57488 2 0 0 7 Copyright © 2008 by the International Bank for Reconstruction and Development/The World Bank 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. All rights reserved Manufactured in the United States of America First printing October 2007 The findings, interpretations, and conclusions expressed in this book are entirely those of the authors and should not be attrib- uted in any manner to the World Bank, to its affi liated organizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility 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 Group any judgment on the legal status of any territory or the endorsement or acceptance of such boundaries. The material in this publication is copyrighted. 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To order Africa Development Indicators 2007, The Little Data Book on Africa 2007, and the Africa Development Indicators 2007--Mul- tiple User CD-ROM, please visit the publications web site at www.worldbank.org/publications. For more information about Africa Development Indicators and its companion products, please visit our web site at www.worldbank. org/africa. You can email us at ADI@worldbank.org. Cover design by Communications Development Incorporated. Photo credits: front cover, Eric Miller/World Bank; back cover, large top inset, Eric Miller/World Bank; bottom, left to right: Arne Hoel/World Bank, Arne Hoel/World Bank, M.Hallahan/Sumitomo Chemical - Olyset® Net, Arne Hoel/World Bank, Arne Hoel/ World Bank. ISBN: 978-0-8213-7283-8 e-ISBN: 978-0-8213-7284-5 DOI: 10.1596/978-0-8213-7283-8 SKU: 17283 Contents Foreword vii Acknowledgments ix Spreading and sustaining growth in Africa 1 Notes 17 References 18 Indicator tables 19 Part I. Basic indicators and national accounts 1. Basic indicators 1.1 Basic indicators 21 2. National accounts 2.1 Gross domestic product, nominal 22 2.2 Gross domestic product, real 23 2.3 Gross domestic product growth 24 2.4 Gross domestic product per capita, real 25 2.5 Gross domestic product per capita growth 26 2.6 Gross national income, nominal 27 2.7 Gross national income, real 28 2.8 Gross national income per capita 29 2.9 Gross domestic product deflator (local currency series) 30 2.10 Gross domestic product deflator (U.S. dollar series) 31 2.11 Gross domestic savings 32 2.12 Gross national savings 33 2.13 General government final consumption 34 2.14 Final consumption expenditure 35 2.15 Final consumption expenditure per capita 36 2.16 Agriculture value added 37 2.17 Industry value added 38 2.18 Services value added 39 2.19 Gross fixed capital formation 40 2.20 General government fixed capital formation 41 2.21 Private sector fixed capital formation 42 2.22 Resource balance (exports minus imports) 43 2.23 Exports of goods and services, nominal 44 2.24 Imports of goods and services, nominal 45 2.25 Exports of goods and services, real 46 2.26 Imports of goods and services, real 47 Part II. Millennium Development Goals 3. Millennium Development Goals 3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger 48 Contents iii 3.2 Millennium Development Goal 2: achieve universal primary education 50 3.3 Millennium Development Goal 3: promote gender equality and empower women 51 3.4 Millennium Development Goal 4: reduce child mortality 52 3.5 Millennium Development Goal 5: improve maternal health 53 3.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases 54 3.7 Millennium Development Goal 7: ensure environmental sustainability 56 3.8 Millennium Development Goal 8: develop a global partnership for development 58 Part III. Development outcomes 4. Paris Declaration indicators 4.1 Status of Paris Declaration indicators 60 5. Private sector development 5.1 Business environment 61 5.2 Investment climate 62 6. Trade 6.1 International trade and tariff barriers 64 6.2 Top three exports and share in total exports, 2005 68 6.3 Regional integration, trade blocs 70 7. Infrastructure 7.1 Water and sanitation 71 7.2 Transportation 72 7.3 Information and communication technology 74 7.4 Energy 76 7.5 Financial sector infrastructure 78 8. Human development 8.1 Education 80 8.2 Health 82 9. Agriculture, rural development, and environment 9.1 Rural development 86 9.2 Agriculture 88 9.3 Environment 90 10. Labor, migration, and population 10.1 Labor force participation 92 10.2 Labor force composition 94 10.3 Migration and population 96 11. HIV/AIDS 11.1 HIV/AIDS 98 12. Malaria 12.1 Malaria 99 13. Capable states and partnership 13.1 Aid and debt relief 100 13.2 Capable states 102 13.3 Governance and anticorruption indicators 104 13.4 Country Policy and Institutional Assessment ratings 106 iv Africa Development Indicators 2007 Part IV. Household welfare 14. Household welfare 14.1 Burkina Faso household survey, 2003 108 14.2 Cameroon household survey, 2001 109 14.3 Ethiopia household survey, 2000 110 14.4 Malawi household survey, 2004 111 14.5 Niger household survey, 2005 112 14.6 Nigeria household survey, 2004 113 14.7 São Tomé and Principe household survey, 2000 114 14.8 Sierra Leone household survey, 2002/03 115 14.9 Uganda household survey, 2002/03 116 Technical notes 117 References 163 User's guide: Africa Development Indicators 2007 CD-ROM 165 Contents v Foreword Something decidedly new is on the horizon it is essential to spread economic growth to in Africa, something that began in the mid- all of Africa and so essential to sustain it, by 1990s. Many African economies appear to avoiding the collapses that have erased past have turned the corner and moved to a path gains. of faster and steadier economic growth. Their Th is year's Africa Development Indicators performance in 1995­2005 reverses the col- essay explores the patterns of growth in Sub- lapses in 1975­85 and the stagnations in Saharan Africa over the past three decades. 1985­95. And for the first time in three de- It fi nds that the volatility of growth--an cades, they are growing in tandem with the outcome of confl ict, governance, and world rest of the world. Average growth in the Sub- commodity prices--has been greater than Saharan economies was 5.4 percent in 2005 in any other region. Volatility has dampened and 2006, and the consensus projections are expectations and investments--and has ob- that growth will remain strong. Leading the scured some periods of good performance way are the oil and mineral exporters, thanks for some countries. The essay shows that to high prices. But 18 nonmineral econo- pickups in growth were seldom sustained-- mies, with 36 percent of Sub-Saharan Africa's indeed, that they were often followed by fe- people, have also been doing well. rocious declines, and hence, Africa's fl at eco- Is this the outcome of good luck or nomic performance over 1975­2005. good policy? Luck certainly has been a fac- The essay shows that avoiding econom- tor. Global economic growth has been fairly ic declines is as important as promoting steady over the last 10 years, trade has ex- growth. Indeed, it may be more important panded rapidly, and foreign direct invest- for the poor, who gain less during the growth ment has rocketed. But policies in many pickups and suffer more during the declines. Sub-Saharan countries have also been get- The essay discusses a key question for eco- ting better. Infl ation, budget deficits, ex- nomic policymakers in Africa: how best to change rates, and foreign debt payments are sustain pickups in growth and its benefits. more manageable. Economies are more open Africa Development Indicators 2007 is the to trade and private enterprise. Governance latest annual report from the World Bank on is also on the mend, with more democracies social and economic conditions across the and more assaults on corruption. Yes, some continent. Along with this book, The Little luck, but policy improvements have also Data Book on Africa 2007, and the Africa made a difference. Development Indicators 2007 CD-ROM, Better economic policy and performance the Africa Development Indicators suite of will also be at the core of improving Afri- products now has a new member: Africa De- can's well-being. More than 40 percent of velopment Indicators Online. the people in Sub-Saharan Africa still live With demand increasing for information on less than $1 a day, life expectancy gains to monitor the African Action Plan, Poverty have stalled in some countries and retreated Reduction Strategy Papers, national develop- in others, and poor health and poor school- ment programs, and the Millennium Devel- ing hold back improvements in people's pro- opment Goals and with access to electronic ductivity--and the chances of meeting the media widening in Africa, the Africa Devel- Millennium Development Goals. That is why opment Indicators products are expected to Foreword vii continue evolving with the goal of offering Indicators Online also brings the Africa De- the most relevant information to monitor velopment Indicators 2007 essay, The Little development progress. Th is will allow us to Data Book on Africa 2007, country-at-a-glance assess the magnitude of problems and chal- tables, technical boxes, and country analyses lenges faced and measure progress in a fea- from African Economic Outlook 2007. sible way. Better statistics are of great value, The Africa Development Indicators suite and this still remains a great challenge for of products is designed to provide all those Africa. interested in Africa with a set of indicators Africa Development Indicators Online, to monitor development outcomes in the re- available by subscription, contains the most gion and is an important reference tool for comprehensive database on Africa, covering those who want a better understanding of more than 1,000 indicators on economics, the economic and social developments oc- human development, private sector devel- curring in Africa. opment, governance, and aid, with time se- It is my hope that the Africa Develop- ries for many indicators going back to 1965. ment Indicators products will contribute to The indicators were assembled from a vari- the way countries, development partners, ety of sources to present a broad picture of analysts, academics, and the general public development across Africa. The Microsoft understand and design development policies WindowsTM­based format permits users to in Africa. search and retrieve data in spreadsheet form, create maps and charts, and import them into other popular software programs for John Page study or presentation. Africa Development Chief Economist, Africa Region viii Africa Development Indicators 2007 Acknowledgments Africa Development Indicators 2007 was pro- Carneiro, Punam Chuhan-Pole, Kene Ezeme- duced by the Office of the Chief Economist nari, Nevin Fahmy, Giuseppe Iarossi, Emily for the Africa Region and the Operational Gosse Kallaur, Caterina Ruggeri Laderchi, Quality and Knowledge Services Group. Sonia Plaza, Quentin Wodon, and Yutaka Jorge Arbache and Rose Mungai were the Yoshino. managers of this book and its companions, Many colleagues from the Office of the Africa Development Indicators Online, Afri- Chief Economist and other units have made ca Development Indicators 2007--Multiple valuable contributions, including Gozde Isik, User CD-ROM, and The Little Data Book on Ann Karasanyi, Vijdan Korman, Lebohang Africa 2007. The core team of Africa Develop- Lijane, Sergio Margulis, Kenneth Omondi, ment Indicators 2007 included Mpho Chiny- Xiao Ye, and Vildan Verbeek-Demiraydin. olo, Francoise Genouille, Jane K. Njuguna, Communications Development Incorpo- Joan Pandit, and Christophe Rockmore. The rated provided overall design direction, ed- work was carried out under the general guid- iting, and layout, led by Bruce Ross-Larson, ance and supervision of John Page, chief Meta de Coquereaumont, and Christopher economist for the Africa Region. Trott. Elaine Wilson created the graphics The Development Data Group of the and laid out the book. Dohatec New Media Development Economics Vice Presidency-- prepared the navigation structure and inter- including Mehdi Akhlaghi, Abdolreza Fari- face design of the Africa Development Indi- vari, Richard Fix, Shelley Lai Fu, Shahin cators Online. Outadi, William C. Prince, Atsushi Shimo, Staff from External Affairs, including and Malarvizhi Veerappan--collaborated in Richard Crabbe, Valentina Kalk, Malika the production of the Africa Development Khek, Mario Trubiano, and Stuart Tucker, Indicators 2007--Multiple User CD-ROM, oversaw publication and dissemination of and The Little Data Book on Africa 2007. the book and its companions. Aby Toure, The boxes in the technical notes ben- from the Africa External Affairs Group efited from contributions from Edward Al- (AFREX), also helped disseminate Africa Hussainy, Thorsten Beck, Francisco Galrão Development Indicators products. Acknowledgments ix Spreading and sustaining growth in Africa Something decidedly new is on the horizon Africans' well-being. About 41 percent of in Africa, something that began in the mid- Sub-Saharan Africa's people live on less than 1990s. Many African economies appear to $1 a day. Because of AIDS, tuberculosis, ma- have turned the corner and moved to a path laria, and other diseases, improvements in of faster and steadier economic growth. Their life expectancy have stalled in some coun- performance over 1995­2005 reverses the tries, retreated in a few others. And despite collapses over 1975­85 and the stagnations substantial progress in primary enrollments, over 1985­95. And for the first time in three educational outcomes are not improving as decades, African economies are growing with quickly as they might. Poor health and poor the rest of the world. Average growth in the schooling naturally hold back improvements Sub-Saharan economies was 5.4 percent in in people's productivity--and the chances 2005 and 2006. The consensus projection of meeting the Millennium Development is 5.3 percent for 2007 and 5.4 percent for Goals. That is why it is so essential to spread 2008. Leading the way: the oil and mineral economic growth to all of Africa and so es- exporters, thanks to high prices. But 18 non- sential to sustain it, by avoiding the collaps- mineral economies, with more than a third es that have erased past gains. of the Sub-Saharan African people, have also This essay explores the patterns of growth been doing well. in Sub-Saharan Africa over the past 30 years. Is this the outcome of good luck or It finds that the volatility of growth--a prod- good policy? Luck certainly has been a fac- uct of confl ict, governance, and world com- tor. Global economic growth has been fairly modity prices--has been greater than in any steady over the last 10 years--at 3.2 percent. other region. That volatility has dampened Global trade has expanded at 40 percent a expectations and investments--and has ob- year. And foreign direct investment rocketed scured some periods of good performance for from 1.15 percent of world GDP in 1995 to some countries. The analysis here finds that more than 2.23 percent in 2005, with pri- pickups in growth were seldom sustained-- vate equity funds scouring the globe for new indeed, that they were often followed by opportunities. Emerging stock markets have ferocious declines. Hence, Africa's fl at eco- also been burgeoning, thanks to global in- nomic performance over 1975­2005. Where vestors searching for high returns. an economy started in 1975 is pretty much But policies in many Sub-Saharan coun- where it ended in 2005. The reason: when tries have also been getting better. Inflation, things go well they do not last, and when budget deficits, exchange rates, and foreign they go wrong they go very wrong. debt payments are more manageable. Econo- So, avoiding a decline from 2 percent GDP mies are more open to trade and private en- growth to ­3 percent is as important as going terprise. Governance is also on the mend, from 2 percent to 7 percent. Indeed, it may with more democracies and more assaults on be more important for poor people, who gain corruption. The conclusion: yes, some luck, much less during growth pickups and suffer but policy improvements have also made a much more during the declines. The question difference. for economic policymakers in Africa, then, is Better economic policy and performance how best to sustain the pickups in growth. will be at the core of continuing to improve The answer: avoid the crushing declines. Spreading and sustaining growth in Africa 1 Growing in tandem with sustained-growth economies (35.6 percent the rest of the world of Africa's population), which have grown Since the mid-1990s average incomes in Af- at more than 4 percent a year for at least 10 rica have been rising in tandem with those years; and oil exporters (27.7 percent of Af- in other regions. Despite an unanticipated oil rica's population) (table 1). shock, growth has remained good. Average Most of the successful growing econo- growth in 2005 was 5.5 percent; it is estimat- mies share some characteristics. They inte- ed at 5.3 percent in 2006 and projected to be grate more with the world economy through 5.3 percent in 2007. More than a third of Af- trade, especially exports. Their investment ricans now live in countries that have grown and productivity are on the rise. And their at more than 4 percent a year for 10 years. institutions are getting better. What do A group of diversified sustained grow- recent data reveal about these aspects of ers has begun to emerge, and natural re- growth in Africa (figures 1­9)? sources have gained new importance. In 2005 growth varied substantially, from ­5.3 Investment and efficiency percent to 20.6 percent, and eight countries Africa's growth deficit is the product of low were near or above the 7 percent threshold productivity and low investment. Growth needed to sustain poverty reduction. Along accounting shows that physical capital per this continuum of growth performance worker has grown less than 0.5 percent a three broad country types are emerging: year, half the world average. Capital shrank slow-growth economies (36.7 percent of Af- between 1990 and 2003, mirroring low capi- rica's population), which include many con- tal investment. But the contribution of hu- fl ict or post-confl ict countries; diversified, man capital to growth has kept pace with the rest of the world, mainly a result of rising Table 1 African GDP growth rates, by country type, 1996­2005 average years of schooling. Indeed, the main culprit in Africa's disappointing growth is Diversified, sustained- total factor productivity, negative since the growth economies Slow-growth economies GDP growth 4 percent 1960s and ­0.4 percent between 1990 and GDP growth less than 4 percent a year a year or more Oil exporters (36.7 percent of population) (35.6 percent of population) (27.7 percent of population) 2003 (Bosworth and Collins 2003). GDP growth GDP growth GDP growth New evidence indicates improvement in Country (percent) Country (percent) Country (percent) these areas. Some improvements in the growth Zambia 3.80 Mozambique 8.3 Equatorial 30.8 of output per worker in Africa were registered Guinea in recent years, and the contribution of total Guinea 3.70 Rwanda 7.6 Chad 9.0 factor productivity dominated this recovery São Tomé and (Berthelemy and Soderling 2001). Niger 3.50 7.1 Angola 8.5 Principe Overall, investment increased between Malawi 3.30 Botswana 6.7 Sudan 6.3 2000 and 2006, from 16.8 percent of GDP Mauritania 3.30 Uganda 6.1 Nigeria 4.3 to 19.5 percent. Sustained-growth countries Togo 3.30 Cape Verde 5.8 Congo, Rep. 3.4 have aggregate efficiency on par with India's Madagascar 3.20 Mali 5.8 Gabon 1.1 and Vietnam's, and they are approaching Lesotho 3.00 Tanzania 5.3 these countries in investment. For the slow Kenya 2.90 Ethiopia 5.2 growers, by contrast, efficiency and invest- Eritrea 2.41 Sierra Leone 5.2 ment were lower. Seychelles 2.30 Burkina Faso 5.0 The aggregate productivity numbers Comoros 2.13 Mauritius 4.8 are supported by fi rm studies (Eifert, Gelb, Central African Republic 0.85 Ghana 4.7 and Ramachandran 2005). Recent research Guinea-Bissau 0.47 Benin 4.6 shows that efficient African enterprises can Burundi 0.43 Senegal 4.5 compete with Chinese and Indian fi rms in Congo, Dem. Rep. 0.08 Cameroon 4.2 factory floor costs (figure 10). They become Zimbabwe ­2.20 Gambia, The 4.2 less competitive, though, due to higher indi- Namibia 4.0 rect business costs, including infrastructure (figures 11 and 12). In China indirect costs Note: GDP growth rates are compound annual averages. Source: World Bank Development Data Platform. are about 8 percent of total costs, but in Af- rican countries they are 18­35 percent. 2 Africa Development Indicators 2007 Trade large declines in governance indicators (Cen- African exports have been growing over the tral African Republic, Côte d'Ivoire, Eritrea, last few years, most dramatically for the oil and Zimbabwe). In mid-2007 there were still exporters, but for the non-oil-producers as 5 civil wars, much fewer than the 16 that ex- well (table 2). Exports rose from $182 billion isted in the late 1990s. in 2004 to $230 billion in 2005, and 38 coun- tries increased their exports, with pockets Doing more business of nontraditional exports (such as clothing In the 2006/07 Doing Business indicators, from Lesotho, Madagascar, and Mauritius). the average rank of African countries was 136 Rwanda, by helping farmers connect to buy- among 178 countries (table 3). Four countries ers of high-quality coffee, boosted its coffee had ranks in the top third--Mauritius 32, exports to the United States by 166 percent South Africa 35, Namibia 43, and Botswana in 2005--driving its impressive growth. In 51. Kenya rose to 72, and Ghana to 87. But Ghana thousands of employees process U.S. all the others had ranks of 90 or higher. health insurance claims around the clock, Before 2005 African countries were and many customers in France do not real- slow to reform, but the pace has picked up ize that they are dealing with call centers in the last two years. Presidential investors' in Senegal. In Kenya exports of cut flowers councils or similar bodies are active in seven more than doubled between 2000 and 2005 countries, among them Mozambique, Rwan- to rank second among its exports, after tea. da, and Tanzania. Benchmarking through While these trends are encouraging, growth the World Bank's Doing Business surveys rates for non-oil-exporters are not yet high and Investment Climate Assessments has enough to constitute an export push. proven very useful in focusing high-level at- tention on the business environment. Policies and governance Forty-six Sub-Saharan countries intro- A central lesson of Africa's growth experi- duced at least one business environment ence is that "policy and governance matter a reform in the past year, and Ghana and Ke- great deal" (Ndulu and others 2007, p. 42). nya were among the top 10 reformers in the Africa today enjoys better growth prospects world in 2006/07. Eleven African countries because its leaders have undertaken major introduced reforms to reduce the time and reforms over the past 10 years. In 2006 Af- cost needed to start a business. For example, rica's best Country Policy and Institutional Assessment (CPIA) ratings were in macro- Table 2 African export growth rates, by country economic management and trade policy. type, 2003­06 (percent) Over 1999­2006 average scores from the Country group 2003 2004 2005 2006 CPIA rose year on year, and the number of All countries 8.2 12.9 14.1 11.3 African countries with scores at or above the Oil exporters 16.7 21.6 19.2 13.5 international "good performance" threshold Non-oil-producers 4.5 7.6 5.7 7.1 of 3.5 on a scale of 1 to 6 increased from 5 to 15. The average African CPIA score in 1995 Source: International Monetary Fund data. was 2.80. By 2006 it had risen to 3.2, and 27 of 36 countries evaluated in both years had improved their scores.1 Table 3 Average ease of doing business rank, by region, 2006/07 Recent data from Global Monitoring Re- port 2007 (World Bank 2007b) provide some Region 2006 evidence of better governance. Measures of East Asia & Pacific 76 bureaucratic capabilities and the quality of Europe & Central Asia 77 checks and balances institutions improved Latin America & the Caribbean 87 in six African countries (The Gambia, Gha- Middle East & North Africa 96 na, Kenya, Madagascar, Senegal, and Tan- South Asia 107 zania). And three of the seven countries Sub-Saharan Africa 136 worldwide showing improved governance in Note: A lower rank is better. a balanced manner over the last decade were Source: World Bank 2007a. in Africa. However, four countries suffered Spreading and sustaining growth in Africa 3 Figure 1 African per capita income is now increasing Figure 2 Macroeconomic management in tandem with other developing countries has improved Annual change in real GDP per capita (%) Inflation, 2000­06 (percent) 10 25 Developing countries Oil-exporting countries Developing countries, excluding China and India Sub-Saharan Africa 20 High-income countries Sub-Saharan Africa 5 15 Oil-importing countries 10 0 5 ­5 0 1990 1995 2000 2005 2008 2000 2001 2002 2003 2004 2005 2006 Source: World Bank Development Data Platform. Source: International Monetary Fund Sub-Saharan Africa Regional Economic Outlook. Figure 3 Structural policies have improved Figure 4 Africa's five fastest growing economies in Sub-Saharan Africa stack up well with Asia . . . Average Country Policy and Institutional Assessment scores Average annual GDP growth, by decade (percent) 3.5 10 3.4 8 3.3 6 Africa top performers 3.2 4 Asia top performers Africa average 3.1 2 China 3.0 0 2000 2001 2002 2003 2004 2005 2006 1960s 1970s 1980s 1990s 2000­05 Source: World Bank 2006. Source: World Bank Development Data Platform. Figure 5 . . . but high population growth Figure 6 Africa's best performers are takes its toll on per capita income on a par with India and Vietnam Average annual GDP per capita growth, by decade (percent) Percent 10 30 Incremental output-capital ratio (percent) 25 8 20 6 Investment (percent of GDP) Africa top performers 15 Asia top performers 4 Africa average 10 2 China 5 0 0 ­2 Africa, Africa, slow Africa, little India Vietnam sustained growth or no growth 1960s 1970s 1980s 1990s 2000­05 growth countries countries countries Source: World Bank Development Data Platform. Source: World Bank Development Data Platform. 4 Africa Development Indicators 2007 Figure 7 Exports are important . . . Figure 8 . . . but are growing slowly . . . Nonoil exports as share of GDP, by region (percent) Average annual growth in exports, by decade (percent) 50 25 40 20 30 15 20 Africa top performers 10 Asia top performers 10 1983­85 1993­95 2003­05 Africa average 5 0 China East Asia Eastern Europe Latin Middle East South Sub-Saharan and Pacific and former America and and North Asia Africa 0 Soviet Union Caribbean Africa 1960s 1970s 1980s 1990s 2000­05 Note: Export shares are unweighted averages. Source: World Bank Development Data Platform. Source: International Monetary Fund World Economic Outlook database. Figure 9 . . . and are declining in importance Figure 10 Factory floor costs in Sub-Saharan Africa for Africa's top performers compare well with those in China and India Exports as a share of GDP (percent) Direct cost per men's shirt ($) 80 0.75 70 60 0.50 50 40 Africa top performers Asia top performers 30 0.25 Africa average 20 10 China 0.00 0 Mada- Kenya Ghana Mozam- Lesotho South India Chinaa 1960s 1970s 1980s 1990s 2000­05 gascar bique Africa a. For factories in an export processing zone. Source: World Bank Development Data Platform. Source: World Bank Development Data Platform. Figure 11 The overall cost structures of firms show Figure 12 And net productivity is much lower than that indirect costs are much higher in Africa "factory floor" (gross) productivity due to high costs of doing business Share of total costs (percent) Material Labor Capital Indirect Total factor productivity (China = 1) Bangladesh Senegal 1.2 India Morocco 1.0 Nicaragua China 0.8 Ethiopia Nigeria 0.6 Bolivia Uganda 0.4 Zambia Tanzania 0.2 Kenya ina Gross Net Eritrea 0.0 Mozambique ia a Eth e ia a ia da Ta a Nic nia ng a Se h Mo al co ia Mo tre iqu eri ny Ba gu es mb iop liv g Ind roc an a ne Ch Ke lad Nig ara Bo Eri nz mb 0 20 40 60 80 100 Za Ug za Source: Eifert, Gelb, and Ramachandran 2005. Source: Eifert, Gelb, and Ramachandran 2005. Spreading and sustaining growth in Africa 5 Burkina Faso created a one-stop shop for Development Association countries on al- business entry, cutting required procedures most all major infrastructure measures.2 In from 12 to 8 and time from 45 days to 34. addition, the quality of service is low, sup- Although fi nancial depth remains low plies are unreliable, and disruptions are fre- in Africa, signs of recovery are encouraging. quent and unpredictable--all pushing up Real private sector credit as a share of GDP production costs, a critical impediment for in low-income African countries has turned investors (table 4). There are also large in- the corner, reaching almost 13 percent in equities in access to household infrastruc- 2005, about a third higher than its low point ture services, with coverage rates in rural in 1996. areas lagging behind those in urban areas. Africa's success in restoring growth is be- The region's unmet infrastructure needs are ginning, however, to reveal some emerging estimated at $22 billion a year (5 percent of constraints to future growth. Infrastructure GDP), plus another $17 billion for operations across the continent is under stress. Skills and maintenance. to build and sustain competitive enterprises Recent progress is encouraging. Except are lacking. And the many small and land- roads, indicators of infrastructure access locked economies face unique challenges rose between the 1990s and the 2000s that can be addressed only through effective (table 5). The Africa Partnership Forum re- regional integration. African agriculture-- ported steady improvements in effectively long neglected--may also emerge as a con- using existing infrastructure and in increas- straint to growth in some economies and as ing public investments. Countries are also the sector for sharing the benefits of growth undertaking regulatory and policy reforms, broadly in others. especially in water, telecommunications, and transport (Africa Partnership Forum Closing the infrastructure gap 2006b). Twenty of the largest African coun- Sub-Saharan Africa lags at least 20 percentage tries have or are formulating reform agendas points behind the average for International for water and sanitation. Compared with other regions, Africa has Table 4 Impact of unreliable infrastructure been slow to mobilize the private sector for services on the productive sector the provision and fi nancing of infrastruc- ture. The Infrastructure Consortium reports Sub-Saharan Developing Service problem Africa countries that private sector interest has gradually Electricity spread. There is an upward trend in private Delay in obtaining electricity connection (days) 79.9 27.5 sector provision and management of infra- Electrical outages (days per year) 90.9 28.7 structure, which stood at $6 billion in 2006, Value of lost output due to electrical outages (percent of turnover) 6.1 4.4 up from $4 billion in 2004. Most private Firms maintaining own generation equipment (percent of total) 47.5 31.8 flows (84 percent) go to telecommunications Telecommunications and energy. Concessions have now been awarded to operate and rehabilitate many Delay in obtaining telephone line (days) 96.6 43.0 African ports and railways and some power Telephone outages (days per year) 28.1 9.1 distribution enterprises, but financial com- Note: Data for Sub-Saharan Africa are for 6 countries; data for developing countries are for 55 countries. mitments by the concessionaire companies Source: World Bank Investment Climate Assessments. are often small. Th is reflects both the value of the management improvements that the concessionaire is expected to bring and the Table 5 Improvements in African infrastructure access limited scale and profitability of the enter- Service 1990s 2000s Percent change prises taken over. An important facilitator Telephones (per 1,000 people) 21 90 328.6 in some cases has been the insurance in- Improved water (percent of households) 55 65 18.1 struments developed over the past 15 years Improved sanitation (percent of households) 31 37 19.3 by such bodies as the U.S. Overseas Private Investment Corporation and the Multilat- Grid electricity (percent of households) 16 23 43.8 eral Investment Guarantee Agency and by Source: World Bank 2006. the World Bank's Partial Risk Guarantee offerings. 6 Africa Development Indicators 2007 There has been significant progress in Integrating the region's economies information and communication technol- The small size of African economies and the ogy. Access to communications services has fact that many countries are landlocked call increased dramatically over the past three for regional approaches to common prob- years, with the proportion of the population lems: infrastructure in trade corridors, com- (excluding South Africa) living under the mon institutional and legal frameworks mobile telephone footprint rising from 3 per- (customs administration, competition policy, cent in 1999 to 50 percent in 2006. Th is has regulation of common property resources been matched by an equally rapid increase in such as fisheries), and transborder solutions the use of communications services. By the to regional health issues. end of 2006 there were 123 million mobile African leaders are more aware of the subscribers. Average penetration rates in the benefits of regional approaches, especially in region doubled between 2004 and 2006 to matters related to trade and infrastructure. reach 16 percent. The New Partnership for Africa's Develop- ment has adopted regional integration as Building skills for one of its core objectives, and the African competitiveness and growth Union is leading efforts to rationalize re- The enrollment trends in secondary and ter- gional economic communities. Most coun- tiary education are positive, though comple- tries in Africa are party to multiple treaties tion rates and quality remain low. The second- or conventions addressing joint develop- ary gross enrollment rate rose from a regional ment and management of shared water re- average of 24 percent in 1999 to 31 percent in sources (including navigation and fisheries), 2004. Still, only 30 percent of each age cohort hydropower, trade corridors, irrigation, and completes junior secondary school and 12 per- flood control. Progress has been most no- cent senior secondary. There is also consider- table in regional infrastructure, particularly able variation. Botswana, Cape Verde, Mau- regional power pools (in West and Southern ritius, Namibia, Seychelles, and South Africa Africa) and in launching customs unions enroll more than 80 percent of the relevant (West, East, and Southern Africa). Progress population in junior secondary schools, while on regional infrastructure is slowed by the Burundi, Burkina Faso, Central African Repub- technical complexity of multicountry proj- lic, Niger, and Rwanda enroll less than 20 per- ects and the time required for decisions by cent. Access to tertiary education has been in- multiple governments. There is less progress creasing at 15 percent a year across the region, in regional approaches to education and in but coverage remains the lowest in the world, systematically addressing regional health less than 5 percent of the relevant age popula- issues. tion. Gender parity in secondary education is improving, with women making up more than Making agriculture more productive 40 percent of enrollments in most countries Sustained growth that reduces rural poverty (up from 20­30 percent 10 years ago). will require that more countries achieve 5 Over the past two years African poli- percent annual growth in agricultural value cymakers and development partners have added. While growth in agricultural value placed greater emphasis on postprimary added has been strong since 2000, averag- education and primary school completion. ing 4.6 percent in 2004, too little of it has National policies are being reoriented to- come from higher productivity or yields.3 ward better tertiary education in Botswa- While land productivity is increasing in 38 of na, The Gambia, Kenya, Nigeria, Rwanda, 46 countries, only 6 have a rate of increase Tanzania, and Uganda. Private secondary of 5 percent or more.4 Labor productivity is education and training are expanding, and increasing in 29 countries, with 10 achieving public-private partnerships are emerging. increases of 3 percent a year or higher.5 Previously neglected issues--such as labor Productivity growth will require an market links among curricula, science and expansion of area irrigated, as well as bet- technology capacities, and research perfor- ter performance of rainfed agriculture. But mance--are emerging in public discussions. less than 4 percent of cultivated land is ir- And private options are increasing. rigated. Because of the long lead time before Spreading and sustaining growth in Africa 7 investments are completed and operational, while the range improved from 11­81 percent this proportion changed little in the past to 26­87 percent. This convergence is the 18 months. Improvements in management result of rising primary school enrollments. of soil fertility have been slow, as has the Regionwide gross enrollment rose from adoption of better seeds. Spending for ag- 79 percent in 1999 to 92 percent in 2004. ricultural research and technology remains Some 87 percent of Africans live in countries low, although it is starting to increase along where the average enrollment rate is above with overall spending on agricultural pro- 75 percent, and fewer than 2 percent live in grams in the region (Africa Partnership countries where the rate is below 50 percent. Forum 2006a). On a positive note there has Six of the seven top countries worldwide in been an increase in the use of water manage- boosting primary completion rates (by more ment techniques (water harvesting, reduced than 10 percent a year between 2000 and tillage). 2005) are in Africa (Benin, Guinea, Madagas- car, Mozambique, Niger, and Rwanda). There Why growth is so important: meeting the have not been comparable improvements Millennium Development Goals to reduce in secondary and tertiary education. While poverty and improve social outcomes East Asian countries increased secondary en- Human development outcomes are improv- rollment rates by 21 percentage points and ing across the region, and progress toward tertiary enrollment rates by 12 percentage the Millennium Development Goals is pick- points over 12 years, Africa raised its second- ing up. In 1990, 47 percent of Africans lived ary rates by only 7 percentage points and its in poverty. In 2004, 41 percent did, and on tertiary rates by 1 percentage point. present trends 37 percent will in 2015. Gross primary school enrollment rates rose from Health 79 percent in 1999 to 92 percent in 2004. Between 1990 and 2005 life expectancy at Health outcomes are more varied but are birth in Sub-Saharan Africa declined from also improving in many countries. In 2005 49.2 years to 47.1. Although life expectancy eight countries were near or above the 7 increased in 25 countries by an average of percent threshold needed to sustain poverty eight years, it declined in 21 more popu- reduction. lous countries by an average of four years. Good economic growth and sustained HIV/AIDS, malaria, and armed conflict have efforts by governments and their develop- contributed to these falling life expectancies. ment partners have accelerated progress Progress against malaria, tuberculosis, and on the Millennium Development Goals. HIV/AIDS is mixed but showing some posi- Although Sub-Saharan Africa is one of two tive signs. The spread of AIDS has slowed regions not expected to reach most of the in Africa, but the continent still bears the Millennium Development Goals by 2015 brunt of the epidemic. Rapid increases in (the other is South Asia), there is substantial tuberculosis infections in Africa are linked variation among countries in both the level to the greater likelihood of tuberculosis ap- of attainment of the goals and the pace of pearing from latent infections among HIV progress. Mauritius has met four goals. Bo- carriers. Malaria remains Africa's leading tswana has met three and will likely meet killer of children under age 5, but a strong one more. And South Africa has met three. new global partnership has formed to ad- Among other countries nine will meet two dress the disease. goals, and 13 will meet at least one. But de- There is evidence that outcomes are im- spite better progress--especially in educa- proving for some of the other health Mil- tion, malaria, and HIV/AIDS--23 African lennium Development Goals. Progress in countries are not likely to meet any of the addressing child mortality has been slow Millennium Development Goals. worldwide, but there are promising signs in Africa. The share of children ages 12­23 Education months immunized against measles went Between 1990 and 2004 the average literacy from 57 percent in 1990 to 64 percent in rate (in the 29 countries for which data are 2005. Some 70 percent of Africans now live available) rose from 54 percent to 62 percent, in countries where under-five mortality has 8 Africa Development Indicators 2007 dropped to 100­200 per 1,000 live births, Figure 13 PPP GDP per capita, unweighted while only 16 percent live in countries with rates above 200. Eritrea, despite a per capita U.S. dollars income of only $190, cut child mortality in 3,000 half between 1990 and 2005. Substantial work is still needed for countries to meet the 2,750 Millennium Development Goal of reducing the rate by two-thirds by 2015. 2,500 Actual Can growth be spread and sustained? Trend Yes, if the past is not prologue 2,250 Africa today is thus very different from the Africa of the early 1990s, when it was com- ing out of the declines after the first two 2,000 1975 1980 1985 1990 1995 2000 2005 oil price shocks and the stagnation of the adjustment years. Whether it can stay dif- Source: Arbache and Page 2007a. ferent will depend on whether it can spread and sustain growth. To find out what that might take, this section looks at the patterns Figure 14 Average GDP per capita as a function of 1975 GDP per capita of long-term growth in Sub-Saharan Africa, using the most recent purchasing power par- ity data for 45 countries (Arbache and Page 10 2007a). 9 Country patterns GDP per capita Sub-Saharan GDP per capita increased only modestly between 1975 and 2005 (figure 8 13). The average GDP per capita of most countries in 2005 closely mirrors that in 7 1975, reflecting inertia, stratification, and initial conditions in economic output (fig- ure 14). Countries that started poor, stayed 6 6 7 8 9 10 poor, and those that started richer, stayed Log of 1975 GDP per capita richer--with few exceptions. Botswana and Source: Arbache and Page 2007a. Namibia saw their GDP per capita shoot up, and Eritrea and Mozambique saw theirs tumble. Accompanying Africa's slow growth is considerable instability in countries. The Figure 15 PPP GDP per capita growth GDP per capita of countries varied wildly, as did the volatility of growth (figure 15). Percent Volatility hit countries at different incomes 10 (Botswana and Malawi) and on different long-term paths (Cape Verde, Comoros, and South Africa). 5 With poorer countries growing more Actual slowly, the gap between their incomes and those of richer countries widened. The rich- Trend 0 est 10 percent of countries had 10.5 times the GDP per capita of the poorest 10 per- cent in 1975 and 18.5 times that in 2005. ­5 So even with country growth rates now 1975 1980 1985 1990 1995 2000 2005 converging, Africa has become more un- equal across countries. The polarization Source: Arbache and Page 2007a. of richer and poorer countries appears to Spreading and sustaining growth in Africa 9 Figure 16 Actual and simulated GDP per capita volatility and growth and between volatility and GDP per capita. Th at could be because U.S. dollars policy and structural characteristics were 3,500 not properly taken into account. It could GDP per capita growth in the no-collapse scenario (1.7 percent a year) also be that African economies are so stuck GDP per capita growth at the in their long-run ruts that short-term vola- observed average (0.7 percent a year) 3,000 Actual GDP per capita tility cannot divert them. Or it could be that volatility and poor growth performance are both symptoms of institutionally weak soci- eties and so are not independent (Acemoglu, 2,500 Johnson, and Robinson 2003). In this view, policies are tools for the groups in power to reap rents and stay in power, adding to the 2,000 difficulty of dealing with political and eco- 1975 1980 1985 1990 1995 2000 2005 nomic shocks, leading to more political and Source: Arbache and Page 2007b. economic instability. Because long-run growth in Africa was both low and volatile, it is a challenge to have increased over 1985­95, when many identify periods of sustained growth or de- countries plunged into confl ict. Emerging cline. In 1975­85 Africa suffered two oil as regional stars are Botswana, Cape Verde, shocks, a plunge in commodity prices, and Gabon, Mauritius, Namibia, Seychelles, and the eruption of confl ict. In 1985­95 it in- South Africa, with 9 percent of the region's troduced structural reforms that brought people but 45 percent of its GDP. austerity to many countries. In 1995­2005 How did countries fare in income per it began to recover. But the economic trajec- capita relative to South Africa, the region's tories for individual countries were far from largest economy (now 38 percent of the re- linear. The volatility of growth, just dis- gion's total GDP)? Nineteen improved, 13 cussed, bears little relation to the long-run stayed put, and 11 saw steep declines. The performance of an economy. mineral exporters Botswana, Cape Verde, Though volatility itself may matter little and Equatorial Guinea registered among the for the overall rate of economic growth-- strongest improvements. But Angola, Chad, and per capita income--for a typical African and Nigeria stayed put, showing that mineral country, it may nevertheless indicate that resources do not always determine success. growth spurts are offset by growth collaps- Th is all suggests that African countries es. Some of these growth accelerations and experience similar economic cycles, in an decelerations may be due to pure bad luck: environment of interdependence, conta- commodity prices rise and then fall. But gion, and other regional spillovers. Among others may be due to policy choices by gov- the channels for the cross-country simi- ernments. Looking at the underlying char- larities in GDP per capita and productivity acteristics of growth accelerations and decel- are worker remittances, temporary migra- erations might thus provide some insights tion, and regional confl icts. Consider Chad into how to sustain the spurts and avoid the and Sudan, Liberia and Sierra Leone, and collapses (Arbache and Page 2007b). the Democratic Republic of Congo and its neighbors. Sustaining the good times and avoiding the bad are what matter Volatility matters little for growth The accelerations and decelerations on a Slow output growth and high volatility are the country's economic path--the good times defining characteristics of the long-run pat- and the bad--show that African countries tern of Sub-Saharan growth just described. experienced several episodes of growth over But does high volatility mean slow growth? 1975­2005. But they have also saw a compa- Not necessarily--or not directly. rable number of collapses, offsetting most of Recent work finds a negative but not sta- the growth. If Africa could have avoided the tistically significant relationship between collapses, it would have grown at 1.7 percent 10 Africa Development Indicators 2007 a year per capita, not 0.7 percent. A percent- Figure 17 Tanzania GDP per capita growth age point might seem small, but it would have added 30 percent to the region's GDP Percent (figure 16). So avoiding the collapses is a ma- 10 jor economic challenge in Africa. What constitutes good times for a given country? Four conditions. First, the four- 5 year forward moving average of GDP per capita growth minus the country's four-year Only the first All four condition is satisfied conditions satisfied backward moving average is greater than (1993­2005) (1998­2005) 0 zero for a given year. Second, the four-year forward moving average of growth is above the country's long-run trend. Third, the four- year forward moving average of GDP per cap- ­5 1989 1991 1993 1995 1997 1999 2001 2003 2005 ita exceeds the four-year backward average. Fourth, the first three conditions are satis- Source: Arbache and Page 2007b. fied for at least three years in a row followed by the three subsequent years after the last year that satisfies the first three conditions. Figure 18 Senegal GDP per capita growth And what constitutes the bad? The opposites of the first three conditions for the good. Percent Consider Tanzania. The first condi- 15 tion was met for the high growth years 1995­2005 (figure 17). But all four condi- 10 tions were met for 1998­2005, which thus qualify as good times. The years just before Growth acceleration 5 (1994­2001) that were a recovery from recession. Now consider Senegal. It contracted dur- 0 ing the bad times of 1998­2004, when the average rate of decline was ­1.4 percent, well ­5 below the trend of 0.35 percent (figure 18). Growth deceleration (1988­94) Then it grew at 1.75 percent during the good ­10 times of 1994­2001, not great but more 1975 1980 1985 1990 1995 2000 2005 than three points better than before. Source: Arbache and Page 2007b. And now South Africa. It contracted at ­1.9 percent during 1982­87 and at ­l.5 percent during 1989­94, below the trend of 0.1 percent (figure 19). Then during the good Figure 19 South Africa GDP per capita growth times of 1999­2005 GDP per capita growth rebounded to 2 percent. Percent Africa the region grew by 3.6 percent 5 a year during good times and shrank by 4 Growth acceleration (1999­2005) ­2.7 percent during the bad. Most of the 3 good times were in 1995­2005, and most 2 of the bad in the preceding two decades. In 1 1975­85 the bad times were 3.5 times more 0 frequent than the good, and in 1985­95, 0.7 ­1 ­2 times more frequent. ­3 For many countries there is no sub- Growth deceleration Growth deceleration ­4 stantial difference in the unconditional (1982­87) (1989­94) ­5 probability of good times or bad, canceling 1975 1980 1985 1990 1995 2000 2005 the benefits of growth (table 6). But for oil exporters and resource-rich countries, the Source: Arbache and Page 2007b. good and bad times are well above the mean, Spreading and sustaining growth in Africa 11 and for confl ict countries the probability of diversified and less exposed to in- bad times is substantially higher than that sects, drought, other natural disas- of good. ters, and swings in the prices for agricultural products. What happens in good times and bad--and · Inflation is higher in bad times. in normal times, neither good nor bad? · Trade is substantially lower in Countries see numerous differences be- bad times, with imports dropping tween normal times and good and bad times sharply. (table 7): · The real effective exchange rate is · Saving and investment (and espe- more competitive in good times, but cially foreign direct investment) are substantially less in bad. It depreci- higher in good times than in nor- ates in the good, appreciates in the mal times--and much lower in bad bad. times. And countries with higher · Official development assistance per savings and investment have more capita is higher in good times, far good times and fewer bad times. So, lower in bad, as is official develop- growth swings seem associated with ment assistance as a percentage of changes in economic fundamentals. GDP. So, official development assis- · Domestic consumption is lower in tance is procyclical, reinforcing the good times than in normal times, importance of predictable aid for probably because more resources sustained growth. are going to investment. But it is · Life expectancy is lower in bad also lower in bad times, probably be- times. cause households have less purchas- · Infant mortality and child mortality ing power. are significantly higher in bad times · The share of agriculture in the econ- (box 1). omy is higher in bad times, as peo- · Primary school completion rates are ple return to the land. The share of significantly lower in bad times. industry is somewhat larger in good · The Country Policy and Institutional times. Assessment score drops in bad times, · Countries that rely less on agri- and countries with lower scores tend culture have more good times, to experience more bad times. Coun- probably because they are more tries that have more good times also have more voice and accountability. Table 6 Frequency of growth acceleration and deceleration, All governance indicators get worse by country category, 1975­2005 during bad times. There is thus a close relationship between gover- Growth acceleration Growth deceleration nance and growth, but it is far more Above or below Above or below Frequency all countries Frequency all countries relevant for understanding the bad Country category (country-years) mean (country-years) mean times than the good. All countries mean 0.25 0.22 Coastal 0.26 Above 0.22 Equal Country differences Landlocked 0.23 Below 0.22 Equal Countries with a high probability of good Coastal without resources 0.24 Below 0.23 Above times tend to have faster growth and a lower Landlocked without resources 0.22 Below 0.22 Equal probability of bad times--while countries Oil exporters 0.29 Above 0.23 Above with a high probability of bad times tend to Non­oil exporters 0.24 Below 0.22 Equal have slow growth. This may sound obvious, Resource countries 0.30 Above 0.21 Below but the wide gap between growth rates during Nonresource countries 0.23 Below 0.23 Above good times and bad times is most important. Major conflict 0.16 Below 0.17 Below True, volatility is at play, but the gap also sug- gests that countries have the capability and Minor conflict 0.19 Below 0.32 Above resilience to grow when internal and external Source: Arbache and Page 2007b. economic conditions and institutions favor them. The gaps tend to be wide for countries 12 Africa Development Indicators 2007 Box 1 Asymmetric impacts of good and bad times on the poor: the case of infant mortality During normal times the average infant mortality rate across Sub- Figure 1 Infant mortality (kernel density estimation) Saharan Africa is 86.2 per 1,000. During good times, the ratio falls during normal times slightly to 84.2, which is not statistically different. But there is a major increase of infant mortality to 114.1 during the bad times. 0.0100 This evidence is illustrated by the kernel density distribution. Dur- ing normal or accelerating times the kernel is right skewed (fig- ures 1 and 2). But during decelerating times the kernel curve is 0.0075 clearly skewed to the left, and a second peak emerges, repre- senting the countries experiencing much worse infant mortality levels (figure 3). Density 0.0050 Among the countries in the second peak are Malawi and Mali in 1980, both of whose infant mortality rate was 176. Remarkably, as growth accelerated these countries experienced substantially lower figures: 115 in 1995 for Malawi and 124 in 2000 for Mali. 0.0025 Other countries in the second peak include Angola in 1990 and 1995, Niger in 1985 and 1990, and Sierra Leone in 1985, 1990, and 1995. These examples highlight the asymmetric relationship 0.0000 0 50 100 150 200 250 between growth acceleration and deceleration and social indica- Infant mortality (per 1,000) tors, suggesting that growth volatility does matter and is margin- Source: Arbache and Page (2007b). ally more important for the poor than growth acceleration. Figure 2 Infant mortality (kernel density estimation) Figure 3 Infant mortality (kernel density estimation) during growth acceleration during growth deceleration 0.0100 0.0150 0.0125 0.0075 0.0100 Density Density 0.0050 0.0075 0.0050 0.0025 0.0025 0.0000 0.0000 0 50 100 150 200 0 50 100 150 200 250 300 Infant mortality (per 1,000) Infant mortality (per 1,000) Source: Arbache and Page (2007b). Source: Arbache and Page (2007b). in conflict and for countries rich in resources What appears to increase the odds for and thus exposed to commodity price vola- bad times? Infl ation and minor confl icts. tility. But they are also wide for landlocked, And what might reduce those odds? Higher resource-poor countries (such as Ethiopia savings, more domestic investment, more and Mali). foreign investment, and more trade. What appears to increase the odds for So, policies to sustain the good times good times? Higher savings. More foreign and hold the bad at bay should increase sav- direct investment. A more competitive ex- ings, investment, and trade; attract foreign change rate. And what might reduce those investment; and reduce confl ict. Contrib- odds? Higher government spending and ma- uting to this are a friendlier business envi- jor confl icts. ronment, stronger institutions, and better Spreading and sustaining growth in Africa 13 Table 7 Difference between sample averages, 1975­2005 Growth acceleration Growth deceleration Normal Variable times Mean t-test Mean t-test Savings (percent of GDP) 11.4 15.3 * 7.09 * Investments (percent of GDP) 20.0 23.1 * 15.5 * Private sector investment (percent of GDP) 12.2 13.8 * 9.17 * Foreign direct investments net flow (percent of GDP) 2.51 4.2 * 0.72 * Consumption (percent of GDP) 93.4 88.8 * 89.7 * Agriculture value added (percent of GDP) 29.8 28.5 31.9 * Industry value added (percent of GDP) 25.3 27.0 ** 24.5 Service value added (percent of GDP) 44.9 44.4 43.5 Consumer price index (percent) 27.2 15.2 184.7 * GDP deflator (percent) 26.9 16.7 175 * Public debt (percent of GNI) 87.3 112.3 * 115.7 * Government consumption (percent of GDP) 17.2 16.0 ** 15.2 * Trade (percent of GDP) 74.7 76.2 58.7 * Exports (percent of GDP) 30.1 31.6 26.5 * Imports (percent of GDP) 44.6 44.4 32.5 * Real effective exchange rate (2000=100) 130.2 115.1 * 186.4 * Terms of trade (2000=100) 109.5 102.2 * 114.5 Current account (percent of GDP) ­5.96 ­5.83 ­6.03 ODA (percent of GDP) 14.2 13.8 12.1 ** ODA per capita ($) 57.3 69.5 * 41.8 * Life expectancy (years) 50.8 51.3 48.2 * Dependency ratio 0.93 0.91 ** 0.93 Under-five mortality (per 1,000) 150.4 145.8 188.7 * Infant mortality (per 1,000 live births) 86.2 84.2 114.1 * Primary completion rate (percent of relevant age group) 53.2 52.7 40.9 * Country Policy and Institutional Assessment (1 low to 6 high) 3.17 3.2 2.75 * Voice and accountability (­2.5 low to 2.5 high) ­0.65 ­0.45 * ­1.08 * Political stability (­2.5 low to 2.5 high) ­0.47 ­0.45 ­1.07 * Government effectiveness (­2.5 low to 2.5 high) ­0.65 ­0.58 ­1.03 * Regulatory quality (­2.5 low to 2.5 high) ­0.61 ­0.49 ­0.97 * Rule of law (­2.5 low to 2.5 high) ­0.62 ­0.65 ­1.14 * Control of corruption (­2.5 low to 2.5 high) ­0.55 ­0.57 ­0.92 * Minor conflict (frequency) 0.09 0.08 0.16 * Major conflict (frequency) 0.12 0.05 * 0.07 * * indicates test that the mean is not equal to the value for normal times, significant at the 5 percent level. ** indicates that the mean is not equal to the value for normal times, significant at the 10 percent level. Source: Arbache and Page 2007b. governance. And more trade can stave off characteristics and accompanied by better collapses in growth. But these are general- fiscal performance and better governance. izations. Only country case work can pro- But driving that growth was the high de- vide a more accurate view. mand for minerals and particularly for oil. Resource-rich countries grew at 3.4 percent So, is Africa's recent growth likely to last? a year, oil-exporting countries at 4.5 percent, Per capita incomes in Africa grew at 1.9 and non-oil-exporting countries at 1.3 per- percent a year during 1995­2005, up from cent. And the unconditional probability of an ­0.1 percent over 1975­95, with growth episode of good times was 55 percent for the shared by countries with very different resource-rich countries, 49 percent for the 14 Africa Development Indicators 2007 Table 8 Difference between sample averages, 1985­94 and 1995­2005 All countries Resource rich Non­resource rich Variable 1995­2005 1985­94 t-test 1995­2005 1985­94 t-test 1995­2005 1985­94 t-test Savings (percent of GDP) 12.05 11.44 14.85 9.31 * 10.88 12.42 ** Investments (percent of GDP) 20.94 19.32 * 25.06 19.04 * 19.19 19.44 Private sector investment (percent of GDP) 12.51 10.88 * 15.43 11.81 * 11.23 10.49 Foreign direct investments net flow (percent of GDP) 4.95 1.48 * 8.22 1.69 * 3.63 1.40 * Consumption (percent of GDP) 91.12 92.45 79.90 85.87 * 95.85 95.19 Trade (percent of GDP) 76.58 67.29 * 85.77 75.29 * 72.73 63.82 * Exports (percent of GDP) 32.27 27.71 * 40.32 34.73 * 28.86 24.67 * Imports (percent of GDP) 44.27 39.57 * 45.25 40.55 ** 43.86 39.15 * Real effective exchange rate (2000=100) 103.52 138.32 * 109.18 145.54 * 100.06 134.38 * Terms of trade (2000=100) 102.40 106.98 * 104.53 113.65 ** 101.63 104.45 Current account (percent of GDP) ­5.58 ­5.18 ­3.71 ­5.22 ­6.43 ­5.16 ** Consumer price index (percent) 33.98 112.52 77.81 56.45 16.98 133.11 GDP deflator (percent) 42.85 106.63 71.73 54.40 30.46 129.07 Public debt (present value, percent of GNI) 128.41 115.16 ** 163.61 146.79 114.53 101.90 ** Government consumption (percent of GDP) 15.48 17.13 * 16.44 20.52 * 15.12 15.64 * indicates that 1985­94 and 1995­2005 values are not equal, significant at the 5 percent level. ** indicates that 1985­94 and 1995­2005 values are not equal, significant at the 10 percent level. Source: Arbache 2007. oil-exporting countries, and 36 percent for Caribbean, a third that in South Asia, and the non-oil-exporting countries. a fifth that in East Asia and Pacific. Savings If growth is now more likely to last, the and investment were well below those in all economic fundamentals should be stronger other regions. Foreign direct investment in 1995­2005 than they were in 1985­95. compared well, but it was concentrated in oil Investments in recent good times were and minerals in only a few countries. Trade slightly higher, but foreign direct investment also compared well, but again it was highly and trade were significantly higher. The ex- concentrated and dependent on few sectors. change rate was more competitive, but the Consumption was higher, reflecting the low terms of trade slightly less favorable. Gov- propensity to save. And infl ation and gov- ernment consumption was down slightly. ernment consumption were higher. And with investment basically at the same The upshot? Africa's economic funda- level, productivity should have increased mentals on average are not much better af- substantially. Indeed, productivity has been ter a decade of growth. Favoring that growth one of the biggest factors behind Africa's re- was certainly better trade conditions, but cent growth (Ndulu and others 2007). not significantly more savings and capital For resource-rich countries savings and accumulation. Statistically it cannot be said investments and most notably foreign in- that growth is more likely to last than it was vestment increased in the last decade, while a decade ago. It remains vulnerable to lower consumption fell (table 8). The exchange rate demand for oil and metals and to other out- became more competitive, and the current side shocks. What can be done to reduce that account improved. vulnerability? For the other countries savings fell and investments remained at the same level, Be sure to avoid the bad times while current account and public debt wors- while pursuing the good ened. But the exchange rate was more com- To spread and sustain growth in Africa, the petitive, and trade increased. evidence here points to three key objectives: How does Africa stand up to other devel- avoiding collapses in growth, accelerating oping regions? Its GDP growth is on par only productivity growth, and increasing private with slow-growing Latin America and the investment. This can be accomplished by Spreading and sustaining growth in Africa 15 increasing the number and variety of firms competitiveness. The potential com- and farms that can compete in the global parative advantage of low wages in economy. For the coastal economies this im- Africa is too often nullified by low plies pushing exports, and for the landlocked, productivity. Surveys of investors increasing their connectivity to regional and show that labor is not cheap where global markets through deeper regional inte- productivity is low. Information and gration. These in turn require adopting the communication technologies can four sets of policies proposed in Challenges be the main driver of productivity of African Growth (Ndulu and others 2007), growth. And there is strong empiri- published this year by the World Bank's Af- cal evidence showing that invest- rica Region. ment in information and commu- · Improving the investment climate re- nication technologies and in higher quires reducing indirect costs to education boosts competitiveness, fi rms, with energy and transporta- making both key parts of the growth tion topping the list of major im- agenda. African countries can make pediments. It also requires reducing a huge leap forward over antiquated and mitigating risks, particularly technology by exploiting the tech- those relating to crime, property nological advantages of information security, political instability, and and communication technologies as macroeconomic instability. Al- late starters. though individual countries are the · Building institutional capacity will focal point of action, their efforts underpin the fi rst three. The World could be pooled to coordinate policy, Bank's Investment Climate Assess- promote investment, improve secu- ment surveys and analysis for World rity, and increase connectivity. Development Report 2005 (World · Improving infrastructure is essential Bank 2004) spotlight costs asso- to reducing the transaction costs ciated with contract enforcement in producing goods and services. difficulties, crime, corruption, and Transportation and energy make regulation as among those weigh- up the largest part of indirect costs ing most heavily on the profitability for businesses, weighing heavily of enterprises. The main focus here on the competitiveness of fi rms in would be to strengthen the capacity most African countries. The focus of relevant public institutions for would be on reducing the high costs protecting property rights and the associated with the remoteness of scrutiny of, and accountability for, landlocked countries to facilitate public action. their trade with neighbors and the Action on these four fronts can accel- rest of the world. Again, there will erate growth in Africa and help countries be a clear need to look beyond coun- break out of the boom-bust-stagnate cycles. try borders and adopt a regional The patterns described in this essay provide approach to coordinating cross- a guide for public policy, not a formula for border infrastructure investment, success. Each country faces its own chal- maintenance, management, and use lenges and opportunities, and each country to lower costs (power pooling is an has to work within its own historical and example). geographical resources and constraints. Sus- · Spurring innovation will require in- tained faster growth in Africa is possible, if vestment in information technol- Africa's economies can meet the challenges ogy and skill formation (higher edu- of avoiding growth collapses, raising produc- cation) to enhance productivity and tivity, and boosting private investment. 16 Africa Development Indicators 2007 Notes 1. CPIA scores in the two years are not strictly com- 3. Growth in Angola, Burkina Faso, Cape Verde, Re- parable because of changes in the composition of public of Congo, Eritrea, Ethiopia, Ghana, Mauri- the index. They are sufficiently comparable, how- tius, Mozambique, Nigeria, and Tanzania has been ever, to show meaningful trends. through an expansion of cropped area. 2. An important exception is the penetration of fixed- 4. Five-year moving average based on 2001­05. line and mobile telephones, where Sub-Saharan Africa leads low-income countries by as much as 5. Five-year moving average based on 2000­04. 13 percent. The largest gaps are for rural roads (29 percentage points) and electricity (21 percent- age points). Notes 17 References Acemoglu, D., S. Johnson, and J. Robinson. 2003. Eifert, Benn, Alan Gelb, and Vijaya Ramachandran. "Institutional Causes, Macroeconomic Symptoms: 2005. "Business Environment and Comparative Volatility, Crises and Growth." Journal of Monetary Advantage in Africa: Evidence from the Investment Economics 50: 49­123. Climate Data." CGD Working Paper 56. Center for Global Development, Washington, D.C. Africa Partnership Forum. 2006a. "Progress Report: Agriculture." Moscow. Ndulu, B.J., L. Chakraborti, L. Lijane, V. Ramachan- dran, and J. Wolgin. 2007. Challenges of African ------. 2006b. "Progress Report: Infrastructure." Growth: Opportunities, Constraints, and Strategic Moscow. Directions. Washington, D.C.: World Bank. Arbache, J.S. 2007. "Is the Recent African Growth Ro- World Bank. 2004. World Development Report 2005: A bust?" World Bank, Washington, D.C. Better Investment Climate for Everyone. Washington, D.C. Arbache, J.S., and J. Page. 2007a. "Patterns of Long Term Growth in Sub-Saharan Africa." ------. 2006. Africa Development Indicators 2006. World Bank, Washington, D.C. [http://ssrn.com/ Washington, D.C. abstract=1014133]. ------. 2007a. Doing Business 2008. Washington, ------. 2007b. "More Growth or Fewer Collapses? A D.C. New Look at Long-Run Growth in Sub-Saharan Af- rica." World Bank, Washington, D.C. ------. 2007b. Global Monitoring Report 2007: Con- fronting the Challenge of Gender Equality and Fragile Berthelemy, Jean-Claude, and Ludvig Soderling. States. Washington, D.C. 2001. "The Role of Capital Accumulation, Adjust- ment and Structural Change for Economic Take-Off: Empirical Evidence from African Growth Episodes." World Development 29 (2): 323­43. Bosworth, B.P., and S.M. Collins. 2003. "The Empirics of Growth: An Update." Brookings Papers on Eco- nomic Activity 2. Washington, D.C. 18 Africa Development Indicators 2007 Indicator tables Part I. Basic indicators and national accounts Participating in growth 1. Basic indicators 1.1 Basic indicators 21 2. National accounts 2.1 Gross domestic product, nominal 22 2.2 Gross domestic product, real 23 2.3 Gross domestic product growth 24 2.4 Gross domestic product per capita, real 25 2.5 Gross domestic product per capita growth 26 2.6 Gross national income, nominal 27 2.7 Gross national income, real 28 2.8 Gross national income per capita 29 2.9 Gross domestic product deflator (local currency series) 30 2.10 Gross domestic product deflator (U.S. dollar series) 31 2.11 Gross domestic savings 32 2.12 Gross national savings 33 2.13 General government final consumption 34 2.14 Final consumption expenditure 35 2.15 Final consumption expenditure per capita 36 2.16 Agriculture value added 37 2.17 Industry value added 38 2.18 Services value added 39 2.19 Gross fixed capital formation 40 2.20 General government fixed capital formation 41 2.21 Private sector fixed capital formation 42 2.22 Resource balance (exports minus imports) 43 2.23 Exports of goods and services, nominal 44 2.24 Imports of goods and services, nominal 45 2.25 Exports of goods and services, real 46 2.26 Imports of goods and services, real 47 Part II. Millennium Development Goals 3. Millennium Development Goals 3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger 48 3.2 Millennium Development Goal 2: achieve universal primary education 50 3.3 Millennium Development Goal 3: promote gender equality and empower women 51 3.4 Millennium Development Goal 4: reduce child mortality 52 3.5 Millennium Development Goal 5: improve maternal health 53 3.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases 54 3.7 Millennium Development Goal 7: ensure environmental sustainability 56 3.8 Millennium Development Goal 8: develop a global partnership for development 58 Part III. Development outcomes Results framework 4. Paris Declaration indicators 4.1 Status of Paris Declaration indicators 60 Indicator tables 19 Drivers of growth 5. Private sector development 5.1 Business environment 61 5.2 Investment climate 62 6. Trade 6.1 International trade and tariff barriers 64 6.2 Top three exports and share in total exports, 2005 68 6.3 Regional integration, trade blocs 70 7. Infrastructure 7.1 Water and sanitation 71 7.2 Transportation 72 7.3 Information and communication technology 74 7.4 Energy 76 7.5 Financial sector infrastructure 78 Participating in growth 8. Human development 8.1 Education 80 8.2 Health 82 9. Agriculture, rural development, and environment 9.1 Rural development 86 9.2 Agriculture 88 9.3 Environment 90 10. Labor, migration, and population 10.1 Labor force participation 92 10.2 Labor force composition 94 10.3 Migration and population 96 11. HIV/AIDS 11.1 HIV/AIDS 98 12. Malaria 12.1 Malaria 99 13. Capable states and partnership 13.1 Aid and debt relief 100 13.2 Capable states 102 13.3 Governance and anticorruption indicators 104 13.4 Country Policy and Institutional Assessment ratings 106 Part IV. Household welfare 14. Household welfare 14.1 Burkina Faso household survey, 2003 108 14.2 Cameroon household survey, 2001 109 14.3 Ethiopia household survey, 2000 110 14.4 Malawi household survey, 2004 111 14.5 Niger household survey, 2005 112 14.6 Nigeria household survey, 2004 113 14.7 São Tomé and Principe household survey, 2000 114 14.8 Sierra Leone household survey, 2002/03 115 14.9 Uganda household survey, 2002/03 116 20 Africa Development Indicators 2007 Participating in growth Table 1.1 Basic indicators Total net GNI per capita official Constant 2000 prices Life development Land area Average expectancy Under-five Adult literacy rate assistance Population (thousands annual at birth mortality rate Gini (% of ages 15 and older) per capita (millions) of sq km) Dollars a growth (%) (years) (per 1,000) coefficient Male Female (current $) 2005 2005 2005 2000­05 2005 2005 2000­05b 2000­05b 2000­05b 2005 SUB­SAHARAN AFRICA 743.7 23,619 572 2.1 46.7 163 .. .. .. 41.3 Excluding South Africa 696.8 22,405 380 2.4 46.6 166 .. .. .. 43.0 Excl. S. Africa & Nigeria 565.3 21,494 362 2.1 47.2 160 .. .. .. 41.7 Angola 15.9 1,247 937 6.9 41.4 260 .. 82.9 54.2 27.7 Benin 8.4 111 323 0.6 55.0 150 36.5 47.9 23.3 41.4 Botswana 1.8 567 4,559 5.5 35.0 120 .. 80.4 81.8 40.2 Burkina Faso 13.2 274 260 2.3 48.5 191 39.5 31.4 16.6 49.9 Burundi 7.5 26 105 ­0.9 44.6 190 .. 67.3 52.2 48.4 Cameroon 16.3 465 739 1.8 46.1 149 44.6 77.0 59.8 25.4 Cape Verde 0.5 4 1,343 2.2 70.7 35 .. 87.8 75.5 316.9 Central African Republic 4.0 623 227 ­2.7 39.4 193 .. 64.8 33.5 23.6 Chad 9.7 1,259 286 12.0 44.0 208 .. 40.8 12.8 39.0 Comoros 0.6 2 386 0.5 62.6 71 .. .. .. 42.0 Congo, Dem. Rep. 57.5 2,267 91 1.5 44.0 205 .. 80.9 54.1 31.8 Congo, Rep. 4.0 342 994 0.9 52.8 108 .. 90.5 79.0 362.3 Côte d'Ivoire 18.2 318 564 ­2.1 46.2 195 44.6 60.8 38.6 6.6 Djibouti 0.8 23 798 0.8 53.4 133 .. .. .. 99.1 Equatorial Guinea 0.5 28 7,533 20.4 42.3 205 .. 93.4 80.5 77.5 Eritrea 4.4 101 172 ­0.9 54.9 78 .. .. .. 80.7 Ethiopia 71.3 1,000 146 2.6 42.7 127 30.0 50.0 22.8 27.2 Gabon 1.4 258 3,991 ­0.1 53.8 91 .. 88.5 79.7 38.9 Gambia, The 1.5 10 335 0.8 56.8 137 .. .. .. 38.3 Ghana 22.1 228 288 2.8 57.5 112 .. 66.4 49.8 50.6 Guinea 9.4 246 385 0.7 54.1 160 38.6 42.6 18.1 19.4 Guinea-Bissau 1.6 28 135 ­3.5 45.1 200 .. .. .. 49.9 Kenya 34.3 569 442 1.1 49.0 120 .. 77.7 70.2 22.4 Lesotho 1.8 30 547 2.9 35.2 132 .. 73.7 90.3 38.3 Liberia 3.3 96 135 ­8.0 42.5 235 .. 58.3 45.7 71.9 Madagascar 18.6 582 233 ­0.8 55.8 119 47.5 76.5 65.3 49.9 Malawi 12.9 94 154 1.2 40.5 125 39.0 .. .. 44.7 Mali 13.5 1,220 244 2.8 48.6 218 40.1 32.7 15.9 51.1 Mauritania 3.1 1,025 429 1.0 53.7 125 39.0 59.5 43.4 62.0 Mauritius 1.2 2 4,404 3.0 73.0 15 .. 88.2 80.5 25.7 Mozambique 19.8 784 288 6.2 41.8 145 47.3 .. .. 65.0 Namibia 2.0 823 2,096 3.3 46.9 62 .. 86.8 83.5 60.7 Niger 14.0 1,267 158 0.2 44.9 256 .. 42.9 15.1 36.9 Nigeria 131.5 911 456 3.4 43.8 194 43.7 78.2 60.1 48.9 Rwanda 9.0 25 260 2.7 44.1 203 46.8 71.4 59.8 63.7 São Tomé and Principe 0.2 1 .. .. 63.5 118 .. 92.2 77.9 203.8 Senegal 11.7 193 503 2.1 56.5 119 41.3 51.1 29.2 59.1 Seychelles 0.1 0 6,666 ­2.9 .. 13 .. 91.4 92.3 222.6 Sierra Leone 5.5 72 217 9.1 41.4 282 .. 46.7 24.2 62.1 Somalia 8.2 627 .. .. 47.7 225 .. .. .. 28.7 South Africa 46.9 1,214 3,429 2.6 47.7 68 57.8 .. .. 14.9 Sudan 36.2 2,376 462 4.1 56.7 90 .. 71.1 51.8 50.5 Swaziland 1.1 17 1,381 0.9 41.5 160 50.4 80.9 78.3 40.7 Tanzania 38.3 884 325 4.5 46.3 122 34.6 77.5 62.2 39.3 Togo 6.1 54 241 ­0.3 55.1 139 .. 68.7 38.5 14.1 Uganda 28.8 197 270 2.0 50.0 136 45.7 76.8 57.7 41.6 Zambia 11.7 743 351 3.0 38.4 182 50.8 .. .. 81.0 Zimbabwe 13.0 387 432 ­6.3 37.3 132 .. 92.7 86.2 28.3 NORTH AFRICA 152.9 5,738 1,928 2.2 71.1 35 .. .. .. 15.4 Algeria 32.9 2,382 2,121 3.6 71.7 39 .. .. .. 11.3 Egypt, Arab Rep. 74.0 995 1,617 1.7 70.5 33 34.4 .. .. 12.5 Libya 5.9 1,760 6,904 0.7 74.4 19 .. .. .. 4.2 Morocco 30.2 446 1,356 2.5 70.4 40 .. .. .. 21.6 Tunisia 10.0 155 2,412 3.5 73.5 24 39.8 .. .. 37.5 ALL AFRICA 896.6 29,358 803 2.0 50.8 149 .. .. .. 36.8 a. Calculated by the World Bank Atlas method. b. Data are for the most recent year available during the period specified. BASIC INDICATORS Part I. Basic indicators and national accounts 21 Table 2.1 Gross domestic product, nominal Current prices ($ millions) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 276,257 301,252 329,679 341,510 334,953 350,017 435,171 533,192 629,793 262,128 317,316 437,439 Excluding South Africa 197,076 189,368 196,523 208,697 216,614 239,369 268,614 316,786 387,869 171,678 183,302 272,991 Excl. S. Africa & Nigeria 129,295 160,961 161,753 162,643 168,541 192,629 210,239 244,390 290,629 135,084 153,325 211,512 Angola .. 10,260 6,155 9,129 8,936 11,432 13,956 19,775 32,811 7,560 7,042 16,007 Benin 1,405 1,845 2,387 2,255 2,372 2,807 3,558 4,047 4,288 1,318 2,005 3,221 Botswana 1,061 3,792 5,623 6,177 6,033 5,933 8,280 9,823 10,445 1,576 4,595 7,782 Burkina Faso 1,929 3,120 2,811 2,601 2,814 3,203 4,182 5,139 5,698 2,002 2,629 3,939 Burundi 920 1,132 808 709 662 628 595 665 800 1,065 979 677 Cameroon 6,741 11,152 10,487 10,075 9,598 10,880 13,622 15,775 16,875 9,159 10,616 12,804 Cape Verde .. 339 583 531 550 616 797 925 999 265 448 737 Central African Republic 797 1,488 1,051 953 968 1,046 1,195 1,307 1,369 929 1,177 1,140 Chad 1,033 1,739 1,537 1,383 1,712 1,994 2,728 4,420 5,896 1,068 1,602 3,022 Comoros 124 250 223 202 220 251 325 362 387 144 233 291 Congo, Dem. Rep. 14,395 9,350 4,711 4,306 4,692 5,548 5,673 6,570 7,104 10,028 7,161 5,649 Congo, Rep. 1,706 2,799 2,354 3,220 2,794 3,020 3,564 4,343 5,971 2,106 2,343 3,819 Côte d'Ivoire 10,175 10,796 12,556 10,425 10,554 11,482 13,734 15,475 16,055 8,609 11,200 12,954 Djibouti .. 452 536 551 572 591 628 666 709 380 490 620 Equatorial Guinea .. 132 872 1,254 1,737 2,166 2,966 4,899 7,520 108 294 3,424 Eritrea .. .. 689 634 671 631 584 635 970 .. 609 687 Ethiopia .. 12,083 7,638 7,903 7,888 7,429 8,030 9,485 11,373 9,256 9,521 8,685 Gabon 4,279 5,952 4,663 5,068 4,713 4,932 6,055 7,178 8,666 3,676 5,064 6,102 Gambia, The 241 317 432 421 418 370 367 401 461 225 374 406 Ghana 4,445 5,886 7,710 4,972 5,313 6,157 7,624 8,872 10,720 4,692 6,576 7,276 Guinea 6,685 2,667 3,461 3,112 3,039 3,208 3,624 4,047 3,327 6,892 3,403 3,393 Guinea-Bissau 111 244 224 216 199 201 235 270 301 156 242 237 Kenya 7,265 8,591 12,896 12,705 12,984 12,915 14,639 16,143 19,193 7,069 9,906 14,763 Lesotho 432 615 904 853 752 687 1,039 1,319 1,457 412 843 1,018 Liberia 954 384 442 561 543 559 410 460 529 935 264 510 Madagascar 4,042 3,081 3,717 3,878 4,530 4,397 5,474 4,364 5,040 3,124 3,326 4,614 Malawi 1,238 1,881 1,776 1,744 1,717 1,935 1,765 1,903 2,076 1,256 1,901 1,856 Mali 1,787 2,421 2,570 2,423 2,630 3,343 4,362 4,874 5,305 1,609 2,486 3,823 Mauritania 709 1,020 1,195 1,081 1,122 1,150 1,285 1,548 1,837 806 1,286 1,337 Mauritius 1,154 2,383 4,259 4,469 4,539 4,549 5,248 6,064 6,290 1,387 3,563 5,193 Mozambique 3,526 2,463 3,985 3,778 3,697 4,092 4,789 5,904 6,823 3,375 2,766 4,847 Namibia 2,169 2,350 3,386 3,414 3,216 3,122 4,473 5,712 6,185 1,859 3,119 4,354 Niger 2,509 2,481 2,018 1,798 1,945 2,171 2,731 2,942 3,398 2,000 2,013 2,498 Nigeria 64,202 28,473 34,776 45,984 48,000 46,711 58,294 72,271 97,018 35,577 30,007 61,380 Rwanda 1,163 2,584 1,931 1,811 1,703 1,732 1,684 1,835 2,154 1,761 1,771 1,820 São Tomé and Principe .. .. .. .. 77 92 99 107 113 .. .. 98 Senegal 3,503 5,717 5,151 4,692 4,878 5,334 6,815 7,947 8,600 3,736 5,172 6,378 Seychelles 147 369 623 615 619 698 706 700 723 197 494 677 Sierra Leone 1,101 650 664 634 806 936 990 1,072 1,193 963 779 938 Somalia 604 917 .. .. .. .. .. .. .. 855 917 .. South Africa 80,710 112,014 133,184 132,878 118,479 110,882 166,654 216,443 242,059 90,894 134,008 164,566 Sudan 7,617 13,167 10,682 12,366 13,362 14,976 17,780 21,690 27,895 12,478 9,659 18,012 Swaziland 543 882 1,377 1,389 1,260 1,192 1,907 2,382 2,613 552 1,186 1,790 Tanzania .. 4,259 8,638 9,079 9,441 9,758 10,283 11,351 12,586 4,760 5,904 10,417 Togo 1,136 1,628 1,576 1,329 1,328 1,476 1,759 2,061 2,109 1,021 1,458 1,677 Uganda 1,245 4,304 5,999 5,926 5,681 5,836 6,250 6,817 8,725 3,611 4,835 6,539 Zambia 3,884 3,288 3,131 3,238 3,637 3,697 4,327 5,423 7,270 3,171 3,350 4,599 Zimbabwe 6,679 8,784 5,964 7,399 10,257 21,897 7,397 4,712 3,418 7,204 7,375 9,180 NORTH AFRICA 131,760 172,192 225,883 241,901 236,696 221,239 243,570 272,517 313,443 141,535 186,168 254,895 Algeria 42,345 62,045 48,641 54,790 55,181 57,053 68,019 85,014 101,786 53,750 48,197 70,307 Egypt, Arab Rep. 22,913 43,130 90,711 99,839 97,632 87,851 82,924 78,845 89,686 31,646 60,220 89,463 Libya 35,545 28,905 30,484 34,495 29,994 19,195 23,822 30,498 41,667 27,793 29,592 29,945 Morocco 18,821 25,821 35,249 33,334 33,901 36,093 43,813 50,031 51,621 16,987 31,337 41,466 Tunisia 8,743 12,291 20,799 19,443 19,988 21,047 24,992 28,129 28,683 8,923 16,823 23,714 ALL AFRICA 406,816 473,347 555,533 583,400 571,637 571,185 678,581 805,454 942,916 403,308 503,383 692,196 a. Provisional. 22 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.2 Gross domestic product, real Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 227,290 273,322 330,095 341,510 354,095 366,236 381,357 402,405 425,186 1.8 2.4 4.4 Excluding South Africa 131,907 162,389 202,585 208,697 217,658 224,762 235,686 249,509 264,506 2.2 2.6 4.8 Excl. S. Africa & Nigeria 99,676 127,371 158,898 162,643 170,180 176,555 182,302 192,868 204,410 2.6 2.7 4.5 Angola 6,746 8,464 8,862 9,129 9,416 10,780 11,137 12,383 14,935 3.5 1.0 9.9 Benin 1,084 1,412 2,131 2,255 2,368 2,474 2,571 2,650 2,727 2.7 4.7 3.9 Botswana 1,208 3,394 5,707 6,177 6,500 6,866 7,290 7,740 8,046 10.9 5.6 5.6 Burkina Faso 1,263 1,750 2,560 2,601 2,754 2,875 3,062 3,205 3,433 3.9 4.1 5.6 Burundi 559 865 715 709 724 756 747 783 790 4.5 ­3.2 2.2 Cameroon 6,339 8,793 9,669 10,075 10,530 10,952 11,393 11,815 12,057 4.5 1.3 3.7 Cape Verde .. 303 498 531 552 577 613 608 681 6.3 5.9 4.7 Central African Republic 730 809 931 953 967 959 886 898 918 1.6 1.8 ­1.4 Chad 664 1,104 1,395 1,383 1,544 1,675 1,922 2,568 2,789 6.7 2.3 15.9 Comoros 136 181 200 202 209 217 223 222 232 2.9 1.2 2.6 Congo, Dem. Rep. 7,016 7,659 4,625 4,306 4,215 4,362 4,614 4,921 5,239 2.1 ­5.0 4.4 Congo, Rep. 1,746 2,796 2,992 3,220 3,342 3,503 3,563 3,691 3,975 3.8 0.8 4.0 Côte d'Ivoire 7,706 8,274 10,787 10,425 10,436 10,266 10,095 10,261 10,230 0.7 3.5 ­0.5 Djibouti .. 660 549 551 563 577 596 614 633 .. ­2.3 2.9 Equatorial Guinea .. 207 1,105 1,254 2,035 2,411 2,694 3,549 3,793 .. 20.7 23.2 Eritrea .. .. 729 634 692 697 739 753 757 .. 7.9 3.5 Ethiopia .. 6,292 7,461 7,903 8,513 8,618 8,315 9,407 10,367 2.0 3.3 4.7 Gabon 3,594 4,298 5,165 5,068 5,176 5,162 5,290 5,361 5,523 0.5 2.9 1.6 Gambia, The 213 305 399 421 445 431 461 484 509 3.5 2.7 3.7 Ghana 2,637 3,263 4,795 4,972 5,171 5,404 5,685 6,003 6,357 2.6 4.3 5.1 Guinea 1,539 2,088 3,055 3,112 3,236 3,372 3,413 3,505 3,621 3.0 4.4 2.9 Guinea-Bissau 115 186 200 215 216 201 202 206 213 3.8 1.4 ­0.5 Kenya 7,087 10,557 12,630 12,705 13,188 13,261 13,657 14,319 15,152 4.1 2.2 3.4 Lesotho 392 602 832 853 868 893 917 954 982 4.1 4.3 2.9 Liberia 1,391 433 446 561 577 599 411 422 444 ­3.3 0.2 ­6.8 Madagascar 3,099 3,266 3,701 3,878 4,111 3,590 3,941 4,149 4,339 0.8 1.7 2.0 Malawi 1,000 1,243 1,716 1,744 1,657 1,704 1,808 1,936 1,990 2.4 3.8 3.4 Mali 1,536 1,630 2,347 2,422 2,716 2,828 3,039 3,105 3,294 0.5 3.9 5.9 Mauritania 693 816 1,062 1,081 1,112 1,125 1,188 1,249 1,317 1.9 2.9 4.0 Mauritius 1,518 2,679 4,296 4,469 4,718 4,846 5,000 5,235 5,475 5.9 5.3 4.0 Mozambique 2,245 2,279 3,706 3,778 4,273 4,621 4,987 5,361 5,695 ­0.9 5.7 8.4 Namibia 2,002 2,263 3,298 3,414 3,495 3,729 3,858 4,088 4,258 1.1 4.0 4.7 Niger 1,523 1,507 1,824 1,798 1,926 1,984 2,071 2,059 2,199 ­0.4 2.4 3.6 Nigeria 31,452 34,978 43,628 45,984 47,409 48,143 53,292 56,543 59,992 0.8 2.4 5.8 Rwanda 1,457 1,782 1,709 1,811 1,933 2,114 2,134 2,218 2,351 2.5 ­1.6 5.1 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2,683 3,463 4,546 4,692 4,907 4,939 5,268 5,562 5,866 2.7 2.8 4.5 Seychelles 290 393 587 615 601 609 573 557 563 3.1 4.5 ­2.1 Sierra Leone 935 1,021 610 634 749 954 1,043 1,120 1,201 0.5 ­5.4 13.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 95,503 110,945 127,577 132,878 136,512 141,549 145,761 152,996 160,793 1.4 2.0 3.9 Sudan 5,555 7,100 11,611 12,366 13,120 13,960 14,742 15,509 16,749 2.4 5.3 6.1 Swaziland 554 1,024 1,361 1,389 1,411 1,452 1,494 1,526 1,562 6.5 3.3 2.5 Tanzania .. 6,801 8,639 9,079 9,646 10,345 10,931 11,667 12,461 .. 2.7 6.5 Togo 964 1,071 1,340 1,329 1,327 1,382 1,419 1,461 1,480 1.5 3.6 2.5 Uganda .. 3,077 5,610 5,926 6,219 6,613 6,926 7,306 7,786 2.3 7.2 5.6 Zambia 2,730 3,028 3,126 3,238 3,396 3,508 3,688 3,887 4,090 1.0 0.2 4.7 Zimbabwe 4,376 6,734 8,034 7,399 7,199 6,883 6,167 5,933 5,618 3.3 2.7 ­5.7 NORTH AFRICA 126,904 176,633 234,042 241,901 251,458 260,036 269,560 282,196 294,925 3.4 3.2 4.0 Algeria 35,291 46,367 53,611 54,790 56,215 58,857 62,918 66,190 69,698 2.9 1.7 5.2 Egypt, Arab Rep. 38,503 65,574 94,738 99,839 103,357 106,649 109,964 114,559 119,714 5.5 4.3 3.6 Libya 14,354 .. 34,104 34,495 36,053 37,228 36,204 38,014 40,409 ­7.0 .. 2.7 Morocco 18,308 26,717 33,018 33,334 35,433 36,563 38,582 40,220 40,910 4.2 2.4 4.3 Tunisia 8,622 12,237 18,571 19,443 20,401 20,738 21,891 23,213 24,194 3.2 4.6 4.5 ALL AFRICA 356,401 451,457 564,126 583,400 605,543 626,261 650,902 684,580 720,082 2.4 2.7 4.3 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 23 Table 2.3 Gross domestic product growth Annual growth (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 4.2 1.1 2.6 3.5 3.7 3.4 4.1 5.5 5.7 2.2 2.0 4.3 Excluding South Africa 2.1 2.1 2.7 3.0 4.3 3.3 4.9 5.9 6.0 2.1 2.5 4.6 Excl. S. Africa & Nigeria 1.2 0.5 3.2 2.4 4.6 3.7 3.3 5.8 6.0 2.6 2.3 4.3 Angola .. ­0.3 3.2 3.0 3.1 14.5 3.3 11.2 20.6 2.7 1.0 9.3 Benin 6.8 3.2 4.7 5.8 5.0 4.5 3.9 3.1 2.9 3.1 4.5 4.2 Botswana 12.0 6.8 7.2 8.2 5.2 5.6 6.2 6.2 4.0 11.5 6.1 5.9 Burkina Faso 0.8 ­1.5 6.7 1.6 5.9 4.4 6.5 4.6 7.1 3.6 3.8 5.0 Burundi 1.0 3.5 ­1.0 ­0.9 2.1 4.4 ­1.2 4.8 0.9 4.3 ­1.4 1.7 Cameroon ­2.0 ­6.1 4.4 4.2 4.5 4.0 4.0 3.7 2.0 4.0 0.4 3.7 Cape Verde .. 0.7 8.6 6.6 3.8 4.6 6.2 ­0.7 11.9 6.4 5.2 5.4 Central African Republic ­4.5 ­2.1 3.6 2.3 1.5 ­0.8 ­7.6 1.3 2.2 0.9 1.3 ­0.2 Chad ­6.0 ­4.2 ­0.7 ­0.9 11.7 8.5 14.7 33.6 8.6 5.4 2.2 12.7 Comoros .. 5.1 2.9 0.9 3.3 4.1 2.5 ­0.2 4.2 2.7 1.6 2.5 Congo, Dem. Rep. 2.2 ­6.6 ­4.3 ­6.9 ­2.1 3.5 5.8 6.6 6.5 1.8 ­5.5 2.2 Congo, Rep. 17.6 1.0 ­2.6 7.6 3.8 4.8 1.7 3.6 7.7 6.8 0.8 4.9 Côte d'Ivoire ­11.0 ­1.1 1.6 ­3.3 0.1 ­1.6 ­1.7 1.6 ­0.3 ­0.2 2.6 ­0.9 Djibouti .. .. 2.2 0.4 2.0 2.6 3.2 3.0 3.2 .. ­2.0 2.4 Equatorial Guinea .. 3.3 41.4 13.5 62.2 18.5 11.7 31.7 6.9 0.9 20.2 24.1 Eritrea .. .. 0.0 ­13.1 9.2 0.7 6.1 1.9 0.5 .. 8.1 0.9 Ethiopia .. 2.0 6.0 5.9 7.7 1.2 ­3.5 13.1 10.2 2.1 2.2 5.8 Gabon 2.6 5.2 ­8.9 ­1.9 2.1 ­0.3 2.5 1.3 3.0 1.9 2.5 1.1 Gambia, The 6.3 3.6 6.4 5.5 5.8 ­3.2 7.0 5.1 5.0 3.9 3.1 4.2 Ghana 0.5 3.3 4.4 3.7 4.0 4.5 5.2 5.6 5.9 2.0 4.3 4.8 Guinea 2.6 4.3 4.7 1.9 4.0 4.2 1.2 2.7 3.3 2.9 4.3 2.9 Guinea-Bissau ­16.0 6.1 7.8 7.5 0.2 ­7.1 0.6 2.2 3.5 2.9 2.0 1.2 Kenya 5.6 4.2 2.3 0.6 3.8 0.6 3.0 4.9 5.8 4.2 2.2 3.1 Lesotho ­2.7 6.4 0.2 2.6 1.8 2.9 2.7 4.0 2.9 3.6 4.0 2.8 Liberia ­4.1 ­51.0 22.9 25.7 2.9 3.7 ­31.3 2.6 5.3 ­4.5 1.2 1.5 Madagascar 0.8 3.1 4.7 4.8 6.0 ­12.7 9.8 5.3 4.6 0.4 1.6 3.0 Malawi 0.4 5.7 3.0 1.6 ­5.0 2.9 6.1 7.1 2.8 1.7 4.1 2.6 Mali ­4.3 ­1.9 6.7 3.2 12.1 4.2 7.4 2.2 6.1 0.6 3.6 5.9 Mauritania 3.4 ­1.8 6.7 1.9 2.9 1.1 5.6 5.2 5.4 2.2 2.6 3.7 Mauritius .. 5.8 5.8 4.0 5.6 2.7 3.2 4.7 4.6 5.9 5.4 4.1 Mozambique .. 1.0 7.5 1.9 13.1 8.2 7.9 7.5 6.2 0.4 5.2 7.5 Namibia .. 2.5 3.4 3.5 2.4 6.7 3.5 6.0 4.2 1.1 4.1 4.4 Niger ­2.2 ­1.3 ­0.6 ­1.4 7.1 3.0 4.4 ­0.6 6.8 0.0 1.9 3.2 Nigeria 4.2 8.2 1.1 5.4 3.1 1.5 10.7 6.1 6.1 0.9 3.1 5.5 Rwanda 9.0 ­2.4 7.6 6.0 6.7 9.4 0.9 4.0 6.0 3.2 2.1 5.5 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal ­0.4 ­0.7 6.3 3.2 4.6 0.7 6.7 5.6 5.5 2.7 2.7 4.4 Seychelles ­4.2 7.0 1.9 4.8 ­2.2 1.3 ­5.9 ­2.9 1.2 2.1 4.9 ­0.6 Sierra Leone 4.8 3.4 ­8.1 3.8 18.1 27.5 9.3 7.4 7.3 1.1 ­4.3 12.2 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6.6 ­0.3 2.4 4.2 2.7 3.7 3.0 5.0 5.1 2.2 1.4 3.9 Sudan 1.5 ­5.5 6.3 6.5 6.1 6.4 5.6 5.2 8.0 3.4 4.5 6.3 Swaziland 12.4 8.6 3.5 2.0 1.6 2.9 2.9 2.1 2.3 6.8 3.8 2.3 Tanzania .. 7.0 3.5 5.1 6.2 7.2 5.7 6.7 6.8 3.8 3.1 6.3 Togo 14.6 ­0.2 2.5 ­0.8 ­0.2 4.1 2.7 3.0 1.2 2.6 2.6 1.7 Uganda .. 6.5 8.1 5.6 4.9 6.3 4.7 5.5 6.6 3.0 6.9 5.6 Zambia 3.0 ­0.5 2.2 3.6 4.9 3.3 5.1 5.4 5.2 1.4 0.4 4.6 Zimbabwe 14.4 7.0 ­3.6 ­7.9 ­2.7 ­4.4 ­10.4 ­3.8 ­5.3 5.2 2.6 ­5.8 NORTH AFRICA 4.6 4.0 4.3 3.4 4.0 3.4 3.7 4.7 4.5 3.4 3.3 3.9 Algeria 0.8 0.8 3.2 2.2 2.6 4.7 6.9 5.2 5.3 2.8 1.6 4.5 Egypt, Arab Rep. 10.0 5.7 6.1 5.4 3.5 3.2 3.1 4.2 4.5 5.9 4.3 4.0 Libya 0.6 .. .. 1.1 4.5 3.3 ­2.8 5.0 6.3 ­6.4 .. 2.9 Morocco 3.6 4.0 ­0.1 1.0 6.3 3.2 5.5 4.2 1.7 3.9 2.7 3.7 Tunisia 7.4 7.9 6.1 4.7 4.9 1.7 5.6 6.0 4.2 3.6 5.1 4.5 ALL AFRICA 4.4 2.1 3.2 3.4 3.8 3.4 3.9 5.2 5.2 2.6 2.5 4.2 a. Provisional. 24 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.4 Gross domestic product per capita, real Constant prices (2000 $) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 590 531 510 515 521 527 536 553 572 ­1.1 ­0.2 2.1 Excluding South Africa 369 339 335 337 343 346 354 366 380 ­0.8 0.0 2.4 Excl. S. Africa & Nigeria 345 327 325 324 331 335 338 349 362 ­0.4 0.1 2.1 Angola 861 804 656 660 662 737 740 799 937 0.5 ­1.8 6.9 Benin 292 273 305 313 319 323 325 324 323 ­0.7 1.3 0.6 Botswana 1,152 2,376 3,288 3,522 3,681 3,877 4,115 4,375 4,559 7.5 3.3 5.5 Burkina Faso 192 205 233 230 237 239 247 250 260 1.3 1.2 2.3 Burundi 135 153 112 109 109 111 106 108 105 1.1 ­4.4 ­0.9 Cameroon 724 755 664 678 695 709 724 737 739 1.6 ­1.2 1.8 Cape Verde .. 852 1,132 1,179 1,196 1,221 1,267 1,229 1,343 4.1 3.4 2.2 Central African Republic 314 270 251 252 252 247 225 225 227 ­1.0 ­0.6 ­2.7 Chad 143 182 176 168 182 190 210 272 286 3.9 ­0.8 12.0 Comoros 405 416 378 374 378 386 387 378 386 0.3 ­1.0 0.5 Congo, Dem. Rep. 251 203 95 86 82 83 85 88 91 ­0.8 ­7.7 1.5 Congo, Rep. 969 1,126 899 937 942 958 945 951 994 0.6 ­2.5 0.9 Côte d'Ivoire 924 654 658 623 612 592 574 574 564 ­3.5 0.6 ­2.1 Djibouti .. 1,183 792 771 767 770 779 788 798 .. ­4.5 0.8 Equatorial Guinea .. 588 2,521 2,794 4,428 5,128 5,598 7,210 7,533 .. 17.8 20.4 Eritrea .. .. 213 178 187 180 182 178 172 .. 6.2 ­0.9 Ethiopia .. 123 119 123 129 128 121 135 146 ­1.0 1.1 2.6 Gabon 5,162 4,490 4,150 3,984 3,990 3,911 3,944 3,935 3,991 ­2.7 ­0.1 ­0.1 Gambia, The 327 326 313 320 328 308 321 328 335 ­0.2 ­0.8 0.8 Ghana 233 211 247 250 255 260 268 277 288 ­0.6 1.7 2.8 Guinea 321 336 370 369 376 383 379 381 385 0.5 1.2 0.7 Guinea-Bissau 144 183 151 158 154 138 135 134 135 1.4 ­1.6 ­3.5 Kenya 435 451 421 414 421 414 417 428 442 0.3 ­0.6 1.1 Lesotho 303 378 469 477 483 496 510 531 547 1.8 3.0 2.9 Liberia 745 203 153 183 183 187 128 130 135 ­4.9 ­3.3 ­8.0 Madagascar 342 271 235 239 247 209 224 229 233 ­2.0 ­1.3 ­0.8 Malawi 162 131 153 151 141 141 147 154 154 ­1.9 2.0 1.2 Mali 220 183 207 208 226 229 239 237 244 ­1.9 1.2 2.8 Mauritania 431 402 413 409 408 401 411 419 429 ­0.5 0.3 1.0 Mauritius 1,572 2,535 3,656 3,766 3,932 4,004 4,089 4,245 4,404 4.9 4.0 3.0 Mozambique 186 170 211 211 234 247 262 276 288 ­1.9 2.5 6.2 Namibia 2,029 1,619 1,780 1,802 1,811 1,902 1,943 2,035 2,096 ­2.3 0.8 3.3 Niger 246 178 160 153 158 157 159 153 158 ­3.4 ­0.9 0.2 Nigeria 460 386 380 391 394 391 423 439 456 ­2.0 ­0.3 3.4 Rwanda 280 251 228 226 231 245 244 250 260 ­1.2 ­1.7 2.7 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 450 434 451 454 463 455 474 489 503 ­0.3 0.2 2.1 Seychelles 4,507 5,614 7,294 7,579 7,405 7,277 6,922 6,654 6,666 2.3 2.9 ­2.9 Sierra Leone 289 250 140 141 160 195 204 210 217 ­1.9 ­6.0 9.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 3,463 3,152 2,972 3,020 3,046 3,123 3,180 3,300 3,429 ­1.2 ­0.3 2.6 Sudan 278 272 361 376 391 408 423 437 462 ­0.4 2.8 4.1 Swaziland 981 1,330 1,335 1,329 1,321 1,334 1,352 1,363 1,381 3.3 0.1 0.9 Tanzania .. 259 254 261 272 286 296 310 325 .. ­0.2 4.5 Togo 346 270 258 248 240 243 243 244 241 ­2.1 0.5 ­0.3 Uganda .. 173 238 244 248 255 258 263 270 ­1.3 3.9 2.0 Zambia 451 361 298 303 311 316 327 339 351 ­2.3 ­2.2 3.0 Zimbabwe 599 637 644 588 567 538 479 459 432 ­0.5 0.8 ­6.3 NORTH AFRICA 1,389 1,505 1,693 1,722 1,762 1,792 1,829 1,875 1,928 0.8 1.4 2.2 Algeria 1,876 1,833 1,785 1,799 1,818 1,876 1,975 2,046 2,121 ­0.1 ­0.3 3.6 Egypt, Arab Rep. 878 1,178 1,435 1,484 1,507 1,526 1,543 1,577 1,617 2.9 2.3 1.7 Libya 4,717 .. 6,555 6,501 6,662 6,745 6,432 6,623 6,904 ­10.6 .. 0.7 Morocco 950 1,117 1,200 1,197 1,258 1,284 1,339 1,349 1,356 2.0 0.8 2.5 Tunisia 1,351 1,501 1,964 2,033 2,109 2,120 2,225 2,337 2,412 0.6 2.9 3.5 ALL AFRICA 748 714 718 726 736 745 758 780 803 ­0.5 0.2 2.0 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 25 Table 2.5 Gross domestic product per capita growth Annual growth (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 1.1 ­1.7 0.1 0.9 1.3 1.1 1.8 3.2 3.3 ­0.8 ­0.6 1.9 Excluding South Africa ­1.0 ­0.8 0.2 0.5 1.8 0.8 2.4 3.4 3.6 ­0.9 ­0.2 2.1 Excl. S. Africa & Nigeria ­1.9 ­2.4 0.6 ­0.1 2.1 1.3 0.8 3.3 3.5 ­0.5 ­0.3 1.8 Angola .. ­3.0 0.9 0.5 0.4 11.3 0.4 8.0 17.2 ­0.3 ­1.7 6.3 Benin 3.5 ­0.3 1.7 2.7 1.8 1.2 0.6 ­0.2 ­0.3 ­0.2 1.1 1.0 Botswana 8.2 3.9 5.7 7.1 4.5 5.3 6.1 6.3 4.2 8.0 3.7 5.6 Burkina Faso ­1.3 ­4.4 3.8 ­1.3 2.7 1.1 3.1 1.4 3.8 1.1 0.9 1.8 Burundi ­1.9 1.0 ­2.1 ­2.5 ­0.2 1.6 ­4.3 1.3 ­2.7 1.0 ­2.8 ­1.1 Cameroon ­4.8 ­8.8 2.2 2.1 2.4 2.0 2.1 1.8 0.3 1.1 ­2.1 1.8 Cape Verde .. ­1.6 6.1 4.1 1.4 2.2 3.7 ­3.0 9.3 4.2 2.8 3.0 Central African Republic ­7.0 ­4.4 1.7 0.6 0.0 ­2.1 ­8.8 0.0 0.9 ­1.6 ­1.1 ­1.6 Chad ­8.0 ­7.0 ­3.8 ­4.1 7.9 4.7 10.7 29.2 5.2 2.7 ­0.8 8.9 Comoros .. 2.4 0.7 ­1.2 1.2 2.0 0.3 ­2.3 2.1 0.1 ­0.6 0.3 Congo, Dem. Rep. ­0.9 ­9.7 ­6.1 ­8.9 ­4.5 0.7 2.8 3.5 3.3 ­1.2 ­8.2 ­0.5 Congo, Rep. 14.0 ­2.2 ­5.7 4.2 0.6 1.6 ­1.3 0.6 4.6 3.5 ­2.4 1.7 Côte d'Ivoire ­15.1 ­4.4 ­0.8 ­5.4 ­1.8 ­3.2 ­3.2 0.1 ­1.9 ­4.4 ­0.3 ­2.5 Djibouti .. .. ­1.3 ­2.6 ­0.5 0.3 1.2 1.1 1.4 .. ­4.3 0.2 Equatorial Guinea .. 1.4 38.1 10.8 58.5 15.8 9.2 28.8 4.5 ­1.6 17.4 21.3 Eritrea .. .. ­3.3 ­16.3 4.8 ­3.7 1.4 ­2.4 ­3.4 .. 6.4 ­3.3 Ethiopia .. ­1.7 3.5 3.4 5.3 ­0.9 ­5.5 11.0 8.2 ­0.9 ­0.1 3.6 Gabon ­0.5 1.8 ­11.1 ­4.0 0.2 ­2.0 0.8 ­0.2 1.4 ­1.3 ­0.5 ­0.6 Gambia, The 2.9 ­0.3 3.0 2.2 2.6 ­6.1 3.9 2.3 2.3 0.3 ­0.4 1.2 Ghana ­2.0 0.5 2.1 1.4 1.7 2.2 3.0 3.4 3.8 ­1.1 1.6 2.6 Guinea ­0.1 0.8 2.6 ­0.2 1.8 1.9 ­1.0 0.5 1.1 0.4 1.1 0.7 Guinea-Bissau ­18.8 3.1 4.9 4.5 ­2.7 ­9.8 ­2.4 ­0.8 0.5 0.4 ­1.0 ­1.8 Kenya 1.7 0.8 0.0 ­1.6 1.6 ­1.6 0.8 2.6 3.4 0.5 ­0.6 0.9 Lesotho ­5.2 5.0 ­0.8 1.8 1.3 2.6 2.7 4.2 3.1 1.3 2.7 2.6 Liberia ­7.2 ­50.5 14.2 19.3 ­0.2 2.2 ­31.6 2.0 3.9 ­6.2 ­3.2 ­0.7 Madagascar ­2.0 0.2 1.6 1.7 3.0 ­15.1 6.8 2.4 1.8 ­2.4 ­1.3 0.1 Malawi ­2.6 1.7 0.2 ­1.1 ­7.3 0.5 3.8 4.8 0.6 ­2.4 2.0 0.2 Mali ­6.5 ­4.3 3.8 0.3 8.9 1.1 4.3 ­0.8 3.0 ­1.8 0.9 2.8 Mauritania 0.9 ­4.0 3.7 ­1.1 ­0.1 ­1.9 2.5 2.1 2.4 ­0.2 0.0 0.6 Mauritius .. 5.0 4.5 3.0 4.4 1.8 2.1 3.8 3.7 4.9 4.2 3.2 Mozambique .. ­0.3 5.2 ­0.2 10.7 6.0 5.8 5.4 4.3 ­0.6 2.4 5.3 Namibia .. ­1.8 0.8 1.2 0.5 5.0 2.1 4.7 3.0 ­2.3 0.8 2.8 Niger ­5.2 ­4.3 ­3.9 ­4.7 3.5 ­0.5 0.9 ­3.9 3.3 ­3.0 ­1.4 ­0.2 Nigeria 1.2 5.1 ­1.3 2.9 0.7 ­0.7 8.3 3.8 3.8 ­1.9 0.4 3.1 Rwanda 5.5 ­2.1 ­1.8 ­1.0 2.2 6.5 ­0.7 2.5 4.2 ­0.3 1.2 2.3 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal ­3.0 ­3.5 3.7 0.7 2.1 ­1.7 4.1 3.1 3.0 ­0.2 0.0 1.9 Seychelles ­5.4 6.1 ­0.1 3.9 ­2.3 ­1.7 ­4.9 ­3.9 0.2 1.2 3.3 ­1.4 Sierra Leone 2.9 1.6 ­10.2 0.8 13.8 22.0 4.4 3.0 3.6 ­1.2 ­5.2 7.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4.2 ­2.3 ­0.1 1.6 0.9 2.5 1.8 3.8 3.9 ­0.3 ­0.8 2.4 Sudan ­1.7 ­7.5 4.0 4.3 4.0 4.4 3.7 3.2 5.9 0.6 2.1 4.2 Swaziland 9.0 5.2 0.6 ­0.4 ­0.6 1.0 1.3 0.8 1.3 3.6 0.6 0.6 Tanzania .. 3.5 1.3 2.9 4.1 5.1 3.6 4.7 4.9 0.4 0.2 4.2 Togo 11.1 ­3.1 ­1.1 ­4.0 ­3.1 1.3 0.0 0.4 ­1.3 ­0.9 ­0.4 ­1.1 Uganda .. 2.7 4.8 2.4 1.6 2.8 1.2 1.9 2.9 ­0.6 3.5 2.1 Zambia ­0.3 ­3.4 0.0 1.5 2.9 1.5 3.4 3.7 3.5 ­1.8 ­2.1 2.7 Zimbabwe 10.5 3.8 ­4.7 ­8.8 ­3.5 ­5.1 ­10.9 ­4.3 ­5.8 1.4 0.6 ­6.4 NORTH AFRICA 1.9 1.7 2.6 1.7 2.3 1.7 2.0 2.5 2.8 0.8 1.4 2.2 Algeria ­2.5 ­1.7 1.8 0.8 1.1 3.1 5.3 3.6 3.7 ­0.3 ­0.4 2.9 Egypt, Arab Rep. 7.5 3.5 4.1 3.4 1.6 1.2 1.1 2.2 2.5 3.4 2.4 2.0 Libya ­3.9 .. .. ­0.8 2.5 1.2 ­4.6 3.0 4.2 ­10.1 .. 0.9 Morocco 1.1 1.9 ­1.2 ­0.2 5.1 2.0 4.3 0.7 0.6 1.6 1.1 2.1 Tunisia 4.6 5.4 4.7 3.5 3.7 0.5 4.9 5.1 3.2 1.0 3.3 3.5 ALL AFRICA 1.3 ­0.6 0.8 1.1 1.5 1.2 1.7 2.9 3.0 ­0.3 0.0 1.9 a. Provisional. 26 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.6 Gross national income, nominal Current prices ($ millions) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 260,874 285,278 317,118 323,171 318,330 332,091 412,296 502,686 593,441 247,283 303,929 413,669 Excluding South Africa 185,300 177,754 187,157 193,501 203,711 224,208 250,318 290,574 356,330 160,445 173,028 253,107 Excl. S. Africa & Nigeria 120,229 152,246 153,861 153,199 159,542 183,398 199,806 229,656 271,336 125,148 145,460 199,490 Angola .. 8,214 4,717 7,449 7,375 9,791 12,230 17,295 28,736 6,696 5,269 13,813 Benin 1,402 1,806 2,372 2,243 2,351 2,781 3,515 4,006 4,259 1,298 1,971 3,192 Botswana 1,028 3,686 5,361 5,826 5,896 5,235 7,564 8,796 9,633 1,461 4,551 7,158 Burkina Faso 1,924 3,113 2,806 2,596 2,808 3,202 4,181 5,139 5,695 1,994 2,622 3,937 Burundi 922 1,117 797 723 650 614 577 646 780 1,051 967 665 Cameroon 5,618 10,674 10,019 9,413 9,179 10,207 13,097 15,374 16,413 8,774 10,032 12,280 Cape Verde .. 340 575 520 544 605 781 907 966 264 444 720 Central African Republic 800 1,465 1,038 937 957 1,039 1,191 1,303 1,367 917 1,159 1,132 Chad 1,038 1,721 1,535 1,366 1,689 1,934 2,270 3,725 4,867 1,069 1,593 2,642 Comoros 124 249 223 202 221 250 323 360 385 144 233 290 Congo, Dem. Rep. 13,899 8,581 4,317 3,918 4,280 5,250 5,485 6,276 6,760 9,482 6,630 5,328 Congo, Rep. 1,544 2,324 1,645 2,275 1,960 2,201 2,679 3,247 4,456 1,961 1,779 2,803 Côte d'Ivoire 9,680 9,209 11,743 9,723 9,920 10,802 13,014 14,711 15,237 7,875 10,136 12,235 Djibouti .. .. 548 567 585 606 679 731 776 .. 510 657 Equatorial Guinea .. 124 417 884 753 1,321 1,296 1,868 3,562 101 225 1,614 Eritrea .. .. 686 634 668 625 574 620 962 .. 678 681 Ethiopia .. 12,016 7,586 7,843 7,836 7,389 7,964 9,421 11,338 9,221 9,449 8,632 Gabon 3,856 5,336 4,094 4,289 4,084 4,453 5,342 5,971 6,678 3,393 4,439 5,136 Gambia, The 237 291 411 400 395 347 348 381 446 218 363 386 Ghana 4,426 5,774 7,546 4,825 5,205 6,028 7,459 8,674 10,533 4,621 6,447 7,121 Guinea .. 2,518 3,377 3,035 2,947 3,170 3,585 3,987 3,279 2,051 3,290 3,334 Guinea-Bissau 105 233 210 203 183 193 225 258 289 153 227 225 Kenya 7,039 8,224 12,723 12,575 12,836 12,793 14,473 16,013 19,084 6,831 9,622 14,629 Lesotho 695 1,022 1,149 1,072 927 848 1,287 1,620 1,761 727 1,198 1,253 Liberia 930 .. 334 389 403 453 350 373 416 807 306 397 Madagascar 4,024 2,958 3,675 3,807 4,470 4,326 5,394 4,285 4,962 3,012 3,203 4,541 Malawi 1,138 1,837 1,734 1,707 1,683 1,890 1,723 1,858 2,033 1,190 1,858 1,816 Mali 1,768 2,405 2,526 2,392 2,464 3,103 4,203 4,679 5,073 1,539 2,457 3,652 Mauritania 672 1,076 1,216 1,092 1,089 1,276 1,343 1,613 1,901 795 1,269 1,386 Mauritius 1,130 2,363 4,235 4,434 4,551 4,541 5,246 6,028 6,285 1,346 3,547 5,181 Mozambique 3,550 2,320 3,777 3,546 3,393 3,919 4,592 5,564 6,409 3,292 2,588 4,570 Namibia 1,818 2,388 3,368 3,447 3,215 3,156 4,702 5,787 6,158 1,672 3,182 4,411 Niger 2,476 2,423 1,999 1,782 1,930 2,146 2,718 3,039 3,397 1,958 1,980 2,502 Nigeria 61,079 25,585 33,300 40,256 44,107 40,806 50,468 60,847 84,820 34,111 27,609 53,551 Rwanda 1,165 2,572 1,920 1,796 1,680 1,713 1,653 1,800 2,128 1,758 1,761 1,795 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 3,403 5,520 5,058 4,601 4,800 5,232 6,710 7,856 8,527 3,589 5,041 6,288 Seychelles 142 355 598 583 601 630 663 667 678 188 480 637 Sierra Leone 1,071 580 639 614 780 906 963 1,039 1,162 928 721 911 Somalia 603 835 .. .. .. .. .. .. .. 818 835 .. South Africa 77,378 107,746 129,975 129,704 114,742 108,093 162,044 212,092 237,179 87,304 130,899 160,642 Sudan 7,508 12,395 9,372 10,479 11,919 13,749 16,446 20,043 25,984 12,026 8,695 16,437 Swaziland 548 941 1,465 1,423 1,362 1,190 1,903 2,402 2,633 575 1,260 1,819 Tanzania .. 4,072 8,543 8,959 9,356 9,579 10,135 11,153 12,383 4,580 5,760 10,261 Togo 1,096 1,598 1,538 1,300 1,298 1,454 1,736 2,033 2,091 979 1,425 1,652 Uganda 1,237 4,227 5,985 5,819 5,571 5,719 6,127 6,678 8,557 3,571 4,787 6,412 Zambia 3,594 3,008 2,975 3,080 3,469 3,542 4,179 4,999 6,804 2,881 3,100 4,345 Zimbabwe 6,610 8,494 5,718 7,145 9,919 21,651 7,207 4,503 3,220 7,010 7,070 8,941 NORTH AFRICA 122,344 159,989 222,024 235,207 235,468 229,591 248,356 276,347 317,215 130,760 175,938 257,031 Algeria 41,147 59,955 46,351 52,080 53,491 54,823 65,319 81,414 96,706 52,260 46,075 67,305 Egypt, Arab Rep. 21,453 42,025 91,923 100,838 98,496 88,763 83,006 78,757 89,532 29,927 60,225 89,899 Libya 35,480 .. .. .. .. .. 24,357 30,253 41,385 27,588 .. 31,998 Morocco 18,402 24,835 34,263 32,462 33,068 35,355 43,024 49,354 51,312 16,320 30,201 40,763 Tunisia 8,450 11,882 19,940 18,526 19,077 20,096 23,957 26,895 27,176 8,559 16,024 22,621 ALL AFRICA 384,281 448,967 539,712 558,248 553,368 562,511 664,599 784,210 916,892 380,575 483,338 673,304 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 27 Table 2.7 Gross national income, real Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 227,290 273,322 330,095 341,510 354,095 366,236 381,357 402,405 425,186 1.8 2.4 4.4 Excluding South Africa 131,907 162,389 202,585 208,697 217,658 224,762 235,686 249,509 264,506 2.2 2.6 4.8 Excl. S. Africa & Nigeria 99,676 127,371 158,898 162,643 170,180 176,555 182,302 192,868 204,410 2.6 2.7 4.5 Angola 6,746 8,464 8,862 9,129 9,416 10,780 11,137 12,383 14,935 3.5 1 9.9 Benin 1,084 1,412 2,131 2,255 2,368 2,474 2,571 2,650 2,727 2.7 4.7 3.9 Botswana 1,208 3,394 5,707 6,177 6,500 6,866 7,290 7,740 8,046 10.9 5.6 5.6 Burkina Faso 1,263 1,750 2,560 2,601 2,754 2,875 3,062 3,205 3,433 3.9 4.1 5.6 Burundi 559 865 715 709 724 756 747 783 790 4.5 ­3.2 2.2 Cameroon 6,339 8,793 9,669 10,075 10,530 10,952 11,393 11,815 12,057 4.5 1.3 3.7 Cape Verde .. 303 498 531 552 577 613 608 681 6.3 5.9 4.7 Central African Republic 730 809 931 953 967 959 886 898 918 1.6 1.8 ­1.4 Chad 664 1,104 1,395 1,383 1,544 1,675 1,922 2,568 2,789 6.7 2.3 15.9 Comoros 136 181 200 202 209 217 223 222 232 2.9 1.2 2.6 Congo, Dem. Rep. 7,016 7,659 4,625 4,306 4,215 4,362 4,614 4,921 5,239 2.1 ­5 4.4 Congo, Rep. 1,746 2,796 2,992 3,220 3,342 3,503 3,563 3,691 3,975 3.8 0.8 4 Côte d'Ivoire 7,706 8,274 10,787 10,425 10,436 10,266 10,095 10,261 10,230 0.7 3.5 ­0.5 Djibouti .. 660 549 551 563 577 596 614 633 .. ­2.3 2.9 Equatorial Guinea .. 207 1,105 1,254 2,035 2,411 2,694 3,549 3,793 .. 20.7 23.2 Eritrea .. .. 729 634 692 697 739 753 757 .. 7.9 3.5 Ethiopia .. 6,292 7,461 7,903 8,513 8,618 8,315 9,407 10,367 2 3.3 4.7 Gabon 3,594 4,298 5,165 5,068 5,176 5,162 5,290 5,361 5,523 0.5 2.9 1.6 Gambia, The 213 305 399 421 445 431 461 484 509 3.5 2.7 3.7 Ghana 2,637 3,263 4,795 4,972 5,171 5,404 5,685 6,003 6,357 2.6 4.3 5.1 Guinea 1,539 2,088 3,055 3,112 3,236 3,372 3,413 3,505 3,621 3 4.4 2.9 Guinea-Bissau 115 186 200 215 216 201 202 206 213 3.8 1.4 ­0.5 Kenya 7,087 10,557 12,630 12,705 13,188 13,261 13,657 14,319 15,152 4.1 2.2 3.4 Lesotho 392 602 832 853 868 893 917 954 982 4.1 4.3 2.9 Liberia 1,391 433 446 561 577 599 411 422 444 ­3.3 0.2 ­6.8 Madagascar 3,099 3,266 3,701 3,878 4,111 3,590 3,941 4,149 4,339 0.8 1.7 2 Malawi 1,000 1,243 1,716 1,744 1,657 1,704 1,808 1,936 1,990 2.4 3.8 3.4 Mali 1,536 1,630 2,347 2,422 2,716 2,828 3,039 3,105 3,294 0.5 3.9 5.9 Mauritania 693 816 1,062 1,081 1,112 1,125 1,188 1,249 1,317 1.9 2.9 4 Mauritius 1,518 2,679 4,296 4,469 4,718 4,846 5,000 5,235 5,475 5.9 5.3 4 Mozambique 2,245 2,279 3,706 3,778 4,273 4,621 4,987 5,361 5,695 ­0.9 5.7 8.4 Namibia 2,002 2,263 3,298 3,414 3,495 3,729 3,858 4,088 4,258 1.1 4 4.7 Niger 1,523 1,507 1,824 1,798 1,926 1,984 2,071 2,059 2,199 ­0.4 2.4 3.6 Nigeria 31,452 34,978 43,628 45,984 47,409 48,143 53,292 56,543 59,992 0.8 2.4 5.8 Rwanda 1,457 1,782 1,709 1,811 1,933 2,114 2,134 2,218 2,351 2.5 ­1.6 5.1 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2,683 3,463 4,546 4,692 4,907 4,939 5,268 5,562 5,866 2.7 2.8 4.5 Seychelles 290 393 587 615 601 609 573 557 563 3.1 4.5 ­2.1 Sierra Leone 935 1,021 610 634 749 954 1,043 1,120 1,201 0.5 ­5.4 13.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 95,503 110,945 127,577 132,878 136,512 141,549 145,761 152,996 160,793 1.4 2 3.9 Sudan 5,555 7,100 11,611 12,366 13,120 13,960 14,742 15,509 16,749 2.4 5.3 6.1 Swaziland 554 1,024 1,361 1,389 1,411 1,452 1,494 1,526 1,562 6.5 3.3 2.5 Tanzania .. 6,801 8,639 9,079 9,646 10,345 10,931 11,667 12,461 .. 2.7 6.5 Togo 964 1,071 1,340 1,329 1,327 1,382 1,419 1,461 1,480 1.5 3.6 2.5 Uganda .. 3,077 5,610 5,926 6,219 6,613 6,926 7,306 7,786 2.3 7.2 5.6 Zambia 2,730 3,028 3,126 3,238 3,396 3,508 3,688 3,887 4,090 1 0.2 4.7 Zimbabwe 4,376 6,734 8,034 7,399 7,199 6,883 6,167 5,933 5,618 3.3 2.7 ­5.7 NORTH AFRICA 126,904 176,633 234,042 241,901 251,458 260,036 269,560 282,196 294,925 3.4 3.2 4 Algeria 35,291 46,367 53,611 54,790 56,215 58,857 62,918 66,190 69,698 2.9 1.7 5.2 Egypt, Arab Rep. 38,503 65,574 94,738 99,839 103,357 106,649 109,964 114,559 119,714 5.5 4.3 3.6 Libya 14,354 .. 34,104 34,495 36,053 37,228 36,204 38,014 40,409 ­7 .. 2.7 Morocco 18,308 26,717 33,018 33,334 35,433 36,563 38,582 40,220 40,910 4.2 2.4 4.3 Tunisia 8,622 12,237 18,571 19,443 20,401 20,738 21,891 23,213 24,194 3.2 4.6 4.5 ALL AFRICA 356,401 451,457 564,126 583,400 605,543 626,261 650,902 684,580 720,082 2.4 2.7 4.3 a. Provisional. 28 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.8 Gross national income per capita Dollarsa Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 664 584 503 488 480 471 514 608 751 587 540 552 Excluding South Africa 535 378 315 306 314 320 352 403 477 424 329 362 Excl. S. Africa & Nigeria 452 401 322 311 310 317 345 396 459 400 343 356 Angola .. 730 390 430 470 620 710 940 1,410 740 452 763 Benin .. .. .. .. 430 460 .. .. .. .. .. 445 Botswana 960 2,450 3,110 3,270 3,470 3,120 3,670 4,380 5,590 1,202 2,923 3,917 Burkina Faso 310 350 260 250 240 250 290 350 400 284 287 297 Burundi 220 210 140 120 110 100 90 90 100 231 166 102 Cameroon 620 960 590 570 640 640 730 890 1,010 883 748 747 Cape Verde .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 340 460 280 270 260 250 260 310 350 345 365 283 Chad 230 260 200 180 190 200 220 330 400 217 238 253 Comoros .. 540 410 400 400 400 470 550 650 383 501 478 Congo, Dem. Rep. 630 230 80 90 80 90 100 120 120 361 151 100 Congo, Rep. 820 880 450 520 570 620 640 750 950 990 658 675 Côte d'Ivoire 1,120 730 710 640 600 570 640 760 870 811 712 680 Djibouti .. .. 810 840 .. .. .. .. .. .. 803 840 Equatorial Guinea .. 360 1,170 .. .. .. .. .. .. 365 582 .. Eritrea .. .. 183 160 160 150 160 180 .. .. 168 162 Ethiopia .. 170 130 130 130 120 110 130 160 154 140 130 Gabon 4,790 4,780 3,180 3,090 3,080 2,990 3,340 4,080 5,010 4,403 4,232 3,598 Gambia, The 380 310 340 340 320 270 300 460 .. 309 337 338 Ghana 430 390 .. 295 351 270 310 .. .. 392 398 307 Guinea .. .. .. .. 370 360 380 410 380 .. .. 380 Guinea-Bissau .. .. .. 160 140 130 140 160 180 .. .. 152 Kenya 450 380 360 350 420 410 430 480 530 374 320 437 Lesotho .. .. .. 640 600 540 590 730 940 .. .. 673 Liberia 620 .. .. 130 130 140 100 120 130 510 .. 125 Madagascar 440 .. .. .. .. 230 290 320 .. 388 260 280 Malawi 190 180 180 150 140 140 150 160 160 168 187 150 Mali 270 270 240 250 230 230 270 .. .. 217 265 245 Mauritania 460 550 400 370 360 340 .. .. .. 469 478 357 Mauritius 1,148 2,478 3,590 .. .. .. .. .. .. 1,358 3,255 .. Mozambique .. 170 220 210 210 220 230 280 .. 267 173 230 Namibia .. .. .. 1,870 1,750 1,650 1,930 2,370 .. 1,474 .. 1,914 Niger 390 280 170 160 160 160 180 210 240 289 213 185 Nigeria 810 280 280 280 330 340 380 430 560 496 270 387 Rwanda 250 370 240 240 220 210 190 200 .. 293 248 212 São Tomé and Principe .. 430 270 290 300 310 340 380 420 490 348 340 Senegal 500 660 460 450 440 420 490 600 700 474 560 517 Seychelles 2,080 5,020 7,290 7,440 7,380 6,840 7,450 8,080 8,180 2,764 6,420 7,562 Sierra Leone 390 200 150 140 160 190 200 210 220 280 175 187 Somalia 110 120 .. .. .. .. .. .. .. 126 120 .. South Africa 2,550 3,390 3,150 3,050 2,830 2,630 2,870 3,700 4,990 2,695 3,472 3,345 Sudan .. 610 330 330 340 380 430 520 650 757 355 442 Swaziland .. .. .. 1,380 1,330 1,240 1,340 1,650 .. .. .. 1,388 Tanzania .. 190 250 260 270 290 300 320 340 .. 188 297 Togo 410 380 290 270 250 240 270 310 350 306 326 282 Uganda .. 190 320 280 240 230 230 250 280 208 243 252 Zambia 630 450 320 310 330 340 380 .. .. 460 368 340 Zimbabwe 930 850 500 460 540 780 790 580 350 858 659 583 NORTH AFRICA 1,276 1,376 1,581 1,644 1,673 1,645 1,699 1,817 2,024 1,305 1,378 1,751 Algeria 2,060 2,420 1,560 1,610 1,690 1,750 1,950 2,290 2,730 2,462 1,758 2,003 Egypt, Arab Rep. 530 810 1,370 1,490 1,530 1,470 1,390 .. .. 671 999 1,470 Libya 10,460 .. .. .. .. .. .. .. .. 7,432 .. .. Morocco 970 1,030 1,190 1,180 1,900 .. .. .. .. 805 1,156 1,540 Tunisia 1,360 1,430 2,090 2,090 2,060 2,000 2,260 2,650 2,880 1,264 1,808 2,323 ALL AFRICA 780 738 694 690 687 674 718 818 971 725 697 760 a. Calculated by the World Bank Atlas method. b. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 29 Table 2.9 Gross domestic product deflator (local currency series) Index (2000 = 100) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 14 38 94 100 105 110 116 122 131 22.6 65.4 113.8 Excluding South Africa 15 39 94 100 105 109 115 121 130 23.6 65.2 113.6 Excl. S. Africa & Nigeria 17 39 95 100 105 109 114 121 130 24.8 65.9 113.3 Angola .. .. 19 100 208 460 931 1,329 1,907 0.0 2.6 822.5 Benin 38 50 97 100 103 111 113 113 116 47.1 72.5 109.5 Botswana 13 41 89 100 106 107 110 117 130 21.8 62.0 111.7 Burkina Faso 45 68 95 100 105 109 111 119 123 59.6 79.2 111.3 Burundi 21 31 88 100 105 107 120 130 151 23.9 50.5 118.9 Cameroon 34 58 97 100 102 106 106 107 112 50.4 78.5 105.6 Cape Verde .. 66 101 100 103 105 106 113 109 59.8 81.0 105.8 Central African Republic 32 70 98 100 103 107 110 108 111 54.5 83.9 106.4 Chad 46 60 95 100 114 116 115 127 156 55.3 77.6 121.5 Comoros 36 70 96 100 109 113 119 121 124 54.3 82.5 114.3 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 29 38 68 100 86 84 82 87 111 38.6 49.8 91.8 Côte d'Ivoire 39 50 101 100 104 109 111 112 116 49.7 75.5 108.8 Djibouti .. 69 98 100 102 102 105 109 112 .. 84.7 105.0 Equatorial Guinea .. 24 68 100 88 88 90 102 147 25.6 40.8 102.5 Eritrea .. .. 80 100 114 131 147 180 206 .. 65.6 146.4 Ethiopia .. 49 94 100 95 90 102 107 116 41.2 80.4 101.7 Gabon 35 53 78 100 94 94 93 99 116 44.3 63.4 99.4 Gambia, The 15 64 96 100 115 134 170 194 203 30.6 81.8 152.7 Ghana 0 11 79 100 135 166 213 244 280 3.0 37.3 189.8 Guinea 5 48 90 100 105 108 121 147 192 17.3 77.0 128.6 Guinea-Bissau 0 6 97 100 95 98 95 97 105 1.0 47.4 98.3 Kenya 10 24 94 100 102 101 107 117 126 15.8 57.2 108.6 Lesotho 12 38 96 100 107 117 124 129 136 21.4 64.7 118.7 Liberia 2 2 101 100 112 141 145 146 166 1.8 22.2 134.9 Madagascar 4 21 93 100 107 124 127 145 172 10.3 53.6 129.2 Malawi 2 7 77 100 126 146 160 180 207 3.3 28.9 153.1 Mali 35 57 95 100 100 116 117 116 119 49.8 79.1 111.4 Mauritania 20 42 99 100 108 116 119 133 157 29.5 75.1 122.2 Mauritius 21 54 96 100 104 111 118 124 130 32.8 74.2 114.5 Mozambique 0 7 91 100 116 136 148 161 179 1.4 48.1 139.9 Namibia 12 39 90 100 114 127 126 130 133 21.4 62.6 121.8 Niger 49 63 96 100 104 107 108 106 114 62.8 77.7 106.6 Nigeria 2 7 72 100 111 115 139 167 209 3.1 41.0 140.1 Rwanda 19 31 97 100 100 100 109 122 131 23.9 65.5 110.3 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 39 63 98 100 103 106 106 106 109 55.1 80.7 105.0 Seychelles 57 88 99 100 105 110 116 121 123 70.8 92.7 112.7 Sierra Leone 0 5 94 100 102 98 106 123 140 0.6 40.9 111.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 9 38 92 100 108 119 125 132 138 18.5 64.9 120.2 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 11 32 89 100 111 125 139 145 153 18.7 57.5 128.9 Tanzania .. 15 93 100 107 114 122 132 142 11.4 49.1 119.7 Togo 35 58 102 100 103 105 101 105 106 48.5 78.3 103.2 Uganda .. 30 96 100 107 102 112 119 129 4.4 71.6 111.6 Zambia 0 1 77 100 124 149 179 214 255 0.2 31.9 170.2 Zimbabwe 2 7 64 100 177 394 1,883 9,064 30,632 3.9 23.6 7,041.8 NORTH AFRICA 30 54 95 100 102 104 111 123 132 43.6 77.0 112.1 Algeria 6 16 80 100 101 103 111 123 142 8.6 50.2 113.3 Egypt, Arab Rep. 13 43 95 100 102 104 111 124 132 20.4 72.6 112.3 Libya 143 .. 81 100 98 128 166 204 263 153.1 80.9 160.0 Morocco 38 75 99 100 102 102 102 104 105 54.8 89.7 102.6 Tunisia 30 64 97 100 103 105 107 110 112 46.2 81.7 106.3 ALL AFRICA 15 39 94 100 104 109 113 122 131 23.5 66.2 113.1 a. Provisional. 30 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.10 Gross domestic product deflator (U.S. dollar series) Index (2000 = 100) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 122 110 100 100 95 96 114 133 148 108 108 114 Excluding South Africa 149 117 97 100 100 107 114 127 147 123 104 116 Excl. S. Africa & Nigeria 130 126 102 100 99 109 115 127 142 121 112 115 Angola .. 121 69 100 95 106 125 160 220 95 91 134 Benin 130 131 112 100 100 113 138 153 157 103 116 127 Botswana 88 112 99 100 93 86 114 127 130 77 108 108 Burkina Faso 153 178 110 100 102 111 137 160 166 130 125 129 Burundi 164 131 113 100 92 83 80 85 101 153 123 90 Cameroon 106 127 108 100 91 99 120 134 140 102 125 114 Cape Verde .. 112 117 100 100 107 130 152 147 95 117 123 Central African Republic 109 184 113 100 100 109 135 146 149 118 144 123 Chad 156 157 110 100 111 119 142 172 211 121 129 143 Comoros 91 138 111 100 106 116 146 163 167 89 125 133 Congo, Dem. Rep. 205 122 102 100 111 127 123 134 136 133 126 122 Congo, Rep. 98 100 79 100 84 86 100 118 150 84 81 106 Côte d'Ivoire 132 130 116 100 101 112 136 151 157 108 123 126 Djibouti .. 69 98 100 102 102 105 109 112 .. 85 105 Equatorial Guinea .. 64 79 100 85 90 110 138 198 54 65 120 Eritrea .. .. 94 100 97 91 79 84 128 .. 99 97 Ethiopia .. 192 102 100 93 86 97 101 110 162 153 98 Gabon 119 138 90 100 91 96 114 134 157 96 104 115 Gambia, The 113 104 108 100 94 86 80 83 91 91 109 89 Ghana 169 180 161 100 103 114 134 148 169 177 166 128 Guinea 434 128 113 100 94 95 106 115 92 420 136 100 Guinea-Bissau 97 131 112 100 92 100 117 131 141 105 116 114 Kenya 103 81 102 100 98 97 107 113 127 86 86 107 Lesotho 110 102 109 100 87 77 113 138 148 92 115 111 Liberia 69 89 99 100 94 93 100 109 119 76 101 103 Madagascar 130 94 100 100 110 122 139 105 116 108 101 115 Malawi 124 151 103 100 104 114 98 98 104 118 132 103 Mali 116 149 110 100 97 118 144 157 161 108 132 129 Mauritania 102 125 113 100 101 102 108 124 139 108 140 112 Mauritius 76 89 99 100 96 94 105 116 115 71 104 104 Mozambique 157 108 108 100 87 89 96 110 120 173 101 100 Namibia 108 104 103 100 92 84 116 140 145 90 111 113 Niger 165 165 111 100 101 109 132 143 155 137 127 123 Nigeria 204 81 80 100 101 97 109 128 162 127 76 116 Rwanda 80 145 113 100 88 82 79 83 92 105 116 87 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 131 165 113 100 99 108 129 143 147 120 136 121 Seychelles 51 94 106 100 103 115 123 126 128 65 103 116 Sierra Leone 118 64 109 100 108 98 95 96 99 98 98 99 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 85 101 104 100 87 78 114 141 151 88 115 112 Sudan 137 185 92 100 102 107 121 140 167 195 108 123 Swaziland 98 86 101 100 89 82 128 156 167 83 101 120 Tanzania .. 63 100 100 98 94 94 97 101 76 77 97 Togo 118 152 118 100 100 107 124 141 143 106 131 119 Uganda .. 140 107 100 91 88 90 93 112 164 114 96 Zambia 142 109 100 100 107 105 117 140 178 111 111 125 Zimbabwe 153 130 74 100 142 318 120 79 61 136 101 137 NORTH AFRICA 104 97 97 100 94 85 90 97 106 96 93 95 Algeria 120 134 91 100 98 97 108 128 146 128 101 113 Egypt, Arab Rep. 60 66 96 100 94 82 75 69 75 62 76 83 Libya 248 .. 89 100 83 52 66 80 103 263 89 81 Morocco 103 97 107 100 96 99 114 124 126 80 106 110 Tunisia 101 100 112 100 98 101 114 121 119 89 111 109 ALL AFRICA 114 105 98 100 94 91 104 118 131 103 102 106 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 31 Table 2.11 Gross domestic savings Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 29.9 19.8 16.0 21.2 19.3 17.9 19.5 20.4 21.3 23.2 16.5 19.9 Excluding South Africa 27.1 17.4 13.9 22.7 19.4 17.0 20.0 23.1 24.5 20.7 14.4 21.1 Excl. S. Africa & Nigeria 22.1 15.2 12.8 17.0 15.0 15.0 16.6 18.3 19.5 20.5 12.4 16.9 Angola .. 29.7 20.7 41.8 15.1 23.9 19.2 25.1 32.8 24.0 22.0 26.3 Benin ­6.3 2.2 4.8 6.0 6.5 3.7 6.0 5.5 6.9 ­2.4 3.8 5.8 Botswana 26.7 42.6 42.2 54.5 56.6 51.4 49.7 50.2 51.8 35.3 39.6 52.3 Burkina Faso ­7.2 5.2 8.1 6.5 5.0 4.7 3.9 .. .. ­2.7 7.6 5.0 Burundi ­0.6 ­5.4 ­2.5 ­6.0 ­7.8 ­9.7 ­8.7 ­11.0 ­23.1 3.1 ­5.2 ­11.1 Cameroon 21.7 20.7 19.2 20.3 19.0 19.0 17.8 18.5 16.2 24.2 18.5 18.5 Cape Verde .. ­8.1 ­17.5 ­14.2 ­15.1 ­15.7 ­15.8 .. .. ­2.2 ­5.6 ­15.2 Central African Republic ­8.9 ­0.6 11.0 7.8 11.1 10.3 .. .. .. ­1.1 3.7 9.7 Chad .. ­7.7 ­0.2 5.5 5.3 .. 18.1 24.5 35.6 ­8.1 ­0.5 17.8 Comoros ­10.1 ­3.2 ­5.7 ­5.7 ­5.2 ­4.0 ­5.8 ­10.6 ­12.9 ­4.5 ­4.5 ­7.4 Congo, Dem. Rep. 10.1 9.3 9.1 4.5 3.2 4.0 5.0 4.0 5.9 10.9 8.8 4.4 Congo, Rep. 35.7 23.8 41.0 59.3 50.5 51.0 51.3 51.3 58.7 31.9 28.8 53.7 Côte d'Ivoire 20.4 11.3 21.3 17.9 19.0 26.3 20.6 20.5 20.4 19.6 17.8 20.8 Djibouti .. ­10.4 ­2.4 ­6.5 ­0.6 4.9 5.2 4.8 7.3 .. ­6.4 2.5 Equatorial Guinea .. ­20.1 .. 72.0 80.8 78.1 78.7 83.0 86.9 .. 13.7 79.9 Eritrea .. .. ­41.2 ­34.7 ­27.0 ­33.7 ­59.7 ­61.4 ­26.8 .. ­30.9 ­40.6 Ethiopia .. 9.6 1.8 6.8 7.4 7.8 6.6 3.7 ­1.6 10.5 7.5 5.1 Gabon 60.6 36.9 47.7 58.3 51.7 43.7 48.2 53.9 67.2 44.3 43.6 53.8 Gambia, The 5.8 10.7 11.0 8.5 12.0 12.9 11.1 10.5 4.4 6.5 7.4 9.9 Ghana 4.9 5.5 3.3 5.3 7.1 7.7 11.4 7.3 3.4 4.8 7.5 7.0 Guinea .. 22.2 15.5 15.4 14.1 9.5 7.8 7.3 11.1 16.6 18.3 10.9 Guinea-Bissau ­1.0 2.8 ­1.2 ­8.5 ­19.3 ­12.1 1.2 ­3.0 1.5 ­0.9 1.5 ­6.7 Kenya 18.1 18.5 10.7 9.4 11.3 13.1 13.3 12.4 9.0 17.9 15.6 11.4 Lesotho ­52.0 ­52.9 ­22.6 ­20.6 ­16.6 ­19.8 ­17.3 ­14.7 ­4.8 ­65.5 ­38.3 ­15.6 Liberia 14.8 .. .. .. ­3.4 ­3.3 ­3.2 ­0.7 2.4 2.2 .. ­1.6 Madagascar ­1.4 5.5 7.2 7.7 15.3 7.7 8.9 9.4 8.4 2.9 4.2 9.6 Malawi 10.8 13.4 ­0.6 3.8 3.8 ­10.1 ­10.7 ­9.1 ­9.5 12.7 3.4 ­5.3 Mali 1.1 6.4 9.5 12.0 14.0 11.3 13.3 8.6 9.8 ­0.4 7.6 11.5 Mauritania ­3.5 4.9 ­1.2 ­8.6 3.1 ­1.9 ­5.0 ­3.1 ­15.0 3.1 2.4 ­5.1 Mauritius 14.5 23.5 23.3 23.9 26.0 25.2 24.8 23.4 18.9 20.0 24.1 23.7 Mozambique ­8.9 ­5.8 13.7 11.6 8.0 11.0 11.7 14.3 11.9 ­6.2 1.0 11.4 Namibia 38.4 18.2 12.5 14.0 17.0 17.8 26.2 26.7 29.5 10.8 12.7 21.9 Niger 14.6 1.2 3.7 3.5 4.4 5.3 5.0 6.1 9.3 7.3 2.7 5.6 Nigeriab 31.4 29.4 19.1 42.3 34.9 25.5 32.1 39.4 39.5 17.5 24.0 35.6 Rwanda 4.2 6.2 0.0 1.3 2.6 0.0 ­0.8 2.4 2.0 5.0 ­5.5 1.2 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2.1 2.4 10.9 11.2 9.4 6.8 8.8 8.0 9.9 4.3 5.4 9.0 Seychelles 27.1 20.3 25.6 22.6 19.1 20.0 21.2 23.0 ­14.7 24.1 21.7 15.2 Sierra Leone 0.9 8.7 ­10.3 ­13.3 ­11.6 ­9.4 ­7.4 ­1.9 ­2.3 9.1 2.9 ­7.6 Somalia ­12.9 ­12.5 .. .. .. .. .. .. .. ­6.3 ­12.5 .. South Africa 37.9 23.2 19.0 18.9 19.2 19.7 18.8 16.8 16.6 28.5 19.4 18.3 Sudan 2.1 .. 7.7 15.9 9.8 13.3 15.7 18.7 13.8 5.0 7.3 14.5 Swaziland 1.2 6.6 0.3 3.0 3.1 19.5 19.9 16.8 13.3 3.7 2.0 12.6 Tanzania .. 1.3 4.5 10.2 8.8 11.8 12.0 11.2 10.9 .. 2.0 10.8 Togo 23.2 14.7 3.2 ­2.2 1.0 0.6 5.3 4.5 4.9 12.3 6.7 2.4 Uganda ­0.4 0.6 7.6 8.1 6.5 4.7 6.3 8.4 7.1 2.3 4.3 6.9 Zambia 19.3 16.6 ­1.1 8.3 17.3 17.7 18.7 18.2 17.0 14.0 7.1 16.2 Zimbabwe 13.8 17.5 16.0 13.3 11.6 7.1 6.2 4.1 0.6 16.5 16.9 7.2 NORTH AFRICA 41.3 22.8 19.7 24.5 23.0 23.6 26.8 30.3 34.9 29.4 20.0 27.2 Algeria 43.1 27.1 31.6 44.8 42.0 40.9 44.9 47.7 54.4 31.5 30.1 45.8 Egypt, Arab Rep. 15.2 16.1 13.4 12.9 13.4 13.9 14.3 15.6 15.7 15.5 14.2 14.3 Libya 56.9 .. .. .. .. .. .. .. .. 46.9 .. .. Morocco 14.9 19.9 18.8 17.1 19.4 19.4 19.9 18.3 18.0 16.7 17.0 18.7 Tunisia 24.0 20.0 24.1 23.7 23.3 21.4 21.2 21.2 20.7 22.7 22.3 21.9 ALL AFRICA 32.8 20.9 17.5 22.5 20.9 20.1 21.9 23.4 25.4 25.0 17.8 22.4 a. Provisional. b. For 1994­2000 Nigeria's values were distorted because the official exchange rate used by the government for oil exports and oil value added was significantly overvalued. 32 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.12 Gross national savings Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 11.8 13.5 13.5 17.1 16.2 14.5 15.9 16.3 17.5 11.9 13.3 16.3 Excluding South Africa 2.6 10.0 11.4 17.4 16.3 13.2 15.7 17.4 19.4 5.4 10.7 16.5 Excl. S. Africa & Nigeria 4.0 8.3 9.7 12.9 12.6 12.5 13.9 14.4 15.7 6.1 9.1 13.6 Angola .. .. ­1.7 23.8 ­1.4 9.8 7.6 12.6 20.5 .. 4.8 12.1 Benin .. .. 9.9 10.9 12.5 7.3 9.4 8.9 10.6 .. 10.8 9.9 Botswana 28.7 43.3 42.0 52.3 57.6 43.2 44.5 45.1 50.5 33.7 41.5 48.9 Burkina Faso .. .. .. .. .. .. .. .. .. .. .. .. Burundi .. .. ­0.1 1.2 1.6 2.9 6.0 5.2 1.1 .. 1.4 3.0 Cameroon 5.1 16.1 15.9 14.7 15.9 15.1 15.5 16.9 14.7 19.2 13.5 15.5 Cape Verde .. 17.6 8.1 9.1 8.0 9.4 9.2 .. .. 24.2 21.2 8.9 Central African Republic .. .. 12.0 8.0 12.3 7.6 .. .. 14.0 .. 12.0 10.5 Chad .. ­2.7 2.4 7.9 6.6 .. 4.9 13.7 21.3 ­3.3 3.5 10.9 Comoros .. 10.1 2.5 9.9 12.5 9.6 7.2 6.5 5.9 .. 6.8 8.6 Congo, Dem. Rep. 7.9 0.8 1.1 ­1.3 0.1 5.3 9.9 6.1 6.1 5.9 1.1 4.4 Congo, Rep. .. .. 11.0 30.1 20.7 24.0 26.7 26.3 33.5 .. 3.2 26.9 Côte d'Ivoire .. ­4.3 11.7 8.0 10.6 16.8 12.3 12.4 10.9 9.2 6.2 11.8 Djibouti .. .. 10.6 5.4 11.6 15.6 20.6 18.5 20.5 .. 11.4 15.4 Equatorial Guinea .. ­22.0 .. 43.1 24.0 38.6 21.5 20.2 33.3 .. 6.0 30.1 Eritrea .. .. 4.9 20.4 28.7 26.3 9.4 0.0 10.6 .. 11.7 15.9 Ethiopia .. 11.9 7.7 14.9 16.6 17.8 19.4 16.1 11.8 11.8 13.2 16.1 Gabon .. 25.4 35.1 40.2 35.4 31.0 33.3 39.9 52.3 23.5 30.0 38.7 Gambia, The .. .. 14.2 13.6 14.8 18.2 18.6 14.3 9.1 .. 16.2 14.8 Ghana .. .. 9.2 15.5 21.3 20.2 24.9 25.7 21.4 .. 14.6 21.5 Guinea .. 14.6 12.9 13.3 12.7 9.2 6.8 5.7 9.1 8.8 14.2 9.5 Guinea-Bissau ­6.3 15.3 ­3.3 ­2.7 ­15.7 ­8.0 5.1 16.2 11.2 ­0.3 5.5 1.0 Kenya 15.4 18.6 13.7 15.2 15.7 16.6 16.4 13.3 11.8 15.8 16.1 14.8 Lesotho 34.5 39.0 22.5 22.8 26.5 24.6 25.3 29.2 37.5 33.8 29.5 27.7 Liberia .. .. .. .. ­21.4 ­11.1 ­6.4 33.1 39.9 .. .. 6.8 Madagascar .. 9.2 9.5 9.4 17.2 8.3 13.0 15.2 11.7 5.0 4.9 12.5 Malawi .. .. ­2.5 2.2 2.4 ­12.0 ­5.1 ­5.2 ­4.3 .. ­0.9 ­3.7 Mali .. .. .. 16.0 12.9 8.5 15.0 9.8 9.9 .. .. 12.0 Mauritania .. .. .. .. .. .. .. .. .. .. .. .. Mauritius .. 26.3 24.9 25.3 27.6 26.6 26.3 23.8 20.1 25.5 26.5 25.0 Mozambique ­6.9 2.1 14.7 15.4 6.4 11.1 12.8 14.2 11.4 ­3.8 4.0 11.9 Namibia 26.9 34.8 24.3 27.7 27.7 27.7 41.6 39.7 40.0 18.5 27.3 34.1 Niger 13.0 ­1.2 2.8 2.8 4.4 4.7 5.5 .. .. 5.5 1.0 4.4 Nigeriab .. .. 13.9 33.6 29.4 15.8 22.2 27.4 30.5 .. 13.9 26.5 Rwanda .. .. ­1.3 ­0.5 0.2 ­2.2 ­3.8 ­0.6 .. .. ­9.2 ­1.4 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 0.1 ­0.5 12.2 14.6 13.9 11.8 16.0 13.3 16.2 0.5 5.3 14.3 Seychelles .. .. 23.2 17.9 16.8 12.0 16.4 20.1 ­18.2 .. 22.0 10.8 Sierra Leone 0.5 2.6 ­5.6 ­7.9 ­3.2 5.2 6.3 8.7 7.6 7.2 0.1 2.8 Somalia ­5.8 .. .. .. .. .. .. .. .. 3.2 .. .. South Africa 33.9 19.1 15.9 15.8 15.4 16.7 15.6 14.1 13.9 24.3 16.6 15.2 Sudan 4.0 .. 0.0 3.5 1.8 9.5 12.3 16.3 13.2 6.5 0.9 9.4 Swaziland 16.7 24.8 16.2 13.2 13.9 24.6 22.1 22.9 18.9 20.2 19.6 19.3 Tanzania .. .. 2.5 10.1 9.1 11.1 11.8 10.7 10.4 .. 2.0 10.5 Togo .. .. 3.8 0.4 3.1 4.1 7.3 6.5 .. .. 4.5 4.3 Uganda ­0.9 0.6 7.7 9.1 7.6 5.9 7.1 9.7 10.0 2.6 6.5 8.2 Zambia 7.3 6.7 ­6.6 2.9 12.1 13.7 15.2 10.0 10.2 2.2 ­1.2 10.7 Zimbabwe .. 15.7 16.1 9.6 9.4 7.0 5.9 4.0 ­0.4 17.3 16.0 5.9 NORTH AFRICA 4.3 11.5 19.9 21.9 22.8 24.3 25.8 27.6 29.3 5.8 18.4 25.3 Algeria .. .. 28.5 41.3 40.1 38.8 43.5 46.3 51.3 .. 27.8 43.6 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. .. .. .. Libya 53.5 .. .. .. .. .. .. .. .. 40.5 .. .. Morocco .. .. 22.1 22.0 27.4 26.6 27.5 26.7 27.9 .. 21.7 26.3 Tunisia .. .. 24.3 23.2 23.7 22.3 22.1 22.4 22.6 .. 21.9 22.7 ALL AFRICA 9.4 12.8 16.1 19.1 18.9 18.3 19.4 20.1 21.5 9.7 15.2 19.6 a. Provisional. b. For 1994­2000 Nigeria's values were distorted because the official exchange rate used by the government for oil exports and oil value added was significantly overvalued. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 33 Table 2.13 General government final consumption Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 13.3 16.6 15.0 15.8 16.7 16.1 17.1 17.0 17.0 14.8 16.1 16.6 Excluding South Africa 12.5 14.6 12.7 14.3 15.8 15.0 15.7 15.2 15.0 13.0 13.5 15.2 Excl. S. Africa & Nigeria 13.1 14.4 12.5 12.3 12.5 12.7 13.4 13.0 12.7 13.0 13.6 12.7 Angola .. 34.5 .. .. .. .. .. .. .. 31.5 40.7 .. Benin 8.6 11.0 10.0 11.6 11.6 12.5 13.3 13.6 15.0 12.7 10.5 12.9 Botswana 21.3 24.1 27.1 22.3 19.7 21.7 22.3 21.8 21.2 24.3 26.8 21.5 Burkina Faso 9.2 13.2 12.5 12.6 12.2 13.1 12.8 .. .. 12.2 13.6 12.7 Burundi 9.2 10.8 18.2 17.5 19.9 19.1 22.7 26.1 26.5 9.3 17.0 22.0 Cameroon 9.7 12.8 9.5 9.5 10.2 10.2 10.0 10.2 10.4 10.0 10.6 10.1 Cape Verde .. 14.7 19.4 21.3 11.3 11.7 14.7 .. .. 13.1 17.0 14.8 Central African Republic 15.1 14.9 11.5 11.3 11.4 11.8 .. .. .. 15.6 13.9 11.5 Chad .. 10.0 6.9 7.7 7.5 7.7 7.6 4.9 4.5 11.3 8.1 6.7 Comoros 30.9 24.5 14.6 11.7 16.2 17.4 14.7 14.3 13.5 28.6 20.3 14.6 Congo, Dem. Rep. 8.4 11.5 6.0 7.5 6.0 5.5 6.3 8.2 8.3 9.0 9.9 7.0 Congo, Rep. 17.6 13.8 15.1 11.6 14.1 18.4 17.0 16.0 13.2 17.7 18.1 15.1 Côte d'Ivoire 16.9 16.8 6.5 7.2 7.5 7.8 8.2 8.3 8.2 16.5 11.9 7.9 Djibouti .. 31.5 29.5 29.7 26.9 28.3 29.3 29.7 27.6 .. 31.8 28.6 Equatorial Guinea .. 39.7 .. 4.6 3.3 5.1 3.8 3.1 3.0 27.4 25.1 3.8 Eritrea .. .. 69.5 63.8 51.5 44.0 51.5 52.6 44.6 .. 39.7 51.3 Ethiopia .. 13.2 16.0 18.5 15.2 16.4 19.1 14.7 13.8 11.2 10.1 16.3 Gabon 13.2 13.4 14.2 9.4 10.0 11.5 10.7 9.3 7.7 18.3 12.6 9.8 Gambia, The 31.2 13.7 13.0 13.7 14.4 12.9 11.0 11.1 .. 29.1 13.8 12.6 Ghana 11.2 9.3 8.0 7.8 7.9 11.5 17.5 16.5 15.3 9.0 11.4 12.8 Guinea .. 11.0 7.0 6.8 6.9 7.5 7.8 6.3 5.6 11.8 8.2 6.8 Guinea-Bissau 27.6 10.3 10.8 14.0 12.6 13.0 12.8 14.5 18.2 18.9 8.4 14.2 Kenya 19.8 18.6 15.8 15.1 16.0 17.4 18.5 17.7 16.7 18.3 15.8 16.9 Lesotho 21.8 14.1 19.5 19.2 18.0 17.7 17.9 17.2 17.0 19.2 16.8 17.9 Liberia 19.1 .. .. .. 14.4 13.7 8.5 10.4 11.2 22.0 .. 11.6 Madagascar 12.1 8.0 7.2 9.0 9.1 8.1 9.2 9.8 8.4 9.8 7.9 8.9 Malawi 19.3 15.1 13.4 14.6 15.8 14.7 16.3 16.9 16.7 17.5 16.6 15.8 Mali 11.6 13.8 15.9 8.6 9.2 8.7 8.4 10.0 9.9 12.3 12.7 9.2 Mauritania 45.3 25.9 20.5 25.8 23.7 22.3 30.1 21.9 22.7 30.6 14.5 24.4 Mauritius 14.4 12.8 12.7 13.1 12.9 12.8 14.1 14.2 14.4 13.5 13.0 13.6 Mozambique 12.2 13.5 9.5 10.1 10.0 9.7 10.3 10.4 10.0 13.8 11.0 10.1 Namibia 17.4 30.6 30.3 28.8 28.4 26.4 26.5 24.5 23.6 27.9 31.0 26.4 Niger 10.4 15.0 14.9 13.0 12.4 12.2 11.3 13.0 11.5 11.9 14.6 12.2 Nigeria 12.1 15.1 13.4 20.9 27.1 24.2 23.7 22.1 21.5 13.9 12.9 23.3 Rwanda 12.5 10.1 11.0 10.5 11.7 11.8 15.1 12.9 13.3 13.0 11.5 12.6 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 24.8 18.4 13.3 12.8 12.6 13.3 13.4 13.9 12.7 19.3 15.0 13.1 Seychelles 28.7 27.7 26.9 24.2 24.8 22.7 25.5 25.4 24.2 33.1 29.0 24.5 Sierra Leone 8.4 7.8 11.5 14.3 17.6 16.4 15.3 13.1 12.3 7.7 10.4 14.8 Somalia 15.6 .. .. .. .. .. .. .. .. 17.6 .. .. South Africa 14.3 19.7 18.4 18.1 18.3 18.4 19.3 19.6 20.0 17.4 19.4 18.9 Sudan 16.0 .. 6.5 7.6 8.6 4.5 10.8 11.8 16.8 12.1 6.0 10.0 Swaziland 27.0 18.1 24.6 24.5 17.7 18.6 18.6 22.2 26.6 20.6 22.6 21.4 Tanzania .. 17.8 7.9 10.6 11.5 12.4 14.8 15.9 17.0 .. 14.0 13.7 Togo 22.4 14.2 9.7 10.2 10.0 8.4 9.8 9.7 10.0 16.9 12.8 9.7 Uganda .. 7.5 12.9 13.7 13.8 15.3 14.8 14.7 14.4 9.9 11.1 14.4 Zambia 25.5 19.0 12.9 9.5 12.8 13.0 13.5 12.7 13.4 23.0 17.7 12.5 Zimbabwe 18.5 19.4 13.6 13.9 17.7 17.9 16.6 23.3 27.2 20.1 17.2 19.4 NORTH AFRICA 14.0 16.2 15.7 14.5 15.0 15.2 15.3 14.9 14.0 17.4 16.1 14.8 Algeria 15.2 16.1 16.8 13.6 14.7 15.4 14.8 13.8 12.2 17.2 16.6 14.1 Egypt, Arab Rep. 15.7 11.3 11.6 11.2 11.3 12.5 12.7 12.8 12.7 16.2 10.9 12.2 Libya 21.8 24.4 21.9 20.5 21.6 16.7 .. .. .. 30.0 24.3 19.6 Morocco 18.3 15.5 19.1 19.1 19.8 20.1 21.0 21.0 22.1 16.6 17.2 20.5 Tunisia 14.5 16.4 15.5 15.6 15.6 15.9 15.7 15.4 15.5 16.5 16.0 15.6 ALL AFRICA 13.3 16.4 15.3 15.3 16.0 15.7 16.5 16.3 16.1 15.5 16.1 16.0 a. Provisional. 34 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.14 Final consumption expenditure Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 70.1 80.2 84.0 78.8 80.7 82.1 80.5 79.6 78.7 76.8 83.5 80.1 Excluding South Africa 72.9 82.6 86.1 77.3 80.6 83.0 80.0 76.9 75.5 79.3 85.6 78.9 Excl. S. Africa & Nigeria 77.9 84.8 87.2 83.0 85.0 85.0 83.4 81.7 80.5 79.5 87.6 83.1 Angola .. 70.3 79.3 58.2 84.9 76.1 80.8 74.9 67.2 76.0 78.0 73.7 Benin 106.3 97.8 95.2 94.0 93.5 96.3 94.0 94.5 93.1 102.4 96.2 94.2 Botswana 73.3 57.4 57.8 45.5 43.4 48.6 50.3 49.8 48.2 64.7 60.4 47.7 Burkina Faso 107.2 94.8 91.9 93.5 95.0 95.3 96.1 .. .. 102.7 92.4 95.0 Burundi 100.6 105.4 102.5 106.0 107.8 109.7 108.7 111.0 123.1 96.9 105.2 111.1 Cameroon 78.3 79.3 80.8 79.7 81.0 81.0 82.2 81.5 83.8 75.8 81.5 81.5 Cape Verde .. 108.1 117.5 114.2 115.1 115.7 115.8 .. .. 102.2 105.6 115.2 Central African Republic 108.9 100.6 89.0 92.2 88.9 89.7 .. .. .. 101.1 96.3 90.3 Chad .. 107.7 100.2 94.5 94.7 .. 81.9 75.5 64.4 108.1 100.5 82.2 Comoros 110.1 103.2 105.7 105.7 105.2 104.0 105.8 110.6 112.9 104.5 104.5 107.4 Congo, Dem. Rep. 89.9 90.7 90.9 95.5 96.8 96.0 95.0 96.0 94.1 89.1 91.2 95.6 Congo, Rep. 64.3 76.2 59.0 40.7 49.5 49.0 48.7 48.7 41.3 68.1 71.2 46.3 Côte d'Ivoire 79.6 88.7 78.7 82.1 81.0 73.7 79.4 79.5 79.6 80.4 82.2 79.2 Djibouti .. 110.4 102.4 106.5 100.6 95.1 94.8 95.2 92.7 .. 106.4 97.5 Equatorial Guinea .. 120.1 .. 28.0 19.2 21.9 21.3 17.0 13.1 .. 86.3 20.1 Eritrea .. .. 141.2 134.7 127.0 133.7 159.7 161.4 126.8 .. 130.9 140.6 Ethiopia .. 90.4 98.2 93.2 92.6 92.2 93.4 96.3 101.6 89.5 92.5 94.9 Gabon 39.4 63.1 52.3 41.7 48.3 56.3 51.8 46.1 32.8 55.7 56.4 46.2 Gambia, The 94.2 89.3 89.0 91.5 88.0 87.1 88.9 89.5 95.6 93.5 92.6 90.1 Ghana 95.1 94.5 96.7 94.7 92.9 92.3 88.6 92.7 96.6 95.2 92.5 93.0 Guinea .. 77.8 84.5 84.6 85.9 90.5 92.2 92.7 88.9 84.9 81.7 89.1 Guinea-Bissau 101.0 97.2 101.2 108.5 119.3 112.1 98.8 103.0 98.5 100.9 98.5 106.7 Kenya 81.9 81.5 89.3 90.6 88.7 86.9 86.7 87.6 91.0 82.1 84.4 88.6 Lesotho 152.0 152.9 122.6 120.6 116.6 119.8 117.3 114.7 104.8 165.5 138.3 115.6 Liberia 85.2 .. .. .. 103.4 103.3 103.2 100.7 97.6 97.8 .. 101.6 Madagascar 101.4 94.5 92.8 92.3 84.7 92.3 91.1 90.6 91.6 97.1 95.8 90.4 Malawi 89.2 86.6 100.6 96.2 96.2 110.1 110.7 109.1 109.5 87.3 96.6 105.3 Mali 98.9 93.6 90.5 88.0 86.0 88.7 86.7 91.4 90.2 100.4 92.4 88.5 Mauritania 103.5 95.1 101.2 108.6 96.9 101.9 105.0 103.1 115.0 96.9 97.6 105.1 Mauritius 85.5 76.5 76.7 76.1 74.0 74.8 75.2 76.6 81.1 80.0 75.9 76.3 Mozambique 108.9 105.8 86.3 88.4 92.0 89.0 88.3 85.7 88.1 106.2 99.0 88.6 Namibia 61.6 81.8 87.5 86.0 83.0 82.2 73.8 73.3 70.5 89.2 87.3 78.1 Niger 85.4 98.8 96.3 96.5 95.6 94.7 95.0 93.9 90.7 92.7 97.3 94.4 Nigeria 68.6 70.6 80.9 57.7 65.1 74.5 67.9 60.6 60.5 82.5 76.0 64.4 Rwanda 95.8 93.8 100.0 98.7 97.4 100.0 100.8 97.6 98.0 95.0 105.5 98.8 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 97.9 97.6 89.1 88.8 90.6 93.2 91.2 92.0 90.1 95.7 94.6 91.0 Seychelles 72.9 79.7 74.4 77.4 80.9 80.0 78.8 77.0 114.7 75.9 78.3 84.8 Sierra Leone 99.1 91.3 110.3 113.3 111.6 109.4 107.4 101.9 102.3 90.9 97.1 107.6 Somalia 112.9 112.5 .. .. .. .. .. .. .. 106.3 112.5 .. South Africa 62.1 76.8 81.0 81.1 80.8 80.3 81.2 83.2 83.4 71.5 80.6 81.7 Sudan 97.9 .. 92.3 84.1 90.2 86.7 84.3 81.3 86.2 95.0 92.7 85.5 Swaziland 98.8 93.4 99.7 97.0 96.9 80.5 80.1 83.2 86.7 96.3 98.0 87.4 Tanzania .. 98.7 95.5 89.8 91.2 88.2 88.0 88.8 89.1 .. 98.0 89.2 Togo 76.8 85.3 96.8 102.2 99.0 99.4 94.7 95.5 95.1 87.7 93.3 97.6 Uganda 100.4 99.4 92.4 91.9 93.5 95.3 93.7 91.6 92.9 97.7 95.7 93.1 Zambia 80.7 83.4 101.1 91.7 82.7 82.3 81.3 81.8 83.0 86.0 92.9 83.8 Zimbabwe 86.2 82.5 84.0 86.7 88.4 92.9 93.8 95.9 99.4 83.5 83.1 92.8 NORTH AFRICA 58.7 77.2 80.3 75.5 77.0 76.4 73.2 69.7 65.1 70.6 80.0 72.8 Algeria 56.9 72.9 68.4 55.2 58.0 59.1 55.1 52.3 45.6 68.5 69.9 54.2 Egypt, Arab Rep. 84.8 83.9 86.6 87.1 86.6 86.1 85.7 84.4 84.3 84.5 85.8 85.7 Libya 43.1 72.8 82.2 67.1 76.5 73.6 .. .. .. 53.1 82.4 72.4 Morocco 85.1 80.1 81.2 82.9 80.6 80.6 80.1 81.7 82.0 83.3 83.0 81.3 Tunisia 76.0 80.0 75.9 76.3 76.7 78.6 78.8 78.8 79.3 77.3 77.7 78.1 ALL AFRICA 67.2 79.1 82.5 77.5 79.1 79.9 78.1 76.6 74.6 75.0 82.2 77.6 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 35 Table 2.15 Final consumption expenditure per capita Dollars Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 502 469 428 406 398 413 493 584 666 458 458 493 Excluding South Africa 401 326 280 260 275 306 323 358 420 335 291 324 Excl. S. Africa & Nigeria 348 351 288 269 279 311 325 362 414 324 308 327 Angola .. 685 361 384 534 595 749 956 1,383 586 478 767 Benin 403 349 325 295 299 353 422 468 473 313 317 385 Botswana 742 1,523 1,872 1,603 1,483 1,629 2,352 2,766 2,853 781 1,735 2,115 Burkina Faso 314 347 236 215 229 254 324 .. .. 277 252 256 Burundi 224 210 130 116 108 101 92 101 130 215 171 108 Cameroon 603 759 582 540 513 570 711 802 867 692 667 667 Cape Verde .. 1,030 1,558 1,347 1,373 1,509 1,910 .. .. 806 1,189 1,535 Central African Republic 373 499 252 233 224 241 .. .. .. 352 344 233 Chad .. 309 194 159 191 .. 245 353 389 221 235 267 Comoros 406 593 445 395 420 464 596 681 728 394 507 547 Congo, Dem. Rep. 462 224 88 82 89 101 99 113 116 288 154 100 Congo, Rep. 609 859 417 381 390 405 460 545 616 681 593 466 Côte d'Ivoire 971 757 603 512 501 488 620 688 704 681 637 586 Djibouti .. 895 792 822 786 750 778 814 828 .. 849 796 Equatorial Guinea .. 450 .. 782 727 1,007 1,315 1,695 1,963 .. 463 1,248 Eritrea .. .. 284 240 230 218 230 242 279 .. 251 240 Ethiopia .. 213 119 115 111 102 109 131 162 189 158 122 Gabon 2,420 3,926 1,961 1,663 1,755 2,102 2,338 2,427 2,051 2,501 2,637 2,056 Gambia, The 348 303 302 293 271 231 227 243 291 276 315 259 Ghana 374 359 384 237 243 274 319 380 468 340 348 320 Guinea .. 334 354 312 303 330 371 408 315 1,127 380 340 Guinea-Bissau 141 233 171 171 169 156 155 181 187 177 205 170 Kenya 365 299 384 375 367 350 388 423 510 301 312 402 Lesotho 508 591 625 575 488 457 677 841 850 472 687 648 Liberia 435 .. .. .. 178 180 131 143 157 446 .. 158 Madagascar 452 242 219 221 230 237 283 218 248 301 231 240 Malawi 179 172 159 146 140 176 158 165 176 150 180 160 Mali 254 255 206 183 188 240 297 339 354 206 230 267 Mauritania 456 478 471 444 399 417 466 535 688 433 551 492 Mauritius 1,020 1,725 2,778 2,865 2,800 2,813 3,226 3,766 4,103 1,078 2,412 3,262 Mozambique 319 194 196 186 186 195 222 261 304 277 173 226 Namibia 1,354 1,376 1,598 1,549 1,383 1,310 1,662 2,083 2,145 1,474 1,666 1,689 Niger 345 289 171 147 152 163 199 205 221 259 202 181 Nigeria 643 222 245 225 260 283 314 340 446 385 221 311 Rwanda 214 342 258 223 198 201 194 202 233 272 283 208 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 575 700 455 403 417 458 559 642 665 521 550 524 Seychelles 1,667 4,196 5,762 5,864 6,166 6,672 6,718 6,442 9,806 2,170 5,142 6,944 Sierra Leone 337 145 167 159 192 209 208 205 221 250 181 199 Somalia 105 155 .. .. .. .. .. .. .. 136 155 .. South Africa 1,818 2,444 2,514 2,450 2,137 1,964 2,951 3,885 4,306 2,094 2,782 2,949 Sudan 373 .. 306 316 359 380 430 497 663 488 314 441 Swaziland 949 1,070 1,347 1,288 1,144 881 1,381 1,770 2,003 812 1,299 1,411 Tanzania .. 160 242 234 243 238 245 268 293 .. 188 253 Togo 314 351 294 253 238 258 285 329 326 270 303 282 Uganda 99 241 235 224 212 214 218 224 281 231 220 229 Zambia 518 328 302 277 276 274 311 386 517 397 330 340 Zimbabwe 788 686 401 509 714 1,591 539 349 261 700 531 661 NORTH AFRICA 846 1,133 1,312 1,301 1,276 1,166 1,209 1,262 1,335 971 1,159 1,258 Algeria 1,281 1,789 1,107 992 1,036 1,075 1,177 1,374 1,413 1,697 1,217 1,178 Egypt, Arab Rep. 443 650 1,190 1,292 1,233 1,082 997 916 1,021 540 843 1,090 Libya 5,040 4,857 4,814 4,360 4,241 2,559 .. .. .. 5,044 5,142 3,720 Morocco 832 865 1,040 992 971 1,021 1,217 1,370 1,403 659 1,001 1,162 Tunisia 1,041 1,205 1,670 1,551 1,584 1,691 2,001 2,231 2,267 961 1,467 1,888 ALL AFRICA 573 592 583 562 550 543 617 703 784 557 585 626 a. Provisional. 36 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.16 Agriculture value added Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 35,543 42,936 56,625 57,696 60,060 61,389 63,115 65,992 69,775 2.2 3.2 3.7 Excluding South Africa 32,459 39,215 52,846 53,736 56,236 57,310 59,150 62,144 65,723 2.1 3.5 3.9 Excl. S. Africa & Nigeria 25,114 30,774 41,452 42,006 44,064 44,612 45,552 47,614 49,916 2.0 3.5 3.2 Angola .. 686 473 517 610 684 767 875 1,024 .. -2.9 14.1 Benin 316 467 774 824 849 911 932 985 1,028 5.2 5.8 4.6 Botswana 132 155 139 139 144 143 147 141 130 1.8 -0.9 -1.1 Burkina Faso 410 531 850 881 848 919 919 .. .. 3.6 4.3 2.1 Burundi 218 300 269 255 247 257 248 247 231 3.1 -1.9 -1.5 Cameroon 1,023 1,298 1,973 2,062 2,139 2,218 2,295 2,395 2,460 2.5 5.3 3.7 Cape Verde .. 50 59 64 64 60 63 .. .. .. 4.2 -0.8 Central African Republic 298 332 450 478 498 517 536 533 547 1.7 3.6 2.6 Chad 306 321 578 563 620 617 648 612 694 3.1 5.5 3.1 Comoros 51 75 90 98 104 109 113 113 118 4.0 2.2 3.5 Congo, Dem. Rep. 1,565 2,011 2,407 2,126 2,043 2,053 2,078 2,090 2,150 2.5 2.2 0.4 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 1,628 1,756 2,216 2,400 2,407 2,351 2,379 2,474 2,438 -0.4 3.2 0.5 Djibouti .. 18 17 17 18 18 19 19 20 .. -1.1 3.6 Equatorial Guinea .. .. .. 121 112 106 102 103 105 .. .. -2.9 Eritrea .. .. 157 89 115 80 89 95 103 .. 5.4 0.8 Ethiopia .. 2,891 3,385 3,494 3,858 3,776 3,345 3,925 4,449 0.1 2.3 3.3 Gabon 236 252 299 315 324 309 313 317 328 1.5 1.9 0.4 Gambia, The 87 89 120 133 145 104 124 138 145 1.2 2.2 1.4 Ghana 1,226 1,268 1,716 1,756 1,820 1,895 1,993 2,133 2,221 0.9 3.3 5.0 Guinea .. 395 594 593 636 668 687 709 731 .. 4.4 4.1 Guinea-Bissau 45 79 108 112 114 113 121 128 136 5.0 4.3 4.0 Kenya 2,192 3,138 3,696 3,649 4,075 3,934 4,031 4,100 4,377 3.2 1.8 2.8 Lesotho 92 122 126 138 126 118 118 116 116 2.1 1.8 -3.2 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 696 860 1,015 1,026 1,068 1,054 1,067 1,100 1,133 2.4 1.8 1.7 Malawi 263 302 590 622 585 600 636 653 594 2.1 8.5 0.5 Mali 656 792 1,046 938 1,043 1,005 1,183 1,128 1,214 2.6 3.1 4.9 Mauritania 239 283 303 277 266 241 252 233 251 2.0 0.6 -2.4 Mauritius 224 274 300 230 304 318 269 278 288 3.1 0.7 2.0 Mozambique .. 698 1,000 887 971 1,076 1,173 1,271 1,292 7.3 5.5 8.3 Namibia 179 223 312 338 304 334 347 336 354 1.3 3.5 1.6 Niger 479 537 743 680 770 785 832 .. .. 1.8 3.6 6.4 Nigeria 7,011 8,447 11,399 11,730 12,176 12,692 13,513 14,392 15,572 2.9 3.3 5.8 Rwanda 535 558 687 750 812 930 901 901 951 0.8 1.5 4.3 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 506 602 773 791 801 623 751 771 843 2.5 2.1 1.1 Seychelles 18 17 17 17 17 17 15 15 15 -1.7 -0.5 -2.9 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 3,073 3,692 3,777 3,956 3,827 4,077 3,991 3,923 4,133 2.9 0.7 0.8 Sudan 1,713 2,144 4,653 4,963 5,239 5,633 .. .. .. 2.3 9.1 .. Swaziland 126 144 156 150 136 138 142 144 147 2.0 1.0 0.3 Tanzania .. 2,767 3,650 3,773 3,980 4,178 4,346 4,597 4,834 .. 3.2 5.0 Togo 220 342 477 455 461 494 489 505 524 5.7 4.5 2.8 Uganda .. 1,401 1,908 2,014 2,111 2,193 2,243 2,361 2,481 1.5 3.6 4.1 Zambia 371 471 634 644 627 616 647 675 671 4.1 4.3 1.4 Zimbabwe 599 858 1,137 1,174 1,128 872 863 838 754 2.8 4.2 -8.5 NORTH AFRICA 17,435 23,679 29,283 28,631 31,174 31,791 35,372 36,251 35,444 4.0 2.4 4.8 Algeria 2,459 3,246 4,842 4,600 5,210 5,144 6,158 6,349 6,469 4.3 4.0 7.3 Egypt, Arab Rep. 8,751 11,474 15,003 15,513 16,088 16,667 17,478 17,723 18,301 2.7 3.1 3.4 Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 4,094 5,925 5,472 4,610 5,882 6,212 7,330 7,469 6,147 7.0 0.1 6.9 Tunisia 1,213 1,787 2,424 2,399 2,351 2,093 2,542 2,800 2,660 2.0 2.1 3.6 ALL AFRICA 52,868 66,572 85,886 86,326 91,207 93,153 98,585 102,295 105,077 2.8 2.9 4.0 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 37 Table 2.17 Industry value added Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 83,896 92,618 102,894 106,656 110,668 112,443 119,788 127,192 134,514 0.9 1.5 4.8 Excluding South Africa 51,217 57,141 66,533 68,388 71,695 72,407 79,658 85,331 91,048 1.1 1.9 6.0 Excl. S. Africa & Nigeria .. 36,718 44,239 44,663 47,380 50,180 52,056 56,555 60,919 4.0 2.5 6.2 Angola .. 4,861 6,359 6,584 6,853 7,850 8,146 9,014 11,126 .. 3.9 10.5 Benin 115 203 287 313 341 362 372 370 387 3.3 4.1 3.8 Botswana 619 1,905 3,037 3,406 3,584 3,706 3,880 4,187 4,394 11.8 5.0 5.2 Burkina Faso 252 333 398 422 457 460 460 .. .. 4.2 2.2 2.7 Burundi 98 159 127 119 111 104 98 92 86 4.6 ­4.7 ­6.2 Cameroon 1,977 3,710 3,196 3,355 3,389 3,416 3,434 3,427 3,365 7.5 ­2.0 0.2 Cape Verde .. 59 97 95 95 108 114 .. .. .. 5.1 7.1 Central African Republic 146 157 159 173 180 187 200 205 210 1.6 0.2 4.2 Chad 61 166 153 151 172 214 341 833 875 7.9 0.5 49.1 Comoros 19 15 23 23 24 25 28 27 25 ­2.3 4.6 2.5 Congo, Dem. Rep. 2,497 2,288 1,061 863 840 909 1,025 1,162 1,265 2.3 ­8.9 9.0 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 1,227 1,575 2,573 2,288 2,214 2,117 1,960 2,034 2,023 5.1 6.6 ­2.7 Djibouti .. 127 71 74 78 81 83 90 95 .. ­6.2 5.1 Equatorial Guinea .. .. .. 1,055 1,829 2,205 2,472 3,320 3,543 .. .. 25.5 Eritrea .. .. 145 135 144 155 158 158 .. .. 18.3 4.1 Ethiopia .. 837 894 942 990 1,072 1,105 1,215 1,313 4.5 3.5 6.8 Gabon 2,276 2,951 3,146 2,851 2,875 2,913 3,035 3,075 3,129 0.4 2.8 2.0 Gambia, The 28 42 46 48 51 56 60 62 64 4.3 0.7 5.9 Ghana 866 962 1,208 1,264 1,325 1,409 1,418 1,483 1,612 2.6 2.3 4.6 Guinea .. 623 918 951 1,002 1,049 1,053 1,086 1,127 .. 4.8 3.2 Guinea-Bissau 24 34 25 26 28 31 30 30 32 1.3 ­2.5 3.6 Kenya 1,189 1,752 1,943 1,908 2,013 2,060 2,187 2,276 2,380 3.7 1.5 4.5 Lesotho 117 186 303 320 335 353 360 381 413 4.1 5.4 4.9 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 450 389 467 500 537 426 488 520 533 0.7 2.0 1.0 Malawi 175 236 285 282 252 248 261 288 333 2.4 2.0 3.8 Mali 166 248 443 464 561 661 599 597 646 4.7 6.4 5.1 Mauritania 155 218 295 298 284 288 300 337 330 5.4 3.3 3.1 Mauritius 319 704 1,158 1,208 1,294 1,328 1,335 1,356 1,335 9.0 5.5 1.9 Mozambique .. 307 817 906 1,095 1,203 1,324 1,392 1,499 ­4.5 11.9 10.0 Namibia 689 670 861 873 905 975 1,021 1,170 1,173 ­0.3 2.5 6.8 Niger 306 275 313 319 327 337 350 .. .. ­2.0 1.8 3.1 Nigeria 22,498 20,202 22,169 23,522 24,141 22,207 27,172 28,433 29,849 ­2.1 0.9 5.5 Rwanda 459 566 358 371 399 431 450 460 493 2.8 ­5.5 5.6 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 489 661 921 960 1,008 1,066 1,128 1,198 1,243 3.3 3.7 5.5 Seychelles 44 65 158 178 177 187 165 171 167 3.8 11.9 ­1.5 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 32,864 35,440 36,511 38,387 39,132 40,177 40,507 42,338 44,053 0.5 0.9 2.7 Sudan .. 1,431 2,558 2,523 2,904 3,122 .. .. .. 1.7 6.0 .. Swaziland 107 313 427 432 444 451 460 468 476 11.8 3.9 1.9 Tanzania .. 967 1,232 1,319 1,410 1,542 1,702 1,871 2,070 .. 2.5 9.6 Togo 216 212 228 237 230 255 291 312 332 0.7 1.7 8.1 Uganda .. 381 1,040 1,095 1,168 1,260 1,351 1,426 1,571 4.4 12.6 7.3 Zambia 886 1,018 709 729 796 873 949 1,050 1,153 0.7 ­4.4 9.6 Zimbabwe 1,235 1,714 1,770 1,586 1,456 1,296 1,108 1,069 944 2.9 1.1 ­10.0 NORTH AFRICA 47,958 64,915 88,156 92,250 94,263 97,787 103,171 107,222 112,842 2.9 3.2 4.2 Algeria 19,854 25,580 29,102 30,360 30,260 31,578 33,802 35,165 37,114 2.9 1.4 4.4 Egypt, Arab Rep. 11,983 17,227 29,112 30,702 31,679 32,838 34,757 35,923 37,781 3.2 5.1 4.3 Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 5,868 8,041 10,347 10,644 11,172 11,472 11,765 12,346 13,087 2.7 3.1 4.0 Tunisia 2,462 3,521 5,275 5,558 5,839 6,013 6,086 6,370 6,529 2.9 4.5 3.1 ALL AFRICA 133,666 159,309 191,911 199,738 205,904 211,068 223,911 235,554 248,591 1.7 2.2 4.5 a. Provisional. 38 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.18 Services value added Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 94,383 121,788 148,351 153,749 159,395 165,905 172,692 180,674 189,788 2.6 2.4 4.3 Excluding South Africa 46,124 60,542 72,954 75,284 78,082 81,180 84,311 88,103 92,102 2.7 2.2 4.1 Excl. S. Africa & Nigeria 41,542 53,976 64,338 65,954 68,442 70,918 73,293 76,285 79,394 2.7 2.1 3.7 Angola .. 2,556 2,035 2,028 1,935 2,233 2,194 2,442 2,792 .. ­2.8 6.7 Benin 679 752 1,073 1,118 1,178 1,195 1,266 1,292 1,304 1.1 4.0 3.2 Botswana 295 1,086 2,121 2,233 2,379 2,557 2,718 2,822 2,924 14.8 8.0 5.6 Burkina Faso 598 877 1,311 1,298 1,439 1,490 1,671 .. .. 3.9 4.6 8.2 Burundi 163 298 242 258 304 338 346 396 438 5.5 ­3.4 10.4 Cameroon 3,345 4,022 3,767 3,899 4,186 4,494 4,827 5,130 5,340 3.5 0.0 6.7 Cape Verde .. 196 342 373 393 407 433 .. .. .. 6.4 5.0 Central African Republic 236 254 233 215 204 173 112 121 121 1.3 ­0.3 ­12.9 Chad 313 537 612 617 690 758 815 878 963 7.4 0.6 9.0 Comoros 66 91 86 81 80 83 82 82 88 3.3 ­0.5 1.4 Congo, Dem. Rep. 3,932 4,607 1,328 1,265 1,250 1,294 1,376 1,497 1,631 2.3 ­13.0 5.5 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 4,857 4,958 6,043 5,738 5,811 5,796 5,741 5,729 5,749 ­0.1 2.5 ­0.1 Djibouti .. 431 392 390 401 406 414 429 438 .. ­1.5 2.3 Equatorial Guinea .. .. .. 52 67 82 93 100 118 .. .. 16.7 Eritrea .. .. 364 365 382 383 417 416 .. .. 6.9 3.5 Ethiopia .. 2,221 2,682 2,934 3,048 3,128 3,263 3,494 3,777 3.6 4.0 5.0 Gabon 1,190 1,325 1,824 1,902 1,961 1,945 1,968 1,995 2,077 0.3 3.2 1.4 Gambia, The 100 136 185 190 203 221 227 232 .. 2.5 3.9 5.4 Ghana 672 1,115 1,865 1,952 2,031 2,120 2,256 2,362 2,521 5.3 5.9 5.3 Guinea .. 1,006 1,356 1,382 1,410 1,438 1,460 1,495 1,518 .. 3.7 1.9 Guinea-Bissau 38 59 54 61 60 67 63 63 63 3.1 ­0.7 0.7 Kenya 2,625 4,235 5,613 5,718 5,710 5,866 6,019 6,322 6,618 4.9 3.4 3.1 Lesotho 149 218 317 314 325 332 348 363 375 3.9 4.8 3.7 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 1,638 1,635 1,896 1,987 2,108 1,773 1,959 2,078 2,198 ­0.2 2.0 1.6 Malawi 445 592 685 669 671 691 701 738 785 3.2 1.9 3.2 Mali 531 636 819 854 905 911 994 1,084 1,144 2.1 2.8 6.2 Mauritania 262 263 387 427 474 508 538 563 602 0.2 4.5 6.8 Mauritius 780 1,319 2,289 2,433 2,587 2,719 2,874 3,049 3,249 4.8 6.4 5.9 Mozambique .. 1,079 1,520 1,611 1,831 1,936 2,024 2,203 2,398 7.2 3.4 7.7 Namibia 775 1,185 1,797 1,869 1,942 2,064 2,135 2,223 2,366 3.7 4.6 4.7 Niger 738 695 768 798 828 862 889 .. .. ­1.3 1.6 3.7 Nigeria 4,512 6,618 8,662 9,367 9,680 10,298 11,024 11,785 12,626 2.7 3.1 6.3 Rwanda 500 712 666 690 721 748 784 860 911 3.9 ­2.5 5.8 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 1,395 1,774 2,294 2,382 2,453 2,577 2,697 2,868 3,042 2.3 2.7 5.1 Seychelles 228 310 412 419 408 405 392 370 381 3.3 2.6 ­2.3 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 48,244 61,244 75,379 78,441 81,288 84,698 88,338 92,515 97,573 2.4 2.6 4.4 Sudan .. 3,072 4,024 4,377 4,530 4,713 .. .. .. 4.3 2.2 .. Swaziland 165 264 367 383 396 414 432 439 444 4.7 3.5 3.1 Tanzania .. 2,444 3,100 3,286 3,467 3,682 3,887 4,131 4,416 .. 2.4 6.1 Togo 539 516 633 637 635 627 631 634 608 ­0.5 3.7 ­0.7 Uganda .. 1,065 2,147 2,285 2,474 2,672 2,841 3,022 3,293 2.1 8.3 7.4 Zambia 1,250 1,159 1,449 1,512 1,585 1,647 1,726 1,763 1,850 0.0 2.0 4.0 Zimbabwe 1,993 2,893 3,856 3,588 3,563 3,692 3,294 2,836 2,019 2.8 3.4 ­10.0 NORTH AFRICA 45,353 77,021 101,057 106,243 110,280 114,034 116,668 123,500 130,498 5.6 3.3 4.1 Algeria 10,412 14,452 16,640 16,843 17,492 18,455 19,118 20,464 21,640 3.3 1.8 5.2 Egypt, Arab Rep. 15,596 32,762 43,441 46,451 48,080 49,413 49,673 52,619 55,561 8.2 3.7 3.4 Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 8,702 13,209 17,386 18,080 18,588 19,122 19,887 20,790 21,789 4.2 2.7 3.8 Tunisia 4,930 6,896 10,860 11,486 12,229 12,680 13,293 14,070 15,070 3.7 5.3 5.4 ALL AFRICA 140,632 199,330 249,513 259,990 269,664 279,982 289,519 304,275 320,358 3.6 2.7 4.2 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 39 Table 2.19 Gross fixed capital formation Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 22.2 17.1 18.3 18.0 18.7 18.4 19.5 19.5 19.4 18.9 17.7 18.9 Excluding South Africa 19.0 16.8 19.7 19.3 20.6 19.6 21.1 20.9 20.2 16.6 18.5 20.3 Excl. S. Africa & Nigeria 17.6 17.2 18.9 19.0 19.6 17.9 20.4 20.5 19.8 16.4 18.2 19.5 Angola .. 11.7 27.1 15.1 13.4 12.6 12.7 9.1 7.5 14.8 19.9 11.7 Benin 15.2 14.2 17.5 18.9 19.2 17.7 18.8 18.2 19.6 15.1 16.3 18.7 Botswana 40.1 37.4 32.6 35.5 40.4 39.9 40.5 38.9 30.7 30.0 29.9 37.6 Burkina Faso 15.1 18.2 23.8 22.7 18.9 17.8 18.7 .. .. 17.4 21.7 19.5 Burundi 13.9 14.5 5.9 6.1 6.2 6.4 10.6 13.3 10.8 16.6 9.1 8.9 Cameroon 21.0 17.8 14.9 16.7 20.3 19.8 17.5 18.9 18.1 23.8 14.8 18.6 Cape Verde .. 22.9 20.9 19.7 18.3 20.9 18.7 .. .. 26.9 29.6 19.4 Central African Republic 7.0 12.3 14.4 10.8 14.0 14.8 .. .. .. 10.9 11.4 13.2 Chad .. 6.8 13.7 23.3 40.3 60.2 52.4 24.3 20.2 6.3 13.1 36.8 Comoros 33.2 19.7 14.9 10.1 10.1 11.0 10.3 9.4 9.3 28.8 18.1 10.0 Congo, Dem. Rep. 10.0 9.1 3.1 3.5 5.2 9.0 12.2 12.8 14.2 11.7 7.6 9.5 Congo, Rep. 35.8 15.9 27.8 22.6 26.4 23.4 25.7 24.2 22.4 32.5 25.9 24.1 Côte d'Ivoire 26.5 6.7 13.1 10.8 11.2 10.1 10.1 10.8 10.3 16.5 11.3 10.5 Djibouti .. 14.1 8.6 8.8 7.9 10.0 14.3 22.0 24.3 .. 11.1 14.5 Equatorial Guinea .. 17.4 .. 58.8 71.5 31.3 58.3 44.5 37.7 .. 59.5 50.4 Eritrea .. .. 36.0 31.9 28.7 26.0 25.4 22.8 20.1 .. 25.0 25.8 Ethiopia .. 12.9 14.4 19.2 19.5 22.4 21.6 21.4 20.5 15.7 14.4 20.8 Gabon 27.5 21.7 26.2 21.9 25.7 24.5 24.0 24.5 22.7 34.6 26.0 23.9 Gambia, The 26.7 22.3 17.8 17.4 17.4 21.2 20.3 28.1 25.0 19.7 20.1 21.6 Ghana 5.6 14.4 20.9 23.9 26.7 19.7 23.2 28.4 29.0 7.8 19.9 25.2 Guinea .. 24.5 19.8 19.7 15.4 13.5 10.2 11.3 13.8 16.4 21.3 14.0 Guinea-Bissau 28.2 29.9 16.8 11.3 15.0 9.6 12.6 13.2 14.6 32.0 25.9 12.7 Kenya 24.5 24.2 15.5 17.4 19.3 16.7 17.9 18.2 16.4 22.7 18.3 17.6 Lesotho 37.0 52.7 49.0 42.6 40.8 41.6 41.2 35.9 35.1 39.9 56.5 39.5 Liberia .. .. .. .. 4.9 4.7 9.4 13.2 16.5 .. .. 9.7 Madagascar 15.0 17.0 14.9 15.0 18.5 14.3 17.9 24.3 22.5 10.6 12.4 18.8 Malawi 24.7 23.0 14.7 13.6 14.9 11.4 11.8 15.3 15.3 19.4 17.7 13.7 Mali 15.5 23.0 21.2 24.6 31.0 18.6 24.2 21.0 22.7 17.2 22.5 23.7 Mauritania 26.3 20.0 12.5 19.4 22.0 21.1 25.9 46.4 44.8 27.5 13.6 29.9 Mauritius 25.4 30.7 25.5 25.9 23.3 21.4 22.7 24.1 23.3 23.5 28.4 23.4 Mozambique 7.6 22.1 36.7 33.5 25.9 29.8 27.4 22.6 21.7 12.2 25.2 26.8 Namibia 30.6 33.7 23.3 19.5 23.4 19.7 29.8 25.5 27.3 18.4 22.6 24.2 Niger 28.1 8.1 10.2 11.4 12.1 14.2 14.2 16.4 18.5 15.3 8.9 14.5 Nigeria 21.3 14.7 23.4 20.3 24.1 26.2 23.9 22.3 21.3 16.5 19.8 23.0 Rwanda 16.1 14.6 17.2 17.5 18.4 16.9 18.4 20.5 22.4 15.3 14.5 19.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 16.6 9.1 17.9 20.5 18.4 17.2 21.0 21.1 25.6 16.5 12.5 20.6 Seychelles 38.3 24.6 43.3 25.2 40.3 25.6 10.0 14.7 12.3 26.4 30.3 21.4 Sierra Leone 16.2 10.0 5.4 8.0 6.7 10.1 13.9 10.7 17.4 12.2 7.4 11.1 Somalia 42.4 15.5 .. .. .. .. .. .. .. 28.8 15.5 .. South Africa 29.9 17.7 16.4 15.9 15.3 16.1 16.9 17.5 18.0 23.4 16.7 16.6 Sudan 14.7 .. 16.8 18.3 17.6 19.5 20.0 22.5 23.6 14.4 15.7 20.2 Swaziland 40.7 19.1 18.7 18.6 18.4 19.8 18.0 18.4 18.0 27.2 21.1 18.5 Tanzania .. 26.1 15.5 17.6 17.0 19.2 18.7 18.3 18.2 .. 21.3 18.2 Togo 28.4 26.6 13.3 17.8 20.4 18.5 18.9 18.0 18.4 19.5 16.3 18.7 Uganda 6.2 12.7 19.5 20.0 18.6 19.3 20.5 22.3 21.2 8.5 16.1 20.3 Zambia 23.3 17.3 17.6 18.7 20.0 23.0 26.1 26.0 25.8 16.1 14.1 23.3 Zimbabwe 16.9 17.4 14.4 13.6 10.3 8.0 11.4 14.2 16.8 17.3 19.5 12.4 NORTH AFRICA 26.6 26.4 22.4 21.1 21.1 22.7 22.5 23.7 22.6 27.4 22.5 22.3 Algeria 39.1 28.6 28.5 25.0 27.3 31.2 30.5 33.3 30.1 33.9 28.5 29.6 Egypt, Arab Rep. 27.5 28.8 21.6 19.6 18.3 18.3 16.9 16.9 18.0 28.6 20.9 18.0 Libya 22.1 18.6 11.2 13.1 12.2 15.0 .. .. .. 26.5 14.0 13.5 Morocco 24.2 25.3 23.1 23.6 22.9 22.7 24.1 25.0 25.9 24.1 22.1 24.0 Tunisia 29.4 27.1 26.3 27.3 27.9 25.7 25.1 24.2 23.4 28.8 26.6 25.6 ALL AFRICA 23.5 20.6 20.0 19.2 19.7 20.1 20.6 21.0 20.5 21.9 19.5 20.2 a. Provisional. 40 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.20 General government fixed capital formation Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA .. .. 4.6 4.5 5.7 5.1 5.0 5.1 5.2 .. 4.3 5.1 Excluding South Africa .. .. 6.7 6.5 7.8 6.5 7.0 7.2 7.2 .. 6.3 7.0 Excl. S. Africa & Nigeria .. 5.6 5.9 5.6 6.1 5.7 6.2 6.6 6.5 5.7 5.8 6.1 Angola .. .. 12.8 6.1 6.4 6.8 7.6 4.9 4.7 .. 7.8 6.1 Benin .. 7.4 6.3 7.6 7.8 6.6 6.1 5.4 6.7 9.1 7.5 6.7 Botswana 0.0 12.7 11.8 10.7 9.3 10.4 11.1 10.4 9.2 0.0 12.4 10.2 Burkina Faso .. 3.9 14.9 12.1 8.2 7.2 7.5 .. .. 6.7 9.5 8.7 Burundi 12.8 12.5 5.4 5.4 3.7 4.6 8.3 10.7 8.8 13.8 9.3 6.9 Cameroon 4.4 5.5 2.4 2.1 2.2 2.3 2.3 2.6 3.1 6.9 2.9 2.4 Cape Verde .. 10.3 6.5 12.5 10.8 13.0 9.8 .. .. 19.3 20.3 11.5 Central African Republic 3.7 4.7 6.8 7.1 7.4 7.6 .. .. .. 5.5 6.2 7.4 Chad .. .. 9.6 10.5 8.8 10.1 12.5 7.8 7.0 3.8 7.4 9.5 Comoros 23.2 5.2 5.4 3.9 4.4 5.8 5.4 4.4 4.5 18.7 7.0 4.7 Congo, Dem. Rep. 5.1 4.0 1.1 0.5 0.1 1.0 2.7 2.8 3.7 4.4 1.7 1.8 Congo, Rep. .. 5.6 6.1 7.0 10.0 8.7 6.5 7.0 5.4 11.1 6.4 7.4 Côte d'Ivoire 11.4 3.6 4.2 2.8 1.9 3.2 2.7 2.8 1.9 7.1 5.6 2.5 Djibouti .. 9.1 3.1 2.7 2.5 4.5 6.7 7.7 9.3 .. 6.1 5.6 Equatorial Guinea .. 10.5 .. 5.1 7.4 8.4 9.8 14.0 10.1 .. 6.9 9.1 Eritrea .. .. 30.3 26.8 23.5 21.7 17.7 17.2 15.4 .. 16.4 20.4 Ethiopia .. 4.0 6.9 7.3 8.6 10.7 9.4 9.0 9.1 4.9 4.8 9.0 Gabon 5.3 3.9 4.1 2.9 4.7 4.0 3.7 4.2 4.2 6.7 6.5 4.0 Gambia, The .. 7.4 4.7 4.6 11.2 7.9 5.7 10.9 9.0 10.4 7.8 8.2 Ghana .. 7.5 8.7 9.2 12.8 6.1 9.2 12.4 12.0 6.3 11.0 10.3 Guinea .. 9.7 4.9 4.9 4.9 4.0 4.4 3.9 3.3 7.5 6.1 4.2 Guinea-Bissau .. 27.4 10.8 10.3 14.8 9.0 13.1 19.9 10.4 33.3 20.2 12.9 Kenya 0.0 9.7 4.5 4.6 6.5 6.3 5.8 5.8 6.5 0.8 7.0 5.9 Lesotho 9.9 23.0 8.8 8.0 10.5 11.2 8.7 7.6 7.8 15.4 16.2 9.0 Liberia .. .. .. .. 0.0 0.0 0.0 0.0 0.0 .. .. 0.0 Madagascar .. 7.9 6.9 6.7 7.3 4.8 7.8 12.5 10.3 6.9 6.9 8.2 Malawi 17.5 7.7 10.3 10.0 10.3 7.7 9.3 12.6 11.0 9.5 9.2 10.2 Mali .. 10.5 9.4 8.6 7.0 7.0 6.9 7.5 7.7 10.2 10.1 7.5 Mauritania .. 6.2 .. .. .. .. .. .. .. 7.6 5.0 .. Mauritius 9.1 4.6 3.4 7.8 6.8 7.0 7.9 7.7 6.6 6.0 3.7 7.3 Mozambique 7.6 12.0 11.6 10.4 15.4 12.5 11.7 9.4 8.1 9.5 11.7 11.3 Namibia 15.7 8.2 11.0 6.1 8.7 6.2 7.0 7.2 7.5 10.7 8.2 7.1 Niger 20.4 7.4 6.4 6.6 7.1 8.8 8.3 9.3 9.8 11.2 5.6 8.3 Nigeria .. .. 10.4 9.6 13.8 10.0 9.7 9.1 9.3 .. 8.7 10.2 Rwanda 12.2 5.9 6.3 6.0 6.6 4.9 5.6 8.5 10.1 12.1 7.2 7.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 4.7 4.1 6.0 4.5 5.1 5.7 6.2 6.7 10.0 3.7 4.5 6.4 Seychelles .. 8.2 1.4 13.8 25.2 9.4 2.2 3.8 5.3 12.0 9.9 9.9 Sierra Leone 5.3 3.9 2.4 6.3 4.4 4.4 4.8 4.6 5.8 4.0 3.3 5.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 0.0 0.0 1.5 1.4 1.9 1.9 2.0 2.0 2.0 0.0 0.3 1.9 Sudan 6.9 .. 1.2 2.3 2.3 3.0 2.9 5.0 5.4 4.3 0.7 3.5 Swaziland 11.9 5.7 6.7 6.3 8.0 7.4 5.7 8.2 8.5 8.0 6.6 7.3 Tanzania .. 10.5 3.1 6.0 5.6 7.6 7.4 7.3 7.2 .. 5.8 6.9 Togo 20.2 7.3 3.1 3.0 2.3 1.4 3.7 5.3 4.2 11.2 3.7 3.3 Uganda .. 6.2 5.4 6.4 5.8 5.3 4.7 5.2 4.6 4.4 5.6 5.3 Zambia .. 6.2 10.6 10.0 11.9 11.8 11.5 9.2 8.9 .. 6.8 10.5 Zimbabwe 1.8 3.4 1.7 0.7 2.1 2.1 2.1 5.1 1.5 2.9 2.9 2.3 NORTH AFRICA .. 9.2 8.6 8.6 8.4 9.3 8.8 8.7 8.4 11.8 8.6 8.7 Algeria 11.0 8.2 5.8 7.8 8.4 10.0 10.8 10.5 9.8 13.8 7.2 9.6 Egypt, Arab Rep. .. 14.7 10.9 9.9 8.7 9.4 8.5 8.7 8.8 16.9 12.0 9.0 Libya 19.4 .. .. .. .. .. .. .. .. 19.4 .. .. Morocco .. 4.8 4.3 4.7 5.1 4.2 3.9 3.9 3.7 7.1 4.3 4.2 Tunisia 15.0 8.7 12.5 12.3 .. .. .. .. .. 14.1 11.5 12.3 ALL AFRICA .. 6.4 6.1 6.1 6.8 6.6 6.4 6.3 6.3 7.7 5.8 6.4 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 41 Table 2.21 Private sector fixed capital formation Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 12.3 12.6 13.1 12.5 12.3 12.7 13.4 13.2 13.3 13.3 13.2 12.9 Excluding South Africa .. 8.6 12.5 11.8 11.9 12.6 13.1 12.5 12.2 8.3 11.2 12.3 Excl. S. Africa & Nigeria .. 9.5 12.4 12.1 12.4 11.7 12.8 12.2 12.3 8.7 11.3 12.2 Angola .. 1.7 16.0 8.9 7.1 5.8 5.1 4.2 2.8 9.2 16.5 5.6 Benin .. 6.0 11.2 11.3 11.4 11.6 12.0 12.1 12.2 4.5 8.3 11.8 Botswana 34.5 19.7 13.2 11.0 11.5 11.6 10.3 9.8 9.7 29.0 14.8 10.6 Burkina Faso .. 13.6 8.9 10.6 10.8 10.6 11.2 .. .. 11.5 12.2 10.8 Burundi 1.1 2.7 0.4 0.8 2.5 1.5 2.3 2.3 1.7 2.3 ­0.3 1.9 Cameroon 15.6 11.9 12.3 13.9 18.1 17.5 15.8 15.7 14.7 14.2 11.7 15.9 Cape Verde .. 12.6 14.4 7.2 7.5 7.9 8.9 .. .. 7.6 9.3 7.9 Central African Republic 3.2 6.7 7.7 3.7 6.6 7.2 .. .. .. 4.7 5.0 5.9 Chad .. .. 7.4 10.5 27.8 49.6 36.3 14.9 12.0 0.6 4.3 25.2 Comoros 5.3 6.7 6.5 6.2 5.6 5.2 4.9 5.0 4.8 5.5 7.7 5.3 Congo, Dem. Rep. 3.7 8.9 2.0 3.0 5.1 8.0 9.5 10.0 10.5 7.1 6.3 7.7 Congo, Rep. .. 11.6 20.4 14.0 16.2 13.8 18.6 16.6 16.6 11.4 18.5 16.0 Côte d'Ivoire 13.0 4.9 10.2 7.5 10.6 6.0 7.8 7.1 8.4 8.7 6.2 7.9 Djibouti .. 5.1 5.6 6.1 5.4 5.6 7.6 14.3 15.0 .. 5.8 9.0 Equatorial Guinea .. 6.9 .. 53.7 64.1 22.9 48.5 30.5 27.6 .. 52.6 41.2 Eritrea .. .. 5.6 5.1 5.2 4.3 7.7 5.6 4.7 .. 8.6 5.4 Ethiopia .. 8.9 7.5 11.8 10.9 11.7 12.2 12.4 11.4 12.8 9.6 11.7 Gabon 21.4 17.6 22.0 19.0 21.0 20.5 20.2 20.3 18.5 27.2 18.9 19.9 Gambia, The .. 14.9 13.1 12.8 6.2 13.3 13.5 13.9 18.5 8.6 12.3 13.0 Ghana .. 6.9 12.0 14.8 13.8 13.6 14.0 16.0 17.0 3.8 8.7 14.9 Guinea .. 8.8 14.0 14.0 9.6 9.2 5.7 7.3 10.4 8.9 11.7 9.4 Guinea-Bissau .. 8.4 6.0 1.0 0.2 0.6 ­0.5 ­6.7 4.2 10.0 7.7 ­0.2 Kenya 8.2 10.9 11.1 12.2 11.6 11.2 10.3 10.5 11.6 10.7 10.6 11.2 Lesotho 25.7 29.7 39.2 36.9 32.9 32.3 31.8 28.2 27.1 24.0 40.8 31.5 Liberia .. .. .. .. 2.0 2.2 4.8 4.2 4.3 .. .. 3.5 Madagascar .. 6.9 8.0 8.3 11.2 9.5 10.1 11.8 12.3 3.6 5.5 10.5 Malawi 4.7 12.4 2.4 2.3 3.5 2.7 1.5 1.9 3.4 6.3 6.0 2.6 Mali .. 12.4 11.8 15.9 24.0 11.6 17.3 13.5 15.0 9.9 12.4 16.2 Mauritania .. 13.7 .. .. .. .. .. .. .. 19.0 13.9 .. Mauritius 15.1 23.7 21.6 17.5 16.3 15.3 14.3 14.5 14.8 15.1 23.4 15.4 Mozambique 0.0 10.1 25.1 23.2 10.5 17.3 15.7 13.2 13.6 2.7 13.6 15.6 Namibia 11.4 13.0 12.0 12.7 13.2 14.9 22.1 17.9 18.8 7.8 12.8 16.6 Niger 5.1 4.0 3.6 4.6 4.8 5.2 5.7 7.1 8.8 3.0 3.4 6.0 Nigeria .. 3.8 13.0 10.7 10.3 16.2 14.2 13.2 12.0 5.9 10.9 12.8 Rwanda .. 8.7 10.9 11.6 11.8 12.0 12.8 12.0 12.2 7.8 7.2 12.1 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 9.9 13.9 16.4 17.9 17.7 19.2 15.1 16.2 15.6 13.7 15.4 16.9 Seychelles .. 14.8 40.1 11.4 15.1 16.2 7.8 11.0 7.1 10.1 19.3 11.4 Sierra Leone 9.5 5.7 3.0 1.7 2.2 5.7 9.0 6.1 11.6 7.3 3.5 6.1 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 25.9 19.1 13.9 13.8 13.2 13.1 13.9 14.3 15.1 23.1 16.0 13.9 Sudan 3.8 .. 9.2 9.7 8.8 10.1 11.2 12.2 13.5 8.9 7.5 10.9 Swaziland 23.1 12.7 12.1 12.3 10.4 12.3 12.3 10.2 9.5 17.3 13.8 11.2 Tanzania .. 15.3 12.3 11.4 11.2 11.4 11.1 10.9 10.8 .. 15.2 11.1 Togo 8.0 18.0 10.3 14.8 19.0 17.4 17.2 15.9 18.1 7.8 11.8 17.1 Uganda .. 6.5 13.8 13.3 12.4 13.7 15.4 16.9 16.4 5.4 10.3 14.7 Zambia .. 7.2 5.4 7.2 6.8 9.8 13.3 15.4 15.8 4.9 5.8 11.4 Zimbabwe 12.3 14.8 11.6 11.1 10.1 8.1 11.7 12.0 19.5 13.1 17.2 12.1 NORTH AFRICA .. 16.0 14.1 12.0 12.2 13.1 12.8 13.2 13.9 13.4 13.6 12.9 Algeria 22.8 18.8 18.6 12.9 14.3 14.5 13.2 13.6 14.1 18.1 19.0 13.8 Egypt, Arab Rep. .. 12.3 9.9 9.1 9.0 8.4 7.9 7.7 9.1 9.3 8.3 8.5 Libya 1.8 .. .. .. .. .. .. .. .. 1.8 .. .. Morocco 16.7 19.2 19.4 19.4 17.2 18.7 20.0 20.7 21.6 16.1 17.7 19.6 Tunisia 13.3 15.6 13.0 13.7 .. .. .. .. .. 13.5 13.8 13.7 ALL AFRICA .. 13.9 13.5 12.4 12.3 12.9 13.4 13.5 13.8 13.4 13.5 13.0 a. Provisional. 42 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.22 Resource balance (exports minus imports) Share of GDP (%) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 2.7 1.7 ­2.4 3.1 0.6 ­0.5 0.0 0.9 2.2 ­0.2 ­0.9 1.1 Excluding South Africa 0.2 ­0.8 ­5.8 3.2 ­1.3 ­2.5 ­1.2 2.1 4.5 ­3.3 ­3.7 0.8 Excl. S. Africa & Nigeria ­4.8 ­3.2 ­6.0 ­2.1 ­4.7 ­2.8 ­3.8 ­2.4 ­0.1 ­4.2 ­5.0 ­2.6 Angola .. 18.0 ­6.4 26.8 1.7 11.3 6.6 16.0 25.3 9.1 2.2 14.6 Benin ­21.5 ­12.0 ­12.7 ­12.9 ­12.7 ­13.9 ­12.8 ­12.7 ­12.6 ­17.5 ­12.5 ­13.0 Botswana ­13.4 5.3 9.6 18.9 16.2 11.5 9.2 11.3 21.1 5.3 9.7 14.7 Burkina Faso ­22.3 ­13.0 ­15.7 ­16.2 ­13.9 ­13.1 ­14.8 .. .. ­20.2 ­14.1 ­14.5 Burundi ­14.5 ­19.9 ­8.4 ­12.2 ­14.0 ­16.2 ­19.3 ­24.3 ­33.9 ­13.5 ­14.4 ­20.0 Cameroon 0.8 2.9 4.3 3.6 ­1.3 ­0.8 0.3 ­0.4 ­1.9 0.4 3.7 ­0.1 Cape Verde .. ­31.0 ­38.4 ­33.9 ­33.4 ­36.6 ­34.5 .. .. ­29.0 ­35.2 ­34.6 Central African Republic ­15.9 ­12.9 ­3.4 ­3.1 ­2.9 ­4.5 .. .. .. ­12.1 ­7.7 ­3.5 Chad ­11.9 ­14.4 ­13.9 ­17.8 ­35.0 .. ­34.3 0.2 15.5 ­13.5 ­13.6 ­14.3 Comoros ­43.2 ­22.9 ­20.7 ­15.8 ­15.3 ­15.0 ­16.1 ­19.9 ­22.2 ­33.3 ­22.6 ­17.4 Congo, Dem. Rep. 0.1 0.3 6.0 1.0 ­2.0 ­4.9 ­7.2 ­8.8 ­8.2 ­0.8 1.2 ­5.0 Congo, Rep. ­0.1 7.9 13.2 36.7 24.1 27.6 25.6 27.0 36.3 ­0.5 2.9 29.5 Côte d'Ivoire ­6.2 4.6 8.2 7.1 7.8 16.2 10.4 9.7 10.2 3.2 6.5 10.2 Djibouti .. ­24.6 ­11.0 ­15.3 ­8.5 ­5.2 ­9.1 ­17.2 ­17.0 .. ­17.5 ­12.0 Equatorial Guinea .. ­37.4 .. 13.2 9.3 46.9 20.3 38.5 49.1 ­28.6 ­45.8 29.5 Eritrea .. .. ­77.2 ­66.6 ­55.7 ­59.7 ­85.1 ­84.2 ­46.9 .. ­55.8 ­66.4 Ethiopia .. ­3.3 ­12.6 ­12.4 ­12.1 ­14.7 ­15.0 ­17.7 ­22.1 ­5.3 ­6.9 ­15.7 Gabon 33.1 15.2 21.5 36.4 26.0 19.2 24.3 29.5 44.5 9.7 17.7 30.0 Gambia, The ­20.9 ­11.7 ­6.8 ­8.9 ­5.4 ­8.3 ­9.2 ­17.6 ­20.6 ­13.2 ­12.6 ­11.7 Ghana ­0.7 ­9.0 ­17.6 ­18.5 ­19.5 ­12.1 ­11.9 ­21.1 ­25.6 ­3.1 ­12.4 ­18.1 Guinea 3.1 ­2.4 ­4.4 ­4.3 ­1.3 ­4.0 ­2.4 ­3.9 ­2.7 0.8 ­3.0 ­3.1 Guinea-Bissau ­29.2 ­27.1 ­18.0 ­19.8 ­34.3 ­21.7 ­11.4 ­16.2 ­13.1 ­32.9 ­24.5 ­19.4 Kenya ­6.4 ­5.6 ­4.9 ­8.0 ­8.0 ­3.6 ­4.6 ­5.8 ­7.4 ­4.9 ­2.7 ­6.2 Lesotho ­89.1 ­105.6 ­71.6 ­63.1 ­57.3 ­61.4 ­58.4 ­50.6 ­39.9 ­105.4 ­94.8 ­55.1 Liberia ­0.1 .. ­27.1 ­4.5 ­8.4 ­8.1 ­12.6 ­13.9 ­14.1 2.9 ­39.6 ­10.2 Madagascar ­16.4 ­11.4 ­7.7 ­7.3 ­3.2 ­6.6 ­9.0 ­14.9 ­14.1 ­7.7 ­8.2 ­9.2 Malawi ­14.0 ­9.6 ­15.3 ­9.7 ­11.1 ­21.4 ­22.5 ­24.4 ­24.8 ­6.7 ­14.3 ­19.0 Mali ­14.4 ­16.6 ­11.7 ­12.6 ­17.0 ­7.3 ­10.9 ­12.4 ­12.9 ­17.6 ­14.9 ­12.2 Mauritania ­29.8 ­15.1 ­13.7 ­28.0 ­18.9 ­23.0 ­30.9 ­49.4 ­59.8 ­24.4 ­11.2 ­35.0 Mauritius ­10.9 ­7.2 ­2.2 ­1.9 2.7 3.8 2.1 ­0.6 ­4.4 ­3.5 ­4.3 0.3 Mozambique ­16.5 ­27.9 ­22.9 ­21.9 ­17.9 ­18.8 ­15.8 ­8.3 ­9.8 ­18.4 ­24.2 ­15.4 Namibia 7.8 ­15.5 ­10.8 ­5.5 ­6.4 ­2.0 ­3.6 1.2 2.3 ­7.6 ­10.0 ­2.3 Niger ­13.5 ­6.9 ­6.5 ­7.9 ­7.7 ­8.9 ­9.2 ­10.3 ­9.2 ­8.0 ­6.2 ­8.9 Nigeriab 10.2 14.6 ­4.2 22.1 10.8 ­0.8 8.2 17.1 18.3 1.1 4.1 12.6 Rwanda ­11.9 ­8.5 ­17.3 ­16.2 ­15.8 ­16.9 ­19.3 ­18.1 ­20.4 ­10.3 ­19.9 ­17.8 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal ­14.5 ­6.8 ­7.0 ­9.3 ­9.0 ­10.4 ­12.2 ­13.1 ­15.7 ­12.1 ­7.1 ­11.6 Seychelles ­11.2 ­4.3 ­17.6 ­2.6 ­21.2 ­5.6 11.1 8.3 ­26.9 ­2.3 ­8.6 ­6.2 Sierra Leone ­15.4 ­1.3 ­15.7 ­21.3 ­18.2 ­19.5 ­21.3 ­15.5 ­18.3 ­3.1 ­4.5 ­19.0 Somalia ­55.3 ­28.0 .. .. .. .. .. .. .. ­35.1 ­28.0 .. South Africa 8.0 5.5 2.6 3.0 3.9 3.7 1.9 ­0.7 ­1.4 5.1 2.8 1.7 Sudan ­12.6 .. ­9.1 ­2.4 ­7.8 ­6.2 ­4.2 ­3.8 ­9.8 ­9.4 ­8.5 ­5.7 Swaziland ­39.4 ­12.5 ­18.4 ­15.6 ­15.4 ­0.3 1.9 ­1.6 ­4.7 ­23.5 ­19.1 ­5.9 Tanzania .. ­24.8 ­11.1 ­7.4 ­8.2 ­7.4 ­6.6 ­7.1 ­7.3 .. ­19.3 ­7.3 Togo ­5.3 ­11.9 ­10.1 ­20.0 ­19.4 ­18.0 ­13.6 ­13.5 ­13.4 ­7.2 ­9.6 ­16.3 Uganda ­6.6 ­12.1 ­12.0 ­11.9 ­12.1 ­14.7 ­14.2 ­13.9 ­14.0 ­6.2 ­11.7 ­13.5 Zambia ­4.0 ­0.7 ­18.7 ­10.4 ­2.8 ­5.3 ­7.4 ­7.7 ­8.8 ­2.1 ­7.0 ­7.1 Zimbabwe ­3.2 0.1 1.6 ­0.3 1.3 ­0.9 ­5.2 ­10.1 ­16.2 ­0.8 ­2.6 ­5.2 NORTH AFRICA 5.0 ­3.6 ­2.6 3.4 1.9 0.8 3.3 4.1 7.7 ­3.2 ­2.4 3.5 Algeria 4.0 ­1.5 3.1 19.8 14.6 9.7 14.4 14.4 24.2 ­2.5 1.6 16.2 Egypt, Arab Rep. ­12.4 ­12.7 ­8.3 ­6.6 ­4.9 ­4.4 ­2.6 ­1.4 ­2.3 ­13.2 ­6.7 ­3.7 Libya 34.8 8.6 6.7 19.8 11.3 11.4 .. .. .. 20.4 3.6 14.1 Morocco ­9.4 ­5.4 ­4.3 ­6.5 ­3.5 ­3.3 ­4.2 ­6.7 ­7.9 ­7.4 ­5.1 ­5.4 Tunisia ­5.4 ­7.0 ­2.3 ­3.6 ­4.6 ­4.3 ­3.9 ­2.9 ­2.6 ­6.1 ­4.3 ­3.7 ALL AFRICA 3.2 ­0.3 ­2.5 3.2 1.1 0.0 1.2 2.0 4.0 ­1.4 ­1.5 1.9 a. Provisional. b. For 1994­2000 Nigeria's values were distorted because the official exchange rate used by the government for oil exports and oil value added was significantly overvalued. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 43 Table 2.23 Exports of goods and services, nominal Current prices ($ millions) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 82,661 79,589 95,222 116,657 109,477 112,721 144,063 183,946 230,383 65,793 87,489 149,541 Excluding South Africa 53,543 52,155 61,417 79,623 73,981 76,452 97,690 126,934 165,571 39,121 55,726 103,375 Excl. S. Africa & Nigeria 34,079 40,240 48,759 54,672 53,218 57,379 68,696 87,565 112,945 31,740 43,513 72,413 Angola .. 3,993 5,311 8,182 6,847 8,406 9,716 13,780 24,121 2,613 4,265 11,842 Benin 222 264 385 342 360 380 487 539 577 214 327 448 Botswana 563 2,087 2,743 3,248 2,933 2,811 3,739 4,491 5,519 999 2,350 3,790 Burkina Faso 173 352 287 237 260 272 357 .. .. 184 287 281 Burundi 81 89 61 55 45 39 50 64 91 111 89 57 Cameroon 1,880 2,251 2,255 2,343 2,104 2,169 2,757 3,061 3,958 2,240 2,198 2,732 Cape Verde .. 43 113 146 167 194 253 .. .. 41 79 190 Central African Republic 201 220 117 126 121 126 .. .. .. 181 185 124 Chad 175 234 282 234 251 253 675 2,255 3,239 153 254 1,151 Comoros 11 36 29 34 34 40 51 46 48 22 40 42 Congo, Dem. Rep. 2,372 2,759 1,109 964 875 1,174 1,483 1,994 2,450 2,016 1,595 1,490 Congo, Rep. 1,024 1,502 1,702 2,585 2,163 2,462 2,825 3,662 5,160 1,092 1,393 3,143 Côte d'Ivoire 3,561 3,421 5,067 4,211 4,357 5,695 6,280 7,445 8,097 3,142 4,129 6,014 Djibouti .. 244 200 193 213 228 248 246 259 .. 210 231 Equatorial Guinea .. 42 .. 1,236 1,760 2,139 2,859 4,766 7,277 32 160 3,340 Eritrea .. .. 66 96 133 128 80 82 85 .. 132 101 Ethiopia .. 672 918 984 980 983 1,140 1,494 1,858 608 715 1,240 Gabon 2,770 2,740 2,780 3,498 2,782 2,642 3,350 4,412 5,844 1,964 2,728 3,755 Gambia, The 103 190 199 202 150 157 158 185 207 108 195 176 Ghana 376 993 2,488 2,440 2,399 2,613 3,073 3,487 3,869 554 1,686 2,980 Guinea 2,084 829 749 735 809 785 806 845 924 2,021 798 817 Guinea-Bissau 14 24 56 68 57 61 77 84 114 15 32 77 Kenya 2,144 2,207 2,687 2,743 2,968 3,281 3,590 4,207 5,126 1,805 2,594 3,652 Lesotho 91 104 216 256 319 390 520 763 695 70 187 490 Liberia 613 .. 64 120 126 111 133 171 201 519 43 144 Madagascar 539 512 909 1,190 1,317 704 1,264 1,425 1,355 414 673 1,209 Malawi 307 447 498 446 480 471 480 511 566 295 465 492 Mali 263 415 680 649 876 1,066 1,153 1,237 1,333 255 514 1,052 Mauritania 261 465 496 500 379 382 356 473 659 387 465 458 Mauritius 539 1,529 2,716 2,801 2,978 2,757 3,099 3,350 3,556 764 2,191 3,090 Mozambique 383 201 586 744 1,004 1,188 1,353 1,828 2,164 215 373 1,380 Namibia 1,712 1,220 1,563 1,558 1,446 1,548 2,300 2,644 2,961 1,139 1,543 2,076 Niger 617 372 321 320 329 330 438 491 512 420 325 403 Nigeria 18,859 12,366 12,832 24,954 20,774 19,093 28,993 39,344 52,575 7,725 12,563 30,955 Rwanda 168 145 114 151 157 133 139 189 228 173 107 166 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 837 1,453 1,445 1,310 1,401 1,523 1,826 2,123 2,221 989 1,347 1,734 Seychelles 100 230 422 464 499 550 671 687 714 123 298 597 Sierra Leone 252 146 84 115 129 153 197 239 281 187 155 186 Somalia 200 90 .. .. .. .. .. .. .. 119 90 .. South Africa 28,555 27,149 33,742 37,034 35,495 36,268 46,372 57,032 64,904 26,088 31,523 46,184 Sudan 806 .. 828 1,891 1,711 1,996 2,613 3,822 4,973 875 658 2,834 Swaziland 405 658 1,006 1,133 1,156 1,131 1,580 1,986 2,095 394 886 1,513 Tanzania .. 538 1,285 1,527 1,505 1,631 2,022 2,538 2,964 .. 962 2,031 Togo 580 545 455 409 421 498 595 691 743 464 441 559 Uganda 242 312 735 663 690 697 778 933 1,145 371 500 818 Zambia 1,608 1,180 701 682 980 875 891 1,059 1,192 1,060 1,083 946 Zimbabwe 1,561 2,009 2,767 2,660 2,369 2,019 1,854 2,002 1,941 1,530 2,469 2,141 NORTH AFRICA 45,633 46,844 53,435 69,926 66,817 66,986 80,271 99,796 125,672 35,544 48,949 84,911 Algeria 14,541 14,546 13,040 22,560 20,002 20,012 26,028 34,067 48,690 12,221 12,420 28,560 Egypt, Arab Rep. 6,992 8,647 13,654 16,175 17,066 16,091 18,074 22,258 27,214 6,654 12,435 19,480 Libya 23,523 11,468 7,275 12,078 9,054 9,164 .. .. .. 17,320 8,527 10,099 Morocco 3,273 6,830 10,624 10,452 11,166 12,198 14,236 16,619 18,809 3,790 8,399 13,913 Tunisia 3,518 5,353 8,843 8,661 9,530 9,520 10,950 13,199 13,766 3,312 7,168 10,938 ALL AFRICA 126,916 126,570 148,632 186,587 176,300 179,710 224,813 284,586 357,053 101,562 136,485 234,842 a. Provisional. 44 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.24 Imports of goods and services, nominal Current prices ($ millions) Annual average 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 75,159 74,458 103,059 105,964 107,564 114,480 144,042 179,001 216,632 66,401 90,312 144,614 Excluding South Africa 53,109 53,700 72,832 72,881 76,712 82,319 100,918 120,374 148,181 44,841 62,469 100,231 Excl. S. Africa & Nigeria 40,315 45,418 58,524 58,058 61,094 62,800 76,622 93,334 113,199 37,338 51,142 77,518 Angola .. 2,147 5,705 5,736 6,697 7,110 8,801 10,621 15,834 1,895 4,032 9,133 Benin 524 486 688 634 662 772 944 1,055 1,119 447 579 864 Botswana 705 1,888 2,204 2,079 1,958 2,130 2,979 3,380 3,313 842 1,896 2,640 Burkina Faso 603 758 729 658 650 693 977 .. .. 579 659 744 Burundi 214 314 129 151 146 151 171 205 291 254 234 186 Cameroon 1,829 1,931 1,800 1,981 2,228 2,254 2,712 3,128 4,282 2,219 1,816 2,764 Cape Verde .. 148 337 326 351 419 529 .. .. 118 237 406 Central African Republic 327 411 153 155 149 174 .. .. .. 292 282 159 Chad 298 485 495 480 850 .. 1,611 2,243 2,328 305 469 1,502 Comoros 64 93 75 66 68 77 103 118 134 67 93 94 Congo, Dem. Rep. 2,354 2,731 827 920 971 1,447 1,892 2,573 2,792 2,107 1,537 1,766 Congo, Rep. 1,026 1,282 1,391 1,404 1,490 1,629 1,913 2,488 2,994 1,093 1,309 1,986 Côte d'Ivoire 4,190 2,927 4,041 3,471 3,529 3,837 4,848 5,939 6,466 2,906 3,406 4,682 Djibouti .. 355 259 278 262 259 305 361 379 .. 295 307 Equatorial Guinea .. 92 .. 1,071 1,599 1,124 2,256 2,882 3,583 61 270 2,086 Eritrea .. .. 597 518 507 505 577 617 540 .. 482 544 Ethiopia .. 1,069 1,882 1,961 1,938 2,073 2,347 3,171 4,367 1,093 1,330 2,643 Gabon 1,354 1,837 1,777 1,656 1,557 1,694 1,882 2,298 1,983 1,586 1,823 1,845 Gambia, The 153 227 228 239 173 188 192 255 302 137 242 225 Ghana 407 1,522 3,841 3,362 3,437 3,355 3,979 5,356 6,610 709 2,511 4,350 Guinea 1,878 892 901 867 849 912 892 1,005 1,012 1,953 905 923 Guinea-Bissau 46 90 96 111 125 105 104 127 153 67 91 121 Kenya 2,608 2,691 3,312 3,757 4,002 3,741 4,257 5,150 6,540 2,154 2,942 4,575 Lesotho 475 753 864 794 750 812 1,127 1,430 1,276 503 977 1,032 Liberia 614 .. 184 146 171 156 184 235 275 491 180 195 Madagascar 1,202 864 1,197 1,474 1,463 993 1,756 2,073 2,067 668 942 1,637 Malawi 480 629 769 616 672 886 878 975 1,080 384 716 851 Mali 520 817 982 954 1,322 1,311 1,630 1,841 2,015 536 882 1,512 Mauritania 473 619 660 803 591 647 753 1,239 1,758 576 607 965 Mauritius 665 1,701 2,808 2,888 2,854 2,584 2,988 3,389 3,830 809 2,334 3,089 Mozambique 965 888 1,500 1,571 1,665 1,958 2,108 2,320 2,830 773 1,001 2,075 Namibia 1,542 1,584 1,927 1,746 1,652 1,610 2,461 2,573 2,819 1,284 1,844 2,144 Niger 957 545 452 462 479 523 688 795 825 583 448 629 Nigeria 12,324 8,203 14,304 14,807 15,601 19,447 24,200 26,965 34,855 7,362 11,214 22,646 Rwanda 307 364 448 445 427 425 464 521 667 354 405 491 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 1,302 1,728 1,806 1,746 1,842 2,078 2,657 3,162 3,431 1,283 1,664 2,486 Seychelles 117 246 531 480 630 589 593 629 908 123 344 638 Sierra Leone 421 154 188 250 276 336 408 406 499 225 191 362 Somalia 534 346 .. .. .. .. .. .. .. 403 346 .. South Africa 22,073 21,016 30,287 33,107 30,889 32,211 43,139 58,561 68,412 21,441 27,961 44,386 Sudan 1,763 .. 1,802 2,189 2,756 2,924 3,367 4,650 7,701 1,853 1,551 3,931 Swaziland 619 768 1,260 1,349 1,350 1,134 1,543 2,023 2,217 515 1,116 1,603 Tanzania .. 1,595 2,241 2,200 2,283 2,353 2,703 3,344 3,881 .. 2,000 2,794 Togo 640 738 615 674 678 763 833 969 1,026 542 586 824 Uganda 324 834 1,455 1,366 1,378 1,554 1,662 1,879 2,370 619 1,042 1,701 Zambia 1,764 1,203 1,287 1,018 1,080 1,072 1,212 1,478 1,835 1,148 1,313 1,282 Zimbabwe 1,771 2,002 2,670 2,680 2,232 2,218 2,238 2,477 2,495 1,598 2,661 2,390 NORTH AFRICA 39,100 53,024 59,364 61,717 62,205 65,195 72,156 88,641 101,546 40,426 53,495 75,243 Algeria 12,847 15,472 11,520 11,700 11,920 14,491 16,239 21,808 24,020 13,875 11,636 16,696 Egypt, Arab Rep. 9,822 14,109 21,144 22,780 21,802 19,917 20,219 23,330 29,246 10,787 16,572 22,882 Libya 11,167 8,996 5,246 5,252 5,674 6,979 .. .. .. 10,722 7,464 5,968 Morocco 5,033 8,227 12,142 12,616 12,363 13,387 16,056 19,989 22,885 4,955 9,980 16,216 Tunisia 3,987 6,220 9,313 9,369 10,446 10,421 11,918 14,026 14,525 3,834 7,842 11,784 ALL AFRICA 114,047 127,869 162,420 167,684 169,771 179,671 216,817 268,476 319,552 107,081 143,927 220,329 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 45 Table 2.25 Exports of goods and services, real Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 72,127 77,415 109,996 119,417 121,895 120,096 130,399 138,648 144,627 1.3 4.8 4.2 Excluding South Africa 53,614 55,278 75,981 82,595 84,450 82,282 93,167 100,845 104,079 1.1 4.4 5.3 Excl. S. Africa & Nigeria 29,742 40,345 55,811 58,367 61,769 62,898 65,889 73,311 77,637 3.1 4.0 5.8 Angola .. .. .. .. .. .. .. .. .. .. .. .. Benin 391 247 344 342 359 359 376 378 397 ­4.5 2.0 2.7 Botswana 691 1,994 2,682 3,248 3,168 3,059 2,961 3,137 3,818 13.8 4.0 2.2 Burkina Faso 226 253 262 237 244 273 300 .. .. ­1.7 ­0.1 8.5 Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 1,054 1,870 2,466 2,343 2,342 2,301 2,299 2,505 2,507 6.5 2.7 1.6 Cape Verde .. 42 96 146 167 182 208 .. .. .. 13.9 12.1 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 160 215 256 234 226 213 476 1,310 1,533 7.4 2.8 55.6 Comoros 9 26 29 34 32 34 35 28 29 11.0 ­2.2 ­3.3 Congo, Dem. Rep. 667 1,224 1,000 964 983 1,062 1,065 1,279 1,391 11.2 ­2.5 7.8 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 3,048 4,084 4,337 4,211 4,147 4,384 4,142 4,855 4,782 1.2 1.5 3.1 Djibouti .. 355 205 193 210 223 238 229 234 .. ­6.1 3.7 Equatorial Guinea .. .. .. 1,236 1,938 2,282 2,633 3,644 3,907 .. .. 24.9 Eritrea .. .. 67 96 131 127 86 80 80 .. ­1.4 ­7.6 Ethiopia .. 579 761 984 1,033 1,172 1,350 1,836 1,904 3.7 5.9 15.9 Gabon 2,157 3,178 3,940 3,498 3,296 2,931 3,166 3,300 3,250 1.8 3.4 ­0.8 Gambia, The 107 174 182 202 167 170 166 192 245 0.5 ­0.9 4.0 Ghana 857 1,010 2,420 2,440 2,441 2,400 2,464 2,746 3,001 1.4 10.5 4.1 Guinea .. 688 743 735 791 780 750 760 803 .. 0.1 0.8 Guinea-Bissau 22 17 55 68 71 71 76 79 83 ­3.9 14.2 4.1 Kenya 1,479 2,374 2,712 2,743 2,943 3,044 3,268 3,700 3,873 3.3 1.2 7.4 Lesotho 58 83 204 256 339 428 396 441 377 4.7 10.7 7.9 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 992 769 1,048 1,190 1,304 706 993 1,008 964 ­1.8 3.3 ­4.1 Malawi 315 354 479 446 491 499 480 465 554 2.1 4.8 2.5 Mali 175 266 657 649 811 1,067 907 905 984 4.7 10.1 6.7 Mauritania 437 532 493 500 491 458 405 442 470 3.5 ­1.5 ­2.1 Mauritius 732 1,739 2,859 2,801 3,101 3,394 3,123 3,060 3,235 10.1 5.8 1.7 Mozambique 436 237 564 744 1,127 1,364 1,776 1,807 1,897 ­8.9 10.5 19.9 Namibia 1,096 954 1,571 1,558 1,525 1,739 2,139 1,994 2,040 1.4 4.3 7.0 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 21,372 15,781 21,442 24,954 23,975 21,360 28,111 28,976 28,466 ­1.5 5.1 4.4 Rwanda 171 210 138 151 264 279 274 304 297 4.2 ­6.4 11.4 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 671 989 1,344 1,310 1,330 1,378 1,376 1,440 1,486 1.8 4.3 2.5 Seychelles .. 252 378 464 503 533 613 638 711 .. 4.9 8.9 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 19,504 22,613 34,192 37,034 37,687 37,888 37,991 38,937 41,527 1.6 5.6 1.9 Sudan 764 334 845 1,891 1,736 1,790 2,135 2,434 2,751 ­5.0 8.8 9.1 Swaziland 424 778 1,006 1,133 1,318 1,345 1,264 1,278 1,355 7.5 3.5 2.1 Tanzania .. 698 1,340 1,527 1,759 1,827 2,214 2,546 2,859 .. 10.3 13.5 Togo 499 414 426 409 460 476 508 523 562 0.4 1.4 6.0 Uganda .. 229 715 663 757 844 911 968 1,011 1.4 16.0 8.7 Zambia 812 559 797 682 880 939 1,034 1,164 1,307 ­3.0 3.5 12.7 Zimbabwe 638 1,011 2,502 2,660 2,606 2,195 1,918 1,952 1,887 4.3 10.8 ­7.5 NORTH AFRICA 31,796 52,076 72,867 76,210 78,842 79,225 83,953 92,313 102,606 4.8 3.8 5.9 Algeria 11,053 16,673 21,303 22,560 21,951 23,158 24,988 25,762 27,257 4.2 2.9 4.4 Egypt, Arab Rep. 6,930 11,111 15,584 16,175 16,707 15,404 17,535 21,974 26,924 4.4 3.3 10.5 Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 2,848 6,337 10,196 10,452 11,529 12,276 11,899 12,309 13,353 7.6 5.5 4.1 Tunisia 3,303 5,408 8,228 8,661 9,660 9,299 9,304 10,027 10,352 5.1 5.1 2.9 ALL AFRICA 104,296 128,692 181,840 194,762 199,759 198,214 213,340 229,613 245,262 2.5 4.4 4.8 a. Provisional. 46 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Table 2.26 Imports of goods and services, real Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 1999 2000 2001 2002 2003 2004 2005a 1980­89 1990­99 2000­05 SUB­SAHARAN AFRICA 86,589 67,505 104,183 104,247 108,582 115,008 124,060 134,976 150,387 ­4.1 5.8 7.6 Excluding South Africa 68,510 49,436 73,473 71,637 76,072 80,894 86,876 92,184 103,385 ­5.4 4.7 7.3 Excl. S. Africa & Nigeria 37,723 39,401 58,144 56,403 59,435 60,546 64,389 69,394 75,384 0.6 4.9 5.8 Angola .. .. .. .. .. .. .. .. .. .. .. .. Benin 797 484 615 634 660 668 670 678 706 ­7.4 1.8 1.8 Botswana 714 1,728 2,171 2,079 2,093 2,287 2,321 2,320 2,265 8.2 3.7 2.2 Burkina Faso 517 650 752 658 718 762 1,000 .. .. 2.6 1.7 14.1 Burundi .. .. .. .. .. .. .. .. .. .. .. .. Cameroon 795 1,261 1,706 1,981 2,259 2,087 2,162 2,427 2,993 4.9 4.1 6.8 Cape Verde .. 145 288 326 351 393 435 .. .. .. 8.2 10.2 Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 320 537 449 480 767 .. 1,135 1,304 1,101 11.5 ­2.8 19.0 Comoros 66 67 60 66 64 67 71 72 80 0.3 ­0.9 4.2 Congo, Dem. Rep. 566 1,063 785 920 1,012 1,387 1,767 2,233 2,626 13.1 ­6.5 25.2 Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 3,347 2,315 4,318 3,471 3,623 3,554 4,005 4,511 4,709 ­2.2 8.9 6.8 Djibouti .. 518 265 278 257 253 292 335 342 .. ­8.0 5.8 Equatorial Guinea .. .. .. 1,071 1,627 1,154 1,986 2,235 2,776 .. .. 19.6 Eritrea .. .. 607 518 520 502 508 434 429 .. 9.9 ­4.1 Ethiopia .. 1,116 1,962 1,961 1,980 2,157 2,271 2,721 3,373 4.6 5.3 11.2 Gabon 1,890 1,967 1,982 1,656 1,662 1,765 1,731 1,817 1,478 ­2.1 1.2 ­0.9 Gambia, The 306 220 236 239 198 217 182 238 263 ­7.5 0.0 2.4 Ghana 1,897 1,548 4,100 3,362 3,622 3,464 3,730 4,064 4,337 ­0.5 11.8 5.0 Guinea .. 1,074 956 867 872 918 844 837 785 .. ­1.0 ­2.0 Guinea-Bissau 79 84 82 111 107 92 84 91 97 1.1 ­2.5 ­3.5 Kenya 1,913 1,854 3,687 3,757 4,080 3,971 3,972 4,450 5,085 0.9 10.3 5.2 Lesotho 520 761 846 794 850 989 966 1,008 856 3.4 2.1 2.5 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 1,911 1,075 1,265 1,474 1,647 1,235 1,638 2,045 2,045 ­7.7 3.3 7.6 Malawi 803 743 787 616 704 907 806 777 786 ­1.5 ­0.3 4.1 Mali 392 683 938 954 1,238 1,110 1,260 1,225 1,270 6.8 3.1 4.4 Mauritania 621 749 579 803 791 812 792 1,129 1,640 0.7 ­0.8 14.1 Mauritius 829 1,793 2,853 2,888 2,955 3,108 3,009 3,003 3,147 9.5 5.2 1.3 Mozambique 1,232 851 1,610 1,571 1,246 1,512 1,710 1,767 1,989 ­4.4 5.5 6.9 Namibia 1,107 1,151 1,842 1,746 1,918 1,983 2,096 1,868 1,897 0.5 6.0 1.1 Niger .. .. .. .. .. .. .. .. .. .. .. .. Nigeria 26,740 9,817 14,933 14,807 16,126 19,442 21,428 21,859 26,518 ­15.4 3.9 11.9 Rwanda 177 210 496 445 447 422 445 490 571 4.3 6.1 4.6 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 987 1,376 1,699 1,746 1,777 1,838 2,025 2,001 2,228 2.0 1.5 4.9 Seychelles .. 208 487 480 629 546 546 590 784 .. 11.1 6.7 Sierra Leone .. .. .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 19,907 18,795 31,429 33,107 33,179 34,859 37,943 43,278 47,642 ­1.0 7.9 8.0 Sudan 1,742 1,083 2,064 2,189 2,182 2,346 2,470 .. .. ­7.5 9.6 4.4 Swaziland 580 770 1,260 1,349 1,480 1,477 1,418 1,437 1,528 2.6 4.7 1.4 Tanzania .. 1,503 2,253 2,200 1,928 2,275 2,413 2,539 2,727 .. 3.8 5.8 Togo 708 803 710 674 681 716 736 758 780 3.0 1.0 3.1 Uganda .. 687 1,397 1,366 1,408 1,667 1,711 1,832 2,229 4.7 10.7 9.8 Zambia 1,977 1,180 1,274 1,018 1,295 1,219 1,264 1,401 1,689 ­1.7 2.6 8.4 Zimbabwe 618 984 2,470 2,680 2,435 2,419 2,255 2,293 2,234 2.7 9.8 ­3.3 NORTH AFRICA 51,425 50,082 60,587 61,336 63,042 64,676 65,187 72,859 82,087 ­1.8 2.4 5.6 Algeria 17,636 14,054 11,806 11,700 12,156 14,527 14,875 16,586 17,880 ­4.0 ­1.4 9.2 Egypt, Arab Rep. 20,494 17,932 23,581 22,780 22,524 21,172 21,443 25,140 31,130 ­2.2 3.3 5.6 Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 4,673 7,642 11,786 12,616 12,714 13,604 13,501 14,795 15,888 4.0 4.7 4.7 Tunisia 4,539 6,476 8,602 9,369 10,641 10,237 10,191 10,552 10,670 0.6 3.5 1.8 ALL AFRICA 137,518 117,568 164,753 165,564 171,612 179,681 189,262 207,874 232,528 ­3.2 4.4 6.9 a. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 47 Table 3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger Share of population below International poverty line national poverty linea Share of population Poverty gap ratio at $1 a daya (poverty headcount ratio) below PPP $1 a day a (incidence × depth of poverty) Surveys 1990­99 Surveys 2000­05 Surveys 1990­99 Surveys 2000­05 Surveys 1990­99 Surveys 2000­05 Year b Percent Year b Percent Year b Percent Year b Percent Year b Percent Year b Percent SUB­SAHARAN AFRICA Angola .. .. .. .. .. .. .. .. .. .. .. .. Benin 1999 29.0 .. .. .. .. 2003 30.9 .. .. 2003 8.2 Botswana .. .. .. .. 1993 28.0 .. .. 1993 9.9 .. .. Burkina Faso 1998 54.6 2003 46.4 1998 44.9 2003 27.2 1998 14.4 2003 7.3 Burundi 1990 36.4 .. .. 1998 54.6 .. .. 1998 22.7 .. .. Cameroon 1996 53.3 2001 40.2 1996 32.5 2001 17.1 1996 9.1 2001 4.1 Cape Verde .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. 1993 66.6 .. .. 1993 38.1 .. .. Chad 1996 64.0 .. .. .. .. .. .. .. .. .. .. Comoros .. .. .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire .. .. .. .. 1998 15.5 2002 14.8 1998 3.8 2002 4.1 Djibouti .. .. .. .. .. .. .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. .. .. .. .. .. Eritrea 1994 53.0 .. .. .. .. .. .. .. .. .. .. Ethiopia 1996 45.5 2000 44.2 1995 31.3 2000 23.0 1995 8.0 2000 4.8 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 1998 57.6 .. .. 1998 59.3 .. .. 1998 28.8 .. .. Ghana 1999 39.5 .. .. 1998 44.8 .. .. 1998 17.3 .. .. Guinea 1994 40.0 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. Kenya 1997 52.0 .. .. 1997 22.8 .. .. 1997 5.9 .. .. Lesotho .. .. .. .. 1995 36.4 .. .. 1995 19.0 .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 1999 71.3 .. .. 1999 66.0 2001 61.0 1999 29.4 2001 27.9 Malawi 1998 65.3 .. .. .. .. 2004 20.8 .. .. 2004 4.7 Mali 1998 63.8 .. .. 1994 72.3 2001 36.1 1994 37.4 2001 12.2 Mauritania 1996 50.0 2000 46.3 1996 28.6 2000 25.9 1996 9.1 2000 7.6 Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mozambique 1997 69.4 .. .. 1997 37.9 2002 36.2 1997 12.0 2002 11.6 Namibia .. .. .. .. 1993 34.9 .. .. 1993 14.0 .. .. Niger 1993 63.0 .. .. 1995 60.6 .. .. 1995 34.0 .. .. Nigeria 1993 34.1 .. .. 1996 77.9 2003 70.8 1996 44.1 2003 34.5 Rwanda 1993 51.2 2000 60.3 .. .. 2000 60.3 .. .. 2000 25.6 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 1992 33.4 .. .. 1995 24.0 2001 17.0 1995 6.3 2001 3.6 Seychelles .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. 2004 70.2 .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. 1995 6.3 2000 10.7 1995 0.6 2000 1.7 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. 1995 8.0 .. .. 1995 2.5 .. .. Tanzania 1991 38.6 2001 35.7 1991 61.5 2000 57.8 1991 22.7 2000 20.7 Togo .. .. .. .. .. .. .. .. .. .. .. .. Uganda .. .. 2003 37.7 .. .. .. .. .. .. .. .. Zambia 1998 72.9 2004 68.0 1998 65.7 2004 63.8 1998 34.0 2004 32.6 Zimbabwe 1996 34.9 .. .. 1996 56.1 .. .. 1996 24.2 .. .. NORTH AFRICA Algeria 1995 22.6 .. .. 1995 2.0 .. .. 1995 0.5 .. .. Egypt, Arab Rep. 1996 22.9 2000 16.7 1995 2.6 2000 3.1 1995 0.5 2000 0.5 Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 1999 19.0 .. .. 1999 2.0 .. .. 1999 0.5 .. .. Tunisia 1995 7.6 .. .. 1995 2.0 2000 2.0 1995 0.5 2000 0.5 a. Data are based on expenditure shares, except for Namibia and Swaziland, for which data are based on income shares. b. Data are for most recent year available during the period specified. 48 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Prevalence of child malnutrition, underweight Population below minimum Share of poorest quintile in national consumption or incomea (% of children under age 5) dietary energy consumption Surveys 1990­99 Surveys 2000­05 Surveys 1990­99 Surveys 2000­05 Share (%) Millions Year b Percent Year b Percent Year b Percent Year b Percent 2004 2004 .. .. .. .. 1996 40.6 2001 30.5 35 4.8 .. .. 2003 7.4 1996 29.2 2005 30.0 12 0.8 1993 3.2 .. .. 1996 17.2 2000 12.5 32 0.6 1998 5.9 2003 6.9 1999 34.3 2003 37.7 15 2.0 1998 5.1 .. .. .. .. 2000 45.1 66 4.5 1996 5.7 2001 5.6 1998 22.2 2004 18.1 26 4.2 .. .. .. .. 1994 13.5 .. .. .. .. 1993 2.0 .. .. 1995 23.2 2000 24.3 44 1.7 .. .. .. .. 1997 38.8 2004 36.7 35 3.0 .. .. .. .. 1996 25.8 2000 25.4 60 0.5 .. .. .. .. 1995 34.4 2001 31.0 74 39.0 .. .. .. .. 1999 13.0 .. .. 33 1.2 1998 5.8 2002 5.2 1999 21.2 2004 17.2 13 2.2 .. .. .. .. 1996 18.2 2002 26.8 24 0.2 .. .. .. .. .. .. 2000 18.6 .. .. .. .. .. .. 1995 43.7 2002 39.6 75 3.1 1995 7.2 2000 9.1 1992 47.7 2005 38.4 46 32.7 .. .. .. .. .. .. 2001 11.9 5 0.1 1998 4.8 .. .. 1996 26.2 2000 17.2 29 0.4 1998 5.6 .. .. 1999 24.9 2003 22.1 11 2.3 .. .. 2003 7.0 1999 23.2 2000 32.7 24 2.0 1993 5.2 .. .. .. .. 2000 25.0 39 0.6 1997 6.0 .. .. 1998 22.1 2003 19.9 31 9.9 1995 1.5 .. .. 1996 16.0 2000 18.0 13 0.2 .. .. .. .. .. .. 2000 26.5 50 1.7 1999 5.9 2001 4.9 1997 40.0 2004 41.9 38 6.6 .. .. 2004 7.0 1995 29.9 2002 21.9 35 4.2 1994 4.6 2001 6.1 1996 26.9 2001 33.2 29 3.8 1996 6.3 2000 6.2 1996 23.0 2001 31.8 10 0.3 .. .. .. .. 1995 14.9 .. .. 5 0.1 1997 6.5 2002 5.4 1997 26.1 2003 23.7 44 8.3 1993 1.4 .. .. 1992 26.2 2000 24.0 24 0.5 1995 2.6 .. .. 1998 49.6 2000 40.1 32 3.9 1996 3.7 2003 5.0 1999 27.3 2003 28.7 9 11.4 .. .. 2000 5.3 1996 27.3 2005 22.5 33 2.8 .. .. .. .. .. .. 2000 12.9 10 0.0 1995 6.5 2001 6.6 1996 22.3 2000 22.7 20 2.1 .. .. .. .. .. .. .. .. 9 0.0 .. .. .. .. 1990 28.7 2000 27.2 51 2.5 .. .. .. .. 1997 18.0 2000 25.8 .. .. 1995 3.6 2000 3.5 1999 11.5 .. .. 3 0.0 .. .. .. .. 1993 33.9 2000 40.7 26 8.7 1995 2.5 2001 4.3 .. .. 2000 10.3 22 0.2 1991 7.4 2000 7.3 1999 29.4 2005 21.8 44 16.4 .. .. .. .. 1998 25.1 .. .. 24 1.2 .. .. 2002 5.7 1995 25.5 2001 22.9 19 4.8 1998 3.4 2004 3.6 1999 25.0 2003 23.0 46 5.0 1996 4.6 .. .. 1999 13.0 .. .. 47 6.0 1995 7.0 .. .. 1995 12.8 2002 10.4 4 1.4 1995 8.8 2000 8.6 1998 10.7 2003 8.6 4 2.6 .. .. .. .. 1995 4.7 .. .. 3 0.0 1999 6.5 .. .. 1997 9.0 2004 10.2 6 1.8 1995 5.6 2000 6.0 1997 3.8 2000 4.0 3 0.0 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 49 Table 3.2 Millennium Development Goal 2: achieve universal primary education Net primary enrollment ratio Primary completion rate Share of cohort reaching grade 5 Youth literacy rate (% of relevant age group) (% of relevant age group) (% of grade 1 students) (% of ages 15­24) 1991 2000 2005 1991 2000 2005 1991 2000 2005a 1991 2000 2000­05b SUB­SAHARAN AFRICA Angola 50.3 .. .. 34.7 .. .. .. .. .. .. .. 72.2 Benin 41.0 51.9 78.2 20.8 34.9 65.0 54.8 84.0 51.6 .. .. 45.3 Botswana 83.4 79.6 83.4 82.9 87.5 92.0 84.0 89.5 .. 89.3 .. 94.0 Burkina Faso 29.2 35.8 45.2 21.3 25.2 30.9 69.7 69.1 75.5 20.2 .. 33.0 Burundi 52.9 43.3 60.5 45.9 25.1 35.7 61.7 56.1 66.9 53.6 73.3 73.3 Cameroon 73.6 .. .. 55.9 53.3 62.4 .. .. .. .. .. .. Cape Verde 91.1 97.7 90.1 .. 101.8 81.4 .. .. 92.5 88.2 .. 96.3 Central African Republic 51.9 .. .. 26.7 .. 22.5 23.0 .. .. .. 58.5 58.5 Chad 34.7 53.7 .. 18.3 22.9 31.6 50.5 53.9 33.2 .. 37.6 37.6 Comoros 56.7 55.1 .. .. .. 50.5 .. .. 80.3 .. .. .. Congo, Dem. Rep. 54.1 .. .. 46.1 .. .. 54.7 .. .. .. .. 70.4 Congo, Rep. 79.4 .. 43.5 54.1 .. 57.5 60.1 .. .. .. .. 97.4 Côte d'Ivoire 44.7 53.0 .. 43.4 39.6 .. 72.5 87.6 .. .. 60.7 60.7 Djibouti 28.7 27.7 33.3 27.1 29.3 31.8 87.3 .. .. 73.2 .. .. Equatorial Guinea 90.6 84.1 .. .. .. 54.3 .. .. .. .. 94.9 94.9 Eritrea 15.5 40.9 47.0 .. 40.3 50.9 .. 60.5 79.1 .. .. .. Ethiopia 22.0 36.1 61.4 .. 36.7 55.0 18.3 .. .. .. .. 49.9 Gabon 85.5 .. .. .. .. .. .. .. .. .. .. 96.2 Gambia, The 48.0 66.7 .. .. .. .. .. .. .. .. .. .. Ghana 53.7 60.7 65.0 62.8 .. 72.1 80.5 66.2 .. .. 70.7 70.7 Guinea 27.2 47.0 65.5 16.8 33.3 54.5 58.6 .. 76.0 .. .. 46.6 Guinea-Bissau 38.1 45.2 .. .. 27.0 .. .. .. .. .. .. .. Kenya .. 66.8 79.9 .. .. 95.0 76.7 .. 82.9 .. 80.3 80.3 Lesotho 71.5 81.6 86.7 58.5 61.2 66.9 65.9 66.7 73.3 .. .. .. Liberia .. 65.7 .. .. .. .. .. .. .. .. .. 67.4 Madagascar 64.2 65.0 92.5 33.3 35.6 57.7 21.1 .. 42.7 .. 70.2 70.2 Malawi 48.4 .. 94.5 28.5 67.2 60.7 64.4 51.9 38.3 .. .. .. Mali 20.9 .. 50.9 10.8 28.5 38.1 69.7 91.7 .. .. .. .. Mauritania 35.3 62.7 72.2 32.9 51.6 44.5 75.3 59.6 52.9 .. 61.3 61.3 Mauritius 91.3 92.9 95.1 106.6 104.7 97.5 97.4 99.3 97.0 91.2 94.5 94.5 Mozambique 42.8 55.5 78.7 27.1 16.2 42.0 34.2 51.9 62.4 .. .. .. Namibia .. 74.1 72.2 .. 85.4 75.3 62.3 94.2 .. 88.1 .. 92.3 Niger 22.3 25.3 39.9 16.5 16.8 28.1 62.4 74.0 64.8 .. .. 36.5 Nigeria 57.8 .. 90.9 .. .. 81.8 89.1 .. .. 71.2 .. 84.2 Rwanda 66.0 .. 73.7 32.9 22.4 39.0 59.9 39.1 .. 74.9 77.6 77.6 São Tomé and Principe .. .. 96.7 .. .. 77.2 .. .. 76.3 93.8 .. 95.4 Senegal 43.5 54.4 76.2 .. 36.0 52.2 84.5 72.3 .. .. .. 49.1 Seychelles .. .. .. .. 112.9 .. 92.7 91.0 .. .. .. 99.1 Sierra Leone 43.3 .. .. .. .. .. .. .. .. .. .. 47.9 Somalia 9.0 .. .. .. .. .. .. .. .. .. .. .. South Africa 89.5 90.4 .. 75.5 89.0 .. .. .. .. .. .. .. Sudan 40.0 43.2 .. 41.4 38.9 49.7 93.8 .. 78.6 .. 77.2 77.2 Swaziland 77.1 76.1 .. 59.9 64.3 .. 77.0 73.9 .. .. 88.4 88.4 Tanzania 49.4 51.4 91.4 61.2 .. 54.2 81.3 81.4 75.8 .. .. 78.4 Togo 64.0 76.6 78.1 34.9 61.0 65.3 48.0 73.8 74.6 .. 74.4 74.4 Uganda .. .. .. .. .. .. 36.0 56.7 .. 69.8 .. 76.6 Zambia .. 62.6 88.9 .. 56.3 77.5 .. .. .. 66.4 .. .. Zimbabwe .. 82.2 .. 98.6 .. .. 76.1 .. .. .. .. 97.7 NORTH AFRICA Algeria 88.8 91.5 96.6 79.5 82.5 95.8 94.5 97.2 95.6 77.3 .. .. Egypt, Arab Rep. 84.1 92.9 .. .. 97.3 .. .. 99.0 .. 61.3 .. .. Libya 95.9 .. .. .. .. .. .. .. .. 91.0 .. .. Morocco 55.9 76.7 86.1 46.6 58.2 80.3 75.1 80.1 79.2 55.3 .. .. Tunisia 94.1 94.4 .. 74.4 87.5 .. 86.4 93.1 .. 84.1 .. .. a. Provisional. b. Data are for most recent year available during the period specified. 50 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Table 3.3 Millennium Development Goal 3: promote gender equity and empower women Ratio of girls to boys in primary Ratio of young literate Share of women employed in and secondary school women to men Women in national parliament the nonagricultural sector (%) (% of ages 15­24) (% of total seats) (%) 1991 2000 2005a 1990 2006a 1990 2000 2006 1990 2000 2004 SUB­SAHARAN AFRICA Angola .. .. .. .. 75.4 15.0 16.0 15.0 .. .. .. Benin 48.6 63.2 72.5 43.6 56.1 3.0 6.0 7.2 46.0 .. .. Botswana 108.0 100.9 102.0 110.0 103.8 5.0 .. 11.1 33.5 40.2 43.0 Burkina Faso 61.1 69.6 76.8 .. 64.6 .. 8.0 11.7 12.5 13.9 14.6 Burundi 81.2 78.1 82.8 76.7 91.6 .. 6.0 30.5 13.3 .. .. Cameroon .. 82.0 83.0 87.9 .. 14.0 6.0 8.9 20.7 21.1 21.6 Cape Verde .. .. 99.7 87.5 .. 12.0 11.0 15.3 39.1 .. .. Central African Republic 58.9 .. .. 60.1 66.6 4.0 7.0 10.5 30.4 .. .. Chad .. 55.0 59.8 64.5 41.7 .. 2.0 6.5 3.8 10.2 12.8 Comoros .. 83.9 83.9 77.8 .. 0.0 .. 3.0 17.0 .. .. Congo, Dem. Rep. .. .. .. 71.8 80.9 5.0 .. 8.4 21.8 20.6 20.1 Congo, Rep. 83.4 82.9 89.1 95.2 .. 14.0 12.0 8.5 26.1 .. .. Côte d'Ivoire .. 67.8 .. 62.0 73.6 6.0 .. 8.5 25.9 .. .. Djibouti 70.4 71.0 75.3 78.1 .. 0.0 0.0 10.8 .. .. .. Equatorial Guinea .. 85.4 .. 91.9 100.2 13.0 5.0 18.0 10.5 .. .. Eritrea .. 76.3 .. 68.0 .. .. 15.0 22.0 .. .. .. Ethiopia 67.6 64.6 76.1 66.1 .. .. 2.0 21.9 40.6 40.7 40.6 Gabon .. 94.0 .. .. .. 13.0 8.0 9.2 37.7 .. .. Gambia, The .. 79.8 .. 67.6 .. 8.0 2.0 13.2 20.9 .. .. Ghana 78.0 87.9 90.8 85.5 86.2 .. 9.0 10.9 56.5 .. .. Guinea 45.2 62.6 73.9 42.5 57.4 .. 9.0 19.3 30.3 .. .. Guinea-Bissau .. 64.7 .. .. .. 20.0 .. 14.0 10.8 .. .. Kenya .. 96.6 .. 93.4 101.1 1.0 4.0 7.3 21.4 33.2 38.7 Lesotho 120.9 105.8 102.8 125.8 .. .. 4.0 11.7 .. .. .. Liberia .. 73.1 .. 51.2 .. .. .. 12.5 23.6 .. .. Madagascar 97.2 95.7 95.9 85.6 93.9 7.0 8.0 6.9 24.2 .. .. Malawi 79.7 91.8 .. 67.6 86.1 10.0 8.0 13.6 10.5 11.8 12.4 Mali 58.1 70.2 74.6 .. 52.3 .. 12.0 10.2 .. .. .. Mauritania 65.4 87.2 95.8 65.0 81.9 .. 4.0 .. 37.0 .. .. Mauritius 101.3 96.3 98.3 99.9 101.7 7.0 8.0 17.1 36.7 38.6 37.5 Mozambique .. 73.6 .. 47.9 .. 16.0 .. 34.8 11.4 .. .. Namibia 108.3 104.5 .. 103.7 102.6 7.0 22.0 26.9 .. 48.8 .. Niger .. 68.2 72.2 37.4 44.2 5.0 1.0 12.4 11.0 8.5 7.8 Nigeria .. .. .. 82.3 .. .. .. 6.4 34.0 .. .. Rwanda .. 94.7 99.1 86.4 97.9 17.0 17.0 48.8 14.6 .. .. São Tomé and Principe .. .. 99.1 .. .. 12.0 9.0 7.3 .. .. .. Senegal .. 80.8 89.6 60.4 70.0 13.0 12.0 19.2 25.7 .. .. Seychelles .. 104.1 .. .. 100.6 16.0 24.0 29.4 .. .. .. Sierra Leone .. .. .. .. 63.0 .. 9.0 14.5 21.2 .. .. Somalia .. .. .. .. .. 4.0 .. 7.8 21.9 .. .. South Africa 102.6 101.3 .. 99.7 100.8 3.0 30.0 32.8 42.6 45.4 45.9 Sudan 77.6 .. 89.0 71.5 84.4 .. .. 14.7 22.2 18.2 16.8 Swaziland 94.3 94.1 .. 100.9 103.2 4.0 3.0 10.8 36.1 31.8 29.9 Tanzania 96.2 96.9 94.9 86.5 94.2 .. 16.0 30.4 .. .. .. Togo 58.2 68.0 72.3 60.1 76.0 5.0 .. 8.6 41.0 .. .. Uganda 81.2 92.2 .. 75.8 86.1 12.0 18.0 29.8 35.6 .. .. Zambia .. 90.4 .. 88.1 91.2 7.0 10.0 14.6 29.4 .. .. Zimbabwe 90.8 93.6 .. 94.6 .. 11.0 14.0 16.0 15.4 20.4 21.8 NORTH AFRICA .. Algeria .. 94.4 102.1 79.1 91.6 2.0 3.0 6.2 14.7 15.8 17.0 Egypt, Arab Rep. 79.0 .. .. 72.0 87.6 4.0 2.0 2.0 20.5 19.0 20.6 Libya .. .. .. 83.5 .. .. .. 7.7 15.0 .. .. Morocco 69.2 82.5 87.6 61.8 74.9 0.0 1.0 10.8 24.8 21.7 21.8 Tunisia 84.4 99.4 .. 81.0 95.7 4.0 12.0 22.8 22.9 24.6 25.0 a. Provisional. MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 51 Table 3.4 Millennium Development Goal 4: reduce child mortality Under-five mortality rate Infant mortality rate Child immunization rate, measles (per 1,000) (per 1,000 live births) (% of children ages 12­23 months) 1990 2000 2005 1990 2000 2005 1990 2000 2005 SUB­SAHARAN AFRICA Angola 260 260 260 154 154 154 38 41 45 Benin 185 160 150 111 95 89 79 68 85 Botswana 58 101 120 45 74 87 87 90 90 Burkina Faso 210 196 191 113 100 96 79 59 84 Burundi 190 190 190 114 114 114 74 75 75 Cameroon 139 151 149 85 88 87 56 49 68 Cape Verde 60 42 35 45 31 26 79 80 65 Central African Republic 168 193 193 102 115 115 83 36 35 Chad 201 205 208 120 122 124 32 28 23 Comoros 120 84 71 88 62 53 87 70 80 Congo, Dem. Rep. 205 205 205 129 129 129 38 46 70 Congo, Rep. 110 108 108 83 81 81 75 34 56 Côte d'Ivoire 157 188 195 103 115 118 56 73 51 Djibouti 175 147 133 116 97 88 85 50 65 Equatorial Guinea 170 200 205 103 120 123 88 51 51 Eritrea 147 97 78 88 61 50 .. 86 84 Ethiopia 204 151 127 122 92 80 38 52 59 Gabon 92 91 91 60 60 60 76 55 55 Gambia, The 151 142 137 103 99 97 86 85 84 Ghana 122 112 112 75 68 68 61 84 83 Guinea 234 183 160 139 110 97 35 42 59 Guinea-Bissau 253 215 200 153 132 124 53 59 80 Kenya 97 117 120 64 77 79 78 75 69 Lesotho 101 108 132 81 86 102 80 74 85 Liberia 235 235 235 157 157 157 .. 52 94 Madagascar 168 137 119 103 84 74 47 56 59 Malawi 221 155 125 131 95 79 81 73 82 Mali 250 224 218 140 124 120 43 49 86 Mauritania 133 125 125 85 79 78 38 62 61 Mauritius 23 18 15 20 16 13 76 84 98 Mozambique 235 178 145 158 122 100 59 71 77 Namibia 86 69 62 60 50 46 57 69 73 Niger 320 270 256 191 159 150 25 34 83 Nigeria 230 207 194 120 107 100 54 35 35 Rwanda 173 203 203 103 118 118 83 74 89 São Tomé and Principe 118 118 118 75 75 75 71 69 88 Senegal 149 133 119 72 66 61 51 48 74 Seychelles 19 15 13 17 13 12 86 97 99 Sierra Leone 302 286 282 175 167 165 .. 37 67 Somalia 225 225 225 133 133 133 30 38 35 South Africa 60 63 68 45 50 55 79 77 82 Sudan 120 97 90 74 65 62 57 47 60 Swaziland 110 142 160 78 98 110 85 72 60 Tanzania 161 141 122 102 88 76 80 78 91 Togo 152 142 139 88 80 78 73 58 70 Uganda 160 145 136 93 85 79 52 61 86 Zambia 180 182 182 101 102 102 90 85 84 Zimbabwe 80 117 132 53 73 81 87 70 85 NORTH AFRICA Algeria 69 44 39 54 37 34 83 80 83 Egypt, Arab Rep. 104 49 33 76 40 28 86 98 98 Libya 41 22 19 35 20 18 89 92 97 Morocco 89 54 40 69 45 36 80 93 97 Tunisia 52 31 24 41 25 20 93 95 96 52 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Table 3.5 Millennium Development Goal 5: improve maternal health Maternal mortality ratio, Births attended by skilled health staff modeled estimate (% of total) (per 100,000 live births) Surveys 1990­99 Surveys 2000­05 2000 Year a Percent Year a Percent SUB­SAHARAN AFRICA Angola 1,700 1996 23 2001 45 Benin 850 1996 60 2005 75 Botswana 100 1996 87 2000 94 Burkina Faso 1,000 1999 31 2003 38 Burundi 1,000 .. .. 2000 25 Cameroon 730 1998 55 2004 62 Cape Verde 150 1998 89 .. .. Central African Republic 1,100 1995 46 2000 44 Chad 1,100 1997 15 2004 14 Comoros 480 1996 52 2000 62 Congo, Dem. Rep. 990 .. .. 2001 61 Congo, Rep. 510 .. .. 2005 86 Côte d'Ivoire 690 1999 47 2004 68 Djibouti 730 .. .. 2003 61 Equatorial Guinea 880 1994 5 2000 65 Eritrea 630 1995 21 2002 28 Ethiopia 850 .. .. 2005 6 Gabon 420 .. .. 2000 86 Gambia, The 540 1990 44 2000 55 Ghana 540 1998 44 2003 47 Guinea 740 1999 35 2003 56 Guinea-Bissau 1,100 1995 25 2000 35 Kenya 1,000 1998 44 2003 42 Lesotho 550 1993 50 2004 55 Liberia 760 .. .. 2000 51 Madagascar 550 1997 47 2004 51 Malawi 1,800 1992 55 2004 56 Mali 1,200 1996 24 2001 41 Mauritania 1,000 1991 40 2001 57 Mauritius 24 1999 99 2004 99 Mozambique 1,000 1997 44 2003 48 Namibia 300 1992 68 2000 76 Niger 1,600 1998 18 2000 16 Nigeria 800 1999 42 2003 35 Rwanda 1,400 1992 26 2005 39 São Tomé and Principe .. .. .. 2003 76 Senegal 690 1997 47 2002 58 Seychelles .. .. .. .. .. Sierra Leone 2,000 .. .. 2000 42 Somalia 1,100 1999 34 2002 25 South Africa 230 1998 84 2003 92 Sudan 590 1993 86 2000 87 Swaziland 370 1994 56 2002 74 Tanzania 1,500 1999 36 2005 43 Togo 570 1998 51 2003 61 Uganda 880 1995 38 2001 39 Zambia 750 1999 47 2002 43 Zimbabwe 1,100 1999 73 .. .. NORTH AFRICA Algeria 140 1992 77 2002 96 Egypt, Arab Rep. 84 1998 55 2005 74 Libya 97 1995 94 .. .. Morocco 220 1995 40 2004 63 Tunisia 120 1995 81 2000 90 a. Data are for most recent year available during the period specified. MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 53 Table 3.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases Contraceptive prevalence Deaths due to malaria Prevalence of HIV (% of women ages 15­49) (per 100,000 people) (% of ages 15­49) Surveys 1990­99 Surveys 2000­05 Surveys 2000­05 2005 Year a Percent Year a Percent Year a Number SUB-SAHARAN AFRICA Angola 3.7 .. .. 2001 6.0 2000 354 Benin 1.8 1996 16.4 2001 18.6 2000 177 Botswana 24.1 .. .. 2000 48.0 2000 15 Burkina Faso 2.0 1999 11.9 2003 13.8 2000 292 Burundi 3.3 .. .. 2000 16.0 2000 143 Cameroon 5.4 1998 19.3 2004 26.0 2000 108 Cape Verde .. 1998 52.9 .. .. 2000 22 Central African Republic 10.7 1995 14.8 2000 28.0 2000 137 Chad 3.5 1997 4.1 2004 2.8 2000 207 Comoros 0.1 1996 21.0 2000 26.0 2000 80 Congo, Dem. Rep. 3.2 .. .. 2001 31.0 2000 224 Congo, Rep. 5.3 .. .. 2005 44.3 2000 78 Côte d'Ivoire 7.1 1999 15.0 .. .. 2000 76 Djibouti 3.1 .. .. 2002 9.0 .. .. Equatorial Guinea 3.2 .. .. .. .. 2000 152 Eritrea 2.4 1995 8.0 2002 8.0 2000 74 Ethiopia .. 1990 4.3 2005 14.7 2000 198 Gabon 7.9 .. .. 2000 32.7 2000 80 Gambia, The 2.4 .. .. 2001 18.0 2000 52 Ghana 2.3 1998 22.0 2003 25.2 2000 70 Guinea 1.5 1999 6.2 2003 7.0 2000 200 Guinea-Bissau 3.8 .. .. 2000 8.0 2000 150 Kenya 6.1 1998 39.0 2003 39.3 2000 63 Lesotho 23.2 1991 23.2 2000 30.0 2000 84 Liberia .. .. .. 2000 10.0 2000 201 Madagascar 0.5 1997 19.4 2004 27.1 2000 184 Malawi 14.1 1996 22.0 2000 30.6 2000 275 Mali 1.7 1996 6.7 2001 8.1 2000 454 Mauritania 0.7 .. .. 2001 8.0 2000 108 Mauritius 0.6 1991 75.0 2002 76.0 .. .. Mozambique 16.1 1997 5.6 2003 25.5 2000 232 Namibia 19.6 1992 28.9 2000 43.7 2000 52 Niger 1.1 1998 8.2 2000 14.0 2000 469 Nigeria 3.9 1999 15.3 2003 12.6 2000 141 Rwanda 3.1 1992 21.2 2005 17.4 2000 200 São Tomé and Principe .. .. .. 2000 29.0 2000 80 Senegal 0.9 1997 12.9 2000 10.5 2000 72 Seychelles .. .. .. .. .. .. .. Sierra Leone 1.6 .. .. 2000 4.0 2000 312 Somalia 0.9 .. .. .. .. 2000 81 South Africa 18.8 1998 56.3 2003 60.3 2000 0 Sudan 1.6 1993 10.0 2000 7.0 2000 70 Swaziland 33.4 .. .. 2002 48.0 2000 0 Tanzania 6.5 1999 25.4 2005 26.4 2000 130 Togo 3.2 1998 23.5 2000 26.0 2000 47 Uganda 6.7 1995 14.8 2001 22.8 2000 152 Zambia 17.0 1999 53.5 2002 34.2 2000 141 Zimbabwe 20.1 1999 53.5 .. .. 2000 1 NORTH AFRICA Algeria 0.1 1992 50.8 2002 57.0 .. .. Egypt, Arab Rep. 0.1 1998 51.7 2005 59.2 .. .. Libya .. 1995 45.0 .. .. .. .. Morocco 0.1 1997 59.0 2004 63.0 .. .. Tunisia 0.1 1995 60.0 2000 66.0 .. .. a. Data are for most recent year available during the period specified. 54 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Children sleeping under insecticide-treated bednets Incidence of tuberculosis Tuberculosis cases detected under DOTS (% of children under age 5) (per 100,000 people) (% of estimated cases) Surveys 2000­05 Surveys 1990­99 Surveys 2000­05 Surveys 1990­99 Surveys 2000­05 Year a Percent Year a Number Yeara Number Yeara Percent Year a Percent 2001 2.0 1999 243.5 2005 268.8 1999 51.3 2005 85.4 2001 7.0 1999 84.0 2005 87.9 1999 84.7 2005 83.3 .. .. 1999 577.2 2005 654.5 1999 68.5 2005 68.5 2003 2.0 1999 178.1 2005 223.3 1999 16.4 2005 17.6 2000 1.0 1999 282.8 2005 333.7 1999 37.1 2005 29.6 2004 1.0 1999 147.7 2005 174.3 1999 21.6 2005 105.8 .. .. 1999 167.1 2005 174.4 .. .. 2005 33.9 2000 2.0 1999 265.9 2005 313.8 1996 61.4 2005 40.4 2000 0.6 1999 230.2 2005 271.6 1999 36.2 2005 21.7 2000 9.0 1999 58.5 2005 45.0 1998 54.5 2005 48.9 2001 1.0 1999 301.9 2005 356.2 1999 53.9 2005 72.4 .. .. 1999 310.7 2005 366.6 1998 50.0 2005 57.4 2004 4.0 1999 324.1 2005 382.4 1999 43.8 2005 37.6 .. .. 1999 690.4 2005 762.3 1999 74.2 2005 42.1 2000 1.0 1999 197.2 2005 232.7 1998 81.0 2004 80.8 2002 4.0 1999 255.3 2005 281.9 1999 13.7 2005 12.5 2005 1.5 1999 291.5 2005 343.9 1999 25.1 2005 32.7 .. .. 1999 209.0 2005 307.5 .. .. 2005 57.5 2000 15.0 1999 219.6 2005 242.4 1998 75.7 2005 69.4 2003 4.0 1999 212.0 2005 205.0 1999 30.8 2005 37.5 2003 4.0 1999 185.6 2005 235.9 1999 52.3 2005 55.6 2000 7.0 1999 186.8 2005 206.3 .. .. 2005 79.1 2003 5.0 1999 390.9 2005 641.0 1999 54.6 2005 42.8 .. .. 1999 527.0 2005 695.8 1998 74.8 2005 84.8 .. .. 1999 255.4 2005 301.4 1998 41.5 2005 50.0 2000 0.2 1999 211.5 2005 233.9 1998 67.6 2005 67.0 2004 15.0 1999 419.4 2005 409.4 1999 41.8 2005 38.6 2003 8.0 1999 288.6 2005 277.8 1999 16.4 2005 21.1 2004 2.0 1999 269.9 2005 298.0 .. .. 2005 28.2 .. .. 1999 64.9 2005 62.3 1999 35.6 2005 31.7 .. .. 1999 379.1 2005 447.3 1999 46.0 2005 48.7 2000 3.0 1999 591.0 2005 697.3 1999 83.2 2005 89.9 2000 6.0 1999 148.1 2005 163.6 1999 35.0 2005 49.6 2003 1.0 1999 239.5 2005 282.6 1999 13.2 2005 21.6 2005 13.0 1999 306.0 2005 361.0 1999 43.5 2005 29.1 2000 22.8 1999 116.2 2005 105.4 .. .. .. .. 2005 14.0 1999 230.7 2005 254.7 1999 48.2 2005 50.7 .. .. 1999 37.2 2005 33.7 1998 69.2 2005 65.3 2000 2.0 1999 349.0 2005 475.4 1998 36.0 2005 37.4 .. .. 1999 262.0 2005 224.1 1999 43.3 2005 85.8 .. .. 1999 508.4 2005 599.9 1999 57.5 2005 103.1 2000 0.0 1999 206.7 2005 228.2 1999 27.6 2005 34.6 2000 0.0 1999 699.3 2005 1261.9 .. .. 2005 42.3 2005 16.0 1999 327.7 2005 342.0 1999 50.6 2005 44.9 2005 54.0 1999 357.7 2005 372.8 1999 11.1 2005 17.9 2001 0.0 1999 323.8 2005 368.8 1999 57.0 2005 45.1 2002 7.0 1999 605.4 2005 600.1 .. .. 2005 51.6 .. .. 1999 614.8 2005 601.0 1999 46.9 2005 41.3 .. .. 1999 46.9 2005 55.3 1997 132.5 2005 105.9 .. .. 1999 31.8 2005 25.0 1999 31.3 2005 62.7 .. .. 1999 23.5 2005 18.4 1999 146.2 2005 177.7 .. .. 1999 113.8 2005 89.2 1999 91.1 2005 101.0 .. .. 1999 26.8 2005 24.4 1999 93.5 2005 82.5 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 55 Table 3.7 Millennium Development Goal 7: ensure environmental sustainability Nationally protected areas Forest area (% of total GDP per unit of energy use (% of total land area) land area) (constant 2000 PPP $ per kg of oil equivalent) 1990 2000 2005 2004 1990 2000 2004 SUB­SAHARAN AFRICA Angola 48.9 47.9 47.4 10.1 3.7 3.1 3.3 Benin 30.0 24.2 21.3 23.9 2.6 3.4 3.3 Botswana 24.2 22.1 21.1 30.9 6.2 7.3 8.6 Burkina Faso 26.1 25.3 24.8 15.4 .. .. .. Burundi 11.3 7.7 5.9 5.7 .. .. .. Cameroon 52.7 48.0 45.6 8.0 4.7 4.4 4.5 Cape Verde 14.4 20.3 20.8 0.2 .. .. .. Central African Republic 37.2 36.8 36.5 16.6 .. .. .. Chad 10.4 9.8 9.5 9.5 .. .. .. Comoros 5.4 3.6 2.2 .. .. .. .. Congo, Dem. Rep. 62.0 59.6 58.9 8.6 5.0 2.3 2.2 Congo, Rep. 66.5 66.0 65.8 18.0 2.3 3.9 3.3 Côte d'Ivoire 32.1 32.5 32.7 17.1 5.2 3.8 3.7 Djibouti 0.3 0.3 0.3 0.4 .. .. .. Equatorial Guinea 66.3 60.9 58.2 16.2 .. .. .. Eritrea .. 15.6 15.4 5.0 .. .. .. Ethiopia 13.7 13.7 13.0 18.6 2.6 2.7 2.8 Gabon 85.1 84.7 84.5 3.4 4.8 5.0 4.9 Gambia, The 44.2 46.1 47.1 3.5 .. .. .. Ghana 32.7 26.8 24.2 16.2 4.6 4.8 5.4 Guinea 30.1 28.1 27.4 6.4 .. .. .. Guinea-Bissau 78.8 75.4 73.7 0.0 .. .. .. Kenya 6.5 6.3 6.2 12.6 2.2 2.1 2.1 Lesotho 0.2 0.2 0.3 0.2 .. .. .. Liberia 42.1 35.9 32.7 15.8 .. .. .. Madagascar 23.5 22.4 22.1 3.1 .. .. .. Malawi 41.4 37.9 36.2 20.6 .. .. .. Mali 11.5 10.7 10.3 3.8 .. .. .. Mauritania 0.4 0.3 0.3 0.2 .. .. .. Mauritius 19.2 18.7 18.2 3.3 .. .. .. Mozambique 25.5 24.9 24.6 5.8 1.3 2.2 2.6 Namibia 10.6 9.8 9.3 5.6 .. 11.1 10.2 Niger 1.5 1.0 1.0 7.7 .. .. .. Nigeria 18.9 14.4 12.2 6.0 1.1 1.2 1.4 Rwanda 12.9 13.9 19.5 7.9 .. .. .. São Tomé and Principe 28.1 28.1 28.1 .. .. .. .. Senegal 48.6 46.2 45.0 11.2 5.0 6.0 6.5 Seychelles 87.0 87.0 87.0 8.3 .. .. .. Sierra Leone 42.5 39.8 38.5 4.5 .. .. .. Somalia 13.2 12.0 11.4 0.3 .. .. .. South Africa 7.6 7.6 7.6 6.1 3.9 3.7 3.7 Sudan 32.1 29.7 28.4 5.2 2.7 4.1 3.7 Swaziland 27.4 30.1 31.5 3.5 .. .. .. Tanzania 46.9 42.2 39.9 42.4 1.4 1.3 1.3 Togo 12.6 8.9 7.1 11.9 4.3 3.7 3.1 Uganda 25.0 20.6 18.4 32.6 .. .. .. Zambia 66.1 60.1 57.1 42.0 1.5 1.3 1.5 Zimbabwe 57.5 49.4 45.3 14.9 3.0 3.1 2.6 NORTH AFRICA Algeria 0.8 0.9 1.0 5.0 5.7 5.6 6.0 Egypt, Arab Rep. 0.0 0.1 0.1 5.6 5.1 5.3 4.9 Libya 0.1 0.1 0.1 0.1 .. .. .. Morocco 9.6 9.7 9.8 1.1 11.9 9.9 10.3 Tunisia 4.1 6.2 6.8 1.5 6.7 7.9 8.2 a. Data are for most recent year available during the period specified. 56 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Solid fuels use Population with sustainable access Population with sustainable access Carbon dioxide emissions (% of to an improved water source to improved sanitation (metric tons per capita) population) (%) (%) 1990 2000 2003 2000­05a 1990 2000 2004 1990 2000 2004 0.4 0.5 0.6 .. 36 46 53 29 30 31 0.1 0.2 0.3 95.6 63 65 67 12 26 33 1.5 2.3 2.3 .. 93 95 95 38 41 42 0.1 0.1 0.1 97.5 38 54 61 7 11 13 0.0 0.0 0.0 .. 69 77 79 44 38 36 0.1 0.2 0.2 82.6 50 61 66 48 50 51 0.2 0.3 0.3 .. .. 80 80 .. 41 43 0.1 0.1 0.1 .. 52 70 75 23 26 27 0.0 0.0 0.0 .. 19 35 42 7 8 9 0.2 0.2 0.2 .. 93 88 86 32 34 33 0.1 0.0 0.0 .. 43 45 46 16 25 30 0.5 0.3 0.4 83.2 .. 57 58 .. 27 27 0.4 0.4 0.3 .. 69 83 84 21 33 37 0.6 0.5 0.5 .. 72 73 73 79 81 82 0.3 0.4 0.3 .. .. 43 43 .. 52 53 .. 0.2 0.2 .. 43 54 60 7 8 9 0.1 0.1 0.1 89.0 23 22 22 3 8 13 6.3 1.2 0.9 34.1 .. 86 88 .. 36 36 0.2 0.2 0.2 .. .. 82 82 .. 53 53 0.2 0.3 0.4 91.8 55 70 75 15 18 18 0.2 0.2 0.1 79.8 44 49 50 14 17 18 0.2 0.2 0.2 .. .. 58 59 .. 34 35 0.2 0.3 0.3 87.1 45 57 61 40 43 43 .. .. .. 62.1 .. 79 79 37 37 37 0.2 0.1 0.1 .. 55 61 61 39 28 27 0.1 0.1 0.1 98.3 40 45 46 14 27 32 0.1 0.1 0.1 97.8 40 64 73 47 58 61 0.0 0.0 0.0 95.9 34 45 50 36 43 46 1.3 0.9 0.9 70.5 38 47 53 31 33 34 1.4 2.3 2.6 .. 100 100 100 .. 94 94 0.1 0.1 0.1 96.9 36 42 43 20 27 32 0.0 0.9 1.2 65.9 57 80 87 24 25 25 0.1 0.1 0.1 .. 39 44 46 7 11 13 0.5 0.4 0.4 76.6 49 49 48 39 42 44 0.1 0.1 0.1 99.4 59 70 74 37 40 42 0.6 0.6 0.6 .. .. 79 79 .. 24 25 0.4 0.4 0.4 58.7 65 73 76 33 50 57 1.6 7.0 6.6 .. 88 87 88 .. .. .. 0.1 0.1 0.1 .. .. 57 57 .. 38 39 0.0 .. .. .. .. 29 29 .. 25 26 8.1 7.4 7.9 .. 83 87 88 69 66 65 0.2 0.2 0.3 .. 64 69 70 33 34 34 0.6 1.0 0.9 .. .. 62 62 .. 48 48 0.1 0.1 0.1 98.1 46 58 62 47 47 47 0.2 0.3 0.4 .. 50 51 52 37 34 35 0.0 0.1 0.1 97.4 44 55 60 42 43 43 0.3 0.2 0.2 83.6 50 55 58 44 51 55 1.6 1.2 0.9 .. 78 80 81 50 52 53 3.0 5.4 5.1 .. 94 89 85 88 91 92 1.4 2.1 2.0 .. 94 97 98 54 65 70 8.7 8.8 8.9 .. 71 71 71 97 97 97 1.0 1.2 1.3 .. 75 79 81 56 69 73 1.6 2.1 2.1 .. 81 90 93 75 83 85 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 57 Table 3.8 Millennium Development Goal 8: develop a global partnership for development Debt sustainability Heavily Indebted Poor Countries Debt service relief (HIPC) Debt Initiative committed Public and publicly guaranteed debt service Decision point Completion point ($ millions) (% of exports) 2006 2006 2006 1990 2000 2004­05a SUB­SAHARAN AFRICA Angola .. .. .. 7.1 20.4 9.0 Benin Jul. 2000 Mar. 2003 460 8.6 10.7 .. Botswana .. 4.3 2.0 0.9 Burkina Faso Jul. 2000 Apr. 2002 930 7.7 15.1 .. Burundi Oct. 2000 Floating 1,472 40.7 25.1 41.2 Cameroon Oct. 2000 Apr. 2006 4,917 12.5 14.0 .. Cape Verde .. .. .. 8.9 10.5 8.3 Central African Republic .. .. .. 7.5 .. .. Chad May 2001 Floating 260 2.4 .. .. Comoros .. .. .. 2.5 .. .. Congo, Dem. Rep. Jul. 2003 Floating 10,389 .. .. .. Congo, Rep. .. .. 2,881 31.6 0.5 2.0 Côte d'Ivoire Mar. 1998 .. .. 14.7 14.9 1.9 Djibouti .. .. .. .. .. .. Equatorial Guinea .. .. .. 2.5 .. .. Eritrea .. .. .. .. 2.8 .. Ethiopia Nov. 2001 Apr. 2004 3,275 33.1 12.2 4.1 Gabon .. .. .. 3.8 8.8 .. Gambia, The Dec. 2000 Floating 90 17.9 .. 14.1 Ghana Feb. 2002 Jul. 2004 3,500 19.9 12.0 5.9 Guinea Dec. 2000 Floating 800 17.7 17.3 .. Guinea-Bissau Dec. 2000 Floating 790 22.0 .. .. Kenya .. .. .. 22.7 15.7 3.8 Lesotho .. .. .. 4.1 10.3 5.0 Liberia .. .. .. .. .. .. Madagascar Dec. 2000 Oct. 2004 1,900 31.9 8.4 14.5 Malawi Dec. 2000 Floating 1,000 22.4 10.8 .. Mali Aug. 2000 Mar. 2003 895 9.7 10.2 .. Mauritania Feb. 2000 Jun. 2002 1,100 24.8 .. .. Mauritius .. .. .. 4.5 16.4 5.4 Mozambique Apr. 2000 Sep. 2001 4,300 17.2 7.0 2.5 Namibia .. .. .. .. .. .. Niger Dec. 2000 Jun. 2004 1,190 3.2 6.0 .. Nigeria .. .. .. 22.3 8.2 16.7 Rwanda Dec. 2000 Jun. 2005 1,316 10.2 14.8 6.8 São Tomé and Principe Dec. 2000 Floating 200 28.6 21.0 .. Senegal Jun. 2000 Apr. 2004 850 13.7 13.2 .. Seychelles .. .. .. 7.6 3.3 6.6 Sierra Leone Mar. 2002 Floating 950 7.8 29.6 7.6 Somalia .. .. .. .. .. South Africa .. .. .. 5.5 1.5 Sudan .. .. 4.5 10.1 7.2 Swaziland .. .. 5.6 2.4 1.8 Tanzania Apr. 2000 Nov. 2001 3,000 25.1 10.8 2.2 Togo .. .. 8.6 3.2 .. Uganda Feb. 2000 May 2000 1,950 47.1 6.5 9.5 Zambia Dec. 2000 Apr. 2005 3,900 12.6 16.9 .. Zimbabwe .. .. .. 18.2 .. .. NORTH AFRICA Algeria .. .. .. 63.3 .. .. Egypt, Arab Rep. .. .. .. 23.2 8.5 6.2 Libya .. .. .. .. .. .. Morocco .. .. .. 23.1 23.0 12.2 Tunisia .. .. .. 23.0 20.0 12.0 Note: 0 indicates less than 1. a. Data are for most recent year available during the period specified. 58 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Youth unemployment rate (ages 15­24) Information and communications Total Male Female Fixed-line and mobile (share of total (share of male (share of female telephone subscribers Personal computers Internet users labor force) labor force) labor force) (per 1,000 people) (per 1,000 people) (per 1,000 people) Year Percent Year Percent Year Percent 1990 2000 2005 1990 2000 2005 1995 1999 2005 .. .. .. .. .. .. 7 7 75 .. 1 .. .. 1 11 .. .. .. .. .. .. 3 15 98 .. 1 4 .. 2 50 2001 39.7 2001 33.9 2001 46.1 19 192 541 .. 34 .. 1 14 34 .. .. .. .. .. .. 2 7 51 0 1 2 .. 1 5 .. .. .. .. .. .. 1 6 .. .. 1 .. 0 1 5 .. .. .. .. .. .. 4 13 .. .. 3 .. .. 3 15 .. .. .. .. .. .. 23 165 302 .. 56 .. .. 18 49 .. .. .. .. .. .. 2 4 27 .. 2 3 .. 1 3 .. .. .. .. .. .. 1 2 .. .. 1 .. .. 0 4 .. .. .. .. .. .. 8 13 55 0 6 .. .. 3 33 .. .. .. .. .. .. 1 1 48 .. .. .. .. 0 2 .. .. .. .. .. .. 6 27 .. .. 3 .. .. 0 13 .. .. .. .. .. .. 6 44 .. .. 5 .. 0 2 11 .. .. .. .. .. .. 10 14 69 2 9 24 0 2 13 .. .. .. .. .. .. 4 25 212 .. 5 .. .. 2 14 .. .. .. .. .. .. .. 9 18 .. 2 8 0 1 16 2005 7.7 2005 4.1 2005 11.2 3 4 14 .. 1 .. 0 0 2 .. .. .. .. .. .. 22 125 498 .. 9 33 .. 12 48 .. .. .. .. .. .. 7 30 192 .. 11 .. 0 9 .. 2000 15.9 2000 12.7 2000 19.4 3 17 143 0 3 .. 0 2 18 .. .. .. .. .. .. 2 8 .. .. 3 .. 0 1 5 .. .. .. .. .. .. 6 8 .. .. .. .. .. 2 20 .. .. .. .. .. .. 8 14 143 0 5 .. 0 3 32 .. .. .. .. .. .. 8 25 163 .. .. .. .. 2 .. .. .. .. .. .. .. 4 3 .. .. .. .. .. 0 .. .. .. .. .. .. .. 3 7 31 .. 2 .. .. 2 5 .. .. .. .. .. .. 3 8 41 .. 1 2 .. 1 4 .. .. .. .. .. .. 1 4 70 .. 1 3 .. 1 4 .. .. .. .. .. .. 3 13 256 .. 10 .. .. 2 7 2005 25.9 2005 20.5 2005 34.3 55 388 863 4 101 .. .. 73 .. .. .. .. .. .. .. 4 8 .. .. 3 .. .. 1 .. 2001 44.8 2001 40.4 2001 49.3 38 101 .. .. 40 .. 0 16 .. .. .. .. .. .. .. 1 2 23 .. 0 1 .. 0 2 .. .. .. .. .. .. 3 5 151 .. 6 .. .. 1 38 .. .. .. .. .. .. 2 7 .. .. .. .. .. 1 6 .. .. .. .. .. .. 19 33 .. .. .. .. .. 47 .. .. .. .. .. .. .. 6 44 171 2 16 21 0 4 46 .. .. .. .. .. .. 124 574 928 .. 136 189 .. 74 249 .. .. .. .. .. .. 3 7 .. .. .. .. 0 1 .. .. .. .. .. .. .. 2 15 73 .. .. .. 0 2 11 2003 60.1 2003 55.8 2003 64.8 94 302 825 7 66 85 7 55 109 .. .. .. .. .. .. 2 13 69 .. 3 90 0 1 77 .. .. .. .. .. .. 18 62 208 .. 12 .. 0 10 .. .. .. .. .. .. .. 3 8 .. .. 3 .. .. 1 .. .. .. .. .. .. .. 3 17 82 .. 19 30 0 19 49 .. .. .. .. .. .. 2 8 56 .. 3 9 0 2 17 .. .. .. .. .. .. 8 17 89 .. 7 .. 0 2 .. 2002 24.9 2002 28.2 2002 21.4 12 41 79 0 16 92 0 4 77 2004 43.4 2004 42.8 2004 46.3 32 61 494 1 7 11 0 5 58 2002 27.1 2002 21.4 2002 40 29 102 325 .. 12 38 0 7 68 .. .. .. .. .. .. 51 122 .. .. .. .. .. 2 .. 2003 17 2003 17.4 2003 15.9 17 135 455 .. 13 25 0 7 153 2005 30.7 2005 31.4 2005 29.3 37 112 692 3 22 57 0 27 95 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 59 Results framework Table 4.1 Status of Paris Declaration indicators PDI-1. Operational PDI-6. Avoidance development strategies PDI-2a. Reliable public of parallel project Comprehensive financial management implementation units PDI-11. Monitorable PDI-12. Mutual Development Framework Benchmark rating of public Number of existing performance assessment accountability composite rating of national expenditure management parallel project frameworks Independent assessment development strategya systems (0­15)b implementation units Overall ratinga framework in place? 2005 2005 2005 2005 2005 SUB­SAHARAN AFRICA Angola .. .. .. .. .. Benin C 4 29 C No Botswana .. .. .. .. .. Burkina Faso C 4 131 C No Burundi D 2.5 37 D No Cameroon .. .. .. .. .. Cape Verde C 3.5 10 D Yes Central African Republic .. .. .. .. .. Chad .. .. .. .. .. Comoros .. .. .. .. .. Congo, Dem. Rep. .. .. 34 .. No Congo, Rep. D 2.5 .. D .. Côte d'Ivoire .. .. .. .. .. Djibouti .. .. .. .. .. Equatorial Guinea .. .. .. .. .. Eritrea .. .. .. .. .. Ethiopia C 3.5 103 C Yes Gabon .. .. .. .. .. Gambia, The .. .. .. .. .. Ghana C 3.5 45 C Yes Guinea .. .. .. .. .. Guinea-Bissau .. .. .. .. .. Kenya D 3.5 17 C No Lesotho .. .. .. .. .. Liberia .. .. .. .. .. Madagascar .. .. .. .. .. Malawi C 3 69 C Yes Mali C 4 65 D No Mauritania B 2 23 C No Mauritius .. .. .. .. .. Mozambique C 3.5 40 C Yes Namibia .. .. .. .. .. Niger C 3.5 52 D No Nigeria .. .. .. .. .. Rwanda B 3.5 48 C No São Tomé and Principe .. .. .. .. .. Senegal C 3.5 23 C No Seychelles .. .. .. .. .. Sierra Leone .. .. .. .. .. Somalia .. .. .. .. .. South Africa .. .. 15 .. Yes Sudan .. .. .. .. .. Swaziland .. .. .. .. .. Tanzania B 4.5 56 B Yes Togo .. .. .. .. .. Uganda B 4 54 B No Zambia C 3 24 D Yes Zimbabwe .. .. .. .. .. Note: See technical notes for further details. PDI is Paris Declaration Indicator. a. Ratings range from A to E, where A means the development strategy substantially achieves good practices; B means it is largely developed toward achieving good practices; C means it reflects action taken toward achieving good practices; D means it incorporates some elements of good practice; and E means it reflects little action toward achieving good practices. b. Ratings range from 0 to 15 and indicate the total number of the 15 required standard benchmarks that a country has met. The higher the number the less system upgrading is required. 60 Part III. Development outcomes PARIS DECLARATION INDICATORS Drivers of growth Table 5.1 Business environment Number Protecting Rigidity of of startup Cost to start Number of Time to Number of Time to investors Time to employment procedures Time to start a business procedures register procedures enforce a disclosure resolve index (0 least to register a business (% of GNI to register property to enforce contract index (0 low insolvency rigid to 100 a business (days) per capita) property (days) a contract (days) to 10 high) (years) most rigid) 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 SUB­SAHARAN AFRICA 11 61 164 7 109 39 595 4 4 47 Angola 13 124 487 7 334 47 1,011 5 6 64 Benin 7 31 173 3 50 49 720 5 4 46 Botswana 11 108 11 4 30 26 501 8 1 20 Burkina Faso 8 34 121 8 107 41 446 6 4 64 Burundi 11 43 222 5 94 47 403 .. 4 59 Cameroon 12 37 152 5 93 58 800 8 3 56 Cape Verde 12 52 46 6 83 40 465 1 .. 44 Central African Republic 10 14 209 3 69 45 660 4 5 73 Chad 19 75 226 6 44 52 743 3 10 60 Comoros 11 23 192 5 24 60 721 6 .. 46 Congo, Dem. Rep. 13 155 481 8 57 51 685 3 5 78 Congo, Rep. 8 71 215 7 137 47 560 4 3 69 Côte d'Ivoire 11 45 134 6 32 25 525 6 2 45 Djibouti 11 37 222 7 49 59 1,225 5 5 46 Equatorial Guinea 20 136 101 6 23 38 553 6 .. 66 Eritrea 13 76 116 12 101 35 305 4 2 20 Ethiopia 7 16 46 13 43 30 690 4 2 34 Gabon 10 60 163 8 60 32 880 5 5 59 Gambia, The 8 27 292 5 371 26 247 2 3 27 Ghana 12 81 50 7 382 29 552 7 2 34 Guinea 13 49 187 6 104 44 276 5 4 41 Guinea-Bissau 17 233 261 9 211 40 1,140 0 .. 77 Kenya 13 54 46 8 73 25 360 4 5 28 Lesotho 8 73 40 6 101 58 695 2 3 35 Liberia .. .. .. .. .. .. .. .. .. .. Madagascar 10 21 35 8 134 29 591 5 .. 57 Malawi 10 37 135 6 118 40 337 4 3 21 Mali 13 42 202 5 33 28 860 6 4 51 Mauritania 11 82 122 4 49 40 400 0 8 59 Mauritius 6 46 8 6 210 37 630 6 2 30 Mozambique 13 113 86 8 42 38 1,010 7 5 54 Namibia 10 95 18 9 23 31 270 5 2 27 Niger 11 24 417 5 49 33 360 4 5 77 Nigeria 9 43 54 16 80 23 457 6 2 21 Rwanda 9 16 188 5 371 27 310 2 .. 49 São Tomé and Principe 10 144 147 7 62 67 405 6 .. 67 Senegal 10 58 113 6 114 33 780 4 3 61 Seychelles 9 38 9 4 33 29 720 4 .. 34 Sierra Leone 9 26 1,195 8 235 58 515 3 3 63 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 9 35 7 6 23 26 600 8 2 41 Sudan 10 39 59 6 9 67 770 0 .. 55 Swaziland 13 61 41 11 46 31 972 1 2 17 Tanzania 13 30 92 10 123 21 393 3 3 67 Togo 13 53 253 7 242 37 535 4 3 58 Uganda 17 30 114 13 227 19 484 7 2 7 Zambia 6 35 30 6 70 21 404 3 3 23 Zimbabwe 10 96 36 4 30 33 410 8 3 34 NORTH AFRICA 10 17 28 8 87 42 626 4 2 52 Algeria 14 24 22 15 51 49 397 6 3 45 Egypt, Arab Rep. 10 19 69 7 193 55 1,010 5 4 53 Libya .. .. .. .. .. .. .. .. .. .. Morocco 6 12 13 4 46 42 615 6 2 63 Tunisia 10 11 9 5 57 21 481 0 1 46 PRIVATE SECTOR DEVELOPMENT Part III. Development outcomes 61 Drivers of growth Table 5.2 Investment climate Domestic Viewed by firms as a major constraint (% of firms) Net foreign credit to Lack of Private direct private confidence in investment investment sector Policy courts to uphold Labor Labor (% of GDP) ($ millions) (% of GDP) uncertainty Corruption Courts property rights Crime Tax rates Finance Electricity regulations skills 2005 a 2005 2005 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 SUB­SAHARAN AFRICA 13.3 20,037.6 .. .. .. .. .. .. .. .. .. .. .. Angola 2.8 1,639.0 4.8 1.6 12.5 .. 51.0 6.2 3.0 11.6 34.5 .. 1.1 Benin 12.2 13.6 16.6 .. .. .. .. .. .. .. .. .. .. Botswana 9.7 59.8 19.0 0.7 7.9 1.4 31.4 10.9 7.3 24.3 1.7 1.5 9.4 Burkina Faso .. 22.8 17.3 .. 5.1 0.7 29.9 1.4 18.8 37.0 19.6 .. .. Burundi 1.7 1.5 20.8 14.3 2.2 0.2 36.9 2.9 3.7 16.0 40.7 .. 0.1 Cameroon 14.7 302.9 9.4 .. 5.2 1.2 37.3 2.9 32.6 13.4 15.1 1.2 .. Cape Verde .. 41.3 39.0 .. .. 2.0 35.2 1.0 13.3 16.3 35.7 1.0 4.1 Central African Republic .. .. 6.7 .. .. .. .. .. .. .. .. .. .. Chad 12.0 123.8 3.1 .. .. .. .. .. .. .. .. .. .. Comoros 4.8 2.0 8.1 .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. 10.5 551.0 1.9 5.3 0.5 .. 56.5 1.8 9.6 14.5 45.5 .. 1.0 Congo, Rep. 16.6 732.3 2.9 .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 8.4 283.1 13.8 .. .. .. .. .. .. .. .. .. .. Djibouti 15.0 60.0 20.1 .. .. .. .. .. .. .. .. .. .. Equatorial Guinea 27.6 1,869.5 5.4 .. .. .. .. .. .. .. .. .. .. Eritrea 4.7 .. 31.0 .. .. .. .. .. .. .. .. .. .. Ethiopia 11.4 149.7 25.3 .. .. .. .. .. .. .. .. .. .. Gabon 18.5 ­276.4 8.9 .. .. .. .. .. .. .. .. .. .. Gambia, The 18.5 46.4 13.0 2.1 0.6 2.3 28.4 2.3 6.5 11.6 53.7 .. 1.7 Ghana 17.0 145.0 15.5 .. .. .. .. .. .. .. .. .. .. Guinea 10.4 63.9 5.1 1.4 2.7 0.4 59.0 1.7 3.1 8.3 61.0 .. .. Guinea-Bissau 4.2 .. 2.1 5.0 6.1 1.4 70.3 0.7 5.3 19.6 41.4 .. .. Kenya 11.6 134.0 25.9 .. .. .. .. .. .. .. .. .. .. Lesotho 27.1 68.7 8.4 .. .. .. .. .. .. .. .. .. .. Liberia 4.3 0.0 6.6 .. .. .. .. .. .. .. .. .. .. Madagascar 12.3 85.7 9.9 .. .. .. .. .. .. .. .. .. .. Malawi 3.4 26.5 10.5 .. .. .. .. .. .. .. .. .. .. Mali 15.0 61.2 18.4 .. .. .. .. .. .. .. .. .. .. Mauritania .. 863.6 .. 0.7 1.5 0.8 35.6 1.2 12.8 21.6 13.0 0.4 3.8 Mauritius 14.8 -32.0 76.7 .. .. .. .. .. .. .. .. .. .. Mozambique 13.6 105.4 11.2 .. .. .. .. .. .. .. .. .. .. Namibia 18.8 252.3 61.4 0.8 9.3 0.6 24.9 20.6 17.2 11.8 3.1 4.4 9.4 Niger 8.8 40.7 6.8 .. .. .. .. .. .. .. .. .. .. Nigeria 12.0 6,409.4 14.9 .. .. .. .. .. .. .. .. .. .. Rwanda 12.2 10.6 13.5 0.9 0.8 .. 34.6 .. 26.9 13.6 31.8 .. 2.8 São Tomé and Principe .. 31.4 44.7 .. .. .. .. .. .. .. .. .. .. Senegal 15.6 100.5 23.8 .. .. .. .. .. .. .. .. .. .. Seychelles 7.1 78.1 40.6 .. .. .. .. .. .. .. .. .. .. Sierra Leone 11.6 27.2 4.5 .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 15.1 2,305.3 143.5 .. .. .. .. .. .. .. .. .. .. Sudan 13.5 2,355.0 10.0 .. .. .. .. .. .. .. .. .. .. Swaziland 9.5 34.1 20.0 0.6 5.2 1.0 56.7 18.5 15.4 10.3 6.8 0.4 2.3 Tanzania 10.8 495.0 10.4 0.5 0.5 .. 35.4 1.9 3.9 9.3 72.9 .. 1.4 Togo 18.1 115.5 16.8 .. .. .. .. .. .. .. .. .. .. Uganda 16.4 245.4 6.7 0.3 2.4 0.1 35.6 0.2 11.0 6.7 63.3 .. 0.4 Zambia 15.8 380.0 7.6 .. .. .. .. .. .. .. .. .. .. Zimbabwe 19.5 13.0 26.9 .. .. .. .. .. .. .. .. .. .. NORTH AFRICA 13.9 7,393.3 .. .. .. .. .. .. .. .. .. .. .. Algeria 14.1 1,020.0 11.8 .. .. .. .. .. .. .. .. .. .. Egypt, Arab Rep. 9.1 3,862.8 52.4 .. .. .. .. .. .. .. .. .. .. Libya .. .. 9.0 .. .. .. .. .. .. .. .. .. .. Morocco 21.6 1,698.1 62.2 .. .. .. .. .. .. .. .. .. .. Tunisia .. 812.4 65.6 .. .. .. .. .. .. .. .. .. .. a. Provisional. 62 Part III. Development outcomes PRIVATE SECTOR DEVELOPMENT Regulation and tax administration Interest rate Highest marginal Time dealing spread Market Time to prepare, tax rate, with officials Average time to Bank branches (lending rate capitalization of Turnover ratio for Number of tax file, and pay taxes Total tax payable corporate rate (% of clear customs (per 100,000 minus deposit Listed domestic listed companies traded stocks payments (hours) (% of profi t) (%) management time) (days) people) rate) companies (% of GDP) (%) 2006 2006 2006 2006 2006 2006 2004 2005 2006a 2005a 2006a 40.8 331 70.6 .. .. 5.0 .. .. .. .. .. 42 272 64.4 .. 7.1 16.5 .. 54.3 .. .. .. 72 270 68.5 38.0 .. .. .. .. .. .. .. 24 140 53.3 15.0 5.0 1.2 3.8 6.5 18.0 23.6 2.4 45 270 51.1 .. 9.5 3.1 .. .. .. .. .. 40 140 286.7 .. 5.7 4.4 .. .. .. .. .. 39 1,300 46.2 .. 12.8 4.3 .. 12.8 .. .. .. 49 100 54.4 .. 12.2 10.0 .. 8.9 .. .. .. 54 504 209.5 .. .. .. .. 12.8 .. .. .. 65 122 68.2 .. .. .. .. 12.8 .. .. .. 20 100 47.5 .. .. .. .. 8.0 .. .. .. 34 312 235.4 40.0 6.3 3.6 .. .. .. .. .. 94 576 57.3 .. .. .. .. 12.8 .. .. .. 71 270 45.7 35.0 .. .. .. .. 40.0 14.2 3.7 36 114 41.7 .. .. .. .. 10.3 .. .. .. 48 212 62.4 .. .. .. .. 12.8 .. .. .. 18 216 86.3 .. .. .. .. .. .. .. .. 20 212 32.8 .. .. .. 0.4 3.5 .. .. .. 27 272 48.3 .. .. .. .. 12.8 .. .. .. 47 376 291.4 .. 7.3 5.0 .. 17.6 .. .. .. 35 304 32.3 25.0 .. .. 1.6 .. 32.0 12.8 3.4 55 416 49.4 .. 2.6 4.1 .. .. .. .. .. 47 208 47.5 .. 2.9 5.6 .. .. .. .. .. 17 432 74.2 .. .. .. 1.4 7.8 51.0 34.1 15.8 21 352 25.6 .. .. .. .. 7.8 .. .. .. .. .. .. .. .. .. .. 13.6 .. .. .. 25 304 43.2 .. .. .. 0.7 8.3 .. .. .. 29 878 32.6 .. .. .. .. 22.2 .. .. .. 60 270 50.0 .. .. .. .. .. .. .. .. 61 696 104.3 .. 5.8 3.9 .. 15.1 .. .. .. 7 158 24.8 25.0 .. .. 11.9 13.8 41.0 41.6 6.0 36 230 39.2 32.0 .. .. .. 11.7 .. .. .. 34 .. 25.6 35.0 2.9 1.3 4.5 4.4 9.0 6.8 4.6 44 270 46.0 .. .. .. .. .. .. .. .. 35 1,120 31.4 .. .. .. 1.6 7.4 202.0 19.6 13.8 43 168 41.1 .. 5.9 6.7 .. .. .. .. .. 42 424 55.2 .. .. .. .. .. .. .. .. 59 696 47.7 .. .. .. .. .. .. .. .. 15 76 48.8 .. .. .. .. 6.3 .. .. .. 20 399 277.0 .. .. .. .. 13.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 23 350 38.3 29.0 .. .. 6.0 4.6 401.0 236.0 49.5 66 180 37.1 .. .. .. .. .. .. .. .. 34 104 39.5 30.0 4.4 1.9 .. 6.6 .. 7.2 .. 48 248 45.0 30.0 4.0 4.8 0.6 10.4 .. 4.9 .. 51 270 48.3 .. .. .. .. .. .. .. .. 31 237 32.2 30.0 5.2 2.9 0.5 10.9 .. 1.2 .. 37 131 22.2 .. .. .. 1.5 17.0 .. 13.6 .. 59 216 37.0 .. .. .. 3.3 144.6 80.0 71.2 7.9 43.8 444 59.6 .. .. .. .. .. .. .. .. 61 504 76.4 .. .. .. .. 6.3 .. .. .. 41 536 50.4 .. .. .. 3.6 5.9 603.0 89.1 55.2 .. .. .. .. .. .. .. 4.0 .. .. .. 28 468 52.7 .. .. .. 6.6 .. 65.0 52.7 32.9 45 268 58.8 .. .. .. .. .. 48.0 10.0 15.2 PRIVATE SECTOR DEVELOPMENT Part III. Development outcomes 63 Drivers of growth Table 6.1 International trade and tariff barriers Trade Annual growth (%) Terms of Merchandise Exports Imports Exports Imports trade index trade (% of GDP) ($ millions) ($ millions) (% of GDP) (% of GDP) Exports Imports (2000=100) 2005a 2005a 2005a 2005a 2005a 2005a 2005a 2005a SUB­SAHARAN AFRICA 71 230,383 216,632 36.6 34.4 .. 11.4 .. Angola 121.8 24,121 15,834 73.5 48.3 .. .. .. Benin 39.6 577 1,119 13.5 26.1 5.0 1.0 91.8 Botswana 84.6 5,519 3,313 52.8 34.6 21.7 1.0 98.8 Burkina Faso .. .. .. .. 21.9 .. .. .. Burundi 47.7 91 291 11.4 36.3 .. .. .. Cameroon 48.8 3,958 4,282 23.5 25.4 0.1 1.2 110.3 Cape Verde .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. Chad 94.4 3,239 2,328 54.9 39.3 17.0 0.8 100.0 Comoros 47.2 48 134 12.5 34.7 2.8 1.1 100.0 Congo, Dem. Rep. 73.8 2,450 2,792 34.5 39.3 8.8 1.2 165.6 Congo, Rep. 136.6 5,160 2,994 86.4 54.8 .. .. .. Côte d'Ivoire 90.7 8,097 6,466 50.4 42.4 -1.5 1.0 123.3 Djibouti 90 259 379 36.5 53.5 2.1 1.0 100.0 Equatorial Guinea 144.4 7,277 3,583 96.8 .. 7.2 1.2 144.3 Eritrea 64.5 85 540 8.8 55.7 -0.1 1.0 84.6 Ethiopia 54.7 1,858 4,367 16.3 39.1 3.7 1.2 75.4 Gabon 90.3 5,844 1,983 67.4 38.5 -1.5 0.8 134.0 Gambia, The 110.2 207 302 44.8 65.4 27.3 1.1 73.5 Ghana 97.7 3,869 6,610 36.1 61.7 9.3 1.1 84.6 Guinea 58.2 924 1,012 27.8 29.6 5.6 0.9 89.1 Guinea-Bissau 88.5 114 153 37.7 55.2 5.0 1.1 86.6 Kenya 60.8 5,126 6,540 26.7 34.9 4.7 1.1 102.9 Lesotho 135.3 695 1,276 47.7 88 -14.4 0.8 123.6 Liberia 90 201 275 38 50.2 .. .. .. Madagascar 67.9 1,355 2,067 26.9 40.3 -4.4 1.0 139.1 Malawi 79.3 566 1,080 27.3 53 19.0 1.0 74.3 Mali 63.1 1,333 2,015 25.1 37.2 8.7 1.0 85.3 Mauritania 131.6 659 1,758 35.9 95 6.2 1.5 131.0 Mauritius 117.4 3,556 3,830 56.5 60.9 5.7 1.0 90.3 Mozambique 73.2 2,164 2,830 31.7 42.3 5.0 1.1 80.2 Namibia 93.5 2,961 2,819 47.9 45 2.3 1.0 97.6 Niger 39.3 512 825 15.1 24.2 .. .. .. Nigeria 90.1 52,575 34,855 54.2 35.2 -1.8 1.2 140.5 Rwanda 41.5 228 667 10.6 31 -2.2 1.2 65.8 São Tomé and Principe .. .. .. .. 99.4 .. .. .. Senegal 65.7 2,221 3,431 25.8 41.6 3.2 1.1 97.1 Seychelles 224.4 714 908 98.7 120.5 11.3 1.3 86.7 Sierra Leone 65.4 281 499 23.5 42.7 .. .. .. Somalia .. .. .. .. .. .. .. .. South Africa 55.1 64,904 68,412 26.8 28.6 6.7 1.1 108.8 Sudan 45.4 4,973 7,701 17.8 28 13.0 .. .. Swaziland 165 2,095 2,217 80.2 95.4 6.0 1.1 106.5 Tanzania 54.4 2,964 3,881 23.5 26.3 12.3 1.1 72.8 Togo 83.9 743 1,026 35.2 46.6 7.5 1.0 100.4 Uganda 40.3 1,145 2,370 13.1 27.2 4.4 1.2 106.6 Zambia 41.6 1,192 1,835 16.4 25.2 12.3 1.2 83.9 Zimbabwe 129.8 1,941 2,495 56.8 52.9 -3.4 1.0 92.2 NORTH AFRICA 72.5 125,672 101,546 40.1 32.4 .. 12.7 .. Algeria 71.4 48,690 24,020 47.8 23.5 5.8 1.1 133.0 Egypt, Arab Rep. 63 27,214 29,246 30.3 32.7 22.5 1.2 107.6 Libya .. .. .. .. .. .. .. .. Morocco 80.8 18,809 22,885 36.4 42.9 8.5 1.1 97.8 Tunisia 98.6 13,766 14,525 48 50.6 3.2 1.0 97.7 a. Provisional. b. Data are for the most recent year available during the period specified. 64 Part III. Development outcomes TRADE Export Structure of merchandise exports Structure of merchandise imports diversification (% of total) (% of total) index Agricultural Ores and Agricultural Ores and (0 low to Food raw materials Fuel metals Manufactures Food raw materials Fuel metals Manufactures 100 high) 2000­05b 2000­05 b 2000­05 b 2000­05b 2000­05b 2000­05b 2000­05 b 2000­05 b 2000­05b 2000­05b 2000­05b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 1.1 24.8 61.0 0.7 0.5 12.8 29.8 4.2 20.4 0.9 43.9 2.9 2.4 0.1 0.0 10.7 86.4 13.9 0.8 4.4 1.1 75.1 1.3 16.4 72.3 2.8 0.6 8.0 12.0 0.6 24.4 0.6 62.5 1.4 86.8 4.2 0.1 2.5 6.2 6.5 1.4 8.5 0.8 82.3 1.3 17.1 13.0 49.6 5.5 3.3 18.0 1.8 26.4 1.1 52.8 3.6 10.4 5.9 48.5 0.1 89.5 30.2 1.9 7.6 0.3 60.0 2.6 0.8 41.2 0.4 16.9 36.1 17.1 27.2 16.9 1.5 36.7 3.5 .. .. .. .. .. .. .. .. .. .. 1.1 88.7 0.0 .. 0.0 8.2 21.9 0.4 4.1 0.2 72.5 2.6 .. .. .. .. .. .. .. .. .. .. 4.1 .. .. .. .. .. .. .. .. .. .. 1.3 55.8 9.2 12.8 0.2 20.0 21.7 0.6 17.1 1.2 48.5 5.6 .. .. .. .. .. .. .. .. .. .. 14.8 .. .. .. .. .. .. .. .. .. .. 1.2 .. .. .. .. .. .. .. .. .. .. 15.4 62.0 25.9 0.0 0.7 11.4 21.5 0.7 12.0 1.5 64.0 3.6 1.2 9.8 76.2 5.5 7.0 24.2 0.6 3.2 1.2 69.7 1.7 78.4 4.3 0.8 0.3 16.9 37.7 1.4 16.1 1.0 43.5 4.6 77.1 5.0 3.3 2.2 12.1 20.8 1.3 1.6 2.1 74.2 4.3 2.0 0.8 0.1 71.6 25.3 23.1 1.2 21.7 0.8 53.0 3.3 .. .. .. .. .. .. .. .. .. .. 1.1 39.7 12.0 23.0 4.2 21.1 10.4 2.1 24.3 1.6 61.3 15.2 .. .. .. .. .. .. .. .. .. .. 5.6 .. .. .. .. .. .. .. .. .. .. 1.8 60.7 6.2 4.4 5.1 22.5 13.5 0.4 23.3 0.4 61.8 12.2 79.5 3.8 0.0 0.2 16.3 18.2 1.0 10.5 0.8 68.3 2.8 9.6 22.3 11.3 0.3 54.6 16.2 0.7 21.9 0.7 60.3 1.5 .. .. .. .. .. .. .. .. .. .. 2.9 27.6 0.5 0.1 0.5 69.9 16.7 1.9 16.5 1.0 63.9 9.9 11.7 3.8 15.0 58.1 7.0 14.4 1.0 1.6 0.4 49.4 1.8 48.3 1.3 1.0 7.3 40.9 14.9 0.7 10.4 3.6 69.4 5.2 30.4 3.6 1.6 54.9 7.9 33.5 4.3 16.9 1.2 44.0 1.6 0.0 0.0 97.9 0.0 2.1 15.5 0.6 16.0 1.6 66.3 1.2 52.3 7.3 6.8 23.3 10.3 11.7 4.0 15.6 2.0 66.7 3.1 .. .. .. .. .. .. .. .. .. .. 3.0 28.8 2.1 21.1 2.8 43.4 28.1 1.6 22.9 2.2 45.1 5.7 57.0 0.0 36.4 0.0 6.5 21.5 1.0 23.5 0.3 48.3 3.5 91.6 0.8 .. 0.1 7.5 22.5 7.6 39.7 0.8 29.3 2.5 .. .. .. .. .. .. .. .. .. .. 5.6 8.5 2.0 10.3 22.4 56.7 4.4 1.1 14.3 1.8 69.6 23.1 6.8 4.8 87.3 0.4 0.1 13.0 0.7 1.2 1.0 83.2 1.3 14.6 7.8 0.7 0.2 76.4 18.2 2.2 12.6 1.0 64.4 18.9 56.7 16.7 0.2 11.7 14.4 11.9 1.3 9.9 1.1 75.7 18.7 21.5 8.9 1.2 10.3 58.1 15.5 0.8 29.0 2.1 52.6 7.6 64.0 11.6 5.1 2.3 17.0 15.0 1.5 16.9 1.2 64.5 5.8 13.1 5.4 0.7 71.9 8.8 6.3 1.3 11.6 2.7 77.9 3.1 30.9 15.7 1.6 23.2 28.5 18.7 1.8 13.7 9.8 54.2 13.9 .. .. .. .. .. .. .. .. .. .. .. 0.2 0.0 97.4 0.4 2.0 21.9 1.9 0.9 1.3 73.9 2.1 9.8 7.0 43.1 3.7 30.6 22.2 5.1 8.3 3.5 49.8 22.6 .. .. .. .. .. 16.8 0.6 0.7 0.9 81.1 1.1 21.5 1.9 2.5 9.0 65.2 10.7 2.8 21.9 2.8 61.7 34.7 11.1 0.7 9.6 1.1 77.6 8.6 2.8 10.3 2.7 75.6 30.6 TRADE Part III. Development outcomes 65 Drivers of growth Table 6.1 International trade and tariff barriers (continued) Competitiveness indicator (%) Tariff barriers, all products (%) Share of lines Share of lines Sectoral Global Binding Simple mean Simple mean Weighted with international with specific effect effect coverage bound rate tariff mean tariff peaks rates 1999­2003 1999­2003 2005 2005 2005 2005 2005 2005 SUB­SAHARAN AFRICA .. .. .. .. .. .. .. .. Angola 13.5 23.4 100.0 59.2 7.6 6.0 10.3 0.0 Benin ­8.3 ­11.0 39.1 28.6 14.4 12.4 57.6 0.0 Botswana 9.7 488.1 96.3 19.0 9.9 11.2 23.6 0.2 Burkina Faso ­7.4 2.2 39.3 41.9 13.1 11.7 48.6 0.0 Burundi 6.9 14.2 20.9 67.5 19.6 19.9 46.5 0.0 Cameroon 4.0 ­7.4 12.6 79.9 18.4 16.5 52.6 0.0 Cape Verde ­4.9 ­1.1 .. .. .. .. .. .. Central African Republic ­1.0 ­20.8 .. .. 17.9 16.8 58.0 0.0 Chad ­10.4 473.8 .. .. 17.2 12.5 48.7 0.0 Comoros ­14.5 ­8.4 .. .. .. .. .. .. Congo, Dem. Rep. 5.8 ­16.4 .. .. .. .. .. .. Congo, Rep. 11.5 10.1 .. .. 19.1 17.7 56.4 0.0 Côte d'Ivoire 1.1 ­6.6 33.2 11.2 12.6 10.3 44.3 0.0 Djibouti ­3.1 ­30.4 .. .. .. .. .. .. Equatorial Guinea 10.4 36.9 .. .. .. .. .. .. Eritrea ­4.9 1.8 .. .. .. .. .. .. Ethiopia ­5.6 19.2 .. .. .. .. .. .. Gabon 11.7 ­12.0 100.0 21.4 19.9 16.8 60.6 0.0 Gambia, The ­7.5 ­6.5 .. .. .. .. .. .. Ghana 0.0 ­4.2 .. .. .. .. .. .. Guinea ­0.7 ­6.7 39.0 20.1 14.2 12.7 58.6 0.0 Guinea-Bissau 10.6 ­18.7 .. .. 14.1 14.0 55.8 0.0 Kenya ­6.7 2.2 14.0 95.1 12.1 7.5 36.4 0.0 Lesotho ­10.2 26.1 .. .. 9.9 16.8 24.1 0.8 Liberia 3.5 ­15.1 .. .. .. .. .. .. Madagascar ­8.7 ­5.4 29.7 27.4 11.6 5.2 37.1 0.0 Malawi ­11.9 3.5 .. .. .. .. .. .. Mali ­4.7 ­7.7 40.7 28.8 12.4 10.7 43.7 0.0 Mauritania 13.3 ­13.6 .. .. .. .. .. .. Mauritius ­5.6 ­7.2 18.0 94.0 8.5 4.7 19.7 0.0 Mozambique ­4.1 18.9 .. .. 13.1 8.6 38.2 0.0 Namibia ­0.8 60.2 96.3 19.4 5.6 1.3 15.2 0.0 Niger 4.6 ­23.4 96.8 44.3 12.7 12.8 47.6 0.0 Nigeria 14.8 ­1.7 18.2 117.8 11.6 10.8 41.0 0.0 Rwanda 41.3 ­50.6 100.0 89.5 17.2 9.7 47.0 0.0 São Tomé and Principe ­1.8 ­16.0 .. .. .. .. .. .. Senegal ­2.8 ­9.9 100.0 30.0 14.0 9.2 53.8 0.0 Seychelles ­5.3 8.5 .. .. 18.3 46.0 23.9 0.0 Sierra Leone 1.3 19.8 .. .. .. .. .. .. Somalia ­4.0 ­7.0 .. .. .. .. .. .. South Africa 2.1 ­5.0 96.3 19.4 8.5 5.4 21.3 1.0 Sudan 11.5 19.5 .. .. .. .. .. .. Swaziland ­5.9 20.2 96.3 19.4 10.8 10.5 26.6 0.0 Tanzania ­2.5 9.5 13.4 120.0 12.9 8.4 38.0 0.0 Togo ­6.0 ­6.4 13.2 80.0 14.6 10.4 55.3 0.0 Uganda ­3.0 1.9 14.9 73.5 12.4 9.0 38.3 0.0 Zambia 7.3 11.2 15.9 105.7 14.6 9.4 34.5 0.0 Zimbabwe ­2.5 ­11.6 .. .. .. .. .. .. NORTH AFRICA .. .. .. .. .. .. .. .. Algeria ­1.4 14.9 .. .. 15.8 10.6 38.6 0.0 Egypt, Arab Rep. ­2.6 13.1 99.1 36.6 18.9 12.0 21.8 0.0 Libya 11.2 4.4 .. .. .. .. .. .. Morocco ­4.3 2.5 100.0 41.3 19.4 13.7 57.0 0.0 Tunisia ­4.0 1.5 57.9 57.7 13.4 9.1 31.0 0.0 a. Provisional. b. Data are for most recent year available during the period specified. 66 Part III. Development outcomes TRADE Average cost to ship 20 ft container Tariff barriers, primary products (%) Tariff barriers, manufactured products (%) from port to final destination ($) Average time to clear customs Simple mean tariff Weighted mean tariff Simple mean tariff Weighted mean tariff Export Import (days) 2005 2005 2005 2005 2006 2006 .. .. .. .. 1,750 2,181 5.0 12.0 13.1 6.8 4.4 1,850 2,325 16.5 15.4 12.0 14.2 12.8 1,167 1,202 .. 5.1 1.0 10.1 12.9 2,328 2,595 1.2 13.6 10.1 13.0 12.6 2,096 3,522 3.1 26.1 25.5 18.5 18.7 2,147 3,705 4.4 20.9 19.5 18.0 15.5 524 1,360 4.3 .. .. .. .. .. .. 10.0 21.8 24.8 17.4 13.2 4,581 4,534 .. 22.1 25.0 16.5 10.3 4,867 5,520 .. .. .. .. .. .. .. .. .. .. .. .. 3,120 3,308 3.6 22.9 22.1 18.5 16.2 2,201 2,201 .. 14.9 11.2 12.2 9.9 1,653 2,457 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 935 1,185 .. .. .. .. .. 1,617 2,793 .. 22.9 19.4 19.3 15.8 .. .. .. .. .. .. .. .. .. 5.0 .. .. .. .. 822 842 .. 16.3 14.3 13.9 11.2 570 995 4.1 16.3 13.3 13.6 14.7 .. .. 5.6 15.9 8.6 11.6 6.7 1,980 2,325 .. 7.4 3.3 10.0 17.6 1,188 1,210 .. .. .. .. .. .. .. .. 16.9 4.1 11.0 5.9 982 1,282 .. .. .. .. .. 1,623 2,500 .. 14.4 11.7 12.2 10.4 1,752 2,680 .. .. .. .. .. 3,733 3,733 3.9 9.0 5.3 8.3 4.2 683 683 .. 16.4 9.1 12.6 8.5 1,155 1,185 .. 3.7 0.5 5.9 1.6 1,539 1,550 1.3 14.9 14.7 12.4 12.1 2,945 2,946 .. 14.9 14.9 11.3 9.3 798 1,460 .. 12.7 5.5 17.7 12.2 3,840 4,080 6.7 .. .. .. .. 690 577 .. 14.9 8.2 13.8 10.4 828 1,720 .. 30.5 110.1 15.7 15.5 .. .. .. .. .. .. .. 1,282 1,242 .. .. .. .. .. .. .. .. 5.4 1.7 8.8 6.5 1,087 1,195 .. .. .. .. .. 1,870 1,970 .. 10.3 4.3 10.8 10.8 .. .. 1.9 18.7 10.6 12.2 7.7 822 917 4.8 15.4 9.7 14.4 11.0 463 695 .. 16.7 10.1 11.9 8.4 1,050 2,945 2.9 14.9 9.3 14.5 9.4 2,098 2,840 .. .. .. .. .. 1,879 2,420 .. .. .. .. .. 1,023 1,259 .. 15.3 9.0 15.7 11.0 1,606 1,886 .. 85.8 16.4 11.6 10.5 1,014 1,049 .. .. .. .. .. .. .. .. 23.3 12.1 18.8 14.3 700 1,500 .. 27.4 13.8 12.0 7.5 770 600 .. TRADE Part III. Development outcomes 67 Drivers of growth Table 6.2 Top three exports and share in total exports, 2005 First Second Share Share of total of total exports exports Product (%) Product (%) SUB­SAHARAN AFRICA Angola Crude petroleum 95.8 Benin Cotton, not carded, combed 55.3 Edible nuts fresh, dried 16.5 Botswana Diamonds excluding industrial 88.2 Nickel mattes, sinters, and the like 8.1 Burkina Faso Cotton, not carded, combed 84.5 Burundi Coffee, not roasted 88.0 Cameroon Crude petroleum 48.8 Wood, nonconiferous, sawn 14.1 Cape Verde Fish, frozen, excluding fillets 61.4 Trousers, breeches, and the like 6.3 Central African Republic Diamonds excluding industrial 40.0 Wood, nonconiferous, rough, untreated 33.8 Chad Crude petroleum 94.9 Comoros Spices, excluding pepper, pimento 57.9 Essential oils 14.2 Congo, Dem. Rep. Diamonds excluding industrial 42.6 Other nonferrous ore, concentrated 17.2 Congo, Rep. Crude petroleum 88.7 Côte d'Ivoire Cocoa beans 38.2 Crude petroleum 12.0 Djibouti Bovine animals, live 20.0 Trousers, breeches, and the like 7.2 Equatorial Guinea Crude petroleum 92.6 Eritrea Natural gums, resins, and the like 17.3 Sesame (sesamum) seeds 8.7 Ethiopia Coffee, not roasted 47.8 Sesame (sesamum) seeds 20.2 Gabon Crude petroleum 76.7 Wood, nonconiferous, rough, untreated 10.6 Gambia, The Edible nuts fresh, dried 43.5 Mechanical shovel and the like, self-propelled 9.9 Ghana Cocoa beans 46.1 Manganese ores, concentrates 7.2 Guinea Aluminium ore, concentrate 50.9 Alumina (aluminium oxide) 17.2 Guinea-Bissau Edible nuts fresh, dried 93.5 Kenya Tea 16.8 Cut flowers and foliage 14.2 Lesotho Jerseys, pullovers, and the like, knit 29.2 Trousers, breeches, and the like 22.0 Liberia Ships, boats, other vessels 73.9 Special purpose vessels and the like 8.9 Madagascar Jerseys, pullovers, and the like, knit 19.4 Crustaceans, frozen 13.2 Malawi Tobacco, stemmed, stripped 59.2 Tea 7.6 Mali Cotton, not carded, combed 81.8 Mauritania Iron ore, concentrates not agglomerated 51.3 Molluscs 24.0 Mauritius Sugars, beet or cane, raw 21.4 T-shirts, other vests knit 18.7 Mozambique Aluminium, aluminium alloy, unwrought 73.4 Crustaceans, frozen 4.7 Namibia Diamonds excluding industrial 39.1 Radioactive chemicals 11.4 Niger Radioactive chemicals 79.5 Nigeria Crude petroleum 92.2 Rwanda Coffee, not roasted 51.9 Ores and concentrates of molybdenum, niobium, and the like 19.0 São Tomé and Principe Cocoa beans 55.2 Vessels, other floating structures 10.9 Senegal Inorganic acid, oxide, and the like 38.8 Molluscs 9.8 Seychelles Fish, prepared, preserved, not elsewhere specified 44.1 Fish, frozen excluding fillets 27.5 Sierra Leone Diamonds excluding industrial 62.7 Cocoa beans 7.2 Somalia Sheep and goats, live 34.6 Bovine animals, live 19.7 South Africa Platinum 12.5 Other coal, not agglomerated 8.0 Sudan Crude petroleum 89.2 Swaziland Sugars, beet or cane, raw 14.1 Food preparations, not elsewhere specified 9.3 Tanzania Gold, nonmonetary excluding ores 10.9 Fish fillets, fresh, chilled 9.7 Togo Cocoa beans 22.4 Natural calcium phosphates 19.8 Uganda Coffee, not roasted 31.1 Fish fillets, fresh, chilled 24.3 Zambia Copper, anodes, alloys 55.8 Cobalt, cadmium, and the like, unwrought 7.0 Zimbabwe Tobacco, stemmed, stripped 13.9 Nickel, nickel alloy, unwrought 12.6 NORTH AFRICA Algeria Crude petroleum 67.2 Natural gas, liquefied 13.2 Egypt, Arab Rep. Natural gas, liquefied 15.8 Crude petroleum 10.3 Libya Crude petroleum 95.3 Morocco Inorganic acid, oxide, and the like 7.2 Insulated wire, and the like, conductor 6.8 Tunisia Crude petroleum 9.0 Trousers, breeches, and the like 8.7 49.2 3.7 AFRICAa Crude petroleum (18.0) Diamonds excluding industrial (12.6) Note: Products are reported when accounting for more than 4 percent of total exports. a. Values in parentheses are Africa's share of total world exports. 68 Part III. Development outcomes TRADE Third Number of exports Share of total accounting for exports 75 percent of Product (%) total exports 1 Other nonferrous metal waste 6.4 3 1 1 1 Bananas, fresh or dried 8.7 4 Gas turbines, not elsewhere specified 4.0 4 Cotton, not carded, combed 8.9 3 1 Fish, frozen excluding fillets 12.7 3 Crude petroleum 16.7 3 1 Cocoa paste 7.7 7 Other ferrous waste, scrap 7.0 17 1 Molluscs 7.6 14 5 Manganese ores, concentrates 6.9 1 Groundnuts (peanuts) 7.7 6 Wood, nonconiferous, sawn 6.7 8 Copper ores, concentrates 7.8 3 1 Other fresh, chilled vegetables 8.1 27 Diamonds excluding industrial 15.0 4 Natural rubber latex 8.0 2 Spices, excluding pepper, pimento 9.0 14 Sugars, beet or cane, raw 5.3 4 1 Fish, frozen excluding fillets 13.5 2 Shirts 7.6 10 2 Zinc, zinc alloy, unwrought 9.7 5 1 1 Tin ores, concentrates 9.8 3 Drawing, measuring instrument 7.6 4 Fish, fresh, chilled, whole 6.4 8 Ships, boats, other vessels 11.0 3 Cultivating machinery and the like 4.1 4 Fish, frozen excluding fillets 7.8 5 Gold, nonmonetary excluding ores 7.9 39 1 Flavors, industrial use 9.0 20 Copper ores, concentrates 8.6 15 Cotton, not carded, combed 18.6 8 Tobacco, stemmed, stripped 7.5 5 Cotton, not carded, combed 5.7 5 Nickel ores, concentrates 12.3 16 Natural gas, gaseous 5.6 2 Portland cement, and the like 4.7 46 1 Natural calcium phosphates 5.6 32 Insulated wire, and the like, conductor 6.7 36 Nickel ores, concentrates 2.8 (17.5) 26 TRADE Part III. Development outcomes 69 Drivers of growth Table 6.3 Regional integration, trade blocs Merchandise exports within bloc Year ($ millions) established 1990 1995 1999 2000 2001 2002 2003 2004 2005 Economic and Monetary Community of Central African States (CEMAC ) 1994 139 120 127 97 118 136 148 176 201 Economic Community of the Countries of the Great Lakes (CEPGL) 1976 7 8 9 10 11 13 15 19 22 Common Market for Eastern and Southern Africa (COMESA) 1994 963 1,386 1,348 1,653 1,819 2,031 2,436 2,849 3,330 Cross-Border Initiative (CBI) 1992 613 1,002 964 1,166 1,070 1,373 1,536 1,705 1,913 East African Community (EAC) 1996 230 530 438 595 664 685 706 750 857 Economic Community of Central African States (ECCAS) 1983 163 163 179 191 203 199 198 238 272 Economic Community of West African States (ECOWAS) 1975 1,557 1,936 2,364 2,835 2,371 3,229 3,140 4,499 5,673 Indian Ocean Commission (IOC) 1984 73 127 91 106 134 105 179 155 159 Mano River Union (MRU) 1973 0 1 4 5 4 5 5 6 6 Southern African Development Community (SADC) 1992 1,630 3,373 4,224 4,282 3,771 4,316 5,377 6,384 6,384 Central African Customs and Economic Union (UDEAC) 1964 139 120 126 96 117 134 146 174 198 West African Economic and Monetary Union (WAEMU/UEMOA) 1994 621 560 805 741 775 857 1,076 1,233 1,390 Merchandise exports within bloc Year (% of total bloc exports) established 1990 1995 1999 2000 2001 2002 2003 2004 2005 Economic and Monetary Community of Central African States (CEMAC ) 1994 2.3 2.1 1.7 1.1 1.4 1.5 1.4 1.3 0.9 Economic Community of the Countries of the Great Lakes (CEPGL) 1976 0.5 0.5 0.8 0.8 0.8 0.9 1.2 1.2 1.3 Common Market for Eastern and Southern Africa (COMESA) 1994 6.6 7.7 7.4 6.1 7.9 7.4 7.4 6.8 5.9 Cross-Border Initiative (CBI) 1992 10.3 11.9 12.1 11.8 11.5 14.5 13.0 13.8 14.0 East African Community (EAC) 1996 13.4 17.4 14.4 20.5 21.4 19.3 18.2 16.6 15.0 Economic Community of Central African States (ECCAS) 1983 1.4 1.5 1.3 1.1 1.3 1.1 1.0 0.9 0.6 Economic Community of West African States (ECOWAS) 1975 7.9 9.0 10.4 7.9 8.5 10.9 8.6 9.4 9.5 Indian Ocean Commission (IOC) 1984 4.1 6.0 4.8 4.4 5.6 4.3 6.2 4.3 4.6 Mano River Union (MRU) 1973 0.0 0.1 0.4 0.4 0.3 0.2 0.3 0.3 0.3 Southern African Development Community (SADC) 1992 17.0 31.6 11.9 9.3 8.6 9.5 9.8 9.5 7.7 Central African Customs and Economic Union (UDEAC) 1964 2.3 2.1 1.7 1.0 1.4 1.4 1.4 1.2 0.9 West African Economic and Monetary Union (WAEMU/UEMOA) 1994 13.0 10.3 13.1 13.1 12.7 12.2 13.3 12.9 13.4 Note: Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; 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, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Tanzania, Zambia, and Zimbabwe; Cross Border Initiative, Burundi, Comoros, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; East African Community (EAC), Kenya, Tanzania, and Uganda; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, 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, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commission, Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière et Economique de l'Afrique Centrale), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. 70 Part III. Development outcomes TRADE Drivers of growth Table 7.1 Water and sanitation Financing Access, Quality Committed Average annual supply side Access, demand side of supply nominal ODA disburse- Internal Population with sustainable access Population with sustainable Water supply investment in ments for fresh water to improved water source access to improved sanitation failure for firms water projects water supply resources per (%) (%) receiving water with private and sanitation capita (cubic (average days participation sector meters) Total Urban Rural Total Urban Rural per year) ($ millions) ($ millions) 2005 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05 a 2000­05 a 2000­05 SUB­SAHARAN AFRICA 56 80 43 37 53 28 93.5 Angola 9,284 53 75 40 31 56 16 .. .. 4.2 Benin 1,221 67 78 57 33 59 11 19.2 .. 5.5 Botswana 1,360 95 100 90 42 57 25 .. .. 2.4 Burkina Faso 945 61 94 54 13 42 6 .. .. 61.3 Burundi 1,338 79 92 77 36 47 35 .. .. 0.4 Cameroon 16,726 66 86 44 51 58 43 .. .. 2.7 Cape Verde 592 80 86 73 43 61 19 .. .. 9.6 Central African Republic 34,921 75 93 61 27 47 12 .. .. 0.0 Chad 1,539 42 41 43 9 24 4 .. .. 14.4 Comoros 1,998 86 92 82 33 41 29 .. .. 5.2 Congo, Dem. Rep. 15,639 46 82 29 30 42 25 .. .. 3.4 Congo, Rep. 55,515 58 84 27 27 28 25 .. .. 0.0 Côte d'Ivoire 4,231 84 97 74 37 46 29 .. .. 0.1 Djibouti 378 73 76 59 82 88 50 .. .. .. Equatorial Guinea 51,637 43 45 42 53 60 46 .. .. 3.5 Eritrea 636 60 74 57 9 32 3 79.2 .. 0.6 Ethiopia 1,712 22 81 11 13 44 7 0.0 .. 9.2 Gabon 118,511 88 95 47 36 37 30 .. .. 17.7 Gambia, The 1,978 82 95 77 53 72 46 .. .. 2.3 Ghana 1,370 75 88 64 18 27 11 .. .. 321.5 Guinea 24,037 50 78 35 18 31 11 .. .. 12.6 Guinea-Bissau 10,086 59 79 49 35 57 23 .. .. 0.5 Kenya 604 61 83 46 43 46 41 85.2 .. 118.7 Lesotho 2,897 79 92 76 37 61 32 19.2 .. 17.2 Liberia 60,915 61 72 52 27 49 7 .. .. 0.3 Madagascar 18,113 46 77 35 32 48 26 5.2 .. 18.7 Malawi 1,250 73 98 68 61 62 61 21.3 .. 2.8 Mali 4,438 50 78 36 46 59 39 2.1 .. 50.2 Mauritania 130 53 59 44 34 49 8 .. .. 3.0 Mauritius 2,252 100 100 100 94 95 94 16.7 .. 0.1 Mozambique 5,068 43 72 26 32 53 19 .. .. 4.0 Namibia 3,052 87 98 81 25 50 13 .. .. 9.2 Niger 251 46 80 36 13 43 4 0.1 3.4 14.4 Nigeria 1,680 48 67 31 44 53 36 .. .. 3.2 Rwanda 1,051 74 92 69 42 56 38 .. .. 22.5 São Tomé and Principe 14,055 79 89 73 25 32 20 .. .. 0.0 Senegal 2,213 76 92 60 57 79 34 5.6 .. 3.2 Seychelles .. 88 100 75 .. .. 100 .. .. 0.1 Sierra Leone 28,957 57 75 46 39 53 30 .. .. 18.2 Somalia 729 29 32 27 26 48 14 .. .. 3.1 South Africa 956 88 99 73 65 79 46 4.8 31.3 67.3 Sudan 828 70 78 64 34 50 24 .. .. 0.3 Swaziland 2,299 62 87 54 48 59 44 .. .. 0.2 Tanzania 2,192 62 85 49 47 53 43 105.0 8.5 4.9 Togo 1,871 52 80 36 35 71 15 .. .. 9.0 Uganda 1,353 60 87 56 43 54 41 2.7 .. 32.2 Zambia 6,873 58 90 40 55 59 52 13.6 .. 38.6 Zimbabwe 946 81 98 72 53 63 47 .. .. 0.2 NORTH AFRICA 91 96 86 77 91 62 .. Algeria 341 85 88 80 92 99 82 31.0 510.0 .. Egypt, Arab Rep. 24 98 99 97 70 86 58 5.2 .. .. Libya 103 71 72 68 97 97 96 .. .. .. Morocco 961 81 99 56 73 88 52 1.3 .. .. Tunisia 419 93 99 82 85 96 65 .. .. .. a. Data are for most recent year available during the period specified. INFRASTRUCTURE Part III. Development outcomes 71 Drivers of growth Table 7.2 Transportation Access, supply side Access, demand side Road density Rural access (% Vehicle fleet (per 1,000 people) Ratio to arable land Ratio to total land of rural population Road network Rail lines (road km/1,000 sq (road km/1,000 sq within 2 km of an Commercial Passenger (km) (km) km arable land) km of land area) all-season road) vehicles vehicles 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a SUB­SAHARAN AFRICA Angola 51,429 2,761 17.1 41.3 .. .. .. Benin 19,000 578 .. 171.8 32.0 .. .. Botswana 24,455 888 66.9 43.2 .. 105.0 42.0 Burkina Faso 15,272 622 .. 55.8 25.0 .. .. Burundi 12,322 .. .. 479.8 .. .. .. Cameroon 50,000 974 .. 107.4 20.0 .. .. Cape Verde 1,350 .. 30.7 335.0 .. .. .. Central African Republic .. .. .. .. .. .. .. Chad .. .. .. .. 5.0 .. .. Comoros .. .. .. .. .. .. .. Congo, Dem. Rep. 153,497 3,641 .. 67.7 26.0 .. .. Congo, Rep. 17,289 795 .. 50.6 .. .. .. Côte d'Ivoire 80,000 639 .. 251.6 .. .. .. Djibouti .. 781 .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. Eritrea .. 306 .. .. .. .. .. Ethiopia 36,469 .. 3.1 36.5 17.0 2.0 1.0 Gabon 9,170 810 26.0 35.6 .. .. .. Gambia, The 3,742 .. 11.9 374.2 .. 7.0 5.0 Ghana 47,787 977 11.4 210.0 .. .. .. Guinea 44,348 1,115 40.3 180.5 .. .. .. Guinea-Bissau 3,455 .. 11.5 122.9 .. .. .. Kenya 63,265 1,917 14.2 111.2 .. 18.0 9.0 Lesotho .. .. .. .. .. .. .. Liberia .. 490 .. .. .. .. .. Madagascar .. 732 .. .. .. .. .. Malawi 15,451 710 6.3 164.2 .. .. .. Mali 18,709 733 3.9 15.3 .. .. .. Mauritania .. 717 .. .. .. .. .. Mauritius 2,015 .. 20.2 992.6 .. 130.0 96.0 Mozambique .. 3,070 .. .. .. .. .. Namibia 42,237 .. 51.8 51.3 .. 82.4 41.6 Niger 14,565 .. .. 11.5 37.0 .. .. Nigeria 193,200 3,528 .. 212.1 47.0 .. 17.0 Rwanda 14,008 .. .. 567.8 .. .. .. São Tomé and Principe .. .. .. .. .. .. .. Senegal 13,576 906 5.5 70.5 .. .. .. Seychelles 458 .. 458.0 995.7 .. .. .. Sierra Leone 11,300 .. 21.1 157.8 .. 3.6 2.2 Somalia .. .. .. .. .. .. .. South Africa 364,131 20,047 24.7 299.8 .. 144.4 91.8 Sudan .. 5,478 .. .. .. .. .. Swaziland 3,594 301 20.2 209.0 .. 83.1 39.9 Tanzania 78,891 2,600 19.7 89.3 38.0 .. .. Togo .. 568 .. .. .. .. .. Uganda 70,746 259 13.6 358.9 .. .. .. Zambia 91,440 1,273 17.4 123.0 .. .. .. Zimbabwe 97,267 .. 30.2 251.4 .. 50.4 43.9 NORTH AFRICA Algeria 108,302 3,572 .. 45.5 .. .. .. Egypt, Arab Rep. 92,370 5,150 31.2 92.8 .. .. .. Libya .. 2,757 .. .. .. .. .. Morocco 57,493 1,907 6.8 128.8 .. .. .. Tunisia 19,232 1,909 6.8 123.8 .. .. .. a. Data are for most recent year available during the period specified. 72 Part III. Development outcomes INFRASTRUCTURE Quality Pricing Financing Roads Average time to ship 20 ft Average cost to ship Price of Price of Committed nominal Average annual ODA Road network Ratio of container from port to 20 ft container from port diesel fuel gasoline investment in transport disbursements for trans- in good or fair paved to total final destination (days) to final destination ($) (U.S. cents/ (U.S. cents/ projects with private portation and storage condition (%) roads (%) Import Export Import Export liter) liter) participation ($ millions) ($ millions) 2000­05a 2000­05a 2006 2006 2006 2006 2004 2004 2003­05a 2003­05 53 43 2,181 1,750 248.3 .. 10.4 58 64 2,325 1,850 29 39 55.0 0.4 .. 9.5 41 34 1,202 1,167 72 77 .. 60.6 .. 36.5 43 33 2,595 2,328 61 66 .. 0.0 69.3 31.2 54 45 3,522 2,096 94 118 .. 27.3 87.6 10.4 71 47 3,705 2,147 108 104 .. 100.5 23.4 10.0 53 39 1,360 524 83 95 .. 13.5 .. 69.0 .. .. .. .. 81 140 .. 2.8 .. .. 66 57 4,534 4,581 114 129 .. 0.5 71.3 .. 102 78 5,520 4,867 101 117 .. 11.7 .. .. .. .. .. .. .. .. 0.5 5.6 23.2 1.8 62 50 3,308 3,120 81 92 .. 9.6 .. 5.0 62 50 2,201 2,201 59 87 .. 0.8 64.6 8.1 43 23 2,457 1,653 95 114 140.0 0.0 .. .. .. .. .. .. .. .. 130.0 .. .. .. .. .. .. .. .. .. 23.0 0.0 .. .. 69 69 1,185 935 40 80 .. 3.6 63.6 19.1 42 46 2,793 1,617 42 60 .. 43.9 78.0 10.2 .. .. .. .. 69 90 91.8 23.3 94.6 19.3 .. .. .. .. 73 75 .. 1.4 71.9 17.9 55 47 842 822 43 49 10.0 79.2 44.2 9.8 32 33 995 570 69 75 .. 60.9 .. 27.9 .. .. .. .. .. .. .. 6.2 67.2 14.1 62 45 2,325 1,980 76 92 .. 209.1 71.5 .. 49 44 1,210 1,188 68 73 .. 17.7 .. .. .. .. .. .. 77 75 .. 0.0 .. .. 48 48 1,282 982 79 105 12.5 126.1 .. 45.0 54 45 2,500 1,623 88 95 .. 6.3 62.0 18.0 65 44 2,680 1,752 90 116 55.4 195.9 .. .. 40 42 3,733 3,733 59 80 .. 21.3 .. 100.0 16 16 683 683 56 74 .. 0.0 63.5 .. 38 27 1,185 1,155 79 88 186.9 34.5 .. 12.8 24 29 1,550 1,539 65 68 .. 20.7 63.1 25.0 68 59 2,946 2,945 91 102 .. 17.3 .. 15.0 53 41 1,460 798 45 39 2,355.4 0.2 .. 19.0 92 63 4,080 3,840 99 98 .. 5.6 .. .. 29 27 577 690 .. .. .. 11.7 27.4 29.3 26 20 1,720 828 90 110 55.4 18.0 .. 96.0 .. .. .. .. .. .. .. 0.0 .. 8.0 34 31 1,242 1,282 89 76 .. 60.8 .. .. .. .. .. .. 49 63 .. 0.3 .. 17.3 35 30 1,195 1,087 80 81 17.0 0.9 .. .. 83 56 1,970 1,870 29 47 .. 9.3 .. .. .. .. .. .. 73 76 .. 0.0 55.0 8.6 51 30 917 822 87 93 27.7 83.4 .. .. 43 34 695 463 83 85 .. 0.0 58.6 23.0 67 42 2,945 1,050 88 102 .. 4.2 .. 22.0 64 53 2,840 2,098 98 110 15.6 6.2 .. 19.0 67 52 2,420 1,879 65 61 .. 0.0 28 20 1,259 1,023 .. .. 70.2 22 15 1,886 1,606 15 32 104.0 .. .. 81.0 29 27 1,049 1,014 10 28 86.0 .. .. .. .. .. .. .. 8 9 .. .. .. 56.9 30 18 1,500 700 70 110 200.0 .. .. 65.8 29 18 600 770 39 68 .. .. INFRASTRUCTURE Part III. Development outcomes 73 Drivers of growth Table 7.3 Information and communication technology Access, supply side Access, demand side Quality Telephone subscribers (per 1,000 people) Households with own telephone Average delay for Internet Duration Telephone Total Urban Rural firm in obtaining users of phone faults Mainline Mobile (% of (% of urban (% of rural a mainline phone (per 1,000 outages (per 100 Total telephone telephone households) households) households) connection (days) people) (hours) mainlines) 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2005 2000­05a 2000­05a SUB­SAHARAN AFRICA 149.7 17.0 124.5 29.0 Angola 74.5 5.9 68.6 .. .. .. .. 11.0 .. .. Benin 97.9 9.0 88.9 4.4 10.3 1.0 159.7 50.4 6.1 5.8 Botswana 541.2 74.8 466.3 .. .. .. .. 34.0 .. .. Burkina Faso 50.6 7.4 43.3 3.7 19.8 0.3 .. 4.9 .. 18.4 Burundi 17.6 3.8 20.3 .. .. .. .. 5.3 .. 6.0 Cameroon 102.0 6.2 138.4 2.3 4.8 0.0 .. 15.3 .. .. Cape Verde 302.2 140.9 161.2 .. .. .. .. 49.3 .. 33.0 Central African Republic 27.2 2.5 24.8 .. .. .. .. 2.7 .. 56.0 Chad 14.4 1.4 21.5 0.9 4.3 0.0 .. 4.1 .. 60.8 Comoros 55.0 28.2 26.8 .. .. .. .. 33.3 .. 55.8 Congo, Dem. Rep. 47.9 0.2 47.7 .. .. .. .. 2.4 .. .. Congo, Rep. 102.4 3.6 122.5 1.3 2.2 0.2 .. 12.5 .. .. Côte d'Ivoire 108.1 14.4 120.6 .. .. .. .. 11.0 .. 81.0 Djibouti 69.1 13.6 55.5 .. .. .. .. 12.6 .. 136.0 Equatorial Guinea 212.3 19.9 192.4 .. .. .. .. 13.9 .. .. Eritrea 17.8 8.6 9.2 .. .. .. 256.3 15.9 .. 54.3 Ethiopia 14.3 8.6 5.8 4.4 35.3 0.2 154.9 2.3 .. 100.0 Gabon 497.8 28.3 469.6 15.3 20.0 1.8 .. 48.4 .. 45.0 Gambia, The 192.1 29.0 163.1 .. .. .. .. .. .. .. Ghana 143.1 14.5 128.5 7.5 17.0 0.7 .. 18.1 .. 5.6 Guinea 19.7 2.8 20.1 7.2 23.7 0.3 .. 5.3 .. 1.6 Guinea-Bissau 7.9 7.1 42.2 .. .. .. .. 19.5 .. 70.5 Kenya 142.9 8.2 134.6 12.3 37.4 6.0 99.4 32.4 27.2 130.4 Lesotho 163.3 26.7 136.5 16.9 45.8 10.6 73.8 .. 26.4 75.0 Liberia 2.8 2.2 48.7 .. .. .. .. .. .. .. Madagascar 30.7 3.6 27.1 4.9 11.9 3.0 63.8 5.4 21.3 59.6 Malawi 41.3 8.0 33.3 6.0 26.7 2.1 107.7 4.1 28.0 .. Mali 69.9 5.5 64.3 3.5 12.8 0.1 70.6 4.4 10.3 177.6 Mauritania 256.3 13.4 243.0 3.6 8.0 0.2 .. 6.5 .. .. Mauritius 862.5 288.8 573.7 .. .. .. 22.6 .. 5.3 41.5 Mozambique 40.0 3.6 61.6 2.1 6.1 0.1 .. .. .. 66.0 Namibia 206.1 63.7 243.7 17.4 43.5 4.5 .. .. .. 40.4 Niger 23.2 1.7 21.5 .. .. .. 60.1 2.1 .. 104.6 Nigeria 150.6 9.3 141.3 5.1 11.7 1.8 .. 38.0 .. 20.6 Rwanda 18.2 2.6 32.1 1.1 6.1 0.2 .. 5.5 .. .. São Tomé and Principe 96.7 46.1 76.7 .. .. .. .. .. .. .. Senegal 171.3 22.9 148.4 19.8 35.9 7.5 12.0 46.3 11.4 17.3 Seychelles 928.0 253.3 674.6 .. .. .. .. 248.5 .. 6.0 Sierra Leone 18.6 4.9 22.1 .. .. .. .. .. .. .. Somalia 72.9 12.2 60.8 .. .. .. .. 10.9 .. .. South Africa 825.1 100.9 724.3 .. .. .. 8.2 108.8 3.9 48.2 Sudan 68.9 18.5 50.4 .. .. .. .. 77.3 .. .. Swaziland 207.8 31.0 176.8 .. .. .. .. .. .. 70.0 Tanzania 55.6 3.9 51.6 9.7 31.4 3.0 23.1 .. 10.8 24.0 Togo 81.7 9.5 72.2 .. .. .. .. 48.8 .. 6.2 Uganda 56.4 3.5 52.9 3.1 18.5 0.9 33.4 17.4 16.9 .. Zambia 89.2 8.1 81.1 4.3 11.2 0.6 88.6 .. 11.7 108.0 Zimbabwe 78.9 25.2 53.7 .. .. .. .. 76.9 .. 7.7 NORTH AFRICA 414.3 105.8 308.4 84.8 Algeria 494.1 78.3 415.8 .. .. .. 174.3 58.4 .. 0.8 Egypt, Arab Rep. 324.5 140.4 184.1 .. .. .. 136.9 67.5 .. 0.1 Libya 155.8 133.2 40.9 .. .. .. .. .. .. .. Morocco 455.2 44.5 410.8 .. .. .. 4.4 152.5 15.0 25.0 Tunisia 691.8 125.4 566.4 .. .. .. .. 95.1 .. 30.0 a. Data are for the most recent year available during the period specified. 74 Part III. Development outcomes INFRASTRUCTURE Pricing Financing Cost of a 3 Cost of 3 minute Annual investment ($ millions) Committed nominal invest- Average annual Price basket minute local cellular local call Cost of 3 minute ment in telecommunica- ODA disbursements for Internet call during during off-peak call to US during Telephone Mobile com- Telecom- tion projects with private for communication ($ per month) peak hours ($) hours ($) peak hours ($) service munication munications participation ($ millions) ($ millions) 2005 2000­05 a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05 a 2000­05 a 2000­05 85.4 20.3 21.7 .. 17.2 34.3 0.1 0.1 3.2 .. .. .. 119.8 0.6 20.7 0.1 0.7 4.8 .. 3.6 26.4 5.8 0.3 21.3 0.1 0.1 2.9 .. .. 19.0 19.0 0.3 90.6 0.2 0.7 1.1 .. 23.2 61.2 5.3 0.3 52.0 0.1 0.5 2.5 .. .. .. 6.0 0.1 44.6 0.1 0.9 .. .. .. 111.2 29.0 0.2 40.3 0.1 0.8 6.1 12.4 1.6 8.9 .. 1.0 147.8 0.6 0.6 2.0 .. .. 0.1 .. 0.2 86.3 0.1 .. 9.1 .. .. .. 1.4 0.2 37.9 0.2 0.7 .. .. .. 4.2 .. 0.1 93.2 .. .. .. .. .. .. 42.0 0.5 84.5 .. .. 5.4 .. .. .. 7.0 0.0 67.1 0.3 0.6 2.2 32.2 83.2 95.2 20.0 0.1 41.1 .. .. 4.7 .. .. .. .. .. 32.7 .. .. .. .. .. .. .. .. 28.6 0.0 0.3 3.6 .. 17.2 17.4 40.0 0.1 23.3 0.0 0.1 4.0 14.5 5.2 35.3 .. 0.4 40.1 0.3 0.5 2.8 .. .. 53.0 9.0 0.2 17.8 0.0 0.5 1.8 .. .. 3.7 6.6 0.0 23.6 0.2 0.1 0.4 .. .. 59.4 51.6 0.4 24.7 0.1 0.4 4.6 .. .. 0.8 32.6 0.1 75.0 .. 0.0 .. .. .. .. 0.6 0.7 75.9 0.1 0.4 3.0 .. .. 80.5 421.0 1.0 38.6 0.2 0.1 3.3 .. .. 7.1 3.0 0.0 .. .. 0.0 .. .. .. .. 15.8 0.0 45.9 0.2 0.7 0.6 .. .. 14.8 12.6 0.2 41.9 0.1 0.4 3.6 .. .. .. 0.9 0.4 28.4 0.1 0.8 12.3 .. .. 17.7 82.6 2.7 54.3 0.1 0.4 .. .. .. 84.7 1.6 0.0 17.5 0.1 0.1 1.6 .. .. 29.7 25.7 0.1 32.9 0.1 0.2 1.2 .. .. 19.7 14.0 9.7 48.7 0.0 0.4 4.3 .. .. 20.5 8.8 0.2 101.8 0.1 0.7 8.8 .. .. .. 47.2 0.5 50.4 0.1 0.1 1.5 .. .. 386.9 2,312.0 0.5 30.1 0.1 0.4 2.4 .. .. .. 33.0 0.0 53.2 0.2 0.0 5.1 .. .. 2.2 .. 0.4 25.6 0.2 0.6 1.0 .. 19.8 106.0 157.0 6.6 31.5 0.2 1.2 3.8 .. .. 4.1 14.9 0.0 10.6 0.0 0.4 .. .. .. .. 0.3 0.6 .. 0.1 0.0 .. .. .. .. 1.4 0.2 63.2 0.2 1.2 0.8 .. 360.3 871.2 1,183.5 7.4 65.5 0.1 0.3 39.2 .. .. 128.5 152.0 0.2 51.7 0.1 0.9 3.0 .. .. 27.6 3.0 0.0 93.6 0.2 0.2 3.2 .. .. 9.4 88.5 0.9 44.7 0.1 0.6 4.0 26.4 .. 30.0 .. 0.2 99.6 0.2 0.5 3.2 .. .. 68.0 77.0 1.3 68.4 0.1 0.4 1.4 .. 36.9 42.5 74.0 0.7 24.6 0.1 1.2 4.4 .. 20.3 21.7 13.0 0.7 .. .. .. .. .. 9.4 .. .. 2.1 .. .. .. 1,272.0 .. 5.0 .. .. 1.5 .. .. .. 1,827.0 .. 22.0 .. .. .. .. .. .. .. .. 26.8 .. .. 1.7 .. .. .. 626.0 .. 12.4 .. .. 2.3 .. .. .. 106.0 .. INFRASTRUCTURE Part III. Development outcomes 75 Drivers of growth Table 7.4 Energy Access, demand side GDP per unit of Access to electricity Solid fuels use Electric power energy use (2000 Total Urban Rural Total Urban Rural consumption PPP $ per kg of (% of total (% of urban (% of rural (% of total (% of urban (% of rural (kWh per capita) oil equivalent) population) population) population) population) population) population) 2000­05 a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a SUB­SAHARAN AFRICA Angola 123.8 3.3 .. .. .. .. .. .. Benin 66.6 3.3 22.0 50.9 5.6 95.6 89.5 99.1 Botswana 1,325.0 8.6 .. .. .. .. .. .. Burkina Faso .. .. 10.2 53.5 0.8 97.5 88.5 99.4 Burundi .. .. .. .. .. .. .. .. Cameroon 207.0 4.5 45.8 76.7 16.3 82.6 67.1 97.3 Cape Verde .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. Chad .. .. 4.3 19.9 0.3 .. .. .. Comoros .. .. .. .. .. .. .. .. Congo, Dem. Rep. 92.9 2.2 .. .. .. .. .. .. Congo, Rep. 131.1 3.3 34.9 51.3 16.4 83.2 71.3 96.5 Côte d'Ivoire 176.1 3.7 .. .. .. .. .. .. Djibouti .. .. .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. Ethiopia 32.7 2.8 12.0 85.9 2.0 89.0 69.7 91.6 Gabon 927.8 4.9 75.2 90.6 31.4 34.1 16.4 84.4 Gambia, The .. .. .. .. .. .. .. .. Ghana 247.0 5.4 44.3 77.0 20.9 91.8 82.7 98.3 Guinea .. .. 20.9 63.5 3.2 79.8 39.3 96.8 Guinea-Bissau .. .. .. .. .. .. .. .. Kenya 140.0 2.1 13.1 51.4 3.6 87.1 46.0 97.3 Lesotho .. .. 5.7 28.1 0.8 62.1 9.5 73.5 Liberia .. .. .. .. .. .. .. .. Madagascar .. .. 18.8 52.0 9.7 98.3 96.3 98.9 Malawi .. .. 7.5 34.0 2.5 97.8 88.7 99.5 Mali .. .. 12.8 41.3 2.7 95.9 97.6 95.3 Mauritania .. .. 23.4 50.7 2.7 70.5 51.9 84.4 Mauritius .. .. .. .. .. .. .. .. Mozambique 366.9 2.6 11.0 29.8 1.5 96.9 91.9 99.5 Namibia 1,388.6 10.2 31.7 74.6 10.4 65.9 18.7 89.3 Niger .. .. .. .. .. .. .. .. Nigeria 104.2 1.4 51.3 84.0 34.6 76.6 52.0 89.0 Rwanda .. .. 5.4 27.2 1.5 99.4 98.3 99.6 São Tomé and Principe .. .. .. .. .. .. .. .. Senegal 176.1 6.5 46.4 82.1 19.0 58.7 24.3 85.2 Seychelles .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. South Africa 4,884.8 3.7 .. .. .. .. .. .. Sudan 92.2 3.7 .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. Tanzania 53.5 1.3 10.6 38.9 1.8 98.1 93.4 99.5 Togo 87.0 3.1 .. .. .. .. .. .. Uganda .. .. 8.4 47.5 2.6 97.4 87.8 98.8 Zambia 692.0 1.5 20.1 50.0 3.5 83.6 58.5 97.6 Zimbabwe 795.1 2.6 .. .. .. .. 5.1 .. NORTH AFRICA Algeria 812.4 6.0 .. .. .. .. .. .. Egypt, Arab Rep. 1,215.3 4.9 .. .. .. .. .. .. Libya 2,519.4 .. .. .. .. .. .. .. Morocco 594.6 10.3 .. .. .. .. .. .. Tunisia 1,157.4 8.2 .. .. .. .. .. .. a. Data are for most recent year available during the period specified. 76 Part III. Development outcomes INFRASTRUCTURE Quality Financing Average delay for Electric power Electrical outages Firms that share Firms identifying electricity Committed nominal invest- Average annual firm in obtaining transmission and of firms or own their own as major or very severe ment in energy projects ODA disbursements electrical connection distribution losses (average number generator obstacle to business with private participation for energy (days) (% of output) of days per year) (%) operation and growth (%) ($ millions) ($ millions) 2000­05a 2000­05a 2000­05a 2000­05a 2000­05 a 2000­05 a 2000­05 .. 14.5 .. .. .. 45.0 0.7 55.6 .. 77.3 29.9 68.5 .. 63.3 .. 10.4 .. .. .. .. 0.0 .. .. .. .. .. .. 7.0 .. .. .. .. .. .. .. .. 19.2 .. .. .. 21.5 0.2 .. .. .. .. .. .. 0.1 .. .. .. .. .. .. 1.3 .. .. .. .. .. .. 0.0 .. .. .. .. .. .. .. .. 3.1 .. .. .. .. 0.0 .. 73.6 .. .. .. .. 12.8 .. 15.7 .. .. .. 20.0 0.5 .. .. .. .. .. .. .. .. .. .. .. .. .. 0.2 65.4 .. 93.9 43.0 36.7 .. 50.4 105.5 10.0 0.0 17.1 42.5 300.0 0.9 .. 17.8 .. .. .. .. .. .. .. .. .. .. .. 0.1 .. 14.7 .. .. .. 184.0 1.6 .. .. .. .. .. .. 6.7 .. .. .. .. .. .. 0.2 43.7 17.4 83.6 70.9 47.1 .. 188.9 43.3 .. 19.1 26.1 35.1 .. 0.0 .. .. .. .. .. .. .. 49.5 .. 78.0 21.5 41.3 .. 2.6 84.4 .. 63.2 49.1 19.2 .. 1.0 32.0 .. 10.5 45.3 24.0 365.9 0.2 .. .. .. .. .. .. 8.8 20.0 .. 6.0 39.5 12.7 .. 131.2 .. 10.1 .. .. .. 5.8 24.5 .. 18.4 .. .. .. 1.0 0.1 16.5 .. 11.1 24.8 1.6 .. 0.2 .. 33.7 .. .. .. 539.0 0.6 .. .. .. .. .. .. 5.4 .. .. .. .. .. 50.0 0.1 10.3 14.7 26.1 62.5 30.5 87.0 6.0 .. .. .. .. .. .. .. .. .. .. .. .. .. 41.2 .. .. .. .. .. .. 0.1 5.3 6.1 5.5 9.5 9.0 7.0 0.1 .. 15.6 .. .. .. .. 0.0 .. .. .. .. .. .. 9.9 44.0 23.3 60.6 55.4 .. 32.0 58.3 .. 34.0 .. .. .. 67.7 .. 25.6 .. 70.8 36.0 .. 124.0 4.9 142.4 4.0 30.0 38.2 39.6 12.4 2.1 .. 15.1 .. .. .. .. 0.0 94.3 15.9 12.4 29.5 11.4 400.0 .. 80.1 12.2 13.9 19.3 26.5 678.0 .. .. 28.4 .. .. .. .. .. 7.7 16.1 5.8 13.8 8.9 360.0 .. .. 11.8 .. .. .. 30.0 .. INFRASTRUCTURE Part III. Development outcomes 77 Drivers of growth Table 7.5 Financial sector infrastructure Macroeconomy Money and quasi Foreign currency sovereign ratings Gross national savings money (M2) Real interest rate Long-term Short-term (% of GDP) (% of GDP) (%) 2007 2007 2005 2005 2005 SUB­SAHARAN AFRICA 17.5 Angola 20.5 11.1 16.9 Benin B B 10.6 25.1 .. Botswana 50.5 26.5 6.4 Burkina Faso .. 20.9 .. Burundi 1.1 25.9 2.1 Cameroon B B 14.7 16.7 12.4 Cape Verde B+ B .. 75.7 11.9 Central African Republic 14.0 16.5 15.0 Chad 21.3 7.6 -2.0 Comoros 5.9 21.4 8.5 Congo, Dem. Rep. 6.1 7.2 .. Congo, Rep. 33.5 14.8 9.8 Côte d'Ivoire 10.9 23.3 .. Djibouti 20.5 73.6 .. Equatorial Guinea 33.3 14.3 .. Eritrea 10.6 138.1 .. Ethiopia 11.8 44.8 1.0 Gabon 52.3 17.6 8.1 Gambia, The CCC C 9.1 43.9 29.4 Ghana B+ B 21.4 27.6 .. Guinea 9.1 14.6 .. Guinea-Bissau 11.2 30.3 .. Kenya 11.8 36.9 8.2 Lesotho BB­ B 37.5 26.9 8.3 Liberia 39.9 17.8 7.4 Madagascar 11.7 19.9 7.3 Malawi B­ B -4.3 20.5 15.3 Mali B­ B 9.9 28.7 .. Mauritania .. .. 3.6 Mauritius 20.1 136.2 15.5 Mozambique B B 11.4 25.9 12.2 Namibia BBB­ F3 40.0 42.6 2.8 Niger .. 13.4 .. Nigeria BB­ B 30.5 18.8 -7.0 Rwanda B­ B .. 19.5 .. São Tomé and Principe .. 60.4 .. Senegal 16.2 34.3 .. Seychelles -18.2 114.6 8.9 Sierra Leone 7.6 18.1 10.2 Somalia .. .. .. South Africa BBB+ F2 13.9 56.4 5.6 Sudan 13.2 17.4 .. Swaziland 18.9 19.1 5.5 Tanzania 10.4 24.8 11.0 Togo .. 26.7 .. Uganda B B 10.0 19.3 11.0 Zambia 10.2 17.4 7.7 Zimbabwe -0.4 44.9 -0.6 NORTH AFRICA 29.3 Algeria 51.3 51.4 .. Egypt, Arab Rep. BB+ B .. 92.4 7.6 Libya .. 28.8 .. Morocco BBB­ F3 27.9 97.4 .. Tunisia BBB F2 22.6 57.3 .. a. Data are consolidated for regional security markets where they exist. 78 Part III. Development outcomes INFRASTRUCTURE Intermediation Capital marketsa Domestic credit Interest rate spread Ratio of bank non- Market capitalization Turnover ratio for to private sector (lending rate minus performing loans to Bank branches Listed domestic of listed companies traded stocks (% of GDP) deposit rate) total gross loans (%) (per 100,000 people) companies, total (% of GDP) (%) 2005 2005 2005 2004 2005 2005 2005 4.8 54.3 .. .. .. .. .. 16.6 .. .. .. .. .. .. 19.0 6.5 .. 3.8 18.0 23.6 1.8 17.3 .. .. .. .. .. .. 20.8 .. .. .. .. .. .. 9.4 12.8 .. .. .. .. .. 39.0 8.9 .. .. .. .. .. 6.7 12.8 .. .. .. .. .. 3.1 12.8 .. .. .. .. .. 8.1 8.0 .. .. .. .. .. 1.9 .. .. .. .. .. .. 2.9 12.8 .. .. .. .. .. 13.8 .. .. .. 39.0 14.2 1.4 20.1 10.3 .. .. .. .. .. 5.4 12.8 .. .. .. .. .. 31.0 .. .. .. .. .. .. 25.3 3.5 .. 0.4 .. .. .. 8.9 12.8 .. .. .. .. .. 13.0 17.6 .. .. .. .. .. 15.5 .. 13.9 1.6 30.0 12.8 2.2 5.1 .. .. .. .. .. .. 2.1 .. .. .. .. .. .. 25.9 7.8 5.2 1.4 47.0 34.1 9.8 8.4 7.8 .. .. .. .. .. 6.6 13.6 .. .. .. .. .. 9.9 8.3 10.1 0.7 .. .. .. 10.5 22.2 .. .. .. .. .. 18.4 .. .. .. .. .. .. .. 15.1 .. .. .. .. .. 76.7 13.8 .. 11.9 42.0 41.6 6.0 11.2 11.7 4.6 .. .. .. .. 61.4 4.4 2.0 4.5 13.0 6.8 1.5 6.8 .. .. .. .. .. .. 14.9 7.4 21.9 1.6 214.0 19.6 11.5 13.5 .. 34.1 .. .. .. .. 44.7 .. .. .. .. .. .. 23.8 .. .. .. .. .. .. 40.6 6.3 .. .. .. .. .. 4.5 13.5 .. .. .. .. .. .. .. .. .. .. .. .. 143.5 4.6 1.5 6.0 388.0 236.0 39.3 10.0 .. .. .. .. .. .. 20.0 6.6 .. .. 6.0 7.2 0.0 10.4 10.4 .. 0.6 6.0 4.9 2.3 16.8 .. .. .. .. .. .. 6.7 10.9 .. 0.5 5.0 1.2 3.1 7.6 17.0 10.8 1.5 12.0 13.6 2.0 26.9 144.6 .. 3.3 79.0 71.2 15.3 11.8 6.3 .. .. .. .. .. 52.4 5.9 25.0 3.6 744.0 89.1 43.0 9.0 4.0 .. .. .. .. .. 62.2 .. 15.7 6.6 56.0 52.7 15.9 65.6 .. 20.9 .. 46.0 10.0 16.5 INFRASTRUCTURE Part III. Development outcomes 79 Participating in growth Table 8.1 Education Primary education Literacy rate (%) Gross enrollment ratio Net enrollment ratio Student- Youth (ages 15­24) Adult (ages 15 and older) (% of relevant age group) (% of relevant age group) teacher Total Male Female Total Male Female Total Male Female Total Male Female ratio 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2000­05a 2005 2005 2005 2005 2005 2005 2005 SUB­SAHARAN AFRICA 98 103 92 Angola 72 84 63 67 83 54 .. .. .. .. .. .. .. Benin 45 59 33 35 48 23 96 107 85 78 86 70 47 Botswana 94 92 96 81 80 82 105 105 104 83 83 83 26 Burkina Faso 33 40 26 24 31 17 58 64 51 45 50 40 47 Burundi 73 77 70 59 67 52 85 91 78 60 63 58 49 Cameroon .. .. .. 68 77 60 117 126 107 .. .. .. 48 Cape Verde 96 96 97 81 88 76 108 111 105 90 91 89 26 Central African Republic 59 70 47 49 65 33 56 67 44 .. .. .. .. Chad 38 56 23 26 41 13 77 92 62 .. .. .. 63 Comoros .. .. .. .. .. .. 85 91 80 .. .. .. 35 Congo, Dem. Rep. 70 78 63 67 81 54 .. .. .. .. .. .. .. Congo, Rep. 97 98 97 85 91 79 88 91 84 44 39 48 83 Côte d'Ivoire 61 71 52 49 61 39 .. .. .. .. .. .. .. Djibouti .. .. .. .. .. .. 40 44 36 33 37 30 35 Equatorial Guinea 95 95 95 87 93 80 114 117 111 .. .. .. .. Eritrea .. .. .. .. .. .. 64 71 57 47 51 43 48 Ethiopia 50 62 39 36 50 23 93 101 86 61 64 59 72 Gabon 96 97 95 84 88 80 .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. .. .. .. .. Ghana 71 76 65 58 66 50 88 90 87 65 65 65 33 Guinea 47 59 34 29 43 18 81 88 74 66 70 61 45 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Kenya 80 80 81 74 78 70 114 116 112 80 80 80 40 Lesotho .. .. .. 82 74 90 132 132 131 87 84 89 42 Liberia 67 65 69 52 58 46 .. .. .. .. .. .. .. Madagascar 70 73 68 71 77 65 138 141 136 92 93 92 54 Malawi .. .. .. .. .. .. 122 121 124 95 92 97 64 Mali .. .. .. 24 33 16 66 74 59 51 56 45 54 Mauritania 61 68 55 51 60 43 93 93 94 72 72 72 40 Mauritius 95 94 95 84 88 81 102 102 102 95 94 96 22 Mozambique .. .. .. .. .. .. 105 114 96 79 82 75 66 Namibia 92 91 93 85 87 83 99 98 100 72 70 75 33 Niger 37 52 23 29 43 15 47 54 39 40 46 33 44 Nigeria 84 87 81 69 78 60 103 111 95 91 .. .. 37 Rwanda 78 79 77 65 71 60 120 119 121 74 72 75 62 São Tomé and Principe 95 96 95 85 92 78 134 135 132 97 97 96 31 Senegal 49 58 41 39 51 29 88 89 86 76 77 75 47 Seychelles 99 99 99 92 91 92 .. .. .. .. .. .. .. Sierra Leone 48 60 37 35 47 24 155 171 139 .. .. .. 67 Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. .. .. .. .. .. .. .. Sudan 77 85 71 61 71 52 60 65 56 .. .. .. 28 Swaziland 88 87 90 80 81 78 .. .. .. .. .. .. .. Tanzania 78 81 76 69 78 62 106 108 104 91 92 91 56 Togo 74 84 64 53 69 38 100 108 92 78 84 72 34 Uganda 77 83 71 67 77 58 118 118 117 .. .. .. 50 Zambia .. .. .. .. .. .. 111 114 108 89 89 89 51 Zimbabwe 98 97 98 89 93 86 .. .. .. .. .. .. .. NORTH AFRICA .. .. .. Algeria .. .. .. .. .. .. 112 116 107 97 98 95 25 Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. 107 108 106 .. .. .. .. Morocco .. .. .. .. .. .. 105 111 99 86 89 83 27 Tunisia .. .. .. .. .. .. .. .. .. .. .. .. .. a. Data are for most recent year available during the period specified. 80 Part III. Development outcomes HUMAN DEVELOPMENT Public spending on Secondary education Tertiary education education (%) Gross enrollment ratio Net enrollment ratio Gross enrollment ratio Share of (% of relevant age group) (% of relevant age group) Student- (% of relevant age group) government Total Male Female Total Male Female teacher ratio Total Male Female expenditure Share of GDP 2005 2005 2005 2005 2005 2005 2005 2004 2004 2004 2000­05a 2000­05a 31 35 28 .. .. .. .. .. .. .. .. .. .. .. 3.0 33 41 23 .. .. .. .. .. .. .. 21.0 3.0 75 73 77 55 51 58 14 5 5 4 28.0 11.0 14 16 12 11 13 9 31 2 3 1 .. 5.0 13 15 11 .. .. .. 19 2 3 1 18.0 5.0 44 49 39 .. .. .. 33 6 7 5 11.0 2.0 68 65 70 58 55 60 23 7 7 7 34.0 7.0 .. .. .. .. .. .. .. 2 .. .. .. .. 16 23 8 .. .. .. 34 1 2 0 .. 2.0 35 40 30 .. .. .. 14 .. .. .. .. 4.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 34 .. .. .. 9.0 2.0 .. .. .. .. .. .. .. .. .. .. 22.0 5.0 24 29 19 23 27 18 .. 2 3 2 34.0 8.0 .. .. .. .. .. .. .. .. .. .. .. 1.0 31 40 23 25 30 20 51 .. .. .. .. 5.0 31 38 24 28 34 22 54 3 4 1 14.0 4.0 .. .. .. .. .. .. .. .. .. .. .. 4.0 .. .. .. .. .. .. 42 .. .. .. 14.0 2.0 44 47 40 37 39 35 19 5 7 4 .. 5.0 30 39 21 24 31 17 33 3 5 1 .. 2.0 .. .. .. .. .. .. .. .. .. .. .. .. 49 50 48 42 42 42 32 .. .. .. 35.0 7.0 39 34 43 25 19 30 26 3 3 4 31.0 13.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 3 3 2 29.0 3.0 28 31 25 24 25 22 .. .. .. .. .. 6.0 24 .. .. .. .. .. .. 3 3 2 .. 4.0 21 22 19 15 17 14 28 3 5 2 13.0 2.0 89 89 88 82 81 82 17 17 15 19 14.0 4.0 14 16 11 7 8 6 .. .. .. .. 26.0 4.0 61 60 61 38 32 44 .. .. .. .. 22.0 7.0 9 10 7 8 9 6 31 1 1 1 .. 2.0 34 37 31 27 29 25 .. .. .. .. .. .. 14 15 13 .. .. .. 26 3 3 2 16.0 4.0 44 43 46 32 30 34 .. .. .. .. .. .. 26 30 23 21 23 18 26 5 .. .. .. 5.0 .. .. .. .. .. .. 14 .. .. .. .. 5.0 30 34 26 .. .. .. .. .. .. .. .. 5.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 19.0 5.0 34 35 33 .. .. .. 25 .. .. .. .. .. .. .. .. .. .. .. .. 4 4 5 18.0 6.0 .. .. .. .. .. .. .. 1 2 1 .. .. 40 54 27 .. .. .. 34 .. .. .. 25.0 3.0 16 18 14 13 14 12 19 .. .. .. 24.0 5.0 28 31 25 26 29 23 34 .. .. .. 17.0 2.0 .. .. .. .. .. .. .. .. .. .. .. 5.0 .. .. .. 83 80 86 .. .. .. 21 20 17 24 .. .. .. .. .. .. .. .. 17 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 50 54 46 .. .. .. 19 11 12 10 32.0 7.0 .. .. .. .. .. .. 18 .. .. .. .. 7.0 HUMAN DEVELOPMENT Part III. Development outcomes 81 Participating in growth Table 8.2 Health Mortality Diseases Life expectancy at birth Under-five mortality rate Infant Maternal mortality Prevalence Incidence of Deaths due (years) (per 1,000) mortality rate ratio, modeled of HIV tuberculosis to malaria (per 1,000 estimate (per (% of ages (per 100,000 (per 100,000 Total Male Female Total Male Female live births) 100,000 live births) 15­49) people) people) 2005 2005 2005 2000­05b 2000­05b 2000­05b 2005 2000­05b 2005 2005 2000­05b SUB­SAHARAN AFRICA Angola 41.4 40.0 42.9 260 276 243 154 1,700 3.7 269 354 Benin 55.0 54.2 55.8 150 152 153 89 850 1.8 88 177 Botswana 35.0 35.4 34.5 120 123 109 87 100 24.1 655 15 Burkina Faso 48.5 47.7 49.3 191 193 191 96 1,000 2.0 223 292 Burundi 44.6 43.7 45.7 190 196 184 114 1,000 3.3 334 143 Cameroon 46.1 45.5 46.6 149 156 143 87 730 5.4 174 108 Cape Verde 70.7 67.7 73.9 35 38 35 26 150 .. 174 22 Central African Republic 39.4 38.8 40.1 193 201 185 115 1,100 10.7 314 137 Chad 44.0 43.0 45.1 208 212 188 124 1,100 3.5 272 207 Comoros 62.6 61.3 64.0 71 76 64 53 480 0.1 45 80 Congo, Dem. Rep. 44.0 43.0 45.1 205 217 192 129 990 3.2 356 224 Congo, Rep. 52.8 51.5 54.1 108 113 103 81 510 5.3 367 78 Côte d'Ivoire 46.2 45.4 46.9 195 225 162 118 690 7.1 382 76 Djibouti 53.4 52.3 54.5 133 131 120 88 730 3.1 762 .. Equatorial Guinea 42.3 42.0 42.6 205 213 195 123 880 3.2 233 152 Eritrea 54.9 53.1 56.8 78 89 75 50 630 2.4 282 74 Ethiopia 42.7 41.9 43.4 127 175 158 80 850 .. 344 198 Gabon 53.8 53.4 54.3 91 102 80 60 420 7.9 308 80 Gambia, The 56.8 55.5 58.2 137 129 115 97 540 2.4 242 52 Ghana 57.5 57.0 58.0 112 113 111 68 540 2.3 205 70 Guinea 54.1 53.8 54.3 160 160 150 97 740 1.5 236 200 Guinea-Bissau 45.1 43.8 46.5 200 212 194 124 1,100 3.8 206 150 Kenya 49.0 49.8 48.1 120 129 110 79 1,000 6.1 641 63 Lesotho 35.2 34.5 35.9 132 87 76 102 550 23.2 696 84 Liberia 42.5 41.7 43.3 235 249 220 157 760 .. 301 201 Madagascar 55.8 54.6 57.1 119 128 117 74 550 0.5 234 184 Malawi 40.5 40.8 40.2 125 179 172 79 1,800 14.1 409 275 Mali 48.6 48.0 49.3 218 230 208 120 1,200 1.7 278 454 Mauritania 53.7 52.1 55.3 125 134 115 78 1,000 0.7 298 108 Mauritius 73.0 69.7 76.5 15 17 14 13 24 0.6 62 .. Mozambique 41.8 41.4 42.3 145 154 150 100 1,000 16.1 447 232 Namibia 46.9 47.0 46.8 62 70 57 46 300 19.6 697 52 Niger 44.9 44.9 45.0 256 256 262 150 1,600 1.1 164 469 Nigeria 43.8 43.7 44.0 194 198 195 100 800 3.9 283 141 Rwanda 44.1 42.6 45.7 203 211 195 118 1,400 3.1 361 200 São Tomé and Principe 63.5 62.4 64.6 118 122 114 75 .. .. 105 80 Senegal 56.5 55.2 57.7 119 141 132 61 690 0.9 255 72 Seychelles .. .. .. 13 14 13 12 .. .. 34 .. Sierra Leone 41.4 40.0 42.8 282 296 269 165 2,000 1.6 475 312 Somalia 47.7 46.5 49.0 225 222 228 133 1,100 0.9 224 81 South Africa 47.7 46.7 48.7 68 72 62 55 230 18.8 600 0 Sudan 56.7 55.3 58.1 90 98 84 62 590 1.6 228 70 Swaziland 41.5 42.2 40.7 160 163 150 110 370 33.4 1,262 0 Tanzania 46.3 46.0 46.6 122 134 117 76 1,500 6.5 342 130 Togo 55.1 53.3 57.0 139 151 128 78 570 3.2 373 47 Uganda 50.0 49.3 50.6 136 144 132 79 880 6.7 369 152 Zambia 38.4 38.9 37.9 182 190 173 102 750 17.0 600 141 Zimbabwe 37.3 37.9 36.6 132 136 121 81 1,100 20.1 601 1 NORTH AFRICA Algeria 71.7 70.4 73.1 39 41 39 34 140 0.1 55 .. Egypt, Arab Rep. 70.5 68.4 72.8 33 36 36 28 84 0.1 25 .. Libya 74.4 72.1 76.8 19 20 19 18 97 .. 18 .. Morocco 70.4 68.2 72.7 40 47 38 36 220 0.1 89 .. Tunisia 73.5 71.5 75.5 24 29 22 20 120 0.1 24 .. a. Diphtheria, pertussis, and tetanus toxoid. b. Data are for most recent year available during the period specified. 82 Part III. Development outcomes HUMAN DEVELOPMENT Prevention and treatment Child immunization rate Contraceptive Children sleeping Tuberculosis Tuberculosis Children under age 5 (% of children ages Malnutrition (% of Births attended prevalence rate under insecticide- cases detected treatment with fever receiving 12­23 months) children under age 5) by skilled health (% of women treated bednets (% of under DOTS (% success rate (% of antimalarial Measles DPTa Stunting Underweight staff (% of total) ages 15­49) children under age 5) of estimated cases) registered cases) drugs (%) 2005 2005 2000­05 b 2000­05 b 2000­05 b 2000­05 b 2000­05 b 2005 2000­05 b 2000­05b 45 47 45.2 30.5 44.7 6.0 2.0 85.4 67.6 63.0 85 93 30.7 30.0 75.0 18.6 7.0 83.3 82.7 60.0 90 97 23.1 12.5 94.2 48.0 .. 68.5 64.6 .. 84 96 38.7 37.7 37.8 13.8 2.0 17.6 67.1 50.0 75 74 56.8 45.1 25.2 16.0 1.0 29.6 78.2 31.0 68 80 31.7 18.1 61.8 26.0 1.0 105.8 70.7 53.0 65 73 .. .. .. .. .. 33.9 71.0 .. 35 40 38.9 24.3 44.1 28.0 2.0 40.4 91.4 69.0 23 20 40.9 36.7 14.4 2.8 0.6 21.7 69.0 44.0 80 80 42.3 25.4 61.8 26.0 9.0 48.9 94.1 63.0 70 73 38.1 31.0 60.7 31.0 1.0 72.4 84.8 45.0 56 65 .. .. 86.2 44.3 .. 57.4 63.0 .. 51 56 .. 17.2 68.0 .. 4.0 37.6 70.6 58.0 65 71 23.0 26.8 60.6 9.0 .. 42.1 79.6 .. 51 33 .. 18.6 64.6 .. 1.0 .. 51.3 49.0 84 83 37.6 39.6 28.3 8.0 4.0 12.5 85.1 4.0 59 69 46.5 38.4 5.7 14.7 1.5 32.7 79.3 3.0 55 38 20.7 11.9 85.5 32.7 .. 57.5 40.3 .. 84 88 19.2 17.2 54.7 18.0 15.0 69.4 85.9 55.0 83 84 29.9 22.1 47.1 25.2 4.0 37.5 71.7 63.0 59 69 .. 32.7 55.5 7.0 4.0 55.6 71.7 56.0 80 80 30.5 25.0 34.7 8.0 7.0 79.1 75.4 58.0 69 76 30.3 19.9 41.6 39.3 5.0 42.8 80.3 27.0 85 83 46.1 18.0 55.4 30.0 .. 84.8 69.2 .. 94 87 39.5 26.5 50.9 10.0 .. 50.0 70.4 .. 59 61 47.7 41.9 51.3 27.1 0.2 67.0 70.8 34.0 82 93 49.0 21.9 56.1 30.6 15.0 38.6 71.0 28.0 86 85 38.2 33.2 40.6 8.1 8.0 21.1 70.6 38.0 61 71 34.5 31.8 56.9 8.0 2.0 28.2 21.9 33.0 98 97 .. .. 99.2 76.0 .. 31.7 88.9 .. 77 72 41.0 23.7 47.7 25.5 .. 48.7 76.7 15.0 73 86 23.6 24.0 75.5 43.7 3.0 89.9 68.4 14.0 83 89 39.7 40.1 15.7 14.0 6.0 49.6 60.7 48.0 35 25 38.3 28.7 35.2 12.6 1.0 21.6 73.4 34.0 89 95 45.3 22.5 38.6 17.4 13.0 29.1 76.5 12.3 88 97 28.9 12.9 76.4 29.0 22.8 .. .. 61.0 74 84 25.4 22.7 58.0 10.5 14.0 50.7 74.4 29.0 99 99 .. .. .. .. .. 65.3 92.3 .. 67 64 33.8 27.2 41.7 4.0 2.0 37.4 81.7 61.0 35 35 23.3 25.8 24.8 .. .. 85.8 90.5 .. 82 94 .. .. 92.0 60.3 .. 103.1 69.7 .. 60 59 43.3 40.7 87.0 7.0 0.0 34.6 77.2 50.0 60 71 30.2 10.3 74.0 48.0 0.0 42.3 50.3 26.0 91 90 37.7 21.8 43.4 26.4 16.0 44.9 81.3 58.2 70 82 .. .. 60.8 26.0 54.0 17.9 66.6 60.0 86 84 39.1 22.9 39.0 22.8 0.0 45.1 70.5 .. 84 80 46.8 23.0 43.4 34.2 7.0 51.6 82.7 52.0 85 90 .. .. .. .. .. 41.3 54.3 .. .. .. .. 83 88 19.1 10.4 95.9 57.0 .. 105.9 90.9 .. 98 98 15.6 8.6 74.2 59.2 .. 62.7 70.1 .. 97 98 .. .. .. .. .. 177.7 63.9 .. 97 98 18.1 10.2 62.6 63.0 .. 101.0 87.0 .. 96 98 12.3 4.0 89.9 66.0 .. 82.5 89.9 .. HUMAN DEVELOPMENT Part III. Development outcomes 83 Participating in growth Table 8.2 Health (continued) Water and sanitation Human resources Population with sustainable access Population with sustainable access to an improved water source (%) to improved sanitation (%) Health workers (per 1,000 people) Total Urban Rural Total Urban Rural Physicians Nurses Midwives 2004 2004 2004 2004 2004 2004 2004 2004 2004 SUB­SAHARAN AFRICA Angola 53 75 40 31 56 16 0.1 .. .. Benin 67 78 57 33 59 11 0.0 0.8 .. Botswana 95 100 90 42 57 25 0.4 2.7 .. Burkina Faso 61 94 54 13 42 6 0.1 0.4 0.1 Burundi 79 92 77 36 47 35 0.0 0.2 .. Cameroon 66 86 44 51 58 43 0.2 1.6 .. Cape Verde 80 86 73 43 61 19 0.5 0.9 .. Central African Republic 75 93 61 27 47 12 0.1 0.3 0.1 Chad 42 41 43 9 24 4 0.0 0.3 0.0 Comoros 86 92 82 33 41 29 0.1 0.7 .. Congo, Dem. Rep. 46 82 29 30 42 25 0.1 0.5 .. Congo, Rep. 58 84 27 27 28 25 0.2 1.0 .. Côte d'Ivoire 84 97 74 37 46 29 0.1 0.6 .. Djibouti 73 76 59 82 88 50 0.2 0.4 0.1 Equatorial Guinea 43 45 42 53 60 46 0.3 0.5 0.1 Eritrea 60 74 57 9 32 3 0.1 0.6 .. Ethiopia 22 81 11 13 44 7 .. 0.2 .. Gabon 88 95 47 36 37 30 0.3 5.2 .. Gambia, The 82 95 77 53 72 46 .. .. .. Ghana 75 88 64 18 27 11 0.2 0.9 .. Guinea 50 78 35 18 31 11 0.1 0.6 0.0 Guinea-Bissau 59 79 49 35 57 23 0.1 0.7 0.0 Kenya 61 83 46 43 46 41 0.1 1.1 .. Lesotho 79 92 76 37 61 32 .. .. .. Liberia 61 72 52 27 49 7 0.0 0.2 0.1 Madagascar 46 77 35 32 48 26 0.3 0.3 .. Malawi 73 98 68 61 62 61 0.0 0.6 .. Mali 50 78 36 46 59 39 0.1 0.5 0.0 Mauritania 53 59 44 34 49 8 0.1 0.6 .. Mauritius 100 100 100 94 95 94 1.1 3.7 0.0 Mozambique 43 72 26 32 53 19 0.0 0.2 0.1 Namibia 87 98 81 25 50 13 0.3 3.1 .. Niger 46 80 36 13 43 4 0.0 0.2 .. Nigeria 48 67 31 44 53 36 .. 1.7 .. Rwanda 74 92 69 42 56 38 0.0 0.4 0.0 São Tomé and Principe 79 89 73 25 32 20 0.5 1.6 0.3 Senegal 76 92 60 57 79 34 0.1 0.3 .. Seychelles 88 100 75 .. .. 100 1.5 7.9 .. Sierra Leone 57 75 46 39 53 30 0.0 0.4 .. Somalia 29 32 27 26 48 14 .. .. .. South Africa 88 99 73 65 79 46 0.8 4.1 .. Sudan 70 78 64 34 50 24 0.2 0.8 0.1 Swaziland 62 87 54 48 59 44 0.2 6.3 .. Tanzania 62 85 49 47 53 43 .. .. .. Togo 52 80 36 35 71 15 0.0 0.4 .. Uganda 60 87 56 43 54 41 0.1 0.6 0.1 Zambia 58 90 40 55 59 52 0.1 1.7 0.3 Zimbabwe 81 98 72 53 63 47 0.2 0.7 .. NORTH AFRICA Algeria 85 88 80 92 99 82 .. .. .. Egypt, Arab Rep. 98 99 97 70 86 58 .. .. .. Libya .. .. .. 97 97 96 .. .. .. Morocco 81 99 56 73 88 52 0.5 0.8 .. Tunisia 93 99 82 85 96 65 1.3 2.9 .. 84 Part III. Development outcomes HUMAN DEVELOPMENT Expenditure on health Out-of-pocket (% of Share of GDP (%) Share of total health expenditure (%) private expenditure Health expenditure Total Public Private Public Private on health) per capita ($) 2004 2004 2004 2004 2004 2004 2004 1.9 1.5 0.4 79.4 20.6 100.0 25.5 4.9 2.5 2.4 51.2 48.8 99.9 24.2 6.4 4.0 2.4 62.9 37.1 27.9 328.6 6.1 3.3 2.8 54.8 45.2 97.9 24.2 3.2 0.8 2.4 26.2 73.8 100.0 3.0 5.2 1.5 3.7 28.0 72.0 94.5 50.7 5.2 3.9 1.3 75.8 24.2 99.8 97.8 4.1 1.5 2.6 36.8 63.2 95.4 13.2 4.2 1.5 2.7 36.9 63.1 95.8 19.6 2.8 1.6 1.2 56.9 43.1 100.0 13.2 4.0 1.1 2.9 28.1 71.9 100.0 4.7 2.5 1.2 1.3 49.2 50.8 100.0 27.6 3.8 0.9 2.9 23.8 76.2 88.7 33.0 6.3 4.4 1.9 69.2 30.8 98.6 53.1 1.6 1.2 0.4 77.1 22.9 75.1 168.2 4.5 1.8 2.7 39.2 60.8 100.0 9.9 5.3 2.7 2.6 51.5 48.5 78.3 5.6 4.5 3.1 1.4 68.8 31.2 100.0 231.3 6.8 1.8 5.0 27.1 72.9 68.2 18.5 6.7 2.8 3.9 42.2 57.8 78.2 27.2 5.3 0.7 4.6 13.2 86.8 99.5 21.8 4.8 1.3 3.5 27.3 72.7 90.0 8.7 4.1 1.8 2.3 42.7 57.3 81.9 20.1 6.5 5.5 1.0 84.2 15.8 18.2 49.4 5.6 3.6 2.0 63.9 36.1 98.5 8.6 3.0 1.8 1.2 59.1 40.9 52.5 7.3 12.9 9.6 3.3 74.7 25.3 35.2 19.3 6.6 3.2 3.4 49.2 50.8 99.5 23.8 2.9 2.0 0.9 69.4 30.6 100.0 14.5 4.3 2.4 1.9 54.7 45.3 80.8 222.3 4.0 2.7 1.3 68.4 31.6 38.5 12.3 6.8 4.7 2.1 69.0 31.0 18.1 189.8 4.2 2.2 2.0 52.5 47.5 85.1 8.6 4.6 1.4 3.2 30.4 69.6 90.4 23.0 7.5 4.3 3.2 56.8 43.2 36.9 15.5 11.5 9.9 1.6 86.2 13.8 100.0 47.8 5.9 2.4 3.5 40.3 59.7 94.5 39.4 6.1 4.6 1.5 75.3 24.7 62.5 534.4 3.3 1.9 1.4 59.0 41.0 100.0 6.6 .. .. .. .. .. .. .. 8.6 3.5 5.1 40.4 59.6 17.2 390.2 4.1 1.5 2.6 35.4 64.6 98.1 24.7 6.3 4.0 2.3 63.8 36.2 40.2 145.8 4.0 1.7 2.3 43.6 56.4 83.2 12.0 5.5 1.1 4.4 20.7 79.3 84.9 17.9 7.6 2.5 5.1 32.7 67.3 51.3 19.0 6.3 3.4 2.9 54.7 45.3 71.4 29.6 7.5 3.5 4.0 46.1 53.9 48.7 27.2 3.6 2.6 1.0 72.5 27.5 94.6 93.9 5.9 2.2 3.7 37.0 63.0 .. 64.0 3.8 2.8 1.0 74.9 25.1 100.0 195.4 5.1 1.7 3.4 34.3 65.7 76.0 82.2 .. .. .. .. .. .. .. HUMAN DEVELOPMENT Part III. Development outcomes 85 Participating in growth Table 9.1 Rural development Share of rural population Rural population (%) Rural population density below the national poverty line (rural population per sq. Share of total population Annual growth km of arable land) Surveys 1990­99 Surveys 2000­05 2005 2005 2000­05a Year a Percent Year a Percent SUB­SAHARAN AFRICA 64.7 1.4 Angola 46.7 1.5 219.0 .. .. .. .. Benin 59.9 2.6 181.0 1999 33.0 .. .. Botswana 42.6 ­2.2 208.1 .. .. .. .. Burkina Faso 81.7 2.7 211.5 1998 61.1 2003 52.4 Burundi 90.0 3.3 643.7 1990 36.0 .. .. Cameroon 45.4 ­0.3 124.8 1996 59.6 2001 49.9 Cape Verde 42.7 0.5 465.4 .. .. .. .. Central African Republic 62.0 1.2 126.8 .. .. .. .. Chad 74.7 2.6 191.4 1996 67.0 .. .. Comoros 63.0 1.1 462.5 .. .. .. .. Congo, Dem. Rep. 67.9 2.3 557.0 .. .. .. .. Congo, Rep. 39.8 2.0 308.9 .. .. .. .. Côte d'Ivoire 55.0 0.8 297.7 .. .. .. .. Djibouti 13.9 ­2.2 11,487.9 .. .. .. .. Equatorial Guinea 61.1 2.2 226.3 .. .. .. .. Eritrea 80.6 3.5 586.0 .. .. .. .. Ethiopia 84.0 1.6 524.0 1996 47.0 2000 45.0 Gabon 16.4 ­2.6 73.5 .. .. .. .. Gambia, The 46.1 0.6 219.2 1998 61.0 .. .. Ghana 52.2 0.6 272.3 1999 49.9 .. .. Guinea 67.0 1.3 534.4 .. .. .. .. Guinea-Bissau 70.4 3.0 350.3 .. .. .. .. Kenya 79.3 2.1 561.0 1997 53.0 .. .. Lesotho 81.3 ­0.4 445.4 .. .. .. .. Liberia 41.9 ­0.5 366.3 .. .. .. .. Madagascar 73.2 2.5 439.3 1999 76.7 .. .. Malawi 82.8 1.7 421.2 1998 66.5 .. .. Mali 69.5 2.2 195.7 1998 75.9 .. .. Mauritania 59.6 2.8 354.3 1996 65.5 2000 61.2 Mauritius 57.6 0.9 716.1 .. .. .. .. Mozambique 65.5 0.7 293.5 1997 71.3 .. .. Namibia 64.9 0.3 160.8 .. .. .. .. Niger 83.2 3.2 75.2 1993 66.0 .. .. Nigeria 51.8 0.8 236.3 1993 36.4 .. .. Rwanda 80.7 0.4 605.0 .. .. 2000 65.7 São Tomé and Principe 42.0 0.1 818.9 .. .. .. .. Senegal 58.4 2.0 265.8 1992 40.4 .. .. Seychelles 47.1 0.2 3,962.8 .. .. .. .. Sierra Leone 59.3 2.2 545.9 .. .. 2004 79.0 Somalia 64.8 2.7 483.6 .. .. .. .. South Africa 40.7 0.0 129.4 .. .. .. .. Sudan 59.2 0.4 125.2 .. .. .. .. Swaziland 75.9 0.8 473.4 .. .. .. .. Tanzania 75.8 2.1 699.6 1991 40.8 2001 38.7 Togo 59.9 1.4 142.5 .. .. .. .. Uganda 87.4 3.4 452.6 .. .. 2003 41.7 Zambia 65.0 1.6 139.7 1998 83.1 2004 78.0 Zimbabwe 64.1 ­0.1 259.4 1996 48.0 .. .. NORTH AFRICA 46.6 0.9 Algeria 36.7 ­0.4 160.9 1995 30.3 .. .. Egypt, Arab Rep. 57.2 1.8 1,411.6 1996 23.3 .. .. Libya 15.2 ­0.3 49.2 .. .. .. .. Morocco 41.3 ­0.7 148.7 1999 27.2 .. .. Tunisia 34.7 ­0.1 125.1 1995 13.9 .. .. a. Data are for most recent year available during the period specified. 86 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT, AND ENVIRONMENT Share of rural population with sustainable access (%) To an improved To improved To To transportation To water source sanitation facilities electricity (within 2 km of an all-season road) landline telephone 2000­05a 2000­05 a 2000­05 a 2000­05a 2000­05a 42.7 28.1 40.0 16.0 .. .. .. 57.0 11.0 5.6 32.0 1.0 90.0 25.0 .. .. .. 54.0 6.0 0.8 25.0 0.3 77.0 35.0 .. .. .. 44.0 43.0 16.3 20.0 0.0 73.0 19.0 .. .. .. 61.0 12.0 .. .. .. 43.0 4.0 0.3 5.0 0.0 82.0 29.0 .. .. .. 29.0 25.0 .. 26.0 .. 27.0 25.0 16.4 .. 0.2 74.0 29.0 .. .. .. 59.0 50.0 .. .. .. 42.0 46.0 .. .. .. 57.0 3.0 .. .. .. 11.0 7.0 2.0 17.0 0.2 47.0 30.0 31.4 .. 1.8 77.0 46.0 .. .. .. 64.0 11.0 20.9 .. 0.7 35.0 11.0 3.2 .. 0.3 49.0 23.0 .. .. .. 46.0 41.0 3.6 .. 6.0 76.0 32.0 0.8 .. 10.6 52.0 7.0 .. .. .. 35.0 26.0 9.7 .. 3.0 68.0 61.0 2.5 .. 2.1 36.0 39.0 2.7 .. 0.1 44.0 8.0 2.7 .. 0.2 100.0 94.0 .. .. .. 26.0 19.0 1.5 .. 0.1 81.0 13.0 10.4 .. 4.5 36.0 4.0 .. 37.0 .. 31.0 36.0 34.6 47.0 1.8 69.0 38.0 1.5 .. 0.2 73.0 20.0 .. .. .. 60.0 34.0 19.0 .. 7.5 75.0 100.0 .. .. .. 46.0 30.0 .. .. .. 27.0 14.0 .. .. .. 73.0 46.0 .. .. .. 64.0 24.0 .. .. .. 54.0 44.0 .. .. .. 49.0 43.0 1.8 38.0 3.0 36.0 15.0 .. .. .. 56.0 41.0 2.6 .. 0.9 40.0 52.0 3.5 .. 0.6 72.0 47.0 .. .. .. 85.9 61.9 80.0 82.0 .. .. .. 97.0 58.0 .. .. .. 68.0 96.0 .. .. .. 56.0 52.0 .. .. .. 82.0 65.0 .. .. .. AGRICULTURE, RURAL DEVELOPMENT, AND ENVIRONMENT Part III. Development outcomes 87 Participating in growth Table 9.2 Agriculture Cereal Trade Agriculture Production index (1999­2001=100) production Agricultural Food value added (thousands of Exports Imports Exports Imports (% of GDP) Crop Food Livestock metric tons) ($ millions) ($ millions) ($ millions) ($ millions) 2005 2004­05a 2004­05a 2004­05a 2004­05a 2004­05 a 2004­05a 2004­05a 2004­05a SUB­SAHARAN AFRICA Angola 7.2 119.2 112.9 100.0 725 6 985 1 741 Benin 32.2 133.9 137.4 116.2 1,109 240 267 44 244 Botswana 1.8 113.1 104.3 102.4 45 52 126 49 92 Burkina Faso .. 130.0 115.2 110.3 2,902 338 169 63 108 Burundi 31.6 104.2 104.4 100.2 280 54 19 1 16 Cameroon 18.7 104.9 104.7 103.2 1,684 608 458 347 428 Cape Verde .. 85.4 91.8 102.1 4 0 113 0 88 Central African Republic 54.7 97.7 108.2 114.7 192 16 33 15 26 Chad 20.9 115.7 112.2 107.6 1,213 123 80 55 65 Comoros 51 105.9 104.6 95.9 21 29 34 26 30 Congo, Dem. Rep. 44.7 96.7 97.5 100.4 1,570 39 336 8 227 Congo, Rep. 4.7 105.5 108.8 121.1 9 40 261 30 301 Côte d'Ivoire 21.7 97.4 101.2 111.0 2,205 3,180 683 2,614 535 Djibouti 3.2 114.6 109.6 108.5 .. 12 163 11 119 Equatorial Guinea 2.8 93.8 93.4 101.9 - 7 57 6 24 Eritrea 20.7 71.5 86.3 99.5 83 3 127 2 126 Ethiopia 42.9 110.5 112.1 115.8 9,280 382 425 125 385 Gabon 4.9 102.3 101.7 101.5 32 17 227 2 185 Gambia, The 29.6 65.6 69.0 103.1 213 17 155 15 127 Ghana 37.5 121.2 121.0 111.8 1,943 1,344 1,074 1,302 729 Guinea 18.6 110.4 113.8 115.3 1,142 70 220 42 154 Guinea-Bissau 59.3 109.8 109.7 109.1 171 62 44 62 35 Kenya 24.1 101.6 104.3 108.7 2,730 1,423 519 393 413 Lesotho 14.6 111.2 106.0 100.0 248 6 60 1 47 Liberia 66 99.3 97.3 110.0 110 96 121 3 105 Madagascar 25.8 108.8 107.6 104.4 3,391 122 90 104 80 Malawi 29.8 91.8 95.6 101.9 1,843 397 100 76 59 Mali 33.7 111.2 109.6 117.9 2,845 334 160 121 123 Mauritania 21.4 100.5 108.8 110.0 125 34 345 16 239 Mauritius 5.3 103.5 105.9 113.6 0 429 426 371 335 Mozambique 19.7 107.4 104.0 101.1 2,007 124 343 .. 301 Namibia 10.7 110.8 114.0 113.9 107 249 278 145 197 Niger .. 122.1 118.4 104.6 2,672 71 262 65 226 Nigeria 23.4 105.9 106.2 108.8 22,783 623 2,285 548 2,024 Rwanda 42.3 113.1 113.2 109.9 319 34 58 1 50 São Tomé and Principe 17 109.3 109.2 107.7 3 4 20 4 14 Senegal 14.4 76.8 81.6 101.1 1,085 156 890 102 798 Seychelles 2.6 93.8 91.6 91.1 - 12 78 6 67 Sierra Leone 43.4 115.0 113.5 105.2 309 14 156 11 98 Somalia .. .. .. .. .. 88 152 85 148 South Africa 2.2 102.6 105.9 108.6 12,352 4,184 2,753 2,680 1,731 Sudan 32.3 109.7 107.8 107.2 3,643 626 642 307 572 Swaziland 6.5 100.8 105.9 111.1 71 272 74 262 54 Tanzania 42.4 106.8 105.6 109.6 5,020 534 342 146 278 Togo 43.6 110.9 104.2 109.2 787 96 85 50 64 Uganda 30 108.7 109.2 110.3 2,625 417 316 78 269 Zambia 16.8 108.2 108.0 98.9 1,364 322 173 159 139 Zimbabwe 13.4 66.1 86.4 99.0 837 847 468 115 371 NORTH AFRICA Algeria 8 128.4 116.8 104.8 3,998 61 3,971 50 3,456 Egypt, Arab Rep. 14 105.5 110.9 122.3 21,315 1,187 3,989 900 3,356 Libya .. 99.8 104.3 100.9 213 12 1,150 1 1,049 Morocco 14.1 148.6 132.1 99.8 8,604 1,430 2,367 1,168 1,776 Tunisia 11.6 101.7 101.6 98.8 2,155 974 1,206 783 874 a. Data are for most recent year available during the period specified. 88 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT, AND ENVIRONMENT Fertilizer Agricultural Share of land area (%) consumption machinery Agricultural (100 grams Tractors per employment Agriculture value Cereal yield Permanent Cereal Irrigated land per hectare of 100 sq km of (% of total Incidence of added per worker (kilograms per cropland cropland (% of cropland) arable land) arable land employment) drought (2000 $) hectare) 2005 2005 2001­03 a 2000­02a 2000­03 a 2000­05a 2000­05a 2003­04a 2005 0.2 1.2 2.2 4.7 31.2 .. No 194 597 2.4 9.2 0.4 187.6 0.7 .. No 622 1,147 0.0 0.1 0.3 122.0 159.2 22.6 No 430 241 0.2 11.3 0.5 3.6 4.1 .. Yes 189 941 14.2 8.1 1.5 25.8 1.7 .. Yes 74 1,329 2.6 2.5 0.4 58.6 0.8 .. No 1,180 1,727 0.7 3.7 6.1 47.8 3.5 .. Yes 1,582 156 0.2 0.3 0.1 3.1 0.2 .. No 422 1,042 0.0 1.9 0.8 48.6 0.5 .. Yes 214 671 23.3 7.0 .. 37.5 0.6 .. No 419 1,338 0.5 0.9 0.1 15.7 3.6 .. No 151 767 0.2 0.0 0.4 4.8 14.1 .. No 368 806 11.3 2.9 1.1 330.3 11.5 .. No 836 1,262 .. 0.0 .. .. 60.0 .. Yes 72 1,500 3.6 .. .. .. 13.1 .. No 654 .. 0.0 3.7 3.7 65.4 8.2 .. Yes 59 405 0.7 9.8 2.5 151.0 2.7 .. Yes 154 1,244 0.7 0.1 1.4 9.2 46.2 .. No 1,929 1,641 0.5 19.8 0.6 25.4 1.4 .. No 240 1,123 9.7 5.9 0.5 74.2 8.6 55.0 Yes 354 1,458 2.6 3.2 5.4 30.5 5.1 .. No 237 1,468 8.9 5.5 4.5 80.0 0.6 .. No 237 1,220 1.0 3.8 2.0 310.3 27.6 .. Yes 326 1,322 0.1 5.1 0.9 342.4 60.6 .. Yes 494 936 2.3 1.2 0.5 .. 8.5 .. No .. 917 1.0 2.6 30.6 30.9 12.0 78.0 Yes 175 2,380 1.5 13.2 2.2 839.2 5.8 .. Yes 137 1,097 0.0 2.6 4.9 89.4 5.4 .. No 229 839 0.0 0.2 9.8 59.4 7.8 .. Yes 338 1,448 3.0 0.0 20.8 2,500.0 37.0 10.0 No 4,967 3,455 0.3 2.5 2.6 59.3 13.2 .. Yes 158 959 0.0 0.3 1.0 3.7 38.7 31.1 No 1,099 447 0.0 6.6 0.5 3.4 0.1 .. Yes 174 394 3.2 20.1 0.8 55.0 9.8 .. No 949 1,057 10.9 14.2 0.6 137.1 0.5 .. Yes 222 1,016 49.0 1.1 18.2 .. 156.3 .. No 221 2,455 0.2 6.2 4.8 136.1 2.8 .. No 259 975 13.0 .. .. 170.0 400.0 .. No 512 .. 1.0 3.5 4.7 5.6 1.4 .. No .. 1,223 0.0 1.2 18.7 4.8 16.3 .. Yes .. .. 0.8 3.5 9.5 654.2 44.4 10.3 No 2,499 3,330 0.2 4.7 10.7 42.8 7.0 .. Yes 728 398 0.8 3.4 26.0 393.3 221.9 .. Yes 1,212 1,160 1.2 4.0 3.6 17.9 19.0 82.1 Yes 303 1,472 2.2 13.7 0.3 67.9 0.3 .. Yes 412 1,058 10.9 8.1 0.1 18.2 9.0 69.1 Yes 237 1,695 0.0 0.8 2.9 123.9 11.4 .. Yes 219 1,595 0.3 4.3 5.2 341.6 74.5 .. Yes 236 717 0.3 1.0 6.9 129.9 128.8 20.7 Yes 2,267 1,466 0.5 3.1 99.9 4,321.5 308.7 29.9 No 2,062 7,516 0.2 0.2 21.9 341.0 219.0 .. No .. 627 2.0 12.2 15.4 475.2 57.8 47.0 Yes 1,739 814 13.8 9.0 8.0 368.1 125.8 .. No 2,874 1,450 Agriculture, rurAl development, And environment Part III. Development outcomes 89 Participating in growth Table 9.3 Environment Renewable internal Average annual freshwater resources Annual fresh water withdrawals deforestation Total (billions of Per capita Total (billions of Share of internal Forest area (% of land area) (% change) cubic meters) (cubic meters) cubic meters) resources (%) 1990 2005 1990­2005 2005 2005 2002 2002 SUB­SAHARAN AFRICA Angola 48.9 47.4 0.2 148 9,284 9,284 0.2 Benin 30.0 21.3 1.9 10 1,221 1,221 1.3 Botswana 24.2 21.1 0.9 2 1,360 1,360 8.1 Burkina Faso 26.1 24.8 0.3 13 945 945 6.4 Burundi 11.3 5.9 3.2 10 1,338 1,338 2.9 Cameroon 52.7 45.6 0.9 273 16,726 16,726 0.4 Cape Verde 14.4 20.8 ­3.0 0 592 592 7.3 Central African Republic 37.2 36.5 0.1 141 34,921 34,921 0.0 Chad 10.4 9.5 0.6 15 1,539 1,539 1.5 Comoros 5.4 2.2 3.9 1 1,998 1,998 0.8 Congo, Dem. Rep. 62.0 58.9 0.3 900 15,639 15,639 0.0 Congo, Rep. 66.5 65.8 0.1 222 55,515 55,515 0.0 Côte d'Ivoire 32.1 32.7 ­0.1 77 4,231 4,231 1.2 Djibouti 0.3 0.3 0.0 0 378 378 6.3 Equatorial Guinea 66.3 58.2 0.8 26 51,637 51,637 0.4 Eritrea .. 15.4 0.3 3 636 636 10.7 Ethiopia 13.7 13.0 0.9 122 1,712 1,712 4.6 Gabon 85.1 84.5 0.0 164 118,511 118,511 0.1 Gambia, The 44.2 47.1 ­0.4 3 1,978 1,978 1.0 Ghana 32.7 24.2 1.7 30 1,370 1,370 3.2 Guinea 30.1 27.4 0.6 226 24,037 24,037 0.7 Guinea-Bissau 78.8 73.7 0.4 16 10,086 10,086 1.1 Kenya 6.5 6.2 0.3 21 604 604 7.6 Lesotho 0.2 0.3 ­4.0 5 2,897 2,897 1.0 Liberia 42.1 32.7 1.5 200 60,915 60,915 0.1 Madagascar 23.5 22.1 0.4 337 18,113 18,113 4.4 Malawi 41.4 36.2 0.8 16 1,250 1,250 6.3 Mali 11.5 10.3 0.7 60 4,438 4,438 10.9 Mauritania 0.4 0.3 2.4 0 130 130 425.0 Mauritius 19.2 18.2 0.3 3 2,252 2,252 21.8 Mozambique 25.5 24.6 0.2 100 5,068 5,068 0.6 Namibia 10.6 9.3 0.8 6 3,052 3,052 4.8 Niger 1.5 1.0 2.3 4 251 251 62.3 Nigeria 18.9 12.2 2.4 221 1,680 1,680 3.6 Rwanda 12.9 19.5 ­3.4 10 1,051 1,051 1.6 São Tomé and Principe 28.1 28.1 0.0 2 14,055 14,055 .. Senegal 48.6 45.0 0.5 26 2,213 2,213 8.6 Seychelles 87.0 87.0 0.0 .. .. .. .. Sierra Leone 42.5 38.5 0.6 160 28,957 28,957 0.2 Somalia 13.2 11.4 0.9 6 729 729 54.8 South Africa 7.6 7.6 0.0 45 956 956 27.9 Sudan 32.1 28.4 0.8 30 828 828 124.4 Swaziland 27.4 31.5 ­1.0 3 2,299 2,299 40.1 Tanzania 46.9 39.9 1.0 84 2,192 2,192 6.2 Togo 12.6 7.1 2.9 12 1,871 1,871 1.5 Uganda 25.0 18.4 1.8 39 1,353 1,353 0.8 Zambia 66.1 57.1 0.9 80 6,873 6,873 2.2 Zimbabwe 57.5 45.3 1.4 12 946 946 34.2 NORTH AFRICA Algeria 0.8 1.0 ­1.8 11 341 341 54.2 Egypt, Arab Rep. 0.0 0.1 ­3.5 2 24 24 3,794.4 Libya 0.1 0.1 0.0 1 103 103 711.3 Morocco 9.6 9.8 ­0.1 29 961 961 43.4 Tunisia 4.1 6.8 ­4.3 4 419 419 62.9 a. Data are for most recent year available during the period specified. 90 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT, AND ENVIRONMENT Water productivity (2000 $ per cubic Carbon dioxide meter of fresh water withdrawal) Emissions of organic Combustible emissions, industrial water pollutants Energy production Energy use renewables and waste (thousands of Total Agriculture Industry (kilograms per day) (kilotons of oil equivalent) (kilotons of oil equivalent) (% of total energy use) metric tons) 2001­03a 2001­03a 2001­03a 1990 2001­03a 1990 2004 1990 2004 1990 2004 1990 2003 30.8 3.3 130.8 .. .. 28,652 57,358 6,285 9,488 68.8 64.7 4,645 8,615 19.0 15.4 12.1 .. .. 1,774 1,623 1,678 2,475 93.2 65.6 714 2,040 35.4 1.8 105.9 4,509 5,499 910 1,008 1,272 1,866 33.1 24.4 2,169 4,114 3.6 1.3 76.7 .. .. .. .. .. .. .. .. 993 1,040 2.6 1.2 6.1 1,570 .. .. .. .. .. .. .. 194 234 11.1 3.0 42.7 13,989 10,032 12,090 12,476 5,032 6,949 75.9 77.8 1,604 3,535 26.2 3.0 269.6 103 .. .. .. .. .. .. .. 84 143 38.4 517.4 46.8 998 .. .. .. .. .. .. .. 198 253 7.2 3.2 .. .. .. .. .. .. .. .. .. 143 117 21.7 23.2 50.4 .. .. .. .. .. .. .. .. 66 88 12.1 18.7 15.2 .. .. 12,019 17,002 11,903 16,559 84.0 92.5 3,971 1,788 76.0 .. .. .. .. 9,005 12,586 1,056 1,063 69.4 61.7 1,172 1,377 11.0 3.9 19.2 .. .. 3,382 7,220 4,408 6,927 72.1 64.9 5,385 5,714 30.4 6.0 .. .. .. .. .. .. .. .. .. 352 366 14.8 105.5 129.7 61 .. .. .. .. .. .. .. 117 165 2.3 0.3 .. .. .. .. .. .. .. .. .. .. 700 1.5 0.7 51.1 18,593 22,085 14,158 19,370 15,151 21,179 92.8 90.4 2,963 7,333 42.1 6.2 291.3 .. .. 14,630 12,107 1,243 1,693 59.8 58.8 5,989 1,223 14.1 5.2 15.7 .. .. .. .. .. .. .. .. 191 282 5.5 2.9 14.8 .. .. 4,392 6,230 5,337 8,354 73.1 69.1 3,766 7,729 2.2 0.5 35.0 .. .. .. .. .. .. .. .. 1,011 1,337 1.1 0.8 3.9 .. .. .. .. .. .. .. .. 209 271 8.4 3.9 20.6 42,588 56,102 10,272 13,675 12,479 16,920 78.4 74.1 5,821 8,773 18.4 11.8 17.6 2,958 .. .. .. .. .. .. .. .. .. 5.4 .. .. .. .. .. .. .. .. .. .. 465 462 0.2 0.1 1.9 .. .. .. .. .. .. .. .. 941 2,341 1.7 0.7 5.0 10,024 .. .. .. .. .. .. .. 601 883 0.4 0.2 11.8 .. .. .. .. .. .. .. .. 421 553 0.7 0.2 5.8 .. .. .. .. .. .. .. .. 2,634 2,498 7.9 .. .. 17,813 .. .. .. .. .. .. .. 1,462 3,143 7.3 2.0 120.3 20,414 10,231 6,846 8,236 7,203 8,571 94.4 84.1 996 1,568 12.4 1.6 69.7 .. .. .. 321 .. 1,337 .. 13.8 7 2,326 0.9 0.4 33.7 .. 386 .. .. .. .. .. .. 1,048 1,205 6.0 2.3 27.4 .. .. 150,453 229,440 70,905 98,989 79.8 80.2 45,326 52,176 14.1 9.1 35.9 .. .. .. .. .. .. .. .. 528 601 .. .. .. .. .. .. .. .. .. .. .. 66 92 2.1 0.3 18.4 10,309 6,603 1,362 1,106 2,238 2,751 60.6 38.9 3,132 4,835 .. .. .. .. .. .. .. .. .. .. .. 114 546 2.5 .. .. .. .. .. .. .. .. .. .. 333 652 .. .. .. .. .. .. .. .. .. .. .. 18 .. 11.3 0.5 53.1 261,618 221,256 114,534 155,998 91,229 131,137 11.4 10.0 285,403 364,157 0.4 0.2 12.0 .. 38,583 8,775 29,330 10,642 17,638 81.7 79.2 5,381 8,989 1.4 0.1 37.6 6,586 .. .. .. .. .. .. .. 425 956 2.0 0.9 61.7 31,125 .. 9,063 17,530 9,808 18,749 91.0 91.6 2,333 3,802 8.2 6.5 63.8 .. .. 1,203 1,910 1,447 2,688 82.6 70.6 751 2,194 22.0 18.3 25.2 .. .. .. .. .. .. .. .. 813 1,711 2.0 0.5 6.7 15,880 .. 4,923 6,360 5,470 6,943 73.4 79.1 2,443 2,194 1.6 0.3 4.3 37,149 .. 8,550 8,600 9,384 9,301 50.4 63.8 16,641 11,465 9.7 1.3 39.5 106,978 .. 104,439 165,728 23,858 32,895 0.1 0.2 76,971 163,634 1.6 0.3 8.2 211,531 186,059 54,869 64,662 31,895 56,881 3.3 2.5 75,414 139,626 8.7 .. .. .. .. 73,173 85,378 11,541 18,193 1.1 0.8 37,762 50,179 2.9 0.6 31.9 41,710 72,126 773 659 6,725 11,452 4.7 3.9 23,480 37,897 7.9 1.0 54.7 .. 55,775 6,127 6,805 5,536 8,703 18.7 12.4 13,256 20,868 AGRICULTURE, RURAL DEVELOPMENT, AND ENVIRONMENT Part III. Development outcomes 91 Participating in growth Table 10.1 Labor force participation Participation rate (%) Labor force ages 15 and older (thousands) Total Ages 15 Ages 65 Total Male Female and older Ages 15­24 Ages 25­54 Ages 55­64 and older 2005 2005 2005 2005 2005 2005 2005 2005 SUB­SAHARAN AFRICA 296,962 170,084 126,878 73.9 65.6 83.5 73.6 48.6 Angola 6,939 3,742 3,197 81.3 79.2 87.7 78.8 54.5 Benin 3,277 2,026 1,251 69.6 60.3 78.3 70.3 53.9 Botswana 612 347 266 55.6 36.4 74.3 55.1 33.1 Burkina Faso 5,796 3,109 2,687 83.0 77.3 90.9 84.1 58.9 Burundi 3,775 1,813 1,962 91.0 87.4 97.6 94.4 79.7 Cameroon 6,317 3,839 2,478 65.8 52.3 78.8 65.6 39.0 Cape Verde 164 112 53 53.6 46.0 65.4 39.4 20.7 Central African Republic 1,831 988 842 79.5 70.1 86.2 86.0 73.0 Chad 3,465 1,779 1,686 67.4 56.0 81.3 82.3 69.9 Comoros 333 200 133 71.9 60.2 82.2 75.2 50.8 Congo, Dem. Rep. 23,003 13,554 9,449 75.8 71.8 81.7 71.0 50.0 Congo, Rep. 1,627 962 665 77.0 58.4 79.9 76.8 72.4 Côte d'Ivoire 6,763 4,820 1,943 64.1 55.7 72.5 66.1 48.8 Djibouti .. .. .. .. 54.7 79.2 66.6 39.4 Equatorial Guinea 196 124 72 69.8 68.6 75.2 73.8 40.6 Eritrea 1,792 1,056 736 73.7 68.8 80.0 67.4 45.1 Ethiopia 34,137 18,880 15,257 79.5 76.9 87.0 71.6 42.7 Gabon 591 339 252 71.3 60.5 83.4 71.1 44.8 Gambia, The 652 382 270 71.8 62.4 79.2 76.1 58.6 Ghana 9,739 5,062 4,676 72.2 50.6 88.6 80.3 56.1 Guinea 4,441 2,387 2,054 84.0 75.7 92.8 82.7 50.6 Guinea-Bissau 643 378 265 77.2 74.3 81.0 71.6 53.8 Kenya 15,413 8,725 6,688 78.7 70.4 88.5 81.5 52.7 Lesotho 634 353 281 57.5 45.9 73.8 59.6 32.9 Liberia 1,202 724 478 69.1 58.1 78.3 69.7 49.2 Madagascar 8,540 4,413 4,127 82.0 68.2 92.2 88.1 71.8 Malawi 5,934 2,987 2,947 87.5 80.3 93.5 90.6 77.6 Mali 5,541 2,956 2,585 79.2 71.3 87.1 76.5 44.9 Mauritania 1,202 717 485 68.7 57.4 79.3 66.9 41.3 Mauritius 554 364 190 59.0 46.8 75.4 44.4 9.8 Mozambique 9,265 4,288 4,977 83.6 67.3 94.4 92.5 82.6 Namibia 641 365 276 53.9 33.0 73.9 48.4 25.8 Niger 5,928 3,443 2,485 83.3 80.3 88.0 80.2 56.9 Nigeria 46,958 30,562 16,395 64.1 53.5 74.9 73.0 46.5 Rwanda 4,161 2,029 2,132 81.5 71.1 94.3 83.0 48.4 São Tomé and Principe .. .. .. .. 37.5 68.4 47.0 21.1 Senegal 4,592 2,678 1,914 68.6 58.9 80.1 63.8 35.7 Seychelles .. .. .. .. .. .. .. .. Sierra Leone 2,342 1,436 906 74.1 73.7 79.1 67.3 50.7 Somalia 3,513 2,133 1,380 76.4 76.9 79.5 68.1 52.7 South Africa 19,780 12,177 7,603 61.9 49.7 76.4 48.9 11.5 Sudan 10,041 7,559 2,482 45.6 32.7 57.5 52.4 37.7 Swaziland 301 204 96 49.3 41.6 64.9 46.4 19.5 Tanzania 19,235 9,718 9,516 87.4 80.6 96.0 90.2 61.8 Togo 2,419 1,530 889 69.7 63.8 76.4 65.7 50.8 Uganda 11,884 6,173 5,711 83.2 75.7 91.4 86.3 59.9 Zambia 4,897 2,831 2,065 77.5 76.8 84.4 67.5 50.8 Zimbabwe 5,659 3,216 2,443 72.5 59.8 87.3 83.8 58.8 NORTH AFRICA 64,379 47,798 16,580 50.5 36.7 63.7 45.4 17.8 Algeria 13,394 9,320 4,074 57.9 48.0 70.4 45.2 15.6 Egypt, Arab Rep. 23,089 17,728 5,361 46.9 29.5 62.2 42.4 10.1 Libya 2,291 1,706 585 56.0 40.1 71.7 50.9 24.2 Morocco 11,686 8,663 3,023 53.9 43.0 64.1 47.6 19.3 Tunisia 3,879 2,822 1,057 51.8 40.4 64.4 37.4 18.1 AFRICA 361,341 217,883 143,458 68.2 59.5 78.5 66.4 39.8 92 Part III. Development outcomes L ABOR, MIGRATION, AND POPULATION Participation rate (%) Male Female Ages 15 Ages 65 Ages 15 Ages 65 and older Ages 15­24 Ages 25­54 Ages 55­64 and older and older Ages 15­24 Ages 25­54 Ages 55­64 and older 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 85.7 73.8 96.5 89.5 67.4 62.4 57.4 70.7 59.4 33.6 89.8 86.1 97.1 88.6 76.1 73.2 72.5 78.6 70.3 37.6 86.0 73.0 95.9 88.4 79.6 53.2 47.3 60.2 55.2 35.5 65.1 40.0 90.5 78.8 48.9 46.8 32.8 59.1 36.3 23.1 89.3 81.3 97.0 92.2 74.2 76.7 73.2 84.9 75.4 46.5 91.4 86.3 99.0 96.9 84.9 90.5 88.5 96.3 92.7 76.5 81.1 61.1 95.3 85.5 61.3 51.0 43.3 62.6 47.7 20.6 78.1 61.1 92.4 66.5 46.5 32.2 30.9 41.9 23.9 6.7 89.4 77.3 98.0 94.2 85.0 70.4 63.1 75.1 79.4 64.4 70.6 54.4 91.7 94.1 83.0 64.2 57.6 71.3 71.8 59.5 86.8 69.7 98.4 95.1 83.7 57.2 50.5 65.9 57.1 24.8 90.9 81.8 97.9 93.4 76.2 61.2 61.7 65.8 51.8 30.3 92.7 69.2 98.6 94.8 88.5 61.8 47.7 61.6 61.2 60.1 88.8 75.8 98.6 91.1 78.2 37.9 35.6 44.2 36.6 17.8 .. 65.4 95.6 86.5 61.9 .. 43.8 62.8 48.2 21.4 90.5 88.6 96.8 95.3 51.3 50.0 48.8 54.1 55.2 32.0 90.4 80.0 98.3 93.0 76.2 58.3 57.7 63.1 47.9 25.4 89.1 81.7 97.2 90.2 62.3 70.1 72.1 77.2 54.5 26.2 82.9 66.5 96.0 82.7 61.9 60.0 54.5 71.1 59.4 31.1 85.6 72.0 94.7 91.2 76.8 58.4 52.9 64.3 62.1 43.0 74.7 50.6 91.7 84.7 65.1 69.8 50.6 85.4 76.1 48.0 88.5 77.6 95.6 90.8 68.9 79.3 73.7 89.9 74.8 34.7 93.0 84.7 98.8 93.8 82.4 62.1 64.1 64.0 51.6 30.6 89.3 79.5 97.5 92.3 78.5 68.1 61.2 79.5 71.8 30.4 72.5 56.3 93.8 81.1 56.2 45.6 35.9 60.4 44.6 15.8 83.9 64.7 97.3 90.4 74.4 54.6 51.4 59.6 50.9 29.1 85.6 68.5 97.3 93.5 81.2 78.4 67.9 87.2 83.2 63.8 89.8 80.2 97.0 93.5 83.3 85.2 80.4 90.2 88.0 72.7 86.5 74.3 93.9 86.5 58.7 72.2 68.1 80.8 68.7 34.6 83.9 68.0 95.8 87.4 63.5 54.3 46.7 63.6 49.1 23 78.8 58.7 96.2 62.8 17.2 39.9 34.7 54.3 28.0 4.7 82.3 61.9 97.0 95.9 87.1 84.7 72.7 92.2 89.8 79.3 62.7 35.5 85.7 64.3 31.6 45.5 30.5 62.7 34.7 21.2 95.1 91.6 98.4 95.1 85.1 71.2 68.4 77.0 67.1 34 83.2 71.3 95.9 91.4 69.5 44.8 35.1 53.6 56.1 27.3 83.6 72.5 96.3 86.7 61.5 79.6 69.8 92.5 79.9 38.5 .. 55.2 95.8 82.6 40.5 .. 19.5 42.6 19.1 4.3 83.0 67.6 96.3 78.8 53.4 55.2 50.2 65.1 52.3 21.5 .. .. .. .. .. .. .. .. .. .. 93.0 86.6 99.5 95.2 87.1 56.1 60.8 59.5 42.7 21.9 94.4 87.8 99.5 96.2 87.3 59.0 66.0 60.2 42.7 23.9 78.6 57.3 96.4 77.1 22.9 46.1 41.9 57.2 25.1 4.2 68.8 46.3 87.4 83.0 62.7 22.5 18.8 27.5 23.9 16.3 71.6 55.6 96.1 77.6 37.6 29.7 27.8 39.9 20.6 5.4 89.5 80.5 97.6 96.0 77.3 85.4 80.7 94.4 85.2 50.0 90.1 80.3 98.1 87.3 78.9 50.1 47.3 55.6 46.4 28.9 87.1 78.8 94.3 90.5 67.5 79.5 72.6 88.4 82.5 53.5 89.7 83.4 98.3 91.3 74.6 65.2 70.2 69.9 46.6 31.7 83.6 71.7 97.2 89.5 62.1 61.7 47.9 77.6 78.9 56.1 75.2 51.3 95.1 77.9 32.6 25.9 21.6 32.1 15.0 5.7 80.4 66.0 95.2 70.9 27.0 35.4 29.3 44.9 21.9 6.2 72.5 41.0 98.4 77.4 19.5 21.7 17.8 25.9 9.0 2.6 80.5 59.5 97.0 79.6 42.4 29.6 19.9 44.9 13.7 4.2 81.2 63.3 95.0 84.3 38.8 27.4 22.2 33.7 15.9 4.7 75.5 49.4 94.8 65.5 35.6 28.2 31.1 33.8 10.8 3.0 83.1 69.0 96.2 86.5 57.4 53.6 49.8 61.0 48.3 25.8 L ABOR, MIGRATION, AND POPULATION Part III. Development outcomes 93 Participating in growth Table 10.2 Labor force composition Sectora Agriculture Industry Services Male (% of male Female (% of female Male (% of male Female (% of female Male (% of male Female (% of female employment) employment) employment) employment) employment) employment) 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b SUB­SAHARAN AFRICA Angola .. .. .. .. .. .. Benin .. .. .. .. .. .. Botswana 25.9 18.6 29.2 13.3 42.8 58.0 Burkina Faso .. .. .. .. .. .. Burundi .. .. .. .. .. .. Cameroon .. .. .. .. .. .. Cape Verde .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. Chad .. .. .. .. .. .. Comoros .. .. .. .. .. .. Congo, Dem. Rep. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. Côte d'Ivoire .. .. .. .. .. .. Djibouti .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. Eritrea .. .. .. .. .. .. Ethiopia .. .. .. .. .. .. Gabon .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. Ghana 59.8 50.3 13.5 14.5 26.5 35.6 Guinea .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. Kenya .. .. .. .. .. .. Lesotho .. .. .. .. .. .. Liberia .. .. .. .. .. .. Madagascar 76.7 79.3 7.4 6.0 16.0 14.6 Malawi .. .. .. .. .. .. Mali .. .. .. .. .. .. Mauritania .. .. .. .. .. .. Mauritius 10.5 8.9 34.2 28.8 55.1 62.2 Mozambique .. .. .. .. .. .. Namibia 32.8 29.1 17.2 6.7 49.4 63.3 Niger .. .. .. .. .. .. Nigeria .. .. .. .. .. .. Rwanda .. .. .. .. .. .. São Tomé and Principe .. .. .. .. .. .. Senegal .. .. .. .. .. .. Seychelles .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. Somalia .. .. .. .. .. .. South Africa 12.6 7.4 33.3 13.6 53.9 78.9 Sudan .. .. .. .. .. .. Swaziland .. .. .. .. .. .. Tanzania 80.2 84.0 4.0 1.2 15.7 14.8 Togo .. .. .. .. .. .. Uganda 60.1 77.3 10.7 4.8 28.2 16.7 Zambia .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. NORTH AFRICA Algeria 20.4 22.3 25.6 28.2 53.8 49.4 Egypt, Arab Rep. 27.7 39.0 22.9 6.2 49.3 54.7 Libya .. .. .. .. .. .. Morocco 41.0 63.0 23.0 15.0 36.0 22.0 Tunisia .. .. .. .. .. .. a. Components may not sum up to 100 percent because of unclassified data. b. Data are for most recent year available during the period specified. 94 Part III. Development outcomes L ABOR, MIGRATION, AND POPULATION Statusa Wage and salaried workers Self-employed workers Contributing family workers Total Male Female Total Male Female Total Male Female (% of total (% of males (% of females (% of total (% of males (% of females (% of total (% of males (% of females employed) employed) employed) employed) employed) employed) employed) employed) employed) 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b 2000­05b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 82.7 83.2 81.9 15.9 15.3 16.8 1.3 1.4 1.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 19.2 29.3 8.7 59.3 57.0 61.7 18.2 9.5 27.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 49.5 52.1 45.9 42.2 40.9 43.7 7.2 5.2 9.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 15.0 17.8 12.0 43.7 51.6 35.4 40.6 29.7 51.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 80.0 77.7 84.9 17.7 21.2 10.4 2.1 0.8 4.7 .. .. .. .. .. .. .. .. .. 61.5 66.7 54.9 16.0 15.0 17.2 16.9 12.8 22.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 81.8 82.4 81.1 17.4 17.1 17.7 0.8 0.5 1.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6.9 9.8 4.0 89.3 87.2 91.4 3.8 3.0 4.6 .. .. .. .. .. .. .. .. .. 14.5 22.2 7.5 59.4 67.5 52.1 26.1 10.3 40.5 18.7 .. .. 59.7 .. .. 19.6 .. .. 37.7 51 23.1 50.4 38.6 63.2 11.9 10.4 13.6 59.8 61.9 49.8 31.7 30.7 36.6 8.2 7.1 13.6 57.9 58.2 56.5 30.0 32.8 17.7 12.2 9.0 25.8 .. .. .. .. .. .. .. .. .. 38.1 39.7 33.6 31.1 37.5 13.4 29.7 21.6 52.5 64.3 .. .. 26.8 .. .. 8.7 .. .. L ABOR, MIGRATION, AND POPULATION Part III. Development outcomes 95 Participating in growth Table 10.3 Migration and population International migration Population Stock Workers Population dynamics remittances, Share of Net received Total Male Female Annual growth Fertility rate population (%) Total migration ($ millions) (millions) (% of total) (% of total) rate (%) (births per woman) 2005 2005 2005 2005 2005 2005 2005 2005 2005 SUB­SAHARAN AFRICA .. .. .. .. 743.7 .. .. .. .. Angola 0.4 56,351 145,000 .. 15.9 49.3 50.7 2.9 6.6 Benin 2.1 174,726 98,831 100.8 8.4 50.4 49.6 3.1 5.6 Botswana 4.5 80,064 ­6,000 .. 1.8 49.1 50.9 ­0.2 3.0 Burkina Faso 5.8 772,817 100,000 49.4 13.2 50.3 49.7 3.1 5.9 Burundi 1.3 100,189 191,600 .. 7.5 48.8 51.2 3.6 6.8 Cameroon 0.8 136,909 13,000 .. 16.3 49.7 50.3 1.8 .. Cape Verde 2.2 11,183 ­5,000 121.0 0.5 47.9 52.1 2.3 3.5 Central African Republic 1.9 76,484 ­45,000 .. 4.0 48.8 51.2 1.3 4.7 Chad 4.5 437,049 270,941 .. 9.7 49.5 50.5 3.1 6.3 Comoros 11.2 67,185 ­10,000 68.2 0.6 50.2 49.8 2.1 3.8 Congo, Dem. Rep. 0.9 538,838 ­321,565 .. 57.5 49.6 50.4 3.0 6.7 Congo, Rep. 7.2 287,603 ­14,000 .. 4.0 49.6 50.4 2.9 5.6 Côte d'Ivoire 13.1 2,371,277 ­371,159 .. 18.2 50.8 49.2 1.6 4.7 Djibouti 2.6 20,272 ­9,794 .. 0.8 49.9 50.1 1.8 4.7 Equatorial Guinea 1.2 5,800 0 .. 0.5 49.6 50.4 2.3 5.9 Eritrea 0.3 14,612 279,932 223.1 4.4 49.1 50.9 3.9 5.2 Ethiopia 0.8 555,054 ­150,335 173.5 71.3 49.7 50.3 1.8 5.3 Gabon 17.7 244,550 ­15,000 4.5 1.4 49.8 50.2 1.6 3.7 Gambia, The 15.3 231,739 31,127 .. 1.5 49.6 50.4 2.6 4.4 Ghana 7.5 1,669,267 11,690 25.6 22.1 50.6 49.4 2.0 4.1 Guinea 4.3 405,772 ­299,219 1.1 9.4 51.2 48.8 1.9 5.6 Guinea-Bissau 1.2 19,171 1,181 16.8 1.6 49.4 50.6 3.0 7.1 Kenya 1 344,857 ­211,519 .. 34.3 50.1 49.9 2.3 5.0 Lesotho 0.3 5,886 ­36,000 8.9 1.8 46.5 53.5 ­0.2 3.4 Liberia 1.5 50,172 ­244,548 .. 3.3 49.9 50.1 1.3 6.8 Madagascar 0.3 62,787 0 .. 18.6 49.7 50.3 2.7 5.0 Malawi 2.2 278,793 ­20,000 161.6 12.9 49.7 50.3 2.2 5.8 Mali 0.3 46,318 ­134,204 .. 13.5 49.8 50.2 3.0 6.7 Mauritania 2.1 65,889 30,000 50.5 3.1 49.5 50.5 2.9 5.6 Mauritius 1.7 20,725 0 154.0 1.2 49.6 50.4 1.1 2.0 Mozambique 2.1 405,904 ­20,000 .. 19.8 48.4 51.6 1.9 5.3 Namibia 7.1 143,275 ­5,500 31.6 2.0 49.6 50.4 1.1 3.7 Niger 0.9 123,687 ­10,000 42.8 14.0 51.1 48.9 3.3 7.7 Nigeria 0.7 971,450 ­170,000 .. 131.5 50.6 49.4 2.4 5.5 Rwanda 1.3 121,183 45,000 .. 9.0 48.5 51.5 1.7 5.8 São Tomé and Principe 4.8 7,499 ­2,000 .. 0.2 49.5 50.5 2.3 3.8 Senegal 2.8 325,940 ­100,000 617.0 11.7 49.2 50.8 2.4 4.9 Seychelles 5.8 4,932 .. .. 0.1 50.3 49.7 1.0 .. Sierra Leone 2.2 119,162 438,215 .. 5.5 49.3 50.7 3.5 6.5 Somalia 3.4 281,702 170,000 .. 8.2 49.6 50.4 3.3 6.2 South Africa 2.4 1,106,214 50,000 .. 46.9 49.1 50.9 1.1 2.8 Sudan 1.8 638,596 ­519,123 .. 36.2 50.3 49.7 2.0 4.1 Swaziland 4 45,459 ­6,000 .. 1.1 48.2 51.8 1.0 3.9 Tanzania 2.1 792,328 ­345,000 10.3 38.3 49.8 50.2 2.6 5.2 Togo 3 183,304 ­3,570 151.3 6.1 49.4 50.6 2.6 5.0 Uganda 1.8 518,158 ­15,000 413.6 28.8 50.0 50.0 3.5 7.1 Zambia 2.4 274,842 ­65,000 .. 11.7 50.1 49.9 1.6 5.4 Zimbabwe 3.9 510,637 ­50,000 .. 13.0 49.6 50.4 0.6 3.3 NORTH AFRICA .. .. .. .. 152.9 .. .. .. .. Algeria 0.7 242,446 ­100,000 .. 32.9 50.5 49.5 1.5 2.4 Egypt, Arab Rep. 0.2 166,047 ­450,000 4,329.5 74.0 50.1 49.9 1.9 3.1 Libya 10.5 617,536 10,000 .. 5.9 51.6 48.4 2.0 2.8 Morocco 0.4 131,654 ­400,000 4,567.4 30.2 49.7 50.3 1.0 2.4 Tunisia 0.4 37,858 ­20,000 1,392.7 10.0 50.4 49.6 1.0 2.0 96 Part III. Development outcomes L ABOR, MIGRATION, AND POPULATION Population Age composition (% of total) Geographic distribution (%) Ages 0­14 Ages 15­64 Ages 65 and older Share of total population Annual growth Dependency Rural Urban Rural Urban Total Male Female Total Male Female Total Male Female ratio population population population population 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 43.5 22.0 21.5 53.4 26.5 26.8 3.1 1.4 1.7 0.90 64.7 35.3 1.4 .. 46.5 23.2 23.3 51.1 25.1 26.0 2.5 1.1 1.4 1.00 46.7 53.3 1.5 4.1 44.2 22.5 21.7 53.1 26.8 26.3 2.7 1.1 1.6 0.90 59.9 40.1 2.6 4.0 37.6 19.0 18.7 59.0 28.9 30.2 3.3 1.3 2.0 0.70 42.6 57.4 ­2.2 1.2 47.2 23.9 23.2 50.1 25.1 25.0 2.7 1.2 1.5 1.00 81.7 18.3 2.7 5.1 45.0 22.5 22.5 52.3 25.2 27.0 2.7 1.0 1.7 0.90 90.0 10.0 3.3 6.4 41.2 20.7 20.5 55.1 27.3 27.8 3.7 1.7 2.0 0.80 45.4 54.6 ­0.3 3.5 39.5 19.8 19.7 56.2 26.6 29.6 4.3 1.5 2.8 0.80 42.7 57.3 0.5 3.7 43.0 21.4 21.6 53.0 25.7 27.3 4.1 1.7 2.4 0.90 62.0 38.0 1.2 1.5 47.3 23.6 23.6 49.7 24.5 25.2 3.0 1.3 1.7 1.00 74.7 25.3 2.6 4.6 41.9 21.3 20.7 55.3 27.7 27.6 2.7 1.2 1.5 0.80 63.0 37.0 1.1 3.9 47.3 23.7 23.6 50.1 24.8 25.3 2.7 1.1 1.5 1.00 67.9 32.1 2.3 4.4 47.1 23.6 23.5 49.9 24.7 25.2 2.9 1.3 1.7 1.00 39.8 60.2 2.0 3.6 41.9 20.9 20.9 54.9 28.2 26.6 3.3 1.7 1.6 0.80 55.0 45.0 0.8 2.5 41.5 20.9 20.6 55.7 27.8 27.9 2.8 1.3 1.6 0.80 13.9 86.1 ­2.2 2.4 44.3 22.3 22.1 51.6 25.4 26.2 3.9 1.8 2.2 0.90 61.1 38.9 2.2 2.3 44.8 22.5 22.2 52.9 25.7 27.3 2.3 0.9 1.4 0.90 80.6 19.4 3.5 5.6 44.5 22.4 22.2 52.5 26.0 26.5 2.9 1.3 1.6 0.90 84.0 16.0 1.6 3.2 40.1 20.2 19.8 55.6 27.6 28.0 4.4 1.9 2.4 0.80 16.4 83.6 ­2.6 2.4 40.1 20.2 19.9 56.1 27.7 28.5 3.7 1.7 2.0 0.80 46.1 53.9 0.6 4.4 39.0 19.9 19.1 57.3 28.9 28.4 3.7 1.7 1.9 0.70 52.2 47.8 0.6 3.7 43.7 22.5 21.2 52.7 27.1 25.7 3.5 1.6 1.9 0.90 67.0 33.0 1.3 3.1 47.5 23.8 23.8 49.4 24.3 25.2 3.1 1.4 1.7 1.00 70.4 29.6 3.0 2.9 42.8 21.5 21.3 54.4 27.2 27.1 2.8 1.3 1.5 0.80 79.3 20.7 2.1 3.3 38.6 19.4 19.2 56.2 24.9 31.3 5.3 2.2 3.0 0.80 81.3 18.7 ­0.4 0.8 47.1 23.6 23.4 50.7 25.3 25.4 2.2 1.0 1.2 1.00 41.9 58.1 ­0.5 2.6 44.0 22.1 21.9 52.9 26.3 26.6 3.1 1.4 1.7 0.90 73.2 26.8 2.5 3.3 47.3 23.8 23.5 49.6 24.4 25.2 3.0 1.4 1.6 1.00 82.8 17.2 1.7 4.6 48.2 24.6 23.7 49.1 24.1 25.0 2.7 1.2 1.5 1.00 69.5 30.5 2.2 4.7 43.0 21.6 21.4 53.6 26.3 27.3 3.4 1.5 1.9 0.90 59.6 40.4 2.8 3.1 24.6 12.5 12.1 68.8 34.4 34.4 6.6 2.7 3.9 0.50 57.6 42.4 0.9 0.7 44.0 22.1 21.9 52.7 24.9 27.8 3.3 1.4 1.9 0.90 65.5 34.5 0.7 4.1 41.5 20.9 20.6 55.0 27.1 27.9 3.5 1.5 1.9 0.80 64.9 35.1 0.3 2.6 49.0 25.2 23.9 49.0 25.1 23.9 2.0 0.9 1.1 1.00 83.2 16.8 3.2 4.1 44.3 22.7 21.6 52.7 26.5 26.2 3.0 1.4 1.7 0.90 51.8 48.2 0.8 4.2 43.5 21.6 21.9 54.0 25.8 28.2 2.5 1.1 1.4 0.90 80.7 19.3 0.4 7.6 39.6 20.1 19.5 56.4 27.7 28.7 4.3 2.0 2.3 0.80 42.0 58.0 0.1 3.9 42.6 21.5 21.1 54.3 26.3 28.0 3.1 1.4 1.7 0.80 58.4 41.6 2.0 2.8 .. .. .. .. .. .. .. .. .. .. 47.1 52.9 0.2 1.7 42.8 21.4 21.4 53.8 26.5 27.4 3.3 1.5 1.9 0.90 59.3 40.7 2.2 5.3 44.1 22.1 22.0 53.3 26.3 27.0 2.6 1.2 1.4 0.90 64.8 35.2 2.7 4.3 32.6 16.4 16.2 63.1 31.0 32.1 4.2 1.7 2.6 0.60 40.7 59.3 0.0 1.9 39.2 20.0 19.3 57.2 28.7 28.5 3.6 1.7 1.9 0.70 59.2 40.8 0.4 4.3 41.0 20.6 20.4 55.5 26.1 29.4 3.5 1.6 2.0 0.80 75.9 24.1 0.8 1.7 42.6 21.4 21.2 54.2 26.9 27.2 3.2 1.4 1.8 0.80 75.8 24.2 2.1 4.1 43.5 21.7 21.7 53.4 26.3 27.1 3.1 1.4 1.8 0.90 59.9 40.1 1.4 4.3 50.5 25.4 25.0 47.1 23.5 23.6 2.5 1.1 1.3 1.10 87.4 12.6 3.4 4.3 45.8 23.0 22.8 51.2 25.7 25.5 3.0 1.3 1.7 1.00 65.0 35.0 1.6 1.8 40.0 20.0 19.9 56.4 27.9 28.5 3.6 1.7 2.0 0.80 64.1 35.9 ­0.1 1.7 31.6 16.1 15.5 63.6 31.9 31.7 4.8 2.2 2.7 0.60 46.6 53.4 0.9 .. 29.6 15.2 14.5 65.8 33.3 32.6 4.5 2.1 2.5 0.50 36.7 63.3 ­0.4 2.6 33.6 17.1 16.4 61.7 30.9 30.8 4.8 2.1 2.7 0.60 57.2 42.8 1.8 2.0 30.1 15.4 14.7 65.9 34.1 31.8 4.1 2.1 1.9 0.50 15.2 84.8 ­0.3 2.4 31.1 15.8 15.3 64.1 31.8 32.3 4.8 2.1 2.7 0.60 41.3 58.7 ­0.7 2.2 25.9 13.4 12.6 67.8 34.1 33.7 6.3 2.9 3.4 0.50 34.7 65.3 ­0.1 1.6 L ABOR, MIGRATION, AND POPULATION Part III. Development outcomes 97 Participating in growth Table 11.1 HIV/AIDS Estimated number of people Estimated prevalence rate (%) Deaths of living with HIV/AIDS (thousands) Adults (ages 15­49) Young men (ages 15­24) Young women (ages 15­24) adults and Adults Women children AIDS (ages (ages Children due to orphans 15 and 15 and (ages Point Low High Point Low High Point Low High HIV/AIDS (ages 0­17, Total older) older) 0­14) estimate estimate estimate estimate estimate estimate estimate estimate estimate (thousands) thousands) 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 2005 SUB­SAHARAN AFRICA 24,500 22,400 13,200 2,000 6.1 5.4 6.8 1.5 1.3 1.7 4.3 3.7 5.1 2,000 12,000 Angola 320 280 170 35 3.7 2.3 5.3 0.9 0.4 1.4 2.5 1.2 4.2 30 160 Benin 87 77 45 10 1.8 1.2 2.5 0.4 0.2 0.6 1.1 0.6 1.8 10 62 Botswana 270 260 140 14 24.1 23.0 32.0 5.7 5.6 7.5 15.3 15.2 20.3 18 120 Burkina Faso 150 140 80 17 2.0 1.5 2.5 0.5 0.3 0.6 1.4 0.8 2.0 12 120 Burundi 150 130 79 20 3.3 2.7 3.8 0.8 0.7 0.9 2.3 2.0 2.7 13 120 Cameroon 510 470 290 43 5.4 4.9 5.9 1.4 1.3 1.6 4.9 4.4 5.3 46 240 Cape Verde .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic 250 230 130 24 10.7 4.5 17.2 2.5 0.9 4.5 7.3 2.7 13.1 24 140 Chad 180 160 90 16 3.5 1.7 6.0 0.9 0.4 1.6 2.2 0.9 3.9 11 57 Comoros <0.5 <0.5 <0.1 <0.1 <0.1 <0.2 <0.2 <0.1 <0.2 <0.2 <0.1 <0.2 <0.2 <0.1 .. Congo, Dem. Rep. 1,000 890 520 120 3.2 1.8 4.9 0.8 0.3 1.3 2.2 1.0 3.8 90 680 Congo, Rep. 120 100 61 15 5.3 3.3 7.5 1.2 0.6 1.9 3.7 1.9 5.7 11 110 Côte d'Ivoire 750 680 400 74 7.1 4.3 9.7 1.7 0.9 2.7 5.1 2.6 7.9 65 450 Djibouti 15 14 8 1 3.1 0.8 6.9 0.7 0.2 1.6 2.1 0.5 4.6 1 6 Equatorial Guinea 9 8 5 <1 3.2 2.6 3.8 0.7 0.6 0.9 2.3 1.8 2.7 <1 5 Eritrea 59 53 31 7 2.4 1.3 3.9 0.6 0.3 1.0 1.6 0.7 2.7 6 36 Ethiopia .. .. .. .. .. 0.9 3.5 .. 0.2 0.8 .. 0.5 2.3 .. .. Gabon 60 56 33 4 7.9 5.1 11.5 1.8 0.9 3.0 5.4 2.7 8.7 5 20 Gambia, The 20 19 11 1 2.4 1.2 4.1 0.6 0.2 1.0 1.7 0.7 2.9 1 4 Ghana 320 300 180 25 2.3 1.9 2.6 0.2 0.2 0.3 1.3 1.1 1.5 29 170 Guinea 85 78 53 7 1.5 1.2 1.8 0.5 0.4 0.5 1.4 1.1 1.6 7 28 Guinea-Bissau 32 29 17 3 3.8 2.1 6.0 0.9 0.4 1.5 2.5 1.1 4.3 3 11 Kenya 1,300 1,200 740 150 6.1 5.2 7.0 1.0 0.9 1.2 5.2 4.5 6.0 140 1,100 Lesotho 270 250 150 18 23.2 21.9 24.7 5.9 5.5 6.2 14.1 13.3 15.0 23 97 Liberia .. .. .. .. .. 2.0 5.0 .. .. .. .. .. .. .. .. Madagascar 49 47 13 2 0.5 0.2 1.2 0.6 0.2 1.3 0.3 0.1 0.6 3 13 Malawi 940 850 500 91 14.1 6.9 21.4 3.4 1.4 5.9 9.6 3.9 16.8 78 550 Mali 130 110 66 16 1.7 1.3 2.1 0.4 0.3 0.5 1.2 0.9 1.5 11 94 Mauritania 12 11 6 1 0.7 0.4 2.8 0.2 0.1 0.3 0.5 0.2 1.0 <1 7 Mauritius 4 4 <1 .. 0.6 0.3 1.8 .. .. .. .. .. .. <0.1 .. Mozambique 1,800 1,600 960 140 16.1 12.5 20.0 3.6 2.0 5.3 10.7 6.0 15.8 140 510 Namibia 230 210 130 17 19.6 8.6 31.7 4.4 1.7 8.1 13.4 5.2 24.7 17 85 Niger 79 71 42 9 1.1 0.5 1.9 0.2 0.1 0.4 0.8 0.3 1.4 8 46 Nigeria 2,900 2,600 1,600 240 3.9 2.3 5.6 0.9 0.4 1.5 2.7 1.3 4.4 220 930 Rwanda 190 160 91 27 3.1 2.9 3.2 0.8 0.7 0.8 1.9 1.9 2.0 21 210 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. Senegal 61 56 33 5 0.9 0.4 1.5 0.2 0.1 0.4 0.6 0.2 1.1 5 25 Seychelles .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone 48 43 26 5 1.6 0.9 2.4 0.4 0.2 0.6 1.1 0.6 1.7 5 31 Somalia 44 40 23 5 0.9 0.5 1.6 0.2 0.1 0.4 0.6 0.3 1.1 4 23 South Africa 5,500 5,300 3,100 240 18.8 16.8 20.7 4.5 4.0 4.9 14.8 13.2 16.3 320 1,200 Sudan 350 320 180 30 1.6 0.8 2.7 .. .. .. .. .. .. 34 .. Swaziland 220 210 120 15 33.4 21.2 45.3 7.7 3.9 12.1 22.7 11.5 35.9 16 63 Tanzania 1,400 1,300 710 110 6.5 5.8 7.2 2.8 2.5 3.1 3.8 3.4 4.2 140 1,100 Togo 110 100 61 10 3.2 1.9 4.7 0.8 0.4 1.2 2.2 1.0 3.6 9 88 Uganda 1,000 900 520 110 6.7 5.7 7.6 2.3 1.9 2.6 5.0 4.2 5.7 91 1,000 Zambia 1,100 1,000 570 130 17.0 15.9 18.1 3.8 3.6 4.0 12.7 11.9 13.6 98 710 Zimbabwe 1,700 1,500 890 160 20.1 13.3 27.6 4.4 2.3 6.9 14.7 7.7 23.2 180 1,100 NORTH AFRICA 440 400 190 31 0.2 0.1 0.4 0.1 0.1 0.2 0.2 0.1 0.3 37 .. Algeria 19 19 4 .. 0.1 0.2 0.2 .. .. .. .. .. .. <0.5 .. Egypt, Arab Rep. 5 5 <1 .. <0.1 0.2 0.2 .. .. .. .. .. .. <0.5 .. Libya .. .. .. .. .. 0.2 0.2 .. .. .. .. .. .. .. .. Morocco 19 19 4 .. 0.1 0.1 0.4 .. .. .. .. .. .. 1 .. Tunisia 9 9 2 .. 0.1 0.1 0.3 .. .. .. .. .. .. <0.1 .. 98 Part III. Development outcomes HIV/AIDS Participating in growth Table 12.1 Malaria Children Children with fever receiving Pregnant Deaths due sleeping under antimalarial treatment within 24 hours women receiving to malaria Under-five insecticide- (% of children under age 5 with fever) two doses of Risk of malaria (% of population) (per mortality treated bednets Effective intermittent Population 100,000 rate (% of children antimalarial Any antimalarial preventive (millions) Endemic Epidemic Negligible population) (per 1,000) under age 5) treatment treatment treatment (%) 2005 2000­05a 2000­05a 2000­05a 2000­05a 2000­05 a 2000­05 a 2000­05 a 2000­05 a 2000­05a SUB­SAHARAN AFRICA 743.7 163 Angola 15.9 90.5 8.4 1.2 354 260 2.0 20 63 .. Benin 8.4 100.0 0.0 0.0 177 150 7.0 19 60 .. Botswana 1.8 0.0 31.5 68.5 15 120 .. .. .. .. Burkina Faso 13.2 100.0 0.0 0.0 292 191 2.0 45 50 .. Burundi 7.5 67.6 17.3 15.2 143 190 1.0 .. 31 .. Cameroon 16.3 93.6 4.4 2.0 108 149 1.0 27 53 .. Cape Verde 0.5 .. .. .. 22 35 .. .. .. .. Central African Republic 4.0 100.0 0.0 0.0 137 193 2.0 .. 66 .. Chad 9.7 96.5 3.5 0.0 207 208 0.6 .. 56 .. Comoros 0.6 0.0 100.0 0.0 80 71 9.0 .. 63 .. Congo, Dem. Rep. 57.5 91.6 2.6 5.8 224 205 1.0 .. 45 .. Congo, Rep. 4.0 100.0 0.0 0.0 78 108 .. 9 48 .. Côte d'Ivoire 18.2 100.0 0.0 0.0 76 195 4.0 .. 58 .. Djibouti 0.8 .. .. .. .. 133 .. .. .. .. Equatorial Guinea 0.5 98.0 1.5 0.5 152 205 1.0 .. 49 .. Eritrea 4.4 92.2 6.9 1.0 74 78 4.0 8 4 .. Ethiopia 71.3 39.7 23.9 36.4 198 127 1.5 1 3 .. Gabon 1.4 96.5 0.0 3.5 80 91 .. .. .. .. Gambia, The 1.5 100.0 0.0 0.0 52 137 15.0 .. 56 .. Ghana 22.1 100.0 0.0 0.0 70 112 4.0 44 63 0.8 Guinea 9.4 100.0 0.0 0.0 200 160 4.0 14 44 5.6 Guinea-Bissau 1.6 99.5 0.0 0.5 150 200 7.0 .. 58 .. Kenya 34.3 53.4 24.4 22.2 63 120 5.0 11 27 3.9 Lesotho 1.8 .. .. .. 84 132 .. .. .. .. Liberia 3.3 100.0 0.0 0.0 201 235 .. .. .. .. Madagascar 18.6 89.1 7.1 3.8 184 119 0.2 .. 34 .. Malawi 12.9 96.7 2.5 0.7 275 125 15.0 23 27 46.5 Mali 13.5 99.1 0.9 0.0 454 218 8.0 .. 38 .. Mauritania 3.1 65.3 34.5 0.2 108 125 2.0 12 33 .. Mauritius 1.2 .. .. .. .. 15 .. .. .. .. Mozambique 19.8 99.5 0.3 0.2 232 145 .. 8 15 .. Namibia 2.0 0.0 40.8 59.2 52 62 3.0 .. .. .. Niger 14.0 97.1 2.8 0.1 469 256 6.0 .. 48 .. Nigeria 131.5 100.0 0.0 0.0 141 194 1.0 25 34 1.1 Rwanda 9.0 53.0 13.6 33.4 200 203 13.0 .. 3 1 São Tomé and Principe 0.2 0.0 100.0 0.0 80 118 22.8 .. 73 .. Senegal 11.7 100.0 0.0 0.0 72 119 14.0 12 36 10.1 Seychelles 0.1 .. .. .. .. 13 .. .. .. .. Sierra Leone 5.5 100.0 0.0 0.0 312 282 2.0 .. 61 .. Somalia 8.2 19.9 79.1 1.1 81 225 .. .. .. .. South Africa 46.9 0.0 19.8 80.2 0 68 .. .. .. .. Sudan 36.2 74.1 24.7 1.3 70 90 0.0 .. 50 .. Swaziland 1.1 0.0 76.6 23.4 0 160 0.0 .. 26 .. Tanzania 38.3 93.1 3.0 3.9 130 122 16.0 49 .. 21.7 Togo 6.1 100.0 0.0 0.0 47 139 54.0 .. 60 .. Uganda 28.8 90.2 2.9 6.9 152 136 0.0 .. .. .. Zambia 11.7 96.1 3.0 0.9 141 182 7.0 .. 52 .. Zimbabwe 13.0 0.0 84.2 15.8 1 132 .. 3 5 6.8 NORTH AFRICA 152.9 35 Algeria 32.9 .. .. .. .. 39 .. .. .. .. Egypt, Arab Rep. 74.0 .. .. .. .. 33 .. .. .. .. Libya 5.9 .. .. .. .. 19 .. .. .. .. Morocco 30.2 .. .. .. .. 40 .. .. .. .. Tunisia 10.0 .. .. .. .. 24 .. .. .. .. a. Data are for the most recent year available during the period specified. MALARIA Part III. Development outcomes 99 Capable states and partnership Table 13.1 Aid and debt relief Net aid (2004 $ millions) From all donors From DAC donors From multilateral donors 2005 2005 2005 SUB­SAHARAN AFRICA 30,686 21,377 9,183 Angola 442 258 183 Benin 349 207 142 Botswana 71 52 19 Burkina Faso 660 339 319 Burundi 365 181 184 Cameroon 414 336 77 Cape Verde 161 104 56 Central African Republic 95 62 33 Chad 380 167 213 Comoros 25 17 8 Congo, Dem. Rep. 1,828 1,034 793 Congo, Rep. 1,449 1,360 89 Côte d'Ivoire 119 151 -32 Djibouti 79 54 23 Equatorial Guinea 39 30 9 Eritrea 355 226 132 Ethiopia 1,937 1,202 706 Gabon 54 30 24 Gambia, The 58 15 43 Ghana 1,120 603 503 Guinea 182 128 54 Guinea-Bissau 79 39 40 Kenya 768 495 260 Lesotho 69 39 30 Liberia 236 149 87 Madagascar 929 500 429 Malawi 575 322 251 Mali 691 378 313 Mauritania 190 125 66 Mauritius 32 22 10 Mozambique 1,286 771 513 Namibia 123 99 23 Niger 515 256 259 Nigeria 6,437 5,966 471 Rwanda 576 292 284 São Tomé and Principe 32 18 13 Senegal 689 440 249 Seychelles 19 8 11 Sierra Leone 343 130 213 Somalia 236 146 90 South Africa 700 486 213 Sudan 1,829 1,472 315 Swaziland 46 20 26 Tanzania 1,505 871 622 Togo 87 59 27 Uganda 1,198 704 492 Zambia 945 836 109 Zimbabwe 368 179 189 NORTH AFRICA 2,349 1,524 727 Algeria 371 290 71 Egypt, Arab Rep. 926 659 238 Libya 24 17 4 Morocco 652 289 309 Tunisia 376 269 105 100 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP Net aid Share of imports Share of central Heavily Indebted Poor Countries Debt service Share of gross of goods and government (HIPC) Debt Initiative relief committed Share of GDP (%) Per capita ($) capital formation (%) services (%) expenditures (%) Decision point Completion point ($ millions) 2005 2005 2005 2005 2005 2006 2006 2006 4.9 41.3 25.1 6.7 28.1 52,315 1.3 27.7 17.9 1.1 .. .. .. .. 8.1 41.4 41.6 20.6 54.3 Jul. 2000 Mar.2003 460 0.7 40.2 2.2 0.8 3.2 .. 11.6 49.9 .. .. .. Jul. 2000 Apr. 2002 930 45.6 48.4 423.5 95.6 172.2 Oct. 2000 Floating 1,472 2.5 25.4 13.6 5.0 23.6 Oct.2000 Apr. 2006 4,917 16.1 316.9 .. .. .. .. .. .. 7 23.6 .. .. .. .. .. .. 6.4 39 31.9 6.8 143.1 May. 2001 Floating 260 6.5 42 70.0 13.8 48.4 .. .. .. 25.7 31.8 181.2 34.9 310.3 Jul. 2003 Floating 10,389 24.3 362.3 108.1 17.8 183.2 .. .. 2,881 0.7 6.6 7.2 0.8 9.0 Mar. 1998 .. .. 11.1 99.1 45.6 12.3 40.2 .. .. 0.5 77.5 1.4 0.4 17.3 .. .. .. 36.6 80.7 182.2 56.8 82.1 .. .. .. 17 27.2 83.1 31.1 123.7 Nov. 2001 Apr. 2004 3,275 0.6 38.9 2.7 0.7 8.1 .. .. .. 12.6 38.3 50.4 11.4 .. Dec. 2000 Floating 90 10.4 50.6 36.0 10.7 68.2 Feb. 2002 Jul. 2004 3,500 5.5 19.4 39.8 9.4 97.4 Dec. 2000 Floating 800 26.3 49.9 180.0 29.7 144.4 Dec. 2000 Floating 790 4 22.4 24.4 6.6 23.9 .. .. .. 4.7 38.3 13.4 3.5 27.8 .. .. .. 44.6 71.9 270.9 49.6 399.7 .. .. .. 18.4 49.9 81.8 27.2 219.2 Dec. 2000 Oct. 2004 1,900 27.7 44.7 181.3 35.0 166.2 Dec. 2000 Floating 1,000 13 51.1 57.5 20.7 131.1 Aug. 2000 Mar.2003 895 10.4 62 23.1 7.9 45.8 Feb. 2000 Jun. 2002 1,100 0.5 25.7 2.2 0.4 3.5 .. .. .. 18.8 65 86.8 25.7 188.8 Apr. 2000 Sep. 2001 4,300 2 60.7 7.3 2.1 8.5 .. .. .. 15.2 36.9 81.8 38.6 131.9 Dec. 2000 Jun. 2004 1,190 6.6 48.9 31.2 7.4 30.8 .. .. .. 26.7 63.7 119.5 64.4 200.6 Dec. 2000 Jun. 2005 1,316 28.3 203.8 .. .. .. Dec. 2000 Floating 200 8 59.1 31.3 12.2 63.3 Jun. 2000 Apr. 2004 850 2.6 222.6 21.2 1.2 10.8 .. .. .. 28.8 62.1 165.9 44.0 233.2 Mar. 2002 Floating 950 .. 28.7 .. .. .. .. .. 0.3 14.9 1.6 0.5 1.4 .. .. 6.6 50.5 27.7 14.4 39.1 .. .. 1.8 40.7 9.8 1.1 6.6 .. .. 12 39.3 65.8 22.0 70.2 Apr. 2000 Nov. 2001 3,000 4.1 14.1 22.4 4.9 40.9 .. .. 13.7 41.6 64.8 34.1 95.4 Feb. 2000 May. 2000 1,950 13 81 50.3 31.2 96.8 Dec. 2000 Apr. 2005 3,900 10.8 28.3 64.0 8.3 39.6 .. .. .. 0.7 15.4 3.3 1.2 5.9 .. 0.4 11.3 1.2 0.5 3.0 .. .. .. 1 12.5 5.7 1.6 8.1 .. .. .. 0.1 4.2 .. .. .. .. .. .. 1.3 21.6 4.9 1.6 5.7 .. .. .. 1.3 37.5 5.6 1.3 8.5 .. .. .. CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 101 Capable states and partnership Table 13.2 Capable states Enforcing contracts Investment climate (viewed by firms as major or very severe constraints, %) Number of Time required Cost Courts Crime procedures (days) (% of debt) 2006 2006 2006 2006 2006 SUB­SAHARAN AFRICA Angola .. 6.2 47 1,011 11.2 Benin .. .. 49 720 29.7 Botswana 1.4 10.9 26 501 24.8 Burkina Faso 0.7 1.4 41 446 95.4 Burundi 0.2 2.9 47 403 32.5 Cameroon 1.2 2.9 58 800 36.4 Cape Verde 2.0 1.0 40 465 15.0 Central African Republic .. .. 45 660 43.7 Chad .. .. 52 743 54.9 Comoros .. .. 60 721 29.4 Congo, Dem. Rep. .. 1.8 51 685 156.8 Congo, Rep. .. .. 47 560 45.6 Côte d'Ivoire .. .. 25 525 29.5 Djibouti .. .. 59 1,225 .. Equatorial Guinea .. .. 38 553 14.5 Eritrea .. .. 35 305 18.6 Ethiopia .. .. 30 690 14.8 Gabon .. .. 32 880 9.8 Gambia, The 2.3 2.3 26 247 35.9 Ghana .. .. 29 552 13.0 Guinea 0.4 1.7 44 276 43.8 Guinea-Bissau 1.4 0.7 40 1,140 27.0 Kenya .. .. 25 360 41.3 Lesotho .. .. 58 695 10.6 Liberia .. .. .. .. .. Madagascar .. .. 29 591 22.8 Malawi .. .. 40 337 136.5 Mali .. .. 28 860 45.0 Mauritania 0.8 1.2 40 400 17.9 Mauritius .. .. 37 630 15.7 Mozambique .. .. 38 1,010 132.1 Namibia 0.6 20.6 31 270 28.3 Niger .. .. 33 360 42.0 Nigeria .. .. 23 457 27.0 Rwanda .. .. 27 310 43.2 São Tomé and Principe .. .. 67 405 69.5 Senegal .. .. 33 780 23.8 Seychelles .. .. 29 720 13.0 Sierra Leone .. .. 58 515 227.3 Somalia .. .. .. .. .. South Africa .. .. 26 600 11.5 Sudan .. .. 67 770 20.6 Swaziland 1.0 18.5 31 972 20.1 Tanzania .. 1.9 21 393 51.5 Togo .. .. 37 535 24.3 Uganda 0.1 0.2 19 484 35.2 Zambia .. .. 21 404 28.7 Zimbabwe .. .. 33 410 .. NORTH AFRICA Algeria .. .. 49 397 .. Egypt, Arab Rep. .. .. 55 1,010 .. Libya .. .. .. .. .. Morocco .. .. 42 615 .. Tunisia .. .. 21 481 .. a. Indexes range from 0 (least desirable) to 10 (most desirable). b. Average of the disclosure, director liability and shareholder suits indexes. 102 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP Corruption Protecting investorsa Regulation and tax administration Extractive Industries Transparency Initiative Perceptions Time to Index transpar- Investor prepare, file, Total tax ency index Disclosure Director Shareholder protection Number of tax and pay taxes payable (mean score, index liability index suits index indexb payments (hours) (% of profit) Endorsed Report produced 0 low to 10 high) 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 5 6 6 5.7 42 272 64.4 No No 2.2 5 8 4 5.7 72 270 68.5 No No 2.5 8 2 3 4.3 24 140 53.3 No No 5.6 6 5 3 4.7 45 270 51.1 No No 3.2 .. .. .. .. 40 140 286.7 No No 2.4 8 2 6 5.3 39 1,300 46.2 Yes Yes 2.3 1 5 6 4.0 49 100 54.4 No No .. 4 6 7 5.7 54 504 209.5 No No 2.4 3 4 7 4.7 65 122 68.2 Yes No 2.0 6 4 5 5.0 20 100 47.5 No No .. 3 3 5 3.7 34 312 235.4 Yes No 2.0 4 5 6 5.0 94 576 57.3 Yes No 2.2 6 5 3 4.7 71 270 45.7 Yes No 2.1 5 2 0 2.3 36 114 41.7 .. .. .. 6 4 5 5.0 48 212 62.4 Yes No 2.1 4 5 5 4.7 18 216 86.3 No No 2.9 4 4 5 4.3 20 212 32.8 No No 2.4 5 4 5 4.7 27 272 48.3 Yes Yes 3.0 2 1 5 2.7 47 376 291.4 No No 2.5 7 5 6 6.0 35 304 32.3 Yes Yes 3.3 5 7 2 4.7 55 416 49.4 Yes Yes 1.9 0 5 6 3.7 47 208 47.5 No No .. 4 2 10 5.3 17 432 74.2 No No 2.2 2 1 8 3.7 21 352 25.6 No No 3.2 .. .. .. .. .. .. .. Yes No .. 5 6 6 5.7 25 304 43.2 Yes No 3.1 4 7 5 5.3 29 878 32.6 No No 2.7 6 5 3 4.7 60 270 50.0 Yes No 2.8 0 0 0 0.0 61 696 104.3 Yes No 3.1 6 8 9 7.7 7 158 24.8 No No 5.1 7 2 6 5.0 36 230 39.2 No No 2.8 5 5 6 5.3 34 .. 25.6 No No 4.1 4 5 5 4.7 44 270 46.0 Yes No 2.3 6 7 4 5.7 35 1,120 31.4 Yes Yes 2.2 2 5 1 2.7 43 168 41.1 No No 2.5 6 1 6 4.3 42 424 55.2 Yes No .. 4 4 4 4.0 59 696 47.7 No No 3.3 4 8 5 5.7 15 76 48.8 No No 3.6 3 6 5 4.7 20 399 277.0 Yes No 2.2 .. .. .. .. .. .. .. No No .. 8 8 8 8.0 23 350 38.3 No No 4.6 0 6 5 3.7 66 180 37.1 No No 2.0 1 1 5 2.3 34 104 39.5 No No 2.5 3 4 7 4.7 48 248 45.0 No No 2.9 4 3 5 4.0 51 270 48.3 No No 2.4 7 5 4 5.3 31 237 32.2 No No 2.7 3 6 7 5.3 37 131 22.2 No No 2.6 8 .. .. .. 59 216 37.0 No No 2.4 .. 6 6 4 5.3 61 504 76.4 .. .. 3.1 5 3 5 4.3 41 536 50.4 .. .. 3.3 .. .. .. .. .. .. .. .. .. 2.7 6 6 1 4.3 28 468 52.7 .. .. 3.2 0 4 6 3.3 45 268 58.8 .. .. 4.6 CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 103 Capable states and partnership Table 13.3 Governance and anticorruption indicators Voice and accountability Political stability and absence of violence Income category 1996 2006 1996 2006 SUB­SAHARAN AFRICA Angola Low income ­1.5 ­1.3 ­2.4 ­0.5 Benin Low income 0.7 0.3 1.0 0.4 Botswana Upper middle income 0.7 0.6 0.7 1.2 Burkina Faso Low income ­0.5 ­0.3 ­0.4 ­0.2 Burundi Low income ­1.3 ­1.0 ­2.0 ­1.4 Cameroon Low income ­1.1 ­1.0 ­1.2 ­0.2 Cape Verde Lower middle income 0.9 0.9 1.0 0.9 Central African Republic Low income ­0.2 ­1.1 ­0.2 ­1.7 Chad Low income ­0.8 ­1.4 ­0.9 ­1.8 Comoros Low income ­0.2 ­0.3 1.0 ­0.2 Congo, Dem. Rep. Low income ­1.3 ­1.6 ­2.0 ­2.3 Congo, Rep. Low income ­1.3 ­1.1 ­0.9 ­1.0 Côte d'Ivoire Low income ­0.3 ­1.4 0.0 ­2.1 Djibouti Lower middle income ­0.9 ­1.0 0.2 ­0.2 Equatorial Guinea Low income ­1.6 ­1.8 ­0.6 ­0.2 Eritrea Low income ­1.2 ­1.8 0.2 ­0.9 Ethiopia Low income ­0.7 ­1.1 ­0.9 ­1.8 Gabon Upper middle income ­0.6 ­1.0 ­0.4 0.1 Gambia, The Low income ­1.4 ­0.9 0.0 0.2 Ghana Low income ­0.4 0.4 ­0.1 0.2 Guinea Low income ­1.2 ­1.2 ­1.5 ­1.7 Guinea-Bissau Low income ­0.6 ­0.4 ­0.8 ­0.6 Kenya Low income ­0.6 ­0.2 ­0.7 ­1.1 Lesotho Low income 0.0 0.3 0.8 0.2 Liberia Low income ­1.5 ­0.6 ­2.7 ­1.2 Madagascar Low income 0.2 ­0.1 ­0.1 0.1 Malawi Low income ­0.5 ­0.3 ­0.2 0.0 Mali Low income 0.3 0.3 0.5 0.0 Mauritania Low income ­0.9 ­1.0 0.5 ­0.3 Mauritius Upper middle income 0.8 0.9 0.9 0.9 Mozambique Low income ­0.3 ­0.1 ­0.6 0.5 Namibia Lower middle income 0.5 0.4 0.6 0.8 Niger Low income ­0.5 ­0.2 ­0.3 ­0.4 Nigeria Low income ­1.6 ­0.8 ­1.8 ­2.0 Rwanda Low income ­1.5 ­1.1 ­1.5 ­0.5 São Tomé and Principe Low income 0.8 0.3 1.0 0.5 Senegal Low income ­0.2 ­0.1 ­0.8 ­0.3 Seychelles Upper middle income 0.1 0.1 1.0 1.1 Sierra Leone Low income ­1.5 ­0.4 ­2.5 ­0.5 Somalia Low income ­2.0 ­2.1 ­2.4 ­2.8 South Africa Lower middle income 0.6 0.6 ­1.2 ­0.1 Sudan Low income ­1.7 ­1.8 ­2.8 ­2.2 Swaziland Lower middle income ­1.4 ­1.1 0.0 ­0.1 Tanzania Low income ­0.9 ­0.3 ­0.2 ­0.2 Togo Low income ­1.1 ­1.2 ­0.7 ­0.9 Uganda Low income ­0.7 ­0.5 ­1.4 ­1.2 Zambia Low income ­0.2 ­0.3 ­0.7 0.3 Zimbabwe Low income ­0.4 ­1.6 ­0.4 ­1.2 NORTH AFRICA Algeria Lower middle income ­1.2 ­0.8 ­2.9 ­0.9 Egypt, Arab Rep. Lower middle income ­0.8 ­1.1 ­0.6 ­0.9 Libya Upper middle income ­1.5 ­1.9 ­1.8 0.2 Morocco Lower middle income ­0.7 ­0.6 ­0.6 ­0.3 Tunisia Lower middle income ­0.6 ­1.2 0.0 0.2 Note: The rating scale for each criterion ranges from ­2.5 (weak performance) to 2.5 (very high performance). 104 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP Government effectiveness Regulatory quality Rule of law Control of corruption 1996 2006 1996 2006 1996 2006 1996 2006 ­1.3 ­1.2 ­1.5 ­1.2 ­1.5 ­1.3 ­1.1 ­1.1 0.0 ­0.5 0.1 ­0.4 ­0.1 ­0.5 .. ­0.8 0.5 0.7 0.6 0.5 0.8 0.6 0.5 0.8 ­0.7 ­0.8 ­0.4 ­0.4 ­0.8 ­0.5 ­0.3 ­0.4 ­1.0 ­1.3 ­1.3 ­1.2 ­0.2 ­1.0 .. ­1.1 ­1.1 ­0.9 ­0.8 ­0.7 ­1.2 ­1.0 ­1.2 ­0.9 ­0.1 0.2 ­0.6 ­0.2 0.0 0.6 .. 0.7 ­0.9 ­1.4 ­0.3 ­1.2 ­0.2 ­1.6 .. ­1.1 ­0.7 ­1.4 0.0 ­1.2 ­0.2 ­1.4 .. ­1.2 ­0.7 ­1.7 ­0.7 ­1.5 .. ­0.9 .. ­0.6 ­1.7 ­1.6 ­2.2 ­1.5 ­1.9 ­1.7 ­2.1 ­1.4 ­1.2 ­1.3 ­0.8 ­1.2 ­1.3 ­1.3 ­0.9 ­1.1 0.1 ­1.4 ­0.1 ­1.1 ­0.7 ­1.5 0.5 ­1.2 ­1.1 ­1.0 0.0 ­0.9 .. ­0.8 .. ­0.7 ­1.5 ­1.3 ­1.0 ­1.4 .. ­1.2 .. ­1.5 ­0.3 ­1.2 ­0.2 ­1.9 ­0.2 ­1.0 .. ­0.2 ­0.6 ­0.6 ­1.3 ­0.8 ­0.3 ­0.6 ­1.1 ­0.6 ­1.0 ­0.6 ­0.5 ­0.5 ­0.4 ­0.6 ­1.3 ­0.8 ­0.3 ­0.7 ­1.6 ­0.4 0.2 ­0.3 0.4 ­0.6 0.1 0.1 0.0 ­0.1 ­0.2 ­0.1 ­0.5 ­0.1 ­1.1 ­1.4 0.0 ­1.1 ­1.1 ­1.4 0.4 ­1.0 ­0.6 ­1.2 0.1 ­1.1 ­1.7 ­1.2 ­1.1 ­1.0 ­0.6 ­0.7 ­0.4 ­0.2 ­0.8 ­1.0 ­1.1 ­1.0 0.1 ­0.3 ­0.7 ­0.6 ­0.4 ­0.3 .. ­0.1 ­1.8 ­1.4 ­3.0 ­1.6 ­2.2 ­1.2 ­1.8 ­0.9 ­1.0 ­0.2 ­0.1 ­0.3 ­0.9 ­0.3 0.4 ­0.3 ­0.7 ­0.9 ­0.4 ­0.6 ­0.3 ­0.5 ­1.1 ­0.7 ­0.7 ­0.4 0.1 ­0.4 ­0.8 ­0.3 ­0.3 ­0.6 0.1 ­0.6 ­0.7 ­0.2 ­0.7 ­0.4 .. ­0.6 0.6 0.6 0.1 0.6 0.7 0.8 0.5 0.4 ­0.5 ­0.3 ­1.1 ­0.5 ­1.3 ­0.6 ­0.5 ­0.6 0.3 0.1 0.0 0.2 0.3 0.2 0.9 0.2 ­1.1 ­0.8 ­1.0 ­0.6 ­1.3 ­0.9 ­0.3 ­1.0 ­1.3 ­1.0 ­1.0 ­0.9 ­1.3 ­1.3 ­1.3 ­1.3 ­1.2 ­0.4 ­1.1 ­0.6 ­0.2 ­0.6 .. ­0.1 ­0.7 ­0.9 ­0.4 ­0.8 .. ­0.5 .. ­0.5 ­0.3 ­0.2 ­0.5 ­0.3 ­0.2 ­0.3 ­0.4 ­0.4 ­0.7 ­0.1 ­1.2 ­0.6 .. 0.0 .. 0.1 ­0.6 ­1.1 ­0.5 ­1.1 ­1.1 ­1.2 ­1.8 ­1.2 ­1.8 ­2.2 ­3.0 ­2.7 ­1.8 ­2.5 ­1.8 ­1.8 0.5 0.8 0.2 0.7 0.3 0.2 0.7 0.6 ­1.3 ­1.1 ­1.7 ­1.2 ­1.5 ­1.3 ­1.2 ­1.1 ­0.4 ­0.7 0.0 ­0.5 0.4 ­0.7 .. ­0.5 ­1.2 ­0.3 ­0.4 ­0.4 ­0.8 ­0.5 ­1.1 ­0.4 ­0.8 ­1.6 0.4 ­0.9 ­1.3 ­1.0 ­1.1 ­1.0 ­0.4 ­0.5 0.1 ­0.2 ­0.9 ­0.5 ­0.5 ­0.7 ­0.8 ­0.7 0.3 ­0.6 ­0.4 ­0.6 ­1.0 ­0.8 0.0 ­1.5 ­0.8 ­2.2 ­0.3 ­1.7 ­0.1 ­1.4 ­0.6 ­0.4 ­0.8 ­0.6 ­0.7 ­0.6 ­0.4 ­0.4 ­0.3 ­0.4 ­0.1 ­0.4 0.2 0.0 0.1 ­0.4 ­0.8 ­0.9 ­1.8 ­1.4 ­1.1 ­0.7 ­1.0 ­0.9 ­0.1 0.0 0.1 ­0.2 0.1 0.0 0.3 ­0.1 0.5 0.6 0.3 0.2 0.0 0.4 0.0 0.2 CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 105 Capable states and partnership Table 13.4 Country Policy and Institutional Assessment ratings CPIA overall Economic management Structural policies rating (IDA resource Macro Business allocation economic Financial regulatory index)a Averageb management Fiscal policy Debt policy Averageb Trade sector environment 2006 2006 2006 2006 2006 2006 2006 2006 2006 SUB­SAHARAN AFRICA Angola 2.7 2.7 3.0 3.0 2.0 2.8 4.0 2.5 2.0 Benin 3.6 4.0 4.5 4.0 3.5 3.8 4.5 3.5 3.5 Botswanac .. .. .. .. .. .. .. .. .. Burkina Faso 3.7 4.3 4.5 4.5 4.0 3.3 4.0 3.0 3.0 Burundi 3.0 3.2 3.5 3.5 2.5 3.0 3.5 3.0 2.5 Cameroon 3.2 3.5 4.0 4.0 2.5 3.2 3.5 3.0 3.0 Cape Verde 4.1 4.3 4.5 4.5 4.0 3.8 4.0 4.0 3.5 Central African Republic 2.4 2.5 3.0 3.0 1.5 2.7 3.5 2.5 2.0 Chad 2.8 3.0 3.5 3.0 2.5 3.0 3.0 3.0 3.0 Comoros 2.4 2.0 2.5 2.0 1.5 2.5 2.5 2.5 2.5 Congo, Dem. Rep. 2.8 3.2 3.5 3.5 2.5 3.0 4.0 2.0 3.0 Congo, Rep. 2.8 2.8 3.5 2.5 2.5 2.8 3.5 2.5 2.5 Côte d'Ivoire 2.5 1.8 2.5 2.0 1.0 3.2 3.5 3.0 3.0 Djibouti 3.1 2.8 3.5 2.5 2.5 3.5 4.0 3.5 3.0 Equatorial Guineac .. .. .. .. .. .. .. .. .. Eritrea 2.5 2.2 2.0 2.0 2.5 1.8 1.5 2.0 2.0 Ethiopia 3.4 3.5 3.0 4.0 3.5 3.2 3.0 3.0 3.5 Gabonc .. .. .. .. .. .. .. .. .. Gambia, The 3.1 3.0 3.5 3.0 2.5 3.3 4.0 3.0 3.0 Ghana 3.9 4.2 4.0 4.5 4.0 3.8 4.0 3.5 4.0 Guinea 2.9 2.7 2.5 3.0 2.5 3.5 4.5 3.0 3.0 GuineaBissau 2.6 2.0 2.0 2.5 1.5 3.2 4.0 3.0 2.5 Kenya 3.7 4.2 4.5 4.0 4.0 3.8 4.0 3.5 4.0 Lesotho 3.5 4.0 4.0 4.0 4.0 3.3 3.5 3.5 3.0 Liberiad .. .. .. .. .. .. .. .. .. Madagascar 3.6 3.5 4.0 3.0 3.5 3.8 4.0 3.5 4.0 Malawi 3.4 3.2 3.5 3.0 3.0 3.5 4.0 3.0 3.5 Mali 3.7 4.3 4.5 4.0 4.5 3.5 4.0 3.0 3.5 Mauritania 3.3 3.3 3.0 3.0 4.0 3.5 4.5 2.5 3.5 Mauritiusc .. .. .. .. .. .. .. .. .. Mozambique 3.5 4.2 4.0 4.0 4.5 3.5 4.5 3.0 3.0 Namibiac .. .. .. .. .. .. .. .. .. Niger 3.3 3.7 4.0 3.5 3.5 3.3 4.0 3.0 3.0 Nigeria 3.2 4.0 4.0 4.0 4.0 3.0 3.0 3.0 3.0 Rwanda 3.6 3.8 4.0 4.0 3.5 3.5 3.5 3.5 3.5 São Tomé and Principe 3.0 2.8 3.0 3.0 2.5 3.2 4.0 2.5 3.0 Senegal 3.7 4.0 4.0 4.0 4.0 3.7 4.0 3.5 3.5 Seychellesc .. .. .. .. .. .. .. .. .. Sierra Leone 3.1 3.7 4.0 3.5 3.5 3.0 3.5 3.0 2.5 Somaliad .. .. .. .. .. .. .. .. .. South Africac .. .. .. .. .. .. .. .. .. Sudan 2.5 2.7 3.5 3.0 1.5 2.8 2.5 3.0 3.0 Swazilandc .. .. .. .. .. .. .. .. .. Tanzania 3.9 4.5 5.0 4.5 4.0 3.7 4.0 3.5 3.5 Togo 2.5 2.0 2.5 2.0 1.5 3.2 4.0 2.5 3.0 Uganda 3.9 4.5 4.5 4.5 4.5 3.8 4.0 3.5 4.0 Zambia 3.4 3.7 4.0 3.5 3.5 3.3 4.0 3.0 3.0 Zimbabwe 1.8 1.0 1.0 1.0 1.0 2.2 2.0 2.5 2.0 NORTH AFRICA Algeriac .. .. .. .. .. .. .. .. .. Egypt, Arab Rep.c .. .. .. .. .. .. .. .. .. Libyac .. .. .. .. .. .. .. .. .. Moroccoc .. .. .. .. .. .. .. .. .. Tunisiac .. .. .. .. .. .. .. .. .. Note: The rating scale for each indicator ranges from 1 (low) to 6 (high). a. Calculated as the average of the average ratings of each cluster. b. All criteria are weighted equally. c. Not an IDA member. d. Not rated in the 2006 International Development Association (IDA) resource allocation index. 106 Part III. Development outcomes Capable states and partnership Policies for social inclusion and equity Public sector management and institutions Equity Policies and Property Quality of Transparency, of public Building Social institutions for rights and budgetary Quality Efficiency accountability, Gender resource human protection environmental rulebased and financial of public of revenue and corruption in Averageb equality use resources and labor sustainability Averageb governance management administration mobilization public sector 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2.7 3.0 2.5 2.5 2.5 3.0 2.4 2.0 2.5 2.5 2.5 2.5 3.2 3.0 3.0 3.5 3.0 3.5 3.3 3.0 3.5 3.0 3.5 3.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.6 3.5 4.0 3.5 3.5 3.5 3.5 3.5 4.0 3.5 3.5 3.0 3.1 3.5 3.0 3.0 3.0 3.0 2.7 2.5 3.0 2.5 3.0 2.5 3.2 3.5 3.0 3.5 3.0 3.0 3.0 2.5 3.5 3.0 3.5 2.5 4.3 4.5 4.5 4.5 4.5 3.5 3.9 4.0 3.5 4.0 3.5 4.5 2.2 2.5 2.0 2.0 2.0 2.5 2.2 2.0 2.0 2.0 2.5 2.5 2.6 2.5 3.0 2.5 2.5 2.5 2.4 2.0 2.5 3.0 2.5 2.0 2.7 3.0 3.0 3.0 2.5 2.0 2.2 2.5 1.5 2.0 2.5 2.5 2.9 3.0 3.0 3.0 3.0 2.5 2.3 2.0 2.5 2.5 2.5 2.0 2.7 3.0 2.5 3.0 2.5 2.5 2.7 2.5 3.0 2.5 3.0 2.5 2.3 2.5 1.5 2.0 2.5 3.0 2.5 2.0 2.5 2.0 4.0 2.0 3.1 3.0 3.0 3.5 3.0 3.0 2.8 2.5 3.0 2.5 3.5 2.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.0 3.5 3.0 3.5 3.0 2.0 2.8 2.5 2.5 3.0 3.5 2.5 3.6 3.0 4.5 3.5 3.5 3.5 3.3 3.0 4.0 3.0 4.0 2.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.1 3.5 3.0 3.5 2.5 3.0 2.9 3.5 2.5 3.0 3.5 2.0 3.8 4.0 4.0 4.0 3.5 3.5 3.9 3.5 4.0 3.5 4.5 4.0 3.0 3.5 3.0 3.0 3.0 2.5 2.6 2.0 2.5 3.0 3.0 2.5 2.6 2.5 3.0 2.5 2.5 2.5 2.6 2.5 2.5 2.5 3.0 2.5 3.2 3.0 3.5 3.5 3.0 3.0 3.4 3.0 3.5 3.5 4.0 3.0 3.4 4.0 3.0 3.5 3.0 3.5 3.4 3.5 3.0 3.0 4.0 3.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.6 3.5 3.5 3.5 3.5 4.0 3.4 3.5 3.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.4 3.5 3.0 3.5 4.0 3.0 3.4 3.5 3.5 3.5 3.5 3.0 3.5 3.5 3.5 3.0 4.0 3.5 3.4 3.5 3.5 3.5 3.0 3.5 2.9 3.0 2.5 3.0 3.5 2.5 .. .. .. .. .. .. .. .. .. .. .. .. 3.3 3.5 3.5 3.5 3.0 3.0 3.1 3.0 3.5 2.5 3.5 3.0 .. .. .. .. .. .. .. .. .. .. .. .. 3.0 2.5 3.5 3.0 3.0 3.0 3.2 3.0 3.5 3.0 3.5 3.0 3.1 3.0 3.5 3.0 3.0 3.0 2.8 2.5 3.0 2.5 3.0 3.0 3.8 3.5 4.5 4.5 3.5 3.0 3.4 3.0 4.0 3.5 3.5 3.0 2.8 3.0 3.5 2.5 2.5 2.5 3.0 2.5 2.5 3.0 3.5 3.5 3.4 3.5 3.5 3.5 3.0 3.5 3.6 3.5 3.5 3.5 4.5 3.0 .. .. .. .. .. .. .. .. .. .. .. .. 2.8 3.0 3.0 3.0 3.0 2.0 2.9 2.5 3.5 3.0 3.0 2.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.3 2.0 2.5 2.5 2.0 2.5 2.3 2.0 2.0 2.5 3.0 2.0 .. .. .. .. .. .. .. .. .. .. .. .. 3.8 4.0 4.0 4.0 3.5 3.5 3.8 3.5 4.5 3.5 4.0 3.5 2.6 3.0 2.0 3.0 2.5 2.5 2.2 2.5 2.0 2.0 2.5 2.0 3.9 3.5 4.5 4.0 3.5 4.0 3.3 3.5 4.0 3.0 3.0 3.0 3.4 3.5 3.5 3.5 3.0 3.5 3.2 3.0 3.5 3.0 3.5 3.0 2.0 2.5 1.5 2.0 1.5 2.5 1.9 1.0 2.0 2.0 3.5 1.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. Capable states and partnership Part III. Development outcomes 107 Table 14.1 Burkina Faso household survey, 2003 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 8,494 5,894 618 853 1,020 1,278 2,125 2,600 253 326 387 573 1,061 Total population (thousands) 11,385 9,317 1,387 1,672 1,804 1,947 2,506 2,068 319 349 390 458 554 Age dependency ratio 1.0 1.1 1.3 1.2 1.2 1.1 0.8 0.6 0.9 0.8 0.7 0.6 0.5 Average household size 6.4 6.6 9.8 8.4 7.5 6.5 4.7 5.6 8.4 7.4 6.8 5.5 3.9 Marital status of head of household (%) Monogamous male 4 3 0 1 1 2 5 10 1 3 4 5 21 Polygamous male 60 59 44 50 57 60 68 63 60 59 67 68 62 Single male 29 33 53 44 37 33 21 13 24 25 18 12 5 De facto female 0 0 0 0 .. .. 0 0 .. .. .. .. 0 De jure female 7 5 3 4 4 5 6 13 14 13 12 14 12 MDG 1: extreme poverty and hunger Mean monthly expenditure (CFA francs) 75,614 65,140 36,960 46,013 58,598 71,470 112,679 129,090 55,311 81,398 106,453 146,524 256,278 Mean monthly share on food (%) 58 65 72 70 69 65 57 42 54 51 48 44 34 Mean monthly share on health (%) 5 5 2 3 3 3 9 6 3 2 6 7 8 Mean monthly share on education (%) 3 1 2 1 2 1 1 8 4 8 8 7 8 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) 63 55 56 58 58 54 53 91 87 86 89 93 93 Net primary enrollment rate (% of relevant age group) Total 93 91 87 90 92 91 93 96 95 95 94 97 97 Male 93 91 88 90 94 90 93 96 95 93 96 96 98 Female 92 91 84 90 90 92 94 95 94 97 93 97 95 Net secondary enrollment rate (% of relevant age group) Total 34 21 16 20 17 23 27 48 24 36 42 52 68 Male 32 21 19 18 14 26 29 47 26 34 41 51 70 Female 36 21 9 24 24 19 23 48 23 38 43 53 66 Tertiary enrollment rate (per 10,000) Adult literacy rate (%) Total 22 13 9 11 10 12 17 56 34 43 49 57 76 Male 29 19 14 18 17 17 23 66 44 54 58 67 83 Female 15 7 4 5 5 7 11 47 25 33 39 49 69 Youth literacy rate (% ages 15­24) Total 31 19 15 20 19 18 20 71 53 70 70 74 80 Male 38 26 22 26 26 24 28 78 58 76 75 83 90 Female 25 13 8 13 12 13 14 65 47 62 63 67 72 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) 65 57 56 55 55 57 59 95 91 89 94 95 97 Morbidity (% of population) 6 6 3 4 6 6 8 7 5 4 6 7 10 Health care provider consulted when sick (%) 64 62 44 49 56 65 71 71 55 54 72 77 77 Type of health care provider consulted (% of total) Public 70 72 57 62 67 70 79 62 66 67 57 64 61 Private, modern medicine 7 2 1 4 2 2 2 25 8 13 27 25 31 Private, traditional healers 17 20 39 28 25 18 14 8 22 14 12 6 3 Missionary or nongovernmental organization .. .. .. .. .. .. .. .. .. .. .. .. .. Child survival and malnutrition (%) Birth assisted by trained staff 52 43 32 42 43 46 50 94 86 94 93 96 98 Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) 43 46 45 46 47 44 47 33 34 29 36 36 31 Wasting (6­59 months) 31 32 35 32 33 32 30 28 24 33 33 28 24 Underweight (6­59 months) 47 50 52 51 51 49 48 35 31 38 43 38 28 MDG 7: environmental sustainability Access to sanitation facilities (% of population) 35 20 12 16 18 20 25 91 70 85 92 95 97 Water source less than 1 hour away (% of population) 90 88 88 90 90 88 85 98 98 97 97 97 98 Market less than 1 hour away (% of population) 83 80 80 80 80 81 79 97 94 96 96 96 98 Access to improved water source (% of population) Totala 27 15 14 16 15 15 16 72 52 63 75 76 77 Own tap 19 5 4 5 4 5 6 70 44 59 71 74 76 Other piped .. .. .. .. .. .. .. .. .. .. .. .. .. Well, protected 9 10 10 11 11 11 10 3 8 4 5 2 1 Traditional fuel use (%) Totala 95 98 99 99 99 99 96 85 99 99 98 93 67 Firewood 91 96 97 98 98 97 94 73 99 94 93 82 47 Charcoal 4 2 2 1 1 2 3 12 1 5 5 11 21 a. Components may not sum to total because of rounding. 108 Part IV. Household welfare HOUSEHOLD WELFARE Table 14.2 Cameroon household survey, 2001 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 10,992 6,017 646 764 1,026 1,217 2,364 4,975 759 786 886 1,061 1,483 Total population (thousands) 15,473 10,089 2,019 2,016 2,019 2,018 2,018 5,383 1,077 1,076 1,076 1,076 1,078 Age dependency ratio 0.9 1.0 1.4 1.3 1.1 0.9 0.6 0.7 1.0 0.8 0.7 0.5 0.4 Average household size 5.0 5.0 7.2 6.8 5.5 5.0 3.0 4.9 7.3 6.3 5.7 4.5 3.1 Marital status of head of household (%) Monogamous male 44 46 50 50 50 48 40 40 47 49 46 38 32 Polygamous male 14 16 22 22 16 17 11 9 16 11 10 9 6 Single male 18 15 5 6 11 11 26 25 15 15 17 26 38 De facto female 4 4 5 5 5 4 3 4 5 4 5 4 4 De jure female 19 19 18 17 18 20 20 21 17 20 22 23 21 MDG 1: extreme poverty and hunger Mean monthly expenditure (CFA francs) 30,619 22,063 6,609 10,217 13,705 18,951 40,025 46,540 11,847 18,846 25,889 37,099 93,334 Mean monthly share on food (%) 59 69 68 71 70 69 68 42 48 45 44 42 36 Mean monthly share on health (%) 7 7 7 6 7 7 8 7 6 6 7 7 8 Mean monthly share on education (%) 4 3 3 3 3 3 3 6 6 7 7 6 5 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) 85 79 75 77 79 77 83 96 96 96 96 95 96 Net primary enrollment rate (% of relevant age group) Total 93 92 92 91 93 93 92 94 94 95 95 93 89 Male 93 93 93 92 94 93 90 94 94 95 95 94 91 Female 92 92 90 90 93 93 93 93 94 96 95 92 87 Net secondary enrollment rate (% of relevant age group) Total 40 29 14 22 28 33 48 57 38 53 59 64 72 Male 39 29 15 22 28 33 49 55 35 49 59 64 73 Female 41 28 12 21 27 33 47 58 40 57 59 64 71 Tertiary enrollment rate (per 10,000) 89 .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 68 56 50 50 55 58 62 88 76 85 89 92 94 Male 77 67 61 60 66 69 72 92 83 91 94 96 96 Female 60 47 42 42 46 49 51 83 70 80 84 88 92 Youth literacy rate (% ages 15­24) Total 82 73 69 69 76 74 78 94 89 93 95 96 97 Male 88 82 76 78 85 84 85 96 90 95 97 97 98 Female 77 66 62 61 69 67 71 93 87 91 93 95 95 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) 90 84 77 83 84 84 88 100 99 100 100 100 100 Morbidity (% of population) 31 31 28 29 31 33 35 31 30 31 31 30 33 Health care provider consulted when sick (%) .. .. .. .. .. .. .. .. .. .. .. .. .. Type of health care provider consulted (% of total) Public 53 55 53 53 53 59 58 48 44 49 51 49 48 Private, modern medicine 13 7 6 5 7 8 9 23 19 20 20 24 31 Private, traditional healers 15 18 18 21 21 15 14 11 18 12 9 7 6 Other 2 3 2 3 4 3 4 1 1 0 1 1 1 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 43 26 13 15 21 29 36 75 58 68 75 79 84 Water source less than 1 hour away (% of population) 68 75 71 80 73 74 76 56 56 59 61 57 50 Market less than 1 hour away (% of population) 90 85 82 85 84 86 88 99 99 99 99 100 99 Access to improved water source (% of population) Totala 66 50 47 44 47 48 58 96 88 94 97 97 98 Own tap 15 6 3 4 4 5 10 32 11 17 24 35 49 Other piped 27 14 12 11 11 13 17 52 58 62 59 51 41 Well, protected 24 31 32 30 32 30 31 12 19 15 14 10 8 Traditional fuel use (%) Totala 75 94 99 99 97 96 86 41 75 58 51 34 17 Firewood 75 93 99 99 96 96 85 40 75 58 49 33 16 Charcoal 0 0 .. .. 0 0 0 1 0 1 2 1 1 a. Components may not sum to total because of rounding. HOUSEHOLD WELFARE Part IV. Household welfare 109 Table 14.3 Ethiopia household survey, 2000 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 16,672 8,459 1,469 1,382 1,519 1,678 2,411 8,213 1,118 1,358 1,506 1,883 2,348 Total population (thousands) 54,756 47,531 9,502 9,513 9,504 9,507 9,505 7,225 1,446 1,443 1,446 1,445 1,445 Age dependency ratio 1.0 1.1 1.3 1.2 1.1 1.0 0.8 0.7 1.0 0.9 0.8 0.6 0.5 Average household size 4.9 4.9 5.9 5.4 5.2 4.8 3.8 4.5 5.6 5.1 4.7 4.3 3.5 Marital status of head of household (%) Monogamous male 68 71 75 72 74 74 64 48 53 50 50 49 41 Polygamous male 1 1 1 1 1 0 1 0 0 0 0 0 0 Single male 6 5 3 4 3 4 8 11 6 4 7 10 23 De facto female 1 1 1 1 1 0 1 3 2 4 4 3 2 De jure female 25 23 20 22 21 22 27 38 39 42 39 38 34 MDG 1: extreme poverty and hunger Mean monthly expenditure (birr) 103 93 42 60 75 95 161 162 49 76 103 147 346 Mean monthly share on food (%) 66 68 72 71 69 68 62 55 66 62 59 53 43 Mean monthly share on health (%) 1 1 1 1 1 1 1 1 1 1 1 1 1 Mean monthly share on education (%) 1 0 0 0 0 0 0 2 2 1 2 2 2 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. .. Net primary enrollment rate (% of relevant age group) Total 30 25 19 23 29 25 32 75 66 70 76 84 85 Male 32 27 20 25 30 27 35 75 68 68 75 85 86 Female 29 22 18 20 28 21 29 75 64 71 77 82 84 Net secondary enrollment rate (% of relevant age group) Total 9 3 2 3 3 3 5 40 30 36 41 50 47 Male 10 4 4 3 3 5 7 43 29 38 47 54 54 Female 8 2 1 2 2 2 3 38 30 35 36 46 42 Tertiary enrollment rate (per 10,000) 10 .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 28 21 15 19 20 23 25 67 54 59 66 71 79 Male 41 34 26 32 33 39 39 81 70 75 80 86 91 Female 17 9 6 8 8 9 11 56 43 47 56 61 69 Youth literacy rate (% ages 15­24) Total 39 29 24 32 29 30 31 84 80 81 86 87 86 Male 50 43 35 47 43 45 42 90 84 86 91 95 95 Female 28 17 12 17 16 16 20 80 76 78 82 81 81 MDGs 4 and 5: child mortality; maternal health Health center less than 5 km away (% of population) 47 38 37 39 40 37 37 98 97 98 99 99 98 Morbidity (% of population) 26 27 27 27 27 26 31 20 20 20 20 19 20 Health care provider consulted when sick (%) 41 39 30 36 40 41 46 67 60 65 68 70 71 Type of health care provider consulted (% of total) Public 45 44 44 49 45 42 41 52 56 59 52 49 43 Private, modern medicine 45 45 46 40 46 46 48 42 36 36 41 43 51 Private, traditional healers 1 1 0 0 1 1 1 1 0 0 1 2 1 Other 6 7 6 7 5 9 7 4 4 3 4 3 4 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds 45 41 35 48 42 38 45 85 81 81 84 96 88 Measles immunization coverage, 1-year-olds 51 47 44 50 47 49 46 90 84 88 90 98 94 Stunting (6­59 months) 59 61 64 60 61 61 55 47 56 51 49 43 29 Wasting (6­59 months) 11 11 12 11 11 9 11 7 8 9 6 4 7 Underweight (6­59 months) 45 46 53 46 48 41 43 27 36 30 27 22 14 MDG 7: environmental sustainability Access to sanitation facilities (% of population) 17 9 7 8 7 9 11 71 48 63 72 78 86 Water source less than 5 km away (% of population) 90 99 90 89 88 90 87 98 97 98 98 98 99 Market less than 5 km away (% of population) 58 52 54 52 52 52 50 98 98 98 99 99 97 Access to improved water source (% of population) Totala 29 19 15 18 18 19 21 92 83 91 93 92 96 Own tap 0 0 0 0 0 0 0 1 1 1 1 2 2 Other piped 17 7 7 7 6 6 8 82 74 79 84 83 88 Well, protected 11 12 8 11 12 13 13 8 9 11 8 7 6 Traditional fuel use (%) Totala 77 78 82 78 77 78 77 66 80 74 70 65 51 Firewood 75 78 82 78 77 78 77 58 75 67 61 57 40 Charcoal 1 0 0 .. .. .. 0 8 5 7 9 8 11 a. Components may not sum to total because of rounding. 110 Part IV. Household welfare HOUSEHOLD WELFARE Table 14.4 Malawi household survey, 2004 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 11,280 9,840 1,495 1,747 1,924 2,106 2,568 1,440 249 246 283 335 327 Total population (thousands) 12,505 11,075 2,187 2,186 2,200 2,219 2,282 1,429 279 281 280 287 299 Age dependency ratio 1.0 1.0 1.4 1.2 1.1 1.0 0.7 0.7 1.1 0.9 0.8 0.6 0.4 Average household size 4.5 4.6 5.9 5.2 4.7 4.2 3.5 4.3 5.3 4.9 4.5 3.7 3.5 Marital status of head of household (%) Monogamous male 63 62 62 63 63 64 58 69 69 81 76 68 57 Polygamous male 8 9 9 11 10 8 8 3 6 3 2 2 2 Single male 6 5 1 1 2 5 13 13 1 4 8 17 27 De facto female 2 2 3 3 2 2 2 1 3 1 2 0 1 De jure female 21 22 25 21 22 21 19 14 20 11 12 12 14 MDG 1: extreme poverty and hunger Mean monthly expenditure (kwacha) 85 72 31 44 58 78 148 184 42 68 98 143 565 Mean monthly share on food (%) 75 76 79 79 78 76 69 64 76 73 67 61 42 Mean monthly share on health (%) 3 3 3 3 3 3 4 3 3 3 3 3 4 Mean monthly share on education (%) 1 1 1 1 1 1 1 2 2 1 2 3 4 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. .. Net primary enrollment rate (% of relevant age group) Total 65 64 57 60 64 69 73 78 71 78 86 79 81 Male 64 63 57 59 63 68 72 78 71 78 86 75 79 Female 66 65 58 61 65 70 74 79 71 78 86 83 83 Net secondary enrollment rate (% of relevant age group) Total 7 5 2 3 3 6 10 20 4 12 16 30 37 Male 7 5 2 3 3 6 10 20 3 14 16 33 35 Female 6 5 2 2 3 6 10 20 4 10 15 28 39 Tertiary enrollment rate (per 10,000) .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 29 29 14 13 13 13 13 17 17 17 15 16 20 Male 43 16 17 16 16 17 15 18 20 17 17 18 18 Female 15 10 10 9 9 9 11 16 13 17 13 13 21 Youth literacy rate (% ages 15­24) Total 35 34 36 35 34 33 32 40 45 42 35 37 41 Male 43 42 44 43 44 45 38 46 52 48 46 50 40 Female 27 26 29 28 26 23 26 33 38 37 25 25 42 MDGs 4 and 5: child mortality; maternal health Health center less than 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 28 29 22 27 29 32 33 17 18 16 18 17 16 Health care provider consulted when sick (%) 87 87 83 86 86 89 88 90 84 90 93 92 89 Type of health care provider consulted (% of total) Public 36 35 39 38 35 32 32 44 41 51 44 50 31 Private, modern medicine 52 52 48 51 53 54 54 51 51 45 48 45 66 Private, traditional healers 5 5 6 4 5 6 3 3 5 2 3 2 1 Missionary or nongovernmental organization 4 4 3 3 3 4 7 2 1 2 4 1 1 Other 4 4 4 3 4 4 3 1 2 .. 1 1 2 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) 39 39 39 39 41 39 36 35 43 36 30 39 19 Wasting (6­59 months) 2 2 3 3 2 2 2 2 2 4 2 0 2 Underweight (6­59 months) 16 16 18 17 15 14 16 15 19 18 12 11 14 MDG 7: environmental sustainability Access to sanitation facilities (% of population) 18 9 6 7 9 9 11 66 57 68 68 73 65 Water source less than 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Access to improved water source (% of population) Totala 67 64 64 63 64 62 67 88 69 86 86 92 97 Own tap 5 2 1 1 1 2 4 28 6 13 16 30 61 Other piped 15 11 10 10 11 9 13 49 42 55 60 56 33 Well, protected 47 51 53 53 52 51 50 11 21 18 10 7 3 Traditional fuel use (%) Totala 97 98 99 99 99 98 98 87 99 95 96 89 62 Firewood 90 97 99 98 98 97 94 38 71 50 40 29 16 Charcoal 7 1 .. 0 0 1 3 49 28 45 56 61 47 Note: Data are provisional. a. Components may not sum to total because of rounding. HOUSEHOLD WELFARE Part IV. Household welfare 111 Table 14.5 Niger household survey, 2005 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 6,690 4,670 682 775 869 1,024 1,320 2,020 277 308 361 453 621 Total population (thousands) 12,627 10,510 2,101 2,103 2,102 2,101 2,102 2,116 424 422 422 423 423 Age dependency ratio 1.1 1.2 1.4 1.1 1.1 0.9 0.8 0.9 1.4 1.2 1.0 0.9 0.7 Average household size 8.4 8.4 10.1 9.4 8.5 7.6 6.4 8.4 9.3 9.4 9.1 8.2 6.1 Marital status of head of household (%) Monogamous male 68 69 72 73 73 67 54 64 60 69 67 64 57 Polygamous male 22 22 12 19 22 28 42 18 9 17 19 24 33 Single male 3 3 5 3 2 2 1 4 6 4 3 4 2 De facto female 1 0 1 0 0 0 1 1 1 1 1 0 1 De jure female 7 5 11 4 4 3 2 13 24 10 10 9 8 MDG 1: extreme poverty and hunger Mean monthly expenditure (CFA francs) 65,877 59,669 23,443 38,353 48,786 61,670 126,084 96,715 36,492 62,921 87,469 112,844 183,902 Mean monthly share on food (%) 85 86 81 84 87 88 89 81 83 85 82 81 75 Mean monthly share on health (%) 1 0 0 0 0 0 0 2 1 1 2 3 3 Mean monthly share on education (%) 0 0 0 0 0 0 0 0 0 0 0 1 1 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. .. Net primary enrollment rate (% of relevant age group) Total 27 24 20 20 24 24 30 47 36 38 46 54 61 Male 30 28 23 25 28 27 34 47 36 38 48 56 58 Female 24 20 17 15 20 21 26 47 37 39 43 52 64 Net secondary enrollment rate (% of relevant age group) Total 7 3 2 2 3 3 5 22 8 15 19 24 37 Male 9 5 4 5 5 5 6 21 5 13 15 25 40 Female 6 2 0 0 2 2 3 22 11 18 23 23 34 Tertiary enrollment rate (per 10,000) .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 31 23 21 21 25 23 31 53 30 32 43 57 65 Male 45 38 35 36 38 39 46 65 41 42 57 71 76 Female 17 9 6 6 11 7 15 41 19 23 30 42 54 Youth literacy rate (% ages 15­24) Total 39 29 26 28 35 23 36 66 45 39 63 76 74 Male 52 44 41 46 52 35 52 72 47 42 68 86 83 Female 26 14 11 9 18 10 21 60 43 36 58 66 66 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 11 11 10 13 14 14 13 9 7 9 9 7 10 Health care provider consulted when sick (%) 8 8 7 10 10 11 11 7 5 7 7 6 10 Type of health care provider consulted (% of total) Public 61 60 55 65 82 53 62 62 63 73 51 73 59 Private, modern medicine 22 19 24 16 7 3 20 33 34 21 44 24 36 Private, traditional healers 17 21 22 20 11 44 18 4 3 6 5 3 5 Missionary or nongovernmental organization .. .. .. .. .. .. .. .. .. .. .. .. .. Other .. .. .. .. .. .. .. .. .. .. .. .. .. Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 21 10 8 9 11 10 14 75 59 72 72 82 91 Water source less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Access to improved water source (% of population) Totala 51 44 47 45 43 41 43 84 89 83 80 84 86 Own tap 8 3 4 3 2 3 3 32 11 15 23 45 64 Other piped 26 21 23 22 22 19 22 51 75 65 55 37 20 Well, protected 17 19 20 20 20 20 18 2 3 3 2 2 2 Traditional fuel use (%) Totala 97 97 96 97 98 97 98 96 97 98 96 97 92 Firewood 96 96 95 96 97 97 97 95 97 97 94 96 90 Charcoal 1 1 1 0 0 0 1 1 1 1 2 2 2 Note: Data are provisional. a. Components may not sum to total because of rounding. 112 Part IV. Household welfare HOUSEHOLD WELFARE Table 14.6 Nigeria household survey, 2004 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 19,158 14,512 2,321 2,446 2,717 3,120 3,908 4,646 783 779 834 988 1,262 Total population (thousands) 126,305 70,599 14,115 14,127 14,116 14,122 14,118 55,706 11,144 11,138 11,140 11,131 11,153 Age dependency ratio 0.8 0.9 1.1 1.0 0.9 0.8 0.6 0.8 0.8 0.9 0.8 0.7 0.5 Average household size 4.7 4.8 6.5 6.0 5.2 4.5 3.4 4.6 5.6 5.7 5.1 4.4 3.3 Marital status of head of household (%) Monogamous male 58 58 54 63 65 62 51 57 56 61 59 59 51 Polygamous male 15 18 32 26 20 14 8 12 16 17 15 10 7 Single male 11 9 4 3 5 8 19 14 10 7 8 13 25 De facto female 3 2 2 2 2 2 3 3 4 3 4 3 3 De jure female 13 12 8 7 9 14 19 14 13 12 14 16 14 MDG 1: extreme poverty and hunger Mean monthly expenditure (Nigerian naira) 11,635 9,924 3,922 6,391 8,008 9,939 16,272 13,705 4,548 8,809 11,580 14,279 22,892 Mean monthly share on food (%) 54 61 57 65 65 64 54 45 36 51 51 50 41 Mean monthly share on health (%) 8 8 3 4 5 7 16 7 4 5 6 6 13 Mean monthly share on education (%) 5 3 4 3 3 3 3 8 11 7 8 7 7 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. .. Net primary enrollment rate (% of relevant age group) .. .. .. .. .. .. .. .. .. .. .. .. .. Total .. .. .. .. .. .. .. .. .. .. .. .. .. Male .. .. .. .. .. .. .. .. .. .. .. .. .. Female .. .. .. .. .. .. .. .. .. .. .. .. .. Net secondary enrollment rate (% of relevant age group) Total .. .. .. .. .. .. .. .. .. .. .. .. .. Male .. .. .. .. .. .. .. .. .. .. .. .. .. Female .. .. .. .. .. .. .. .. .. .. .. .. .. Tertiary enrollment rate (per 10,000) .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 62 50 38 42 48 55 63 75 71 68 73 80 83 Male 69 57 44 49 55 62 71 83 78 77 81 86 89 Female 54 43 31 36 41 49 54 68 65 59 65 73 75 Youth literacy rate (% ages 15­24) Total 78 68 55 60 66 72 81 88 84 86 89 93 89 Male 82 74 60 67 75 81 86 90 85 88 92 96 92 Female 73 62 50 53 58 65 77 86 82 84 85 90 87 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 12 12 8 10 11 14 21 11 7 9 10 11 17 Health care provider consulted when sick (%) 57 57 31 41 50 62 74 57 30 50 56 58 71 Type of health care provider consulted (% of total) Public 38 37 27 26 31 32 47 40 36 41 41 39 40 Private, modern medicine 57 58 69 69 63 64 49 55 58 54 56 56 53 Private, traditional healers 2 2 1 1 2 1 2 1 2 0 1 2 Other 3 3 3 4 4 3 3 4 6 4 3 4 4 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 60 50 47 48 50 50 52 72 73 71 71 72 75 Water source less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Access to improved water source (% of population) Totala 61 42 41 41 43 41 43 83 81 82 82 86 84 Own tap 13 4 3 3 4 3 5 23 18 21 23 24 28 Other piped 11 4 3 4 5 4 5 18 24 18 17 17 16 Well, protected 38 34 35 35 35 34 33 42 39 43 42 45 40 Traditional fuel use (%) Totala 65 88 92 93 91 89 79 38 44 52 43 36 24 Firewood 64 87 92 93 90 89 79 37 42 51 42 35 23 Charcoal 1 0 0 0 1 0 1 1 2 1 1 1 2 Note: Data are provisional. a. Components may not sum to total because of rounding. HOUSEHOLD WELFARE Part IV. Household welfare 113 Table 14.7 São Tomé and Principe household survey, 2000 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 2,416 1,173 179 197 215 244 338 1,243 187 202 242 264 348 Total population (thousands) 128 57 11 11 11 11 11 71 14 14 14 14 14 Age dependency ratio 0.9 1.0 1.3 1.1 1.0 1.0 0.6 0.8 1.1 1.0 0.8 0.8 0.6 Average household size 4.6 4.5 6.3 5.7 4.9 4.2 3.0 4.6 6.2 5.5 4.9 4.4 3.3 Marital status of head of household (%) Monogamous male 51 53 62 66 66 48 37 50 51 50 46 56 46 Polygamous male .. .. .. .. .. .. .. .. .. .. .. .. .. Single male 16 18 9 5 10 16 36 15 4 9 12 14 26 De facto female 7 6 5 5 5 8 7 8 7 11 12 5 8 De jure female 25 23 25 24 19 27 20 27 37 29 30 25 20 MDG 1: extreme poverty and hunger Mean monthly expenditure (dobras) 451,490 318,313 80,362 128,371 175,196 243,054 679,373 560,829 108,471 179,366 252,850 359,041 1,403,366 Mean monthly share on food (%) 72 75 78 77 78 76 71 69 76 74 69 68 62 Mean monthly share on health (%) 3 3 3 3 2 3 3 4 3 3 4 3 5 Mean monthly share on education (%) 2 2 2 2 2 2 1 3 2 3 3 3 2 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) 34 33 46 44 37 35 16 35 51 39 35 38 23 Net primary enrollment rate (% of relevant age group) Total 70 67 68 68 63 68 67 73 71 73 78 73 74 Male 71 70 67 75 62 71 70 73 72 71 75 80 66 Female 69 64 68 60 63 64 63 73 69 75 81 65 79 Net secondary enrollment rate (% of relevant age group) Total 43 29 13 26 23 34 50 52 32 39 64 62 64 Male 43 29 15 24 24 42 47 52 30 41 65 66 66 Female 42 28 11 28 22 25 51 52 35 37 62 59 63 Tertiary enrollment rate (per 10,000) .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 83 80 76 82 79 77 85 86 78 83 85 89 91 Male 92 89 87 89 89 87 92 94 90 92 92 95 97 Female 76 72 67 76 70 69 77 79 68 75 80 84 84 Youth literacy rate (% ages 15­24) Total 94 92 90 92 91 91 95 96 91 94 98 98 96 Male 95 93 95 91 90 94 96 96 94 96 97 98 98 Female 93 91 86 92 92 88 95 95 88 92 98 98 95 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) 84 81 77 74 81 82 85 87 86 90 85 89 87 Morbidity (% of population) 18 15 12 14 14 17 20 19 12 19 19 22 24 Health care provider consulted when sick (%) 48 45 41 45 40 50 47 50 38 44 50 56 57 Type of health care provider consulted (% of total) Public 70 81 94 88 78 83 68 64 80 78 68 62 53 Private, modern medicine 25 14 4 9 16 10 27 31 15 18 29 32 43 Private, traditional healers 3 2 .. 3 .. 3 4 4 5 1 3 6 2 Other 1 2 2 .. 6 3 1 1 .. 3 .. .. 2 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 28 21 18 12 20 20 27 35 14 26 36 41 46 Water source less than 1 hour away (% of population) 88 93 93 94 93 95 92 84 82 80 87 86 85 Market less than 1 hour away (% of population) 87 81 74 73 80 86 86 92 90 88 91 93 94 Access to improved water source (% of population) Totala 77 67 74 70 64 70 63 84 82 79 81 89 88 Own tap 20 10 7 9 7 13 12 27 12 20 26 29 40 Other piped 8 13 19 15 15 11 10 4 4 3 5 5 4 Well, protected 49 44 48 46 42 46 41 53 65 56 49 56 43 Traditional fuel use (%) Totala 84 95 100 98 99 94 88 75 96 83 81 72 57 Firewood 73 91 98 96 97 90 82 59 88 74 63 50 36 Charcoal 11 4 1 2 2 4 6 16 8 9 18 22 20 a. Components may not sum to total because of rounding. 114 Part IV. Household welfare HOUSEHOLD WELFARE Table 14.8 Sierra Leone household survey, 2002/03 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 3,713 2,396 412 451 453 511 569 1,317 223 246 277 276 295 Total population (thousands) 5,337 3,440 688 689 688 688 688 1,897 379 379 380 379 380 Age dependency ratio 0.9 1.0 1.1 1.0 1.0 0.9 0.9 0.8 1.0 1.0 0.8 0.7 0.6 Average household size 7.4 7.3 8.2 7.6 7.5 6.8 6.3 7.5 8.4 7.6 7.1 7.2 7.4 Marital status of head of household (%) Monogamous male 61 60 52 56 61 65 64 63 56 62 66 67 64 Polygamous male 19 23 31 28 26 19 15 10 13 13 13 8 6 Single male 4 3 2 2 3 3 4 6 2 3 3 7 14 De facto female 2 2 3 1 1 2 2 2 1 3 2 2 1 De jure female 14 12 12 13 10 11 15 19 27 19 16 16 16 MDG 1: extreme poverty and hunger Mean monthly expenditure (leones) 294,515 239,364 103,175 150,703 197,851 237,999 438,780 378,978 154,151 242,246 322,612 385,918 685,453 Mean monthly share on food (%) 52 59 60 61 62 61 53 42 49 46 45 43 32 Mean monthly share on health (%) 10 2 6 9 7 10 14 13 9 10 12 12 19 Mean monthly share on education (%) 4 2 3 2 2 2 2 6 5 6 6 6 5 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) Net primary enrollment rate (% of relevant age group) Total 73 67 62 64 67 69 75 86 78 85 89 87 91 Male 72 66 58 65 66 70 72 85 78 83 88 88 93 Female 74 68 66 63 68 67 77 86 78 87 90 87 89 Net secondary enrollment rate (% of relevant age group) Total 19 10 7 7 11 10 18 33 27 23 24 37 51 Male 22 13 9 10 12 13 22 36 31 28 24 47 48 Female 17 7 4 3 9 7 13 30 23 18 24 27 54 Tertiary enrollment rate (per 10,000) .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 27 13 11 10 11 14 20 49 32 37 41 52 75 Male 35 20 17 17 17 21 27 58 43 50 49 59 81 Female 19 8 6 5 6 8 14 40 24 26 33 46 68 Youth literacy rate (% ages 15­24) Total 40 23 18 17 17 28 35 62 49 51 56 62 81 Male 47 31 26 24 25 36 42 68 59 62 64 65 85 Female 33 16 12 11 11 20 27 55 39 42 48 60 78 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 44 42 34 40 42 42 49 45 37 44 45 45 54 Health care provider consulted when sick (%) 59 65 49 64 67 68 75 56 41 50 49 58 75 Type of health care provider consulted (% of total) Public 53 55 50 39 53 51 61 51 51 52 49 55 51 Private, modern medicine 30 27 16 31 27 33 25 36 18 32 28 31 48 Private, traditional healers 9 11 23 16 12 8 9 4 6 5 12 5 Other 8 7 11 14 8 9 5 8 25 11 12 10 2 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds 72 72 74 57 64 71 96 73 70 75 71 63 87 Measles immunization coverage, 1-year-olds 16 16 16 24 15 13 8 18 19 17 21 21 9 Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 4 2 2 2 1 2 4 7 1 2 4 5 23 Water source less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Access to improved water source (% of population) Totala 37 25 24 25 23 22 31 59 40 51 52 67 79 Own tap 7 1 0 1 1 0 4 18 0 3 10 20 49 Other piped 12 5 6 8 5 3 5 24 19 23 19 33 23 Well, protected 18 19 18 17 17 20 22 17 21 24 22 15 7 Traditional fuel use (%) Totala 97 99 99 99 99 99 98 95 99 98 98 95 86 Firewood 93 98 98 98 98 98 97 83 98 96 91 83 55 Charcoal 5 1 1 1 1 0 1 12 1 2 7 12 32 Note: Data are provisional. a. Components may not sum to total because of rounding. HOUSEHOLD WELFARE Part IV. Household welfare 115 Table 14.9 Uganda household survey, 2002/03 Expenditure quintile Rural Urban National Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic indicators Sample size (households) 9,710 5,648 937 1,019 1,036 1,182 1,474 4,062 894 877 766 701 824 Total population (thousands) 25,273 21,795 4,359 4,358 4,358 4,363 4,357 3,477 695 696 696 696 695 Age dependency ratio 1.2 1.3 1.7 1.5 1.4 1.2 0.9 0.8 1.3 1.1 0.8 0.6 0.4 Average household size 5.1 5.3 6.4 5.9 5.8 5.3 4.0 4.1 5.7 4.6 4.3 4.0 3.0 Marital status of head of household (%) Monogamous male 54 56 56 61 60 57 50 45 52 53 45 45 37 Polygamous male 12 13 13 12 14 14 12 7 9 8 7 8 3 Single male 8 7 3 3 4 6 15 12 5 6 13 14 18 De facto female 8 8 10 9 8 8 7 9 8 8 6 7 12 De jure female 18 16 18 15 14 15 17 27 25 25 28 27 30 MDG 1: extreme poverty and hunger Mean monthly expenditure (Ugandan shillings) 1,523 1,322 593 854 1,121 1,393 2,175 2,499 864 1,208 1,689 2,281 4,926 Mean monthly share on food (%) 56 58 60 62 61 59 52 43 52 49 45 42 34 Mean monthly share on health (%) 4 4 2 3 3 4 6 7 5 6 6 6 9 Mean monthly share on education (%) 4 4 4 3 4 4 5 4 3 3 5 4 3 MDGs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. .. Net primary enrollment rate (% of relevant age group) Total 63 62 50 59 63 67 73 73 64 69 80 75 82 Male 62 61 51 58 63 65 72 71 63 65 77 75 78 Female 64 63 50 60 64 68 74 75 64 73 82 75 86 Net secondary enrollment rate (% of relevant age group) Total 13 11 2 5 10 15 22 26 15 19 27 30 40 Male 13 11 1 4 7 19 21 26 13 23 26 29 39 Female 14 12 2 5 14 11 23 27 17 16 28 31 41 Tertiary enrollment rate (per 10,000) 3 .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 70 66 49 61 66 71 79 87 72 83 86 92 95 Male 80 77 66 75 75 81 85 91 80 91 90 93 95 Female 61 56 34 48 58 62 73 84 65 77 82 92 94 Youth literacy rate (% ages 15­24) Total 80 78 62 73 79 81 86 90 78 89 88 94 95 Male 85 83 72 84 83 86 88 91 80 92 88 93 96 Female 76 73 52 63 76 77 84 89 77 86 88 94 95 MDGs 4 and 5: child mortality; maternal health Health center less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 29 29 28 28 28 29 33 28 25 29 29 28 28 Health care provider consulted when sick (%) 93 92 87 91 94 94 95 94 91 91 96 96 97 Type of health care provider consulted (% of total) Public 30 32 44 36 29 25 26 18 28 23 18 15 10 Private, modern medicine 64 62 51 59 63 67 65 76 66 72 77 78 84 Private, traditional healers 1 1 1 1 2 1 1 1 2 1 0 0 0 Missionary or nongovernmental organization 5 5 4 3 5 6 7 5 4 3 5 7 4 Other 0 0 0 1 0 0 1 1 0 0 0 .. 2 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 76 72 50 67 74 77 84 95 86 94 96 98 99 Water source less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Access to improved water source (% of population) Totala 60 56 57 55 55 56 57 81 77 79 81 82 85 Own tap .. .. .. .. .. .. .. .. .. .. .. .. .. Other piped 9 2 1 1 1 2 5 48 22 34 46 58 65 Well, protected 51 54 56 54 54 54 52 34 55 45 35 24 20 Traditional fuel use (%) Totala 97 98 99 99 99 99 97 89 99 97 93 93 73 Firewood 79 90 97 96 97 92 76 22 54 33 19 12 7 Charcoal 18 8 1 3 3 8 20 67 45 64 74 81 66 Note: The survey did not collect data in the Kitgum, Gulu, Kasese, and Bundibugio districts. a. Components may not sum to total because of rounding. 116 Part IV. Household welfare HOUSEHOLD WELFARE Technical notes General notes Gross national income (GNI) per capita is .. means that data are not available or the total domestic and foreign value added that aggregates cannot be calculated because claimed by residents, which comprises gross of missing data in the years shown domestic product plus net factor income $ means U.S. dollars from abroad (the income residents receive A blank means not applicable or, for an ag- from abroad for factor services including la- gregate, not analytically meaningful. bor and capital) less similar payments made A billion is 1,000 million. to nonresidents who contribute to the do- mestic economy, divided by midyear popu- 1. Basic indicators lation. It is calculated using the World Bank Atlas method with constant 2000 exchange Table .. Basic indicators rates (box 1). Growth rates are shown in real Population is World Bank estimates, usu- terms. They have been calculated by the least- ally projected from the most recent popu- squares method using constant 2000 GNI lation censuses or surveys (mostly from per capita dollars (see also table 2.8). 1980­2005). Refugees not permanently Life expectancy at birth is the number of settled in the country of asylum are gener- years a newborn infant would live if prevail- ally considered to be part of the population ing patterns of mortality at the time of its of their country of origin. birth were to remain the same throughout Land area is the land surface area of a coun- its life. Data are World Bank estimates based try, excluding inland waters, national claims on data from the United Nations Population to continental shelf, and exclusive economic Division, the United Nations Statistics Divi- zones. sion, and national statistical offices. Box 1 The World Bank Atlas method for converting gross national income to a common denominator The World Bank Atlas method uses a three-year The following formulas describe the proce- average of conversion factors to convert gross dures for computing the conversion factor for national income (GNI) data, expressed in differ- year t: ent national currencies, to a common denomina- tion, conventionally U.S. dollars. The Atlas con- version factor for any year is the average of the official exchange rate or alternative conversion and for calculating per capita GNI in U.S. dol- factor for that year and for the two preceding lars for year t: years, after adjusting them for differences in rela- tive inflation between that country and the United Y $ = (Yt /Nt) + e* t t­2,t States. This three-year average smoothes fluc- tuations in prices and exchange rates for each where Y is current GNI (local currency) for year t country. The resulting GNI in U.S. dollars is di- t, P is the GNI deflator for year t, Nt is midyear t vided by the midyear population for the latest of population for year t, and P $ is the U.S. GNI t the three years to derive GNI per capita. deflator for year t. Technical notes 117 Under-five mortality rate is the probability The sum of the components of GDP by that a newborn baby will die before reaching industrial origin (presented here as value age 5, if subject to current age-specific mor- added) will not normally equal total GDP for tality rates. The probability is expressed as a several reasons. First, components of GDP rate per 1,000. by expenditure are individually rescaled and Gini coefficient is the most commonly used summed to provide a partially rebased series measure of inequality. The coefficient ranges for total GDP. Second, total GDP is shown from 0, which reflects complete equality, to at purchaser value, while value added com- 1, which indicates complete inequality (one ponents are conventionally reported at pro- person has all the income or consumption, ducer prices. As explained above, purchaser all others have none). Graphically, the Gini values exclude net indirect taxes, while pro- coefficient can be easily represented by the ducer prices include indirect taxes. Third, cer- area between the Lorenz curve and the line tain items, such as imputed bank charges, are of equality. added in total GDP. Adult literacy rate is the percentage of adults ages 15 and older who can, with un- Source: World Bank country desk data. derstanding, read and write a short, simple statement on their everyday life. Table .. Gross domestic product, real Net official development assistance per capita Gross domestic product (GDP), real, is obtained is net disbursements of loans and grants from by converting national currency GDP series all official sources on concessional financial to U.S. dollars using constant (2000) ex- terms divided by the midyear population for change rates. For countries where the official the corresponding year exchange rate does not effectively reflect the Regional aggregates for GNI per capita, life rate applied to actual foreign exchange trans- expectancy at birth, and adult literacy rates actions, an alternative currency conversion are weighted by population. factor has been used. Source: Data on population, land area, GNI Source: World Bank country desk data. per capita, life expectancy at birth, under-five mortality, Gini coefficient, and adult literacy Table .. Gross domestic product are from the World Bank's World Develop- growth ment Indicators database. Data on aid flows Gross domestic product (GDP) growth is the av- are from the Organisation for Economic Co- erage annual growth rate of real GDP (table operation and Development's Geographic 2.2) at market prices based on constant local Distribution of Aid Flows to Developing currency. Aggregates are based on constant Countries database. 2000 U.S. dollars. 2. National accounts Source: World Bank country desk data. Table .. Gross domestic product, Table .. Gross domestic product per nominal capita, real Gross domestic product (GDP), nominal, is Gross domestic product (GDP) per capita, real, is the total output of goods and services for calculated by dividing real GDP (table 2.2) by final use produced by residents and non- corresponding midyear population. residents, regardless of the allocation to domestic and foreign claims. It is calculated Source: World Bank country desk data. without making deductions for deprecia- tion of fabricated capital assets or depletion Table .. Gross domestic product per and degradation of natural resources. GDP capita growth figures are shown at market prices (also Gross domestic product (GDP) per capita growth known as purchaser values) and converted is the average annual growth rate of real GDP from national currency GDP series in cur- per capita (table 2.4). rent prices to U.S. dollars at official annual exchange rates. Source: World Bank country desk data. 118 Africa Development Indicators 2007 Table .. Gross national income, as an index with base year 2000. The series nominal shows the effects of domestic price changes Gross national income, nominal, is the sum of and exchange rate variations. value added by all resident producers plus any product taxes (less subsidies) not included in Source: World Bank country desk data. the valuation of output plus net receipts of primary income (compensation of employees Table .. Gross domestic savings and property income) from abroad. Data are Gross domestic savings is calculated by de- converted from national currency in current ducting total consumption (table 2.13) from prices to U.S. dollars at official annual ex- nominal gross domestic product (table 2.1). change rates. See box 2 for a discussion of the differences between gross domestic product Source: World Bank country desk data. and gross national income. Table .. Gross national savings Source: World Bank and Organisation for Gross national savings is the sum of gross do- Economic Co-operation and Development mestic savings (table 2.11), net factor income (OECD) national accounts data. from abroad, and net private transfers from abroad. The estimate here also includes net Table .. Gross national income, real public transfers from abroad. Gross national income, real, is obtained by converting national currency gross national Source: World Bank country desk data. income series to U.S. dollars using constant (2000) exchange rates. Table .. General government final consumption Source: World Bank and OECD national ac- General government consumption is all current counts data. expenditure for purchases of goods and ser- vices by all levels of government, including Table .. Gross national income per capital expenditure on national defense and capita security. Other capital expenditure by gov- Gross national income (GNI) per capita is calcu- ernment is included in capital formation. lated using the World Bank Atlas method (see box 1). It similar in concept to GNI per capita Source: World Bank country desk data. in current prices, except that the use of three- year averages of exchange rates smoothes out Table .. Final consumption sharp fluctuations from year to year. expenditure Final consumption expenditure (formerly to- Source: World Bank country desk data. tal consumption) is the sum of household final consumption expenditure (private Table .. Gross domestic product de- consumption) and general government fi- flator (local currency series) nal consumption expenditure (table 2.13), Gross domestic product (GDP) deflator (local shown as a share of gross domestic prod- currency series) is nominal GDP in current lo- uct. This estimate includes any statistical cal currency divided by real GDP in constant discrepancy in the use of resources relative 2000 local currency, expressed as an index to the supply of resourcesPrivate consump- with base year 2000. tion, not separately shown here, is the value of all goods and services purchased or re- Source: World Bank country desk data. ceived as income in kind by households and nonprofit institutions. It excludes purchases Table .. Gross domestic product of dwellings, but includes imputed rent for deflator (U.S. dollar series) owner-occupied dwellings. In practice, it in- Gross domestic product (GDP) deflator (U.S. cludes any statistical discrepancy in the use dollar series) is nominal GDP in current U.S. of resources. dollars (table 2.1) divided by real GDP in con- stant 2000 U.S. dollars (table 2.2), expressed Source: World Bank country desk data. Technical notes 119 Box 2 Gross domestic product and gross national product Gross domestic product (GDP) is the broadest quantitative mea- a country is counted. For GDP production by foreigners within a sure of a nation's total economic activity. It measures, in market country is included and production by nationals outside a country prices, the value of economic activity within a country's geographic is not. For GNP production by foreigners within a country is not borders, including all final goods and services produced over a included but production by nationals outside a country is. Thus, period of time (usually a year). There are two ways of calculating while GDP is the value of goods and services produced within GDP. The expenditure approach sums consumption, investment, a country, GNP is the value of goods and services produced by government expenditure, and net exports. The income approach citizens of a country. sums wages, rents, interests, profits, nonincome charges, and net This distinction matters little for countries such as the United foreign factor income earned. Both methods should yield the same States, where payments to U.S. residents, including U.S.-based results because total expenditure on goods and services by defini- firms, from their activities in the rest of the world are roughly the tion must equal the value of goods and services produced, which same as payments to foreign residents from their activities in the must equal the total income paid to the factors that produced the United States. But for developing countries GDP may be a poor goods and services. indicator of financial performance. For example, a country with GDP is just one way of measuring the total output of an econ- large amounts of foreign direct investment, the profits of which are omy. Gross national product (GNP) is another. It measures the repatriated, will have a high GDP but will not see a commensurate value of all goods and services produced by permanent residents raise in available capital or living standards. A similar situation of a country regardless of their location. For example, the income occurs in oil-producing developing countries; a large share of oil of a U.S. citizen working in Paris would count toward U.S. GNP-- profits is repatriated by foreign oil companies. but also French GDP. To take another example, revenue from ac- Figure 1 shows how new foreign direct investment is rap- tivities of Euro Disney in Paris would count toward U.S. GNP be- idly flowing to mineral exporters in Africa. Figure 2 shows the cause the Walt Disney Company is a U.S.-owned company, but difference between GDP and GNP for African economies that because the activities take place in Paris, it would count toward rely heavily on foreign direct investment. This comparison is im- French GDP. portant because it shows the difference between how much in- The distinction between GDP and GNP is the difference in how come is generated in a particular country and how much income production by foreigners in a country and by nationals outside of is repatriated. Figure 1 Foreign direct investment Figure 2 Differences between gross domestic product and gross national product for Foreign direct investment inflows, oil-exporting countries in Sub-Saharan Africa, select African countries 2000­02 (% of GDP, three-year average) 30 Gross domestic product and gross national product, 2000­02 ($ billions, three-year average) 10 25 20 8 15 6 10 4 5 2 0 GNP GDP Angola Cameroon Chad Congo, Equatorial Nigeria Sudan Sub- Rep. Guinea Saharan 0 Africaa Angola Equatorial Guinea Congo, Rep. Gabon a. Data are weighted by GDP. Source: World Bank Development Data Platform. Source: World Bank country desk data. Table .. Final consumption expendi- U.S. dollars (table 2.14) divided by midyear ture per capita population. Final consumption expenditure per capita is final consumption expenditure in current Source: World Bank country desk data. 120 Africa Development Indicators 2007 Table .. Agriculture value added the public sector (table 2.20) and the pri- Agriculture value added is the gross output of vate sector (table 2.21). Examples include forestry, hunting, and fishing less the value improvements in land, dwellings, machin- of their intermediate inputs. It is shown at ery, and other equipment. For some coun- factor cost for most countries, but it is shown tries the sum of gross private investment at market prices, that is, including interme- and gross public investment does not total diate inputs, for Botswana, Cameroon, Chad, gross domestic investment due to statistical Democratic Republic of Congo, Republic of discrepancies. Congo, Gabon, Guinea, Madagascar, Mali, Morocco, Niger, Rwanda, Senegal, Togo, and Source: World Bank country desk data. Zambia. Table .. General government fixed Source: World Bank country desk data. capital formation General government fixed capital formation is Table .. Industry value added gross domestic fixed capital formation (see Industry value added is the gross output of table 2.19) for the public sector. mining, manufacturing, construction, elec- tricity, water, and gas, less the value of their Source: World Bank country desk data. intermediate inputs. It is shown at factor cost for most countries, but it is shown at Table 1 Method used to calculate regional aggregates market prices, that is, including intermedi- and period averages in section 2 tables ate inputs, for Botswana, Cameroon, Chad, Democratic Republic of Congo, Republic of Method Method Method Method Method Method Congo, Gabon, Guinea, Madagascar, Mali, Table 1 2 3 4 5 6 Morocco, Niger, Rwanda, Senegal, Togo, and 2.1 Gross domestic product, nominal × × Zambia. 2.2 Gross domestic product, real × × 2.3 Gross domestic product growth × × Source: World Bank country desk data. 2.4 Gross domestic product per capita, real × × 2.5 Gross domestic product per capita, growth × × Table .. Services value added 2.6 Gross national income, nominal × × Services value added is the gross output of all 2.7 Gross national income, real × × other branches of economic activity, includ- 2.8 Gross national income per capita × × ing government, less the value of their in- 2.9 Gross domestic product deflator (local currency series) × × termediate inputs. It is shown at factor cost 2.10 Gross domestic product deflator (U.S. series) × × for most countries, but it is shown at market 2.11 Gross domestic savings × × 2.12 Gross national savings × × prices, that is, including intermediate inputs, 2.13 General government final consumption × × for Botswana, Cameroon, Chad, Democratic 2.14 Final consumption expenditure × × Republic of Congo, Republic of Congo, Ga- 2.15 Final consumption expenditure per capita × × bon, Guinea, Madagascar, Mali, Morocco, 2.16 Agriculture value added × × Niger, Rwanda, Senegal, Togo, and Zambia. 2.17 Industry value added × × Other items, such as imputed bank service 2.18 Services value added × × charges (which are difficult to assess in the 2.19 Gross fixed capital formation × × same fashion for all countries) and any cor- 2.20 General government fixed capita formation × × rections for statistical discrepancies, are not 2.21 Private sector fixed capital formation × × included. 2.22 Resource balance (exports minus imports) × × 2.23 Exports of goods and services, nominal × × Source: World Bank country desk data. 2.24 Imports of goods and services, nominal × × 2.25 Exports of goods and services, real × × Table .. Gross fixed capital 2.26 Imports of goods and services, real × × formation Note: Method 1 is the simple total of the gap-filled indicator; method 2 is the simple total of the gap-filled main indicator divided by the simple Gross fixed capital formation consists of gross total of the gap-filled secondary indicator; method 3 is the simple total of the first gap-filled main indicator minus the simple total of the second domestic fixed capital formation plus net gap-filled main indicator, divided by the simple total of the secondary indicator; method 4 is the arithmetic mean (using the same series as shown in the table; that is, ratio if the rest of the table is shown as ratio, level if the rest of the table is shown as level, growth rate if the rest is changes in the level of inventories. Gross shown as growth rate, and so on); method 5 is the least-squares growth rate (using the main indicator); method 6 is the median. capital formation comprises outlays by Technical notes 121 Table .. Private sector fixed capital Share of population below purchasing power formation parity (PPP) $1 a day is the percentage of the Private sector fixed capital formation is gross population living on less than $1.08 a day at domestic fixed capital formation (see table 1993 international prices. As a result of revi- 2.19) for the private sector. sions in PPP exchange rates, poverty rates for individual countries cannot be compared with Source: World Bank country desk data. poverty rates reported in earlier editions. Poverty gap ratio at $1 a day is the mean Table .. Resource balance (exports shortfall from the poverty line (counting the minus imports) nonpoor as having zero shortfall), expressed Resource balance is the difference between free as a percentage of the poverty line. This mea- on board exports (table 2.23) and cost, insur- sure reflects the depth of poverty as well as ance, and freight imports (table 2.24) of goods its incidence. and services (or the difference between gross Share of poorest quintile in national consump- domestic savings and gross capital formation). tion or income is the share of consumption, or The resource balance is shown as a share of in some cases income, that accrues to the nominal gross domestic product (table 2.1). poorest 20 percent of the population. Prevalence of child malnutrition, under- Source: World Bank country desk data. weight, is the percentage of children under age 5 whose weight for age is more than two Tables . and .. Exports and im- standard deviations below the median for the ports of goods and services, nominal international reference population ages 0­59 Exports and imports of goods and services, months. The reference population, adopted nominal, comprise all transactions between by the World Health Organization in 1983, residents of an economy and the rest of the is based on children from the United States, world involving a change in ownership of who are assumed to be well nourished. general merchandise, goods sent for process- Population below minimum dietary energy ing and repairs, nonmonetary gold, and ser- consumption (also referred to as prevalence of vices expressed in current U.S dollars. undernourishment) is the population whose food intake is insufficient to meet dietary en- Source: World Bank country desk data. ergy requirements continuously. Tables . and .. Exports and im- Source: Data on poverty measures are ports of goods and services, real prepared by the World Bank's Development Exports and imports of goods and services, real, Research Group. The national poverty lines are defined as in tables 2.23 and 2.24, but ex- are based on the World Bank's country pov- pressed in constant 2000 U.S. dollars. erty assessments. The international poverty lines are based on nationally representative Source: World Bank country desk data. primary household surveys conducted by national statistical offices or by private agen- 3. Millennium Development Goals cies under the supervision of government or international agencies and obtained from Table .. Millennium Development government statistical offices and World Goal : eradicate extreme poverty and Bank country departments. The World Bank hunger has prepared an annual review of its poverty Share of population below national poverty line work since 1993. For details on data sourc- (poverty headcount ratio) is the percentage es and methods used in deriving the World of the population living below the national Bank's latest estimates, see Chen and Raval- poverty line. National estimates are based lion (2004). on population-weighted subgroup estimates Data have been compiled by World Bank from household surveys. See box 3 for a staff from primary and secondary sources. discussion of cross-country comparisons of Efforts have been made to harmonize these poverty and box 4 for a discussion of objec- data series with those published on the Unit- tive and subjective measures of poverty. ed Nations Millennium Development Goals 122 Africa Development Indicators 2007 Box 3 Using simple cross-country comparisons to guide measurement: poverty in the CFA franc zone Three things are needed to measure poverty in a country: an in- dicator of well-being or welfare, such as consumption per capita Poverty in the CFA Franc zone: Estimates by country or per equivalent adult; a threshold, or poverty line, to which each Natural household's welfare can be compared; and a poverty measure that log of GDP Share of Household GDP per per capita Method for population aggregates the information on poverty obtained for each house- survey capita divided measuring in poverty Gini hold into meaningful statistics for a country as a whole. Different Country year ($) by 100 poverty (%) index poverty estimates can result depending on the indicator, thresh- Benin 2003 325 1.18 Relative 39.0 0.36 old, or poverty measure used. Standard measures used to monitor Cost of global poverty trends, such as the share of the population living on Burkina Faso 2003 247 0.90 basic needs 46.4 0.46 less than $1 or $2 a day, are typically not used for country-specific Cost of Cameroon 2001 695 1.94 basic needs 40.2 0.41 work. It is indeed better for country work to adapt the methodology Central African Cost of used for estimating poverty to country specifics, be it to country Republic 2003 225 0.81 basic needs 67.2 0.44 characteristics or data quality. Still, this does not mean that cross- Cost of country comparisons are not useful. They can be used to suggest Chad 2003 211 0.75 basic needs 55.0 0.37 revisions in poverty estimates, as in the CFA franc zone. Cost of The table and figure show World Bank poverty estimates from Congo, Rep. 2005 994 2.30 basic needs 50.7 0.46 a series of recent poverty assessments for countries of the CFA Côte d'Ivoire 2002 592 1.78 Relative 38.4 0.50 franc zone. Poverty comparisons between the countries are facili- Cost of Gabon 2005 3,991 3.69 basic needs 33.2 0.44 tated by the countries' shared currency, similar inflation rates, and free trade between member countries. Each country has a slightly Guinea-Bissau 2002 138 0.33 $1 a day 65.7 0.36 different methodology for estimating poverty. Most use a poverty Cost of Mali 2001 226 0.82 basic needs 55.6 0.38 line based on the cost of basic needs method, although they dif- Cost of fer in whether they use consumption per capita or per equivalent Niger 2005 158 0.45 basic needs 62.1 0.47 adult and in the caloric requirement norm used to determine what Cost of households should be able to purchase. The surveys used in each Senegal 2001 442 1.49 basic needs 57.1 0.34 country also differ. But an inverse relationship clearly exists be- Cost of tween the natural log of GDP per capita and the share of the popu- Togo 2006 238 0.87 basic needs 61.7 0.32 lation living in poverty.1 The curve fitted through the scatter plot in Note: Recent household survey data are not available for Equatorial Guinea. the figure gives a very rough idea of the poverty level expected for Source: Wodon 2007b. a given GDP per capita. Divergence from this curve may stem from issues of data quality or from different levels of inequality between countries, for example. Poverty and per capita GDP These simple comparisons of poverty levels between coun- tries have actually been used to suggest changes in methodolo- Share of population in poverty (%) gies for measuring poverty at the country level in the CFA franc 70 zone. Preliminary estimates for Togo presented at a February 2007 workshop were much higher than those reported in the table and suggested that Togo had by far the highest poverty rate in the CFA 60 franc zone--a surprising finding given the country's relative GDP per capita. The data in the table led to a downward revision of Togo's poverty estimates. Similarly, previous estimates suggested that Mali 50 had a much higher poverty rate than shown in the table. The data helped in suggesting alternative poverty estimates at a September 2007 workshop in Bamako. Obviously, caution should be exercised 40 in making cross-country poverty comparisons. But given the dif- ferent assumptions that countries use to estimate poverty and their debatable strengths and weaknesses, it is often useful to use sim- 30 ple cross-country comparisons to help inform the methodological 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 choices made for poverty measurement in any given country. Natural log of per capita GDP divided by 100 ($) Source: Wodon 2007b. 1. GDP per capita is expressed in U.S. dollars for simplicity, despite the fact that the CFA franc appreciated against the dollar in recent years. Technical notes 123 Box 4 Comparing objective and subjective measures of poverty Several African countries have succeeded at increasing their eco- Objective and subjective perceptions of nomic growth rate, translating into substantial poverty reduction. poverty in West Africa, by country (percent) At the same time people have not felt their poverty situation im- proving, a source of concern to elected policymakers. To what Measure or Senegal perception Cameroon Guinea Mauritania extent is there a divergence between objective and subjective of poverty 1996­2001 1994­2002 1990­2000 1994­2001 2001­06 measures of poverty, and what explains it? Growth and objective poverty Data from household surveys can help answer this ques- Cumulative growth tion. The table below provide poverty estimates from selected in GDP per capita (1) 12.7 16.7 16.8 18.9 9.3 World Bank poverty assessments in countries with high growth Initial poverty rates between repeated household surveys with consumption incidence (2) 53.3 62.6 56.6 67.8 57.1 data. (Growth vanished in Guinea and slowed down in Cameroon Final poverty incidence (3) 40.2 49.1 46.7 57.1 50.8 after 2001­02). The table also provides data on poverty as mea- Poverty reduction sured in the household surveys and on subjective perceptions (4) = [(3)­(2)]/(2) ­0.246 ­0.216 ­0.175 ­0.158 ­0.110 regarding poverty trends. In all four countries growth significantly Elasticity of poverty reduced poverty, often with an elasticity of poverty reduction to GDP growth (4)/(1) ­1.94 ­1.19 ­1.04 ­0.84 ­1.19 to growth of ­1. Inequality increased in some countries but de- Gini index of inequality creased in others, suggesting no general pattern. But percep- Initial Gini index 40.6 45.8 33.8a 32.6 34.1 tions regarding poverty were not as favorable: a majority of re- Final Gini index 40.8 41.0 39.0 34.2 32.0 spondents declared that poverty had worsened in their country Perception regarding poverty or community. Even for Senegal in 2001­06, a larger share of the Deterioration 54.1 23.1 30.9 64.3 43.9 population cited a deteriorating poverty situation in their com- No change 17.4 49.5 40.8 12.8 22.1 munity (although perceptions seem to have improved over those Improvement 17.3 24.5 28.3 19.0 31.2 for 1994­2001). Four tentative explanations can explain this apparent dis- No opinion 11.2 2.9 -- 4.0 2.8 connect between a substantial decline in objective poverty a. Data are for 1996. measures and perceptions of a deterioration in the countries' or Source: Wodon 2007a. communities' poverty situation. First, when assessing trends in poverty subjectively, households may be influenced by persis- tent and in some cases increasing inequality. In a relative depri- perceptions of poverty may also account for nonmonetary as- vation framework growth without a reduction in inequality may pects of well-being. Low levels of satisfaction with publicly pro- lead to higher feelings of deprivation over time. Second, even if vided services for education, health, and basic infrastructure many households benefit from higher consumption levels over may affect negatively perceptions in most countries. Fourth, time, their vulnerability to shocks remains very high. West Afri- even if the share of the population living in poverty is reduced can countries, among others, have been subjected to weather over time, the number of the poor is increasing due to high popu- and commodity price shocks in recent years. Third, subjective lation growth. website (www.un.org/millenniumgoals), but Net primary enrollment ratio is the ratio of some differences in timing, sources, and defi- children of official primary school age based nitions remain. on the International Standard Classification Data on child malnutrition and population of Education 1997 who are enrolled in pri- below minimum dietary energy consumption mary school to the population of the corre- are from the Food and Agriculture Organiza- sponding official primary school age. tion (see www.fao.org/faostat/foodsecurity/ Primary completion rate is the percentage of index_en.htm). students completing the last year of primary school. It is calculated as the total number of Table .. Millennium Development Goal students in the last grade of primary school : achieve universal primary education minus the number of repeaters in that grade Primary education provides children with ba- divided by the total number of children of of- sic reading, writing, and mathematics skills ficial graduation age. along with an elementary understanding of Share of cohort reaching grade 5 is the per- such subjects as history, geography, natural centage of children enrolled in grade 1 of science, social science, art, and music. primary school who eventually reach grade 5. 124 Africa Development Indicators 2007 The estimate is based on the reconstructed Infant mortality rate is the number of in- cohort method. fants dying before reaching one year of age, Youth literacy rate is the percentage of peo- per 1,000 live births. ple ages 15­24 who can, with understanding, Child immunization rate, measles, is the per- both read and write a short, simple statement centage of children ages 12­23 months who about their everyday life. received vaccinations for measles before 12 months or at any time before the survey. A Source: Data are from the United Nations child is considered adequately immunized Educational, Scientific, and Cultural Orga- against measles after receiving one dose of nization Institute for Statistics. Data have vaccine. been compiled by World Bank staff from primary and secondary sources. Efforts have Source: Data on under-five and infant mor- been made to harmonize these data series tality are the harmonized estimates of the with those published on the United Nations World Health Organization, United Nations Millennium Development Goals website Children's Fund (UNICEF), and the World (www.un.org/millenniumgoals), but some Bank, based mainly on household surveys, differences in timing, sources, and defini- censuses, and vital registration, supplement- tions remain. ed by the World Bank's estimates based on household surveys and vital registration. Oth- Table .. Millennium Development er estimates are compiled and produced by the Goal : promote gender equality and World Bank's Human Development Network empower women and Development Data Group in consultation Ratio of girls to boys in primary and secondary with its operational staff and country offices. school is the ratio of female to male gross Data on child immunization are from the enrollment rate in primary and secondary World Health Organization and UNICEF esti- school. mates of national immunization coverage. Ratio of young literate women to men is the ratio of the female to male youth literacy Table .. Millennium Development rate. Goal : improve maternal health Women in national parliament are the per- Maternal mortality ratio, modeled estimate, is centage of parliamentary seats in a single or the number of women who die from preg- lower chamber occupied by women. nancy-related causes during pregnancy and Share of women employed in the nonagri- childbirth, per 100,000 live births. cultural sector is women wage employees in Births attended by skilled health staff are the the nonagricultural sector as a share of total percentage of deliveries attended by person- nonagricultural employment. nel trained to give the necessary supervision, care, and advice to women during pregnancy, Source: Data on net enrollment and lit- labor, and the postpartum period; to con- eracy are from the United Nations Educa- duct deliveries on their own; and to care for tional, Scientific, and Cultural Organization newborns. Institute for Statistics. Data on women in national parliaments are from the Inter- Source: Data on maternal mortality are Parliamentary Union. Data on women's em- from AbouZahr and Wardlaw (2003). Data ployment are from the International Labour on births attended by skilled health staff are Organization's Key Indicators of the Labour from the United Nations Children's Fund's Market, fourth edition. State of the World's Children 2006 and Child- info, and Demographic and Health Surveys Table .. Millennium Development by Macro International. Goal : reduce child mortality Under-five mortality rate is the probability Table .. Millennium Development that a newborn baby will die before reaching Goal : combat HIV/AIDS, malaria, and age 5, if subject to current age-specific mor- other diseases tality rates. The probability is expressed as a Prevalence of HIV is the percentage of people rate per 1,000. ages 15­49 who are infected with HIV. Technical notes 125 Contraceptive prevalence rate is the percent- Gross domestic product (GDP) per unit of age of women who are practicing, or whose energy use is the GDP in purchasing power sexual partners are practicing, any form of parity (PPP) U.S. dollars per kilogram of oil contraception. It is usually measured for equivalent of energy use. PPP GDP is gross married women ages 15­49 only. domestic product converted to 2000 con- Deaths due to malaria is the number of ma- stant international dollars using purchasing laria deaths per 100,000 people. power parity rates. An international dollar Share of children under age 5 sleeping under has the same purchasing power over GDP as insecticide-treated bednets is the percentage of a U.S. dollar has in the United States. children under age 5 with access to an insec- Carbon dioxide emissions are those stem- ticide-treated bednet to prevent malaria. ming from the burning of fossil fuels and the Incidence of tuberculosis is the estimated manufacture of cement. They include carbon number of new tuberculosis cases (pulmo- dioxide produced during consumption of sol- nary, smear positive, and extrapulmonary), id, liquid, and gas fuels and gas flaring. per 100,000 people. Solid fuel use is the percentage of the popu- Tuberculosis cases detected under DOTS is lation using solid fuels as opposed to modern the percentage of estimated new infectious fuels. Solid fuels are defined to include fuel tuberculosis cases detected under DOTS, the wood, straw, dung, coal, and charcoal. Mod- internationally recommended tuberculosis ern fuels are defined to include electricity, liq- control strategy. uefied petroleum gas, natural gas, kerosene, and gasoline. Source: Data on HIV prevalence are from Population with sustainable access to an im- the Joint United Nations Programme on proved water source is the percentage of the HIV/AIDS and the World Health Organiza- population with reasonable access to an ad- tion's (WHO) 2006 Report on the Global equate amount of water from an improved AIDS Epidemic. Data on contraceptive preva- source, such as a household connection, lence are from household surveys, including public standpipe, borehole, protected well or Demographic and Health Surveys by Macro spring, or rainwater collection. Unimproved International and Multiple Indicator Cluster sources include vendors, tanker trucks, and Surveys by the United Nations Children's unprotected wells and springs. Reasonable Fund (UNICEF). Data on deaths due to ma- access is defined as the availability of at least laria are from the WHO. Data on insecticide- 20 liters a person a day from a source within treated bednet use are from UNICEF's State 1 kilometer of the dwelling. of the World's Children 2006 and Childinfo, Population with sustainable access to im- and Demographic and Health Surveys by proved sanitation is the percentage of the Macro International. Data on tuberculosis population with at least adequate access to are from the WHO's Global Tuberculosis excreta disposal facilities that can effectively Control Report 2006. prevent human, animal, and insect contact with excreta. Improved facilities range from Table .. Millennium Develop- simple but protected pit latrines to flush toi- ment Goal : ensure environment lets with a sewerage connection. The excreta sustainability disposal system is considered adequate if it Forest area is land under natural or planted is private or shared (but not public) and if it stands of trees, whether productive or not. hygienically separates human excreta from Nationally protected areas are totally or par- human contact. To be effective, facilities tially protected areas of at least 1,000 hect- must be correctly constructed and properly ares that are designated as scientific reserves maintained. with limited public access, national parks, natural monuments, nature reserves or wild- Source: Data on forest area are from the life sanctuaries, and protected landscapes. Food and Agricultural Organization's Global Marine areas, unclassified areas, and litoral Forest Resources Assessment. Data on na- (intertidal) areas are not included. The data tionally protected areas are from the United also do not include sites protected under lo- Nations Environment Programme and the cal or provincial law. World Conservation Monitoring Centre. 126 Africa Development Indicators 2007 Data on energy use are from electronic files to the public switched telephone network, or of the International Energy Agency. Data to a public mobile telephone service, which on carbon dioxide emissions are from the uses cellular technology. Carbon Dioxide Information Analysis Cen- Personal computers are self-contained ter, Environmental Sciences Division, Oak computers designed for use by a single Ridge National Laboratory, in the U.S. state individual. of Tennessee. Data on solid fuel use are from Internet users are people with access to the household survey data, supplemented by worldwide network. World Bank estimates. Data on access to wa- ter and sanitation are from the World Health Source: Data on HIPC countries are from Organization and United Nations Children's the IMF's March 2006 "HIPC Status Re- Fund's Meeting the MDG Drinking Water ports." Data on external debt are mainly from and Sanitation Target (www.unicef.org/wes/ reports to the World Bank through its Debtor mdgreport). Reporting System from member countries that have received International Bank for Re- Table .. Millennium Development construction and Development loans or In- Goal : develop a global partnership ternational Development Association credits, for development as well as World Bank and IMF files. Data on Heavily Indebted Poor Countries (HIPC) Debt youth unemployment are from the Interna- Initiative decision point is the date at which tional Labour Organization's Key Indicators a HIPC with an established track record of of the Labour Market, fourth edition. Data good performance under adjustment pro- on phone subscribers, personal computers, grams supported by the International Mon- and Internet users are from the International etary Fund (IMF) and the World Bank com- Telecommunication Union's (ITU) World mits to undertake additional reforms and to Telecommunication Development Report develop and implement a poverty reduction database and World Bank estimates. strategy. HIPC completion point is the date at which 4. Paris Declaration indicators the country successfully completes the key structural reforms agreed on at the decision Table .. Status of Paris Declaration point, including developing and implement- indicators ing its poverty reduction strategy. The coun- The Paris Declaration is the outcome of the try then receives the bulk of debt relief under 2005 Paris High-Level Forum on Aid Effec- the HIPC Initiative without further policy tiveness. In the Declaration 60 partner coun- conditions. tries, 30 donor countries, and 30 develop- Debt service relief committed is the amount ment agencies committed to specific actions of debt service relief, calculated at the En- to further country ownership, harmoniza- hanced HIPC Initiative decision point, tion, alignment, managing for development that will allow the country to achieve debt results, and mutual accountability for the use sustainability at the completion point. of aid. Participants agreed on 12 indicators Public and publicly guaranteed debt service is of aid effectiveness. These indicators include the sum of principal repayments and interest good national development strategies, reliable actually paid on total long-term debt (public country systems for procurement and public and publicly guaranteed and private non- financial management, the development and guaranteed), use of IMF credit, and interest use of results frameworks, and mutual assess- on short-term debt. ment of progress. Qualitative desk reviews by Youth unemployment rate is the percentage the Organisation for Economic Co-operation of the labor force ages 15­24 without work and Development's Development Assistance but available for and seeking employment. Committee and the World Bank and a survey Definitions of labor force and unemployment questionnaire for governments and donors may differ by country. are used to calculate the indicators. Table 4.1 Fixed-line and mobile telephone subscribers includes five of these indicators. are subscribers to a fixed-line telephone ser- Operational development strategies mea- vice, which connects a customer's equipment sure the extent to which a country has an Technical notes 127 operational development strategy to guide commitment to establishing performance the aid coordination effort and the coun- frameworks has been realized. The indicator try's overall development. The score is based relies on the scorings of the 2005 Compre- on the World Bank's 2005 Comprehensive hensive Development Framework Progress Development Framework Progress Report. Report and considers three criteria: the qual- An operational strategy calls for a coherent ity of development information, stakeholder long-term vision and a medium-term strat- access to development information, and egy derived from it; specific targets serving a coordinated country-level monitoring and holistic, balanced and well sequenced devel- evaluation. The assessments therefore reflect opment strategy; and capacity and resources both the extent to which sound data on de- for its implementation. velopment outputs, outcomes, and impacts Reliable public financial management is the are collected, and various aspects of the way World Bank's annual Country Policy and In- information is used, disseminated among stitutional Assessment rating for the quality stakeholders, and fed back into policy. of public financial management. Measured Mutual accountability indicates whether on a scale of 1 (worst) to 5 (best), its focus there is a mechanism for mutual review of is on how much existing systems adhere to progress on aid effectiveness commitments. broadly accepted good practices and whether This is an important innovation of the Paris a reform program is in place to promote im- Declaration because it develops the idea that proved practices. aid is more effective when both donors and Avoidance of parallel project implementation partner governments are accountable to units (PIUs) is the number of parallel project their constituents for the use of resources to implementation units. "Parallel" indicates achieve development results and when they that the units were created outside existing are accountable to each other. The specific country institutional structures. The survey focus is mutual accountability for the imple- guidance distinguishes between PIUs and ex- mentation of the partnership commitments ecuting agencies and describes three typical included in the Paris Declaration and any local features of parallel PIUs: they are accountable agreements on enhancing aid effectiveness. to external funding agencies rather than to country implementing agencies (ministries, Source: Overview of the Results 2006 Sur- departments, agencies, and the like), most vey on Monitoring the Paris Declaration and of the professional staff are appointed by World Bank data. the donor, and the personnel salaries often exceeds those of civil service personnel. In- 5. Private sector development terpretation of the Paris Declaration survey question on this subject was controversial Table .. Business environment in a number of countries. It is unclear that Number of startup procedures to register a busi- within countries all donors applied the same ness is the number of procedures required criteria with the same degree of rigor or that to start a business, including interactions to across countries the same standards were obtain necessary permits and licenses and to used. In several cases the descriptive part of complete all inscriptions, verifications, and the survey results indicates that some donors notifications to start operations. applied a legalistic criterion of accountability Time to start a business is the number of to the formal executing agency, whereas the calendar days needed to complete the proce- national coordinator and other donors would dures to legally operate a business. If a pro- have preferred greater recognition of the cedure can be speeded up at additional cost, substantive reality of accountability to the the fastest procedure, independent of cost, is donor. Some respondents may have confused chosen. the definitional question ("Is the unit `paral- Cost to start a business is normalized by lel'?") with the aid management question ("Is presenting it as a percentage of gross nation- the parallelism justified in terms of the devel- al income (GNI) per capita. opmental benefits and costs?"). Number of procedures to register property is Monitorable performance assessment frame- the number of procedures required for a busi- works measure the extent to which a country's ness to secure rights to property. 128 Africa Development Indicators 2007 Time to register property is the number of repayment. For some countries these claims calendar days needed for a business to secure include credit to public enterprises. rights to property. Policy uncertainty is the share of senior man- Number of procedures to enforce a contract is agers who ranked economic and regulatory the number of independent actions, mandat- policy uncertainty as a major or very severe ed by law or courts, that demand interaction constraint. See box 6 for a discussion of how between the parties of a contract or between good policies matter more for the business cli- them and the judge or court officer. mate than natural resources or geography, a Time to enforce a contract is the number of finding of Africa Competitiveness Report 2007. calendar days from the filing of the lawsuit Corruption is the share of senior managers in court until the final determination and, in who ranked corruption as a major or very se- appropriate cases, payment. vere constraint. Protecting investors disclosure index mea- Courts are the share of senior managers sures the degree to which investors are pro- who ranked courts and dispute resolution tected through disclosure of ownership and systems as a major or very severe constraint. financial information. Lack of confidence in courts to uphold property Time to resolve insolvency is the number of rights is the share of senior managers who do years from the filing for insolvency in court not agree with the statement: "I am confident until the resolution of distressed assets. that the judicial system will enforce my contrac- Rigidity of employment index measures the tual and property rights in business disputes." regulation of employment, specifically the Crime is the share of senior managers who hiring and firing of workers and the rigidity ranked crime, theft, and disorder as a major of working hours. This index is the average of or very severe constraint. three subindexes: a difficulty of hiring index, Tax rates are the share of senior managers a rigidity of hours index, and a difficulty of who ranked tax rates as a major or very se- firing index. vere constraint. Finance is the share of senior managers Source: Data are from the World Bank's Do- who ranked access to finance or cost of fi- ing Business project (http://rru.worldbank. nance as a major or very severe constraint. org/DoingBusiness/). Electricity is the share of senior managers who ranked electricity as a major or severe Table .. Investment climate constraint. Private investment is private sector fixed capi- Labor regulation is the share of senior man- tal formation (table 2.21) divided by nominal agers who ranked labor regulations as a ma- gross domestic product (table 2.1). jor or severe constraint. Net foreign direct investment is invest- Labor skills are the share of senior manag- ment by residents of the Organisation for ers who ranked skills of available workers as Economic Co-operation and Development's a major or severe constraint. (OECD) Development Assistance Commit- Number of tax payments is the number of tee (DAC) member countries to acquire a taxes paid by businesses, including electronic lasting management interest (at least 10 filing. The tax is counted as paid once a year percent of voting stock) in an enterprise even if payments are more frequent. operating in the recipient country. The data Time to prepare, file, and pay taxes is the reflect changes in the net worth of subsid- number of hours it takes to prepare, file, and iaries in recipient countries whose parent pay (or withhold) three major types of taxes: company is in the DAC source country. See the corporate income tax, the value added or box 5 for a discussion of the availability sales tax, and labor taxes, including payroll and accuracy of statistics on foreign direct taxes and social security contributions. investment. Total tax payable is the total amount of tax- Domestic credit to private sector is financial es payable by the business (except for labor resources provided to the private sector, such taxes) after accounting for deductions and as through loans, purchases of nonequity exemptions as a percentage of profit. For fur- securities, and trade credits and other ac- ther details on the method used for assessing counts receivable, that establish a claim for the total tax payable. Technical notes 129 Box 5 Availability and accuracy of statistics on foreign direct investment With foreign direct investment (FDI) flows to African countries be- coming an important source of foreign capital and technologies, World foreign direct investment reliable and accurate statistics are crucial for sound FDI policies. Foreign direct investment inflows and outflows, 2002­04 ($ billions) Despite major achievements around the world in gathering FDI 1,000 data, the availability and quality of FDI data remain an issue in developing countries, including African countries. The main source of data for estimating aggregate levels of 800 FDI for most countries are central bank foreign exchange records, which are collected as part of balance of payments data. FDI sta- 600 tistics based on balance of payments data are becoming increas- ingly standardized as the International Monetary Fund's Balance of Payments Manual and the Organisation for Economic Co-oper- 400 ation and Development's Benchmark Definition of Foreign Direct Investment are used as the main sources. 200 Outflows Inflows However, balance of payments data often fail to capture for- eign residents' investment activities that do not involve direct 0 cross-border capital transactions--for example, reinvested earn- 2002 2003 2004 ings, where investment in a company is based on its own profits made from past investments in the same host country. Other ex- Source: World Bank World Development Indicators database. amples are equity in the form of machinery (investment in kind) and intracompany debt. More countries--but not all--have begun incorporating these Another problem with the administrative source­based esti- elements of FDI. Only a few countries in Africa do. According to a mation is that many countries lack the details required to match in- recent survey by the United Nations Conference on Trade and Devel- ternational standards, which leads to inconsistency across coun- opment (UNCTAD 2005), only Botswana and Nigeria report all these tries in compiling FDI data. Also, administrative source­based FDI elements, and South Africa and Tunisia report reinvested earnings. data often have some flaws in valuing investment projects due Failure to include these elements has resulted in discrepan- to the time gap between project approval and actual investment cies in FDI data. For example, in theory total worldwide FDI inflows activity. should equal total worldwide FDI outflows. But a significant dis- Some countries have implemented firm-level investor surveys, crepancy between them exists because of the omission of these such as censuses, to supplement their balance of payments­ and components (see figure). administrative data­based FDI statistics and improve the overall Another source of FDI data is government administrative re- quality of their FDI statistics. These surveys can collect data on cords, such as approval data of investment projects by foreign reinvested earnings and depreciation of FDI stocks. The downside companies. The advantage of this source is that it incorporates of this approach, however, is the extreme difficulty in tracking all sectoral and geographical information (origins and destinations) firms that conduct FDI transactions. The process is also so costly of foreign investments, which is useful information for microeco- that only a handful countries have implemented them. nomic analysis of implications of FDI in countries' economic and Many countries in Africa lack sufficient human and institu- industrial growth. tional capacity to address the availability and quality of FDI data. But only a few countries publish sectoral and geographical Capacity building at the national level is very much needed in FDI distributions of their FDI inflows and outflows. Availability of such statistics. Collaboration among agencies--the ministry of finance, data in African countries is particularly limited; only five countries ministry of commerce, ministry of industries, central banks, fiscal report their FDI inflows by origin, and only three report inflows by and tax authorities, and investment promotion agencies--is also sector (UNCTAD 2005). Availability of cross-sectional information important. of sectoral and geographical distributions of FDI flows is limited mostly to developed countries. Source: IMF 2003; UNCTAD 2005. Highest marginal tax rate, corporate, is spent in a typical week dealing with require- the highest rate shown on the schedule of ments imposed by government regulations tax rates applied to the taxable income of (for example, taxes, customs, labor regula- corporations. tions, licensing, and registration), including Time dealing with officials is the average per- dealings with officials, completing forms, and centage of senior management's time that is the like. 130 Africa Development Indicators 2007 Box 6 Findings of Africa Competitiveness Report 2007 The first message of Africa Competitiveness Report 2007 (World delays. Improvements in infrastructure would have a substantial Economic Forum 2007) is that good policies matter more for the impact on firm competitiveness, increasing total factor productiv- investment climate than resource abundance or sea access. By ity by 5 percent and employment by 7 percent. combining different variables representative of the business cli- Quality of public institutions comprises law and order, cor- mate into one composite indicator, the report shows that resource- ruption, court efficiency, and quality in the provision of public ser- endowed countries have a similar quality of business climate as vices. Across African countries corruption in particular remains a resource-scarce countries. So do landlocked countries and coun- serious obstacle--viewed as one of the top five overall constraints tries with sea access. For improving the investment climate, geog- among business owners, irrespective of gender and firm size. Per- raphy and geology count less than good policies (figure 1). formance indicators show that a 10 percent improvement in the The World Bank's Enterprise Survey questionnaire asks re- spondents to rank a list of issues based on how constraining they Figure 2 Top constraints to business operations are to the operations and growth of their business. Although sub- growth in Africa stantial country variation exists, access to finance, infrastructure, institutions, and skills are the constraints most often reported as Share of firms reporting constraint as major or very severe, various years (percent) "major" or "very severe" by entrepreneurs, both male and female, 60 across Africa (figure 2). Half of respondents report access to finance as a top con- 50 straint. Across countries access to finance appears more acute in resource rich countries and low-income countries. Within coun- 40 tries access to finance is problematic for small firms and locally owned enterprises. In addition, expanding firms are 10 percent 30 more likely to report access to finance as a major constraint. Per- formance indicators show that better access to finance is associ- 20 Upper middle income ated with both higher productivity and employment growth. Low income Infrastructure remains one of the tightest bottlenecks to busi- 10 Overall nesses in Africa. In low-income countries electricity is the top re- ported constraint. Moreover, unreliable power supply is a con- 0 Access to Electricity Corruption Crime Skills Labor straint that affects all firms, regardless of size. Transportation, by finance regulations contrast, affects landlocked countries and small and medium-size Source: World Economic Forum 2007. firms more. Firms in Africa report losing as much as 8 percent of sales due to power outages and 3 percent due to transportation Figure 3 Skills and labor regulation constraints, Figure 1 Differences in overall investment climate by size of firm measure, by country grouping, various years Share of firms reporting constraint as major or very severe, various years (percent) Investment climate composite measure (0, weak, to 4, strong) 60 4 50 3 40 30 Lack of skills available 2 20 Labor regulations Resource poor Resource rich 1 10 Landlocked Not free Coastal Good Poor Free 0 0 Small Medium Large Very large Resource Geographic Regulatory Economic (6­10 (11­50 (51­150 (151 or more endowment location environment freedom employees) employees) employees) employees) Source: World Economic Forum 2007. Source: World Economic Forum 2007. (continued) Technical notes 131 Box 6 Findings of Africa Competitiveness Report 2007 (continued) Figure 4 Enterprises owned by women in select African countries Share of total, various years (percent) 70 60 50 40 30 20 10 0 . Sw p. co ria So enya a l nin li Ta r Ma ia De ius u Ma ia nia ia rab i nd da a Na r bia a so Ca ndi n Bo e Ca ana e a ga w ine Rep ge a Ma ric Ma gol Bu and iqu rd sa Mo roo itre an mb mb Re sc yp Mala Fa roc ge ila it ne ita mi Be ru Ni Ve Bis Af tsw ur nz An mb ga me Er K az Za Ga Ug Ni m. Bu ur na Se Mo pe uth a- za rki t, A o, Gu ng Eg Co Source: World Economic Forum 2007. objective measure of corruption and regulation is associated with Finally, the report highlights the critical importance of informa- about a 2 percent increase in productivity. tion and communication technologies for boosting efficiency, boost- Lack of skills remains a critical problem in Africa. Large firms ing skill, and technology levels and moving into higher value prod- are almost 60 percent more likely to report skills availability and ucts. It recognizes African governments' shifting role in information labor regulations as constraining factors (figure 3). With larger and communication technologies from owning and operating to workforces and more stringent hiring and firing requirements, promoting competitiveness by establishing a sound policy frame- this is not surprising. Increasing the supply of skilled workers has work and stable institutions--particularly in the mobile telephone shown a positive impact on employment growth. A 10 percent im- market. This has substantially transformed the structure of the mo- provement in the objective measure of the supply of skilled work- bile telephone market in Africa over the last decade (figure 7). ers will increase employment by 1 percent. These efforts have resulted in strong growth in African The report identifies increased entrepreneurial participation markets--particularly in mobile telephone technologies (figure 8). of women as Africa's hidden growth potential. Albeit with large variation across countries, female entrepreneurs in Africa remain Figure 5 Performance of men- and women-owned a minority compared with their male counterparts in most African enterprises countries (figure 4). The report suggests that there is no clear gen- der-distinct pattern of constraints faced by firms across Africa. But Median value added per worker, by sex of the business owner, 2005 ($) female entrepreneurs tend to be younger, less likely to be married, 30 and more likely to be engaged in family enterprises. The report concludes that once a firm is in business, enter- prises managed by women are as productive as those run by men, Women-owned enterprises based on productivity indicators such as value added per worker 20 and total factor productivity (figure 5). This finding highlights the considerable hidden growth potential of women-owned enter- prises once entry barriers to women's entrepreneurial participa- tion are removed. 10 The report also compares Africa's four largest economies, Al- geria, Egypt, Nigeria, and South Africa, with Brazil, China, India, and Russia., four of the largest developing and transition econo- mies It argues that the four African economies together have the 0 size and scale to become drivers of Africa's economic growth. But 10 20 30 0 key obstacles to competitiveness in their investment climates and Men-owned enterprises very low intra-African trade hinder their capacities to act as effec- Source: World Economic Forum 2007. tive growth poles (figure 6). 132 Africa Development Indicators 2007 Box 6 Findings of Africa Competitiveness Report 2007 (continued) Figure 6 Intraregional trade patterns Figure 8 Mobile telephone markets Intraregional exports, various years (percent of total exports) Average annual growth in mobile telephone network subscribers, 2000­05 Mobile telephone subscribers as a share of total telephone subscribers, 2005 60 100 50 80 40 60 30 20 40 Egypt, Arab Rep. South Africa 10 20 Algeria Nigeria China Brazil India 0 Intra-African trade Intra-Asian trade Intra­Latin 0 American Oceania Europe Americas World Asia Africa trade Source: World Economic Forum 2007. Source: World Economic Forum 2007. Figure 7 Structure of the mobile telephone market in Africa Number of countries, 1993­2006 No network No competition Competition 55 50 45 40 35 30 25 20 15 10 5 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Source: World Economic Forum 2007. Average time to clear customs is the number Listed domestic companies are domestically of days to clear an imported good through incorporated companies listed on a coun- customs. try's stock exchanges at the end of the year. Bank branches are deposit money bank They exclude investment companies, mu- branches. tual funds, and other collective investment Interest rate spread is the interest rate vehicles. charged by banks on loans to prime custom- Market capitalization of listed companies, ers minus the interest rate paid by commer- also known as market value, is the share price cial or similar banks for demand, time, or of a listed domestic company's stock times savings deposits. the number of shares outstanding. Technical notes 133 Turnover ratio for traded stocks is the total 6. Trade value of shares traded during the period di- vided by the average market capitalization Table .. International trade and for the period. Average market capitalization tariff barriers is calculated as the average of the end-of- Merchandise trade is the sum of imports and period values for the current period and the exports of divided by nominal gross domes- previous period. tic product. Exports and imports comprise all transac- Source: Data on private investment are tions between residents of an economy and from the World Bank's World Development the rest of the world involving a change in Indicators database. Data on net foreign di- ownership of general merchandise, goods sent rect investment are from the World Bank's for processing and repairs, and nonmonetary World Development Indicators database. gold. Data are shown in current U.S. dollars. Data on domestic credit to the private sec- Exports and imports as a share of gross domes- tor are from the International Monetary tic product (GDP) are calculated as merchan- Fund's International Financial Statistics dise exports and imports divided by nominal database and data files, World Bank and GDP. Annual growth of exports and imports is OECD gross domestic product (GDP) es- calculated using the real imports and exports timates, and the World Bank's World De- series in tables 2.25 and 2.26. See box 7 for a velopment Indicators database. Data on discussion of the importance of cross-border investment climate constraints to firms trade for Rwanda's exports and imports. are based on enterprise surveys conducted Terms of trade index measures the relative by the World Bank and its partners during movement of export and import prices. This 2001­05 (http://rru.worldbank.org/Enter- series is calculated as the ratio of a country's priseSurveys). Data on regulation and tax export unit values or prices to its import unit administration and highest marginal cor- values or prices shows changes over a base porate tax rates are from the World Bank's year (2000) in the level of export unit values Doing Business project (http://rru.world- as a percentage of import unit values. bank.org/DoingBusiness). Data on time Structure of merchandise exports and imports dealing with officials and average time to components may not sum to 100 percent be- clear customs are from World Bank Enter- cause of unclassified trade. prise Surveys (http://rru.worldbank.org/ Food comprises the commodities in Stan- EnterpriseSurveys/). Data on bank branches dard International Trade Classification are from surveys of banking and regulatory (SITC) sections 0 (food and live animals), 1 institutions by the World Bank's Research (beverages and tobacco), and 4 (animal and Department and Financial Sector and Op- vegetable oils and fats) and SITC division 22 erations Policy Department and the World (oil seeds, oil nuts, and oil kernels). Development Indicators database. Data on Agricultural raw materials comprise the interest rate spreads are from the IMF's commodities in SITC section 2 (crude ma- International Financial Statistics database terials except fuels), excluding divisions 22, and data files and the World Bank's World 27 (crude fertilizers and minerals excluding Development Indicators database. Data on coal, petroleum, and precious stones), and 28 listed domestic companies and turnover ra- (metalliferous ores and scrap). tios for traded stocks are from Standard & Fuels comprise SITC section 3 (mineral Poor's Emerging Stock Markets Factbook and fuels). supplemental data and the World Bank's Ores and metals comprise the commodities World Development Indicators database. in SITC sections 27, 28, and 68 (nonferrous Data on market capitalization of listed com- metals). panies are from Standard & Poor's Emerging Manufactures comprise the commodities in Stock Markets Factbook and supplemental SITC sections 5 (chemicals), 6 (basic manu- data, World Bank and OECD estimates of factures), 7 (machinery and transport equip- GDP, and the World Bank's World Develop- ment), and 8 (miscellaneous manufactured ment Indicators database. goods), excluding division 68. 134 Africa Development Indicators 2007 Box 7 The importance of cross-border trade: the case of Rwanda Cross-border trade in Rwanda--an important source of income for Table 1 Exports and imports and cross-border trade less well-off households--has been growing over the last decade as a share of GDP, 2001­04 (percent) (tables 1 and 2). Cross-border trade is trade between Rwanda and Cross-border tradea its immediate neighbors (Burundi, Democratic Republic of Congo, Exports of Imports of Year goods goods Exports Imports Tanzania, and Uganda). The income earned by a large number of 2001 4.83 12.69 0.28 0.69 small traders and family-operated ventures augments household income and agricultural production (through the provision of fertil- 2002 3.18 11.03 0.46 0.98 izers and livestock feed), thereby helping reduce poverty. Cross- 2003 2.77 10.21 0.46 1.68 border activities also support regional food security. 2004 4.41 11.62 0.58 2.06 Cross-border trade is also an important source of government Average, 2001­04 3.80 11.39 0.50 1.58 revenue. Overall, the customs post at Gikongo, in Kigali, which clears 90­95 percent of imports, accounts for 90 percent of total a. Value of included transactions exceeded 200,000 francs ($370). customs duties. The airport custom post accounts for 7.5 percent Source: World Bank 2007 and National Bank of Rwanda. of customs duties, and border customs posts account for 2.5 per- cent of customs duties. Table 2 Growth rates of exports and imports Most cross-border trade in Rwanda takes place between Ki- as a share of GDP, 2001­04 (percent) gali and neighboring countries. According to a 2001/02 National Cross-border tradea Bank of Rwanda survey, these transactions account for about Exports of goods Imports of goods and services and services Exports Imports 99 percent of cross-border exchanges. Transactions between 4.04 ­2.23 30.85 45.45 customs posts and neighboring countries account for the remain- ing 1 percent of cross-border transactions. a. Value of included transactions exceeded 200,000 francs ($370). The main goods traded are agricultural commodities (maize, Source: World Bank 2007 and National Bank of Rwanda. sugar, milk, rice, wheat, and flour), industrial goods (petroleum products, machinery, cement, shoes, plate, lamp, and pan), water airplanes and vehicles to Burundi and the Democratic Republic resources (fish), forest resources (cassiterite), and services (mobile of Congo. telephony, human skills, and banking activities). Rwanda also re- exports some goods, including secondhand clothes and fuel for Source: Coulibaly, Ezemenari, and Maburuki 2007. Export diversification index measures the Simple mean bound rate is the unweighted extent to which exports are diversified. It is average of all the lines in the tariff schedule constructed as the inverse of a Herfindahl in which bound rates have been set. index, using disaggregated exports at four Simple mean tariff is the unweighted aver- digits (following the SITC3). A higher index age of effectively applied rates or most fa- indicates more export diversification. vored nation rates for all products subject to Competitiveness indicator has two as- tariffs calculated for all traded goods. pects: sectoral effect and global competi- Weighted mean tariff is the average of ef- tiveness effect. To calculate both indicators, fectively applied rates or most favored nation growth of exports is decomposed into three rates weighted by the product import shares components: the growth rate of total in- corresponding to each partner country. ternational trade over the reference period Share of lines with international peaks is the (2001­05); the sectoral effect, which mea- share of lines in the tariff schedule with tariff sures the contribution to a country's ex- rates that exceed 15 percent. port growth of the dynamics of the sectoral Share of lines with specific rates is the share markets in which the country sells its prod- of lines in the tariff schedule that are set on ucts, assuming that sectoral market shares a per unit basis or that combine ad valorem are constant; and the competitiveness effect, and per unit rates. which measures the contribution of changes Primary products are commodities classified in in sectoral market shares to a country's ex- SITC revision 2 sections 0­4 plus division 68. port growth. Manufactured products are commodities Binding coverage is the percentage of prod- classified in SITC revision 2 sections 5­8 ex- uct lines with an agreed bound rate. cluding division 68. Technical notes 135 Tariff barriers are a form of duty based on Source: Data on merchandise trade flows the value of the import. are published in the International Mon- Average cost to ship 20 ft container from port etary Fund's (IMF) Direction of Trade Statis- to destination is the cost of all operations as- tics Yearbook and Direction of Trade Statistics sociated with moving a container from on- Quarterly. The data in the table were cal- board a ship to the considered economic cen- culated using the IMF's Direction of Trade ter, weighted based on container traffic for database. The United Nations Conference each corridor. on Trade and Development publishes data Average time to clear customs is the number on intraregional trade in its Handbook of of days to clear an imported good through International Trade and Development Statis- customs. tics. The information on trade bloc mem- bership is from World Bank (2000), the Source: All indicators in the table were cal- World Bank's Global Economic Prospects culated by World Bank staff using the World 2005, and the World Bank's International Integrated Trade Solution system. Data on Trade Unit. the export diversification index and the com- petitiveness indicator are from the Organisa- 7. Infrastructure tion for Economic Co-operation and Devel- opment. Data on tariffs are from the United Table .. Water and sanitation Nations Conference on Trade and Develop- Internal fresh water resources per capita is the ment and the World Trade Organization. sum of total renewable resources, which in- Data on global imports are from the United clude internal flows of rivers and ground- Nations Statistics Division's COMTRADE water from rainfall in the country, and river database. Data on merchandise exports and flows from other countries. imports are from World Bank country desks. Population with sustainable access to an im- Data on shipping costs are from the World proved water source is the percentage of popu- Bank's Sub-Saharan Africa Transport Policy lation with reasonable access to an adequate Program (SSATP). Data on average time to amount of water from an improved source, clear customs are from World Bank Enter- such as a household connection, public prise Surveys (http://rru.worldbank.org/ standpipe, borehole, protected well or spring, EnterpriseSurveys/). or rainwater collection. Unimproved sources include vendors, tanker trucks, and unpro- Table . Top three exports and share tected wells and springs. Reasonable access is in total exports, defined as the availability of at least 20 liters Top exports and share of total exports are based a person a day from a source within 1 kilome- on exports disaggregated at the four-digit ter of the user's dwelling. level (following the Standard International Population with sustainable access to im- Trade Classification Revision 3). proved sanitation is the percentage of the Number of exports accounting for 75 percent population with at least adequate access to of total exports is the number of exports in a excreta disposal facilities that can effectively country that account for 75 percent of the prevent human, animal, and insect contact country's exports. with excreta. Improved facilities range from simple but protected pit latrines to flush toi- Source: All indicators in the table are from lets with a sewerage connection. The excreta the Organisation for Economic Co-operation disposal system is considered adequate if it and Development. is private or shared (but not public) and if it hygienically separates human excreta from Table . Regional integration, trade human contact. To be effective, facilities blocs must be correctly constructed and properly Merchandise exports within bloc are the sum of maintained. merchandise exports by members of a trade Water supply failure for firms receiving water bloc to other members of the bloc. They are is the average number of days per year that shown both in U.S. dollars and as a percent- firms experienced insufficient water supply age of total merchandise exports by the bloc. for production. 136 Africa Development Indicators 2007 Committed nominal investment in water proj- Passenger vehicles are road motor vehicles, ects with private participation is annual com- other than two-wheelers, intended for the mitted investment in water projects with carriage of passengers and designed to seat private investment, including projects for po- no more than nine people (including the table water generation and distribution and driver). sewerage collection and treatment projects. Road network in good or fair condition is the Average annual official development assis- length of the national road network, includ- tance (ODA) disbursements for water and sani- ing the interurban classified network without tation are average annual ODA for water and the urban and rural network, that is in good sanitation, including bilateral, multilateral, or fair condition, as defined by each country's and other donors. road agency. Ratio of paved to total roads is the length of Source: Data on fresh water resources are paved roads--which are those surfaced with from the World Bank's World Development In- crushed stone (macadam) and hydrocarbon dicators database. Data on access to water and binder or bituminized agents, with concrete, sanitation are from the World Health Organi- or with cobblestones--as a percentage of all zation and United Nations Children's Fund's the country's roads. Meeting the MDG Drinking Water and Sanita- Average time to ship 20 ft container from port tion Target (www.unicef.org/wes/mdgreport). to final destination is the time in days from Data on water supply failure are from World when the ship is available for unloading (be Bank Investment Climate Surveys. Data on it moored at the berth or offshore) until the committed nominal investment in potable content of the container is made available to water projects with private participation are the final customer at the destination in the from the World Bank's Private Participation considered economic center, weighted based in Infrastructure database. Data on ODA dis- on container traffic for each corridor. bursements are from the Organisation for Average cost to ship 20 ft container from Economic Co-operation and Development. port to final destination is the costs of all op- erations associated with bringing a container Table .. Transportation from onboard a ship to the considered eco- Road network is the length of motorways, nomic center, weighted based on container highways, main or national roads, secondary traffic for each corridor. or regional roads, and other roads. Price of diesel fuel and super gasoline is the Rail lines are the length of railway route price as posted at filling stations in a coun- available for train service, irrespective of the try's capital city. When several fuel prices for number of parallel tracks. major cities were available, the unweighted Road density, ratio to arable land is the total average is used. Since super gasoline (95 length of national road network per 1,000 octane/A95/premium) is not available ev- square kilometers of arable land area. The use erywhere, it is sometime replaced by regu- of arable land area in the denominator focus- lar gasoline (92 octane/A92), premium plus es on inhabited sectors of total land area by gasoline (98 octane/A98), or an average of excluding wilderness areas. the two. Road density, ratio to total land is the total Committed nominal investment in transport length of national road network per 1,000 projects with private participation is annual square kilometers of total land area. committed investment in transport projects Rural access is the percentage of the rural with private investment, including projects population who live within 2 kilometers of for airport runways and terminals, railways an all-season passable road as a share of the (including fixed assets, freight, intercity pas- total rural population. senger, and local passenger), toll roads, bridg- Vehicle fleet is motor vehicles, including es, and tunnels. cars, buses, and freight vehicles but not two- Average annual official development assis- wheelers. tance (ODA) disbursements for transportation Commercial vehicles are the number of com- and storage are average annual ODA for trans- mercial vehicles that use at least 24 liters of portation and storage, including bilateral, diesel fuel per 100 kilometers. multilateral, and other donors. Technical notes 137 Source: Data on length of road network Price basket for Internet is calculated based and size of vehicle fleet are from the Inter- on the cheapest available tariff for accessing national Road Federation's World Road Statis- the Internet 20 hours a month (10 hours tics. Data on rail lines and ratio of paved to peak and 10 hours off-peak). The basket does total roads are from the World Bank's World not include telephone line rental but does in- Development Indicators database. Data clude telephone usage charges if applicable. on road density and rural access to roads Data are compiled in the national currency are from the World Bank's Sub-Saharan Af- and converted to U.S. dollars using the an- rica Transport Policy Program (SSATP) and nual average exchange rate. World Development Indicators database. Cost of 3 minute local phone call during peak Data on length of national network in good hours is the cost of a three-minute local call or fair condition and average time and costs during peak hours. Local call refers to a call are from the World Bank's SSATP. Data on within the same exchange area using the sub- fuel and gasoline prices are from the German scriber's own terminal (that is, not from a Society for Technical Cooperation (GTZ). public telephone). Data on committed nominal investment in Cost of 3 minute cellular local call during off- transport projects with private participation peak hours is the cost of a three-minute cel- are from the World Bank's Private Participa- lular local call during off-peak hours. tion in Infrastructure database. Data on ODA Cost of 3 minute phone call to the United disbursements are from the Organisation for States (US) during peak hours is the cost of a Economic Co-operation and Development. three-minute call to the United States during peak hours. Table .. Information and communica- Annual investment in telephone service is the tion technology annual investment in equipment for fixed Telephone subscribers are subscribers to a main telephone service. telephone line service, which connects a cus- Annual investment in mobile communication tomer's equipment to the public switched is the capital investment on equipment for telephone network, or to a cellular telephone mobile communication networks. service, which uses cellular technology. Annual investment in telecommunications is Households with own telephone is the percent- the expenditure associated with acquiring the age of households possessing a telephone. ownership of telecommunication equipment Average delay for firm in obtaining a tele- infrastructure (including supporting land phone connection is the average actual delay and buildings and intellectual and non-tan- in days that firms experience when obtaining gible property such as computer software). It a telephone connection, measured from the includes expenditure on initial installations day the establishment applied to the day it and on additions to existing installations. received the service or approval. Committed nominal investment in telecom- Internet users are people with access to the munication projects with private participation worldwide network. is annual committed investment in telecom- Duration of telephone outages is the average munication projects with private investment, duration in hours of instances of telephone including projects for fixed or mobile local te- unavailability related to production. lephony, domestic long-distance telephony, Telephone faults are the total number of re- and international long-distance telephony. ported faults for the year divided by the total Average annual official development assis- number of mainlines in operation multiplied tance (ODA) disbursements for communications by 100. The definition of fault can vary. Some are average annual ODA for communica- countries include faulty customer equip- tions, including bilateral, multilateral, and ment; others distinguish between reported other donors. and actual found faults. There is also some- times a distinction between residential and Source: Data on telephone subscribers, business lines. Another consideration is the reported phone faults, cost of local and cel- time period: some countries report this indi- lular calls, and investment in telephone cator on a monthly basis; in these cases data service, mobile communication, and tele- are converted to yearly estimates. communications are from the International 138 Africa Development Indicators 2007 Telecommunications Union. Data on house- Electrical outages of firms are the average holds with own telephone are from Demo- number of days per year that establishments graphic and Health Surveys. Data on delays experienced power outages or surges from for firms in obtaining a telephone connection the public grid. and duration of telephone outages, are from Firms that share or own their own genera- World Bank Investment Climate Assess- tor is the percentage of firms that responded ments. Data on Internet users and pricing "Yes"to the following question: "Does your are from the International Telecommunica- establishment own or share a generator?" tion Union, World Telecommunication Develop- Firms identifying electricity as major or very ment Report and database, and World Bank severe obstacle to business operation and growth estimates. Data on cost of a call to the Unit- is the percentage of firms that responded ed States are from the World Bank's Global "major" or "very severe" obstacle to the fol- Development Finance and World Develop- lowing question: "Please tell us if any of the ment Indicator databases. Data on commit- following issues are a problem for the opera- ted nominal investment are from the World tion and growth of your business. If an issue Bank's Private Participation in Infrastructure (infrastructure, regulation, and permits) pos- database. Data on ODA disbursements are es a problem, please judge its severity as an from the Organisation for Economic Co-op- obstacle on a five-point scale that ranges from eration and Development. 0 = no obstacle to 5 = very severe obstacle." Committed nominal investment in energy Table .. Energy projects with private participation is annual Electric power consumption is the production committed investment in energy projects of power plants and combined heat and pow- with private investment, including projects er plants, less distribution losses and own for electricity generation, transmission, and use by heat and power plants. distribution as well as natural gas transmis- GDP per unit of energy use is nominal GDP sion and distribution. in purchasing power parity (PPP) U.S. dollars Average annual official development assis- divided by apparent consumption, which is tance (ODA) disbursements for energy are aver- equal to indigenous production plus imports age annual overseas ODA for energy, includ- and stock changes minus exports and fuels ing bilateral, multilateral, and other donors). supplied to ships and aircraft engaged in international transport. Source: Data on electric power consump- Access to electricity is the percentage of the tion and PPP GDP per unit of energy use are population living in households with access from the World Bank's World Development to electricity. Indicators database. Data on access to elec- Solid fuels use is the percentage of the tricity and solid fuels use are from household population using solid fuels as opposed to survey data, supplemented by World Bank modern fuels. Solid fuels include fuel wood, Project Appraisal Documents. Data on de- straw, dung, coal, and charcoal. Modern fuels lays for firms in obtaining an electrical con- include electricity, liquefied petroleum gas, nection, electrical outages of firms, firms natural gas, kerosene, and gasoline. that share or own their own generator, and Average delay for firm in obtaining electrical firms identifying electricity as a major or connection is the average actual delay in days very severe obstacle to business operation that firms experience when obtaining an elec- and growth are from World Bank Investment trical connection, measured from the day the Climate Assessments. Data on transmission establishment applied to the day it received and distribution losses are from the World the service or approval. Bank's World Development Indicators data- Electric power transmission and distribution base, supplemented by World Bank Project losses are technical and nontechnical losses, Appraisal Documents. Data on committed including electricity losses due to operation nominal investment are from the World of the system and the delivery of electricity Bank's Private Participation in Infrastruc- as well as those caused by unmetered supply. ture database. Data on ODA disbursements This comprises all losses due to transport and are from the Organisation for Economic Co- distribution of electrical energy and heat. operation and Development. Technical notes 139 Table .. Financial sector infrastruc- Turnover ratio for traded stocks is the total ture value of shares traded during the period di- Sovereign ratings are long- and short-term for- vided by the average market capitalization eign currency ratings. for the period. Average market capitalization Gross national savings are the sum of gross is calculated as the average of the end-of- domestic savings (table 2.12) and net fac- period values for the current period and the tor income and net private transfers from previous period. abroad. The estimate here also includes net public transfers from abroad. Source: Data on sovereign ratings are from Money and quasi money (M2) are the sum Fitch Ratings. Data on gross national savings of currency outside banks, demand deposits are from World Bank country desks. Data on other than those of the central government, money and quasi money and domestic credit and the time, savings, and foreign currency to the private sector are from the IMF's In- deposits of resident sectors other than the ternational Financial Statistics database and central government. This definition of mon- data files, World Bank and OECD estimates ey supply is frequently called M2 and corre- of GDP, and the World Bank's World Devel- sponds to lines 34 and 35 in the IMF's Inter- opment Indicators database. Data on real in- national Financial Statistics. terest rates are from the IMF's International Real interest rate is the lending interest Financial Statistics database and data files rate adjusted for inflation as measured by the using World Bank data on the GDP deflator gross domestic product (GDP) deflator. and the World Bank's World Development Domestic credit to private sector is financial Indicators database. Data on interest rate resources provided to the private sector, such spreads are from the IMF's International as through loans, purchases of nonequity Financial Statistics database and data files securities, and trade credits and other ac- and the World Bank's World Development counts receivable, that establish a claim for Indicators database. Data on ratios of bank repayment. For some countries these claims nonperforming loans to total are from the include credit to public enterprises. IMF's Global Financial Stability Report and the Interest rate spread is the interest rate World Bank's World Development Indicators charged by banks on loans to prime custom- database. Data on bank branches are from ers minus the interest rate paid by commer- surveys of banking and regulatory institu- cial or similar banks for demand, time, or tions by the World Bank's Research Depart- savings deposits. ment and Financial Sector and Operations Ratio of bank nonperforming loans to total Policy Department and the World Develop- gross loans is the value of nonperforming ment Indicators database. Data on listed loans divided by the total value of the loan domestic companies and turnover ratios for portfolio (including nonperforming loans be- traded stocks are from Standard & Poor's fore the deduction of specific loan-loss provi- Emerging Stock Markets Factbook and supple- sions). The loan amount recorded as nonper- mental data and the World Bank's World forming should be the gross value of the loan Development Indicators database. Data on as recorded on the balance sheet, not just the market capitalization of listed companies are amount that is overdue. from Standard & Poor's Emerging Stock Mar- Bank branches are deposit money bank kets Factbook and supplemental data, World branches. See box 8 for a discussion of infor- Bank and OECD estimates of GDP, and the mal finance. World Bank's World Development Indicators Listed domestic companies are domestically database. incorporated companies listed on a country's stock exchanges at the end of the year. They 8. Human development exclude investment companies, mutual funds, and other collective investment vehicles. Table .. Education Market capitalization of listed companies, Youth literacy rate is the percentage of people also known as market value, is the share price ages 15­24 who can, with understanding, of a listed domestic company's stock times both read and write a short, simple state- the number of shares outstanding. ment about their everyday life. 140 Africa Development Indicators 2007 Box 8 What is informal finance? Financial services such as payment, savings, credit, and insurance financial providers will become formal and thus subject to regula- services are an important lubricant for a vibrant market-based tory and supervisory frameworks. economy. By facilitating the exchange of goods and services be- tween people and over time, pooling savings and intermediating Collecting data them to investment projects, and insuring people against shocks While aggregate data on formal financial institutions and markets and allowing them to save for retirement, financial institutions and are readily available, they are not for informal financial institutions. markets are important drivers of economic development. They It is thus impossible to quantify the importance of informal com- are especially important in Africa's fight to reach the Millennium pared with formal finance. Data on access to and use of formal fi- Development Goal target of halving poverty by 2015. While ac- nancial services have only recently become available, and data on ademics and policymakers are typically concerned with formal use of informal financial services are still limited to a few individual finance--with financial institutions and markets that are regulated countries. But estimates suggest that in many Sub-Saharan coun- and supervised by government authorities--a wide array of infor- tries less than 20 percent of the population have access to formal mal and semiformal institutions and markets also provide impor- financial services, leaving some 80 percent of the population to tant services. use at least one informal financial service. While surveys of small Informal finance is a broad concept, encompassing a wide formal enterprises indicate that less than 10 percent of working variety of services and relationships ranging from loans from capital and new investment is financed with resources from infor- friends and family to informal savings and credit clubs to mon- mal lenders and friends and families, this share is likely higher for eylenders. Informal does not necessarily mean illegal, but rather informal enterprises, though, there is significant overlap between financial service provision outside the oversight of any govern- the clientele of formal and informal financial service providers. ment authority. This includes professional money lenders, credit While household and firm-level surveys can give a good cross- linked to trade or rent agreements, deposit collectors (also known sectional snapshot of how much of the population uses formal as susu or esusu collectors in West Africa), informal burial so- and informal financial services, financial diaries can document the cieties, hawala and other ethnically based international money financial life of low-income people over time (see, for example, transfer businesses, and a variety of savings and credit associa- www.financialdiaries.com). In South Africa diaries have shown that tions, including rotating credit and savings associations, stokvels, low-income people use an average of 4 savings, 2 insurance, and and tontines. Unlike most formal financial relationships, informal 11 credit instruments and a mix of formal and informal providers. finance is based on personal relationships and socioeconomic Diaries can also help explain the needs for financial services and proximity. Most providers focus on only one service--savings, advice to help policymakers and commercial financial institutions credit, payment, or insurance rather than offering a bundle of develop policies and products. services as many formal financial providers do. The relative im- portance of different informal financial providers varies across Overcoming data challenges countries and regions. An array of data collection efforts is needed to provide a better The line between formal and informal finance, however, is not picture of who has access to and uses which financial services. clear cut. A number of financial institutions could be described as While investment climate surveys have gone far as a consistent semiformal, such as savings and credit cooperative societies or cross-country source of firm financing data, a similar instrument is microcredit projects that have to register with public authorities, still in development on the household side. Micro studies are time- but are not subject to any regulation or supervision. and cost-intensive and typically cannot be undertaken frequently. Informal finance is an important stage in the development pro- But combining household- or firm-level data with aggregate data cess. In 19th century Western Europe informal financial arrange- such as deposit or loan accounts can help track countries more ment flourished in a wide range of institutional forms. In France, frequently and allow cross-country comparisons. for example, notaries were financial intermediaries for the nascent While cross-country comparisons are important, country- manufacturing and trade sectors (Cull and others 2006). But even specific details must be taken into account, including barriers that today, unregistered (and therefore usually illegal) moneylenders impede clients from accessing formal financial services. This is continue to operate in deprived neighborhoods of even the richest important for policymakers because formal financial services, able economies. While precise data are missing, anecdotal evidence to reach out beyond limited sociogeographic areas, are generally suggests that the importance of informal finance decreases as more efficient. Also, little information is available on costs and economies develop; the formal financial system becomes more ef- interest rates in informal finance beyond the anecdotal evidence ficient in reaching out and formal financial services become more that moneylenders charge very high interest rates, while friends affordable to larger shares of the population. Also, some informal and family often charge no interest. Technical notes 141 Adult literacy rate is the proportion of United Nations Statistics Division, and na- adults ages 15 and older who can, with un- tional statistical offices. derstanding, read and write a short, simple Under-five mortality rate is the probability statement on their everyday life. that a newborn baby will die before reaching Primary education provides children with age 5, if subject to current age-specific mor- basic reading, writing, and mathematics skills tality rates. The probability is expressed as a along with an elementary understanding of rate per 1,000. such subjects as history, geography, natural Infant mortality rate is the number of in- science, social science, art, and music. fants dying before reaching one year of age, Secondary education completes the provi- per 1,000 live births. sion of basic education that began at the pri- Maternal mortality ratio, modeled estimate, mary level and aims to lay the foundations is the number of women who die from preg- for lifelong learning and human develop- nancy-related causes during pregnancy and ment by offering more subject- or skill-ori- childbirth, per 100,000 live births. ented instruction using more specialized Prevalence of HIV is the percentage of peo- teachers. ple ages 15­49 who are infected with HIV. Tertiary education, whether or not at an Incidence of tuberculosis is the number of advanced research qualification, normally tuberculosis cases (pulmonary, smear posi- requires, as a minimum condition of admis- tive, and extrapulmonary) in a population sion, the successful completion of education at a given point in time, per 100,000 people. at the secondary level. This indicator is sometimes referred to as Gross enrollment ratio is the ratio of total "point prevalence." Estimates include cases enrollment, regardless of age, to the popu- of tuberculosis among people with HIV. lation of the age group that officially corre- Deaths due to malaria is the number of ma- sponds to the level of education shown. laria deaths per 100,000 people. Net enrollment ratio is the ratio of children Child immunization rate is the percentage of official school age based on the Interna- of children ages 12­23 months who received tional Standard Classification of Education vaccinations before 12 months or at any time 1997 who are enrolled in school to the popu- before the survey for four diseases--measles lation of the corresponding official school and diphtheria, pertussis (whooping cough), age. and tetanus (DPT). A child is considered ad- Student-teacher ratio is the number of stu- equately immunized against measles after re- dents enrolled in school divided by the num- ceiving one dose of vaccine and against DPT ber of teachers, regardless of their teaching after receiving three doses. assignment. Stunting is the percentage of children under Public spending on education is current and age 5 whose height for age is more than two capital public expenditure on education plus standard deviations below the median for the subsidies to private education at the pri- international reference population ages 0­59 mary, secondary, and tertiary levels by local, months. For children up to two years of age regional, and national government, includ- height is measured by recumbent length. For ing municipalities. It excludes household older children height is measured by stature contributions. while standing. The reference population ad- opted by the World Health Organization in Source: United Nations Educational, Scien- 1983 is based on children from the United tific, and Cultural Organization Institute for States, who are assumed to be well nourished. Statistics. Underweight is the percentage of children under age 5 whose weight for age is more Table .. Health than two standard deviations below the me- Life expectancy at birth is the number of years dian reference standard for their age as estab- a newborn infant would live if prevailing pat- lished by the World Health Organization, the terns of mortality at the time of its birth were U.S. Centers for Disease Control and Preven- to remain the same throughout its life. Data tion, and the U.S. National Center for Health are World Bank estimates based on data from Statistics. Data are based on children under the United Nations Population Division, the age 3, 4, or 5, depending on the country. 142 Africa Development Indicators 2007 Births attended by skilled health staff are the simple but protected pit latrines to flush toi- percentage of deliveries attended by person- lets with a sewerage connection. The excreta nel trained to give the necessary supervision, disposal system is considered adequate if it care, and advice to women during pregnancy, is private or shared (but not public) and if it labor, and the postpartum period; to con- hygienically separates human excreta from duct deliveries on their own; and to care for human contact. To be effective, facilities newborns. must be correctly constructed and properly Contraceptive prevalence rate is the percent- maintained. age of women who are practicing, or whose Physicians are the number of physicians, sexual partners are practicing, any form of including generalists and specialists. contraception. It is usually measured for Nurses are the number of nurses, includ- married women ages 15­49 only. ing professional nurses, auxiliary nurses, en- Children sleeping under insecticide-treated rolled nurses, and other nurses, such as den- bednets is the percentage of children under tal nurses and primary care nurses. age 5 with access to an insecticide-treated Midwives are the number of midwives, bednet to prevent malaria. including professional midwives, auxiliary Tuberculosis cases detected under DOTS are midwives, and enrolled midwives. Tradition- the percentage of estimated new infectious al birth attendants, who are counted as com- tuberculosis cases detected under DOTS, the munity health workers, are not included. internationally recommended tuberculosis Total health expenditure is the sum of public control strategy. and private health expenditure. It covers the Tuberculosis treatment success rate is the provision of health services (preventive and percentage of new smear-positive tuber- curative), family planning activities, nutri- culosis cases registered under DOTS in a tion activities, and emergency aid designated given year that successfully completed treat- for health but does not include provision of ment, whether with bacteriologic evidence water and sanitation. of success ("cured") or without ("treatment Public health expenditure consists of recur- completed"). rent and capital spending from government Children with fever receiving antimalarial (central and local) budgets, external borrow- drugs are the percentage of children under ing and grants (including donations from in- age 5 in malaria-risk areas with fever being ternational agencies and nongovernmental treated with effective antimalarial drugs. organizations), and social (or compulsory) Population with sustainable access to an im- health insurance funds. proved water source is the percentage of the Private health expenditure includes direct population with reasonable access to an ad- household (out-of-pocket) spending, private equate amount of water from an improved insurance, charitable donations, and direct source, such as a household connection, service payments by private corporations. public standpipe, borehole, protected well or Out-of-pocket health expenditure is any di- spring, or rainwater collection. Unimproved rect outlay by households, including gratu- sources include vendors, tanker trucks, and ities and in-kind payments, to health prac- unprotected wells and springs. Reasonable titioners and suppliers of pharmaceuticals, access is defined as the availability of at therapeutic appliances, and other goods and least 20 liters a person a day from a source services whose primary intent is to contrib- within 1 kilometer of the dwelling. See box ute to the restoration or enhancement of 9 for a discussion of using Demographic and the health status of individuals or popula- Health Surveys to measure access to infra- tion groups. It is a part of private health structure, including water and sanitation expenditure. infrastructure. Health expenditure per capita is the total Population with sustainable access to im- health expenditure is the sum of public and proved sanitation is the percentage of the private health expenditures as a ratio of total population with at least adequate access to population. It covers the provision of health excreta disposal facilities that can effectively services (preventive and curative), family prevent human, animal, and insect contact planning activities, nutrition activities, and with excreta. Improved facilities range from emergency aid designated for health but does Technical notes 143 Box 9 Measuring trends in access to basic infrastructure in Sub-Saharan Africa: results from Demographic and Health Surveys Household surveys have long been used to estimate poverty and Trends in access to basic infrastructure inequality trends but not to the same extent to assess trends in ac- services in Africa, 1990­2005 (percent) cess to infrastructure. A recent study in Africa used Demographic Piped water Electricity Flush toilet and Health Surveys from 22 countries that have conducted at least Subgroup 2001­ 2001­ 2001­ two surveys between 1990 and 2005 to collect comparable infor- and method 05 05 05 mation across countries on access to water, electricity, and sanita- National tion over time. To conduct a distributional analysis of access, an Method 1 12 13 10 19 29 34 7 8 10 asset index was constructed using principal components analysis, Method 2 17 17 15 23 28 31 10 10 11 and households were divided into five quintiles of population by Method 3 17 17 16 23 28 29 10 10 12 their level of wealth or assets. Urban The difficulty in estimating the Africawide trend in access rates Method 1 38 34 25 67 72 72 26 27 26 stems from the fact that the panel of countries or surveys is not Method 2 49 44 37 70 70 70 35 32 30 balanced. Countries have observations for different years. So three Method 3 49 44 40 70 70 70 35 32 30 alternative methods were used to estimate overall access trends. Rural The first method includes only the 11 countries for which there are Method 1 4 4 4 5 13 16 1 2 3 data for three time periods, 1990­95, 1996­2000, and 2001­05. The second method includes countries with data for only one or Method 2 4 4 4 6 10 13 1 1 2 two time periods. For countries with data for only one time period Method 3 4 4 4 6 10 13 1 1 2 the data are used for all three time periods, assuming no change Poorest quintile over time in access. If data are available for two periods, the annual Method 1 0 0 0 0 1 5 0 0 0 growth rate in coverage between the two periods is used to esti- Method 2 0 0 0 0 2 3 0 0 0 mate the rate for the third period. The third method is similar but as- Method 3 0 0 0 0 2 3 0 0 0 sumes that access rates cannot fall more than population growth. Second quintile If access rates in the third period drop by more than what would be Method 1 1 2 1 2 8 19 0 0 1 observed assuming no growth in the total number of connections, Method 2 3 4 3 2 8 32 1 1 1 the survey data for the third period are replaced with the coverage Method 3 3 4 4 2 8 32 1 1 1 rate in the second period times the ratio of the population in the Third quintile second period divided by the population in the third period. Method 1 3 3 4 6 20 22 2 1 2 Issues of comparability between surveys in some countries and the need to correct for some outliers mean that the preferred Method 2 9 8 19 12 19 25 4 4 13 estimates for this analysis are from the third method. Method 3 9 8 20 12 19 25 4 4 13 The results from all three methods suggest that access rates Fourth quintile for electricity and a flush toilet have improved slightly over time but Method 1 14 12 13 24 41 45 7 5 7 that rates for access to piped water have not (see table). Access Method 2 33 19 19 27 36 40 15 12 17 rates within urban and rural areas have not changed much (except Method 3 33 19 20 27 36 40 15 12 17 for countries with rural electrification projects), which suggests Richest quintile that migration from rural to urban areas has contributed to the Method 1 42 46 35 63 73 77 27 36 41 higher access rates. Finally, the gains in access to electricity have Method 2 47 48 42 65 69 71 31 34 36 been better shared across wealth groups (except for the very poor) Method 3 47 48 48 65 69 71 31 34 36 than have the gains in access to flush toilets, which tend to have benefited the richest households the most. Among the poorest quintile access to all three basic infrastructure services remains virtually inexistent. Source: Banerjee and others 2007. not include provision of water and sanitation. www3.who.int/whosis/core/core_select.cfm Data are in current U.S. dollars. ?path=whosis,core&language=english). Data on health expenditure are from the World Source: Data are from the latest Core Health Health Organization' World Health Report and Indicators from World Health Organization updates and from the Organisation for Eco- sources, including World Health Statistics nomic Co-operation and Development for its 2006 and World Health Report 2006 (http:// member countries, supplemented by World 144 Africa Development Indicators 2007 Bank poverty assessments and country and Share of rural population with access to trans- sector studies, and household surveys con- portation is the percentage of the rural popu- ducted by governments or by statistical or lation who live within 2 kilometers of an all- international organizations. season passable road as a share of the total rural population. 9. Agriculture, rural development, Share of rural households with access to a and environment landline telephone is the percentage of rural households possessing a telephone. Table .. Rural development Rural population is the difference between the Source: Data on rural population are calcu- total population and the urban population. lated from urban population shares from the Rural population density is the rural popula- United Nations Population Division's World tion divided by the arable land area. Arable Urbanization Prospects and from total popu- land includes land defined by the Food and lation figures from the World Bank. Data on Agriculture Organization (FAO) as land un- rural population density are from the FAO der temporary crops (double-cropped areas and World Bank population estimates. Data are counted once), temporary meadows for on rural population below the poverty line mowing or for pasture, land under market or are national estimates based on population- kitchen gardens, and land temporarily fallow. weighted subgroup estimates from house- Land abandoned as a result of shifting culti- hold surveys. Data on rural population with vation is excluded. access to water and rural population with Rural population below the national poverty access to sanitation are from World Health line is the percentage of the rural population Organization and United Nations Children's living below the national poverty line. Fund's Meeting the MDG Water and Sanita- Share of rural population with sustainable ac- tion Target (www.unicef.org/wes/mdgre- cess to an improved water source is the percent- port). Data on rural population with access age of the rural population with reasonable to electricity are from household survey data, access to an adequate amount of water from supplemented by World Bank Project Ap- an improved source, such as a household praisal Documents. Data on rural population connection, public standpipe, borehole, pro- with access to transport are from the World tected well or spring, or rainwater collection. Bank's Sub-Saharan Africa Transport Policy Unimproved sources include vendors, tanker Program (SSATP). Data on rural households trucks, and unprotected wells and springs. with own telephone are from Demographic Reasonable access is defined as the availabil- and Health Surveys. ity of at least 20 liters a person a day from a source within 1 kilometer of the dwelling. Table .. Agriculture Share of rural population with sustainable ac- Agriculture value added is shown at factor cost cess to improved sanitation facilities is the per- in current U.S. dollars divided by nominal centage of the rural population with at least gross domestic product. Value added in agri- adequate access to excreta disposal facilities culture comprises the gross output of forest- that can effectively prevent human, animal, ry, hunting, and fishing less the value of their and insect contact with excreta. Improved intermediate inputs. However, for Botswana, facilities range from simple but protected Cameroon, Chad, Democratic Republic of pit latrines to flush toilets with a sewerage Congo, Republic of Congo, Gabon, Guinea, connection. The excreta disposal system is Madagascar, Mali, Morocco, Niger, Rwanda, considered adequate if it is private or shared Senegal, Togo, and Zambia, it is shown at (but not public) and if it hygienically sepa- market prices, that is, including intermediate rates human excreta from human contact. To inputs. be effective, facilities must be correctly con- Crop production index shows agricultural structed and properly maintained. production for each year relative to the base Share of rural population with access to elec- period 1999­2001. It includes all crops ex- tricity is the percentage of the rural popu- cept fodder crops. Regional and income group lation living in households with access to aggregates for the Food and Agriculture Or- electricity. ganization's (FAO) production indexes are Technical notes 145 calculated from the underlying values in in- Agricultural employment includes people ternational dollars, normalized to the base who work for a public or private employer period 1999­2001. and who receive remuneration in wages, sal- Food production index covers food crops that ary, commission, tips, piece rates, or pay in are considered edible and that contain nutri- kind. Agriculture corresponds to division 1 ents. Coffee and tea are excluded because, al- (International Standard Industrial Classifica- though edible, they have no nutritive value. tion, ISIC, revision 2) or tabulation categories Livestock production index includes meat A and B (ISIC revision 3) and includes hunt- and milk from all sources, dairy products ing, forestry, and fishing. such as cheese, and eggs, honey, raw silk, Incidence of drought shows whether a coun- wool, and hides and skins. try experienced a significant shortage of Cereal production is crops harvested for rain that unfavorably affected agricultural dry grain only. Cereals include wheat, rice, production. maize, barley, oats, rye, millet, sorghum, Agriculture value added per worker is the buckwheat, and mixed grains. Cereal crops output of the agricultural sector (ISIC divi- harvested for hay or harvested green for sions 1­5) less the value of intermediate food, feed, or silage and those used for graz- inputs. Agriculture comprises value added ing are excluded. from forestry, hunting, and fishing as well as Agricultural exports and imports are ex- cultivation of crops and livestock production. pressed in current U.S. dollars at free on Data are in constant 2000 U.S. dollars. board prices. The term agriculture in trade Cereal yield is includes wheat, rice, maize, refers to both food and agriculture and does barley, oats, rye, millet, sorghum, buckwheat, not include forestry and fishery products. and mixed grains. Production data on cereals Food exports and imports are expressed in relate to crops harvested for dry grain only. current U.S. dollars at free on board prices. Cereal crops harvested for hay or harvested Permanent cropland is land cultivated with green for food, feed, or silage and those used crops that occupy the land for long periods for grazing are excluded. and need not be replanted after each harvest, such as cocoa, coffee, and rubber. It includes Source: Data on agriculture value added land under flowering shrubs, fruit trees, nut are from World Bank country desks. Data on trees, and vines, but excludes land under crop, food, livestock, and cereal production, trees grown for wood or timber. agricultural exports and imports, permanent Cereal cropland refers to harvested area, al- cropland, cereal cropland, and agricultural though some countries report only sown or machinery are from the FAO. Data on irrigat- cultivated area. ed land are from the FAO's Production Yearbook Irrigated land is areas equipped to provide and data files. Data on fertilizer consumption water to the crops, including areas equipped are from the FAO database for the Fertilizer for full and partial control irrigation, spate Yearbook. Data on agricultural employment irrigation areas, and equipped wetland or in- are from the International Labour Organiza- land valley bottoms. tion. Data on incidence of drought are from Fertilizer consumption is the aggregate of ni- the Southern Africa Flood and Drought Net- trogenous, phosphate, and potash fertilizers. work and East Africa Drought (CE). Data on Agricultural machinery refers to the num- agriculture value added per worker are from ber of wheel and crawler tractors (excluding World Bank national accounts files and the garden tractors) in use in agriculture at the FAO's Production Yearbook and data files. end of the calendar year specified or during the first quarter of the following year. Arable Table .. Environment land includes land defined by the FAO as Forest area is land under natural or planted land under temporary crops (double-cropped stands of trees, whether productive or not. areas are counted once), temporary meadows Average annual deforestation refers to the for mowing or for pasture, land under market permanent conversion of natural forest area or kitchen gardens, and land temporarily fal- to other uses, including shifting cultivation, low. Land abandoned as a result of shifting permanent agriculture, ranching, settlements, cultivation is excluded. and infrastructure development. Deforested 146 Africa Development Indicators 2007 areas do not include areas logged but intend- industrial waste, and municipal waste, mea- ed for regeneration or areas degraded by fuel- sured as a percentage of total energy use. wood gathering, acid precipitation, or forest Carbon dioxide emissions are those stem- fires. Negative numbers indicate an increase ming from the burning of fossil fuels and the in forest area. manufacture of cement. They include carbon Renewable internal fresh water resources re- dioxide produced during consumption of sol- fer to internal renewable resources (internal id, liquid, and gas fuels and gas flaring. river flows and groundwater from rainfall) in the country. Source: Data on forest area and deforesta- Annual fresh water withdrawals refer to to- tion are from the Food and Agriculture Or- tal water withdrawals, not counting evapora- ganization's (FAO) Global Forest Resources tion losses from storage basins. Withdrawals Assessment 2005. Data on fresh water re- also include water from desalination plants in sources and withdrawals are from the World countries where they are a significant source. Resources Institute, supplemented by the Withdrawals can exceed 100 percent of total FAO's AQUASTAT data. Data on emissions of renewable resources where extraction from organic water pollutants are from the World nonrenewable aquifers or desalination plants Bank. Data on energy production and use and is considerable or where there is significant combustible renewables and waste are from water reuse. Withdrawals for agriculture and the International Energy Agency. Data on industry are total withdrawals for irrigation carbon dioxide emissions are from Carbon Di- and livestock production and for direct in- oxide Information Analysis Center, Environ- dustrial use (including withdrawals for cool- mental Sciences Division, Oak Ridge National ing thermoelectric plants). Withdrawals for Laboratory, in the U.S. state of Tennessee. domestic uses include drinking water, munic- ipal use or supply, and use for public services, 10. Labor, migration, and population commercial establishments, and homes. Water productivity is calculated as gross do- Table .. Labor force participation mestic product in constant prices divided by Labor force is people ages 15 and older who annual total water withdrawal. Sectoral wa- meet the International Labour Organization ter productivity is calculated as annual value (ILO) definition of the economically active added in agriculture or industry divided by population. It includes both the employed and water withdrawal in each sector. the unemployed. While national practices vary Emissions of organic water pollutants are in the treatment of such groups as the armed measured in terms of biochemical oxygen de- forces and seasonal or part-time workers, the mand, which refers to the amount of oxygen labor force generally includes the armed forces, that bacteria in water will consume in break- the unemployed, and first-time job-seekers, but ing down waste. This is a standard water- excludes homemakers and other unpaid care- treatment test for the presence of organic givers and workers in the informal sector. See pollutants. box 10 for a discussion of employment in the Energy production refers to forms of prima- informal sector and informal employment. ry energy--petroleum (crude oil, natural gas Participation rate is the percentage of the liquids, and oil from nonconventional sourc- population ages 15­64 that is economically es), natural gas, solid fuels (coal, lignite, and active, that is, all people who supply labor for other derived fuels), and combustible renew- the production of goods and services during ables and waste--and primary electricity, all a specified period. converted into oil equivalents. Energy use refers to use of primary energy Source: International Labour Organization, before transformation to other end-use fuels, Global Employment Trends Model 2006, which is equal to indigenous production plus Employment Trends Team. imports and stock changes, minus exports and fuels supplied to ships and aircraft en- Table .. Labor force composition gaged in international transport. Agriculture corresponds to division 1 (Inter- Combustible renewables and waste com- national Standard Industrial Classification, prise solid biomass, liquid biomass, biogas, ISIC, revision 2) or tabulation categories A Technical notes 147 Box 10 Employment in the informal sector and informal employment The informal sector accounts for the bulk of nonagricultural em- The definition of informal employment recognizes that infor- ployment in developing countries and is particularly notable in the mal jobs may also be found in production units that are not in the poorest countries, where it has grown in recent decades. In Sub- informal sector. Thus, in addition to employment in the informal Saharan Africa the informal sector accounts for as much as 78 sector, the definition includes informal employment outside the percent of nonagricultural employment and 41 percent of gross informal sector, characterized as "an employment relation that is, domestic product (ILO 2002), and serves as the main source of in law or practice, not subject to national labour legislation, income job creation. Given its vital role, measuring and describing infor- taxation, social protection or entitlement to certain employment mal activity have become increasingly important to designing pov- benefits" (Hussmanns 2004, p. 6). erty reduction and growth strategies and to understanding labor In practice, informal employment outside the informal sector markets. comprises employees in formal enterprises or households whose Defining and quantifying informality are complicated by the employment relations do not comply with labor regulations, con- informal sector's high degree of heterogeneity. The concept is in- tributing family workers in either formal or informal enterprises tuitively linked to a variety of characteristics that range from op- (because they typically do not have written contracts of employ- erating outside existing labor regulation, not paying payroll taxes, ment), and own-account workers engaged in production of goods not having a license or registration, not being firmly established, for final consumption for the households when such production having very low productivity, being owner operated and having "represents an important contribution to the total consumption of few employees, and the like. Some of those characteristics can be the household" (Hussmanns 2004, p. 6). seen as pertaining to a firm's operation, and others to the contrac- Labor force surveys are the best means for monitoring these tual relationship between the employee and the employers. The indicators, though difficulties arise when employees are unaware informal sector could therefore be understood to include those of the information used to identify informal firms (such as legal or- who are self-employed or wage workers in informal firms, workers ganization, bookkeeping, and registration), when lack of probing at formal firms without legal protections or permanent contracts, leads to productive activities that would be considered informal "homeworkers" (home-based industrial outworkers), apprentices, being reported as formal employment, when information is lim- unpaid family workers, and domestic workers. ited to main jobs, and when information is unavailable for periods The International Labour Organization, in two International longer than a weekly recall, given the seasonal nature of some ac- Conferences of Labour Statisticians (ICLS 15 and 17), has ratio- tivities. It is also practically impossible to estimate the number of nalized the framework for measuring this phenomenon by adopt- informal sector enterprises from labor force surveys because the ing a resolution on the statistical definition of employment in the surveys are household based rather than firm based. informal sector, first, and then broadening the concept to arrive Moreover, data on informal workers beyond those working at measures of informal employment. Such definitions might not for informal firms (such as homeworkers and casual workers) are do justice to the variety of meanings attached to the concept (for quite scarce. Efforts to measure and characterize the informal example, it has been argued that formality should be seen as a sector should therefore recognize limitations inherent in the avail- continuum rather than a clearly defined state), but they offer a able data and use innovative methods to attempt to capture its solid basis for international comparisons. Because people can heterogeneity. hold multiple jobs, the unit of measurement for informal employ- One example of how to address these issues in practice ment is jobs rather than employed persons. comes from Ethiopia. The Ethiopian Central Statistical Author- Employment in the informal sector is defined as "all jobs in ity's Labor Force Survey and Urban Employment and Unemploy- informal sector enterprises or all persons who, during a given ref- ment Survey use a definition of the informal sector based on the erence period, were employed in at least one informal sector en- characteristics of the firm in which respondents work. They define terprises, irrespective of their status in employment and whether informal businesses as those that do not keep proper accounts, it was their main or secondary jobs" (Hussmans 2004). that do not have a business license, or that have fewer than 10 em- Informal sector enterprises are those that satisfy four ployees. Because the answers to these questions are not recorded criteria: separately,1 it is impossible to change the criterion for identifica- · They are private unincorporated enterprises (because tion (and there is no separate information on size of the firm, for they are not separate legal entities it is impossible to sep- example, which could allow for a fine tuning of the definition or arate their activities from other activities of their owner). adoption of a different cutoff). This definition leads to an estimate · They produce at least some goods meant for sale. of urban employment in the informal sector of 44 percent of people · They are limited in employment size, with the thresh- ages 15 and older (see table). This estimate is considerably lower old determined by national circumstances (though an than the available estimates for Africa as a whole, which is about expert working group recommended that international 60 percent in urban areas. comparisons be conducted on the basis of less than five The surveys do not systematically collect information on the workers). contractual relationship with the employer to fully capture infor- · They are engaged in nonagricultural activities. mal employment. Nevertheless additional categories of workers in 148 Africa Development Indicators 2007 Box 10 Employment in the informal sector and informal employment (continued) more likely to be informal, and the surveys' likely neglect of mi- Ethiopia: employment in the informal sector and informal employment, urban, grants, who might not be adequately covered in existing sample ages 15 and older, 2004 (percent) frames. Extending sample frames to marginal areas, particularly at times of rapid urbanization, and exploring alternative survey Employment in the Informal informal sectora employmentb techniques, such as recapture methods, can reach a better un- Total 43.5 54.2 derstanding of the size of important informal activities such as Male 39.4 52.1 street vending. Challenges also typically arise in comparing wages in the for- Female 49.1 57.0 mal and informal sectors. In Ethiopia's labor force surveys, for a. Employed in a business that holds no account book, has no license, or employs fewer than 10 employees. example, wage information is not collected for several categories b. Employed in a business that holds no account book, has no license, employs fewer than 10 employees; is an employee domestic, self-employed, apprentice, or unpaid family worker; or paid only in kind. of informal workers, given the difficulties in accounting for in-kind Note: Data are for employment in the seven days before the survey. payments and the lack of complete bookkeeping in informal firms. Source: Ethiopia Urban Employment and Unemployment Survey 2004. However, knowing about wages is important for understanding well-being among informal sector workers and thus for guiding policymaking. Complementing data analysis with qualitative work informal contractual relationships (or very likely to be) can be iden- may be the best way to arrive at a more nuanced, country-specific tified, such as domestic employees, self-employed people (who assessment of informality. appear to be negligible in the survey), apprentices, unpaid family workers, and workers paid only in kind. Adding these categories to 1. The questions in the Labor Force Survey were nested so that if the firm bring the measurement closer to the International Labour Organi- did not keep books, respondents were asked if the firm had less than 10 zation definition raises the rate of informality to 54 percent of work- employees; if so, respondents were asked if it had a license. Such ques- ers. Such a significant increase underscores the need for caution in tioning restricts the definition to keeping books as the broadest possible making cross-country comparisons using nonstandardized data. criterion. Important limitations of these estimates are the surveys' focus on primary jobs only, excluding secondary activities, which are Source: Hussmans 2004. and B (ISIC revision 3) and includes hunting, self-employed workers with without employ- forestry, and fishing. ees (own-account workers), and members of Industry corresponds to divisions 2­5 producer cooperatives. Although the contrib- (ISIC revision 2) or tabulation categories C­F uting family workers category is technically (ISIC revision 3) and includes mining and part of the self-employed according to the quarrying (including oil production), manu- classification used by the International La- facturing, construction, and public utilities bour Organization (ILO), and could therefore (electricity, gas, and water). be combined with the other self-employed Services correspond to divisions 6­9 (ISIC categories to derive the total self-employed, revision 2) or tabulation categories G­P (ISIC they are reported here as a separate category revision 3) and include wholesale and retail in order to emphasize the difference between trade and restaurants and hotels; transport, the two statuses, since the socioeconomic storage, and communications; financing, in- implications associated with each status can surance, real estate, and business services; and be significantly varied. This practice follows community, social, and personal services. that of the ILO's Key Indicators of the Labour Wage and salaried workers (employees) are Market. workers who hold the type of jobs defined Contributing family workers (unpaid work- as paid employment jobs, where incumbents ers) are workers who hold self-employment hold explicit (written or oral) or implicit em- jobs as own-account workers in a market- ployment contracts that give them a basic re- oriented establishment operated by a related muneration that is not directly dependent on person living in the same household. the revenue of the unit for which they work. Self-employed workers are self-employed Source: Data are from the ILO's Key Indica- workers with employees (employers), tors of the Labour Market, fourth edition. Technical notes 149 Table .. Migration and population Deaths due to HIV/AIDS are the estimated Stock is the number of people born in a coun- number of adults and children that have died try other than that in which they live. It in- in a specific year based in the modeling of cludes refugees. HIV surveillance data using standard and ap- Net migration is the net average annual num- propriate tools. ber of migrants during the period, that is, the AIDS orphans are the estimated number of annual number of immigrants less the annual children who have lost their mother or both number of emigrants, including both citizens parents to AIDS before age 17 since the epi- and noncitizens. Data are five-year estimates. demic began in 1990. Some of the orphaned Workers remittances received comprise cur- children included in this cumulative total are rent transfers by migrant workers and wages no longer alive; others are no longer under and salaries by nonresident workers. See box age 17. 11 for a discussion of remittances in Africa. Population is World Bank estimates, usu- Source: The Joint United Nations Pro- ally projected from the most recent popu- gramme on HIV/AIDS and the World Health lation censuses or surveys (mostly from Organization's 2006 Report on the Global 1980­2004). Refugees not permanently AIDS Epidemic. settled in the country of asylum are gener- ally considered to be part of the population 12. Malaria of their country of origin. Fertility rate is the number of children that Table .. Malaria would be born to a woman if she were to live Population is World Bank estimates, usu- to the end of her childbearing years and bear ally projected from the most recent popu- children in accordance with current age-spe- lation censuses or surveys (mostly from cific fertility rates. 1980­2004). Refugees not permanently Age composition refers to the percentage settled in the country of asylum are gener- of the total population that is in specific age ally considered to be part of the population groups. of their country of origin. Dependency ratio is the ratio of depen- Endemic risk of malaria is the percentage of dents--people younger than 15 or older than the population living in areas with significant 64--to the working-age population--those annual transmission of malaria, be it season- ages 15­64. al or perennial. Rural population is calculated as the differ- Epidemic risk of malaria is the percent- ence between the total population and the age of the population living in areas prone urban population. to distinct interannual variation, with no Urban population is midyear population of transmission taking place at all in some areas defined as urban in each country. years. Negligible risk of malaria is the percentage Source: World Bank's World Development of the population living in areas where ma- Indicators database. laria is ordinarily not present and where the risk of malaria outbreaks is negligible. 11. HIV/AIDS Deaths due to malaria are the number of malaria deaths per 100,000 people. Table .. HIV/AIDS Under-five mortality rate is the probability Estimated number of people living with HIV/ that a newborn baby will die before reaching AIDS is the number of people in the relevant age 5, if subject to current age-specific mor- age group living with HIV. tality rates. The probability is expressed as a Estimated prevalence rate is the percentage of rate per 1,000. the population of the relevant age group who Children sleeping under insecticide-treated are infected with HIV. Depending on the reli- bednets is the percentage of children under ability of the data available, there may be more age 5 with access to an insecticide-treated or less uncertainty surrounding each estimate. bednet to prevent malaria. Therefore, plausible bounds have been present- Children with fever receiving antimalarial ed for adult rate (low and high estimate). treatment within 24 hours are the percentage 150 Africa Development Indicators 2007 Box 11 Remittances in Africa Workers remittances have emerged as a major source of external foreign direct investment, investment income, and portfolio invest- development finance in recent years. Because of the large size of ment (Economist Intelligence Unit 2007). Bank of Ghana officials remittances, governments in developing and developed countries acknowledged that the flows that they register represent only a frac- have focused on the development impact of remittances and on tion of what is probably entering into the country. Their figure does regulatory issues in sending and receiving remittances. Reliable not include all unofficial remittances, which arrive through money- data on remittances are hard to come by. While the International transfer bureaus, transfers such as deposits into personal accounts, Monetary Fund publishes statistics on "workers remittances, com- and physical movements of cash or goods across borders. pensation of employees, and migrants transfers," the data are not comprehensively reported, nor do they capture monetary flows Unrecorded remittances outside formal financial channels. Official data on remittances are believed to be underestimated, perhaps severely, but there is little agreement on the size of the Issues to consider in quantifying remittances undercounting. A recent International Monetary Fund study (El- Workers remittances have been understood to be a migrants' Qorchi, Maimbo and Wilson 2003) estimated that unofficial trans- earnings sent from abroad to relatives in their country of origin fers of remittances to developing countries amount to $10 billion to meet economic and financial obligations. Interest has been a year. Another study estimates that global remittances are about growing in the concept of residence and in information on migrant 2.5 times the size of recorded remittances reported in the Inter- workers and their associated remittances flows. One problem with national Monetary Fund's balance of payments data (AITE 2005). measurement is the difficulty of determining actual length of stay These estimates differ by a factor of 25. Freund and Spatafora and of applying the concept of residency to distinguish between (2005) estimate that informal remittances to Sub-Saharan Africa compensation of employees and workers remittances. are relatively high--45­65 percent of formal flows--compared with A way around this is to use the concept of household to house- only about 5­20 percent in Latin America. Adams and Page (2003) hold transactions to capture the component of personal transfers and Page and Plaza (2006) also find that unrecorded remittances instead. It is important to use broader definitions of remittances, are large--48 percent worldwide--ranging from 73 percent in Sub- including personal remittances and institutional remittances. The Saharan Africa to a negligible amount in South Asia.1 Sub-Saharan International Monetary Fund recently completed a draft of the Africa has the highest share of unrecorded remittances, which sixth edition of the Balance of Payments and International Invest- may reflect the fact that informal channels are common in many ment Position Manual which introduces the term personal trans- African countries because the formal financial infrastructure is lim- fers, which comprises all current transfers in cash or in kind made ited (Page and Plaza 2006). or received by resident households to or form other nonresident Undercounting arises from two sources. First, most remittance households (IMF Committee on Balance of Payments Statistics source countries do not require "small" transactions to be reported.2 2006). This term will replace the component workers' remittances. Remittances through post offices, exchange bureaus, and other The sixth edition will include a new concept of remittances for money transfer companies are often not reflected in the official sta- measuring and analyzing international remittances and resource tistics (World Bank 2006).3 Second, official data do not capture re- flows to households and nonprofit institutions serving households. mittance flows through informal channels. Remittances transferred Three categories of remittances would be introduced: personal through agents such as informal operators or hand carried by travel- remittances, total remittances, and total remittances and transfers ers may be nearly as large as remittances through official channels. to nonprofit institutions serving households (IMF 2007). A recent World Bank study (Sander and Maimbo 2003) reports Countries do not apply the concepts uniformly. Data deficien- that unrecorded flows appear to be high in Africa. In Sudan, for cies and data omissions further cloud the picture. Data inaccuracy example, informal remittances are estimated at 85 percent of total stems from problems associated with knowing the universe of re- remittance receipts. Preliminary findings from Mazzucato, van den mitters and the intermediaries facilitating the process, enforcing Boom, and Nsowah-Nuamah (2004) of the Ghana Transnational data collection, maintaining a line of communication with interme- Networks research program in Amsterdam show that as much as diaries and other relevant organizations, and possessing the ap- 65 percent of total remittances to Ghana may be sent informally, propriate methodologies to capture the data (Orozco 2005). and the Bank of Ghana estimates that informal flows are at least Consider Ghana. The Bank of Ghana reports the value of inter- as high as recorded flows. In South Africa an informal money re- national remittances for 2005 as $99.2 million, but the Ghana min- mittance system exists side by side with the formal system, and ister of finance recently announced that international remittances the bulk of remittances to neighboring countries flows through in 2006 totaled $1.8 billion, leading to confusion. In April the Bank informal channels (Genesis Analytics 2003). In Comoros informal of Ghana reported that it estimated total private transfers made transfers account for approximately 80 percent of remittances (da to "nongovernmental organizations, embassies, service providers, Cruz, Fegler, Schwartzman, 2004). The weakness of the Comoros individuals, and the like" at $5.8 billion. Only a portion of these trans- banking sector--Comoros has only one commercial bank--may fers are remittances; the rest are payments to service providers, account for the wide use of informal channels. (continued) Technical notes 151 Box 11 Remittances in Africa (continued) One example of an informal remittance transfer system is While some efforts have been made to improve the develop- the Somali xawilaad. Operated by Somalis and used mainly by ment impact of remittances in developing countries (such as new Somalis, the xawilaad is an informal system of value transfer that definitions in the balance of payments), a need remains to improve operates in almost every part of the world (Horst and Van Hear data on remittances in Sub-Saharan Africa. 2002). Interviews conducted in Virginia, in the United States (one of the areas with the largest Somali migrant population), report 1. The zero estimate for South Asia is a result of the estimating technique. that two large companies provide transfers of remittances to The country observations for which data are available form a portion of the the Somali community: Dahbbshil and Amal. (After September "outer bound" regression plane, and hence their officially recorded remit- 11 one of the largest xawilaad companies, Al Barakat, closed tances are accepted as total remittances. This is not strictly true, but the down.) The system relies heavily on telecommunications, so pattern does conform to the observation that remittances in South Asia xawilaad companies have invested in telephones, mobile radio increasingly have moved through recorded channels. systems, computer networks, and satellite telecommunications 2. For example, the reporting threshold (typically per person per day) is facilities (Montclos and Kagwanja 2000; Gundel 2003). Trans- $10,000 in the United States, 12,500 euros in Western Europe, and 3 mil- fers by xawiilaad are fast and effi cienct (Montclos 2002). But lion yen in Japan. it is very diffi cult to estimate the amount of remittances sent 3. The Bank of Ghana is one of the few banks that collect statistics in through this system to Kenya (the largest refugee site of Soma- remittances and require information from registered banks and transfer lis) and Somalia. agencies. of children under age 5 in malaria-risk areas 13. Capable states and partnership with fever being treated with antimalarial drugs. Table .. Aid and debt relief Pregnant women receiving two doses of inter- Net aid from all donors is net aid from the Or- mittent preventive treatment are the number ganisation for Economic Co-operation and of pregnant women who receive at least two Development's (OECD) Development Assis- preventive treatment doses of an effective tance Committee (DAC), non-DAC bilateral antimalarial drug during routine antenatal (Organization of Petroleum Exporting Coun- clinic visits. This approach has been shown to tries [OPEC], the former Council for Mutual be safe, inexpensive, and effective. Economic Assistance [CMEA] countries, and China [OECD data]), and multilateral donors. Source: Data on population are from the OPEC countries are Algeria, Iran, Iraq, Ku- World Bank's Development Data Platform. wait, Libya, Nigeria, Qatar, Saudi Arabia, the Data on risk of malaria, children with fever United Arab Emirates, and Venezuela. The receiving antimalarial drugs, and pregnant former CMEA countries are Bulgaria, Czecho- women receiving two doses of intermittent slovakia, the former German Democratic Re- preventive treatment are from Demographic public, Hungary, Poland, Romania, and the Health Surveys, Multiple Indicator Cluster former Soviet Union). See box 12 for a discus- Surveys, and national statistical offices. Data sion of accounting for debt forgiveness in of- on deaths due to malaria are from the United ficial development assistance statistics. Nations Statistics Division based on World Net aid from DAC donors is net aid from Health Organization (WHO) estimates. Data OECD's DAC donors, which include Aus- on under-five mortality are harmonized es- tralia, Austria, Belgium, Canada, Denmark, timates of the WHO, United Nations Chil- Finland, France, Germany, Italy, Japan, the dren's Fund, and the World Bank, based Netherlands, Norway, Sweden, Switzerland, mainly on household surveys, censuses, and the United Kingdom, and the United States. vital registration, supplemented by World Ireland and New Zealand have been excluded Bank estimates based on household surveys in this compilation because their aid to Africa and vital registration. Data on insecticide- is negligible. treated bednet use are from Demographic Net aid from multilateral donors is net aid and Health Surveys and Multiple Indicator from multilateral sources, such as the Af- Cluster Surveys. rican Development Fund, the European 152 Africa Development Indicators 2007 Box 12 Accounting for debt forgiveness in official development assistance statistics A surge in debt forgiveness grants beginning in 2002 has drawn at- Development Assistance Committee debt forgiveness tention to their treatment in official development assistance (ODA) grants and net official development assistance statistics. These grants from the Organisation for Economic Co- to Sub-Saharan Africa ($ billions, 2000­05) operation and Development's Development Assistance Commit- Debt Offsetting Net debt Total net official tee (DAC) countries have ballooned from a modest $2.5 billion in forgiveness entries for forgiveness development Year grants debt relief grants assistance 2001 to $25 billion in 2005 (measured in gross terms). One-half to 2000 1.23 0.43 0.80 8.14 three-quarters of these grants have been allocated to Sub-Saha- 2001 1.28 0.30 0.98 8.17 ran Africa, except in 2005 when large debt relief operations for Iraq amounted to nearly $14 billion. The prominence of debt relief in aid 2002 2.96 0.37 2.59 11.40 flows in recent years is evident: bilateral ODA to Africa doubled 2003 6.48 0.39 6.10 17.24 (in nominal terms) over 2000­05, and about half of the expansion 2004 4.97 1.73 3.24 16.71 represented debt relief (see table). 2005 9.46 1.51 7.94 22.51 There are several conceptual and measurement issues with Source: OECD-DAC database. DAC debt forgiveness statistics. Depetris Chauvin and Kraay (2005, 2006) argue that the standard data do not provide a reli- able estimate of the value of debt relief--that is, in present value were ODA loans. The country received Naples Terms--67 percent terms. They have developed their own present value estimates of of commercial credits were cancelled and the remaining 33 per- debt relief. Another problem with DAC debt relief statistics is that cent were rescheduled; and ODA credits were rescheduled. (In No- forgiveness of outstanding amounts, debt service flows, and ar- vember 2003 the country received Cologne Terms from Paris Club rears is treated in the same way, even though the cash flow impli- creditors.) The resulting DAC data for ODA disbursements in 2003 cations for borrowers' budgets are quite different. Despite these (when the bulk of relief granted under the Paris Club agreement methodological issues, DAC debt forgiveness statistics are widely was reported in the DAC statistics) show debt forgiveness grants of used for analytical and monitoring purposes. $4.441 billion and offsetting entries for debt relief of only $4.9 mil- Debt relief from the donors' perspective--budget effort--can be lion. Together, these two items account for $4.44 billion of net ODA quite different from that from the recipients' perspective--availabil- flows. The country did not receive additional resources of anything ity of resources. One important question that arises then is whether close to this amount. However, the country's debt burden was sub- ODA debt forgiveness grants represent additional flows (cross-bor- stantially reduced, and it was able to normalize relations with the der flows) to recipients. Several advocacy groups have argued that international community, improving its prospects for growth. ODA statistics are misleading because debt cancellations do not Although debt cancellation may not deliver additional flows represent "genuine" aid (see ActionAid International 2005). to borrowers, it does reflect government budget effort. The ex- DAC statistical guidelines allow debt cancellation to be re- tent of the budget effort will depend on the terms of government ported as debt forgiveness when the action on debt occurs within guarantees for export and commercial credits and on the timing the "framework of a bilateral agreement and is implemented for of writeoffs for official loans--some may already have been written the purpose of promoting the development or welfare of the re- down (see also OECD 2007). Because of differences in practices cipient" (OECD­DAC 2000a,b). Thus, forgiveness of ODA, other across donors, the extent of the budget effort for a particular debt official flows, and private claims--principal, interest, and arrears-- action varies across countries. is captured in DAC statistics under "Debt forgiveness grants."1 Appropriate offsetting items (or counter entries) for principal and This note is adapted from box 4.1 of Global Monitoring Report 2007: Con- interest of each type of claim are reported, but not all are ODA fronting the Challenges of Gender Equality and Fragile States (World Bank 2007). A host of debt actions are presented in Development Assistance flows--only forgiven principal on ODA loans is included under "off- Committee statistics; the focus here is on debt forgiveness. setting entry for debt forgiveness" in ODA flows.2 1. Reorganization of other official funds and private claims within the frame- Most debt forgiveness grants in DAC statistics represent for- work of the Paris Club often involves concessionality in the form of debt giveness of other official flows and private claims typically under the reduction, debt service reduction, and capitalization of moratorium inter- est. The cancellation of part of the claims (or the amount equivalent to the framework of the Paris Club. The counter entries are not ODA flows, reduction in net present value) is treated as debt forgiveness in ODA with so there is concern that recent debt actions assign a large amount no offsetting items in ODA flows. Amounts of other official funds and pri- of flows to recipients that do not represent any new transfer of re- vate claims that are rescheduled are not part of ODA and are included as "Rescheduling" loans under other official funds flows. sources. This point is well illustrated by the 2002 Paris Club debt re- 2. Forgiven other official funds principal is reported under "Offsetting entries lief agreement for Democratic Republic of Congo. The country had for debt relief" in other official funds flows and forgiven private principal is an unbearable debt burden and under reasonable conditions was accounted in "Offsetting entry for debt relief" under private flows. There are clearly unable to meet its obligations to external creditors. The Paris no offsets to forgiven interest in ODA, other official funds, or private flows. Instead, appropriate counter entries "Offsetting entry for forgiven interest" Club agreement restructured $8.98 billion of debt--$8.49 billion in are to be noted in memo items--the data for which are usually incomplete. principal and interest arrears and $490 million of future payments The result is that the treatment of debt cancellation in ODA statistics assigns (Paris Club 2002). Only about $1.4 billion of the outstanding claims a larger amount of net flows to recipients than amounts actually received. Technical notes 153 Development Fund for the Commission of Net aid as a share of imports of goods and ser- the European Communities, the Internation- vices is calculated by dividing nominal total al Development Association, the Internation- net aid by imports of goods and services. al Fund for Agricultural Development, Arab Net aid as a share of central government and OPEC financed multilateral agencies, expenditure is calculated by dividing nomi- and UN programs and agencies. Aid flows nal total net aid by central government from the International Monetary Fund's expenditure. (IMF) Trust Fund and Structural Adjustment Heavily Indebted Poor Countries (HIPC) Debt Facility are also included. UN programs and Initiative decision point is the date at which a agencies include the United Nations Techni- HIPC with an established track record of cal Assistance Programme, the United Na- good performance under adjustment pro- tions Development Programme, the United grams supported by the International Mon- Nations Office of the High Commissioner etary Fund and the World Bank commits to for Refugees, the United Nations Children's undertake additional reforms and to develop Fund, and the World Food Programme. Arab and implement a poverty reduction strategy. and OPEC financed multilateral agencies in- HIPC Debt Initiative completion point is the clude the Arab Bank for Economic Develop- date at which the country successfully com- ment in Africa, the Arab Fund for Economic pletes the key structural reforms agreed on at and Social Development, the Islamic Devel- the decision point, including developing and opment Bank, the OPEC Fund for Interna- implementing its poverty reduction strategy. tional Development, the Arab Authority for The country then receives the bulk of debt Agricultural Investment and Development, relief under the HIPC Initiative without fur- the Arab Fund for Technical Assistance to ther policy conditions. African and Arab Countries, and the Islamic Debt service relief committed is the amount Solidarity Fund. of debt service relief, calculated at the deci- Net aid as a share of gross domestic product sion point, that will allow the country to (GDP) is calculated by dividing the nominal achieve debt sustainability at the completion total net aid from all donors by nominal GDP. point. For a given level of aid flows, devaluation of a recipient's currency may inflate the ratios Source: OECD and World Bank data. shown in the table. Thus, trends for a given country and comparisons across countries Table .. Capable states that have implemented different exchange Courts are the share of senior managers who rate policies should be interpreted carefully. ranked courts and dispute resolution systems Net aid per capita is calculated by dividing as a major or very severe constraint. the nominal total net aid by midyear popu- Crime is the share of senior managers who lation. These ratios offer some indication of ranked crime, theft, and disorder as a major the importance of aid flows in sustaining per or very severe constraint. capita income and consumption levels, al- Number of procedures to enforce a contract is though exchange rate fluctuations, the actual the number of independent actions, mandat- rise of aid flows, and other factors vary across ed by law or courts, that demand interaction countries and over time. between the parties of a contract or between Net aid as a share of gross capital formation them and the judge or court officer. is calculated by dividing the nominal total Time required to enforce a contract is the net aid by gross capital formation. These data number of calendar days from the filing of highlight the relative importance of the indi- the lawsuit in court until the final determina- cated aid flows in maintaining and increasing tion and, in appropriate cases, payment. investment in these economies. The same Cost to enforce a contract is court and attor- caveats mentioned above apply to their in- ney fees, where the use of attorneys is man- terpretation. Furthermore, aid flows do not datory or common, or the cost of an adminis- exclusively finance investment (for example, trative debt recovery procedure, expressed as food aid finances consumption), and the a percentage of the debt value. share of aid going to investment varies across Protecting investors disclosure index mea- countries. sures the degree to which investors are 154 Africa Development Indicators 2007 protected through disclosure of ownership Corruption Perceptions Index transparency and financial information. index is the annual Transparency Interna- Director liability index measures a plaintiff 's tional corruption perceptions index, which ability to hold directors of firms liabile for ranks more than 150 countries in terms of damages to the company). perceived levels of corruption, as determined Shareholder suits index measures share- by expert assessments and opinion surveys. holders' ability to sue officers and directors for misconduct. Source: Data on investment climate con- Investor protection index measures the de- straints to firms are based on enterprise gree to which investors are protected through surveys conducted by the World Bank and disclosure of ownership and financial infor- its partners during 2001­05 (http://rru. mation regulations. worldbank.org/EnterpriseSurveys). Data on Number of tax payments is the number of enforcing contracts, protecting investors, and taxes paid by businesses, including electronic regulation and tax administration are from filing. The tax is counted as paid once a year the World Bank's Doing Business project even if payments are more frequent. (http://rru.worldbank.org/DoingBusiness/). Time to prepare, file, and pay taxes is the Data on the EITI are from the EITI website, number of hours it takes to prepare, file, and www.eitransparency.org. Data on corrup- pay (or withhold) three major types of taxes: tion perceptions index are from Transpar- the corporate income tax, the value added or ency International (www.transparency.org/ sales tax, and labor taxes, including payroll policy_research/surveys_indices/cpi). taxes and social security contributions. Total tax payable is the total amount of tax- Table .. Governance and anti- es payable by the business (except for labor corruption indicators taxes) after accounting for deductions and Voice and accountability measures the extent exemptions as a percentage of gross profit. to which a country's citizens are able to par- For further details on the method used for ticipate in selecting their government and to assessing the total tax payable, see the World enjoy freedom of expression, freedom of as- Bank's Doing Business 2006. sociation, and a free media. Extractive Industries Transparency Initiative Political stability and absence of violence mea- (EITI) Endorsed indicates whether a coun- sures the perceptions of the likelihood that try has implemented or endorsed the EITI, a the government will be destabilized or over- multistakeholder approach to increasing gov- thrown by unconstitutional or violent means, ernance and transparency in extractive indus- including domestic violence or terrorism. tries. It includes civil society, the private sector, Government effectiveness measures the qual- and government and requires a work plan with ity of public services, the quality and degree of timeline and budget to ensure sustainability, independence from political pressures of the independent audit of payments and disclosure civil service, the quality of policy formulation of revenues, publication of results in a publicly and implementation, and the credibility of the accessible manner, and an approach that covers government's commitment to such policies. all companies and government agencies. EITI Regulatory quality measures the ability of supports improved governance in resource- the government to formulate and implement rich countries through the verification and full sound policies and regulations that permit publication of company payments and govern- and promote private sector development. ment revenues from oil, gas, and mining. EITI Rule of law measures the extent to which is a global initiative and the EITI Secretariat agents have confidence in and abide by the has developed an EITI Source Book that pro- rules of society, in particular the quality of con- vides guidance for countries and companies tract enforcement, the police, and the courts, wishing to implement the initiative (http:// as well as the likelihood of crime and violence. www.eitransparency.org/section/abouteiti). Control of corruption measures the extent EITI report produced indicates whether the to which public power is exercised for private country has publicly released an EITI report. gain, including petty and grand forms of cor- Generally, a report is produced after the EITI ruption, as well as "capture" of the state by principles are adopted. elites and private interests. Technical notes 155 Source: Data are from the World Bank In- risk management, processes duty stitute's Worldwide Governance Indicators collections and refunds promptly, database, which relies on 33 sources, includ- and operates transparently. ing surveys of enterprises and citizens, and · Financial sector assesses the struc- expert polls, gathered from 30 organizations ture of the financial sector and the around the world. policies and regulations that af- fect it. It covers three dimensions: Table 13.4. Country Policy and Institu- financial stability; the sector's ef- tional Assessment ratings ficiency, depth, and resource mo- The Country Policy and Institutional Assess- bilization strength; and access to ment (CPIA) assesses the quality of a coun- financial services. try's present policy and institutional frame- · Business regulatory environment as- work. "Quality" means how conducive that sesses the extent to which the legal, framework is to fostering sustainable, pover- regulatory, and policy environment ty-reducing growth and the effective use of de- helps or hinders private business in velopment assistance. The CPIA is conducted investing, creating jobs, and becom- annually for all International Bank for Recon- ing more productive. The emphasis struction and Development and International is on direct regulations of business Development Association borrowers (except activity and regulation of goods Liberia and Somalia), but results are reported and factor markets. It measures only for International Development Asso- three subcomponents: regulations ciation members. It has evolved into a set of affecting entry, exit, and competi- criteria grouped into four clusters with 16 cri- tion; regulations of ongoing busi- teria that reflect a balance between ensuring ness operations; and regulations of that all key factors that foster pro-poor growth factor markets (labor and land). and poverty alleviation are captured, without · Policies for social inclusion and equity overly burdening the evaluation process. · Gender equality assesses the extent · Economic management to which the country has enacted · Macroeconomic management assess- and put in place institutions and es the quality of the monetary, ex- programs to enforce laws and poli- change rate, and aggregate demand cies that promote equal access for policy framework. men and women to human capital · Fiscal policy assesses the short- and development and to productive medium-term sustainability of and economic resources and that fiscal policy (taking into account give men and women equal status monetary and exchange rate policy and protection under the law. and the sustainability of the public · Equity of public resource use assesses debt) and its impact on growth. the extent to which the pattern of · Debt policy assesses whether the debt public expenditures and revenue management strategy is conducive collection affects the poor and is to minimize budgetary risks and en- consistent with national poverty re- sure long-term debt sustainability duction priorities. The assessment · Structural policies of the consistency of government · Trade assesses how the policy frame- spending with the poverty reduc- work fosters trade in goods. It covers tion priorities takes into account the two areas: trade regime restrictive- extent to which individuals, groups, ness--which focuses on the height or localities that are poor, vulnerable, of tariffs barriers, the extent to which or have unequal access to services nontariff barriers are used, and the and opportunities are identified; a transparency and predictability of national development strategy with the trade regime--and customs and explicit interventions to assist those trade facilitation--which includes individuals, groups, and localities has the extent to which the customs ser- been adopted; and the composition vice is free of corruption, relies on and incidence of public expenditures 156 Africa Development Indicators 2007 are tracked systematically and their is, for air, water, waste, conserva- results feedback into subsequent re- tion management, coastal zones source allocation decisions. The as- management, and natural resources sessment of the revenue collection management). dimension takes into account the · Public sector management and incidence of major taxes--for ex- institutions ample, whether they are progressive · Property rights and rule-based gover- or regressive--and their alignment nance assess the extent to which pri- with the poverty reduction priorities. vate economic activity is facilitated When relevant, expenditure and rev- by an effective legal system and rule- enue collection trends at the national based governance structure in which and subnational levels should be con- property and contract rights are reli- sidered. The expenditure component ably respected and enforced. Three receives two-thirds of the weight in dimensions are rated separately: computing the overall rating. legal basis for secure property and · Building human resources assesses contract rights; predictability, trans- the national policies and public parency, and impartiality of laws and private sector service delivery and regulations affecting economic that affect access to and quality of activity, and their enforcement by health and nutrition services, in- the legal and judicial system; and cluding population and reproduc- crime and violence as an impedi- tive health; education, early child- ment to economic activity. hood development, and training · Quality of budgetary and financial and literacy programs; and preven- management assesses the extent to tion and treatment of HIV/AIDS, which there is a comprehensive and tuberculosis, and malaria. credible budget, linked to policy pri- · Social protection and labor assess orities; effective financial manage- government policies in the area of ment systems to ensure that the social protection and labor market budget is implemented as intended regulation, which reduce the risk in a controlled and predictable way; of becoming poor, assist those who and timely and accurate accounting are poor to better manage further and fiscal reporting, including timely risks, and ensure a minimal level and audited public accounts and ef- of welfare to all people. Interven- fective arrangements for follow-up. tions include social safety net pro- · Efficiency of revenue mobilization as- grams, pension and old age savings sesses the overall pattern of reve- programs, protection of basic labor nue mobilization--not only the tax standards, regulations to reduce structure as it exists on paper, but segmentation and inequity in la- revenue from all sources as they are bor markets, active labor market actually collected. programs (such as public works or · Quality of public administration as- job training), and community driv- sesses the extent to which civilian en initiatives. In interpreting the central government staffs (including guidelines it is important to take teachers, health workers, and police) into account the size of the econo- are structured to design and imple- my and its level of development. ment government policy and deliver · Policies and institutions for environmen- services effectively. Civilian central tal sustainability assess the extent to government staffs include the cen- which environmental policies foster tral executive together with all other the protection and sustainable use ministries and administrative de- of natural resources and the man- partments, including autonomous agement of pollution. Assessment agencies. It excludes the armed of environmental sustainability re- forces, state-owned enterprises, and quires multidimension criteria (that subnational government. Technical notes 157 · Transparency, accountability, and cor- De facto female refers to a household with- ruption in the public sector assess the out a resident male head or where the male extent to which the executive branch head is not present and the wife is the head can be held accountable for its use of by default and serves as the main decision funds and the results of its actions maker in his absence or a household where by the electorate and by the legisla- the resident male head has lost most of his ture and judiciary, and the extent to functions as the economic provider due to which public employees within the infirmity, inability to work, or the like. executive are required to account for De jure female refers to a household headed the use of resources, administrative by a woman who is widowed, separated, or decisions, and results obtained. Both divorced or who has never been married. levels of accountability are enhanced Mean monthly expenditure is the average by transparency in decisionmaking, monthly expenditure on both food and non- public audit institutions, access to food items. See box 14 for a discussion of us- relevant and timely information, ing income data to inform policy. and public and media scrutiny. Mean monthly share on food is total monthly food expenditure and food own consumption Source: World Bank's Country Policy and as a share of total household expenditure. Institutional Assessment 2005. Mean monthly share on health is total health expenditure (consultation, medical 14. Household welfare procedure, among other) as a share of total household expenditure. Health expenditure The questions asked in household surveys excludes hospitalization. vary by country. Quintiles are derived by Mean monthly share on education is total edu- ranking weighted sample population by area cation expenditure (tuition, transport, and the of residence (rural and urban) and per capita like) as a share of total household expenditure expenditure. Two sets of quintiles are calcu- Primary school within 30 minutes is the lated, one for rural and one for urban. Each share of households that live within 30 min- quintile contains an equal number of people utes of a primary school. rather than households. The definition of ru- Net primary enrollment rate is the ratio of ral and urban also vary by country. See box 13 children of a country's official primary school for a discussion of the West and Central Af- age who are enrolled in primary school to rica Poverty Mapping Initiative, which com- the total population of the corresponding of- bines census and household survey informa- ficial primary school age. Primary education tion to construct detailed poverty maps. provides children with basic reading, writ- Sample size is the number of households ing, and mathematics skills along with an surveyed in the country. elementary understanding of such subjects Total population is the weighted estimate as history, geography, natural science, social of all the surveyed population in the country science, art, and music. based on the survey--that is, it is the weight- Net secondary enrollment rate is the ratio ed sample population. of children of a country's official secondary Age dependency ratio is the ratio of depen- school age who are enrolled in secondary dents--people younger than 15 or older than school to the total population of the corre- 64--to the working-age population--those sponding official secondary school age. Sec- ages 15­64. ondary education completes the provision Average household size is the average num- of basic education that began at the primary ber of people in a household. level and aims to lay the foundations for life- Monogamous male is a household headed by long learning and human development by of- man who has no more than one spouse (wife). fering more subject- or skill-oriented instruc- Polygamous male is a household headed by tion using more specialized teachers. a man who has more than one spouse (wife). Tertiary enrollment rate is the number of Single male is a household headed by a man students currently in tertiary education per who is widowed or divorced or who has never 10,000 people. Tertiary education, whether married. or not to an advanced research qualification, 158 Africa Development Indicators 2007 Box 13 Combining census and household survey data for better targeting: the West and Central Africa Poverty Mapping Initiative There are often large regional differences in social indicators within limiting the set of explanatory variables to ones common to both a country. But geographic poverty profiles based on household the survey and the latest census. Second, the coefficients from that surveys tend to be limited to broad areas because survey sample regression are applied to the census data to predict the expendi- sizes prevent analysts from constructing valid estimates of poverty ture level of each household in the census. Third, the predicted at the local level. At the same time policymakers often need finely household expenditures are used to construct a series of poverty disaggregated information at the neighborhood, town, or village and inequality indicators for different geographical population sub- level to implement antipoverty programs. Following a methodol- groups. Although the idea behind the methodology is simple, its ogy developed by Elbers, Lanjouw, and Lanjouw (2003), the World proper implementation requires complex computations. Bank's Africa Region launched the West and Central Africa Pov- The table below lists the countries participating in the initia- erty Mapping Initiative to combine census and household survey tive, the year of the census and survey data used, and the ad- data to construct detailed poverty maps. ministrative level at which poverty estimates are available. The The methodology is straightforward. First, a regression of adult map below--based on the data from the 1998/99 Ghana Living equivalent consumption is estimated using household survey data, Standards Survey and the 2000 Housing and Population Census-- shows poverty by district in Ghana. Countries participating in the West and Central Africa Poverty Mapping Initiative Poverty in Ghana, by district, 2000 Census Survey Country year year Administrative levels measured (number) Incidence of Burkina Faso 1996 1998 Region (13), Province (45), Department (383) poverty (%) Cape Verde 2000 2000/01 Ilho (9), Concelho (17), Freguesi (31) 5.2­26.4 Region (19), Department (58), 26.4­39.7 Côte d'Ivoire 1998 2002 Sous-prefecture (254), Secteur (444) 39.7­52.6 Gabon 2003 2005 Province (9), Department (48), Canton (219) 52.6­68.4 Gambia 2003 2003/04 Local government area (8), District (39) 2000 1998/99 Region (10), District (138) 68.4­86.1 Ghana 2003a 2005/06 Region (10), District (110) Guinea 1996 2002 Region (8), Prefecture (38), Commune (341) Mali 1998 2001 Region (9), Cercle (49), Commune (703) Mauritania 2000 2004 Wilaya (13), Moughata (53), Commune (216) Department (8), Arrondissement Niger 2001 2005 (36), Canton/commune (175) Nigeria 2006a 2003/04 State (37), Senatorial (109) Rwanda 2002 2000/01 Province (5), District (30), Secteur (416) Region (11), Department (34), Senegal 2001 2001/02 Arrondissement (133), Commune (426) Sierra Leone 2004 2002/03 Province (4), District (14), Chiefdom (166) a. Core Welfare Indicator Questionnaire. Source: 1998/99 Ghana Living Standards Survey Source: Coulombe and Wodon 2007. and the 2000 Housing and Population Census. normally requires, as a minimum condition Health center less than 5 km away is the of admission, the successful completion of percentage of the population living less than education at the secondary level. 5 kilometers away from a health center. Adult literacy rate is the percentage of Morbidity is the percentage of the popula- adults ages 15 and older who can both read tion who were sick or injured within a given and write a simple sentence in any language. number of weeks before the survey. Youth literacy rate is the percentage of Health care provider consulted when sick is youth ages 15­24 who can both read and the percentage of sick people who took any write a simple sentence in any language. remedial action when sick. Health center less than 1 hour away is the Type of health care provider consulted is the percentage of the population living less than type of facility visited by a sick household mem- 1 hour away from a health center. ber. Public includes fully government-owned Technical notes 159 Box 14 Using income data to inform policy: the case of cotton producer prices In many African countries household surveys are well designed to Data on income (and thus implicitly on production levels) can measure consumption and poverty as well as human development also be used to assess who would benefit from higher producer outcomes and access to basic infrastructure. But detailed infor- prices. The table suggests that except for Burkina Faso about mation on the sources of income and the livelihoods of households two-thirds of cotton production is accounted for by households in and individuals are still often lacking. This is problematic because the bottom three quintiles of per capita consumption. About two- income data is essential to identify the links between growth and thirds of the additional income that would be generated by higher poverty reduction, to determine ways to improve the household cotton producer prices would benefit these households, which are well-being, and to understand the potential impacts of economic often considered vulnerable because many are poor and those shocks. who are not have consumption levels close to the poverty line. To show how simple tabulations on income sources can in- Finally, although not shown in the table, the same data can form policy debates, consider cotton. World cotton prices (as be used to simulate the impact of changes in producer prices on measured by the Cotlook A Index) have been declining for most of poverty among producers and among the population as a whole. the past decade, and farmers in West Africa especially have suf- Because cotton typically accounts for only about half the total fered from low producer prices. Income data can first be used to income of households producing cotton, and total income also identify cotton producers in household surveys. The table below accounts for only half the consumption of households observed provides data for the "cotton-4" countries--Benin, Burkina Faso, in the surveys, poverty measures tend not to change dramatically Chad, and Mali. It suggests that cotton producers are on average with producer prices. At the same time, even small differences more likely to be poor than the population as a whole, except in in income or consumption levels can make a big difference for Burkina Faso. households that have to survive on very meager resources. Poverty among cotton producers and distribution of production, select West African countries, various years (percent) Benin (2003) Burkina Faso (2003) Chad (2003) Mali (2006) Prevalence of poverty Whole population 39.0 46.4 55.0 47.4 Cotton producers 53.6 47.2 72.7 77.8 Share of cotton production Bottom population quintile 22.0 13.1a 24.6 23.2 Bottom two population quintiles 44.4 32.3a 51.7 48.6 Bottom three population quintiles 65.9 49.9a 67.3 71.6 a. Data are from the 1997/98 priority survey. Source: Tsimpo and Wodon 2007. as well as semi-public health facilities. Private, Immunization coverage, 1-year-olds, is the modern medicine, is facilities set up with profit percentage of children ages 12­23 months at as their main focus and includes private doc- the time of survey who received one dose of tors. Private, traditional healers refer to health Bacille Calmette Guerin vaccine, three doses care providers whose knowledge, skills, and of polio vaccine, three doses of diphtheria, practices are based on the experiences indig- pertussis, and tetanus vaccine, and one does enous to different cultures and whose ser- of measles vaccine. vices are directed toward the maintenance of Measles immunization coverage, 1-year-olds, health, as well as the prevention, diagnosis, is the percentage of children ages 12­23 and improvement of physical and mental ill- months at the time of survey who received a ness. Other is other types of health providers dose of measles vaccine. A child is considered that cannot be classified by the categories de- adequately immunized against measles after scribed above. receiving one dose of vaccine. Birth assisted by trained staff are the percent- Stunting is the percentage of children under age of deliveries attended by personnel trained age 5 whose height for age is more than two to give the necessary supervision, care, and standard deviations below the median for the advice to women during pregnancy, labor, and international reference population ages 6­59 the postpartum period; to conduct deliveries months. The reference population, adopted on their own; and to care for newborns. by the World Health Organization in 1983, 160 Africa Development Indicators 2007 is based on children from the United States, Source: Burkina Faso's Institut National de who are assumed to be well nourished. la Statistique et de la Démographie carried Wasting is the percentage of children under out the Enquête Prioritaire II sur les Condi- age 5 whose weight for height is more than two tions de Vie des Ménages au Burkina. Data standard deviations below the median for the were collected in 2003. The project was fund- international reference population ages 6­59 ed by the government of Burkina Faso, the months. The reference population, adopted World Bank, the African Development Bank, by the World Health Organization in 1983, and the United Nations through the United is based on children from the United States, Nations Development Programme. who are assumed to be well nourished. Underweight is the percentage of children Table .. Cameroon household sur- under age 5 whose weight for age is more than vey, two standard deviations below the median for Household is people who live under the same the international reference population ages roof, take their meals together or in little 6­59 months. The reference population, ad- groups, and put some or all of their incomes opted by the World Health Organization in together for the group's spending purposes, 1983, is based on children from the United at the head of household's discretion. States, who are assumed to be well nourished. Water source less than 1 hour away is the Source: Cameroon's Bureau Central des Re- percentage of the population living less than censements et des Enquêtes of the Direction 1 hour away from a water source. de la Statistique et de la Comptabilité carried Water source less than 5 km away is the per- out the Enquête Camerounaise auprès des centage of the population living less than 5 Ménages in 2001. kilometers away from a water source. Market less than 1 hour away is the percent- Table .. Ethiopia household survey, age of the population living less than 1 hour away from a market. Household is a person or a group of people Market less than 5 km away is the percent- who live under the same roof, share the age of the population living less than 5 kilo- same meals, and recognize one person as the meters away from a market. head. Access to improved water source refers to the percentage of the population with reasonable Source: The 1999/2000 Household Income, access to an adequate amount of water from Consumption, and Expenditure Survey was an improved source, such as a household carried out by the Central Statistical Office. connection, public standpipe, borehole, pro- The data collection process was carried out tected well or spring, or rainwater collection. from June 1999 to February 2000. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Table .. Malawi household survey, Own tap is a household water connection. Other piped is a public water connection. Well, Household is a person living alone or a group protected, is a ground water source. of people, either related or unrelated, who Traditional fuel use is the percentage of the live together as a single unit in the sense that population using traditional fuels such as they have common housekeeping arrange- firewood and charcoal as the main source of ments (that is, share or are supported by a cooking fuel. common budget). Someone who did not live with the household during the survey period Table .. Burkina Faso household was not counted as a current member of the survey, household. Household is the basic socioeconomic unit in Literacy measures the ability to read and which the different members--related or living write a simple sentence for those who had in the same house or property--put together not attended school in the past two months their resources and jointly meet their basic and was defined based on education attain- needs, including food, under the authority of ment for those who had attended school in one person who is recognized as the head. the past two months. Technical notes 161 Source: The Malawi National Statistics Of- Literacy measures the number of people with fice carried out the Integrated Household the ability to read and write a simple sentence. Survey in 2004/5. Source: The Instituto Nacional de Estatis- Table .. Niger household survey, tica of the Ministério de Planomento, Finan- ças e Cooperaçao carried out the Enquête sur Household is the set of people who partly les Conditions de Vie des Ménages in 2000. or totally shared their expenditures, had The project was financed by the government not been absent for more than 6 of the 12 of São Tomé and Principe with assistance months preceding the survey, and were not from the African Development Bank and the domestic help. For polygamous households United Nations Development Programme. each wife and her children were considered Technical assistance was provided by the In- to be a separate household. ternational Labour Organization. Literacy measures the number of people with ability to read and write in Portuguese. Table .. Sierra Leone household survey, / Source: Direction de la Statistique et des Household is a group of people who normal- comptes nationaux carried out the Enquete ly cook, eat, and live together. Number of Nationale sur les Conditions de vie des Me- months sharing in these activities was anoth- nages from April 14 to July 11, 2005. er criterion used to qualify as a household a member (minimum three months). However, Table .. Nigeria household survey, all heads of households irrespective of num- ber of months living elsewhere were included Household is a group of persons who nor- as household members. These people may or mally cook, eat, and live together. Number may not be related by blood, but make com- of months sharing in these activities was mon provision for food or other essentials for another criterion used to qualify as a house- living, and they have one person whom they hold a member (minimum of three months). all regarded as the head of the household. However, all heads of households irrespec- Literacy measures the number of people tive of number of months living elsewhere with the ability to read and write a sim- were included as household members. These ple sentence in either English or the local people may or may not be related by blood, languages. but make common provision for food or other essentials for living, and they have one Source: The Sierra Leone Central Statisti- person whom they all regard as the head of cal Office carried out the Living Conditions the household. Monitoring Survey. Data were collected be- Literacy measures the number of people tween 2002 and 2003. with the ability to read and write either in English or any of the local languages. Table .. Uganda household survey, / Source: The Federal Office of Statistics, Household is individuals who normally eat Abuja, of Nigeria carried out the Nigeria Liv- and live together. ing Standards Survey, an integrated survey. Literacy measures the number of people Data were collected between September 2003 who responded that they could both read and and August 2004. write. The level of education was also used to determine literacy. Table .. São Tomé and Principe household survey, Source: The Uganda Bureau of Statistics Household is the set of people, related or not, carried out the National Household Survey. who live together under the same roof, put Data collection occurred between May 2002 their resources together, and address as a and April 2003. The project was funded by the unit their primary needs, under the author- government of Uganda and the World Bank. ity of one person whom they recognize as the Statistics Denmark and the World Bank pro- head of the household. vided consultants for technical support. 162 Africa Development Indicators 2007 References ActionAid International. 2005. "Real Aid: An Agenda for Making Aid Freund, Caroline L., and Nikola Spatafora. 2005. "Remittances: Work" Johannesburg, South Africa. Transaction Costs, Determinants, and Informal Flows." Policy Research Working Paper 3704. World Bank, Washington, D.C. Adams, Richard, Jr., and John Page. 2003. 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"Draft Balance of Payments and International Medium-Size Enterprises." Journal of Banking and Finance 30 Investment Position Manual." Washington, D.C. (11): 3017­42. IMF (International Monetary Fund) Committee on Balance of Da Cruz, Vincent, Wolfang Fengler, and Adam Schwartzman. Payments Statistics. 2006. "Definitions of Remittances." 2004. "Remittance to Comoros: Volume, Trends, Impact and Nineteenth Meeting Outcome Paper. October 23­26, Frankfurt, Implications." Africa Region Working Paper N075. World Bank, Germany. Washington, D.C. Mazzucato, V., B. van den Boom, and N.N.N. Nsowah-Nuamah. Depetris Chuavin, Nicolas, and Aart Kraay. 2005. "What Has 2004. "The Impact of International Remittances on Local Living 100 Billion Dollars Worth of Debt Relief Done for Low-Income Standards: Evidence for Households in Ghana." Paper presented Countries?" Unpublished manuscript. World Bank, Washington, at the United Nations Development Programme Conference on D.C. 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"Informal Funds Transfer Systems: An Analysis Development­Development Assistance Committee). 2000. of the Informal Hawala System." IMF Occasional Paper 222. "Handbook for Reporting Debt Reorganization on the DAC International Monetary Fund, Washington, D.C. Questionnaire." Paris. References 163 ------. 2000. "DAC Statistical Reporting Directives." Paris. and Policy Issues." Note by the UNCTAD Secretariat for Expert Meeting on Capacity Building in the Area of FDI: Data Orozco, Manuel. 2005. "Conceptual Considerations, Empirical Compilation and Policy Formulation in Developing Countries, Challenges and Solutions in Measuring Remittances." Report 12­14 December, Geneva. presented at the Centro de Estudios Monetarios Latinoamericanos meeting in September 1, Mexico City. Wodon, Q. 2007a. "Is There a Divergence between Objective and Subjective Measures of Poverty? Evidence from West Africa." Page, John, and S. Plaza. 2006. "Migration, Remittances and World Bank, Washington, D.C. 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"FDI Statistics: Data Compilation Geneva. 164 Africa Development Indicators 2007 User's Guide Africa Development Indicators 2007 CD-ROM Introduction Panel, Add/Remove Programs. To reinstall category. Periods: Select time periods from This CD-ROM is part of the Africa it, reboot your computer first. the Periods list box. Development Indicators suite of products. It was produced by the Office of the Chief Operation Creating your own country or indicator list. Economist for the Africa Region and the To start the CD-ROM, go to the WB You can create your own group of countries, Operational Quality and Knowledge Services Development Data program group and click series, or periods by saving your query on Group in collaboration with the Development on the Africa Development Indicators 2007 the appropriate screen. You can also save all Data Group of the Development Economics icon. elements of the query on the Query screen. Vice Presidency. It uses the latest version Note that standard WindowsTM controls You can reload a saved query in a future of the World Bank's *STARS* data retrieval are used for most functions. For detailed session. system, Win*STARS version 5.0. instructions, refer to the on-screen Help The CD-ROM contains about 1,000 menu or tool tips (on-screen explanations of To save a query: macroeconomic, sectoral, and social buttons that are displayed when the cursor 1. Highlight items on any of the Countries, indicators, covering 53 African countries. rolls over them). Series, or Periods (or any two or all three) Time series include data from 1965 to 2005. selection screens and click on Select to Win*STARS 5.0 features mapping and Features and instructions place them in the Selected box. charting and several data export formats Win*STARS has four main functions--Home, 2. Click on the Save Query icon and follow (AccessTM, ASCII, dBASETM, ExcelTM, and Query, Result, and Map. Move among them at the naming prompts. SASTM). We invite you to explore it. any time by clicking on the respective tabs. To load a query: A note about the data Home 1. Go to the selection screen in which Users should note that the data for the Africa On the Home screen you can access each your query is saved. For example, if you Development Indicators suite of products element of the Africa Development Indicators have saved a set of countries, go to the are drawn from the same database. The 2007 CD-ROM. Use the browser controls Countries selection screen. general cutoff date for data is July 2007. to link to the Africa Development Indicators 2. Click on the Load Query icon, select the Data for African Economic Outlook 2007 tables, The Little Data Book on Africa query you want, and click on OK. country analyses may differ from those for 2007, time series database, maps, African the Africa Development Indicators suite of Economic Outlook 2007 country analyses, To modify a saved query: products because of different data sources or and other related information. 1. Load the query. methodologies. 2. In the Selected box, highlight the items to Query be removed and click on the Remove icon. Help 1. Click on the Query button to start your 3. Add new items if necessary. This guide explains how to use the main time series selection. 4. Resave the query. functions of the CD-ROM. For details about 2. Click on each of the Country, Series, and additional features, click Help on the menu Periods buttons and make your selections Result bar or the Help icon; or call one of the hotline on each screen. There are many ways to On the Result screen, data are presented numbers listed in the Help menu and on the make a selection--see below, or use the in a three-dimensional spreadsheet and, copyright page of this booklet. Help menu. initially, in scientific notation. Data for the 3. Highlight the items you want. third dimension are presented on separate Installation 4. Click on the Select button to move them screens. You can change the selection As is usual for WindowsTM products, you into the Selected box. displayed by clicking on the third dimension should make sure that other applications are 5. Deselect items at any time by highlighting scroll box. You can also change the scale and closed while you install the CD-ROM. them and clicking on the Remove icon. the number of digits after the decimal. If the To install the single-user version: 6. When selection is complete, click on OK column is too narrow to present all the digits, 1. Insert the CD-ROM into your CD drive. to return to the main Query screen. they will appear as a series of ######. 2. Click on Start and select Run. Type 7. If you want to, you can display information Double click on the column's guideline to D:\SETUP.EXE (where D: is your CD-ROM on data availability by clicking on the widen it, or choose a larger scale (millions, drive letter), click OK and follow the Availability icon. You can choose to count for example). To scale series individually, instructions. For Windows VistaTM, click time series or total observations. click Options and check Enable Series-Level the Computer icon on your desktop, 8. Click on View Data to see the data on the Scaling. Click the far right scroll box to view navigate to your CD-ROM drive, and Result screen. the percentage change over each selected launch the Setup application. period or to index the data. 3. Win*STARS 5.0 requires Microsoft Making selections. Countries: You can Internet ExplorerTM 4.0 or higher. If you select countries from an alphabetical list, Changing the orientation. You can view the do not have Internet Explorer, it may be by Classification (region, income group, result in six different orientations (countries downloaded at no charge from www. or lending category), by Criteria (up to two down/periods across, series down/countries microsoft.com. It does not need to be can be specified), or by Group (aggregates across, etc.). To change the orientation, click your default browser. If you do not wish to have been calculated only when there were on the Orientation scroll box. use Internet Explorer, you have the option adequate data). Series: You can choose to install Win*STARS 4.2. from an alphabetical list or by Category. Charting and mapping data. On the Result You can delete this program at any time When selecting series by category, the screen, you can chart or map the data by clicking on Start, Settings, Control subcategory buttons change with each displayed. Highlight a set of cells for charting Users guide 165 or a particular cell for mapping. Click on the 1. LICENSE. In consideration of your by a copy of your receipt. The Bank's Chart or Map icon on the toolbar accordingly. payment of the required license fee, the entire liability and your exclusive remedy The charting function has many features. WORLD BANK (the "Bank") hereby grants shall be the replacement of any CD-ROMs After you have displayed a chart, right click you a nonexclusive license to use the that do not meet the Bank's limited on the chart to open the Chart Wizard for enclosed data and Win*STARS retrieval warranty. Defective CD-ROMs should be more options. Mapping is described on page program (collectively, the "Program") returned within the warranty period, with 8. From this screen you can choose to map subject to the terms and conditions set a copy of your receipt, to the address all countries or only your selected countries. forth in this license agreement. specified in section 9 below. Cutting, pasting, printing, and saving. You EXCEPT AS SPECIFIED ABOVE, THE can cut, paste, and print the result, or you 2. OWNERSHIP. As a licensee you own the PRODUCT IS PROVIDED "AS IS" can save the spreadsheet in another format. physical media on which the Program WITHOUT WARRANTY OF ANY KIND, Click on the appropriate icon on the toolbar is originally or subsequently recorded. EITHER EXPRESSED OR IMPLIED, and follow the prompts. Click on Help for The Bank, however, retains the title and INCLUDING, BUT NOT LIMITED TO, more details. ownership of the program recorded on THE IMPLIED WARRANTIES OF the original CD-ROMs and all subsequent MERCHANTABILITY AND FITNESS Map copies of the Program. This license is not FOR A PARTICULAR PURPOSE. THE On the Map screen, you can select a country considered to be a sale of the Program or BANK DOES NOT WARRANT THAT and view a set of tables describing it, or you any copy thereof. THE FUNCTIONS CONTAINED IN can map a series for all countries. In the THE PROGRAM WILL MEET YOUR upper left corner of the screen, the country 3. COPY RESTRICTIONS. The Program REQUIREMENTS OR THAT THE name will appear as the cursor rolls slowly and accompanying written materials are OPERATION OF THE PROGRAM WILL BE over the map. To zoom in for a closer look at copyrighted. You may make one copy of UNINTERRUPTED OR ERROR-FREE. the map, click on the Zoom icon. the Program solely for backup purposes. IN NO EVENT WILL THE BANK BE LIABLE Unauthorized copying of the Program TO YOU FOR ANY DAMAGES ARISING Selecting a country or viewing country tables. or of the written materials is expressly OUT OF THE USE OF OR THE INABILITY To highlight a country and view any of its forbidden. TO USE THE PROGRAM. tables, click on the country on the map or THE ABOVE WARRANTY GIVES YOU select it in the Locate a Country scroll box in 4. USE. You may not modify, adapt, SPECIFIC LEGAL RIGHTS IN THE UNITED the upper right corner. translate, reverse-engineer, decompile, STATES THAT MAY VARY FROM STATE or disassemble the Program. You may TO STATE. BECAUSE SOME STATES Mapping a series. On the Map screen, click not modify, adapt, translate, or create DO NOT ALLOW THE EXCLUSION OF on the Series icon. A list of key indicators derivative works based on any written IMPLIED WARRANTIES OR LIMITATION will be displayed. (To show all available materials without the prior written consent OF EXCLUSION OF LIABILITY FOR indicators, click on the box by Show default of the Bank. If you have purchased the INCIDENTAL OR CONSEQUENTIAL series to remove the X.) Highlight a series, single-user version of this product, you DAMAGES, PARTS OF THE ABOVE select a period from the Available Periods may use the Program only on a single LIMITATIONS AND EXCLUSIONS MAY list box (the default is the latest available) laptop/desktop computer. You may NOT APPLY TO YOU. and click on Paint Map. The map will be not distribute copies of the Program colored according to the legend settings, or accompanying written materials to 7. TERMINATION. This license is effective any of which you can change. Note that as others. You may not use the Program on from the date you open the package the cursor moves across the map, the series any network, including an Intranet or the until the license is terminated. You may value is now also displayed in the upper left Internet, without obtaining prior written terminate it by destroying the Program corner. permission from the Bank. If you have and its documentation and any backup purchased the multiple-user version of this copy thereof or by returning these Changing the map legend and colors. The product, your license is valid only up to 15 materials to the Bank. If any of the terms default interval range is an equal number users. Should you need to add additional or conditions of this license are broken, of countries. To set an equal interval range users, please send a request, indicating the Bank may terminate the license and or to map multiple periods, click on the the number of users you would like to add, demand that you return the Program. Recalculate icon. Set your own intervals by to: World Bank Publications, Marketing and editing the legend. To change map colors, Rights, 1818 H Street, N.W., Washington, 8. GOVERNING LAW. This license shall be double click on the legend color boxes. Press D.C. 20433, fax: 202-522-2422, email: governed by the laws of the District of the Remap icon to see your changes. pubrights@worldbank.org. Columbia, without reference to conflicts of law thereof. Printing and saving. Click on the appropriate 5. TRANSFER RESTRICTIONS. This icon to print the map or save it as a bitmap Program is licensed only to you, the 9. GENERAL. If you have any questions or metafile. licensee, and may not be transferred to concerning this product, you may anyone without prior written consent of contact the Bank by writing to World License agreement the Bank. Bank Publications, CD-ROM Inquiries, You must read and agree to the terms of this The World Bank, 1818 H Street, N.W., License Agreement prior to using this CD- 6. LIMITED WARRANTY AND Washington, D.C. 20433, email: ROM product. Use of the software and data LIMITATIONS OF REMEDIES. The Bank data@worldbank.org. All queries on rights contained on the CD-ROM is governed by the warrants that under normal use the CD- and licenses should be addressed to terms of this License Agreement. If you do ROMs on which the Program is furnished World Bank Publications, Marketing and not agree with these terms, you may return are free from defects in materials and Rights, 1818 H Street, N.W., Washington, the product unused to the World Bank for a workmanship for a period of ninety (90) D.C. 20433, fax: 202-522-2422, full refund of the purchase price. days from delivery to you, as evidenced email: pubrights@worldbank.org. 166 Africa Development Indicators 2007