2 .. 7 .. .. 104 .. 102 100 9 4 Gabon .. 48 .. .. 8.8 58 75 .. .. 92 91 Gambia, The 4.8 .. .. .. 15.4 44 63 66 102 153 113 Georgia 5.4 .. 64 .. .. .. 85 98 103 46 32 Germany 8.5 .. 6 .. .. 100 95 99 99 9 4 Ghana 5.6 .. .. .. 18.8 61 71 79 95f 120 120 Greece 6.7 40 28 .. .. 99 100 99 99 11 4 Guatemala 3.9 .. 55 27.8 17.7 .. 77 .. 92 82 41 Guinea 7.0 .. .. .. 22.5 17 64 45 74 235 161 Guinea-Bissau 5.2 .. .. .. 21.9 .. .. .. .. 240 200 Haiti 2.4 .. .. .. 18.9 27 .. 94 .. 152 80 18 2008 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 Prevalence of in national Vulnerable malnutrition Ratio of girls to boys consumption employment Underweight Primary enrollments in primary Under-fi ve or income Unpaid family workers % of children completion ratea and secondary schoola mortality rate % 1992­ % of total employment under age 5 % % per 1,000 2005b,c 1990 2005 1990 2000­06b 1991 2006d 1991 2006d 1990 2006 Honduras 3.4 49 49 .. 8.6 64 89 106 109 58 27 Hungary 8.6 7 8 2.3 .. 93 94 100 99 17 7 India 8.1 .. .. .. 43.5 64 85 70 91 115 76 Indonesia 7.1 .. .. 31.0 24.4 91 99 93 97 91 34 Iran, Islamic Rep. 6.5 .. .. .. .. 91 101 85 105 72 34 Iraq .. .. .. .. .. 59 .. 78 78 53 .. Ireland 7.4 20 12 .. .. .. 97 104 103 9 5 Israel 5.7 .. 8 .. .. .. 101 105 100 12 5 Italy 6.5 16 13 .. .. 104 100 100 99 9 4 Jamaica 5.3 42 34 .. 3.1 90 82 102 101 33 31 Japan 10.6 19 12 .. .. 101 .. 101 100 6 4 Jordan 6.7 .. .. .. 3.6 72 100 101 102 40 25 Kazakhstan 7.4 .. 36 .. .. .. 101f 102 99 f 60 29 Kenya 6.0 .. .. 20.1 16.5 .. 93 94 96 97 121 Korea, Dem. Rep. .. .. .. .. 17.8 .. .. .. .. 55 55 Korea, Rep. 7.9 .. 26 .. .. 98 101 99 96 9 5 Kuwait .. .. .. .. .. .. 91 97 102 16 11 Kyrgyz Republic 8.9 .. 50 .. .. .. 99 .. 100 75 41 Lao PDR 8.1 .. .. .. 36.4 43 75 76 85 163 75 Latvia 6.8 .. 8 .. .. .. 92 101 99 18 9 Lebanon .. .. .. .. .. .. 80 .. 103 37 30 Lesotho 1.5 38 .. .. 16.6 59 78 123 104 101 132 Liberia .. .. .. .. 22.8 .. 63 .. .. 235 235 Libya .. .. .. .. .. .. .. .. 105 41 18 Lithuania 6.8 .. .. .. .. 89 91 .. 100 13 8 Macedonia, FYR 6.1 .. 22 .. 1.2 98 97 99 99 38 17 Madagascar 4.9 .. 82 35.5 36.8 33 57 98 96 168 115 Malawi 7.0 .. .. 24.4 18.4 29 55 81 100 221 120 Malaysia 4.4 .. 20 .. .. 91 95 101 105 22 12 Mali 6.1 .. .. 29.0 30.1 13 49 57 74 250 217 Mauritania 6.2 .. .. .. 30.4 34 47 71 102 133 125 Mauritius .. .. 17 .. .. 107 92 102 103 23 14 Mexico 4.3 37 31 13.9 3.4 88 103 97 99 53 35 Moldova 7.8 .. 36 .. 3.2 .. 90 106 102 37 19 Mongolia 7.5 .. 60 .. 4.8 .. 109 109 108 109 43 Morocco 6.5 .. 58 8.1 9.9 48 84 70 87 89 37 Mozambique 5.4 .. .. .. 21.2 26 42 71 85 235 138 Myanmar .. .. .. .. 29.6 .. 95 97 101 130 104 Namibia 1.4 .. .. .. 20.3 78 76 106 104 86 61 Nepal 6.0 .. .. .. 38.8 51 76 59 93 142 59 Netherlands 7.6 .. .. .. .. .. 100 97 98 9 5 New Zealand 6.4 13 12 .. .. 100 .. 100 104 11 6 Nicaragua 5.6 .. 38 .. 7.8 42 73 109 102 68 36 Niger 2.6 .. .. 41.0 39.9 18 33 53 70 320 253 Nigeria 5.0 .. .. 35.1 27.2 .. 76 77 83 230 191 Norway 9.6 .. .. .. .. 100 99 102 101 9 4 Oman .. .. .. .. .. 74 94 89 98 32 12 Pakistan 9.1 .. 61 39.0 31.3 .. 62 .. 78 130 97 Panama 2.5 34 32 .. .. 86 94 .. 101 34 23 Papua New Guinea 4.5 .. .. .. .. 46 .. 80 .. 94 73 Paraguay 2.4 23 50 2.8 .. 68 94 98 99 41 22 Peru 3.7 36 36 8.8 5.2 .. 100 96 101 78 25 Philippines 5.4 .. 45 .. 20.7 86 96 100 103 62 32 Poland 7.4 28 22 .. .. 98 97 101 99 18 7 Portugal 5.8 19 19 .. .. 95 104 103 102 14 5 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 19 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 Prevalence of in national Vulnerable malnutrition Ratio of girls to boys consumption employment Underweight Primary enrollments in primary Under-fi ve or income Unpaid family workers % of children completion ratea and secondary schoola mortality rate % 1992­ % of total employment under age 5 % % per 1,000 2005b,c 1990 2005 1990 2000­06b 1991 2006d 1991 2006d 1990 2006 Romania 8.2 27 33 .. 3.5 96 99 99 100 31 18 Russian Federation 6.1 1 6 .. .. 93 94 104 99 27 16 Rwanda 5.3 .. .. 24.3 18.0 35 35 92 102 176 160 Saudi Arabia .. .. .. .. .. 55 85 84 95 44 25 Senegal 6.6 83 .. 21.9 14.5 39 49 69 91 149 116 Serbia 8.3g .. .. .. .. .. .. .. .. .. 8 Sierra Leone 6.5 .. .. .. 24.7 .. 81f 67 86f 290 270 Singapore 5.0 8 9 .. 3.3 .. .. 95 101 8 3 Slovak Republic 8.8 .. 9 .. .. 96 94 .. 100 14 8 Slovenia 8.3 12 11 .. .. 95 99 .. 100 10 4 Somalia .. .. .. .. .. .. .. .. .. 203 145 South Africa 3.5 .. 19 .. .. 76 100 104 100 60 69 Spain 7.0 22 13 .. .. .. 103 104 103 9 4 Sri Lanka 7.0 .. 39 29.3 22.8 102 108 102 104 32 13 Sudan .. .. .. .. 38.4 42 47 77 89 120 89 Swaziland 4.3 .. .. .. 9.1 60 67 98 95 110 164 Sweden 9.1 .. .. .. .. 96 .. 102 100 7 3 Switzerland 7.6 9 10 .. .. 53 91 97 97 9 5 Syrian Arab Republic .. .. .. .. .. 89 115 85 95 38 14 Tajikistan 7.8 .. .. .. .. .. 106 .. 88 115 68 Tanzania 7.3 .. .. 25.1 16.7 62 85f 97 .. 161 118 Thailand 6.3 70 53 17.4 .. .. .. 97 104 31 8 Timor-Leste .. .. .. .. 40.6 .. .. .. 95 177 55 Togo .. .. .. 21.2 .. 35 67 59 73 149 108 Trinidad and Tobago 5.9 22 16 4.7 4.4 101 88 101 101 34 38 Tunisia 6.0 .. .. 8.5 .. 74 99 86 104 52 23 Turkey 5.3 .. 41 8.7 .. 90 86 81 89 82 26 Turkmenistan 6.1 .. .. .. .. .. .. .. .. 99 51 Uganda 5.7 .. 85 19.7 19.0 .. 54 82 98 160 134 Ukraine 9.0 .. .. .. 4.1 94 105 .. 99 25 24 United Arab Emirates .. .. .. .. .. 103 100 104 101 15 8 United Kingdom 6.1 .. .. .. .. .. .. 102 101 10 6 United States 5.4 .. .. .. 1.1 .. .. 100 100 11 8 Uruguay 5.0e .. 25 .. 6.0 94 93 .. 106 23 12 Uzbekistan 7.2 .. .. .. .. .. 98 94 98f 74 43 Venezuela, RB 3.3 .. 35 .. .. 43 96 105 103 33 21 Vietnam 7.1 .. 74 36.9 26.7 .. 92 .. 97 53 17 West Bank and Gaza .. .. 38 .. .. .. 89 .. 104 40 22 Yemen, Rep. 7.2 .. .. .. .. .. 60 .. 66 139 100 Zambia 3.6 65 79 21.2 23.3 .. 84 .. 96 180 182 Zimbabwe 4.6 .. 62 8.0 14.0 97 81 92 96 76 105 World .. w .. w .. w 23.5 w 79 w 86 w 86 w 95 w 92 w 73 w Low income .. .. .. 35.3 57 73 73 89 143 112 Middle income .. .. .. 9.5 93 97 91 99 56 33 Lower middle income .. .. .. 10.7 95 97 89 98 60 36 Upper middle income .. 24 .. .. 88 99 99 100 47 26 Low & middle income .. .. .. 24.5 78 85 84 94 101 79 East Asia & Pacific .. .. .. 12.9 101 98 89 99 56 29 Europe & Central Asia .. 18 .. .. 93 95 98 96 49 26 Latin America & Carib. 36 32 .. 5.1 82 99 99 101 55 26 Middle East & N. Africa .. .. .. .. 77 91 82 94 77 42 South Asia .. .. .. 41.0 62 80 70 90 123 83 Sub-Saharan Africa .. .. .. 27.0 51 60 79 86 184 157 High income .. .. .. .. .. 97 100 100 12 7 Euro area .. 12 .. .. 100 .. 101 .. 9 4 a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data before 1998 are not fully comparable with data from 1999 onward. b. Data are for the most recent year available. c. See table 2.8 for survey year and whether share is based on income or consumption expenditure. d. Provisional data. e. Urban data. f. Data are for 2007. g. Includes Montenegro. 20 2008 World Development Indicators 1.2 WORLD VIEW Millennium Development Goals: eradicating poverty and saving lives About the data Definitions This table and the two following present indicators for undernourished mothers who give birth to under- · Share of poorest quintile in national consumption 17 of the 21 targets specified by the Millennium Devel- weight children. or income is the share of the poorest 20 percent of opment Goals. Each of the eight goals includes one or Progress toward universal primary education is the population in consumption or, in some cases, more targets, and each target has several associated measured by the primary completion rate. Because income. · Vulnerable employment is the sum of indicators for monitoring progress toward the target. many school systems do not record school comple- unpaid family workers and own-account workers as Most of the targets are set as a value of a specific indi- tion on a consistent basis, it is estimated from the a percentage of total employment. · Prevalence of cator to be attained by a certain date. In some cases gross enrollment rate in the final grade of primary malnutrition is the percentage of children under age the target value is set relative to a level in 1990. In oth- school, adjusted for repetition. Official enrollments five whose weight for age is more than two standard ers it is set at an absolute level. Some of the targets sometimes differ significantly from attendance, and deviations below the median for the international for goals 7 and 8 have not yet been quantified. even school systems with high average enrollment reference population ages 0­59 months. The data The indicators in this table relate to goals 1­4. ratios may have poor completion rates. are based on the new international child growth stan- Goal 1 has three targets between 1990 and 2015: Eliminating gender disparities in education would dards for infants and young children, called the Child to reduce by half the proportion of people whose help to increase the status and capabilities of women. Growth Standards, released in 2006 by the World income is less than $1 a day, to achieve full and The ratio of female to male enrollments in primary and Health Organization. · Primary completion rate is productive employment and decent work for all, secondary school provides an imperfect measure of the percentage of students completing the last year and to reduce by half the proportion of people who the relative accessibility of schooling for girls. of primary school. It is calculated as the total num- suffer from hunger. Estimates of poverty rates are The targets for reducing under-five mortality rates ber of students in the last grade of primary school, in table 2.7. The indicator shown here, the share are among the most challenging. Under-five mortal- minus the number of repeaters in that grade, divided of the poorest quintile in national consumption, ity rates are harmonized estimates produced by a by the total number of children of official graduation is a distributional measure. Countries with more weighted least squares regression model and are age. · Ratio of girls to boys enrollments in primary unequal distributions of consumption (or income) available at regular intervals for most countries. and secondary school is the ratio of the female to have a higher rate of poverty for a given average Most of the 60 indicators relating to the Millennium male gross enrollment rate in primary and secondary income. Vulnerable employment measures the Development Goals can be found in World Develop- school. · Under-five mortality rate is the probability portion of the labor force that receives the low- ment Indicators. Table 1.2a shows where to find the that a newborn baby will die before reaching age five, est wages and least security in employment. No indicators for the first four goals. For more informa- if subject to current age-specific mortality rates. The single indicator captures the concept of suffering tion about data collection methods and limitations, probability is expressed as a rate per 1,000. from hunger. Child malnutrition is a symptom of see About the data for the tables listed there. For inadequate food supply, lack of essential nutri- information about the indicators for goals 5, 6, 7, and ents, illnesses that deplete these nutrients, and 8, see 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 1.1 Proportion of population below $1 a day 2.7* 1.2 Poverty gap ratio 2.7 1.3 Share of poorest quintile in national consumption 1.2, 2.8 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 Data sources 1.8 Prevalence of underweight in children under age five 1.2, 2.18, 2.20 1.9 Proportion of population below minimum level of dietary energy consumption 2.18 The indicators here and throughout this book have Goal 2. Achieve universal primary education been compiled by World Bank staff from primary 2.1 Net enrollment ratio in primary education 2.11 and secondary sources. Data on primary school 2.2 Proportion of pupils starting grade 1 who reach last grade of primary 2.12 completion rates are provided by the United 2.3 Literacy rate of 15- to 24-year-olds 2.13 Goal 3. Promote gender equality and empower women Nations Educational, Scientifi c, and Cultural 3.1 Ratio of girls to boys in primary, secondary, and tertiary education 1.2, 2.11* Organization Institute of Statistics and national 3.2 Share of women in wage employment in the nonagricultural sector 1.5, 2.3* sources. Efforts have been made to harmonize 3.3 Proportion of seats held by women in national parliament 1.5 the data series used to compile this table with Goal 4. Reduce child mortality those published on the United Nations Millen- 4.1 Under-five mortality rate 1.2, 2.20, 2.21 4.2 Infant mortality rate 2.20, 2.21 nium Development Goals Web site (www.un.org/ 4.3 Proportion of one-year-old children immunized against measles 2.16, 2.20 millenniumgoals), but some differences in timing, -- No data are available in the World Development Indicators database. * Table shows information on related indicators. sources, and definitions remain. 2008 World Development Indicators 21 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 Fixed-line and estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved mobile phone per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities subscribers live births ages 15­49 ages 15­49 people metric tons % % of population per 100 peoplea 2005 1990 2000­06b 2005 2006 1990 2004 2007 1990 2004 2006 Afghanistan .. .. .. .. .. .. .. 0.8 .. .. 10 Albania 92 .. 60 0.2 19 2.2 1.2 1.3 .. 91 60 Algeria 180 47 61 0.1 56 3.0 6.0 2.0 88 92 71 Angola 1,400 .. 6 3.7 285 0.4 0.5 1.4 29 31 14 Argentina 77 .. .. 0.6 39 3.4 3.7 1.8 81 91 105 Armenia 76 .. 53 0.1 72 1.2 1.2 0.9 .. 83 30 Australia 4 .. .. 0.1 6 16.3 16.2 3.7 100 100 143 Austria 4 .. .. 0.3 13 7.5 8.5 1.8 100 100 155 Azerbaijan 82 .. 55 0.1 77 7.5 3.8 0.8 .. 54 53 Bangladesh 570 40 58 <0.1 225 0.1 0.2 1.8 20 39 13 Belarus 18 .. 73 0.3 61 10.6 6.6 .. .. 84 96 Belgium 8 78 .. 0.3 13 10.1 9.7 1.4 .. .. 136 Benin 840 .. 17 1.8 90 0.1 0.3 1.5 12 33 13 Bolivia 290 30 58 0.1 198 0.8 0.8 0.8 33 46 36 Bosnia and Herzegovina 3 .. 36 <0.1 51 1.6 4.0 14.4 .. 95 73 Botswana 380 33 44 24.1 551 1.6 2.4 0.6 38 42 60 Brazil 110 59 .. 0.5 50 1.4 1.8 1.2 71 75 73 Bulgaria 11 .. .. <0.1 40 8.6 5.5 1.2 99 99 138 Burkina Faso 700 .. 17 2.0 248 0.1 0.1 0.9 7 13 8 Burundi 1,100 .. 9 3.3 367 0.0 0.0 1.6 44 36 2 Cambodia 540 .. 40 1.6 500 0.0 0.0 17.4 .. 17 8 Cameroon 1,000 16 29 5.5c 192 0.1 0.2 5.4 48 51 13 Canada 7 .. .. 0.3 5 15.0 20.0 2.0 100 100 117 Central African Republic 980 .. 19 10.7 345 0.1 0.1 0.7 23 27 3 Chad 1,500 .. 3 3.5 299 0.0 0.0 1.0 7 9 5 Chile 16 56 .. 0.3 15 2.7 3.9 2.3 84 91 96 China 45 85 87 0.1d 99 2.1 3.9 2.3 23 44 63 Hong Kong, China .. 86 .. .. 62 4.6 5.5 11.8 .. .. 193 Colombia 130 66 78 0.6 45 1.7 1.2 1.1 82 86 83 Congo, Dem. Rep. 1,100 8 21e 3.2 392 0.1 0.0 2.5 16 30 7 Congo, Rep. 740 .. 44 5.3 403 0.5 1.0 1.1 .. 27 14 Costa Rica 30 .. 96 0.3 14 0.9 1.5 1.8 .. 92 64 Côte d'Ivoire 810 .. 13 7.1 420 0.4 0.3 3.9 21 37 23 Croatia 7 .. 69 <0.1 40 5.1 5.3 1.7 100 100 142 Cuba 45 .. 73 0.1 9 3.0 2.3 4.0 98 98 10 Czech Republic 4 78 .. 0.1 10 15.6 11.5 1.8 99 98 147 Denmark 3 78 .. 0.2 8 9.7 9.8 1.6 .. .. 164 Dominican Republic 150 56 61 1.1 89 1.3 2.1 1.9 52 78 57 Ecuador 210 53 73 0.3 128 1.6 2.3 10.3 63 89 78 Egypt, Arab Rep. 130 47 59 <0.1 24 1.4 2.2 2.3 54 70 39 El Salvador 170 47 67 0.9 50 0.5 0.9 1.6 51 62 72 Eritrea 450 .. 8 2.4 94 .. 0.2 6.8 7 9 2 Estonia 25 .. .. 1.3 39 18.1 14.0 0.7 97 97 164 Ethiopia 720 4 15 1.4f 378 0.1 0.1 1.4 3 13 2 Finland 7 77 .. 0.1 5 10.3 12.6 1.2 100 100 144 France 8 81 .. 0.4 14 6.4 6.2 2.3 .. .. 140 Gabon 520 .. 33 7.9 354 6.5 1.1 2.0 .. 36 61 Gambia, The 690 12 18 2.4 257 0.2 0.2 2.1 .. 53 27 Georgia 66 .. 47 0.2 84 3.2 0.9 1.0 97 94 51 Germany 4 75 .. 0.1 6 12.3 9.8 2.2 100 100 168 Ghana 560 13 17 2.3 203 0.2 0.3 3.7 15 18 24 Greece 3 .. .. 0.2 18 7.1 8.7 1.9 .. .. 155 Guatemala 290 .. 43 0.9 79 0.6 1.0 2.3 58 86 65 Guinea 910 .. 9 1.5 265 0.2 0.2 2.2 14 18 2 Guinea-Bissau 1,100 .. 10 3.8 219 0.2 0.2 2.1 .. 35 7 Haiti 670 10 32 2.2g 299 0.1 0.2 2.2 24 30 7 22 2008 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 Fixed-line and estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved mobile phone per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities subscribers live births ages 15­49 ages 15­49 people metric tons % % of population per 100 peoplea 2005 1990 2000­06b 2005 2006 1990 2004 2007 1990 2004 2006 Honduras 280 47 65 1.5 76 0.5 1.1 3.2 50 69 42 Hungary 6 .. .. 0.1 19 5.8 5.7 2.1 .. 95 132 India 450 43 56 0.9 168 0.8 1.2 2.8 14 33 19 Indonesia 420 50 57 0.1 234 1.2 1.7 2.7 46 55 35 Iran, Islamic Rep. 140 49 74 0.2 22 4.0 6.4 0.9 83 .. 51 Iraq .. 14 .. .. .. 2.6 .. 8.0 81 .. 6 Ireland 1 60 .. 0.2 13 8.7 10.4 1.1 .. .. 159 Israel 4 68 .. 0.2 8 7.1 10.5 2.7 .. .. 162 Italy 3 .. .. 0.5 7 6.9 7.7 2.2 .. .. 165 Jamaica 170 55 69 1.5 7 3.3 4.0 7.4 75 80 118 Japan 6 58 56 <0.1 22 8.7 9.8 3.2 100 100 123 Jordan 62 40 56 0.2 5 3.2 3.1 1.7 93 93 90 Kazakhstan 140 .. 51 0.1 130 17.6 13.3 1.1 72 72 70 Kenya 560 27 39 6.1 384 0.2 0.3 3.4 40 43 19 Korea, Dem. Rep. 370 62 .. 0.2 178 12.1 3.4 1.4 .. 59 .. Korea, Rep. 14 79 .. <0.1 88 5.6 9.7 1.6 .. .. 139 Kuwait 4 .. .. 0.2 24 20.4 40.4 .. .. .. 114 Kyrgyz Republic 150 .. 48 0.1 123 2.8 1.1 0.8 60 59 19 Lao PDR 660 .. 32 0.1 152 0.1 0.2 1.1 .. 30 13 Latvia 10 .. .. 0.8 57 5.4 3.1 1.5 .. 78 124 Lebanon 150 .. 58 0.1 11 3.1 4.1 1.1 .. 98 44 Lesotho 960 23 37 23.4 c 635 .. .. 0.6 37 37 15 Liberia 1,200 .. 10 .. 331 0.2 0.1 3.6 39 27 .. Libya 97 .. .. 0.2 18 8.7 10.3 1.4 97 97 73 Lithuania 11 .. .. 0.2 62 6.6 3.9 .. .. .. 162 Macedonia, FYR 10 .. 14 <0.1 29 8.1 5.1 0.9 .. .. 94 Madagascar 510 17 27 0.5 248 0.1 0.2 5.5 14 32 6 Malawi 1,100 13 42 14.1 377 0.1 0.1 3.3 47 61 4 Malaysia 62 50 .. 0.5 103 3.1 7.0 5.5 .. 94 91 Mali 970 .. 8 1.7 280 0.1 0.1 1.1 36 46 13 Mauritania 820 3 8 0.7 316 1.4 0.9 .. 31 34 36 Mauritius 15 75 76 0.6 23 1.4 2.6 17.0 .. 94 90 Mexico 60 .. 71 0.3 21 5.0 4.3 3.0 58 79 74 Moldova 22 .. 68 1.1 141 5.4 2.0 1.4 .. 68 62 Mongolia 46 .. 66 <0.1 188 4.7 3.4 1.1 .. 59 28 Morocco 240 42 63 0.1 93 1.0 1.4 1.8 56 73 57 Mozambique 520 .. 17 16.1 443 0.1 0.1 2.1 20 32 11 Myanmar 380 17 34 1.3 171 0.1 0.2 1.9 24 77 1 Namibia 210 29 44 19.6 767 0.0 1.2 2.0 24 25 31 Nepal 830 23 48 0.5 176 0.0 0.1 1.1 11 35 6 Netherlands 6 76 .. 0.2 8 9.4 8.7 1.5 100 100 144 New Zealand 9 .. .. 0.1 9 6.6 7.7 5.2 .. .. 127 Nicaragua 170 .. 69 0.2 58 0.6 0.7 1.2 45 47 38 Niger 1,800 4 11 1.1 174 0.1 0.1 1.1 7 13 3 Nigeria 1,100 6 13 3.9 311 0.5 0.8 4.2 39 44 24 Norway 7 74 .. 0.1 6 7.8 19.1 1.5 .. .. 152 Oman 64 9 32 0.2 13 5.6 12.5 3.2 83 .. 82 Pakistan 320 15 28 0.1 181 0.6 0.8 1.4 37 59 25 Panama 130 .. .. 0.9 45 1.3 1.8 2.8 71 73 67 Papua New Guinea 470 .. .. 1.8 250 0.6 0.4 2.4 44 44 2 Paraguay 150 48 73 0.4 71 0.5 0.7 0.6 58 80 59 Peru 240 59 46 0.6 162 1.0 1.2 2.6 52 63 39 Philippines 230 36 49 <0.1 287 0.7 1.0 4.8 57 72 54 Poland 8 49 .. 0.1 25 9.1 8.0 1.4 .. .. 126 Portugal 11 .. .. 0.4 32 4.3 5.6 2.9 .. .. 155 Puerto Rico 18 .. .. .. 5 3.3 0.5 3.5 .. .. 112 2008 World Development Indicators 23 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 Fixed-line and estimate rate % of of tuberculosis Carbon dioxide emissions with Access to improved mobile phone per 100,000 % of married women population per 100,000 per capita extinction sanitation facilities subscribers live births ages 15­49 ages 15­49 people metric tons % % of population per 100 peoplea 2005 1990 2000­06b 2005 2006 1990 2004 2007 1990 2004 2006 Romania 24 .. 70 <0.1 128 6.7 4.2 1.7 .. .. 100 Russian Federation 28 34 .. 1.1 107 15.3 10.6 1.3 87 87 112 Rwanda 1,300 21 17 3.0 f 397 0.1 0.1 1.6 37 42 3 Saudi Arabia 18 .. .. 0.2 44 15.6 13.7 1.9 91 99 100 Senegal 980 .. 12 0.7f 270 0.4 0.4 2.1 33 57 27 Serbia 14h .. 41 0.2h 32h 12.4 6.6 2.0h 87h 87h 99 Sierra Leone 2,100 .. 5 1.6 517 0.1 0.2 3.3 .. 39 .. Singapore 14 65 .. 0.3 26 14.8 12.3 3.6 100 100 148 Slovak Republic 6 74 .. <0.1 15 9.7 6.7 1.3 99 99 112 Slovenia 6 .. .. <0.1 13 9.0 8.1 .. .. .. 132 Somalia 1,400 1 15 0.9 218 0.0 .. 1.9 .. 26 7 South Africa 400 57 60 18.8 940 9.4 9.4 1.6 69 65 83 Spain 4 .. .. 0.6 30 5.5 7.7 3.8 100 100 146 Sri Lanka 58 .. 70 <0.1 60 0.2 0.6 12.0 69 91 37 Sudan 450 9 8 1.6 242 0.2 0.3 1.5 33 34 14 Swaziland 390 20 48 33.4 1,155 0.6 0.9 0.8 .. 48 26 Sweden 3 .. .. 0.2 6 5.8 5.9 1.4 100 100 165 Switzerland 5 .. .. 0.4 7 6.4 5.5 1.3 100 100 166 Syrian Arab Republic 130 .. 58 0.2 32 2.8 3.7 1.7 73 90 41 Tajikistan 170 .. 38 0.1 204 4.4 0.8 0.8 .. 51 8 Tanzania 950 10 26 6.5 312 0.1 0.1 4.7 47 47 15 Thailand 110 .. 77 1.4 142 1.8 4.3 1.9 80 99 75 Timor-Leste 380 .. 10 0.2 556 .. 0.2 .. .. 36 .. Togo 510 34 17 3.2 389 0.2 0.4 1.1 37 35 12 Trinidad and Tobago 45 .. 43 2.6 8 13.8 24.7 1.4 100 100 149 Tunisia 100 50 63 0.1 25 1.6 2.3 2.0 75 85 85 Turkey 44 63 71 0.2 29 2.6 3.2 1.3 85 88 98 Turkmenistan 130 .. 48 <0.1 65 8.7 8.7 11.2 .. 62 10 Uganda 550 5 24 6.4i 355 0.0 0.1 2.7 42 43 7 Ukraine 18 .. 66 1.4 106 13.2 6.9 1.1 .. 96 131 United Arab Emirates 37 .. .. 0.2 16 30.8 37.8 .. 97 98 161 United Kingdom 8 .. 84 0.2 15 10.1 9.8 2.2 .. .. 171 United States 11 71 .. 0.6 4 19.3 20.6 5.7 100 100 135 Uruguay 20 .. .. 0.5 27 1.3 1.7 2.4 100 100 100 Uzbekistan 24 .. 65 0.2 121 6.3 5.3 0.9 51 67 10 Venezuela, RB 57 .. .. 0.7 41 5.9 6.6 1.0 .. 68 85 Vietnam 150 53 76 0.5f 173 0.3 1.2 2.6 36 61 31 West Bank and Gaza .. .. 50 .. 20 .. .. .. .. 73 31 Yemen, Rep. 430 10 23 0.2 78 0.8 1.0 9.8 32 43 14 Zambia 830 15 34 17.0 553 0.3 0.2 0.8 44 55 15 Zimbabwe 880 43 60 18.1g 557 1.6 0.8 1.0 50 53 9 World 400 w 57 w 60 w 1.0 w 139 w 4.3 w 4.5 w 45 w 57 w 59 w Low income 650 33 44 1.7 221 0.8 0.9 21 38 17 Middle income 160 68 75 0.7 114 3.6 4.0 47 62 66 Lower middle income 180 73 76 0.3 116 2.3 3.4 37 55 60 Upper middle income 97 51 .. 1.7 109 6.9 5.6 77 81 88 Low & middle income 440 54 60 1.1 161 2.4 2.6 36 51 44 East Asia & Pacific 150 75 79 0.2 135 1.9 3.3 30 51 58 Europe & Central Asia 43 46 63 0.6 82 10.3 7.1 84 85 88 Latin America & Carib. 130 57 69 0.6 57 2.4 2.5 67 77 73 Middle East & N. Africa 200 41 60 0.1 42 2.5 3.9 70 76 53 South Asia 500 40 53 0.7 174 0.7 1.0 17 37 19 Sub-Saharan Africa 900 15 22 5.8 368 0.9 0.9 31 37 15 High income 9 71 .. 0.4 16 11.9 13.2 100 100 143 Euro area 5 .. .. 0.3 13 8.4 8.2 100 100 153 a. Data are from the International Telecommunication Union's World Telecommunication Development Report database. b. Data are for the most recent year available. c. Survey data, 2004. d. Includes Hong Kong, China. e. Data are for 2007. f. Survey data, 2005. g. Survey data, 2005­06. h. Includes Montenegro. i. Survey data, 2004­05. 24 2008 World Development Indicators 1.3 WORLD VIEW Millennium Development Goals: protecting our common environment About the data Definitions The Millennium Development Goals address con- HIV/AIDS, which has a long latency between contrac- · Maternal mortality ratio is the number of women cerns common to all economies. Diseases and envi- tion of the virus and the appearance of symptoms, who die from pregnancy-related causes during preg- ronmental degradation do not respect national bound- or malaria, which has periods of dormancy, can be nancy and childbirth, per 100,000 live births. Data aries. Epidemic diseases, wherever they occur, pose particularly difficult. The table shows the estimated are from various years and adjusted to a common a threat to people everywhere. And environmental prevalence of HIV among adults ages 15­49. Preva- 2000 base year. The values are modeled estimates damage in one location may affect the well-being of lence among older populations can be affected by (see About the data for table 2.17). · Contracep- plants, animals, and humans far away. The indicators life-prolonging treatment. The incidence of tubercu- tive prevalence rate is the percentage of women in the table relate to goals 5, 6, and 7 and the targets losis is based on case notifications and estimates ages 15­49 married or in-union who are practicing, of goal 8 that address access to new technologies. of cases detected in the population. or whose sexual partners are practicing, any form of For the other targets of goal 8, see table 1.4. Carbon dioxide emissions are the primary source contraception. · HIV prevalence is the percentage The target of achieving universal access to repro- of greenhouse gases, which contribute to global of people ages 15­49 who are infected with HIV. ductive health has been added to goal 5 to address warming, threatening human and natural habitats. · Incidence of tuberculosis is the estimated number the importance of family planning and health service In recognition of the vulnerability of animal and plant of new tuberculosis cases (pulmonary, smear posi- in improving maternal health and preventing maternal species, a new target of reducing biodiversity loss tive, and extrapulmonary). · Carbon dioxide emis- death. Women with multiple pregnancies are more has been added to goal 7. sions are those stemming from the burning of fossil likely to die in childbirth. Access to contraception is Access to reliable supplies of safe drinking water and fuels and the manufacture of cement. They include an important way to limit and space births. sanitary disposal of excreta are two of the most impor- emissions produced during consumption of solid, Measuring the prevalence or incidence of a dis- tant means of improving human health and protecting liquid, and gas fuels and gas flaring (see table 3.8). ease can be difficult. Most developing economies the environment. Improved sanitation facilities prevent · Proportion of species threatened with extinction lack reporting systems for monitoring diseases. Esti- human, animal, and insect contact with excreta. is the total number of threatened mammal (exclud- mates are often derived from surveys and reports Fixed telephone lines and mobile phones are ing whales and porpoises), bird, and higher native, from sentinel sites that must be extrapolated to among the telecommunications technologies that vascular plant species as a percentage of the total the general population. Tracking diseases such as are changing the way the global economy works. number of known species of the same categories. · Access to improved sanitation facilities is the Location of indicators for Millennium Development Goals 5­7 1.3a percentage of the population with at least adequate access to excreta disposal facilities (private or Goal 5. Improve maternal health shared, but not public) that can effectively prevent 5.1 Maternal mortality ratio 1.3, 2.17 human, animal, and insect contact with excreta 5.2 Proportion of births attended by skilled health personnel 2.17, 2.20 (facilities do not have to include treatment to ren- 5.3 Contraceptive prevalence rate 1.3, 2.17, 2.20 5.4 Adolescent fertility rate 2.17 der sewage outflows innocuous). Improved facilities 5.5 Antenatal care coverage 1.5, 2.17, 2.20 range from simple but protected pit latrines to flush 5.6 Unmet need for family planning 2.17 toilets with a sewerage connection. To be effective, Goal 6. Combat HIV/AIDS, malaria, and other diseases facilities must be correctly constructed and properly 6.1 HIV prevalence among pregnant women ages 15­24 1.3*, 2.19* maintained. · Fixed-line and mobile phone subscrib- 6.2 Condom use at last high-risk sex 2.19* ers are telephone mainlines connecting a customer's 6.3 Proportion of population ages 15­24 with comprehensive correct -- equipment to the public switched telephone network knowledge of HIV/AIDS 6.4 Ratio of school attendance of orphans to school attendance of -- and users of portable telephones subscribing to an nonorphans ages 10­14 automatic public mobile telephone service using cel- 6.5 Proportion of population with advanced HIV infection with access -- lular technology that provides access to the public to antiretroviral drugs switched telephone network. 6.6 Incidence and death rates associated with malaria -- 6.7 Proportion of children under age 5 sleeping under insecticide-treated bednets and proportion of children under age 5 with fever who are treated with appropriate antimalarial drugs 2.16 6.8 Incidence, prevalence, and death rates associated with tuberculosis 1.3, 2.19 6.9 Proportion of tuberculosis cases detected and cured under directly observed treatment short course 2.16 Goal 7. Ensure environmental sustainability Data sources 7.1 Proportion of land area covered by forest 3.1 7.2 Carbon dioxide emissions, total, per capita, and per $1 GDP, and consumption The indicators here and throughout this book have of ozone-depleting substances 3.8 7.3 Proportion of fish stocks within safe biological limits -- been compiled by World Bank staff from primary 7.4 Proportion of total water resources used 3.5 and secondary sources. Efforts have been made 7.5 Proportion of terrestrial and marine areas protected 3.4 to harmonize the data series used to compile this 7.6 Proportion of species threatened with extinction 1.3 table with those published on the United Nations 7.7 Proportion of population using and improved drinking water source 1.3, 2.16, 3.5 Millennium Development Goals Web site (www. 7.8 Proportion of population using an improved sanitation facility 1.3, 2.16, 3.11 7.9 Proportion of urban population living in slums un.org/millenniumgoals), but some differences in -- No data are available in the World Development Indicators database. * Table shows information on related indicators. timing, sources, and definitions remain. 2008 World Development Indicators 25 1.4 Millennium Development Goals: overcoming obstacles Development Assistance Committee members Official development Least developed countries' access Support to assistance (ODA) to high-income markets agriculture by donor For basic Average tariff on exports of Net social servicesa Goods least developed countries % of % of total (excluding arms) donor sector-allocable admitted free of tariffs Agricultural products Textiles Clothing GNI ODA % % % % % of GDP 2006 2006 1999 2005 1999 2005 1999 2005 1999 2005 2006b Australia 0.30 15.4 96.3 100.0 13.7 0.0 6.3 0.0 25.5 0.0 0.22 Canada 0.29 24.3 45.7 99.7 9.3 0.7 7.5 0.2 19.8 1.7 0.80 European Union 96.9 97.8 1.0 1.2 0.0 0.1 0.0 1.2 1.10 Austria 0.47 14.9 Belgium 0.50 18.5 Denmark 0.80 26.8 Finland 0.40 15.7 France 0.47 11.1 Germany 0.36 13.3 Greece 0.17 16.4 Ireland 0.54 22.8 Italy 0.20 11.6 Luxembourg 0.89 26.3 Netherlands 0.81 42.6 Portugal 0.21 4.8 Spain 0.32 13.4 Sweden 1.02 13.6 United Kingdom 0.21 12.9 Japan 0.25 18.6 58.0 23.2 3.7 2.5 5.1 2.8 0.4 0.1 1.11 New Zealandc 0.27 21.0 93.8 99.2 0.0 6.7 9.6 0.0 13.0 0.0 0.25 Norway 0.89 11.9 97.5 99.1 3.3 0.4 4.8 0.0 1.5 1.0 0.99 Switzerland 0.39 8.8 99.9 96.7 1.5 0.9 0.0 0.0 0.0 0.0 1.46 United States 0.18 13.5 53.4 76.7 9.4 7.9 7.1 5.7 14.3 11.7 0.73 Heavily indebted poor countries (HIPCs) HIPC HIPC HIPC MDRI HIPC HIPC HIPC MDRI decision completion Initiative assistancef decision completion Initiative assistancef pointd pointd assistancee pointd pointd assistancee $ millions $ millions $ millions $ millions Afghanistan Jul. 2007 Floating 546 .. Haiti Nov. 2006 Floating 140 .. Benin Jul. 2000 Mar. 2003 344 570 Honduras Jul. 2000 Apr. 2005 729 1,474 Boliviag Feb. 2000 Jun. 2001 1,752 1,526 Madagascar Dec. 2000 Oct. 2004 1,096 1,205 Burkina Fasog,h Jul. 2000 Apr. 2002 725 564 Malawih Dec. 2000 Aug. 2006 1,278 662 Burundi Aug. 2005 Floating 864 .. Malig Sep. 2000 Mar. 2003 707 982 Cameroon Oct. 2000 Apr. 2006 1,662 687 Mauritania Feb. 2000 Jun. 2002 816 422 Central African Republic Sep. 2007 Floating 583 .. Mozambiqueg Apr. 2000 Sep. 2001 2,758 1,004 Chad May 2001 Floating 214 .. Nicaragua Dec. 2000 Jan. 2004 4,340 900 Congo, Dem. Rep. Jul. 2003 Floating 7,229 .. Nigerh Dec. 2000 Apr.2004 853 477 Congo, Rep. Apr. 2006 Floating 1,757 .. Rwandah Dec. 2000 Apr. 2005 872 200 Ethiopiah Nov. 2001 Apr. 2004 2,446 1,366 São Tomé & Principeh Dec. 2000 Mar. 2007 156 22 Gambia, The Dec. 2000 Dec. 2007 81 201 Senegal Jun. 2000 Apr. 2004 641 1,298 Ghana Feb. 2002 Jul. 2004 2,742 1,938 Sierra Leone Mar. 2002 Dec. 2006 809 316 Guinea Dec. 2000 Floating 716 .. Tanzania Apr. 2000 Nov. 2001 2,658 1,907 Guinea-Bissau Dec. 2000 Floating 546 .. Ugandag Feb. 2000 May 2000 1,349 1,713 Guyanag Nov. 2002 Dec. 2003 824 382 Zambia Dec. 2000 Apr. 2005 3,279 1,437 a. Includes basic health, education, nutrition, and water and sanitation services. b. Preliminary. c. Estimates of market access for least developed countries are 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. Total HIPC assistance (committed debt relief) assuming full participation of creditors, in end-2006 net present value terms. Topping-up assistance and assistance provided under the original HIPC Initiative were committed in net present value terms as of the decision point and are converted to end-2006 terms. f. Multilateral Debt Relief Initiative (MDRI) assistance has been delivered in full to all post-completion point countries, shown in end-2006 net present value terms. g. Also reached completion point under the original HIPC Initiative. The assistance includes original debt relief. h. Assistance includes topping up at completion point. 26 2008 World Development Indicators 1.4 WORLD VIEW Millennium Development Goals: overcoming obstacles About the data Definitions Achieving the Millennium Development Goals requires falling, averages may disguise high tariffs on specific · Net offi cial development assistance (ODA) is an open, rule-based global economy in which all goods (see table 6.7 for each country's share of tariff grants and loans (net of repayments of principal) countries, rich and poor, participate. Many poor lines with "international peaks"). The averages in the that meet the DAC definition of ODA and are made countries, lacking the resources to finance develop- table include ad valorem duties and equivalents. to countries and territories on the DAC list of recipi- ment, burdened by unsustainable debt, and unable Subsidies to agricultural producers and exporters ent countries. · ODA for basic social services is to compete globally, need assistance from rich coun- in OECD countries are another barrier to developing aid reported by DAC donors for basic health, educa- tries. For goal 8--develop a global partnership for economies' exports. The table shows the total sup- tion, nutrition, and water and sanitation services. development--many indicators therefore monitor the port to agriculture as a share of the economy's gross · Goods admitted free of tariffs are exports of goods actions of members of the Organisation for Economic domestic product (GDP). Agricultural subsidies in OECD (excluding arms) from least developed countries Co-operation and Development's (OECD) Develop- economies are estimated at $372 billion in 2006. admitted without tariff as a share of total exports ment Assistance Committee (DAC). The Debt Initiative for Heavily Indebted Poor Coun- from least developed countries. · Average tariff is Official development assistance (ODA) has risen tries (HIPCs), an important step in placing debt relief the unweighted average of the effectively applied in recent years as a share of donor countries' gross within the framework of poverty reduction, is the first rates for all products subject to tariffs. · Agricultural national income (GNI), but the poorest countries comprehensive approach to reducing the external products are plant and animal products, including need additional assistance to achieve the Millen- debt of the world's poorest, most heavily indebted tree crops but excluding timber and fish products. nium Development Goals. After rising to a record countries. A 1999 review led to an enhancement of · Textiles and clothing are natural and synthetic $106 billion in 2005, ODA fell 4.5 percent in 2006 the framework. In 2005, to further reduce the debt fi bers and fabrics and articles of clothing made to $104 billion in nominal terms. of HIPCs and provide resources for meeting the Mil- from them. · Support to agriculture is the value of One important action that high-income economies lennium Development Goals, the Multilateral Debt gross transfers from taxpayers and consumers aris- can take is to reduce barriers to low- and middle- Relief Initiative (MDRI), proposed by the Group of ing from policy measures that support agriculture, income economy exports. The European Union has Eight countries, was launched. Under the MDRI the net of associated budgetary receipts, regardless of begun to eliminate tariffs on developing country International Development Association (IDA), Interna- their objectives and impacts on farm production and exports of "everything but arms," and the United tional Monetary Fund (IMF), and African Development income or consumption of farm products. · HIPC States offers special concessions to Sub-Saharan Fund (AfDF) provide 100 percent debt relief on eligible decision point is the date when a heavily indebted African exports. However, these programs still have debts due to them from countries that completed poor country with an established track record of many restrictions. the HIPC Initiative process. Debt relief under the two good performance under adjustment programs sup- Average tariffs in the table reflect high-income OECD initiatives is expected to reduce the debt stocks of ported by the IMF and the World Bank commits to member tariff schedules for exports of countries the 32 HIPCs that have reached the decision point additional reforms and a poverty reduction strategy. designated least developed countries by the United by almost 90 percent. Twenty-two countries have · HIPC completion point is the date when a country Nations. Agricultural commodities, textiles, and cloth- reached the completion point and have received successfully completes the key structural reforms ing are three of the most important exports of devel- nearly $45 billion in HIPC Initiative assistance and agreed on at the decision point, including developing oping economies. Although average tariffs have been $42 billion in MDRI assistance in nominal terms. and implementing a poverty reduction strategy. The country then receives the bulk of debt relief under Location of indicators for Millennium Development Goal 8 1.4a the HIPC Initiative without further policy conditions. · HIPC Initiative assistance is the net present value Goal8. Develop a global partnership for development Table of debt relief committed as of the decision point and 8.1 Net ODA as a percentage of DAC donors' gross national income 1.4, 6.12 converted to end-2006 values. · MDRI assistance is 8.2 Proportion of ODA for basic social services 1.4, 6.13b* the net present value of debt relief from IDA, IMF, and 8.3 Proportion of ODA that is untied 6.13b 8.4 Proportion of ODA received in landlocked countries as a percentage of GNI -- AfDF, delivered to countries having reached the HIPC 8.5 Proportion of ODA received in small island developing states as a percentage of GNI -- completion point converted to end-2006 values. 8.6 Proportion of total developed country imports (by value, excluding arms) from least developed countries admitted free of duty 1.4 8.7 Average tariffs imposed by developed countries on agricultural products and Data sources textiles and clothing from least developed countries 1.4, 6.7* Data on ODA are from the OECD. Data on goods 8.8 Agricultural support estimate for OECD countries as a percentage of GDP 1.4 admitted free of tariffs and average tariffs are from 8.9 Proportion of ODA provided to help build trade capacity -- the World Trade Organization, in collaboration with 8.10 Number of countries reaching HIPC decision and completion points 1.4 8.11 Debt relief committed under new HIPC initiative 1.4 the United Nations Conference on Trade and Devel- 8.12 Debt services as a percentage of exports of goods and services 6.9* opment and the International Trade Centre. These 8.13 Proportion of population with access to affordable, essential drugs on a data are available electronically at www.mdg-trade. sustainable basis -- org. Data on subsidies to agriculture are from the 8.14 Telephone lines per 100 people 1.3*, 5.10 OECD's Producer and Consumer Support Estimates, 8.15 Cellular subscribers per 100 people 1.3*, 5.10 OECD Database 1986­2006. Data on the HIPC Ini- 8.16 Internet users per 100 people 5.11 tiative and MDRI are from the World Bank's Eco- -- No data are available in the World Development Indicators database. * Table shows information on related indicators. nomic Policy and Debt Department. 2008 World Development Indicators 27 1.5 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving 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 2006 2006 2006 2000­06a 2000­06a 2005 2000­05a 2000­05a 1990 2007 Afghanistan .. .. .. 16 .. .. .. .. 4 27 Albania 50.0 73 80 97 .. 33 .. .. 29 7 Algeria 49.4 71 73 89 .. 14 7.2 7.2 2 8 Angola 50.7 41 44 66 .. .. .. .. 15 15 Argentina 50.8 71 79 99 .. 45 0.7b 1.9b 6 35 Armenia 53.2 68 75 93 5 .. 1.1 0.8 36 9 Australia 49.7 79 83 .. .. 49 0.2 0.4 6 25 Austria 50.5 77 83 .. .. 47 1.0 1.9 12 32 Azerbaijan 51.3 70 75 70 .. 49 .. .. .. 11 Bangladesh 48.8 63 65 48 33 .. 9.9 48.0 10 15 Belarus 53.2 63 74 99 .. 53 .. .. .. 29 Belgium 50.5 77 82 .. .. 45 0.4 3.4 9 35 Benin 49.6 55 57 84 21 .. .. .. 3 8 Bolivia 50.1 63 67 79 16 32 12.6 34.8 9 17 Bosnia and Herzegovina 51.2 72 77 99 .. .. .. .. .. 14 Botswana 50.3 50 50 97 .. 40 2.3 2.2 5 11 Brazil 50.5 69 76 97 .. .. 5.4b 9.1b 5 9 Bulgaria 51.0 69 76 .. .. 53 0.9 2.2 21 22 Burkina Faso 49.9 50 53 85 23 .. .. .. .. 15 Burundi 51.1 48 50 92 .. .. .. .. .. 31 Cambodia 51.2 57 61 69 8 52 31.6 53.3 .. 10 Cameroon 50.0 50 51 82 28 .. 9.5 27.2 14 14 Canada 50.0 78 83 .. .. 49 0.1 0.2 13 21 Central African Republic 51.2 43 46 69 .. .. .. .. 4 11 Chad 50.3 49 52 39 37 .. .. .. .. 7 Chile 50.3 75 81 .. .. 38 1.4 3.2 .. 15 China 48.2 70 74 90 .. .. .. .. 21 20 Hong Kong, China 51.6 79 85 .. .. 48 0.2 1.4 .. .. Colombia 50.6 69 76 94 21 48 3.5 7.7 5 8 Congo, Dem. Rep. 50.5 45 47 85c .. .. .. .. 5 8 Congo, Rep. 50.4 54 56 86 27 .. .. .. 14 7 Costa Rica 49.0 76 81 92 .. 40 1.7 3.5 11 39 Côte d'Ivoire 49.2 47 49 85 .. .. .. .. 6 9 Croatia 51.5 73 79 100 4 44 1.1d 3.6d .. 19 Cuba 49.5 76 80 100 .. 43 .. .. 34 36 Czech Republic 50.8 73 80 .. .. 47 0.3 1.3 .. 16 Denmark 50.0 76 80 .. .. 49 0.2 1.3 31 37 Dominican Republic 49.6 69 75 99 23 38 2.8 4.9 8 20 Ecuador 49.7 72 78 84 .. 42 3.0 b 9.4b 5 25 Egypt, Arab Rep. 49.8 69 73 70 9 20 9.4 32.2 4 2 El Salvador 50.8 69 75 86 .. 35 7.7 7.7 12 17 Eritrea 50.9 55 60 70 14 .. .. .. .. 22 Estonia 53.6 67 78 .. .. 53 0.3 0.2 .. 22 Ethiopia 50.2 51 54 28 17 41 34.6 68.5 .. 22 Finland 50.6 76 83 .. .. 51 0.6 0.4 32 42 France 50.7 77 84 .. .. 48 0.5 1.6 7 19 Gabon 49.9 56 57 94 33 .. .. .. 13 13 Gambia, The 49.8 58 60 98 .. .. .. .. 8 9 Georgia 52.5 67 75 94 .. 49 19.0 39.0 .. 9 Germany 50.7 76 82 .. .. 47 0.5 1.9 .. 32 Ghana 49.3 59 60 92 14 .. .. .. .. 11 Greece 49.9 77 82 .. .. 41 3.3 11.2 7 16 Guatemala 51.1 66 74 84 .. .. 21.3 24.5 7 12 Guinea 49.5 54 57 82 32 .. .. .. .. 19 Guinea-Bissau 50.5 45 48 78 .. .. .. .. 20 14 Haiti 50.4 59 62 85 14 .. .. .. .. 4 28 2008 World Development Indicators 1.5 WORLD VIEW Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving 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 2006 2006 2006 2000­06a 2000­06a 2005 2000­05a 2000­05a 1990 2007 Honduras 50.2 66 73 92 22 45 12.1b 8.3b 10 23 Hungary 52.0 69 77 .. .. 49 0.3 0.7 21 10 India 48.1 63 66 74 .. 18 .. .. 5 8 Indonesia 49.9 66 70 92 10 .. .. .. 12 11 Iran, Islamic Rep. 49.2 69 72 .. .. .. .. .. 2 4 Iraq .. .. .. 84 .. .. .. .. 11 26 Ireland 49.8 77 82 .. .. 48 0.6 0.9 8 13 Israel 50.1 78 82 .. .. 49 0.2 0.5 7 14 Italy 50.7 78 84 .. .. 43 1.2 2.8 13 17 Jamaica 50.3 70 73 91 .. 47 0.4 2.5 5 13 Japan 50.5 79 86 .. .. 41 1.5 8.6 1 9 Jordan 48.5 71 74 99 4 .. .. .. 0 6 Kazakhstan 52.1 61 72 100 7 49 1.0 1.3 .. 16 Kenya 50.1 52 55 88 23 .. .. .. 1 7 Korea, Dem. Rep. 50.6 65 69 .. .. .. .. .. 21 20 Korea, Rep. 49.8 75 82 .. .. 42 1.3 14.0 2 13 Kuwait 39.8 76 80 .. .. .. .. .. .. 2 Kyrgyz Republic 50.6 64 72 97 .. 52 9.6 21.8 .. 0 Lao PDR 50.1 63 65 27 .. .. .. .. 6 25 Latvia 53.6 65 77 .. .. 53 2.5 2.1 .. 19 Lebanon 50.8 70 74 96 .. .. .. .. 0 5 Lesotho 52.9 43 43 90 20 .. .. .. .. 24 Liberia 50.0 44 46 85 .. .. .. .. .. 13 Libya 48.1 71 77 .. .. .. .. .. .. 8 Lithuania 53.1 65 77 .. .. 51 2.1 3.9 .. 25 Macedonia, FYR 49.9 72 76 98 .. 44 6.4 16.7 .. 28 Madagascar 50.2 57 61 80 34 46 29.7 51.9 7 8 Malawi 50.3 47 48 92 31 .. .. .. 10 14 Malaysia 49.1 72 76 79 .. 38 2.2 9.6 5 9 Mali 51.2 52 56 57 40 50 18.4 10.2 .. 10 Mauritania 49.3 62 66 64 16 .. .. .. .. 18 Mauritius 50.2 70 77 .. .. 37 0.9 4.7 7 17 Mexico 51.0 72 77 .. .. 39 5.5 11.0 12 23 Moldova 52.0 65 72 98 6 55 0.8 1.4 .. 22 Mongolia 50.0 66 69 99 .. 53 18.4 31.7 25 7 Morocco 50.7 69 73 68 7 22 22.8 55.7 0 11 Mozambique 51.5 42 43 85 41 .. .. .. 16 35 Myanmar 50.3 59 65 76 .. .. .. .. .. .. Namibia 50.6 52 53 91 18 .. 12.8 22.0 7 27 Nepal 50.4 63 64 44 19 .. .. .. 6 17 Netherlands 50.1 78 82 .. .. 47 0.2 1.0 21 37 New Zealand 50.3 78 82 .. .. 47 0.4 0.9 14 32 Nicaragua 50.0 70 76 86 25 .. 3.1 4.2 15 19 Niger 49.2 57 56 46 39 .. .. .. 5 12 Nigeria 50.0 46 47 58 25 21 .. .. .. 7 Norway 49.7 78 83 .. .. 49 0.2 0.3 36 38 Oman 44.0 74 77 100 .. .. .. .. .. 0 Pakistan 48.5 65 66 36 .. 10 18.3 52.8 10 21 Panama 49.4 73 78 .. .. 43 2.8 5.5 8 17 Papua New Guinea 49.2 55 60 .. .. .. .. .. 0 1 Paraguay 49.3 69 74 94 .. .. 10.9b 8.7b 6 10 Peru 49.8 69 74 92 26 38 1.6b 7.0 b 6 29 Philippines 49.6 69 74 88 8 42 8.9 18.7 9 22 Poland 51.4 71 80 .. .. 47 3.8 7.0 14 20 Portugal 51.1 75 82 .. .. 47 0.9 2.1 8 21 Puerto Rico 51.6 74 83 .. .. 40 0.1 0.9 .. .. 2008 World Development Indicators 29 1.5 Women in development Female Life Pregnant Teenage Women in Unpaid family Women in population expectancy women mothers nonagricultural sector workers parliaments at birth receiving 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 2006 2006 2006 2000­06a 2000­06a 2005 2000­05a 2000­05a 1990 2007 Romania 51.0 69 76 94 .. 46 7.8 21.2 34 11 Russian Federation 53.5 59 73 .. .. 51 0.1 0.1 .. 10 Rwanda 51.8 44 47 94 4 .. .. .. 17 49 Saudi Arabia 44.8 71 75 .. .. .. .. .. .. 0 Senegal 50.0 61 65 87 19 .. .. .. 13 22 Serbia 50.2 70 76 98 .. .. .. .. .. 20 Sierra Leone 50.7 41 44 81 .. 23 .. .. .. 13 Singapore 49.4 78 82 .. .. 48 0.3 1.2 5 25 Slovak Republic 51.2 70 78 .. .. 51 0.0 b 0.1b .. 19 Slovenia 50.9 74 81 .. .. 47 3.1 6.4 .. 12 Somalia 50.3 47 49 26 .. .. .. .. 4 8 South Africa 50.8 49 53 92 .. 43 0.4 1.1 3 33 Spain 50.1 78 84 .. .. 42 1.1 2.4 15 36 Sri Lanka 50.4 72 78 100 .. 40 4.2b 20.9b 5 5 Sudan 49.6 57 60 70 .. .. .. .. .. 18 Swaziland 51.6 42 40 90 .. .. .. .. 4 11 Sweden 49.6 79 83 .. .. 51 0.2 0.2 38 47 Switzerland 50.7 79 84 .. .. 47 1.3 2.9 14 30 Syrian Arab Republic 49.4 72 76 84 .. .. 10.8 44.2 9 12 Tajikistan 50.3 64 69 77 .. .. .. .. .. 18 Tanzania 50.2 51 53 78 26 .. .. .. .. 30 Thailand 51.1 66 75 98 .. 48 14.7 31.4 3 9 Timor-Leste 49.2 56 58 61 .. .. .. .. .. 28 Togo 50.5 56 60 89 .. .. .. .. 5 7 Trinidad and Tobago 50.6 68 72 96 .. 44 0.3 1.7 17 19 Tunisia 49.5 72 76 92 .. 25 .. .. 4 23 Turkey 49.5 69 74 81 .. 20 7.0 41.7 1 9 Turkmenistan 50.7 59 67 99 4 .. .. .. 26 16 Uganda 49.9 50 51 94 25 39 10.3b 40.5b 12 30 Ukraine 53.6 62 74 99 .. 55 0.5 0.5 .. 9 United Arab Emirates 32.2 77 82 .. .. .. .. .. 0 23 United Kingdom 50.4 77 81 .. .. 49 0.3 0.5 6 20 United States 50.3 75 81 .. .. 48 0.1 0.1 7 16 Uruguay 51.3 72 80 .. .. 48 0.7b 2.2b 6 11 Uzbekistan 50.2 64 71 99 .. .. .. .. .. 18 Venezuela, RB 49.6 72 77 94 .. .. 2.0 3.9 10 19 Vietnam 49.8 68 73 91 3 46 18.9 47.2 18 26 West Bank and Gaza 49.1 71 74 99 .. 18 6.4 32.2 .. .. Yemen, Rep. 49.4 61 64 41 .. .. .. .. 4 0e Zambia 50.1 41 42 93 32 .. .. .. 7 15 Zimbabwe 50.2 43 42 94 21 .. 10.4 13.6 11 17 World 49.4 w 66 w 70 w 80 w .. w .. w .. w 13 w 18 w Low income 49.0 59 62 69 24 .. .. 11 16 Middle income 49.6 68 73 90 .. .. .. 14 16 Lower middle income 49.0 69 73 89 .. .. .. 14 16 Upper middle income 51.0 67 74 .. 44 3.8 7.9 12 15 Low & middle income 49.3 64 68 80 .. .. .. 13 16 East Asia & Pacific 48.7 69 73 89 .. .. .. 17 18 Europe & Central Asia 51.9 65 74 91 48 2.8 6.9 .. 15 Latin America & Carib. 50.4 70 76 95 .. 4.6 8.4 12 20 Middle East & N. Africa 49.5 68 72 76 .. .. .. 4 9 South Asia 48.3 63 66 66 17 .. .. 6 14 Sub-Saharan Africa 50.2 49 52 72 .. .. .. .. 17 High income 50.1 76 82 .. 46 0.6 2.6 12 23 Euro area 50.5 77 83 .. 46 0.8 2.3 12 25 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2007. d. Data are for 2006. e. Less than 0.5. 30 2008 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 restricted lation that is female. · Life expectancy at birth is life--worldwide. But while disparities exist through- access to education and vocational training, heavy the number of years a newborn infant would live if out the world, they are most prevalent in developing workloads at home and in nonpaid domestic and prevailing patterns of mortality at the time of its birth countries. Gender inequalities in the allocation of market activities, and labor market discrimination were to stay the same throughout its life. · Pregnant such resources as education, health care, nutrition, often limit women's participation in paid economic women receiving prenatal care are the percentage and political voice matter because of the strong activities, lower their productivity, and reduce their of women attended at least once during pregnancy association with well-being, productivity, and eco- wages. When women are in salaried employment, by skilled health personnel for reasons related to nomic growth. These patterns of inequality begin at they tend to be concentrated in the nonagricultural pregnancy. · Teenage mothers are the percentage of an early age, with boys routinely receiving a larger sector. However, in many developing countries women ages 15­19 who already have children or are share of education and health spending than do girls, women are a large part of agricultural employment, currently pregnant. · Women in nonagricultural sec- for example. often as unpaid family workers. Among people who tor are female wage employees in the nonagricultural Because of biological differences girls are are unsalaried, women are more likely than men to sector as a percentage of total nonagricultural wage expected to experience lower infant and child mor- be unpaid family workers, while men are more likely employment. · Unpaid family workers are those who tality rates and to have a longer life expectancy than women to be self-employed or employers. There work without pay in a market-oriented establishment than boys. This biological advantage, however, may are several reasons for this. or activity operated by a related person living in the be overshadowed by gender inequalities in nutri- Few women have access to credit markets, capital, same household. · Women in parliaments are the tion and medical interventions and by inadequate land, training, and education, which may be required percentage of parliamentary seats in a single or care during pregnancy and delivery, so that female to start a business. Cultural norms may prevent lower chamber held by women. rates of illness and death sometimes exceed male women from working on their own or from super- rates, particularly during early childhood and the vising other workers. Also, women may face time reproductive years. In high-income countries women constraints due to their traditional family respon- tend to outlive men by four to eight years on aver- sibilities. Because of biases and misclassification age, while in low-income countries the difference is substantial numbers of employed women may be narrower--about two to three years. The difference underestimated or reported as unpaid family workers in child mortality rates (table 2.21) is another good even when they work in association or equally with indicator of female social disadvantage because their husbands in the family enterprise. nutrition and medical interventions are particularly Women are vastly underrepresented in decision- important for the 1­4 age group. Female child mor- making positions in government, although there is tality rates that are as high as or higher than male some evidence of recent improvement. Gender parity child mortality rates may indicate discrimination in parliamentary representation is still far from being against girls. realized. In 2007 women accounted for 18 percent Having a child during the teenage years limits of parliamentarians worldwide, compared with 9 per- girls' opportunities for better education, jobs, and cent in 1987. Without representation at this level, it income. Pregnancy is more likely to be unintended is difficult for women to influence policy. during the teenage years, and births are more likely For information on other aspects of gender, see Data sources to be premature and are associated with greater tables 1.2 (Millennium Development Goals: eradi- risks of complications during delivery and of death. cating poverty and saving lives), 2.3 (Employment Data on female population and life expectancy are In many countries maternal mortality (tables 1.3 and by economic activity), 2.4 (Decent work and produc- from the World Bank's population database. Data 2.17) is a leading cause of death among women of tive employment), 2.5 (Unemployment), 2.6 (Children on pregnant women receiving prenatal care are reproductive age. Most maternal deaths result from at work), 2.9 (Assessing vulnerability and security), from household surveys, including Demographic preventable causes--hemorrhage, infection, and 2.12 (Education efficiency), 2.13 (Education comple- and Health Surveys by Macro International and complications from unsafe abortions. Prenatal care tion and outcomes), 2.14 (Education gaps by income Multiple Indicator Cluster Surveys by the United is essential for recognizing, diagnosing, and promptly and gender), 2.17 (Reproductive health), 2.19 Nations Children's Fund (UNICEF), and UNICEF's treating complications that arise during pregnancy. (Health risk factors and public health challenges), State of the World's Children 2008. Data on teen- In high-income countries most women have access 2.20 (Health gaps by income and gender), and 2.21 age mothers are from Demographic and Health to health care during pregnancy, but in developing (Mortality). Surveys by Macro International. Data on labor countries an estimated 200 million women suffer force and employment are from the International pregnancy-related complications, and over half a mil- Labour Organization's Key Indicators of the Labour lion die every year (Glasier and others 2006). This Market, fi fth edition. Data on women in parlia- is reflected in the differences in maternal mortality ments are from the Inter-Parliamentary Union. ratios between high- and low-income countries. 2008 World Development Indicators 31 1.6 Key indicators for other economies Population Surface Population Gross national Gross domestic Life Adult Carbon area density income product expectancy literacy dioxide at birth rate emissions PPPa 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 2006 2006 2006 2006b 2006b 2006 2006 2005­06 2005­06 2006 2005 2004 American Samoa 60 0.2 298 .. ..c .. .. .. .. .. .. 41 Andorra 67 0.5 142 .. ..d .. .. .. .. .. .. .. Antigua and Barbuda 84 0.4 191 929 11,050e 1,273f 15,130 f 11.5 10.1 .. .. 414 Aruba 101 0.2 562 .. ..d .. .. .. .. .. .. 2,154 Bahamas, The 327 13.9 33 .. ..d ..f ..f .. .. 73 .. 2,007 Bahrain 739 0.7 1,041 14,022 19,350 24,869 34,310 7.8 5.6 76 .. 16,934 Barbados 293 0.4 681 .. ..d 4,422 f 15,150 f .. .. 77 .. 1,267 Belize 298 23.0 13 1,114 3,740 2,108f 7,080 f 5.6 3.5 72 .. 791 Bermuda 64 0.1 1,276 .. ..d .. .. .. .. 79 .. 549 Bhutan 649 47.0 14 928 1,430 2,596 4,000 8.5 6.5 65 60 414 Brunei Darussalam 382 5.8 72 10,287 26,930 19,059 49,900 5.1 2.9 77 .. 8,802 Cape Verde 519 4.0 129 1,105 2,130 1,344 2,590 6.1 3.7 71 81 275 Cayman Islands 46 0.3 177 .. ..d .. .. .. .. .. .. 311 Channel Islands 149 0.2 784 .. ..d .. .. .. .. 79 .. .. Comoros 614 1.9 330 406 660 698 1,140 0.5 ­1.6 63 .. 88 Cyprus 771 9.3 83 17,948 23,270 19,328 25,060 4.0 2.2 79 .. 6,744 Djibouti 819 23.2 35 864 1,060 1,787 2,180 4.9 3.0 54 .. 366 Dominica 72 0.8 97 300 4,160 566f 7,870 f 4.0 3.4 .. .. 106 Equatorial Guinea 496 28.1 18 4,216 8,510 8,238 16,620 ­5.6 ­7.8 51 .. 5,421 Faeroe Islands 48 1.4 35 .. ..d .. .. .. .. 79 .. 659 Fiji 833 18.3 46 3,098 3,720 g 3,707 4,450 3.6 2.9 69 .. 1,070 French Polynesia 259 4.0 71 .. ..d .. .. .. .. 74 .. 670 Greenland 57 410.5 0h .. ..d .. .. .. .. .. .. 571 Grenada 108 0.3 318 495 4,650 934f 8,770 f 0.7 ­0.8 .. .. 216 Guam 171 0.5 317 .. ..d .. .. .. .. 75 .. 4,081 Guyana 739 215.0 4 849 1,150 2,522f 3,410 f 4.8 4.9 66 .. 1,443 Iceland 302 103.0 3 15,078 49,960 10,181 33,740 2.6 0.9 81 .. 2,227 Isle of Man 77 0.6 135 3,088 40,600 .. .. 5.9 4.9 .. .. .. About the data Definitions The table shows data for 56 economies with popu- · Population is based on the de facto definition of net receipts of primary income (compensation of lations between 30,000 and 1 million and smaller population, which counts all residents regardless employees and property income) from abroad. Data economies if they are members of the World Bank. of legal status or citizenship--except for refugees are in current U.S. dollars converted using the World Where data on gross national income (GNI) per capita not permanently settled in the country of asylum, Bank Atlas method (see Statistical methods). · GNI are not available, the estimated range is given. For who are generally considered part of the popula- per capita is GNI divided by midyear population. more information on the calculation of GNI (gross tion of their country of origin. The values shown are GNI per capita in U.S. dollars is converted using national product, or GNP, in the System of National midyear estimates. See also table 2.1. · Surface the World Bank Atlas method. · Purchasing power Accounts 1968) and purchasing power parity (PPP) area is a country's total area, including areas under parity (PPP) GNI is GNI converted to international conversion factors, see About the data for table 1.1. inland bodies of water and some coastal waterways. dollars using PPP rates. An international dollar has Since 2000 the table has excluded France's over- · Population density is midyear population divided the same purchasing power over GNI that a U.S. seas departments--French Guiana, Guadeloupe, by land area in square kilometers. · Gross national dollar has in the United States. · Gross domestic Martinique, and Réunion--for which GNI and other income (GNI) is the sum of value added by all resi- product (GDP) is the sum of value added by all economic measures are now included in the French dent producers plus any product taxes (less sub- resident producers plus any product taxes (less national accounts. sidies) not included in the valuation of output plus subsidies) not included in the valuation of output. 32 2008 World Development Indicators 1.6 WORLD VIEW Key indicators for other economies Population Surface Population Gross national Gross domestic Life Adult Carbon area density income product expectancy literacy dioxide at birth rate emissions PPPa 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 2006 2006 2006 2006b 2006b 2006 2006 2005­06 2005­06 2006 2005 2004 Kiribati 100 0.8 124 124 1,240 624f 6,230 f 5.8 4.5 .. .. 29 Liechtenstein 35 0.2 218 .. ..d .. .. .. .. .. .. .. Luxembourg 462 2.6 178 32,904 71,240 28,117 60,870 6.2 5.0 79 .. 11,267 Macao, China 478 0.0 16,934 .. ..d .. .. 16.6 15.5 80 .. 2,205 Maldives 300 0.3 1,001 903 3,010 1,424 4,740 23.5 21.5 68 .. 725 Malta 406 0.3 1,269 6,216 15,310 8,523 20,990 3.4 2.8 79 .. 2,451 Marshall Islands 65 0.2 363 195 2,980 525f 8,040 f 3.0 ­0.3 .. .. .. Mayotte 187 0.4 499 .. ..c .. .. .. .. .. .. .. Micronesia, Fed. Sts. 111 0.7 158 264 2,390 672f 6,070 f ­0.7 ­1.2 68 .. .. Monaco 33 0.0 16,718 .. ..d .. .. .. .. .. .. .. Montenegro 601 14.0 44 2,481 4,130 5,366 8,930 16.2 17.5 74 .. .. Netherlands Antilles 189 0.8 236 .. ..d .. .. .. .. 75 96 4,084 New Caledonia 238 18.6 13 .. ..d .. .. .. .. 75 .. 2,575 Northern Mariana Islands 82 0.5 178 .. ..c .. .. .. .. .. .. .. Palau 20 0.5 44 161 7,990 290 f 14,340 f 5.7 5.2 .. .. 238 Qatar 821 11.0 75 .. ..d .. .. 6.1 1.8 75 89 52,857 Samoa 185 2.8 66 421 2,270 943f 5,090 f 2.3 1.5 71 99 150 San Marino 29 0.1 477 1,291 45,130 .. .. 5.0 3.5 82 .. .. São Tomé and Principe 155 1.0 162 124 800 231 1,490 7.0 5.3 65 .. 92 Seychelles 85 0.5 184 751 8,870 1,215f 14,360 f 5.3 3.2 72 .. 546 Solomon Islands 484 28.9 17 333 690 896f 1,850 f 6.1 3.6 63 .. 176 St. Kitts and Nevis 48 0.3 186 406 8,460 597f 12,440 f 5.8 4.9 .. .. 125 St. Lucia 166 0.6 272 833 5,060 1,400 f 8,500 f 4.5 3.7 74 .. 366 St. Vincent & Grenadines 120 0.4 307 395 3,320 741f 6,220 f 1.5 1.0 71 .. 198 Suriname 455 163.3 3 1,918 4,210g 3,514f 7,720 f 5.8 5.1 70 90 2,282 Tonga 100 0.8 139 225 2,250 546f 5,470 f 1.4 0.9 73 .. 117 Vanuatu 221 12.2 18 373 1,690 768f 3,480 f 7.2 4.6 70 .. 88 Virgin Islands (U.S.) 109 0.4 310 .. ..d .. .. .. .. 79 .. 13,524 a. PPP is purchasing power parity, see Definitions. b. Calculated using the World Bank Atlas method. c. Estimated to be upper middle income ($3,596­$11,115). d. Estimated to be high income ($11,116 or more). e. Included in the aggregates for high-income economies based on earlier data. f. Based on regression; others are extrapolated from the 2005 International Comparison Program benchmark estimates. g. Included in the aggregates for lower middle-income economies based on earlier data. h. Less than 0.5. Growth is calculated from constant price GDP data in local 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 patterns of mortality at the time of Data sources its birth were to stay the same throughout its life. · Adult literacy rate is the percentage of adults The indicators here and throughout the book ages 15 and older who can, with understanding, are compiled by World Bank staff from primary read and write a short, simple statement about their and secondary sources. More information about everyday life. · Carbon dioxide emissions are those the indicators and their sources can be found in stemming from the burning of fossil fuels and the the About the data, Definitions, and Data sources manufacture of cement. They include carbon dioxide entries that accompany each table in subsequent produced during consumption of solid, liquid, and sections. gas fuels and gas fl aring. 2008 World Development Indicators 33 Text figures, tables, and boxes PEOPLE 2 Introduction Introduction R eproductive health Keeping mothers alive and healthy is good for women, their families, and society. Prioritizing women's health will help countries meet many of the Millennium Development Goals--first improved maternal and child health, then reduced poverty, universal education, and gender equality. Poor people tend to have large families, suffer disproportionately from illness, and use fewer health services during pregnancy and childbirth. Reproductive health care can enhance poor people's overall health care and help families escape the poverty impact of having many children. When financial resources are divided among fewer family members, more is left for education, health care, and savings, decreasing vulnerability and insecurity (UN Millennium Project 2005a). This important link between reproductive health and development outcomes was first articu- lated in 1994 at the International Conference on Population and Development in Cairo. But as fertility declined in many countries and new priorities arose, reproductive health and fam- ily planning fell steadily in international priority. Complicating this was the lack of sectoral ownership of reproductive health and the requirement for multisectoral action. The targets for the Millennium Development Goals, drafted in 2000, ignored the overarching Cairo goal of universal access to sexual and reproductive health services, instead focusing on the target of reducing maternal mortality, a problem of immense magnitude in poor coun- tries (figures 2a and 2b). Millennium Development Goal 5 in 2000 identified two indicators to measure progress: maternal mortality ratios and the proportion of births attended by skilled staff. At an analytical level, however, it is impossible to disentangle maternal health from reproductive health, of which maternal health is just one facet. Most maternal deaths . . . especially in Sub-Saharan occur in developing countries . . . 2a Africa and South Asia 2b Maternal mortality ratio, by income group (per 100,000 live births) 1990 2005 Maternal mortality ratio, by region (per 100,000 live births) 1990 2005 800 1,000 800 600 600 400 400 200 200 0 0 Low-income Lower Upper High-income East Asia Europe & Latin Middle South Sub-Saharan middle-income middle-income & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Source: Estimates from the World Health Organization, United Nations Children's Source: Estimates from the World Health Organization, United Nations Children's Fund, United Nations Population Fund, and the World Bank. Fund, United Nations Population Fund, and the World Bank. 2008 World Development Indicators 35 Why reproductive health now? Pregnancy and childbirth are leading causes of death and Poor women disproportionately bear the burden of disability disability for women of reproductive age in developing coun- and loss of productive life. Women in low-income countries tries. In 2005 more than half a million women died from face a 1 in 40 risk of a pregnancy-related death; those in high- pregnancy-related causes, and about 200 million women suf- income countries, a 1 in 6,700 risk (figure 2c). The contrast fered life-threatening complications and disabilities (Glasier is also large within countries. In Peru the poorest women are and others 2006). As a result of reproductive health prob- about 7 times more likely than the richest to die of pregnancy- lems an estimated 250 million years of productive life are related causes (Ronsman and Graham 2006). Even though lost every year (UNFPA 2005). Over 99 percent of all mater- cheap and easy methods to prevent unintended or unwanted nal deaths occur in developing countries, the majority in Sub- pregnancies are available, 120 million couples hoping to avoid Saharan Africa and South Asia (Glasier and others 2006). pregnancy did not use contraception. As a result, 80 million In 2005 Millennium Development Goal 5--improved women became pregnant against their will, and 45 million maternal health--was expanded to include family planning sought abortions, about 20 million of them unsafe, performed and reproductive health services. Reproductive health care by untrained providers (Glasier and others 2006). was recognized as important for improving maternal health Progress in maternal and reproductive health in recent and preventing maternal deaths, but also as essential for years has been mixed in developing countries. Several mid- achieving all the Millennium Development Goals. A new dle-income countries have made rapid progress in reducing target was introduced for universal access to reproductive maternal deaths, but maternal mortality ratios and the lifetime health by 2015, along with indicators measuring adolescent risk of dying in childbirth remain unacceptably high in Sub- fertility, prenatal care, unmet need for contraception, and Saharan Africa and South Asia (figure 2d). Within countries, contraceptive prevalence. poorer women are more vulnerable than wealthier women. Women in developing countries are more likely to die of pregnancy- East Asia and Pacific leads in contraceptive related causes than women in high-income countries 2c use among married women ages 15­49 2e Lifetime risk of dying from pregnancy-related causes, by income group, 2005 (%) Contraceptive prevalence rate, by region 3 (% of married women ages 15­49) 1990 2006 100 1 in 40 2 75 50 1 25 1 in 270 1 in 540 1 in 6,700 0 0 Low-income Lower Upper High-income East Asia Europe & Latin Middle South Sub-Saharan middle-income middle-income & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Source: Estimates from the World Health Organization, United Nations Children's Fund, United Nations Population Fund, and the World Bank. Source: Household surveys. The lifetime risk of dying from pregnancy-related causes is Women from the richest households are more likely to use unacceptably high in Sub-Saharan Africa and South Asia 2d contraception--but contraceptive prevalence rates remain low 2f Lifetime risk of dying from pregnancy-related causes, by region, 2005 (%) Contraceptive prevalence rate, by region and wealth 5 quintile (% of married women ages 15­49) Poorest 20% Richest 20% 1 in 22 100 4 75 3 2 1 in 59 50 1 1 in 160 25 1 in 340 1 in 280 1 in 1,400 0 East Asia Europe & Latin Middle South Sub-Saharan 0 & Pacific Central Asia America & East & Asia Africa East Asia Europe & Latin Middle South Sub-Saharan Caribbean North Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Source: Estimates from the World Health Organization, United Nations Children's Fund, United Nations Population Fund, and the World Bank. Source: Gwatkin and others 2007. 36 2008 World Development Indicators Maternal and reproductive health: current status The vast majority of maternal deaths and disabilities can be Despite the benefits, many countries continue to face prevented through appropriate reproductive health services major challenges in meeting their family planning needs before, during, and after pregnancy. Key among them is ex- (figure 2g), and rates of unmet need for family planning in panding family planning to allow women to space or limit developing countries remain high (figure 2h). According to their births. surveys, one married woman in seven in these countries has Contraceptive use among women in developing countries an unmet need for contraception, and in Sub-Saharan Africa has increased steadily, from about 14 percent of married nearly one in four does. Regional aggregates mask wide dif- women ages 15­49 in 1965 to 60 percent in 2006. But ferences: in Asia only 5 percent of women in Vietnam have use is uneven across and within countries. In Sub-Saharan an unmet need, compared with 28 percent in Nepal (Sedgh Africa only 22 percent of married women use contraception, and others 2007b). Preventing unplanned pregnancies alone compared with 63 percent in Europe and Central Asia, about could avert around one-quarter of maternal deaths, including 70 percent in Latin America and the Caribbean, and about those from unsafe abortions (Sedgh and others 2007b). 80 percent in East Asia and the Pacific (figure 2e). Young girls are particularly vulnerable to maternal death. Contraceptive use follows the distribution of wealth, and They have limited information, means, and access to contra- the poorest women come up short. Differences are especially ception and even less access to good quality maternal health stark in South Asia and Sub-Saharan Africa (figure 2f). In Sub- care, especially if they are not married. In regions where the Saharan Africa women from richer households are three times adolescent fertility rate is high (figure 2i), many young women more likely to use contraception, but prevalence is still less and their children, particularly very young women, face higher than 30 percent of eligible women. In South Asia richer women risks of death and disability (box 2j). Young girls either are twice as likely as poorer women to use contraception. continue unintended pregnancies, giving up opportunities Meeting family planning needs remains a challenge-- High adolescent fertility rates mean young women and despite benefits such as reduced fertility 2g their children are at higher risk of death and disability 2i Contraceptive prevalence rate, 2006 Total fertility rate, 2006 Adolescent fertility rate, by region (per 1,000 women ages 15­19) 1997 2006 (% of married women ages 15­49) (births per woman) 150 100 8 75 6 100 50 4 50 25 2 0 0 0 East Asia Europe & Latin Middle South Sub-Saharan East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Caribbean North Africa Source: Household surveys and World Development Indicators data files. Source: United Nations Population Division 2007. Many women in developing countries Age-specific fertility have an unmet need for contraception 2h for girls ages 15­17 Box 2j Married women with an unmet need for contraception (%) 1990­95 2000­05 The age below which giving birth is physically risky for a woman var- 30 ies depending on general health conditions and access to prenatal care. Although the physical risk of giving birth during adolescence 20 is not high for women in countries with good nutritional levels and extensive access to prenatal care, the risk rises in societies where anemia and malnutrition are prevalent and where access to health care is generally poor. The adolescent fertility rate for ages 15­19 is 10 now included as a Millennium Development Goal indicator. However, the fertility rate of girls ages 15­17 is argued to be a better indica- 0 tor, as this age group is at higher risk of suffering pregnancy-related Latin America North Africa South and Sub-Saharan complications and having very low birthweight babies. Even when and the and West Asia Southeast Asia Africa Caribbean very young adolescents deliver their babies in health facilities, they suffer higher rates of mortality than older women do. Note: Regions are Guttmacher Institute regions, which differ from World Bank regions. Source: Sedgh and others 2007b. Source: Lule and others 2005. 2008 World Development Indicators 37 An improvement, but is it enough? for education and employment, or seek unsafe abortions. Both preventive and strategic interventions are needed to Forty percent of all the abortions are performed on women treat the many factors that contribute to maternal mortality. under age 25 (Glasier and others 2006). The expanded Millennium Development Goal 5 indicators are Prenatal care, long at the core of maternal health services, mainly process indicators to assess reproductive health and identifies risks, helps plan for safe delivery, and provides entry address preventive interventions: preparing for birth, including into the health care system. All regions but Sub-Saharan Africa timing and spacing of births for both adults and adolescents; have made progress in providing prenatal care to women at recognizing danger signs in the prenatal period and respond- least once during pregnancy (figure 2k). In South Asia, with the ing appropriately; and having skilled health staff at delivery. slowest progress, 66 percent of pregnant women have at least Equally important are the strategic interventions, espe- one prenatal care visit. But rich women are three times more cially during labor and delivery. Among these are obstetric care, likely to get prenatal care than are poor women (figure 2l). including timely and safe transfers of mothers to a hospital or A key factor in lowering maternal mortality is the pres- health care center with the necessary staff, equipment, drugs, ence of a skilled attendant during childbirth. Nearly half of and other supplies. The World Health Organization (WHO) has maternal deaths in developing countries occur during labor proposed that national public health administrators monitor and delivery or just after delivery (Lule and others 2005). The the availability of essential obstetric care and access to emer- proportion of attended births remains low in South Asia and gency obstetric care at the country level (box 2o). An estimated Sub-Saharan Africa (figure 2m) and is even lower in the poorer 15 percent of pregnancies result in complications (Nanda, segments of these countries (figure 2n). Other regions have Switlick, and Lule 2005). But data on complications are col- made impressive gains, with countries in Europe and Central lected only by ad hoc studies, usually in limited areas of coun- Asia providing skilled care to nearly all women giving birth. tries, and no standard definition or methodology is followed. All regions have made progress in providing prenatal The proportion of births attended by skilled health staff care to women at least once during their pregnancy 2k remains low in South Asia and Sub-Saharan Africa 2m Pregnant women receiving prenatal care, by region (%) 1990 2006 Births attended by skilled health staff, by region (%) 1990 2006 100 100 75 75 50 50 25 25 0 0 East Asia Europe & Latin Middle South Sub-Saharan East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Caribbean North Africa Source: Household surveys. Source: Household surveys. In South Asia rich women are three times more Nearly all women in Europe and Central Asia have births attended likely to receive prenatal care than are poor women 2l by skilled health staff--but even there poor women lag behind 2n Pregnant women receiving prenatal care, Births attended by skilled health staff, various years, by region and wealth quintile (%) Poorest 20% Richest 20% various years, by wealth quintile (%) Poorest 20% Richest 20% 100 100 75 75 50 50 25 25 0 0 East Asia Europe & Latin Middle South Sub-Saharan East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa & Pacific Central Asia America & East & Asia Africa Caribbean North Africa Caribbean North Africa Source: Gwatkin and others 2007. Source: Gwatkin and others 2007. 38 2008 World Development Indicators Challenges ahead Complications from abortion are also now recognized as The interventions to prevent the vast majority of conditions a major public and reproductive health problem, especially that kill women of reproductive age--and to enable health in developing countries. Abortions, especially unsafe ones, systems to protect and promote women's health--have al- account for 13 percent of maternal deaths, and good qual- ready been identified. Some are simple, low-tech, and cost- ity post-abortion services and family planning services to effective, such as the provision and use of contraception. avoid unwanted pregnancies are essential. Of an estimated Yet many people in developing countries, especially in South 20 million unsafe abortions worldwide each year, the major- Asia and Sub-Saharan Africa, do not benefit. Behind the ity are in developing countries (Nanda, Switlick, and Lule failure of these health systems are weak commitments to 2005) (figure 2p). Abortion information is particularly difficult improving maternal health, poor management systems, inad- to gather because abortion is restricted and stigmatized in equate human and medical resources and equipment, and, many countries, leading to false reporting by women and for most of the poor, the inability to pay for services. service providers. Regional estimates of abortion rates are Underlying the failures of the health system is the lack of available from the WHO, UN agencies, national authorities, reliable data for monitoring progress in maternal and repro- and nongovernmental organizations. But reliable country ductive health and in other safe motherhood indicators. And data are not routinely collected. most developing countries have inadequate health informa- In addition to definitional gaps, data collection for these tion systems or lack them altogether. So, providing timely two indicators faces additional hurdles because the infrastruc- and reliable information often depends on local, one-off data ture for collecting data is weak or because there is political, collection, such as household surveys, which are both costly cultural, or moral hesitation. Obtaining accurate values also and unsustainable because they do not establish permanent requires significant clinical resources and technical skills. health information structures. Ideally, there would be vital registration systems, hospital and health service data, and The importance of household surveys. emergency obstetric care Box 2o Least available are data on maternal deaths, needed to Emergency obstetric care encompasses a set of functions performed monitor the Millennium Development Goal target of cutting at health facilities that can prevent the death of women experiencing obstetric complications. Basic emergency obstetric care, usually pro- maternal mortality ratios by 75 percent. While vital registra- vided at health centers and small maternity homes, includes admin- tion systems are a rich and valuable source of health data in istering certain drugs and performing lifesaving procedures, such as for preeclampsia and eclampsia. Comprehensive emergency obstet- developed countries, they are incomplete in developing coun- ric care, usually provided at subdistrict or district hospitals, also includes providing Caesarean sections and blood transfusions. tries. For example, the share of developing countries with at More maternal health programs now recognize that emergency least 90 percent complete vital registration increased from obstetric care is critical to reducing maternal death and disability. Much can be accomplished by upgrading existing facilities. In pro- 45 percent in 1988 to 62 percent in 2006. Still, some of gramming for emergency obstetric care, bottlenecks in accessing services are often assessed using the "three delays" model: delays the most populous countries--China, India, Indonesia, Bra- in the decision to seek care, delays in arrival at a health facility, and zil, Pakistan, Bangladesh, Nigeria--do not have complete delays in the provision of adequate care at the facility. Source: Nanda, Switlick, and Lule 2005. vital registration systems. Hospital or other health service records are sometimes a source of information. But these Most unsafe abortions take place in developing countries, especially in Latin America and the Caribbean and Africa 2p record only women who have access to health services, and Incidence of unsafe abortion, 2003 (per 1,000 women) a large number of women, especially in rural areas, do not. 30 Household surveys for estimating maternal mortality ratios are costly and yield unreliable estimates. 20 The evidence base should be strong enough to provide 10 crucial information on who dies and why--and to generate insights about interventions that are available, accessible, 0 World Developed Developing Africa Asia Europe Latin Oceania appropriate, and affordable. countries countries America & the Caribbean Note: Regions are World Health Organization regions, which differ from World Bank regions. Source: WHO 2007. 2008 World Development Indicators 39 Tables 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate dependents as % proportion of Ages Ages Ages working-age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2006 2015 1990­2006 2006­15 2006 2006 2006 2006 2006 2006 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.3 3.2 3.3 ­0.2 0.4 25.5 65.8 8.7 0.4 0.1 6 16 Algeria 25.3 33.4 38.0 1.7 1.5 28.9 66.5 4.6 0.4 0.1 5 21 Angola 10.5 16.6 21.2 2.8 2.8 46.3 51.3 2.4 0.9 0.0a 21 48 Argentina 32.6 39.1 42.5 1.1 0.9 26.1 63.6 10.3 0.4 0.2 8 18 Armenia 3.5 3.0 3.0 ­1.0 ­0.2 20.0 67.9 12.1 0.3 0.2 9 12 Australia 17.1 20.7 22.4 1.2 0.9 19.3 67.4 13.3 0.3 0.2 7 13 Austria 7.7 8.3 8.4 0.4 0.2 15.6 68.0 16.4 0.2 0.2 9 9 Azerbaijan 7.2 8.5 9.2 1.1 0.9 24.2 68.5 7.2 0.4 0.1 6 18 Bangladesh 113.0 156.0 180.0 2.0 1.6 34.7 61.7 3.6 0.6 0.1 8 25 Belarus 10.2 9.7 9.2 ­0.3 ­0.6 15.3 70.4 14.3 0.2 0.2 15 9 Belgium 10.0 10.5 10.7 0.3 0.1 16.9 65.8 17.3 0.3 0.3 10 12 Benin 5.2 8.8 11.3 3.3 2.9 44.0 53.3 2.7 0.8 0.1 11 41 Bolivia 6.7 9.4 10.9 2.1 1.6 37.7 57.7 4.6 0.7 0.1 8 28 Bosnia and Herzegovina 4.3 3.9 3.9 ­0.6 ­0.2 17.3 68.6 14.1 0.3 0.2 9 9 Botswana 1.4 1.9 2.1 1.9 1.2 35.1 61.5 3.4 0.6 0.1 15 25 Brazil 149.5 189.3 209.4 1.5 1.1 27.6 66.2 6.3 0.4 0.1 6 19 Bulgaria 8.7 7.7 7.1 ­0.8 ­0.8 13.6 69.2 17.3 0.2 0.2 15 9 Burkina Faso 8.9 14.4 18.6 3.0 2.9 46.0 51.0 3.1 0.9 0.1 15 44 Burundi 5.7 8.2 11.2 2.3 3.5 44.7 52.7 2.6 0.8 0.0a 16 47 Cambodia 9.7 14.2 16.6 2.4 1.8 36.7 60.1 3.2 0.6 0.1 9 27 Cameroon 12.2 18.2 21.5 2.5 1.9 41.5 55.0 3.5 0.8 0.1 15 35 Canada 27.8 32.6 35.1 1.0 0.8 17.3 69.4 13.3 0.2 0.2 7 11 Central African Republic 3.0 4.3 5.0 2.2 1.8 42.5 53.7 3.9 0.8 0.1 18 37 Chad 6.1 10.5 13.4 3.4 2.7 46.2 50.9 2.9 0.9 0.1 16 46 Chile 13.2 16.4 17.8 1.4 0.9 24.3 67.4 8.3 0.4 0.1 5 15 China 1,135.2 1,311.8 1,382.5 0.9 0.6 21.1 71.1 7.8 0.3 0.1 7 12 Hong Kong, China 5.7 6.9 7.4 1.2 0.9 14.8 73.2 12.1 0.2 0.2 5 10 Colombia 34.9 45.6 50.6 1.7 1.2 29.8 65.0 5.2 0.5 0.1 6 19 Congo, Dem. Rep. 37.9 60.6 78.5 2.9 2.9 47.3 50.1 2.6 0.9 0.1 18 44 Congo, Rep. 2.4 3.7 4.5 2.6 2.1 41.9 54.9 3.2 0.8 0.1 12 36 Costa Rica 3.1 4.4 5.0 2.2 1.4 27.8 66.3 5.9 0.4 0.1 4 18 Côte d'Ivoire 12.8 18.9 22.3 2.5 1.9 41.4 55.4 3.2 0.7 0.1 16 36 Croatia 4.8 4.4 4.3 ­0.5 ­0.3 15.3 67.4 17.3 0.2 0.3 11 9 Cuba 10.6 11.3 11.2 0.4 ­0.1 18.9 69.7 11.4 0.3 0.2 8 11 Czech Republic 10.4 10.3 10.2 ­0.1 ­0.1 14.5 71.2 14.3 0.2 0.2 10 10 Denmark 5.1 5.4 5.5 0.4 0.1 18.7 66.0 15.4 0.3 0.2 10 12 Dominican Republic 7.3 9.6 10.9 1.7 1.4 33.2 61.1 5.7 0.5 0.1 6 24 Ecuador 10.3 13.2 14.6 1.6 1.1 32.2 61.7 6.0 0.5 0.1 5 21 Egypt, Arab Rep. 55.1 74.2 86.2 1.9 1.7 33.0 62.1 4.9 0.5 0.1 6 24 El Salvador 5.1 6.8 7.6 1.8 1.3 33.7 60.7 5.6 0.6 0.1 6 23 Eritrea 3.2 4.7 6.2 2.5 3.0 42.9 54.8 2.3 0.8 0.0a 9 40 Estonia 1.6 1.3 1.3 ­1.0 ­0.4 14.9 68.4 16.7 0.2 0.2 13 11 Ethiopia 51.2 77.2 96.0 2.6 2.4 44.2 52.9 2.9 0.8 0.1 13 39 Finland 5.0 5.3 5.4 0.3 0.2 17.2 66.7 16.1 0.3 0.2 9 11 France 56.7 61.3 63.1 0.5 0.3 18.3 65.4 16.3 0.3 0.2 9 13 Gabon 0.9 1.3 1.5 2.2 1.5 35.4 60.0 4.6 0.6 0.1 12 26 Gambia, The 1.0 1.7 2.1 3.4 2.5 41.0 55.2 3.8 0.7 0.1 11 36 Georgia 5.5 4.4 4.2 ­1.3 ­0.7 18.4 67.3 14.4 0.3 0.2 12 11 Germany 79.4 82.4 81.1 0.2 ­0.2 14.1 66.6 19.2 0.2 0.3 10 8 Ghana 15.6 23.0 27.3 2.4 1.9 38.6 57.7 3.7 0.7 0.1 9 30 Greece 10.2 11.1 11.2 0.6 0.0a 14.2 67.4 18.4 0.2 0.3 9 10 Guatemala 8.9 13.0 16.2 2.4 2.4 42.9 52.8 4.3 0.8 0.1 6 34 Guinea 6.0 9.2 11.4 2.6 2.4 43.3 53.7 3.1 0.8 0.1 12 40 Guinea-Bissau 1.0 1.6 2.2 3.0 3.0 47.6 49.4 3.0 1.0 0.1 19 50 Haiti 7.1 9.4 11.0 1.8 1.7 37.5 58.3 4.2 0.6 0.1 9 28 40 2008 World Development Indicators 2.1 PEOPLE Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate dependents as % proportion of Ages Ages Ages working-age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2006 2015 1990­2006 2006­15 2006 2006 2006 2006 2006 2006 2006 Honduras 4.9 7.0 8.2 2.2 1.8 39.4 56.4 4.2 0.7 0.1 6 28 Hungary 10.4 10.1 9.7 ­0.2 ­0.4 15.5 69.1 15.4 0.2 0.2 13 10 India 849.5 1,109.8 1,233.2 1.7 1.2 32.5 62.4 5.0 0.5 0.1 8 24 Indonesia 178.2 223.0 245.1 1.4 1.0 28.0 66.3 5.6 0.4 0.1 7 20 Iran, Islamic Rep. 54.4 70.1 78.9 1.6 1.3 27.8 67.8 4.5 0.4 0.1 5 18 Iraq 18.5 .. .. .. .. .. .. .. .. .. .. .. Ireland 3.5 4.3 4.8 1.2 1.3 20.7 68.2 11.1 0.3 0.2 6 15 Israel 4.7 7.0 8.0 2.6 1.5 27.9 62.0 10.1 0.4 0.2 6 21 Italy 56.7 58.8 58.4 0.2 ­0.1 13.9 66.1 19.9 0.2 0.3 9 10 Jamaica 2.4 2.7 2.8 0.7 0.4 31.3 61.2 7.5 0.5 0.1 6 17 Japan 123.5 127.8 124.5 0.2 ­0.3 13.8 66.0 20.3 0.2 0.3 9 9 Jordan 3.2 5.5 6.8 3.5 2.2 36.5 60.2 3.3 0.6 0.1 4 29 Kazakhstan 16.3 15.3 16.4 ­0.4 0.8 23.9 68.2 8.0 0.4 0.1 10 20 Kenya 23.4 36.6 46.1 2.8 2.6 42.6 54.7 2.7 0.8 0.0a 12 39 Korea, Dem. Rep. 20.1 23.7 24.4 1.0 0.3 23.6 67.5 8.8 0.4 0.1 10 14 Korea, Rep. 42.9 48.4 49.2 0.8 0.2 18.1 72.0 9.8 0.3 0.1 5 9 Kuwait 2.1 2.6 3.2 1.3 2.2 23.6 74.6 1.9 0.3 0.0a 2 21 Kyrgyz Republic 4.4 5.2 5.7 1.0 1.0 30.4 63.8 5.8 0.5 0.1 7 23 Lao PDR 4.1 5.8 6.7 2.2 1.7 38.9 57.5 3.5 0.7 0.1 7 27 Latvia 2.7 2.3 2.2 ­1.0 ­0.6 14.0 69.2 16.8 0.2 0.2 15 10 Lebanon 3.0 4.1 4.4 1.9 1.0 28.2 64.5 7.3 0.4 0.1 7 18 Lesotho 1.6 2.0 2.1 1.4 0.6 40.1 55.1 4.7 0.7 0.1 19 29 Liberia 2.1 3.6 5.1 3.2 3.9 47.0 50.8 2.2 0.9 0.0a 19 50 Libya 4.4 6.0 7.1 2.0 1.8 30.2 65.9 3.9 0.5 0.1 4 24 Lithuania 3.7 3.4 3.2 ­0.5 ­0.5 16.2 68.3 15.5 0.2 0.2 13 9 Macedonia, FYR 1.9 2.0 2.0 0.4 ­0.0 b 19.2 69.5 11.3 0.3 0.2 9 11 Madagascar 12.0 19.2 24.1 2.9 2.5 43.6 53.3 3.2 0.8 0.1 10 37 Malawi 9.4 13.6 17.0 2.3 2.5 47.0 49.9 3.0 0.9 0.1 15 41 Malaysia 18.1 26.1 30.0 2.3 1.5 31.0 64.6 4.4 0.5 0.1 4 21 Mali 7.7 12.0 15.7 2.8 3.0 47.6 48.8 3.6 1.0 0.1 15 48 Mauritania 1.9 3.0 3.8 2.8 2.4 40.1 56.3 3.6 0.7 0.1 8 33 Mauritius 1.1 1.3 1.3 1.1 0.7 24.0 69.3 6.7 0.3 0.1 8 15 Mexico 83.2 104.2 113.7 1.4 1.0 30.2 63.8 6.0 0.5 0.1 5 19 Moldova 4.4 3.8 3.6 ­0.8 ­0.8 19.4 69.5 11.1 0.3 0.2 12 11 Mongolia 2.1 2.6 2.9 1.3 1.1 28.0 68.1 4.0 0.4 0.1 6 18 Morocco 24.2 30.5 33.9 1.5 1.2 29.7 65.0 5.3 0.5 0.1 6 22 Mozambique 13.5 21.0 24.7 2.7 1.8 44.3 52.5 3.2 0.8 0.1 20 40 Myanmar 40.1 48.4 51.9 1.2 0.8 26.7 67.7 5.6 0.4 0.1 10 18 Namibia 1.4 2.0 2.3 2.3 1.2 38.3 58.2 3.5 0.7 0.1 13 26 Nepal 19.1 27.6 32.2 2.3 1.7 38.5 57.8 3.7 0.7 0.1 8 29 Netherlands 15.0 16.3 16.5 0.6 0.1 18.3 67.4 14.3 0.3 0.2 8 11 New Zealand 3.4 4.2 4.5 1.2 0.8 21.2 66.5 12.3 0.3 0.2 7 14 Nicaragua 4.1 5.5 6.3 1.8 1.4 37.2 58.7 4.0 0.6 0.1 5 25 Niger 7.8 13.7 18.5 3.5 3.3 48.0 48.8 3.2 1.0 0.1 14 49 Nigeria 94.5 144.7 175.6 2.7 2.1 44.1 53.0 2.9 0.8 0.1 17 40 Norway 4.2 4.7 4.9 0.6 0.6 19.4 65.9 14.7 0.3 0.2 9 12 Oman 1.8 2.5 3.0 2.0 2.0 33.1 64.1 2.7 0.5 0.0a 3 22 Pakistan 108.0 159.0 191.9 2.4 2.1 36.4 59.7 3.9 0.6 0.1 7 26 Panama 2.4 3.3 3.8 1.9 1.5 30.1 63.8 6.1 0.5 0.1 5 21 Papua New Guinea 4.1 6.2 7.3 2.5 1.8 40.3 57.3 2.4 0.7 0.0a 10 30 Paraguay 4.2 6.0 7.0 2.2 1.7 35.4 59.8 4.8 0.6 0.1 6 25 Peru 21.8 27.6 30.7 1.5 1.2 31.2 63.1 5.7 0.5 0.1 6 21 Philippines 61.2 86.3 101.0 2.1 1.8 35.8 60.3 3.9 0.6 0.1 5 26 Poland 38.1 38.1 37.4 0.0a ­0.2 15.9 70.8 13.3 0.2 0.2 10 10 Portugal 9.9 10.6 10.8 0.4 0.2 15.6 67.4 17.0 0.2 0.3 10 10 Puerto Rico 3.5 3.9 4.1 0.7 0.5 21.6 65.7 12.7 0.3 0.2 8 13 2008 World Development Indicators 41 2.1 Population dynamics Population Average annual Population age Dependency Crude Crude population growth composition ratio death birth rate rate dependents as % proportion of Ages Ages Ages working-age population per 1,000 per 1,000 millions % 0­14 15­64 65+ Young Old people people 1990 2006 2015 1990­2006 2006­15 2006 2006 2006 2006 2006 2006 2006 Romania 23.2 21.6 20.5 ­0.5 ­0.6 15.4 69.8 14.9 0.2 0.2 12 10 Russian Federation 148.3 142.5 135.2 ­0.2 ­0.6 14.9 71.4 13.7 0.2 0.2 15 10 Rwanda 7.3 9.5 12.1 1.6 2.8 43.1 54.5 2.5 0.8 0.0a 17 44 Saudi Arabia 16.4 23.7 28.5 2.3 2.1 34.0 63.2 2.8 0.5 0.0a 4 25 Senegal 7.9 12.1 15.4 2.7 2.7 41.9 53.8 4.3 0.8 0.1 9 36 Serbia 7.5c 7.4 c 7.3c ­0.1c ­0.2c 18.4 d 66.9d 14.7d 0.3d 0.2d 14 c 10 c Sierra Leone 4.1 5.7 6.9 2.1 2.1 42.8 53.9 3.3 0.8 0.1 22 46 Singapore 3.0 4.5 4.8 2.4 0.8 18.8 72.4 8.8 0.3 0.1 4 10 Slovak Republic 5.3 5.4 5.4 0.1 ­0.0 b 16.3 71.8 11.8 0.2 0.2 10 10 Slovenia 2.0 2.0 2.0 0.0a ­0.1 13.9 70.3 15.8 0.2 0.2 9 9 Somalia 6.7 8.4 10.9 1.4 2.8 44.2 53.2 2.6 0.8 0.0a 17 43 South Africa 35.2 47.4 49.1 1.9 0.4 31.9 63.7 4.4 0.5 0.1 21 23 Spain 38.8 44.1 45.7 0.8 0.4 14.5 68.7 16.9 0.2 0.2 9 11 Sri Lanka 17.0 19.9 20.5 1.0 0.3 23.7 69.7 6.6 0.3 0.1 6 19 Sudan 25.9 37.7 45.6 2.3 2.1 40.3 56.1 3.6 0.7 0.1 10 32 Swaziland 0.8 1.1 1.2 2.4 0.5 39.2 57.5 3.3 0.7 0.1 22 33 Sweden 8.6 9.1 9.4 0.4 0.4 17.1 65.5 17.4 0.3 0.3 10 12 Switzerland 6.7 7.5 7.6 0.7 0.2 16.5 67.9 15.7 0.2 0.2 8 10 Syrian Arab Republic 12.7 19.4 23.5 2.6 2.1 36.0 60.8 3.2 0.6 0.1 3 27 Tajikistan 5.3 6.6 7.7 1.4 1.6 38.7 57.4 3.9 0.7 0.1 6 28 Tanzania 25.5 39.5 48.9 2.7 2.4 44.4 52.6 3.0 0.8 0.1 13 40 Thailand 54.3 63.4 66.6 1.0 0.5 21.4 70.6 8.0 0.3 0.1 8 15 Timor-Leste 0.7 1.0 1.4 2.0 3.7 44.7 52.6 2.7 0.8 0.1 15 51 Togo 4.0 6.4 8.0 3.0 2.5 43.0 53.9 3.1 0.8 0.1 10 37 Trinidad and Tobago 1.2 1.3 1.4 0.5 0.4 21.7 71.7 6.6 0.3 0.1 8 15 Tunisia 8.2 10.1 11.2 1.4 1.1 25.4 68.3 6.3 0.4 0.1 6 17 Turkey 56.2 73.0 81.0 1.6 1.2 27.9 66.5 5.7 0.4 0.1 6 19 Turkmenistan 3.7 4.9 5.5 1.8 1.3 30.9 64.5 4.6 0.5 0.1 8 22 Uganda 17.8 29.9 40.7 3.2 3.4 49.3 48.3 2.5 1.0 0.1 14 47 Ukraine 51.9 46.8 43.4 ­0.6 ­0.8 14.3 69.5 16.2 0.2 0.2 16 10 United Arab Emirates 1.8 4.2 5.3 5.5 2.4 19.6 79.3 1.1 0.2 0.0a 1 15 United Kingdom 57.6 60.6 62.4 0.3 0.3 17.8 66.1 16.1 0.3 0.2 10 12 United States 249.6 299.4 323.9 1.1 0.9 20.7 67.0 12.3 0.3 0.2 8 14 Uruguay 3.1 3.3 3.4 0.4 0.2 23.6 62.8 13.6 0.4 0.2 9 15 Uzbekistan 20.5 26.5 29.6 1.6 1.2 32.4 62.9 4.7 0.5 0.1 6 19 Venezuela, RB 19.8 27.0 31.1 2.0 1.5 30.9 64.0 5.1 0.5 0.1 5 22 Vietnam 66.2 84.1 93.7 1.5 1.2 28.9 65.6 5.6 0.4 0.1 5 17 West Bank and Gaza 2.0 3.8 4.7 4.1 2.5 45.6 51.4 3.0 0.9 0.1 3 32 Yemen, Rep. 12.3 21.7 28.2 3.6 2.9 45.4 52.2 2.3 0.9 0.0a 8 38 Zambia 8.1 11.7 13.8 2.3 1.9 45.6 51.4 2.9 0.9 0.1 19 40 Zimbabwe 10.5 13.2 14.8 1.5 1.3 39.0 57.5 3.5 0.7 0.1 18 28 World 5,263.9 s 6,538.1 s 7,200.7 s 1.4 w 1.1 w 28.0 w 64.6 w 7.4 w 0.4 w 0.1 w 8w 20 w Low income 1,747.9 2,419.7 2,815.3 2.0 1.7 36.3 59.4 4.3 0.6 0.1 10 29 Middle income 2,599.1 3,087.7 3,313.9 1.1 0.8 24.7 67.9 7.4 0.4 0.1 8 16 Lower middle income 1,899.6 2,276.5 2,456.3 1.1 0.8 24.7 68.3 7.0 0.4 0.1 7 16 Upper middle income 699.5 811.3 857.7 0.9 0.6 24.6 66.9 8.6 0.4 0.1 9 17 Low & middle income 4,347.0 5,507.4 6,129.2 1.5 1.2 29.8 64.2 6.0 0.5 0.1 8 22 East Asia & Pacific 1,595.9 1,898.9 2,032.7 1.1 0.8 23.5 69.4 7.1 0.3 0.1 7 14 Europe & Central Asia 451.8 460.5 460.7 0.1 0.0a 19.4 68.9 11.6 0.3 0.2 12 13 Latin America & Carib. 436.9 556.1 616.5 1.5 1.1 29.6 64.1 6.3 0.5 0.1 6 20 Middle East & N. Africa 225.6 310.7 361.9 2.0 1.7 32.7 63.0 4.3 0.5 0.1 6 24 South Asia 1,120.1 1,499.4 1,694.9 1.8 1.4 33.4 61.9 4.7 0.5 0.1 8 24 Sub-Saharan Africa 516.7 781.8 962.6 2.6 2.3 43.3 53.6 3.1 0.8 0.1 15 39 High income 916.9 1,030.7 1,071.5 0.7 0.4 17.9 67.1 14.9 0.3 0.2 8 12 Euro area 296.2 316.7 319.7 0.4 0.1 15.5 66.7 17.8 0.2 0.3 9 10 a. Less than 0.05. b. More than ­0.05. c. Excludes Kosovo and Metohija. d. Includes Kosovo and Metohija. 42 2008 World Development Indicators 2.1 PEOPLE Population dynamics About the data Definitions Population estimates are usually based on national mortality rates are now reflected in the larger share · Population is based on the de facto definition of population censuses, but their frequency and quality of the working-age population. population, which counts all residents regardless of vary by country. Most countries conduct a complete Dependency ratios account for variations in the legal status or citizenship--except for refugees not enumeration no more than once a decade. Estimates proportions of children, elderly people, and working- permanently settled in the country of asylum, who for the years before and after the census are inter- age people in the population. Calculations of young are generally considered part of the population of polations or extrapolations based on demographic and old-age dependency suggest the dependency their country of origin. The values shown are mid- models. Errors and undercounting occur even in high- burden that the working-age population must bear in year estimates for 1990 and 2006 and projections income countries; in developing countries errors may relation to children and the elderly. But dependency for 2015. · Average annual population growth is be substantial because of limits in the transport, ratios show only the age composition of a population, the exponential change for the period indicated. See communications, and other resources required to not economic dependency. Some children and elderly Statistical methods for more information. · Popula- conduct and analyze a full census. people are part of the labor force; many working-age tion age composition is the percentage of the total The quality and reliability of official demographic people are not. population that is in specific age groups. · Depen- data are also affected by public trust in the govern- The vital rates in the table are based on data from dency ratio is the ratio of dependents--people ment, government commitment to accurate enumera- birth and death registration systems, censuses, and younger than 15 or older than 64--to the working- tion, confidentiality and protection against misuse of sample surveys by national statistical offices and other age population--those ages 15­64. · Crude death census data, and census agencies' independence organizations, or on demographic analysis. The 2006 rate and crude birth rate are the number of deaths from political influence. Moreover, comparability of estimates for many countries are projections based on and the number of live births occurring during the population indicators is limited by differences in the extrapolations of levels and trends from earlier years or year, per 1,000 population, estimated at midyear. concepts, definitions, collection procedures, and esti- interpolations of population estimates and projections Subtracting the crude death rate from the crude birth mation methods used by national statistical agencies from the United Nations Population Division. rate provides the rate of natural increase, which is and other organizations that collect the data. Vital registers are the preferred source for these equal to the population growth rate in the absence Of the 153 economies in the table, 131 (about data, but in many developing countries systems for of migration. 86 percent) conducted a census between 1995 and registering births and deaths are absent or incom- 2006. The currentness of censuses and the availabil- plete because of defi ciencies in the coverage of ity of complementary data from surveys or registra- events or geographic areas. Many developing coun- tion systems are objective ways to judge demographic tries carry out special household surveys that ask data quality. Some European countries' registration respondents about births and deaths in the recent systems offer complete information on population past. Estimates derived in this way are subject to in the absence of a census. See Primary data docu- sampling errors and errors due to inaccurate recall. mentation for the most recent census or survey year The United Nations Statistics Division monitors and for the completeness of registration. the completeness of vital registration systems. The Current population estimates for developing coun- share of countries with at least 90 percent complete tries that lack recent census-based data and pre- and vital registration rose from 45 percent in 1988 to 62 post-census estimates for countries with census data percent in 2006. Still, some of the most populous Data sources are provided by the United Nations Population Division developing countries--China, India, Indonesia, Brazil, and other agencies. The standard estimation method Pakistan, Bangladesh, Nigeria--lack complete vital The World Bank's population estimates are com- requires fertility, mortality, and net migration data, registration systems. From 2003 to 2006, 51 percent piled and produced by its Human Development often collected from sample surveys, which can be of births and deaths and 48 percent of infant deaths Network and Development Data Group in consulta- small or limited in coverage. Population estimates are worldwide were registered and reported. tion with its operational staff and country offices. from demographic modeling and so are susceptible International migration is the only other factor Important inputs to the World Bank's demographic to biases and errors from shortcomings in the model besides birth and death rates that directly determines work come from the United Nations Population as well as in the data. Population projections use a country's population growth. From 1990 to 2005 the Division's World Population Prospects: The 2006 the cohort component method. Because of a drastic number of immigrants in high-income countries rose Revision; census reports and other statistical reduction in estimated mortality due partly to revised by 40 million. About 190 million people (3 percent publications from national statistical offi ces; lower estimates of HIV prevalence, populations of of the world's population) currently live outside their household surveys conducted by national agen- several countries, notably in Sub-Saharan Africa, home country. Estimating international migration is cies, Macro International, and the U.S. Centers for have been revised upward from previous estimates. difficult. At any time many people are located outside Disease Control and Prevention; Eurostat, Demo- The growth rate of the total population conceals their home country as tourists, workers, or refugees graphic Statistics (various years); Centro Latino- the fact that different age groups may grow at differ- or for other reasons. Standards for the duration and americano de Demografía, Boletín Demográfico ent rates. In many developing countries the under-15 purpose of international moves that qualify as migra- (various years); and U.S. Bureau of the Census, population was growing rapidly but has begun to tion vary, and estimates require information on flows International Database. shrink. Previously high fertility rates and declining into and out of countries that is difficult to collect. 2008 World Development Indicators 43 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 2006 1990 2006 1990 2006 1990­2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. Albania 83 70 58 49 1.6 1.4 ­0.7 40.2 41.8 Algeria 78 80 23 37 7.2 13.9 4.1 22.6 31.0 Angola 90 92 74 74 4.5 7.3 3.0 46.4 45.8 Argentina 78 76 38 54 13.0 18.8 2.3 34.4 43.1 Armenia 87 60 72 48 1.9 1.3 ­2.6 47.7 48.9 Australia 75 70 52 56 8.4 10.5 1.4 41.3 44.8 Austria 70 66 43 50 3.5 4.0 0.8 40.8 44.4 Azerbaijan 78 73 64 61 3.3 4.3 1.6 47.4 47.7 Bangladesh 89 86 63 52 51.2 71.0 2.0 40.2 36.7 Belarus 76 64 61 53 5.3 4.8 ­0.7 48.8 49.1 Belgium 61 60 37 44 3.9 4.5 0.9 39.1 43.6 Benin 90 86 58 54 2.0 3.4 3.3 40.8 38.3 Bolivia 80 84 49 63 2.5 4.3 3.3 39.2 43.5 Bosnia and Herzegovina 78 68 60 59 2.2 2.0 ­0.6 44.4 48.4 Botswana 77 70 57 46 0.5 0.7 2.1 44.5 40.3 Brazil 85 79 45 57 62.5 93.1 2.5 35.0 42.9 Bulgaria 68 52 60 40 4.4 3.1 ­2.3 48.0 45.0 Burkina Faso 91 89 77 78 3.9 6.5 3.2 47.5 47.1 Burundi 90 93 91 92 2.8 4.2 2.5 52.5 51.4 Cambodia 85 80 78 75 4.4 6.9 2.9 52.4 50.7 Cameroon 82 80 56 52 4.6 7.0 2.6 41.3 39.6 Canada 76 72 58 61 14.7 17.9 1.2 44.0 46.1 Central African Republic 89 89 71 71 1.4 2.0 2.3 47.0 46.0 Chad 80 78 64 66 2.4 4.0 3.3 45.7 46.8 Chile 77 70 32 37 5.0 6.6 1.7 30.5 35.4 China 85 82 73 69 650.6 780.5 1.1 44.8 44.1 Hong Kong, China 80 70 47 54 2.9 3.6 1.5 36.3 45.5 Colombia 81 81 46 62 14.0 22.8 3.0 37.0 44.8 Congo, Dem. Rep. 91 91 61 61 15.2 24.2 2.9 41.6 41.3 Congo, Rep. 86 88 58 57 1.0 1.5 2.9 41.3 40.1 Costa Rica 84 81 33 46 1.2 2.0 3.5 27.6 35.6 Côte d'Ivoire 90 89 44 39 4.7 7.1 2.6 30.0 29.3 Croatia 71 60 47 45 2.2 1.9 ­0.8 42.1 44.8 Cuba 73 73 39 44 4.6 5.3 0.9 34.6 37.3 Czech Republic 73 67 61 52 5.4 5.2 ­0.3 47.5 44.9 Denmark 75 69 62 59 2.9 2.8 ­0.2 46.1 46.4 Dominican Republic 84 82 36 47 2.7 4.1 2.5 29.6 36.4 Ecuador 85 82 33 61 3.7 6.4 3.5 27.8 42.7 Egypt, Arab Rep. 75 73 27 20 16.5 23.1 2.1 26.3 21.7 El Salvador 80 75 51 48 2.0 2.7 2.1 41.2 40.7 Eritrea 92 90 61 58 1.3 2.0 2.7 41.8 41.0 Estonia 77 65 64 52 0.9 0.7 ­1.6 49.8 48.9 Ethiopia 91 89 72 71 22.6 34.4 2.6 44.9 44.9 Finland 70 66 58 57 2.6 2.7 0.2 47.2 47.4 France 65 61 46 48 24.8 27.3 0.6 43.3 45.5 Gabon 84 83 63 62 0.4 0.6 2.7 43.8 42.7 Gambia, The 86 86 63 59 0.4 0.7 3.5 42.6 40.8 Georgia 72 76 69 49 2.9 2.2 ­1.6 52.3 42.7 Germany 72 65 44 51 38.3 41.0 0.4 40.4 45.1 Ghana 80 75 76 70 6.7 10.3 2.6 48.8 47.8 Greece 67 65 36 44 4.2 5.2 1.4 36.2 40.7 Guatemala 89 83 29 34 2.9 4.2 2.4 24.7 31.3 Guinea 90 87 80 80 2.8 4.4 2.7 47.3 47.5 Guinea-Bissau 91 93 58 61 0.4 0.7 2.9 40.3 40.8 Haiti 83 84 58 56 2.8 4.1 2.4 42.7 41.3 44 2008 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 2006 1990 2006 1990 2006 1990­2006 1990 2006 Honduras 87 89 33 55 1.6 3.0 4.0 27.9 39.4 Hungary 64 58 46 42 4.5 4.2 ­0.5 44.5 45.0 India 85 82 37 34 325.6 438.0 1.9 28.4 28.1 Indonesia 81 85 50 51 75.3 109.2 2.3 38.4 37.9 Iran, Islamic Rep. 81 74 22 40 15.6 29.1 3.9 20.2 34.3 Iraq 76 .. 16 .. 4.7 .. .. 16.8 .. Ireland 70 72 36 54 1.3 2.1 2.9 34.3 43.0 Israel 62 59 41 51 1.6 2.8 3.3 40.5 47.0 Italy 66 61 36 38 23.9 24.8 0.2 37.1 39.9 Jamaica 80 74 66 54 1.1 1.2 0.2 46.8 43.3 Japan 77 73 50 48 63.9 66.2 0.2 40.6 40.8 Jordan 69 77 18 28 0.8 1.9 5.7 18.8 25.4 Kazakhstan 78 75 61 65 7.7 8.1 0.3 46.3 49.4 Kenya 90 90 75 70 9.8 16.7 3.3 45.9 44.2 Korea, Dem. Rep. 82 78 52 48 9.9 11.4 0.9 40.6 39.3 Korea, Rep. 73 74 47 50 19.1 24.5 1.6 39.3 40.8 Kuwait 82 85 35 50 0.9 1.4 3.2 21.8 25.7 Kyrgyz Republic 74 74 59 55 1.8 2.3 1.5 46.2 44.0 Lao PDR 80 80 53 54 1.5 2.4 2.8 40.6 41.0 Latvia 77 64 63 49 1.5 1.1 ­1.8 49.7 48.0 Lebanon 78 80 32 34 1.0 1.6 2.9 31.2 31.0 Lesotho 86 74 57 46 0.6 0.7 0.8 46.5 43.5 Liberia 85 83 55 55 0.8 1.3 3.2 39.4 39.7 Libya 79 82 19 35 1.3 2.5 4.2 16.9 27.8 Lithuania 75 64 59 52 1.9 1.6 ­1.0 48.1 49.0 Macedonia, FYR 73 65 48 41 0.9 0.9 0.1 40.0 39.0 Madagascar 83 86 79 79 5.4 8.9 3.2 49.2 48.3 Malawi 91 90 85 86 4.4 6.3 2.2 50.2 50.0 Malaysia 81 81 44 47 7.1 11.6 3.0 34.8 36.0 Mali 89 82 72 72 3.2 4.8 2.5 46.9 49.2 Mauritania 86 84 56 54 0.8 1.3 3.1 40.2 39.2 Mauritius 82 79 42 43 0.5 0.6 1.4 33.9 35.7 Mexico 84 80 34 40 29.9 43.1 2.3 30.0 35.2 Moldova 75 68 61 54 2.1 1.9 ­0.8 48.5 46.8 Mongolia 82 82 56 54 0.8 1.3 2.5 41.0 40.1 Morocco 81 80 24 27 7.6 11.3 2.5 23.7 26.1 Mozambique 88 83 88 85 6.4 9.8 2.7 54.0 53.4 Myanmar 88 86 69 68 20.2 27.3 1.9 44.7 44.9 Namibia 65 63 49 47 0.4 0.7 2.7 45.0 43.8 Nepal 80 78 48 50 7.1 10.8 2.6 37.9 40.5 Netherlands 71 73 44 57 6.9 8.6 1.3 39.1 44.4 New Zealand 74 74 53 61 1.7 2.2 1.8 43.1 46.1 Nicaragua 86 86 35 36 1.3 2.1 2.8 29.7 30.0 Niger 95 95 71 71 3.3 5.9 3.6 43.7 42.4 Nigeria 86 85 48 46 33.9 52.7 2.8 36.5 35.3 Norway 73 73 57 64 2.2 2.6 0.9 44.7 46.8 Oman 83 81 15 24 0.6 1.0 3.3 11.1 17.3 Pakistan 86 83 28 33 35.0 59.6 3.3 23.3 27.3 Panama 79 79 39 52 0.9 1.5 3.0 32.5 39.2 Papua New Guinea 75 75 72 72 1.8 2.7 2.7 46.7 48.7 Paraguay 83 84 52 65 1.7 2.9 3.5 38.1 43.3 Peru 80 82 47 60 8.5 13.4 2.8 37.0 42.5 Philippines 83 83 47 56 23.5 38.4 3.1 36.5 40.2 Poland 74 61 57 47 18.6 17.2 ­0.5 45.8 45.7 Portugal 73 70 50 56 4.8 5.6 1.0 42.7 46.2 Puerto Rico 61 59 31 38 1.2 1.5 1.5 35.8 41.4 2008 World Development Indicators 45 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 2006 1990 2006 1990 2006 1990­2006 1990 2006 Romania 71 62 54 50 11.0 10.1 ­0.5 44.3 45.9 Russian Federation 77 68 60 55 77.3 73.5 ­0.3 48.4 48.8 Rwanda 87 84 86 80 3.1 4.4 2.1 51.8 51.4 Saudi Arabia 80 80 15 18 5.1 8.4 3.2 11.4 14.2 Senegal 87 81 61 56 3.2 4.8 2.5 40.8 41.2 Serbia 72a 70a 50a 51a 3.5b 3.6b 0.2b 41.8b 42.9b Sierra Leone 90 94 53 56 1.7 2.5 2.3 38.5 38.5 Singapore 80 76 50 50 1.6 2.3 2.4 38.8 39.9 Slovak Republic 75 68 60 52 2.6 2.7 0.1 46.3 44.9 Slovenia 70 67 54 54 1.0 1.0 0.4 45.5 46.0 Somalia 96 95 61 59 2.9 3.6 1.5 39.9 39.2 South Africa 79 79 54 46 14.4 20.0 2.1 41.6 37.9 Spain 69 67 34 45 15.9 21.1 1.8 34.4 40.6 Sri Lanka 79 76 45 35 7.2 8.4 1.0 36.0 32.3 Sudan 79 71 27 24 7.7 10.7 2.0 26.0 24.9 Swaziland 78 75 38 32 0.2 0.4 2.9 38.0 32.3 Sweden 72 67 63 59 4.7 4.7 0.0 47.7 46.6 Switzerland 80 75 52 61 3.7 4.2 0.9 40.4 46.1 Syrian Arab Republic 82 88 29 39 3.6 7.9 4.9 26.0 30.5 Tajikistan 74 62 52 46 1.9 2.2 0.9 42.2 43.7 Tanzania 91 90 88 86 12.4 19.3 2.8 50.2 49.7 Thailand 88 81 75 66 31.4 36.5 0.9 46.9 46.7 Timor-Leste 79 83 50 56 0.3 0.4 1.9 37.5 39.5 Togo 90 90 54 50 1.5 2.5 3.2 38.5 36.7 Trinidad and Tobago 75 77 42 47 0.5 0.6 2.0 37.0 38.9 Tunisia 76 75 21 29 2.4 3.9 3.0 21.6 27.9 Turkey 82 76 34 28 21.0 27.4 1.7 29.4 26.5 Turkmenistan 77 73 64 61 1.5 2.3 2.4 46.9 46.5 Uganda 92 86 80 80 8.0 12.6 2.9 47.2 48.4 Ukraine 73 64 58 50 26.3 22.5 ­1.0 49.3 48.1 United Arab Emirates 92 93 25 41 0.9 2.7 6.7 9.8 14.6 United Kingdom 75 69 53 55 29.7 30.8 0.2 43.3 45.4 United States 76 73 57 60 129.3 157.0 1.2 44.3 45.9 Uruguay 76 78 46 57 1.4 1.7 1.3 39.9 44.4 Uzbekistan 76 73 60 57 8.2 11.6 2.2 45.4 44.6 Venezuela, RB 81 84 38 59 7.3 13.3 3.8 31.8 41.3 Vietnam 81 78 74 72 31.3 44.8 2.2 48.4 48.2 West Bank and Gaza 64 66 9 10 0.4 0.8 4.4 11.9 13.2 Yemen, Rep. 74 75 28 30 3.0 6.3 4.6 27.5 28.2 Zambia 90 91 66 66 3.4 5.0 2.3 43.1 42.6 Zimbabwe 80 85 70 64 4.2 6.0 2.2 47.0 43.6 World 81 w 79 w 54 w 53 w 2,386.6 t 3,081.8 t 1.6 w 39.7 w 39.9 w Low income 85 83 48 46 694.0 995.4 2.3 35.1 35.0 Middle income 82 79 59 57 1,258.0 1,582.6 1.4 41.7 41.9 Lower middle income 83 81 63 60 954.4 1,208.6 1.5 42.4 42.0 Upper middle income 79 74 48 49 303.7 374.0 1.3 39.5 41.5 Low & middle income 83 81 55 53 1,952.1 2,578.0 1.7 39.4 39.2 East Asia & Pacific 85 82 69 66 858.7 1,074.1 1.4 44.1 43.5 Europe & Central Asia 75 68 56 49 216.4 214.6 ­0.1 45.7 44.7 Latin America & Carib. 83 80 41 53 171.1 257.4 2.6 33.9 40.8 Middle East & N. Africa 78 77 23 30 64.9 111.8 3.4 22.9 28.0 South Asia 85 82 39 36 430.6 597.1 2.0 29.7 29.3 Sub-Saharan Africa 87 85 63 61 210.3 323.0 2.7 43.0 42.2 High income 73 70 49 52 434.5 503.8 0.9 41.4 43.4 Euro area 68 64 41 47 131.8 148.8 0.8 39.6 43.4 a. Includes Montenegro. b. Excludes Kosovo and Metohija. 46 2008 World Development Indicators 2.2 PEOPLE Labor force structure About the data Definitions The labor force is the supply of labor available for pro- The labor force participation rates in the table are · Labor force participation rate is the proportion ducing goods and services in an economy. It includes from Key Indicators of the Labour Market, 5th edition. of the population ages 15 and older that is eco- people who are currently employed and people who These harmonized estimates use strict data selec- nomically active: all people who supply labor for the are unemployed but seeking work as well as first-time tion criteria and enhanced methods to ensure compa- production of goods and services during a specified job-seekers. Not everyone who works is included, rability across countries and over time, including col- period. · Total labor force comprises people ages however. Unpaid workers, family workers, and stu- lection and tabulation methodologies and methods 15 and older who meet the ILO definition of the dents are often omitted, and some countries do not applied to such country-specific factors as military economically active population. It includes both the count members of the armed forces. Labor force size service requirements. Estimates are based mainly on employed and the unemployed. · Average annual tends to vary during the year as seasonal workers labor force surveys, with other sources (population percentage growth of the labor force is calculated enter and leave. censuses and nationally reported estimates) used using the exponential endpoint method (see Statisti- Data on the labor force are collected from labor only when no survey data are available. cal methods for more information). · Females as a force surveys, censuses, establishment censuses Participation rates indicate the relative size of the percentage of the labor force show the extent to and surveys, and administrative records such as labor supply. The indicator in this edition is for the which women are active in the labor force. employment exchange registers and unemployment population ages 15 and older, to include people who insurance schemes. For some countries a combina- continue working past age 65. In previous editions tion of these sources is used. Labor force surveys the indicator was for the population ages 15­64, are the most comprehensive source for internation- so participation rates are not comparable across ally comparable labor force data. They can cover all editions. noninstitutionalized civilians, all branches and sec- The labor force estimates in the table were cal- tors of the economy, and all categories of workers, culated by applying labor force participation rates including people holding multiple jobs. By contrast, from the International Labour Organization (ILO) data- labor force data from population censuses are often base to World Bank population estimates to create a based on a limited number of questions on the eco- series consistent with these population estimates. nomic characteristics of individuals, with little scope This procedure sometimes results in labor force to probe. The resulting data often differ from labor estimates that differ slightly from those in the ILO's force survey data and vary considerably by country, Yearbook of Labour Statistics and its database Key depending on the census scope and coverage. Estab- Indicators of the Labour Market. lishment censuses and surveys provide data only on Estimates of women in the labor force and employ- the employed population, not unemployed workers, ment are generally lower than those of men and are workers in small establishments, or workers in the not comparable internationally, reflecting that demo- informal sector (International Labour Organization, graphic, social, legal, and cultural trends and norms Key Indicators of the Labour Market 2001­2002). determine whether women's activities are regarded The reference period of a census or survey is as economic. In many countries many women work another important source of differences: in some on farms or in other family enterprises without pay, countries data refer to people's status on the day and others work in or near their homes, mixing work of the census or survey or during a specific period and family activities during the day. before the inquiry date, while in others data are recorded without reference to any period. In devel- 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. Differing definitions of employment age also affect Data sources comparability. For most countries the working age is 15 and older, but in some developing countries chil- Data on labor force participation rates are from dren younger than 15 work full- or part-time and are the ILO database Key Indicators of the Labour included in the estimates. Similarly, some countries Market, 5th edition. Labor force numbers were have an upper age limit. As a result, calculations may calculated by World Bank staff, applying labor systematically over- or underestimate actual rates. force participation rates from the ILO database For further information on source, reference period, to population estimates. or definition, consult the original source. 2008 World Development Indicators 47 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 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. 23 .. 11 .. 24 .. 25 .. 53 .. 64 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 0 b,c 2c 0 b,c 1c 40 c 33c 18 c 11c 59c 66c 81c 88 c Armenia .. .. .. .. .. .. .. .. .. .. .. .. Australia 6 5 4 3 32 31 12 9 61 65 84 88 Austria 6 6c 8 6c 47 40 c 20 13c 46 55c 72 81c Azerbaijan .. 41 .. 37 .. 15 .. 9 .. 44 .. 54 Bangladesh 54 50 85 59 16 12 9 18 25 38 2 23 Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 3c 2c 2c 2c 41c 35c 16c 11c 56c 62c 81c 86c Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 3c .. 1c .. 42c .. 17c .. 55c .. 82c .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 29 .. 13 .. 28 .. 17 .. 43 .. 71 Brazil 31c 25c 25c 16c 27c 27c 10 c 13c 43c 48 c 65c 71c Bulgaria .. 11 .. 7 .. 39 .. 29 .. 50 .. 64 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 53 .. 68 .. 14 .. 4 .. 26 .. 23 .. Canada 6c 4c 2c 2c 31c 32c 11c 11c 64 c 64 c 87c 88 c Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 24 17 6 6 32 29 15 12 45 54 79 83 China .. .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China 1 0b 0b 0b 37 22 27 7 63 77 73 93 Colombia 2 32b 1b,c 8b,c 35 21 25 16 63 48 74 76 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 32 21 5 5 27 26 25 13 41 52 69 82 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatia .. 16c .. 19c .. 37c .. 18 c .. 47c .. 63c Cuba .. 28 .. 10 .. 23 .. 14 .. 50 .. 76 Czech Republic 9 5 7 3 55 49 33 27 36 46 61 71 Denmark 7 4 3 2 37 34 16 12 56 62 81 86 Dominican Republic 26 21 3 3 23 26 21 15 52 53 76 82 Ecuador 10 c 11c 2c 4c 29c 27c 17c 12c 62c 62c 81c 84 c Egypt, Arab Rep. 35 28 52 39 25 23 10 6 41 49 37 55 El Salvador 48 c 28 15c 5 23c 25 23c 22 29c 45 63c 75 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 23 7 13 4 42 44 30 24 36 49 57 72 Ethiopia .. 84 c .. 76c .. 5c .. 8c .. 10 c .. 16c Finland 11 7 6 3 38 38 15 12 51 56 78 84 France .. 5 .. 2 .. 35 .. 12 .. 60 .. 85 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 52 .. 57 .. 14 .. 4 .. 34 .. 38 Germany 4 3 4 2 50 41 24 16 47 56 72 82 Ghana 66 .. 59 .. 10 .. 10 .. 23 .. 32 .. Greece 20 c 12c 26c 14 c 32c 30 c 17c 10 c 48 c 58 c 56c 76c Guatemala .. .. .. .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 48 2008 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 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Honduras 53 51 6 13 18 20 25 23 29 29 69 63 Hungary .. 7c .. 3c .. 42c .. 21c .. 51c .. 76c India .. .. .. .. .. .. .. .. .. .. .. .. Indonesia 54 43 57 41 15 20 13 15 31 37 31 44 Iran, Islamic Rep. .. 23 .. 34 .. 31 .. 28 .. 46 .. 37 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 19 9 3 1 33 39 18 12 48 51 78 86 Israel 5 3 2 1 38 31 15 11 57 65 83 88 Italy 8 5 9 3 37 39 22 18 55 56 70 79 Jamaica 36 25 16 9 25 27 12 5 39 48 72 86 Japan 6 4 7 5 40 35 27 18 54 59 65 77 Jordan .. 4 .. 2 .. 23 .. 12 .. 73 .. 84 Kazakhstan .. 33 .. 30 .. 25 .. 12 .. 42 .. 58 Kenya 19 c .. 20 c .. 23c .. 9c .. 58 c .. 71c .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 14 7 18 9 40 34 28 17 46 59 54 74 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 39 .. 39 .. 23 .. 11 .. 38 .. 50 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia .. 15c .. 8c .. 35c .. 16c .. 49c .. 75c Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 25 17c 15 11c 46 37c 31 21c 29 46c 54 68 c Macedonia, FYR .. 20 .. 19 .. 34 .. 30 .. 46 .. 51 Madagascar .. 77 .. 79 .. 7 .. 6 .. 16 .. 15 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23 16 20 11 31 35 32 27 46 49 48 62 Mali .. 50 .. 30 .. 18 .. 15 .. 32 .. 55 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 15 11 13 9 36 34 48 29 48 55 39 62 Mexico 34 21 11 5 25 30 19 19 41 49 70 76 Moldova .. 41 .. 40 .. 21 .. 12 .. 38 .. 48 Mongolia .. 43 .. 37 .. 19 .. 15 .. 38 .. 48 Morocco .. 38 .. 63 .. 22 .. 14 .. 40 .. 23 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 45 .. 52 .. 21 .. 8 .. 34 .. 40 .. Nepal 75 .. 91 .. 4 .. 1 .. 20 .. 8 .. Netherlands 5 4 3 2 33 30 10 8 60 62 82 86 New Zealand 13 9 8 5 31 32 13 11 56 59 80 84 Nicaragua .. 41 .. 10 .. 19 .. 17 .. 33 .. 52 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 7 5 3 2 34 32 10 8 58 63 86 90 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 45 38 69 67 20 21 15 15 35 41 16 18 Panama 35 22 3 4 20 22 11 9 45 56 85 86 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 3c 39c 0 b,c 20 c 33c 19c 19c 10 c 64 c 42c 80 c 70 c Peru 1c 1c 0 b,c 0 b,c 30 c 31c 13c 13c 69c 68 c 87c 86c Philippines 53c 45 32c 25 17c 17 14 c 12 29c 39 55c 64 Poland .. 18 c .. 17c .. 39c .. 17c .. 43c .. 66c Portugal 10 c 11c 13c 13c 39c 41c 24 c 19c 51c 48 c 63c 68 c Puerto Rico 5 3 0b 0b 27 25 19 11 67 72 80 89 2008 World Development Indicators 49 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 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a 1990­92a 2003­06a Romania 29 c 31 38 c 33 44 c 35 30 c 25 28 c 34 33c 42 Russian Federation .. 12 .. 8 .. 38 .. 21 .. 50 .. 71 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 5 .. 0b .. 11 .. 1 .. 85 .. 99 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 1 0 0b 0 36 36 32 21 63 63 68 79 Slovak Republic .. 6c .. 3c .. 50 c .. 25c .. 44 c .. 72c Slovenia .. 9 .. 9 .. 47 .. 25 .. 43 .. 65 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 13 .. 7 .. 33 .. 14 .. 54 .. 79 Spain 11c 6c 8c 4c 41c 41c 16c 12c 49c 52c 76c 84 c Sri Lanka .. .. .. .. .. .. .. .. .. .. .. .. Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5c 3c 2c 1c 40 c 34 c 12c 9c 55c 63c 86c 90 c Switzerland 4c 5c 4c 3c 37c 32c 15c 11c 59c 63c 81c 86c Syrian Arab Republic 23 23 54 49 28 29 8 8 49 48 38 43 Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 78 c .. 90 c .. 7c .. 1c .. 15c .. 8c .. Thailand 60 44 62 41 18 22 13 19 22 34 25 41 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 15 6 6 2 34 41 14 16 51 52 80 82 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 33 22 72 52 26 28 11 15 41 50 17 33 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 91 60c 91 77c 4 11c 6 5c 5 29c 3 18 c Ukraine .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 3 2 1 1 41 33 16 9 55 65 82 90 United States 4 2 1 1 34 30 14 10 62 68 85 90 Uruguay 7c 7c 1c 2c 36c 29c 21c 13c 57c 64 c 78 c 86c Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 17 16c 2 2c 32 25c 16 11c 52 59c 82 86c Vietnam .. 56 .. 60 .. 21 .. 14 .. 23 .. 26 West Bank and Gaza .. 12 .. 34 .. 28 .. 8 .. 59 .. 56 Yemen, Rep. 44 .. 83 .. 14 .. 2 .. 38 .. 13 .. Zambia .. .. .. .. .. .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. .. .. .. .. .. .. .. .. .. .. .. Lower middle income .. .. .. .. .. .. .. .. .. .. .. .. Upper middle income .. 20 .. 14 .. 31 .. 17 .. 49 .. 68 Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific .. .. .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 19 .. 18 .. 34 .. 19 .. 47 .. 62 Latin America & Carib. 20 21 14 10 30 27 14 15 50 52 72 76 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. .. .. South Asia .. .. .. .. .. .. .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 6 4 5 3 38 34 19 13 56 62 76 85 Euro area 7 5 7 3 42 38 20 14 50 56 72 82 Note: Data across sectors may not sum to 100 percent because of workers not classified by sectors. a. Data are for the most recent year available. b. Less than 0.5. c. Limited coverage. 50 2008 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 database by major divisions of the ISIC revision 2 or revision 3. Key Indicators of the Labour Market, 5th edition. In the table the reported divisions or categories are 2008 World Development Indicators 51 2.4 Decent work and productive employment Employment to Vulnerable Labor population ratio employment productivity Unpaid family workers and own-account workers GDP per person employed Male Female Index % ages 15 and older % ages 15­24 % of male employment % of female employment 1990 PPP $a 1980 = 100 1991 2006 1991 2006 1990 2005 1990 2005 1990 2006 1990 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 62 51 45 37 .. .. .. .. 2,499 3,502 107 149 Algeria 37 50 22 33 .. 29 .. 32 2,946 3,401 94 108 Angola 74 75 64 66 .. .. .. .. 869 1,143 90 119 Argentina 54 60 45 46 .. 23b .. 19b 6,436 8,915 78 109 Armenia 68 49 51 19 .. .. .. .. 6,066 8,428 .. .. Australia 57 60 56 63 12 12 8 7 17,106 24,603 119 171 Austria 54 55 61 50 .. 9 .. 8 16,895 22,708 123 165 Azerbaijan 59 61 39 41 .. .. .. .. 4,639 5,954 .. .. Bangladesh 73 67 64 57 .. 60 .. 73 640 1,014 117 185 Belarus 59 52 40 36 .. .. .. .. 7,184 9,491 .. .. Belgium 46 48 32 28 .. 11 .. 10 17,197 22,582 119 156 Benin 67 64 55 49 .. .. .. .. .. .. .. .. Bolivia 61 70 44 51 32b .. 50 b .. 2,197 2,764 85 107 Bosnia and Herzegovina 58 55 37 37 .. .. .. .. 3,737 6,469 .. .. Botswana 57 44 38 21 .. 7 .. 17 .. .. .. .. Brazil 60 61 54 49 29b 34b 30 b 32b 4,923 5,812 95 112 Bulgaria 50 41 31 20 .. 11 .. 9 5,597 7,780 93 129 Burkina Faso 81 82 74 73 .. .. .. .. 810 1,135 111 155 Burundi 83 84 67 71 .. .. .. .. .. .. .. .. Cambodia 79 76 69 63 .. .. .. .. 880 1,827 106 220 Cameroon 63 61 48 44 .. .. .. .. 1,222 1,155 102 97 Canada 59 62 57 59 .. .. .. .. 18,872 24,633 117 152 Central African Republic 73 72 56 57 .. .. .. .. .. .. .. .. Chad 66 65 44 45 .. .. .. .. .. .. .. .. Chile 51 49 34 22 .. 29 .. 24 6,402 12,207 113 215 China 76 73 73 65 .. .. .. .. 1,871 6,352 176 599 Hong Kong, China 63 58 54 39 .. 10 .. 5 17,541 27,769 167 264 Colombia 54 63 41 46 30 b 44 26 b 44 4,840 5,767 114 135 Congo, Dem. Rep. 67 68 56 58 .. .. .. .. 510 224 85 38 Congo, Rep. 66 66 49 48 .. .. .. .. .. .. .. .. Costa Rica 55 60 48 44 26 20 20 23 4,747 7,321 97 149 Côte d'Ivoire 62 58 47 45 .. .. .. .. 1,363 1,310 65 63 Croatia 52 45 34 27 .. 19 .. 21 7,351 8,326 .. .. Cuba 54 58 39 37 .. .. .. .. 2,948 3,008 112 114 Czech Republic 62 55 51 30 .. 15 .. 8 8,895 11,688 .. .. Denmark 62 61 65 61 .. .. .. .. 18,452 24,816 121 163 Dominican Republic 49 53 32 32 42 49 30 31 2,473 4,344 104 183 Ecuador 55 66 43 48 33b 30 b 41b 39b 3,903 4,831 95 117 Egypt, Arab Rep. 43 42 22 20 .. 21 .. 46 2,522 3,386 122 164 El Salvador 58 57 41 37 .. 29 .. 45 .. .. .. .. Eritrea 68 66 60 56 .. .. .. .. .. .. .. .. Estonia 68 54 51 29 2 7 3 4 10,820 20,795 .. .. Ethiopia 77 76 74 71 .. 89 .. 93 578 702 89 108 Finland 59 56 45 43 .. .. .. .. 16,866 23,358 130 180 France 50 49 28 23 .. 8 .. 5 18,093 22,402 120 148 Gabon 60 59 42 39 .. .. .. .. .. .. .. .. Gambia, The 68 66 52 51 .. .. .. .. .. .. .. .. Georgia 60 53 37 24 .. 64 .. 65 7,616 4,721 .. .. Germany 56 52 58 41 .. 7 .. 6 16,306 20,018 .. .. Ghana 72 66 51 42 .. .. .. .. 1,063 1,485 92 128 Greece 46 50 31 28 .. 29 .. 28 10,015 15,440 112 172 Guatemala 58 55 52 49 .. .. .. .. 3,631 4,554 83 104 Guinea 82 81 72 70 .. .. .. .. .. .. .. .. Guinea-Bissau 67 69 56 60 .. .. .. .. .. .. .. .. Haiti 60 65 39 50 .. .. .. .. .. .. .. .. 52 2008 World Development Indicators 2.4 PEOPLE Decent work and productive employment Employment to Vulnerable Labor population ratio employment productivity Unpaid family workers and own-account workers GDP per person employed Male Female Index % ages 15 and older % ages 15­24 % of male employment % of female employment 1990 PPP $a 1980 = 100 1991 2006 1991 2006 1990 2005 1990 2005 1990 2006 1990 2006 Honduras 57 69 48 60 48b 48b 50 b 51b .. .. .. .. Hungary 49 46 39 24 8 9 7 6 6,459 9,291 102 147 India 59 56 46 40 .. .. .. .. 1,309 2,611 140 278 Indonesia 63 61 45 37 .. .. .. .. 2,526 4,126 135 220 Iran, Islamic Rep. 46 51 33 34 .. .. .. .. 3,503 5,786 89 146 Iraq 33 .. 20 .. .. .. .. .. 2,458 .. 39 .. Ireland 45 60 38 48 25 17 9 5 11,818 27,768 138 325 Israel 46 50 24 25 .. 9 .. 5 12,968 17,548 118 160 Italy 44 46 30 26 .. 15 .. 11 16,313 19,653 124 150 Jamaica 61 57 39 30 46 37 37 31 3,786 3,751 121 120 Japan 63 58 43 41 15 11 26 14 18,789 22,461 140 167 Jordan 39 47 26 31 .. .. .. .. 3,792 4,591 85 103 Kazakhstan 63 65 45 44 .. 33 .. 39 7,458 8,954 .. .. Kenya 64 63 44 43 .. .. .. .. 1,117 1,060 106 101 Korea, Dem. Rep. 64 60 49 33 .. .. .. .. .. .. .. .. Korea, Rep. 59 60 36 34 .. 24 .. 29 8,704 18,086 212 440 Kuwait 65 71 34 38 .. .. .. .. 6,121 11,806 46 89 Kyrgyz Republic 59 59 41 41 .. 50 .. 50 3,602 2,464 .. .. Lao PDR 65 66 53 54 .. .. .. .. .. .. .. .. Latvia 61 51 46 31 .. 9 .. 7 9,916 13,514 .. .. Lebanon 47 51 32 32 .. .. .. .. .. .. .. .. Lesotho 54 37 40 25 .. .. .. .. .. .. .. .. Liberia 63 63 48 47 .. .. .. .. .. .. .. .. Libya 47 54 30 33 .. .. .. .. .. .. .. .. Lithuania 55 53 35 24 .. .. .. .. 8,663 10,309 .. .. Macedonia, FYR 40 33 20 13 .. 23 .. 21 3,972 3,538 .. .. Madagascar 77 78 61 63 .. 79 .. 86 799 675 76 64 Malawi 80 80 66 66 .. .. .. .. 554 620 86 96 Malaysia 61 62 47 44 .. 20 .. 21 5,132 9,782 140 268 Mali 75 70 67 58 .. .. .. .. 747 1,026 102 140 Mauritania 64 64 49 48 .. .. .. .. .. .. .. .. Mauritius 53 55 39 35 .. 18 .. 15 .. .. .. .. Mexico 57 57 50 40 37 30 36 34 6,085 7,816 96 124 Moldova 58 56 36 36 .. 37 .. 36 6,165 3,057 .. .. Mongolia 50 59 39 44 .. 62 .. 57 .. .. .. .. Morocco 46 47 39 36 .. 54 .. 67 2,596 2,998 114 132 Mozambique 80 77 62 55 .. .. .. .. 1,115 1,783 91 146 Myanmar 75 75 63 58 .. .. .. .. 778 2,387 95 291 Namibia 46 38 23 18 .. .. .. .. .. .. .. .. Nepal 59 58 48 44 .. .. .. .. .. .. .. .. Netherlands 53 61 55 69 .. .. .. .. 17,262 23,385 117 159 New Zealand 57 65 54 58 15 15 10 9 13,909 18,306 113 148 Nicaragua 56 56 45 44 .. .. .. .. .. .. .. .. Niger 78 79 68 71 .. .. .. .. 540 514 67 64 Nigeria 60 59 44 43 .. .. .. .. 1,214 1,329 85 93 Norway 60 66 49 60 .. .. .. .. 18,466 28,044 123 186 Oman 52 52 28 28 .. .. .. .. 6,479 7,528 159 185 Pakistan 54 55 44 44 .. 60 .. 69 1,589 2,278 137 196 Panama 50 59 34 36 44 35 19 26 .. .. .. .. Papua New Guinea 71 71 58 58 .. .. .. .. .. .. .. .. Paraguay 62 69 51 58 17b 50 b 31b 52b .. .. .. .. Peru 56 64 40 43 30 b 34b 45b 39b 3,021 4,272 71 100 Philippines 59 64 42 44 .. 43 .. 48 2,224 2,734 94 115 Poland 55 46 35 22 .. 23 .. 20 5,113 8,999 89 157 Portugal 59 58 53 38 18b 18 21b 20 10,826 14,174 135 176 Puerto Rico 38 43 21 30 .. .. .. .. 10,539 15,026 129 184 2008 World Development Indicators 53 2.4 Decent work and productive employment Employment to Vulnerable Labor population ratio employment productivity Unpaid family workers and own-account workers GDP per person employed Male Female Index % ages 15 and older % ages 15­24 % of male employment % of female employment 1990 PPP $a 1980 = 100 1991 2006 1991 2006 1990 2005 1990 2005 1990 2006 1990 2006 Romania 58 52 47 22 7b 33 10 b 34 3,511 4,305 85 104 Russian Federation 58 56 36 33 1 6 1 6 7,779 7,297 .. .. Rwanda 79 73 63 58 .. .. .. .. .. .. .. .. Saudi Arabia 51 51 26 25 .. .. .. .. 8,993 8,691 68 66 Senegal 67 62 55 47 77 .. 91 .. 1,279 1,433 101 113 Serbia 49c 51c 28 c 33c .. .. .. .. 5,160 c 2,935c .. .. Sierra Leone 64 68 51 60 .. .. .. .. .. .. .. .. Singapore 64 60 56 41 10 12 6 6 14,220 24,688 157 273 Slovak Republic 56 52 41 30 .. 13b .. 5b 7,763 11,057 .. .. Slovenia 55 57 37 33 .. 12 .. 10 10,860 16,136 .. .. Somalia 70 69 64 63 .. .. .. .. .. .. .. .. South Africa 48 45 31 27 .. 18 .. 20 3,842 4,821 88 110 Spain 43 51 37 36 20 14 24 11 12,055 17,110 131 186 Sri Lanka 52 52 32 37 .. 39 b .. 39 b 2,448 4,193 132 227 Sudan 47 43 33 26 .. .. .. .. 743 947 80 102 Swaziland 42 39 26 22 .. .. .. .. .. .. .. .. Sweden 65 59 59 44 .. .. .. .. 17,609 23,831 118 160 Switzerland 65 65 68 63 8 9 11 10 21,487 23,475 114 125 Syrian Arab Republic 51 56 40 43 .. .. .. .. 5,701 7,015 88 108 Tajikistan 54 48 37 28 .. .. .. .. 2,979 1,318 .. .. Tanzania 87 84 77 72 .. .. .. .. 551 690 92 115 Thailand 77 72 70 46 67 51 74 55 4,633 7,888 181 309 Timor-Leste 62 67 46 57 .. .. .. .. .. .. .. .. Togo 65 63 53 51 .. .. .. .. .. .. .. .. Trinidad and Tobago 48 58 33 46 22 17 21 13 9,272 23,233 75 188 Tunisia 41 45 29 29 .. .. .. .. 3,337 5,362 113 182 Turkey 53 47 48 39 .. 36 .. 55 5,445 8,080 136 201 Turkmenistan 58 60 36 37 .. .. .. .. 3,626 2,609 .. .. Uganda 83 81 74 71 .. 77b .. 92b 598 889 104 154 Ukraine 60 52 43 34 .. .. .. .. 6,027 4,154 .. .. United Arab Emirates 72 76 43 47 .. .. .. .. 13,070 22,700 47 82 United Kingdom 58 59 66 59 .. .. .. .. 16,430 22,967 127 178 United States 61 63 56 55 .. .. .. .. 23,201 31,245 125 168 Uruguay 55 62 49 50 .. 27b .. 22b 6,474 8,313 98 126 Uzbekistan 56 58 36 37 .. .. .. .. 4,241 4,202 .. .. Venezuela, RB 55 60 38 41 .. 33 .. 40 8,313 8,815 82 87 Vietnam 75 73 75 66 .. 70 .. 79 1,025 2,458 135 325 West Bank and Gaza 29 28 18 15 .. 37 .. 43 .. .. .. .. Yemen, Rep. 44 47 32 32 .. .. .. .. 2,272 2,861 99 125 Zambia 63 70 48 61 56 .. 81 .. 810 719 89 79 Zimbabwe 71 70 50 52 .. .. .. .. 1,356 910 105 70 World 63 w 62 w 53 w 47 w .. w .. w .. w .. w 5,408 w 7,629 w 106 m 146 m Low income 63 61 51 47 .. .. .. .. 1,175 1,937 95 115 Middle income 66 64 57 48 .. .. .. .. 3,208 5,775 97 120 Lower middle income 69 67 61 52 .. .. .. .. 2,353 5,348 103 120 Upper middle income 57 55 44 38 .. 26 .. 24 6,099 7,245 96 123 Low & middle income 65 63 54 48 .. .. .. .. 2,507 4,356 96 120 East Asia & Pacific 74 71 68 58 .. .. .. .. 2,006 6,352 135 279 Europe & Central Asia 57 53 40 33 .. 19 .. 17 6,359 6,704 .. .. Latin America & Carib. 57 60 47 45 .. 33 .. 34 5,186 6,452 96 117 Middle East & N. Africa 43 46 29 30 .. .. .. .. 3,110 4,253 96 125 South Asia 60 57 47 43 .. .. .. .. 1,266 2,611 135 212 Sub-Saharan Africa 67 66 54 52 .. .. .. .. 1,061 1,192 90 102 High income 57 57 47 45 .. .. .. .. 18,145 24,534 123 167 Euro area 50 51 41 35 .. 13 .. 10 15,772 20,101 123 169 a. Based on extrapolated PPPs from the 1993 ICP. b. Limited coverage. c. Includes Montenegro. 54 2008 World Development Indicators 2.4 PEOPLE Decent work and productive employment About the data Definitions At the 2005 World Summit four targets were added within a country. Information from labor force surveys · Employment to population ratio is the proportion to the UN Millennium Declaration. One was full and is not always consistent in terms of what is included of a country's population that is employed. Ages 15 productive employment and decent work for all, in employment. For example, information provided and older are generally considered the working-age which is seen as the main route for people to escape by the Organisation for Economic Co-operation and population. Ages 15­24 are generally considered poverty. The four indicators for this target have an Development relates only to civilian employment, the youth population. · Vulnerable employment is economic focus, and three of them are presented which can result in an underestimation of "employ- unpaid family workers and own-account workers as in the table. ees" and "workers not classified by status," espe- a percentage of total employment · Labor productiv- The employment to population ratio indicates how cially in countries with large armed forces. While ity is gross domestic product (GDP) divided by total efficiently an economy provides jobs for people who the categories of unpaid family workers and self- employment in the economy. Purchasing power parity want to work. A high ratio means that a large propor- employed workers, which include own-account work- (PPP) GDP is GDP converted to 1990 constant inter- tion of the population is employed. But this indicator ers, would not be affected, their relative shares would national dollars using PPP rates. An international dol- has a gender bias because women who do not con- be. Geographic coverage is another factor that can lar has the same purchasing power over GDP that a sider their work employment or who are not perceived limit cross-country comparisons. The employment by U.S. dollar has in the United States. as working tend to be undercounted. This bias has status data for most Latin American countries covers different effects across countries. urban areas only. Similarly, in some countries in Sub- Comparability of employment ratios across coun- Saharan Africa, where limited information is available tries is also affected by variations in definitions of anyway, the members of producer cooperatives are employment and population (see About the data for usually excluded from the self-employed category. table 2.3). The biggest difference results from the For detailed information on definitions and coverage, age range used to define labor force activity. The consult the original source. population base for employment ratios can also Labor productivity, measured as output per per- vary (see table 2.1). Most countries use the resi- son employed, can be used to assess a country's dent, noninstitutionalized population of working age economic ability to create and sustain decent living in private households, excluding members of employment opportunities with fair and equitable the armed forces and individuals residing in mental, remuneration. For comparability of individual sectors penal, or other types of institutions. But some coun- labor productivity is estimated according to national tries include members of the armed forces in the accounts conventions. However, there are still signifi - population base of their employment ratio while still cant limitations on the availability of reliable data, as excluding them from employment data (International the information on consistent series of output in both Labour Organization, Key Indicators of the Labour national currencies and purchasing power parity U.S. Market, 5th edition). dollars is not easily available, especially in devel- The proportion of unpaid family workers and own- oping countries, because the definition, coverage, account workers in total employment is derived from and methodology are not always consistent across information on status in employment. Each status countries. For example, countries employ different group faces different economic risks, and unpaid methodologies for estimating the missing values family workers and own-account workers are the for the nonmarket service sectors and use different most vulnerable--and therefore the most likely to definitions of the informal sector (see About the data fall into poverty. They are the least likely to have for- for tables 4.1 and 4.14). mal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of gen- erating sufficient savings to offset these shocks. A high proportion of unpaid family workers in a country indicates weak development, little job growth, and often a large rural economy. Data on employment by status are drawn from labor Data sources force surveys and household surveys, supplemented by official estimates and censuses for a small group Data on decent work and productive employment of countries. The labor force survey is the most are from the International Labour Organization comprehensive source for international comparable database Key Indicators of the Labour Market, employment, but there are still some limitations for 5th edition. comparing data across countries and over time even 2008 World Development Indicators 55 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 2003­05a 1990­92a 2003­05a 1990­92a 2003­05a 2000­05a 2000­05a 2000­05a 2003­05a 2003­05a 2003­05a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 14.4 .. 12.4 .. 17.5 .. .. .. 98.3 .. 1.7 Algeria .. 15.3 .. 14.9 .. 17.5 .. .. .. 59.3 23.0 11.4 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 6.6b 10.2b 6.8b 9.2b 6.3b 12.5b .. .. .. 40.3b 39.8b 18.4b Armenia .. .. .. .. .. .. 71.6b 72.2b 70.8b 6.2 79.8 14.0 Australia 10.8 5.1 11.4 4.9 10.0 5.3 17.7b 20.2b 14.9b 51.4 29.1 19.3 Austria 3.6 5.2 3.5 4.9 3.8 5.5 25.3 25.6 24.9 35.2b 55.0 b 9.6b Azerbaijan .. 8.6 .. 7.6 .. 9.5 .. .. .. 4.4 30.2 65.4 Bangladesh 1.9 4.3 2.0 4.2 1.9 4.9 .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. 10.2 40.6 49.1 Belgium 6.7 8.1 4.8 7.4 9.5 9.0 51.6 50.4 52.7 42.1 38.4 19.6 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 5.5b .. 5.5b .. 5.6b .. .. .. .. .. .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 14.2 23.8 12.1 21.4 17.5 26.3 .. .. .. 65.5 27.3 .. Brazil 6.4b 8.9b 5.4b 6.8b 7.9b 11.7b .. .. .. 53.4b 30.4b 3.0 b Bulgaria .. 10.1 .. 10.3 .. 9.9 .. .. .. 38.6 51.0 10.3 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 11.2b 6.8b 12.0 b 7.0 b 10.2b 6.5b 9.6b 10.1b 9.1b 27.1b 31.2b 41.7b Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 4.4 6.9 3.9 6.1 5.3 8.5 .. .. .. 16.1 58.9 24.5 China 2.3b 4.2b .. .. .. .. .. .. .. .. .. .. Hong Kong, China 2.0 5.6 2.0 6.5 1.9 4.4 .. .. .. 46.3b 39.7b 12.6b Colombia 9.4b 9.5 6.7b 7.4 13.0 b 12.3 .. .. .. 58.4 .. 15.6 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 4.0 6.6 3.4 5.0 5.4 9.6 10.9 8.9 13.3 64.0 20.5 12.0 Côte d'Ivoire 6.7 .. .. .. .. .. .. .. .. .. .. .. Croatia .. 11.2c .. 10.1c .. 13.2c 53.7c 52.7c 54.5c 22.0 c 69.1c 9.8 c Cuba .. 1.9 .. 1.7 .. 2.2 .. .. .. 50.6 44.7 4.7 Czech Republic .. 7.9 .. 6.5 .. 9.8 53.6 52.9 54.2 24.1 72.0 4.1 Denmark 9.0 4.8 8.3 4.1 9.9 5.6 25.9 29.7 22.7 27.7 44.8 27.5 Dominican Republic 20.7 17.9 12.0 11.3 35.2 28.8 1.6 2.2 1.3 .. .. .. Ecuador 8.9b 7.7b 6.0 b 5.6b 13.2b 10.8b .. .. .. 76.0 b .. 22.5b Egypt, Arab Rep. 9.1 10.7 6.5 6.8 17.3 24.4 .. .. .. .. .. .. El Salvador 7.9b 6.6 8.4b 8.5 7.2b 3.9 .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 3.7 7.9 3.9 8.8 3.5 7.1 .. .. .. 15.7 64.4 19.9 Ethiopia .. 5.4 .. 2.7 .. 8.2 24.4 24.3 24.4 35.9 13.3 3.2 Finland 11.7 8.4 13.6 8.2 9.7 8.7 24.9 27.9 21.9 35.5 46.8 17.7 France 10.0 b 9.8b 7.9b 9.0 b 12.7b 10.8b 42.5 41.8 43.2 40.6 39.4 18.7 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia .. 13.8 .. 14.8 .. 12.7 .. .. .. 4.8 56.0 38.8 Germany 6.6 11.1 5.3 11.3 8.4 10.9 54.0 53.8 54.4 27.1 60.5 12.4 Ghana .. .. .. .. .. .. .. .. .. .. .. .. Greece 7.8 9.6 4.9 5.8 12.9 15.2 53.7 43.1 59.6 30.8 49.7 19.1 Guatemala 3.2b 3.4 2.6b 2.5 4.6b 4.9 .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 12.2 .. 11.2 .. 13.6 .. .. .. .. .. .. .. 56 2008 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 2003­05a 1990­92a 2003­05a 1990­92a 2003­05a 2000­05a 2000­05a 2000­05a 2003­05a 2003­05a 2003­05a Honduras 3.2b 4.2b 3.3b 3.2b 3.0 b 6.2b .. .. .. .. .. .. Hungary 9.9 7.2 11.0 7.0 8.7 7.5 46.1 47.9 44.2 30.2 62.2 7.6 India .. 5.0 b .. 4.9b .. 5.3b .. .. .. 27.0 41.1 31.9 Indonesia 2.8 10.3c 2.7 8.5c 3.0 13.4 c .. .. .. 48.7c 38.0 c 6.2c Iran, Islamic Rep. 11.1 11.5 9.5 10.1 24.4 17.1 .. .. .. 41.8 34.7 19.6 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 15.2 4.3 15.2 4.6 15.2 3.8 34.3 42.4 21.1 45.0 25.6 26.1 Israel 11.2b 9.0 b 9.2b 8.5b 13.9 b 9.5b .. .. .. 20.6 48.7 25.9 Italy 11.5 7.7 8.1 6.2 17.3 10.1 52.2 50.5 53.8 48.1 39.4 10.7 Jamaica 15.7 10.9 9.5 7.4 22.8 15.3 31.7 24.4 36.2 12.9 4.2 9.2 Japan 2.2 4.4 2.1 4.6 2.2 4.2 33.3 40.3 22.6 67.7 .. 29.9 Jordan .. 12.4 .. 11.8 .. 16.5 .. .. .. .. .. .. Kazakhstan .. 7.8 c .. 6.4 c .. 9.2c .. .. .. 7.1c 49.0 c 43.9c Kenya .. .. .. .. .. .. .. .. .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2.5 3.7 2.8 4.0 2.1 3.4 0.8 1.0 0.4 17.4 53.2 29.4 Kuwait .. 1.7 .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic .. 8.5 .. 8.0 .. 9.3 .. .. .. 9.9 79.5 10.7 Lao PDR .. 1.4 .. 1.3 .. 1.4 .. .. .. .. .. .. Latvia .. 8.7 .. 9.0 .. 8.4 .. .. .. 23.6 65.6 10.7 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 8.3 .. 8.2 .. 8.3 57.8 .. .. 16.4 69.5 14.1 Macedonia, FYR .. 37.3 .. 36.5 .. 38.4 .. .. .. .. .. .. Madagascar .. 5.0 .. 3.8 .. 6.2 .. .. .. 61.5 .. 6.1 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 3.7 3.5 .. 3.6 .. 3.6 .. .. .. 32.0 48.8 15.6 Mali .. 8.8 .. 7.2 .. 10.9 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 3.1 9.6 3.2 5.8 3.1 16.5 .. .. .. 48.6 44.9 5.4 Mexico 3.1 3.5 2.7 3.4 4.0 3.6 2.4b 2.3b 2.6b 51.7 24.4 21.5 Moldova .. 7.3 .. 8.7 .. 6.0 .. .. .. .. .. .. Mongolia .. 14.2 .. 14.3 .. 14.1 .. .. .. 35.1 45.8 18.5 Morocco 16.0 b 9.7c 13.0 b 9.7c 25.3b 9.7c .. .. .. 51.1b 22.4b 21.6b Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.0 .. 4.7 .. 8.8 .. .. .. .. .. .. .. Namibia 19.1 .. 19.6 .. 18.6 .. .. .. .. .. .. .. Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 5.5 5.2 4.3 4.9 7.3 5.6 40.1 44.7 35.0 40.7 39.1 17.9 New Zealand 10.4b 3.7b 11.0 b 3.4b 9.6b 4.0 b 9.4b 12.6b 6.2b 0.0 52.7 14.4 Nicaragua 14.4 8.0 11.3 7.9 19.4 8.1 .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway 5.9 4.6 6.6 4.8 5.1 4.4 9.5 10.4 8.5 24.3 54.1 18.9 Oman .. .. .. .. .. .. .. .. .. .. .. .. Pakistan 5.2 7.7 3.8 6.6 14.0 12.8 .. .. .. 13.1 12.3 29.1 Panama 14.7 10.3 10.8 8.1 22.3 14.0 29.3 24.0 35.7 31.7 38.4 29.1 Papua New Guinea 7.7 .. 9.0 .. 5.9 .. .. .. .. .. .. .. Paraguay 5.0 b 7.9b 6.0 b 6.6b 3.7b 10.0 b .. .. .. .. .. .. Peru 9.4b 11.4b 7.5b 9.7b 12.5b 13.7b .. .. .. 69.6b .. 30.0 b Philippines 8.6 7.4 7.9 7.4 9.9 7.3 .. .. .. 15.2 45.2 38.9 Poland 13.3 17.7 12.2 16.6 14.7 19.1 52.2 51.3 53.1 17.7 74.8 7.6 Portugal 4.1b 7.6 3.5b 6.7 5.0 b 8.7 48.6 47.1 49.9 70.2 15.3 10.9 Puerto Rico 17.0 11.3 19.3 12.2 13.3 10.2 .. .. .. .. .. .. 2008 World Development Indicators 57 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 2003­05a 1990­92a 2003­05a 1990­92a 2003­05a 2000­05a 2000­05a 2000­05a 2003­05a 2003­05a 2003­05a Romania .. 7.2 .. 7.7 .. 6.4 .. .. .. 23.1 69.1 6.6 Russian Federation 5.3 7.9 5.4 7.8 5.2 8.0 .. .. .. .. .. .. Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia .. 6.2 .. 4.7 .. 14.7 .. .. .. 12.3 43.9 40.0 Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia .. 15.2d .. 14.4 d .. 16.4 d .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 2.7 4.2 2.7 3.7 2.6 5.0 .. .. .. 20.2 25.7 59.2 Slovak Republic .. 16.2 .. 15.4 .. 17.2 68.1 68.7 67.4 27.1b 68.3b 4.5b Slovenia .. 5.8 .. 5.5 .. 6.0 .. .. .. 22.4 69.0 8.6 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 26.7 .. 26.8 .. 26.6 .. .. .. 50.2 41.0 5.1 Spain 18.1 9.2 13.9 7.0 25.8 12.2 32.6 28.2 36.0 53.9 22.1 23.1 Sri Lanka 13.3b 7.6b 10.1b 5.5b 19.9b 11.9 b .. .. .. 41.7b .. 58.3b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 5.7 7.7 6.7 7.8 4.6 7.6 18.9 20.9 16.4 25.9 54.4 17.8 Switzerland 2.8 4.4 2.3 3.9 3.5 5.1 38.8 37.1 40.4 28.6 53.5 17.3 Syrian Arab Republic .. 12.3 .. 9.0 .. 28.3 .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 3.6b .. 2.8b .. 4.3b .. .. .. .. .. .. .. Thailand 1.4 1.3 1.3 1.5 1.5 1.2 .. .. .. 39.7 46.3 0.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 19.6 8.0 17.0 5.8 23.9 11.0 27.6 20.3 34.7 .. .. .. Tunisia .. 14.2 .. 13.1 .. 17.3 .. .. .. 79.1 .. 13.6 Turkey 8.5 10.3 8.8 10.3 7.8 10.3 39.6 36.9 47.4 54.3 28.1 11.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 3.2 .. 2.5 .. 3.9 .. .. .. .. .. .. Ukraine .. 7.2 .. 7.5 .. 6.8 .. .. .. 10.9 53.2 35.8 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 9.7 4.6 11.5 5.0 7.3 4.1 22.4 26.2 16.9 36.7 46.1 16.2 United States 7.5b 5.1b 7.9b 5.1b 7.0 b 5.1b 11.8b 12.6b 10.8b 19.1b 35.5b 45.4b Uruguay 9.0 b 12.2b 6.8b 9.5b 11.8b 15.3b .. .. .. .. .. .. Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 7.7 15.0 8.2 13.4 6.8 17.6 .. .. .. .. .. .. Vietnam .. 2.1 .. 1.9 .. 2.4 .. .. .. .. .. .. West Bank and Gaza .. 26.8 .. 28.1 .. 20.1 .. .. .. 58.5 13.1 18.9 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 18.9 .. 16.3 .. 22.4 .. .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World .. w 6.7 w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income 3.9 6.4 .. .. .. .. .. .. .. .. .. .. Lower middle income 3.2 5.7 .. .. .. .. .. .. .. .. .. .. Upper middle income 6.3 9.8 6.0 9.0 7.0 11.4 .. .. .. 44.0 41.2 8.7 Low & middle income .. 6.8 .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 2.5 4.9 .. .. .. .. .. .. .. .. .. .. Europe & Central Asia .. 10.0 .. 10.0 .. 9.9 .. .. .. .. .. .. Latin America & Carib. 6.7 8.9 5.5 7.1 8.4 11.5 .. .. .. 56.6 31.9 12.7 Middle East & N. Africa .. 13.8 .. 12.8 .. 18.7 .. .. .. .. .. .. South Asia .. 5.3 .. 5.1 .. 6.3 .. .. .. .. .. .. Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 7.4 6.2 7.0 6.0 7.9 6.6 26.4 28.0 24.0 36.3 38.1 29.1 Euro area 9.5 9.0 7.5 8.1 12.5 10.3 45.8 44.6 46.5 45.8 35.5 17.2 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2006. d. Includes Montenegro and excludes Kosovo and Metohija. 58 2008 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 are may also refl ect changes in reporting practices. working part-time or in temporary jobs in the informal Paradoxically, low unemployment rates can disguise sector, despite the instability of these jobs or their substantial poverty in a country, while high unemploy- active search for more secure employment. ment rates can occur in countries with a high level Long-term unemployment is measured by the of economic development and low rates of poverty. length of time that an unemployed person has been In countries without unemployment or welfare ben- without work and looking for a job. The data in the efits people eke out a living in the informal sector. table are from labor force surveys. The underlying In 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 2.10. 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 database that follows the international recommendations more Key Indicators of the Labour Market, 5th edition. closely than that used by other sources and therefore 2008 World Development Indicators 59 2.6 Children at work Survey Economically active children Employment by economic activitya year % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Work Agriculture Manufacturing Services Study Total Male Female only and work Male Female Male Female Male Female Afghanistan .. .. .. .. .. .. .. .. .. .. .. Albania 2000 36.6 41.1 31.8 43.1 56.9 .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. Angolab 2001 30.1 30.0 30.1 26.6 73.4 .. .. .. .. .. .. Argentina 2004 15.1 18.0 12.0 4.1 95.9 .. .. .. .. .. .. Armenia .. .. .. .. .. .. .. .. .. .. .. Australia .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 2000 9.7 12.0 7.3 4.2 95.8 .. .. .. .. .. .. Bangladesh 2003 17.5 20.9 13.9 63.3 36.7 61.4 64.0 11.6 15.5 25.2 18.3 Belarus .. .. .. .. .. .. .. .. .. .. .. Belgium .. .. .. .. .. .. .. .. .. .. .. Benin .. .. .. .. .. .. .. .. .. .. .. Bolivia 2002 23.2 24.0 22.5 15.2 84.8 78.8 73.4 4.5 3.8 15.5 22.6 Bosnia and Herzegovina 2000 20.2 22.8 17.6 4.0 96.0 .. .. .. .. .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. Brazil 2004 7.0 9.4 4.6 7.2 92.8 66.2 48.9 5.2 9.7 26.4 40.8 Bulgaria .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 2004 50.0 49.0 51.0 98.1 1.9 98.4 96.1 0.2 0.5 1.3 3.1 Burundi 2000 37.0 38.4 35.7 48.3 51.7 .. .. .. .. .. .. Cambodia 2001 52.3 52.4 52.1 16.5 83.5 78.5 73.6 4.7 5.4 15.7 20.4 Cameroonc 2001 15.9 14.5 17.4 52.5 47.5 90.4 86.3 1.9 2.3 5.1 8.8 Canada .. .. .. .. .. .. .. .. .. .. .. Central African Republic 2000 67.0 66.5 67.6 54.9 45.1 .. .. .. .. .. .. Chad 2004 60.4 64.4 56.2 59.0 41.0 .. .. .. .. .. .. Chile 2003 4.1 5.1 3.1 3.2 96.8 31.0 12.2 8.2 4.5 57.8 81.5 China .. .. .. .. .. .. .. .. .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 2005 4.0 6.2 1.8 32.8 67.2 .. .. .. .. .. .. Congo, Dem. Rep. 2000 39.8 39.9 39.8 35.7 64.3 .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. Costa Ricac 2004 5.7 8.1 3.5 44.6 55.4 48.0 19.4 9.5 9.6 40.8 71.1 Côte d'Ivoire 2000 40.7 40.9 40.5 46.4 53.6 .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. Dominican Republicc 2002 3.5 5.9 0.9 11.4 88.6 .. .. .. .. .. .. Ecuador 2004 12.0 14.6 9.3 27.0 73.0 71.2 68.0 5.0 4.1 21.1 27.8 Egypt, Arab Rep. 2005 7.9 11.5 4.3 21.0 79.0 .. .. .. .. .. .. El Salvador 2003 12.7 17.1 8.1 19.5 80.5 66.4 17.6 10.8 16.1 21.2 66.3 Eritrea .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. Ethiopia 2005 56.0 64.3 47.1 69.4 30.6 96.8 91.4 0.6 2.8 2.4 5.6 Finland .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. Gambia, The 2000 25.3 25.4 25.3 41.6 58.4 .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. Germany .. .. .. .. .. .. .. .. .. .. .. Ghana 2003 6.0 6.0 5.9 71.2 28.8 89.0 67.9 1.5 4.1 7.5 23.5 Greece .. .. .. .. .. .. .. .. .. .. .. Guatemala 2003 21.1 26.2 16.0 33.8 66.2 74.2 43.0 6.0 20.1 16.5 36.9 Guinea 1994 48.3 47.2 49.5 98.6 1.4 .. .. .. .. .. .. Guinea-Bissau 2000 67.5 67.4 67.5 63.7 36.3 .. .. .. .. .. .. Haiti 2005 33.4 37.3 29.6 17.7 82.3 .. .. .. .. .. .. 60 2008 World Development Indicators 2.6 PEOPLE Children at work Survey Economically active children Employment by economic activitya year % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Work Agriculture Manufacturing Services Study Total Male Female only and work Male Female Male Female Male Female Honduras 2004 6.8 10.4 3.2 48.6 51.4 76.9 20.2 5.3 17.9 13.9 59.4 Hungary .. .. .. .. .. .. .. .. .. .. .. India 2000 5.2 5.3 5.1 89.8 10.2 70.5 76.6 10.0 15.4 15.9 6.5 Indonesia 2000 8.9 8.8 9.1 24.9 75.1 .. .. .. .. .. .. Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. Jamaica 2002 1.1 1.5 0.6 17.1 82.9 36.8 17.1 6.2 11.6 43.6 71.3 Japan .. .. .. .. .. .. .. .. .. .. .. Jordan .. .. .. .. .. .. .. .. .. .. .. Kazakhstan 1996 29.7 30.3 29.1 4.4 95.6 .. .. .. .. .. .. Kenya 1999 6.7 6.9 6.4 44.8 55.2 87.3 74.4 2.5 0.3 8.8 25.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 1998 8.6 9.7 7.6 7.0 93.0 93.0 96.3 0.0 0.0 7.0 2.7 Lao PDR .. .. .. .. .. .. .. .. .. .. .. Latvia .. .. .. .. .. .. .. .. .. .. .. Lebanon .. .. .. .. .. .. .. .. .. .. .. Lesotho 2000 30.8 34.2 27.5 17.6 82.4 .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. Madagascar 2001 25.6 26.1 25.1 85.1 14.9 94.1 93.9 0.6 1.4 2.0 2.9 Malawi 2004 42.6 45.0 40.3 13.9 86.1 .. .. .. .. .. .. Malaysia .. .. .. .. .. .. .. .. .. .. .. Mali 2005 70.9 71.2 70.7 53.3 46.7 78.4 41.8 1.4 3.2 19.6 54.6 Mauritania .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. Mexicod 2004 8.9 12.2 5.6 34.1 65.9 46.4 20.6 12.6 11.5 38.6 68.0 Moldova 2000 33.5 34.1 32.8 3.8 96.2 .. .. .. .. .. .. Mongolia 2000 22.0 23.5 20.6 28.2 71.8 .. .. .. .. .. .. Morocco 1998­99 13.2 13.5 12.8 93.2 6.8 60.8 60.3 8.1 8.5 13.5 6.4 Mozambique .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. Namibia 1999 15.4 16.2 14.7 9.5 90.5 91.5 91.7 0.4 0.4 8.1 8.0 Nepal 1999 47.2 42.2 52.4 35.6 64.4 89.0 86.1 1.2 1.5 9.7 12.3 Netherlands .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. Nicaragua 2001 12.1 17.5 6.5 33.3 66.7 73.2 32.0 3.0 10.2 23.3 57.8 Niger .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. .. .. Pakistan .. .. .. .. .. .. .. .. .. .. .. Panamac 2003 5.1 7.7 2.2 38.4 61.6 62.0 41.3 2.5 5.2 34.0 53.5 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. Paraguayc 2005 15.3 22.6 7.7 24.2 75.7 69.8 33.9 6.0 6.9 34.0 59.3 Peru 2000 24.1 25.7 22.3 4.8 95.2 75.4 69.1 3.1 2.5 21.2 28.4 Philippines 2001 13.3 16.3 10.0 14.8 85.2 72.6 53.6 3.6 5.3 22.1 41.0 Poland .. .. .. .. .. .. .. .. .. .. .. Portugal 2001 3.6 4.6 2.6 3.6 96.4 52.7 40.7 11.4 10.7 25.6 47.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 61 2.6 Children at work Survey Economically active children Employment by economic activitya year % of economically active % of economically active % of children children ages 7­14 children ages 7­14 ages 7­14 Work Agriculture Manufacturing Services Study Total Male Female only and work Male Female Male Female Male Female Romania 2000 1.4 1.7 1.1 20.7 79.3 96.4 98.1 0.0 0.0 2.6 1.9 Russian Federation .. .. .. .. .. .. .. .. .. .. .. Rwanda 2000 33.1 36.1 30.3 27.5 72.5 .. .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. Senegal 2005 18.5 24.4 12.6 61.9 38.1 85.2 67.0 6.5 2.3 6.7 28.5 Serbia .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 2000 65.0 64.7 65.4 53.8 46.2 .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 1999 27.7 29.0 26.4 5.1 94.9 .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 1998 17.0 20.4 13.4 5.4 94.6 71.1 71.4 12.0 15.0 15.8 13.5 Sudane 2000 19.1 21.5 16.8 55.9 44.1 .. .. .. .. .. .. Swaziland 2000 11.2 11.4 10.9 14.0 86.0 .. .. .. .. .. .. Sweden .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. Tajikistanf 1999 7.3 7.9 6.8 11.2 88.8 23.8 35.3 .. .. 76.2 64.7 Tanzania 2001 40.4 41.5 39.2 40.0 60.0 83.5 73.1 0.1 0.2 16.3 26.7 Thailand .. .. .. .. .. .. .. .. .. .. .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 2006 39.6 40.5 38.5 30.2 69.8 89.7 77.2 0.9 1.5 8.3 20.8 Trinidad and Tobago 2000 3.9 5.2 2.8 12.8 87.2 .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. Turkey 1999 4.5 5.2 3.8 66.8 33.2 52.7 83.4 19.9 10.2 10.2 1.8 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. Uganda 2005­06 38.2 39.8 36.5 7.7 92.3 96.0 94.9 1.0 1.7 2.7 3.3 Ukraine .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. Uruguay .. .. .. .. .. .. .. .. .. .. .. Uzbekistan 2000 18.1 22.0 14.0 4.1 95.9 .. .. .. .. .. .. Venezuela, RBc 2003 9.1 11.4 6.6 17.6 82.4 35.2 9.2 7.3 9.5 53.9 81.0 Vietnam .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1999 13.1 12.4 14.0 64.3 35.7 87.2 96.6 1.2 0.8 10.8 1.8 Zambia 2005 47.9 48.9 46.8 25.9 74.1 96.5 95.3 0.7 0.5 2.8 4.2 Zimbabwe 1999 14.3 15.3 13.3 12.0 88.0 .. .. .. .. .. .. a. Shares by major industrial category 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. Covers children ages 12­14. e. Covers northern Sudan only. f. Covers children ages 11­14. 62 2008 World Development Indicators 2.6 PEOPLE Children at work About the data Definitions The indicators in the table refer to children's eco- recalculated to present statistics for children ages · Survey year is the year in which the underlying data nomic activity, a broader concept than child labor. 7­14. were collected. · Economically active children are According to a gradually emerging consensus, child Although efforts are made to harmonize the defini- children involved in economic activity for at least one labor is a subset of children's economic activity. tion of employment and the questions on employ- hour in the reference week of the survey. · Work only Based on International Labour Organization (ILO) ment used in survey questionnaires, substantial dif- refers to children involved in economic activity and Conventions 138 and 182, child labor is work that ferences remain among the survey instruments used not attending school. · Study and work refer to chil- is damaging to the child and therefore targeted for to collect data on working children and the sampling dren attending school in combination with economic elimination. design underlying these surveys. Differences exist activity. · Employment by economic activity is the In line with the defi nition of economic activity not only among different household surveys in the distribution of economically active children by the adopted by the Thirteenth International Conference same country, but also within the same type of sur- major industrial categories (ISIC revision 2 or revi- of Labour Statisticians and set by the 1993 United vey carried out in different countries. sion 3). · Agriculture corresponds to division 1 (ISIC Nations System of National Accounts, the threshold Because of differences in the underlying survey revision 2) or categories A and B (ISIC revision 3) for classifying a person as employed is spending at instruments and survey dates, estimates of working and includes agriculture and hunting, forestry and least one hour during the reference period in the children are not fully comparable across countries. logging, and fishing. · Manufacturing corresponds production of goods and services. Economic activity Great caution should be exercised in drawing conclu- to division 3 (ISIC revision 2) or category D (ISIC covers all market production and certain types of sions concerning relative levels of child economic revision 3). · Services correspond to divisions 6­9 nonmarket production, including the production of activity across countries or regions based on the (ISIC revision 2) or categories G­P (ISIC revision 3) goods for own use. It excludes household chores published data. and include wholesale and retail trade, hotels and performed in one's own household. The table aggregates the distribution of working restaurants, transport, financial intermediation, real The data used to develop the indicators are from children by the industrial categories of the Interna- estate, public administration, education, health and household surveys conducted by the ILO, the United tional Standard Industrial Classification (ISIC): agri- social work, other community services, and private Nations Children's Fund (UNICEF), the World Bank, culture, industry, and services. A residual category, household activity. and national statistical offices. These surveys yield which includes mining and quarrying; electricity, gas, data on education, employment, health, expenditure, and water; construction; extraterritorial organization; and consumption that relate to child work. and other inadequately defined activities, is not pre- Household survey data generally include informa- sented in the table, and so the broad groups do not tion on work type--for example, whether a child is add up to 100 percent. The use of either ISIC revision working for pay in cash or in kind or is involved in 2 or revision 3 is strictly related to the codification unpaid work, whether a child is working for someone applied by each country in describing the economic who is not a member of the household, whether a activity. The use of two different classifications does child is involved in any type of family work (on the not affect the definition of the groups presented in farm or in a business), and the like. The age used the table. in country surveys to define child labor ranges from 5 to 17 years old. The data in the table have been In developing countries the majority of child workers Data sources ages 5­14 are involved in unpaid family work 2.6a Data on children at work are estimates produced Share of child workers (%) Unpaid Self-employed Wage and Unclassified by the Understanding Children's Work project family workers workers salary workers based on household survey data sets made avail- 100 able by the ILO's International Programme on the 80 Elimination of Child Labour under its Statistical Monitoring Programme on Child Labour, UNICEF 60 under its Multiple Indicator Cluster Survey pro- 40 gram, the World Bank under its Living Standards Measurement Study program, and national sta- 20 tistical offices. Information on how the data were 0 collected and some indication of their reliability Argentina Cambodia Ethiopia Philippines Turkey Yemen, Rep. can be found at www.ilo.org/public/english/ The incidence of child work varies substantially by country, as does status in employment for working chil- standards/ipec/simpoc/, www.childinfo.org, and dren. A majority of children are unpaid family workers, with self-employed workers the next largest group. www.worldbank.org/lsms. Detailed country statis- Source: Understanding Children's Work. tics can be found at www.ucw-project.org. 2008 World Development Indicators 63 2.7 Poverty Population below national poverty line Poverty gap at national poverty line Survey % Survey % Survey % year Rural Urban National year Rural Urban National year Rural Urban National Afghanistan .. .. .. .. .. .. .. .. .. Albania 2002 29.6 19.8 25.4 .. .. .. 2002 6.6 .. 5.7 Algeria 1988 16.6 7.3 12.2 1995 30.3 14.7 22.6 1995 4.5 1.8 3.2 Angola .. .. .. .. .. .. .. .. .. Argentina 1995 .. 28.4 .. 1998 .. 29.9 .. 1998 .. 11.6 .. Armenia 1998­99 50.8 58.3 55.1 2001 48.7 51.9 50.9 2001 .. .. 15.1 Australia .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. Azerbaijan 1995 .. .. 68.1 2001 42.0 55.0 49.6 2001 .. .. 15.5 Bangladesh 1995­96 55.2 29.4 51.0 2000 53.0 36.6 49.8 2000 13.8 9.5 12.9 Belarus 2000 .. .. 41.9 2002 .. .. 18.5 2002 .. .. 20.0 Belgium .. .. .. .. .. .. .. .. .. Benin 1995 25.2 28.5 26.5 1999 33.0 23.3 29.0 1999 9.4 6.9 .. Bolivia 1999 84.0 51.4 63.5 2002 83.5 53.9 65.2 2002 43.4 23.8 31.2 Bosnia and Herzegovina 2001­02 19.9 13.8 19.5 .. .. .. 2001­02 4.9 2.8 4.6 Botswana .. .. .. .. .. .. .. .. .. 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 1994 .. .. 47.0 2004 38.0 18.0 35.0 2004 7.8 1.2 6.7 Cameroon 1996 59.6 41.4 53.3 2001 49.9 22.1 40.2 .. .. .. Canada .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. Chad 1995­96 48.6 .. 43.4 .. .. .. 1995­96 26.3 .. 27.5 Chile 1996 .. .. 19.9 1998 .. .. 17.0 1998 .. .. 5.7 China 1998 4.6 .. 4.6 2004 .. .. 2.8 .. .. .. Hong Kong, China .. .. .. .. .. .. .. .. .. Colombia 1995 79.0 48.0 60.0 1999 79.0 55.0 64.0 1999 44.0 26.0 34.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. Costa Rica 1992 25.5 19.2 22.0 2004 28.3 20.8 23.9 2004 10.8 7.0 8.6 Côte d'Ivoire .. .. .. .. .. .. .. .. .. Croatia .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. Dominican Republic 2000 45.3 18.2 27.7 2004 55.7 34.7 42.2 2004 24.0 12.9 16.8 Ecuador 1995 56.0 19.0 34.0 1998 69.0 30.0 46.0 1998 29.0 9.0 18.0 Egypt, Arab Rep. 1995­96 23.3 22.5 22.9 1999­2000 .. .. 16.7 1999­2000 .. .. 3.0 El Salvador 1995 64.8 38.9 50.6 2002 49.8 28.5 37.2 2002 24.2 11.1 16.5 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 Finland .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. 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 .. .. .. Germany .. .. .. .. .. .. .. .. .. Ghana 1998­99 49.6 19.4 39.5 2005­06 39.2 10.8 28.5 2005­06 13.5 3.1 9.6 Greece .. .. .. .. .. .. .. .. .. Guatemala 1989 71.9 33.7 57.9 2000 74.5 27.1 56.2 2000 .. .. 22.6 Guinea 1994 .. .. 40.0 .. .. .. .. .. .. Guinea-Bissau 2002 .. 52.6 65.7 .. .. .. 2000 .. 17.5 25.7 Haiti 1987 .. .. 65.0 1995 66.0 .. .. .. .. .. 64 2008 World Development Indicators 2.7 PEOPLE Poverty Population below national poverty line Poverty gap at national poverty line Survey % Survey % Survey % year Rural Urban National year Rural Urban National year Rural Urban National 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 .. .. 17.5 2004 .. .. 16.7 2004 .. .. 2.9 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. Jamaica 1995 37.0 18.7 27.5 2000 25.1 12.8 18.7 .. .. .. Japan .. .. .. .. .. .. .. .. .. 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 1994 47.0 29.0 40.0 1997 53.0 49.0 52.0 .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. 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 .. .. 33.0 2002­03 .. .. 8.0 Latvia 2002 11.6 .. 7.5 2004 12.7 .. 5.9 2004 .. .. 1.2 Lebanon .. .. .. .. .. .. .. .. .. Lesotho 1993 53.9 27.8 49.2 1999 .. .. 68.0 .. .. .. Liberia .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. Lithuania .. .. .. .. .. .. .. .. .. Macedonia, FYR 2002 25.3 .. 21.4 2003 22.3 .. 21.7 2003 6.5 .. 6.7 Madagascar 1997 76.0 63.2 73.3 1999 76.7 52.1 71.3 1999 36.1 21.4 32.8 Malawi 1990­91 .. .. 54.0 1997­98 66.5 54.9 65.3 .. .. .. 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 .. .. .. .. .. .. .. .. .. Mexico 2002 34.8 11.4 20.3 2004 27.9 11.3 17.6 2002 12.2 2.8 6.3 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 55.3 51.5 54.1 2002­03 20.9 19.7 20.5 Myanmar .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. .. .. .. .. .. Nepal 1995­96 43.3 21.6 41.8 2003­04 34.6 9.6 30.9 2003­04 8.5 2.2 7.5 Netherlands .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. 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 .. .. .. Norway .. .. .. .. .. .. .. .. .. Oman .. .. .. .. .. .. .. .. .. 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 .. .. .. 1997 32.1 3.9 16.4 Papua New Guinea 1996 41.3 16.1 37.5 .. .. .. 1996 13.8 4.3 12.4 Paraguaya 1990 28.5 19.7 20.5 .. .. .. 1990 10.5 5.6 6.0 Peru 2001 77.1 42.0 54.3 2004 72.1 42.9 53.1 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 .. .. .. Portugal .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 65 2.7 Poverty Population below national poverty line Poverty gap at national poverty line Survey % Survey % Survey % year Rural Urban National year Rural Urban National year Rural Urban National Romania 1995 .. .. 25.4 2002 .. .. 28.9 2002 .. .. 7.6 Russian Federation 1998 .. .. 31.4 2002 .. .. 19.6 2002 .. .. 5.1 Rwanda 1993 .. .. 51.2 1999­2000 65.7 14.3 60.3 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. Senegal 1992 40.4 23.7 33.4 .. .. .. 1992 16.4 3.1 13.9 Serbia .. .. .. .. .. .. .. .. .. Sierra Leone 1989 .. .. 82.8 2003­04 79.0 56.4 70.2 2003­04 34.0 .. 29.0 Singapore .. .. .. .. .. .. .. .. .. Slovak Republic .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. .. .. .. Spain .. .. .. .. .. .. .. .. .. Sri Lanka 1995­96 27.0 15.0 25.0 2002 7.9 24.7 22.7 2002 .. .. 5.1 Sudan .. .. .. .. .. .. .. .. .. Swaziland 2000­01 75.0 49.0 69.2 .. .. .. 2000­01 .. .. 32.9 Sweden .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. Tajikistan 1999 .. .. 74.9 2003 .. .. 44.4 2003 .. .. 12.7 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 Turkmenistan .. .. .. .. .. .. .. .. .. Uganda 1999­2000 37.4 9.6 33.8 2002­03 41.7 12.2 37.7 2002­03 12.6 3.0 11.3 Ukraine 2000 34.9 .. 31.5 2003 28.4 .. 19.5 .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. Uruguay 1994 .. 20.2 .. 1998 .. 24.7 .. 1998 .. 8.6 .. Uzbekistan 2000 30.5 22.5 27.5 .. .. .. .. .. .. Venezuela, RB 1989 .. .. 31.3 .. .. .. 1989 .. 24.0 .. Vietnam 1998 45.5 9.2 37.4 2002 35.6 6.6 28.9 2002 8.7 1.3 6.9 West Bank and Gaza .. .. .. .. .. .. .. .. .. 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. Covers Asunción metropolitan area only. 66 2008 World Development Indicators 2.7 PEOPLE Poverty About the data The World Bank periodically prepares poverty surveys can differ widely. Even similar surveys may from household survey data. Detailed information is assessments for member countries in which it has not be strictly comparable because of differences in available from the site. an active program in close collaboration with national timing or in the quality and training of enumerators. Estimation from distributional data requires an institutions, other development agencies, and civil Comparisons of countries at different levels of interpolation method. The method chosen was Lorenz society groups, including poor people's organiza- development also pose a potential problem because curves with fl exible functional forms, which have tions. Poverty assessments assess the extent and of differences in the relative importance of consump- proved reliable in past work. The Lorenz curve can causes of poverty and propose strategies to reduce tion of nonmarket goods. The local market value of be graphed as the cumulative percentages of total it. Since 1992 the World Bank has conducted about all consumption in kind (including own production, consumption or income against the cumulative num- 180 poverty assessments, which are the source of particularly important in underdeveloped rural econo- ber of people, starting with the poorest individual. all poverty estimates based on national poverty lines mies) should be included in total consumption expen- The empirical Lorenz curves estimated by PovcalNet presented in the table. diture. Similarly, imputed profit from the production are weighted by household size, so they are based on The World Bank published its first systematic review of nonmarket goods should be included in income. percentiles of population, not households. of poverty for developing countries in World Devel- This is not always done, though such omissions were PovcalNet also allows users to calculate poverty opment Report 1990 using household survey data a far bigger problem in surveys before the 1980s. measures under different assumptions. For exam- for 22 countries (Ravallion, Datt, and van de Walle Most survey data now include valuations for con- ple, users can specify different poverty lines and 1991). Since then the number of countries that field sumption or income from own production, but valu- aggregate the estimates using alternative country such surveys has increased considerably, as have ation methods vary. groupings (for example, UN groupings or groupings the frequency of the surveys and the quality of the The statistics reported here are based on con- based on average incomes) or a selected set of data. Household survey data sets rose dramatically sumption data or, when unavailable, on income individual countries. PovcalNet is available online at from 10 between 1979 and 1981 to 111 between surveys. Analysis of some 20 countries for which http://iresearch.worldbank.org/povcalnet/. It will be 2000 and 2002. Fewer surveys are available after income and consumption expenditure data were both updated using the 2005 PPP results along with the 2002, reflecting the lag between data collection and available from the same surveys found income to World Development Indicators supplemental publica- availability for analysis, not a reduction in collection yield a higher mean than consumption but also found tion later this year. effort. Coverage is improving in all regions, but Sub- higher inequality. When poverty measures based on Definitions Saharan Africa continues to lag, with only 21 of 48 consumption and income were compared, the two countries having at least one data set available since effects roughly cancelled each other out: there was · Survey year is the year in which the underlying data 2000. Overall more than 550 surveys representing no significant statistical difference. were collected. · Rural population below national about 100 developing countries are now included poverty line is the percentage of the rural population in the World Bank's data sets. Some 1.1 million International poverty lines and the 2005 living below the national rural poverty line. · Urban randomly sampled households were interviewed in International Comparison Project population below national poverty line is the per- these surveys, representing 93 percent of the popu- This year's table does not include poverty estimates centage of the urban population living below the lation of developing countries. A complete overview using the international poverty lines of $1 a day and national urban poverty line. · National population of data availability by year and country is available at $2 a day, which were based on 1993 purchasing below national poverty line is the percentage of the http://iresearch.worldbank.org/povcalnet/. power parities (PPPs). The International Comparison country's population living below the national poverty These household surveys ask detailed questions Program recently released new PPP estimates bench- line. National estimates are based on population- on sources of income and how income was spent and marked to 2005 (see introduction to World View). weighted subgroup estimates from household sur- on household characteristics such as the number Poverty estimates using new international poverty veys. · Poverty gap at national poverty line is the of people sharing that income. Most interviews are lines based on PPPs will be published later as a mean shortfall from the poverty line (counting the conducted by staff of government statistics offices. supplement to World Development Indicators. nonpoor as having zero shortfall) as a percentage of As data coverage and quality have improved, so has the poverty line. This measure reflects the depth of the underlying methodology, resulting in more com- Do it yourself: PovcalNet poverty as well as its incidence. prehensive estimates. The World Bank's Development Research Group Data sources Estimating poverty and comparing poverty rates developed PovcalNet, an interactive Web-based tool is difficult. In addition to survey data availability are that allows users to replicate the calculations by The poverty measures are prepared by the data quality issues that arise in measuring household the World Bank's researchers in estimating abso- World Bank's Development Research Group. The living standards. One concerns the choice of income lute poverty in the world. PovcalNet is self-contained national poverty lines are based on the World or consumption as a welfare indicator. Income is and powered by built-in software that performs the Bank's country poverty assessments. For details generally more difficult to measure accurately, and calculations from a primary database. The under- on data sources and methods used in deriving consumption comes closer to the notion of living lying software can also be downloaded from the the World Bank's latest estimates, see Chen and standards. And income can vary over time even if PovcalNet site and used with distributional data of Ravallion's "How Have the World's Poorest Fared living standards do not. But consumption data are various formats. The PovcalNet primary database Since the Early 1980s?" not always available. Another issue is that household consists of distributional data calculated directly 2008 World Development Indicators 67 2.8 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 2004b 31.1 3.4 8.2 12.6 17.0 22.6 39.5 24.4 Algeria 1995b 35.3 2.8 7.0 11.6 16.1 22.7 42.6 26.8 Angola .. .. .. .. .. .. .. .. Argentinac 2004 d 51.3 0.9 3.1 7.6 12.8 21.1 55.4 38.2 Armenia 2003b 33.8 3.6 8.5 12.3 15.7 20.6 42.8 29.0 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 2001b 36.5 3.1 7.4 11.5 15.3 21.2 44.5 29.5 Bangladesh 2005b 33.2 3.8 8.8 12.2 15.6 20.9 42.5 28.0 Belarus 2005b 28.0 3.6 8.8 13.7 17.7 23.0 36.8 22.1 Belgium 2000 d 33.0 3.4 8.5 13.0 16.3 20.8 41.4 28.1 Benin 2003b 36.5 3.1 7.4 11.3 15.4 21.5 44.5 29.0 Bolivia 2002d 60.1 0.3 1.5 5.9 10.9 18.7 63.0 47.2 Bosnia and Herzegovina 2005b 35.8 2.7 7.0 11.6 15.9 22.3 43.2 27.5 Botswana 1993b 60.5 1.2 3.2 6.0 9.7 16.0 65.1 51.0 Brazil 2005d 56.6 0.9 2.9 6.5 11.1 18.7 60.8 44.9 Bulgaria 2003b 29.2 3.4 8.7 13.7 17.2 22.1 38.3 23.9 Burkina Faso 2003b 39.5 2.8 6.9 10.9 14.5 20.5 47.2 32.2 Burundi 1998 b 42.4 1.7 5.1 10.3 15.1 21.5 48.0 32.8 Cambodia 2004b 41.7 2.9 6.8 10.2 13.7 19.6 49.6 34.8 Cameroon 2001b 44.6 2.3 5.6 9.3 13.7 20.4 50.9 35.4 Canada 2000 d 32.6 2.6 7.2 12.7 17.2 23.0 39.9 24.8 Central African Republic 1993b 61.3 0.7 2.0 4.9 9.6 18.5 65.0 47.7 Chad .. .. .. .. .. .. .. .. Chile 2003d 54.9 1.4 3.8 7.3 11.1 17.8 60.0 45.0 China 2004 d 46.9 1.6 4.3 8.5 13.7 21.7 51.9 34.9 Hong Kong, China 1996d 43.4 2.0 5.3 9.4 13.9 20.7 50.7 34.9 Colombia 2004 d 56.2 0.8 2.9 6.9 11.0 18.3 60.9 45.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. Costa Rica 2004 d 48.2 1.4 4.1 8.5 13.2 20.9 53.3 36.7 Côte d'Ivoire 2002b 44.6 2.0 5.2 9.1 13.7 21.3 50.7 34.0 Croatia 2005b 29.0 3.6 8.8 13.3 17.3 22.7 37.9 23.1 Cuba .. .. .. .. .. .. .. .. Czech Republic 1996d 25.4 4.3 10.3 14.5 17.7 21.7 35.9 22.4 Denmark 1997d 24.7 2.6 8.3 14.7 18.2 22.9 35.8 21.3 Dominican Republic 2005d 49.9 1.5 4.1 8.1 12.6 19.9 55.3 39.0 Ecuador 1998b 53.6 0.9 3.3 7.5 11.7 19.4 58.0 41.6 Egypt, Arab Rep. 2004­05b 34.4 3.8 8.9 12.7 16.0 20.8 41.5 27.6 El Salvador 2002d 52.4 0.7 2.7 7.5 12.8 21.2 55.9 38.8 Eritrea .. .. .. .. .. .. .. .. Estonia 2004b 36.0 2.6 6.8 11.7 16.2 22.0 43.3 27.8 Ethiopia 1999­2000 b 30.0 3.9 9.1 13.2 16.8 21.5 39.4 25.5 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 .. .. .. .. .. .. .. .. Gambia, The 2003­04b 47.4 1.8 4.8 8.7 13.0 20.7 52.9 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 1998­99b 40.8 2.1 5.6 10.1 14.9 22.9 46.6 30.0 Greece 2000 d 34.3 2.5 6.7 11.9 16.8 23.0 41.5 26.0 Guatemala 2004 d 49.4 1.3 3.9 8.2 13.1 20.6 54.1 38.0 Guinea 2003b 38.6 2.9 7.0 10.8 14.7 21.4 46.1 30.7 Guinea-Bissau 1993b 47.0 2.1 5.2 8.8 13.1 19.4 53.4 39.3 Haiti 2001d 59.2 0.7 2.4 6.2 10.4 17.7 63.4 47.7 68 2008 World Development Indicators 2.8 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% Honduras 2003d 53.8 1.2 3.4 7.1 11.6 19.6 58.3 42.2 Hungary 2004b 30.1 3.5 8.6 13.1 17.1 22.3 38.9 24.2 India 2004­05b 36.8 3.6 8.1 11.3 14.9 20.4 45.3 31.1 Indonesia 2005b 39.4 3.0 7.1 10.7 14.4 20.5 47.3 32.3 Iran, Islamic Rep. 2005b 38.4 2.5 6.5 10.9 15.4 22.1 45.1 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 5.3 9.2 13.2 20.6 51.6 35.8 Japan 1993d 24.9 4.8 10.6 14.2 17.6 22.0 35.7 21.7 Jordan 2002­03b 38.8 2.7 6.7 10.8 14.9 21.3 46.3 30.6 Kazakhstan 2003b 33.9 3.0 7.4 11.9 16.4 22.8 41.5 25.9 Kenya 1997b 42.5 2.5 6.0 9.8 14.3 20.8 49.1 33.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1998d 31.6 2.9 7.9 13.6 18.0 23.1 37.5 22.5 Kuwait .. .. .. .. .. .. .. .. Kyrgyz Republic 2003b 30.3 3.8 8.9 12.8 16.4 22.5 39.4 24.3 Lao PDR 2002b 34.6 3.4 8.1 11.9 15.6 21.1 43.3 28.5 Latvia 2004b 35.8 2.6 6.8 11.7 16.2 22.3 42.9 27.5 Lebanon .. .. .. .. .. .. .. .. Lesotho 1995b 63.2 0.5 1.5 4.3 8.9 18.8 66.5 48.3 Liberia .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 2004b 35.8 2.6 6.8 11.7 16.1 22.4 43.0 27.5 Macedonia, FYR 2003b 39.0 2.4 6.1 10.8 15.5 22.2 45.5 29.6 Madagascar 2001b 47.5 1.9 4.9 8.5 12.7 20.4 53.5 36.6 Malawi 2004­05b 39.0 2.9 7.0 10.8 14.8 20.7 46.6 31.8 Malaysia 1997d 49.2 1.7 4.4 8.1 12.9 20.3 54.3 38.4 Mali 2001b 40.1 2.4 6.1 10.2 14.7 22.2 46.6 30.2 Mauritania 2000 b 39.0 2.5 6.2 10.6 15.2 22.3 45.7 29.5 Mauritius .. .. .. .. .. .. .. .. Mexico 2004b 46.1 1.6 4.3 8.3 12.6 19.7 55.1 39.4 Moldova 2003b 33.2 3.2 7.8 12.2 16.5 22.1 41.4 26.4 Mongolia 2002b 32.8 3.0 7.5 12.2 16.8 23.1 40.5 24.6 Morocco 1998­99b 39.5 2.6 6.5 10.6 14.8 21.3 46.6 30.9 Mozambique 2002­03b 47.3 2.1 5.4 9.3 13.0 18.7 53.6 39.4 Myanmar .. .. .. .. .. .. .. .. Namibia 1993d 74.3 0.5 1.4 3.0 5.4 11.5 78.7 64.5 Nepal 2003­04b 47.2 2.6 6.0 9.0 12.4 18.0 54.6 40.6 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 2001b 43.1 2.2 5.6 9.8 14.2 21.1 49.3 33.8 Niger 1995b 50.5 0.8 2.6 7.1 13.9 23.1 53.3 35.4 Nigeria 2003b 43.7 1.9 5.0 9.6 14.5 21.7 49.2 33.2 Norway 2000 d 25.8 3.9 9.6 14.0 17.2 22.0 37.2 23.4 Oman .. .. .. .. .. .. .. .. Pakistan 2005b 31.2 3.9 9.1 12.9 16.1 21.1 40.8 26.5 Panama 2003d 56.1 0.7 2.5 6.6 11.4 19.6 59.9 43.0 Papua New Guinea 1996b 50.9 1.7 4.5 7.9 11.9 19.2 56.5 40.5 Paraguay 2003d 58.4 0.7 2.4 6.3 10.8 18.6 61.9 46.1 Peru 2003d 52.0 1.3 3.7 7.7 12.2 19.7 56.7 40.9 Philippines 2003b 44.5 2.2 5.4 9.1 13.6 21.3 50.6 34.2 Poland 2005b 34.9 3.0 7.4 11.7 16.1 22.3 42.5 27.2 Portugal 1997d 38.5 2.0 5.8 11.0 15.5 21.9 45.9 29.8 Puerto Rico .. .. .. .. .. .. .. .. 2008 World Development Indicators 69 2.8 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% Romania 2005b 31.5 3.3 8.2 12.8 16.9 22.1 40.0 25.4 Russian Federation 2002b 39.9 2.4 6.1 10.5 14.9 21.8 46.6 30.6 Rwanda 2000 b 46.8 2.1 5.3 9.1 13.2 19.4 53.0 38.2 Saudi Arabia .. .. .. .. .. .. .. .. Senegal 2001b 41.3 2.7 6.6 10.3 14.2 20.6 48.4 33.4 Serbiae 2003b 30.0 3.4 8.3 13.0 17.3 23.0 38.4 23.4 Sierra Leone 2003b 40.0 2.6 6.5 10.5 14.5 21.2 47.3 31.2 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 18.7 22.8 34.8 20.9 Slovenia 2004b 30.9 3.4 8.3 12.8 16.7 22.6 39.6 24.6 Somalia .. .. .. .. .. .. .. .. South Africa 2000 b 57.8 1.4 3.5 6.3 10.0 18.0 62.2 44.7 Spain 2000 d 34.7 2.6 7.0 12.1 16.4 22.5 42.0 26.6 Sri Lanka 2002b 40.2 3.0 7.0 10.5 14.2 20.4 48.0 32.7 Sudan .. .. .. .. .. .. .. .. Swaziland 2000­01d 50.4 1.6 4.3 8.2 12.3 18.9 56.3 40.7 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 2.9 7.3 12.0 16.1 22.3 42.4 26.9 Thailand 2002b 42.0 2.7 6.3 9.9 14.0 20.8 49.0 33.4 Timor-Leste .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. Trinidad and Tobago 1992d 38.9 2.2 5.9 10.8 15.3 23.1 44.9 28.8 Tunisia 2000 b 39.8 2.3 6.0 10.3 14.8 21.7 47.3 31.5 Turkey 2003b 43.6 2.0 5.3 9.7 14.2 21.0 49.7 34.1 Turkmenistan 1998b 40.8 2.6 6.1 10.2 14.7 21.5 47.5 31.7 Uganda 2002b 45.7 2.3 5.7 9.4 13.2 19.1 52.5 37.7 Ukraine 2005b 28.2 3.8 9.0 13.5 17.4 22.7 37.4 22.6 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 Uruguayc 2003d 44.9 1.9 5.0 9.1 14.0 21.5 50.5 34.0 Uzbekistan 2003b 36.8 2.8 7.2 11.7 15.4 21.0 44.7 29.6 Venezuela, RB 2003d 48.2 0.7 3.3 8.7 13.9 22.0 52.1 35.2 Vietnam 2004b 37.0 2.9 7.1 11.1 15.1 21.8 44.8 28.9 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. 2005b 37.7 2.9 7.2 11.4 15.3 20.8 45.3 30.9 Zambia 2004b 50.8 1.2 3.6 7.9 12.6 20.8 55.1 38.8 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. e. Includes Montenegro. 70 2008 World Development Indicators 2.8 PEOPLE Distribution of income or consumption About the data Definitions Inequality in the distribution of income is reflected but achieving strict comparability is still impossible · Survey year is the year in which the underlying in the percentage shares of income or consumption (see About the data for table 2.7). data were collected. · Gini index measures the accruing to portions of the population ranked by Two sources of noncomparability should be noted extent to which the distribution of income (or con- income or consumption levels. The portions ranked in particular. First, the surveys can differ in many sumption expenditure) among individuals or house- lowest by personal income receive the smallest respects, including whether they use income or con- holds within an economy deviates from a perfectly shares of total income. The Gini index provides a con- sumption expenditure as the living standard indi- equal distribution. A Lorenz curve plots the cumula- venient summary measure of the degree of inequal- cator. The distribution of income is typically more tive percentages of total income received against the ity. Data on the distribution of income or consump- unequal than the distribution of consumption. In cumulative number of recipients, starting with the tion come from nationally representative household addition, the definitions of income used differ more poorest individual. The Gini index measures the area surveys. Where the original data from the house- often among surveys. Consumption is usually a between the Lorenz curve and a hypothetical line hold survey were available, they have been used to much better welfare indicator, particularly in devel- of absolute equality, expressed as a percentage of directly calculate the income or consumption shares oping countries. Second, households differ in size the maximum area under the line. Thus a Gini index by quintile. Otherwise, shares have been estimated (number of members) and in the extent of income of 0 represents perfect equality, while an index of from the best available grouped data. sharing among members. And individuals differ in 100 implies perfect inequality. · Percentage share The distribution data have been adjusted for age and consumption needs. Differences among of income or consumption is the share of total household size, providing a more consistent mea- countries in these respects may bias comparisons income or consumption that accrues to subgroups of sure of per capita income or consumption. No adjust- of distribution. population indicated by deciles or quintiles. 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 estimation method consistent with that applied for in method and type of data collected, the distribu- developing countries. tion data are not strictly comparable across coun- tries. These problems are diminishing as survey methods improve and become more standardized, The Gini coefficient and ratio of income or consumption of the richest quintile to the poorest quintiles are closely correlated 2.8a Gini coefficient (%) 80 70 60 50 40 30 20 0 10 20 30 40 50 60 Ratio of income or consumption of richest quintile to poorest quintile 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. 2008 World Development Indicators 71 2.9 Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female % of pension Male Female labor force labor force % of % of working- % of per 1998­ ages 15­24 ages 15­24 total labor age % of capita 1998­ 2005a 2005a 2003­05a 2003­05a 2003­05a Year force population Year GDP Year income Afghanistan .. .. .. .. .. .. .. 2005 0.5 .. Albania .. .. .. .. .. 2004 48.9 33.0 2004 4.6 .. Algeria .. .. 43 46 .. 2002 36.7 22.1 2002 3.2 2002 89.1 Angola .. .. .. .. .. .. .. .. .. Argentina .. .. 22b 28b .. 2004 35.0 25.9 1994 6.2 2002 73.7 Armenia .. .. .. .. 36 2002 64.4 48.3 2004 3.4 .. Australia .. .. 11b 11b .. 2005 92.6 69.6 2003 5.4 2002 52.4 Austria .. .. 11 10 .. 2005 96.4 68.7 2003 14.6 2002 93.2 Azerbaijan .. .. .. .. .. 1996 52.0 46.0 1996 2.5 .. Bangladesh .. .. 7 6 10 2004 2.8 2.1 1992 0.0 .. Belarus .. .. .. .. 54 1992 97.0 94.0 1997 7.7 .. Belgium .. .. 21 19 .. 2005 94.2 61.6 2003 11.3 2002 62.8 Benin 50 b 41b .. .. 23 1996 4.8 .. 1993 0.4 .. Bolivia .. .. .. .. 20 2002 10.1 7.8 2000 4.5 .. Bosnia and Herzegovina .. .. .. .. .. 2004 36.0 27.0 2004 8.8 .. Botswana .. .. .. .. .. .. .. .. .. Brazil .. .. 14b 23b .. 2004 52.6 39.1 2004 12.6 .. Bulgaria .. .. 23 21 .. 1994 64.0 63.0 2005 8.9 2002 75.2 Burkina Faso .. .. .. .. 9 1993 3.1 3.0 1992 0.3 .. Burundi .. .. .. .. .. 1993 3.3 3.0 1991 0.2 .. Cambodia .. .. .. .. 24 .. .. .. .. Cameroon .. .. .. .. 24 1993 13.7 11.5 2001 0.8 .. Canada .. .. 14b 11b .. 2005 90.5 71.4 2003 5.4 2002 57.1 Central African Republic .. .. .. .. .. .. .. 1990 0.3 .. Chad .. .. .. .. 20 1990 1.1 1.0 1997 0.1 .. Chile .. .. 15 21 .. 2003 58.0 35.2 2001 2.9 2002 53.5 China .. .. .. .. .. 2005 20.5 17.2 1996 2.7 .. Hong Kong, China .. .. 14 8 .. .. .. .. .. Colombia .. .. 12 19 30 2000 19.0 14.0 1994 1.1 2002 54.4 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. 23 1992 5.8 5.6 1992 0.9 .. Costa Rica .. .. 11 22 .. 2004 55.3 37.6 1997 4.2 2002 103.1 Côte d'Ivoire .. .. .. .. .. 1997 9.3 9.1 1997 0.3 .. Croatia .. .. 27c 31c .. 2005 77.0 50.0 2005 12.3 2002 61.6 Cuba .. .. .. .. 46 .. .. 1992 12.6 .. Czech Republic .. .. 19 19 .. 2003 86.3 61.5 2003 8.7 2002 58.2 Denmark .. .. 6 10 .. 2005 94.6 75.0 2003 11.0 2002 54.1 Dominican Republic .. .. .. .. 28 2005 27.2 18.6 2000 0.8 2002 55.9 Ecuador 32b 42b 12b 21b .. 2004 27.0 20.8 2002 2.5 .. Egypt, Arab Rep. .. .. .. .. 12 2004 55.5 27.7 2004 4.1 2002 119.8 El Salvador 43c 55c 13c 10 c .. 2005 29.8 19.7 1997 1.3 2002 39.3 Eritrea .. .. .. .. 47 .. .. 2001 0.3 .. Estonia .. .. 16 15 .. 2004 95.2 68.6 2003 6.0 2002 60.9 Ethiopia 33b 46b 4 11 23 .. .. 1993 0.9 .. Finland .. .. 21 19 .. 2005 88.7 67.2 2003 11.2 2002 78.8 France .. .. 21 25b .. 2005 89.9 61.4 2003 13.1 2002 65.0 Gabon .. .. .. .. 26 1995 15.0 14.0 .. .. Gambia, The .. .. .. .. .. 2003 3.8 2.9 .. .. Georgia 21b 7b 27 31 .. 2004 29.9 22.7 2004 3.0 .. Germany .. .. 16 14 .. 2005 88.2 65.5 2003 13.3 2002 71.8 Ghana .. .. .. .. 34 2003 9.1 7.1 2002 1.3 .. Greece .. .. 18 35 .. 2005 85.2 58.5 2003 12.8 2002 99.9 Guatemala .. .. .. .. .. 2000 19.0 11.7 1995 0.7 .. Guinea .. .. .. .. 17 1993 1.5 1.8 .. .. Guinea-Bissau .. .. .. .. .. 2004 1.9 1.5 2005 2.1 .. Haiti .. .. .. .. 44 .. .. .. .. 72 2008 World Development Indicators 2.9 PEOPLE Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female % of pension Male Female labor force labor force % of % of working- % of per 1998­ ages 15­24 ages 15­24 total labor age % of capita 1998­ 2005a 2005a 2003­05a 2003­05a 2003­05a Year force population Year GDP Year income Honduras .. .. 5b 11b 26 1999 20.6 17.7 1994 0.6 .. Hungary .. .. 20 19 .. 2002 56.3 34.0 2003 9.1 2002 90.5 India 54b 41b 10 b 11b .. 2004 9.0 5.7 .. .. Indonesia .. .. 25 34 12 2002 15.5 11.3 .. .. Iran, Islamic Rep. .. .. 20 32 .. 2001 35.0 20.0 2000 1.1 2002 124.2 Iraq .. .. .. .. 11 .. .. .. .. Ireland .. .. 9 7 .. 2005 88.0 63.9 2003 4.1 2002 36.6 Israel .. .. 17 19 .. 1992 82.0 63.0 1996 5.9 .. Italy .. .. 22 27 .. 2005 92.4 58.4 2003 14.7 2002 88.8 Jamaica .. .. 22 36 .. 2004 17.4 12.6 .. .. Japan .. .. 10 b 7b .. 2005 95.3 75.0 2003 8.9 2002 59.1 Jordan .. .. .. .. 12 2003 30.3 17.4 2001 2.2 2002 76.1 Kazakhstan .. .. 10 c 15c .. 2004 33.8 26.4 2004 4.9 .. Kenya .. .. .. .. 32 2005 8.0 6.7 1993 0.5 .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. 12 9 .. 2005 74.3 52.0 2003 1.3 2002 43.3 Kuwait .. .. .. .. .. .. .. 1990 3.5 .. Kyrgyz Republic 33b 25b 14 18 .. 2006 42.2 28.9 2006 4.8 .. Lao PDR .. .. .. .. .. .. .. .. .. Latvia .. .. 12 14 .. 2003 92.4 66.5 2002 7.5 2002 81.8 Lebanon .. .. .. .. .. 2003 33.1 19.9 2003 2.1 .. Lesotho .. .. .. .. 37 2005 5.7 3.6 .. .. Liberia .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. 2003 65.5 38.1 2001 2.1 2002 91.2 Lithuania 50b 27b 16 15 .. 2004 79.7 56.0 2003 6.2 2002 71.3 Macedonia, FYR .. .. 63 62 8 2000 63.8 38.9 1998 8.7 .. Madagascar .. .. 7 7 22 1993 5.4 4.8 1990 0.2 .. Malawi .. .. .. .. 25 .. .. .. .. Malaysia .. .. .. .. .. 1993 48.7 37.8 1999 6.5 .. Mali .. .. .. .. 11 1990 2.5 2.0 1991 0.4 .. Mauritania .. .. .. .. 29 1995 5.0 4.0 1992 0.2 .. Mauritius .. .. 21 34 .. 2000 51.4 33.6 1999 4.4 .. Mexico 18b 22b 6 7 .. 2002 34.5 22.7 2003 1.3 2002 45.1 Moldova .. .. 19 18 .. 2000 60.6 43.1 2003 8.0 .. Mongolia .. .. 20 21 .. 2002 61.4 49.1 2002 5.8 .. Morocco .. .. 18c 14 c 17 2003 22.4 12.8 2003 1.9 2002 74.1 Mozambique .. .. .. .. 26 1995 2.0 2.1 1996 0.0 .. Myanmar .. .. .. .. .. .. .. .. .. Namibia .. .. .. .. 42 .. .. .. .. Nepal 60 b 76b .. .. 23 2003 2.1 1.4 2003 0.3 .. Netherlands .. .. 10 10 .. 2005 90.3 70.4 2003 12.8 2002 84.1 New Zealand .. .. 9b 10 b .. .. .. 2003 7.4 2002 39.5 Nicaragua .. .. 11 16 31 2005 17.9 11.5 1996 2.5 .. Niger .. .. .. .. 19 1992 1.3 1.5 2005 0.2 .. Nigeria .. .. .. .. 17 2005 1.7 1.2 1991 0.1 .. Norway .. .. 13 12 .. 2005 90.8 75.7 2003 10.7 2002 65.1 Oman .. .. .. .. .. .. .. .. .. Pakistan 44b 22b 11 15 .. 2004 6.4 4.0 1993 0.9 .. Panama .. .. 19 30 .. 1998 51.6 40.7 1996 4.3 .. Papua New Guinea .. .. .. .. .. .. .. .. .. Paraguay .. .. 12b 21b .. 2004 11.6 9.1 2001 1.2 .. Peru 56b 55b 21b 21b 22 2003 16.3 12.3 2000 2.6 2002 43.9 Philippines .. .. 15 19 15 2000 27.1 18.7 1993 1.0 .. Poland .. .. 37 39 .. 2005 84.9 54.5 2003 13.9 2002 69.7 Portugal .. .. 14 19 .. 2005 91.4 71.9 2003 11.9 2002 79.8 Puerto Rico .. .. 25b 21b .. .. .. .. .. 2008 World Development Indicators 73 2.9 Assessing vulnerability and security Urban informal Youth Female-headed Pension Public expenditure sector employment unemployment households contributors on pensions % of urban Male Female Average employment % of male % of female % of pension Male Female labor force labor force % of % of working- % of per 1998­ ages 15­24 ages 15­24 total labor age % of capita 1998­ 2005a 2005a 2003­05a 2003­05a 2003­05a Year force population Year GDP Year income Romania .. .. 21 18 .. 2005 57.6 39.1 2003 6.9 .. Russian Federation .. .. .. .. .. .. 2004 5.8 .. .. Rwanda .. .. .. .. 34 2004 4.8 4.1 .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. Senegal .. .. .. .. 23 2003 5.3 3.9 2003 1.3 .. Serbia .. .. .. .. 26 2003 46.0 d 32.2d 2003 12.4 d .. Sierra Leone .. .. .. .. .. 2004 4.6 3.6 .. .. Singapore .. .. 4 6 .. 1995 73.0 56.0 1996 1.4 .. Slovak Republic .. .. 31 29 .. 2003 78.5 55.3 2003 8.5 2002 60.2 Slovenia .. .. 11 12 .. 1995 86.0 68.7 2003 10.1 .. Somalia .. .. .. .. .. .. .. .. .. South Africa 16 28 56 65 .. .. .. .. .. Spain .. .. 17 24 .. 2005 91.0 63.2 2003 9.2 2002 88.3 Sri Lanka .. .. 20 b 37b .. 2004 35.6 22.2 1996 2.4 .. Sudan .. .. .. .. 19 1995 12.1 12.0 .. .. Swaziland .. .. .. .. .. .. .. .. .. Sweden .. .. 23 22 .. 2005 91.0 72.3 2003 12.7 2002 68.2 Switzerland .. .. 9 9 .. 2005 100.0 79.1 2003 12.1 2002 67.3 Syrian Arab Republic .. .. .. .. .. 2004 17.4 11.4 2004 1.3 .. Tajikistan .. .. .. .. .. .. .. 1996 3.0 .. Tanzania .. .. .. .. 25 1996 2.0 2.0 .. .. Thailand .. .. 5 5 30 2003 22.5 18.0 .. .. Timor-Leste .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. 1997 15.9 15.0 1997 0.6 .. Trinidad and Tobago .. .. .. .. .. 2004 55.6 .. 1996 0.6 .. Tunisia .. .. 31 29 .. 2004 45.3 25.4 2003 4.3 2002 72.7 Turkey 10 b 6b 19 19 .. 2002 45.0 24.3 2002 7.1 2002 103.3 Turkmenistan .. .. .. .. 27 .. .. 1996 2.3 .. Uganda .. .. .. .. 30 2004 10.7 9.3 2003 0.3 .. Ukraine 3b 3b 15 14 .. 2005 76.0 52.3 2005 15.4 .. United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom .. .. 13 10 .. 2005 92.7 71.4 2003 10.9 2002 47.6 United States .. .. 12b 10 b .. 2005 92.5 72.5 2003 7.5 2002 51.0 Uruguay .. .. 25 35 .. 2004 55.0 44.3 1996 15.0 2002 125.4 Uzbekistan .. .. .. .. .. .. .. 1995 5.3 .. Venezuela, RB .. .. 24 35 .. 2004 31.8 23.8 2001 2.7 .. Vietnam .. .. 4 5 27 2005 13.2 10.8 1998 1.6 .. West Bank and Gaza .. .. 39 45 .. 2000 18.8 7.8 2001 0.8 .. Yemen, Rep. .. .. .. .. .. 2005 10.0 5.5 1994 0.1 2002 106.3 Zambia .. .. .. .. 23 2000 5.9 4.9 1993 0.1 .. Zimbabwe .. .. .. .. 38 1995 12.0 10.0 2002 2.3 .. World .. w .. w Low income .. .. Middle income .. .. Lower middle income .. .. Upper middle income 21 26 Low & middle income .. .. East Asia & Pacific .. .. Europe & Central Asia .. .. Latin America & Carib. 14 20 Middle East & N. Africa .. .. South Asia 11 12 Sub-Saharan Africa .. .. High income 14 13 Euro area 18 20 a. Data are for the most recent year available. b. Limited coverage. c. Data are for 2006. d. Includes Montenegro. 74 2008 World Development Indicators 2.9 PEOPLE Assessing vulnerability and security About the data Definitions As traditionally measured, poverty is a static con- likely to include school leavers, the level of youth · Urban informal sector employment is all people cept, and vulnerability a dynamic one. Vulnerabil- unemployment varies considerably over the year as a who, during a given reference period, were employed ity reflects a household's resilience in the face of result of different school opening and closing dates. in at least one informal enterprise, irrespective of shocks and the likelihood that a shock will lead to a The youth unemployment rate shares similar limita- their status in employment and whether it was their decline in well-being. Thus, it depends primarily on tions on comparability as the general unemployment main or secondary job. · Youth unemployment is the the household's assets and insurance mechanisms. rate. For further information, see About the data for share of the labor force ages 15­24 without work but Because poor people have fewer assets and less table 2.5 and the original source. available for and seeking employment. · Female- diversified sources of income than do the better-off, The definition of female-headed household differs headed households are the percentage of house- fluctuations in income affect them more. greatly across countries, making cross-country com- holds with a female head. · Pension contributors are Enhancing security for poor people means reduc- parison difficult. In some cases it is assumed that a the share of the labor force or working-age population ing their vulnerability to such risks as ill health, pro- woman cannot be the head of any household with an (here defined as ages 15­64) covered by a pension viding them the means to manage risk themselves, adult male, because of sex-biased stereotype. Cau- scheme. · Public expenditure on pensions is all and strengthening market or public institutions for tion should be exercised in interpreting the data. government expenditures on cash transfers to the managing risk. Tools include microfinance programs, Pension scheme coverage may be broad or even elderly, the disabled, and survivors and the adminis- public provision of education and basic health care, universal where eligibility is determined by citizen- trative costs of these programs. · Average pension and old age assistance (see tables 2.10 and 2.15). ship, residency, or income status. In contribution- is estimated by dividing total pension expenditure by Poor households face many risks, and vulnerability related schemes, however, eligibility is usually the number of pensioners. is thus multidimensional. The indicators in the table restricted to individuals who have contributed for focus on individual risks--informal sector employ- a minimum number of years. Definitional issues-- ment, youth unemployment, female-headed house- relating to the labor force, for example--may arise in holds, income insecurity in old age--and the extent comparing coverage by contribution-related schemes to which publicly provided services may be capable over time and across countries (for country-specific of mitigating some of these risks. Poor people face information, see Palacios and Pallares-Miralles labor market risks, often having to take up precari- 2000). The share of the labor force covered by a ous, low-quality jobs in the informal sector and to pension scheme may be overstated in countries that increase their household's labor market participa- do not try to count informal sector workers as part tion by sending their children to work (see table 2.6). of the labor force. Income security is a prime concern for the elderly. Public interventions and institutions can provide Data on informal sector employment are from a services directly to poor people, although whether variety of sources, including labor force and special these interventions and institutions work well for the informal sector surveys, household surveys, surveys poor is debated. State action is often ineffective, of household industries or economic activities, sur- in part because governments can influence only a veys of small enterprises and microenterprises, and few of the many sources of well-being and in part official estimates. In most countries data on the infor- because of difficulties in delivering goods and ser- mal economy are collected on an ad hoc basis or less vices. The effectiveness of public provision is further frequently than annually. The international compara- constrained by the fiscal resources at governments' bility of the data is affected by differences among disposal and the fact that state institutions may not countries in definitions and coverage and in treatment be responsive to the needs of poor people. of domestic workers. The data in the table are based The data on public pension spending cover non- on national definitions of informal sector and urban contributory pensions or social assistance targeted areas established by countries, and therefore data to the elderly and disabled and spending by social may not be comparable across countries. For details insurance schemes for which contributions had previ- Data sources on these definitions, consult the original source. ously been made. A country's pattern of spending is Youth unemployment is an important policy issue correlated with its demographic structure--spending Data on urban informal sector employment and for many economies. Experiencing unemployment increases as the population ages. youth unemployment are from the ILO database may permanently impair a young person's produc- Key Indicators of the Labour Market, 5th edi- tive potential and future employment opportunities. tion. Data on female-headed household are from The table presents unemployment among youth ages Demographic and Health Surveys by Macro Inter- 15­24, but the lower age limit for young people in national. Data on pension contributors and pen- a country could be determined by the minimum sion spending are from the World Bank Pensions age for leaving school, so age groups could dif- Database (available June 2008). fer across countries. Also, since this age group is 2008 World Development Indicators 75 2.10 Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2006b 1999 2006b 1999 2006b 2006b 2006b 2006b 2006b Afghanistan .. .. .. .. .. .. .. .. 36.5 83 Albania .. .. .. .. .. .. .. .. .. 21 Algeria 26.5 .. .. .. .. .. .. .. 99.3 24 Angola .. .. .. .. .. 65.5 2.4 .. .. .. Argentina .. 11.3 16.4 15.7 17.7 11.8 3.8 13.1 .. 17 Armenia .. .. 12.4 .. 29.1 .. .. .. 77.5 21 Australia .. 15.9 14.5 14.5 25.7 22.5 4.6 .. .. .. Austria 18.2 22.5 29.9 27.2 51.6 48.5 5.4 10.8 .. 12 Azerbaijan .. 5.5 17.0 8.5 19.1 9.4 2.1 17.4 100.0 13 Bangladesh .. 7.6 12.4 14.6 46.3 49.4 2.5 14.2 48.3 51 Belarus .. 14.3 .. 27.0 .. 29.0 6.1 12.9 99.6 16 Belgium 15.8 20.0 23.7 33.5 38.3 35.1 6.0 12.2 .. 11 Benin .. 11.5 26.1 .. 202.9 .. 4.4 17.1 72.2 47 Bolivia .. .. 11.7 .. 44.1 .. .. .. .. 24 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana .. 15.7 .. 40.2 .. 438.4 8.7c 21.0 c 96.7 25 Brazil .. 12.8 9.5 11.5 57.0 32.6 4.0 .. .. 21 Bulgaria .. 11.9 18.8 10.8 17.9 17.8 2.5 .. .. 16 Burkina Faso .. 27.4 .. 20.5 .. 208.1 4.2 15.4 86.9 46 Burundi 13.4 19.1 .. 74.5 1,051.9 348.8 5.1 17.7 87.5 54 Cambodia .. 5.6 11.4 .. 43.8 .. 1.7 .. 98.3 50 Cameroon .. 6.3 16.5 22.8 63.0 94.1 3.3 16.8 61.8 44 Canada .. .. .. .. 47.9 .. .. .. .. .. Central African Republic 11.9 10.5 .. .. .. 291.3 1.4 .. 49.7 .. Chad 8.0 6.8 27.5 28.0 .. 333.9 1.9 10.1 26.8 63 Chile .. 11.9 14.8 13.1 19.4 11.6 3.4 18.5 .. 26 China .. .. 11.5 .. 90.1 .. .. .. .. 18 Hong Kong, China .. 14.1 17.7 18.2 .. 58.3 3.9 23.9 94.8 18 Colombia .. 19.2 16.9 18.0 39.6 23.6 4.7 11.1 .. 28 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. 3.4 .. .. 404.9 .. 1.9 8.1 89.0 55 Costa Rica 7.8 17.0 23.2 17.1 55.0 35.9 4.7 29.8 88.0 20 Côte d'Ivoire .. .. 54.5 .. 212.8 .. .. .. .. 46 Croatia .. 23.7 .. 22.5 41.5 27.9 4.4 9.1 100.0 15 Cuba 21.6 33.8 41.3 43.0 86.4 34.5 9.1 14.2 100.0 10 Czech Republic .. 12.8 21.7 23.3 33.7 30.4 4.4 10.0 .. 16 Denmark .. 24.8 38.1 35.3 65.9 62.5 8.4 15.3 .. .. Dominican Republic .. 8.2 .. 5.9 .. .. 3.6 16.8 88.3 23 Ecuador .. .. 9.7 .. .. .. .. .. 71.1 23 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. 26 El Salvador .. 10.0 7.9 9.3 9.4 16.6 3.1 20.0 94.0 40 Eritrea .. 9.3 37.3 9.3 429.4 1,082.5 5.3 .. 87.5 47 Estonia .. 19.2 27.9 25.5 32.6 18.2 5.1 14.9 .. .. Ethiopia 22.1 14.1 .. 13.7 .. 747.7 6.0 17.5 .. 59 Finland 21.7 18.8 26.2 32.9 40.9 36.7 6.5 12.8 .. 16 France 11.8 17.8 28.6 29.0 29.7 34.0 5.8 10.9 .. 19 Gabon .. .. .. .. .. .. .. .. .. 36 Gambia, The 13.2 7.4 .. 9.1 .. 238.0 2.0 .. 76.3 35 Georgia .. .. .. .. .. .. 3.1 9.3 .. 15 Germany .. 16.3 20.5 21.7 .. .. 4.6 9.8 .. 14 Ghana .. 17.8 .. 28.0 .. 209.4 5.4 .. 53.0 c 32c Greece 7.5 16.5 17.0 22.6 28.7 27.1 4.2 8.5 .. 11 Guatemala .. 9.2 4.2 4.1 .. 34.9 2.6 .. .. 31 Guinea .. .. .. .. .. 188.8 1.6 .. 67.7 44 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti 9.1 .. .. .. .. .. .. .. .. .. 76 2008 World Development Indicators 2.10 PEOPLE Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2006b 1999 2006b 1999 2006b 2006b 2006b 2006b 2006b Honduras .. .. .. .. .. .. .. .. 87.2 28 Hungary 21.2 23.3 19.1 23.5 34.2 24.3 5.4 11.1 .. 10 India .. 9.2 24.9 27.0 90.8 61.0 3.8 .. .. 40 Indonesia .. .. 7.3 .. 21.3 .. .. .. .. 20 Iran, Islamic Rep. .. 13.6 9.8 11.1 34.6 30.0 5.1 18.6 70.4 19 Iraq .. .. .. .. .. .. .. .. 100.0 17 Ireland 11.5 14.3 16.8 21.1 28.5 23.9 4.7 14.0 .. 18 Israel 12.6 22.3 23.3 22.7 32.9 25.6 6.9 .. .. 13 Italy 14.9 24.9 27.7 27.2 27.6 22.7 4.6 9.6 .. 10 Jamaica 9.9 14.6 23.6 21.5 79.0 .. 5.3 8.8 .. 28 Japan .. 22.7 21.0 22.7 15.2 20.8 3.7 9.8 .. 19 Jordan .. 14.6 15.8 17.6 .. .. .. .. .. 20 Kazakhstan .. 9.8 .. 7.7 .. 5.6 3.2 15.8 .. 17c Kenya 12.9 21.0 14.8 20.7 204.8 284.5 6.9 17.9 98.8 40 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 11.8 19.2 15.7 25.0 8.4 8.9 4.6 16.5 .. 28 Kuwait 35.4 9.6 .. 13.9 .. 80.5 3.8 12.9 100.0 10 Kyrgyz Republic .. .. 11.9 .. 27.7 21.8 4.9 .. 61.3 24 Lao PDR .. 9.1 4.3 4.7 66.5 25.2 3.0 14.0 85.8 31 Latvia .. 20.7 23.7 24.0 27.9 12.4 5.1 14.2 .. 12 Lebanon .. 8.3 .. 8.8 14.2 17.2 2.7 11.0 12.6 14 Lesotho .. 22.2 69.0 44.2 1,247.8 1,012.0 13.0 29.8 66.1 40 Liberia .. .. .. .. .. .. .. .. .. 19 Libya .. .. .. .. 23.8 .. .. .. .. .. Lithuania .. 15.0 .. 21.2 34.2 20.0 5.2 15.6 .. 14 Macedonia, FYR .. .. .. .. .. .. .. .. .. 19 Madagascar .. 8.1 39.9 15.3 180.9 187.8 3.1 25.3 36.5 48 Malawi 7.2 .. .. .. .. .. .. .. .. .. Malaysia 10.1 14.5 22.3 21.1 83.3 71.0 6.2 25.2 .. 17 Mali .. 24.5 61.6 36.4 265.0 .. 4.5 16.8 .. 56 Mauritania .. 10.0 36.4 25.1 80.1 40.6 2.9 10.1 100.0 41 Mauritius 10.1 10.3 15.3 17.4 40.4 29.8 3.9 12.7 100.0 22 Mexico 4.8 14.9 14.2 15.7 47.8 41.3 5.4 25.6 .. 28 Moldova .. .. .. .. .. 43.8 7.6 20.2 .. 17 Mongolia .. 14.0 .. 13.0 .. 22.4 5.2 .. .. 33 Morocco 15.4 22.9 50.1 39.7 107.0 84.3 6.8 27.2 100.0 27 Mozambique .. 15.0 .. 94.8 .. 361.2 5.0 19.5 64.6 67 Myanmar .. .. 7.0 .. 28.6 .. .. .. 98.3 30 Namibia .. 20.0 36.4 19.9 157.6 .. .. .. 92.4 31 Nepal .. .. 13.1 .. 141.7 .. .. .. 30.5 40 Netherlands 12.1 17.9 20.9 24.0 42.3 40.6 5.2 11.2 .. .. New Zealand 17.2 19.3 24.3 22.5 41.6 25.2 6.5 .. .. 16 Nicaragua .. 9.2 .. 4.2 .. .. .. .. 73.6 33 Niger .. 32.4 64.4 49.1 .. 384.9 3.6 15.0 91.9 40 Nigeria .. .. .. .. .. .. .. .. 49.8 37 Norway 32.7 20.3 27.0 30.5 46.1 52.2 7.6 16.6 .. 11 Oman 10.5 15.4 22.2 12.9 .. 14.2 4.7 31.1 100.0 14 Pakistan .. .. .. .. .. .. 2.6 12.2 84.6 39 Panama 11.3 9.7 19.1 12.3 33.6 26.5 3.8 8.9 91.1 25 Papua New Guinea .. .. .. .. .. .. .. .. .. 36 Paraguay .. .. 18.4 .. 58.9 .. .. .. .. 28 Peru .. 6.6 10.8 8.9 21.2 9.0 2.7 17.0 .. 23 Philippines .. 9.2 10.7 9.0 15.0 12.4 2.7 16.4 .. 35 Poland 12.9 22.8 16.5 21.6 21.1 21.5 5.4 12.7 .. 12 Portugal 16.3 23.2 27.5 34.9 28.1 23.5 5.4 11.5 .. 11 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 77 2.10 Education inputs Public expenditure Public expenditure Trained Primary per studenta on education teachers school in primary pupil-teacher education ratio % of total % of GDP per capita government pupils per Primary Secondary Tertiary % of GDP expenditure % of total teacher 1991 2006b 1999 2006b 1999 2006b 2006b 2006b 2006b 2006b Romania .. 9.9 16.0 14.7 32.6 22.1 3.3 8.6 .. 17 Russian Federation .. .. .. .. .. 10.8 3.5 12.9 .. 17 Rwanda .. 10.4 28.4 18.4 657.6 404.5 3.8 19.0 c 98.3 66 Saudi Arabia .. .. .. .. .. .. 6.8 27.6 .. 15 Senegal 18.9 18.3 .. 35.0 .. 235.3 5.0 26.3 100.0 39 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. 3.8 .. 50.1c 44 c Singapore .. .. 17.9 .. .. .. .. .. .. 24 Slovak Republic .. 11.9 18.3 16.7 32.6 32.2 4.2 10.8 .. 18 Slovenia 17.4 25.9 26.5 30.6 28.8 25.8 6.0 .. .. 15 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 20.2 14.3 20.0 17.6 60.7 50.1 5.4 17.6 .. 36 Spain 11.3 19.0 24.4 23.8 19.6 22.7 4.3 11.0 .. 14 Sri Lanka .. .. .. .. .. .. .. .. .. 22 Sudan .. .. .. .. .. .. .. .. 58.7 34 Swaziland 6.7 14.2 26.1 38.8 388.4 320.6 7.0 .. 90.8 33 Sweden 45.8 25.7 26.1 34.5 52.7 43.7 7.3 12.9 .. 10 Switzerland 36.1 25.0 27.7 28.0 54.5 63.1 6.0 .. .. 13 Syrian Arab Republic .. .. 22.1 .. .. .. .. .. .. .. Tajikistan .. 8.8 .. 11.4 .. 11.2 3.4 19.0 93.0 22 Tanzania .. .. .. .. .. .. .. .. 100.0c 53c Thailand 11.6 14.1 15.7 15.5 35.5 25.0 4.2 25.0 .. 18 Timor-Leste .. .. .. .. .. .. .. .. .. 34 Togo .. .. 30.9 .. 317.9 .. .. .. 36.8 38 Trinidad and Tobago .. .. 12.2 .. 147.6 .. .. .. 81.0 17 Tunisia .. 21.1 27.1 24.4 89.4 56.4 7.3 20.8 .. 20 Turkey 10.7 14.1 14.3 17.8 45.5 40.7 4.0 .. .. .. Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda .. 11.3 .. 34.0 .. 188.9 5.2 18.3 84.8 49 Ukraine .. 16.0 11.2 24.5 36.5 31.5 6.3 19.3 99.6 17 United Arab Emirates .. 7.1 11.5 9.2 41.5 .. 1.3 27.4 60.0 15 United Kingdom 15.0 18.0 24.4 27.0 26.2 27.6 5.4 11.7 .. 17 United States .. 22.0 22.5 24.7 27.0 23.5 5.6 14.4 .. 14 Uruguay 7.8 7.6 11.3 8.7 19.1 20.1 2.6 14.1 .. 21 Uzbekistan .. .. .. .. .. .. .. .. 100.0 c 18 c Venezuela, RB .. 8.0 .. 8.3 .. 34.3 3.7 .. 83.1 17 Vietnam .. .. .. .. .. .. .. .. 95.6 21 West Bank and Gaza .. .. .. .. .. .. .. .. 100.0 32 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. Zambia .. 5.4 19.9 8.2 168.2 .. 2.0 14.8 .. 51 Zimbabwe 20.7 .. 19.5 .. 195.2 .. .. .. .. .. World .. m 14.5 m .. m 21.1 m .. m .. m 4.6 m .. m 30 w Low income .. .. .. .. .. .. .. .. 41 Middle income .. 13.0 16.6 17.1 37.2 25.9 4.3 .. .. Lower middle income .. .. .. .. .. .. .. .. 19 Upper middle income .. 13.2 17.1 16.7 31.8 23.3 4.1 14.1 21 Low & middle income .. .. .. .. .. .. 4.1 .. 33 East Asia & Pacific .. .. 7.0 .. 32.2 .. 3.5 .. 19 Europe & Central Asia .. 13.6 .. 18.2 .. 21.8 4.2 13.1 16 Latin America & Carib. .. 11.4 14.8 14.1 37.1 .. 4.0 .. 24 Middle East & N. Africa .. .. .. .. .. .. .. .. 23 South Asia .. .. 13.1 .. 90.8 .. 2.2 .. 41 Sub-Saharan Africa .. 11.8 .. .. .. .. 4.2 .. 47 High income 15.8 19.2 24.3 24.8 32.8 29.0 5.4 12.5 16 Euro area 14.9 18.9 25.3 27.2 28.7 27.1 5.3 11.0 14 a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Data are for 2007. 78 2008 World Development Indicators 2.10 PEOPLE Education inputs About the data Definitions Data on education are compiled by the United The general quality of the data on education finance · Public expenditure per student is public current Nations Educational, Scientific, and Cultural Organi- is poor. This is partly because ministries of educa- and capital spending on education divided by the zation (UNESCO) Institute for Statistics from official tion, from which the UNESCO Institute for Statistics number of students by level as a percentage of gross responses to surveys and from reports provided by collects data, are not necessarily the best source for domestic product (GDP) per capita. · Public expen- education authorities in each country. The data are education finance data. Other agencies, particularly diture on education is current and capital public used for monitoring, policymaking, and resource allo- ministries of finance, need to be consulted, but coor- expenditure on education as a percentage of GDP cation. For a variety of reasons, however, education dination is not easy. It is also difficult to track actual and as a percentage of total government expendi- statistics generally fail to provide a complete and spending from the central government to local institu- ture. · Trained teachers in primary education are accurate picture of a country's education system. tions. And private spending adds to the complexity of the percentage of primary school teachers who have Statistics often lag by one to two years, though collecting accurate data on public spending. received the minimum organized teacher training efforts have been made to shorten the delay. More- The share of trained teachers in primary educa- (pre-service or in-service) required for teaching in over, coverage and data collection methods vary tion measures the quality of the teaching staff. It their country. · Primary school pupil-teacher ratio across countries and over time within countries, so does not take account of competencies acquired by is the number of pupils enrolled in primary school comparisons should be interpreted with caution. teachers through their professional experience or divided by the number of primary school teachers For most countries the data on education spending self-instruction or of such factors as work experi- (regardless of their teaching assignment). in the table refer to public spending--government ence, teaching methods and materials, or classroom spending on public education plus subsidies for pri- conditions, which may affect the quality of teaching. vate education--and generally exclude foreign aid for Since the training teachers receive varies greatly education. They may also exclude spending by reli- (pre-service or in-service), care should be taken in gious schools, which play a significant role in many making comparisons across countries. developing countries. Data for some countries and The primary school pupil-teacher ratio refl ects some years refer to ministry of education spending the average number of pupils per teacher. It differs only and exclude education expenditures by other from the average class size because of the differ- ministries and local authorities. ent practices countries employ, such as part-time Many developing countries seek to supplement teachers, school shifts, and multigrade classes. The public funds for education, some with tuition fees comparability of pupil-teacher ratios across coun- to recover part of the cost of providing education tries is affected by the definition of teachers and by services or to encourage development of private differences in class size by grade and in the number schools. Fees raise diffi cult questions of equity, of hours taught, as well as the different practices efficiency, access, and taxation, however, and some mentioned above. Moreover, the underlying enroll- governments have used scholarships, vouchers, and ment levels are subject to a variety of reporting errors other public finance methods to counter criticism. For (for further discussion of enrollment data, see About most countries the data reflect only public spend- the data for table 2.11). While the pupil-teacher ratio ing. Data for a few countries include private spend- is often used to compare the quality of schooling ing, although countries vary on whether parents or across countries, it is often weakly related to the schools pay for books, uniforms, and other supplies. 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. Thus the The share of public expenditure devoted to edu- time-series data for the years through 1997 are not cation allows an assessment of the priority a gov- comparable with those for 1999 onward. Any time- ernment assigns to education relative to other series analysis should therefore be undertaken with public investments, as well as a government's extreme caution. 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 Data sources 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 2005/06, for example, are Data on education inputs are from the UNESCO cannot be understood simply by comparing current now listed for 2006. This change was implemented Institute for Statistics, which compiles inter- education indicators with national income. It takes to present the most recent data available and to align national data on education in cooperation with a long time before currently enrolled children can the data reporting with that of other international orga- national commissions and national statistical productively contribute to the national economy nizations (in particular the Organisation for Economic services. (Hanushek 2002). Co-operation and Development and Eurostat). 2008 World Development Indicators 79 2.11 Participation in education Gross enrollment Net enrollment Total net enrollment Children out of ratio ratioa ratio, primary school 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 2006b 2006b 2006b 2006b 1991 2006b 1991 2006b 2006b 2006b 2006b 2006b Afghanistan .. .. .. . .. .. .. .. .. .. .. .. Albania 49 105 77 19 95 94 .. 73 94 93 8 8 Algeria 14 110 83 22 89 95 53 66 100 98 26 62 Angola .. .. .. 3 50 .. .. .. .. .. .. .. Argentina 64 113 86 65 .. 99 .. 79 .. .. .. .. Armenia 36 98 90 32 .. 82 .. 86 84 88 7 4 Australia 104 104 149 73 99 96 80 86 96 97 35 27 Austria 88 102 102 49 88 97 .. .. 96 98 8 4 Azerbaijan 32 96 83 15 89 85 .. 78 87 84 38 43 Bangladesh 10 103 44 6 .. 89 .. 41 91 94 842 529 Belarus 103 96 96 66 85 89 .. 88 91 88 18 21 Belgium 120 102 109 62 96 98 86 97 98 98 9 7 Benin 5 96 32 .. 41 78 .. .. 89 71 79 198 Bolivia 50 109 82 41 .. 95 .. 71 96 97 30 22 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana .. 108 75 5 88 86 39 61 88 89 19 17 Brazil 63 140 106 24 85 95 17 78 95 97 336 224 Bulgaria 80 102 105 44 85 93 63 89 95 94 8 8 Burkina Faso 2 60 15 2 27 47 .. 12 49 39 562 653 Burundi 2 103 14 2 53 75 .. .. 61 56 154 170 Cambodia 11 122 38 5 72 90 .. 24 97 96 98 114 Cameroon 22 106 41 7 69 .. .. .. .. .. .. .. Canada 68 100 117 62 98 .. 89 .. .. .. 0 .. Central African Republic 2 61 .. 1 52 45 .. .. .. .. 160 212 Chad 1 76 15 1 34 .. .. .. .. .. .. .. Chile 55 104 91 48 89 .. 55 .. 95 94 44 53 China 39 111 76 22 98 .. .. .. .. .. .. .. Hong Kong, China .. .. 85 33 .. .. .. 78 97 93 .. .. Colombia 40 116 82 31 68 88 34 65 92 92 193 174 Congo, Dem. Rep. .. .. .. .. 54 .. .. .. .. .. .. .. Congo, Rep. 9 108 43 .. 82 55 .. .. 49 60 116 133 Costa Rica 70 111 86 25 87 .. 38 .. .. .. .. .. Côte d'Ivoire 3 71 .. .. 45 .. .. .. .. .. .. .. Croatia 53 93 89 46 79 .. 63 .. .. .. .. .. Cuba 113 101 94 88 94 97 73 87 97 97 15 12 Czech Republic 114 102 96 48 87 93 .. .. 91 94 22 15 Denmark 94 99 124 81 98 96 87 91 96 97 9 6 Dominican Republic 32 98 69 35 56 77 .. 52 78 81 139 116 Ecuador 80 117 65 .. 98 97 .. 55 99 100 12 0 Egypt, Arab Rep. 17 102 86 35 86 94 .. 83 100 94 10 256 El Salvador 51 114 64 21 .. 94 .. 54 96 97 21 18 Eritrea 14 62 31 1 15 47 .. 25 53 45 145 163 Estonia 116 100 100 66 100 95 .. 91 97 97 1 1 Ethiopia 2 83 27 2 22 65 .. 24 70 65 2,047 2,426 Finland 59 100 111 92 98 99 93 95 99 99 3 2 France 117 110 114 56 100 99 .. 99 99 99 19 9 Gabon .. 152 .. .. 94 .. .. .. .. .. .. .. Gambia, The 17 74 45 1 46 62 .. 38 .. .. 49 41 Georgia 55 96 85 38 97 89 .. 79 87 88 19 14 Germany 97 101 100 .. 84 .. .. .. .. .. .. .. Ghana 55 98 c 47c 5 54 66c .. 38 64 65 572c 569c Greece 68 102 102 90 95 100 83 91 100 100 0 1 Guatemala 29 114 53 9 .. 94 .. 38 97 93 21 62 Guinea 7 88 35 3 27 72 .. 28 76 64 159 230 Guinea-Bissau .. .. .. .. 38 .. .. .. .. .. .. .. Haiti .. .. .. .. 21 .. .. .. .. .. .. .. 80 2008 World Development Indicators 2.11 PEOPLE Participation in education Gross enrollment Net enrollment Total net enrollment Children out of ratio ratioa ratio, primary school 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 2006b 2006b 2006b 2006b 1991 2006b 1991 2006b 2006b 2006b 2006b 2006b Honduras 38 118 76 17 88 96 21 .. 96 97 21 11 Hungary 84 98 96 65 91 89 75 90 96 96 10 9 India 39 115 54 11 .. 88 .. .. 96 92 2,780 4,713 Indonesia 33 115 62 17 96 95 39 57 99 96 142 544 Iran, Islamic Rep. 53 118 81 27 92 94 .. 77 91 100 305 0 Iraq .. .. .. . 94 .. .. .. .. .. .. .. Ireland .. 104 112 58 90 95 80 87 94 95 13 11 Israel 93 110 93 58 92 97 .. 89 97 98 11 7 Italy 104 102 99 65 100 99 .. 92 100 99 4 12 Jamaica 92 95 87 .. 96 90 64 78 91 91 16 15 Japan 85 100 102 55 100 100 97 100 100 100 12 0 Jordan 32 97 89 40 94 91 .. 79 95 96 23 17 Kazakhstan 36 105c 93c 51c 88 90 c .. 86c 98 99 6c 3c Kenya 50 108 48 3 .. 76 .. 42 76 77 670 649 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 96 105 96 91 100 98 86 94 .. .. .. .. Kuwait 75 96 89 18 49 83 .. .. 89 88 12 12 Kyrgyz Republic 14 97 86 43 92 86 .. 80 94 93 14 14 Lao PDR 11 116 43 9 62 84 .. 35 85 80 54 71 Latvia 87 95 99 75 94 90 .. .. 90 94 4 3 Lebanon 64 94 81 48 66 82 .. 73 83 83 40 40 Lesotho 18 114 37 4 72 72 15 24 73 78 55 48 Liberia 100 91 .. .. .. 39 .. .. .. .. 177 179 Libya 9 110 111 .. 93 .. .. .. .. .. .. .. Lithuania 65 94 100 76 .. 88 .. 94 90 91 8 7 Macedonia, FYR 33 98 84 30 94 92 .. 81 97 97 2 1 Madagascar 8 139 24 3 64 96 .. .. 93 93 54 52 Malawi .. 119 29 0d 49 91 .. 24 91 96 136 66 Malaysia 122 100 72 31 .. 99 .. 72 99 99 11 15 Mali 3 80 28 3 25 61 6 .. 67 52 328 466 Mauritania 2 102 22 4 36 79 .. 16 75 79 52 40 Mauritius 101 102 86 17 91 95 .. 79 94 96 3 2 Mexico 96 112 85 25 98 98 45 69 100 99 15 52 Moldova 68 91 82 36 88 83 .. 75 85 85 14 13 Mongolia 54 101 89 47 90 91 .. 82 95 99 6 1 Morocco 59 106 52 12 56 88 .. .. 90 85 168 261 Mozambique .. 105 16 1 42 69 .. 4 80 73 568 662 Myanmar 6 114 49 .. 99 100 .. 46 98 100 16 0 Namibia 31 107 57 6 .. 76 .. 35 74 79 49 40 Nepal 27 126 43 6 .. 79 .. .. 85 75 267 436 Netherlands 90 107 118 59 95 98 84 87 99 97 8 15 New Zealand 93 102 121 82 98 99 85 .. 99 99 1 1 Nicaragua 52 116 66 .. 70 90 .. 43 93 94 38 34 Niger 2 51 11 1 24 43 6 9 49 36 565 680 Nigeria 14 96 32 10 55 63 .. 26 70 60 3,550 4,547 Norway 88 98 113 78 100 98 88 96 98 98 5 4 Oman 8 82 89 25 69 74 .. 77 76 77 44 38 Pakistan 52 84 30 5 33 66 .. 30 76 58 2,705 4,116 Panama 67 112 70 45 .. 98 .. 64 99 99 1 2 Papua New Guinea .. 55 .. .. .. .. .. .. .. .. .. .. Paraguay 34 112 67 25 94 94 26 .. 94 95 24 21 Peru 66 116 92 34 .. 96 .. 70 98 100 30 2 Philippines 40 111 85 28 96 93 .. 60 92 95 463 315 Poland 55 98 100 64 97 97 76 93 97 97 50 38 Portugal 79 116 97 55 98 98 .. 82 100 99 1 3 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 81 2.11 Participation in education Gross enrollment Net enrollment Total net enrollment Children out of ratio ratioa ratio, primary school 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 2006b 2006b 2006b 2006b 1991 2006b 1991 2006b 2006b 2006b 2006b 2006b Romania 74 105 86 45 81 91 .. 81 95 95 24 24 Russian Federation 88 129 91 70 98 92 .. .. 92 93 170 140 Rwanda .. 140 13 3 67 91 8 .. 72 75 78 45 Saudi Arabia 12 108 96 27 87 93 39 60 87 87 110 108 Senegal 9 80 22 6 45 71 .. 17 75 71 250 262 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. 145c 32c .. 43 .. .. 23c .. .. .. .. Singapore .. 78 63 .. .. .. .. .. .. .. .. .. Slovak Republic 95 99 96 41 .. 92 .. .. 92 92 10 9 Slovenia 78 98 96 79 96 96 .. 91 97 97 1 1 Somalia .. .. .. .. 9 19 .. .. .. .. .. .. South Africa 38 106 95 15 90 88 45 .. 93 94 262 207 Spain 119 105 118 66 100 100 .. 94 100 99 3 6 Sri Lanka .. 108 87 .. .. 97 .. .. .. .. .. .. Sudan 24 66 34 .. 40 54 .. 19 .. .. .. .. Swaziland 17 106 47 4 75 78 30 32 76 77 23 22 Sweden 93 98 103 82 100 97 85 99 97 97 10 10 Switzerland 96 98 93 45 84 90 80 82 94 94 16 14 Syrian Arab Republic 11 126 70 .. 91 .. 43 63 .. .. .. .. Tajikistan 9 100 83 19 77 97 .. 80 99 95 2 17 Tanzania 28 112c .. 1 51 100 c .. .. 99 97 0c 10 c Thailand 92 108 78 46 88 94 .. 71 100 100 0 1 Timor-Leste 10 99 53 .. .. 68 .. .. 70 67 28 29 Togo 2 102 40 .. 64 80 15 .. 87 74 58 120 Trinidad and Tobago 85 95 76 11 89 85 .. 65 89 90 8 7 Tunisia .. 110 83 30 93 97 .. .. 98 99 12 6 Turkey 10 94 74 31 89 90 42 66 92 88 329 499 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 3 117 18 3 .. .. .. 16 .. .. .. .. Ukraine 90 102 93 73 81 90 .. 84 91 91 81 79 United Arab Emirates 78 104 90 .. 99 88 60 79 93 92 7 6 United Kingdom 71 107 105 59 98 99 80 95 100 100 3 0d United States 61 98 94 82 97 92 84 88 93 94 954 750 Uruguay 67 113 107 42 91 94 .. .. 97 98 5 4 Uzbekistan 27 95c 102c 10 c 78 .. .. .. .. .. .. .. Venezuela, RB 60 104 78 52 87 91 18 67 91 91 123 103 Vietnam 60 90 76 16 90 84 .. 69 .. .. .. .. West Bank and Gaza 30 83 94 41 .. 76 .. 90 80 80 48 45 Yemen, Rep. 1 87 46 9 50 75 .. 37 86 62 275 632 Zambia .. 117 36 .. .. 92 .. 28 92 94 96 54 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. World 40 w 106 w 65 w 24 w 84 w 86 w .. w 58 w 90 w 87 w Low income 34 102 45 9 .. 78 .. 39 84 78 Middle income .. 112 78 27 93 93 .. 70 95 94 Lower middle income 41 112 73 23 93 93 .. 68 94 94 Upper middle income 63 113 92 40 91 94 .. 76 96 95 Low & middle income 34 107 61 19 82 85 .. 54 89 86 East Asia & Pacific 41 111 72 20 96 93 .. 68 94 94 Europe & Central Asia 54 103 89 51 90 91 .. 81 93 91 Latin America & Carib. 62 119 89 30 85 94 31 69 96 96 Middle East & N. Africa 23 104 74 24 82 91 .. 67 94 90 South Asia 41 110 49 9 .. 85 .. .. 92 87 Sub-Saharan Africa 16 93 31 5 49 68 .. 25 72 66 High income 78 101 101 67 95 95 85 91 96 96 Euro area 103 .. .. .. 95 .. .. .. .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Data are for 2007. d. Less than 0.5. 82 2008 World Development Indicators 2.11 PEOPLE Participation in education About the data Definitions School enrollment data are reported to the United Overage or underage enrollments are frequent, par- · Gross enrollment ratio is the ratio of total enroll- Nations Educational, Scientific, and Cultural Organiza- ticularly when, for cultural or economic reasons, par- ment, regardless of age, to the population of the age tion (UNESCO) Institute for Statistics by national edu- ents prefer children to start school at other than the group that officially corresponds to the level of educa- cation authorities and statistical offices. Enrollment official age. Age at enrollment may be inaccurately tion shown. · Preprimary education refers to the ini- ratios help monitor whether a country is on track to estimated or misstated, especially in communities tial stage of organized instruction, designed primarily achieve the Millennium Development Goal of univer- where registration of births is not strictly enforced. to introduce very young children to a school-type envi- sal primary education by 2015, which implies achiev- Other problems of cross-country comparison of ronment. · Primary education provides children with ing a net primary enrollment ratio of 100 percent, enrollment data stem from errors in school-age popu- basic reading, writing, and mathematics skills along and whether an education system has the capacity lation estimates. Age-sex structures drawn from cen- with an elementary understanding of such subjects to meet the needs of universal primary education, as suses or vital registrations, the primary data sources as history, geography, natural science, social sci- indicated in part by its gross enrollment ratios. on school-age population, commonly underenumer- ence, art, and music. · Secondary education com- Enrollment ratios, while a useful measure of par- ate (especially young children) to circumvent laws or pletes the provision of basic education that began ticipation in education, have limitations. They are regulations. Errors are also introduced when parents at the primary level and aims at laying the founda- based on data from annual school surveys, which round children's ages. While census data are often tions for lifelong learning and human development are typically conducted at the beginning of the school adjusted for age bias, adjustments are rarely made by offering more subject- or skill-oriented instruction year. They do not reflect actual attendance or drop- for inadequate vital registration systems. Compound- using more specialized teachers. · Tertiary educa- out rates during the year. And school administrators ing these problems, pre- and postcensus estimates tion refers to a wide range of post-secondary educa- may exaggerate enrollments, especially if there is a of school-age children are model interpolations or tion institutions, including technical and vocational financial incentive to do so. projections that may miss important demographic education, colleges, and universities, whether or not Also, as international indicators, the gross and net events (see discussion of demographic data in About leading to an advanced research qualification, that primary enrollment ratios have an inherent weakness: the data for table 2.1). normally require as a minimum condition of admis- the length of primary education differs across coun- Gross enrollment ratios indicate the capacity of sion the successful completion of education at the tries, although the International Standard Classifica- each level of the education system, but a high ratio secondary level. · Net enrollment ratio is the ratio tion of Education tries to minimize the difference. A may reflect a substantial number of overage children of total enrollment of children of official school age relatively short duration for primary education tends enrolled in each grade because of repetition rather based on the International Standard Classification of to increase the ratio; a relatively long one to decrease than a successful education system. The net enroll- Education 1997 to the population of the age group it (in part because more older children drop out). ment ratio excludes overage and underage students that officially corresponds to the level of education to capture more accurately the system's coverage and shown. · Total net enrollment ratio, primary, is the internal efficiency but does not account for children ratio of total enrollment of children of official school In some countries close to 10 percent of primary-school-age children are who fall outside the official school age because of age for primary education who are enrolled in primary enrolled in secondary school 2.11a late or early entry rather than grade repetition. Differ- or secondary education to the total primary-school- ences between gross and net enrollment ratios show age population. · Children out of school are the Net enrollment ratio, primary the incidence of overage and underage enrollments. number of primary-school-age children not enrolled Percent Total net enrollment ratio, primary 100 Total net primary enrollment was recently added in primary or secondary school. as a Millennium Development Goal indicator. It cap- tures the children of primary-school age who have progressed to secondary education, which the tradi- tional net enrollment ratio excludes. Children out of school are primary-school-age chil- 50 dren not enrolled in primary or secondary education. The data are calculated by the UNESCO Institute for Statistics using administrative data. Children out of school include dropouts, children never enrolled, and children of primary age enrolled in preprimary educa- 0 tion. Large numbers of children out of school create Hungary Kazakhstan Kyrgyz Republic pressure to enroll children and provide classrooms, The difference between net enrollment and total teachers, and educational materials, a task made primary net enrollment is small in most coun- difficult in many developing countries by limited edu- tries. But it is larger in some countries because cation budgets. However, getting children into school many children start primary school earlier than is a high priority for countries and crucial for achiev- Data sources the official entrance age and are younger than the ing the Millennium Development Goal of universal official age when they reach secondary school. primary education. Data on gross and net enrollment ratios and out In 2006 the UNESCO Institute for Statistics changed of school children are from the UNESCO Institute Source: United Nations Educational, Scientific, and Cultural Organization Institute for Statistics. its convention for citing the reference year. For more for Statistics. information, see About the data for table 2.10. 2008 World Development Indicators 83 2.12 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 5a primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2006b 2006b 1991 2005b 1991 2005b 2005b 2005b 2006b 2006b 2005b 2005b Afghanistan .. .. .. .. .. .. .. .. 18 14 .. .. Albania 100 99 .. .. .. .. 89 91 3 2 100 99 Algeria 99 97 95 95 94 96 90 92 14 9 74 79 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 110 109 .. 96 .. 98 94 97 8 5 93 96 Armenia 102 106 .. .. .. .. 100 99 0c 0c 100 99 Australia 106 105 98 .. 99 .. .. .. .. .. .. .. Austria 102 100 .. .. .. .. 97 100 1 1 .. .. Azerbaijan 99 97 .. .. .. .. 100 94 0c 0c 100 98 Bangladesh 122 124 .. 63 .. 67 63 67 7 7 86 92 Belarus 102 100 .. .. .. .. 99 100 0c 0c 99 100 Belgium 97 99 90 .. 92 .. .. .. 3 3 .. .. Benin 109 96 54 53 56 50 48 44 17 17 .. .. Bolivia 122 122 .. 85 .. 85 83 81 1 1 90 90 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 111 104 81 89 87 92 83 88 5 4 95 95 Brazil 106 97 .. .. .. .. .. .. 20 20 .. .. Bulgaria 97 94 91 .. 90 .. 91 93 3 2 95 96 Burkina Faso 79 67 71 71 68 74 63 66 12 12 45 43 Burundi 164 164 65 66 58 68 57 61 29 28 37 31 Cambodia 135 127 .. 61 .. 64 54 57 14 11 83 80 Cameroon 111 97 .. .. .. .. .. .. 28 23 43 47 Canada 97 95 95 .. 98 .. .. .. .. .. .. .. Central African Republic 73 55 24 .. 22 .. .. .. 29 30 46 52 Chad 109 79 56 34 41 32 27 23 22 24 56 42 Chile 101 99 94 100 91 99 98 98 3 2 96 98 China 88 87 58 .. 78 .. .. .. 0c 0c .. .. Hong Kong, China .. .. .. 99 .. 100 99 100 1 1 100 100 Colombia 127 123 .. 78 .. 86 78 86 4 3 99 100 Congo, Dem. Rep. .. .. 58 .. 50 .. .. .. .. .. .. .. Congo, Rep. 78 78 56 .. 65 .. .. .. 21 21 58 58 Costa Rica 108 108 83 93 85 95 89 92 8 6 100 97 Côte d'Ivoire 73 61 75 .. 70 .. .. .. 23 24 .. .. Croatia .. .. .. .. .. .. .. .. 0c 1 100 d 100 d Cuba 102 104 .. 96 .. 98 96 98 1 0c 98 99 Czech Republic 102 103 .. 98 .. 99 98 99 1 1 99 100 Denmark 98 97 94 93 94 93 92 92 .. .. 100 99 Dominican Republic 102 100 .. 66 .. 71 58 65 10 6 81 87 Ecuador 133 131 .. 75 .. 77 75 77 2 1 81 76 Egypt, Arab Rep. 106 102 .. 98 .. 99 98 99 3 2 72 82 El Salvador 121 116 56 70 60 74 65 70 9 6 91 92 Eritrea 53 45 .. 77 .. 69 77 69 15 15 86 79 Estonia 100 97 .. 98 .. 99 99 99 2 1 96 99 Ethiopia 125 113 16 57d 23 59d 62 63 6 5 91 91 Finland 98 98 100 99 100 100 99 100 1 0c 100 100 France .. .. 69 .. 95 .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 65 71 .. .. .. .. .. .. 6 6 .. .. Georgia 97 103 .. 86 .. 90 83 89 0c 0c 98 100 Germany 104 103 .. .. .. .. 99 100 1 1 99 99 Ghana 105 110 81 .. 79 .. .. .. 6 6 .. .. Greece 100 100 100 98 100 100 98 100 1 0c 99 100 Guatemala 125 122 .. 70 .. 68 65 62 13 11 92 90 Guinea 94 87 64 83 48 78 79 72 8 9 75 66 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 84 2008 World Development Indicators 2.12 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 5a primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2006b 2006b 1991 2005b 1991 2005b 2005b 2005b 2006b 2006b 2005b 2005b Honduras 139 134 .. 80 .. 87 77 85 8 6 68 74 Hungary 97 95 77 .. 98 .. 98 98 3 2 99 99 India 132 125 .. 73 .. 73 73 73 3 3 87 83 Indonesia 120 116 34 92 78 87 88 83 6 4 79 78 Iran, Islamic Rep. 112 150 91 .. 89 .. .. .. 3 1 93 83 Iraq .. .. .. 87 .. 73 78 61 9 7 73 66 Ireland 99 99 99 100 100 100 .. .. 1 1 .. .. Israel 96 99 .. 100 .. 100 100 100 2 1 74 73 Italy 104 102 .. 100 .. 100 100 100 0c 0c 100 99 Jamaica 94 92 .. .. .. .. .. .. 3 2 100 97 Japan 98 99 100 .. 100 .. .. .. .. .. .. .. Jordan 92 92 .. 97 .. 96 96 95 1 1 96 97 Kazakhstan 107 107 .. .. .. .. 100 d 100 d 0 c,e 0 c,e 100 d 100 d Kenya 112 108 75 81 78 85 74 71 6 6 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 105 106 99 99 100 99 99 99 0c 0c 99 99 Kuwait 96 93 .. 95 .. 97 95 97 2 2 95 100 Kyrgyz Republic 98 97 .. .. .. .. 97 100 0c 0c 100 100 Lao PDR 129 120 .. 62 .. 62 62 62 19 17 79 75 Latvia 94 93 .. .. .. .. 99 98 4 2 97 98 Lebanon 86 86 .. 88 .. 94 83 91 11 8 83 88 Lesotho 105 99 58 68 73 80 53 71 21 16 67 65 Liberia 109 106 .. .. .. .. .. .. 6 6 .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 94 93 .. .. .. .. 98 98 1 0c 98 99 Macedonia, FYR 99 99 .. .. .. .. 98 99 0c 0c 100 99 Madagascar 181 176 22 35 21 37 35 37 20 19 56 54 Malawi 145 156 71 44 57 44 36 36 21 20 74 71 Malaysia 102 101 97 .. 97 .. .. .. .. .. .. .. Mali 89 76 71 83 67 79 75 70 17 17 63 48 Mauritania 124 129 76 59 75 56 46 43 10 10 51 45 Mauritius 104 104 97 98 98 100 97 100 5 4 61 72 Mexico 111 109 35 93 71 94 91 92 6 4 95 93 Moldova 90 87 .. .. .. .. 96 98 0c 0c 98 99 Mongolia 117 119 .. .. .. .. 91 91 0c 0c 95 99 Morocco 104 100 75 82 76 79 76 72 15 10 78 77 Mozambique 153 143 36 60 32 55 41 39 5 5 52 56 Myanmar 139 136 .. 71 .. 72 71 72 1 0c 76 72 Namibia 104 105 60 84 65 90 73 80 19 14 72 77 Nepal 160 160 51 75 51 83 75 83 21 20 79 74 Netherlands 101 100 .. 99 .. 100 .. .. .. .. 96 100 New Zealand 105 104 .. .. .. .. .. .. .. .. .. .. Nicaragua 173 163 11 50 37 57 46 55 11 8 .. .. Niger 76 59 61 58 65 54 55 50 5 5 61 58 Nigeria 116 99 .. 71 .. 75 61 64 3 3 .. .. Norway 97 97 99 100 100 100 100 100 .. .. 100 100 Oman 76 76 97 100 96 100 100 99 0c 1 99 98 Pakistan 125 100 .. 68 .. 72 68 72 2 2 69 75 Panama 116 114 .. 87 .. 89 84 86 7 5 92 95 Papua New Guinea .. .. 70 .. 68 .. .. .. .. .. .. .. Paraguay 117 114 73 79 75 83 74 79 8 5 90 90 Peru 110 112 .. 91 .. 90 86 85 9 9 96 94 Philippines 137 128 .. 71 .. 80 66 77 3 2 91 92 Poland 97 98 89 .. 96 .. .. .. 1 0c .. .. Portugal 106 106 .. .. .. .. .. .. 13 7 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 85 2.12 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 5a primary education age group enrollment % Male Female Male Female Male Female Male Female Male Female 2006b 2006b 1991 2005b 1991 2005b 2005b 2005b 2006b 2006b 2005b 2005b Romania 97 96 .. .. .. .. 94 95 3 2 98 98 Russian Federation .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 209 206 61 43 59 49 30 32 15 15 .. .. Saudi Arabia 102 105 82 100 84 93 100 94 6 4 93 97 Senegal 95 98 .. 65 .. 65 54 53 11 10 52 48 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. 10e 10e .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 100 98 .. .. .. .. 97 98 3 2 98 99 Slovenia 98 96 .. .. .. .. .. .. 1 0c .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 118 112 .. 82 .. 83 75 79 8 8 89 91 Spain 103 101 .. 100 .. 100 100 100 3 2 .. .. Sri Lanka 109 109 92 .. 93 .. .. .. 1 1 .. .. Sudan 67 58 90 78 99 79 73 75 1 2 94 100 Swaziland 111 103 74 81 80 87 66 75 19 15 88 89 Sweden 96 95 100 .. 100 .. .. .. .. .. .. .. Switzerland 86 91 .. .. .. .. .. .. 2 1 99 100 Syrian Arab Republic 125 122 97 .. 95 .. 92 93 7 5 95 97 Tajikistan 103 99 .. .. .. .. 100 97 0c 0c 98 97 Tanzania 105 104 81 85d 82 89d 81d 85d 4e 4e 47 45 Thailand .. .. .. .. .. .. .. .. .. .. .. .. Timor-Leste 118 105 .. .. .. .. .. .. .. .. .. .. Togo 101 95 52 79 42 70 74 62 23 23 68 61 Trinidad and Tobago 96 92 .. 90 .. 92 80 87 6 4 94 92 Tunisia 100 101 94 97 77 97 93 95 10 7 86 90 Turkey 97 93 98 97 97 97 95 93 3 3 93 90 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 145 147 .. 49 .. 49 26 25 30 29 42 43 Ukraine 99 99 .. .. .. .. .. .. 0c 0c 100 100 United Arab Emirates 103 101 80 98 80 100 98 100 2 2 99 100 United Kingdom .. .. .. .. .. .. .. .. 0 0 .. .. United States 102 100 .. .. .. .. .. .. .. .. .. .. Uruguay 107 105 96 90 98 93 88 91 9 6 75 87 Uzbekistan 97 94 .. .. .. .. .. .. 0e 0e .. .. Venezuela, RB 102 99 82 90 90 94 87 93 8 5 99 99 Vietnam 99 94 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza 78 78 .. .. .. .. 97 100 1 1 98 99 Yemen, Rep. 122 102 .. 67 .. 65 61 57 5 4 83 82 Zambia 119 125 .. 92 .. 87 79 73 7 6 49 60 Zimbabwe .. .. 72 .. 81 .. .. .. .. .. .. .. World 116 w 111 w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 126 116 .. 71 .. 71 69 69 6 6 79 77 Middle income .. .. 61 .. 80 .. .. .. .. .. .. .. Lower middle income 94 95 59 .. 79 .. .. .. 3 2 .. .. Upper middle income 105 101 .. .. .. .. .. .. 10 9 .. .. Low & middle income .. .. .. .. .. .. .. .. .. .. .. .. East Asia & Pacific 91 90 55 .. 78 .. .. .. 1 1 .. .. Europe & Central Asia .. .. .. .. .. .. .. .. .. .. .. .. Latin America & Carib. 112 108 .. .. .. .. .. .. 10 9 .. .. Middle East & N. Africa 108 110 .. 90 .. 87 87 84 7 4 82 83 South Asia 130 120 .. 72 .. 73 72 73 4 4 84 82 Sub-Saharan Africa 117 108 .. .. .. .. .. .. 9 9 .. .. High income 101 101 .. .. .. .. .. .. .. .. .. .. Euro area 103 102 .. .. .. .. .. .. 1 1 .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Less than 0.5. d. Data are for 2006. e. Data are for 2007. 86 2008 World Development Indicators 2.12 PEOPLE Education efficiency About the data Definitions The United Nations Educational, Scientific, and Cul- power and internal effi ciency. Rates approaching · Gross intake rate in grade 1 is the number of tural Organization (UNESCO) Institute for Statistics 100 percent indicate high retention and low dropout new entrants in the first grade of primary education estimates indicators of students' progress through levels. Cohort survival rates are typically estimated regardless of age as a percentage of the population school. These indicators measure an education sys- from data on enrollment and repetition by grade for of the official primary school entrance age. · Cohort tem's success in reaching all students, efficiently two consecutive years. This procedure, called the survival rate is the percentage of children enrolled moving students from one grade to the next, and reconstructed cohort method, makes three simplify- in the first grade of primary school who eventually imparting a particular level of education. ing assumptions: dropouts never return to school; reach grade 5 or the last grade of primary educa- The gross intake rate indicates the level of access promotion, repetition, and dropout rates remain con- tion. The estimate is based on the reconstructed to primary education and the education system's stant over the period in which the cohort is enrolled cohort method (see About the data). · Repeaters in capacity to provide access to primary education. in school; and the same rates apply to all pupils primary school are the number of students enrolled Low gross intake rates in grade 1 reflect the fact enrolled in a grade, regardless of whether they previ- in the same grade as in the previous year as a per- that many children do not enter primary school even ously repeated a grade (Fredricksen 1993). Cross- centage of all students enrolled in primary school. though school attendance, at least through the pri- country comparisons should thus be made with cau- · Transition to secondary school is the number of mary level, is mandatory in all countries. Because tion, because other flows--caused by new entrants, new entrants to the first grade of secondary school the gross intake rate includes all new entrants reentrants, grade skipping, migration, or transfers in a given year as a percentage of the number of regardless of age, it can exceed 100 percent. Once during the school year--are not considered. students enrolled in the final grade of primary school enrolled, students drop out for a variety of reasons, Research suggests that five to six years of school- in the previous year. including low quality schooling, relevance of cur- ing, which is how long primary education lasts in most riculum (real or perceived by parents or students), countries, is a critical threshold for achieving sus- repetition, discouragement over poor performance, tainable basic literacy and numeracy skills. But the and direct and indirect schooling costs. Students' indicator only indirectly reflects the quality of school- progress to higher grades may also be limited by the ing, and a high rate does not guarantee these learn- availability of teachers, classrooms, and materials. ing outcomes. Measuring actual learning outcomes The cohort survival rate is the estimated proportion requires setting curriculum standards and measuring of an entering cohort of grade 1 students that eventu- students' learning progress against those standards ally reaches grade 5 or the last grade of primary edu- through standardized assessments, actions that cation. It measures an education system's holding many countries do not systematically undertake. Data on repeaters are often used to indicate an In Lesotho more girls who enroll education system's internal efficiency. Repeaters not in primary school stay in and only increase the cost of education for the family complete school than boys do 2.12a and the school system, but also use limited school Percent Girls Boys resources. Country policies on repetition and promo- 120 tion differ; in some cases the number of repeaters is controlled because of limited capacity. Care should be taken in interpreting this indicator. The transition rate from primary to secondary 80 school conveys the degree of access or transition between the two levels. As completing primary edu- cation is a prerequisite for participating in lower 40 secondary school, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low transi- 0 tion rate can signal such problems as an inadequate Gross intake Primary school Primary examination and promotion system or insufficient rate in repeaters completion grade 1 rate secondary school capacity. The quality of data on In many developing countries, especially in the transition rate is affected when new entrants and Sub-Saharan Africa, fewer girls than boys enroll repeaters are not correctly distinguished in the first and stay in school. But in Lesotho more girls com- grade of secondary school. Students who interrupt plete primary school because they repeat grades their studies after completing primary school could also affect data quality. Data sources less often and are less likely to drop out. In 2006 the UNESCO Institute for Statistics changed Data on education efficiency are from the UNESCO Source: United Nations Educational, Scientific, and its convention for citing the reference year. For more Institute for Statistics. Cultural Organization Institute for Statistics. information, see About the data for table 2.10. 2008 World Development Indicators 87 2.13 Education completion and outcomes Primary completion Youth literacy Adult literacy ratea rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2006b 1991 2006b 1991 2006b 1990 2005 1990 2005 2005 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. 96 .. 97 .. 96 .. 99 .. 99 99 98 Algeria 80 85 86 86 73 84 86 94 62 86 80 60 Angola 35 .. .. .. .. .. .. 84 .. 63 83 54 Argentina .. 99 .. 97 .. 102 98 99 99 99 97 97 Armenia 90 91 .. 90 .. 93 100 100 100 100 100 99 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. 103 .. 103 .. 102 .. .. .. .. .. .. Azerbaijan .. 92 .. 94 .. 90 .. .. .. .. .. .. Bangladesh 49 72 .. 70 .. 74 52 67 38 60 54 41 Belarus 94 95 95 96 96 93 100 .. 100 .. .. .. Belgium 79 .. 76 .. 82 .. .. .. .. .. .. .. Benin 21 65 28 78 13 51 55 59 27 33 48 23 Bolivia .. 101 .. 102 .. 100 96 99 92 96 93 81 Bosnia and Herzegovina .. .. .. .. .. .. .. 100 .. 100 99 94 Botswana 89 95 82 75 97 115 86 92 92 96 80 82 Brazil 93 105 .. .. .. .. .. 96 .. 98 88 89 Bulgaria 84 99 86 98 83 99 .. 98 .. 98 99 98 Burkina Faso 20 31 24 35 15 28 27 40 14 26 31 17 Burundi 46 36 49 40 43 32 59 77 48 70 67 52 Cambodia .. 87 .. 87 .. 86 .. 88 .. 79 85 64 Cameroon 53 58 57 65 49 51 .. .. .. .. 77 60 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 27 24 35 31 18 18 63 70 35 47 65 33 Chad 18 31 29 41 7 21 .. 56 .. 23 41 13 Chile .. 123 .. 130 .. 116 98 99 99 99 96 96 China 105 .. .. .. .. .. 97 99 91 99 95 87 Hong Kong, China 102 .. .. .. .. .. .. .. .. .. .. .. Colombia 70 105 67 103 73 107 89 98 92 98 93 93 Congo, Dem. Rep. 46 38 58 46 34 31 .. 78 .. 63 81 54 Congo, Rep. 54 73 59 77 49 69 .. 98 .. 97 91 79 Costa Rica 79 89 77 87 81 91 .. 97 .. 98 95 95 Côte d'Ivoire 43 43 55 53 32 33 60 71 38 52 61 39 Croatia 85 92 .. 93 .. 92 100 100 100 100 99 97 Cuba 99 92 .. 92 .. 91 .. 100 .. 100 100 100 Czech Republic .. 102 .. 102 .. 102 .. .. .. .. .. .. Denmark 98 99 98 99 98 99 .. .. .. .. .. .. Dominican Republic 61 83 .. 80 .. 87 .. 93 .. 95 87 87 Ecuador 91 106 91 105 92 106 97 96 96 96 92 90 Egypt, Arab Rep. .. 98 .. 102 .. 94 .. 90 .. 79 83 59 El Salvador 41 88 38 88 43 88 85 87 85 90 82 79 Eritrea 19 48 22 56 17 41 .. .. .. .. .. .. Estonia 93 106 93 107 94 104 100 100 100 100 100 100 Ethiopia 26 49 32 55 19 42 .. 62 .. 39 50 23 Finland 97 100 98 101 97 99 .. .. .. .. .. .. France 104 .. .. .. .. .. .. .. .. .. .. .. Gabon 58 75 55 73 61 76 94 97 92 95 88 80 Gambia, The 44 63 55 62 33 64 .. .. .. .. .. .. Georgia .. 85 .. 83 .. 86 .. .. .. .. .. .. Germany 100 95 99 94 100 95 .. .. .. .. .. .. Ghana 61 71 69 73 54 68 .. 76 .. 65 66 50 Greece 99 100 99 100 98 100 99 99 99 99 98 94 Guatemala .. 77 .. 80 .. 73 .. 86 .. 78 75 63 Guinea 17 64 25 74 9 53 .. 59 .. 34 43 18 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 27 .. 29 .. 26 .. .. .. .. .. .. .. 88 2008 World Development Indicators 2.13 PEOPLE Education completion and outcomes Primary completion Youth literacy Adult literacy ratea rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2006b 1991 2006b 1991 2006b 1990 2005 1990 2005 2005 2005 Honduras 64 89 67 86 61 91 .. 87 .. 91 80 80 Hungary 93 94 88 94 90 94 .. .. .. .. .. .. India 64 85 75 87 52 82 74 84c 49 68c 73c 48c Indonesia 91 99 .. 99 .. 100 97 99 95 99 94 87 Iran, Islamic Rep. 91 101 97 95 85 108 92 98 81 97 88 77 Iraq 59 .. 64 .. 53 .. .. .. .. .. .. .. Ireland .. 97 .. 96 .. 97 .. .. .. .. .. .. Israel .. 101 .. 101 .. 101 .. .. .. .. .. .. Italy 104 100 104 100 104 99 .. 100 .. 100 99 98 Jamaica 90 82 86 81 94 84 .. .. .. .. .. .. Japan 101 .. 101 .. 102 .. .. .. .. .. .. .. Jordan 72 100 69 100 77 101 .. 99 .. 99 95 87 Kazakhstan .. 101d .. 100 d .. 101d 100 .. 100 .. .. .. Kenya .. 93 .. 94 .. 92 .. 80 .. 81 78 70 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 98 101 98 107 98 95 .. .. .. .. .. .. Kuwait .. 91 .. 90 .. 92 .. 100 .. 100 94 91 Kyrgyz Republic .. 99 .. 99 .. 100 .. .. .. .. .. .. Lao PDR 43 75 48 80 38 70 .. 83 .. 75 77 61 Latvia .. 92 .. 93 .. 92 100 100 100 100 100 100 Lebanon .. 80 .. 79 .. 82 .. .. .. .. .. .. Lesotho 59 78 42 65 76 92 .. .. .. .. 74 90 Liberia .. 63 .. 69 .. 58 .. 65 .. 69 58 46 Libya .. .. .. .. .. .. .. 100 .. 96 93 75 Lithuania 89 91 .. 91 .. 91 100 100 100 100 100 100 Macedonia, FYR 98 97 .. 96 .. 98 .. 99 .. 98 98 94 Madagascar 33 57 33 57 34 57 .. 73 .. 68 77 65 Malawi 29 55 36 55 21 55 70 .. 49 .. .. .. Malaysia 91 95 91 95 91 95 96 97 95 97 92 85 Mali 13 49 15 59 10 40 .. .. .. .. 33 16 Mauritania 34 47 41 47 27 47 .. 68 .. 55 60 43 Mauritius 107 92 107 91 107 94 91 94 92 95 88 81 Mexico 88 103 89 102 90 103 96 98 95 98 93 90 Moldova .. 90 .. 90 .. 91 100 100 100 100 100 99 Mongolia .. 109 .. 108 .. 110 .. 97 .. 98 98 98 Morocco 48 84 57 88 39 80 .. 81 .. 60 66 40 Mozambique 26 42 32 49 21 35 .. .. .. .. .. .. Myanmar .. 95 .. 93 .. 98 .. 96 .. 93 94 86 Namibia 78 76 70 73 86 80 86 91 90 93 87 83 Nepal 51 76 68 80 40 72 68 81 33 60 63 35 Netherlands .. 100 .. 101 .. 99 .. .. .. .. .. .. New Zealand 100 .. 101 .. 99 .. .. .. .. .. .. .. Nicaragua 42 73 43 70 59 77 .. 84 .. 89 77 77 Niger 18 33 22 39 13 26 .. 52 .. 23 43 15 Nigeria .. 76 .. 83 .. 68 81 87 62 81 78 60 Norway 100 99 100 99 100 98 .. .. .. .. .. .. Oman 74 94 78 95 70 92 .. 98 .. 97 87 74 Pakistan .. 62 .. 70 .. 53 .. 77 .. 53 64 35 Panama 86 94 86 94 86 95 95 97 95 96 93 91 Papua New Guinea 46 .. 51 .. 42 .. .. 69 .. 64 63 51 Paraguay 68 94 68 94 69 95 96 96 95 96 94 93 Peru .. 100 .. 100 .. 100 97 98 94 96 94 82 Philippines 86 96 84 92 84 100 96 94 97 97 92 94 Poland 98 97 .. .. .. .. .. .. .. .. .. .. Portugal 95 104 94 102 95 107 99 100 99 100 96 92 Puerto Rico .. .. .. .. .. .. 92 86 94 86 90 90 2008 World Development Indicators 89 2.13 Education completion and outcomes Primary completion Youth literacy Adult literacy ratea rate rate % of relevant age group % ages 15­24 % ages 15 and older Total Male Female Male Female Male Female 1991 2006b 1991 2006b 1991 2006b 1990 2005 1990 2005 2005 2005 Romania 96 99 96 99 96 98 99 98 99 98 98 96 Russian Federation 93 94 92 .. 93 .. 100 100 100 100 100 99 Rwanda 35 35 40 36 31 35 .. 79 .. 77 71 60 Saudi Arabia 55 85 60 .. 51 .. 94 97 81 95 88 78 Senegal 39 49 47 51 30 47 49 58 28 41 51 29 Serbia .. .. .. .. .. .. .. 99e .. 99e 99e 94 e Sierra Leone .. 81d .. 92d .. 70 d .. 60 .. 37 47 24 Singapore .. .. .. .. .. .. 99 99 99 100 97 89 Slovak Republic 96 94 95 95 96 94 .. .. .. .. .. .. Slovenia 95 99 .. 100 .. 99 100 100 100 100 100 100 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 76 100 71 100 80 100 .. .. .. .. .. .. Spain .. 103 .. 103 .. 103 100 .. 100 .. .. .. Sri Lanka 102 108 103 107 102 108 .. 95f .. 96f 92 f 89 f Sudan 42 47 47 50 37 43 .. 85g .. 71g 71g 52g Swaziland 60 67 57 64 63 69 .. 87 .. 90 81 78 Sweden 96 .. 96 .. 96 .. .. .. .. .. .. .. Switzerland 53 91 53 91 54 92 .. .. .. .. .. .. Syrian Arab Republic 89 115 94 116 84 113 .. 95 .. 90 88 74 Tajikistan .. 106 .. 108 .. 104 100 100 100 100 100 99 Tanzania 62 85d 62 87d 63 83d 86 81 78 76 78 62 Thailand .. .. .. .. .. .. .. 98 .. 98 95 91 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 35 67 48 78 22 56 .. 84 .. 64 69 38 Trinidad and Tobago 101 88 98 86 104 90 .. 99 .. 99 99 98 Tunisia 74 99 79 98 70 100 .. 96 .. 92 83 65 Turkey 90 86 93 90 86 82 97 98 88 93 95 80 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. 54 .. 57 .. 51 77 83 63 71 77 58 Ukraine 94 105 98 105 97 105 .. 100 .. 100 100 99 United Arab Emirates 103 100 104 101 103 100 .. 98 .. 95 89 88 United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 94 93 91 92 96 93 .. .. .. .. .. .. Uzbekistan .. 98 .. 98 .. 98 .. .. .. .. .. .. Venezuela, RB 43 96 37 93 49 98 95 96 96 98 93 93 Vietnam .. 92 .. 103 .. 97 94 .. 93 .. .. .. West Bank and Gaza .. 89 .. 89 .. 89 .. 99 .. 99 97 88 Yemen, Rep. .. 60 .. 74 .. 46 .. 91 .. 59 73 35 Zambia .. 84 .. 89 .. 79 67 .. 66 .. .. .. Zimbabwe 97 81 99 83 96 79 97 97 94 98 93 86 World 79 w 86 w 85 w 88 w 74 w 84 w 88 w 91 w 79 w 84 w 87 w 77 w Low income 57 73 68 77 48 69 72 80 54 66 72 50 Middle income 93 97 96 98 90 97 95 97 91 96 93 87 Lower middle income 95 97 98 97 90 96 95 97 90 95 93 85 Upper middle income 88 99 88 99 88 99 97 98 96 98 94 92 Low & middle income 78 85 84 87 72 83 86 90 76 82 85 73 East Asia & Pacific 101 98 103 98 95 98 97 98 92 98 95 87 Europe & Central Asia 93 95 94 96 91 94 99 99 98 98 99 96 Latin America & Carib. 82 99 82 98 83 100 93 96 94 96 91 89 Middle East & N. Africa 77 91 83 93 71 88 84 93 68 84 83 63 South Asia 62 80 75 83 52 76 71 81 48 65 70 46 Sub-Saharan Africa 51 60 56 65 46 55 71 76 58 64 69 50 High income .. 97 .. 99 .. 96 99 99 99 99 99 98 Euro area 100 .. .. .. .. .. .. .. .. .. .. .. a. Because of the change from International Standard Classification of Education 1976 (ISCED76) to ISCED97 in 1998, data for 1991 are not fully comparable with data from 1999 onward. b. Provisional data. c. Excludes Mao Maram, Paomata, and Purul of Senapati district of Manipur. d. Data are for 2007. e. Includes Montenegro and excludes Kosovo and Metohija. f. Covers 18 of 25 districts. g. Covers northern Sudan only. 90 2008 World Development Indicators 2.13 PEOPLE Education completion and outcomes About the data Definitions Many governments publish statistics that indi- proxy rates should be taken as an upper estimate of · Primary completion rate is the percentage of stu- cate how their education systems are working and the actual primary completion rate. dents completing the last year of primary school. It is developing--statistics on enrollment and such effi - There are many reasons why the primary comple- calculated by taking the total number of students in ciency indicators as repetition rates, pupil-teacher tion rate can exceed 100 percent. The numerator the last grade of primary school, minus the number of ratios, and cohort progression. The World Bank may include late entrants and overage children who repeaters in that grade, divided by the total number and the United Nations Educational, Scientific, and have repeated one or more grades of primary school of children of official completing age. · Youth literacy Cultural Organization (UNESCO) Institute for Statis- as well as children who entered school early, while rate is the percentage of people ages 15­24 that tics jointly developed the primary completion rate the denominator is the number of children of official can, with understanding, both read and write a short, indicator. Increasingly used as a core indicator of completing age. Other data limitations contribute to simple statement about their everyday life. · Adult an education system's performance, it reflects an completion rates exceeding 100 percent, such as the literacy rate is the literacy rate among people ages education system's coverage and the educational use of estimates for the population of varying reli- 15 and older. attainment of students. The indicator is a key mea- ability, the conduct of school and population surveys sure of educational outcome at the primary level at different times of year, and other discrepancies in and of progress on the Millennium Development the numbers used in the calculation. Goals and the Education for All initiative. However, Basic student outcomes include achievements in because curricula and standards for school comple- reading and mathematics judged against established tion vary across countries, a high primary comple- standards. National assessments are enabling many tion rate does not necessarily mean high levels of countries' ministries of education to monitor progress student learning. in these outcomes. International comparable assess- The primary completion rate reflects the primary ments are not yet available, although a few exist for cycle as defined by the International Standard Clas- some countries. The UNESCO Institute for Statistics sification of Education, ranging from three or four has established literacy as an outcome indicator years of primary education (in a very small number based on an internationally agreed definition. of countries) to five or six years (in most countries) The literacy rate is the percentage of people who and seven (in a small number of countries). can, with understanding, both read and write a short, The table shows the proxy primary completion rate, simple statement about their everyday life. In prac- calculated by subtracting the number of repeaters tice, literacy is difficult to measure. To estimate lit- in the last grade of primary school from the total eracy using such a definition requires census or sur- number of students in that grade and dividing by the vey measurements under controlled conditions. Many total number of children of official graduation age. countries estimate the number of literate people from Data limitations preclude adjusting for students who self-reported data. Some use educational attainment drop out during the final year of primary school. Thus data as a proxy but apply different lengths of school attendance or levels of completion. Because defini- In 2005 more than 770 million tions and methodologies of data collection differ people were illiterate--64 percent across countries, data should be used cautiously. of them women, a share unchanged since 1990 2.13a The reported literacy data are compiled by the UNESCO Institute for Statistics based on national Millions Female Male censuses and household surveys during 1985­2005. 900 For detailed information on sources and definitions, consult the original source. Literacy statistics for most countries cover the pop- 600 ulation ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The literacy data in the narrower age 300 range of 15­24 captures the ability of participants in the formal education system better and reflects recent progress in education. The youth literacy rate reported in the table measures the accumulated out- Data sources 0 1990 2005 comes of primary education over the previous 10 Data on primary completion rates and lit- years or so by indicating the proportion of people who Source: United Nations Educational, Scientific, and eracy rates are from the UNESCO Institute for Cultural Organization Institute for Statistics. have passed through the primary education system Statistics. and acquired basic literacy and numeracy skills. 2008 World Development Indicators 91 2.14 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 children age group age group Ages 15­24 % of relevant age group ages 6­11 Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile Male Female quintile quintile Armenia 2000 105 93 177 181 9 11 96 98 96 98 14 13 Bangladesh 2004 193 156 107 120 3 8 26 70 47 58 25 10 Benin 2001 74 112 51 115 1 6 7 45 23 15 66 21 Bolivia 2003 98 95 92 98 6 11 48 90 75 75 24 5 Burkina Faso 2003 24 97 20 98 1 6 8 52 24 20 87 32 Cambodia 2000 146 187 78 134 2 7 4 45 18 17 50 12 Cameroon 2004 115 100 94 122 3 9 12 69 36 37 42 4 Central African Republic 1994­95 103 118 57 130 2 6 0a 18 8 6 65 21 Chad 2004 3 14 15 98 0a 5 1 36 15 8 91 36 Colombia 2005 157 85 126 99 6 11 50 90 70 77 8 1 Comoros 1996 84 119 56 147 2 6 4 29 12 12 72 26 Côte d'Ivoire 1994 26 39 41 103 2 6 6 41 25 17 70 23 Dominican Republic 2002 170 103 149 156 6 11 38 87 57 69 14 4 Egypt, Arab Rep. 2003 87 120 96 103 6 11 58 87 77 71 24 5 Eritrea 1995 55 117 42 154 1 7 3 65 21 24 84 10 Ethiopia 2000 87 257 61 186 1 5 4 44 15 12 87 42 Gabon 2000 .. .. 155 140 5 8 12 60 35 40 8 3 Ghana 2003 90 90 71 108 4 9 15 66 38 41 57 20 Guatemala 1995 114 124 62 122 2 9 9 76 41 40 58 8 Guinea 1999 13 39 10 38 1 5 3 27 18 9 95 77 Haiti 2000 141 200 94 152 3 8 1 40 13 18 64 21 India 1999 99 72 87 122 3 10 31 87 64 55 35 2 Indonesia 2002­03 85 92 103 104 7 11 75 97 86 89 19 6 Jordan 2002 .. .. 101 99 10 12 93 98 97 97 11 9 Kazakhstan 1999 .. .. 125 130 10 11 98 100 98 99 24 18 Kenya 2003 128 123 104 118 5 9 14 57 30 36 24 4 Kyrgyz Republic 1997 .. .. 133 138 10 10 86 88 85 87 21 18 Madagascar 1997 84 87 59 134 2 7 1 47 13 16 60 6 Malawi 2002 180 226 103 126 4 8 10 52 32 14 29 9 Mali 2001 45 89 36 101 1 5 3 37 16 11 75 29 Morocco 2003­04 109 85 98 116 2 9 17 78 47 46 26 2 Mozambique 2003 104 134 79 150 2 5 2 17 8 7 59 13 Namibia 1992 .. .. 138 116 5 8 15 65 25 34 22 9 Nepal 2001 240 249 116 160 3 7 18 59 37 28 33 6 Nicaragua 2001 127 108 79 104 3 10 14 88 47 59 46 5 Niger 1998 11 69 15 77 1 4 8 46 22 13 90 44 Nigeria 2003 77 106 67 111 4 10 16 70 39 37 56 5 Pakistan 1990­91 68 173 45 127 2 8 11 55 32 22 72 13 Paraguay 1990 137 106 103 114 5 10 29 77 49 54 21 10 Peru 2000 114 94 112 109 6 11 41 93 72 72 9 1 Philippines 2003 131 102 103 102 6 11 46 88 67 79 17 2 Rwanda 2000 216 197 100 126 3 6 7 28 14 14 43 23 Tanzania 1999 95 231 63 119 4 7 27 55 34 34 74 27 Uganda 2000­01 145 127 106 120 4 8 7 43 19 21 28 6 Uzbekistan 1996 .. .. 102 114 10 10 84 87 84 86 29 23 Vietnam 2002 121 105 139 127 5 10 58 97 84 84 8 2 Zambia 2001­02 83 119 74 112 4 9 16 79 38 43 61 18 Zimbabwe 1994 138 114 104 109 7 10 36 80 51 57 22 8 a. Less than 0.5. 92 2008 World Development Indicators 2.14 PEOPLE Education gaps by income and gender About the data Definitions The data in the table describe basic information on performed. In particular, the use of a unified index · Survey year is the year in which the underlying data school participation and attainment by individuals does not permit a disaggregated analysis to examine were collected. · Gross intake rate in grade 1 is in different socioeconomic groups within countries. which asset indicators have a more or less important the number of students in the first grade of primary The data are from Demographic and Health Surveys association with education status. In addition, some education regardless of age as a percentage of the conducted by Macro International with the support asset indicators may reflect household wealth better population of the official primary school entrance of the U.S. Agency for International Development. in some countries than in others--or reflect differ- age. These data may differ from those in table 2.12. These large-scale household sample surveys, con- ent degrees of wealth in different countries. Taking · Gross primary participation rate is the ratio of ducted periodically in developing countries, collect such information into account and creating country- total students attending primary school regardless information on a large number of health, nutrition, specific asset indexes with country-specific choices of age to the population of the age group that offi - and population measures as well as on respondents' of asset indicators might produce a more effective cially corresponds to primary education. · Average social, demographic, and economic characteristics and accurate index for each country. The asset index years of schooling are the years of formal school- using a standard set of questionnaires. The data used in the table does not have this flexibility. ing received, on average, by youths and adults ages presented here draw on responses to individual and The analysis was carried out for 48 countries. The 15­24. · Primary completion rate is the percentage household questionnaires. table shows the estimates for the poorest and rich- of children of the official primary school completing Typically, Demographic and Health Surveys collect est quintiles only; the full set of estimates for 32 indi- age to the official primary school completing age plus basic information on educational attainment and cators is available in the country reports (see Data four who have completed the last year of primary enrollment levels from every household member sources). The data in the table differ from data for school or higher. These data differ from those in ages 5 or 6 and older as background characteris- similar indicators in preceding tables either because table 2.13 because the definition and methodology tics. As the surveys are intended for the collection of the indicator refers to a period a few years preceding are different. · Children out of school are the per- demographic and health information, the education the survey date or because the indicator definition centage of children ages 6­11 who are not in school. section of the survey is not as robust and detailed or methodology is different. Findings should be inter- These data differ from those in table 2.11 because as the health section; however, it still provides useful preted with caution because of measurement error the definition and methodology are different. micro-level information on education that cannot be inherent in the use of survey data. explained by aggregate national-level data. Socioeconomic status as displayed in the table is based on a household's assets, including ownership of consumer items, features of the household's dwell- ing, and other characteristics related to wealth. Each household asset on which information was collected was assigned a weight generated through principal- component analysis. The resulting scores were stan- dardized in relation to a standard normal distribution with a mean of zero and a standard deviation of one. The standardized scores were then used to create break-points defining wealth quintiles, expressed as quintiles of individuals in the population. The choice of the asset index for defining socio- economic status was based on pragmatic rather than conceptual considerations: Demographic and Health Surveys do not collect income or consumption data but do have detailed information on households' own- ership of consumer goods and access to a variety of goods and services. Like income or consumption, the asset index defines disparities primarily in eco- nomic terms. It therefore excludes other possibilities Data sources of disparities among groups, such as those based on gender, education, ethnic background, or other Data on education gaps by income and gender are facets of social exclusion. To that extent the index from an analysis of Demographic and Health Sur- provides only a partial view of the multidimensional veys by Macro International and the World Bank. concepts of poverty, inequality, and inequity. Country reports are available at www.worldbank. Creating one index that includes all asset indi- org/education/edstats/. cators limits the types of analysis that can be 2008 World Development Indicators 93 2.15 Health expenditure, services, and use Health Health workers Hospital expenditure beds Public per 1,000 people % of Out of External Community Total government pocket resourcesa Per capita Nurses and health per 1,000 % of GDP % of GDP % of total expenditure % of private % of total $ Physicians midwives workers people 2005 2005 2005 2005 2005 2005 2005 2000­06b 2000­06b 2000­06b 2000­06b Afghanistan 5.2 1.0 20.0 3.3 97.4 13.1 .. .. .. .. .. Albania 6.5 2.6 40.3 8.6 97.0 1.9 169 1.2 4.7 .. 3.0 Algeria 3.5 2.6 75.3 9.5 94.6 0.1 108 1.1 2.2 0.0 c 1.7 Angola 1.8 1.5 81.5 4.7 100.0 7.3 36 0.1 1.4 .. .. Argentina 10.2 4.5 43.9 14.2 43.4 0.0 484 .. .. .. 4.1 Armenia 5.4 1.8 32.9 8.2 89.2 12.7 88 3.7 4.9 .. 4.5 Australia 8.8 5.9 67.0 17.0 55.2 0.0 3,181 2.5 9.7 0.2 4.0 Austria 10.2 7.7 75.7 15.5 67.4 0.0 3,788 3.7 6.6 .. 7.7 Azerbaijan 3.9 1.0 24.8 3.8 84.6 0.4 62 3.6 8.4 .. 8.2 Bangladesh 2.8 0.8 29.1 5.5 88.3 12.2 12 0.3 0.3 0.2 0.3 Belarus 6.6 5.0 75.8 10.5 69.0 .. 204 4.8 12.5 .. 11.1 Belgium 9.6 6.9 71.4 13.9 78.7 0.0 3,451 4.2 14.2 .. 5.3 Benin 5.4 3.0 55.6 13.5 99.9 19.7 28 0.0 c 0.8 0.0 c 0.5 Bolivia 6.9 4.3 61.6 12.4 81.4 6.8 71 1.2 2.1 0.1 1.0 Bosnia and Herzegovina 8.8 5.2 58.7 14.0 100.0 0.6 243 1.4 4.7 .. 3.0 Botswana 7.0 4.5 63.6 12.4 26.2 4.7 362 0.4 2.7 .. 2.2 Brazil 7.9 3.5 44.1 6.7 54.6 0.0 371 1.2 3.8 .. 2.6 Bulgaria 7.7 4.7 60.6 12.1 96.3 1.1 272 0.3 4.6 .. 6.4 Burkina Faso 6.7 4.0 59.5 18.4 94.2 29.5 27 0.1 0.5 0.1 .. Burundi 3.4 1.0 28.6 2.3 100.0 50.9 3 0.0 c 0.2 0.1 0.7 Cambodia 6.4 1.5 24.2 12.0 79.3 25.7 29 0.2 0.9 .. 0.6 Cameroon 5.2 1.5 28.0 11.0 94.6 5.3 49 0.2 1.6 .. .. Canada 9.7 6.8 70.3 17.5 48.7 0.0 3,430 1.9 10.1 .. 3.6 Central African Republic 4.0 1.5 37.5 10.9 95.3 38.5 13 0.1 0.4 0.1 .. Chad 3.7 1.5 39.8 9.5 96.2 12.5 22 0.0 c 0.3 0.0 c 0.4 Chile 5.4 2.8 51.4 13.2 54.3 0.1 397 1.1 0.6 .. 2.4 China 4.7 1.8 38.8 1.0 85.3 0.1 81 1.5 1.0 0.1 2.5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. Colombia 7.3 6.2 84.8 17.7 45.1 0.0 201 1.4 0.6 .. 1.2 Congo, Dem. Rep. 4.2 1.5 34.6 7.2 100.0 23.6 5 0.1 0.5 .. .. Congo, Rep. 1.9 0.9 47.1 4.0 100.0 4.7 31 0.2 1.0 0.0 c .. Costa Rica 7.1 5.4 76.0 21.0 79.4 0.2 327 1.3 0.9 1.3 1.4 Cote d'Ivoire 3.9 0.8 21.5 4.2 87.8 6.6 34 0.1 0.6 .. .. Croatia 8.4 d 6.3d 75.5d 13.1d 93.6 0.0 812d 2.5 5.5 .. 5.5 Cuba 7.6 6.9 90.8 11.7 93.2 0.3 310 5.9 7.4 .. 4.9 Czech Republic 7.1 6.3 88.6 14.4 95.3 0.0 868 3.6 8.9 .. 8.4 Denmark 9.1 7.7 84.1 14.4 90.1 0.0 4,350 3.6 10.1 .. 3.8 Dominican Republic 5.4 1.7 31.1 9.3 86.4 2.5 197 1.9 1.8 .. 2.2 Ecuador 5.3 2.1 40.0 8.0 85.0 0.4 147 1.5 1.7 .. 1.4 Egypt, Arab Rep. 6.1 2.3 38.0 7.3 94.9 0.9 78 2.4 3.4 .. 2.2 El Salvador 8.1d 4.0 d 50.0 d 22.0 d 94.0 d 2.7d 220 d 1.5 0.8 .. 0.9 Eritrea 3.7 1.7 44.9 4.2 100.0 50.5 8 0.1 0.6 .. .. Estonia 5.0 3.8 76.9 11.5 88.7 0.3 516 3.3 7.0 0.0 c 5.8 Ethiopia 4.9 3.0 61.0 10.8 80.6 37.9 6 0.0 c 0.2 0.3 0.2 Finland 7.5 5.8 77.8 11.6 80.0 0.0 2,824 3.3 8.9 .. 7.0 France 11.1 8.9 79.8 16.5 34.2 0.0 3,807 3.4 8.0 .. 7.5 Gabon 4.1 3.0 74.0 13.9 100.0 1.5 276 0.3 5.0 .. .. Gambia, The 5.2 3.4 65.4 11.2 70.3 29.3 15 0.1 1.3 0.7 0.8 Georgia 8.6 1.7 19.5 6.7 95.7 5.1 123 4.7 4.0 .. 3.8 Germany 10.7 8.2 76.9 17.6 56.8 0.0 3,628 3.4 8.0 .. 8.4 Ghana 6.2 2.1 34.1 6.9 79.1 26.0 30 0.2 0.9 .. 0.9 Greece 10.1 4.3 42.8 11.5 62.0 .. 2,580 5.0 3.6 .. 4.7 Guatemala 5.2 2.0 37.9 15.7 92.2 1.1 132 .. .. .. 0.7 Guinea 5.6 0.7 11.9 4.7 99.5 12.2 21 0.1 0.5 0.0 c .. Guinea-Bissau 5.2 1.7 31.9 4.0 85.7 31.8 10 0.1 0.7 2.9 .. Haiti 6.2 3.2 51.3 27.7 90.1 18.9 28 .. .. .. 0.8 94 2008 World Development Indicators 2.15 PEOPLE Health expenditure, services, and use Health Health workers Hospital expenditure beds Public per 1,000 people % of Out of External Community Total government pocket resourcesa Per capita Nurses and health per 1,000 % of GDP % of GDP % of total expenditure % of private % of total $ Physicians midwives workers people 2005 2005 2005 2005 2005 2005 2005 2000­06b 2000­06b 2000­06b 2000­06b Honduras 7.5 3.8 50.6 16.1 87.0 6.8 91 0.6 1.3 .. 1.0 Hungary 7.8 5.5 70.8 11.1 86.8 .. 855 3.0 9.2 .. 7.9 India 5.0 1.0 19.0 3.5 94.0 0.4 36 0.6 1.3 0.1 0.9 Indonesia 2.1 1.0 46.6 5.1 66.4 4.6 26 0.1 0.8 0.0 .. Iran, Islamic Rep. 7.8 4.4 55.8 9.2 94.8 0.1 212 0.9 1.6 0.4 1.7 Iraq 4.1e 3.1e 74.4 e 3.4 e 100.0e 4.9e .. .. .. .. .. Ireland 8.2 6.5 79.5 19.0 59.3 0.0 3,993 2.9 19.5 .. 5.7 Israel 7.9 4.8 61.3 10.4 61.0 0.0 1,533 3.7 6.2 .. 6.3 Italy 8.9 6.8 76.6 14.1 86.6 0.0 2,692 3.7 7.2 .. 4.0 Jamaica 4.7 2.3 48.8 3.5 63.6 1.8 170 0.9 1.7 .. 1.7 Japan 8.2 6.7 82.2 17.8 83.5 0.0 2,936 2.1 9.5 .. 14.3 Jordan 10.5f 4.8f 45.3f 9.5f 76.1 4.5 241 2.4 3.2 0.2 1.7 Kazakhstan 4.1d 2.2d 67.4 d 10.8d 100.0 d 0.3d 204 d 3.9 7.6 .. 7.7 Kenya 4.5 2.1 46.6 6.1 80.0 18.1 24 0.1 1.2 .. 1.9 Korea, Dem. Rep. 3.5 3.0 85.6 6.0 100.0 36.6 0g 3.3 4.1 .. 13.2 Korea, Rep. 5.9 3.1 53.0 10.9 80.1 0.0 973 1.6 1.9 .. 7.1 Kuwait 2.2 1.7 77.2 6.2 91.6 0.0 687 1.8 3.7 .. 1.9 Kyrgyz Republic 6.1 2.5 40.3 8.6 95.0 7.5 29 2.4 5.8 .. 5.1 Lao PDR 3.6 0.7 20.6 4.1 92.7 11.3 18 0.4 1.0 .. 0.9 Latvia 6.4 3.9 60.5 10.8 97.7 0.3 443 3.1 5.6 .. 7.7 Lebanon 8.7 3.8 43.5 11.9 74.7 2.3 460 2.4 1.3 .. 3.6 Lesotho 9.4 8.5 90.1 18.2 18.3 10.7 69 0.1 0.6 .. .. Liberia 6.4 4.4 68.2 36.3 98.7 41.2 10 0.0 c 0.3 0.0 c .. Libya 3.2 2.2 69.5 6.5 100.0 0.0 223 1.3 4.8 .. 3.4 Lithuania 5.9 4.0 67.3 11.9 98.6 0.0 448 4.0 7.7 .. 8.1 Macedonia, FYR 7.8 5.5 70.4 15.8 100.0 1.0 224 2.6 4.3 .. 4.7 Madagascar 3.2 2.0 62.5 9.6 52.6 46.1 9 0.3 0.3 0.0c 0.4 Malawi 12.2 8.7 71.3 16.6 30.6 61.2 19 0.0 c 0.6 .. .. Malaysia 4.2 1.9 44.8 7.0 75.7 0.0 222 0.7 1.8 .. 1.8 Mali 5.8 2.9 50.6 12.0 99.5 15.6 28 0.1 0.6 0.0c .. Mauritania 2.7 1.7 63.2 5.0 100.0 26.1 17 0.1 0.6 0.1 0.6 Mauritius 4.3 2.2 51.5 9.2 81.4 1.1 218 1.1 3.7 0.2 3.0 Mexico 6.4 2.9 45.5 12.5 93.9 0.0 474 1.5 0.9 .. 1.0 Moldova 7.5 4.2 55.5 11.3 96.4 2.6 58 2.7 6.2 .. 6.4 Mongolia 4.3 3.3 77.5 11.0 86.5 1.5 35 2.6 3.5 1.5 7.5 Morocco 5.3 1.9 36.6 5.5 76.0 1.0 89 0.5 0.8 .. 0.9 Mozambique 4.3 2.7 63.6 12.6 40.5 66.5 14 0.0 c 0.3 .. .. Myanmar 2.2 0.3 11.6 1.2 99.4 12.7 4 0.4 1.0 1.0 0.6 Namibia 5.3 3.5 65.2 10.1 15.5 13.5 165 0.3 3.1 .. .. Nepal 5.8 1.6 28.1 8.4 87.0 16.4 16 0.2 0.5 0.6 0.2 Netherlands 9.2 6.0 64.9 13.2 21.9 0.0 3,560 3.7 14.6 .. 5.0 New Zealand 8.9 6.9 77.4 18.0 74.4 0.0 2,403 2.2 8.9 1.4 6.0 Nicaragua 8.3 4.1 49.6 13.7 96.2 9.2 75 0.4 1.1 .. 0.9 Niger 3.8 1.9 50.5 10.2 85.2 17.0 9 0.0 c 0.2 .. .. Nigeria 3.9 1.2 30.9 3.5 90.4 4.8 27 0.3 1.7 0.9 1.2 Norway 9.0 7.5 83.6 17.9 95.3 0.0 5,910 3.8 16.2 .. 4.2 Oman 2.5 2.1 85.0 6.1 64.4 0.0 312 1.7 3.7 .. 2.1 Pakistan 2.1 0.4 17.5 1.5 98.0 3.6 15 0.8 0.5 0.4 0.7 Panama 7.3 5.0 68.9 12.3 80.8 0.2 351 1.5 2.8 0.5 2.4 Papua New Guinea 4.2 3.6 86.2 9.6 42.5 37.0 34 0.1 0.5 .. .. Paraguay 7.3 2.7 36.5 15.3 87.7 0.6 92 1.1 1.8 1.2 1.2 Peru 4.3 2.1 49.0 8.4 80.0 1.7 125 .. .. .. 1.1 Philippines 3.2 1.2 36.6 5.5 80.3 5.1 37 1.2 6.1 .. 1.2 Poland 6.2 4.3 69.3 9.9 85.1 0.1 495 2.0 5.2 .. 5.3 Portugal 10.2 7.4 72.3 15.5 79.8 0.0 1,800 3.4 4.7 .. 3.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 95 2.15 Health expenditure, services, and use Health Health workers Hospital expenditure beds Public per 1,000 people % of Out of External Community Total government pocket resourcesa Per capita Nurses and health per 1,000 % of GDP % of GDP % of total expenditure % of private % of total $ Physicians midwives workers people 2005 2005 2005 2005 2005 2005 2005 2000­06b 2000­06b 2000­06b 2000­06b Romania 5.5 3.9 70.3 12.4 85.0 0.8 250 1.9 4.2 .. 6.6 Russian Federation 5.2 3.2 62.0 10.1 82.4 0.0 277 4.3 8.5 3.0 9.7 Rwanda 7.2 4.1 56.9 16.9 36.9 43.9 19 0.1 0.4 1.4 1.7 Saudi Arabia 3.4 2.6 76.2 8.7 16.5 0.0 448 1.7 3.0 .. 2.3 Senegal 5.4 1.7 31.7 6.7 90.3 13.0 38 0.1 0.3 .. .. Serbia 8.0h 5.8h 71.9h 15.1h 86.7h 0.5h 212h 2.0 4.3 .. 5.9 Sierra Leone 3.7 1.9 51.5 7.8 100.0 41.0 8 0.0 c 0.5 0.1 0.4 Singapore 3.5 1.1 31.9 5.6 93.8 0.0 944 1.5 4.5 .. 2.8 Slovak Republic 7.0 5.2 74.4 13.9 88.1 0.0 626 3.1 6.6 .. 6.9 Slovenia 8.5 6.2 72.4 13.4 45.0 0.1 1,495 2.4 8.0 .. 4.8 Somalia .. .. .. .. .. .. .. .. .. .. .. South Africa 8.7 3.6 41.7 9.9 17.4 0.5 437 0.8 4.1 0.2 .. Spain 8.2 5.9 71.4 15.4 73.1 0.0 2,152 3.3 7.6 .. 3.5 Sri Lanka 4.1 1.9 46.2 7.8 86.0 1.2 51 0.6 1.7 .. 3.1 Sudan 3.8 1.4 37.6 7.0 98.3 6.8 29 0.3 0.9 0.2 0.7 Swaziland 6.3 4.0 64.1 10.9 41.7 5.6 146 0.2 6.3 4.3 .. Sweden 8.9 7.5 84.6 13.6 89.6 0.0 3,598 3.3 10.9 .. 3.6 Switzerland 11.4 6.8 59.7 18.7 75.7 0.0 5,694 4.0 11.0 .. 5.7 Syrian Arab Republic 4.2 2.1 50.5 6.8 100.0 0.5 61 0.5 1.4 .. 1.3 Tajikistan 5.0 1.1 22.8 5.0 96.6 11.8 18 2.0 5.0 .. 6.2 Tanzania 5.1 2.9 56.9 12.6 83.4 27.8 17 0.0 c 0.4 .. .. Thailand 3.5 2.2 63.9 11.3 76.6 0.2 98 0.4 2.8 0.1 2.2 Timor-Leste 13.7 11.9 86.6 19.1 37.2 57.2 45 0.1 2.2 2.0 .. Togo 5.3 1.4 25.5 6.9 84.7 13.3 18 0.0 c 0.4 0.1 0.9 Trinidad and Tobago 4.5 2.4 53.7 8.3 87.8 2.4 513 .. .. .. 3.3 Tunisia 5.5 2.4 44.3 6.5 82.2 0.8 158 1.3 2.9 .. 1.8 Turkey 7.6 5.4 71.4 13.9 69.5 0.0 383 1.6 2.9 .. 2.6 Turkmenistan 4.8 3.2 66.7 14.9 100.0 0.3 156 2.5 4.7 .. 4.9 Uganda 7.0 2.0 28.6 10.0 51.8 33.1 22 0.1 0.7 .. 0.7 Ukraine 7.0 3.7 52.8 8.4 84.8 0.6 128 3.1 8.5 .. 8.7 United Arab Emirates 2.6 1.9 71.6 8.6 77.9 0.0 833 1.7 3.5 .. 2.2 United Kingdom 8.2 7.1 87.1 16.2 92.1 0.0 3,064 2.2 .. .. 3.9 United States 15.9 7.2 45.4 0.7 23.9 0.0 6,657 2.3 9.4 .. 3.3 Uruguay 8.1 3.4 42.5 10.1 31.1 0.6 404 3.7 0.9 .. 2.4 Uzbekistan 5.0 2.4 47.7 7.4 97.1 3.5 26 2.7 10.9 .. 5.2 Venezuela, RB 4.7 2.1 45.3 7.9 88.2 0.1 247 1.9 1.1 .. 0.9 Vietnam 6.0 1.5 25.7 5.1 86.1 2.0 37 0.6 0.8 .. 1.4 West Bank and Gaza .. .. .. .. .. .. .. 0.8 .. .. .. Yemen, Rep. 5.1 2.1 41.8 5.6 95.2 15.0 39 0.3 0.7 0.3 0.6 Zambia 5.6 2.7 49.0 10.7 71.5 40.5 36 0.1 2.0 .. 2.0 Zimbabwe 8.1 3.6 44.8 8.9 52.0 20.6 21 0.2 0.7 0.0c .. World 10.1 w 6.0 w 59.3 w 10.4 w 43.5 w 0.1 w 703 w .. w .. w .. w .. w Low income 4.6 1.2 24.9 6.9 92.0 5.6 27 0.5 .. 0.2 .. Middle income 5.8 2.9 51.1 8.2 74.5 0.4 162 1.6 .. .. 3.1 Lower middle income 4.8 2.2 46.9 5.9 84.9 0.8 86 1.3 1.0 .. 2.7 Upper middle income 6.7 3.6 53.8 .. 66.8 0.1 374 2.3 .. .. .. Low & middle income 5.6 2.7 48.1 7.3 77.4 1.0 104 .. .. .. .. East Asia & Pacific 4.3 1.8 40.3 2.1 83.8 0.7 70 1.5 1.0 0.1 2.5 Europe & Central Asia 6.2 4.1 66.2 10.5 82.8 0.2 279 3.1 6.8 .. 7.2 Latin America & Carib. 7.1 3.3 47.9 .. 68.0 0.2 329 .. .. .. .. Middle East & N. Africa 5.8 3.0 53.4 8.2 90.5 1.1 123 .. .. .. .. South Asia 4.5 0.9 20.2 3.5 93.9 1.3 31 0.6 1.3 0.1 0.9 Sub-Saharan Africa 6.1 2.6 42.9 .. 45.7 7.4 49 .. .. .. .. High income 11.4 7.0 60.9 10.9 36.8 0.0 3,979 2.6 .. .. 6.2 Euro area 9.9 7.4 75.1 15.6 58.2 0.0 3,155 3.5 .. .. 6.6 a. 0.0 is not applicable or less than 0.05. b. Data are for the most recent year available. c. Less than 0.05. d. Data are for 2006. e. Excludes northern Iraq. f. Includes contributions from the United Nations Relief and Works Agency for Palestine Refugees. g. Less than 0.5. h. Excludes Kosovo and Metohija. 96 2008 World Development Indicators 2.15 PEOPLE Health expenditure, services, and use About the data Definitions National health accounts track financial flows in the the data for nurses and midwives, because for some · Total health expenditure is the sum of public and health sector, including public and private expendi- countries the available information does not clearly private health expenditure. It covers the provision tures, by source of funding. In contrast with high- distinguish between the two groups. There is no uni- of health services (preventive and curative), family income countries, few developing countries have versally accepted definition of hospital beds. More- planning and nutrition activities, and emergency aid health accounts that are methodologically consis- over, fi gures on physicians and hospital beds are for health but excludes provision of water and sani- tent with national accounting approaches. Efforts indicators of availability, not of quality or use. They tation. · Public health expenditure is recurrent and are needed to standardize and harmonize the various do not show how well trained the physicians are or capital spending from central and local governments, competing national health account methodologies. how well equipped the hospitals or medical centers external borrowing and grants (including donations The difficulties in creating national health accounts are. And physicians and hospital beds tend to be from international agencies and nongovernmental go beyond data collection. To establish a national concentrated in urban areas, so these indicators give organizations), and social (or compulsory) health health accounting system, a country needs to define only a partial view of health services available to the insurance funds. · Out of pocket health expendi- the boundaries of the health care system and to entire population. ture, part of private health expenditure, is direct define a taxonomy of health care delivery institutions. Meeting the minimum of 2.5 physicians, nurses, household outlays including gratuities and in-kind The accounting system should be comprehensive and midwives per 1,000 people is critical for coun- payments to health practitioners and pharmaceutical and standardized, providing not only accurate mea- tries to provide the adequate primary health care suppliers, therapeutic appliances, and other goods sures of financial flows but also information on the interventions needed to achieve the health-related and services whose primary intent is to contribute equity and efficiency of health financing to inform Millennium Development Goals (WHO, World Health to health restoration or enhancement. · External health policy. Report 2006). resources for health, part of total health expendi- The absence of consistent national health account- ture, are funds or services in kind provided by enti- ing systems in most developing countries makes ties not part of the country. Resources may come cross-country comparisons of health spending dif- from international organizations, other countries, ficult. Compiling estimates of public health expen- or foreign nongovernmental organizations. · Health ditures is complicated in countries where state or expenditure per capita is total health expenditure provincial and local governments are involved in divided by population. · Physicians are graduates financing and delivering health care, often because of any faculty or school of medicine working in the the data on public spending are not aggregated. country in any medical field (practice, teaching, or There are a number of potential data sources related research). · Nurses and midwives are professional to external resources for health, including govern- nurses, auxiliary nurses, enrolled nurses, and other ment expenditure accounts, government records nurses, such as dental nurses and primary care on external assistance, routine surveys of external nurses, and professional midwives, auxiliary mid- financing assistance, and special surveys. Survey wives, and enrolled midwives. · Community health data are the major source of information about out workers are traditional medicine practitioners, faith of pocket expenditure on health. The data in the healers, assistant and community health education table are the product of an effort by the World Health workers, community health officers, family health Organization (WHO), the Organisation for Economic workers, lady health visitors, health extension pack- Co-operation and Development (OECD), and the age workers, community midwives, and traditional World Bank to collect all available information on birth attendants. · Hospital beds are inpatient beds health expenditures from national and local govern- for both acute and chronic care available in public, ment budgets, national accounts, household sur- private, general, and specialized hospitals and reha- veys, insurance publications, international donors, bilitation centers. and existing tabulations. Indicators on health services (physicians, nurses and midwives, community health workers, and Data sources hospital beds) are compiled by the WHO based on Data on health expenditure come mostly from the household and labor force surveys, censuses, and WHO's National Health Account database (www. professional and administrative records. Data com- who.int/nha/en) and from the OECD for its mem- parability is limited by differences in definitions. In ber countries, supplemented by country data. estimates of health personnel, for example, some Data on physicians, nurses and midwives, com- countries incorrectly include retired physicians munity health workers, and hospital beds are from (because deletions to physician rosters are made the WHO, OECD, and TransMONEE, supplemented only periodically) or physicians working outside the by country data. health sector. Caution must be exercised in using 2008 World Development Indicators 97 2.16 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % 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 2004 1990 2004 2006 2006 2000­06c 2000­06c 2000­06c 2000­06c 2005 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. 90 66 Albania 96 96 .. 91 97 98 45 50 .. .. 77 37 Algeria 94 85 88 92 91 95 53 24 .. .. 87 102 Angola 36 53 29 31 48 44 58 32 2.3 63.0 72 76 Argentina 94 96 81 91 97 91 .. .. .. .. 53 71 Armenia .. 92 .. 83 92 87 36 59 .. .. 72 59 Australia 100 100 100 100 94 92 .. .. .. .. 80 40 Austria 100 100 100 100 80 83 .. .. .. .. 75 46 Azerbaijan 68 77 .. 54 96 95 36 40 1.4 0.8 59 50 Bangladesh 72 74 20 39 81 88 30 49 .. .. 91 65 Belarus 100 100 .. 84 97 99 90 54 .. .. 73 40 Belgium .. .. .. .. 88 97 .. 42 .. .. 66 55 Benin 63 67 12 33 89 93 36 42 20.1 54.0 87 86 Bolivia 72 85 33 46 81 81 52 54 .. .. 78 69 Bosnia and Herzegovina 97 97 .. 95 90 87 91 53 .. .. 97 62 Botswana 93 95 38 42 90 97 14 7 .. .. 70 80 Brazil 83 90 71 75 99 99 .. .. .. .. 77 55 Bulgaria 99 99 99 99 96 95 .. .. .. .. 86 94 Burkina Faso 38 61 7 13 88 95 39 42 9.6 48.0 71 17 Burundi 69 79 44 36 75 74 38 23 8.3 30.0 79 24 Cambodia .. 41 .. 17 78 80 48 59 4.2 0.2 93 62 Cameroon 50 66 48 51 73 81 35 22 13.1 57.8 74 91 Canada 100 100 100 100 94 94 .. .. .. .. 68 55 Central African Republic 52 75 23 27 35 40 32 47 15.1 57.0 65 69 Chad 19 42 7 9 23 20 7 27 0.6 44.0 69 19 Chile 90 95 84 91 91 94 .. .. .. .. 78 141 China 70 77 23 44 93 93 .. .. .. .. 94 79 Hong Kong, China .. .. .. .. .. .. .. .. .. .. 77 56 Colombia 92 93 82 86 88 86 62 39 0.7 .. 71 83 Congo, Dem. Rep. 43 46 16 30 73 77 36 17 5.8d 29.8d 85 61 Congo, Rep. .. 58 .. 27 66 79 48 39 6.1 48.0 28 51 Costa Rica .. 97 .. 92 89 91 .. .. .. .. 89 102 Côte d'Ivoire 69 84 21 37 73 77 35 45 5.9 36.0 75 37 Croatia 100 100 100 100 96 96 .. .. .. .. .. .. Cuba .. 91 98 98 96 89 .. .. .. .. 91 94 Czech Republic 100 100 99 98 97 98 .. .. .. .. 72 57 Denmark 100 100 .. .. 99 93 .. .. .. .. 83 62 Dominican Republic 84 95 52 78 99 81 64 42 .. .. 85 66 Ecuador 73 94 63 89 97 98 .. .. .. .. 83 34 Egypt, Arab Rep. 94 98 54 70 98 98 63 27 .. .. 79 59 El Salvador 67 84 51 62 98 96 62 .. .. .. 91 61 Eritrea 43 60 7 9 95 97 44 54 4.2 3.6 88 35 Estonia 100 100 97 97 96 95 .. .. .. .. 72 66 Ethiopia 23 22 3 13 63 72 19 15 1.5 3.0 78 27 Finland 100 100 100 100 97 97 .. .. .. .. .. .. France 100 100 .. .. 87 98 .. .. .. .. .. .. Gabon .. 88 .. 36 55 38 48 44 .. .. 46 58 Gambia, The .. 82 .. 53 95 95 69 38 49.0 62.6 87 64 Georgia 80 82 97 94 95 87 99 .. .. .. 73 109 Germany 100 100 100 100 94 90 .. .. .. .. 71 54 Ghana 55 75 15 18 85 84 59 29 21.8 60.8 73 38 Greece .. .. .. .. 88 88 .. .. .. .. .. .. Guatemala 79 95 58 86 95 80 64 .. .. .. 85 56 Guinea 44 50 14 18 67 71 42 38 0.3 43.5 72 55 Guinea-Bissau .. 59 .. 35 60 77 57 25 39.0 45.7 69 64 Haiti 47 54 24 30 58 53 35 43 .. 5.1 81 55 98 2008 World Development Indicators 2.16 PEOPLE Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % 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 2004 1990 2004 2006 2006 2000­06c 2000­06c 2000­06c 2000­06c 2005 2006 Honduras 84 87 50 69 91 87 56 49 .. 0.5 88 85 Hungary 99 99 .. 95 99 99 .. .. .. .. 45 49 India 70 86 14 33 59 55 69 32 .. 12.0 86 64 Indonesia 72 77 46 55 72 70 61 56 0.1 0.7 91 73 Iran, Islamic Rep. 92 94 83 .. 99 99 93 .. .. .. 83 69 Iraq 83 .. 81 .. .. .. .. .. .. .. 86 40 Ireland .. .. .. .. 86 91 .. .. .. .. .. .. Israel 100 100 .. .. 95 95 .. .. .. .. 78 31 Italy .. .. .. .. 87 96 .. .. .. .. 74 71 Jamaica 92 93 75 80 87 85 75 39 .. .. 57 73 Japan 100 100 100 100 99 99 .. .. .. .. 60 79 Jordan 97 97 93 93 99 98 72 44 .. .. 83 76 Kazakhstan 87 86 72 72 99 99 71 48 .. .. 74 e 69 Kenya 45 61 40 43 77 80 49 33 4.6 26.5 82 70 Korea, Dem. Rep. 100 100 .. 59 96 89 93 .. .. .. 89 97 Korea, Rep. .. 92 .. .. 99 98 .. .. .. .. 83 18 Kuwait .. .. .. .. 99 99 .. .. .. .. 63 95 Kyrgyz Republic 78 77 60 59 97 92 62 22 .. .. 85 63 Lao PDR .. 51 .. 30 48 57 36 37 17.7 8.7 90 77 Latvia 99 99 .. 78 95 98 .. .. .. .. 74 85 Lebanon 100 100 .. 98 96 92 74 .. .. .. 92 55 Lesotho .. 79 37 37 85 83 59 53 .. .. 73 79 Liberia 55 61 39 27 94 88 70 .. 2.6 .. 76 55 Libya 71 .. 97 97 98 98 .. .. .. .. 69 156 Lithuania .. .. .. .. 97 94 .. .. .. .. 70 109 Macedonia, FYR .. .. .. .. 94 93 93 45 .. .. 84 66 Madagascar 40 46 14 32 59 61 48 47 0.2 34.2 74 73 Malawi 40 73 47 61 85 99 51 26 23.0 23.9 73 42 Malaysia 98 99 .. 94 90 96 .. .. .. .. 70 80 Mali 34 50 36 46 86 85 43 45 8.4 38.0 75 26 Mauritania 38 53 31 34 62 68 41 9 2.1 33.4 55 34 Mauritius 100 100 .. 94 99 97 .. .. .. .. 86 67 Mexico 82 97 58 79 96 98 .. .. .. .. 77 118 Moldova .. 92 .. 68 96 97 60 48 .. .. 62 69 Mongolia 63 62 .. 59 99 99 63 47 .. .. 88 97 Morocco 75 81 56 73 95 97 38 46 .. .. 81 95 Mozambique 36 43 20 32 77 72 55 47 .. 15.0 79 47 Myanmar 57 78 24 77 78 82 66 65 .. .. 85 109 Namibia 57 87 24 25 63 74 53 39 3.4 14.4 75 83 Nepal 70 90 11 35 85 89 43 43 .. .. 88 64 Netherlands 100 100 100 100 96 98 .. .. .. .. 84 36 New Zealand 97 .. .. .. 82 89 .. .. .. .. 60 61 Nicaragua 70 79 45 47 99 87 57 49 .. 1.8 85 89 Niger 39 46 7 13 47 39 47 43 7.4 33.0 74 49 Nigeria 49 48 39 44 62 54 33 28 1.2 33.9 75 20 Norway 100 100 .. .. 91 93 .. .. .. .. 91 39 Oman 80 .. 83 .. 96 98 .. .. .. .. 90 122 Pakistan 83 91 37 59 80 83 .. .. .. .. 83 50 Panama 90 90 71 73 94 99 .. .. .. .. 80 134 Papua New Guinea 39 39 44 44 65 75 .. .. .. .. 71 21 Paraguay 62 86 58 80 88 73 .. .. .. .. 91 48 Peru 74 83 52 63 99 94 68 71 .. .. 91 96 Philippines 87 85 57 72 92 88 55 76 .. .. 89 77 Poland .. .. .. .. 99 99 .. .. .. .. 77 67 Portugal .. .. .. .. 93 93 .. .. .. .. 89 88 Puerto Rico .. .. .. .. .. .. .. .. .. .. 75 82 2008 World Development Indicators 99 2.16 Disease prevention coverage and quality Access to Access to Child Children Children with Children Children Tuberculosis DOTS an improved improved immunization with acute diarrhea who sleeping with fever treatment detection water source sanitation rate respiratory received oral under receiving success rate facilities infection rehydration treated antimalarial rate taken to and continuous bednetsa drugs health feeding provider % 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 2004 1990 2004 2006 2006 2000­06c 2000­06c 2000­06c 2000­06c 2005 2006 Romania .. 57 .. .. 95 97 .. .. .. .. 82 79 Russian Federation 94 97 87 87 99 99 .. .. 13.0 .. 58 44 Rwanda 59 74 37 42 95 99 28 24 5.0 12.3 83 27 Saudi Arabia 94 96 91 99 95 96 .. .. .. .. 65 40 Senegal 65 76 33 57 80 89 47 43 7.1 26.8 74 48 Serbia 93f 93f 87f 87f 88 92 93 31 .. .. 85 79 Sierra Leone .. 57 .. 39 67 64 48 31 5.3 51.9 86 35 Singapore 100 100 100 100 93 95 .. .. .. .. 83 107 Slovak Republic 100 100 99 99 98 99 .. .. .. .. 92 43 Slovenia .. .. .. .. 96 97 .. .. .. .. 84 71 Somalia .. 29 .. 26 35 35 13 7 9.2 7.9 89 83 South Africa 83 88 69 65 85 99 .. .. .. .. 71 71 Spain 100 100 100 100 97 98 .. .. .. .. .. .. Sri Lanka 68 79 69 91 99 99 .. .. .. .. 86 85 Sudan 64 70 33 34 73 78 57 56 27.6 54.2 82 30 Swaziland .. 62 .. 48 57 68 60 24 0.1 25.5 42 49 Sweden 100 100 100 100 95 99 .. .. .. .. 64 58 Switzerland 100 100 100 100 86 95 .. .. .. .. .. .. Syrian Arab Republic 80 93 73 90 98 99 77 34 .. .. 89 48 Tajikistan .. 59 .. 51 87 86 64 22 1.3 1.2 86 33 Tanzania 46 62 47 47 93 90 57 53 16.0 58.2 82 46 Thailand 95 99 80 99 96 98 84 46 .. .. 75 73 Timor-Leste .. 58 .. 36 64 67 24 .. 8.0 47.4 82 33 Togo 50 52 37 35 83 87 23 22 38.4 47.7 71 19 Trinidad and Tobago 92 91 100 100 89 92 74 32 .. .. .. .. Tunisia 81 93 75 85 98 99 43 .. .. .. 90 81 Turkey 85 96 85 88 98 90 41 .. .. .. 89 80 Turkmenistan .. 72 .. 62 99 98 83 25 .. .. 81 58 Uganda 44 60 42 43 89 80 74 28 9.7 61.8 73 44 Ukraine .. 96 .. 96 98 98 .. .. .. .. .. 65 United Arab Emirates 100 100 97 98 92 94 .. .. .. .. 73 17 United Kingdom 100 100 .. .. 85 92 .. .. .. .. .. .. United States 100 100 100 100 93 96 .. .. .. .. 64 88 Uruguay 100 100 100 100 94 95 .. .. .. .. 84 77 Uzbekistan 94 82 51 67 95 95 68 28 .. .. 81 48 Venezuela, RB .. 83 .. 68 55 71 72 51 .. .. 83 71 Vietnam 65 85 36 61 93 94 71 65 5.1 2.6 92 85 West Bank and Gaza .. 92 .. 73 .. .. 65 .. .. .. 100 5 Yemen, Rep. 71 67 32 43 80 85 47 18 .. .. 80 43 Zambia 50 58 44 55 84 80 69 48 22.8 57.9 84 53 Zimbabwe 78 81 50 53 90 90 26 .. 2.9 4.7 68 42 World 76 w 83 w 45 w 57 w 80 w 80 w .. w 85 w 62 w Low income 64 75 21 38 69 68 21.1 84 54 Middle income 78 84 47 62 91 91 .. 86 74 Lower middle income 74 81 37 55 90 89 .. 90 77 Upper middle income 88 93 77 81 94 95 .. 71 66 Low & middle income 72 80 36 51 79 79 .. 85 62 East Asia & Pacific 72 79 30 51 89 89 .. 91 78 Europe & Central Asia 92 92 84 85 97 95 .. 70 56 Latin America & Carib. 83 91 67 77 93 92 .. 79 69 Middle East & N. Africa 88 89 70 76 92 93 .. 83 69 South Asia 71 84 17 37 65 64 12.0 86 63 Sub-Saharan Africa 49 56 31 37 71 72 34.5 76 46 High income 100 99 100 100 93 96 .. 68 52 Euro area 100 100 100 100 91 95 .. .. 33 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 2007. e. Data are for 2006. f. Includes Montenegro. 100 2008 World Development Indicators 2.16 PEOPLE Disease prevention coverage and quality About the data People's health is influenced by the environment in rehydration salts at home. However, recommenda- least 20 liters a person a day from a source within which they live. Lack of clean water and basic sanita- tions for the use of oral rehydration therapy have 1 kilometer of the dwelling. · Access to improved tion is the main reason diseases transmitted by feces changed over time based on scientific progress, so sanitation facilities is the percentage of people with are so common in developing countries. Access to it is difficult to accurately compare use rates across at least adequate access to excreta disposal facilities drinking water from an improved source and access countries. Until the current recommended method for that can effectively prevent human, animal, and insect to improved sanitation do not ensure safety or ade- home management of diarrhea is adopted and applied contact with excreta. Improved facilities range from quacy, as these characteristics are not tested at in all countries, the data should be used with caution. protected pit latrines to flush toilets. · Child immuni- the time of the surveys. But improved drinking water Also, the prevalence of diarrhea may vary by season. zation rate is the percentage of children ages 12­23 technologies and improved sanitation facilities are Since country surveys are administered at different months who, before 12 months or at any time before more likely than those characterized as unimproved times, data comparability is further affected. the survey, had received measles vaccine and three to provide safe drinking water and to prevent con- Malaria is endemic to the poorest countries in the doses of diphtheria, pertussis (whooping cough), and tact with human excreta. The data are derived by world, mainly in tropical and subtropical regions of tetanus (DTP3) vaccine. One dose of measles vac- the Joint Monitoring Programme (JMP) of the World Africa, Asia, and the Americas. An estimated 300­500 cine and three doses of DTP vaccine are considered Health Organization (WHO) and United Nations Chil- million clinical malaria cases and more than 1 million adequate. · Children with acute respiratory infection dren's Fund (UNICEF) based on national censuses malaria deaths occur each year--the vast majority in taken to a health provider are the percentage of chil- and nationally representative household surveys. The Sub-Saharan Africa and among children under age dren under age 5 with acute respiratory infection in coverage rates for water and sanitation are based on 5. Insecticide-treated bednets, if properly used and the two weeks before the survey who were taken to an information from service users on the facilities their maintained, are one of the most important malaria- appropriate health provider. · Children with diarrhea households actually use rather than on information preventive strategies to limit human-mosquito con- who received oral rehydration and continuous feeding from service providers, which may include nonfunc- tact. Studies have emphasized that mortality rates are the percentage of children under age 5 with diar- tioning systems. While the estimates are based on could be reduced by about 25­30 percent if every rhea in the two weeks before the survey who received use, the JMP reports use as access, because access child under age 5 in malaria-risk areas such as Africa either oral rehydration therapy or increased fluids, is the term used in the Millennium Development Goal slept under a treated bednet every night. with continuous feeding. · Children sleeping under target for drinking water and sanitation. Prompt and effective treatment of malaria is a criti- treated bednets are the percentage of children under Governments in developing countries usually cal element of malaria control. It is vital that suffer- age 5 who slept under an insecticide-treated bednet finance immunization against measles and diphthe- ers, especially children under age 5, start treatment to prevent malaria in the two weeks before the survey. ria, pertussis (whooping cough), and tetanus (DTP) within 24 hours of the onset of symptoms, to pre- · Children with fever receiving antimalarial drugs as part of the basic public health package. In many vent progression--often rapid--to severe malaria are the percentage of children under age 5 who were developing countries lack of precise information on and death. ill with fever in the two weeks before the survey and the size of the cohort of one-year-old children makes Data on the success rate of tuberculosis treatment received any appropriate (locally defined) antimalarial immunization coverage diffi cult to estimate from are provided for countries that have implemented drugs. · Tuberculosis treatment success rate is the program statistics. The data shown here are based DOTS, the internationally recommended tubercu- percentage of new registered infectious tuberculosis on an assessment of national immunization cover- losis control strategy. The treatment success rate cases that were cured or completed a full course of age rates by the WHO and UNICEF. The assessment for tuberculosis provides a useful indicator of the treatment. · DOTS detection rate is the percentage of considered both administrative data from service quality of health services. A low rate or no success estimated new infectious tuberculosis cases detected providers and household survey data on children's suggests that infectious patients may not be receiv- under the internationally recommended tuberculosis immunization histories. Based on the data available, ing adequate treatment. An essential complement detection and treatment strategy. consideration of potential biases, and contributions to the tuberculosis treatment success rate is the of local experts, the most likely true level of immuni- DOTS detection rate, which indicates whether there Data sources zation coverage was determined for each year. is adequate coverage by the recommended case Data on access to water and sanitation are from Acute respiratory infection continues to be a lead- detection and treatment strategy. A country with a the WHO and UNICEF's Meeting the MDG Drinking ing cause of death among young children, killing high treatment success rate may still face big chal- Water and Sanitation Target (www.who.int/water_ about 2 million children under age 5 in developing lenges if its DOTS detection rate remains low. sanitation_health/monitoring). Data on immuniza- countries each year. An estimated 60 percent of For indicators that are from household surveys, the tion are from WHO and UNICEF estimates (www.who. these deaths can be prevented by the selective use year in the table refers to the survey year. For more int/immunization_monitoring). Data on children with of antibiotics by appropriate health care providers. information, consult the original sources. acute respiratory infection, with diarrhea, sleeping Data are drawn mostly from household health sur- under treated bednets, and receiving antimalarial Definitions veys in which mothers report on number of episodes drugs are from UNICEF's State of the World's Chil- and treatment for acute respiratory infection. · Access to an improved water source is the percent- dren 2008, Childinfo, and Demographic and Health Since 1990 diarrhea-related deaths among chil- age of people with reasonable access to water from an Surveys by Macro International. Data on tuberculo- dren have declined tremendously. Most diarrhea- improved source, such as piped water into a dwelling; sis are from the WHO's Global Tuberculosis Control related deaths are due to dehydration, and many of public tap; tubewell; protected dug well; and rainwater Report 2008: Surveillance, Planning, Financing. these deaths can be prevented with the use of oral collection. Reasonable access is the availability of at 2008 World Development Indicators 101 2.17 Reproductive health Total fertility Adolescent Unmet Contraceptive Newborns Pregnant Births attended Maternal rate fertility rate need for prevalence rate protected women by skilled mortality contraception against receiving health staff ratio tetanus prenatal care births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Modeled woman ages 15­19 15­49 15­49 % of births % % of total estimates estimates 1990 2006 2006 2000­06a 2000­06a 2006 2000­06a 1990 2000­06 a 1990­2006a 2005 Afghanistan .. .. .. .. .. .. 16 .. 14 .. .. Albania 2.9 1.4 16 .. 60 87 97 .. 100 16 92 Algeria 4.6 2.4 8 .. 61 70 89 77 95 117 180 Angola 7.1 6.5 139 .. 6 80 66 .. 45 .. 1,400 Argentina 3.0 2.3 58 .. .. .. 99 96 99 39 77 Armenia 2.5 1.3 30 13 53 .. 93 .. 98 16 76 Australia 1.9 1.8 15 .. .. .. .. 100 100 .. 4 Austria 1.5 1.4 12 .. .. .. .. .. .. .. 4 Azerbaijan 2.7 2.3 29 .. 55 .. 70 .. 100 26 82 Bangladesh 4.3 2.9 129 11 58 92 48 .. 20 322 570 Belarus 1.9 1.3 22 .. 73 .. 99 .. 100 10 18 Belgium 1.6 1.7 7 .. .. 94 .. .. .. .. 8 Benin 6.7 5.5 123 30 17 84 84 .. 79 498 840 Bolivia 4.9 3.6 79 23 58 .. 79 43 67 230 290 Bosnia and Herzegovina 1.7 1.2 21 23 36 85 99 97 100 3 3 Botswana 4.6 3.0 54 .. 44 71 97 77 94 326 380 Brazil 2.8 2.3 89 .. .. 84 97 72 97 76 110 Bulgaria 1.8 1.4 41 .. .. 65 .. .. 99 10 11 Burkina Faso 7.3 6.1 129 29 17 .. 85 .. 54 484 700 Burundi 6.8 6.8 55 .. 9 84 92 .. 34 615 1,100 Cambodia 5.7 3.3 43 25 40 80 69 .. 44 472 540 Cameroon 5.9 4.4 122 20 29 52 82 58 63 669 1,000 Canada 1.8 1.5 14 .. .. 82 .. .. 100 .. 7 Central African Republic 5.6 4.7 119 .. 19 74 69 .. 53 543 980 Chad 6.7 6.3 169 21 3 60 39 .. 14 1,099 1,500 Chile 2.6 2.0 60 .. .. .. .. .. 100 17 16 China 2.1 1.8 7 .. 87 .. 90 50 98 48 45 Hong Kong, China 1.3 1.0 5 .. .. .. .. .. 100 .. .. Colombia 3.0 2.3 67 6 78 88 94 82 96 78 130 Congo, Dem. Rep. 6.7 6.3 224 .. 21b 77 85b .. 74b 1,289 1,100 Congo, Rep. 5.3 4.6 118 16 44 84 86 .. 86 781 740 Costa Rica 3.1 2.1 73 .. 96 .. 92 98 99 36 30 Cote d'Ivoire 6.5 4.6 112 29 13 .. 85 .. 57 543 810 Croatia 1.6 1.4 13 .. 69 .. 100 100 100 7 7 Cuba 1.7 1.5 48 8 73 .. 100 .. 100 37 45 Czech Republic 1.9 1.3 11 .. .. .. .. .. 100 5 4 Denmark 1.7 1.9 6 .. .. .. .. .. .. 10 3 Dominican Republic 3.3 2.8 109 11 61 85 99 93 96 92 150 Ecuador 3.6 2.6 83 .. 73 66 84 .. 75 107 210 Egypt, Arab Rep. 4.3 2.9 41 10 59 86 70 37 74 84 130 El Salvador 3.7 2.7 82 .. 67 91 86 52 92 71 170 Eritrea 6.2 5.1 75 27 8 79 70 .. 28 998 450 Estonia 2.0 1.5 22 .. .. .. .. .. 100 29 25 Ethiopia 6.8 5.3 97 34 15 80 28 .. 6 673 720 Finland 1.8 1.8 10 .. .. .. .. .. 100 6 7 France 1.8 2.0 7 .. .. .. .. .. .. 10 8 Gabon 4.7 3.1 85 28 33 63 94 .. 86 519 520 Gambia, The 6.0 4.8 106 .. 18 94 98 44 57 730 690 Georgia 2.1 1.4 31 .. 47 87 94 .. 92 23 66 Germany 1.5 1.3 10 .. .. .. .. .. 100 8 4 Ghana 5.7 3.9 58 34 17 .. 92 40 50 .. 560 Greece 1.4 1.4 9 .. .. 69 .. .. .. 1 3 Guatemala 5.6 4.2 109 .. 43 91 84 .. 41 153 290 Guinea 6.6 5.5 153 21 9 79 82 31 38 980 910 Guinea-Bissau 7.1 7.1 190 .. 10 91 78 .. 39 405 1,100 Haiti 5.4 3.6 48 38 32 94 85 23 26 630 670 102 2008 World Development Indicators 2.17 PEOPLE Reproductive health Total fertility Adolescent Unmet Contraceptive Newborns Pregnant Births attended Maternal rate fertility rate need for prevalence rate protected women by skilled mortality contraception against receiving health staff ratio tetanus prenatal care births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Modeled woman ages 15­19 15­49 15­49 % of births % % of total estimates estimates 1990 2006 2006 2000­06a 2000­06a 2006 2000­06a 1990 2000­06 a 1990­2006a 2005 Honduras 5.1 3.4 95 17 65 .. 92 45 67 108 280 Hungary 1.8 1.4 20 .. .. .. .. .. 100 4 6 India 3.8 2.5 63 .. 56 83 74 .. 47 301 450 Indonesia 3.1 2.2 41 9 57 87 92 32 72 307 420 Iran, Islamic Rep. 4.8 2.1 21 .. 74 .. .. .. 90 37 140 Iraq 5.9 .. .. .. .. .. 84 54 89 .. .. Ireland 2.1 1.9 17 .. .. .. .. .. 100 6 1 Israel 2.8 2.7 14 .. .. .. .. .. .. 5 4 Italy 1.3 1.4 6 .. .. 52 .. .. 99 7 3 Jamaica 2.9 2.3 80 .. 69 72 91 79 97 95 170 Japan 1.5 1.3 3 .. 56 86 .. 100 100 8 6 Jordan 5.4 3.2 25 11 56 .. 99 87 100 41 62 Kazakhstan 2.7 2.1 30 .. 51 .. 100 .. 100 70 140 Kenya 5.8 5.0 104 25 39 74 88 50 42 414 560 Korea, Dem. Rep. 2.4 1.9 1 .. .. .. .. .. 97 105 370 Korea, Rep. 1.6 1.1 4 .. .. .. .. 98 100 20 14 Kuwait 3.5 2.3 13 .. .. 90 .. .. 100 5 4 Kyrgyz Republic 3.7 2.4 31 1 48 82 97 .. 98 104 150 Lao PDR 6.1 3.3 75 .. 32 52 27 .. 19 405 660 Latvia 2.0 1.4 15 .. .. .. .. .. 100 10 10 Lebanon 3.1 2.2 25 .. 58 72 96 .. 98 .. 150 Lesotho 4.9 3.5 77 31 37 72 90 .. 55 762 960 Liberia 6.9 6.8 220 .. 10 .. 85 .. 51 .. 1,200 Libya 4.7 2.8 3 .. .. .. .. .. .. 77 97 Lithuania 2.0 1.3 19 .. .. .. .. .. 100 16 11 Macedonia, FYR 2.0 1.5 22 34 14 .. 98 .. 98 21 10 Madagascar 6.2 4.9 136 24 27 67 80 57 51 469 510 Malawi 6.9 5.7 140 28 42 84 92 55 54 984 1,100 Malaysia 3.7 2.7 13 .. .. 88 79 .. 98 28 62 Mali 7.4 6.6 183 29 8 .. 57 .. 41 582 970 Mauritania 5.8 4.5 88 32 8 94 64 40 57 747 820 Mauritius 2.3 2.0 41 .. 76 .. .. 91 99 22 15 Mexico 3.4 2.2 66 .. 71 87 .. .. 83 62 60 Moldova 2.3 1.2 33 .. 68 .. 98 .. 100 19 22 Mongolia 4.0 2.3 46 14 66 87 99 .. 99 93 46 Morocco 4.0 2.4 19 10 63 .. 68 31 63 227 240 Mozambique 6.2 5.2 155 18 17 85 85 .. 48 408 520 Myanmar 3.4 2.1 17 .. 34 87 76 .. 68 316 380 Namibia 5.7 3.3 61 22 44 81 91 68 76 271 210 Nepal 5.1 3.1 116 25 48 83 44 7 19 281 830 Netherlands 1.6 1.7 5 .. .. .. .. .. 100 7 6 New Zealand 2.2 2.1 23 .. .. .. .. .. 97 15 9 Nicaragua 4.7 2.8 114 15 69 94 86 .. 67 87 170 Niger 7.9 7.0 201 16 11 53 46 15 18 648 1,800 Nigeria 6.7 5.4 131 17 13 71 58 33 36 .. 1,100 Norway 1.9 1.9 9 .. .. .. .. 100 .. 6 7 Oman 6.5 3.1 11 .. 32 94 100 .. 98 15 64 Pakistan 5.8 3.9 33 .. 28 80 36 19 31 533 320 Panama 3.0 2.6 84 .. .. .. .. .. 91 40 130 Papua New Guinea 4.8 3.9 55 .. .. 81 .. .. 42 .. 470 Paraguay 4.7 3.2 74 .. 73 82 94 66 77 174 150 Peru 3.9 2.6 61 8 46 64 92 80 87 185 240 Philippines 4.3 3.3 48 17 49 57 88 .. 60 172 230 Poland 2.0 1.3 13 .. .. .. .. .. 100 4 8 Portugal 1.4 1.4 14 .. .. .. .. 98 100 8 11 Puerto Rico 2.2 1.8 50 .. .. .. .. .. 100 .. 18 2008 World Development Indicators 103 2.17 Reproductive health Total fertility Adolescent Unmet Contraceptive Newborns Pregnant Births attended Maternal rate fertility rate need for prevalence rate protected women by skilled mortality contraception against receiving health staff ratio tetanus prenatal care births per % of married % of married per 100,000 live births births per 1,000 women women ages women ages National Modeled woman ages 15­19 15­49 15­49 % of births % % of total estimates estimates 1990 2006 2006 2000­06a 2000­06a 2006 2000­06a 1990 2000­06 a 1990­2006a 2005 Romania 1.8 1.3 33 .. 70 .. 94 .. 98 17 24 Russian Federation 1.9 1.3 28 .. .. .. .. .. 99 23 28 Rwanda 7.4 5.9 41 38 17 82 94 26 39 750 1,300 Saudi Arabia 5.9 3.4 29 .. .. 56 .. .. 96 10 18 Senegal 6.5 5.3 91 32 12 86 87 .. 52 434 980 Serbia 1.8 1.4 25 29 41 .. 98 .. 99 7c 14 c Sierra Leone 6.5 6.5 166 .. 5 .. 81 .. 43 1,800 2,100 Singapore 1.9 1.3 5 .. .. 4 .. .. 100 6 14 Slovak Republic 2.1 1.2 20 .. .. 73 .. .. 100 6 6 Slovenia 1.5 1.3 7 .. .. 74 .. 100 100 17 6 Somalia 6.8 6.1 67 .. 15 .. 26 .. 33 1,044 1,400 South Africa 3.3 2.7 63 .. 60 88 92 .. 92 150 400 Spain 1.3 1.4 9 .. .. 72 .. .. .. 6 4 Sri Lanka 2.5 1.9 26 .. 70 93 100 .. 96 43 58 Sudan 5.9 4.3 59 6 8 .. 70 69 49 .. 450 Swaziland 5.3 3.5 34 .. 48 .. 90 .. 74 229 390 Sweden 2.1 1.9 4 .. .. 86 .. .. .. 5 3 Switzerland 1.6 1.4 4 .. .. 93 .. .. 100 5 5 Syrian Arab Republic 5.4 3.2 38 .. 58 87 84 .. 93 65 130 Tajikistan 5.1 3.4 28 .. 38 88 77 .. 83 97 170 Tanzania 6.1 5.3 123 22 26 .. 78 53 46 578 950 Thailand 2.1 1.8 42 .. 77 .. 98 .. 97 24 110 Timor-Leste 4.9 7.3 56 .. 10 63 61 .. 18 .. 380 Togo 6.4 4.9 92 .. 17 84 89 31 62 480 510 Trinidad and Tobago 2.4 1.6 35 .. 43 .. 96 .. 98 45 45 Tunisia 3.5 2.0 7 .. 63 89 92 69 90 69 100 Turkey 3.0 2.2 39 .. 71 67 81 .. 83 29 44 Turkmenistan 4.2 2.6 16 10 48 .. 99 .. 100 14 130 Uganda 7.1 6.7 156 41 24 88 94 38 42 505 550 Ukraine 1.8 1.3 28 .. 66 .. 99 .. 100 13 18 United Arab Emirates 4.3 2.3 19 .. .. .. .. .. 100 3 37 United Kingdom 1.8 1.9 24 .. 84 .. .. .. .. 7 8 United States 2.1 2.1 43 .. .. .. .. 99 99 8 11 Uruguay 2.5 2.0 62 .. .. .. .. .. 99 26 20 Uzbekistan 4.1 2.4 34 8 65 87 99 .. 100 28 24 Venezuela, RB 3.4 2.6 90 .. .. 88 94 .. 95 60 57 Vietnam 3.6 2.1 18 5 76 61 91 .. 88 162 150 West Bank and Gaza 6.3 4.6 82 .. 50 .. 99 .. 99 .. .. Yemen, Rep. 8.0 5.6 73 .. 23 .. 41 16 27 365 430 Zambia 6.4 5.3 130 27 34 90 93 51 43 729 830 Zimbabwe 5.1 3.8 62 13 60 80 94 70 80 555 880 World 3.1 w 2.5 w 52 w 60 w .. w 80 w .. w 65 w 400 w Low income 4.7 3.5 82 44 81 69 .. 43 650 Middle income 2.7 2.1 32 75 .. 90 53 88 160 Lower middle income 2.6 2.1 24 76 .. 89 50 86 180 Upper middle income 2.7 2.0 56 .. .. .. .. 94 97 Low & middle income 3.4 2.7 56 60 .. 80 .. 62 440 East Asia & Pacific 2.4 2.0 16 79 .. 89 47 87 150 Europe & Central Asia 2.3 1.6 29 63 .. 91 81 95 43 Latin America & Carib. 3.2 2.4 77 69 84 95 73 88 130 Middle East & N. Africa 4.8 2.9 30 60 .. 76 48 77 200 South Asia 4.1 2.8 69 53 84 66 32 41 500 Sub-Saharan Africa 6.2 5.2 122 22 76 72 44 45 900 High income 1.8 1.7 22 .. .. .. .. 99 9 Euro area 1.5 1.5 8 .. .. .. .. .. 5 a. Data are for most recent year available. b. Data are for 2007. c. Includes Montenegro. 104 2008 World Development Indicators 2.17 PEOPLE Reproductive health About the data Definitions Reproductive health is a state of physical and men- these data cannot be compared with those in previ- · Total fertility rate is the number of children that tal well-being in relation to the reproductive system ous editions. would be born to a woman if she were to live to the and its functions and processes. Means of achieving Good prenatal and postnatal care improve mater- end of her childbearing years and bear children in reproductive health include education and services nal health and reduce maternal and infant mortality. accordance with current age-specific fertility rates. during pregnancy and childbirth, safe and effective But data may not reflect such improvements because · Adolescent fertility rate is the number of births per contraception, and prevention and treatment of sexu- health information systems are often weak, mater- 1,000 women ages 15­19. · Unmet need for contra- ally transmitted diseases. Pregnancy and childbirth nal deaths are underreported, and rates of maternal ception is the percentage of fertile, married women complications are the leading cause of death and mortality are difficult to measure. of reproductive age who do not want to become preg- disability among women of reproductive age in devel- The share of births attended by skilled health staff nant and are not using contraception. · Contracep- oping countries. is an indicator of a health system's ability to provide tive prevalence rate is the percentage of women Total and adolescent fertility rates are based on adequate care for pregnant women. Maternal mor- married or in-union ages 15­49 who are practicing, data on registered live births from vital registration tality ratios are generally of unknown reliability, as or whose sexual partners are practicing, any form systems or, in the absence of such systems, from are many other cause-specific mortality indicators. of contraception. · Newborns protected against censuses or sample surveys. The estimated rates Household surveys such as Demographic and Health tetanus are the percentage of births by women of are generally considered reliable measures of fertility Surveys attempt to measure maternal mortality by child-bearing age who are immunized against teta- in the recent past. Where no empirical information asking respondents about survivorship of sisters. nus. · Pregnant women receiving prenatal care are on age- specific fertility rates is available, a model is The main disadvantage of this method is that the the percentage of women attended at least once used to estimate the share of births to adolescents. estimates of maternal mortality that it produces during pregnancy by skilled health personnel for For countries without vital registration systems fertil- pertain to 12 years or so before the survey, making reasons related to pregnancy. · Births attended by ity rates are generally based on extrapolations from them unsuitable for monitoring recent changes or skilled health staff are the percentage of deliveries trends observed in censuses or surveys from earlier observing the impact of interventions. In addition, attended by personnel trained to give the necessary years. measurement of maternal mortality is subject to care to women during pregnancy, labor, and post- More couples in developing countries want to limit many types of errors. Even in high-income countries partum; to conduct deliveries on their own; and to or postpone childbearing but are not using effec- with vital registration systems, misclassification of care for newborns. · Maternal mortality ratio is the tive contraception. These couples have an unmet maternal deaths has been found to lead to serious number of women who die from pregnancy-related need for contraception. Common reasons are lack underestimation. causes during pregnancy and childbirth per 100,000 of knowledge about contraceptive methods and The national estimates of maternal mortality live births. concerns about possible side effects. This indica- ratios in the table are based on national surveys, Data sources tor excludes women not exposed to the risk of unin- vital registration records, and surveillance data or tended pregnancy because of menopause, infertility, are derived from community and hospital records. Data on fertility rates are compiled and estimated or postpartum anovulation. The modeled estimates are based on an exercise by by the World Bank's Development Data Group. Contraceptive prevalence reflects all methods-- the World Health Organization (WHO), United Nations Inputs come from the United Nations Population ineffective traditional methods as well as highly Children's Fund (UNICEF), United Nations Popula- Division's World Population Prospects: The 2006 effective modern methods. Contraceptive prevalence tion Fund (UNFPA), and World Bank. For countries Revision, census reports and other statistical rates are obtained mainly from household surveys, with complete vital registration systems with good publications from national statistical offi ces, including Demographic and Health Surveys, Multiple attribution of cause of death information, the data and household surveys such as Demographic Indicator Cluster Surveys, and contraceptive preva- are used as reported. For countries with national and Health Surveys. Data on women with unmet lence surveys (see Primary data documentation for data, either from complete vital registration systems need for contraception and contraceptive preva- the most recent survey year). Unmarried women are with uncertain or poor attribution of cause of death lence rates are from household surveys, including often excluded from such surveys, which may bias information, or from household surveys, reported Demographic and Health Surveys by Macro Inter- the estimates. maternal mortality was adjusted usually by a fac- national and Multiple Indicator Cluster Surveys by An important cause of infant mortality in some tor of underenumeration and misclassification. For UNICEF. Data on tetanus vaccinations, pregnant developing countries, neonatal tetanus can be pre- countries with no empirical national data (about 35 women receiving prenatal care, births attended vented through immunization of the mother during percent of countries), maternal mortality was esti- by skilled health staff, and national estimates of pregnancy. The data on tetanus in this year's edition mated with a regression model using socioeconomic maternal mortality ratios are from UNICEF's State are estimated by the "protection at birth" model, information, including fertility, birth attendants, and of the World's Children 2008 and Childinfo and which tracks the immunization status of women of GDP. Neither set of ratios can be assumed to provide Demographic and Health Surveys by Macro Inter- child-bearing age. The estimates account for the an exact estimate of maternal mortality for any of the national. Modeled estimates for maternal mortal- number of doses received and the time since the countries in the table. ity ratios are from "Maternal Mortality in 2005: mother's last immunization. A currently immune For the indicators that are from household surveys, Estimates Developed by WHO, UNICEF, UNFPA and woman's child is considered protected. Because the year in the table refers to the survey year. For the World Bank" (2007). the methodology behind this indicator has changed, more information, consult the original sources. 2008 World Development Indicators 105 2.18 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2002­04a 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2005 Afghanistan .. .. .. .. .. .. .. 28 .. Albania 5c 6 17.0 39.2 30.0 7 2 62 .. Algeria 5 4 10.2 21.6 15.4 6 7 61 .. Angola 58 35 27.5 50.8 5.3 12 11 35 79 Argentina <2.5 3 2.3 8.2 9.9 7 .. .. .. Armenia 52c 24 4.2 18.2 11.7 8 33 97 .. Australia <2.5 <2.5 .. .. .. 7 .. .. .. Austria <2.5 <2.5 .. .. .. 7 .. .. .. Azerbaijan 34 c 7 14.0 24.1 6.2 12 7 26 29d Bangladesh 35 30 39.2 47.8 0.9 22 37 84 83 Belarus <2.5c 4 .. .. .. 4 9 55 .. Belgium <2.5 <2.5 .. .. .. .. .. .. .. Benin 20 12 21.5 39.1 3.0 13 70 55 94 Bolivia 28 23 5.9 32.5 9.2 7 54 90 39 Bosnia and Herzegovina 9c 9 4.2 12.1 16.3 5 18 62 .. Botswana 23 32 10.7 29.1 10.4 10 34 66 62 Brazil 12 7 3.7 .. .. 8 .. 88 .. Bulgaria 8c 8 1.6 8.8 13.6 10 .. 100 .. Burkina Faso 21 15 35.2 43.1 5.4 16 7 34 95 Burundi 48 66 38.9 63.1 1.4 11 45 98 69 Cambodia 43 33 28.4 43.7 1.7 11 60 73 79 Cameroon 33 26 15.1 35.4 8.7 11 21 49 95 Canada <2.5 <2.5 .. .. .. 6 .. .. .. Central African Republic 50 44 21.8 44.6 10.8 13 23 62 79 Chad 58 35 33.9 44.8 4.4 22 2 56 95 Chile 8 4 .. .. .. 6 63 100 .. China 16 12 6.8 21.8 9.2 2 51 90 .. Hong Kong, China .. .. .. .. .. 5 .. .. .. Colombia 17 13 5.1 16.2 4.2 6 47 90 .. Congo, Dem. Rep. 31 74 33.6 44.4 6.5 12 36e 72 92 Congo, Rep. 54 33 11.8 31.2 8.5 13 19 82 90 Costa Rica 6 5 .. .. .. 7 .. .. 60 Côte d'Ivoire 18 13 .. .. .. 17 4 84 95 Croatia 16c 7 .. .. .. 6 .. .. .. Cuba 7 <2.5 .. .. .. 5 26 88 .. Czech Republic <2.5c <2.5 2.1 2.6 4.4 7 .. .. .. Denmark <2.5 <2.5 .. .. .. 5 .. .. .. Dominican Republic 27 29 4.2 11.7 8.6 11 4 19 40 Ecuador 8 6 6.2 29.0 5.1 .. 40 .. .. Egypt, Arab Rep. 4 4 5.4 23.8 14.1 14 38 78 .. El Salvador 12 11 6.1 24.6 5.8 7 24 62 .. Eritrea 70 c 75 34.5 43.7 1.6 14 52 68 57 Estonia 9c <2.5 .. .. .. 4 .. .. .. Ethiopia 69c 46 34.6 50.7 5.1 14 49 20 59 Finland <2.5 <2.5 .. .. .. 4 .. .. .. France <2.5 <2.5 .. .. .. .. .. .. .. Gabon 10 5 8.8 26.3 5.6 14 6 36 30 Gambia, The 22 29 15.4 24.1 3.0 20 41 7 95 Georgia 44 c 9 .. .. .. 7 .. 91 .. Germany <2.5 <2.5 .. .. .. .. .. .. .. Ghana 37 11 18.8 35.6 4.5 9 54 32 95 Greece <2.5 <2.5 .. .. .. .. .. .. .. Guatemala 16 22 17.7 54.3 5.6 12 51 67 44 d Guinea 39 24 22.5 39.3 5.1 12 27 51 95 Guinea-Bissau 24 39 21.9 36.1 5.1 24 16 1 64 Haiti 65 46 18.9 29.7 3.9 25 41 3 42 106 2008 World Development Indicators 2.18 PEOPLE Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2002­04a 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2005 Honduras 23 23 8.6 29.9 5.8 10 30 .. 40 Hungary <2.5c <2.5 .. .. .. 9 .. .. .. India 25 20 43.5 47.9 1.9 .. 46 51 64 d Indonesia 9 6 24.4 28.6 5.1 9 40 73 76 Iran, Islamic Rep. 4 4 .. .. .. .. 44 99 .. Iraq .. .. .. .. .. .. .. 28 .. Ireland <2.5 <2.5 .. .. .. .. .. .. .. Israel <2.5 <2.5 .. .. .. 8 .. .. .. Italy <2.5 <2.5 .. .. .. .. .. .. .. Jamaica 14 9 3.1 4.5 7.5 12 15 .. .. Japan <2.5 <2.5 .. .. .. 8 .. .. .. Jordan 4 6 3.6 12.0 4.7 12 27 88 .. Kazakhstan <2.5c 6 .. .. .. 6 17 92 .. Kenya 39 31 16.5 35.8 5.8 10 13 91 69 Korea, Dem. Rep. 18 33 17.8 44.7 0.9 7 65 40 95 Korea, Rep. <2.5 <2.5 .. .. .. 4 .. .. .. Kuwait 24 5 .. .. .. .. .. .. .. Kyrgyz Republic 21c 4 .. .. .. 5 32 76 88 Lao PDR 29 19 36.4 48.2 2.7 14 23 75 63 Latvia 3c 3 .. .. .. 5 .. .. .. Lebanon <2.5 3 .. .. .. 6 27 92 .. Lesotho 17 13 16.6 45.2 6.8 13 36 91 9 Liberia 34 50 22.8 45.3 4.6 .. 35 .. 95 Libya <2.5 <2.5 .. .. .. .. .. .. .. Lithuania 4c <2.5 .. .. .. 4 .. .. .. Macedonia, FYR 15c 5 1.2 1.2 7.9 6 16 94 95 Madagascar 35 38 36.8 52.8 6.2 17 67 75 95 Malawi 50 35 18.4 52.5 10.2 13 56 48 94 Malaysia 3 3 .. .. .. 9 .. .. .. Mali 29 29 30.1 42.7 3.1 23 25 74 66 Mauritania 15 10 30.4 39.5 3.8 .. 20 2 96 Mauritius 6 5 .. .. .. 14 21 .. .. Mexico 5 5 3.4 15.5 7.6 8 .. 91 68 Moldova 5c 11 3.2 11.3 9.1 6 46 60 .. Mongolia 34 27 4.8 23.5 6.1 6 57 83 92 Morocco 6 6 9.9 23.1 13.3 15 31 59 .. Mozambique 66 44 21.2 47.0 6.3 15 30 54 95 Myanmar 10 5 29.6 40.6 2.4 15 15 60 95 Namibia 34 24 20.3 29.5 3.3 14 19 63 68 Nepal 20 17 38.8 49.3 0.6 21 53 63 96 Netherlands <2.5 <2.5 .. .. .. .. .. .. .. New Zealand <2.5 <2.5 .. .. .. 6 .. .. .. Nicaragua 30 27 7.8 25.2 7.1 12 31 97 98 Niger 41 32 39.9 54.8 3.5 13 14 46 94 Nigeria 13 9 27.2 43.0 6.2 14 17 97 73 Norway <2.5 <2.5 .. .. .. 5 .. .. .. Oman .. .. .. .. .. 8 .. .. 95 Pakistan 24 24 31.3 41.5 4.8 .. .. 17 95 Panama 21 23 .. .. .. 10 .. .. 4 Papua New Guinea .. .. .. .. .. .. .. .. 90 Paraguay 18 15 .. .. .. 9 22 88 .. Peru 42 12 5.2 31.3 11.8 7 63 91 .. Philippines 26 18 20.7 33.8 2.4 20 34 56 85 Poland <2.5c <2.5 .. .. .. 6 .. .. .. Portugal <2.5 <2.5 .. .. .. 8 .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 107 2.18 Nutrition Prevalence of Prevalence of child Prevalence Low- Exclusive Consumption Vitamin A undernourishment malnutrition of overweight birthweight breastfeeding of iodized supplemen- children babies salt tation % of children under age 5 % of children % of children % of % of children % of population Underweight Stunting under age 5 % of births under 6 months households 6­59 months 1990­92 2002­04a 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2005 Romania <2.5c <2.5 3.5 12.8 8.3 8 16 74 .. Russian Federation 4c 3 .. .. .. 6 .. 35 .. Rwanda 43 33 18.0 51.7 6.7 6 88 88 100 Saudi Arabia 4 4 .. .. .. .. .. .. .. Senegal 23 20 14.5 20.1 2.4 19 34 41 95 Serbia 5c,f 9f .. .. .. 5 15 .. .. Sierra Leone 46 51 24.7 38.4 4.7 24 8 45 95 Singapore .. .. 3.3 4.4 2.6 8 .. .. .. Slovak Republic 4c 7 .. .. .. 7 .. .. .. Slovenia 3c 3 .. .. .. 6 .. .. .. Somalia .. .. .. .. .. 11 9 1 6 South Africa <2.5 <2.5 .. .. .. .. 7 .. 33 Spain <2.5 <2.5 .. .. .. .. .. .. .. Sri Lanka 28 22 22.8 18.4 1.0 22 53 94 64d Sudan 31 26 38.4 47.6 5.2 .. 34 11 90 Swaziland 14 22 9.1 36.6 14.9 9 24 59 59 Sweden <2.5 <2.5 .. .. .. .. .. .. .. Switzerland <2.5 <2.5 .. .. .. .. .. .. .. Syrian Arab Republic 5 4 .. .. .. 9 29 79 .. Tajikistan 22c 56 .. .. .. 10 25 46 98 Tanzania 37 44 16.7 44.4 4.9 10 41 43 95 Thailand 30 22 .. .. .. 9 5 58 .. Timor-Leste 11 9 40.6 55.7 5.7 12 31 72 91 Togo 33 24 .. .. .. 12 28 25 95 Trinidad and Tobago 13 10 4.4 5.3 4.9 19 13 28 .. Tunisia <2.5 <2.5 .. .. .. 7 47 97 .. Turkey <2.5 3 .. .. .. .. 21 64 .. Turkmenistan 12c 7 .. .. .. 4 11 87 .. Uganda 24 19 19.0 44.8 4.9 12 60 96 78 Ukraine <2.5c <2.5 4.1 22.9 26.5 4 6 18 .. United Arab Emirates 4 3 .. .. .. .. .. .. .. United Kingdom <2.5 <2.5 .. .. .. 8 .. .. .. United States <2.5 <2.5 1.1 3.3 7.0 8 .. .. .. Uruguay 7 <2.5 6.0 13.9 9.4 8 .. .. .. Uzbekistan 8c 25 .. .. .. 5 26 53 82 Venezuela, RB 11 18 .. .. .. 9 .. .. .. Vietnam 31 16 26.7 43.4 2.5 7 17 93 99d West Bank and Gaza .. 16 .. .. .. 7 27 86 .. Yemen, Rep. 34 38 .. .. .. .. 12 30 15d Zambia 48 46 23.3 52.5 5.9 12 40 77 66 Zimbabwe 45 47 14.0 35.8 9.1 .. 22 .. 81 World 17 w 14 w 23.5 w .. w 5.5 w 10 w 39 w 68 w .. w Low income 27 24 35.3 45.9 3.4 .. 38 55 76 Middle income 14 10 9.5 23.8 8.5 7 40 79 .. Lower middle income 16 11 10.7 24.8 8.5 7 41 81 .. Upper middle income .. 5 .. .. .. 8 .. 72 .. Low & middle income 20 16 24.5 37.1 5.4 10 39 68 .. East Asia & Pacific 17 12 12.9 26.2 7.3 6 44 84 .. Europe & Central Asia 6c 6 .. .. .. 6 .. 50 .. Latin America & Carib. 13 10 5.1 .. .. 9 .. 85 .. Middle East & N. Africa 6 7 .. .. .. 12 30 72 .. South Asia 26 21 41.0 46.7 2.1 .. 45 51 72 Sub-Saharan Africa 29 30 27.0 44.5 5.7 13 31 61 79 High income 3 3 .. .. .. .. .. .. .. Euro area 3 3 .. .. .. .. .. .. .. a. Preliminary data. b. Data are for the most recent year available. c. Data are for 1993­95. d. Country's vitamin A supplementation programs do not target children all the way up to 59 months of age. e. Data are for 2007. f. Includes Montenegro. 108 2008 World Development Indicators 2.18 PEOPLE Nutrition About the data Definitions Data on undernourishment are produced by the Food more by nutrition, feeding practices, environment, · Prevalence of undernourishment is the percent- and Agriculture Organization (FAO) of the United and healthcare than by genetics or ethnicity. The age of the population that is undernourished--whose Nations based on the calories available from local data reported previously were based on the U.S. dietary energy consumption is continuously below a food production, trade, and stocks; the number of National Center for Health Statistics­WHO growth minimum dietary energy requirement for maintaining calories needed by different age and gender groups; reference. Because of the change in standards, the a healthy life and carrying out light physical activity. the proportion of the population represented by data in this edition should not be compared with data · Prevalence of child malnutrition is the percent- each age group; and a coefficient of distribution to in previous editions. age of children under age 5 whose weight for age account for inequality in access to food (FAO, State Low birthweight, which is associated with maternal (underweight) or height for age (stunting) is more of Food Insecurity in the World 2000). From a policy malnutrition, raises the risk of infant mortality and than two standard deviations below the median for and program standpoint, however, this measure has stunts growth in infancy and childhood. There is also the international reference population ages 0­59 its limits. First, food insecurity exists even where emerging evidence that low-birthweight babies are months. For children up to two years old height is food availability is not a problem because of inad- more prone to noncommunicable diseases such as measured by recumbent length. For older children equate access of poor households to food. Second, diabetes and cardiovascular diseases. Estimates of height is measured by stature while standing. The food insecurity is an individual or household phe- low-birthweight infants are drawn mostly from hos- table presents data for the WHO's new child growth nomenon, and the average food available to each pital records and household surveys. Many births in standards released in 2006. · Prevalence of over- person, even corrected for possible effects of low developing countries take place at home, and these weight children is the percentage of children under income, is not a good predictor of food insecurity births are seldom recorded. A hospital birth may indi- age 5 whose weight for height is more than two stan- among the population. And third, nutrition security cate higher income and therefore better nutrition, or dard deviations above the median for the interna- is determined not only by food security but also by it could indicate a higher risk birth, possibly skewing tional reference population of the corresponding age the quality of care of mothers and children and the the data on birthweights downward. The data should as established by the WHO's new child growth stan- quality of the household's health environment (Smith therefore be used with caution. dards released in 2006. · Low-birthweight babies and Haddad 2000). Improved breastfeeding practice can save an esti- are the percentage of newborns weighing less than Estimates of child malnutrition, based on weight for mated 1.3 million children a year. Breast milk alone 2.5 kilograms, with the measurement taken within age (underweight) and height for age (stunting), are contains all the nutrients, antibodies, hormones, and the first hours of life, before significant postnatal from national survey data. The proportion of children antioxidants an infant needs to thrive. It protects weight loss has occurred. · Exclusive breastfeeding who are underweight is the most common indica- babies from diarrhea and acute respiratory infections, is the percentage of children less than six months tor of malnutrition. Being underweight, even mildly, stimulates their immune systems and response to old who were fed breast milk alone (no other liquids) increases the risk of death and inhibits cognitive vaccination, and according to some studies confers in the past 24 hours. · Consumption of iodized salt development in children. Moreover, it perpetuates cognitive benefits as well. The data on breastfeeding is the percentage of households that use edible salt the problem from one generation to the next, as mal- are derived from national surveys. fortified with iodine. · Vitamin A supplementation nourished women are more likely to have low-birth- Iodine defi ciency is the single most important is the percentage of children ages 6­59 months old weight babies. Height for age reflects linear growth cause of preventable mental retardation, and it con- who received at least one dose of vitamin A in the achieved pre- and postnatally, and a deficit indicates tributes significantly to the risk of stillbirth and mis- previous six months, as reported by mothers. long-term, cumulative effects of inadequacies of carriage. Widely used and inexpensive, iodized salt health, diet, or care. It is often argued that stunting is the best source of iodine, and a global campaign is a proxy for multifaceted deprivation and is a better to iodize edible salt is significantly reducing the risks indicator of long-term changes in malnutrition. (UNICEF, Childinfo 2006). The data on iodized salt are Estimates of children who are overweight are also derived from household surveys. from national survey data. Overweight children have Vitamin A is essential for the functioning of the become a growing concern in developing countries. immune system. Besides being a leading cause of Researchers show an association between obesity blindness, vitamin A deficiency causes a 23 percent Data sources in childhood and a high prevalence of diabetes, greater risk of dying from a range of childhood ail- respiratory disease, high blood pressure, and psy- ments such as measles, malaria, and diarrhea. Giv- Data on undernourishment are from www.fao. chosocial and orthopedic disorders (de Onis and ing vitamin A to new mothers who are breastfeeding org/faostat/foodsecurity/index_en.htm. Data Blössner 2000). helps protect their children during the first months on malnutrition and overweight children are from New international child growth reference standards of life. Food fortification with vitamin A is being intro- the WHO's Global Database on Child Growth and for infants and young children were released in 2006 duced in many developing countries. Malnutrition (www.who.int/nutgrowthdb). Data on by the World Health Organization (WHO) as a tool for For indicators from household surveys, the year in low-birthweight babies, breastfeeding, iodized salt monitoring the nutritional status of children. They are the table refers to the survey year. For more informa- consumption, and vitamin A supplementation are also key in measuring and monitoring health targets tion, consult the original sources. from the United Nations Children's Fund's State of for the Millennium Development Goals. The differ- the World's Children 2008 and Childinfo. ences in children's growth to age 5 are influenced 2008 World Development Indicators 109 2.19 Health risk factors and public health 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 2000­05a 2000­05a 2006 2007 2003 2005 2003 2005 2005 2005 2000­06a 2000­06a Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 60 18 19 4.5 0.2 0.2 .. .. .. .. .. .. Algeria 32 0b 56 8.4 0.1 0.1 20.6 21.6 .. .. .. .. Angola .. .. 285 3.3 3.7 3.7 59.3 60.7 0.9 2.5 .. .. Argentina 32 25 39 5.6 0.6 0.6 26.7 27.7 .. .. .. .. Armenia 62 2 72 7.7 0.1 0.1 .. .. .. .. 32 7 Australia 19 16 6 5.0 0.1 0.1 .. .. .. .. .. .. Austria .. .. 13 7.9 0.3 0.3 19.2 19.2 .. .. .. .. Azerbaijan .. 1 77 7.3 <0.1 0.1 .. .. .. .. .. .. Bangladesh 55 27 225 5.3 <0.1 <0.1 .. 12.7 .. .. .. .. Belarus 53 7 61 7.6 0.3 0.3 24.4 25.5 .. .. .. .. Belgium 30 25 13 5.2 0.2 0.3 45.5 38.6 .. .. .. .. Benin .. .. 90 4.4 2.0 1.8 59.3 58.4 0.4 1.1 32 8 Bolivia .. .. 198 5.8 0.1 0.1 27.0 27.9 .. .. 29 10 Bosnia and Herzegovina 49 30 51 7.0 .. <0.1 .. .. .. .. .. .. Botswana .. .. 551 5.2 24.0 24.1 56.0 53.8 5.7 15.3 .. .. Brazil 22 14 50 6.2 0.5 0.5 34.5 36.1 .. .. .. .. Bulgaria 44 23 40 7.6 .. <0.1 .. .. .. .. .. .. Burkina Faso .. .. 248 3.7 1.8 c 2.0 59.2 57.1 0.5 1.4 54 17 Burundi .. .. 367 1.7 3.3 3.3 60.8 60.8 0.8 2.3 .. .. Cambodia .. .. 500 5.0 2.0 1.6 46.4 45.4 .. .. .. 3 Cameroon .. .. 192 3.7 5.5 5.5d 62.2 61.7 1.4 4.9 52 24 Canada 22 17 5 7.4 0.3 0.3 12.2 16.3 .. .. .. .. Central African Republic .. .. 345 4.4 10.8 10.7 59.1 56.5 2.5 7.3 .. .. Chad .. .. 299 3.6 3.4 3.5 54.7 56.3 0.9 2.2 18 7 Chile 48 37 15 5.6 0.3 0.3 26.4 27.1 .. .. .. .. China 67 4 99 4.1 0.1e 0.1e 24.5e 27.7e .. .. .. .. Hong Kong, China 22 4 62 8.2 .. .. .. .. .. .. .. .. Colombia .. .. 45 5.0 0.5 0.6 26.4 28.1 .. .. .. 23 Congo, Dem. Rep. .. .. 392 3.0 3.2 3.2 59.0 58.4 0.8 2.2 .. .. Congo, Rep. .. .. 403 5.0 5.4 5.3 58.6 61.0 1.2 3.7 36 16 Costa Rica 29 10 14 9.3 0.3 0.3 27.0 27.4 .. .. .. .. Côte d'Ivoire .. .. 420 4.6 7.0 7.1 57.8 58.8 1.7 5.1 .. .. Croatia 32 23 40 7.1 .. <0.1 .. .. .. .. .. .. Cuba .. .. 9 9.3 0.1 0.1 54.8 55.3 .. .. .. .. Czech Republic 31 20 10 7.6 <0.1 0.1 .. .. .. .. .. .. Denmark 31 25 8 5.5 <0.1 0.2 24.0 23.6 .. .. .. .. Dominican Republic 16 11 89 8.7 1.0 f 1.1 49.2 50.0 .. .. 40 10 Ecuador .. .. 128 5.7 0.3 0.3 52.4 54.5 .. .. .. .. Egypt, Arab Rep. 40 18 24 11.0 <0.1 <0.1 .. .. .. .. .. .. El Salvador 42 15 50 9.0 0.9 0.9 27.1 28.3 0.6 0.4 .. .. Eritrea .. .. 94 2.3 2.4 2.4 59.2 58.5 0.6 1.6 .. 2 Estonia 45 18 39 7.6 1.1 1.3 22.1 24.0 .. .. .. .. Ethiopia 6 0b 378 2.3 .. 1.4g .. .. 0.2 1.1 18 2 Finland 26 19 5 5.9 0.1 0.1 .. .. .. .. .. .. France 30 21 14 5.9 0.4 0.4 33.3 34.6 .. .. .. .. Gabon .. .. 354 4.9 7.7 7.9 59.6 58.9 1.8 5.4 .. .. Gambia, The .. .. 257 4.1 2.2 2.4 58.8 57.9 0.6 1.7 .. .. Georgia 53 6 84 7.4 0.1 0.2 .. .. .. .. .. .. Germany 37 28 6 7.9 0.1 0.1 29.5 30.6 .. .. .. .. Ghana 7 1 203 4.2 2.2c 2.3 60.7 60.0 0.2 1.3 45 19 Greece 47 29 18 5.9 0.2 0.2 20.7 21.5 .. .. .. .. Guatemala 21 2 79 8.6 0.9 0.9 26.4 27.1 .. .. .. .. Guinea .. .. 265 4.1 1.6 1.5 68.9 67.9 0.6 1.2 35 10 Guinea-Bissau .. .. 219 3.8 3.8 3.8 59.3 58.6 0.9 2.5 .. .. Haiti 15 6 299 9.0 3.8 2.2h 52.9 53.3 0.6 1.5 28 20 110 2008 World Development Indicators 2.19 PEOPLE Health risk factors and public health 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 2000­05a 2000­05a 2006 2007 2003 2005 2003 2005 2005 2005 2000­06a 2000­06a Honduras .. .. 76 9.1 1.5 1.5 25.0 26.2 .. .. .. 7 Hungary 41 28 19 7.6 0.1 0.1 .. .. .. .. .. .. India 47 17 168 6.7 0.9 0.9 28.8 28.6 .. .. .. .. Indonesia 58 3 234 2.3 0.1 0.1 13.6 17.1 .. .. .. 1 Iran, Islamic Rep. 22 2 22 7.8 0.1 0.2 13.0 16.7 .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 28 26 13 5.1 0.2 0.2 32.0 36.0 .. .. .. .. Israel 32 18 8 6.9 0.2 0.2 .. .. .. .. .. .. Italy 31 17 7 5.8 0.5 0.5 33.6 33.3 .. .. .. .. Jamaica .. .. 7 10.3 1.5 1.5 27.1 27.6 .. .. .. .. Japan 47 15 22 4.9 <0.1 <0.1 56.5 58.2 .. .. .. .. Jordan 51 8 5 9.8 0.2 0.2 .. .. .. .. .. 4 Kazakhstan 65 9 130 5.6 0.1 0.1 56.0 56.7 .. .. .. .. Kenya 21 1 384 3.3 6.7c 6.1 64.2 61.7 1.0 5.2 39 9 Korea, Dem. Rep. .. .. 178 5.2 0.2 0.2 .. .. .. .. .. .. Korea, Rep. .. .. 88 7.8 <0.1 <0.1 59.1 56.9 .. .. .. .. Kuwait .. .. 24 14.4 0.2 0.2 .. .. .. .. .. .. Kyrgyz Republic 51 5 123 5.1 <0.1 0.1 .. .. .. .. .. .. Lao PDR 59 13 152 3.1 0.1 0.1 .. .. .. .. .. .. Latvia 51 19 57 7.6 0.6 0.8 20.3 22.0 .. .. .. .. Lebanon 42 31 11 7.7 0.1 0.1 .. .. .. .. .. .. Lesotho .. .. 635 3.8 23.7 23.4d 56.0 60.0 5.9 14.1 44 26 Liberia .. .. 331 4.6 .. .. .. .. .. .. .. .. Libya .. .. 18 4.4 0.2 0.2 .. .. .. .. .. .. Lithuania 44 13 62 7.6 0.1 0.2 .. .. .. .. .. .. Macedonia, FYR .. .. 29 7.1 <0.1 <0.1 .. .. .. .. .. .. Madagascar .. .. 248 3.0 0.5 0.5 28.2 27.7 0.6 0.3 8 2 Malawi 21 5 377 2.1 14.2 14.1 59.3 58.8 3.4 9.6 28 9 Malaysia 43 2 103 10.7 0.4 0.5 25.0 25.4 .. .. .. .. Mali .. .. 280 4.1 1.8i 1.7 57.3 60.0 0.4 1.2 32 9 Mauritania .. .. 316 4.6 0.7 0.7 59.2 57.3 0.2 0.5 .. .. Mauritius 32 1 23 11.1 0.2 0.6 .. .. .. .. .. .. Mexico 13 5 21 10.6 0.3 0.3 20.0 23.3 .. .. .. .. Moldova 34 2 141 7.6 0.9 1.1 56.5 57.1 .. .. 55 22 Mongolia 68 26 188 1.9 <0.1 <0.1 .. .. .. .. .. .. Morocco 29 0b 93 8.1 0.1 0.1 18.2 21.1 .. .. .. .. Mozambique .. .. 443 3.7 16.0 16.1 57.5 60.0 3.6 10.7 27 12 Myanmar 36 12 171 3.2 1.4 1.3 31.6 31.4 .. .. .. .. Namibia 23 10 767 4.2 19.5 19.6 60.0 61.9 4.4 13.4 65 42 Nepal 49 24 176 4.2 0.5 0.5 20.3 21.6 .. .. 24 8 Netherlands 36 28 8 5.2 0.2 0.2 33.8 34.7 .. .. .. .. New Zealand 24 22 9 6.4 0.1 0.1 .. .. .. .. .. .. Nicaragua .. 5 58 10.1 0.2 0.2 22.4 23.6 .. .. .. 7 Niger .. .. 174 3.7 1.1 1.1 59.7 59.2 0.2 0.8 .. .. Nigeria .. 1 311 4.5 3.7 3.9 58.3 61.5 0.9 2.7 38 8 Norway 27 25 6 3.6 0.1 0.1 .. .. .. .. .. .. Oman .. .. 13 13.1 0.2 0.2 .. .. .. .. .. .. Pakistan .. .. 181 9.6 0.1 0.1 13.3 16.7 .. .. .. .. Panama .. .. 45 9.7 0.9 0.9 26.0 25.3 .. .. .. .. Papua New Guinea .. .. 250 2.9 1.6 1.8 59.2 59.6 .. .. .. .. Paraguay 23 7 71 4.8 0.4 0.4 27.3 26.9 .. .. .. .. Peru .. .. 162 6.0 0.5 0.6 26.8 28.6 .. .. .. 9 Philippines 41 8 287 7.6 <0.1 <0.1 20.2 28.3 .. .. 13 3 Poland 40 25 25 7.6 0.1 0.1 30.0 30.0 .. .. .. .. Portugal .. .. 32 5.7 0.4 0.4 3.9 4.1 .. .. .. .. Puerto Rico 17 10 5 10.7 .. .. .. .. .. .. .. .. 2008 World Development Indicators 111 2.19 Health risk factors and public health 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 2000­05a 2000­05a 2006 2007 2003 2005 2003 2005 2005 2005 2000­06a 2000­06a Romania 32 10 128 7.6 .. <0.1 .. .. .. .. .. .. Russian Federation 60 16 107 7.6 0.9 1.1 21.1 22.3 .. .. .. .. Rwanda .. .. 397 1.5 3.8 3.0 g 52.6 56.9 0.4 1.5 19 5 Saudi Arabia 19 8 44 16.7 0.2 0.2 .. .. .. .. .. .. Senegal .. .. 270 4.6 0.9 0.7g 58.5 58.9 0.1 0.4 48 5 Serbia 48j 34j 32j 7.1j 0.2j 0.2j 22.2j 20.0j .. .. .. .. Sierra Leone .. .. 517 4.3 1.6 1.6 60.0 60.5 0.4 1.1 .. .. Singapore 24 4 26 10.1 0.3 0.3 25.5 27.3 .. .. .. .. Slovak Republic .. .. 15 7.6 <0.1 <0.1 .. .. .. .. .. .. Slovenia 28 20 13 7.6 <0.1 <0.1 .. .. .. .. .. .. Somalia .. .. 218 2.8 0.9 0.9 60.5 57.5 0.2 0.6 .. .. South Africa 23 8 940 4.4 15.6f 18.8 56.9 58.5 4.5 14.8 57 46 Spain 39 25 30 5.7 0.7 0.6 22.9 22.9 .. .. .. .. Sri Lanka 23 2 60 8.4 <0.1 <0.1 .. .. .. .. .. .. Sudan .. .. 242 4.0 1.6 1.6 56.7 56.3 .. .. .. .. Swaziland 11 3 1,155 4.0 32.4 33.4 63.2 57.1 7.7 22.7 .. .. Sweden 17 18 6 5.2 0.2 0.2 31.3 31.3 .. .. .. .. Switzerland 27 23 7 7.9 0.4 0.4 36.0 36.9 .. .. .. .. Syrian Arab Republic .. .. 32 10.6 0.2 0.2 .. .. .. .. .. .. Tajikistan .. .. 204 4.9 <0.1 0.1 .. .. .. .. .. .. Tanzania .. .. 312 2.9 7.0k 6.5 52.3 54.6 2.8 3.8 36 13 Thailand 49 3 142 6.9 1.4 1.4 38.6 39.3 .. .. .. .. Timor-Leste .. .. 556 1.7 0.2 0.2 .. .. .. .. .. .. Togo .. .. 389 4.1 3.2 3.2 58.9 61.0 0.8 2.2 .. .. Trinidad and Tobago .. .. 8 11.5 2.6 2.6 56.0 57.7 .. .. .. .. Tunisia 50 2 25 5.2 0.1 0.1 .. 22.1 .. .. .. .. Turkey 49 18 29 7.8 0.2 0.2 .. .. .. .. .. .. Turkmenistan .. .. 65 5.2 .. <0.1 .. .. .. .. .. 1 Uganda 25 3 355 2.0 6.8 6.4l 57.6 57.8 1.1 4.3 38 15 Ukraine 53 11 106 7.6 1.3 1.4 47.4 48.8 .. .. .. .. United Arab Emirates 17 1 16 19.5 0.2 0.2 .. .. .. .. .. .. United Kingdom 27 25 15 2.9 0.2 0.2 .. .. .. .. .. .. United States 24 19 4 7.8 0.6 0.6 25.5 25.0 .. .. .. .. Uruguay 35 24 27 5.6 0.4 0.5 55.6 55.8 .. .. .. .. Uzbekistan 24 1 121 5.1 0.1 0.2 .. 13.2 .. .. 18 2 Venezuela, RB .. .. 41 5.4 0.6 0.7 27.7 28.2 .. .. .. .. Vietnam 35 2 173 2.9 0.4 0.5g 30.5 33.6 0.8 .. .. 8 West Bank and Gaza .. .. 20 8.4 .. .. .. .. .. .. .. .. Yemen, Rep. .. .. 78 2.9 0.2 0.2 .. .. .. .. .. .. Zambia 16 1 553 3.8 15.6m 17.0 56.3 57.0 3.8 12.7 36 19 Zimbabwe 20 2 557 4.0 22.1 18.1h 58.1 59.3 4.4 14.7 52 9 World .. w .. w 139 w 5.8 w 1.0 w 1.0 w 30.4 w 31.4 w Low income .. 15 221 5.7 1.7 1.7 35.8 34.3 Middle income .. .. 114 5.6 0.6 0.7 26.1 28.7 Lower middle income .. .. 116 5.0 0.3 0.3 24.9 27.9 Upper middle income .. .. 109 7.3 1.6 1.7 29.9 31.3 Low & middle income .. .. 161 5.6 1.1 1.1 29.9 31.1 East Asia & Pacific 67 4 135 4.2 0.2 0.2 24.3 27.4 Europe & Central Asia .. .. 82 7.3 0.5 0.6 .. .. Latin America & Carib. .. .. 57 7.1 0.5 0.6 30.3 31.9 Middle East & N. Africa .. .. 42 8.7 0.1 0.1 .. .. South Asia 47 18 174 6.9 0.7 0.7 26.9 25.4 Sub-Saharan Africa .. .. 368 3.6 6.4 5.8 57.6 58.5 High income .. .. 16 6.8 0.4 0.4 33.1 33.3 Euro area .. .. 13 6.4 0.4 0.3 29.3 29.7 a. Data are for the most recent year available. b. Less than 0.5. c. Survey data, 2003. d Survey data, 2004. e. Includes Hong Kong, China. f. Survey data, 2002. g. Survey data, 2005. h. Survey data, 2005-06. i. Survey data, 2001. j. Includes Montenegro. k. Survey data, 2003­04. l. Survey data, 2004­05. m. Survey data, 2001­02. 112 2008 World Development Indicators 2.19 PEOPLE Health risk factors and public health challenges About the data Definitions The limited availability of data on health status is a They often disguise serious epidemics that are ini- · Prevalence of smoking is the percentage of men major constraint in assessing the health situation in tially concentrated in certain localities or among and women who smoke cigarettes. The age range var- developing countries. Surveillance data are lacking specific population groups and threaten to spill over ies, but in most countries is 18 and older or 15 and for many major public health concerns. Estimates into the wider population. In many developing coun- older. · Incidence of tuberculosis is the estimated of prevalence and incidence are available for some tries most new infections occur in young adults, with number of new tuberculosis cases (pulmonary, smear diseases but are often unreliable and incomplete. young women especially vulnerable. positive, extrapulmonary). · Prevalence of diabetes National health authorities differ widely in their The current HIV estimates from the Joint United refers to the percentage of people ages 20­79 who capacity and willingness to collect or report infor- Nations Programme on HIV/AIDS (UNAIDS) and the have type 1 or type 2 diabetes. · Prevalence of HIV mation. To compensate for the paucity of data and WHO are lower than the previous estimates, due is the percentage of people who are infected with ensure reasonable reliability and international com- mostly to increased availability of reliable data, HIV. Total and youth rates are as a percentage of the parability, the World Health Organization (WHO) pre- including more population-based HIV prevalence relevant age group. Female rate is as a percentage pares estimates in accordance with epidemiological surveys, new and improved HIV surveillance data, of the total population with HIV. · Condom use is the models and statistical standards. and improved quality and coverage of sentinel sur- percentage of the population ages 15­24 who used Smoking is the most common form of tobacco use veillance in many countries, including rural areas, condom at last intercourse in the last 12 months. in many countries, and the prevalence of smoking where prevalence is known to be lower. is therefore a good measure of the extent of the Estimates from recent Demographic and Health tobacco epidemic (Corrao and others 2000). Tobacco Surveys that have collected data on HIV/AIDS dif- use causes heart and other vascular diseases and fer somewhat from those of UNAIDS and the WHO, cancers of the lung and other organs. Given the long which are based on surveillance systems that focus delay between starting to smoke and the onset of on pregnant women who attend sentinel antenatal disease, the health impact of smoking in develop- clinics. Caution should be exercised in about com- ing countries will increase rapidly in the next few paring the two sets of estimates. Demographic and decades. Because the data present a one-time esti- Health Surveys are household surveys that use a mate, with no information on the intensity or duration representative sample from the whole population, of smoking, and because the definition of adult var- whereas surveillance data from antenatal clinics are ies across countries, the data should be interpreted limited to pregnant women. Representative house- with caution. hold surveys also frequently provide better coverage Tuberculosis is one of the main causes of death of rural populations. However, the fact that some from a single infectious agent among adults in devel- respondents refuse to participate or are absent oping countries. In high-income countries tubercu- from the household adds considerable uncertainty losis has reemerged largely as a result of cases to survey-based HIV estimates, because the possible among immigrants. The estimates of tuberculosis association of absence or refusal with higher HIV incidence in the table are based on a new approach prevalence is unknown. UNAIDS and the WHO use a in which reported cases are adjusted using the ratio methodology to estimate HIV prevalence for the adult of case notifications to the estimated share of cases population (ages 15­49) that assumes that preva- detected by panels of 80 epidemiologists convened lence among pregnant women is a good approxima- by the WHO. tion of prevalence among men and women. However, Diabetes, an important cause of ill health and a this assumption might not apply to all countries or risk factor for other diseases in developed countries, over time. There are also other potential biases asso- Data sources is spreading rapidly in developing countries. While ciated with the use of antenatal clinic data, such diabetes is most common among the elderly, preva- as differences among women who attend antenatal Data on smoking are from J. McCay, M. Erkson, lence rates are rising among younger and produc- clinics and those who do not. and O. Shafey's Tobacco Atlas, 2nd edition (2006). tive populations in developing countries. Economic Data on condom use are from household surveys Data on tuberculosis are from the WHO's Global development has led to the spread of Western life- and refer to condom use at last intercourse. However, Tuberculosis Control Report 2008: Surveillance, styles and diet to developing countries, resulting in condoms are not as effective at preventing the trans- Planning, Financing. Data on diabetes are from a substantial increase in diabetes. Without effective mission of HIV unless used consistently. Some sur- the International Diabetes Federation's Diabetes prevention and control programs, diabetes will likely veys have tried to ask directly about consistent use, Atlas, 3rd edition. Data on prevalence of HIV are continue to increase. Data are estimated based on but the question is subject to recall and other biases. from UNAIDS and the WHO's 2006 Report on the sample surveys. Caution should be used in interpreting the data. Global AIDS Epidemic. Data on condom use are Adult HIV prevalence rates reflect the rate of HIV For indicators from household surveys, the year in from Demographic and Health Surveys by Macro infection in each country's population. Low national the table refers to the survey year. For more informa- International. prevalence rates can be very misleading, however. tion, consult the original sources. 2008 World Development Indicators 113 2.20 Health gaps by income and gender Survey Prevalence of Child Infant Under-five year child malnutrition immunization rate mortality rate mortality rate Moderate underweight % of children % of children under age 5 ages 12­23 monthsa New reference Old reference Measles DTP3 per 1,000 live births per 1,000 Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Armenia 2000 3 2 3 1 68 74b 89 84b 52 27 61 30 Bangladesh 2004 36 19 41 24 60 91 71 91 90 65 121 71 Benin 2001 18 6 21 9 57 83 63 89 112 50 198 93 Bolivia 2003 7 1 10 1 62 74 64 85 87 32 119 37 Brazil 1996 7 2 10 3 78 90 66 82 83 29 99 33 Burkina Faso 2003 19 13 26 16 48 71 45 73 97 78 206 144 Cambodia 2000 27 23 35 28 44 82 39 75 110 50 155 64 Cameroon 2004 .. .. .. .. 57 86 55 86 101 52 189 88 Central African Republic 1994­95 20 11 25 15 31 80 27 76 132 54 193 98 Chad 2004 24 16 27 19 8 38 5 42 109 101 176 187 Colombia 2005 7 2 11 3 70 91 73 91 32 14 39 16 Côte d'Ivoire 1994 17 7 21 10 31 79 26 74 117 63 190 97 Dominican Republic 2002 7 1 9 1 83 94 46 66 50 20 66 22 Egypt, Arab Rep. 2000 4 2 5 2 95 99 94 93 76 30 98 34 Eritrea 1995 .. .. .. .. 37 92 30 89 74 68 152 104 Ethiopia 2000 25 22 32 29 18 52 14 43 93 95 159 147 Gabon 2000 10 4 14 7 34 71 18 49 57 36 93 55 Ghana 2003 17 6 22 10 74 88 64 87 61 58 128 88 Guatemala 1998­99 21 9 26 10 80 91 74 76 58 39 78 39 Guinea 1999 17 9 22 13 33 73 30 69 119 70 230 133 Haiti 2000 14 4 18 6 43 63 31 58 100 97 164 109 India 1998­99 28 16 33 21 28 81 36 85 97 38 141 46 Indonesia 2002­03 .. .. .. .. 59 85 42 72 61 17 77 22 Jordan 1997 .. .. .. .. 90 93 98 93 35 23 42 25 Kazakhstan 1999 3 5 5 6 74 76b 90 82b 68 42 82 45 Kenya 2003 17 6 22 7 54 88 56 73 96 62 149 91 Kyrgyz Republic 1997 6 5 10 7 82 81 82 87 83 46 96 49 Madagascar 1997 24 18 29 24 32 79 32 81 119 58 195 101 Malawi 2000 18 9 24 12 80 90 79 93 132 86 231 149 Mali 2001 20 10 26 13 40 77 28 71 137 90 248 148 Mauritania 2000­01 18 11 23 15 42 86 18 61 61 62 98 79 Morocco 2003­04 11 2 13 3 83 98 89 98 62 24 78 26 Mozambique 2003 16 5 21 7 61 96 52 96 143 71 196 108 Namibia 2000 17 6 22 9 76 86 76 83 36 23 55 31 Nepal 2001 34 20 40 26 61 83 62 85 86 53 130 68 Nicaragua 2001 9 2 13 2 76 94 77 83 50 16 64 19 Niger 1998 27 18 30 26 23 66 9 68 131 86 282 184 Nigeria 2003 20 9 24 10 16 71 7 61 133 52 257 79 Pakistan 1990­91 28 14 33 19 28 75 24 64 89 63 125 74 Paraguay 1990 3 1 5 1 48 69 40 69 43 16 57 20 Peru 2000 9 1 13 1 81 92 76 93 64 14 93 18 Philippines 2003 .. .. .. .. 70 89 64 92 42 19 66 21 Rwanda 2000 15 8 19 12 84 89 80 89 139 88 246 154 Senegal 1997 .. .. .. .. .. .. .. .. 85 45 181 70 South Africa 1998 .. .. .. .. 74 85 64 85 62 17 87 22 Tanzania 2004 14 8 20 11 65 91 34 36 88 64 137 93 Togo 1998 17 8 23 10 35 63 29 68 84 66 168 97 Turkey 1998 .. .. .. .. 64 89 45 81 68 30 85 33 Turkmenistan 2000 .. .. .. .. 91 80 97 86 89 58 106 70 Uganda 2000­01 16 7 21 10 49 65 35 55 106 60 192 106 Uzbekistan 1996 11 8 15 10 96 93 89 82 54 46 70 50 Vietnam 2002 .. .. .. .. 64 98 53 94 39 14 53 16 Yemen, Rep. 1997 .. .. 36 24 16 73 14 71 109 60 163 73 Zambia 2001­02 18 12 24 17 81 88 74 89 115 57 192 92 Zimbabwe 1999 10 5 16 6 80 86 81 86 59 44 100 62 a. Refers to children who were immunized at any time before the survey. b. The data contain large sampling errors because of the small number of cases. 114 2008 World Development Indicators 2.20 PEOPLE Health gaps by income and gender Survey Prevalence of child Child Infant Under-five year malnutrition immunization rate mortality rate mortality rate Old reference Moderate underweight % of children % of children ages 12­23 monthsa under age 5 Measles DTP3 per 1,000 live births per 1,000 Male Female Male Female Male Female Male Female Male Female Armenia 2000 2 3 71 79 90 89 46 42 51 45 Bangladesh 2004 34 35 76 76 81 81 80 64 102 91 Benin 2001 19 17 69 67 74 71 98 92 162 163 Bolivia 2003 6 6 65 63 70 73 71 64 94 91 Brazil 1996 6 5 87 87 82 80 52 44 60 53 Burkina Faso 2003 25 23 54 58 57 57 95 89 195 192 Cambodia 2000 32 33 57 54 50 47 103 82 133 110 Cameroon 2004 14 15 65 66 65 68 88 74 154 141 Central African Republic 1994­95 21 19 52 53 49 46 109 94 165 152 Chad 2004 23 23 23 23 20 21 122 108 207 198 Colombia 2005 6 6 83 82 84 81 26 18 30 21 Côte d'Ivoire 1994 19 16 54 52 49 45 99 83 163 137 Dominican Republic 2002 5 5 89 88 54 61 38 31 46 40 Egypt, Arab Rep. 2000 4 3 97 97 94 94 55 55 69 70 Eritrea 1995 26 27 52 50 49 49 82 69 163 141 Ethiopia 2000 32 31 28 26 22 19 124 101 197 178 Gabon 2000 10 9 55 55 40 33 74 49 103 80 Ghana 2003 17 17 82 83 81 77 70 59 111 108 Guatemala 1998­99 21 18 82 87 73 74 50 48 64 65 Guinea 1999 17 19 52 52 46 47 112 101 202 188 Haiti 2000 14 13 54 54 43 43 97 83 143 132 India 1998­99 28 30 52 50 56 54 75 71 98 105 Indonesia 2002­03 .. .. 73 71 58 59 46 40 58 51 Jordan 1997 4 5 90 90 96 96 34 23 38 30 Kazakhstan 1999 4 4 79 78 89 88 62 47 72 53 Kenya 2003 18 14 73 72 71 74 84 67 122 103 Kyrgyz Republic 1997 11 8 84 85 83 81 72 60 81 70 Madagascar 1997 27 27 47 45 48 49 109 90 176 152 Malawi 2000 20 19 83 83 84 85 117 108 207 199 Mali 2001 24 21 49 48 41 38 136 116 250 226 Mauritania 2000­01 22 22 61 63 39 41 74 59 110 94 Morocco 2003­04 9 8 88 92 95 95 51 37 59 48 Mozambique 2003 18 17 77 76 73 71 127 120 181 176 Namibia 2000 19 18 79 82 78 81 45 34 67 54 Nepal 2001 35 36 73 69 74 70 79 75 105 112 Nicaragua 2001 9 7 87 86 84 81 39 32 48 41 Niger 1998 29 30 36 34 25 25 141 131 299 306 Nigeria 2003 19 20 34 38 19 24 116 102 222 212 Pakistan 1990­91 27 27 55 46 45 40 102 86 122 119 Paraguay 1990 3 4 56 61 50 57 39 33 49 45 Peru 2000 6 6 84 85 85 84 46 40 64 57 Philippines 2003 .. .. 78 81 78 80 35 25 48 34 Rwanda 2000 19 19 86 88 85 87 123 112 215 198 Senegal 1997 .. .. .. .. .. .. 74 65 144 134 South Africa 1998 .. .. 84 81 74 78 49 35 66 48 Tanzania 2004 18 18 80 80 37 33 83 82 135 130 Togo 1998 19 18 45 40 43 41 89 71 156 132 Turkey 1998 7 7 79 78 60 57 51 46 61 58 Turkmenistan 2000 11 10 87 88 93 92 83 60 101 76 Uganda 2000­01 18 17 56 57 45 48 93 85 164 149 Uzbekistan 1996 15 13 91 92 87 90 50 37 65 46 Vietnam 2002 .. .. 84 82 72 73 25 25 34 31 Yemen, Rep. 1997 33 30 45 40 41 39 98 80 128 114 Zambia 2001­02 21 21 83 86 78 82 95 93 176 160 Zimbabwe 1999 12 11 77 81 80 82 63 56 95 85 a. Refers to children who were immunized at any time before the survey. 2008 World Development Indicators 115 2.20 Health gaps by income and gender Survey Pregnant women Contraceptive Births attended by Total fertility Exclusive year receiving prevalence skilled health staffa rate breastfeeding prenatal care rate modern methods % of married women % of children % ages 15­49 % of total births per woman under 4 months Poorest Richest Poorest Richest Poorest Richest Poorest Richest Poorest Richest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile Armenia 2000 85 97 16 29 93 100 2.5 1.6 .. .. Bangladesh 2004 25 81 45 50 3 39 4.1 2.2 62 31 Benin 2001 73 100 4 15 50 99 7.2 3.5 50 42b Bolivia 2003 62 98 23 49 27 98 6.7 2.0 79 31 Brazil 1996 72 98 56 77 72 99 4.8 1.7 33 60 b Burkina Faso 2003 56 96 2 27 19 84 6.6 3.6 17 28 Cambodia 2000 22 80 13 25 15 81 4.7 2.2 14 18 Cameroon 2004 65 97 2 27 29 95 6.5 3.2 33 30 b Central African Republic 1994­95 39 91 1 9 14 82 5.1 4.9 9 4 Chad 2004 9 77 0 7 1 51 5.1 6.0 1 2 Colombia 2005 84 99 60 72 72 99 4.1 1.4 60 64 Côte d'Ivoire 1994 62 98 1 13 17 84 6.4 3.7 0 5 Dominican Republic 2002 97 99 59 70 94 100 4.5 2.1 18 6 Egypt, Arab Rep. 2000 31 84 43 61 31 94 4.0 2.9 72 57 Eritrea 1995 34 90 0c 19 5 74 8.0 3.7 64 73 Ethiopia 2000 15 60 3 23 1 25 6.3 3.6 63 46 Gabon 2000 85 98 6 18 67 97 6.3 3.0 6 5b Ghana 2003 83 98 9 26 21 90 6.4 2.8 62b .. Guatemala 1998­99 37 97 5 60 9 92 7.6 2.9 62 .. Guinea 1999 58 97 1 9 12 82 5.8 4.0 9 8 Haiti 2000 65 91 17 24 4 70 6.8 2.7 40 15b India 1998­99 44 93 29 55 16 84 3.4 1.8 64 37 Indonesia 2002­03 78 99 49 58 40 94 3.0 2.2 58 35 Jordan 1997 93 97 28 47 91 99 5.2 3.1 14 14b Kazakhstan 1999 97 91 49 55 99 99 3.4 1.2 .. .. Kenya 2003 75 94 12 44 17 75 7.6 3.1 22 17 Kyrgyz Republic 1997 96 99 44 54 96 100 4.6 2.0 18b .. Madagascar 1997 67 96 2 24 30 89 8.1 3.4 57 65 Malawi 2000 89 98 20 40 43 83 7.1 4.8 53 72 Mali 2001 42 92 4 18 22 89 7.3 5.3 38 18 Mauritania 2000­01 33 89 0c 17 15 93 5.4 3.5 28 30 Morocco 2003­04 40 93 51 57 29 95 3.3 1.9 53 36 Mozambique 2003 67 98 14 37 25 89 6.3 3.8 47 27 Namibia 2000 81 96 29 64 55 97 6.0 2.7 100 b 85b Nepal 2001 30 80 24 55 4 45 5.3 2.3 76 67 Nicaragua 2001 69 97 50 71 78 99 5.6 2.1 53 15b Niger 1998 24 85 1 18 4 63 8.4 5.7 1 3 Nigeria 2003 37 96 4 21 13 85 6.5 4.2 15 34 Pakistan 1990­91 8 72 1 23 5 55 5.1 4.0 36 9 Paraguay 1990 73 98 21 46 41 98 7.9 2.7 7 0 Peru 2000 41 74 37 58 13 88 5.5 1.6 88 59 Philippines 2003 72 97 24 35 25 92 5.9 2.0 60 20 Rwanda 2000 90 95 2 15 17 60 6.0 5.4 89 79 Senegal 1997 67 97 1 24 20 86 7.4 3.6 13 19 South Africa 1998 96 94 34 70 68 98 4.8 1.9 15 11b Tanzania 2004 91 97 11 36 31 87 7.3 3.3 58 55 Togo 1998 69 97 3 13 25 91 7.3 2.9 7 34 Turkey 1998 38 96 24 48 53 98 3.9 1.7 10 4b Turkmenistan 2000 98 97 51 50 97 98 3.4 2.1 11 28b Uganda 2000­01 88 98 11 41 20 77 8.5 4.1 73 59 Uzbekistan 1996 93 96 46 52 92 100 4.4 2.2 .. .. Vietnam 2002 68 100 58 52 58 100 2.2 1.4 18 .. Yemen, Rep. 1997 17 68 1 24 7 50 7.3 4.7 20 13 Zambia 2001­02 89 99 11 53 20 91 7.3 3.6 39 70 b Zimbabwe 1999 94 97 41 67 57 94 4.9 2.6 36 46b a. Based on births in the fi ve years before the survey. b. The data contain large sampling errors because of the small number of cases. c. Less than 0.5. 116 2008 World Development Indicators 2.20 PEOPLE Health gaps by income and gender About the data Definitions The data in the table describe the health status and account and creating country-specific asset indexes · Survey year is the year in which the underlying use of health services by individuals in different with country-specifi c choices of asset indicators data were collected. · Prevalence of child malnu- socioeconomic groups within countries. The data are might produce a more effective and accurate index trition is the percentage of children under age 5 from Demographic and Health Surveys conducted for each country. The asset index used in the table whose weight for age is two to three standard devia- by Macro International with the support of the U.S. does not have this flexibility. tions below the median reference standard for their Agency for International Development. These large- The analysis was carried out for 56 countries, age. New international child growth standards were scale household sample surveys, conducted peri- with the results issued in country reports. The table released in 2006 by the World Health Organization. odically in developing countries, collect information shows the estimates for the poorest and richest quin- The table presents malnutrition data using both the on a large number of health, nutrition, and popula- tiles and by sex only; the full set of estimates for up new and old reference standards. For more informa- tion measures as well as on respondents' social, to 117 indicators is available in the country reports tion about the change in standards, see About the demographic, and economic characteristics using a (see Data sources). data for table 2.18. · Child immunization rate is the standard set of questionnaires. The data presented Demographic and Health Surveys try to collect percentage of children ages 12­23 months at the here draw on responses to individual and household cross-country comparable data, but the age group time of the survey who, at any time before the survey, questionnaires. of the reference population could differ across coun- had received measles vaccine and three doses of Socioeconomic status as displayed in the table is tries. Caution should be exercised when comparing diphtheria, tetanus, and pertussis (whooping cough) based on a household's assets, including ownership the data. The estimates in the table are based on vaccine (DTP3). · Infant mortality rate is the num- of consumer items, features of the household's dwell- survey data, which refer to a period preceding the ber of infants dying before reaching one year of age, ing, and other characteristics related to wealth. Each survey date, or use a definition or methodology dif- per 1,000 live births. · Under-five mortality rate is household asset on which information was collected ferent from the estimates in tables 2.16­2.18 and the probability that a newborn baby will die before was assigned a weight generated through principal- 2.21. Thus the estimates may differ from those in reaching age 5, per 1,000, if subject to current age- component analysis. The resulting scores were stan- the other tables, and caution should be exercised in specific mortality rates. · Pregnant women receiv- dardized in relation to a standard normal distribution using the data. ing prenatal care are the percentage of women with with a mean of zero and a standard deviation of one. one or more births during the five years preceding The standardized scores were then used to create the survey who were attended at least once during break-points defining wealth quintiles, expressed as pregnancy by skilled health personnel for reasons quintiles of individuals in the population rather than related to pregnancy. · Contraceptive prevalence quintiles of individuals at risk with respect to any rate is the percentage of women married or in-union one health indicator. ages 15­49 who are practicing, or whose sexual The choice of the asset index for defining socio- partners are practicing, any modern method of con- economic status was based on pragmatic rather than traception. · Births attended by skilled health staff conceptual considerations: Demographic and Health are the percentage of deliveries attended by person- Surveys do not collect income or consumption data nel trained to give the necessary supervision, care, but do have detailed information on households' own- and advice to women during pregnancy, labor, and ership of consumer goods and access to a variety the postpartum period; to conduct deliveries on their of goods and services. Like income or consumption, own; and to care for newborns. Skilled health staff the asset index defines disparities primarily in eco- include doctors, nurses, and trained midwives, but nomic terms. It therefore excludes other possibilities exclude trained or untrained traditional birth atten- of disparities among groups, such as those based dants. · Total fertility rate is the number of children on gender, education, ethnic background, or other that would be born to a woman if she were to live to facets of social exclusion. To that extent the index the end of her childbearing years and bear children provides only a partial view of the multidimensional in accordance with current age-specific fertility rates. concepts of poverty, inequality, and inequity. · Exclusive breastfeeding refers to the percentage Creating one index that includes all asset indica- of children ages 0­3 months who received only tors limits the types of analysis that can be per- breast milk in the 24 hours preceding the survey. formed. In particular, the use of a unified index does Data sources not permit a disaggregated analysis to examine which asset indicators have a more or less impor- Data on health gaps by income and gender are tant association with health status or use of health from an analysis of Demographic and Health Sur- services. In addition, some asset indicators may veys by the World Bank and Macro International. reflect household wealth better in some countries Country reports are available at www.worldbank. than in others--or reflect different degrees of wealth org/povertyandhealth/countrydata. in different countries. Taking such information into 2008 World Development Indicators 117 2.21 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 2006 1990 2006 1990 2006 1997­2006a 1997­2006a 2004­06a 2004­06a 2006 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 72 76 37 15 45 17 .. .. 108 52 81 90 Algeria 67 72 54 33 69 38 .. .. 123 105 77 81 Angola 40 42 154 154 260 260 .. .. 486 437 30 35 Argentina 72 75 25 14 29 16 .. .. 168 80 74 87 Armenia 68 72 47 21 56 24 8 3 197 88 68 83 Australia 77 81 8 5 10 6 .. .. 85 49 87 93 Austria 76 80 8 4 10 5 .. .. 111 55 84 92 Azerbaijan 71 72 84 73 105 88 .. .. 218 103 62 77 Bangladesh 55 64 100 52 149 69 24 29 235 203 61 66 Belarus 71 69 20 12 24 13 .. .. 368 128 52 82 Belgium 76 79 8 4 10 4 .. .. 114 62 84 92 Benin 53 56 111 88 185 148 64 65 287 239 52 58 Bolivia 59 65 89 50 125 61 25 29 238 178 63 71 Bosnia and Herzegovina 72 75 18 13 22 15 .. .. 148 77 76 86 Botswana 63 50 45 90 58 124 .. .. 586 575 31 35 Brazil 67 72 48 19 57 20 .. .. 234 123 66 80 Bulgaria 72 73 15 12 19 14 .. .. 221 92 69 86 Burkina Faso 50 52 123 122 206 204 110 113 288 187 46 57 Burundi 46 49 114 109 190 181 .. .. 412 377 39 44 Cambodia 55 59 85 65 116 82 20 20 359 248 49 61 Cameroon 55 50 85 87 139 149 73 72 416 420 41 43 Canada 77 80 7 5 8 6 .. .. 95 57 86 91 Central African Republic 50 44 114 115 173 175 .. .. 566 536 28 33 Chad 51 51 120 124 201 209 96 101 352 303 43 50 Chile 74 78 18 8 21 9 .. .. 130 62 80 89 China 69 72 36 20 45 24 .. .. 153 92 75 82 Hong Kong, China 77 82 .. .. .. .. .. .. 78 34 87 94 Colombia 68 73 26 17 35 21 4 3 206 97 71 83 Congo, Dem. Rep. 46 46 129 129 205 205 .. .. 439 401 35 40 Congo, Rep. 57 55 67 79 103 126 49 43 402 377 44 50 Costa Rica 76 79 16 11 18 12 .. .. 116 62 82 89 Côte d'Ivoire 53 48 105 90 153 127 83 58 429 408 38 42 Croatia 72 76 11 5 12 6 .. .. 159 62 75 89 Cuba 75 78 11 5 13 7 .. .. 119 74 82 88 Czech Republic 71 76 11 3 13 4 .. .. 139 60 78 90 Denmark 75 78 8 4 9 5 .. .. 116 68 83 88 Dominican Republic 68 72 50 25 65 29 9 9 222 134 68 78 Ecuador 69 75 43 21 57 24 .. .. 171 91 75 85 Egypt, Arab Rep. 62 71 67 29 91 35 10 10 158 94 72 82 El Salvador 66 72 47 22 60 25 .. .. 209 127 70 80 Eritrea 49 57 88 48 147 74 55 50 430 326 41 54 Estonia 69 73 12 5 16 7 .. .. 282 100 59 84 Ethiopia 48 52 122 77 204 123 56 56 367 329 44 49 Finland 75 79 6 3 7 4 .. .. 132 57 83 92 France 77 81 7 4 9 4 .. .. 127 57 84 93 Gabon 61 57 60 60 92 91 32 33 378 374 49 51 Gambia, The 51 59 103 84 153 113 .. .. 221 180 57 62 Georgia 70 71 39 28 46 32 .. .. 214 82 67 83 Germany 75 79 7 4 9 4 .. .. 112 58 84 92 Ghana 57 60 76 76 120 120 44 52 289 283 56 58 Greece 77 79 9 4 11 4 .. .. 93 42 85 92 Guatemala 63 70 60 31 82 41 15 18 237 131 67 79 Guinea 47 56 139 98 235 161 89 86 275 236 52 58 Guinea-Bissau 42 46 142 119 240 200 .. .. 447 396 35 41 Haiti 55 60 105 60 152 80 33 36 309 245 54 62 118 2008 World Development Indicators 2.21 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 2006 1990 2006 1990 2006 1997­2006a 1997­2006a 2004­06a 2004­06a 2006 2006 Honduras 66 70 45 23 58 27 8 9 245 141 65 78 Hungary 69 73 15 6 17 7 .. .. 256 107 66 85 India 59 64 80 57 115 76 25 37 260 168 59 69 Indonesia 62 68 60 26 91 34 13 11 172 123 71 79 Iran, Islamic Rep. 65 71 54 30 72 34 .. .. 155 104 73 81 Iraq 62 .. 42 .. 53 .. .. .. .. .. .. .. Ireland 75 79 8 4 9 5 .. .. 90 52 85 91 Israel 77 80 10 4 12 5 .. .. 82 39 87 93 Italy 77 81 8 4 9 4 .. .. 86 45 85 93 Jamaica 71 71 28 26 33 31 .. .. 222 140 70 79 Japan 79 82 5 3 6 4 .. .. 93 45 87 94 Jordan 68 72 33 21 40 25 5 5 167 116 73 80 Kazakhstan 68 66 51 26 60 29 11 6 369 147 49 76 Kenya 59 53 64 79 97 121 42 39 432 408 42 47 Korea, Dem. Rep. 70 67 42 42 55 55 .. .. 182 128 65 75 Korea, Rep. 71 78 8 5 9 5 .. .. 114 47 81 92 Kuwait 75 78 14 9 16 11 .. .. 87 53 85 89 Kyrgyz Republic 68 68 63 36 75 41 10 11 281 132 57 75 Lao PDR 55 64 120 59 163 75 .. .. 238 196 61 67 Latvia 69 71 14 8 18 9 .. .. 311 111 63 86 Lebanon 69 72 32 26 37 30 .. .. 155 103 73 82 Lesotho 59 43 81 102 101 132 22 19 715 698 20 24 Liberia 43 45 157 157 235 235 .. .. 466 430 33 37 Libya 68 74 35 17 41 18 .. .. 152 94 74 83 Lithuania 71 71 10 7 13 8 .. .. 326 110 62 86 Macedonia, FYR 71 74 33 15 38 17 .. .. 137 81 77 85 Madagascar 51 59 103 72 168 115 45 45 289 231 54 61 Malawi 49 48 131 76 221 120 101 67 540 525 32 37 Malaysia 70 74 16 10 22 12 .. .. 156 89 75 85 Mali 48 54 140 119 250 217 132 125 263 184 48 58 Mauritania 58 64 85 78 133 125 38 38 177 111 64 73 Mauritius 69 73 20 13 23 14 .. .. 210 108 68 82 Mexico 71 74 42 29 53 35 .. .. 144 81 78 86 Moldova 67 69 30 16 37 19 7 4 296 140 58 77 Mongolia 63 67 79 34 109 43 .. .. 268 175 59 70 Morocco 64 71 69 34 89 37 9 11 151 101 73 82 Mozambique 44 42 158 96 235 138 61 64 609 589 25 29 Myanmar 59 62 91 74 130 104 .. .. 304 192 54 66 Namibia 62 52 60 45 86 61 22 20 523 508 37 41 Nepal 54 63 99 46 142 59 21 19 235 211 61 65 Netherlands 77 80 7 4 9 5 .. .. 83 53 86 91 New Zealand 75 80 8 5 11 6 .. .. 81 53 86 91 Nicaragua 64 72 52 29 68 36 10 9 214 124 70 80 Niger 47 56 191 148 320 253 138 136 169 182 59 57 Nigeria 47 47 120 99 230 191 120 123 432 410 37 40 Norway 77 80 7 3 9 4 .. .. 86 53 86 92 Oman 70 76 25 10 32 12 .. .. 103 76 82 87 Pakistan 59 65 100 78 130 97 .. .. 177 145 66 69 Panama 72 75 27 18 34 23 .. .. 140 75 78 87 Papua New Guinea 55 57 69 54 94 73 .. .. 422 305 41 55 Paraguay 68 72 33 19 41 22 .. .. 176 129 72 79 Peru 66 71 58 21 78 25 19 8 201 125 70 79 Philippines 66 71 41 24 62 32 14 9 161 107 73 81 Poland 71 75 19 6 18 7 .. .. 190 66 71 89 Portugal 74 78 11 3 14 5 .. .. 139 58 83 91 Puerto Rico 75 78 .. .. .. .. .. .. 138 54 79 91 2008 World Development Indicators 119 2.21 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 2006 1990 2006 1990 2006 1997­2006a 1997­2006a 2004­06a 2004­06a 2006 2006 Romania 70 72 27 16 31 18 .. .. 205 87 69 85 Russian Federation 69 66 23 14 27 16 .. .. 429 158 43 77 Rwanda 32 46 106 98 176 160 90 87 471 422 33 39 Saudi Arabia 68 73 35 21 44 25 3 4 143 93 75 84 Senegal 57 63 72 60 149 116 69 69 174 106 63 72 Serbia 71 73 .. 7 .. 8 .. .. 159 85 74 85 Sierra Leone 39 42 169 159 290 270 .. .. 412 349 33 39 Singapore 74 80 7 2 8 3 .. .. 83 47 86 92 Slovak Republic 71 74 12 7 14 8 .. .. 196 76 71 88 Slovenia 73 78 8 3 10 4 .. .. 149 57 80 91 Somalia 42 48 121 90 203 145 .. .. 389 339 39 44 South Africa 62 51 45 56 60 69 18 13 605 568 29 36 Spain 77 81 8 4 9 4 .. .. 110 45 85 94 Sri Lanka 71 75 26 11 32 13 .. .. 241 102 66 83 Sudan 53 58 74 61 120 89 38 30 311 270 52 58 Swaziland 57 41 78 112 110 164 .. .. 750 727 17 21 Sweden 78 81 6 3 7 3 .. .. 78 49 88 93 Switzerland 77 82 7 4 9 5 .. .. 80 47 87 93 Syrian Arab Republic 68 74 31 12 38 14 .. .. 126 86 78 85 Tajikistan 63 67 91 56 115 68 .. .. 213 141 63 73 Tanzania 51 52 102 74 161 118 56 52 444 412 40 45 Thailand 67 70 26 7 31 8 .. .. 276 162 63 77 Timor-Leste 46 57 133 47 177 55 .. .. 272 237 56 61 Togo 58 58 88 69 149 108 73 65 280 235 54 61 Trinidad and Tobago 70 70 30 33 34 38 .. .. 243 191 66 73 Tunisia 70 74 41 19 52 23 .. .. 126 73 78 86 Turkey 66 71 67 24 82 26 10 13 154 87 73 84 Turkmenistan 63 63 81 45 99 51 19 17 300 144 53 72 Uganda 50 51 93 78 160 134 71 61 446 433 39 43 Ukraine 70 68 22 20 25 24 .. .. 375 132 51 80 United Arab Emirates 73 79 13 8 15 8 .. .. 75 50 86 91 United Kingdom 76 79 8 5 10 6 .. .. 88 56 85 90 United States 75 78 9 6 11 8 .. .. 140 82 81 88 Uruguay 73 76 20 11 23 12 .. .. 145 68 76 88 Uzbekistan 69 67 61 38 74 43 .. .. 242 138 61 74 Venezuela, RB 71 74 27 18 33 21 .. .. 181 96 73 84 Vietnam 65 71 38 15 53 17 10 7 139 93 78 84 West Bank and Gaza 69 73 34 20 40 22 .. .. 131 95 77 83 Yemen, Rep. 54 62 98 75 139 100 33 36 259 210 58 65 Zambia 48 42 101 102 180 182 89 74 636 632 23 26 Zimbabwe 61 43 52 68 76 105 21 21 706 729 21 21 World 65 w 68 w 63 w 50 w 92 w 73 w 226 w 155 w 67 w 76 w Low income 57 60 93 74 143 112 285 223 56 63 Middle income 68 71 43 26 56 33 195 116 70 80 Lower middle income 67 71 44 27 60 36 173 108 72 81 Upper middle income 69 70 38 22 47 26 260 137 64 80 Low & middle income 63 66 69 54 101 79 232 159 64 73 East Asia & Pacific 67 71 42 24 56 29 165 104 73 81 Europe & Central Asia 69 69 40 22 49 26 298 122 58 81 Latin America & Carib. 68 73 43 22 55 26 197 107 71 82 Middle East & N. Africa 64 70 58 34 77 42 166 115 71 79 South Asia 59 64 86 62 123 83 251 172 60 68 Sub-Saharan Africa 50 50 109 94 184 157 421 391 40 45 High income 76 79 9 6 12 7 117 63 83 91 Euro area 76 80 8 4 9 4 112 54 84 92 a. Data are for the most recent year available. 120 2008 World Development Indicators 2.21 PEOPLE Mortality About the data Definitions Mortality rates for different age groups (infants, interventions are more important in this age group. · Life expectancy at birth is the number of years children, and adults) and overall mortality indicators Where female child mortality is higher, as in some a newborn infant would live if prevailing patterns of (life expectancy at birth or survival to a given age) countries in South Asia, girls probably have unequal mortality at the time of its birth were to stay the are important indicators of a country's health sta- access to resources. Child mortality rates in the same throughout its life. · Infant mortality rate is tus. Because data on disease incidence and preva- table are not compatible with infant mortality and the number of infants dying before reaching one year lence are frequently unavailable, mortality rates under-five mortality rates because of differences in of age per 1,000 live births in a given year. · Under- are often used to identify vulnerable populations. methodologies and reference years. Child mortality five mortality rate is the probability per 1,000 that They are among the indicators most frequently used data were directly estimated from surveys, based a newborn baby will die before reaching age 5, if sub- to compare socioeconomic development across on vital events that occurred during the 10 years ject to current age-specific mortality rates. · Child countries. preceding the survey. The reference year for the child mortality rate is the probability per 1,000 of dying The main sources of mortality data are vital reg- mortality data is the survey year. between ages 1 and 5--that is, the probability of a istration systems and direct or indirect estimates Adult mortality rates increased in many countries in 1-year-old dying before reaching age 5--if subject to based on sample surveys or censuses. A "complete" Sub-Saharan Africa and in Europe and Central Asia. current age-specific mortality rates. · Adult mortal- vital registration system--covering at least 90 per- In Sub-Saharan Africa the increase stems from AIDS- ity rate is the probability per 1,000 of dying between cent of vital events in the population--is the best related mortality and affects both men and women. the ages of 15 and 60--that is, the probability of a source of age-specific mortality data. Where reliable In Europe and Central Asia the causes are more 15-year-old dying before reaching age 60--if subject age-specific mortality data are available, life expec- diverse (high prevalence of smoking, high-fat diet, to current age-specific mortality rates between those tancy at birth is directly estimated from the life table excessive alcohol use, stressful conditions related to ages. · Survival to age 65 refers to the percentage constructed based on age-specific mortality data. the economic transition) and affect men more. of a cohort of newborn infants that would survive to But "complete" vital registration systems are fairly The percentage of a cohort surviving to age 65 age 65, if subject to current age-specific mortality uncommon in developing countries. Thus estimates reflects both child and adult mortality rates. Like life rates. must be obtained from sample surveys or derived by expectancy, it is a synthetic measure based on cur- applying indirect estimation techniques to registra- rent age-specific mortality rates. It shows that even tion, census, or survey data (see Primary data docu- in countries where mortality is high, a certain share mentation). Survey data are subject to recall error, of the current birth cohort will live well beyond the life and surveys estimating infant deaths require large expectancy at birth, while in low-mortality countries samples because households in which a birth or an close to 90 percent will reach at least age 65. infant death has occurred during a given year cannot Revised lower estimates of HIV prevalence have Data sources ordinarily be preselected for sampling. Indirect esti- led adult mortality estimates for many countries, mates rely on estimated actuarial "life" tables that notably in Sub-Saharan Africa, to be revised drasti- Data on infant and under-five mortality rates are may be inappropriate for the population concerned. cally downward from previous estimates from 1990 the harmonized estimates of the World Health Because life expectancy at birth is estimated using onward and life expectancy at birth and survival to Organization, UNICEF, and the World Bank, based infant mortality data and model life tables for many age 65 to be revised upward. mainly on household surveys, censuses, and vital developing countries, similar reliability issues arise registration data, supplemented by the World for this indicator. Extrapolations based on outdated Bank's estimates based on household surveys surveys may not be reliable for monitoring changes in and vital registration and sample registration data. health status or for comparative analytical work. Data on child mortality rates are from Demographic To produce harmonized estimates of infant and and Health Surveys by Macro International. Other under-five mortality rates that transparently use all estimates are compiled and produced by the World available information, the United Nations Children's Bank's Human Development Network and Develop- Fund (UNICEF) and the World Bank developed a meth- ment Data Group in consultation with its opera- odology that fits a regression line to the relationship tional staff and country offices. Important inputs to between mortality rates and their reference dates the World Bank's demographic work come from the using weighted least squares. (For further discus- United Nations Population Division's World Popula- sion of childhood mortality estimates, see UNICEF, tion Prospects: The 2006 Revision, census reports WHO, World Bank, and United Nations Population and other statistical publications from national sta- Division 2007.) tistical offices, Eurostat, Demographic and Health Infant and child mortality rates are higher for boys Surveys by Macro International, and the Human than for girls in countries in which parental gender Mortality Database by the University of California, preferences are insignificant. Child mortality cap- Berkeley, and the Max Planck Institute for Demo- tures the effect of gender discrimination better than graphic Research (www.mortality.org). does infant mortality, as malnutrition and medical 2008 World Development Indicators 121 Text figures, tables, and boxes ENVIRONMENT 3 Introduction C limate change by the numbers Greenhouse gas emissions by sector and by activity 3a Sector End use/activity Greenhouse gas Industry and mining 34.3% Energy-related and industrial processes Carbon dioxide 77% 64.7% Buildings 15.3% Methane 14% Transport 13.8% Land use change 18.2% Deforestation 18.3% Nitrous oxide 8% Agriculture 13.5% Agriculture and livestock 14.9% Fluorinated gases 1% Waste 3.6% Landfills and other waste disposal 3.6% Source: World Resources Institute 2005. Numbers tell the story. The natural climate has changed, and the change is accelerating as our planet warms. The rate of warming has been nearly twice as fast in the last 50 years as in the last 100 years, with the 13 warmest years since 1880 experienced in the last 15 years. Since 1978 annual mean arctic sea ice has been declining. Temperatures at the top of the permafrost have increased by up to 3 degrees centigrade. Sea levels rose more from 1993 to 2003 than in the previous 30 years. Concentration of atmospheric carbon dioxide, the main cause of global warming, increased one-third faster in the last decade than over the last 50 years (IPCC 2007a). Climate change poses risks for the environment and for development in most economies, disproportionately affecting those with the lowest capacity to adapt to such impacts. That makes climate change a development issue critical to poverty reduction. It is also an envi- ronmental issue vital to sustaining growth and preserving the ecosystem. Countries need measures to mitigate it--and to adapt to its unavoidable outcomes. Knowledge about climate change has grown greatly in the last few years. The most compre- hensive treatment is in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), which presents the findings of hundreds of experts in the field: · All greenhouse gas concentrations--the main causes of climate change--have increased since the start of the industrial revolution. From 1750 to 2005 carbon dioxide grew from 280 parts per million to 379, methane from 715 parts per billion to 1,774, and nitrous oxide from 270 parts per billion to 319. · Warming of the climate system is unequivocal--now evident in global averages of air, surface, and ocean temperatures; in widespread melting of snow and ice; and in rising global mean sea level. · The likely consequences of climate change are uneven across regions, with more profound negative impacts for developing countries and for more vulnerable socioeconomic groups. · It is very likely (90+ percent confidence) that human activities are causing global warming. · Changes in technology, management, and behavior can mitigate climate change. · Even with mitigation, climate change will continue, and adaptation will be needed. 2008 World Development Indicators 123 Why the natural climate has changed The IPCC's assessment concluded that global greenhouse emissions has been more than offset by per capita income gas emissions have drastically increased since preindustrial growth (67 percent) and population growth (73 percent). times, with a 70 percent increase between 1970 and 2004. Country trends and contributions to climate change vary More than 75 percent of these emissions come from carbon substantially, with the United States and China contributing dioxide, mainly from burning fossil fuels, manufacturing ce- most (figure 3c and tables 3.7 and 3.8). The average resident ment, and cutting forests. Carbon dioxide emissions grew by of a rich country produces far more carbon dioxide than does about 80 percent, accelerating in recent years (a 28 percent the average resident of a low- and middle-income country. increase since 1990). Per capita emissions of carbon dioxide in 2004 averaged 0.9 The other major greenhouse gases are methane and metric tons in low-income countries, 4.0 metric tons in mid- nitrous oxide, mainly from agriculture, energy use, industrial dle-income countries, and 13.2 metric tons in high-income processes, waste, and savannah burning (see figure 3a for countries (figure 3d). High-income economies, with 15 per- a schematic representation of greenhouse gas emissions). cent of the world's people, produced 55 percent of global Their emissions have grown as well (table 3.9). But emissions GDP (in purchasing power parity terms) and emitted nearly of ozone-depleting substances, also greenhouse gases, have half of the global carbon dioxide emissions in 2004 (figure declined significantly since the 1990s, controlled under the 3e and table 3.8). international treaty known as the Montreal Protocol. By 2005 Global trends in emissions of greenhouse gas sources consumption of these substances was less than 10 percent also vary substantially. The power sector contributes almost of their 1990 level (figure 3b). a quarter of global greenhouse gases, and transport, indus- Global energy intensity declined 33 percent during try, buildings, and other energy-related activities account for 1970­2004. But the favorable impact on carbon dioxide another 41 percent (figure 3f). The biggest growth between Use of ozone-depleting substances High-income countries produce far more carbon dioxide has dropped substantially since 1990 3b emissions per capita than low- or middle-income countries 3d Ozone-depleting potential (metric tons) 1990 2000 2005 Carbon dioxide emissions per capita (metric tons) 1990 2004 1,250 25 1,000 20 750 15 High-income average, 2004: 13.2 500 10 Middle- income Low-income average, 250 5 average, 2004: 4.0 2004: 0.9 0 0 Developing High-income World India China Russian United Germany Japan World economies economies Federation States Source: United Nations Millennium Development Goals database. Source: Table 3.8. The United States and China lead the High-income economies emitted half the world in carbon dioxide emissions 3c global carbon dioxide emissions in 2004 3e Carbon dioxide emissions (billions of metric tons) Other low-income 3% India 5% 30 United United States States 21% 20 China Other middle-income Russian Federation 26% Former Soviet Union India 10 Japan Other high-income 28% China 17% Rest of the world 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2004 Source: Carbon Dioxide Information Analysis Center. Source: Table 3.8 and Carbon Dioxide Information Analysis Center. 124 2008 World Development Indicators 1970 and 2004 was from power generation (145 percent) (12 percent; figure 3i). By far the largest source of trans- followed by transport (120 percent). Fossil fuels account for port emissions is North America, producing 37 percent of three-quarters of the energy used in the power sector, with the global total. This partly reflects the fact that the United coal dominant (figure 3g and table 3.10 ). Coal is respon- States has the highest vehicle ownership in the world (814 sible for the majority of emissions from the power sector vehicles per 1,000 people, compared with 604 in the Euro- (figure 3h). Almost half the electricity and heat produced is pean Union and 15 in China) and also lags in fuel efficiency, used in buildings (residential and commercial), and around which is about two-thirds that in the European Union (An and one-third in industry (WRI 2006). Sauer 2004). North America accounts for by far the largest amount Agriculture and deforestation are responsible for one- of power sector emissions (3 gigatons of carbon dioxide third of greenhouse gas emissions. In many countries soil equivalent), followed by China (1.7 gigatons), European Union degradation, along with the loss of agricultural land through (1.6 gigatons), and transition economies (1.4 gigatons). urbanization and population growth, has led to substantial North America also has among the highest emissions per deforestation. The global forested area in 2005 was about capita (7 tons of carbon dioxide per person), more than twice 4 billion hectares, covering 30 percent of total land area those of the European Union and six times those of China (table 3.4). But deforestation continues at about 13 million (WRI 2006). hectares a year. Reforestation reduced the net loss of for- Transport accounts for 14 percent of global greenhouse est areas to 7.3 million hectares a year during 2000­05, an gas emissions, behind power and land use change but improvement from losses of 8.9 million hectares a year dur- about the same as agriculture (see figure 3f). Most of these ing 1990­2000. Sub-Saharan Africa and Latin America con- emissions are from road transport (76 percent) and aviation tinued to have the largest forest loss after 1990. Power generation and land use change were the two largest Coal was responsible for the majority of sources of greenhouse gas emissions in 2000 3f emissions from the power sector in 2002 3h Waste 3% Oil 5% Agriculture 14% Power 24% Gas 27% Land use change 18% Transport Coal 14% 68% Other Industry energy-related 5% Buildings 14% 8% Source: WRI 2006. Source: WRI 2006. Fossil fuels accounted for three-quarters Road transport accounted for more than three-quarters of the fuel used in the power sector in 2002 3g of total transport carbon dioxide emissions in 2000 3i Rail 2% Nuclear Water 12% Domestic air 5% 10% Renewables International air 7% Coal 13% 42% Cars and vans Two- and three-wheelers 2% 45% Oil 4% Buses 6% Gas Freight trucks 29% 23% Source: WRI 2006. Source: WRI 2006. 2008 World Development Indicators 125 Climate change and vulnerable people and regions Climate change will have different effects on different regions water resources will change drastically. While water's (depending on geography) and different income groups (de- availability could increase in the moist tropics and in high pending on livelihoods and adaptive capacity). The effects latitudes, it will decline in the midlatitudes and in semiarid will also vary by the extent of adaptation, exposure to tem- low altitudes, increasing droughts and water shortages. perature change, and socioeconomic conditions. Potential Accelerated glacial melt in the Himalayas will compound impacts could include: severe ecological problems in northern China, India, and · Lower agricultural productivity. Climate change has the Pakistan, increasing floods but reducing water flow to potential to drastically affect food production (figure 3j). major river systems vital for irrigation. In Latin America In parts of Sub-Saharan Africa and South and East Asia accelerated melting of tropical glaciers will threaten water losses in agricultural productivity are linked to drought supplies for urban populations, agriculture, and hydroelec- and rainfall variation. Drought has already become more tricity, especially in the Andean region. Water shortages frequent in Sub-Saharan Africa (figure 3k). Because a could contribute to regional conflicts. large share of the world's poor people depend directly on · Heightened health risks. Climate change will affect human agriculture, drought and other negative effects of climate health. Globally, 220­400 million more people could be change put poverty reduction efforts at risk. But global at increased risk of malaria, particularly in Sub-Saharan warming could potentially benefit agriculture in some tem- Africa, where exposure to malaria is projected to increase perate areas--mostly in developed countries. 16­28 percent (UNDP 2007b; IPCC 2007b). Climate change · Greater water scarcity. The rise in global temperature is could also increase the incidence of malnutrition, diarrhea, accelerating (figure 3l). If it exceeds the 2° C threshold and infectious diseases--and change the distribution of (as some scenarios project), the distribution of the world's disease vectors, adding to the burden on health services. Climate change would hurt developing countries' agricultural output 3j Change in agricultural output potential in 2080s (% of 2000 potential) World Industrial economies Developing economies South and East Asia and Pacific Middle East and North Africa Latin America and the Caribbean Sub-Saharan Africa ­20 ­15 ­10 ­5 0 5 10 Source: Cline 2007. Less rain is falling in the Sahel, with dire consequences 3k Mean normalized rainfall, 1950­2000, June­October 3.0 2.0 1.0 0.0 ­1.0 ­2.0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Note: The averages are standardized for the period 1950­2000 so that the mean of the series is zero and the standard deviation is one. Source: World Bank 2002e. 126 2008 World Development Indicators · More exposure to climate disasters. Climate-related disas- The negative impacts will not occur everywhere (IPCC ters, mainly floods and droughts, have already increased. 2007a). These impacts depend on two main factors: expo- On average 262 million people a year were affected sure to the effects of climate change and capacity to adapt between 2000 and 2004, more than twice the number in to them. the 1980s (figure 3m), and most of them (98 percent) live Exposure is partly determined by environmental factors. in developing countries (figure 3n). Temperature increases People, flora, and fauna in areas prone to flooding or facing greater than 2° C would accelerate the rise in sea level, water scarcity have far greater exposure. The level of expo- causing widespread displacement of people in countries sure also depends on the population density or the infrastruc- such as Bangladesh, Egypt, and Vietnam and the inun- ture in environmentally sensitive areas. Adaptive capacity is dation of several small-island economies. Rising sea lev- the ability to deal with climate change, such as by building els and more intense tropical storm activity could raise levies to combat flooding or irrigation systems to deal with the number of people experiencing coastal flooding by drought. It is closely associated with a society's wealth, edu- 180­230 million (Dasgupta and others 2007; Anthoff and cation, institutional strength, and access to technology (Bur- others 2006; UNDP 2007b). ton, Diringer, and Smith 2006; IPCC 2007e). · Harm to ecosystems. Coral reef systems, already in decline, High exposure and low adaptive capacity occur mostly in would suffer extensive bleaching, transforming marine developing countries, making them highly vulnerable to cli- ecologies, with large losses of biodiversity and ecosystem mate change. services. This would adversely affect hundreds of millions Poverty and political instability make the negative impacts of people dependent on fish for their livelihoods and nutri- of climate change more severe and weaken the ability to tion (UNDP 2007b). adapt. The rise in global mean surface temperature is accelerating 3l Degrees centigrade 14.75 Annual average 14.50 14.25 10-year average 14.00 13.75 13.50 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2005 Source: Goddard Institute for Space Science Studies Analysis. Climate disasters are affecting more and more Developing countries are exposed people, mostly in developing countries 3m to higher risk of natural disaster 3n People affected by hydrometeorological disasters (millions per year, five-year average) Developing countries Developing countries High-income OECD and Europe and Central Asia People affected by natural disaster (per 100,000) High-income OECD 300 6,000 200 4,000 100 2,000 0 0 1975­79 1980­84 1985­89 1990­94 1995­99 2000­04 1980­84 2000­04 Source: UNDP 2007b, based on OFDA and CRED 2007. Source: UNDP 2007b, based on OFDA and CRED 2007. 2008 World Development Indicators 127 The enormous costs of inaction The impacts of climate change are costly--so is mitigating reduce pollution while economizing on exhaustible resources. the causes of the climate change or adapting to the unavoid- Preventing deforestation is important because forests pro- able outcomes of change. There is substantial economic and tect biodiversity and provide livelihood for millions of poor social justification for mitigating global greenhouse gases people (figure 3o). But taking a low carbon path by shifting emissions over the coming decades (IPCC 2007d), offsetting to alternative energy may be difficult for many developing the projected growth of global emissions or even reducing countries that need to grow but can afford to use only fossil emissions below current levels. The costs of mitigation de- fuels--particularly coal, the "dirtiest" of energy sources. pend on the level at which emissions stabilize. With some 1.6 billion people lacking electricity (figure 3p; But the cost of inaction is significantly higher. The range IEA 2006b), cheap and abundant coal is the fuel of choice in of estimates is wide, depending on underlying assumptions, much of the world, powering economic booms in most devel- on which consensus is lacking. For example, the Stern Review oping economies, notably China and India, that have lifted (Stern 2006) estimates that without action the overall costs millions of people out of poverty. Low-income countries use of climate change will be equivalent to losing at least 5 per- coal to generate 47 percent of their electricity. Coal generates cent of global GDP each year, now and forever. They would be 78 percent of China's electricity and 69 percent of India's (fig- much higher under a wider range of risks and impacts. ure 3q). Worldwide, coal demand is projected to rise about Some steps to reduce carbon dioxide are economically 60 percent by 2030, to 6.9 billion metric tons a year, most and socially desirable, regardless of their mitigating impact. of it going to electrical plants. So, greater coal efficiency can Conserving energy and promoting new technologies and reduce carbon dioxide emissions (figure 3r). energy alternatives (such as capturing and storing carbon and Burning coal does more than add to global warming-- shifting to renewable and cleaner sources of energy) would it is also linked to other environmental and health issues, Forested areas are shrinking in Latin America China and India generate more than and Sub-Saharan Africa--recovering in East Asia 3o two-thirds of their electricity from coal 3q Forested area (million square kilometers) 1990 2000 2005 Share of coal in generating electricity, 10 by income group and selected countries (%) 1990 2004 80 8 60 6 4 40 2 20 0 High- East Asia Europe & Latin Middle East South Sub-Saharan 0 income & Pacific Central America & & North Asia Africa Low- Lower Upper High- China India Asia Caribbean Africa income middle- middle- income income income Source: Table 3.4. Source: Table 3.10. The vast majority of people without access to Greater coal efficiency can electricity in 2004 lived in developing countries 3p reduce carbon dioxide emissions 3r Total: 1.6 billion Reference scenario Projected carbon dioxide emissions from coal-fired With policy change power generation, 2030 (millions of metric tons) With improved technology Others 6% 6,000 East Asia 14% 4,000 South Asia 45% 2,000 Sub-Saharan Africa 35% 0 China India Source: IEA 2006b. Source: Watson 2007; UNDP 2007b. 128 2008 World Development Indicators including acid rain and asthma. Air pollution prematurely unintended victims of industrialized economies' past energy kills more than 2 million people a year. In China the health consumption. costs attributable to air pollution are estimated at $68 bil- With poor adaptive capacity, inadequate social protec- lion a year, nearly 4 percent of its economic output (World tion, and gaps in climate information, developing countries Bank 2007c). And acid rain has contaminated one-third of will find it difficult to respond (figures 3s and 3t). Because the country, destroying some $4 billion worth of crops every climate change crosses national borders, a coordinated year. Chinese authorities have closed some polluting facto- program of funding and new technologies is required. But ries and by 2010 will retire 50 gigawatts of inefficient power the funding needed for adaptation is enormous, and the plants (about 8 percent of the power grid; Pew Center on amount available for climate adaptation in developing coun- Global Climate Change 2007). The authorities have also tries is still insufficient. In June 2007 pledges totaled less mandated that solar, wind, hydroelectric, and other forms of than $220 million, with even smaller amounts allocated and renewable energy provide 10 percent of the nation's power disbursed (figure 3u). The Netherlands has already spent by 2010--and ordered key industries to reduce energy con- $2.2 billion for flood protection, and Austria has a $1.3 bil- sumption by 20 percent. lion project to deal with water scarcity and extreme weather There is considerable agreement and much evidence (WRI 2007). that, even with current mitigation policies, global greenhouse There is still a window of opportunity to act before the gas emissions will continue to grow over the coming decades economic and human costs become insurmountable (Stern (IPCC 2007d). So, countries need to adapt to the unavoidable 2006; IPCC 2007c). But action requires measuring and effects of climate change that are already affecting the well- monitoring the state of the environment and human well- being of their people, particularly those who are poor, the being and how they are changing. There are still information gaps, and many of the available data are not up to date. The Social insurance spending is lower in developing countries, where impacts of carbon dioxide emissions are not well quantified, people are exposed to higher risk of climate change impact 3s especially in developing countries. The impacts of extreme Social insurance spending (% of GDP) climate events are poorly tracked. Local impacts are not 15 widely researched. Few projections on aquatic resources are available. Research on adaptation is still not comprehensive 10 across a range of climate and socioeconomic futures. There is much to be learned about the impacts on biofuel and 5 industrial crops. 0 Numbers tell the story. But we still lack many of the num- OECD Europe & Central East Asia & Latin America & Middle East & South Sub-Saharan Asia Africa bers to tell the whole story. Asia Pacific Caribbean North Africa Source: World Bank 2005d. The climate information gap Adaptation is expensive, and funding makes adaptation more difficult 3t for developing countries is inadequate 3u Meteorological stations per 10,000 square kilometers $ millions, 2007 15 1,500 12 1,000 9 6 500 3 0 0 United Kingdom Venice flood gate Donors' adaptation annual flood defense (annually, 2006­11) fund pledges The United Cuba Africa Mozambique Tanzania Sudan Niger (2004­05) (June 2007) Netherlands Kingdom average Source: UNDP 2007b; WMO 2007; UN 2007. Source: UNDP 2007b; Abott 2004; DEFRA 2007; GEF 2007. 2008 World Development Indicators 129 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 2006 1990­2006 2006 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Afghanistan .. .. .. 652.1 2.0 1.3 0.2 0.2 12.1 12.1 .. .. Albania 63.6 53.9 ­1.3 27.4 28.8 29.0 4.6 4.5 21.1 21.1 18.7 18.4 Algeria 47.9 36.1 0.0 2,381.7 0.8 1.0 0.2 0.4 3.0 3.1 24.5 23.1 Angola 62.9 46.0 0.9 1,246.7 48.9 47.4 0.4 0.2 2.3 2.6 21.2 21.1 Argentina 13.0 9.7 ­0.7 2,736.7 12.9 12.1 0.4 0.4 9.6 10.4 75.2 74.0 Armenia 32.5 36.0 ­0.4 28.2 12.3 10.0 2.7 2.1 17.7 17.6 16.1c 16.4 Australia 14.6 11.6 ­0.2 7,682.3 21.9 21.3 0.0 0.0 6.2 6.4 248.9 240.6 Austria 34.2 33.9 0.4 82.5 45.8 46.8 1.0 0.8 17.3 16.8 17.3 17.0 Azerbaijan 46.3 48.4 1.3 82.7 11.3 11.3 3.7 2.7 20.5 22.3 22.6 22.2 Bangladesh 80.2 74.5 1.5 130.2 6.8 6.7 2.3 3.5 70.2 61.1 5.7 5.3 Belarus 33.6 27.3 ­1.6 207.5 35.6 38.0 0.9 0.6 29.3 26.3 58.4 c 56.2 Belgium 3.6 2.8 ­1.3 30.2 22.4 22.1 .. 0.8 .. 27.9 8.2 8.1 Benin 65.5 59.5 2.7 110.6 30.0 21.3 0.9 2.4 14.6 24.9 33.0 33.0 Bolivia 44.4 35.3 0.7 1,084.4 57.9 54.2 0.1 0.2 1.9 2.8 34.9 33.9 Bosnia and Herzegovina 60.8 53.7 ­1.4 51.2 43.2 42.7 2.9 1.9 16.6 19.5 26.1c 25.9 Botswana 58.1 41.8 ­0.1 566.7 24.2 21.1 0.0 0.0 0.7 0.7 21.5 20.8 Brazil 25.2 15.3 ­1.6 8,459.4 61.5 56.5 0.8 0.9 6.0 7.0 33.1 32.0 Bulgaria 33.6 29.7 ­1.5 108.6 30.1 33.4 2.7 1.9 34.9 29.2 43.4 42.0 Burkina Faso 86.2 81.3 2.6 273.6 26.1 24.8 0.2 0.2 12.9 17.7 35.9 35.9 Burundi 93.7 89.7 2.0 25.7 11.3 5.9 14.0 14.2 36.2 37.8 14.2 13.0 Cambodia 87.4 79.7 1.8 176.5 73.3 59.2 0.6 0.9 20.9 21.0 28.4 27.0 Cameroon 59.3 44.5 0.7 465.4 52.7 45.6 2.6 2.6 12.8 12.8 36.7 34.2 Canada 23.4 19.8 0.0 9,093.5 34.1 34.1 0.7 0.7 5.0 5.0 147.4 142.8 Central African Republic 63.2 61.8 2.0 623.0 37.2 36.5 0.1 0.1 3.1 3.1 49.1 46.8 Chad 79.2 74.2 3.0 1,259.2 10.4 9.5 0.0 0.0 2.6 3.3 40.7 40.1 Chile 16.7 12.1 ­0.6 748.8 20.4 21.5 0.3 0.5 3.7 2.6 12.7 12.2 China 72.6 58.7 ­0.4 9,360.8a 16.8 21.2 0.8 1.4 13.3 15.4 11.1 11.0 Hong Kong, China 0.5 0.0 .. 1.0 .. .. .. .. .. .. .. .. Colombia 31.3 27.0 0.7 1,109.5 55.4 54.7 1.5 1.5 3.0 1.8 5.9 4.9 Congo, Dem. Rep. 72.2 67.3 2.5 2,267.1 62.0 58.9 0.5 0.5 2.9 3.0 12.9 11.8 Congo, Rep. 45.7 39.4 1.7 341.5 66.5 65.8 0.1 0.1 1.4 1.4 15.0 14.0 Costa Rica 49.3 37.8 0.6 51.1 50.2 46.8 4.9 6.5 5.1 4.4 5.6 5.3 Côte d'Ivoire 60.3 54.6 1.8 318.0 32.1 32.7 11.0 11.3 7.6 11.0 18.2 18.8 Croatia 46.0 43.2 ­0.8 55.9 37.8 38.2 2.0 2.1 21.7 19.8 32.7c 27.6 Cuba 26.6 24.6 ­0.1 109.8 18.7 24.7 7.4 6.1 27.6 33.4 32.8 32.7 Czech Republic 24.8 26.5 0.4 77.3 34.0 34.3 .. 3.1 .. 39.4 30.1 29.9 Denmark 15.2 14.3 0.0 42.4 10.5 11.8 0.2 0.2 60.4 52.7 42.6 41.8 Dominican Republic 44.8 32.5 ­0.3 48.4 28.4 28.4 9.3 10.3 18.6 16.9 9.2 8.8 Ecuador 44.9 36.7 0.3 276.8 49.9 39.2 4.8 4.4 5.8 4.9 12.0 10.1 Egypt, Arab Rep. 56.5 57.0 1.9 995.5 0.0 0.1 0.4 0.5 2.3 3.0 4.2 4.1 El Salvador 50.8 39.9 0.2 20.7 18.1 14.4 12.5 12.1 26.5 31.9 10.4 10.0 Eritrea 84.2 80.2 2.2 101.0 16.0 15.4 .. 0.0 .. 6.3 14.6 14.0 Estonia 28.9 30.9 ­0.6 42.4 51.0 53.9 0.3 0.3 26.3 13.9 52.1c 40.9 Ethiopia 87.4 83.7 2.3 1,000.0 13.7 13.0 0.6 0.8 9.8 13.1 15.1 16.7 Finland 38.6 38.8 0.4 304.6 72.9 73.9 0.0 0.0 7.4 7.3 42.2 42.5 France 25.9 23.1 ­0.2 550.1 26.4 28.3 2.2 2.1 32.7 33.6 31.1 30.5 Gabon 30.9 15.9 ­1.9 257.7 85.1 84.5 0.6 0.7 1.1 1.3 27.0 25.6 Gambia, The 61.7 45.3 1.5 10.0 44.2 47.1 0.5 0.5 18.2 35.0 21.3 21.9 Georgia 44.8 47.7 ­0.9 69.5 39.7 39.7 4.8 3.8 11.4 11.5 17.1c 17.8 Germany 26.6 24.7 ­0.2 348.8 30.8 31.8 1.3 0.6 34.3 34.1 14.3 14.4 Ghana 63.5 51.5 1.1 227.5 32.7 24.2 6.6 9.7 11.9 18.4 19.7 19.0 Greece 41.2 40.9 0.5 128.9 25.6 29.1 8.3 8.8 22.5 20.4 24.9 24.1 Guatemala 58.9 52.3 1.6 108.4 43.8 36.3 4.5 5.6 12.0 13.3 12.2 11.6 Guinea 72.0 66.5 2.1 245.7 30.1 27.4 2.0 2.7 3.0 4.9 12.1 13.2 Guinea-Bissau 71.9 70.3 2.9 28.1 78.8 73.7 4.2 8.9 10.7 10.7 21.2 19.4 Haiti 70.5 60.5 0.8 27.6 4.2 3.8 11.6 11.6 28.3 28.3 8.9 8.5 130 2008 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 2006 1990­2006 2006 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Honduras 59.7 53.0 1.5 111.9 66.0 41.5 3.2 3.2 13.1 9.5 16.9 15.9 Hungary 34.2 33.3 ­0.4 89.6 20.0 22.1 2.6 2.3 56.2 51.3 45.2 45.5 India 74.5 71.0 1.4 2,973.2 21.5 22.8 2.2 3.4 54.8 53.7 15.5 14.8 Indonesia 69.4 50.8 ­0.6 1,811.6 64.3 48.8 6.5 7.5 11.2 12.7 10.3 10.6 Iran, Islamic Rep. 43.7 32.6 ­0.3 1,628.6 6.8 6.8 0.8 1.0 9.3 10.2 24.0 24.0 Iraq 30.3 .. .. 437.4 1.8 1.9 0.7 0.6 12.1 13.1 22.0 .. Ireland 43.1 39.2 0.6 68.9 6.4 9.7 0.0 0.0 15.1 17.6 29.7 29.5 Israel 9.6 8.4 1.7 21.6 7.1 7.9 4.1 3.5 15.9 14.6 5.3 4.8 Italy 33.3 32.2 0.0 294.1 28.5 33.9 10.1 8.6 30.6 26.3 14.7 13.6 Jamaica 50.6 46.6 0.2 10.8 31.9 31.3 9.2 10.2 11.0 16.1 6.7 6.6 Japan 36.9 34.0 ­0.3 364.5 68.4 68.2 1.3 0.9 13.1 12.0 3.5 3.4 Jordan 27.8 17.4 0.6 88.2 0.9 0.9 0.8 1.0 2.0 2.1 3.9 3.6 Kazakhstan 43.7 42.4 ­0.6 2,699.7 1.3 1.2 0.1 0.1 13.0 8.3 148.7 149.3 Kenya 81.8 79.0 2.6 569.1 6.5 6.2 0.8 0.8 8.8 9.2 15.7 15.1 Korea, Dem. Rep. 41.6 38.0 0.5 120.4 68.1 51.4 1.5 1.7 19.0 23.3 11.4 11.7 Korea, Rep. 26.2 19.0 ­1.3 98.7 64.5 63.5 1.6 2.0 19.8 16.4 3.6 3.4 Kuwait 2.0 1.7 0.2 17.8 0.2 0.3 0.1 0.2 0.2 0.8 0.6c 0.6 Kyrgyz Republic 62.2 64.0 1.2 191.8 4.4 4.5 0.4 0.4 6.9 6.7 27.2c 25.9 Lao PDR 84.6 79.0 1.7 230.8 75.0 69.9 0.3 0.4 3.5 4.3 17.0 17.8 Latvia 30.7 32.1 ­0.7 62.3 44.7 47.2 0.4 0.2 27.2 17.5 41.0 c 44.1 Lebanon 16.9 13.3 0.4 10.2 11.8 13.3 11.9 13.9 17.9 18.2 4.7 4.7 Lesotho 82.8 81.0 1.2 30.4 0.2 0.3 0.1 0.1 10.4 10.9 17.3 16.8 Liberia 54.7 41.2 1.5 96.3 42.1 32.7 2.2 2.3 4.2 4.0 12.0 11.4 Libya 21.4 14.9 ­0.2 1,759.5 0.1 0.1 0.2 0.2 1.0 1.0 33.3 30.6 Lithuania 32.4 33.4 ­0.3 62.7 31.0 33.5 0.7 0.6 46.0 30.4 58.8 c 49.0 Macedonia, FYR 42.2 30.4 ­1.6 25.4 35.6 35.6 2.2 1.8 23.8 22.3 27.9c 27.9 Madagascar 76.4 72.9 2.6 581.5 23.5 22.1 1.0 1.0 4.7 5.1 17.6 16.3 Malawi 88.4 82.3 1.8 94.1 41.4 36.2 1.2 1.5 19.3 27.6 18.4 19.8 Malaysia 50.2 31.8 ­0.6 328.6 68.1 63.6 16.0 17.6 5.2 5.5 7.6 7.1 Mali 76.7 68.9 2.1 1,220.2 11.5 10.3 0.0 0.0 1.7 3.9 45.3 42.6 Mauritania 60.3 59.4 2.7 1,030.7 0.4 0.3 0.0 0.0 0.4 0.5 18.5 17.1 Mauritius 56.1 57.5 1.2 2.0 19.2 18.2 3.0 3.0 49.3 49.3 8.3 8.1 Mexico 27.5 23.7 0.5 1,944.0 35.5 33.0 1.0 1.3 12.5 12.9 25.4 24.6 Moldova 53.2 53.0 ­0.9 32.9 9.7 10.0 14.2 9.1 52.8 56.2 45.1c 47.1 Mongolia 43.0 43.1 1.3 1,566.5 7.3 6.5 0.0 0.0 0.9 0.7 49.1 46.7 Morocco 51.6 40.7 0.0 446.3 9.6 9.8 1.6 2.1 19.5 19.0 29.7 28.4 Mozambique 78.9 64.7 1.5 786.4 25.4 24.5 0.3 0.3 4.4 5.6 21.6 21.8 Myanmar 75.1 68.7 0.6 657.6 59.6 49.0 0.8 1.4 14.5 15.3 21.4 21.1 Namibia 72.3 64.3 1.6 823.3 10.6 9.3 0.0 0.0 0.8 1.0 42.7 40.9 Nepal 91.1 83.7 1.8 143.0 33.7 25.4 0.5 0.9 16.0 16.5 9.4 8.9 Netherlands 31.3 19.3 ­2.5 33.9 10.2 10.8 0.9 1.0 25.9 26.8 5.7 5.6 New Zealand 15.3 13.7 0.5 267.7 28.8 31.0 5.1 7.1 9.9 5.6 38.5 36.7 Nicaragua 46.9 40.6 0.9 121.4 53.9 42.7 1.6 1.9 10.7 15.9 37.1 35.7 Niger 84.6 83.0 3.4 1,266.7 1.5 1.0 0.0 0.0 8.7 11.4 125.7 113.1 Nigeria 65.0 51.0 1.2 910.8 18.9 12.2 2.8 3.3 32.4 35.1 22.6 22.6 Norway 28.0 22.5 ­0.8 304.3 30.0 30.8 .. .. 2.8 2.8 19.6 19.0 Oman 34.6 28.5 0.8 309.5 0.0 0.0 0.1 0.1 0.1 0.2 1.6 2.2 Pakistan 69.4 64.7 2.0 770.9 3.3 2.5 0.6 1.0 26.6 27.6 15.2 14.1 Panama 46.1 28.4 ­1.1 74.4 58.8 57.7 2.1 2.0 6.7 7.4 18.1 17.3 Papua New Guinea 86.9 86.5 2.5 452.9 69.6 65.0 1.3 1.4 0.4 0.5 3.8 3.9 Paraguay 51.3 40.9 0.8 397.3 53.3 46.5 0.2 0.2 5.3 10.6 61.2 70.2 Peru 31.1 27.2 0.6 1,280.0 54.8 53.7 0.3 0.5 2.7 2.9 14.2 13.7 Philippines 51.2 36.6 0.0 298.2 35.5 24.0 14.8 16.8 18.4 19.1 7.3 6.9 Poland 38.7 37.8 ­0.2 306.3 29.2 30.0 1.1 1.2 47.3 39.6 35.3 32.6 Portugal 52.1 41.8 ­1.0 91.5 33.9 41.3 8.5 7.1 25.6 13.8 15.4 13.3 Puerto Rico 27.8 2.2 ­15.3 8.9 45.5 46.0 5.6 4.7 7.3 8.0 1.7 1.8 2008 World Development Indicators 131 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 2006 1990­2006 2006 1990 2005 1990 2005 1990 2005 1990­92 2003­05 Romania 45.7 46.1 ­0.4 230.0 27.8 27.7 2.6 2.3 41.2 40.4 42.4 43.2 Russian Federation 26.6 27.1 ­0.1 16,381.4 49.4 49.4 0.1 0.1 8.1 7.4 84.9c 84.9 Rwanda 94.6 79.8 0.6 24.7 12.9 19.5 12.4 11.1 35.7 48.6 11.8 13.2 Saudi Arabia 23.4 18.8 0.9 2,000.0 b 1.4 1.4 0.0 0.1 1.7 1.8 17.0 15.7 Senegal 61.0 58.1 2.4 192.5 48.6 45.0 0.1 0.2 12.1 13.2 22.9 21.8 Serbiad 49.1 47.6 .. 102.0 25.1 26.4 3.5 3.1 36.5 34.4 41.9c 42.4 Sierra Leone 69.9 58.6 1.0 71.6 42.5 38.5 0.8 1.1 6.8 8.4 10.8 11.0 Singapore 0.0 0.0 .. 0.7 3.0 2.9 1.5 0.3 1.5 0.9 0.0 0.0 Slovak Republic 43.5 43.7 0.2 48.1 40.0 40.1 .. 0.5 .. 28.9 27.1 26.0 Slovenia 49.6 48.8 ­0.1 20.1 59.0 62.8 1.8 1.3 9.9 8.7 8.6c 8.7 Somalia 70.3 64.3 0.9 627.3 13.2 11.4 0.0 0.0 1.6 2.2 15.1 16.5 South Africa 48.0 40.2 0.8 1,214.5 7.6 7.6 0.7 0.8 11.1 12.1 33.0 31.8 Spain 24.6 23.2 0.4 499.2 27.0 35.9 9.7 9.9 30.7 27.4 32.2 32.0 Sri Lanka 82.8 84.9 1.1 64.6 36.4 29.9 15.9 15.5 13.5 14.2 4.7 4.7 Sudan 73.4 58.3 0.9 2,376.0 32.1 28.4 0.0 0.1 5.4 8.2 48.1 51.2 Swaziland 77.1 75.6 2.3 17.2 27.4 31.5 0.7 0.8 10.5 10.3 16.7 15.9 Sweden 16.9 15.7 ­0.1 410.3 66.7 67.1 0.0 0.0 6.9 6.6 30.3 29.8 Switzerland 31.6 24.4 ­0.9 40.0 28.9 30.5 0.5 0.6 9.8 10.3 5.7 5.5 Syrian Arab Republic 51.1 49.2 2.4 183.8 2.0 2.5 4.0 4.7 26.6 26.5 27.1 25.9 Tajikistan 68.5 75.4 2.0 140.0 2.9 2.9 0.9 0.9 6.1 6.6 14.9c 14.4 Tanzania 81.1 75.4 2.3 885.8 46.8 39.8 1.1 1.3 10.2 10.4 25.9 24.5 Thailand 70.6 67.4 0.7 510.9 31.2 28.4 6.1 7.0 34.2 27.8 25.9 22.7 Timor-Leste 79.2 73.1 1.5 14.9 65.0 53.7 3.9 4.6 7.4 8.2 15.2 13.2 Togo 69.9 59.2 2.0 54.4 12.6 7.1 1.7 2.6 38.6 45.8 45.1 41.2 Trinidad and Tobago 91.5 87.5 0.2 5.1 45.8 44.1 9.0 9.2 14.4 14.6 5.7 5.7 Tunisia 40.4 34.3 0.3 155.4 4.1 6.8 12.5 13.9 18.7 17.6 29.0 27.9 Turkey 40.8 32.2 0.2 769.6 12.6 13.2 3.9 3.6 32.0 31.0 34.8 33.2 Turkmenistan 54.9 53.4 1.6 469.9 8.8 8.8 0.1 0.1 2.9 4.9 40.5c 46.9 Uganda 88.9 87.3 3.1 197.1 25.0 18.4 9.4 11.2 25.4 27.4 20.0 18.9 Ukraine 33.2 32.0 ­0.9 579.4 16.0 16.5 1.9 1.6 57.6 56.0 66.9c 68.4 United Arab Emirates 20.9 23.3 6.1 83.6 2.9 3.7 0.2 2.3 0.4 0.8 2.0 1.6 United Kingdom 11.3 10.2 ­0.3 241.9 10.8 11.8 0.3 0.2 27.4 23.7 9.7 9.6 United States 24.7 18.9 ­0.5 9,161.9 32.6 33.1 0.2 0.3 20.3 19.0 61.6 59.6 Uruguay 11.0 7.9 ­1.7 175.0 5.2 8.6 0.3 0.2 7.2 7.8 41.5 41.5 Uzbekistan 59.9 63.3 2.0 425.4 7.2 7.7 0.9 0.8 10.5 11.0 18.0 c 18.2 Venezuela, RB 16.0 6.3 ­3.9 882.1 59.0 54.1 0.9 0.9 3.2 3.0 10.5 10.1 Vietnam 79.7 73.1 1.0 310.1 28.8 41.7 3.2 7.6 16.4 21.3 8.2 8.0 West Bank and Gaza 32.1 28.3 3.3 6.0 1.5 1.5 19.1 19.1 18.4 17.8 3.4 3.0 Yemen, Rep. 79.1 72.3 3.0 528.0 1.0 1.0 0.2 0.3 2.9 2.9 8.1 7.4 Zambia 60.6 64.9 2.7 743.4 66.1 57.1 0.0 0.0 7.1 7.1 49.3 46.7 Zimbabwe 71.0 63.6 0.8 386.9 57.5 45.3 0.3 0.3 7.5 8.3 25.2 24.7 World 57.0 w 50.9 w 0.6 w 129,644.6 w 31.5 w 30.5 w 1.1 w 1.1 w 10.9 w 11.0 w 23.0 w 22.3 w Low income 74.6 69.6 1.6 28,147.5 26.2 23.9 1.0 1.3 13.2 14.1 17.4 16.9 Middle income 55.8 45.5 ­0.2 68,468.3 34.8 33.8 1.4 1.2 9.1 9.7 22.4 21.8 Lower middle income 64.9 52.7 ­0.2 27,976.6 27.0 26.5 1.6 1.9 9.4 11.4 14.5 14.2 Upper middle income 30.6 25.0 ­0.3 40,491.7 40.2 38.8 1.1 0.7 8.9 8.5 44.6 43.2 Low & middle income 63.3 56.1 0.7 96,615.8 32.2 30.9 1.2 1.2 10.6 11.0 20.3 19.7 East Asia & Pacific 71.2 57.6 ­0.2 15,871.1 28.8 28.4 2.2 2.9 12.1 13.5 11.6 11.4 Europe & Central Asia 37.0 36.2 0.0 23,247.6 38.2 38.3 0.4 0.4 12.3 11.1 57.7 57.0 Latin America & Carib. 29.0 22.3 ­0.1 20,156.5 48.8 45.4 0.9 1.0 6.5 7.2 27.5 26.7 Middle East & N. Africa 48.0 42.5 1.2 8,953.2 2.2 2.4 0.8 0.9 5.6 5.9 18.1 17.5 South Asia 75.1 71.2 1.5 4,781.3 16.5 16.8 1.8 2.6 42.6 41.9 14.5 13.8 Sub-Saharan Africa 72.0 64.2 1.9 23,606.1 29.2 26.5 0.8 0.9 6.7 8.0 25.5 25.0 High income 26.4 22.4 ­0.3 33,028.8 29.1 29.5 0.7 0.7 11.4 11.0 37.3 36.4 Euro area 29.0 26.5 ­0.2 2,464.9 33.4 37.2 4.7 4.4 27.1 25.4 20.4 20.1 a. Includes Taiwan, China; Macao, China; and Hong Kong, China. b. Provisional estimate. c. Data for all three years are not available. d. Includes Montenegro. 132 2008 World Development Indicators 3.1 ENVIRONMENT Rural population and land use About the data Definitions With 3 billion people, including 70 percent of the Satellite images show land use that differs from · Rural population is calculated as the difference world's poor people, living in rural areas, adequate that of ground-based measures in area under cultiva- between the total population and the urban popula- indicators to monitor progress in rural areas are tion and type of land use. Moreover, land use data tion (see Definitions for tables 2.1 and 3.11). · Land 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. indicators of rural population and land use. Rural government revenue, the quality and coverage of land In most cases definitions 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.) · Land use can rural population, it is not precise (see box 3.1a for and differences in definitions (see About the data be broken into several categories, three of which further discussion). for table 3.4). FAO's Global Forest Resources Assess- are presented in the table (not shown are land used The data in the table show that land use patterns ment 2005 aims to address this limitation. The as permanent pasture and land under urban devel- are changing. They also indicate major differences FAO has been coordinating global forest resources opments). · Forest area is land under natural or in resource endowments and uses among countries. assessments every 5­10 years since 1946. Global planted stands of trees, whether productive or not. True comparability of the data is limited, however, Forest Resources Assessment 2005, conducted dur- · Permanent cropland is land cultivated with crops by variations in definitions, statistical methods, and ing 2003­05, covers 229 countries and territories that occupy the land for long periods and need not quality of data. Countries use different definitions of at three points: 1990, 2000, and 2005. The most be replanted after each harvest, such as cocoa, cof- rural and urban population and land use. The Food comprehensive assessment of forests, forestry, and fee, and rubber. Land under flowering shrubs, fruit and Agriculture Organization of the United Nations the benefits of forest resources in both scope and trees, nut trees, and vines is included, but land under (FAO), the primary compiler of the data, occasion- number of countries and people involved, it exam- trees grown for wood or timber is not. · Arable land ally adjusts its definitions of land use categories ines status and trends for about 40 variables on the is land defined by the FAO as under temporary crops and revises earlier data. Because the data reflect extent, condition, uses, and values of forests and (double-cropped areas are counted once), temporary changes in reporting procedures as well as actual other wooded land. meadows for mowing or for pasture, land under mar- changes in land use, apparent trends should be inter- ket or kitchen gardens, and land temporarily fallow. preted cautiously. Land abandoned as a result of shifting cultivation is excluded. What is rural? Urban? 3.1a The rural population identified in table 3.1 is approximated as the difference between total population and urban population, calculated using the urban share reported by the United Nations Population Division. There is no universal standard for distinguishing rural from urban areas, and any urban-rural dichotomy is an oversimplification (see About the data for table 3.11). The two distinct images--isolated farm, thriving 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 and 3.11 is inadequate. A recent 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 Data sources 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 on urban population shares used to estimate unit cost of delivering most social services and many types of infrastructure is high. Where large urban rural population come from the United Nations areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and Population Division's World Urbanization Prospects: it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population The 2005 Revision. The total population figures are density and remoteness together define a set of rural areas that face special development challenges. World Bank estimates. Data on land area and land Using these criteria and the Gridded Population of the World (CIESIN 2005), the authors' estimates of use are from the FAO's electronic files. The FAO the rural population for Latin America and the Caribbean differ substantially from those in table 3.1. Their gathers these data from national agencies through estimates range from 13 percent of the population, based on a population density of less than 20 people annual questionnaires and by analyzing the results per square kilometer, to 64 percent, based on a population density of more than 500 people per square of national agricultural censuses. Data on forest kilometer. Taking remoteness into account, the estimated rural population would be 13­52 percent. The area are from the FAO's Global Forest Resources estimate for Latin America and the Caribbean in table 3.1 is 22 percent. Assessment 2005. 2008 World Development Indicators 133 3.2 Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural landa land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1990­92 2003­05 1990­92b 2003­05b,c 1990­92 2004­06 1990­92b 2003­05b 1990­92 2003­05 1990­92 2001­03 Afghanistan 58.3 58.3 33.9 33.8 2,283 2,702 59 .. .. .. 1 1 Albania 41.1 40.9 55.6 49.5 243 145 903 924 .. 58.3 177 141 Algeria 16.3 17.1 6.4 6.9 3,105 2,675 144 137 .. 21.1 128 129 Angola 46.1 46.2 2.3 2.3 893 1,441 29 29 .. .. 35 33 Argentina 46.6 47.2 5.6 4.7d 8,510 9,309 73 480 0.4 1.2 103 107 Armenia 44.7d 49.3 49.9d 51.2 163d 171 502d 232 .. 46.5 293 289 Australia 60.5 57.5 4.2 4.9 12,814 19,004 275 469 5.5 3.8 67 65 Austria 42.5 40.0 0.3 2.5d,e 903 798 1,995 2,309 7.5 5.4 2,367 2,380 Azerbaijan 53.4 d 57.5 68.0 d 69.1 627 791 440 d 134 32.5 39.6 195d 164 Bangladesh 73.5 69.2 33.8 54.3 10,985 11,312 1,136 2,094 66.4 51.7 6 7 Belarus 45.3d 42.7 2.1d 2.0 2,578d 2,186 2,293d 1,886 21.7 .. 207d 111 Belgium .. 46.0 .. 4.6 .. 320 .. .. 2.8 1.9 .. 1,146 Benin 20.6 31.9 0.6 0.4 660 937 78 3 .. .. 1 1 Bolivia 32.9 34.6 5.5 4.1 633 846 42 62 1.7 .. 25 20 Bosnia and Herzegovina 43.0 d 42.1 0.2d 0.3 305d 318 .. 453 .. .. 235d 289 Botswana 45.9 45.8 0.2 0.3 140 75 22 .. .. 21.2 143 159 Brazil 28.9 31.2 4.6 4.4 19,633 19,368 656 1,539 25.6 20.9 142 137 Bulgaria 55.7 48.5 29.6 16.5 2,174 1,701 1,194 1,541 19.7 9.6 128 95 Burkina Faso 34.9 39.8 0.6 0.5 2,725 3,249 60 75 .. .. 3 4 Burundi 82.9 90.9 1.2 1.6 219 209 34 16 .. .. 2 2 Cambodia 25.5 29.6 6.6 7.0 1,801 2,431 19 50 c .. .. 3 7 Cameroon 19.7 19.7 0.3 0.4 816 1,107 34 94 60.6 .. 1 1 Canada 7.5 7.4 1.4 1.5 20,864 16,772 476 581 4.2 2.7 162 160 Central African Republic 8.0 8.4 0.0 0.1 104 177 5 .. .. .. 0 0 Chad 38.4 38.9 0.5 0.8 1,242 2,264 25 .. .. .. 1 0 Chile 21.0 20.4 57.1 81.1 742 663 1,215 2,910 18.8 13.4 144 272 China 57.0 59.5 36.9 35.5 93,430 81,957 2,321 3,214 53.5 .. 64 65 Hong Kong, China .. .. .. .. .. .. .. .. 0.8 0.3 .. .. Colombia 40.5 38.2 14.3 23.3 1,598 1,210 1,822 2,940 1.4 21.3 98 91 Congo, Dem. Rep. 10.1 10.1 0.1 0.1 1,868 1,964 8 .. .. .. 4 4 Congo, Rep. 30.8 30.9 0.3 0.4 9 12 35 .. .. .. 15 14 Costa Rica 55.7 56.5 15.2 20.4 83 62 4,522 8,528 25.2 15.0 259 311 Côte d'Ivoire 59.8 63.4 1.1 1.1 1,434 842 151 203 .. .. 15 12 Croatia 43.0d 50.8 0.2d 0.4 593d 598 1,514 d 1,337 .. 16.8 35d 25 Cuba 61.5 60.0 22.6 19.5 235 286 1,288 156 25.1 21.5 250 209 Czech Republic .. 55.2 .. 0.7 .. 1,589 .. 1,404 10.1 4.3 .. 305 Denmark 65.4 62.0 16.9 9.7 1,581 1,499 2,249 1,159 5.4 3.0 625 540 Dominican Republic 71.6 70.7 16.5 20.9 134 156 1,003 .. 19.5 14.4 25 23 Ecuador 28.6 26.9 27.9 28.3 861 881 508 1,731 7.0 8.9 67 106 Egypt, Arab Rep. 2.7 3.5 100.0 100.0 2,410 2,918 3,977 6,707 36.2 29.9 251 309 El Salvador 71.1 82.2 4.9 4.9 453 335 1,336 904 17.9 18.7 60 52 Eritrea .. 75.1 .. 3.6 .. 371 .. 13 .. .. .. 8 Estonia 32.4 d 19.1 0.5d 0.6 454 274 1,011d 4,846 19.5 5.8 419d 889 Ethiopia 51.0 33.0 1.4 2.6 4,586 9,126 .. 32 .. 44.1 4 3 Finland 7.9 7.4 2.8 2.9 1,050 1,154 1,647 1,286 8.8 4.9 1,040 882 France 55.3 53.9 11.0 13.3 9,212 9,226 2,918 2,162 .. 4.0 784 685 Gabon 20.0 20.0 1.1 1.4 14 20 25 38 .. .. 50 46 Gambia, The 63.2 80.7 0.9 0.6 90 200 44 .. .. .. 2 1 Georgia 46.5d 43.3 39.9d 44.1 249 314 906d 319 .. 54.4 296d 254 Germany 49.8 48.8 4.0 4.0 6,673 6,829 2,616 2,208 4.0 2.4 1,253 801 Ghana 55.7 64.8 0.7 0.5 1,078 1,377 38 63 62.0 .. 15 9 Greece 71.3 65.2 31.1 37.4 1,455 1,156 2,289 1,691 22.7 13.4 774 939 Guatemala 39.5 42.9 6.8 6.4 768 790 1,072 1,285 .. .. 33 30 Guinea 48.9 51.0 7.0 5.6 774 1,398 16 27 .. .. 5 5 Guinea-Bissau 53.2 58.0 4.1 4.6 112 139 15 .. .. .. 1 1 Haiti 57.9 57.7 8.0 8.4 406 444 35 .. .. .. 2 2 134 2008 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural landa land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1990­92 2003­05 1990­92b 2003­05b,c 1990­92 2004­06 1990­92b 2003­05b 1990­92 2003­05 1990­92 2001­03 Honduras 29.8 26.2 3.8 5.6 502 354 203 545 42.1 37.2 31 49 Hungary 70.7 65.4 4.1 2.5 2,803 2,939 796 1,197 11.3 5.3 158 247 India 60.9 60.6 28.3 32.7 100,760 97,347 758 1,140 68.1 .. 65 141 Indonesia 23.5 26.3 14.5 12.7 13,861 15,151 1,330 1,449 54.9 44.5 18 41 Iran, Islamic Rep. 38.5 36.1 39.9 47.2 9,612 9,056 750 571 25.6 24.9 136 158 Iraq 21.9 22.9 63.0 58.6 3,506 3,509 347 .. .. 17.0 72 80 Ireland 70.2 62.4 .. .. 298 287 6,591 4,529 14.1 6.3 1,667 1,324 Israel 26.7 24.4 44.4 40.9 108 88 2,836 20,008 3.7 2.0 763 714 Italy 55.4 50.7 22.9 25.8d 4,347 4,025 2,195 1,817 8.4 4.6 1,619 2,031 Jamaica 44.0 47.4 11.0 8.8 3 1 1,737 432 27.3 19.0 242 177 Japan 15.5 12.9 54.3 35.8 2,439 2,015 3,779 3,924 6.8 4.5 4,297 4,588 Jordan 12.0 11.5 25.0 27.5 112 57 969 7,295 .. 3.6 352 308 Kazakhstan 82.0 d 76.9 9.8d 15.7 22,152d 14,517 136d 68 .. 33.7 62d 22 Kenya 47.3 47.4 1.1 1.7 1,766 2,103 209 442 19.0 .. 20 25 Korea, Dem. Rep. 21.0 24.9 58.2 50.9 1,569 1,282 3,522 .. .. .. 297 241 Korea, Rep. 21.9 19.2 47.1 47.1 1,368 1,072 4,932 4,379 16.7 8.3 275 1,239 Kuwait 7.9 8.6 60.0 77.0 0 1 2,000 d 15,602c,e .. 0.0 215 69 Kyrgyz Republic 52.6d 56.2 72.6d 73.1 579d 611 242d 152 35.5 43.4 189d 167 Lao PDR 7.2 8.5 16.2 17.2 630 770 31 .. .. .. 11 12 Latvia 40.8d 26.5 1.1d 2.1 699d 475 995d 876 .. 13.0 364 d 580 Lebanon 31.1 38.1 28.1 32.3 41 64 1,639 1,619 .. .. 188 465 Lesotho 76.7 76.9 0.6 0.9 178 182 167 .. .. .. 57 61 Liberia 27.1 27.0 0.5 0.5 135 .. 8 .. .. .. 8 9 Libya 8.8 8.8 21.8 21.9 355 356 458 506 .. .. 187 219 Lithuania 54.1d 42.5 0.5d 0.4 1,134 d 933 541d 1,470 18.8 15.9 256d 641 Macedonia, FYR 51.4 d 48.8 12.1d 9.0 235d 192 .. 200 .. 19.4 730 d 954 Madagascar 62.5 70.2 30.7 30.6 1,321 1,486 34 32 .. 78.0 11 12 Malawi 40.2 48.3 1.2 2.3 1,443 1,491 351 236 .. .. 8 6 Malaysia 22.7 24.0 4.8 4.8 699 692 5,264 8,536 23.9 14.6 161 241 Mali 26.3 32.4 3.7 5.0 2,393 3,206 91 .. .. 41.5 11 6 Mauritania 38.5 38.6 11.8 9.8 133 203 132 .. .. .. 8 8 Mauritius 55.7 55.7 16.0 20.1 1 0 2,732 2,301 14.7 10.0 36 37 Mexico 53.8 55.3 22.0 22.8 10,075 9,941 686 733 24.7 15.9 128 129 Moldova 77.9d 76.7 14.2d 11.5 676d 969 776d 122 .. 41.4 310 d 221 Mongolia 79.9 83.3 5.8 7.0 620 153 111 38 .. 40.6 73 42 Morocco 68.2 68.1 13.2 15.5 5,374 5,584 353 570 .. 45.0 46 58 Mozambique 60.7 61.8 2.8 2.7 1,509 2,046 12 51 .. .. 16 14 Myanmar 15.8 17.1 10.1 17.9 5,283 7,670 79 11 69.4 .. 12 10 Namibia 47.0 47.2 0.7 1.0 215 290 .. 22 48.2 .. 47 39 Nepal 29.0 29.5 43.0 47.0 2,957 3,352 340 124 81.9 .. 23 24 Netherlands 58.9 56.8 61.0 60.0 185 215 6,298 5,839 4.3 2.9 2,056 1,645 New Zealand 65.0 64.5 7.6 11.4d 153 114 1,911 6,741 10.7 7.6 324 507 Nicaragua 33.5 43.5 4.0 2.8 299 484 270 317 38.7 29.0 20 15 Niger 27.0 30.4 0.5 0.5 7,011 7,666 1 4 .. .. 0 0 Nigeria 79.4 80.4 0.7 0.8 16,417 18,399 142 64 .. .. 8 10 Norway 3.3 3.4 .. .. 361 324 2,362 1,886 5.9 3.5 1,723 1,486 Oman 3.5 5.1 71.6 88.4 4 5 2,441 3,424 .. .. 42 50 Pakistan 33.7 35.2 78.5 84.2 11,777 12,714 962 1,621c,e 48.9 42.7 133 149 Panama 28.7 30.0 4.8 6.2 182 188 666 421 25.8 16.4 103 148 Papua New Guinea 2.0 2.3 .. .. 2 3 622 1,806 .. .. 59 53 Paraguay 56.0 60.7 2.9 1.8 455 791 92 581 1.7 31.5 72 46 Peru 17.1 16.6 29.9 27.9 683 1,110 246 854 1.0 0.7 36 36 Philippines 37.4 40.9 15.7 14.5 6,957 6,632 935 1,579 45.3 37.1 20 20 Poland 61.6 52.8 0.7 0.6d,e 8,523 8,362 895 1,297 25.2 17.9 821 1,034 Portugal 42.8 41.2 20.5 23.8d 780 403 1,123 1,884 15.6 12.1 569 1,100 Puerto Rico 47.5 25.1 36.8 15.7d 0 0 .. .. 3.5 2.1 478 449 2008 World Development Indicators 135 3.2 Agricultural inputs Agricultural Irrigated Land under Fertilizer Agricultural Agricultural landa land cereal production consumption employment machinery hundred grams Tractors % of % of thousand per hectare % of total per 100 sq. km land area cropland hectares of arable land employment of arable land 1990­92 2003­05 1990­92b 2003­05b,c 1990­92 2004­06 1990­92b 2003­05b 1990­92 2003­05 1990­92 2001­03 Romania 64.4 63.8 31.0 3.2 5,842 5,663 788 429 30.6 33.1 146 179 Russian Federation 13.5d 13.2 4.2d 3.6 59,541d 40,742 417 137 .. 10.4 98d 52 Rwanda 75.6 78.6 0.3 0.7 258 336 20 .. .. .. 1 1 Saudi Arabia .. .. 44.2 42.7 1,062 666 1,446 1,060 .. .. 20 28 Senegal 41.9 42.6 3.3 4.6 1,154 1,133 65 221 .. .. 2 3 Serbia .. .. .. .. .. 1,856d .. .. .. .. .. .. Sierra Leone 38.3 40.0 5.2 5.0 452 576 23 .. .. .. 3 2 Singapore 2.2 1.2 .. .. .. .. 54,333 160,533 0.3 0.2 637 794 Slovak Republic .. 42.3 .. 3.8 1d 782 .. 965 .. 5.2 .. 159 Slovenia 28.0 d 25.0 0.8 d 1.2 112d 97 3,168 3,835 .. 8.9 .. .. Somalia 70.2 70.7 19.2 16.9 531 704 26d .. .. .. 21 15 South Africa 80.2 82.0 8.3 9.5 5,736 3,875 549 521 .. 10.3 101 46 Spain 60.8 58.3 16.9 20.6 7,588 6,485 1,186 1,472 10.7 5.5 494 712 Sri Lanka 36.2 36.5 28.0 34.4 834 879 2,016 2,873 44.3 33.9 71 113 Sudan 51.9 57.2 14.1 10.9 6,267 7,883 51 36 .. .. 8 7 Swaziland 75.8 80.9 24.1 26.0 69 54 688 .. .. .. 251 222 Sweden 8.2 7.8 4.1 4.3 1,184 1,040 1,112 1,051 3.3 2.1 604 615 Switzerland 46.9 38.1 6.0 5.8 207 164 4,032 2,100 4.2 4.0 2,870 2,649 Syrian Arab Republic 73.7 75.6 14.3 24.0 3,812 3,214 621 857 28.2 27.0 137 224 Tajikistan 32.1d 30.4 72.9d 68.2 266d 392 1,488d .. 57.9 .. 415d 233 Tanzania 38.4 38.8 1.4 1.8 3,003 3,519 53 70 84.2 .. 7 8 Thailand 41.9 36.3 21.0 26.6 10,594 11,252 598 1,411 61.7 43.3 39 144 Timor-Leste 21.9 22.9 .. .. 84 115 .. .. .. .. 10 9 Togo 58.7 66.7 0.3 0.3 610 729 56 61 .. .. 0 0 Trinidad and Tobago 25.7 25.9 3.3 3.3 6 2 1,111 6,764 11.8 4.9 354 360 Tunisia 58.4 63.0 7.3 7.2 1,525 1,457 330 461 .. .. 88 126 Turkey 51.8 53.3 14.8 19.7 13,760 13,929 757 836 46.5 32.5 287 410 Turkmenistan 68.6d 70.2 106.1d 89.2 331d 1,013 1,296d .. .. .. 465d 256 Uganda 61.0 63.9 0.1 0.1 1,098 1,611 1 15 91.5 69.1 9 9 Ukraine 72.4 d 71.4 7.6d 6.8 12,542d 14,144 807d 157 20.0 19.8 153d 124 United Arab Emirates 3.7 6.7 106.7 29.2 1 0 4,810 5,531 .. .. 50 55 United Kingdom 75.0 70.2 2.5 3.0 3,549 2,970 3,323 3,020 2.2 1.3 762 878 United States 46.6 45.3 11.3 12.5 64,547 56,333 1,015 1,153 2.9 1.6 245 270 Uruguay 84.7 85.4 10.2 14.3 509 557 610 1,257 1.5 4.6 259 241 Uzbekistan 65.2d 65.6 87.3d 87.4 1,225d 1,632 1,632 .. .. .. 402d 373 Venezuela, RB 24.7 24.6 13.9 16.9 799 1,119 1,388 1,747 12.6 10.7 176 189 Vietnam 21.0 30.8 44.6 33.9 6,730 8,393 1,299 3,309 73.8 58.8 60 247 West Bank and Gaza 62.5 61.8 .. 6.9 31 33 .. .. .. 15.8 441 710 Yemen, Rep. 33.4 33.6 24.3 31.4 738 710 127 25 52.6 .. 40 43 Zambia 31.4 34.4 0.7 2.8 813 647 131 .. .. .. 11 11 Zimbabwe 34.1 39.9 3.6 5.2 1,431 1,606 508 316 .. .. 61 75 World 38.6 w 37.5 w 17.4 w 18.1 w 632,022 s 677,485 s 958 w 1,145 w 42.5 w .. w 189 w 191 w Low income 43.3 45.0 21.5 24.0 209,966 229,649 522 .. 66.5 .. 46 82 Middle income 37.3 35.3 19.4 18.2 279,067 312,815 1,096 1,289 45.9 .. 125 123 Lower middle income 40.5 42.4 27.1 26.3 170,383 175,242 1,502 1,949 49.5 .. 75 90 Upper middle income 34.5 30.5 8.5 9.0 108,684 137,573 644 694 .. 17.1 187 153 Low & middle income 39.3 38.2 20.2 20.4 489,033 542,464 851 1,104 51.9 .. 91 108 East Asia & Pacific 48.3 50.7 .. .. 142,273 136,511 .. .. 54.3 .. 56 72 Europe & Central Asia 47.8 28.4 10.5 10.9 67,977 114,139 782 371 .. 20.0 175 171 Latin America & Carib. 34.4 35.7 11.3 12.5 47,713 49,115 586 1,091 17.4 16.5 123 122 Middle East & N. Africa 22.8 22.9 29.6 33.8 30,625 29,638 643 928 .. .. 116 143 South Asia 54.7 54.7 33.9 39.2 129,690 128,361 767 1,220 66.1 .. 67 129 Sub-Saharan Africa 42.5 43.8 3.3 3.5 70,755 84,700 130 .. .. .. 17 13 High income 36.9 35.5 10.9 .. 142,990 135,021 1,206 1,260 5.8 3.4 415 434 Euro area 49.9 47.4 14.9 16.8 32,589 31,089 2,303 2,027 7.3 4.5 986 1,003 a. Includes permanent pastures, arable land, and land under permanent crops. b. Time series have been revised but are available only from 2001 onward; data for earlier years are from the Food and Agriculture Organization's previous release of time series data. c. The averages in italics are for years other than those specified. d. Data for all three years are not available. e. The average is not for consecutive years. 136 2008 World Development Indicators 3.2 ENVIRONMENT Agricultural inputs About the data Definitions Agriculture is still a major sector in many economies, machinery. There is no single correct mix of inputs: · Agricultural land is the share of land area that and agricultural activities provide developing coun- appropriate levels and application rates vary by coun- is permanent pastures, arable, or under permanent tries with food and revenue. But agricultural activities try and over time and depend on the type of crops, crops. Permanent pasture is land used for fi ve or also can degrade natural resources. Poor farming the climate and soils, and the production process more years for forage, including natural and culti- practices can cause soil erosion and loss of soil used. vated crops. Arable land includes land defined by the fertility. Efforts to increase productivity through the The data shown here and in table 3.3 are col- FAO as land under temporary crops (double-cropped use of chemical fertilizers, pesticides, and inten- lected by the Food and Agriculture Organization of areas are counted once), temporary meadows for sive irrigation have environmental costs and health the United Nations (FAO) through annual question- mowing or for pasture, land under market or kitchen impacts. Excessive use of chemical fertilizers can naires. The FAO tries to impose standard definitions gardens, and land temporarily fallow. Land aban- alter the chemistry of soil. Pesticide poisoning is and reporting methods, but complete consistency doned as a result of shifting cultivation is excluded. common in developing countries. And salinization of across countries and over time is not possible. For Land under permanent crops is land cultivated with irrigated land diminishes soil fertility. Thus inappro- example, despite standard definitions, data on agri- crops that occupy the land for long periods and need priate use of inputs for agricultural production has cultural land in different climates may not be com- not be replanted after each harvest, such as cocoa, far-reaching effects. parable. For example, permanent pastures are quite coffee, and rubber. Land under flowering shrubs, fruit The table provides indicators of major inputs to different in nature and intensity in African countries trees, nut trees, and vines is included, but land under agricultural production: land, fertilizer, labor, and and dry Middle Eastern countries. Data on agricul- trees grown for wood or timber is not. · Irrigated land tural employment, in particular, should be used with refers to areas purposely provided with water, includ- Nearly 40 percent of land globally caution. In many countries much agricultural employ- ing land irrigated by controlled flooding. · Cropland is devoted to agriculture 3.2a ment is informal and unrecorded, including substan- is arable land and permanent cropland (see table tial work performed by women and children. 3.1). · Land under cereal production refers to har- Total land area in 2005: 130 million sq. km Fertilizer consumption measures the quantity of vested areas, although some countries report only plant nutrients. Consumption is calculated as pro- sown or cultivated area. · Fertilizer consumption duction plus imports minus exports. Because some is the quantity of plant nutrients used per unit of Permanent Others pastures 32.0% chemical compounds used for fertilizers have other arable land. Fertilizer products cover nitrogen, pot- 25.4% industrial applications, the consumption data may ash, and phosphate fertilizers (including ground rock Arable land overstate the quantity available for crops. The FAO phosphate). Traditional nutrients--animal and plant 11.0% recently revised the time series for fertilizer con- manures--are not included. The time reference for Forests 30.5% sumption and irrigation but only for 2001 onward. fertilizer consumption is the crop year (July through Permanent The data for earlier years are from the FAO's previous June). · Agricultural employment is employment in crops 1.1% releases and are not necessarily comparable with agriculture, forestry, hunting, and fishing (see table Note: Agricultural land includes permanent pastures, later data. Caution should thus be exercised when 2.3). · Agricultural machinery refers to wheel and arable land, and land under permanent crops. Source: Tables 3.1 and 3.2. comparing data over time. crawler tractors (excluding garden tractors) in use in To smooth annual fluctuations in agricultural activ- agriculture at the end of the calendar year specified Developing regions lag in agricultural ity, the indicators in the table have been averaged or during the first quarter of the following year. machinery, which reduces their over three years. agricultural productivity 3.2b Tractors per 100 square kilometers of arable land 1990­92 2001­03 500 400 300 200 100 0 East Europe Latin Middle South Sub- High Data sources Asia & & America East & Asia Saharan income Pacific Central & North Africa Asia Caribbean Africa Data on agricultural inputs are from electronic files Source: Table 3.2. that the FAO makes available to the World Bank. 2008 World Development Indicators 137 3.3 Agricultural output and productivity Crop production Food production Livestock Cereal Agricultural index index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2002­04 1990­92 2004­06a 1990­92 2002­04 1990­92 2004­06 1990­92 2003­05 Afghanistan .. .. .. .. .. .. 1,153 1,571 .. .. Albania 86.2 100.6 74.2 105.1 66.6 108.9 2,372 3,492 778 1,449 Algeria 85.4 122.9 81.7 116.8 80.7 103.3 915 1,449 1,911 2,225 Angola 60.5 119.4 65.0 112.9 75.6 100.0 378 522 165 174 Argentina 67.2 106.4 73.6 102.0 89.2 92.0 2,652 3,857 6,767 10,072 Armenia 106.5 119.2 112.9 140.6 118.9 123.2 1,843b 2,036 1,476 3,692 Australia 59.7 81.6 69.2 91.9 83.3 96.9 1,739 1,560 22,523 34,880 Austria 93.4 99.1 89.8 102.2 92.5 99.6 5,400 6,128 12,048 22,203 Azerbaijan 137.4 122.7 104.8 121.1 98.2 113.6 2,112b 2,594 1,084 1,143 Bangladesh 75.4 104.7 73.8 104.6 73.8 103.2 2,567 3,648 254 338 Belarus 107.3 124.7 136.1b 116.0 146.5 99.7 2,739b 2,801 1,977b 3,153 Belgium 77.6 106.0 91.3 101.0 94.3 99.7 .. 8,680 21,479 41,631 Benin 57.7 125.4 62.7 137.4 89.1 109.2 880 1,136 326 519 Bolivia 63.6 116.4 70.0 110.3 77.2 107.9 1,385 1,865 670 773 Bosnia and Herzegovina 107.2 101.1 120.3b 98.0 122.7b 86.6 3,548b 4,326 .. 8,270 Botswana 96.2 111.5 114.8 104.3 118.8 103.4 312 363 536 390 Brazil 77.2 119.6 70.4 124.3 65.5 116.8 1,916 3,076 1,506 3,126 Bulgaria 149.2 110.9 137.5 107.7 147.1 96.2 3,639 3,679 2,500 7,159 Burkina Faso 67.0 126.6 68.7 115.2 70.7 108.1 783 1,065 110 173 Burundi 112.4 107.0 112.1 104.4 135.1 100.2 1,370 1,328 108 70 Cambodia 65.2 105.8 65.1 105.4 65.7 103.5 1,356 2,356 .. 306 Cameroon 71.2 103.0 73.9 104.7 84.1 103.1 1,166 1,459 389 652 Canada 87.9 93.8 84.1 101.6 78.3 103.6 2,559 3,114 28,243 43,055 Central African Republic 74.4 97.7 69.9 108.2 68.1 113.5 884 1,033 287 381 Chad 69.0 110.9 72.5 112.2 84.5 105.4 636 727 173 215 Chile 78.2 110.5 74.0 112.8 68.0 107.0 3,949 5,822 3,600 5,308 China 69.6 110.6 60.1 117.8 49.4 116.1 4,307 5,237 254 401 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 98.4 107.4 83.9 109.7 80.6 107.1 2,492 3,725 3,405 2,847 Congo, Dem. Rep. 124.7 97.2 121.4 97.5 100.8 99.2 794 776 184 149 Congo, Rep. 80.2 105.1 79.0 108.8 76.0 114.5 688 794 .. .. Costa Rica 71.4 99.6 72.2 99.4 79.9 101.4 3,188 3,135 3,143 4,499 Côte d'Ivoire 73.0 96.2 72.9 101.2 74.9 110.9 869 1,708 598 795 Croatia 79.9 97.2 99.0 96.7 126.6b 108.2 3,975b 5,233 4,921b 9,987 Cuba 112.1 112.6 111.5 109.6 130.0 92.7 2,092 2,755 .. .. Czech Republic .. 94.8 .. 104.6 .. 95.8 .. 4,785 .. 5,423 Denmark 103.5 97.7 97.6 101.4 89.0 102.8 5,448 5,976 15,190 40,780 Dominican Republic 119.1 110.0 104.0 102.6 79.5 103.7 4,078 4,262 2,268 4,586 Ecuador 80.1 95.9 72.4 107.2 65.1 115.3 1,724 2,779 1,686 1,676 Egypt, Arab Rep. 69.2 104.2 67.5 110.9 65.4 115.3 5,738 7,536 1,528 2,072 El Salvador 102.2 90.6 86.4 104.8 74.5 108.5 1,871 2,639 1,633 1,638 Eritrea .. 67.7 .. 86.3b .. 97.1 .. 343 .. 61 Estonia 121.4 89.9 181.3b 102.1 193.3 101.7 1,304b 2,412 2,747 3,235 Ethiopia .. 106.7 .. 112.1 .. 117.7 1,234 1,374 135 145 Finland 97.5 102.4 104.0 103.6 106.5 104.3 3,246 3,309 18,822 31,214 France 94.0 98.8 97.4 101.6 97.3 100.4 6,370 7,099 22,234 44,017 Gabon 87.2 101.9 89.1 101.7 86.5 100.5 1,712 1,574 1,176 1,592 Gambia, The 55.8 65.2 60.2 69.0 98.8 102.6 1,114 1,145 224 233 Georgia 120.6 91.9 102.7 100.8 78.9 110.3 1,998b 1,858 2,443b 1,790 Germany 83.7 95.1 98.0 102.9 107.5 101.0 5,578 6,855 13,724 26,549 Ghana 59.1 117.0 61.1 121.0 89.8 108.7 1,084 1,380 293 320 Greece 86.9 90.4 93.7 95.3 101.5 98.2 3,589 3,951 7,668 9,011 Guatemala 77.6 102.6 75.4 104.4 76.6 100.6 1,882 1,542 2,120 2,547 Guinea 73.7 107.5 72.9 113.8 60.5 111.8 1,423 1,599 142 190 Guinea-Bissau 71.1 104.9 73.1 109.7 81.2 106.6 1,529 1,460 205 238 Haiti 108.5 98.8 99.8 100.6 69.8 111.6 997 882 .. .. 138 2008 World Development Indicators 3.3 ENVIRONMENT Agricultural output and productivity Crop production Food production Livestock Cereal Agricultural index index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2002­04 1990­92 2004­06a 1990­92 2002­04 1990­92 2004­06 1990­92 2003­05 Honduras 92.9 118.9 86.5 111.0 69.3 105.8 1,371 1,471 977 1,197 Hungary 114.0 99.7 117.0 111.9 125.5 101.9 4,551 5,403 4,105 6,987 India 79.6 100.0 75.9 104.7 69.4 110.5 1,947 2,428 324 392 Indonesia 82.8 112.7 83.8 117.4 85.8 127.3 3,826 4,354 484 583 Iran, Islamic Rep. 73.8 118.1 72.2 115.4 68.8 103.3 1,523 2,462 1,954 2,542 Iraq .. .. .. .. .. .. 872 1,014 .. 1,756 Ireland 92.7 100.3 95.3 98.4 94.3 96.1 6,653 7,473 .. 17,879 Israel 97.8 103.3 82.8 108.2 72.4 113.1 3,132 3,096 .. .. Italy 97.3 92.6 97.0 98.1 95.1 99.4 4,340 5,368 11,542 23,967 Jamaica 84.9 96.7 85.7 99.4 87.2 102.8 1,298 1,099 2,016 1,889 Japan 112.9 95.0 108.4 97.7 106.8 100.2 5,713 5,983 20,445 35,517 Jordan 100.1 136.6 85.4 118.2 71.2 94.1 1,168 1,267 1,892 1,360 Kazakhstan 163.8 108.4 163.0 b 103.1 178.5 111.6 1,338b 975 1,795b 1,557 Kenya 86.9 103.2 85.7 104.3 83.9 110.4 1,645 1,709 335 333 Korea, Dem. Rep. 126.2 108.4 119.6 109.7 145.1 114.2 5,073 3,787 .. .. Korea, Rep. 88.2 91.3 79.8 92.1 68.1 100.4 5,885 6,400 5,679 11,286 Kuwait 33.6 110.6 26.4 125.9 27.9 115.7 3,112 2,440 .. 13,521b Kyrgyz Republic 68.5 102.9 74.0 b 97.9 106.9 98.4 2,771b 2,696 675b 979 Lao PDR 62.2 115.3 59.1 116.8b 60.6 107.5 2,341 3,804 360 458 Latvia 128.7 119.4 222.3b 117.4 273.8 101.1 1,641b 2,499 1,790 b 2,704 Lebanon 109.7 94.1 100.4 100.8 65.6 120.4 2,001 2,708 .. 30,099 Lesotho 67.5 100.8 87.8 106.0 b 115.0 100.0 716 589 422 418 Liberia 62.3 97.7 80.5 97.3 90.4 107.8 951 .. .. .. Libya 79.2 96.9 77.1 104.3 75.9 101.0 706 619 .. .. Lithuania 80.2 113.1 159.9b 112.2 187.0 107.8 1,938b 2,708 .. 4,703 Macedonia, FYR 107.4 93.3 107.8 108.5 105.1b 103.3 2,652b 3,345 2,256b 3,487 Madagascar 93.6 103.5 90.4 107.6 93.3 97.1 1,935 2,440 186 174 Malawi 57.5 84.3 49.6 95.6 85.4 101.8 871 1,099 72 116 Malaysia 74.4 114.0 70.5 120.0 81.3 115.1 2,827 3,317 3,803 5,126 Mali 73.8 107.4 78.6 109.6b 81.3 112.9 840 1,008 208 241 Mauritania 63.2 97.2 84.2 108.8b 87.4 109.3 802 771 574 356 Mauritius 110.7 101.6 101.1 105.9 71.1 116.8 4,117 7,269 3,942 5,011 Mexico 82.8 103.8 77.7 107.8 71.4 107.8 2,520 3,083 2,256 2,792 Moldova 136.6 112.2 153.3b 115.7 198.7 103.2 2,928b 2,721 1,286b 816 Mongolia 246.9 107.3 98.3 93.6 93.9 95.9 967 791 870 907 Morocco 101.1 133.4 94.3 132.1 81.3 102.0 1,095 1,307 1,430 1,775 Mozambique 64.7 106.1 70.5 104.0 94.8 100.9 330 938 109 153 Myanmar 61.5 114.7 62.3 115.4 65.0 115.1 2,739 3,424 .. .. Namibia 71.9 111.4 99.5 114.0 104.1 109.3 381 403 820 1,103 Nepal 73.5 111.2 75.2 110.5 80.1 107.3 1,831 2,304 192 209 Netherlands 93.7 97.9 105.5 95.1 105.3 92.6 7,145 8,287 24,914 42,198 New Zealand 78.9 101.9 77.8 116.4 80.7 112.1 5,257 6,876 19,869 25,978 Nicaragua 76.6 115.3 64.0 123.1 57.5 119.9 1,529 1,808 .. 2,071 Niger 71.4 119.5 75.4 118.4 82.0 104.7 323 463 152 157b Nigeria 68.9 103.4 69.1 106.2 76.9 106.6 1,135 1,420 562 950 Norway 120.7 103.4 104.1 99.5 98.2 97.3 3,744 4,085 19,500 37,776 Oman 62.8 87.3 60.2 92.1b 65.7 94.0 2,411 2,621 1,005 1,302 Pakistan 80.6 102.5 70.6 110.6 67.6 109.1 1,818 2,533 594 696 Panama 110.9 104.2 94.8 103.7 76.3 101.1 1,862 1,845 2,363 3,914 Papua New Guinea 78.5 101.6 79.9 107.7b 80.8 110.1 2,504 3,848 390 490 b Paraguay 85.8 120.7 77.4 115.0 87.3 98.2 1,905 2,283 1,596 2,052 Peru 52.6 108.1 57.1 110.2 68.3 114.1 2,463 3,433 930 1,498 Philippines 84.2 109.6 77.9 115.5 62.1 120.7 2,070 3,074 905 1,075 Poland 109.1 91.6 110.0 106.7 114.8 105.0 2,958 3,123 1,502b 2,182 Portugal 103.1 98.6 98.7 98.9 85.7 98.2 1,939 2,744 4,612 5,980 Puerto Rico 167.7 114.6 127.6 98.2 118.4 94.1 1,100 2,119 .. .. 2008 World Development Indicators 139 3.3 Agricultural output and productivity Crop production Food production Livestock Cereal Agricultural index index production index yield productivity Agriculture value added kilograms per worker 1999­2001 = 100 1999­2001 = 100 1999­2001 = 100 per hectare 2000 $ 1990­92 2002­04 1990­92 2004­06a 1990­92 2002­04 1990­92 2004­06 1990­92 2003­05 Romania 92.2 112.2 97.7 123.2 114.5 107.6 2,777 3,478 2,196 4,646 Russian Federation 125.8 116.0 132.6b 111.4 152.1 103.2 1,743b 1,879 1,825b 2,518 Rwanda 111.4 117.6 107.3 113.2 77.7 107.3 1,088 1,087 191 214 Saudi Arabia 120.7 114.8 105.2 118.6 67.8 104.9 4,212 4,545 7,875 15,780 Senegal 73.0 68.3 71.9 81.6 74.8 98.2 803 1,018 225 215 Serbiac 97.6 110.0 109.2 114.2 103.8 94.9 2,926 4,910 .. 1,679 Sierra Leone 128.1 113.5 118.9 113.5 86.1 105.2 1,223 1,971 .. .. Singapore 157.1 100.0 352.1 70.2b 396.3 74.2 .. .. 22,695 40,323 Slovak Republic .. .. .. .. .. .. 1,031b 4,383 .. 5,026 Slovenia 93.1 110.2 77.2b 108.5 73.6 103.6 3,270 b 5,668 11,531b .. Somalia .. .. .. .. .. .. 622 558 .. .. South Africa 79.6 102.4 84.2 105.9 94.6 108.2 1,602 3,076 1,786 2,484 Spain 87.9 106.1 87.1 105.9 79.5 107.2 2,310 3,008 9,511 19,030 Sri Lanka 86.2 98.8 88.9 95.6 94.6 109.9 2,950 3,550 679 700 Sudan 68.9 110.8 66.7 107.8 67.6 106.3 596 663 418 666 Swaziland 106.6 100.1 108.9 105.9 130.3 111.9 1,299 1,030 1,225 1,243 Sweden 102.2 102.1 97.9 99.4 95.7 97.7 4,272 4,711 21,463 33,023 Switzerland 112.4 95.3 104.9 99.6 104.8 101.9 6,102 6,393 22,344 23,418 Syrian Arab Republic 73.6 117.1 75.1 121.7 75.0 115.6 947 1,711 2,344 3,261 Tajikistan 123.6 132.9 138.1 145.8 192.6 139.2 1,037b 2,211 397b 465 Tanzania 92.7 103.6 88.7 105.6 82.9 109.4 1,276 1,477 238 295 Thailand 82.0 106.1 84.1 104.7 86.8 105.5 2,186 2,976 497 621 Timor-Leste 93.5 107.2 102.2 112.9 101.6 117.9 1,694 1,322 .. 281 Togo 73.4 110.3 74.1 104.2 87.9 106.7 791 1,155 312 347 Trinidad and Tobago 116.3 91.9 88.7 117.5 73.5 142.6 3,159 3,341 1,666 1,989 Tunisia 104.6 104.2 91.2 101.6 60.3 99.9 1,401 1,360 2,422 2,719 Turkey 88.0 104.0 89.5 103.9 92.2 101.6 2,192 2,514 1,890 1,891 Turkmenistan 111.4 116.5 57.1b 131.0 64.0 121.7 2,210 b 3,057 1,222b .. Uganda 78.0 106.6 79.5 109.2 82.3 112.9 1,487 1,508 184 229 Ukraine 130.6 114.0 146.0 b 115.4 170.0 108.1 2,834b 2,636 1,195b 1,702 United Arab Emirates 23.4 56.0 26.5 63.7b 57.5 116.9 2,042 7,333 10,454 25,841 United Kingdom 104.9 100.3 107.2 98.0 105.6 98.5 6,321 7,169 22,659 26,933 United States 88.4 101.5 84.8 107.5 83.4 102.6 4,875 6,538 20,793 41,797 Uruguay 70.4 112.7 76.7 115.5 84.2 98.3 2,445 4,203 5,714 7,973 Uzbekistan 107.8 109.0 91.3b 105.2 99.7 104.7 1,777 3,839 1,272b 1,800 Venezuela, RB 79.5 96.0 73.9 98.3 73.5 100.4 2,561 3,401 4,483 6,292 Vietnam 60.1 116.6 63.1 124.4 57.9 124.9 3,096 4,717 214 305 West Bank and Gaza .. .. .. .. .. .. 1,105 2,037 .. .. Yemen, Rep. 75.0 100.1 71.5 110.5 66.3 115.5 906 798 271 328b Zambia 80.7 102.4 84.3 108.0 80.1 99.2 1,251 1,822 159 206 Zimbabwe 69.2 69.3 77.3 86.4 90.1 100.1 1,123 663 240 222 World 82.5 w 105.7 w 82.0 w 106.2 w 83.4 w 107.0 w 2,866 w 3,306 w 742 w 914 w Low income 78.5 103.5 76.1 105.2 73.5 109.6 1,752 2,105 303 376 Middle income 80.9 110.2 79.8 110.5 81.2 111.0 2,986 3,354 531 763 Lower middle income 77.5 111.7 72.8 112.5 67.9 114.1 3,424 3,956 388 561 Upper middle income 93.1 104.9 101.8 104.2 115.8 102.7 2,318 2,602 2,163 2,999 Low & middle income 80.1 108.1 78.7 108.9 79.3 110.6 2,451 2,827 438 591 East Asia & Pacific 71.8 110.8 64.5 112.4 52.4 116.6 3,816 4,518 303 445 Europe & Central Asia 113.2 107.1 127.1 106.1 149.3 104.1 2,652 2,359 1,903 2,195 Latin America & Carib. 78.2 111.5 74.4 110.4 72.9 108.9 2,234 3,194 2,151 3,057 Middle East & N. Africa 78.8 113.7 75.7 112.5 70.4 107.7 1,632 2,360 1,576 2,198 South Asia 79.9 101.0 75.5 103.5 69.1 109.8 1,992 2,513 335 406 Sub-Saharan Africa 75.9 103.9 77.6 105.1 84.5 107.1 984 1,120 277 335 High income 89.9 98.2 89.7 99.9 90.1 101.2 4,254 5,160 15,072 26,940 Euro area 91.5 97.8 94.6 98.8 97.9 99.7 4,632 5,664 12,701 23,097 a. Aggregates are for 2002­04. b. Data for all three years are not available. c. Includes Montenegro. 140 2008 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 for prepared by the Food and Agriculture Organization of for seed and feed are subtracted from the produc- each period relative to the base period 1999­2001. the United Nations (FAO). The FAO obtains data from tion data to avoid double counting. The resulting It includes all crops except fodder crops. The regional official and semiofficial reports of crop yields, area aggregate represents production available for any and income group aggregates for the FAO's produc- under production, and livestock numbers. If data are use except as seed and feed. The FAO's indexes tion indexes are calculated from the underlying unavailable, the FAO makes estimates. The indexes may differ from those from other sources because 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 pro- value. · Livestock production index includes meat duction. These prices, expressed in international and milk from all sources, dairy products such as dollars (equivalent in purchasing power to the U.S. cheese, and eggs, honey, raw silk, wool, and hides Cereal yield in low-income countries dollar), are derived using a Geary-Khamis formula and skins. · Cereal yield, measured in kilograms was only 40 percent of the yield in high-income countries 3.3a applied to agricultural outputs (see Inter-Secretariat per hectare of harvested land, includes wheat, rice, Working Group on National Accounts 1993, sections maize, barley, oats, rye, millet, sorghum, buckwheat, Kilograms per hectare 16.93­96). This method assigns a single price to and mixed grains. Production data on cereals refer (thousands) 1990­92 2004­06 6 each commodity so that, for example, one metric ton to crops harvested for dry grain only. Cereal crops of wheat has the same price regardless of where it harvested for hay or harvested green for food, feed, 5 was produced. The use of international prices elimi- or silage, and those used for grazing, are excluded. nates fluctuations in the value of output due to transi- The FAO allocates production data to the calendar 4 tory movements of nominal exchange rates unrelated year in which the bulk of the harvest took place. But 3 to the purchasing 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 had the averaged over three years. should be made with caution. lowest yield, while East Asia and Pacific is closing the gap with high-income countries 3.3b Kilograms per hectare (thousands) 1990­92 2004­06 6 5 4 3 Data sources 2 Data on agricultural production indexes, cereal 1 yield, and agricultural employment are from elec- tronic files that the FAO makes available to the 0 World Bank. The files may contain more recent East Europe & Latin Middle South Sub- Asia & Central America & East & Asia Saharan information than published versions. Data on agri- Pacific Asia Caribbean North Africa Africa cultural value added are from the World Bank's Source: Table 3.3. national accounts files. 2008 World Development Indicators 141 3.4 Deforestation and biodiversity Forest Average annual Animal Higher GEF Nationally Marine area deforestationa species plantsb benefits protected areas protected index for areas biodiversity 0­100 (no Total Total biodiversity % of % of thousand known Threatened known Threatened to maximum thousand total land thousand surface sq. km % species species species species biodiversity) sq. km area sq. km area 1990 2005 1990­2000 2000­05 2004 2007 2004 2007 2005 2004 c 2004 c 2004 2004 Afghanistan 13 9 2.5 3.1 578 33 4,000 2 3.6 2.2 0.3 .. .. Albania 8 8 0.3 ­0.6 376 45 3,031 0 0.2 0.7 2.7 0.3 1.0 Algeria 18 23 ­1.8 ­1.2 472 71 3,164 3 3.0 118.6 5.0 0.9 0.0 Angola 610 591 0.2 0.2 1,226 62 5,185 26 9.6 125.5 10.1 29.1 2.3 Argentina 353 330 0.4 0.4 1,413 152 9,372 42 18.5 174.5 6.4 7.8 0.3 Armenia 3 3 1.3 1.5 380 35 3,553 1 0.3 3.0 10.6 .. .. Australia 1,679 1,637 0.2 0.1 1,227 568 15,638 55 95.8 745.3 9.7 680.8 8.8 Austria 38 39 ­0.2 ­0.1 513 62 3,100 4 0.3 23.5 28.5 .. .. Azerbaijan 9 9 0.0 0.0 446 38 4,300 0 0.9 4.0 4.8 1.2 1.4 Bangladesh 9 9 0.0 0.3 735 89 5,000 12 1.6 0.7 0.5 0.3 0.2 Belarus 74 79 ­0.6 ­0.1 297 17 2,100 .. 0.0 13.2 6.3 .. .. Belgiumd 7 7 0.1 0.0 519 29 1,550 1 0.0 1.0 3.5 0.0 0.0 Benin 33 24 2.1 2.5 644 34 2,500 14 0.2 26.4 23.9 .. .. Bolivia 628 587 0.4 0.5 1,775 80 17,367 71 13.8 211.0 19.5 .. .. Bosnia and Herzegovina 22 22 0.1 0.0 390 55 .. 1 0.4 0.3 0.5 .. .. Botswana 137 119 0.9 1.0 739 18 2,151 0 1.5 174.9 30.9 .. .. Brazil 5,200 4,777 0.5 0.6 2,290 343 56,215 382 100.0 1,532.6 18.1 47.4 0.6 Bulgaria 33 36 ­0.1 ­1.4 485 47 3,572 0 0.9 11.2 10.3 0.0 0.0 Burkina Faso 72 68 0.3 0.3 581 13 1,100 2 0.3 42.1 15.4 .. .. Burundi 3 2 3.7 5.2 713 48 2,500 2 0.5 1.5 5.7 .. .. Cambodia 129 104 1.1 2.0 648 82 .. 31 3.9 41.5 23.5 1.9 1.1 Cameroon 245 212 0.9 1.0 1,258 157 8,260 355 13.3 37.4 8.0 3.9 0.8 Canada 3,101 3,101 0.0 0.0 683 77 3,270 1 22.2 628.7 6.9 362.7 3.6 Central African Republic 232 228 0.1 0.1 850 17 3,602 15 1.7 103.3 16.6 .. .. Chad 131 119 0.6 0.7 635 21 1,600 2 2.1 119.8 9.5 .. .. Chile 153 161 ­0.4 ­0.4 604 95 5,284 39 16.2 26.9 3.6 114.5 15.1 China 1,571 1,973 ­1.2 ­2.2 1,801 351 32,200 446 64.8 1,100.7 11.8 16.0 0.2 Hong Kong, China .. .. .. .. 363 37 .. 6 .. 0.3 24.7 0.3 .. Colombia 614 607 0.1 0.1 2,288 382 51,220 222 57.3 825.3 74.4 8.1 0.7 Congo, Dem. Rep. 1,405 1,336 0.4 0.2 1,578 126 11,007 65 17.0 194.4 8.6 .. .. Congo, Rep. 227 225 0.1 0.1 763 37 6,000 35 3.4 61.3 18.0 .. .. Costa Rica 26 24 0.8 ­0.1 1,070 131 12,119 111 11.1 12.1 23.6 4.8 9.4 Côte d'Ivoire 102 104 ­0.1 ­0.1 931 73 3,660 105 3.9 54.5 17.1 0.3 0.1 Croatia 21 21 ­0.1 ­0.1 461 78 4,288 1 0.5 3.6 6.5 2.5 4.4 Cuba 21 27 ­1.7 ­2.2 423 115 6,522 163 13.5 1.5 1.4 31.7 28.6 Czech Republic 26 26 0.0 ­0.1 474 39 1,900 4 0.1 14.4 18.7 .. .. Denmark 4 5 ­0.9 ­0.6 508 28 1,450 3 0.2 10.9 25.7 5.1 11.8 Dominican Republic 14 14 0.0 0.0 260 81 5,657 30 6.8 11.9 24.6 8.6 17.6 Ecuador 138 109 1.5 1.7 1,856 340 19,362 1,838 30.0 67.2 24.3 141.0 49.7 Egypt, Arab Rep. 0 1 ­3.0 ­2.6 599 59 2,076 2 3.2 56.0 5.6 76.7 7.7 El Salvador 4 3 1.5 1.7 571 29 2,911 26 0.8 0.4 1.9 0.1 0.4 Eritrea 16 16 0.3 0.3 607 38 .. 3 0.9 5.0 5.0 .. .. Estonia 22 23 ­0.4 ­0.4 334 14 1,630 0 0.0 8.9 21.1 .. .. Ethiopia 151 130 1.0 1.1 1,127 86 6,603 22 8.5 186.2 18.6 .. .. Finland 222 225 ­0.1 0.0 501 19 1,102 1 0.2 29.5 9.7 1.1 0.3 France 145 156 ­0.5 ­0.3 665 117 4,630 7 3.9 16.2 3.0 0.5 0.1 Gabon 219 218 0.0 0.0 798 43 6,651 108 3.4 8.8 3.4 1.0 0.4 Gambia, The 4 5 ­0.4 ­0.4 668 31 974 4 0.1 0.3 3.5 0.2 1.9 Georgia 28 28 0.0 0.0 366 46 4,350 0 0.7 3.0 4.3 0.0 0.1 Germany 107 111 ­0.3 0.0 613 59 2,682 12 0.7 111.5 32.0 9.1 2.6 Ghana 74 55 2.0 2.0 978 56 3,725 117 2.0 36.9 16.2 .. .. Greece 33 38 ­0.9 ­0.8 530 95 4,992 11 3.0 4.3 3.3 2.5 1.9 Guatemala 47 39 1.2 1.3 877 133 8,681 84 8.9 25.4 23.4 0.1 0.1 Guinea 74 67 0.7 0.5 855 61 3,000 22 2.6 15.6 6.4 .. .. Guinea-Bissau 22 21 0.4 0.5 560 29 1,000 4 0.7 0.0 0.0 .. .. Haiti 1 1 0.6 0.7 312 91 5,242 29 5.8 0.1 0.3 .. .. 142 2008 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity Forest Average annual Animal Higher GEF Nationally Marine area deforestationa species plantsb benefits protected areas protected index for areas biodiversity 0­100 (no Total Total biodiversity % of % of thousand known Threatened known Threatened to maximum thousand total land thousand surface sq. km % species species species species biodiversity) sq. km area sq. km area 1990 2005 1990­2000 2000­05 2004 2007 2004 2007 2005 2004 c 2004 c 2004 2004 Honduras 74 46 3.0 3.1 900 102 5,680 110 7.9 23.4 21.0 1.9 1.7 Hungary 18 20 ­0.6 ­0.7 455 55 2,214 1 0.2 8.3 9.3 .. .. India 639 677 ­0.6 0.0 1,602 313 18,664 247 43.9 156.3 5.3 16.1 0.5 Indonesia 1,166 885 1.7 2.0 2,271 464 29,375 386 90.0 259.9 14.3 130.1 6.8 Iran, Islamic Rep. 111 111 0.0 0.0 656 75 8,000 1 7.9 105.5 6.5 6.2 0.4 Iraq 8 8 ­0.2 ­0.1 498 40 .. 0 1.7 0.0 0.0 .. .. Ireland 4 7 ­3.3 ­1.9 471 15 950 1 0.7 0.8 1.1 0.0 0.0 Israel 2 2 ­0.6 ­0.8 649 79 2,317 0 0.9 4.6 21.3 0.1 0.6 Italy 84 100 ­1.2 ­1.1 610 119 5,599 19 4.4 32.4 11.0 1.5 0.5 Jamaica 3 3 0.1 0.1 333 61 3,308 209 4.9 1.8 16.2 8.2 74.5 Japan 250 249 0.0 0.0 763 190 5,565 12 41.4 52.2 14.3 10.6 2.8 Jordan 1 1 0.0 0.0 490 43 2,100 0 0.3 9.7 11.0 0.0 0.0 Kazakhstan 34 33 0.2 0.2 642 55 6,000 16 5.4 77.4 2.9 0.5 0.0 Kenya 37 35 0.3 0.3 1,510 172 6,506 103 9.9 71.9 12.6 3.1 0.5 Korea, Dem. Rep. 82 62 1.8 1.9 474 44 2,898 3 0.7 3.2 2.6 .. .. Korea, Rep. 64 63 0.1 0.1 512 54 2,898 0 1.8 3.5 3.6 3.5 3.5 Kuwait 0 0 ­5.2 ­3.7 381 23 234 .. 0.1 0.0 0.0 0.3 1.5 Kyrgyz Republic 8 9 ­0.3 ­0.3 265 22 4,500 14 1.2 7.2 3.7 .. .. Lao PDR 173 161 0.5 0.5 919 77 8,286 21 5.4 37.4 16.2 .. .. Latvia 28 29 ­0.4 ­0.4 393 23 1,153 0 0.0 9.7 15.6 0.2 0.2 Lebanon 1 1 ­0.8 ­0.8 447 38 3,000 0 0.2 0.1 0.7 0.0 0.0 Lesotho 0 0 ­3.4 ­2.7 370 11 1,591 1 0.3 0.1 0.2 .. .. Liberia 41 32 1.6 1.8 759 60 2,200 46 2.9 15.2 15.8 0.6 0.5 Libya 2 2 0.0 0.0 413 31 1,825 1 1.7 1.2 0.1 0.5 0.0 Lithuania 19 21 ­0.4 ­0.8 298 20 1,796 .. 0.0 5.9 9.5 0.5 0.8 Macedonia, FYR 9 9 0.0 0.0 380 34 3,500 0 0.2 2.0 7.9 .. .. Madagascar 137 128 0.5 0.3 427 262 9,505 280 31.4 18.3 3.1 0.2 0.0 Malawi 39 34 0.9 0.9 865 141 3,765 14 3.9 19.4 20.6 .. .. Malaysia 224 209 0.4 0.7 1,083 225 15,500 686 14.8 100.8 30.7 5.0 1.5 Mali 141 126 0.7 0.8 758 21 1,741 6 1.6 46.7 3.8 .. .. Mauritania 4 3 2.7 3.4 615 44 1,100 .. 1.4 2.5 0.2 15.0 1.5 Mauritius 0 0 0.3 0.5 151 65 750 88 4.2 0.1 3.3 0.1 4.4 Mexico 690 642 0.5 0.4 1,570 579 26,071 261 75.8 99.0 5.1 82.1 4.2 Moldova 3 3 ­0.2 ­0.2 253 28 1,752 0 0.0 0.5 1.4 .. .. Mongolia 115 103 0.7 0.8 527 38 2,823 0 4.4 217.9 13.9 .. .. Morocco 43 44 ­0.1 ­0.2 559 76 3,675 2 4.0 4.7 1.1 0.5 0.1 Mozambique 200 193 0.3 0.3 913 93 5,692 46 8.2 45.3 5.8 22.5 2.8 Myanmar 392 322 1.3 1.4 1,335 118 7,000 38 10.6 35.3 5.4 0.2 0.0 Namibia 88 77 0.9 0.9 811 55 3,174 24 5.9 46.0 5.6 74.0 9.0 Nepal 48 36 2.1 1.4 477 72 6,973 7 2.2 26.6 18.6 .. .. Netherlands 3 4 ­0.4 ­0.3 539 26 1,221 0 0.1 9.5 28.0 0.8 1.9 New Zealand 77 83 ­0.6 ­0.2 424 124 2,382 21 22.3 64.7 24.2 22.7 8.4 Nicaragua 65 52 1.6 1.3 813 59 7,590 39 3.6 28.1 23.1 1.3 1.0 Niger 19 13 3.7 1.0 616 20 1,460 2 0.9 96.9 7.7 .. .. Nigeria 172 111 2.7 3.3 1,189 79 4,715 171 6.6 55.0 6.0 .. .. Norway 91 94 ­0.2 ­0.2 525 32 1,715 2 1.6 19.7 6.5 1.3 0.4 Oman 0 0 0.0 0.0 557 50 1,204 6 4.4 0.2 0.1 29.6 9.6 Pakistan 25 19 1.8 2.1 820 78 4,950 2 5.1 73.1 9.5 2.2 0.3 Panama 44 43 0.2 0.1 1,145 121 9,915 194 11.7 13.1 17.6 10.0 13.3 Papua New Guinea 315 294 0.5 0.5 980 158 11,544 142 27.7 7.3 1.6 3.5 0.8 Paraguay 212 185 0.9 0.9 864 39 7,851 10 3.3 16.6 4.2 .. .. Peru 702 687 0.1 0.1 2,222 238 17,144 274 36.3 216.1 16.9 3.4 0.3 Philippines 106 72 2.8 2.1 812 253 8,931 213 33.7 24.3 8.2 16.6 5.5 Poland 89 92 ­0.2 ­0.3 534 38 2,450 4 0.6 70.3 23.1 0.7 0.2 Portugal 31 38 ­1.5 ­1.1 606 147 5,050 16 3.8 4.7 5.1 2.0 2.2 Puerto Rico 4 4 ­0.1 0.0 348 47 2,493 53 3.8 0.3 3.5 1.7 19.1 2008 World Development Indicators 143 3.4 Deforestation and biodiversity Forest Average annual Animal Higher GEF Nationally Marine area deforestationa species plantsb benefits protected areas protected index for areas biodiversity 0­100 (no Total Total biodiversity % of % of thousand known Threatened known Threatened to maximum thousand total land thousand surface sq. km % species species species species biodiversity) sq. km area sq. km area 1990 2005 1990­2000 2000­05 2004 2007 2004 2007 2005 2004 c 2004 c 2004 2004 Romania 64 64 0.0 0.0 466 64 3,400 1 .. 5.8 2.5 6.1 2.6 Russian Federation 8,090 8,088 0.0 0.0 941 153 11,400 7 37.1 1,287.0 7.9 301.8 1.8 Rwanda 3 5 ­0.8 ­6.9 871 49 2,288 3 1.1 1.9 7.9 .. .. Saudi Arabia 27 27 0.0 0.0 527 45 2,028 3 3.4 819.1 41.0 5.2 0.2 Senegal 93 87 0.5 0.5 803 55 2,086 7 1.3 21.6 11.2 0.9 0.4 Serbiae 26 27 ­0.3 ­0.3 477 91 4,082 1 .. 3.8 3.7 0.1 0.1 Sierra Leone 30 28 0.7 0.7 823 48 2,090 47 1.5 3.2 4.5 .. .. Singapore 0 0 0.0 0.0 473 44 2,282 54 0.1 0.0 4.2 0.0 0.1 Slovak Republic 19 19 0.0 ­0.1 419 44 3,124 2 0.1 11.0 22.8 .. .. Slovenia 12 13 ­0.4 ­0.4 437 80 3,200 .. 0.2 2.9 14.5 0.0 0.0 Somalia 83 71 1.0 1.0 824 55 3,028 17 6.7 1.9 0.3 3.3 0.5 South Africa 92 92 0.0 0.0 1,149 323 23,420 73 23.5 74.0 6.1 3.4 0.3 Spain 135 179 ­2.0 ­1.7 647 170 5,050 49 6.6 46.2 9.3 1.8 0.4 Sri Lanka 24 19 1.2 1.5 504 177 3,314 280 6.6 17.7 27.3 2.3 3.5 Sudan 764 675 0.8 0.8 1,254 47 3,137 17 5.5 123.0 5.2 0.3 0.0 Swaziland 5 5 ­0.9 ­0.9 614 16 2,715 11 0.1 0.6 3.5 .. .. Sweden 274 275 0.0 0.0 542 30 1,750 3 0.3 44.8 10.9 4.3 1.0 Switzerland 12 12 ­0.4 ­0.4 475 44 3,030 3 0.2 11.9 29.6 .. .. Syrian Arab Republic 4 5 ­1.5 ­1.3 432 59 3,000 0 0.9 2.7 1.5 .. .. Tajikistan 4 4 0.0 0.0 427 27 5,000 14 0.7 26.0 18.6 .. .. Tanzania 414 353 1.0 1.1 1,431 299 10,008 240 15.1 374.3 42.3 2.3 0.2 Thailand 160 145 0.7 0.4 1,271 157 11,625 86 8.0 80.3 15.7 5.8 1.1 Timor-Leste 10 8 1.2 1.3 .. .. .. .. .. 1.9 12.6 .. .. Togo 7 4 3.4 4.5 740 33 3,085 10 0.4 6.5 11.9 .. .. Trinidad and Tobago 2 2 0.3 0.2 551 38 2,259 1 2.4 0.2 4.7 0.1 1.3 Tunisia 6 11 ­4.1 ­1.9 438 52 2,196 0 0.5 2.3 1.5 0.2 0.1 Turkey 97 102 ­0.4 ­0.2 581 121 8,650 3 6.0 20.3 2.6 4.5 0.6 Turkmenistan 41 41 0.0 0.0 421 44 .. 3 2.0 19.8 4.2 .. .. Uganda 49 36 1.9 2.2 1,375 131 4,900 38 3.3 64.3 32.6 .. .. Ukraine 93 96 ­0.3 ­0.1 445 58 5,100 1 0.4 19.4 3.3 3.1 0.5 United Arab Emirates 2 3 ­2.4 ­0.1 298 27 .. .. 0.2 0.2 0.2 .. .. United Kingdom 26 28 ­0.7 ­0.4 660 38 1,623 13 2.1 60.5 25.0 22.5 9.2 United States 2,986 3,031 ­0.1 ­0.1 1,356 937 19,473 242 90.3 1,490.1 16.3 909.5 9.4 Uruguay 9 15 ­4.5 ­1.3 532 66 2,278 1 1.4 0.7 0.4 0.1 0.0 Uzbekistan 30 33 ­0.5 ­0.5 434 33 4,800 15 1.2 20.5 4.8 .. .. Venezuela, RB 520 477 0.6 0.6 1,745 166 21,073 68 26.8 644.4 73.1 21.3 2.3 Vietnam 94 129 ­2.3 ­2.0 1,116 152 10,500 146 11.7 13.6 4.4 0.7 0.2 West Bank and Gaza 0 0 0.0 0.0 .. .. .. .. .. .. .. .. .. Yemen, Rep. 5 5 0.0 0.0 459 47 1,650 159 3.4 0.0 0.0 .. .. Zambia 491 425 0.9 1.0 1,025 38 4,747 8 5.0 312.3 42.0 .. .. Zimbabwe 222 175 1.5 1.7 883 35 4,440 17 2.1 57.5 14.9 .. .. World 40,679 s 39,426 s 0.2 w 0.2 w 15,050.8 s 11.6 w 4,348.9 s 3.8 w Low income 7,392 6,714 0.6 0.7 2,794.9 9.9 73.8 .. Middle income 23,770 23,086 0.2 0.1 7,975.0 11.7 1,233.1 1.9 Lower middle income 7,550 7,413 0.2 ­0.1 3,585.7 12.8 632.6 1.7 Upper middle income 16,220 15,673 0.2 0.3 4,389.3 10.9 600.6 2.1 Low & middle income 31,161 29,799 0.3 0.3 10,769.9 11.2 1,307.0 1.6 East Asia & Pacific 4,581 4,507 0.3 ­0.2 1,926.6 12.1 192.1 1.3 Europe & Central Asia 8,845 8,869 0.0 0.0 1,630.1 7.0 321.6 1.4 Latin America & Carib. 9,834 9,147 0.5 0.5 3,966.0 19.7 495.7 2.7 Middle East & N. Africa 200 211 ­0.4 ­0.3 301.1 3.4 114.7 1.5 South Asia 789 801 ­0.2 0.1 288.6 6.0 20.9 0.5 Sub-Saharan Africa 6,913 6,263 0.7 0.6 2,657.5 11.3 162.0 .. High income 9,492 9,600 ­0.1 ­0.1 4,277.1 13.0 3,042.0 8.8 Euro area 822 915 ­0.8 ­0.6 283.9 11.5 19.5 0.8 a. Negative values indicate an increase in forest area. b. Flowering plants only. c. Data may refer to earlier years. They are the most recent reported by the World Conservation Monitoring Centre in 2004. d. Includes Luxembourg. e. Includes Montenegro. 144 2008 World Development Indicators 3.4 ENVIRONMENT Deforestation and biodiversity About the data Definitions Biological diversity is defined in terms of variability More than information about species richness is · Forest area is land under natural or planted stands in genes, species, and ecosystems. As threats to needed to set priorities for conserving biodiversity. of trees, whether productive or not. · Average biodiversity mount, the international community is The Global Environment Facility's (GEF) benefi ts annual deforestation is the permanent conversion increasingly focusing on conserving diversity. Defor- index for biodiversity is a comprehensive indicator of natural forest area to other uses, including agri- estation is a major cause of loss of biodiversity, and of national biodiversity status and is used to guide culture, ranching, settlements, and infrastructure. habitat conservation is vital for stemming this loss. its biodiversity priorities. The indicator incorporates Deforested areas do not include areas logged but Conservation efforts have focused on protecting information on individual species range maps avail- intended for regeneration or areas degraded by fuel- areas of high biodiversity. able from the IUCN for virtually all mammals (4,863), wood gathering, acid precipitation, or forest fires. The Food and Agriculture Organization's (FAO) amphibians (5,915), and endangered birds (1,098); · Animal species are mammals (excluding whales Global Forest Resources Assessment 2005 provides country data from the World Resources Institute and porpoises) and birds (included within a country's detailed information on forest cover in 2005 and for reptiles and vascular plants; country data from breeding or wintering ranges). · Higher plants are adjusted estimates of forest cover in 1990 and FishBase for 31,190 fish species; and the ecological native vascular plant species. · Threatened species 2000. The current survey uses a uniform definition characteristics of 867 world terrestrial ecoregions are the number of species classified by the IUCN as of forest. Because of space limitations, the table from WWF International. For each country the bio- endangered, vulnerable, rare, indeterminate, out of does not break down forest cover between natural diversity indicator incorporates the best available danger, or insufficiently known. · GEF benefits index forest and plantation, a breakdown the FAO provides and comparable information in four relevant dimen- for biodiversity is a composite index of relative biodi- for developing countries. Thus the deforestation data sions: represented species, threatened species, rep- versity potential based on the species represented in in the table may underestimate the rate at which resented ecoregions, and threatened ecoregions. To each country and their threat status and diversity of natural forest is disappearing in some countries. combine these dimensions into one measure, the habitat types. The index has been normalized from Measures of species richness are a straightforward indicator uses dimensional weights that reflect the 0 (no biodiversity potential) to 100 (maximum biodi- way to indicate an area's importance for biodiversity. consensus of conservation scientists at the GEF, versity potential). · Nationally protected areas are The number of threatened species is also an important IUCN, WWF International, and other nongovernmen- totally or partially protected areas of at least 1,000 measure of the immediate need for conservation in an tal organizations. hectares that are designated as scientific reserves area. Global analyses of the status of threatened spe- The World Conservation Monitoring Centre (WCMC) with limited public access, national parks, natural cies have been carried out for few groups of organisms. compiles data on protected areas, numbers of cer- monuments, nature reserves or wildlife sanctuaries, Only for mammals, birds, and amphibians has the tain species, and numbers of those species under and protected landscapes. Marine areas, unclassi- status of virtually all known species been assessed. threat from various sources. Because of differences fied areas, littoral (intertidal) areas, and sites pro- Threatened species are defined using the World Con- in definitions, reporting practices, and reporting peri- tected under local or provincial law are excluded. servation Union's (IUCN) classification: endangered (in ods, cross-country comparability is limited. Total area protected is a percentage of total land danger of extinction and unlikely to survive if causal Nationally protected areas are defined using the area (see table 3.1). · Marine protected areas are factors continue operating); vulnerable (likely to move six IUCN management categories for areas of at areas of intertidal or subtidal terrain--and overlying into the endangered category in the near future if least 1,000 hectares: scientific reserves and strict water and associated flora and fauna and historical causal factors continue operating); rare (not endan- nature reserves with limited public access; national and cultural features--that have been reserved to gered or vulnerable but at risk); indeterminate (known parks of national or international significance and not protect part or all of the enclosed environment. to be endangered, vulnerable, or rare but not enough materially affected by human activity; natural monu- information is available to say which); out of danger ments and natural landscapes with unique aspects; (formerly included in one of the above categories but managed nature reserves and wildlife sanctuaries; now considered relatively secure because appropriate protected landscapes (which may include cultural conservation measures are in effect); and insufficiently landscapes); and areas managed mainly for the sus- known (suspected but not definitely known to belong tainable use of natural systems to ensure long-term Data sources to one of the above categories). protection and maintenance of biological diversity. Data on forest area and deforestation are from the Unlike birds and mammals, it is difficult to accu- Designating land as a protected area does not mean FAO's Global Forest Resources Assessment 2005. rately count plants. The number of plant species is that protection is in force. And for small countries Data on species are from the electronic files of the highly debated. The IUCN's 2007 IUCN Red List of that only have protected areas smaller than 1,000 United Nations Environmental Program and WCMC Threatened Species, the result of more than 20 years' hectares, the size limit in the definition leads to an and 2007 IUCN Red List of Threatened Species. The work by botanists worldwide, is the most comprehen- underestimate of protected areas. GEF benefits index for biodiversity is from Kiran sive list of threatened species on a global scale. Only Due to variations in consistency and methods of Dev Pandey, Piet Buys, Ken Chomitz, and David 5 percent of plant species have been evaluated, and collection, data quality is highly variable across coun- Wheeler's, "Biodiversity Conservation Indicators: 70 percent are threatened with extinction. Plant spe- tries. Some countries update their information more cies data may not be comparable across countries frequently than others, some have more accurate New Tools for Priority Setting at the Global Environ- because of differences in taxonomic concepts and data on extent of coverage, and many underreport ment Facility" (2006). Data on protected areas are coverage and so should be interpreted with caution. the number or extent of protected areas. from the United Nations Environment Programme However, the data identify countries that are major and WCMC, as compiled by the World Resources sources of global biodiversity and that show national Institute. commitments to habitat protection. 2008 World Development Indicators 145 3.5 Freshwater Renewable internal 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 2005 2005 1987­2002b 1987­2002b 1987­2002b 1987­2002b 1987­2002b 2002 2004 2004 Afghanistan 55 .. 23.3 42.3 98 0 2 .. .. .. Albania 27 8,530 1.7 6.4 62 11 27 2.4 99 94 Algeria 11 341 6.1 54.2 65 13 22 9.7 88 80 Angola 148 9,195 0.4 0.2 60 17 23 30.8 75 40 Argentina 276 7,123 29.2 10.6 74 9 17 8.3 98 80 Armenia 9 3,016 3.0 32.4 66 4 30 0.8 99 80 Australia 492 24,118 23.9 4.9 75 10 15 17.9 100 100 Austria 55 6,680 2.1 3.8 1 64 35 93.4 100 100 Azerbaijan 8 965 17.3 213.0 68 28 5 0.4 95 59 Bangladesh 105 685 79.4 75.6 96 1 3 0.7 82 72 Belarus 37 3,805 2.8 7.5 30 47 23 5.0 100 100 Belgium 12 1,145 .. .. .. .. .. .. 100 .. Benin 10 1,213 0.1 1.3 45 23 32 19.0 78 57 Bolivia 304 33,054 1.4 0.5 81 7 13 6.1 95 68 Bosnia and Herzegovina 36 9,067 .. .. .. .. .. .. 99 96 Botswana 2 1,307 0.2 8.1 41 18 41 35.4 100 90 Brazil 5,418 29,000 59.3 1.1 62 18 20 11.3 96 57 Bulgaria 21 2,713 10.5 50.0 19 78 3 1.3 100 97 Burkina Faso 13 897 0.8 6.4 86 1 13 3.6 94 54 Burundi 10 1,285 0.3 2.9 77 6 17 2.6 92 77 Cambodia 121 8,642 4.1 3.4 98 0 1 1.0 64 35 Cameroon 273 15,341 1.0 0.4 74 8 18 11.1 86 44 Canada 2,850 88,203 46.0 1.6 12 69 20 16.5 100 99 Central African Republic 141 33,640 0.0 0.0 4 16 80 38.3 93 61 Chad 15 1,478 0.2 1.5 83 0 17 7.3 41 43 Chile 884 54,249 12.6 1.4 64 25 11 6.4 100 58 China 2,812 2,156 630.3 22.4 68 26 7 2.2 93 67 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia 2,112 46,990 10.7 0.5 46 4 50 8.1 99 71 Congo, Dem. Rep. 900 15,322 0.4 0.0 31 17 53 12.1 82 29 Congo, Rep. 222 61,498 0.0 0.0 9 22 70 76.1 84 27 Costa Rica 112 25,975 2.7 2.4 53 17 29 6.2 100 92 Côte d'Ivoire 77 4,132 0.9 1.2 65 12 24 11.0 97 74 Croatia 38 8,485 .. .. .. .. .. .. 100 100 Cuba 38 3,384 8.2 21.5 69 12 19 .. 95 78 Czech Republic 13 1,290 2.6 19.5 2 57 41 23.0 100 100 Denmark 6 1,108 1.3 21.2 43 25 32 127.5 100 100 Dominican Republic 21 2,218 3.4 16.1 66 2 32 6.3 97 91 Ecuador 432 33,076 17.0 3.9 82 5 12 1.0 97 89 Egypt, Arab Rep. 2 25 68.3 3,794.4 86 6 8 1.6 99 97 El Salvador 18 2,669 1.3 7.2 59 16 25 10.7 94 70 Eritrea 3 619 0.3 10.7 97 0 3 2.3 74 57 Estonia 13 9,435 0.2 1.2 5 38 57 41.4 100 99 Ethiopia 122 1,623 5.6 4.6 94 0 6 1.6 81 11 Finland 107 20,396 2.5 2.3 3 84 14 51.3 100 100 France 179 2,932 40.0 22.4 10 74 16 34.2 100 100 Gabon 164 127,064 0.1 0.1 42 8 50 43.0 95 47 Gambia, The 3 1,855 0.0 1.0 65 12 23 14.1 95 77 Georgia 58 12,988 3.6 6.2 59 21 20 0.9 96 67 Germany 107 1,297 47.1 44.0 20 68 12 40.9 100 100 Ghana 30 1,345 1.0 3.2 66 10 24 5.5 88 64 Greece 58 5,223 7.8 13.4 80 3 16 20.1 .. .. Guatemala 109 8,592 2.0 1.8 80 13 6 10.0 99 92 Guinea 226 25,104 1.5 0.7 90 2 8 2.2 78 35 Guinea-Bissau 16 10,019 0.2 1.1 82 5 13 1.1 79 49 Haiti 13 1,398 1.0 7.6 94 1 5 3.8 52 56 146 2008 World Development Indicators 3.5 ENVIRONMENT Freshwater Renewable internal 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 2005 2005 1987­2002b 1987­2002b 1987­2002b 1987­2002b 1987­2002b 2002 2004 2004 Honduras 96 14,033 0.9 0.9 80 12 8 7.3 95 81 Hungary 6 595 7.6 127.3 32 59 9 6.8 100 98 India 1,261 1,152 645.8 51.2 86 5 8 0.8 95 83 Indonesia 2,838 12,867 82.8 2.9 91 1 8 2.2 87 69 Iran, Islamic Rep. 129 1,860 72.9 56.7 91 2 7 1.5 99 84 Iraq 35 .. 42.7 121.3 92 5 3 0.5 .. .. Ireland 49 11,781 1.1 2.3 0 77 23 95.7 100 .. Israel 1 116 2.1 256.3 62 7 31 58.1 100 100 Italy 183 3,114 44.4 24.3 45 37 18 25.3 100 .. Jamaica 9 3,541 0.4 4.4 49 17 34 20.2 98 88 Japan 430 3,365 88.4 20.6 62 18 20 53.0 100 100 Jordan 1 129 1.0 144.3 75 4 21 9.3 99 91 Kazakhstan 75 4,978 35.0 46.4 82 17 2 0.7 97 73 Kenya 21 581 1.6 7.6 64 6 30 8.4 83 46 Korea, Dem. Rep. 67 2,837 9.0 13.5 55 25 20 .. 100 100 Korea, Rep. 65 1,344 18.6 28.6 48 16 36 30.6 97 71 Kuwait .. .. 0.4 .. 52 2 45 90.8 .. .. Kyrgyz Republic 47 9,041 10.1 21.7 94 3 3 0.1 98 66 Lao PDR 190 33,616 3.0 1.6 90 6 4 0.6 79 43 Latvia 17 7,259 0.3 1.8 13 33 53 30.0 100 96 Lebanon 5 1,197 1.4 28.8 67 1 33 13.2 100 100 Lesotho 5 2,625 0.1 1.0 20 40 40 17.9 92 76 Liberia 200 58,109 0.1 0.1 55 18 27 5.4 72 52 Libya 1 101 4.3 711.3 83 3 14 8.7 72 68 Lithuania 16 4,569 0.3 1.7 7 15 78 48.2 .. .. Macedonia, FYR 5 2,655 .. .. .. .. .. .. .. .. Madagascar 337 18,077 15.0 4.4 96 2 3 0.2 77 35 Malawi 16 1,217 1.0 6.3 80 5 15 1.6 98 68 Malaysia 580 22,609 9.0 1.6 62 21 17 10.5 100 96 Mali 60 5,167 6.5 10.9 90 1 9 0.4 78 36 Mauritania 0 135 1.7 425.0 88 3 9 0.7 59 44 Mauritius 3 2,252 0.6 21.8 .. .. .. 7.9 100 100 Mexico 409 3,967 78.2 19.1 77 5 17 7.5 100 87 Moldova 1 258 2.3 231.0 33 58 10 0.6 97 88 Mongolia 35 13,626 0.4 1.3 52 27 20 2.7 87 30 Morocco 29 962 12.6 43.4 87 3 10 3.3 99 56 Mozambique 100 4,885 0.6 0.6 87 2 11 8.2 72 26 Myanmar 881 18,358 33.2 3.8 98 1 1 .. 80 77 Namibia 6 3,070 0.3 4.8 71 5 24 12.4 98 81 Nepal 198 7,315 10.2 5.1 96 1 3 0.6 96 89 Netherlands 11 674 7.9 72.2 34 60 6 49.5 100 100 New Zealand 327 79,102 2.1 0.6 42 9 48 27.0 100 .. Nicaragua 190 34,727 1.3 0.7 83 2 15 3.1 90 63 Niger 4 264 2.2 62.3 95 0 4 0.9 80 36 Nigeria 221 1,563 8.0 3.6 69 10 21 6.0 67 31 Norway 382 82,625 2.2 0.6 11 67 23 79.6 100 100 Oman 1 399 1.4 136.0 90 2 7 16.1 85 73 Pakistan 52 336 169.4 323.3 96 2 2 0.5 96 89 Panama 147 45,613 0.8 0.6 28 5 67 14.6 99 79 Papua New Guinea 801 131,967 .. .. .. .. .. .. 88 32 Paraguay 94 15,936 0.5 0.5 71 8 20 14.7 99 68 Peru 1,616 59,250 20.1 1.2 82 10 8 2.8 89 65 Philippines 479 5,664 28.5 6.0 74 9 17 2.8 87 82 Poland 54 1,404 16.2 30.2 8 79 13 10.9 100 .. Portugal 38 3,602 11.3 29.6 78 12 10 10.3 .. .. Puerto Rico 7 1,815 .. .. .. .. .. .. .. .. 2008 World Development Indicators 147 3.5 Freshwater Renewable internal 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 2005 2005 1987­2002b 1987­2002b 1987­2002b 1987­2002b 1987­2002b 2002 2004 2004 Romania 42 1,955 23.2 54.8 57 34 9 1.8 91 16 Russian Federation 4,313 30,127 76.7 1.8 18 63 19 3.7 100 88 Rwanda 10 1,029 0.2 1.6 68 8 24 14.1 92 69 Saudi Arabia 2 104 17.3 721.7 89 1 10 11.0 97 63 Senegal 26 2,192 2.2 8.6 93 3 4 2.2 92 60 Serbiac 44 5,456 .. .. .. .. .. .. 99 86 Sierra Leone 160 28,641 0.4 0.2 92 3 5 2.5 75 46 Singapore 1 138 .. .. .. .. .. .. 100 .. Slovak Republic 13 2,339 .. .. .. .. .. .. 100 99 Slovenia 19 9,348 .. .. .. .. .. .. .. .. Somalia 6 732 3.3 54.8 100 0 0 .. 32 27 South Africa 45 955 12.5 27.9 63 6 31 11.3 99 73 Spain 111 2,562 35.6 32.0 68 19 13 17.3 100 100 Sri Lanka 50 2,542 12.6 25.2 95 2 2 1.3 98 74 Sudan 30 813 37.3 124.4 97 1 3 0.4 78 64 Swaziland 3 2,299 1.0 40.1 97 1 2 1.4 87 54 Sweden 171 18,949 3.0 1.7 9 54 37 84.3 100 100 Switzerland 40 5,432 2.6 6.4 2 74 24 97.0 100 100 Syrian Arab Republic 7 370 20.0 285.0 95 2 3 1.1 98 87 Tajikistan 66 10,122 12.0 18.0 92 5 4 0.1 92 48 Tanzania 84 2,183 5.2 6.2 89 0 10 2.0 85 49 Thailand 210 3,333 87.1 41.5 95 2 2 1.5 98 100 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 12 1,843 0.2 1.5 45 2 53 8.2 80 36 Trinidad and Tobago 4 2,871 0.3 8.2 6 26 68 29.6 92 88 Tunisia 4 419 2.6 62.9 82 4 14 7.9 99 82 Turkey 227 3,150 37.5 16.5 74 11 15 5.3 98 93 Turkmenistan 1 290 24.7 1,760.7 98 1 2 .. 93 54 Uganda 39 1,347 0.3 0.8 40 17 43 22.1 87 56 Ukraine 53 1,127 37.5 70.7 52 35 12 1.0 99 91 United Arab Emirates 0 49 2.3 1,150.0 68 9 23 34.0 100 100 United Kingdom 145 2,408 9.5 6.6 3 75 22 157.9 100 100 United States 2,800 9,443 479.3 17.1 41 46 13 20.9 100 100 Uruguay 59 17,848 3.2 5.3 96 1 3 5.6 100 100 Uzbekistan 16 623 58.3 357.9 93 2 5 0.3 95 75 Venezuela, RB 723 27,185 8.4 1.2 47 7 46 13.2 85 70 Vietnam 367 4,410 71.4 19.5 68 24 8 0.5 99 80 West Bank and Gaza .. .. .. .. .. .. .. .. 94 88 Yemen, Rep. 4 194 6.6 161.7 95 1 4 1.5 71 65 Zambia 80 6,987 1.7 2.2 76 7 17 2.0 90 40 Zimbabwe 12 938 4.2 34.2 79 7 14 1.6 98 72 World 43,507 s 6,778 w 3,807.4 s 9.1 w 70 w 20 w 10 w 8.6 w 94 w 72 w Low income 7,404 3,077 1,240.7 18.9 89 5 6 0.8 88 69 Middle income 26,662 8,754 1,667.0 6.3 71 19 10 3.3 95 72 Lower middle income 18,455 5,769 1,337.3 7.3 75 17 8 2.1 93 71 Upper middle income 8,207 17,199 329.6 4.0 54 29 18 6.7 98 78 Low & middle income 34,066 6,268 2,907.6 8.8 78 13 8 2.3 93 70 East Asia & Pacific 9,454 5,022 958.8 11.1 74 20 7 2.1 92 70 Europe & Central Asia 5,255 11,473 383.2 7.5 59 31 10 2.5 99 80 Latin America & Carib. 13,429 24,471 265.3 2.0 71 10 19 7.8 96 73 Middle East & N. Africa 228 757 239.8 105.0 89 4 7 2.0 96 81 South Asia 1,816 1,230 941.1 51.8 90 4 6 0.7 94 81 Sub-Saharan Africa 3,884 5,093 119.3 3.1 87 3 10 3.1 80 42 High income 9,441 9,579 899.7 10.2 42 42 15 28.3 100 98 Euro area 929 2,951 199.7 22.3 38 48 15 30.7 100 100 a. Excludes river flows from other countries because of data unreliability. b. Data are for the most recent year available (see Primary data documentation). c. Includes Montenegro. 148 2008 World Development Indicators 3.5 ENVIRONMENT Freshwater About the data Definitions The data on freshwater resources are based on Caution should also be exercised in comparing data · Renewable internal freshwater resources estimates of runoff into rivers and recharge of on annual freshwater withdrawals, which are subject flows are internal renewable resources (internal river groundwater. These estimates are based on differ- to variations in collection and estimation methods. flows and groundwater from rainfall) in the country. ent sources and refer to different years, so cross- In addition, inflows and outflows are estimated at · Renewable internal freshwater resources per country comparisons should be made with caution. different times and at different levels of quality and capita are calculated using the World Bank's popula- Because the data are collected intermittently, they precision, requiring caution in interpreting the data, tion estimates (see table 2.1). · Annual freshwater may hide significant variations in total renewable particularly for water-short countries, notably in the withdrawals are total water withdrawals, not count- water resources from year to year. The data also Middle East and North Africa. ing evaporation losses from storage basins. With- fail to distinguish between seasonal and geographic Water productivity is an indication only of the drawals also include water from desalination plants variations in water availability within countries. Data effi ciency by which each country uses its water in countries where they are a significant source. With- for small countries and countries in arid and semiarid resources. Given the different economic structure drawals can exceed 100 percent of total renewable zones are less reliable than those for larger countries of each country, these indicators should be used resources where extraction from nonrenewable aqui- and countries with greater rainfall. carefully, taking into account the countries' sectoral fers or desalination plants is considerable or where activities and natural resource endowments. water reuse is significant. Withdrawals for agriculture The data on access to an improved water source and industry are total withdrawals for irrigation and Agriculture is still the largest user measure the percentage of the population with ready livestock production and for direct industrial use of water, accounting for some access to water for domestic purposes. The data (including for cooling thermoelectric plants). With- 70 percent of global withdrawals 3.5a are based on surveys and estimates provided by drawals for domestic uses include drinking water, Percent Industry Domestic Agriculture governments to the Joint Monitoring Programme of municipal use or supply, and use for public services, 100 the World Health Organization (WHO) and the United commercial establishments, and homes. · Water Nations Children's Fund (UNICEF). The coverage productivity is calculated as GDP in constant prices 80 rates are based on information from service users divided by annual total water withdrawal. · Access to on actual household use rather than on information an improved water source is the percentage of the 60 from service providers, which may include nonfunc- population with reasonable access to an adequate tioning systems. Access to drinking water from an amount of water from an improved source, such as 40 improved source does not ensure that the water piped water into a dwelling, plot, or yard; public tap is safe or adequate, as these characteristics are or standpipe; tubewell or borehole; protected dug 20 not tested at the time of survey. While information well or spring; and rainwater collection. Unimproved on access to an improved water source is widely sources include unprotected dug wells or springs, 0 used, it is extremely subjective, and such terms as carts with small tank or drum, bottled water, and Low- Lower Upper High- World income middle- middle- income safe, improved, adequate, and reasonable may have tanker trucks. Reasonable access is defined as the income income different meaning in different countries despite offi - availability of at least 20 liters a person a day from Source: Table 3.5. cial WHO definitions (see Definitions). Even in high- a source within 1 kilometer of the dwelling. The share of withdrawals for income countries treated water may not always be agriculture approaches 90 percent safe to drink. Access to an improved water source is in some developing regions 3.5b equated with connection to a supply system; it does Percent Industry Domestic Agriculture not take into account variations in the quality and Data sources 100 cost (broadly defined) of the service. Data on freshwater resources and withdrawals are 80 compiled by the World Resources Institute from various sources and published in World Resources 60 2005 (produced in collaboration with the United Nations Environment Programme, United Nations 40 Development Programme, and World Bank). These data are supplemented by the Food and Agriculture 20 Organization's AQUASTAT data. The GDP estimates used to calculate water productivity are from the 0 World Bank national accounts database. Data on East Europe Latin Middle South Sub- Asia & America East & Asia Saharan access to water are from WHO and UNICEF's Meet- & Central & North Africa Pacific Asia Caribbean Africa ing the MDG Drinking Water and Sanitation Target Source: Table 3.5. (www.unicef.org/wes/mdgreport). 2008 World Development Indicators 149 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 2004a 1990 2004a 2004a 2004a 2004a 2004a 2004a 2004a 2004a 2004a Afghanistan 5.9 0.2 0.16 0.21 .. 37.7 17.5 31.1 0.4 13.2 .. .. Albania 34.8 2.0 0.14 0.20 0.0 0.0 0.0 25.0 0.0 75.0 0.0 0.0 Algeria 107.0 .. 0.25 .. .. .. .. .. .. .. .. .. Angola 4.5 .. 0.19 .. .. .. .. .. .. .. .. .. Argentina 186.7 164.3 0.20 0.23 5.6 14.6 8.6 58.9 0.1 7.6 1.1 3.5 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 94.1 36.9 0.15 0.08 14.6 21.0 7.8 34.9 0.3 3.3 6.1 12.1 Azerbaijan 53.3 16.1 0.15 0.17 9.9 2.4 21.0 15.0 5.9 14.3 1.0 30.4 Bangladesh 171.1 .. 0.17 .. .. .. .. .. .. .. .. .. Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 118.0 102.3 0.16 0.17 13.6 18.4 11.2 40.3 0.2 5.9 2.2 8.2 Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 8.4 11.5 0.24 0.25 1.2 15.1 6.8 64.9 0.2 8.7 2.3 0.7 Bosnia and Herzegovina 50.7 .. 0.14 .. .. .. .. .. .. .. .. .. Botswana 4.5 3.3 0.19 0.34 0.0 3.4 0.0 69.5 0.0 5.6 0.0 21.4 Brazil 780.4 .. 0.19 .. .. .. .. .. .. .. .. .. Bulgaria 149.4 101.9 0.11 0.17 7.9 9.5 6.6 46.1 0.2 22.2 2.3 5.2 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi 1.6 .. 0.24 .. .. .. .. .. .. .. .. .. Cambodia 11.8 .. 0.14 .. .. .. .. .. .. .. .. .. 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 321.5 312.5 0.17 0.16 9.6 22.1 8.6 39.5 0.1 5.8 5.4 8.9 Central African Republic 1.0 .. 0.18 .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 66.8 72.9 0.22 0.24 6.9 11.3 8.9 62.7 0.1 5.0 2.6 2.5 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, 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 93.3 93.9 0.19 0.21 3.1 16.2 9.7 53.2 0.2 14.2 1.0 2.4 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 80.0 42.9 0.15 0.17 6.1 15.9 7.5 48.4 0.2 12.0 3.6 6.3 Cuba 173.0 .. 0.25 .. .. .. .. .. .. .. .. .. Czech Republic 205.1 .. 0.13 .. .. .. .. .. .. .. .. .. Denmark 91.9 .. 0.18 .. .. .. .. .. .. .. .. .. Dominican Republic 47.9 .. 0.36 .. .. .. .. .. .. .. .. .. Ecuador 25.6 41.5 0.23 0.28 1.9 7.5 12.6 45.4 4.6 12.9 2.6 12.5 Egypt, Arab Rep. 211.5 186.1 0.20 0.20 10.8 8.2 9.0 50.7 0.3 17.7 0.6 2.8 El Salvador 5.5 .. 0.22 .. .. .. .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 18.6 22.1 0.23 0.23 2.3 11.0 5.5 61.0 0.3 17.3 2.0 0.7 Finland 79.5 67.4 0.18 0.16 8.7 40.1 7.6 26.6 0.2 2.4 3.9 10.6 France 653.5 564.6 0.15 0.15 7.2 13.8 12.9 49.5 0.2 2.9 2.3 11.1 Gabon 2.0 .. 0.25 .. .. .. .. .. .. .. .. .. Gambia, The 0.8 .. 0.34 .. .. .. .. .. .. .. .. .. Georgia .. .. .. .. .. .. .. .. .. .. .. .. Germany 835.0 966.7 0.12 0.14 9.3 20.4 11.8 38.7 0.2 2.3 2.1 15.1 Ghana 16.5 .. 0.20 .. .. .. .. .. .. .. .. .. Greece 63.5 .. 0.18 .. .. .. .. .. .. .. .. .. Guatemala 21.6 .. 0.23 .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 5.4 .. 0.20 .. .. .. .. .. .. .. .. .. 150 2008 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 2004a 1990 2004a 2004a 2004a 2004a 2004a 2004a 2004a 2004a 2004a Honduras 17.8 .. 0.23 .. .. .. .. .. .. .. .. .. Hungary 178.0 60.7 0.16 0.10 6.4 11.8 7.6 49.1 0.2 12.8 2.4 9.8 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 495.6 733.0 0.19 0.18 2.5 8.2 9.2 53.7 0.1 19.4 4.5 2.4 Iran, Islamic Rep. 102.7 164.8 0.16 0.15 15.6 8.0 10.7 46.7 0.7 9.5 0.9 8.1 Iraq 26.7 .. 0.19 .. .. .. .. .. .. .. .. .. Ireland 34.6 11.6 0.18 0.21 .. 58.5 10.4 22.9 0.7 .. 7.5 .. Israel 46.4 54.0 0.16 0.16 3.6 22.3 10.5 45.5 0.1 6.0 1.9 10.1 Italy 358.1 488.9 0.13 0.12 9.4 16.6 10.7 30.8 0.3 15.0 3.9 13.3 Jamaica 18.7 .. 0.29 .. .. .. .. .. .. .. .. .. Japan 1,556.6 1,184.7 0.14 0.15 7.1 19.0 9.4 45.7 0.2 4.8 1.6 12.3 Jordan 8.3 25.3 0.19 0.18 2.7 6.5 15.5 21.8 11.6 16.9 2.4 22.7 Kazakhstan .. .. .. .. .. .. .. .. .. .. .. .. 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. 369.2 315.2 0.12 0.12 11.4 18.9 13.0 25.8 0.2 13.6 1.5 15.7 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 30.9 19.1 0.12 0.21 7.3 7.8 3.5 65.4 0.4 11.0 0.9 3.7 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 39.9 29.2 0.12 0.19 4.1 15.4 3.6 53.8 0.1 9.6 9.7 3.7 Lebanon .. .. .. .. .. .. .. .. .. .. .. .. Lesotho 3.0 .. 0.16 .. .. .. .. .. .. .. .. .. Liberia 0.6 .. 0.30 .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 53.8 43.9 0.13 0.17 0.8 4.8 6.8 20.5 3.8 22.9 11.2 29.2 Macedonia, FYR 32.4 .. 0.18 .. .. .. .. .. .. .. .. .. Madagascar 11.0 67.2 0.27 0.14 0.3 1.7 12.4 7.6 2.8 58.9 6.3 10.0 Malawi 10.0 .. 0.29 .. .. .. .. .. .. .. .. .. Malaysia 104.7 183.8 0.13 0.12 7.8 14.9 15.5 33.7 0.2 8.3 6.8 12.8 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius 17.8 17.7 0.16 0.15 0.9 6.6 2.6 32.8 0.1 55.4 0.6 1.1 Mexico 174.3 296.1 0.18 0.20 7.8 12.5 10.4 55.6 0.2 7.5 0.9 5.1 Moldova 55.9 21.6 0.15 0.45 .. 2.2 .. 97.7 .. .. .. 0.1 Mongolia 10.2 .. 0.18 .. .. .. .. .. .. .. .. .. Morocco 41.7 91.0 0.14 0.18 1.2 3.0 8.5 21.8 6.0 43.2 1.8 14.5 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 20.9 26.9 0.13 0.16 3.5 9.7 5.9 55.1 1.4 21.7 1.7 1.0 Netherlands 136.7 .. 0.18 .. .. .. .. .. .. .. .. .. New Zealand 50.2 46.1 0.22 0.22 3.2 21.7 5.2 57.3 0.1 4.6 3.6 4.2 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 55.0 51.7 0.20 0.20 9.0 31.3 4.7 42.8 0.1 1.4 3.1 7.5 Oman 0.4 5.8 0.11 0.17 7.3 13.3 10.1 54.3 0.9 8.3 2.4 3.4 Pakistan 104.1 .. 0.18 .. .. .. .. .. .. .. .. .. Panama 9.7 11.7 0.26 0.32 1.5 13.2 4.6 76.6 0.2 3.2 0.4 0.4 Papua New Guinea 5.7 .. 0.25 .. .. .. .. .. .. .. .. .. Paraguay 3.3 .. 0.28 .. .. .. .. .. .. .. .. .. Peru 56.1 .. 0.20 .. .. .. .. .. .. .. .. .. Philippines 228.3 .. 0.21 .. .. .. .. .. .. .. .. .. Poland 428.9 329.4 0.14 0.17 7.5 11.7 7.6 52.2 0.2 9.1 4.3 7.3 Portugal 147.9 127.5 0.15 0.15 3.1 16.4 4.9 37.8 0.4 26.1 5.3 6.0 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 2008 World Development Indicators 151 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 2004a 1990 2004a 2004a 2004a 2004a 2004a 2004a 2004a 2004a 2004a Romania 413.9 241.5 0.12 0.14 4.8 3.0 6.6 12.2 4.2 30.9 5.4 32.9 Russian Federation 1,911.3 1,470.8 0.13 0.18 9.9 4.4 11.5 18.5 8.0 7.7 4.6 35.4 Rwanda 1.6 .. 0.25 .. .. .. .. .. .. .. .. .. Saudi Arabia 18.5 .. 0.15 .. .. .. .. .. .. .. .. .. Senegal 10.3 6.6 0.32 0.30 5.8 8.4 10.7 70.1 0.1 4.2 0.4 0.3 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 4.2 .. 0.32 .. .. .. .. .. .. .. .. .. Singapore 32.4 34.3 0.09 0.10 1.4 24.6 16.0 25.4 0.1 3.9 1.6 26.9 Slovak Republic 77.2 43.3 0.13 0.14 2.9 16.9 8.4 43.7 0.3 12.2 4.0 11.6 Slovenia 55.6 38.4 0.16 0.16 33.7 14.7 8.3 23.7 0.2 10.8 2.0 6.7 Somalia 6.2 .. 0.38 .. .. .. .. .. .. .. .. .. South Africa 261.6 181.7 0.17 0.17 6.8 7.4 10.4 16.7 5.0 7.1 4.7 41.9 Spain 320.3 352.9 0.17 0.15 7.5 20.6 9.5 39.6 0.4 8.6 4.3 9.6 Sri Lanka 53.0 78.4 0.19 0.18 0.5 7.2 6.6 51.5 0.2 31.6 1.1 1.2 Sudan .. 38.6 .. 0.29 0.7 2.5 3.1 88.6 0.4 3.2 0.6 1.1 Swaziland 6.6 .. 0.33 .. .. .. .. .. .. .. .. .. Sweden 109.6 103.9 0.15 0.14 11.3 35.0 7.8 26.6 0.1 1.3 3.0 14.9 Switzerland 146.0 .. 0.16 .. .. .. .. .. .. .. .. .. Syrian Arab Republic 21.7 .. 0.22 .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. 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 291.6 .. 0.17 .. .. .. .. .. .. .. .. .. Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 10.0 7.9 0.26 0.23 6.5 18.8 11.9 55.3 0.2 3.8 2.0 1.5 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 177.3 172.2 0.18 0.16 11.4 4.8 8.0 43.7 0.3 26.4 0.4 5.0 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 16.7 .. 0.30 .. .. .. .. .. .. .. .. .. Ukraine 692.4 525.1 0.14 0.19 14.3 4.1 9.7 18.9 6.4 7.0 2.3 37.2 United Arab Emirates 5.6 .. 0.14 .. .. .. .. .. .. .. .. .. United Kingdom 739.6 331.0 0.15 0.12 9.0 48.0 17.5 0.6 0.3 5.2 4.0 15.4 United States 2,565.2 1,805.9 0.15 0.13 9.6 10.6 14.0 42.1 0.2 5.4 4.2 13.9 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 .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6.9 15.4 0.27 0.23 .. 7.7 6.8 74.6 0.4 7.6 0.9 2.0 Zambia 15.9 .. 0.23 .. .. .. .. .. .. .. .. .. Zimbabwe 37.1 .. 0.20 .. .. .. .. .. .. .. .. .. 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. 152 2008 World Development Indicators 3.6 ENVIRONMENT Water pollution About the data Definitions Emissions of organic pollutants from industrial are fairly reliable because sampling techniques for · Emissions of organic water pollutants are mea- activities are a major cause of degradation of water measuring water pollution are more widely under- sured as biochemical oxygen demand, or the amount quality. Water quality and pollution levels are gener- stood and much less expensive than those for air of oxygen that bacteria in water will consume in ally measured as concentration or load--the rate of pollution. breaking down waste, a standard water treatment occurrence of a substance in an aqueous solution. Hettige, Mani, and Wheeler (1998) used plant- and test for the presence of organic pollutants. Emis- Polluting substances include organic matter, metals, sector-level information on emissions and employ- sions per worker are total emissions divided by the minerals, sediment, bacteria, and toxic chemicals. ment from 13 national environmental protection number of industrial workers. · Industry shares of The table focuses on organic water pollution result- agencies and sector-level information on output emissions of organic water pollutants are emissions ing from industrial activities. Because water pollu- and employment from the United Nations Industrial from manufacturing activities as defined by two-digit tion tends to be sensitive to local conditions, the Development Organization (UNIDO). Their economet- divisions of the International Standard Industrial national-level data in the table may not reflect the ric analysis found that the ratio of BOD to employ- Classification (ISIC) revision 3. quality of water in specific locations. ment in each industrial sector is about the same The data in the table come from an international across countries. This finding allowed the authors to study of industrial emissions that may be the first estimate BOD loads across countries and over time. to include data from developing countries (Hettige, The estimated BOD intensities per unit of employ- Mani, and Wheeler 1998). These data were updated ment were multiplied by sectoral employment num- through 2004 by the World Bank's Development bers from UNIDO's industry database for 1980­98. Research Group. Unlike estimates from earlier stud- These estimates of sectoral emissions were then ies based on engineering or economic models, these used to calculate kilograms of emissions of organic estimates are based on actual measurements of water pollutants per day for each country and year. plant-level water pollution. The focus is on organic The data in the table were derived by updating these water pollution caused by organic waste, measured in estimates through 2004. terms of biochemical oxygen demand (BOD), because 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. Such data Emissions of organic water pollutants declined in most countries from 1990 to 2004, even in some of the top emitters 3.6a Kilograms per day (millions) 1990 2004 8 Data sources 6 Data on water pollutants come from the 1998 study by Hemamala Hettige, Muthukumara Mani, 4 and David Wheeler, "Industrial Pollution in Eco- nomic Development: Kuznets Revisited" (avail- able at www.worldbank.org/nipr). The data were 2 updated through 2004 by the World Bank's Devel- opment Research Group using the same meth- 0 odology as the initial study. Data on industrial China United States Russian Japan India Germany Indonesia Federation sectoral employment are from UNIDO's industry Source: Table 3.6. database. 2008 World Development Indicators 153 3.7 Energy production and use Total energy Energy production use Total % of total million million Per capita metric tons of metric tons of kilograms of Combustible oil equivalent oil equivalent oil equivalent Fossil fuel renewables and waste Clean energy 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 2.4 1.2 2.7 2.4 809 762 76.5 69.8 13.6 9.6 9.2 19.2 Algeria 104.4 175.1 23.9 34.8 944 1,058 99.8 99.6 0.1 0.2 0.1 0.1 Angola 28.7 70.7 6.3 9.9 597 615 30.2 34.7 68.8 63.8 1.0 1.5 Argentina 48.5 81.0 46.1 63.7 1,415 1,644 88.6 88.5 3.7 3.5 7.5 7.4 Armenia 0.1 0.9 7.9 2.6 2,240 848 97.3 66.4 0.1 0.0 1.7 33.6 Australia 157.5 271.0 87.5 122.0 5,130 5,978 94.0 94.5 4.5 4.3 1.4 1.1 Austria 8.1 9.8 25.1 34.4 3,251 4,174 79.4 78.1 9.8 11.6 10.8 9.1 Azerbaijan 21.3 27.3 26.0 13.8 3,637 1,649 98.8 97.3 0.0 0.0 0.2 1.9 Bangladesh 10.8 19.3 12.8 24.2 113 158 45.9 65.2 53.5 34.3 0.6 0.5 Belarus 3.3 3.8 42.2 26.6 4,139 2,720 97.6 93.9 0.5 4.8 0.0 0.0 Belgium 13.1 13.9 49.2 56.7 4,932 5,407 75.8 74.1 1.5 2.8 22.7 21.9 Benin 1.8 1.7 1.7 2.6 324 304 5.8 33.3 93.2 64.7 0.0 0.0 Bolivia 4.9 13.9 2.8 5.3 416 578 69.1 81.9 27.2 14.0 3.6 4.1 Bosnia and Herzegovina 4.6 3.3 7.0 5.0 1,633 1,268 93.9 86.6 2.3 3.7 3.7 9.4 Botswana 0.9 1.1 1.3 1.9 930 1,032 66.3 68.0 33.1 24.1 0.0 0.0 Brazil 98.1 187.8 134.0 209.5 896 1,122 53.5 56.7 31.1 26.5 13.7 15.1 Bulgaria 9.6 10.6 28.8 20.1 3,306 2,592 84.5 70.0 0.6 3.7 13.8 26.3 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 12.1 11.9 5.0 7.0 411 392 19.5 16.6 75.9 78.6 4.5 4.8 Canada 273.7 401.3 209.4 272.0 7,535 8,417 74.7 75.1 3.9 4.6 21.4 20.3 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 7.6 9.1 14.1 29.6 1,067 1,815 74.8 76.9 19.0 15.5 6.2 7.0 China 886.3 1,640.9 863.2 1,717.2 760 1,316 75.5 84.2 23.2 13.0 1.3 2.8 Hong Kong, China 0.0 0.0 10.7 18.1 1,869 2,653 99.5 96.6 0.5 0.3 0.0 0.0 Colombia 48.2 79.5 24.7 28.6 710 636 68.1 73.6 22.3 14.4 9.6 12.0 Congo, Dem. Rep. 12.0 17.4 11.9 17.0 314 289 11.9 3.8 84.0 92.5 4.1 3.7 Congo, Rep. 9.0 13.7 1.1 1.2 436 332 26.4 38.2 69.4 56.3 4.0 2.6 Costa Rica 1.0 1.8 2.0 3.8 658 883 48.3 51.9 36.6 7.0 14.4 40.6 Côte d'Ivoire 3.4 8.2 4.4 7.8 345 422 24.8 40.1 72.1 58.3 2.6 1.6 Croatia 5.1 3.8 9.1 8.9 1,897 2,000 86.6 84.9 3.4 4.0 3.6 6.1 Cuba 6.6 5.5 16.8 10.2 1,587 906 65.1 79.6 34.9 20.3 0.0 0.1 Czech Republic 40.1 32.9 49.0 45.2 4,728 4,417 93.1 81.3 0.0 3.9 6.9 14.8 Denmark 10.0 31.3 17.9 19.6 3,482 3,621 89.9 83.2 6.4 13.2 0.0 0.0 Dominican Republic 1.0 1.5 4.1 7.4 567 777 75.1 79.2 24.2 18.6 0.7 2.2 Ecuador 16.5 28.6 6.1 10.4 597 799 79.5 87.9 13.5 5.1 7.0 5.7 Egypt, Arab Rep. 54.9 76.0 31.9 61.3 578 841 94.0 95.9 3.3 2.3 2.7 1.8 El Salvador 1.7 2.5 2.5 4.6 496 694 32.0 44.4 48.2 32.4 19.8 22.6 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 5.0 3.7 9.6 5.1 6,107 3,786 98.0 87.9 2.0 12.1 0.0 0.0 Ethiopia 14.2 19.9 15.2 21.6 296 288 6.6 8.2 92.8 90.6 0.6 1.1 Finland 12.1 16.6 29.2 35.0 5,851 6,664 60.9 55.0 15.6 19.6 20.4 20.7 France 112.4 136.9 227.8 276.0 4,015 4,534 56.9 51.4 5.1 4.3 38.0 44.3 Gabon 14.6 12.1 1.2 1.7 1,354 1,333 35.3 37.2 59.8 58.7 4.9 4.1 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 1.8 1.3 12.3 3.2 2,259 718 88.8 59.3 3.7 20.1 5.3 17.0 Germany 186.2 134.5 356.2 344.7 4,485 4,180 87.0 82.9 1.3 3.5 11.6 12.9 Ghana 4.4 6.4 5.3 8.9 343 397 17.7 28.7 73.1 66.0 9.2 5.1 Greece 9.2 10.3 22.2 31.0 2,183 2,790 94.7 93.6 4.0 3.3 0.7 1.4 Guatemala 3.4 5.4 4.5 8.0 503 628 28.8 43.3 67.8 53.2 3.4 3.5 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1.3 1.9 1.6 2.5 223 269 20.9 23.2 76.5 75.9 2.5 0.9 154 2008 World Development Indicators 3.7 ENVIRONMENT Energy production and use Total energy Energy production use Total % of total million million Per capita metric tons of metric tons of kilograms of Combustible oil equivalent oil equivalent oil equivalent Fossil fuel renewables and waste Clean energy 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 1.7 1.8 2.4 3.9 494 566 29.9 53.9 62.0 42.0 8.1 4.0 Hungary 14.3 10.3 28.6 27.8 2,753 2,752 82.4 80.6 1.3 4.0 12.9 13.4 India 291.1 419.0 319.9 537.3 377 491 55.8 68.0 41.7 29.4 2.4 2.4 Indonesia 170.3 263.4 103.2 179.5 579 814 54.9 67.8 43.6 28.5 1.5 3.7 Iran, Islamic Rep. 179.7 303.8 68.8 162.5 1,264 2,352 98.2 98.6 1.0 0.5 0.8 0.9 Iraq 104.9 96.0 19.1 30.8 1,029 .. 98.7 99.4 0.1 0.1 1.2 0.1 Ireland 3.5 1.7 10.4 15.3 2,957 3,676 98.4 96.3 1.0 1.6 0.6 0.4 Israel 0.4 2.1 12.1 19.5 2,599 2,816 97.3 97.0 0.0 0.0 0.0 0.0 Italy 25.3 27.6 148.0 185.2 2,609 3,160 93.5 91.2 0.6 2.3 3.8 4.1 Jamaica 0.5 0.5 2.9 3.8 1,231 1,445 83.5 87.4 16.2 12.2 0.3 0.3 Japan 75.2 99.8 444.5 530.5 3,598 4,152 84.7 81.9 1.1 1.2 13.9 16.8 Jordan 0.2 0.3 3.5 7.1 1,103 1,311 98.3 98.1 0.1 0.0 0.0 0.1 Kazakhstan 90.5 121.7 73.7 52.4 4,506 3,462 97.0 98.6 0.2 0.1 0.9 1.3 Kenya 10.3 13.9 12.5 17.2 532 484 17.6 19.5 78.4 74.6 4.0 5.9 Korea, Dem. Rep. 28.7 20.2 32.9 21.2 1,632 898 93.0 89.8 2.9 4.9 4.1 5.3 Korea, Rep. 22.6 42.9 93.4 213.8 2,178 4,426 83.9 80.9 0.8 1.0 15.3 18.0 Kuwait 50.4 146.3 8.5 28.1 3,984 11,100 99.9 100.0 0.1 0.0 0.0 0.0 Kyrgyz Republic 2.5 1.4 7.6 2.8 1,723 544 88.6 56.1 0.1 0.1 11.3 43.8 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1.2 2.3 7.8 4.7 2,916 2,050 82.2 59.8 8.5 30.2 5.0 6.1 Lebanon 0.1 0.2 2.3 5.6 776 1,391 93.6 95.3 4.5 2.3 1.9 1.6 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 73.2 95.0 11.5 19.0 2,645 3,218 98.9 99.2 1.1 0.8 0.0 0.0 Lithuania 4.9 3.9 16.2 8.6 4,377 2,515 70.2 59.3 1.8 8.3 28.0 32.4 Macedonia, FYR 1.5 1.5 2.7 2.7 1,421 1,346 98.2 84.2 0.0 5.6 1.5 5.1 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 50.3 93.9 23.3 61.3 1,288 2,389 89.4 94.6 9.1 4.5 1.5 0.8 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 194.8 259.2 124.3 176.5 1,494 1,712 88.3 88.8 5.9 4.7 5.8 6.5 Moldova 0.1 0.1 10.0 3.6 2,277 917 99.4 90.1 0.4 2.1 0.2 0.1 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 0.8 1.0 6.7 13.8 278 458 93.6 95.2 4.7 3.3 1.6 0.9 Mozambique 6.8 11.7 7.2 10.2 532 497 5.1 3.4 94.4 85.4 0.3 11.2 Myanmar 10.7 22.1 10.7 14.7 266 307 14.6 28.6 84.4 69.6 1.0 1.8 Namibia 0.0 0.3 0.0 1.4 0 683 62.0 66.9 16.0 13.5 17.5 10.4 Nepal 5.5 8.2 5.8 9.2 304 338 5.3 11.1 93.4 86.6 1.3 2.3 Netherlands 60.5 61.9 66.8 81.8 4,464 5,015 96.0 93.3 1.4 3.2 1.4 1.3 New Zealand 12.0 12.2 13.8 16.9 3,990 4,090 65.3 71.1 4.0 5.1 30.7 23.4 Nicaragua 1.5 2.0 2.1 3.3 512 611 29.2 41.3 53.2 50.5 17.3 8.1 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 150.5 231.8 70.9 103.8 751 734 19.7 21.3 79.8 78.0 0.5 0.7 Norway 120.3 233.7 21.5 32.1 5,072 6,948 46.8 59.5 4.8 4.1 48.4 36.4 Oman 38.3 59.6 4.6 14.0 2,475 5,570 100.0 100.0 0.0 0.0 0.0 0.0 Pakistan 34.4 61.3 43.4 76.3 402 490 53.3 60.2 43.2 35.5 3.5 4.3 Panama 0.6 0.7 1.5 2.6 618 804 58.4 71.6 28.3 16.1 12.8 12.3 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 4.6 6.6 3.1 4.0 731 674 21.6 29.2 72.3 54.8 .. .. Peru 10.6 10.8 10.0 13.8 457 506 64.1 70.7 26.9 16.4 9.0 12.4 Philippines 13.7 24.2 26.2 44.7 427 528 50.8 54.9 29.2 24.4 20.0 20.7 Poland 99.4 78.6 99.9 93.0 2,620 2,436 97.7 94.7 2.2 5.1 0.1 0.2 Portugal 3.4 3.6 17.7 27.2 1,793 2,575 81.5 84.7 14.0 10.8 4.5 1.7 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 155 3.7 Energy production and use Total energy Energy production use Total % of total million million Per capita metric tons of metric tons of kilograms of Combustible oil equivalent oil equivalent oil equivalent Fossil fuel renewables and waste Clean energy 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 40.8 27.9 62.4 38.3 2,689 1,772 96.2 83.0 1.0 8.5 1.6 8.5 Russian Federation 1,280.3 1,184.9 878.3 646.7 5,923 4,517 93.4 90.5 1.4 1.1 5.2 8.4 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 370.8 576.7 61.3 140.3 3,744 6,068 100.0 100.0 0.0 0.0 0.0 0.0 Senegal 1.4 1.3 2.2 3.0 283 258 39.4 58.8 60.6 39.2 0.0 0.8 Serbiaa 13.2 11.5 21.5 16.2 2,044 2,004 .. .. 1.8 4.9 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 0.0 0.0 13.4 30.1 4,384 6,933 100.0 100.0 0.0 0.0 0.0 0.0 Slovak Republic 5.3 6.6 21.3 18.8 4,035 3,496 81.6 70.6 0.8 2.4 15.5 27.0 Slovenia 2.9 3.4 5.6 7.3 2,801 3,657 69.1 68.3 4.8 6.7 26.1 25.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 114.5 158.6 91.2 127.6 2,592 2,722 86.1 87.0 11.4 10.5 2.5 2.5 Spain 34.6 30.3 91.1 145.2 2,345 3,346 77.6 83.8 4.5 3.5 17.9 11.5 Sri Lanka 4.2 5.3 5.5 9.4 324 477 24.1 43.9 71.0 52.9 4.9 3.2 Sudan 8.8 31.1 10.6 18.4 410 499 17.5 19.9 81.7 79.5 0.8 0.6 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 29.8 34.8 47.6 52.2 5,557 5,782 37.9 34.6 11.6 17.2 50.5 48.2 Switzerland 9.7 10.9 25.0 27.2 3,723 3,651 61.1 57.9 3.7 7.1 35.2 32.9 Syrian Arab Republic 22.3 29.1 11.7 17.9 918 948 98.0 98.3 0.0 0.0 2.0 1.7 Tajikistan 2.0 1.5 5.6 3.5 1,055 528 72.8 57.9 0.0 0.0 25.4 41.5 Tanzania 9.1 19.1 9.8 20.4 385 530 7.6 7.1 91.0 92.1 1.4 0.7 Thailand 26.5 54.0 43.9 100.0 808 1,588 65.5 82.7 33.4 16.5 1.0 0.5 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 1.2 1.6 1.4 2.0 365 320 15.5 18.1 82.6 79.4 0.6 0.3 Trinidad and Tobago 12.6 31.4 6.0 12.7 4,934 9,599 99.2 99.8 0.8 0.2 0.0 0.0 Tunisia 6.1 6.7 5.5 8.5 679 843 81.2 86.5 18.7 13.3 0.1 0.1 Turkey 25.8 23.6 53.0 85.2 943 1,182 81.8 88.2 13.6 6.3 4.6 5.2 Turkmenistan 74.9 61.1 19.6 16.3 5,353 3,381 99.7 100.0 0.0 0.0 0.3 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 133.7 81.0 251.7 143.2 4,851 3,041 91.7 82.9 0.1 0.2 8.2 16.9 United Arab Emirates 109.4 167.9 22.5 46.9 12,716 11,436 100.0 100.0 0.0 0.0 0.0 0.0 United Kingdom 208.0 204.3 212.2 233.9 3,686 3,884 90.9 88.6 0.3 1.7 8.3 9.3 United States 1,650.3 1,630.7 1,927.5 2,340.3 7,721 7,893 86.5 86.2 3.2 3.2 10.2 10.4 Uruguay 1.1 1.0 2.3 2.9 725 875 58.7 62.5 24.3 15.4 26.8 19.9 Uzbekistan 38.6 56.6 46.4 47.0 2,262 1,798 98.8 98.9 0.0 0.0 1.2 1.1 Venezuela, RB 148.9 204.7 43.9 60.9 2,224 2,293 91.5 88.5 1.2 0.9 7.2 10.6 Vietnam 24.7 69.5 24.3 51.3 367 617 20.4 49.7 77.7 46.7 1.9 3.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 9.4 20.4 2.6 6.7 208 319 97.0 98.8 3.0 1.1 0.0 0.0 Zambia 4.9 6.5 5.5 7.1 673 621 14.1 10.6 73.4 78.7 12.5 10.7 Zimbabwe 8.6 8.9 9.4 9.7 895 741 45.3 30.3 50.4 61.9 4.0 5.2 World 8,804.7 t 11,441.1 t 8,610.9 t 11,209.7 t 1,682 w 1,796 w 81.3 w 81.1 w 10.0 w 9.7 w 8.7 w 9.1 w Low income 739.4 1,147.0 723.3 1,110.7 426 486 49.1 55.5 48.3 41.7 2.6 2.8 Middle income 4,367.6 5,790.6 3,495.9 4,544.6 1,346 1,486 83.9 84.1 11.7 10.3 4.4 5.6 Lower middle income 1,982.3 3,133.1 1,701.7 2,747.8 894 1,216 78.6 83.3 18.4 12.8 3.0 3.9 Upper middle income 2,385.3 2,657.5 1,794.2 1,796.9 2,586 2,248 88.7 85.4 5.3 6.5 5.8 8.0 Low & middle income 5,106.5 6,929.5 4,213.0 5,639.6 993 1,071 78.2 78.8 17.7 16.2 4.1 5.0 East Asia & Pacific 1,222.6 2,209.1 1,138.3 2,210.7 717 1,182 71.7 81.4 26.5 15.5 1.8 3.1 Europe & Central Asia 1,879.2 1,729.1 1,734.7 1,287.5 3,878 2,826 93.2 89.1 1.5 2.2 5.3 8.7 Latin America & Carib. 604.3 908.0 452.9 656.9 1,039 1,198 72.4 74.3 18.4 14.8 9.2 10.8 Middle East & N. Africa 601.9 874.1 194.4 386.8 861 1,270 97.1 98.0 1.8 1.1 1.0 0.8 South Asia 348.8 517.3 390.7 661.9 350 453 54.0 65.9 43.5 31.5 2.5 2.6 Sub-Saharan Africa 481.8 748.0 317.4 462.6 685 681 41.2 41.3 56.6 56.3 2.2 2.4 High income 3,723.0 4,545.8 4,426.7 5,609.3 4,841 5,498 84.2 83.4 2.8 3.2 13.0 13.2 Euro area 471.3 450.5 1,052.7 1,244.4 3,562 3,961 80.2 77.5 3.2 4.3 16.4 17.5 a. Includes Montenegro. 156 2008 World Development Indicators 3.7 ENVIRONMENT Energy production and use About the data Definitions In developing countries growth in energy use is energy--primary energy and primary electricity--are · Total energy production refers to forms of primary closely related to growth in the modern sectors-- converted into oil equivalents. A notional thermal effi - energy--petroleum (crude oil, natural gas liquids, industry, motorized transport, and urban areas-- ciency of 33 percent is assumed to convert nuclear and oil from nonconventional sources), natural gas, but energy use also reflects climatic, geographic, electricity into oil equivalents and 100 percent effi - solid fuels (coal, lignite, and other derived fuels), and economic factors (such as the relative price of ciency to convert hydroelectric power. and combustible renewables and waste--and pri- energy). Energy use has been growing rapidly in low- The IEA makes these estimates in consultation mary electricity, all converted into oil equivalents and middle-income countries, but high-income coun- with national statistical offices, oil companies, elec- (see About the data). · Energy use refers to the use tries still use more than five times as much energy tric utilities, and national energy experts. The IEA of primary energy before transformation to other end- on a per capita basis. occasionally revises its time series to reflect politi- use fuels, which is equal to indigenous production Energy data are compiled by the International cal changes, and energy statistics undergo contin- plus imports and stock changes, minus exports and Energy Agency (IEA). IEA data for countries that ual changes in coverage or methodology as more fuels supplied to ships and aircraft engaged in inter- are not members of the Organisation for Economic detailed energy accounts become available. Breaks national transport (see About the data). · Fossil fuel Co-operation and Development (OECD) are based in series are therefore unavoidable. comprises coal, oil, petroleum, and natural gas prod- on national energy data adjusted to conform to ucts. · Combustible renewables and waste com- annual questionnaires completed by OECD member prise solid biomass, liquid biomass, biogas, indus- governments. trial waste, and municipal waste. · Clean energy Total energy use refers to the use of primary energy is noncarbohydrate energy that does not produce before transformation to other end-use fuels (such carbon dioxide when generated. It includes hydro- as electricity and refined petroleum products). It power and nuclear, geothermal, and solar power, includes energy from combustible renewables and 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. (The data series published in World Development Indica- tors 1998 and earlier editions did not include energy from combustible renewables and waste.) 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 A person in a high-income economy uses an average of more than 11 times as much energy as a person in a low-income economy 3.7a Energy use per capita (thousands of kilograms of oil equivalent) 1990 2005 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. 2008 World Development Indicators 157 3.8 Energy dependency and efficiency and carbon dioxide emissions Net energy Energy GDP per unit of Carbon dioxide importsa use energy use emissions average 2005 PPP $ From solid fuel kilograms per annual per kilogram Total consumption Per capita 2005 PPP $ % of energy use % growth of oil equivalent million metric tons % of total metric tons of GDP 1990 2005 1990­2005 1990 2005 1990 2004 1990 2004 1990 2004 1990 2004 Afghanistan .. .. .. .. .. 2.6 0.7 10.7 13.2 .. .. .. 0.0 Albania 8 51 2.0 4.3 7.2 7.3 3.7 38.2 3.0 2.2 1.2 0.6 0.2 Algeria ­338 ­404 2.4 5.6 5.7 77.0 193.8 3.9 1.0 3.0 6.0 0.6 1.0 Angola ­356 ­614 3.1 5.4 6.1 4.6 7.9 0.0 0.0 0.4 0.5 0.1 0.2 Argentina ­5 ­27 1.9 5.3 6.6 109.7 141.7 2.8 1.2 3.4 3.7 0.5 0.4 Armenia 98 66 ­5.8 1.3 4.9 4.2 3.6 10.2 0.0 1.2 1.2 0.4 0.3 Australia ­80 ­122 2.2 4.8 5.7 278.4 326.5 52.4 50.3 16.3 16.2 0.7 0.5 Austria 68 71 2.0 8.1 8.2 57.6 69.8 28.3 21.0 7.5 8.5 0.3 0.3 Azerbaijan 18 ­97 ­4.0 1.3 2.8 53.7 31.3 0.1 0.0 7.5 3.8 1.5 1.0 Bangladesh 16 20 4.5 6.1 6.8 15.4 37.1 6.9 3.6 0.1 0.2 0.2 0.2 Belarus 92 86 ­2.8 1.6 3.1 107.8 64.8 8.3 3.4 10.6 6.6 1.6 0.9 Belgium 73 75 1.2 5.1 5.9 100.6 100.6 40.8 22.9 10.1 9.7 0.4 0.3 Benin ­6 35 2.8 3.2 4.0 0.7 2.4 0.0 0.0 0.1 0.3 0.1 0.2 Bolivia ­77 ­161 4.0 7.3 6.4 5.5 7.0 0.0 0.0 0.8 0.8 0.3 0.2 Bosnia and Herzegovina 35 33 0.9 .. 4.7 6.9 15.6 52.1 70.7 1.6 4.0 .. 0.7 Botswana 28 45 2.7 7.3 11.6 2.2 4.3 100.0 57.3 1.6 2.4 0.2 0.2 Brazil 27 10 3.2 8.1 7.6 209.5 331.5 20.4 18.2 1.4 1.8 0.2 0.2 Bulgaria 67 47 ­1.8 2.3 3.6 75.3 42.5 46.8 63.8 8.6 5.5 1.1 0.6 Burkina Faso .. .. .. .. .. 1.0 1.1 0.0 0.0 0.1 0.1 0.2 0.1 Burundi .. .. .. .. .. 0.2 0.2 7.5 3.3 0.0 0.0 0.1 0.1 Cambodia .. .. .. .. .. 0.5 0.5 0.0 0.0 0.0 0.0 .. 0.0 Cameroon ­140 ­71 2.4 5.1 5.1 1.6 3.8 0.2 0.0 0.1 0.2 0.1 0.1 Canada ­31 ­48 1.7 3.6 4.2 415.7 638.8 22.0 15.6 15.0 20.0 0.6 0.6 Central African Republic .. .. .. .. .. 0.2 0.3 0.0 0.0 0.1 0.1 0.1 0.1 Chad .. .. .. .. .. 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 Chile 46 69 5.2 6.2 6.7 35.6 62.4 30.3 23.6 2.7 3.9 0.4 0.3 China ­3 4 3.9 1.5 3.1 2,398.2 5,005.7 80.5 71.9 2.1 3.9 1.9 1.0 Hong Kong, China 100 100 3.2 12.0 13.5 26.2 37.4 69.2 53.2 4.6 5.5 0.2 0.2 Colombia ­95 ­178 0.4 7.0 9.2 58.0 53.6 23.9 13.7 1.7 1.2 0.3 0.2 Congo, Dem. Rep. ­1 ­2 2.4 1.9 0.9 4.0 2.1 19.3 45.1 0.1 0.0 0.2 0.1 Congo, Rep. ­753 ­1,041 0.1 7.8 9.8 1.2 3.5 0.0 0.0 0.5 1.0 0.1 0.3 Costa Rica 49 52 4.6 9.1 9.9 2.9 6.4 0.0 2.3 0.9 1.5 0.2 0.2 Côte d'Ivoire 23 ­5 3.6 5.4 3.8 5.4 5.2 0.0 0.0 0.4 0.3 0.2 0.2 Croatia 43 57 1.2 6.0 6.6 24.6 23.5 10.0 13.2 5.1 5.3 0.5 0.4 Cuba 61 46 ­1.5 .. .. 32.0 25.8 1.2 0.1 3.0 2.3 .. .. Czech Republic 18 27 ­0.1 3.4 4.6 161.7 116.9 77.6 66.7 15.6 11.5 1.0 0.6 Denmark 44 ­60 0.3 7.4 9.3 49.7 52.9 47.2 37.1 9.7 9.8 0.4 0.3 Dominican Republic 75 79 4.6 5.6 6.7 9.6 19.6 0.3 10.5 1.3 2.1 0.4 0.4 Ecuador ­169 ­174 3.6 9.2 8.4 16.7 29.2 0.0 0.0 1.6 2.3 0.3 0.4 Egypt, Arab Rep. ­72 ­24 4.6 5.7 5.4 75.4 158.1 4.5 2.1 1.4 2.2 0.4 0.5 El Salvador 32 45 3.9 8.2 7.8 2.6 6.2 0.0 0.0 0.5 0.9 0.1 0.2 Eritrea .. .. .. .. .. .. 0.8 .. 0.0 .. 0.2 .. 0.2 Estonia 47 27 ­3.1 1.7 4.3 28.3 18.9 76.9 75.1 18.1 14.0 1.8 0.9 Ethiopia 7 8 2.6 1.7 2.0 3.0 8.0 0.0 0.0 0.1 0.1 0.1 0.2 Finland 59 53 1.8 4.0 4.6 51.2 65.7 39.6 44.9 10.3 12.6 0.4 0.4 France 51 50 1.2 6.2 6.7 363.7 373.4 21.7 15.1 6.4 6.2 0.3 0.2 Gabon ­1,077 ­604 2.1 11.2 10.4 6.0 1.4 0.0 0.0 6.5 1.1 0.4 0.1 Gambia, The .. .. .. .. .. 0.2 0.3 0.0 0.0 0.2 0.2 0.2 0.2 Georgia 85 60 ­9.2 2.4 4.9 17.3 3.9 6.4 0.7 3.2 0.9 0.6 0.3 Germany 48 61 0.0 5.5 7.3 980.3 808.0 48.6 41.4 12.3 9.8 0.5 0.3 Ghana 18 29 3.6 2.5 2.9 3.8 7.2 0.2 0.0 0.2 0.3 0.3 0.3 Greece 59 67 2.5 9.4 10.5 72.4 96.6 43.8 39.8 7.1 8.7 0.3 0.3 Guatemala 24 32 4.2 8.2 7.8 5.1 12.2 0.0 10.0 0.6 1.0 0.1 0.2 Guinea .. .. .. .. .. 1.0 1.3 0.0 0.0 0.2 0.2 0.2 0.1 Guinea-Bissau .. .. .. .. .. 0.2 0.3 0.0 0.0 0.2 0.2 0.3 0.4 Haiti 21 23 3.3 8.1 4.4 1.0 1.8 3.3 0.0 0.1 0.2 0.1 0.2 158 2008 World Development Indicators 3.8 ENVIRONMENT Energy dependency and efficiency and carbon dioxide emissions Net energy Energy GDP per unit of Carbon dioxide importsa use energy use emissions average 2005 PPP $ From solid fuel kilograms per annual per kilogram Total consumption Per capita 2005 PPP $ % of energy use % growth of oil equivalent million metric tons % of total metric tons of GDP 1990 2005 1990­2005 1990 2005 1990 2004 1990 2004 1990 2004 1990 2004 Honduras 30 54 3.0 5.7 5.8 2.6 7.6 0.0 6.1 0.5 1.1 0.2 0.4 Hungary 50 63 0.0 4.5 6.2 60.1 57.1 31.2 22.3 5.8 5.7 0.5 0.3 India 9 22 3.5 3.2 4.5 681.5 1,341.8 69.9 69.6 0.8 1.2 0.7 0.6 Indonesia ­65 ­47 3.6 3.6 3.9 213.8 377.9 3.7 10.0 1.2 1.7 0.6 0.6 Iran, Islamic Rep. ­161 ­87 5.5 5.0 4.0 218.2 433.2 2.1 1.0 4.0 6.4 0.6 0.7 Iraq ­451 ­212 3.3 .. .. 48.5 81.6 0.0 0.0 2.6 .. .. .. Ireland 67 89 3.3 5.9 10.3 30.6 42.3 43.0 22.6 8.7 10.4 0.5 0.3 Israel 96 89 3.6 6.8 8.0 33.1 71.2 29.7 48.5 7.1 10.5 0.4 0.5 Italy 83 85 1.6 9.1 8.8 389.6 449.5 13.1 14.5 6.9 7.7 0.3 0.3 Jamaica 84 87 2.3 4.9 5.0 8.0 10.6 1.7 1.7 3.3 4.0 0.6 0.6 Japan 83 81 1.2 7.2 7.3 1,070.4 1,256.8 28.5 38.0 8.7 9.8 0.3 0.3 Jordan 95 96 4.0 3.0 3.3 10.2 16.5 0.0 0.0 3.2 3.1 1.0 0.8 Kazakhstan ­23 ­132 ­3.8 1.6 2.5 288.1 200.1 56.9 54.4 17.6 13.3 2.5 1.7 Kenya 18 19 2.2 2.7 2.8 5.8 10.6 6.9 2.7 0.2 0.3 0.2 0.2 Korea, Dem. Rep. 13 5 ­2.8 .. .. 244.6 79.0 91.2 92.1 12.1 3.4 .. .. Korea, Rep. 76 80 5.6 4.9 4.8 241.1 465.2 38.7 43.8 5.6 9.7 0.5 0.5 Kuwait ­495 ­420 8.3 3.0 3.9 43.4 99.3 0.0 0.0 20.4 40.4 0.6 1.0 Kyrgyz Republic 67 48 ­5.6 1.5 3.2 12.6 5.7 31.4 37.1 2.8 1.1 1.1 0.6 Lao PDR .. .. .. .. .. 0.2 1.3 1.6 60.2 0.1 0.2 0.1 0.1 Latvia 85 51 ­3.0 3.5 6.4 14.5 7.1 10.6 2.7 5.4 3.1 0.5 0.3 Lebanon 94 96 5.4 6.7 6.9 9.1 16.2 0.0 3.3 3.1 4.1 0.6 0.4 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. 0.5 0.5 0.0 0.0 0.2 0.1 0.4 0.5 Libya ­534 ­399 2.8 .. 3.4 37.8 59.9 0.0 0.0 8.7 10.3 .. 1.0 Lithuania 70 54 ­3.1 2.9 5.6 24.3 13.3 8.5 5.9 6.6 3.9 0.5 0.3 Macedonia, FYR 46 47 ­0.4 5.7 5.5 15.5 10.4 67.7 77.6 8.1 5.1 1.0 0.7 Madagascar .. .. .. .. .. 0.9 2.7 3.5 0.9 0.1 0.2 0.1 0.2 Malawi .. .. .. .. .. 0.6 1.0 7.9 14.4 0.1 0.1 0.1 0.1 Malaysia ­116 ­53 6.0 5.2 4.9 55.3 177.4 9.8 19.8 3.1 7.0 0.5 0.6 Mali .. .. .. .. .. 0.4 0.6 0.0 0.0 0.1 0.1 0.1 0.1 Mauritania .. .. .. .. .. 2.6 2.6 0.6 0.7 1.4 0.9 0.9 0.5 Mauritius .. .. .. .. .. 1.5 3.2 13.5 24.0 1.4 2.6 0.2 0.3 Mexico ­57 ­47 2.0 6.1 6.6 413.1 437.6 2.2 4.5 5.0 4.3 0.5 0.4 Moldova 99 98 ­6.7 1.7 2.4 23.8 7.7 19.2 4.1 5.4 2.0 1.4 1.0 Mongolia .. .. .. .. .. 10.0 8.5 73.2 79.5 4.7 3.4 2.0 1.4 Morocco 89 93 4.2 9.9 7.8 23.5 41.1 19.9 35.3 1.0 1.4 0.4 0.4 Mozambique 5 ­15 2.8 0.8 1.4 1.0 2.2 15.4 2.9 0.1 0.1 0.2 0.2 Myanmar 0 ­50 2.0 1.3 2.7 4.3 9.8 6.4 3.9 0.1 0.2 0.3 0.3 Namibia 67 76 5.1 8.1 6.7 0.0 2.5 0.0 0.4 0.0 1.2 0.0 0.3 Nepal 5 11 3.2 2.3 2.8 0.6 3.0 5.2 26.3 0.0 0.1 0.0 0.1 Netherlands 9 24 1.2 5.8 6.9 141.0 141.9 28.7 22.2 9.4 8.7 0.4 0.3 New Zealand 13 28 1.6 4.7 6.0 22.6 31.5 14.6 14.5 6.6 7.7 0.3 0.3 Nicaragua 29 41 3.1 4.2 4.3 2.6 4.0 0.0 0.0 0.6 0.7 0.3 0.3 Niger .. .. .. .. .. 1.0 1.2 43.7 39.0 0.1 0.1 0.2 0.2 Nigeria ­112 ­123 2.3 1.7 2.1 45.3 113.9 0.3 0.0 0.5 0.8 0.4 0.6 Norway ­459 ­627 2.0 6.4 6.8 33.2 87.5 9.9 4.0 7.8 19.1 0.2 0.4 Oman ­740 ­327 7.0 5.7 3.7 10.3 30.9 0.0 0.0 5.6 12.5 0.4 0.6 Pakistan 21 20 3.7 4.2 4.5 68.0 125.6 12.5 13.5 0.6 0.8 0.4 0.4 Panama 59 72 3.8 9.1 10.5 3.1 5.7 2.9 0.1 1.3 1.8 0.2 0.2 Papua New Guinea .. .. .. .. .. 2.4 2.4 0.2 0.1 0.6 0.4 0.4 0.2 Paraguay ­48 ­66 1.7 5.4 5.7 2.3 4.2 0.0 0.0 0.5 0.7 0.1 0.2 Peru ­6 22 2.1 9.8 12.7 21.0 31.5 2.6 9.0 1.0 1.2 0.2 0.2 Philippines 48 46 4.0 5.7 5.6 43.9 80.4 13.0 28.7 0.7 1.0 0.3 0.3 Poland 1 15 ­0.8 3.1 5.6 347.5 307.0 82.9 71.0 9.1 8.0 1.1 0.6 Portugal 81 87 3.2 8.6 7.7 42.3 58.9 26.0 22.0 4.3 5.6 0.3 0.3 Puerto Rico .. .. .. .. .. 11.8 2.1 .. .. 3.3 0.5 .. .. 2008 World Development Indicators 159 3.8 Energy dependency and efficiency and carbon dioxide emissions Net energy Energy GDP per unit of Carbon dioxide importsa use energy use emissions average 2005 PPP $ From solid fuel kilograms per annual per kilogram Total consumption Per capita 2005 PPP $ % of energy use % growth of oil equivalent million metric tons % of total metric tons of GDP 1990 2005 1990­2005 1990 2005 1990 2004 1990 2004 1990 2004 1990 2004 Romania 35 27 ­2.6 2.9 5.3 155.0 90.3 30.9 35.1 6.7 4.2 0.9 0.5 Russian Federation ­46 ­83 ­1.8 2.1 2.6 2,261.7 1,523.6 24.9 22.2 15.3 10.6 1.2 1.0 Rwanda .. .. .. .. .. 0.5 0.6 0.0 0.0 0.1 0.1 0.1 0.1 Saudi Arabia ­505 ­311 4.7 5.1 3.5 254.7 308.1 0.0 0.0 15.6 13.7 0.8 0.7 Senegal 39 58 2.4 4.8 6.0 3.1 5.0 0.0 0.0 0.4 0.4 0.3 0.3 Serbiab 38 29 ­0.5 .. .. 130.4 53.3 54.7 68.7 12.4 6.6 .. .. Sierra Leone .. .. .. .. .. 0.3 1.0 0.0 0.0 0.1 0.2 0.1 0.3 Singapore 100 100 3.6 5.3 6.0 45.1 52.2 0.2 0.0 14.8 12.3 0.6 0.3 Slovak Republic 75 65 ­0.2 3.1 4.5 51.4 36.3 49.0 43.4 9.7 6.7 0.8 0.4 Slovenia 48 53 2.3 5.7 6.2 18.0 16.2 42.9 41.3 9.0 8.1 0.6 0.4 Somalia .. .. .. .. .. 0.0 .. 0.0 .. 0.0 .. .. .. South Africa ­26 ­24 2.1 3.0 3.1 331.7 436.6 79.9 82.2 9.4 9.4 1.2 1.2 Spain 62 79 3.4 8.4 8.1 212.1 330.2 35.6 25.2 5.5 7.7 0.3 0.3 Sri Lanka 24 44 3.8 6.0 7.2 3.8 11.5 0.1 2.2 0.2 0.6 0.1 0.2 Sudan 18 ­69 3.8 2.5 3.4 5.4 10.4 0.0 0.0 0.2 0.3 0.2 0.2 Swaziland .. .. .. .. .. 0.4 1.0 100.0 100.0 0.6 0.9 0.1 0.2 Sweden 37 33 0.6 4.5 5.5 49.4 53.0 21.3 20.9 5.8 5.9 0.2 0.2 Switzerland 61 60 0.7 8.9 9.6 42.7 40.4 3.3 1.3 6.4 5.5 0.2 0.2 Syrian Arab Republic ­91 ­63 2.9 3.2 4.2 35.9 68.4 0.0 0.0 2.8 3.7 1.0 0.9 Tajikistan 64 56 ­3.3 2.9 2.8 23.4 5.0 3.5 5.7 4.4 0.8 1.4 0.6 Tanzania 8 6 4.9 2.0 1.8 2.3 4.3 0.5 4.0 0.1 0.1 0.1 0.1 Thailand 40 46 5.2 5.1 4.4 95.7 267.8 14.2 21.6 1.8 4.3 0.4 0.6 Timor-Leste .. .. .. .. .. .. 0.2 .. 0.0 .. 0.2 .. 0.1 Togo 17 20 2.3 2.3 2.3 0.8 2.3 0.0 0.0 0.2 0.4 0.2 0.5 Trinidad and Tobago ­109 ­147 5.7 1.6 1.6 16.9 32.5 0.0 0.0 13.8 24.7 1.7 1.7 Tunisia ­11 21 3.3 5.8 7.6 13.3 22.9 2.0 0.0 1.6 2.3 0.4 0.4 Turkey 51 72 3.4 6.0 6.6 146.1 225.9 42.1 38.6 2.6 3.2 0.5 0.4 Turkmenistan ­281 ­274 0.8 .. .. 32.0 41.7 2.5 0.0 8.7 8.7 .. .. Uganda .. .. .. .. .. 0.8 1.8 0.0 0.0 0.0 0.1 0.1 0.1 Ukraine 47 43 ­3.7 1.7 1.8 684.0 329.7 45.9 38.0 13.2 6.9 1.6 1.3 United Arab Emirates ­385 ­258 4.8 2.7 2.9 54.7 149.1 0.0 0.0 30.8 37.8 0.9 1.2 United Kingdom 2 13 0.6 6.2 8.1 579.2 586.7 41.5 25.8 10.1 9.8 0.4 0.3 United States 14 30 1.4 4.1 5.3 4,816.9 6,044.0 37.1 35.7 19.3 20.6 0.6 0.5 Uruguay 49 65 1.1 9.7 10.6 3.9 5.5 0.1 0.1 1.3 1.7 0.2 0.2 Uzbekistan 17 ­20 0.7 0.9 1.1 129.2 137.8 6.0 2.0 6.3 5.3 3.1 2.8 Venezuela, RB ­239 ­236 1.6 4.3 4.3 117.3 172.5 1.0 0.0 5.9 6.6 0.6 0.7 Vietnam ­2 ­36 5.2 2.5 3.5 21.4 98.6 51.5 40.1 0.3 1.2 0.4 0.6 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. ­266 ­203 6.3 8.6 6.9 9.6 21.1 0.0 0.0 0.8 1.0 0.4 0.5 Zambia 10 9 1.7 1.8 1.9 2.4 2.3 34.0 14.9 0.3 0.2 0.2 0.2 Zimbabwe 9 9 ­0.1 0.3 0.2 16.6 10.5 87.5 80.8 1.6 0.8 6.0 4.3 World ­2c w ­2c w 1.7 w 4.1 w 5.0 w 22,695.9d t 28,974.3d t 38.5d w 35.9d w 4.3d w 4.5d w 0.6d w 0.5d w Low income ­2 ­3 2.9 2.8 3.8 1,337.9 2,082.9 63.1 58.5 0.8 0.9 0.6 0.5 Middle income ­25 ­27 1.6 3.0 4.0 9,187.1 11,936.3 42.2 46.0 3.6 4.0 0.9 0.7 Lower middle income ­17 ­14 2.9 2.5 3.6 4,388.4 7,508.5 55.1 55.5 2.3 3.4 1.1 0.8 Upper middle income ­33 ­48 0.1 3.4 4.6 4,798.7 4,427.8 33.1 31.1 6.9 5.6 0.8 0.6 Low & middle income ­21 ­23 1.8 2.9 4.0 10,525.0 14,019.1 44.6 47.7 2.4 2.6 0.8 0.7 East Asia & Pacific ­7 0 3.9 2.0 3.3 3,091.4 6,111.6 71.6 63.6 1.9 3.3 1.3 0.9 Europe & Central Asia ­8 ­34 ­1.8 2.3 3.3 4,566.4 3,187.7 35.5 31.9 10.3 7.1 1.2 0.8 Latin America & Carib. ­33 ­38 2.4 6.7 7.0 1,066.0 1,381.8 7.9 8.3 2.4 2.5 0.4 0.3 Middle East & N. Africa ­210 ­126 4.4 5.4 4.7 569.0 1,143.9 3.9 3.0 2.5 3.9 0.6 0.7 South Asia 11 22 3.5 3.4 4.6 772.2 1,520.8 63.0 62.8 0.7 1.0 0.6 0.5 Sub-Saharan Africa ­52 ­62 2.4 2.5 2.7 460.0 673.2 88.4 79.2 0.9 0.9 0.6 0.6 High income 16 19 1.6 5.2 6.0 10,929.8 13,382.1 34.7 32.7 11.9 13.2 0.5 0.4 Euro area 55 64 1.2 6.6 7.5 2,469.2 2,564.5 33.8 27.5 8.4 8.2 0.4 0.3 a. Negative values indicate that a country is a net exporter. b. Includes Montenegro. c. Deviation from zero is due to statistical errors and changes in stock. d. Includes emissions not allocated to specific countries. 160 2008 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 have larger error bounds. Trends estimated from a duction and use need to be distinguished. Net energy amounts of carbon dioxide for the same level of consistent time series tend to be more accurate than imports show the extent to which an economy's use energy use: oil releases about 50 percent more car- individual values. Each year the CDIAC recalculates exceeds its production. High-income countries are bon dioxide than natural gas, and coal releases about the entire time series since 1949, incorporating net energy importers; middle-income countries are twice as much. Cement manufacturing releases recent findings and corrections to its database. Esti- their main suppliers. about half a metric ton of carbon dioxide for each mates exclude fuels supplied to ships and aircraft The ratio of gross domestic product (GDP) to energy metric ton of cement produced. in international transport because of the difficulty use indicates energy efficiency. To produce compa- The U.S. Department of Energy's Carbon Diox- of apportioning these fuels among the benefi ting rable and consistent estimates of real GDP across ide Information Analysis Center (CDIAC) calculates countries. countries relative to physical inputs to GDP--that is, annual anthropogenic emissions from data on fossil Definitions units of energy use--GDP is converted to 2005 con- fuel consumption (from the United Nations Statistics stant international dollars using purchasing power Division's World Energy Data Set) and world cement · Net energy imports are estimated as energy use parity (PPP) rates. Differences in this ratio over time manufacturing (from the U.S. Bureau of Mines's less production, both measured in oil equivalents. and across countries reflect structural changes in Cement Manufacturing Data Set). Carbon dioxide · Energy use refers to the use of primary energy the economy, changes in sectoral energy efficiency, emissions are often calculated and reported as before transformation to other end-use fuel, which and differences in fuel mixes. elemental carbon. For the table these values were is equal to indigenous production plus imports and Carbon dioxide emissions, largely byproducts of converted to actual carbon dioxide mass by multiply- stock changes minus exports and fuel supplied to energy production and use (see table 3.7), account ing them by 3.664 (the ratio of the mass of carbon to ships and aircraft engaged in international transport for the largest share of greenhouse gases, which that of carbon dioxide). Although estimates of global (see About the data for table 3.7). · GDP per unit of are associated with global warming. Anthropogenic carbon dioxide emissions are probably accurate energy use is the ratio of gross domestic product carbon dioxide emissions result primarily from fos- within 10 percent (as calculated from global aver- (GDP) per kilogram of oil equivalent of energy use, sil fuel combustion and cement manufacturing. In age fuel chemistry and use), country estimates may with GDP converted to 2005 constant international dollars using purchasing power parity (PPP) rates. An High-income economies depend on imported energy . . . 3.8a international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States. Net energy imports (% of energy use) 1990 2005 · Carbon dioxide emissions are emissions from Low-income the burning of fossil fuels and the manufacture of cement and include carbon dioxide produced during Lower middle-income consumption of solid, liquid, and gas fuels and gas Upper middle-income flaring. · Carbon dioxide emissions from solid fuel consumption refer mainly to emissions from use of High-income coal as an energy source. Euro area ­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 countries in the Middle East and North Africa and Latin America and the Caribbean 3.8b Net energy imports (% of energy use) 1990 2005 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, in Tennessee, United States. 2008 World Development Indicators 161 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total thousand metric thousand metric thousand metric average annual tons of carbon Industrial Agricultural tons of carbon Industrial Agricultural tons of carbon % growth dioxide equivalent % of total % of total dioxide equivalent % of total % of total dioxide equivalent 1990­ 1970­90 2004 1990 2005 2005 2005 1990 2005 2005 2005 1990 2005 Afghanistan 3.2 ­11.0 .. .. .. .. .. .. .. .. .. .. Albania 3.5 ­2.0 2,230 2,170 11.5 70.0 2,340 1,390 0.0 97.1 0 50 Algeria 7.3 8.8 18,570 24,310 66.3 15.3 8,780 10,330 7.2 89.1 230 110 Angola 1.5 4.1 13,630 37,020 11.6 39.1 5,110 28,350 0.0 35.9 0 0 Argentina 1.3 1.4 82,110 94,340 13.0 63.9 65,060 83,410 0.2 97.7 1,880 930 Armenia 2.6 ­0.5 3,090 2,300 25.2 50.9 910 450 0.0 93.3 0 10 Australia 3.1 1.2 104,050 116,840 24.6 61.5 106,090 114,500 1.3 94.9 2,620 4,580 Austria ­0.1 1.4 8,210 7,210 14.6 50.1 5,740 4,620 9.1 85.3 1,210 3,310 Azerbaijan 2.6 ­4.5 14,510 11,550 45.2 45.4 4,060 4,040 0.0 93.6 180 50 Bangladesh 7.8 6.9 81,620 92,530 11.6 69.2 22,420 37,100 0.0 91.9 0 0 Belarus 2.6 ­3.8 19,230 16,620 43.0 38.8 15,270 10,360 32.3 65.6 0 440 Belgium ­1.9 ­0.4 10,230 7,610 17.0 59.7 11,250 9,650 12.0 65.4 130 9,380 Benin 4.3 7.9 2,730 4,840 8.9 47.5 2,120 4,660 0.0 68.0 0 0 Bolivia 1.9 2.6 15,550 27,120 2.8 34.5 14,310 28,300 0.0 43.3 0 0 Bosnia and Herzegovina 3.4 12.5 2,000 2,850 51.9 32.6 1,140 1,020 0.0 82.4 460 850 Botswana 21.1 4.3 130 4,480 17.9 71.9 0 2,460 0.0 96.3 0 0 Brazil 3.9 3.6 285,650 421,820 3.0 67.1 227,790 300,300 3.2 74.4 5,290 7,760 Bulgaria 1.7 ­3.4 9,560 6,140 31.9 32.7 13,250 5,880 29.9 64.5 0 650 Burkina Faso 8.7 1.3 .. .. .. .. .. .. .. .. .. .. Burundi 7.4 1.2 .. .. .. .. .. .. .. .. .. .. Cambodia 7.2 1.1 .. 14,890 5.4 71.5 .. 3,820 0.0 74.1 0 0 Cameroon 9.3 5.0 10,500 15,110 17.9 56.0 8,290 14,540 0.0 85.0 810 890 Canada 0.7 3.6 83,000 103,830 46.6 22.2 50,700 51,390 4.5 86.7 12,810 11,010 Central African Republic 1.9 2.0 .. .. .. .. .. .. .. .. .. .. Chad ­0.5 2.7 .. .. .. .. .. .. .. .. .. .. Chile 0.2 4.8 14,190 19,560 12.1 29.9 8,170 12,590 6.9 88.7 0 10 China 5.7 4.0 895,350 995,760 34.2 50.0 455,150 566,680 3.0 92.7 8,640 119,720 Hong Kong, China 6.9 2.5 1,180 1,090 40.4 0.9 210 200 0.0 5.0 0 330 Colombia 3.7 ­0.8 49,180 61,690 15.7 55.1 21,140 24,530 0.9 78.0 190 330 Congo, Dem. Rep. 1.5 ­6.1 2,670 5,750 49.6 11.8 820 2,250 0.0 15.6 0 0 Congo, Rep. 2.9 8.8 27,720 50,320 7.7 26.3 19,390 38,680 0.0 23.2 0 0 Costa Rica 2.8 5.0 3,720 2,450 1.6 58.0 3,440 2,850 0.0 98.9 0 0 Côte d'Ivoire 5.3 0.5 5,410 15,320 11.2 20.6 2,460 12,350 0.0 25.0 0 0 Croatia 3.4 1.8 3,950 3,690 44.2 29.8 3,390 3,590 22.3 63.8 670 720 Cuba 3.0 ­1.8 9,890 9,490 6.4 62.4 13,650 8,330 8.0 87.4 0 110 Czech Republic 0.6 ­2.0 22,250 14,930 58.7 17.2 10,740 6,570 16.4 75.0 20 3,530 Denmark ­0.5 ­1.0 5,650 4,920 16.3 67.7 10,000 7,380 7.7 78.6 260 1,460 Dominican Republic 4.6 6.1 5,280 5,960 4.0 62.1 4,140 2,850 0.0 96.1 0 0 Ecuador 8.8 2.8 12,170 12,890 16.3 57.4 8,840 8,500 0.0 97.6 0 0 Egypt, Arab Rep. 7.3 5.6 23,250 32,960 31.2 44.2 16,980 27,810 11.5 85.6 2,250 1,820 El Salvador 1.7 5.8 2,740 3,200 12.5 48.1 2,050 2,250 0.0 95.1 0 0 Eritrea .. 12.3 2,090 2,410 7.5 77.6 1,340 2,350 0.0 99.1 0 0 Estonia 2.6 ­3.3 2,610 1,230 43.1 35.0 1,630 610 0.0 83.6 0 60 Ethiopia 3.5 8.1 39,110 47,740 10.0 77.2 50,730 63,130 0.0 98.6 0 0 Finland 0.7 1.5 7,400 5,470 10.2 30.3 .. .. 27.0 59.5 220 1,030 France ­1.6 0.1 56,710 43,520 10.7 71.1 88,450 78,090 12.1 77.3 10,740 27,010 Gabon 4.5 ­9.0 3,120 2,040 79.9 4.4 1,850 420 0.0 57.1 0 0 Gambia, The 7.0 3.3 .. .. .. .. .. .. .. .. .. .. Georgia 2.6 ­10.2 5,790 4,330 29.6 51.7 3,390 3,390 17.4 49.3 0 10 Germany ­0.2 ­1.0 109,870 58,100 45.7 39.2 77,470 69,470 13.3 74.2 11,230 41,980 Ghana 1.7 5.3 5,310 8,630 10.7 49.6 4,540 10,520 0.0 88.6 190 170 Greece 4.8 2.5 6,390 7,410 9.7 39.1 13,060 13,090 3.3 91.3 790 1,620 Guatemala 2.6 6.7 5,920 8,990 11.3 42.7 4,780 7,980 0.0 70.8 0 0 Guinea 1.4 2.1 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 5.4 2.0 .. .. .. .. .. .. .. .. .. .. Haiti 5.3 7.0 2,870 3,740 6.4 61.2 2,470 4,290 0.0 98.4 0 0 162 2008 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 thousand metric thousand metric thousand metric average annual tons of carbon Industrial Agricultural tons of carbon Industrial Agricultural tons of carbon % growth dioxide equivalent % of total % of total dioxide equivalent % of total % of total dioxide equivalent 1990­ 1970­90 2004 1990 2005 2005 2005 1990 2005 2005 2005 1990 2005 Honduras 3.0 7.7 5,020 5,380 5.8 71.9 3,550 3,860 0.0 97.9 0 0 Hungary 0.1 ­0.4 14,220 11,050 52.6 18.3 11,950 8,760 20.7 76.0 760 1,540 India 6.6 4.8 625,420 712,330 14.7 64.8 225,250 300,680 0.5 93.0 8,010 9,510 Indonesia 8.0 3.8 180,250 224,330 36.2 41.2 60,220 69,910 0.3 72.6 1,380 900 Iran, Islamic Rep. 2.6 5.0 54,730 95,060 64.7 21.8 48,620 66,140 0.9 97.6 2,130 1,560 Iraq 3.5 3.7 11,120 10,980 48.7 14.7 6,570 3,990 0.0 93.0 390 470 Ireland 2.0 2.8 11,560 3,660 24.3 32.0 12,840 12,320 0.2 92.6 110 2,050 Israel 3.2 5.6 1,010 1,170 9.4 36.8 1,900 1,820 0.0 83.5 840 1,140 Italy 1.0 1.0 42,310 36,670 19.1 37.7 35,560 37,200 23.7 70.5 4,770 27,710 Jamaica ­1.0 2.3 1,220 1,160 3.4 47.4 1,220 1,020 0.0 96.1 0 0 Japan 1.0 1.1 57,690 53,480 30.0 13.4 31,970 23,590 8.4 49.3 26,560 70,570 Jordan 11.3 3.5 1,080 1,610 13.0 24.2 1,160 1,240 0.0 93.5 0 10 Kazakhstan 2.6 ­3.8 55,300 28,270 49.1 37.9 23,600 5,530 0.0 90.2 0 0 Kenya 1.6 4.9 19,410 20,310 18.0 65.0 21,830 19,060 0.0 96.4 0 0 Korea, Dem. Rep. 5.1 ­11.2 9,800 10,650 29.0 36.4 9,190 23,160 0.0 97.5 300 860 Korea, Rep. 7.8 4.4 27,430 31,280 18.5 31.1 9,480 22,020 56.6 36.1 5,400 8,700 Kuwait 2.7 12.2 6,800 11,200 93.9 1.5 250 540 0.0 81.5 250 390 Kyrgyz Republic 2.6 ­6.3 4,680 3,520 10.5 72.2 4,240 3,260 0.0 98.8 0 60 Lao PDR ­4.2 15.8 .. .. .. .. .. .. .. .. .. .. Latvia 2.6 ­6.1 4,320 2,290 40.6 29.3 2,690 1,390 0.0 88.5 0 110 Lebanon 2.4 4.5 730 980 12.2 18.4 740 1,020 0.0 93.1 0 0 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia ­5.4 3.2 .. .. .. .. .. .. .. .. .. .. Libya 4.4 4.0 8,750 8,540 77.6 8.9 2,860 2,050 0.0 91.7 100 290 Lithuania 2.6 ­4.7 7,740 3,650 44.1 38.1 4,160 2,860 0.0 90.2 0 150 Macedonia, FYR 3.4 ­0.8 .. .. .. .. .. .. .. .. .. .. Madagascar ­0.2 8.6 .. .. .. .. .. .. .. .. .. .. Malawi 0.6 4.4 .. .. .. .. .. .. .. .. .. .. Malaysia 6.4 6.9 21,300 25,510 57.2 22.3 11,600 9,920 3.9 64.3 960 530 Mali 2.9 2.2 .. .. .. .. .. .. .. .. .. .. Mauritania 9.6 ­1.1 .. .. .. .. .. .. .. .. .. .. Mauritius 3.4 6.3 .. .. .. .. .. .. .. .. .. .. Mexico 7.1 0.3 95,840 120,100 22.2 39.6 70,240 75,500 1.2 90.1 1,930 3,160 Moldova 2.6 ­9.1 4,780 2,590 43.6 30.9 3,270 970 0.0 94.8 0 360 Mongolia 7.4 ­2.1 7,380 4,840 2.9 83.9 10,000 22,850 0.0 99.6 0 0 Morocco 5.9 3.7 9,070 13,240 2.6 41.6 14,380 15,510 0.0 75.2 0 0 Mozambique ­6.8 4.2 9,430 11,680 16.9 64.3 2,950 9,930 0.0 99.7 0 0 Myanmar 1.2 6.2 40,170 60,840 6.8 70.0 14,390 25,900 0.0 66.8 0 10 Namibia .. 57.6 4,320 4,260 4.7 89.9 4,240 4,620 0.0 99.1 0 0 Nepal 6.9 10.3 33,810 36,040 10.4 80.5 5,700 7,100 0.0 88.5 0 0 Netherlands ­0.4 0.2 19,320 15,180 23.6 49.2 19,320 16,800 33.8 51.5 5,950 5,300 New Zealand 1.9 3.1 27,370 27,490 10.4 82.3 33,920 27,960 0.1 99.4 400 820 Nicaragua 1.4 4.7 4,690 6,350 4.7 80.2 3,750 3,210 0.0 96.9 0 0 Niger 9.4 1.1 .. .. .. .. .. .. .. .. .. .. Nigeria 2.7 6.8 59,690 78,290 45.5 33.7 28,050 39,030 0.0 87.1 120 80 Norway 2.4 7.4 7,620 12,080 61.8 14.3 5,290 4,680 37.8 53.0 4,980 1,770 Oman 11.2 8.6 2,020 4,260 76.1 12.9 870 1,140 0.0 96.5 0 0 Pakistan 6.6 4.0 82,830 110,300 14.1 66.3 55,400 80,040 0.8 96.4 700 620 Panama 0.3 4.9 2,970 3,040 4.3 72.4 2,520 2,070 0.0 95.7 0 0 Papua New Guinea 4.7 0.0 .. .. .. .. .. .. .. .. .. .. Paraguay 6.4 4.3 11,690 17,750 1.7 70.9 9,980 12,870 0.0 81.8 0 0 Peru 0.9 2.7 17,260 21,510 6.4 48.1 14,300 18,720 0.0 89.4 0 80 Philippines 1.6 4.6 38,830 44,860 8.0 66.7 17,990 18,940 0.1 95.6 100 350 Poland 1.6 ­1.2 90,010 60,060 67.0 18.4 31,570 26,110 22.3 72.5 460 1,270 Portugal 5.0 2.8 7,450 7,140 8.0 52.9 6,920 7,000 9.9 80.7 130 1,050 Puerto Rico 0.7 ­4.1 .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 163 3.9 Trends in greenhouse gas emissions Carbon dioxide Methane Nitrous oxide Other greenhouse emissions emissions emissions gas emissions Total Total thousand metric thousand metric thousand metric average annual tons of carbon Industrial Agricultural tons of carbon Industrial Agricultural tons of carbon % growth dioxide equivalent % of total % of total dioxide equivalent % of total % of total dioxide equivalent 1990­ 1970­90 2004 1990 2005 2005 2005 1990 2005 2005 2005 1990 2005 Romania 2.4 ­3.7 42,300 23,260 52.4 30.1 24,700 11,790 25.9 69.6 1,500 2,220 Russian Federation 2.6 ­2.7 631,450 501,380 77.3 7.9 129,210 42,650 8.0 76.2 19,380 56,600 Rwanda 14.2 1.6 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 8.9 ­1.1 39,710 63,500 91.8 1.9 8,230 7,720 0.0 92.1 2,260 1,530 Senegal 4.4 2.4 5,550 6,340 4.7 75.9 6,220 10,250 0.0 99.0 0 10 Serbiaa 3.4 ­3.1 12,860 6,720 16.4 59.2 9,070 4,700 11.1 81.5 340 840 Sierra Leone ­0.7 5.0 .. .. .. .. .. .. .. .. .. .. Singapore 3.6 1.0 740 1,260 27.0 4.8 180 7,970 95.7 0.8 400 1,300 Slovak Republic 0.6 ­1.8 7,450 5,290 54.3 19.5 4,650 2,760 32.2 58.0 10 710 Slovenia 3.4 1.5 1,740 1,630 20.9 47.9 1,070 1,100 0.0 88.2 580 210 Somalia 2.5 .. .. .. .. .. .. .. .. .. .. .. South Africa 4.6 1.8 52,260 59,200 54.3 23.8 26,460 29,250 7.3 82.7 1,450 2,600 Spain 2.2 3.3 31,640 38,010 11.3 44.1 35,290 48,520 3.5 85.7 4,440 15,050 Sri Lanka 1.0 8.5 10,280 10,280 12.3 61.8 2,410 3,130 0.0 89.1 0 0 Sudan ­1.3 5.9 39,760 67,310 21.5 73.3 39,400 59,750 0.0 96.2 0 0 Swaziland 1.0 11.5 .. .. .. .. .. .. .. .. .. .. Sweden ­2.9 0.1 7,670 6,460 6.5 41.5 6,330 6,070 8.4 76.8 990 1,620 Switzerland ­0.1 ­0.2 4,790 4,150 8.7 68.0 3,170 2,840 8.1 78.2 760 3,310 Syrian Arab Republic 9.7 2.6 5,810 7,960 33.8 34.7 7,860 9,430 2.8 94.9 0 0 Tajikistan 2.6 ­11.1 3,690 3,270 10.1 68.5 3,110 1,590 0.0 99.4 80 120 Tanzania 0.0 3.4 26,860 39,460 20.3 63.5 23,300 31,690 0.0 84.3 0 0 Thailand 7.6 6.3 68,930 78,840 9.4 76.1 21,330 27,990 0.7 87.9 1,580 940 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 4.7 8.3 1,790 2,840 14.8 48.6 1,990 5,470 0.0 88.8 0 0 Trinidad and Tobago 4.5 4.7 2,510 3,820 78.0 1.0 340 360 0.0 91.7 0 0 Tunisia 7.0 3.3 3,740 4,390 32.1 34.2 4,260 7,230 4.1 94.2 0 30 Turkey 5.8 3.5 27,050 23,140 15.3 59.5 44,270 47,950 9.0 88.0 2,840 1,480 Turkmenistan 2.6 3.0 33,230 23,060 81.8 15.2 4,150 3,200 20.0 78.8 0 250 Uganda ­3.5 7.1 .. .. .. .. .. .. .. .. .. .. Ukraine 2.6 ­5.6 146,380 75,640 68.9 15.7 69,380 23,270 41.6 54.2 60 1,390 United Arab Emirates 4.6 9.3 19,110 34,250 96.8 1.7 930 2,730 0.0 90.5 220 480 United Kingdom ­0.6 ­0.2 67,750 39,400 35.7 50.7 68,470 65,480 37.1 52.2 5,880 14,030 United States 0.3 1.9 857,660 810,280 56.4 18.4 412,740 456,210 5.5 74.7 91,230 108,420 Uruguay ­2.7 1.1 14,110 17,700 0.6 90.3 15,170 15,630 0.0 99.6 0 20 Uzbekistan 2.6 0.9 41,610 51,480 70.1 23.2 14,330 14,660 0.3 98.3 0 760 Venezuela, RB 3.7 1.8 41,520 65,730 42.0 33.6 21,700 26,460 0.1 77.8 1,330 2,300 Vietnam ­0.3 11.9 52,990 75,080 17.8 66.8 13,920 37,470 0.0 94.9 0 10 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 10.0 5.1 4,620 9,040 44.5 27.7 5,110 7,080 0.0 98.9 0 10 Zambia ­2.5 ­1.6 9,820 16,770 5.7 68.6 4,800 11,410 3.7 65.1 0 0 Zimbabwe 3.3 ­3.1 10,850 10,400 24.8 60.4 8,970 10,160 0.0 97.1 0 20 World 1.8 w 1.6 w 6,174,140 s 6,607,490 s 34.8 w 43.1 w 3,323,000 s 3,787,800 s 82.6 w 47.9 w 265,210 s 601,890 s Low income 4.9 2.8 1,231,970 1,526,640 18.1 62.6 585,050 861,010 91.5 63.3 9,400 12,240 Middle income 3.6 1.4 3,270,800 3,491,860 36.1 41.9 1,660,270 1,809,370 82.8 48.4 56,960 213,780 Lower middle income 4.6 3.1 1,749,030 1,973,890 32.8 46.1 908,360 1,085,030 84.0 46.1 18,400 130,730 Upper middle income 2.9 ­0.6 1,521,770 1,517,970 40.4 36.5 751,910 724,340 81.1 51.8 38,560 83,050 Low & middle income 3.8 1.6 4,502,770 5,018,500 30.6 48.2 2,245,320 2,670,380 85.6 53.2 66,360 226,020 East Asia & Pacific 5.7 3.6 .. .. 30.5 51.9 .. .. 90.0 52.3 .. .. Europe & Central Asia 2.5 ­2.6 1,174,570 867,600 68.8 16.2 419,030 226,870 77.2 30.6 26,400 69,800 Latin America & Carib. 4.1 1.7 683,590 929,970 10.7 57.9 518,270 645,520 80.8 59.0 10,620 14,700 Middle East & N. Africa 4.7 5.3 143,490 213,330 52.5 25.7 118,190 152,970 92.0 28.7 5,100 4,300 South Asia 6.6 4.8 833,960 961,480 14.1 66.0 311,180 428,050 93.4 65.7 8,710 10,130 Sub-Saharan Africa 3.9 2.6 .. .. 24.4 49.4 .. .. 78.5 54.4 .. .. High income 0.6 1.5 1,671,370 1,588,990 47.8 27.1 1,077,680 1,117,420 75.3 35.3 198,850 375,870 Euro area 0.0 0.4 313,430 232,220 22.2 47.6 313,640 303,960 76.4 49.3 40,300 135,750 a. Includes Montenegro. 164 2008 World Development Indicators 3.9 ENVIRONMENT Trends in greenhouse gas emissions About the data Greenhouse gases--which include carbon dioxide, at trapping heat in the earth's atmosphere as a kilo- Energy Data Set) and data on world cement manufac- methane, nitrous oxide, hydrofluorocarbons, per- gram of carbon dioxide within 100 years. turing data (from the U.S. Bureau of Mines's Cement fluorocarbons, and sulfur hexafluoride--contribute Nitrous oxide emissions are mainly from fossil fuel Manufacturing Data Set). Carbon dioxide emissions to climate change. combustion, fertilizers, rainforest fires, and animal are often calculated and reported as elemental car- Carbon dioxide emissions, largely a by-product of waste. Nitrous oxide is a powerful greenhouse gas, bon. For the table these values were converted to energy production and use (see table 3.7), account with an estimated atmospheric lifetime of 114 years, actual carbon dioxide mass by multiplying by 3.664 for the largest share of greenhouse gases. Anthro- compared with 12 years for methane. The per kilo- (the ratio of the mass of carbon to that of carbon pogenic carbon dioxide emissions result primarily gram global warming potential of nitrous oxide is dioxide). Although estimates of global carbon dioxide from fossil fuel combustion and cement manufactur- nearly 310 times that of carbon dioxide within 100 emissions are probably accurate within 10 percent, ing. Burning oil releases more carbon dioxide than years. country estimates may have larger error bounds. burning natural gas, and burning coal releases even Other greenhouse gases covered under the Kyoto Trends estimated from a consistent time series tend more for the same level of energy use. Cement manu- Protocol are hydrofluorocarbons, perfluorocarbons, to be more accurate than individual values. Each facturing releases about half a metric ton of carbon and sulfur hexafluoride. Although emissions of these year the CDIAC recalculates the entire time series, dioxide for each metric ton of cement produced. artificial gases are small, they are more powerful incorporating recent findings and corrections to the Methane emissions result largely from agricultural greenhouse gases than carbon dioxide, with much database. Estimates exclude fuels supplied to ships activities, industrial production landfills and waste- higher atmospheric lifetime and high global warming and aircraft in international transport because of the water treatment, and other sources such as tropi- potential. difficulty of apportioning these fuels among benefit- cal forest and other vegetation fires. The emissions The Carbon Dioxide Information Analysis Cen- ing countries. are usually expressed in carbon dioxide equivalents ter (CDIAC), sponsored by the U.S. Department of Definitions using the global warming potential, which allows the Energy, calculates annual anthropogenic emissions effective contributions of different gases to be com- of carbon dioxide from fossil fuel consumption data · Carbon dioxide emissions are emissions from pared. A kilogram of methane is 21 times as effective (from the United Nations Statistics Division's World the burning of fossil fuels and the manufacture of cement and include carbon dioxide produced during The 10 largest contributors to methane emissions consumption of solid, liquid, and gas fuels and gas account for about 62 percent of emissions 3.9a flaring. · Methane emissions are emissions from human activities such as agriculture and from indus- Methane emissions, 2005 (million metric tons of carbon dioxide equivalent) trial methane production. · Industrial methane emis- 1,000 sions are emissions from the handling, transmission, and combustion of fossil fuels and biofuels. · Agri- 750 cultural methane emissions are emissions from animals, animal waste, rice production, agricultural 500 waste burning (nonenergy, on-site), and savannah 250 burning. · Nitrous oxide emissions are emissions from agricultural biomass burning, industrial activi- 0 ties, and livestock management. · Industrial nitrous China United India Russian Brazil Indonesia Mexico Australia Pakistan Canada States Federation oxide emissions are emissions produced during the manufacturing of adipic acid and nitric acid. · Agri- Source: Table 3.9. cultural nitrous oxide emissions are emissions pro- duced through fertilizer use (synthetic and animal The 10 largest contributors to nitrous oxide emissions account for about 56 percent of emissions 3.9b manure), animal waste management, agricultural waste burning (nonenergy, on-site), and savannah Nitrous oxide emissions, 2005 (million metric tons of carbon dioxide equivalent) burning. · Other greenhouse gas emissions are by- 600 product emissions of hydrofluorocarbons, perfluoro- carbons, and sulfur hexafluoride. 400 Data sources Data on carbon dioxide emissions are from the 200 CDIAC, Environmental Sciences Division, Oak Ridge National Laboratory, Tennessee, United 0 States. Data on methane, nitrous oxide, and other China United India Brazil Australia Argentina Pakistan France Mexico Indonesia States greenhouse gases emissions are compiled by the Source: Table 3.9. International Energy Agency. 2008 World Development Indicators 165 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 3.2 5.4 0.0 0.0 0.0 0.0 10.9 1.3 89.1 98.7 0.0 0.0 Algeria 16.1 33.9 0.0 0.0 93.7 96.2 5.4 2.2 0.8 1.6 0.0 0.0 Angola 0.8 2.7 0.0 0.0 0.0 0.0 13.8 34.2 86.2 65.8 0.0 0.0 Argentina 51.0 105.8 1.3 2.1 39.0 52.3 9.7 5.4 35.6 32.4 14.3 6.5 Armenia 10.4 6.3 0.0 0.0 16.4 28.9 68.6 0.0 15.0 28.1 0.0 43.0 Australia 154.3 250.9 77.1 80.1 10.6 11.7 2.7 0.8 9.2 6.3 0.0 0.0 Austria 49.3 63.0 14.2 13.5 15.7 20.7 3.8 2.6 63.9 57.0 0.0 0.0 Azerbaijan 23.2 21.2 0.0 0.0 0.0 58.1 97.0 27.7 3.0 14.2 0.0 0.0 Bangladesh 7.7 22.6 0.0 0.0 84.3 87.6 4.3 6.7 11.4 5.7 0.0 0.0 Belarus 29.5 31.0 0.0 0.0 44.0 91.4 55.9 8.4 0.1 0.1 0.0 0.0 Belgium 70.3 85.7 28.2 12.2 7.7 26.7 1.9 2.0 0.4 0.3 60.8 55.5 Benin 0.0 0.1 0.0 0.0 0.0 0.0 100.0 99.1 0.0 0.9 0.0 0.0 Bolivia 2.1 5.2 0.0 0.0 37.6 32.3 5.3 16.7 55.3 47.8 0.0 0.0 Bosnia and Herzegovina 14.6 12.7 71.8 56.0 0.0 0.0 7.3 1.1 20.9 42.9 0.0 0.0 Botswana 0.9 1.0 88.1 99.4 0.0 0.0 11.9 0.6 0.0 0.0 0.0 0.0 Brazil 222.8 403.0 2.0 2.5 0.0 4.7 2.2 2.9 92.8 83.7 1.0 2.4 Bulgaria 42.1 44.0 50.3 42.4 7.6 3.9 2.9 1.4 4.5 9.9 34.8 42.4 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. 0.9 .. .. .. .. .. 41.0 .. 1.9 .. .. Cameroon 2.7 4.1 0.0 0.0 0.0 0.0 1.5 5.6 98.5 94.4 0.0 0.0 Canada 481.9 628.1 17.1 16.9 2.0 5.8 3.4 3.1 61.6 57.9 15.1 14.7 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 18.4 49.9 34.3 16.7 1.3 29.9 7.6 3.4 55.3 48.1 0.0 0.0 China 621.2 2,497.4 71.3 79.0 0.4 0.5 7.9 2.4 20.4 15.9 0.0 2.1 Hong Kong, China 28.9 38.5 98.3 62.6 0.0 36.8 1.7 0.6 0.0 0.0 0.0 0.0 Colombia 36.4 51.6 10.1 8.2 12.4 13.3 1.0 0.2 75.6 77.2 0.0 0.0 Congo, Dem. Rep. 5.7 7.4 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 0.0 0.6 0.3 99.4 99.7 0.0 0.0 Costa Rica 3.5 8.3 0.0 0.0 0.0 0.0 2.5 3.3 97.5 79.6 0.0 0.0 Côte d'Ivoire 2.0 5.6 0.0 0.0 0.0 74.1 33.3 0.1 66.7 25.8 0.0 0.0 Croatia 9.2 12.4 6.8 18.8 20.2 14.7 31.6 15.0 41.3 51.3 0.0 0.0 Cuba 15.0 15.3 0.0 0.0 0.2 0.0 91.5 97.5 0.6 0.6 0.0 0.0 Czech Republic 62.3 81.9 76.4 60.8 0.6 4.8 0.9 0.4 1.9 2.9 20.2 30.2 Denmark 26.0 36.3 90.7 42.6 2.7 24.3 3.4 3.8 0.1 0.1 0.0 0.0 Dominican Republic 3.7 12.9 1.2 10.0 0.0 0.3 88.6 74.6 9.4 14.7 0.0 0.0 Ecuador 6.3 13.4 0.0 0.0 0.0 7.7 21.5 41.0 78.5 51.4 0.0 0.0 Egypt, Arab Rep. 42.3 108.7 0.0 0.0 39.6 74.3 36.9 13.6 23.5 11.6 0.0 0.0 El Salvador 2.2 4.8 0.0 0.0 0.0 0.0 6.9 42.6 73.5 35.0 0.0 0.0 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 17.2 10.2 86.8 91.2 4.8 7.4 8.4 0.3 0.0 0.2 0.0 0.0 Ethiopia 1.2 2.9 0.0 0.0 0.0 0.0 11.6 0.7 88.4 99.3 0.0 0.0 Finland 54.4 70.6 33.0 16.5 8.6 15.9 3.1 0.7 20.0 19.5 35.3 33.0 France 417.2 570.6 8.5 5.4 0.7 4.0 2.1 1.3 12.9 9.1 75.3 79.1 Gabon 1.0 1.6 0.0 0.0 16.4 15.9 11.2 31.7 72.1 51.9 0.0 0.0 Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 13.7 7.3 0.0 0.0 15.6 13.3 29.2 0.9 55.2 85.8 0.0 0.0 Germany 547.7 613.2 58.7 49.8 7.4 11.3 1.9 1.7 3.2 3.2 27.8 26.6 Ghana 5.7 6.8 0.0 0.0 0.0 0.0 0.0 21.5 100.0 78.5 0.0 0.0 Greece 34.8 59.4 72.4 59.8 0.3 13.7 22.3 15.5 5.1 8.4 0.0 0.0 Guatemala 2.3 7.6 0.0 13.9 0.0 0.0 9.0 31.4 76.0 42.8 0.0 0.0 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 0.6 0.6 0.0 0.0 0.0 0.0 20.6 52.3 76.5 47.7 0.0 0.0 166 2008 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 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Honduras 2.3 5.6 0.0 0.0 0.0 0.0 1.7 66.4 98.3 32.3 0.0 0.0 Hungary 28.4 35.8 30.5 20.0 15.7 34.6 4.8 1.3 0.6 0.6 48.3 38.7 India 289.4 699.0 66.2 68.7 3.4 8.9 3.5 4.5 24.8 14.3 2.1 2.5 Indonesia 33.3 127.4 31.5 40.7 2.3 13.8 42.7 31.9 20.2 8.4 0.0 0.0 Iran, Islamic Rep. 59.1 180.4 0.0 0.0 52.5 73.0 37.3 18.0 10.3 8.9 0.0 0.0 Iraq 24.0 34.0 0.0 0.0 0.0 0.0 89.2 98.5 10.8 1.5 0.0 0.0 Ireland 14.2 25.6 57.4 34.5 27.7 45.2 10.0 13.0 4.9 2.5 0.0 0.0 Israel 20.9 49.8 50.1 71.1 0.0 11.4 49.9 17.5 0.0 0.1 0.0 0.0 Italy 213.1 294.4 16.8 16.8 18.6 50.7 48.2 16.0 14.8 11.4 0.0 0.0 Jamaica 2.5 7.4 0.0 0.0 0.0 0.0 92.4 96.6 3.6 2.0 0.0 0.0 Japan 836.7 1,094.2 14.0 28.3 19.9 21.1 18.2 9.5 10.7 7.1 24.2 27.9 Jordan 3.6 9.7 0.0 0.0 11.9 57.3 87.8 42.1 0.3 0.6 0.0 0.0 Kazakhstan 87.4 67.9 71.1 70.3 10.5 10.7 10.0 7.4 8.4 11.6 0.0 0.0 Kenya 3.0 6.0 0.0 0.0 0.0 0.0 7.6 29.5 81.6 50.4 0.0 0.0 Korea, Dem. Rep. 27.7 22.9 40.1 39.0 0.0 0.0 3.6 3.6 56.3 57.3 0.0 0.0 Korea, Rep. 105.4 387.9 16.8 38.4 9.1 16.0 17.9 6.3 6.0 0.9 50.2 37.8 Kuwait 18.5 43.7 0.0 0.0 45.7 17.9 54.3 82.1 0.0 0.0 0.0 0.0 Kyrgyz Republic 15.7 16.4 13.1 3.6 23.5 9.5 0.0 0.0 63.5 86.9 0.0 0.0 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 6.6 4.9 0.9 0.0 26.1 30.3 5.4 0.1 67.6 67.8 0.0 0.0 Lebanon 1.5 10.1 0.0 0.0 0.0 0.0 66.7 89.7 33.3 10.3 0.0 0.0 Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 10.2 22.5 0.0 0.0 0.0 28.2 100.0 71.8 0.0 0.0 0.0 0.0 Lithuania 28.4 14.4 0.0 0.0 23.8 20.9 14.6 2.8 1.5 3.1 60.0 71.7 Macedonia, FYR 5.8 6.9 89.7 78.3 0.0 0.0 1.8 0.2 8.5 21.5 0.0 0.0 Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysia 23.0 87.3 12.3 26.5 22.0 64.0 48.4 2.9 17.3 6.6 0.0 0.0 Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mexico 124.1 234.9 6.3 14.0 11.6 36.1 56.7 29.3 18.9 11.8 2.4 4.6 Moldova 15.5 3.9 32.3 0.0 39.5 98.1 26.6 0.2 1.7 1.6 0.0 0.0 Mongolia .. .. .. .. .. .. .. .. .. .. .. .. Morocco 9.6 22.6 23.0 69.2 0.0 0.0 64.4 23.6 12.7 6.3 0.0 0.0 Mozambique 0.5 13.3 13.9 0.0 0.0 0.1 23.6 0.1 62.6 99.8 0.0 0.0 Myanmar 2.5 6.0 1.6 0.0 39.3 39.8 10.9 10.3 48.1 49.8 0.0 0.0 Namibia 0.0 1.7 1.5 0.4 0.0 0.0 3.3 2.6 95.2 97.0 0.0 0.0 Nepal 0.9 2.4 0.0 0.0 0.0 0.0 0.1 0.2 99.9 99.8 0.0 0.0 Netherlands 71.9 100.2 38.3 26.9 50.9 57.7 4.3 2.3 0.1 0.1 4.9 4.0 New Zealand 32.3 43.0 1.9 13.5 17.6 22.0 0.0 0.0 72.3 54.6 0.0 0.0 Nicaragua 1.4 2.9 0.0 0.0 0.0 0.0 39.8 69.8 28.8 15.1 0.0 0.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 13.5 23.5 0.1 0.0 53.7 53.6 13.7 12.7 32.6 33.8 0.0 0.0 Norway 121.6 137.3 0.1 0.1 0.0 0.3 0.0 0.0 99.6 98.9 0.0 0.0 Oman 4.5 12.6 0.0 0.0 81.6 82.0 18.4 18.0 0.0 0.0 0.0 0.0 Pakistan 37.7 93.8 0.1 0.1 33.6 44.0 20.6 20.3 44.9 32.9 0.8 2.6 Panama 2.7 5.8 0.0 0.0 0.0 0.0 14.7 35.7 83.2 63.9 0.0 0.0 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 27.2 51.2 0.0 0.0 0.0 0.0 0.0 0.0 99.9 100.0 0.0 0.0 Peru 13.8 25.5 0.0 3.2 1.7 9.7 21.5 8.2 75.8 78.3 0.0 0.0 Philippines 25.2 56.5 7.7 27.0 0.0 29.8 46.7 10.9 24.0 14.8 0.0 0.0 Poland 134.4 155.4 97.5 93.4 0.1 2.3 1.2 1.5 1.1 1.4 0.0 0.0 Portugal 28.4 46.2 32.1 33.0 0.0 29.5 33.1 19.0 32.3 10.2 0.0 0.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 167 3.10 Sources of electricity Electricity Sources of production electricitya % of total billion kilowatt hours Coal Gas Oil Hydropower Nuclear power 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 1990 2005 Romania 64.3 59.4 28.8 37.3 35.1 16.2 18.4 3.2 17.7 34.0 0.0 9.3 Russian Federation 1,082.2 951.2 14.5 17.4 47.3 46.2 11.9 2.2 15.3 18.2 10.9 15.7 Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 69.2 176.1 0.0 0.0 48.1 49.1 51.9 50.9 0.0 0.0 0.0 0.0 Senegal 0.9 2.5 0.0 0.0 2.4 2.4 97.6 79.4 0.0 10.5 0.0 0.0 Serbiab 43.2 35.4 79.8 69.9 1.6 1.5 1.2 0.8 22.3 27.9 0.0 0.0 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 15.7 38.2 0.0 0.0 0.0 74.4 100.0 25.6 0.0 0.0 0.0 0.0 Slovak Republic 25.5 31.4 31.9 19.1 7.1 7.0 6.4 2.4 7.4 14.8 47.2 56.5 Slovenia 12.0 15.1 31.9 34.9 0.0 2.2 4.8 0.3 24.7 22.9 38.7 38.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 165.4 242.9 94.3 94.1 0.0 0.0 0.0 0.0 0.6 0.9 5.1 4.6 Spain 151.2 290.6 40.1 27.8 1.0 27.2 5.7 8.4 16.8 6.7 35.9 19.8 Sri Lanka 3.2 8.8 0.0 0.0 0.0 0.0 0.2 60.6 99.8 39.4 0.0 0.0 Sudan 1.5 4.1 0.0 0.0 0.0 0.0 36.8 70.0 63.2 30.0 0.0 0.0 Swaziland .. .. .. .. .. .. .. .. .. .. .. .. Sweden 146.0 158.4 1.1 1.2 0.3 0.4 0.9 0.9 49.7 46.0 46.7 45.7 Switzerland 55.0 57.8 0.1 0.0 0.6 1.5 0.7 0.3 54.2 54.1 43.0 40.4 Syrian Arab Republic 11.6 34.9 0.0 0.0 20.5 37.1 56.0 53.0 23.5 9.9 0.0 0.0 Tajikistan 18.1 17.1 0.0 0.0 9.1 2.3 0.0 0.0 90.9 97.7 0.0 0.0 Tanzania 1.6 3.0 0.0 3.3 0.0 0.0 4.9 38.2 95.1 58.6 0.0 0.0 Thailand 44.2 132.2 25.0 15.1 40.2 71.4 23.5 6.6 11.3 4.4 0.0 0.0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 0.2 0.2 0.0 0.0 0.0 0.0 39.9 60.4 60.1 39.6 0.0 0.0 Trinidad and Tobago 3.6 7.1 0.0 0.0 99.0 99.5 0.1 0.2 0.0 0.0 0.0 0.0 Tunisia 5.8 13.7 0.0 0.0 63.7 90.4 35.5 8.2 0.8 1.1 0.0 0.0 Turkey 57.5 162.0 35.1 26.7 17.7 45.3 6.9 3.4 40.2 24.4 0.0 0.0 Turkmenistan 14.6 12.8 0.0 0.0 95.2 100.0 0.0 0.0 4.8 0.0 0.0 0.0 Uganda .. .. .. .. .. .. .. .. .. .. .. .. Ukraine 298.6 185.9 38.2 26.7 16.8 18.7 16.1 0.3 3.5 6.7 25.5 47.7 United Arab Emirates 17.1 60.7 0.0 0.0 96.3 97.9 3.7 2.1 0.0 0.0 0.0 0.0 United Kingdom 317.8 397.6 65.0 34.3 1.6 38.5 10.9 1.4 1.6 1.2 20.7 20.5 United States 3,202.8 4,268.4 53.1 50.5 11.9 18.3 4.1 3.3 8.5 6.4 19.1 19.0 Uruguay 7.4 7.7 0.0 0.0 0.0 0.0 5.1 12.5 94.2 87.0 0.0 0.0 Uzbekistan 56.3 47.7 7.4 4.7 76.4 68.8 4.4 13.6 11.8 12.8 0.0 0.0 Venezuela, RB 59.3 101.5 0.0 0.0 26.2 15.6 11.5 10.5 62.3 73.9 0.0 0.0 Vietnam 8.7 53.5 23.1 16.7 0.1 38.5 15.0 4.6 61.8 40.1 0.0 0.0 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1.7 4.7 0.0 0.0 0.0 0.0 100.0 100.0 0.0 0.0 0.0 0.0 Zambia 8.0 8.9 0.5 0.2 0.0 0.0 0.3 0.4 99.2 99.4 0.0 0.0 Zimbabwe 9.4 10.3 53.3 43.0 0.0 0.0 0.0 0.2 46.7 56.8 0.0 0.0 World 11,735.9 s 18,155.6 s 37.5 w 40.3 w 14.6 w 19.8 w 10.4 w 6.2 w 18.1 w 16.0 w 17.2 w 15.2 w Low income 520.1 1,082.4 41.5 46.7 16.5 18.3 5.7 7.4 35.0 24.8 1.2 1.8 Middle income 3,709.5 6,599.4 32.7 44.1 22.0 20.3 15.4 7.1 22.0 21.5 7.4 6.0 Lower middle income 1,447.7 3,765.8 41.9 56.9 12.6 13.9 20.2 7.8 19.3 16.8 5.3 3.8 Upper middle income 2,261.8 2,833.5 26.8 27.0 28.0 28.9 12.3 6.1 23.7 27.8 8.7 9.0 Low & middle income 4,229.7 7,681.8 33.8 44.4 21.3 20.0 14.2 7.1 23.6 22.0 6.6 5.5 East Asia & Pacific 785.8 2,984.2 61.4 70.4 3.5 7.4 12.6 4.1 21.7 15.6 0.0 1.8 Europe & Central Asia 2,085.5 1,913.2 27.3 27.3 34.0 35.8 13.1 2.9 13.6 17.5 12.1 16.0 Latin America & Carib. 605.1 1,120.8 3.8 5.4 9.2 18.0 18.9 13.6 63.9 57.6 2.1 2.5 Middle East & N. Africa 190.0 487.9 1.2 3.2 38.4 60.0 48.2 29.3 12.2 7.4 0.0 0.0 South Asia 338.9 826.7 56.6 58.1 8.6 14.9 5.4 6.9 27.6 16.7 1.9 2.4 Sub-Saharan Africa 224.4 349.0 72.1 67.1 3.3 4.9 2.2 4.1 18.4 20.1 3.8 3.2 High income 7,506.2 10,473.9 39.5 37.3 10.8 19.6 8.2 5.6 15.1 11.5 23.1 22.4 Euro area 1,665.0 2,238.0 34.4 26.3 8.6 20.7 9.5 5.2 11.1 8.4 35.5 33.6 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 Montenegro. 168 2008 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. Since 1990, for example, the IEA has con- combustible renewables and waste. Production warming--as does burning an equivalent amount structed 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 2005 Other 2% Other 3% Nuclear Nuclear power power 17% 15% Coal 38% Coal Hydropower 40% Hydropower 16% 18% Oil Gas Oil 6% Gas 10% 15% 20% Source: Table 3.10. . . . with low-income countries relying more on coal 3.10b Low-income countries 1990 2005 Nuclear power 1% Other 0.1% Nuclear power 2% Other 1% Hydropower Hydropower 25% 35% Coal 41% Coal Data sources 47% Oil Data on electricity production are from the IEA's 7% electronic files and its annual publications Energy Oil Gas Gas 18% 6% 17% Statistics and Balances of Non-OECD Countries, Energy Statistics of OECD Countries, and Energy Source: Table 3.10. Balances of OECD Countries. 2008 World Development Indicators 169 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 2006 1990 2006 1990­2006 1990 2005 1990 2005 1990 2004 1990 2004 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. .. Albania 1.2 1.5 36 46 1.3 .. .. .. .. 99 99 .. 84 Algeria 13.2 21.3 52 64 3.0 8 10 14 15 99 99 77 82 Angola 3.9 8.9 37 54 5.2 15 17 40 32 61 56 18 16 Argentina 28.3 35.3 87 90 1.4 39 39 37 36 86 92 45 83 Armenia 2.4 1.9 68 64 ­1.4 33 37 49 57 96 96 .. 61 Australia 14.6 18.3 85 88 1.4 60 60 25 24 100 100 100 100 Austria 5.1 5.5 66 66 0.5 27 27 41 42 100 100 100 100 Azerbaijan 3.8 4.4 54 52 0.8 24 22 45 43 .. 73 .. 36 Bangladesh 22.4 39.8 20 26 3.6 8 12 29 32 55 51 12 35 Belarus 6.8 7.1 66 73 0.3 16 18 24 25 .. 93 .. 61 Belgium 9.6 10.2 96 97 0.4 10 10 10 10 .. .. .. .. Benin 1.8 3.5 35 41 4.3 .. .. .. .. 32 59 2 11 Bolivia 3.7 6.0 56 65 3.1 25 31 29 26 49 60 14 22 Bosnia and Herzegovina 1.7 1.8 39 46 0.5 .. .. .. .. 99 99 .. 92 Botswana 0.6 1.1 42 58 4.0 .. .. .. .. 61 57 21 25 Brazil 111.8 160.3 75 85 2.2 34 37 13 12 82 83 37 37 Bulgaria 5.8 5.4 66 70 ­0.4 14 14 21 20 100 100 96 96 Burkina Faso 1.2 2.7 14 19 4.9 .. .. 49 36 32 42 3 6 Burundi 0.4 0.8 6 10 5.3 .. .. .. .. 42 47 44 35 Cambodia 1.2 2.9 13 20 5.4 6 10 49 50 .. 53 .. 8 Cameroon 5.0 10.1 41 55 4.4 14 18 19 18 59 58 40 43 Canada 21.3 26.2 77 80 1.3 40 44 18 21 100 100 99 99 Central African Republic 1.1 1.6 37 38 2.4 .. .. .. .. 34 47 17 12 Chad 1.3 2.7 21 26 4.7 .. .. 38 35 28 24 2 4 Chile 11.0 14.4 83 88 1.7 35 35 42 40 91 95 52 62 China 311.0 541.8 27 41 3.5 13 18 3 3 64 69 7 28 Hong Kong, China 5.7 6.9 100 100 1.2 100 100 100 100 .. .. .. .. Colombia 24.0 33.3 69 73 2.0 30 36 20 24 95 96 52 54 Congo, Dem. Rep. 10.5 19.8 28 33 3.9 15 16 35 32 53 42 1 25 Congo, Rep. 1.3 2.2 54 61 3.3 29 32 53 54 .. 28 .. 25 Costa Rica 1.6 2.7 51 62 3.5 24 28 47 46 .. 89 97 97 Côte d'Ivoire 5.1 8.6 40 45 3.3 16 19 41 43 37 46 10 29 Croatia 2.6 2.5 54 57 ­0.1 .. .. .. .. 100 100 100 100 Cuba 7.8 8.5 73 75 0.5 20 19 27 26 99 99 95 95 Czech Republic 7.8 7.5 75 74 ­0.2 12 11 16 16 99 99 97 97 Denmark 4.4 4.7 85 86 0.4 26 20 31 23 .. .. .. .. Dominican Republic 4.0 6.5 55 68 3.0 21 21 38 32 60 81 43 73 Ecuador 5.7 8.4 55 63 2.4 26 30 28 29 77 94 45 82 Egypt, Arab Rep. 24.0 31.9 44 43 1.8 22 20 38 36 70 86 42 58 El Salvador 2.5 4.1 49 60 3.0 19 23 39 38 70 77 33 39 Eritrea 0.5 0.9 16 20 3.9 .. .. .. .. 44 32 0 3 Estonia 1.1 0.9 71 69 ­1.2 .. .. .. .. 97 97 96 96 Ethiopia 6.4 12.6 13 16 4.2 3 4 28 24 13 44 2 7 Finland 3.1 3.2 61 61 0.3 17 21 28 34 100 100 100 100 France 42.0 47.1 74 77 0.7 23 22 22 21 .. .. .. .. Gabon 0.6 1.1 69 84 3.5 .. .. .. .. .. 37 .. 30 Gambia, The 0.4 0.9 38 55 5.7 .. .. .. .. .. 72 .. 46 Georgia 3.0 2.3 55 52 ­1.6 22 23 41 45 99 96 94 91 Germany 58.3 62.0 73 75 0.4 8 8 6 5 100 100 100 100 Ghana 5.7 11.2 37 49 4.2 12 16 21 18 23 27 10 11 Greece 6.0 6.6 59 59 0.6 30 29 51 49 .. .. .. .. Guatemala 3.7 6.2 41 48 3.3 .. .. 22 16 73 90 47 82 Guinea 1.7 3.1 28 33 3.7 15 16 53 48 27 31 10 11 Guinea-Bissau 0.3 0.5 28 30 3.4 .. .. .. .. .. 57 .. 23 Haiti 2.1 3.7 30 39 3.6 16 23 54 59 25 57 23 14 170 2008 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 2006 1990 2006 1990­2006 1990 2005 1990 2005 1990 2004 1990 2004 Honduras 2.0 3.3 40 47 3.2 .. .. 29 29 77 87 31 54 Hungary 6.8 6.7 66 67 ­0.1 19 17 29 25 100 100 .. 85 India 216.6 321.6 26 29 2.5 10 12 6 6 45 59 3 22 Indonesia 54.5 109.8 31 49 4.4 9 12 14 12 65 73 37 40 Iran, Islamic Rep. 30.6 47.3 56 67 2.7 23 23 21 16 86 .. 78 .. Iraq 12.9 .. 70 .. .. 26 .. 32 .. 95 .. 48 .. Ireland 2.0 2.6 57 61 1.6 26 25 46 41 .. .. .. .. Israel 4.2 6.5 90 92 2.7 43 44 48 47 100 100 .. .. Italy 37.8 39.9 67 68 0.3 19 17 9 8 .. .. .. .. Jamaica 1.2 1.4 49 53 1.2 .. .. .. .. 86 91 64 69 Japan 78.0 84.3 63 66 0.5 46 48 42 42 100 100 100 100 Jordan 2.3 4.6 72 83 4.3 27 24 37 29 97 94 82 87 Kazakhstan 9.2 8.8 56 58 ­0.3 7 8 12 13 87 87 52 52 Kenya 4.3 7.7 18 21 3.7 6 8 32 38 48 46 37 41 Korea, Dem. Rep. 11.8 14.7 58 62 1.4 15 19 21 23 .. 58 .. 60 Korea, Rep. 31.6 39.2 74 81 1.3 51 51 33 25 .. .. .. .. Kuwait 2.1 2.6 98 98 1.3 65 71 67 73 .. .. .. .. Kyrgyz Republic 1.7 1.9 38 36 0.7 .. .. 38 43 75 75 51 51 Lao PDR 0.6 1.2 15 21 4.1 .. .. .. .. .. 67 .. 20 Latvia 1.9 1.6 69 68 ­1.1 .. .. .. .. .. 82 .. 71 Lebanon 2.5 3.5 83 87 2.2 43 44 52 51 100 100 .. 87 Lesotho 0.3 0.4 17 19 2.0 .. .. .. .. 61 61 32 32 Liberia 1.0 2.1 45 59 4.9 .. .. 55 47 59 49 24 7 Libya 3.4 5.1 79 85 2.5 48 54 44 42 97 97 96 96 Lithuania 2.5 2.3 68 67 ­0.6 .. .. .. .. .. .. .. .. Macedonia, FYR 1.1 1.4 58 70 1.6 .. .. .. .. .. .. .. .. Madagascar 2.8 5.2 24 27 3.8 8 9 33 32 27 48 10 26 Malawi 1.1 2.4 12 18 4.9 .. .. .. .. 64 62 45 61 Malaysia 9.0 17.8 50 68 4.3 6 5 12 8 95 95 .. 93 Mali 1.8 3.7 23 31 4.6 10 12 42 39 50 59 32 39 Mauritania 0.8 1.2 40 41 2.9 .. .. .. .. 42 49 22 8 Mauritius 0.5 0.5 44 42 0.9 .. .. .. .. 95 95 .. 94 Mexico 60.3 79.5 73 76 1.7 32 35 25 25 75 91 13 41 Moldova 2.1 1.8 47 47 ­0.8 .. .. .. .. .. 86 .. 52 Mongolia 1.2 1.5 57 57 1.3 .. .. 48 60 .. 75 .. 37 Morocco 11.7 18.1 48 59 2.7 16 16 23 18 87 88 27 52 Mozambique 2.9 7.4 21 35 5.9 6 6 27 19 49 53 12 19 Myanmar 10.0 15.1 25 31 2.6 7 9 29 28 48 88 16 72 Namibia 0.4 0.7 28 36 3.9 .. .. .. .. 70 50 8 13 Nepal 1.7 4.5 9 16 6.1 .. .. 23 19 48 62 7 30 Netherlands 10.3 13.2 69 81 1.6 14 14 10 9 100 100 100 100 New Zealand 2.9 3.6 85 86 1.3 25 28 30 32 .. .. 88 .. Nicaragua 2.2 3.3 53 59 2.5 18 21 33 36 64 56 24 34 Niger 1.2 2.3 15 17 4.1 .. .. 36 38 35 43 2 4 Nigeria 33.1 70.9 35 49 4.8 11 13 14 16 51 53 33 36 Norway 3.1 3.6 72 78 1.0 .. .. 22 22 .. .. .. .. Oman 1.2 1.8 65 72 2.6 .. .. .. .. 97 97 61 .. Pakistan 33.0 56.2 31 35 3.3 16 18 22 21 82 92 17 41 Panama 1.3 2.4 54 72 3.7 35 38 65 53 89 89 51 51 Papua New Guinea 0.5 0.8 13 14 2.7 .. .. .. .. 67 67 41 41 Paraguay 2.1 3.6 49 59 3.4 22 31 45 54 72 94 45 61 Peru 15.0 20.1 69 73 1.8 27 26 39 36 69 74 15 32 Philippines 29.9 54.7 49 63 3.8 14 14 27 20 66 80 48 59 Poland 23.4 23.7 61 62 0.1 4 4 7 7 .. .. .. .. Portugal 4.7 6.2 48 58 1.6 37 39 54 45 .. .. .. .. Puerto Rico 2.6 3.8 72 98 2.6 44 67 60 68 .. .. .. .. 2008 World Development Indicators 171 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 2006 1990 2006 1990­2006 1990 2005 1990 2005 1990 2004 1990 2004 Romania 12.6 11.6 54 54 ­0.5 8 9 14 17 .. 89 .. .. Russian Federation 108.8 103.9 73 73 ­0.3 18 19 8 10 93 93 70 70 Rwanda 0.4 1.9 5 20 9.9 .. .. 56 44 49 56 36 38 Saudi Arabia 12.5 19.2 77 81 2.7 30 36 19 22 100 100 .. .. Senegal 3.1 5.1 39 42 3.1 18 18 45 44 53 79 19 34 Serbiaa 5.4 4.2 51 52 .. 11 14 22 26 97 97 77 77 Sierra Leone 1.2 2.4 30 41 4.1 .. .. 43 35 .. 53 .. 30 Singapore 3.0 4.5 100 100 2.4 99 100 99 100 100 100 .. .. Slovak Republic 3.0 3.0 57 56 0.1 .. .. .. .. 100 100 98 98 Slovenia 1.0 1.0 50 51 0.1 .. .. .. .. .. .. .. .. Somalia 2.0 3.0 30 36 2.6 14 16 47 46 .. 48 .. 14 South Africa 18.3 28.3 52 60 2.7 25 30 10 12 85 79 53 46 Spain 29.3 33.9 75 77 0.9 22 24 15 17 100 100 100 100 Sri Lanka 2.9 3.0 17 15 0.2 .. .. .. .. 89 98 64 89 Sudan 6.9 15.7 27 42 5.1 9 12 34 30 53 50 26 24 Swaziland 0.2 0.3 23 24 2.8 .. .. .. .. .. 59 .. 44 Sweden 7.1 7.7 83 84 0.5 17 19 21 22 100 100 100 100 Switzerland 4.6 5.7 68 76 1.3 14 15 20 20 100 100 100 100 Syrian Arab Republic 6.2 9.9 49 51 2.9 26 25 25 26 97 99 50 81 Tajikistan 1.7 1.6 32 25 ­0.1 .. .. .. .. .. 70 .. 45 Tanzania 4.8 9.7 19 25 4.4 5 7 27 29 52 53 45 43 Thailand 16.0 20.7 29 33 1.6 11 10 37 32 95 98 74 99 Timor-Leste 0.2 0.3 21 27 3.6 .. .. .. .. .. 66 .. 33 Togo 1.2 2.6 30 41 4.9 16 21 52 53 71 71 24 15 Trinidad and Tobago 0.1 0.2 9 13 2.9 .. .. .. .. 100 100 100 100 Tunisia 4.9 6.7 60 66 2.0 .. .. .. .. 95 96 47 65 Turkey 33.2 49.4 59 68 2.5 22 26 20 20 96 96 70 72 Turkmenistan 1.7 2.3 45 47 2.0 .. .. .. .. .. 77 .. 50 Uganda 2.0 3.8 11 13 4.1 4 5 38 36 54 54 41 41 Ukraine 34.7 31.8 67 68 ­0.5 12 13 7 8 98 98 .. 93 United Arab Emirates 1.4 3.3 79 77 5.3 27 32 34 42 98 98 95 95 United Kingdom 51.1 54.4 89 90 0.4 26 26 15 16 .. .. .. .. United States 188.0 242.8 75 81 1.6 41 43 9 8 100 100 100 100 Uruguay 2.8 3.1 89 92 0.6 41 38 46 42 100 100 99 99 Uzbekistan 8.2 9.8 40 37 1.1 10 8 25 23 69 78 39 61 Venezuela, RB 16.6 25.3 84 94 2.6 34 37 17 12 .. 71 .. 48 Vietnam 13.4 22.6 20 27 3.3 13 13 30 23 58 92 30 50 West Bank and Gaza 1.3 2.7 68 72 4.4 .. .. .. .. .. 78 .. 61 Yemen, Rep. 2.6 6.0 21 28 5.3 5 9 25 31 82 86 19 28 Zambia 3.2 4.1 39 35 1.6 9 11 24 31 63 59 31 52 Zimbabwe 3.0 4.8 29 36 2.9 10 12 34 32 69 63 42 47 World 2,250.7 s 3,197.7 s 43 w 49 w 2.2 w 18 w 20 w 17 w 16 w 77 w 79 w 23 w 38 w Low income 444.5 735.8 25 30 3.1 10 12 17 18 50 60 12 28 Middle income 1,146.2 1,679.3 44 55 2.4 .. .. 15 14 78 81 24 41 Lower middle income 666.2 1,077.3 35 47 3.0 14 18 14 12 73 76 20 39 Upper middle income 480.0 602.1 69 75 1.4 .. .. 17 17 87 89 53 60 Low & middle income 1,590.7 2,415.1 37 44 2.6 14 17 16 15 70 74 18 34 East Asia & Pacific 459.9 804.8 29 42 3.5 .. .. 9 8 65 72 15 36 Europe & Central Asia 279.7 288.6 63 64 0.2 15 17 13 15 94 93 .. 70 Latin America & Carib. 310.3 432.2 71 78 2.1 32 34 24 22 81 86 35 49 Middle East & N. Africa 117.2 178.6 52 57 2.6 20 20 27 25 87 92 52 58 South Asia 279.1 431.4 25 29 2.7 10 12 10 11 50 63 6 27 Sub-Saharan Africa 144.5 279.6 28 36 4.1 .. .. 26 25 52 53 24 28 High income 660.0 782.6 74 78 1.1 .. .. 20 19 100 100 .. .. Euro area 210.2 232.7 71 73 0.6 18 18 15 15 .. .. .. .. a. Includes Montenegro. 172 2008 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. Most countries use an urban classification accounted for about 43 percent of China's popula- tion in urban agglomerations of more than 1 million related to the size or characteristics of settlements. tion, more than double the 20 percent considered is the percentage of a country's population living in Some define urban areas based on the presence of urban in 1994. In addition to the continuous migra- metropolitan areas that in 2005 had a population of certain infrastructure and services. And other coun- tion of people from rural to urban areas, one of the more than 1 million. · Population in largest city is tries designate urban areas based on administrative main reasons for this shift was the rapid growth the percentage of a country's urban population living arrangements. in the hundreds of towns reclassified as cities in in that country's largest metropolitan area. · Access The population of a city or metropolitan area recent years. Because the estimates in the table to improved sanitation facilities is the percentage depends on the boundaries chosen. For example, in are based on national definitions of what constitutes of the urban or rural population with access to at 1990 Beijing, China, contained 2.3 million people in a city or metropolitan area, cross-country compari- least adequate excreta disposal facilities (private or 87 square kilometers of "inner city" and 5.4 million in sons should be made with caution. To estimate urban shared but not public) that can effectively prevent 158 square kilometers of "core city." The population populations, UN ratios of urban to total population human, animal, and insect contact with excreta. of "inner city and inner suburban districts" was 6.3 were applied to the World Bank's estimates of total Improved facilities range from simple but protected million and that of "inner city, inner and outer sub- population (see table 2.1). pit latrines to flush toilets with a sewerage connec- urban districts, and inner and outer counties" was The table shows access to improved sanitation tion. To be effective, facilities must be correctly con- 10.8 million. (Most countries use the last definition.) facilities for both urban and rural populations to structed and properly maintained. For further discussion of urban-rural issues see box allow comparison of access. Definitions of access 3.1a in About the data for table 3.1. and urban areas vary, however, so comparisons Estimates of the world's urban population would between countries can be misleading. change significantly if China, India, and a few other Developing economies had the largest increase in urban population between 1990 and 2006 3.11a Urban population (millions) 1990 2006 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 2006 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 2005 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 Meeting the MDG Drinking Water Source: Table 3.11. and Sanitation Target. 2008 World Development Indicators 173 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 1991 2.6 .. 2 .. .. .. .. .. 50 .. 13 .. 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 1998 5.2 .. .. .. .. .. .. .. .. .. .. .. 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, 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 .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti 1982 4.2 .. 26 .. .. .. 92 68 .. .. 9 19 174 2008 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 Honduras 2001 4.4 .. .. .. 69 85 .. .. .. .. 14 .. Hungary 1990 2.7 .. .. .. .. .. .. .. .. .. 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 1994 6.2 6.0 1 .. 97 97 69 64 57 67 .. .. 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 .. .. .. .. .. .. .. .. .. .. .. 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 2000 4.4 .. 27b .. 87 .. 78 .. 6 .. .. .. 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 1990 5.3 5.3 .. .. 62 .. 83 76 6 11 4 4 Poland 1988 3.2 .. .. .. .. .. .. .. .. .. 1 .. Portugal 2001 2.8 .. .. .. .. .. 76 .. 86 .. .. .. Puerto Rico 1990 3.3 .. .. .. .. .. 72 .. .. .. 11 .. 2008 World Development Indicators 175 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 1992 3.1 3.1 .. .. 58 .. 87 77 39 71 6 4 Russia 2002 2.8 2.7 7 5 .. .. .. .. 73 86 .. .. Rwanda 1991 4.7 .. .. .. 79 78 92 73 19 25 .. .. 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 1991 3.1 .. .. .. .. .. 69 .. 37 .. 9 .. Somalia 1975 .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2001 4.0 .. .. .. .. .. .. .. 7 .. .. .. Spain 1991 3.3 .. 0 .. .. .. 78 .. .. .. .. .. 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 1991 4.9 4.0b .. .. 21b .. 80 b 24b 0b 2b .. .. Ukraine 2003 .. .. .. .. .. .. .. .. .. .. .. .. United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom 2001 .. 2.4 .. .. .. .. .. 69 .. 19 .. .. United States 2000 2.7 .. .. .. .. .. 66 .. .. .. 9 7 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. 176 2008 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. 2008 World Development Indicators 177 3.13 Traffic and congestion Motor Passenger Road Fuel Particulate matter vehicles cars density prices concentration km. of road Urban-population- per 100 weighted PM10 per 1,000 per kilometer per 1,000 sq. km. of $ per liter micrograms per people of road people land area Gasoline Diesel cubic meter 1990 2005 1990 2005 1990 2005 2005 2006 2006 1990 2005 Afghanistan .. .. 3 9 .. .. 5 0.68 0.65 79 44 Albania 11 85 3 15 2 61 66 1.44 1.29 92 50 Algeria 55 91 15 27 26 58 5 0.32 0.19 115 71 Angola 19 .. 3 .. .. 8 4 0.50 0.36 142 80 Argentina 181 .. 27 .. 134 146 8 0.62 0.48 105 76 Armenia 5 .. 2 .. 1 .. 27 0.96 0.77 456 68 Australia 530 671 11 17 450 542 11 0.93 0.94 22 15 Austria 421 599 30 36 387 503 162 1.32 1.26 38 34 Azerbaijan 52 61 7 10 36 57 72 0.46 0.41 169 59 Bangladesh 1 1 0 1 0 0 184 0.79 0.45 231 140 Belarus 61 .. 13 21 59 181 46 0.79 0.55 23 7 Belgium 423 529 30 37 385 468 498 1.63 1.34 31 23 Benin 3 .. 2 .. 2 13 17 0.81 0.81 75 41 Bolivia 41 49 6 7 .. 15 6 0.54 0.47 120 97 Bosnia and Herzegovina 114 .. 24 .. 101 .. 43 1.34 1.24 36 19 Botswana 18 113 3 8 10 47 4 0.78 0.74 95 68 Brazil 88 170 8 18 84 136 21 1.26 0.84 40 26 Bulgaria 163 360 39 63 146 314 40 1.05 1.08 111 60 Burkina Faso 4 7 3 7 2 5 34 1.15 1.12 149 94 Burundi .. .. 3 .. .. 1 48 1.20 1.22 56 30 Cambodia .. 36 0 37 .. 25 22 1.01 0.78 116 62 Cameroon 10 11 3 3 6 11 11 1.14 1.07 116 65 Canada .. 582 20 13 468 561 15 0.84 0.78 25 19 Central African Republic 1 .. 0 .. 1 1 4 1.37 1.27 61 49 Chad 2 .. 0 .. 1 .. 3 1.31 1.20 215 123 Chile 81 135 13 26 52 88 11 1.09 0.86 88 53 China 5 24 4 16 1 15 21 0.69 0.61 114 75 Hong Kong, China 66 72 253 254 42 53 188 1.69 1.06 .. .. Colombia 39 59 13 16 21 35 15 0.98 0.57 39 23 Congo, Dem. Rep. .. .. 9 .. 17 .. 7 0.94 1.00 73 50 Congo, Rep. 18 .. 3 .. .. 8 5 0.96 0.67 132 91 Costa Rica 87 198 7 24 55 146 69 0.98 0.67 45 37 Côte d'Ivoire 24 .. 6 .. .. 7 25 1.20 1.06 94 48 Croatia .. 349 34 55 185 312 51 1.34 1.22 50 31 Cuba 37 .. 16 .. 18 .. 55 1.10 0.91 44 17 Czech Republic 246 394 46 31 228 363 165 1.30 1.29 68 22 Denmark 368 437 27 33 320 354 170 1.58 1.45 30 19 Dominican Republic 75 115 48 .. 21 78 26 1.03 0.75 44 23 Ecuador 35 55 8 17 31 32 16 0.47 0.39 38 26 Egypt, Arab Rep. 29 .. 33 .. 21 27 9 0.30 0.12 222 128 El Salvador 33 .. 14 .. 17 24 48 0.82 0.80 46 35 Eritrea 1 .. 1 .. 1 .. 4 1.90 0.81 121 61 Estonia 211 477 22 11 154 367 135 1.23 1.22 45 14 Ethiopia 1 2 2 4 1 1 4 0.93 0.62 112 74 Finland 441 531 29 35 386 460 26 1.55 1.26 24 18 France 494 596 32 38 405 494 173 1.48 1.33 18 14 Gabon 32 .. 4 .. 19 .. 4 0.64 0.39 10 7 Gambia, The .. 7 .. 3 .. 5 37 1.08 1.01 142 95 Georgia .. 71 .. 16 .. 56 29 0.86 0.89 208 51 Germany 405 585 53 208 386 550 .. 1.55 1.38 27 19 Ghana 8 21 4 9 5 5 25 0.86 0.84 39 34 Greece 248 497 22 47 171 388 91 1.16 1.19 64 36 Guatemala 21 68 16 53 11 53 13 0.78 0.64 63 62 Guinea 4 14 1 4 2 8 18 0.79 0.82 107 77 Guinea-Bissau .. .. .. .. .. .. 12 0.00 0.00 118 80 Haiti 8 .. 14 .. 5 .. 15 0.88 0.60 70 39 178 2008 World Development Indicators 3.13 ENVIRONMENT Traffic and congestion Motor Passenger Road Fuel Particulate matter vehicles cars density prices concentration km. of road Urban-population- per 100 weighted PM10 per 1,000 per kilometer per 1,000 sq. km. of $ per liter micrograms per people of road people land area Gasoline Diesel cubic meter 1990 2005 1990 2005 1990 2005 2005 2006 2006 1990 2005 Honduras 22 67 10 31 5 52 12 0.89 0.73 45 46 Hungary 212 316 21 20 188 274 178 1.30 1.31 36 18 India 4 12 2 3 2 8 114 1.01 0.75 112 68 Indonesia 16 109 10 62 7 .. 21 0.57 0.44 138 96 Iran, Islamic Rep. 34 .. 14 .. .. 24 11 0.09 0.03 86 55 Iraq 14 .. 6 .. 1 .. 10 0.03 0.01 146 126 Ireland 270 447 10 20 227 382 140 1.34 1.35 26 17 Israel 210 293 74 115 174 239 81 1.47 1.27 71 31 Italy 529 667 99 81 476 595 165 1.56 1.49 42 28 Jamaica 52 .. 7 .. 43 135 199 0.82 0.75 58 38 Japan 469 586 52 63 283 441 323 1.09 0.90 43 31 Jordan 60 115 26 83 44 78 9 0.86 0.45 110 52 Kazakhstan 76 116 8 17 50 93 3 0.70 0.45 43 19 Kenya 12 18 5 10 .. 9 11 1.12 0.98 66 38 Korea, Dem. Rep. .. .. .. .. .. .. 26 0.71 0.79 181 73 Korea, Rep. 79 319 60 151 48 230 104 1.65 1.33 51 37 Kuwait .. 422 .. 181 .. 349 32 0.22 0.21 82 101 Kyrgyz Republic .. 39 .. 10 .. 39 10 0.64 0.54 76 24 Lao PDR 9 57 3 10 6 .. 14 0.86 0.73 73 47 Latvia 135 377 6 12 106 323 112 1.20 1.15 38 15 Lebanon 321 .. 183 .. 300 403 68 0.74 0.62 43 40 Lesotho 11 .. 4 .. 3 .. 20 0.89 0.88 85 42 Liberia 14 .. 4 .. .. 6 11 0.79 0.85 60 45 Libya 165 257 10 .. 96 232 5 0.13 0.13 106 94 Lithuania 160 467 12 20 133 426 127 1.08 1.09 53 19 Macedonia, FYR 132 163 30 25 121 150 52 1.23 1.09 46 20 Madagascar 6 .. 2 .. 4 .. 9 1.15 1.00 77 35 Malawi 4 .. 4 .. 2 .. 16 1.17 1.12 75 34 Malaysia 124 272 26 72 101 225 30 0.53 0.40 37 25 Mali 3 .. 2 .. 2 .. 2 1.22 1.04 271 171 Mauritania 10 .. 3 .. 7 .. 1 0.97 0.84 145 104 Mauritius 59 130 35 79 44 96 99 0.74 0.56 23 17 Mexico 119 208 41 90 82 137 18 0.74 0.52 69 40 Moldova 53 94 17 31 48 70 39 0.45 0.31 98 38 Mongolia 21 43 1 2 6 28 3 0.88 0.87 65 64 Morocco 37 59 15 29 28 46 13 1.22 0.87 32 22 Mozambique 4 .. 2 .. 3 .. 4 1.15 1.06 110 28 Myanmar 2 5 3 .. 1 4 4 0.66 0.75 116 63 Namibia 71 85 1 4 39 42 5 0.87 0.87 74 42 Nepal .. .. .. .. .. 3 12 0.94 0.73 67 36 Netherlands 405 486 58 62 368 429 372 1.70 1.32 46 35 New Zealand 524 720 19 32 436 607 35 0.98 0.70 16 15 Nicaragua 19 46 5 13 10 18 15 0.67 0.58 49 30 Niger 6 5 .. 4 5 4 1 1.14 1.11 217 149 Nigeria 30 .. 21 .. 12 17 21 0.51 0.66 176 62 Norway 458 546 22 27 380 439 31 1.80 1.66 24 20 Oman 130 .. 9 .. 83 156 11 0.31 0.39 148 132 Pakistan 6 14 4 8 4 10 34 1.01 0.64 212 120 Panama 75 103 18 27 60 73 16 0.70 0.60 58 35 Papua New Guinea 27 .. 6 .. .. 5 4 0.94 0.64 34 24 Paraguay 27 85 4 15 16 50 7 0.97 0.77 106 84 Peru .. 47 43 16 .. 30 6 1.22 0.86 98 61 Philippines 10 34 4 14 7 9 67 0.76 0.67 55 26 Poland 168 386 18 35 138 323 138 1.30 1.30 59 37 Portugal 222 507 34 67 162 471 86 1.56 1.10 52 28 Puerto Rico 295 .. 79 .. 242 .. 289 0.65 0.78 27 21 2008 World Development Indicators 179 3.13 Traffic and congestion Motor Passenger Road Fuel Particulate matter vehicles cars density prices concentration km. of road Urban-population- per 100 weighted PM10 per 1,000 per kilometer per 1,000 sq. km. of $ per liter micrograms per people of road people land area Gasoline Diesel cubic meter 1990 2005 1990 2005 1990 2005 2005 2006 2006 1990 2005 Romania 72 185 11 20 56 149 86 1.26 1.24 36 14 Russian Federation 87 174 14 48 65 161 3 0.77 0.66 41 19 Rwanda 2 3 1 .. 1 1 57 1.11 1.08 49 28 Saudi Arabia 165 .. 19 .. 98 415 8 0.16 0.07 161 120 Senegal 11 14 6 9 8 10 7 1.31 1.09 95 95 Serbiaa 137 199 31 102 133 181 44 1.48 1.31 30 14 Sierra Leone .. 4 .. 2 .. 2 16 0.98 0.98 91 57 Singapore 130 137 142 183 89 101 469 0.92 0.63 106 40 Slovak Republic 194 256 57 32 163 222 89 1.35 1.43 41 16 Slovenia 306 523 42 27 289 471 191 1.23 1.21 40 31 Somalia 2 .. 1 .. 1 .. 4 0.74 0.67 78 32 South Africa 139 143 26 16 97 98 30 0.85 0.84 34 22 Spain 360 550 43 35 309 445 133 1.15 1.10 42 34 Sri Lanka 21 42 4 9 7 13 151 0.88 0.55 95 94 Sudan 9 .. 22 .. 8 .. 1 0.72 0.49 326 173 Swaziland 66 84 18 25 35 40 21 0.80 0.85 60 31 Sweden 464 513 29 11 426 460 104 1.46 1.44 15 12 Switzerland 491 563 46 59 449 520 178 1.27 1.36 37 25 Syrian Arab Republic 26 36 10 7 10 12 52 0.60 0.13 159 79 Tajikistan 3 .. 1 .. 0 19 20 0.80 0.74 104 52 Tanzania 5 .. 2 .. 1 1 9 1.04 0.99 57 24 Thailand 46 .. 36 .. 14 54 11 0.70 0.65 88 77 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 24 .. 11 .. .. 10 14 1.03 1.01 56 36 Trinidad and Tobago 117 .. 19 .. 98 .. 162 0.43 0.24 142 107 Tunisia 48 95 19 49 23 83 12 0.83 0.57 71 32 Turkey 50 117 8 20 34 80 55 1.88 1.62 75 43 Turkmenistan .. .. .. .. .. .. 5 0.02 0.01 177 56 Uganda 2 5 .. 4 1 2 36 1.17 1.01 27 12 Ukraine 63 128 20 36 63 118 29 0.81 0.87 72 23 United Arab Emirates 121 .. 52 .. 97 228 5 0.37 0.53 264 135 United Kingdom 400 517 64 80 341 457 160 1.63 1.73 25 16 United States 756b 814b 30 31 536b 461b,c 71 0.63 0.69 30 22 Uruguay 138 176 45 .. 122 151 44 1.23 0.94 237 161 Uzbekistan .. .. .. .. .. .. 19 0.85 0.54 84 61 Venezuela, RB 93 .. 25 .. 73 94 11 0.03 0.02 22 11 Vietnam .. 8 .. .. .. .. 72 0.67 0.53 124 61 West Bank and Gaza .. 36 .. 26 .. 29 83 1.29 0.98 .. .. Yemen, Rep. 34 .. 8 .. 14 19 14 0.30 0.28 141 82 Zambia 14 .. 3 .. 8 .. 12 1.31 1.22 95 44 Zimbabwe 32 .. 4 .. 29 45 25 0.61 0.65 35 27 World 117 w 148 w .. .. 91 w 118 w 23 w 0.97 m 0.84 m 80 w 53 w Low income 5 9 .. .. 3 8 21 0.98 0.84 130 74 Middle income 36 66 .. .. 22 50 13 0.86 0.74 85 56 Lower middle income 14 31 .. .. 8 21 16 0.85 0.70 107 69 Upper middle income 111 174 .. .. 87 140 12 0.92 0.79 54 33 Low & middle income 24 39 .. .. 15 39 15 0.89 0.79 98 61 East Asia & Pacific 9 24 .. .. 4 14 22 0.53 0.40 112 73 Europe & Central Asia 93 167 .. .. 75 152 9 1.14 1.09 62 29 Latin America & Carib. 100 155 .. .. 71 115 18 0.82 0.67 59 37 Middle East & N. Africa 37 .. .. .. 25 34 7 0.46 0.34 124 77 South Asia 4 12 .. .. 2 8 110 0.91 0.65 132 81 Sub-Saharan Africa 21 .. .. .. 14 .. 7 1.03 0.98 115 60 High income 496 600 .. .. 388 467 55 1.33 1.24 37 27 Euro area 428 604 .. .. 379 418d 139 1.52 1.29 33 24 a. Includes Montenegro. b. Data are from the U.S. Federal Highway Administration. c. Excludes personal passenger vans, passenger minivans, and utility-type vehicles, which are all treated as trucks. d. Data are from the European Commission and the European Road Federation. 180 2008 World Development Indicators 3.13 ENVIRONMENT Traffic and congestion About the data Definitions Traffic congestion in urban areas constrains eco- Because this effort covers data for 1999­2005 only, · Motor vehicles include cars, buses, and freight vehi- nomic productivity, damages people's health, and data in the table for 1990 and 2005 may not be com- cles but not two-wheelers. Population figures refer to degrades the quality of life. The particulate air pollu- parable. Another reason is coverage. For example, for the midyear population in the year for which data are tion emitted by motor vehicles--the dust and soot in the United States the 2005 estimate for passenger available. Roads refer to motorways, highways, main exhaust--is far more damaging to human health than cars from the U.S. Federal Highway Administration or national roads, and secondary or regional roads. A once believed. (For information on particulate matter excludes personal passenger vans, passenger mini- motorway is a road designed and built for motor traf- and other air pollutants, see table 3.14.) vans, and utility-type vehicles, which are all treated fic that separates the traffic flowing in opposite direc- In recent years ownership of passenger cars has as trucks. Moreover, the data do not cover vehicle tions. · Passenger cars are road motor vehicles, increased, and the expansion of economic activ- quality or age. Road density is a rough indicator of other than two-wheelers, intended for the carriage ity has led to the transport by road of more goods accessibility and does not capture the width, type, or of passengers and designed to seat no more than and services over greater distances (see table 5.9). condition of roads. Thus comparisons over time and nine people (including the driver). · Road density These developments have increased demand for across countries should be made with caution. is the ratio of the length of the country's total road roads and vehicles, adding to urban congestion, air Data on fuel prices are compiled by the German network to the country's land area. The road network pollution, health hazards, and traffic accidents and Agency for Technical Cooperation (GTZ), from its global includes all roads in the country-- motorways, high- injuries. Congestion, the most visible cost of expand- network of regional offices and representatives, and ways, main or national roads, secondary or regional ing vehicle ownership, is reflected in the indicators in other sources, including the Allgemeiner Deutscher roads, and other urban and rural roads. · Fuel prices the table. Other relevant indicators--such as aver- Automobile Club (for Europe) and a project of the Latin are the pump prices of the most widely sold grade age vehicle speed in major cities and the cost of American Energy Organization for Latin America. Local of gasoline and of diesel fuel. Prices are converted traffic congestion, which takes a heavy toll on eco- prices are converted to U.S. dollars using the exchange from the local currency to U.S. dollars (see About nomic productivity--are not included because data rate in the Financial Times international monetary table the data). · Particulate matter concentration is fine are incomplete or difficult to compare. on the survey date. When multiple exchange rates exist, suspended particulates of less than 10 microns in The data in the table--except those on fuel prices the market, parallel, or black market rate is used. diameter (PM10) that are capable of penetrating and particulate matter--are compiled by the Interna- Considerable uncertainty surrounds estimates deep into the respiratory tract and causing significant tional Road Federation (IRF) through questionnaires of particulate matter concentrations, and caution health damage. Data are urban-population-weighted sent to national organizations. The IRF uses a hierar- should be used in interpreting them. They allow for PM10 levels in residential areas of cities with more chy of sources to gather as much information as pos- cross-country comparisons of the relative risk of par- than 100,000 residents. The estimates represent sible. Primary sources are national road associations. ticulate matter pollution facing urban residents. Major the average annual exposure level of the average Where an association lacks data or does not respond, sources of urban outdoor particulate matter pollution urban resident to outdoor particulate matter. other agencies are contacted, including road director- are traffic and industrial emissions, but nonanthro- ates, ministries of transport or public works, and cen- pogenic sources such as dust storms may also be tral statistical offices. As a result, data are of uneven a substantial contributor for some cities. Country quality. The coverage of each indicator may differ technology and pollution controls are important deter- across countries because of different definitions. minants of particulate matter. Data on particulate Comparability is also limited when time series data matter for selected cities are in table 3.14. Estimates are reported. The IRF is taking steps to improve the of economic damages from death and illness due to quality of the data in its 2006 World Road Statistics. particulate matter pollution are in table 3.16. 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 2005 Data on vehicles and road density are from the 150 IRF's electronic files and its annual World Road Statistics, except where footnoted. Data on fuel 100 prices are from the GTZ's electronic files. Data on particulate matter concentrations are from Kiran Dev Pandey, David Wheeler, Bart Ostro, 50 Uwe Deichmann, Kirk Hamilton, and Katie Bolt's "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)" (2006). 2008 World Development Indicators 181 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 2005 2004 2001 a 2001 a open fires or stoves without chimneys lead to indoor air Argentina Córdoba 1,423 58 .. 97 pollution, which is responsible for 1.6 million deaths a Australia Melbourne 3,626 12 .. 30 year--one every 20 seconds. In many urban areas air Perth 1,474 12 5 19 pollution exposure is the main environmental threat to Sydney 4,331 20 28 81 health. Long-term exposure to high levels of soot and Austria Vienna 2,260 41 14 42 small particles contributes to a range of health effects, Belgium Brussels 1,012 28 20 48 including respiratory diseases, lung cancer, and heart Brazil Rio de Janeiro 11,469 35 129 .. São Paulo 18,333 40 43 83 disease. Particulate pollution, alone or with sulfur diox- Bulgaria Sofia 1,093 61 39 122 ide, creates an enormous burden of ill health. Canada Montréal 3,640 19 10 42 Sulfur dioxide and nitrogen dioxide emissions Toronto 5,312 22 17 43 lead to deposition of acid rain and other acidic com- Vancouver 2,188 13 14 37 pounds over long distances, which can lead to the Chile Santiago 5,683 61 29 81 leaching of trace minerals and nutrients critical to China Anshan 1,611 82 115 88 trees and plants. Sulfur dioxide emissions can dam- Beijing 10,717 89 90 122 Changchun 3,046 74 21 64 age human health, particularly that of the young and Chengdu 4,065 86 77 74 old. Nitrogen dioxide is emitted by bacteria, motor Chongqing 6,363 123 340 70 vehicles, industrial activities, nitrogen fertilizers, fuel Dalian 3,073 50 61 100 and biomass combustion, and aerobic decomposi- Guangzhou 8,425 63 57 136 tion of organic matter in soils and oceans. Guiyang 3,447 70 424 53 Where coal is the primary fuel for power plants Harbin 3,695 77 23 30 Jinan 2,743 94 132 45 without effective dust controls, steel mills, industrial Kunming 2,837 70 19 33 boilers, and domestic heating, high levels of urban Lanzhou 2,411 91 102 104 air pollution are common-- especially particulates Liupanshui 1,149 59 102 .. and sulfur dioxide. Elsewhere the worst emissions Nanchang 2,188 78 69 29 are from petroleum product combustion. Pingxiang 905 67 75 .. Sulfur dioxide and nitrogen dioxide concentration Quingdao 2,817 68 190 64 data are based on average observed concentrations Shanghai 14,503 73 53 73 Shenyang 4,720 101 99 73 at urban monitoring sites, which not all cities have. Taiyuan 2,794 88 211 55 The data on particulate matter are estimated aver- Tianjin 7,040 125 82 50 age annual concentrations in residential areas away Wulumqi 2,025 57 60 70 from air pollution "hotspots," such as industrial Wuhan 7,093 79 40 43 districts and transport corridors. The data are from Zhengzhou 2,590 97 63 95 the World Bank's Development Research Group and Zibo 2,982 74 198 43 Environment Department estimates of annual ambi- Colombia Bogotá 7,747 31 .. .. Croatia Zagreb 908b 33 31 .. ent concentrations of particulate matter in cities Cuba Havana 2,189 21 1 5 with populations exceeding 100,000 (Pandey and Czech Republic Prague 1,171 23 14 33 others 2006b). A country's technology and pollution Denmark Copenhagen 1,088 21 7 54 controls are important determinants of particulate Ecuador Guayaquil 2,387 23 15 .. matter concentrations. Quito 1,514 30 22 .. Pollutant concentrations are sensitive to local con- Egypt, Arab Rep. Cairo 11,128 169 69 .. Finland Helsinki 1,091 21 4 35 ditions, and even monitoring sites in the same city France Paris 9,820 11 14 57 may register different levels. Thus these data should Germany Berlin 3,389 22 18 26 be considered only a general indication of air qual- Frankfurt 668b 19 11 45 ity, and comparisons should be made with caution. Munich 1,263 20 8 53 Current World Health Organization (WHO) air quality Ghana Accra 1,981 33 .. .. guidelines are annual mean concentrations of 20 Greece Athens 3,230 43 34 64 micrograms per cubic meter for particulate matter Hungary Budapest 1,693 19 39 51 Iceland Reykjavik 164b 18 5 42 less than 10 microns in diameter and 40 micrograms India Ahmadabad 5,120 83 30 21 for nitrogen dioxide and daily mean concentrations of Bengaluru 6,462 45 .. .. 20 micrograms per cubic meter for sulfur dioxide. 182 2008 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- 2005 2004 2001 a 2001 a ticulates of less than 10 microns in diameter (PM10) India Chennai 6,916 37 15 17 that are capable of penetrating deep into the respi- Delhi 15,048 150 24 41 ratory tract and causing significant health damage. Hyderabad 6,115 41 12 17 Data are urban- population-weighted PM10 levels in Kanpur 3,018 109 15 14 Kolkata 14,277 128 49 34 residential areas of cities with more than 100,000 Lucknow 2,566 109 26 25 residents. The estimates represent the average Mumbai 18,196 63 33 39 annual exposure level of the average urban resident Nagpur 2,350 56 6 13 to outdoor particulate matter. · Sulfur dioxide is an Pune 4,409 47 .. .. air pollutant produced when fossil fuels containing Indonesia Jakarta 13,215 104 .. .. Iran, Islamic Rep. Tehran 7,314 58 209 .. sulfur are burned. · Nitrogen dioxide is a poisonous, Ireland Dublin 1,037 19 20 .. pungent gas formed when nitric oxide combines with Italy Milan 2,953 30 31 248 hydrocarbons and sunlight, producing a photochemi- Rome 3,348 29 .. .. cal reaction. These conditions occur in both natural Turin 1,660 44 .. .. and anthropogenic activities. Japan Osaka-Kobe 11,268 35 19 63 Tokyo 35,197 40 18 68 Yokohama 3,366b 31 100 13 Kenya Nairobi 2,773 43 .. .. Korea, Rep. Pusan 3,554 44 60 51 Seoul 9,645 41 44 60 Taegu 2,511 50 81 62 Malaysia Kuala Lumpur 1,405 29 24 .. Mexico Mexico City 19,411 51 74 130 Netherlands Amsterdam 1,147 34 10 58 New Zealand Auckland 1,148 14 3 20 Norway Oslo 802 14 8 43 Philippines Manila 10,686 39 33 .. Poland Katowice 2,914b 39 83 79 Lódz 776 39 21 43 Warsaw 1,680 43 16 32 Portugal Lisbon 2,761 23 8 52 Romania Bucharest 1,934 18 10 71 Russian Federation Moscow 10,654 21 109 .. Omsk 1,132 22 20 34 Singapore Singapore 4,326 44 20 30 Slovak Republic Bratislava 456b 15 21 27 South Africa Cape Town 3,083 16 21 72 Durban 2,631 32 31 .. Johannesburg 3,254 33 19 31 Data sources Spain Barcelona 4,795 35 11 43 Madrid 5,608 30 24 66 Data on city population are from the United Sweden Stockholm 1,708 11 3 20 Nations Population Division. Data on particulate Switzerland Zurich 1,144 23 11 39 matter concentrations are from Kiran D. Pandey, Thailand Bangkok 6,593 79 11 23 David Wheeler, Bart Ostro, Uwe Deichman, Kirk Turkey Ankara 3,573 46 55 46 Istanbul 9,712 55 120 .. Hamilton, and Kathrine Bolt's "Ambient Particulate Ukraine Kiev 2,672 35 14 51 Matter Concentration in Residential and Pollution United Kingdom Birmingham 2,280 25 9 45 Hotspot Areas of World Cities: New Estimates London 8,505 21 25 77 Based on the Global Model of Ambient Particu- Manchester 2,228 15 26 49 lates (GMAPS)" (2006). Data on sulfur dioxide United States Chicago 8,814 25 14 57 Los Angeles 12,298 34 9 74 and nitrogen dioxide concentrations are from the New York-Newark 18,718 21 26 79 WHO's Healthy Cities Air Management Information Venezuela, RB Caracas 2,913 10 33 57 System and the World Resources Institute. a. Data are for the most recent year available. b. Data are for 2000. 2008 World Development Indicators 183 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 2004 Afghanistan 2002 2004f 2004f 2002 1985f 1995f Albania 1993 1995 1999 f 1999 f 2003f 1994f 2005f 2003f 2000 f 2004 Algeria 2001 1994 1992f 1992f 1996 1995 2005f 1983f 1996 2006 Angola 2000 2000 f 2000 f 1994 1998 2007 1997 2006 Argentina 1992 1994 1990 1990 1995 1994 2001 1981 1997 2005 Armenia 1994 1999 f 1999 f 2002f 1993d 2003f 1997 2003 Australia 1992 1994 1994 1987f 1989 1994 1993 2007d 1976 2000 2004 Austria 1994 1987 1989 1995 1994 2002 1982f 1997f 2002 Azerbaijan 1998 1995 1996f 1996f 2000e 2000 f 1998f 1998f 2004f Bangladesh 1991 1990 1994 1990 f 1990 f 2001 1994 2001f 1981 1996 2007 Belarus 2000 1986d 1988d 2006f 1993 2005d 1995f 2001f 2004f Belgium 1996 1988 1988 1998 1996 2002 1983 1997f 2006 Benin 1993 1994 1993f 1993f 1997 1994 2002f 1984f 1996 2004 Bolivia 1994 1988 1995 1994f 1994f 1995 1994 1999 1979 1996 2003 Bosnia and Herzegovina 2000 1992g 1992g 1994g 2002f 2007 2002 2002f Botswana 1990 1991 1994 1991f 1991f 1994 1995 2003f 1977f 1996 2002f Brazil 1988 1994 1990 f 1990 f 1994 1994 2002 1975 1997 2004 Bulgaria 1994 1995 1990 f 1990 f 1996 1996 2002 1991f 2001f 2004 Burkina Faso 1993 1994 1989 1989 2005 1993 2005f 1989 f 1996 2004 Burundi 1994 1989 1997 1997f 1997f 1997 2001f 1988f 1997 2005 Cambodia 1999 1996 2001f 2001f 1995f 2002f 1997 1997 2006 Cameroon 1989 1995 1989 f 1989 f 1994 1994 2002f 1981f 1997 Canada 1990 1994 1994 1986 1988 2003 1992 2002 1975 1995 2001 Central African Republic 1995 1993f 1993f 1995 1980 f 1996 Chad 1990 1994 1989 f 1994 1994 1989 f 1996 2004 Chile 1993 1995 1990 1990 1997 1994 2002 1975 1997 2005 China 1994 1994 1994 1989 f 1991f 1996 1993 2002e 1981f 1997 2004 Hong Kong, China Colombia 1998 1988 1995 1990 f 1993f 1994 2001f 1981 1999 Congo, Dem. Rep. 1990 1995 1994f 1994f 1995 1996 2005f 1976f 1997 2005f Congo, Rep. 1990 1997 1994f 1994f 1994 2007 1983f 1999 2007 Costa Rica 1990 1992 1994 1991f 1991f 1994 1994 2002 1975 1998 2007 Côte d'Ivoire 1994 1991 1995 1993f 1993f 1994 1994 2007 1994f 1997 2004 Croatia 2001 2000 1996 1991d 1991d 1994g 1996 2007d 2000 f 2000 d 2007 Cuba 1994 1992f 1992f 1994 1994 2002 1990 f 1997 Czech Republic 1994 1994 1993d 1993d 1996 1993e 2001e 993g 2000 f 2002 Denmark 1994 1994 1988 1988 2004 1993 2002 1977 1995f 2003 Dominican Republic 1995 1999 1993f 1993f 1996 2002f 1986f 1997f 2007 Ecuador 1993 1995 1994 1990 f 1990 f 1993 2000 1975 1995 2004 Egypt, Arab Rep. 1992 1988 1995 1988 1988 1994 1994 2005f 1978 1995 2003 El Salvador 1994 1988 1996 1992 1992 1994 1998 1987f 1997f Eritrea 1995 1995 2005f 2005f 1996f 2005f 1994f 1996 2005f Estonia 1998 1994 1996f 1996f 2005f 1994 2002 1992f Ethiopia 1994 1991 1994 1994f 1994f 1994 2005f 1989 f 1997 2003 Finland 1995 1994 1986 1988 1996 1994 d 2002 1976f 1995d 2002d France 1990 1994 1987e 1988e 1996 1994 2002e 978 1997 2004 e Gabon 1990 1998 1994f 1994f 1998 1997 2006d 1989 f 1996f 2007 Gambia, The 1992 1989 1994 1990 f 1990 f 1994 1994 2001f 1977f 1996 2006 Georgia 1998 1994 1996f 1996f 1996f 1994f 1999 f 1996f 1999 2006 Germany 1994 1988 1988 1994f 1993 2002 1976 1996 2002 Ghana 1992 1988 1995 1989 f 1989 1994 1994 2003f 1975 1996 2003 Greece 1994 1988 1988 1995 1994 2002 1992f 1997 2006 Guatemala 1994 1988 1996 1987f 1989 f 1997 1995 1999 1979 1998f Guinea 1994 1988 1994 1992f 1992f 1994 1993 2000 f 1981f 1997 Guinea-Bissau 1993 1991 1996 2002 f 2002f 1994 1995 2005d 1990 f 1995 Haiti 1999 1996 2000 f 2000 f 1996 1996 2005f 1996 184 2008 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 2004 Honduras 1993 1996 1993f 1993f 1994 1995 2000 1985f 1997 2005 Hungary 1995 1994 1988f 1989 f 2002 1994 2002f 1985f 1999 f India 1993 1994 1994 1991f 1992f 1995 1994 2002f 1976 1996 2006 Indonesia 1993 1993 1994 1992f 1992 1994 1994 2004 1978f 1998 Iran, Islamic Rep. 1996 1990 f 1990 f 1996 2005f 1976 1997 2006 Iraq 1994 Ireland 1994 1988f 1988 1996 1996 2002 2002 1997 Israel 1996 1992f 1992 1995 2004 1979 1996 Italy 1994 1988 1988 1995 1994 2002 1979 1997 Jamaica 1994 1995 1993f 1993f 1994 1995 1999 f 1997f 1997f 2007 Japan 1994 1988 f 1988 1996 1993d 2002d 1980 1998d 2002f Jordan 1991 1994 1989 f 1989 f 1995f 1993 2003f 1978f 1996 2004 Kazakhstan 1995 1998f 1998f 1994 2000 f 1997 Kenya 1994 1992 1994 1988f 1988 1994 1994 2005f 1978 1997 2004 Korea, Dem. Rep. 1995 1995f 1995f 1994 e 2005f 2003f 2002f Korea, Rep. 1994 1992 1992 1996 1994 2002 1993f 1999 2007 Kuwait 1995 1992f 1992f 1994 2002 2005f 2002 1997 2006 Kyrgyz Republic 1995 2000 2000 f 2000 f 1996e 2003f 1997f 2006 Lao PDR 1995 1995 1998f 1998f 1998 1996e 2003f 2004f 1996d 2006 Latvia 1995 1995f 1995f 2004f 1995 2002 1997f 2002f 2004 Lebanon 1995 1993f 1993f 1995 1994 2006 1996 2003 Lesotho 1989 1995 1994f 1994f 1995 2000 f 2003 1995 2002 Liberia 2003 1996f 1996f 2000 2002f 2005f 1998f 2002f Libya 1999 1990 f 1990 f 2001 2006 2003f 1996 2005f Lithuania 1995 1995f 1995f 2003f 1996 2003 2001f 2003f 2006 Macedonia, FYR 1998 1994g 1994g 1994g 1997f 2004f 2000 f 2002f 2004 Madagascar 1988 1991 1999 1996f 1996f 2001 1996 2003f 1975 1997 Malawi 1994 1994 1991f 1991f 1994 2001f 1982f 1996 Malaysia 1991 1988 1994 1989 f 1989 f 1996 1994 2002 1977f 1997 Mali 1989 1995 1994f 1994f 1994 1995 2002 1994f 1995 2003 Mauritania 1988 1994 1994f 1994f 1996 1996 2005f 1998f 1996 2005 Mauritius 1990 1994 1992f 1992f 1994 1992 2001f 1975 1996 2004 Mexico 1988 1994 1987 1988 1994 1993 2000 1991f 1995 2003 Moldova 2002 1995 1996f 1996f 2007 1995 2003f 2001f 1999 f 2004 Mongolia 1995 1994 1996f 1996f 1996 1993 1999 f 1996f 1996 2004 Morocco 1988 1996 1995 1995 1995 2002f 1975 1996 2004 Mozambique 1994 1995 1994f 1994f 1997 1995 2005f 1981f 1997 2005 Myanmar 1989 1995 1993f 1993f 1996 1995 2003f 1997f 1997f 2004f Namibia 1992 1995 1993f 1993f 1994 1997 2003f 1990 f 1997 2005f Nepal 1993 1994 1994f 1994f 1998 1993 2005f 1975f 1996 2007 Netherlands 1994 1994 1988f 1988d 1996 1994 d 2002f 1984 1995d 2002d New Zealand 1994 1994 1987 1988 1996 1993 2002 1989 f 2000 f 2004 Nicaragua 1994 1996 1993f 1993f 2000 1995 1999 1977f 1998 Niger 1991 1995 1992f 1992f 1995 2004 1975 1996 2006 Nigeria 1990 1992 1994 1988f 1988f 1994 1994 2004f 1974 1997 2004 Norway 1994 1994 1986 1988 1996 1993 2002 1976 1996 2002 Oman 1995 1999 f 1999 f 1994 1995 2005f 1996f 2005 Pakistan 1994 1991 1994 1992f 1992f 1997 1994 2005f 1976f 1997 Panama 1990 1995 1989 f 1989 1996 1995 1999 1978 1996 2003 Papua New Guinea 1992 1993 1994 1992f 1992f 1997 1993 2002 1975f 2000 f 2003 Paraguay 1994 1992f 1992f 1994 1994 1999 1976 1997 2004 Peru 1988 1994 1989 1993f 1993 2002 1975 1995 2005 Philippines 1989 1989 1994 1991f 1991 1994 1993 2003 1981 2000 2004 Poland 1993 1991 1994 1990 f 1990 f 1998 1996 2002 1989 2001f Portugal 1995 1994 1988f 1988 1997 1993 2002e 1980 1996 2004 d Puerto Rico 2008 World Development Indicators 185 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 2004 Romania 1995 1994 1993f 1993f 1996 1994 2001 1994f 1998f 2004 Russian Federation 1999 1994 1995 1986d 1988d 1997 1995 2004 1992 2003f Rwanda 1991 1998 2001f 2001f 1996 2004f 1980 f 1998 2002f Saudi Arabia 1995 1993f 1993f 1996 2001e 2005f 1996f 1997f Senegal 1984 1991 1995 1993f 1993 1994 1994 2001f 1977f 1995 2003 Serbia 2001 2001g 2001g 2001g 2002 2002 2002 Sierra Leone 1994 1995 2001f 2001f 1994 1994 e 2006f 1994f 1997 2003f Singapore 1993 1995 1997 1989 f 1989 f 1994 1995 2006f 1986f 1999 f 2005 Slovak Republic 1994 1993g 1993g 1996 1994 e 2002 1993 2001f 2002 Slovenia 1994 1996 1992g 1992g 1995g 1996 2002 2000 f 2001f 2004 Somalia 2001f 2001f 1994 1985f 2002f South Africa 1993 1997 1990 f 1990 f 1997 1995 2002f 1975 1997 2002 Spain 1994 1988f 1988 1997 1995 2002 1986f 1996 2004 Sri Lanka 1994 1991 1994 1989 f 1989 f 1994 1994 2002f 1979 f 1998f Sudan 1994 1993f 1993f 1994 1995 2004f 1982 1995 2006 Swaziland 1997 1992f 1992f 1994 1997f 1996 2006 Sweden 1994 1986 1988 1996 1993 2002 1974 1995 2002 Switzerland 1994 1987 1988 1994 2006f 1974 1996 2003 Syrian Arab Republic 1999 1996 1989 f 1989 f 1996 2006f 2003f 1997 2005 Tajikistan 1998 1996f 1998f 1997e 1997f 2007 Tanzania 1994 1988 1996 1993f 1993f 1994 1996 2002f 1979 1997 2004 Thailand 1995 1989 f 1989 2004 2002 1983 2001f 2005 Timor-Leste Togo 1991 1995 1991f 1991 1994 1995d 2004f 1978 1995d 2004 Trinidad and Tobago 1994 1989 f 1989 f 1994 1996 1999 1984f 2000 f 2002f Tunisia 1994 1988 1994 1989 f 1989 f 1994 1993 2003f 1974 1995 2004 Turkey 1998 2004 1991f 1991f 1997 1996f 1998 Turkmenistan 1995 1993f 1993f 1996e 1999 1996 Uganda 1994 1988 1994 1988f 1988 1994 1993 2002f 1991f 1997 2004f Ukraine 1999 1997 1986d 1988d 1999 1995 2004 1999 f 2002f United Arab Emirates 1996 1989 f 1989 f 2000 2005f 1990 f 1998f 2002 United Kingdom 1995 1994 1994 1987 1988 1997f 1994 2002 1976 1996 2005 United States 1995 1995 1994 1986 1988 1974 2000 Uruguay 1994 1989 f 1991f 1994 1993 2001 1975 1999 f 2004 Uzbekistan 1994 1993f 1993f 1995e 1999 1997f 1995 Venezuela 1995 1988f 1989 1994 1977 1998f 2005 Vietnam 1993 1995 1994f 1994f 2006f 1994 2002 1994f 1998f 2002 West Bank and Gaza Yemen, Rep. 1996 1992 1996 1996f 1996f 1994 1996 2004f 1997f 1997f 2004 Zambia 1994 1994 1990 f 1990 f 1994 1993 2006f 1980 f 1996 2006 Zimbabwe 1987 1994 1992f 1992f 1994 1994 1981f 1997 a. Ratification of the treaty. b. Year the treaty entered into force in the country. c. Convention became effective November 16, 1994. d. Acceptance. e. Approval. f. Accession. g. Succession. 186 2008 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 2008 World Development Indicators 187 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Afghanistan .. 7.7 .. .. 0.0 .. 0.9 0.1 0.9 .. Albania 16.7 10.5 6.1 2.8 2.0 0.0 0.0 0.2 0.2 6.5 Algeria .. 11.8 .. 4.5 58.1 0.1 0.1 1.3 0.3 .. Angola 42.2 12.2 30.0 3.0 68.8 0.0 0.0 0.2 1.6 ­37.6 Argentina 27.1 12.0 15.1 4.0 12.8 1.0 0.0 0.5 1.6 3.2 Armenia 29.3 10.2 19.2 3.0 0.0 2.2 0.0 0.6 1.8 17.6 Australia 21.9a 15.1 6.8 4.7 3.6 5.1 0.0 0.4 0.1 2.4 Austria 25.8 14.2 11.6 5.3 0.3 0.0 0.0 0.2 0.3 16.1 Azerbaijan 57.0 12.1 44.9 2.8 83.8 0.0 0.0 2.4 1.1 ­39.7 Bangladesh 31.6 8.1 23.6 1.9 5.6 0.0 0.7 0.4 0.5 18.3 Belarus 26.4 11.2 15.2 5.7 2.1 0.0 0.0 1.6 .. 17.2b Belgium 23.9 15.4 8.5 5.9 0.0 0.0 0.0 0.2 0.2 14.1 Benin .. 8.5 .. 3.6 0.0 0.0 0.9 0.3 0.4 .. Bolivia 26.9 9.8 17.1 6.3 40.6 4.9 0.0 0.7 1.4 ­24.2 Bosnia and Herzegovina 6.0 10.4 ­4.4 .. 0.3 0.0 .. 1.3 0.1 .. Botswana 55.8 12.5 43.3 8.6 0.3 7.0 0.0 0.3 .. 44.2b,c Brazil 17.8 12.0 5.8 4.3 3.7 2.3 0.0 0.2 0.3 3.5 Bulgaria 15.5 11.9 3.6 4.2 0.9 2.0 0.0 1.2 1.6 2.1 Burkina Faso .. 8.1 .. 4.5 0.0 0.0 0.9 0.1 1.4 .. Burundi 2.0 6.4 ­4.4 5.1 0.0 0.8 10.5 0.2 0.1 ­10.9 Cambodia 18.1 8.7 9.5 1.8 0.0 0.0 0.3 0.1 0.4 10.5 Cameroon 17.5 9.4 8.1 1.6 14.9 0.1 0.0 0.2 0.8 ­6.2 Canada 23.7a 14.6 9.1 5.2 7.4 1.1 0.0 0.3 0.1 5.4 Central African Republic .. 7.8 .. 1.6 0.0 0.0 0.0 0.1 0.4 .. Chad 29.5 10.9 18.6 1.3 65.4 0.0 0.0 0.0 1.1 ­46.6 Chile 27.6 14.2 13.4 3.7 0.6 27.5 0.0 0.4 0.6 ­12.1 China 53.8 10.2 43.6 1.8 5.8 0.7 0.0 1.3 1.5 36.1 Hong Kong, China 31.9 13.9 18.1 3.4 0.0 0.0 0.0 0.2 .. 21.3b Colombia 20.9 11.4 9.5 5.0 9.7 1.7 0.0 0.3 0.1 2.5 Congo, Dem. Rep. 9.3 6.8 2.5 0.9 4.8 4.2 0.0 0.2 0.6 ­6.3 Congo, Rep. .. .. .. 2.6 .. .. .. .. 1.0 .. Costa Rica 19.4 6.2 13.2 4.0 0.0 0.0 0.2 0.2 0.3 16.5 Côte d'Ivoire 15.1 9.6 5.6 4.7 12.5 0.0 0.0 0.3 0.4 ­3.0 Croatia 24.7 12.9 11.8 4.5 2.1 0.0 0.2 0.4 0.5 13.0 Cuba .. .. .. 7.1 .. .. .. .. 0.1 .. Czech Republic 25.4 13.7 11.7 4.2 0.3 0.0 0.1 0.7 0.1 14.7 Denmark 25.0 15.0 10.0 8.1 3.4 0.0 0.0 0.1 0.1 14.4 Dominican Republic 20.7 11.6 9.1 1.9 0.0 3.4 0.0 0.6 0.1 6.9 Ecuador 28.1 11.4 16.7 1.4 28.8 0.4 0.0 0.5 0.1 ­11.8 Egypt, Arab Rep. 22.1 9.8 12.3 4.4 24.4 0.2 0.2 1.1 1.0 ­10.2 El Salvador 11.9 11.0 0.9 2.8 0.0 0.0 0.5 0.3 0.3 2.7 Eritrea 8.7 7.2 1.5 4.0 0.0 0.0 1.1 0.4 0.4 3.5 Estonia 25.7 12.9 12.8 5.1 37.6 0.0 0.0 1.1 0.0 ­20.9 Ethiopia 9.4 6.9 2.5 4.0 0.0 0.6 6.8 0.5 0.3 ­1.7 Finland 26.5 15.8 10.8 6.0 0.0 0.2 0.0 0.2 0.1 16.3 France 18.8 12.5 6.3 5.3 0.0 0.0 0.0 0.1 0.0 11.4 Gabon 52.4 15.3 37.1 3.3 60.8 0.0 0.0 0.2 .. ­20.6b Gambia, The 10.3 7.8 2.5 2.0 0.0 0.0 0.6 0.5 0.8 2.7 Georgia 7.7 10.4 ­2.7 2.8 0.4 0.0 0.0 0.5 1.4 ­2.2 Germany 22.9 14.7 8.2 4.5 0.3 0.0 0.0 0.2 0.1 12.1 Ghana 27.4 8.5 18.9 4.7 0.0 7.7 1.6 0.5 0.1 13.8 Greece 16.4 6.9 9.5 3.4 0.2 0.2 0.0 0.3 0.7 11.6 Guatemala 14.5 10.8 3.7 1.6 1.1 0.0 0.7 0.2 0.4 2.9 Guinea 8.6 7.9 0.7 2.0 0.0 9.5 2.1 0.3 0.4 ­9.7 Guinea-Bissau 23.5 7.1 16.4 2.3 0.0 0.0 0.0 0.7 1.0 17.0 Haiti .. 9.6 .. 1.5 0.0 0.0 0.8 0.3 0.4 .. 188 2008 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Honduras 32.3 10.0 22.3 3.5 0.0 2.4 0.0 0.5 0.4 22.5 Hungary 20.3 13.6 6.6 5.5 1.3 0.0 0.0 0.4 0.1 10.3 India 33.7 9.0 24.7 3.9 4.3 1.2 0.5 1.3 0.7 20.6 Indonesia 27.6 10.4 17.2 0.9 11.4 3.1 0.0 0.7 1.2 1.7 Iran, Islamic Rep. 40.7 11.0 29.7 4.4 54.2 0.5 0.0 1.3 0.8 ­22.7 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 37.7a 10.7 27.0 5.3 3.4 0.4 0.0 0.2 0.0 28.2 Israel .. 16.7 .. 6.7 0.3 0.0 0.0 0.4 0.4 .. Italy 19.3 13.4 5.9 4.5 0.3 0.0 0.0 0.2 0.2 9.7 Jamaica .. 7.6 .. 4.5 0.0 3.5 0.0 0.8 0.2 .. Japan 27.3a 13.9 13.4 3.1 0.0 0.0 0.0 0.2 0.5 15.8 Jordan 13.8 10.2 3.6 5.6 0.4 0.0 0.0 1.0 0.7 7.1 Kazakhstan 34.5 13.1 21.3 4.4 52.4 4.2 0.0 2.1 0.3 ­33.2 Kenya 10.1 9.6 0.5 6.3 0.0 0.1 1.0 0.4 0.1 5.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 30.5 13.3 17.1 4.0 0.1 0.0 0.0 0.4 0.6 20.0 Kuwait .. .. .. 4.2 .. .. 0.0 .. 1.4 .. Kyrgyz Republic 4.5 8.5 ­4.0 4.4 1.1 0.0 0.0 1.3 0.3 ­2.3 Lao PDR 21.8 9.7 12.1 1.1 0.0 0.0 0.0 0.4 0.8 12.1 Latvia 17.5 18.2 ­0.7 5.6 0.0 0.0 0.7 0.3 0.0 3.8 Lebanon ­4.5 11.9 ­16.4 2.5 0.0 0.0 0.0 0.6 0.9 ­15.4 Lesotho 21.8 7.1 14.7 9.3 0.0 0.0 1.4 0.0 0.2 22.4 Liberia .. 8.7 .. .. 0.0 0.1 6.0 0.6 0.5 .. Libya .. 12.2 .. .. 81.3 0.0 0.0 0.8 .. .. Lithuania 14.5 13.0 1.5 5.1 0.3 0.0 0.1 0.4 0.2 5.6 Macedonia, FYR 22.1 10.8 11.3 4.9 0.0 0.0 0.2 1.4 0.1 14.5 Madagascar 16.3 7.6 8.7 2.7 0.0 0.0 0.0 0.3 0.2 10.9 Malawi 15.7 7.3 8.4 4.9 0.0 0.0 0.6 0.2 0.2 12.2 Malaysia 32.7 12.1 20.6 5.8 22.1 0.0 0.0 0.8 0.1 3.3 Mali 13.8 8.8 5.0 3.6 0.0 0.0 0.0 0.1 1.6 7.0 Mauritania 27.5 8.7 18.8 2.4 0.0 24.2 0.4 0.8 2.8 ­7.0 Mauritius 18.9 11.5 7.4 3.8 0.0 0.0 0.0 0.4 .. 10.8b Mexico 22.2 12.4 9.8 5.3 10.6 0.6 0.0 0.4 0.4 3.1 Moldova 20.3 8.1 12.2 3.6 0.1 0.0 0.0 1.7 0.7 13.3 Mongolia 45.3 7.9 37.3 5.1 2.8 26.4 0.0 2.5 1.1 9.7 Morocco 35.0 10.5 24.5 6.5 0.2 0.8 0.0 0.5 0.1 29.4 Mozambique 3.5 8.5 ­5.1 3.7 11.5 0.0 0.5 0.2 0.2 ­13.8 Myanmar .. .. .. 0.8 .. .. .. .. 0.6 .. Namibia 42.7 11.0 31.6 7.3 0.0 5.2 0.0 0.3 0.1 33.4 Nepal 28.0 7.6 20.4 2.6 0.0 0.0 2.1 0.3 0.1 20.5 Netherlands 28.7 14.5 14.2 5.2 2.5 0.0 0.0 0.2 0.6 16.0 New Zealand 21.2a 13.7 7.5 7.0 1.5 0.3 0.0 0.3 0.0 12.4 Nicaragua 13.6 9.5 4.1 3.0 0.0 1.1 0.0 0.6 0.1 5.3 Niger .. 7.4 .. 2.3 0.0 0.0 2.6 0.3 0.9 .. Nigeria 38.8 10.2 28.5 0.9 57.7 0.0 0.2 0.4 0.7 ­29.6 Norway 38.8a 13.3 25.4 7.0 23.0 0.0 0.0 0.1 0.1 9.2 Oman .. .. .. 3.7 .. .. 0.0 .. 1.4 .. Pakistan 23.1 8.4 14.7 1.8 7.2 0.0 0.4 0.8 1.5 6.8 Panama 18.6 12.3 6.3 4.4 0.0 0.0 0.0 0.3 0.2 10.2 Papua New Guinea .. 10.2 .. .. 23.8 48.5 0.0 0.4 0.0 .. Paraguay 7.3 9.9 ­2.6 4.1 0.0 0.0 0.0 0.3 0.7 0.5 Peru 25.1 12.0 13.1 2.5 3.2 14.8 0.0 0.3 0.7 ­3.4 Philippines 30.5 8.4 22.1 2.4 0.7 1.2 0.1 0.5 0.3 21.7 Poland 18.8 12.8 6.0 5.4 1.3 1.1 0.0 0.8 0.4 7.8 Portugal 12.7 17.4 ­4.7 5.7 0.0 0.2 0.0 0.2 0.4 0.1 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 189 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Romania 13.0 12.0 0.9 3.3 4.5 0.2 0.0 0.7 0.0 ­1.2 Russian Federation 30.7 7.0 23.7 3.5 37.5 1.9 0.0 1.4 0.3 ­13.8 Rwanda 13.9 7.4 6.4 3.5 0.0 0.0 2.3 0.2 0.1 7.3 Saudi Arabia .. .. .. 7.2 .. .. 0.0 .. 1.4 .. Senegal 18.8 9.0 9.8 4.6 0.0 0.1 0.0 0.4 1.1 12.6 Serbiad 9.0 .. .. .. 2.2 0.1 .. 1.5 .. .. Sierra Leone 9.7 7.5 2.2 4.5 0.0 0.0 1.7 0.5 1.1 3.5c Singapore 47.8a 15.0 32.8 2.5 0.0 0.0 0.0 0.4 0.8 34.0 Slovak Republic 21.2 21.9 ­0.8 4.1 0.1 0.0 0.4 0.6 0.0 2.2 Slovenia 26.3 13.5 12.8 5.6 5.1 0.0 0.2 0.3 0.2 12.5 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 14.2 11.9 2.3 5.3 3.5 3.1 0.1 1.1 0.1 ­0.3 Spain 22.4 14.5 7.9 3.9 0.0 0.0 0.0 0.2 0.4 11.2 Sri Lanka 24.9 9.7 15.1 2.6 0.0 0.0 0.3 0.3 0.4 16.7 Sudan 15.5 10.0 5.4 0.9 21.6 0.2 0.0 0.2 0.4 ­16.2 Swaziland 18.6 10.4 8.2 6.2 0.0 0.0 0.0 0.3 0.1 14.1 Sweden 24.8 12.1 12.7 7.3 0.0 0.5 0.0 0.1 0.0 19.4 Switzerland .. 13.5 .. 5.1 0.0 0.0 0.0 0.1 0.2 .. Syrian Arab Republic 17.6 10.3 7.3 2.6 31.7 0.0 0.0 1.2 0.9 ­24.0 Tajikistan 12.2 8.3 3.9 3.2 0.6 0.0 0.0 1.6 0.4 4.5 Tanzania 11.4 7.8 3.6 2.4 0.3 4.7 0.0 0.2 0.1 0.6 Thailand 32.1 11.2 20.9 4.7 5.8 0.0 0.2 1.0 0.4 18.1 Timor-Leste 104.5 3.3 101.3 .. .. .. .. .. .. .. Togo .. 7.8 .. 2.5 0.0 0.3 2.8 0.6 0.2 .. Trinidad and Tobago .. 12.1 .. 4.0 71.7 0.0 0.0 1.6 0.2 .. Tunisia 26.9 11.4 15.5 6.7 7.4 0.4 0.1 0.6 0.3 13.4 Turkey 16.6 11.7 4.9 3.5 0.4 0.1 0.0 0.5 1.2 6.2 Turkmenistan .. 11.0 .. .. .. 0.0 .. 2.9 1.0 .. Uganda 14.7 7.7 7.0 4.0 0.0 0.0 4.5 0.2 .. 6.3b Ukraine 23.2 10.6 12.6 4.4 9.7 0.0 0.0 2.8 0.5 4.1 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 14.2 10.2 4.0 5.3 2.2 0.0 0.0 0.2 0.0 6.9 United States 14.1a 12.2 1.9 4.8 1.8 0.1 0.0 0.3 0.3 4.1 Uruguay 14.3 12.1 2.3 2.6 0.0 0.0 0.2 0.2 1.9 2.6 Uzbekistan 36.0 8.7 27.4 9.4 .. 0.0 0.0 6.3 0.9 .. Venezuela, RB 39.8 12.0 27.8 4.4 39.8 1.1 0.0 0.7 0.0 ­9.5 Vietnam 37.7 9.0 28.7 2.8 17.9 0.1 0.4 1.1 0.6 11.6 West Bank and Gaza 10.0 9.1 1.0 .. 0.0 0.0 .. .. .. .. Yemen, Rep. .. 9.8 .. .. 42.8 0.0 0.0 0.7 0.9 .. Zambia 25.3 10.1 15.3 2.2 0.1 31.0 0.0 0.2 0.7 ­14.4 Zimbabwe .. .. .. 6.9 .. .. .. .. 0.1 .. World 21.8 w 12.4 w 9.3 w 4.4 w 4.1 w 0.5 w 0.0 w 0.4 w 0.4 w 8.3 w Low income 30.5 9.0 21.5 3.4 9.4 1.3 0.6 1.0 0.7 11.9 Middle income 30.5 10.9 19.6 3.5 12.8 1.6 0.0 0.9 0.8 7.0 Lower middle income 41.4 10.4 31.0 2.5 11.1 1.1 0.0 1.2 1.1 18.9 Upper middle income 22.3 11.4 10.9 4.4 14.4 2.0 0.0 0.7 0.4 ­2.2 Low & middle income 30.5 10.7 19.8 3.5 12.3 1.5 0.1 0.9 0.8 7.6 East Asia & Pacific 47.2 10.3 36.9 2.1 7.1 0.9 0.0 1.2 1.3 28.5 Europe & Central Asia 22.6 10.3 12.3 4.1 18.4 1.1 0.0 1.1 0.5 ­4.9 Latin America & Carib. 22.4 12.1 10.4 4.4 9.1 3.0 0.0 0.4 0.4 1.8 Middle East & N. Africa .. 10.9 .. 4.6 40.0 0.3 0.1 1.1 0.6 .. South Asia 32.1 8.9 23.2 3.5 4.5 0.9 0.5 1.1 0.8 18.8 Sub-Saharan Africa 19.4 10.7 8.7 3.8 18.7 2.3 0.4 0.6 0.4 ­10.0 High income 19.9 13.0 6.9 4.7 1.5 0.2 0.0 0.3 0.3 9.3 Euro area 21.8 13.8 8.0 4.8 0.4 0.0 0.0 0.2 0.2 12.0 a. World Bank staff estimates. b. Excludes particulate emissions damage. c. Likely to be overestimated because mineral depletion excludes diamonds. d. Includes Montenegro. 190 2008 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 rents because they are not produced; in contrast, for · Gross savings are the difference between gross a specified set of assets, excluding capital gains. If produced goods and services competitive forces will national income and public and private consump- a country's net savings are positive and the account- expand supply until economic profits are driven to tion, plus net current transfers. · Consumption of ing includes a sufficiently broad range of assets, zero. For each type of resource and each country, unit fi xed capital is the replacement value of capital economic theory suggests that the present value resource rents are derived by taking the difference used up in production. · Net national savings are of social welfare is increasing. Conversely, persis- between world prices and the average unit extrac- gross savings minus consumption of fixed capital. tently negative adjusted net savings indicate that an tion or harvest costs (including a "normal" return on · Education expenditure is public current operating economy is on an unsustainable path. capital). Unit rents are then multiplied by the physi- expenditures in education, including wages and sala- The table provides a test to check the extent cal quantity extracted or harvested in order to arrive ries and excluding capital investments in buildings to which today's rents from a number of natural at a depletion figure. This figure is one of a range and equipment. · Energy depletion is unit resource resources and changes in human capital are bal- of possible depletion estimates, depending on the rents times the physical quantities of extracted coal, anced by net savings, that is, this generation's assumptions made about future quantities, prices, crude oil, and natural gas. · Mineral depletion is bequest to future generations. and costs, and there is reason to believe that it is at unit resource rents times the physical quantities of Adjusted net savings are derived from standard the high end of the range. World prices are used in extracted tin, gold, lead, zinc, iron, copper, nickel, sil- national accounting measures of gross savings by order to reflect the social opportunity cost of deplet- ver, bauxite, and phosphate. · Net forest depletion making four adjustments. First, estimates of capital ing minerals and energy. is unit resource rents times the excess of roundwood consumption of produced assets are deducted to A positive net depletion figure for forest resources harvest over natural growth. · Carbon dioxide dam- obtain net savings. Second, current public expen- implies that the harvest rate exceeds the rate of age is estimated at $20 per ton of carbon (in 1995 ditures on education are added to net savings (in natural growth; this is not the same as deforesta- U.S. dollars) times tons of carbon emitted. · Par- standard national accounting these expenditures tion, which represents a change in land use (see ticulate emission damage is the willingness to pay are treated as consumption). Third, estimates of Definitions for table 3.4). In principle, there should to avoid illness and death attributable to particulate the depletion of a variety of natural resources are be an addition to savings in countries where growth emissions.· Adjusted net savings are net savings deducted to reflect the decline in asset values asso- exceeds harvest, but empirical estimates suggest plus education expenditure minus energy depletion, ciated with their extraction and harvest. And fourth, that most of this net growth is in forested areas that mineral depletion, net forest depletion, and carbon deductions are made for damages from carbon diox- cannot currently be exploited economically. Because dioxide and particulate emissions damage. ide and particulate emissions. the depletion estimates reflect only timber values, The exercise treats public education expenditures they ignore all the external and nontimber benefits Data sources as an addition to savings effort. However, because associated with standing forests. of the wide variability in the effectiveness of govern- Pollution damage from emissions of carbon dioxide Data on gross savings are from World Bank national ment education expenditures, these figures cannot is calculated as the marginal social cost per unit mul- accounts data files, described in the Economy sec- be construed as the value of investments in human tiplied by the increase in the stock of carbon dioxide. tion. Data on consumption of fixed capital are from capital. Current expenditure of $1 on education The unit damage figure represents the present value the United Nations Statistics Division's National does not necessarily yield $1 of human capital. The of global damage to economic assets and to human Accounts Statistics: Main Aggregates and Detailed calculation should also consider private education welfare over the time the unit of pollution remains Tables, 1997, extrapolated to 2006. Data on edu- expenditure, but data are not available for a large in the atmosphere. cation expenditure are from the United Nations number of countries. Pollution damage from particulate emissions is Statistics Division's Statistical Yearbook 1997 and While extensive, the accounting of natural resource estimated by valuing the human health effects from from the United Nations Educational, Scientific, depletion and pollution costs still has some gaps. exposure to particulate matter pollution in urban and Cultural Organization Institute for Statistics' Key estimates missing on the resource side include areas. The estimates are calculated as willingness online database. The data sources and methods the value of fossil water extracted from aquifers, net to pay to avoid illness and death from cardiopulmo- used to estimate resource depletion are described depletion of fish stocks, and depletion and degrada- nary disease and lung cancer in adults and acute in Kunte and others' "Estimating National Wealth" tion of soils. Important pollutants affecting human respiratory infections in children that is attributable (1998). The unit damage figure for carbon diox- health and economic assets are excluded because to particulate emissions. ide emissions is from Frankhauser's "Fractales, no internationally comparable data are widely avail- For a detailed note on methodology, see www. tissues urbains et reseaux de transport" (1994). able on damage from ground-level ozone or sulfur worldbank.org/data. The estimates of particulate emissions damage oxides. are from Pandey and others' "The Human Costs Estimates of resource depletion are based on of Air Pollution: New Estimates for Developing the calculation of unit resource rents. An economic Countries" (2006). The conceptual underpinnings rent represents an excess return to a given factor of of the savings measure appear in Hamilton and production--in this case the returns from resource Clemens' "Genuine Savings Rates in Developing extraction or harvest are higher than the normal rate Countries" (1999). of return on capital. Natural resources give rise to 2008 World Development Indicators 191 Text figures, tables, and boxes ECONOMY 4 Introduction A portrait of the global economy The world's output grew 4.8 percent in 2006, half a percentage point faster than in 2005, to reach nearly $59 trillion. That was an increase of almost 50 percent since 1995, measured in purchasing power parity and 2005 prices (figure 4a). Low- and middle-income economies--whose share of global output increased from 34 percent to 41 percent--grew faster on average than high-income economies. Setting the pace were East Asia and Pacific, whose developing economies more than doubled their output and increased their share of global output from 9 percent to 14 percent, and South Asia, whose share increased from 4 percent to 6 percent. Dominating the growth in these two regions were China and India. Growing less rapidly, Europe and Central Asia gained a percentage point, Sub-Saharan Africa and the Middle East and North Africa saw their shares stay the same, while Latin America and the Caribbean saw its share of global output decline from 9 percent to 8 percent. The statistics in this section measure the size and structure of the world's economies and how they are managed. The national accounts record the sources of economic growth. The balance of payments tracks the flow of goods and services between countries. The fiscal and monetary accounts, interest rates, and exchange rates reflect the domestic and international forces acting on the economy and the responses of politicians and policymakers. Viewed over time, macroeconomic statistics show the health of an economy and the quality of macroeconomic management. Viewed across countries, they reveal the many varied pat- terns of development. Together they inform citizens, businesses, and governments of the results of their efforts and guide them in their future choices. Developing economies increased their share of world output 4a 1995 world output: $39.4 trillion 2006 world output: $58.6 trillion East Asia Europe & & Pacific Central Asia 7% East Asia 9% & Pacific 14% Europe & Latin Central Asia America & 8% Caribbean 9% Middle East & Latin High-income North Africa 3% America & 66% Caribbean 8% High-income South Asia 4% 59% Sub-Saharan Africa 2% Middle East & North Africa 3% South Asia 6% Sub-Saharan Africa 2% Note: World output is measured in 2005 international dollars (GDP in purchasing power parity terms). Source: World Development Indicators data files. 2008 World Development Indicators 193 Better policies to achieve Long-term trends macroeconomic stability Developing economies are expected to continue growing Developing economies are running lower fiscal and external faster than high-income economies thanks to labor surplus- deficits, accumulating larger reserves, and pursuing more es, higher returns to physical capital, and ready access to prudent monetary and fiscal policies. These policies mean technology already developed and amortized in high-income less vulnerability and volatility and increased investor con- economies. With adequate investment in physical and human fidence. Since the high inflation and the debt crises of the capital, developing economies should close the gap with rich- 1970s and 1980s--and the rapid inflation in Europe and er economies in the long run. Central Asia after the Soviet Union's collapse--better fiscal, Average growth of low- and lower middle-income economies monetary, and exchange rate policies have reduced inflation has been rising, surpassing that of upper middle-income and in most developing countries. These shocks also revealed the high-income economies in the last three decades (figure 4b). importance of reliable, publicly available data for monitoring Since 2000 annual GDP growth in low-income economies has governments and private agents. The number of countries averaged 6.5 percent, compared with 5.6 percent in middle- with double-digit inflation dropped from 61 in the 1990s to income economies and 2.3 percent in high-income econo- 27 in 2000­06, and inflation averaged less than 9 percent mies. A few large countries drive these averages: China, India, in all developing regions in 2006 (table 4d). But higher pric- and the Russian Federation, which have performed exception- es for oil and other commodities pushed inflation back up in ally well and carry large weights in the aggregates. Growth three regions in 2006. remains uneven across regions (figure 4c) and economies. In Better macroeconomic management has also lowered the last decade 20, mostly small, economies graduated from real interest rates in many developing economies, encourag- the World Bank's low- and middle-income economies clas- ing investment and faster growth. For the poorest and most sification. Some of the most successful economies are now indebted, Heavily Indebted Poor Country and Multilateral classified as high-income. But poverty traps, exclusion from Debt Relief Initiatives led by the World Bank and International global markets, internal conflicts, resource constraints, poor Monetary Fund have reduced debt burdens. Reforms under policies, and market failures have limited growth and poverty these programs have improved Sub-Saharan Africa's growth reduction in low-income economies, especially in Africa. prospects. Low- and lower middle-income Inflation is now less than economies have had the strongest growth 4b 9 percent in all developing regions 4d Average annual growth (%) 1976­86 1986­96 1996­2006 8 Region 1975 1985 1995 2000 2005 2006 East Asia & Pacific .. 3 8 3 6 5 6 Europe & Central Asia .. .. 56 13 7 8 4 Latin America & Caribbean 15 16 12 7 6 7 2 Middle East & North Africa 5 4 8 7 6 7 South Asia 24 7 9 4 6 6 0 Low-income Lower Upper High-income Sub-Saharan Africa 11 10 10 6 8 7 middle-income middle-income Source: World Development Indicators data files. Source: World Development Indicators data files. Patterns of regional Real interest rates have fallen growth vary widely 4c in many developing economies 4e Average annual growth (%) 1976­86 1986­96 1996­2006 Economy 1985 1990 1995 2000 2005 2006 10 Argentina .. .. 14 10 ­2 ­4 High-income average, 1976­2006 (2.8%) Algeria .. .. ­8 ­12 ­7 ­1 5 Brazil .. .. .. 48 45 45 China ­2 3 ­1 4 1 2 India 9 5 6 8 6 5 0 Indonesia .. 12 8 ­2 ­1 2 Nigeria 6 17 ­23 ­12 ­6 8 ­5 Russian Federation .. .. 72 ­10 ­7 ­5 East Asia Europe & Latin Middle South Sub-Saharan & Pacific Central Asia America & East & Asia Africa South Africa 4 5 7 5 6 4 Caribbean North Africa Ukraine .. .. ­57 15 ­7 1 Source: World Development Indicators data files. Source: World Development Indicators and International Monetary Fund data files. 194 2008 World Development Indicators The contribution of trade Globalization has elevated the importance of trade for devel- For some countries the terms of trade effect can be quite oping economies. The rapid industrialization of many large large. The terms of trade adjustment accounted for 33 per- developing economies has increased demand for primary cent of Zambia's GDI between 2000 and 2006 (table 4h). commodities. The prices of oil, metals, and minerals have Real growth rates, taking account of the terms of trade effect, increased rapidly since 2002, allowing commodity producers may differ substantially from constant price growth rates. to invest and produce more (figure 4f). Tajikistan's GDP increased 9.1 percent a year from 2000 to As a result, many primary commodity­exporting econo- 2006, but the real growth of GDI was only 0.6 percent. This mies have experienced strong GDP growth, while oil- and represented a terms-of-trade loss of 8.5 percent, the largest metal-importing economies have seen price increases of any economy over the period. (figure 4g). Most oil-exporting economies have seen rising terms of Such changes in the terms of trade affect the real growth trade in recent years. Some metal-exporting economies, such of GDP. When export prices rise faster than import prices, as Chile and Zambia, have also experienced favorable terms the terms of trade improve, an economy's capacity to import of trade thanks to recent increases in copper prices. But rises, and the real value of its output increases. some oil-importing economies have weathered the worsen- One commonly used measure of the terms of trade ing terms of trade by rapidly expanding manufactured goods effect is the difference between the value of exports exports (China) or services (India). Of 147 economies with deflated by the import price index and the value of exports data, 65 experienced a loss in income due to the terms of in constant prices. Adding the terms of trade adjustment to trade effect (6 greater than 2 percent), 68 economies gained GDP in constant prices yields real gross domestic income (18 more than 2 percent), and 14 economies had no appre- (GDI). ciable terms of trade effect. Oil, metal, and mineral prices Terms of trade, gross domestic product, and have increased since 1990 4f gross domestic income growth for selected economies 4h World Bank commodity price index (1990 = 100) Terms of trade Gross Gross Terms of trade 300 Oil domestic domestic gain or loss Metals product income and minerals average 200 2000 annual % average annual Raw = 100 growth % growth % % of GDI materials 2000­ 2000­ 2000­ 2000­ 2000­ Economy 2006 06 06 06 06 06 100 MUV G-5 index Agriculture Oil-exporting economies Azerbaijan 214 14.2 15.6 23.5 7.9 13.5 0 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 Equatorial Guinea 168 9.3 19.4 30.1 10.7 16.1 Note: The MUV G-5 index, or the manufacturers unit value index, a proxy for the price of Iran, Islamic Rep. 172 11.1 5.6 8.7 3.1 7.6 developing country imports of manufactures, is a weighted average of the export prices of the Group of Five economies (France, Germany, Japan, United Kingdom, and United States). Russian Federation 149 7.8 6.4 10.7 4.4 6.9 Source: World Development Indicators data files. Venezuela, RB 215 15.8 3.4 8.9 5.4 18.5 Metal- and primary commodity-exporting economies Oil-exporting economies have experienced gains 4g Burkina Faso 96 ­1.5 6.2 6.0 ­0.2 ­0.8 Chile 174 10.2 4.3 8.6 4.3 5.5 Terms of trade index (2000 = 100) 250 Côte d'Ivoire 82 ­3.0 0.1 ­2.3 ­2.3 ­17.7 Venezuela, RB Tajikistan 42 ­11.7 9.1 0.6 ­8.5 1.7 200 Russian Zambia 142 4.7 5.0 15.7 10.8 32.8 Federation 150 Oil-importing economies Brazil 100 China 87 ­2.7 9.8 8.5 ­1.3 ­2.9 China India Costa Rica 87 ­2.4 4.8 3.4 ­1.4 ­2.5 50 Brazil 110 1.5 3.0 3.2 0.2 ­3.5 0 India 87 ­2.8 7.4 6.9 ­0.5 0.3 1996 1998 2000 2002 2004 2006 South Africa 113 2.0 4.1 4.7 0.6 1.7 Source: World Development Indicators data files. Source: World Development Indicators data files. 2008 World Development Indicators 195 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 of import % growth % growth % growth % growth % of GDP $ millions coverage 2006 2007a 2006 2007a 2006 2007a 2006 2007a 2006 2007a 2007 2007a Algeria 3.0 3.0 .. ­0.1 .. 38.8 9.1 7.8 .. 23.6 110,600 41.3 Angola 18.6 23.4 .. .. .. .. 14.7 0.2 23.7 6.4 .. .. Argentina 8.5 8.5 7.4 .. 15.2 .. 13.5 12.8 3.8 2.6 44,779 12.0 Armenia 13.3 13.7 ­4.3 2.0 6.7 25.0 4.6 3.9 ­1.8 ­5.0 1,657 5.1 Azerbaijan 34.5 25.0 29.3 37.4 12.3 19.8 5.3 7.7 18.7 30.1 4,273 5.8 Bangladesh 6.6 6.5 25.8 27.0 18.2 23.6 5.2 5.6 1.9 1.4 5,077 3.3 Belarus 9.9 8.1 9.9 8.2 9.9 6.1 10.8 11.1 ­4.1 ­6.6 4,182 1.7 Bolivia 4.6 4.2 9.6 ­3.2 4.2 2.7 12.2 14.0 11.8 9.1 4,636 11.8 Bosnia and Herzegovina 6.0 6.0 13.7 12.6 ­6.9 14.2 6.5 2.5 ­10.1 12.1 5,621 17.3 Botswana 2.1 4.3 8.0 12.6 ­4.4 16.5 13.5 8.4 18.3 20.4 9,907 24.4 Brazil 3.7 5.3 4.6 6.3 18.1 20.0 4.3 4.4 1.3 0.3 179,433 13.4 Bulgaria 6.1 6.0 9.0 6.0 15.2 11.0 8.1 8.0 ­15.9 ­20.2 15,876 6.0 Cameroon 3.8 2.7 1.9 7.7 10.4 39.9 3.7 1.8 .. ­1.4 1,896 16.9 Chile 4.0 5.2 4.2 8.1 9.4 15.1 11.7 5.5 3.6 3.8 .. .. China 10.7 11.4 23.3 21.2 14.3 16.5 3.6 5.2 9.4 11.0 8,249 17.8 Colombia 6.8 6.6 7.8 15.2 20.8 27.3 5.4 5.1 ­2.0 ­3.9 20,955 5.6 Congo, Dem. Rep. 4.9 6.5 2.5 9.9 5.7 9.2 13.1 17.0 0.0 ­7.8 522 1.5 Congo, Rep. 6.4 3.7 .. .. .. .. 15.2 ­16.4 .. .. 2,362 5.8 Costa Rica 8.2 6.8 9.9 14.2 8.9 11.8 10.1 9.0 ­5.0 ­5.9 4,114 3.6 Côte d'Ivoire 0.9 1.7 ­1.6 ­0.4 2.4 2.6 5.6 1.7 3.0 2.3 .. .. Croatia 4.8 5.8 6.9 6.9 7.3 7.4 3.4 3.2 ­7.5 ­8.2 12,210 4.7 Dominican Republic 10.7 8.0 5.8 7.6 12.6 6.7 7.6 7.0 ­2.5 ­2.0 2,946 1.8 Ecuador 3.9 2.2 8.6 2.6 9.2 6.5 7.2 4.7 3.6 3.3 3,521 3.5 Egypt, Arab Rep. 6.8 7.1 21.3 23.3 21.8 28.8 7.4 10.5 2.5 2.1 28,589 7.3 El Salvador 4.2 4.2 8.1 7.8 8.4 9.6 4.9 4.1 ­4.6 5.9 2,158 3.0 Gabon 1.2 5.6 ­9.7 4.2 8.6 118.3 7.9 1.1 .. 13.2 1,689 5.2 Ghana 6.2 6.3 10.3 10.0 8.9 14.0 14.6 13.0 ­8.1 ­13.6 2,500 2.7 Guatemala 4.5 5.7 6.5 12.5 5.2 7.5 6.3 5.4 ­4.5 ­5.1 4,320 4.0 Honduras 6.0 6.0 4.8 7.9 13.5 15.6 5.1 9.4 ­2.1 ­10.0 2,733 3.1 Hungary 3.9 1.7 18.9 15.5 14.5 13.2 3.7 6.4 ­6.6 ­4.9 24,121 2.9 India 9.2 8.7 8.6 6.4 11.4 6.4 5.9 4.5 ­1.0 ­1.4 295,000 12.0 Indonesia 5.5 6.3 9.2 8.4 7.6 7.4 13.6 10.5 2.7 2.5 56,920 5.7 Iran, Islamic Rep. 4.6 6.2 36.7 3.3 38.9 ­6.5 11.0 21.4 .. 11.1 78,112 14.2 Jamaica 2.5 1.4 .. .. .. .. 6.3 6.6 ­11.7 ­17.0 1,878 3.8 Jordan 5.7 6.5 6.7 4.1 ­0.2 0.0 5.8 4.4 ­13.5 ­13.8 7,585 6.0 Kazakhstan 10.7 8.7 6.9 9.3 12.1 9.6 21.6 18.6 ­2.2 ­7.3 17,392 4.9 Kenya 6.1 5.5 0.7 12.5 18.1 9.6 7.1 ­0.6 ­2.3 ­13.3 3,015 3.7 Latvia 11.9 11.9 5.3 9.5 17.5 22.1 11.1 12.3 ­22.5 ­24.8 5,758 4.6 Lebanon 0.0 1.0 8.5 13.2 ­4.9 15.7 5.6 3.4 ­5.9 ­15.0 14,649 15.4 Lesotho 7.2 4.9 7.9 6.6 1.8 19.7 4.2 6.2 4.5 1.7 829 6.3 196 2008 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 of import % growth % growth % growth % growth % of GDP $ millions coverage 2006 2007a 2006 2007a 2006 2007a 2006 2007a 2006 2007a 2007 2007a Lithuania 7.7 8.7 12.2 8.6 13.8 16.0 6.6 6.1 ­10.8 ­14.0 7,721 3.8 Macedonia, FYR 3.0 5.1 12.9 16.5 12.0 12.1 2.7 4.2 ­0.4 ­2.3 2,239 5.3 Malawi 7.4 7.4 ­11.8 15.8 ­13.1 5.7 18.5 7.6 .. ­16.3 215 2.5 Malaysia 5.9 5.7 7.4 6.0 8.6 5.0 4.1 2.6 16.9 15.9 92,791 6.5 Mauritius 3.5 4.9 8.0 6.2 9.0 5.6 4.1 5.0 ­9.6 ­7.4 1,273 3.0 Mexico 4.8 3.2 11.1 5.1 12.2 7.7 4.4 2.0 ­0.2 ­0.8 77,990 3.3 Moldova 4.0 5.0 3.0 28.0 16.7 32.0 12.6 10.9 ­11.5 ­9.7 1,334 3.0 Morocco 8.0 2.0 10.5 3.5 6.7 11.0 1.9 2.0 2.8 1.0 24,760 8.3 Montenegro 16.2 7.5 .. 15.2 .. 38.1 2.6 2.4 .. ­45.9 515 2.0 Nicaragua 3.7 3.8 10.5 8.9 6.1 8.1 10.6 12.5 ­16.1 ­15.8 930 2.5 Nigeria 5.2 6.3 .. .. .. .. 7.9 4.8 .. 0.9 51,000 11.0 Pakistan 6.9 6.4 9.9 0.4 18.7 1.3 9.3 7.8 ­5.4 ­4.9 14,287 4.4 Panama 8.1 9.5 11.1 8.6 10.0 10.5 2.1 3.7 ­3.2 ­6.2 1,628 1.2 Papua New Guinea 2.6 6.2 .. 18.8 .. 19.2 9.7 2.4 .. 4.3 2,109 4.7 Paraguay 4.3 6.4 14.2 .. 33.3 .. 10.8 8.6 ­2.3 5.1 2,462 4.2 Peru 7.7 8.5 1.1 6.0 11.8 19.0 7.3 0.6 2.8 1.0 27,720 17.0 Philippines 5.4 6.5 11.2 5.4 1.9 2.5 5.2 3.3 5.0 4.6 30,249 5.3 Poland 6.1 6.5 14.5 11.7 15.8 12.3 1.0 1.4 ­3.3 ­5.1 61,236 3.8 Romania 7.7 6.1 10.3 8.0 8.6 18.6 10.1 6.8 ­10.5 ­14.6 39,423 6.5 Russian Federation 6.7 8.1 7.2 7.4 21.7 ­30.4 16.1 12.8 9.6 6.0 476,391 20.3 Senegal 2.3 5.0 ­8.6 3.6 1.0 3.0 2.9 5.9 .. ­8.1 1,686 4.1 Serbia 5.7 7.5 5.7 33.2 2.8 32.5 15.6 8.9 .. ­16.1 14,218 8.0 Seychelles 5.3 5.3 17.8 ­8.8 11.9 16.9 2.2 7.3 ­21.2 ­38.7 41 0.4 Slovak Republic 8.3 10.3 20.7 16.6 17.8 12.6 2.7 3.6 .. ­4.0 22,148 4.3 South Africa 5.0 4.8 5.5 7.0 18.4 7.6 6.9 8.1 ­6.5 ­6.7 28,613 3.3 Sri Lanka 7.4 6.5 4.8 5.9 8.3 5.8 10.3 11.5 ­4.9 ­4.2 3,238 2.8 Sudan 11.8 10.5 0.4 15.5 8.2 ­4.1 7.0 3.8 ­12.6 ­11.8 1,000 1.0 Swaziland 2.1 2.3 6.0 10.0 5.5 15.0 5.7 8.3 3.7 ­0.7 637 3.4 Syrian Arab Republic 5.1 3.9 ­10.0 2.5 ­4.5 8.4 9.1 3.8 2.8 2.5 2,689 2.1 Thailand 5.0 4.3 8.6 6.5 1.6 3.2 5.0 3.0 1.1 2.2 92,574 6.5 Tunisia 5.2 5.7 3.9 4.2 1.4 5.3 3.0 1.8 ­2.1 ­1.4 7,348 4.5 Turkey 6.1 5.0 8.5 11.2 7.1 10.7 11.5 7.0 ­8.1 ­7.7 74,692 5.9 Uganda 5.4 6.2 4.0 15.6 7.0 12.8 7.3 8.9 ­2.5 ­6.8 2,160 6.0 Ukraine 7.1 7.3 ­4.9 3.3 6.5 15.2 13.7 21.4 ­1.5 ­3.2 32,500 5.6 Uruguay 7.0 7.3 7.6 19.0 16.0 16.0 6.8 9.5 ­2.3 ­1.7 4,121 7.4 Uzbekistan 7.3 9.5 2.0 17.6 1.2 10.0 21.5 23.9 .. 21.1 6,500 14.9 Venezuela, RB 10.3 8.4 ­4.2 ­5.3 31.4 31.9 16.9 23.4 14.9 11.1 33,477 5.7 Vietnam 8.2 8.5 22.7 21.1 21.5 34.0 7.3 8.2 .. ­9.0 21,565 4.3 Zambia 6.2 5.7 21.0 10.8 14.3 20.0 12.2 10.1 8.8 ­4.0 1,080 3.6 a. Data are preliminary estimates. Source: World Development Indicators data files. 2008 World Development Indicators 197 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 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Afghanistan .. 10.7 .. 0.4 .. 21.1 .. 13.8 .. 21.9 Albania 3.5 5.3 4.3 1.4 ­0.5 2.9 .. ­0.2 6.9 7.6 Algeria 1.9 5.0 3.6 7.3 1.8 4.4 ­2.1 2.4 1.8 5.2 Angolaa 1.6 11.5 ­1.4 13.8 4.4 12.2 ­0.3 17.1 ­2.2 6.7 Argentina 4.3 3.6 3.5 3.0 3.8 5.3 2.7 5.0 4.5 2.4 Armenia ­1.9 12.5 0.5 7.8 ­7.8 16.4 ­4.3 7.7 6.4 13.4 Australia 4.0 3.2 3.7 1.7 2.8 2.7 2.1 1.1 4.4 3.6 Austria 2.4 1.7 1.6 0.0 2.7 2.2 2.7 1.3 2.3 1.6 Azerbaijan ­6.3 15.6 ­2.1 5.9 ­0.8 20.1 ­12.0 8.8 ­2.3 12.2 Bangladesh 4.8 5.6 2.9 2.8 7.3 7.7 7.2 7.3 4.5 5.8 Belarus ­1.7 8.1 ­4.0 6.5 ­1.8 11.5 ­0.7 11.6 ­0.4 6.1 Belgium 2.1 1.7 2.7 1.0 1.8 0.8 3.1 0.4 1.9 2.0 Benina 4.8 3.8 5.8 4.6 4.1 3.8 5.8 2.7 4.2 3.2 Bolivia 4.0 3.3 2.9 3.7 4.1 3.7 3.8 3.7 4.3 2.3 Bosnia and Herzegovina .. 5.1 .. 4.6 .. 6.6 .. 7.3 .. 3.6 Botswana 6.0 5.1 ­1.2 ­1.6 5.8 4.8 4.4 2.1 7.8 5.4 Brazil 2.7 3.0 3.6 4.2 2.4 2.9 2.0 3.0 3.8 3.1 Bulgaria ­1.8 5.5 3.0 ­1.1 ­5.0 5.1 .. 6.0 ­5.2 5.9 Burkina Faso 5.5 6.2 5.9 6.2 5.9 7.3 5.9 6.3 3.9 5.5 Burundi ­2.9 2.5 ­1.9 ­1.5 ­4.3 ­6.2 ­8.7 .. ­2.8 10.4 Cambodia 7.0 9.5 3.7 5.2 14.3 14.6 18.6 14.2 7.5 10.0 Cameroon 1.7 3.6 5.5 3.8 ­0.9 2.6 1.4 5.0 0.2 7.3 Canada 3.1 2.6 1.1 1.7 3.2 1.3 4.5 ­0.1 3.0 3.0 Central African Republic 2.0 ­0.7 3.8 0.3 0.7 ­0.4 ­0.2 ­0.1 0.2 ­2.6 Chad 2.2 14.1 4.9 3.4 0.6 41.7 .. .. 0.8 8.5 Chile 6.6 4.3 2.2 6.2 5.6 3.7 4.4 4.0 6.9 4.3 Chinaa 10.6 9.8 4.1 4.2 13.7 11.2 12.9 11.1 10.2 10.1 Hong Kong, China 4.1 4.8 .. ­1.1 .. ­3.0 .. ­4.2 .. 4.1 Colombia 2.8 3.9 ­2.6 1.6 1.5 5.5 ­2.5 4.9 4.5 3.1 Congo, Dem. Rep. ­4.9 4.7 1.4 0.8 ­8.0 9.3 ­8.7 5.7 ­12.3 6.2 Congo, Rep.a 1.0 4.4 0.7 .. 1.7 .. ­2.4 .. ­0.7 .. Costa Rica 5.3 4.8 4.1 3.0 6.2 5.2 6.8 5.2 4.7 5.3 Côte d'Ivoirea 3.2 0.1 3.5 1.1 6.3 ­1.3 5.5 ­3.0 2.0 0.1 Croatia 0.6 4.8 ­2.1 1.1 ­1.1 5.9 ­3.5 5.5 1.3 4.6 Cubaa 4.2 3.4 .. .. .. .. .. .. .. .. Czech Republic 1.1 4.2 0.0 2.9 0.2 5.8 4.3 7.1 1.2 3.5 Denmark 2.7 1.6 4.6 2.8 2.5 0.6 2.5 ­0.4 2.7 1.5 Dominican Republica 6.0 3.9 3.9 3.8 7.0 1.0 4.9 1.7 6.0 5.6 Ecuador a 1.9 5.3 ­1.7 4.8 2.6 6.1 1.5 5.2 2.4 4.8 Egypt, Arab Rep. 4.4 4.0 3.1 3.3 5.1 3.6 6.4 3.5 4.1 4.6 El Salvador 4.8 2.5 1.2 2.2 5.1 2.2 5.2 2.2 4.0 2.7 Eritrea 5.7 2.7 1.5 2.0 15.0 4.1 10.6 6.6 5.7 3.5 Estonia 0.2 8.6 ­3.4 ­0.1 ­3.3 9.4 5.9 10.5 3.1 8.7 Ethiopia 4.0 5.7 2.4 5.0 4.5 7.0 4.0 4.4 5.5 5.7 Finland 2.6 2.9 ­1.1 ­0.2 4.1 4.2 6.4 4.0 2.5 1.8 France 1.9 1.7 2.0 ­0.3 1.0 1.3 .. 1.1 2.2 1.8 Gabona 2.3 1.7 2.0 0.8 1.6 1.3 3.0 3.5 3.1 2.1 Gambia, The 3.0 3.9 3.3 2.5 1.0 5.9 0.9 4.2 3.7 5.4 Georgia ­7.1 7.8 ­11.0 1.9 ­8.1 13.0 .. 7.7 ­0.3 8.3 Germany 1.8 0.8 0.1 1.0 ­0.1 1.0 0.2 1.2 2.9 1.0 Ghanaa 4.3 5.3 3.4 3.6 2.7 7.5 ­4.5 .. 5.6 6.1 Greece 2.2 4.4 0.5 ­3.1 1.0 2.9 .. 1.2 2.6 4.8 Guatemalaa 4.2 2.7 2.8 2.6 4.3 2.0 2.8 1.8 4.7 3.1 Guinea 4.4 2.9 4.3 4.0 4.9 3.4 4.0 2.2 3.6 1.8 Guinea-Bissau 1.2 0.4 3.9 4.4 ­3.1 3.7 ­2.0 3.7 ­0.6 0.6 Haiti ­1.5 ­0.3 .. .. .. .. .. .. .. .. 198 2008 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 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Honduras 3.2 4.0 2.2 3.7 3.6 4.0 4.0 4.3 3.8 4.7 Hungary 1.5 4.3 ­2.4 8.2 3.6 3.7 7.9 7.0 1.3 4.1 India 5.9 7.4 3.2 2.7 6.1 8.0 6.7 7.7 7.7 8.9 Indonesiaa 4.2 4.9 2.0 3.1 5.2 4.0 6.7 5.1 4.0 6.5 Iran, Islamic Rep. 3.1 5.6 3.2 5.5 2.6 6.7 5.1 9.3 3.8 5.1 Iraq .. ­11.4 .. ­3.6 .. ­17.0 .. ­12.8 .. 5.9 Ireland 7.5 5.1 0.8 ­1.8 12.7 4.9 .. .. 8.1 5.6 Israel 5.4 2.6 .. .. .. .. .. .. .. .. Italy 1.5 0.7 2.1 ­0.4 0.8 ­0.3 1.4 ­1.2 1.7 1.1 Jamaica 1.8 1.8 ­0.3 ­1.3 ­1.0 1.8 ­2.2 ­0.2 2.3 1.8 Japan 1.1 1.5 ­1.3 ­1.9 ­0.3 0.9 .. 1.9 2.0 1.6 Jordan 5.0 6.1 ­3.0 9.4 5.2 8.8 5.6 10.8 5.0 5.5 Kazakhstan ­4.1 10.1 ­8.0 4.7 0.6 11.4 2.7 8.8 0.3 10.8 Kenya 2.2 3.9 1.9 3.2 1.2 4.6 1.3 3.8 3.2 3.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 5.8 4.6 1.6 0.1 6.0 6.4 7.3 7.3 5.6 3.7 Kuwait a 4.9 7.3 1.0 15.1 0.3 1.9 ­0.1 2.5 3.5 10.2 Kyrgyz Republic ­4.1 3.8 1.5 2.3 ­10.3 ­0.7 ­7.5 ­1.9 ­4.9 7.7 Lao PDR 6.5 6.4 4.8 2.6 11.1 12.9 11.7 10.2 6.6 6.8 Latvia ­1.5 8.6 ­5.2 3.2 ­8.3 8.4 ­7.3 7.1 2.7 9.0 Lebanon 6.1 3.7 1.8 0.8 ­1.3 4.2 ­5.1 3.8 3.7 2.9 Lesotho 3.9 3.4 2.4 ­2.7 5.0 5.2 6.6 3.2 4.4 3.9 Liberiaa 4.1 ­4.7 .. .. .. .. .. .. .. .. Libya .. 3.2 .. .. .. .. .. .. .. .. Lithuania ­2.7 8.0 ­0.3 3.3 3.3 10.2 7.0 10.1 5.4 6.6 Macedonia, FYR ­0.8 2.2 0.2 0.8 ­2.3 1.4 ­5.3 0.6 0.5 2.3 Madagascar 2.0 2.7 1.9 1.9 2.4 1.6 2.0 1.6 2.3 2.7 Malawi 3.7 2.4 8.6 ­0.8 2.0 3.9 0.5 1.5 1.6 3.6 Malaysiaa 7.0 5.0 0.3 3.6 8.6 4.9 9.5 5.7 7.3 5.3 Mali 4.1 5.7 2.6 5.0 6.4 4.8 ­1.4 5.3 3.0 6.3 Mauritania 2.9 5.0 ­0.2 ­0.4 3.4 4.0 5.8 ­2.5 4.9 7.2 Mauritius 5.2 4.0 ­0.5 1.1 5.5 1.4 5.3 0.2 6.4 5.9 Mexico 3.1 2.3 1.5 1.9 3.8 1.3 4.3 0.8 2.9 2.8 Moldova ­9.6 6.8 ­11.2 1.8 ­13.6 1.2 ­7.1 5.8 0.7 10.1 Mongolia 1.0 7.1 2.5 2.7 ­2.5 7.9 ­9.7 7.5 0.7 8.6 Moroccoa 2.4 5.1 ­0.4 8.7 3.2 4.1 2.6 3.4 3.1 4.7 Mozambique 5.7 8.2 4.9 7.9 12.8 9.6 10.2 12.4 2.8 7.8 Myanmar a 6.9 9.2 5.7 .. 10.5 .. 7.9 .. 7.2 .. Namibia 4.0 4.8 3.8 1.5 2.4 6.0 2.6 3.4 4.5 5.4 Nepal 4.9 3.3 2.4 3.6 7.2 2.4 8.9 0.6 6.4 3.4 Netherlands 3.2 1.3 1.8 1.5 1.7 0.1 2.6 0.1 3.6 1.8 New Zealand 3.2 3.3 2.9 0.3 2.4 3.2 2.2 2.6 3.5 4.0 Nicaragua 3.7 3.3 4.7 3.0 5.5 4.2 5.3 5.2 5.0 3.4 Niger a 2.4 3.9 3.0 6.4 2.0 3.1 2.6 3.9 1.9 3.7 Nigeria 2.5 6.0 3.4 5.8 1.0 5.5 1.1 8.8 3.3 6.5 Norway 3.9 2.3 2.6 4.4 3.8 0.8 1.5 2.8 3.9 2.7 Omana 4.5 4.2 5.0 2.2 3.9 ­0.5 6.0 9.3 5.0 7.5 Pakistan 3.8 5.5 4.4 2.5 4.1 7.9 3.8 10.0 4.4 6.1 Panama 4.7 5.0 3.1 4.4 6.0 2.7 2.7 ­0.4 4.5 5.6 Papua New Guinea 4.3 1.9 4.0 2.2 5.6 ­3.6 5.5 ­1.1 1.5 1.4 Paraguaya 2.2 2.9 3.3 4.9 0.6 1.8 1.4 1.4 2.5 2.5 Peru 4.7 4.9 5.5 3.5 5.4 5.8 3.8 5.4 4.0 4.5 Philippinesa 3.3 4.9 1.7 3.8 3.5 3.5 3.0 4.5 4.0 6.3 Poland 4.7 3.7 0.5 3.4 7.1 4.3 9.9 7.0 5.1 3.2 Portugal 2.8 0.7 ­0.3 ­0.9 3.1 ­0.8 3.6 0.0 2.4 1.4 Puerto Ricoa 4.2 .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 199 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 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Romania ­0.6 6.0 ­1.9 8.1 ­1.2 5.6 .. .. 0.9 5.4 Russian Federation ­4.7 6.4 ­4.9 3.9 ­7.1 6.0 .. .. ­1.7 6.6 Rwandaa ­0.3 5.0 2.6 3.6 ­3.7 6.2 ­6.0 3.0 ­1.2 6.2 Saudi Arabiaa 2.1 4.4 1.6 1.5 2.2 4.9 5.6 6.0 2.2 4.2 Senegal 3.0 4.5 2.4 1.6 3.8 3.9 3.1 1.7 3.0 5.5 Serbia .. 5.3 .. .. .. .. .. .. .. .. Sierra Leone ­5.1 12.3 ­13.0 .. ­4.5 .. 6.1 .. ­2.9 .. Singapore 7.6 5.0 ­2.4 1.8 7.8 4.6 7.0 6.5 7.8 5.3 Slovak Republica 2.1 5.1 0.4 10.0 3.8 9.6 9.3 10.7 5.3 2.1 Slovenia 2.7 3.7 0.0 0.0 1.3 4.0 1.1 4.4 3.4 2.0 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.1 4.1 1.0 ­0.2 1.1 3.1 1.6 2.9 2.7 4.8 Spain 2.7 3.3 3.1 ­1.7 2.3 2.6 .. 0.7 2.7 3.5 Sri Lanka 5.3 4.8 1.8 1.2 6.9 4.4 8.1 3.5 5.7 6.3 Sudan 5.6 7.0 7.4 1.7 8.6 14.2 8.9 8.8 1.9 6.2 Swaziland 3.3 2.4 1.2 0.7 3.7 1.9 2.8 1.9 3.6 2.8 Sweden 2.1 2.7 ­1.1 2.0 4.2 4.3 8.6 4.4 1.9 2.1 Switzerland 1.0 1.3 ­2.0 ­1.7 0.4 1.1 1.2 0.8 1.2 0.9 Syrian Arab Republic 5.1 4.2 6.0 3.8 9.2 2.2 .. 16.1 1.5 7.3 Tajikistan ­10.4 9.1 ­6.9 9.4 ­10.8 11.2 ­10.0 9.8 ­12.6 7.4 Tanzaniab 2.9 6.5 3.2 4.9 3.1 9.6 2.7 8.0 2.7 6.2 Thailanda 4.2 5.4 1.0 2.4 5.7 6.8 6.9 7.0 3.7 4.6 Timor-Leste .. ­0.7 .. 4.3 .. ­5.3 .. ­0.2 .. ­1.8 Togoa 3.5 2.6 4.0 2.8 1.8 8.1 1.8 7.5 3.9 ­0.7 Trinidad and Tobago 3.2 9.5 2.7 ­5.6 3.2 13.3 4.9 9.5 3.2 5.7 Tunisiaa 4.7 4.6 2.3 3.4 4.6 3.2 5.5 3.1 5.3 5.6 Turkey 3.8 5.6 1.3 1.6 4.6 6.4 4.7 6.7 3.9 5.2 Turkmenistan ­4.8 .. ­5.7 .. ­3.4 .. .. .. ­5.4 .. Uganda 7.1 5.6 3.7 4.3 12.2 7.5 14.1 5.6 8.2 7.6 Ukraine ­9.3 7.8 ­5.6 3.2 ­12.6 6.3 ­11.2 11.4 ­8.1 7.3 United Arab Emirates 4.8 8.2 13.2 2.9 3.0 5.6 11.9 8.5 7.2 9.3 United Kingdom 2.7 2.5 ­0.3 0.6 1.5 0.1 1.3 ­0.4 3.2 3.4 United States 3.5 2.6 3.7 2.5 3.7 1.5 .. 2.2 3.4 2.7 Uruguay 3.4 2.3 2.8 6.3 1.1 2.5 ­0.1 4.2 3.7 1.2 Uzbekistan ­0.2 5.7 0.5 6.9 ­3.4 4.3 0.7 1.8 0.4 5.7 Venezuela, RB 1.6 3.4 1.2 3.0 1.2 1.6 4.5 2.5 ­0.1 4.9 Vietnama 7.9 7.6 4.3 3.9 11.9 10.3 11.2 11.7 7.5 7.2 West Bank and Gazaa 7.3 0.2 .. .. .. .. .. .. .. .. Yemen, Rep. 6.0 3.9 5.6 0.3 8.2 ­0.1 5.7 3.0 5.0 8.1 Zambia 0.5 5.0 4.2 2.1 ­4.2 9.2 0.8 5.4 2.5 5.9 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 3.0 w 2.0 w 2.5 w 2.4 w 2.6 w .. w 2.9 w 3.1 w 2.8 w Low income 4.7 6.5 3.3 3.1 4.9 7.6 5.9 7.7 5.8 7.6 Middle income 3.8 5.6 2.0 3.7 4.6 6.6 6.3 6.9 4.0 5.4 Lower middle income 6.2 7.6 2.7 4.0 8.0 8.7 8.6 9.4 6.0 7.7 Upper middle income 2.2 3.9 0.8 3.3 1.6 3.9 3.8 3.8 3.0 3.8 Low & middle income 3.9 5.7 2.4 3.6 4.6 6.7 6.2 6.9 4.2 5.7 East Asia & Pacific 8.5 8.6 3.4 3.9 11.0 9.7 10.9 9.8 8.1 8.8 Europe & Central Asia ­0.9 5.8 ­1.8 3.6 ­2.9 6.2 .. .. 0.9 5.4 Latin America & Carib. 3.2 3.1 2.1 3.2 3.1 2.9 2.9 2.8 3.6 3.1 Middle East & N. Africa 3.8 4.2 2.9 4.7 4.1 2.5 3.8 5.7 3.5 5.0 South Asia 5.5 7.0 3.3 2.7 6.0 7.9 6.4 7.8 6.9 8.2 Sub-Saharan Africa 2.5 4.7 3.3 3.5 1.7 5.4 2.1 3.4 2.5 4.7 High income 2.7 2.3 1.4 0.5 1.9 1.4 .. 1.8 2.9 2.4 Euro area 2.1 1.5 1.6 ­0.4 1.0 1.2 2.2 0.6 2.5 1.7 a. Components are at producer prices. b. Covers mainland Tanzania only. 200 2008 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 year fabricated capital assets or for depletion and deg- and real gross national income. The volume of GDP without major shocks or distortions. Some developing radation of natural resources. Value added is the is the sum of value added, measured at constant countries have not rebased their national accounts net output of an industry after adding up all outputs prices, by households, government, and industries for many years. Using an old base year can be mis- and subtracting intermediate inputs. The industrial operating in the economy. leading because implicit price and volume weights origin of value added is determined by the Interna- Each industry's contribution to growth in the econ- become progressively less relevant and useful. tional Standard Industrial Classification (ISIC) revi- omy's output is measured by growth in the industry's To obtain comparable series of constant price data, sion 3. · Agriculture corresponds to ISIC divisions value added. In principle, value added in constant the World Bank rescales GDP and value added by 1­5 and includes forestry and fishing. · Industry prices can be estimated by measuring the quantity industrial origin to a common reference year. This corresponds to ISIC divisions 10­45, which cover of goods and services produced in a period, valu- year's World Development Indicators continues to mining, manufacturing (also reported separately), ing them at an agreed set of base year prices, and use 2000 as the reference year. Because rescaling construction, electricity, water, and gas. · Manu- subtracting the cost of intermediate inputs, also in changes the implicit weights used in forming regional facturing corresponds to industries belonging to constant prices. This double-deflation method, rec- and income group aggregates, aggregate growth rates ISIC divisions 15­37. · Services correspond to ISIC ommended by the 1993 SNA and its predecessors, in this year's edition are not comparable with those divisions 50­99. This sector is derived as a residual requires detailed information on the structure of from earlier editions with different base years. (from GDP less agriculture and industry) and may not prices of inputs and outputs. Rescaling may result in a discrepancy between properly reflect the sum of services output, including In many industries, however, value added is the rescaled GDP and the sum of the rescaled com- banking and financial services. For some countries extrapolated from the base year using single volume ponents. Because allocating the discrepancy would it includes product taxes (minus subsidies) and may indexes of outputs or, less commonly, inputs. Par- cause distortions in the growth rates, the discrep- also include statistical discrepancies. ticularly in the services industries, including most of ancy is left unallocated. As a result, the weighted government, value added in constant prices is often average of the growth rates of the components gen- imputed from labor inputs, such as real wages or erally will not equal the GDP growth rate. number of employees. In the absence of well defined measures of output, measuring the growth of ser- Computing growth rates vices remains difficult. Growth rates of GDP and its components are calcu- Moreover, technical progress can lead to improve- lated using the least squares method and constant ments in production processes and in the quality of price data in the local currency. Constant price U.S. goods and services that, if not properly accounted dollar series are used to calculate regional and Data sources for, can distort measures of value added and thus income group growth rates. Local currency series are of growth. When inputs are used to estimate output, converted to constant U.S. dollars using an exchange Data on national accounts for most developing as for nonmarket services, unmeasured technical rate in the common reference year. The growth rates countries are collected from national statistical progress leads to underestimates of the volume of in the table are average annual compound growth organizations and central banks by visiting and res- output. Similarly, unmeasured improvements in qual- rates. Methods of computing growth rates and the ident World Bank missions. Data for high-income ity lead to underestimates of the value of output and alternative conversion factor are described in Sta- economies come from Organisation for Economic value added. The result can be underestimates of tistical methods. Co-operation and Development (OECD) data files growth and productivity improvement and overesti- (see Annual National Accounts for OECD Member mates of inflation. Changes in the System of National Accounts Countries: Data from 1970 Onwards). The World Informal economic activities pose a particular World Development Indicators adopted the termi- Bank rescales constant price data to a common measurement problem, especially in developing nology of the 1993 SNA in 2001. Although many reference year. The complete national accounts countries, where much economic activity is unre- countries continue to compile their national accounts time series is available on the World Development corded. A complete picture of the economy requires according to the SNA version 3 (referred to as the Indicators 2008 CD-ROM. The United Nations estimating household outputs produced for home 1968 SNA), more and more are adopting the 1993 Statistics Division publishes detailed national use, sales in informal markets, barter exchanges, SNA. Some low-income countries still use concepts accounts for UN member countries in National and illicit or deliberately unreported activities. The from the even older 1953 SNA guidelines, including Accounts Statistics: Main Aggregates and Detailed consistency and completeness of such estimates valuations such as factor cost, in describing major Tables and publishes updates in the Monthly Bul- depend on the skill and methods of the compiling economic aggregates. Countries that use the 1993 letin of Statistics. statisticians. SNA are identified in Primary data documentation. 2008 World Development Indicators 201 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. 8,399 .. 36 .. 24 .. 15 .. 39 Albania 2,424 9,098 56 23 22 22 14 .. 22 56 Algeria 41,764 114,727 10 8 50 61 11 6 39 30 Angolaa 5,040 45,163 7 9 66 70 4 4 26 21 Argentina 258,032 214,241 6 8 28 36 18 22 66 56 Armenia 1,468 6,387 42 20 32 44 25 17 26 37 Australia 384,095 780,531 4 3 28 28 15 11 68 69 Austria 239,560 322,001 3 2 30 31 19 19 67 67 Azerbaijan 3,052 19,851 27 7 34 70 13 6 39 22 Bangladesh 37,940 61,897 26 20 25 28 15 17 49 52 Belarus 13,973 36,945 17 9 37 42 31 33 46 49 Belgium 284,321 394,033 2 1 28 24 20 17 70 75 Benina 2,009 4,775 34 32 15 13 9 8 51 54 Bolivia 6,715 11,162 17 14 33 34 19 15 50 52 Bosnia and Herzegovina 1,867 12,255 21 10 26 25 11 12 54 65 Botswana 4,774 10,598 4 2 51 53 5 4 45 45 Brazil 768,951 1,067,472 6 5 28 31 19 18 67 64 Bulgaria 13,107 31,483 14 9 35 31 24 19 50 60 Burkina Faso 2,380 6,173 35 33 21 22 15 14 44 45 Burundi 1,000 903 48 35 19 20 9 9 33 45 Cambodia 3,441 7,258 48 30 14 26 9 19 38 44 Cameroon 8,733 18,323 24 20 31 33 22 18 45 47 Canada 590,517 1,271,593 3 .. 31 .. 18 .. 66 .. Central African Republic 1,122 1,494 46 56 21 15 10 8 33 29 Chad 1,446 6,541 36 21 14 55 11 5 51 25 Chile 71,349 145,843 9 4 35 48 18 14 55 48 Chinaa 728,011 2,644,681 20 12 47 48 34 33 33 40 Hong Kong, China 144,230 189,799 0 0 15 9 8 3 85 91 Colombia 92,503 153,405 15 12 32 36 16 17 53 52 Congo, Dem. Rep. 5,643 8,543 57 46 17 28 9 6 26 27 Congo, Rep.a 2,116 7,385 10 4 45 73 8 5 45 22 Costa Rica 11,722 22,229 14 9 30 29 22 22 57 62 Côte d'Ivoirea 11,000 17,551 25 23 21 26 15 18 55 51 Croatia 18,808 42,925 11 7 34 32 24 21 55 61 Cubaa .. .. 6 .. 45 .. 38 .. 49 .. Czech Republic 55,257 143,018 5 3 38 39 24 27 57 58 Denmark 181,984 275,366 3 2 25 26 17 14 71 72 Dominican Republica 12,585 31,846 13 12 33 26 18 14 55 62 Ecuador a 20,206 41,402 17 7 25 35 14 9 58 59 Egypt, Arab Rep. 60,159 107,484 17 14 32 38 17 17 51 48 El Salvador 9,500 18,654 14 11 30 29 23 22 56 60 Eritrea 578 1,085 21 17 17 23 9 9 62 60 Estonia 4,331 16,410 8 3 29 29 18 17 63 68 Ethiopia 7,606 13,315 57 47 10 13 5 5 33 39 Finland 130,605 210,652 4 3 33 32 25 23 63 65 France 1,569,983 2,248,091 3 2 25 21 .. 12 72 77 Gabona 4,959 9,546 8 5 52 61 5 4 40 34 Gambia, The 382 511 30 33 13 13 6 5 57 54 Georgia 2,694 7,744 52 13 16 25 17 13 32 62 Germany 2,522,792 2,896,876 1 1 32 30 23 23 67 69 Ghanaa 6,457 12,906 39 37 24 25 9 8 37 37 Greece 151,184 308,449 8 3 21 21 .. 10 71 76 Guatemalaa 14,657 35,325 24 22 20 19 14 12 56 59 Guinea 3,694 3,317 19 13 29 37 4 4 52 50 Guinea-Bissau 254 304 55 62 12 11 8 7 33 27 Haiti 2,908 4,975 25 .. 32 .. 20 .. 44 .. 202 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 3,911 9,235 22 14 31 31 18 20 48 55 Hungary 44,656 112,920 7 4 32 30 24 23 61 66 India 356,299 911,813 26 18 28 28 18 16 46 55 Indonesiaa 202,132 364,790 17 13 42 47 24 28 41 40 Iran, Islamic Rep. 90,829 217,898 18 10 34 45 12 12 47 45 Iraq 10,114 .. 9 .. 75 .. 1 .. 16 .. Ireland 67,105 220,137 7 2 38 36 30 25 55 62 Israel 93,992 140,457 .. .. .. .. .. .. .. .. Italy 1,126,042 1,850,961 3 2 30 27 22 18 66 71 Jamaica 5,813 10,023 9 6 37 33 16 13 54 61 Japan 5,247,610 4,368,435 2 2 34 30 23 21 64 69 Jordan 6,727 14,101 4 3 29 30 15 19 67 67 Kazakhstan 20,374 81,003 13 6 32 42 15 12 55 52 Kenya 9,046 22,779 31 27 16 19 10 11 53 54 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 517,118 888,024 6 3 42 40 28 28 52 57 Kuwait a 27,192 80,781 0 .. 55 .. 4 .. 45 .. Kyrgyz Republic 1,661 2,818 44 33 20 20 9 13 37 47 Lao PDR 1,764 3,437 56 42 19 32 14 21 25 26 Latvia 5,236 20,116 9 4 30 21 21 12 61 75 Lebanon 11,719 22,722 7 7 27 24 15 11 66 70 Lesotho 931 1,494 18 16 39 43 16 18 43 40 Liberiaa 135 631 82 66 5 16 3 12 13 18 Libya 25,541 50,320 .. .. .. .. .. .. .. .. Lithuania 7,621 29,766 11 5 32 35 20 18 56 59 Macedonia, FYR 4,449 6,217 13 13 30 29 23 19 57 58 Madagascar 3,160 5,499 27 28 9 15 8 13 64 57 Malawi 1,397 3,164 30 34 20 20 16 14 50 46 Malaysiaa 88,832 150,672 13 9 41 50 26 30 46 41 Mali 2,466 5,866 50 37 19 24 8 3 32 39 Mauritania 1,415 2,663 37 13 25 48 8 5 37 39 Mauritius 3,820 6,347 10 6 32 27 23 19 58 68 Mexico 286,698 839,182 6 4 28 27 21 18 66 69 Moldova 1,753 3,356 33 18 32 15 26 14 35 67 Mongolia 1,227 3,132 41 22 29 42 12 4 30 36 Morocco 32,986 65,401 15 16 34 28 19 16 51 57 Mozambique 2,247 6,833 37 28 15 26 8 16 48 46 Myanmar a .. .. 60 .. 10 .. 7 .. 30 .. Namibia 3,503 6,566 12 11 28 31 13 14 60 58 Nepal 4,401 8,938 42 34 23 16 10 8 36 49 Netherlands 418,969 662,296 3 2 27 25 17 14 69 73 New Zealand 61,281 104,519 7 .. 27 .. 19 .. 66 .. Nicaragua 3,191 5,301 23 20 27 30 19 18 49 51 Niger a 1,881 3,663 40 .. 17 .. 6 .. 43 .. Nigeria 28,109 115,338 32 23 47 57 5 .. 22 20 Norway 148,920 334,942 3 2 34 45 13 9 63 54 Omana 13,803 30,835 3 2 46 55 5 8 51 43 Pakistan 60,636 126,836 26 19 24 27 16 19 50 53 Panama 7,906 17,097 8 8 18 19 9 8 74 73 Papua New Guinea 4,601 5,654 32 42 36 39 10 6 31 19 Paraguaya 8,066 9,275 21 21 23 18 16 12 56 61 Peru 53,674 92,416 9 7 31 38 17 17 60 55 Philippinesa 74,120 117,562 22 14 32 32 23 23 46 54 Poland 139,095 338,733 8 5 35 32 21 19 57 64 Portugal 112,960 194,726 6 3 28 25 19 .. 66 72 Puerto Ricoa 42,647 .. 1 .. 44 .. 42 .. 55 .. 2008 World Development Indicators 203 4.2 Structure of output Gross domestic product Agriculture Industry Manufacturing Services $ millions % of GDP % of GDP % of GDP % of GDP 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 35,477 121,609 21 11 43 38 29 26 36 52 Russian Federation 395,529 986,940 7 5 37 39 .. 19 56 56 Rwandaa 1,293 2,494 44 41 16 21 10 8 40 38 Saudi Arabiaa 142,458 349,138 6 3 49 65 10 9 45 32 Senegal 4,879 9,186 21 16 24 23 17 14 55 61 Serbia 19,681 31,989 .. 13 .. 26 .. .. .. 62 Sierra Leone 871 1,450 43 46 39 25 9 .. 18 29 Singapore 84,291 132,158 0 0 35 35 27 29 65 65 Slovak Republica 19,715 55,049 5 4 34 32 24 20 61 65 Slovenia 20,288 37,303 4 2 35 35 26 25 61 63 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 151,113 255,155 4 3 35 31 21 18 61 66 Spain 596,751 1,224,676 5 3 29 30 .. 15 66 67 Sri Lanka 13,030 26,964 23 16 27 27 16 14 50 56 Sudan 7,288 37,442 44 32 15 29 9 6 41 39 Swaziland 1,364 2,648 15 11 44 46 36 37 40 43 Sweden 250,640 383,799 3 1 30 29 22 20 67 70 Switzerland 314,799 380,412 2 1 31 28 21 20 66 70 Syrian Arab Republic 11,397 33,407 32 18 20 32 15 7 48 49 Tajikistan 1,232 2,811 38 25 39 27 28 19 22 48 Tanzaniab 5,255 12,784 47 45 14 17 7 7 38 37 Thailanda 167,896 206,338 10 11 41 45 30 35 50 45 Timor-Leste .. 356 .. 32 .. 13 .. 3 .. 55 Togoa 1,309 2,206 38 44 22 24 10 10 40 32 Trinidad and Tobago 5,329 18,136 2 1 47 62 9 6 51 38 Tunisiaa 18,031 30,298 11 11 29 28 19 17 59 60 Turkey 169,708 402,710 16 10 27 27 23 22 56 63 Turkmenistan 2,482 10,496 17 20 63 40 40 22 20 40 Uganda 5,756 9,419 49 32 14 18 7 9 36 49 Ukraine 48,214 106,469 15 9 43 35 35 21 42 57 United Arab Emirates 42,807 129,702 3 2 52 56 10 14 45 42 United Kingdom 1,135,785 2,376,984 2 1 32 24 22 14 66 75 United States 7,342,300 13,163,870 2 1 26 23 19 14 72 76 Uruguay 18,348 19,308 9 9 29 32 20 23 62 58 Uzbekistan 13,350 17,178 32 26 28 27 12 11 40 46 Venezuela, RB 74,889 181,862 6 4 41 55 15 18 53 40 Vietnama 20,736 60,999 27 20 29 42 15 21 44 38 West Bank and Gazaa 3,220 4,059 .. .. .. .. .. .. .. .. Yemen, Rep. 4,236 19,057 20 .. 32 .. 14 .. 48 .. Zambia 3,478 10,734 18 22 36 33 11 11 46 45 Zimbabwe 7,111 3,418 15 19 29 24 22 14 56 57 World 29,613,549 t 48,461,854 t 4w 3w 31 w 28 w 20 w 18 w 65 w 69 w Low income 665,159 1,618,703 29 20 27 28 16 16 44 52 Middle income 4,510,786 10,059,157 12 8 36 37 23 20 52 54 Lower middle income 1,814,273 4,735,728 18 12 41 44 27 27 41 45 Upper middle income 2,695,409 5,324,615 7 6 31 32 20 19 61 62 Low & middle income 5,177,822 11,678,579 14 10 34 36 22 19 51 54 East Asia & Pacific 1,313,304 3,616,708 19 12 44 47 31 32 37 41 Europe & Central Asia 987,511 2,499,359 12 7 34 33 23 20 54 60 Latin America & Carib. 1,751,110 2,964,189 7 6 29 31 19 18 64 62 Middle East & N. Africa 329,469 734,423 15 12 35 40 14 13 50 48 South Asia 476,196 1,146,716 26 18 27 28 17 17 46 54 Sub-Saharan Africa 320,739 712,731 19 15 32 30 15 14 49 55 High income 24,431,143 36,794,507 2 2 30 26 20 17 68 72 Euro area 7,273,737 10,636,418 3 2 29 27 22 18 68 72 a. Components are at producer prices. b. Covers mainland Tanzania only. 204 2008 World Development Indicators 4.2 ECONOMY Structure of output About the data Definitions An economy's gross domestic product (GDP) repre- Ideally, industrial output should be measured · Gross domestic product (GDP) at purchaser prices sents the sum of value added by all producers in through regular censuses and surveys of fi rms. is the sum of gross value added by all resident pro- the economy. Value added is the value of the gross But in most developing countries such surveys are ducers in the economy plus any product taxes (less output of producers less the value of intermediate infrequent, so earlier survey results must be extrapo- subsidies) not included in the valuation of output. It goods and services consumed in production, before lated using an appropriate indicator. The choice of is calculated without deducting for depreciation of taking account of the consumption of fixed capital sampling unit, which may be the enterprise (where fabricated assets or for depletion and degradation of in the production process. The United Nations Sys- responses may be based on financial records) or natural resources. Value added is the net output of tem of National Accounts calls for estimates of value the establishment (where production units may be an industry after adding up all outputs and subtract- added to be valued at either basic prices (excluding recorded separately), also affects the quality of ing intermediate inputs. The industrial origin of value net taxes on products) or producer prices (including the data. Moreover, much industrial production is added is determined by the International Standard net taxes on products paid by producers but excluding organized in unincorporated or owner-operated ven- Industrial Classification (ISIC) revision 3. · Agricul- sales or value added taxes). Both valuations exclude tures that are not captured by surveys aimed at the ture corresponds to ISIC divisions 1­5 and includes transport charges that are invoiced separately by pro- formal sector. Even in large industries, where regu- forestry and fishing. · Industry corresponds to ISIC ducers. Total GDP shown in the table and elsewhere lar surveys are more likely, evasion of excise and divisions 10­45, which cover mining, manufacturing in this volume is measured at purchaser prices. Value other taxes and nondisclosure of income lower the (also reported separately), construction, electricity, added by industry is normally measured at basic estimates of value added. Such problems become water, and gas. · Manufacturing corresponds to ISIC prices. When value added is measured at producer more acute as countries move from state control divisions 15­37. · Services correspond to ISIC divi- prices, this is noted in Primary data documentation. of industry to private enterprise, because new firms sions 50­99. This sector is derived as a residual While GDP estimates based on the production enter business and growing numbers of established (from GDP less agriculture and industry) and may not approach are generally more reliable than estimates firms fail to report. In accordance with the System properly reflect the sum of services output, including compiled from the income or expenditure side, dif- of National Accounts, output should include all such banking and financial services. For some countries ferent countries use different definitions, methods, unreported activity as well as the value of illegal it includes product taxes (minus subsidies) and may and reporting standards. World Bank staff review the activities and other unrecorded, informal, or small- also include statistical discrepancies. quality of national accounts data and sometimes scale operations. Data on these activities need to be make adjustments to improve consistency with collected using techniques other than conventional international guidelines. Nevertheless, significant surveys of firms. discrepancies remain between international stan- In industries dominated by large organizations and dards and actual practice. Many statistical offices, enterprises, such as public utilities, data on output, especially those in developing countries, face severe employment, and wages are usually readily available limitations in the resources, time, training, and bud- and reasonably reliable. But in the services industry gets required to produce reliable and comprehensive the many self-employed workers and one-person busi- series of national accounts statistics. nesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone Data problems in measuring output to report their full earnings. Compounding these prob- Among the difficulties faced by compilers of national lems are the many forms of economic activity that Data sources accounts is the extent of unreported economic activ- go unrecorded, including the work that women and ity in the informal or secondary economy. In develop- children do for little or no pay. For further discussion Data on national accounts for most developing ing countries a large share of agricultural output is of the problems of using national accounts data, see countries are collected from national statistical either not exchanged (because it is consumed within Srinivasan (1994) and Heston (1994). organizations and central banks by visiting and the household) or not exchanged for money. resident World Bank missions. Data for high- Agricultural production often must be estimated Dollar conversion income economies come from Organisation for indirectly, using a combination of methods involv- To produce national accounts aggregates that are Economic Co-operation and Development (OECD) ing estimates of inputs, yields, and area under cul- measured in the same standard monetary units, data files (see Annual National Accounts for OECD tivation. This approach sometimes leads to crude the value of output must be converted to a single Member Countries: Data from 1970 Onwards). The approximations that can differ from the true values common currency. The World Bank conventionally complete national accounts time series is avail- over time and across crops for reasons other than uses the U.S. dollar and applies the average official able on the World Development Indicators 2008 climate conditions or farming techniques. Similarly, exchange rate reported by the International Monetary CD-ROM. The United Nations Statistics Division agricultural inputs that cannot easily be allocated to Fund for the year shown. An alternative conversion publishes detailed national accounts for UN mem- specific outputs are frequently "netted out" using factor is applied if the official exchange rate is judged ber countries in National Accounts Statistics: Main equally crude and ad hoc approximations. For further to diverge by an exceptionally large margin from the 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. 2008 World Development Indicators 205 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicalsa Other value added beverages, clothinga and transport manufacturingb and tobacco equipmenta $ millions % of total % of total % of total % of total % of total 1995 2006 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Afghanistan 1,053 .. .. .. .. .. .. .. .. .. .. Albania 405 .. .. 17 .. 27 .. 4 .. 5 .. 48 Algeria 4,366 5,404 .. .. .. .. .. .. .. .. .. .. Angola 202 1,922 .. .. .. .. .. .. .. .. .. .. Argentina 44,502 44,048 30 31 7 6 10 8 15 16 38 39 Armenia 356 985 .. .. .. .. .. .. .. .. .. .. Australia 51,314 74,569 20 19 6 4 11 17 8 7 55 53 Austria 41,681 52,934 11 10 6 3 27 31 2 2 54 54 Azerbaijan 352 1,055 .. .. .. .. .. .. .. .. .. .. Bangladesh 5,586 10,262 28 .. 44 .. 4 .. 11 .. 13 .. Belarus 3,909 10,382 .. .. .. .. .. .. .. .. .. .. Belgium 51,721 56,400 13 .. 6 .. 22 .. 8 .. 51 .. Benin 174 322 .. .. .. .. .. .. .. .. .. .. Bolivia 1,123 1,286 36 .. 5 .. 1 .. 3 .. 55 .. Bosnia and Herzegovina 213 1,176 .. .. .. .. .. .. .. .. .. .. Botswana 242 352 44 20 9 4 6 .. 4 .. 36 76 Brazil 124,976 169,164 21 18 8 6 23 22 13 12 35 43 Bulgaria 2,015 4,764 .. .. .. .. .. .. .. .. .. .. Burkina Faso 336 775 .. .. .. .. .. .. .. .. .. .. Burundi 83 64 .. .. .. .. .. .. .. .. .. .. Cambodia 315 1,349 .. .. .. .. .. .. .. .. .. .. Cameroon 1,758 3,084 .. .. .. .. .. .. .. .. .. .. Canada 100,393 .. 13 13 4 4 23 25 10 8 50 50 Central African Republic 108 106 .. .. .. .. .. .. .. .. .. .. Chad 159 342 .. .. .. .. .. .. .. .. .. .. Chile 10,594 18,654 .. .. .. .. .. .. .. .. .. .. China 244,997 751,172 .. .. .. .. .. .. .. .. .. .. Hong Kong, China 10,524 5,856 .. .. .. .. .. .. .. .. .. .. Colombia 13,506 23,047 .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. 510 526 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 172 360 .. .. .. .. .. .. .. .. .. .. Costa Rica 2,339 4,344 .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 1,655 3,205 .. .. .. .. .. .. .. .. .. .. Croatia 3,666 7,400 .. .. .. .. .. .. .. .. .. .. Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 12,124 34,965 .. .. .. .. .. .. .. .. .. .. Denmark 26,924 31,100 20 14 2 2 25 21 1 2 52 60 Dominican Republic 2,286 4,444 .. .. .. .. .. .. .. .. .. .. Ecuador 2,830 3,725 26 32 7 3 4 3 4 3 59 58 Egypt, Arab Rep. 9,829 16,737 19 20 13 10 12 10 18 22 38 38 El Salvador 2,026 3,845 .. .. .. .. .. .. .. .. .. .. Eritrea 47 86 .. .. .. .. .. .. .. .. .. .. Estonia 684 2,410 .. .. .. .. .. .. .. .. .. .. Ethiopia 344 647 .. .. .. .. .. .. .. .. .. .. Finland 28,814 39,106 10 7 3 2 27 37 4 3 57 51 France .. 248,295 13 14 5 2 28 30 12 12 42 41 Gabon 224 391 .. .. .. .. .. .. .. .. .. .. Gambia, The 20 22 65 .. 8 .. 1 .. 9 .. 17 .. Georgia 523 862 .. .. .. .. .. .. .. .. .. .. Germany 516,542 584,442 .. 9 .. 2 .. 42 .. 10 .. 37 Ghana 602 1,093 .. 32 .. 6 .. 1 .. 12 .. 49 Greece .. 24,626 25 .. 15 .. 13 .. 10 .. 38 .. Guatemala 2,069 4,405 .. .. .. .. .. .. .. .. .. .. Guinea 142 116 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau 19 22 .. .. .. .. .. .. .. .. .. .. Haiti 558 .. .. .. .. .. .. .. .. .. .. .. 206 2008 World Development Indicators 4.3 ECONOMY Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicalsa Other value added beverages, clothinga and transport manufacturingb and tobacco equipmenta $ millions % of total % of total % of total % of total % of total 1995 2006 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Honduras 607 1,638 .. .. .. .. .. .. .. .. .. .. Hungary 8,839 22,028 19 16 3 5 10 34 13 10 55 35 India 57,917 134,725 .. 10 .. 10 .. 20 .. 17 .. 43 Indonesia 48,781 102,323 .. 23 .. 13 .. 18 .. 9 .. 36 Iran, Islamic Rep. 10,918 25,354 15 10 12 5 18 28 15 13 40 44 Iraq 67 .. .. .. .. .. .. .. .. .. .. .. Ireland 18,096 43,393 .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy 225,513 299,459 9 9 14 11 27 26 8 8 41 46 Jamaica 865 1,163 .. .. .. .. .. .. .. .. .. .. Japan 1,077,348 954,411 11 12 4 3 37 39 10 11 39 35 Jordan 866 2,393 30 24 7 12 5 5 15 16 44 42 Kazakhstan 2,976 9,423 .. .. .. .. .. .. .. .. .. .. Kenya 757 2,316 .. 30 .. 5 .. 5 .. 6 .. 53 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 128,839 219,771 8 8 10 7 39 46 8 9 34 30 Kuwait 1,032 .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 142 318 .. .. .. .. .. .. .. .. .. .. Lao PDR 245 711 .. .. .. .. .. .. .. .. .. .. Latvia 965 2,101 .. .. .. .. .. .. .. .. .. .. Lebanon 1,577 2,217 .. .. .. .. .. .. .. .. .. .. Lesotho 129 237 .. .. .. .. .. .. .. .. .. .. Liberia 4 66 .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,351 4,733 .. 21 .. 12 .. 14 .. 4 .. 49 Macedonia, FYR 873 980 35 .. 17 .. 9 .. 8 .. 31 .. Madagascar 233 672 .. 42 .. 27 .. 1 .. 2 .. 29 Malawi 195 384 .. .. .. .. .. .. .. .. .. .. Malaysia 23,432 44,884 .. 8 .. 3 .. 39 .. 10 .. 40 Mali 174 167 .. .. .. .. .. .. .. .. .. .. Mauritania 107 84 .. .. .. .. .. .. .. .. .. .. Mauritius 765 1,060 25 24 52 51 2 2 .. .. 21 24 Mexico 54,546 135,863 26 .. 4 .. 22 .. 15 .. 33 .. Moldova 400 404 .. .. .. .. .. .. .. .. .. .. Mongolia 143 109 23 .. 62 .. 1 .. 1 .. 12 .. Morocco 6,056 9,610 .. 32 .. 18 .. 9 .. 14 .. 28 Mozambique 166 960 .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 403 826 .. .. .. .. .. .. .. .. .. .. Nepal 393 661 35 45 34 19 2 2 6 10 23 23 Netherlands 65,999 78,537 18 19 3 2 15 14 16 9 48 56 New Zealand 10,517 .. 29 .. .. .. .. .. .. .. 71 .. Nicaragua 533 859 .. .. .. .. .. .. .. .. .. .. Niger 120 .. .. .. .. .. .. .. .. .. .. .. Nigeria 1,495 .. .. .. .. .. .. .. .. .. .. .. Norway 17,018 28,060 17 .. 2 .. 24 .. 9 .. 48 .. Oman 643 2,045 .. .. .. .. .. .. .. .. .. .. Pakistan 8,864 23,178 .. .. .. .. .. .. .. .. .. .. Panama 694 1,204 54 .. 7 .. .. .. 7 .. 32 .. Papua New Guinea 388 249 .. .. .. .. .. .. .. .. .. .. Paraguay 1,280 1,094 .. .. .. .. .. .. .. .. .. .. Peru 8,105 13,743 28 .. 9 .. 7 .. 9 .. 48 .. Philippines 17,043 26,916 29 23 6 5 7 13 2 2 57 56 Poland 25,891 56,009 .. .. .. .. .. .. .. .. .. .. Portugal 18,383 .. 13 14 22 18 18 17 6 5 41 46 Puerto Rico 17,867 .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 207 4.3 Structure of manufacturing Manufacturing Food, Textiles and Machinery Chemicalsa Other value added beverages, clothinga and transport manufacturingb and tobacco equipmenta $ millions % of total % of total % of total % of total % of total 1995 2006 1995 2004 1995 2004 1995 2004 1995 2004 1995 2004 Romania 9,387 26,495 28 13 13 18 19 21 7 5 33 43 Russian Federation .. 163,520 .. 16 .. 2 .. 9 .. 2 .. 71 Rwanda 132 212 .. .. .. .. .. .. .. .. .. .. Saudi Arabia 13,714 33,087 .. .. .. .. .. .. .. .. .. .. Senegal 730 1,104 .. 41 .. 3 .. 2 .. 29 .. 26 Serbia .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 75 .. .. .. .. .. .. .. .. .. .. .. Singapore 20,799 36,496 4 4 1 1 60 50 9 24 26 20 Slovak Republic 4,704 10,923 11 9 7 6 14 22 9 3 59 60 Slovenia 4,556 8,005 .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 29,274 41,198 15 17 8 4 19 16 10 7 47 56 Spain .. 156,192 16 15 7 6 23 22 10 9 43 48 Sri Lanka 1,836 3,329 .. .. .. .. .. .. .. .. .. .. Sudan 799 2,173 .. .. .. .. .. .. .. .. .. .. Swaziland 398 596 .. .. .. .. .. .. .. .. .. .. Sweden 48,628 60,294 7 8 1 1 33 30 3 3 56 58 Switzerland 63,668 66,928 .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 1,574 2,687 .. .. .. .. .. .. .. .. .. .. Tajikistan 331 471 .. .. .. .. .. .. .. .. .. .. Tanzaniac 349 819 .. .. .. .. .. .. .. .. .. .. Thailand 50,194 72,318 21 .. 9 .. 29 .. 6 .. 35 .. Timor-Leste .. 9 .. .. .. .. .. .. .. .. .. .. Togo 130 214 .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 439 1,029 .. 25 .. 1 .. 1 .. 22 .. 52 Tunisia 3,419 5,279 .. .. .. .. .. .. .. .. .. .. Turkey 38,296 84,983 15 .. 17 .. 16 .. 10 .. 42 .. Turkmenistan 948 1,399 .. .. .. .. .. .. .. .. .. .. Uganda 359 786 .. .. .. .. .. .. .. .. .. .. Ukraine 14,922 19,068 .. .. .. .. .. .. .. .. .. .. United Arab Emirates 4,452 18,770 .. .. .. .. .. .. .. .. .. .. United Kingdom 219,282 269,610 13 14 5 4 28 27 11 11 43 44 United States 1,289,100 1,662,800 12 .. 4 .. 33 .. 12 .. 38 .. Uruguay 3,614 4,484 .. 40 .. 12 .. 4 .. 11 .. 33 Uzbekistan 1,376 1,675 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 10,668 18,507 .. .. .. .. .. .. .. .. .. .. Vietnam 3,109 12,963 .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 599 .. .. 48 .. 7 .. 0 .. 1 .. 43 Zambia 344 1,112 .. .. .. .. .. .. .. .. .. .. Zimbabwe 1,370 324 30 .. 7 .. 29 .. 6 .. 29 .. World 5,489,148 t 7,440,831 t Low income 93,977 216,271 Middle income 935,581 1,874,533 Lower middle income 462,950 1,076,557 Upper middle income 481,141 909,966 Low & middle income 1,029,570 2,062,844 East Asia & Pacific 390,751 973,648 Europe & Central Asia .. .. Latin America & Carib. 290,974 467,711 Middle East & N. Africa 40,026 77,125 South Asia 75,044 173,275 Sub-Saharan Africa 45,485 73,246 High income 4,480,402 5,382,611 Euro area 1,340,064 1,609,628 a. When data are shown as not available, they are included in other manufacturing. b. Includes unallocated data. c. Covers mainland Tanzania only. 208 2008 World Development Indicators 4.3 ECONOMY Structure of manufacturing About the data Definitions The data on the distribution of manufacturing value accord with revision 3. Concordances matching ISIC · Manufacturing value added is the sum of gross added by industry are provided by the United Nations categories to national classification systems and to output less the value of intermediate inputs used in Industrial Development Organization (UNIDO). UNIDO related systems such as the Standard International production for industries classified in ISIC major divi- obtains the data from a variety of national and inter- Trade 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 division 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 division 17 to 19. · Machinery Economic Co-operation and Development, and the described and the units whose activities are to and transport equipment correspond to ISIC 29, 30, International Monetary Fund. To improve comparabil- be reported. There are many possibilities, and the 32, 34, and 35. · Chemicals correspond to ISIC divi- ity over time and across countries, UNIDO supple- choices affect how the statistics can be interpreted sion 24. · Other manufacturing, a residual, covers ments these data with information from industrial and how useful they are in analyzing economic wood and related products (ISIC division 20), paper censuses, statistics from national and international behavior. The ISIC emphasizes commonalities in the and related products (ISIC division 21 and 22), petro- organizations, unpublished data that it collects in the production process and is explicitly not intended to leum and related products (ISIC division 23), basic field, and estimates by the UNIDO Secretariat. Nev- measure outputs (for which there is a newly devel- metals and mineral products (ISIC divisions 27), fab- ertheless, coverage may be incomplete, particularly oped Central Product Classification). Nevertheless, ricated metal products and professional goods (ISIC for the informal sector. When direct information on the ISIC views an activity as defined by "a process division 28), and other industries (ISIC divisions 25, inputs and outputs is not available, estimates may resulting in a homogeneous set of products" (UN 26, 31, 33, 36, and 37). be used, which may result in errors in industry totals. 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 manufacturer or producer prices) to estimate value added. (See engages in forging, welding, and painting as well as also About the data for table 4.2.) advertising, accounting, and other service activities. The data on manufacturing value added in U.S. dol- Collecting data at such a detailed level is not practical, lars are from the World Bank's national accounts files nor is it useful to record production data at the highest and may differ from those UNIDO uses to calculate level of a large, multiplant, multiproduct firm. The ISIC shares of value added by industry, in part because has therefore adopted as the definition of an estab- of differences in exchange rates. Thus value added lishment "an enterprise or part of an enterprise which in a particular industry estimated by applying the independently engages in one, or predominantly one, shares to total manufacturing value added will not kind of economic activity at or from one location . . . match those from UNIDO sources. Classification of for which data are available . . ." (UN 1990, p. 25). manufacturing industries in the table accords with By design, this definition matches the reporting unit the United Nations International Standard Industrial required for the production accounts of the United Classification (ISIC) revision 3 for the first time. Pre- Nations System of National Accounts. The ISIC system vious editions of World Development Indicators used is described in the United Nations' International Stan- revision 2, first published in 1948. Revision 3 was dard Industrial Classification of All Economic Activi- completed in 1989, and many countries now use it. ties, Third Revision (1990). The discussion of the ISIC But revision 2 is still widely used for compiling cross- draws on Jacob Ryten's "Fifty Years of ISIC: Historical country data. UNIDO has converted these data to Origins and Future Perspectives" (1998). Manufacturing continues to show strong growth in East Asia 4.3a Value added in manufacturing (index, 1990 = 100) 500 East Asia & Pacific 400 300 South Asia Middle East & North Africa 200 Latin America & Caribbean Data sources 100 Data on manufacturing value added are from the Sub-Saharan Africa World Bank's national accounts files. Data used 0 to calculate shares of industry value added are 1990 1995 2000 2006 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 percent a year between 1990 and 2006. UNIDO's International Yearbook of Industrial Sta- Source: World Development Indicators data files. tistics 2007. 2008 World Development Indicators 209 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan 156 430 .. .. .. .. .. .. .. .. .. .. Albania 202 793 11 8 9 13 3 14 12 38 65 27 Algeria 10,258 54,613 1 0 0 0 95 98 1 1 4 1 Angola 3,642 35,000 .. .. .. .. .. .. .. .. .. .. Argentina 20,967 46,569 50 45 4 1 10 15 2 5 34 32 Armenia 271 1,004 11 12 5 3 1 2 26 25 54 56 Australia 53,111 123,269 22 15 8 3 19 25 18 25 30 23 Austria 57,738 140,397 4 6 3 2 1 5 3 3 88 80 Azerbaijan 635 6,372 4 5 8 1 66 85 1 1 20 8 Bangladesh 3,501 11,802 10 6 3 1 0 0 0 0 85 92 Belarus 4,803 19,739 .. 7 .. 2 .. 38 .. 0 .. 50 Belgium 178,265a 369,166 .. 8 .. 1 .. 8 .. 4 .. 77 Benin 420 560 14 26 75 64 5 .. 0 1 6 9 Bolivia 1,100 3,863 21 15 10 2 15 52 35 24 19 7 Bosnia and Herzegovina 152 3,312 .. 5 .. 8 .. 8 .. 17 .. 62 Botswana 2,142 4,670 .. .. .. .. .. .. .. .. .. .. Brazil 46,506 137,470 29 25 5 4 1 8 10 11 54 51 Bulgaria 5,355 15,064 18 9 3 2 7 13 10 20 60 53 Burkina Faso 276 440 25 16 69 72 0 3 0 1 6 8 Burundi 105 59 91 87 4 4 0 0 1 2 3 6 Cambodia 855 3,800 .. 1 .. 2 .. 0 .. 0 .. 97 Cameroon 1,651 3,573 27 12 28 16 29 62 8 5 8 3 Canada 192,197 389,538 8 7 9 4 9 20 7 7 63 56 Central African Republic 171 120 4 1 20 41 1 0 30 17 45 36 Chad 243 3,750 .. .. .. .. .. .. .. .. .. .. Chile 16,024 58,116 24 15 12 5 0 2 48 64 13 11 China 148,780 968,936 8 3 2 0 4 2 2 2 84 92 Hong Kong, Chinab 173,871 322,669 3 3 0 1 0 2 1 4 94 91 Colombia 10,056 24,388 31 16 5 5 28 40 1 2 35 37 Congo, Dem. Rep. 1,563 2,300 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 1,172 6,400 1 .. 8 .. 88 .. 0 .. 3 .. Costa Rica 3,453 8,216 63 30 5 3 1 1 1 2 25 65 Côte d'Ivoire 3,806 8,420 63 35 20 8 10 37 0 0 7 15 Croatia 4,633 10,376 11 11 5 3 9 15 2 4 74 66 Cuba 1,600 2,678 .. 30 .. 0 .. 1 .. 48 .. 22 Czech Republic 21,335 95,077 6 3 4 1 4 3 3 2 82 89 Denmark 50,906 92,752 24 18 3 3 3 10 1 2 60 65 Dominican Republic 3,780 6,440 19 .. 0 .. 0 .. 0 .. 78 .. Ecuador 4,307 12,658 53 27 3 4 36 59 0 1 8 10 Egypt, Arab Rep. 3,450 13,702 10 7 6 2 37 56 6 2 40 21 El Salvador 1,652 3,513 57 36 1 1 0 3 3 4 39 55 Eritrea 86 10 .. .. .. .. .. .. .. .. .. .. Estonia 1,840 9,469 16 7 10 5 6 16 3 3 65 64 Ethiopia 422 1,014 73 .. 13 .. 3 .. 0 .. 11 .. Finland 40,490 77,032 2 2 8 6 2 5 3 5 83 81 France 301,162 490,368 14 10 1 1 2 4 3 3 79 79 Gabon 2,713 5,600 0 1 13 7 83 86 2 3 2 4 Gambia, The 16 10 60 81 1 4 0 .. 1 1 36 14 Georgia 151 993 29 25 3 2 19 3 8 22 41 48 Germany 523,461 1,111,969 5 4 1 1 1 2 3 3 87 83 Ghana 1,724 3,703 58 61 15 4 5 1 9 3 13 31 Greece 11,054 20,898 30 20 4 2 7 13 7 10 50 52 Guatemala 2,155 6,025 65 50 4 5 2 9 0 1 28 35 Guinea 702 970 8 .. 1 .. 0 .. 67 .. 24 .. Guinea-Bissau 24 75 89 .. 11 .. .. .. .. .. 0 .. Haiti 110 507 37 .. 0 .. 0 .. 0 .. 62 .. Data for Taiwan, China 113,047 223,766 3 1 2 1 1 5 1 2 93 90 210 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 1,220 1,929 87 64 3 3 0 3 0 9 9 21 Hungary 12,865 74,478 21 6 2 0 3 2 5 2 68 84 India 30,630 120,254 19 9 1 2 2 11 3 7 74 70 Indonesia 45,417 103,487 11 12 7 6 25 27 6 10 51 45 Iran, Islamic Rep. 18,360 73,700 4 4 1 0 86 83 1 2 9 10 Iraq 496 29,597 .. .. .. .. .. .. .. .. .. .. Ireland 44,705 111,066 19 10 1 0 0 1 1 1 72 85 Israel 19,046 46,449 5 2 2 1 0 0 1 1 89 82 Italy 233,766 410,572 7 6 1 1 1 4 1 2 89 85 Jamaica 1,427 1,980 22 17 0 0 1 14 6 11 71 58 Japan 443,116 649,931 0 0 1 1 1 1 1 2 95 91 Jordan 1,769 5,175 25 15 2 0 0 1 24 13 49 71 Kazakhstan 5,250 40,470 10 3 3 1 25 69 24 15 38 13 Kenya 1,878 3,437 56 52 7 16 6 1 3 6 28 26 Korea, Dem. Rep. 959 1,980 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 125,058 325,465 2 1 1 1 2 6 1 3 93 89 Kuwait 12,785 55,673 0 .. 0 .. 95 .. 0 .. 5 .. Kyrgyz Republic 409 796 23 19 13 10 11 18 13 6 40 46 Lao PDR 311 874 .. .. .. .. .. .. .. .. .. .. Latvia 1,305 6,153 14 12 23 14 2 5 1 4 58 60 Lebanon 816 2,814 20 16 2 1 0 0 8 12 70 70 Lesotho 160 694 .. .. .. .. .. .. .. .. .. .. Liberia 820 181 .. .. .. .. .. .. .. .. .. .. Libya 8,975 39,500 0 .. 0 .. 95 .. 0 .. 5 .. Lithuania 2,705 14,113 18 14 8 3 11 24 5 2 58 58 Macedonia, FYR 1,204 2,401 18 16 5 1 0 9 18 4 58 69 Madagascar 507 953 69 35 6 4 1 6 7 4 14 41 Malawi 405 540 90 83 2 3 0 0 0 0 7 13 Malaysia 73,914 160,676 10 7 6 3 7 14 1 1 75 74 Mali 441 1,350 23 14 75 74 0 1 0 0 2 10 Mauritania 499 1,290 57 25 0 0 1 .. 42 69 0 0 Mauritius 1,538 2,173 29 29 1 1 0 0 0 1 70 69 Mexico 79,542 250,441 8 5 1 0 10 16 3 2 78 76 Moldova 745 1,052 72 63 2 1 1 0 3 5 23 31 Mongolia 473 1,543 2 2 28 13 0 6 60 66 10 13 Morocco 6,881 12,707 31 19 3 2 2 2 12 9 51 68 Mozambique 168 2,398 66 16 16 3 2 15 2 60 13 5 Myanmar 860 4,250 .. .. .. .. .. .. .. .. .. .. Namibia 1,409 2,648 .. 26 .. 1 .. 0 .. 26 .. 47 Nepal 345 760 8 .. 1 .. 0 .. 0 .. 84 .. Netherlands 203,171 462,410 20 13 4 3 7 13 3 4 63 66 New Zealand 13,645 22,432 45 52 19 11 2 2 5 5 29 27 Nicaragua 466 1,027 75 86 3 2 1 1 1 2 21 9 Niger 288 540 17 24 1 4 0 2 80 54 1 14 Nigeria 12,342 52,000 2 .. 2 .. 96 .. 0 .. 1 .. Norway 41,992 121,505 8 5 2 0 47 68 9 7 27 16 Oman 6,068 21,585 5 2 0 0 79 95 2 1 14 3 Pakistan 8,029 16,930 12 12 4 1 1 5 0 1 83 81 Panama 625 1,048 75 84 0 1 3 1 1 4 20 10 Papua New Guinea 2,654 4,122 13 .. 20 .. 38 .. 25 .. 4 .. Paraguay 919 1,906 44 76 36 7 0 0 0 1 19 16 Peru 5,575 23,431 31 18 3 2 5 10 46 57 15 14 Philippines 17,502 47,037 13 5 1 1 2 2 4 4 42 87 Poland 22,895 110,303 10 9 3 1 8 4 7 5 71 79 Portugal 22,783 43,323 7 8 5 2 3 5 2 4 83 74 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 211 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 7,910 32,336 7 3 3 2 8 10 3 5 78 79 Russian Federation 81,095 304,520 2 2 3 3 43 63 10 8 26 17 Rwanda 54 138 57 .. 16 .. 0 .. 12 .. 14 .. Saudi Arabia 50,040 209,483 1 1 0 0 88 91 1 0 10 8 Senegal 993 1,550 9 44 7 5 22 0 12 7 48 44 Serbia .. 6,428 .. .. .. .. .. .. .. .. .. .. Sierra Leone 42 216 .. .. .. .. .. .. .. .. .. .. Singaporeb 118,268 271,772 4 2 1 0 7 13 2 1 84 80 Slovak Republic 8,580 41,721 6 4 4 1 4 5 4 3 82 85 Slovenia 8,316 23,257 4 3 2 1 1 3 3 5 90 87 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 27,853c 58,412 8c 7 4c 2 9c 9 8c 29 44 c 53 Spain 97,849 205,455 15 14 2 1 2 4 2 3 78 76 Sri Lanka 3,798 6,886 21 22 4 2 0 0 1 4 73 70 Sudan 555 5,657 44 7 47 5 0 87 0 0 6 0 Swaziland 866 2,060 .. 17 .. 8 .. 1 .. 0 .. 74 Sweden 80,440 147,377 2 4 6 4 2 5 3 4 79 78 Switzerland 81,641 147,457 3 3 1 0 0 3 3 4 94 91 Syrian Arab Republic 3,563 8,750 12 17 7 2 63 40 1 1 17 32 Tajikistan 750 1,399 .. .. .. .. .. .. .. .. .. .. Tanzania 682 1,690 65 53 23 11 0 0 0 17 10 18 Thailand 56,439 130,790 19 11 5 5 1 5 1 2 73 76 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 378 617 19 21 42 9 0 1 32 10 7 58 Trinidad and Tobago 2,455 14,147 8 2 0 0 48 76 0 0 43 21 Tunisia 5,475 11,513 10 10 1 1 8 13 2 1 79 75 Turkey 21,637 85,479 20 7 1 0 1 0 3 1 74 42 Turkmenistan 1,880 5,260 1 .. 13 .. 77 .. 1 .. 8 .. Uganda 460 1,004 90 62 5 9 0 5 1 2 4 21 Ukraine 13,128 38,368 19 12 1 1 4 6 7 6 68 73 United Arab Emirates 28,364 139,353 8 .. 0 .. 9 .. 55 .. 28 .. United Kingdom 237,953 448,291 8 5 1 1 6 10 3 3 81 77 United States 584,743 1,038,278 11 7 4 2 2 4 3 4 77 79 Uruguay 2,106 3,953 44 56 15 8 1 4 1 1 39 32 Uzbekistan 3,430 5,617 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 18,457 65,210 3 0 0 0 77 93 6 2 14 5 Vietnam 5,449 39,605 30 20 3 3 18 26 0 1 44 50 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,945 7,285 3 4 1 0 95 94 1 0 1 1 Zambia 1,040 3,689 3 6 1 3 3 1 87 85 7 6 Zimbabwe 2,118 1,950 43 30 7 8 1 0 12 23 37 38 World 5,172,060 t 12,084,582 t 9w 6w 3w 2w 7w 11 w 3w 4w 76 w 73 w Low income 94,379 323,066 18 17 5 4 26 15 4 5 47 59 Middle income 880,315 3,312,091 14 8 3 2 12 21 5 6 63 60 Lower middle income 390,379 1,689,637 13 8 3 2 12 16 3 4 65 69 Upper middle income 490,027 1,621,751 15 9 4 2 12 25 6 7 62 53 Low & middle income 974,709 3,635,152 15 9 3 2 12 21 5 6 63 60 East Asia & Pacific 355,216 1,468,949 11 6 4 2 6 8 2 3 74 80 Europe & Central Asia 205,007 834,785 10 5 3 2 22 32 7 6 51 46 Latin America & Carib. 223,378 663,606 20 15 3 2 15 21 7 9 55 53 Middle East & N. Africa 68,070 280,990 6 5 1 0 73 76 2 2 17 15 South Asia 46,647 157,637 17 11 2 2 1 9 3 6 76 72 Sub-Saharan Africa 76,692 231,263 18 .. 7 .. 37 .. 8 .. 28 .. High income 4,196,970 8,451,209 8 6 2 2 6 8 3 4 79 77 Euro area 1,733,625 3,492,756 11 8 2 1 2 5 2 3 81 79 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). 212 2008 World Development Indicators 4.4 ECONOMY Structure of merchandise exports About the data Definitions Data on merchandise trade are from customs reports c are classified as re-exports. Because of differences · Merchandise exports are the f.o.b. value of goods of goods moving into or out of an economy or from in reporting practices, data on exports may not be provided to the rest of the world, valued in U.S. dol- reports of financial transactions related to merchan- fully comparable across economies. lars. · Food corresponds to the commodities in SITC dise trade recorded in the balance of payments. The data on total exports of goods (merchandise) sections 0 (food and live animals), 1 (beverages and Because of differences in timing and definitions, are from the World Trade Organization (WTO), which tobacco), and 4 (animal and vegetable oils and fats) estimates of trade flows from customs reports may uses two main sources: national statistical offices and SITC division 22 (oil seeds, oil nuts, and oil ker- differ from those based on the balance of payments. and the IMF's International Financial Statistics. It nels). · Agricultural raw materials correspond to Moreover, several international agencies process supplements these with the Comtrade database and SITC section 2 (crude materials except fuels) exclud- trade data, each correcting unreported or misre- publications or databases of regional organizations, ing divisions 22, 27 (crude fertilizers and minerals ported data, leading to other differences. specialized agencies, economic groups, and private excluding coal, petroleum, and precious stones), and The most detailed source of data on international sources (such as Eurostat, the Food and Agriculture 28 (metalliferous ores and scrap). · Fuels correspond trade in goods is the Commodity Trade (Comtrade) Organization, and country reports of the Economist to SITC section 3 (mineral fuels). · Ores and met- database maintained by the United Nations Statistics Intelligence Unit). Country websites and direct con- als correspond to the commodities in SITC divisions Division. In addition, the International Monetary Fund tact through email have improved collection of up- 27, 28, and 68 (nonferrous metals). · Manufactures (IMF) collects customs-based data on exports and to-date statistics for many countries, reducing the correspond to the commodities in SITC sections 5 imports of goods. The value of exports is recorded proportion of estimated figures. The WTO database (chemicals), 6 (basic manufactures), 7 (machinery as the cost of the goods delivered to the frontier of now covers most of the major traders in Africa, Asia, and transport equipment), and 8 (miscellaneous the exporting country for shipment--the free on board and Latin America, which together with high-income manufactured goods), excluding division 68. (f.o.b.) value. Many countries report trade data in U.S. countries account for nearly 95 percent of world dollars. When countries report in local currency, the trade. The availability of reliable figures for countries United Nations Statistics Division applies the average in Europe and Central Asia has also improved. official exchange rate for the period shown. The shares of exports by major commodity group are Countries may report trade according to the gen- from Comtrade. The values of total exports reported eral or special system of trade (see Primary data here have not been fully reconciled with the estimates documentation). Under the general system exports of exports of goods and services from the national comprise outward-moving goods that are (a) goods accounts or from the balance of payments. wholly or partly produced in the country; (b) foreign The classification of commodity groups is based goods, neither transformed nor declared for domes- on the Standard International Trade Classification tic consumption in the country, that move outward (SITC) revision 1. Most countries now use later revi- from customs storage; and (c) goods previously sions of the SITC or the Harmonized System. Con- included as imports for domestic consumption but cordance tables are used to convert data reported subsequently exported without transformation. in one system to another. This may introduce some Under the special system exports comprise catego- classification errors, but conversions from later to ries a and c. In some compilations categories b and earlier systems are generally reliable. Developing economies' share of world merchandise exports continues to expand 4.4a Data sources 1995 2006 ($5.2 billion) ($12.1 billion) Data on merchandise exports are from the WTO. East Asia & Pacific 12% Data on shares of exports by major commodity East Asia & Pacific 7% Europe & Europe & Central Asia 4% Central Asia 7% group are from Comtrade. The WTO publishes Latin America & Caribbean 4% Sub-Saharan Africa 1% Latin America data on world trade in its Annual Report. The IMF Middle East & N. Africa 1% & Caribbean 5% publishes estimates of total exports of goods in South Asia 1% Sub-Saharan Africa 2% High-income Middle East & N. Africa 2% its International Financial Statistics and Direction 82% High-income South Asia 1% of Trade Statistics, as does the United Nations 71% Statistics Division in its Monthly Bulletin of Statis- tics. And the United Nations Conference on Trade and Development publishes data on the structure Developing economies' share of world merchandise exports increased 11 percentage points from 1995 to of exports in its Handbook of International Trade 2006. East Asia and Pacific was the biggest gainer, capturing an additional 5 percentage points. Except and Development Statistics. Tariff line records of South Asia, every other region increased its share in world trade. exports are compiled in the United Nations Sta- Source: World Development Indicators data files and World Trade Organization. tistics Division's Comtrade database. 2008 World Development Indicators 213 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan 387 2,960 .. .. .. .. .. .. .. .. .. .. Albania 714 3,058 34 20 1 1 2 12 1 3 61 64 Algeria 10,100 21,456 29 19 3 2 1 1 2 2 65 76 Angola 1,468 11,600 .. .. .. .. .. .. .. .. .. .. Argentina 20,122 34,158 5 3 2 1 4 5 2 3 86 87 Armenia 674 2,194 31 16 0 1 27 17 0 2 39 60 Australia 61,283 139,252 5 5 2 1 5 14 1 1 86 79 Austria 66,237 140,258 6 6 3 2 4 14 4 5 82 73 Azerbaijan 668 5,268 39 10 1 1 4 12 2 2 53 74 Bangladesh 6,694 16,086 17 16 3 8 8 8 2 2 69 65 Belarus 5,564 22,323 .. 9 .. 2 .. 33 .. 4 .. 48 Belgium 164,934 a 353,720 .. 7 .. 1 .. 13 .. 5 .. 72 Benin 746 990 27 30 3 4 9 20 1 1 59 44 Bolivia 1,424 2,819 10 9 2 1 5 10 3 1 82 79 Bosnia and Herzegovina 1,082 7,305 .. 17 .. 1 .. 15 .. 3 .. 63 Botswana 1,911 3,160 .. .. .. .. .. .. .. .. .. .. Brazil 54,137 95,886 11 4 3 2 12 19 3 5 71 70 Bulgaria 5,660 23,136 8 5 3 1 34 5 4 9 48 62 Burkina Faso 455 1,450 21 12 2 1 14 24 1 1 62 62 Burundi 234 431 21 6 2 1 11 8 1 1 64 82 Cambodia 1,187 4,900 .. 8 .. 2 .. 10 .. 0 .. 79 Cameroon 1,199 2,990 17 18 3 2 3 31 2 1 76 48 Canada 168,426 357,652 6 6 2 1 4 9 3 3 83 79 Central African Republic 175 240 16 17 10 27 9 17 2 2 64 37 Chad 365 1,250 24 .. 1 .. 18 .. 1 .. 56 .. Chile 15,900 38,409 7 7 2 1 9 24 2 3 79 65 China 132,084 791,461 7 3 5 4 4 12 4 9 79 71 Hong Kong, China 196,072 335,754 5 3 2 1 2 3 2 3 88 91 Colombia 13,853 26,046 9 9 3 2 3 3 2 3 78 82 Congo, Dem. Rep. 871 2,800 .. .. .. .. .. .. .. .. .. .. Congo, Rep. 670 1,700 21 .. 1 .. 20 .. 1 .. 58 .. Costa Rica 4,036 11,520 10 6 1 1 9 12 2 2 78 78 Côte d'Ivoire 2,931 5,310 21 17 1 1 19 32 1 1 57 43 Croatia 7,510 21,488 12 8 2 1 12 16 3 3 67 72 Cuba 2,825 9,410 .. 22 .. 1 .. 23 .. 1 .. 53 Czech Republic 25,085 93,217 7 5 3 1 8 9 4 4 77 79 Denmark 45,939 86,273 12 11 3 2 3 6 2 2 73 77 Dominican Republic 5,170 11,190 .. .. .. .. .. .. .. .. .. .. Ecuador 4,152 12,049 8 7 3 1 6 17 2 1 82 74 Egypt, Arab Rep. 11,760 20,595 28 19 7 4 1 16 3 3 61 43 El Salvador 3,329 7,628 15 13 2 2 9 18 2 1 72 66 Eritrea 454 540 .. .. .. .. .. .. .. .. .. .. Estonia 2,546 13,277 14 7 3 3 11 16 1 1 71 67 Ethiopia 1,145 4,594 14 .. 2 .. 11 .. 1 .. 72 .. Finland 29,470 68,873 6 5 4 2 9 15 6 9 74 66 France 289,391 534,894 11 7 3 1 7 15 4 3 76 73 Gabon 882 1,728 19 17 1 0 4 4 1 1 75 77 Gambia, The 182 255 36 31 1 2 14 17 0 1 46 49 Georgia 392 3,678 36 16 0 0 39 19 0 1 24 61 Germany 463,872 908,630 10 6 3 1 6 12 4 5 73 66 Ghana 1,906 5,497 8 13 1 1 6 14 0 1 77 70 Greece 25,898 63,185 16 11 2 1 7 19 3 4 71 65 Guatemala 3,292 11,920 12 10 2 1 12 20 1 1 73 68 Guinea 819 930 31 .. 1 .. 19 .. 1 .. 47 .. Guinea-Bissau 133 110 44 .. 0 .. 16 .. 0 .. 40 .. Haiti 653 1,705 .. .. .. .. .. .. .. .. .. .. Data for Taiwan, China 103,558 203,017 6 3 4 1 7 18 6 8 75 68 214 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 1,642 5,418 13 16 1 1 12 20 1 1 74 62 Hungary 15,465 76,963 6 4 3 1 12 7 4 3 75 75 India 34,707 174,845 4 3 4 2 24 36 7 5 54 52 Indonesia 40,630 80,333 9 9 6 3 8 32 4 4 73 53 Iran, Islamic Rep. 13,882 51,100 21 2 2 1 2 4 3 0 71 16 Iraq 665 27,935 .. .. .. .. .. .. .. .. .. .. Ireland 32,340 72,806 8 8 1 1 3 8 2 2 76 76 Israel 29,578 49,985 7 6 2 1 6 16 2 2 82 75 Italy 205,990 437,386 12 8 6 3 7 12 5 6 68 64 Jamaica 2,818 5,648 14 14 2 2 13 25 1 1 68 57 Japan 335,882 579,574 16 9 6 2 16 28 7 8 54 52 Jordan 3,697 11,447 21 13 2 1 13 24 3 2 61 57 Kazakhstan 3,807 24,956 10 7 2 1 25 13 5 1 59 78 Kenya 2,991 7,311 10 10 2 2 15 24 2 2 71 61 Korea, Dem. Rep. 1,380 3,010 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 135,119 309,383 6 4 6 2 14 28 6 8 68 58 Kuwait 7,790 15,991 16 .. 1 .. 1 .. 2 .. 81 .. Kyrgyz Republic 522 1,718 18 14 3 1 36 29 3 2 40 53 Lao PDR 589 1,060 .. .. .. .. .. .. .. .. .. .. Latvia 1,815 11,510 10 10 2 2 21 13 1 2 66 70 Lebanon 7,278 9,647 21 16 2 1 9 22 2 2 66 58 Lesotho 1,107 1,465 .. .. .. .. .. .. .. .. .. .. Liberia 510 444 .. .. .. .. .. .. .. .. .. .. Libya 5,392 6,950 23 17 1 1 0 1 1 1 75 81 Lithuania 3,650 19,300 13 9 4 2 19 22 4 2 58 64 Macedonia, FYR 1,719 3,763 17 12 3 1 12 20 3 4 64 63 Madagascar 628 1,487 16 15 2 1 14 19 1 1 65 65 Malawi 475 1,209 14 15 1 1 11 11 1 1 73 71 Malaysia 77,691 131,152 5 5 1 1 2 9 3 5 86 78 Mali 772 1,860 20 14 1 1 16 21 1 1 62 64 Mauritania 494 974 24 25 1 1 22 27 0 0 53 47 Mauritius 1,976 3,630 17 17 3 2 7 17 1 1 72 64 Mexico 75,858 268,169 6 6 2 1 2 6 2 3 80 83 Moldova 840 2,693 8 11 3 2 46 24 2 1 42 62 Mongolia 415 1,486 14 12 1 0 19 29 1 0 65 58 Morocco 10,023 23,574 20 9 6 3 14 22 4 3 56 63 Mozambique 704 2,807 22 14 3 1 10 17 1 0 62 48 Myanmar 1,348 2,460 .. .. .. .. .. .. .. .. .. .. Namibia 1,616 2,920 .. 16 .. 1 .. 3 .. 1 .. 78 Nepal 1,333 2,100 12 .. 3 .. 12 .. 3 .. 46 .. Netherlands 185,232 416,445 14 9 2 2 8 17 3 4 72 68 New Zealand 13,957 26,434 7 8 1 1 5 15 3 2 83 74 Nicaragua 975 2,988 18 12 1 0 18 25 1 0 63 61 Niger 374 950 32 34 1 4 13 15 3 1 51 46 Nigeria 8,222 21,809 18 .. 1 .. 1 .. 2 .. 77 .. Norway 32,968 64,120 7 7 3 1 3 5 6 7 81 80 Oman 4,379 10,915 20 11 1 1 2 3 2 5 70 79 Pakistan 11,515 29,825 18 10 6 4 16 26 3 3 57 56 Panama 2,510 4,863 11 11 1 0 14 18 1 1 73 69 Papua New Guinea 1,452 2,252 .. .. .. .. .. .. .. .. .. .. Paraguay 3,144 5,879 19 6 0 1 7 13 1 1 74 80 Peru 7,584 15,327 14 10 2 2 9 19 1 1 75 68 Philippines 28,341 51,522 8 7 2 1 9 15 3 2 58 75 Poland 29,050 125,997 10 6 3 2 9 10 3 4 74 74 Portugal 32,610 66,618 14 11 4 1 8 15 2 3 72 64 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 215 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 10,278 51,106 8 6 2 1 21 14 4 3 63 77 Russian Federation 60,945 163,867 18 14 1 1 3 1 2 2 45 76 Rwanda 236 501 19 .. 3 .. 12 .. 3 .. 64 .. Saudi Arabia 28,091 66,307 17 13 1 1 0 0 4 5 76 80 Senegal 1,412 3,434 25 23 2 2 30 26 1 1 42 48 Serbia .. 13,172 .. .. .. .. .. .. .. .. .. .. Sierra Leone 133 389 .. .. .. .. .. .. .. .. .. .. Singapore 124,507 238,652 5 3 1 0 8 19 2 2 83 74 Slovak Republic 8,770 45,870 9 5 3 1 13 14 6 3 70 76 Slovenia 9,492 24,104 8 6 5 2 7 11 4 6 74 74 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 30,546b 77,280 7b 4 2b 1 8b 18 2b 2 78b 66 Spain 113,537 316,448 14 8 3 1 8 16 4 4 71 70 Sri Lanka 5,306 10,258 16 12 2 1 6 13 1 3 75 69 Sudan 1,218 8,074 24 13 2 1 14 1 0 1 59 83 Swaziland 1,008 2,200 .. 18 .. 1 .. 12 .. 1 .. 66 Sweden 65,036 126,738 7 7 2 1 6 12 4 4 80 72 Switzerland 80,152 141,374 6 5 2 1 3 8 3 6 85 80 Syrian Arab Republic 4,709 9,670 17 13 3 3 1 27 1 3 76 52 Tajikistan 810 1,723 .. .. .. .. .. .. .. .. .. .. Tanzania 1,675 4,253 10 12 1 1 1 24 4 1 84 61 Thailand 70,786 128,636 4 4 4 2 7 20 3 5 81 68 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 594 1,100 18 16 2 1 30 29 1 2 49 53 Trinidad and Tobago 1,714 6,485 16 8 1 1 1 35 6 5 77 52 Tunisia 7,902 14,865 13 8 4 3 7 14 3 3 73 72 Turkey 35,709 138,290 7 2 6 2 13 5 6 5 68 41 Turkmenistan 1,365 4,057 24 .. 0 .. 3 .. 2 .. 71 .. Uganda 1,056 2,505 16 14 3 1 2 21 2 1 78 63 Ukraine 15,484 45,035 8 7 2 1 48 28 3 3 38 60 United Arab Emirates 23,778 97,754 15 .. 0 .. 4 .. 6 .. 75 .. United Kingdom 267,250 619,385 10 8 2 1 4 9 3 3 80 67 United States 770,852 1,919,427 5 4 2 1 8 18 3 3 79 71 Uruguay 2,867 4,757 10 8 4 3 10 28 1 1 74 60 Uzbekistan 2,750 3,996 .. .. .. .. .. .. .. .. .. .. Venezuela, RB 12,649 33,616 14 8 4 1 1 1 4 1 77 69 Vietnam 8,155 44,410 5 6 2 4 10 15 2 3 76 71 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 1,582 4,935 29 21 2 1 8 22 1 1 59 55 Zambia 700 2,920 10 8 2 1 13 15 2 2 72 74 Zimbabwe 2,660 2,250 6 10 2 2 9 15 2 40 78 32 World 5,228,938 t 12,326,824 t 9w 6w 3w 1w 7w 15 w 4w 4w 75 w 70 w Low income 111,167 389,128 11 7 4 2 19 27 5 4 58 58 Middle income 940,577 2,958,062 8 6 3 2 7 12 3 4 76 70 Lower middle income 428,941 1,487,837 9 6 5 3 6 16 4 6 75 66 Upper middle income 511,391 1,466,978 8 6 3 1 7 9 3 4 77 74 Low & middle income 1,051,772 3,347,357 8 6 3 2 8 12 3 4 74 70 East Asia & Pacific 366,057 1,245,694 6 4 4 3 5 15 4 7 78 71 Europe & Central Asia 224,595 841,512 10 7 3 1 14 10 3 3 64 66 Latin America & Carib. 241,125 613,382 8 6 2 1 5 10 2 3 78 77 Middle East & N. Africa 81,546 213,435 22 12 3 2 6 13 2 2 66 50 South Asia 60,322 237,321 8 5 4 2 21 32 6 5 56 55 Sub-Saharan Africa 78,560 201,872 12 10 2 1 10 15 2 3 73 65 High income 4,176,841 8,984,577 9 6 3 1 7 15 4 4 76 70 Euro area 1,635,980 3,440,926 11 8 3 2 7 14 4 5 73 69 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). 216 2008 World Development Indicators 4.5 ECONOMY Structure of merchandise imports About the data Definitions Data on imports of goods are derived from the domestic consumption from bonded warehouses · Merchandise imports are the c.i.f. value of goods same sources as data on exports. In principle, world and free trade zones. Goods transported through a purchased from the rest of the world valued in U.S. exports and imports should be identical. Similarly, country en route to another are excluded. dollars. · Food corresponds to the commodities in exports from an economy should equal the sum of The data on total imports of goods (merchandise) SITC sections 0 (food and live animals), 1 (beverages imports by the rest of the world from that economy. in the table come from the World Trade Organization and tobacco), and 4 (animal and vegetable oils and But differences in timing and definitions result in dis- (WTO). For further discussion of the WTO's sources fats) and SITC division 22 (oil seeds, oil nuts, and oil crepancies in reported values at all levels. For further and methodology, see About the data for table 4.4. kernels). · Agricultural raw materials correspond to discussion of indicators of merchandise trade, see The shares of imports by major commodity group SITC section 2 (crude materials except fuels) exclud- About the data for tables 4.4 and 6.2. are from the United Nations Statistics Division's ing divisions 22, 27 (crude fertilizers and minerals The value of imports is generally recorded as the Commodity Trade (Comtrade) database. The values excluding coal, petroleum, and precious stones), and cost of the goods when purchased by the importer of total imports reported here have not been fully 28 (metalliferous ores and scrap). · Fuels correspond plus the cost of transport and insurance to the fron- reconciled with the estimates of imports of goods to SITC section 3 (mineral fuels). · Ores and met- tier of the importing country--the cost, insurance, and services from the national accounts (shown in als correspond to the commodities in SITC divisions and freight (c.i.f.) value, corresponding to the landed table 4.8) or those from the balance of payments 27, 28, and 68 (nonferrous metals). · Manufactures cost at the point of entry of foreign goods into the (table 4.15). correspond to the commodities in SITC sections 5 country. A few countries, including Australia, Canada, The classification of commodity groups is based (chemicals), 6 (basic manufactures), 7 (machinery and the United States, collect import data on a free on the Standard International Trade Classification and transport equipment), and 8 (miscellaneous on board (f.o.b.) basis and adjust them for freight and (SITC) revision 1. Most countries now use later revi- manufactured goods), excluding division 68. insurance costs. Many countries collect and report sions of the SITC or the Harmonized System. Con- trade data in U.S. dollars. When countries report in cordance tables convert data reported in one system local currency, the United Nations Statistics Division to another. The conversion process may introduce applies the average official exchange rate for the some classifi cation errors, but conversions from period shown. later to earlier systems are generally reliable. Countries may report trade according to the gen- eral or special system of trade (see Primary data documentation). Under the general system imports include goods imported for domestic consumption 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 Top 10 developing country exporters of merchandise goods in 2006 4.5a Merchandise exports ($ billions) 1995 2006 Data sources 1,000 Data on merchandise imports are from the WTO. 800 Data on shares of imports by major commodity group are from Comtrade. The WTO publishes data on world trade in its Annual Report. The 600 International Monetary Fund publishes estimates of total imports of goods in its International Finan- 400 cial Statistics and Direction of Trade Statistics, as does the United Nations Statistics Division in 200 its Monthly Bulletin of Statistics. And the United Nations Conference on Trade and Development 0 China Russian Mexico Malaysia Brazil Thailand India Poland Indonesia Turkey publishes data on the structure of imports in its Federation Handbook of International Trade and Development China continues to dominate merchandise exports among developing countries. Even when developed Statistics. Tariff line records of imports are com- countries are included, China ranks as the third leading merchandise exporter. piled in the United Nations Statistics Division's Source: World Development Indicators data files and World Trade Organization. Comtrade database. 2008 World Development Indicators 217 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 94 1,481 19.1 11.0 69.3 68.3 1.4 2.0 10.2 18.7 Algeria .. .. .. .. .. .. .. .. .. .. Angola 113 1,484 31.8 1.4 .. 5.0 9.2 .. 59.0 93.6 Argentina 3,676 7,542 27.4 18.9 60.5 43.9 0.2 0.1 11.9 37.2 Armenia 27 475 53.4 21.5 5.2 57.0 .. 3.9 41.3 17.6 Australia 16,076 32,439 29.3 19.6 50.6 55.0 5.4 4.0 14.8 21.4 Austria 31,692 45,202 11.8 20.1 42.4 29.4 3.9 6.6 41.9 44.0 Azerbaijan 166 841 45.9 48.4 42.3 13.9 0.1 1.8 11.7 35.9 Bangladesh 469 603 15.0 14.7 5.3 13.3 0.1 5.7 79.6 66.3 Belarus 466 2,276 64.8 68.9 5.0 11.9 0.5 0.2 29.7 19.0 Belgium 33,619a 57,285 29.4 a 27.3 17.4 a 17.9 14.8a 7.9 38.4 a 46.9 Benin 159 179 25.8 18.3 53.2 57.7 6.9 2.3 14.1 21.7 Bolivia 174 419 44.8 26.6 31.5 48.0 9.8 12.2 13.9 13.2 Bosnia and Herzegovina 457 1,112 3.8 8.3 54.1 53.1 2.6 4.2 39.5 34.5 Botswana 236 771 16.2 10.5 68.5 69.7 7.8 2.9 7.5 16.9 Brazil 6,005 17,946 43.3 19.2 16.2 24.1 16.9 5.9 23.6 50.9 Bulgaria 1,431 5,041 34.5 27.3 33.0 51.8 .. 1.6 32.5 19.4 Burkina Faso 38 .. 17.3 .. 47.8 .. .. .. 34.8 .. Burundi 4 6 46.2 14.7 32.4 23.5 0.5 0.9 21.0 60.9 Cambodia 103 1,244 30.5 13.4 51.7 77.5 .. 1.2 17.7 8.0 Cameroon 242 869 48.3 16.8 14.8 18.2 7.2 5.7 29.7 59.3 Canada 25,425 57,750 20.7 18.5 31.1 25.4 11.4 9.3 36.8 46.8 Central African Republic .. .. 34.1 .. 33.9 .. 19.6 .. 12.5 .. Chad 23 .. 4.5 .. 49.8 .. 1.7 .. 43.9 .. Chile 3,249 7,406 36.8 60.3 28.0 16.4 7.4 2.7 27.8 20.5 China 18,430 91,421 18.2 23.0 47.4 37.1 10.1 0.8 24.4 39.1 Hong Kong, China 33,790 72,283 32.5 31.9 16.8 16.2 9.2 10.6 41.5 41.4 Colombia 1,641 3,297 34.4 27.3 40.0 47.0 6.5 1.8 19.1 23.9 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 61 223 52.2 34.3 22.4 15.1 0.0 .. 25.4 50.6 Costa Rica 957 2,916 14.0 9.9 71.2 59.4 -0.2 0.4 14.9 30.4 Côte d'Ivoire 426 680 28.9 26.2 20.9 12.4 12.3 .. 37.9 61.4 Croatia 2,223 10,808 31.8 11.5 60.7 73.9 1.3 0.7 6.2 13.9 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 6,638 13,296 22.0 28.1 43.4 37.8 1.1 3.0 33.5 31.1 Denmark 15,171 52,679 44.6 47.1 24.3 15.6 .. .. 31.0 37.4 Dominican Republic 1,894 4,153 2.2 3.3 82.9 91.3 .. 0.7 14.9 4.7 Ecuador 687 939 46.8 37.5 37.1 52.2 0.0 0.0 16.0 10.3 Egypt, Arab Rep. 8,262 15,834 38.8 34.7 32.5 47.9 1.0 1.2 27.8 16.2 El Salvador 342 1,464 28.3 24.0 25.0 59.5 7.8 2.3 39.0 14.2 Eritrea 49 .. 70.4 .. 3.1 .. .. .. 26.5 .. Estonia 868 3,451 43.0 41.5 41.1 30.0 0.4 2.4 15.5 26.1 Ethiopia 310 890 76.9 65.6 5.3 18.2 1.5 2.3 16.4 13.9 Finland 7,334 15,981 28.1 17.3 22.4 14.9 2.0 0.9 47.5 67.0 France 83,108 117,586 24.6 22.3 33.2 39.5 5.3 1.9 36.9 36.3 Gabon 191 136 46.4 59.8 9.0 7.2 3.3 17.1 41.3 15.9 Gambia, The 38 92 21.7 17.5 73.4 71.9 0.3 0.4 4.7 10.3 Georgia 188 817 48.2 52.2 25.0 38.3 .. 4.0 26.9 5.5 Germany 73,576 166,926 27.0 25.0 24.5 19.7 5.0 7.0 43.5 48.3 Ghana 139 1,301 58.7 15.8 7.9 66.2 3.0 0.8 30.3 17.3 Greece 9,528 35,671 3.9 50.4 43.4 40.4 0.3 1.0 52.4 8.2 Guatemala 628 1,292 8.6 9.5 33.9 75.0 4.0 7.7 53.6 7.8 Guinea 17 31 75.3 21.8 5.1 .. 1.4 0.4 18.2 77.8 Guinea-Bissau 2 6 18.2 22.9 .. 16.6 .. 19.5 81.8 41.0 Haiti 98 150 5.1 .. 91.9 90.5 0.6 .. 2.4 9.5 218 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 221 709 25.6 8.5 36.3 68.9 2.0 2.3 36.1 20.3 Hungary 5,086 13,191 8.0 18.1 57.6 32.3 3.2 1.3 31.3 48.3 India 6,763 75,057 28.0 10.2 38.2 11.9 2.5 4.2 31.4 73.7 Indonesia 5,342 11,091 .. 19.0 97.9 40.1 .. 1.9 2.1 39.0 Iran, Islamic Rep. 533 .. 25.9 .. 12.6 .. 8.8 .. 52.7 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland 4,799 68,660 22.2 4.3 46.1 7.8 .. 27.4 31.7 60.5 Israel 7,741 19,229 26.0 19.3 38.7 14.4 0.2 0.1 35.1 66.1 Italy 61,173 97,151 17.7 16.2 47.0 39.4 6.6 3.9 28.8 40.5 Jamaica 1,568 2,613 16.0 17.6 68.2 71.6 1.1 2.1 14.7 8.8 Japan 63,966 115,140 35.2 32.7 5.0 7.4 0.9 6.7 58.8 53.2 Jordan 1,689 2,432 24.8 21.7 39.1 67.5 .. .. 36.1 10.8 Kazakhstan 535 2,584 65.7 56.4 22.7 32.4 0.0 1.2 11.6 9.9 Kenya 851 2,011 33.2 50.8 57.1 34.2 2.3 0.4 7.4 14.6 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 22,133 50,385 41.9 51.3 23.3 10.6 0.4 5.8 34.5 32.3 Kuwait 1,124 6,024 83.6 38.6 10.7 3.4 5.7 1.6 .. 56.4 Kyrgyz Republic 39 351 39.6 16.1 11.9 47.5 .. 1.0 48.4 35.4 Lao PDR 68 .. 22.8 .. 76.0 .. 0.6 .. 0.6 .. Latvia 718 2,613 91.9 54.1 2.8 18.4 2.4 7.3 3.0 20.3 Lebanon .. 11,609 .. 4.1 .. 43.2 .. 2.2 .. 50.5 Lesotho 30 51 7.0 1.3 90.9 53.8 1.4 -0.4 0.7 45.3 Liberia .. .. .. .. .. .. .. .. .. .. Libya 20 385 62.7 33.2 12.0 49.4 .. 14.3 25.3 3.1 Lithuania 482 3,583 59.6 54.3 16.0 29.0 0.9 0.5 23.5 16.2 Macedonia, FYR 151 581 32.0 32.0 13.6 22.2 3.6 1.9 50.7 43.9 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 .. .. .. .. .. Malaysia 11,438 21,722 21.6 19.5 34.7 48.0 .. 1.7 43.7 30.9 Mali 68 253 32.5 13.8 37.3 58.5 5.1 2.9 25.2 24.7 Mauritania 19 .. 9.1 .. 57.9 .. .. .. 33.0 .. Mauritius 773 1,663 25.8 21.8 55.6 60.5 0.0 1.7 18.5 16.1 Mexico 9,585 16,372 12.1 11.7 64.5 74.4 6.7 7.7 16.7 6.2 Moldova 143 471 29.5 47.5 39.8 23.7 11.6 1.0 19.1 27.7 Mongolia 47 483 31.7 44.4 43.6 46.6 5.3 2.0 19.5 7.0 Morocco 2,020 9,318 20.3 15.9 64.2 64.2 1.4 0.8 14.2 19.0 Mozambique 242 355 24.8 29.6 .. 39.4 .. 0.5 75.2 30.5 Myanmar 353 256 6.5 50.8 42.7 18.1 0.0 .. 50.9 31.2 Namibia 301 509 .. 20.6 92.4 75.0 1.5 0.3 6.2 4.1 Nepal 592 252 9.3 14.0 30.0 50.8 .. 0.6 60.7 34.6 Netherlands 44,646 80,180 40.4 25.7 14.7 14.2 1.2 1.9 43.7 58.2 New Zealand 4,401 7,776 34.7 21.3 52.7 58.7 0.1 1.4 12.6 18.7 Nicaragua 94 302 17.7 12.8 52.5 76.3 2.5 1.1 27.4 9.7 Niger 12 84 3.3 10.1 57.8 51.2 0.0 1.1 38.9 37.6 Nigeria 608 4,164 16.4 17.5 2.8 0.4 0.6 0.2 80.2 81.8 Norway 13,458 32,730 63.3 46.6 16.6 11.0 3.7 3.3 16.4 39.1 Oman 13 913 7.7 34.8 81.2 59.0 1.1 0.6 10.0 5.7 Pakistan 1,432 2,246 58.0 49.6 7.7 11.3 1.0 3.7 33.4 35.4 Panama 1,298 3,897 60.4 56.9 23.8 24.6 6.1 7.9 9.6 10.6 Papua New Guinea 321 285 10.8 10.9 7.8 1.3 1.2 5.4 80.2 82.4 Paraguay 566 735 13.3 13.2 24.3 12.3 5.0 4.2 57.4 70.3 Peru 1,042 2,323 32.5 22.6 41.1 59.4 7.2 4.7 19.3 13.3 Philippines 9,323 6,453 2.9 17.8 12.2 54.3 0.7 2.0 84.2 25.9 Poland 10,637 20,522 28.6 34.0 21.7 35.3 8.3 1.5 41.4 29.2 Portugal 8,161 17,624 18.6 22.7 59.2 47.6 4.5 2.1 17.7 27.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 219 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 1,476 7,005 31.9 26.9 40.0 18.7 5.4 2.0 22.7 52.5 Russian Federation 10,568 30,691 35.8 32.8 40.8 24.9 0.6 3.1 22.8 39.2 Rwanda 11 74 60.6 40.6 21.9 42.2 .. 3.8 17.6 13.4 Saudi Arabia 3,475 7,297 .. .. .. .. .. .. .. .. Senegal 364 598 15.4 16.1 46.1 35.3 0.6 1.6 37.9 47.0 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone 71 40 13.7 34.2 80.5 57.9 0.3 6.1 5.6 1.8 Singapore 25,404 58,957 32.7 35.6 30.0 12.0 8.5 9.4 28.9 43.1 Slovak Republic 2,378 3,270 25.9 43.2 26.2 26.4 4.9 2.3 43.0 28.0 Slovenia 2,016 4,337 25.1 30.7 53.8 41.4 0.6 1.2 20.6 26.7 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 4,414 11,712 24.2 12.7 48.2 67.2 9.9 7.3 17.7 12.7 Spain 40,019 105,483 15.8 17.2 63.4 48.6 3.9 4.4 16.9 29.7 Sri Lanka 800 1,604 41.9 46.8 28.2 25.6 3.4 3.6 26.5 24.0 Sudan 82 178 0.9 10.5 9.7 70.6 3.7 14.1 85.8 4.8 Swaziland 150 274 18.2 3.9 32.2 27.1 0.0 13.8 49.6 55.1 Sweden 15,336 49,921 32.2 18.5 22.6 18.3 2.4 5.7 42.7 57.5 Switzerland 25,179 50,729 15.1 9.3 37.6 21.0 27.8 33.4 19.5 36.4 Syrian Arab Republic 1,632 2,649 14.5 8.2 77.1 76.4 .. 2.4 8.4 12.9 Tajikistan .. 110 .. 56.0 .. 1.9 .. 8.1 .. 34.0 Tanzania 566 1,422 0.3 24.0 88.6 64.3 0.0 1.1 11.1 10.7 Thailand 14,652 23,944 16.8 22.5 54.8 51.9 0.7 1.1 27.7 24.6 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 64 145 33.9 39.1 19.9 14.0 1.8 1.0 44.3 45.9 Trinidad and Tobago 331 883 58.6 24.4 23.4 51.3 9.2 15.3 8.8 9.0 Tunisia 2,401 4,162 24.9 29.9 63.7 54.7 1.5 2.7 9.8 12.7 Turkey 14,475 24,233 11.8 17.5 34.2 69.5 1.5 2.1 52.4 10.9 Turkmenistan 79 .. 79.9 .. 9.3 .. 0.9 .. 10.0 .. Uganda 104 476 17.9 2.3 75.1 74.5 .. 5.2 7.0 18.0 Ukraine 2,846 10,822 75.6 49.4 6.7 32.2 2.7 1.4 15.0 17.0 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 77,549 225,868 20.7 13.6 26.4 15.0 17.5 26.0 35.4 45.4 United States 198,501 397,833 22.7 17.2 37.7 26.8 4.2 11.7 35.5 44.3 Uruguay 1,309 1,259 30.5 34.4 46.7 47.5 1.5 5.3 21.3 12.8 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 1,529 1,469 38.2 27.8 55.5 52.3 0.1 0.1 6.1 19.7 Vietnam 2,243 4,176 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 141 468 21.9 6.7 35.3 38.6 .. .. 42.8 54.7 Zambia 112 238 64.3 35.7 25.9 46.2 .. 7.4 9.8 10.7 Zimbabwe 353 .. 26.4 .. 50.6 .. 0.3 .. 22.7 .. World 1,210,617 t 2,767,235 t 26.9 w 23.2 w 32.5 w 27.6 w 5.9 w 7.5 w 36.2 w 41.7 w Low income 18,274 111,021 26.7 19.7 25.2 18.2 2.2 3.5 46.3 58.8 Middle income 183,341 459,244 25.0 22.9 45.6 45.8 6.0 2.9 26.4 28.4 Lower middle income 87,021 232,986 21.6 23.2 47.9 42.5 6.6 1.3 27.9 32.9 Upper middle income 96,501 227,532 27.4 22.7 43.7 48.3 5.5 4.1 25.1 25.0 Low & middle income 201,502 566,671 25.1 22.7 44.0 44.2 5.7 3.0 27.9 30.2 East Asia & Pacific 62,745 163,462 17.4 21.5 49.2 42.8 7.1 1.2 30.6 34.5 Europe & Central Asia 56,445 149,843 33.6 32.3 34.9 33.3 2.6 2.2 29.1 32.1 Latin America & Carib. 37,663 79,286 24.0 18.9 51.3 56.6 6.9 5.2 17.9 19.3 Middle East & N. Africa .. .. .. .. .. .. .. .. .. .. South Asia 10,333 80,602 31.8 19.3 29.7 13.7 2.1 4.2 36.4 62.8 Sub-Saharan Africa 11,933 35,650 25.7 16.9 31.8 43.0 5.8 4.6 40.2 36.1 High income 1,006,903 2,200,476 27.4 23.4 29.1 22.7 6.0 8.8 38.7 45.1 Euro area 419,928 872,274 25.6 22.3 31.5 26.7 5.6 5.5 37.5 45.4 a. Includes Luxembourg. 220 2008 World Development Indicators 4.6 ECONOMY Structure of service exports About the data Definitions Balance of payments statistics, the main source of affiliates. Another important dimension of service · Commercial service exports are total service information on international trade in services, have trade not captured by conventional balance of pay- exports minus exports of government services not many weaknesses. Some large economies--such ments statistics is establishment trade--sales in included elsewhere. International transactions in ser- as the former Soviet Union--did not report data on the host country by foreign affiliates. By contrast, vices are defined by the IMF's Balance of Payments trade in services until recently. Disaggregation of cross-border intrafirm transactions in merchandise Manual (1993) as the economic output of intangible important components may be limited and varies may be reported as exports or imports in the balance commodities that may be produced, transferred, and considerably across countries. There are inconsis- of payments. consumed at the same time. Definitions may vary tencies in the methods used to report items. And the The data on exports of services in the table and among reporting economies. · Transport covers all recording of major flows as net items is common (for on imports of services in table 4.7, unlike those in transport services (sea, air, land, internal waterway, example, insurance transactions are often recorded editions before 2000, include only commercial ser- space, and pipeline) performed by residents of one as premiums less claims). These factors contribute vices and exclude the category "government services economy for those of another and involving the car- to a downward bias in the value of the service trade not included elsewhere." The data are compiled by riage of passengers, movement of goods (freight), reported in the balance of payments. the IMF based on returns from national sources. rental of carriers with crew, and related support and Efforts are being made to improve the coverage, Data on total trade in goods and services from the auxiliary services. Excluded are freight insurance, quality, and consistency of these data. Eurostat and IMF's Balance of Payments database are shown in which is included in insurance services; goods pro- the Organisation for Economic Co-operation and table 4.15. cured in ports by nonresident carriers and repairs of Development, for example, are working together transport equipment, which are included in goods; to improve the collection of statistics on trade in repairs of harbors, railway facilities, and airfield facili- services in member countries. In addition, the Inter- ties, which are included in construction services; and national Monetary Fund (IMF) has implemented rental of carriers without crew, which is included the new classifi cation of trade in services intro- in other services. · Travel covers goods and ser- duced in the fifth edition of its Balance of Payments vices acquired from an economy by travelers in that Manual (1993). economy for their own use during visits of less than Still, difficulties in capturing all the dimensions of one year for business or personal purposes. Travel international trade in services mean that the record services include the goods and services consumed is likely to remain incomplete. Cross-border intrafirm by travelers, such as meals, lodging, and transport service transactions, which are usually not captured (within the economy visited), including car rental. in the balance of payments, have increased in recent · Insurance and fi nancial services cover freight years. An example is transnational corporations' use insurance on goods exported and other direct insur- of mainframe computers around the clock for data ance such as life insurance; financial intermediation processing, exploiting time zone differences between services such as commissions, foreign exchange their home country and the host countries of their transactions, and brokerage services; and auxil- iary services such as financial market operational Top 10 developing country exporters of commercial services in 2006 4.6a and regulatory services. · Computer, information, communications, and other commercial services Commercial service exports ($ billions) 1995 2006 100 include such activities as international telecommu- nications and postal and courier services; computer 80 data; news-related service transactions between residents and nonresidents; construction services; 60 royalties and license fees; miscellaneous business, professional, and technical services; and personal, 40 cultural, and recreational services. 20 0 Data sources China India Russian Turkey Thailand Malaysia Poland Brazil Mexico Egypt, Federation Arab. Rep Data on exports of commercial services are from The top 10 developing country exporters of commercial services accounted for almost 60 percent of the IMF, which publishes balance of payments developing country commercial service exports and 12 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. 2008 World Development Indicators 221 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania 98 1,552 61.4 16.1 6.7 62.1 22.1 3.6 9.8 18.1 Algeria .. .. .. .. .. .. .. .. .. .. Angola 1,665 6,860 18.2 23.7 4.5 2.2 2.7 6.1 74.6 68.0 Argentina 6,992 8,222 30.1 27.8 46.9 38.1 7.1 4.4 15.9 29.7 Armenia 52 600 82.6 38.6 6.2 47.7 10.3 5.2 0.9 8.5 Australia 16,979 31,631 36.9 35.8 30.4 37.0 7.2 3.6 25.6 23.6 Austria 27,552 32,398 11.9 14.9 39.5 22.4 5.6 6.6 43.0 56.0 Azerbaijan 297 2,784 31.1 18.3 49.1 7.2 0.8 4.3 19.0 70.2 Bangladesh 1,192 2,111 65.0 76.1 19.6 6.6 5.6 9.3 9.7 8.0 Belarus 276 1,454 35.9 24.9 31.5 50.5 3.6 2.4 29.0 22.1 Belgium 32,511a 52,285 24.1a 24.9 27.7a 29.6 10.2a 7.7 38.0a 37.8 Benin 235 267 59.2 65.2 14.7 10.1 10.4 11.2 15.7 13.5 Bolivia 321 787 65.9 38.0 15.0 28.7 9.3 15.5 9.9 17.8 Bosnia and Herzegovina 262 493 51.5 42.5 30.9 32.1 9.5 12.2 8.1 13.2 Botswana 440 834 42.6 38.6 33.0 33.2 8.1 3.5 16.3 24.7 Brazil 13,161 27,149 44.1 24.2 25.8 21.2 9.6 6.0 20.6 48.6 Bulgaria 1,278 4,103 41.5 32.6 15.3 35.9 .. 4.6 43.2 26.9 Burkina Faso 116 .. 56.0 .. 19.6 .. 4.8 .. 19.6 .. Burundi 62 190 49.4 26.5 41.0 66.0 5.9 2.5 3.8 5.0 Cambodia 181 746 46.4 58.4 4.6 16.4 4.3 6.0 44.7 19.2 Cameroon 485 1,454 35.4 26.4 21.7 22.2 7.2 7.2 35.7 44.2 Canada 32,985 71,746 24.1 23.6 31.1 28.6 11.3 10.8 33.5 37.0 Central African Republic 114 .. 43.7 .. 38.0 .. 7.9 .. 10.4 .. Chad 174 .. 55.0 .. 14.9 .. 1.5 .. 28.6 .. Chile 3,524 8,289 54.0 54.9 19.9 15.1 4.1 9.7 21.9 20.3 China 24,635 100,327 38.7 34.3 15.0 24.2 17.3 9.7 29.0 31.8 Hong Kong, China 24,962 36,533 22.2 30.9 54.0 39.3 6.2 5.9 17.6 23.8 Colombia 2,813 5,425 42.4 41.5 31.2 24.5 11.9 8.8 14.5 25.1 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 690 1,550 18.6 19.9 7.5 6.6 7.3 .. 66.6 73.5 Costa Rica 895 1,608 41.4 38.9 36.1 30.2 4.6 7.1 17.9 23.8 Côte d'Ivoire 1,235 2,073 50.5 52.3 15.4 17.4 11.0 .. 23.2 30.3 Croatia 1,327 3,491 29.5 20.3 31.8 21.1 3.4 5.3 35.3 53.3 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republic 4,860 11,726 16.5 23.4 33.7 22.8 5.2 11.3 44.7 42.5 Denmark 13,945 46,137 45.1 43.4 30.8 21.8 .. .. 24.1 34.8 Dominican Republic 957 1,486 61.1 62.5 18.1 22.4 10.2 8.2 10.6 6.9 Ecuador 1,141 2,265 42.4 51.7 20.6 20.6 5.9 6.3 31.1 21.4 Egypt, Arab Rep. 4,511 10,288 35.1 44.0 28.3 17.3 4.6 10.2 32.0 28.5 El Salvador 488 1,458 55.1 40.5 14.9 35.5 11.0 8.3 19.0 15.7 Eritrea 45 .. .. .. 6.9 .. .. .. 93.1 .. Estonia 420 2,427 52.9 42.5 21.5 24.4 4.7 2.2 20.9 30.9 Ethiopia 337 1,154 63.4 54.9 7.5 8.4 7.4 5.7 21.7 31.0 Finland 9,418 15,571 22.8 28.5 24.2 22.0 5.0 1.3 48.0 48.2 France 64,523 106,949 32.9 27.5 25.4 29.2 6.1 5.7 35.6 37.5 Gabon 832 921 17.7 33.5 16.5 23.2 8.6 5.8 57.2 37.5 Gambia, The 47 94 59.6 36.1 30.4 6.8 5.8 5.4 4.2 51.7 Georgia 249 687 27.0 56.4 62.8 24.3 8.4 12.3 1.8 7.0 Germany 130,490 213,283 18.2 24.1 46.2 34.8 1.5 3.7 34.1 37.5 Ghana 331 1,442 61.3 51.3 6.2 23.9 6.5 4.8 26.0 20.0 Greece 4,003 15,899 29.9 55.2 33.1 18.9 4.5 6.8 32.5 19.2 Guatemala 672 1,628 41.4 52.0 21.0 30.4 8.7 12.6 28.9 5.1 Guinea 252 195 58.4 47.3 8.4 12.8 7.2 12.7 26.0 27.2 Guinea-Bissau 27 42 53.1 53.5 14.1 30.9 4.7 0.4 28.1 15.1 Haiti 236 370 77.6 79.5 14.7 15.1 1.7 1.7 5.9 3.7 222 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 326 994 60.4 48.7 17.5 28.5 2.5 .. 19.7 22.8 Hungary 3,765 11,485 12.8 21.0 39.8 18.5 4.9 4.0 42.5 56.5 India 10,062 63,053 56.7 40.0 9.9 11.7 5.6 6.3 27.9 42.1 Indonesia 13,230 21,406 36.7 38.2 16.4 16.8 3.4 3.5 43.4 41.5 Iran, Islamic Rep. 2,192 .. 43.0 .. 11.0 .. 9.9 .. 36.1 .. Iraq .. .. .. .. .. .. .. .. .. .. Ireland 11,252 78,460 15.9 3.2 18.1 8.7 1.4 17.5 64.6 70.5 Israel 8,134 14,704 44.9 32.5 26.1 20.3 3.0 2.8 26.0 44.4 Italy 54,613 98,005 24.5 23.1 27.2 23.6 9.7 4.0 38.6 49.3 Jamaica 1,073 1,969 46.3 45.0 13.8 13.9 9.2 10.7 30.8 30.5 Japan 121,547 133,899 29.6 32.0 30.2 20.1 2.4 5.6 37.8 42.3 Jordan 1,385 2,596 52.3 57.4 30.7 24.1 6.1 8.9 10.9 9.7 Kazakhstan 776 8,581 38.4 17.6 36.4 9.6 .. 5.0 25.2 67.8 Kenya 733 1,265 58.8 53.0 19.8 14.1 9.8 13.1 11.6 19.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 25,394 69,787 38.0 33.5 25.0 26.1 1.5 2.2 35.5 38.2 Kuwait 3,826 8,359 39.4 34.0 58.8 62.8 1.7 1.6 0.1 1.6 Kyrgyz Republic 193 456 27.1 39.0 3.4 20.1 4.3 4.8 65.3 36.1 Lao PDR 119 .. 43.3 .. 25.0 .. 4.0 .. 27.7 .. Latvia 225 1,962 68.2 32.6 10.8 35.9 7.0 2.9 14.0 28.7 Lebanon .. 8,692 .. 16.9 .. 34.6 .. 3.2 .. 45.3 Lesotho 58 75 74.9 72.9 22.6 25.1 0.2 .. 2.4 2.0 Liberia .. .. .. .. .. .. .. .. .. .. Libya 510 2,324 60.4 50.9 15.0 28.7 .. 8.5 24.7 11.9 Lithuania 457 2,462 63.9 45.1 23.3 36.9 1.1 2.5 11.7 15.4 Macedonia, FYR 300 548 49.6 42.3 8.8 12.9 20.7 4.4 20.9 40.4 Madagascar 277 462 55.6 48.5 21.1 15.9 3.7 1.0 19.6 34.7 Malawi 151 .. 66.8 .. 26.0 .. 0.1 .. 7.2 .. Malaysia 14,821 23,493 37.8 40.8 15.6 17.1 .. 3.1 46.5 39.0 Mali 412 583 59.6 61.9 11.9 13.2 1.4 6.8 27.1 18.0 Mauritania 197 .. 61.5 .. 11.6 .. 1.4 .. 25.4 .. Mauritius 630 1,319 39.9 40.5 25.2 24.8 4.6 5.4 30.3 29.3 Mexico 9,021 22,329 38.0 12.0 35.1 36.3 12.5 43.2 14.4 8.4 Moldova 193 455 51.6 37.7 29.2 41.2 9.3 2.3 9.9 18.7 Mongolia 87 514 69.6 49.5 22.3 36.5 .. 3.9 8.1 10.1 Morocco 1,350 3,568 48.1 49.1 22.4 19.7 3.5 2.6 25.9 28.6 Mozambique 350 729 32.7 37.5 .. 24.6 2.2 2.0 65.1 35.9 Myanmar 233 547 11.0 46.5 7.7 6.8 0.5 .. 80.8 46.8 Namibia 538 421 36.5 35.6 16.7 28.1 9.5 5.5 37.3 30.8 Nepal 305 488 36.3 38.2 44.7 37.9 3.0 5.9 15.9 18.0 Netherlands 43,618 78,730 28.9 22.1 26.8 21.7 3.0 2.6 41.3 53.7 New Zealand 4,571 7,675 41.2 33.7 27.5 32.9 5.2 3.7 26.1 29.7 Nicaragua 207 457 39.1 56.0 19.3 21.2 3.3 9.6 38.3 13.2 Niger 120 277 74.4 77.1 11.1 11.0 2.6 2.2 12.0 9.7 Nigeria 4,398 7,321 22.4 20.7 20.6 15.1 2.5 .. 54.4 64.2 Norway 13,052 30,776 38.2 30.4 32.4 37.6 5.6 4.3 23.7 27.7 Oman 985 3,740 41.8 33.1 4.8 18.4 4.6 9.2 48.8 39.4 Pakistan 2,431 8,087 67.0 37.4 18.4 19.1 4.3 3.1 10.3 40.4 Panama 1,049 1,666 71.0 56.9 11.5 16.3 8.8 14.3 8.7 12.4 Papua New Guinea 642 1,151 25.2 24.2 9.1 4.8 2.8 10.3 63.0 60.7 Paraguay 676 405 66.4 61.9 19.7 22.5 12.4 12.3 1.4 3.3 Peru 1,781 3,269 50.8 44.7 16.7 23.3 10.2 8.8 22.3 23.3 Philippines 6,906 6,024 29.7 56.3 6.1 20.5 1.6 4.1 62.6 19.2 Poland 7,008 17,949 25.2 23.7 5.9 32.1 13.6 4.3 55.3 39.9 Portugal 6,339 11,314 26.8 31.5 33.1 29.2 8.9 4.4 31.1 35.0 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 223 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 1,801 6,901 33.5 34.8 38.7 19.0 5.3 5.4 22.4 40.8 Russian Federation 20,206 43,703 16.4 15.4 57.4 41.7 0.4 3.7 25.9 39.2 Rwanda 58 214 72.8 51.7 17.1 16.4 .. 6.9 10.1 25.0 Saudi Arabia 8,670 19,390 25.3 27.3 .. .. 2.8 3.0 71.9 69.6 Senegal 405 681 57.1 55.8 17.7 8.4 7.0 10.2 18.2 25.6 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone 79 75 17.4 58.8 62.5 16.7 3.8 10.1 16.3 14.4 Singapore 20,728 61,745 44.8 37.2 22.5 16.8 10.1 6.6 22.6 39.5 Slovak Republic 1,800 3,012 17.0 29.8 17.8 19.0 4.9 8.7 60.2 42.4 Slovenia 1,429 3,222 30.6 23.5 40.2 30.2 1.8 2.5 27.4 43.8 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 5,756 13,949 39.9 47.5 32.1 24.3 14.1 5.5 13.8 22.7 Spain 22,354 77,902 31.1 26.4 20.3 21.4 7.4 7.8 41.2 44.4 Sri Lanka 1,169 2,359 58.1 62.0 15.9 15.8 5.4 5.9 20.5 16.2 Sudan 150 2,718 27.3 45.8 28.7 51.6 0.3 0.4 43.7 2.3 Swaziland 206 360 15.7 10.9 20.7 13.4 4.3 18.5 59.2 57.2 Sweden 17,112 39,638 28.4 16.3 31.8 29.1 1.4 3.7 38.4 50.9 Switzerland 14,899 28,616 35.2 21.3 49.8 34.7 1.1 6.1 13.9 37.9 Syrian Arab Republic 1,358 2,437 57.2 51.5 36.7 22.2 .. 15.2 6.1 11.2 Tajikistan .. 393 .. 61.5 .. 1.5 .. 8.1 .. 28.9 Tanzania 729 1,212 29.8 34.5 49.4 44.1 2.7 4.6 18.0 16.8 Thailand 18,629 32,241 41.8 50.3 22.9 14.4 5.2 5.6 30.2 29.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 148 276 70.8 77.3 12.5 2.8 4.4 12.0 12.3 7.9 Trinidad and Tobago 223 471 42.2 40.7 31.0 38.2 7.9 6.5 18.8 14.6 Tunisia 1,245 2,338 45.3 52.8 20.1 17.5 6.5 8.9 28.1 20.8 Turkey 4,654 10,152 30.3 42.4 19.6 27.0 8.4 13.5 41.7 17.0 Turkmenistan 403 .. 40.4 .. 18.2 .. 6.9 .. 34.6 .. Uganda 563 975 38.2 48.4 14.3 14.0 4.2 6.3 43.3 31.2 Ukraine 1,334 8,582 34.0 37.4 15.7 33.0 7.3 6.3 42.9 23.3 United Arab Emirates .. .. .. .. .. .. .. .. .. .. United Kingdom 62,524 170,962 27.1 20.9 39.9 37.0 4.4 7.3 28.7 34.7 United States 129,227 308,349 32.3 30.1 35.8 24.9 5.9 13.6 26.0 31.4 Uruguay 814 863 46.2 49.1 29.0 24.7 4.5 4.6 20.2 21.6 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 4,654 5,797 30.7 47.8 36.8 21.2 2.6 8.5 29.9 22.5 Vietnam 2,304 5,282 .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 604 1,800 35.6 40.9 12.5 9.0 7.1 7.5 44.8 42.6 Zambia 282 560 78.9 57.6 9.2 9.4 0.0 10.1 11.9 23.0 Zimbabwe 645 .. 56.0 .. 18.7 .. 2.9 .. 22.5 .. World 1,220,158 t 2,580,923 t 31.2 w 28.8 w 30.9 w 26.6 w 6.2 w 8.7 w 32.1 w 36.1 w Low income 32,423 122,594 52.6 45.4 14.4 14.5 5.1 6.8 28.3 34.3 Middle income 210,450 497,838 37.0 33.4 23.8 25.8 9.8 12.6 30.1 28.3 Lower middle income 99,850 250,874 40.3 41.4 17.1 22.4 10.8 7.8 31.9 28.5 Upper middle income 110,799 247,787 34.6 27.3 28.9 28.4 9.0 16.1 28.8 28.1 Low & middle income 242,630 616,140 38.4 34.3 23.0 25.0 9.4 12.1 30.0 28.7 East Asia & Pacific 82,593 194,456 38.0 40.0 15.5 20.6 12.1 6.9 36.7 32.5 Europe & Central Asia 49,260 141,959 26.7 28.5 27.6 30.3 6.7 6.4 39.4 34.9 Latin America & Carib. 52,171 98,845 41.3 25.6 31.2 30.1 10.1 25.2 17.4 19.3 Middle East & N. Africa 20,192 49,265 44.6 .. 20.2 .. .. .. 29.7 .. South Asia 15,377 77,047 58.6 45.4 13.4 13.1 5.3 6.1 22.6 35.3 Sub-Saharan Africa 24,433 58,709 40.2 45.5 24.1 22.8 8.8 6.0 27.7 26.1 High income 976,943 1,969,987 29.2 27.3 33.0 27.0 5.4 7.8 32.7 38.1 Euro area 421,365 818,771 24.9 24.9 31.8 27.6 5.4 5.2 38.0 42.4 a. Includes Luxembourg. 224 2008 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. International transactions in ser- in the producing country (for example, use of a hotel vices are defined by the IMF's Balance of Payments room) but are classified as imports of the traveler's Manual (1993) as the economic output of intangible country. In other cases services may be supplied commodities that may be produced, transferred, and from a remote location; for example, insurance consumed at the same time. Definitions may vary services may be supplied from one location and among reporting economies. · Transport covers all consumed in another. For further discussion of the transport services (sea, air, land, internal waterway, problems of measuring trade in services, see About space, and pipeline) performed by residents of one the data for table 4.6. economy for those of another and involving the car- The data on imports of services in the table and on riage of passengers, movement of goods (freight), exports of services in table 4.6, unlike those in edi- rental of carriers with crew, and related support and tions before 2000, include only commercial services auxiliary services. Excluded are freight insurance, and exclude the category "government services not which is included in insurance services; goods pro- included elsewhere." The data are compiled by the cured in ports by nonresident carriers and repairs of International Monetary Fund (IMF) based on returns transport equipment, which are included in goods; from national sources. repairs of harbors, railway facilities, and airfield facili- ties, which are included in construction services; and rental of carriers without crew, which is included in other services. · Travel covers goods and ser- vices acquired from an economy by travelers in that economy for their own use during visits of less than one year for business or personal purposes. Travel services include the goods and services consumed by travelers, such as meals, lodging, and transport (within the economy visited), including car rental. · Insurance and fi nancial services cover freight insurance on goods imported and other direct insur- ance such as life insurance; financial intermediation services such as commissions, foreign exchange transactions, and brokerage services; and auxil- iary services such as financial market operational and regulatory services. · Computer, information, The mix of commercial service imports by developing countries is changing 4.7a communications, and other commercial services include such activities as international telecommu- 1995 2006 nications, and postal and courier services; computer ($1.2 billion) ($2.6 billion) data; news-related service transactions between residents and nonresidents; construction services; Other royalties and license fees; miscellaneous business, 30% Transport 38% Other 29% Transport professional, and technical services; and personal, 34% Travel cultural, and recreational services. Insurance and financial 9% 23% Insurance and Travel financial 12% 25% Data sources Between 1995 and 2006 developing economies' commercial service imports more than doubled. 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. 2008 World Development Indicators 225 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 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 Afghanistan .. 110 .. 9 .. 25 .. 12 .. 56 .. 24 Albania 87 90 14 9 21 25 12 25 35 49 21 17 Algeria 55 33 17 12 31 30 26 48 29 24 26 51 Angola 51 50 ..a ..a 35 14 82 74 68 38 30 37 Argentina 69 59 13 12 18 24 10 25 10 19 16 26 Armenia 109 70 11 11 18 34 24 22 62 36 ­7 30 Australia 59 57 18 18 23 27 19 20 20 22 19 21 Austria 57 56 20 18 23 21 35 58 35 52 21 26 Azerbaijan 77 31 13 8 24 32 28 70 42 41 14 50 Bangladesh 83 76 5 6 19 25 11 19 17 25 21 34 Belarus 59 54 21 20 25 30 50 60 54 64 21 26 Belgium 54 53 22 23 20 22 68 88 63 85 25 24 Benin 82 78 11 15 20 20 20 13 33 26 8 11 Bolivia 76 63 14 15 15 12 23 42 27 33 11 26 Bosnia and Herzegovina 131 82 ..a 24 20 16 20 25 71 47 10 7 Botswana 34 28 29 20 25 26 51 55 38 29 36 52 Brazil 62 60 21 20 18 17 7 15 9 12 16 17 Bulgaria 71 70 15 17 16 32 45 64 46 83 12 16 Burkina Faso 63 77 25 21 24 17 14 11 27 25 18 6 Burundi 89 91 19 29 6 17 13 11 27 48 4 1 Cambodia 95 82 6 3 15 21 31 69 47 76 6 17 Cameroon 72 72 9 11 13 18 24 26 18 27 14 17 Canada 57 55 21 19 19 22 37 38 34 34 18 24 Central African Republic 79 88 15 10 14 9 20 14 28 22 6 6 Chad 91 52 7 6 13 22 22 59 34 38 5 23 Chile 61 55 10 10 26 20 29 45 27 31 25 24 China 42 33 14 14 42 45 23 40 21 32 43 54 Hong Kong, China 62 59 8 8 34 21 143 205 148 194 31 32 Colombia 65 61 15 18 26 24 15 22 21 25 18 20 Congo, Dem. Rep. 81 88 5 7 9 16 28 29 24 41 1 9 Congo, Rep. 49 17 13 14 37 24 65 91 64 46 ­3 20 Costa Rica 71 66 14 14 18 27 38 50 40 56 15 19 Côte d'Ivoire 66 72 11 8 16 10 42 51 34 41 12 14 Croatia 64 56 29 20 18 33 39 48 49 57 11 24 Cuba 71 .. 24 .. 7 .. 13 .. 16 .. .. .. Czech Republic 51 48 21 21 33 27 51 76 55 73 29 24 Denmark 51 49 25 26 20 23 38 52 34 49 22 25 Dominican Republic 79 80 5 7 19 20 31 33 34 40 18 18 Ecuador 68 65 13 11 22 23 26 34 28 33 17 27 Egypt, Arab Rep. 74 71 11 12 20 19 23 30 28 32 22 22 El Salvador 87 94 9 10 20 16 22 27 38 47 15 12 Eritrea 94 81 44 42 23 19 22 8 83 50 4 9 Estonia 54 55 27 17 27 38 68 80 76 90 22 25 Ethiopia 80 94 8 12 18 20 10 16 16 42 21 9 Finland 52 51 23 21 18 21 36 44 29 38 21 27 France 57 57 24 24 19 21 23 27 22 28 19 19 Gabon 41 27 12 8 23 23 59 65 36 24 33 41 Gambia, The 90 96 14 ..a 20 25 49 45 73 65 6 10 Georgia 102 82 11 15 4 27 26 33 42 57 ­7 7 Germany 58 58 20 18 22 18 24 45 23 40 20 23 Ghana 76 79 12 13 20 32 24 39 33 64 18 27 Greece 74 68 14 14 20 26 15 19 23 27 20 16 Guatemala 86 90 6 6 15 19 19 16 25 31 11 14 Guinea 74 84 8 5 21 13 21 32 25 35 14 8 Guinea-Bissau 95 76 6 18 22 17 12 42 35 53 5 23 Haiti 87 91 7 9 24 29 9 14 27 43 10 .. 226 2008 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 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 Honduras 64 79 9 14 32 33 44 41 48 66 27 31 Hungary 66 64 11 10 23 25 45 78 45 77 19 19 India 64 58 11 11 27 34 11 23 12 26 27 34 Indonesia 62 62 8 9 32 25 26 31 28 26 28 26 Iran, Islamic Rep. 46 46 16 12 29 34 22 42 13 34 37 40 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 54 44 16 16 18 27 76 81 64 69 23 24 Israel 55 55 28 27 25 18 29 45 38 44 15 .. Italy 58 59 18 20 20 21 26 28 22 29 22 19 Jamaica 70 66 11 18 29 33 51 46 61 63 24 26 Japan 55 57 15 18 28 23 9 14 8 13 30 27 Jordan 65 89 24 22 33 27 52 55 73 92 29 14 Kazakhstan 68 46 14 10 23 33 39 51 44 40 18 31 Kenya 70 74 15 16 22 19 33 26 39 36 16 13 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 52 54 11 15 38 30 29 43 30 42 36 30 Kuwait 43 28 32 15 15 20 52 68 42 30 .. .. Kyrgyz Republic 75 101 20 19 18 17 29 39 42 76 9 4 Lao PDR .. 65 .. 9 .. 33 23 36 37 42 15 19 Latvia 63 65 24 17 14 38 43 44 45 64 14 17 Lebanon 101 89 15 15 36 12 11 24 62 40 ­3 ­4 Lesotho 120 97 18 18 61 33 21 51 120 99 26 27 Liberia .. 86 .. 11 .. 16 9 38 72 52 .. 40 Libya 59 .. 22 .. 12 .. 29 .. 22 .. .. .. Lithuania 67 65 22 18 22 27 49 60 60 70 12 13 Macedonia, FYR 70 79 19 19 21 21 33 50 43 68 14 22 Madagascar 90 78 7 9 11 25 24 30 32 41 1 16 Malawi 79 77 21 12 17 24 30 17 48 29 ­4 15 Malaysia 48 50 12 12 44 21 94 117 98 100 34 32 Mali 83 75 10 10 23 23 21 32 36 40 14 13 Mauritania 77 61 11 20 20 23 37 55 45 59 17 29 Mauritius 63 68 13 14 29 25 58 60 64 67 26 19 Mexico 67 68 10 12 20 22 30 32 28 33 19 22 Moldova 57 95 27 18 25 34 49 46 58 93 19 23 Mongolia 56 48 13 11 32 35 48 65 49 60 35 44 Morocco 68 55 17 18 21 32 27 33 34 38 17 34 Mozambique 90 76 8 11 27 19 16 41 41 47 1 3 Myanmar 87 .. ..a .. 14 .. 1 .. 2 .. 14 .. Namibia 54 48 30 24 22 29 49 54 56 55 31 42 Nepal 75 83 9 9 25 26 25 14 35 32 23 28 Netherlands 49 47 24 25 21 20 59 74 54 66 27 30 New Zealand 58 60 17 18 23 25 29 28 28 30 18 15 Nicaragua 83 89 11 12 22 29 19 31 35 61 ­1 13 Niger 86 79 14 11 7 18 17 15 24 24 ­4 12 Nigeria 70 56 11 ..a 16 22 44 56 42 35 11 34 Norway 50 41 22 19 22 22 38 46 32 29 26 37 Oman 51 35 25 19 15 18 44 63 36 36 .. .. Pakistan 72 75 12 11 19 22 17 15 19 23 21 24 Panama 52 66 15 12 30 20 101 73 98 71 30 18 Papua New Guinea 42 .. 17 .. 22 .. 62 .. 43 .. 35 .. Paraguay 76 86 10 10 26 21 59 49 71 66 18 7 Peru 71 61 10 10 25 20 13 29 18 20 25 23 Philippines 74 77 11 10 22 14 36 46 44 48 19 33 Poland 60 62 20 19 19 20 23 41 21 41 20 18 Portugal 65 65 18 21 23 22 29 31 35 39 23 12 Puerto Rico .. .. .. .. .. .. 72 .. 97 .. .. .. 2008 World Development Indicators 227 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 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 1990 2006 Romania 68 73 14 13 24 24 28 34 33 44 19 13 Russian Federation 52 50 19 18 25 20 29 34 26 21 28 30 Rwanda 97 85 10 13 13 21 5 12 26 32 12 14 Saudi Arabia 47 25 24 25 20 18 38 62 28 31 20 .. Senegal 80 80 13 10 14 29 31 26 37 44 8 18 Serbia 73 78 23 21 12 21 17 27 24 47 6 10 Sierra Leone 88 85 14 13 6 15 19 23 26 36 ­3 9 Singapore 41 38 8 11 34 19 .. 253 .. 221 52 .. Slovak Republic 52 57 22 19 24 29 57 86 55 90 27 20 Slovenia 60 54 19 19 23 27 51 69 53 70 23 25 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 63 63 18 19 18 20 23 30 22 33 17 14 Spain 60 58 18 18 22 31 22 26 22 32 22 22 Sri Lanka 73 74 11 9 26 29 36 32 46 43 20 25 Sudan 83 70 6 16 20 25 9 16 19 27 ­4 10 Swaziland 76 60 22 28 20 17 75 81 93 86 18 19 Sweden 50 47 27 27 17 18 39 51 33 43 20 25 Switzerland 60 60 12 11 23 22 35 48 30 41 30 36 Syrian Arab Republic 66 67 13 13 27 16 31 39 38 36 23 17 Tajikistan 62 109 16 10 29 15 66 23 72 58 22 12 Tanzaniab 86 70 12 18 20 19 24 24 42 31 0 11 Thailand 55 57 10 12 42 28 42 74 49 70 34 31 Timor-Leste .. 68 .. 50 .. 19 .. .. .. .. .. 249 Togo 77 85 12 10 16 18 32 35 37 49 11 11 Trinidad and Tobago 53 51 12 13 21 16 54 65 39 43 26 32 Tunisia 63 62 16 14 25 24 45 54 49 54 20 25 Turkey 68 71 11 13 25 24 20 28 24 36 25 17 Turkmenistan 44 46 8 13 49 23 75 72 75 54 50 34 Uganda 85 77 11 14 12 23 12 15 21 29 8 15 Ukraine 55 60 21 19 27 24 47 47 50 50 24 23 United Arab Emirates 48 46 16 11 30 24 69 94 63 76 .. .. United Kingdom 64 64 20 22 17 18 28 29 29 33 15 14 United States 68 71 15 16 18 19 11 11 12 16 16 13 Uruguay 73 73 12 11 15 16 19 30 19 30 14 14 Uzbekistan 51 51 22 15 27 22 28 38 28 26 27 36 Venezuela, RB 69 48 7 11 18 25 27 37 22 21 21 40 Vietnam 74 62 8 6 27 36 33 73 42 77 19 37 West Bank and Gaza 98 95 18 32 35 27 16 16 68 70 11 10 Yemen, Rep. 71 .. 14 .. 22 .. 51 .. 58 .. 20 .. Zambia 72 57 15 10 16 24 36 38 40 30 5 23 Zimbabwe 65 72 18 27 20 17 38 57 41 73 17 0 World 61 w 61 w 17 w 17 w 22 w 22 w 21 w 27 w 21 w 27 w 22 w 21 w Low income 68 64 11 11 24 30 17 27 19 30 22 30 Middle income 59 55 15 15 27 27 25 36 25 33 26 30 Lower middle income 53 48 13 13 35 35 26 40 27 36 34 41 Upper middle income 63 61 16 15 21 21 24 33 24 30 20 22 Low & middle income 60 56 14 14 26 27 24 35 25 33 25 30 East Asia & Pacific 47 41 13 13 40 39 29 47 29 40 38 47 Europe & Central Asia 61 61 17 16 23 23 29 40 31 40 23 22 Latin America & Carib. 66 63 15 15 20 21 18 26 19 23 18 22 Middle East & N. Africa 62 57 16 14 25 26 27 38 29 35 25 30 South Asia 67 62 10 11 25 32 12 22 15 26 25 32 Sub-Saharan Africa 69 67 15 17 18 21 28 35 30 36 14 18 High income 61 62 17 18 21 21 21 26 20 26 21 19 Euro area 57 57 20 20 21 21 29 40 28 38 21 22 a. Data for general government final consumption expenditure are not available separately; they are included in household final consumption expenditure. b. Covers mainland Tanzania only. 228 2008 World Development Indicators 4.8 ECONOMY Structure of demand About the data Definitions Gross domestic product (GDP) from the expenditure guidelines are capital outlays on defense establish- · Household final consumption expenditure is the side is made up of household final consumption ments that may be used by the general public, such market value of all goods and services, including expenditure, general government final consumption as schools, airfields, and hospitals, and intangibles durable products (such as cars and computers), expenditure, gross capital formation (private and such as computer software and mineral exploration purchased by households. It excludes purchases public investment in fixed assets, changes in inven- outlays. Data on capital formation may be estimated of dwellings but includes imputed rent for owner- tories, and net acquisitions of valuables), and net from direct surveys of enterprises and administrative occupied dwellings. It also includes government fees exports (exports minus imports) of goods and ser- records or based on the commodity flow method using for permits and licenses. Expenditures of nonprofit vices. Such expenditures are recorded in purchaser data from production, trade, and construction activi- institutions serving households are included, even prices and include net taxes on products. ties. The quality of data on government fixed capital when reported separately. Household consumption Because policymakers have tended to focus on formation depends on the quality of government expenditure may include any statistical discrepancy fostering the growth of output, and because data accounting systems (which tend to be weak in devel- in the use of resources relative to the supply of on production are easier to collect than data on oping countries). Measures of fixed capital formation resources. · General government fi nal consump- spending, many countries generate their primary by households and corporations--particularly capital tion expenditure is all government current expendi- estimate of GDP using the production approach. outlays by small, unincorporated enterprises--are tures for purchases of goods and services (including Moreover, many countries do not estimate all the usually unreliable. compensation of employees). It also includes most components of national expenditures but instead Estimates of changes in inventories are rarely expenditures on national defense and security but derive some of the main aggregates indirectly using complete but usually include the most important excludes military expenditures with potentially wider GDP (based on the production approach) as the activities or commodities. In some countries these public use that are part of government capital forma- control total. Household final consumption expen- estimates are derived as a composite residual along tion. · Gross capital formation is outlays on addi- diture (private consumption in the 1968 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 Data sources 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 on national accounts indicators for most Gross capital formation (gross domestic investment editions before 2006. The change was made to con- developing countries are collected from national in the 1968 SNA) consists of outlays on additions form to SNA concepts and definitions. For further statistical organizations and central banks by to the economy's fixed assets plus net changes in discussion of the problems in compiling national visiting and resident World Bank missions. Data the level of inventories. It is generally obtained from accounts, see Srinivasan (1994), Heston (1994), for high-income economies come from Organisa- reports by industry of acquisition and distinguishes and Ruggles (1994). For an analysis of the reliability tion for Economic Co-operation and Development only the broad categories of capital formation. The of foreign trade and national income statistics, see (OECD) data files (see Annual National Accounts 1993 SNA recognizes a third category of capital Morgenstern (1963). for OECD Member Countries: Data from 1970 formation: net acquisitions of valuables. Included Onwards). in gross capital formation under the 1993 SNA 2008 World Development Indicators 229 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 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 4.3 5.1 5.2 4.6 2.4 2.1 25.8 4.6 18.9 11.2 15.7 12.8 Algeria ­0.1 .. ­1.9 .. 3.6 .. ­0.6 .. 3.2 .. ­1.0 .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 2.8 1.9 1.5 0.9 2.2 1.3 7.4 6.2 8.7 6.5 15.6 2.3 Armenia ­0.5 8.1 1.1 8.5 ­1.5 10.9 ­1.9 20.7 ­18.4 15.3 ­12.7 10.8 Australia 3.6 .. 2.4 .. 3.0 .. 5.7 .. 7.4 .. 8.1 .. Austria 1.9 1.3 1.5 0.8 2.5 1.0 .. .. 5.5 6.2 5.0 5.0 Azerbaijan 1.5 11.3 0.4 10.5 ­1.7 10.0 42.9 32.0 6.8 16.4 15.5 20.3 Bangladesh 2.6 4.0 0.5 2.1 4.7 9.6 9.2 8.0 13.1 11.2 9.7 8.4 Belarus ­0.5 10.9 ­0.3 11.4 ­1.9 2.0 ­7.5 12.5 ­4.8 8.0 ­8.7 10.1 Belgium 1.8 1.3 1.5 0.8 1.4 1.7 2.8 2.8 4.7 2.9 4.5 2.9 Benin 2.6 .. ­0.8 .. 4.4 .. 12.2 7.6 1.8 .. 2.1 .. Bolivia 3.6 2.4 1.3 0.5 3.6 3.2 8.5 ­1.8 4.5 9.8 6.0 5.3 Bosnia and Herzegovina .. .. .. .. .. .. .. 5.8 .. 8.4 .. 0.7 Botswana 2.5 4.6 0.1 3.4 7.1 2.0 6.4 ­2.7 4.7 4.0 3.8 0.7 Brazila 3.7 2.4 2.2 1.0 1.0 3.0 4.2 0.5 5.9 9.2 11.6 4.4 Bulgaria ­3.7 5.2 ­3.0 6.0 ­8.4 3.5 ­5.0 13.6 3.9 9.4 2.7 12.3 Burkina Faso 5.7 4.4 2.7 1.2 2.9 8.8 3.1 7.5 4.4 9.6 1.9 6.0 Burundi ­4.9 .. .. .. ­2.6 .. ­0.5 .. ­1.2 .. ­1.6 .. Cambodiaa 6.0 7.7 3.4 5.9 7.2 2.3 10.3 13.9 21.7 16.0 14.8 14.0 Cameroon 3.1 4.1 0.5 1.8 0.7 4.9 0.4 9.3 3.2 1.4 5.1 8.5 Canada 2.6 .. 1.6 .. 0.3 .. 4.5 .. 8.7 .. 7.1 .. Central African Republica .. 0.3 .. ­1.4 .. ­4.9 .. ­1.3 .. ­2.6 .. ­4.0 Chada 1.5 3.2 ­1.8 ­0.3 ­8.3 6.5 4.0 10.4 2.3 32.5 ­1.8 13.4 Chile 7.3 5.1 5.6 4.0 3.7 3.8 9.3 7.5 9.4 5.6 11.7 9.4 China 8.9 7.2 7.8 6.6 9.7 8.8 11.5 12.4 12.9 21.1 14.3 16.9 Hong Kong, China 3.9 2.5 2.1 2.0 3.3 1.3 7.7 1.0 7.8 8.7 8.3 7.6 Colombia 2.2 4.2 0.4 2.7 10.5 1.3 2.0 13.4 5.3 4.4 9.0 11.3 Congo, Dem. Rep.a ­4.5 .. ­7.2 .. ­17.4 .. ­0.7 .. ­0.5 6.5 ­2.4 18.3 Congo, Rep.a ­1.8 .. .. .. ­4.4 .. 10.4 .. 3.0 .. 2.0 .. Costa Ricaa 5.1 3.2 2.5 1.4 2.0 1.6 5.1 11.4 10.9 5.7 9.2 6.3 Côte d'Ivoire 4.1 0.0 1.2 ­1.7 0.8 2.9 8.1 ­0.9 1.9 7.2 8.2 3.4 Croatia 2.7 4.6 3.2 4.9 1.3 0.4 5.4 13.2 5.9 6.1 4.6 8.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 3.0 3.3 3.0 3.3 ­0.9 2.5 4.6 5.1 8.7 10.4 12.0 9.9 Denmark 2.2 2.4 1.8 2.1 2.4 1.5 .. .. 5.1 4.1 6.1 6.2 Dominican Republica 5.3 3.3 3.4 1.8 5.2 6.1 10.4 0.7 9.1 2.6 9.4 0.5 Ecuador a 2.1 5.8 0.3 4.7 ­1.5 2.5 ­0.6 10.7 5.3 6.5 2.8 11.0 Egypt, Arab Rep. 4.0 3.6 2.1 1.8 4.4 3.5 6.0 3.6 3.6 11.4 3.1 8.5 El Salvador 5.3 3.1 3.3 1.6 2.8 1.6 7.1 2.5 13.4 4.5 11.6 4.6 Eritrea ­5.0 .. ­6.7 .. 22.6 .. 19.1 ­3.9 ­2.5 ­3.2 7.5 ­3.3 Estonia 0.6 8.9 2.2 9.3 4.9 1.8 0.1 13.7 11.2 9.3 12.0 10.3 Ethiopia 3.5 7.3 1.1 4.7 9.5 ­2.2 2.3 4.6 7.1 11.1 5.8 12.5 Finland 1.7 3.2 1.4 2.9 0.6 1.6 .. .. 10.3 4.7 6.4 5.1 France 1.6 2.2 1.2 1.6 1.4 1.6 1.8 2.0 6.9 2.5 5.7 3.9 Gabona ­0.3 2.7 ­2.8 1.0 3.7 4.2 3.0 3.3 2.1 ­2.9 0.1 ­0.5 Gambia, The 3.6 .. ­0.1 .. ­2.2 .. 1.9 8.2 0.1 4.3 0.1 0.8 Georgia 6.1 7.9 7.5 8.9 12.0 4.3 ­12.5 16.1 12.2 5.6 11.2 6.9 Germany 1.9 0.3 1.6 0.3 1.9 0.5 .. .. 6.0 6.8 5.8 4.8 Ghana 4.1 4.5 1.4 2.3 4.8 ­0.8 4.3 17.2 10.1 3.5 10.4 6.8 Greece 2.1 4.0 1.4 3.6 2.1 1.9 .. .. 7.6 1.5 7.4 2.1 Guatemalaa 4.2 3.8 1.8 1.3 5.1 ­0.3 6.1 5.6 6.1 0.1 9.2 5.3 Guinea 5.2 4.3 2.0 2.5 ­0.5 0.0 0.1 ­9.1 0.3 1.3 ­1.1 ­1.4 Guinea-Bissau 2.6 6.8 ­0.4 3.8 1.9 ­2.9 ­6.5 0.8 15.4 4.1 ­0.4 ­0.6 Haiti .. .. .. .. .. .. .. .. .. .. .. .. 230 2008 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 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Hondurasa 3.0 5.4 0.6 3.5 2.0 5.4 6.9 3.0 1.6 6.0 3.8 8.2 Hungary ­0.1 4.9 0.1 5.1 0.9 2.9 9.6 ­0.6 9.9 10.1 11.4 8.9 India 4.7 5.3 2.9 3.8 6.4 5.2 6.9 11.8 12.3 11.6 14.4 10.2 Indonesia 6.6 3.8 5.0 2.5 0.1 8.1 ­0.6 5.0 5.9 7.0 5.7 8.0 Iran, Islamic Rep. 3.2 6.7 1.6 5.2 1.6 2.8 ­0.1 7.2 1.2 10.3 ­6.8 20.2 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 5.3 .. 4.5 .. 4.2 .. .. .. 15.7 .. 14.5 .. Israel 4.6 2.9 2.0 1.0 2.9 1.6 1.7 ­1.8 10.9 3.3 7.5 1.6 Italy 1.5 0.8 1.5 0.2 ­0.3 1.7 .. .. 5.1 0.3 3.8 1.2 Jamaica .. .. .. .. .. .. .. .. .. .. .. .. Japan 1.5 .. 1.3 .. 2.9 .. ­0.8 .. 4.1 .. 4.2 .. Jordan 4.9 5.6 1.1 3.2 4.7 2.7 0.3 7.0 2.6 8.2 1.5 6.6 Kazakhstana ­8.1 9.8 ­7.0 9.3 ­7.1 7.4 ­18.3 20.1 ­2.6 6.5 ­11.2 5.1 Kenya 3.6 3.9 0.6 1.3 6.9 1.5 6.1 7.1 1.0 6.6 9.4 6.4 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 4.9 3.1 3.9 2.6 4.7 4.8 3.4 3.0 16.0 10.3 10.0 8.4 Kuwait 4.5 .. 0.6 .. ­2.4 .. 1.0 .. ­1.6 .. 0.8 .. Kyrgyz Republic ­6.5 10.6 ­7.4 9.7 ­8.8 0.3 ­3.9 ­1.4 ­1.6 1.6 ­8.2 11.0 Lao PDR .. 1.6 .. 0.0 .. 9.4 .. 13.6 .. .. .. .. Latvia ­3.9 10.0 ­2.7 10.6 1.8 2.6 ­3.7 16.1 4.3 8.4 7.6 12.6 Lebanon 1.3 3.0 ­0.5 1.8 10.5 2.7 ­7.7 ­3.8 15.1 11.6 ­2.8 3.3 Lesotho 0.5 4.0 ­1.2 3.0 6.2 2.3 1.5 ­1.9 11.1 9.0 0.9 3.7 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuaniaa 5.3 8.9 6.0 9.4 1.9 4.1 11.1 12.1 4.9 12.6 7.5 14.2 Macedonia, FYR 2.2 2.6 1.7 2.3 ­0.4 1.0 3.6 0.9 4.2 1.5 7.5 1.9 Madagascar 2.2 2.5 ­0.8 ­0.3 0.0 6.9 3.3 12.1 3.8 0.1 4.1 6.1 Malawi 5.4 4.1 3.4 1.5 ­4.4 5.1 ­8.4 21.3 4.0 ­9.8 ­1.1 4.3 Malaysia 5.3 6.3 2.6 4.4 4.8 9.2 5.3 1.2 12.0 5.3 10.3 6.0 Mali 3.0 0.8 0.3 ­2.2 3.2 16.8 0.4 8.3 9.9 8.7 3.5 5.4 Mauritania .. .. .. .. .. .. .. .. ­1.3 11.9 0.6 .. Mauritius 5.1 4.9 3.9 4.0 4.8 4.7 4.7 3.5 5.4 3.7 5.2 2.9 Mexico 3.9 3.0 2.2 2.0 1.8 0.7 4.7 0.5 14.6 4.8 12.3 5.2 Moldovaa 9.9 9.6 10.7 10.9 ­12.4 13.1 ­15.5 9.8 0.7 13.0 5.6 15.1 Mongoliaa .. .. .. .. .. .. .. .. .. .. .. .. Morocco 1.8 3.9 0.1 2.8 3.9 2.9 2.5 9.7 5.9 6.0 5.1 6.3 Mozambiquea 3.9 4.5 0.8 2.1 4.2 8.5 10.0 1.5 13.1 18.9 5.0 5.2 Myanmar 3.9 .. .. .. .. .. 15.3 .. 10.0 .. 5.8 .. Namibia 4.8 2.0 1.9 0.6 3.3 1.8 6.9 10.3 3.8 6.0 5.4 5.1 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands 3.1 0.4 2.4 0.0 2.0 3.2 4.4 0.2 7.3 4.1 7.6 3.9 New Zealand 3.2 .. 2.0 .. 2.5 .. 6.1 .. 5.2 .. 6.2 .. Nicaraguaa 6.1 3.3 3.9 2.0 ­1.5 1.2 11.3 ­0.3 9.3 7.9 12.2 4.0 Niger 1.8 .. .. .. 0.8 .. 4.0 .. 3.1 .. ­2.1 .. Nigeria 0.2 .. .. .. ­1.8 .. 5.4 .. 5.0 .. 4.0 .. Norway 3.5 3.5 2.9 2.9 2.7 2.4 6.1 5.1 5.5 1.2 5.8 4.9 Oman 5.4 .. 2.6 .. 2.4 .. 4.0 .. 6.2 .. 5.9 .. Pakistan 4.9 4.4 2.3 2.1 0.7 9.6 1.8 5.3 1.7 10.5 2.5 9.7 Panamaa 6.4 5.9 4.2 4.1 1.7 4.2 10.4 1.7 ­0.4 4.3 1.2 4.3 Papua New Guinea 5.6 .. .. .. 2.7 .. 0.5 .. 4.3 .. 2.8 .. Paraguay 2.6 3.4 0.2 1.5 2.5 ­1.8 0.7 3.6 3.1 4.4 2.9 5.0 Perua 4.0 4.0 2.3 2.7 5.2 4.0 7.4 6.0 8.5 8.1 9.0 6.7 Philippines 3.7 4.7 1.5 2.7 3.8 0.4 4.1 ­1.4 7.8 5.7 7.8 4.8 Polanda 5.2 3.1 5.1 3.3 3.7 3.3 10.6 1.6 11.3 9.2 16.7 6.6 Portugal 3.0 1.4 2.7 0.8 2.8 1.7 .. .. 5.3 3.5 7.3 2.0 Puerto Rico .. .. .. .. .. .. .. .. 1.6 .. 4.5 .. 2008 World Development Indicators 231 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 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Romaniaa 1.3 6.1 1.7 6.8 0.8 5.1 ­5.1 9.7 8.1 10.3 6.0 11.5 Russian Federation ­0.9 7.6 ­0.7 8.0 ­2.2 1.7 ­19.1 9.4 0.8 8.3 ­6.1 17.2 Rwandaa 1.1 3.5 0.1 1.0 ­1.7 9.3 1.4 6.0 ­3.8 15.7 5.0 6.6 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2.6 4.3 ­0.2 1.8 0.9 3.8 3.5 10.0 4.1 0.8 2.0 4.3 Serbia .. 4.7 .. 4.9 .. 4.2 .. 20.8 .. 9.1 .. 13.1 Sierra Leone ­4.4 .. .. .. 10.4 .. ­5.6 .. ­11.2 .. ­0.2 .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 5.6 4.5 5.4 4.5 2.0 3.3 8.1 7.7 8.8 10.9 12.2 10.8 Slovenia 3.9 2.7 3.9 2.6 2.1 3.0 10.9 5.1 1.7 7.8 5.2 7.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2.9 5.0 0.6 3.7 0.3 5.0 5.0 7.9 5.6 3.2 7.1 9.0 Spain 2.4 3.5 2.0 1.9 2.7 4.7 .. .. 10.5 3.5 9.4 6.4 Sri Lankaa 5.7 .. .. .. 7.5 .. 6.9 5.9 7.5 3.9 8.6 5.0 Sudan 3.8 6.7 1.2 4.7 5.5 9.8 21.5 12.3 11.6 8.2 8.4 14.3 Swazilanda 3.8 1.6 0.6 0.4 5.5 ­0.5 2.7 3.7 3.8 4.0 4.5 3.0 Sweden 1.3 1.8 0.9 1.4 0.6 1.1 1.8 2.4 8.6 5.2 6.3 3.6 Switzerland 1.1 .. 0.5 .. 0.8 .. .. .. 4.0 .. 4.2 .. Syrian Arab Republic 3.0 6.1 0.3 3.4 2.0 5.0 3.3 14.5 12.0 0.6 4.4 11.5 Tajikistan ­4.2 10.6 ­5.6 9.4 ­19.1 0.9 ­17.6 5.8 ­1.3 7.8 ­3.9 8.3 Tanzaniab 4.9 2.5 2.0 ­0.1 ­7.0 15.1 ­1.6 7.1 9.3 10.4 3.9 4.2 Thailand 3.7 4.8 2.5 4.1 5.1 4.5 ­4.0 6.2 9.5 5.9 4.5 6.4 Timor-Leste .. ­7.7 .. ­12.2 .. 6.4 .. ­4.5 .. .. .. .. Togo 5.0 .. 1.7 .. 0.0 .. ­0.1 .. 1.2 .. 1.1 .. Trinidad and Tobago 0.7 .. 0.1 .. 0.3 .. 12.5 .. 6.9 .. 9.9 .. Tunisia 4.3 4.7 2.6 3.7 4.1 4.1 3.6 1.9 5.1 3.6 3.8 2.4 Turkey 3.6 3.7 1.7 2.3 4.9 1.0 5.0 5.7 11.6 10.1 11.0 8.3 Turkmenistan .. .. .. .. .. .. 1.9 .. ­6.1 .. 0.6 .. Uganda 6.7 4.8 3.3 1.6 7.1 5.9 8.9 8.5 14.7 7.7 10.0 6.6 Ukraine ­6.9 11.8 ­6.4 12.7 ­4.1 3.2 ­18.5 8.3 ­3.6 3.5 ­6.6 5.3 United Arab Emirates 7.1 .. 0.7 .. 6.8 .. 5.5 .. 5.5 .. 6.4 .. United Kingdom 2.9 2.7 2.6 2.4 1.0 2.9 5.0 3.6 6.6 4.8 6.8 5.9 United States 3.6 .. 2.4 .. 0.7 .. 7.4 .. 7.3 .. 9.8 .. Uruguaya 5.0 0.6 4.3 0.5 2.3 ­1.9 6.3 3.1 6.0 5.4 9.9 2.3 Uzbekistan .. .. .. .. .. .. ­2.5 5.1 2.4 4.4 ­1.2 4.6 Venezuela, RB 0.6 7.0 ­1.5 5.2 3.7 6.5 11.0 7.1 1.0 ­1.0 8.2 10.7 Vietnam 5.4 6.8 3.9 5.4 3.2 7.0 19.8 10.5 24.1 16.2 28.2 17.1 West Bank and Gaza 5.3 ­1.8 0.9 ­5.8 12.7 1.0 9.2 ­4.3 8.7 ­1.4 7.5 ­2.5 Yemen, Rep. 3.2 .. ­0.7 .. 1.7 .. 11.4 .. 16.6 .. 8.3 .. Zambia 2.4 0.3 ­0.2 ­1.6 ­8.1 21.1 13.3 ­5.2 6.7 20.5 15.5 15.0 Zimbabwe 0.0 .. ­1.9 .. ­2.2 .. ­2.5 .. 10.5 .. 9.4 .. World 3.0 w .. w 1.5 w .. w 1.7 w .. w 3.5 w .. w 6.9 w 6.2 w 7.0 w .. w Low income 4.2 5.0 2.1 3.1 4.2 5.9 6.4 10.6 8.7 10.2 9.4 10.5 Middle income 4.0 4.7 2.7 3.8 3.3 4.7 2.6 7.8 7.3 10.5 6.5 9.9 Lower middle income 5.5 6.0 4.2 5.0 6.5 6.9 5.6 10.6 7.3 14.1 5.8 12.1 Upper middle income 3.0 3.8 2.0 3.0 1.3 2.8 ­0.1 4.1 7.2 6.8 7.0 7.9 Low & middle income 4.0 4.7 2.4 3.4 3.3 4.8 3.0 8.2 7.4 10.5 6.7 9.9 East Asia & Pacific 7.5 6.4 6.1 5.6 8.1 8.3 8.1 11.0 11.0 14.9 10.3 12.5 Europe & Central Asia 1.0 5.6 0.9 5.6 0.1 2.5 ­7.8 7.3 3.1 8.9 1.2 10.6 Latin America & Carib. 3.6 3.1 2.0 1.7 2.1 2.4 5.4 3.1 8.5 5.4 10.8 5.7 Middle East & N. Africa 3.0 4.6 0.9 2.8 3.4 3.7 1.2 7.1 4.2 .. 0.2 10.7 South Asia 4.5 5.1 2.5 3.4 5.6 5.9 6.5 10.9 10.0 11.0 11.1 9.6 Sub-Saharan Africa 3.1 4.3 0.4 1.8 0.4 4.9 4.5 7.6 5.0 4.5 5.6 8.3 High income 2.8 .. 2.0 .. 1.5 .. 3.7 .. 6.8 .. 7.0 .. Euro area 1.9 1.4 1.6 0.9 1.4 1.8 .. .. 6.6 4.2 6.1 4.1 a. Household final consumption expenditure includes statistical discrepancy. b. Covers mainland Tanzania only. 232 2008 World Development Indicators 4.9 ECONOMY Growth of consumption and investment About the data Definitions Measures of growth in consumption and capital for- technique captures improvements in productivity · Household final consumption expenditure is the mation are subject to two kinds of inaccuracy. The or changes in the quality of government services. market value of all goods and services, including first stems from the difficulty of measuring expendi- Deflators for household consumption are usually cal- durable products (such as cars and computers), tures at current price levels, as described in About culated on the basis of the consumer price index. purchased by households. It excludes purchases the data for table 4.8. The second arises in deflat- Many countries estimate household consumption of dwellings but includes imputed rent for owner- ing current price data to measure volume growth, as a residual that includes statistical discrepancies occupied dwellings. It also includes government fees where results depend on the relevance and reliability associated with the estimation of other expenditure for permits and licenses. Expenditures of nonprofit of the price indexes and weights used. Measuring items, including changes in inventories; thus these institutions serving households are included, even price changes is more difficult for investment goods estimates lack detailed breakdowns of household when reported separately. Household consumption than for consumption goods because of the one-time consumption expenditures. expenditure may include any statistical discrepancy nature of many investments and because the rate in the use of resources relative to the supply of of technological progress in capital goods makes resources. · Household final consumption expen- capturing change in quality diffi cult. (An example diture per capita is household final consumption is computers--prices have fallen as quality has expenditure divided by midyear population. · Gen- improved.) Several countries estimate capital forma- eral government final consumption expenditure is tion from the supply side, identifying capital goods all government current expenditures for goods and entering an economy directly from detailed produc- services (including compensation of employees). It tion and international trade statistics. This means also includes most expenditures on national defense that the price indexes used in deflating production and security but excludes military expenditures with and international trade, reflecting delivered or offered potentially wider public use that are part of govern- prices, will determine the deflator for capital forma- ment capital formation. · Gross capital formation is tion expenditures on the demand side. outlays on additions to fixed assets of the economy, Growth rates of household final consumption expen- net changes in inventories, and net acquisitions diture, household final consumption expenditure per of valuables. Fixed assets include land improve- capita, general government final consumption expen- ments (fences, ditches, drains); plant, machinery, diture, gross capital formation, and exports and and equipment purchases; and construction (roads, imports of goods and services are estimated using railways, schools, buildings, and so on). Inventories constant price data. (Consumption, capital forma- are goods held to meet temporary or unexpected tion, and exports and imports of goods and services fluctuations in production or sales, and "work in prog- as shares of GDP are shown in table 4.8.) ress." · Exports and imports of goods and services To obtain government consumption in constant are the value of all goods and other market services prices, countries may defl ate current values by provided to or received from the rest of the world. applying a wage (price) index or extrapolate from They include the value of merchandise, freight, insur- the change in government employment. Neither ance, transport, travel, royalties, license fees, and other services (communication, construction, finan- Investment is rising rapidly in Asia 4.9a cial, information, business, personal, government services, and so on). They exclude compensation of Gross capital formation (2000 $ billions) 1,200 employees and investment income (factor services East Asia & Pacific in the 1968 SNA) and transfer payments. 1,000 800 Data sources 600 Latin America & Caribbean Data on national accounts indicators for most Europe & Central Asia 400 developing countries are collected from national South Asia statistical organizations and central banks by Middle East & North Africa 200 visiting and resident World Bank missions. Data 0 Sub-Saharan Africa for high-income economies come from Organisa- 1990 1995 2000 2006 tion for Economic Co-operation and Development Between 1990 and 2006 investment increased nearly sixfold in East Asia and Pacific and threefold in (OECD) data files (see Annual National Accounts South Asia. for OECD Member Countries: Data from 1970 Source: World Development Indicators data files. Onwards). 2008 World Development Indicators 233 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 2006 2006 Afghanistanb .. 7.4 .. 17.1 .. ­1.7 .. 0.3 .. 2.1 9.3 0.2 Albaniab 21.2 23.6 25.6 21.9 ­8.9 ­3.0 7.4 1.9 2.1 1.0 .. 15.5 Algeriab 30.2 43.1 24.2 17.5 ­1.3 13.8 ­7.4 3.3 8.6 ­2.0 .. 1.9 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 18.1 .. 18.3 .. ­0.5 .. 0.5 .. 1.5 .. 26.5 Armeniab .. 18.8 .. 16.2 .. ­0.3 .. 0.3 .. 1.0 .. 1.8 Australia .. 26.0 .. 24.2 .. 1.7 .. .. .. .. 20.5 3.6 Austria 38.4 39.8 44.2 42.0 ­5.4 ­1.6 .. .. .. .. 63.4 6.9 Azerbaijanb 18.0 .. 19.8 .. ­3.1 .. .. .. .. .. .. .. Bangladeshb .. 10.0 .. 8.8 .. ­0.7 .. 2.3 .. 0.9 36.2 16.4 Belarusb 30.0 36.0 28.7 30.4 ­2.7 1.4 2.2 1.2 0.4 0.0 .. 0.9 Belgium 41.5 41.2 45.6 41.4 ­3.8 0.3 .. .. .. .. 84.8 9.2 Beninb .. 16.7 .. 13.4 .. 0.2 .. ­2.6 .. 2.4 .. 1.3 Bolivia .. 23.8 .. 24.6 .. 12.5 .. 0.7 .. ­11.2 .. 7.9 Bosnia and Herzegovina .. 39.8 .. 35.7 .. 2.9 .. ­0.5 .. 0.1 .. 1.4 Botswanab 40.5 .. 30.4 .. 4.9 .. 0.2 .. ­0.4 .. .. .. Brazilb 26.9 .. 32.9 .. ­2.7 .. .. .. .. .. .. .. Bulgariab 35.5 36.8 39.4 31.9 ­5.1 3.4 7.4 ­0.9 ­0.8 ­1.2 .. 3.4 Burkina Faso .. 12.1 .. 12.0 .. ­5.7 .. 0.1 .. 4.0 .. 3.1 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 19.5 24.2 17.9 ­4.3 1.5 4.9 0.1 0.0 0.3 48.6 6.9 Central African Republicb .. 8.1 .. 9.4 .. ­0.5 .. 1.2 .. 0.1 .. 8.0 Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 25.9 .. 17.1 .. 7.7 .. ­1.9 .. ­0.1 .. 2.7 Chinab 5.4 9.6 .. 10.8 .. ­1.6 1.6 1.7 .. 0.0 .. 4.4 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 26.0 .. 28.7 .. ­3.9 .. 9.7 .. 1.5 68.0 35.5 Congo, Dem. Rep.b 5.3 .. 8.2 .. 0.0 .. 0.0 .. 0.2 .. .. .. Congo, Rep. .. 30.9 .. 19.9 .. 6.4 .. .. .. .. 0.2 18.1 Costa Ricab .. 24.1 .. 22.1 .. 1.2 .. .. ­0.8 .. .. 15.9 Côte d'Ivoireb 20.1 17.4 .. 19.0 .. ­1.4 ­1.2 ­0.1 3.8 1.2 107.9 8.2 Croatiab 43.1 40.0 42.5 39.4 ­1.3 ­1.8 ­2.7 2.1 0.8 ­1.4 .. 5.4 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republicb 33.2 30.6 32.6 35.5 ­0.9 ­4.3 ­0.5 2.4 ­0.4 1.0 24.6 3.1 Denmark 39.1 36.0 38.2 32.2 1.5 5.1 .. .. .. .. 29.0 5.5 Dominican Republicb .. 17.9 .. 16.5 .. ­1.2 .. ­1.0 .. 2.5 .. 8.5 Ecuador b 30.9 .. 26.3 .. 0.1 .. .. .. .. .. .. .. Egypt, Arab Rep.b 25.9 24.1 23.8 26.8 ­1.1 ­5.8 .. .. .. .. .. .. El Salvador .. 17.2 .. 19.3 .. ­3.2 .. 1.5 .. 2.5 43.3 15.3 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 31.8 .. 26.6 .. 3.6 .. .. .. .. 7.0 0.3 Ethiopiab .. .. .. .. .. .. .. .. .. .. .. .. Finland 39.9 38.3 38.6 35.2 1.9 3.9 0.3 ­1.0 ­1.3 2.3 39.7 3.6 France 43.3 43.0 47.6 45.6 ­4.1 ­2.3 .. .. .. .. 67.4 5.6 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Theb 23.7 .. .. .. .. .. .. .. .. .. .. .. Georgiab 12.2 22.5 15.4 20.3 ­4.3 1.6 2.2 ­0.2 2.4 ­0.4 28.0 3.1 Germany 29.9 28.9 38.6 30.6 ­8.3 ­1.4 .. 1.6 .. ­0.1 43.5 5.9 Ghanab 17.0 23.8 .. 20.9 .. ­2.9 .. .. .. 3.3 .. 14.4 Greece 30.6 33.6 37.1 35.8 ­8.0 ­4.5 .. .. .. .. 102.1 11.4 Guatemalab 8.4 10.7 7.6 11.6 ­0.5 ­1.7 .. 1.6 0.4 1.0 19.0 10.7 Guineab 11.2 .. 12.1 .. ­4.3 .. ­0.1 .. 4.5 .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 234 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 2006 2006 Honduras .. 19.4 .. 20.8 .. ­1.3 .. 1.4 .. 1.0 .. 4.7 Hungary 42.6 35.8 49.6 44.1 ­4.7 ­8.6 3.9 2.0 ­0.7 4.4 70.2 10.7 Indiab 12.3 12.7 14.4 15.1 ­2.2 ­2.8 5.1 3.2 0.0 0.2 60.0 25.8 Indonesiab 17.7 18.4 9.7 16.9 3.0 ­1.1 ­0.6 0.0 ­0.4 ­0.4 28.8 14.8 Iran, Islamic Rep.b 24.2 36.2 15.8 24.8 1.1 3.3 .. 1.4 0.1 0.0 .. 0.8 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 33.6 34.2 37.5 30.9 ­2.2 2.7 .. .. .. .. 28.2 2.9 Israel .. 39.9 .. 44.0 .. ­1.6 .. .. .. .. .. 10.7 Italy 40.4 37.2 48.0 40.8 ­7.5 ­3.3 .. .. .. .. 109.8 11.9 Jamaicab .. 39.2 33.3 38.7 .. 0.3 .. .. .. .. 140.1 37.6 Japan 20.7 .. .. .. .. .. 1.5 .. .. .. .. .. Jordanb 28.2 31.7 26.1 35.0 0.9 ­3.9 ­2.5 3.1 6.1 ­3.0 77.5 7.7 Kazakhstanb 14.0 16.8 18.7 14.6 ­1.8 1.6 0.8 ­0.5 2.8 0.0 7.1 1.5 Kenyab 21.6 19.8 25.9 17.8 ­5.1 1.5 3.9 ­0.5 ­1.3 ­3.8 .. 8.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.b 17.8 23.3 14.3 21.3 2.4 0.7 ­0.3 ­0.1 ­0.1 ­0.3 .. 5.4 Kuwait 36.8 37.2 46.4 26.2 ­13.6 8.2 .. .. .. .. .. 0.1 Kyrgyz Republicb .. 18.5 .. 17.5 .. ­0.6 .. .. .. .. .. 4.4 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latviab 25.8 27.0 28.3 28.7 ­2.7 ­0.5 2.4 0.6 1.5 0.4 .. 1.6 Lebanon .. 21.6 .. 26.5 .. ­8.5 .. ­1.3 .. 12.4 .. 56.0 Lesothob 49.9 50.0 34.5 40.3 5.1 4.1 0.0 .. 6.3 .. .. 4.8 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 29.0 .. 28.2 .. ­0.2 .. ­3.3 .. 4.1 20.0 2.4 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 11.7 .. 11.6 .. 9.9 .. 0.8 .. 3.0 .. 11.2 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiab 24.4 23.7 17.2 20.1 2.4 ­4.3 .. .. ­0.8 .. .. 10.5 Mali .. 16.7 .. 15.7 .. 32.1 .. ­1.0 .. ­34.0 .. 0.9 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusb 21.6 21.5 19.9 22.2 ­1.3 ­3.0 3.1 4.7 ­0.6 ­0.6 43.2 12.0 Mexicob 15.3 .. 15.0 .. ­0.6 .. .. .. 5.5 .. .. .. Moldovab 28.4 33.8 38.4 32.4 ­6.3 0.2 3.0 0.0 2.7 ­0.5 29.6 2.8 Mongolia .. 33.3 .. 27.1 .. ­0.4 .. 9.9 .. ­6.0 105.5 3.1 Moroccob .. 25.1 .. 25.9 .. ­1.8 .. 1.0 .. ­0.5 43.7 12.6 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 6.4 8.0 .. 3.4 .. ­1.8 .. 1.8 .. 0.0 .. .. Namibiab 31.7 28.1 35.7 31.1 ­5.0 ­6.8 .. ­20.0 .. ­0.1 .. 9.1 Nepalb 10.5 10.9 .. 14.7 .. ­1.6 0.6 0.7 2.5 0.2 50.3 7.3 Netherlands 41.5 42.2 50.8 41.5 ­9.2 0.5 .. .. .. .. 49.0 4.4 New Zealand .. 39.6 .. 33.8 .. 4.7 .. ­1.7 .. 2.8 45.9 3.9 Nicaraguab 12.8 18.8 14.2 19.2 0.6 0.1 .. .. 3.4 .. .. 7.9 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 50.4 .. 32.1 .. 17.9 .. 0.5 .. 15.1 48.4 2.3 Omanb 27.8 .. 32.4 .. ­8.9 .. ­0.1 .. 0.0 .. .. .. Pakistanb 17.2 13.5 19.1 15.3 ­5.3 ­4.2 .. .. .. .. .. 33.9 Panamab 26.1 .. 22.0 .. 1.5 .. .. .. .. .. .. .. Papua New Guineab 23.9 .. 25.8 .. ­0.5 .. 1.5 .. ­0.7 .. .. .. Paraguay b .. 21.3 .. 16.7 .. 1.2 .. 1.0 .. ­0.3 .. 4.6 Perub 17.4 17.6 17.4 17.3 ­1.3 ­0.8 .. 1.9 3.9 ­1.2 .. 10.6 Philippinesb 17.7 16.2 15.9 17.5 ­0.8 ­1.3 ­0.5 ­0.1 ­0.7 2.0 77.7 33.1 Poland .. 32.2 .. 36.2 .. ­3.6 .. 2.9 .. 2.2 47.9 7.1 Portugal 35.3 38.6 37.8 42.3 ­3.0 ­3.9 ­3.5 0.3 4.1 6.3 72.2 6.9 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 235 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 2006 2006 Romania .. 24.5 .. 24.0 .. ­1.0 .. ­1.0 .. 0.1 .. 4.1 Russian Federation 21.3 28.8 11.6 19.6 9.3 8.1 .. 0.5 .. ­2.8 .. 2.2 Rwandab 10.6 .. 15.0 .. ­5.6 .. 2.9 .. .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegalb 15.2 .. .. .. .. .. .. .. .. .. .. .. Serbiab .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leoneb 9.4 12.3 .. 23.8 .. ­2.5 0.3 .. .. .. .. 21.0 Singaporeb 26.7 19.9 12.4 13.8 19.8 7.0 10.3 6.1 0.0 .. 104.0 0.5 Slovak Republic .. 30.5 .. 33.6 .. ­3.4 .. 4.5 .. 0.4 42.4 5.2 Sloveniab 36.7 40.2 35.2 40.3 ­0.2 ­0.8 ­0.4 1.5 0.3 ­0.4 .. 3.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 31.8 .. 30.4 .. 1.2 .. 0.2 .. 0.1 .. 9.5 Spain 32.0 27.2 37.1 25.2 ­5.8 1.9 .. .. .. .. 39.8 4.8 Sri Lankab 20.4 17.0 26.0 22.2 ­7.6 ­7.2 5.2 6.1 3.2 1.5 93.0 29.7 Sudanb 7.2 .. 6.8 .. ­0.4 .. 0.3 .. .. .. .. .. Swazilandb .. 26.6 .. 24.4 .. ­2.6 .. .. .. .. .. 4.5 Sweden 40.4 37.9 39.0 35.4 2.2 1.9 .. .. .. .. 48.5 4.3 Switzerlandb 22.7 18.6 25.8 19.5 ­0.6 ­0.4 ­0.5 0.3 .. .. 28.6 4.3 Syrian Arab Republicb 22.9 .. .. .. .. .. .. .. .. .. .. .. Tajikistanb 9.3 13.5 11.4 13.7 ­3.3 ­6.6 0.1 .. 2.3 .. .. 5.1 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 20.2 .. 16.2 .. 1.9 .. 3.0 .. ­0.6 26.2 7.1 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togob .. 17.7 .. 17.8 .. ­0.1 .. .. .. .. .. .. Trinidad and Tobagob 27.2 32.5 25.3 24.0 ­0.1 6.1 2.8 .. 2.6 .. .. 8.2 Tunisiab 30.0 30.1 28.4 29.4 ­2.5 ­2.8 0.9 0.9 2.9 ­2.3 55.1 9.3 Turkey b .. 32.9 .. 29.1 .. 2.5 .. 2.2 .. ­0.1 67.8 25.4 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandab 10.6 13.5 .. 17.4 .. ­2.0 .. 1.7 .. 1.6 .. 7.8 Ukraineb .. 36.6 .. 37.2 .. ­1.0 .. ­0.4 .. 0.9 .. 1.7 United Arab Emiratesb 10.1 .. 9.3 .. 0.5 .. .. .. .. .. .. .. United Kingdom 37.2 38.8 37.1 41.2 0.3 ­2.8 ­0.3 3.5 0.0 0.0 49.9 5.3 United States .. 19.3 .. 21.3 .. ­2.0 .. 1.4 .. 0.6 46.9 10.9 Uruguay b 27.6 27.7 27.1 27.2 ­1.2 ­0.9 7.9 0.5 1.1 ­1.9 70.0 15.4 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBb 16.9 28.4 18.5 25.2 ­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 17.7 21.4 20.0 ­3.1 ­2.8 28.0 .. 16.2 .. .. 13.7 Zimbabweb 26.7 .. 32.1 .. ­5.4 .. ­1.4 .. 1.6 .. .. .. World .. w 27.0 w .. w 28.0 w .. w ­1.2 w .. m .. m .. m .. m .. m 5.8 m Low income 13.3 12.9 15.4 15.2 ­2.6 ­2.6 .. .. .. .. .. .. Middle income 17.2 .. .. .. .. .. .. 1.3 .. 0.1 .. 7.1 Lower middle income 11.5 16.0 .. 15.8 .. ­0.9 .. 1.4 .. 0.3 .. 7.7 Upper middle income .. .. .. .. .. .. .. 0.6 .. 0.0 .. 5.4 Low & middle income 16.5 .. .. .. .. .. .. .. .. .. .. 9.4 East Asia & Pacific 8.4 11.0 .. 11.7 .. ­1.3 .. 2.1 .. 0.0 .. 7.6 Europe & Central Asia .. 31.5 .. 28.9 .. 1.8 .. 0.4 .. 0.0 .. 2.8 Latin America & Carib. 21.2 .. 23.4 .. ­1.5 .. .. 1.5 .. 1.2 .. 9.6 Middle East & N. Africa 26.1 31.8 .. 25.0 .. 1.0 .. .. .. .. .. .. South Asia 13.1 12.9 15.3 15.4 ­2.7 ­3.1 3.8 1.9 1.1 0.8 55.1 25.8 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income .. 27.2 .. 28.6 .. ­1.3 .. .. .. .. 47.6 5.1 Euro area 34.7 35.5 42.2 37.1 ­7.4 ­1.3 .. .. .. .. 63.4 5.7 a. Excludes grants. b. Data were reported on a cash basis and have been adjusted to the accrual framework. 236 2008 World Development Indicators 4.10 ECONOMY Central government finances About the data Definitions Tables 4.10­4.12 present an overview of the size periods can also be used. The definition of govern- · Revenue is cash receipts from taxes, social con- and role of central governments relative to national ment excludes public corporations and quasi corpo- tributions, and other revenues such as fines, fees, economies. The tables are based on the concepts rations (such as the central bank). rent, and income from property or sales. Grants, usu- and recommendations of the International Monetary Units of government meeting this definition exist ally considered revenue, are excluded. · Expense is Fund's (IMF) Government Finance Statistics Manual at many levels, from local administrative units to cash payments for government operating activities in 2001, 2nd edition. Before 2005 World Development the national government, but inadequate statistical providing goods and services. It includes compensa- Indicators reported data derived on the basis of the coverage precludes the presentation of subnational tion of employees, interest and subsidies, grants, 1986 manual. The 2001 manual, harmonized with data. Although data for general government are avail- social benefi ts, and other expenses such as rent the 1993 System of National Accounts, recommends able for a few countries under the 2001 manual, only and dividends. · Cash surplus or deficit is revenue an accrual accounting method over the cash-based data for the central government are shown to mini- (including grants) minus expense, minus net acquisi- method of the 1986 manual. The new manual focuses mize disparities. Still, different accounting concepts tion of nonfinancial assets. In editions before 2005 on all economic events affecting assets, liabilities, of central government make cross-country compari- nonfinancial assets were included under revenue revenues, and expenses, not only those represented sons potentially misleading. and expenditure in gross terms. This cash surplus by cash transactions. It takes all stocks into account, Central government can refer to consolidated or or deficit is close to the earlier overall budget balance so that stock data at the end of an accounting period budgetary accounting concepts. For most countries (still missing is lending minus repayments, which are are equal to stock data at the beginning of the period central government finance data have been consoli- brought in below as a financing item under net acqui- plus flows during the period. The 1986 manual consid- dated into one account, but for others only budgetary sition of financial assets). · Net incurrence of liabili- ered only the debt stock data. Further, the new manual central government accounts are available. Countries ties is domestic financing (obtained from residents) does not distinguish between current and capital reve- reporting budgetary data are noted in Primary data and foreign financing (obtained from nonresidents), nue or expenditures, unlike the 1986 manual. The new documentation. Because budgetary accounts do not or the means by which a government provides finan- manual also introduces the concepts of nonfinancial necessarily include all central government units (such cial resources to cover a budget deficit or allocates and financial assets. Most countries still follow the as extrabudgetary accounts and social security funds), financial resources arising from a budget surplus. 1986 manual, however. The IMF has reclassified his- the picture they provide is usually incomplete. The net incurrence of liabilities should be offset by torical Government Finance Statistics Yearbook data to Data on government revenue and expense are col- the net acquisition of financial assets (a third financ- conform to the format of the 2001 manual. Because lected by the IMF through questionnaires to mem- ing item). The difference between the cash surplus of differences in reporting, the reclassified data under- ber countries and by the Organisation for Economic or deficit and the three financing items is the net state both revenue and expense. Co-operation and Development. Despite IMF efforts change in the stock of cash. · Total debt is the entire The 2001 manual describes the economic func- to standardize the collection of public finance data, stock of direct government fi xed-term contractual tions of a government as the provision of goods and statistics are often incomplete, untimely, and not obligations to others outstanding on a particular services to the community on a nonmarket basis for comparable across countries. date. It includes domestic and foreign liabilities collective or individual consumption, and the redis- Government finance statistics are reported in local such as currency and money deposits, securities tribution of income and wealth through transfer pay- currency. The indicators here are shown as percent- other than shares, and loans. It is the gross amount ments. Government activities are financed mainly by ages of GDP. Many countries report government finance of government liabilities reduced by the amount of taxation and other income transfers, though other data by fiscal year; see Primary data documentation for equity and financial derivatives held by the govern- forms of financing such as borrowing for temporary information on fiscal year end by country. ment. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day Fifteen developing economies had a total debt to GDP ratio of 50 percent or higher 4.10a of the fiscal year. · Interest payments are interest payments on government debt--including long-term Central government total debt to GDP ratio, 2006 (%) 150 bonds, long-term loans, and other debt instruments --to domestic and foreign residents. 100 Data sources Data on central government finances are from 50 the IMF's Government Finance Statistics Yearbook 2007 and data files. Each country's accounts 0 are reported using the system of common defi - a ire lia ka es an an y ay a ey a a s l pa ar ve c bi di si rk u ai go an in rd ut nitions and classifications in the IMF's Govern- Ivo ni m In Ne ng ug di Tu m pp Jo Bh on Tu iL lo al d' Hu Ur Ja ili Co M M Sr te Ph ment Finance Statistics Manual 2001. See these Cô sources for complete and authoritative explana- Note: Data are for the most recent year available for 2004­06. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2008 World Development Indicators 237 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistana .. 57 .. 37 .. 0 .. 6 .. 0 Albaniaa 18 12 14 30 9 17 59 42 0 0 Algeriaa 6 6 39 28 13 5 34 31 8 29 Angola .. .. .. .. .. .. .. .. .. .. Argentina .. 5 .. 12 .. 26 .. 50 .. 7 Armeniaa .. 37 .. 23 .. 2 .. 33 .. 5 Australia .. 10 .. 10 .. 4 .. 70 .. 6 Austria 5 5 13 12 8 7 65 65 11 12 Azerbaijana 49 .. 10 .. 0 .. 41 .. 0 .. Bangladesha .. 17 .. 25 .. 20 .. 29 .. 9 Belarusa 39 12 5 12 1 1 55 69 0 5 Belgium 3 2 7 7 18 9 71 79 3 3 Benina .. 31 .. 40 .. 2 .. 8 .. 20 Bolivia .. 14 .. 21 .. 13 .. 47 .. 5 Bosnia and Herzegovina .. 25 .. 29 .. 2 .. 41 .. 4 Botswanaa 32 .. 30 .. 2 .. 36 .. 2 .. Brazila 5 .. 8 .. 45 .. 45 .. 1 .. Bulgariaa 18 14 7 18 37 4 38 61 2 3 Burkina Faso .. 21 .. 39 .. 4 .. 35 .. 0 Burundia 20 .. 30 .. 6 .. 14 .. 10 .. Cambodia .. 41 .. 33 .. 2 .. 19 .. 5 Cameroona 17 .. 40 .. 26 .. 14 .. .. .. Canadaa 8 8 10 12 18 8 64 67 .. 6 Central African Republica .. 27 .. 53 .. 9 .. .. .. 11 Chad .. .. .. .. .. .. .. .. .. .. Chile .. 11 .. 21 .. 4 .. 57 .. 11 Chinaa .. 28 .. 1 .. 4 .. 62 .. 5 Hong Kong, China .. .. .. .. .. .. .. .. .. .. Colombia .. 5 .. 19 .. 32 .. 41 .. 3 Congo, Dem. Rep.a 37 .. 58 .. 1 .. 2 .. .. .. Congo, Rep. .. 29 .. 37 .. 29 .. 5 .. 0 Costa Ricaa .. 11 .. 42 .. 17 .. 16 .. 14 Côte d'Ivoirea .. 22 .. 34 .. 8 .. 16 .. 20 Croatiaa 35 9 27 26 3 5 32 53 3 6 Cuba .. .. .. .. .. .. .. .. .. .. Czech Republica 7 6 9 8 3 3 75 69 5 13 Denmark 8 10 13 14 13 6 64 68 4 4 Dominican Republica .. 16 .. 25 .. 9 .. 42 .. 7 Ecuador a 6 .. 49 .. 26 .. .. .. .. .. Egypt, Arab Rep.a 21 9 26 28 31 .. 7 33 .. 12 El Salvador .. 16 .. 37 .. 14 .. 25 .. 10 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. 15 .. 21 .. 0 .. 46 .. 4 Ethiopiaa .. .. .. .. .. .. .. .. .. .. Finland 10 10 10 10 9 4 68 71 7 7 France 8 6 23 22 6 5 59 53 6 2 Gabon .. .. .. .. .. .. .. .. .. .. Gambia, Thea .. .. .. .. .. .. .. .. .. .. Georgiaa 52 24 11 16 10 4 26 49 .. 7 Germany 4 5 5 5 6 6 67 82 20 3 Ghanaa .. .. .. 45 .. 21 .. 5 .. .. Greece 10 10 22 25 27 11 36 43 5 3 Guatemalaa 15 13 50 24 12 10 18 25 6 27 Guineaa 17 .. 34 .. 28 .. 9 .. 1 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. 238 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras .. 15 .. 46 .. 5 .. 22 .. 12 Hungary 8 9 10 13 20 9 57 62 13 10 Indiaa 14 13 10 8 27 22 33 34 0 0 Indonesiaa 21 8 20 13 16 16 41 63 2 0 Iran, Islamic Rep.a 21 11 56 40 0 1 .. 29 .. 19 Iraq .. .. .. .. .. .. .. .. .. .. Ireland 5 12 15 26 14 3 33 36 1 1 Israel .. 27 .. 24 .. 10 .. 32 .. 9 Italy 4 4 14 15 24 11 54 61 6 10 Jamaicaa 22 19 24 32 32 38 1 3 21 8 Japan .. .. .. .. .. .. .. .. .. .. Jordana 7 7 67 40 11 8 12 31 4 16 Kazakhstana .. 20 .. 8 3 2 58 54 .. 15 Kenyaa 15 23 28 60 46 10 .. 5 2 2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 16 10 15 11 3 6 63 52 3 21 Kuwait 33 21 31 32 5 0 24 26 7 21 Kyrgyz Republica .. 26 .. 26 .. 5 .. 35 .. 8 Lao PDR .. .. .. .. .. .. .. .. .. .. Latviaa 20 13 20 16 3 2 56 40 0 30 Lebanon .. 3 .. 33 .. 46 .. 16 .. 2 Lesothoa 32 28 45 37 5 6 8 29 3 .. Liberia .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Lithuania .. 14 .. 20 .. 3 .. 60 .. 6 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar .. 14 .. 43 .. 21 .. 14 .. 8 Malawi .. .. .. .. .. .. .. .. .. .. Malaysiaa 23 26 34 30 17 12 27 31 1 1 Mali .. 32 .. 33 .. 3 .. 19 .. 13 Mauritania .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 13 45 36 12 12 28 35 2 4 Mexicoa 9 .. 19 .. 19 .. .. .. .. .. Moldovaa 10 19 8 16 11 3 71 57 1 6 Mongolia .. 36 .. 30 .. 4 .. 31 .. 0 Moroccoa .. 12 .. 43 .. 12 .. 27 .. 6 Mozambique .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. Namibiaa 28 28 53 49 1 8 .. 14 4 2 Nepala .. .. .. .. .. 6 .. .. .. .. Netherlands 5 7 8 8 9 5 77 80 3 3 New Zealand .. 30 .. 25 .. 5 .. 37 .. 6 Nicaraguaa 14 15 25 34 17 9 29 35 14 7 Niger .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. Norway .. 11 .. 16 .. 4 .. 67 .. 5 Omana 55 .. 30 .. 7 .. 8 .. 0 .. Pakistana .. 37 .. 4 28 31 2 28 .. .. Panamaa 16 .. 45 .. 8 .. 30 .. 1 .. Papua New Guineaa 19 .. 36 .. 20 .. 26 .. 1 .. Paraguaya .. 12 .. 53 .. 6 .. 24 .. 5 Perua 20 20 19 20 19 11 33 45 8 4 Philippinesa 15 19 34 31 33 31 15 18 .. 2 Poland .. 7 .. 12 .. 7 .. 70 .. 7 Portugal 7 6 30 28 10 6 43 48 11 2 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 239 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania .. 21 .. 19 .. 4 .. 44 .. 12 Russian Federation 27 19 .. 20 .. 3 .. 55 .. 3 Rwandaa 52 .. 36 .. 12 .. 5 .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. Senegala .. .. .. .. .. .. .. .. .. .. Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leonea .. 28 .. 26 .. 19 .. 9 .. 18 Singaporea 38 40 39 34 8 1 15 26 .. .. Slovak Republic .. 11 .. 14 .. 5 .. 63 .. 7 Sloveniaa 19 12 21 19 3 4 55 62 3 3 Somalia .. .. .. .. .. .. .. .. .. .. South Africa .. 11 .. 14 .. 10 .. 58 .. 8 Spain 5 5 14 9 11 5 42 75 2 8 Sri Lankaa 23 13 20 28 22 24 24 26 10 9 Sudana 44 .. 38 .. 8 .. 10 .. .. .. Swazilanda .. 29 .. 42 .. 5 .. 21 .. 2 Sweden 11 11 9 10 13 5 64 52 5 3 Switzerlanda 24 8 6 7 4 4 66 75 0 5 Syrian Arab Republica .. .. .. .. .. .. .. .. .. .. Tajikistana 47 29 8 9 12 5 33 27 .. 30 Tanzania .. .. .. .. .. .. .. .. .. .. Thailand .. 21 .. 40 .. 9 .. 29 .. 4 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togoa .. .. .. .. .. .. .. .. .. .. Trinidad and Tobagoa 20 19 36 31 20 11 24 37 1 1 Tunisiaa 7 6 37 39 13 10 36 36 7 9 Turkeya .. 9 .. 22 .. 29 .. 40 .. 1 Turkmenistan .. .. .. .. .. .. .. .. .. .. Ugandaa .. 30 .. 12 .. 8 .. 49 .. 0 Ukrainea .. 12 .. 13 .. 2 .. 69 .. 4 United Arab Emiratesa 50 .. 37 .. .. .. .. .. .. .. United Kingdom 22 18 7 15 9 5 54 30 9 1 United States .. 15 .. 13 .. 10 .. 61 .. 2 Uruguaya 13 15 17 22 6 16 64 46 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 28 35 37 16 15 19 15 0 5 Zimbabwea 16 .. 34 .. 31 .. 19 .. .. .. World .. m 13 m .. m 21 m .. m 6m .. m 45 m .. m 5m Low income .. .. .. .. .. .. .. .. .. .. Middle income .. 13 .. 24 .. 7 .. 40 .. 7 Lower middle income .. 14 .. 28 .. 9 .. 32 .. 7 Upper middle income .. 11 .. 20 .. 5 .. 55 .. 7 Low & middle income .. 16 .. 26 .. 10 .. 34 .. .. East Asia & Pacific .. 27 .. 31 .. 7 .. 31 .. 0 Europe & Central Asia .. 14 .. 16 .. 3 .. 55 .. 6 Latin America & Carib. .. 14 .. 29 .. 11 .. 30 .. 8 Middle East & N. Africa 8 8 39 40 13 .. .. 25 .. 11 South Asia .. 25 .. 18 27 22 24 27 .. 0 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. High income 7 10 15 14 8 5 59 62 5 4 Euro area 5 6 14 12 11 5 55 65 3 3 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. 240 2008 World Development Indicators 4.11 ECONOMY Central government expenses About the data Definitions The term expense has replaced expenditure in the fixed capital. Purchases from a third party and cash · Goods and services are all government payments table since the 2005 edition of World Development transfers to households are shown as subsidies and in exchange for goods and services used for the Indicators in accordance with use in the International other transfers, and other expenses. The economic production of market and nonmarket goods and ser- Monetary Fund's (IMF) Government Finance Statis- classification can be problematic. For example, the vices. Own-account capital formation is excluded. tics Manual 2001. Government expenses include distinction between current and capital expense may · Compensation of employees is all payments in all nonrepayable payments, whether current or be arbitrary, and subsidies to public corporations or cash, as well as in kind (such as food and hous- capital, requited or unrequited. Total central govern- banks may be disguised as capital financing. Subsi- ing), to employees in return for services rendered, ment expense as presented in the IMF's Govern- dies may also be hidden in special contractual pric- and government contributions to social insurance ment Finance Statistics Yearbook is comparable to ing for goods and services. For further discussion of schemes such as social security and pensions that the concept used in the 1993 System of National government finance statistics, see About the data for provide benefits to employees. · Interest payments Accounts. tables 4.10 and 4.12. 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 2.10; for more on employer social benefits in cash and in kind. · Other health expenses, see table 2.15. 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 refl ected in compensation of employees, use of goods and services, and consumption of Interest payments are a large part of government expenses for some developing countries 4.11a Central government interest payments as a share of total expense, 2006 (%) 50 40 30 20 Data sources 10 Data on central government expenses are from 0 the IMF's Government Finance Statistics Yearbook es on ca a n y p. a ka a a ar vis ke bi ta in di an Re sc in ai an an Ne m nt In s r Gh 2007 and data files. Each country's accounts Tu pp m a ki iL lo b ge o, ag Ja Pa d Le ili Co ng Sr Ar an ad Ph Co are reported using the system of common defi - M s itt .K St nitions and classifications in the IMF's Govern- Interest payments accounted for more than 20 percent of total expenses in 2006 for 13 countries. ment Finance Statistics Manual 2001. See these sources for complete and authoritative explana- Note: Data are for the most recent year for 2004­06. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2008 World Development Indicators 241 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistana .. 4 .. 6 .. 11 .. 2 .. 0 .. 76 Albaniaa 8 15 39 49 14 8 1 1 15 18 22 10 Algeriaa 65 6 10 64 18 3 1 1 .. .. 5 26 Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina .. 19 .. 29 .. 16 .. 14 .. 17 .. 5 Armeniaa .. 20 .. 32 .. 3 .. 20 .. 12 .. 14 Australia .. 65 .. 24 .. 2 .. 0 .. .. .. 9 Austria 20 24 21 22 0 0 5 4 41 38 14 12 Azerbaijana 31 .. 34 .. 33 .. 2 .. 23 .. 0 .. Bangladesha .. 12 .. 29 .. 33 .. 4 .. .. .. 22 Belarusa 16 7 33 40 6 7 11 8 31 33 3 6 Belgium 36 37 23 25 .. .. 2 1 36 35 3 2 Benina .. 19 .. 36 .. 24 .. 6 .. .. .. 15 Bolivia .. 7 .. 29 .. 2 .. 6 .. 5 .. 52 Bosnia and Herzegovina .. 2 .. 42 .. 0 .. 11 .. 33 .. 11 Botswanaa 21 .. 4 .. 15 .. 0 .. .. .. 59 .. Brazila 14 .. 24 .. 2 .. 4 .. 31 .. 26 .. Bulgariaa 17 14 28 46 8 2 3 0 21 23 23 15 Burkina Faso .. 15 .. 35 .. 13 .. 2 .. .. .. 35 Burundia 14 .. 30 .. 20 .. 1 .. 5 .. 30 .. Cambodia .. 10 .. 40 .. 22 .. 0 .. .. .. 28 Cameroona 17 .. 25 .. 28 .. 3 .. 2 .. 25 .. Canadaa 50 55 17 16 2 1 .. .. 22 22 10 6 Central African Republica .. 14 .. 23 .. 19 .. 4 .. 6 .. 34 Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile .. 41 .. 34 .. 2 .. 3 .. 5 .. 15 Chinaa 9 24 61 79 7 ­16 0 0 .. .. 22 12 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia .. 18 .. 23 .. 9 .. 4 .. 4 .. 42 Congo, Dem. Rep.a 21 .. 12 .. 21 .. 5 .. 1 .. 41 .. Congo, Rep. .. 4 .. 16 .. 7 .. 1 .. 4 .. 69 Costa Ricaa .. 14 .. 37 .. 5 .. 2 .. 31 .. 11 Côte d'Ivoirea 15 15 14 15 58 44 3 11 5 8 5 8 Croatiaa 11 8 42 47 9 2 1 1 33 34 4 8 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republica 15 18 32 27 4 0 1 1 40 46 8 8 Denmark 34 37 40 44 .. .. 7 3 5 4 14 12 Dominican Republica .. 20 .. 54 .. 14 .. 4 .. 1 .. 8 Ecuador a 50 .. 26 .. 11 .. 1 .. .. .. 12 .. Egypt, Arab Rep.a 22 32 17 23 13 6 13 3 .. .. 35 35 El Salvador .. 24 .. 44 .. 6 .. 1 .. 11 .. 14 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia .. 11 .. 30 .. 0 .. 0 .. 33 .. .. Ethiopiaa .. .. .. .. .. .. .. .. .. .. .. .. Finland 21 20 34 34 0 .. 2 2 32 31 12 13 France 17 25 25 24 0 0 3 4 47 42 8 6 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea 14 .. 32 .. 42 .. 0 .. 0 .. 7 .. Georgiaa 7 10 48 51 10 4 .. 0 13 15 22 20 Germany 16 17 20 22 .. .. 0 .. 58 57 6 4 Ghanaa 15 22 31 22 24 29 .. 2 .. .. 9 26 Greece 17 19 32 29 0 0 3 3 31 34 16 11 Guatemalaa 19 28 46 55 23 9 3 1 2 2 6 4 Guineaa 8 .. 4 .. 62 .. 2 .. 1 .. 23 .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 242 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras .. 25 .. 52 .. 6 .. 1 .. .. .. 15 Hungary 18 20 28 34 8 0 1 2 33 35 12 10 Indiaa 23 39 28 30 24 15 0 0 0 0 25 16 Indonesiaa 46 28 33 32 4 3 1 4 6 3 9 30 Iran, Islamic Rep.a 12 13 5 2 9 6 1 1 6 11 66 68 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 37 38 35 35 0 0 2 6 17 17 9 4 Israel .. 33 .. 28 .. 1 .. 5 .. 16 .. 17 Italy 32 34 21 22 .. .. 5 5 35 35 6 4 Jamaicaa .. 14 .. 32 .. 8 .. 21 .. 9 .. 17 Japan 35 .. 14 .. 1 .. 5 .. 26 .. 18 .. Jordana 10 12 23 38 22 10 9 15 .. 0 36 24 Kazakhstana 11 42 28 42 3 6 5 0 48 .. 6 9 Kenyaa 35 33 40 44 14 10 1 0 0 0 10 12 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep.a 31 29 32 28 7 3 10 7 8 16 12 16 Kuwait 1 0 0 .. 2 1 0 0 .. .. 97 98 Kyrgyz Republica .. 11 .. 52 .. 13 .. .. .. .. .. 24 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latviaa 7 13 41 40 3 1 0 0 35 29 13 17 Lebanon .. 11 .. 45 .. 8 .. 12 .. 1 .. 24 Lesothoa 15 19 12 16 49 49 1 0 .. .. 24 15 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania .. 24 .. 36 .. 0 .. 0 .. 31 .. 10 Macedonia, FYR .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 9 .. 12 .. 25 .. 4 .. .. .. 50 Malawi .. .. .. .. .. .. .. .. .. .. .. .. Malaysiaa 37 47 26 21 12 6 5 0 1 .. 19 26 Mali .. 5 .. 15 .. 4 .. 3 .. .. .. 73 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritiusa 12 17 25 44 34 17 6 5 6 5 16 11 Mexicoa 27 .. 54 .. 4 .. 2 .. 14 .. 16 .. Moldovaa 6 3 38 50 5 4 1 0 38 28 2 15 Mongolia .. 16 .. 35 .. 6 .. 0 .. 16 .. 27 Moroccoa .. 33 .. 38 .. 9 .. 7 .. .. .. 12 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar 20 25 26 31 12 2 .. .. .. .. 42 42 Namibiaa 27 38 32 20 28 32 2 2 .. 1 11 8 Nepala 10 11 33 34 26 18 4 5 .. .. 27 32 Netherlands 26 26 24 27 .. 1 2 3 40 35 8 9 New Zealand .. 58 .. 26 .. 2 .. 0 .. 0 .. 14 Nicaraguaa 9 23 52 49 7 5 0 0 11 19 31 23 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. 33 .. 23 .. 0 .. 1 .. 17 .. 25 Omana 21 .. 1 .. 3 .. 2 .. .. .. 74 .. Pakistana 18 20 27 33 24 13 7 1 .. .. 24 33 Panamaa 20 .. 17 .. 11 .. 3 .. 16 .. 34 .. Papua New Guineaa 40 .. 8 .. 27 .. 2 .. 0 .. 23 .. Paraguaya .. 9 .. 35 .. 8 .. 4 .. 15 .. 28 Perua 15 24 46 40 10 6 8 6 10 9 11 15 Philippinesa 33 39 26 25 29 20 4 4 .. .. 8 12 Poland .. 14 .. 38 .. 0 .. 1 .. 37 .. 10 Portugal 23 21 32 34 0 0 2 2 29 32 14 14 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 243 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania .. 13 .. 33 .. 3 .. 0 .. 39 .. 13 Russian Federation .. 7 .. 21 .. 29 .. 0 .. 19 .. 24 Rwandaa 11 .. 25 .. 23 .. 3 .. 2 .. 36 .. Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegala 17 .. 19 .. 36 .. 2 .. .. .. 26 .. Serbiaa .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leonea 15 16 34 9 39 27 0 .. .. .. 12 48 Singaporea 26 30 20 23 1 0 15 10 .. .. 38 36 Slovak Republic .. 11 .. 35 .. 0 .. 0 .. 40 .. 15 Sloveniaa 13 18 33 32 9 0 0 3 42 38 3 9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. 51 .. 33 .. 4 .. 3 .. 2 .. 7 Spain 28 26 21 18 0 0 0 0 40 46 .. 7 Sri Lankaa 12 16 49 51 17 15 4 3 1 1 18 14 Sudana 17 .. 41 .. 27 .. 1 .. .. .. 14 .. Swazilanda .. 28 .. 19 .. 48 .. 0 .. .. .. 5 Sweden 15 10 26 34 .. .. 12 12 35 34 13 10 Switzerlanda 11 19 21 33 1 1 2 2 49 37 17 8 Syrian Arab Republica 23 .. 37 .. 13 .. 8 .. 0 .. 19 .. Tajikistana 6 3 63 54 12 11 0 1 13 12 5 18 Tanzania .. .. .. .. .. .. .. .. .. .. .. .. Thailand .. 36 .. 40 .. 6 .. 1 .. 5 .. 12 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togoa .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobagoa 50 52 26 15 6 5 1 15 2 4 15 10 Tunisiaa 16 26 20 33 28 6 4 4 15 19 17 12 Turkeya .. 22 .. 49 .. 1 .. 7 .. .. .. 21 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Ugandaa 10 19 45 30 7 21 2 0 .. .. 37 30 Ukrainea .. 13 .. 31 .. 4 .. 0 .. 35 .. 16 United Arab Emiratesa .. .. 15 .. .. .. .. .. 1 .. 84 .. United Kingdom 39 39 31 31 .. .. 6 6 19 21 5 4 United States .. 57 .. 3 .. 1 .. 1 .. 36 .. 3 Uruguaya 10 11 32 49 4 5 10 4 31 21 8 10 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RBa 38 21 33 25 9 5 0 4 4 2 19 43 Vietnam .. .. .. .. .. .. .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep.a 17 .. 10 .. 18 .. 3 .. .. .. 51 .. Zambiaa 27 33 22 36 36 9 0 0 0 .. 15 21 Zimbabwea 36 .. 22 .. 17 .. 3 .. 2 .. 19 .. World .. m 21 m .. m 34 m .. m 6m .. m 2m .. m .. m .. m 14 m Low income .. .. .. .. .. .. .. .. .. .. .. .. Middle income .. 16 .. 38 .. 5 .. 2 .. 15 .. 14 Lower middle income 19 17 34 39 14 6 .. 4 .. 13 16 15 Upper middle income .. 14 .. 38 .. 2 .. 1 .. 27 .. 11 Low & middle income .. 16 .. 36 .. 8 .. 2 .. .. .. 17 East Asia & Pacific 35 26 26 35 12 6 .. 1 .. .. 20 22 Europe & Central Asia .. 13 .. 40 .. 3 .. 0 .. 32 .. 15 Latin America & Carib. .. 19 .. 41 .. 6 .. 3 .. 7 .. 15 Middle East & N. Africa 19 11 14 36 16 8 4 4 .. .. 35 27 South Asia 15 16 31 33 24 15 4 2 .. 0 25 32 Sub-Saharan Africa .. .. .. .. .. .. .. .. .. .. .. .. High income 24 26 24 27 .. 1 3 3 33 34 8 9 Euro area 26 24 23 26 0 0 2 3 40 35 7 7 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. 244 2008 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 countries 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, 2006 (%) ship of property); and voluntary, unrequited, nonre- 70 Australia payable receipts other than grants. 60 United States South Africa 50 India 40 30 Data sources 20 Data on central government revenues are from 10 the IMF's Government Finance Statistics Yearbook 2007 and data files. Each country's accounts Kuwait 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 2004­06. Source: International Monetary Fund, Government Finance Statistics data files, and World Development Indicators data files. tions of concepts, definitions, and data sources. 2008 World Development Indicators 245 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 51.8 16.0 1.8 12.4 ­8.3 1.9 15.3 5.2 19.7 12.9 8.9 10.8 Algeria 9.6 20.6 1.0 3.4 ­10.0 ­11.0 16.0 1.8 18.4 8.0 ­7.9 ­1.0 Angola 4,105.6 57.3 471.4 34.5 119.5 ­85.0 125.9 4.5 206.3 19.5 ­84.7 4.2 Argentina ­2.8 20.3 ­1.1 13.1 7.8 ­15.0 11.9 6.4 17.9 8.6 14.2 ­4.3 Armenia 64.3 32.9 70.3 14.8 7.2 ­10.1 63.2 5.8 111.9 16.5 ­18.9 11.4 Australia 8.5 15.2 12.5 18.1 0.4 ­0.9 6.1 4.0 10.7 9.4 8.2 4.6 Austriaa .. .. .. .. .. .. 2.2 .. 6.4 .. 6.1 .. Azerbaijan 25.4 86.9 6.1 55.7 ­32.7 5.5 .. 10.6 .. 17.9 .. 10.1 Bangladesh 12.1 20.2 25.0 12.2 4.8 6.3 6.0 9.1 14.0 15.3 6.2 9.7 Belarus 158.4 39.9 61.4 45.5 44.7 ­4.3 100.8 7.7 175.0 8.8 ­63.9 ­1.7 Belgiuma .. .. .. .. .. .. 4.0 1.6 8.4 7.5 7.1 5.4 Benin ­1.8 14.5 2.2 6.4 6.0 ­13.3 3.5 3.5 16.8 .. 13.0 .. Bolivia 7.7 24.0 13.7 4.3 1.1 ­11.5 18.9 4.0 51.0 11.9 35.5 ­0.3 Bosnia and Herzegovina 22.0 25.7 23.9 20.4 ­0.4 ­0.9 51.9 3.7 73.5 8.0 76.3 1.4 Botswana 12.3 67.4 ­1.7 14.2 10.0 ­55.0 9.8 8.9 14.4 16.5 5.2 2.6 Brazil 44.3 18.9 40.5 14.3 14.6 10.8 52.2 13.9 78.2 50.8 65.5 44.6 Bulgaria 40.5 27.6 22.1 17.8 ­7.2 ­7.0 35.9 3.2 79.4 8.9 10.1 0.7 Burkina Faso 22.3 11.0 2.9 12.1 ­7.3 ­7.5 3.5 3.5 16.8 .. 16.6 .. Burundi ­10.2 24.6 ­9.9 13.6 ­2.2 27.6 .. .. 15.3 17.1 ­0.7 14.1 Cambodia 43.6 40.5 12.5 25.5 1.2 ­9.8 8.7 1.8 18.7 16.4 6.4 11.2 Cameroon ­6.2 10.3 0.3 1.8 ­2.2 ­22.6 5.5 4.3 16.0 15.3 ­0.8 11.2 Canada 4.8 12.6 3.8 13.3 0.2 1.2 5.3 1.8 8.7 5.8 6.2 3.4 Central African Republic 4.3 ­4.2 3.9 2.2 ­7.9 5.7 5.5 4.3 16.0 15.3 5.2 11.0 Chad 48.8 52.3 6.4 ­1.3 ­18.6 ­25.1 5.5 4.3 16.0 15.3 6.6 5.2 Chile 24.3 16.1 34.9 20.4 ­2.0 ­4.5 13.7 5.1 18.2 8.0 7.0 ­3.3 China 29.5 16.0 21.1 10.1 0.7 0.1 11.0 2.5 12.1 6.1 ­1.5 2.4 Hong Kong, China 10.6 16.2 9.8 1.0 ­2.4 ­0.7 5.6 2.7 8.8 7.8 6.1 7.9 Colombia 28.2 20.2 34.3 43.9 2.9 ­10.6 32.3 6.3 42.7 12.9 20.1 7.1 Congo, Dem. Rep. 357.6 57.5 59.6 19.3 ­7.9 13.6 60.0 .. 293.9 .. ­30.5 .. Congo, Rep. ­0.1 45.7 6.3 1.5 2.0 ­89.6 5.5 4.3 16.0 15.3 12.2 0.1 Costa Rica 4.8 26.3 0.0 21.7 5.7 ­2.2 23.9 9.8 36.7 22.2 11.9 11.0 Côte d'Ivoire 18.1 10.3 13.3 4.8 0.3 ­1.7 3.5 3.5 16.8 .. 16.8 .. Croatia 40.4 18.0 30.5 20.6 ­2.4 ­1.5 5.5 1.7 20.2 9.9 14.2 6.3 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 29.3 9.9 15.8 10.5 2.1 2.3 7.0 1.2 12.8 5.6 ­3.6 3.5 Denmark 6.2 9.6 2.6 39.7 ­1.5 ­3.3 3.9 2.4 10.3 .. 9.0 .. Dominican Republic 17.8 12.5 15.3 7.0 ­2.6 37.5 14.9 9.8 30.7 19.5 16.0 11.1 Ecuador 6.8 13.1 15.1 14.1 ­74.8 ­5.5 43.3 4.1 55.7 9.5 45.7 2.2 Egypt, Arab Rep. 9.9 15.0 12.1 5.5 0.6 6.8 10.9 6.0 16.5 12.6 4.5 4.9 El Salvador 13.5 11.9 22.6 9.9 ­0.9 ­2.5 14.4 .. 19.1 .. 7.8 .. Eritrea 21.0 5.8 27.8 0.9 20.5 8.6 .. .. .. .. .. .. Estonia 27.5 28.2 22.0 74.3 ­5.5 ­1.2 8.7 2.8 19.0 5.0 ­9.4 ­1.0 Ethiopia 9.0 20.0 13.4 14.7 ­3.5 5.4 11.5 3.6 15.1 7.0 2.1 ­0.7 Finlanda .. .. .. .. .. .. 3.2 1.0 7.8 3.7 2.9 3.0 Francea .. .. .. .. .. .. 4.5 2.4 8.1 6.6 6.7 4.9 Gabon 10.1 16.4 11.9 10.1 5.8 ­13.7 5.5 4.3 16.0 15.3 14.5 6.9 Gambia, The 14.2 26.2 ­5.0 8.3 15.2 3.8 12.5 12.7 25.0 29.8 20.3 24.7 Georgia 40.2 39.7 ­11.1 50.5 73.8 ­11.2 31.0 11.4 58.2 18.8 10.6 9.5 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 6.8 12.1 3.1 Guatemala 15.6 13.4 36.1 12.0 ­7.1 0.8 7.9 4.5 21.2 12.8 11.5 6.1 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 4.4 ­6.7 5.7 ­20.4 ­1.5 3.5 3.5 32.9 .. ­8.2 .. Haiti 27.1 4.6 15.7 3.1 0.1 ­5.4 10.7 6.2 24.8 43.3 ­2.4 27.2 246 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 29.2 22.5 16.5 24.0 ­7.4 ­1.6 12.0 9.3 27.0 17.4 1.7 11.7 Hungary 20.9 14.3 4.9 17.1 20.2 5.5 24.4 7.4 32.6 8.1 4.6 4.2 India 11.0 21.6 6.0 16.8 3.4 2.4 .. .. 15.5 11.2 5.9 5.0 Indonesia 27.5 14.9 25.9 7.4 ­2.3 0.8 16.7 11.4 18.9 16.0 8.3 2.1 Iran, Islamic Rep. 30.1 29.1 9.8 27.0 17.3 ­8.4 .. 11.8 .. 14.0 .. 2.7 Iraq .. 30.8 .. 6.7 .. ­29.9 .. .. .. 14.4 .. .. Irelanda .. .. .. .. .. .. 0.4 0.0 6.6 2.7 3.4 ­0.8 Israel 21.7 5.1 18.3 3.4 ­0.5 ­1.8 14.1 3.2 20.2 7.4 1.9 5.0 Italya .. .. .. .. .. .. 6.4 0.9 13.2 5.6 7.9 3.8 Jamaica 28.0 15.8 18.0 11.5 6.1 ­3.5 23.2 7.0 43.6 17.6 17.5 10.7 Japan 4.1 ­0.7 1.3 ­0.1 2.5 ­0.3 0.9 0.7 3.5 1.7 4.0 2.5 Jordan 5.7 12.8 9.6 15.6 ­3.8 ­1.0 7.7 4.6 10.7 8.2 8.6 2.3 Kazakhstan 108.2 78.1 ­72.5 105.2 24.7 ­44.4 .. .. .. .. .. .. Kenya 29.0 18.0 26.7 10.2 6.6 3.1 13.6 5.1 28.8 13.6 15.8 14.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 15.6 4.4 21.6 19.5 ­1.2 1.2 8.8 4.5 9.0 6.0 1.5 6.4 Kuwait 9.4 21.7 10.9 23.7 ­0.2 ­5.7 6.5 4.9 8.4 8.6 3.4 ­13.6 Kyrgyz Republic 14.8 51.5 0.1 18.1 62.6 ­0.5 36.7 5.6 65.0 23.2 21.9 12.8 Lao PDR 16.4 26.7 18.1 ­1.5 ­7.2 1.8 14.0 5.0 25.7 30.0 5.0 24.2 Latvia ­21.4 38.7 ­23.8 88.8 6.5 ­2.3 14.8 3.5 34.6 7.3 5.5 ­3.4 Lebanon 16.4 7.8 13.1 1.9 6.0 6.6 16.3 8.0 24.7 10.3 12.8 4.4 Lesotho 9.8 35.3 ­2.3 4.8 ­18.7 ­22.7 13.3 4.5 16.4 12.2 6.5 7.7 Liberia 29.5 34.6 ­6.0 15.9 37.2 76.6 6.4 3.4 15.6 15.5 8.5 2.7 Libya 9.6 14.1 3.1 1.0 3.6 ­112.6 5.5 2.5 7.0 6.3 .. ­7.4 Lithuania 28.9 22.5 12.7 40.3 ­2.4 ­8.3 20.1 1.2 27.1 5.1 ­14.5 ­1.4 Macedonia, FYR 11.7 21.5 ­147.3 16.5 ­243.6 ­4.7 24.1 6.6 46.0 12.2 24.6 8.1 Madagascar 16.2 26.4 9.6 9.6 ­13.1 ­17.4 18.5 22.3 37.5 29.5 ­5.3 16.4 Malawi 56.2 16.4 2.8 19.0 ­10.4 ­9.8 37.3 11.0 47.3 32.3 ­16.9 11.6 Malaysia 18.5 11.5 29.2 6.2 ­0.7 1.6 5.9 3.1 8.7 6.5 4.9 2.3 Mali 7.3 6.0 18.9 1.4 ­11.6 ­9.9 3.5 3.5 16.8 .. 14.5 .. Mauritania ­5.1 10.5 ­42.5 18.7 ­28.9 ­15.8 9.0 8.0 20.3 23.1 17.0 4.3 Mauritius 18.6 10.1 8.7 7.2 3.0 0.5 12.2 9.6 20.8 21.1 16.1 16.3 Mexico 31.9 11.1 ­2.9 17.7 27.6 3.2 39.8 3.3 59.4 7.5 15.6 2.9 Moldova 65.3 23.5 34.6 21.5 19.1 ­0.7 25.4 11.9 36.7 18.1 7.7 4.9 Mongolia 32.6 30.8 14.4 31.8 ­31.8 ­37.3 74.6 13.0 134.4 21.4 46.9 ­1.4 Morocco 7.0 17.0 6.9 10.4 5.1 0.0 7.3 3.7 10.0 11.5 8.3 11.9 Mozambique 47.7 22.6 21.8 13.4 ­12.5 ­8.3 38.8 10.4 24.4 18.6 18.0 11.9 Myanmar 36.5 27.3 13.4 6.8 19.7 23.5 9.8 9.5 16.5 15.0 ­2.4 ­2.2 Namibia 22.6 29.6 30.5 20.3 1.7 ­7.2 10.8 6.3 18.5 11.2 12.1 1.9 Nepal 15.6 14.7 18.0 11.0 3.6 0.1 9.6 2.3 12.9 8.0 4.7 1.2 Netherlandsa .. .. .. .. .. .. 4.4 3.0 7.2 3.5 5.0 2.0 New Zealand 9.3 16.0 15.8 16.6 ­3.9 ­1.0 8.5 6.9 12.1 12.3 9.9 10.8 Nicaragua 35.1 8.4 30.3 22.5 ­21.5 ­10.0 11.1 4.9 19.9 11.6 5.7 0.9 Niger 3.8 14.8 ­22.8 15.2 10.2 ­31.6 3.5 3.5 16.8 .. 15.5 .. Nigeria 19.4 ­33.9 22.3 19.5 ­9.1 ­23.6 13.5 9.7 20.2 16.9 ­22.9 8.3 Norway 3.8 3.4 9.5 10.4 ­1.9 ­5.3 5.0 1.8 7.6 4.0 4.4 ­4.2 Oman 7.7 24.6 9.3 20.6 ­2.3 ­5.5 6.5 4.0 9.4 7.4 7.5 ­1.4 Pakistan 13.8 14.6 10.8 10.6 8.7 1.6 .. 4.2 .. 11.0 .. 1.6 Panama 8.4 22.3 14.5 13.8 ­4.3 0.1 7.2 3.8 11.1 8.4 10.6 6.1 Papua New Guinea 13.7 38.9 0.2 16.1 5.0 ­3.0 7.3 1.0 13.1 10.6 0.0 0.8 Paraguay 0.5 8.7 4.9 6.0 0.1 ­3.1 21.2 6.7 33.9 30.1 17.9 17.5 Peru 29.3 11.8 31.1 4.2 ­8.1 ­5.1 9.6 3.2 36.2 23.9 20.5 15.5 Philippines 23.9 19.6 27.9 3.7 3.0 1.2 8.4 5.3 14.7 9.8 6.6 4.3 Poland 35.6 14.8 19.1 15.7 3.1 2.1 26.8 2.8 33.5 5.5 ­5.2 4.5 Portugala .. .. .. .. .. .. 8.4 .. 13.8 .. 10.0 .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 247 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 69.6 36.2 23.1 34.6 11.6 ­0.9 .. .. .. .. .. .. Russian Federation 112.6 40.5 46.2 37.2 73.6 ­21.8 102.0 4.1 320.3 10.4 72.3 ­4.9 Rwanda 69.5 18.0 32.7 14.5 ­41.0 ­13.8 10.9 7.9 18.5 16.1 6.9 6.4 Saudi Arabia 3.4 20.4 3.4 7.3 1.4 ­17.7 6.2 5.0 .. .. .. .. Senegal 7.4 12.5 1.2 4.8 1.0 2.7 3.5 3.5 16.8 .. 17.8 .. Serbia 33.0 38.3 88.5 18.5 34.1 ­16.0 19.1 5.1 78.0 16.6 .. 0.8 Sierra Leone 19.6 21.4 1.6 4.1 ­101.6 ­62.0 7.0 10.4 28.8 24.0 ­3.6 8.6 Singapore 8.5 19.4 19.7 4.2 ­8.1 2.5 3.5 0.6 6.4 5.3 4.0 5.1 Slovak Republic 18.4 14.5 3.4 14.0 ­4.8 ­1.1 9.0 3.6 16.8 7.7 6.3 4.8 Slovenia 31.5 8.4 36.8 27.7 5.8 ­2.3 15.4 2.8 23.4 7.4 ­1.5 5.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 16.0 21.2 18.9 29.2 ­4.1 ­5.2 13.5 7.1 17.9 11.2 6.9 4.0 Spaina .. .. .. .. .. .. 7.7 .. 10.1 .. 4.9 .. Sri Lanka 35.8 19.0 75.4 15.9 5.4 3.1 12.1 10.2 18.0 7.0 8.0 ­2.7 Sudan 72.7 29.7 10.6 26.3 389.1 17.7 .. .. .. .. .. .. Swaziland 3.9 25.1 1.3 22.5 ­14.8 ­24.6 9.4 4.9 17.1 11.2 ­0.2 5.2 Sweden 3.1 11.9 ­1.1 24.5 ­4.0 0.2 6.2 0.8 11.1 3.3 7.3 2.1 Switzerland 4.6 4.9 4.0 10.0 0.2 0.4 1.3 1.4 5.5 3.0 4.6 1.6 Syrian Arab Republic 9.2 7.3 3.9 2.9 6.1 0.9 4.0 1.0 9.0 8.0 2.2 ­4.4 Tajikistan .. 59.7 .. 45.5 .. ­13.8 23.9 9.1 75.5 24.4 6.2 3.4 Tanzania 33.0 18.3 ­3.9 13.2 16.3 ­16.2 24.6 6.6 42.8 15.4 12.6 8.6 Thailand 17.7 6.7 40.3 3.5 ­4.2 ­1.8 11.6 4.4 13.3 7.4 7.3 2.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 22.3 22.6 17.6 0.0 14.9 0.1 3.5 3.5 17.5 .. 13.8 .. Trinidad and Tobago 4.0 21.7 9.0 16.0 0.6 ­18.3 6.9 4.8 15.2 10.9 10.7 3.1 Tunisia 6.6 11.6 10.4 8.0 ­1.2 3.4 .. .. .. .. .. .. Turkey 104.2 32.6 66.9 24.8 30.1 4.3 76.0 21.6 .. .. .. .. Turkmenistan 449.5 .. 76.3 .. ­573.1 .. .. .. .. .. .. .. Uganda 13.9 17.6 9.6 10.8 ­41.2 ­10.1 7.6 9.1 20.2 18.7 9.9 10.7 Ukraine 115.5 34.3 7.7 51.4 95.4 ­0.5 70.3 7.6 122.7 15.2 ­56.8 1.3 United Arab Emirates 10.2 23.2 10.7 29.5 ­4.3 1.1 .. .. .. .. .. .. United Kingdom 20.3 11.9 19.6 16.4 9.5 ­1.1 4.1 .. 6.7 4.7 3.9 2.2 United States 6.9 9.0 6.0 7.3 0.2 0.8 .. .. 8.8 8.0 6.7 4.6 Uruguay 39.0 11.7 34.2 5.3 1.0 ­12.8 57.7 1.8 93.1 9.3 36.9 2.3 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 36.6 75.4 15.3 38.1 32.8 11.2 24.7 10.3 39.7 15.5 ­7.9 ­1.2 Vietnam 25.7 29.7 12.7 21.8 0.8 0.9 8.5 7.6 20.1 11.2 10.5 3.6 West Bank and Gaza .. 5.6 .. 2.9 .. 2.4 .. 3.0 .. 7.7 .. 8.0 Yemen, Rep. 50.7 26.1 6.0 3.6 13.3 ­5.2 23.8 13.0 31.5 18.0 ­3.2 4.1 Zambia 55.5 44.2 34.2 22.4 185.8 ­36.9 30.2 10.3 45.5 23.2 5.4 9.8 Zimbabwe 25.5 1,453.0 25.5 624.0 ­0.3 274.6 25.9 203.4 34.7 496.5 23.0 ­0.7 a. As members of the euro area, these countries share a single currency, the euro. 248 2008 World Development Indicators 4.13 ECONOMY Monetary indicators About the data Definitions Money and the financial accounts that record the during the reporting period. The valuation of finan- · Money and quasi money are the sum of currency supply of money lie at the heart of a country's cial derivatives and the net liabilities of the banking outside banks, demand deposits other than those of financial system. There are several commonly used system can also be difficult. The quality of commer- the central government, and the time, savings, and defi nitions of the money supply. The narrowest, cial bank reporting also may be adversely affected foreign currency deposits of resident sectors other M1, encompasses currency held by the public and by delays in reports from bank branches, especially than the central government. This definition of the demand deposits with banks. M2 includes M1 plus in countries where branch accounts are not com- money supply, often called M2, corresponds to lines time and savings deposits with banks that require puterized. Thus the data in the balance sheets of 34 and 35 in the IMF's International Financial Statis- a prior notice for withdrawal. M3 includes M2 as commercial banks may be based on preliminary esti- tics (IFS). The change in money supply is measured well as various money market instruments, such as mates subject to constant revision. This problem is as the difference in end-of-year totals relative to M2 certificates of deposit issued by banks, bank depos- likely to be even more serious for nonbank financial in the preceding year. · Claims on private sector its denominated in foreign currency, and deposits intermediaries. (IFS line 32d) include gross credit from the finan- with financial institutions other than banks. However Many interest rates coexist in an economy, reflect- cial system to individuals, enterprises, nonfinancial defined, money is a liability of the banking system, ing competitive conditions, the terms governing loans public entities not included under net domestic distinguished from other bank liabilities by the spe- and deposits, and differences in the position and credit, and financial institutions not included else- cial role it plays as a medium of exchange, a unit of status of creditors and debtors. In some economies where. · Claims on governments and other public account, and a store of value. interest rates are set by regulation or administra- entities (IFS line 32an + 32b + 32bx + 32c) usually The banking system's assets include its net for- tive fiat. In economies with imperfect markets, or comprise direct credit for specific purposes, such eign assets and net domestic credit. Net domestic where reported nominal rates are not indicative of as financing the government budget deficit; loans credit includes credit extended to the private sector effective rates, it may be difficult to obtain data on to state enterprises; advances against future credit and general government and credit extended to the interest rates that reflect actual market transactions. authorizations; and purchases of treasury bills and nonfinancial public sector in the form of investments Deposit and lending rates are collected by the Inter- bonds, net of deposits by the public sector. Public in short- and long-term government securities and national Monetary Fund (IMF) as representative inter- sector deposits with the banking system also include loans to state enterprises; liabilities to the public est rates offered by banks to resident customers. sinking funds for the service of debt and temporary and private sectors in the form of deposits with the The terms and conditions attached to these rates deposits of government revenues. · Deposit interest banking system are netted out. Net domestic credit differ by country, however, limiting their comparabil- rate is the rate paid by commercial or similar banks also includes credit to banking and nonbank financial ity. Real interest rates are calculated by adjusting for demand, time, or savings deposits. · Lending institutions. nominal rates by an estimate of the inflation rate in interest rate is the rate charged by banks on loans to Domestic credit is the main vehicle through which the economy. A negative real interest rate indicates prime customers. · Real interest rate is the lending changes in the money supply are regulated, with cen- a loss in the purchasing power of the principal. The interest rate adjusted for inflation as measured by tral bank lending to the government often playing the real interest rates in the table are calculated as the GDP deflator. most important role. The central bank can regulate (i ­ P) / (1 + P), where i is the nominal lending inter- lending to the private sector in several ways--for est rate and P is the inflation rate (as measured by example, by adjusting the cost of the refinancing the GDP deflator). 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 Financial Statistics Manual (2000) for guidelines nonperforming assets. Valuation errors typically for the presentation of monetary and financial sta- arise with respect to foreign exchange transactions, tistics. Data on real interest rates are derived from particularly in countries with flexible exchange rates World Bank data on the GDP deflator. or in those that have undergone currency devaluation 2008 World Development Indicators 249 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 2006 2007a 1995 2006 2006 2006 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Afghanistan 49.50 .. .. 17.5 0.4 .. .. 11.8 .. .. .. .. Albania 98.10 83.02 27.1 48.0 0.5 .. 38.0 3.8 31.5 3.0 .. 5.1 Algeria 72.65 67.07 18.2 39.6 0.5 83.2 18.5 8.1 17.3 2.6 2.1 3.5 Angola 80.37 75.02 0.0 49.4 0.6 .. 739.4 67.2 711.0 65.1 .. .. Argentina 3.05 3.12 0.9 1.4 0.5 .. 5.2 12.2 8.9 10.9 0.3 19.8 Armenia 416.04 304.10 116.3 181.0 0.4 106.3 212.5 4.2 103.3 3.4 13.2 0.9 Australia 1.33 1.15 1.3 1.4 1.1 124.7 1.5 3.7 2.1 3.0 1.1 3.2 Austriab 0.80 0.69 1.0 0.9 1.1 105.4 1.7 1.6 2.2 1.9 0.3 2.2 Azerbaijan 0.89 0.85 0.2 0.3 0.4 .. 203.0 7.3 192.4 5.5 .. .. Bangladesh 68.93 68.59 19.2 23.1 0.3 .. 4.0 4.1 5.5 5.9 .. .. Belarus 2,144.56 2,153.40 3.5 836.5 0.4 .. 355.1 31.3 267.6 25.7 266.4 30.0 Belgiumb 0.80 0.69 0.9 0.9 1.1 109.4 1.8 2.0 1.9 2.0 1.2 2.0 Benin 522.89 449.94 187.1 225.7 0.4 .. 8.7 3.3 8.3 2.7 .. .. Bolivia 8.01 7.65 1.6 2.4 0.3 79.5 8.6 6.0 8.7 3.4 .. .. Bosnia and Herzegovina 1.56 1.34 0.6 0.8 0.5 .. 3.3 3.4 .. .. .. .. Botswana 5.84 6.03 1.4 2.7 0.5 .. 9.7 6.1 10.4 8.4 .. .. Brazil 2.18 1.79 0.7 1.4 0.6 .. 211.9 9.2 199.5 8.4 204.9 13.2 Bulgaria 1.56 1.34 0.0 0.6 0.4 125.4 103.3 4.5 117.5 5.3 85.7 5.0 Burkina Faso 522.89 449.94 184.3 198.9 0.4 .. 3.7 3.1 5.5 2.6 .. .. Burundi 1,028.43 1,137.21 126.3 341.2 0.3 73.2 13.4 8.3 16.1 7.3 .. .. Cambodia 4,103.25 3,999.00 1,140.0 1,297.1 0.3 94.3 3.4 3.4 4.9 3.1 .. .. Cameroon 522.89 449.94 236.0 252.4 0.5 113.2 6.3 2.4 6.5 2.1 .. .. Canada 1.13 1.00 1.2 1.2 1.1 126.8 1.5 2.5 1.7 2.3 2.7 1.0 Central African Republic 522.89 449.94 267.5 265.2 0.5 129.3 4.5 2.1 5.3 2.1 6.3 4.4 Chad 522.89 449.94 134.9 221.1 0.4 126.7 7.1 8.8 6.9 2.8 .. .. Chile 530.29 499.28 261.8 361.2 0.7 96.6 7.9 6.8 8.9 2.6 7.0 5.7 China 7.97 7.37 3.3 3.5 0.4 94.4 7.9 3.5 8.6 1.5 .. .. Hong Kong, China 7.77 7.80 8.2 5.5 0.7 .. 4.0 ­2.9 5.9 ­1.0 0.6 0.1 Colombia 2,361.14 2,016.70 484.1 1,104.8 0.5 103.6 21.7 6.7 20.3 6.1 16.4 6.0 Congo, Dem. Rep. 468.28 .. 0.0 234.8 0.5 32.8 964.9 35.7 932.8 41.1 .. .. Congo, Rep. 522.89 449.94 153.5 300.1 0.6 .. 9.0 4.7 9.6 2.7 0.6 .. Costa Rica 511.30 498.69 106.3 270.2 0.5 92.7 15.9 9.8 15.6 11.2 14.1 11.8 Côte d'Ivoire 522.89 449.94 261.4 294.1 0.6 115.9 9.2 3.1 7.2 3.0 .. .. Croatia 5.84 5.02 3.1 3.9 0.7 111.8 86.0 3.7 86.2 2.5 83.7 2.3 Cuba .. .. .. .. .. .. 2.5 2.6 .. .. .. .. Czech Republic 22.60 18.04 11.2 14.2 0.6 132.3 12.8 2.3 6.9 2.0 8.2 2.0 Denmark 5.95 5.12 8.4 8.4 1.4 108.4 1.6 2.3 2.1 1.9 1.1 1.9 Dominican Republic 33.37 33.76 6.9 18.7 0.6 99.1 9.4 19.0 8.7 19.0 .. .. Ecuador 1.00 1.00 0.4 0.4 0.4 147.1 4.3 10.6 37.1 8.9 .. 8.5 Egypt, Arab Rep. 5.73 5.53 1.2 1.7 0.3 .. 8.7 6.4 8.8 5.8 6.1 9.5 El Salvador 1.00 1.00 0.4 0.5 0.5 .. 6.2 3.2 8.5 3.4 .. 3.9 Eritrea 15.38 15.38 1.8 5.2 0.3 .. 6.4 15.4 .. .. .. .. Estonia 12.47 10.74 4.6 8.0 0.6 .. 53.8 4.0 23.1 3.4 8.2 2.0 Ethiopia 8.70 9.12 2.2 2.4 0.3 100.0 5.8 4.6 5.5 7.1 .. .. Finlandb 0.80 0.69 1.1 1.0 1.2 104.0 2.0 0.8 1.5 1.1 1.0 1.3 Franceb 0.80 0.69 1.0 0.9 1.1 107.6 1.3 1.9 1.6 1.9 .. 1.6 Gabon 522.89 449.94 187.6 268.0 0.5 102.1 7.0 4.3 4.6 1.2 .. .. Gambia, The 28.07 22.24 3.9 7.6 0.3 54.3 4.2 14.3 4.1 10.6 .. .. Georgia 1.78 1.60 0.4 0.8 0.4 .. 356.7 6.5 27.1 5.6 .. .. Germany b 0.80 0.69 1.0 0.9 1.1 106.7 1.7 0.9 2.1 1.6 0.4 2.4 Ghana 0.92 0.97 573.5 4,133.3 0.5 116.1 26.7 21.1 28.4 18.2 .. .. Greeceb 0.80 0.69 0.6 0.7 0.9 114.6 9.2 3.4 9.0 3.4 3.0 3.8 Guatemala 7.60 7.63 2.3 4.0 0.5 .. 10.4 7.1 10.1 7.2 .. .. Guinea 3,644.33 .. 645.8 1,635.0 0.3 .. 5.5 17.2 .. .. .. .. Guinea-Bissau 522.89 449.94 116.0 202.6 0.4 .. 32.5 0.7 34.0 1.2 .. .. Haiti 40.41 36.22 5.4 17.4 0.4 .. 22.8 17.4 21.9 20.4 .. .. 250 2008 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 2006 2007a 1995 2006 2006 2006 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Honduras 18.90 18.90 2.8 7.1 0.4 .. 18.9 7.8 22.8 8.0 .. .. Hungary 210.39 173.86 60.2 129.1 0.6 127.0 19.6 5.3 20.3 5.4 16.8 2.9 India 45.31 39.44 11.2 15.1 0.3 .. 8.1 4.2 9.1 4.2 7.4 4.9 Indonesia 9,159.32 9,333.60 1,025.3 4,332.6 0.5 .. 15.8 9.6 13.7 9.3 15.4 8.6 Iran, Islamic Rep. 9,170.94 9,368.13 564.5 2,877.9 0.3 135.2 27.7 17.4 26.0 14.1 28.4 10.2 Iraq 1,467.42 .. .. 558.7 .. .. 13.9 0.3 .. .. .. .. Irelandb 0.80 0.69 0.8 1.0 1.3 125.9 3.5 3.3 2.3 3.4 1.6 0.1 Israel 4.46 3.90 3.1 3.7 0.8 78.0 10.8 1.3 9.7 1.6 8.1 4.5 Italy b 0.80 0.69 0.8 0.9 1.1 110.8 3.8 2.8 3.7 2.4 2.9 2.4 Jamaica 65.74 71.17 14.2 32.6 0.5 .. 23.0 10.0 23.5 10.8 .. .. Japan 116.30 112.25 174.9 124.5 1.1 72.0 0.1 ­1.3 0.8 ­0.3 ­0.9 0.0 Jordan 0.71 0.71 0.4 0.4 0.6 .. 3.2 2.6 3.5 2.9 .. 8.2 Kazakhstan 126.09 120.78 17.4 67.9 0.5 .. 204.7 13.5 86.7 7.1 12.6 11.5 Kenya 72.10 63.30 15.4 30.6 0.4 .. 16.6 5.3 15.6 8.9 .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 954.79 930.76 735.8 761.8 0.8 .. 5.7 2.0 5.1 3.2 3.6 2.3 Kuwait 0.29 0.27 0.1 0.2 0.7 .. 0.8 8.3 2.0 1.8 1.4 2.3 Kyrgyz Republic 40.15 35.01 3.5 12.0 0.3 .. 110.6 5.3 18.1 4.0 36.0 7.2 Lao PDR 10,159.92 9,541.42 308.9 3,032.3 0.3 .. 27.0 10.3 28.2 10.3 .. .. Latvia 0.56 0.48 0.2 0.3 0.6 .. 48.0 6.1 33.8 4.5 15.2 5.3 Lebanon 1,507.50 1,507.50 841.3 867.1 0.6 .. 17.8 1.7 21.3 .. .. .. Lesotho 6.77 6.81 2.1 3.5 0.5 129.4 9.8 5.5 9.9 8.1 .. .. Liberia 58.01 60.77 0.6 30.6 0.5 .. 51.8 10.1 .. .. .. .. Libya 1.31 1.22 .. 0.9 0.7 .. .. 22.8 5.6 ­3.0 .. .. Lithuania 2.75 2.37 1.2 1.5 0.6 .. 75.1 2.0 40.5 1.1 32.6 3.5 Macedonia, FYR 48.80 42.02 17.1 19.0 0.4 100.0 79.3 2.3 14.4 1.8 10.9 0.7 Madagascar 2,142.30 1,792.15 286.8 700.4 0.3 .. 19.1 11.5 18.7 10.5 .. .. Malawi 136.01 140.17 3.9 45.3 0.3 73.3 33.6 23.6 33.8 13.8 .. .. Malaysia 3.67 3.33 1.4 1.7 0.5 99.0 3.9 4.9 3.6 1.9 3.4 4.3 Mali 522.89 449.94 226.3 242.2 0.5 .. 7.0 3.7 5.2 1.7 .. .. Mauritania 265.53 .. 62.2 124.3 0.5 .. 8.7 11.4 6.1 7.3 .. .. Mauritius 31.71 29.04 10.5 14.8 0.5 .. 6.4 5.4 6.9 5.4 .. .. Mexico 10.90 10.85 2.7 7.2 0.7 .. 19.0 6.7 19.4 4.7 18.4 6.5 Moldova 13.13 11.29 1.2 4.8 0.4 102.9 119.6 10.9 14.5 10.7 .. .. Mongolia 1,165.37 1,187.63 158.3 497.8 0.4 .. 57.8 13.3 39.8 6.5 .. .. Morocco 8.80 7.78 4.9 4.8 0.5 92.9 4.0 1.0 3.8 1.7 2.9 ­0.6 Mozambique 25.40 25.84 3,938.1 11,203.4 0.4 .. 34.7 8.0 31.8 12.2 .. .. Myanmar 5.78 5.45 40.9 254.4 .. .. 25.5 21.1 25.9 23.7 .. .. Namibia 6.77 6.81 2.5 4.5 0.7 .. 10.4 5.2 .. 4.3 .. .. Nepal 72.76 63.63 15.5 23.4 0.3 .. 8.0 5.2 8.7 4.7 .. .. Netherlandsb 0.80 0.69 0.9 0.9 1.1 111.9 2.1 2.3 2.4 2.2 1.3 2.3 New Zealand 1.54 1.30 1.5 1.5 1.0 128.1 1.7 2.4 1.7 2.5 1.4 2.5 Nicaragua 17.57 18.87 3.0 6.0 0.3 88.2 42.4 7.2 .. 7.4 .. .. Niger 522.89 449.94 203.1 221.8 0.4 .. 6.0 2.3 6.1 2.0 .. .. Nigeria 128.65 118.21 17.3 63.0 0.5 133.1 29.5 15.8 32.5 14.6 .. .. Norway 6.41 5.50 7.0 9.2 1.4 111.3 2.7 3.9 2.2 1.7 1.6 5.8 Oman 0.39 0.39 0.2 0.2 0.6 .. 0.1 4.3 .. 0.7 .. .. Pakistan 60.27 61.22 10.1 20.2 0.3 97.0 11.1 6.1 9.7 5.6 10.4 6.7 Panama 1.00 1.00 0.5 0.6 0.6 .. 3.6 1.7 1.1 1.1 1.0 2.0 Papua New Guinea 3.06 2.83 0.8 1.5 0.5 101.3 7.0 7.9 9.3 7.1 .. .. Paraguay 5,635.46 4,731.70 966.4 2,153.9 0.4 88.1 11.5 10.9 13.1 8.8 5.8 13.1 Peru 3.27 2.98 1.2 1.5 0.5 .. 26.7 3.4 27.3 2.0 23.7 2.2 Philippines 51.31 41.74 14.1 22.2 0.4 102.5 8.3 5.3 7.7 5.3 5.0 9.0 Poland 3.10 2.48 1.2 1.9 0.6 109.8 24.7 2.3 25.3 2.3 19.8 2.8 Portugalb 0.80 0.69 0.6 0.7 0.9 111.5 5.2 3.1 4.5 3.0 .. 2.3 Puerto Rico 1.00 1.00 .. .. .. .. 3.0 .. .. .. .. .. 2008 World Development Indicators 251 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 2006 2007a 1995 2006 2006 2006 1990­2000 2000­06 1990­2000 2000­06 1990­2000 2000­06 Romania 2.81 2.43 0.1 1.5 0.5 128.9 98.0 19.6 100.5 15.6 93.8 19.3 Russian Federation 27.19 24.57 1.5 14.3 0.5 163.4 161.5 17.0 108.0 13.6 110.8 17.2 Rwanda 551.71 .. 133.6 196.9 0.4 .. 14.6 6.6 15.8 7.4 .. .. Saudi Arabia 3.75 3.75 1.8 2.5 0.7 81.7 1.6 7.5 1.0 0.5 1.3 1.5 Senegal 522.89 449.94 252.4 250.9 0.5 .. 6.0 1.7 5.4 1.4 .. .. Serbia 67.15 54.68 .. 30.5 0.5 .. .. 21.9 42.4 20.3 .. .. Sierra Leone 2,961.91 2,982.38 382.8 1,188.0 0.4 73.5 32.1 8.3 29.3 7.3 .. .. Singapore 1.59 1.45 1.3 1.0 0.7 94.3 1.3 0.2 1.7 0.7 ­1.0 3.3 Slovak Republic 29.70 22.64 12.6 17.1 0.6 142.7 11.2 4.4 7.4 5.8 9.5 5.3 Slovenia 191.03c 0.69d 96.0 145.8 0.8 .. 28.7 4.8 11.9 4.9 9.0 4.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6.77 6.81 2.3 4.0 0.6 104.2 9.9 6.5 8.7 4.9 7.4 5.2 Spainb 0.80 0.69 0.7 0.8 1.0 114.9 3.9 4.1 3.8 3.2 2.4 2.8 Sri Lanka 103.91 109.13 18.9 37.6 0.4 .. 9.1 8.8 9.9 9.7 8.1 9.7 Sudan 217.15 2.03 15.3 111.6 0.5 .. 76.8 9.8 71.9 7.8 .. .. Swaziland 6.77 6.81 1.6 3.4 0.5 .. 12.5 8.4 9.4 6.5 .. .. Sweden 7.38 6.47 10.0 9.1 1.2 96.8 2.2 1.4 1.9 1.4 2.4 2.2 Switzerland 1.25 1.14 2.0 1.7 1.4 101.7 1.0 0.9 1.6 0.9 ­0.4 0.6 Syrian Arab Republic 11.23 11.23 12.7 20.8 0.4 .. 7.9 6.0 6.4 4.8 4.7 2.2 Tajikistan 3.30 3.46 0.0 0.9 0.3 .. 235.0 20.3 .. .. .. .. Tanzania 1,251.90 1,158.93 172.6 407.6 0.3 .. 21.6 7.3 20.9 3.7 .. .. Thailand 37.88 33.66 15.1 16.2 0.4 .. 4.2 2.7 4.9 2.6 3.8 5.2 Timor-Leste .. .. .. 0.2 .. .. .. 1.5 .. .. .. .. Togo 522.89 449.94 238.0 232.0 0.4 112.4 7.0 0.7 8.5 2.3 .. .. Trinidad and Tobago 6.31 6.31 3.7 4.9 0.8 112.6 5.4 4.8 5.7 5.1 2.8 2.0 Tunisia 1.33 1.23 0.5 0.6 0.4 84.6 4.4 2.4 4.4 2.9 3.6 3.5 Turkey 1.43 1.18 0.0 0.9 0.7 .. 76.1 21.7 79.9 23.5 .. 9.5 Turkmenistan .. .. .. 4,306.3 0.4 .. 408.0 .. .. .. .. .. Uganda 1,831.45 1,747.17 483.6 644.1 0.4 87.8 11.8 5.5 10.5 4.7 .. .. Ukraine 5.05 5.05 0.3 1.9 0.4 110.7 271.0 12.6 190.4 7.8 198.5 11.4 United Arab Emirates 3.67 3.67 2.7 3.5 0.9 .. 2.2 4.9 .. .. .. .. United Kingdom 0.54 0.49 0.6 0.6 1.2 103.1 2.9 2.7 2.9 2.6 2.4 1.6 United States 1.00 1.00 1.0 1.0 1.0 92.4 2.0 2.5 2.7 2.6 1.2 3.9 Uruguay 24.07 21.69 5.7 13.7 0.6 78.1 31.1 10.0 33.9 10.5 27.2 17.1 Uzbekistan .. .. 11.1 356.9 0.3 .. 245.8 27.7 .. .. .. .. Venezuela, RB 2,147.00 2,147.00 72.7 1,306.5 0.6 73.4 45.3 28.2 49.0 20.8 44.1 29.6 Vietnam 15,994.25 .. 3,162.7 4,899.4 0.3 .. 15.2 6.3 3.3 5.2 .. .. West Bank and Gaza .. .. 1.1 1.3 0.3 .. 4.9 3.2 4.0 3.8 .. .. Yemen, Rep. 197.05 199.33 21.8 76.3 0.4 .. 22.4 13.0 26.3 12.8 .. .. Zambia 3,603.07 3,834.24 396.9 2,625.9 0.7 176.7 52.1 19.4 57.0 18.9 68.8 .. Zimbabwe 22,364.00 255.00 25.7 33,068.2 1.5 .. 26.7 232.0 29.0 296.4 25.3 .. Note: The differences in the growth rates of the GDP deflator and consumer and wholesale price indexes are due mainly to data availability of each of the indexes during the period. a. December or latest monthly data available. b. As members of the euro area, these countries share a single currency, the euro. c. Tolars. d. Euros. 252 2008 World Development Indicators 4.14 ECONOMY Exchange rates and prices About the data Definitions In a market-based economy household, producer, and countries and the euro area. For most high-income · Official exchange rate is the exchange rate deter- government choices about resource allocation are influ- countries weights are derived from industrial coun- mined by national authorities or the rate determined enced by relative prices, including the real exchange try trade in manufactured goods. Data are compiled in the legally sanctioned exchange market. It is cal- rate, real wages, real interest rates, and other prices in from the nominal effective exchange rate index and a culated as an annual average based on monthly the economy. Relative prices also largely reflect these cost indicator of relative normalized unit labor costs averages (local currency units relative to the U.S. agents' choices. Thus relative prices convey vital infor- in manufacturing. For selected other countries the dollar). · Purchasing power parity (PPP) conversion mation about the interaction of economic agents in an nominal effective exchange rate index is based on factor is the number of units of a country's currency economy and with the rest of the world. manufactured goods and primary products trade with required to buy the same amount of goods and ser- The exchange rate is the price of one currency partner or competitor countries. For these countries vices in the domestic market that a U.S. dollar would in terms of another. Offi cial exchange rates and the real effective exchange rate index is the nomi- buy in the United States. · Ratio of PPP conver- exchange rate arrangements are established by nal index adjusted for relative changes in consumer sion factor to market exchange rate is the result governments. Other exchange rates recognized by prices; an increase represents an appreciation of obtained by dividing the PPP conversion factor by the governments include market rates, which are deter- the local currency. Because of conceptual and data market exchange rate. · Real effective exchange mined largely by legal market forces, and for coun- limitations, changes in real effective exchange rates rate is the nominal effective exchange rate (a mea- tries with multiple exchange arrangements, principal should be interpreted with caution. sure of the value of a currency against a weighted rates, secondary rates, and tertiary rates. (Also see Inflation is measured by the rate of increase in a average of several foreign currencies) divided by Statistical methods for alternative conversion factors price index, but actual price change can be nega- a price deflator or index of costs. · GDP implicit in the World Bank Atlas method of calculating gross tive. The index used depends on the prices being deflator measures the average annual rate of price national income (GNI) per capita in U.S. dollars.) examined. The GDP deflator reflects price changes change in the economy as a whole for the periods Official or market exchange rates are often used for total GDP. The most general measure of the over- shown. · Consumer price index reflects changes to compare prices across currencies. Since rates all price level, it accounts for changes in government in the cost to the average consumer of acquiring a reflect at best the relative prices of tradable goods, consumption, capital formation (including inventory basket of goods and services that may be fixed or the volume of goods and services that a U.S. dollar appreciation), international trade, and the main com- may change at specified intervals, such as yearly. buys in the United States may not correspond to ponent, household final consumption expenditure. The Laspeyres formula is generally used. · Whole- what a U.S. dollar converted to another country's The GDP deflator is usually derived implicitly as the sale price index refers to a mix of agricultural and currency at the official exchange rate would buy in ratio of current to constant price GDP--or a Paasche industrial goods at various stages of production and that country. Since identical volumes of goods and index. It is defective as a general measure of inflation distribution, including import duties. The Laspeyres services in different countries correspond to differ- for policy use because of long lags in deriving esti- formula is generally used. ent values (and vice versa) when official exchange mates and because it is often an annual measure. rates are used, an alternative method to compare Consumer price indexes are produced more fre- prices across countries converts national currency quently and so are more current. They are also con- estimates of GNI to a common unit of account using structed explicitly, based on surveys of the cost of conversion factors that reflect equivalent purchas- a defined basket of consumer goods and services. ing power. Based on price and expenditure surveys Nevertheless, consumer price indexes should be conducted by the International Comparison Program, interpreted with caution. The definition of a house- purchasing power parity (PPP) conversion factors are hold, the basket of goods, and the geographic (urban applied to equalize price levels across countries. See or rural) and income group coverage of consumer About the data for table 1.1 for further discussion. price surveys can vary widely by country. In addi- The ratio of the PPP conversion factor to the market tion, weights are derived from household expendi- exchange rate--or the national price level--allows ture surveys, which, for budgetary reasons, tend to comparison of the cost of the bundle of goods that be conducted infrequently in developing countries, make up gross domestic product (GDP) across coun- impairing comparability over time. Although useful for tries. The market exchange rate (or alternative conver- measuring consumer price inflation within a country, sion factor) is the official exchange rate adjusted by consumer price indexes are of less value in compar- World Bank staff for some countries to reflect actual ing countries. price changes. National price levels vary systemati- Wholesale price indexes are based on the prices of cally, rising with GNI per capita. The real effective commodities that are significant in a country's output Data sources exchange rate is a nominal effective exchange rate or consumption at the first commercial transaction. index adjusted for relative movements in national Prices are farm-gate prices for agricultural commodi- Data on official and real effective exchange rates price or cost indicators of the home country, selected ties and ex-factory prices for industrial goods. Prefer- and consumer and wholesale price indexes are countries, and the euro area. A nominal effective ence is given to indexes with the broadest coverage from the International Monetary Fund's Interna- exchange rate index is the ratio (expressed on the of the economy. tional Financial Statistics. PPP conversion factors base 2000 = 100) of an index of a currency's period- The least-squares method is used to calculate and GDP deflators are from the World Bank's data average exchange rate to a weighted geometric aver- growth rates of the GDP implicit deflator, consumer files. age of exchange rates for currencies of selected price index, and wholesale price index. 2008 World Development Indicators 253 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania 304 2,297 836 4,500 44 263 477 1,270 ­12 ­671 265 1,813 Algeria .. .. .. .. .. .. .. .. .. .. 4,164 81,463 Angola 3,836 33,346 3,519 16,289 ­767 ­6,178 156 ­190 ­295 10,690 213 8,599 Argentina 24,987 54,123 26,066 41,088 ­4,636 ­5,440 597 497 ­5,118 8,092 15,979 32,022 Armenia 300 1,510 726 2,536 40 215 168 694 ­218 ­117 111 1,072 Australia 69,710 158,002 74,841 166,759 ­14,036 ­32,076 ­109 ­213 ­19,277 ­41,046 14,952 55,079 Austria 89,906 179,503 92,055 166,059 ­1,597 ­1,830 ­1,702 ­1,355 ­5,448 10,259 23,369 12,911 Azerbaijan 785 13,955 1,290 8,133 ­6 ­2,681 111 566 ­401 3,708 121 2,500 Bangladesh 4,431 12,888 7,589 16,784 68 ­841 2,265 5,933 ­824 1,196 2,376 3,877 Belarus 5,269 22,137 5,752 23,723 ­51 ­107 76 182 ­458 ­1,512 377 1,417 Belgium 190,686b 340,727 178,798b 330,926 6,808b 7,531 ­4,463b ­6,661 14,232b 10,671 24,120 13,437 Benin 614 772 895 1,145 ­8 ­18 121 164 ­167 ­226 198 912 Bolivia 1,234 4,297 1,574 3,437 ­207 ­364 244 822 ­303 1,319 1,005 3,194 Bosnia and Herzegovina .. 4,496 .. 8,187 .. 409 .. 2,049 .. ­1,233 80 3,372 Botswana 2,421 5,292 2,050 3,451 ­32 ­772 ­39 871 300 1,940 4,695 7,992 Brazil 52,641 157,270 63,293 120,466 ­11,105 ­27,489 3,621 4,306 ­18,136 13,621 51,477 85,843 Bulgaria 6,776 20,108 6,502 25,985 ­432 47 132 821 ­26 ­5,010 1,635 11,756 Burkina Faso 272 .. 483 .. ­29 .. 255 .. 15 .. 347 555 Burundi 129 93 259 448 ­13 ­9 153 229 10 ­135 216 131 Cambodia 969 4,989 1,375 5,539 ­57 ­290 277 503 ­186 ­337 192 1,411 Cameroon 2,040 3,630 1,608 3,970 ­412 ­443 69 176 90 ­608 15 1,735 Canada 219,501 461,118 200,991 429,289 ­22,721 ­10,416 ­117 ­616 ­4,328 20,797 16,369 35,063 Central African Republic 179 .. 244 .. ­23 .. 63 .. ­25 .. 238 132 Chad 190 .. 411 .. ­7 .. 191 .. ­38 .. 147 632 Chile 19,358 65,620 18,301 44,329 ­2,714 ­19,392 307 3,357 ­1,350 5,256 14,860 19,397 China 147,240 1,061,682 135,282 852,769 ­11,774 11,755 1,435 29,199 1,618 249,866 80,288 1,080,756 Hong Kong, China .. 389,883 .. 368,167 .. 657 .. ­2,222 .. 20,151 55,424 133,211 Colombia 12,294 28,554 16,012 30,352 ­1,596 ­6,003 799 4,743 ­4,516 ­3,057 8,452 15,437 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 157 .. Congo, Rep. 1,374 4,964 1,346 2,917 ­695 ­1,122 42 ­22 ­625 903 64 1,848 Costa Rica 4,451 11,023 4,717 12,422 ­226 ­68 134 349 ­358 ­1,118 1,060 3,117 Côte d'Ivoire 4,337 9,010 3,806 7,256 ­787 ­728 ­237 ­496 ­492 529 529 1,798 Croatia 6,972 21,454 9,106 24,678 ­53 ­1,384 802 1,389 ­1,385 ­3,220 1,896 11,488 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 28,202 108,450 30,044 103,940 ­104 ­8,204 572 ­891 ­1,374 ­4,586 14,613 31,457 Denmark 65,655 143,295 57,860 134,061 ­4,549 2,611 ­1,391 ­4,506 1,855 7,339 11,652 31,084 Dominican Republic 5,731 10,664 6,137 12,748 ­769 ­1,735 992 3,033 ­183 ­786 373 2,127 Ecuador 5,196 14,141 5,708 13,737 ­930 ­1,950 442 3,049 ­1,000 1,503 1,788 2,027 Egypt, Arab Rep. 13,260 36,680 17,140 40,553 ­405 738 4,031 5,770 ­254 2,635 17,122 26,007 El Salvador 2,040 5,070 3,623 8,741 ­67 ­519 1,389 3,335 ­262 ­855 940 1,963 Eritrea 135 .. 498 .. 8 .. 324 .. ­32 .. 40 25 Estonia 2,573 13,128 2,860 14,833 3 ­751 126 11 ­158 ­2,446 583 2,786 Ethiopia 768 2,199 1,446 5,276 ­19 18 736 1,274 39 ­1,786 815 833 Finland 47,973 93,630 37,705 81,955 ­4,440 885 ­597 ­1,682 5,231 10,878 10,657 7,499 France 362,717 601,590 333,746 628,801 ­8,964 26,452 ­9,167 ­27,555 10,840 ­28,315 58,510 98,239 Gabon 2,945 4,228 1,723 2,155 ­665 ­965 ­42 ­184 515 924 153 1,122 Gambia, The 177 201 232 316 ­5 ­38 52 87 ­8 ­66 106 121 Georgia 575 2,567 1,413 4,413 127 169 197 522 ­514 ­1,154 199 931 Germany 603,815 1,304,419 592,056 1,149,108 ­2,737 28,805 ­38,769 ­33,370 ­29,746 150,745 121,816 111,637 Ghana 1,582 5,125 2,120 8,286 ­129 ­127 523 2,248 ­144 ­1,040 804 2,269 Greece 15,523 56,063 24,711 80,952 ­1,684 ­8,958 8,008 4,282 ­2,864 ­29,565 16,119 2,850 Guatemala 2,823 7,420 3,728 12,750 ­159 ­379 491 4,117 ­572 ­1,592 783 4,055 Guinea 700 811 1,011 964 ­85 ­27 179 18 ­216 ­162 87 97 Guinea-Bissau 30 83 89 127 ­21 ­10 46 67 ­35 14 20 82 Haiti 192 698 802 2,086 ­31 7 553 1,382 ­87 1 199 254 Data for Taiwan, China 128,369 253,061 124,171 234,046 4,188 9,581 ­2,912 ­3,935 5,474 24,661 95,559 274,800 254 2008 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 1,635 3,796 1,852 6,055 ­226 ­287 243 2,352 ­201 ­195 270 2,642 Hungary 19,765 87,643 19,916 87,169 ­1,701 ­8,344 203 449 ­1,650 ­7,421 12,017 21,590 India 38,013 198,971 48,225 230,232 ­3,734 ­4,264 8,382 26,109 ­5,563 ­9,415 22,865 178,050 Indonesia 52,923 115,032 54,461 95,493 ­5,874 ­14,465 981 4,863 ­6,431 9,937 14,908 42,597 Iran, Islamic Rep. 18,953 .. 15,113 .. ­478 .. ­4 .. 3,358 .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. 8,347 19,655 Ireland 49,439 173,857 42,169 151,307 ­7,325 ­31,101 1,776 ­544 1,721 ­9,095 8,770 832 Israel 27,482 62,992 35,290 61,892 ­2,655 ­576 5,673 7,466 ­4,790 7,990 8,123 29,153 Italy 295,618 515,634 250,319 529,153 ­15,644 ­17,118 ­4,579 ­16,675 25,076 ­47,312 60,690 75,773 Jamaica 3,394 4,782 3,729 7,098 ­371 ­603 607 1,749 ­99 ­1,170 681 2,318 Japan 493,991 733,111 419,556 670,065 44,285 118,156 ­7,676 ­10,684 111,044 170,517 192,620 895,321 Jordan 3,479 7,693 4,903 12,972 ­279 581 1,444 2,790 ­259 ­1,909 2,279 6,982 Kazakhstan 5,975 41,570 6,102 32,840 ­146 ­9,317 59 ­1,207 ­213 ­1,795 1,660 19,127 Kenya 2,948 5,963 3,542 8,200 ­325 ­70 518 1,781 ­400 ­526 384 2,416 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 147,761 383,718 155,104 373,268 ­1,303 ­539 ­19 ­3,820 ­8,665 6,092 32,804 239,148 Kuwait 14,215 65,610 12,615 24,542 4,881 13,385 ­1,465 ­3,457 5,016 50,996 4,543 14,180 Kyrgyz Republic 448 1,185 726 2,253 ­35 ­34 79 716 ­235 ­386 134 817 Lao PDR 408 .. 748 .. ­6 .. 110 .. ­237 .. 99 460 Latvia 2,088 8,783 2,193 13,251 19 ­532 71 479 ­16 ­4,522 602 4,511 Lebanon .. 14,417 .. 17,253 .. 210 .. 1,280 .. ­1,347 8,100 19,239 Lesotho 199 754 1,046 1,456 314 379 210 390 ­323 67 457 658 Liberia .. .. .. .. .. .. .. .. .. .. 28 72 Libya 7,513 37,962 5,755 15,783 133 ­595 ­220 586 1,672 22,170 7,415 62,229 Lithuania 3,191 17,774 3,902 20,900 ­13 ­817 109 725 ­614 ­3,218 829 5,773 Macedonia, FYR 1,302 2,998 1,773 4,258 ­30 ­3 213 1,239 ­288 ­24 275 1,889 Madagascar 749 1,332 987 2,042 ­167 ­80 129 236 ­276 ­554 109 583 Malawi 470 .. 660 .. ­44 .. 157 .. ­78 .. 115 142 Malaysia 83,369 182,673 86,851 147,865 ­4,144 ­4,729 ­1,017 ­4,591 ­8,644 25,488 24,699 82,876 Mali 529 1,375 991 1,833 ­41 ­207 219 228 ­284 ­438 323 970 Mauritania 504 .. 510 .. ­48 .. 76 .. 22 .. 90 .. Mauritius 2,349 4,004 2,454 4,736 ­19 50 101 71 ­22 ­611 887 1,309 Mexico 89,321 266,390 82,168 278,963 ­12,689 ­13,544 3,960 24,124 ­1,576 ­1,993 17,046 76,329 Moldova 884 1,542 1,006 3,129 ­18 401 56 800 ­85 ­387 257 775 Mongolia 508 2,031 521 1,880 ­25 ­145 77 215 39 222 158 1,062 Morocco 9,044 21,751 11,243 25,811 ­1,318 ­421 2,330 6,333 ­1,186 1,851 3,874 20,791 Mozambique 411 2,767 1,055 3,407 ­140 ­496 339 501 ­445 ­634 195 1,216 Myanmar 1,307 4,834 2,020 2,906 ­110 ­1,248 562 122 ­261 802 651 1,383 Namibia 1,734 3,177 2,100 2,974 139 ­85 403 946 176 1,064 221 450 Nepal 1,029 1,234 1,624 2,934 9 62 230 1,787 ­356 150 646 1,565 Netherlands 241,517 469,195 216,558 421,267 7,247 20,371 ­6,434 ­12,504 25,773 55,795 47,162 23,902 New Zealand 17,882 30,364 17,248 32,376 ­3,957 ­7,878 255 509 ­3,068 ­9,381 4,410 14,068 Nicaragua 662 2,319 1,150 3,905 ­372 ­124 138 856 ­722 ­855 142 922 Niger 321 565 457 1,049 ­47 ­10 31 182 ­152 ­312 95 371 Nigeria 12,342 52,233 12,841 24,609 ­2,878 ­6,732 799 3,310 ­2,578 24,202 1,709 42,735 Norway 56,058 155,654 46,848 94,494 ­1,919 ­2,574 ­2,059 ­3,372 5,233 55,213 22,976 56,842 Oman 6,078 22,499 5,035 13,636 ­374 ­1,698 ­1,469 ­2,788 ­801 4,377 1,943 5,014 Pakistan 10,214 20,507 14,185 35,112 ­1,939 ­3,129 2,562 10,940 ­3,349 ­6,795 2,528 12,878 Panama 7,610 12,415 7,768 11,928 ­466 ­1,298 153 258 ­471 ­552 781 1,335 Papua New Guinea 2,992 3,580 1,905 2,692 ­488 ­538 75 291 674 640 267 1,441 Paraguay 4,802 5,645 5,200 6,197 110 ­51 195 386 ­92 ­217 1,106 1,702 Peru 6,622 26,251 9,597 18,266 ­2,482 ­7,581 832 2,185 ­4,625 2,589 8,653 17,442 Philippines 26,795 52,979 33,317 59,463 3,662 ­799 880 13,180 ­1,980 5,897 7,781 22,963 Poland 35,716 138,052 33,825 142,839 ­1,995 ­14,500 958 8,203 854 ­11,084 14,957 48,474 Portugal 32,260 61,387 39,545 76,063 21 ­6,753 7,132 3,147 ­132 ­18,281 22,063 9,883 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 255 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 9,404 39,368 11,306 54,199 ­241 ­4,079 369 6,125 ­1,774 ­12,785 2,624 30,206 Russian Federation 92,987 334,853 82,809 209,431 ­3,369 ­29,628 156 ­1,537 6,965 94,257 18,024 303,773 Rwanda 75 276 374 731 7 ­21 350 296 57 ­180 99 440 Saudi Arabia 53,450 218,602 44,874 104,466 2,800 641 ­16,694 ­15,711 ­5,318 99,066 10,399 30,445 Senegal 1,506 2,180 1,821 3,194 ­124 ­131 195 632 ­244 ­513 272 1,334 Serbia .. .. .. .. .. .. .. .. .. .. .. 11,889 Sierra Leone 128 313 260 434 ­30 ­41 43 62 ­118 ­101 35 184 Singapore 157,658 334,055 144,520 292,161 2,130 ­4,185 ­894 ­1,383 14,373 36,326 68,816 136,259 Slovak Republic 10,969 .. 10,658 .. ­14 .. 93 .. 390 .. 3,863 13,364 Slovenia 10,377 25,741 10,749 26,109 201 ­506 95 ­214 ­75 ­1,088 1,821 7,139 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 34,402 75,855 33,375 84,232 ­2,875 ­5,293 ­645 ­2,817 ­2,493 ­16,487 4,464 25,593 Spain 133,910 322,761 135,000 395,527 ­5,402 ­26,454 4,525 ­7,125 ­1,967 ­106,344 40,531 19,340 Sri Lanka 4,617 8,508 5,982 11,621 ­137 ­388 732 2,169 ­770 ­1,334 2,112 2,943 Sudan 681 5,862 1,238 9,894 ­3 ­2,014 60 1,324 ­500 ­4,722 2 1,660 Swaziland 1,020 2,259 1,274 2,329 81 1 144 168 ­30 98 298 373 Sweden 95,525 199,130 81,142 167,115 ­6,473 1,095 ­2,970 ­4,696 4,940 28,413 25,870 28,017 Switzerland 123,320 219,219 108,916 190,987 10,708 36,938 ­4,409 ­10,321 20,703 54,849 68,620 64,461 Syrian Arab Republic 5,757 13,169 5,541 11,879 ­560 ­935 607 565 263 920 .. .. Tajikistan .. 1,646 .. 2,349 .. ­64 .. 746 .. ­21 39 204 Tanzania 1,265 3,206 2,139 5,113 ­110 ­85 395 550 ­590 ­1,442 270 2,259 Thailand 70,292 152,059 82,246 146,408 ­2,114 ­6,844 487 3,368 ­13,582 2,175 36,939 67,008 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 465 837 671 1,451 ­34 ­35 118 188 ­122 ­461 130 375 Trinidad and Tobago 2,799 10,569 2,110 6,265 ­390 ­760 ­4 50 294 3,594 379 6,608 Tunisia 7,979 15,802 8,811 16,489 ­716 ­1,586 774 1,639 ­774 ­634 1,689 6,912 Turkey 36,581 116,484 40,113 144,361 ­3,204 ­6,584 4,398 1,687 ­2,338 ­32,774 13,891 63,265 Turkmenistan 1,774 .. 1,796 .. 17 .. 5 .. 0 .. 1,168 .. Uganda 664 1,494 1,490 3,229 ­96 ­225 639 1,720 ­281 ­240 459 1,811 Ukraine 17,090 50,239 18,280 53,307 ­434 ­1,722 472 3,173 ­1,152 ­1,617 1,069 22,360 United Arab Emirates .. .. .. .. .. .. .. .. .. .. 7,778 27,617 United Kingdom 322,114 679,164 327,000 768,279 3,393 33,509 ­11,943 ­21,943 ­13,436 ­77,548 49,144 47,039 United States 794,397 1,445,702 890,784 2,204,226 20,899 36,633 ­38,073 ­89,595 ­113,561­811,486 175,996 221,089 Uruguay 3,507 5,660 3,568 5,762 ­227 ­469 76 134 ­213 ­436 1,813 3,091 Uzbekistan .. .. .. .. .. .. .. .. .. .. .. .. Venezuela, RB 20,753 66,782 16,905 38,503 ­1,943 ­1,092 109 ­38 2,014 27,149 10,715 36,715 Vietnam 9,498 36,618 12,334 38,562 ­384 ­1,219 1,200 3,380 ­2,020 217 1,324 13,384 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 2,160 7,865 2,471 7,781 ­561 ­1,234 1,056 1,356 184 206 638 7,543 Zambia 1,222 4,125 1,338 3,222 ­249 ­124 182 171 ­182 950 223 720 Zimbabwe 2,344 .. 2,515 .. ­294 .. 40 .. ­425 .. 888 .. World 6,395,987 t 14,635,235 t 6,247,521 t 14,403,234 t Low income 111,208 453,874 145,057 504,594 Middle income 1,085,148 3,830,081 1,112,568 3,340,141 Lower middle income 485,240 1,991,516 508,950 1,683,108 Upper middle income 598,809 1,861,137 604,464 1,666,426 Low & middle income 1,196,157 4,281,393 1,256,657 3,837,134 East Asia & Pacific 397,583 1,632,160 413,802 1,371,821 Europe & Central Asia 269,117 1,014,166 278,118 973,486 Latin America & Carib. 272,866 760,863 288,144 692,844 Middle East & N. Africa .. .. 108,418 257,095 South Asia 58,893 243,917 78,652 300,538 Sub-Saharan Africa 89,634 230,089 97,459 248,989 High income 5,194,902 10,422,094 4,989,710 10,616,832 Euro area 2,090,190 4,175,306 1,968,796 4,061,245 a. International reserves including gold valued at London gold price. b. Includes Luxembourg. 256 2008 World Development Indicators 4.15 ECONOMY Balance of payments current account About the data Definitions The balance of payments records an economy's system, external debt records, information provided · Exports and imports of goods and services are all transactions with the rest of the world. Balance of by enterprises, surveys to estimate service transac- transactions between residents of an economy and payments accounts are divided into two groups: tions, and foreign exchange records. Differences in the rest of the world involving a change in ownership the current account, which records transactions collection methods--such as in timing, definitions of general merchandise, goods sent for processing in goods, services, income, and current transfers, of residence and ownership, and the exchange rate and repairs, nonmonetary gold, and services. · Net and the capital and financial account, which records used to value transactions--contribute to net errors income is receipts and payments of employee com- capital transfers, acquisition or disposal of non- and omissions. In addition, smuggling and other ille- pensation for nonresident workers, and investment produced, nonfinancial assets, and transactions in gal or quasi-legal transactions may be unrecorded or income (receipts and payments on direct investment, financial assets and liabilities. The table presents misrecorded. For further discussion of issues relat- portfolio investment, and other investments and data from the current account plus gross interna- ing to the recording of data on trade in goods and receipts on reserve assets). Income derived from tional reserves. services, see About the data for tables 4.4­4.7. the use of intangible assets is recorded under busi- The balance of payments is a double-entry account- The concepts and definitions underlying the data ness services. · Net current transfers are recorded ing system that shows all flows of goods and services in the table are based on the fi fth edition of the in the balance of payments whenever an economy into and out of an economy; all transfers that are the International Monetary Fund's (IMF) Balance of Pay- provides or receives goods, services, income, or counterpart of real resources or financial claims pro- ments Manual (1993). That edition redefined as capi- financial items without a quid pro quo. All transfers vided to or by the rest of the world without a quid pro tal transfers some transactions previously included not considered to be capital are current. · Current quo, such as donations and grants; and all changes in the current account, such as debt forgiveness, account balance is the sum of net exports of goods in residents' claims on and liabilities to nonresidents migrants' capital transfers, and foreign aid to acquire and services, net income, and net current transfers. that arise from economic transactions. All transac- capital goods. Thus the current account balance now · Total reserves are holdings of monetary gold, tions are recorded twice--once as a credit and once reflects more accurately net current transfer receipts special drawing rights, reserves of IMF members as a debit. In principle the net balance should be in addition to transactions in goods, services (pre- held by the IMF, and holdings of foreign exchange zero, but in practice the accounts often do not bal- viously nonfactor services), and income (previously under the control of monetary authorities. The gold ance, requiring inclusion of a balancing item, net factor income). Many countries maintain their data component of these reserves is valued at year-end errors and omissions. collection systems according to the fourth edition (December 31) London prices ($386.75 an ounce in Discrepancies may arise in the balance of pay- of the Balance of Payments Manual (1977). Where 1995 and $635.70 an ounce in 2006). ments because there is no single source for balance necessary, the IMF converts such reported data to of payments data and therefore no way to ensure conform to the fifth edition (see Primary data docu- that the data are fully consistent. Sources include mentation). Values are in U.S. dollars converted at customs data, monetary accounts of the banking market exchange rates. Top 15 economies with the largest current account surplus--and top 15 economies with the largest current account deficit in 2006 4.15a Surplus Share of GDP Deficit Share of GDP Economy ($ billions) (%) Economy ($ billions) (%) China 249.9 9.4 United States ­811.5 ­6.2 Japan 170.5 3.9 Spain ­106.3 ­8.7 Data sources Germany 150.7 5.2 United Kingdom ­77.5 ­3.3 Saudi Arabia 99.1 Italy ­47.3 ­2.6 Data on the balance of payments are published in Russian Federation 94.3 9.6 Australia ­41.0 ­5.3 the IMF's Balance of Payments Statistics Yearbook Netherlands 55.8 8.4 Turkey ­32.8 ­8.1 and International Financial Statistics. The World Bank exchanges data with the IMF through elec- Norway 55.2 16.5 Greece ­29.6 ­9.6 tronic files that in most cases are more timely and Switzerland 54.8 14.4 France ­28.3 ­1.3 cover a longer period than the published sources. Kuwait 51.0 Portugal ­18.3 ­9.4 More information about the design and compila- Singapore 36.3 27.5 Congo, Dem. Rep ­16.5 ­6.5 tion of the balance of payments can be found in Sweden 28.4 7.4 Romania ­12.8 ­10.5 the IMF's Balance of Payments Manual, fifth edition Venezuela, RB 27.1 14.9 Poland ­11.1 ­3.3 (1993), Balance of Payments Textbook (1996), and Malaysia 25.5 16.9 India ­9.4 ­1.0 Balance of Payments Compilation Guide (1995). Taiwan, China 24.7 6.7 New Zealand ­9.4 ­9.0 The IMF's International Financial Statistics and Libya 22.2 44.1 Ireland ­9.1 ­4.1 Balance of Payments databases are available on CD-ROM. Source: International Monetary Fund balance of payments data files and World Development Indicators data files. 2006 World Development Indicators 257 STATES AND MARKETS 5 Introduction M easuring governance The breakup of the Soviet Union and the emergence of democracies in many develop- ing countries have increased interest in governance. Good governance, strong institutions, and control of corruption are important for development success. Failures of the state can negate development gains, particularly in low-income economies, many of them fragile states. Improvements in data and econometric techniques have permitted large cross-country stud- ies on the impact of governance and institutions on investment and growth. This research has produced strong evidence that the quality of governance has a big impact on economic growth, a relationship that is robust over time and across countries (figure 5a). It shows that corruption discourages private investment and distorts resource allocation in ways that hurt the poor. Research also finds that public spending to expand primary education and reduce child and infant mortality produces more benefits in countries with less corruption. And it finds that good governance in a country increases the likelihood of development projects succeeding. The World Bank defines governance as the way public officials and institutions acquire and exercise authority to provide public goods and services, including education, health care, infra- structure, and a sound investment climate. Bad governance is often equated with corruption. But the concepts, while related, are different. Corruption, the abuse of public office for private gain, is an outcome of poor governance, reflecting the breakdown of accountability. Fighting corruption requires addressing underlying failures of governance. As citizens, investors, policymakers, and donors become more aware of the importance of good governance to development, they increasingly demand information that better tracks progress and increases the transparency of public sector management and anticorruption programs (box 5b). The growing interest in the quality of governance has driven what a recent Organisation for Economic Co-operation and Develop- Governance and growth go together 5a ment publication describes Growth of GDP per capita, 1982­2006 (%) as "explosive growth in the 10 China use of quantitative indica- 8 tors in developing countries" 6 (OECD 2006, p. 13). At least 4 Indonesia 140 sets of governance Egypt, Arab Rep. 2 Austria indicators, with thousands Hungary 0 Senegal of individual indicators, are Papua New Guinea ­2 Togo now publicly available. Some ­4 look at rules, some at how Congo, Dem. Rep. ­6 the rules are implemented, 0 10 20 30 40 50 60 some at outcomes, and Quality of initial governance, 1982 (International Country Risk Guide index) some are aggregate mea- Over a very long period countries with better governance at the beginning of the period grew faster. The International Country Risk Guide index comprises fi ve elements of governance: corruption in sures, summarizing more government, rule of law, risk of expropriation, repudiation of contracts by government, and quality of the bureaucracy. specific indicators. Source: World Bank staff estimates. 2008 World Development Indicators 259 Types of governance indicators Rules indicators attempt to establish the presence or ab- The Doing Business indicators in table 5.3 are based on sence of rules and processes. Do countries have laws guar- information collected by local experts. The methodology uses anteeing the right to information? Do they have indepen- factual information about laws and regulations to assess dent anticorruption commissions? Are budget documents the business climate of a country. The results at the two published? extremes are far from surprising. New Zealand, Singapore, Such indicators are used to measure specific institutional and the United States are the easiest countries to do busi- reforms. They require narrow and explicit definitions of what ness in, while the fragile states of Democratic Republic of is being measured. Typically, these indicators are prepared by Congo, Central African Republic, and Guinea-Bissau are the country experts and validated by outside experts. most difficult. However, China and India, two of the fastest Interpreting these indicators is not easy. There may be growing economies in the world, rank 83rd and 120 th, sug- clarity about the existence of a specific rule, law, or legal gesting either that their rules are not a serious impediment to body, but this does not make the resulting indicators more growth or that the business environment is not as unfavorable objective than perception-based indicators. Those who frame as these rankings imply. the questions have a concept of a "good system" and may Part of the explanation may lie in what the data represent. impose their own prejudices and values. Nor do formal rules For comparability, the data refer to businesses in each coun- necessarily lead to desired outcomes. An anticorruption com- try's most populous city, which may not be representative. The mission, for example, may not guarantee less corruption reports cover only domestically owned, limited liability compa- (figure 5c). And while the rules may have normative values nies and a limited set of transactions. Indicators of the time of their own--access to budget documents, for instance, is it takes to start a business involve judgment by local experts. desirable in itself--it is not clear how they influence gover- Businesses may get things done faster, if they deploy "speed nance outcomes or reforms. Most important, assessments of money," or slower, if they are poorly informed about policies complicated rules are subject to errors of fact and judgment, and procedures. For the serious analyst the indicators are only particularly when the analyst has to determine the net effect a starting point. Understanding what the data say opens doors of many conflicting rules and regulations. to better understanding governance. Who uses governance indicators? Box 5b · Citizens are more conscious of the need to hold their gov- performance to improve the effectiveness of their policies ernments accountable, and governance indicators increase and institutions and to better understand how outcomes awareness of the quality of governance. The indicators can can be improved. Governance indicators can provide provide citizens with information to monitor service delivery benchmarks against which governments can measure their and measure how their government--local, provincial, or progress. national--is performing. Citizens can compare indicators · Donors are accountable to their citizens for the develop- with those of similar countries. ment assistance they provide. They are thus anxious to · Investors, lenders, and businesses, both domestic and know that the resources that they provide will be used for foreign, know that the quality of governance influences the intended purposes and to compare performance across the investment climate and the return on investments. countries. In preparing their development assistance They want to be better informed about the governance and strategies, they rely on governance assessments that use corruption risks that they are likely to face. Many of the a wide range of governance indicators. These governance earliest efforts to provide governance indicators came from assessments are used to inform country programming and credit and investment risk evaluation agencies in response assistance priorities, allocate aid money using transparent to these commercial needs. and consistent criteria, provide a basis for a dialogue with · Governments, following the maxim that "what you cannot partner governments, and assess political and fi duciary measure you cannot manage," need to monitor their own risks, among other purposes. 260 2008 World Development Indicators Outcome indicators--some highly specific, others more because they are able to act on their beliefs. If they believe general--attempt to measure the consequences of gov- the courts are highly corrupt, they will avoid seeking legal ernance. Typically, they are perceptions-based indicators recourse through the courts and instead choose arbitration that capture the views of relevant stakeholders or inter- or informal means of settling disputes. While governments ested observers, including experts, officials, researchers, may discount outsiders' views, citizens and firms' views decisionmakers, opinion makers, businesses, and citizens. matter. The indicators provide information on how the rules operate in There are few household surveys on governance, but practice (figure 5d). But they have some problems. It is difficult many firm-level surveys. The World Bank's Enterprise Surveys to identify a connection between particular rules and particu- provide an overview of the international investment climate, lar outcomes. And outcome indicators are often measured on reporting on some governance outcomes, such as unoffi - a cardinal scale--say, from 1 to 5 or 10. Unless the criteria cial payments as a share of firms' sales, the time required for assigning specific scores are clear and independently veri- to resolve disputes in court, the cost of providing security fied, there is a risk of arbitrary scoring and confusion about against crime, and the efficiency and client orientation of the the relative importance of scores. tax system. Four frequently used sets of outcome indicators--covering The distinction between rules and outcome indicators is civil and political rights, political risk, corruption, and overall not absolute. Some rules indicators also implicitly measure governance (table 5e)--rely on expert assessments or a com- outcomes. As noted, the time required to register a business bination of expert assessments and surveys of firms, house- is the outcome of applicable regulations and not a measure holds, and opinion makers. Expert assessments are cheaper of the time it actually takes. and with careful benchmarking may be used for cross-country Actionable indicators or second-generation indicators comparisons. But experts often disagree, so it is best not to stem from the desire to identify specific policies, procedures, rely on any one set of experts. and institutional arrangements that contribute to the overall Surveys of firms and households may be better grounded quality of governance. Actionable indicators have received in country realities. The views of respondents matter, greater attention as part of the World Bank's Governance and Not producing Governance in theory the desired results 5c and in practice 5d Global Integrity Index Rating of the anticorruption agency Global Integrity Index Legal framework Practical implementation 100 100 Japan Pakistan Romania Ecuador Costa Rica Canada Mexico India Spain Moldova Kenya Bulgaria 75 75 Indonesia Ghana Nigeria Costa Rica Ukraine Colombia Nicaragua Thailand Kenya Cameroon 50 50 Pakistan Mexico Peru Vietnam 25 25 Congo, Dem. Rep. 0 0 0 25 50 75 100 0 2 4 6 8 10 Global Corruption Barometer Proportion of people who think government Transparency International Corruption Perception Index anticorruption efforts are "effective" (percent) Rules indicators and outcome indicators produce different assessments. Global Integrity produces summary indexes of countries' legal frameworks and practical Anticorruption agencies should help reduce corruption, but even when agency rules and implementation of controls on corruption. Scores on the practical implementa- implementation are rated highly by experts, citizens are not convinced that their govern- tion measure generally lie below the legal framework measure. And the practical ments' efforts are effective. This appears to confirm other research findings that cast measure is more strongly correlated with Transparency International's broad-based doubt on the effectiveness of such agencies. Citizens may also be using their survey Corruption Perception Index, suggesting that the Transparency International sources responses to send a message to their governments about the need to do more. put more weight on outcomes than on rules. Source: World Bank staff estimates. Source: World Bank staff estimates. 2008 World Development Indicators 261 Anticorruption Strategy. These indicators look beyond the Efforts like those described in table 5f are planned or under rules to how they are actually implemented (table 5f). Some way in other areas, including public accountability, human examples of these indicators follow: resources management, and provincial and local governance. · The Public Expenditure and Financial Accountability pro- Despite these efforts, major gaps remain in topical coverage gram aims to provide governments and donors a shared (such as legal and judicial reforms), country coverage, periodic- pool of information on public financial management perfor- ity, and methods. Actionable indicators are subject to many of mance and a common platform for policy dialogue. the same measurement errors as other governance indicators. · The Global Integrity Index is based on six key aspects of Experts may disagree even over narrowly defined assessments. global integrity: civil society; public information and media; The coverage of countries and years, while expanding, is still elections; government accountability, administration. and limited. The Global Integrity Index provides two observations for civil service; oversight and regulation; and anticorruption only 25 countries and three observations for only 8. Much work and rule of law. These six aspects cover 23 subcategories remains to be done in understanding which of the profusion of and 290 indicators, all narrowly and explicitly defined. "actionable" indicators are also "action worthy," in the sense Such indicators are called "actionable" for four reasons: of leading to desired governance and development outcomes. · They provide more clarity about the steps governments Progress is bound to be gradual, a long-term undertaking need- can take to improve their ratings. ing the support of key development institutions. · They shed light on the efficacy of certain public sector Aggregate indicators are composite measures combining reforms in improving governance. the scores on many separate indicators. Among the most · They are replicable--that is, independent observers can widely used and cited governance indicators are the World arrive at roughly the same scores when the questions are Bank's Worldwide Governance Indicators, which draw on 33 explicit and precise. sources to produce indicators on six dimensions of gover- · They allow meaningful discussion between the raters and nance for 212 countries and territories, and Transparency those being rated and thus stimulate policy dialogue on International's Corruption Perceptions Index, which draws on these issues. 12 sources and covers 180 countries. Examples of governance outcome indicators 5e Indicator or objective Nature and number of indicators Country coverage Since 1972 Freedom House has produced Countries are scored on political rights and civil 193 countries and 15 Freedom in the World, an annual survey that liberties outcomes on a 1­7 scale and then rated related and disputed provides an "evaluation of the state of global not free, partly free, or free. The ratings are based territories. freedom as experienced by individuals." on a checklist of 10 political rights and 15 civil http://www.freedomhouse.org liberties. Since 1980 Political Risk Services Group has The political risk guide assigns points to 12 risk 140 countries monthly and produced International Country Risk Guide (ICRG) components relevant to governance. 21 annually. to meet the needs of clients for an in-depth analysis of potential risks to international business. http://www.prsgroup.com Since 1995 Transparency International has ranked The CPI is a composite, a poll of polls, that 180 countries. countries by the degree to which corruption is draws on corruption-related data from expert and perceived to exist among public officials and business surveys by a variety of independent politicians. The Corruption Perceptions Index institutions. The CPI reflects views from around (CPI) defines corruption as "the abuse of public the world, including in-country experts. The office for private gain," encompassing both 2007 CPI draws on 14 polls and surveys from 12 administrative and political corruption. independent institutions. http://www.transparency.org Since 1999 Worldwide Governance Indicators have Governance is measured along six dimensions: 212 countries. provided aggregate governance outcomes from voice and accountability, political stability and 1996 onward. absence of violence, government effectiveness, http://www.govindicators.org regulatory quality, rule of law, and control of corruption. 262 2008 World Development Indicators Drilling down: the Worldwide Governance Indicators 5g Aggregation is not unique to governance indicators. Weighted averages or more complex statistical methods are Worldwide Governance Indicators Governance Standard used to produce broad indicators of social conditions. The Indonesia 2006 Sources Year score error United Nations Development Programme's Human Develop- Voice and accountability 14 2006 ­0.25 0.14 ment Index is an example. Aggregation is also necessary to Political stability 10 2006 ­1.17 0.22 Government summarize the results of large sets of "actionable indicators." effectiveness 14 2006 ­0.38 0.15 For example, the World Bank uses the aggregate Country Regulatory quality 12 2006 ­0.26 0.17 Rule of law 19 2006 ­0.82 0.13 Policy and Institutional Assessment (CPIA) rating, an average Control of corruption 17 2006 ­0.77 0.13 of 16 more detailed components, to allocate concessional lending across countries. Properly designed, aggregation can WGI sources (partial list) Type Values Bertelsmann Transformation Index Experts 0.61 provide estimates of the variance of the underlying indicators. Institute for Management and Development But it also loses some of the detail, reducing its usefulness World Competitiveness Yearbook Survey 0.38 International Budget Project Open Budget Index Experts 0.41 as a policy tool. It is important, therefore, to provide access Political Risk Services International to the underlying indicators, as the Worldwide Governance Country Risk Guide Experts 0.41 Indicators now do in most cases (figure 5g). Open Budget Index 2006 (partial list) Aggregate indicators, despite their limitations, have Executive's budget proposal Questions 1­55, 67, 68, 69 opened doors to much research and analysis on governance Citizens budget Question 61 Pre-budget statement Questions 72, 73, 74 and corruption. They provide a starting point for drilling down Auditors report Questions 112­114, 116, 120­122 deeper into country governance systems. And the increasing variety and richness of disaggregated indicators--covering 61. Does the executive publish a "citizens budget" or some nontechnical presentation intended for a wide audience more topics in more depth for more countries over longer peri- that describes the budget and its proposals? ods, using a variety of methods--enables drilling down even Starting from the Worldwide Governance Indicator of Voice and accountability, it is further and increasing understanding of the factors driving possible to drill down to the underlying indicators on which it is based. And for some it is possible to go farther, to the scoring of individual questions. Good documenta- aggregate success or failure. tion and access to the original data make aggregate indicators more useful. Selected actionable governance indicators 5f Indicator or objective Nature and number of indicators Country coverage Public Expenditure and Financial Accountability 28 high-level indicators that capture 67 completed, of which 26 Assessment, initiated in 2001, measures critical six dimensions of public financial are publicly available. dimensions of open and orderly public fi nancial management. management systems. www.pefa.org. OECD Assessment Methodology for Public Procurement 12 indicators with 54 subindicators 22 countries participating Systems, developed over 2003­04 through an Organisation in four broad areas: legislative and in pilot program; reports for Economic Co-operation and Development Development regulatory framework, institutional available online for 9. Assistance Committee­ and World Bank­led roundtable and framework and management capacity, now being piloted, measures compliance, performance, and procurement operations and market transparency and integrity of public procurement systems. practice, and integrity and transparency. www.oecd.org/dac. Open Budget Index, launched in October 2006 by 122 items that assess public 59 in 2006; 88 targeted and civil society organizations in 59 counties, provides availability of key budget documents, 80 expected for 2008 comprehensive practical information to gauge a quality of information, and timeliness of government's commitment to budget transparency and dissemination. accountability. www.openbudgetindex.org Global Integrity Index, launched in 2002 by the Washington, More than 290 discrete integrity 25 countries in 2004, 41 D.C.,­based Center for Public Integrity and a new indicators generate the index, which is in 2006, 48 in 2007, 33 independent nonprofit called Global Integrity formally organized into six broad categories. assessed at least twice. started in 2005, assesses the existence and effectiveness of anticorruption mechanisms that promote public integrity. The index evaluates the existence of laws, regulations, and institutions; their implementation; and the access average citizens have to those mechanisms. www.globalintegrity.org 2008 World Development Indicators 263 Why governance is difficult to measure Measuring governance is not easy. A broad concept, gover- the equality of all citizens in the eyes of the law so that no nance embraces many institutions and the formal and infor- individual, however powerful, stands above the law. Reaching mal rules that guide their operation. Governance also involves a consensus on such concepts is not easy (figure 5i). Because a range of players--citizens, their elected leaders, public of- most definitions tend to be broad, the boundaries between ficials, and those delivering services--who respond to the in- different indicators risk being blurred. centives created by these rules. Formal rules are more readily That governance is difficult to measure does not imply observed. Informal rules, less easily measured, may have a that governance is not measurable. Nor should demonstrable greater influence on the quality of governance and require a errors of measurement deter the effort. All indicators are much deeper understanding of the workings of society. That subject to error. The national accounts reported in World is why many governance measures rely on the views of ex- Development Indicators are estimated and later subject to perts or the managers of firms--because they understand revision, at times very large. Because it is difficult and costly the principles of governance or have practical experience of to obtain reliable data through surveys and official records, the formal and informal rules of the game (figure 5h). Demand maternal mortality is often estimated from models. Poverty for such measures comes from a variety of stakeholders (see estimates depend on surveys of household consumption box 5b). patterns and the judgment of experts about an appropriate Measuring governance can involve assessing how public poverty line. institutions work as a whole or in their many parts, such as the Still, measuring corruption is particularly problematic. effectiveness of the judiciary or the bureaucracy or the process Those with direct knowledge of corruption are likely to want for setting and monitoring the budget. Because the concepts to keep it secret. In some cases administrative corruption are so broad, the same terms may be applied in many different can be gauged through surveys of citizens and business or ways. Thus, the rule of law may be interpreted narrowly--to the judgments of informed experts. But often the state's mean whether the country's laws are clear and well under- capture by special interests is difficult to assess because stood, whether property rights and contracts are effectively that lies outside the direct experience of citizens and small enforced. Or they may be interpreted more broadly--to mean businesses. Experts generally agree on governance . . . but experts can still disagree, even assessments at the aggregate level . . . 5h using a very specific assessment protocol 5i World Bank overall CPIA score, Sub-Saharan African IDA countries, 2005 World Bank CPIA item 13, Sub-Saharan African IDA countries, 2005 6 6 5 5 4 4 3 3 2 2 1 1 1 2 3 4 5 6 1 2 3 4 5 6 African Development Bank overall CPIA score, Sub-Saharan African countries, 2005 African Development Bank CPIA item 13, Sub-Saharan African countries, 2005 On CPIA item 13, which assesses the quality of budgetary and financial manage- The World Bank and African Development Bank rate countries independently using ment, differences between the scores assigned by the World Bank and African similar Country Performance and Institutional Assessments (CPIA), an aggregation Development Bank for an individual component differ by as much as 1.5 points on a of 16 specific scores. Overall scores are normalized to a scale of 1 to 6. scale of 1 to 6. Source: World Bank staff estimates. Source: World Bank staff estimates. 264 2008 World Development Indicators Measurement errors All governance indicators are subject to significant measure- In explicitly measuring margins of error, the Worldwide ment errors, but these errors are rarely reported. Measures Governance Indicators inform users of the uncertainty sur- based on sample surveys are subject to sampling error, and rounding the estimates. For some countries with similar those based on expert assessments to informant error. Be- scores, overlapping confidence intervals make comparisons cause any indicator is an imperfect measure of the broader of differences meaningless. But statistically reliable state- concepts it pertains to, a third source of error might be called ments can be made in many cases when scores differ by proxy error. High levels of overall corruption in the customs larger amounts. Figure 5j shows the World Governance Indica- service, even if accurately measured, might not reflect corrup- tors government effectiveness scores and margins of error tion in the country. To increase the reliability of governance for 212 countries. The 81 countries at the lower end of the measures, measurement errors should be quantified and re- distribution of governance have scores that are almost cer- ported where possible. tainly below the median, and the 85 countries at the upper In combining information from different sources, aggre- end of the distribution are almost certainly above the median gate indicators can smooth the idiosyncrasies of their under- (with a probability of 90 percent or higher). But for the 46 lying components. The Worldwide Governance Indicators, for countries in the middle of the distribution there is at least a instance, draw on indicators from 33 sources to produce six 10 percent chance that a score below the median could be aggregate indicators. The statistical model for combining the above it, or vice versa. indicators assumes that the observed empirical indicators of Recognition of measurement errors should discourage governance provide noisy or imperfect signals of the funda- naïve ranking of countries on governance performance. mentally unobservable concept of governance. The model esti- Transparency International, which uses country rankings as mates the variance of the aggregate estimate for each coun- a way of shaming countries into fighting corruption, never- try, conditional on the observed data, and provides estimates theless cautions users against comparing countries with of the variance of the underlying indicators as well (Kaufmann close scores. Its country rankings also cannot be compared and Kraay forthcoming). The more the individual indicators from year to year as country coverage keeps changing and agree, the smaller is the measured error of the aggregate. expanding. Comparing governance scores in the light of uncertainty 5j Worldwide Governance Indicators, Government Effectiveness, normalized governance score, 2006 3 90% confidence interval Score Above median Below median Indeterminate 2 1 0 Median governance score ­1 ­2 ­3 0 25 50 75 100 Percentile rank Countries' scores on the Worldwide Governance Indicators aggregate indicator of government effectiveness are shown in rank order. The error bars show a 90 percent confidence interval around each score. Because of measurement error, differences in scores cannot be determined with certainty. In this example the scores of the 46 countries in the middle of the distribution cannot be determined to be significantly above or below the median value. Source: World Bank staff estimates. 2008 World Development Indicators 265 Looking ahead The proliferation of governance indicators has led to several Decentralization is particularly promising, because it enables recent efforts to take stock of where this work stands and central governments to monitor the performance of provincial what the next areas of emphasis should be (see UNDP 2007a; and local governments, improving information on governance Knack, Kugler, and Manning 2003; Arndt and Oman 2006; in the country as a whole. World Bank 2006g; Kaufmann and Kraay forthcoming; Levy Third, one difficulty with the proliferation of disaggregated, 2007; Thomas 2006). specific indicators is that they do not provide guidance to Four priorities stand out. users on which of the many subindicators are most critical to First, it is important to evaluate all governance indica- particular governance outcomes. Research on this is a high tors, exposing them to peer review and strengthening them priority, to identify a core set of the most important indicators to increase public confidence in their use. The methods and that influence governance outcomes, allowing governments underlying assumptions used to produce them should be and donors to focus their reforms on those critical areas. carefully reviewed. The quality of the underlying data should Fourth, given the growing recognition of how understanding be evaluated, including the role of experts and surveys. And a country's political economy can produce better development methods of better estimating the uncertainties associated outcomes, the quality of current efforts to measure political with all measures of governance should be studied so that trends and outcomes should be reviewed for their capacity to users of data are aware of the uncertainties they are dealing shed light on development prospects and outcomes. with. These and other issues could be part of a program of Second, given the strong interest from policymakers in work led by the World Bank, as a major user and producer of indicators of remediable policy or institutional failures, prog- governance indicators (box 5k). ress on action-worthy indicators is a high priority. To build This section of World Development Indicators includes a on the promise of the initial round of Public Expenditure broad range of indicators that shed light on the effectiveness and Financial Accountability (PEFA) Assessments, formally and accountability of governments and their interaction with launched two years ago, it will be important to extend them the private sector. Tables 5.2­5.6 provide an overview of the to more countries, to conduct regular periodic assessments, climate for investment and doing business and of the tax and and to ensure that results are disseminated. The example of regulatory roles of the state. Table 5.8 provides the World PEFA generating information on the quality of public financial Bank's Country Policy and Institutional Assessment data for systems also opens the door to similar approaches in other 77 International Development Association­eligible countries. areas. The World Bank has already identified some key areas Other tables show data on financial markets, public and private for undertaking similar assessments, including decentraliza- provision of infrastructure, and defense, all of which depend tion, public accountability, and human resources management. on effective government spending and oversight. 266 2008 World Development Indicators The World Bank and governance indicators 5k Governance indicators are now routinely collected and used by the Country governance monitoring. Diagnosing governance obstacles at World Bank for a number of purposes. the country level and designing and monitoring reforms, now a re- quirement under the World Bank's new Governance and Anticorrup- Resource allocation. The Bank's Country Policy and Institutional As- tion Strategy, employ a range of aggregate and actionable indicators sessment Indicators (CPIA) enter into the International Development including the Worldwide Governance Indicators, the Transparency Association (IDA) country performance rating (CPR) with an effective International indicator, Public Expenditure and Financial Accountabil- weight of 67 percent. The CPR is used as part of the IDA performance ity indicators, the Doing Business indicators, the investment climate assessment, which is used to allocate IDA resources among eligible assessments, public financial management studies, the World Bank countries. Institute Governance and Anticorruption diagnostic surveys, and quan- titative service delivery surveys and report cards. These feature in the Global monitoring. The 2006 Global Monitoring Report included 13 Bank's analytical and advisory assistance, project documents, and governance indicators in its statistical appendix (see table). country assistance strategies. Governance indicators from Global Monitoring Report Actionable indicators. The Bank's new Governance and Anticorrup- tion Strategy calls for the development and promotion of actionable Category Indicator indicators, including decentralization, public accountability, human resources management, and the Public Expenditure and Financial Overall 1. Control of corruption (Worldwide Accountability (PEFA). This work includes extending the coverage of governance Governance Indicators) PEFA and the Global Integrity Index to more countries and encouraging performance 2. Corruption perceptions index (Transparency countries to permit the publication of PEFA data. International) 3. Unofficial payments (Enterprise Surveys) Research. In studies on governance outcomes World Bank research 4. Policy outcome (CPIA cluster a­c average) increasingly uses large cross-country governance databases including 5. Aggregate public institutions (CPIA cluster d) Polity IV, the database of political institutions; the Worldwide Gover- 6. Licensing time (Doing Business) nance Indicators; and Transparency International's Corruption Percep- 7. Time spent on regulations (Enterprise tions Index. Surveys) Bureaucratic 8. Budget/financial management (CPIA 13) Data. Bank staff manage, produce, and analyze several databases on capability 9. Public administration (CPIA 15) governance: the Investment Climate Assessments, the Doing Busi- ness database, the Database of Political Institutions, and the annual Checks and 10. Voice and accountability (Worldwide Governance Matters report (Kaufmann, Kraay, and Mastruzzi 2007, balances Governance Indicators) Governance Matters VI), which since 2003 has generated annual ag- institutions 11. Rule of law (Worldwide Governance gregate indicators on worldwide governance based on external data Indicators) sources. 12. Property rights and rule-based governance (CPIA 12) 13. Executive constraints (Polity IV) 2008 World Development Indicators 267 Tables 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses Micro, projects with private participationa credit to registered small, and private medium-size sector enterprises $ millions Water and per 1,000 Telecommunications Energy Transport sanitation % of GDP New Total Total people 1995­99 2000­06 1995­99 2000­06 1995­99 2000­06 1995­99 2000­06 2006 2005 2005 2000­05b 2000­05b Afghanistan .. 747.5 .. 1.6 .. .. .. .. .. .. .. .. .. Albania .. 569.2 0.0 789.0 .. 308.0 .. 8.0 21.8 2,388 16,423 38,331 12.2 Algeria .. 4,124.5 .. 2,720.0 .. 120.9 .. 510.0 12.5 12,164 103,482 580,000 18.7 Angola .. 528.7 .. 54.4 .. 55.0 .. .. 7.5 .. .. .. .. Argentina 10,498.6 6,859.8 12,992.6 5,642.1 6,996.5 522.2 3,307.1 791.6 13.0 53,000 450,535 .. .. Armenia 112.5 317.1 0.0 67.0 .. 63.0 .. 0.0 8.8 9,667 123,951 99,805 33.1 Australia .. .. .. .. .. .. .. .. 109.6 81,079 935,047 1,269,000 63.0 Austria .. .. .. .. .. .. .. .. 114.9 14,669 172,602 252,399 30.9 Azerbaijan 122.0 769.2 .. 375.2 .. .. .. 0.0 12.2 .. .. 49,527 6.0 Bangladesh 438.1 2,187.3 554.9 501.5 0.0 0.0 .. .. 36.2 5,328 67,459 177,000 1.2 Belarus 20.0 955.8 500.0 .. .. .. .. .. 20.2 .. .. 25,108 2.5 Belgium .. .. .. .. .. .. .. .. 83.3 25,492 343,761 686,533 66.2 Benin .. 133.9 .. 590.0 .. .. .. .. 16.7 .. .. .. .. Bolivia 528.0 594.3 2,777.3 934.3 168.7 16.6 682.0 .. 36.1 1,625 24,649 .. .. Bosnia and Herzegovina 0.0 860.5 .. 277.9 .. .. .. .. 48.4 1,409 34,035 14,986 3.8 Botswana 97.0 122.0 .. .. .. .. .. .. 19.6 7,301 79,543 13,137 7.2 Brazil 45,135.2 46,959.3 33,042.3 29,351.3 16,960.8 4,060.7 1,850.0 1,215.3 36.5 .. .. 4,903,268 27.4 Bulgaria 202.5 2,641.1 .. 3,566.1 .. 533.7 .. 152.0 47.4 .. .. 216,489 27.7 Burkina Faso .. 331.9 5.6 .. 63.3 .. .. .. 16.7 .. .. .. .. Burundi .. 53.6 .. .. .. .. .. .. 21.0 .. .. .. .. Cambodia 102.4 198.1 143.0 88.1 120.0 325.3 .. .. 9.1 1,551 10,349 .. .. Cameroon 12.7 457.4 .. 531.8 90.0 0.0 .. .. 9.0 .. .. .. .. Canada .. .. .. .. .. .. .. .. 195.3 85,083 1,357,881 2,245,245 69.5 Central African Republic 1.1 0.0 .. .. .. .. .. .. 6.6 .. .. .. .. Chad 2.0 37.4 .. 0.0 .. .. .. .. 2.5 .. .. .. .. Chile 673.5 1,485.6 6,594.1 1,525.1 3,104.1 4,936.2 4,190.3 1,495.2 82.4 31,088 170,636 700,000 43.4 China 5,970.0 8,548.0 17,166.6 10,847.0 10,852.5 20,347.4 985.9 4,300.4 113.6 .. .. 8,000,000 6.3 Hong Kong, China .. .. .. .. .. .. .. .. 139.5 74,122 557,002 263,959 38.9 Colombia 1,384.3 3,012.0 6,985.4 695.0 995.5 1,919.8 321.0 619.3 35.7 987 20,026 664,000 15.2 Congo, Dem. Rep. 48.0 547.4 .. .. 0.0 .. .. .. 2.9 .. .. .. .. Congo, Rep. 54.7 71.8 325.0 .. .. .. .. 0.0 2.2 2,160 34,514 .. .. Costa Rica .. .. 301.2 160.0 .. 508.2 .. .. 39.1 44,301 392,726 40,921 9.6 Côte d'Ivoire 752.3 147.9 260.6 0.0 241.3 140.0 .. .. 14.1 .. .. .. .. Croatia 978.0 1,602.1 368.5 7.1 672.2 451.0 .. 298.7 68.7 8,733 113,708 94,088 21.2 Cuba .. 60.0 165.0 .. .. 0.0 .. 600.0 .. .. .. .. .. Czech Republic 6,178.5 8,996.0 944.1 3,865.3 283.7 106.7 135.5 263.7 40.9 30,945 273,688 .. .. Denmark .. .. .. .. .. .. .. .. 185.1 33,047 234,432 257,950 47.8 Dominican Republic 163.0 424.0 979.0 1,306.6 .. 1,148.9 .. .. 25.8 .. .. .. .. Ecuador 696.4 588.6 30.0 431.0 686.8 1,651.0 .. 500.0 24.0 .. .. 1,043,440 83.7 Egypt, Arab Rep. 1,914.5 7,222.9 634.0 678.0 123.9 821.5 .. .. 55.3 9,595 367,559 .. .. El Salvador 720.2 1,282.1 900.2 85.0 .. .. .. .. 42.9 2,617 40,739 461,642 73.3 Eritrea .. 40.0 .. .. .. .. .. .. 29.0 .. .. .. .. Estonia 628.2 467.1 26.5 .. 1.0 298.4 .. 115.0 78.4 9,945 73,999 65,194 48.4 Ethiopia .. .. .. .. .. .. .. .. 27.2 .. .. .. .. Finland .. .. .. .. .. .. .. .. 77.8 7,710 114,061 221,000 42.4 France .. .. .. .. .. .. .. .. 98.7 144,521 1,225,291 2,612,960 43.2 Gabon 8.4 26.6 294.0 0.0 46.7 177.4 .. .. 9.3 .. .. .. .. Gambia, The .. 6.6 .. 0.0 .. .. .. .. 15.6 .. .. .. .. Georgia 61.0 493.8 159.0 134.5 .. 168.5 .. .. 19.5 5,035 56,840 33,860 7.6 Germany .. .. .. .. .. .. .. .. 109.8 66,747 465,615 3,162,111 38.3 Ghana 491.1 371.5 110.0 590.0 .. 10.0 .. 0.0 18.0 6,189 100,272 25,679 1.2 Greece .. .. .. .. .. .. .. .. 72.3 2,381 33,839 771,000 69.9 Guatemala 1,366.3 836.1 1,223.2 110.0 33.8 .. .. .. 26.8 4,251 68,451 .. .. Guinea 120.3 98.6 36.4 .. .. .. .. .. 5.0 .. .. .. .. Guinea-Bissau .. 6.9 .. .. .. .. .. .. 4.0 .. .. .. .. Haiti 102.5 148.0 4.7 5.5 .. .. .. .. 13.3 9 300 .. .. 268 2008 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy Investment commitments in infrastructure Domestic Businesses Micro, projects with private participationa credit to registered small, and private medium-size sector enterprises $ millions Water and per 1,000 Telecommunications Energy Transport sanitation % of GDP New Total Total people 1995­99 2000­06 1995­99 2000­06 1995­99 2000­06 1995­99 2000­06 2006 2005 2005 2000­05b 2000­05b Honduras 51.3 224.2 112.1 358.8 10.5 120.0 .. 207.9 49.0 .. .. 257,953 41.6 Hungary 6,430.2 5,798.1 3,812.1 2,090.6 135.0 3,297.5 205.8 0.0 55.4 22,251 240,556 .. .. India 7,456.8 27,912.6 7,096.7 11,572.2 1,349.1 11,365.7 .. 2.1 45.0 38,129 712,800 .. .. Indonesia 8,847.5 8,108.1 9,942.1 2,485.7 1,530.8 2,400.7 955.2 36.7 24.6 19,851 259,799 41,362,315 195.3 Iran, Islamic Rep. 28.0 695.0 .. 650.0 .. .. .. .. 47.3 .. .. .. .. Iraq .. 1,074.0 .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. 183.4 17,234 160,707 97,000 24.3 Israel .. .. .. .. .. .. .. .. 89.6 14,687 379,503 468,338 67.6 Italy .. .. .. .. .. .. .. .. 95.6 104,364 1,688,198 4,486,000 77.9 Jamaica .. 701.0 43.0 279.0 0.0 565.0 .. .. 27.9 .. .. .. .. Japan .. .. .. .. .. .. .. .. 182.0 114,013 2,572,088 5,712,191 44.7 Jordan 39.9 1,952.6 .. .. 182.0 0.0 0.0 169.0 98.0 7,706 102,716 141,327 26.7 Kazakhstan 1,633.5 1,788.9 1,825.0 300.0 .. .. .. 100.0 47.8 3,302 32,150 .. .. Kenya 193.0 2,053.0 238.0 116.7 53.4 404.0 0.0 .. 27.7 7,371 125,102 2,800,000 85.1 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. 102.0 .. .. 2,998,223 62.4 Kuwait .. .. .. .. .. .. .. .. 63.1 .. .. .. .. Kyrgyz Republic 100.8 47.4 .. .. .. .. .. .. 10.5 .. .. 142,475 28.3 Lao PDR 100.1 97.7 535.5 2,050.0 0.0 0.0 .. .. 6.0 .. .. .. .. Latvia 600.9 817.4 106.0 71.1 75.0 135.0 .. .. 86.8 10,856 193,893 32,571 13.8 Lebanon 485.7 138.1 .. .. .. 153.0 .. 0.0 77.9 3,127 63,423 .. .. Lesotho 15.7 93.9 .. 0.0 .. .. .. .. 8.9 .. .. .. .. Liberia .. 80.8 .. .. .. .. .. .. 8.4 .. .. .. .. Libya .. .. .. .. .. .. .. .. 15.5 .. .. .. .. Lithuania 832.7 1,112.0 10.0 399.3 .. .. .. .. 50.6 4,507 71,085 56,428 16.5 Macedonia, FYR .. 808.6 .. 391.0 .. .. .. .. 30.2 10,814 157,973 55,742 27.5 Madagascar 30.0 12.6 .. 0.0 .. 48.5 .. .. 10.2 1,234 19,305 .. .. Malawi 23.1 66.8 .. 0.0 6.0 .. .. .. 8.7 420 5,595 747,396 64.3 Malaysia 3,188.6 3,770.8 1,610.2 6,840.6 8,135.6 4,992.4 10.0 6,502.2 108.1 .. .. 518,996 20.2 Mali .. 82.6 .. 365.9 .. 55.4 .. .. 17.2 .. .. .. .. Mauritania .. 92.1 .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 393.0 109.3 0.0 42.6 .. .. .. 78.0 .. .. 75,267 62.2 Mexico 10,757.5 20,763.4 2,120.8 6,795.3 4,706.1 5,388.4 305.0 548.7 22.1 306,400 4,290,000 2,891,300 28.3 Moldova 84.6 80.1 60.0 25.3 38.0 0.0 .. .. 27.9 5,033 61,333 25,667 6.5 Mongolia 21.9 22.1 .. .. .. .. .. .. 32.8 .. .. .. .. Morocco 1,240.0 6,715.1 5,978.0 1,049.0 .. 340.0 .. .. 58.1 13,407 155,947 450,000 15.4 Mozambique 29.0 138.6 .. 1,205.8 441.0 334.6 25.5 .. 13.8 .. .. .. .. Myanmar .. .. 719.0 .. 50.0 .. .. .. 5.6 .. .. .. .. Namibia 55.0 35.0 4.0 1.0 .. .. .. 0.0 61.7 .. .. .. .. Nepal .. 97.3 98.2 39.0 .. .. .. .. 37.7 .. .. 3,040 0.1 Netherlands .. .. .. .. .. .. .. .. 176.2 116,000 1,030,000 735,160 45.0 New Zealand .. .. .. .. .. .. .. .. 144.2 62,695 388,846 334,031 81.7 Nicaragua 24.5 294.3 232.4 126.3 .. 104.0 .. .. 33.8 .. .. .. .. Niger .. 85.5 .. .. .. .. .. 3.4 8.3 .. .. .. .. Nigeria 69.0 9,485.8 .. 1,920.0 .. 2,617.6 .. .. 15.0 .. .. .. .. Norway .. .. .. .. .. .. .. .. .. 47,436 298,360 316,243 68.4 Oman .. 1,047.0 183.0 1,364.3 77.5 473.8 .. 0.0 34.9 .. .. 7,373 3.0 Pakistan 75.5 9,068.0 4,298.3 800.7 421.3 322.0 .. .. 29.0 4,227 44,897 2,956,704 19.0 Panama 1,429.2 307.9 669.2 455.5 994.6 51.4 25.0 .. 88.6 .. .. .. .. Papua New Guinea .. .. 65.0 .. .. .. 71.0 .. 17.1 .. .. .. .. Paraguay 259.3 365.5 .. .. 58.0 .. .. .. 16.9 .. .. 548,000 98.4 Peru 4,774.5 2,643.2 3,004.9 2,511.2 86.3 1,537.5 .. 152.0 17.8 33,349 554,135 658,837 24.4 Philippines 5,358.3 5,235.3 6,998.0 4,275.2 1,364.0 1,260.5 7,567.2 503.9 30.0 13,328 .. 808,634 10.0 Poland 4,913.2 18,179.1 628.1 2,352.7 169.4 1,672.0 6.1 64.3 33.6 23,864 509,894 1,654,822 43.3 Portugal .. .. .. .. .. .. .. .. 157.4 16,770 262,686 693,000 66.4 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. 2,069 0.5 2008 World Development Indicators 269 5.1 Private sector in the economy Investment commitments in infrastructure Domestic Businesses Micro, projects with private participationa credit to registered small, and private medium-size sector enterprises $ millions Water and per 1,000 Telecommunications Energy Transport sanitation % of GDP New Total Total people 1995­99 2000­06 1995­99 2000­06 1995­99 2000­06 1995­99 2000­06 2006 2005 2005 2000­05b 2000­05b Romania 2,072.8 4,179.9 100.0 2,065.6 23.4 .. .. 1,116.0 26.3 91,386 851,562 392,544 18.1 Russian Federation 5,639.1 27,700.4 2,281.3 1,726.0 406.0 253.4 108.0 938.5 30.8 446,605 4,767,300 6,891,300 48.1 Rwanda 8.0 82.3 .. 1.6 .. .. .. .. 13.5 .. .. .. .. Saudi Arabia .. .. .. .. .. .. .. .. 50.7 .. .. .. .. Senegal 273.9 805.1 124.0 93.3 .. 55.4 20.0 0.0 23.1 23 1,000 .. .. Serbia 1,590.0 3,197.0 .. .. .. .. .. 0.0 26.8 14,608 270,872 68,220 9.1 Sierra Leone 7.0 88.8 .. .. .. .. .. .. 4.4 .. .. .. .. Singapore .. .. .. .. .. .. .. .. 98.6 19,501 102,662 136,363 32.2 Slovak Republic 488.5 2,993.9 .. 4,459.6 .. 42.0 0.0 13.6 39.2 7,507 81,775 70,553 13.1 Slovenia .. .. .. .. .. .. .. .. 68.8 3,237 40,560 91,066 45.6 Somalia 0.0 13.4 .. .. .. .. .. .. .. .. .. .. .. South Africa 2,975.3 6,856.5 3.0 1,261.2 1,386.4 3,987.7 56.9 31.3 160.8 41,356 553,425 .. .. Spain .. .. .. .. .. .. .. .. 167.4 139,119 2,193,691 3,168,735 73.0 Sri Lanka 559.9 938.2 192.3 270.8 240.0 .. .. .. 32.8 4,754 58,518 121,426 6.3 Sudan 18.3 1,454.0 .. .. .. 30.0 .. .. 0.1 .. .. 22,460 0.7 Swaziland 21.2 27.7 .. .. .. .. .. .. 23.7 .. .. .. .. Sweden .. .. .. .. .. .. .. .. 117.3 21,695 301,814 898,454 99.6 Switzerland .. .. .. .. .. .. .. .. 174.3 8,998 140,580 344,000 46.9 Syrian Arab Republic .. 628.0 .. .. .. 37.0 .. .. 14.9 216 2,268 .. .. Tajikistan 1.2 8.5 .. 16.0 .. .. .. .. 16.0 .. .. 92,964 14.7 Tanzania 100.2 585.3 127.0 376.4 16.5 27.7 .. 8.5 12.2 3,933 59,163 2,700,000 75.8 Thailand 2,735.2 6,732.7 6,875.4 4,693.3 1,941.1 939.0 289.0 306.5 88.0 .. .. 842,360 13.7 Timor-Leste .. 0.0 .. .. .. .. .. .. .. .. .. 4,138 4.5 Togo 5.0 0.0 0.0 657.7 0.0 .. .. .. 16.9 .. .. .. .. Trinidad and Tobago 0.0 190.0 207.0 39.0 .. .. 0.0 120.0 34.3 .. .. 19,150 14.5 Tunisia .. 3,094.0 291.0 30.0 .. .. .. .. 65.0 6,353 62,563 .. .. Turkey 3,269.7 14,780.3 2,992.2 6,084.5 610.0 4,160.6 942.0 .. 34.1 86,900 593,166 210,134 3.1 Turkmenistan .. 36.3 .. .. .. .. .. .. .. .. .. .. .. Uganda 119.3 387.6 .. 125.7 .. 404.0 0.0 0.0 7.9 8,096 89,503 160,453 6.1 Ukraine 1,094.6 4,028.1 .. 160.0 .. .. .. .. 44.9 28,716 471,839 343,786 7.3 United Arab Emirates .. .. .. .. .. .. .. .. 60.9 .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. 175.8 333,700 2,160,000 4,415,260 73.8 United States .. .. .. .. .. .. .. .. 201.1 676,830 5,156,000 5,868,737 20.0 Uruguay 63.7 144.2 86.0 330.0 20.0 251.1 .. 368.0 26.2 .. .. 125,000 37.9 Uzbekistan 513.8 385.6 .. .. .. .. .. 0.0 .. .. .. 212,424 8.3 Venezuela, RB 4,877.9 4,428.0 103.0 39.5 268.0 34.0 25.0 15.0 17.1 .. .. 11,314 0.5 Vietnam 256.0 690.0 435.5 2,279.0 85.0 20.0 38.8 174.0 71.3 .. .. 90,935 1.1 West Bank and Gaza 265.0 279.8 .. 150.0 .. .. 0.0 .. 8.0 .. .. 97,194 27.7 Yemen, Rep. .. 647.6 .. 15.8 190.0 .. .. .. 6.9 1,800 21,332 310,000 16.1 Zambia 64.2 446.3 274.0 3.0 .. 15.6 .. 0.0 9.7 3,389 65,155 .. .. Zimbabwe 46.0 92.0 600.0 .. 85.0 .. .. .. 26.6 .. .. .. .. World .. s .. s .. s .. s .. s .. s .. s .. s 136.7 w 3,658,665 s 38,885,427 s Low income 11,569.9 60,085.8 15,726.4 23,465.5 3,121.9 16,176.2 155.3 188.0 38.3 75,510 1,221,960 Middle income 147,572.8 255,063.3 136,018.9 114,473.2 63,231.9 70,618.4 21,831.5 21,613.6 60.1 1,389,189 16,463,270 Lower middle income 38,688.6 76,783.1 65,675.8 37,500.4 18,401.9 34,404.2 10,800.3 7,913.7 81.3 182,097 2,737,021 Upper middle income 108,884.2 178,280.2 70,343.1 76,972.8 44,829.9 36,214.2 11,031.2 13,699.9 41.4 1,207,092 13,726,249 Low & middle income 159,142.7 315,149.1 151,745.3 137,938.7 66,353.8 86,794.6 21,986.8 21,801.6 57.3 1,464,699 17,685,230 East Asia & Pacific 26,616.5 33,533.1 44,490.3 33,565.9 24,079.0 30,285.2 9,917.1 11,823.7 98.7 21,402 270,148 Europe & Central Asia 30,761.6 94,150.4 12,842.2 25,358.4 2,129.0 11,084.7 1,261.9 2,691.1 35.1 783,581 8,648,355 Latin America & Carib. 83,557.9 92,346.7 72,527.6 51,226.9 35,089.7 22,852.5 10,705.4 6,562.5 30.9 477,627 5,971,458 Middle East & N. Africa 3,973.1 27,618.6 7,086.0 6,657.1 573.4 2,426.2 0.0 679.0 41.3 54,368 879,290 South Asia 8,530.3 41,008.4 12,240.4 13,185.8 2,010.4 11,687.7 .. 2.1 42.6 52,438 883,674 Sub-Saharan Africa 5,703.3 26,491.9 2,558.8 7,944.5 2,472.2 8,458.3 102.4 43.2 78.4 75,283 1,032,305 High income .. .. .. .. .. .. .. .. 161.7 2,193,966 21,200,197 Euro area .. .. .. .. .. .. .. .. 115.7 658,331 7,178,119 a. Data refer to total for the period shown. Includes projects that became privatized during financial closure years 1990­2006. b. Data are for the most recent year available. 270 2008 World Development Indicators 5.1 STATES AND MARKETS Private sector in the economy About the data Definitions Private sector development and investment--tapping include credit to state-owned or partially state-owned · Investment commitments in infrastructure projects private sector initiative and investment for socially enterprises. with private participation refers to infrastructure proj- useful purposes--are critical for poverty reduction. Entrepreneurship is essential to the dynamism of ects in telecommunications, energy (electricity and In parallel with public sector efforts, private invest- the modern market economy, and a greater entry rate natural gas transmission and distribution), transport, ment, especially in competitive markets, has tre- of new businesses can foster competition and eco- and water and sanitation that have reached financial mendous potential to contribute to growth. Private nomic growth. The table includes data on business closure and directly or indirectly serve the public. markets are the engine of productivity growth, creat- registrations from the 2007 World Bank Group Entre- Incinerators, movable assets, standalone solid waste ing productive jobs and higher incomes. And with gov- preneurship Survey, which includes entrepreneurial projects, and small projects such as windmills are ernment playing a complementary role of regulation, activity in 84 countries for 2003­05. Survey data are excluded. Included are operation and management funding, and service provision, private initiative and used to analyze firm creation, its relationship to eco- contracts, operation and management contracts with investment can help provide the basic services and nomic growth and poverty reduction, and the impact major capital expenditure, greenfield projects (new conditions that empower poor people--by improving of regulatory and institutional reforms. The 2007 sur- facilities built and operated by a private entity or a pub- health, education, and infrastructure. vey improves on the 2006 survey's methodology and lic-private joint venture), and divestitures. Investment Investment in infrastructure projects with private country coverage for better cross-country comparabil- commitments are the sum of investments in facili- participation has made important contributions to ity. Data on total and newly registered businesses ties and investments in government assets. Invest- easing fiscal constraints, improving the efficiency were collected directly from national registrars of com- ments in facilities are resources the project company of infrastructure services, and extending delivery panies. For cross-country comparability, only limited commits to invest during the contract period in new to poor people. Developing countries have been in liability corporations that operate in the formal sector facilities or in expansion and modernization of existing the forefront, pioneering better approaches to infra- are included. For additional information on sources, facilities. Investments in government assets are the structure services and reaping the benefits of greater methodology, calculation of entrepreneurship rates, resources the project company spends on acquiring competition and customer focus. Between 1990 and and data limitations see www.ifc.org/ifcext/sme.nsf/ government assets such as state-owned enterprises, 2006 more than 3,800 projects in more than 139 Content/Entrepreneurship+Database. rights to provide services in a specific area, or use developing countries introduced private participation Formal and informal micro, small, and medium- of specific radio spectrums. · Domestic credit to in at least one infrastructure sector. size enterprises employ more than half the working private sector is financial resources provided to the The data on investment in infrastructure projects population in many market economies and account private sector--such as through loans, purchases with private participation refer to all investment (pub- for about 90 percent of firms. And they contribute of nonequity securities, and trade credits and other lic and private) in projects in which a private company significantly to innovation. If small businesses are accounts receivable--that establish a claim for repay- assumes operating risk during the operating period allowed to compete on a level playing field, the good ment. For some countries these claims include credit or development and operating risk during the con- ones can become larger, workers can earn higher to public enterprises. · New businesses registered tract period. Investment refers to commitments not wages, and productivity will increase. A good invest- are the number of limited liability firms registered in disbursements. Foreign state-owned companies are ment climate--one that provides opportunities and the calendar year. · Total businesses registered are considered private entities for the purposes of this incentives for firms, reduces legal and regulatory the year-end stock of total registered limited liability measure. The data are from the World Bank's Private costs, lowers the costs of providing financial ser- firms. · Micro, small, and medium-size enterprises Participation in Infrastructure (PPI) Project Database, vices, and facilitates the transfer of technology and are business that may be defined by the number of which tracks more than 3,800 projects, newly owned knowledge and the upgrading of capabilities in small employees. There is no international standard defi - or managed by private companies, that reached finan- and medium-size firms--is important for economic nition of firm size; however, many institutions that cial closure in developing economies in 1990­2006. progress, better jobs, and a more inclusive society. collect information use the following size categories: Geographic and income aggregates are calculated by Data on the business registration of micro, small, micro enterprises, 0­9 employees; small enterprises, the World Bank's Development Data Group. For more and medium-size enterprises are collected by govern- 10­49 employees; and medium-size enterprises, information, see http://ppi.worldbank.org/. ments, international organizations, foundations, and 50­249 employees. Credit is an important link in money transmission; small business organizations. These data have been Data sources it finances production, consumption, and capital for- collated by the International Finance Corporation (IFC) mation, which in turn affect economic activity. The and are available in two databases: Entrepreneurship Data on investment commitments in infrastruc- data on domestic credit to the private sector are Data, and Micro, Small, and Medium Enterprises: A ture projects with private participation are from taken from the banking survey of the International Collection of Published Data. This IFC initiative is a the World Bank's PPI Project database (http://ppi. Monetary Fund's (IMF) International Financial Statis- work in progress, improved and updated as new data worldbank.org). Data on domestic credit are from tics or, when unavailable, from its monetary survey. become available. Because the concepts and defini- the IMF's International Financial Statistics. Data The monetary survey includes monetary authorities tions of micro, small, and medium-size enterprises on business registration and micro, small, and (the central bank), deposit money banks, and other vary by source, using these data for precise country medium-size enterprises are from the IFC's Micro, banking institutions, such as finance companies, rankings may be inappropriate. See www. Ifc.org/ Small, and Medium Enterprises database (www. development banks, and savings and loan institu- ifcext/sme.nsf/Content/Resources for additional ifc.org/ifcext/sme.nsf/Content/Resources). tions. Credit to the private sector may sometimes information on sources and precise firm size. 2008 World Development Indicators 271 5.2 Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Average Time Losses due Firms that time to Average dealing with Time required Unofficial to theft, do not Firms with Firms using clear Firms number officials to obtain payments robbery, report all female banks to Value lost due ISO exports offering of times operating to public vandalism, sales for tax participation finance to electrical certification through formal % of management license officials and arson purposes in ownership investment outages ownership customs training management met with tax time officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albaniaa 2005 10.4 6.6 .. 64.3 0.0 66.2 14.1 27.9 10.9 16.7 1.4 47.5 Algeria 2002 .. .. .. 75.2 0.5 70.4 .. 16.9 4.3 .. 8.6 31.8 Angolaa 2006 7.1 5.2 24.1 46.3 2.4 67.8 23.4 2.1 3.7 5.1 16.5 19.4 Argentinaa 2006 14.1 4.6 175.8 18.7 3.7 49.1 30.3 6.9 1.4 26.9 5.5 52.2 Armeniaa 2005 3.0 2.9 .. 24.6 0.0 26.2 12.5 35.0 2.5 5.7 5.0 35.9 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijana 2005 5.2 1.3 .. 37.8 0.2 38.7 14.4 0.6 5.9 10.3 1.6 16.3 Bangladesha 2007 3.2 1.4 6.1 82.2 1.2 .. 16.1 11.6 10.6 7.8 8.4 16.2 Belarusa 2005 3.6 3.1 .. 26.2 0.2 20.0 23.8 10.5 3.8 8.9 3.0 49.7 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 2004 6.5 6.3 39.9 57.7 0.3 39.6 .. 20.8 6.5 2.7 6.3 35.3 Boliviaa 2006 13.5 3.5 30.0 32.0 3.3 51.4 41.1 21.1 4.4 13.8 15.3 53.9 Bosnia and Herzegovinaa 2005 4.3 1.9 .. 24.1 0.4 29.2 25.2 17.5 2.4 14.5 2.0 47.2 Botswanaa 2006 5.0 2.4 13.7 27.6 3.2 65.3 40.9 11.3 1.4 12.7 1.4 37.7 Brazil 2003 7.2 .. .. .. 0.4 82.8 .. 22.9 1.6 19.1 8.2 67.1 Bulgariaa 2005 2.8 4.7 .. 36.1 0.3 39.7 36.5 24.7 1.3 11.0 2.0 32.3 Burkina Fasoa 2006 9.5 2.5 .. 87.0 1.8 58.8 23.3 22.3 3.9 7.4 2.8 43.1 Burundia 2006 5.7 2.1 27.3 56.5 4.9 42.7 34.8 12.3 10.7 7.1 .. 22.1 Cambodia 2003 8.6 7.2 .. 82.4 1.6 91.0 .. 6.8 2.2 2.8 .. 22.5 Cameroona 2006 12.8 6.4 15.6 77.4 3.8 38.7 35.3 18.0 3.9 16.4 4.3 42.4 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chilea 2006 9.0 5.4 67.7 8.2 1.3 27.9 27.8 29.0 1.8 22.0 5.8 46.9 China 2003 18.3 14.4 11.8 72.6 0.1 49.5 .. 9.8 1.3 35.9 6.7 84.8 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombiaa 2006 14.3 2.5 28.2 8.2 2.9 38.7 43.0 30.5 2.3 5.9 7.1 39.5 Congo, Dem. Rep.a 2006 6.3 10.0 17.8 83.8 6.5 65.4 21.2 3.3 5.6 4.3 3.6 11.4 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2005 9.6 0.7 .. 33.8 0.4 68.3 34.7 9.3 1.9 10.5 3.5 46.4 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Croatiaa 2005 2.7 2.3 .. 20.8 0.2 33.3 20.0 29.7 2.4 16.1 2.0 59.9 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republica 2005 2.1 1.7 .. 25.5 0.4 51.1 21.8 11.4 1.6 12.5 3.6 60.3 Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 2005 8.8 2.7 .. 26.3 0.7 73.6 .. 3.6 15.2 9.6 11.4 53.3 Ecuador a 2006 17.3 2.6 19.9 20.7 3.0 37.6 32.7 23.8 2.7 18.2 7.0 61.6 Egypt, Arab Rep. 2004 .. 7.2 112.8 21.2 .. 33.0 .. 7.9 4.5 12.0 4.8 13.4 El Salvador a 2006 9.2 4.1 35.4 27.3 5.6 42.3 39.6 17.3 2.9 11.0 2.6 49.6 Eritrea 2002 3.8 .. .. 6.8 .. 84.2 5.3 30.5 .. 6.6 .. 20.3 Estoniaa 2005 2.3 2.2 .. 16.2 0.4 24.7 34.1 17.8 1.1 13.2 1.8 64.9 Ethiopiaa 2006 3.8 1.8 11.4 12.4 1.4 51.6 30.9 11.0 0.9 4.2 4.3 38.2 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, Thea 2006 7.3 3.2 9.1 52.1 8.7 88.1 21.3 7.6 11.8 22.2 5.0 25.6 Georgiaa 2005 3.1 7.9 .. 11.1 0.3 36.0 36.9 12.5 9.2 13.0 3.4 24.0 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghanaa 2007 4.0 4.6 6.4 38.8 3.7 59.2 44.0 16.0 6.0 6.8 7.8 33.0 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemalaa 2006 9.2 3.9 75.4 13.0 5.2 44.2 28.4 12.8 4.5 8.0 4.5 28.1 Guineaa 2006 2.7 3.6 13.0 84.8 8.3 95.4 25.4 0.9 14.0 5.2 4.3 21.1 Guinea-Bissaua 2006 2.9 4.4 30.4 62.2 3.3 68.2 19.9 0.7 5.3 8.4 5.6 12.4 Haiti .. .. .. .. .. .. .. .. .. .. .. .. 272 2008 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 Average Time Losses due Firms that time to Average dealing with Time required Unofficial to theft, do not Firms with Firms using clear Firms number officials to obtain payments robbery, report all female banks to Value lost due ISO exports offering of times operating to public vandalism, sales for tax participation finance to electrical certification through formal % of management license officials and arson purposes in ownership investment outages ownership customs training management met with tax time officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Hondurasa 2006 4.6 2.4 31.6 12.7 6.1 36.0 39.9 8.5 3.8 16.5 6.0 33.3 Hungarya 2005 4.0 2.5 .. 32.1 0.1 40.0 40.1 22.3 1.4 23.1 4.5 39.9 India 2006 6.7 3.1 .. 47.5 0.1 59.2 9.1 19.4 6.6 22.5 15.6 15.9 Indonesia 2003 4.0 2.0 18.6 44.2 0.2 44.0 .. 13.9 3.3 22.1 4.1 23.8 Iran, Islamic Rep. .. .. .. .. .. .. .. .. .. .. .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 2005 6.3 2.2 .. 17.7 1.1 28.8 32.2 10.6 11.8 16.4 4.3 53.5 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 2006 6.7 2.2 6.4 4.1 1.3 13.0 13.1 8.6 1.7 15.5 3.8 23.9 Kazakhstana 2005 3.1 4.0 .. 45.1 0.3 23.2 36.1 15.4 2.2 9.9 6.8 30.7 Kenya 2003 11.7 5.5 11.6 63.0 0.8 45.9 .. 25.7 8.1 .. 4.7 48.5 Korea, Dem. Rep. 2005 3.2 .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 2005 3.2 2.4 .. 14.1 0.0 43.7 19.1 11.5 .. 17.6 7.2 39.5 Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republica 2005 6.1 3.5 43.9 66.3 0.7 43.2 27.3 7.9 4.1 11.9 4.1 47.0 Lao PDR 2005 4.5 3.8 .. 31.2 1.5 14.9 .. 13.8 4.3 3.3 2.0 28.2 Latviaa 2005 2.9 2.2 .. 31.3 0.5 26.3 42.3 15.1 1.4 9.3 2.0 51.7 Lebanon 2006 12.0 4.7 .. 51.2 0.5 67.5 27.9 26.8 6.0 20.9 7.4 67.8 Lesotho 2003 19.8 14.3 .. 33.3 0.1 35.4 .. 6.7 8.5 8.6 2.3 24.6 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuaniaa 2005 5.1 4.2 55.5 44.6 0.4 39.0 25.5 15.6 1.2 15.1 1.8 52.6 Macedonia, FYRa 2005 8.2 2.7 .. 26.0 0.3 52.2 17.5 9.0 1.8 11.0 2.4 37.4 Madagascar 2005 20.8 2.7 .. 24.5 1.9 21.0 .. 13.0 6.6 6.6 3.5 48.5 Malawia 2006 5.8 8.9 17.4 35.7 2.3 55.3 15.8 20.6 22.6 17.2 3.5 51.6 Malaysia 2002 7.3 5.2 .. .. 0.3 .. .. 23.8 1.8 31.4 2.5 42.0 Mali 2003 7.5 6.9 8.1 59.6 0.5 55.1 .. 16.8 1.7 6.5 8.1 25.5 Mauritaniaa 2006 5.8 1.9 10.7 82.1 5.6 82.5 17.3 3.2 1.6 5.9 3.9 25.5 Mauritius 2005 9.6 2.1 .. 17.5 0.1 26.3 .. 36.3 2.9 28.4 4.4 62.1 Mexicoa 2006 20.5 2.3 11.9 20.0 3.4 57.7 24.8 2.6 2.4 20.3 5.4 24.6 Moldovaa 2005 3.6 2.7 44.7 36.0 0.1 40.2 27.5 17.7 2.7 6.9 2.6 32.5 Mongolia 2004 6.0 7.3 .. .. 0.6 80.4 .. 32.8 1.5 20.5 3.5 46.2 Morocco 2004 9.2 0.8 4.9 .. 0.0 10.7 .. 24.7 0.7 22.3 2.2 33.5 Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibiaa 2006 2.9 1.6 9.6 11.4 3.0 45.5 33.4 8.1 0.7 17.6 1.5 44.5 Nepal .. .. .. .. .. .. .. .. .. .. .. .. Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaraguaa 2006 9.3 2.5 19.7 16.8 3.8 60.4 41.4 13.0 8.7 18.7 5.0 28.9 Niger a 2006 11.5 4.3 10.9 69.7 6.1 29.7 10.0 14.4 2.5 4.8 7.4 34.4 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman .. 5.2 11.8 33.2 .. 42.5 .. 6.5 4.2 10.8 4.2 20.9 Pakistan 2002 8.7 4.2 35.2 57.0 0.1 .. .. 3.6 4.9 17.0 9.7 11.1 Panamaa 2006 10.3 2.7 41.2 24.2 2.7 54.2 37.1 19.2 2.4 14.7 5.7 43.9 Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguaya 2006 7.9 2.2 37.8 68.0 3.1 42.8 44.8 8.0 2.5 7.1 5.5 46.9 Perua 2006 13.5 2.5 81.1 9.2 2.4 27.2 32.8 30.8 3.2 14.6 5.6 57.7 Philippines 2003 6.9 3.9 25.0 44.7 0.9 57.9 .. 5.5 5.9 15.8 6.6 21.7 Polanda 2005 3.0 2.7 16.5 23.7 0.4 43.9 33.6 20.7 1.6 13.9 3.3 48.4 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 273 5.2 Business environment: enterprise surveys Survey Regulations Permits Corruption Crime Informality Gender Finance Infrastructure Innovation Trade Workforce year and tax and licenses Average Time Losses due Firms that time to Average dealing with Time required Unofficial to theft, do not Firms with Firms using clear Firms number officials to obtain payments robbery, report all female banks to Value lost due ISO exports offering of times operating to public vandalism, sales for tax participation finance to electrical certification through formal % of management license officials and arson purposes in ownership investment outages ownership customs training management met with tax time officials days % of firms % of sales % of firms % of firms % of firms % of sales % of firms days % of firms Romaniaa 2005 1.1 1.8 .. 33.1 0.2 26.9 27.7 23.2 2.1 16.8 2.4 32.7 Russian Federationa 2005 6.3 2.5 .. 59.9 0.5 40.3 28.6 10.2 2.0 9.3 8.2 37.3 Rwandaa 2006 5.9 4.0 6.5 20.0 7.1 28.9 41.0 15.9 8.7 10.8 6.7 27.6 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2003 .. 6.7 30.5 25.3 0.6 .. .. 26.3 4.3 6.1 6.6 32.7 Serbiaa 2005 8.1 4.1 .. 31.8 0.6 33.3 25.0 16.7 2.4 11.7 3.2 47.5 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republica 2005 3.0 1.8 .. 34.3 0.4 22.0 18.2 13.2 1.2 10.0 5.8 79.4 Slovenia 2005 3.7 1.4 .. 11.2 0.2 35.6 34.5 29.6 1.1 20.2 2.9 69.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 2003 9.2 3.3 6.4 2.1 0.5 15.9 .. 24.2 0.4 42.4 4.5 64.0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 2004 3.5 5.1 49.5 16.3 0.5 42.0 .. 16.2 .. .. 7.6 32.6 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 2006 4.4 1.9 24.0 40.6 3.4 74.6 28.6 7.7 2.5 22.1 4.0 51.0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 2003 10.3 6.0 .. .. .. 79.9 .. 2.9 8.6 7.4 6.3 21.0 Tajikistan 2005 3.3 3.0 15.3 45.7 0.3 34.5 21.8 1.0 7.3 6.5 5.4 30.9 Tanzania 2006 4.0 3.3 15.9 49.1 3.9 71.0 30.9 6.8 9.6 14.7 5.7 36.5 Thailand 2004 1.3 1.7 37.1 .. 0.1 .. .. 74.7 1.4 44.6 1.4 76.3 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. .. .. Turkey 2005 10.8 2.2 .. 45.7 0.2 63.1 8.9 7.5 2.2 12.6 4.5 25.5 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 2006 5.2 2.9 9.3 50.6 4.1 74.5 34.7 7.7 10.2 15.5 4.7 35.0 Ukraine 2005 8.1 4.7 .. 48.0 0.4 24.4 34.9 14.7 2.7 10.8 4.7 44.0 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 2006 7.0 2.2 133.8 7.1 2.1 45.5 41.6 6.8 0.9 6.8 2.8 24.6 Uzbekistan 2005 2.5 3.5 .. 36.8 0.1 14.6 17.2 3.3 2.7 8.7 5.1 16.2 Venezuela, RB 2006 33.6 3.4 41.6 .. 6.8 .. .. 35.7 4.4 12.5 14.1 42.3 Vietnam 2005 3.1 2.2 .. 67.2 0.1 70.3 27.4 29.2 .. 11.4 4.9 44.0 West Bank and Gaza 2006 5.7 5.2 21.3 5.2 7.5 25.7 18.0 4.2 4.6 18.2 6.0 26.5 Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 2002 13.0 2.9 .. 44.4 2.8 53.5 .. 17.4 3.8 5.8 2.3 34.2 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. a. Representative sample of the nonagricultural economy, excluding financial and public services. 274 2008 World Development Indicators 5.2 STATES AND MARKETS Business environment: enterprise surveys About the data Definitions The World Bank Group's Enterprise Surveys collect electricity, and transport--can be more productive. · Survey year is the year in which the underlying data firm-level data on the business environment to ana- Firm-level innovation and use of modern technology were collected. · Time dealing with officials is the lyze how it changes and affects firm performance and may improve enterprises' ability to compete in the time senior management spends dealing with the growth. Enterprise Surveys cover 11 dimensions of business environment. requirements of government regulation. · Average the business environment and are available for more Delays in clearing customs can be costly, deterring number of times management met with tax officials than 70,000 firms in 104 countries. firms from engaging in foreign trade or making them is the average number of visits or required meetings Firms evaluating alternative investment options, gov- uncompetitive in foreign markets. Ill-considered labor with tax officials. · Time required to obtain operat- ernments interested in improving business conditions, regulations discourage firms from creating jobs, and ing license is the average wait to obtain an operating and economists seeking to explain economic perfor- while employed workers may benefit, unemployed, license from the day the establishment applied for it mance have all grappled with defining and measuring low-skilled, and informally employed workers will not. to the day it was granted. · Unofficial payments to the business environment. The firm-level data from A trained labor force enables firms to thrive, com- public officials are the percentage of firms expected Enterprise Surveys provide a useful tool for bench- pete, innovate, and adopt new technology. to make informal payments to public officials to "get marking performance and monitoring progress. The table presents data for 27 countries in Europe things done" with regard to customs, taxes, licenses, Most countries can improve regulation and taxa- and Central Asia and 2 comparator countries in Asia regulations, services, and the like. · Losses due to tion without compromising broader social interests. (Republic of Korea and Vietnam) that are based on the theft, robbery, vandalism, and arson are the esti- Excessive regulation may harm business perfor- joint European Bank for Reconstruction and Develop- mated losses from those causes that occurred on mance and growth. For example, time spent with tax ment (EBRD)­World Bank Business Environment and establishments' premises as a percentage of annual officials is a burden firms may face in paying taxes. Enterprise Performance Surveys (BEEPS). All other sales. · Firms that do not report all sales for tax The business environment suffers when governments data are from the World Bank Financial and Private purposes are the percentage of firms that expressed increase uncertainty and risks or impose unneces- Sector Development Group's Enterprise Surveys. All that a typical firm reports less than 100 percent of sary costs and unsound regulation and taxation. The BEEPS economies project plus the Latin American sales for tax purposes; such firms are termed "infor- time needed to obtain licenses and permits and the and Caribbean and Sub-Saharan African countries mal firms." · Firms with female participation in own- associated red tape constrains firm operations. for 2006 (except Burkina Faso, Cameroon, and Cape ership are the percentage of firms with a woman In some countries doing business requires unof- Verde), Jordan, and the 2007 surveys for Bangladesh among the principal owners. · Firms using banks ficial payments or gifts to "get things done" in cus- and Ghana draw a sample from the universe of regis- to finance investment are the percentage of firms toms, taxes, licenses, regulations, services, and the tered nonagricultural businesses, excluding the finan- using banks to finance investments. · Value lost due like. Corruption such as this harms the business envi- cial and public sectors. Economies in the table with to electrical outages is the percentage of sales lost ronment by distorting policymaking, undermining gov- samples that are representative of the economy are due to power outages. · ISO certification ownership ernment credibility, and diverting public resources. footnoted. Samples for most of the remaining econo- is the percentage of firms that have earned a quality Crime, theft, and disorder may also impose costs on mies were drawn from the manufacturing sector. certification recognized by the International Organi- businesses and society. Samples are selected by simple random sampling zation for Standardization (ISO). · Average time to In many developing countries informal businesses or stratified random sampling. Typical sample sizes clear exports through customs is the average num- operate without licenses, which constrains private range from 100 to 1,800, depending on the size of ber of days to clear direct exports through customs. sector growth because these firms have less access the economy. BEEPS use a simple random sample · Firms offering formal training are the percentage to financial and public services and can engage in method based on GDP contributions, and therefore of firms offering formal training programs for their fewer types of contracts and investments. samples are self-weighted. Latin American and Carib- permanent, full-time employees. Equal opportunities for men and women contribute bean and Sub-Saharan African countries (except to development. The table shows female participa- Burkina Faso, Cameroon, and Cape Verde), Bangla- tion in firm ownership as a measure of women's inte- desh, and Jordan use stratified random sampling, gration as decisionmakers in business. with three levels of stratification: sector, firm size, When financial markets work well, they connect and geographic region. At the sector level the strata firms to lenders and investors, allowing firms to seize were defined by a few selected manufacturing indus- opportunities and grow their businesses: creditwor- tries, the retail industry (to represent the services thy firms can obtain credit from financial intermedi- sector), and a residual stratum for the rest of the aries at competitive prices. But too often market economy. Firm size is stratified into small, medium, imperfections and government-induced distortions and large. Geographic stratifi cation is defined by limit a firm's access to credit and thus restrain pri- country. Stratified random sampling allows indica- Data sources vate sector development and economic growth. tors to be computed by sector, size, and geographic The reliability and availability of infrastructure region. Economywide indicators can also be com- Data on the business environment are from the benefi t households and are crucial for develop- puted with more precision than under simple random World Bank Group's Enterprise Surveys website ment. Firms with access to modern and effi cient sampling when individual observations are properly (www.enterprisesurveys.org). infrastructure -- telecommunications, reliable weighted. 2008 World Development Indicators 275 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property licenses workers contracts investors business 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 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Afghanistan 4 9 84.6 9 250 13 340 23 47 1,642 0 .. Albania 10 36 20.9 7 47 24 331 35 39 390 0 .. Algeria 14 24 13.2 14 51 22 240 48 47 630 6 2.5 Angola 12 119 343.7 7 334 14 337 69 46 1,011 5 6.2 Argentina 14 31 9.7 5 65 28 338 41 36 590 6 2.8 Armenia 9 18 4.8 3 4 19 116 31 50 285 5 1.9 Australia 2 2 0.8 5 5 16 221 3 28 262 8 1.0 Austria 8 28 5.4 3 32 13 194 37 26 397 3 1.1 Azerbaijan 13 30 6.9 7 61 31 207 38 39 267 4 2.7 Bangladesh 8 74 46.2 8 425 14 252 35 41 1,442 6 4.0 Belarus 10 48 8.8 7 231 17 350 27 28 225 5 5.8 Belgium 3 4 5.3 7 132 14 169 20 27 505 8 0.9 Benin 7 31 195.0 3 118 15 332 40 42 720 6 4.0 Bolivia 15 50 134.1 7 92 17 249 79 37 591 1 1.8 Bosnia and Herzegovina 12 54 30.1 7 331 16 467 46 38 595 3 3.3 Botswana 11 108 9.9 4 30 24 167 20 29 987 8 1.7 Brazil 18 152 10.4 14 45 18 411 46 45 616 6 4.0 Bulgaria 9 32 8.4 9 19 22 131 29 40 564 10 3.3 Burkina Faso 6 18 82.1 8 182 32 226 61 37 446 6 4.0 Burundi 11 43 251.0 5 94 20 384 41 44 558 4 .. Cambodia 10 86 190.3 7 56 23 709 45 44 401 5 .. Cameroon 13 37 129.2 5 93 15 426 46 43 800 6 3.2 Canada 2 3 0.9 6 17 14 75 4 36 570 8 0.8 Central African Republic 10 14 205.4 3 69 21 239 61 43 660 6 4.8 Chad 19 75 188.8 6 44 9 181 46 41 743 6 .. Chile 9 27 8.6 6 31 18 155 24 36 480 7 4.5 China 13 35 8.4 4 29 37 336 24 35 406 10 1.7 Hong Kong, China 5 11 3.1 5 54 23 155 0 24 211 10 1.1 Colombia 11 42 19.3 9 23 14 146 27 34 1,346 8 3.0 Congo, Dem. Rep. 13 155 487.2 8 57 14 322 74 43 685 3 5.2 Congo, Rep. 10 37 150.1 7 137 14 169 69 44 560 6 3.0 Costa Rica 12 77 21.3 6 21 23 178 32 40 877 2 3.5 Côte d'Ivoire 10 40 135.8 7 62 21 628 38 33 770 6 2.2 Croatia 8 40 11.7 5 174 22 255 50 38 561 1 3.1 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 10 17 10.6 4 123 36 180 31 27 820 2 6.5 Denmark 4 6 0.0 6 42 6 69 10 34 380 7 1.1 Dominican Republic 9 22 31.1 7 60 17 214 32 34 460 5 3.5 Ecuador 14 65 29.2 10 17 19 148 51 39 498 1 5.3 Egypt, Arab Rep. 7 9 28.6 7 193 28 249 27 42 1,010 7 4.2 El Salvador 9 26 73.1 5 31 34 155 24 30 786 5 4.0 Eritrea 13 84 125.8 12 101 .. .. 20 39 405 4 .. Estonia 5 7 2.0 3 51 13 117 58 36 425 8 3.0 Ethiopia 7 16 41.3 13 43 12 128 34 39 690 4 3.0 Finland 3 14 1.0 3 14 18 38 48 33 235 6 0.9 France 5 7 1.1 9 123 13 137 56 30 331 10 1.9 Gabon 9 58 164.0 8 60 14 210 59 38 1,070 6 5.0 Gambia, The 9 32 279.0 5 371 17 146 23 32 434 2 3.0 Georgia 5 11 9.5 5 5 12 113 7 36 285 8 3.3 Germany 9 18 5.7 4 40 12 100 44 33 394 5 1.2 Ghana 11 42 41.4 5 34 18 220 37 36 487 7 1.9 Greece 15 38 23.3 12 23 15 169 55 39 819 1 2.0 Guatemala 11 26 47.3 5 30 22 235 28 28 1,459 3 3.0 Guinea 13 41 138.3 6 104 32 255 41 50 276 6 3.8 Guinea-Bissau 17 233 255.5 9 211 15 167 66 41 1,140 6 .. Haiti 12 202 133.9 5 405 11 1,179 21 35 508 2 5.7 276 2008 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 licenses workers contracts investors business 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 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Honduras 13 21 59.9 7 24 17 125 43 45 480 1 3.8 Hungary 6 16 17.7 4 63 31 211 30 33 335 2 2.0 India 13 33 74.6 6 62 20 224 30 46 1,420 7 10.0 Indonesia 12 105 80.0 7 42 19 196 44 39 570 9 5.5 Iran, Islamic Rep. 8 47 5.3 9 36 19 670 40 39 520 5 4.5 Iraq 11 77 93.5 5 8 14 215 38 51 520 4 .. Ireland 4 13 0.3 5 38 11 185 17 20 515 10 0.4 Israel 5 34 4.4 7 144 20 235 24 35 890 7 4.0 Italy 9 13 18.7 8 27 14 257 38 41 1,210 7 1.8 Jamaica 6 8 8.7 5 54 10 236 4 34 565 4 1.1 Japan 8 23 7.5 6 14 15 177 17 30 316 7 0.6 Jordan 10 14 66.2 8 22 18 122 30 39 689 5 4.3 Kazakhstan 8 21 7.6 8 52 38 231 20 38 230 7 3.3 Kenya 12 44 46.1 8 64 10 100 21 44 465 3 4.5 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 10 17 16.9 7 11 13 34 37 35 230 7 1.5 Kuwait 13 35 1.6 8 55 25 104 13 50 566 7 4.2 Kyrgyz Republic 8 21 8.8 4 4 20 291 38 39 177 8 4.0 Lao PDR 8 103 16.5 9 135 24 172 37 42 443 0 .. Latvia 5 16 3.0 8 54 26 188 43 27 279 5 3.0 Lebanon 6 46 94.1 8 25 20 211 25 37 721 9 4.0 Lesotho 8 73 37.4 6 101 15 601 24 41 695 2 2.6 Liberia 12 99 493.3 13 50 25 398 31 41 1,280 4 3.0 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 7 26 3.0 3 3 17 156 48 30 210 5 1.7 Macedonia, FYR 9 15 6.6 6 98 19 192 50 39 385 5 3.7 Madagascar 5 7 22.7 8 134 16 268 63 38 871 5 .. Malawi 10 37 188.7 6 88 21 213 25 42 432 4 2.6 Malaysia 9 24 18.1 5 144 25 285 10 30 600 10 2.3 Mali 11 26 132.1 5 29 14 208 38 39 860 6 3.6 Mauritania 11 65 56.2 4 49 25 201 45 46 400 5 8.0 Mauritius 6 7 5.3 6 210 18 107 23 37 750 6 1.7 Mexico 8 27 13.3 5 74 11 131 48 38 415 8 1.8 Moldova 9 23 11.5 6 48 30 292 38 31 365 7 2.8 Mongolia 8 20 4.3 5 11 21 126 34 32 314 5 4.0 Morocco 6 12 11.5 8 47 19 163 63 40 615 6 1.8 Mozambique 10 29 21.6 8 42 17 361 54 31 1,010 5 5.0 Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 10 99 22.3 9 23 12 139 20 33 270 5 1.5 Nepal 7 31 73.9 3 5 15 424 52 39 735 6 5.0 Netherlands 6 10 6.0 2 5 18 230 42 25 514 4 1.1 New Zealand 2 12 0.1 2 2 7 65 7 30 216 10 1.3 Nicaragua 6 39 119.1 8 124 17 219 27 35 540 4 2.2 Niger 11 23 174.8 5 32 16 293 70 39 545 6 5.0 Nigeria 9 34 56.6 14 82 18 350 7 39 457 5 2.0 Norway 6 10 2.3 1 3 14 252 47 33 310 7 0.9 Oman 9 34 4.3 2 16 16 242 24 51 598 8 4.0 Pakistan 11 24 14.0 6 50 12 223 43 47 880 6 2.8 Panama 7 19 22.0 7 44 25 149 69 31 686 1 2.5 Papua New Guinea 8 56 26.4 4 72 24 217 10 43 591 5 3.0 Paraguay 7 35 77.6 6 46 13 291 59 38 591 6 3.9 Peru 10 72 29.9 5 33 21 210 55 41 468 8 3.1 Philippines 15 58 26.8 8 33 21 177 35 37 842 1 5.7 Poland 10 31 21.2 6 197 30 308 37 38 830 7 3.0 Portugal 7 7 3.4 5 42 20 327 48 35 577 6 2.0 Puerto Rico 7 7 0.8 8 194 22 209 21 41 620 7 3.8 2008 World Development Indicators 277 5.3 Business environment: Doing Business indicators Starting a Registering Dealing with Employing Enforcing Protecting Closing a business property licenses workers contracts investors business 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 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 Romania 6 14 4.7 8 150 17 243 66 32 537 9 3.3 Russian Federation 8 29 3.7 6 52 54 704 44 37 281 6 3.8 Rwanda 9 16 171.5 5 371 16 227 42 24 310 2 .. Saudi Arabia 7 15 32.3 4 4 18 125 13 44 635 7 2.8 Senegal 10 58 107.0 6 114 14 217 61 44 780 6 3.0 Serbia 11 23 8.9 6 111 20 204 46 36 635 7 2.7 Sierra Leone 9 26 1,075.2 8 235 47 235 51 40 515 3 2.6 Singapore 5 5 0.8 3 9 11 102 0 22 120 10 0.8 Slovak Republic 9 25 4.2 3 17 13 287 36 30 565 3 4.0 Slovenia 9 60 8.5 6 391 15 208 63 32 1,350 3 2.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 8 31 7.1 6 24 17 174 42 30 600 8 2.0 Spain 10 47 15.1 4 18 11 233 56 39 515 5 1.0 Sri Lanka 5 39 8.5 8 83 22 214 27 40 1,318 4 1.7 Sudan 10 39 57.9 6 9 19 271 36 53 810 0 .. Swaziland 13 61 38.7 11 46 13 93 17 40 972 0 2.0 Sweden 3 15 0.6 1 2 8 116 39 30 508 6 2.0 Switzerland 6 20 2.1 4 16 14 154 17 32 417 0 3.0 Syrian Arab Republic 13 43 55.7 4 34 21 128 37 55 872 6 4.1 Tajikistan 13 49 39.6 6 37 32 191 51 34 295 0 3.0 Tanzania 12 29 47.1 10 119 21 308 63 38 462 3 3.0 Thailand 8 33 5.6 2 2 11 156 18 35 479 10 2.7 Timor-Leste 9 82 11.9 .. .. 22 208 34 51 1,800 3 .. Togo 13 53 245.7 5 295 15 277 54 41 588 6 3.0 Trinidad and Tobago 9 43 0.9 8 162 20 261 7 42 1,340 4 .. Tunisia 10 11 8.3 5 49 20 93 49 39 565 0 1.3 Turkey 6 6 20.7 6 6 25 188 42 36 420 8 3.3 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 18 28 92.0 13 227 16 143 3 38 535 2 2.2 Ukraine 10 27 7.8 10 93 29 429 45 30 354 1 2.9 United Arab Emirates 11 62 36.9 3 6 21 125 20 50 607 4 5.1 United Kingdom 6 13 0.8 2 21 19 144 7 30 404 10 1.0 United States 6 6 0.7 4 12 19 40 0 32 300 7 1.5 Uruguay 11 44 46.0 8 66 30 234 31 40 720 3 2.1 Uzbekistan 7 15 14.2 12 78 26 260 34 42 195 4 4.0 Venezuela, RB 16 141 28.2 8 47 11 395 79 29 510 3 4.0 Vietnam 11 50 20.0 4 67 13 194 27 34 295 6 5.0 West Bank and Gaza 12 92 280.4 10 72 21 199 31 44 700 6 .. Yemen, Rep. 12 63 178.8 6 21 13 107 33 37 520 6 3.0 Zambia 6 33 30.5 6 70 17 254 34 35 471 3 2.7 Zimbabwe 10 96 21.3 4 30 19 952 33 38 410 8 3.3 World 9u 44 u 61.1 u 6u 81 u 18 u 223 u 34 u 38 u 605 u 5u 3.0 u Low income 10 54 134.9 7 114 19 288 40 40 645 5 3.8 Middle income 9 48 43.1 6 76 19 216 34 38 621 5 3.1 Lower middle income 10 53 56.5 6 86 18 218 33 39 635 4 3.3 Upper middle income 9 41 24.0 6 61 21 212 35 38 602 5 2.9 Low & middle income 10 50 75.9 7 89 19 241 36 39 630 5 3.3 East Asia & Pacific 9 47 40.8 5 112 19 179 21 37 591 5 3.1 Europe & Central Asia 9 26 11.6 6 81 24 261 39 36 392 5 3.2 Latin America & Carib. 10 73 47.8 7 67 17 242 34 39 692 4 3.2 Middle East & N. Africa 10 39 80.5 7 47 19 218 38 43 707 6 3.5 South Asia 8 33 40.7 6 134 16 247 27 44 1,047 4 5.0 Sub-Saharan Africa 11 56 148.1 7 105 18 262 43 39 643 5 3.4 High income 7 22 6.6 5 51 17 158 28 34 516 6 2.0 Euro area 7 22 7.4 6 70 14 190 45 31 591 6 1.4 278 2008 World Development Indicators 5.3 STATES AND MARKETS Business environment: Doing Business indicators About the data Definitions These indicators on the environment for doing busi- is reflected in two indicators: the number of judicial · Number of procedures for starting a business is the ness identify regulations that enhance or constrain procedures to resolve a commercial dispute and the number of procedures required to start a business, business investment, productivity, and growth. The time to enforce a commercial contract. including interactions to obtain necessary permits and data are from the World Bank's Doing Business data- What companies disclose to the public has a large licenses and to complete all inscriptions, verifications, base, which includes data on 178 economies. The impact on investor protection. Both investors and and notifications to start operations. Data are for busi- indicators in the table point to the administrative and entrepreneurs benefi t greatly from such legal pro- nesses with specific characteristics of ownership, size, regulatory reforms and institutions needed to create tection. The disclosure index is based on several and type of production. · Time required for starting a a favorable environment for doing business. measures that cover disclosure of ownership and business is the number of calendar days to complete When entrepreneurs start a business, the first interests in related party transactions to reduce the procedures for legally operating a business. If a obstacles they face are the administrative and legal expropriation of minority investors. procedure can be expedited at additional cost, the fast- procedures required to register the new firm. Coun- Unviable businesses prevent assets and human est procedure, independent of cost, is chosen. · Cost tries differ widely in how they regulate the entry of capital from being allocated to more productive uses for starting a business is normalized as a percentage new businesses. In some countries the process is in new companies or in viable companies that are of gross national income (GNI) per capita. · Number of straightforward and affordable. In others the proce- financially distressed. The time to close a business procedures for registering property is the number of dures are so burdensome that entrepreneurs may opt (resolve insolvency) captures the average time to procedures required for a business to legally transfer to run their business informally. The data on starting complete a procedure, as estimated by insolvency property. · Time required for registering property is a business cover the number of start-up procedures, lawyers. Delays due to legal derailment tactics that the number of calendar days for a business to legally the time required, and the cost to complete them. parties to the insolvency may use, in particular exten- transfer property. · Number of procedures for deal- Property registries were developed to raise tax rev- sion of response periods or appeals, are taken into ing with licenses to build a warehouse is the number enue, but they have benefited entrepreneurs as well. account. of interactions of a company's employees or manag- Securing rights to legally transfer land and buildings, To ensure cross-country comparability, several ers with external parties, including government staff, a major source of wealth in most countries, strength- standard characteristics of a company are defined public inspectors, notaries, land registry and cadastre ens incentives to invest and facilitates trade. More in all surveys, such as size, ownership, location, staff, and technical experts apart from architects and complex procedures to register property are associ- legal status, and type of activities undertaken. For engineers. · Time required for dealing with licenses ated with less perceived security of property rights, example, for the starting a business data, standard to build a warehouse is the number of calendar days to more informality, and more corruption. The data characteristics include that the business is a lim- complete the required procedures for building a ware- cover the number of procedures required and time ited liability company; operates in the country's most house. If a procedure can be expedited at additional required to legally transfer property. populous city; is 100 percent domestically owned cost, the fastest procedure, independent of cost, is Construction is a large sector in most economies, and has fi ve owners, none of them a legal entity; chosen. · Rigidity of employment index, a measure and the table includes data on the number of proce- has start-up capital of 10 times income per capita; of employment regulation, is the average of three sub- dures and time required for a business in the con- has paid-in cash; performs general industrial or indexes: a difficulty of hiring index, a rigidity of hours struction industry to complete the legal procedures commercial activities, such as production or sale of index, and a difficulty of firing index. Higher values indi- to build a standardized warehouse. These include products or services to the public; does not perform cate more rigid regulations. · Number of procedures obtaining all necessary licenses and permits, com- foreign trade activities or handle products subject to for enforcing contracts is the number of independent pleting all required notifi cations and inspections, a special tax regime; does not use heavily polluting actions, mandated by law or court regulation, that and submitting the relevant documents to the production processes; leases the commercial plant demand interaction between the parties to a contract authorities. and offices and is not a proprietor of real estate; or between them and the judge or court officer. · Time Every economy has a complex system of laws and does not qualify for investment incentives or any required for enforcing contracts is the number of cal- institutions to protect the interests of workers and special benefits; has up to 50 employees within one endar days from the time of the filing of a lawsuit in guarantee a minimum standard of living for its popu- month of commencement of operations, all of them court to the final determination and payment. · Disclo- lation. The rigidity of employment index focuses on nationals; has turnover at least 100 times income sure index measures the degree to which investors are the regulation of employment. The index is the aver- per capita; and has a company deed at least 10 protected through disclosure of ownership and financial age of three subindexes: a difficulty of hiring index, a pages long. The data were collected through a study information. Higher values indicate more disclosure. rigidity of working hours index, and a difficulty of firing of laws and regulations in each country, surveys of · Time to resolve insolvency is the number of years index. All subindexes have several components and regulators or private sector professionals on each from time of filing for insolvency in court until resolution take values between 0 and 100, with higher values topic, and cooperative arrangements with private of distressed assets and payment of creditors. indicating more rigid regulation. consulting firms and business and law associations. Data sources Contract enforcement is critical to enable busi- Note that some of these assumptions do not apply nesses to engage with new borrowers or custom- to all Doing Business indicators. Data on the business environment are from ers. The institution that enforces contracts between For more information on the methodology, see the World Bank's Doing Business project (www. debtors and creditors, and suppliers and customers, www.doingbusiness.org/. doingbusiness.org). is the court. The efficiency of contract enforcement 2008 World Development Indicators 279 5.4 Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2007 2000 2006 2000 2006 2000 2007 2000 2007 2006 2007 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. .. .. Algeria .. .. .. .. .. .. .. .. .. .. .. .. Angola .. .. .. .. .. .. .. .. .. .. .. .. Argentina 166,068 86,684 58.4 37.2 2.1 2.1 4.8 9.8 127 107 57.6 0.7a Armenia 2 60 0.1 0.9 0.0 0.1 4.6 9.4 105 35 .. .. Australia 372,794 1,095,858 93.3 140.4 56.6 105.9 56.5 87.0 1,330 1,751 .. .. Austria 29,935 191,300 15.4 59.4 4.8 24.7 29.8 50.4 97 96 .. .. Azerbaijan 4 .. 0.1 .. .. .. .. .. 2 .. .. .. Bangladesh 1,186 6,793 2.5 5.8 1.6 1.5 74.4 95.5 221 278 12.9 126.4b Belarus .. .. .. .. .. .. .. .. .. .. .. .. Belgium 182,481 396,220 78.7 100.6 16.4 42.1 20.7 48.5 174 153 .. .. Benin .. .. .. .. .. .. .. .. .. .. .. .. Bolivia 1,742 2,223 20.7 19.9 0.8 0.0 0.1 0.0 26 35 .. .. Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. .. .. Botswana 978 5,887 15.8 37.2 0.8 0.7 4.8 2.1 16 18 53.0 37.2b Brazil 226,152 1,370,377 35.1 66.6 15.7 23.8 43.5 56.2 459 442 43.1 74.7a Bulgaria 617 21,793 4.9 32.8 0.5 4.8 9.2 34.1 503 369 31.4 39.0b Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cambodia .. .. .. .. .. .. .. .. .. .. .. .. Cameroon .. .. .. .. .. .. .. .. .. .. .. .. Canada 841,385 1,700,708 116.1 133.7 87.6 101.5 77.3 81.1 1,418 3,790 .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 60,401 212,910 79.7 119.7 8.0 19.7 9.4 21.8 258 238 28.6 22.6a China 580,991 6,226,305 48.5 91.7 60.2 61.8 158.3 197.5 1,086 1,530 80.7 66.6a Hong Kong, China 623,398 1,714,953 368.6 903.6 223.4 437.7 61.3 60.0 779 1,165 .. .. Colombia 9,560 101,956 11.4 36.6 0.5 7.4 3.8 15.4 126 96 12.7 12.7b Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Costa Rica 2,924 1,944 18.3 8.7 0.7 0.2 12.0 3.1 21 16 .. .. Côte d'Ivoire 1,185 8,353 11.4 23.7 0.3 0.6 2.6 2.3 41 38 35.6 115.6b Croatia 2,742 65,977 14.9 67.6 1.0 4.2 7.4 7.2 64 353 85.2 68.1b Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic 11,002 73,420 19.4 34.0 11.6 23.0 60.3 73.4 131 32 30.9 49.7a Denmark 107,666 231,015 67.3 83.9 57.2 64.2 86.0 86.4 225 201 .. .. Dominican Republic 141 .. 0.8 .. .. .. .. .. 6 .. .. .. Ecuador 704 4,266 4.4 9.8 0.1 0.7 5.5 7.0 30 35 32.0 3.8 b Egypt, Arab Rep. 28,741 139,289 28.8 87.0 11.1 44.2 34.7 48.3 1,076 435 10.2 52.2a El Salvador 2,041 5,465 15.5 29.3 0.2 0.9 1.3 3.7 40 48 .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 1,846 6,037 32.8 36.3 5.8 5.9 18.9 31.6 23 18 30.3 ­15.5b Ethiopia .. .. .. .. .. .. .. .. .. .. .. .. Finland 293,635 265,477 241.0 126.0 169.6 169.4 64.3 150.2 154 134 .. .. France 1,446,634 2,428,572 108.9 108.0 81.6 111.4 74.1 119.6 808 717 .. .. Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. Georgia 24 668 0.8 8.6 0.1 1.2 .. 18.6 269 231 .. .. Germany 1,270,243 1,637,826 66.8 56.5 56.3 85.8 79.1 173.9 1,022 656 .. .. Ghana 502 2,380 10.1 25.0 0.2 0.4 1.5 5.1 22 32 9.7 21.6b Greece 110,839 208,284 76.9 67.5 66.0 34.8 63.7 60.8 329 318 .. .. Guatemala 240 .. 1.2 .. 0.0 .. 0.0 .. 44 .. .. .. Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Haiti .. .. .. .. .. .. .. .. .. .. .. .. 280 2008 World Development Indicators 5.4 STATES AND MARKETS Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2007 2000 2006 2000 2006 2000 2007 2000 2007 2006 2007 Honduras 458 .. 8.8 .. .. .. .. .. 46 .. .. .. Hungary 12,021 47,651 25.1 37.1 25.3 27.6 90.7 102.6 60 41 31.4 13.1a India 148,064 1,819,101 32.2 89.8 110.8 70.0 133.6 95.9 5,937 4,887 46.7 78.6a Indonesia 26,834 211,693 16.3 38.1 8.7 13.4 32.9 66.7 290 383 67.9 49.3a Iran, Islamic Rep. 7,350 37,943 7.3 17.4 1.1 2.2 12.7 12.7 304 332 .. .. Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 81,882 163,358 85.0 74.2 15.0 36.3 19.2 57.6 76 57 .. .. Israel 64,081 236,361 53.0 123.4 19.3 63.2 36.3 54.8 654 654 ­6.3 34.3a Italy 768,364 1,026,640 70.0 55.5 70.9 73.8 104.0 149.7 291 284 .. .. Jamaica 3,582 12,335 44.6 122.5 0.9 3.0 2.5 3.1 46 41 ­1.5 0.3b Japan 3,157,222 4,726,269 67.6 108.2 57.7 143.1 69.9 132.1 2,561 3,362 5.9 ­5.2b Jordan 4,943 41,216 58.4 210.8 4.9 142.2 7.7 52.2 163 245 ­36.0 32.6b Kazakhstan 1,342 43,688 7.3 53.9 0.5 4.9 25.1 14.7 23 67 .. ..c Kenya 1,283 13,387 10.1 49.9 0.4 5.7 3.6 11.6 57 51 60.3 11.8b Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 171,587 1,123,633 33.5 94.1 208.7 150.9 233.2 191.6 1,308 1,767 13.3 27.7a Kuwait 20,772 188,046 55.1 161.0 11.2 116.4 21.3 74.0 77 181 ­4.6 39.9b Kyrgyz Republic 4 93 0.3 3.3 1.7 3.5 .. 148.2 80 8 .. .. Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 563 3,111 7.2 13.4 2.9 0.6 48.6 4.7 64 41 1.5 1.9 b Lebanon 1,583 10,858 9.4 36.4 0.7 9.0 6.7 10.9 12 11 ­9.2 40.5b Lesotho .. .. .. .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 1,588 10,134 13.9 34.2 1.8 7.0 14.8 9.2 54 40 9.7 14.3b Macedonia, FYR 7 1,098 0.2 17.7 3.3 3.1 6.6 22.4 1 43 .. .. Madagascar .. .. .. .. .. .. .. .. .. .. .. .. Malawi .. 587 .. 18.6 .. 0.5 13.8 3.5 .. 10 .. .. Malaysia 116,935 325,663 129.5 156.2 64.8 44.4 44.6 51.6 795 1,036 34.6 44.6a Mali .. .. .. .. .. .. .. .. .. .. .. .. Mauritania 1,090 .. 97.2 .. .. .. .. .. 40 .. .. .. Mauritius 1,331 5,666 29.8 56.7 1.7 2.2 5.0 8.7 40 41 44.3 94.0 b Mexico 125,204 397,725 21.5 41.5 7.8 9.5 32.3 29.5 179 125 41.1 12.8a Moldova 392 .. 30.4 22.1 1.9 0.8 5.8 5.9 36 .. .. .. Mongolia 37 113 3.4 3.6 0.7 0.3 7.3 13.5 410 386 .. .. Morocco 10,899 75,495 29.4 75.5 3.0 20.6 9.2 39.6 53 74 78.5 45.3a Mozambique .. .. .. .. .. .. .. .. .. .. .. .. Myanmar .. .. .. .. .. .. .. .. .. .. .. .. Namibia 311 702 9.1 8.3 0.6 0.3 4.5 3.5 13 9 12.8 39.4b Nepal 790 1,805 14.4 20.2 0.6 0.8 6.9 4.4 110 135 .. .. Netherlands 640,456 779,645 166.3 117.7 175.9 165.5 101.4 159.7 234 226 .. .. New Zealand 18,866 44,940 35.8 43.0 20.5 18.9 45.9 44.7 142 154 .. .. Nicaragua .. .. .. .. .. .. .. .. .. .. .. .. Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 4,237 86,347 9.2 28.5 0.6 3.1 7.3 28.7 195 212 34.0 108.3b Norway 65,034 281,081 38.6 83.9 35.7 104.8 93.4 148.7 191 195 .. .. Oman 3,463 23,060 17.4 49.5 2.8 10.4 14.2 30.9 131 125 7.9 67.0 b Pakistan 6,581 70,262 8.9 35.9 44.6 99.8 475.5 167.3 762 654 1.3 41.7b Panama 2,794 5,716 24.0 33.4 1.3 0.8 1.7 2.7 29 22 .. .. Papua New Guinea 1,520 6,632 49.6 117.3 0.0 0.4 .. 0.5 7 9 .. .. Paraguay 224 409 3.5 4.4 0.1 0.0 3.5 0.5 56 55 .. .. Peru 10,562 105,960 19.8 64.6 2.9 4.6 12.6 7.8 230 190 82.5 66.4 a Philippines 25,957 103,224 34.2 58.2 10.8 9.6 15.8 33.1 228 242 50.3 36.0a Poland 31,279 207,322 18.3 44.0 8.5 16.2 49.9 44.1 225 328 38.1 23.2a Portugal 60,681 104,201 53.9 53.5 48.3 36.1 85.5 82.1 109 47 .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 281 5.4 Stock markets Market Market Turnover Listed domestic S&P/EMDB capitalization liquidity ratio companies indexes Value of Value of shares traded shares traded % of market $ millions % of GDP % of GDP capitalization number % change 2000 2007 2000 2006 2000 2006 2000 2007 2000 2007 2006 2007 Romania 1,069 44,925 2.9 27.0 0.6 3.5 23.1 19.2 5,555 2,096 54.2 32.8 b Russian Federation 38,922 1,503,011 15.0 107.1 7.8 52.1 36.9 63.9 249 328 62.0 21.9a Rwanda .. .. .. .. .. .. .. .. .. .. .. .. Saudi Arabia 67,171 515,111 35.6 93.6 9.2 401.9 27.1 199.2 75 111 ­48.9 35.6b Senegal .. .. .. .. .. .. .. .. .. .. .. .. Serbia 734 10,985 4.6 34.3 0.1 4.2 0.0 16.3 6 1,111 .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Singapore 152,827 276,329 164.8 209.1 98.7 139.5 52.1 62.2 418 461 .. .. Slovak Republic 1,217 6,971 6.0 10.1 4.4 0.2 129.8 0.5 493 153 24.0 57.4b Slovenia 2,547 28,963 13.2 40.7 2.4 2.7 20.7 10.7 38 87 74.3 95.0 b Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 204,952 833,548 154.2 280.2 58.3 122.5 33.9 52.5 616 422 17.2 15.5a Spain 504,219 1,323,090 86.8 108.0 169.8 157.6 210.7 169.1 1,019 3,339 .. .. Sri Lanka 1,074 7,553 6.6 28.8 0.9 3.7 11.0 12.3 239 235 45.3 ­10.6b Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 73 200 5.3 7.5 0.0 0.0 9.8 0.0 6 6 .. .. Sweden 328,339 573,250 135.7 149.4 161.2 176.4 111.2 138.6 292 321 .. .. Switzerland 792,316 1,212,508 322.0 318.7 247.6 338.3 82.0 119.6 252 256 .. .. Syrian Arab Republic .. .. .. .. .. .. .. .. .. .. .. .. Tajikistan .. .. .. .. .. .. .. .. .. .. .. .. Tanzania 233 541 2.6 4.2 0.4 0.1 2.4 2.1 4 6 .. .. Thailand 29,489 196,046 24.0 68.4 19.0 48.9 53.2 62.0 381 475 6.2 39.4a Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. .. .. .. Trinidad and Tobago 4,330 15,605 53.1 85.9 1.7 2.3 3.1 2.4 27 37 ­6.5 ­2.8b Tunisia 2,828 5,355 14.5 14.7 3.2 1.7 23.3 13.0 44 50 47.9 15.6b Turkey 69,659 286,572 34.9 40.3 89.7 56.5 206.2 134.2 315 319 ­4.0 74.8a Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda 35 116 0.6 1.2 0.0 0.1 .. 5.2 2 5 .. .. Ukraine 1,881 111,757 6.0 40.3 0.9 1.1 19.6 2.7 139 276 48.6 112.2b United Arab Emirates 5,727 224,675 8.1 173.9 0.2 110.4 3.9 85.0 54 90 ­44.6 52.1b United Kingdom 2,576,992 3,794,310 178.7 159.6 127.2 178.5 66.6 123.8 1,904 2,913 26.2 5.6d United States 15,104,037 19,425,855 154.7 147.6 326.3 252.7 200.8 182.8 7,524 5,133 13.6 3.5e Uruguay 161 125 0.8 0.6 0.0 0.0 0.5 1.6 16 10 .. .. Uzbekistan 32 715 0.2 4.2 0.1 0.1 .. 5.9 5 114 .. .. Venezuela, RB 8,128 8,251 6.9 4.5 0.6 0.4 8.9 1.3 85 53 79.0 .. Vietnam .. 19,542 .. 14.9 .. 1.8 .. 85.6 .. 121 .. 10.7b West Bank and Gaza 765 2,729 18.6 67.2 4.6 26.3 10.0 29.7 24 33 .. .. Yemen, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Zambia 236 1,186 7.3 11.0 0.2 0.2 20.8 2.1 9 14 .. .. Zimbabwe 2,432 5,333 32.9 70.3 3.8 9.7 10.8 11.0 69 82 912.3 ­83.8b World 32,187,756 s 54,194,991 s 102.7 w 113.9 w 152.8 w 143.4 w 122.1 w 94.3 w 47,877 s 50,212 s Low income 166,802 967,029 23.9 67.0 78.1 55.0 151.9 93.3 7,922 6,911 Middle income 1,833,330 7,056,701 37.2 74.2 26.8 36.8 71.5 94.5 15,335 13,195 Lower middle income 751,235 3,186,679 35.8 74.5 37.5 44.5 107.8 146.4 4,940 5,205 Upper middle income 1,082,095 3,870,022 38.3 74.0 18.7 30.6 46.0 50.8 10,395 7,990 Low & middle income 2,000,132 8,023,730 35.6 73.3 33.2 39.2 81.4 94.3 23,257 20,106 East Asia & Pacific 780,487 3,026,517 47.2 85.1 49.9 52.4 125.2 163.5 3,190 4,080 Europe & Central Asia 163,360 1,603,092 19.0 66.7 26.3 35.1 83.9 64.1 8,141 6,070 Latin America & Carib. 620,263 1,470,534 31.8 51.7 8.4 13.5 27.4 34.8 1,762 1,509 Middle East & N. Africa 60,573 242,122 19.7 48.9 5.0 19.4 12.6 28.3 1,807 1,443 South Asia 157,695 877,581 26.1 77.2 90.2 67.5 167.9 101.3 7,269 6,089 Sub-Saharan Africa 217,754 803,885 89.9 159.9 32.3 65.4 22.2 30.1 1,088 915 High income 30,187,624 46,171,261 117.3 126.1 178.8 174.6 130.7 150.2 24,620 30,106 Euro area 5,432,330 8,639,721 87.0 81.2 80.4 96.4 90.4 139.0 4,535 6,318 a. Data refer to the S&P/IFC investable index. b. Data refer to the S&P/IFC Global index. c. Data refer to the Nikkei 225 index. d. Data refer to the FT 100 index. e. Data refer to the S&P 500 index. 282 2008 World Development Indicators 5.4 STATES AND MARKETS Stock markets About the data Definitions The development of an economy's financial markets companies is another measure of market size. Mar- · Market capitalization (also known as market is closely related to its overall development. Well ket size is positively correlated with the ability to value) is the share price times the number of shares functioning financial systems provide good and eas- mobilize capital and diversify risk. outstanding. · Market liquidity is the total value ily accessible information. That lowers transaction Market liquidity, the ability to easily buy and sell of shares traded during the period divided by gross costs, which in turn improves resource allocation and securities, is measured by dividing the total value domestic product (GDP). This indicator complements boosts economic growth. Both banking systems and of shares traded by GDP. The turnover ratio--the the market capitalization ratio by showing whether stock markets enhance growth, the main factor in value of shares traded as a percentage of market market size is matched by trading. · Turnover ratio poverty reduction. At low levels of economic develop- capitalization--is also a measure of liquidity as well is the total value of shares traded during the period ment commercial banks tend to dominate the finan- as of transaction costs. (High turnover indicates low divided by the average market capitalization for the cial system, while at higher levels domestic stock transaction costs.) The turnover ratio complements period. Average market capitalization is calculated as markets tend to become more active and efficient the ratio of value traded to GDP, because the turn- the average of the end-of-period values for the cur- relative to domestic banks. over ratio is related to the size of the market and rent period and the previous period. · Listed domes- Open economies with sound macroeconomic poli- the value traded ratio to the size of the economy. A tic companies are the domestically incorporated cies, good legal systems, and shareholder protection small, liquid market will have a high turnover ratio companies listed on the country's stock exchanges attract capital and therefore have larger financial mar- but a low value of shares traded ratio. Liquidity is at the end of the year. This indicator does not include kets. Recent research on stock market development an important attribute of stock markets because, investment companies, mutual funds, or other col- shows that modern communications technology and in theory, liquid markets improve the allocation of lective investment vehicles. · S&P/EMDB indexes increased financial integration have resulted in more capital and enhance prospects for long-term eco- measure the U.S. dollar price change in the stock cross-border capital flows, a stronger presence of nomic growth. A more comprehensive measure of markets covered by the S&P/IFCI country index and financial firms around the world, and the migration of liquidity would include trading costs and the time S&P/IFCG indexes. stock exchange activities to international exchanges. and uncertainty in finding a counterpart in settling Many firms in emerging markets now cross-list on trades. international exchanges, which provides them with The S&P/EMDB, the source for all the data in lower cost capital and more liquidity-traded shares. the table, provides regular updates on 58 emerg- However, this also means that exchanges in emerg- ing stock markets encompassing more than 3,800 ing markets may not have enough financial activity stocks. Standard & Poor's maintains a series of to sustain them, putting pressure on them to rethink indexes for investors interested in investing in stock their operations. markets in developing countries. At the core of the The stock market indicators in the table include S&P/EMDB indexes, the Global (S&P/IFCG) index measures of size (market capitalization, number of is intended to represent the most active stocks in listed domestic companies) and liquidity (value of the markets it covers and to be the broadest pos- shares traded as a percentage of gross domestic sible indicator of market movements. The Investable product, value of shares traded as a percentage of (S&P/IFCI) index, which applies the same calculation market capitalization). The comparability of such indi- methodology as the S&P/IFCG index, is designed to cators between countries may be limited by concep- measure the returns that foreign portfolio investors tual and statistical weaknesses, such as inaccurate might receive from investing in emerging market reporting and differences in accounting standards. stocks that are legally and practically open to foreign The percentage change in stock market prices in portfolio investment. These indexes are widely used U.S. dollars, from the Standard & Poor's Emerg- benchmarks for international portfolio management. ing Markets Data Base (S&P/EMDB) indexes, is an See Standard & Poor's (2000) for further information important measure of overall performance. Regula- on the indexes. Data sources tory and institutional factors that can affect investor Because markets included in Standard & Poor's confidence, such as entry and exit restrictions, the emerging markets category vary widely in level of Data on stock markets are from Standard & Poor's existence of a securities and exchange commission, development, it is best to look at the entire category Global Stock Markets Factbook 2007, which draws and the quality of laws to protect investors, may influ- to identify the most significant market trends. And on the Emerging Markets Data Base, supple- ence the functioning of stock markets but are not it is useful to remember that stock market trends mented by other data from Standard & Poor's. included in the table. may be distorted by currency conversions, espe- The firm collects data through an annual survey Stock market size can be measured in various cially when a currency has registered a significant of the world's stock exchanges, supplemented by ways, and each may produce a different ranking of devaluation. information provided by its network of correspon- countries. Market capitalization shows the overall About the data is based on Demirgüç-Kunt and dents and by Reuters. Data on GDP are from the size of the stock market in U.S. dollars and as a Levine (1996), Beck and Levine (2001), and Claes- World Bank's national accounts data files. percentage of GDP. The number of listed domestic sens, Klingebiel, and Schmukler (2002). 2008 World Development Indicators 283 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 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 2007 June 2007 June 2007 June 2007 2006 2006 2006 2006 2006 Afghanistan 0 0 0.0 0.0 .. .. .. .. .. Albania 9 0 0.0 0.0 6.2 3.1 54.5 7.7 7.5 Algeria 3 2 0.2 0.0 .. .. 4.0 6.3 5.9 Angola 3 4 2.3 0.0 11.3 13.3 ­3.6 15.0 .. Argentina 3 6 25.5 100.0 13.6 3.4 30.8 2.2 .. Armenia 5 5 2.8 13.5 22.9 2.5 8.1 10.7 11.7 Australia 9 5 0.0 100.0 4.9 0.2 115.0 5.5 .. Austria 5 6 1.3 40.6 5.2 2.6 128.4 .. .. Azerbaijan 7 4 1.4 0.0 14.2 7.2 13.6 7.3 7.8 Bangladesh 7 2 0.7 0.0 4.0 13.2 58.1 6.2 .. Belarus 2 3 0.0 0.0 17.8 1.2 27.2 1.2 .. Belgium 5 4 57.2 0.0 3.7 1.8 110.2 .. 4.8 Benin 4 1 7.8 0.0 .. .. 10.2 .. .. Bolivia 1 5 12.1 22.6 10.0 8.7 39.4 7.9 7.3 Bosnia and Herzegovina 7 5 0.0 63.7 13.8 4.0 47.8 4.3 .. Botswana 7 4 0.0 58.3 9.7 2.8 ­14.3 7.6 .. Brazil 2 5 17.1 46.4 9.9 4.1 81.7 36.9 36.4 Bulgaria 6 6 25.4 3.0 10.4 2.2 43.0 5.7 6.3 Burkina Faso 4 1 2.1 0.0 .. .. 14.4 .. .. Burundi 1 1 0.2 0.0 .. .. 42.1 .. .. Cambodia 0 0 0.0 0.0 .. .. 6.0 14.6 .. Cameroon 3 2 1.0 0.0 .. .. 8.2 11.0 .. Canada 7 6 0.0 100.0 5.7 0.4 220.8 4.0 1.8 Central African Republic 3 2 1.4 0.0 .. .. 17.5 11.0 .. Chad 3 1 0.2 0.0 .. .. 4.7 11.0 .. Chile 4 5 26.2 33.5 6.8 0.8 83.5 2.9 .. China 3 4 49.2 0.0 6.1 7.5 136.9 3.6 .. Hong Kong, China 1 5 0.0 64.7 11.8 1.1 134.6 5.1 4.5 Colombia 2 5 0.0 39.9 10.8 2.6 48.0 6.6 .. Congo, Dem. Rep. 3 0 0.0 0.0 .. .. 4.6 .. .. Congo, Rep. 3 2 2.4 0.0 .. .. ­9.3 11.0 .. Costa Rica 4 5 6.1 52.7 10.2 1.5 44.7 12.4 .. Côte d'Ivoire 3 1 2.8 0.0 .. .. 17.8 .. .. Croatia 6 3 0.0 72.4 10.3 5.2 80.6 8.2 .. Cuba .. .. .. .. .. .. .. .. .. Czech Republic 6 5 4.2 53.0 6.2 4.1 48.4 4.4 3.1 Denmark 8 4 0.0 11.5 6.2 0.4 189.3 .. .. Dominican Republic 4 6 13.3 35.4 10.0 4.5 47.1 9.6 .. Ecuador 1 5 37.9 44.1 13.7 3.3 17.5 5.4 .. Egypt, Arab Rep. 1 4 1.6 0.0 5.5 24.7 99.3 6.6 3.1 El Salvador 3 6 17.2 74.6 11.8 1.9 45.7 .. .. Eritrea 3 0 0.0 0.0 .. .. 139.0 .. .. Estonia 4 5 0.0 19.7 8.4 0.2 81.6 2.2 .. Ethiopia 4 2 0.1 0.0 .. .. 53.7 3.4 6.9 Finland 6 5 0.0 14.9 9.2 0.3 81.3 2.7 .. France 6 4 24.8 0.0 5.8 3.2 115.5 4.3 .. Gabon 3 2 2.4 0.0 .. 11.1 8.3 11.0 .. Gambia, The 4 0 0.0 0.0 .. .. 27.0 17.1 .. Georgia 5 4 0.0 0.2 18.8 2.5 23.9 7.3 12.1 Germany 8 6 0.7 98.1 4.7 4.0 132.0 .. .. Ghana 5 0 0.0 0.0 12.4 7.9 32.4 .. .. Greece 3 4 0.0 38.7 5.2 5.5 90.5 .. .. Guatemala 3 5 20.7 13.1 8.2 4.6 32.8 8.3 .. Guinea 4 0 0.0 0.0 .. .. 15.7 .. .. Guinea-Bissau 3 1 0.9 0.0 .. .. 10.5 .. .. Haiti 3 2 0.7 0.0 .. .. 27.8 37.1 32.7 284 2008 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 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 2007 June 2007 June 2007 June 2007 2006 2006 2006 2006 2006 Honduras 6 6 12.7 58.0 8.4 6.6 40.6 8.1 .. Hungary 6 5 0.0 6.9 8.7 2.5 68.1 0.6 1.2 India 6 4 0.0 10.8 6.6 3.5 63.4 .. .. Indonesia 5 3 20.5 0.2 10.7 13.1 41.7 4.6 .. Iran, Islamic Rep. 5 3 22.2 0.0 .. .. 49.2 4.2 .. Iraq 4 0 0.0 0.0 .. .. .. .. .. Ireland 8 5 0.0 100.0 4.3 0.7 182.0 2.6 .. Israel 8 5 0.0 91.6 5.9 1.9 76.6 3.2 2.1 Italy 3 5 11.0 71.5 7.1 5.3 112.9 .. 2.4 Jamaica 5 0 0.0 0.0 8.7 2.6 63.9 10.6 4.9 Japan 6 6 0.0 68.3 5.3 2.5 307.7 1.0 1.2 Jordan 5 2 0.8 0.0 10.7 4.3 116.5 3.6 .. Kazakhstan 5 4 0.0 13.7 8.9 4.8 32.5 .. .. Kenya 8 4 0.0 1.5 .. 5.2 40.3 8.5 6.9 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. Korea, Rep. 5 5 0.0 74.2 9.2 0.8 107.1 1.5 .. Kuwait 4 4 0.0 14.5 12.0 3.9 71.7 3.7 .. Kyrgyz Republic 5 3 0.0 1.6 .. .. 11.7 17.6 18.4 Lao PDR 2 0 0.0 0.0 .. .. 7.3 25.0 11.7 Latvia 8 4 2.6 0.0 7.6 0.4 89.0 3.8 3.2 Lebanon 4 5 4.7 0.0 8.4 13.5 196.2 2.3 5.0 Lesotho 5 0 0.0 0.0 .. 1.0 ­5.7 7.6 5.3 Liberia 4 0 0.0 0.0 .. .. 177.8 13.6 .. Libya .. .. .. .. .. .. ­53.8 3.8 0.6 Lithuania 4 6 6.6 7.3 7.1 1.0 49.5 4.5 2.2 Macedonia, FYR 6 3 4.0 0.0 .. 11.2 23.7 5.5 .. Madagascar 1 0 0.1 0.0 6.2 10.1 9.7 7.2 8.3 Malawi 7 0 0.0 0.0 .. .. 13.9 21.3 13.0 Malaysia 8 6 44.5 .. 7.6 8.5 119.4 3.3 3.3 Mali 3 1 2.5 0.0 .. .. 13.6 .. .. Mauritania 4 1 0.2 0.0 .. .. .. 15.1 11.2 Mauritius 5 1 38.6 0.0 .. .. 111.1 11.5 .. Mexico 3 6 0.0 61.2 13.2 2.1 39.3 4.2 0.3 Moldova 6 0 0.0 0.0 17.0 4.3 35.0 6.2 10.8 Mongolia 5 3 9.5 0.0 .. .. 20.9 8.4 .. Morocco 3 1 2.3 0.0 7.4 10.9 78.5 7.9 .. Mozambique 3 3 0.9 0.0 6.4 3.7 10.0 8.2 3.5 Myanmar .. .. .. .. .. .. 28.1 5.5 .. Namibia 5 5 0.0 59.9 8.3 2.9 63.9 4.9 3.9 Nepal 4 2 0.0 0.2 .. .. 50.4 5.9 6.0 Netherlands 7 5 0.0 78.1 4.0 1.0 186.7 0.6 .. New Zealand 9 5 0.0 100.0 .. .. 142.3 5.3 5.2 Nicaragua 3 5 14.8 100.0 8.8 8.0 74.4 6.7 .. Niger 3 1 1.0 0.0 .. .. 7.9 .. .. Nigeria 7 0 0.0 0.0 14.7 21.9 .. 7.2 6.9 Norway 6 4 0.0 100.0 5.0 0.6 .. 2.2 .. Oman 4 2 12.4 0.0 13.2 7.8 34.9 3.4 .. Pakistan 4 4 4.6 1.4 8.8 7.7 43.0 6.8 2.4 Panama 6 6 0.0 41.6 11.3 1.5 90.8 4.6 .. Papua New Guinea 5 0 0.0 0.0 .. .. 23.2 9.6 6.6 Paraguay 3 6 11.0 48.7 12.5 3.3 18.0 23.4 .. Peru 4 6 20.7 33.0 9.5 1.6 15.0 20.7 .. Philippines 3 3 0.0 5.5 11.7 18.6 48.6 4.5 4.5 Poland 4 4 0.0 51.5 7.9 9.4 42.4 4.0 1.3 Portugal 4 4 67.1 11.3 6.4 1.3 160.9 .. .. Puerto Rico 6 5 0.0 62.0 .. .. .. .. .. 2008 World Development Indicators 285 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 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 2007 June 2007 June 2007 June 2007 2006 2006 2006 2006 2006 Romania 7 5 4.1 10.9 8.9 8.4 26.8 .. .. Russian Federation 3 4 0.0 4.4 12.5 2.6 21.4 6.4 .. Rwanda 1 2 0.2 0.0 9.2 27.2 9.7 8.1 6.2 Saudi Arabia 3 6 0.0 23.5 9.3 2.0 37.3 .. .. Senegal 3 1 4.0 0.0 8.1 16.0 23.3 .. .. Serbia 7 5 0.1 51.3 15.6 21.4 23.6 11.5 6.3 Sierra Leone 5 0 0.0 0.0 19.0 20.9 10.6 13.6 6.3 Singapore 9 4 0.0 42.7 9.6 2.8 72.6 4.7 2.4 Slovak Republic 9 4 1.2 56.0 8.0 3.7 50.4 4.1 .. Slovenia 6 2 2.5 0.0 7.4 4.9 76.3 4.6 4.1 Somalia .. .. .. .. .. .. .. .. .. South Africa 5 6 0.0 52.1 7.8 1.2 197.4 4.0 3.8 Spain 6 6 44.9 8.3 7.2 0.6 177.7 .. .. Sri Lanka 3 3 0.0 2.9 6.7 9.6 44.1 ­3.2 ­2.0 Sudan 4 0 0.0 0.0 .. .. 0.2 .. .. Swaziland 5 5 0.0 37.6 .. 2.0 15.7 6.2 3.6 Sweden 6 4 0.0 100.0 4.9 0.5 125.8 2.5 1.6 Switzerland 6 5 0.0 24.0 4.9 0.3 187.4 1.6 1.7 Syrian Arab Republic 3 0 0.0 0.0 .. .. 33.5 7.0 .. Tajikistan 4 0 0.0 0.0 .. .. 15.4 15.3 .. Tanzania 5 0 0.0 0.0 .. .. 11.2 8.8 3.8 Thailand 5 5 0.0 27.9 9.2 7.5 101.3 2.9 .. Timor-Leste 2 0 0.0 0.0 .. .. .. .. .. Togo 3 1 2.7 0.0 .. .. 17.4 .. .. Trinidad and Tobago 5 4 0.0 34.4 .. .. 21.0 6.1 4.8 Tunisia 2 4 13.7 0.0 7.7 19.2 72.4 .. .. Turkey 3 5 10.3 2.7 11.3 3.2 60.2 .. .. Turkmenistan .. .. .. .. .. .. .. .. .. Uganda 3 0 0.0 0.0 9.7 2.8 9.4 9.6 10.6 Ukraine 8 0 0.0 0.0 12.1 17.8 46.2 7.6 .. United Arab Emirates 3 2 1.4 0.0 12.6 6.3 59.5 .. .. United Kingdom 10 6 0.0 84.6 8.9 0.9 176.9 .. 0.0 United States 7 6 0.0 100.0 10.5 0.8 230.8 .. 3.2 Uruguay 5 6 14.1 93.8 9.8 1.9 32.2 7.4 4.7 Uzbekistan 2 0 0.0 0.0 .. .. .. .. .. Venezuela, RB 4 0 0.0 0.0 9.8 1.1 18.8 5.2 .. Vietnam 6 3 9.2 0.0 .. .. 75.0 3.5 6.4 West Bank and Gaza 5 3 1.8 0.0 .. .. 9.2 4.8 .. Yemen, Rep. 3 0 0.1 0.0 .. .. 4.8 5.0 2.4 Zambia 6 0 0.0 0.0 .. 10.8 16.6 12.8 12.8 Zimbabwe 6 0 0.0 0.0 12.1 23.2 93.1 293.1 174.1 World 4.6 u 2.7 u 4.6 u 19.6 u 8.9 m 3.0 m 187.9 w 6.6 m Low income 3.8 0.9 1.0 0.3 .. .. 53.2 11.3 Middle income 4.4 2.9 6.2 17.5 10.0 3.4 76.7 6.6 Lower middle income 4.0 2.7 5.9 13.9 10.7 4.0 99.1 7.2 Upper middle income 5.0 3.3 6.6 22.8 9.8 3.2 63.4 5.9 Low & middle income 4.2 2.2 4.3 11.3 9.4 5.3 76.7 7.3 East Asia & Pacific 3.9 1.6 6.6 3.9 .. .. 119.6 6.5 Europe & Central Asia 5.6 3.3 2.4 14.4 10.3 3.2 37.6 6.8 Latin America & Carib. 4.0 3.4 8.9 32.1 10.1 2.6 54.9 7.4 Middle East & N. Africa 3.5 2.1 4.6 0.0 .. .. 49.8 4.3 South Asia 3.9 1.9 0.7 1.9 6.6 7.7 60.6 6.7 Sub-Saharan Africa 4.0 1.3 2.1 4.6 .. .. 94.4 9.6 High income 5.8 4.4 5.7 49.9 6.2 1.1 194.3 4.4 Euro area 5.6 4.3 16.1 35.5 5.2 1.6 132.2 .. 286 2008 World Development Indicators 5.5 STATES AND MARKETS Financial access, stability, and efficiency About the data Definitions Financial sector development has positive impacts and total regulatory capital, which includes several · Legal rights index measures the degree to which on economic growth and poverty. The size of the sec- specified types of subordinated debt instruments collateral and bankruptcy laws protect the rights tor determines the amount of resources mobilized for that need not be repaid if the funds are required of borrowers and lenders and thus facilitate lend- investment. Access to finance can expand opportuni- to maintain minimum capital levels (these comprise ing. Higher values indicate that the laws are better ties for all--not just the rich and well connected-- tier 2 and tier 3 capital). Total assets include all designed to expand access to credit. · Credit infor- with higher levels of access and use of banking nonfinancial and financial assets. Data are from inter- mation index measures rules affecting the scope, services associated with lower financing obstacles nally consistent financial statements, to enhance the accessibility, and quality of information available for people and businesses. A stable financial sys- quality and analytical usefulness of the indicator. through public or private credit registries. Higher tem that promotes efficient savings and investment The ratio of bank nonperforming loans to total values indicate the availability of more credit informa- is also crucial for a thriving democracy and market gross loans is a measure of bank health and effi - tion. · Public credit registry coverage is the number economy. The banking system is the largest sector ciency. It helps to identify problems with asset quality of individuals and firms listed in a public credit reg- in the financial system in most countries, so most in the loan portfolio. A high ratio may signal deteriora- istry with current information on repayment history, indicators in the table cover the banking system. tion in the quality of the credit portfolio. International unpaid debts, or credit outstanding as a percentage There are several aspects of access to financial ser- guidelines recommend that loans be classified as of the adult population. · Private credit bureau cov- vices: availability, cost, and quality of services. The nonperforming when payments of principal and inter- erage is the number of individuals or firms listed by development and growth of credit markets depend est are past due by 90 days or more or when future a private credit bureau with current information on on access to timely, reliable, and accurate data on payments are not expected to be received in full. See repayment history, unpaid debts, or credit outstand- borrowers' credit experiences. For secured transac- the International Monetary Fund's (IMF) Global Finan- ing as a percentage of the adult population. · Bank tions, such as mortgages or vehicle loans, having cial Stability Report for detailed information. capital to asset ratio is the ratio of bank capital rapid access to information in property registries is Domestic credit provided by the banking sector as and reserves to total assets. Capital and reserves also vital, and for small business loans corporate a share of GDP is a measure of banking sector depth include funds contributed by owners, retained earn- registry data are needed. An effective way to improve and financial sector development in terms of size. ings, general and special reserves, provisions, and access to credit is to increase information about In a few countries governments may hold interna- valuation adjustments. · Ratio of bank nonperform- potential borrowers' creditworthiness and make it tional reserves as deposits in the banking system ing loans to total gross loans is the value of non- easy to create and enforce collateral agreements. rather than in the central bank. Since the claims on performing loans divided by the total value of the Lenders look at a borrower's credit history and col- the central government are a net item (claims on loan portfolio (including nonperforming loans before lateral when extending loans. Where credit registries the central government minus central government the deduction of loan loss provisions). The amount and effective collateral laws are absent--as in many deposits), this net figure may be negative, resulting recorded as nonperforming should be the gross value developing countries--banks make fewer loans. Indi- in a negative figure of domestic credit provided by of the loan as recorded on the balance sheet, not just cators that cover financial access, or getting credit, the banking sector. the amount overdue. · Domestic credit provided by include the legal rights index (ranges from 0, weaker, The interest rate spread--the margin between banking sector is all credit to various sectors on a to 10, stronger), credit information index (ranges the cost of mobilizing liabilities and the earnings gross basis, except to the central government, which from 0, less, to 6, more), public registry coverage, on assets--is a measure of the efficiency by which is net. The banking sector includes monetary authori- and private bureau coverage. The legal rights index the financial sector intermediates funds. A narrow ties, deposit money banks, and other banking insti- is based on seven aspects related to legal rights in interest rate spread means low transaction costs, tutions for which data are available. · Interest rate collateral law and three aspects in bankruptcy law. which lowers the overall cost of funds for investment, spread is the interest rate charged by banks on loans The depth of credit information index assesses six crucial to economic growth. to prime customers minus the interest rate paid by features of the public registry or the private credit The risk premium on lending is the spread between commercial or similar banks for demand, time, or bureau. For more information on these indexes, see the lending rate to the private sector and the "risk- savings deposits. · Risk premium on lending is the www.doingbusiness.org/MethodologySurveys/. free" government rate. A small spread indicates that interest rate charged by banks on loans to prime The size and mobility of international capital flows the market considers its best corporate customers private sector customers minus the "risk-free" trea- have made it increasingly important to monitor the to be low risk. Interest rate spreads are expressed sury bill interest rate at which short-term government strength of financial systems. Robust financial sys- as annual averages. In some countries this spread securities are issued or traded in the market. tems can increase economic activity and welfare, may be negative, indicating that the market consid- Data sources but instability in the financial system can disrupt ers its best corporate clients to be lower risk than financial activity and impose huge and widespread the government. Data on getting credit are from the World Bank's costs on the economy. The ratio of bank capital to Doing Business project (www.doingbusiness.org). assets, a measure of bank solvency and resiliency, Data on bank capital and nonperforming loans are provides a measure of the extent to which banks can from the IMF's Global Financial Stability Report. deal with unexpected losses. Capital includes tier 1 Data on credit and interest rates are from the capital (paid-up shares and common stock), which is IMF's International Financial Statistics. a common feature in all countries' banking systems, 2008 World Development Indicators 287 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 2006 June 2007 June 2007 June 2007 2006 2006 2006 Afghanistanb .. 5.8 6 275 35.5 .. .. .. Albaniab 16.1 17.3 44 240 46.8 20 2,003 20 Algeriab 36.9 32.1 33 451 72.6 .. .. .. Angola .. .. 31 272 53.2 .. .. .. Argentina 9.8 14.2 19 615 112.9 35 41,379 35 Armeniab .. 14.4 50 1,120 36.6 .. .. .. Australia 22.1 23.7 12 107 50.6 47 72,519 30 Austria 19.6 20.0 22 170 54.6 50 63,750 25 Azerbaijanb 12.7 .. 38 952 40.9 35 12,632 22 Bangladeshb 7.6 8.1 17 400 39.5 .. .. .. Belarusb 16.6 22.2 124 1,188 144.4 .. .. .. Belgium 27.4 26.1 11 156 64.3 50 39,625 34 Beninb 15.5 15.8 55 270 73.3 35 .. 38 Bolivia 13.2 17.3 41 1,080 78.1 .. .. 25 Bosnia and Herzegovina .. 22.4 51 368 44.1 15 .. 30 Botswanab .. .. 19 140 17.2 25 19,569 15 Brazilb 11.3 .. 11 2,600 69.2 28 11,486 15 Bulgariab 18.3 23.7 17 616 36.7 24 4,586 15 Burkina Fasob .. 11.2 45 270 48.9 .. .. .. Burundib 13.6 .. 32 140 278.7 .. .. .. Cambodia 8.2 8.2 27 137 22.6 20 36,652 20 Cameroonb 11.2 .. 41 1,400 51.9 .. .. .. Canadab 15.0 14.1 9 119 45.9 29 97,756 22 Central African Republicb .. 6.0 54 504 203.8 .. .. .. Chad .. .. 54 122 63.7 .. .. .. Chile 16.7 20.7 10 316 25.9 40 6,127 17 Chinab 6.8 8.7 35 872 73.9 45 8,637 .. Hong Kong, China .. .. 4 80 24.4 20 11,568 18 Colombia 13.3 14.1 69 268 82.4 22 43,154 39 Congo, Dem. Rep.b 3.5 .. 32 308 229.8 50 4,920 40 Congo, Rep. 9.2 .. 89 606 65.4 .. .. .. Costa Ricab .. 14.1 43 402 55.7 25 19,414 30 Côte d'Ivoireb 14.6 14.9 66 270 45.4 10 4,550 35 Croatiab 26.2 23.3 28 196 32.5 45 3,765 20 Cuba .. .. .. .. .. .. .. .. Czech Republicb 15.4 14.6 12 930 48.6 32 13,823 24 Denmark 31.0 31.2 9 135 33.3 59 53,117 28 Dominican Republicb .. 16.8 74 286 40.2 30 29,596 30 Ecuador b .. .. 8 600 35.3 25 61,440 25 Egypt, Arab Rep.b 14.6 15.8 36 711 47.9 20 6,920 .. El Salvador 10.7 13.4 66 224 33.8 .. .. .. Eritrea .. .. 18 216 84.5 .. .. .. Estonia 15.9 16.6 10 81 49.2 23 1,908 23 Ethiopiab 10.7 .. 20 198 31.1 35c .. 30 c Finland 24.6 21.9 20 269 47.8 28 72,750 26 France 23.2 22.7 23 132 66.3 48 60,673 33 Gabon .. .. 28 272 44.2 .. .. .. Gambia, Theb .. .. 50 376 286.7 .. .. .. Georgiab 7.7 15.5 29 387 38.6 12 .. 20 Germany 11.9 11.4 16 196 50.8 42 65,190 25 Ghanab 17.2 22.4 32 304 32.9 25 10,581 25 Greece 20.2 17.2 21 264 48.6 40 28,750 29 Guatemalab 10.1 10.2 39 344 37.5 31 38,663 31 Guineab 11.1 .. 56 416 49.9 .. .. .. Guinea-Bissau .. .. 46 208 45.9 .. .. .. Haiti .. .. 53 160 40.0 .. .. .. 288 2008 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 2006 June 2007 June 2007 June 2007 2006 2006 2006 Honduras .. 17.9 47 424 51.4 25 26,553 25 Hungary b 21.9 20.1 24 340 55.1 36 7,766 16 Indiab 9.0 10.7 60 271 70.6 30 5,669 34 Indonesiab 11.6 12.3 51 266 37.3 35 20,608 30 Iran, Islamic Rep.b 6.3 7.6 22 292 47.4 35 114,101 25 Iraq .. .. 13 312 24.7 .. .. .. Ireland 26.1 26.7 9 76 28.9 42 40,000 13 Israel 29.6 28.6 33 230 36.0 49 94,530 31 Italy 23.2 22.9 15 360 76.2 43 125,000 33 Jamaicab 24.7 29.2 72 414 51.3 25 1,993 33 Japanb .. .. 13 350 52.0 37 163,310 30 Jordanb 19.0 26.2 26 101 31.1 .. .. .. Kazakhstanb 10.2 16.3 9 271 36.7 20 55,810 30 Kenyab 16.8 18.3 41 432 50.9 30 5,841 30 Korea, Dem. Rep.b .. .. .. .. .. .. .. .. Korea, Rep.b 16.1 15.7 48 290 34.9 35 78,116 25 Kuwait 1.3 1.0 14 118 14.4 0 .. 0 Kyrgyz Republicb .. 14.3 75 202 61.4 .. .. .. Lao PDR .. .. 34 672 35.5 .. .. .. Latvia 14.2 15.7 7 219 32.6 25 .. 15 Lebanon 12.2 16.3 19 180 35.4 .. .. .. Lesotho 32.7 44.3 22 342 20.8 .. .. .. Liberia .. .. 37 158 81.6 .. .. .. Libya .. .. .. .. .. .. .. .. Lithuania 14.6 18.1 24 166 48.3 33 .. 15 Macedonia, FYRb .. .. 52 96 49.8 24 14,610 15 Madagascar 11.3 10.7 26 238 46.5 .. .. .. Malawi .. .. 30 370 32.2 .. .. .. Malaysiab 14.3 .. 35 166 36.0 28 65,963 28 Mali 13.2 15.7 58 270 51.4 .. .. .. Mauritania .. .. 38 696 107.5 .. .. .. Mauritiusb 18.2 18.2 7 161 21.7 30 16,949 25 Mexicob 11.7 .. 27 552 51.2 29 9,470 29 Moldovab 14.7 19.9 49 218 44.0 20 1,667 15 Mongolia .. .. 42 204 38.4 .. .. .. Morocco 19.9 22.5 28 358 53.1 .. .. .. Mozambique .. .. 37 230 34.3 32 43,710 32 Myanmar b 3.0 4.7 .. .. .. .. .. .. Namibiab 30.0 .. 37 .. 26.5 35 31,447 35 Nepalb 8.7 8.9 33 408 32.5 .. .. .. Netherlands 22.3 23.7 9 180 43.4 52 65,285 30 New Zealand 29.5 34.2 8 70 35.1 39 42,254 33 Nicaraguab 13.8 17.5 64 240 63.2 30 29,886 30 Niger .. .. 42 270 42.4 .. .. .. Nigeria .. .. 35 1,120 29.9 .. .. .. Norway 27.4 29.2 4 87 42.0 .. .. 28 Omanb 7.2 .. 14 62 21.6 0 .. 12 Pakistanb 10.1 9.5 47 560 40.7 35 11,763 37 Panamab 10.2 .. 59 482 50.8 30 200,000 30 Papua New Guineab 19.4 .. 33 206 41.7 .. .. .. Paraguay b .. 12.1 35 328 35.3 10 .. 0 Perub 12.2 13.5 9 424 41.5 30 49,899 30 Philippinesb 13.7 14.3 47 195 52.8 32 9,076 35 Poland 16.0 17.5 41 418 38.4 40 22,854 19 Portugal 21.5 22.0 8 328 44.8 42 75,000 25 Puerto Rico .. .. 16 140 44.3 33 50,000 20 2008 World Development Indicators 289 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 2006 June 2007 June 2007 June 2007 2006 2006 2006 Romania 11.7 12.2 96 202 46.9 16 4,617 16 Russian Federation 13.7 16.7 22 448 51.4 13 .. 24 Rwanda .. .. 34 168 33.8 .. .. .. Saudi Arabia .. .. 14 79 14.5 0 .. 0 Senegalb 16.1 .. 59 696 46.0 0 .. .. Serbiab .. .. 66 279 35.8 .. .. .. Sierra Leoneb 10.2 11.0 22 399 233.5 .. .. .. Singaporeb 15.4 12.7 5 49 23.2 21 192,771 20 Slovak Republic .. 14.0 31 344 50.5 19 14,087 19 Sloveniab 21.2 21.9 22 260 39.2 50 .. 25 Somalia .. .. .. .. .. .. .. .. South Africa 24.0 29.0 11 350 37.1 40 47,170 29 Spain 16.2 12.9 8 298 62.0 29 58,524 35 Sri Lankab 14.5 15.3 62 256 63.7 35 4,975 35 Sudanb 6.4 .. 42 180 31.6 .. .. .. Swazilandb .. .. 33 104 36.6 33 11,792 30 Sweden 19.7 21.3 2 122 54.5 25 61,673 28 Switzerlandb 11.3 10.5 24 63 29.1 .. .. 9 Syrian Arab Republicb 17.4 .. 21 336 46.7 .. .. .. Tajikistan 7.7 9.8 54 224 82.2 .. .. .. Tanzania .. .. 48 172 44.3 30 5,740 30 Thailand .. 16.9 35 264 37.7 37 99,453 30 Timor-Leste .. .. 15 640 28.3 .. .. .. Togob .. 13.9 53 270 48.2 .. .. .. Trinidad and Tobagob 22.1 27.9 40 114 33.1 25 7,937 25 Tunisiab 21.3 21.0 46 268 61.0 .. .. .. Turkey b .. 25.9 15 223 45.1 35 100,298 30 Turkmenistan .. .. .. .. .. .. .. .. Ugandab 10.9 13.0 33 237 32.3 30 2,763 30 Ukraineb 14.1 18.0 99 2,085 57.3 13 .. 25 United Arab Emiratesb 1.7 .. 14 12 14.4 0 .. .. United Kingdom 29.0 28.8 8 105 35.7 40 60,545 30 United States 12.7 11.9 10 325 46.2 35 326,450 35 Uruguay b 16.7 19.3 53 304 40.7 0 .. 30 Uzbekistan .. .. 118 196 96.3 29 960 12 Venezuela, RBb 13.3 15.6 70 864 53.3 34 93,767 34 Vietnamb .. .. 32 1,050 41.1 40 5,044 28 West Bank and Gaza .. .. 27 154 17.1 .. .. .. Yemen, Rep.b 9.4 .. 32 248 41.4 .. .. .. Zambiab 18.6 17.2 37 132 16.1 30 368 35 Zimbabweb .. .. 52 256 53.0 45 26,249 30 World 15.7 w 16.8 w 34 u 323 u 50.7 u Low income 9.5 10.7 41 327 67.4 Middle income .. .. 37 377 45.3 Lower middle income 9.4 11.4 40 401 45.8 Upper middle income .. .. 32 344 44.5 Low & middle income .. .. 38 359 53.2 East Asia & Pacific 7.7 9.5 31 295 39.9 Europe & Central Asia 16.1 18.9 50 455 51.4 Latin America & Carib. 11.4 .. 40 435 48.7 Middle East & N. Africa 15.7 17.3 27 276 41.4 South Asia 9.3 10.6 31 306 41.4 Sub-Saharan Africa .. .. 39 321 68.0 High income 16.5 16.7 17 188 41.5 Euro area 19.1 18.5 16 211 50.9 a. Data are from PriceWaterhouseCoopers's World Wide Tax Summaries Online. 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. 290 2008 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 governments. The sources of tax revenue and their ees, and turnover. For details about the assump- refers to compulsory transfers to the central gov- relative contributions are determined by government tions, see Doing Business 2008. ernment for public purposes. Certain compulsory policy choices about where and how to impose taxes A potentially important influence on both domestic transfers such as fines, penalties, and most social and by changes in the structure of the economy. Tax and international investors is a tax system's progres- security contributions are excluded. Refunds and policy may refl ect concerns about distributional sivity, as reflected in the highest marginal tax rate corrections of erroneously collected tax revenue are effects, economic efficiency (including corrections levied at the national level on individual and corpo- treated as negative revenue. The analytic framework for externalities), and the practical problems of rate income. Data for individual marginal tax rates of the International Monetary Fund's (IMF) Govern- administering a tax system. There is no ideal level generally refer to employment income. In some coun- ment Finance Statistics Manual 2001 (GFSM 2001) of taxation. But taxes influence incentives and thus tries the highest marginal tax rate is also the basic is based on accrual accounting and balance sheets. the behavior of economic actors and the economy's or flat rate, and other surtaxes, deductions, and the For countries still reporting government finance data competitiveness. like may apply. And in many countries several differ- on a cash basis, the IMF adjusts reported data to the The level of taxation is typically measured by tax ent corporate tax rates may be levied, depending on GFSM 2001 accrual framework. These countries are revenue as a share of gross domestic product (GDP). the type of business (mining, banking, insurance, footnoted in the table. · Number of tax payments Comparing levels of taxation across countries pro- agriculture, manufacturing), ownership (domestic or by businesses is the total number of taxes paid by vides a quick overview of the fiscal obligations and foreign), volume of sales, and whether surtaxes or businesses during one year. When electronic filing is incentives facing the private sector. The table shows exemptions are included. The corporate tax rates in available, the tax is counted as paid once a year even only central government data, which may significantly the table are mainly general rates applied to domes- if payments are more frequent. · Time to prepare, understate the total tax burden, particularly in coun- tic companies. For more detailed information, see file, and pay taxes is the time, in hours per year, it tries where provincial and municipal governments are the country's laws, regulations, and tax treaties and takes to prepare, file, and pay (or withhold) three large or have considerable tax authority. PricewaterhouseCoopers's Worldwide Tax Summaries major types of taxes: the corporate income tax, the Low ratios of tax revenue to GDP may reflect weak Online (www.pwc.com). value-added or sales tax, and labor taxes, includ- administration and large-scale tax avoidance or eva- ing payroll taxes and social security contributions. sion. Low ratios may also reflect a sizable parallel · Total tax rate is the total amount of taxes payable economy with unrecorded and undisclosed incomes. by businesses (except for consumption taxes) after Tax revenue ratios tend to rise with income, with accounting for deductions and exemptions as a per- higher income countries relying on taxes to finance centage of profit. For further details on the method a much broader range of social services and social used for assessing the total tax payable, see Doing security than lower income countries are able to. Business 2008. · Highest marginal tax rate is the The indicators covering taxes payable by busi- highest rate shown on the national schedule of tax nesses measure all taxes and contributions that rates applied to the annual taxable income of indi- are government mandated (at any level--federal, viduals and corporations. Also presented are the state, or local), apply to standardized businesses, income levels for individuals above which the high- and have an impact in their income statements. The est marginal tax rates levied at the national level taxes covered go beyond the definition of a tax for apply. government national accounts (compulsory, unre- quited payments to general government) and also measure any imposts that affect business accounts. The main differences are in labor contributions and value-added taxes. The indicators account for government-mandated contributions paid by the employer to a requited private pension fund or work- Data sources ers insurance fund but exclude value-added taxes because they do not affect the accounting profits of Data on central government tax revenue are from the business--that is, they are not reflected in the print and electronic editions of the IMF's Govern- income statement. ment Finance Statistics Yearbook. Data on taxes To make the data comparable across countries, payable by businesses are from Doing Business several assumptions are made about businesses. 2008 (www.doingbusiness.org). Data on individ- The main assumptions are that they are limited liabil- ual and corporate tax rates are from Pricewater- ity companies, they operate in the country's most houseCoopers's Worldwide Tax Summaries Online populous city, they are domestically owned, they per- (www.pwc.com). form general industrial or commercial activities, and 2008 World Development Indicators 291 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. 9.9 .. .. 383 51 6.2 0.6 0 .. .. 28 Albania 2.1 1.6 8.2 6.3 87 12 6.0 0.8 .. .. 24 42 Algeria 3.0 2.7 12.2 15.3 163 334 1.8 2.4 .. .. 365 173 Angola 8.1 5.4 .. .. 122 110 2.3 1.5 .. .. 1 22 Argentina 1.6 0.9 .. 5.9 99 107 0.7 0.6 3 .. 75 53 Armenia 4.1 2.8 .. 17.3 61 47 4.2 3.7 .. .. 49 151 Australia 1.9 1.8 .. 7.5 57 51 0.6 0.5 28 4 149 777 Austria 1.1 0.8 2.4 2.0 56 40 1.4 1.0 11 61 24 21 Azerbaijan 2.7 3.3 13.8 .. 127 82 3.8 1.9 .. .. 25 45 Bangladesh 1.4 1.1 .. 13.6 171 214 0.3 0.3 .. .. 121 208 Belarus 1.6 1.7 5.7 5.5 106 183 2.1 3.8 8 24 .. 254 Belgium 1.6 1.1 3.4 2.7 47 40 1.1 0.9 299 50 16 4 Benin .. .. .. .. 7 8 0.3 0.2 .. .. .. .. Bolivia 1.8 1.5 .. 5.9 64 83 2.2 1.9 .. .. 1 25 Bosnia and Herzegovina .. 1.6 .. 4.8 92 9 5.3 0.4 .. .. 52 .. Botswana 3.5 3.0 11.5 .. 9 11 1.5 1.6 .. .. 7 9 Brazil 1.9 1.5 4.8 .. 681 754 0.9 0.8 28 1 259 323 Bulgaria 2.6 2.3 6.6 7.1 136 75 3.5 2.5 2 .. 1 20 Burkina Faso 1.5 1.4 .. 11.5 10 11 0.2 0.2 .. .. .. 19 Burundi 4.2 5.5 17.8 .. 15 82 0.5 2.1 .. .. .. .. Cambodia 5.4 1.7 .. 19.4 309 191 6.2 2.8 0 .. 33 .. Cameroon 1.3 1.4 11.8 .. 24 23 0.4 0.3 .. .. 4 5 Canada 1.6 1.2 6.4 6.5 76 64 0.5 0.4 326 227 356 109 Central African Republic 1.2 1.1 .. 12.3 5 3 0.3 0.2 .. .. .. 9 Chad 1.7 0.9 .. .. 35 35 1.2 0.9 .. .. 1 2 Chile 3.1 3.6 .. 21.2 130 103 2.3 1.6 30 .. 459 1,125 China 1.7a 1.9a ..a 18.2a 4,130 3,605 0.6 0.5 1,017 564 641 3,261 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 2.6 3.5 .. 12.1 233 398 1.4 1.7 .. .. 37 33 Congo, Dem. Rep. 1.5 0.0 13.5 .. 65 65 0.4 0.3 .. .. 0 13 Congo, Rep. .. 1.1 .. .. 17 12 1.4 0.8 .. .. 27 4 Costa Rica .. .. .. .. 16 10 1.2 0.5 .. .. 3 .. Côte d'Ivoire 0.8 .. .. .. 15 19 0.3 0.3 .. .. 2 14 Croatia 9.4 1.6 22.2 4.1 150 21 7.2 1.1 .. .. 22 8 Cuba .. .. .. .. 124 76 2.5 1.4 .. .. .. .. Czech Republic 1.9 1.7 5.9 4.9 92 26 1.8 0.5 122 56 0 65 Denmark 1.7 1.4 .. 4.4 33 30 1.2 1.1 8 3 130 133 Dominican Republic 0.6 0.5 .. 3.1 40 65 1.3 1.6 .. .. 4 27 Ecuador 2.4 2.3 6.0 .. 57 57 1.3 0.9 .. .. 11 33 Egypt, Arab Rep. 3.9 2.7 16.3 9.9 610 866 3.5 3.7 7 .. 1,698 538 El Salvador 1.0 0.6 .. 3.0 39 28 1.8 1.0 0 .. 3 .. Eritrea 20.8 .. .. .. 55 202 4.4 10.6 .. .. 3 70 Estonia 1.0 1.4 .. 5.4 6 7 0.8 1.1 8 .. 17 8 Ethiopia 1.6 2.6 .. .. 120 183 0.5 0.5 .. .. 70 162 Finland 1.5 1.4 .. 3.8 35 32 1.4 1.2 20 31 159 84 France 3.0 2.4 6.2 5.3 502 354 2.0 1.3 795 1,557 43 121 Gabon .. 1.2 .. .. 10 7 2.1 1.2 .. .. .. 63 Gambia, The 0.8 0.5 .. .. 1 1 0.2 0.1 .. .. .. 7 Georgia 2.2 3.1 8.2 15.2 14 33 0.5 1.5 .. 7 8 70 Germany 1.6 1.3 4.2 4.3 365 246 0.9 0.6 1,430 1,855 252 216 Ghana 0.8 0.7 .. 3.8 13 7 0.2 0.1 .. .. 7 27 Greece 3.3 3.2 9.0 9.1 202 161 4.5 3.1 18 23 870 1,452 Guatemala 1.0 0.4 13.1 3.6 57 35 1.8 0.8 .. .. 3 .. Guinea 1.4 2.0 .. .. 19 13 0.6 0.3 .. .. .. .. Guinea-Bissau 0.9 4.0 .. .. 9 9 1.9 1.4 .. .. 4 .. Haiti .. .. .. .. 7 5 0.2 0.1 .. .. .. .. 292 2008 World Development Indicators 5.7 STATES AND MARKETS Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras .. 0.6 .. 2.8 24 20 1.3 0.7 .. .. .. .. Hungary 1.6 1.2 3.0 2.7 73 44 1.7 1.0 6 68 24 337 India 2.7 2.7 18.4 17.7 2,150 2,589 0.6 0.6 2 11 968 1,710 Indonesia 1.6 1.2 16.2 8.3 461 582 0.5 0.5 25 8 319 54 Iran, Islamic Rep. 2.4 4.8 15.2 19.4 763 585 4.4 2.0 1 9 355 891 Iraq .. .. .. .. 407 495 7.0 6.0 .. .. .. 195 Ireland 1.0 0.5 2.7 1.7 13 10 0.9 0.5 .. .. 43 11 Israel 8.6 8.4 .. 19.0 178 185 8.5 6.7 113 287 308 994 Italy 1.7 1.7 3.6 4.1 585 440 2.6 1.8 365 878 332 697 Jamaica 0.6 0.6 1.7 1.4 4 3 0.3 0.3 .. .. .. 25 Japan 1.0 0.9 .. .. 252 252 0.4 0.4 158 .. 1,254 392 Jordan 5.8 4.9 22.3 14.1 129 111 10.3 5.9 77 13 19 117 Kazakhstan 1.1 0.9 5.7 6.2 75 81 1.0 1.0 25 5 99 53 Kenya 1.6 1.6 6.4 9.3 29 29 0.2 0.2 .. .. 12 25 Korea, Dem. Rep. .. .. .. .. 1,243 1,295 12.0 11.4 52 13 82 5 Korea, Rep. 2.8 2.7 19.4 12.1 641 692 3.0 2.8 21 89 1,788 1,292 Kuwait 13.6 4.8 29.3 17.1 22 23 2.5 1.6 .. .. 608 107 Kyrgyz Republic 3.5 3.1 .. 17.5 7 21 0.4 0.9 61 .. .. 1 Lao PDR 2.9 .. .. .. 137 129 7.9 5.5 .. .. 14 4 Latvia 0.9 1.6 3.1 5.7 11 17 0.9 1.5 8 .. 16 4 Lebanon 6.4 4.1 .. 16.8 63 76 5.0 4.7 .. .. 34 1 Lesotho 3.7 2.4 10.7 5.9 2 2 0.3 0.3 .. .. .. 1 Liberia 31.2 .. .. .. 21 2 2.6 0.2 .. .. .. .. Libya 4.1 1.5 .. .. 81 76 5.1 3.0 8 24 .. 5 Lithuania 0.4 1.2 .. 4.2 9 24 0.5 1.5 .. .. 4 33 Macedonia, FYR 3.0 2.0 .. .. 18 19 2.2 2.2 0 29 0 .. Madagascar 0.9 1.0 .. 8.5 29 22 0.5 0.3 .. .. 19 .. Malawi 0.8 .. .. .. 10 7 0.2 0.1 .. .. 2 .. Malaysia 2.8 2.0 16.0 .. 140 134 1.7 1.2 0 .. 876 654 Mali 2.2 2.2 .. 14.3 15 12 0.4 0.2 .. .. 7 13 Mauritania 2.0 2.5 .. .. 21 21 2.4 1.7 .. .. 2 .. Mauritius 0.4 0.2 1.8 0.9 2 2 0.4 0.4 .. .. 30 .. Mexico 0.6 0.4 3.8 .. 189 280 0.5 0.6 .. .. 42 68 Moldova 0.9 0.3 2.4 0.9 15 8 0.8 0.4 36 4 6 .. Mongolia 1.7 1.3 .. .. 31 16 3.3 1.3 .. .. .. .. Morocco 5.9 3.7 .. 14.2 238 246 2.7 2.2 .. .. 30 49 Mozambique 1.5 0.9 .. .. 12 11 0.2 0.1 .. .. .. .. Myanmar 3.7 .. .. .. 371 513 1.7 1.9 .. .. 245 7 Namibia 2.0 2.9 .. .. 8 15 1.5 2.2 .. .. 4 13 Nepal 0.9 1.9 .. 12.8 63 131 0.8 1.2 .. .. 1 4 Netherlands 1.9 1.5 3.8 3.6 78 46 1.0 0.5 421 1,481 47 171 New Zealand 1.4 1.0 .. 3.0 10 9 0.6 0.4 3 1 7 8 Nicaragua 1.1 0.7 7.8 3.4 12 14 0.8 0.7 5 .. .. .. Niger 1.0 1.1 .. .. 11 10 0.3 0.2 .. .. 3 .. Nigeria 0.7 0.7 .. .. 89 162 0.2 0.3 .. .. 2 72 Norway 2.4 1.5 .. 4.7 31 16 1.4 0.6 22 2 84 509 Oman 14.6 11.8 45.2 .. 48 47 6.2 4.9 1 1 182 406 Pakistan 6.0 3.8 31.4 24.9 846 923 2.2 1.5 1 17 .. .. Panama 1.2 .. 5.6 .. 12 12 1.1 0.8 .. .. 0 .. Papua New Guinea 1.0 0.5 3.9 .. 4 3 0.2 0.1 .. .. 0 .. Paraguay 1.4 0.8 .. 4.9 28 26 1.4 0.9 .. .. 2 1 Peru 1.9 1.2 10.7 7.9 178 198 1.8 1.5 .. 5 32 365 Philippines 1.4 0.9 8.5 5.0 149 147 0.5 0.4 .. .. 30 43 Poland 2.0 2.0 .. 5.4 302 148 1.7 0.9 176 169 195 224 Portugal 2.4 2.1 5.7 5.0 104 91 2.1 1.6 1 .. 24 431 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 293 5.7 Military expenditures and arms transfers Military expenditures Armed forces personnel Arms transfers $ millions % of central government % of 1990 prices % of GDP expenditure thousands labor force Exports Imports 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 2.8 1.9 .. 8.2 297 154 2.4 1.5 6 .. 3 131 Russian Federation 4.4 4.0 38.3 20.5 1,800 1,446 2.5 2.0 3,363 6,623 40 4 Rwanda 4.4 2.7 .. .. 47 53 1.9 1.2 .. .. 1 .. Saudi Arabia 9.3 8.5 .. .. 178 240 3.0 2.8 2 36 987 148 Senegal 1.7 1.6 .. .. 17 19 0.5 0.4 .. .. 2 .. Serbia 4.3 2.1 .. .. 165 24 .. .. 2 5 20 .. Sierra Leone 2.9 1.0 .. 5.1 7 11 0.4 0.4 .. .. 15 .. Singapore 4.4 4.7 35.1 34.0 66 167 3.7 7.3 0 3 269 54 Slovak Republic 3.2 1.7 .. 5.1 51 17 2.1 0.6 91 79 218 4 Slovenia 1.6 1.7 4.7 4.2 13 11 1.3 1.1 .. .. 19 2 Somalia .. .. .. .. 225 0 8.4 0.0 .. .. .. .. South Africa 2.2 1.4 .. 4.5 277 103 1.7 0.5 15 115 38 862 Spain 1.4 1.0 3.9 4.2 282 222 1.7 1.1 82 803 363 378 Sri Lanka 5.3 2.4 20.3 11.0 236 213 3.3 2.5 .. .. 49 20 Sudan 2.7 .. .. .. 134 123 1.6 1.2 .. .. 3 48 Swaziland 2.4 1.9 .. .. 3 .. 1.1 .. .. .. .. .. Sweden 2.3 1.4 .. 4.0 100 25 2.2 0.5 222 472 96 122 Switzerland 1.3 0.9 5.2 4.9 31 23 0.8 0.5 36 144 93 72 Syrian Arab Republic 6.2 3.8 .. .. 531 401 11.4 5.1 0 3 43 9 Tajikistan 1.0 2.2 .. 15.8 18 17 0.9 0.8 .. .. 27 13 Tanzania 1.6 1.1 .. .. 36 28 0.2 0.1 .. .. 3 .. Thailand 2.3 1.1 .. 6.8 421 420 1.3 1.2 .. 5 520 47 Timor-Leste .. .. .. .. .. 1 .. 0.3 .. .. .. .. Togo 2.4 1.6 .. .. 8 10 0.4 0.4 .. .. 3 .. Trinidad and Tobago .. .. .. .. 7 3 1.3 0.5 .. .. .. .. Tunisia 1.9 1.4 6.7 4.8 59 48 2.1 1.2 .. .. 46 16 Turkey 3.9 2.9 .. 9.8 690 612 3.0 2.2 0 45 1,580 486 Turkmenistan 2.3 .. .. .. 11 22 0.7 1.0 .. .. .. 10 Uganda 2.2 2.1 .. 12.0 52 47 0.6 0.4 .. .. 32 15 Ukraine 2.8 2.1 .. 5.7 519 215 2.0 1.0 215 133 0 29 United Arab Emirates 5.2 2.0 49.2 .. 71 51 5.5 1.9 27 7 427 2,439 United Kingdom 3.0 2.6 .. 6.3 233 181 0.8 0.6 1,402 1,063 659 463 United States 3.8 4.1 .. 19.5 1,636 1,498 1.2 1.0 11,288 7,938 767 443 Uruguay 2.1 1.2 7.9 4.3 27 25 1.8 1.5 .. .. 8 7 Uzbekistan 1.1 .. .. .. 42 87 0.5 0.7 .. 4 .. .. Venezuela, RB 1.6 1.1 8.7 4.4 80 82 0.9 0.6 .. 5 5 388 Vietnam 2.6 .. .. .. 622 495 1.8 1.1 .. .. 269 179 West Bank and Gaza .. .. .. .. .. 56 .. 7.0 .. .. 1 0 Yemen, Rep. 6.4 6.0 33.4 .. 70 138 1.7 2.2 .. .. 175 308 Zambia 1.6 2.3 .. 11.3 23 16 0.6 0.3 .. .. 5 15 Zimbabwe 19.1 2.3 0.1 .. 68 51 1.4 0.9 .. .. 1 20 World 2.5 w 2.5 w .. w 11.2 w 30,182 s 27,030 s 1.2 w 0.9 w 21,064 s 22,904 s 22,357 s 26,241 s Low income 2.7 2.4 19.4 18.3 7,694 7,160 1.0 0.9 115 11 2,250 2,681 Middle income 2.3 2.0 .. .. 16,027 14,271 1.2 0.9 4,996 7,717 8,509 11,361 Lower middle income 2.1 2.0 .. 15.7 10,405 9,878 1.0 0.8 1,304 730 4,302 6,115 Upper middle income 2.5 2.0 .. .. 5,622 4,393 1.7 1.2 3,692 6,987 4,207 5,246 Low & middle income 2.4 2.1 .. .. 23,721 21,431 1.1 0.9 5,111 7,728 10,759 14,042 East Asia & Pacific 1.8 1.8 .. 17.2 8,021 7,535 0.9 0.7 1,039 572 2,994 4,250 Europe & Central Asia 3.5 2.9 .. 13.0 4,874 3,434 2.3 1.6 4,011 6,975 2,301 1,560 Latin America & Carib. 1.7 1.3 5.1 .. 2,105 2,281 1.0 0.9 36 6 935 2,412 Middle East & N. Africa 4.2 3.5 17.6 16.2 3,172 3,479 4.2 3.1 8 49 2,951 2,399 South Asia 3.0 2.7 20.4 18.4 3,852 4,121 0.8 0.7 2 11 1,463 2,247 Sub-Saharan Africa 2.0 1.3 .. .. 1,698 582 0.7 0.5 15 115 115 1,174 High income 2.5 2.6 .. 10.6 6,461 5,599 1.4 1.1 15,953 15,176 11,598 12,199 Euro area 2.0 1.6 4.0 4.3 2,298 1,707 1.7 1.1 3,235 4,884 2,149 3,351 Note: For some countries data are partial or uncertain or based on rough estimates; see SIPRI (2007). a. Estimates differ from official statistics of the government of China, which has published the following estimates: military expenditure as 1.0 percent of GDP in 1995 and 1.4 percent in 2005 and 9.3 percent of central government expenditure in 1995 and 7.3 percent in 2005 (see National Bureau of Statistics of China, www.stats.gov.cn). 294 2008 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 2007: 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 2008. 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 2008 World Development Indicators 295 5.8 Public policies and institutions IDA Economic management Structural policies Resource 1­6 (low to high) 1­6 (low to high) Allocation Index 1­6 (low to high) Business Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2006 2006 2006 2006 2006 2006 2006 2006 2006 Afghanistan 2.6 4.0 3.0 3.0 3.3 3.0 2.0 2.5 2.5 Albania 3.7 4.5 3.5 4.0 4.0 5.0 4.0 3.5 4.2 Angola 2.7 3.0 3.0 2.0 2.7 4.0 2.5 2.0 2.8 Armenia 4.3 5.5 5.0 5.5 5.3 4.5 3.5 4.0 4.5 Azerbaijan 3.7 4.5 4.5 4.5 4.5 4.0 3.0 3.5 3.5 Bangladesh 3.4 4.0 3.5 4.5 4.0 3.5 3.0 3.5 3.3 Benin 3.6 4.5 4.0 3.5 4.0 4.5 3.5 3.5 3.8 Bhutan 3.8 4.5 4.0 4.0 4.2 3.0 3.0 3.5 3.2 Bolivia 3.7 4.0 4.0 4.5 4.2 5.0 3.5 2.5 3.7 Bosnia and Herzegovina 3.7 4.5 3.5 4.0 4.0 3.5 4.0 3.5 3.7 Burkina Faso 3.7 4.5 4.5 4.0 4.3 4.0 3.0 3.0 3.3 Burundi 3.0 3.5 3.5 2.5 3.2 3.5 3.0 2.5 3.0 Cambodia 3.2 4.0 3.0 3.5 3.5 3.5 2.5 3.5 3.2 Cameroon 3.2 4.0 4.0 2.5 3.5 3.5 3.0 3.0 3.2 Cape Verde 4.1 4.5 4.5 4.0 4.3 4.0 4.0 3.5 3.8 Central African Republic 2.4 3.0 3.0 1.5 2.5 3.5 2.5 2.0 2.7 Chad 2.8 3.5 3.0 2.5 3.0 3.0 3.0 3.0 3.0 Comoros 2.4 2.5 2.0 1.5 2.0 2.5 2.5 2.5 2.5 Congo, Dem. Rep. 2.8 3.5 3.5 2.5 3.2 4.0 2.0 3.0 3.0 Congo, Rep. 2.8 3.5 2.5 2.5 2.8 3.5 2.5 2.5 2.8 Côte d'Ivoire 2.5 2.5 2.0 1.0 1.8 3.5 3.0 3.0 3.2 Djibouti 3.1 3.5 2.5 2.5 2.8 4.0 3.5 3.0 3.5 Dominica 3.8 4.0 4.5 3.0 3.8 4.0 4.0 4.5 4.2 Eritrea 2.5 2.0 2.0 2.5 2.2 1.5 2.0 2.0 1.8 Ethiopia 3.4 3.0 4.0 3.5 3.5 3.0 3.0 3.5 3.2 Gambia, The 3.1 3.5 3.0 2.5 3.0 4.0 3.0 3.0 3.3 Georgia 4.1 4.5 4.5 4.5 4.5 4.5 3.5 4.5 4.2 Ghana 3.9 4.0 4.5 4.0 4.2 4.0 3.5 4.0 3.8 Grenada 3.8 4.0 3.0 3.0 3.3 4.0 3.5 4.5 4.0 Guinea 2.9 2.5 3.0 2.5 2.7 4.5 3.0 3.0 3.5 Guinea-Bissau 2.6 2.0 2.5 1.5 2.0 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 3.9 4.5 4.0 4.0 4.2 5.0 3.5 4.0 4.2 India 3.8 4.5 3.5 4.5 4.2 3.5 4.0 3.5 3.7 Indonesia 3.7 4.5 4.0 4.5 4.3 4.5 3.5 3.0 3.7 Kenya 3.7 4.5 4.0 4.0 4.2 4.0 3.5 4.0 3.8 Kiribati 3.1 2.5 2.0 5.0 3.2 3.0 3.0 3.0 3.0 About the data The International Development Association (IDA) IDA eligible. Serbia and Montenegro as a unified inclusion and equity, and public sector management is the part of the World Bank Group that helps the country was IDA eligible in 2005, rated in that year's and institutions. IDA resources are allocated to a poorest countries reduce poverty by providing con- exercise, and included in last year's table, but neither country on per capita terms based on its IDA country cessional loans and grants for programs aimed at Serbia nor Montenegro is IDA eligible as an indepen- performance rating and, to a limited extent, based boosting economic growth and improving living con- dent country and thus neither is rated in the 2006 on its per capita gross national income. This ensures ditions. IDA funding helps these countries deal with exercise nor included in this year's table. Afghanistan that good performers receive a higher IDA allocation the complex challenges they face in striving to meet and Timor-Leste are included in this year's table. in per capita terms. The IRAI is a key element in the the Millennium Development Goals. Country assessments have been carried out annually country performance rating. The World Bank's IDA Resource Allocation Index by World Bank staff since the mid-1970s. Over time The CPIA exercise is intended to capture the qual- (IRAI), which is presented in the table, is based on the criteria have been revised from a largely macro- ity of a country's policies and institutional arrange- the results of the annual Country Policy and Insti- economic focus to include governance aspects and ments, focusing on key elements that are within the tutional Assessment (CPIA) exercise, which covers a broader coverage of social and structural dimen- country's control, rather than on outcomes (such as the IDA-eligible countries. The table does not include sions. Country performance is assessed against a economic growth rates) that are influenced by events Liberia, Myanmar, and Somalia because they were set of 16 criteria grouped into four clusters: economic beyond the country's control. More specifically, the not rated in the 2006 exercise even though they are management, structural policies, policies for social CPIA measures the extent to which a country's policy 296 2008 World Development Indicators 5.8 STATES AND MARKETS Public policies and institutions IDA Economic management Structural policies Resource 1­6 (low to high) 1­6 (low to high) Allocation Index 1­6 (low to high) Business Macroeconomic Fiscal Debt Financial regulatory management policy policy Average Trade sector environment Average 2006 2006 2006 2006 2006 2006 2006 2006 2006 Kyrgyz Republic 3.6 4.5 3.5 4.0 4.0 5.0 3.5 3.5 4.0 Lao PDR 3.1 4.0 3.5 3.5 3.7 3.5 2.0 3.0 2.8 Lesotho 3.5 4.0 4.0 4.0 4.0 3.5 3.5 3.0 3.3 Madagascar 3.6 4.0 3.0 3.5 3.5 4.0 3.5 4.0 3.8 Malawi 3.4 3.5 3.0 3.0 3.2 4.0 3.0 3.5 3.5 Maldives 3.6 3.0 2.5 3.5 3.0 4.0 4.0 4.0 4.0 Mali 3.7 4.5 4.0 4.5 4.3 4.0 3.0 3.5 3.5 Mauritania 3.3 3.0 3.0 4.0 3.3 4.5 2.5 3.5 3.5 Moldova 3.7 4.0 4.0 4.0 4.0 3.5 3.5 3.5 3.5 Mongolia 3.4 4.0 3.0 3.0 3.3 4.5 3.0 3.5 3.7 Mozambique 3.5 4.0 4.0 4.5 4.2 4.5 3.0 3.0 3.5 Nepal 3.4 4.5 3.5 3.5 3.8 4.0 3.0 3.0 3.3 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 3.2 4.0 4.0 4.0 4.0 3.0 3.0 3.0 3.0 Pakistan 3.6 4.0 3.5 4.5 4.0 4.0 4.5 4.0 4.2 Papua New Guinea 3.1 4.0 3.5 4.0 3.8 4.0 3.0 3.0 3.3 Rwanda 3.6 4.0 4.0 3.5 3.8 3.5 3.5 3.5 3.5 Samoa 3.9 4.0 3.5 4.0 3.8 4.5 4.0 3.5 4.0 São Tomé and Principe 3.0 3.0 3.0 2.5 2.8 4.0 2.5 3.0 3.2 Senegal 3.7 4.0 4.0 4.0 4.0 4.0 3.5 3.5 3.7 Sierra Leone 3.1 4.0 3.5 3.5 3.7 3.0 3.0 2.5 3.0 Solomon Islands 2.8 3.5 3.5 2.5 3.2 3.5 3.0 2.5 2.8 Sri Lanka 3.6 3.0 3.0 3.5 3.2 4.0 4.0 4.0 3.8 St. Lucia 3.9 4.5 3.5 4.0 4.0 4.0 4.0 4.5 4.2 St. Vincent & Grenadines 3.8 4.0 3.5 3.5 3.7 2.5 4.0 4.5 4.2 Sudan 2.5 3.5 3.0 1.5 2.7 4.0 3.0 3.0 2.8 Tajikistan 3.3 4.5 4.0 4.0 4.2 4.0 3.0 3.5 3.5 Tanzania 3.9 5.0 4.5 4.0 4.5 3.5 3.5 3.5 3.7 Timor-Leste 2.7 2.5 3.0 3.5 3.0 3.5 2.5 1.5 2.5 Togo 2.5 2.5 2.0 1.5 2.0 4.0 2.5 3.0 3.2 Tonga 2.9 3.0 2.0 3.0 2.7 3.0 3.0 3.0 3.0 Uganda 3.9 4.5 4.5 4.5 4.5 4.0 3.5 4.0 3.8 Uzbekistan 3.0 3.0 3.5 4.0 3.5 2.5 2.5 2.5 2.5 Vanuatu 3.1 3.5 3.0 4.0 3.5 3.5 3.0 3.0 3.2 Vietnam 3.9 5.5 4.5 4.0 4.7 3.5 3.0 3.5 3.3 Yemen, Rep. 3.3 3.5 3.0 4.5 3.7 4.5 2.5 3.0 3.3 Zambia 3.4 4.0 3.5 3.5 3.7 4.0 3.0 3.0 3.3 Zimbabwe 1.8 1.0 1.0 1.0 1.0 2.0 2.5 2.0 2.2 and institutional framework supports sustainable a rating. The ratings reflect a variety of indicators, that the assessments are informed by up-to-date infor- growth and poverty reduction and, consequently, the observations, and judgments based on country knowl- mation. To ensure that scores are consistent across effective use of development assistance. edge and on relevant publicly available indicators. In countries, the process involves two key phases. In the All criteria within each cluster receive equal weight, interpreting the assessment scores, it should be benchmarking phase a small representative sample and each cluster has a 25 percent weight in the over- noted that the criteria are designed in a developmen- of countries drawn from all regions is rated. Country all score, which is obtained by averaging the average tally neutral manner. Accordingly, higher scores can be teams prepare proposals that are reviewed first at the scores of the four clusters. For each of the 16 criteria attained by a country that, given its stage of develop- regional level and then in a Bankwide review process. A countries are rated on a scale of 1 (low) to 6 (high). ment, has a policy and institutional framework that similar process is followed to assess the performance The scores depend on the level of performance in more strongly fosters growth and poverty reduction. of the remaining countries, using the benchmark a given year assessed against the criteria, rather The country teams that prepare the ratings are very countries' scores as guideposts. The final ratings are than on changes in performance compared with the familiar with the country, and their assessments are determined following a Bankwide review. The overall previous year. All 16 CPIA criteria contain a detailed based on country diagnostic studies prepared by the numerical IRAI score and the separate criteria scores description of each rating level. In assessing country World Bank or other development organizations and on were first publicly disclosed in June 2006. performance, World Bank staff evaluate the country's their own professional judgment. An early consultation See IDA's website at www.worldbank.org/ida for actual performance on each of the criteria and assign is conducted with country authorities to make sure more information. 2008 World Development Indicators 297 5.8 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Afghanistan 2.0 2.5 3.0 2.0 2.0 2.3 1.5 3.0 2.5 2.0 2.5 2.3 Albania 4.0 3.5 3.5 3.5 3.0 3.5 3.0 4.0 4.0 3.0 2.5 3.3 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 3.5 4.2 3.5 4.0 3.5 4.0 3.5 3.5 Azerbaijan 4.0 4.0 3.0 4.0 3.0 3.6 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 2.5 3.0 3.0 3.0 2.5 2.8 Benin 3.0 3.0 3.5 3.0 3.5 3.2 3.0 3.5 3.5 3.0 3.5 3.3 Bhutan 4.0 4.0 4.5 3.5 4.5 4.1 3.5 3.5 4.0 4.0 4.0 3.8 Bolivia 4.0 4.0 4.5 3.0 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 4.5 3.0 3.0 3.4 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 3.5 3.0 3.0 3.0 3.0 3.1 2.5 3.0 3.0 2.5 2.5 2.7 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 3.5 3.0 3.5 3.0 3.0 3.2 2.5 3.5 3.5 3.0 2.5 3.0 Cape Verde 4.5 4.5 4.5 4.5 3.5 4.3 4.0 3.5 3.5 4.0 4.5 3.9 Central African Republic 2.5 2.0 2.0 2.0 2.5 2.2 2.0 2.0 2.5 2.0 2.5 2.2 Chad 2.5 3.0 2.5 2.5 2.5 2.6 2.0 2.5 2.5 3.0 2.0 2.4 Comoros 3.0 3.0 3.0 2.5 2.0 2.7 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 2.5 2.0 2.3 Congo, Rep. 3.0 2.5 3.0 2.5 2.5 2.7 2.5 3.0 3.0 2.5 2.5 2.7 Côte d'Ivoire 2.5 1.5 2.0 2.5 3.0 2.3 2.0 2.5 4.0 2.0 2.0 2.5 Djibouti 3.0 3.0 3.5 3.0 3.0 3.1 2.5 3.0 3.5 2.5 2.5 2.8 Dominica 4.0 3.5 4.0 3.5 3.5 3.7 4.0 3.0 3.5 3.5 4.0 3.6 Eritrea 3.5 3.0 3.5 3.0 2.0 3.0 2.5 2.5 3.5 3.0 2.5 2.8 Ethiopia 3.0 4.5 3.5 3.5 3.5 3.6 3.0 4.0 4.0 3.0 2.5 3.3 Gambia, The 3.5 3.0 3.5 2.5 3.0 3.1 3.5 2.5 3.5 3.0 2.0 2.9 Georgia 4.5 4.5 4.0 4.0 3.5 4.1 3.5 4.0 4.0 3.5 3.5 3.7 Ghana 4.0 4.0 4.0 3.5 3.5 3.8 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 2.5 3.0 3.0 2.5 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 3.5 3.0 3.0 3.4 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 3.0 3.7 3.5 4.0 4.0 3.0 3.0 3.5 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 Indonesia 3.5 4.0 3.5 3.5 3.0 3.5 2.5 3.5 3.5 3.5 3.0 3.2 Kenya 3.0 3.5 3.5 3.0 3.0 3.2 3.0 3.5 4.0 3.5 3.0 3.4 Kiribati 3.0 3.0 2.5 3.0 3.0 2.9 3.5 3.0 3.0 3.0 3.5 3.2 Definitions · IDA Resource Allocation Index is obtained by sustainability. · Structural policies cluster: Trade · Equity of public resource use assesses the extent calculating the average score for each cluster and assesses how the policy framework fosters trade in to which the pattern of public expenditures and rev- then by averaging those scores. For each of 16 cri- goods. · Financial sector assesses the structure of enue collection affects the poor and is consistent teria countries are rated on a scale of 1 (low) to the financial sector and the policies and regulations with national poverty reduction priorities. · Build- 6 (high) · Economic management cluster: Macro- that affect it. · Business regulatory environment ing human resources assesses the national policies economic management assesses the monetary, assesses the extent to which the legal, regulatory, and public and private sector service delivery that exchange rate, and aggregate demand policy frame- and policy environments help or hinder private busi- affect the access to and quality of health and edu- work. · Fiscal policy assesses the short- and nesses in investing, creating jobs, and becoming cation services, including prevention and treatment medium-term sustainability of fiscal policy (taking more productive. · Policies for social inclusion of HIV/AIDS, tuberculosis, and malaria. · Social into account monetary and exchange rate policy and equity cluster: Gender equality assesses the protection and labor assess government policies in and the sustainability of the public debt) and its extent to which the country has installed institutions social protection and labor market regulations that impact on growth. · Debt policy assesses whether and programs to enforce laws and policies that pro- reduce the risk of becoming poor, assist those who the debt management strategy is conducive to mini- mote equal access for men and women in educa- are poor to better manage further risks, and ensure mizing budgetary risks and ensuring long-term debt tion, health, the economy, and protection under law. a minimal level of welfare to all people. · Policies 298 2008 World Development Indicators 5.8 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Kyrgyz Republic 4.5 3.5 3.5 3.5 3.0 3.6 2.5 3.0 3.5 2.5 2.5 2.8 Lao PDR 3.5 3.5 3.0 2.0 3.5 3.1 3.0 3.0 2.5 3.0 2.0 2.7 Lesotho 4.0 3.0 3.5 3.0 3.5 3.4 3.5 3.0 4.0 3.0 3.5 3.4 Madagascar 3.5 3.5 3.5 3.5 4.0 3.6 3.5 3.0 3.5 3.5 3.5 3.4 Malawi 3.5 3.5 3.5 3.5 3.5 3.5 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 4.0 3.0 3.5 3.5 Mauritania 3.5 3.5 3.5 3.0 3.5 3.4 3.0 2.5 3.5 3.0 2.5 2.9 Moldova 5.0 3.5 4.0 3.5 3.5 3.9 3.5 3.5 3.0 3.0 3.0 3.2 Mongolia 3.5 3.0 3.5 3.5 2.5 3.2 3.0 4.0 3.5 3.5 2.5 3.3 Mozambique 3.5 3.5 3.5 3.0 3.0 3.3 3.0 3.5 3.5 2.5 3.0 3.1 Nepal 3.5 3.5 3.5 3.0 3.0 3.3 3.0 3.5 3.5 3.0 3.0 3.2 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 3.0 3.0 3.1 2.5 3.0 3.0 2.5 3.0 2.8 Pakistan 2.0 3.5 3.5 3.0 3.5 3.1 3.0 3.5 3.5 3.5 2.5 3.2 Papua New Guinea 2.5 3.0 2.5 3.0 1.5 2.5 2.0 3.5 3.5 2.5 3.0 2.9 Rwanda 3.5 4.5 4.5 3.5 3.0 3.8 3.0 4.0 3.5 3.5 3.0 3.4 Samoa 4.0 4.0 4.0 3.5 4.0 3.9 4.0 3.5 4.0 4.0 4.0 3.9 São Tomé and Principe 3.0 3.5 2.5 2.5 2.5 2.8 2.5 2.5 3.5 3.0 3.5 3.0 Senegal 3.5 3.5 3.5 3.0 3.5 3.4 3.5 3.5 4.5 3.5 3.0 3.6 Sierra Leone 3.0 3.0 3.0 2.5 2.0 2.8 2.5 3.5 2.5 3.0 2.5 2.9 Solomon Islands 3.0 2.5 3.0 3.5 2.0 2.6 2.5 2.5 3.5 2.0 3.0 2.5 Sri Lanka 4.0 3.5 4.0 4.0 3.5 3.7 3.5 4.0 3.5 3.0 3.5 3.5 St. Lucia 4.0 3.5 4.0 3.5 3.5 3.8 4.0 3.5 3.5 3.5 4.5 3.8 St. Vincent & Grenadines 4.5 3.5 4.0 2.0 3.5 3.8 4.0 3.5 3.0 3.5 4.0 3.7 Sudan 2.0 2.5 2.5 3.5 2.5 2.3 2.0 2.0 3.0 2.5 2.0 2.3 Tajikistan 3.5 3.0 3.0 3.5 2.5 3.1 2.5 3.0 4.0 2.5 2.0 2.6 Tanzania 4.0 4.0 4.0 2.5 3.5 3.8 3.5 4.5 3.0 3.5 3.5 3.8 Timor-Leste 3.0 3.0 2.5 2.5 2.0 2.6 1.5 3.0 3.0 2.5 3.0 2.6 Togo 3.0 2.0 3.0 2.5 2.5 2.6 2.5 2.0 2.5 2.0 2.0 2.2 Tonga 2.5 3.5 4.0 3.0 3.0 3.2 3.5 2.5 3.0 2.5 2.0 2.7 Uganda 3.5 4.5 4.0 3.5 4.0 3.9 3.5 4.0 3.0 3.0 3.0 3.3 Uzbekistan 3.5 3.5 4.0 3.5 3.5 3.6 2.0 3.0 3.0 2.5 1.5 2.4 Vanuatu 3.0 3.5 2.5 2.0 3.0 2.8 3.0 3.5 3.5 2.5 3.0 3.1 Vietnam 4.5 4.5 4.0 3.0 3.5 3.9 3.5 4.0 3.5 3.5 3.0 3.5 Yemen, Rep. 2.5 3.5 3.0 3.5 3.0 3.1 2.5 3.0 3.0 3.0 3.0 2.9 Zambia 3.5 3.5 3.5 3.0 3.5 3.4 3.0 3.5 3.5 3.0 3.0 3.2 Zimbabwe 2.5 1.5 2.0 1.5 2.5 2.0 1.0 2.0 3.5 2.0 1.0 1.9 and institutions for environmental sustainability accurate accounting and fiscal reporting, including to which public employees within the executive are assess the extent to which environmental policies timely and audited public accounts. · Effi ciency required to account for administrative decisions, foster the protection and sustainable use of natural of revenue mobilization assesses the overall pat- use of resources, and results obtained. The three resources and the management of pollution. · Public tern of revenue mobilization--not only the de facto main dimensions assessed are the accountability sector management and institutions cluster: Prop- tax structure, but also revenue from all sources as of the executive to oversight institutions and of pub- erty rights and rule-based governance assess the actually collected. · Quality of public administration lic employees for their performance, access of civil extent to which private economic activity is facili- assesses the extent to which civilian central govern- society to information on public affairs, and state tated by an effective legal system and rule-based ment staff is structured to design and implement capture by narrow vested interests. governance structure in which property and contract government policy and deliver services effectively. rights are reliably respected and enforced. · Quality · Transparency, accountability, and corruption in Data sources of budgetary and financial management assesses the public sector assess the extent to which the Data on public policies and institutions are from the extent to which there is a comprehensive and executive can be held accountable for its use of the World Bank Group's CPIA database available credible budget linked to policy priorities, effective funds and for the results of its actions by the elector- at www.worldbank.org/ida. fi nancial management systems, and timely and ate, the legislature, and the judiciary and the extent 2008 World Development Indicators 299 5.9 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 2000­05a 2000­05a 2000­05a 2000­05a 2000­06a 2000­06a 2000­06a 2006 2006 2006 2006 Afghanistan 34,782 23.7 .. .. .. .. .. .. .. .. .. Albania 18,000 39.0 197 2,200 447 73 26 .. 4 213 0 Algeria 108,302 70.2 .. .. 3,572 929 1,471 .. 45 2,900 24 Angola 51,429 10.4 166,045 4,709 2,761 .. .. .. 5 263 81 Argentina 231,374 30.0 .. .. .. .. .. 1,758 74 6,612 125 Armenia 7,515 90.0 2,131 231 711 27 654 .. 6 606 7 Australia 812,972 .. 290,280 168,630 9,528 1,290 46,164 5,689 353 46,952 2,570 Austria 133,928 100.0 69,000 26,411 5,690 8,470 17,036 .. 150 8,785 572 Azerbaijan 59,141 49.4 10,892 7,536 2,122 878 10,067 .. 13 1,253 16 Bangladesh 239,226 9.5 .. .. 2,855 4,164 817 904 8 1,729 191 Belarus 94,797 88.6 9,231 15,055 5,498 13,568 43,559 .. 6 307 1 Belgium 150,567 78.0 126,680 54,856 3,542 9,150 8,130 8,672 158 3,641 740 Benin 19,000 9.5 .. .. 578 66 86 .. .. .. .. Bolivia 62,479 7.0 .. .. .. .. .. .. 22 1,443 11 Bosnia and Herzegovina 21,846 52.3 .. 300 1,000 53 1,173 .. .. .. .. Botswana 24,455 33.2 .. .. 888 171 842 .. 7 214 0 Brazil 1,751,868 5.5 .. .. .. .. .. 6,305 561 40,945 1,412 Bulgaria 44,033 99.0 14,401 6,840 4,154 2,389 5,164 .. 12 808 3 Burkina Faso 92,495 4.2 .. .. 622 .. .. .. 2 73 0 Burundi 12,322 10.4 .. .. .. .. .. .. .. .. .. Cambodia 38,257 6.3 201 3 650 45 92 .. 4 256 1 Cameroon 50,000 10.0 .. .. 1,016 357 1,076 .. 11 425 29 Canada 1,408,900 39.9 493,814 184,774 67,346 1,430 445,689 4,309 1,042 46,727 1,503 Central African Republic 24,307 .. .. .. .. .. .. .. .. .. .. Chad 33,400 0.8 .. .. .. .. .. .. .. .. .. Chile 79,604 20.2 .. .. 2,035 737 1,241 2,127 95 6,017 1,028 China 1,930,544 81.6 929,210 869,320 62,200 666,200 2,170,700 84,686 1,543 158,013 7,692 Hong Kong, China 1,955 100.0 .. .. .. .. .. 23,539 130 21,796 8,326 Colombia 164,257 .. 157 38,199 2,137 .. 7,751 1,511 175 10,616 1,051 Congo, Dem. Rep. 153,497 1.8 .. .. 3,641 140 444 .. .. .. .. Congo, Rep. 17,289 5.0 .. .. 795 135 231 .. .. .. .. Costa Rica 35,330 24.4 .. .. .. .. .. 834 36 943 10 Côte d'Ivoire 80,000 8.1 .. .. 639 10 129 710 .. .. .. Croatia 28,472 84.4 3,403 9,328 2,726 1,266 2,835 .. 22 1,389 2 Cuba 60,856 49.0 .. .. .. .. .. .. 12 812 30 Czech Republic 127,781 100.0 90,055 46,600 9,513 6,631 14,385 .. 75 4,922 39 Denmark 72,257 100.0 70,635 11,058 2,212 5,459 2,030 669 14 582 1 Dominican Republic 12,600 49.4 .. .. 1,743 .. .. 537 .. .. .. Ecuador 43,197 15.0 10,641 5,453 966 .. .. 671 31 2,110 6 Egypt, Arab Rep. 92,370 81.0 .. .. 5,150 40,837 3,917 4,916 47 4,988 309 El Salvador 10,029 19.8 .. .. 283 .. .. .. 25 2,579 20 Eritrea 4,010 21.8 .. .. 306 .. .. .. .. .. .. Estonia 57,016 22.7 3,190 7,641 959 248 10,311 .. 8 598 1 Ethiopia 37,018 13.4 219,113 2,456 .. .. .. .. 34 1,720 157 Finland 78,821 65.0 70,300 27,800 5,732 3,478 9,706 1,401 115 7,597 409 France 950,985 100.0 771,000 193,000 29,286 76,159 41,898 4,005 806 59,538 6,135 Gabon 9,170 10.2 .. .. 810 95 2,219 .. 9 508 78 Gambia, The 3,742 19.3 16 .. .. .. .. .. .. .. .. Georgia 20,247 39.4 5,200 570 1,515 720 6,127 .. 5 272 3 Germany .. 100.0 1,062,700 237,609 34,218 72,554 88,022 15,053 1,085 99,647 8,134 Ghana 57,613 17.9 .. .. 977 85 242 .. 1 96 7 Greece 117,533 91.8 .. 18,360 2,576 1,854 613 1,769 133 9,481 71 Guatemala 14,095 34.5 .. .. 886 .. .. 800 .. .. .. Guinea 44,348 9.8 .. .. 1,115 .. .. .. .. .. .. Guinea-Bissau 3,455 27.9 .. .. .. .. .. .. .. .. .. Haiti 4,160 24.3 .. .. .. .. .. .. .. .. .. 300 2008 World Development Indicators 5.9 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 2000­05a 2000­05a 2000­05a 2000­05a 2000­06a 2000­06a 2000­06a 2006 2006 2006 2006 Honduras 13,600 20.4 .. .. 699 .. .. 553 .. .. .. Hungary 159,568 43.9 13,300 12,505 7,730 6,953 8,537 .. 46 2,592 20 India 3,383,344 47.4 .. .. 63,465 575,702 407,398 6,190 454 40,289 843 Indonesia 372,929 55.3 .. .. .. 14,345 4,430 3,740 357 29,867 469 Iran, Islamic Rep. 179,388 67.4 .. .. 7,131 11,149 19,127 1,529 136 13,623 92 Iraq 45,550 84.3 .. .. 1,963 570 1,682 .. .. .. .. Ireland 96,602 100.0 .. 15,900 1,919 1,781 303 1,065 350 50,738 131 Israel 17,589 100.0 .. .. 899 1,618 1,149 1,774 36 4,357 1,124 Italy 484,688 100.0 97,560 192,700 16,225 46,144 20,131 9,963 448 36,709 1,377 Jamaica 21,532 73.9 .. .. 272 .. .. 2,150 21 1,527 15 Japan 1,177,278 77.7 947,562 327,632 20,052 245,957 22,632 18,274 670 102,845 8,480 Jordan 7,601 100.0 .. .. 293 .. 1,024 .. 27 2,046 259 Kazakhstan 90,800 83.0 91,651 47,100 14,205 12,129 191,200 .. 19 1,283 16 Kenya 63,265 14.1 .. 22 1,917 226 1,399 .. 29 2,685 301 Korea, Dem. Rep. 31,200 6.4 .. .. 5,214 .. .. .. 2 105 2 Korea, Rep. 102,293 76.8 91,665 12,545 3,392 31,004 10,108 15,711 224 34,843 7,752 Kuwait 5,749 85.0 .. .. .. .. .. 750 21 2,628 239 Kyrgyz Republic 18,500 91.1 5,874 1,336 424 50 561 .. 5 219 1 Lao PDR 31,210 14.4 .. .. .. .. .. .. 10 327 3 Latvia 69,829 100.0 2,869 2,767 2,375 894 17,921 .. 29 1,410 13 Lebanon 6,970 .. .. .. 401 .. .. .. 11 969 74 Lesotho 5,940 18.3 .. .. .. .. .. .. .. .. .. Liberia 10,600 6.2 .. .. 490 .. .. .. .. .. .. Libya 83,200 57.2 .. .. 2,757 .. .. .. 13 1,152 0 Lithuania 79,497 78.2 38,484 15,908 1,772 428 12,457 .. 11 430 1 Macedonia, FYR 13,182 .. 842 4,100 699 94 441 .. 2 209 0 Madagascar 49,827 11.6 .. .. 732 10 12 .. 14 573 19 Malawi 15,451 45.0 .. .. 710 26 38 .. 6 146 2 Malaysia 98,721 81.3 .. .. 1,667 1,181 1,178 13,419 164 17,833 2,597 Mali 18,709 18.0 .. .. 733 196 189 .. .. .. .. Mauritania 7,660 11.3 .. .. 717 .. .. .. 2 149 0 Mauritius 2,015 100.0 .. .. .. .. .. .. 14 1,056 195 Mexico 355,796 37.0 422,915 204,217 .. .. .. 2,680 318 21,243 457 Moldova 12,737 86.3 1,640 1,577 1,075 355 2,980 .. 4 274 1 Mongolia 49,250 3.5 557 242 1,810 1,228 8,857 .. 6 348 6 Morocco 57,626 61.9 .. 1,256 1,907 2,987 5,919 561 55 4,109 51 Mozambique 30,400 18.7 .. .. 3,070 172 768 .. 10 350 5 Myanmar 27,966 11.4 .. .. .. .. .. .. 29 1,621 3 Namibia 42,237 12.8 47 591 .. .. .. .. 7 401 0 Nepal 17,280 56.9 .. .. 59 .. .. .. 7 510 7 Netherlands 126,100 90.0 .. 77,100 2,813 14,730 4,331 10,044 251 27,454 4,959 New Zealand 93,460 64.9 .. .. .. .. 4,078 1,718 221 12,382 819 Nicaragua 18,669 11.4 .. .. 6 .. .. .. .. .. .. Niger 18,423 20.6 .. .. .. .. .. .. .. .. .. Nigeria 193,200 15.0 .. .. 3,528 174 77 513 16 1,308 11 Norway 92,864 77.5 58,247 14,966 4,087 2,440 9,568 .. 254 12,277 177 Oman 34,965 27.7 .. .. .. .. .. 2,620 32 3,267 235 Pakistan 258,340 64.7 209,959 .. 7,791 24,237 5,013 1,699 51 5,715 427 Panama 11,643 34.6 .. .. 355 .. .. 3,383 33 2,029 36 Papua New Guinea 19,600 3.5 .. .. .. .. .. .. 22 919 22 Paraguay 29,500 50.8 .. .. 441 .. .. .. 10 433 0 Peru 78,829 14.4 .. .. .. .. .. 1,085 56 4,218 112 Philippines 200,037 9.9 .. .. 491 144 1 3,596 62 8,305 319 Poland 423,997 69.7 29,314 119,740 19,507 16,742 45,438 428 83 3,626 80 Portugal 78,470 86.0 .. 23,187 2,839 3,412 2,422 1,012 120 9,441 294 Puerto Rico 25,645 95.0 .. 10 96 .. .. 1,729 .. .. .. 2008 World Development Indicators 301 5.9 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 2000­05a 2000­05a 2000­05a 2000­05a 2000­06a 2000­06a 2000­06a 2006 2006 2006 2006 Romania 198,817 30.2 9,438 37,220 10,844 7,960 16,032 1,018 43 2,047 5 Russian Federation 537,289 .. .. 25,200 85,245 177,639 1,950,900 2,326 421 28,837 1,926 Rwanda 14,008 19.0 .. .. .. .. .. .. .. .. .. Saudi Arabia 152,044 29.9 .. .. 1,020 393 1,192 3,919 132 16,831 1,066 Senegal 13,576 29.3 .. .. 906 88 265 .. 0 501 0 Serbia 45,290 62.4 3,865 3,100 3,809 852 3,482 .. 20 1,042 4 Sierra Leone 11,300 8.0 .. .. .. .. .. .. 0 19 10 Singapore 3,234 100.0 .. .. .. .. .. 24,792 85 19,566 7,981 Slovak Republic 43,000 87.3 32,214 18,517 3,659 2,166 9,326 .. 15 780 0 Slovenia 38,485 100.0 848 11,033 1,228 777 3,245 .. 20 861 2 Somalia 22,100 11.8 .. .. .. .. .. .. .. .. .. South Africa 364,131 17.3 .. 434 20,247 991 109,721 3,552 147 12,933 1,233 Spain 666,292 99.0 397,117 132,868 14,484 21,047 11,586 10,033 603 53,122 1,100 Sri Lanka 97,286 81.0 21,067 .. 1,200 4,358 135 3,079 21 3,101 325 Sudan 11,900 36.3 .. .. 5,478 40 766 .. 9 563 51 Swaziland 3,594 30.0 .. .. 301 .. 11,394 .. .. .. .. Sweden 425,383 31.5 112,010 39,373 9,867 5,673 13,120 1,281 190 11,624 257 Switzerland 71,296 100.0 97,996 15,753 3,011 13,830 8,571 .. 125 10,647 1,039 Syrian Arab Republic 94,890 20.1 589 .. 1,888 607 2,256 .. 17 1,252 16 Tajikistan 27,767 .. .. .. 616 50 1,117 .. 3 394 13 Tanzania 78,891 8.6 .. .. 4,582b 946b 1,990 b .. 5 190 2 Thailand 57,403 98.5 .. .. 4,044 9,195 4,037 5,574 127 20,102 2,107 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. Togo 7,520 31.6 .. .. 568 .. .. .. .. .. .. Trinidad and Tobago 8,320 51.1 .. .. .. .. .. .. 14 1,024 46 Tunisia 19,232 65.8 .. 16,611 1,909 1,319 2,067 .. 22 2,055 19 Turkey 426,914 .. 182,152 166,831 8,697 6,183 9,078 3,648 177 19,361 464 Turkmenistan 24,000 81.2 .. .. 2,529 1,286 8,670 .. 16 1,843 10 Uganda 70,746 23.0 .. .. 259 .. 218 .. 0 55 34 Ukraine 169,323 97.4 51,820 23,895 22,001 52,655 223,980 730 49 2,802 44 United Arab Emirates 4,030 100.0 .. .. .. .. .. 10,967 87 14,314 3,734 United Kingdom 388,008 100.0 736,000 163,000 15,810 43,200 22,110 8,226 1,037 97,545 6,215 United States 6,544,257 65.3 7,814,575 2,116,532 153,787 47,717 2,589,349c 40,875 9,739d 725,531d 39,882d Uruguay 77,732 10.0 .. .. 3,003 12 331 .. 9 569 4 Uzbekistan 81,600 87.3 .. 1,200 4,014 2,012 18,007 .. 22 1,665 68 Venezuela, RB 96,155 33.6 .. .. 336 .. 54 1,218 140 5,226 2 Vietnam 222,179 .. .. .. 2,671 4,558 2,928 3,000 51 5,284 216 West Bank and Gaza 4,996 100.0 .. .. .. .. .. .. .. .. .. Yemen, Rep. 71,300 8.7 .. .. .. .. .. .. 19 1,162 66 Zambia 91,440 22.0 .. .. 1,273 183 .. .. 6 59 0 Zimbabwe 97,267 19.0 .. .. .. .. .. .. 7 239 9 World 35.9 m .. m .. m .. s 2,278 m 7,751 m 414,087 s 24,843 s 2,072,237 s 143,212 s Low income 12.1 .. .. .. .. .. 9,772 846 69,322 2,469 Middle income 44.0 .. .. .. 1,286 5,542 158,956 5,580 467,938 23,065 Lower middle income 65.8 .. .. .. 1,286 3,449 114,068 3,034 285,540 13,248 Upper middle income 34.1 .. .. .. 1,716 9,202 44,888 2,546 182,398 9,817 Low & middle income 26.8 .. .. .. .. .. 170,749 6,426 537,260 25,535 East Asia & Pacific 11.4 .. .. .. 4,558 1,902 114,016 2,454 244,449 13,538 Europe & Central Asia .. 9,859 13,124 196,529 1,286 8,874 5,530 1,032 73,664 2,700 Latin America & Carib. 24.3 .. .. .. .. .. 24,523 1,621 107,627 4,346 Middle East & N. Africa 70.2 .. .. .. .. 2,256 .. 392 34,257 909 South Asia 56.9 .. .. .. 14,297 2,915 11,872 549 51,488 1,793 Sub-Saharan Africa 11.9 .. .. .. .. .. .. 378 25,776 2,249 High income 90.9 .. 51,147 .. 6,152 10,311 243,338 18,417 1,534,977 117,678 Euro area 100.0 126,680 51,147 120,827 8,810 9,706 64,550 4,314 371,383 29,254 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. 302 2008 World Development Indicators 5.9 STATES AND MARKETS Transport services About the data Definitions Transport infrastructure--highways, railways, ports air transport for passengers and freight and sea · Total road network covers motorways, highways, and waterways, and airports and air traffic control transport for freight tend to be more competitive. main or national roads, secondary or regional roads, systems--and the services that fl ow from it are The railways indicators in the table focus on scale and all other roads in a country. · Paved roads are crucial to the activities of households, producers, and output measures: total route-kilometers, pas- roads surfaced with crushed stone (macadam) and and governments. Because performance indicators senger-kilometers, and goods (freight) hauled in hydrocarbon binder or bituminized agents, with con- vary widely by transport mode and focus (whether ton-kilometers. crete, or with cobblestones. · Passengers carried physical infrastructure or the services flowing from Measures of port container traffi c, much of it by road are the number of passengers transported that infrastructure), highly specialized and carefully commodities of medium to high value added, give by road times kilometers traveled. · Goods hauled specified indicators are required. The table provides some indication of economic growth in a country. by road are the volume of goods transported by road selected indicators of the size, extent, and produc- But when traffic is merely transshipment, much of vehicles, measured in millions of metric tons times tivity of roads, railways, and air transport systems the economic benefit goes to the terminal operator kilometers traveled. · Rail lines are the length of rail- and of the volume of traffic in these modes as well and ancillary services for ships and containers rather way route available for train service, irrespective of as in ports. than to the country more broadly. In transshipment the number of parallel tracks. · Passengers carried Data for transport sectors are not always inter- centers empty containers may account for as much by railway are the number of passengers transported nationally comparable. Unlike for demographic sta- as 40 percent of traffic. by rail times kilometers traveled. · Goods hauled tistics, national income accounts, and international The air transport data represent the total (interna- by railway are the volume of goods transported by trade data, the collection of infrastructure data has tional and domestic) scheduled traffic carried by the railway, measured in metric tons times kilometers not been "internationalized." But data on roads are air carriers registered in a country. Countries submit traveled. · Port container traffic measures the flow collected by the International Road Federation (IRF), air transport data to ICAO on the basis of standard of containers from land to sea transport modes and and data on air transport by the International Civil instructions and definitions issued by ICAO. In many vice versa in twenty-foot-equivalent units (TEUs), a Aviation Organization (ICAO). cases, however, the data include estimates by ICAO standard-size container. Data cover coastal shipping National road associations are the primary source for nonreporting carriers. Where possible, these esti- as well as international journeys. Transshipment traf- of IRF data. In countries where a national road asso- mates are based on previous submissions supple- fic is counted as two lifts at the intermediate port ciation is lacking or does not respond, other agencies mented by information published by the air carriers, (once to off-load and again as an outbound lift) and are contacted, such as road directorates, ministries such as flight schedules. includes empty units. · Registered carrier depar- of transport or public works, or central statistical The data cover the air traffic carried on scheduled tures worldwide are domestic takeoffs and takeoffs offices. As a result, definitions and data collection services, but changes in air transport regulations abroad of air carriers registered in the country. · Pas- methods and quality differ, and the compiled data in Europe have made it more diffi cult to classify sengers carried by air include both domestic and are of uneven quality. Moreover, the quality of trans- traffic as scheduled or nonscheduled. Thus recent international passengers of air carriers registered port service (reliability, transit time, and condition of increases shown for some European countries may in the country. · Air freight is the volume of freight, goods delivered) is rarely measured, though it may be be due to changes in the classification of air traffic express, and diplomatic bags carried on each flight as important as quantity in assessing an economy's rather than actual growth. For countries with few air stage (operation of an aircraft from takeoff to its next transport system. Several new initiatives are under carriers or only one, the addition or discontinuation landing), measured in metric tons times kilometers way to improve data availability and consistency. The of a home-based air carrier may cause significant traveled. IRF is collaborating with national and international changes in air traffic. development agencies to improve the quality and coverage of road statistics. To improve measures of progress and performance, the World Bank is also working on better measures of access, affordabil- Data sources ity, effi ciency, quality, and fiscal and institutional aspects of infrastructure. Data on roads are from the IRF's World Road Unlike the road sector, where numerous qualified Statistics, supplemented by World Bank staff motor vehicle operators can operate anywhere on estimates. Data on railways are from a database the road network, railways are a restricted transport maintained by the World Bank's Transport and system with vehicles confined to a fixed guideway. Urban Development Department, Transport Divi- Considering the cost and service characteristics, sion, based on data from the International Union railways generally are best suited to carry--and can of Railways. Data on port container traffic are from effectively compete for--bulk commodities and con- Containerisation International's Containerisation tainerized freight for distances of 500­5,000 kilo- International Yearbook. Data on air transport are meters, and passengers for distances of 50­1,000 from the ICAO's Civil Aviation Statistics of the World kilometers. Below these limits road transport tends and ICAO staff estimates. to be more competitive, while above these limits 2008 World Development Indicators 303 5.10 Power and communications Electric power Telephones Access Quality Affordability and efficiency Inter- Transmission Population national $ per month and covered voice Price Cost of Telecom- Consumption distribution per 100 people by mobile traffic a Faults basket for Price call to U.S.a munications Subscrib- per capita losses Fixed Mobile telephonya minutes per 100 residential basket for $ per revenuea ers per kWh % of output mainlinesa subscribersa % per person mainlinesa fi xed lineb mobilea 3 minutes % of GDP employeea 2005 2005 2006 2006 2006 2006 2006 2006 2006 2005 2006 2006 Afghanistan .. .. 1 10 .. 1 25.0 0.1 10.8 0.39 5.1 60 Albania 1,167 39 11 49 97 160 .. 5.1 22.1 1.34 .. 623 Algeria 899 13 9 63 75 17 0.8 6.3 7.5 2.08 4.7 302 Angola 141 14 1 14 .. .. .. .. 12.2 3.23 2.0 586 Argentina 2,418 15 24 81 .. 33 .. 6.8 7.8 .. 3.4 972 Armenia 1,503 16 20 11 88 128 64.4 2.4 8.7 2.42 3.0 173 Australia 11,481 7 48 95 98 .. 12.0 30.5 18.2 .. 3.6 317 Austria 7,889 5 43 112 99 .. 5.7 29.0 23.2 0.71 2.3 642 Azerbaijan 2,407 10 14 39 99 33 48.1 5.3 15.1 4.18 1.3 229 Bangladesh 136 8 1 12 90 6 .. 4.0 2.6 2.02 1.5 .. Belarus 3,209 12 35 61 93 64 23.1 1.6 11.8 1.90 2.1 280 Belgium 8,510 5 45 92 99 .. 6.3 33.1 18.5 0.75 3.2 634 Benin 69 .. 1 12 .. 6 7.5 16.1 13.0 4.80 1.6 621 Bolivia 479 16 7 29 .. 49 .. 8.5 5.6 .. 5.7 810 Bosnia and Herzegovina 2,316 18 25 48 97 208 .. 6.3 6.6 3.62 5.5 366 Botswana 1,406 15 7 53 99 74 .. 10.2 8.7 2.88 2.6 1,101 Brazil 2,008 17 20 53 88 .. 1.6 15.6 26.2 0.71 3.4 1,545 Bulgaria 4,121 11 31 107 100 72 2.8 10.0 16.2 0.57 6.2 347 Burkina Faso .. .. 1 7 26 11 18.4 16.9 12.8 1.14 3.7 440 Burundi .. .. 0 2 .. .. .. 2.6 12.2 2.45 .. 234 Cambodia .. .. 0 8 .. 10 .. .. 5.1 2.94 .. .. Cameroon 196 16 1 13 73 9 .. 9.3 16.3 .. 3.1 730 Canada 17,285 7 64 53 97 .. .. .. 6.9 .. 2.5 425 Central African Republic .. .. 0 2 .. .. .. .. 12.4 1.99 1.1 .. Chad .. .. 0 4 .. .. .. 16.9 13.2 .. .. .. Chile 3,074 4 20 76 100 48 .. 9.7 11.8 .. .. .. China 1,781 7 28 35 .. 7 .. .. 2.9 2.90 3.1 1,043 Hong Kong, China 5,878 13 56 136 100 1,179 1.3 12.6 2.2 0.77 3.7 657 Colombia 890 19 17 65 80 68 27.9 8.0 10.4 .. 5.3 .. Congo, Dem. Rep. 91 4 0 7 50 5 .. .. 11.0 .. 6.4 1,428 Congo, Rep. 160 56 0 14 80 .. .. .. 11.0 5.39 2.9 .. Costa Rica 1,719 10 31 33 86 127 3.8 6.0 1.9 .. 2.4 459 Côte d'Ivoire 170 18 1 21 55 17 .. 22.5 21.8 2.25 5.4 1,442 Croatia 3,475 17 41 101 100 231 12.0 13.1 14.5 .. 5.5 540 Cuba 1,152 15 9 1 71 31 9.2 13.1 22.6 7.49 2.6 58 Czech Republic 6,343 6 31 118 100 95 6.1 24.1 17.2 1.06 3.9 768 Denmark 6,663 4 57 107 .. 318 .. 30.7 6.0 0.89 2.6 474 Dominican Republic 1,000 27 9 48 90 .. .. 18.2 8.6 0.22 0.5 .. Ecuador 714 43 13 64 67 216 3.8 7.9 18.9 .. 3.8 660 Egypt, Arab Rep. 1,245 16 15 24 98 30 0.1 4.0 5.8 1.45 3.8 443 El Salvador 666 13 15 57 95 410 1.7 2.0 8.5 2.40 4.6 1,182 Eritrea .. .. 1 1 .. 9 73.7 6.2 .. 3.59 2.4 80 Estonia 5,567 11 40 124 99 109 .. 15.6 8.6 0.90 5.4 641 Ethiopia 34 10 1 1 .. 3 .. 2.2 3.1 4.01 2.4 142 Finland 16,120 4 36 108 99 .. .. 28.7 6.7 1.80 2.7 451 France 7,938 6 55 84 99 183 .. 29.0 29.4 0.84 2.3 582 Gabon 999 18 3 58 78 74 13.4 32.4 14.9 2.77 1.5 244 Gambia, The .. .. 3 24 .. .. .. .. .. 1.81 .. .. Georgia 1,672 16 12 38 96 .. .. 9.7 44.1 .. 7.3 197 Germany 7,111 5 66 102 99 .. .. 26.5 17.0 0.43 2.9 559 Ghana 266 14 2 23 69 20 5.6 9.8 7.0 0.39 .. 563 Greece 5,242 9 55 100 100 182 12.8 21.1 23.1 1.09 3.4 632 Guatemala 522 8 10 55 .. 195 .. 9.8 6.1 1.21 .. .. Guinea .. .. 0 2 .. .. .. .. 7.7 .. .. .. Guinea-Bissau .. .. 1 6 .. .. .. .. 21.9 .. .. .. Haiti 37 38 2 5 .. .. .. .. 4.5 2.15 .. .. 304 2008 World Development Indicators 5.10 STATES AND MARKETS Power and communications Electric power Telephones Access Quality Affordability and efficiency Inter- Transmission Population national $ per month and covered voice Price Cost of Telecom- Consumption distribution per 100 people by mobile traffic a Faults basket for Price call to U.S.a munications Subscrib- per capita losses Fixed Mobile telephonya minutes per 100 residential basket for $ per revenuea ers per kWh % of output mainlinesa subscribersa % per person mainlinesa fi xed lineb mobilea 3 minutes % of GDP employeea 2005 2005 2006 2006 2006 2006 2006 2006 2006 2005 2006 2006 Honduras 626 24 10 32 .. 96 .. 5.9 10.8 2.52 7.1 187 Hungary 3,771 11 33 99 99 105 8.2 23.6 12.1 1.01 4.4 780 India 480 25 4 15 61 .. .. 3.3 2.5 1.19 2.0 .. Indonesia 509 12 7 29 90 5 .. 5.8 4.3 2.79 2.2 1,084 Iran, Islamic Rep. 2,117 19 31 19 90 9 .. 2.1 2.7 0.55 1.4 856 Iraq 1,188 6 4 2 72 .. .. .. 2.6 .. .. .. Ireland 6,234 8 49 110 99 .. 3.2 39.5 19.3 0.71 2.4 406 Israel 6,759 3 43 119 100 364 .. 10.5 9.3 0.59 4.2 692 Italy 5,669 7 43 122 100 .. .. 24.9 14.1 0.79 3.0 1,116 Jamaica 2,474 12 12 106 95 .. .. 9.1 7.5 0.87 4.9 .. Japan 8,233 5 43 80 99 43 0.0 26.1 20.4 1.63 3.7 1,722 Jordan 1,676 14 11 78 99 139 7.9 10.0 6.9 1.44 7.8 707 Kazakhstan 3,206 10 19 51 .. .. .. .. 11.4 .. 2.6 98 Kenya 138 18 1 18 .. 6 145.4 13.9 16.6 3.00 4.6 220 Korea, Dem. Rep. 817 16 .. .. .. .. .. .. .. .. .. .. Korea, Rep. 7,779 4 55 83 99 92 .. 8.3 14.2 0.76 4.9 .. Kuwait 15,345 11 20 94 100 .. 4.0 10.5 75.0 1.51 3.4 387 Kyrgyz Republic 1,842 26 9 11 90 30 .. 4.7 6.4 5.40 4.5 134 Lao PDR .. .. 1 11 55 7 .. .. 3.8 1.11 1.7 496 Latvia 2,702 17 29 95 98 67 1.1 13.3 9.3 1.63 3.9 731 Lebanon 2,242 16 17 27 100 279 .. 15.0 20.1 2.19 5.0 .. Lesotho .. .. 2 13 29 18 60.0 18.4 14.8 3.28 .. 1,111 Liberia .. .. .. 5 .. .. .. .. .. .. .. .. Libya 3,299 13 8 65 71 66 .. 1.9 6.1 .. .. 1,566 Lithuania 3,104 8 23 139 100 49 9.3 17.7 8.9 1.55 3.3 .. Macedonia, FYR 3,417 23 24 70 99 63 9.0 10.5 14.8 .. 7.1 .. Madagascar .. .. 1 5 .. 1 .. 10.5 8.1 0.59 2.6 .. Malawi .. .. 1 3 .. .. .. 5.8 10.2 .. 3.3 .. Malaysia 3,262 4 17 75 .. .. 22.5 8.7 5.0 0.71 4.6 770 Mali .. .. 1 13 .. .. .. 16.1 13.5 .. 5.2 .. Mauritania .. .. 1 35 .. .. 5.5 11.6 .. .. 5.9 1,003 Mauritius .. .. 29 62 100 150 23.0 7.9 4.2 1.59 3.7 492 Mexico 1,899 16 19 55 100 174 1.4 16.1 13.9 0.83 3.0 691 Moldova 1,428 38 27 35 97 110 5.1 5.3 17.1 1.46 10.2 250 Mongolia .. .. 6 22 .. 5 18.5 1.6 5.4 .. 3.6 147 Morocco 644 18 4 52 98 65 25.0 23.0 15.9 1.69 4.5 821 Mozambique 450 12 0 11 .. 13 46.0 13.1 10.0 1.17 1.5 980 Myanmar 82 35 1 0 .. 3 125.0 1.3 .. 0.17 0.6 81 Namibia 1,428 18 7 25 88 .. 35.0 9.1 14.2 .. 4.8 470 Nepal 70 20 2 4 1 6 68.0 3.1 2.1 2.04 0.9 145 Netherlands 6,988 4 47 97 100 .. .. .. 22.9 0.32 .. .. New Zealand 9,656 7 42 85 98 361 .. 28.6 19.4 1.30 .. 962 Nicaragua 414 22 4 33 60 62 4.8 9.2 15.1 3.15 .. .. Niger .. .. 0 2 15 .. .. 9.5 16.5 .. 2.2 .. Nigeria 127 24 1 22 58 .. .. .. 10.7 1.49 3.5 .. Norway 25,137 7 44 108 .. 193 .. 37.9 19.8 .. 1.4 445 Oman 3,757 26 11 71 92 189 89.7 12.1 5.5 1.87 2.4 583 Pakistan 456 24 3 22 36 10 .. 4.1 2.4 1.03 2.5 433 Panama 1,500 16 13 52 89 .. 12.2 10.3 16.7 .. 3.9 330 Papua New Guinea .. .. 1 1 .. .. .. .. 14.6 .. .. .. Paraguay 849 5 6 54 .. 31 8.2 6.4 3.4 0.90 4.4 .. Peru 848 9 8 31 .. 99 .. 18.8 23.0 1.80 2.7 670 Philippines 588 12 4 50 99 28 4.5 11.6 5.3 1.20 4.4 1,555 Poland 3,437 9 30 96 99 .. 5.0 .. 7.6 1.35 3.8 .. Portugal 4,663 9 40 115 99 178 10.4 31.8 23.1 1.04 4.9 1,126 Puerto Rico .. .. 27 86 100 .. .. 33.5 .. .. .. .. 2008 World Development Indicators 305 5.10 Power and communications Electric power Telephones Access Quality Affordability and efficiency Inter- Transmission Population national $ per month and covered voice Price Cost of Telecom- Consumption distribution per 100 people by mobile traffic a Faults basket for Price call to U.S.a munications Subscrib- per capita losses Fixed Mobile telephonya minutes per 100 residential basket for $ per revenuea ers per kWh % of output mainlinesa subscribersa % per person mainlinesa fi xed lineb mobilea 3 minutes % of GDP employeea 2005 2005 2006 2006 2006 2006 2006 2006 2006 2005 2006 2006 Romania 2,342 10 19 81 98 .. 10.4 7.2 10.5 0.82 5.0 .. Russian Federation 5,785 12 28 84 .. .. 7.1 .. 5.9 2.03 2.9 439 Rwanda .. .. 0 3 75 .. .. 6.6 12.3 2.43 3.3 .. Saudi Arabia 6,813 11 17 83 96 216 .. 11.7 9.7 .. 3.1 927 Senegal 151 30 2 25 85 39 2.0 15.4 9.4 1.02 9.1 1,100 Serbia .. .. 36 70 99 .. .. .. 5.8 .. 0.0 605 Sierra Leone .. .. .. .. .. .. .. .. 70.9 .. .. .. Singapore 8,358 5 41 107 100 1,045 0.3 6.3 6.1 0.69 3.1 .. Slovak Republic 4,920 5 22 91 100 90 7.9 19.8 12.2 1.06 3.6 559 Slovenia 6,918 6 42 91 99 .. 13.4 17.6 10.1 0.65 3.5 1,225 Somalia .. .. 1 6 .. .. .. .. 5.1 .. .. .. South Africa 4,847 6 10 72 96 .. .. 22.7 13.8 0.79 6.4 1,145 Spain 6,147 9 42 105 99 173 .. 25.8 21.7 0.60 4.3 656 Sri Lanka 378 15 9 27 85 28 8.6 8.2 1.2 2.11 2.6 619 Sudan 94 16 2 12 .. 12 95.5 6.3 4.0 .. 7.6 624 Swaziland .. .. 4 22 .. .. 90.0 8.3 13.5 2.97 12.3 .. Sweden 15,440 7 59 106 99 .. .. 26.7 6.0 0.41 2.8 764 Switzerland 8,305 7 67 99 100 .. .. 29.5 28.0 0.32 3.3 537 Syrian Arab Republic 1,411 24 17 24 99 44 50.0 2.7 10.0 .. 3.0 221 Tajikistan 2,267 15 4 4 4 .. .. .. 23.3 7.84 2.9 114 Tanzania 61 27 0 15 .. .. 26.0 14.0 10.0 3.17 .. .. Thailand 1,988 8 11 64 31 14 2.7 8.3 4.3 0.67 3.2 1,850 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 94 46 1 11 85 21 .. 15.4 12.1 3.98 6.3 432 Trinidad and Tobago 5,038 6 25 125 .. 376 .. 7.0 6.7 2.19 2.4 .. Tunisia 1,194 12 13 72 100 73 20.0 2.9 5.3 .. 4.4 915 Turkey 1,898 15 26 72 96 27 5.6 14.7 12.7 2.40 2.9 1,032 Turkmenistan 1,731 12 8 2 14 .. .. .. 17.2 .. 0.7 72 Uganda .. .. 0 7 80 .. .. 13.8 9.4 3.21 3.4 255 Ukraine 3,246 13 26 105 96 57 41.3 .. 9.4 1.65 5.8 210 United Arab Emirates 13,708 7 31 130 100 .. 0.3 17.4 4.1 1.73 2.7 587 United Kingdom 6,253 8 55 115 99 .. .. 28.2 13.7 0.77 3.7 .. United States 13,648 6 57 78 99 279 13.8 25.0 5.2 .. 3.0 389 Uruguay 2,007 23 30 70 100 121 .. 10.7 16.1 0.52 .. .. Uzbekistan 1,659 9 7 3 .. 12 92.2 0.9 1.8 .. 2.5 117 Venezuela, RB 2,848 25 16 70 .. .. .. .. 1.2 0.84 3.6 677 Vietnam 573 11 19 18 .. .. .. 2.7 6.3 1.95 4.7 .. West Bank and Gaza .. .. 9 22 95 66 23.0 1.0 9.6 1.17 0.8 903 Yemen, Rep. 174 23 5 9 68 .. .. 2.8 4.2 2.39 1.2 .. Zambia 721 5 1 14 65 .. 108.0 7.7 14.2 1.41 2.5 175 Zimbabwe 953 7 3 6 .. 25 57.0 4.3 3.4 .. 4.3 375 World 2,678 w 9w 20 w 40 w .. w .. w .. m 10.0 m 10.4 m 1.42 m 2.9 w 572 m Low income 391 22 3 14 40 .. .. 6.1 10.0 1.99 4.0 .. Middle income 1,928 11 22 44 .. 31 8.2 9.2 10.2 1.65 2.1 586 Lower middle income 1,502 9 22 38 .. 21 22.0 8.2 9.8 2.08 2.1 599 Upper middle income 3,131 13 22 66 95 .. 7.5 11.4 10.9 1.06 3.6 594 Low & middle income 1,290 12 13 31 .. .. .. 8.7 10.0 1.81 2.5 492 East Asia & Pacific 1,492 7 23 35 .. 8 .. 5.8 5.0 1.16 2.7 849 Europe & Central Asia 3,633 12 25 63 .. .. 9.5 7.2 11.8 1.55 1.7 314 Latin America & Carib. 1,715 16 18 55 90 .. .. 9.5 10.4 1.21 4.3 642 Middle East & N. Africa 1,358 17 17 36 84 36 23.5 5.2 6.5 1.66 1.5 466 South Asia 432 24 3 15 60 .. .. 4.0 2.4 2.02 2.1 433 Sub-Saharan Africa 542 9 1 14 .. .. .. 11.6 12.3 2.43 3.2 586 High income 9,760 6 53 90 99 204 .. 26.6 17.0 0.77 4.4 641 Euro area 6,926 6 54 99 99 .. 8.3 28.8 20.5 0.73 3.3 638 a. Data are from the International Telecommmunication Union's (ITU) World Telecommunication Development Report database. Please cite the ITU for third-party use of these data. b. Calculated by the World Bank based on ITU data. 306 2008 World Development Indicators 5.10 STATES AND MARKETS Power and communications About the data Definitions The quality of an economy's infrastructure, includ- Operators are the main source of telecommunica- · Electric power consumption per capita measures ing power and communications, is an important ele- tions data, so information on subscribers is widely the production of power plants and combined heat ment in investment decisions for both domestic and available for most countries. This gives a general and power plants less transmission, distribution, foreign investors. Government effort alone is not idea of access, but a more precise measure is the and transformation losses and own use by heat and enough to meet the need for investments in modern penetration rate--the share of households with power plants divided by midyear population. · Elec- infrastructure; public-private partnerships, especially access to telecommunications. Also important are tric power transmission and distribution losses are those involving local providers and financiers, are data on actual use of telecommunications equip- losses in transmission between sources of supply critical for lowering costs and delivering value for ment. Ideally, statistics on telecommunications (and and points of distribution and in distribution to con- money. In telecommunications, competition in the other information and communications technologies) sumers, including pilferage. · Fixed telephone main- marketplace, along with sound regulation, is lower- should be compiled for all three measures: subscrip- lines are telephone lines connecting a subscriber ing costs, improving quality, and easing access to tion and possession, access, and use. The quality of to the telephone exchange equipment. · Mobile services around the globe. data varies among reporting countries as a result of telephone subscribers are subscribers to a public An economy's production and consumption of elec- differences in regulations covering data provision. mobile telephone service using cellular technology. tricity are basic indicators of its size and level of Globally, there have been huge improvements in · Population covered by mobile telephony is the development. Although a few countries export elec- access to telecommunications, driven mainly by percentage of people within range of a mobile cellular tric power, most production is for domestic consump- mobile telephony. By 2002 access to mobiles out- signal regardless of whether they are subscribers. tion. Expanding the supply of electricity to meet the paced access to fi xed-line telephones in develop- · International voice traffic is the sum of interna- growing demand of increasingly urbanized and indus- ing countries, and rural areas are catching up with tional incoming and outgoing telephone traffic (in trialized economies without incurring unacceptable urban areas (although gaps are still large). By 2004 minutes) divided by total population. · Telephone social, economic, and environmental costs is one of approximately 98 percent of the population in high- mainline faults are the number of reported faults for the great challenges facing developing countries. income countries and about 64 percent of the popu- the year per 100 telephone mainlines. · Price bas- Data on electric power production and consump- lation in developing countries were covered by mobile ket for residential fixed line is calculated as one-fifth tion are collected from national energy agencies by telephony (within range of a mobile cellular signal). of the installation charge, the monthly subscription the International Energy Agency (IEA) and adjusted Indeed, in many developing countries, especially in charge, and the cost of local calls (15 peak and 15 by the IEA to meet international definitions (for data Sub-Saharan Africa, the number of mobile phones off-peak calls of three minutes each). · Price bas- on electricity production, see table 3.10). Electricity has overtaken the number of fixed-line phones. ket for mobile is calculated as the prepaid price for consumption is equivalent to production less power Telephone mainline faults are a measure of tele- 25 calls per month spread over the same mobile plants' own use and transmission, distribution, and communications quality. The definition varies among network, other mobile networks, and mobile to fixed transformation losses less exports plus imports. It countries: some operators define faults as includ- calls and during peak, off-peak, and weekend times. includes consumption by auxiliary stations, losses ing malfunctioning customer equipment while others It also includes 30 text messages per month. · Cost in transformers that are considered integral parts include only technical faults. of call to U.S. is the cost of a three-minute, peak of those stations, and electricity produced by pump- Although access is the key to delivering telecom- rate, fixed-line call from the country to the United ing installations. Where data are available, it covers munications services to people, if the service is not States. · Telecommunications revenue is the rev- electricity generated by primary sources of energy-- affordable to most people, then goals of universal enue from the provision of telecommunications ser- coal, oil, gas, nuclear, hydro, geothermal, wind, tide usage will not be met. Three indicators of tele- vices such as fi xed-line, mobile, and data divided and wave, and combustible renewables. Neither pro- communications affordability are presented in the by GDP. · Subscribers per employee are telephone duction nor consumption data capture the reliability table: price basket for fixed-line telephone service, subscribers (fi xed-line plus mobile) divided by the of supplies, including breakdowns, load factors, and price basket for mobile service, and the cost of an total number of telecommunications employees. frequency of outages. international call. Telecommunications effi ciency Over the past decade new financing and technol- is measured by total telecommunications revenue ogy, along with privatization and liberalization, have divided by GDP and by total telephone subscribers Data sources spurred dramatic growth in telecommunications per employee. in many countries. With the rapid development of Data on electricity consumption and losses are mobile telephony and the global expansion of the from the IEA's Energy Statistics and Balances of Internet, information and communication technolo- Non-OECD Countries 2004­2005, the IEA's Energy gies are increasingly recognized as essential tools of Statistics of OECD Countries 2004­2005, and the development, contributing to global integration and United Nations Statistics Division's Energy Statis- enhancing public sector effectiveness, efficiency, tics Yearbook. Data on telecommunications are and transparency. The table presents telecommu- from the International Telecommunication Union's nications indicators covering access, quality, and World Telecommunication Development Report affordability and efficiency. database and World Bank estimates. 2008 World Development Indicators 307 5.11 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisionb technology expenditures Access Quality Application Affordability Broadband per 100 people subscribers b International Secure Price basket per 1,000 Personal Internet per 100 Internet bandwidthb Internet servers for Internet b Per capita people % computersb usersb people bits per capita per million people $ per month % of GDP $ 2000­06a 2006 2006 2006 2006 2006 December 2007 2006 2006 2006 Afghanistan .. 6 0.4 2.1 0.00 0 0 .. .. .. Albania 25 90 1.7 14.9 0.01 4 2 16.3 .. .. Algeria .. 90 1.1 7.4 0.59 5 0 9.3 2.4 84 Angola 2 9 0.7 0.5 0.00 12 0 0.2 .. .. Argentina 36 97 9.0 20.9 4.01 690 12 5.4 6.9 379 Armenia 8 91 9.8 5.7 0.07 22 3 56.6 .. .. Australia 156 99 75.7 73.9 18.84 11,593 579 22.5 6.4 2,413 Austria 315 98 60.7 50.7 17.24 6,634 284 15.7 5.5 2,137 Azerbaijan 16 99 2.3 9.8 0.03 36 0 10.0 .. .. Bangladesh .. 23 2.2 0.3 0.00 8 0 24.0 2.7 11 Belarus 82 97 0.8 56.3 0.12 192 1 10.5 .. .. Belgium 164 98 37.7 45.8 19.19 11,279 146 37.6 5.9 2,203 Benin 0 20 0.4 8.0 0.00 5 0 11.4 .. .. Bolivia .. 50 2.4 6.2 0.12 43 3 12.1 4.9 58 Bosnia and Herzegovina .. 87 5.4 24.2 1.02 40 4 7.6 .. .. Botswana 43 10 4.7 3.3 0.09 16 1 18.2 .. .. Brazil 36 91 16.1 22.5 3.13 150 16 10.1 6.4 363 Bulgaria 79 97 6.3 24.3 5.00 1,756 11 7.4 3.4 141 Burkina Faso .. 8 0.2 0.6 0.01 15 0 33.9 .. .. Burundi .. 14 0.7 0.7 0.00 1 0 40.0 .. .. Cambodia .. 43 0.3 0.3 0.01 1 0 9.9 .. .. Cameroon .. 26 1.1 2.0 0.00 9 0 17.6 5.1 52 Canada 175 99 87.6 68.1 23.51 6,732 644 9.5 5.7 2,201 Central African Republic .. 5 0.3 0.3 0.00 0 0 100.1 .. .. Chad .. 4 0.2 0.6 0.00 1 .. 86.3 .. .. Chile 51 90 14.1 25.3 5.95 780 22 26.7 5.2 465 China 74 89 4.3 10.4 3.88 196 0 10.0 5.4 108 Hong Kong, China 223 99 61.2 55.0 26.20 13,439 194 3.9 8.8 2,428 Colombia 22 90 4.2 14.7 1.38 560 6 7.5 7.1 239 Congo, Dem. Rep. .. 4 0.0 0.3 0.00 0 0 14.0 .. .. Congo, Rep. .. 7 0.5 1.9 0.00 0 0 64.9 .. .. Costa Rica 65 89 23.1 27.6 1.34 176 67 18.3 7.3 368 Côte d'Ivoire .. 35 1.7 1.6 0.01 3 0 67.7 .. .. Croatia .. 98 19.9 35.5 5.67 1,074 48 16.5 0.0 0 Cuba 65 70 3.3 2.1 0.00 14 0 30.0 .. .. Czech Republic 182 .. 27.4 34.5 10.58 2,170 64 19.9 7.3 1,020 Denmark 352 97 69.6 58.3 31.79 34,796 614 23.4 6.0 3,036 Dominican Republic 42 76 2.2 20.8 0.69 6 6 12.3 .. .. Ecuador 99 80 6.6 11.7 0.21 227 5 20.2 3.0 93 Egypt, Arab Rep. .. 88 3.7 8.1 0.28 126 1 5.0 1.4 20 El Salvador 37 83 5.2 9.6 0.63 23 6 22.6 .. .. Eritrea .. 16 0.6 2.1 0.00 2 .. 13.0 .. .. Estonia 192 .. 48.3 56.6 17.00 11,175 163 10.9 .. .. Ethiopia 5 4 0.4 0.2 0.00 0 0 6.7 .. .. Finland 431 94 50.0 55.5 27.12 4,311 380 22.5 6.7 2,689 France 165 97 57.5 49.1 20.73 3,286 96 12.6 6.3 2,324 Gabon .. 56 3.5 6.2 0.09 153 5 39.2 .. .. Gambia, The .. 12 1.5 3.6 0.00 6 1 6.8 .. .. Georgia 4 89 4.7 7.5 0.61 7 5 9.9 .. .. Germany 267 98 60.6 46.9 17.10 6,864 349 7.5 6.2 2,174 Ghana .. 26 0.6 2.7 0.06 9 0 11.9 .. .. Greece .. 100 9.2 18.4 4.38 587 40 10.2 3.2 875 Guatemala .. 50 2.1 10.1 0.21 56 6 54.3 .. .. Guinea .. 11 0.5 0.5 0.00 0 .. 5.9 .. .. Guinea-Bissau .. 31 0.2 2.2 0.00 1 .. 15.0 .. .. Haiti .. 27 0.2 6.9 0.00 17 1 12.0 .. .. 308 2008 World Development Indicators 5.11 STATES AND MARKETS The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisionb technology expenditures Access Quality Application Affordability Broadband per 100 people subscribers b International Secure Price basket per 1,000 Personal Internet per 100 Internet bandwidthb Internet servers for Internet b Per capita people % computersb usersb people bits per capita per million people $ per month % of GDP $ 2000­06a 2006 2006 2006 2006 2006 December 2007 2006 2006 2006 Honduras .. 58 1.8 4.8 0.00 6 5 12.0 4.6 60 Hungary 217 96 14.9 34.8 9.70 993 36 10.5 6.0 669 India 73 32 1.6 5.5 0.21 24 1 6.6 6.1 50 Indonesia .. 65 1.5 7.3 0.05 7 1 6.6 3.1 51 Iran, Islamic Rep. .. .. 10.6 25.7 0.66 53 0 2.3 2.4 76 Iraq .. .. .. 0.1 0.00 .. 0 .. .. .. Ireland 182 98 52.8 33.7 12.12 5,912 415 31.4 4.3 2,207 Israel .. 92 122.1 26.9 20.16 2,455 182 22.2 7.9 1,570 Italy 138 96 36.7 49.0 14.68 2,044 53 25.0 4.3 1,363 Jamaica .. 70 6.7 46.4 1.70 15,822 18 26.5 10.2 383 Japan 551 99 67.6 68.5 20.16 1,038 331 13.1 7.9 2,688 Jordan .. 96 6.6 14.4 0.88 57 4 10.9 8.0 204 Kazakhstan .. .. .. 8.1 0.20 63 1 15.8 .. .. Kenya .. 18 1.4 7.6 0.00 21 0 15.8 2.4 15 Korea, Dem. Rep. .. .. .. .. 0.00 .. .. .. .. .. Korea, Rep. .. .. 53.2 70.5 29.00 1,028 60 34.6 6.6 1,214 Kuwait .. 95 23.7 31.4 0.99 348 35 13.7 1.4 466 Kyrgyz Republic 1 .. 1.9 5.7 0.05 39 1 12.0 .. .. Lao PDR 3 30 1.8 0.4 0.00 4 0 25.0 .. .. Latvia 154 98 24.6 46.8 4.79 3,230 46 12.6 .. .. Lebanon 61 96 10.2 23.4 4.19 111 10 10.0 .. .. Lesotho .. 2 0.1 2.6 0.00 2 0 38.6 .. .. Liberia .. .. .. .. 0.00 .. .. .. .. .. Libya .. 50 2.2 3.9 0.00 21 0 22.1 .. .. Lithuania 108 98 18.0 31.9 10.86 2,714 26 7.3 .. .. Macedonia, FYR 89 98 22.2 13.2 1.79 17 2 25.3 .. .. Madagascar .. 10 0.5 0.6 0.00 2 0 2.8 .. .. Malawi .. 3 0.2 0.4 0.00 1 0 22.5 .. .. Malaysia 111 95 21.8 43.2 3.44 124 17 2.7 6.7 388 Mali .. 17 0.4 0.6 0.02 26 0 28.7 .. .. Mauritania .. 25 2.6 3.3 0.02 30 1 16.0 .. .. Mauritius 77 93 16.9 14.5 1.75 153 18 16.2 .. .. Mexico 92 93 13.6 17.5 3.58 109 10 17.3 3.3 266 Moldova .. 82 9.0 19.0 0.57 147 4 13.3 .. .. Mongolia 19 63 13.3 10.5 0.07 13 4 10.7 .. .. Morocco 11 78 2.5 20.0 1.28 377 1 26.8 5.6 119 Mozambique 3 6 1.4 0.9 0.00 1 0 32.9 .. .. Myanmar .. 3 0.8 0.2 0.00 2 0 1.5 .. .. Namibia 28 39 12.3 4.0 0.00 18 8 48.7 .. .. Nepal .. 13 0.5 0.9 0.00 5 1 8.0 .. .. Netherlands 308 99 85.4 89.0 31.78 20,501 413 8.8 6.2 2,531 New Zealand 185 98 50.2 76.5 13.77 1,107 588 11.0 10.6 2,635 Nicaragua .. 60 4.0 2.8 0.34 1 3 10.0 .. .. Niger 0 7 0.1 0.3 0.00 2 0 101.8 .. .. Nigeria .. 32 0.8 5.5 0.00 1 0 11.3 3.4 27 Norway 517 100 59.4 87.4 27.43 9,305 389 29.8 4.9 3,556 Oman .. 79 5.2 12.5 0.60 174 4 5.2 .. .. Pakistan 51 46 0.5 7.5 0.04 5 0 9.5 6.9 55 Panama 65 79 4.6 6.7 0.54 287 57 38.5 8.2 425 Papua New Guinea 9 10 6.4 1.8 0.00 1 1 12.9 .. .. Paraguay .. 82 7.8 4.3 0.27 83 1 0.2 .. .. Peru .. 71 10.3 22.1 1.76 367 6 11.5 5.9 199 Philippines 80 63 5.3 5.5 0.15 38 3 2.0 6.7 91 Poland 113 91 24.2 28.8 6.92 560 38 11.7 4.2 369 Portugal .. 99 13.3 30.3 13.79 829 65 28.7 4.3 797 Puerto Rico .. 97 0.8 23.4 3.02 511 33 .. .. .. 2008 World Development Indicators 309 5.11 The information age Daily Households Personal computers and the Internet Information and newspapers with communications televisionb technology expenditures Access Quality Application Affordability Broadband per 100 people subscribers b International Secure Price basket per 1,000 Personal Internet per 100 Internet bandwidthb Internet servers for Internet b Per capita people % computersb usersb people bits per capita per million people $ per month % of GDP $ 2000­06a 2006 2006 2006 2006 2006 December 2007 2006 2006 2006 Romania 70 94 12.9 32.4 8.19 1,503 7 6.0 3.2 180 Russian Federation 92 98 12.2 18.0 2.04 100 3 12.7 3.2 222 Rwanda .. 2 0.2 0.7 0.02 7 .. 30.1 .. .. Saudi Arabia .. 99 13.6 19.8 0.92 126 5 5.3 2.1 308 Senegal 9 31 2.1 5.4 0.24 103 0 25.8 8.5 64 Serbia .. .. 5.2 20.3 .. 95 3 6.9 .. .. Sierra Leone .. .. .. 0.2 0.00 .. 0 10.6 .. .. Singapore 361 98 68.2 38.3 17.76 7,052 291 13.2 9.3 2,743 Slovak Republic 125 98 35.8 41.8 5.88 2,913 28 19.8 5.5 557 Slovenia 175 96 40.4 62.3 13.14 1,255 95 18.8 3.1 575 Somalia .. 8 0.9 1.1 0.00 0 0 .. .. .. South Africa 30 59 8.5 10.9 0.35 19 23 11.6 10.0 537 Spain 145 99 27.7 42.1 15.08 2,776 100 32.0 3.6 1,004 Sri Lanka 24 32 3.7 2.2 0.15 25 2 4.4 5.4 73 Sudan .. 16 11.2 9.3 0.01 5 0 52.5 .. .. Swaziland 26 18 3.7 3.7 0.00 1 4 15.1 .. .. Sweden 480 94 83.6 76.9 25.83 17,468 405 19.2 7.2 3,052 Switzerland 429 99 86.5 58.2 28.57 9,609 576 7.9 7.7 3,914 Syrian Arab Republic .. 95 4.2 7.7 0.03 8 0 9.2 .. .. Tajikistan .. 79 1.3 0.3 0.00 0 .. 12.3 .. .. Tanzania 2 14 0.9 1.0 0.00 0 0 36.0 .. .. Thailand .. 92 7.0 13.3 0.17 156 6 5.8 4.0 129 Timor-Leste .. .. .. .. 0.00 .. 1 5.0 .. .. Togo 2 16 3.0 5.0 0.00 16 0 10.7 .. .. Trinidad and Tobago 151 88 9.7 12.3 1.55 370 27 12.6 .. .. Tunisia 23 92 6.3 12.8 0.18 126 2 3.1 6.0 180 Turkey .. 92 5.7 16.8 3.80 631 25 6.7 8.2 452 Turkmenistan 9 .. 7.2 1.3 0.00 16 .. 23.1 .. .. Uganda .. 10 1.7 2.5 0.00 4 0 99.6 .. .. Ukraine 132 97 4.5 11.9 0.00 17 2 2.1 7.8 177 United Arab Emirates .. 86 25.6 40.2 5.66 2,371 59 5.4 3.6 1,201 United Kingdom 292 98 75.8 55.4 21.46 13,062 560 27.6 6.9 2,721 United States 194 99 76.2 69.5 19.42 3,307 868 15.0 8.7 3,846 Uruguay .. 92 13.6 22.8 3.23 484 30 23.9 7.8 454 Uzbekistan .. .. 3.1 6.4 0.03 9 0 5.7 .. .. Venezuela, RB 93 90 9.3 15.3 1.99 50 5 12.5 3.7 248 Vietnam .. 83 1.4 17.5 0.61 84 0 1.8 15.1 110 West Bank and Gaza 10 93 5.4 7.0 0.68 199 1 15.6 .. .. Yemen, Rep. 4 43 1.9 1.2 0.00 0 0 6.0 .. .. Zambia 5 .. 1.1 4.3 0.02 11 0 33.3 .. .. Zimbabwe .. 34 6.5 9.2 0.08 4 0 1.3 12.7 17 World 105 w 83 m 10.6 w 21.4 w 5.46 w 529 w 74 w 12.1 m 6.7 w 564 w Low income .. 16 1.4 4.2 0.18 22 0 12.0 6.1 47 Middle income 71 89 6.6 14.1 3.33 144 5 11.2 5.1 166 Lower middle income 73 80 4.3 11.4 3.23 189 1 10.0 5.0 103 Upper middle income 68 93 13.3 22.2 3.57 242 15 11.7 5.2 339 Low & middle income 67 60 4.3 8.0 2.04 143 3 11.7 5.2 121 East Asia & Pacific 74 63 4.1 11.1 3.56 182 1 5.8 5.3 105 Europe & Central Asia 99 97 10.2 19.2 3.64 268 11 11.1 4.6 291 Latin America & Carib. 64 79 11.3 18.4 2.95 269 12 12.2 5.3 304 Middle East & N. Africa .. 90 5.6 13.8 0.63 126 1 9.2 2.9 72 South Asia 70 32 1.4 4.9 0.18 22 1 6.6 6.0 47 Sub-Saharan Africa .. 14 1.8 3.8 0.03 5 2 15.9 .. .. High income 263 98 56.7 59.3 19.20 4,346 441 13.7 7.2 2,555 Euro area 203 98 47.6 47.9 17.33 4,830 185 20.7 5.4 1,813 a. Data are for the most recent year available. b. Data are from the International Telecommunication Union's (ITU) World Telecommunication Development Report database. Please cite ITU for third-party use of these data. 310 2008 World Development Indicators 5.11 STATES AND MARKETS The information age About the data Definitions The digital and information revolution has changed personal computers sold each year in a country. · Daily newspapers are newspapers that report the way the world learns, communicates, does busi- Annual shipment data can also be multiplied by an mainly on events occurring in the 24-hour period ness, and treats illnesses. New information and com- estimated average useful lifespan before replace- before going to press and that are issued at least munications technologies offer vast opportunities for ment to approximate the number of personal com- four times a week. The indicator is average circula- progress in all walks of life in all countries--opportu- puters. There is no precise method for determining tion (or copies printed) per 1,000 people. · House- nities for economic growth, improved health, better replacement rates, but in general personal comput- holds with television are the percentage of house- service delivery, learning through distance educa- ers are replaced every three to five years. holds with a television set. · Personal computers tion, and social and cultural advances. Data on Internet users and related Internet indica- are self-contained computers designed for use by The table presents indicators of the penetration tors are based on nationally reported data. Some a single individual, including laptops and notebooks of the information economy (newspapers, televi- countries derive these data from surveys, but since and excluding terminals connected to mainframe and sions, personal computers, and Internet use), qual- survey questions and definitions differ, the estimates minicomputers intended primarily for shared use and ity (broadband subscribers, international Internet may not be strictly comparable. For example, ques- devices such as smart phones and personal digital bandwidth, and secure Internet servers), and some tions on the age of Internet users and frequency of assistants. · Internet users are people with access of the economics of the information age (Internet use vary by country. Countries without surveys gener- to the worldwide network. · Broadband subscribers access charges and spending on information and ally derive their estimates by multiplying subscriber are the number of broadband subscribers with a digi- communications technologies). counts reported by Internet service providers by a tal subscriber line, cable modem, or other high-speed Comparable statistics on access, use, quality, multiplier. This method may undercount the actual technologies. · International Internet bandwidth is and affordability of information and communica- number of people using the Internet, particularly in the contracted capacity of international connections tions technologies are needed to formulate growth- developing countries, where many commercial sub- between countries for transmitting Internet traffic. enabling policies for the sector and to monitor and scribers rent out computers connected to the Internet · Secure Internet servers are servers using encryp- evaluate the sector's impact on economic and social or prepaid cards are used to access the Internet. tion technology in Internet transactions. · Price bas- development. Although basic access data are avail- Broadband refers to technologies that provide ket for Internet is based on the cheapest available able for many countries, in most developing coun- Internet speeds of at least 256 kilobits per sec- tariff for accessing the Internet 20 hours a month (10 tries little is known about who uses information and ond of upstream and downstream capacity. These hours peak and 10 hours off-peak). The basket does communications technologies (especially by age technologies--including digital subscriber lines, not include telephone line rental but does include and gender); what they are used for (school, work, cable modems, satellite broadband Internet, fiber- any telephone usage charges. · Information and business, research, government, and the like); and to-home Internet access, ethernet local access communications technology expenditures include how they affect people and businesses. To close networks, and wireless area networks--improve the computer hardware (computers, storage devices, this data gap, the global Partnership on Measuring online experience. Bandwidth, another measure of printers, and other peripherals); computer software ICT for Development is helping to set standards and quality, refers to the range of frequencies available to (operating systems, programming tools, utilities, harmonize information and communications technol- be occupied by signals. The higher the bandwidth, the applications, and internal software development); ogy statistics and to build capacity for compiling sta- more information that can be transmitted at one time. computer services (information technology consult- tistics in developing countries. For more information Reporting countries may have different definitions of ing, computer and network systems integration, Web see www.itu.int/ITU-D/ict/partnership/. broadband, so data are not strictly comparable. hosting, data processing services, and other ser- Data on the number of daily newspapers in circula- The number of secure Internet servers, from the vices); and communications services (voice and data tion are from surveys by the United Nations Educa- Netcraft Secure Server Survey, gives an indication communications services) and wired and wireless tional, Scientific, and Cultural Organization (UNESCO) of how many companies are conducting encrypted communications equipment. Institute for Statistics that cover such areas as news- transactions over the Internet. The Netcraft survey Data sources paper circulation, online newspaper titles, journal- examines the use of encrypted transactions on the ists, community newspapers, and news agencies. Internet through extensive automated exploration, tal- Data on newspapers are compiled by the UNESCO Estimates of households with television are derived lying the number of Web sites using a secure socket Institute for Statistics. Data on televisions, per- from household surveys. Some countries report only layer (SSL). Some countries, such as the Republic sonal computers, Internet users, broadband the number of households with a color television set, of Korea, establish the encryption channel by using subscribers, international Internet bandwidth, and so the true number may be higher than reported. application layers, which are SSL equivalent. and price basket for Internet are from the ITU's Estimates of personal computers are from an According to the World Information Technology and World Telecommunication Development Report annual International Telecommunication Union (ITU) Services Alliance's (WITSA) Digital Planet 2006, the database. Data on secure Internet servers are questionnaire sent to member states, supplemented global marketplace for information and communica- from Netcraft (www.netcraft.com/) and offi cial by other sources. Many governments lack the capac- tions technologies was expected to top $3 trillion in government sources. Data on information and ity to survey all places where personal computers 2006 and to rise to almost $4 trillion by 2009. The communications technology expenditures are from are used--homes, schools, businesses, government data on information and communications technol- WITSA's Digital Planet 2006: The Global Informa- offices, libraries, Internet cafes, and the like--so ogy expenditures cover the world's 75 largest buyers tion Economy and from Global Insight, Inc. most estimates are derived from the number of among countries and regions. 2008 World Development Indicators 311 5.12 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 Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 2000­05d 2000­05d 2005 2000­05d 2006 2006 2006 2006 2005 2005 2005 2005 Afghanistan .. .. .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. 8 13 1 7 .. .. .. .. Algeria 170 35 350 0.16 11 2 .. .. 58 455 1,488 3,369 Angola .. .. .. .. .. .. 1,340 1 .. .. .. .. Argentina 768 338 3,058 0.44 994 7 71 807 .. .. 61,953 19,139 Armenia .. .. 180 0.21 5 1 .. .. 206 2 1,088 364 Australia 4,099 .. 15,957 1.77 3,371 12 621 2,221 8,630 22,562 38,728 17,053 Austria 3,444 1,477 4,566 2.35 14,037 13 177 1,334 1,904 601 7,565 1,018 Azerbaijan .. .. 116 0.23 8 2 0 1 281 6 774 823 Bangladesh .. .. 193 .. 21 0 0 5 .. .. .. .. Belarus .. .. 490 0.69 268 3 6 50 1,065 382 2,410 3,556 Belgium 3,067 1,473 6,841 1.82 22,644 8 1,544 1,075 533 175 20,831e 30,665e Benin .. .. .. .. 0 0 .. 2 .. .. .. .. Bolivia 120 6 .. 0.28 13 4 2 14 .. .. .. .. Bosnia and Herzegovina .. .. .. .. 62 3 .. .. 66 306 295 902 Botswana .. .. .. 0.39 .. .. 0 7 .. .. .. .. Brazil 462 395 9,889 0.91 8,426 12 150 1,664 3,821 2,560 83,117 15,981 Bulgaria 1,301 478 764 0.50 486 6 11 69 261 52 6,731 1,252 Burkina Faso 19 17 .. 0.18 3 10 .. .. .. .. .. .. Burundi .. .. .. .. 0 4 0 .. .. .. .. .. Cambodia 17 13 .. 0.05 4 0 0 7 .. .. 409 1,638 Cameroon 28 .. 131 .. 3 3 0 2 .. .. .. .. Canada 3,922 1,467 25,836 2.01 32,740 15 3,245 7,320 3,942 35,946 17,719 22,169 Central African Republic .. .. .. .. 0 0 .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. .. .. .. Chile 833 302 1,559 0.68 401 7 55 381 361 2,646 .. .. China 708 .. 41,596 1.34 271,170 30 205 6,634 93,172 80,155 593,382 63,902 Hong Kong, China 2,096 417 .. 0.74 1,788 11 245 1,289 156 11,607 8,173 20,877 Colombia 125 95 400 0.17 349 4 11 127 .. .. .. .. Congo, Dem. Rep. .. .. .. 0.48 .. .. .. .. .. .. .. .. Congo, Rep. 30 32 .. .. .. .. .. .. .. .. .. .. Costa Rica .. .. 105 0.37 2,088 45 0 87 .. .. .. .. Côte d'Ivoire .. .. .. .. 521 42 0 10 .. .. .. .. Croatia 1,573 567 953 1.22 691 10 47 175 355 657 1,180 831 Cuba .. .. 261 0.56 59 12 .. .. 94 191 301 482 Czech Republic 2,365 1,348 3,169 1.42 11,897 14 31 526 586 244 9,279 973 Denmark 5,190 .. 5,040 2.45 11,455 20 .. .. 1,655 168 4,585 1,289 Dominican Republic .. .. .. .. .. .. 0 32 .. .. .. .. Ecuador 50 .. .. 0.06 96 8 0 44 11 580 5,907 2,148 Egypt, Arab Rep. .. .. 1,658 0.19 15 1 138 159 428 1,008 .. .. El Salvador 47 .. .. .. 22 3 1 27 .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Estonia 2,505 490 439 0.99 771 13 6 29 23 15 1,241 1,737 Ethiopia 21 10 88 0.20 .. .. 0 1 .. .. .. .. Finland 7,541 .. 4,811 3.52 13,990 22 1,494 1,901 1,827 232 2,820 661 France 3,320 .. 30,309 2.13 80,525 21 6,230 3,298 14,230 3,060 62,330 3,224 Gabon .. .. .. .. 71 32 .. .. .. .. .. .. Gambia, The 30 18 .. .. 0 1 .. .. .. .. .. .. Georgia .. .. 145 0.18 74 16 13 5 225 22 507 518 Germany 3,242 1,056 44,145 2.51 154,757 17 5,888 7,843 47,537 12,685 67,208 3,718 Ghana .. .. 81 .. 1 0 0 .. .. .. .. .. Greece 1,531 831 4,291 0.61 1,139 11 67 406 487 50 5,872 893 Guatemala .. .. .. .. 35 3 0 0 10 267 .. .. Guinea .. .. .. .. .. .. .. 0 .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. 0 .. .. .. .. Haiti .. .. .. .. .. .. 4 1 .. .. .. .. 312 2008 World Development Indicators 5.12 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 Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 2000­05d 2000­05d 2005 2000­05d 2006 2006 2006 2006 2005 2005 2005 2005 Honduras .. .. .. 0.05 3 1 0 25 .. .. 1,149 3,388 Hungary 1,572 466 2,614 0.95 14,915 24 627 1,056 697 505 3,515 659 India .. .. 14,608 0.61 3,511 5 112 949 6,795 10,671 .. .. Indonesia 202 .. 205 0.05 5,900 13 14 870 234 4,069 .. .. Iran, Islamic Rep. .. .. 2,635 0.59 375 6 .. .. .. .. 17,607 1,356 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland 2,688 654 2,120 1.24 31,840 34 1,028 20,815 789 75 1,285 2,677 Israel .. .. 6,309 4.95 5,565 14 596 679 1,329 5,124 2,816 6,159 Italy 1,241 .. 24,645 1.10 25,046 7 1,116 1,840 .. .. .. .. Jamaica .. .. .. 0.07 1 0 12 11 10 59 .. .. Japan 5,294 528 55,471 3.18 126,618 22 20,096 15,500 359,382 67,696 114,015 11,792 Jordan .. .. 275 0.34 35 1 .. .. .. .. .. .. Kazakhstan 803 86 96 0.28 987 21 0 48 1,696 102 2,908 1,070 Kenya .. .. 226 .. 17 3 10 50 .. .. .. .. Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,760 567 16,396 2.99 92,945 32 2,011 4,487 121,942 38,979 99,435 16,454 Kuwait 74 95 233 0.18 .. .. 0 0 .. .. .. .. Kyrgyz Republic .. .. .. 0.20 6 3 2 19 179 1 133 345 Lao PDR .. .. .. .. .. .. .. .. .. .. .. .. Latvia 1,423 460 134 0.57 242 7 11 20 112 57 1,367 487 Lebanon .. .. 234 .. 26 2 0 0 .. .. .. .. Lesotho .. .. .. 0.06 .. .. 18 .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 361 493 .. .. .. .. .. 0 .. .. .. .. Lithuania 2,226 419 406 0.76 653 8 1 24 68 47 1,839 411 Macedonia, FYR 547 83 .. 0.25 18 1 3 9 37 415 619 437 Madagascar 43 6 .. 0.16 4 1 2 9 16 22 439 419 Malawi .. .. .. .. 9 11 .. .. .. .. .. .. Malaysia 509 64 615 0.63 63,411 54 26 1,052 .. .. 10,479 11,668 Mali .. .. .. .. 2 4 0 1 .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. .. .. 0.38 360 24 0 4 .. .. .. .. Mexico 321 147 3,902 0.41 35,732 19 171 503 549 13,887 45,736 22,962 Moldova .. .. 89 .. 10 5 2 4 377 11 1,941 474 Mongolia .. .. .. 0.32 3 2 .. .. 100 87 369 1,854 Morocco .. .. 443 0.75 830 10 5 48 139 521 .. .. Mozambique .. .. .. 0.52 3 2 1 2 .. .. .. .. Myanmar 17 133 .. 0.16 .. .. .. .. .. .. .. .. Namibia .. .. .. .. 102 7 .. 3 .. .. .. .. Nepal 59 137 .. .. .. .. .. .. .. .. .. .. Netherlands 2,309 1,765 13,885 1.79 69,210 28 4,126 3,865 2,217 633 .. .. New Zealand 3,945 833 2,983 1.14 587 11 123 487 1,856 5,149 8,269 8,564 Nicaragua .. .. .. 0.05 5 7 0 .. .. .. .. .. Niger 7 10 .. .. 5 11 0 1 .. .. .. .. Nigeria .. .. 362 .. .. .. .. 45 .. .. .. .. Norway 4,729 .. 3,644 1.51 3,577 19 760 553 1,143 4,843 .. 5,996 Oman .. .. 111 .. 4 1 .. .. .. .. .. .. Pakistan 80 41 492 0.43 197 1 53 106 .. 1,081 8,319 5,117 Panama 97 387 .. 0.24 0 0 0 50 .. .. .. .. Papua New Guinea .. .. .. .. .. .. .. .. .. .. .. .. Paraguay 82 118 .. 0.08 25 8 236 2 .. .. .. .. Peru .. .. 133 0.15 57 2 3 86 27 993 10,468 8,353 Philippines .. .. 178 0.14 27,626 68 6 349 157 1,731 7,031 5,526 Poland 1,613 232 6,844 0.57 3,284 4 38 1,313 2,028 4,555 13,828 984 Portugal 2,001 307 2,910 0.81 2,971 9 82 349 158 47 8,589 1,134 Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 313 5.12 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 Receipts Payments Non- Non- people people % of GDP $ millions exports $ millions $ millions Residents residents Residents residents 2000­05d 2000­05d 2005 2000­05d 2006 2006 2006 2006 2005 2005 2005 2005 Romania 976 254 887 0.39 1,129 4 35 236 916 68 11,121 2,090 Russian Federation 3,244 553 14,412 1.07 4,755 9 299 2,002 23,588 8,665 26,460 7,926 Rwanda .. .. .. .. .. .. 0 1 .. .. .. .. Saudi Arabia .. .. 575 .. 148 1 0 0 .. .. .. .. Senegal .. .. 83 0.09 17 6 .. 7 .. .. .. .. Serbia .. .. 849 1.41 176 4 .. .. 381 658 1,089 736 Sierra Leone .. .. .. .. .. .. 1 1 .. .. .. .. Singapore 5,500 381 3,609 2.36 124,133 58 730 10,470 435 8,170 4,839 26,986 Slovak Republic 2,022 416 919 0.52 2,196 6 .. .. 154 96 2,740 1,146 Slovenia 1,949 1,264 1,035 1.22 941 5 17 154 323 27 1,399 417 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 379 110 2,392 0.87 1,799 6 46 1,282 .. 5,554 .. 28,331 Spain 2,549 888 18,336 1.12 10,037 6 922 2,504 3,027 326 54,268 2,541 Sri Lanka 130 72 136 0.19 99 2 .. .. 95 189 3,989 1,773 Sudan .. .. .. 0.30 0 1 .. .. .. .. .. .. Swaziland .. .. .. .. 2 0 0 106 .. .. .. .. Sweden 5,977 .. 10,012 3.86 18,078 16 3,964 1,618 2,512 448 .. 9,864 Switzerland 3,508 2,366 8,749 2.94 29,261 22 .. .. 1,643 455 9,393 4,479 Syrian Arab Republic .. .. 77 .. 29 1 .. 20 .. .. .. .. Tajikistan .. .. .. 0.10 .. .. 1 0 32 2 63 277 Tanzania .. .. 107 .. 1 0 0 1 .. .. .. .. Thailand 287 208 1,249 0.25 26,953 27 46 2,046 891 5,449 22,612 9,241 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo .. .. .. .. 0 0 0 3 .. .. .. .. Trinidad and Tobago .. .. .. 0.12 30 1 .. .. .. 205 .. .. Tunisia 1,450 41 571 1.03 344 4 14 11 56 282 .. .. Turkey 469 37 7,815 0.67 258 .. 0 531 465 383 48,981 3,096 Turkmenistan .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. 93 1.25 60 34 2 11 .. .. .. .. Ukraine .. .. 2,105 1.07 926 3 32 428 3,535 2,057 13,184 3,182 United Arab Emirates .. .. 229 .. 10 .. .. .. .. .. .. .. United Kingdom .. .. 45,572 1.75 115,464 34 13,588 9,962 17,488 10,500 24,163 4,529 United States 4,605 .. 205,320 2.68 219,179 30 62,378 26,433 202,776 187,957 224,269 28,359 Uruguay 366 50 204 0.26 36 3 0 7 37 514 5,626 8,189 Uzbekistan .. .. 157 .. .. .. .. .. 264 180 349 611 Venezuela, RB .. .. 534 0.25 80 2 0 257 .. .. .. .. Vietnam 115 .. 221 0.19 869 5 .. .. .. .. .. .. West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. .. .. .. 3 5 149 9 .. .. .. .. Zambia .. .. .. 0.03 4 2 .. 0 .. .. .. .. Zimbabwe .. .. .. .. 8 2 .. .. .. .. .. .. World .. w .. w 708,086 s 2.10 w 1,418,509 s 21 w 135,278 s 148,518 s 915,598 s 553,167 s 1,584,746 s 420,729 s Low income .. .. 16,711 0.57 .. 6 334 1,163 364 267 1,157 2,884 Middle income 803 .. 112,719 0.85 478,215 20 3,743 22,719 132,662 137,246 898,687 200,348 Lower middle income 500 .. 53,423 1.03 272,746 24 2,154 11,140 99,752 97,897 634,878 93,599 Upper middle income 1,285 372 59,296 0.72 143,179 16 1,589 11,579 32,910 39,349 263,809 106,749 Low & middle income .. .. 129,430 0.83 .. 20 4,077 23,882 133,026 137,513 899,844 203,232 East Asia & Pacific 704 .. 44,064 1.34 .. 33 297 10,959 94,397 91,491 611,261 82,950 Europe & Central Asia 2,019 371 39,975 0.87 31,160 9 1,129 5,998 33,133 17,286 136,989 30,048 Latin America & Carib. 392 256 20,045 0.59 48,368 12 753 4,146 4,873 20,916 151,155 58,115 Middle East & N. Africa .. .. 6,354 .. 1,263 5 306 247 623 2,266 17,607 3,369 South Asia .. .. 15,429 0.59 .. 4 175 1,060 6,795 11,752 8,319 5,117 Sub-Saharan Africa .. .. 3,563 .. .. .. 1,417 1,471 16 5,554 439 28,750 High income 3,731 .. 578,656 2.38 1,322,714 21 131,201 124,636 782,572 415,654 684,902 217,497 Euro area 2,734 .. 158,066 2.02 428,463 16 23,049 44,309 58,359 14,865 148,179 43,724 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 European Patent Office (33,410 by residents, 95,303 by nonresidents) and the Eurasian Patent Organization (1,940 by nonresidents). c. Excludes applications filed under the auspices of the EU Office for Harmonization in the Internal Market (64,798 by nonresidents). d. Data are for the most recent year available. e. Includes Luxembourg and the Netherlands. 314 2008 World Development Indicators 5.12 STATES AND MARKETS Science and technology About the data Definitions Technological innovation, often fueled by government- more appropriate than the sectoral approach for · Researchers in R&D are professionals engaged in led research and development (R&D), has been the analyzing international trade. This method takes conceiving of or creating new knowledge, products, driving force for industrial growth. The best oppor- only R&D intensity into account, but other charac- processes, methods, and systems and in managing tunities to improve living standards, including new teristics of high technology are also important, such the projects concerned. Postgraduate students at the ways of reducing poverty, will come from science and as know-how, scientifi c and technical personnel, doctoral level (ISCED97 level 6) engaged in R&D are technology. Science is playing a growing economic and technology embodied in patents. Considering considered researchers. · Technicians in R&D and role: countries able to access, generate, and apply these characteristics would yield a different list. (See equivalent staff are people whose main tasks require scientific knowledge will have a competitive edge. Hatzichronoglou 1997 for further details.) Moreover, technical knowledge and experience in engineering, And there is greater appreciation of the need for high- the R&D for high-technology exports may not have physical and life sciences (technicians), and social quality scientific input into public policy issues such occurred in the reporting country. sciences and humanities (equivalent staff). They as regional and global environmental concerns. A patent is an exclusive right granted for an inven- engage in R&D by performing scientific and techni- Science and technology cover a range of issues tion (a product or process that provides a new way cal tasks involving the application of concepts and too broad and complex to be quantified by a single of doing something or a new technical solution to a operational methods, normally under the supervision set of indicators, but those in the table shed light on problem). It must be of practical use and display a of researchers. · Scientific and technical journal countries' technology base. characteristic unknown in the body of existing knowl- articles are published articles in physics, biology, The United Nations Educational, Scientifi c, and edge in its technical field. A patent grants protection chemistry, mathematics, clinical medicine, biomedical Cultural Organization (UNESCO) Institute for Statis- for the invention to the owner of the patent for a research, engineering and technology, and earth and tics collects data on researchers, technicians, and specified period, generally 20 years. space sciences. · Expenditures for R&D are current expenditure on R&D from around the world, through Most countries have systems to protect patentable and capital expenditures on creative work undertaken questionnaires and surveys and from other interna- inventions. The Patent Cooperation Treaty provides a systematically to increase the stock of knowledge, tional sources. Data on researchers and technicians system for filing patent applications. It consists of an including knowledge of humanity, culture, and soci- are normally calculated as full-time equivalents. international phase followed by a national or regional ety, and the use of knowledge to devise new applica- Scientific and technical article counts are from a phase. An applicant files an international application tions. R&D covers basic research, applied research, set of journals classified and covered by the Institute and designates the countries in which patent protec- and experimental development. · High-technology for Scientific Information's Science Citation Index tion is sought (since 2004 all eligible countries are exports are products with high R&D intensity, such as (SCI) and Social Sciences Citation Index (SSCI). automatically designated in every application under in aerospace, computers, pharmaceuticals, scientific Counts are based on fractional assignments; for the treaty). The application is searched, published, instruments, and electrical machinery. · Royalty and example, an article with two authors from different and, optionally, an international preliminary examina- license fees are payments and receipts between resi- countries is counted as one-half of an article for each tion is conducted. In the national (or regional) phase dents and nonresidents for authorized use of intan- country (see Definitions for fields covered). The SCI the applicant requests national processing of the gible, nonproduced, nonfinancial assets and propri- and SSCI databases cover the core set of scientific application, pays additional fees, and initiates the etary rights (such as patents, copyrights, trademarks, journals but may exclude some of regional or local national search and granting procedure. International franchises, and industrial processes) and for the use, importance. They may also reflect some bias toward applications under the treaty provide for a national through licensing agreements, of produced originals of English-language journals. patent grant only--there is no international patent. prototypes (such as films and manuscripts). · Patent R&D expenditures include all expenditures for R&D The national phase filing represents the applicant's applications filed are worldwide patent applications performed within a country, including capital expendi- seeking of patent protection for a given territory, filed through the Patent Cooperation Treaty procedure tures and current costs (annual wages and salaries whereas international filings, while they represent a or with a national patent office. · Trademark applica- and all associated costs of researchers, technicians, legal right, do not accurately reflect where patent pro- tions filed are applications to register a trademark and supporting staff and other current costs, includ- tection is eventually sought. Resident filings are those with a national or regional trademark office. ing noncapital purchases of materials, supplies, and from residents of the country or region concerned. R&D equipment such as utilities, books, journals, Nonresident filings are from applicants outside the Data sources reference materials, subscriptions to libraries and country or region. For regional offices such as the Data on R&D are provided by the UNESCO Insti- scientific societies, and materials for laboratories). European Patent Office, applications from residents tute for Statistics. Data on scientific and tech- The method used for determining a country's high- of any member state of the regional patent convention nical journal articles are from the U.S. National technology exports was developed by the Organisa- are considered a resident filing. Some offices (nota- Science Foundation's Science and Engineering tion for Economic Co-operation and Development bly the U.S. Patent and Trademark Office) use the Indicators 2008. Data on high-technology exports in collaboration with Eurostat. Termed the "prod- residence of the inventor rather than the applicant to are from the United Nations Statistics Division's uct approach" to distinguish it from a "sectoral classify resident and nonresident filings. A trademark Commodity Trade (Comtrade) database. Data on approach," the method is based on R&D intensity protects its owner by ensuring exclusive right to use it royalty and license fees are from the International (R&D expenditure divided by total sales) for groups to identify goods or services or to authorize another to Monetary Fund's Balance of Payments Statistics of products from six countries (Germany, Italy, Japan, use it in return for payment. The period of protection Yearbook. Data on patents and trademarks are the Netherlands, Sweden, and the United States). varies, but a trademark can be renewed indefinitely for from the World Intellectual Property Organization's Because industrial sectors specializing in a few a fee. Trademarks help consumers identify a product WIPO Patent Report: Statistics on Worldwide Patent high-technology products may also produce many or service whose nature and quality, indicated by its Activity (2007 edition) and www.wipo.int. low-technology products, the product approach is unique trademark, meet their needs. 2008 World Development Indicators 315 Text figures, tables, and boxes GLOBAL LINKS 6 Introduction T he world economy expands and economies grow closer Economic integration is the widening and deepening of the ties that link national economies. Trade, finance, movement of people, and transportation and communication infrastructure are the mechanisms. But integration is not a simple or certain process. Politi- cal and cultural connections underpin economic alliances. Geography may pose obstacles to integration, while technology can overcome them. The past two decades have seen an enormous increase in the size of the global economy and of the economic ties between countries. Between 1990 and 2006 East Asia and Pacific's trade increased from 47 percent of its gross domestic product (GDP) to 87 percent, and gross private capital flows from international sources increased from 7 percent of GDP to 11 percent. In Sub-Saharan Africa trade within the region and with the rest of the world increased from 52 percent of GDP to 72 percent, and gross private capital flows rose from 12 percent to 14 percent. Evidence of integration? Yes, but the two regions have had much different experiences. Each had about 3.5 percent of global exports in 1980, but by 2006 East Asia and Pacific's share had grown to 10.8 percent while Sub-Saharan Africa's had fallen to 1.9 percent. As global integration proceeds, developing countries are likely to expand their share of the global economy, especially regional centers with large populations and a significant economic base, such as Brazil, China, India, the Russian Federation, and South Africa. But even small and remote economies can take part. Better air and ocean transport gets products to mar- kets faster and with more precise timing. Better transportation has been complemented by improvements in technology and favorable regulatory environments, reducing the costs of global communication, information dissemination, and management of economic activities. But as Dollar (2005, p. 148) notes, "As in previous waves of integration . . . change is driven partly by technological advances in transport and communications and partly by deliberate policy choices." Integration does not happen automatically. All developing countries have the potential to gain from an integrated global environment. Like all economic forces, global integration may produce winners and losers. To realize the benefits of integration, countries need the capacity to absorb new technologies, use capital productively, and increase their labor force's knowledge and skills. Countries do not start with the same endowments--and wars, political divisions, and plain bad luck may blight their opportunities. The challenge is to ensure sustainable and widely shared growth. Monitoring the development of global links provides the underpinning for policies aimed at managing challenges and aiding integration that is inclusive for all. The data in this section provide a snapshot of the world's integration and and a framework for measuring it. 2008 World Development Indicators 317 Developing countries' Financial integration: growing world trade resilient and unabated International trade is a critical channel for integration. It in- More access to international capital markets and foreign direct creases economic efficiency and brings producers and con- investment (FDI) has helped developing countries surmount sumers together. Developing countries' share in world trade their less developed capital markets. Developing countries has been rising from 16 percent in 1990 to 30 percent in have decreased their vulnerability to financial crises by reduc- 2006, led by China, whose exports now rival those of the ing their external debt burden from 39 percent of gross national United States, and with Brazil and India not far behind (fig- income in 1995 to 26 percent in 2006 and increasing foreign ure 6a). Projections of further increases in developing coun- exchange reserves to 92 percent of long-term debt and 423 per- tries' share, to 45 percent by 2030 (Global Economic Pros- cent of more volatile, short-term debt in 2006 (figure 6c). pects 2007), reflect increasing integration. Private capital flows to developing countries increased Developing country trade integration, measured by the more than 10-fold between 1990 and 2006. In 2006 develop- share of imports plus exports in GDP, has been rising rapidly, ing countries received almost one-third of global FDI, though increasing from 40 percent of GDP in 1990 to almost 67 per- just over one-tenth of that went to low-income economies. cent in 2006, surpassing the share in high-income econo- Sub-Saharan Africa's 34 low-income economies received mies. Developing country exports are changing as well. The only 1 percent. The main source of external financing for share of manufactured goods in exports is large and rising low-income countries remains official development assis- while that of food and commodities (excluding fuels) is small tance (ODA). ODA, however, includes debt relief, technical and falling (figure 6b). And despite the attention given to the assistance, and emergency relief, which do not provide the spread of offshore services, trade in goods remains many long-term investment needed to raise productive capacity. In times greater than trade in services. India is a notable excep- constant prices ODA has risen more than 50 percent since tion: its service sector now produces almost 40 percent of 2000, but excluding debt, technical assistance, and emer- its exports. gency relief, it has risen only 25 percent (figure 6d). Developing countries' share Rising reserves and falling debt make of global trade is rising 6a developing countries less vulnerable to crises 6c Share of global exports (%) Ratio of reserves to debt stocks (%) 30 500 Ratio to short-term debt 400 20 300 All other developing countries 200 10 Ratio to total debt 100 Brazil, China, India, Russian Federation, and South Africa 0 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 1990 1992 1994 1996 1998 2000 2002 2004 2006 Source: World Development Indicators data files. Source: World Development Indicators and Global Development Finance data files. Manufactured goods dominate Private financing has long exceeded official the exports of developing countries 6b development assistance to developing countries 6d Structure of exports for 10 largest developing country exporters (%) Net inflows ($ billions) 80 750 Manufactures Equity flows 60 500 Bonds Foreign direct investment 40 Commercial banks and other lending 250 20 Services Official development assistance Food and commodities (excluding fuel) 0 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 1990 1992 1994 1996 1998 2000 2002 2004 2006 Source: International Monetary Fund and United Nations Conference on Trade and Source: Organisation for Economic Co-operation and Development International Development data files. Development Statistics and Global Development Finance data files. 318 2008 World Development Indicators Movement of people facilitates The role of information and common economic and social goals communication technologies is expanding Movement of people as tourists, migrants, or business trav- Communication and information networks are crucial for over- elers raises awareness and spreads knowledge, important coming geographic barriers, bringing people and markets elements of globalization. These movements link diverse closer. These networks enable effective management of en- populations with common economic and social goals. Global terprises across borders and participation in global produc- tourism increased 5.6 percent in 2006, a pace well above its tion and service supply chains. Deregulation and competition long-term average. Tourist departures from developing econo- have reduced communication costs. The average cost of a mies have risen 43 percent since 2000, and 7 of the top 15 three-minute call to the United States fell from $4.00 in 1999 tourist destinations are in developing economies. to $1.40 in 2004. Migration increased sharply over the past two decades. Over that period the share of people with access to the Like other elements of globalization, migration patterns are Internet tripled. The Internet promises to be an even greater shaped by market forces and official policies. Opportunities force for globalization and development. But diffusion of tech- in high-income economies are a strong lure (figure 6e), and nology around the world and within countries is unequal (fig- a need for workers has led many countries to relax entry ures 6g and 6h). Average contracted capacity for international barriers. Successful migration requires resources, skills, Internet connections in developing economies grew from 3 bits and adaptation to a new culture. So, the largest net flows of per second per person in 2000 to 140 in 2006, still far short migrants are from middle-income economies. of the estimated 5,000 high-income average. Low-income Migration facilitates cross-border remittances, a major economies' Internet capacity was still less than 20 bits per source of foreign earnings for many developing countries. second per person in 2006, and international voice traffic less Remittances to developing countries almost quadrupled than 5 percent of the high-income average. Capital, policies, between 1995 and 2006, to more than $220 billion (fig- and infrastructure are needed to develop, adapt, and diffuse ure 6f), rivaling other forms of private financing. communication networks to accelerate development. More migrants in Europe and Central Asia and Latin America and the Caribbean high-income economies . . . 6e lead other developing regions in access to the Internet . . . 6g Net migration (millions) 1985­90 1990­95 1995­2000 2000­05 Internet users (per 100 people) 20 20 Europe & Central Asia Latin America & Caribbean Middle East & 10 15 North Africa East Asia 0 10 & Pacific ­10 5 South Asia Sub-Saharan Africa ­20 0 Low-income Middle-income High-income 2000 2001 2002 2003 2004 2005 2006 Source: International Telecommunication Union and World Development Indicators Source: United Nations Population Division and World Development Indicators data files. data files. . . . are sending more . . . and in international remittances to developing countries 6f bandwith per capita 6h Workers' remittances ($ billions) Low-income Middle-income International bandwidth per capita (bits per second) 250 300 200 Europe & Central Asia Latin America & Caribbean 200 150 East Asia & Pacific 100 100 Middle East & North Africa 50 South Asia 0 0 Sub-Saharan Africa 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2000 2001 2002 2003 2004 2005 2006 Source: International Telecommunication Union and World Development Indicators Source: World Development Indicators data files. data files. 2008 World Development Indicators 319 Tables 6.1 Integration with the global economy Trade International finance Movement of people International communication % of GDP Financing through Workers' international remittances capital and Cost of call Voice Internet markets Foreign direct compensation to U.S.a traffic a bandwidtha % of GDP Gross investment of employees Net International tourists $ per minutes bits per Merchandise Services inflows Net inflows Net outflows received migration Inbound Outbound 3 minutes per person capita 2006 2006 2006 2006 2006 2006 2000­05 2006 2006 2005 2006 2006 Afghanistan 40.4 .. 0.0 .. .. .. 1,112 .. .. 0.4 1 0 Albania 42.3 34.0 0.2 3.6 0.1 14.9 ­110 60 b 2,616 1.3 160 4 Algeria 66.3 .. 0.9 1.6 .. 2.2c ­140 1,443d,e 1,513 2.1 17 5 Angola 103.2 19.9 3.6 ­0.1 0.4 .. 175 121 .. 3.2 .. 12 Argentina 37.7 7.5 1.5 2.3 1.0 0.3 ­100 4,156 4,009 .. 33 690 Armenia 50.1 17.2 0.4 5.4 0.0 18.4 c ­100 381 329 2.4 128 22 Australia 33.6 8.4 .. 3.4 3.0 0.4 593 5,064f 4,941 .. .. 11,593 Austria 87.2 24.1 .. 0.0 1.2 0.6 180 20,261 g 10,042 0.7 .. 6,634 Azerbaijan 58.6 19.2 28.2 ­2.9 3.6 4.1 ­100 1,194 1,836 4.2 33 36 Bangladesh 45.1 5.9 0.2 1.1 0.0 8.8 ­500 200 1,819 2.0 6 8 Belarus 113.8 10.2 0.9 1.0 0.0 0.9 0 89 525 1.9 64 192 Belgium 183.5 28.6 .. 15.7 14.0 1.9 180 6,995g 7,852 0.8 .. 11,279 Benin 32.5 11.0 2.1 1.3 0.0 3.6c 99 180 .. 4.8 6 5 Bolivia 59.9 11.1 0.0 2.2 0.0 5.5 ­100 515 466 .. 49 43 Bosnia and Herzegovina 86.6 13.2 0.8 3.5 0.0 16.9 115 256g .. 3.6 208 40 Botswana 73.9 15.2 0.0 4.6 ­0.5 1.1 20 1,675 .. 2.9 74 16 Brazil 21.9 4.6 6.0 1.8 2.6 0.4 ­229 5,019 4,825 0.7 .. 150 Bulgaria 121.3 29.1 9.0 16.4 0.5 5.4 ­43 5,158 4,180 0.6 72 1,756 Burkina Faso 30.6 .. 0.6 0.4 .. 0.8 c 100 264h .. 1.1 11 15 Burundi 54.2 25.8 0.0 0.0 .. 0.0 192 201e .. 2.5 .. 1 Cambodia 119.9 28.7 1.5 6.7 0.1 4.1 10 1,700 427 2.9 10 1 Cameroon 35.8 15.3 0.0 1.7 0.0 0.6c 6 176h .. .. 9 9 Canada 58.8 10.4 .. 5.4 3.6 .. 1,041 18,265 22,732 .. .. 6,732 Central African Republic 24.1 .. 0.0 1.6 .. .. ­45 12 f 7 2.0 .. 0 Chad 76.4 .. 0.0 10.7 .. .. 219 29h .. .. .. 1 Chile 66.2 10.9 6.2 5.5 2.0 0.0 30 2,027 2,651 .. 48 780 China 66.6 7.3 2.6 3.0 0.7 0.9c ­1,900 49,913 34,524 2.9 7 196 Hong Kong, China 346.9 57.3 .. 22.6 22.9 0.2 300 15,821 75,812 0.8 1,179 13,439 Colombia 32.9 5.8 3.4 4.2 0.7 2.6 ­120 1,053d 1,553 .. 68 560 Congo, Dem. Rep. 59.7 .. 0.0 2.1 .. .. ­237 61f .. .. 5 0 Congo, Rep. 109.7 30.1 0.0 4.7 0.1 0.2c ­10 .. .. 5.4 .. 0 Costa Rica 88.8 20.5 1.1 6.6 0.4 2.3 84 1,725 485 .. 127 176 Côte d'Ivoire 78.2 17.3 0.0 1.8 .. 0.9 ­339 .. .. 2.2 17 3 Croatia 74.2 33.4 6.1 7.9 0.5 2.9 100 8,659g .. .. 231 1,074 Cuba .. .. .. .. .. .. ­129 2,150 f 199 7.5 31 14 Czech Republic 131.7 17.6 .. 4.2 0.9 0.8 67 6,435g .. 1.1 95 2,170 Denmark 65.0 35.9 .. 1.2 3.0 0.3 46 4,699g 5,469 0.9 318 34,796 Dominican Republic 55.4 18.2 4.3 3.7 0.0 9.6 ­148 3,965e,f 420 0.2 .. 6 Ecuador 59.7 8.1 0.2 0.7 0.0 7.1 ­400 841d,i 733 .. 216 227 Egypt, Arab Rep. 31.9 25.8 5.3 9.3 0.1 5.0 ­525 8,646 4,531 1.5 30 126 El Salvador 59.7 16.0 7.7 1.1 ­0.3 17.8 ­143 1,138 1,382 2.4 410 23 Eritrea 50.7 .. 0.0 0.3 .. .. 229 78d,e .. 3.6 9 2 Estonia 138.6 36.3 3.6 9.7 6.3 2.4 1 1,940 .. 0.9 109 11,175 Ethiopia 42.1 17.6 0.0 2.7 0.0 1.3 ­140 290e .. 4.0 3 0 Finland 69.3 15.1 .. 2.5 0.8 0.3 33 3,375 5,756 1.8 .. 4,311 France 45.6 10.1 .. 3.6 5.2 0.6 722 79,083 22,466 0.8 183 3,286 Gabon 76.8 15.3 0.6 2.8 ­0.3 0.1c 10 .. .. 2.8 74 153 Gambia, The 51.9 36.4 0.0 16.1 .. 12.5 31 125 .. 1.8 .. 6 Georgia 60.3 21.0 2.1 13.7 ­0.2 6.3 ­248 983d .. .. .. 7 Germany 69.8 13.4 .. 1.5 2.7 0.2 1,000 23,569g 71,200 0.4 .. 6,864 Ghana 71.3 22.7 7.1 3.4 0.0 0.8 12 429e .. 0.4 20 9 Greece 27.3 16.9 .. 1.8 1.4 0.5 154 16,039 .. 1.1 182 587 Guatemala 50.8 8.7 0.0 1.0 0.0 10.3 ­300 1,502 1,055 1.2 195 56 Guinea 57.3 9.1 0.0 3.3 .. 1.3c ­425 46f .. .. .. 0 Guinea-Bissau 60.8 19.3 0.0 13.8 ­2.8 9.2c 1 12f .. .. .. 1 Haiti 44.5 14.9 2.7 3.2 .. 21.5 ­140 112 .. 2.2 .. 17 320 2008 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy Trade International finance Movement of people International communication % of GDP Financing through Workers' international remittances capital and Cost of call Voice Internet markets Foreign direct compensation to U.S.a traffic a bandwidtha % of GDP Gross investment of employees Net International tourists $ per minutes bits per Merchandise Services inflows Net inflows Net outflows received migration Inbound Outbound 3 minutes per person capita 2006 2006 2006 2006 2006 2006 2000­05 2006 2006 2005 2006 2006 Honduras 79.6 19.2 0.0 4.2 0.0 25.6 ­150 739 308 2.5 96 6 Hungary 134.1 22.1 10.3 5.4 14.5 0.3 65 9,259 17,612 1.0 105 993 India 32.4 15.2 4.2 1.9 1.1 2.8 ­1,350 4,447i 8,340 1.2 .. 24 Indonesia 50.4 9.1 3.7 1.5 0.7 1.6 ­1,000 4,871 4,106 2.8 5 7 Iran, Islamic Rep. 57.3 .. 0.5 0.4 .. 0.5c ­1,250 1,659 .. 0.5 9 53 Iraq .. .. .. .. .. .. ­375 .. .. .. .. .. Ireland 83.5 67.1 .. ­0.4 6.7 0.2 188 8,001 6,848 0.7 .. 5,912 Israel 68.7 24.3 .. 10.2 10.3 0.8 115 1,825i 3,713 0.6 364 2,455 Italy 45.8 10.8 .. 2.1 2.3 0.1 1,125 41,058 25,697 0.8 .. 2,044 Jamaica 76.1 46.6 11.0 8.8 0.9 19.4 ­100 1,679e,f .. 0.9 .. 15,822 Japan 28.1 5.8 .. ­0.2 1.1 0.0 270 7,334 d,i 17,535 1.6 43 1,038 Jordan 117.9 36.9 0.4 22.8 ­1.0 20.4 130 3,225e 1,628 1.4 139 57 Kazakhstan 80.8 14.2 25.3 7.6 ­0.5 0.2 ­200 3,143 3,004 .. .. 63 Kenya 47.2 17.1 1.4 0.2 0.1 5.0 c 25 1,536 .. 3.0 6 21 Korea, Dem. Rep. .. .. .. .. .. .. 0 .. .. .. .. .. Korea, Rep. 71.5 13.8 .. 0.4 0.8 0.1 ­80 6,155d,e 11,610 0.8 92 1,028 Kuwait 75.1 16.5 .. 0.3 6.4 .. 264 91h 1,928 1.5 .. 348 Kyrgyz Republic 89.2 29.6 0.0 6.5 2.0 17.1 ­75 766 454 5.4 30 39 Lao PDR 56.3 .. 0.0 5.5 .. 0.0 c ­115 842 .. 1.1 7 4 Latvia 87.8 23.0 7.5 8.3 0.9 2.4 ­20 1,535 3,151 1.6 67 3,230 Lebanon 54.8 89.5 18.3 12.3 0.3 22.9 0 1,063 .. 2.2 279 111 Lesotho 144.5 10.4 0.0 5.2 0.0 24.2 ­36 347 .. 3.3 18 2 Liberia 99.0 .. 246.8 ­13.0 .. .. ­119 .. .. .. .. .. Libya 92.3 6.1 0.0 .. 0.9 0.0 10 149 .. .. 66 21 Lithuania 112.3 20.7 4.3 6.1 1.0 3.3 ­30 2,000 .. 1.6 49 2,714 Macedonia, FYR 99.1 18.9 1.1 5.6 0.0 4.3 ­10 202g .. .. 63 17 Madagascar 44.4 22.1 0.0 4.2 .. 0.2c ­5 312f .. 0.6 1 2 Malawi 55.3 .. 0.0 0.9 .. 0.0 c ­30 438 .. .. .. 1 Malaysia 193.7 30.2 7.0 4.0 4.0 1.0 150 17,547 30,761 0.7 .. 124 Mali 54.7 16.3 0.0 3.2 0.0 3.0 c ­134 153f,h .. .. .. 26 Mauritania 85.0 .. 0.0 ­0.1 .. 0.1c 30 .. .. .. .. 30 Mauritius 91.4 47.2 2.8 1.7 0.2 3.4 c 0 788 186 1.6 150 153 Mexico 61.8 4.7 4.6 2.3 0.7 3.0 ­3,983 21,353e 14,002 0.8 174 109 Moldova 111.6 29.0 0.0 7.2 0.0 35.2 ­250 13 68 1.5 110 147 Mongolia 96.7 32.2 0.0 11.0 0.0 5.8 ­50 386 .. .. 5 13 Morocco 55.5 21.9 1.4 4.1 0.7 8.3 ­550 6,558e 2,247 1.7 65 377 Mozambique 76.2 16.7 0.6 2.2 0.0 1.2 ­20 578 .. 1.2 13 1 Myanmar .. .. .. .. .. .. ­99 264 .. 0.2 3 2 Namibia 84.8 14.6 0.0 .. ­0.2 0.3 ­1 833 .. .. .. 18 Nepal 32.0 9.8 0.0 ­0.1 .. 16.3 ­100 375 373 2.0 6 5 Netherlands 132.7 24.4 .. 1.1 7.0 0.4 110 10,739g 16,695 0.3 .. 20,501 New Zealand 46.8 15.0 .. 7.6 0.7 0.6 102 2,409d 1,861 1.3 361 1,107 Nicaragua 75.8 15.6 1.6 5.3 0.0 12.4 ­210 773e 788 3.2 62 1 Niger 40.7 10.7 0.0 0.6 0.3 1.8 c ­28 60 .. .. .. 2 Nigeria 64.0 11.7 1.0 4.7 .. 2.9c ­170 1,010 .. 1.5 .. 1 Norway 55.4 19.2 .. 1.4 4.6 0.2 84 3,945 3,193 .. 193 9,305 Oman 89.2 12.3 22.8 2.9 0.4 0.1 ­150 1,306h .. 1.9 189 174 Pakistan 36.9 9.4 3.0 3.4 0.1 4.0 ­1,239 898 .. 1.0 10 5 Panama 34.6 33.1 7.6 15.1 0.0 0.9 8 843 284 .. .. 287 Papua New Guinea 112.7 29.7 1.4 0.6 0.1 0.2c 0 78 .. .. .. 1 Paraguay 83.9 13.3 0.0 2.0 0.0 4.7 ­45 388i 210 0.9 31 83 Peru 41.9 6.3 3.6 3.8 .. 2.0 ­510 1,635 1,857 1.8 99 367 Philippines 83.8 10.7 8.6 2.0 0.1 13.0 ­900 2,843e 2,144 1.2 28 38 Poland 69.8 11.5 3.6 5.7 2.7 1.3 ­200 15,670 44,696 1.4 .. 560 Portugal 56.5 15.1 .. 3.8 1.8 1.7 276 11,282e 18,378 1.0 178 829 Puerto Rico .. .. .. .. .. .. ­10 3,722f 1,468 .. .. 511 2008 World Development Indicators 321 6.1 Integration with the global economy Trade International finance Movement of people International communication % of GDP Financing through Workers' international remittances capital and Cost of call Voice Internet markets Foreign direct compensation to U.S.a traffic a bandwidtha % of GDP Gross investment of employees Net International tourists $ per minutes bits per Merchandise Services inflows Net inflows Net outflows received migration Inbound Outbound 3 minutes per person capita 2006 2006 2006 2006 2006 2006 2000­05 2006 2006 2005 2006 2006 Romania 68.6 11.6 1.2 9.4 0.3 5.5 ­270 6,037d 8,906 0.8 .. 1,503 Russian Federation 47.5 7.7 7.9 3.1 2.3 0.3 917 22,486 29,107 2.0 .. 100 Rwanda 25.6 15.0 0.0 0.5 ­0.6 0.8 43 .. .. 2.4 .. 7 Saudi Arabia 79.0 13.7 .. 0.2 0.0 .. 285 8,620 2,000 .. 216 126 Senegal 54.3 17.2 1.0 0.6 0.2 6.9c ­100 769 .. 1.0 39 103 Serbia 61.3 .. 0.0 16.0 .. 14.7c,j ­339 469g .. .. .. 95 Sierra Leone 41.7 8.5 0.0 4.1 0.0 2.3 472 34f 67 .. .. .. Singapore 386.2 91.6 .. 18.3 6.5 .. 200 7,588 5,533 0.7 1,045 7,052 Slovak Republic 159.1 .. 2.4 7.6 .. 0.8 c 3 1,612g 22,688 1.1 90 2,913 Slovenia 127.0 20.4 .. 1.7 2.4 0.8 22 1,617g 2,680 0.7 .. 1,255 Somalia .. .. .. .. .. .. 100 .. .. .. .. 0 South Africa 53.2 10.3 10.3 0.0 2.6 0.3 75 8,396 .. 0.8 .. 19 Spain 42.6 15.1 .. 1.6 7.2 0.7 2,846 58,451 10,676 0.6 173 2,776 Sri Lanka 63.6 14.9 0.1 1.8 0.1 8.7 ­442 560i 757 2.1 28 25 Sudan 36.7 8.0 0.2 9.4 0.0 3.1 ­532 328e .. .. 12 5 Swaziland 160.9 24.6 0.0 1.4 0.1 3.7 ­6 873h 1,072 3.0 .. 1 Sweden 71.4 23.5 .. 7.1 6.2 0.1 152 3,270g 12,591 0.4 .. 17,468 Switzerland 75.9 21.2 .. 7.1 18.6 0.5 100 7,863h .. 0.3 .. 9,609 Syrian Arab Republic 55.1 16.3 0.0 1.8 0.0 2.4 200 4,422 4,042 .. 44 8 Tajikistan 111.0 18.8 0.0 12.0 0.0 36.2 ­345 .. .. 7.8 .. 0 Tanzania 46.5 21.4 0.0 3.7 0.0 0.1 ­345 622 .. 3.2 .. 0 Thailand 125.7 27.4 3.2 4.4 0.5 0.6 231 13,822i 3,382 0.7 14 156 Timor-Leste .. .. 0.0 .. .. .. 100 .. .. .. .. .. Togo 77.8 21.6 0.0 2.6 ­0.6 8.7c ­4 81h .. 4.0 21 16 Trinidad and Tobago 113.8 9.5 13.5 6.2 ­2.3 0.5c ­20 463f .. 2.2 376 370 Tunisia 87.1 22.3 1.6 10.8 0.1 5.0 ­29 6,549i 2,241 .. 73 126 Turkey 55.6 8.9 9.1 5.0 0.2 0.3 ­30 18,916 8,275 2.4 27 631 Turkmenistan 88.8 .. 0.0 7.0 .. .. ­10 12 33 .. .. 16 Uganda 37.2 15.7 0.0 4.2 0.0 8.6 ­5 539 254 3.2 .. 4 Ukraine 78.3 19.2 5.3 5.3 ­0.1 0.8 ­173 18,900 16,875 1.6 57 17 United Arab Emirates 155.7 .. .. .. .. .. 577 7,126b,e .. 1.7 .. 2,371 United Kingdom 44.9 17.1 .. 5.9 5.4 0.3 948 30,654 69,536 0.8 .. 13,062 United States 22.5 5.8 .. 1.4 1.8 0.0 6,493 50,978 63,662 .. 279 3,307 Uruguay 45.1 11.3 13.0 7.0 0.0 0.5 ­104 1,749 666 0.5 121 484 Uzbekistan 56.0 .. 0.2 1.0 .. .. ­300 262 455 .. 12 9 Venezuela, RB 54.3 4.2 0.3 ­0.3 1.1 0.1 40 748 1,095 0.8 .. 50 Vietnam 137.7 17.8 2.7 3.8 0.1 7.9c ­200 3,583d .. 1.9 .. 84 West Bank and Gaza .. .. 0.0 .. .. 14.7c 11 123h .. 1.2 66 199 Yemen, Rep. 64.1 12.6 0.3 5.9 .. 6.7 ­100 382h .. 2.4 .. 0 Zambia 61.6 8.3 8.5 5.4 .. 0.5 ­82 669 .. 1.4 .. 11 Zimbabwe 121.4 .. 0.0 3.0 .. .. ­75 2,287d .. .. 25 4 World 49.9 w 11.3 w .. w 2.8 w 3.0 w 0.6 w ..k s 850,778 t 1,030,976 t 1.4 m .. w 529 w Low income 44.1 14.3 3.1 2.6 0.9 3.6 ­4,690 27,246 .. 2.0 .. 22 Middle income 61.8 9.8 4.7 3.3 1.4 1.7 ­14,021 301,883 344,318 1.6 31 144 Lower middle income 66.5 10.5 2.9 3.0 0.6 2.2 ­9,750 148,352 107,329 2.1 21 189 Upper middle income 57.6 9.2 6.3 3.5 2.0 1.2 ­4,271 155,980 222,638 1.1 .. 242 Low & middle income 59.4 10.3 4.5 3.2 1.3 1.9 ­18,711 332,275 419,006 1.8 .. 143 East Asia & Pacific 75.7 9.9 3.1 2.9 0.8 1.5 ­3,847 98,476 81,142 1.2 8 182 Europe & Central Asia 66.2 11.5 7.3 5.0 2.1 1.4 ­1,730 108,942 176,948 1.6 .. 268 Latin America & Carib. 43.1 6.2 4.5 2.4 1.6 1.9 ­6,811 55,387 38,100 1.2 .. 269 Middle East & N. Africa 59.8 .. 2.0 4.2 .. 3.9 ­2,768 36,214 26,968 1.7 36 126 South Asia 34.4 14.1 3.7 2.0 0.9 3.5 ­2,484 7,296 12,998 2.0 .. 22 Sub-Saharan Africa 60.8 13.7 4.8 2.4 0.4 1.6 ­1,070 27,486 .. 2.4 .. 5 High income 46.9 11.6 .. 2.7 3.5 0.2 18,604 510,271 533,390 0.8 204 4,346 Euro area 65.2 16.1 .. 3.8 5.3 0.5 6,887 284,903 194,611 0.7 .. 4,830 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. Arrivals in hotels only. c. World Bank estimates. d. Refers to arrivals of nonresident visitors at national borders. e. Includes nationals residing abroad. f. Arrivals by air only. g. Arrivals in all types of accommodation establishments. h. Arrivals in hotels and similar establishments. i. Excludes nationals residing abroad. j. Includes Montenegro. k. 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. 322 2008 World Development Indicators 6.1 GLOBAL LINKS Integration with the global economy About the data Globalization--the integration of the world foreign affiliates. Distinguished from other kinds of received are current transfers by migrant workers and economy--has been a persistent theme of the past international investment, FDI is made to establish a wages and salaries earned by nonresident workers. quarter century. Growth of cross-border economic lasting interest in or effective management control Workers' remittances are current private transfers activity has changed the structure of economies and over an enterprise in another country. FDI may be from migrant workers resident in the host country for the political and social organization of countries. Not understated in many developing countries because more than a year, irrespective of their immigration all effects of globalization can be measured directly. some countries fail to report reinvested earnings status, to recipients in their country of origin. Com- But the scope and pace of change can be monitored and because the definition of long-term loans differs pensation of employees is the income of migrants along four key dimensions: trade in goods and services, across countries. However, the quality and coverage of who have lived in the host country for less than a year. financial flows, movement of people, and communica- the data are improving as a result of continuous efforts · Net migration is the total number of immigrants tion. Globalization has created opportunities and chal- by international and national statistics agencies. See minus the total number of emigrants, including citi- lenges for developing countries, but many poor people About the data for table 6.10 for more information. zens and noncitizens, for the five-year period. · Inter- and countries have been unable to take full advantage Workers' remittances comprise workers' remit- national inbound tourists (overnight visitors) are the of globalization's opportunities and benefits. tances, compensation of employees, and migrants' number of tourists who travel to a country other than Trade data are based on gross flows that capture the transfers. Migration and tourism have increased in that in which they have their usual residence, but out- two-way flow of goods and services. In conventional importance over time, now accounting for a substan- side their usual environment, for less than 12 months balance of payments accounting exports are recorded tial part of global integration. See About the data and whose main purpose in visiting is not for paid work. as a credit and imports as a debit. See tables 4.4 Definitions for tables 6.16 and 6.17 for information When data on the number of tourists are not avail- and 4.5 for data on the main trade components of on migration and tourism. able, the number of day visitors, which includes tour- merchandise trade and tables 4.6 and 4.7 for data on Well developed communications infrastructure ists, cruise passengers, and crew members, is shown the main trade components of services trade. attracts investments and allows investors to capitalize instead. · International outbound tourists are the Financing through international capital markets on benefits offered by the digital age. See About the number of departures that people make from their includes gross bond issuance, bank lending, and data for tables 5.10 and 5.11 for more information. country of usual residence to any other country for any new equity placement as reported by Dealogic, a purpose other than paid work. · Cost of call to U.S. Definitions company specializing in the investment banking is the cost of a three-minute, peak rate, fixed-line call industry. In financial accounting inward investment · Trade in merchandise is the sum of merchandise from the country to the United States. · International is a credit and outward investment a debit. Gross exports and imports. · Trade in services is the sum voice traffic is the sum of international incoming and flow is a better measure of integration than net flow of services exports and imports. · Financing through outgoing telephone traffic (in minutes) divided by total because gross flow shows the total value of financial international capital markets is the sum of the abso- population. · International Internet bandwidth is transactions over a given period, while net flow is the lute values of new bond issuance, syndicated bank the contracted capacity of international connections sum of credits and debits and represents a balance lending, and new equity placements. · Foreign direct between countries for transmitting Internet traffic. in which many transactions are canceled out. investment net inflows are net inflows of investment Data sources Components of fi nancing through international in the reporting economy. FDI is the sum of equity capital markets are reported in U.S. dollars by mar- capital, reinvestment of earnings, and other short- Data on merchandise trade are from the World ket sources. and long-term capital. · Foreign direct investment Trade Organization's (WTO) Annual Report. Data on Foreign direct investment (FDI) has three compo- net outflows are net outflows of investment from the trade in services are from the International Mon- nents: equity investment, reinvested earnings, and reporting economy to the rest of the world. · Work- etary Fund's (IMF) Balance of Payments database. short-and long-term loans between parent firms and ers' remittances and compensation of employees Data on international capital market financing are based on data reported by Dealogic. Data on FDI Trade and international finance are leading globalization 6.1a are based on balance of payments data reported by the IMF, supplemented by staff estimates using Foreign direct investment net inflows as a share of GDP (%) Trade as a share of GDP (%) data reported by the United Nations Conference Low-income Middle-income High-income Low-income Middle-income High-income 8 80 on Trade and Development and official national sources. Data on workers' remittances are World 6 60 Bank staff estimates based on IMF balance of payments data. Data on net migration are from 4 40 the United Nations Population Division's World Population Prospects: The 2006 Revision. Data on 2 20 international tourism are from the WTO's Yearbook of Tourism Statistics and Compendium of Tourism 0 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Statistics 2008 and electronic updates. Data on Trade in low- and middle-income economies has grown faster than trade in high-income economies since cost of call to U.S., international voice traffic, and 2000. FDI net inflows in low-income economies soared in 2006. international Internet bandwidth are from the Inter- national Telecommunication Union's International Source: World Development Indicators data files. Development Report database. 2008 World Development Indicators 323 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 1985­95 1995­2006 1985­95 1995­2006 1985­95 1995­2006 1985­95 1995­2006 1995 2006 Afghanistan .. .. .. .. .. .. .. .. .. .. Albania .. .. .. .. .. .. .. .. .. .. Algeria 2.5 2.6 1.4 5.8 1.3 14.2 4.4 6.1 57.9 178.9 Angola 8.1 7.7 ­1.4 15.7 6.9 19.8 2.3 17.5 80.8 196.6 Argentina 6.9 5.4 17.8 0.4 10.4 5.9 20.3 ­0.7 91.6 112.9 Armenia .. .. .. .. .. .. .. .. .. .. Australiaa 6.9 4.9 6.9 6.9 9.1 7.9 10.0 7.0 99.4 145.5 Austriaa 5.1 6.1 3.6 4.8 .. .. .. .. .. .. Azerbaijan .. .. .. .. .. .. .. .. .. .. Bangladesh 13.4 8.3 4.9 2.9 14.1 9.3 8.1 7.9 111.8 73.2 Belarus .. .. .. .. .. .. .. .. .. .. Belgiuma 2.9 5.6 2.4 5.4 3.4 7.7 3.3 8.1 104.3 99.1 Benin 13.0 3.8 3.5 1.9 17.4 2.7 6.8 2.6 106.6 80.8 Bolivia 8.9 8.1 4.4 2.8 5.5 10.9 8.5 3.4 89.4 134.6 Bosnia and Herzegovina .. .. .. .. .. .. .. .. .. .. Botswana 0.1 4.9 3.0 4.1 6.3 5.4 9.3 4.5 89.3 94.5 Brazil 4.5 9.8 15.0 1.6 6.4 9.8 11.9 2.8 110.4 103.8 Bulgaria .. .. .. .. .. .. .. .. .. .. Burkina Faso 0.6 10.0 1.6 8.4 3.7 7.3 1.7 9.7 131.0 89.8 Burundi 4.2 6.2 2.4 13.5 ­3.1 ­4.1 1.7 5.2 163.6 125.5 Cambodia .. 16.4 .. 11.9 50.3 17.0 25.4 14.9 .. 88.7 Cameroon 9.7 ­0.4 ­4.5 8.9 10.4 4.8 ­3.4 9.4 90.4 136.1 Canadaa 6.3 4.3 6.5 5.7 7.9 5.5 8.2 6.0 103.2 115.8 Central African Republic 9.2 4.9 1.9 0.5 5.1 ­1.7 ­0.4 0.4 193.0 90.2 Chad .. .. .. .. .. .. .. .. .. .. Chile 11.0 7.7 9.4 5.2 13.3 10.5 17.1 5.7 135.6 183.7 China 15.1 19.5 11.8 17.6 17.7 18.8 12.7 19.3 101.9 82.1 Hong Kong, China 16.1 6.9 17.1 6.0 19.1 5.4 20.2 4.7 99.1 96.8 Colombia 8.7 4.2 9.5 4.0 8.9 7.0 13.1 4.2 86.8 115.2 Congo, Dem. Rep. ­9.9 10.6 ­13.4 28.3 ­4.7 2.8 ­7.1 12.7 79.8 125.9 Congo, Rep. 1.5 3.3 ­7.6 9.9 1.6 15.0 ­2.8 9.3 52.0 184.4 Costa Rica 11.3 8.4 13.3 8.9 13.0 6.3 15.0 8.7 104.6 85.8 Côte d'Ivoire 1.7 2.6 ­5.0 1.0 ­0.2 6.4 2.2 3.2 122.0 135.4 Croatia .. .. .. .. .. .. .. .. .. .. Cuba .. ­3.0 .. 5.5 ­18.9 2.8 ­15.0 8.7 .. 148.8 Czech Republic .. .. .. .. .. .. .. .. .. .. Denmarka 5.0 4.3 3.3 4.3 4.8 5.9 2.4 5.7 102.1 105.0 Dominican Republic 0.3 1.1 7.7 4.9 ­1.6 1.2 11.9 5.3 98.1 94.9 Ecuador 9.0 5.6 5.4 9.1 5.9 8.5 7.9 10.1 80.6 109.9 Egypt, Arab Rep. ­5.7 7.9 ­9.3 ­1.6 ­3.1 12.6 ­4.6 1.9 116.3 127.0 El Salvador 1.0 4.4 11.0 5.6 2.9 3.7 11.7 7.2 121.1 95.5 Eritrea .. .. .. .. .. .. .. .. .. .. Estonia .. .. .. .. .. .. .. .. .. .. Ethiopia 9.8 8.6 6.7 10.9 17.5 6.3 8.0 12.9 151.0 98.9 Finland .. .. .. .. .. .. .. .. .. .. Francea 5.1 7.5 5.0 8.6 2.9 5.8 1.8 7.1 106.4 102.2 Gabon 7.4 4.7 ­3.1 3.4 6.9 5.9 ­0.1 3.7 125.4 179.1 Gambia, The ­8.5 ­6.9 4.1 ­1.3 ­3.4 ­7.6 9.0 ­0.6 100.0 80.5 Georgia .. .. .. .. .. .. .. .. .. .. Germanya .. .. .. .. .. .. .. .. 107.5 96.6 Ghana 6.8 3.6 7.8 8.4 6.6 6.8 11.5 9.8 106.7 132.0 Greecea 6.4 .. 9.6 .. 15.1 .. 17.2 .. 112.1 .. Guatemala 4.6 5.6 9.3 8.6 6.1 4.3 11.8 9.9 117.9 89.6 Guinea 5.6 ­2.3 5.3 4.8 3.8 1.7 8.2 1.9 89.6 204.0 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. Haiti ­3.4 12.3 ­2.3 6.8 ­8.6 13.0 0.1 9.4 113.2 88.9 Data for Taiwan, China 17.0 3.7 21.3 2.9 11.5 5.9 16.2 5.8 89.9 84.4 324 2008 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 1985­95 1995­2006 1985­95 1995­2006 1985­95 1995­2006 1985­95 1995­2006 1995 2006 Honduras 3.7 4.9 4.3 10.4 1.9 2.6 4.6 10.0 96.3 83.2 Hungarya ­0.1 14.5 2.6 13.0 2.7 15.4 6.0 14.6 104.3 95.5 Indiaa .. .. .. .. .. .. .. .. 107.7 105.2 Indonesia 10.4 1.8 10.1 1.1 11.7 5.6 15.3 3.2 90.4 100.9 Iran, Islamic Rep. 7.4 2.3 .. 14.3 7.7 12.7 4.3 13.1 .. 155.3 Iraq .. .. .. .. .. .. .. .. .. .. Irelanda 10.5 8.9 6.9 6.6 10.8 9.0 8.2 7.0 98.9 94.4 Israela 6.2 7.1 8.6 3.7 21.6 12.3 22.8 8.7 92.1 93.8 Italya 5.6 0.7 4.8 2.8 9.7 4.5 7.1 6.7 95.9 97.5 Jamaica 7.2 ­5.5 .. 0.6 8.7 1.0 9.4 4.8 .. 192.0 Japana 2.4 3.5 7.3 3.9 1.5 4.2 2.1 4.9 105.5 91.8 Jordan 5.1 10.8 3.8 7.6 7.5 10.7 3.8 10.4 115.6 84.3 Kazakhstan .. .. .. .. .. .. .. .. .. .. Kenya 7.7 4.6 9.0 4.8 5.8 5.1 4.2 7.2 103.9 91.3 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 10.6 14.2 13.3 6.9 13.0 8.8 15.0 7.6 138.5 73.2 Kuwait 9.7 5.5 .. 13.7 0.6 13.3 2.5 7.0 .. 195.2 Kyrgyz Republic .. .. .. .. .. .. .. .. .. .. Lao PDR .. 9.0 .. 7.7 21.7 5.7 12.7 2.6 .. 117.6 Latviaa ­6.8 8.9 .. .. ­1.7 9.9 .. .. .. .. Lebanon .. 17.8 .. 2.1 1.7 15.9 13.9 2.7 .. 101.1 Lesotho 12.1 19.2 5.3 4.3 21.0 17.1 12.1 4.2 100.0 81.1 Liberia .. .. .. .. .. .. .. .. .. .. Libya .. 4.7 1.6 5.3 ­0.2 12.8 1.7 7.9 .. 171.7 Lithuania .. .. .. .. .. .. .. .. .. .. Macedonia, FYR .. .. .. .. .. .. .. .. .. .. Madagascar 2.5 11.0 1.6 9.2 4.6 12.4 7.3 11.4 79.6 76.5 Malawi 2.6 3.3 0.8 5.4 5.1 1.8 7.8 7.1 105.7 84.4 Malaysia 10.3 8.7 18.8 5.6 18.0 6.8 22.3 4.8 108.5 98.7 Mali 10.5 10.5 4.8 8.1 11.8 11.1 7.7 8.5 109.6 107.2 Mauritania ­0.9 ­0.7 7.3 7.6 2.9 0.9 9.2 5.5 102.2 169.7 Mauritius 5.6 0.7 7.9 5.6 10.4 1.2 12.5 3.5 88.5 115.2 Mexico 12.2 7.7 14.7 9.6 12.0 9.4 18.6 10.4 92.5 104.0 Moldova .. .. .. .. .. .. .. .. .. .. Mongolia .. 4.1 .. 9.1 ­7.7 10.1 ­14.9 11.7 .. 162.4 Morocco 6.7 4.4 7.9 7.9 9.3 3.9 9.9 8.0 89.1 86.2 Mozambique 10.0 28.6 3.2 11.7 8.1 29.1 6.2 14.1 151.1 130.6 Myanmar 14.3 18.6 11.8 ­1.1 14.8 18.0 18.3 2.6 214.3 111.1 Namibia 5.9 3.4 4.2 5.8 3.5 4.3 3.8 4.2 82.6 127.3 Nepal .. ­1.9 .. ­1.8 11.7 7.8 10.4 3.5 .. 79.0 Netherlandsa 5.8 5.3 5.1 4.5 4.0 6.4 3.4 6.0 103.2 100.5 New Zealanda 4.1 3.5 5.4 6.1 8.2 4.8 7.7 6.3 101.8 111.5 Nicaragua 2.6 7.8 ­1.4 5.8 2.9 5.9 0.1 8.6 128.9 79.4 Niger 1.7 ­0.5 ­3.9 5.6 ­0.5 4.9 0.5 8.5 121.4 163.7 Nigeria ­2.0 ­1.6 ­9.1 10.5 5.3 10.2 3.3 13.2 55.6 160.6 Norwaya 6.8 2.6 2.5 5.8 5.4 9.4 3.6 5.1 60.3 139.2 Oman 12.4 ­0.4 .. 8.9 10.2 11.6 6.9 8.0 .. 182.3 Pakistan 9.8 7.5 4.1 4.4 10.7 6.5 7.2 8.0 119.2 76.2 Panama 3.2 3.4 6.6 2.4 7.1 4.0 9.2 3.9 100.0 90.9 Papua New Guinea 6.3 ­6.4 .. 5.2 11.4 3.2 3.6 1.8 .. 160.4 Paraguay 5.5 6.7 19.4 0.7 12.4 6.2 19.9 2.2 118.3 95.5 Peru 4.4 10.5 8.5 1.1 6.1 12.1 13.2 2.3 123.4 151.1 Philippines 12.8 7.2 16.0 4.7 13.1 7.7 18.0 4.3 80.2 84.1 Polanda 4.8 13.0 7.9 10.5 4.6 15.3 7.1 12.1 101.7 107.1 Portugala 6.8 ­0.5 8.8 ­1.0 11.0 ­4.7 10.2 ­5.1 104.7 101.6 Puerto Rico .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 325 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 1985­95 1995­2006 1985­95 1995­2006 1985­95 1995­2006 1985­95 1995­2006 1995 2006 Romania .. .. .. .. .. .. .. .. .. .. Russian Federation .. .. .. .. .. .. .. .. .. .. Rwanda ­13.2 3.8 0.1 2.2 ­12.6 6.3 ­5.4 4.5 110.1 128.7 Saudi Arabia 12.7 0.2 .. 11.5 9.3 13.5 3.6 7.6 .. 205.1 Senegal 2.0 6.9 ­0.5 6.9 4.2 4.8 2.7 9.7 156.3 101.8 Serbia .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. .. Singapore 16.0 9.1 13.5 4.7 17.9 7.0 17.3 4.8 104.3 86.1 Slovak Republic .. .. .. .. .. .. .. .. .. .. Slovenia .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. South Africa 2.2 4.6 5.2 7.1 4.2 6.5 7.7 8.0 106.0 125.3 Spaina 8.7 6.7 12.1 8.5 11.0 8.0 12.0 10.2 104.3 103.5 Sri Lanka 6.3 4.5 6.8 3.2 12.1 4.4 11.6 5.3 99.0 80.0 Sudan 14.9 19.2 16.7 15.4 1.2 26.3 3.1 17.0 100.0 189.5 Swaziland 7.9 9.3 5.9 6.6 13.9 9.3 11.8 7.4 100.0 93.3 Swedena 3.8 6.3 3.1 4.8 8.0 3.9 6.4 4.5 109.5 88.1 Switzerlanda 2.3 4.5 1.2 4.1 .. .. .. .. .. .. Syrian Arab Republic 24.4 1.2 .. 10.1 11.1 7.2 5.9 6.1 .. 133.0 Tajikistan .. .. .. .. .. .. .. .. .. .. Tanzania 6.0 7.6 ­0.4 8.0 8.2 10.0 7.9 9.2 98.0 115.7 Thailand 17.3 7.1 17.3 3.3 22.2 7.8 23.1 6.3 116.0 92.3 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 4.0 4.6 ­4.2 3.9 3.6 4.5 ­0.7 5.9 99.1 78.0 Trinidad and Tobago 1.4 8.7 ­5.8 3.1 2.6 16.2 0.6 10.3 .. 122.7 Tunisia 9.5 7.4 6.2 6.2 12.1 7.3 11.5 6.1 95.8 94.3 Turkey 11.6 12.6 14.0 9.2 9.9 12.8 11.6 10.6 105.7 96.2 Turkmenistan .. .. .. .. .. .. .. .. .. .. Uganda 7.2 9.7 8.7 3.7 ­2.5 4.8 6.7 5.6 197.2 102.0 Ukraine .. .. .. .. .. .. .. .. .. .. United Arab Emirates 8.2 7.9 .. 17.6 10.5 15.1 14.7 15.1 .. 152.7 United Kingdoma 4.6 4.0 4.5 6.7 8.8 4.7 8.3 7.1 100.1 104.3 United Statesa 8.2 3.6 5.0 7.1 10.3 4.0 7.3 8.1 103.3 96.0 Uruguay 5.9 4.6 12.9 ­0.4 7.4 3.1 14.9 0.4 116.2 88.7 Uzbekistan .. .. .. .. .. .. .. .. .. .. Venezuela, RB 6.7 0.1 ­0.2 5.9 5.1 9.9 3.6 6.2 63.4 184.4 Vietnam .. 13.2 .. 13.9 22.7 18.1 12.1 15.8 .. 96.8 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. Yemen, Rep. .. ­3.2 2.3 13.3 8.1 12.1 4.3 11.4 .. 150.3 Zambia ­3.1 9.1 ­9.7 12.5 2.3 8.1 ­0.1 13.5 189.7 187.0 Zimbabwe 6.5 0.1 13.8 ­1.4 4.4 ­1.3 11.5 ­2.8 96.8 93.4 a. Data are from the International Monetary Fund's International Financial Statistics database. 326 2008 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 quan- from each country's balance of payments and IMF's International Financial Statistics database, the tity of goods traded. They are derived from UNCTAD's customs records. While the balance of payments United Nations Economic Commission for Latin Amer- quantum index series and are the ratio of the export focuses on the financial transactions that accom- ica and the Caribbean, the United Nations Statistics or import value indexes to the corresponding unit pany trade, customs data record the direction of Division's Monthly Bulletin of Statistics database, value indexes. Unit value indexes are based on data trade and the physical quantities and value of goods the World Bank Africa Database, the U.S. Bureau reported by countries that demonstrate consistency entering or leaving the customs area. Customs data of Labor Statistics, Japan Customs, and UNCTAD's under UNCTAD quality controls, supplemented by may differ from data recorded in the balance of pay- Commodity Price Statistics. The IMF also compiles UNCTAD's estimates using the previous year's trade ments because of differences in valuation and time data on trade prices and volumes in its International values at the Standard International Trade Classifi - of recording. The 1993 System of National Accounts Financial Statistics (IFS) database. cation three-digit level as weights. For economies and the fifth edition of the International Monetary Unless otherwise noted, the growth rates and for which UNCTAD does not publish data, the export Fund's (IMF) Balance of Payments Manual (1993) terms of trade in the table were calculated from and import volume indexes (lines 72 and 73) in the attempted to reconcile defi nitions and reporting index numbers compiled by UNCTAD. The growth IMF's International Financial Statistics are used to standards for international trade statistics, but dif- rates and terms of trade for selected economies calculate the average annual growth rates. · Export ferences in sources, timing, and national practices were calculated from index numbers compiled in and import values are the current value of exports limit comparability. Real growth rates derived from the IMF's International Financial Statistics. In some (f.o.b.) or imports (c.i.f.), converted to U.S. dollars trade volume indexes and terms of trade based on cases price and volume indexes from different and expressed as a percentage of the average for unit price indexes may therefore differ from those sources vary significantly as a result of differences the base period (2000). UNCTAD's export or import derived from national accounts aggregates. in estimation procedures. Because the IMF does not value indexes are reported for most economies. For Trade in goods, or merchandise trade, includes all publish trade value indexes, for selected economies selected economies for which UNCTAD does not pub- goods that add to or subtract from an economy's the trade value indexes were derived from the vol- lish data, the value indexes are derived from export material resources. Trade data are collected on the ume and price indexes. All indexes are rescaled to or import volume indexes (lines 72 and 73) and cor- basis of a country's customs area, which in most a 2000 base year. responding unit value indexes of exports or imports cases is the same as its geographic area. Goods The terms of trade measures the relative prices of (lines 74 and 75) in the IMF's International Financial provided as part of foreign aid are included, but a country's exports and imports. There are several Statistics. · Net barter terms of trade index is calcu- goods destined for extraterritorial agencies (such ways to calculate it. The most common is the net lated as the percentage ratio of the export unit value as embassies) are not. barter (or commodity) terms of trade index, or the indexes to the import unit value indexes, measured Collecting and tabulating trade statistics are dif- ratio of the export price index to the import price relative to the base year 2000. ficult. Some developing countries lack the capacity index. When a country's net barter terms of trade 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, 2008 World Development Indicators 327 6.3 Direction and growth of merchandise trade Direction of trade High-income importers % of world trade, 2006 European United Other high- Union Japan States income Total Source of exports High-income economies 29.1 2.6 8.7 10.3 50.8 European Union 22.8 0.5 2.8 2.5 28.6 Japan 0.8 .. 1.2 1.6 3.6 United States 1.9 0.5 .. 3.3 5.7 Other high-income economies 3.6 1.7 4.7 2.9 12.9 Low- and middle-income economies 8.2 1.8 6.3 5.1 21.4 East Asia & Pacific 2.0 1.3 2.4 3.7 9.5 China 1.5 0.8 1.7 2.5 6.4 Europe & Central Asia 3.7 0.1 0.2 0.2 4.2 Russian Federation 1.3 0.0 0.1 0.1 1.5 Latin America & Caribbean 0.8 0.1 2.7 0.4 4.1 Brazil 0.3 0.0 0.2 0.1 0.6 Middle East & N. Africa 0.9 0.1 0.3 0.3 1.6 Algeria 0.2 0.0 0.1 0.0 0.4 South Asia 0.3 0.0 0.2 0.3 0.9 India 0.2 0.0 0.2 0.3 0.7 Sub-Saharan Africa 0.5 0.1 0.5 0.1 1.2 South Africa 0.2 0.1 0.1 0.0 0.3 World 37.3 4.4 15.1 15.4 72.2 Low- and middle-income importers % of world trade, 2006 Europe Latin Middle East Asia & Central America East & South Sub-Saharan & Pacific Asia & Caribbean N. Africa Asia Africa Total Source of exports High-income economies 7.3 4.0 3.1 1.1 1.0 0.9 17.4 European Union 1.0 3.4 0.7 0.7 0.3 0.5 6.5 Japan 1.3 0.1 0.2 0.0 0.1 0.1 1.8 United States 0.7 0.2 1.8 0.1 0.1 0.1 3.0 Other high-income economies 4.4 0.4 0.5 0.3 0.5 0.3 6.2 Low- and middle-income economies 2.3 2.6 1.5 0.7 0.6 0.6 8.3 East Asia & Pacific 1.2 0.5 0.4 0.2 0.3 0.2 2.8 China 0.4 0.4 0.3 0.1 0.2 0.2 1.6 Europe & Central Asia 0.2 1.8 0.0 0.2 0.1 0.0 2.4 Russian Federation 0.1 0.7 0.0 0.0 0.0 0.0 0.9 Latin America & Caribbean 0.3 0.1 1.0 0.1 0.0 0.1 1.5 Brazil 0.1 0.0 0.3 0.0 0.0 0.0 0.5 Middle East & N. Africa 0.2 0.1 0.0 0.2 0.0 0.1 0.6 Algeria 0.0 0.0 0.0 0.0 0.0 0.0 0.0 South Asia 0.1 0.0 0.0 0.0 0.1 0.1 0.4 India 0.1 0.0 0.0 0.0 0.1 0.1 0.3 Sub-Saharan Africa 0.2 0.0 0.1 0.0 0.0 0.2 0.5 South Africa 0.0 0.0 0.0 0.0 0.0 0.1 0.1 World 9.7 6.6 4.7 1.7 1.6 1.5 25.7 328 2008 World Development Indicators 6.3 GLOBAL LINKS Direction and growth of merchandise trade Nominal growth of trade High-income importers annual % growth, 1996­2006 European United Other high- Union Japan States income Total Source of exports High-income economies 6.8 3.7 6.2 5.7 6.3 European Union 7.0 2.1 8.6 5.7 6.9 Japan 3.5 .. 2.7 4.4 3.6 United States 5.0 ­1.2 .. 4.8 4.2 Other high-income economies 7.4 6.6 6.0 7.4 6.8 Low- and middle-income economies 14.1 8.5 13.1 13.6 13.1 East Asia & Pacific 16.2 8.5 15.9 13.8 13.8 China 23.8 11.5 22.5 18.8 19.3 Europe & Central Asia 16.3 6.3 9.2 13.4 15.4 Russian Federation 16.3 4.9 3.3 7.5 14.1 Latin America & Caribbean 9.1 5.2 10.6 11.8 10.2 Brazil 8.7 2.5 10.2 14.0 9.5 Middle East & N. Africa 11.8 8.5 28.5 13.4 13.4 Algeria 15.2 6.9 24.0 25.4 18.1 South Asia 9.9 3.4 11.3 13.9 11.1 India 11.3 6.1 13.0 16.2 13.0 Sub-Saharan Africa 10.7 25.3 15.9 15.4 13.7 South Africa 6.9 7.1 4.7 1.3 5.6 World 7.9 5.3 8.5 7.6 7.8 Low- and middle-income importers annual % growth, 1996­2006 Europe Latin Middle East Asia & Central America East & South Sub-Saharan & Pacific Asia & Caribbean N. Africa Asia Africa Total Source of exports High-income economies 10.0 11.9 6.8 7.9 9.3 7.5 9.4 European Union 7.8 12.3 5.8 7.0 8.5 6.6 9.4 Japan 7.2 16.7 5.5 5.5 5.0 4.9 7.2 United States 8.6 6.5 7.3 6.0 10.0 7.0 7.6 Other high-income economies 11.8 10.5 6.7 12.1 10.6 10.5 11.1 Low- and middle-income economies 17.8 14.1 12.2 16.1 17.0 17.9 15.2 East Asia & Pacific 17.1 27.5 22.8 20.1 20.1 22.2 20.0 China 21.5 31.6 27.7 26.8 26.5 26.0 26.2 Europe & Central Asia 11.1 12.4 10.7 15.0 15.7 14.8 12.6 Russian Federation 11.3 11.8 9.5 17.7 15.1 15.0 12.0 Latin America & Caribbean 19.0 13.1 9.3 9.9 18.7 15.6 11.2 Brazil 16.3 16.3 11.4 16.2 14.4 18.6 13.4 Middle East & N. Africa 22.8 11.1 13.9 18.7 12.2 23.2 17.5 Algeria 43.3 4.3 13.2 18.1 8.0 23.1 11.3 South Asia 19.0 11.2 21.9 15.6 17.0 16.3 17.0 India 20.5 12.1 25.9 19.0 14.9 17.0 18.0 Sub-Saharan Africa 30.1 17.2 22.7 8.8 2.9 15.3 18.6 South Africa 6.7 8.1 10.5 11.7 7.1 4.1 5.7 World 11.4 12.8 8.2 10.3 11.6 10.1 10.9 2008 World Development Indicators 329 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 developed and 23 devel- able, monthly average rates in New York. Because (see inside front cover). · European Union is defined oping countries report trade on a timely basis, cover- imports are reported at cost, insurance, and freight as all high-income EU members: Austria, Belgium, ing about 80 percent of trade for recent years. Trade (c.i.f.) valuations, and exports at free on board (f.o.b.) Cyprus, Czech Republic, Denmark, Estonia, Finland, by less timely reporters and by countries that do not valuations, the IMF adjusts country reports of import France, Germany, Greece, Ireland, Italy, Luxembourg, report is estimated using reports of trading partner values by dividing them by 1.10 to estimate equiva- Malta, the Netherlands, Portugal, Slovenia, Spain, countries. Because the largest exporting and import- lent export values. The accuracy of this approxima- Sweden, and the United Kingdom. · Other high- ing countries are reliable reporters, a large portion tion depends on the set of partners and the items income economies include all high-income econo- of the missing trade flows can be estimated from traded. Other factors affecting the accuracy of trade mies (both Organisation for Economic Co-operation partner reports. Partner country data may introduce data include lags in reporting, recording differences and Development members and others) except the discrepancies due to smuggling, confidentiality, dif- across countries, and whether the country reports high-income European Union, Japan, and the United ferent exchange rates, overreporting of transit trade, trade according to the general or special system of States. · Low- and middle-income regional group- inclusion or exclusion of freight rates, and different trade. (For further discussion of the measurement of ings are based on World Bank classifications and points of valuation and times of recording. exports and imports, see About the data for tables may differ from those used by other organizations. In addition, estimates of trade within the European 4.4 and 4.5.) Union (EU) have been significantly affected by changes The regional trade flows in the table are calculated in reporting methods following the creation of a cus- from current price values. The growth rates are in toms union. The current system for collecting data on nominal terms; that is, they include the effects of trade between EU members--Intrastat, introduced in changes in both volumes and prices. 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 More than half of the world's merchandise trade takes place between high-income economies. But integration of low- and middle-income economies in global merchandise trade increased substantially during 1996­2006 6.3a 1996 2006 Low- and middle-income to Unspecified 3.0% Low- and middle-income to Unspecified 2.1% low- and middle-income 4.5% low- and middle-income 8.3% Low- and middle-income to high-income 14.1% Low- and middle-income to high-income High-income High-income to High-income 21.4% to high-income low- and middle- to high-income 50.8% income 62.4% 16.0% High-income to low- and middle-income 17.4% Trade between low- and middle-income economies accounted for about 8.3 percent of world merchandise Data sources trade in 2006, compared with 4.5 percent in 1996. The share of trade from low- and middle-income Data on the direction and growth of merchandise economies to high-income economies increased 7.3 percentage points between 1996 and 2006. trade were calculated using the IMF's Direction of Source: International Monetary Fund's Direction of Trade database. Trade database. 330 2008 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 Uniona Japan United States 1996 2006 1996 2006 1996 2006 1996 2006 Total ($ billions) 73.8 165.0 34.5 68.7 8.3 14.5 8.9 18.8 % of total exports Food 8.3 5.7 8.8 6.3 0.5 0.4 16.5 10.6 Cereals 3.2 1.8 3.0 1.7 0.2 0.1 12.8 6.1 Agricultural raw materials 2.1 1.9 1.2 1.4 1.5 1.4 3.9 4.1 Ores and nonferrous metals 2.2 3.3 1.6 2.2 0.8 1.5 1.8 2.5 Fuels 5.2 9.5 3.3 5.5 1.2 0.9 1.4 2.9 Crude petroleum 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.9 6.7 3.2 5.3 1.1 0.5 1.3 2.3 Manufactured goods 80.4 75.9 83.2 81.6 94.8 92.6 72.7 72.1 Chemical products 12.0 11.9 12.1 11.0 7.2 7.2 11.7 10.7 Iron and steel 3.8 3.3 4.3 3.0 6.7 9.4 1.6 1.4 Machinery and transport equipment 43.9 42.1 43.3 44.4 66.6 61.8 49.7 47.6 Furniture 0.2 0.2 0.3 0.4 0.1 0.2 0.1 0.2 Textiles 4.3 3.1 1.7 1.3 3.7 3.3 1.5 1.6 Footwear 0.1 0.1 0.2 0.1 0.0 0.0 0.1 0.1 Other 16.0 15.2 21.2 21.6 10.5 10.6 7.9 10.6 Miscellaneous goods 1.8 3.7 1.9 3.1 1.1 3.2 3.7 7.8 Imports from low-income economies Total ($ billions) 81.4 217.1 38.1 81.2 8.5 15.7 18.3 77.2 % of total imports Food 18.9 9.7 23.6 13.8 25.9 14.1 8.9 4.6 Cereals 0.9 0.5 0.4 0.4 0.1 0.3 0.2 0.2 Agricultural raw materials 6.2 1.9 6.9 3.1 8.0 1.6 2.6 0.9 Ores and nonferrous metals 4.7 4.8 4.1 6.3 13.6 9.0 1.8 0.7 Fuels 21.1 31.7 15.5 18.6 16.5 39.8 36.0 43.2 Crude petroleum 19.6 25.0 15.0 13.0 13.4 29.3 33.5 39.6 Petroleum products 1.3 4.8 0.4 2.3 2.1 8.4 2.4 2.4 Manufactured goods 48.7 51.4 49.6 57.8 35.8 35.0 50.2 50.0 Chemical products 2.7 3.9 2.3 4.1 1.3 2.8 2.3 3.0 Iron and steel 1.1 2.0 0.7 2.3 2.0 1.0 1.1 1.7 Machinery and transport equipment 3.6 5.9 3.7 6.0 1.4 10.7 2.2 4.4 Furniture 0.4 1.5 0.3 1.5 0.8 1.6 0.2 1.9 Textiles 24.1 22.2 25.0 27.2 17.7 8.6 27.4 25.5 Footwear 1.7 2.9 2.8 5.2 0.6 2.1 0.7 1.6 Other 15.2 13.0 14.7 11.6 12.0 8.2 16.2 12.0 Miscellaneous goods 0.3 0.6 0.3 0.4 0.2 0.5 0.5 0.7 Simple applied tariff rates on imports from low-income economies (%)b Food 9.2 5.9 9.7 6.5 13.0 5.6 4.6 3.1 Cereals 14.9 7.4 42.7 20.6 14.4 13.4 6.5 1.3 Agricultural raw materials 2.4 1.8 0.3 0.3 1.1 0.5 1.1 0.3 Ores and nonferrous metals 1.8 1.6 0.6 0.5 2.7 0.0 0.3 0.3 Fuels 3.8 1.8 0.0 0.1 1.3 0.3 2.5 1.4 Crude petroleum 7.8 1.0 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.7 2.1 0.0 0.2 1.7 0.6 2.5 2.1 Manufactured goods 5.0 3.7 1.6 1.1 4.2 2.2 6.9 4.1 Chemical products 3.1 2.5 1.4 1.8 1.1 0.1 2.4 0.9 Iron and steel 3.2 2.2 0.4 0.3 0.3 0.2 3.6 0.2 Machinery and transport equipment 2.4 2.0 0.4 0.4 0.0 0.0 1.6 0.4 Furniture 3.7 3.4 0.2 0.0 0.2 0.0 4.6 0.8 Textiles 8.8 6.4 4.2 2.6 6.9 4.9 13.7 9.5 Footwear 11.9 6.7 4.1 2.5 11.0 9.1 19.6 9.9 Other 3.0 2.5 0.6 0.4 1.4 0.9 3.2 0.9 Miscellaneous goods 5.3 1.3 0.9 0.4 0.0 0.0 3.5 0.1 Average 5.4 3.9 2.5 1.7 5.3 2.4 6.2 3.8 2008 World Development Indicators 331 6.4 High-income economy trade with low- and middle-income economies Exports to middle-income economies High-income economies European Uniona Japan United States 1996 2006 1996 2006 1996 2006 1996 2006 Total ($ billions) 693.4 1,698.0 292.9 760.2 98.3 199.6 157.2 305.0 % of total exports Food 7.9 4.8 8.4 5.0 0.3 0.4 11.9 8.6 Cereals 2.2 0.9 1.5 0.6 0.0 0.0 5.0 2.5 Agricultural raw materials 2.0 1.9 1.4 1.4 1.1 0.9 3.1 3.8 Ores and nonferrous metals 1.9 3.8 1.6 2.7 1.4 3.4 1.7 4.3 Fuels 2.5 4.4 1.8 2.6 0.7 0.9 2.6 4.9 Crude petroleum 0.1 0.3 0.2 0.1 0.0 0.0 0.0 0.0 Petroleum products 1.8 3.5 1.4 2.2 0.6 0.9 1.8 4.4 Manufactured goods 83.4 82.2 84.4 85.3 95.1 90.7 76.9 74.5 Chemical products 10.8 12.8 12.2 13.0 6.6 9.0 11.0 12.7 Iron and steel 2.8 3.4 3.0 3.7 5.7 6.1 1.1 1.2 Machinery and transport equipment 48.5 48.1 46.0 47.9 67.7 61.7 45.8 45.0 Furniture 0.6 0.5 1.0 0.8 0.1 0.2 0.6 0.4 Textiles 5.7 3.5 5.8 4.2 3.0 1.9 4.8 3.0 Footwear 0.3 0.2 0.5 0.4 0.0 0.0 0.1 0.0 Other 14.6 13.7 16.1 15.3 11.9 11.8 13.7 12.3 Miscellaneous goods 2.3 3.0 2.4 3.1 1.5 3.8 3.8 3.8 Imports from middle-income economies Total ($ billions) 848.3 2,679.3 281.6 1,006.7 107.2 234.9 247.2 826.2 % of total imports Food 10.9 5.9 14.0 7.1 15.4 8.3 7.8 4.6 Cereals 0.3 0.3 0.3 0.3 0.4 0.3 0.2 0.1 Agricultural raw materials 3.1 1.4 3.9 1.8 5.1 2.3 1.7 1.0 Ores and nonferrous metals 5.2 5.4 6.5 5.9 8.7 11.0 2.9 2.9 Fuels 14.6 18.8 18.8 22.8 18.1 16.4 13.4 18.9 Crude petroleum 9.4 12.7 12.2 15.3 9.5 7.9 10.1 15.1 Petroleum products 2.5 3.4 3.4 4.4 1.4 1.5 3.0 3.2 Manufactured goods 64.3 67.0 54.1 60.8 51.5 60.8 72.1 70.6 Chemical products 3.6 3.4 4.7 3.4 2.8 3.8 2.3 2.5 Iron and steel 2.7 2.8 2.7 3.4 1.7 1.4 2.1 2.3 Machinery and transport equipment 24.1 33.2 16.0 28.3 15.1 26.8 32.9 35.7 Furniture 1.5 2.1 1.7 2.0 1.5 1.6 1.7 3.1 Textiles 13.2 8.5 13.5 8.5 14.9 10.5 11.9 8.1 Footwear 2.8 1.5 1.6 1.3 1.7 1.1 4.0 2.0 Other 16.5 15.4 13.9 14.0 13.9 15.6 17.2 16.8 Miscellaneous goods 1.8 1.4 2.8 1.5 1.2 1.3 2.0 2.0 Simple applied tariff rates on imports from middle-income economiesb (%) Food 12.5 7.3 20.3 11.9 14.4 7.7 2.9 2.4 Cereals 16.9 10.2 42.2 28.5 22.8 12.0 1.6 0.8 Agricultural raw materials 2.4 1.9 1.1 0.4 0.6 0.6 0.5 0.4 Ores and nonferrous metals 1.4 1.1 1.1 0.7 0.5 0.1 0.5 0.5 Fuels 3.5 1.7 0.1 0.1 0.6 0.3 0.9 1.4 Crude petroleum 13.4 1.2 0.0 0.0 0.0 0.0 0.0 0.0 Petroleum products 3.6 2.2 0.1 0.2 1.1 0.6 1.2 2.1 Manufactured goods 4.6 3.5 2.3 1.1 1.9 2.2 3.7 2.8 Chemical products 3.1 2.4 1.7 1.8 1.4 0.3 1.4 1.0 Iron and steel 2.7 1.5 0.8 0.2 0.6 0.2 3.2 0.2 Machinery and transport equipment 2.7 2.1 0.8 0.4 0.0 0.0 0.6 0.3 Furniture 4.5 3.9 0.4 0.0 0.0 0.0 0.5 0.3 Textiles 9.0 6.9 6.2 3.0 4.9 6.8 10.6 8.5 Footwear 10.4 7.0 6.1 3.0 15.4 17.9 13.0 8.3 Other 3.2 2.7 1.1 0.4 0.5 0.7 1.0 0.7 Miscellaneous goods 4.0 1.2 2.2 0.4 0.0 0.0 0.7 0.3 Average 5.3 3.8 3.9 2.1 3.2 2.6 3.4 2.6 a. Tariff data are from the Trade Analysis and Information System (TRAINS) database and may have a different country coverage than that for the 20 EU members whose trade values are reported. b. Includes ad valorem equivalents of specific rates. 332 2008 World Development Indicators 6.4 GLOBAL LINKS High-income economy trade with low- and middle-income economies About the data Definitions Developing countries are becoming increasingly low- and middle-income economies have grown. And The product groups in the table are defined in accor- important in the global trading system. Since the trade between developing economies has grown dance with the SITC revision 1: food (0, 1, 22, and early 1990s trade between high-income economies substantially over the past decade, a result of their 4) and cereals (04); agricultural raw materials (2 and low- and middle-income economies has grown increasing share of world output and liberalization of excluding 22, 27, and 28); ores and nonferrous faster than trade among high-income economies. The trade, among other influences. metals (27, 28, and 68); fuels (3), crude petroleum increased trade benefits consumers and producers. Yet trade barriers remain high. The table includes (331), and petroleum products (332); manufactured But as was apparent at the World Trade Organization's information about tariff rates by selected product goods (5­8 excluding 68), chemical products (5), (WTO) Ministerial Conferences in Doha, Qatar, in Octo- groups. Applied tariff rates are the tariffs in effect iron and steel (67), machinery and transport equip- ber 2001, Cancun, Mexico, in September 2003, and for partners in preferential trade agreements such ment (7), furniture (82), textiles (65 and 84), foot- Hong Kong, China, in December 2005, achieving a as the North American Free Trade Agreement. When wear (85), and other manufactured goods (6 and 8 more pro-development outcome from trade remains these rates are unavailable, most favored nation excluding 65, 67, 68, 82, 84, and 85); and miscel- a challenge. Meeting it will require strengthening rates are used. The difference between most favored laneous goods (9). · Exports are all merchandise international consultation. After the Doha meetings nation and applied rates can be substantial. Simple exports by high-income economies to low-income negotiations were launched on services, agriculture, averages of applied rates are shown because they and middle-income economies as recorded in the manufactures, WTO rules, the environment, dispute are generally a better indicator of tariff protection United Nations Statistics Division's Comtrade data- settlement, intellectual property rights protection, and than weighted average rates are. base. Exports are recorded free on board (f.o.b.). disciplines on regional integration. At the most recent The data are from the United Nations Conference · Imports are all merchandise imports by high- negotiations in Hong Kong, China, trade ministers on Trade and Development (UNCTAD). Partner coun- income economies from low-income and middle- agreed to eliminate subsidies of agricultural exports try reports by high-income economies were used for income economies as recorded in the United Nations by 2013; to abolish cotton export subsidies and grant both exports and imports. Because of differences Statistics Division's Commodity Trade (Comtrade) unlimited export access to selected cotton-growing in sources of data, timing, and treatment of miss- database. Imports include insurance and freight countries in Sub-Saharan Africa; to cut more domestic ing data, the numbers in the table may not be fully charges (c.i.f.). · High-, middle-, and low-income farm supports in the European Union, Japan, and the comparable with those used to calculate the direc- economies are those classified as such by the World United States; and to offer more aid to developing tion of trade statistics in table 6.3 or the aggregate Bank (see inside front cover). · European Union countries to help them compete in global trade. flows in tables 4.4, 4.5, and 6.2. Tariff line data is defined as all high-income EU members: Austria, Trade flows between high-income and low- and were matched to Standard International Trade Clas- Belgium, Cyprus, Czech Republic, Denmark, Estonia, middle-income economies reflect the changing mix of sification (SITC) revision 1 codes to define commodity Finland, France, Germany, Greece, Ireland, Italy, Lux- exports to and imports from developing economies. groups. For further discussion of merchandise trade embourg, Malta, the Netherlands, Portugal, Slovenia, While food and primary commodities have continued statistics, see About the data for tables 4.4, 4.5, Spain, Sweden, and the United Kingdom. to fall as a share of high-income economies' imports, 6.2, and 6.3, and for information about tariff barri- manufactures as a share of goods imports from both ers, see table 6.7. The composition of high-income economies' imports from low- and middle-income economies has changed over the last decade 6.4a Share of total imports (%) 1996 2006 70 60 50 40 30 20 10 0 Food Agricultural Fuels Crude Manu- Machinery Textiles Food Agricultural Fuels Crude Manu- Machinery Textiles Data sources raw petroleum factured and raw petroleum factured and materials goods transport materials goods transport equipment equipment Data on trade values are from United Nations Imports from low-income economies Imports from middle-income economies Statistics Division's Comtrade database. Data The shares of high-income economies' imports of food, agricultural raw materials, and textiles from low- on tariffs are from UNCTAD's Trade Analysis and and middle-income economies dropped noticeably between 1996 and 2006, while the shares of fuels Information System database and are calculated (especially crude petroleum) and machinery and transport equipment have increased considerably. by World Bank staff using the World Integrated Source: United Nations Statistics Division's Comtrade database. Trade Solution system. 2008 World Development Indicators 333 6.5 Primary commodity prices 1970 1980 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 World Bank commodity price index (1990 = 100) Nonenergy commodities 156 159 100 104 89 84 89 91 100 114 140 158 Agriculture 163 175 100 112 90 84 93 95 98 106 116 131 Beverages 203 230 100 129 91 76 91 87 88 109 113 125 Food 166 177 100 100 87 91 97 96 103 103 111 131 Raw materials 130 133 100 116 93 81 89 98 99 107 126 134 Fertilizers 108 164 100 88 109 105 108 106 118 126 126 204 Metals and minerals 144 120 100 87 85 80 78 82 105 133 198 220 Petroleum 19 204 100 64 127 113 117 126 154 218 258 279 Steel productsa 111 100 100 91 79 71 73 79 114 129 124 121 MUV G-5 index 28 79 100 117 97 94 93 100 107 107 109 111 Commodity prices (1990 prices) Agricultural raw materials Cotton (cents/kg) 225 260 182 182 134 112 109 140 128 114 116 125 Logs, Cameroon ($/cu. m)a 153 319 344 290 283 282 253 279 310 312 293 343 Logs, Malaysian ($/cu. m) 154 248 177 218 195 169 175 187 184 190 220 241 Rubber (cents/kg) 145 181 86 135 69 61 82 108 122 140 194 206 Sawnwood, Malaysian ($/cu. m) 625 503 533 632 612 510 565 550 543 616 689 725 Tobacco ($/mt) 3,836 2,887 3,392 2,258 3,063 3,185 2,947 2,643 2,560 2,606 2,730 2,966 Beverages (cents/kg) Cocoa 240 330 127 122 93 113 191 175 145 144 146 176 Coffee, robustas 330 411 118 237 94 64 71 81 74 104 137 172 Coffee, Arabica 409 440 197 285 198 146 146 141 166 237 232 245 Tea, avg., 3 auctions 298 211 206 127 193 169 162 151 157 154 172 183 Energy Coal, Australian ($/mt) 0 51 40 34 27 34 27 26 49 44 45 59 Coal, U.S. ($/mt) 0 55 42 33 34 48 43 .. .. .. .. .. Natural gas, Europe ($/mmbtu) 0 4 3 2 4 4 3 4 4 6 8 8 Natural gas, U.S. ($/mmbtu) 1 2 2 1 4 4 4 5 6 8 6 6 Petroleum ($/bbl) 4 47 23 15 29 26 27 29 35 50 59 64 About the data Primary commodities--raw or partially processed importers are used. Annual price series are gen- Separate indexes are compiled for petroleum and materials that will be transformed into fi nished erally simple averages based on higher frequency steel products, which are not included in the nonen- goods--are often the most signifi cant exports of data. The constant price series in the table is ergy commodity price index. developing countries, and revenues obtained from defl ated using the manufactures unit value (MUV) The MUV index is a composite index of prices them have an important effect on living standards. index for the Group of Five (G-5) countries (see for manufactured exports from the fi ve major Price data for primary commodities are collected from below). (G-5) industrial economies (France, Germany, Japan, a variety of sources, including trade journals, inter- The commodity price indexes are calculated as the United Kingdom, and the United States) to low- national study groups, government market surveys, Laspeyres index numbers, in which the fixed weights and middle-income economies, valued in U.S. dol- newspaper and wire service reports, and commodity are the 1987­89 export values for low- and middle- lars. The index covers products in groups 5­8 of the exchange spot and near-term forward prices. income economies rebased to 1990. Each index Standard International Trade Classification revision The table is based on frequently updated price represents a fi xed basket of primary commodity 1. To construct the MUV G-5 index, unit value indexes reports. When available, the prices received by exports. The nonenergy commodity price index con- for each country are combined using weights deter- exporters are used; otherwise, the prices paid by tains 37 price series for 31 nonenergy commodities. mined by each country's export share. 334 2008 World Development Indicators 6.5 GLOBAL LINKS Primary commodity prices 1970 1980 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 Commodity prices (continued) (1990 prices) Fertilizers ($/mt) Phosphate rock 39 59 41 30 45 44 43 38 38 39 41 64 Triple superphosphate 152 229 132 128 142 135 143 149 174 188 185 305 Food Fats and oils ($/mt) Coconut oil 1,417 855 337 572 463 337 452 467 617 576 558 826 Groundnut oil 1,350 1,090 964 846 734 721 738 1,242 1,085 991 892 1,216 Palm oil 927 740 290 536 319 303 419 443 440 394 440 701 Soybeans 417 376 247 221 218 208 228 264 286 257 247 345 Soybean meal 367 332 200 168 195 192 188 211 225 200 192 276 Soybean oil 1,021 758 447 534 348 375 488 553 576 509 550 792 Grains ($/mt) Sorghum 185 164 104 102 91 101 109 106 103 90 113 146 Maize 208 159 109 105 91 95 107 105 104 92 112 147 Rice 450 521 271 274 208 183 206 197 222 267 280 293 Wheat 196 219 136 151 117 134 159 146 147 142 177 229 Other food Bananas ($/mt) 590 481 541 380 436 618 568 374 490 563 623 608 Beef (cents/kg) 465 350 256 163 199 226 226 198 235 245 234 234 Oranges ($/mt) 599 496 531 454 374 631 606 680 803 817 763 860 Sugar, EU domestic (cents/kg) 40 62 58 59 57 56 59 60 63 62 59 61 Sugar, U.S. domestic (cents/kg) 59 84 51 43 44 50 50 47 42 44 45 41 Sugar, world (cents/kg) 29 80 28 25 19 20 16 16 15 20 30 20 Metals and minerals Aluminum ($/mt) 1,982 1,847 1,639 1,542 1,594 1,531 1,449 1,430 1,603 1,774 2,363 2,372 Copper ($/mt) 5,038 2,768 2,662 2,508 1,866 1,673 1,674 1,777 2,678 3,437 6,182 6,399 Iron ore (cents/dmtu) 35 36 33 24 30 32 31 32 35 61 71 76 Lead (cents/kg) 108 115 81 54 47 50 49 51 83 91 119 232 Nickel ($/mt) 10,148 8,270 8,864 7,028 8,888 6,303 7,271 9,617 12,915 13,776 22,305 33,462 Tin (cents/kg) 1,310 2,128 609 531 559 475 436 489 795 690 807 1,307 Zinc (cents/kg) 105 97 151 88 116 94 84 83 98 129 301 291 a. Series not included in the nonenergy index. Definitions · Nonenergy commodity price index covers the lead, nickel, tin, and zinc. · Petroleum price index data" (also known as the "Pink Sheet") at the Global 31 nonenergy primary commodities that make up refers to the average spot price of Brent, Dubai, and Prospects Web site (www.worldbank.org/prospects, the agriculture, fertilizer, and metals and minerals West Texas Intermediate crude oils, equally weighted. click on Products). indexes. · Agriculture includes beverages, food, · Steel products price index is the composite price and agricultural raw materials. · Beverages include index for eight steel products based on quotations cocoa, coffee, and tea. · Food includes rice, wheat, free on board (f.o.b.) Japan excluding shipments to Data sources maize, sorghum, soybeans, soybean oil, soybean China and the United States, weighted by product meal, palm oil, coconut oil, groundnut oil, bananas, shares of apparent combined consumption (volume Data on commodity prices and the MUV G-5 index beef, oranges, and sugar. · Agricultural raw mate- of deliveries) for Germany, Japan, and the United are compiled by the World Bank's Development rials include cotton, timber (logs and sawnwood), States. · MUV G-5 index is the manufactures Prospects Group. Monthly updates of commodity natural rubber, and tobacco. · Fertilizers include unit value index for G-5 country exports to low- and prices are available on the Web at www.worldbank. phosphate rock and triple superphosphate. · Metals middle-income economies. · Commodity prices-- org/prospects. and minerals include aluminum, copper, iron ore, for definitions and sources, see "Commodity price 2008 World Development Indicators 335 6.6 Regional trade blocs Merchandise exports within bloc Year of entry into Type of force of the the most $ millions Year of most recent recent creation agreement agreementa 1990 1995 2000 2003 2004 2005 2006 High-income and low- and middle-income economies APECb 1989 None 901,560 1,688,708 2,261,791 2,436,516 2,924,291 3,309,117 3,763,569 CEFTAc 1992 1994 FTA 322 2,886 2,136 3,147 3,915 5,382 6,474 CIS 1991 1994 FTA .. 31,529 28,753 38,576 43,446 59,423 66,583 EEA 1994 1994 EIA 1,070,201 1,444,732 1,680,468 2,175,403 2,589,764 2,780,586 3,142,002 EFTA 1960 2002 EIA 782 925 831 967 1,128 1,252 1,524 European Unionc 1957 1958 EIA, CU 1,022,933 1,385,805 1,608,174 2,087,311 2,482,418 2,649,078 2,987,188 NAFTA 1994 1994 FTA 226,273 394,472 676,141 651,060 737,591 824,550 902,085 SPARTECA 1981 1981 PS 4,737 8,535 8,139 10,864 13,047 14,413 14,531 Trans-Pacific SEP 2006 2006 EIA, FTA 1,110 2,614 1,438 1,621 2,096 2,345 2,927 Latin America and the Caribbean Andean Community 1969 1988 CU 1,312 4,812 5,293 5,064 7,619 8,676 11,300 CACM 1961 1961 CU 667 1,594 2,586 3,156 3,574 4,064 5,022 CARICOM 1973 1997 EIA 456 877 1,078 1,419 1,746 2,090 2,429 LAIA (ALADI) 1980 1981 PS 13,350 35,986 44,252 40,425 57,732 71,711 91,651 MERCOSUR 1991 2005 EIA 4,909 16,811 20,082 13,765 19,675 24,211 30,902 OECS 1981d 1981d NNA 29 39 38 48 60 68 84 Middle East and Asia ASEAN 1967 1992 FTA 27,365 79,544 98,060 116,831 141,934 165,169 194,321 Bangkok Agreement 1975 1976 PS 2,429 21,728 37,895 70,845 99,369 127,277 150,545 EAEC 1997 1997 CU .. 13,556 15,467 19,933 17,291 27,297 27,930 ECO 1985 2003d PS 1,243 4,746 4,518 7,468 9,989 13,936 19,053 GCC 1981 2003 CU 6,906 6,832 7,954 9,915 12,532 16,507 20,050 PAFTA (GAFTA) 1997 1998 FTA 13,204 12,948 16,140 21,918 35,328 44,468 54,862 SAARCe 1985 1995 PS 863 2,024 2,680 4,954 5,830 7,266 9,109 UMA 1989 1994 d NNA 958 1,109 1,094 1,338 1,375 1,926 2,400 Sub-Saharan Africa CEMAC 1994 1999 CU 139 120 96 146 174 198 245 COMESA 1994 1994 FTA 1,164 1,390 1,448 2,041 2,427 2,869 3,546 EAC 1996 2000 CU 230 530 595 706 750 857 1,059 ECCAS 1983 2004 d NNA 163 163 191 198 240 271 334 ECOWAS 1975 1993 PS 1,532 1,875 2,715 3,037 4,366 5,497 5,957 Indian Ocean Commission 1984 2005d NNA 73 127 106 179 155 159 172 SADC 1992 2000 FTA 677 1,015 4,383 5,609 6,590 7,668 8,571 UEMOA 1994 2000 CU 621 560 741 1,076 1,233 1,390 1,545 Note: Regional bloc memberships are as follows: Andean Community, Bolivia, Colombia, Ecuador, Peru, and Bolivarian Republic of Venezuela; Arab Maghreb Union (UMA), Algeria, Libyan Arab Republic, Mauritania, Morocco, and Tunisia; Asia Pacific Economic Cooperation (APEC), Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong (China), Indonesia, Japan, the Republic of Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, the Philippines, the Russian Federa- tion, Singapore, Taiwan (China), Thailand, the United States, and Vietnam; 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; Bangkok Agreement, Bangladesh, China, India, the Republic of Korea, the Lao People's Democratic Republic, and Sri Lanka; 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), Bulgaria, Croatia, Macedonia, Romania, and Slovenia; Common Market for Eastern and Southern Africa (COMESA), Angola, 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, Russian Federation, Tajikistan, Turkmenistan, Ukraine, and Uzbekistan; East African Community (EAC), Kenya, Tanzania, and Uganda; Economic and Monetary Community of Central Africa (CEMAC; formerly Union Douanière et Economique de l'Afrique Centrale [UDEAC]), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; Economic Community of Central African States (ECCAS), Angola, Burundi, Camer- oon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, Rwanda, 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 336 2008 World Development Indicators 6.6 GLOBAL LINKS Regional trade blocs Merchandise exports within bloc Year of entry into force of the % of total bloc exports Year of most recent Type of creation agreement agreementa 1990 1995 2000 2003 2004 2005 2006 High-income and low- and middle-income economies APECb 1989 None 68.3 71.7 73.1 72.7 72.2 70.8 69.4 CEFTAc 1992 1994 FTA 4.1 10.5 7.3 7.0 6.8 7.8 7.9 CIS 1991 1994 FTA .. 28.6 20.0 20.3 17.6 18.0 16.5 EEA 1994 1994 EIA 68.6 67.5 68.1 68.4 68.1 67.5 67.7 EFTA 1960 2002 EIA 0.8 0.7 0.6 0.6 0.5 0.5 0.6 European Unionc 1957 1958 EIA, CU 67.1 66.1 66.8 67.2 66.8 66.0 66.2 NAFTA 1994 1994 FTA 41.4 46.2 55.7 56.1 55.9 55.8 53.8 SPARTECA 1981 1981 PS 9.4 12.1 10.2 11.8 11.6 10.8 9.6 Trans-Pacific SEP 2006 2006 EIA, FTA 1.5 1.7 0.8 0.8 0.8 0.8 0.8 Latin America and the Caribbean Andean Community 1969 1988 CU 4.1 12.0 8.9 8.9 9.7 8.2 8.1 CACM 1961 1961 CU 15.3 21.8 19.1 20.2 20.9 18.9 16.2 CARICOM 1973 1997 EIA 8.0 12.1 14.4 12.0 12.2 11.5 11.1 LAIA (ALADI) 1980 1981 PS 11.6 17.3 13.2 11.5 13.2 13.6 14.3 MERCOSUR 1991 2005 EIA 7.6 18.9 16.4 10.3 11.1 11.0 11.6 OECS 1981a 1981 NNA 8.1 12.6 10.0 7.6 11.7 11.4 8.0 Middle East and Asia ASEAN 1967 1992 FTA 18.9 24.5 23.0 24.7 24.9 25.3 24.9 Bangkok Agreement 1975 1976 NNA 1.6 6.8 8.0 10.0 10.6 11.0 10.7 EAEC 1997 1997 CU .. 14.8 12.5 12.6 8.5 9.6 8.0 ECO 1985 2003d PS 3.2 7.9 5.6 6.6 6.7 7.6 8.5 GCC 1981 2003 CU 8.0 6.8 4.8 5.2 5.0 4.8 4.8 PAFTA (GAFTA) 1997 1998 FTA 10.2 9.8 7.2 8.7 10.0 9.8 9.7 SAARCe 1985 1995 PS 3.2 4.4 4.2 5.8 5.7 5.6 5.6 UMA 1989 1994 d NNA 2.9 3.8 2.3 2.4 1.9 2.0 2.0 Sub-Saharan Africa CEMAC 1994 1999 CU 2.3 2.1 1.0 1.4 1.2 0.9 0.9 COMESA 1994 1994 FTA 4.2 5.4 3.7 4.4 4.1 3.4 3.2 EAC 1996 2000 CU 13.4 17.4 20.5 18.3 16.7 15.1 16.5 ECCAS 1983 2004 d NNA 1.4 1.5 1.1 1.0 0.9 0.6 0.6 ECOWAS 1975 1993 PS 8.0 9.0 7.6 8.5 9.3 9.3 8.3 Indian Ocean Commission 1984 2005d NNA 4.1 6.0 4.4 6.2 4.3 4.6 4.7 SADC 1992 2000 FTA 6.8 9.2 9.4 10.1 9.7 9.2 9.1 UEMOA 1994 2000 CU 13.0 10.3 13.1 13.3 12.9 13.4 13.1 Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, and Uzbekistan; Eurasian Economic Community (EAEC), Belarus, Kazakhstan, Kyrgyz Republic, Russian Federation, Tajikistan, and Uzbekistan; European Economic Area (EEA), European Union plus Iceland, Liechtenstein, and Norway; European Free Trade Association (EFTA), Iceland, Liechtenstein, Norway, and Switzerland; European Union (EU; formerly European Economic Community and European Community), Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Malta, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, and the United Kingdom; Gulf Cooperation 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; 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; 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, Micronesia (Federated States of), 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, Malawi, Madagascar, Mauritius, Mozambique, Namibia, 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. 2008 World Development Indicators 337 6.6 Regional trade blocs Merchandise exports by bloc Year of entry into force of the % of world exports Year of most recent Type of creation agreement agreementa 1990 1995 2000 2003 2004 2005 2006 High-income and low- and middle-income economies APECb 1989 None 39.0 46.3 48.5 44.6 44.4 45.1 45.3 CEFTAc 1992 1994 FTA 0.2 0.5 0.5 0.6 0.6 0.7 0.7 CIS 1991 1994 FTA 0.0 2.2 2.2 2.5 2.7 3.2 3.4 EEA 1994 1994 EIA 46.1 42.1 38.7 42.3 41.6 39.7 38.8 EFTA 1960 2002 EIA 2.9 2.4 2.2 2.3 2.3 2.3 2.3 European Unionc 1957 1958 EIA, CU 45.1 41.3 37.7 41.4 40.7 38.7 37.7 NAFTA 1994 1994 FTA 16.2 16.8 19.0 15.4 14.5 14.3 14.0 SPARTECA 1981 1981 PS 1.5 1.4 1.3 1.2 1.2 1.3 1.3 Trans-Pacific SEP 2006 2006 EIA, FTA 2.2 3.0 2.7 2.7 2.8 2.9 3.0 Latin America and the Caribbean Andean Community 1969 1988 CU 0.9 0.8 0.9 0.8 0.9 1.0 1.2 CACM 1961 1961 CU 0.1 0.1 0.2 0.2 0.2 0.2 0.3 CARICOM 1973 1997 EIA 0.2 0.1 0.1 0.2 0.2 0.2 0.2 LAIA (ALADI) 1980 1981 PS 3.4 4.1 5.3 4.7 4.8 5.1 5.4 MERCOSUR 1991 2005 EIA 1.9 1.8 1.9 1.8 1.9 2.1 2.2 OECS 1981 1981d NNA 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Middle East and Asia ASEAN 1967 1992 FTA 4.3 6.4 6.7 6.3 6.2 6.3 6.5 Bangkok Agreement 1975 1976 NNA 4.5 6.3 7.4 9.4 10.3 11.2 11.8 EAEC 1997 1997 CU 0.0 1.8 1.9 2.1 2.2 2.7 2.9 ECO 1985 2003d PS 1.1 1.2 1.3 1.5 1.6 1.8 1.9 GCC 1981 2003 CU 2.6 2.0 2.6 2.5 2.7 3.3 3.5 PAFTA (GAFTA) 1997 1998 FTA 3.8 2.6 3.5 3.4 3.9 4.4 4.7 SAARCe 1985 1995 PS 0.8 0.9 1.0 1.1 1.1 1.3 1.3 UMA 1989 1994 d NNA 1.0 0.6 0.8 0.7 0.8 0.9 1.0 Sub-Saharan Africa CEMAC 1994 1999 CU 0.2 0.1 0.1 0.1 0.2 0.2 0.2 COMESA 1994 1994 FTA 0.8 0.5 0.6 0.6 0.7 0.8 0.9 EAC 1996 2000 CU 0.1 0.1 0.0 0.1 0.0 0.1 0.1 ECCAS 1983 2004 d NNA 0.3 0.2 0.3 0.3 0.3 0.4 0.5 ECOWAS 1975 1993 PS 0.6 0.4 0.6 0.5 0.5 0.6 0.6 Indian Ocean Commission 1984 2005d NNA 0.1 0.0 0.0 0.0 0.0 0.0 0.0 SADC 1992 2000 FTA 0.3 0.2 0.7 0.7 0.7 0.8 0.8 UEMOA 1994 2000 CU 0.1 0.1 0.1 0.1 0.1 0.1 0.1 a. FTA is free trade agreement, CU is customs union, EIA is economic integration agreement, PS is partial scope 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. Members changed and new agreements entered into force in 2007, but are not reflected in the data shown. d. Years of the most recent agreement are collected from official trade bloc website. e. Free trade agreement was signed in 2006 but has not entered into force yet. 338 2008 World Development Indicators 6.6 GLOBAL LINKS Regional trade blocs About the data Trade blocs are groups of countries that have estab- The table shows the value of merchandise intra- An economic integration agreement liberalizes trade lished special preferential arrangements governing trade (service exports are excluded) for important in services among members and covers a substantial trade between members. Although in some cases regional trade blocs and the size of intratrade rela- number of sectors, affects a sufficient volume of the preferences--such as lower tariff duties or tive to each bloc's exports of goods and the share trade, includes substantial modes of supply, and is exemptions from quantitative restrictions--may be of the bloc's exports in world exports. Although the nondiscriminatory (in the sense that similarly situ- no greater than those available to other trading part- Asia Pacific Economic Cooperation (APEC) has no ated service suppliers are treated the same). Partial ners, such arrangements are intended to encourage preferential arrangements, it is included because of scope agreements are preferential trade agreements exports by bloc members to one another--some- the volume of trade between its members. notified to the World Trade Organization (WTO) that times called intratrade. The data on country exports are from the Inter- are not a free trade agreement, a customs union, or Most countries are members of a regional trade national Monetary Fund's (IMF) Direction of Trade an economic integration agreement. Unless other- bloc, and more than a third of the world's trade takes database and should be broadly consistent with wise indicated in the footnotes, information on the place within such arrangements. While trade blocs those from sources such as the United Nations type of agreement and date of enforcement are based vary in structure, they all have the same objective: Statistics Division's Commodity Trade (Comtrade) on the WTO's list of regional trade agreements. to reduce trade barriers between member countries. database. However, trade flows between many devel- Although bloc exports have been calculated back But effective integration requires more than reducing oping countries, particularly in Sub-Saharan Africa, to 1990 on the basis of current membership, several tariffs and quotas. Economic gains from competition are not well recorded, so the value of intratrade for blocs came into existence after that and membership and scale may not be achieved unless other barri- certain groups may be understated. Data on trade may have changed over time. For this reason, and ers that divide markets and impede the free flow between developing and high-income countries are because systems of preferences also change over of goods, services, and investments are lifted. For generally complete. time, intratrade in earlier years may not have been example, many regional trade blocs retain contingent Membership in the trade blocs shown is based affected by the same preferences as in recent years. protections on intrabloc trade, including antidumping, on the most recent information available (see Data In addition, some countries belong to more than countervailing duties, and "emergency protection" to sources). The table includes the date of each bloc's one trade bloc, so shares of world exports exceed address balance of payments problems or protect an creation, the date of entry into force of the most 100 percent. Exports of blocs include all commod- industry from import surges. Other barriers include recent preferential trade agreement, and the type ity trade, which may include items not specified in differing product standards, discrimination in public of the agreement. Other types of preferential trade trade bloc agreements. Differences from previously procurement, and cumbersome border formalities. agreements may have entered into force earlier than published estimates may be due to changes in mem- Membership in a regional trade bloc may reduce those shown in the table and are still effective. bership or revisions in underlying data. the frictional costs of trade, increase the credibility Under a free trade agreement members substan- Definitions of reform initiatives, and strengthen security among tially eliminate all tariff and nontariff barriers but partners. But making it work effectively is challenging. set tariffs on imports from nonmembers. Under a · Merchandise exports within bloc are the sum of All economic sectors may be affected, and some may customs union members substantially eliminate all merchandise exports by members of a trade bloc to expand while others contract, so it is important to weigh tariff and nontariff barriers among themselves and other members of the bloc. They are shown both in the potential costs and benefits of membership. establish a common external tariff for nonmembers. U.S. dollars and as a percentage of total merchan- dise exports by the bloc. · Merchandise exports by The number of trade agreements has increased bloc as a share of world exports are the bloc's total rapidly since 1990, especially free trade agreements 6.6a merchandise exports (within the bloc and to the rest Free trade Economic integration Partial scope Customs of the world) as a share of total merchandise exports Cumulative agreements agreements agreements agreements unions 200 by all economies in the world. 150 Data sources Data on merchandise trade flows are published in 100 the IMF's Direction of Trade Statistics Yearbook and Direction of Trade Statistics Quarterly; the data in 50 the table were calculated using the IMF's Direc- 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 2007 Report Trade Blocs (2000a), UNCTAD's Trade and Note: Data are the cumulative number of bilateral and multilateral trade agreements notified to the General Agreement on Development Report 2007, WTO's Web portal on Tariffs and Trade/World Trade Organization at the time they entered into force. Agreements on accessions of new members regional trade agreements, and the World Bank's to existing agreement are not included. Movements from one kind of agreement to another are taken into account. Source: World Bank staff calculations based on World Trade Organization's Web portal on regional trade agreements. International Trade Unit. 2008 World Development Indicators 339 6.7 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 .. .. .. .. .. .. .. .. .. .. Albania 2005 100.0 7.0 6.3 7.1 0.0 6.0 7.3 6.5 6.1 7.3 Algeria 2006 .. .. 15.8 10.7 38.7 0.0 15.5 9.3 15.8 11.1 Angola 2006a 100.0 59.2 7.6 6.5 10.4 0.8 11.5 13.1 6.9 5.0 Antigua and Barbuda 2006 97.9 58.7 11.6 12.5 39.7 0.0 13.6 12.0 11.1 12.7 Argentina 2006 100.0 31.9 10.1 5.0 22.1 0.0 7.4 1.4 10.4 5.6 Armenia 2006 100.0 8.5 3.6 1.8 0.0 0.5 5.1 1.5 3.4 2.0 Australia 2006 97.1 10.0 3.9 2.6 5.6 0.3 1.3 0.4 4.3 3.0 Azerbaijan 2005a .. .. 10.4 5.8 0.0 2.8 12.0 5.4 10.2 6.0 Bahamas, The 2006a .. .. 28.5 17.0 77.1 1.4 22.2 20.1 29.8 15.0 Bahrain 2006 72.5 35.8 4.4 5.0 0.2 0.6 5.2 5.5 4.2 4.0 Bangladesh 2006 15.1 161.7 15.5 19.9 41.5 0.1 15.6 8.8 15.5 26.1 Barbados 2006 97.8 78.2 15.0 12.7 45.3 1.0 23.6 10.0 13.8 14.2 Belarus 2002a .. .. 11.3 8.9 16.4 2.2 11.1 7.1 11.3 10.3 Belize 2006 97.9 58.2 11.9 10.2 36.0 1.4 15.7 6.7 11.4 12.4 Benin 2006 39.1 28.6 13.4 11.3 53.4 0.0 13.2 10.9 13.4 11.7 Bermuda 2005a .. .. 17.3 27.0 61.5 2.2 9.0 14.0 18.8 28.2 Bhutan 2005 .. .. 22.2 21.5 61.6 0.0 42.6 37.7 17.5 14.5 Bolivia 2006 100.0 40.0 6.5 4.1 0.0 0.0 6.4 3.4 6.5 4.3 Bosnia and Herzegovina 2006a .. .. 7.7 6.4 0.0 5.4 4.0 4.0 8.1 7.5 Botswana 2006 96.3 19.0 8.7 10.5 20.9 1.3 3.6 0.8 9.2 12.4 Brazil 2006 100.0 31.4 12.1 6.7 25.6 0.0 7.7 1.2 12.6 9.0 Brunei 2006 95.3 24.3 2.9 4.0 23.2 1.5 0.1 0.0 3.5 5.0 Bulgaria 2006 100.0 24.7 4.0 1.9 11.6 2.4 8.6 4.2 3.5 1.3 Burkina Faso 2006 38.9 42.2 12.2 9.8 43.5 0.0 11.3 7.8 12.4 11.0 Burundi 2006a 21.2 66.7 14.7 13.5 27.9 0.0 15.1 11.7 14.6 13.8 Cambodia 2005 100.0 19.1 14.1 10.8 20.8 0.0 16.2 11.2 13.8 10.6 Cameroon 2005a 12.6 79.9 19.2 14.5 55.9 0.0 23.0 14.0 18.7 14.8 Canada 2006 99.7 5.1 3.7 0.9 6.5 3.5 1.8 0.3 4.0 1.0 Central African Republic 2005a 62.2 36.2 18.8 17.3 58.3 0.0 23.1 20.2 18.2 15.2 Chad 2005a 12.7 79.9 17.9 13.3 52.5 0.0 23.0 21.7 17.3 11.4 Chile 2006 100.0 25.1 2.3 2.1 0.0 0.0 2.1 1.8 2.3 2.3 China 2006 100.0 10.0 8.9 4.3 12.2 0.2 8.9 3.5 8.9 4.5 Hong Kong, China 2006a 45.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Colombia 2006 100.0 42.8 11.2 8.8 19.2 0.0 10.0 7.9 11.3 8.9 Congo, Dem. Rep. 2006a 100.0 96.2 13.1 11.4 43.3 0.2 14.2 11.3 12.8 11.5 Congo, Rep. 2005a 15.2 27.5 19.3 17.3 57.1 0.0 23.3 21.4 18.7 15.9 Costa Rica 2005a 100.0 42.9 7.0 4.1 0.5 0.0 10.4 6.1 6.6 3.7 Côte d'Ivoire 2006 33.0 11.1 13.5 7.3 49.9 0.0 15.4 4.2 13.1 9.5 Croatia 2006 100.0 5.9 2.4 1.2 3.1 4.0 4.8 2.2 2.1 0.8 Cuba 2006 31.0 21.3 11.3 7.8 11.9 0.0 11.2 5.2 11.3 10.1 Cyprus 2002a 86.2 40.4 9.0 10.4 12.2 4.5 40.4 27.2 5.1 5.1 Czech Republic 2003a 100.0 5.0 5.0 4.4 4.8 0.0 5.6 4.1 5.0 4.4 Djibouti 2006 100.0 41.0 30.2 29.1 87.9 6.3 23.1 23.2 31.3 31.0 Dominica 2006 94.7 58.7 12.3 7.8 38.9 0.0 19.3 5.6 10.9 9.1 Dominican Republic 2006 100.0 34.9 9.3 8.5 28.6 0.2 12.7 7.3 8.8 9.0 Ecuador 2006 99.9 21.8 9.8 6.2 17.7 0.0 9.1 4.4 9.9 6.8 Egypt, Arab Rep. 2005 99.1 36.5 19.1 13.3 23.0 0.1 84.8 17.8 12.0 11.9 El Salvador 2006 99.9 36.6 5.0 4.1 6.6 0.8 5.7 2.5 4.9 5.2 Equatorial Guinea 2005a .. .. 19.0 15.3 56.1 0.0 23.4 18.5 18.3 14.3 Estonia 2003a 100.0 8.6 1.0 0.9 5.4 0.0 8.1 4.0 0.0 0.0 Ethiopiab 2006a .. .. 16.4 10.7 49.2 0.1 18.1 12.6 16.3 10.4 European Unionc 2005 100.0 4.2 2.8 2.1 6.8 9.0 8.2 2.5 1.7 1.8 Gabon 2005a 100.0 21.4 20.1 16.5 61.3 0.0 23.2 19.4 19.6 15.6 Gambia 2003 13.0 101.8 .. .. .. .. .. .. .. .. Georgia 2006 100.0 7.2 5.6 4.7 3.3 0.0 8.6 4.4 5.3 4.8 Ghana 2004 a 13.5 92.1 13.2 11.0 45.3 0.3 17.4 17.1 12.4 8.8 Grenada 2006 100.0 56.8 10.7 9.5 35.3 0.0 13.9 9.7 10.2 9.4 Guatemala 2005a 100.0 42.2 6.7 5.8 1.0 0.0 8.8 5.5 6.5 6.0 Data for Taiwan, China 2006 100.0 5.9 5.4 2.4 7.7 1.5 8.3 2.2 5.0 2.4 340 2008 World Development Indicators 6.7 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 2005a 38.6 20.3 14.2 12.7 58.6 0.7 16.3 14.3 13.9 11.2 Guinea-Bissau 2006 97.7 48.7 12.7 9.1 50.1 0.0 14.3 9.0 12.4 9.2 Guyana 2006 100.0 56.6 11.4 6.2 34.5 0.0 17.8 4.1 10.6 7.9 Honduras 2005a 100.0 32.5 6.7 6.0 0.2 0.0 9.7 7.2 6.4 5.3 Hungary 2002a 96.2 9.8 8.9 7.9 10.9 0.0 17.9 6.7 7.8 8.1 Iceland 2006 95.0 13.5 2.6 1.0 2.0 2.9 2.9 1.4 2.5 0.9 India 2005 73.8 49.6 16.8 14.5 15.7 0.0 24.4 16.5 15.7 12.7 Indonesia 2006 96.6 37.1 6.0 4.3 7.9 0.3 6.6 3.3 5.9 4.6 Iran, Islamic Rep. 2004 a .. .. 18.7 13.8 43.4 0.5 14.9 11.2 19.1 14.6 Israel 2006 76.3 20.9 4.5 2.0 1.0 3.5 5.1 1.8 4.4 2.1 Jamaica 2006 100.0 49.6 9.2 9.7 36.1 0.2 16.0 10.1 8.5 9.2 Japan 2006 99.7 3.0 2.7 1.5 7.0 5.8 5.0 1.6 2.3 1.5 Jordan 2006 100.0 16.3 10.9 5.6 31.8 0.1 14.3 3.4 10.4 7.1 Kazakhstan 2004 a .. .. 2.4 1.9 0.0 1.5 3.4 3.4 2.3 1.5 Kenya 2006 14.0 95.1 11.9 6.6 36.2 0.4 14.8 6.4 11.6 6.6 Korea, Rep. 2006 94.5 15.7 9.1 7.4 5.5 0.0 21.2 11.4 7.3 4.5 Kuwait 2006 99.9 100.0 4.6 4.5 0.0 0.7 3.7 3.2 4.7 4.7 Kyrgyz Republic 2006 99.9 7.4 3.0 1.2 0.1 1.2 4.5 0.9 2.8 1.4 Lao PDR 2006 .. .. 6.5 9.3 16.4 0.0 10.7 11.7 5.9 8.0 Latvia 2001a 100.0 12.7 3.3 2.6 3.0 0.0 8.1 5.4 2.5 1.6 Lebanon 2006 .. .. 6.1 4.6 10.0 0.3 10.5 3.6 5.5 5.4 Lesotho 2006 100.0 78.4 9.9 16.5 24.2 1.9 7.5 3.2 10.0 17.3 Libya 2006a .. .. 0.0 0.0 0.0 0.0 0.0 15.1 0.0 0.0 Lithuania 2003 100.0 9.2 1.3 0.7 3.3 0.0 3.6 1.3 1.0 0.4 Macedonia, FYR 2006 100.0 6.9 5.5 4.2 11.7 2.5 8.8 5.6 5.1 3.4 Madagascar 2006 29.7 27.4 13.3 8.7 43.5 0.0 14.2 3.0 13.2 12.2 Malawi 2006 30.2 74.9 12.9 8.1 40.3 0.0 12.8 6.1 12.9 8.9 Malaysia 2006 84.2 14.6 6.2 3.4 22.9 0.8 3.0 2.4 6.8 3.7 Maldives 2006 97.1 37.0 21.3 20.5 72.3 0.0 17.8 18.0 22.2 22.0 Mali 2006 40.7 29.3 12.6 8.5 46.3 0.0 11.5 8.6 12.7 8.5 Malta 2003a 97.1 48.3 6.7 5.7 7.5 0.0 5.8 4.6 6.9 6.0 Mauritania 2006a 39.4 19.6 11.6 7.2 44.3 3.9 11.5 9.3 11.6 6.6 Mauritius 2006 18.0 94.0 4.2 1.6 8.5 8.1 6.1 1.5 3.9 1.7 Mexico 2006 100.0 35.0 8.0 2.4 10.7 0.3 6.8 1.7 8.1 2.5 Moldova 2006 99.9 6.7 4.4 1.7 16.0 2.1 7.3 1.4 4.0 1.9 Mongolia 2006a 100.0 17.5 4.2 4.4 0.0 0.0 5.0 5.1 4.1 3.9 Montserrat 1999a .. .. 18.2 13.3 41.2 31.0 22.3 15.5 16.4 12.2 Morocco 2006 100.0 41.3 15.5 11.0 45.3 2.0 21.9 11.7 14.9 10.6 Mozambique 2006 12.9 97.4 12.7 8.3 38.2 0.0 15.4 8.9 12.3 8.0 Myanmar 2006 16.8 83.4 4.4 3.9 4.0 0.0 6.5 4.2 4.1 3.7 Namibia 2006 96.3 19.4 5.8 0.8 15.7 2.6 3.5 0.6 6.2 0.9 Nepal 2006 99.4 26.0 12.5 13.4 16.8 0.6 12.5 9.9 12.5 15.2 New Zealand 2006 99.9 10.3 3.7 2.7 8.0 2.4 1.8 0.5 4.0 3.3 Nicaragua 2005a 100.0 41.7 6.8 5.4 0.5 0.0 10.6 5.4 6.4 5.4 Niger 2006 96.8 44.6 13.1 9.8 50.3 0.0 13.1 10.0 13.1 9.7 Nigeria 2006a 18.1 118.5 11.7 11.6 41.5 0.0 14.8 15.1 11.4 10.2 Norway 2006 100.0 3.0 0.5 0.4 0.6 5.8 1.9 1.3 0.3 0.2 Oman 2006 100.0 13.7 3.8 3.2 0.1 0.6 4.1 2.9 3.8 3.3 Pakistan 2006 44.8 52.2 14.8 12.2 43.2 0.5 14.1 8.8 14.9 14.6 Panama 2006 99.9 23.4 7.4 6.9 1.8 0.0 11.2 7.9 7.0 6.5 Papua New Guinea 2006 100.0 31.7 4.8 1.7 14.1 0.7 12.1 2.6 3.9 1.3 Paraguay 2006 100.0 33.6 7.2 3.2 15.7 0.0 5.4 1.1 7.3 3.8 Peru 2006 100.0 30.1 8.6 5.3 10.0 0.0 9.2 2.5 8.5 6.6 Philippines 2006 67.0 25.6 5.4 3.2 4.8 0.0 6.9 5.3 5.2 2.8 Poland 2003 96.2 11.9 7.6 4.4 10.2 3.5 45.7 18.2 2.5 1.2 Qatar 2006 100.0 15.9 4.0 4.2 0.1 0.8 3.7 3.7 4.1 4.3 Romania 2005 100.0 39.8 6.6 3.1 21.0 0.0 13.3 7.2 5.7 1.8 Russian Federation 2005a .. .. 11.4 9.6 17.9 2.6 10.7 12.2 11.5 8.9 Rwanda 2006 100.0 89.4 19.7 14.4 52.2 0.1 17.4 14.0 20.0 14.5 2008 World Development Indicators 341 6.7 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 Saudi Arabia 2006 .. .. 4.1 4.1 0.0 0.5 3.2 2.7 4.3 4.4 Senegal 2006 100.0 30.0 13.5 9.4 51.3 0.0 14.4 8.5 13.4 10.3 Serbiad 2005a .. .. 8.1 6.0 17.8 0.0 10.9 4.5 7.8 6.8 Seychelles 2006a .. .. 6.3 30.7 12.2 1.6 12.8 49.6 4.9 6.7 Sierra Leone 2004 100.0 47.4 .. .. .. .. .. .. .. .. Singapore 2006 69.8 7.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 Slovak Republic 2002a 100.0 5.0 5.0 4.6 4.3 0.0 5.5 3.7 4.9 4.9 Slovenia 2003 100.0 23.7 4.4 1.8 11.4 1.5 7.0 4.0 4.0 1.2 Solomon Islands 2006 100.0 78.7 14.6 11.7 53.1 1.6 16.8 10.4 14.2 12.8 South Africa 2006 96.3 19.4 8.3 5.1 19.3 2.1 5.5 1.7 8.6 6.4 Sri Lanka 2006 36.8 29.6 11.0 7.0 23.1 1.5 15.2 9.5 10.5 5.9 St. Kitts and Nevis 2006 97.9 75.9 12.8 12.4 39.0 0.8 14.1 11.6 12.5 12.7 St. Lucia 2006 99.6 62.0 10.5 9.9 37.2 0.0 12.9 6.0 10.1 12.3 St. Vincent & Grenadines 2006 99.7 62.5 3.8 3.8 14.3 0.0 13.6 6.4 1.9 2.7 Sudan 2006 .. .. 17.1 15.3 38.1 0.0 22.9 19.7 16.6 14.6 Suriname 2000a 25.0 18.5 14.8 12.9 6.6 70.5 23.8 13.7 11.7 11.6 Swaziland 2006 96.3 19.4 10.3 9.2 25.0 2.4 8.0 3.8 10.5 9.6 Switzerland 2005 99.8 0.0 4.7 2.3 13.3 35.1 23.1 12.7 1.4 0.2 Syrian Arab Republic 2002a .. .. 14.7 15.5 23.3 0.1 14.4 11.7 14.7 17.1 Tajikistan 2006 .. .. 4.7 3.7 0.0 1.4 5.1 2.5 4.7 4.3 Tanzania 2006 13.4 120.0 12.5 7.2 37.6 0.4 16.9 7.7 12.0 7.0 Thailand 2006 75.1 25.7 10.8 4.7 22.8 1.1 13.5 2.3 10.4 5.8 Timor-Leste .. .. .. .. .. .. .. .. .. .. Togo 2006 13.2 80.0 14.0 9.7 52.3 0.0 13.9 8.7 14.0 10.7 Trinidad and Tobago 2006 100.0 55.8 9.4 5.1 35.2 0.6 13.1 3.5 8.9 6.7 Tunisia 2006 57.9 57.7 22.9 18.5 55.5 0.0 33.1 14.7 22.0 20.0 Turkey 2006 47.7 29.6 1.8 1.7 2.6 0.6 11.3 3.4 1.1 1.1 Turkmenistan 2002a .. .. 5.4 2.9 14.8 0.3 14.8 12.6 3.8 1.1 Uganda 2006 14.9 73.5 12.0 7.4 37.1 0.5 14.6 7.0 11.7 7.6 Ukraine 2006 .. .. 4.8 3.0 4.5 3.9 4.7 0.7 4.8 4.5 United Arab Emirates 2006 100.0 14.7 4.7 4.6 0.2 0.8 4.8 4.7 4.7 4.6 United States 2006 100.0 3.6 3.0 1.6 3.7 6.4 2.4 1.0 3.1 1.8 Uruguay 2006 100.0 31.6 9.6 3.3 26.0 0.0 5.6 1.2 10.0 4.7 Uzbekistan 2006 .. .. 11.3 7.3 20.9 5.7 10.8 4.8 11.3 7.8 Vanuatu 2006 .. .. 16.7 8.2 45.0 5.0 19.9 18.0 16.1 7.0 Venezuela, RB 2006 99.9 36.8 11.1 10.2 16.8 0.0 10.5 8.9 11.2 10.5 Vietnam 2006 .. .. 13.1 13.3 33.5 0.0 17.8 14.6 12.4 12.8 Yemen 2006a .. .. 6.7 6.9 1.8 0.5 9.6 8.6 6.3 5.6 Zambia 2005 16.0 105.8 14.6 9.4 34.5 0.0 14.9 9.3 14.6 9.4 Zimbabwe 2003a 20.8 90.7 16.7 17.3 38.8 6.5 19.5 19.8 16.3 15.3 World 77.3 31.5 7.5 3.1 13.7 0.5 9.3 3.1 7.3 3.2 Low income 48.2 47.1 12.7 12.4 29.5 0.6 15.0 13.0 12.3 12.1 Middle income 86.6 30.9 8.5 4.9 16.2 0.7 11.3 4.4 8.1 5.0 Lower middle-income 83.8 31.6 10.1 5.3 19.6 0.0 13.2 4.4 9.7 5.6 Upper middle income 90.2 30.0 6.9 4.3 13.2 1.3 9.5 4.4 6.6 4.2 Low & middle income 74.4 34.9 9.2 5.6 17.9 0.6 11.8 5.6 8.9 5.5 East Asia & Pacific 79.0 32.4 8.4 4.4 16.7 0.0 9.4 3.7 8.2 4.7 Europe & Central Asia 74.8 10.9 6.0 4.6 8.9 2.0 8.3 5.3 5.7 4.4 Latin America & Carib. 97.0 41.5 8.6 4.5 15.1 0.0 9.7 2.9 8.5 4.9 Middle East & N. Africa 93.4 34.8 11.3 8.9 28.4 0.0 16.3 8.4 10.7 9.2 South Asia 61.1 42.7 14.9 13.9 32.0 1.5 17.8 14.2 14.5 13.7 Sub-Saharan Africa 47.6 42.8 12.1 7.9 34.8 0.0 13.5 7.5 11.9 8.0 High-income 87.7 22.6 3.8 1.9 5.3 0.2 5.3 2.0 3.6 1.8 OECD 98.6 7.4 3.8 2.1 4.8 0.0 3.8 2.0 3.8 2.1 Non-OECD 78.1 34.1 4.6 1.2 7.3 0.6 7.2 1.8 4.1 1.1 Note: Tariff rates include ad valorem equivalents of specific rates whenever available. a. Rates are most favored nation rates. b. Excludes Eritrea. c. Refers to all member states of the European Union except Bulgaria and Romania. d. Includes Montenegro. 342 2008 World Development Indicators 6.7 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- regions new trade policies could make the difference indication of how selectively tariffs are applied. The cific rates is the share of lines in the tariff schedule between achieving important Millennium Develop- effective rate of protection--the degree to which the that are set on a per unit basis or that combine ad ment Goals--reducing poverty, lowering maternal value added in an industry is protected--may exceed valorem and per unit rates. · Primary products are and child mortality rates, improving educational the nominal rate if the tariff system systematically commodities classified in SITC revision 2 sections 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 2 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 1 codes to define commod- cator, they are not included in the table. ity groups and import weights. Import weights were Unless specified as most favored nation rates, the calculated using the United Nations Statistics Divi- tariff rates used in calculating the indicators in the sion's Commodity Trade (Comtrade) database. Data table are effectively applied rates. Effectively applied are shown only for the last year for which complete rates are those in effect for partners in preferen- data are available. tial trade arrangements such as the North Ameri- can Free Trade Agreement. The difference between most favored nation and applied rates can be sub- stantial. As more countries report their free trade agreements, suspensions of tariffs, or other spe- cial preferences, World Development Indicators will Data sources include their effectively applied rates. All estimates are calculated using the most recent information, All indicators in the table were calculated by World which is not necessarily revised every year. As a Bank staff using the World Integrated Trade Solu- result, data for the same year may differ from data tion system. Data on tariffs were provided by the in last year's edition. United Nations Conference on Trade and Develop- Three measures of average tariffs are shown: sim- 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 2008 World Development Indicators 343 6.8 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. 1,771 .. 1,761 .. 358 .. 0 .. 11 .. 0 Albania 456 2,340 330 1,588 109 729 0 84 62 575 65 93 Algeria 33,042 5,583 31,303 3,738 2,049 119 0 1,304 261 541 1,478 0 Angola 11,500 9,563 9,543 7,398 81 347 0 0 1,958 2,165 0 0 Argentina 98,465 122,190 54,913 64,711 4,913 6,206 16,066 22,441 21,355 35,039 6,131 0 Armenia 371 2,073 298 1,037 96 847 0 574 2 298 70 164 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 321 1,900 206 1,359 30 588 0 104 14 302 101 134 Bangladesh 15,927 20,521 15,106 18,866 5,692 9,297 0 0 199 1,178 622 476 Belarus 1,694 6,124 1,301 846 116 50 0 855 110 4,423 283 0 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 1,614 824 1,483 782 498 126 0 0 47 39 84 3 Bolivia 5,272 5,292 4,459 3,203 865 233 239 1,855 307 220 268 15 Bosnia and Herzegovina .. 5,669 .. 2,830 472 1,449 .. 1,675 .. 1,144 48 20 Botswana 717 408 707 384 108 7 0 0 10 24 0 0 Brazil 160,469 194,150 98,260 84,936 6,038 9,694 30,830 88,889 31,238 20,325 142 0 Bulgaria 10,379 20,925 8,808 5,001 444 1,331 342 7,543 512 8,040 717 341 Burkina Faso 1,271 1,142 1,140 1,022 608 361 0 0 56 85 75 35 Burundi 1,162 1,411 1,099 1,291 591 797 0 0 15 38 48 83 Cambodia 2,284 3,527 2,110 3,318 65 500 0 0 102 209 72 0 Cameroon 10,632 3,171 9,301 2,078 1,067 216 288 489 991 596 51 8 Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 946 1,020 854 863 414 395 0 0 57 115 35 42 Chad 912 1,772 843 1,686 379 956 0 0 20 18 49 68 Chile 22,038 47,977 7,178 9,454 1,383 349 11,429 29,112 3,431 9,411 0 0 China 118,090 322,845 94,674 85,802 14,248 21,412 1,090 63,666 22,325 173,377 0 0 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 25,044 39,698 13,946 25,764 2,559 4,566 5,553 9,112 5,545 4,822 0 0 Congo, Dem. Rep. 13,239 11,201 9,636 9,848 1,413 2,251 0 0 3,118 520 485 833 Congo, Rep. 5,982 6,130 4,942 5,328 279 295 0 0 1,022 767 19 35 Costa Rica 3,802 6,832 3,133 3,669 303 54 214 837 430 2,326 24 0 Côte d'Ivoire 18,899 13,840 11,902 10,830 2,386 2,303 2,660 847 3,910 2,013 427 150 Croatia 3,830 37,480 1,860 10,235 117 1,028 1,257 21,674 492 5,571 221 0 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 4,447 8,905 3,653 6,571 300 448 19 414 616 1,461 160 459 Ecuador 13,994 16,536 12,068 10,108 1,108 760 440 4,981 1,312 1,424 173 23 Egypt, Arab Rep. 33,499 29,339 30,710 26,072 2,356 2,024 313 1,633 2,372 1,635 103 0 El Salvador 2,509 9,136 1,979 5,504 327 428 5 2,401 525 1,230 0 0 Eritrea 37 800 37 781 24 419 0 0 0 19 0 0 Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 10,308 2,326 9,774 2,212 1,470 553 0 0 460 114 73 0 Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 4,360 4,350 3,976 3,860 110 19 0 0 287 434 97 57 Gambia, The 426 725 385 689 162 263 0 0 15 18 26 18 Georgia 1,240 1,964 1,039 1,457 84 785 0 159 85 111 116 236 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 5,495 3,192 4,200 1,891 2,434 810 27 0 620 1,143 648 159 Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 3,282 5,496 2,328 3,921 158 642 142 91 812 1,484 0 0 Guinea 3,242 3,281 2,987 2,980 847 1,259 0 0 161 229 94 72 Guinea-Bissau 898 711 798 695 210 297 0 0 95 8 6 8 Haiti 738 1,189 683 1,034 307 238 0 0 27 123 29 32 344 2008 World Development Indicators 6.8 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 4,797 4,076 4,193 2,986 828 349 123 527 382 533 99 31 Hungary 31,650 107,677 23,974 28,017 2,218 137 4,089 64,681 3,203 14,979 385 0 India 94,464 153,075 80,422 59,570 27,348 30,236 6,618 81,535 5,049 11,971 2,374 0 Indonesia 124,398 130,956 65,309 67,273 13,259 8,741 33,123 30,683 25,966 33,000 0 0 Iran, Islamic Rep. 21,879 20,113 15,116 11,090 316 559 314 59 6,449 8,964 0 0 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 4,577 7,994 3,716 6,010 595 387 128 811 492 1,173 240 0 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 7,661 8,000 6,624 7,143 806 939 0 0 785 699 251 158 Kazakhstan 3,750 74,148 2,834 2,136 295 502 103 59,433 381 12,579 432 0 Kenya 7,309 6,534 5,857 5,807 2,412 2,764 445 0 634 574 374 153 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 609 2,382 472 1,860 141 612 0 251 13 108 124 163 Lao PDR 2,165 2,985 2,091 2,191 285 643 0 762 10 5 64 27 Latvia 463 22,795 271 1,555 55 100 0 10,764 31 10,476 160 0 Lebanon 2,966 23,963 1,550 18,958 113 314 50 805 1,365 4,200 0 0 Lesotho 684 670 642 633 207 284 0 0 4 0 38 36 Liberia 2,154 2,674 1,161 1,115 269 256 0 0 657 1,223 336 336 Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 769 18,955 430 3,244 62 81 29 8,011 49 7,700 262 0 Macedonia, FYR 1,277 2,661 788 1,498 181 642 289 805 143 303 57 56 Madagascar 4,302 1,453 3,687 1,236 1,121 636 0 0 542 189 73 29 Malawi 2,239 850 2,079 767 1,306 157 0 0 44 64 116 19 Malaysia 34,343 52,526 16,023 21,899 1,059 437 11,046 18,824 7,274 11,803 0 0 Mali 2,958 1,436 2,739 1,411 863 282 0 0 72 17 147 8 Mauritania 2,396 1,630 2,127 1,401 347 130 0 0 169 229 100 0 Mauritius 1,757 1,997 1,148 585 157 71 267 49 342 1,363 0 0 Mexico 165,379 160,700 93,902 96,304 13,823 4,418 18,348 57,050 37,300 7,346 15,828 0 Moldova 695 2,416 450 735 152 393 9 718 6 822 230 141 Mongolia 531 1,444 472 1,361 59 301 0 3 12 50 47 31 Morocco 23,771 18,493 23,190 14,108 3,999 2,285 331 2,588 198 1,797 52 0 Mozambique 7,458 3,265 5,209 2,511 890 655 1,769 0 279 744 202 10 Myanmar 5,771 6,828 5,378 5,234 777 776 0 0 393 1,595 0 0 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 2,410 3,409 2,339 3,285 1,023 1,468 0 0 23 81 48 43 Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 10,390 4,391 8,566 3,425 341 256 0 288 1,785 615 39 63 Niger 1,572 805 1,315 703 598 189 133 26 72 49 52 27 Nigeria 34,092 7,693 28,140 3,800 3,489 2,074 301 0 5,651 3,893 0 0 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 5,776 4,819 2,637 819 25 0 2,598 2,047 541 1,953 0 0 Pakistan 30,229 35,909 23,788 32,309 6,403 10,015 1,593 907 3,235 1,230 1,613 1,462 Panama 6,099 9,989 3,782 7,774 175 185 0 1,694 2,207 505 111 15 Papua New Guinea 2,506 1,675 1,668 1,225 407 322 711 283 78 167 50 0 Paraguay 2,574 3,426 1,453 2,235 189 254 338 480 784 711 0 0 Peru 30,833 28,174 18,931 21,825 1,729 2,633 1,288 3,318 9,659 3,011 955 20 Philippines 39,379 60,324 28,525 36,793 5,185 2,886 4,847 18,522 5,279 5,009 728 0 Poland 44,080 125,831 40,890 39,248 2,067 1,961 1,012 65,228 2,178 21,355 0 0 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 345 6.8 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 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 6,832 55,114 3,957 14,204 844 2,481 534 23,081 1,303 17,725 1,038 104 Russian Federation 121,401 251,067 101,582 50,254 1,524 4,759 0 160,364 10,201 40,448 9,617 0 Rwanda 1,029 419 971 390 512 169 0 0 32 25 26 4 Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,906 1,984 3,256 1,712 1,160 495 44 151 260 95 347 26 Serbia 10,785a 13,831 6,788a 7,686 1,252a 3,072 1,773a 4,105 2,139a 1,796 84 a 244 Sierra Leone 1,250 1,428 1,058 1,323 234 533 0 0 27 70 165 35 Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 5,744 27,085 3,488 4,508 263 298 85 6,994 1,714 15,584 457 0 Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia 2,678 2,836 1,961 1,923 432 435 0 0 551 745 166 168 South Africa 25,358 35,549 9,837 13,940 0 29 4,935 6,349 9,673 15,260 913 0 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 8,395 11,446 7,175 10,140 1,512 2,245 90 206 535 855 595 244 Sudan 17,603 19,158 9,779 11,609 1,279 1,271 496 496 6,368 6,535 960 518 Swaziland 291 544 279 494 25 26 0 0 11 51 0 0 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 21,415 6,502 16,853 5,576 471 21 0 0 4,562 925 0 0 Tajikistan 634 1,154 590 982 0 339 0 33 43 95 0 44 Tanzania 7,421 4,240 6,217 2,929 2,269 1,056 44 6 963 1,293 197 13 Thailand 100,039 55,233 16,826 11,914 1,906 405 39,117 25,507 44,095 17,812 0 0 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 1,476 1,806 1,286 1,565 541 696 0 0 85 233 105 8 Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 10,818 18,480 9,215 15,144 1,766 1,470 0 0 1,310 3,336 293 0 Turkey 73,781 207,854 50,317 67,214 5,069 6,919 7,079 87,563 15,701 42,315 685 10,762 Turkmenistan 402 881 385 725 1 21 0 4 17 152 0 0 Uganda 3,609 1,264 3,089 1,107 1,792 436 0 0 103 148 417 9 Ukraine 8,429 49,887 6,581 9,538 491 2,362 84 24,158 223 15,361 1,542 830 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 5,318 9,804 3,833 7,211 513 653 127 385 1,336 2,208 21 0 Uzbekistan 1,799 3,892 1,415 3,322 157 342 15 403 212 166 157 0 Venezuela, RB 35,538 44,635 28,223 27,180 1,639 51 2,013 5,606 3,063 11,848 2,239 0 Vietnam 25,428 20,202 21,778 17,518 231 3,663 0 0 3,272 2,504 377 181 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 6,217 5,563 5,528 5,000 827 1,894 0 0 689 318 0 246 Zambia 6,958 2,325 5,291 1,003 1,434 260 13 826 415 455 1,239 41 Zimbabwe 4,989 4,677 3,462 3,452 896 946 381 19 685 1,093 461 113 World .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s .. s Low income 366,231 375,060 298,243 240,914 77,144 85,474 15,299 86,554 39,576 41,895 13,113 5,697 Middle income 1,585,166 2,608,599 1,035,719 1,026,218 105,438 109,106 202,465 951,635 299,174 616,340 47,809 14,406 Lower middle income 698,032 921,093 460,313 427,332 60,740 63,722 88,238 198,074 141,256 292,828 8,226 2,860 Upper middle income 887,133 1,687,506 575,406 598,886 44,697 45,384 114,226 753,561 157,918 323,512 39,583 11,547 Low & middle income 1,951,397 2,983,659 1,333,962 1,267,133 182,582 194,579 217,764 1,038,189 338,750 658,235 60,922 20,103 East Asia & Pacific 455,619 659,985 255,393 255,163 37,604 40,233 90,050 158,260 108,839 246,324 1,337 239 Europe & Central Asia 331,945 1,047,027 259,527 261,993 16,242 31,826 16,699 549,276 38,868 222,425 16,851 13,333 Latin America & Carib. 608,475 734,499 371,682 397,231 38,402 32,992 87,303 230,312 122,859 106,219 26,632 737 Middle East & N. Africa 167,325 141,318 142,996 108,074 12,776 9,759 3,606 8,435 18,546 24,386 2,177 423 South Asia 151,740 227,303 129,135 126,989 42,036 53,764 8,301 82,647 9,051 15,435 5,252 2,232 Sub-Saharan Africa 236,293 173,526 175,229 117,683 35,521 26,005 11,804 9,258 40,587 43,445 8,673 3,140 High income Euro area a. Includes Montenegro. 346 2008 World Development Indicators 6.8 GLOBAL LINKS External debt About the data Definitions A country's external indebtedness plays an important a single currency (U.S. dollars) to produce summary · Total external debt is debt owed to nonresidents role in its creditworthiness and in perceptions by tables. Stock figures (amount of debt outstanding) repayable in foreign currency, goods, or services. It investors. Data on the external debt of developing are converted using end-of-period exchange rates, is the sum of public, publicly guaranteed, and private countries are gathered by the World Bank through its as published in the IMF's International Financial Sta- nonguaranteed long-term debt, short-term debt, and Debtor Reporting System. Indebtedness is calculated tistics (line ae). Flow figures are converted at annual use of IMF credit. · Long-term debt is debt that has using loan-by-loan reports submitted by countries on average exchange rates (line rf). Projected debt an original or extended maturity of more than one long-term public and publicly guaranteed borrowing, service is converted using end-of-period exchange year. It has three components: public, publicly guar- along with information on short-term debt collected rates. Debt repayable in multiple currencies, goods, anteed, and private nonguaranteed debt. · Public by the countries or collected from creditors through or services and debt with a provision for maintenance and publicly guaranteed debt comprises the long- the reporting systems of the Bank for International of the value of the currency of repayment are shown term external obligations of public debtors, including Settlements and the Organisation for Economic Co- at book value. the national government and political subdivisions operation and Development. These data are sup- Because flow data are converted at annual aver- (or an agency of either) and autonomous public bod- plemented by information from major multilateral age exchange rates and stock data at end-of-period ies, and the external obligations of private debtors banks and official lending agencies in major creditor exchange rates, year-to-year changes in debt out- that are guaranteed for repayment by a public entity. countries as well as estimates by World Bank and standing and disbursed are sometimes not equal to · IBRD loans and IDA credits are extended by the International Monetary Fund (IMF) staff. In addition, net flows (disbursements less principal repayments); World Bank. The International Bank for Reconstruc- the table includes data on long-term private nonguar- similarly, changes in debt outstanding, including tion and Development (IBRD) lends at market rates. anteed debt that is either reported to the World Bank undisbursed debt, differ from commitments less The International Development Association (IDA) pro- or estimated by its staff. repayments. Discrepancies are particularly notable vides credits at concessional rates. · Private non- The coverage, quality, and timeliness of data vary when exchange rates have moved sharply during guaranteed debt consists of the long-term external across countries. Coverage varies for both debt the year. Cancellations and reschedulings of other obligations of private debtors that are not guaran- instruments and borrowers. The widening spec- liabilities into long-term public debt also contribute teed for repayment by a public entity. · Short-term trum of debt instruments and investors alongside to the differences. debt is debt owed to nonresidents having an original the expansion of private nonguaranteed borrowing Variations in reporting rescheduled debt also affect maturity of one year or less and interest in arrears makes comprehensive coverage of external debt cross-country comparability. For example, reschedul- on long-term debt. · Use of IMF credit denotes more complex. Reporting countries differ in their ing under the auspices of the Paris Club of official repurchase obligations to the IMF for all uses of IMF capacity to monitor debt, especially private nonguar- creditors may be subject to lags between the com- resources (excluding those resulting from drawings anteed debt. Even data on public and publicly guar- pletion of the general rescheduling agreement and on the reserve tranche). These obligations, shown for anteed debt are affected by coverage and accuracy the completion of the specific bilateral agreements the end of the year specified, comprise purchases in reporting--again because of monitoring capacity that define the terms of the rescheduled debt. Other outstanding under the credit tranches (including and sometimes because of an unwillingness to pro- areas of inconsistency include country treatment of enlarged access resources) and all special facilities vide information. A key part often underreported is arrears and of nonresident national deposits denomi- (the Buffer Stock, Compensatory and Contingency military debt. nated in foreign currency. Financing, Extended Fund, Supplemental Reserve, Because debt data are normally reported in the Oil, Supplementary Financing, Policy on Enlarged currency of repayment, they have to be converted into Access, and Systemic Transformation), trust fund loans, and operations under the structural adjust- Financial integration has complemented growth 6.8a ment and poverty reduction and growth facilities. Total external debt ($ trillions) External debt as a share of GNI (%) Data sources 3.5 50 Data on external debt are mainly reports to the Total external debt 3.0 45 World Bank through its Debtor Reporting System from member countries that have received IBRD 2.5 40 loans or IDA credits, with additional information from the files of the World Bank, the IMF, the Afri- 2.0 35 can Development Bank and African Development Fund, the Asian Development Bank and Asian 1.5 30 External debt Development Fund, and the Inter-American Devel- 1.0 25 opment Bank. Summary tables of the external debt 1990 1995 2000 2006 of developing countries are published annually in For developing countries economic growth has exceeded debt accumulation since 1999. the World Bank's Global Development Finance and Source: World Development Indicators data files. on its Global Development Finance CD-ROM. 2008 World Development Indicators 347 6.9 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 and goods and services publicly guaranteed services and % of GNI and incomea debt service % of total debt % of total reserves % of GNI incomea 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 2006b 2006b Afghanistan .. 21.1 .. .. .. 100.0 .. 0.6 .. .. 5c 22c Albania 18.4 25.2 1.4 3.5 11.4 44.6 13.7 24.6 23.5 31.7 21 55 Algeria 83.5 5.2 .. .. 17.7 18.7 0.8 9.7 6.3 0.7 5 10 Angola 311.9 24.1 12.0 12.8 0.6 0.5 17.0 22.6 919.7 25.2 33 39 Argentina 38.9 58.6 30.1 31.6 21.6 80.5 21.7 28.7 133.6 109.4 68 230 Armenia 25.3 32.0 3.1 7.6 69.8 87.5 0.6 14.4 1.9 27.8 29 78 Australia .. .. .. .. .. .. .. .. .. .. .. .. Austria .. .. .. .. .. .. .. .. .. .. .. .. Azerbaijan 10.6 11.1 1.3 1.6 21.8 39.2 4.4 15.9 11.6 12.1 12 15 Bangladesh 40.7 31.1 13.2 3.7 27.1 74.7 1.3 5.7 8.4 30.4 22 91 Belarus 12.2 16.6 3.4 3.3 55.4 12.8 6.5 72.2 29.2 312.2 17 28 Belgium .. .. .. .. .. .. .. .. .. .. .. .. Benin 82.1 17.5 6.8 .. 54.8 47.9 2.9 4.8 23.7 4.3 14 c 70 c Bolivia 81.2 49.0 29.4 8.5 75.5 91.1 5.8 4.2 30.5 6.9 51c 123c Bosnia and Herzegovina .. 44.2 .. 8.7 .. 52.4 .. 20.2 .. 33.9 43 84 Botswana 15.1 4.1 3.1 0.9 76.0 70.7 1.4 5.9 0.2 0.3 4 6 Brazil 21.2 18.7 36.6 37.3 18.5 7.8 19.5 10.5 60.7 23.7 26 158 Bulgaria 81.8 66.5 16.5 12.4 10.5 59.3 4.9 38.4 31.3 68.4 74 110 Burkina Faso 53.6 18.5 .. .. 76.7 77.6 4.4 7.5 16.1 15.4 13c 110 c Burundi 117.6 162.2 27.6 40.4 70.6 88.3 1.3 2.7 6.9 28.7 119 c 1,061c Cambodia 67.5 50.6 0.7 0.6 11.9 67.7 4.5 5.9 53.1 14.8 48 66 Cameroon 129.6 17.5 20.8 .. 60.8 39.3 9.3 18.8 6,444.5 34.3 18 c 70 c Canada .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 85.9 68.4 .. .. 100.0 100.0 6.0 11.2 24.0 86.6 57c 597c Chad 63.3 34.2 .. .. 87.1 76.9 2.2 1.0 13.7 2.9 24 c 36c Chile 32.1 37.9 24.5 20.0 76.2 5.0 15.6 19.6 23.1 48.5 42 86 China 16.5 12.2 9.9 2.5 7.6 26.0 18.9 53.7 27.8 16.0 14 35 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. Colombia 27.5 26.9 31.5 31.3 32.7 27.0 22.1 12.1 65.6 31.2 32 143 Congo, Dem. Rep. 271.4 137.5 .. .. .. 38.1 23.6 4.6 1,980.9 .. 130 c 388 c Congo, Rep. 487.1 .. 13.1 .. 21.0 60.6 17.1 12.5 1,606.5 41.5 104 c 104 c Costa Rica 33.1 31.9 13.8 5.0 50.7 66.0 11.3 34.0 40.6 74.6 35 66 Côte d'Ivoire 188.7 82.6 23.1 1.4 59.3 71.8 20.7 14.5 739.1 112.0 82c 150 c Croatia 20.4 90.2 4.8 33.1 73.1 10.9 12.8 14.9 25.9 48.5 93 168 Cuba .. .. .. .. .. .. .. .. .. .. .. .. Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. Denmark .. .. .. .. .. .. .. .. .. .. .. .. Dominican Republic 37.8 29.6 6.1 9.6 39.8 17.1 13.8 16.4 165.3 68.7 35 66 Ecuador 72.6 41.9 24.9 24.1 31.7 30.7 9.4 8.6 73.4 70.3 52 129 Egypt, Arab Rep. 55.8 27.4 13.2 4.9 26.3 17.2 7.1 5.6 13.9 6.3 28 69 El Salvador 26.7 50.4 8.9 13.1 55.1 50.8 20.9 13.5 55.9 62.7 55 119 Eritrea 6.3 74.1 0.1 .. 100.0 75.8 0.0 2.3 0.0 73.2 52c 742c Estonia .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 136.6 17.5 18.4 6.8 41.7 55.0 4.5 4.9 56.5 13.7 16c 84 c Finland .. .. .. .. .. .. .. .. .. .. .. .. France .. .. .. .. .. .. .. .. .. .. .. .. Gabon 101.6 57.9 15.3 .. 17.9 100.0 6.6 10.0 187.8 38.7 64 79 Gambia, The 113.0 145.2 15.5 12.4 49.1 59.2 3.5 2.5 14.0 15.3 108 c 191c Georgia 48.2 26.2 .. 8.8 0.4 30.2 6.9 5.7 43.0 12.0 22 57 Germany .. .. .. .. .. .. .. .. .. .. .. .. Ghana 86.9 24.9 24.0 4.9 48.4 44.6 11.3 35.8 77.1 50.4 24 c 59c Greece .. .. .. .. .. .. .. .. .. .. .. .. Guatemala 22.6 15.7 11.1 4.8 47.7 58.6 24.7 27.0 103.7 36.6 18 59 Guinea 89.8 100.2 25.0 .. 30.4 55.8 5.0 7.0 185.6 .. 71c 261c Guinea-Bissau 380.7 241.2 51.9 .. 88.3 45.2 10.5 1.1 467.0 9.9 169c 360 c Haiti 25.3 27.5 50.4 3.2 92.0 83.0 3.6 10.4 13.4 48.5 24 c 64 c 348 2008 World Development Indicators 6.9 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 and goods and services publicly guaranteed services and % of GNI and incomea debt service % of total debt % of total reserves % of GNI incomea 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 2006b 2006b Honduras 131.5 45.7 34.0 5.1 52.6 63.9 8.0 13.1 141.7 20.2 41c 61c Hungary 73.7 102.7 33.8 33.1 20.1 8.8 10.1 13.9 26.7 69.4 100 127 India 26.8 16.9 29.7 7.7 24.3 24.6 5.3 8.9 22.1 6.7 15 63 Indonesia 63.4 37.5 29.9 16.6 28.4 48.3 20.9 25.2 174.2 77.5 45 122 Iran, Islamic Rep. 24.3 9.3 30.2 .. 1.3 4.5 29.5 44.6 .. .. 10 27 Iraq .. .. .. .. .. .. .. .. .. .. .. .. Ireland .. .. .. .. .. .. .. .. .. .. .. .. Israel .. .. .. .. .. .. .. .. .. .. .. .. Italy .. .. .. .. .. .. .. .. .. .. .. .. Jamaica 82.2 85.6 16.2 11.9 40.6 28.2 10.7 14.7 72.2 50.6 99 144 Japan .. .. .. .. .. .. .. .. .. .. .. .. Jordan 118.8 54.5 12.4 6.1 33.5 47.0 10.2 8.7 34.4 10.0 58 77 Kazakhstan 18.6 103.4 3.9 33.7 7.8 60.8 10.2 17.0 23.0 65.8 132 222 Kenya 83.8 28.6 30.4 6.5 32.5 64.5 8.7 8.8 164.9 23.7 26 87 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 37.5 85.6 13.2 5.7 59.0 93.5 2.1 4.5 9.7 13.2 71c 126c Lao PDR 123.2 98.6 6.3 .. 37.4 65.7 0.5 0.2 10.2 1.1 87 245 Latvia 8.8 117.2 1.6 33.3 60.3 45.0 6.7 46.0 5.2 232.2 135 266 Lebanon 24.3 107.0 .. 21.0 13.2 3.8 46.0 17.5 16.9 21.8 116 128 Lesotho 51.9 35.8 6.1 4.0 60.3 54.7 0.6 0.0 0.9 0.0 25 38 Liberia .. 541.3 .. .. .. 100.0 30.5 45.7 2,340.6 1,699.1 674 c 2,030 c Libya .. .. .. .. .. .. .. .. .. .. .. .. Lithuania 10.2 68.9 1.3 22.1 31.8 14.5 6.4 40.6 6.0 133.4 79 121 Macedonia, FYR 29.0 42.8 .. 15.7 99.9 16.5 11.2 11.4 51.9 16.0 50 102 Madagascar 143.3 26.8 7.6 .. 74.3 66.4 12.6 13.0 497.1 32.4 20 c 65c Malawi 165.8 27.2 24.9 .. 51.4 89.6 1.9 7.5 37.8 45.1 21c 79c Malaysia 40.6 36.0 7.0 4.0 15.5 5.7 21.2 22.5 29.5 14.2 39 31 Mali 122.3 26.0 13.4 .. 45.5 76.4 2.4 1.2 22.2 1.8 20c 63c Mauritania 175.3 58.9 22.9 .. 49.6 88.1 7.1 14.0 187.9 .. 60 c 121c Mauritius 46.2 31.2 9.4 7.1 34.5 25.6 19.5 68.2 38.5 104.1 31 50 Mexico 60.5 19.5 27.0 18.9 19.5 29.6 22.6 4.6 218.8 9.6 21 62 Moldova 40.3 64.3 7.9 12.2 79.1 48.7 0.9 34.0 2.3 105.9 65 90 Mongolia 44.2 47.4 10.2 2.2 2.8 38.6 2.2 3.5 7.4 4.7 43 57 Morocco 75.1 28.7 33.4 12.2 30.3 40.6 0.8 9.7 5.1 8.6 30 72 Mozambique 360.6 53.2 34.5 1.9 17.4 69.6 3.7 22.8 142.8 61.1 45c 115c Myanmar .. .. 17.8 1.7 15.0 3.4 6.8 23.4 60.4 115.3 70 202 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Nepal 54.7 37.8 7.5 5.1 54.2 68.8 0.9 2.4 3.5 .. 28 c 93c Netherlands .. .. .. .. .. .. .. .. .. .. .. .. New Zealand .. .. .. .. .. .. .. .. .. .. .. .. Nicaragua 368.3 84.8 38.7 4.1 30.3 59.5 17.2 14.0 1,256.8 66.7 72c 131c Niger 85.9 22.1 16.7 .. 95.5 76.0 4.6 6.1 75.6 13.3 17c 93c Nigeria 131.7 7.6 13.8 .. 45.4 6.9 16.6 50.6 330.7 9.1 9 13 Norway .. .. .. .. .. .. .. .. .. .. .. .. Oman 43.1 .. 14.8 1.3 7.3 58.6 9.4 40.5 27.9 38.9 16 25 Pakistan 49.5 27.8 26.5 8.6 43.2 60.5 10.7 3.4 128.0 9.6 26 123 Panama 80.9 62.2 3.4 24.7 52.7 7.0 36.2 5.1 282.4 37.9 77 94 Papua New Guinea 56.5 33.0 20.8 .. 31.7 68.7 3.1 10.0 29.1 11.6 35 42 Paraguay 31.5 36.9 5.6 6.8 48.0 47.5 30.4 20.8 70.8 41.8 43 69 Peru 60.3 33.3 15.9 12.9 49.9 41.3 31.3 10.7 111.6 17.3 42 140 Philippines 51.7 47.1 16.1 19.6 29.2 12.6 13.4 8.3 67.8 21.8 57 101 Poland 32.2 38.7 11.0 24.7 13.5 4.8 4.9 17.0 14.6 44.1 41 97 Portugal .. .. .. .. .. .. .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. .. .. .. .. 2008 World Development Indicators 349 6.9 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 and goods and services publicly guaranteed services and % of GNI and incomea debt service % of total debt % of total reserves % of GNI incomea 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 2006b 2006b Romania 19.4 46.6 10.5 18.4 21.3 32.4 19.1 32.2 49.7 58.7 58 148 Russian Federation 31.0 26.2 6.3 13.8 9.7 3.0 8.4 16.1 56.6 13.3 34 88 Rwanda 79.2 16.9 20.5 9.6 99.0 86.1 3.1 6.0 32.3 5.7 13c 100 c Saudi Arabia .. .. .. .. .. .. .. .. .. .. .. .. Senegal 82.7 22.0 16.8 .. 62.2 64.2 6.7 4.8 95.6 7.1 17c 46c Serbia .. 43.8 .. .. 100.0d 76.0 19.8d 13.0 .. 15.1 52 127 Sierra Leone 152.7 100.9 54.3 9.6 8.3 84.0 2.2 4.9 77.8 37.8 83c 349c Singapore .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic 29.2 51.1 11.3 .. 7.5 17.1 29.8 57.5 44.4 116.6 58 67 Slovenia .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. 20.6 26.3 .. .. .. .. South Africa 17.1 14.2 9.5 6.7 0.0 1.6 38.1 42.9 216.7 59.6 15 51 Spain .. .. .. .. .. .. .. .. .. .. .. .. Sri Lanka 65.3 42.6 8.0 8.6 14.0 22.6 6.4 7.5 25.3 29.1 40 92 Sudan 276.2 55.5 6.7 4.1 100.0 10.2 36.2 34.1 .. 393.7 69 c 304 c Swaziland 20.1 20.4 1.8 1.8 55.0 62.1 3.9 9.3 3.8 13.6 21 23 Sweden .. .. .. .. .. .. .. .. .. .. .. .. Switzerland .. .. .. .. .. .. .. .. .. .. .. .. Syrian Arab Republic 184.8 20.0 4.3 1.3 66.6 30.1 21.3 14.2 .. .. 23 51 Tajikistan 53.6 42.5 .. 5.1 .. 18.1 6.8 8.2 .. 46.4 36 42 Tanzania 144.6 33.6 17.9 3.4 66.7 79.2 13.0 30.5 356.6 57.2 29c 116c Thailand 60.6 27.3 11.6 9.4 20.9 8.0 44.1 32.2 119.4 26.6 30 40 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 116.7 82.8 6.0 .. 75.5 60.5 5.8 12.9 65.1 62.2 74 c 154 c Trinidad and Tobago .. .. .. .. .. .. .. .. .. .. .. .. Tunisia 63.0 64.5 16.9 14.4 43.8 48.1 12.1 18.1 77.6 48.3 66 112 Turkey 43.0 51.7 27.7 33.2 20.7 13.3 21.3 20.4 113.0 66.9 61 200 Turkmenistan 16.1 8.9 .. .. 1.9 6.0 4.3 17.2 1.5 .. 11 15 Uganda 63.3 13.6 19.8 4.8 69.7 68.6 2.8 11.7 22.4 8.2 11c 46c Ukraine 17.6 47.6 6.6 18.1 13.6 22.2 2.6 30.8 20.9 68.7 58 106 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom .. .. .. .. .. .. .. .. .. .. .. .. United States .. .. .. .. .. .. .. .. .. .. .. .. Uruguay 29.4 52.1 22.1 87.8 27.3 31.7 25.1 22.5 73.7 71.4 66 185 Uzbekistan 13.5 22.7 .. .. 1.9 17.1 11.8 4.3 .. .. 26 67 Venezuela, RB 48.7 24.7 22.9 13.3 11.5 9.2 8.6 26.5 28.6 32.3 34 83 Vietnam 124.0 33.9 .. .. 2.9 12.9 12.9 12.4 247.2 18.7 33 45 West Bank and Gaza .. .. .. .. .. .. .. .. .. .. .. .. Yemen, Rep. 169.0 31.6 3.1 2.4 78.3 56.1 11.1 5.7 107.9 4.2 25 46 Zambia 215.1 23.9 .. 3.6 50.6 69.0 6.0 19.6 186.2 63.2 29c 70 c Zimbabwe 73.5 .. .. .. 33.6 0.0 13.7 23.4 77.2 .. 110 248 World .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w .. w Low income 56.3 23.7 22.9 6.6 32.9 28.4 10.8 11.2 78.5 13.5 Middle income 36.3 26.8 16.7 13.4 21.0 18.5 18.9 23.6 66.2 25.1 Lower middle income 39.9 19.9 15.3 7.0 22.0 24.7 20.2 31.8 64.7 19.3 Upper middle income 33.9 33.0 17.8 20.1 20.2 15.7 17.8 19.2 67.5 34.0 Low & middle income 38.9 26.4 17.3 12.6 22.7 19.4 17.4 22.1 67.2 23.9 East Asia & Pacific 35.5 18.4 12.7 5.0 18.2 22.3 23.9 37.3 64.9 18.7 Europe & Central Asia 33.9 43.2 12.2 20.0 16.9 9.8 11.7 21.2 48.0 38.1 Latin America & Carib. 35.9 25.8 26.2 23.0 26.2 23.2 20.2 14.5 88.6 34.0 Middle East & N. Africa 58.4 21.9 19.0 10.4 19.4 20.5 11.1 17.3 18.9 8.3 South Asia 32.0 19.8 25.5 7.5 27.4 33.9 6.0 6.8 29.5 7.7 Sub-Saharan Africa 77.9 26.2 15.9 .. 35.0 15.1 17.2 25.0 164.3 34.6 High income Euro area a. Includes workers' remittances. b. The numerator refers to 2006, whereas the denominator is a three-year average of 2004­06 data. c. Data are from debt sustainability analyses undertaken as part of the Heavily Indebted Poor Countries Initiative. Present value estimates for these countries are for public and publicly guaranteed debt only. d. Includes Montenegro. 350 2008 World Development Indicators 6.9 GLOBAL LINKS Ratios for external debt About the data Definitions A country's external debt burden, both debt outstand- vulnerability, it is compared with the total debt and · Total external debt is debt owed to nonresidents ing and debt service, affects a country's creditworthi- foreign exchange reserves that are instrumental in and comprises public, publicly guaranteed, and pri- ness and vulnerability. The table shows total exter- providing coverage for such obligations. The present vate nonguaranteed long-term debt, short-term debt, nal debt relative to a country's size--gross national value of external debt provides a measure of future and use of IMF credit. It is presented as a share of income (GNI). Total debt service is contrasted with debt service obligations. gross national income (GNI). · Total debt service is countries' ability to obtain foreign exchange through The present value of external debt is calculated by the sum of principal repayments and interest actually exports of goods, services, income, and workers' discounting the debt service (interest plus amortiza- paid on total long-term debt (public and publicly guar- remittances. The ratios shown here may differ from tion) due on long-term external debt over the life of anteed and private nonguaranteed), use of IMF credit, those published elsewhere because estimates of existing loans. Short-term debt is included at face and interest on short-term debt. · Exports of goods, exports and GNI have been revised to incorporate value. The data on debt are in U.S. dollars converted services, and income refer to international trans- data available as of February 15, 2008. at official exchange rates (see About the data for table actions involving a change in ownership of general Multilateral debt service (shown as a share of the 6.8). The discount rate on long-term debt depends on merchandise, goods sent for processing and repairs, country's total public and publicly guaranteed debt the currency of repayment and is based on commercial nonmonetary gold, services, receipts of employee service) are obligations to international financial interest reference rates established by the Organisa- compensation for nonresident workers, investment institutions, such as the World Bank, the Interna- tion for Economic Co-operation and Development. income, and workers' remittances. · Multilateral tional Monetary Fund (IMF), and regional develop- Loans from the International Bank for Reconstruction debt service is the repayment of principal and inter- ment banks. Multilateral debt service takes priority and Development (IBRD), credits from the International est to the World Bank, regional development banks, over private and bilateral debt service, and borrowers Development Association (IDA), and obligations to the and other multilateral and intergovernmental agen- must stay current with multilateral debts to remain IMF are discounted using a special drawing rights refer- cies. · Short-term debt includes all debt having an creditworthy. While bilateral and private creditors ence rate. When the discount rate is greater than the original maturity of one year or less and interest in often write off debts, international financial institu- loan interest rate, the present value is less than the arrears on long-term debt. · Total reserves comprise tion bylaws prohibit granting debt relief or canceling nominal sum of future debt service obligations. holdings of monetary gold, special drawing rights, debts directly. However, the recent decrease in multi- Debt ratios are used to assess the sustainability of reserves of IMF members held by the IMF, and hold- lateral debt service ratios for some countries reflects a country's debt service obligations, but no absolute ings of foreign exchange under the control of mon- debt relief from special programs, such as the Heav- rules determine what values are too high. Empirical etary authorities. · Present value of debt is the sum ily Indebted Poor Countries (HIPC) Debt Initiative and analysis of developing countries' experience and of short-term external debt plus the discounted sum the Multilateral Debt Relief Initiative (MDRI) (see debt service performance shows that debt service of total debt service payments due on public, publicly table 1.4.) Other countries have accelerated repay- difficulties become increasingly likely when the pres- guaranteed, and private nonguaranteed long-term ment of debt outstanding. Indebted countries may ent value of debt reaches 200 percent of exports. external debt over the life of existing loans. also apply to the Paris and London Clubs to renegoti- Still, what constitutes a sustainable debt burden var- ate obligations to public and private creditors. ies by country. Countries with fast-growing econo- Because short-term debt poses an immediate mies and exports are likely to be able to sustain burden and is particularly important for monitoring higher debt levels. Data sources Developing countries have reduced financial vulnerability 6.9a Data on external debt are mainly from reports to the World Bank through its Debtor Reporting Sys- Foreign reserves (% of short-term debt) Total debt service (% of exports of goods, services, and income) 500 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 400 25 IMF, the African Development Bank and African Total debt service Development Fund, the Asian Development Bank 300 20 and Asian Development Fund, and the Inter Ameri- can Development Bank. Data on GNI, exports of 200 15 goods and services, and total reserves are from Foreign reserves the World Bank's national accounts files and the 100 10 IMF's Balance of Payments and International 1990 1995 2000 2006 Financial Statistics databases. Summary tables Since 1990 developing countries have increased their buffer for external debt and its service. Total debt of the external debt of developing countries are services have decreased significantly since 1999, due largely to debt relief initiatives by multilateral published annually in the World Bank's Global and bilateral donors. Development Finance and on its Global Develop- Source: World Bank's Global Development Finance. ment Finance CD-ROM. 2008 World Development Indicators 351 6.10 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. .. .. 0 .. 0 .. 0 Albania 70 325 0 0 0 0 0 ­11 Algeria 0 1,795 0 0 ­278 0 788 ­1,348 Angola 472 ­38 0 0 0 0 123 ­1,517 Argentina 5,609 4,840 1,552 662 3,705 1,690 754 825 Armenia 25 343 0 ­1 0 0 0 108 Australia 12,026 26,599 .. .. .. .. .. .. Austria 1,901 157 .. .. .. .. .. .. Azerbaijan 330 ­584 0 1 0 0 0 ­100 Bangladesh 2 697 ­15 31 0 0 ­21 ­24 Belarus 15 354 0 ­1 0 0 103 264 Belgium 10,689a 61,990 .. .. .. .. .. .. Benin 13 63 0 2 0 0 0 0 Bolivia 393 240 0 0 0 0 41 93 Bosnia and Herzegovina 0 423 0 0 .. 0 .. ­185 Botswana 70 486 6 36 0 0 ­6 ­2 Brazil 4,859 18,782 2,775 7,716 2,636 ­7,136 8,283 13,333 Bulgaria 90 5,172 0 95 ­6 166 ­93 2,062 Burkina Faso 10 26 0 0 0 0 0 0 Burundi 2 0 0 0 0 0 ­1 ­2 Cambodia 151 483 0 0 0 0 13 0 Cameroon 7 309 0 0 0 0 ­65 ­122 Canada 9,319 69,068 .. .. .. .. .. .. Central African Republic 6 24 0 0 0 0 0 0 Chad 33 700 0 0 0 0 0 ­1 Chile 2,957 7,952 ­249 63 489 580 1,773 ­278 China 35,849 78,095 0 42,861 317 1,705 4,696 5,795 Hong Kong, China .. 42,891 .. .. .. .. .. .. Colombia 968 6,463 165 ­30 1,008 642 1,250 ­789 Congo, Dem. Rep. 122 180 0 0 0 0 0 ­6 Congo, Rep. 125 344 0 0 0 0 ­50 0 Costa Rica 337 1,469 0 0 ­4 ­25 ­9 251 Côte d'Ivoire 211 315 1 48 0 0 14 0 Croatia 114 3,376 4 411 0 ­280 265 3,745 Cuba .. .. .. .. .. .. .. .. Czech Republic 2,568 6,021 .. .. .. .. .. .. Denmark 4,139 3,343 .. .. .. .. .. .. Dominican Republic 414 1,183 0 0 0 716 ­31 ­429 Ecuador 452 271 13 0 0 ­740 63 434 Egypt, Arab Rep. 598 10,043 0 502 0 0 ­311 ­250 El Salvador 38 204 0 0 0 504 ­31 290 Eritrea 37 4 0 0 0 0 0 0 Estonia 201 1,600 .. .. .. .. .. .. Ethiopia 14 364 0 0 0 0 ­48 ­45 Finland 1,044 5,311 .. .. .. .. .. .. France 23,736 81,045 .. .. .. .. .. .. Gabon ­315 268 0 0 0 0 ­75 21 Gambia, The 8 82 0 0 0 0 0 0 Georgia 6 1,060 0 118 0 0 0 37 Germany 11,985 43,410 .. .. .. .. .. .. Ghana 107 435 0 0 0 0 38 9 Greece 1,053 5,401 .. .. .. .. .. .. Guatemala 75 354 0 0 44 0 ­32 ­25 Guinea 1 108 0 0 0 0 ­15 0 Guinea-Bissau 0 42 0 0 0 0 0 0 Haiti 7 160 0 0 0 0 0 0 352 2008 World Development Indicators 6.10 GLOBAL LINKS Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 50 385 0 0 ­13 0 38 17 Hungary 4,804 6,098 ­62 917 2,120 6,315 781 30,327 India 2,144 17,453 1,591 9,549 286 3,206 967 12,892 Indonesia 4,346 5,580 1,493 1,898 2,248 3,784 55 992 Iran, Islamic Rep. 17 901 0 0 0 0 ­115 ­158 Iraq .. .. .. .. .. .. .. .. Ireland 1,447 ­882 .. .. .. .. .. .. Israel 1,351 14,302 .. .. .. .. .. .. Italy 4,842 38,884 .. .. .. .. .. .. Jamaica 147 882 0 0 13 880 15 27 Japan 39 ­6,784 .. .. .. .. .. .. Jordan 13 3,219 0 144 0 ­1 ­201 ­11 Kazakhstan 964 6,143 0 2,797 0 6,219 240 19,549 Kenya 32 51 6 2 0 0 ­163 ­69 Korea, Dem. Rep. .. .. .. .. .. .. .. .. Korea, Rep. 1,776 3,645 .. .. .. .. .. .. Kuwait 7 110 .. .. .. .. .. .. Kyrgyz Republic 96 182 0 0 0 0 0 82 Lao PDR 95 187 0 0 0 0 0 15 Latvia 180 1,664 0 22 43 240 3 3,987 Lebanon 35 2,794 0 551 350 834 333 ­36 Lesotho 275 78 0 0 0 0 12 ­8 Liberia 5 ­82 0 0 0 0 0 0 Libya .. .. .. .. .. .. .. .. Lithuania 73 1,812 6 72 0 1,256 55 3,222 Macedonia, FYR 9 351 0 77 0 0 0 ­61 Madagascar 10 230 0 0 0 0 ­4 ­3 Malawi 6 30 0 0 0 0 ­23 ­2 Malaysia 4,178 6,064 0 2,392 2,440 363 1,231 1,822 Mali 111 185 0 6 0 0 0 1 Mauritania 7 ­3 0 0 0 0 0 ­2 Mauritius 19 107 22 32 150 0 126 ­102 Mexico 9,526 19,222 519 2,805 3,758 ­9,727 1,401 5,747 Moldova 26 242 ­1 2 0 ­6 24 137 Mongolia 10 344 0 0 0 0 ­14 15 Morocco 92 2,699 20 ­309 0 0 158 ­825 Mozambique 45 154 0 0 0 0 24 0 Myanmar 280 279 0 0 0 0 36 ­8 Namibia .. .. .. .. .. .. .. .. Nepal 19 ­7 0 0 0 0 ­5 0 Netherlands 12,206 7,197 .. .. .. .. .. .. New Zealand 3,316 7,941 .. .. .. .. .. .. Nicaragua 89 282 0 0 0 0 ­81 ­9 Niger 7 20 0 1 0 0 ­24 ­7 Nigeria 1,079 5,445 0 0 0 ­1,442 ­448 ­60 Norway 2,393 4,653 .. .. .. .. .. .. Oman 46 952 0 1,020 0 25 ­15 505 Pakistan 723 4,273 10 1,152 0 1,050 317 ­233 Panama 223 2,574 0 0 0 186 ­12 ­10 Papua New Guinea 455 32 0 0 ­32 0 ­311 ­110 Paraguay 103 189 0 0 0 0 ­16 ­18 Peru 2,557 3,467 171 182 0 ­90 43 151 Philippines 1,478 2,345 0 2,388 1,110 1,734 ­215 ­2,725 Poland 3,659 19,198 219 ­2,134 250 3,036 228 13,987 Portugal 685 7,366 .. .. .. .. .. .. Puerto Rico .. .. .. .. .. .. .. .. 2008 World Development Indicators 353 6.10 Global private financial flows Equity flows Debt flows $ millions $ millions Foreign direct investment Portfolio equity Bonds Commercial bank and other lending 1995 2006 1995 2006 1995 2006 1995 2006 Romania 419 11,394 0 301 0 0 413 4,800 Russian Federation 2,065 30,827 46 6,149 ­810 12,175 444 14,591 Rwanda 2 11 0 0 0 0 0 0 Saudi Arabia ­1,875 660 .. .. .. .. .. .. Senegal 32 58 4 0 0 0 ­25 18 Serbia 45b 5,128 0b 0 0b 0 0b 3,786 Sierra Leone 7 59 0 0 0 0 ­28 0 Singapore 11,566 24,191 .. .. .. .. .. .. Slovak Republic 236 4,165 ­16 0 0 ­351 245 2,271 Slovenia 150 649 .. .. .. .. .. .. Somalia 1 96 0 0 0 0 0 0 South Africa 1,248 ­120 2,914 14,959 731 1,576 748 ­553 Spain 8,086 20,167 .. .. .. .. .. .. Sri Lanka 56 480 0 ­304 0 0 103 ­83 Sudan 12 3,534 0 ­35 0 0 0 0 Swaziland 52 36 1 0 0 0 0 6 Sweden 14,939 27,299 .. .. .. .. .. .. Switzerland 4,158 27,185 .. .. .. .. .. .. Syrian Arab Republic 100 600 0 0 0 0 ­5 ­1 Tajikistan 10 339 0 0 0 0 0 3 Tanzania 120 474 0 3 0 0 15 1 Thailand 2,068 9,010 2,123 5,300 2,123 ­2,036 3,702 3,729 Timor-Leste .. .. .. .. .. .. .. .. Togo 26 57 0 14 0 0 0 0 Trinidad and Tobago 299 940 .. .. .. .. .. .. Tunisia 264 3,270 12 65 588 ­301 ­96 36 Turkey 885 20,070 195 1,939 627 4,773 174 28,627 Turkmenistan 233 731 0 0 0 0 20 ­76 Uganda 121 392 0 19 0 0 ­9 ­1 Ukraine 267 5,604 0 322 ­200 360 ­19 9,118 United Arab Emirates .. .. .. .. .. .. .. .. United Kingdom 21,731 139,745 .. .. .. .. .. .. United States 57,800 180,580 .. .. .. .. .. .. Uruguay 157 1,346 0 ­2 144 320 39 ­233 Uzbekistan ­24 164 0 0 0 0 201 ­460 Venezuela, RB 985 ­543 270 41 ­468 ­4,738 ­247 ­355 Vietnam 1,780 2,315 0 0 0 ­26 356 ­41 West Bank and Gaza .. .. .. .. .. .. .. .. Yemen, Rep. ­218 1,121 0 0 0 0 ­2 7 Zambia 97 575 0 2 0 0 ­37 221 Zimbabwe 118 40 0 0 ­30 0 140 ­10 World 328,368 s 1,352,442 s .. s .. s .. s .. s .. s .. s Low income 7,878 41,711 1,597 10,793 224 2,788 944 12,182 Middle income 96,122 325,781 12,198 94,056 23,114 24,633 27,003 164,242 Lower middle income 52,300 142,109 3,997 53,211 6,959 7,151 9,938 12,478 Upper middle income 43,822 183,673 8,201 40,845 16,155 17,482 17,066 151,765 Low & middle income 104,001 367,492 13,794 104,849 23,338 27,421 27,947 176,424 East Asia & Pacific 50,798 104,972 3,616 54,837 8,206 5,525 9,529 9,482 Europe & Central Asia 14,598 124,581 392 11,085 1,958 33,902 3,084 139,815 Latin America & Carib. 30,202 70,457 5,216 11,440 11,311 ­16,952 13,225 18,969 Middle East & N. Africa 952 27,503 32 1,971 660 557 534 ­2,080 South Asia 2,931 22,916 1,585 10,428 286 4,256 1,362 12,556 Sub-Saharan Africa 4,520 17,063 2,954 15,088 851 5,802 213 ­2,316 High income 224,367 984,950 .. .. .. .. .. .. Euro area 78,196 400,472 .. .. .. .. .. .. a. Includes Luxembourg. b. Includes Montenegro. 354 2008 World Development Indicators 6.10 GLOBAL LINKS Global private financial flows About the data Definitions Private fi nancial fl ows account for the bulk of countries. In addition, FDI data capture only cross- · Foreign direct investment is net inflows of invest- development finance and are split into two broad border investment flows involving equity participation ment to acquire a lasting interest in or management categories--equity and debt. Equity flows comprise and thus omit nonequity crossborder transactions control over an enterprise operating in an economy foreign direct investment (FDI) and portfolio equity. such as intrafirm flows of goods and services. For other than that of the investor. It is the sum of equity Debt flows are financing raised through bond issu- a detailed discussion of the data issues, see the capital, reinvestment of earnings, other long-term ance, bank lending, and supplier credits. World Bank's World Debt Tables 1993­94 (vol. 1, capital, and short-term capital, as shown in the The data on FDI and portfolio equity are based on chap. 3). balance of payments. · Portfolio equity includes balance of payments data reported by the Interna- Statistics on bonds, bank lending, and supplier net inflows from equity securities other than those tional Monetary Fund (IMF). These data are supple- credits are produced by aggregating individual trans- recorded as direct investment and including shares, mented by staff estimates using data from the United actions of public and publicly guaranteed debt and stocks, depository receipts and direct purchases of Nations Conference on Trade and Development and private nonguaranteed debt. Data on public and pub- shares in local stock markets by foreign investors official national sources for FDI data and from market licly guaranteed debt are reported through the Debtor · Bonds are securities issued with a fixed rate of sources for portfolio equity data. Reporting System by World Bank member economies interest for a period of more than one year. They Under the internationally accepted definition of FDI, that have received either loans from the International include net flows through cross-border public and provided in the fifth edition of the IMF's Balance of Bank for Reconstruction and Development or cred- publicly guaranteed and private nonguaranteed Payments Manual (1993), FDI has three components: its from the International Development Association. bond issues. · Commercial bank and other lending equity investment, reinvested earnings, and short- These reports are cross-checked with data reported includes net commercial bank lending (public and and long-term loans between parent firms and foreign from market sources that also provide transactional publicly guaranteed and private nonguaranteed) and affiliates. Distinguished from other kinds of interna- data. Information on private nonguaranteed bonds other private credits. tional investment, FDI is made to establish a last- and bank lending is collected from market sources, ing interest in or effective management control over because official national sources reporting to the an enterprise in another country. As a guideline the Debtor Reporting System are not asked to report the IMF suggests that investments should account for breakdown between private nonguaranteed bonds at least 10 percent of voting stock to be counted as and private nonguaranteed loans. FDI. In practice many countries set a higher thresh- The volume of global private financial flows reported old. Also, many countries fail to report reinvested by the World Bank generally differs from that reported earnings, and the definition of long-term loans differs by other sources because of differences in sources, among countries. classifi cation of economies, and method used to FDI data do not give a complete picture of inter- adjust and disaggregate reported information. In national investment in an economy. Balance of addition, particularly for debt financing, differences payments data on FDI do not include capital raised may also result based on whether particular install- locally, which has become an important source of ments of the transactions are included and how cer- financing for investment projects in some developing tain offshore issuances are treated. Financial integration of low-income economies remains marginal 6.10a Net private financial flows ($ billions) Low-income Lower middle-income Upper middle-income 700 600 500 400 300 200 Data sources 100 Data on equity and debt flows are compiled from a 0 variety of public and private sources, including the 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 World Bank's Debtor Reporting System, the IMF's Since 2002 net private financial flows to developing countries have risen sharply, driven by increased International Financial Statistics and Balance of foreign direct investment. However, financial integration of low-income economies remains marginal. Payments databases, and Dealogic. These data Note: Net private financial flows are the sum of net flows of foreign direct investment, portfolio equity, bonds, and are also published in the World Bank's Global commercial bank and other lending. Source: World Bank Debtor Reporting System. Development Finance 2008. 2008 World Development Indicators 355 6.11 Net official financial flows Total International financial institutions United Nationsa $ millions $ millions Regional From IMF development banksa $ millions From bilateral multilateral World Bank Conces- Non- Conces- Non- Other sources sourcesa,b IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA WFP Others 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Afghanistan 39.7 163.1 37.0 0.0 0.0 0.0 66.2 0.0 0.1 18.5 0.0 1.5 39.8 Albania 25.6 97.3 40.9 0.0 ­7.6 3.6 0.0 17.2 37.5 1.1 0.0 0.0 4.6 Algeria ­8,452.4 ­2,262.6 0.0 ­661.0 0.0 0.0 0.0 ­234.1 ­1,377.7 1.3 0.0 2.2 6.7 Angola ­685.3 42.1 12.4 0.0 0.0 0.0 1.7 ­1.6 ­6.0 10.2 0.0 1.8 23.6 Argentina ­1.2 ­10,548.3 0.0 ­674.6 0.0 ­9,793.3 0.0 ­85.0 0.0 0.6 0.0 0.0 4.0 Armenia 9.1 49.1 57.7 ­0.7 ­21.5 0.0 0.0 ­7.9 7.6 0.8 0.0 1.2 11.9 Australia Austria Azerbaijan ­18.6 48.2 56.3 5.4 ­24.1 ­13.1 4.0 7.2 ­1.3 1.5 0.0 1.7 10.6 Bangladesh 115.6 639.9 225.7 0.0 150.0 0.0 106.1 81.8 23.9 11.8 0.0 5.1 35.5 Belarus 19.3 ­14.1 0.0 ­10.3 0.0 0.0 0.0 ­7.9 0.0 0.6 0.0 0.0 3.5 Belgium Benin ­16.5 52.2 24.2 0.0 1.3 0.0 17.5 ­0.2 ­9.2 4.3 0.0 2.5 11.8 Bolivia 57.4 9.1 22.4 0.0 0.0 0.0 42.0 ­54.7 ­11.0 1.5 0.0 2.9 6.0 Bosnia and Herzegovina ­26.4 11.1 22.9 ­23.6 0.0 ­43.9 0.0 2.5 38.7 1.1 0.0 0.0 13.4 Botswana ­7.4 ­23.8 ­0.5 ­1.1 0.0 0.0 ­2.2 ­8.9 ­17.2 1.0 0.0 0.0 5.1 Brazil ­2,658.8 2,255.6 0.0 1,460.1 0.0 0.0 0.0 794.0 ­9.7 2.2 0.0 0.0 9.0 Bulgaria 20.0 ­686.1 0.0 ­237.3 0.0 ­346.1 0.0 ­7.1 ­95.6 .. .. .. 0.0 Burkina Faso 29.1 170.4 28.1 0.0 19.0 0.0 48.2 0.0 43.6 7.3 0.0 2.4 21.8 Burundi 0.0 69.1 12.5 0.0 21.0 0.0 9.3 0.0 1.2 8.0 0.0 1.5 15.6 Cambodia 67.6 94.9 15.1 0.0 0.0 0.0 51.0 0.0 4.3 5.0 0.0 2.7 16.8 Cameroon ­84.6 1.2 20.7 ­38.7 ­17.3 0.0 19.5 ­15.4 10.5 3.6 0.0 1.8 16.5 Canada Central African Republic 0.0 ­24.8 ­46.7 0.0 ­6.0 10.2 0.0 0.0 0.0 2.7 0.0 3.7 11.3 Chad 23.6 65.7 20.5 ­4.7 ­15.6 0.0 27.7 0.0 16.3 5.8 0.0 3.4 12.3 Chile ­19.6 70.8 ­0.7 56.8 0.0 0.0 ­1.0 13.7 0.0 0.4 0.0 0.0 1.6 China 81.0 868.6 ­208.2 233.2 0.0 0.0 0.0 800.5 ­5.3 11.1 0.0 0.0 37.3 Hong Kong, China .. .. .. .. .. .. .. .. .. .. .. .. .. Colombia ­101.2 1,224.1 ­0.7 683.9 0.0 0.0 ­14.3 698.2 ­151.9 1.1 0.0 1.7 6.1 Congo, Dem. Rep. ­114.6 163.1 106.4 0.0 0.0 0.0 3.1 0.0 ­13.8 26.1 0.0 2.0 39.3 Congo, Rep. ­18.3 1.5 3.1 0.0 7.5 0.0 ­0.9 ­17.2 ­2.3 1.7 0.0 0.6 9.0 Costa Rica 0.1 ­114.9 ­0.2 ­5.7 0.0 0.0 ­11.6 ­44.2 ­57.5 0.6 0.0 0.0 3.7 Côte d'Ivoire 12.5 ­12.6 0.0 0.0 ­57.5 0.0 0.0 ­1.4 19.1 6.4 0.0 3.3 17.5 Croatia ­134.4 454.2 0.0 169.5 0.0 0.0 0.0 94.5 185.1 0.3 0.0 0.0 4.8 Cuba .. 8.3 .. .. .. .. .. .. .. 0.7 0.0 3.4 4.2 Czech Republic .. .. .. .. .. .. .. .. .. .. .. .. .. Denmark Dominican Republic 244.3 111.6 ­0.7 32.7 0.0 37.0 ­21.0 59.1 ­0.6 1.0 0.0 0.0 4.1 Ecuador ­247.0 284.1 ­1.1 ­53.5 0.0 ­58.3 ­26.5 26.4 391.3 1.0 0.0 1.0 3.8 Egypt, Arab Rep. ­954.3 ­62.9 14.4 42.0 0.0 0.0 1.5 ­34.2 ­109.5 3.0 0.0 1.1 18.8 El Salvador ­37.7 50.7 ­0.8 ­19.1 0.0 0.0 ­23.1 24.5 61.5 0.8 0.0 1.0 5.9 Eritrea 2.4 48.6 19.5 0.0 0.0 0.0 5.1 0.0 4.3 3.9 0.0 0.7 15.1 Estonia .. .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 74.1 327.6 156.5 0.0 0.0 0.0 35.2 ­18.5 59.0 25.8 0.0 16.0 53.6 Finland France Gabon 14.4 ­0.8 0.0 ­11.8 0.0 ­14.4 ­0.2 ­1.1 20.9 0.6 0.0 0.0 5.2 Gambia, The 0.8 43.9 7.7 0.0 ­4.0 0.0 6.7 0.0 24.9 1.2 0.0 1.4 6.0 Georgia ­66.0 42.4 72.3 0.0 ­8.0 0.0 0.0 3.1 ­35.0 1.0 0.0 1.2 7.8 Germany Ghana ­66.2 413.9 231.6 0.0 116.4 0.0 47.8 ­16.7 6.4 4.5 0.0 2.4 21.5 Greece Guatemala ­62.1 427.8 0.0 162.6 0.0 0.0 ­18.6 83.9 181.6 1.0 0.0 4.9 12.4 Guinea ­40.6 ­15.7 ­1.8 0.0 ­19.4 0.0 9.5 ­7.3 ­24.2 4.4 0.0 4.3 18.8 Guinea-Bissau ­10.5 9.1 ­0.3 0.0 ­3.8 0.0 0.8 0.0 1.9 2.0 0.0 1.7 6.8 Haiti ­4.3 65.8 ­9.1 0.0 ­4.4 14.9 43.5 0.0 ­1.3 2.8 0.0 2.8 16.6 356 2008 World Development Indicators 6.11 GLOBAL LINKS Net official financial flows Total International financial institutions United Nationsa $ millions $ millions Regional From IMF development banksa $ millions From bilateral multilateral World Bank Conces- Non- Conces- Non- Other sources sourcesa,b IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA WFP Others 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Honduras ­13.4 156.3 49.9 0.0 15.0 0.0 76.5 ­19.0 24.1 1.0 0.0 0.6 8.2 Hungary ­33.2 134.2 0.0 ­39.0 0.0 0.0 0.0 162.4 10.8 .. .. .. 0.0 India 703.1 1,553.5 239.2 606.2 0.0 0.0 0.0 564.0 31.1 38.0 0.0 9.6 65.4 Indonesia 154.3 ­8,026.3 288.3 ­706.4 0.0 ­8,037.1 75.8 242.0 0.0 7.7 0.0 67.1 36.3 Iran, Islamic Rep. ­36.4 190.0 0.0 181.2 0.0 0.0 0.0 0.0 ­9.0 2.1 0.0 0.2 15.5 Iraq .. 12.6 .. .. .. .. .. .. .. 2.2 0.0 0.6 9.8 Ireland Israel .. .. .. .. .. .. .. .. .. .. .. .. .. Italy Jamaica ­87.4 ­52.9 0.0 ­25.3 0.0 0.0 ­5.3 ­34.7 10.1 0.8 0.0 0.0 1.5 Japan Jordan ­89.1 42.9 ­2.6 ­35.1 0.0 ­88.4 0.0 0.0 63.5 0.8 100.8 0.4 3.5 Kazakhstan 30.6 ­47.9 0.0 ­101.1 0.0 0.0 ­0.9 ­3.6 51.8 1.1 0.0 0.0 4.8 Kenya 12.0 ­102.4 ­18.4 0.0 ­13.6 0.0 9.8 ­8.4 ­126.4 6.9 0.0 14.0 33.7 Korea, Dem. Rep. .. 13.9 .. .. .. .. .. .. .. 1.7 0.0 1.8 10.4 Korea, Rep. .. .. .. .. .. .. .. .. .. .. .. .. .. Kuwait .. .. .. .. .. .. .. .. .. .. .. .. .. Kyrgyz Republic 8.8 35.6 19.4 0.0 ­23.7 0.0 40.5 ­8.8 ­0.2 1.1 0.0 0.0 7.3 Lao PDR ­20.4 92.5 16.9 0.0 ­3.3 0.0 57.9 5.0 ­2.7 2.0 0.0 3.0 13.7 Latvia ­0.5 95.2 0.0 ­19.0 0.0 0.0 0.0 ­0.8 115.0 .. .. .. 0.0 Lebanon ­54.1 0.5 0.0 ­52.5 0.0 0.0 0.0 0.0 ­15.6 2.0 62.0 0.0 4.6 Lesotho ­8.6 16.8 5.6 ­3.6 ­0.5 0.0 8.2 ­1.0 ­1.2 1.1 0.0 2.5 5.7 Liberia 0.0 27.2 0.0 0.0 0.0 ­0.7 0.0 0.0 0.0 4.1 0.0 3.1 20.7 Libya .. 2.0 .. .. .. .. .. .. .. 0.0 0.0 0.4 1.6 Lithuania ­158.4 ­26.0 0.0 ­8.6 0.0 0.0 0.0 ­2.8 ­14.6 .. .. .. 0.0 Macedonia, FYR ­30.0 28.7 1.8 ­0.3 ­8.5 ­1.0 0.0 12.7 16.5 0.6 0.0 0.0 6.9 Madagascar 8.4 266.1 162.2 0.0 11.6 0.0 60.1 0.0 4.2 6.0 0.0 2.9 19.1 Malawi ­0.9 64.0 20.3 0.0 3.7 ­6.4 16.9 ­1.8 ­0.7 7.8 0.0 4.4 19.8 Malaysia ­278.3 ­131.3 0.0 ­96.3 0.0 0.0 0.0 ­54.6 14.1 0.6 0.0 0.0 4.9 Mali 20.6 155.4 93.4 0.0 5.9 0.0 22.6 0.0 4.0 9.5 0.0 2.5 17.5 Mauritania 3.6 118.7 42.0 0.0 ­23.0 0.0 6.9 ­7.6 82.9 1.8 0.0 4.7 11.0 Mauritius ­50.3 ­35.3 ­0.6 ­7.3 0.0 0.0 ­0.1 ­29.1 ­1.0 0.0 0.0 0.0 2.8 Mexico ­272.7 ­8,302.2 0.0 ­4,671.1 0.0 0.0 0.0 ­3,641.6 0.0 0.8 0.0 0.0 9.7 Moldova ­19.4 53.2 22.6 ­13.9 59.8 ­20.1 0.0 ­5.4 1.2 0.8 0.0 0.0 8.2 Mongolia 10.5 45.8 10.4 0.0 ­6.0 0.0 23.3 0.0 7.3 0.9 0.0 0.0 9.9 Morocco 23.2 448.7 ­1.4 ­154.0 0.0 0.0 ­0.8 364.0 231.0 1.6 0.0 0.0 8.3 Mozambique ­5.7 411.3 215.3 0.0 4.8 0.0 102.4 20.0 11.0 9.5 0.0 8.5 39.8 Myanmar ­51.1 33.9 0.0 0.0 0.0 0.0 0.0 0.0 ­2.2 9.9 0.0 1.1 25.1 Namibia .. 8.4 .. .. .. .. .. .. .. 1.5 0.0 1.0 5.9 Nepal ­31.2 150.8 12.4 0.0 21.2 0.0 75.2 0.0 1.2 6.3 0.0 7.5 27.0 Netherlands New Zealand Nicaragua 8.4 295.3 56.3 0.0 61.5 0.0 111.9 ­8.8 61.0 1.3 0.0 0.6 11.5 Niger ­10.4 3.1 46.6 0.0 ­105.5 0.0 21.6 ­2.5 6.1 12.2 0.0 7.3 17.3 Nigeria ­4,336.1 133.8 342.7 ­210.8 0.0 0.0 7.3 ­78.8 0.0 31.1 0.0 0.0 42.3 Norway Oman 14.6 ­39.6 0.0 0.0 0.0 0.0 0.0 0.0 ­41.3 0.1 0.0 0.0 1.6 Pakistan ­49.7 1,274.8 688.3 ­128.6 ­78.9 ­28.2 105.8 448.3 172.8 14.9 0.0 10.9 69.5 Panama ­11.2 44.0 0.0 ­26.2 0.0 ­9.8 ­7.9 70.0 13.4 0.4 0.0 0.3 3.8 Papua New Guinea ­15.8 ­27.2 ­3.6 ­5.6 0.0 0.0 6.1 ­29.3 ­2.6 1.9 0.0 0.0 5.9 Paraguay ­20.8 14.5 ­1.5 5.8 0.0 0.0 ­15.1 21.2 ­0.4 1.0 0.0 0.0 3.5 Peru ­305.3 ­144.1 0.0 ­182.5 0.0 ­39.4 ­8.2 202.3 ­144.7 1.5 0.0 0.6 26.3 Philippines ­213.0 ­192.4 ­6.8 ­250.8 0.0 ­400.3 ­25.0 468.3 ­2.4 3.3 0.0 1.7 19.6 Poland ­1,991.5 15.3 0.0 15.3 0.0 0.0 0.0 0.0 0.0 .. .. .. 0.0 Portugal Puerto Rico 2008 World Development Indicators 357 6.11 Net official financial flows Total International financial institutions United Nationsa $ millions $ millions Regional From IMF development banksa $ millions From bilateral multilateral World Bank Conces- Non- Conces- Non- Other sources sourcesa,b IDA IBRD sional concessional sional concessional institutions UNICEF UNRWA WFP Others 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Romania 17.9 ­44.6 0.0 ­54.3 0.0 ­167.3 6.8 ­32.9 203.1 .. .. .. 0.0 Russian Federation ­25,232.5 ­221.1 0.0 ­369.6 0.0 0.0 0.0 119.9 28.6 .. .. .. 0.0 Rwanda ­3.9 85.5 28.6 0.0 2.5 0.0 24.3 0.0 ­3.3 6.1 0.0 5.4 21.9 Saudi Arabia .. 2.8 .. .. .. .. .. .. .. 0.0 0.0 0.0 2.8 Senegal ­19.5 183.9 115.8 0.0 20.4 0.0 21.3 ­12.5 9.6 4.0 0.0 3.9 21.4 Serbia 7.5 ­651.5 56.5 ­250.2 0.0 ­652.9 0.0 53.9 118.2 1.0 0.0 0.0 22.0 Sierra Leone 0.0 69.3 7.2 0.0 8.9 0.0 16.7 0.0 6.4 5.3 0.0 4.8 20.0 Singapore .. .. .. .. .. .. .. .. .. .. .. .. .. Slovak Republic ­63.9 ­42.8 0.0 ­32.9 0.0 0.0 0.0 ­3.9 ­6.0 .. .. .. 0.0 Slovenia .. .. .. .. .. .. .. .. .. .. .. .. .. Somalia 0.0 24.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.4 0.0 4.4 12.8 South Africa 0.0 31.7 0.0 ­1.8 0.0 0.0 0.0 24.5 0.0 1.2 0.0 0.0 7.8 Spain Sri Lanka 86.8 70.3 57.1 0.0 0.0 ­153.6 88.4 39.1 14.4 0.9 0.0 0.8 23.2 Sudan ­41.7 229.5 ­2.0 0.0 0.0 ­27.0 0.0 0.0 136.9 17.4 0.0 55.9 48.3 Swaziland ­5.4 45.6 ­0.3 ­1.2 0.0 0.0 ­1.0 11.5 30.6 1.0 0.0 0.0 5.0 Sweden Switzerland Syrian Arab Republic ­92.5 6.1 ­1.5 0.0 0.0 0.0 0.0 0.0 ­34.8 1.6 35.2 0.9 4.7 Tajikistan 46.8 97.1 16.2 0.0 14.4 0.0 35.2 ­1.4 20.6 2.6 0.0 1.2 8.3 Tanzania 54.1 522.4 384.9 0.0 4.1 0.0 44.4 ­0.9 42.0 12.9 0.0 5.5 29.5 Thailand ­512.6 ­171.2 ­3.4 ­50.2 0.0 0.0 ­2.9 ­117.1 ­14.0 1.8 0.0 0.0 14.6 Timor-Leste .. 9.1 .. .. .. .. .. .. .. 1.6 0.0 0.2 7.3 Togo ­1.8 13.0 0.0 0.0 ­6.4 0.0 0.1 ­1.4 9.8 2.2 0.0 0.4 8.3 Trinidad and Tobago .. 0.7 .. .. .. .. .. .. .. 0.0 0.0 0.0 0.7 Tunisia ­29.4 ­174.0 ­2.1 ­254.6 0.0 0.0 0.0 ­124.9 203.2 1.0 0.0 0.0 3.4 Turkey ­323.1 ­3,496.3 ­5.9 989.1 0.0 ­4,552.0 0.0 0.0 60.0 2.0 0.0 0.0 10.5 Turkmenistan ­125.5 ­6.3 0.0 ­8.2 0.0 0.0 0.0 0.0 ­2.8 1.1 0.0 0.0 3.6 Uganda ­32.5 266.8 131.4 0.0 2.9 0.0 50.6 ­2.4 29.3 11.7 0.0 9.7 33.6 Ukraine ­279.8 ­546.7 0.0 ­85.8 0.0 ­410.5 0.0 ­48.2 ­13.0 1.4 0.0 0.0 9.4 United Arab Emirates .. .. .. .. .. .. .. .. .. .. .. .. .. United Kingdom United States Uruguay ­9.8 ­2,934.8 0.0 ­162.8 0.0 ­2,372.1 ­2.4 ­401.7 0.5 0.5 0.0 0.0 3.2 Uzbekistan ­106.4 71.1 13.4 11.2 0.0 0.0 0.2 22.8 13.3 2.5 0.0 0.0 7.7 Venezuela, RB 226.0 179.5 0.0 ­149.7 0.0 0.0 0.0 ­164.3 484.1 0.8 0.0 0.0 8.6 Vietnam 357.4 500.1 317.4 0.0 ­32.8 0.0 159.0 10.1 19.0 3.9 0.0 0.0 23.5 West Bank and Gaza .. 408.0 .. .. .. .. .. .. .. 5.0 402.0 0.9 0.1 Yemen, Rep. 45.1 172.8 129.2 0.0 ­47.1 ­13.0 0.0 0.0 71.1 5.2 0.0 6.6 20.8 Zambia ­24.6 120.2 23.1 0.0 24.3 0.0 27.9 ­14.8 11.6 5.8 0.0 16.4 25.9 Zimbabwe 12.4 22.8 0.0 0.0 ­0.4 ­3.1 0.0 0.0 1.6 2.5 0.0 8.3 13.9 World .. s .. s .. s .. s .. s .. s .. s .. s .. s 740.1 s 599.9 s 473.5 s 2,099.4 s Low income ­3,358.6 8,923.0 3,879.8 267.8 ­18.0 ­53.1 1,415.6 937.2 708.1 398.4 0.0 261.8 1,125.4 Middle income ­42,730.3 ­30,303.7 679.4 ­5,522.5 87.5 ­27,136.2 287.0 ­798.2 643.9 114.5 599.9 108.3 632.7 Lower middle income ­11,843.3 ­6,312.8 616.3 ­1,234.0 81.8 ­9,225.0 314.2 2,362.3 ­441.3 91.8 538.0 107.6 475.5 Upper middle income ­30,887.0 ­24,025.7 63.1 ­4,288.4 5.7 ­17,911.2 ­27.2 ­3,160.4 1,085.1 17.7 62.0 0.7 127.2 Low & middle income ­46,088.9 ­20,714.6 4,559.2 ­5,254.7 69.5 ­27,189.3 1,702.6 139.0 1,352.0 738.2 599.9 473.3 2,095.7 East Asia & Pacific ­421.0 ­6,844.8 430.3 ­878.3 ­42.0 ­8,437.4 345.4 1,330.9 34.7 54.7 0.0 77.5 239.4 Europe & Central Asia ­28,383.9 ­4,526.2 374.0 ­64.4 ­19.2 ­6,203.2 85.7 367.1 758.1 24.2 0.0 5.3 146.2 Latin America & Carib. ­3,235.8 ­16,719.7 127.3 ­3,572.8 105.0 ­12,224.2 161.0 ­2,454.8 896.7 23.7 0.0 20.2 198.2 Middle East & N. Africa ­9,625.6 ­1,168.0 143.8 ­934.0 ­48.7 ­101.4 1.0 ­21.4 ­997.7 27.4 599.9 14.4 148.7 South Asia 893.5 3,917.0 1,271.4 477.7 92.3 ­181.7 449.3 1,133.1 271.3 92.6 0.0 36.7 274.3 Sub-Saharan Africa ­5,316.0 4,063.2 2,212.3 ­282.9 ­17.9 ­41.3 660.2 ­216.0 389.0 293.3 0.0 217.4 849.1 High income .. 5.7 .. .. .. .. .. .. .. 1.8 0.0 0.2 3.7 Euro area a. Aggregates include amounts for economies not specified elsewhere. b. World and income group aggregates include flows not allocated by country or region. 358 2008 World Development Indicators 6.11 GLOBAL LINKS Net official financial flows About the data Definitions The table shows fi nancing from offi cial bilateral income (GNI) per capita and performance standards · Total net official financial flows are disbursements and multilateral sources. It shows concessional assessed by World Bank staff. The cutoff for IDA eligi- of public or publicly guaranteed loans and credits, and nonconcessional financial flows from the major bility is set at the beginning of the World Bank's fiscal less repayments of principal. · IDA is the Interna- multilateral institutions--the World Bank, the Inter- year. Since July 1, 2007, the GNI per capita cutoff tional Development Association, the concessional national Monetary Fund (IMF), regional development has been $1,065, measured in 2006 U.S. dollars loan window of the World Bank Group. · IBRD is the banks, other international financial institutions, and using the World Bank Atlas method (see Users guide). International Bank for Reconstruction and Develop- UN agencies. In exceptional circumstances IDA extends temporary ment, the founding and largest member of the World The multilateral development banks fund their non- eligibility to countries above the cutoff and that are Bank Group. · IMF is the International Monetary concessional lending operations primarily by selling undertaking major adjustment efforts but are not Fund, which provides concessional lending through low-interest, highly rated bonds backed by prudent creditworthy for International Bank for Reconstruction the Poverty Reduction and Growth Facility and the lending and financial policies and the strong financial and Development (IBRD) lending. Exceptions are also IMF Trust Fund and nonconcessional lending through support of their members. Funds are then on-lent made for small island economies. The IBRD lends to the credit it provides to its members, mainly to meet at slightly higher interest rates with 15- to 20-year creditworthy countries at an initial interest rate that balance of payments needs. · Regional develop- maturities to developing countries. Lending terms consists of a variable base rate of six-month LIBOR, ment banks are the African Development Bank, in vary with market conditions and bank policies. and a spread, either variable or fixed, for the life of the Tunis, Tunisia, which serves all of Africa, including Concessional flows from multilateral development loan. The lending rate is reset every six months on the North Africa; the Asian Development Bank, in Manila, banks are credits provided through their concessional interest payment dates for the loan and applies to the Philippines, which serves South and Central Asia and lending facilities. The cost of these loans is reduced interest period beginning on that date. Although some East Asia and Pacific; the European Bank for Recon- through subsidies from donors or other resources. outstanding IBRD loans have a low enough interest struction and Development, in London, United King- Grants from multilateral agencies are not included rate to be classified as concessional under the DAC dom, which serves Europe and Central Asia; and the in the net flows. Concessional flows from bilateral definition, all IBRD loans in the table are classified as Inter-American Development Bank, in Washington, donors are defined by the Organisation for Economic nonconcessional. Lending by the International Finance D.C., which serves the Americas. · Concessional Co-operation and Development's (OECD) Develop- Corporation is not included in the table. financial fl ows are disbursements made through ment Assistance Committee (DAC) as financial flows The IMF makes concessional funds available concessional lending facilities. · Nonconcessional with a grant element of at least 25 percent. The grant through its Poverty Reduction and Growth Facility and financial flows are all disbursements that are not element is evaluated assuming a 10 percent nominal the IMF Trust Fund. Eligibility is based principally on a concessional. · Other institutions is a residual cat- discount rate. The grant element is nil for a loan with country's per capita income and eligibility under IDA. egory in the World Bank's Debtor Reporting System a 10 percent interest rate and 100 percent for a Regional development banks also maintain conces- that includes other multilateral institutions such grant, which requires no repayment. sional windows. Loans from the major regional devel- as the Caribbean Development Fund, Council of All World Bank concessional lending is carried out opment banks are recorded in the table according to Europe, European Development Fund, Islamic Devel- by the International Development Association (IDA). each institution's classification and not according to opment Bank, Nordic Development Fund, and the Eligibility for IDA resources is based on gross national the DAC definition. like. · United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and While net financial flows to middle-income economies are falling, low-income Works Agency for Palestine Refugees in the Near economies are still borrowing from international financial institutions 6.11a East (UNRWA), World Food Programme (WFP), and Middle-income Low-income other UN agencies, such as the International Fund for $ billions World Bank IMF Regional development banks Other international financial institutions Agricultural Development, United Nations Develop- 40 8 ment Programme, United Nations Population Fund, 30 7 United Nations Refugee Agency, and United Nations 20 6 Regular Programme for Technical Assistance. 10 5 0 4 Data sources ­10 3 Data on net financial flows from international finan- ­20 2 cial institutions are from the World Bank's Debtor ­30 1 Reporting System and published in the World Bank's ­40 0 Global Development Finance 2008 and electroni- ­50 ­1 1990 1995 2000 2006 1990 1995 2000 2006 cally as GDF Online. Data on aid from UN agencies In recent years, as many middle-income economies paid off loans from international financial institutions, are from the DAC annual Development Cooperation net disbursement fell sharply. But international financial institutions still maintain a positive flow of net Report and are available electronically on the OECD's disbursement to low-income economies. International Development Statistics CD-ROM and at Source: World Bank Debtor Reporting System. www.oecd.org/dac/stats/idsonline. 2008 World Development Indicators 359 6.12 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 $ millions Australia 9,120 2,123 1,773 23 327 308 6,074 4,968 978 .. 129 615 Austria 3,215 1,498 1,101 ­9 407 ­448 2,045 1,613 0 .. 433 119 Belgium 5,309 1,978 1,365 ­7 620 ­434 3,514 3,533 0 .. ­19 251 Canada 14,234 3,684 2,573 ­42 1,153 356 9,093 7,717 427 .. 950 1,100 Denmark 2,686 2,236 1,525 ­61 772 ­77 454 454 0 .. .. 73 Finland 1,413 834 442 13 380 .. 553 402 137 .. 14 25 France 22,329 10,601 8,422 ­503 2,681 ­1,388 13,116 10,589 3,983 .. ­1,456 .. Germany 27,203 10,435 7,576 ­542 3,401 ­5,728 21,149 12,401 7,672 1,057 19 1,348 Greece 2,896 424 189 .. 235 8 2,454 2,454 0 .. .. 10 Ireland 5,237 1,022 632 .. 389 .. 3,877 .. 3,877 .. .. 339 Italy 5,512 3,641 2,147 ­146 1,640 ­957 2,705 1,151 ­1,049 .. 2,602 123 Japan 26,230 11,187 7,660 ­347 3,874 2,438 12,290 14,144 ­1,201 ­928 275 315 Luxembourg 299 291 205 .. 86 .. .. .. .. .. .. 8 Netherlands 28,616 5,452 4,415 ­133 1,169 343 22,544 6,351 10,728 ­248 5,713 277 New Zealand 338 259 203 .. 56 7 24 24 0 .. .. 48 Norway 4,304 2,954 2,119 79 756 5 1,345 1,351 0 .. ­6 .. Portugal 666 396 198 14 185 ­20 286 44 0 .. 243 4 Spain 11,146 3,814 2,012 80 1,722 .. 7,333 7,608 0 .. ­275 .. Sweden 4,175 3,955 2,838 14 1,103 ­2 210 333 0 .. ­123 12 Switzerland 11,306 1,646 1,241 13 392 17 9,241 10,001 0 ­239 ­521 402 United Kingdom 26,941 12,459 8,809 ­92 3,741 ­187 14,127 7,530 11,292 .. ­4,696 543 United States 90,897 23,532 22,005 ­843 2,370 ­4,017 62,345 36,624 23,662 3,156 ­1,097 9,037 Total 304,074 104,421 79,450 ­2,490 27,461 ­9,774 194,779 129,291 60,507 2,798 2,183 14,648 Official development assistance Commitmentsb Gross Net disbursementsb disbursements % of general per capitab government $ millions $ millions $ millionsb $ % of GNIa disbursementsa 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 Australia 1,793 2,058 1,545 2,058 1,545 2,058 80 100 0.27 0.30 0.72 0.82 Austria 841 1,485 649 1,476 645 1,465 80 177 0.23 0.47 0.44 0.94 Belgium 1,253 2,343 1,253 1,988 1,219 1,921 119 182 0.36 0.50 0.72 1.03 Canada 2,746 3,520 2,434 3,426 2,400 3,385 78 103 0.25 0.29 0.59 0.74 Denmark 2,390 2,051 2,549 2,249 2,523 2,173 472 399 1.06 0.80 1.93 1.58 Finland 502 947 537 824 527 820 102 156 0.31 0.40 0.63 0.81 France 6,960 14,617 7,422 12,417 6,094 10,313 103 163 0.30 0.47 0.60 0.88 Germany 8,119 13,005 8,241 11,844 7,140 10,257 87 124 0.27 0.36 0.59 0.79 Greece 354 407 354 407 354 407 32 37 0.20 0.17 0.39 0.37 Ireland 378 984 378 984 378 984 100 232 0.29 0.54 0.77 1.36 Italy 2,512 4,015 2,485 3,884 2,139 3,533 37 60 0.13 0.20 0.27 0.39 Japan 15,627 18,520 14,885 18,276 12,335 11,946 97 94 0.28 0.25 0.74 0.70 Luxembourg 191 269 191 269 191 269 433 584 0.71 0.89 1.61 1.75 Netherlands 5,305 12,343 4,975 5,757 4,833 5,329 303 326 0.84 0.81 1.84 1.76 New Zealand 212 378 200 275 200 275 52 66 0.25 0.27 0.55 0.60 Norway 1,798 3,148 2,029 2,732 2,020 2,732 450 584 0.76 0.89 1.77 2.16 Portugal 655 390 655 390 426 385 42 37 0.26 0.21 0.56 0.44 Spain 2,293 3,974 2,293 3,974 1,974 3,643 49 81 0.22 0.32 0.53 0.80 Sweden 1,907 4,141 2,386 3,854 2,386 3,854 269 423 0.80 1.02 1.30 1.85 Switzerland 1,276 1,874 1,257 1,652 1,254 1,641 175 219 0.34 0.39 1.07 1.24 United Kingdom 6,225 12,630 6,225 12,630 6,156 12,034 105 200 0.32 0.51 0.84 1.16 United States 14,215 25,920 12,246 23,834 11,223 22,863 41 76 0.10 0.18 0.30 0.49 Total 77,553 129,018 75,187 115,201 67,961 102,287 81 115 0.22 0.31 0.57 0.75 Note: Components may not sum to totals because of gaps in reporting. a. At current prices and exchange rates. b. At 2005 prices and exchange rates. 360 2008 World Development Indicators 6.12 GLOBAL LINKS Financial flows from Development Assistance Committee members About the data The flows of official and private financial resources advanced developing countries and territories. are concessional funding received by multilateral from the members of the Development Assistance This distinction has been dropped. ODA recipients institutions from DAC members as grants or capital Committee (DAC) of the Organisation for Economic now comprise all low- and middle-income countries subscriptions. · Other offi cial fl ows are transac- Co-operation and Development (OECD) to developing except those that are members of the Group of Eight tions by the official sector whose main objective is economies are compiled by DAC, based principally on or the European Union (including countries with a firm other than development or whose grant element is reporting by DAC members using standard question- date for EU accession). The content and structure less than 25 percent. · Private flows are flows at naires issued by the DAC Secretariat. of tables 6.12 through 6.15 have been revised to market terms financed from private sector resources The table shows data reported by DAC member reflect this change. Because official aid flows are in donor countries. They include changes in hold- economies and does not include aid provided by the quite small relative to ODA, the net effect of these ings of private long-term assets by reporting country Commission of the European Communities--a multi- changes is believed to be minor. residents. · Foreign direct investment is investment lateral member of DAC. Flows are transfers of resources, either in cash or by residents of DAC member countries to acquire DAC exists to help its members coordinate their in the form of commodities or services measured on a lasting management interest (at least 10 per- development assistance and to encourage the a cash basis. Short-term capital transactions (with cent of voting stock) in an enterprise operating in expansion and improve the effectiveness of the one year or less maturity) are not counted. Repay- the recipient country. The data reflect changes in aggregate resources flowing to recipient economies. ments of the principal (but not interest) of ODA loans the net worth of subsidiaries in recipient countries In this capacity DAC monitors the flow of all financial are recorded as negative flows. Proceeds from offi - whose parent company is in the DAC source country. resources, but its main concern is official develop- cial equity investments in a developing country are · Bilateral portfolio investment covers bank lending ment assistance (ODA). Grants or loans to countries reported as ODA, while proceeds from their later sale and the purchase of bonds, shares, and real estate and territories on the DAC list of aid recipients have are recorded as negative flows. by residents of DAC member countries in recipient to meet three criteria to be counted as ODA. They Because the table is based on donor country countries. · Multilateral portfolio investment are are undertaken by the official sector. They promote reports, it does not provide a complete picture of the transactions of private banks and nonbanks in DAC economic development and welfare as the main resources received by developing economies for two member countries in the securities issued by multi- objective. And they are provided at concessional reasons. First, flows from DAC members are only part lateral institutions. · Private export credits are financial terms (loans must have a grant element of of the aggregate resource flows to these economies. loans extended to recipient countries by the private at least 25 percent, calculated at a discount rate of Second, the data that record contributions to multi- sector in DAC member countries to promote trade; 10 percent). The DAC Statistical Reporting Directives lateral institutions measure the flow of resources they may be supported by an official guarantee. · Net provide the most detailed explanation of this defini- made available to those institutions by DAC mem- grants by nongovernmental organizations (NGOs) tion and all ODA-related rules. bers, not the flow of resources from those institu- are private grants by NGOs, net of subsidies from This definition excludes nonconcessional fl ows tions to developing and transition economies. the official sector. · Commitments are obligations, from official creditors, which are classified as "other Aid as a share of gross national income (GNI), aid expressed in writing and backed by funds, undertaken official flows," and aid for military purposes. Transfer per capita, and ODA as a share of the general gov- by an official donor to provide specified assistance payments to private individuals, such as pensions, ernment disbursements of the donor are calculated to a recipient country or multilateral organization. reparations, and insurance payouts, are in general by the OECD. The denominators used in calculating · Gross disbursements are the international trans- not counted. In addition to financial flows, technical these ratios may differ from corresponding values fer of financial resources and goods and services, cooperation is included in ODA. Most expenditures elsewhere in this book because of differences in tim- valued at the cost to the donor. for peacekeeping under UN mandates and assis- ing or definitions. tance to refugees are counted in ODA. Also included Definitions are contributions to multilateral institutions, such as the United Nations and its specialized agencies, · Net disbursements are gross disbursements of and concessional funding to multilateral develop- grants and loans minus repayments of principal on ment banks. earlier loans. · Total net fl ows comprise ODA or DAC has revised the list of countries and territories official aid flows, other official flows, private flows, Data sources that are counted as aid recipients. These revisions and net grants by nongovernmental organizations. will govern aid reporting for three years, starting with · Offi cial development assistance comprises Data on financial flows are compiled by OECD- 2005 flows. In the past DAC distinguished aid going flows that meet the DAC definition of ODA and are DAC and published in its annual statistical report, to Part I and Part II countries. Part I countries, the made to countries and territories on the DAC list of Geographical Distribution of Financial Flows to Aid recipients of ODA, comprised many of the countries aid recipients. · Bilateral grants are transfers of Recipients, and its annual Development Coop- classified by the World Bank as low- and middle- money or in kind for which no repayment is required. eration Report. Data are available electronically income economies. Part II countries, whose assis- · Bilateral loans are loans extended by governments on the OECD's International Development Statis- tance was designated official aid, included the more or official agencies that have a grant element of at tics CD-ROM and at www.oecd.org/dac/stats/ advanced countries of Central and Eastern Europe, least 25 percent (calculated at a 10 percent discount idsonline. countries of the former Soviet Union, and certain rate). · Contributions to multilateral institutions 2008 World Development Indicators 361 6.13 Allocation of bilateral aid from Development Assistance Committee members 6.13a 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 2006 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 Australia 758 1,796 27.8 21.6 55.1 48.0 1.1 15.4 9.7 10.6 6.2 4.3 Austria 273 1,092 28.7 6.8 41.8 19.4 20.4 69.3 2.7 1.5 6.4 2.9 Belgium 477 1,357 33.6 10.0 46.9 50.3 6.6 29.3 5.4 6.4 7.5 4.0 Canada 1,160 2,531 39.6 44.2 43.0 27.3 1.1 10.3 5.0 9.1 11.4 9.0 Denmark 1,024 1,464 65.8 63.3 25.3 11.1 1.0 7.7 0.0 10.3 8.0 7.6 Finland 217 455 40.8 54.6 41.4 22.6 0.0 0.0 10.5 15.5 7.2 7.4 France 2,829 7,919 25.4 9.5 50.6 41.4 17.0 44.2 0.4 0.6 6.7 4.3 Germany 2,687 7,034 16.8 8.9 63.8 44.9 6.6 37.8 4.1 5.1 8.7 3.2 Greece 99 189 69.6 29.8 23.8 49.9 0.0 0.0 6.4 10.2 0.2 10.1 Ireland 154 632 79.1 75.8 0.4 4.9 0.0 0.0 15.5 13.8 5.1 5.4 Italy 377 2,001 10.2 4.8 8.1 8.6 57.5 80.2 18.3 3.7 5.9 2.8 Japan 9,768 7,313 60.4 19.2 24.9 25.4 4.2 43.8 0.9 2.5 9.5 9.1 Luxembourg 99 205 84.4 71.9 3.2 3.6 0.8 0.0 10.4 18.1 1.2 6.4 Netherlands 2,243 4,282 41.1 62.8 33.7 14.7 6.8 7.3 9.1 9.3 9.4 5.9 New Zealand 85 203 39.7 51.7 48.1 29.7 0.0 0.0 3.4 10.6 8.8 8.0 Norway 934 2,198 57.9 57.1 23.0 20.4 1.0 1.0 11.3 14.1 6.9 7.5 Portugal 179 211 30.4 34.5 50.4 55.9 14.6 0.2 1.9 3.3 2.7 6.1 Spain 720 2,092 69.3 39.7 17.9 24.3 2.3 24.6 3.7 6.6 6.8 4.8 Sweden 1,242 2,852 60.9 61.6 13.6 11.0 3.1 10.3 14.6 10.3 7.7 6.8 Switzerland 627 1,254 58.6 49.8 19.4 24.2 0.9 7.8 20.2 14.0 0.9 4.1 United Kingdom 2,710 8,718 47.7 34.7 25.5 10.0 5.7 40.2 12.7 9.6 8.4 5.5 United States 7,405 21,162 14.6 26.5 64.4 46.2 1.7 8.0 9.6 14.3 9.7 5.1 Total 36,064 76,960 40.5 29.1 39.4 31.6 5.4 25.0 6.1 8.8 8.6 5.5 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 6.12. 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 tion or training at home or abroad, and payments and other activities with finance and commodity its annual statistical report, Geographical Distribu- to consultants, advisers, and similar personnel and supply. · Technical cooperation is the provision of tion of Financial Flows to Aid Recipients, and its to teachers and administrators serving in recipient resources whose main aim is to augment the stock of annual Development Cooperation Report. Data are countries. Technical cooperation is spent mostly in human intellectual capital, such as the level of knowl- available electronically on the OECD's International the donor economy. edge, skills, and technical know-how in the recipient Development Statistics CD-ROM and at www.oecd. Two other types of aid are presented because they country (including the cost of associated equipment). org/dac/stats/idsonline. serve distinctive purposes. Debt-related aid aims to Contributions take the form mainly of the supply of 362 2008 World Development Indicators 6.13 GLOBAL LINKS Allocation of bilateral aid from Development Assistance Committee members 6.13b 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 commitment (%) 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Australia 68.7 53.2 8.6 11.5 2.5 0.4 24.8 8.0 1.8 4.1 7.5 .. Austria 24.8 19.8 9.8 1.7 0.4 1.9 5.3 3.0 0.2 0.9 2.0 89.5 Belgium 53.6 39.0 11.0 6.7 1.7 4.0 10.4 11.4 2.1 4.5 3.1 90.7 Canada 61.5 44.6 11.9 8.0 1.9 0.7 18.3 9.6 1.0 5.5 7.3 62.9 Denmark 55.2 32.4 1.2 7.8 2.2 10.8 9.0 17.6 4.5 5.3 5.2 95.3 Finland 67.7 39.5 5.7 8.3 1.4 7.6 14.8 14.2 1.6 4.1 14.0 86.5 France 47.4 29.5 18.0 2.7 0.0 2.4 1.4 8.4 4.3 1.7 9.5 95.6 Germany 60.1 34.5 14.5 2.6 2.4 5.3 7.2 19.9 3.2 3.8 5.7 93.3 Greece 75.0 59.1 12.5 12.7 4.2 0.5 24.7 10.1 7.6 1.2 5.7 39.1 Ireland 67.2 56.2 10.1 12.1 12.6 2.7 13.2 5.9 0.6 4.3 5.0 100.0 Italy 29.0 12.1 1.7 3.8 0.2 2.2 1.6 12.2 5.9 1.2 4.7 77.0 Japan 61.1 22.6 6.8 2.3 0.1 9.4 2.8 34.5 16.4 4.5 4.0 95.6 Luxembourg 67.4 50.2 16.0 15.9 6.3 5.4 3.1 8.0 1.2 2.9 9.2 100.0 Netherlands 47.1 35.7 17.5 5.3 1.8 4.3 6.0 8.1 0.2 1.2 3.3 100.0 New Zealand 59.6 45.1 20.5 5.5 3.3 1.4 12.8 10.8 2.8 3.0 3.7 90.2 Norway 69.6 45.6 9.2 8.6 2.5 1.3 20.1 13.8 1.2 4.0 10.3 99.8 Portugal 85.5 65.1 30.3 4.8 0.0 0.3 20.6 13.7 11.8 0.7 6.7 61.3 Spain 61.1 33.2 9.6 4.6 1.8 3.0 8.1 20.2 8.4 3.2 7.6 82.8 Sweden 54.1 35.2 4.6 5.8 3.5 2.4 15.5 11.2 1.6 3.8 7.8 100.0 Switzerland 52.3 22.1 3.8 3.3 0.2 2.5 11.4 15.5 1.0 4.7 14.7 96.3 United Kingdom 37.5 30.5 4.9 4.6 3.3 0.6 15.8 5.3 0.8 1.6 1.7 100.0 United States 69.4 44.1 2.0 5.6 11.7 3.4 11.1 18.6 3.8 2.6 6.7 .. Total 56.9 34.9 8.7 4.7 4.1 4.0 8.9 16.2 4.7 2.9 5.8 94.5 a. Excludes technical cooperation and administrative costs. 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 cover road, rail, water, and air transport; post and transport and storage are examples. The third level of the time required for completing disbursements. telecommunications; and radio, television, and print comprises subsectors such as basic education and · Total sector-allocable aid is the sum of aid that media. · Agriculture includes 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 includes support for form of aid by donors may not be complete. Also, tial of aid recipients. · Education includes 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 Data sources recording time. the sector concerned. · Health covers 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 insurance Organisation for Economic Co-operation and obtaining the best value for their money. Tying programs. · Population covers all activities related Development (OECD) DAC in Geographical Dis- requires recipients to purchase goods and services to family planning and research into population tribution of Financial Flows to Aid Recipients and from the donor country or from a specified group of problems. · Water supply and sanitation cover Development Cooperation Report. Data are avail- countries. Such arrangements prevent a recipient assistance for water supply and use, sanitation, able electronically on the OECD's International from misappropriating or mismanaging aid receipts, and water resources development (including rivers). Development Statistics CD-ROM and at www.oecd. but they may also be motivated by a desire to benefit · Government and civil society include assistance org/dac/stats/idsonline. donor country suppliers. to strengthen government administrative apparatus 2008 World Development Indicators 363 6.14 Aid dependency Net official Aid per Aid dependency development capita ratios assistancea Aid as % of imports Aid as Aid as % of gross of goods, services, Aid as % of central $ millions $ % of GNI capital formation and income government expense 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 Afghanistan 136 3,000 .. .. .. 35.7 .. .. .. .. .. .. Albania 317 321 103 101 8.4 3.5 34.8 14.1 21.0 7.0 .. .. Algeria 201 209 7 6 0.4 0.2 1.5 .. .. .. 1.8 1.0 Angola 302 171 22 10 4.1 0.4 22.0 2.8 4.1 0.8 .. .. Argentina 53 114 1 3 0.0 0.1 0.1 0.2 0.1 0.2 .. .. Armenia 216 213 70 71 11.0 3.3 60.6 9.9 21.2 7.2 .. 20.7 Australia Austria Azerbaijan 139 206 17 24 2.8 1.2 12.8 3.3 5.8 1.9 .. .. Bangladesh 1,168 1,223 8 8 2.4 1.9 10.8 8.0 11.7 6.9 .. .. Belarus 40 73 4 7 0.3 0.2 1.2 0.6 0.5 0.3 1.5 0.6 Belgium Benin 238 375 33 43 10.6 8.0 55.9 .. 32.1 .. .. 58.4 Bolivia 472 581 57 62 5.8 5.4 31.0 43.1 19.3 14.5 .. 21.2 Bosnia and Herzegovina 737 494 195 126 12.4 3.9 65.1 24.9 17.4 5.8 .. 11.3 Botswana 31 65 18 35 0.5 0.7 1.4 2.4 1.0 1.4 .. .. Brazil 232 82 1 0 0.0 0.0 0.2 0.0 0.2 0.1 .. .. Bulgariab 311 .. 39 .. 2.5 .. 13.5 .. 3.7 .. 7.6 .. Burkina Faso 335 871 28 61 12.9 14.1 76.6 83.3 48.5 .. .. 117.5 Burundi 93 415 14 51 12.8 47.7 212.6 275.7 56.1 89.9 .. .. Cambodia 396 529 31 37 11.2 7.6 61.8 33.9 16.1 8.9 .. 84.7 Cameroon 379 1,684 24 93 4.0 9.3 22.5 51.0 12.8 .. .. .. Canada Central African Republic 75 134 19 31 8.0 9.0 82.4 101.3 .. .. .. .. Chad 130 284 15 27 9.5 5.5 40.4 20.2 .. .. .. .. Chile 49 83 3 5 0.1 0.1 0.3 0.3 0.2 0.1 0.3 0.3 China 1,728 1,245 1 1 0.1 0.0 0.4 0.1 0.6 0.1 .. .. Hong Kong, Chinab 4 .. 1 .. 0.0 .. 0.0 .. 0.0 .. .. .. Colombia 187 988 4 22 0.2 0.7 1.6 2.7 1.1 2.6 .. 2.5 Congo, Dem. Rep. 177 2,056 3 34 4.5 25.2 119.1 148.8 .. .. 15.2 .. Congo, Rep. 33 254 10 69 1.5 .. 4.6 14.4 1.6 .. .. .. Costa Rica 11 24 3 5 0.1 0.1 0.4 0.4 0.1 0.2 .. 0.5 Côte d'Ivoire 351 251 21 13 3.6 1.5 31.2 14.7 7.9 3.1 .. 7.5 Croatia 66 200 15 45 0.4 0.5 1.8 1.4 0.6 0.7 0.8 1.2 Cuba 44 78 4 7 .. .. .. .. .. .. .. .. Czech Republicb 438 .. 43 .. 0.8 .. 2.6 .. 1.1 .. 2.3 .. Denmark Dominican Republic 56 53 6 6 0.3 0.2 1.2 0.8 0.5 0.4 .. 1.0 Ecuador 146 189 12 14 1.0 0.5 4.6 2.0 2.3 1.2 .. .. Egypt, Arab Rep. 1,328 873 20 12 1.3 0.8 6.8 4.3 5.6 2.1 6.6 3.0 El Salvador 180 157 29 23 1.4 0.9 8.1 5.2 3.0 1.7 .. 38.2 Eritrea 176 129 48 28 27.7 12.0 86.9 63.6 34.5 .. .. .. Estoniab 64 .. 47 .. 1.2 .. 4.0 .. 1.2 .. 3.8 .. Ethiopia 686 1,947 10 25 8.7 14.7 45.3 74.0 41.0 36.6 .. .. Finland France Gabon 12 31 10 24 0.3 0.4 1.1 1.4 0.5 .. .. .. Gambia, The 49 74 35 45 12.2 14.8 66.9 59.7 .. 20.7 .. .. Georgia 169 361 36 81 5.3 4.8 20.8 17.4 13.6 7.9 47.9 22.9 Germany Ghana 600 1,176 30 51 12.4 9.2 50.2 28.1 17.3 13.9 .. .. Greece Guatemala 263 487 23 37 1.4 1.4 7.7 7.4 4.4 3.6 12.5 11.9 Guinea 153 164 19 18 5.0 5.0 24.9 38.3 15.7 .. .. .. Guinea-Bissau 80 82 59 50 39.5 27.9 329.8 157.2 .. .. .. .. Haiti 208 581 24 62 5.4 13.4 20.8 40.6 15.1 27.7 .. .. 364 2008 World Development Indicators 6.14 GLOBAL LINKS Aid dependency Net official Aid per Aid dependency development capita ratios assistancea Aid as % of imports Aid as Aid as % of gross of goods, services, Aid as % of central $ millions $ % of GNI capital formation and income government expense 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 Honduras 449 587 72 84 7.7 6.6 24.5 19.3 12.7 9.0 .. 30.3 Hungary b 252 .. 25 .. 0.6 .. 1.7 .. 0.6 .. 1.3 .. India 1,463 1,379 1 1 0.3 0.2 1.3 0.4 1.8 0.6 2.0 1.0 Indonesia 1,654 1,405 8 6 1.1 0.4 4.5 1.6 2.5 1.2 .. .. Iran, Islamic Rep. 130 121 2 2 0.1 0.1 0.4 0.2 0.7 .. 0.2 0.2 Iraq 100 8,661 .. .. .. .. .. .. .. .. .. .. Ireland Israelb 800 .. 127 .. 0.7 .. 3.2 .. 1.4 .. 1.5 .. Italy Jamaica 10 37 4 14 0.1 0.4 0.5 1.1 0.2 0.5 0.4 0.9 Japan .. .. Jordan 552 580 115 105 6.4 3.9 29.2 15.4 8.7 4.3 24.1 11.8 Kazakhstan 189 172 13 11 1.1 0.2 5.7 0.6 1.8 0.4 7.5 1.4 Kenya 510 943 16 26 4.1 4.1 23.0 21.4 12.9 11.3 23.9 .. Korea, Dem. Rep. 73 55 3 2 .. .. .. .. .. .. .. .. Korea, Rep.b ­198 .. ­4 .. 0.0 .. ­0.1 .. ­0.1 .. ­0.2 .. Kuwait 3 .. 1 .. 0.0 .. 0.1 .. 0.0 .. .. .. Kyrgyz Republic 215 311 44 60 16.7 11.2 78.3 63.4 28.5 13.4 .. 62.9 Lao PDR 282 364 54 63 16.9 12.0 77.7 32.6 44.1 .. .. .. Latviab 91 .. 38 .. 1.2 .. 4.9 .. 2.3 .. 4.1 .. Lebanon 199 707 53 174 1.2 3.2 5.9 25.5 .. 3.7 3.8 .. Lesotho 37 72 19 36 3.4 3.8 10.1 14.5 4.4 4.8 .. .. Liberia 67 269 22 75 17.4 54.4 .. .. .. .. .. .. Libya 14 37 3 6 .. 0.1 0.3 .. 0.2 0.2 .. .. Lithuaniab 99 .. 28 .. 0.9 .. 4.4 .. 1.6 .. 3.2 .. Macedonia, FYR 251 200 125 98 7.1 3.2 31.5 15.4 10.6 4.5 .. .. Madagascar 322 754 20 39 8.4 13.9 55.1 55.3 20.3 .. 78.1 117.9 Malawi 446 669 38 49 26.1 21.4 188.7 89.1 65.7 .. .. .. Malaysia 45 240 2 9 0.1 0.2 0.2 0.8 0.0 0.1 0.3 .. Mali 359 825 36 69 15.0 14.9 60.4 61.5 34.4 .. 127.7 89.7 Mauritania 211 188 82 62 19.4 6.8 101.0 30.3 .. .. .. .. Mauritius 20 19 17 15 0.5 0.3 1.8 1.2 0.7 0.4 2.2 1.4 Mexico ­56 247 ­1 2 0.0 0.0 0.0 0.1 0.0 0.1 ­0.1 .. Moldova 123 228 30 60 9.4 6.1 39.7 19.8 11.3 6.8 32.9 21.0 Mongolia 217 203 91 78 20.1 6.7 68.8 18.4 27.5 9.9 .. .. Morocco 419 1,046 15 34 1.2 1.6 4.4 5.1 3.1 3.9 .. 6.2 Mozambique 876 1,611 48 77 21.8 26.2 66.6 122.0 49.7 39.7 .. .. Myanmar 106 147 2 3 .. .. .. .. 4.0 3.4 .. .. Namibia 152 145 81 71 4.4 2.2 22.8 7.5 8.2 4.4 14.1 .. Nepal 387 514 16 19 7.0 5.7 29.0 22.1 21.2 17.0 .. 39.4 Netherlands New Zealand Nicaragua 561 733 110 132 15.0 14.2 47.2 47.0 23.5 18.0 86.5 71.8 Niger 208 401 19 29 11.7 11.0 101.4 .. 43.0 .. .. .. Nigeria 174 11,434 1 79 0.4 11.3 1.9 45.1 1.1 .. .. .. Norway Oman 45 35 19 14 0.2 .. 1.9 .. 0.6 0.2 0.9 .. Pakistan 692 2,147 5 14 0.9 1.7 5.4 7.8 4.8 5.5 5.6 11.1 Panama 16 30 5 9 0.1 0.2 0.6 0.9 0.2 0.2 0.6 .. Papua New Guinea 275 279 51 45 8.4 5.5 .. .. 13.7 .. 26.2 .. Paraguay 82 56 15 9 1.1 0.6 6.1 2.9 2.3 0.9 .. 3.6 Peru 398 468 15 17 0.8 0.6 3.7 2.5 3.4 1.7 4.2 .. Philippines 575 562 8 7 0.7 0.4 3.6 3.3 1.1 0.9 4.3 2.7 Polandb 1,396 .. 36 .. 0.8 .. 3.3 .. 2.3 .. .. .. Portugal Puerto Rico 2008 World Development Indicators 365 6.14 Aid dependency Net official Aid per Aid dependency development capita ratios assistancea Aid as % of imports Aid as Aid as % of gross of goods, services, Aid as % of central $ millions $ % of GNI capital formation and income government expense 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 Romaniab 432 .. 19 .. 1.2 .. 6.0 .. 2.9 .. .. .. Russian Federationb 1,561 .. 11 .. 0.6 .. 3.2 .. 2.2 .. 2.8 .. Rwanda 321 585 39 62 17.9 23.6 101.3 109.4 71.2 75.1 .. .. Saudi Arabia 22 25 1 1 0.0 0.0 0.1 0.0 0.0 0.0 .. .. Senegal 423 825 41 68 9.2 9.1 44.1 30.8 21.9 .. 70.9 .. Serbia 1,134 c 1,586 151c 213 12.6c 5.0 150.1c 23.4 .. .. .. .. Sierra Leone 181 364 40 63 29.4 25.7 356.3 163.6 68.8 74.8 98.8 .. Singaporeb 1 .. 0 .. 0.0 .. 0.0 .. 0.0 .. 0.0 .. Slovak Republicb 113 .. 21 .. 0.6 .. 2.1 .. 0.7 .. .. .. Sloveniab 61 .. 31 .. 0.3 .. 1.2 .. 0.5 .. 0.8 .. Somalia 101 392 14 46 .. .. .. .. .. .. .. .. South Africa 487 718 11 15 0.4 0.3 2.3 1.4 1.3 0.8 1.3 0.9 Spain Sri Lanka 276 796 14 40 1.7 3.0 6.0 10.3 3.2 6.5 7.3 13.3 Sudan 220 2,058 7 55 2.1 6.0 9.7 22.3 8.5 17.2 .. .. Swaziland 13 35 13 30 0.9 1.3 5.1 7.6 0.9 1.4 .. .. Sweden Switzerland Syrian Arab Republic 158 27 10 1 0.9 0.1 4.7 0.5 2.4 0.2 .. .. Tajikistan 124 240 20 36 13.7 8.8 109.9 58.8 .. 9.9 160.3 .. Tanzania 1,019 1,825 30 46 11.4 14.5 63.7 77.0 45.7 34.6 .. .. Thailand 698 ­216 12 ­3 0.6 ­0.1 2.5 ­0.4 0.9 ­0.1 .. ­0.6 Timor-Leste 231 210 295 204 71.6 24.7 285.9 310.3 .. .. .. .. Togo 70 79 13 12 5.4 3.6 29.4 .. 10.5 .. .. 20.1 Trinidad and Tobago ­2 13 ­1 10 0.0 0.1 ­0.1 .. 0.0 .. .. .. Tunisia 222 432 23 43 1.2 1.5 4.2 5.9 2.1 2.4 4.1 4.8 Turkey 327 570 5 8 0.2 0.1 0.7 0.6 0.5 0.4 .. 0.5 Turkmenistan 31 26 7 5 1.2 0.3 3.1 .. .. .. .. .. Uganda 817 1,551 33 52 14.0 16.7 69.1 70.3 51.9 44.0 92.4 95.0 Ukraine 541 484 11 10 1.8 0.5 8.8 1.9 2.8 0.9 6.4 1.2 United Arab Emiratesb 3 .. 1 .. 0.0 .. 0.0 .. .. .. .. .. United Kingdom United States Uruguay 17 21 5 6 0.1 0.1 0.6 0.7 0.3 0.3 0.3 0.4 Uzbekistan 186 149 8 6 1.4 0.9 8.3 3.9 .. .. .. .. Venezuela, RB 76 58 3 2 0.1 0.0 0.3 0.1 0.3 0.1 0.3 .. Vietnam 1,681 1,846 22 22 5.5 3.1 18.2 8.5 9.3 .. .. .. West Bank and Gaza 637 1,449 215 384 13.3 34.6 47.4 132.6 .. .. .. .. Yemen, Rep. 263 284 14 13 3.0 1.6 14.3 .. 6.2 3.0 .. .. Zambia 795 1,425 76 122 25.8 14.6 140.8 55.4 53.1 42.0 .. 66.5 Zimbabwe 176 280 14 21 2.5 .. 17.5 .. .. .. .. .. World 57,760 s 105,292 s 10 w 16 w 0.2 w 0.2 w 0.8 w .. w 0.6 w 0.6 w .. w .. w Low income 18,665 48,150 9 20 2.3 3.0 9.8 10.1 9.2 8.8 .. .. Middle income 24,441 34,522 8 11 0.5 0.3 1.9 1.2 1.5 0.9 .. .. Lower middle income 15,763 27,649 7 12 0.7 0.6 2.3 1.6 2.1 1.5 .. .. Upper middle income 7,518 5,722 10 7 0.3 0.1 1.3 0.5 0.8 0.3 .. .. Low & middle income 55,463 105,252 11 19 0.9 0.9 3.8 3.2 3.0 2.5 .. .. East Asia & Pacific 8,589 7,888 5 4 0.5 0.2 1.6 0.6 1.4 0.5 .. .. Europe & Central Asia 10,327 6,224 22 14 1.2 0.3 5.2 1.1 2.8 0.6 .. .. Latin America & Carib. 4,835 6,923 9 12 0.2 0.2 1.2 1.2 0.9 0.8 .. .. Middle East & N. Africa 4,534 16,778 16 54 1.0 2.1 4.0 7.9 3.3 6.1 .. .. South Asia 4,194 9,277 3 6 0.7 0.8 2.9 2.5 3.6 2.9 .. .. Sub-Saharan Africa 13,194 40,516 20 52 4.1 6.0 21.5 27.1 10.9 13.9 .. .. High income 2,297 40 2 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. The distinction between official aid, for countries on the Part II list of the Organisation for Economic Co-operation and Development Development Assistance Committee (DAC), and official development assistance was dropped in 2005. b. No longer on the DAC list of eligible official development assistance recipients. Data for 2000 are official aid. c. Includes Montenegro. 366 2008 World Development Indicators 6.14 GLOBAL LINKS Aid dependency About the data Unless otherwise noted, aid includes official devel- Ratios of aid to gross national income (GNI), gross made directly by the donor. Similarly, grant commod- opment assistance (ODA; see About the data for capital formation, imports, and government spending ity aid may not always be recorded in trade data or in table 6.12). The data cover loans and grants from provide measures of recipient country dependency the balance of payments. Moreover, DAC statistics Development Assistance Committee (DAC) member on aid. But care must be taken in drawing policy con- exclude purely military aid. countries, multilateral organizations, and non-DAC clusions. For foreign policy reasons some countries The nominal values used here may overstate the donors. They do not reflect aid given by recipient have traditionally received large amounts of aid. Thus real value of aid to recipients. Changes in interna- countries to other developing countries. As a result, aid dependency ratios may reveal as much about a tional prices and exchange rates can reduce the pur- some countries that are net donors (such as Saudi donor's interest as about a recipient's needs. Ratios chasing power of aid. Tying aid, still prevalent though Arabia) are shown in the table as aid recipients are generally much higher in Sub-Saharan Africa than declining in importance, also tends to reduce its pur- (see table 6.14a). Data before 2005 for countries in other regions, and they increased in the 1980s. chasing power (see About the data for table 6.13). that were Part II recipients (see About the data for High ratios are due only in part to aid flows. Many The aggregates refer to World Bank definitions. table 6.12 for more information) are defined as African countries saw severe erosion in their terms Therefore the ratios shown may differ from those official aid. of trade in the 1980s, which, along with weak poli- of the Organisation for Economic Co-operation and The table does not distinguish types of aid (pro- cies, contributed to falling incomes, imports, and Development (OECD). gram, project, or food aid; emergency assistance; investment. Thus the increase in aid dependency Definitions postconflict peacekeeping assistance; or technical ratios reflects events affecting both the numerator cooperation), which may have different effects on the (aid) and the denominator (GNI). · Net official development assistance is flows (net economy. Expenditures on technical cooperation do Because the table relies on information from of repayment of principal) that meet the DAC defini- not always directly benefit the economy to the extent donors, it is not necessarily consistent with infor- tion of ODA and are made to countries and territories that they defray costs incurred outside the country on mation recorded by recipients in the balance of pay- on the DAC list of aid recipients. See About the data salaries and benefits of technical experts and over- ments, which often excludes all or some technical for table 6.12. · Aid per capita is ODA divided by head costs of firms supplying technical services. assistance--particularly payments to expatriates midyear population. · Aid dependency ratios are calculated using values in U.S. dollars converted at official exchange rates. Imports of goods, services, Official development assistance from non-DAC donors, 2002­06 6.14a and income refer to international transactions involv- Net disbursements ($ millions) ing a change in ownership of general merchandise, goods sent for processing and repairs, nonmonetary 2002 2003 2004 2005 2006 gold, services, receipts of employee compensation OECD members (non-DAC) for nonresident workers, and investment income. For Czech Republic 45 91 108 135 161 definitions of GNI, gross capital formation, and cen- Hungary .. 21 70 100 149 tral government expense, see Definitions for tables Iceland 13 18 21 27 41 1.1, 4.8, and 4.10. Korea, Rep. 279 366 423 752 455 Poland 14 27 118 205 297 Slovak Republic 7 15 28 56 55 Turkey 73 67 339 601 714 Arab countries Kuwait 20 138 161 218 158 Data sources Saudi Arabia 2,478 2,391 1,734 1,005 2,095 United Arab Emirates 156 188 181 141 249 Data on fi nancial fl ows are compiled by DAC Other donors and published in its annual statistical report, Israela 131 112 84 95 90 Geographical Distribution of Financial Flows to Aid Recipients, and in its annual Development Taiwan, China .. .. 421 483 513 Cooperation Report. Data are available electroni- Thailand .. .. .. .. 74 cally on the OECD's International Development Sta- Other donors 3 4 22 86 121 tistics CD-ROM and at www.oecd.org/dac/stats/ Total 3,218 3,436 3,712 3,905 5,172 idsonline. Data on population, GNI, gross capital Note: The table does not reflect aid provided by several major emerging non­Organisation for Economic Co-operation formation, imports of goods and services, and and Development donors because information on their aid has not been disclosed. a. Includes $87.8 million in 2002, $68.8 million in 2003, $47.9 million in 2004, $49.2 million in 2005, and $45.5 central government expense used in computing million in 2006 for first-year sustenance expenses for people arriving from developing countries (many of which are the ratios are from World Bank and International 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. Monetary Fund databases. 2008 World Development Indicators 367 6.15 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Commission Kingdom France Japan Germany Netherlands Sweden Canada Norway $ millions 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Afghanistan 2,625.5 1,403.7 220.9 246.5 14.6 107.4 118.0 87.3 46.4 140.3 69.7 167.3 Albania 248.3 40.6 71.4 3.6 4.5 1.9 29.6 5.3 12.5 0.6 5.7 72.5 Algeria 190.8 0.8 ­13.8 0.0 173.4 ­11.7 ­25.5 0.1 0.5 ­3.5 1.0 69.4 Angola ­6.6 32.9 48.6 12.6 ­97.1 12.4 11.4 2.1 7.2 1.4 23.4 ­61.5 Argentina 105.6 2.0 24.6 0.0 15.9 8.0 10.9 0.1 0.4 1.5 0.0 42.1 Armenia 156.1 64.9 21.1 8.4 14.1 7.7 16.7 8.5 2.3 0.1 5.2 7.2 Australia Austria Azerbaijan 113.9 56.5 18.7 0.0 10.7 4.1 12.0 0.1 0.6 0.1 5.6 5.6 Bangladesh 557.3 41.8 100.9 139.1 ­2.2 ­7.3 29.1 67.5 38.4 56.7 21.4 71.3 Belarus 53.6 4.4 15.5 0.0 5.1 0.2 16.0 0.0 7.8 0.1 0.2 4.4 Belgium Benin 263.5 20.3 35.1 2.3 73.8 10.1 26.5 24.5 0.2 6.0 0.0 64.9 Bolivia 621.7 193.1 52.0 8.7 39.8 100.4 47.0 34.4 17.9 17.0 3.6 107.7 Bosnia and Herzegovina 409.5 66.0 89.3 5.9 2.9 16.1 26.7 18.9 40.2 7.6 19.0 116.8 Botswana 63.8 24.8 27.5 0.1 1.5 0.3 2.7 0.6 1.6 1.7 2.0 1.1 Brazil 83.4 ­67.9 8.6 1.6 30.9 ­13.0 65.6 2.0 3.2 7.1 2.8 42.3 Bulgaria Burkina Faso 519.5 21.9 133.7 2.8 131.4 18.5 29.5 55.1 15.0 17.1 0.4 94.1 Burundi 269.5 46.6 47.1 26.9 13.9 15.4 14.5 17.0 8.2 4.5 13.1 62.3 Cambodia 376.3 57.9 28.8 22.3 29.8 106.3 27.6 2.0 17.1 8.1 3.1 70.0 Cameroon 1,549.0 13.6 43.7 169.6 243.6 18.8 228.1 19.0 12.9 206.9 0.7 592.2 Canada Central African Republic 79.2 21.0 13.9 0.9 26.8 0.1 4.9 0.0 1.8 0.6 5.6 3.6 Chad 210.2 37.2 57.7 2.4 42.1 8.7 26.7 6.1 3.6 2.2 1.6 21.9 Chile 76.6 ­0.3 12.3 0.9 9.9 8.1 36.3 0.1 0.3 2.5 0.0 6.3 China 1,215.8 18.9 42.1 52.3 142.8 569.4 244.9 30.5 11.9 29.6 14.4 57.4 Hong Kong, China Colombia 986.9 719.8 69.8 0.6 24.4 ­5.8 22.4 33.5 18.3 10.6 9.9 83.3 Congo, Dem. Rep. 1,722.6 838.5 222.2 139.9 57.2 23.2 35.7 29.9 40.0 28.1 20.7 287.2 Congo, Rep. 225.0 9.0 55.9 0.6 123.4 0.4 3.8 0.1 3.2 3.7 1.0 23.9 Costa Rica 27.4 ­9.6 7.3 ­0.5 5.4 6.1 7.4 1.0 0.9 2.8 0.1 6.5 Côte d'Ivoire 275.4 30.9 76.5 1.9 106.8 13.0 12.5 1.0 7.8 3.4 3.8 17.8 Croatia 189.9 30.9 121.7 0.3 3.4 ­0.1 6.9 0.0 5.1 0.4 14.9 6.2 Cuba 59.7 14.0 2.8 ­2.9 3.2 3.4 3.8 0.4 0.9 7.6 1.0 25.5 Czech Republic Denmark Dominican Republic 61.9 30.3 48.9 ­71.9 7.3 6.2 23.2 0.1 0.0 1.7 0.8 15.4 Ecuador 199.0 70.1 28.5 ­2.0 1.8 4.5 16.5 1.9 0.6 2.2 1.9 72.8 Egypt, Arab Rep. 765.2 195.6 228.5 18.8 62.7 ­5.2 140.6 13.1 2.0 15.9 0.5 92.8 El Salvador 167.6 24.5 17.0 11.3 3.3 29.8 9.2 0.9 4.3 3.0 0.5 63.8 Eritrea 78.1 6.6 14.9 5.5 0.9 9.9 4.8 3.3 2.1 0.8 17.9 11.5 Estonia Ethiopia 1,218.5 315.8 194.4 164.6 17.4 57.9 56.8 49.8 41.5 62.5 41.8 216.0 Finland France Gabon 34.6 1.1 2.7 0.0 30.1 ­0.3 ­0.2 0.0 0.0 1.3 0.0 ­0.2 Gambia, The 26.5 4.7 1.5 4.1 0.6 11.0 1.3 0.1 0.8 1.2 0.3 1.1 Georgia 265.5 103.2 55.1 4.9 4.4 11.6 46.4 11.1 9.4 0.8 7.4 11.1 Germany Ghana 656.5 68.4 61.9 167.2 23.2 43.7 59.8 97.0 0.8 53.9 1.0 79.7 Greece Guatemala 476.8 67.3 31.7 ­4.7 3.6 38.9 17.9 20.0 32.6 10.2 14.9 243.6 Guinea 124.2 34.9 21.3 1.0 20.6 17.1 14.0 0.1 1.5 7.2 0.6 6.1 Guinea-Bissau 72.6 5.5 33.3 0.0 9.9 0.0 0.3 0.0 0.0 0.7 0.4 22.5 Haiti 445.7 190.7 82.4 2.0 26.9 5.5 3.3 0.2 2.9 97.5 8.0 26.3 368 2008 World Development Indicators 6.15 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 Commission Kingdom France Japan Germany Netherlands Sweden Canada Norway $ millions 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Honduras 417.0 84.1 32.3 1.1 34.8 138.0 13.7 1.1 18.7 15.1 1.6 76.4 Hungary India 862.8 96.8 209.7 349.3 4.3 29.6 55.7 13.1 17.1 25.5 18.3 42.6 Indonesia 825.7 190.1 137.2 101.6 ­55.7 ­73.9 50.4 75.6 23.9 37.3 10.3 320.2 Iran, Islamic Rep. 90.9 2.3 20.1 0.0 15.4 ­7.3 38.4 1.0 0.0 0.0 2.6 18.3 Iraq 8,495.8 4,781.8 8.0 203.0 790.7 780.8 388.2 3.2 278.3 17.7 22.6 1,219.0 Ireland Israel Italy Jamaica 32.2 9.4 32.9 14.5 ­1.3 ­16.2 ­7.6 ­3.0 0.1 3.3 0.4 ­0.2 Japan Jordan 412.1 329.5 50.2 0.9 ­3.5 ­15.6 17.2 0.3 0.4 5.5 3.8 23.4 Kazakhstan 108.4 51.5 12.4 0.2 3.0 24.9 11.3 0.3 0.9 0.1 2.6 1.4 Kenya 818.6 282.4 57.4 107.8 20.1 106.2 45.4 26.3 51.9 24.2 12.3 83.3 Korea, Dem. Rep. 40.9 0.4 12.1 0.0 0.6 0.0 2.9 0.6 5.1 0.3 3.8 14.9 Korea, Rep. Kuwait Kyrgyz Republic 135.5 50.3 12.0 11.2 1.1 17.2 17.9 0.1 5.0 0.1 2.3 18.2 Lao PDR 196.4 4.3 8.8 0.3 22.9 64.1 18.3 0.1 23.7 2.9 11.4 37.1 Latvia Lebanon 600.0 91.4 211.4 6.9 74.3 5.3 28.9 13.4 9.7 16.4 27.8 113.8 Lesotho 42.5 3.2 4.0 7.6 ­1.2 4.8 6.6 0.1 0.0 0.9 1.3 15.2 Liberia 231.7 88.4 44.2 15.3 2.1 17.4 9.0 6.5 15.2 1.6 8.9 23.1 Libya 34.2 25.1 0.8 0.0 2.4 0.1 3.9 0.0 0.0 0.0 0.1 1.9 Lithuania Macedonia, FYR 189.7 39.2 58.7 0.6 3.3 9.5 17.2 11.4 13.4 0.1 12.4 24.0 Madagascar 428.0 61.1 162.4 5.1 103.8 43.8 11.2 0.2 13.1 1.7 16.1 9.6 Malawi 476.6 64.0 78.7 170.9 0.6 23.4 23.8 10.4 17.4 12.5 50.3 24.3 Malaysia 231.6 3.2 1.4 9.9 ­3.0 201.9 8.0 0.2 0.5 0.2 2.0 7.3 Mali 525.1 65.0 126.7 4.1 81.6 26.7 40.2 66.1 25.4 27.3 17.0 44.9 Mauritania 119.9 12.2 26.2 1.0 31.6 12.1 13.8 0.4 1.1 2.3 0.5 18.8 Mauritius 23.1 0.4 14.6 ­0.1 2.7 4.0 ­0.1 0.0 0.0 0.4 0.0 1.2 Mexico 226.2 153.5 17.3 0.0 22.2 21.4 25.8 ­0.2 0.1 6.8 0.0 ­21.0 Moldova 109.7 23.9 26.2 3.4 6.6 6.1 9.4 7.0 11.9 0.2 2.7 12.2 Mongolia 129.8 12.4 3.1 0.4 1.4 47.0 29.7 8.0 2.6 1.3 1.1 22.3 Morocco 905.4 ­6.5 338.7 0.0 301.4 61.1 104.5 0.4 1.5 7.2 0.1 97.0 Mozambique 1,112.9 108.9 174.6 99.4 9.0 106.8 64.9 59.7 91.8 49.4 64.3 284.2 Myanmar 103.1 10.9 11.1 13.5 1.5 30.9 5.5 0.7 3.8 0.1 8.1 16.5 Namibia 110.5 50.6 4.8 1.5 2.0 1.0 13.9 0.9 9.0 1.4 1.7 23.8 Nepal 341.9 61.5 24.4 74.8 ­2.4 41.7 33.0 4.2 2.1 11.1 25.0 65.2 Netherlands New Zealand Nicaragua 472.1 67.5 86.6 9.3 1.9 35.9 22.8 34.6 40.2 11.2 25.2 136.3 Niger 322.4 30.6 87.2 6.1 88.8 12.1 21.3 0.0 0.1 6.8 2.5 67.0 Nigeria 10,969.6 787.2 150.0 3,185.7 2,027.2 1,631.6 1,710.4 228.8 1.0 15.7 2.9 1,229.0 Norway Oman ­14.5 ­17.1 0.0 0.0 0.8 1.5 0.3 0.0 0.0 0.0 0.0 0.0 Pakistan 1,202.8 477.7 57.9 203.2 15.8 225.0 59.5 20.8 11.8 43.4 18.7 68.4 Panama 32.0 18.7 12.8 ­10.7 0.3 2.1 1.1 0.1 0.0 0.9 0.0 6.8 Papua New Guinea 264.9 0.2 16.7 ­0.4 0.1 ­9.0 ­0.9 0.3 0.0 0.5 0.6 243.3 Paraguay 64.3 17.6 2.2 ­0.4 0.8 25.9 4.2 0.0 1.6 0.9 0.8 10.7 Peru 428.9 187.3 54.1 22.1 11.4 ­0.5 25.1 ­0.3 4.0 14.5 1.4 108.6 Philippines 540.0 97.8 20.5 0.9 ­9.3 263.6 47.2 16.6 5.9 19.9 4.7 67.7 Poland Portugal Puerto Rico 2008 World Development Indicators 369 6.15 Distribution of net aid by Development Assistance Committee members Ten major DAC donors $ millions Other Total United European United DAC donors $ millions States Commission Kingdom France Japan Germany Netherlands Sweden Canada Norway $ millions 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 Romania Russian Federation Rwanda 386.1 77.6 65.0 95.4 10.6 12.7 19.4 24.7 17.5 6.2 4.0 53.0 Saudi Arabia 11.2 0.7 0.0 0.0 4.5 4.6 1.1 0.0 0.0 0.0 0.0 0.3 Senegal 542.8 37.7 33.7 10.1 287.5 34.5 34.8 19.5 0.6 17.1 0.6 66.7 Serbia 1,503.7 147.0 334.5 180.5 109.8 8.4 202.5 21.3 44.3 49.9 32.6 373.0 Sierra Leone 258.4 21.0 59.3 65.6 1.9 62.7 10.8 5.8 3.9 5.2 2.7 19.5 Singapore Slovak Republic Slovenia Somalia 351.7 95.2 88.5 53.2 1.7 0.2 7.0 14.1 13.3 7.1 33.8 37.4 South Africa 697.3 140.5 136.7 1.5 158.8 15.9 40.5 53.2 22.1 11.1 14.1 101.5 Spain Sri Lanka 509.8 29.2 24.5 6.9 ­0.7 202.7 63.9 15.0 20.6 15.4 37.3 93.6 Sudan 1,817.6 738.8 299.4 215.6 14.7 42.7 50.7 96.1 47.5 79.3 106.9 123.9 Swaziland 22.7 1.9 10.4 0.2 0.2 11.6 ­3.0 0.0 0.0 1.0 0.4 0.0 Sweden Switzerland Syrian Arab Republic 17.2 0.6 28.6 0.0 27.4 ­41.5 ­9.4 0.1 0.8 0.1 0.9 9.6 Tajikistan 125.3 43.6 33.5 7.0 0.6 8.0 8.7 0.0 8.9 0.4 1.7 13.0 Tanzania 1,180.5 121.6 188.8 218.9 2.0 39.4 49.3 114.6 111.7 41.7 75.4 215.6 Thailand ­262.2 25.0 30.6 ­2.3 64.9 ­453.3 26.8 9.3 7.9 2.6 6.4 19.9 Timor-Leste 199.3 20.6 25.6 4.2 0.1 21.8 5.4 0.2 1.6 1.8 14.5 98.2 Togo 65.1 2.0 10.4 0.7 33.3 0.4 8.0 0.0 0.7 2.0 0.1 7.5 Trinidad and Tobago 11.4 0.2 7.3 0.1 1.2 1.3 0.3 0.0 0.0 0.7 0.0 0.2 Tunisia 436.3 ­12.8 149.3 16.5 176.3 18.6 39.6 ­2.1 0.7 0.7 0.0 49.4 Turkey 530.5 ­20.1 383.3 ­1.4 32.9 62.3 ­52.8 5.0 3.2 ­2.3 1.8 118.5 Turkmenistan 7.2 3.1 1.8 0.0 0.8 0.6 0.8 0.0 0.0 0.0 0.0 0.0 Uganda 1,093.7 246.2 155.5 214.4 5.4 21.8 54.6 82.4 62.6 14.1 50.5 186.1 Ukraine 414.0 130.2 133.4 12.0 14.3 6.6 58.7 0.3 18.4 15.8 0.5 23.7 United Arab Emirates United Kingdom United States Uruguay 17.6 0.4 6.8 0.0 6.3 2.3 0.3 0.0 0.4 1.1 0.0 0.1 Uzbekistan 105.1 49.2 12.6 0.1 2.7 18.6 15.8 0.0 1.4 0.0 0.5 4.2 Venezuela, RB 48.5 9.8 15.6 0.0 6.1 2.8 6.0 0.1 0.0 0.6 0.2 7.4 Vietnam 1,348.3 45.2 41.8 82.2 159.4 562.9 86.8 61.1 42.8 34.4 15.2 213.9 West Bank and Gaza 1,012.3 205.5 257.9 35.1 40.0 78.2 67.7 32.2 51.0 34.6 87.8 121.8 Yemen, Rep. 157.6 31.8 22.7 15.0 6.1 5.6 41.4 28.7 0.6 1.5 0.3 4.0 Zambia 1,213.5 309.9 98.3 86.8 63.7 31.5 287.5 55.7 48.3 10.6 66.2 154.2 Zimbabwe 254.5 36.4 54.7 69.9 3.6 6.5 9.9 7.4 17.8 6.8 11.2 29.5 World 86,449.2 s 21,162.1 s 9,489.1 s 8,717.6 s 7,919.4 s 7,313.1 s 7,034.0 s 4,282.2 s 2,851.9 s 2,531.0 s 2,197.6 s 12,748.4 s Low income 37,148.5 7,237.9 3,827.2 6,316.1 3,619.5 3,751.0 3,319.1 1,397.0 895.2 946.0 856.8 4,921.8 Middle income 30,193.8 9,045.4 4,136.1 903.3 3,381.0 2,325.8 2,462.0 559.2 801.0 676.5 483.6 5,310.3 Lower middle income 23,889.9 8,184.8 2,515.9 680.7 2,383.5 1,939.1 1,889.8 454.4 693.6 533.2 344.6 4,216.7 Upper middle income 5,237.0 636.7 1,415.6 215.6 861.9 383.8 405.3 97.1 95.0 104.0 101.1 898.0 Low & middle income 86,425.0 21,159.1 9,485.2 8,717.4 7,913.5 7,305.0 7,032.6 4,282.2 2,851.9 2,530.1 2,197.6 12,747.7 East Asia & Pacific 6,195.1 725.1 459.8 289.0 473.5 1,420.4 580.8 207.6 161.9 140.4 99.6 1,490.1 Europe & Central Asia 5,017.7 1,034.8 1,465.5 236.6 224.7 204.1 471.8 90.5 190.1 83.3 131.8 884.5 Latin America & Carib. 6,076.9 1,952.9 827.3 58.7 303.8 428.1 446.7 167.8 187.4 368.7 94.5 1,236.0 Middle East & N. Africa 13,614.2 5,740.8 1,486.2 304.5 1,759.5 878.6 860.4 91.2 354.8 102.3 151.4 1,880.5 South Asia 6,264.8 2,165.9 650.9 1,019.7 32.2 624.8 377.4 212.5 143.7 297.3 192.6 539.0 Sub-Saharan Africa 33,091.4 5,592.7 3,370.5 5,404.0 4,362.1 2,553.6 3,191.7 1,330.3 881.6 1,041.6 773.8 4,579.5 High income 24.2 3.1 3.9 0.2 5.9 8.1 1.4 0.0 0.0 1.0 0.0 0.7 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. 370 2008 World Development Indicators 6.15 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- information on such aid expenditures as develop- · Net aid comprises net bilateral official development income economies from members of the Develop- ment-oriented research, stipends and tuition costs assistance that meets the DAC definition of official ment Assistance Committee (DAC) of the Organisa- for aid-financed students in donor countries, and development assistance and are made to countries tion for Economic Co-operation and Development payment of experts hired by donor countries. More- and territories on the DAC list of aid recipients. See (OECD). The data include aid to some countries and over, a full accounting would include donor country About the data for table 6.12 · Other DAC donors territories not shown in the table and aid to unspeci- contributions to multilateral institutions, the flow of are Australia, Austria, Belgium, Denmark, Finland, fied economies recorded only at the regional or global resources from multilateral institutions to recipient Greece, Ireland, Italy, Luxembourg, New Zealand, level. Aid to countries and territories not shown in the countries, and flows from countries that are not mem- Portugal, Spain, and Switzerland. table has been assigned to regional totals based bers of DAC. Previous editions of the table included on the World Bank's regional classification system. only DAC member economies. This year's edition Aid to unspecified economies is included in regional includes net aid from the European Commission--a totals and, when possible, income group totals. Aid multilateral member of DAC. not allocated by country or region--including admin- The expenditures that countries report as official istrative costs, research on development, and aid to development assistance (ODA) have changed. For nongovernmental organizations--is included in the example, some DAC members have reported as world total. Thus regional and income group totals ODA the aid provided to refugees during the first 12 do not sum to the world total. months of their stay within the donor's borders. The table is based on donor country reports of Some of the aid recipients shown in the table are bilateral programs, which may differ from reports by also aid donors. See table 6.14a for a summary of recipient countries. Recipients may lack access to ODA from non-DAC countries. Debt relief and political interests have shaped the allocation of official development assistance 6.15a Share of official development assistance (ODA) net disbursements received 2000 2002 Indonesia 4% Serbia and Montenegro 5% Russian Federation 4% Mozambique 4% Poland 4% China 3% China 4% Russian Federation 3% Indonesia 3% Vietnam 4% Egypt, Arab Rep. 3% Egypt, Arab Rep. 3% Afghanistan 3% Serbia and Montenegro 3% Others Others 74% 76% 2004 2006 Afghanistan 4% Iraq 8% China 3% Nigeria Poland 3% 16% Congo, Dem. Rep. 3% Egypt, Arab Rep. 2% Iraq 13% Others Others 77% 62% Afghanistan 4% Sudan 3% Congo, Dem. Rep. 2% Data sources Data on financial flows are compiled by DAC and One-time disbursements of debt relief to Iraq and Nigeria increased their share of ODA in 2006. Large published in its annual statistical report, Geograph- aid flows also went to fragile states and international hot spots. Some changes reflect administrative ical Distribution of Financial Flows to Aid Recipients, decisions: since 2005 aid to the Russian Federation and the new member states of the European Union and its annual Development Cooperation Report. are no longer counted as ODA. Data are available electronically on the OECD's International Development Statistics CD-ROM and Note: Only ODA allocated to specific economies are included in the denominators. Source: Organisation for Economic Co-operation and Development Development Assistance Committee. at www.oecd.org/dac/stats/idsonline. 2008 World Development Indicators 371 6.16 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 2006 1995 2006 1995 2006 1995 2006 Afghanistan 3,313 1,112 35 43 2,679.1 2,107.5 19.6 .. .. .. .. .. Albania ­409 ­110 71 83 5.8 14.1 4.7 0.1 427 1,359 .. 27 Algeria ­50 ­140 299 242 1.5 8.4 192.5 94.2 1,120a 2,527a .. .. Angola 143 175 38 56 246.7 206.5 10.9 13.1 5 .. 210 413 Argentina 50 ­100 1,590 1,500 0.3 0.9 10.3 3.2 56 541 190 366 Armenia ­500 ­100 455 235 201.4 14.9 219.0 113.7 65a 1,175a 17 154 Australia 519 593 4,068 4,097 .. .. 62.2 68.9 1,651 3,133 700 2,815 Austria 262 180 717 1,234 .. .. 34.4 25.5 1,012 1,989 346 1,533 Azerbaijan ­116 ­100 292 182 200.5 126.1 233.7 2.6 3 813 9 301 Bangladesh ­260 ­500 1,006 1,032 .. .. 51.1 26.3 1,202 5,428 1 3 Belarus 0 0 1,269 1,191 0.1 9.4 29.0 0.7 29 334 12 93 Belgium 85 180 909 719 .. 0.1 31.7 16.8 4,937 7,476 3,252 2,669 Benin 105 99 146 175 .. .. 23.8 10.8 100a 173a 26a 40a Bolivia ­100 ­100 70 116 0.2 0.4 0.7 0.6 7 612 9 73 Bosnia and Herzegovina ­1,000 115 73 41 .. .. .. 10.3 .. 2,068 .. 55 Botswana 14 20 39 80 .. .. 0.3 3.2 59 117 200 118 Brazil ­184 ­229 730 641 0.1 0.7 2.1 3.5 3,315 4,253 347 691 Bulgaria ­349 ­43 47 104 4.2 3.4 1.3 4.5 42 1,695 34 47 Burkina Faso ­128 100 464 773 0.1 0.4 29.8 0.5 80a 50a 51a 44 a Burundi ­250 192 295 100 350.6 396.5 173.0 13.2 .. 0 5 0 Cambodia 150 10 116 304 61.2 18.0 .. 0.1 12 297 52 158 Cameroon ­5 6 159 137 2.0 10.4 45.8 35.1 11a 103a 22a 42a Canada 643 1,041 5,003 6,106 .. 0.1 152.1 151.8 .. .. .. .. Central African Republic 37 ­45 67 76 57.0 7.8 33.9 12.4 0 .. 27 .. Chad ­10 219 78 437 59.7 36.3 0.1 286.7 1 .. 15 .. Chile 90 30 136 231 14.3 0.8 0.3 1.1 .. 3 7 6 China ­1,281 ­1,900 441 596 104.7 140.6 288.3 301.0 1,053a 23,319a 19 3,025 Hong Kong, China 300 300 2,432 2,999 0.2 .. 1.5 1.9 .. 297 .. 365 Colombia ­250 ­120 108 123 .. .. 0.2 0.1 815 3,928 150 66 Congo, Dem. Rep. 1,208 ­237 2,049 539 .. .. 1,433.8 208.4 .. .. .. .. Congo, Rep. 14 ­10 169 288 0.1 0.2 19.4 55.8 4a 11a 27a 45a Costa Rica 62 84 228 441 0.2 0.3 24.2 11.5 123 513 36 246 Côte d'Ivoire 214 ­339 2,314 2,371 .. .. 297.9 27.3 151 164 457 17 Croatia 153 100 721 661 .. .. 198.7 2.4 544 1,234 17 274 Cuba ­98 ­129 90 74 24.9 33.6 1.8 0.7 .. .. .. .. Czech Republic 8 67 454 453 769.8 199.9 2.7 1.9 191 1,186 101 2,831 Denmark 58 46 250 389 .. .. 64.8 36.7 523 869 209 1,763 Dominican Republic ­129 ­148 118 156 .. 0.2 1.0 .. 839 3,044 7 27 Ecuador ­50 ­400 88 114 0.2 0.9 0.2 11.8 386 2,922 4 62 Egypt, Arab Rep. ­600 ­525 172 166 .. .. 5.4 88.0 3,226 5,330 223 135 El Salvador ­90 ­143 26 24 .. .. 0.2 .. 1,064 3,329 1 29 Eritrea ­359 229 12 15 286.7 193.7 1.1 4.6 .. .. .. .. Estonia ­108 1 309 202 0.4 0.6 .. .. 1 402 3 75 Ethiopia 868 ­140 795 555 101.0 74.0 393.5 97.0 27 172 1 14 Finland 43 33 103 156 .. .. 10.2 11.8 74 698 54 251 France 424 722 6,089 6,471 .. 0.1 155.3 146.0 4,640 12,479 4,935 4,330 Gabon 20 10 164 245 .. 0.1 0.8 8.4 4a 7a 99a 110a Gambia, The 45 31 148 232 0.2 1.3 6.6 13.8 19 64 .. 1 Georgia ­560 ­248 250 191 0.3 6.3 0.1 1.4 284 485 12 24 Germany 2,688 1,000 9,092 10,144 0.4 0.1 1,267.9 605.4 4,523 6,667 11,270 12,344 Ghana 40 12 1,038 1,669 13.6 10.0 83.2 44.9 17 105 5a 6a Greece 470 154 549 974 .. .. 4.4 2.3 3,286 1,543 300 982 Guatemala ­360 ­300 45 53 .. .. 1.5 0.4 358 3,626 8 35 Guinea 350 ­425 870 406 0.4 6.8 672.3 31.5 1a 42a 10a 48a Guinea-Bissau 20 1 32 19 0.8 1.0 15.4 7.8 2a 28a 3a 5a Haiti ­133 ­140 22 30 13.9 20.8 .. .. 109 1,070 .. 68 372 2008 World Development Indicators 6.16 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 2006 1995 2006 1995 2006 1995 2006 Honduras ­120 ­150 31 26 1.2 1.0 0.1 .. 124 2,367 8 1 Hungary 101 65 293 316 .. .. 11.4 8.1 152 363 146 190 India ­960 ­1,350 6,951 5,700 5.0 17.8 227.5 158.4 6,223 25,426 419 1,580 Indonesia ­725 ­1,000 219 160 9.8 34.7 .. 0.3 651 5,722 .. 1,359 Iran, Islamic Rep. ­1,587 ­1,250 2,478 1,959 .. 0.1 2,072.0 968.4 1,600a 1,032a .. .. Iraq 170 ­375 134 28 1.9 72.8 116.7 44.4 .. .. .. .. Ireland ­1 188 264 585 .. .. 0.4 7.9 347 532 173 1,947 Israel 484 115 1,919 2,661 0.9 0.9 .. 0.8 702 1,063 1,408 2,428 Italy 573 1,125 1,483 2,519 0.1 0.1 74.3 26.9 2,364 2,626 1,824 8,216 Jamaica ­100 ­100 20 18 .. 0.7 .. .. 653 1,946 74 385 Japan 248 270 1,261 2,048 .. 0.2 5.4 1.8 1,151 1,380 1,820 3,476 Jordan 509 130 1,618 2,225 0.5 1.6 1,288.9b 2,358.6b 1,441 2,883 107 402 Kazakhstan ­1,509 ­200 3,295 2,502 0.1 7.4 15.6 4.4 116 187 503 3,036 Kenya 222 25 366 345 9.3 5.4 234.7 272.5 298a 1,128a 4 25 Korea, Dem. Rep. 0 0 35 37 .. 0.4 .. .. .. .. .. .. Korea, Rep. ­115 ­80 584 551 .. 1.3 .. 0.1 1,080 917 634 4,245 Kuwait ­598 264 996 1,669 0.8 0.6 3.3 0.1 .. .. 1,354 3,021 Kyrgyz Republic ­273 ­75 482 288 0.2 26.3 13.4 0.4 1 481 41 145 Lao PDR ­30 ­115 23 25 245.6 93.8 .. .. 22a 1a 9a 1a Latvia ­134 ­20 713 449 0.2 1.4 .. .. 41 482 1 30 Lebanon 230 0 594 657 13.5 12.3 348.1b 428.6b 1,225 5,202 .. 4,134 Lesotho ­84 ­36 5 6 .. .. .. .. 411 361 75 11 Liberia ­283 ­119 199 50 744.6 160.5 120.1 16.2 .. .. .. .. Libya 10 10 506 618 0.6 1.6 4.0 2.8 .. 16 222 945 Lithuania ­99 ­30 272 165 0.1 0.9 .. 0.5 1 994 1 426 Macedonia, FYR ­27 ­10 114 121 42.9 6.5 9.1 1.2 68 267 1 18 Madagascar ­7 ­5 60 63 0.1 0.3 .. .. 14a 11a 11a 21a Malawi ­920 ­30 325 279 .. 0.1 1.0 3.9 1a 1a 1a 1a Malaysia 287 150 1,135 1,639 0.1 0.6 5.3 37.2 716 1,535 1,329 5,560 Mali ­260 ­134 63 46 77.2 0.6 17.9 10.6 112a 177a 42a 69a Mauritania ­15 30 118 66 84.3 33.4 34.4 0.8 5a 2a 14 .. Mauritius ­7 0 12 21 .. 0.1 .. .. 132a 215a 1 13 Mexico ­1,792 ­3,983 467 644 0.4 3.3 38.7 3.3 4,368 25,052 .. .. Moldova ­121 ­250 473 440 0.5 11.7 .. 0.2 1 1,182 1 86 Mongolia ­59 ­50 7 9 .. 0.9 .. .. .. 181 .. 77 Morocco ­450 ­550 103 132 0.3 4.7 0.1 0.5 1,970 5,454 20 41 Mozambique 650 ­20 246 406 125.6 0.2 0.1 2.6 59 80 21 26 Myanmar ­126 ­99 112 117 152.3 202.8 .. .. 81 116 .. 32 Namibia 3 ­1 124 143 .. 1.2 1.7 5.5 16 17 11 20 Nepal ­101 ­100 625 819 .. 2.6 124.8 128.2 57 1,453 9 79 Netherlands 190 110 1,387 1,638 0.1 .. 80.0 100.6 1,359 2,412 2,802a 6,802a New Zealand 94 102 732 642 .. .. 3.8 4.9 1,858 650 584 865 Nicaragua ­115 ­210 27 28 23.9 1.8 0.6 0.2 75 656 .. .. Niger ­3 ­28 139 124 10.3 0.8 27.6 0.3 8a 66a 29a 29a Nigeria ­96 ­170 582 971 1.9 13.3 8.1 8.8 804 a 3,329a 5a 18a Norway 42 84 231 344 .. .. 47.6 43.3 239 524 603 2,620 Oman 23 ­150 573 628 .. .. .. .. 39 39 1,537 2,788 Pakistan ­2,611 ­1,239 4,077 3,254 5.3 25.6 1,202.5 1,044.5 1,712 5,121 4 2 Panama 8 8 73 102 0.2 0.1 0.9 1.8 112 149 20 121 Papua New Guinea 0 0 32 25 2.0 .. 9.6 10.2 16a 13a 16a 135a Paraguay ­30 ­45 183 168 0.1 0.1 0.1 0.1 287 432 .. .. Peru ­441 ­510 51 42 5.9 7.0 0.6 0.9 599 1,837 34 133 Philippines ­900 ­900 214 374 0.5 0.9 0.8 0.1 5,360 15,251 151 20 Poland ­77 ­200 963 703 19.7 13.5 0.6 6.8 724 4,370 262 800 Portugal ­7 276 528 764 .. .. 0.3 0.3 3,953 3,328 527 1,386 Puerto Rico ­4 ­10 351 418 .. .. .. .. .. .. .. .. 2008 World Development Indicators 373 6.16 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 2006 1995 2006 1995 2006 1995 2006 Romania ­529 ­270 135 133 17.0 7.2 0.2 1.7 9 6,718 2 57 Russian Federation 2,263 917 11,707 12,080 207.0 159.4 .. 1.4 2,503 3,091 3,939 11,438 Rwanda ­1,714 43 60 121 1,819.4 93.0 7.8 49.2 21 21 1 47 Saudi Arabia ­500 285 4,611 6,361 0.3 0.6 13.2 240.8 .. .. 16,594 15,611 Senegal ­100 ­100 320 326 17.6 15.2 66.8 20.6 146a 633a 76a 77a Serbia 451 ­339 .. .. 86.1c 174.0 650.7c 99.0 1,295a,c 4,703a,c .. .. Sierra Leone ­380 472 55 119 379.5 42.9 4.7 27.4 24 33 .. 35 Singapore 250 200 992 1,843 .. 0.1 0.1 .. .. .. .. .. Slovak Republic 9 3 114 124 .. 0.7 2.3 0.2 26a 424 a 3a 16a Slovenia 38 22 200 167 12.9 1.8 22.3 0.3 272 282 31 129 Somalia ­1,193 100 18 282 638.7 464.0 0.6 0.7 .. .. .. .. South Africa 1,125 75 1,098 1,106 0.5 0.5 101.4 35.1 105 734 629 1,068 Spain 292 2,846 1,009 4,790 .. 2.4 5.9 5.3 3,235 8,863 868 11,005 Sri Lanka ­256 ­442 428 368 107.6 117.0 .. 0.2 809 2,349 16 283 Sudan ­168 ­532 1,111 639 445.3 686.3 674.1 196.2 346 1,156 1 2 Swaziland ­38 ­6 38 45 .. .. 0.7 0.8 83 99 4 17 Sweden 151 152 906 1,117 .. .. 199.2 79.9 288 336 336 589 Switzerland 200 100 1,471 1,660 .. .. 82.9 48.5 1,473 1,859 10,114 13,805 Syrian Arab Republic ­70 200 801 985 0.2 0.1 373.5b 1,144.6b 339 795 15 235 Tajikistan ­313 ­345 305 306 59.0 0.6 0.6 0.9 .. 1,019 .. 395 Tanzania 591 ­345 1,130 792 0.1 1.7 829.7 485.3 1 15 1 30 Thailand 172 231 568 1,050 0.2 3.3 106.6 133.1 1,695 1,333 .. .. Timor-Leste 0 100 6 6 .. 0.3 .. .. .. .. .. .. Togo ­122 ­4 169 183 93.2 27.3 10.9 6.3 15a 193a 5a 35a Trinidad and Tobago ­24 ­20 46 38 .. 0.2 .. .. 32a 92a 14 .. Tunisia ­22 ­29 38 38 0.3 2.8 0.2 0.1 680 1,510 36 16 Turkey 109 ­30 1,210 1,328 44.9 227.2 12.8 2.6 3,327 1,111 .. 107 Turkmenistan 50 ­10 260 224 .. 0.7 23.3 0.8 4 .. 7 .. Uganda 120 ­5 610 518 24.2 21.8 229.4 272.0 .. 814 .. 322 Ukraine 100 ­173 7,063 6,833 1.7 63.7 5.2 2.3 6 829 1 30 United Arab Emirates 340 577 1,716 3,212 .. 0.3 0.4 0.2 .. .. .. .. United Kingdom 167 948 4,198 5,408 0.1 0.2 90.9 301.6 2,469 6,954 2,581 4,525 United States 5,200 6,493 28,522 38,355 0.2 1.4 623.3 843.5 2,179 2,880 22,181 42,222 Uruguay ­20 ­104 93 84 0.3 0.2 0.1 0.1 .. 89 .. 3 Uzbekistan ­340 ­300 1,474 1,268 0.1 9.1 2.6 1.4 .. .. .. .. Venezuela, RB 40 40 1,019 1,010 0.5 3.8 1.6 0.7 2 165 203 253 Vietnam ­256 ­200 27 21 2.3 3.1 34.4 2.4 .. 4,800a .. .. West Bank and Gaza 1 11 1,201 1,680 23.5 6.4 1,201.0 b 1,739.3b 626a 598a .. 16a Yemen, Rep. 650 ­100 228 265 0.4 1.4 53.5 95.8 1,080 1,283 61 120 Zambia ­11 ­82 271 275 .. 0.2 130.0 120.3 .. 58 59 115 Zimbabwe ­192 ­75 638 511 .. 12.8 0.5 3.5 44 .. 7 .. World ..d s ..d s 164,017 s 189,693 s 18,068.7b,e s 14,326.1b,e s 18,068.7b,f s 14,326.1b,f s 101,562 s 296,757 s 98,648 s 207,865 s Low income ­3,098 ­4,690 30,412 27,110 8,567.8 4,838.6 7,304.4 3,724.7 12,776 55,239 1,342 3,828 Middle income ­9,432 ­14,021 48,539 49,582 1,544.3 1,643.6 7,723.3 7,819.9 44,744 166,674 11,029 40,774 Lower middle income ­9,775 ­9,750 20,522 21,249 1,133.4 1,021.7 6,282.9 7,140.4 27,044 102,551 1,341 7,884 Upper middle income 344 ­4,271 28,017 28,333 410.9 621.9 1,440.5 679.5 17,700 64,123 9,688 32,890 Low & middle income ­12,529 ­18,711 78,951 76,692 10,112.1 6,482.2 15,027.7 11,544.6 57,520 221,912 12,371 44,602 East Asia & Pacific ­2,828 ­3,847 3,001 4,432 578.9 501.1 447.0 484.4 9,701 52,847 1,618 10,431 Europe & Central Asia ­3,106 ­1,730 32,049 29,970 891.8 884.6 1,434.3 274.5 7,928 35,385 4,920 17,747 Latin America & Carib. ­3,847 ­6,811 5,280 5,713 87.5 78.0 94.0 40.5 13,335 56,860 1,114 2,646 Middle East & N. Africa ­1,201 ­2,768 8,780 9,642 60.8 112.7 5,683.1 6,974.5 13,358 26,697 2,239 8,837 South Asia ­976 ­2,484 13,133 11,229 2,901.8 2,378.6 1,625.5 1,357.6 10,005 39,779 475 2,031 Sub-Saharan Africa ­572 ­1,070 16,707 15,706 5,591.3 2,527.2 5,743.8 2,413.1 3,193 10,344 2,005 2,911 High income 12,513 18,604 85,065 113,001 786.3 211.2 3,041.9 2,781.9 44,042 74,844 86,277 163,263 Euro area 5,078 6,849 22,466 30,335 13.5 4.6 1,688.3 954.6 30,071 50,391 26,443 59,452 a. World Bank estimates. b. Includes Palestinian refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East, who are not included in data from the UN Refugee Agency (UNHCR). c. Includes Montenegro. d. 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. e. Includes refugees without specified country of origin. f. Regional and income group totals do not sum to the world total because of rounding. 374 2008 World Development Indicators 6.16 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 overes- total number of emigrants, including both citizens and try and their country of origin. Yet reliable statistics timate the number of refugees because it is easier to noncitizens. Data are five-year estimates. · Interna- on migration are difficult to collect and are often register than to de-register. The UN Refugee Agency tional migrant stock is the number of people born in incomplete, making international comparisons a (UNHCR) collects and maintains data on refugees, a country other than that in which they live. It includes challenge. except for Palestinian refugees residing in areas refugees. · Refugees are people who are recognized The United Nations Population Division provides under the mandate of the United Nations Relief and as refugees under the 1951 Convention Relating to data on net migration and migration stock. To derive Works Agency for Palestine Refugees in the Near the Status of Refugees or its 1967 Protocol, the estimates of net migration, the organization takes East (UNRWA). The UNRWA provides services to Pal- 1969 Organization of African Unity Convention Gov- into account the past migration history of a country estinian refugees who live in certain areas and who erning the Specific Aspects of Refugee Problems in or area, the migration policy of a country, and the register with the agency. Registration is voluntary, Africa, people recognized as refugees in accordance influx of refugees in recent periods. The data to cal- and estimates by the UNRWA are not an accurate with the UNHCR statute, people granted refugee-like culate these official estimates come from a variety count of the Palestinian refugee population. The humanitarian status, and people provided temporary of sources, including border statistics, administra- table shows estimates of refugees collected by protection. Asylum seekers are people who have tive records, surveys, and censuses. When no offi - the UNHCR, complemented by estimates of Pales- applied for asylum or refugee status and who have cial estimates can be made because of insufficient tinian refugees under the UNRWA mandate. Thus, not yet received a decision or who are registered as data, net migration is derived through the balance the aggregates differ from those published by the asylum seekers. Palestinian refugees are people (and equation, which is the difference between overall UNHCR. their descendants) whose residence was Palestine population growth and the natural increase during Workers' remittances and compensation of between June 1946 and May 1948 and who lost their the 1990­2000 intercensal period. employees are World Bank staff estimates based homes and means of livelihood as a result of the The data used to estimate the international migrant on data from the International Monetary Fund's (IMF) 1948 Arab-Israeli conflict. · Country of origin refers stock at a particular time are obtained mainly from Balance of Payments Yearbook. The IMF data are sup- to the nationality or country of citizenship of a claim- population censuses. The estimates are derived plemented by World Bank staff estimates for missing ant. · Country of asylum is the country where an from the data on foreign-born population--people data for countries where workers' remittances are asylum claim was filed. · Workers' remittances and who have residence in one country but were born in important. The data reported here are the sum of compensation of employees received and paid com- another country. When data on the foreign-born popu- three items defined in the IMF's Balance of Payments prise current transfers by migrant workers and wages lation are not available, data on foreign population-- Manual (fifth edition): workers' remittances, compen- and salaries earned by nonresident workers. Remit- that is, people who are citizens of a country other sation of employees, and migrants' transfers. tances are classified as current private transfers from than the country in which they reside--are used as The distinction between these three items is not migrant workers resident in the host country for more estimates. always consistent in the data reported by countries than a year, irrespective of their immigration status, After the breakup of the Soviet Union in 1991 peo- to the IMF. In some cases countries compile data on to recipients in their country of origin. Migrants' trans- ple living in one of the newly independent countries the basis of the citizenship of migrant workers rather fers are defined as the net worth of migrants who are who were born in another were classified as interna- than their residency status. Some countries also expected to remain in the host country for more than tional migrants. Estimates of migration stock in the report remittances entirely as workers' remittances one year that is transferred to another country at the newly independent states from 1990 on are based or compensation of employees. Following the fifth time of migration. Compensation of employees is the on the 1989 census of the Soviet Union. edition of the Balance of Payments Manual in 1993, income of migrants who have lived in the host country For countries with information on the interna- migrants' transfers are considered a capital transac- for less than a year. tional migrant stock for at least two points in time, tion, but previous editions regarded them as current Data sources interpolation or extrapolation was used to estimate transfers. For these reasons the figures presented in the international migrant stock on July 1 of the refer- the table take all three items into account. Data on net migration are from the United Nations ence years. For countries with only one observation, Population Division's World Population Prospects: estimates for the reference years were derived using The 2006 Revision. Data on migration stock come 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. A Trends in Total Migrant Stock: The 2005 Revision. model was used to estimate migration for countries Data on refugees are from the UNHCR's Statisti- that had no data. cal Yearbook 2006, 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 2008 World Development Indicators 375 6.17 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Afghanistan .. .. .. .. .. .. .. .. 1 .. .. .. Albania 40a 60a 12 2,616 70 1,057 23.2 46.0 19 989 2.3 22.0 Algeria 520b,c 1,443b,c 1,090 1,513 32d 184d .. .. 186d 370d .. .. Angola 9 121 3 .. 27 91 0.7 0.3 113 393 3.2 2.4 Argentina 2,289 4,156 3,815 4,009 2,550 3,914 10.2 7.2 4,013 4,143 15.4 10.1 Armenia 12 381 .. 329 14 193 4.7 12.8 12 133 1.7 5.2 Australia 3,726e 5,064 e 2,519 4,941 11,915 23,732 17.1 15.0 7,260 16,382 9.7 9.8 Austria 17,173f 20,261f 3,713 10,042 14,529 19,310 16.2 11.3 11,686 12,755 12.7 7.8 Azerbaijan 93 1,194 432 1,836 88 201 11.2 1.4 165 256 12.8 3.1 Bangladesh 156 200 830 1,819 25 80 0.6 0.6 234 444 3.1 2.6 Belarus 161 89 626 525 28 386 0.5 1.7 101 823 1.8 3.5 Belgium 5,560 f 6,995f 5,645 7,852 4,548 12,680 2.4 3.7 8,115 19,557 4.5 5.9 Benin 138 180 .. .. 85 108 13.8 14.0 48 58 5.4 5.1 Bolivia 284 515 249 466 92 287 7.5 6.7 72 328 4.6 9.5 Bosnia and Herzegovina 115f 256f .. .. .. 643 .. 14.3 .. 198 .. 2.4 Botswana 521 1,675 .. .. 176 539 7.3 10.2 153 285 7.5 8.3 Brazil 1,991 5,019 2,600 4,825 1,085 4,577 2.1 2.9 3,982 7,501 6.3 6.2 Bulgaria 3,466 5,158 3,524 4,180 662 3,315 9.8 16.5 312 2,092 4.8 8.1 Burkina Faso 124g 264g .. .. .. .. .. .. .. .. .. .. Burundi 34 c 201c 36 .. 2 2 1.9 1.7 25 126 9.7 28.1 Cambodia 220e 1,700 31 427 71 1,080 7.3 21.6 22 176 1.6 3.2 Cameroon 100 g 176g .. .. 75 212 3.7 5.8 140 394 8.7 9.9 Canada 16,932 18,265 18,206 22,732 9,176 16,976 4.2 3.7 12,658 25,994 6.3 6.1 Central African Republic 26e 12e .. 7 4d 4d .. .. 43d 32d .. .. Chad 19g 29g .. .. 43d .. .. .. 38d .. .. .. Chile 1,540 2,027 1,070 2,651 1,186 1,816 6.1 2.8 934 1,581 5.1 3.6 China 20,034 49,913 4,520 34,524 8,730 37,132 5.9 3.5 3,688 28,242 2.7 3.3 Hong Kong, China 10,200 15,821 3,023 75,812 9,604d 15,311d .. 3.9d 10,497d,h 13,974 d,h .. 3.8d,h Colombia 1,433b 1,053b 1,057 1,553 887 2,005 7.2 7.0 1,162 1,796 7.3 5.9 Congo, Dem. Rep. 35e 61e .. .. .. .. .. .. .. .. .. .. Congo, Rep. 37g .. .. .. 15 34 1.1 0.7 69 103 5.1 3.5 Costa Rica 785 1,725 273 485 763 1,890 17.1 17.1 336 577 7.1 4.6 Côte d'Ivoire 188 .. .. .. 103 84 2.4 0.9 312 361 8.2 5.0 Croatia 1,485f 8,659 f .. .. 1,349 8,296 19.3 38.7 422 770 4.6 3.1 Cuba 742e 2,150e 72 199 1,100 d 2,404 d .. .. .. .. .. .. Czech Republic 3,381f 6,435f .. .. 2,880 5,869 10.2 5.4 1,635 2,781 5.4 2.7 Denmark 2,124f 4,699 f 5,035 5,469 3,691h 4,493h 5.6h 3.5h 4,288h 5,690 h 7.4h 5.0 h Dominican Republic 1,776c,e 3,965c,e 168 420 1,571h 3,792h 27.4h 35.6h 267 499 4.4 3.9 Ecuador 440 b,i 841b,i 271 733 315 492 6.1 3.5 331 706 5.8 5.1 Egypt, Arab Rep. 2,871 8,646 2,683 4,531 2,954 8,133 22.3 22.2 1,371 2,156 8.0 5.3 El Salvador 235 1,138 348 1,382 152 1,175 7.5 23.2 99 601 2.7 6.9 Eritrea 315b,c 78b,c .. .. 58d 60 d 43.1d .. .. .. .. .. Estonia 530 1,940 1,764 .. 452 1,372 17.6 10.5 121 705 4.2 4.8 Ethiopia 103e 290 c 120 .. 177 639 23.1 29.1 30 97 2.1 1.8 Finland .. 3,375 5,147 5,756 2,384 3,326 5.0 3.6 2,853 3,988 7.6 4.9 France 60,033 79,083 18,686 22,466 31,295 54,033 8.6 9.0 20,699 37,793 6.2 6.0 Gabon 125e .. .. .. 94 74 3.2 1.8 183 275 10.6 12.8 Gambia, The 45 125 .. .. 28 69 15.8 34.4 16 8 6.9 2.5 Georgia 85b 983b 228 .. 75 361 13.1 14.1 171 257 12.1 5.8 Germany 14,847f 23,569 f 55,800 71,200 24,052 42,792 4.0 3.3 66,527 84,205 11.2 7.3 Ghana 286c 429c .. .. 30 910 1.9 17.8 74 575 3.5 6.9 Greece 10,130 16,039 .. .. 4,182 14,495 26.9 25.9 1,495 3,004 6.0 3.7 Guatemala 563 1,502 333 1,055 216 1,008 7.7 13.6 167 572 4.5 4.5 Guinea 12e 46e .. .. 1 70 0.1 3.7 29 41 2.9 3.0 Guinea-Bissau .. 12e .. .. 3 2 5.3 2.6 6 18 6.7 17.3 Haiti 145 112 .. .. 90h 135h 46.8h 19.3h 35 233 4.4 11.2 376 2008 World Development Indicators 6.17 GLOBAL LINKS Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Honduras 271 739 149 308 85 490 5.2 12.9 99 353 5.3 5.8 Hungary .. 9,259 13,083 17,612 2,938 5,223 14.9 6.0 1,501 3,076 7.5 3.5 India 2,124i 4,447i 3,056 8,340 2,582 9,227 6.8 4.6 996 9,296 2.1 4.0 Indonesia 4,324 4,871 1,782 4,106 5,229 4,890 9.9 4.3 2,172 5,028 4.0 5.3 Iran, Islamic Rep. 489 1,659 1,000 .. 205 1,513 1.1 .. 247 5,004 1.6 .. Iraq 61b .. .. .. 18d,h .. .. .. 117d,h .. .. .. Ireland 4,818 8,001 2,547 6,848 2,698 7,664 5.5 4.4 2,034 6,978 4.8 4.6 Israel 2,215i 1,825i 2,259 3,713 3,491 3,319 12.7 5.3 2,626 3,870 7.4 6.3 Italy 31,052 41,058 18,173 25,697 30,426 41,644 10.3 8.1 17,219 27,437 6.9 5.2 Jamaica 1,147c,e 1,679c,e .. .. 1,199 2,094 35.3 43.8 173 315 4.6 4.4 Japan 3,345b,i 7,334b,i 15,298 17,535 4,894 11,490 1.0 1.6 46,966 37,659 11.2 5.6 Jordan 1,075 3,225c 1,128 1,628 973 2,008 28.0 26.1 719 698 14.7 5.4 Kazakhstan .. 3,143 523 3,004 155 973 2.6 2.3 296 1,060 4.9 3.2 Kenya 896 1,536 .. .. 590 1,182 20.0 19.8 183 178 5.2 2.2 Korea, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Korea, Rep. 3,753b,c 6,155b,c 3,819 11,610 6,670 8,069 4.5 2.1 6,947 20,386 4.5 5.5 Kuwait 72g 91g 878 1,928 307 470 2.2 0.7 2,514 5,753 19.9 23.4 Kyrgyz Republic 36 766 42 454 5 176 1.1 14.8 7 115 1.0 5.1 Lao PDR 60 842 .. .. 52 173 12.8 .. 34 .. 4.5 .. Latvia 539 1,535 1,812 3,151 37 622 1.8 7.1 62 788 2.8 5.9 Lebanon 450 1,063 .. .. 710 5,491 .. 38.1 .. 3,783 .. 21.9 Lesotho 87 347 .. .. 29 28 14.6 3.7 17 22 1.6 1.5 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Libya 56 149 484 .. 4 244 0.1 0.6 98 915 1.7 5.8 Lithuania 650 2,000 1,925 .. 102 1,077 3.2 6.1 107 931 2.7 4.5 Macedonia, FYR 147f 202f .. .. 19 156 2.7 5.2 27 110 1.7 2.6 Madagascar 75e 312e 39 .. 106 386 14.2 21.8 79 86 8.0 3.9 Malawi 192 438 .. .. 22 43 4.7 .. 53 75 8.0 .. Malaysia 7,469 17,547 20,642 30,761 5,044 12,355 6.1 6.8 2,722 4,847 3.1 3.3 Mali 42e,g 153e,g .. .. 26 167 4.9 10.8 74 133 7.5 7.3 Mauritania .. .. .. .. 11h .. 2.2h .. 30 .. 5.9 .. Mauritius 422 788 107 186 616 1,302 26.2 32.5 184 347 7.5 7.3 Mexico 20,241c 21,353c 8,450 14,002 6,847 13,341 7.7 5.0 3,587 9,399 4.4 3.4 Moldova 32 13 71 68 71 145 8.0 9.4 73 220 7.3 7.0 Mongolia 108 386 .. .. 33 261 6.5 12.9 22 212 4.2 11.3 Morocco 2,602c 6,558 c 1,317 2,247 1,469 6,899 16.2 31.7 356 1,123 3.2 4.4 Mozambique .. 578 .. .. 49 145 10.2 5.2 68 205 6.6 6.0 Myanmar 117 264 .. .. 169 59 12.9 1.2 18 40 0.9 1.4 Namibia 272 833 .. .. 278 473 16.0 14.9 90h 118h 4.3h 4.0h Nepal 363 375 100 373 232 157 22.5 12.7 167 261 10.3 8.9 Netherlands 6,574f 10,739 f 12,313 16,695 10,611 11,548 4.4 2.5 13,151 17,125 6.1 4.1 New Zealand 1,409b 2,409b 920 1,861 2,318h 4,563h 13.0h 15.0h 1,289h 2,526h 7.5h 7.8h Nicaragua 281 773c 255 788 51 237 7.7 10.2 56 177 4.9 4.5 Niger 35 60 10 .. 7 35 2.2 7.8 26 54 5.7 4.0 Nigeria 656 1,010 .. .. 47 46 0.4 0.1 939 1,385 7.3 5.6 Norway 2,880a 3,945 590 3,193 2,730 4,224 4.9 2.7 4,481 11,400 9.6 12.1 Oman 279g 1,306g .. .. 193 743 2.5 3.3 47 868 0.9 6.4 Pakistan 378 898 .. .. 582 919 5.7 4.5 654 2,029 4.6 5.8 Panama 345 843 185 284 372 1,446 4.9 11.6 181 401 2.3 3.4 Papua New Guinea 42 78 51 .. 25 4 0.8 0.1 58 56 3.0 2.1 Paraguay 438i 388i 427 210 162 111 3.4 2.0 173 143 3.3 2.3 Peru 479 1,635 508 1,857 521 1,586 7.9 6.0 428 1,005 4.5 5.5 Philippines 1,760 c 2,843c 1,615 2,144 1,141 3,063 4.3 5.8 551 1,550 1.7 2.6 Poland 19,215 15,670 36,387 44,696 6,927 8,121 19.4 5.9 5,865 6,151 17.3 4.3 Portugal 9,511i 11,282c .. 18,378 5,646 10,036 17.5 16.3 2,540 4,050 6.4 5.3 Puerto Rico 3,131e 3,722e 1,237 1,468 1,828d 3,369d .. .. 1,155d 1,752d .. .. 2008 World Development Indicators 377 6.17 Travel and tourism International tourists Inbound tourism expenditure Outbound tourism expenditure thousands Inbound Outbound $ millions % of exports $ millions % of imports 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 1995 2006 Romania 5,445b 6,037b 5,737 8,906 689 1,650 7.3 4.2 749 1,457 6.6 2.7 Russian Federation 10,290 22,486 21,329 29,107 4,312 9,720 4.6 2.9 11,599 19,601 14.0 9.4 Rwanda .. .. .. .. 4 31 5.4 11.2 13 35 3.5 4.8 Saudi Arabia 3,325 8,620 .. 2,000 .. 4,961d .. 2.3d .. 1,806d .. 1.7d Senegal .. 769 .. .. 168 334 11.2 13.2 154 144 8.5 4.3 Serbia .. 469 f .. .. .. 398d .. .. .. 322d .. .. Sierra Leone 38e 34 e 6 67 57h 23h 44.4h 7.4h 51 15 19.4 3.5 Singapore 6,070 7,588 2,867 5,533 7,611h 7,069h 4.8h 2.1h 4,663h 10,384h 3.2h 3.6h Slovak Republic 903f 1,612f 218 22,688 630 1,513 5.7 .. 338 1,055 3.2 .. Slovenia 732f 1,617f .. 2,680 1,128 1,911 10.9 7.4 606 1,058 5.6 4.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4,488 8,396 2,520 .. 2,655 8,967 7.7 11.8 2,414 5,230 7.2 6.2 Spain 34,920 58,451 3,648 10,676 27,369 57,537 20.4 17.8 5,826 20,348 4.3 5.1 Sri Lanka 403i 560i 504 757 367 733 7.9 8.6 279 666 4.7 5.7 Sudan 29i 328 c 195 .. 8h 126h 1.2h 2.1h 43h 1,403h 3.5h 14.2h Swaziland 300a 873g .. 1,072 54 74 5.3 3.3 45 53 3.5 2.3 Sweden 2,310 f 3,270 f 10,127 12,591 4,390 10,437 4.6 5.2 6,816 12,844 8.4 7.7 Switzerland 6,946g 7,863g 11,148 .. 11,354 12,755 9.2 5.8 9,478 11,866 8.7 6.2 Syrian Arab Republic .. 4,422 1,746 4,042 1,258 2,113 21.9 16.0 498 585 9.0 4.9 Tajikistan .. .. .. .. .. 11 .. 0.7 .. 6h .. 0.3h Tanzania 285 622 157 .. 502 950 39.7 29.6 360 571 16.8 11.2 Thailand 6,952c 13,822i 1,820 3,382 9,257 15,559 13.2 10.2 4,791 6,140 5.8 4.2 Timor-Leste .. .. .. .. .. .. .. .. .. .. .. .. Togo 53g 81g .. .. 13 27 2.8 3.2 40 42 6.0 2.9 Trinidad and Tobago 260e 463e 261 .. 232 593 8.3 5.6 91 234 4.3 3.7 Tunisia 4,120i 6,549i 1,778 2,241 1,838 2,999 23.0 19.0 294 498 3.3 3.0 Turkey 7,083 18,916 3,981 8,275 4,957 18,441 13.6 15.8 911 3,154 2.3 2.2 Turkmenistan 218 12 21 33 13 .. 0.7 .. 74 .. 4.1 .. Uganda 160 539 148 254 78 356 11.7 23.8 80 210 5.4 6.5 Ukraine 3,716 18,900 6,552 16,875 191 4,018 1.1 8.0 210 3,202 1.1 6.0 United Arab Emirates 2,315a,c 7,126a,c .. .. 632d 4,972d .. .. .. 8,827d .. .. United Kingdom 21,719 30,654 41,345 69,536 27,577 43,041 8.6 6.3 30,749 78,325 9.4 10.2 United States 43,490 50,978 51,285 63,662 93,700 128,922 11.8 8.9 60,924 104,310 6.8 4.7 Uruguay 2,022 1,749 562 666 725 706 20.7 12.5 332 306 9.3 5.3 Uzbekistan 92 262 246 455 15d 57d .. .. .. .. .. .. Venezuela, RB 700 748 534 1,095 995 745 4.8 1.1 1,852 1,807 11.0 4.7 Vietnam 1,351b 3,583b .. .. .. 3,200 d .. 5.1d .. .. .. .. West Bank and Gaza 220 g 123g .. .. 255h 121h .. .. 162h 265h .. .. Yemen, Rep. 61g 382g .. .. 50h 181h 2.3h 2.3h 76 225 3.1 2.9 Zambia 163 669 .. .. 29h 110h 2.4h 2.7h 83 96 6.2 3.0 Zimbabwe 1,416b 2,287b 256 .. 145d 338d .. .. 106d .. .. .. World 538,992 t 850,778 t 579,267 t 1,030,976 t 486,150 t 887,743 t 7.6 w 6.0 w 458,239 t 803,866 t 7.5 w 5.6 w Low income 11,056 27,246 .. .. 7,285 22,549 6.5 5.7 6,477 24,213 4.2 4.4 Middle income 156,970 301,883 179,154 344,318 90,126 231,020 8.4 6.3 64,580 148,415 6.0 4.6 Lower middle income 60,125 148,352 35,370 107,329 42,277 111,524 9.1 6.1 20,190 67,636 3.9 3.8 Upper middle income 97,893 155,980 134,188 222,638 47,852 119,501 7.9 6.4 44,492 80,578 7.5 5.4 Low & middle income 170,318 332,275 212,104 419,006 97,598 253,983 8.3 6.2 71,208 171,125 5.8 4.6 East Asia & Pacific 44,243 98,476 36,056 81,142 31,197 78,567 7.8 4.8 14,769 48,335 3.5 3.5 Europe & Central Asia 56,887 108,942 101,318 176,948 24,108 68,438 9.1 7.0 24,473 49,564 9.4 5.8 Latin America & Carib. 38,965 55,387 21,780 38,100 21,591 45,333 7.5 5.6 18,751 33,091 6.5 4.9 Middle East & N. Africa 13,617 36,214 13,353 26,968 9,947 30,744 12.8 16.2 4,459 13,835 4.3 6.4 South Asia 3,819 7,296 5,151 12,998 4,016 11,608 6.8 4.8 2,393 12,923 3.0 4.2 Sub-Saharan Africa 12,878 27,486 .. .. 6,729 19,170 7.6 10.0 6,766 15,177 7.0 5.5 High income 361,206 510,271 320,789 533,390 388,504 633,422 7.5 5.9 386,329 632,672 7.9 6.0 Euro area 201,613 284,903 139,868 194,611 163,394 285,919 7.8 6.5 154,655 243,434 7.8 5.9 a. Arrivals in hotels only. b. Arrivals of nonresident visitors at national borders. c. Includes nationals residing abroad. d. Country estimates. e. Arrivals by air only. f. Arrivals in all types of accommodation establishments. g. Arrivals in hotels and similar establishments. h. Expenditure on travel-related items only; excludes passenger transport items. i. Excludes nationals residing abroad. 378 2008 World Development Indicators 6.17 GLOBAL LINKS Travel and tourism About the data Definitions Tourism is defined as the activities of people trav- the arrivals of international visitors, which include · International inbound tourists (overnight visitors) are eling to and staying in places outside their usual tourists, same-day visitors, cruise passengers, and the number of tourists who travel to a country other environment for no more than one year for leisure, crew members. than that in which they have their usual residence, business, and other purposes not related to an activ- Sources and collection methods for data on arriv- but outside their usual environment, for a period not ity remunerated from within the place visited. The als differ across countries. In some cases data are exceeding 12 months and whose main purpose in social and economic phenomenon of tourism has obtained from border statistics (police, immigration, visiting is other than an activity remunerated from grown substantially over the past quarter century. and the like) and supplemented by border surveys. In within the country visited. When data on number of Statistical information on tourism is based mainly other cases data are obtained from tourism accom- tourists are not available, the number of visitors, which on data on arrivals and overnight stays along with modation establishments. For some countries num- includes tourists, same-day visitors, cruise passen- balance of payments information. But these data ber of arrivals is limited to arrivals by air and for gers, and crew members, is shown instead. · Interna- do not completely capture the economic phenom- others to arrivals staying in hotels. Some countries tional outbound tourists are the number of departures enon of tourism or give governments, businesses, include arrivals of nationals residing abroad while that people make from their country of usual residence and citizens the information needed for effective others do not. Comparison of arrivals across coun- to any other country for any purpose other than a public policies and efficient business operations. tries should thus be treated with caution. remunerated activity in the country visited. · Inbound Credible data are needed on the scale and signifi - The World Tourism Organization is improving its tourism expenditure is expenditures by international cance of tourism. Information on the role of tour- coverage of tourism expenditure data. It is now using inbound visitors, including payments to national carri- ism in national economies is particularly deficient. balance of payments data from the International ers for international transport. These receipts include Although the World Tourism Organization reports that Monetary Fund (IMF), supplemented by data received any other prepayment made for goods or services progress has been made in harmonizing definitions from individual countries. The new data, shown in the received in the destination country. They also may and measurement, differences in national practices table, include travel and passenger transport items include receipts from same-day visitors, except when still prevent full international comparability. as defined in the IMF's Balance of Payments Manual. these are important enough to justify separate classifi- The data in the table are from the World Tourism When the IMF does not report data on passenger cation. For some countries they do not include receipts Organization, an agency of the United Nations. The transport items, expenditure data for travel items for passenger transport items. Their share in exports is data on international inbound and outbound tour- are shown instead. calculated as a ratio to exports of goods and services, ists refer to the number of arrivals and departures Aggregates are based on the World Bank's classifi - which comprise all transactions between residents of of visitors, not to the number of people traveling. cation of countries and differ from those of the World a country and the rest of the world involving a change Thus a person who makes several trips to a coun- Tourism Organization. Countries not shown in the of ownership from residents to nonresidents of general try during a given period is counted each time as table but for which data are available are included merchandise, goods sent for processing and repairs, a new arrival. Unless otherwise indicated in the in the regional and income group totals. The aggre- nonmonetary gold, and services. · Outbound tourism footnotes, the data on inbound tourism show the gates are calculated using the World Bank's weighted expenditure is expenditures of international outbound arrivals of nonresident tourists (overnight visitors) aggregation methodology (see Statistical methods) visitors in other countries, including payments to for- at national borders. When data on international tour- and differ from the World Tourism Organization's eign carriers for international transport. These expen- ists are unavailable or incomplete, the table shows aggregates. ditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For Developing countries are spending more on tourism in other countries 6.17a some countries they do not include expenditures for passenger transport items. Their share in imports is $ billions calculated as a ratio to imports of goods and services, 200 which comprise all transactions between residents of South Asia Sub-Saharan Africa a country and the rest of the world involving a change 150 Middle East & N. Africa of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, 100 nonmonetary gold, and services. Data sources 50 Latin America & Caribbean Europe & Central Asia Data on visitors and tourism expenditure are from the World Tourism Organization's Yearbook East Asia & Pacific 0 of Tourism Statistics and Compendium of Tourism 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Statistics 2008. Data in the table are updated Although almost 80 percent of the world's expenditure on tourism in other countries originated in high- from electronic files provided by the World Tour- income countries in 2006, developing countries' share has been gradually rising. Developing countries' ism Organization. Data on exports and imports expenditures on tourism in other countries nearly doubled between 2000 and 2006. are from the IMF's Balance of Payments Statistics Source: World Bank staff calculations based on World Tourism Organization data. Yearbook and data files. 2008 World Development Indicators 379 Text figures, tables, and boxes PRIMARY DATA DOCUMENTATION The World Bank is not a primary data collection agency for most areas other than business and investment climate surveys, living standards surveys, and external debt. As a major user of socioeconomic data, however, the World Bank recog- nizes the importance of data documentation to inform users of differences in the methods and conventions used by primary data collectors--usually national statistical agencies, 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 quality 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. 2008 World Development Indicators 381 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 Preliminary C G Albania Albanian lek a 1996 b VAB 2005 BPM5 Actual G C G Algeria Algerian dinar 1980 VAB BPM5 Actual S B Angola Angolan kwanza 1997 VAP 1991­96 2005 BPM5 Actual S 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 Australia Australian dollar a 2000 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 Bangladesh Bangladesh taka 1995/96 b VAB 2005 BPM5 Actual 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 Benin CFA franc 1985 VAP 1992 2005 BPM5 Preliminary S B G Bolivia Boliviano 1990 b VAB 1960­85 2005 BPM5 Actual S C G Bosnia and Herzegovina Konvertible mark a 1996 b VAB 2005 BPM5 Actual C Botswana Botswana pula 1993/94 b VAB 2005 BPM5 Actual G B G Brazil Brazilian real a 2000 b VAB 2005 BPM5 Actual S C S 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 Preliminary S B G Canada Canadian dollar 2000 b VAB 2005 BPM5 G C S Central African Republic CFA franc 2000 VAB 2005 BPM4 Estimate S B G Chad CFA franc 1995 VAB 2005 BPM5 Preliminary S C G Chile Chilean peso 1996 b VAB 2005 BPM5 Actual S C S China Chinese yuan 2000 b VAP 1978­93 2005 BPM5 Preliminary S B G Hong Kong, China Hong Kong dollar 2000 b VAB 2005 BPM5 G C S Colombia Colombian peso 1994 b VAB 1992­94 2005 BPM5 Actual S B S Congo, Dem. Rep. Congo franc 1987 VAB 1999­2001 2005 BPM5 Estimate S C G Congo, Rep. CFA Franc 1978 VAP 2005 BPM5 Estimate 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 Actual G C S Cuba Cuban peso 1984 VAP G Czech Republic Czech koruna 2000 1995 b VAB 2005 BPM5 G C S Denmark Danish krone 2000 b VAB 2005 BPM5 G C S Dominican Republic Dominican peso 1990 VAP 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 1982­90 BPM5 Actual S C S Eritrea Eritrean nakfa 1992 VAB BPM4 Actual Estonia Estonian kroon 2000 b VAB 1991­95 2005 BPM5 G C S Ethiopia Ethiopian birr 1999/2000 b VAB 2005 BPM5 Actual G C G Finland Euro 2000 b VAB 2005 BPM5 G C S France Euro a 2000 b VAB 2005 BPM5 S C S Gabon CFA franc 1991 VAP 1993 2005 BPM5 Estimate S B G Gambia, The Gambian dalasi 1987 VAB 2005 BPM5 Actual G B G Georgia Georgian lari a 1994 b VAB 1990­95 2005 BPM5 Actual G C G Germany Euro 2000 b VAB 2005 BPM5 S C S Ghana Ghanaian cedi 1975 VAP 1973­87 2005 BPM5 Actual G B G Greece Euro a 2000 VAB 2005 BPM5 S C S Guatemala Guatemalan quetzal 1958 VAP BPM5 Actual S B G Guinea Guinean franc 1996 1994 VAB 2005 BPM5 Preliminary S B G Guinea-Bissau CFA franc 1986 VAB 2005 BPM5 Estimate G G 382 2008 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 1977 1987 Albania 2001 RHS, 2002 LSMS, 2004 Yes 1998 2004 2006 1995 Algeria 1998 MICS, 2000 HLSS, 1995 2001 2006 1995 Angola 1970 MICS, 2001 1964­65 1991 1987 Argentina 2001 EPH, 2003 Yes 2002 2002 2006 1995 Armenia 2001 DHS, 2005 ILCS, 2003 Yes 2006 1994 Australia 2006 SIHC, 1994 Yes 2001 2003 2006 1985 Austria 2001 Microcensus, 2000 Yes 1999­2000 2003 2006 1991 Azerbaijan 1999 RHS, 2001 HBS, 2003 Yes 2006 1995 Bangladesh 2001 DHS, 2004; MICS 2006 HES, 2005 2005 1998 2004 1990 Belarus 1999 MICS, 2005 IES, 2005 Yes 1994 2006 1990 Belgium 2001 ECHP, 2000 Yes 1999­2000c 2001 2006 Benin 2002 DHS, 2001 CWIQ, 2003 1992 2005 1994 Bolivia 2001 DHS, 2003 MECOVI, 2002 1984­88 2001 2006 1987 Bosnia and Herzegovina 1991 MICS, 2006 LSMS, 2005 Yes 2006 1995 Botswana 2001 MICS, 2000 HIES, 1993/94 1993 2004 2006 1992 Brazil 2000 DHS, 1996 PNAD, 2005 1996 2004 2006 1992 Bulgaria 2001 HBS, 2003 Yes 2006 1988 Burkina Faso 2006 DHS, 2003 EVCBM, 2003 1993 2004 1992 Burundi 1990 MICS, 2000 Priority survey, 1998 2005 1987 Cambodia 1998 DHS, 2005 SES, 2004 2004 1987 Cameroon 1987 DHS, 2004 Priority survey, 2001 1984 2006 1987 Canada 2006 SLID, 2000 Yes 1996/2001 2002 2006 1991 Central African Republic 2003 MICS, 2006 EPI, 1993 1985 2005 1987 Chad 1993 DHS, 2004 ECOSIT, 1995 1995 1987 Chile 2002 CASEN, 2003 Yes 1996­97 2006 1987 China 2000 Intercensal survey, 1995 HHS (Rural/Urban), 2004 1997 2006 1993 Hong Kong, China 2006 Yes 2006 Colombia 2005­06 DHS, 2005 ECV, 2004 2001 2000 2006 1996 Congo, Dem. Rep. 1984 MICS, 2001 1990 1986 1990 Congo, Rep. 1996 DHS, 2005 1985­86 1995 1987 Costa Rica 2000 RHS, 1993 EHPM, 2004 Yes 1973 2006 1997 Côte d'Ivoire 1998 MICS, 2006; AIS, 2005 LSMS, 2002 2001 2006 1987 Croatia 2001 HBS, 2005 Yes 2003 2006 1996 Cuba 2002 MICS, 2006 Yes 2004 1995 Czech Republic 2001 RHS, 1993 Microcensus, 1996/97 Yes 2000 2006 1991 Denmark 2001 Income Tax Register, 1997 Yes 1999­2000 2003 2006 1990 Dominican Republic 2002 DHS, 2002; ENFT, 2005 1971 2001 1994 ENHOGAR, 2006 Ecuador 2001 RHS, 2004 LSMS, 1998 1999­2000 2004 2006 1997 Egypt, Arab Rep. 2006 DHS, 2005; SPA 2004 HECS, 2004/05 Yes 1999­2000 2002 2006 1996 El Salvador 1992 RHS, 2002/03 EHPM, 2002 Yes 1970­71 2006 1992 Eritrea 1984 DHS, 2002 2003 Estonia 2000 HBS, 2004 Yes 2001 2006 1995 Ethiopia 1994 DHS, 2005 ICES, 2000 2001­02 2006 1987 Finland 2000 IDS, 2000 Yes 1990­2000 2002 2006 1991 France 2004 HBS, 1994/95 Yes 1999­2000 2003 2006 1999 Gabon 2003 DHS, 2000 1974­75 2006 1987 Gambia, The 2003 MICS, 2005/06 HHS, 2003/04 2001­02 1995 2006 1982 Georgia 2002 MICS, 1999; RHS, 1999 SGH, 2005 Yes 2006 1990 Germany 2004 GSOEP, 2000 Yes 1999­2000 2003 2006 1991 Ghana 2000 DHS, 2003; MICS, 2006 LSMS, 1998/99 1984 2003 2006 1997 Greece 2001 ECHP, 2000 Yes 1999­2000 1998 2006 1980 Guatemala 2002 RHS, 2002 ENEI-2, 2004 Yes 2003 2006 1992 Guinea 1996 DHS, 2005 LSMS, 2003 2000­01 2002 1987 Guinea-Bissau 1991 MICS, 2000 IES, 1993 1988 1995 1991 2008 World Development Indicators 383 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 Haiti Haitian gourde 1975/76 VAB 1991 BPM5 Preliminary G Honduras Honduran lempira 1978 VAB 1988­89 BPM5 Actual S B G Hungary Hungarian forint a 2000 b VAB 2005 BPM5 Actual S 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 1980­90 2005 BPM5 Actual G C Iraq Iraqi dinar 1997 VAB 2005 S Ireland Euro 2000 b VAB 2005 BPM5 G C S Israel Israeli new shekel 2005 b VAP 2005 BPM5 S C S Italy Euro 2000 b VAB 2005 BPM5 S C S Jamaica Jamaica dollar 1996 VAB 1996 BPM5 Actual G C G Japan Japanese yen 2000 VAB 2005 BPM5 G C S Jordan Jordan dinar 1994 VAB 2005 BPM5 Actual G B G Kazakhstan Kazakh tenge a 1995 b VAB 1987­95 2005 BPM5 Actual G C S Kenya Kenya shilling 2001 b VAB 2005 BPM5 Actual G B G Korea, Dem. Rep. Democratic Republic BPM5 of Korea won Korea, Rep. Korean won 2000 b VAB 2005 BPM5 S C S 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 lat 2000 b VAB 1991­95 2005 BPM5 Actual S C S Lebanon Lebanese pound 2004 VAB 2005 BPM5 Actual G B G Lesotho Lesotho loti 1995 b VAB 2005 BPM5 Actual G C G Liberia Liberian dollar 1992 VAB 2005 Estimate G Libya Libyan dinar 1975 VAB 1986 BPM5 G Lithuania Lithuanian litas 2000 b VAB 1990­95 2005 BPM5 Actual G C S 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 Estimate G B G Malaysia Malaysian ringgit 1987 VAP 2005 BPM5 Estimate G C S Mali CFA franc 1987 VAB 2005 BPM4 Actual G B G Mauritania Mauritanian ouguiya 1985 VAB 2005 BPM4 Actual G G Mauritius Mauritian rupee 1997/98 VAB 2005 BPM5 Actual G C G Mexico Mexican new peso 1993 b VAB 2005 BPM5 Actual G C S Moldova Moldovan leu a 1996 b VAB 1987­95 2005 BPM5 Actual G C S Mongolia Mongolian tugrik 2005 b VAB 2005 BPM5 Actual S C G Morocco Moroccan dirham 1998 VAB 2005 BPM5 Actual S C S Mozambique Mozambican metical 1995 VAB 1992­95 2005 BPM5 Preliminary S G Myanmar Myanmar kyat 1985/86 VAP BPM5 Estimate G C Namibia Namibia dollar 1995/96 b VAB 2005 BPM5 B G Nepal Nepalese rupee 1994/95 VAB 2005 BPM5 Actual S C G Netherlands Euro a 2000 b VAB 2005 BPM5 S C S New Zealand New Zealand dollar 2000/01 VAB 2005 BPM5 G C Nicaragua Nicaraguan gold cordoba 1994 b VAB 1965­93 BPM5 Actual S B G Niger CFA franc 1987 VAP 1993 2005 BPM5 Preliminary S G Nigeria Nigerian naira 1987 VAB 1971­98 2005 BPM5 Preliminary G G Norway Norwegian krone a 2000 b VAB 2005 BPM5 G C S Oman Rial Omani 1988 VAP 2005 BPM5 Actual G B G Pakistan Pakistan rupee 1999/2000 b VAB 2005 BPM5 Actual G C G Panama Panamanian balboa 1996 b VAB 1996 BPM5 Actual S C G Papua New Guinea Papua New Guinea kina 1983 VAB 1989 BPM5 Actual G B Paraguay Paraguayan guarani 1994 b VAP 1982­88 2005 BPM5 Actual S C G Peru Peruvian new sol 1994 VAB 1985­91 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 384 2008 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 Haiti 2003 DHS, 2005 ECVH, 2001 1971 1997 1991 Honduras 2001 DHS, 2005 EPHPM, 2003 1993 2006 1992 Hungary 2001 FBS, 2004 Yes 2000 2002 2006 1991 India 2001 DHS, 2005/06 NSS, 2004/05 1995­1996/ 2003 2006 1990 2000­2001 Indonesia 2000 DHS, 2002/03 SUSENAS, 2005 2003 2003 2006 1990 Iran, Islamic Rep. 2006 DHS, 2000 SECH, 2005 Yes 2003 2003 2006 1993 Iraq 1997 MICS, 2006 1981 1976 1990 Ireland 2006 ECHP, 2000 Yes 2000 2006 1980 Israel 1995 HES, 2001 Yes 1981 2006 1997 Italy 2001 SHIW, 2000 Yes 2000 2003 2006 1998 Jamaica 2001 RHS, 2002/03; MICS 2005 LSMS, 2004 1978­79 2006 1993 Japan 2005 Yes 2000 2002 2006 1992 Jordan 2004 DHS, 2002 HIES, 2002/03 1997 2004 2006 1993 Kazakhstan 1999 DHS, 1999; MICS, 2006 HBS, 2003 Yes 2006 1993 Kenya 1999 DHS, 2003; SPA, 2004 WMS II, 1997 1977­79 2004 2004 1990 Korea, Dem. Rep. 1993 MICS, 2000 1987 Korea, Rep. 2005 NSFIE, 1998/99 Yes 2000 2002 2006 1994 Kuwait 2005 FHS, 1996 Yes 1970 2001 1994 Kyrgyz Republic 1999 DHS, 1997; MICS 2005/06 HBS, 2003 Yes 2002 2001 2006 1994 Lao PDR 2005 MICS, 2000 ECS I, 2002 1998­99 1975 1987 Latvia 2000 HBS, 2005 Yes 2001 2006 1994 Lebanon 1970 MICS, 2000 1998­99 2004 1996 Lesotho 2006 DHS, 2004 HBS, 1995 1999­2000 2002 1987 Liberia 1984 MICS, 1995 1984 1987 Libya 1995 MICS, 2000 2001 2004 1999 Lithuania 2001 HBS, 2004 Yes 1994 2004 2006 1995 Macedonia, FYR 2002 HBS, 2003 Yes 1994 2001 2006 1996 Madagascar 1993 DHS, 2003/04 Priority survey, 2001 1984­85 2004 2006 1984 Malawi 1998 DHS, 2004; MICS 2006 HHS, 2004/05 1993 2001 2006 1994 Malaysia 2000 HIBAS, 1997 Yes 2003 2006 1995 Mali 1998 DHS, 2001 EMCES, 2001 1984 2004 1987 Mauritania 2000 DHS, 2000/01 LSMS, 2000 1984­85 2006 1985 Mauritius 2000 Yes 2002 2006 Mexico 2005 ENPF, 1995 ENIGH, 2004 1991 2000 2006 1998 Moldova 2004 DHS, 2005 HBS, 2003 Yes 2006 1992 Mongolia 2000 MICS, 2005 LSMS/Integrated Survey, 2002 Yes 2000 2006 1993 Morocco 2004 DHS, 2003/04 LSMS, 1998/99 1996 2004 2006 1998 Mozambique 2007 DHS, 2003 NHS, 2002/03 1999­2000 2006 1992 Myanmar 1983 MICS, 2000 2003 1992 1987 Namibia 2001 DHS, 2000 NHIES, 1993 1996­97 2006 1991 Nepal 2001 DHS, 2006 LSMS, 2003/04 2002 2002 2003 1994 Netherlands 2001 ECHP, 1999 Yes 1999­2000c 2003 2006 1991 New Zealand 2006 Yes 2002 2002 2006 1991 Nicaragua 2005 DHS, 2001 LSMS, 2001 Yes 2001 2006 1998 Niger 2001 DHS/MICS, 2006 1980 2005 1988 Nigeria 2006 DHS, 2003 LSMS, 2003 1960 2003 1987 Norway 2001 IF 2000 Yes 1999 2001 2006 1985 Oman 2003 FHS, 1995 1978­79 2006 1991 Pakistan 1998 RHS, 2000/01 PIHS, 2005 2000 2006 1991 Panama 2000 LSMS, 2003 EH, 2003 2001 2001 2006 1990 Papua New Guinea 2000 DHS, 1996 HHS, 1996 2004 1987 Paraguay 2002 RHS, 2004 EIH, 2003 1991 2006 1987 Peru 2005 DHS, 2004 ENAHO, 2003 1994 1996 2006 1992 Philippines 2000 DHS, 2003 FIES, 2003 Yes 2002 2003 2006 1995 Poland 2002 HBS, 2005 Yes 1996/2002 2006 1991 2008 World Development Indicators 385 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 Portugal Euro 2000 b VAB 2005 BPM5 S C S Puerto Rico U.S. dollar 1954 VAP G Romania New Romanian leu a 1999 b VAB 1987­89, 2005 BPM5 Preliminary S C S 1992 Russian Federation Russian ruble 2003 2000 b VAB 1987­95 2005 BPM5 Preliminary G C S Rwanda Rwanda franc 1995 VAP 2005 BPM5 Preliminary G C G Saudi Arabia Saudi Arabian riyal 1999 VAP 2005 BPM4 G Senegal CFA franc 1999 b VAB 2005 BPM5 Actual S B G Serbia Serbian dinar 2002 VAB 2005 Actual Sierra Leone Sierra Leonean leone 2001 1990 b VAB 2005 BPM5 Preliminary G B G Singapore Singapore dollar 2000 b VAB 2005 BPM5 G C S Slovak Republic Slovak koruna 2000 1995 b VAP 2005 BPM5 Actual G C S Slovenia Euro a 2000 b VAB 2005 BPM5 S C S Somalia Somali shilling 1985 VAB 1977­90 Estimate South Africa South African rand 2000 b VAB 2005 BPM5 Preliminary S C S Spain Euro 2000 b VAB 2005 BPM5 S C S Sri Lanka Sri Lankan rupee 1996 VAB 2005 BPM5 Actual G B G Sudan Sudanese dinar 1981/82d 1982 VAB 2005 BPM5 Actual G B G Swaziland Lilangeni 1985 VAB 2005 Actual 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 1970­2006 2005 BPM5 Estimate S C G Tajikistan Tajik somoni a 1997 b VAB 1990­95 2005 BPM5 Preliminary G C G Tanzania Tanzania shilling 1992 VAB 2005 BPM5 Estimate S G Thailand Thai baht 1988 VAP 2005 BPM5 Actual G C S Timor-Leste U.S. dollar 2000 VAP Togo CFA franc 1978 VAP 2005 BPM5 Actual S B G Trinidad and Tobago Trinidad and 2000 b VAB 1996 BPM5 S C G Tobago dollar Tunisia Tunisian dinar 1990 VAP 2005 BPM5 Actual G C S Turkey New Turkish lira 1987 VAB 2005 BPM5 Actual S B S Turkmenistan Turkmen manat a 1987 b VAB 1987­95, 2000 BPM5 Actual G 1997­2006 Uganda Uganda shilling 1997/98 VAB 2005 BPM5 Actual G B G Ukraine Ukrainian hryvnia a 2003 b VAB 1990­95 2005 BPM5 Actual G C S United Arab Emirates U.A.E. dirham 1995 VAB BPM4 G C 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 1983 VAB 2005 BPM5 Actual S C S Uzbekistan Uzbek sum a 1997 b VAB 1990­95 2000 BPM5 Actual G Venezuela, RB Venezuelan bolivar 1997 VAB 2005 BPM5 Actual G C G Vietnam Vietnamese dong 1994 b VAP 1991 2005 BPM4 Actual G C G West Bank and Gaza Israeli new shekel 1997 VAB B G Yemen, Rep. Yemen rial 1990 VAP 1991­96 2005 BPM5 Actual G B G Zambia Zambian kwacha 1994 VAB 1990­92 2005 BPM5 Actual G B G Zimbabwe Zimbabwe dollar 1990 VAB 1991, 1998 2005 BPM5 Actual G C G 386 2008 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 Portugal 2001 Yes 1999 2003 2006 1990 Puerto Rico 2000 RHS, 1995/96 Yes 1997/2002 Romania 2002 RHS, 1999 LSMS, 2005 Yes 2002 2004 2006 1994 Russian Federation 2002 RHS, 1996 LMS, Round 9, 2002 Yes 1994­95 2004 2006 1994 Rwanda 2002 DHS, 2005 LSMS, 1999/2000 1984 2003 1993 Saudi Arabia 2004 Demographic survey, 1999 1999 2006 1992 Senegal 2002 DHS, 2005 ESASM, 1995 1998­99 2002 2006 1987 Serbia 2002 MICS, 2000 Yes 2006 Sierra Leone 2004 MICS, 2005 SLIHS, 2003 1984­85 2002 1987 Singapore 2000 General household, 2005 Yes 2003 2006 1975 Slovak Republic 2001 Microcensus, 1996 Yes 2001 2003 2006 1991 Slovenia 2002 HBS, 2004 Yes 2000 2006 1996 Somalia 1987 MICS, 1999 1982 1987 South Africa 2001 DHS, 1998 IES, 2000 2002 2004 2006 1990 Spain 2001 ECHP, 2000 Yes 1999 2003 2006 1997 Sri Lanka 2001 DHS, 1987 HIES, 2002 Yes 2002 2005 1990 Sudan 1993 MICS, 2000 2006 1995 Swaziland 2007 MICS, 2000 SHIES, 2000/01 2000 2005 .. Sweden 2005 HINK, 2000 Yes 1999­2000 2002 2006 1991 Switzerland 2000 EVE, 2000 Yes 2000 2006 1991 Syrian Arab Republic 1994 MICS, 2006 1981 2006 1995 Tajikistan 2000 MICS, 2005 LSMS, 2004 Yes 1994 2000 1994 Tanzania 2002 DHS, 2004; AIS 2003 HIES, 2000/01 2002­03 2006 1994 Thailand 2000 DHS, 1987; MICS 2005/06 SES, 2002 2003 2000 2006 1990 Timor-Leste 2004 Togo 1981 MICS, 2006 1996 2005 1987 Trinidad and Tobago 2000 MICS, 2000 LSMS, 1992 Yes 2004 2002 2006 1997 Tunisia 2004 MICS, 2000 LSMS, 2000 2004 2005 1996 Turkey 2000 DHS, 2003 LSMS, 2003 2001 2001 2006 1997 Turkmenistan 1995 DHS,2000 LSMS, 1998 Yes 2000 1994 Uganda 2002 DHS, 2006; AIS, 2004 NIHS III, 2002 1991 2006 1970 Ukraine 2001 MICS, 2000 HBS, 2003 Yes 2004 2006 1992 United Arab Emirates 2005 1998 2005 1995 United Kingdom 2001 FRS, 1999 Yes 1999­2000c 2002 2006 1991 United States 2000 CPS (monthly) CPS, 2000 Yes 1997/2002 2001 2006 1990 Uruguay 2004 ECH, 2003 Yes 2000 2003 2006 1965 Uzbekistan 1989 MICS, 2006; FBS, 2003 Yes 1994 DHS special, 2002 Venezuela, RB 2001 MICS, 2000 EHM, 2003 Yes 1997 2006 1970 Vietnam 1999 DHS 2002; AIS 2005 LSMS, 2004 2001 2005 1990 West Bank and Gaza 1997 PAPFAM, 2006 1971 Yemen, Rep. 2004 DHS, 1997 HBS, 2005 2002 2003 2006 1990 Zambia 2000 DHS, 2001/02, SPA, 2005 LCMS II, 2004 1990 2006 1994 Zimbabwe 2002 DHS, 2005/06 LCMS III, 1995 1960 1996 2005 1987 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. Conducted annually. d. Reporting period switch from fiscal year to calendar year from 1996. Pre-1996 data converted to calendar year. 2008 World Development Indicators 387 Primary data documentation notes · Base year is the base or pricing period used for goods entered into customs storage are recorded as Cluster Survey (Encuesta Nacional de Hogares de constant price calculations in the country's national imports at arrival. Under the special trade system Propósitos Múltiples), ENPF is National Family Plan- accounts. Price indexes derived from national goods are recorded as imports when declared for ning Survey (Encuesta Nacional de Planificacion Fami- accounts aggregates, such as the implicit deflator for domestic consumption whether at time of entry or on liar), FHS is Family Health Survey, LSMS is Living gross domestic product (GDP), express the price level withdrawal from customs storage. Exports under the Standards Measurement Survey, MICS is Multiple relative to base year prices. · Reference year is the general system comprise outward-moving goods: Indicator Cluster Survey, PAPFAM is Pan Arab Project year in which the local currency, constant price series (a) national goods wholly or partly produced in the for Family Health, RHS is Reproductive Health Survey, of a country is valued. The reference year is usually country; (b) foreign goods, neither transformed nor and SPA is Service Provision Assessments. Detailed the same as the base year used to report the constant declared for domestic consumption in the country, information for AIS, DHS, and SPA are available at price series. However, when the constant price data that move outward from customs storage; and www.measuredhs.com/aboutsurveys; for MICS at are chain linked, the base year is changed annually, (c) nationalized goods that have been declared for www.childinfo.org; and for RHS at www.cdc. gov/repro- so the data are rescaled to a specific reference year domestic consumption and move outward without ductivehealth/surveys. · Source of most recent to provide a consistent time series. When the country being transformed. Under the special system of trade, income and expenditure data shows household sur- has not rescaled following a change in base year, exports are categories a and c. In some compilations veys that collect income and expenditure data. CASEN World Bank staff rescale the data to maintain a longer categories b and c are classified as re-exports. Direct is Caracterizacion Socioeconomica Nacional, CPS is historical series. To allow for cross-country compari- transit trade--goods entering or leaving for transport Current Population Survey, CWIQ is Core Welfare Indi- son and data aggregation, constant price data only--is excluded from both import and export statis- cators Questionnaire, ECH is Encuesta Continua de reported in World Development Indicators are rescaled tics. See About the data for tables 4.4, 4.5, and 6.2 Hogares, ECHP is European Community Household to a common reference year (2000) and currency (U.S. for further discussion. · Government finance account- Panel, ECOSIT is Enquête sur la Consommation des dollars). · System of National Accounts identifies ing concept is the accounting basis for reporting cen- Ménages et le Secteur Informel au Tchad, ECS is countries that use the 1993 System of National tral government financial data. For most countries Expenditure and Consumption Survey, ECV is Encuesta Accounts (1993 SNA), the terminology applied in World government finance data have been consolidated (C) Nacional de Calidad de Vida, ECVH is Enquête sur les Development Indicators since 2001, to compile into one set of accounts capturing all central govern- Conditions de Vie en Haïti, EH is Encuesta de Hogares, national accounts. Although more countries are adopt- ment fiscal activities. Budgetary central government EHM is Encuesta de Hogares por Muestreo, EHPM is ing the 1993 SNA, many still follow the 1968 SNA, accounts (B) exclude some central government units. Encuesta de Hogares de Propositos Multiples, EIH is and some low-income countries use concepts from See About the data for tables 4.10, 4.11, and 4.12 for Encuesta Integrada de Hogares, EMCES is Enquête the 1953 SNA. · SNA price valuation shows whether further details. · IMF data dissemination standard Malienne de Conjoncture Economique et Sociale, value added in the national accounts is reported at shows the countries that subscribe to the IMF's Spe- ENAHO is Enquesta Nacional de Hogares, ENEI is basic prices (VAB) or producer prices (VAP). Producer cial Data Dissemination Standard (SDDS) or General Encuesta Nacional de Empleo e Ingresos, ENFT is prices include taxes paid by producers and thus tend Data Dissemination System (GDDS). S refers to coun- Encuesta Nacional de Fuerza de Trabajo, ENIGH is to overstate the actual value added in production. tries that subscribe to the SDDS and have posted data Encuesta Nacional de Ingreso-Gasto de los Hogares, However, VAB can be higher than VAP in countries with on the Dissemination Standards Bulletin Board at EPH is Encuesta Permanente de Hogares, EPHPM is high agricultural subsidies. See About the data for http://dsbb.imf.org. G refers to countries that sub- Encuesta Permanente de Hogares de Propositos Mul- tables 4.1 and 4.2 for further discussion of national scribe to the GDDS. The SDDS was established for tiples, EPI is Enquête Prioritaire sur les Conditions de accounts valuation. · Alternative conversion factor member countries that have or might seek access to Vie des Ménages, ESASM is Enquête Sénégalaise identifies the countries and years for which a World international capital markets to guide them in provid- Auprès des Ménages, EVCBM is Enquête Burkinabé Bank­estimated conversion factor has been used in ing their economic and financial data to the public. The sur les Conditions de Die des Ménages, EVE is Ein- place of the official exchange rate (line rf in the Inter- GDDS helps countries disseminate comprehensive, kommens- und Verbraucherserhebung, FBS and HBS national Monetary Fund's [IMF] International Financial timely, accessible, and reliable economic, financial, are Household Budget Survey, FIES is Family Income Statistics). See Statistical methods for further discus- and sociodemographic statistics. IMF member coun- and Expenditure Survey, FRS is Family Resources Sur- sion of alternative conversion factors. · Purchasing tries elect to participate in either the SDDS or the vey, GSOEP is German Socio-Economic Panel, HECS power parity (PPP) survey year is the latest available GDDS. Both standards enhance the availability of is Household Expenditure and Consumption Survey, survey year for the International Comparison Pro- timely and comprehensive data and therefore contrib- HES is Household Expenditure Survey, HHS is House- gram's estimates of PPPs. See About the data for table ute to the pursuit of sound macroeconomic policies. hold Survey, HIBAS is Household Income and Basic 1.1 for a more detailed description of PPPs. · Balance The SDDS is also expected to improve the functioning Amenities Survey, HIES is Household Income and of Payments Manual in use refers to the classification of financial markets. · Latest population census Expenditure Survey, HINK is Household Income Sur- system used to compile and report data on balance shows the most recent year in which a census was vey, HLSS is Household Living Standards Survey, ICES of payments items in table 4.15. BPM4 refers to the conducted and in which at least preliminary results is Income, Consumption, and Expenditure Survey, IDS 4th edition of the IMF's Balance of Payments Manual were released. It includes registration-based cen- is Income Distribution Survey, IES is Income and (1977), and BPM5 to the 5th edition (1993). · Exter- suses. Some countries with complete population reg- Expenditure Survey, IF is Inntekts- og formuesunder- nal debt shows debt reporting status for 2006 data. istration systems produce similar tables every 5 or 10 søkelsen for husholdninger, ILCS is Integrated Survey Actual indicates that data are as reported, preliminary years instead of conducting regular censuses. of Living Standards, LCMS is Living Conditions Moni- that data are preliminary and include an element of · Latest demographic, education, or health house- toring Survey, LMS is Longitudinal Measurement Sur- staff estimation, and estimate that data are World hold survey indicates the household surveys used to vey, LSMS is Living Standards Measurement Study, Bank staff estimates. · System of trade refers to the compile the demographic, education, and health data MECOVI is Measurement of Living Conditions in Latin United Nations general trade system (G) or special in section 2. AIS is AIDS Indicator Survey, CPS is Cur- America and the Caribbean, NHS is National House- trade system (S). Under the general trade system rent Population Survey, DHS is Demographic and hold Survey, NIHS is National Integrated Household goods entering directly for domestic consumption and Health Survey, ENHOGAR is National Multiple Indicator Survey, NSFIE is National Survey of Family Income and 388 2008 World Development Indicators Primary data documentation notes Expenditures, NSS is National Sample Survey of are shown in the second year of the period. Balance statistical discrepancy. The Central Statistical Office Households, PIHS is Pakistan Integrated Household of payments data are reported in World Development published large-scale revisions of constant price dis- Survey, PNAD is Pesquisa Nacional por Amostra de Indicators by calendar year and so are not comparable crepancy in GDP for 1996/97­2004/05 in April 2006 Domicilios, SECH is Socioeconomic Characteristics of to the national accounts data of the countries that and May 2007. · Brazil. The Institute of Geography and Households, SES is Socioeconomic Survey, SGH is report their national accounts on a fiscal year basis. Statistics revised its national accounts data. Among Survey of Georgian Households, SHIW is Survey of the changes are new sources and a change in base Household Income and Wealth, SIHC is Survey of Economies with exceptional reporting periods year to 2000. · Burkina Faso. National accounts value Income and Housing Costs, SLID is Survey of Labour added and expenditure data have been revised from Reporting period and Income Dynamics, SLIHS is Sierra Leone Inte- Fiscal for national 1985­2006 according to recently released data from Economy year end accounts data grated Household Survey, SUSENAS is Socioeconomic the Ministry of Economy and Finance. Constant price Afghanistan Mar. 20 FY Survey, and WMS is Welfare Monitoring Survey. series have been linked back since 1984. Valuation Australia Jun. 30 FY Detailed information on household surveys for devel- is value added at basic prices, and the new base year Bangladesh Jun. 30 FY oping countries can be found on the website of the is 1999. · Chile. Data from 2003 onward reflect the Botswana Jun. 30 FY International Household Survey Network (www.survey- Central Bank's new series using 2003 as the base Canada Mar. 31 CY network.org). · Vital registration complete identifies year. · China. The base year for constant price data Egypt, Arab Rep. Jun. 30 FY countries judged to have at least 90 percent complete changed from 1990 to 2000. · Côte d'Ivoire. Data Ethiopia Jul. 7 FY registries of vital (birth and death) statistics by the for 1999­2006 were revised using data from the IMF, Gambia, The Jun. 30 CY United Nations Statistics Division and reported in national authorities, and World Bank staff estimates. Haiti Sep. 30 FY Population and Vital Statistics Reports. Countries with · Egypt. Constant price data are updated from official India Mar. 31 FY complete vital statistics registries may have more published national accounts. Constant price imports Indonesia Mar. 31 CY accurate and more timely demographic indicators than and exports data have been revised based on data from Iran, Islamic Rep. Mar. 20 FY other countries. · Latest agricultural census shows the Central Bank website (www.cbe.org.eg), which lists Japan Mar. 31 CY the most recent year in which an agricultural census the constant price expenditure components of GDP. Kenya Jun. 30 CY was conducted and reported to the Food and Agricul- · Fiji. Data revisions reflect changes in sources. Data Kuwait Jun. 30 CY ture Organization of the United Nations. · Latest for 1996­2005 were revised using data from the Asian Lesotho Mar. 31 CY industrial data show the most recent year for which Development Bank's Key Indicators 2007. · India. In Malawi Mar. 31 CY manufacturing value added data at the three-digit May 2007 the Central Statistical Organization pub- Mauritius Jun. 30 FY level of the International Standard Industrial Classifi - lished revised national accounts data for 1951­99 Myanmar Mar. 31 FY cation (ISIC, revision 2 or 3) are available in the United consistent with the new series of national accounts Namibia Mar. 31 CY Nations Industrial Development Organization data- statistics released on January 31, 2006. · Jordan. Nepal Jul. 14 FY base. · Latest trade data show the most recent year New Zealand Mar. 31 FY Data have been revised by the Central Bank and the for which structure of merchandise trade data from Pakistan Jun. 30 FY Department of Statistics. · Lebanon. Data have been the United Nations Statistics Division's Commodity Puerto Rico Jun. 30 FY revised by the Central Bank. · Malawi. The central sta- Trade (Comtrade) database are available. · Latest Sierra Leone Jun. 30 CY tistics office, with assistance from Norway, revised its water withdrawal data show the most recent year for Singapore Mar. 31 CY national accounts data. The initial outcome is that GDP which data on freshwater withdrawals have been com- South Africa Mar. 31 CY will increase by approximately 37 percent. · Morocco. piled from a variety of sources. See About the data for Swaziland Mar. 31 CY The government revised national accounts data from table 3.5 for more information. Sweden Jun. 30 CY 1998 onward. National accounts value added data Thailand Sep. 30 CY switched from producer prices to basic prices. The Exceptional reporting periods Uganda Jun. 30 FY new base year is 1998. · São Tomé and Principe. Data In most economies the fiscal year is concurrent with United States Sep. 30 CY have been revised by the National Statistics Institute. the calendar year. Exceptions are shown in this table. Zimbabwe Jun. 30 CY Revised GDP estimates are much higher (47.5 per- The ending date reported here is for the fiscal year of cent for the new base year 2001) than those of the the central government. Fiscal years for other levels of Revisions to national accounts data previous series and reflect improvements in coverage. government and reporting years for statistical surveys National accounts data are revised by national statisti- · Senegal. National accounts data have been revised may differ. And some countries that follow a fiscal cal offices when methodologies change or data sources to conform to 1993 SNA methodology, and the base year report their national accounts data on a calendar improve. National accounts data in World Development year has changed to 1999. Value added data are now year basis as shown in the reporting period column. Indicators are also revised when data sources change. in basic prices. Agricultural sector data are entered The reporting period for national accounts data is The following notes, while not comprehensive, provide in the year of production (N) in the 1999 base year of designated as either calendar year basis (CY) or fiscal information on revisions from previous data. the SNA as opposed to the year following the year of year basis (FY). Most economies report their national · Bhutan. Data revisions reflect changes in production (N+1) in base year 1987. · Sudan. Expen- accounts and balance of payments data using calen- sources. Current and constant price value added data diture items in both current and constant prices for dar years, but some use fiscal years. In World Devel- from 1980 to 2006 are from the government of Bhu- 1988­95 were revised using recent United Nations opment Indicators fiscal year data are assigned to tan. Current price expenditure data for 1989­2005 Statistics Division and IMF World Economic Outlook the calendar year that contains the larger share of and constant price expenditure data for 2000­05 are estimates. · Tanzania. National accounts expenditure the fiscal year. If a country's fiscal year ends before from the Asian Development Bank's Key Indicators data in current and constant prices have been revised June 30, data are shown in the first year of the fiscal 2007. · Botswana. Large changes in constant price from 1995 onward. Data are from IMF and World Bank period; if the fiscal year ends on or after June 30, data consumption indicators from 1998­2006 are due to staff estimates and Tanzanian authorities. 2008 World Development Indicators 389 STATISTICAL METHODS This section describes some of the statistical procedures used in preparing the World indicator as a weight) and denoted by a u when calculated as unweighted Development Indicators. It covers the methods employed for calculating regional and averages. The aggregate ratios are based on available data, including data income group aggregates and for calculating growth rates, and it describes the World for economies not shown in the main tables. Missing values are assumed Bank Atlas method for deriving the conversion factor used to estimate gross national to have the same average value as the available data. No aggregate is cal- income (GNI) and GNI per capita in U.S. dollars. Other statistical procedures and culated if missing data account for more than a third of the value of weights 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 · Aggregate growth rates are denoted by a w when calculated as a weighted economies appear at the end of most tables. The countries included in these clas- average of growth rates. In a few cases growth rates may be computed from sifications are shown on the flaps on the front and back covers of the book. Most time series of group totals. Growth rates are not calculated if more than half tables also include the aggregate euro area. This aggregate includes the member the observations in a period are missing. For further discussion of methods states of the Economic and Monetary Union (EMU) of the European Union that have of computing growth rates see below. adopted the euro as their currency: Austria, Belgium, Cyprus, Finland, France, Ger- · Aggregates denoted by an m are medians of the values shown in the table. many, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovenia, No value is shown if more than half the observations for countries with a and Spain. Other classifications, such as the European Union and regional trade population of more than 1 million are missing. 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 judgment Because of missing data, aggregates for groups of economies should be of World Bank analysts, the aggregates may be based on as little as 50 percent of treated as approximations of unknown totals or average values. Regional and the available data. In other cases, where missing or excluded values are judged to be income group aggregates are based on the largest available set of data, including small or irrelevant, aggregates are based only on the data shown in the tables. values for the 153 economies shown in the main tables, other economies shown in table 1.6, and Taiwan, China. The aggregation rules are intended to yield esti- Growth rates mates for a consistent set of economies from one period to the next and for all Growth rates are calculated as annual averages and represented as percentages. indicators. Small differences between sums of subgroup aggregates and overall Except where noted, growth rates of values are computed from constant price totals and averages may occur because of the approximations used. In addition, series. Three principal methods are used to calculate growth rates: least squares, compilation errors and data reporting practices may cause discrepancies in theo- exponential endpoint, and geometric endpoint. Rates of change from one period retically identical aggregates such as world exports and world imports. to the next are calculated as proportional changes from the earlier period. Five methods of aggregation are used in World Development Indicators: · For group and world totals denoted in the tables by a t, missing data are Least-squares growth rate. Least-squares growth rates are used wherever imputed based on the relationship of the sum of available data to the total there is a sufficiently long time series to permit a reliable calculation. No growth in the year of the previous estimate. The imputation process works forward rate is calculated if more than half the observations in a period are missing. and backward from 2000. Missing values in 2000 are imputed using one of The least-squares growth rate, r, is estimated by fitting a linear regression trend several proxy variables for which complete data are available in that year. The line to the logarithmic annual values of the variable in the relevant period. The imputed value is calculated so that it (or its proxy) bears the same relation- regression equation takes the form ship to the total of available data. Imputed values are usually not calculated if missing data account for more than a third of the total in the benchmark ln Xt = a + bt year. The variables used as proxies are GNI in U.S. dollars, total population, exports and imports of goods and services in U.S. dollars, and value added which is equivalent to the logarithmic transformation of the compound growth in agriculture, industry, manufacturing, and services in U.S. dollars. 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 100 390 2008 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. 2008 World Development Indicators 391 CREDITS World Development Indicators draws on a wide range of World Bank reports and Indikadahena (health expenditure), Monika Bloessner and Mercedes de Onis numerous external sources, listed in the bibliography following this section. (malnutrition and overweight), Neeru Gupta (health workers), Mie Inoue (hospital Many people inside and outside the World Bank helped in writing and producing beds), and Seyed Mehran Hosseini (tuberculosis); and Khin Wityee Oo of the the book. The team would like to particularly acknowledge the help and encour- United Nations Children's Fund (health). agement of Alan Gelb, Acting Senior Vice President and Chief Economist of the World Bank, and Shaida Badiee, Director, Development Data Group. The team is 3. Environment also grateful to the people who provided valuable comments on the entire book. Section 3 was prepared by Mehdi Akhlaghi and M. H. Saeed Ordoubadi in partner- This note identifies many of those who made specific contributions. Numerous ship with the World Bank's Sustainable Development Network. Important contri- others, too many to acknowledge here, helped in many ways for which the team butions were made by Carola Fabi and Edward Gillin of the Food and Agriculture is extremely grateful. Organization of the United Nations; Ricardo Quercioli of the International Energy Agency; Amay Cassara, Christian Layke, Daniel Prager, and Robin White of the 1. World view World Resources Institute; Laura Battlebury of the World Conservation Monitoring The introduction to section 1 was prepared by Sebastien Dessus and Eric Centre; and Gerhard Metchies of German Technical Cooperation (GTZ). The World Swanson. Sarwar Lateef provided valuable suggestions. Rafael de Hoyos and Bank's Environment Department devoted substantial staff resources to the book, Maurizio Bussolo of the Development Economics Prospects Group helped in com- for which the team is very grateful. M.H. Saeed Ordoubadi wrote the introduction puting the inequality estimates. Yuri Dikhanov and the International Comparison with valuable comments from Sarwar Lateef, Jeffrey Lewis, Bruce Ross-Larson, Program team provided the new estimates of purchasing power parities (PPP) and and Eric Swanson. Other contributions were made by Susmita Dasgupta, Kirk Sup Lee prepared the special PPP table. Changqing Sun prepared the estimates Hamilton, Craig Meisner, Kiran Pandey, Giovanni Ruta, and Jana Stover. of gross national income in PPP terms. K.M. Vijayalakshmi prepared tables 1.1 and 1.6. Uranbileg Batjargal prepared table 1.4, with valuable assistance from 4. Economy Azita Amjadi. Tables 1.2, 1.3, and 1.5 were prepared by Masako Hiraga. Dominic Section 4 was prepared by K.M. Vijayalakshmi in close collaboration with Patrick Mellor of the World Bank's Economic Policy and Debt Department provided the Sustainable Development and Economic Data Team of the World the estimates of debt relief for the Heavily Indebted Poor Countries Debt Initiative Bank's Development Data Group, led by Soong Sup Lee. Eric Swanson and and Multilateral Debt Relief Initiative. The team is grateful to Yasmin Ahmad and K.M. Vijayalakshmi wrote the introduction with valuable suggestions from Aimee Nichols at the Organisation for Economic Co-operation and Development Sarwar Lateef and Soong Sup Lee. Contributions to the section were provided for data and advice on official development assistance flows and agricultural by Azita Amjadi (trade). The national accounts data for low- and middle-income support estimates. economies were gathered by the World Bank's regional staff through the annual Unifi ed Survey. Maja Bresslauer, Mahyar Eshragh-Tabary, Victor Gabor, and 2. People Soong Sup Lee worked on updating, estimating, and validating the databases Section 2 was prepared by Masako Hiraga and Sulekha Patel in partnership with for national accounts. The team is grateful to the International Monetary Fund, the World Bank's Human Development Network and the Development Research Organisation for Economic Co-operation and Development, United Nations Group in the Development Economics Vice Presidency. Kyoko Okamoto and Industrial Development Organization, and World Trade Organization for access William Prince provided invaluable assistance in data and table preparation, and to the databases. Kiyomi Horiuchi prepared the demographic estimates and projections. Masako Hiraga and Sulekha Patel wrote the introduction with valuable inputs and com- 5. States and markets ments from Sadia Chowdhury, Sarwar Lateef, and Eric Swanson. The poverty Section 5 was prepared by David Cieslikowski and Raymond Muhula, in partner- estimates were prepared by Shaohua Chen and and Prem Sangraula of the World ship with the World Bank's Financial and Private Sector Development Network, Bank's Poverty Monitoring Group and Changquin Sun. The data for table 2.6 on Poverty Reduction and Economic Management Network, Sustainable Development children at work were prepared by Lorenzo Guarcello and Furio Rosati from the Network, the International Finance Corporation, and external partners. Sarwar Understanding Children's Work project. The data on health gaps by income and Lateef wrote the introduction to the section with input from David Cieslikowski, gender were based on data prepared by Darcy Gallucio and Davidson Gwatkin of Alan Gelb, Steve Knack, Aart Kraay, Brian Levy, and Eric Swanson. Other con- the Human Development Network. Other contributions were provided by Eduard tributors include Ada Karina Izaguirre (privatization and infrastructure projects); Bos and Emi Suzuki (population, health, and nutrition); Montserrat Pallares- Shokraneh Minovi and Leora Klapper (micro, small, and medium-size enterprises); Miralles (vulnerability and security); Lawrence Jeffrey Johnson of the International Jorge Luis Rodriguez Meza and Federica Saliola (Enterprise Surveys); Svetlana Labour Organization (labor force); Juan Cruz Perusia and Jose Pessoae of the Bagaudinova (Doing Business); Alka Banerjee and Isilay Cabuk (Standard & United Nations Educational, Scientific, and Cultural Organization Institute for Poor's global stock market indexes); Himmat Kalsi (financial); Rui Coutinho Statistics (education and literacy); the World Health Organization's Chandika (public policies and institutions); Nigel Adderley of the International Institute 392 2008 World Development Indicators for Strategic Studies (military personnel); Bjorn Hagelin and Petter Stålenheim Design, production, and editing of the Stockholm International Peace Research Institute (military expenditures Richard Fix and Beatriz Prieto-Oramas coordinated all stages of production with and arms transfers); Henrich Bofinger, Tsukasa Hattori, and Helene Stephan Communications Development Incorporated, which provided overall design direc- (transport); Jane Degerlund of Containerisation International (ports); Vanessa tion, editing, and layout, led by Meta de Coquereaumont, Bruce Ross-Larson, Grey and Esperanza Magpantay of the International Telecommunication Union and and Christopher Trott. Elaine Wilson created the graphics and typeset the book. Mark Williams (communications and information); Ernesto Fernandez Polcuch of Joseph Caponio and Amye Kenall provided proofreading and production assis- the United Nations Educational, Scientific, and Cultural Organization Institute for tance. Communications Development's London partner, Peter Grundy of Peter Statistics (research and development, researchers, and technicians); and Anders Grundy Art & Design, provided art direction and design. Staff from External Affairs Halvorsen of the World Information Technology and Services Alliance (information oversaw printing and dissemination of the book. and communication technology expenditures). Client services 6. Global links The Development Data Group's Client Services and Communications Team (Azita Section 6 was prepared by Uranbileg Batjargal and Azita Amjadi in partnership Amjadi, Richard Fix, Buyant Erdene Khaltarkhuu, William Prince, and Beatriz with the World Bank's Development Research Group (trade), Prospects Group Prieto-Oramas) contributed to the design and planning of World Development (commodity prices), and external partners. Eric Swanson and Himmat Kalsi wrote Indicators 2008 and helped coordinate work with the Office of the Publisher. the introduction, with assistance from Uranbileg Batjargal, David Cieslikowski, Ibrahim Levent, and K.M. Vijayalakshmi and comments from Sarwar Lateef and Administrative assistance and office technology support Changqing Sun. Substantial input for the data came from Azita Amjadi (trade), Awatif Abuzeid and Estela Zamora provided administrative assistance. Jean-Pierre Jerzy Rozanski (tariffs), and Ibrahim Levent and Gloria Moreno (external debt and Djomalieu, Gytis Kanchas, Nacer Megherbi, and Shahin Outadi provided informa- financial data). Other contributors include David Cristallo and Henri Laurencin of tion technology support. the United Nations Conference on Trade and Development, Rohini Acharya and Hubert Escaith of the World Trade Organization, and Francis Ng (trade); Betty Dow Publishing and dissemination (commodity prices); Dilek Aykut (foreign direct investment flows); Eung Ju Kim The Office of the Publisher, under the direction of Carlos Rossel, provided valu- (financing through capital markets); Yasmin Ahmad, Elena Bernaldo, and Aimee able assistance throughout the production process. Stephen McGroarty, Randi Nichols of the Organisation for Economic Co-operation and Development and Park, and Nora Ridolfi coordinated printing and supervised marketing and distribu- Malvina Pollock (aid); Nanasamudd Chhim, Nevin Fahmy, and Nino Kostova (debt); tion. Merrell Tuck-Primdahl of the Development Economics Vice President's Office Henrik Pilgaard of the United Nations Refugee Agency (refugees); Bela Hovy of the managed the communications strategy. United Nations Population Division (migration); K.M. Vijayalakshmi (remittances); David Cieslikowski (table 6.1); and Teresa Ciller of the World Tourism Organization World Development Indicators CD-ROM (tourism). Quality assurance of tables was provided by the Social Indicators team, Programming and testing were carried out by Reza Farivari and his team: Azita led by Sulekha Patel, and the Financial Data team, lead by Ibrahim Levent. Mehdi Amjadi, Ying Chi, Ramgopal Erabelly, Nacer Megherbi, Shahin Outadi, and William Akhlaghi, Joseph Judkins, Gytis Kanchas, William Prince, and Atsushi Shimo Prince. Masako Hiraga produced the social indicators tables. William Prince provided valuable technical assistance. coordinated user interface design and overall production and provided quality assurance. Photo credits belong to the World Bank photo library. The interactive Other parts of the book text was produced by Dohatec. Jeff Lecksell of the World Bank's Map Design Unit coordinated preparation of the maps on the inside covers. David Cieslikowski prepared the Users guide. Eric WDI Online Swanson wrote Statistical methods. K.M. Vijayalakshmi coordinated preparation Design, programming, and testing were carried out by Reza Farivari and his team: of Primary data documentation, and Uranbileg Batjargal assisted in updating Mehdi Akhlaghi, Azita Amjadi, and Shahin Outadi. William Prince coordinated the Primary data documentation table. Richard Fix and Beatriz Prieto-Oramas production and provided quality assurance. Valentina Kalk and Triinu Tombak of prepared Partners and Index of indicators. the Office of the Publisher were responsible for implementation of WDI Online and management of the subscription service. Database management Mehdi Akhlaghi coordinated management of the integrated World Development Client feedback Indicators database with assistance from William Prince. Operation of the data- The team is grateful to the many people who have taken the time to provide base management system was made possible by the Data and Information assistance on its publications. Their feedback and suggestions have helped Systems Team under the leadership of Reza Farivari. improve this year's edition. 2008 World Development Indicators 393 BIBLIOGRAPHY Abbott, Alison. 2004. "Saving Venice." Nature. January 10. [www.nature.com/ Brown, Lester R., Christopher Flavin, Hilary F. French, and others. 1998. State of news/2004/040112/full/040112-8.html;jsessionid=26CC93DEBA2BEDF87 the World 1998: A Worldwatch Institute Report on Progress toward a Sustainable 62546E0413759D5]. January 2007. Society. New York: W.W. Norton. AbouZahr, Carla, and Tessa Wardlaw. 2003. Maternal Mortality in 2000: Estimates Bulatao, Rodolfo. 1998. The Value of Family Planning Programs in Developing Coun- Developed by WHO, UNICEF, and UNFPA. Geneva: World Health Organization. tries. RAND Monograph Report. Santa Monica, Calif.: RAND Corporation. Ahmad, Sultan. 1992. "Regression Estimates of Per Capita GDP Based on Purchas- Burton, Ian, Elliot Diringer, and Joel Smith. 2006. "Climate Change: International ing Power Parities." Policy Research Working Paper 956. World Bank, Interna- Policy Options." Pew Center on Global Climate Change, Arlington, Va. tional Economics Department, Washington, D.C. Caiola, Marcello. 1995. A Manual for Country Economists. Training Series 1. Vol. ------. 1994. "Improving Inter-Spatial and Inter-Temporal Comparability of National 1. Washington, D.C.: International Monetary Fund. Accounts." Journal of Development Economics 44 (1): 53­75. CAWMA (Comprehensive Assessment of Water Management in Agriculture). An, F., and A. Sauer. 2004. "Comparison of Passenger Vehicle Fuel Economy and 2007. Water for Food, Water for Life. London: Earthscan. Greenhouse Gas Emission Standards around the World." Pew Center on Global Carr, Dara. 2004. "Improving the Health of the World's Poorest People." Health Climate Change, Arlington, Va. Bulletin 1. Population Reference Bureau, Washington, D.C. Anthoff, David, Robert J. Nichols, Richard S. J. Tol, and Athanasios T. Vafeidis. Centro Latinoamericano de Demografia. Various issues. Boletín Demografico. 2006. "Global and Regional Exposure to Large Rises in Sea-level: A Sensitiv- Chen, Shaohua, and Martin Ravallion. 2004. "How Have the World's Poorest Fared ity Analysis." Working Paper 96. Tyndall Centre for Climate Change Research, since the 1980s?" World Bank Research Observer 19 (2): 141­69. University of East Anglia, Norwich, U.K. Chomitz, Kenneth M., Piet Buys, and Timothy S. Thomas. 2005. "Quantifying the Arndt, Christiane, and Charles Oman. 2006. Uses and Abuses of Governance Indica- Rural-Urban Gradient in Latin America and the Caribbean." Policy Research Work- tors. Paris: Organisation for Economic Co-operation and Development. . ing Paper 3634. World Bank, Development Research Group, Washington, D.C. Ashford, Lori S., Davidson R. Gwatkin, and Abdo S. Yazbeck. 2006. Designing CIESIN (Center for International Earth Science Information Network). 2005. Grid- Health and Population Programs to Reach the Poor. Washington, D.C.: Popula- ded Population of the World. Columbia University and Centro Internacional de tion Reference Bureau. Agricultura Tropical. [http://sedac.ciesin.columbia.edu/gpw/]. Baeza, Cristian C., and Truman G. Packard. 2006. Beyond Survival: Protecting Claessens, Stijn, Daniela Klingebiel, and Sergio L. Schmukler. 2002. "Explaining Households from Health Shocks in Latin America. Washington, D.C.: World the Migration of Stocks from Exchanges in Emerging Economies to Interna- Bank. tional Centers." Policy Research Working Paper 2816. World Bank, Development Ball, Nicole. 1984. "Measuring Third World Security Expenditure: A Research Note." Research Group, Washington, D.C. World Development 12 (2): 157­64. Cleland J., S. Bernstein, A. Ezeh, A. Faundes, A. Glasier, and J. Innis. 2006. "Family Barro, Robert J. 1991. "Economic Growth in a Cross-Section of Countries." Quar- Planning: The Unfinished Agenda." Lancet 368 (9549): 1810­27 terly Journal of Economics 106 (2): 407­43. Cline, William. 2007. Global Warming and Agriculture: Impact Estimate by Country. Beck, Thorsten, and Ross Levine. 2001. "Stock Markets, Banks, and Growth: Washington, D.C.: Center for Global Development, Peterson Institute for Inter- Correlation or Causality?" Policy Research Working Paper 2670. World Bank, national Economics. Development Research Group, Washington, D.C. Commission of the European Communities, IMF (International Monetary Fund), Bhalla, Surjit. 2002. Imagine There Is No Country: Poverty, Inequality, and Growth OECD (Organisation for Economic Co-operation and Development), United in the Era of Globalization. Washington, D.C.: Institute for International Nations, and World Bank. 2002. System of Environmental and Economic Economics. Accounts: SEEA 2000. New York. Bilsborrow, R. E., Graeme Hugo, A. S. Oberai, and Hania Zlotnik. 1997. Interna- Containerisation International. 2008. Containerisation International Yearbook tional Migration Statistics. Geneva: International Labour Office. 2008. London: Informa Maritime and Transport. Bloom, David E., and Jeffrey G. Williamson. 1998. "Demographic Transitions and Corrao, Marlo Ann, G. Emmanuel Guindon, Namita Sharma, and Donna Fakhrabadi Economic Miracles in Emerging Asia." World Bank Economic Review 12 (3): Shokoohi, eds. 2000. Tobacco Control Country Profiles. Atlanta, Ga.: American 419­55. Cancer Society. Brown, Lester R., Michael Renner, and Brian Halweil. 1999. Vital Signs 1999: The CSD (Commission on Sustainable Development). 1997. Comprehensive Assess- Environmental Trends that Are Shaping Our Future. New York: W.W. Norton. ment of the Freshwater Resources of the World. Report of the Secretary-General. Brown, Lester R., Michael Renner, and Christopher Flavin. 1998. Vital Signs New York. 1998: The Environmental Trends that Are Shaping Our Future. New York: W.W. Dasgupta, Susmita, Benoit Laplante, Craig Meisner, David Wheeler, and Jian- Norton. ping Yan. 2007. "The Impact of Sea Level Rise on Developing Countries: A 394 2008 World Development Indicators Comparative Analysis." Policy Research Working Paper 4136. World Bank, Devel- FAO (Food and Agriculture Organization). 1995. Programme for the World Census opment Research Group, Washington, D.C. of Agriculture 2000. FAO Statistical Development Series 5. Rome. Deaton, Angus. 2002. "Counting the World's Poor: Problems and Possible Solu- ------. 1996. Food Aid in Figures 1994. Vol. 12. Rome. tions." World Bank Research Observer 16 (2): 125­47. ------. 2001. Agriculture: Towards 2015/30. Rome. DEFRA (Department for Environment, Food and Rural Affairs). 2007. "New Bill and ------. 2003. State of the World's Forests 2003. Rome. Strategy Lay Foundations for Tackling Climate Change­Miliband." News Release. ------. 2005. Global Forest Resources Assessment 2005. Rome. March 13. London. [www.defra.gov.uk/news/2007/070313a.htm]. ------. Various years. Fertilizer Yearbook. FAO Statistics Series. Rome. Demirgüç-Kunt, Asli, and Ross Levine. 1996. "Stock Market Development and ------. Various years. Production Yearbook. FAO Statistics Series. Rome. Financial Intermediaries: Stylized Facts." World Bank Economic Review 10 (2): ------. Various years. State of Food Insecurity in the World. Rome. 291­321. ------. Various years. Trade Yearbook. FAO Statistics Series. Rome De Onis, Mercedes, and Monika Blössner. 2000. "The WHO Global Database on Frankhauser, Pierre. 1994. "Fractales, tissus urbains et reseaux de transport." Child Growth and Malnutrition: Methodology and Applications." International Revue d'economie politique 104: 435­55. Journal of Epidemiology 32: 518­26. Fredricksen, Birger. 1993. Statistics of Education in Developing Countries: An Intro- De Onis, Mercedes, Adelheid W. Onyango, Elaine Borghi, Cutberto Garza, and Hong duction to Their Collection and Analysis. Paris: United Nations Educational, Sci- Yang. 2006. "Comparison of the World Health Organization (WHO) Child Growth entific, and Cultural Organization. Standards and the National Center for Health Statistics/WHO International Gallup, John L., and Jeffrey D. Sachs. 1998. "The Economic Burden of Malaria." Growth Reference: Implications for Child Health Programmes." Public Health Harvard Institute for International Development, Cambridge, Mass. Nutrition 9 (7): 942­47. Gannon, Colin, and Zmarak Shalizi. 1995. "The Use of Sectoral and Project Per- Development Committee. 2003. "Supporting Sound Policies with Adequate and formance Indicators in Bank-Financed Transport Operations." TWU Discussion Appropriate Financing: Implementing the Monterrey Consensus at the Coun- Paper 21. World Bank, Transportation, Water, and Urban Development Depart- try Level." SecM2003-0370. World Bank and International Monetary Fund, ment, Washington, D.C. Washington, D.C. Gardner-Outlaw, Tom, and Robert Engelman. 1997. "Sustaining Water, Easing Disease Control Priorities Project. 2006. Global Burden of Disease and Risk Fac- Scarcity: A Second Update." Population Action International, Washington, D.C. tors. Washington, D.C.: Oxford University Press and World Bank. GEF (Global Environmental Facility). 2007. "Pledging Meeting for Climate Change Dollar, David. 2005. "Globalization, Poverty, and Inequality since 1980." World Bank Funds." 15 June. GEF Secretariat, Washington, D.C. Research Observer 20 (2): 145­75. Glasier A., A. M. Gulmezoglu, G. Schmid, C. Garcia Moreno, and P. F. A. van Look. Doyle, John J., and Gabrielle J. Persley, eds. 1996. Enabling the Safe Use of Bio- 2006. "Sexual and Reproductive Health: A Matter of Life and Death." Lancet technology: Principles and Practice. Environmentally Sustainable Development 368 (9547): 1595­1607. Studies and Monographs Series 10. Washington, D.C.: World Bank. Goldfinger, Charles. 1994. L'utile et le futile: L'économie de l'immatériel. Paris: Easterly, William. 2000. "Growth Implosions, Debt Explosions, and My Aunt Editions Odile Jacob. Marilyn: Do Growth Slowdowns Cause Public Debt Crises?" Policy Research Grimes, D.A., J. Bensen, S. Singh, M. Romero, B. Ganatra, F. E. Okonofua, and Working Paper 2531. World Bank, Development Research Group, Washing- I. H. Shah. 2006. "Unsafe abortion: the preventable pandemic." Lancet 368 ton, D.C. (9550): 1908­19. Eastwood, Robert, and Michael Lipton. 1999. "The Impact of Changes in Human GTZ (German Agency for Technical Cooperation). 2004. Fuel Prices and Taxation. Fertility on Poverty." Journal of Development Studies 36 (1): 1­30. Eschborn, Germany. EIA (Energy Information Administration). 2006. "Emission of Greenhouse Gases Gupta, Sanjeev, Brian Hammond, and Eric Swanson. 2000. "Setting the Goals." in the United States 2005." Washington, DC. OECD Observer 223: 15­17. Eurostat (Statistical Office of the European Communities). Various years. Demo- Gwatkin, Davidson R., Shea Rutstein, Kiersten Johnson, Eldaw Suliman, Adam graphic Statistics. Luxembourg. Wagstaff, and Agbessi Amouzou. 2007. Socio Economic Differences in Health, ------. Various years. Statistical Yearbook. Luxembourg. Nutrition, and Population. Washington, D.C.: World Bank. Faiz, Asif, Christopher S. Weaver, and Michael P. Walsh. 1996. Air Pollution from Habyarimana, James, Jishnu Das, Stefan Dercon, and Pramila Krishnan. 2003. Motor Vehicles: Standards and Technologies for Controlling Emissions. Washing- "Sense and Absence: Absenteeism and Learning in Zambian Schools." World ton, D.C.: World Bank. Bank, Washington, D.C. Fankhauser, Samuel. 1995. Valuing Climate Change: The Economics of the Green- Hamilton, Kirk, and Michael Clemens. 1999. "Genuine Savings Rates in Developing house. London: Earthscan. Countries." World Bank Economic Review 13 (2): 333­56. 2008 World Development Indicators 395 BIBLIOGRAPHY Hanushek, Eric. 2002. The Long-Run Importance of School Quality. NBER Working ------. Various years. International Financial Statistics Yearbook. Washington, D.C. Paper 9071. Cambridge, Mass.: National Bureau of Economic Research. International Budget Project. 2006. "Open Budget Initiative" website. [www.open- Happe, Nancy, and John Wakeman-Linn. 1994. "Military Expenditures and Arms budgetindex.org]. Trade: Alternative Data Sources." IMF Working Paper 94/69. International Mon- International Civil Aviation Organization. 2007. Civil Aviation Statistics of the World. etary Fund, Policy Development and Review Department, Washington, D.C. Montreal. Hatzichronoglou, Thomas. 1997. "Revision of the High-Technology Sector and International Diabetes Federation. Various years. Diabetes Atlas. Brussels. Product Classification." STI Working Paper 1997/2. Organisation for Economic International Institute for Strategic Studies. 2008. The Military Balance 2008. Co-operation and Development, Directorate for Science, Technology, and Indus- London: Oxford University Press. try, Paris. International Road Federation. 2007. World Road Statistics 2007. Geneva. Heston, Alan. 1994. "A Brief Review of Some Problems in Using National Accounts International Trade Center, UNCTAD (United Nations Conference on Trade and Data in Level of Output Comparisons and Growth Studies." Journal of Develop- Development), and WTO (World Trade Organization). The Millennium Develop- ment Economics 44 (1): 29­52. ment Goals. Online database. [www.mdg-trade.org/] Hettige, Hemamala, Muthukumara Mani, and David Wheeler. 1998. "Industrial Pol- International Working Group of External Debt Compilers (Bank for International lution in Economic Development: Kuznets Revisited." Policy Research Working Settlements, International Monetary Fund, Organisation for Economic Co- Paper 1876. World Bank, Development Research Group, Washington, D.C. operation and Development, and World Bank). 1987. External Debt Definitions. IEA (International Energy Agency). 2002. World Energy Outlook: Energy and Pov- Washington, D.C. erty. Paris. Inter-Secretariat Working Group on National Accounts (Commission of the Euro- ------. 2006. World Energy Outlook. OECD (Organisation for Economic Co-operation pean Communities, International Monetary Fund, Organisation for Economic and Development)/IEA, Paris. Co-operation and Development, United Nations, and World Bank). 1993. System ------. Various years. Energy Balances of OECD Countries. Paris. of National Accounts. Brussels, Luxembourg, New York, and Washington, D.C. ------. Various years. Energy Statistics and Balances of Non-OECD Countries. IPCC (Intergovernmental Panel on Climate Change). 2001a. Climate Change 2001. Paris. Cambridge, U.K.: Cambridge University Press. ------. Various years. Energy Statistics of OECD Countries. Paris. ------. 2001b. Climate Change 2001: The Scientific Basis; Contribution of Working ILO (International Labour Organization). Various years. Key Indicators of the Labour Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Market. Geneva: International Labour Office. Change. Cambridge, U.K.: Cambridge University Press. ------. Various years. Yearbook of Labour Statistics. Geneva: International Labour ------. 2001c. Climate Change 2001: Impacts, Adaptation, and Vulnerability; Contri- Office. bution of Working Group II to the Third Assessment Report of the Intergovernmen- ------. 2006. The End of Child Labour within Reach. Geneva. tal Panel on Climate Change. Cambridge, U.K.: Cambridge University Press. IMF (International Monetary Fund). 1977. Balance of Payments Manual. 4th ed. ------. 2001d. Climate Change 2001: Mitigation; Contribution of Working Group II to Washington, D.C. the Third Assessment Report of the Intergovernmental Panel on Climate Change. ------. 1993. Balance of Payments Manual. 5th ed. Washington, D.C. Cambridge, U.K.: Cambridge University Press. ------. 1995. Balance of Payments Compilation Guide. Washington, D.C. ------. 2007a. Climate Change 2007: The Physical Science Basis. Contribution of ------. 1996. Balance of Payments Textbook. Washington, D.C. Working Group I to the Fourth Assessment Report of the Intergovernmental Panel ------. 2000. Monetary and Financial Statistics Manual. Washington, D.C. on Climate Change. Cambridge, U.K.: Cambridge University Press. ------. 2001. Government Finance Statistics Manual. Washington, D.C. ------. 2007b. "Summary for Policymakers." In Climate Change 2007: The Physical ------. 2004a. Compilation Guide on Financial Soundness Indicators. Washington, Science Basis. Contribution of Working Group I to the Fourth Assessment Report D.C. of the Intergovernmental Panel on Climate Change. Cambridge, U.K.: Cambridge ------. 2004b. World Economic Outlook. Chapter 3. Washington, DC. University Press. ------. 2007. Global Financial Stability Report. Washington, D.C. ------. 2007c. "Summary for Policymakers." In S. Solomon, D. Qin, M. Manning, Z. ------. Various issues. Direction of Trade Statistics. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, eds., Climate Change ------. Various issues. International Financial Statistics. 2007: Climate Change Impacts, Adaptation and Vulnerability. Working Group II ------. Various years. Balance of Payments Statistics Yearbook. Parts 1 and 2. Contribution to the Fourth Assessment Report of the Intergovernmental Panel on Washington, D.C. Climate Change. Cambridge, U.K.: Cambridge University Press. ------. Various years. Direction of Trade Statistics Yearbook. Washington, D.C. ------. 2007d. "Summary for Policymakers." In S. Solomon, D. Qin, M. Manning, ------. Various years. Government Finance Statistics Yearbook. Washington, D.C. Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H.L. Miller, eds., Climate 396 2008 World Development Indicators Change 2007: Mitigation of Climate Change. Working Group III Contribution to the Lanjouw, Peter, and Gershon Feder. 2001. "Rural Nonfarm Activities and Rural Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Development: From Experience toward Strategy." Rural Strategy Discussion Cambridge, U.K.: Cambridge University Press. Paper 4. World Bank, Washington, D.C. ------. 2007e. "Technical Summary." In S. Solomon, D. Qin, M. Manning, Z. Chen, Levy, Brian. 2007. Governance Reform, Bridging, Monitoring and Action. Washington, M. Marquis, K.B. Averyt, M. Tignor and H. L. Miller, eds., Climate Change 2007: D.C.: World Bank. Climate Change Impacts, Adaptation and Vulnerability. WorkingGroup II Contribu- Lopez, Alan D., Colin D. Mathers, Majad Ezzati, Dean T. Jamison, and Christopher tion to the Fourth Assessment Report of the Intergovernmental Panel on Climate J. L. Murray. 2006. Global Burden of Disease and Risk Factors. Washington, Change. Cambridge, U.K.: Cambridge University Press. D.C.: World Bank. ITU (International Telecommunication Union). 2007. World Telecommunication Lovei, Magdolna. 1997. "Toward Effective Pollution Management." Environment Indicators database. Geneva. Matters (Fall): 52­53. IUCN (World Conservation Union). 2007. 2007 IUCN Red List of Threatened Spe- Lule, Elizabbeth, G. N. V. Ramana, Nandini Ooman, Joanne Epp, Dale Huntington, cies. Gland, Switzerland. and James E. Rosen. 2005. Achieving the Millennium Development Goals of Jamison, Dean T., Joel G. Breman, Anthony R. Measham, and others, eds. 2006. Improving Maternal Health: Determinants, Interventions and Challenges. Wash- Priorities in Health. Washington, D.C.: World Bank. ington, D.C.: World Bank. Johnson, Simon. Forthcoming. "Worldwide Governance Indicators: A Comment." Mani, Muthukumara, and David Wheeler. 1997. "In Search of Pollution Havens? World Bank Research Observer. Dirty Industry in the World Economy, 1960­95." World Bank, Policy Research Johnston, Michael. 2001. "Measuring Corruption: Numbers versus Knowledge Department, Washington, D.C. versus Understanding." In Arvind K. Jain, ed., The Political Economy of Corrup- Mankiw, G., D. Romer, and D. Weil. 1992. "A Contribution to the Empirics of Eco- tion. London and New York: Routledge. nomic Growth." The Quarterly Journal of Economics 107 (2): 407­37. Joint Learning Initiative. 2004. Human Resources for Health: Overcoming the Crisis. McCarthy, F. Desmond, and Holger Wolf. 2001. "Comparative Life Expectancy Boston, Mass. in Africa." Policy Research Working Paper 2668. World Bank, Development Kaufmann, Daniel. 2005. "Click Refresh Button: Investment Climate Reconsidered." Research Group, Washington, D.C. Development Outreach, March. McCay, J., M. Erkson, and O. Shafey. 2006. Tobacco Atlas. 2nd ed. Atlanta, Ga.: Kaufmann, Daniel, and Aart Kraay. 2007a. "The Worldwide Governance Indicators American Cancer Society. Project: Answering the Critics." Policy Research Working Paper 4370. World Morgenstern, Oskar. 1963. On the Accuracy of Economic Observations. Princeton, Bank, Washington, D.C. N.J.: Princeton University Press. ------. 2007b. "Governance Matters VI, Aggregate and Individual Governance Morisset, Jacques. 2000. "Foreign Direct Investment in Africa: Policies Also Matter." Indicators 1996­2006." Policy Research Working Paper 4280. World Bank, Policy Research Working Paper 2481. World Bank, Washington, D.C. Washington, D.C. Nanda, Geeta, Kimberly Switlick, and Elizabeth Lule. 2005. "Accelerating Progress ------. Forthcoming. "Governance Indicators: Where Are We, Where Should We Be towards Achieving the MDG to Improve Maternal Health: A Collection of Promis- Going?" World Bank Research Observer. ing Approaches." HNP Discussion Paper. World Bank, Washington D.C. Kaufmann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2005. "Governance Mat- National Science Board. 2008. Science and Engineering Indicators 2008. Arlington, ters IV: Governance Indicators for 1996­2004." Policy Research Working Paper Va.: National Science Foundation. 3630. World Bank, Washington, D.C. Netcraft. 2007. "Netcraft Secure Server Survey." [www.netcraft.com/]. Knack, Stephen. 2007. "Measuring Corruption: A Critique of Indicators in Eastern Newfarmer, Richard, ed. 2006. Trade, Doha, and Development: A Window into the Europe and Central Asia." Journal of Public Policy 27 (3): 255­91. Issues. Washington, D.C.: World Bank. Knack, Stephen, M. Kugler, and N. Manning. 2003. "Second Generation Governance NRI (National Research Institute) and World Bank. 2003. "Public Expenditure and Indicators." International Review of Administrative Sciences 69 (3): 345­64. Service Delivery in Papua New Guinea: Draft." Washington, D.C. Kozak, Marta. 2005. "Micro, Small, and Medium Enterprises: A Collection of Pub- NREL (National Renewable Energy Laboratory) Energy Analysis Office. 2005. lished Data." International Finance Corporation, Washington, D.C. Renewable Energy Cost Trends. Presentation. [www.nrel.gov/analysis/docs/ Kunte, Arundhati, Kirk Hamilton, John Dixon, and Michael Clemens. 1998. "Esti- cost_curves_2005.ppt]. mating National Wealth: Methodology and Results." Environmental Economics OECD (Organisation for Economic Co-operation and Development). 1996. Trade, Series 57. World Bank, Environment Department, Washington, D.C. Employment, and Labour Standards: A Study of Core Workers' Rights and Inter- Lanjouw, Jean O., and Peter Lanjouw. 2001. "The Rural Non-Farm Sector: Issues and national Trade. Paris. Evidence from Developing Countries." Agricultural Economics 26 (1): 1­23. ------. 1997. Employment Outlook. Paris. 2008 World Development Indicators 397 BIBLIOGRAPHY ------. 2006. OECD Health Data 2006. Paris. Ratha, Dilip, and William Shaw. 2007. "South-South Migration and Remittances." ------. 2007. Agricultural Policies in OECD Countries: Monitoring and Evaluation World Bank, Development Prospects Group, Washington, D.C. 2007, Paris Ravallion, Martin, and Shaohua Chen. 1996. "What Can New Survey Data Tell Us ------. Various issues. Main Economic Indicators. Paris. about the Recent Changes in Living Standards in Developing and Transitional ------. Various years. National Accounts. Vol. 1, Main Aggregates. Paris Economies?" World Bank, Policy Research Department, Washington, D.C. ------. Various years. National Accounts. Vol. 2, Detailed Tables. Paris. Ravallion, Martin, Gaurav Datt, and Dominique van de Walle. 1991. "Quantifying ------. Various years. Producer and Consumer Support Estimates. Paris. Absolute Poverty in the Developing World." Review of Income & Wealth 37 (4): ------. Various years. Trends in International Migration: Continuous Reporting Sys- 345­61. tem on Migration. Paris Ronsman, Carine S., and Wendy J. Graham. 2006. "Maternal Mortality: Who, When, OECD (Organisation for Economic Co-operation and Development) DAC (Devel- Where and Why." Lancet 368: 1189­1200. opment Assistance Committee). Various years. International Development Rosengrant, M. W. and P. B. R. Hazel. 2000. Transforming the Rural Asia Economy: Statistics. CD-ROM. The Unfinished Revolution. Hong Kong, China: Oxford University Press. ------. Various years. International Development Statistics Online. Database. Rouen, Ren, and Kai Chen. 1995. "China's GDP in U.S. Dollars, Based on Purchas- [www.oecd.org/dac/stats/idsonline]. ing Power Parity." Policy Research Working Paper 1415. Washington, D.C. ------. Various years. Development Cooperation Report. Paris Ruggles, Robert. 1994. "Issues Relating to the UN System of National Accounts ------. Various years. Geographical Distribution of Financial Flows to Aid Recipients and Developing Countries." Journal of Development Economics 44 (1): 77­85. Paris Ryten, Jacob. 1998. "Fifty Years of ISIC: Historical Origins and Future Perspectives." OFDA (Office of U.S. Foreign Disaster Assistance) and CRED (Centre for Research ECA/STAT.AC. 63/22. United Nations Statistics Division, New York. on the Epidemiology of Disasters). 2007. Emergency Events Database (EM- Saghir, Jamal. 2005. "Energy and Poverty: Myths, Links, and Policy Issues. Energy DAT). Database. [www.em-dat.net/who.htm]. Working Notes 4. World Bank, Washington, D.C. Özden, Çaglar, and Maurice Schiff, eds. 2005. International Migration, Remittances, Sala-i-Martin, Xavier. 2002. The Disturbing "Rise" in Global Income Inequality. and the Brain Drain. New York: Palgrave Macmillan. NBER Working Paper 8904. Cambridge, Mass.: National Bureau of Economic Palacios, Robert, and Montserrat Pallares-Miralles. 2000. "International Patterns Research. of Pension Provision." Social Protection Discussion Paper 0009. World Bank, Salomon, Joshua A., Daniel R. Hogan, John Stover, Karen A. Stanecki, Neff Walker, Human Development Network, Washington, D.C. Peter D. Ghys, and Bernhard Schwartländer. 2005. "Integrating HIV Prevention Pandey, Kiran D., Piet Buys, Kenneth Chomitz, and David Wheeler. 2006a. "Bio- and Treatment: From Slogans to Impact." PLoS Medicine 2 (1): e16. diversity Conservation Indicators: New Tools for Priority Setting at the Global Scherr, Sara J. 1999. "Soil Degradation: A Threat to Developing-Country Food Secu- Environmental Facility." World Bank, Development Economics Research Group rity by 2020." 2020 Vision for Food, Agriculture, and Environment Discussion and Environment Department, Washington, D.C. Paper 27. International Food Policy Research Institute, Washington, D.C. Pandey, Kiran D., Bart Ostro, David Wheeler, Uwe Deichmann, Kirk Hamilton, and Sedgh G, S. Henshaw, S. Singh, E. Ahman, and I. Shah. 2007a. "Induced Abortion: Katie Bolt. 2006b. "Ambient Particulate Matter Concentrations in Residential Estimated Rates and Trends Worldwide." Lancet 370: 1338­45. and Pollution Hotspots of World Cities: New Estimates Based on the Global Sedgh, G., Rubina Hussain, Akinrinola Bankole, and Susheela Singh. 2007b. Model of Ambient Particulates (GMAPS)." World Bank, Development Economics "Women with an Unmet Need for Contraception in Developing Countries and Research Group and Environment Department, Washington, D.C. Their Reasons for Not Using a Method." New York: Guttmacher Institute. Pandey, Kiran Dev, Katharine Bolt, Uwe Deichmann, Kirk Hamilton, Bart Ostro, SIPRI (Stockholm International Peace Research Institute). 2007. SIPRI Yearbook and David Wheeler. 2003. "The Human Cost of Air Pollution: New Estimates for 2007: Armaments, Disarmament, and International Security. Oxford, U.K.: Oxford Developing Countries." World Bank, Development Research Group and Environ- University Press. ment Department, Washington, D.C. Smith, Lisa, and Laurence Haddad. 2000. "Overcoming Child Malnutrition in Devel- Pew Center on Global Climate Change. 2007. A Look at Emission Targets. [www. oping Countries: Past Achievements and Future Choices." 2020 Brief 64. Inter- pewclimate.org/what_s_being_done/targets]. national Food Policy Research Institute, Washington, D.C. Pricewaterhouse Coopers. 2006. Worldwide Summaries Online. New York. [www. Srinivasan, T. N. 1994. "Database for Development Analysis: An Overview." Journal pwc.com/extweb/pwcpublications.nsf/docid/9B2B76032544964C852571 of Development Economics 44 (1): 3­28. 7E00606CBD]. Standard & Poor's. 2000. The S&P Emerging Market Indices: Methodology, Defini- Rama, Martin, and Raquel Artecona. 2002. "A Database of Labor Market Indicators tions, and Practices. New York. across Countries." World Bank, Development Research Group, Washington, D.C. ------. 2007. Global Stock Markets Factbook 2007. New York. 398 2008 World Development Indicators Stern, Nicholas. 2006. The Economics of Climate Change: The Stern Review. London: ------. 2007a. Governance Indicators, A User's Guide. 2nd ed. [www.undp.org/ Cambridge University Press. oslocentre]. Tarmann, Allison. 2002. Response to Hunger Tests New Priorities. Population Today, ------. 2007b. Human Development Report 2007/2008: Fighting Climate Change: November/December 2001. Human Solidarity in a Divided World. New York. Thomas, M. A. 2006. "What Do the Worldwide Governance Indicators Mea- UNEP (United Nations Environment Programme). 2002. Global Environment Out- sure?" Draft. John Hopkins University. [http://siteresources.worldbank. look 3. London: Earthscan. org/INTWBIGOVANTCOR/Resources/1740479-1149112210081/2604389 UNESCO (United Nations Educational, Scientific, and Cultural Organization). -1167941884942/what_do_wgi_measure.pdf]. 1997. International Standard Classification of Education. Paris Transparency International. 2007. "Corruption Perceptions Index." [www. ------. 2005. Literacy for Life. Paris. transparency.org]. ------. 2006. EFA Global Monitoring Report. Paris. UN (United Nations). 1947. Measurement of National Income and the Construction UNESCO (United Nations Educational, Scientific, and Cultural Organization) Insti- of Social Accounts. New York. tute for Statistics. Various years. Global Education Digest. Paris. ------. 1968. "A System of National Accounts: Studies and Methods." Series F, ------. Online database. [www.uis.unesco.org/]. no. 2, rev. 3. New York. UNESCWA (United Nations Economic and Social Commission for Western Asia). ------. 1990. International Standard Industrial Classification of All Economic Activi- 1997. "Purchasing Power Parities: Volume and Price Level Comparisons for the ties, Third Revision. Statistical Papers Series M, no. 4, rev. 3. New York. Middle East, 1993." E/ESCWA/STAT/1997/2. Amman, Jordan. ------. 1992. "Handbook of the International Comparison Programme." Studies UNFCCC (United Nations Framework Convention on Climate Change). 2005. in Methods Series F, no. 62. New York. "Kyoto Protocol to the United Nations Framework Convention on Climate ------. 1993. "SNA Handbook on Integrated Environmental and Economic Account- Change." Bonn, Germany. ing." Series F, no. 61. Statistical Office, New York. UNFPA (United Nations Population Fund). 2005. State of World Population. New ------. 1999. "Integrated Environmental and Economic Accounting: An Operational York. Manual." Studies in Methods Series F, no. 78. New York. UN-HABITAT (United Nations Human Settlements Programme). 2003. Global ------. 2000. We the Peoples: The Role of the United Nations in the 21st Century. Report on Human Settlements. Nairobi. New York. UNHCR (United Nations High Commissioner for Refugees). Various years. Statisti- ------. 2004. "Trends in Total Migrant Stock: The 2003 Revision." POP/DB/MIG/ cal Yearbook. Geneva. Rev.2003. Department of Economic and Social Affairs, New York. UNICEF (United Nations Children's Fund). Various years. State of the World's Chil- ------. 2005a. "The Energy Challenge for Achieving the Millennium Development dren. New York: Oxford University Press. Goals." New York. ------. n.d. Childinfo. [www.childinfo.org]. ------. 2005b. The Millennium Development Goals Report. New York. UNICEF (United Nations Children's Fund), WHO (World Health Organization), World ------. 2007. The Millennium Development Goals Report. New York. Bank, and United Nations Population Division. 2007. "Levels and Trends of UNACC/SCN (United Nations Administrative Committee on Coordination, Sub- Child Mortality in 2006: Estimates Developed by the Inter-agency Group for committee on Nutrition). Various years. Update on the Nutrition Situation. Child Mortality Estimation." Working Paper. New York. Geneva. UNIDO (United Nations Industrial Development Organization). Various years. Inter- UNAIDS (Joint United Nations Programme on HIV/AIDS) and WHO (World Health national Yearbook of Industrial Statistics. Vienna. Organization). 2005. AIDS Epidemic Update: December 2005. Geneva. UNIFEM (United Nations Development Fund for Women). 2005. Progress of the ------. Various years. Report on the Global AIDS Epidemic. Geneva. World's Women. New York. UNCTAD (United Nations Conference on Trade and Development). 2003. The Least United Nations Population Division. 2002. International Migration Report 2002. Developed Countries Report. Geneva New York. ------. Various years. Handbook of Statistics. Geneva. ------. 2007. World Population Prospects: The 2006 Revision Highlights. New ------ 2007a. Trade and Development Report, 2007. New York and Geneva. York. ------ 2007b. World Investment Report, 2007. New York and Geneva. ------. Various years. Levels and Trends of Contraceptive Use. New York. Understanding Children's Work (UCW). n.d. Online database. [www.ucw-project. ------. Various years. Trends in Total Migrant Stock. New York. org]. ------. Various years. World Population Prospects. Department of Economic and UNDP (United Nations Development Programme). 2006. Human Development Social Affairs, New York. Report 2006: Beyond Scarcity: Power, Poverty and Global Water Crisis. New York. ------. Various years. World Urbanization Prospects. New York. 2008 World Development Indicators 399 BIBLIOGRAPHY United Nations Statistics Division. 1985. National Accounts Statistics: Compendium ------. Various years. Global Tuberculosis Control Report. Geneva. of Income Distribution Statistics. New York. ------. Various years. World Health Report. Geneva. ------. Various issues. Monthly Bulletin of Statistics. New York. ------. Various years. World Health Statistics. Geneva. ------. Various years. Energy Statistics Yearbook. New York. WHO (World Health Organization) and UNICEF (United Nations Children's Fund). ------. Various years. International Trade Statistics Yearbook. New York 2003. The Africa Malaria Report 2003. Geneva. ------. Various years. National Accounts Statistics: Main Aggregates and Detailed ------. 2004. Beyond the Numbers: Reviewing Maternal Deaths and Complications Tables. Parts 1 and 2. New York. to Make Pregnancy Safer. Geneva. ------. Various years. National Income Accounts. New York. ------. 2006. Meeting the MDG Drinking Water and Sanitation Target. Geneva. ------. Various years. Population and Vital Statistics Report. New York. ------. n.d. "Immunization, Surveillance, Assessment, and Monitoring." Online ------. Various years. Statistical Yearbook. New York database. [www.who.int/immunization_monitoring]. University of California, Berkeley, and Max Planck Institute for Demographic WHO (World Health Organization), UNICEF (United Nations Children's Fund), Research. n.d. Human Mortality Database. [www.mortality.org or www.human- UNFPA (United Nations Population Fund), and World Bank. 2007. Maternal mortality.de] (accessed December 9, 2007). Mortality in 2005: Estimates Developed by WHO, UNICEF, UNFPA, and the World UN Millennium Project. 2005a. Investing in Development: A Practical Plan to Achieve Bank. Geneva. the Millennium Development Goals. New York. WHO (World Health Organization) and World Bank. 2004. World Report on Road ------. 2005b. Taking Action: Achieving Gender Equality and Empowering Women. Traffic Injury Prevention. Geneva. Task Force on Education and Gender Equality. London: Earthscan. WIPO (World Intellectual Property Organization). 2007. WIPO Patent Report: Sta- U.S. Census Bureau. International Data Base (IDB). [www.census.gov/ipc/www/ tistics on Worldwide Patent Activity. Geneva. idb/]. WITSA (World Information Technology and Services Alliance). 2006. Digital Planet U.S. Centers for Disease Control and Prevention. Various years. International 2006: The Global Information Economy. Vienna, Va. Reproductive Health Surveys. [www.cdc.gov/reproductivehealth/surveys]. WMO (World Meteorological Organization). 2006. Statement on the Status of the U.S. Environmental Protection Agency. 1995. National Air Quality and Emissions Global Climate in 2005. Geneva. Trends Report 1995. Washington, D.C. ------. 2007. "Observing Stations." Publication No. 9, Volume A, (9 July 2007). Walsh, Michael P. 1994. "Motor Vehicle Pollution Control: An Increasingly Critical [www.wmo.int/pages/prog/www/ois/volumea/vola-home.htm]. Issue for Developing Countries." World Bank, Washington, D.C. Wolf, Holger C. 1997. Patterns of Intra- and Inter-State Trade. NBER Working Paper Watson, Jim, Gordon MacKerron, David Ockwell, and Tao Wang. 2007. "Technology 5939. Cambridge, Mass.: National Bureau of Economic Research. and Carbon Mitigation in Developing Countries: Are Cleaner Coal Technologies a World Bank. 1990. World Development Report 1990: Poverty. New York: Oxford Viable Option?" Background paper for United Nations Development Programme, University Pres 2007, Human Development Report 2007. [http://hdr.undp.org/en/reports/ ------. 1991. Managing Development: The Governance Dimension. Discussion global/hdr2007-2008/papers/watson_mackerron_ockwell_wang.pdf]. Paper 34899. World Bank, Task Force from Operations; Policy, Research and Watson, Robert, John A. Dixon, Steven P. Hamburg, Anthony C. Janetos, and External Affairs; Legal; Corporate Planning and Budget; and Finance Complexes, Richard H. Moss. 1998. Protecting Our Planet, Securing Our Future: Linkages Washington, D.C. among Global Environmental Issues and Human Needs. Nairobi and Washington, ------. 1992. World Development Report 1992: Development and the Environment. D.C.: United Nations Environment Programme, U.S. National Aeronautics and New York: Oxford University Press. Space Administration, and World Bank. ------. 1996a. Environment Matters (summer). Environment Department, Wash- Whitehouse, Edward. 2007. Pensions Panorama: Retirement-Income Systems in 53 ington, D.C. Countries. Washington, D.C.: World Bank. ------. 1996b. "Livable Cities for the 21st Century: A Directions in Development WHO (World Health Organization). 1983. International Classification of Diseases. book." Washington, D.C. 10th rev. Geneva. ------. 1996c. "National Environmental Strategies: Learning from Experience." ------. 2003. "Poverty and Health: Report by the Director-General." Geneva. [www. Environment Department, Washington, D.C. who.int/gb/EB_WHA/PDF/EB105/ee5.pdf]. ------. 1997a. Can the Environment Wait? Priorities for East Asia. Washington, ------. 2006. Reproductive Health Indicators: Guidelines for their Generation, Inter- D.C. pretation and Analysis for Global Monitoring. Geneva. ------. 1997b. "Expanding the Measure of Wealth: Indicators of Environmentally ------. 2007. Unsafe Abortion: Global and Regional Incidence of Unsafe Abortion Sustainable Development." Environmentally Sustainable Development Studies and Associated Mortality in 2003. 5th ed. Geneva. and Monographs Series, no. 17. Washington, D.C. 400 2008 World Development Indicators ------. 1997c. "Rural Development: From Vision to Action." Environmentally Sustain- ------. 2006b. "Debt Relief for the Poorest: An Evaluation Update of the HIPC able Development Studies and Monographs Series, no. 12. Washington, D.C. Initiative." Intependent Evaluation Group, Washington, D.C. ------. 1997d. World Development Report 1997: The State in a Changing World. ------. 2006c. Doing Business 2007: How to Reform. Washington, D.C. ------. 1999a. "Fuel for Thought: Environmental Strategy for the Energy Sector." ------. 2006d. Private Participation in Infrastructure Project Database. [http:// Environment Department, Energy, Mining, and Telecommunications Department ppi.worldbank.org/]. and International Finance Corporation, Washington, D.C. ------. 2006e. Where is the Wealth of Nations? Measuring Capital for the 21st ------. 1999b. Greening Industry: New Roles for Communities, Markets, and Govern- Century. Washington, D.C. ments. New York: Oxford University Press. ------. 2006f. Health Financing Revisited. ------. 2000a. Trade Blocs. New York: Oxford University Press. ------. 2006g. Global Monitoring Report 2006: Millennium Development Goals, ------. 2000b. World Development Report 2000/2001: Attacking Poverty. New Strengthening Mutual Accountability, Aid, Trade and Governance. Washington York: Oxford University Press. D.C.: World Bank. ------. 2001. World Development Report 2002: Building Institutions for Markets. ------. 2007a. "Enterprise Surveys Online" [www.enterprisesurveys.org]." New York: Oxford University Press. ------. 2007b. "Performance Assessments and Allocation of IDA Resources". ------. 2002a. A Case for Aid: Building a Consensus for Development Assistance. Online database. [www.worldbank.org/ida]. Washington, D.C. Washington, D.C. ------. 2007c. Global Monitoring Report. Washington, D.C. ------. 2002b. "The Environment and the Millennium Development Goals." Wash- ------. 2007d. "An Investment Framework for Clean Energy and Development. ington, D.C. A Platform for Convergence of Public and Private Investments." Washington, ------. 2002c. "Financial Impact of the HIPC Initiative: First 24 Country Cases." D.C. Washington, D.C. ------. 2007e. Doing Business 2008. Washington, D.C. ------. 2002d. Globalization, Growth, and Poverty: Building an Inclusive World ------. 2008. Global Purchasing Parities and Real Expenditures: 2005 International Economy. New York: Oxford University Press. Comparison Program. Washington, D.C. ------. 2002e. World Development Report 2003: Sustainable Development in a ------. Forthcoming. Progress Report of Pension Indicators. Washington, D.C. [www. Dynamic World. New York: Oxford University Press. worldbank.org/pensions]. ------. 2003a. "The Millennium Development Goals for Health: Rising to the Chal- ------. Various issues. Global Commodity Markets. lenges." Washington, D.C. ------. Various years. Global Development Finance. Washington, D.C. ------. 2003b. World Bank Atlas. Washington, D.C. ------. Various years. Global Economic Prospects and the Developing Countries. ------. 2003c. World Development Report 2004: Making Services Work for the Poor. Washington, D.C. New York: Oxford University Press. ------. Various years. World Debt Tables. Washington, D.C. ------. 2004a. "Measuring Results: Improving National Statistics in IDA Countries." ------. Various years. World Development Indicators. Washington, D.C. International Development Association, Washington, D.C. [http://siteresources. World Bank and IMF (International Monetary Fund). 2005a. Global Monitoring worldbank.org/IDA/Resources/MeasuringResultsStatistics.pdf]. Report 2005: Millennium Development Goals; From Consensus to Momentum. ------. 2004b. Partnerships in Development: Progress in the Fight against Poverty. Washington, D.C. Washington, D.C. ------. 2005b. "HIPC (Heavily Indebted Poor Countries) Public Expenditure Man- ------. 2004c. World Bank Atlas. Washington, D.C. agement Assessment and Action Plans." Washington, D.C. [www.worldbank. ------. 2005a. "Country Policy and Institutional Assessments, 2005 Assessment org/hipc]. Questionnaire." Operational Policy and Country Services, Washington, D.C. ------. 2007. "Heavily Indebted Poor Countries (HIPC) Initiative and Multilateral ------. 2005b. "Meeting the Challenge of Africa's Development: A World Bank Debt Relief Initiative (MDRI)--Status of Implementation." Washington, D.C. Group Action Plan." Africa Region, World Bank, Washington, D.C. World Bank and European Bank for Reconstruction and Development. 2006. ------. 2005c. Rolling Back Malaria: The World Bank Global Strategy and Booster Business Environment and Enterprise Performance Survey (BEEPS), BEEPS Program. Washington, D.C. Interactive Dataset. [http://info.worldbank.org/governance/beeps/]. ------. 2005d. World Development Report 2006: Equity and Development. New World Energy Council. 1995. Global Energy Perspectives to 2050 and Beyond. York: Oxford University Press. London. ------. 2006a. "Anticorruption Diagnostic Surveys." World Bank Institute's Gov- World Tourism Organization. Various years. Compendium of Tourism Statistics. ernance Diagnostic Capacity Building Program website. [www.worldbank. org/ Madrid. wbi/governance]. ------. Various years. Yearbook of Tourism Statistics. Vols. 1 and 2. Madrid. 2008 World Development Indicators 401 BIBLIOGRAPHY WRI (World Resources Institute). 2005. "Navigating the Numbers." Washington, WRI (World Resources Institute), UNEP (United Nations Environment Programme), D.C. UNDP (United Nations Development Programme), and World Bank. Various ------. 2007a. Climate Analysis Indicators Tool (CAIT). Online database. [www.wri. years. World Resources: A Guide to the Global Environment. New York: Oxford org/climate/project_description2.cfm?pid=93]. University Press. ------. 2007b. Earth Trends, the Environmental Information Portal. Online data- WTO (World Trade Organization). Various years. Annual Report. Geneva. base. (accessed July 2007). ------. n.d. "Regional Trade Agreements Gateway." Geneva. [www.wto.org/english/ tratop_e/region_e/region_e.htm]. 402 2008 World Development Indicators INDEX OF INDICATORS References are to table numbers. A Agriculture Aid per worker 3.3 agricultural raw materials by recipient commodity prices 6.5 aid dependency ratios 6.14 exports per capita 6.14 as share of total exports 4.4 total 6.14 from high-income economies as share of total exports 6.4 net concessional flows imports from international financial institutions 6.11 as share of total imports 4.4 from UN agencies 6.11 by high-income economies as share of total exports 6.4 official development assistance by DAC members tariff rates applied by high-income countries 6.4 administrative costs, as share of net bilateral cereal ODA disbursements 6.13a area under production 3.2 bilateral aid 6.13a, 6.13b, 6.15 exports from high-income economies as share of total exports 6.4 by purpose 6.13a imports, by high-income economies as share of total imports 6.4 by sector 6.13b tariff rates applied by high-income countries 6.4 commitments 6.12, 6.13b yield 3.3 debt-related aid, as share of net bilateral ODA disbursements 6.13a employment, as share of total 3.2 development projects, programs, and other resource provisions, fertilizer as share of net bilateral ODA disbursements 6.13a commodity prices 6.5 for basic social services, as share of sector-allocable bilateral consumption, per hectare of arable land 3.2 ODA commitments 1.4 food gross disbursements 6.12 beverages and tobacco 4.3 humanitarian assistance, as share of net bilateral commodity prices 6.5 ODA disbursements 6.13a exports from high-income economies as share of total exports 4.4, 6.4 net disbursements imports by high-income economies as share of total imports 4.5, 6.4 as share of general government disbursements 6.12 tariff rates applied by high-income countries 6.4 as share of GNI of donor country 1.4, 6.12 freshwater withdrawals for, as share of total 3.5 from major donors, by recipient 6.15 land per capita of donor country 6.12 agricultural, as share of land area 3.2 total 6.12, 6.13a arable, as share of land area 3.1 technical cooperation, as share of net bilateral arable, per 100 people 3.1 ODA disbursements 6.13a area under cereal production 3.2 total sector allocable, as share of bilateral ODA commitments 6.13b irrigated, as share of cropland 3.2 untied aid 6.13b permanent cropland, as share of land area 3.1 official development assistance by non-DAC members 6.14a machinery tractors per 100 square kilometers of arable land 3.2 AIDS--see HIV, prevalence production indexes crop 3.3 Air pollution--see Pollution food 3.3 livestock 3.3 Air transport value added air freight 5.9 annual growth 4.1 passengers carried 5.9 as share of GDP 4.2 registered carrier departures worldwide 5.9 2008 World Development Indicators 403 INDEX OF INDICATORS Animal species crime threatened 3.4 losses due to theft, robbery, vandalism, and arson 5.2 total known 3.4 customs average time to clear exports 5.2 Asylum seekers--see Migration; Refugees dealing with licenses to build a warehouse number of procedures 5.3 B Balance of payments time required employing workers rigidity of employment index 5.3 5.3 current account balance 4.15 enforcing contracts exports and imports of goods and services 4.15 number of procedures 5.3 net current transfers 4.15 time required 5.3 net income 4.15 finance total reserves 4.15 firms using banks to finance investment 5.2 See also Exports; Imports; Investment; Private financial flows; Trade gender female participation in ownership 5.2 Beverages informality commodity prices 6.5 firms that do not report all sales for tax purposes 5.2 infrastructure Biodiversity--see Biological diversity value lost due to electrical outages 5.2 innovation Biological diversity ISO certification ownership 5.2 assessment, date prepared, by country 3.15 permits and licenses GEF benefits index 3.4 time required to obtain operating license 5.2 threatened species 3.4 protecting investors disclosure, index 5.3 animal 3.4 registering property higher plants 3.4 number of procedures 5.3 treaty 3.15 time to register 5.3 regulation and tax Birth rate, crude 2.1 average number of times firms spend meeting with tax officials 5.2 time dealing with officials 5.2 Births attended by skilled health staff 2.17, 2.20 starting a business cost to start a business 5.3 Birthweight, low 2.18 number of start-up procedures 5.3 time to start a business 5.3 Bonds--see Debt flows; Private financial flows workforce, firms offering formal training 5.2 Breastfeeding, exclusive Business environment 2.18, 2.20 C Carbon dioxide businesses registered damage 3.16 new 5.1 emissions total 5.1 per 2005 PPP dollar of GDP 3.8 closing a business per capita 1.3, 3.8 time to resolve insolvency 5.3 solid fuel consumption as share of total 3.8 corruption total 1.6, 3.8 unofficial payments to public officials 5.2 404 2008 World Development Indicators Children at work Corruption, unofficial payments to public officials 5.2 by economic activity 2.6 male and female 2.6 Country Policy and Institutional Assessment (CPIA)--see Economic study and work 2.6 management; Social inclusion and equity policies; Public sector management total 2.6 and institutions; Structural policies work only 2.6 Credit Cities getting credit air pollution 3.14 credit information index 5.5 population legal rights index 5.5 in largest city 3.11 private credit registry coverage 5.5 in selected cities 3.14 public credit registry coverage 5.5 in urban agglomerations of more than 1 million 3.11 provided by banking sector 5.5 urban population 3.11 to private sector 5.1 See also Urban environment Crime, losses due to 5.2 Closing a business--see Business environment Current account balance 4.15 Commercial banks and other lending 6.10 See also Balance of payments See also Debt flows; Private financial flows Customs, average time to clear 5.2 Commodity prices and price indexes 6.5 Communications--see Internet; Newspapers, daily; Telephones; Television, households with D DAC (Development Assistance Committee)--see Aid Compensation of government employees 4.11 Death rate, crude 2.1 See also Mortality rate Computers (personal) per 100 people 5.11 Debt, external Consumption as share of GNI 6.9 distribution--see Income distribution debt ratios 6.9 fixed capital 3.16 debt service government, general multilateral, as share of public and publicly guaranteed annual growth 4.9 debt service 6.9 as share of GDP 4.8 total, as share of exports of goods and services and income 6.9 household IMF credit, use of 6.8 average annual growth 4.9 long-term per capita 4.9 private nonguaranteed 6.8 as share of GDP 4.8 public and publicly guaranteed See also Purchasing power parity (PPP) IBRD loans and IDA credits 6.8 total 6.8 Contraceptive prevalence rate 1.3, 2.17, 2.20 present value as share of GNI 6.9 Contract enforcement as share of exports of goods and services and income 6.9 number of procedures 5.3 total 6.8 time required for 5.3 2008 World Development Indicators 405 INDEX OF INDICATORS short-term 6.8 per student, as share of GDP per capita, by level 2.10 as share of total debt 6.9 pupil-teacher ratio, primary level 2.10 as share of total reserves 6.9 repeaters, primary level, male and female 2.12 total 6.8 teachers, primary, trained 2.10 transition to secondary school, male and female 2.12 Debt flows unemployment by level of educational attainment 2.5 bonds 6.10 years of schooling, average 2.14 commercial banks and other lending 6.10 See also Private financial flows Electricity consumption 5.10 Deforestation, average annual 3.4 production share of total 3.10 Density--see Population, density sources 3.10 transmissions and distribution losses 5.10 Dependency ratio--See Population value lost due to outages 5.2 Development assistance--see Aid Emissions Carbon dioxide Disease--see Health risks average annual growth 3.9 per capita 3.8 Distribution of income or consumption--see Income distribution total 3.8 Methane E Economic management (Country Policy and Institutional Assessment) agricultural as share of total industrial as share of total total 3.9 3.9 3.9 debt policy 5.8 Nitrous oxide economic management cluster average 5.8 agricultural as share of total 3.9 fiscal policy 5.8 industrial as share of total 3.9 macroeconomic management 5.8 total 3.9 Other greenhouse gases 3.9 Education enrollment ratio Employment girls to boys enrollment in primary and secondary schools 1.2 economically active children 2.6 gross, by level 2.11 in agriculture, as share of total employment 3.2 net, by level 2.11 in agriculture, male and female 2.3 total net, primary 2.11 in industry, male and female 2.3 gross intake rate, grade 1 2.12, 2.14 in informal sector, urban, male and female 2.9 gross primary participation rate 2.14 in services, male and female 2.3 out of school children, male and female 2.11, 2.14 rigidity index 5.3 primary completion rate 1.2, 2.13, 2.14 to population ratio 2.4 male and female 2.13, 2.14 vulnerable 2.4 progression See also Labor force; Unemployment share of cohort reaching grade 5, male and female 2.12 share of cohort reaching last grade of primary, male and female 2.12 Employing workers public expenditure on rigidity of employment index 5.3 as share of GDP 2.10 as share of total government expenditure 2.10 Endangered species--see Animal species; Biological diversity; Plants, higher 406 2008 World Development Indicators Energy high-technology commodity prices 6.5 share of manufactured exports 5.12 depletion, as share of GNI 3.16 total 5.12 emissions--see Pollution merchandise imports, net 3.8 annual growth 6.3 production 3.7 by high-income countries, by product 6.4 use by regional trade blocs 6.6 2005 PPP dollar of GDP per unit 3.8 direction of trade 6.3 average annual growth 3.8 structure 4.4 clean energy consumption as share of total 3.7 total 4.4 combustible renewables and waste as share of total 3.7 value, average annual growth 6.2 fossil fuel consumption as share of total 3.7 volume, average annual growth 6.2 total 3.7 services See also Electricity; Fuels structure 4.6 total 4.6 Enforcing contracts--see Business environment transport 4.6 travel 4.6, 6.17 Enrollment--see Education See also Trade Entry regulations for business--see Business environment Environmental strategy, year adopted 3.15 F Female-headed households 2.8 Equity flows Fertility rate foreign direct, net inflows 6.10 adolescent 2.17 portfolio equity 6.10 total 2.17, 2.20 See also Private financial flows Finance, firms using banks to finance investment 5.2 European Commission distribution of net aid from 6.15 Financial access, stability, and efficiency bank capital to asset ratio 5.5 Exchange rates bank nonperforming loans 5.5 official, local currency units to U.S. dollar 4.14 ratio of PPP conversion factor to official exchange rate 4.14 Financial flows, net real effective 4.14 from DAC members 6.12 See also Purchasing power parity (PPP) official from bilateral sources 6.11 Export credits from international financial institutions 6.11 private, from DAC members 6.12 from multilateral sources 6.11 total 6.11 Exports from UN agencies 6.11 arms 5.7 official development assistance and official aid goods and services grants from NGOs 6.12 as share of GDP 4.8 other official flows 6.12 average annual growth 4.9 private 6.12 total 4.15 total 6.12 See also Aid 2008 World Development Indicators 407 INDEX OF INDICATORS Financing through international capital markets 6.1 Gender differences See also Private financial flows in child employment 2.4 in education Food--see Agriculture, production indexes; Commodity prices and price indexes enrollment, primary and secondary 1.2, 2.11 in employment 2.3 Foreign direct investment, net--see Investment; Private financial flows in HIV prevalence 2.19 in labor force participation 2.2 Forest in life expectancy at birth 1.5 area, as share of total land area 3.1 in literacy deforestation, average annual 3.4 adult 2.13 net depletion 3.16 youth 2.13 in mortality Freshwater adult 2.21 annual withdrawals child 2.21 amount 3.5 in smoking 2.19 as share of internal resources 3.5 in survival to age 65 2.21 for agriculture 3.5 in youth unemployment 2.9 for domestic use 3.5 unpaid family workers 1.5 for industry 3.5 women in nonagricultural sector 1.5 renewable internal resources women in parliaments 1.5 flows 3.5 per capita 3.5 Gini index 2.7 See also Water, access to improved source of Government, central Fuels cash surplus or deficit 4.10 exports debt as share of total exports 4.4 as share of GDP 4.10 crude petroleum, from high-income economies, as share interest, as share of revenue 4.10 of total exports 6.4 interest, as share of total expenses 4.11 from high-income economies, as share of total exports 6.4 expense petroleum products, from high-income economies, as share as share of GDP 4.10 of total exports 6.4 by economic type 4.11 imports military 5.7 as share of total imports 4.4 net incurrence of liabilities, as share of GDP crude petroleum, by high-income economies, as share domestic 4.10 of total imports 6.4 foreign 4.10 by high-income economies, as share of total imports 6.4 revenues, current petroleum products, by high-income economies, as share as share of GDP 4.10 of total imports 6.4 grants and other 4.12 prices 3.13 social contributions 4.12 tariff rates applied by high-income countries 6.4 tax, as share of GDP 5.6 tax, by source 4.12 G GEF benefits index for biodiversity 3.4 Greenhouse gases--see Emissions Gross capital formation Gender, female participation in ownerhsip 5.2 annual growth 4.9 408 2008 World Development Indicators as share of GDP 4.8 low-birthweight babies 2.18 maternal mortality ratio 1.3, 2.17 Gross domestic product (GDP) unmet need for contraception 2.17 annual growth 1.1, 1.6, 4.1 tuberculosis implicit deflator--see Prices DOTS detection rate 2.16 per capita, annual growth 1.1, 1.6 incidence 1.3, 2.19 total 4.2 treatment success rate 2.16 Gross enrollment--see Education Health expenditure as share of GDP 2.15 Gross national income (GNI) external resources 2.15 per capita out of pocket 2.15 PPP dollars 1.1, 1.6 per capita 2.15 rank 1.1 public 2.15 U.S. dollars 1.1, 1.6 total 2.15 rank PPP dollars 1.1 Health risks U.S. dollars 1.1 child malnutrition, prevalence 1.2, 2.18, 2.20 total condom use 2.19 PPP dollars 1.1, 1.6 diabetes, prevalence 2.19 U.S. dollars 1.1, 1.6 HIV, prevalence 1.3, 2.19 overweight children, prevalence 2.18 Gross savings smoking, prevalence 2.19 as share of GDP 4.8 tuberculosis, incidence 1.3, 2.19 as share of GNI 3.16 undernourishment, prevalence 2.18 H Health care Heavily indebted poor countries (HIPCs) assistance completion point 1.4 1.4 children sleeping under treated bednets 2.16 decision point 1.4 children with acute respiratory infection taken to health provider 2.16 Multilateral Debt Relief Initiative (MDRI) assistance 1.4 children with diarrhea who received oral rehydration and continued feeding 2.16 HIV children with fever receiving antimalarial drugs 2.16 prevalence 1.3, 2.19 community health workers 2.15 female 2.19 hospital beds per 1,000 people 2.15 population ages 15­24, male and female 2.19 immunization 2.16, 2.17, 2.20 total 2.19 newborns protected against tetanus 2.17 prevention physicians, nurses, and midwives 2.15 condom use, male and female 2.19 physicians per 1,000 people 2.15 pregnant women receiving prenatal care 1.5, 2.17, 2.20 Hospital beds--see Health care reproductive births attended by skilled health staff 1.2, 2.17, 2.20 Housing conditions, national and urban contraceptive prevalence rate 1.3, 2.17, 2.20 durable dwelling units 3.12 fertility rate home ownership 3.12 adolescent 2.17 household size 3.12 total 2.17, 2.20 multiunit dwellings 3.12 2008 World Development Indicators 409 INDEX OF INDICATORS overcrowding 3.12 Information and communications technology expenditures vacancy rate 3.12 as share of GDP 5.11 per capita 5.11 I IDA Resource Allocation Index (IRAI) 5.8 Innovation, ISO certification ownership 5.2 Integration, global economic, indicators 6.1 Immunization rate, child DPT, share of children ages 12­23 months 2.16, 2.20 Interest payments--see Government, central, debt measles, share of children ages 12­23 months 2.16, 2.20 tetanus, newborns protected against 2.17 Interest rates deposit 4.13 Imports lending 4.13 arms 5.7 real 4.13 energy, net, as share of total energy use 3.8 risk premium on lending 5.5 goods and services spread 5.5 as share of GDP 4.8 average annual growth 4.9 International Bank for Reconstruction and Development (IBRD) total 4.15 IBRD loans and IDA credits 6.8 merchandise net financial flows from 6.11 annual growth 6.3 by high-income countries, by product 6.4 International Development Association (IDA) direction of trade 6.3 IBRD loans and IDA credits 6.8 structure 4.5 net concessional flows from 6.11 tariffs 6.4, 6.7 total 4.5 International migrant stock 6.16 value, average annual growth 6.2 See also Migration volume, average annual growth 6.2 services International Monetary Fund (IMF) structure 4.7 net financial flows from 6.11 total 4.7 use of IMF credit 6.8 transport 4.7 travel 4.7, 6.17 Internet See also Trade broadband subscribers 5.11 price basket 5.11 Income distribution secure servers 5.11 Gini index 2.8 users 5.11 percentage of 1.2, 2.8 international bandwidth 5.11, 6.1 schools connected 5.11 Industry annual growth 4.1 Investment as share of GDP 4.2 foreign direct, net inflows employment, male and female 2.3 as share of GDP 6.1 from DAC members 6.12 Inflation--see Prices total 6.10 foreign direct, net outflows Informal economy, firms that do not report all sales for tax purposes 5.2 as share of GDP 6.1 410 2008 World Development Indicators infrastructure, private participation in children with fever receiving antimalarial drugs 2.16 energy 5.1 telecommunications 5.1 Management time dealing with officials 5.2 transport 5.1 water and sanitation 5.1 Manufacturing See also Gross capital formation; Private financial flows chemicals 4.3 exports 4.4, 6.4 Iodized salt, consumption of 2.18 food 4.3 imports 4.5, 6.4 L Labor force machinery structure textile 4.3 4.3 4.3 annual growth 2.2 value added armed forces 5.7 annual growth 4.1 children at work 2.6 as share of GDP 4.2 female 2.2 total 4.3 participation of population ages 15+, male female 2.2 See also Merchandise total 2.2 See also Employment; Migration; Unemployment Market access to high-income countries goods admitted free of tariffs 1.4 Land area support to agriculture 1.4 arable--see Agriculture, land; Land use tariffs on exports from low- and middle-income countries See also Protected areas; Surface area agricultural products 1.4 textiles and clothing 1.4 Land use arable land, as share of total land 3.1 Merchandise area under cereal production 3.2 exports by type 3.1 agricultural raw materials 4.4, 6.4 forest area, as share of total land 3.1 by regional trade blocs 6.6 irrigated land 3.2 cereals 6.4 permanent cropland, as share of total land 3.1 chemicals 6.4 total area 3.1 crude petroleum 6.4 food 4.4, 6.4 Life expectancy at birth footwear 6.4 male and female 1.5 fuels 4.4 total 1.6, 2.21 furniture 6.4 iron and steel 6.4 Literacy machinery and transport equipment 6.4 adult, male and female 1.6, 2.13 manufactures 4.4 youth, male and female 1.6, 2.13 ores and metals 4.4 ores and nonferrous materials 6.4 M Malnutrition, in children under age 5 1.2, 2.18, 2.20 petroleum products textiles total 6.4 6.4 4.4 value, average annual growth 6.2 Malaria volume, average annual growth 6.2 children sleeping under treated bednets 2.16 within regional trade blocs 6.6 2008 World Development Indicators 411 INDEX OF INDICATORS imports arms transfers agricultural raw materials 4.5 exports 5.7 cereals 6.4 imports 5.7 chemicals 6.4 military expenditure crude petroleum 6.4 as share of central government expenditure 5.7 food 4.5 as share of GDP 5.7 footwear 6.4 fuels 4.5 Millennium Development Goals, indicators for furniture 6.4 access to improved sanitation facilities 1.3, 2.16 iron and steel 6.4 access to improved water source 2.16, 3.5 machinery and transport equipment 6.4 aid manufactures 4.5 as share of GNI of donor country 1.4, 6.10 ores and metals 4.5 as share of total ODA commitments 1.4 ores and nonferrous materials 6.4 births attended by skilled health staff 2.17 petroleum products 6.4 carbon dioxide emissions per capita 1.3, 3.8 textiles 6.4 children sleeping under treated bednets 2.16 total 4.5 contraceptive prevalence rate 1.3, 2.17 value, average annual growth 6.2 employment to population ratio 2.4 volume, average annual growth 6.2 enrollment ratio, net, primary 2.11 trade female to male enrollments, primary and secondary 1.2 direction 6.3 fertility rate, adolescent 2.17 growth 6.3 heavily indebted poor countries (HIPCs) merchandise, as share of GDP 6.1 completion point 1.4 regional trade blocs 6.6 decision point 1.4 services, as share of GDP 6.1 nominal debt service relief 1.4 immunization Metals and minerals DPT 2.16, 2.20 commodity prices 6.5 Measles 2.16, 2.20 income or consumption, national share of poorest quintile 1.2, 2.8 Methane emissions infant mortality rate 2.20, 2.21 agricultural as share of total 3.9 labor productivity, GDP per person employed 2.4 industrial as share of total 3.9 literacy rate of 15­24 year olds 2.13 total 3.9 malnutrition, prevalence 1.2, 2.18, 2.20 malaria Micro, small, and medium-size enterprises children under age 5 sleeping under insecticide treated bednets 2.16 per 1,000 people 5.1 children under age 5 with fever who are treated with appropriate total 5.1 antimalarial drugs 2.16 maternal mortality ratio 1.3, 2.17 Migration national parliament seats held by women 1.5 international migrant stock 6.16 poverty gap 2.7 net 6.1, 6.16 pregnant women receiving prenatal care 1.5, 2.17, 2.20 See also Refugees; Remittances share of cohort reaching last grade of primary 2.12 telephone lines, fixed-line and mobile 1.3, 5.10 Military tuberculosis armed forces personnel DOTS detection rate 2.16 as share of labor force 5.7 incidence 1.3, 2.19 total 5.7 treatment success rate 2.16 412 2008 World Development Indicators under-five mortality rate 1.2, 2.21 vitamin A supplementation 2.18 undernourishment, prevalence 2.18 unmet need for contraception vulnerable employment women in wage employment in the nonagricultural sector 2.17 1.2, 2.4 1.5 O Official development assistance--see Aid Minerals, depletion of 3.15 Official flows, other 6.12 Monetary indicators claims on governments and other public entities claims on private sector 4.13 4.13 P Passenger cars per 1,000 people 3.13 Money and quasi money, annual growth 4.13 Particulate matter emission damage 3.16 Mortality rate selected cities 3.14 adult, male and female 2.21 urban-population-weighted PM10 3.13 child, male and female 2.21 children under age 5 1.2, 2.20, 2.21 Patent applications filed 5.12 infant 2.21 maternal 1.3, 2.17 Pension average, as share of per capita income 2.9 Motor vehicles contributors passenger cars 3.13 as share of labor force 2.9 per 1,000 people 3.13 as share of working age population 2.9 per kilometer of road 3.13 public expenditure on, as share of GDP 2.9 road density 3.13 See also Roads; Traffic Permits and licenses, time required to obtain operating license 5.2 N Net enrollment--see Education Physicians--see Health care Plants, higher species 3.4 Net national savings 3.16 threatened species 3.4 Newspapers, daily 5.11 Pollution carbon dioxide Nitrous oxide emissions damage, as share of GNI 3.16 agricultural as share of total 3.9 emissions industrial as share of total 3.9 per 2005 PPP dollar of GDP 3.8 total 3.9 per capita 3.8 total 3.8 Nutrition methane emissions breastfeeding 2.18, 2.20 agricultural as share of total 3.9 iodized salt consumption 2.18 industrial as share of total 3.9 malnutrition, child 1.2, 2.18, 2.120 total 3.9 overweight children, prevalence 2.18 nitrogen dioxide, selected cities 3.14 undernourishment, prevalence 2.18 2008 World Development Indicators 413 INDEX OF INDICATORS nitrous oxide emissions urban 2.7 agricultural as share of total 3.9 industrial as share of total 3.9 Power--see Electricity, production total 3.9 organic water pollutants, emissions Prenatal care, pregnant women receiving 1.5, 2.17, 2.20 by industry 3.6 per day 3.6 Prices per worker 3.6 commodity prices and price indexes 6.5 particulate matter, selected cities 3.14 consumer, annual growth 4.14 sulfur dioxide, selected cities 3.14 GDP implicit deflator, annual growth 4.14 urban-population-weighted PM10 3.13 terms of trade 6.2 wholesale, annual growth 4.14 Population age dependency ratio 2.1 Primary education--see Education annual growth 2.1 by age group Private financial flows 0­14 2.1 debt flows 15­64 2.1 bonds 6.10 65 and older 2.1 commercial banks and other lending 6.10 density 1.1, 1.6 equity flows female, as share of total 1.5 foreign direct investment, net inflows 6.10 rural portfolio equity 6.10 annual growth 3.1 financing through international capital markets, as share of GDP 6.1 as share of total 3.1 from DAC members 6.12 total 1.1, 1.6, 2.1 See also Investment urban as share of total 3.11 Productivity average annual growth 3.11 in agriculture in largest city 3.11 value added per worker 3.3 in selected cities 3.14 labor productivity, GDP per person employed 2.4 in urban agglomerations 3.11 water productivity, total 3.5 total 3.11 See also Migration Protected areas marine Portfolio--see Equity flows; Private financial flows as share of total surface area 3.4 total 3.4 Ports, container traffic in 5.9 national as share of total land area 3.4 Poverty total 3.4 national poverty line population below 2.7 Protecting investors disclosure index 5.3 national 2.7 rural 2.7 Public sector management and institutions (Country Policy and urban 2.7 Institutional Assessment) poverty gap at efficiency of revenue mobilization 5.8 national 2.7 property rights and rule-based governance 5.8 rural 2.7 public sector management and institutions cluster average 5.8 414 2008 World Development Indicators quality of budgetary and financial management 5.8 paved, as share of total 5.9 quality of public administration 5.8 total network 5.9 transparency, accountability, and corruption in the public sector 5.8 traffic 3.13 Purchasing power parity (PPP) Royalty and license fees conversion factor 4.14 payments 5.12 gross national income 1.1, 1.6 receipts 5.12 R Railways Rural environment access to improved sanitation facilities population 3.11 goods hauled by 5.9 annual growth 3.1 lines, total 5.9 as share of total 3.1 passengers carried 5.9 Refugees by country of asylum 6.16 S S&P/EMDB Indexes 5.4 by country of origin 6.16 Sanitation, access to improved facilities, population with Regional development banks, net financial flows from 6.11 rural 3.11 total 1.3, 2.16 Registering property urban 3.11 number of procedures 5.3 time to register 5.3 Savings gross, as share of GDP 4.8 Regulation and tax administration gross, as share of GNI 3.16 management time dealing with officials 5.2 net 3.16 meeting with tax officials, number of times 5.2 Schooling--see Education Relative prices (PPP)--see Purchasing power parity (PPP) Science and technology Remittances scientific and technical journal articles 5.12 workers' remittances and compensation of employees See also Research and development as share of GDP 6.1 paid 6.16 Secondary education--see Education received 6.16 Services Research and development employment, male and female 2.3 expenditures 5.12 exports researchers 5.12 structure 4.6 technicians 5.12 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 5.9 value added passengers carried 5.9 annual growth 4.1 2008 World Development Indicators 415 INDEX OF INDICATORS as share of GDP 4.2 applied rates on imports from low- and middle-income economies 6.4 manufactured products Smoking, prevalence, male and female 2.18 simple mean tariff 6.7 weighted mean tariff 6.7 Social inclusion and equity policies (Country Policy and Institutional on exports of least developed countries 1.4 Assessment) primary products building human resources 5.8 simple mean tariff 6.7 equity of public resource use 5.8 weighted mean tariff 6.7 gender equity 5.8 policy and institutions for environmental sustainability 5.8 Taxes and tax policies social inclusion and equity cluster average 5.8 business taxes social protection and labor 5.8 average number of times firms spent meeting tax officials 5.2 number of payments 5.6 Starting a business--see Business environment time to prepare, file, and pay 5.6 total tax rate, share of gross profit 5.6 Stock markets goods and services taxes, domestic 4.12 listed domestic companies 5.4 highest marginal tax rate market capitalization corporate 5.6 as share of GDP 5.4 individual 5.6 total 5.4 income, profit, and capital gains taxes as share of revenue 4.12 market liquidity 5.4 international trade taxes 4.12 S&P/EMDB Indices 5.4 other taxes 4.12 turnover ratio 5.4 social contributions 4.12 tax revenue, as share of GDP 5.6 Structural policies (Country Policy and Institutional Assessment) business regulating environment 5.8 Technology--see Computers; Exports, high-technology; Internet; financial sector 5.8 Research and development; Science and technology structural policies cluster average 5.8 trade 5.8 Telephones cost of call to U.S. 5.10, 6.1 Sulfur dioxide emissions--see Pollution international voice traffic 5.10, 6.1 mainlines Surface area 1.1, 1.6 faults per 100 5.10 See also Land use per 100 people 5.10 price basket 5.10 Survival to age 65, male and female 2.21 mobile per 100 people 1.3, 5.10 Suspended particulate matter--see Pollution population covered 5.10 price basket 5.10 T Tariffs total revenue total subscribers per employee 5.10 5.10 all products Television, households with 5.11 binding coverage 6.7 simple mean board rate 6.7 Terms of trade, net barter 6.2 simple mean tariff 6.7 weighted mean tariff 6.7 Tertiary education--see Education 416 2008 World Development Indicators Tetanus vaccinations, newborns protected against 2.17 Transport--see Air transport; Railways; Roads; Traffic; Urban environment Threatened species--see Animal species; Biological diversity; Plants, higher Treaties, participation in biological diversity 3.15 Tourism, international CFC control 3.15 expenditures in the country climate change 3.15 as share of exports 6.17 Convention on International Trade on Endangered Species (CITES) 3.15 total 6.17 Convention to Combat Desertification (CCD) 3.15 expenditures in other countries Kyoto Protocol 3.15 as share of imports 6.17 Law of the Sea 3.15 total 6.17 ozone layer 3.15 inbound tourists, by country 6.1, 6.17 Stockholm Convention on Persistent Organic Pollutants 3.15 outbound tourists, by country 6.1, 6.17 Tuberculosis, incidence 1.3, 2.19 Trade arms merchandise as share of GDP 5.7 6.1 U UN agencies, net concessional flows from 6.13 direction of, by region 6.3 high-income economy with low- and middle-income economies, Undernourishment, prevalence of 2.18 by product 6.4 nominal growth, by region 6.3 Unemployment regional trading blocs 6.6 incidence of long-term, total, male, and female 2.5 services by level of educational attainment, primary, secondary, tertiary 2.5 as share of GDP 6.1 total, male, and female 2.5 computer, information, communications, and other 4.6, 4.7 youth, male, and female 1.3, 2.9 insurance and financial 4.6, 4.7 transport 4.6, 4.7 UNFPA, net concessional flows from 6.13 travel 4.6, 4.7 See also Balance of payments; Exports; Imports; Manufacturing; UNICEF, net concessional flows from 6.13 Merchandise; Terms of trade; Trade blocs UNRWA Trade blocs, regional net concessional flows from 6.11 exports within bloc 6.6 refugees under the mandate of 6.16 total exports, by bloc 6.6 type of agreement 6.6 Urban environment year of creation 6.6 access to sanitation 3.11 year of entry into force of the most recent agreement 6.6 employment, informal sector 2.8 population Trademark applications filed 5.12 as share of total 3.11 average annual growth 3.11 Trade policies--see Tariffs in largest city 3.11 in urban agglomerations 3.11 Traffic total 3.11 road traffic 3.13 selected cities road traffic injury and mortality 2.18 nitrogen dioxide 3.14 See also Roads particulate matter 3.14 2008 World Development Indicators 417 INDEX OF INDICATORS population 3.14 pollution--see Pollution, organic water pollutants sulfur dioxide 3.14 productivity 3.5 See also Pollution; Population; Sanitation; Water WFP, net concessional flows from 6.11 V Value added Women in development female-headed households 2.9 as share of GDP female population 1.5 in agriculture 4.2 life expectancy at birth 1.5 in industry 4.2 pregnant women receiving prenatal care 1.5 in manufacturing 4.2 teenage mothers 1.5 in services 4.2 unpaid family workers 1.5 growth women in nonagricultural sector 1.5 in agriculture 4.1 women in parliaments 1.5 in industry 4.1 in manufacturing 4.1 Workforce, firms offering formal training 5.2 in services 4.1 per worker World Bank commodity price index in agriculture 3.3 MUV G-5 index 6.5 total, in manufacturing 4.3 nonenergy commodities 6.5 petroleum 6.5 Vulnerable employment 1.2, 2.4 steel products 6.5 W Water World Bank, net financial flows from See also International Bank for Reconstruction and Development; International Development Association 6.11 access to improved source of, population with 1.3, 2.15 418 2008 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 REGION MAP The world by region East Asia and Pacific Bolivia Botswana Ireland* American Samoa Brazil Burkina Faso Italy* Cambodia Chile Burundi Japan China Colombia Cameroon Korea, Rep. Fiji Costa Rica Cape Verde Luxembourg* Indonesia Cuba Central African Republic Netherlands* Kiribati Dominica Chad New Zealand Korea, Dem. Rep. Dominican Republic Comoros Norway Lao PDR Ecuador Congo, Dem. Rep. Portugal* Malaysia El Salvador Congo, Rep. Spain* Marshall Islands Grenada Côte d'Ivoire Sweden Micronesia, Fed. Sts. Guatemala Equatorial Guinea Switzerland Mongolia Guyana Eritrea United Kingdom Myanmar Haiti Ethiopia United States Northern Mariana Islands Honduras Gabon Palau Jamaica Gambia, The Other high income Papua New Guinea Mexico Ghana Andorra Philippines Nicaragua Guinea Antigua and Barbuda Samoa Panama Guinea-Bissau Aruba Solomon Islands Paraguay Kenya Bahamas, The Thailand Peru Lesotho Bahrain Timor-Leste St. Kitts and Nevis Liberia Barbados Tonga St. Lucia Madagascar Bermuda Vanuatu St. Vincent and the Malawi Brunei Darussalam Vietnam Grenadines Mali Cayman Islands Suriname Mauritania Channel Islands Europe and Uruguay Mauritius Cyprus* Central Asia Venezuela, RB Mayotte Estonia Albania Mozambique Faeroe Islands Armenia Middle East and Namibia French Polynesia Azerbaijan North Africa Niger Greenland Belarus Algeria Nigeria Guam Bosnia and Herzegovina Djibouti Rwanda Hong Kong, China Bulgaria Egypt, Arab Rep. São Tomé and Principe Isle of Man Croatia Iran, Islamic Rep. Senegal Israel Georgia Iraq Seychelles Kuwait Hungary Jordan Sierra Leone Liechtenstein Kazakhstan Lebanon Somalia Macao, China Kyrgyz Republic Libya South Africa Malta* Latvia Morocco Sudan Monaco Lithuania Oman Swaziland Netherlands Antilles Macedonia, FYR Syrian Arab Republic Tanzania New Caledonia Moldova Tunisia Togo Puerto Rico Montenegro West Bank and Gaza Uganda Qatar Poland Yemen, Rep. Zambia San Marino Romania Zimbabwe Saudi Arabia Russian Federation South Asia Singapore Serbia Afghanistan High-income OECD Slovenia* Slovak Republic Bangladesh Australia Trinidad and Tobago Tajikistan Bhutan Austria* United Arab Emirates Turkey India Belgium* Virgin Islands (U.S.) Turkmenistan Maldives Canada Ukraine Nepal Czech Republic Uzbekistan Pakistan Denmark Sri Lanka Finland* Latin America and France* the Caribbean Sub-Saharan Africa Germany* Argentina Angola Greece* Belize Benin Iceland *Member of the Euro area The World Bank 1818 H Street N.W. Washington, D.C. ISBN 978-0-8213-7386-6 20433 USA Telephone: 202 473 1000 Fax: 202 477 6391 Web site: www.worldbank.org SKU 17386 Email: feedback@worldbank.org The World Development Indicators Includes more than 800 indicators for 153 economies Provides definitions, sources, and other information about the data Organizes the data into six thematic areas WORLD VIEW Living standards and development progress PEOPLE Gender, health, and employment ENVIRONMENT Natural resources and environmental changes ECONOMY New opportunities for growth STATES & MARKETS Elements of a good investment climate GLOBAL LINKS Evidence on globalization Saved: 70 trees 3,290 pounds of solid waste 25,621 gallons of waste water 6,172 pounds of net greenhouse gases 49 million BTUs of total energy