46768 2008/09 Youth and Employment in Africa: e Potential, the Problem, the Promise YOUTH AND EMPLOYMENT IN AFRICA The Potential, the Problem, the Promise THE WORLD BANK Copyright © 2009 the International Bank for Reconstruction and Development/ e World Bank 1818 H Street, N.W. Washington, D.C. 20433, U.S.A. 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To subscribe to Africa Development Indicators On- line please visit: http://publications.worldbank.org/ADI For more information about Africa Development Indicators and its companion products, please visit www.worldbank.org/africa. You can email us at ADI@worldbank.org. Cover design by Word Express, Inc. Dohatec New Media prepared the navigation structure and interface design of the Africa Development Indicators 2008/09 CD- ROM. Photo credits: front cover Jonathan Ernst and back cover Arne Hoel/World Bank. e map of Africa is provided by e Map Design Unit/World Bank. ISBN: 978-0-8213-7787-1 e-ISBN: 978-0-8213-7795-6 DOI: 10.1596/978-0-8213-7787-1 SKU: 17787 Contents Foreword vi Acknowledgements vii Youth and Employment in Africa ­ e Potential, the Problem, the Promise 1 Introduction 1 Stylized facts about youth and labor markets in Africa 5 Policy response requires an integrated, multi-sector approach 11 and close monitoring Conclusions 23 Essay references 25 Indicator tables 27 Part I. Basic indicators and national accounts 1. Basic indicators 1.1 Basic indicators 28 2. National accounts 2.1 Gross domestic product, nominal 29 2.2 Gross domestic product, real 30 2.3 Gross domestic product growth 31 2.4 Gross domestic product per capita, real 32 2.5 Gross domestic product per capita growth 33 2.6 Gross national income, nominal 34 2.7 Gross national income, Atlas method 35 2.8 Gross national income per capita 36 2.9 Gross domestic product deflator (local currency series) 37 2.10 Gross domestic product deflator (U.S. dollar series) 38 2.11 Consumer price index 39 2.12 Price indices 40 2.13 Gross domestic savings 41 2.14 Gross national savings 42 2.15 General government final consumption expenditure 43 2.16 Household final consumption expenditure 44 2.17 Final consumption expenditure plus discrepancy 45 2.18 Final consumption expenditure plus discrepancy per capita 46 2.19 Agriculture value added 47 2.20 Industry value added 48 2.21 Services plus discrepancy value added 49 2.22 Gross fixed capital formation 50 2.23 Gross general government fixed capital formation 51 2.24 Private sector fixed capital formation 52 2.25 Resource balance (exports minus imports) 53 Contents iii 2.26 Exports of goods and services, nominal 54 2.27 Imports of goods and services, nominal 55 2.28 Exports of goods and services 56 2.29 Imports of goods and services 57 2.30 Balance of payment and current account 58 2.31 Structure of demand 59 2.32 Exchange rates and Purchasing Power Parity 60 Part II. Millennium Developing Goals 3. Millennium Developing Goals 3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger 62 3.2 Millennium Development Goal 2: achieve universal primary education 64 3.3 Millennium Development Goal 3: promote gender equity and empower women 65 3.4 Millennium Development Goal 4: reduce child mortality 66 3.5 Millennium Development Goal 5: improve maternal health 67 3.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases 68 3.7 Millennium Development Goal 7: ensure environmental sustainability 70 3.8 Millennium Development Goal 8: develop a global partnership for development 72 Part III. Development outcomes 4. Paris Declaration indicators 4.1 Status of Paris Declaration Indicators 74 5. Private sector development 5.1 Business environment 76 5.2 Investment climate 78 6. Trade 6.1 International trade and tariff barriers 80 6.2 Top three exports and share in total exports, 2006 84 6.3 Regional integration, trade blocs 87 7. Infrastructure 7.1 Water and sanitation 88 7.2 Transportation 90 7.3 Information and communication technology 92 7.4 Energy 94 7.5 Financial sector infrastructure 96 8. Human development 8.1 Education 98 8.2 Health 100 9. Agriculture, rural development, and environment 9.1 Rural development 104 9.2 Agriculture 106 9.3 Environment 108 9.4 Climate change 112 10. Labor, migration, and population 10.1 Labor force participation 114 10.2 Labor force composition 116 10.3 Unemployment 118 10.4 Migration and population 120 iv Africa Development Indicators 2008/09 11. HIV/AIDS 11.1 HIV/AIDS 122 12. Malaria 12.1 Malaria 124 13. Capable states and partnership 13.1 Aid and debt relief 126 13.2 Capable states 128 13.3 Governance and anticorruption indicators 130 13.4 Country policy and institutional assessment ratings 132 13.5 Polity indicators 134 Part IV. Household welfare 14. Household welfare 14.1 Burkina Faso household survey, 2003 135 14.2 Cameroon household survey, 2001 136 14.3 Ethiopia household survey, 1999/00 137 14.4 Liberia household survey, 2007 138 14.5 Malawi household survey, 2003/04 139 14.6 Niger household survey, 2005 140 14.7 Nigeria household survey, 2003/04 141 14.8 São Tomé and Principe household survey, 2000/01 142 14.9 Sierra Leone household survey, 2002/03 143 14.10 Tanzania household survey, 2000/01 144 14.11 Uganda household survey, 2005/06 145 Users Guide 147 Technical notes 151 Technical notes references 203 User's Guide: Africa Development Indicators 2008/09 CD-ROM 205 Contents v Foreword For centuries, data have been used as an in- To effectively serve as a tool for transpar- strument for decision-making. To choose be- ency, any data set must meet certain criteria. tweentwopublicpolicyoptions--whetheror First, it must be accurate. All data in the ADI not to build a bridge, for example--analysts are rigorously checked and cross-checked; use data to evaluate the costs and benefits of only those data that pass various statistical each option and inform the decision-maker tests make it in the document. Second, the accordingly. More recently, data have taken data must be accessible to the public. is on a new role: as an instrument for holding is why ADI is disseminated worldwide; the policymakers accountable. When data are new, improved on-line version permits easy made publicly available, the public can use access and manipulation of the data to suit data to question policymakers' decisions, individual needs and tastes. ird, the data and hold them accountable, if not imme- must be salient--it must be about issues diately then periodically through the ballot that people care about. is year's version box. e publication of citizen report cards includes new datasets on climate change, in Bangalore, India; the public expenditure conflict, and governance, among others. tracking surveys in Uganda; and Transpar- Following a two-year-old tradition, the ency International's worldwide corruption ADI also has an essay: "Youth and Employ- indices are but three examples where data ment in Africa-- e Potential, the Problem, have empowered citizens to hold public of- the Promise." e choice of this topic is obvi- ficers to account. ous. Finding productive employment for the e Africa Development Indicators 200 million Africans between the ages of 15 (ADI) seeks to fulfill both roles. Originally and 24 is surely one of the continent's great- intended as a tool for aiding decision-mak- est challenges. What the essay shows, how- ing by presenting cross-country compari- ever, is that the median young person in Af- sons of various data (to discern patterns in rica is a poor, out-of-school female living in African development, as well as exceptions a rural area. is finding--based on a careful to those patterns), ADI has evolved into a examination of the data--has important tool for transparency as well. Journalists, implications for policy design, as well as for researchers, students, Civil Society Organi- the politics of youth-sensitive policies. Once zations and other citizens use the compara- again, data can play the dual role of inform- tive data in ADI to ask questions such as: ing policy choices and empowering citizens why their country is not performing as well to hold politicians accountable. in some dimensions as other, comparable countries; or alternatively, why their coun- try is in fact doing so well but getting very Obiageli K. Ezekwesili little credit for it. Vice President, Africa Region vi Africa Development Indicators 2008/09 Acknowledgments Africa Development Indicators (ADI) is a e boxes in the book and in the techni- product of the Africa Region of the World cal notes benefited from contributions from Bank. Aziz Bouzaher (climate change), Sebastien Jorge Saba Arbache was the manager of Dessus (PPP), John May (demographic tran- this book and its companions--Africa De- sition), Gary Milante (conflicts), Deepak velopment Indicators Online 2008/09, Afri- Mishra and Mesfin Girma Bezawagaw (in- ca Development Indicators 2008/09--Mul- flation), Pierella Paci and Catalina Gutierrez tiple User CD-ROM, and Little Data Book (unemployment), David Wilson and Eliza- on Africa 2008/09. Rose Mungai led the beth Lule (HIV prevalence and incidence), work on data gathering, consistency checks Quentin Wodon (food prices, role of faith- and compilation. e core team included inspired organizations, and poverty and mi- Mpho Chinyolo, Francoise Genouille, Jane gration), and Ali Zafar (franc zone). K. Njuguna, and Christophe Rockmore. Aziz Bouzaher provided guidance for the e overall work was carried out under the table on climate change, and Gary Milante guidance and supervision of Shantayanan for the tables on conflicts and polity. Devarajan, Chief Economist of the Africa Ann Karasanyi and Ken Omondi pro- Region. vided administrative and logistical support. Pablo Suarez Robles provided research Delfin Go and Yutaka Yoshino provided gen- assistance, and Harold Alderman, Mayra Bu- eral comments and suggestions. vinic, Louise Fox, Caterina R. Laderchi, and Several institutions provided data to ADI. Paul Moreno-Lopez provided useful com- eir contribution is very much appreciated. ments on an earlier draft of the essay. e Word Express, Inc. provided design Azita Amjadi, Abdolreza Farivari, Richard direction, editing, and layout. Dohatec New Fix, Shelley Lai Fu, Shahin Outadi, William Media prepared the navigation structure and Prince, Atsushi Shimo and Malarvizhi Veer- interface design of the Africa Development appan collaborated in the data production. Indicators 2008/09 CD-ROM. Maja Bresslauer, Mahyar Eshragh-Tabary, Staff from External Affairs, including Victor Gabor and Soong Sup Lee collaborat- Herbert Boh, Richard Crabbe, Lillian Foo, ed in the update of the Live Data Base. Me- Gozde Isik, Valentina Kalk, and Malika Khek hdi Akhlaghi collaborated in the production oversaw publication and dissemination of of the Little Data Book on Africa 2008/09. the book and its companions. Acknowledgments vii Youth and Employment in Africa ­ e Potential, the Problem, the Promise Introduction Today's world population counts an estimat- Table 1 Incidence of poverty among young people (in %) ed 1.2 billion people at the ages of 15 to 24 in Sub-Saharan Africa years, an increase of 17% compared to 1995, or 18% of the world population. About 87% Country Less than US$ 2 per day of these young people live in countries with Burundi, 1998 85.7 developing economies. In Africa, 200 million Côte d'Ivoire, 1998 46.5 people are in this age range, comprising more Cameroon, 2001 49.1 than 20% of the population (United Nations Ethiopia, 2000 70.7 2007). In 2005, 62% of Africa's overall popu- Ghana, 1998 66.5 lation fell below the age of 25. e still very Kenya, 1997 54.4 high fertility rate along with a demographic Madagascar, 2001 81.7 transition that is slowly taking place in the Mozambique, 1996 75.4 region are likely to increase the pressure Af- Malawi, 1997 66.3 rican countries face for job creation over the Nigeria, 1996 92.9 coming decades.1 Sierra Leone, 2003 68.0 Worldwide, and in Africa as well, the ra- Uganda, 1999 93.8 tio of the youth-to-adult unemployment rate Zambia, 1998 86.3 equals three (ILO 2006), which clearly points SSA-13 (mean) 72.1 out the substantial difficulties of youth par- SSA-13 (median) 70.7 ticipation in the labor market. Yet, the youth employment elasticity to GDP growth is low Source: World Bank Survey-Based Harmonized Indicators Program (SHIP). Note: A person is considered poor if per capita total annual household expenditure divided by 365 falls below the poverty line. The "$2-a-day" and only a fifth of that observed for all work- poverty line--$2.17 per day in purchasing power parity (PPP) at 1993 prices--is defined as 2.17 times the product of the 1993 consumption PPP ers (Kapsos 2005). As a consequence, youth exchange rate and the ratio of the average consumer price index for the year of the survey to the average consumer price index for 1993. CPIs and PPP exchange rates were respectively taken from World Development Indicators 2007 and PovCalNet (World Bank). made up 43.7% of the total unemployed people in the world despite accounting for only 25% of the working population. More than one third of the youth in the world is a rural area, and literate but not attending either seeking but unable to find work, has school (Table 2).2 given up on the job search entirely, or is As a way to escape poverty, many youth working but still living below the $2 a day look for better opportunities by migrating. poverty line. In Sub-Saharan Africa, 3 in 5 of Indeed, migration to urban areas is unavoid- the total unemployed are youth (ILO 2006) able and even desirable as a way to improve and on average 72% of the youth population allocation of human resources, especially in live with less than $2 a day (Table 1). land-scarce countries. While youth are more Young people in Africa are not a homo- likely than older people to move from rural geneous group and their employment pros- to urban areas or to move across urban areas, pects vary according to region, gender, age, educational level, ethnicity, and health sta- 1 e definition of youth is age 15 to 24 years, and tus, thus requiring different sets of policy adults 25­64. interventions. However, the typical African 2 Higher death rate of males due to homicides, youth, as given by medians, is easily identifi- war-related conflicts, diseases and other causes able: she is an 18.5-year-old female, living in help explain this pattern. Introduction 1 Table 2 Typical African youth ­ median Country Location Sex Age Literate Attending school SHIP data Burundi, 1998 Rural 93.9% Female 54.9% 18 Yes 71.4% No 25.6% Côte d'Ivoire, 1998 Urban 46.8% Female 51.9% 19 Yes 60.7% No 27.6% Cameroon, 2001 Rural 56.4% Female 52.5% 19 Yes 82.4% No 46.2% Ghana, 1998 Rural 57.8% Male 49.7% 18 Yes 65.9% No 41.3% Guinea, 1994 Rural 57.2% Female 50.6% 19 No 30.6% No 18.4% Kenya, 1997 Rural 81.0% Female 51.9% 19 Yes 93.5% No 42.0% Mozambique, 1996 Rural 76.9% Female 52.3% 19 Yes 51.1% No 19.2% Mauritania, 2000 Rural 55.5% Female 52.9% 18 Yes 70.2% No 27.6% Malawi, 1997 Rural 87.4% Female 52.7% 19 Yes 62.9% No 40.1% Nigeria, 1996 Rural 56.4% Female 53.8% 18 Yes 74.3% No 46.7% Sierra Leone, 2003 Rural 51.9% Female 52.4% 18 No 43.2% No 42.8% São Tomé and Principe, 2000 Urban 40.9% Male 49.9% 19 Yes 94.1% No 25.0% Uganda, 1999 Rural 82.8% Female 51.3% 18 Yes 79.0% No 43.7% Zambia, 1998 Rural 59.8% Female 52.8% 19 -- -- No 30.2% SSA-14 (mean) -- 64.6% -- 52.1% -- 67.6% -- 34.0% SSA-14 (median) -- 57.5% -- 52.4% -- 70.2% -- 35.1% LFS data Ethiopia, 2005 Rural 79.6% Female 53.2% 19 No 49.9% -- -- Madagascar, 2005 Rural 76.0% Female 51.7% 19 Yes 75.2% No 23.0% Tanzania, 2005 Rural 70.5% Female 53.1% 19 Yes 83.0% No 28.7% Source: World Bank Survey-Based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Tanzania ILFS 2005/06 and Madagascar EPM 2005. Note: -- Not available. this increased youth migration has a wide and labor market opportunities. How easily impact. It increases the strain for jobs with- and how effectively young people find jobs is out necessarily improving the job conditions also dependent on how well the labor market of those who are left in rural areas; impacts is prepared to receive them, and on how well provision of public goods, education, utili- they are prepared for the labor market. ties, housing, and infrastructure; and affects A large group of young people enter the demographic and skills composition in both labor market very early, which affects their urban and rural areas. Given that about 70% progress in the labor market. In the short of the African youth population is still in term, poor families gain from child labor; rural areas, and that urban areas have been thus, there are short-term welfare losses very slow to create job opportunities for for rural families from sanctions on child most new job seekers, there is a need for an labor.3 For long-term development, how- integrated, coherent approach in which poli- ever, child labor elicits a cost in terms of cies appropriate for the youth in urban areas foregone education and persistence of long- are closely connected with policies appropri- term poverty. ate for the youth in rural areas. is type of Post-conflict settings pose specific chal- approach is essential if governments want to lenges for the youth (e.g., recently disarmed smooth the deleterious impacts of rapid mi- idle men and displaced young men) as these gration while preparing the rural youth for a settings have prominently young popula- more rewarding mobility. tions, many of whom have been deprived of While in some countries demographic education, have grown up in violent societ- change is the main factor behind high youth unemploymentandunderemploymentrates, 3 Participation rate of children 5­14 years old is much of the youth employment challenges about 30% in Sub-Saharan Africa (World Bank can also be related to labor market dynamics 2008). 2 Youth and Employment in Africa ­ The Potential, the Problem, the Promise ies, and often have been combatants them- of information on what their options are, selves. Employment and the creation of jobs what works in different situations, and what for young people should therefore form a key has been tried and failed. component of any peace building processes. is essay examines these issues. e e energy, skills and aspirations of first part presents stylized facts of youth young people are invaluable assets that no and labor markets in Africa. e second part country can afford to squander, and helping discusses past youth employment interven- them to realize their full potential by gain- tions in the region. It argues for the need of ing access to employment is a precondition an integrated approach should governments for poverty eradication, sustainable develop- want to tackle youth employment issues in a ment, and lasting peace. Given the immense sustainable manner. Indeed, in African coun- challenges youth face to get a job, youth em- tries, with large informal sectors and domi- ployment has obtained growing prominence nance of rural population, solely reforming on development agendas after having been labor market institutions and implement- largely neglected in national development ing active labor market policies are likely to strategies in the past. have limited impact. It argues that the most e youth employment challenge con- needed and well-rounded approaches are: fronts all countries in Africa, regardless expanding job and education alternatives in of their stage of socio-economic develop- the rural areas--where most youth live; pro- ment, but the socio-economic context has moting and encouraging mobility; creating a an important contribution on the nature conducive business environment; encourag- and extent of the problem. As they consider ing the private sector; improving the access measures to help young people make the and quality of skills formation; taking care transition into the labor market and obtain of demographic issues that more directly af- work, policymakers are hampered by a lack fects the youth; and reducing child labor. Introduction 3 Stylized facts about youth and labor markets in Africa In 2005, the labor force participation rate of Table 3 Distribution of youth and adults by job status (in %) young males was 73.7% (ILO 2006), one of the highest in the world (ILO 2006, United Employed Unemployed Out of the labor force Nations 2007). Youth Adults Youth Adults Youth Adults Youth make up 36.9% of the working- SHIP data age population, but 59.5% of the total un- Burundi, 1998 70.4 95.8 0.3 0.4 29.3 3.8 employed, which is much higher than the Côte d'Ivoire, 1998 51.4 81.8 3.0 2.9 45.6 15.3 world's average for 2005 (43.7%), reflecting Cameroon, 2001 42.7 80.9 7.2 4.7 50.1 14.4 serious labor demand deficiencies in the re- Ghana, 1998 17.7 78.4 31.3 8.7 51.0 12.9 gion (ILO 2006). e share of unemployed Guinea, 1994 69.9 87.8 8.3 5.3 21.8 6.9 youth among the total unemployed can be as Kenya, 1997 20.8 58.2 3.7 1.1 75.5 40.7 high as 83% in Uganda, 68% in Zimbabwe, Mozambique, 1996 22.0 59.5 2.2 1.4 75.8 39.1 and 56% in Burkina.4 Mauritania, 2000 28.4 50.4 3.1 3.4 68.6 46.2 Unemployment among youth is often Malawi, 1997 20.3 58.8 1.3 1.5 78.4 39.7 higher than among adults (Table 3). Nigeria, 1996 23.1 76.7 5.5 1.2 71.4 22.1 Youth unemployment is more preva- Sierra Leone, 2003 40.4 85.4 52.5 10.2 7.1 4.4 lent in urban areas (Table 4) and is higher São Tomé and Principe, 2000 32.8 68.1 4.1 0.8 63.1 31.1 among those with higher education attain- Uganda, 1999 17.9 66.0 0.7 0.6 81.4 33.4 ment and those in wealthy households. On Zambia, 1998 38.7 77.7 6.7 4.2 54.6 18.1 average, unemployment among those with SSA-14 (mean) 35.5 73.3 9.3 3.3 55.3 23.4 secondary education or above is three times SSA-14 (median) 30.6 77.2 3.9 2.2 58.9 20.1 higher than among those with no education LFS data attainment, and unemployment is twice as Ethiopia, 2005 72.8 86.5 2.9 1.9 24.3 11.6 high among youth from households in the Madagascar, 2005 71.7 93.3 1.7 2.6 26.6 4.1 fifth (or highest) income quintile as com- Tanzania, 2005 74.4 93.5 4.9 1.9 20.7 4.6 pared to those in the first income quintile (Figure 1).5 Source: World Bank Survey-Based Harmonized Indicators Program (SHIP)), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. Youth are more likely than adults to be in the informal sector, and less likely to be wage employed or self-employed. In 2005 in Ethio- 4 Unemployment as defined by ILO is increas- of underemployment. As an illustration, youth ingly seen as inadequate to characterize low in- unemployment was only 0.8% in Malawi, 2.1% come countries' labor markets (Cling et al. 2006; in Burkina, and 0.7% in Rwanda (United Nations Fares et al. 2006; World Bank 2006, inter alia). 2007). erefore, unemployment should not be Youth unemployment does not provide a full and the main component of a youth employment adequate description of the difficulties youth face strategy or the main results performance indica- in the labor market. In fact, in countries with tor of labor markets in Africa. widespread poverty, looking at the unemploy- 5 Distinguishing between who is rural and urban ment rate may be misleading because most youth is increasingly difficult, especially with the expan- cannot afford to be unemployed. e difficulties sion of semi-urban areas where large proportions in the labor market may be better reflected by of populations rely on agricultural activities to measures of quality of employment or measures meet their livelihood needs. Stylized facts about youth and labor markets in Africa 5 Table 4 Distribution of urban and rural youth by job status (in %) cure work arrangements, characterized by low productivity and meager earnings. Un- Employed Unemployed Out of the labor force deremployment is more prevalent among Urban Rural Urban Rural Urban Rural youth than adults, and is more prevalent SHIP data in rural rather than urban areas (Figures 2 Burundi, 1998 14.9 74.0 5.2 0.0 79.9 26.0 and 3).6 Côte d'Ivoire, 1998 31.5 73.6 5.1 0.6 63.4 25.9 Youth are employed primarily in agricul- Cameroon, 2001 25.9 55.6 12.5 3.1 61.6 41.3 ture (Figure 4), in which they account for Ghana, 1998 16.2 18.7 36.7 27.4 47.1 53.9 65% of total employment (ILO 2007). Guinea, 1994 40.0 92.2 16.2 2.4 43.8 5.4 In rural areas the youth work longer Kenya, 1997 36.2 17.2 8.4 2.6 55.4 80.2 hours and spend a lot of their time in house- Mozambique, 1996 20.9 22.4 3.5 1.7 75.6 75.9 hold work. In rural Ethiopia they work 43 hours a week in contrast to the 31 hours Mauritania, 2000 17.6 37.0 5.4 1.2 77.0 61.8 worked in urban areas. Of those 43 hours Malawi, 1997 14.5 21.2 2.6 1.1 82.9 77.7 worked, the rural youth spends 31 hours in Nigeria, 1996 22.9 23.3 6.4 4.8 70.7 71.9 household work (fetching water, collecting Sierra Leone, 2003 22.9 56.6 67.9 38.2 9.2 5.2 fire wood, and other domestic activities), in São Tomé and Principe, 2000 30.5 36.3 3.2 5.4 66.4 58.3 contrast to the 22 spent on these tasks in Uganda, 1999 25.6 16.3 2.3 0.4 72.1 83.3 urban areas. Zambia, 1998 16.6 53.6 11.9 3.2 71.5 43.2 Rural youth attached to agriculture are SSA-14 (mean) 24.0 42.7 13.4 6.6 62.6 50.7 disadvantaged in terms of employment sta- SSA-14 (median) 22.9 36.7 5.9 2.5 68.6 56.1 tus as compared to those engaged in non- LFS data farm activities (Table 5). Ethiopia, 2005 40.7 81.1 10.0 1.0 49.3 17.9 Sub-Saharan Africa has the lowest pri- Madagascar, 2005 50.5 78.4 4.6 0.7 44.9 20.9 mary education completion rate of any re- Tanzania, 2005 56.0 82.1 13.4 1.3 30.6 16.6 gion (60% compared to 91% in MENA, 98% Source: World Bank Survey-Based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. 6 Underemployment rate refers to total under- employed expressed as a proportion of total em- ployed. A person is classified as underemployed pia, respectively 81.4% and 12.5% of youth if the total number of hours worked during the week is less than 30. As regards Ethiopia and Tan- were in the informal and self-employment zania, a person is classified as underemployed if sectors, against 43% and 49.6% of adults. in addition he or she is available for more work. Young people are more likely to work is information is not available for the other longer hours under intermittent and inse- countries. Figure 1 Youth unemployment ratios by level of education and weighted quintiles of the per capita total annual household expenditure SSA-16 countries (average) SSA-14 countries (average) 20 20 15 15 10 10 5 5 0 0 No education Primary not Primary Secondary Quint 1 Quint 2 Quint 3 Quint 4 Quint 5 completed completed completed or tertiary Source: World Bank Survey-Based Harmonized Indicators Program (SHIP). 6 Youth and Employment in Africa ­ The Potential, the Problem, the Promise in EAP, 99% in LAC, and 86% in all regions Underemployment rates among youth and adults in 2005). More than a third of the youth Figure 2 population in the region was still illiterate in Youth Adults 2002 (ILO 2006). Urban youth enjoy greater educational 70 opportunities, stay longer in school and join 60 the labor force later than rural youth. In Bu- 50 rundi 57% of urban youth are in school in contrast to 23% in rural areas; in Cameroon 40 48% and 24%; in Mozambique 30% and 15% 30 (Garcia and Fares 2008). Young women are more likely to be un- 20 deremployed, and more likely to be out of 10 the labor force (Tables 6 and 7). 5 Women work more hours than males 01 05 98 94 05 96 00 97 96 03 05 and are more likely to engage in non market Ghana Guinea Malawi Nigeria Leone activities. In Ethiopia they work 48 hours a Ethiopia Tanzania Cameroon Mauritania week versus 32 for males. Of those hours Madagascar Mozambique Sierra they spend 36 in household activities in Source: World Bank Survey-based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. contrast to the 15 males work in these tasks (Ethiopia LFS 2005). Underemployment rates among urban and rural youth Young women have lower levels of Figure 3 school attainment and school enrollment. Urban Rural In Sierra Leone (2003) 53% of young men and 33% of young women were attending 70 school while in Uganda (1999), the figures 60 were 53% and 35%, respectively (SHIP 50 data). In 2005, the male and female net school enrollment ratios in Africa were 71% 40 and 65% in primary education and 28% and 30 23% in secondary education, respectively. e male gross school enrollment ratio in 20 tertiary education was 6%, while that of 10 women was 4%. 5 Africa's youth follow two paths in their 01 05 98 94 05 96 00 97 96 03 05 transitions to working life: many go to work Ghana Guinea Malawi Nigeria Leone directly, with little benefit of formal school- Ethiopia Tanzania Cameroon Mauritania ing, while others join the work force after a Madagascar Mozambique Sierra time in the formal school system. e esti- Source: World Bank Survey-based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. mated school life expectancy ranges from 2.9 years for Niger (2002) and 4.4 for DRC (1999), to 11.7 for Mauritius (2002) and this number reaches 50% (Garcia and Fares, 12.4 for South Africa (2001).7 With a few 2008). exceptions, the estimated school life expec- Many youth move from rural to urban tancy is higher for males. areas in search of greater opportunities. ose who enter the labor market directly are unprepared, making them more vulner- able to demographic and demand changes. 7Definition of School Life Expectancy from us, they are more likely to be stuck in low UNESCO: "Number of years a child is expected productivity jobs. to remain at school, or university, including years spent on repetition. It is the sum of the age- Children and young people start to work specific enrolment ratios for primary, secondary, early--a quarter of children ages 5­14 are post-secondary non-tertiary and tertiary educa- working, and among children ages 10­14, tion." (Source: UNESCO Institute for Statistics 31% are estimated to be working. In Burundi Database). Stylized facts about youth and labor markets in Africa 7 In Ethiopia, 53% of the rural-to-urban mi- Figure 4 Share of young workers in agriculture (in %) grants are youth, and the main reasons that push them to migrate are access to education 100 (57%) and search for work (22%) (Ethiopia 90 LFS 2005). 80 Young male migrants are more likely to 70 be unemployed and out of the labor force 60 than their non-migrant counterparts (Garcia 50 and Fares 2008). 40 Urban residents are less likely to be em- 30 ployed than recent rural-to-urban youth mi- 20 grants. However, recent migrants who are 10 employed are more likely to work in insecure 5 98 98 01 05 98 94 97 05 00 97 03 and 00 05 99 98 jobs. In Ethiopia they are three times more likely to be engaged in informal activities. Burundi d'Ivoire Ghana Ethiopia Guinea Kenya Malawi Leone Tomé Principe Uganda Zambia Tanzania Cameroon Mauritania Recent youth rural migrants are more Côte Madagascar São Sierra educated than rural residents, but less edu- Source: World Bank Survey-based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. cated than native urban residents, thus sug- gesting self-selection. In Ethiopia, 74.9% of recent young migrants were illiterate or had only primary education, compared to Table 5 Distribution of rural young workers in the agricultural and 57% of native youth urban residents, and non-agricultural sectors by employment status (in %) 97.7% of rural youth residents (Ethiopia LFS 2005). Wage employment Self-employment Unpaid family workers In 1999­2003 the youth employment Agric. Non-agric. Agric. Non-agric. Agric. Non-agric. elasticity of GDP growth in Sub-Saharan Af- Ethiopia, 2005 2.2 20.2 14.8 45.1 83.0 34.7 rica was 0.62, down from 0.90 in 1995­1999 Madagascar, 2005 3.6 37.7 10.0 22.7 86.4 39.6 (Kapsos 2005). Tanzania, 2005 1.5 19.6 82.5 42.1 16.0 38.3 Before the age of 24, most female youth Source: Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. have already been married, but in many countries they get married even earlier: In Mozambique, 47% of females were already married before the age of 19; in Chad 49%; Table 6 Underemployment rates among young men and women (in %) in Guinea, 46%; in Mali, 50%; in Sierra Le- one, 46%; in Niger, 62% (United Nations Men Women 2007). In rural areas, the median age of SHIP data first marriage for women is as low as 15.2 Cameroon, 2001 26.3 40.7 in Niger (1998), 15.8 in Chad (1997), 16.1 Ghana, 1998 22.2 30.0 in Guinea (1999), 16.3 in Mali (2001), Guinea, 1994 4.4 16.3 and 16.7 in Ethiopia (2000) and Senegal Mozambique, 1996 33.6 42.0 (1997).8 Mauritania, 2000 63.1 66.9 Motherhood starts very early. In 2003 in Malawi, 1997 26.5 36.7 Mozambique, 58% of females in the range of Nigeria, 1996 6.1 6.7 15­24 had already given birth at least once, Sierra Leone, 2003 2.9 7.4 and 18% of males at this age were parents. SSA-8 (mean) 23.1 30.8 ese figures are respectively 57% and 17% in SSA-8 (median) 24.3 33.4 Malawi (2004), 57% and 7% in Niger (2006), LFS data 53% and 10% in Chad (2004), 47% and 15% Ethiopia, 2005 12.0 16.4 inUganda(2006),and47%and17%inGabon Madagascar, 2005 15.1 23.2 Tanzania, 2005 13.5 12.4 8For the cohort of women age 25­29 at the time Source: World Bank Survey-Based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. of the survey. (Source: ORC Macro, 2004. Mea- Source: World Bank Survey-Based Harmonized Indicators Program (SHIP). sure DHS STATcompiler). 8 Youth and Employment in Africa ­ The Potential, the Problem, the Promise (2000). e median age at first birth is 17.9 Table 7 Distribution of young men and women by job status (in %) in Niger (1998), 18.2 in Chad (1997), 18.6 in Guinea (1999), and 18.7 in Gabon (2000), Employed Unemployed Out of the labor force Mali (2001) and Mozambique (1997).9 Men Women Men Women Men Women Young women who have given birth SHIP data have substantially lower education attain- Burundi, 1998 67.4 72.8 0.4 0.3 32.2 26.9 ment than those who have not given birth Côte d'Ivoire, 1998 53.4 49.4 3.0 3.0 43.6 47.6 (Table 9). Cameroon, 2001 43.6 41.8 8.5 6.0 47.9 52.2 ese stylized facts suggest that the Ghana, 1998 15.7 19.7 29.0 33.7 55.3 46.6 youth at large comprise a vulnerable group Guinea, 1994 66.3 73.4 6.6 9.9 27.1 16.7 facing challenges in labor markets, but also Kenya, 1997 23.5 18.3 4.6 2.8 71.9 78.9 indicate that youth attached to agriculture Mozambique, 1996 28.6 16.0 3.7 0.7 67.7 83.2 and female youth face particularly stronger Mauritania, 2000 36.4 21.2 4.1 2.2 59.5 76.6 challenges. Malawi, 1997 21.9 18.9 2.0 0.8 76.1 80.3 Nigeria, 1996 27.1 19.6 7.9 3.4 65.0 77.0 Sierra Leone, 2003 31.5 48.5 60.6 45.2 7.9 6.3 São Tomé and Principe, 2000 47.4 18.2 5.3 2.8 47.3 78.9 Uganda, 1999 22.1 13.9 1.0 0.5 76.9 85.6 Zambia, 1998 38.1 39.3 8.1 5.5 53.8 55.3 SSA-14 (mean) 37.4 33.6 10.3 8.3 52.3 58.0 SSA-14 (median) 34.0 20.5 5.0 2.9 54.6 66.0 LFS data Ethiopia, 2005 78.7 67.7 2.2 3.4 19.1 28.9 Madagascar, 2005 72.3 71.1 1.3 2.0 26.4 26.9 Tanzania, 2005 74.9 74.0 4.1 5.5 21.0 20.5 Source: World Bank Survey-Based Harmonized Indicators Program (SHIP), Ethiopia LFS 2005, Madagascar EPM 2005 and Tanzania ILFS 2005/06. Table 8 School enrollment ratios, 2005 School enrollment, primary Male (% gross) 99 Female (% gross) 87 Male (% net) 71 Female (% net) 65 School enrollment, secondary Male (% gross) 35 Female (% gross) 28 Male (% net) 28 Female (% net) 23 School enrollment, tertiary Male (% gross) 6 Female (% gross) 4 Source: WDI 2007. Note: Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Net enrollment ratio is the ratio of children of official school age based on the International Standard Classification of education 1997 who are enrolled in school to the population of the corresponding official school age. 9 For the cohort of women age 25­29 at the time of the survey. (Source: ORC Macro, 2004. Mea- sure DHS STATcompiler). Stylized facts about youth and labor markets in Africa 9 Table 9 Distribution of young women who have given birth or not by highest education level attended (in %) No education Primary Secondary Higher Have given Have not Have given Have not Have given Have not Have given Have not birth given birth birth given birth birth given birth birth given birth Benin, 2006 69.2 32.7 20.6 26.0 9.9 39.3 0.3 2.0 Burkina Faso, 2003 82.4 60.9 12.7 19.0 4.8 19.3 0.0 0.8 Cameroon, 2004 24.5 9.4 43.0 32.4 32.2 55.5 0.3 2.7 Chad, 2004 71.8 58.9 21.4 26.3 6.6 14.4 0.2 0.4 Ethiopia, 2005 73.6 36.1 20.4 41.5 5.6 20.7 0.3 1.7 Gabon, 2000 5.6 3.4 37.4 29.7 56.0 64.4 1.0 2.5 Ghana, 2003 26.7 10.9 27.6 19.4 45.3 66.3 0.4 3.4 Guinea, 2005 79.4 47.4 12.3 23.9 8.0 28.3 0.2 0.4 Kenya, 2003 10.3 4.7 70.2 60.8 18.3 29.5 1.2 5.0 Lesotho, 2004 1.4 0.3 62.2 53.1 36.1 45.9 0.3 0.7 Madagascar, 2004 28.1 13.9 54.5 42.8 17.0 40.7 0.3 2.7 Malawi, 2004 12.5 4.0 70.8 66.2 16.5 28.7 0.1 1.1 Mali, 2006 79.9 55.0 12.3 17.1 7.6 26.8 0.2 1.1 Mozambique, 2003 38.1 19.7 56.4 60.3 5.4 19.7 0.1 0.4 Namibia, 2000 9.2 2.9 28.4 31.2 61.7 65.3 0.7 0.6 Niger, 2006 86.9 65.3 10.1 17.1 3.0 17.2 0.0 0.5 Nigeria, 2003 53.7 17.5 21.0 18.6 24.0 57.9 1.3 6.0 Rwanda, 2005 24.4 9.2 69.9 80.9 5.4 9.5 0.3 0.4 Senegal, 2005 70.4 42.4 24.1 33.3 5.3 23.4 0.2 0.9 Swaziland, 2006 6.4 1.7 35.1 32.2 56.6 63.3 1.9 2.8 Tanzania, 2004 27.5 16.7 68.2 66.8 3.8 15.3 0.5 1.2 Uganda, 2006 12.1 2.9 68.0 58.9 18.0 33.2 1.9 5.0 Zambia, 2002 12.8 6.4 61.1 50.4 25.0 41.2 1.1 2.0 23-SSA (average) 39.4 22.7 39.5 39.5 20.5 35.9 0.6 1.9 23-SSA (median) 27.5 13.9 35.1 32.4 16.5 29.5 0.3 1.2 Source: Demographic and Health Surveys. Note: Young women who have already given birth at least once are those young women for whom the total number of children ever born (including children who have died) is at least equal to one. 10 Youth and Employment in Africa -- The Potential, the Problem, the Promise Policy response requires an integrated, multi-sector approach and close monitoring Given the challenges faced by the youth in cultural, health, or family status, which also labor markets in Africa, as suggested by the require decisive action. stylized facts, assuring that young people can achieve success in pursuing employment may require long term, concerted actions, Past interventions to support young that span a wide range of policies and pro- workers in Sub-Saharan Africa: What do grams. Indeed, success will not be achieved we know? and sustained through fragmented and iso- e World Bank has compiled a world-wide lated interventions. Policy response will re- inventory of interventions designed to in- quire working with households and commu- tegrate young people into the labor market nities across multiple sectors in both rural (Puerto 2007, Rother 2006). Eleven out of and urban areas, and creating policies that the 29 programs in Sub-Saharan Africa cov- will evolve over extended and varied time eredintheinventoryhaveacomprehensive, periods. An over-arching, but most essential multiple service approach. In most cases, guideline for addressing the youth employ- these programs depend almost entirely on ment challenge is the need for an integrated external funding from international donor strategy for rural development, growth and institutions, bilaterals and their national job creation. is type of integrated policy implementation agencies. Such programs would cover both the demand and the sup- included elements targeted at helping ply sides of the labor market and take into young people to start their own businesses, account the youth mobility from rural to ur- combined with elements of skills develop- ban areas. It should, then, be combined with ment and training. Seven programs focused targeted interventions to help young people exclusively at improving chances for young overcome disadvantages in entering and re- entrepreneurs. ey typically encompassed maining in the labor market. modules such as supporting young people Targeted, but coherent interventions in starting their own business, includ- should address the gender and age specific ing providing training on writing project challenges of young people; the aspirations proposals and business plans; conducting of youth which do not match the realities in feasibility studies; counseling on legal re- the labor market; the lack of job experience quirements; and improving their access to which makes them less attractive to employ- credit/start-up loans. An example of this ers; the difficult access to and low quality of category of programs is the Youth Dairy education and training, especially in rural Farm Project in Uganda, which supports areas; the lack of organization and voice to youth by training them in the management ensure their needs are addressed in policies of husbandry and farm products, which and programs; the demographic bulge and the youth then sell. Six programs focused migration from rural to urban areas; early mainly on skills training for young people motherhood; and the difficult access to the and four programs adopted the objective means needed for the youth to engage pro- of making existing training systems work ductively in the economy. ese challenges better for young people. e latter intend- are amplified by conflicts and by discrimina- ed to improve highly fragmented, input- tion based on sex, ethnicity, race, religious, orientated training systems by upgrading Policy response requires an integrated, multi-sector approach and close monitoring 11 training facilities; improving the quality of However, evaluations of the impact of training centers; enhancing the quality of the programs--an invaluable element for instruction; and upgrading the matching guiding policy--have been very low in Sub- processes between labor demand and sup- Saharan Africa, and lower than in any other ply through better coordination and infor- developing region. is can be explained mation systems. Finally, one program was partially by the low number of youth em- categorized as making the labor market ployment programs in the region, poor data better for young people: e public works availability, and the fact that evaluations program in South Africa covers infrastruc- rarely tracked post-program outcomes. ture projects, the environmental sector, In general, programs in Sub-Saharan and the social sector, and seeks to increase Africa included in the inventory were not the labor intensity of government-funded evaluated appropriately. For example, while programs and to create work opportunities 11 programs included information on gross in public environmental programs. labor market outcomes, 16 programs lacked Interventions in the poorest countries any information on results or the level of have generally focused mainly on young en- evaluation was unknown. Only two had trepreneurs and followed a scheme of multi- enough information to suggest a positive service programs. is contrasts with the impact. In the case of 10 programs--includ- situation of middle-income countries such ing three entrepreneurship programs, three as South Africa and Namibia, where multi- skills training programs, one "making train- service comprehensive approaches were used ing systems work better for young people" predominantly to integrate unemployed program, one public works program, and youth into the labor force, mainly through two multiple service programs--a tentative the provision of skills training programs. assessment based on limited available in- Young workers were the primary focus formation would suggest a positive impact of most employment interventions includ- on labor market outcomes. However, it is ed in the inventory. Twenty-two out of the not clear whether the benefits exceeded the 29 programs (76%) targeted young workers costs associated with the programs' imple- exclusively, while seven programs were open mentation in all cases. to unemployed workers of all age groups. Two programs (PCY Uganda and the Most of the programs targeting youth work- Swiss-South African Co-operation Initiative) ers aimed at improving employment pros- that have a comprehensive, multiple service pects for young entrepreneurs, skills train- approach, for which an impact analysis was ing, or implementing the multiple service conducted,wereawardedthehighestrankings approach. Eight programs focused on urban for the quality of their intervention. ese areas, six on rural areas, and fifteen on both programs had a positive impact on labor mar- areas. ket outcomes and were cost-effective. PCY Eleven out of the 29 programs were di- pursues an integrated, multi-dimensional rected towards young women, and three approach to promoting the needs of children programs targeted young workers with dis- and youth in the areas of social work for and abilities. Moreover, 12 programs were aimed with young people, information and counsel- at young people with low-income, and 17 at ing, entrepreneurship and self-employment youths with low levels of formal education. activities, and local skills development. By contrast, ethnicity did not appear to be According to the inventory, it appears a selection criterion. In general, significant that successful interventions in the region complementarities can be observed in pro- are often associated with a multifaceted, grams targets. For example, the majority of integrated bundle of services such as skills programs that target low-income youth also training, promoting entrepreneurship and target youth with low-levels of education. A addressing social elements. Moreover, pro- similar trend is observable for programs tar- grams aimed at strengthening entrepreneur- geting women or disabled youth, which fo- ship also seem to deliver satisfactory results cus at the same time on young people from in many cases. low-income families with no or only low lev- Accounting for all programs with net els of education. impacts evaluations included in the inven- 12 Youth and Employment in Africa ­ The Potential, the Problem, the Promise tory--73 out of 289 programs implemented ere are conceptual issues relating to the in 84 countries around the world--it ap- specificities of African economies and labor pears that comprehensive, multiple service markets.Indeed,labormarketsindeveloping approaches did better than average. In Latin countries, and particularly in Sub-Saharan America, the Jovenes Programs, for exam- Africa, differ from those in other countries ple, have been widely analyzed and cited as in that most of the labor force is either in a successful story in assisting young workers informal jobs, self employment, or inactive. in developing countries (World Bank 2007). Rural wage labor markets are very thin and ey use a demand-driven model that targets almost all occupied youth are in subsistence economically disadvantaged youth, fosters agriculture or unremunerated home produc- private sector participation, and promotes tion activities, and unemployment there is competition among training providers. It has typically very low. Working conditions in been successful in improving job placement agriculture are particularly unfavorable and and earnings, but has become particularly can be hazardous. is fact, along with low expensive for some countries where it has income and limited perspectives of improv- been replaced by smaller and more focused ing living standards and educational attain- interventions.10 Entrepreneurship programs ment, encourages youth to migrate. also performed better than average. Despite Making well balanced choices for employ- its low frequency in interventions and evalu- ment-intensive investments in agriculture ations, this category of program shows im- and other rural non-farm activities can cre- provements in employment and earning of ate immediate short term employment op- young people. Overall, training-related pro- portunities which can be more easily tapped grams were relatively less successful than by young people. Combined with appropriate average. local economic development strategies, this As long as youth employment programs approach can generate more and sustainable in Sub-Saharan Africa countries are relative- jobs. Indeed, an study in Liberia (FAO/ILO/ ly rare, more systematic and careful evalu- Ministry of Agriculture 2007) demonstrated ations of program performance are needed that modern agriculture has considerable po- to draw strong policy conclusions. To the ex- tential for job and wealth creation and may tent that evaluations exist, they typically fail absorb large numbers of would-be youth to analyze the effect of policy interventions. migrants or youths who currently crowd the e particularities of African countries cities with underemployment. However, this and the lack of more systematic information requires strategies to make agriculture an at- and evaluation of what works leaves the need tractive enough option for youth to engage to discuss and explore areas for interven- tions. In what follows, this essay discusses 10 areas that should be included as important Estimates on unit cost for the Jovenes Pro- grams range from the upper US$ 600s to about elements in development agendas aimed at US$2,000 per participant served. Across pro- tackling youth employment in Africa. grams, there is impact evaluation evidence of in- creased employment probability and earnings of participants upon graduation compared to their Expanding rural job opportunities control group. Although expensive, they proved Africa's rural population is very high and to be cost-effective. Early evidence from Peru a substantial share of the labor force is at- indicates that the positive earnings effect shall last at least 7 years for PROJoven to yield a posi- tached to agriculture, making rural activi- tive net gain. A recent longitudinal version of ties a major part of the equation of youth propensity score matching of PROJoven shows employment. Unless urban areas can create a positive internal rate of return, consistently a massive number of jobs, which is unlikely above 4%. In Dominican Republic, the invest- because most countries have not yet initiat- ment on training is recuperated after 2 years. ed their transition to industrialization, any Although the program has yielded positive re- sults in Latin America, it does not mean they development agenda must recognize that in will necessarily work in other contexts. Piloting the short term only rural activities, farm or and carefully evaluating results are therefore non-farm, can effectively create occupation needed should governments want to replicate for most new job seekers. them in Africa. Policy response requires an integrated, multi-sector approach and close monitoring 13 in, including moving away from subsistence likely have a particularly pronounced effect agriculture, and introducing commercializa- on youth, even if not specifically targeted at tionandproductivityimprovementsthrough them. For example, the promotion of small technological changes and infrastructure and medium rural enterprises that use new support. Recent changes in the global food technologies could have a differential impact market, in science and technology, and in a on youth, given their advantage in using range of institutions that affect competitive- them. ness are creating new challenges to the com- By creating jobs and educational oppor- petitiveness of smallholders, but are creating tunities, rural areas can increase their attrac- income opportunities, too. tiveness to young workers, thus eventually In order to create jobs, accelerated prog- delaying the rural-urban migration. is is a ress will be needed to increase agricultural very critical issue that governments should productivity and to connect poor people to attempt to mitigate in order to prevent the markets. Sustained growth that reduces ru- growth of urban youth unemployment and ral poverty will require significant growth underemployment, and the worsening of in agricultural value-added and multi-sector well being in already congested African cit- approaches that solve gaps as well as sup- ies. port agribusiness, and rural diversification. Youth migration can significantly change To create jobs that will increase rural income the composition of the rural population, and welfare and, thereby, retain young peo- which poses its own challenges for rural de- ple, it is necessary to increase investments in velopment because migration is often selec- irrigation, water resources management, and tive. ose who leave are generally younger, research and extension; increase rural public better educated, and more skilled. Youth services; and increase use of improved seeds, migration can thus diminish entrepreneur- fertilizers and better agricultural practices. It ship and education level among the remain- is also necessary to address vulnerability to ing population. In addition, migration can weather-related shocks and limited farmer change the gender composition of rural pop- capacity, distorted incentives (including ulations. But migration has several benefits Government policies) that keep farmers in too, as it diversifies risks, contributes to rural subsistence farming, poorly functioning in- income through remittances, and increases put/output markets, and weak institutional knowledge and opportunities. e challenge, capacity to manage the risk of food insecuri- then, is to find the appropriate set of incen- ty. Increased investment in rural roads, rural tives that makes youth migration contribute electricity, and communications will permit the most to lift the livelihoods in both rural rural areas to become better connected to and urban areas. market opportunities. Employment opportunities for the rural Investments in rural education are also youth are not only in agriculture but also necessary to increase rural productivity and non-farm. Including rural towns, the rural enhance the well being of the rural residents. non-farm sector accounts for about 20% of As the rural young workers today may be the employment opportunities in Sub-Saharan urban workers tomorrow, investing in hu- Africa. e history of economic development man capital in rural areas is important not hasshownthatdevelopmentofthenon-farm only as a way out of poverty in the agricul- sector is tied to improved productivity on ture sector, but also as a way to create oppor- the farm. As technological innovations raise tunities for people to migrate more success- productivity on the farm, labor is freed up to fully and contribute to the economic growth move to the non-farm sector. e range of of cities (World Bank 2009). Indeed, better opportunities in rural areas is far wider than educated migrants are more likely to have a might be apparent at first glance. successful migration outcome. e rural non-farm economy can gen- Because young people are the most mo- erate a significant share of rural incomes, bile, they are the most likely to switch sec- shares that have grown in many countries. tors to take advantage of new opportuni- Earnings are significantly higher in non-farm ties (see Box 1). So policies to designed to activities mostly due to skills differences. In develop the farm and non-farm sectors will some instances, this higher income share is a 14 Youth and Employment in Africa ­ The Potential, the Problem, the Promise Box 1 Escaping poverty through migration: Insights from Burkina Faso In a recent study the International Movement ATD Fourth World tells the life story of Paul, a youth who lived in the streets of Ouagadougou when he left his village about 35 km from the capital in search of opportunities. Like other children from the streets, Paul never accepted to be called a `Street Child' as this was a pejorative label. After meeting the Fourth World team, it took Paul several years to return to his village in part due to the shame that he had felt from having ended up in the streets. But he did manage to stabilize his life by going back to his village, and he is now back in the city and doing well. Paul's story highlights how employment conditions for youths in poverty are often made up of a stream of activities in a process of trial and errors in search of a decent livelihood. During four years spent in his village after coming back from Ouagadougou, Paul engaged did seasonal gardening in his uncle's garden. Then this uncle gave Paul the opportunity to grow and sell vegetables on his own and he made a small profit from it. He later helped another uncle to work with a tractor and was paid for this work. Still with another uncle he sold clothes at a local market, and another relative introduced him to buying chickens and selling them to merchants who came from the city. With friends and relatives he made bricks of clay to sell as building material. He also raised chickens, goats, and pigs. Basically every activity that Paul participated in involved a relative or a member of his community, suggesting that the quality of social links between community members is essential for access to work and training for youths. After four years in his village, Paul left again for Ouagadougou. As he explained it, "In the village you can't earn a living by farming without trading. I came to Ouagadougou in search of money to start trading. I want to sell new clothes in the village: hats, caps, and Nike sportswear. I expect to work in my village and I want to help my parents and my brothers." Shortly after his arrival in the city, Paul found a job in a res- taurant. He also found accommodation in a yard in the same area. Paul's job consists in selling roasted chicken. He is in charge of the cash register, which is a sign that the owner of the restaurant trusts him. Paul still goes back to the village every third Sunday, which is a market day. He visits his parents and grandmother. He leaves them some money. He leaves his savings with an uncle in the village. Thus, even though Paul went back to live in the city as it did provide better job opportunities, he still identifies himself as a member of his family and as a villager. He describes his future project as follows: "I will begin trading during the holiday season. Once I begin trading, I will stop working at the restaurant. I will look after my relatives during the rainy season and provide them with food. My young brothers can learn trading with me. They can come and help me, take things to sell and go around the market." Can it be concluded from Paul's life story that he and his family moved out of poverty and that migration and employment in the capital ultimately played a positive role in this process? Probably yes, at least to some extent. Nowadays, Paul is in a better economic situation than before, even if he still has an informal and potentially unstable job, as most other youngsters in Burkina Faso. Today, Paul keeps as one of his key objectives the ability for him to support his family. He actually measures the success of his activities in part by his ability to support his parents and brothers. As Paul said as a conclusion to his experiences, "I have a job, that's good. If you don't have a job, you don't know what you are going to do to earn money. What is also good is that I am not far from my village: I can go see my family and come back. I am lucky because if I go back to live in the village, I will always find work and the people will always show me what to do." At the same time, the story shows that migration for the sake of better employment is often an iterative process, especially when the migrant stays within his/her country, and that it may entail substantial costs, as evidenced by the period during which Paul was forced to live in the streets of Ouaga- dougou, lost contacts with his family, and thereby lacked the support that such contacts may bring. Source: Wodon (2008). result of crop failures or other adverse shocks substantially to job creation and income. to the farm sector. In most cases, however, In Latin America, for example, about half rising productivity growth in the agriculture the youth population in rural areas, and sector raises farm income and hence the more than 65% of those ages 25­34, work demand for goods and services produced in nonagricultural activities (World Bank outside agriculture. International evidence 2007). shows that labor productivity is higher there e demand for youth labor will not in- as measured by value added per worker. crease without a dynamic rural economy in Although agriculture is still the largest both the agriculture and non-farm sectors. source of rural income in Africa, the shares An appropriate investment climate along of incomes from non-farm rural activities in with adequate infrastructures that prepare total income are already relatively high and towns and cities for business and urban- increasing. e small participation of the ization is therefore critical. Indeed, rural non-agricultural sector in employment sug- Investment Climate Assessments reveal gests that it has the potential to contribute substantial constraints on rural investment, Policy response requires an integrated, multi-sector approach and close monitoring 15 including access to credit, land titling, inade- upturns in both male and female secondary quate supply of energy, poor quality of roads school enrollment. For the average country, and infrastructure, lack of well-functioning a doubling of apparel and footwear exports legal structures, and weak governance. as a share of GDP raises female secondary school attendance by 20­25% (Gruben and McLeod, 2006). Improving the investment and e poor job creation observed in vir- macroeconomic environments tually all Sub-Saharan African economies, ough improving the investment climate is whatever their geographic and demographic not youth specific, it can have a significant characteristics, income level, and whether or impact on youth by creating more and bet- not reforms were undertaken, suggests that ter jobs. Indeed, economic growth and job the supply side explanation may be incom- creation benefit most participants in the plete. Yet small domestic and regional mar- labor market, youth included. When labor ket sizes and low purchase power of con- demand is strong, youth employment and sumers trap firms in low scale production labor force participation for both males and and low productivity and help explain the females increases while the unemployment limited labor demand and the types of jobs rate for youth tends to go down.11 created. Limited economic activity is there- Governments should create a better in- fore an important determinant of youth vestment climate by tackling unjustified unemployment and underemployment. costs, risks, and barriers to competition. Resolving these problems requires growth ey can do this by ensuring political stabil- of employment at a sustained level. Well- ity and security, improving the regulatory designed macroeconomic policies that bal- and tax climate for investment, and provid- ance objectives of macroeconomic stability ing needed infrastructure. Trade facilitation with employment generation are of primary and adequate industrial policies can also importance. Given that youth employment play a key role in the business environment is highly dependent on the general employ- in the region. In the case of Africa, which ment situation, policies to boost and sustain is a high risk and high cost place for doing job-rich economic growth are fundamental business, improvements in the investment for young peoples' successful integration in climate can rapidly be accompanied by cre- the labor market. ation of jobs. During recent decades, growth in Sub- Expanding world trade has shifted pro- Saharan Africa has been both low and highly duction around the world. Because the volatile, which helps explain the poor in- young are the most able to respond to the vestment climate and gloomy job creation growing demand for labor, these shifts can (Arbache and Page, 2008). Indeed, volatility favor them. e young may also be particu- reduces the time horizon and incentives for larly attractive to firms in new and growing long-term investments, and increases risks. sectors of the economy because they are Africa's poor long-term growth was a product more adaptable than older workers to new of good and bad times for its economies that production methods. featured surprisingly high rates of growth Industrial growth led by foreign direct and decline that occurred with almost equal investment can be stimulated partly by the frequency (Arbache and Page, 2007). ere availability of cheaper young labor. How- is now sufficient evidence that economic, so- ever, as the dynamic growth process occurs, cial, governance, and institutional variables the demand for a more educated labor force are significantly different during growth ac- able to adapt to new technology with ap- celeration and deceleration episodes, and propriate knowledge, skills, and behavior that reducing growth volatility and prevent- will increase. Indeed, opportunities in more ing growth collapses turns out to be critical dynamic sectors can provide incentives for for sustainable growth and job creation. youth to acquire more skills. Among 48 de- veloping countries, increases in apparel and 11 In poor countries where few can afford to be shoe exports as a share of GDP were found unemployed, increasing labor demand is likely to to be positively associated with subsequent impact primarily quality than quantity of jobs. 16 Youth and Employment in Africa ­ The Potential, the Problem, the Promise Encouraging and supporting well integrated services such as management entrepreneurship training, business mentoring programs, fi- A main factor behind the high rate of youth nancial services, support in gaining access underemployment in Africa is the lack of markets, and networking opportunities (see productive jobs to meet the supply of youth. Box 2). An alternative to reducing the underem- In most countries, the fastest growing ployment is encouraging entrepreneurship, form of employment is the non-agricultural a driving force for initiating business ideas, household enterprise. is sector already ac- mobilizing human, financial and physical re- counts for 24% of the labor force in Uganda, sources, and for establishing and expanding and 30% in Senegal. is employment is enterprises. Entrepreneurship is not youth mostly urban, although there is an impor- specific, but can unleash the economic po- tant rural non-farm sector as well. Informal tential of young people and provide living al- economy agents should be strengthened ternatives for them. An enterprise and entre- to improve the quality of employment and preneurial culture is of primary importance. increase productivity. e service sector of- Societies that appreciate entrepreneurship fers immense possibilities in both rural and and promote its values and norms can create urban areas. By encouraging informal sector a dynamic and vibrant class of young entre- enterprises to grow and succeed, without en- preneurs. Empirical evidence shows that ed- couraging illegal activities and tax evasion, ucating young minds in enterprising behav- several productive jobs can be created for the ior, thus boosting confidence for calculated youth. risk taking, increases incidence of adopting entrepreneurship as a career option. Improving access to education and skills Young people, and in particular young A young person's employment prospects are women in rural areas, face particular chal- closely related to the education received. Ac- lenges. ey have less capital in the form of cess to basic education is widely recognized skills, knowledge and experience, savings as an effective means of combating child and credit, and more difficult access to busi- labor and eradicating poverty. Indeed, edu- ness networks and sources of information. cation and skills development generate im- Weak representation of young people in portant economic as well as social benefits. policy and decision-making is another issue. Unskilled youth workers are more vulnerable ey lack the influence and the connection to economic shocks, less likely to find work, with representative business associations more likely to get stuck in low quality jobs and networks that generally work with the with few opportunities to develop their hu- government on relevant policies. Enhancing man capital, and are also more vulnerable to their capacity for participation in association demographic changes. In Ethiopia, young- building and policy advocacy can address age cohorts have a much larger impact on this disadvantaged position. Young women the probability of unskilled youth in urban entrepreneurs face additional hurdles, as in areas to find jobs than those more educated many cultures their roles in the family and (Garcia and Fares, 2008). society keep them from tapping opportuni- Education and skills are central to in- ties in business development. Because of creasing productivity and income. Boosting this, they are more likely to be in the infor- productivity requires technological change, mal economy, in self-employment activities, which in turn is only possible if new and and are less likely to be entrepreneurs em- higher skills are available at large. Invest- ploying others. ment climate surveys show that more than Youth entrepreneurship can be maxi- a fifth of all firms in developing countries mized through programs and strategies that as diverse as Algeria and Zambia rate inad- address the barriers to doing business, iden- equate skills and education of workers as a tify youth with entrepreneurial drive and major or severe obstacle to their operations talent to be nurtured, build the appropriate (World Bank, 2007). It is important to take skills, and help new entrepreneurs develop these factors into account in policy planning. their businesses. Successful development of Capital investment and introduction of new youth business hinges upon good access to technologies without having a locally skilled Policy response requires an integrated, multi-sector approach and close monitoring 17 and educated workforce available means ment systems such as: national occupational that local youth will be ill-equipped to take standards; curriculum development which up emerging jobs. On the other hand, having emphasizes both the acquisition of knowl- highly-skilled and educated persons avail- edge and understanding and the demon- able without job opportunities will lead to stration of occupational performance; skills outward migration, or trigger frustrations assessment based on demonstration of com- with negative consequences. petencies; additional skills for employability ere have been significant improve- along with occupational training; funding ments in primary school enrollment in most focused on performance and outcomes of parts of the region, but access to and qual- the Technical and Vocational Education and ity of education are still major issues, espe- Training(TVET)institutions(seeBox2);and cially in rural areas. Lack of access to educa- skills recognition and certification to help tion has been shown to be among the most youth to seek jobs in the formal economy. important reasons for youth migration; it e provision of public technical and voca- was cited by 57% of Ethiopian youth who tional training has, however, been less than recently migrated from rural to urban areas adequate as it often offers insufficient op- (Ethiopia LFS 2005). Although the enter- portunities for practice and is biased toward prise surveys in Africa suggest that skills of white collar jobs in the urban wage sector; workers is low on their list of complaints, a provides courses that are often rigid and too finer look reveals that the better companies, standardized to meet the multi-skill needs such as the large foreign owned companies of the workplace; and often includes little in demanding sectors (export industries as accountability and few incentives to moni- opposed to retail), do complain quite a bit tor and adjust to changes in the demand for about skills. skills of formal and informal sectors (Adams Reaction to improved access to education 2008). can be significant as suggested by the result Informal apprenticeship is a major pro- of the elimination of school fees for primary vider of skills in the informal economy, most- education in Kenya and Uganda. is action ly for the poorer and less educated youth. produced large increases in school enroll- Governments and social partners need to ment and had large impacts on completion review informal apprenticeship systems and rates for fourth and fifth graders from poor provide guidance and support to this system households. Other costs, however, can still through introduction of regulations (such as hinder the chances of the poor to attend the maximum duration of training per trade school. For example, in some countries, in order to prevent exploitation of apprentic- distance to school was found to be a major es); improvement of the learning processes correlate of program uptake (World Bank through training of master craftsmen and 2008). provision of incentives to these craftsmen; Providing specific technical skills in high assistance with the testing and certification demand by the private sector (e.g. English of graduate apprentices; inclusion of evening proficiency, plumbers, mechanics and ac- literacy or theoretical classes to apprentices countants) and in rural areas are also impor- in the public education/training institu- tant for successful youth employment poli- tions; and offering a fiscal allowance to ap- cies. It is necessary to expand public training prentices, giving many more youth a chance opportunities to provide better access to dis- pay for their training. advantaged urban and rural youth, the less Developing second chance education educated, and girls.12 Indeed, to the extent programs for dropouts should also be an that women engaged in the labor market important element of an effective program. have lower fertility, higher bargaining power Examples of such are in Uganda and Malawi, and improved allocation of resources at the household level, targeted job opportunity 12Low rural educational levels, poor learning out- programs for girls may have far-reaching comes, scattered populations, limited demand, beneficial consequences. and low cost-recovery are challenges in providing Policies should include the introduction quality training services in rural areas (Bennell of new fundamentals into the skills develop- 2007). 18 Youth and Employment in Africa ­ The Potential, the Problem, the Promise Box 2 Jua Kali Voucher Program in Kenya One of the best known programs under this heading is Kenya's Jua Kali voucher program, established in 1997 as a pilot program, under the auspices of the Micro and Small Enterprise Training and Technology Project. Under this type of program, vouchers are issued to un- employed youth, who can personally select a training provider based on their needs and objectives, rather than having them chosen by a bureaucratic institution. The voucher program intends to empower recipients with the capacity to buy training on the open market and thereby promote competition between private and public suppliers. The approach should improve the quality of training and bring down the costs, while at the same time ensuring a better match between the participant and the training course. Under the Jua Kali pilot program, anyone eligible for training is given a voucher which can be cashed in at the chosen training provider. Participants pay only 10% of the cost of the voucher with the government subsidizing the remaining 90%. Master craftsmen were the major providers of training, responding to demand from clients. Although the Jua Kali voucher scheme did not focus entirely on youth, the majority of those trained were young and disadvantaged. Under this program, 37,606 vouchers were issued to entrepreneurs and employees in enterprises with fifty workers or less over the 1997­2001 period. There is evidence that the scheme has had a positive impact on those who were trained and that it has boosted employment, assets, and business for enterprises which participated (in comparison with a control group). These findings relate to a small population served by the pilot program; there is no evidence of outcomes/impact in a large (national) sample. The scheme was complex and costly to establish, and it has proven to be difficult to phase out the subsidization of the vouchers. Lessons learned from the experience include the following: such schemes should be administered through the private sector rather than (as in Kenya) through a government ministry; the scheme should include provision for upgrading of training providers, especially those from small enterprises; and it should promote the willingness of clients to pay for training. An exit strategy is needed unless subsidies are to last forever. But, overall, the Jua Kali experience suggests that there is scope for the use of vouchers in a system more precisely targeted at the most vulnerable. Source: Johanson and Adams (2004). where social funds projects are providing makes youth the most abundant asset that training to local youth at community owned the region can claim, thus making it a win- training centers. is training could have a dow of opportunity. Indeed, East Asia put rapid, strong effect on key sensitive popu- the right policies and institutions in place lations (including pregnant girls and young and was able to reap the demographic divi- mothers). Half of the 19-year-olds in school dend from a large work force with fewer de- are at the primary level in Malawi (World pendents, and part of the Asian Miracle is of- Bank, 2007). It is important, however, to ten attributed to the demographic dividend. carefully evaluate the cost/benefit ratios of e demographic pressure from a large these programs, which tend to be expensive. youth cohort entering the labor market can To make optimal use of investments in adversely affect youth employment pros- education and training systems, policies re- pects. In Ethiopia, the size of the youth lated to education and skills need to be fully cohort has already reduced the probability synchronized with other policies and pro- of their employment. In Tanzania, the in- grams for productivity, income growth, and crease in the size of the youth cohort has job creation. ese policies must also con- increased the incidence of unemployment sider the flow of capital investments in the among urban youth, particularly among ur- economy. erefore, inter-ministerial coor- ban females, and increased inactivity among dination and collaboration among different urban males (Garcia and Fares 2008). Given stakeholders becomes crucial. the large and increasing size of the youth population, African countries will have to recognize that finding proper jobs for most Addressing demographic issues new job seekers, especially in cities, will be a Africa's population is growing fast and is ex- challenge, and that it is likely that the infor- periencing a slow demographic transition. mal sector will continue to play a key role as e projections are that this will not stabilize a means of job opportunities for a long time before 2050. is transition has fiscal, po- to come (Fox and Gaal, 2008). litical, and social implications, ranging from increased education and health costs to risks Although crude birth rates have been of social unrest. e demographic transition declining, especially among young women, Policy response requires an integrated, multi-sector approach and close monitoring 19 Box 3 The Adolescent Girl Initiative young people do so because of poverty. e region has thousands of ex-young combat- The World Bank's Adolescent Girl Initiative began as a US$3 million public-private ants--100,000 in Sudan alone. In one study, sector partnership between the Government of Liberia, the World Bank Group and crippling poverty and hopelessness were the Nike Foundation. In a pilot-phase, it will expand to at least six other low-income unanimously identified as key motivators or post-conflict countries, adding the participation of new donors, governments, for the 60 combatants interviewed (Human foundations and corporations. This initiative promotes the economic empowerment Rights Watch, 2005). of young women by smoothing their path to productive employment. A new model It is becoming increasingly recognized of skills training matched to market needs for women aged 15­24 in Liberia has been that non-economic aspects of poverty, such developed with incentive structures in place to maximize access to wage jobs or as the absence or inadequacy of essential ser- successful self-employment. This model will be brought to the other pilot countries vices, the lack of livelihood and educational and, if successful there, to many more. In addition, depending on the economic envi- opportunities, and the non-participation of ronment, interventions such as business development skills training, job placement youth in decision--and policy-making are incentives and assistance, access to micro finance, and mentoring and apprentice- conditions that promote the involvement of ship programs will be added. young people in conflict. Conflict prevents Source: Gender Equality as Smart Economics Newsletter, World Bank Group Action Plan, Sep- children from obtaining a decent educa- tember 2008. tion and learning useful skills. Lacking any real social capital, many feel excluded from mainstream society and seek to become part of an armed militia, where they feel accepted (Integrated Regional Information Networks, they are still quite high as compared to other 2007). Whatever the cause, conflict creates regions--39 per 1,000 in Sub-Saharan Af- heavy losses in resources, thereby deepen- rica compared to 14 in East Asia and Pacific, ing poverty. Combined with poverty, conflict 20 in Latin America and the Caribbean, 24 exacerbates the alienation of young people in Middle East and North Africa and South from society and hampers their ability to Asia, and 20 in all regions in 2006.13 is has participate fully in development, even after labor market repercussions for the mother, the conflict is over. the father and the children. Indeed, early ere is a need for programs specifi- motherhood, a serious issue in Africa, has cally designed to meet the needs of youth in substantial impact on skills development conflict-affected countries. Such programs and labor market and career development, should include the recognition of prior skills thus compromising the likelihood of young through certification (e.g. Eritrea); and vo- mothers to invest in education and find cational training of ex-combatants with good jobs (see Box 3). Evidence shows that disabilities, such as in Sierra Leone. ese high fertility traps young mothers, especially programs should be more gender balanced from rural areas, into household and low and should not ignore the huge employment productivity activities. Easily accessible and needs of young women. effective sexual and reproductive health pro- grams targeted to young women can play a key role in addressing this issue. Improving the labor market conditions Active labor market policies and programs have increasingly been used in several Addressing youth in violent and countries to raise demand for young work- post conflict settings ers and enhance their employability. eir Sub-Saharan Africa has been the site of nu- function is to mediate between labor sup- merous armed conflicts in which young peo- ply and demand, to mitigate education and ple have been both the victims and the per- labor market failures, and to promote effi- petrators of violence. e period 1990­2000 ciency, equity, and growth. If properly tar- alone saw 19 major armed conflicts in Africa, geted and implemented, these programs ranging from civil wars to the 1998­2000 war between Eritrea and Ethiopia. Children 13Crude birth rate indicates the number of live and youth are increasingly participating births occurring during the year, per 1,000 mid- in armed conflicts as active soldiers. Many year population. (WDI 2007). 20 Youth and Employment in Africa ­ The Potential, the Problem, the Promise can effectively benefit disadvantaged youth. skills acquired through training in the job. ey can also assist rural workers in finding To be effective, employment services have to better employment opportunities by link- keep up with the changing requirements of ing them to jobs in semi-urban and urban the labor market and offer targeted packages areas, thus helping households transition of services that meet both the young people's out of poverty. ese programs are useful, and the employers' needs. however, in countries where mismatch be- Labor market regulations are also an tween job-seekers and existing vacant jobs is important element of policies to promote a significant problem, which is not the case efficiency and equity in the labor market. of most African countries. In spite of this, However, youth wages and employment active labor market policies can play a role protection legislation continue to attract in improving labor market conditions in the controversy in the debate on youth employ- rapidly growing urban areas of the region ment. In countries where labor law compli- and where demand for skilled people is on ance is weak and wage jobs are very limited, the rise. as is the case of many African countries, this One barrier to matching the supply of is less than a problem. Labor codes have, young labor to demand is the lack of both nevertheless, often been considered a po- labor market information and job search tential cause of high youth unemployment. skills. Regardless of a country's stage of de- e question for developing countries is not velopment, labor market information, job- whether to regulate or not, but what kind search techniques, and career guidance play and what level of regulations are appropriate an important role in helping young people to get the best forms of protection for young in their career choices and can bring about people, who are usually vulnerable and inse- better labor market outcomes should jobs cure, without inhibiting formal firms from become available. Labor market informa- hiring. tion improves the quantity and quality of job Good and effective public governance matches between employers and jobseekers, are critical for the successful design, imple- reduces unemployment spells, and increases mentation and impact of labor regulations, labor market efficiency. e collection, analy- policies and programs. Key aspects of good sis and dissemination of labor market infor- governance include the rule of law, and in- mation have a pivotal role in informing young stitutions for the representation of all inter- jobseekers about employment opportunities ests and for social dialogue. Social dialogue and in providing indications for policy and is a central element in the development program design. Furthermore, the availabil- of effective and credible interventions to ity of reliable and up-to-date labor market promote employment for young people. It information is essential for the design and requires strong, independent and well in- monitoring of youth employment interven- formed partners. Participation of young tions. Youth should also be given access to vo- people in membership-based organizations cational and labor market guidance in order and their engagement in decision-making to understand labor markets and select the processes affecting their employment and right occupation for which to train. is will working conditions are also important to reduce the time required for the job search fostering social inclusion and advancing de- and permit the utilization of knowledge and mocratization. Policy response requires an integrated, multi-sector approach and close monitoring 21 Conclusions Successfully addressing the youth employ- chronized with other economic policies, and ment challenge requires a coherent and challenges must be well understood so that integrated response that recognizes the interventions are effective, in particular as particularities of Africa, especially the very many countries are about to reforming their large share of rural youth population, gen- training systems. Specific attention has to der and demographic traits, and tiny labor be given to training needs in the informal markets. In many countries interventions economy. have focused on programs that are narrow ere is also heightened recognition of in scope, limited in time, and biased to- the need to work in partnership. Clearly the ward urban areas. Increasingly, the politi- primary responsibility for promoting youth cal priority attached to youth employment employment lies with governments. ere- has brought policy-makers to recognize fore, coherence, coordination and coopera- that achieving productive employment and tion are needed across different government work for young people entails long-term ac- institutions and agencies, at central and lo- tion covering a range of economic and so- cal levels. e challenge at stake, however, is cial policies focusing on labor demand and daunting and the responsibility reaches be- supply, and addressing both quantitative yond the national level. is calls for renewed and qualitative dimensions of youth em- efforts to work together in a concerted and ployment. Such policies and programs need effective way. Governments, the social part- to be integrated in broader development ners, civil society, the international commu- frameworks, and be made up of two key el- nity, as well as young people themselves, all ements: an integrated strategy for growth have an important contribution to make to and job-creation in both rural and urban ar- this process. eas, as well as targeted interventions to help Finally, as seen above, youth employ- young people overcome the specific barriers ment is not an isolated issue; it reflects eco- they face in entering and remaining in the nomic, geographic, demographic, and other labor market. conditions, and the particularities of each Job creation can be supported through country. Youth specific policies will be more employment rich growth, with specific focus effective when they are aligned with other on sector attractive to youth, and choices policies and priorities and when they take for employment intensive investment. e into account the economic and social con- potential of entrepreneurship is high, but texts. e main challenge for governments, to be well tapped, specific support measures however, is to determine how to bridge the are needed. Training is a key intervention short to the long term perspective, and to area, but it is not a panacea. Planning of identify the appropriate policies to absorb training interventions needs to be well syn- the youth in the economy. Policy response requires an integrated, multi-sector approach and closeConclusions monitoring 23 Essay references Adams, Arvil V. 2008. "A Framework for the Garcia, Marito and Jean Fares. 2008. "Youth Study of Skills Development in the In- in Africa's Labor market." 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Washington, DC. 26 Youth and Employment in Africa ­ The Potential, the Problem, the Promise Indicator tables Indicator tables 27 Participating in growth ableT1.1 Basic Indicators GDP per capita Under­ Constant 2000 prices Life five Net Land area Average expectancy mortality Adult literacy rate ODA aid Population (thousands annual at birth rate Gini (% ages 15 and older) per capita (millions) of sq km) Dollars growth (%) (years) (per 1,000) index Male Female (current $) 2006 2006 2006 2000­06 2006 2006 2000­06a 2000­06a 2000­06a 2006 SUB-SAHARAN AFRICA 782.5 23,629 580 2.3 50.5 157 .. .. .. 48.5 Excl. South Africa 735.1 22,414 388 2.7 50.5 160 .. .. .. 50.7 Excl. South Africa & Nigeria 590.3 21,504 371 2.3 51.4 152 .. .. .. 43.7 Angola 16.6 1,247 1,070 8.3 42.4 260 .. 82.9 54.2 10.3 Benin 8.8 111 323 0.5 56.2 148 36.5 47.9 23.3 42.8 Botswana 1.9 567 4,511 4.2 49.8 124 .. 80.4 81.8 35.0 Burkina Faso 14.4 274 258 2.7 51.9 204 39.5 31.4 16.6 60.6 Burundi 8.2 26 102 ­1.0 49.0 181 .. 67.3 52.2 50.8 Cameroon 18.2 465 687 1.3 50.3 149 44.6 77.0 59.8 92.7 Cape Verde 0.5 4 1,384 2.2 71.0 34 50.5 87.8 75.5 266.6 Central African Republic 4.3 623 223 ­2.3 44.4 175 .. 64.8 33.5 31.4 Chad 10.5 1,259 266 10.1 50.6 209 .. 40.8 12.8 27.1 Comoros 0.6 2 382 0.3 63.2 68 .. .. .. 49.5 Congo, Dem. Rep. 60.6 2,267 91 1.6 46.1 205 .. 80.9 54.1 33.9 Congo, Rep. 3.7 342 1,145 2.0 54.8 127 .. 90.5 79.0 69.0 Côte d'Ivoire 18.9 318 549 ­1.7 48.1 127 44.6 60.8 38.6 13.3 Djibouti 0.8 23 817 1.3 54.5 130 .. .. .. 143.2 Equatorial Guinea 0.5 28 7,470 16.6 51.1 206 .. 93.4 80.5 54.1 Eritrea 4.7 101 160 ­1.4 57.3 74 .. .. .. 27.5 Ethiopia 77.2 1,000 161 3.9 52.5 123 .. 50.0 22.8 25.2 Gabon 1.3 258 4,263 ­0.1 56.7 91 .. 88.5 79.7 23.7 Gambia, The 1.7 10 326 1.0 59.1 113 47.4 .. .. 44.5 Ghana 23.0 228 294 3.0 59.7 120 .. 66.4 49.8 51.1 Guinea 9.2 246 406 1.1 55.5 161 38.6 42.6 18.1 17.8 Guinea-Bissau 1.6 28 131 ­3.2 46.2 200 .. .. .. 50.0 Kenya 36.6 569 440 1.3 53.4 121 .. 77.7 70.2 25.8 Lesotho 2.0 30 528 2.5 42.9 132 .. 73.7 90.3 36.0 Liberia 3.6 96 134 ­6.9 45.3 235 .. 58.3 45.7 75.1 Madagascar 19.2 582 238 ­0.2 59.0 115 47.5 76.5 65.3 39.4 Malawi 13.6 94 145 ­0.2 47.6 120 39.0 .. .. 49.3 Mali 12.0 1,220 290 2.6 53.8 217 40.1 32.7 15.9 69.0 Mauritania 3.0 1,031 483 2.0 63.7 125 39.0 59.5 43.4 61.6 Mauritius 1.3 2 4,522 3.0 73.2 14 .. 88.2 80.5 14.8 Mozambique 21.0 786 330 5.6 42.5 138 47.3 .. .. 76.8 Namibia 2.0 823 2,166 3.3 52.5 61 .. 86.8 83.5 71.0 Niger 13.7 1,267 169 0.3 56.4 253 .. 42.9 15.1 29.2 Nigeria 144.7 911 454 4.1 46.8 191 43.7 78.2 60.1 79.0 Rwanda 9.5 25 263 3.5 45.6 160 46.8 71.4 59.8 61.8 São Tomé and Principe 0.2 1 .. .. 65.2 96 .. 92.2 77.9 138.9 Senegal 12.1 193 499 1.9 62.8 116 41.3 51.1 29.2 68.3 Seychelles 0.1 0 7,005 ­1.7 72.2 13 .. 91.4 92.3 164.9 Sierra Leone 5.7 72 225 7.7 42.2 270 37.0 46.7 24.2 63.4 Somalia 8.4 627 .. .. 47.7 146 .. .. .. 46.4 South Africa 47.4 1,214 3,562 2.8 50.7 69 57.8 .. .. 15.1 Sudan 37.7 2,376 489 4.4 58.1 89 .. 71.1 51.8 54.6 Swaziland 1.1 17 1,297 0.2 40.8 164 50.4 80.9 78.3 30.3 Tanzania 39.5 886 339 4.0 51.9 118 34.6 77.5 62.2 46.3 Togo 6.4 54 240 ­0.3 58.2 108 .. 68.7 38.5 12.3 Uganda 29.9 197 274 2.3 50.7 134 40.8 76.8 57.7 51.9 Zambia 11.7 743 371 3.0 41.7 182 50.8 .. .. 121.8 Zimbabwe 13.2 387 .. ­6.4 42.7 105 .. 92.7 86.2 21.2 NORTH AFRICA 154.2 5,738 2,060 2.7 71.5 35 .. .. .. 16.8 Algeria 33.4 2,382 2,123 3.3 72.0 38 .. 79.6 60.1 6.3 Egypt, Arab Rep. 74.2 995 1,724 2.2 71.0 35 34.4 83.0 59.4 11.8 Libya 6.0 1,760 7,040 1.1 74.0 18 .. 92.8 74.8 6.2 Morocco 30.5 446 1,667 3.9 70.7 37 .. 65.7 39.6 34.3 Tunisia 10.1 155 2,518 3.6 73.6 23 39.8 83.4 65.3 42.7 AFRICA 936.6 29,367 823 2.2 53.9 144 .. .. .. 43.3 a. Data are for the most recent year available during the period specified. 28 Part I. Basic indicators and national accounts BASIC INDICATORS ableT2.1 Gross domestic product, nominal Current prices ($ millions) Average annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 276,319 297,064 342,099 335,631 363,915 446,080 550,041 645,195 744,731 0.6 1.7 15.6 Excl. South Africa 197,202 185,167 209,287 217,294 253,313 279,554 333,682 403,314 490,318 3.0 1.4 15.6 Excl. South Africa & Nigeria 129,406 156,754 163,234 169,222 194,086 211,757 245,610 290,742 342,847 2.1 1.1 13.5 Angola .. 10,260 9,129 8,936 11,432 13,956 19,775 30,632 45,163 .. ­3.8 32.2 Benin 1,405 1,845 2,255 2,372 2,807 3,558 4,047 4,287 4,623 2.3 3.5 14.1 Botswana 1,061 3,792 6,177 6,033 5,933 8,278 9,827 10,513 11,006 12.6 4.4 12.7 Burkina Faso 1,929 3,101 2,611 2,813 3,290 4,270 5,109 5,427 5,771 4.8 0.0 15.9 Burundi 920 1,132 709 662 628 595 664 796 903 2.2 ­3.3 4.2 Cameroon 6,741 11,152 10,075 9,598 10,880 13,622 15,775 16,588 17,953 7.3 ­2.5 12.1 Cape Verde .. 339 531 550 616 797 925 1,006 1,182 .. 6.4 15.4 Central African Republic 797 1,488 959 968 1,042 1,195 1,307 1,350 1,477 8.1 ­4.3 8.1 Chad 1,033 1,739 1,385 1,709 1,988 2,737 4,415 5,873 6,300 5.7 ­1.3 32.2 Comoros 124 250 202 220 251 324 362 387 403 8.0 ­2.0 13.6 Congo, Dem. Rep. 14,395 9,350 4,306 4,692 5,548 5,673 6,570 7,104 8,545 ­6.2 ­7.1 11.5 Congo, Rep. 1,706 2,799 3,220 2,794 3,020 3,564 4,343 6,087 7,731 2.3 ­2.4 17.6 Côte d'Ivoire 10,175 10,796 10,417 10,545 11,487 13,737 15,481 16,345 17,268 2.0 2.2 10.1 Djibouti .. 452 551 572 591 622 666 709 769 .. 1.7 5.7 Equatorial Guinea .. 132 1,254 1,737 2,166 2,966 4,899 7,528 8,565 .. 22.5 40.5 Eritrea .. .. 634 671 631 584 635 970 1,085 .. 7.2 8.8 Ethiopia .. 12,083 8,180 8,169 7,791 8,558 10,054 12,305 15,166 5.8 ­5.7 11.0 Gabon 4,279 5,952 5,068 4,713 4,932 6,055 7,178 8,666 9,546 0.6 ­1.7 13.3 Gambia, The 241 317 421 418 370 367 401 461 511 1.7 3.6 3.1 Ghana 4,445 5,886 4,977 5,309 6,160 7,624 8,872 10,720 12,715 3.2 2.6 17.8 Guinea 6,684 2,667 3,112 3,039 3,208 3,619 3,938 3,261 3,204 ­15.6 3.0 1.6 Guinea-Bissau 111 244 215 199 201 235 270 301 308 4.3 ­0.6 8.0 Kenya 7,265 8,591 12,604 12,983 13,152 14,986 16,199 18,730 22,779 2.5 7.6 10.5 Lesotho 431 615 853 752 687 1,039 1,319 1,425 1,494 1.3 4.2 13.2 Liberia 954 384 561 543 559 410 460 530 614 ­0.5 1.8 0.7 Madagascar 4,042 3,081 3,878 4,529 4,397 5,474 4,364 5,040 5,499 ­5.2 3.9 4.6 Malawi 1,238 1,881 1,744 1,717 2,665 2,425 2,625 2,855 3,164 1.7 ­0.1 10.5 Mali 1,787 2,421 2,422 2,630 3,343 4,362 4,874 5,305 5,866 3.4 0.1 17.2 Mauritania 709 1,020 1,081 1,122 1,150 1,285 1,548 1,837 2,663 3.7 1.4 15.3 Mauritius 1,153 2,383 4,469 4,539 4,549 5,248 6,064 6,290 6,347 9.2 6.7 7.4 Mozambique 3,526 2,463 4,249 4,075 4,201 4,666 5,698 6,579 6,833 ­5.7 8.3 10.1 Namibia 2,169 2,350 3,414 3,216 3,122 4,473 5,649 6,230 6,566 1.5 4.6 14.9 Niger 2,509 2,481 1,798 1,945 2,170 2,639 2,897 3,330 3,597 1.8 ­1.8 13.1 Nigeria 64,202 28,472 45,984 48,000 59,117 67,656 87,845 112,249 146,867 ­12.0 3.2 22.0 Rwanda 1,163 2,584 1,735 1,675 1,641 1,777 1,971 2,379 2,869 8.6 ­1.5 11.8 São Tomé and Principe .. .. .. 76 91 98 107 114 123 .. .. 9.4 Senegal 3,503 5,717 4,692 4,878 5,334 6,858 8,030 8,688 9,269 6.2 ­1.4 14.8 Seychelles 147 369 615 622 698 706 700 723 775 10.0 6.1 3.6 Sierra Leone 1,101 650 634 806 936 991 1,073 1,215 1,420 ­4.3 0.1 12.8 Somalia 604 917 .. .. .. .. .. .. .. 6.4 .. .. South Africa 80,710 112,014 132,878 118,479 110,874 166,654 216,443 242,059 254,993 4.8 2.1 14.8 Sudan 7,617 9,016 12,366 13,362 14,976 17,780 21,684 27,386 36,402 7.6 7.8 19.7 Swaziland 543 882 1,389 1,317 1,188 1,821 2,377 2,613 2,784 1.9 5.9 16.0 Tanzania .. 4,259 9,079 9,441 9,758 10,283 11,351 14,142 14,178 .. 8.9 8.8 Togo 1,136 1,628 1,329 1,328 1,476 1,759 2,061 2,154 2,218 4.5 ­0.1 10.3 Uganda 1,245 4,304 5,927 5,681 5,836 6,250 6,817 8,738 9,495 20.7 8.5 9.5 Zambia 3,884 3,288 3,238 3,637 3,716 4,374 5,525 7,349 10,886 ­3.1 0.2 21.4 Zimbabwe 6,679 8,784 7,399 10,256 21,897 7,397 4,712 3,418 .. ­0.1 ­2.9 ­18.8 NORTH AFRICA 131,760 172,192 245,626 240,561 225,619 249,576 278,878 321,692 370,017 2.7 4.2 7.5 Algeria 42,345 62,045 54,790 55,181 57,053 68,019 85,014 102,339 116,459 4.5 ­1.2 14.9 Egypt, Arab Rep. 22,912 43,130 99,839 97,632 87,851 82,924 78,845 89,686 107,484 6.8 10.8 ­0.2 Libya 35,545 28,905 34,495 29,994 19,195 23,822 30,498 41,743 49,711 ­6.2 ­0.9 8.3 Morocco 18,821 25,821 37,059 37,766 40,472 49,819 56,392 58,956 65,401 6.1 5.2 11.0 Tunisia 8,743 12,291 19,443 19,988 21,047 24,992 28,129 28,968 30,962 2.3 6.0 9.1 AFRICA 407,093 469,167 587,721 576,188 589,450 695,491 828,652 966,561 1,114,365 1.4 2.6 12.5 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 29 ableT2.2 Gross domestic product, real Constant prices (2000 $ millions) Average annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 227,238 273,268 342,099 354,758 366,789 382,293 405,831 428,769 453,537 1.8 2.4 4.8 Excl. South Africa 131,915 162,335 209,287 218,323 225,347 236,450 252,945 268,099 284,894 2.1 2.7 5.3 Excl. South Africa & Nigeria 99,157 127,317 163,234 170,846 177,140 183,259 194,103 206,081 219,033 2.6 2.8 4.9 Angola .. 8,464 9,129 9,416 10,780 11,137 12,383 14,935 17,707 .. 1 11.5 Benin 1,084 1,412 2,255 2,368 2,474 2,571 2,650 2,727 2,831 2.7 4.7 3.8 Botswana 1,209 3,395 6,177 6,499 6,867 7,281 7,730 8,105 8,382 10.9 5.6 5.4 Burkina Faso 1,101 1,556 2,611 2,785 2,915 3,150 3,296 3,505 3,698 4 5.5 6 Burundi 559 865 709 724 756 747 783 790 830 4.5 ­3.2 2.5 Cameroon 6,339 8,793 10,075 10,530 10,952 11,393 11,815 12,087 12,476 4.5 1.3 3.6 Cape Verde .. 303 531 552 577 613 608 648 717 .. 5.9 4.7 Central African Republic 735 815 959 962 956 883 895 914 950 1.6 1.8 ­0.7 Chad 665 1,106 1,385 1,547 1,678 1,925 2,572 2,776 2,780 6.7 2.3 14.1 Comoros 136 181 202 209 217 223 222 232 234 2.9 1.2 2.5 Congo, Dem. Rep. 7,016 7,659 4,306 4,215 4,362 4,614 4,921 5,239 5,505 2.1 ­5 4.7 Congo, Rep. 1,746 2,796 3,220 3,342 3,503 3,563 3,691 3,975 4,223 3.8 0.8 4.4 Côte d'Ivoire 7,727 8,298 10,417 10,415 10,266 10,106 10,287 10,409 10,382 0.7 3.5 0 Djibouti .. 660 551 563 577 596 619 638 669 .. ­2.3 3.3 Equatorial Guinea .. 207 1,254 2,036 2,454 2,775 3,668 3,920 3,702 .. 20.7 19.4 Eritrea .. .. 634 692 697 739 753 757 749 .. 7.9 2.7 Ethiopia .. 6,234 8,180 8,859 8,993 8,798 9,993 11,174 12,387 2.1 3.7 6.7 Gabon 3,594 4,298 5,068 5,176 5,162 5,290 5,361 5,523 5,588 0.5 2.9 1.7 Gambia, The 213 305 421 445 431 461 484 509 542 3.5 2.7 4.1 Ghana 2,640 3,267 4,977 5,177 5,410 5,691 6,010 6,364 6,771 2.6 4.3 5.3 Guinea 1,539 2,088 3,112 3,236 3,372 3,440 3,534 3,651 3,730 3 4.4 3 Guinea-Bissau 115 186 215 216 201 199 204 211 215 3.8 1.4 ­0.2 Kenya 7,060 10,518 12,604 13,168 13,240 13,628 14,321 15,140 16,065 4.1 2.2 4 Lesotho 392 602 853 868 893 917 954 982 1,053 4.1 4.3 3.4 Liberia 1,391 433 561 577 599 411 422 444 479 ­3.3 0.2 ­4.7 Madagascar 3,099 3,266 3,878 4,111 3,590 3,941 4,149 4,339 4,551 0.8 1.7 2.7 Malawi 1,000 1,243 1,744 1,657 1,584 1,683 1,779 1,819 1,963 2.4 3.8 2.4 Mali 1,536 1,630 2,422 2,716 2,828 3,039 3,105 3,294 3,469 0.5 3.9 5.7 Mauritania 693 816 1,081 1,112 1,125 1,188 1,249 1,317 1,471 1.9 2.9 5 Mauritius 1,518 2,679 4,469 4,718 4,846 5,000 5,235 5,475 5,668 5.9 5.3 4 Mozambique 2,581 2,620 4,249 4,754 5,173 5,485 5,918 6,414 6,925 ­0.9 5.5 8.2 Namibia 2,002 2,263 3,414 3,495 3,729 3,858 4,114 4,308 4,433 1.1 4 4.8 Niger 1,523 1,507 1,798 1,926 1,984 2,071 2,054 2,206 2,320 ­0.4 2.4 3.9 Nigeria 31,452 34,978 45,984 47,409 48,143 53,102 58,731 61,902 65,740 0.8 2.4 6.7 Rwanda 1,368 1,673 1,735 1,882 2,089 2,096 2,207 2,363 2,492 2.5 ­1.6 5.9 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2,683 3,463 4,692 4,907 4,939 5,268 5,579 5,893 6,029 2.7 2.8 4.5 Seychelles 292 395 615 601 608 572 556 563 593 3.1 4.5 ­1.2 Sierra Leone 935 1,022 634 749 955 1,043 1,120 1,202 1,290 0.5 ­5.4 12.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 95,503 110,945 132,878 136,512 141,520 145,935 152,996 160,793 168,809 1.4 2 4.1 Sudan 5,523 7,059 12,366 13,133 13,842 14,825 15,581 16,562 18,434 2.4 5.4 6.6 Swaziland 490 1,033 1,389 1,289 1,317 1,367 1,402 1,435 1,476 7.2 3.1 1.6 Tanzania .. 6,801 9,079 9,646 10,345 10,931 11,667 12,526 13,370 .. 2.7 6.7 Togo 964 1,071 1,329 1,327 1,382 1,419 1,461 1,480 1,541 1.5 3.6 2.6 Uganda .. 3,077 5,927 6,220 6,618 6,930 7,306 7,794 8,190 2.3 7.2 5.6 Zambia 2,730 3,028 3,238 3,396 3,488 3,686 3,886 4,088 4,341 1 0.2 5 Zimbabwe 4,376 6,734 7,399 7,199 6,883 6,167 5,933 5,618 .. 3.3 2.7 ­5.7 NORTH AFRICA 128,670 179,235 245,626 255,900 264,663 274,772 288,005 300,872 317,549 3.4 3.2 4.3 Algeria 35,291 46,367 54,790 56,215 58,857 62,918 66,190 69,565 70,817 2.9 1.7 4.8 Egypt, Arab Rep. 38,519 65,600 99,839 103,357 106,649 110,055 114,611 119,681 127,872 5.5 4.3 4 Libya 14,354 .. 34,495 36,053 37,228 36,204 38,014 40,409 42,511 ­7 .. 3.2 Morocco 20,068 29,286 37,059 39,875 41,191 43,704 45,976 47,080 50,846 4.2 2.4 5.1 Tunisia 8,622 12,237 19,443 20,401 20,738 21,891 23,213 24,136 25,503 3.2 4.6 4.6 AFRICA 357,720 453,974 587,721 610,657 631,452 657,062 693,824 729,620 771,057 2.4 2.7 4.6 a. Provisional 30 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.3 Gross domestic product growth Annual growth (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 4.2 1.1 3.5 3.7 3.4 4.2 6.2 5.7 5.8 2.2 2.0 4.6 Excl. South Africa 2.0 2.1 3.1 4.3 3.2 4.9 7.0 6.0 6.3 2.1 2.5 5.0 Excl. South Africa & Nigeria 1.1 0.6 2.5 4.7 3.7 3.5 5.9 6.2 6.3 2.6 2.3 4.7 Angola .. ­0.3 3.0 3.1 14.5 3.3 11.2 20.6 18.6 4.2 1.0 10.6 Benin 6.8 3.2 5.8 5.0 4.5 3.9 3.1 2.9 3.8 3.1 4.5 4.1 Botswana 12.0 6.8 8.2 5.2 5.7 6.0 6.2 4.9 3.4 11.5 6.1 5.7 Burkina Faso 0.8 ­0.6 1.8 6.6 4.7 8.0 4.6 6.4 5.5 3.7 5.1 5.4 Burundi 1.0 3.5 ­0.9 2.1 4.4 ­1.2 4.8 0.9 5.1 4.3 ­1.4 2.2 Cameroon ­2.0 ­6.1 4.2 4.5 4.0 4.0 3.7 2.3 3.2 4.0 0.4 3.7 Cape Verde .. 0.7 6.6 3.8 4.6 6.2 ­0.7 6.5 10.7 4.8 5.2 5.4 Central African Republic ­4.5 ­2.1 2.3 0.3 ­0.6 ­7.6 1.3 2.1 4.0 0.9 1.3 0.3 Chad ­6.0 ­4.2 ­0.9 11.7 8.5 14.7 33.6 7.9 0.2 5.4 2.2 10.8 Comoros .. 5.1 0.9 3.3 4.1 2.5 ­0.2 4.2 1.2 2.7 1.6 2.3 Congo, Dem. Rep. 2.2 ­6.6 ­6.9 ­2.1 3.5 5.8 6.6 6.5 5.1 1.8 ­5.5 2.6 Congo, Rep. 17.6 1.0 7.6 3.8 4.8 1.7 3.6 7.7 6.2 6.8 0.8 5.1 Côte d'Ivoire ­11.0 ­1.1 ­3.7 0.0 ­1.4 ­1.6 1.8 1.2 ­0.3 ­0.2 2.6 ­0.6 Djibouti .. .. 0.4 2.0 2.6 3.2 3.8 3.2 4.8 .. ­2.0 2.9 Equatorial Guinea .. 3.3 13.5 62.3 20.6 13.1 32.2 6.9 ­5.6 0.9 20.2 20.4 Eritrea .. .. ­13.1 9.2 0.7 6.1 1.9 0.5 ­1.0 .. 8.1 0.6 Ethiopia .. 2.7 6.1 8.3 1.5 ­2.2 13.6 11.8 10.9 2.4 2.7 7.1 Gabon 2.6 5.2 ­1.9 2.1 ­0.3 2.5 1.3 3.0 1.2 1.9 2.5 1.1 Gambia, The 6.3 3.6 5.5 5.8 ­3.2 7.0 5.1 5.0 6.5 3.9 3.1 4.5 Ghana 0.5 3.3 3.7 4.0 4.5 5.2 5.6 5.9 6.4 2.0 4.3 5.0 Guinea .. 4.3 1.9 4.0 4.2 2.0 2.7 3.3 2.2 3.0 4.3 2.9 Guinea-Bissau ­16.0 6.1 7.5 0.2 ­7.1 ­0.6 2.2 3.5 1.8 2.9 2.0 1.1 Kenya 5.6 4.2 0.5 4.5 0.5 2.9 5.1 5.7 6.1 4.2 2.2 3.6 Lesotho ­2.7 6.4 2.6 1.8 2.9 2.7 4.0 2.9 7.2 3.6 4.0 3.4 Liberia ­4.1 ­51.0 25.7 2.9 3.7 ­31.3 2.6 5.3 7.8 ­4.5 1.2 2.4 Madagascar 0.8 3.1 4.8 6.0 ­12.7 9.8 5.3 4.6 4.9 0.4 1.6 3.2 Malawi 0.4 5.7 1.6 ­5.0 ­4.4 6.3 5.7 2.3 7.9 1.7 4.1 2.0 Mali ­4.3 ­1.9 3.2 12.1 4.2 7.4 2.2 6.1 5.3 0.6 3.6 5.8 Mauritania 3.4 ­1.8 1.9 2.9 1.1 5.6 5.2 5.4 11.7 2.2 2.6 4.8 Mauritius .. 5.8 4.0 5.6 2.7 3.2 4.7 4.6 3.5 5.9 5.4 4.0 Mozambique .. 1.0 1.1 11.9 8.8 6.0 7.9 8.4 8.0 0.4 5.1 7.4 Namibia .. 2.5 3.5 2.4 6.7 3.5 6.6 4.7 2.9 1.1 4.1 4.3 Niger ­2.2 ­1.3 ­1.4 7.1 3.0 4.4 ­0.8 7.4 5.2 0.0 1.9 3.6 Nigeria 4.2 8.2 5.4 3.1 1.5 10.3 10.6 5.4 6.2 0.9 3.1 6.1 Rwanda 9.0 ­2.4 8.1 8.5 11.0 0.3 5.3 7.1 5.5 3.2 2.1 6.5 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal ­3.3 ­0.7 3.2 4.6 0.7 6.7 5.9 5.6 2.3 2.4 2.7 4.1 Seychelles ­4.2 7.0 4.3 ­2.3 1.2 ­5.9 ­2.9 1.2 5.3 2.1 4.9 0.1 Sierra Leone 4.8 3.4 3.8 18.2 27.5 9.3 7.4 7.3 7.4 1.1 ­4.3 11.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6.6 ­0.3 4.2 2.7 3.7 3.1 4.8 5.1 5.0 2.2 1.4 4.1 Sudan 1.5 ­5.5 8.4 6.2 5.4 7.1 5.1 6.3 11.3 3.4 4.4 7.1 Swaziland 12.4 8.9 2.6 ­7.1 2.2 3.8 2.6 2.4 2.8 8.2 3.6 1.3 Tanzania .. 7.0 5.1 6.2 7.2 5.7 6.7 7.4 6.7 3.8 3.1 6.4 Togo 14.6 ­0.2 ­0.8 ­0.2 4.1 2.7 3.0 1.3 4.1 2.6 2.6 2.0 Uganda .. 6.5 5.6 4.9 6.4 4.7 5.4 6.7 5.1 3.0 6.9 5.6 Zambia 3.0 ­0.5 3.6 4.9 2.7 5.7 5.4 5.2 6.2 1.4 0.4 4.8 Zimbabwe 14.4 7.0 ­7.9 ­2.7 ­4.4 ­10.4 ­3.8 ­5.3 .. 5.2 2.6 ­5.8 NORTH AFRICA 4.6 4.0 3.5 4.2 3.4 3.8 4.8 4.5 5.5 3.4 3.3 4.2 Algeria 0.8 0.8 2.2 2.6 4.7 6.9 5.2 5.1 1.8 2.8 1.6 4.1 Egypt, Arab Rep. 10.0 5.7 5.4 3.5 3.2 3.2 4.1 4.4 6.8 5.9 4.3 4.4 Libya 0.6 .. 1.1 4.5 3.3 ­2.7 5.0 6.3 5.2 ­6.4 .. 3.2 Morocco 3.6 4.0 1.8 7.6 3.3 6.1 5.2 2.4 8.0 3.9 2.8 4.9 Tunisia 7.4 7.9 4.7 4.9 1.7 5.6 6.0 4.0 5.7 3.6 5.1 4.6 AFRICA 4.4 2.1 3.5 3.9 3.4 4.1 5.6 5.2 5.7 2.6 2.5 4.5 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 31 ableT2.4 Gross domestic product per capita, real Constant prices (2000 $) Average annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 593 532 508 513 517 526 545 562 580 ­1.0 ­0.6 2.2 Excl. South Africa 371 339 332 338 340 347 362 374 388 ­0.9 ­0.3 2.6 Excl. South Africa & Nigeria 348 331 323 329 333 335 346 358 371 ­0.3 ­0.2 2.3 Angola .. 804 655 658 732 734 792 928 1,070 .. ­2.3 8.2 Benin 292 273 312 317 321 323 322 321 323 ­0.7 1.2 0.6 Botswana 1,214 2,483 3,573 3,707 3,869 4,056 4,259 4,415 4,511 7.6 3.3 3.9 Burkina Faso 161 175 220 227 230 241 244 252 258 1.3 2.6 2.6 Burundi 135 152 106 106 107 102 103 101 102 1.2 ­3.7 ­0.8 Cameroon 698 718 635 648 659 670 679 679 687 1.3 ­1.6 1.3 Cape Verde .. 852 1,179 1,196 1,221 1,267 1,229 1,279 1,384 .. 3.2 2.7 Central African Republic 316 271 248 245 239 218 217 218 223 ­1.2 ­1.0 ­1.8 Chad 144 181 164 176 184 203 262 274 266 3.3 ­0.6 8.1 Comoros 405 416 374 378 386 387 378 386 382 0.0 ­1.0 0.4 Congo, Dem. Rep. 250 202 85 81 82 84 87 89 91 ­1.2 ­8.6 1.1 Congo, Rep. 969 1,154 1,005 1,017 1,040 1,033 1,046 1,101 1,145 2.2 ­2.1 2.2 Côte d'Ivoire 926 649 611 599 580 562 563 560 549 ­3.4 0.0 ­1.8 Djibouti .. 1,177 755 753 757 767 783 794 817 .. ­4.7 1.3 Equatorial Guinea .. 611 2,913 4,618 5,438 6,008 7,756 8,098 7,470 .. 16.2 15.7 Eritrea .. .. 172 181 174 177 173 167 160 .. .. ­1.2 Ethiopia .. 130 124 131 129 123 137 149 161 .. ­0.8 4.3 Gabon 5,271 4,683 4,287 4,294 4,205 4,235 4,221 4,279 4,263 ­1.5 ­0.5 ­0.1 Gambia, The 318 316 304 311 292 302 308 315 326 0.0 ­0.7 1.1 Ghana 232 210 247 251 257 264 273 282 294 ­1.2 1.7 2.9 Guinea 336 346 379 387 396 397 400 406 406 0.2 1.0 1.1 Guinea-Bissau 144 183 157 153 138 133 132 132 131 2.3 ­2.2 ­3.1 Kenya 434 449 403 411 402 403 413 425 440 0.3 ­0.9 1.4 Lesotho 302 376 452 454 462 470 485 496 528 1.9 2.0 2.6 Liberia 745 203 183 181 184 125 126 129 134 ­6.6 ­3.1 ­5.2 Madagascar 342 271 240 247 209 224 229 233 238 ­2.6 ­1.6 ­0.1 Malawi 161 132 150 139 129 134 138 138 145 ­2.4 1.6 ­0.6 Mali 253 213 242 264 267 278 276 284 290 ­1.5 1.4 3.0 Mauritania 461 419 421 421 413 424 433 445 483 ­0.6 0.2 2.3 Mauritius 1,572 2,535 3,766 3,932 4,004 4,089 4,245 4,404 4,522 4.8 4.1 3.1 Mozambique 213 193 234 255 270 280 295 312 330 ­1.0 2.3 5.8 Namibia 2,017 1,596 1,816 1,827 1,920 1,960 2,064 2,133 2,166 ­2.4 1.3 2.9 Niger 263 193 162 167 166 168 160 166 169 ­3.0 ­1.4 0.7 Nigeria 443 370 369 370 367 394 426 438 454 ­2.5 ­0.3 3.5 Rwanda 263 229 212 221 239 235 244 256 263 ­1.3 ­1.0 3.6 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 457 439 454 463 454 471 486 501 499 ­0.1 0.3 1.6 Seychelles 4,532 5,645 7,579 7,400 7,267 6,913 6,740 6,789 7,005 1.8 2.9 ­1.3 Sierra Leone 289 250 140 159 194 202 208 215 225 ­1.8 ­6.5 7.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 3,463 3,152 3,020 3,046 3,128 3,186 3,301 3,429 3,562 ­0.8 ­0.7 2.8 Sudan 281 272 371 386 398 418 431 449 489 0.5 2.8 4.6 Swaziland 867 1,342 1,329 1,207 1,210 1,236 1,252 1,269 1,297 4.3 ­0.1 ­0.4 Tanzania .. 267 268 278 291 299 311 326 339 .. ­0.2 3.9 Togo 346 270 246 238 241 240 241 237 240 ­2.4 ­0.6 ­0.4 Uganda .. 173 240 244 252 255 261 269 274 .. 3.4 2.2 Zambia 459 373 310 318 321 333 345 356 371 ­1.9 ­2.2 3.0 Zimbabwe 601 642 585 564 535 477 456 428 .. 0.3 0.0 .. NORTH AFRICA 1,409 1,531 1,749 1,794 1,826 1,867 1,927 1,982 2,060 0.7 1.3 2.7 Algeria 1,876 1,834 1,796 1,816 1,874 1,973 2,045 2,117 2,123 ­0.1 ­0.3 2.8 Egypt, Arab Rep. 882 1,190 1,501 1,525 1,546 1,566 1,602 1,643 1,724 2.9 2.2 2.3 Libya 4,686 .. 6,453 6,608 6,686 6,371 6,555 6,828 7,040 .. .. 1.5 Morocco 1,036 1,212 1,302 1,383 1,411 1,481 1,541 1,562 1,667 1.5 0.7 4.1 Tunisia 1,351 1,501 2,033 2,109 2,120 2,225 2,337 2,407 2,518 0.6 3.0 3.6 AFRICA 753 719 722 732 739 752 776 797 823 ­0.5 ­0.1 2.2 a. Provisional. 32 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.5 Gross domestic product per capita growth Annual growth (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 1.1 ­1.7 0.8 1.1 0.8 1.7 3.6 3.1 3.2 ­0.8 ­0.7 2.0 Excl. South Africa ­1.0 ­0.8 0.4 1.6 0.6 2.3 4.3 3.3 3.6 ­0.9 ­0.3 2.3 Excl. South Africa & Nigeria ­1.9 ­2.3 ­0.2 1.9 1.0 0.8 3.2 3.5 3.6 ­0.5 ­0.4 2.0 Angola .. ­3.0 0.4 0.3 11.2 0.3 7.9 17.2 15.3 1.6 ­1.8 7.5 Benin 3.5 ­0.3 2.6 1.7 1.2 0.6 ­0.2 ­0.3 0.6 ­0.2 1.1 0.9 Botswana 8.0 3.7 6.5 3.7 4.4 4.8 5.0 3.7 2.2 7.9 3.5 4.3 Burkina Faso ­1.5 ­3.4 ­1.2 3.3 1.4 4.6 1.3 3.1 2.4 1.1 2.1 2.1 Burundi ­1.9 0.8 ­2.8 ­0.5 1.3 ­4.5 1.1 ­2.9 1.1 1.0 ­3.1 ­1.0 Cameroon ­4.8 ­8.9 1.8 2.1 1.6 1.6 1.4 0.1 1.1 0.9 ­2.2 1.4 Cape Verde .. ­1.6 4.1 1.4 2.2 3.7 ­3.0 4.1 8.2 2.6 2.8 3.0 Central African Republic ­7.0 ­4.5 0.3 ­1.5 ­2.2 ­9.0 ­0.2 0.4 2.2 ­1.6 ­1.3 ­1.4 Chad ­8.1 ­7.2 ­4.3 7.6 4.5 10.5 28.9 4.4 ­2.9 2.5 ­1.0 7.0 Comoros .. 2.4 ­1.2 1.2 2.0 0.3 ­2.3 2.1 ­0.9 0.1 ­0.6 0.2 Congo, Dem. Rep. ­0.9 ­9.7 ­9.1 ­4.6 0.6 2.7 3.4 3.2 1.8 ­1.2 ­8.2 ­0.3 Congo, Rep. 14.1 ­1.9 4.8 1.2 2.2 ­0.7 1.2 5.3 3.9 3.7 ­2.0 2.6 Côte d'Ivoire ­15.1 ­4.6 ­5.8 ­2.0 ­3.1 ­3.1 0.2 ­0.5 ­2.0 ­4.5 ­0.4 ­2.3 Djibouti .. .. ­2.4 ­0.4 0.5 1.3 2.0 1.4 3.0 .. ­4.5 0.8 Equatorial Guinea .. 1.5 10.8 58.5 17.8 10.5 29.1 4.4 ­7.8 ­1.4 17.5 17.6 Eritrea .. .. ­16.2 5.0 ­3.5 1.6 ­2.3 ­3.3 ­4.5 .. 6.5 ­3.3 Ethiopia .. ­0.5 2.9 5.2 ­1.2 ­4.7 10.8 8.9 8.0 ­0.9 ­0.5 4.3 Gabon ­0.2 2.1 ­3.9 0.2 ­2.1 0.7 ­0.3 1.4 ­0.4 ­1.1 ­0.2 ­0.6 Gambia, The 2.9 ­0.3 2.0 2.4 ­6.3 3.7 2.0 2.0 3.5 0.3 ­0.6 1.3 Ghana ­2.0 0.5 1.3 1.6 2.1 2.9 3.3 3.7 4.2 ­1.1 1.6 2.7 Guinea .. 0.7 ­0.1 2.0 2.3 0.2 0.8 1.4 0.2 0.3 1.0 1.0 Guinea-Bissau ­18.8 3.1 4.5 ­2.7 ­9.9 ­3.6 ­0.9 0.4 ­1.2 0.3 ­1.0 ­1.9 Kenya 1.7 0.8 ­2.1 1.8 ­2.0 0.3 2.4 3.0 3.3 0.5 ­0.8 1.0 Lesotho ­5.2 4.9 1.0 0.4 1.7 1.7 3.2 2.2 6.4 1.3 2.3 2.4 Liberia ­7.2 ­50.5 18.9 ­0.7 1.6 ­32.2 0.9 2.4 3.7 ­6.2 ­3.1 ­0.8 Madagascar ­2.0 0.2 1.7 3.0 ­15.1 6.7 2.4 1.7 2.1 ­2.4 ­1.3 0.4 Malawi ­2.6 1.8 ­1.4 ­7.5 ­6.9 3.6 3.1 ­0.3 5.2 ­2.4 1.9 ­0.6 Mali ­6.4 ­4.3 0.3 8.9 1.1 4.3 ­0.9 2.9 2.2 ­1.7 0.9 2.7 Mauritania 0.6 ­4.3 ­1.1 ­0.1 ­1.8 2.6 2.2 2.6 8.7 ­0.4 ­0.2 1.9 Mauritius .. 5.0 3.0 4.4 1.8 2.1 3.8 3.7 2.7 4.9 4.2 3.1 Mozambique .. ­0.3 ­1.4 9.1 6.1 3.5 5.4 6.0 5.7 ­0.6 2.2 4.9 Namibia .. ­1.7 1.4 0.6 5.1 2.1 5.3 3.4 1.6 ­2.4 1.0 2.8 Niger ­5.2 ­4.4 ­4.9 3.4 ­0.6 0.8 ­4.2 3.7 1.5 ­2.9 ­1.6 0.0 Nigeria 1.2 5.1 2.7 0.5 ­1.0 7.6 7.9 2.9 3.7 ­1.9 0.2 3.5 Rwanda 5.4 ­2.3 1.3 4.0 8.1 ­1.4 3.7 5.0 2.9 ­0.5 1.2 3.4 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal ­6.0 ­3.5 0.5 1.9 ­2.0 3.9 3.2 3.0 ­0.2 ­0.6 0.0 1.5 Seychelles ­5.4 6.1 3.3 ­2.4 ­1.8 ­4.9 ­2.5 0.7 3.2 1.2 3.3 ­0.6 Sierra Leone 2.9 1.6 0.6 13.6 21.7 4.2 2.8 3.5 4.4 ­1.2 ­5.2 7.3 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 4.2 ­2.3 1.6 0.9 2.7 1.9 3.6 3.9 3.9 ­0.3 ­0.8 2.6 Sudan ­1.7 ­7.7 6.0 4.0 3.3 5.0 3.0 4.1 8.9 0.5 1.8 4.9 Swaziland 9.0 5.6 0.1 ­9.1 0.3 2.1 1.3 1.3 2.2 4.9 0.4 ­0.3 Tanzania .. 3.7 2.6 3.6 4.5 3.0 4.0 4.7 4.1 0.6 0.2 3.8 Togo 11.1 ­3.1 ­4.1 ­3.3 1.1 ­0.1 0.2 ­1.4 1.3 ­0.9 ­0.5 ­0.9 Uganda .. 2.6 2.5 1.7 3.1 1.4 2.1 3.3 1.7 ­0.5 3.4 2.3 Zambia ­0.3 ­3.3 1.3 2.8 0.8 3.8 3.5 3.3 4.2 ­1.7 ­2.2 2.8 Zimbabwe 10.4 3.8 ­8.9 ­3.5 ­5.1 ­11.0 ­4.4 ­6.0 .. 1.4 0.5 ­6.5 NORTH AFRICA 1.9 1.8 1.8 2.5 1.8 2.2 3.2 2.9 3.9 0.9 1.4 2.6 Algeria ­2.5 ­1.7 0.7 1.1 3.2 5.3 3.6 3.5 0.3 ­0.3 ­0.4 2.5 Egypt, Arab Rep. 7.6 3.5 3.5 1.6 1.3 1.3 2.3 2.6 4.9 3.5 2.4 2.5 Libya ­4.0 .. ­0.9 2.4 1.2 ­4.7 2.9 4.2 3.1 ­10.2 .. 1.2 Morocco 1.1 2.0 0.4 6.2 2.1 4.9 4.1 1.4 6.7 1.6 1.1 3.7 Tunisia 4.6 5.4 3.5 3.7 0.5 4.9 5.1 3.0 4.6 1.0 3.3 3.6 AFRICA 1.4 ­0.6 1.0 1.4 1.0 1.7 3.2 2.8 3.3 ­0.3 ­0.1 2.0 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 33 ableT2.6 Gross national income, nominal Current prices ($ millions) Average annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 261,181 281,251 323,815 319,118 345,348 423,590 521,464 607,922 706,165 0.8 1.8 15.5 Excl. South Africa 185,642 173,533 194,148 204,504 237,516 261,645 309,373 370,913 456,982 ­1.1 1.7 15.5 Excl. South Africa & Nigeria 120,565 148,013 153,848 160,336 184,716 201,540 231,074 271,216 320,956 2.6 1.3 13.2 Angola .. 8,214 7,449 7,375 9,791 12,230 17,295 26,601 39,660 .. ­2.4 33.8 Benin 1,402 1,806 2,243 2,351 2,781 3,515 4,006 4,259 4,623 2.1 3.7 14.2 Botswana 1,028 3,686 5,826 5,896 5,235 7,562 8,801 9,702 10,234 10.8 3.9 12.1 Burkina Faso 1,924 3,094 2,606 2,807 3,288 4,269 5,102 5,411 5,756 4.8 0.0 15.9 Burundi 922 1,117 723 650 614 577 646 776 870 1.9 ­3.3 3.5 Cameroon 5,618 10,674 9,464 9,177 10,207 13,097 15,374 16,126 17,702 9.0 ­2.4 13.0 Cape Verde .. 340 520 544 605 781 907 972 1,137 .. 6.2 15.0 Central African Republic 800 1,465 947 960 1,033 1,193 1,306 1,379 1,554 7.8 ­4.3 9.1 Chad 1,038 1,721 1,368 1,687 1,928 2,279 3,720 4,847 4,942 5.5 ­1.2 26.7 Comoros 124 249 202 221 250 323 360 385 404 7.9 ­2.0 13.5 Congo, Dem. Rep. 14,102 8,579 3,918 4,280 5,250 5,485 6,276 6,760 8,145 ­6.8 ­7.0 12.5 Congo, Rep. 1,544 2,324 2,275 1,960 2,201 2,679 3,247 4,509 5,979 1.9 ­5.2 19.4 Côte d'Ivoire 9,680 9,209 9,715 9,912 10,807 13,018 14,763 15,625 16,473 1.3 3.3 10.5 Djibouti .. .. 567 585 606 673 731 776 854 .. 1.3 7.3 Equatorial Guinea .. 124 884 864 1,181 1,406 1,970 3,483 5,241 .. 16.9 36.1 Eritrea .. .. 634 668 625 574 620 962 1,079 .. .. 8.6 Ethiopia .. 12,016 8,119 8,117 7,751 8,492 9,990 12,269 15,127 5.8 ­5.8 11.1 Gabon 3,856 5,336 4,289 4,081 4,450 5,299 5,971 6,678 7,511 ­0.1 ­1.9 11.1 Gambia, The 237 291 400 395 347 348 381 446 460 1.6 3.7 2.7 Ghana 4,426 5,774 4,831 5,201 6,030 7,459 8,674 10,533 12,596 2.9 2.6 18.1 Guinea .. 2,518 3,035 2,947 3,170 3,580 3,879 3,212 3,257 .. 3.3 2.1 Guinea-Bissau 105 233 203 183 193 225 258 289 298 3.4 ­0.7 8.8 Kenya 7,043 8,224 12,474 12,836 13,031 14,820 16,069 18,766 22,850 2.6 8.2 10.5 Lesotho 695 1,022 1,072 927 848 1,287 1,620 1,729 1,874 1.3 1.2 13.6 Liberia 930 .. 389 403 453 350 373 417 477 ­3.2 .. 1.8 Madagascar 4,024 2,958 3,807 4,470 4,326 5,394 4,285 4,962 5,419 ­6.0 3.8 4.6 Malawi 1,138 1,837 1,707 1,683 2,621 2,385 2,582 2,813 3,125 2.2 0.2 10.6 Mali 1,768 2,405 2,392 2,464 3,103 4,203 4,679 5,099 5,524 2.8 0.1 16.9 Mauritania 672 1,076 1,092 1,089 1,276 1,343 1,613 1,901 2,769 4.7 1.8 15.9 Mauritius 1,130 2,363 4,434 4,551 4,541 5,246 6,028 6,285 6,391 7.7 6.6 7.5 Mozambique 3,550 2,320 4,017 3,771 4,028 4,469 5,358 6,095 6,141 ­5.6 8.6 9.4 Namibia 1,818 2,388 3,447 3,215 3,156 4,702 5,733 6,118 6,494 0.2 4.5 14.5 Niger 2,476 2,423 1,782 1,930 2,146 2,718 3,039 3,397 3,707 0.1 ­1.7 14.0 Nigeria 61,079 25,585 40,256 44,107 52,716 59,996 78,110 99,421 135,425 ­12.5 3.7 22.4 Rwanda 1,165 2,572 1,720 1,652 1,622 1,746 1,936 2,354 2,850 8.5 ­2.0 9.0 São Tomé and Principe .. .. .. .. .. .. .. 111 120 .. .. .. Senegal 3,403 5,520 4,601 4,800 5,232 6,753 7,938 8,532 9,107 6.1 ­1.6 13.8 Seychelles 142 355 583 605 630 663 666 683 731 8.9 5.9 3.6 Sierra Leone 1,071 580 614 780 906 964 1,041 1,177 1,385 ­4.8 1.4 12.9 Somalia 603 835 .. .. .. .. .. .. .. 5.5 .. .. South Africa 77,425 107,918 129,702 114,739 108,079 162,044 212,125 237,120 249,710 4.3 2.2 15.7 Sudan 7,508 8,245 10,479 11,919 13,749 16,446 19,990 25,397 33,503 7.2 8.4 21.2 Swaziland 548 941 1,423 1,419 1,186 1,862 2,396 2,633 2,799 1.9 5.9 15.2 Tanzania .. 4,072 8,959 9,356 9,579 10,135 11,153 14,002 14,097 .. 9.3 8.6 Togo 1,096 1,598 1,300 1,298 1,454 1,736 2,033 2,118 2,180 4.6 ­0.1 10.8 Uganda 1,237 4,227 5,819 5,571 5,719 6,127 6,694 8,504 9,257 20.7 9.1 8.9 Zambia 3,594 3,008 3,082 3,472 3,565 4,231 5,128 6,740 9,885 ­4.1 0.7 20.4 Zimbabwe 6,610 8,494 7,145 9,919 21,651 7,207 4,503 3,220 .. ­0.2 ­2.6 ­19.2 NORTH AFRICA 122,344 159,989 239,394 239,753 234,377 254,995 283,367 326,056 378,147 2.8 5.4 8.1 Algeria 41,147 59,955 52,080 53,491 54,823 65,319 81,414 97,259 111,939 4.5 ­1.3 14.9 Egypt, Arab Rep. 21,453 42,025 100,838 98,496 88,763 83,006 78,758 89,474 107,219 7.5 11.2 ­0.5 Libya 35,480 .. .. .. .. 24,357 30,253 41,462 50,765 ­6.0 .. 28.6 Morocco 18,402 24,835 36,091 36,784 39,504 48,779 55,404 58,307 64,469 3.3 5.3 11.3 Tunisia 8,450 11,882 18,526 19,077 20,096 23,957 26,895 27,309 29,553 2.0 6.0 9.0 AFRICA 384,584 444,713 562,782 558,146 580,935 682,652 810,310 940,432 1,091,816 1.6 3.0 12.8 a. Provisional 34 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.7 Gross national income, Atlas method Dollarsa Average annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006b 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 256,706 297,018 325,070 327,620 332,558 374,889 462,821 574,961 673,572 0.8 1.5 13.9 Excl. South Africa 192,397 177,663 190,670 200,701 213,284 243,604 294,693 349,399 418,531 ­1.4 1.0 14.5 Excl. South Africa & Nigeria 131,765 152,225 157,226 160,546 167,829 187,883 221,098 261,031 303,269 1.8 0.7 12.2 Angola .. 7,700 5,905 6,642 9,133 10,678 14,638 21,938 32,646 .. ­2.3 33.0 Benin 1,433 1,723 2,426 2,424 2,523 2,985 3,708 4,316 4,671 1.1 3.2 13.3 Botswana 1,012 3,505 5,729 6,135 5,557 6,534 7,840 9,847 10,546 9.4 4.4 11.8 Burkina Faso 2,016 2,923 2,885 2,922 3,003 3,684 4,634 5,527 5,990 3.4 ­1.1 14.9 Burundi 897 1,187 803 712 666 623 666 723 815 3.6 ­4.1 0.3 Cameroon 5,433 11,129 9,814 9,962 9,816 11,393 14,184 16,293 17,782 8.5 ­2.2 11.8 Cape Verde .. 334 575 570 571 678 807 972 1,135 .. 6.3 13.1 Central African Republic 785 1,384 1,027 988 965 1,017 1,219 1,424 1,566 6.9 ­4.1 8.3 Chad 1,086 1,591 1,506 1,620 1,755 1,999 3,254 4,204 4,594 4.4 ­1.2 23.3 Comoros .. 234 216 220 228 271 326 389 411 10.5 ­1.7 13.0 Congo, Dem. Rep. 17,086 8,371 4,249 3,987 4,555 5,455 6,365 6,949 7,742 ­8.4 ­6.3 12.3 Congo, Rep. 1,471 2,185 1,767 2,020 2,257 2,440 2,929 3,799 5,037 2.2 ­5.8 18.1 Côte d'Ivoire 9,319 9,253 10,784 10,262 9,935 11,191 13,655 15,676 16,354 0.8 2.9 9.0 Djibouti .. .. 552 571 593 675 754 803 864 .. 0.6 8.4 Equatorial Guinea .. 124 663 879 1,201 1,241 1,675 2,665 4,216 .. 15.5 33.5 Eritrea .. .. 624 666 653 652 656 750 888 .. .. 4.8 Ethiopia .. 12,201 8,392 8,566 8,195 8,169 9,956 12,195 14,301 8.9 ­5.5 9.3 Gabon 3,337 4,577 3,801 4,176 4,392 4,689 5,342 6,072 7,032 0.3 ­1.4 10.5 Gambia, The 243 292 424 420 372 383 403 442 454 0.7 3.5 1.4 Ghana 4,643 5,847 6,459 5,832 5,501 6,549 8,144 9,964 11,713 4.0 1.9 12.3 Guinea .. 2,588 3,367 3,180 3,171 3,392 3,824 3,862 3,722 .. 4.1 3.2 Guinea-Bissau 115 219 217 199 185 199 240 282 303 3.4 ­1.1 7.2 Kenya 7,446 8,848 13,063 12,986 12,876 14,058 16,152 18,696 21,159 2.4 5.6 9.0 Lesotho 594 1,019 1,121 1,040 947 1,042 1,295 1,670 1,957 2.4 1.9 11.0 Liberia 989 .. 385 416 459 342 365 407 462 ­3.3 .. 1.0 Madagascar 4,018 2,785 3,870 4,173 3,842 4,858 5,184 5,377 5,343 ­4.3 4.1 6.5 Malawi 1,169 1,723 1,750 1,655 1,801 2,243 2,822 2,860 3,148 2.0 0.7 12.5 Mali 1,752 2,270 2,593 2,580 2,702 3,477 4,366 5,194 5,546 1.2 0.6 16.0 Mauritania 719 1,102 1,208 1,120 1,246 1,310 1,532 1,791 2,325 4.8 3.1 11.7 Mauritius .. 2,433 4,438 4,635 4,621 4,997 5,759 6,526 6,812 10.6 7.0 8.1 Mozambique .. 2,338 4,199 4,224 4,331 4,491 5,147 5,986 6,453 ­2.2 6.7 8.0 Namibia .. 2,429 3,538 3,410 3,408 3,948 4,802 5,959 6,573 1.9 4.7 12.6 Niger 2,442 2,368 1,912 1,973 2,002 2,425 2,856 3,351 3,725 ­0.3 ­2.4 13.0 Nigeria 55,754 25,520 33,452 40,122 45,396 55,622 73,423 88,155 114,771 ­10.9 2.7 22.8 Rwanda 1,298 2,546 1,993 1,877 1,804 1,759 1,932 2,280 2,615 8.2 ­3.5 4.6 São Tomé and Principe .. .. .. .. .. .. .. 117 123 .. .. .. Senegal 3,485 5,335 5,045 4,993 4,898 5,875 7,370 8,669 9,192 4.9 ­1.6 12.6 Seychelles 134 351 602 599 573 620 680 714 751 9.5 5.9 4.3 Sierra Leone 1,243 802 631 760 929 1,032 1,107 1,208 1,344 ­6.6 0.0 12.8 Somalia 656 959 .. .. .. .. .. .. .. 5.9 .. .. South Africa 69,325 119,499 134,405 126,984 119,406 131,455 168,297 225,657 255,331 4.6 2.2 13.0 Sudan 9,049 10,358 10,270 11,467 12,959 15,292 18,507 22,938 29,253 7.9 3.7 19.1 Swaziland 544 925 1,440 1,401 1,263 1,507 1,853 2,465 2,793 1.9 6.3 13.3 Tanzania .. 4,836 8,943 9,448 9,772 10,464 11,565 13,380 14,623 .. 6.5 8.7 Togo 1,137 1,516 1,454 1,360 1,375 1,561 1,877 2,122 2,272 3.2 ­0.3 9.5 Uganda .. 5,638 6,391 6,012 5,959 6,218 6,884 7,934 8,895 24.5 5.5 6.2 Zambia 3,610 3,491 3,132 3,302 3,454 4,014 4,635 5,756 7,524 ­6.1 ­0.2 15.5 Zimbabwe 6,775 8,996 5,740 6,816 9,972 9,874 7,334 4,466 .. 0.1 ­2.7 ­2.9 NORTH AFRICA 116,591 161,543 235,949 243,979 243,400 256,171 280,610 317,668 359,629 4.1 4.2 7.2 Algeria 38,814 61,138 48,967 52,145 54,983 62,070 73,991 89,341 103,752 6.3 ­2.5 13.8 Egypt, Arab Rep. 21,726 42,481 97,361 100,364 97,705 93,417 90,758 92,790 100,912 8.6 9.5 ­0.4 Libya 31,832 .. .. .. .. 24,289 25,652 34,721 44,651 ­4.4 .. 23.7 Morocco 18,734 24,777 38,268 39,040 38,789 44,335 53,147 59,911 65,793 1.9 4.7 10.5 Tunisia 8,689 11,649 19,954 19,961 19,531 22,258 26,325 28,750 30,761 2.0 6.2 8.7 AFRICA 372,610 462,779 560,938 571,025 575,715 632,234 745,555 895,852 1,037,152 2.0 2.4 11.3 a. Calculated using the World Bank Atlas method. b. Provisional. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 35 ableT2.8 Gross national income per capita Dollarsa Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006b 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 669 578 482 474 469 516 621 753 861 590 534 597 Excl. South Africa 541 371 303 310 321 358 422 488 569 426 324 396 Excl. South Africa & Nigeria 463 396 311 309 315 344 394 454 514 406 340 377 Angola .. 730 420 460 620 700 940 1,360 1,970 740 452 924 Benin 390 330 340 320 330 370 450 510 530 319 328 407 Botswana 1,020 2,560 3,310 3,500 3,130 3,640 4,320 5,360 5,680 1,263 3,019 4,134 Burkina Faso 300 330 240 240 240 280 340 400 420 270 269 309 Burundi 220 210 120 100 90 90 90 90 100 231 165 97 Cameroon 600 910 620 610 590 670 810 920 980 848 753 743 Cape Verde .. 940 1,280 1,240 1,210 1,400 1,630 1,920 2,190 910 1,122 1,553 Central African Republic 340 460 270 250 240 250 300 340 370 345 362 289 Chad 240 260 180 180 190 210 330 410 440 218 235 277 Comoros .. 540 400 400 400 470 550 650 670 383 501 506 Congo, Dem. Rep. 610 220 80 80 90 100 110 120 130 350 151 101 Congo, Rep. 820 900 550 610 670 710 830 1,050 1,370 998 684 827 Côte d'Ivoire 1,120 720 630 590 560 620 750 840 860 808 703 693 Djibouti .. .. 760 760 780 870 950 1,000 1,060 .. 790 883 Equatorial Guinea .. 360 1,540 1,990 2,660 2,690 3,540 5,500 8,510 353 570 3,776 Eritrea .. .. 170 170 160 160 150 170 190 .. 202 167 Ethiopia .. 250 130 130 120 110 140 160 190 233 186 140 Gabon 4,890 4,990 3,220 3,460 3,580 3,750 4,210 4,700 5,360 4,541 4,450 4,040 Gambia, The 360 300 310 290 250 250 260 270 270 295 322 271 Ghana 410 380 320 280 260 300 370 440 510 365 376 354 Guinea .. 430 410 380 370 390 430 430 410 538 476 403 Guinea-Bissau 150 220 160 140 130 130 150 180 180 178 202 153 Kenya 460 380 420 400 390 420 470 530 580 381 345 459 Lesotho 460 640 590 540 490 530 660 840 980 517 708 661 Liberia 530 .. 130 130 140 100 110 120 130 399 120 123 Madagascar 440 230 240 250 220 280 290 290 280 320 233 264 Malawi 190 180 150 140 150 180 220 220 230 168 186 184 Mali 290 300 260 250 250 320 390 450 460 236 293 340 Mauritania 480 570 470 420 460 470 530 600 760 483 593 530 Mauritius .. 2,300 3,740 3,860 3,820 4,090 4,670 5,250 5,430 1,390 3,202 4,409 Mozambique .. 170 230 230 230 230 260 290 310 250 170 254 Namibia .. 1,710 1,880 1,780 1,750 2,010 2,410 2,950 3,210 1,449 1,981 2,284 Niger 420 300 170 170 170 200 220 250 270 314 230 207 Nigeria 780 270 270 310 350 410 530 620 790 478 258 469 Rwanda 250 350 240 220 210 200 210 250 280 284 274 230 São Tomé and Principe .. .. .. .. .. .. .. 760 800 .. .. 780 Senegal 590 680 490 470 450 530 640 740 760 548 591 583 Seychelles 2,080 5,020 7,420 7,380 6,850 7,490 8,240 8,610 8,870 2,764 6,420 7,837 Sierra Leone 380 200 140 160 190 200 210 220 230 280 177 193 Somalia 100 140 .. .. .. .. .. .. .. 127 .. .. South Africa 2,510 3,390 3,050 2,830 2,640 2,870 3,630 4,810 5,390 2,805 3,473 3,603 Sudan 460 400 310 340 370 430 510 620 780 511 255 480 Swaziland 960 1,200 1,380 1,310 1,160 1,360 1,650 2,180 2,450 948 1,404 1,641 Tanzania .. 190 260 270 270 290 310 350 370 .. 189 303 Togo 410 380 270 240 240 260 310 340 350 306 326 287 Uganda .. 320 260 240 230 230 250 270 300 283 243 254 Zambia 610 430 300 310 320 360 410 500 640 443 356 406 Zimbabwe 930 860 450 530 780 760 560 340 .. 862 661 570 NORTH AFRICA 1,277 1,379 1,680 1,710 1,679 1,740 1,877 2,093 2,333 1,307 1,385 1,873 Algeria 2,060 2,420 1,610 1,680 1,750 1,950 2,290 2,720 3,110 2,462 1,759 2,159 Egypt, Arab Rep. 500 770 1,460 1,480 1,420 1,330 1,270 1,270 1,360 636 962 1,370 Libya 10,390 .. .. .. .. 4,270 4,420 5,870 7,390 7,730 .. 5,488 Morocco 970 1,030 1,340 1,350 1,330 1,500 1,780 1,990 2,160 801 1,174 1,636 Tunisia 1,360 1,430 2,090 2,060 2,000 2,260 2,650 2,870 3,040 1,264 1,808 2,424 AFRICA 785 733 689 684 674 723 834 979 1,107 728 692 813 a. Calculated using the World Bank Atlas method. b. Provisional. 36 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.9 Gross domestic product deflator (local currency series) Index (2000=100) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 13 38 100 105 111 117 123 131 140 22.0 66.0 118.0 Excl. South Africa 14 39 100 105 110 117 122 130 139 23.0 66.0 118.0 Excl. South Africa & Nigeria 15 39 100 105 110 117 121 130 139 24.0 66.0 117.0 Angola .. .. 100 208 460 931 1,329 1,780 2,041 .. 3.0 979.0 Benin 38 50 100 103 111 113 113 116 120 47.0 73.0 111.0 Botswana 13 41 100 106 107 110 117 130 150 22.0 62.0 117.0 Burkina Faso 52 76 100 104 110 111 115 115 115 67.0 84.0 110.0 Burundi 21 31 100 105 107 120 130 151 155 24.0 51.0 124.0 Cameroon 34 58 100 102 105 106 107 110 115 50.0 79.0 107.0 Cape Verde .. 66 100 103 105 106 113 115 121 60.0 81.0 109.0 Central African Republic 32 70 100 104 107 110 108 109 114 54.0 83.0 108.0 Chad 46 60 100 114 116 116 127 157 166 55.0 78.0 128.0 Comoros 36 70 100 109 113 119 121 124 126 54.0 82.0 116.0 Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. 29 38 100 86 84 82 87 113 134 39.0 50.0 98.0 Côte d'Ivoire 39 50 100 105 110 111 112 117 122 50.0 75.0 111.0 Djibouti .. 69 100 102 102 104 108 111 115 .. 85.0 106.0 Equatorial Guinea .. 24 100 88 86 87 99 142 170 26.0 41.0 110.0 Eritrea .. .. 100 114 131 147 180 206 231 .. 66.0 158.0 Ethiopia .. 49 100 94 91 102 106 117 130 42.0 79.0 106.0 Gabon 35 53 100 94 94 93 99 116 125 44.0 63.0 103.0 Gambia, The 15 64 100 115 134 170 194 203 207 31.0 82.0 160.0 Ghana .. 11 100 135 166 213 244 280 316 3.0 37.0 208.0 Guinea 5 48 100 105 108 120 145 186 256 17.0 77.0 145.0 Guinea-Bissau .. 6 100 95 98 96 98 106 105 1.0 47.0 100.0 Kenya 10 24 100 102 103 109 117 123 134 16.0 57.0 112.0 Lesotho 12 38 100 107 117 124 129 133 139 21.0 65.0 121.0 Liberia 2 2 100 112 141 145 146 166 182 2.0 22.0 142.0 Madagascar 4 21 100 107 124 127 145 172 191 10.0 54.0 138.0 Malawi 2 7 100 126 217 236 270 312 368 3.0 29.0 233.0 Mali 35 57 100 100 116 117 116 119 124 50.0 79.0 113.0 Mauritania 20 42 100 108 116 119 133 157 203 29.0 75.0 134.0 Mauritius 21 54 100 104 111 117 124 130 136 33.0 74.0 118.0 Mozambique .. 6 100 115 124 131 141 153 162 1.0 45.0 132.0 Namibia 12 39 100 114 127 126 128 133 145 21.0 63.0 125.0 Niger 49 63 100 104 107 104 105 112 114 63.0 78.0 106.0 Nigeria 2 7 100 111 146 162 195 234 280 3.0 41.0 175.0 Rwanda 20 33 100 101 96 117 132 144 163 25.0 70.0 122.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 39 63 100 103 106 107 107 110 113 55.0 81.0 106.0 Seychelles 56 87 100 106 110 117 121 124 126 70.0 92.0 115.0 Sierra Leone .. 5 100 102 98 106 123 139 155 1.0 41.0 118.0 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 9 38 100 108 119 124 132 138 147 18.0 65.0 124.0 Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland 12 32 100 127 137 145 158 167 184 19.0 57.0 145.0 Tanzania .. 15 100 107 114 122 132 159 166 11.0 49.0 129.0 Togo 35 58 100 103 105 101 105 108 106 49.0 78.0 104.0 Uganda .. 30 100 107 102 112 119 129 140 4.0 72.0 116.0 Zambia .. 1 100 124 151 181 218 258 290 0.0 32.0 189.0 Zimbabwe 2 7 100 177 394 1,883 9,064 30,632 .. 4.0 24.0 7042.0 NORTH AFRICA 30 54 100 101 104 111 123 132 142 44.0 77.0 116.0 Algeria 6 16 100 101 103 111 123 143 159 9.0 50.0 120.0 Egypt, Arab Rep. 13 43 100 102 104 111 124 132 142 20.0 73.0 117.0 Libya 143 .. 100 98 128 166 204 264 300 153.0 81.0 180.0 Morocco 35 68 100 101 102 103 102 104 106 50.0 84.0 103.0 Tunisia 30 64 100 103 105 107 110 114 118 46.0 82.0 108.0 AFRICA 14 39 100 104 110 116 123 132 141 23.0 67.0 118.0 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 37 ableT2.10 Gross domestic product deflator (U.S. dollar series) Index (2000=100) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 122 109 100 95 99 117 136 150 164 108 108 123 Excl. South Africa 149 114 100 100 112 118 132 150 172 123 103 126 Excl. South Africa & Nigeria 131 123 100 99 110 116 127 141 157 120 111 121 Angola .. 121 100 95 106 125 160 205 255 95 91 149 Benin 130 131 100 100 113 138 153 157 163 103 116 132 Botswana 88 112 100 93 86 114 127 130 131 77 108 112 Burkina Faso 175 199 100 101 113 136 155 155 156 147 135 131 Burundi 164 131 100 92 83 80 85 101 109 153 123 93 Cameroon 106 127 100 91 99 120 134 137 144 102 125 118 Cape Verde .. 112 100 100 107 130 152 155 165 95 117 130 Central African Republic 108 183 100 101 109 135 146 148 155 117 143 128 Chad 155 157 100 111 118 142 172 212 227 121 129 154 Comoros 91 138 100 106 116 146 163 167 172 89 125 138 Congo, Dem. Rep. 205 122 100 111 127 123 134 136 155 133 126 127 Congo, Rep. 98 100 100 84 86 100 118 153 183 84 81 118 Côte d'Ivoire 132 130 100 101 112 136 150 157 166 108 123 132 Djibouti .. 69 100 102 102 104 108 111 115 .. 85 106 Equatorial Guinea .. 64 100 85 88 107 134 192 231 54 65 134 Eritrea .. .. 100 97 91 79 84 128 145 .. 99 103 Ethiopia .. 194 100 92 87 97 101 110 122 167 151 101 Gabon 119 138 100 91 96 114 134 157 171 96 104 123 Gambia, The 113 104 100 94 86 80 83 91 94 91 109 90 Ghana 168 180 100 103 114 134 148 168 188 177 166 136 Guinea 434 128 100 94 95 105 111 89 86 420 136 97 Guinea-Bissau 97 131 100 92 100 118 132 143 143 105 116 118 Kenya 103 82 100 99 99 110 113 124 142 86 86 112 Lesotho 110 102 100 87 77 113 138 145 142 92 115 115 Liberia 69 89 100 94 93 100 109 119 128 76 101 106 Madagascar 130 94 100 110 122 139 105 116 121 108 101 116 Malawi 124 151 100 104 168 144 148 157 161 118 132 140 Mali 116 149 100 97 118 144 157 161 169 108 132 135 Mauritania 102 125 100 101 102 108 124 139 181 108 140 122 Mauritius 76 89 100 96 94 105 116 115 112 71 104 105 Mozambique 137 94 100 86 81 85 96 103 99 150 92 93 Namibia 108 104 100 92 84 116 137 145 148 90 111 117 Niger 165 165 100 101 109 127 141 151 155 137 127 126 Nigeria 204 81 100 101 123 127 150 181 223 127 76 144 Rwanda 85 154 100 89 79 85 89 101 115 112 123 94 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 131 165 100 99 108 130 144 147 154 120 136 126 Seychelles 50 93 100 104 115 123 126 128 131 65 103 118 Sierra Leone 118 64 100 108 98 95 96 101 110 97 98 101 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 85 101 100 87 78 114 141 151 151 88 115 117 Sudan 138 128 100 102 108 120 139 165 197 186 89 133 Swaziland 111 85 100 102 90 133 170 182 189 84 100 138 Tanzania .. 63 100 98 94 94 97 113 106 76 77 100 Togo 118 152 100 100 107 124 141 145 144 106 131 123 Uganda .. 140 100 91 88 90 93 112 116 164 114 99 Zambia 142 109 100 107 107 119 142 180 251 111 111 144 Zimbabwe 153 130 100 142 318 120 79 61 .. 136 101 137 NORTH AFRICA 102 96 100 94 85 91 97 107 117 95 92 99 Algeria 120 134 100 98 97 108 128 147 164 128 101 120 Egypt, Arab Rep. 59 66 100 94 82 75 69 75 84 62 76 83 Libya 248 .. 100 83 52 66 80 103 117 263 89 86 Morocco 94 88 100 95 98 114 123 125 129 73 99 112 Tunisia 101 100 100 98 101 114 121 120 121 89 111 111 AFRICA 114 103 100 94 93 106 119 132 145 102 101 113 a. Provisional 38 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.11 Consumer price index* Annual (% change) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA Excl. South Africa Excl. South Africa & Nigeria Angola .. .. 325.0 169.7 95.6 98.2 43.5 24.8 11.7 .. 1,122.5 109.8 Benin .. .. 4.2 4.0 2.5 1.5 0.9 5.4 3.8 .. 9.7 3.2 Botswana 13.6 11.4 8.6 6.6 8.0 9.2 6.9 8.6 11.6 10.8 10.8 8.5 Burkina Faso 12.2 ­0.8 ­0.3 5.0 2.2 2.0 ­0.4 6.4 2.3 5.0 4.5 2.5 Burundi 2.5 7.0 24.3 9.2 ­1.3 7.9 10.7 13.5 2.8 7.2 13.5 9.6 Cameroon 9.6 1.1 1.2 4.4 2.8 0.6 0.2 2.0 5.1 9.1 5.6 2.4 Cape Verde .. 10.7 ­2.5 3.3 1.9 1.2 ­1.9 0.4 5.4 6.7 6.4 1.1 Central African Republic .. ­0.4 3.2 3.8 2.3 4.1 ­2.1 2.9 .. 3.7 3.9 2.4 Chad .. ­0.7 3.8 12.4 5.2 ­1.8 ­5.4 7.9 8.0 3.0 5.5 4.3 Comoros .. .. .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. 46.6 81.3 550.0 313.7 38.1 12.9 4.0 21.3 .. 57.0 3,367.2 156.7 Congo, Rep. .. 2.9 ­0.8 0.0 3.7 2.3 1.0 5.3 3.4 1.0 8.5 2.1 Côte d'Ivoire 14.7 ­0.8 2.5 4.3 3.1 3.3 1.4 3.9 2.5 6.7 6.0 3.0 Djibouti 12.1 .. .. .. .. .. .. .. .. 5.3 .. .. Equatorial Guinea .. 0.9 4.8 8.8 7.6 7.3 4.2 .. .. ­5.5 6.6 6.6 Eritrea .. .. .. .. .. .. .. .. .. .. .. .. Ethiopia 4.5 5.2 0.7 ­8.2 1.7 17.8 3.3 11.6 12.3 4.6 8.0 5.6 Gabon 12.3 7.7 0.5 2.1 0.0 2.2 0.4 1.2 ­1.4 6.5 3.7 0.7 Gambia, The 6.8 12.2 0.2 4.4 8.6 17.0 14.2 3.2 .. 17.5 5.5 7.9 Ghana 50.1 37.3 25.2 32.9 14.8 26.7 12.6 15.1 10.9 48.3 27.6 19.7 Guinea .. .. .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. 33.0 8.6 3.3 3.3 ­3.5 0.9 3.3 2.0 70.5 37.4 2.6 Kenya 13.9 17.8 10.0 5.7 2.0 9.8 11.6 10.3 14.5 11.8 17.4 9.1 Lesotho 16.3 11.6 6.1 ­9.6 33.8 6.7 5.0 3.4 6.0 13.9 12.4 7.4 Liberia 14.7 .. .. .. .. .. .. .. .. 5.9 .. .. Madagascar 18.2 11.8 12.0 6.9 15.9 ­1.2 13.8 18.5 10.8 18.6 17.3 11.0 Malawi .. 11.8 29.6 22.7 14.7 9.6 11.4 15.4 14.0 16.8 31.0 16.8 Mali .. 0.6 ­0.7 5.2 5.0 ­1.3 ­3.1 6.4 1.5 ­0.1 4.2 1.9 Mauritania .. 6.6 3.3 4.7 3.9 5.2 10.4 12.1 6.2 7.5 6.4 6.5 Mauritius 42.0 13.5 4.2 5.4 6.5 3.9 4.8 4.9 8.9 11.2 7.6 5.5 Mozambique .. 47.0 12.7 9.0 16.8 13.4 12.7 7.2 13.2 45.1 34.5 12.2 Namibia .. .. .. .. .. 7.2 4.1 2.3 5.1 .. .. 4.7 Niger 10.3 ­0.8 2.9 4.0 2.6 ­1.6 0.3 7.8 0.0 3.6 4.3 2.3 Nigeria 10.0 7.4 6.9 18.9 12.9 14.0 15.0 17.9 8.2 20.9 30.6 13.4 Rwanda 7.2 4.2 4.3 3.0 2.3 7.1 12.3 9.0 8.9 4.7 8.6 6.7 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 8.7 0.3 0.7 3.1 2.2 0.0 0.5 1.7 2.1 6.9 4.4 1.5 Seychelles 13.6 3.9 6.3 6.0 0.2 3.3 3.8 0.9 ­0.3 4.0 2.0 2.9 Sierra Leone 12.9 110.9 ­0.8 2.1 ­3.3 7.6 14.2 12.1 9.5 63.0 45.9 5.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 13.7 14.3 5.3 5.7 9.2 5.9 1.4 3.4 4.6 14.6 9.9 5.1 Sudan 25.4 65.2 6.9 5.8 9.8 6.5 8.3 8.5 7.2 36.2 80.4 7.6 Swaziland 18.7 13.1 12.2 5.9 12.0 7.3 3.4 4.8 5.3 15.0 9.5 7.3 Tanzania 30.2 35.8 5.9 5.1 1.0 3.5 0.0 8.6 6.4 30.1 23.1 4.4 Togo 12.3 1.0 1.9 3.9 3.1 ­1.0 0.4 6.8 2.2 5.0 7.1 2.5 Uganda .. 33.1 2.8 2.0 ­0.3 7.8 3.3 8.2 6.8 111.2 15.9 4.4 Zambia .. 107.0 26.0 21.4 22.2 21.4 18.0 18.3 9.0 69.3 76.2 19.5 Zimbabwe 5.4 17.4 55.9 76.7 140.1 431.7 282.4 302.1 1,096.7 12.8 28.6 340.8 NORTH AFRICA Algeria 9.5 16.6 0.3 4.2 1.4 2.6 3.6 1.6 2.5 9.0 18.6 2.3 Egypt, Arab Rep. 20.8 16.8 2.7 2.3 2.7 4.5 11.3 4.9 7.6 17.4 10.5 5.1 Libya 12.4 8.5 ­2.9 ­8.8 ­9.9 ­2.1 ­2.2 2.0 3.4 8.1 6.7 ­2.9 Morocco 9.4 6.9 1.9 0.6 2.8 1.2 1.5 1.0 3.3 7.6 4.5 1.7 Tunisia .. 6.5 2.9 2.0 2.7 2.7 3.6 2.0 4.5 7.6 4.9 2.9 AFRICA a. Provisional * For a discussion on the impacts of the recent food prices acceleration, see Box 3 in the technical notes. NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 39 ableT2.12 Price indices* Inflation, Exports price index Imports price index GDP deflator Consumer price index (goods and services, (goods and services, (annual %) (2000 = 100) 2000 = 100) 2000 = 100) 2005 2006 2005 2006 2005 2006 2005 2006 SUB-SAHARAN AFRICA 8.8 7.4 128.1 139.5 .. .. 139.8 147.5 Excl. South Africa 8.8 7.9 128.1 140.9 .. .. 137.0 146.0 Excl. South Africa & Nigeria 8.8 7.2 127.4 139.5 .. .. 137.0 146.0 Angola 34.0 14.7 1,872.8 2,091.5 .. .. .. .. Benin 2.8 3.0 115.0 119.3 145.5 .. 158.5 .. Botswana 11.1 15.6 146.0 162.9 144.5 139.2 146.2 141.7 Burkina Faso ­0.3 ­0.1 116.0 118.7 160.1 158.0 158.2 164.4 Burundi 16.6 2.6 146.1 150.2 .. .. .. .. Cameroon 2.6 3.9 110.5 116.1 133.6 160.5 144.0 148.7 Cape Verde 2.0 5.2 105.0 110.6 63.0 63.5 107.4 108.3 Central African Republic 1.0 4.3 111.5 .. 120.2 127.8 161.8 177.6 Chad 23.1 6.2 118.7 128.2 211.6 226.6 211.6 226.6 Comoros 2.3 2.0 .. .. 167.2 172.0 167.2 172.0 Congo, Dem. Rep. 21.6 13.1 813.6 .. 161.2 176.6 106.3 126.5 Congo, Rep. 29.9 18.5 112.8 116.7 .. .. .. .. Côte d'Ivoire 4.2 5.0 117.1 120.0 126.9 139.0 171.9 169.2 Djibouti 3.2 3.5 .. .. 111.0 115.0 111.0 115.0 Equatorial Guinea 43.5 19.4 .. .. 186.3 232.6 129.1 138.6 Eritrea 14.9 12.0 .. .. 106.5 110.0 126.0 127.8 Ethiopia 9.9 11.6 126.6 142.2 97.6 110.4 129.5 139.2 Gabon 17.0 7.9 106.1 104.6 179.8 212.5 134.2 141.3 Gambia, The 4.3 2.1 156.4 .. 84.5 .. 114.8 .. Ghana 15.0 12.7 250.6 278.0 142.3 168.9 143.4 163.9 Guinea 28.6 37.4 .. .. 115.1 135.2 129.2 145.5 Guinea-Bissau 7.6 ­0.5 107.4 109.5 136.1 146.6 157.1 151.3 Kenya 4.4 9.4 145.8 166.9 125.7 131.6 134.9 151.2 Lesotho 3.4 4.2 140.2 148.6 174.5 172.3 146.0 148.1 Liberia 13.8 9.2 .. .. .. .. .. .. Madagascar 18.4 11.3 165.2 183.0 140.6 137.0 101.1 106.0 Malawi 15.6 18.0 198.4 226.1 216.8 216.6 115.6 117.2 Mali 2.4 4.1 112.4 114.1 138.1 172.0 155.9 178.5 Mauritania 18.0 29.8 141.6 150.4 140.4 142.0 107.2 .. Mauritius 4.8 4.1 128.1 139.5 109.9 109.0 121.7 124.1 Mozambique 8.8 6.0 174.4 197.5 101.1 122.5 145.9 151.1 Namibia 3.7 9.1 114.1 119.9 151.2 159.8 150.9 153.4 Niger 6.8 1.8 113.5 113.5 .. .. .. .. Nigeria 19.8 19.6 207.4 224.5 .. .. .. .. Rwanda 8.9 13.1 138.1 150.3 77.0 80.7 116.2 108.3 São Tomé and Principe 7.6 19.9 .. .. .. .. .. .. Senegal 2.3 3.4 107.7 110.0 155.5 170.9 165.5 180.2 Seychelles 2.0 2.2 114.9 114.5 128.4 130.7 128.4 130.7 Sierra Leone 12.9 11.6 135.9 148.9 .. .. .. .. Somalia .. .. .. .. .. .. .. .. South Africa 4.8 6.8 128.1 134.0 156.2 169.3 143.5 149.3 Sudan 12.2 6.5 145.5 155.9 161.8 194.0 161.8 194.1 Swaziland 5.7 10.3 138.0 145.3 106.5 95.2 112.8 98.8 Tanzania 20.2 4.2 119.4 127.1 103.7 108.9 142.3 139.6 Togo 3.0 ­1.9 113.7 116.3 132.1 .. 131.6 .. Uganda 7.9 8.6 122.5 130.8 114.2 133.5 103.8 105.1 Zambia 18.1 12.6 251.4 274.1 100.3 137.6 91.2 97.8 Zimbabwe 237.9 .. 34,682.2 415,034.4 102.9 .. 111.7 .. NORTH AFRICA 6.3 7.4 113.8 117.0 .. .. 121.0 122.2 Algeria 16.5 10.8 114.1 117.0 178.6 .. 134.3 .. Egypt, Arab Rep. 6.3 7.4 128.1 137.9 103.0 100.5 93.9 89.5 Libya 29.1 13.7 80.3 83.0 .. .. .. .. Morocco 2.1 1.9 107.2 110.8 139.1 145.7 132.1 139.7 Tunisia 3.1 3.8 113.8 118.9 133.0 153.2 136.1 152.0 AFRICA 8.3 7.4 126.6 132.4 .. .. 132.5 138.8 *For a discussion on the perception of the public about inflation, see Box 4 in the technical notes. 40 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.13 Gross domestic savings Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 35.4 16.2 20.5 19.9 21.9 21.7 22.6 25.1 29.0 22.3 14.6 23.0 Excl. South Africa 31.5 9.9 20.9 18.5 20.1 22.5 26.3 29.8 34.8 16.3 10.7 24.7 Excl. South Africa & Nigeria 14.4 12.8 16.9 14.2 14.5 16.1 17.9 20.2 23.5 13.3 12.5 17.6 Angola .. 29.7 41.8 15.1 23.9 19.2 25.1 37.9 49.5 24.0 22.0 30.4 Benin ­6.3 2.2 6.0 6.5 3.7 6.0 5.5 6.9 .. ­2.4 3.8 5.8 Botswana 26.7 42.6 53.9 55.9 52.2 50.4 50.7 52.4 52.4 35.3 39.7 52.6 Burkina Faso ­7.2 5.4 0.6 ­0.1 3.7 4.5 1.8 4.8 2.8 ­1.6 9.0 2.6 Burundi ­0.6 ­5.4 ­6.0 ­7.8 ­9.7 ­8.7 ­11.0 ­23.1 ­20.2 3.1 ­5.2 ­12.4 Cameroon 21.7 20.7 20.3 19.0 19.0 17.8 18.5 18.1 18.9 24.2 18.5 18.8 Cape Verde .. ­8.1 ­14.2 ­15.1 ­15.7 ­15.8 ­1.5 4.4 4.7 ­2.2 ­5.6 ­7.6 Central African Republic ­8.9 ­0.6 5.2 3.9 4.3 1.7 0.1 0.2 1.1 ­1.1 3.7 2.4 Chad .. ­7.7 5.5 5.3 ­40.8 18.5 24.5 35.5 39.8 ­8.1 ­0.5 12.6 Comoros ­10.1 ­3.2 ­5.7 ­5.2 ­4.0 ­5.8 ­10.6 ­12.9 ­14.0 ­4.5 ­4.5 ­8.3 Congo, Dem. Rep. 10.1 9.3 4.5 3.2 4.0 5.0 4.0 6.5 4.7 10.9 8.8 4.5 Congo, Rep. 35.7 23.8 59.3 50.5 51.0 51.3 51.3 57.6 65.7 31.9 28.8 55.2 Côte d'Ivoire 20.4 11.3 17.9 19.5 26.7 21.0 20.0 17.2 20.4 19.6 17.8 20.4 Djibouti .. ­10.4 ­6.5 ­0.6 4.9 5.3 4.3 8.6 12.1 .. ­6.4 4.0 Equatorial Guinea .. ­20.1 74.5 81.2 79.0 79.8 83.5 87.3 86.0 .. 13.7 81.6 Eritrea .. .. ­34.7 ­27.0 ­33.7 ­59.7 ­61.4 ­26.8 ­23.3 .. ­30.9 ­38.1 Ethiopia .. 9.6 8.3 9.7 9.9 7.7 8.8 2.6 1.5 10.5 9.7 7.0 Gabon 60.6 36.9 58.3 51.7 43.7 48.2 53.9 67.2 64.7 44.3 43.6 55.4 Gambia, The 5.8 10.7 8.5 12.0 12.9 11.1 10.5 4.4 .. 6.5 7.4 9.9 Ghana 4.9 5.5 5.6 7.0 7.4 7.0 7.3 3.4 7.9 4.8 7.5 6.5 Guinea .. 22.2 15.4 14.1 9.5 7.8 7.4 11.3 10.5 15.1 18.3 10.9 Guinea-Bissau ­1.0 2.8 ­8.5 ­19.3 ­12.1 1.2 ­3.0 1.5 6.3 ­0.9 1.5 ­4.8 Kenya 18.1 18.5 9.4 7.0 8.4 10.4 9.3 6.1 9.5 18.3 15.5 8.6 Lesotho ­52.0 ­52.9 ­20.6 ­16.6 ­19.8 ­17.3 ­12.4 ­16.2 ­15.0 ­65.5 ­38.3 ­16.8 Liberia 14.8 .. .. ­3.4 ­3.3 ­3.2 ­0.7 2.4 .. 2.2 .. ­1.6 Madagascar ­1.4 5.5 7.7 15.3 7.7 8.9 9.4 8.4 13.6 2.9 4.2 10.2 Malawi 10.8 13.4 3.8 3.8 .. ­3.4 2.0 7.7 11.2 12.7 3.4 4.2 Mali 1.1 6.4 12.0 14.0 11.3 13.3 8.6 11.0 14.8 ­0.4 7.6 12.1 Mauritania ­3.5 4.9 ­8.6 3.1 ­1.9 ­5.0 ­3.1 ­15.0 18.8 3.1 2.4 ­1.7 Mauritius 14.5 23.5 23.9 26.0 25.2 24.8 23.4 18.9 17.5 20.0 24.1 22.8 Mozambique ­8.9 ­5.8 11.5 3.7 11.6 6.1 10.0 9.3 13.3 ­6.2 ­2.9 9.4 Namibia 38.4 18.2 14.0 17.0 17.8 26.2 20.0 24.2 28.4 10.8 12.7 21.1 Niger 14.6 1.2 3.5 4.4 5.3 5.2 4.1 13.7 .. 7.3 2.7 6.0 Nigeriab .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 4.2 6.2 1.3 2.9 ­0.5 0.4 3.5 3.3 3.2 5.0 ­5.5 2.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 2.1 2.4 11.2 9.4 6.8 8.8 7.9 14.1 10.5 4.3 5.4 9.8 Seychelles 27.1 20.3 21.9 19.2 24.4 21.5 14.7 3.8 10.2 24.1 21.7 16.5 Sierra Leone 0.9 8.7 ­14.4 ­11.6 ­9.4 ­7.4 ­1.7 ­0.6 1.7 9.1 2.8 ­6.2 Somalia ­12.9 ­12.5 .. .. .. .. .. .. .. ­6.3 .. .. South Africa 37.9 23.2 18.9 19.3 19.9 19.2 17.2 17.3 17.1 28.5 19.4 18.4 Sudan 2.1 3.1 15.9 9.8 13.3 15.7 18.7 14.2 14.4 4.0 7.6 14.6 Swaziland 1.2 6.6 3.0 2.9 19.6 20.8 15.9 9.0 8.1 3.7 2.0 11.3 Tanzania .. 1.3 10.2 8.8 11.8 12.0 11.2 9.7 10.8 .. 2.0 10.7 Togo 23.2 14.7 ­2.2 1.0 0.6 5.3 4.5 6.9 .. 12.3 6.7 2.7 Uganda ­0.4 0.6 8.1 6.5 4.7 6.3 8.4 7.6 8.1 2.3 4.3 7.1 Zambia 19.3 16.6 3.0 2.8 7.9 13.0 19.6 21.2 30.9 14.0 9.0 14.0 Zimbabwe 13.8 17.5 13.3 11.6 7.1 6.2 4.1 0.6 .. 16.5 16.9 7.2 NORTH AFRICA 41.3 22.8 24.8 23.6 24.3 27.5 31.1 35.5 37.0 29.4 20.2 29.1 Algeria 43.1 27.1 44.8 42.0 40.9 44.9 47.7 54.1 .. 31.5 30.1 45.7 Egypt, Arab Rep. 15.2 16.1 12.9 13.4 13.9 14.3 15.6 15.7 17.1 15.5 14.2 14.7 Libya 56.9 27.2 32.9 23.5 26.4 .. .. .. .. 46.9 17.6 27.6 Morocco 14.9 19.9 20.1 23.5 23.7 24.5 23.5 24.1 26.2 16.7 17.8 23.7 Tunisia 24.0 20.0 23.7 23.3 21.4 21.2 21.2 20.5 23.6 22.7 22.3 22.1 AFRICA 37.7 19.3 22.4 21.6 23.2 24.3 26.0 29.1 32.2 25.4 17.3 25.5 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 41 ableT2.14 Gross national savings Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 13.3 12.9 12.9 11.4 10.9 12.3 11.8 9.8 9.0 13.1 12.1 11.1 Excl. South Africa 4.7 9.0 11.0 9.2 8.2 10.1 10.1 7.2 6.4 7.1 8.8 8.9 Excl. South Africa & Nigeria 7.1 10.7 14.1 11.8 10.7 13.4 13.8 10.0 9.2 8.9 10.5 11.9 Angola .. 9.0 23.8 ­1.4 9.8 7.6 12.6 24.8 37.0 13.7 ­3.7 16.3 Benin 1.1 9.9 10.9 12.5 9.3 10.5 10.3 10.6 .. 2.1 7.7 10.7 Botswana 28.7 43.3 51.7 56.9 44.1 45.3 45.6 51.3 53.3 33.7 41.6 49.7 Burkina Faso ­1.6 13.7 5.1 4.1 7.5 9.1 5.1 7.9 6.3 4.7 15.8 6.4 Burundi .. .. 0.5 1.6 2.9 6.0 5.2 1.3 .. .. 1.4 2.9 Cameroon 6.3 16.1 16.1 16.7 15.1 15.5 16.9 16.6 18.9 19.8 13.6 16.5 Cape Verde .. 17.6 9.1 8.0 9.4 8.4 21.8 28.8 26.8 24.2 21.3 16.0 Central African Republic 1.6 ­0.4 8.2 6.7 7.4 3.9 4.4 4.6 11.7 5.6 4.1 6.7 Chad .. ­2.7 7.9 6.6 ­40.2 5.4 12.6 20.3 20.4 ­3.3 2.8 4.7 Comoros .. ­1.3 9.9 12.5 9.6 7.2 6.5 6.0 4.9 .. 3.8 8.1 Congo, Dem. Rep. 9.3 0.8 ­3.5 ­1.7 5.3 9.9 6.1 6.7 8.9 7.4 0.3 4.5 Congo, Rep. .. 6.6 30.1 20.9 24.2 26.7 26.0 31.7 43.0 18.0 4.9 28.9 Côte d'Ivoire .. ­4.3 10.0 12.4 19.3 14.5 14.2 11.7 14.7 9.2 7.0 13.8 Djibouti .. .. 5.4 11.6 15.6 17.6 16.2 20.1 20.7 .. 11.4 15.3 Equatorial Guinea .. ­22.0 45.7 30.8 33.0 26.3 22.8 32.7 46.2 .. 6.0 33.9 Eritrea .. .. 20.4 28.7 26.3 ­6.5 ­20.9 10.3 8.7 .. 11.7 9.6 Ethiopia .. 11.9 16.3 18.7 19.5 20.6 21.5 16.8 15.1 11.8 15.7 18.3 Gabon .. 24.2 41.7 36.6 31.3 32.7 34.7 42.1 41.3 21.8 29.3 37.2 Gambia, The .. 5.3 13.6 13.8 18.4 18.4 21.1 15.0 10.0 .. 11.6 15.8 Ghana 4.5 7.0 15.7 21.2 20.0 20.5 25.7 21.4 27.4 4.7 12.5 21.7 Guinea .. 14.6 13.3 12.7 9.2 6.8 5.7 9.3 11.8 8.8 14.2 9.8 Guinea-Bissau ­6.3 15.3 ­2.7 ­15.7 ­8.0 5.1 16.2 11.2 22.7 ­0.3 5.5 4.1 Kenya 15.4 18.6 15.2 11.4 11.9 13.4 10.2 9.7 12.5 16.2 16.0 12.0 Lesotho 49.6 59.5 21.4 24.6 21.3 22.7 29.6 25.3 27.3 41.8 41.1 24.6 Liberia .. .. .. ­21.4 ­11.1 ­6.4 33.1 39.8 .. .. .. 6.8 Madagascar ­2.4 9.2 9.4 17.2 10.0 13.0 15.2 11.7 16.0 2.1 4.9 13.2 Malawi .. 13.6 2.2 2.4 .. 0.7 4.8 13.0 15.5 .. 2.3 6.4 Mali 1.9 15.1 16.0 12.8 9.0 14.4 8.6 11.4 13.0 2.6 14.4 12.1 Mauritania 3.9 17.6 0.8 11.4 16.8 9.9 8.4 ­5.4 28.7 17.1 9.0 10.1 Mauritius 14.0 26.3 25.3 27.6 26.6 26.3 23.8 19.7 19.0 19.7 26.5 24.0 Mozambique ­6.9 2.1 6.0 ­3.7 7.5 1.8 4.0 1.9 3.1 ­3.8 0.1 3.0 Namibia 26.9 34.8 26.2 26.6 26.6 40.2 33.3 33.5 42.2 18.5 27.0 32.7 Niger 17.1 ­2.1 2.8 4.4 4.7 10.8 11.7 .. .. 9.9 0.3 6.9 Nigeriab .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 13.3 11.3 12.9 13.0 10.3 11.3 17.6 18.9 13.8 10.9 8.1 14.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 0.1 ­0.5 14.6 13.9 11.8 15.9 14.9 21.2 18.3 0.5 5.3 15.8 Seychelles .. 21.7 17.2 16.9 16.4 16.7 12.4 2.6 9.4 .. 21.5 13.1 Sierra Leone 0.5 2.6 ­9.1 ­3.2 5.3 6.3 5.2 9.1 9.5 7.2 0.2 3.3 Somalia ­5.8 .. .. .. .. .. .. .. .. 3.2 .. .. South Africa 34.1 19.6 15.8 15.6 16.9 15.8 14.4 14.2 13.9 25.0 16.7 15.2 Sudan 7.0 ­4.4 3.5 1.7 9.5 12.3 16.0 13.2 10.3 6.8 ­2.5 9.5 Swaziland 16.7 24.8 13.2 13.3 24.7 25.6 20.6 14.7 14.5 20.2 19.6 18.1 Tanzania .. 7.7 8.5 7.6 9.7 10.6 9.6 8.8 10.7 .. 4.2 9.4 Togo .. 20.4 0.3 4.1 5.0 10.8 9.9 12.0 .. 11.7 9.1 7.0 Uganda ­1.0 1.2 9.3 7.6 6.0 7.3 9.5 7.2 12.9 2.9 6.7 8.5 Zambia 7.3 6.9 ­1.3 ­0.7 5.9 10.7 12.7 14.4 23.3 2.2 0.6 9.3 Zimbabwe .. 15.7 9.6 9.4 7.0 5.9 4.0 ­0.4 .. 17.3 16.0 5.9 NORTH AFRICA 17.5 14.2 12.9 14.2 15.0 14.6 14.4 13.8 14.6 15.6 11.7 14.2 Algeria 40.8 26.2 41.3 40.1 38.8 43.5 46.3 51.0 .. 30.3 28.1 43.5 Egypt, Arab Rep. .. 21.4 18.7 18.4 20.0 19.5 20.5 21.3 22.0 20.4 22.6 20.1 Libya 53.5 .. .. .. .. .. .. .. .. 40.5 .. .. Morocco 18.6 25.1 24.2 30.4 29.6 30.6 30.4 32.2 34.5 20.4 21.6 30.3 Tunisia .. 21.7 23.2 23.7 22.3 22.3 22.3 20.0 25.4 22.2 21.8 22.7 AFRICA 14.7 13.4 12.9 12.6 12.4 13.1 12.7 11.1 10.8 13.9 12.0 12.2 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. 42 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.15 General government final consumption expenditure Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 12.4 17.4 14.6 14.4 13.8 15.0 15.2 14.6 13.7 15.2 16.8 14.5 Excl. South Africa 11.7 15.5 11.9 12.0 11.8 12.0 11.7 11.0 10.3 13.7 14.2 11.5 Excl. South Africa & Nigeria 14.6 15.0 12.5 12.6 12.6 13.0 13.0 12.5 12.1 14.2 13.9 12.6 Angola .. 34.5 .. .. .. .. .. .. .. 31.5 40.7 .. Benin 8.6 11.0 11.6 11.6 12.5 13.3 13.6 15.0 .. 12.7 10.5 12.9 Botswana 21.3 24.1 22.9 20.4 20.9 21.5 21.3 20.6 19.2 24.3 26.7 20.9 Burkina Faso 9.2 21.1 20.8 21.7 25.2 22.2 21.6 22.3 22.0 15.6 22.5 22.3 Burundi 9.2 10.8 17.5 19.9 19.1 22.7 26.1 26.5 29.3 9.3 17.0 23.0 Cameroon 9.7 12.8 9.5 10.2 10.2 10.0 10.2 10.0 9.6 10.0 10.6 9.9 Cape Verde .. 14.7 21.3 11.3 11.7 14.7 20.6 20.3 20.7 13.1 17.0 17.2 Central African Republic 15.1 14.9 14.0 11.9 12.9 10.5 10.2 13.3 10.6 15.6 13.9 11.9 Chad .. 10.0 7.7 7.5 7.7 7.6 4.9 4.5 5.9 11.3 8.1 6.5 Comoros 30.9 24.5 11.7 16.2 17.4 14.7 14.3 13.5 12.6 28.6 20.3 14.3 Congo, Dem. Rep. 8.4 11.5 7.5 6.0 5.5 6.3 8.2 8.3 7.3 9.0 9.9 7.0 Congo, Rep. 17.6 13.8 11.6 14.1 18.4 17.0 16.0 13.0 13.2 17.7 18.1 14.8 Côte d'Ivoire 16.9 16.8 7.2 7.5 7.8 8.2 8.3 8.3 8.4 16.5 11.9 8.0 Djibouti .. 31.5 29.7 26.9 28.3 29.5 29.7 27.1 28.0 .. 31.8 28.5 Equatorial Guinea .. 39.7 4.6 3.3 5.1 3.8 3.1 3.0 2.9 27.4 25.1 3.7 Eritrea .. .. 63.8 51.5 44.0 51.5 52.6 44.6 42.4 .. 39.7 50.0 Ethiopia .. 13.2 17.9 14.6 14.8 13.4 13.1 12.3 12.1 11.2 9.8 14.0 Gabon 13.2 13.4 9.6 11.5 11.0 10.1 9.3 8.3 8.4 18.3 13.2 9.7 Gambia, The 31.2 13.7 13.7 14.4 12.9 11.0 11.1 .. .. 29.1 13.8 12.6 Ghana 11.2 9.3 10.2 9.7 9.9 11.5 12.2 11.7 13.4 9.0 11.7 11.2 Guinea .. 11.0 6.8 6.9 7.5 7.8 6.3 5.7 5.6 11.8 8.2 6.7 Guinea-Bissau 27.6 10.3 14.0 12.6 13.0 12.8 14.5 18.2 17.7 18.9 8.4 14.7 Kenya 19.8 18.6 15.3 16.0 17.1 18.0 17.6 17.1 16.3 18.3 16.0 16.8 Lesotho 21.8 14.1 19.2 18.0 17.7 17.9 17.2 18.1 18.1 19.2 16.8 18.0 Liberia 19.1 .. .. 14.4 13.7 8.5 10.4 11.1 .. 22.0 .. 11.6 Madagascar 12.1 8.0 9.0 9.1 8.1 9.2 9.8 8.4 8.8 9.8 7.9 8.9 Malawi 19.3 15.1 14.6 15.8 10.7 11.9 12.2 12.1 11.8 17.5 16.6 12.7 Mali 11.6 13.8 8.6 9.2 8.7 8.4 10.0 9.9 9.9 12.3 12.7 9.3 Mauritania 45.3 25.9 25.8 23.7 22.3 30.1 21.9 22.7 19.9 30.6 14.5 23.8 Mauritius 14.4 12.8 13.1 12.9 12.8 14.1 14.2 14.4 14.5 13.5 13.0 13.7 Mozambique 12.2 13.5 9.0 9.1 9.4 10.6 10.8 10.4 11.1 13.8 9.7 10.1 Namibia 17.4 30.6 28.8 28.4 26.4 26.5 24.7 24.6 23.7 27.9 31.0 26.2 Niger 10.4 15.0 13.0 12.4 12.2 11.7 13.2 11.7 .. 11.9 14.6 12.4 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 12.5 10.1 11.0 11.9 12.5 14.4 12.0 12.0 11.7 13.0 11.5 12.2 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 24.8 18.4 12.8 12.6 13.3 13.3 13.7 9.6 9.6 19.3 15.0 12.1 Seychelles 28.7 27.7 24.2 24.6 22.7 25.5 28.3 26.0 24.7 33.1 29.0 25.1 Sierra Leone 8.4 7.8 14.3 17.6 16.4 15.6 13.5 13.4 13.1 7.7 10.6 14.9 Somalia 15.6 .. .. .. .. .. .. .. .. 17.6 .. .. South Africa 14.3 19.7 18.1 18.3 18.4 19.3 19.6 19.6 19.5 17.4 19.4 19.0 Sudan 16.0 7.3 7.6 8.6 4.5 10.8 11.8 17.1 16.7 11.1 6.7 11.0 Swaziland 27.0 18.1 24.5 17.0 18.6 19.5 22.3 20.8 19.5 21.5 22.6 20.3 Tanzania .. 17.8 10.6 11.5 12.4 14.8 15.9 15.2 16.3 .. 14.0 13.8 Togo 22.4 14.2 10.2 10.0 8.4 9.8 9.7 9.8 .. 16.9 12.8 9.7 Uganda .. 7.5 13.7 13.8 15.3 14.8 14.7 14.4 14.7 9.9 11.1 14.5 Zambia 25.5 19.0 9.5 10.2 11.8 14.4 17.7 9.4 10.0 23.0 17.7 11.9 Zimbabwe 18.5 19.4 13.9 17.7 17.9 16.6 23.3 27.2 .. 20.1 17.2 19.4 NORTH AFRICA 14.0 16.2 14.5 14.9 14.9 14.8 14.6 13.6 .. 17.4 16.1 14.5 Algeria 15.2 16.1 13.6 14.7 15.4 14.8 13.8 12.1 .. 17.2 16.6 14.1 Egypt, Arab Rep. 15.7 11.3 11.2 11.3 12.5 12.7 12.8 12.7 12.3 16.2 10.9 12.2 Libya 21.8 24.4 20.5 21.6 16.7 .. .. .. .. 30.0 24.3 19.6 Morocco 18.3 15.5 18.4 18.6 18.3 18.0 18.8 19.2 18.3 16.6 17.0 18.5 Tunisia 14.5 16.4 15.6 15.6 15.9 15.7 15.4 15.4 13.4 16.5 16.0 15.3 AFRICA 12.5 16.8 14.5 14.6 14.2 14.9 14.9 14.2 13.3 15.7 16.4 14.4 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 43 ableT2.16 Household final consumption expenditure Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 58.9 65.5 68.5 68.6 68.1 67.4 67.8 68.0 66.9 63.3 68.1 67.9 Excl. South Africa .. 72.8 73.6 74.3 74.1 72.7 72.0 72.3 70.3 72.5 74.3 72.8 Excl. South Africa & Nigeria 71.4 72.8 73.6 74.3 74.1 72.7 72.0 72.3 70.3 72.4 74.3 72.8 Angola .. 35.8 .. .. .. .. .. .. .. 44.5 42.6 .. Benin 97.7 86.8 82.4 81.9 83.8 80.7 80.9 78.1 .. 89.7 85.7 81.3 Botswana 52.0 33.2 23.2 23.7 26.9 28.1 28.0 27.0 28.4 40.4 33.6 26.5 Burkina Faso 98.0 73.5 78.5 78.4 71.2 73.3 76.6 72.8 75.2 86.0 68.5 75.2 Burundi 91.4 94.5 88.5 88.0 90.6 85.9 84.9 96.6 90.9 87.5 88.3 89.4 Cameroon 68.6 66.6 70.2 70.7 70.8 72.2 71.4 72.0 71.5 65.8 70.9 71.3 Cape Verde .. 93.4 92.9 103.8 104.0 101.1 80.9 75.3 74.6 89.1 88.6 90.4 Central African Republic 93.7 85.7 80.8 84.2 82.8 87.8 89.6 86.5 88.3 85.5 82.4 85.7 Chad .. 97.6 86.8 87.3 133.1 74.4 70.6 59.8 50.5 96.8 92.5 80.3 Comoros 79.2 78.7 94.0 89.0 86.6 91.1 96.2 99.5 101.4 75.9 84.2 94.0 Congo, Dem. Rep. 81.5 79.1 88.0 90.8 90.4 88.7 87.8 85.2 88.1 80.0 81.3 88.4 Congo, Rep. 46.8 62.4 29.1 35.4 30.7 31.7 32.7 29.4 21.1 50.3 53.1 30.0 Côte d'Ivoire 62.8 71.9 74.9 72.9 65.5 70.8 71.7 74.5 71.2 63.9 70.3 71.7 Djibouti .. 78.9 76.8 73.7 66.8 65.2 66.0 64.2 59.9 .. 73.8 67.5 Equatorial Guinea .. 80.3 20.9 15.6 15.9 16.5 13.3 9.7 11.1 .. 61.2 14.7 Eritrea .. .. 70.9 75.6 89.6 108.2 108.8 82.2 80.9 .. 91.2 88.0 Ethiopia .. 77.2 73.8 75.6 75.2 78.8 78.2 85.1 86.4 78.4 80.5 79.0 Gabon 26.1 49.7 32.2 36.8 45.3 41.7 36.8 24.4 26.9 37.4 43.2 34.9 Gambia, The 63.0 75.6 77.8 73.6 74.3 78.0 78.5 .. .. 64.4 78.8 76.4 Ghana 83.9 85.2 84.3 83.3 82.7 81.5 80.5 84.9 78.7 86.2 80.8 82.2 Guinea .. 66.9 77.7 79.1 83.0 84.4 86.3 82.9 83.8 73.1 73.5 82.5 Guinea-Bissau 73.3 86.9 94.6 106.7 99.1 86.0 88.5 80.3 76.0 82.0 90.1 90.2 Kenya 62.1 62.8 75.2 77.1 74.5 71.6 73.1 76.8 74.3 63.3 68.5 74.6 Lesotho 130.2 138.8 101.3 98.5 102.1 99.4 95.2 98.1 96.9 146.4 121.5 98.8 Liberia 66.1 .. .. 89.1 89.7 94.7 90.3 86.4 .. 75.8 .. 90.0 Madagascar 89.3 86.4 83.2 75.6 84.2 81.9 80.8 83.2 77.6 87.2 87.9 80.9 Malawi 69.9 71.5 81.6 80.4 .. 91.6 85.8 80.1 77.0 69.8 80.0 82.8 Mali 87.4 79.8 79.4 76.7 80.0 78.3 81.4 79.1 75.3 88.1 79.7 78.6 Mauritania 58.2 69.2 82.8 73.2 79.7 74.9 81.2 92.3 61.3 66.3 83.0 77.9 Mauritius 71.0 63.7 63.0 61.1 62.0 61.1 62.4 66.7 68.1 66.5 62.9 63.5 Mozambique 96.7 92.3 79.5 87.2 78.9 83.3 79.2 80.4 75.6 92.3 93.2 80.6 Namibia 44.2 51.2 57.1 54.6 55.8 47.3 55.2 51.3 47.9 61.3 56.3 52.8 Niger 75.1 83.8 83.4 83.2 82.5 83.1 82.7 74.6 .. 80.8 82.7 81.6 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 83.3 83.7 87.7 85.2 88.0 85.3 84.6 84.7 85.1 82.0 94.0 85.8 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 73.1 79.2 76.0 78.0 80.0 77.9 78.4 76.3 79.9 76.4 79.6 78.1 Seychelles 44.2 52.0 53.9 56.2 52.9 52.9 57.0 70.2 65.2 42.7 49.3 58.3 Sierra Leone 90.7 83.5 100.1 94.0 93.0 91.8 88.2 87.1 85.3 83.2 86.6 91.4 Somalia 97.3 .. .. .. .. .. .. .. .. 100.6 .. .. South Africa 47.8 57.1 63.0 62.4 61.7 61.5 63.2 63.2 63.5 54.2 61.2 62.6 Sudan 81.9 89.6 76.5 81.7 82.2 73.5 69.5 68.7 68.9 84.9 85.7 74.4 Swaziland 71.8 75.2 72.4 80.1 61.8 59.7 61.9 70.2 72.3 74.7 75.4 68.3 Tanzania .. 80.9 79.2 79.7 75.8 73.1 72.9 75.1 72.8 .. 84.0 75.5 Togo 54.5 71.1 92.0 89.0 91.0 84.8 85.8 83.3 .. 70.8 80.5 87.6 Uganda .. 91.9 78.2 79.7 80.1 78.8 76.9 78.0 77.2 87.2 84.6 78.4 Zambia 55.2 64.4 87.4 87.1 80.3 72.6 62.7 69.4 59.1 62.9 73.3 74.1 Zimbabwe 67.7 63.1 72.8 70.7 75.0 77.2 72.6 72.2 .. 63.4 65.9 73.4 NORTH AFRICA 56.0 64.1 61.3 62.0 61.8 61.4 60.3 59.0 .. 61.1 65.6 61.0 Algeria 41.7 56.8 41.6 43.4 43.7 40.4 38.5 33.8 .. 51.3 53.3 40.2 Egypt, Arab Rep. 69.2 72.6 75.9 75.3 73.6 73.0 71.7 71.6 70.6 68.3 75.0 73.1 Libya 21.3 48.4 46.6 54.9 56.9 .. .. .. .. 23.1 58.1 52.8 Morocco 66.8 64.6 61.5 57.8 57.9 57.5 57.6 56.6 55.5 66.7 65.3 57.8 Tunisia 61.5 63.6 60.7 61.1 62.7 63.1 63.4 64.1 63.0 60.8 61.7 62.6 AFRICA 57.5 64.8 65.2 65.6 65.3 64.9 64.7 64.2 66.6 62.3 67.0 65.2 a. Provisional 44 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.17 Final consumption expenditure plus discrepancy Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 64.6 83.8 79.5 80.1 78.1 78.3 77.4 74.9 71.0 77.7 85.4 77.0 Excl. South Africa 68.5 90.1 79.1 81.5 79.9 77.5 73.7 70.2 65.2 83.7 89.3 75.3 Excl. South Africa & Nigeria 85.6 87.2 83.1 85.8 85.5 83.9 82.1 79.8 76.5 86.7 87.5 82.4 Angola .. 70.3 58.2 84.9 76.1 80.8 74.9 62.1 50.5 76.0 78.0 69.6 Benin 106.3 97.8 94.0 93.5 96.3 94.0 94.5 93.1 .. 102.4 96.2 94.2 Botswana 73.3 57.4 46.1 44.1 47.8 49.6 49.3 47.6 47.6 64.7 60.3 47.4 Burkina Faso 107.2 94.6 99.4 100.1 96.3 95.5 98.2 95.2 97.2 101.6 91.0 97.4 Burundi 100.6 105.4 106.0 107.8 109.7 108.7 111.0 123.1 120.2 96.9 105.2 112.4 Cameroon 78.3 79.3 79.7 81.0 81.0 82.2 81.5 81.9 81.1 75.8 81.5 81.2 Cape Verde .. 108.1 114.2 115.1 115.7 115.8 101.5 95.6 95.3 102.2 105.6 107.6 Central African Republic 108.9 100.6 94.8 96.1 95.7 98.3 99.9 99.8 98.9 101.1 96.3 97.6 Chad .. 107.7 94.5 94.7 140.8 81.5 75.5 64.5 60.2 108.1 100.5 87.4 Comoros 110.1 103.2 105.7 105.2 104.0 105.8 110.6 112.9 114.0 104.5 104.5 108.3 Congo, Dem. Rep. 89.9 90.7 95.5 96.8 96.0 95.0 96.0 93.5 95.3 89.1 91.2 95.5 Congo, Rep. 64.3 76.2 40.7 49.5 49.0 48.7 48.7 42.4 34.3 68.1 71.2 44.8 Côte d'Ivoire 79.6 88.7 82.1 80.5 73.3 79.0 80.0 82.8 79.6 80.4 82.2 79.6 Djibouti .. 110.4 106.5 100.6 95.1 94.7 95.7 91.4 87.9 .. 106.4 96.0 Equatorial Guinea .. 120.1 25.5 18.8 21.0 20.2 16.5 12.7 14.0 .. 86.3 18.4 Eritrea .. .. 134.7 127.0 133.7 159.7 161.4 126.8 123.3 .. 130.9 138.1 Ethiopia .. 90.4 91.7 90.3 90.1 92.3 91.2 97.4 98.5 89.5 90.3 93.0 Gabon 39.4 63.1 41.7 48.3 56.3 51.8 46.1 32.8 35.3 55.7 56.4 44.6 Gambia, The 94.2 89.3 91.5 88.0 87.1 88.9 89.5 95.6 .. 93.5 92.6 90.1 Ghana 95.1 94.5 94.4 93.0 92.6 93.0 92.7 96.6 92.1 95.2 92.5 93.5 Guinea .. 77.8 84.6 85.9 90.5 92.2 92.6 88.7 89.5 84.9 81.7 89.1 Guinea-Bissau 101.0 97.2 108.5 119.3 112.1 98.8 103.0 98.5 93.7 100.9 98.5 104.8 Kenya 81.9 81.5 90.6 93.0 91.6 89.6 90.7 93.9 90.5 81.7 84.5 91.4 Lesotho 152.0 152.9 120.6 116.6 119.8 117.3 112.4 116.2 115.0 165.5 138.3 116.8 Liberia 85.2 .. .. 103.4 103.3 103.2 100.7 97.6 .. 97.8 .. 101.6 Madagascar 101.4 94.5 92.3 84.7 92.3 91.1 90.6 91.6 86.4 97.1 95.8 89.8 Malawi 89.2 86.6 96.2 96.2 .. 103.4 98.0 92.3 88.8 87.3 96.6 95.8 Mali 98.9 93.6 88.0 86.0 88.7 86.7 91.4 89.0 85.2 100.4 92.4 87.9 Mauritania 103.5 95.1 108.6 96.9 101.9 105.0 103.1 115.0 81.2 96.9 97.6 101.7 Mauritius 85.5 76.5 76.1 74.0 74.8 75.2 76.6 81.1 82.5 80.0 75.9 77.2 Mozambique 108.9 105.8 88.5 96.3 88.4 93.9 90.0 90.7 86.7 106.2 102.9 90.6 Namibia 61.6 81.8 86.0 83.0 82.2 73.8 80.0 75.8 71.6 89.2 87.3 78.9 Niger 85.4 98.8 96.5 95.6 94.7 94.8 95.9 86.3 .. 92.7 97.3 94.0 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 95.8 93.8 98.7 97.1 100.5 99.6 96.5 96.7 96.8 95.0 105.5 98.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 97.9 97.6 88.8 90.6 93.2 91.2 92.1 85.9 89.5 95.7 94.6 90.2 Seychelles 72.9 79.7 78.1 80.8 75.6 78.5 85.3 96.2 89.8 75.9 78.3 83.5 Sierra Leone 99.1 91.3 114.4 111.6 109.4 107.4 101.7 100.6 98.3 90.9 97.2 106.2 Somalia 112.9 112.5 .. .. .. .. .. .. .. 106.3 .. .. South Africa 62.1 76.8 81.1 80.7 80.1 80.8 82.8 82.7 82.9 71.5 80.6 81.6 Sudan 97.9 96.9 84.1 90.2 86.7 84.3 81.3 85.8 85.6 96.0 92.4 85.4 Swaziland 98.8 93.4 97.0 97.1 80.4 79.2 84.1 91.0 91.9 96.3 98.0 88.7 Tanzania .. 98.7 89.8 91.2 88.2 88.0 88.8 90.3 89.2 .. 98.0 89.3 Togo 76.8 85.3 102.2 99.0 99.4 94.7 95.5 93.1 .. 87.7 93.3 97.3 Uganda 100.4 99.4 91.9 93.5 95.3 93.7 91.6 92.4 91.9 97.7 95.7 92.9 Zambia 80.7 83.4 97.0 97.2 92.1 87.0 80.4 78.8 69.1 86.0 91.0 86.0 Zimbabwe 86.2 82.5 86.7 88.4 92.9 93.8 95.9 99.4 .. 83.5 83.1 92.8 NORTH AFRICA 58.7 77.2 75.2 76.4 75.7 72.5 68.9 64.5 63.0 70.6 79.8 70.9 Algeria 56.9 72.9 55.2 58.0 59.1 55.1 52.3 45.9 .. 68.5 69.9 54.3 Egypt, Arab Rep. 84.8 83.9 87.1 86.6 86.1 85.7 84.4 84.3 82.9 84.5 85.8 85.3 Libya 43.1 72.8 67.1 76.5 73.6 .. .. .. .. 53.1 82.4 72.4 Morocco 85.1 80.1 79.9 76.5 76.3 75.5 76.5 75.9 73.8 83.3 82.2 76.3 Tunisia 76.0 80.0 76.3 76.7 78.6 78.8 78.8 79.5 76.4 77.3 77.7 77.9 AFRICA 62.3 80.7 77.6 78.4 76.8 75.7 74.0 70.9 67.8 74.6 82.7 74.5 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 45 ableT2.18 Final consumption expenditure plus discrepancy per capita Dollars Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 465 485 404 389 401 480 572 633 676 462 464 508 Excl. South Africa 380 348 263 274 305 318 352 395 435 349 300 335 Excl. South Africa & Nigeria 389 356 269 280 312 325 360 404 444 357 306 342 Angola .. 685 381 530 590 743 948 1,182 1,376 586 477 821 Benin 403 349 293 297 351 420 465 470 .. 313 317 383 Botswana 781 1,591 1,647 1,517 1,597 2,287 2,668 2,724 2,817 821 1,789 2,180 Burkina Faso 303 331 218 230 250 312 371 371 391 260 235 306 Burundi 224 210 113 104 98 89 98 125 133 215 169 108 Cameroon 581 723 506 479 530 658 739 764 802 663 631 640 Cape Verde .. 1,030 1,347 1,373 1,509 1,910 1,895 1,898 2,172 807 1,189 1,729 Central African Republic 373 497 236 237 249 289 317 321 343 352 341 285 Chad .. 306 155 184 307 236 340 373 362 221 232 280 Comoros 406 593 395 420 464 596 682 728 749 394 507 576 Congo, Dem. Rep. 461 223 81 87 99 98 111 113 134 287 153 103 Congo, Rep. 609 881 409 421 440 503 600 715 719 688 617 544 Côte d'Ivoire 971 750 502 488 476 603 678 728 727 680 628 600 Djibouti .. 891 805 771 737 759 806 805 826 .. 833 787 Equatorial Guinea .. 467 742 742 1,008 1,300 1,705 1,969 2,413 .. 482 1,411 Eritrea .. .. 232 222 211 223 235 272 285 .. 241 240 Ethiopia .. 227 114 109 101 111 125 159 194 201 161 130 Gabon 2,471 4,095 1,789 1,889 2,260 2,511 2,603 2,199 2,573 2,582 2,784 2,261 Gambia, The 338 294 278 257 218 214 229 273 .. 268 303 245 Ghana 371 357 233 239 270 329 373 459 509 337 345 345 Guinea .. 344 321 312 341 385 413 321 312 1,173 390 344 Guinea-Bissau 141 233 171 168 155 155 180 186 175 177 205 170 Kenya 365 299 365 377 366 398 424 494 564 299 310 427 Lesotho 506 588 545 459 426 625 754 836 861 471 674 644 Liberia 435 .. .. 177 178 129 138 150 .. 446 .. 154 Madagascar 453 242 221 230 237 283 218 248 248 301 231 241 Malawi 178 172 144 138 .. 200 200 199 207 150 180 181 Mali 291 296 213 220 280 346 396 407 418 238 267 326 Mauritania 489 499 458 411 431 482 554 713 711 459 571 537 Mauritius 1,020 1,725 2,865 2,800 2,813 3,226 3,766 4,103 4,180 1,078 2,412 3,393 Mozambique 316 193 207 210 194 224 255 291 283 275 187 238 Namibia 1,346 1,357 1,562 1,396 1,322 1,677 2,266 2,339 2,299 1,459 1,659 1,837 Niger 371 314 156 161 172 202 217 217 .. 279 217 188 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 214 332 209 191 188 199 210 249 294 268 275 220 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 584 707 403 417 457 560 645 634 687 527 555 543 Seychelles 1,667 4,196 5,919 6,193 6,301 6,688 7,234 8,386 8,228 2,170 5,142 6,993 Sierra Leone 337 145 160 191 208 206 203 219 243 250 181 204 Somalia 105 154 .. .. .. .. .. .. .. 136 .. .. South Africa 1,818 2,444 2,450 2,133 1,963 2,941 3,865 4,270 4,463 2,094 2,782 3,155 Sudan 380 337 312 354 374 423 488 637 826 493 257 488 Swaziland 949 1,070 1,288 1,197 878 1,304 1,786 2,102 2,248 812 1,300 1,543 Tanzania .. 165 241 248 242 248 269 332 320 .. 194 271 Togo 314 351 251 236 256 282 324 321 .. 270 303 278 Uganda 99 240 221 209 212 216 223 279 292 231 217 236 Zambia 527 338 300 332 315 344 394 505 644 406 334 405 Zimbabwe 791 691 507 710 1,582 536 347 259 .. 703 532 657 NORTH AFRICA 847 1,136 1,316 1,288 1,179 1,228 1,286 1,367 1,511 973 1,165 1,311 Algeria 1,281 1,789 991 1,034 1,074 1,176 1,374 1,430 .. 1,697 1,217 1,180 Egypt, Arab Rep. 445 656 1,307 1,248 1,096 1,011 930 1,038 1,201 545 851 1,119 Libya 5,007 4,824 4,328 4,206 2,537 .. .. .. .. 4,992 5,113 3,691 Morocco 827 856 1,040 1,001 1,057 1,274 1,445 1,484 1,582 654 1,006 1,269 Tunisia 1,041 1,206 1,551 1,584 1,691 2,001 2,231 2,296 2,337 961 1,467 1,956 AFRICA 534 600 560 542 530 602 686 749 806 554 585 639 a. Provisional 46 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.19 Agriculture value added Share of GDP (%) Annual average (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 17.4 21.6 17.1 18.2 18.1 16.0 14.0 13.9 14.5 19.8 20.0 15.9 Excl. South Africa 21.8 32.1 26.0 26.4 24.3 23.6 21.3 20.8 20.7 27.4 31.9 23.3 Excl. South Africa & Nigeria 25.9 29.7 26.1 26.5 24.8 25.2 23.7 23.1 22.8 27.0 29.8 24.6 Angola .. 17.9 5.7 8.2 7.9 8.3 8.6 7.7 8.9 15.2 11.3 7.9 Benin 35.4 36.1 36.5 35.5 33.8 32.1 32.1 32.2 .. 33.8 36.1 33.7 Botswana 12.7 4.5 2.3 2.2 2.2 2.2 2.0 1.8 1.7 7.8 3.9 2.1 Burkina Faso 28.4 28.0 27.5 34.5 32.6 33.4 30.6 33.0 32.8 28.9 32.0 32.1 Burundi 57.6 51.1 36.0 35.6 36.5 36.1 36.1 31.6 .. 53.1 46.0 35.3 Cameroon 28.7 24.0 20.5 20.4 20.4 20.1 19.0 19.0 19.3 24.4 22.9 19.8 Cape Verde .. 14.4 12.0 7.8 7.1 6.8 9.7 9.2 8.8 16.6 12.9 8.8 Central African Republic 37.6 43.8 50.0 51.5 51.0 54.2 53.9 53.7 53.4 41.4 45.6 52.5 Chad 45.7 27.9 40.7 40.3 37.9 32.3 22.9 20.9 20.9 36.3 35.4 30.8 Comoros 34.0 41.4 48.6 50.0 50.2 50.5 50.9 51.0 45.2 36.3 40.2 49.5 Congo, Dem. Rep. 25.3 30.1 49.4 58.7 50.1 50.1 46.3 44.7 43.3 29.0 46.5 48.9 Congo, Rep. 11.7 12.9 5.3 5.8 6.3 6.4 5.9 4.6 4.0 10.0 10.5 5.5 Côte d'Ivoire 25.9 32.5 24.2 24.7 25.7 25.6 23.2 22.8 23.1 27.1 27.2 24.2 Djibouti .. 2.7 3.1 3.1 3.1 3.1 3.1 3.1 3.1 2.8 3.0 3.1 Equatorial Guinea .. 58.9 9.6 6.7 6.2 5.6 4.0 2.8 2.7 62.4 40.3 5.4 Eritrea .. .. 14.0 16.6 11.5 13.2 12.4 20.7 16.0 .. 20.6 14.9 Ethiopia .. 51.7 46.6 44.2 40.4 38.9 40.4 43.0 44.4 53.3 55.4 42.6 Gabon 6.8 7.3 6.2 6.4 6.1 6.1 5.6 4.9 4.9 7.7 7.7 5.7 Gambia, The 27.0 24.3 31.5 32.5 24.9 28.2 30.0 29.6 .. 29.2 24.6 29.4 Ghana 57.9 44.8 35.3 35.2 35.1 36.5 38.0 37.5 38.0 51.9 39.5 36.5 Guinea .. 24.7 19.0 19.7 18.7 21.5 15.0 19.0 12.7 24.2 19.5 17.9 Guinea-Bissau 42.2 56.9 52.1 50.1 55.7 61.3 58.9 59.3 60.3 47.0 53.7 56.8 Kenya 27.8 25.3 29.4 27.9 25.9 25.7 24.8 24.6 24.0 28.0 27.0 26.0 Lesotho 22.4 19.6 16.2 15.5 14.8 15.5 15.0 15.0 14.4 20.6 15.4 15.2 Liberia 32.2 54.4 72.0 73.3 75.5 71.6 68.2 65.8 .. 33.5 67.2 71.1 Madagascar 26.7 26.1 26.5 25.7 29.8 26.8 26.2 25.8 25.1 30.5 26.4 26.6 Malawi 39.2 38.5 35.7 35.3 34.6 33.8 32.8 29.3 30.5 39.2 33.5 33.1 Mali 43.6 44.1 38.7 35.0 32.3 35.8 33.4 33.7 34.1 41.1 42.9 34.7 Mauritania 28.5 26.6 25.6 24.6 23.6 25.1 23.1 21.4 12.1 27.5 31.0 22.2 Mauritius 14.0 11.0 5.2 6.0 6.3 5.4 5.4 5.3 4.9 12.9 9.1 5.5 Mozambique 33.9 34.1 20.9 20.0 25.4 25.4 24.8 24.5 25.6 37.8 32.3 23.8 Namibia 10.5 10.6 9.9 9.3 10.0 10.6 9.4 10.9 9.9 10.4 10.1 10.0 Niger 43.1 35.3 37.8 40.0 39.6 41.3 .. .. .. 38.6 39.4 39.7 Nigeria .. .. .. .. 47.1 41.5 33.4 32.4 31.7 .. .. 37.2 Rwanda 45.8 32.5 37.2 37.3 35.5 38.5 38.8 38.9 41.3 40.2 40.6 38.2 São Tomé and Principe .. .. .. 19.7 19.9 21.1 22.6 16.8 .. .. .. 20.0 Senegal 17.9 17.9 16.9 16.3 13.6 15.4 13.9 14.5 13.6 19.6 17.7 14.9 Seychelles 6.8 4.8 3.0 3.0 3.0 3.0 3.0 3.0 3.0 6.1 3.9 3.0 Sierra Leone 30.4 44.0 55.0 44.0 44.9 44.2 43.5 43.5 45.1 37.4 45.6 45.7 Somalia 64.4 62.7 .. .. .. .. .. .. .. 62.7 .. .. South Africa 5.8 4.2 3.0 3.2 3.8 3.2 2.8 2.4 2.4 5.0 3.8 3.0 Sudan 29.9 .. 40.1 41.0 40.2 36.9 33.1 32.3 31.3 31.8 43.8 36.4 Swaziland 19.5 10.8 10.8 8.8 8.8 7.8 7.1 6.9 6.1 16.4 12.2 8.0 Tanzania .. 42.0 41.6 41.2 41.2 41.4 42.3 37.7 37.9 .. 43.0 40.5 Togo 27.5 33.8 34.2 37.7 38.1 40.8 41.2 42.7 .. 31.8 37.4 39.1 Uganda 71.8 53.3 34.0 33.5 28.5 29.9 29.6 30.3 28.7 54.8 44.3 30.6 Zambia 14.0 18.2 19.9 19.7 19.9 20.5 21.1 20.5 19.9 14.3 18.8 20.2 Zimbabwe 15.1 14.8 15.9 15.7 12.7 15.0 14.1 13.4 .. 14.8 15.0 14.5 NORTH AFRICA 9.6 12.8 11.8 12.4 12.6 12.7 11.8 10.2 10.3 11.1 12.6 11.7 Algeria 7.9 10.4 8.4 9.7 9.2 9.7 9.4 7.9 .. 9.1 10.3 9.0 Egypt, Arab Rep. 17.4 18.4 15.5 15.4 15.4 15.3 14.3 14.0 13.2 18.9 16.2 14.7 Libya 1.6 .. .. .. .. .. .. .. .. 3.3 .. .. Morocco 18.4 17.7 13.3 14.7 14.7 15.5 14.7 12.0 14.0 16.2 16.9 14.1 Tunisia 14.1 15.7 12.3 11.6 10.3 12.1 12.7 11.5 11.1 13.8 14.0 11.7 AFRICA 14.5 18.0 14.9 15.7 15.9 14.7 13.2 12.7 13.1 16.3 17.0 14.3 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 47 ableT2.20 Industry value added Share of GDP (%) Annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 29.5 32.7 29.9 28.9 28.5 29.6 31.3 33.2 34.0 30.7 30.9 30.8 Excl. South Africa 21.1 29.6 30.7 29.1 28.8 31.3 35.0 38.2 39.1 24.0 28.8 33.2 Excl. South Africa & Nigeria 22.4 24.3 27.4 26.0 26.2 27.3 29.8 32.9 34.5 21.2 24.0 29.2 Angola .. 40.8 72.1 64.9 68.2 67.4 66.1 72.6 69.7 39.4 56.9 68.7 Benin 12.3 13.2 13.9 14.4 13.7 13.7 13.3 13.4 .. 14.0 13.7 13.7 Botswana 43.9 56.9 55.1 55.0 50.7 47.6 48.2 49.3 50.9 52.4 50.5 51.0 Burkina Faso 19.8 20.4 23.1 18.1 19.8 20.3 21.6 21.9 22.0 20.4 20.0 21.0 Burundi 11.7 17.3 16.8 17.1 16.7 17.0 17.0 18.2 .. 13.7 16.9 17.1 Cameroon 23.5 28.8 33.3 30.1 29.6 28.4 28.4 29.7 31.5 30.2 28.6 30.1 Cape Verde .. 21.4 17.9 14.7 16.1 19.7 15.2 16.7 16.3 19.0 19.7 16.7 Central African Republic 18.9 18.1 14.9 14.8 14.3 14.2 14.2 14.8 14.8 15.2 19.1 14.6 Chad 9.0 16.9 10.9 13.1 14.3 23.4 46.0 52.8 55.7 13.2 13.3 30.9 Comoros 13.2 8.3 11.5 11.7 11.6 12.7 12.2 11.0 11.8 12.5 11.4 11.8 Congo, Dem. Rep. 33.1 28.2 20.1 19.8 21.1 21.1 24.0 26.4 26.3 28.7 20.4 22.7 Congo, Rep. 46.6 40.6 72.2 65.5 63.3 61.5 63.6 68.6 70.2 45.1 45.4 66.4 Côte d'Ivoire 19.7 23.2 24.9 24.1 22.9 21.6 23.1 25.8 26.8 20.8 22.2 24.2 Djibouti .. 19.2 13.4 13.8 14.0 14.0 14.5 14.8 14.6 17.6 14.6 14.2 Equatorial Guinea .. 10.2 84.1 87.5 87.3 87.5 91.1 93.4 93.6 8.5 37.9 89.2 Eritrea .. .. 21.4 20.8 22.2 22.8 23.0 20.8 21.1 .. 16.5 21.7 Ethiopia .. 10.6 11.5 12.1 12.9 13.1 12.8 11.9 11.7 11.0 9.6 12.3 Gabon 60.4 43.0 56.3 51.3 51.7 52.0 55.3 61.4 61.2 53.7 48.2 55.6 Gambia, The 13.0 11.0 11.5 11.6 13.1 12.7 12.1 11.9 .. 11.8 11.7 12.1 Ghana 11.9 16.8 25.4 25.2 25.3 25.2 24.7 25.1 25.8 12.8 22.5 25.2 Guinea .. 34.6 30.6 30.9 30.4 28.9 29.5 33.1 36.7 33.8 29.2 31.4 Guinea-Bissau 18.7 17.4 12.0 12.5 12.8 13.1 11.7 11.5 11.2 15.2 12.0 12.1 Kenya 17.8 16.3 15.5 15.3 15.5 15.6 16.0 16.9 16.7 16.8 15.6 15.9 Lesotho 24.2 27.0 37.5 38.4 38.3 36.8 35.5 37.2 38.3 23.1 33.9 37.4 Liberia 25.2 16.8 11.6 9.6 8.0 10.6 13.4 15.7 .. 25.8 11.0 11.5 Madagascar 14.3 11.7 12.9 13.5 13.6 14.1 14.5 14.2 13.9 12.3 11.2 13.8 Malawi 20.2 24.7 16.2 15.2 15.6 16.8 16.7 18.2 17.6 20.2 20.4 16.6 Mali 11.9 15.4 19.1 24.4 25.4 21.8 21.9 22.3 22.2 13.7 15.6 22.4 Mauritania 24.4 25.8 27.6 24.1 25.0 21.5 25.4 26.4 44.0 24.4 24.3 27.7 Mauritius 22.3 27.7 27.0 27.4 27.5 26.6 25.7 24.5 23.5 24.1 28.0 26.0 Mozambique 31.5 16.9 21.3 23.0 21.2 23.6 24.8 23.0 23.4 23.4 15.8 22.9 Namibia 52.7 34.3 25.6 27.7 28.7 26.2 26.8 25.3 27.6 41.4 27.4 26.9 Niger 22.9 16.2 17.8 17.0 17.0 17.4 .. .. .. 19.8 17.4 17.3 Nigeria .. .. .. .. 29.6 35.7 41.1 43.0 41.6 .. .. 38.2 Rwanda 21.5 24.6 13.6 14.2 13.9 12.8 13.7 14.0 13.3 21.0 19.5 13.6 São Tomé and Principe .. .. .. 16.9 17.1 17.8 21.0 20.5 .. .. .. 18.7 Senegal 17.9 19.9 20.5 21.7 22.3 21.3 21.8 20.7 19.7 18.4 21.1 21.1 Seychelles 15.6 16.3 29.0 28.1 30.3 27.4 28.2 26.8 25.5 16.5 21.5 27.9 Sierra Leone 20.2 18.0 26.8 24.0 23.2 23.3 23.5 23.5 24.3 14.8 30.8 24.1 Somalia 7.5 .. .. .. .. .. .. .. .. 7.5 .. .. South Africa 45.5 36.4 28.9 29.4 30.1 28.6 27.6 27.3 27.3 40.4 31.9 28.5 Sudan 12.9 .. 20.7 18.3 19.7 20.9 24.2 26.7 27.7 14.4 14.7 22.6 Swaziland 25.9 34.3 31.1 38.3 38.0 39.0 36.8 34.8 34.9 27.2 33.8 36.1 Tanzania .. 16.1 14.5 14.7 14.9 15.2 15.2 13.8 14.5 .. 14.3 14.7 Togo 24.8 22.5 17.8 17.2 18.5 22.2 22.8 23.5 .. 22.0 21.0 20.3 Uganda 4.5 10.4 18.5 18.6 19.8 19.6 19.5 16.8 16.7 8.9 13.8 18.5 Zambia 39.1 45.3 22.5 22.7 23.4 24.1 25.5 27.8 31.3 40.9 34.8 25.3 Zimbabwe 27.9 29.8 21.4 20.2 18.8 19.2 18.0 16.8 .. 27.6 27.1 19.1 NORTH AFRICA 47.9 35.4 37.0 36.0 38.3 39.1 40.2 41.9 .. 40.1 34.8 38.8 Algeria 53.8 44.0 55.4 49.7 48.7 50.6 52.3 57.4 .. 47.9 45.5 52.3 Egypt, Arab Rep. 35.1 27.3 30.8 30.9 32.6 33.4 34.7 34.1 36.2 29.2 29.9 33.2 Libya 74.2 .. .. .. .. .. .. .. .. 59.7 .. .. Morocco 30.9 32.4 25.8 24.5 24.3 25.0 25.0 26.1 24.8 32.9 30.5 25.1 Tunisia 31.1 29.8 28.6 28.8 29.6 28.3 28.2 28.4 27.8 31.5 28.8 28.5 AFRICA 35.7 33.6 32.9 31.9 32.2 33.1 34.6 36.4 37.4 34.0 32.2 34.1 a. Provisional 48 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.21 Services plus discrepancy value added Share of GDP (%) Average annual growth (%) 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 30.6 43.6 45.5 44.8 43.4 44.7 45.1 43.5 42.6 37.1 47.0 44.2 Excl. South Africa 26.2 40.9 36.8 37.9 38.5 37.3 36.9 35.1 35.2 32.9 40.7 36.8 Excl. South Africa & Nigeria 34.1 41.2 40.2 41.5 42.8 41.7 40.5 38.4 37.8 35.3 41.5 40.4 Angola .. 41.2 22.2 27.0 24.0 24.3 25.3 19.8 21.4 45.4 31.8 23.4 Benin 52.3 50.7 49.6 50.0 52.6 54.2 54.6 54.4 .. 52.2 50.2 52.6 Botswana 29.9 31.8 36.1 36.8 39.9 41.8 41.2 40.5 39.6 30.6 37.5 39.4 Burkina Faso 48.5 48.8 44.1 41.6 41.1 40.2 41.0 41.8 43.7 47.8 42.8 41.9 Burundi 23.2 23.0 36.4 37.4 36.8 36.9 36.9 41.0 .. 24.5 27.7 37.6 Cameroon 39.6 44.9 38.7 41.7 42.4 44.1 45.3 48.7 46.6 40.4 42.9 43.9 Cape Verde .. 64.3 70.2 77.4 76.9 73.4 75.1 74.1 74.9 64.4 67.4 74.6 Central African Republic 37.6 30.2 29.2 28.5 28.6 27.0 27.2 28.0 27.4 37.0 29.6 28.0 Chad 46.7 50.5 44.6 43.0 43.9 40.4 28.7 24.4 25.1 48.6 47.9 35.7 Comoros 52.8 50.3 39.9 38.3 38.3 36.7 36.9 38.0 .. 51.2 48.4 38.0 Congo, Dem. Rep. 36.1 39.0 29.4 19.8 27.0 27.0 27.7 27.0 25.3 37.8 32.0 26.2 Congo, Rep. 41.7 46.5 22.5 28.7 30.4 32.0 30.5 26.8 25.8 44.9 44.1 28.1 Côte d'Ivoire 54.4 44.3 50.9 51.2 51.4 52.8 53.7 51.4 50.2 52.0 50.6 51.7 Djibouti .. 65.4 70.8 71.3 70.4 69.5 69.4 71.0 71.4 65.1 70.1 70.6 Equatorial Guinea .. 26.6 4.2 4.2 5.0 5.0 3.7 2.9 2.9 23.9 19.4 4.0 Eritrea .. .. 57.6 55.1 55.2 53.9 53.4 50.2 54.5 .. 52.7 54.3 Ethiopia .. 32.8 35.3 36.4 39.5 40.9 38.2 37.1 36.5 30.1 29.7 37.7 Gabon 32.8 49.7 37.5 42.3 42.2 41.9 39.1 33.8 33.9 38.6 44.0 38.7 Gambia, The 47.6 48.6 45.0 45.5 52.2 49.8 48.6 49.2 .. 45.1 49.1 48.4 Ghana 30.2 38.4 39.3 39.5 39.6 38.2 37.3 37.4 36.3 35.3 38.0 38.2 Guinea .. 44.5 44.4 43.4 43.9 43.0 49.4 41.8 45.0 42.7 48.4 44.4 Guinea-Bissau 34.4 19.3 28.2 34.8 31.9 24.9 27.8 27.6 26.2 34.8 29.7 28.8 Kenya 39.7 44.1 43.8 45.7 47.4 47.7 48.3 49.7 48.0 41.7 44.3 47.2 Lesotho 44.5 35.8 36.8 37.0 36.6 37.9 37.3 35.7 35.8 42.2 37.0 36.7 Liberia 32.3 28.8 16.4 17.1 16.4 17.7 18.4 18.4 .. 34.0 21.8 17.4 Madagascar 47.8 53.5 51.2 53.1 50.8 50.8 50.4 51.6 52.2 46.2 54.9 51.4 Malawi 30.3 22.3 38.4 40.5 41.4 39.2 39.7 41.4 41.2 29.5 36.1 40.2 Mali 34.7 37.4 35.3 33.1 34.5 34.7 36.5 36.2 36.0 37.6 33.5 35.2 Mauritania 41.0 37.3 39.5 42.5 42.9 44.5 41.8 42.5 35.9 38.5 36.0 41.4 Mauritius 48.5 45.0 54.4 54.9 55.2 55.6 56.0 57.2 59.0 47.2 49.7 56.0 Mozambique 26.0 40.9 49.0 48.9 45.0 41.8 41.2 43.4 41.4 34.0 43.8 44.4 Namibia 31.2 45.3 54.7 52.9 51.7 55.4 54.0 53.9 52.9 41.7 51.9 53.6 Niger 34.0 48.6 44.4 43.0 43.4 41.4 .. .. .. 41.6 43.2 43.1 Nigeria .. .. .. .. 20.3 19.9 23.2 23.5 25.9 .. .. 22.5 Rwanda 32.6 42.8 39.8 38.6 39.3 48.8 47.5 47.2 45.4 38.8 39.9 43.8 São Tomé and Principe .. .. .. 63.5 63.0 61.2 56.4 62.7 .. .. .. 61.3 Senegal 53.3 52.0 50.8 50.3 51.8 51.1 51.9 51.7 53.1 51.0 50.9 51.5 Seychelles 77.5 78.9 68.0 68.9 66.7 69.6 68.8 70.2 71.5 77.4 74.6 69.1 Sierra Leone 41.4 31.8 12.5 25.4 25.8 27.0 27.8 28.0 25.8 41.3 18.7 24.6 Somalia 22.2 .. .. .. .. .. .. .. .. 23.5 .. .. South Africa 42.7 50.2 59.0 58.4 57.1 58.8 59.3 59.2 58.9 46.8 55.6 58.7 Sudan 48.3 .. 35.4 36.4 35.7 37.4 36.5 37.1 38.0 47.0 37.7 36.7 Swaziland 40.5 36.4 27.6 35.1 35.5 35.2 35.1 35.4 34.2 40.4 33.6 34.0 Tanzania .. 33.3 36.2 36.3 36.1 35.3 34.1 30.3 31.2 .. 34.8 34.2 Togo 47.7 43.7 47.9 45.0 43.3 37.1 36.0 31.7 .. 46.2 41.7 40.2 Uganda 23.4 30.5 38.6 39.9 43.6 42.9 42.9 45.1 46.7 31.2 34.3 42.8 Zambia 39.7 24.8 46.7 46.5 46.8 46.2 45.0 44.6 43.9 34.8 35.2 45.7 Zimbabwe 53.1 45.4 48.5 54.4 58.1 55.0 52.2 40.0 .. 49.0 46.3 51.4 NORTH AFRICA 33.4 41.9 43.4 45.0 46.8 45.1 43.8 41.9 40.9 39.0 43.3 43.8 Algeria 31.6 37.0 30.7 33.6 33.8 32.0 31.0 28.1 .. 34.9 35.8 31.6 Egypt, Arab Rep. 42.9 49.5 46.5 46.5 45.6 44.9 45.1 45.9 44.7 47.5 47.8 45.6 Libya 21.4 .. .. .. .. .. .. .. .. 34.8 .. .. Morocco 50.3 46.8 49.8 49.7 50.0 49.1 50.1 51.8 50.4 49.9 47.8 50.1 Tunisia 54.8 54.5 59.1 59.6 60.2 59.7 59.1 60.1 61.1 54.8 57.3 59.8 AFRICA 31.5 43.1 44.6 44.9 44.7 45.1 45.1 43.5 42.5 37.8 45.8 44.3 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 49 ableT2.22 Gross fixed capital formation Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 17.7 18.1 16.0 16.2 15.5 16.6 16.6 16.8 17.6 18.5 17.4 16.5 Excl. South Africa 14.0 17.6 16.8 17.3 16.2 17.3 17.2 16.9 17.4 16.0 18.4 17.0 Excl. South Africa & Nigeria 17.5 17.0 17.7 18.2 17.4 18.7 19.1 19.2 20.4 16.6 18.0 18.7 Angola .. 11.1 15.1 13.4 12.6 12.7 9.1 8.1 13.7 14.2 23.2 12.1 Benin .. 13.4 18.9 19.2 18.1 18.1 17.5 18.9 .. 14.8 15.7 18.5 Botswana 34.5 32.4 21.7 20.8 21.9 21.5 20.2 19.0 17.9 29.0 27.2 20.4 Burkina Faso 14.1 17.7 18.7 14.7 17.1 17.5 19.3 19.4 20.8 17.4 21.2 18.2 Burundi 13.9 15.2 6.1 6.2 6.1 10.6 13.0 10.5 16.7 16.1 9.0 9.9 Cameroon 20.0 17.3 16.0 20.3 19.8 18.1 18.3 17.7 16.7 21.1 14.5 18.1 Cape Verde .. 22.9 19.7 18.3 20.9 18.7 37.4 37.1 38.1 26.9 29.6 27.2 Central African Republic 6.9 11.4 9.5 8.4 9.0 6.0 6.1 9.0 8.9 10.2 11.2 8.1 Chad .. 4.8 20.9 36.6 59.7 48.6 22.7 19.1 21.2 4.4 11.0 32.7 Comoros 28.5 11.9 10.1 10.1 11.0 10.3 9.4 9.3 9.8 24.3 14.6 10.0 Congo, Dem. Rep. 8.8 12.8 3.5 5.2 9.0 12.2 12.8 .. .. 11.4 8.0 8.5 Congo, Rep. 35.8 17.2 20.9 26.3 22.5 25.1 23.6 21.5 22.4 32.5 24.9 23.2 Côte d'Ivoire 24.4 8.5 11.2 9.9 10.9 9.7 9.8 9.7 9.9 15.8 11.4 10.2 Djibouti .. 14.1 8.8 7.9 10.0 14.4 21.5 19.0 29.6 .. 11.1 15.9 Equatorial Guinea .. 17.4 61.3 71.9 32.1 59.4 45.1 38.2 41.6 .. 59.5 49.9 Eritrea .. .. 31.9 28.7 26.0 25.4 22.8 20.1 18.7 .. 25.0 24.8 Ethiopia .. 12.9 20.3 21.5 23.9 21.8 25.5 23.0 24.2 15.7 16.5 22.9 Gabon 26.7 21.4 21.9 25.7 24.5 24.0 24.5 22.7 23.1 33.8 25.4 23.8 Gambia, The .. 22.3 17.4 17.4 21.2 19.2 24.8 .. .. 18.9 20.1 20.0 Ghana 6.1 14.4 23.1 27.1 18.8 22.9 28.4 29.0 32.9 7.9 19.7 26.0 Guinea .. 22.9 18.9 14.5 13.2 10.1 11.3 14.0 13.3 16.4 20.0 13.6 Guinea-Bissau 28.2 29.9 11.3 15.0 9.6 12.6 13.2 14.6 17.2 32.0 25.9 13.4 Kenya 18.3 20.6 16.8 18.2 17.2 15.8 16.1 18.6 18.8 18.8 17.6 17.4 Lesotho 35.6 52.8 44.9 43.4 43.5 40.5 35.7 34.4 33.3 39.5 57.0 39.4 Liberia .. .. .. 4.9 4.7 9.4 13.2 16.4 .. .. .. 9.7 Madagascar 14.4 14.8 15.0 18.5 14.3 17.9 24.3 22.5 24.8 10.8 12.4 19.6 Malawi 22.2 20.1 12.3 13.8 .. 16.2 18.2 21.5 21.8 15.8 15.2 17.3 Mali 15.5 23.0 24.6 31.0 18.6 24.2 21.0 22.6 22.9 17.2 22.5 23.6 Mauritania .. 20.0 19.4 22.0 21.1 25.9 46.4 44.8 23.3 26.6 13.6 29.0 Mauritius 24.2 28.3 25.3 23.1 22.3 22.2 22.1 21.3 22.9 21.1 27.1 22.8 Mozambique 7.6 22.1 31.0 20.0 30.0 22.3 18.6 18.7 19.3 12.2 20.7 22.8 Namibia 27.2 21.2 18.8 21.9 21.2 29.2 25.7 26.3 28.2 18.6 21.0 24.5 Niger 25.5 11.4 11.2 11.9 14.0 14.5 16.6 18.9 .. 14.2 9.0 14.5 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 12.2 14.6 18.3 19.0 17.7 18.6 20.4 21.6 20.3 14.4 14.5 19.4 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 14.6 18.0 22.4 22.7 24.8 21.2 22.7 29.7 28.9 17.4 19.9 24.6 Seychelles 36.5 23.0 25.2 40.7 25.6 10.4 12.7 30.2 32.7 25.6 29.2 25.3 Sierra Leone 14.9 9.6 6.9 6.7 10.1 13.9 10.7 17.4 15.5 11.4 7.2 11.6 Somalia 43.1 14.9 .. .. .. .. .. .. .. 26.9 .. .. South Africa 25.9 19.1 15.1 15.1 15.0 15.9 16.1 17.0 18.7 23.1 16.3 16.1 Sudan 10.8 8.2 12.1 11.1 13.2 14.1 17.2 19.2 21.0 12.6 10.8 15.4 Swaziland 35.0 18.4 18.6 17.6 19.9 18.8 18.4 18.0 15.8 25.4 20.5 18.2 Tanzania .. 25.8 17.4 16.8 19.0 18.5 18.2 16.1 16.6 .. 21.0 17.5 Togo 28.2 25.3 17.8 21.3 18.8 20.9 21.2 21.8 .. 19.0 15.6 20.3 Uganda .. 12.7 19.6 18.2 18.9 20.1 22.1 21.3 23.0 9.3 15.9 20.5 Zambia 18.2 13.5 16.0 17.7 20.6 24.1 22.6 22.3 22.7 12.4 12.4 20.8 Zimbabwe 14.1 18.2 11.8 12.1 10.2 13.8 17.1 21.0 .. 16.0 20.1 14.3 NORTH AFRICA 23.4 24.1 20.1 20.0 21.2 20.5 20.9 21.0 21.3 25.6 21.4 20.7 Algeria 33.8 27.0 20.7 22.7 24.4 24.0 24.1 23.8 .. 31.9 26.2 23.3 Egypt, Arab Rep. 24.6 26.9 18.9 17.7 17.8 16.3 16.4 17.9 18.7 27.8 20.4 17.7 Libya 21.2 13.9 12.9 11.9 14.4 .. .. .. .. 25.8 12.7 13.1 Morocco 22.2 24.0 26.0 24.8 25.2 25.2 26.7 28.5 28.7 23.1 22.2 26.4 Tunisia 28.3 24.4 26.0 26.2 25.4 23.4 22.6 22.4 22.7 27.5 25.3 24.1 AFRICA 19.3 20.4 17.8 17.8 17.7 18.0 18.1 18.2 18.9 21.0 18.9 18.1 a. Provisional 50 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.23 Gross general government fixed capital formation Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA .. 5.0 4.2 4.4 4.3 4.4 4.4 4.4 5.2 5.4 4.5 4.5 Excl. South Africa .. 5.9 5.5 5.7 5.4 5.8 6.0 5.7 6.8 6.4 6.1 5.8 Excl. South Africa & Nigeria .. 5.8 5.7 6.0 5.8 6.2 6.6 6.5 7.9 6.0 6.0 6.4 Angola .. .. 6.1 6.4 6.8 7.6 4.9 5.0 11.3 .. 7.8 6.9 Benin .. 7.4 7.6 7.8 6.6 6.1 5.4 6.7 .. 9.1 7.5 6.7 Botswana .. 8.6 10.0 10.2 10.6 10.2 8.7 7.7 8.3 9.7 11.7 9.4 Burkina Faso .. 9.7 7.7 6.1 6.4 6.3 7.2 7.4 8.0 10.4 10.5 7.0 Burundi 12.8 12.5 5.4 3.7 4.6 8.3 10.7 8.8 .. 13.8 9.3 6.9 Cameroon 4.4 5.5 2.1 2.2 2.3 2.3 2.6 2.5 2.4 6.9 2.9 2.3 Cape Verde .. 10.3 12.5 10.8 13.0 9.8 7.7 8.9 8.7 19.3 20.3 10.2 Central African Republic 3.7 4.7 4.7 3.5 4.8 2.1 2.0 4.0 3.3 5.5 6.2 3.5 Chad .. .. 10.5 8.8 10.1 12.5 7.8 7.0 8.0 3.8 7.4 9.2 Comoros 23.2 5.2 3.9 4.4 5.8 5.4 4.4 4.5 5.0 18.7 7.0 4.8 Congo, Dem. Rep. 5.1 4.0 0.5 0.1 1.0 2.7 2.8 .. .. 4.4 1.7 1.4 Congo, Rep. .. 5.6 7.0 10.0 8.7 6.5 7.0 5.3 8.6 11.1 6.4 7.6 Côte d'Ivoire 11.4 3.6 2.8 1.9 3.2 2.7 2.8 2.7 3.1 7.1 5.6 2.7 Djibouti .. 9.1 2.7 2.5 4.5 6.7 7.7 9.3 7.5 .. 6.1 5.8 Equatorial Guinea .. 10.5 5.1 7.4 8.4 9.8 14.0 10.1 15.1 .. 6.9 10.0 Eritrea .. .. 26.8 23.5 21.7 17.7 17.2 15.4 14.0 .. 16.4 19.5 Ethiopia .. 4.0 12.2 13.1 14.0 12.8 15.7 14.7 16.7 4.9 6.6 14.2 Gabon 5.3 3.9 2.9 4.7 4.0 3.7 4.2 4.2 4.8 6.7 6.5 4.1 Gambia, The .. 7.4 4.6 11.2 7.9 5.7 10.9 9.0 7.9 10.4 7.8 8.2 Ghana .. 7.5 10.4 10.4 9.6 8.9 12.4 12.0 14.6 6.3 11.1 11.2 Guinea .. 9.7 4.9 4.9 4.0 4.4 4.0 3.4 3.2 7.5 6.1 4.1 Guinea-Bissau .. 27.4 10.0 13.7 9.0 11.1 11.1 14.1 10.8 33.3 20.2 11.4 Kenya .. 9.7 4.6 4.4 4.3 4.2 4.2 3.8 3.6 0.8 7.1 4.2 Lesotho 9.9 23.0 8.0 10.5 11.2 8.7 7.4 7.5 7.2 15.4 16.2 8.6 Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar .. 7.9 6.7 7.3 4.8 7.8 12.5 10.3 10.3 6.9 6.9 8.5 Malawi 17.5 7.7 10.0 10.3 .. 2.4 2.0 7.0 7.4 9.5 9.2 6.5 Mali .. 10.5 8.6 7.0 7.0 6.9 7.5 7.7 8.6 10.2 10.1 7.6 Mauritania .. 6.2 .. 7.2 9.1 12.0 9.1 8.1 5.6 7.6 5.0 8.5 Mauritius 9.1 4.6 7.8 6.8 7.0 7.9 7.7 6.6 7.0 6.0 3.7 7.3 Mozambique 7.6 12.0 9.2 14.0 12.2 12.0 9.7 8.6 12.3 9.5 12.1 11.1 Namibia 15.7 8.2 6.1 8.7 6.2 7.0 7.3 7.4 8.2 10.7 8.2 7.3 Niger 20.4 7.4 6.6 7.1 8.8 8.6 5.4 6.4 .. 11.2 5.6 7.1 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda 12.2 5.9 6.2 6.7 5.2 5.4 7.9 9.1 7.5 12.1 7.2 6.9 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 4.7 4.1 4.5 5.1 5.7 6.2 6.7 10.0 9.8 3.7 4.5 6.8 Seychelles .. 8.2 11.4 25.0 7.4 1.7 3.1 5.6 10.1 12.0 9.9 9.2 Sierra Leone 5.3 3.9 5.2 4.4 4.4 4.8 4.6 5.8 5.1 4.0 3.8 4.9 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 6.4 3.9 2.7 2.5 2.5 2.6 2.6 2.7 2.9 5.7 2.8 2.6 Sudan 6.9 .. 2.3 2.3 3.0 2.9 5.0 5.5 6.4 4.3 0.8 3.9 Swaziland 11.9 5.7 6.3 10.0 12.4 12.9 10.2 9.5 8.4 8.0 6.6 9.9 Tanzania .. 10.5 6.0 5.6 7.6 7.4 7.3 6.4 6.7 .. 5.8 6.7 Togo 20.2 7.3 3.0 2.3 1.4 3.7 5.3 4.1 .. 11.2 3.7 3.3 Uganda .. 6.2 6.4 5.8 5.3 4.7 5.2 4.7 4.9 4.4 5.6 5.3 Zambia .. 6.2 10.0 11.9 11.8 11.4 8.6 6.9 3.9 .. 6.8 9.2 Zimbabwe 1.8 3.4 0.7 2.1 2.1 2.1 5.1 1.5 .. 2.9 2.9 2.3 NORTH AFRICA .. 9.2 8.6 8.2 9.1 8.7 8.6 8.3 .. 11.8 8.5 8.6 Algeria 11.0 8.2 7.8 8.4 10.0 10.8 10.5 9.7 .. 13.8 7.2 9.5 Egypt, Arab Rep. .. 14.7 9.9 8.7 9.4 8.5 8.7 8.8 7.3 16.9 12.0 8.8 Libya 19.4 .. .. .. .. .. .. .. .. 19.4 .. .. Morocco .. 4.8 4.7 4.7 3.9 3.8 3.9 3.8 3.8 7.1 4.2 4.1 Tunisia 15.0 8.7 12.3 .. .. .. .. .. .. 14.1 11.5 12.3 AFRICA .. 6.5 6.0 6.0 6.2 6.0 5.9 5.8 6.3 7.9 6.0 6.0 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 51 ableT2.24 Private sector fixed capital formation Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 11.2 12.5 11.7 11.8 11.1 12.2 12.2 12.4 12.5 12.1 12.6 12.0 Excl. South Africa .. 10.4 11.3 11.5 10.8 11.5 11.2 11.2 10.6 9.4 11.8 11.2 Excl. South Africa & Nigeria .. 10.1 11.9 12.1 11.6 12.5 12.5 12.7 12.4 8.9 11.6 12.2 Angola .. 1.7 8.9 7.1 5.8 5.1 4.2 3.0 2.4 9.2 16.5 5.2 Benin .. 6.0 11.3 11.4 11.6 12.0 12.1 12.2 .. 4.5 8.3 11.8 Botswana 34.5 23.8 11.6 10.6 11.3 11.3 11.5 11.3 9.6 19.4 15.5 11.0 Burkina Faso .. 8.0 11.0 8.6 10.8 11.1 .. .. .. 8.8 10.8 10.4 Burundi 1.1 2.7 0.8 2.5 1.5 2.3 2.3 1.7 .. 2.3 ­0.3 1.9 Cameroon 15.6 11.9 13.9 18.1 17.5 15.8 15.7 15.2 14.3 14.2 11.7 15.8 Cape Verde .. 12.6 7.2 7.5 7.9 8.9 29.7 28.2 29.4 7.6 9.3 17.0 Central African Republic 3.2 6.7 4.8 4.9 4.2 3.9 4.1 4.9 5.6 4.7 5.0 4.7 Chad .. .. 10.5 27.8 49.6 36.1 14.9 12.0 13.2 0.6 4.3 23.5 Comoros 5.3 6.7 6.2 5.6 5.2 4.9 5.0 4.8 4.9 5.5 7.7 5.2 Congo, Dem. Rep. 3.7 8.9 3.0 5.1 8.0 9.5 10.0 .. .. 7.1 6.3 7.1 Congo, Rep. .. 11.6 14.0 16.2 13.8 18.6 16.6 16.2 13.8 11.4 18.5 15.6 Côte d'Ivoire 13.0 4.9 8.4 8.0 7.7 7.0 7.1 7.0 6.8 8.7 6.2 7.4 Djibouti .. 5.1 6.1 5.4 5.6 7.7 13.8 9.7 22.0 .. 5.8 10.0 Equatorial Guinea .. 6.9 56.2 64.5 23.7 49.6 31.0 28.1 26.6 .. 52.6 40.0 Eritrea .. .. 5.1 5.2 4.3 7.7 5.6 4.7 4.2 .. 8.6 5.3 Ethiopia .. 8.9 8.1 8.3 9.9 9.0 9.7 8.3 7.6 12.8 9.9 8.7 Gabon 21.4 17.6 19.0 21.0 20.5 20.2 20.3 18.5 18.3 27.2 18.9 19.7 Gambia, The .. 14.9 12.8 6.2 13.3 13.5 13.9 18.5 14.9 8.6 12.3 13.3 Ghana .. 6.9 12.7 16.7 9.2 14.0 16.0 17.0 18.3 3.8 8.6 14.8 Guinea .. 8.8 14.0 9.6 9.2 5.7 7.4 10.7 10.2 8.9 11.7 9.5 Guinea-Bissau .. 8.4 1.3 1.3 0.6 1.5 2.1 0.5 6.4 10.0 7.7 2.0 Kenya 8.2 10.9 12.2 13.7 12.9 11.5 11.9 14.7 15.2 10.7 10.6 13.2 Lesotho 25.7 29.7 36.9 32.9 32.3 31.8 28.4 26.9 26.1 24.0 40.8 30.8 Liberia .. .. .. 2.0 2.2 4.8 4.2 4.3 .. .. .. 3.5 Madagascar .. 6.9 8.3 11.2 9.5 10.1 11.8 12.3 14.5 3.6 5.5 11.1 Malawi 4.7 12.4 2.3 3.5 .. 13.8 16.2 14.5 14.4 6.3 6.0 10.8 Mali .. 12.4 15.9 24.0 11.6 17.3 13.5 15.0 14.3 9.9 12.4 15.9 Mauritania .. 13.7 .. 14.8 11.9 13.9 37.3 36.7 17.7 19.0 13.9 22.1 Mauritius 15.1 23.7 17.5 16.3 15.3 14.3 14.5 14.8 15.9 15.1 23.4 15.5 Mozambique .. 10.1 21.7 6.0 17.7 10.2 8.9 10.1 7.1 2.7 8.6 11.7 Namibia 11.4 13.0 12.7 13.2 14.9 22.1 18.3 18.9 20.0 7.8 12.8 17.2 Niger 5.1 4.0 4.6 4.8 5.2 5.9 11.3 12.5 .. 3.0 3.4 7.4 Nigeria .. .. .. .. .. .. .. .. .. .. .. .. Rwanda .. 8.7 12.1 12.3 12.5 13.3 12.5 12.5 12.8 7.8 7.2 12.6 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 9.9 13.9 17.9 17.7 19.2 15.0 16.0 19.7 19.1 13.7 15.4 17.8 Seychelles .. 14.8 13.8 15.7 18.2 8.7 9.7 24.6 22.6 10.1 19.3 16.2 Sierra Leone 9.5 5.7 1.7 2.2 5.7 9.0 6.1 11.6 10.4 7.3 3.3 6.7 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 19.5 15.3 12.4 12.5 12.5 13.3 13.5 14.3 15.8 17.4 13.5 13.5 Sudan 3.8 .. 9.7 8.8 10.1 11.2 12.2 13.7 14.5 8.9 10.2 11.5 Swaziland 23.1 12.7 12.3 7.7 7.5 5.9 8.2 8.5 7.5 17.3 13.8 8.2 Tanzania .. 15.3 11.4 11.2 11.4 11.1 10.9 9.6 9.9 .. 15.2 10.8 Togo 8.0 18.0 14.8 19.0 17.4 17.2 15.9 17.7 .. 7.8 11.8 17.0 Uganda .. 6.5 13.3 12.4 13.7 15.4 16.9 16.6 18.2 5.4 10.3 15.2 Zambia .. 7.2 6.0 5.8 8.8 12.7 14.1 15.3 18.8 4.9 5.7 11.6 Zimbabwe 12.3 14.8 11.1 10.1 8.1 11.7 12.0 19.5 .. 13.1 17.2 12.1 NORTH AFRICA .. 16.0 12.5 12.9 13.8 13.4 14.0 14.8 .. 13.4 13.8 13.6 Algeria 22.8 18.8 12.9 14.3 14.5 13.2 13.6 14.1 .. 18.1 19.0 13.7 Egypt, Arab Rep. .. 12.3 9.1 9.0 8.4 7.9 7.7 9.1 11.4 9.3 8.3 8.9 Libya 1.8 .. .. .. .. .. .. .. .. 1.8 .. .. Morocco 16.7 19.2 21.3 20.1 21.2 21.4 22.8 24.7 24.9 16.1 18.0 22.4 Tunisia 13.3 15.6 13.7 .. .. .. .. .. .. 13.5 13.8 13.7 AFRICA 12.1 13.8 12.1 12.2 12.1 12.7 12.9 13.3 13.7 12.6 13.1 12.7 a. Provisional 52 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.25 Resource balance (exports minus imports) Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 2.4 1.7 3.1 0.3 ­1.1 ­0.9 0.6 2.1 2.6 ­0.5 ­0.9 1.0 Excl. South Africa ­0.1 ­0.8 3.1 ­1.7 ­3.2 ­2.8 1.2 3.9 5.8 ­3.7 ­3.7 0.9 Excl. South Africa & Nigeria ­5.4 ­3.2 ­2.2 ­5.2 ­4.0 ­4.4 ­2.9 ­0.6 1.9 ­4.6 ­5.0 ­2.5 Angola .. 18.0 26.8 1.7 11.3 6.6 16.0 29.8 35.8 9.1 2.2 18.3 Benin ­21.5 ­12.0 ­12.9 ­12.7 ­13.9 ­12.8 ­12.7 ­12.6 .. ­17.5 ­12.5 ­13.0 Botswana ­13.4 5.3 18.9 16.2 11.5 8.7 9.9 17.2 22.5 5.3 9.7 15.0 Burkina Faso ­22.3 ­13.5 ­16.1 ­13.9 ­12.4 ­12.9 ­13.5 ­15.6 ­15.3 ­19.6 ­13.5 ­14.2 Burundi ­14.5 ­19.9 ­12.2 ­14.0 ­16.2 ­19.3 ­24.3 ­33.9 ­36.9 ­13.5 ­14.4 ­22.4 Cameroon 0.8 2.9 3.6 ­1.3 ­0.8 0.3 ­0.4 ­1.0 2.1 0.4 3.7 0.3 Cape Verde .. ­31.0 ­33.9 ­33.4 ­36.6 ­34.5 ­38.9 ­32.7 ­33.4 ­29.0 ­35.2 ­34.8 Central African Republic ­15.9 ­12.9 ­4.3 ­4.5 ­4.6 ­4.3 ­6.0 ­8.8 ­7.9 ­12.1 ­7.7 ­5.8 Chad ­11.9 ­14.4 ­17.8 ­35.0 ­101.0 ­34.1 0.2 15.5 21.3 ­13.5 ­13.6 ­21.5 Comoros ­43.2 ­22.9 ­15.8 ­15.3 ­15.0 ­16.1 ­19.9 ­22.2 ­23.8 ­33.3 ­22.6 ­18.3 Congo, Dem. Rep. 0.1 0.3 1.0 ­2.0 ­4.9 ­7.2 ­8.8 ­7.7 ­11.5 ­0.8 1.2 ­5.9 Congo, Rep. ­0.1 7.9 36.7 24.1 27.6 25.6 27.0 35.6 42.9 ­0.5 2.9 31.4 Côte d'Ivoire ­6.2 4.6 7.1 8.4 16.6 10.9 9.2 7.5 10.5 3.2 6.5 10.0 Djibouti .. ­24.6 ­15.3 ­8.5 ­5.2 ­9.2 ­17.2 ­10.3 ­17.4 .. ­17.5 ­11.9 Equatorial Guinea .. ­37.4 13.2 9.3 46.9 20.3 38.5 49.2 44.4 ­28.6 ­45.8 31.7 Eritrea .. .. ­66.6 ­55.7 ­59.7 ­85.1 ­84.2 ­46.9 ­42.0 .. ­55.8 ­62.9 Ethiopia .. ­3.3 ­11.9 ­11.7 ­14.0 ­14.1 ­16.7 ­20.4 ­22.7 ­5.3 ­6.8 ­15.9 Gabon 33.1 15.2 36.4 26.0 19.2 24.3 29.5 44.5 41.6 9.7 17.7 31.6 Gambia, The ­20.9 ­11.7 ­8.9 ­5.4 ­8.3 ­9.2 ­17.6 ­20.6 .. ­13.2 ­12.6 ­11.7 Ghana ­0.7 ­9.0 ­18.4 ­19.6 ­12.3 ­15.9 ­21.1 ­25.6 ­24.9 ­3.1 ­12.4 ­19.7 Guinea 3.1 ­2.4 ­4.3 ­1.3 ­4.0 ­2.4 ­4.0 ­2.7 ­2.8 0.8 ­3.0 ­3.1 Guinea-Bissau ­29.2 ­27.1 ­19.8 ­34.3 ­21.7 ­11.4 ­16.2 ­13.1 ­10.9 ­32.9 ­24.5 ­18.2 Kenya ­6.4 ­5.6 ­8.1 ­11.8 ­6.7 ­6.0 ­7.6 ­10.7 ­12.4 ­4.9 ­2.8 ­9.1 Lesotho ­89.1 ­105.6 ­63.1 ­57.3 ­61.4 ­58.4 ­48.3 ­50.3 ­48.0 ­105.4 ­94.8 ­55.3 Liberia ­0.1 .. ­4.5 ­8.4 ­8.1 ­12.6 ­13.9 ­14.0 ­45.3 2.9 ­39.6 ­15.2 Madagascar ­16.4 ­11.4 ­7.3 ­3.2 ­6.6 ­9.0 ­14.9 ­14.1 ­11.2 ­7.7 ­8.2 ­9.5 Malawi ­14.0 ­9.6 ­9.7 ­11.1 ­24.9 ­21.8 ­18.2 ­15.7 ­12.5 ­6.7 ­14.3 ­16.3 Mali ­14.4 ­16.6 ­12.6 ­17.0 ­7.3 ­10.9 ­12.4 ­11.7 ­8.1 ­17.6 ­14.9 ­11.4 Mauritania ­29.8 ­15.1 ­28.0 ­18.9 ­23.0 ­30.9 ­49.4 ­59.8 ­4.5 ­24.4 ­11.2 ­30.6 Mauritius ­10.9 ­7.2 ­1.9 2.7 3.8 2.1 ­0.6 ­4.4 ­7.1 ­3.5 ­4.3 ­0.8 Mozambique ­16.5 ­27.9 ­19.5 ­16.2 ­18.3 ­16.2 ­8.6 ­9.4 ­6.1 ­18.4 ­23.6 ­13.5 Namibia 7.8 ­15.5 ­5.5 ­6.4 ­2.0 ­3.6 ­6.1 ­3.5 ­1.0 ­7.6 ­10.0 ­4.0 Niger ­13.5 ­6.9 ­7.9 ­7.7 ­8.9 ­9.5 ­10.5 ­9.4 .. ­8.0 ­6.2 ­9.0 Nigeriab 10.2 14.6 21.9 10.7 ­0.7 2.3 12.9 15.5 14.9 1.1 4.1 11.1 Rwanda ­11.9 ­8.5 ­17.0 ­16.1 ­18.2 ­18.3 ­16.9 ­18.4 ­17.1 ­10.3 ­19.9 ­17.4 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal ­14.5 ­6.8 ­9.3 ­9.0 ­10.4 ­12.1 ­12.9 ­15.6 ­18.5 ­12.1 ­7.1 ­12.5 Seychelles ­11.2 ­4.3 ­3.3 ­21.5 ­1.2 11.2 2.0 ­26.4 ­22.5 ­2.3 ­8.6 ­8.8 Sierra Leone ­15.4 ­1.3 ­21.3 ­18.2 ­19.5 ­21.3 ­12.4 ­18.0 ­13.8 ­3.1 ­4.5 ­17.8 Somalia ­55.3 ­28.0 .. .. .. .. .. .. .. ­35.1 .. .. South Africa 8.0 5.5 3.0 4.0 3.8 2.3 ­0.4 ­0.9 ­3.4 5.1 2.8 1.2 Sudan ­12.6 ­4.2 ­2.4 ­7.8 ­6.2 ­4.2 ­3.8 ­9.9 ­10.9 ­8.5 ­9.4 ­6.5 Swaziland ­39.4 ­12.5 ­15.6 ­14.7 ­0.3 2.0 ­2.6 ­8.9 ­7.7 ­23.5 ­19.1 ­6.8 Tanzania .. ­24.8 ­7.4 ­8.2 ­7.4 ­6.6 ­7.1 ­6.5 ­5.9 .. ­19.3 ­7.0 Togo ­5.3 ­11.9 ­20.0 ­19.4 ­18.0 ­13.6 ­13.5 ­13.2 .. ­7.2 ­9.6 ­16.3 Uganda ­6.6 ­12.1 ­11.9 ­12.1 ­14.7 ­14.2 ­13.9 ­13.9 ­15.3 ­6.2 ­11.7 ­13.7 Zambia ­4.0 ­0.7 ­14.4 ­16.3 ­14.0 ­12.4 ­4.3 ­2.0 8.3 ­2.1 ­5.6 ­7.9 Zimbabwe ­3.2 0.1 ­0.3 1.3 ­0.9 ­5.2 ­10.1 ­16.2 .. ­0.8 ­2.6 ­5.2 NORTH AFRICA 5.0 ­3.6 3.4 2.0 0.9 3.4 4.2 7.6 .. ­3.2 ­2.4 3.6 Algeria 4.0 ­1.5 19.8 14.6 9.7 14.4 14.4 24.1 .. ­2.5 1.6 16.2 Egypt, Arab Rep. ­12.4 ­12.7 ­6.6 ­4.9 ­4.4 ­2.6 ­1.4 ­2.3 ­1.6 ­13.2 ­6.7 ­3.4 Libya 34.8 8.6 19.8 11.3 11.4 .. .. .. .. 20.4 3.6 14.1 Morocco ­9.4 ­5.4 ­5.4 ­2.6 ­2.2 ­3.0 ­5.2 ­6.1 ­5.4 ­7.4 ­4.9 ­4.3 Tunisia ­5.4 ­7.0 ­3.6 ­4.6 ­4.3 ­3.9 ­2.9 ­2.6 0.1 ­6.1 ­4.3 ­3.1 AFRICA 3.0 ­0.2 3.2 1.0 ­0.3 0.6 1.8 3.9 4.6 ­1.6 ­1.4 2.1 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 53 ableT2.26 Exports of goods and services, nominal Current prices ($ millions) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 83,550 79,605 116,864 109,587 113,346 144,936 184,890 231,798 276,758 66,400 87,538 168,311 Excl. South Africa 54,743 52,178 79,830 73,890 76,767 98,175 127,193 165,291 200,761 39,893 55,790 117,415 Excl. South Africa & Nigeria 34,970 40,246 55,014 53,265 57,948 69,295 88,593 113,059 137,379 32,457 43,560 82,079 Angola .. 3,993 8,182 6,847 8,406 9,716 13,780 24,286 33,317 2,613 4,265 14,934 Benin 222 264 342 360 380 487 539 577 .. 214 327 448 Botswana 563 2,087 3,248 2,933 2,811 3,697 4,357 5,120 5,581 999 2,350 3,964 Burkina Faso 173 340 237 260 290 376 549 542 665 189 286 417 Burundi 81 89 55 45 39 50 64 91 99 111 89 63 Cameroon 1,880 2,251 2,343 2,104 2,169 2,757 3,061 3,393 4,130 2,240 2,198 2,851 Cape Verde .. 43 146 167 194 253 138 171 229 41 79 185 Central African Republic 201 220 190 160 162 154 168 170 207 181 185 173 Chad 175 234 234 251 252 674 2,252 3,234 3,852 153 254 1,536 Comoros 11 36 34 34 40 51 46 48 47 22 40 43 Congo, Dem. Rep. 2,372 2,759 964 875 1,174 1,483 1,994 2,242 2,517 2,016 1,595 1,607 Congo, Rep. 1,024 1,502 2,585 2,163 2,462 2,825 3,662 5,160 6,717 1,092 1,393 3,654 Côte d'Ivoire 3,561 3,421 4,211 4,412 5,747 6,297 7,517 8,354 9,004 3,142 4,129 6,506 Djibouti .. 244 193 213 228 248 246 288 307 .. 210 246 Equatorial Guinea .. 42 1,236 1,760 2,139 2,859 4,766 7,285 8,096 32 160 4,020 Eritrea .. .. 96 133 128 80 82 85 87 .. 132 99 Ethiopia .. 672 984 980 982 1,140 1,498 1,858 2,097 608 715 1,363 Gabon 2,770 2,740 3,498 2,782 2,642 3,350 4,412 5,844 6,238 1,964 2,728 4,110 Gambia, The 103 190 202 150 157 158 185 207 .. 108 195 176 Ghana 376 993 2,429 2,401 2,625 3,101 3,487 3,869 5,063 554 1,684 3,282 Guinea 2,084 829 735 809 785 806 829 925 1,073 2,021 798 852 Guinea-Bissau 14 24 68 57 61 77 84 114 129 15 32 84 Kenya 2,144 2,207 2,817 2,866 3,105 3,557 4,248 5,004 5,720 1,805 2,599 3,902 Lesotho 91 104 256 319 390 520 763 708 755 70 187 530 Liberia 613 .. 120 126 111 133 171 201 175 519 43 148 Madagascar 539 512 1,190 1,317 704 1,264 1,425 1,355 1,635 414 673 1,270 Malawi 307 447 446 480 907 723 655 610 537 295 465 623 Mali 263 415 649 876 1,066 1,153 1,237 1,359 1,884 255 514 1,175 Mauritania 261 465 500 379 382 356 473 659 1,453 387 465 600 Mauritius 539 1,529 2,801 2,978 2,757 3,099 3,350 3,556 3,809 764 2,191 3,193 Mozambique 383 201 744 1,004 1,188 1,353 1,828 2,164 2,831 215 373 1,587 Namibia 1,712 1,220 1,558 1,446 1,548 2,300 2,594 2,967 3,577 1,139 1,543 2,284 Niger 617 372 320 329 330 438 491 512 .. 420 325 403 Nigeria 18,859 12,366 24,821 20,637 18,839 28,891 38,609 52,238 63,391 7,725 12,563 35,347 Rwanda 168 145 151 157 133 139 189 229 296 173 107 185 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 837 1,453 1,310 1,401 1,523 1,826 2,123 2,340 2,351 989 1,347 1,839 Seychelles 100 230 481 508 586 671 684 717 860 123 298 644 Sierra Leone 252 146 115 129 153 197 233 268 333 187 155 204 Somalia 200 90 .. .. .. .. .. .. .. 119 .. .. South Africa 28,555 27,149 37,034 35,695 36,578 46,760 57,700 66,523 76,106 26,088 31,523 50,914 Sudan 806 499 1,892 1,714 1,996 2,613 3,822 4,992 6,014 841 579 3,292 Swaziland 405 658 1,133 1,156 1,131 1,580 2,056 1,897 1,774 394 886 1,532 Tanzania .. 538 1,527 1,505 1,631 2,022 2,538 2,964 3,106 .. 962 2,185 Togo 580 545 409 421 498 595 691 743 .. 464 441 559 Uganda 242 312 663 690 697 778 933 1,154 1,403 371 500 903 Zambia 1,608 1,180 878 1,021 1,030 1,256 2,079 2,482 4,120 1,060 1,099 1,838 Zimbabwe 1,561 2,009 2,660 2,369 2,019 1,854 2,002 1,941 .. 1,530 2,469 2,141 NORTH AFRICA 45,633 46,844 69,807 66,721 66,896 80,121 99,631 125,521 .. 35,544 48,909 84,783 Algeria 14,541 14,546 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 16,175 17,066 16,091 18,074 22,258 27,214 32,191 6,654 12,435 21,296 Libya 23,523 11,468 12,078 9,054 9,164 .. .. .. .. 17,320 8,527 10,099 Morocco 3,273 6,830 10,333 11,069 12,109 14,092 16,458 18,656 21,592 3,790 8,360 14,901 Tunisia 3,518 5,353 8,661 9,530 9,520 10,950 13,199 13,766 16,477 3,312 7,168 11,729 AFRICA 127,967 126,562 186,675 176,313 180,245 225,548 285,379 358,340 426,668 102,286 136,482 262,738 a. Provisional 54 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.27 Imports of goods and services, nominal Current prices ($ millions) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­05 SUB-SAHARAN AFRICA 76,888 74,423 106,369 108,470 117,220 148,888 181,663 218,410 257,218 67,756 90,329 162,606 Excl. South Africa 54,934 53,569 73,286 77,610 84,950 105,971 123,151 149,702 172,411 46,247 62,444 112,440 Excl. South Africa & Nigeria 41,909 45,298 58,543 62,096 65,676 78,554 95,829 114,795 130,745 38,672 51,150 86,605 Angola .. 2,147 5,736 6,697 7,110 8,801 10,621 15,144 17,129 1,895 4,032 10,177 Benin 524 486 634 662 772 944 1,055 1,119 .. 447 579 864 Botswana 705 1,888 2,079 1,958 2,130 2,979 3,380 3,308 3,109 842 1,896 2,706 Burkina Faso 603 758 658 650 697 928 1,240 1,390 1,547 567 640 1,016 Burundi 214 314 141 138 140 165 225 360 432 254 234 229 Cameroon 1,829 1,931 1,981 2,228 2,254 2,712 3,128 3,562 3,762 2,219 1,816 2,804 Cape Verde .. 148 326 351 419 529 497 500 624 118 237 464 Central African Republic 327 411 231 203 210 205 246 289 324 292 282 244 Chad 298 485 480 849 2,259 1,608 2,241 2,324 2,509 305 469 1,753 Comoros 64 93 66 68 77 103 118 134 143 67 93 101 Congo, Dem. Rep. 2,354 2,731 920 971 1,447 1,892 2,573 2,792 3,499 2,107 1,537 2,013 Congo, Rep. 1,026 1,282 1,404 1,490 1,629 1,913 2,488 2,994 3,398 1,093 1,309 2,188 Côte d'Ivoire 4,190 2,927 3,471 3,529 3,837 4,796 6,093 7,132 7,189 2,906 3,406 5,150 Djibouti .. 355 278 262 259 305 361 361 441 .. 295 324 Equatorial Guinea .. 92 1,071 1,599 1,124 2,256 2,882 3,583 4,295 61 270 2,401 Eritrea .. .. 518 507 505 577 617 540 543 .. 482 544 Ethiopia .. 1,069 1,961 1,938 2,073 2,346 3,175 4,366 5,539 1,093 1,330 3,057 Gabon 1,354 1,837 1,656 1,557 1,694 1,882 2,298 1,983 2,267 1,586 1,823 1,905 Gambia, The 153 227 239 173 188 192 255 302 .. 137 242 225 Ghana 407 1,522 3,347 3,441 3,380 4,316 5,356 6,610 8,234 709 2,509 4,955 Guinea 1,878 892 867 849 912 892 986 1,013 1,163 1,953 905 955 Guinea-Bissau 46 90 111 125 105 104 127 153 162 67 91 127 Kenya 2,608 2,691 3,840 4,403 3,986 4,456 5,485 7,012 8,534 2,154 2,963 5,388 Lesotho 475 753 794 750 812 1,127 1,400 1,426 1,472 503 977 1,112 Liberia 614 .. 146 171 156 184 235 275 453 491 180 232 Madagascar 1,202 864 1,474 1,463 993 1,756 2,073 2,067 2,252 668 942 1,725 Malawi 480 629 616 672 1,570 1,251 1,134 1,058 932 384 716 1,033 Mali 520 817 954 1,322 1,311 1,630 1,841 1,979 2,360 536 882 1,628 Mauritania 473 619 803 591 647 753 1,239 1,758 1,573 576 607 1,052 Mauritius 665 1,701 2,888 2,854 2,584 2,988 3,389 3,830 4,257 809 2,334 3,256 Mozambique 965 888 1,571 1,665 1,958 2,108 2,320 2,783 3,245 773 1,001 2,236 Namibia 1,542 1,584 1,746 1,652 1,610 2,461 2,940 3,186 3,644 1,284 1,844 2,463 Niger 957 545 462 479 523 688 795 825 .. 583 448 629 Nigeria 12,324 8,203 14,728 15,499 19,245 27,360 27,282 34,849 41,518 7,362 11,214 25,783 Rwanda 307 364 445 427 430 464 522 666 787 354 405 534 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 1,344 1,840 1,746 1,842 2,078 2,657 3,162 3,694 4,062 1,408 1,719 2,749 Seychelles 117 246 501 641 594 593 671 908 1,034 123 344 706 Sierra Leone 421 154 250 276 336 408 366 487 529 225 191 379 Somalia 534 346 .. .. .. .. .. .. .. 403 .. .. South Africa 22,073 21,016 33,107 30,897 32,316 42,967 58,544 68,754 84,692 21,441 27,961 50,183 Sudan 1,763 877 2,189 2,760 2,924 3,367 4,650 7,701 9,995 1,744 1,289 4,798 Swaziland 619 768 1,349 1,350 1,134 1,543 2,117 2,130 1,988 515 1,116 1,659 Tanzania .. 1,595 2,200 2,283 2,353 2,703 3,344 3,881 3,941 .. 2,000 2,958 Togo 640 738 674 678 763 833 969 1,026 .. 542 586 824 Uganda 324 834 1,366 1,378 1,554 1,662 1,879 2,369 2,854 619 1,042 1,866 Zambia 1,764 1,203 1,343 1,612 1,552 1,796 2,319 2,631 3,221 1,148 1,283 2,068 Zimbabwe 1,771 2,002 2,680 2,232 2,218 2,238 2,477 2,495 .. 1,598 2,661 2,390 NORTH AFRICA 39,100 53,024 61,430 61,875 64,800 71,674 88,038 100,933 115,394 40,426 53,419 80,592 Algeria 12,847 15,472 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 22,780 21,802 19,917 20,219 23,330 29,246 33,931 10,787 16,572 24,461 Libya 11,167 8,996 5,252 5,674 6,979 .. .. .. .. 10,722 7,464 5,968 Morocco 5,033 8,227 12,329 12,033 12,992 15,579 19,393 22,272 25,125 4,955 9,905 17,103 Tunisia 3,987 6,220 9,369 10,446 10,421 11,918 14,026 14,525 16,449 3,834 7,842 12,450 AFRICA 115,856 127,727 167,801 170,345 182,017 221,305 270,566 320,728 375,095 108,542 143,841 243,980 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 55 ableT2.28 Exports of goods and services Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 30.2 26.8 34.2 32.7 31.1 32.5 33.6 35.9 37.2 25.3 27.6 33.9 Excl. South Africa 27.8 28.2 38.1 34.0 30.3 35.1 38.1 41.0 40.9 23.2 30.7 36.8 Excl. South Africa & Nigeria 27.0 25.7 33.7 31.5 29.9 32.7 36.1 38.9 40.1 24.2 28.7 34.7 Angola .. 38.9 89.6 76.6 73.5 69.6 69.7 79.3 73.8 34.8 63.4 76.0 Benin 15.8 14.3 15.2 15.2 13.5 13.7 13.3 13.5 .. 16.6 16.4 14.1 Botswana 53.1 55.1 52.6 48.6 47.4 44.7 44.3 48.7 50.7 62.0 51.2 48.1 Burkina Faso 9.0 11.0 9.1 9.2 8.8 8.8 10.7 10.0 11.5 9.5 11.1 9.7 Burundi 8.8 7.9 7.8 6.9 6.2 8.4 9.6 11.4 10.9 10.4 9.0 8.7 Cameroon 27.9 20.2 23.3 21.9 19.9 20.2 19.4 20.5 23.0 25.7 20.9 21.2 Cape Verde .. 12.7 27.5 30.3 31.5 31.7 14.9 17.0 19.4 15.5 16.9 24.6 Central African Republic 25.2 14.8 19.8 16.5 15.5 12.9 12.8 12.6 14.0 20.5 16.2 14.9 Chad 16.9 13.5 16.9 14.7 12.7 24.6 51.0 55.1 61.1 14.3 16.1 33.7 Comoros 8.7 14.3 16.7 15.5 15.7 15.8 12.7 12.5 11.7 14.7 17.4 14.4 Congo, Dem. Rep. 16.5 29.5 22.4 18.6 21.2 26.1 30.4 31.6 29.5 21.4 23.1 25.7 Congo, Rep. 60.0 53.7 80.3 77.4 81.5 79.3 84.3 84.8 86.9 52.0 60.2 82.1 Côte d'Ivoire 35.0 31.7 40.4 41.8 50.0 45.8 48.6 51.1 52.1 37.1 36.8 47.1 Djibouti .. 53.8 35.1 37.3 38.6 39.9 37.0 40.6 39.9 .. 43.2 38.3 Equatorial Guinea .. 32.2 98.6 101.3 98.7 96.4 97.3 96.8 94.5 35.9 52.9 97.7 Eritrea .. .. 15.1 19.9 20.3 13.6 12.9 8.8 8.0 .. 22.0 14.1 Ethiopia .. 5.6 12.0 12.0 12.6 13.3 14.9 15.1 13.8 6.6 8.1 13.4 Gabon 64.7 46.0 69.0 59.0 53.6 55.3 61.5 67.4 65.4 53.3 54.0 61.6 Gambia, The 42.7 59.9 48.0 35.9 42.5 43.1 46.0 44.8 .. 47.8 52.6 43.4 Ghana 8.5 16.9 48.8 45.2 42.6 40.7 39.3 36.1 39.8 11.2 25.2 41.8 Guinea 31.2 31.1 23.6 26.6 24.5 22.3 21.0 28.4 33.5 29.6 23.8 25.7 Guinea-Bissau 12.7 9.9 31.8 28.6 30.2 32.9 31.0 37.7 41.8 9.9 13.3 33.4 Kenya 29.5 25.7 22.3 22.1 23.6 23.7 26.2 26.7 25.1 25.7 27.7 24.3 Lesotho 21.0 16.8 30.0 42.4 56.8 50.0 57.9 49.7 50.5 16.7 21.7 48.2 Liberia 64.3 .. 21.5 23.2 19.9 32.4 37.3 37.9 28.6 55.3 11.4 28.7 Madagascar 13.3 16.6 30.7 29.1 16.0 23.1 32.6 26.9 29.7 13.6 20.1 26.9 Malawi 24.8 23.8 25.6 28.0 34.0 29.8 25.0 21.4 17.0 23.7 25.1 25.8 Mali 14.7 17.1 26.8 33.3 31.9 26.4 25.4 25.6 32.1 15.8 20.8 28.8 Mauritania 36.8 45.6 46.2 33.8 33.3 27.7 30.6 35.9 54.6 47.9 36.7 37.4 Mauritius 46.8 64.2 62.7 65.6 60.6 59.1 55.2 56.5 60.0 53.1 61.5 60.0 Mozambique 10.9 8.2 17.5 24.6 28.3 29.0 32.1 32.9 41.4 6.8 12.8 29.4 Namibia 78.9 51.9 45.6 45.0 49.6 51.4 45.9 47.6 54.5 61.2 49.7 48.5 Niger 24.6 15.0 17.8 16.9 15.2 16.6 16.9 15.4 .. 21.0 16.2 16.5 Nigeria 29.4 43.4 54.0 43.0 31.9 42.7 44.0 46.5 43.2 21.4 42.0 43.6 Rwanda 14.4 5.6 8.7 9.4 8.1 7.9 9.6 9.6 10.3 10.4 6.0 9.1 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 23.9 25.4 27.9 28.7 28.5 26.6 26.4 26.9 25.4 27.4 26.4 27.2 Seychelles 68.0 62.5 78.2 81.6 84.0 95.1 97.8 99.2 111.0 62.1 59.9 92.4 Sierra Leone 22.9 22.4 18.1 16.0 16.4 19.9 21.7 22.1 23.4 19.5 19.8 19.7 Somalia 33.2 9.8 .. .. .. .. .. .. .. 15.5 .. .. South Africa 35.4 24.2 27.9 30.1 33.0 28.1 26.7 27.5 29.8 28.8 23.5 29.0 Sudan 10.6 5.5 15.3 12.8 13.3 14.7 17.6 18.2 16.5 7.7 7.4 15.5 Swaziland 74.6 74.6 81.6 87.8 95.2 86.8 86.5 72.6 63.7 70.2 74.8 82.0 Tanzania .. 12.6 16.8 15.9 16.7 19.7 22.4 21.0 21.9 .. 16.4 19.2 Togo 51.1 33.5 30.7 31.7 33.8 33.8 33.5 34.5 .. 46.1 30.2 33.0 Uganda 19.4 7.2 11.2 12.1 11.9 12.4 13.7 13.2 14.8 11.6 9.8 12.8 Zambia 41.4 35.9 27.1 28.1 27.7 28.7 37.6 33.8 37.8 34.4 32.8 31.5 Zimbabwe 23.4 22.9 35.9 23.1 9.2 25.1 42.5 56.8 .. 21.4 34.1 32.1 NORTH AFRICA 34.6 27.2 28.4 27.7 29.7 32.1 35.7 39.0 .. 25.5 26.4 32.1 Algeria 34.3 23.4 41.2 36.2 35.1 38.3 40.1 47.6 .. 23.8 25.8 39.7 Egypt, Arab Rep. 30.5 20.0 16.2 17.5 18.3 21.8 28.2 30.3 29.9 22.2 21.8 23.2 Libya 66.2 39.7 35.0 30.2 47.7 .. .. .. .. 54.7 28.7 37.6 Morocco 17.4 26.5 27.9 29.3 29.9 28.3 29.2 31.6 33.0 22.2 25.9 29.9 Tunisia 40.2 43.6 44.5 47.7 45.2 43.8 46.9 47.5 53.2 36.9 42.5 47.0 AFRICA 31.4 27.0 31.8 30.6 30.6 32.4 34.4 37.1 38.3 25.4 27.1 33.6 a. Provisional 56 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.29 Imports of goods and services Share of GDP (%) Annual average 1980 1990 2000 2001 2002 2003 2004 2005 2006a 1980­89 1990­99 2000­06 SUB-SAHARAN AFRICA 27.8 25.1 31.1 32.3 32.2 33.4 33.0 33.9 34.5 25.9 28.5 32.9 Excl. South Africa 27.9 28.9 35.0 35.7 33.5 37.9 36.9 37.1 35.2 26.9 34.4 35.9 Excl. South Africa & Nigeria 32.4 28.9 35.9 36.7 33.8 37.1 39.0 39.5 38.1 28.9 33.7 37.2 Angola .. 20.9 62.8 74.9 62.2 63.1 53.7 49.4 37.9 25.6 61.3 57.7 Benin 37.3 26.3 28.1 27.9 27.5 26.5 26.1 26.1 .. 34.1 28.9 27.0 Botswana 66.4 49.8 33.7 32.5 35.9 36.0 34.4 31.5 28.2 56.7 41.5 33.2 Burkina Faso 31.3 24.5 25.2 23.1 21.2 21.7 24.3 25.6 26.8 29.2 24.6 24.0 Burundi 23.3 27.8 19.9 20.9 22.3 27.7 33.9 45.3 47.8 23.8 23.4 31.1 Cameroon 27.1 17.3 19.7 23.2 20.7 19.9 19.8 21.5 21.0 25.3 17.2 20.8 Cape Verde .. 43.7 61.4 63.7 68.1 66.3 53.8 49.7 52.8 44.6 52.1 59.4 Central African Republic 41.1 27.6 24.1 21.0 20.1 17.2 18.8 21.4 21.9 32.5 24.0 20.7 Chad 28.9 27.9 34.7 49.7 113.7 58.7 50.8 39.6 39.8 27.7 29.7 55.3 Comoros 51.9 37.1 32.5 30.8 30.8 31.8 32.6 34.7 35.5 47.9 40.0 32.7 Congo, Dem. Rep. 16.4 29.2 21.4 20.7 26.1 33.3 39.2 39.3 41.0 22.2 21.9 31.6 Congo, Rep. 60.1 45.8 43.6 53.3 53.9 53.7 57.3 49.2 43.9 52.6 57.3 50.7 Côte d'Ivoire 41.2 27.1 33.3 33.5 33.4 34.9 39.4 43.6 41.6 33.9 30.3 37.1 Djibouti .. 78.4 50.4 45.8 43.7 49.1 54.2 50.9 57.3 .. 60.7 50.2 Equatorial Guinea .. 69.6 85.4 92.0 51.9 76.1 58.8 47.6 50.1 64.5 98.6 66.0 Eritrea .. .. 81.8 75.6 80.0 98.7 97.1 55.7 50.0 .. 77.8 77.0 Ethiopia .. 8.8 24.0 23.7 26.6 27.4 31.6 35.5 36.5 11.9 14.9 29.3 Gabon 31.6 30.9 32.7 33.0 34.3 31.1 32.0 22.9 23.7 43.6 36.3 30.0 Gambia, The 63.6 71.6 56.8 41.3 50.8 52.3 63.7 65.4 .. 61.0 65.3 55.1 Ghana 9.2 25.9 67.2 64.8 54.9 56.6 60.4 61.7 64.8 14.3 37.6 61.5 Guinea 28.1 33.4 27.9 27.9 28.4 24.6 25.0 31.1 36.3 28.8 26.9 28.8 Guinea-Bissau 41.8 37.0 51.6 63.0 51.9 44.3 47.2 50.8 52.7 42.8 37.7 51.6 Kenya 35.9 31.3 30.5 33.9 30.3 29.7 33.9 37.4 37.5 30.6 30.6 33.3 Lesotho 110.1 122.4 93.1 99.7 118.2 108.4 106.2 100.0 98.5 122.1 116.5 103.5 Liberia 64.4 .. 26.0 31.6 27.9 44.9 51.2 51.9 73.8 52.4 51.0 43.9 Madagascar 29.7 28.0 38.0 32.3 22.6 32.1 47.5 41.0 40.9 21.3 28.3 36.3 Malawi 38.8 33.4 35.3 39.1 58.9 51.6 43.2 37.1 29.4 30.4 39.4 42.1 Mali 29.1 33.7 39.4 50.3 39.2 37.4 37.8 37.3 40.2 33.4 35.7 40.2 Mauritania 66.7 60.7 74.2 52.7 56.2 58.6 80.0 95.7 59.1 72.2 47.9 68.1 Mauritius 57.6 71.4 64.6 62.9 56.8 56.9 55.9 60.9 67.1 56.6 65.8 60.7 Mozambique 27.4 36.1 37.0 40.9 46.6 45.2 40.7 42.3 47.5 25.1 36.4 42.9 Namibia 71.1 67.4 51.2 51.4 51.6 55.0 52.0 51.1 55.5 68.7 59.7 52.5 Niger 38.1 22.0 25.7 24.6 24.1 26.1 27.4 24.8 .. 29.0 22.4 25.4 Nigeria 19.2 28.8 32.0 32.3 32.6 40.4 31.1 31.0 28.3 20.3 38.5 32.5 Rwanda 26.4 14.1 25.7 25.5 26.2 26.1 26.5 28.0 27.4 20.7 26.0 26.5 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 38.4 32.2 37.2 37.8 39.0 38.7 39.4 42.5 43.8 39.6 33.5 39.8 Seychelles 79.1 66.7 81.4 103.0 85.2 84.0 95.8 125.7 133.5 64.4 68.4 101.2 Sierra Leone 38.2 23.8 39.4 34.3 35.9 41.2 34.1 40.1 37.2 22.5 24.3 37.5 Somalia 88.5 37.7 .. .. .. .. .. .. .. 50.6 .. .. South Africa 27.3 18.8 24.9 26.1 29.1 25.8 27.0 28.4 33.2 23.8 20.7 27.8 Sudan 23.1 9.7 17.7 20.7 19.5 18.9 21.4 28.1 27.5 16.2 16.8 22.0 Swaziland 114.0 87.1 97.2 102.5 95.5 84.7 89.1 81.5 71.4 93.7 93.9 88.8 Tanzania .. 37.5 24.2 24.2 24.1 26.3 29.5 27.4 27.8 .. 35.6 26.2 Togo 56.4 45.3 50.7 51.1 51.7 47.4 47.0 47.6 .. 53.3 39.8 49.3 Uganda 26.0 19.4 23.0 24.3 26.6 26.6 27.6 27.1 30.1 17.8 21.6 26.5 Zambia 45.4 36.6 41.5 44.3 41.8 41.1 42.0 35.8 29.6 36.5 38.4 39.4 Zimbabwe 26.5 22.8 36.2 21.8 10.1 30.3 52.6 73.0 .. 22.2 36.7 37.3 NORTH AFRICA 29.7 30.8 25.0 25.7 28.7 28.7 31.6 31.4 31.2 28.7 28.8 28.9 Algeria 30.3 24.9 21.4 21.6 25.4 23.9 25.7 23.5 .. 26.3 24.2 23.6 Egypt, Arab Rep. 42.9 32.7 22.8 22.3 22.7 24.4 29.6 32.6 31.6 35.4 28.5 26.6 Libya 31.4 31.1 15.2 18.9 36.4 .. .. .. .. 34.3 25.1 23.5 Morocco 26.7 31.9 33.3 31.9 32.1 31.3 34.4 37.8 38.4 29.6 30.9 34.2 Tunisia 45.6 50.6 48.2 52.3 49.5 47.7 49.9 50.1 53.1 43.0 46.8 50.1 AFRICA 28.5 27.2 28.6 29.6 30.9 31.8 32.7 33.2 33.7 27.0 28.6 31.5 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 57 ableT2.30 Balance of payment and current account Current prices ($ millions) Exports of goods Imports of goods Net Net current Current account Total reserves and services and services income transfers balance including gold 2005 2006a 2005 2006a 2005 2006a 2005 2006a 2005 2006a 2005 2006a SUB-SAHARAN AFRICA 231,798 276,758 218,410 257,218 .. .. .. .. 11,361 ­14,234 83,557 113,863 Excl. South Africa 165,291 200,761 149,702 172,411 ­30,902 ­32,092 .. .. 21,084 2,253 62,928 88,250 Excl. South Africa & Nigeria 113,059 137,379 114,795 130,745 ­18,075 ­20,792 .. .. ­3,118 2,253 34,296 45,515 Angola 24,286 33,317 15,144 17,129 ­4,031 ­6,178 27 ­190 5,138 10,690 3,197 8,599 Benin 577 .. 1,119 .. ­18 .. 164 .. ­226 .. 529 496 Botswana 5,120 5,581 3,308 3,109 ­836 ­772 678 871 1,597 1,940 6,309 7,992 Burkina Faso 542 665 1,390 1,547 .. .. .. .. .. .. .. .. Burundi 91 99 360 432 ­18 ­9 239 229 ­11 ­135 101 131 Cameroon 3,393 4,130 3,562 3,762 .. .. .. .. .. .. 801 1,026 Cape Verde 171 229 500 624 ­33 ­45 279 295 ­35 ­40 181 .. Central African Republic 170 207 289 324 .. .. .. .. .. .. 145 132 Chad 3,234 3,852 2,324 2,509 .. .. .. .. .. .. 371 459 Comoros 48 47 134 143 .. .. .. .. .. .. 86 94 Congo, Dem. Rep. 2,242 2,517 2,792 3,499 .. .. .. .. .. .. 360 470 Congo, Rep. 5,160 6,717 2,994 3,398 ­1,122 .. ­22 .. 903 .. 740 1,998 Côte d'Ivoire 8,354 9,004 7,132 7,189 ­653 ­710 ­462 ­531 40 479 1,322 1,798 Djibouti 288 307 361 441 21 23 73 79 20 ­17 88 117 Equatorial Guinea 7,285 8,096 3,583 4,295 .. .. .. .. .. .. 2,102 3,067 Eritrea 85 87 540 543 .. .. .. .. .. .. 16 16 Ethiopia 1,858 2,097 4,366 5,539 ­5 18 1,402 1,274 ­1,568 ­1,786 1,555 1,158 Gabon 5,844 6,238 1,983 2,267 .. .. .. .. .. .. 675 1,122 Gambia, The 207 .. 302 .. ­32 ­38 69 87 ­44 ­66 85 95 Ghana 3,869 5,063 6,610 8,234 ­187 ­127 1,794 2,248 ­1,105 ­1,040 1,951 2,084 Guinea 925 1,073 1,013 1,163 .. .. .. .. .. .. 125 232 Guinea-Bissau 114 129 153 162 .. .. .. .. .. .. 213 210 Kenya 5,004 5,720 7,012 8,534 ­108 ­70 1,253 1,781 ­261 ­526 2,043 2,654 Lesotho 708 755 1,426 1,472 305 379 301 390 ­98 67 447 455 Liberia 201 175 275 453 ­113 ­131 318 307 7 ­138 10 .. Madagascar 1,355 1,635 2,067 2,252 ­80 .. 236 .. ­554 .. 498 532 Malawi 610 537 1,058 932 .. .. .. .. .. .. .. .. Mali 1,359 1,884 1,979 2,360 ­207 ­269 228 325 ­438 ­231 929 977 Mauritania 659 1,453 1,758 1,573 .. .. .. .. .. .. 70 .. Mauritius 3,556 3,809 3,830 4,257 ­8 50 61 71 ­324 ­611 1,487 1,391 Mozambique 2,164 2,831 2,783 3,245 ­360 ­496 403 501 ­761 ­634 1,103 1,241 Namibia 2,967 3,577 3,186 3,644 ­127 ­85 673 946 334 1,064 316 513 Niger 512 .. 825 .. ­10 .. 182 .. ­312 .. 250 371 Nigeria 52,238 63,391 34,849 41,518 ­6,732 .. 3,310 .. 24,202 .. 28,632 42,735 Rwanda 229 296 666 787 ­16 ­21 366 296 ­52 ­180 406 440 São Tomé and Principe .. .. .. .. ­3 3 3 0 ­36 ­58 27 35 Senegal 2,340 2,351 3,694 4,062 .. .. .. .. .. .. 1,261 897 Seychelles 717 860 908 1,034 ­40 ­44 31 48 ­195 ­164 56 111 Sierra Leone 268 333 487 529 ­51 ­41 137 62 ­104 ­101 112 142 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa 66,523 76,106 68,754 84,692 ­4,929 ­5,293 ­2,801 ­2,817 ­9,723 ­16,487 20,629 25,613 Sudan 4,992 6,014 7,701 9,995 ­1,362 ­2,014 1,446 1,324 ­2,768 ­4,722 554 .. Swaziland 1,897 1,774 2,130 1,988 62 1 131 168 86 98 230 209 Tanzania 2,964 3,106 3,881 3,941 ­117 ­85 496 550 ­881 ­1,442 2,049 2,259 Togo 743 .. 1,026 .. ­35 .. 188 .. ­461 .. .. .. Uganda 1,154 1,403 2,369 2,854 ­249 ­226 833 1,429 ­414 ­323 1,168 1,398 Zambia 2,482 4,120 2,631 3,221 ­595 ­1,168 107 362 ­600 128 331 595 Zimbabwe 1,941 .. 2,495 .. .. .. .. .. .. .. .. .. NORTH AFRICA 125,521 .. 100,933 115,394 ­7,141 ­9,572 .. .. 17,854 26,023 99,382 133,559 Algeria 48,690 .. 24,020 .. .. .. .. .. .. .. 59,167 81,463 Egypt, Arab Rep. 27,214 32,191 29,246 33,931 ­35 738 5,748 5,770 2,103 2,635 19,322 26,660 Libya .. .. .. .. ­281 ­595 ­634 586 14,945 22,170 .. .. Morocco 18,656 21,592 22,272 25,125 ­314 ­421 5,375 6,333 1,110 1,851 17,936 18,613 Tunisia 13,766 16,477 14,525 16,449 ­1,668 ­1,586 1,502 1,639 ­304 ­634 2,957 6,824 AFRICA 358,340 426,668 320,728 375,095 ­42,983 ­46,947 .. .. 29,215 11,789 182,939 247,423 a. Provisional 58 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS ableT2.31 Structure of demand Share of GDP % Household final General government consumption final consumption Gross fixed Exports of goods Imports of goods Gross national expenditure expenditure capital formation and services and services savings 1990 2000 2006a 1990 2000 2006a 1990 2000 2006a 1990 2000 2006a 1990 2000 2006a 1990 2000 2006a SUB-SAHARAN AFRICA 65.5 68.5 66.9 17.4 14.6 13.7 18.1 16.0 17.6 26.8 34.2 37.2 25.1 31.1 34.5 12.9 12.9 9.0 Excl. South Africa 72.8 73.6 70.3 15.5 11.9 10.3 17.6 16.8 17.4 28.2 38.1 40.9 28.9 35.0 35.2 9.0 11.0 6.4 Excl. South Africa & Nigeria 72.8 73.6 70.3 15.0 12.5 12.1 17.0 17.7 20.4 25.7 33.7 40.1 28.9 35.9 38.1 10.7 14.1 9.2 Angola 35.8 .. .. 34.5 .. .. 11.1 15.1 13.7 38.9 89.6 73.8 20.9 62.8 37.9 9.0 23.8 37.0 Benin 86.8 82.4 .. 11.0 11.6 .. 13.4 18.9 .. 14.3 15.2 .. 26.3 28.1 .. 9.9 10.9 .. Botswana 33.2 23.2 28.4 24.1 22.9 19.2 32.4 21.7 17.9 55.1 52.6 50.7 49.8 33.7 28.2 43.3 51.7 53.3 Burkina Faso 73.5 78.5 75.2 21.1 20.8 22.0 17.7 18.7 20.8 11.0 9.1 11.5 24.5 25.2 26.8 13.7 5.1 6.3 Burundi 94.5 88.5 90.9 10.8 17.5 29.3 15.2 6.1 16.7 7.9 7.8 10.9 27.8 19.9 47.8 .. 0.5 .. Cameroon 66.6 70.2 71.5 12.8 9.5 9.6 17.3 16.0 16.7 20.2 23.3 23.0 17.3 19.7 21.0 16.1 16.1 18.9 Cape Verde 93.4 92.9 74.6 14.7 21.3 20.7 22.9 19.7 38.1 12.7 27.5 19.4 43.7 61.4 52.8 17.6 9.1 26.8 Central African Republic 85.7 80.8 88.3 14.9 14.0 10.6 11.4 9.5 8.9 14.8 19.8 14.0 27.6 24.1 21.9 ­0.4 8.2 11.7 Chad 97.6 86.8 50.5 10.0 7.7 5.9 4.8 20.9 21.2 13.5 16.9 61.1 27.9 34.7 39.8 ­2.7 7.9 20.4 Comoros 78.7 94.0 101.4 24.5 11.7 12.6 11.9 10.1 9.8 14.3 16.7 11.7 37.1 32.5 35.5 ­1.3 9.9 4.9 Congo, Dem. Rep. 79.1 88.0 88.1 11.5 7.5 7.3 12.8 3.5 .. 29.5 22.4 29.5 29.2 21.4 41.0 0.8 ­3.5 8.9 Congo, Rep. 62.4 29.1 21.1 13.8 11.6 13.2 17.2 20.9 22.4 53.7 80.3 86.9 45.8 43.6 43.9 6.6 30.1 43.0 Côte d'Ivoire 71.9 74.9 71.2 16.8 7.2 8.4 8.5 11.2 9.9 31.7 40.4 52.1 27.1 33.3 41.6 ­4.3 10.0 14.7 Djibouti 78.9 76.8 59.9 31.5 29.7 28.0 14.1 8.8 29.6 53.8 35.1 39.9 78.4 50.4 57.3 .. 5.4 20.7 Equatorial Guinea 80.3 20.9 11.1 39.7 4.6 2.9 17.4 61.3 41.6 32.2 98.6 94.5 69.6 85.4 50.1 ­22.0 45.7 46.2 Eritrea .. 70.9 80.9 .. 63.8 42.4 .. 31.9 18.7 .. 15.1 8.0 .. 81.8 50.0 .. 20.4 8.7 Ethiopia 77.2 73.8 86.4 13.2 17.9 12.1 12.9 20.3 24.2 5.6 12.0 13.8 8.8 24.0 36.5 11.9 16.3 15.1 Gabon 49.7 32.2 26.9 13.4 9.6 8.4 21.4 21.9 23.1 46.0 69.0 65.4 30.9 32.7 23.7 24.2 41.7 41.3 Gambia, The 75.6 77.8 .. 13.7 13.7 .. 22.3 17.4 .. 59.9 48.0 .. 71.6 56.8 .. 5.3 13.6 10.0 Ghana 85.2 84.3 78.7 9.3 10.2 13.4 14.4 23.1 32.9 16.9 48.8 39.8 25.9 67.2 64.8 7.0 15.7 27.4 Guinea 66.9 77.7 83.8 11.0 6.8 5.6 22.9 18.9 13.3 31.1 23.6 33.5 33.4 27.9 36.3 14.6 13.3 11.8 Guinea-Bissau 86.9 94.6 76.0 10.3 14.0 17.7 29.9 11.3 17.2 9.9 31.8 41.8 37.0 51.6 52.7 15.3 ­2.7 22.7 Kenya 62.8 75.2 74.3 18.6 15.3 16.3 20.6 16.8 18.8 25.7 22.3 25.1 31.3 30.5 37.5 18.6 15.2 12.5 Lesotho 138.8 101.3 96.9 14.1 19.2 18.1 52.8 44.9 33.3 16.8 30.0 50.5 122.4 93.1 98.5 59.5 21.4 27.3 Liberia .. .. .. .. .. .. .. .. .. .. 21.5 28.6 .. 26.0 73.8 .. .. .. Madagascar 86.4 83.2 77.6 8.0 9.0 8.8 14.8 15.0 24.8 16.6 30.7 29.7 28.0 38.0 40.9 9.2 9.4 16.0 Malawi 71.5 81.6 77.0 15.1 14.6 11.8 20.1 12.3 21.8 23.8 25.6 17.0 33.4 35.3 29.4 13.6 2.2 15.5 Mali 79.8 79.4 75.3 13.8 8.6 9.9 23.0 24.6 22.9 17.1 26.8 32.1 33.7 39.4 40.2 15.1 16.0 13.0 Mauritania 69.2 82.8 61.3 25.9 25.8 19.9 20.0 19.4 23.3 45.6 46.2 54.6 60.7 74.2 59.1 17.6 0.8 28.7 Mauritius 63.7 63.0 68.1 12.8 13.1 14.5 28.3 25.3 22.9 64.2 62.7 60.0 71.4 64.6 67.1 26.3 25.3 19.0 Mozambique 92.3 79.5 75.6 13.5 9.0 11.1 22.1 31.0 19.3 8.2 17.5 41.4 36.1 37.0 47.5 2.1 6.0 3.1 Namibia 51.2 57.1 47.9 30.6 28.8 23.7 21.2 18.8 28.2 51.9 45.6 54.5 67.4 51.2 55.5 34.8 26.2 42.2 Niger 83.8 83.4 .. 15.0 13.0 .. 11.4 11.2 .. 15.0 17.8 .. 22.0 25.7 .. ­2.1 2.8 .. Nigeria .. .. .. .. .. .. .. .. .. 43.4 54.0 43.2 28.8 32.0 28.3 .. .. .. Rwanda 83.7 87.7 85.1 10.1 11.0 11.7 14.6 18.3 20.3 5.6 8.7 10.3 14.1 25.7 27.4 11.3 12.9 13.8 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. Senegal 79.2 76.0 79.9 18.4 12.8 9.6 18.0 22.4 28.9 25.4 27.9 25.4 32.2 37.2 43.8 ­0.5 14.6 18.3 Seychelles 52.0 53.9 65.2 27.7 24.2 24.7 23.0 25.2 32.7 62.5 78.2 111.0 66.7 81.4 133.5 21.7 17.2 9.4 Sierra Leone 83.5 100.1 85.3 7.8 14.3 13.1 9.6 6.9 15.5 22.4 18.1 23.4 23.8 39.4 37.2 2.6 ­9.1 9.5 Somalia .. .. .. .. .. .. 14.9 .. .. 9.8 .. .. 37.7 .. .. .. .. .. South Africa 57.1 63.0 63.5 19.7 18.1 19.5 19.1 15.1 18.7 24.2 27.9 29.8 18.8 24.9 33.2 19.6 15.8 13.9 Sudan 89.6 76.5 68.9 7.3 7.6 16.7 8.2 12.1 21.0 5.5 15.3 16.5 9.7 17.7 27.5 ­4.4 3.5 10.3 Swaziland 75.2 72.4 72.3 18.1 24.5 19.5 18.4 18.6 15.8 74.6 81.6 63.7 87.1 97.2 71.4 24.8 13.2 14.5 Tanzania 80.9 79.2 72.8 17.8 10.6 16.3 25.8 17.4 16.6 12.6 16.8 21.9 37.5 24.2 27.8 7.7 8.5 10.7 Togo 71.1 92.0 .. 14.2 10.2 .. 25.3 17.8 .. 33.5 30.7 .. 45.3 50.7 .. 20.4 0.3 .. Uganda 91.9 78.2 77.2 7.5 13.7 14.7 12.7 19.6 23.0 7.2 11.2 14.8 19.4 23.0 30.1 1.2 9.3 12.9 Zambia 64.4 87.4 59.1 19.0 9.5 10.0 13.5 16.0 22.7 35.9 27.1 37.8 36.6 41.5 29.6 6.9 ­1.3 23.3 Zimbabwe 63.1 72.8 .. 19.4 13.9 .. 18.2 11.8 .. 22.9 35.9 .. 22.8 36.2 .. 15.7 9.6 .. NORTH AFRICA 64.1 61.3 .. 16.2 14.5 .. 24.1 20.1 21.3 27.2 28.4 .. 30.8 25.0 31.2 14.2 12.9 14.6 Algeria 56.8 41.6 .. 16.1 13.6 .. 27.0 20.7 .. 23.4 41.2 .. 24.9 21.4 .. 26.2 41.3 .. Egypt, Arab Rep. 72.6 75.9 70.6 11.3 11.2 12.3 26.9 18.9 18.7 20.0 16.2 29.9 32.7 22.8 31.6 21.4 18.7 22.0 Libya 48.4 46.6 .. 24.4 20.5 .. 13.9 12.9 .. 39.7 35.0 .. 31.1 15.2 .. .. .. .. Morocco 64.6 61.5 55.5 15.5 18.4 18.3 24.0 26.0 28.7 26.5 27.9 33.0 31.9 33.3 38.4 25.1 24.2 34.5 Tunisia 63.6 60.7 63.0 16.4 15.6 13.4 24.4 26.0 22.7 43.6 44.5 53.2 50.6 48.2 53.1 21.7 23.2 25.4 AFRICA 64.8 65.2 66.6 16.8 14.5 13.3 20.4 17.8 18.9 27.0 31.8 38.3 27.2 28.6 33.7 13.4 12.9 10.8 a. Provisional NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 59 ableT2.32 Exchange rates and Purchasing Power Parity* Purchasing Ratio of PPP Official power parity conversion factor exchange (PPP) conversion to market rate factor exchange rate local currency units local currency to US$ units to international $ 2005 2006 2005 2006 2005 2006 SUB-SAHARAN AFRICA Excl. South Africa .. .. .. .. .. .. Excl. South Africa & Nigeria .. .. .. .. .. .. Angola 44.5 49.4 87.2 80.4 0.5 0.6 Benin 219.6 219.2 527.5 522.9 0.4 0.4 Botswana 2.4 2.7 5.1 5.8 0.5 0.5 Burkina Faso 200.2 193.9 527.5 522.9 0.4 0.4 Burundi 343.0 341.2 1,081.6 1,028.7 0.3 0.3 Cameroon 251.0 252.9 527.5 522.9 0.5 0.5 Cape Verde 69.4 70.7 88.7 87.9 0.8 0.8 Central African Republic 263.7 266.5 527.5 522.9 0.5 0.5 Chad 208.0 214.0 527.5 522.9 0.4 0.4 Comoros 226.2 223.6 395.6 392.2 0.6 0.6 Congo, Dem. Rep. 214.3 234.9 473.9 468.3 0.5 0.5 Congo, Rep. 268.8 308.7 527.5 522.9 0.5 0.6 Côte d'Ivoire 287.5 292.6 527.5 522.9 0.5 0.6 Djibouti 84.7 84.9 177.7 177.7 0.5 0.5 Equatorial Guinea 287.4 332.7 527.5 522.9 0.5 0.6 Eritrea 6.1 6.6 15.4 15.4 0.4 0.4 Ethiopia 2.3 2.4 8.7 8.7 0.3 0.3 Gabon 256.2 268.0 527.5 522.9 0.5 0.5 Gambia, The 7.6 7.5 28.6 28.1 0.3 0.3 Ghana 3,720.6 4,064.5 0.9 0.9 0.4 0.4 Guinea 1,219.3 1,623.6 3,644.3 .. 0.3 0.3 Guinea-Bissau 217.3 209.4 527.5 522.9 0.4 0.4 Kenya 29.5 31.3 75.6 72.1 0.4 0.4 Lesotho 3.5 3.5 6.4 6.8 0.5 0.5 Liberia 28.1 29.7 57.1 58.0 0.5 0.5 Madagascar 649.6 700.4 2,003.0 2,142.3 0.3 0.3 Malawi 39.5 45.1 118.4 136.0 0.3 0.3 Mali 240.1 242.2 527.5 522.9 0.5 0.5 Mauritania 98.8 124.3 265.5 .. 0.4 0.5 Mauritius 14.7 14.8 29.5 31.7 0.5 0.5 Mozambique 10,909.4 11,203.4 23.1 25.4 0.5 0.4 Namibia 4.3 4.5 6.4 6.8 0.7 0.7 Niger 226.7 223.7 527.5 522.9 0.4 0.4 Nigeria 60.2 69.8 131.3 128.7 0.5 0.5 Rwanda 186.2 204.1 557.8 551.7 0.3 0.4 São Tomé and Principe 5,558.1 6,456.4 10,558.0 12,448.6 0.5 0.5 Senegal 251.7 252.1 527.5 522.9 0.5 0.5 Seychelles 3.8 3.8 5.5 5.5 0.7 0.7 Sierra Leone 1,074.1 1,161.9 2,889.6 2,961.9 0.4 0.4 Somalia .. .. .. .. .. .. South Africa 3.9 4.0 6.4 6.8 0.6 0.6 Sudan 107.7 111.1 2.4 2.2 0.4 0.5 Swaziland 3.3 3.5 6.4 6.8 0.5 0.5 Tanzania 395.6 399.4 1,128.9 1,251.9 0.4 0.3 Togo 240.4 228.5 527.5 522.9 0.5 0.4 Uganda 619.6 652.4 1,780.7 1,831.5 0.4 0.4 Zambia 2,414.8 2,635.0 4,463.5 3,603.1 0.5 0.7 Zimbabwe .. .. 22.4 164.4 .. .. NORTH AFRICA Algeria 31.0 33.3 73.3 72.6 0.4 0.5 Egypt, Arab Rep. 1.6 1.7 5.8 5.7 0.3 0.3 Libya 0.7 0.8 1.3 1.3 0.5 0.6 Morocco 4.9 4.8 8.9 8.8 0.6 0.5 Tunisia 0.6 0.6 1.3 1.3 0.4 0.4 AFRICA * For a discussion on the new purchase power parity data, and on the exchange rate in Franc Zone countries, see Boxes 5 and 6 in the technical notes. 60 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS Real effective exchange rate Gross domestic product Index 2000=100 PPP $ billions Per capita PPP $ 2005 2006 2005 2006 2005 2006 1,329 1,455 1,741 1,860 .. .. 938 1,031 1,309 1,403 .. .. 690 759 1,199 1,286 .. .. 60 73 3,729 4,435 .. .. 10 11 1,213 1,259 .. .. 22 24 12,088 12,744 .. .. 14 16 1,026 1,084 71.0 73.2 3 3 319 333 109.7 113.2 35 37 1,959 2,043 .. .. 1 1 2,538 2,833 122.3 129.3 3 3 644 679 119.8 126.7 15 15 1,468 1,470 .. .. 1 1 1,127 1,152 29.4 32.8 16 17 268 281 .. .. 12 13 3,309 3,550 116.4 115.9 30 31 1,614 1,632 .. .. 1 2 1,850 1,965 147.7 150.8 14 13 28,536 27,161 .. .. 2 3 544 536 91.2 99.6 47 54 628 700 103.8 100.5 18 19 13,821 14,209 54.5 54.3 2 2 1,078 1,152 109.7 115.3 26 29 1,160 1,247 .. .. 10 10 1,081 1,118 .. .. 1 1 458 467 .. .. 48 52 1,346 1,436 132.8 129.4 3 3 1,311 1,440 .. .. 1 1 313 335 .. .. 16 17 834 878 75.2 73.3 9 10 648 703 .. .. 12 13 1,004 1,058 .. .. 5 6 1,684 1,890 .. .. 12 13 9,975 10,571 .. .. 14 15 677 739 .. .. 9 10 4,599 4,820 .. .. 8 8 584 612 124.2 133.1 245 268 1,731 1,853 .. .. 7 8 772 820 .. .. 0 0 1,417 1,534 .. .. 18 19 1,547 1,592 .. .. 1 1 12,459 13,265 70.9 73.5 3 4 585 631 .. .. .. .. .. .. 108.5 104.2 398 431 8,478 9,087 .. .. 62 71 1,679 1,887 .. .. 5 5 4,462 4,705 .. .. 40 44 1,049 1,126 113.7 112.5 5 5 758 792 88.7 87.8 24 27 846 888 134.7 176.6 14 15 1,183 1,273 .. .. .. .. .. .. 823 894 5,424 5,800 83.2 83.2 242 254 7,370 7,626 .. .. 333 367 4,574 4,953 .. .. 76 83 12,866 13,688 91.8 92.9 107 119 3,554 3,915 85.3 84.6 65 70 6,445 6,958 2,151 2,347 2,350 2,506 NATIONALACCOUNTS Part I. Basic indicators and national accounts 61 Millennium Development Goal 1: ableT3.1 eradicate extreme poverty and hunger* National poverty line Share of population below national Share of urban population below national Share of rural population below national poverty linea (poverty headcount ratio) poverty linea (poverty headcount ratio) poverty linea (poverty headcount ratio) Surveys 1990­99 Surveys 2000­06 Surveys 1990­99 Surveys 2000­06 Surveys 1990­99 Surveys 2000­06 Yearb Percent Yearb Percent Yearb Percent Yearb Percent Yearb Percent Yearb Percent SUB-SAHARAN AFRICA Angola .. .. .. .. .. .. .. .. .. .. .. .. Benin 1999 29.0 .. .. 1999 23.3 .. .. 1999 33.0 .. .. Botswana .. .. .. .. .. .. .. .. .. .. .. .. Burkina Faso 1998 54.6 2003 46.4 1998 22.4 2003 19.2 1998 61.1 2003 52.4 Burundi 1998 68.0 .. .. 1998 66.5 .. .. 1998 64.6 .. .. Cameroon 1996 53.3 2001 40.2 1996 41.4 2001 22.1 1996 59.6 2001 49.9 Cape Verde .. .. .. .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. Chad 1996 64.0 .. .. 1996 63.0 .. .. 1996 67.0 .. .. Comoros .. .. .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. 2005 50.7 .. .. 2005 35.2 .. .. 2005 64.8 Côte d'Ivoire .. .. .. .. .. .. .. .. .. .. .. .. Djibouti .. .. .. .. .. .. .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. .. .. .. .. .. Eritrea 1994 53.0 .. .. .. .. .. .. .. .. .. .. Ethiopia 1996 45.5 2000 44.2 1996 33.3 2000 37.0 1996 47.0 2000 45.0 Gabon .. .. .. .. .. .. .. .. .. .. .. .. Gambia, The 1998 57.6 2003 61.3 1998 48.0 .. .. 1998 61.0 2003 63.0 Ghana 1997 39.5 2005 28.5 1997 19.4 2005 10.8 1997 49.6 2005 39.2 Guinea 1994 40.0 .. .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. 2002 65.7 .. .. .. .. .. .. .. .. Kenya 1997 52.3 2005 45.9 1997 49.2 2005 33.7 1997 52.9 2005 49.1 Lesotho 1999 68.0 .. .. 1993 27.8 .. .. 1993 53.9 .. .. Liberia .. .. .. .. .. .. .. .. .. .. .. .. Madagascar 1999 71.3 .. .. 1999 52.1 .. .. 1999 76.7 .. .. Malawi 1998 65.3 .. .. 1998 54.9 .. .. 1998 66.5 .. .. Mali 1998 63.8 .. .. 1998 30.1 .. .. 1998 75.9 .. .. Mauritania 1996 50.0 2000 46.3 1996 30.1 2000 25.4 1996 65.5 2000 61.2 Mauritius .. .. .. .. .. .. .. .. .. .. .. .. Mozambique 1996 69.4 2002 54.1 1996 62.0 2002 51.5 1996 71.3 2002 55.3 Namibia .. .. .. .. .. .. .. .. .. .. .. .. Niger 1993 63.0 .. .. 1993 52.0 .. .. 1993 66.0 .. .. Nigeria 1992 34.1 2003 54.7 1992 30.4 2003 43.1 1992 36.4 2003 63.8 Rwanda 1993 51.2 2000 60.3 .. .. 2000 14.3 .. .. 2000 65.7 São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. Senegal 1992 33.4 .. .. 1992 23.7 .. .. 1992 40.4 .. .. Seychelles .. .. .. .. .. .. .. .. .. .. .. .. Sierra Leone .. .. 2003 65.9 .. .. 2003 56.4 .. .. 2003 78.5 Somalia .. .. .. .. .. .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. .. .. .. .. .. .. Sudan .. .. .. .. .. .. .. .. .. .. .. .. Swaziland .. .. 2001 69.2 .. .. .. .. .. .. 2001 75.0 Tanzania 1991 38.6 2001 35.7 1991 31.2 2001 29.5 1991 40.8 2001 38.7 Togo .. .. .. .. .. .. .. .. .. .. .. .. Uganda 1999 33.8 2005 31.1 1999 9.6 2005 13.4 1999 37.4 2005 34.2 Zambia 1998 72.9 2006 64.0 1998 56.0 2004 53.0 1998 83.1 2004 78.0 Zimbabwe 1996 34.9 .. .. 1996 7.9 .. .. 1996 48.0 .. .. NORTH AFRICA Algeria 1995 22.6 .. .. 1995 14.7 .. .. 1995 30.3 .. .. Egypt, Arab Rep. 1996 22.9 2000 16.7 1996 22.5 .. .. 1996 23.3 .. .. Libya .. .. .. .. .. .. .. .. .. .. .. .. Morocco 1999 19.0 .. .. 1999 12.0 .. .. 1999 27.2 .. .. Tunisia 1995 7.6 .. .. 1995 3.6 .. .. 1995 13.9 .. .. a. Data are based on expenditure shares, except for Namibia and Swaziland, where data are based on income shares. b. Data are for most recent year available during the period specified. * For a discussion on service delivery in Africa, see Box 7 in the technical notes. 62 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Share of poorest quintile in national Prevalence of child malnutrition, underweight Population below minimum consumption or incomea (% of children under age 5) dietary energy consumption Surveys 1990­99 Surveys 2000­06 Surveys 1990­99 Surveys 2000­06 Share (%) Millions Yearb Percent Yearb Percent Yearb Percent Yearb Percent 2004 2004 .. .. .. .. 1996 37.0 2001 27.5 35 4.8 .. .. 2003 7.4 .. .. 2001 21.5 12 0.8 1993 3.2 .. .. .. .. 2000 10.7 32 0.6 1998 5.9 2003 6.9 .. .. 2003 35.2 15 2.0 1998 5.1 .. .. .. .. 2000 38.9 66 4.5 1996 5.7 2001 5.6 1998 17.8 2004 15.1 26 4.2 .. .. 2001 4.4 .. .. .. .. .. .. 1993 2.0 .. .. 1995 23.3 2000 21.8 44 1.7 .. .. .. .. 1997 34.3 2004 33.9 35 3.0 .. .. .. .. 1996 22.3 2000 25.0 60 0.5 .. .. .. .. .. .. 2001 33.6 74 39.0 .. .. .. .. .. .. 2005 11.8 33 1.2 1998 5.8 2002 5.2 1999 18.2 2006 20.2 13 2.2 .. .. .. .. .. .. 2006 25.6 24 0.2 .. .. .. .. .. .. 2000 15.7 .. .. .. .. .. .. 1996 38.3 2002 34.5 75 3.1 1999 9.1 .. .. .. .. 2005 34.6 46 32.7 .. .. .. .. .. .. 2001 8.8 5 0.1 1998 4.0 2003 4.8 .. .. 2000 15.4 29 0.4 1998 5.6 .. .. 1999 20.3 2003 18.8 11 2.3 .. .. 2003 7.0 1999 21.2 2005 22.5 24 2.0 1993 5.2 .. .. .. .. 2000 21.9 39 0.6 1997 6.0 .. .. 1998 17.6 2003 16.5 31 9.9 1995 1.5 .. .. .. .. 2005 16.6 13 0.2 .. .. .. .. .. .. 2000 22.8 50 1.7 1999 5.9 2001 4.9 1997 35.5 2004 36.8 38 6.6 .. .. 2004 7.0 1992 24.4 2005 18.4 35 4.2 1994 4.6 2001 6.1 1996 38.2 2001 30.1 29 3.8 1996 6.3 2000 6.2 .. .. 2001 30.4 10 0.3 .. .. .. .. .. .. .. .. 5 0.1 1997 5.6 2002 5.4 1997 28.1 2003 21.2 44 8.3 1993 1.4 .. .. 1992 21.5 2000 20.3 24 0.5 1995 2.6 .. .. 1998 45.0 2006 39.9 32 3.9 1996 5.0 2003 5.0 1990 35.1 2003 27.2 9 11.4 .. .. 2000 5.3 1992 24.3 2005 18.0 33 2.8 .. .. .. .. .. .. 2000 10.1 10 .. 1995 6.5 2001 6.6 1993 21.9 2005 14.5 20 2.1 .. .. .. .. .. .. .. .. 9 .. 1990 1.1 2003 6.5 .. .. 2000 24.7 51 2.5 .. .. .. .. .. .. 2006 32.8 .. .. 1995 3.6 2000 3.5 .. .. .. .. 3 .. .. .. .. .. .. .. 2000 38.4 26 8.7 1995 2.7 2000 4.3 .. .. 2000 9.1 22 0.2 1991 7.4 2000 7.3 1999 25.3 2005 16.7 44 16.4 .. .. .. .. 1998 23.2 .. .. 24 1.2 1999 6.0 2002 5.7 1995 21.5 2001 19.0 19 4.8 1998 3.4 2004 3.6 1997 19.6 2002 23.3 46 5.0 1995 4.6 .. .. 1999 11.5 2006 14.0 47 6.0 1995 7.0 .. .. 1995 11.3 2002 10.2 4 1.4 1995 8.8 2004 8.9 .. .. 2005 5.4 4 2.6 .. .. .. .. 1995 4.3 .. .. 3 .. 1998 6.5 .. .. 1992 8.1 2004 9.9 6 1.8 1995 5.6 2000 6.0 .. .. .. .. 3 .. MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 63 Millennium Development Goal 2: ableT3.2 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 2006 1991 2000 2006 1991 2000 2006a 1991 2000 2000­06b SUB-SAHARAN AFRICA Angola 50.3 .. .. 34.7 .. .. .. .. .. .. .. 72.2 Benin 41.1 51.8 80.2 20.7 34.9 64.4 54.8 84.0 71.5 .. .. 45.3 Botswana 88.3 81.6 .. 89.5 89.9 .. 84.0 89.5 .. 89.3 .. 94.0 Burkina Faso 27.0 35.4 46.9 19.5 25.0 31.3 69.7 69.1 72.5 20.2 .. 33.0 Burundi 53.0 42.6 74.6 45.9 24.9 36.3 61.7 56.1 87.9 .. 73.3 73.3 Cameroon 69.4 .. .. 53.0 49.9 51.8 .. .. .. .. .. .. Cape Verde 91.1 97.7 87.8 .. 101.8 92.3 .. .. 91.9 .. .. 96.3 Central African Republic 51.8 .. 45.6 26.7 .. 24.4 23.0 .. 49.8 .. 58.5 58.5 Chad 33.9 53.1 .. 17.9 22.3 .. 50.5 53.9 .. .. 37.6 37.6 Comoros 56.7 55.1 .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. 53.9 .. .. 45.9 .. .. 54.7 .. .. .. .. 70.4 Congo, Rep. 81.9 .. 54.7 54.3 .. 73.2 60.1 .. .. .. .. 97.4 Côte d'Ivoire 44.6 52.2 .. 43.4 39.1 42.8 72.5 87.6 .. .. 60.7 60.7 Djibouti 28.5 26.9 37.8 26.9 28.0 35.5 87.3 .. 89.9 .. .. .. Equatorial Guinea 96.2 90.7 .. .. .. .. .. .. .. .. 94.9 94.9 Eritrea 14.7 37.8 46.5 .. 36.4 48.9 .. 60.5 73.7 .. .. .. Ethiopia 21.9 38.4 65.2 .. 21.6 43.0 18.3 64.6 65.3 .. .. 49.9 Gabon 93.9 .. .. .. .. .. .. .. .. .. .. 96.2 Gambia, The 46.4 64.0 61.8 .. .. 63.0 .. .. .. .. .. .. Ghana 53.6 60.2 63.6 61.2 .. .. 80.5 66.2 .. .. 70.7 70.7 Guinea 27.4 47.9 71.6 17.4 32.8 63.7 58.6 .. 80.9 .. .. 46.6 Guinea-Bissau 37.9 45.2 .. .. 26.9 .. .. .. .. .. .. .. Kenya 75.6 66.2 75.5 .. .. .. 76.7 .. .. .. 80.3 80.3 Lesotho 72.0 77.7 72.4 58.9 60.1 78.3 65.9 66.7 73.7 .. .. .. Liberia .. 66.2 39.5 .. .. 63.4 .. .. .. .. .. 67.4 Madagascar 64.3 64.6 95.9 33.3 35.5 56.9 21.1 .. 35.8 .. 70.2 70.2 Malawi 48.5 .. 91.1 28.7 65.7 55.1 64.4 51.9 44.2 .. .. .. Mali 24.6 .. 60.5 12.6 32.8 49.4 69.7 91.7 81.2 .. .. .. Mauritania 36.4 64.5 79.5 34.1 52.6 47.1 75.3 59.6 57.4 .. 61.3 61.3 Mauritius 91.3 92.9 95.0 106.6 104.6 92.3 97.4 99.3 98.9 .. 94.5 94.5 Mozambique 42.1 56.1 76.0 26.4 16.1 41.8 34.2 51.9 57.6 .. .. .. Namibia 85.9 75.4 76.4 .. 81.6 76.4 62.3 94.2 86.8 88.1 .. 92.3 Niger 24.1 27.2 43.5 17.6 18.4 32.8 62.4 74.0 56.5 .. .. 36.5 Nigeria 55.2 59.7 .. .. .. .. 89.1 .. .. 71.2 .. 84.2 Rwanda 66.9 .. .. 35.4 20.7 .. 59.9 39.1 .. 74.9 77.6 77.6 São Tomé and Principe 95.6 .. 96.2 .. .. 73.9 .. .. .. 93.8 .. 95.4 Senegal 45.3 56.5 70.7 .. 37.7 48.7 84.5 72.3 65.0 .. .. 49.1 Seychelles .. .. .. .. 112.9 .. 92.7 91.0 .. .. .. 99.1 Sierra Leone 43.1 .. .. .. .. .. .. .. .. .. .. 47.9 Somalia 8.9 .. 23.0 .. .. .. .. .. .. .. .. .. South Africa 90.2 91.7 .. 75.8 90.1 .. .. .. .. .. .. .. Sudan 40.4 41.2 53.7 42.0 37.5 .. 93.8 .. .. .. 77.2 77.2 Swaziland 74.8 75.4 .. 59.9 64.3 .. 77.0 73.9 .. .. 88.4 88.4 Tanzania 50.5 53.4 97.8 62.4 .. 74.3 81.3 81.4 85.0 .. .. 78.4 Togo 64.0 76.5 80.1 34.9 61.0 67.2 48.0 73.8 .. .. 74.4 74.4 Uganda 51.1 .. .. .. .. .. 36.0 56.7 .. 69.8 .. 76.6 Zambia 78.0 67.2 92.0 .. 60.1 84.0 .. .. 89.3 .. .. .. Zimbabwe 84.1 83.5 87.8 97.2 .. .. 76.1 .. .. .. .. 97.7 NORTH AFRICA Algeria 88.9 91.6 95.2 79.5 82.6 85.2 94.5 97.2 95.2 .. .. 90.1 Egypt, Arab Rep. 86.2 93.4 93.9 .. 98.1 93.8 .. 99.0 96.2 .. .. 84.9 Libya 93.2 .. .. .. .. .. .. .. .. .. .. 98.0 Morocco 56.1 75.8 88.1 48.1 56.7 84.0 75.1 80.1 80.3 .. .. 70.5 Tunisia 93.5 93.8 96.1 74.2 86.7 119.6 86.4 93.1 96.7 .. .. 94.3 a. Provisional b. Data are for most recent year available during the period specified. 64 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Millennium Development Goal 3: ableT3.3 promote gender equity and empower women Ratio of girls to boys in primary Ratio of young literate females Women in national Share of women employed in and secondary school (%) to males (% ages 15­24) parliament (% of total seats) the nonagricultural sector (%) 1991 2000 2006a 1990 2006a 1990 2000 2006 1990 2000 2004 SUB-SAHARAN AFRICA Angola .. .. .. .. 75.4 15.0 16.0 15.0 .. .. .. Benin 49.5 64.2 .. .. 56.1 3.0 6.0 7.2 46.0 .. .. Botswana 108.7 101.6 .. .. 103.8 5.0 .. 11.1 33.5 .. 39.5 Burkina Faso 62.3 70.0 79.9 .. 65.6 .. 8.0 11.7 12.5 .. .. Burundi 81.8 .. 88.6 80.7 91.6 .. 6.0 30.5 13.3 .. .. Cameroon 83.0 .. 82.8 .. .. 14.0 6.0 8.9 20.7 .. .. Cape Verde .. .. 103.1 96.1 101.0 12.0 11.0 15.3 .. 38.9 .. Central African Republic 59.8 .. .. .. 66.6 4.0 7.0 10.5 30.4 .. .. Chad 41.6 55.9 .. .. 41.7 .. 2.0 6.5 3.8 .. .. Comoros 71.1 84.1 .. .. .. .. .. 3.0 .. .. .. Congo, Dem. Rep. .. .. .. .. 80.9 5.0 .. 8.4 26.1 .. .. Congo, Rep. 85.1 84.5 .. .. 98.1 14.0 12.0 8.5 26.1 .. .. Côte d'Ivoire 65.3 69.1 .. .. 73.6 6.0 .. 8.5 .. .. .. Djibouti 70.5 71.0 75.8 .. .. .. .. 10.8 .. .. .. Equatorial Guinea .. 86.3 .. .. 100.2 13.0 5.0 18.0 10.5 .. .. Eritrea .. 77.4 72.0 .. .. .. 15.0 22.0 .. .. .. Ethiopia 68.4 65.1 80.5 .. 61.9 .. 2.0 21.9 .. .. 40.6 Gabon .. 95.8 .. .. 97.7 13.0 8.0 9.2 37.7 .. .. Gambia, The 65.6 81.7 101.8 .. .. 8.0 2.0 13.2 20.9 .. .. Ghana 78.5 89.4 93.9 .. 86.2 .. 9.0 10.9 56.5 .. .. Guinea 44.9 61.3 74.4 .. 57.4 .. 9.0 19.3 30.3 .. .. Guinea-Bissau .. 65.0 .. .. .. 20.0 .. 14.0 10.8 .. .. Kenya 93.6 97.6 96.1 .. 101.1 1.0 4.0 7.3 21.4 .. .. Lesotho 123.5 107.2 103.9 .. .. .. 4.0 11.7 .. .. .. Liberia .. 72.7 .. .. 106.4 .. .. 12.5 .. .. .. Madagascar 97.5 .. 96.0 .. 93.9 7.0 8.0 6.9 .. 43.7 .. Malawi 81.3 92.6 100.0 .. .. 10.0 8.0 13.6 10.5 .. .. Mali 57.1 68.5 74.4 .. .. .. 12.0 10.2 .. .. 49.7 Mauritania 71.3 95.0 101.5 .. 81.9 .. 4.0 .. .. 35.8 .. Mauritius 101.6 98.2 .. 101.1 101.7 7.0 8.0 17.1 36.7 38.6 37.5 Mozambique 71.5 74.9 84.7 .. .. 16.0 .. 34.8 11.4 .. .. Namibia 106.4 103.3 103.8 .. 102.6 7.0 22.0 26.9 .. 47.5 .. Niger 53.3 65.8 70.5 .. 44.2 5.0 1.0 12.4 11.0 .. .. Nigeria 77.2 .. .. .. 93.5 .. .. 6.4 .. 18.6 21.0 Rwanda 92.1 96.1 .. .. 97.9 17.0 17.0 48.8 .. 33.0 .. São Tomé and Principe .. .. 99.0 .. 98.8 12.0 9.0 7.3 .. 34.8 36.8 Senegal 68.8 82.0 92.4 .. 70.0 13.0 12.0 19.2 .. .. .. Seychelles .. 101.4 .. .. 100.6 16.0 24.0 29.4 .. .. .. Sierra Leone 66.8 .. .. .. 62.7 .. 9.0 14.5 .. .. 23.2 Somalia .. 55.0 .. .. .. 4.0 .. 7.8 21.7 .. .. South Africa 103.9 100.4 .. .. .. 3.0 30.0 32.8 .. .. 42.7 Sudan 77.5 .. 89.3 .. 84.4 .. .. 14.7 22.2 .. .. Swaziland 97.7 95.4 .. .. 103.2 4.0 3.0 10.8 .. .. .. Tanzania 96.7 .. .. .. 94.2 .. 16.0 30.4 .. .. .. Togo 58.9 68.8 .. .. 76.0 5.0 .. 8.6 41.0 .. .. Uganda 81.7 92.8 .. .. 86.1 12.0 18.0 29.8 .. .. .. Zambia .. 91.3 .. 97.4 .. 7.0 10.0 14.6 29.4 .. .. Zimbabwe 92.1 94.5 97.2 .. 100.5 11.0 14.0 16.0 15.4 20.4 .. NORTH AFRICA Algeria 82.9 .. .. .. 91.6 2.0 3.0 6.2 .. 13.0 14.4 Egypt, Arab Rep. 81.4 92.4 .. .. 87.6 4.0 2.0 2.0 20.5 19.0 .. Libya .. .. 105.4 .. 96.9 .. .. 7.7 .. .. .. Morocco 69.6 82.4 .. .. 74.9 .. 1.0 10.8 24.8 21.7 22.3 Tunisia 86.1 100.0 103.7 .. 95.7 4.0 12.0 22.8 .. 24.6 .. a. Provisional MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 65 Millennium Development Goal 4: ableT3.4 reduce child mortality Child immunization rate, measles Under­five mortality rate (per 1,000) Infant mortality rate (per 1,000 live births) (% of children ages 12­23 months) 1990 2000 2006 1990 2000 2006 1990 2000 2006 SUB-SAHARAN AFRICA Angola 260 260 260 154 154 154 38 41 48 Benin 185 160 148 111 95 88 79 68 89 Botswana 58 101 124 45 74 90 87 90 90 Burkina Faso 206 194 204 123 116 122 79 59 88 Burundi 190 181 181 114 109 109 74 75 75 Cameroon 139 151 149 85 88 87 56 49 73 Cape Verde 60 42 34 45 31 25 79 80 65 Central African Republic 173 186 175 114 120 115 83 36 35 Chad 201 205 209 120 122 124 32 28 23 Comoros 120 84 68 88 62 51 87 70 66 Congo, Dem. Rep. 205 205 205 129 129 129 38 46 73 Congo, Rep. 103 117 126 67 74 79 75 34 66 Côte d'Ivoire 153 136 127 105 95 90 56 73 73 Djibouti 175 147 130 116 97 86 85 50 67 Equatorial Guinea 170 200 206 103 120 124 88 51 51 Eritrea 147 97 74 88 61 48 .. 86 95 Ethiopia 204 151 123 122 92 77 38 52 63 Gabon 92 91 91 60 60 60 76 55 55 Gambia, The 153 132 113 103 94 84 86 85 95 Ghana 120 113 120 76 72 76 61 84 85 Guinea 235 184 161 139 111 98 35 42 67 Guinea-Bissau 240 218 200 142 129 119 53 59 60 Kenya 97 117 121 64 77 79 78 75 77 Lesotho 101 108 132 81 86 102 80 74 85 Liberia 235 235 235 157 157 157 .. 52 94 Madagascar 168 137 115 103 84 72 47 56 59 Malawi 221 155 120 131 95 76 81 73 85 Mali 250 224 217 140 124 119 43 49 86 Mauritania 133 125 125 85 79 78 38 62 62 Mauritius 23 18 14 20 16 13 76 84 99 Mozambique 235 178 138 158 122 96 59 71 77 Namibia 86 69 61 60 50 45 57 69 63 Niger 320 270 253 191 159 148 25 34 47 Nigeria 230 207 191 120 107 99 54 35 62 Rwanda 176 183 160 106 110 98 83 74 95 São Tomé and Principe 100 97 96 65 64 63 71 69 85 Senegal 149 133 116 72 66 60 51 48 80 Seychelles 19 15 13 17 13 12 86 97 99 Sierra Leone 290 277 270 169 162 159 .. 37 67 Somalia 203 165 145 121 100 90 30 38 35 South Africa 60 63 69 45 50 56 79 77 85 Sudan 120 97 89 74 65 61 57 58 73 Swaziland 110 142 164 78 98 112 85 72 57 Tanzania 161 141 118 102 88 74 80 78 93 Togo 149 124 108 88 78 69 73 58 83 Uganda 160 145 134 93 85 78 52 61 89 Zambia 180 182 182 101 102 102 90 85 84 Zimbabwe 76 105 105 52 68 68 87 70 90 NORTH AFRICA Algeria 69 44 38 54 37 33 83 80 91 Egypt, Arab Rep. 91 51 35 67 40 29 86 98 98 Libya 41 22 18 35 20 17 89 92 98 Morocco 89 54 37 69 45 34 79 93 95 Tunisia 52 31 23 41 25 19 93 95 98 66 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Millennium Development Goal 5: ableT3.5 improve maternal health Maternal mortality ratio, Maternal mortality ratio Births attended by skilled health staff (% of total) modeled estimate (national estimate, (per 100,000 live births) per 100,000 live births) Surveys 1990­99 Surveys 2000­06 2005 2000­06a Yeara Percent Yeara Percent SUB-SAHARAN AFRICA Angola 1,400 .. 1996 23 2001 45 Benin 840 .. 1996 60 2006 79 Botswana 380 .. 1996 87 2000 94 Burkina Faso 700 .. 1999 31 2005 54 Burundi 1,100 615 .. .. 2005 34 Cameroon 1,000 669 1998 55 2006 63 Cape Verde 210 76 1998 89 .. .. Central African Republic 980 543 1995 46 2006 53 Chad 1,500 1,099 1997 15 2004 14 Comoros 400 380 1996 52 2000 62 Congo, Dem. Rep. 1,100 1,289 .. .. 2001 61 Congo, Rep. 740 781 .. .. 2005 86 Côte d'Ivoire 810 543 1999 47 2006 57 Djibouti 650 .. .. .. 2006 93 Equatorial Guinea 680 .. 1994 5 2000 65 Eritrea 450 .. 1995 21 2002 28 Ethiopia 720 673 .. .. 2005 6 Gabon 520 519 .. .. 2000 86 Gambia, The 690 730 1990 44 2006 57 Ghana 560 .. 1998 44 2006 50 Guinea 910 980 1999 35 2005 38 Guinea-Bissau 1,100 405 1995 25 2006 39 Kenya 560 414 1998 44 2003 42 Lesotho 960 762 1993 50 2004 55 Liberia 1,200 .. .. .. 2000 51 Madagascar 510 469 1997 47 2004 51 Malawi 1,100 984 1992 55 2006 54 Mali 970 582 1996 40 2001 41 Mauritania 820 747 1991 40 2001 57 Mauritius 15 22 1999 99 2005 99 Mozambique 520 408 1997 44 2003 48 Namibia 210 271 1992 68 2000 76 Niger 1,800 648 1998 18 2006 18 Nigeria 1,100 .. 1999 42 2003 36 Rwanda 1,300 750 1992 26 2005 39 São Tomé and Principe .. 148 .. .. 2006 81 Senegal 980 434 1999 48 2005 52 Seychelles .. 57 .. .. .. .. Sierra Leone 2,100 1,800 .. .. 2005 43 Somalia 1,400 1,044 1999 32 2006 33 South Africa 400 .. 1998 84 2003 92 Sudan 450 .. 1999 57 2006 49 Swaziland 390 .. 1994 56 2002 74 Tanzania 950 578 1999 44 2005 43 Togo 510 .. 1998 51 2006 62 Uganda 550 505 1995 38 2006 42 Zambia 830 729 1999 47 2002 43 Zimbabwe 880 555 1999 73 2006 80 NORTH AFRICA Algeria 180 .. 1992 77 2006 95 Egypt, Arab Rep. 130 84 1998 55 2005 74 Libya 97 .. 1999 99 .. .. Morocco 240 227 1995 40 2004 63 Tunisia 100 .. 1995 81 2000 90 a. Data are for most recent year available during the period specified. MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 67 Millennium Development Goal 6: ableT3.6 combat HIV/AIDS, malaria, and other diseases Deaths due to malaria Prevalence of HIV Contraceptive prevalence (% of women ages 15­49) (per 100,000 people) (% of ages 15­49) Surveys 1990­99 Surveys 2000­06 Surveys 2000­06 2007 Yeara Percent Yeara Percent Yeara Number SUB-SAHARAN AFRICA Angola 2.1 1996 8.1 2001 6.2 2000 354 Benin 1.2 1996 16.4 2006 17.2 2000 177 Botswana 23.9 .. .. 2000 44.4 2000 15 Burkina Faso 1.6 1999 11.9 2006 17.4 2000 292 Burundi 2.0 .. .. 2005 9.1 2000 143 Cameroon 5.1 1998 19.3 2006 29.2 2000 108 Cape Verde .. 1998 52.9 .. .. 2000 22 Central African Republic 6.3 1995 14.8 2006 19.0 2000 137 Chad 3.5 1997 4.2 2004 2.8 2000 207 Comoros <0.1 1996 21.0 2000 25.7 2000 80 Congo, Dem. Rep. .. 1991 7.7 2001 31.4 2000 224 Congo, Rep. 3.5 .. .. 2005 44.3 2000 78 Côte d'Ivoire 3.9 1999 15.0 2006 12.9 2000 76 Djibouti 3.1 .. .. 2006 17.8 .. .. Equatorial Guinea 3.4 .. .. .. .. 2000 152 Eritrea 1.3 1995 8.0 2002 8.0 2000 74 Ethiopia 2.1 1990 4.3 2005 14.7 2000 198 Gabon 5.9 .. .. 2000 32.7 2000 80 Gambia, The 0.9 1990 11.8 2001 17.5 2000 52 Ghana 1.9 1999 22.0 2006 16.7 2000 70 Guinea 1.6 1999 6.2 2005 9.1 2000 200 Guinea-Bissau 1.8 .. .. 2006 10.3 2000 150 Kenya .. 1998 39.0 2003 39.3 2000 63 Lesotho 23.2 1992 23.2 2004 37.3 2000 84 Liberia 1.7 .. .. 2000 10.0 2000 201 Madagascar 0.1 1997 19.3 2004 27.1 2000 184 Malawi 11.9 1996 21.9 2006 41.7 2000 275 Mali 1.5 1996 6.7 2001 8.1 2000 454 Mauritania 0.8 1991 3.3 2001 8.0 2000 108 Mauritius 1.7 1999 26.0 2002 75.9 .. .. Mozambique 12.5 1997 5.6 2004 16.5 2000 232 Namibia 15.3 1992 28.9 2000 43.7 2000 52 Niger 0.8 1998 8.2 2006 11.2 2000 469 Nigeria 3.1 1999 15.3 2003 12.6 2000 141 Rwanda 2.8 1996 13.7 2005 17.4 2000 200 São Tomé and Principe .. .. .. 2006 30.3 2000 80 Senegal 1.0 1999 10.5 2005 11.8 2000 72 Seychelles .. .. .. .. .. .. .. Sierra Leone 1.7 .. .. 2005 5.3 2000 312 Somalia 0.5 1999 7.9 2006 14.6 2000 81 South Africa 18.1 1998 56.3 2003 60.3 2000 .. Sudan 1.4 1993 9.9 2006 7.6 2000 70 Swaziland 26.1 .. .. 2002 48.1 2000 .. Tanzania 6.2 1999 25.4 2005 26.4 2000 130 Togo 3.3 1999 23.5 2006 16.8 2000 47 Uganda 5.4 1995 14.8 2006 23.7 2000 152 Zambia 15.2 1999 22.0 2002 34.2 2000 141 Zimbabwe 15.3 1999 53.5 2006 60.2 2000 1 NORTH AFRICA Algeria 0.1 1995 52.0 2006 61.4 .. .. Egypt, Arab Rep. .. 1998 51.7 2005 59.2 .. .. Libya .. 1995 45.2 .. .. .. .. Morocco 0.1 1997 58.8 2004 63.0 .. .. Tunisia 0.1 1995 60.0 2001 62.6 .. .. a. Data are for most recent year available during the period specified. 68 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Children sleeping under insecticide­treated Tuberculosis cases detected under DOTS bednets (% of children under age 5) Incidence of tuberculosis (per 100,000 people) (% of estimated cases) Surveys 2000­06 Surveys 1990­99 Surveys 2000­06 Surveys 1990­99 Surveys 2000­06 Yeara Percent Yeara Number Yeara Number Yeara Percent Yeara Percent 2001 2.3 1999 245.8 2006 285.3 1999 49.7 2006 75.8 2006 20.1 1999 83.8 2006 89.9 1999 85.5 2006 85.9 .. .. 1999 573.1 2006 550.5 1999 70.8 2006 80.2 2006 9.6 1999 177.6 2006 248.5 1999 16.0 2006 17.2 2005 8.3 1999 293.7 2006 366.9 1999 34.8 2006 23.5 2006 13.1 1999 153.4 2006 191.6 1999 19.4 2006 91.2 .. .. 1999 165.5 2006 168.4 .. .. 2006 33.3 2006 15.1 1999 276.1 2006 345.0 1996 58.4 2006 68.6 2000 0.6 1999 239.0 2006 298.6 1999 33.9 2005 18.6 2000 9.3 1999 58.7 2006 43.8 1998 54.4 2006 41.6 2001 0.7 1999 313.4 2006 391.6 1999 51.0 2006 60.6 .. .. 1999 322.6 2006 403.0 1998 51.7 2006 51.2 2006 5.9 1999 336.5 2006 420.4 1999 41.4 2006 37.1 2006 1.3 1999 697.0 2006 809.0 1999 71.6 2006 39.6 2000 0.7 1999 204.8 2006 255.8 1998 82.8 2004 74.4 2002 4.2 1999 83.6 2006 93.8 1999 39.8 2006 34.7 2005 1.5 1999 302.6 2006 378.1 1999 23.9 2006 27.0 .. .. 1999 210.1 2006 353.6 .. .. 2006 57.5 2006 49.0 1999 221.7 2006 257.3 1998 72.2 2006 63.9 2006 21.8 1999 211.8 2006 202.9 1999 30.3 2006 37.6 2005 0.3 1999 188.7 2006 264.9 1999 52.5 2006 54.6 2006 39.0 1999 188.6 2006 218.9 .. .. 2006 64.3 2003 4.6 1999 396.5 2006 384.5 1999 58.2 2006 70.0 .. .. 1999 519.1 2006 635.1 1998 72.8 2006 79.1 2005 2.6 1999 265.2 2006 331.3 1998 40.2 2006 55.1 2004 0.2 1999 213.4 2006 247.8 1998 67.2 2006 73.2 2006 23.0 1999 417.3 2006 377.1 1999 45.5 2006 42.0 2003 8.4 1999 289.4 2006 279.6 1999 18.9 2006 25.5 2004 2.1 1999 272.5 2006 316.3 .. .. 2006 34.5 .. .. 1999 24.2 2006 22.7 1999 95.5 2006 67.1 .. .. 1999 354.3 2006 442.7 1999 47.6 2006 46.9 2000 3.4 1999 613.6 2006 766.6 1999 79.5 2006 82.9 2006 7.4 1999 149.6 2006 173.6 1999 36.6 2006 49.5 2003 1.2 1999 248.6 2006 310.6 1999 11.9 2006 20.2 2000 5.0 1999 317.7 2006 396.9 1999 44.5 2006 27.4 2006 41.7 1999 115.9 2006 102.8 .. .. .. .. 2005 7.1 1999 232.9 2006 270.4 1999 47.6 2005 48.2 .. .. 1999 37.1 2006 32.9 1998 67.1 2005 62.1 2005 5.3 1999 351.6 2006 517.0 1998 35.5 2006 35.0 2006 9.2 1999 262.0 2006 218.4 1999 42.9 2006 83.0 .. .. 1999 478.8 2006 940.2 1999 61.3 2006 71.2 2006 27.6 1999 208.7 2006 242.2 1999 26.9 2006 30.0 2000 0.1 1999 690.5 2006 1155.3 .. .. 2006 48.9 2005 16.0 1999 326.5 2006 312.1 1999 52.0 2006 46.4 2006 38.4 1999 360.5 2006 388.8 1999 10.9 2006 19.4 2006 9.7 1999 324.1 2006 354.7 1999 55.7 2006 44.3 2006 22.8 1999 603.3 2006 552.6 .. .. 2006 52.5 2006 2.9 1999 613.1 2006 557.3 1999 47.5 2006 42.4 .. .. 1999 46.8 2006 56.1 1997 132.5 2006 101.6 .. .. 1999 31.7 2006 24.0 1999 31.7 2006 59.3 .. .. 1999 23.2 2006 17.5 1999 146.8 2006 156.3 .. .. 1999 114.4 2006 93.3 1999 91.6 2006 94.9 .. .. 1999 26.8 2006 24.7 1999 93.5 2006 81.4 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 69 Millennium Development Goal 7: ableT3.7 ensure environmental sustainability Nationally protected areas GDP per unit of energy use (constant Forest area (% of total land area) (% of total land area) 2000 PPP $ per kg of oil equivalent) 1990 2000 2005 2004 1990 2000 2005 SUB-SAHARAN AFRICA Angola 48.9 47.9 47.4 10.1 5.4 4.6 6.1 Benin 30.0 24.2 21.3 23.9 3.2 4.2 4.0 Botswana 24.2 22.1 21.1 30.9 7.3 9.2 11.7 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 5.0 4.6 5.0 Cape Verde 14.3 20.4 20.7 0.2 .. .. .. Central African Republic 37.2 36.8 36.5 16.6 .. .. .. Chad 10.4 9.8 9.5 9.5 .. .. .. Comoros 6.4 4.3 3.0 21.7 .. .. .. Congo, Dem. Rep. 62.0 59.6 58.9 8.6 1.9 0.9 0.9 Congo, Rep. 66.5 66.1 65.8 18.0 8.0 11.4 10.0 Côte d'Ivoire 32.1 32.5 32.7 17.1 5.4 4.4 3.8 Djibouti 0.2 0.2 0.2 0.4 .. .. .. Equatorial Guinea 66.3 60.9 58.2 16.2 .. .. .. Eritrea .. 15.6 15.4 5.0 .. .. .. Ethiopia 15.2 13.7 13.0 18.6 1.7 1.8 2.2 Gabon 85.1 84.7 84.5 3.4 11.2 10.6 10.4 Gambia, The 44.2 46.1 47.1 3.5 .. .. .. Ghana 32.7 26.8 24.2 16.2 2.5 2.6 2.9 Guinea 30.1 28.1 27.4 6.4 .. .. .. Guinea-Bissau 78.8 75.4 73.7 .. .. .. .. Kenya 6.5 6.3 6.2 12.6 2.7 2.7 2.8 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 25.4 24.8 24.5 5.8 0.8 1.1 1.4 Namibia 10.6 9.8 9.3 5.6 .. 7.1 6.7 Niger 1.5 1.0 1.0 7.7 .. .. .. Nigeria 18.9 14.4 12.2 6.0 1.9 2.0 2.4 Rwanda 12.9 13.9 19.5 7.9 .. .. .. São Tomé and Principe 28.5 28.5 28.5 .. .. .. .. Senegal 48.6 46.2 45.0 11.2 4.8 5.5 6.0 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 3.0 3.0 3.1 Sudan 32.1 29.7 28.4 5.2 2.5 3.4 3.4 Swaziland 27.4 30.1 31.5 3.5 .. .. .. Tanzania 46.8 42.1 39.8 42.3 2.2 2.2 2.0 Togo 12.6 8.9 7.1 11.9 2.4 2.4 2.4 Uganda 25.0 20.6 18.4 32.6 .. .. .. Zambia 66.1 60.1 57.1 42.0 1.8 1.7 1.9 Zimbabwe 57.5 49.4 45.3 14.9 .. .. .. NORTH AFRICA Algeria 0.8 0.9 1.0 5.0 6.8 6.5 7.0 Egypt, Arab Rep. 0.0 0.1 0.1 5.6 5.7 6.1 5.4 Libya 0.1 0.1 0.1 0.1 .. 3.8 4.0 Morocco 9.6 9.7 9.8 1.1 9.9 8.5 7.8 Tunisia 4.1 6.2 6.8 1.5 5.9 6.9 7.6 a. Data are for most recent year available during the period specified. 70 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Carbon dioxide emissions Solid fuels use Population with sustainable Population with sustainable (metric tons per capita) (% of population) access to improved water source (%) access to improved sanitation (%) 1990 2000 2004 2000­06a 1990 2000 2006 1990 2000 2006 0.4 0.5 0.5 .. 39.0 44.0 51.0 26.0 40.0 50.0 0.1 0.2 0.3 95.6 63.0 64.0 65.0 12.0 24.0 30.0 1.6 2.5 2.4 .. 93.0 95.0 96.0 38.0 45.0 47.0 0.1 0.1 0.1 97.5 34.0 56.0 72.0 5.0 9.0 13.0 0.0 0.0 0.0 .. 70.0 71.0 71.0 44.0 42.0 41.0 0.1 0.2 0.2 82.6 49.0 63.0 70.0 39.0 47.0 51.0 0.2 0.4 0.6 .. .. 80.0 .. .. 41.0 .. 0.1 0.1 0.1 .. 58.0 63.0 66.0 11.0 22.0 31.0 0.0 0.0 0.0 .. .. 34.0 48.0 5.0 7.0 9.0 0.2 0.2 0.1 .. 93.0 88.0 85.0 18.0 29.0 35.0 0.1 0.0 0.0 .. 43.0 45.0 46.0 15.0 25.0 31.0 0.5 0.7 1.0 83.2 .. 70.0 71.0 .. 20.0 20.0 0.4 0.3 0.3 .. 67.0 75.0 81.0 20.0 22.0 24.0 0.6 0.5 0.5 .. 76.0 83.0 92.0 .. 65.0 67.0 0.3 0.6 11.5 .. 43.0 43.0 43.0 51.0 51.0 51.0 .. 0.2 0.2 .. 43.0 54.0 60.0 3.0 4.0 5.0 0.1 0.1 0.1 89.0 13.0 29.0 42.0 4.0 7.0 11.0 6.5 1.2 1.1 34.1 .. 85.0 87.0 .. 36.0 36.0 0.2 0.2 0.2 .. .. 86.0 86.0 .. 49.0 52.0 0.2 0.3 0.3 91.8 56.0 72.0 80.0 6.0 9.0 10.0 0.2 0.2 0.2 79.8 45.0 61.0 70.0 13.0 16.0 19.0 0.2 0.2 0.2 .. .. 58.0 57.0 .. 30.0 33.0 0.2 0.3 0.3 87.1 41.0 51.0 57.0 39.0 41.0 42.0 .. .. .. 62.1 .. 77.0 78.0 .. 34.0 36.0 0.2 0.1 0.1 .. 57.0 63.0 64.0 40.0 32.0 32.0 0.1 0.1 0.2 98.3 39.0 45.0 47.0 8.0 11.0 12.0 0.1 0.1 0.1 97.8 41.0 63.0 76.0 46.0 55.0 60.0 0.1 0.1 0.1 95.9 33.0 51.0 60.0 35.0 42.0 45.0 1.4 1.0 0.9 70.5 37.0 50.0 60.0 20.0 22.0 24.0 1.4 2.3 2.6 .. 100.0 100.0 100.0 94.0 94.0 94.0 0.1 0.1 0.1 96.9 .. 41.0 42.0 .. 27.0 31.0 0.0 0.9 1.2 65.9 57.0 81.0 93.0 26.0 32.0 35.0 0.1 0.1 0.1 .. 41.0 41.0 42.0 3.0 5.0 7.0 0.5 0.7 0.8 76.6 50.0 49.0 47.0 26.0 28.0 30.0 0.1 0.1 0.1 99.4 65.0 65.0 65.0 29.0 25.0 23.0 0.6 0.6 0.6 .. .. 82.0 86.0 .. 22.0 24.0 0.4 0.4 0.4 58.7 67.0 72.0 77.0 26.0 27.0 28.0 1.6 7.0 6.6 .. .. 87.0 .. .. .. .. 0.1 0.1 0.2 .. .. 57.0 53.0 .. 12.0 11.0 0.0 .. .. .. .. 23.0 29.0 .. 21.0 23.0 9.4 9.0 9.4 .. 81.0 89.0 93.0 55.0 57.0 59.0 0.2 0.2 0.3 .. 64.0 69.0 70.0 33.0 34.0 35.0 0.6 1.0 0.9 .. .. 59.0 60.0 .. 50.0 50.0 0.1 0.1 0.1 98.1 49.0 53.0 55.0 35.0 34.0 33.0 0.2 0.3 0.4 .. 49.0 55.0 59.0 13.0 12.0 12.0 0.0 0.1 0.1 97.4 43.0 56.0 64.0 29.0 32.0 33.0 0.3 0.2 0.2 83.6 50.0 54.0 58.0 42.0 49.0 52.0 1.6 1.2 0.8 .. 78.0 80.0 81.0 44.0 45.0 46.0 3.0 6.4 6.0 .. 94.0 89.0 85.0 88.0 92.0 94.0 1.4 2.1 2.2 .. 94.0 97.0 98.0 50.0 61.0 66.0 8.7 10.3 10.3 .. 71.0 71.0 .. 97.0 97.0 97.0 1.0 1.2 1.4 .. 75.0 80.0 83.0 52.0 65.0 72.0 1.6 2.1 2.3 .. 82.0 90.0 94.0 74.0 81.0 85.0 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 71 Millennium Development Goal 8: ableT3.8 develop a global partnership for development Debt sustainability Heavily indebted Poor Country (HIPC) Debt Initiative Debt service relief Public and publicly guaranteed Decision point Completion point committed ($ millions) debt service (% of exports) 2006 2006 2006 1990 2000 2004­06a SUB-SAHARAN AFRICA Angola .. .. .. 7.1 20.4 12.6 Benin Jul. 2000 Mar. 2003 460 8.6 10.9 7.5 Botswana .. .. .. 4.3 2.0 0.9 Burkina Faso Jul. 2000 Apr. 2002 930 7.7 15.1 10.1 Burundi Oct. 2000 .. 1,472 40.7 25.1 40.0 Cameroon Oct. 2000 Apr. 2006 4,917 12.4 13.9 12.7 Cape Verde Sep. 2008 .. .. 8.9 10.5 5.5 Central African Republic .. .. .. 7.5 .. .. Chad May 2001 .. 260 2.4 .. .. Comoros .. .. .. 2.5 .. .. Congo, Dem. Rep. Jul. 2003 Floating 10,389 .. .. .. Congo, Rep. Mar. 2006 Floating 2,881 31.6 0.5 2.0 Côte d'Ivoire Mar. 1998 .. .. 14.7 14.9 0.2 Djibouti .. .. .. .. 4.8 5.9 Equatorial Guinea .. .. .. 2.5 .. .. Eritrea .. .. .. .. 2.8 2.8 Ethiopia Nov. 2001 Apr. 2004 3,275 33.1 12.2 7.1 Gabon .. .. .. 3.8 8.8 4.6 Gambia, The Dec. 2000 Dec. 2008 90 17.9 .. 13.7 Ghana Feb. 2002 Jul. 2004 3,500 19.9 12.0 4.5 Guinea Dec. 2000 Floating 800 17.7 17.3 18.1 Guinea-Bissau Dec. 2000 Floating 790 22.0 .. 46.4 Kenya .. .. .. 22.7 15.7 6.5 Lesotho .. .. .. 4.1 10.3 4.0 Liberia .. .. .. .. .. 0.0 Madagascar Dec. 2000 Oct. 2004 1,900 31.9 8.4 4.9 Malawi Dec. 2000 Aug.2006 1,000 22.4 10.8 5.8 Mali Sep. 2000 Mar.2003 895 9.7 10.2 4.2 Mauritania Feb. 2000 Jun. 2002 1,100 24.8 .. .. Mauritius .. .. .. 4.5 16.3 5.4 Mozambique Apr. 2000 Sep. 2001 4,300 17.2 7.0 1.8 Namibia .. .. .. .. .. .. Niger Dec. 2000 Apr. 2004 1,190 3.2 6.0 4.0 Nigeria .. .. .. 22.3 8.2 16.7 Rwanda Dec. 2000 Apr. 2005 1,316 9.4 14.8 10.0 São Tomé and Principe Dec. 2000 Mar. 2008 200 28.6 21.0 39.2 Senegal Jun. 2000 Apr. 2004 850 13.7 13.2 12.0 Seychelles .. .. .. 7.6 3.3 18.2 Sierra Leone Mar. 2002 Dec. 2006 950 7.8 29.6 8.4 Somalia .. .. .. .. .. South Africa .. .. .. 5.5 3.2 Sudan .. .. 4.5 10.1 4.5 Swaziland .. .. 5.6 2.3 1.7 Tanzania Apr. 2000 Nov. 2001 3,000 25.1 10.8 3.0 Togo .. .. 8.6 3.2 0.5 Uganda Feb. 2000 May. 2000 1,950 47.1 6.5 6.4 Zambia Dec. 2000 Apr. 2005 3,900 12.6 17.4 2.1 Zimbabwe .. .. .. 18.2 .. .. NORTH AFRICA Algeria .. .. .. 63.3 .. .. Egypt, Arab Rep. .. .. .. 23.2 8.5 5.3 Libya .. .. .. .. .. .. Morocco .. .. .. 23.1 23.0 9.0 Tunisia .. .. .. 23.9 20.5 14.6 a. Data are for most recent year available during the period specified. 72 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS Youth unemployment rate (ages 15­24) Information and communications Total Male Female Share of total Share of male Share of female Fixed line and mobile phone Personal computers Internet users labor force labor force labor force subscribers (per 100 people) (per 100 people) (per 100 people) Year Percent Year Percent Year Percent 1990 2000 2006 1990 2000 2005­06 1995 2000 2006 .. .. .. .. .. .. 0.7 0.7 14.3 .. 0.1 0.7 .. 0.1 0.6 .. .. .. .. .. .. 0.3 1.5 12.9 .. 0.1 0.6 .. 0.2 1.4 2001 39.6 2001 33.9 2001 46.1 1.9 20.7 51.4 .. 3.5 4.8 0.1 2.9 4.3 .. .. .. .. .. .. 0.2 0.7 7.7 0.0 0.1 0.6 .. 0.1 0.6 .. .. .. .. .. .. 0.1 0.5 2.9 .. 0.1 0.8 .. 0.1 0.7 .. .. .. .. .. .. 0.3 1.3 18.0 .. 0.3 1.1 .. 0.3 2.0 .. .. .. .. .. .. 2.3 16.5 34.8 .. 5.5 12.0 .. 1.8 6.4 .. .. .. .. .. .. 0.2 0.4 2.9 .. 0.2 0.3 .. 0.1 0.3 .. .. .. .. .. .. 0.1 0.2 4.6 .. 0.1 0.2 .. 0.0 0.6 .. .. .. .. .. .. 0.8 1.3 9.1 0.0 0.6 0.9 .. 0.3 3.4 .. .. .. .. .. .. 0.1 0.0 7.3 .. .. 0.0 .. 0.0 0.3 .. .. .. .. .. .. 0.7 2.9 .. .. 0.3 0.5 .. 0.0 1.9 .. .. .. .. .. .. 0.6 4.3 22.9 .. 0.5 1.7 0.0 0.2 1.6 .. .. .. .. .. .. 1.0 1.4 .. 0.2 0.9 2.4 0.0 0.2 1.3 .. .. .. .. .. .. 0.4 2.6 .. .. 0.5 1.9 .. 0.2 1.6 .. .. .. .. .. .. .. 0.8 2.1 .. 0.2 0.6 .. 0.1 2.1 2005 7.7 2005 4.1 2005 11.2 0.3 0.4 2.1 .. 0.1 0.6 0.0 0.0 0.3 .. .. .. .. .. .. 2.3 13.4 71.3 .. 1.0 3.6 .. 1.3 6.2 .. .. .. .. .. .. 0.6 2.8 27.1 .. 1.1 1.9 0.0 0.9 4.9 .. .. .. .. .. .. 0.3 1.7 24.2 0.0 0.3 0.6 0.0 0.1 2.7 .. .. .. .. .. .. 0.2 0.8 .. .. 0.4 0.5 0.0 0.1 0.5 .. .. .. .. .. .. 0.6 0.8 10.0 .. .. 0.2 .. 0.2 2.2 .. .. .. .. .. .. 0.7 1.3 20.9 0.0 0.5 1.4 0.0 0.3 7.6 .. .. .. .. .. .. 0.8 2.3 20.6 .. .. 0.3 .. 0.2 3.0 .. .. .. .. .. .. 0.4 0.3 .. .. .. .. .. 0.0 0.0 2003 7.0 2003 6.7 2003 7.3 0.3 0.7 6.1 .. 0.2 0.5 .. 0.2 0.6 .. .. .. .. .. .. 0.3 0.8 6.1 .. 0.1 0.2 .. 0.1 0.4 .. .. .. .. .. .. 0.1 0.5 13.3 .. 0.1 0.4 .. 0.1 0.7 .. .. .. .. .. .. 0.3 1.3 36.0 .. 1.0 4.6 .. 0.2 1.0 2005 25.9 2005 20.5 2005 34.3 5.5 38.8 90.1 0.4 10.1 17.6 .. 7.3 25.5 .. .. .. .. .. .. 0.4 0.8 11.5 .. 0.3 1.4 .. 0.1 0.9 2001 44.8 2001 40.4 2001 49.3 3.7 10.2 36.4 .. 4.0 19.5 0.0 1.6 4.4 .. .. .. .. .. .. 0.1 0.2 .. .. 0.0 0.1 .. 0.0 0.3 .. .. .. .. .. .. 0.3 0.5 23.5 .. 0.6 0.8 .. 0.1 5.5 .. .. .. .. .. .. 0.1 0.7 3.5 .. .. 0.3 .. 0.1 1.1 .. .. .. .. .. .. 1.9 3.3 16.8 .. .. 3.9 .. 4.6 14.2 .. .. .. .. .. .. 0.6 4.4 27.0 0.2 1.5 2.1 0.0 0.4 5.4 .. .. .. .. .. .. 12.4 57.4 107.6 .. 13.6 20.1 .. 7.4 34.3 .. .. .. .. .. .. 0.3 0.7 .. .. .. .. .. 0.1 0.2 .. .. .. .. .. .. 0.2 1.5 7.7 .. .. 0.9 .. 0.2 1.1 2003 60.1 2003 55.8 2003 64.7 9.4 30.2 93.6 0.7 6.6 8.5 0.7 5.5 7.8 .. .. .. .. .. .. 0.2 1.2 13.4 .. 0.3 11.2 .. 0.0 8.5 .. .. .. .. .. .. 1.8 6.2 25.8 .. 1.1 3.7 0.0 1.0 3.7 .. .. .. .. .. .. 0.3 0.8 15.0 .. 0.3 0.9 .. 0.1 1.0 .. .. .. .. .. .. 0.3 1.7 12.3 .. 1.9 3.0 .. 1.9 5.0 .. .. .. .. .. .. 0.2 0.8 7.1 .. 0.2 1.7 0.0 0.2 5.0 .. .. .. .. .. .. 0.8 1.7 15.0 .. 0.7 1.1 0.0 0.2 4.3 2002 24.9 2002 28.2 2002 21.4 1.2 4.1 9.0 0.0 1.5 6.5 0.0 0.4 9.2 2004 43.4 2004 42.8 2004 46.3 3.2 6.1 71.5 0.1 0.7 1.1 0.0 0.5 7.4 2002 27.1 2002 21.4 2002 40.0 2.9 10.3 38.8 .. 1.2 4.3 0.0 0.7 8.1 .. .. .. .. .. .. 5.0 12.1 .. .. .. 2.2 .. 0.2 4.3 2006 16.6 2006 17.5 2006 14.1 1.7 13.2 56.6 .. 1.2 3.0 0.0 0.7 20.0 2005 30.7 2005 31.4 2005 29.3 3.7 11.2 85.0 0.3 2.2 6.3 0.0 2.7 12.8 MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 73 Results framework ableT4.1 Status of Paris Declaration Indicators PDI-1 PDI-2 PDI-3 PDI-4 PDI-5 Technical Aid for government Aid for assistance sectors uses government Country with Reliable public Reliable Government aligned and country public sectors uses operational national financial country budget estimates co-ordinated with financial of country development management procurement comprehensive country programmes management procurement strategies (rating) (rating) systems (rating) and realistic (%) (%) systems (%) systems (%) 2007 2007 2007 2007 2007 2007 2007 SUB-SAHARAN AFRICA Angola .. .. .. .. .. .. .. Benin C 3.5 .. 28.5 53.9 47.5 63.3 Botswana .. .. .. .. .. .. .. Burkina Faso B 4.0 .. 92.2 56.4 43.2 53.8 Burundi C 3.0 .. 53.9 41.0 32.7 34.6 Cameroon C 3.5 B 85.7 29.9 53.1 63.1 Cape Verde C 4.0 .. 90.2 39.3 22.5 22.1 Central African Republic D 2.0 .. 36.4 36.5 23.8 10.2 Chad C .. .. .. .. .. .. Comoros .. .. .. .. .. .. .. Congo, Dem. Rep. D 2.5 .. 58.3 38.1 .. 0.8 Congo, Rep. .. .. .. .. .. .. .. Côte d'Ivoire E 2.0 .. 64.4 30.9 .. 9.3 Djibouti .. .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. Ethiopia B 4.0 .. 61.7 66.8 46.7 41.4 Gabon N/A 6.0 .. 22.4 70.4 4.7 32.3 Gambia, The .. .. .. .. .. .. .. Ghana B 4.0 C 94.3 74.7 50.9 56.2 Guinea .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. Kenya C 3.5 .. 67.4 58.1 53.1 35.7 Lesotho .. .. .. .. .. .. .. Liberia D 6.0 .. .. 35.3 32.0 .. Madagascar C 3.5 .. 87.0 70.9 21.5 25.9 Malawi C 3.0 C 63.7 52.3 49.9 35.4 Mali C 3.5 .. 72.6 75.4 34.4 34.8 Mauritania C 2.5 .. 57.4 53.4 8.3 22.2 Mauritius .. .. .. .. .. .. .. Mozambique C 3.5 .. 82.5 27.1 43.5 53.8 Namibia .. .. .. .. .. .. .. Niger C 3.5 B 90.7 50.2 25.5 36.5 Nigeria C 3.0 .. 6.3 70.6 .. .. Rwanda B 4.0 B 51.0 83.6 42.0 42.9 São Tomé and Principe .. .. .. .. .. .. .. Senegal C 3.5 B 87.7 54.1 19.0 41.3 Seychelles .. .. .. .. .. .. .. Sierra Leone C 3.5 B 53.6 22.5 20.1 38.3 Somalia .. .. .. .. .. .. .. South Africa .. .. .. .. .. .. .. Sudan D 2.0 .. 84.6 53.2 3.1 0.4 Swaziland .. .. .. .. .. .. .. Tanzania B 4.0 B 83.6 60.5 71.5 68.5 Togo N/A 2.0 .. 68.9 28.9 4.4 15.5 Uganda B 4.0 B 98.4 58.1 57.0 36.9 Zambia B 3.5 C 73.5 34.5 59.4 71.0 Zimbabwe .. .. .. .. .. .. .. NORTH AFRICA Algeria .. .. .. .. .. .. .. Egypt, Arab Rep. N/A 6.0 .. 57.4 86.2 12.0 22.7 Libya .. .. .. .. .. .. .. Morocco N/A 6.0 .. 80.0 82.0 79.0 81.0 Tunisia .. .. .. .. .. .. .. Note: See technical notes for further details. PDI is a Paris Declaration Indicator. PDI-1, PDI-11 and PDI-12. Ratings from A (the highest) to E (lowest) PDI-2a. Rating rating scale ranges from 1 (low) to 6 (high). PDI-2b. Ratings from A (the highest) to D (lowest) 74 Part III. Development outcomes PARIS DECLARATION INDICATORS PDI-6 PDI-7 PDI-8 PDI-9 PDI-10 PDI-11 PDI-12 Project Aid Aid provided Existence of implementation disbursements Bilateral in the a monitorable Existence of units parallel on schedule aid framework Donor Country- performance a mutual to country and recorded that is of programme- missions analysis assessment accountability structures by government untied based co-ordinated co-ordinated framework review (number) (%) (%) appproaches (%) (%) (%) (rating) (rating) 2007 2007 2007 2007 2007 2007 2007 2007 .. .. .. .. .. .. .. .. 58 31.6 98.8 49.0 25.1 44.0 C B .. .. .. .. .. .. .. .. 102 91.6 91.8 57.2 12.8 39.0 C B 29 44.4 90.6 35.5 13.5 73.8 D A 38 50.8 98.5 39.6 25.8 49.2 D B 18 96.4 60.3 30.9 43.4 64.5 C B 11 45.2 86.7 34.3 9.8 23.2 D B .. .. .. .. .. .. D .. .. .. .. .. .. .. .. .. 146 19.5 93.9 20.8 21.3 22.9 D B .. .. .. .. .. .. D .. 29 67.0 91.7 2.6 65.0 75.0 E B .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 56 73.4 82.2 65.6 19.1 52.1 C A 5 16.8 99.7 .. 4.7 36.8 N/A B .. .. .. .. .. .. .. .. 16 83.2 91.8 68.9 39.0 59.8 C A .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 21 49.2 84.5 27.5 17.8 63.8 C B .. .. .. .. .. .. .. .. 16 .. 82.4 21.3 11.0 65.6 D B 48 79.5 83.9 43.5 23.8 41.6 C B 51 58.1 90.5 42.0 22.3 60.8 C A 60 68.2 93.4 40.6 15.2 39.3 D B 27 52.1 67.0 35.1 11.4 25.4 C B .. .. .. .. .. .. .. .. 26 73.7 90.8 46.4 16.8 31.7 B A .. .. .. .. .. .. .. .. 47 77.5 84.3 49.0 15.4 31.8 D B 23 7.1 99.2 3.9 19.1 32.8 C B 41 66.8 95.1 38.4 13.5 31.5 C B .. .. .. .. .. .. .. .. 55 60.8 93.0 38.9 16.6 28.1 C B .. .. .. .. .. .. .. .. 2 29.7 91.6 26.9 27.1 56.3 D B .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 105 51.6 79.9 19.2 14.9 44.7 D B .. .. .. .. .. .. .. .. 28 60.8 98.9 60.8 15.8 64.9 B A 13 14.3 56.1 38.9 15.1 20.7 N/A B 55 74.4 85.4 65.7 21.0 54.0 B B 34 85.1 99.6 46.8 10.4 34.8 C B .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 32 78.9 75.0 48.9 14.1 42.0 N/A B .. .. .. .. .. .. .. .. 47 68.0 90.0 70.0 12.0 25.0 N/A B .. .. .. .. .. .. .. .. PARIS DECLARATION INDICATORS Part III. Development outcomes 75 Drivers of growth ableT5.1 Business environment Starting a business Registering property Enforcing contracts Number of Average time Minimum procedures spent for each Cost (% capital Cost (% of Time to register procedure of GNI per (% of income Number of Time property Number of required Cost (% a business (days) capita) per capita) procedures (days) value) procedures (days) of debt) 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 SUB-SAHARAN AFRICA 11 56 160.3 196.1 7 103 11.3 39 673 48.6 Angola 12 119 343.7 50.5 7 334 11.1 46 1,011 44.4 Benin 7 31 195.0 354.2 4 120 11.9 42 825 64.7 Botswana 11 108 9.9 .. 4 11 5.0 29 987 28.1 Burkina Faso 6 18 82.1 415.7 8 182 12.2 37 446 107.4 Burundi 11 43 251.0 .. 5 94 11.5 44 558 38.6 Cameroon 13 37 129.2 177.1 5 93 17.8 43 800 46.6 Cape Verde 12 52 40.1 53.4 6 83 7.8 37 425 21.8 Central African Republic 10 14 205.4 531.2 5 75 18.6 43 660 82.0 Chad 19 75 188.8 398.4 6 44 21.2 41 743 77.4 Comoros 11 23 188.4 280.3 5 24 20.8 43 506 89.4 Congo, Dem. Rep. 13 155 487.2 .. 8 57 9.4 43 685 151.8 Congo, Rep. 10 37 150.1 206.3 7 137 27.3 44 560 53.2 Côte d'Ivoire 10 40 135.8 219.8 6 62 13.9 33 770 41.7 Djibouti 11 37 206.6 530.8 7 40 13.2 40 1,225 34.0 Equatorial Guinea 20 136 105.1 23.2 6 23 6.3 40 553 18.5 Eritrea 13 84 125.8 488.0 12 101 5.3 39 405 22.6 Ethiopia 7 16 41.3 960.0 13 43 7.5 39 690 15.2 Gabon 9 58 25.6 38.2 8 60 10.5 38 1,070 34.3 Gambia, The 9 32 279.0 .. 5 371 4.6 32 434 37.9 Ghana 11 42 41.4 20.9 5 34 1.3 36 487 23.0 Guinea 13 41 138.3 466.5 6 104 13.9 50 276 45.0 Guinea-Bissau 17 233 255.5 1,006.6 9 211 5.4 41 1,140 25.0 Kenya 12 44 46.1 .. 8 64 4.2 44 465 26.7 Lesotho 8 73 37.4 14.3 6 101 8.2 41 695 19.5 Liberia 12 99 493.3 .. 13 50 14.9 41 1,280 35.0 Madagascar 5 7 22.7 333.4 8 134 11.6 38 871 42.4 Malawi 10 37 188.7 .. 6 88 3.3 42 432 142.4 Mali 11 26 132.1 434.6 5 29 21.2 39 860 52.0 Mauritania 11 65 56.2 503.1 4 49 5.2 46 400 23.2 Mauritius 6 7 5.3 .. 6 210 10.8 37 750 17.4 Mozambique 10 29 21.6 115.8 8 42 12.3 31 1,010 142.5 Namibia 10 99 22.3 .. 9 23 9.9 33 270 29.9 Niger 11 23 174.8 735.6 4 35 11.1 39 545 59.6 Nigeria 9 34 56.6 .. 14 82 22.2 39 457 32.0 Rwanda 9 16 171.5 .. 5 371 9.4 24 310 78.7 São Tomé and Principe 10 144 94.5 .. 7 62 12.6 43 1,185 34.8 Senegal 10 58 107.0 255.0 6 145 19.5 44 780 26.5 Seychelles 9 38 8.7 .. 4 33 7.0 38 720 14.3 Sierra Leone 9 26 1,075.2 .. 8 235 14.9 40 515 149.5 Somalia .. .. .. .. .. .. .. .. .. .. South Africa 8 31 7.1 .. 6 24 8.8 30 600 33.2 Sudan 10 39 57.9 .. 6 9 3.2 53 810 19.8 Swaziland 13 61 38.7 0.6 11 46 7.1 40 972 23.1 Tanzania 12 29 47.1 .. 9 73 4.4 38 462 14.3 Togo 13 53 245.7 546.4 5 295 13.9 41 588 47.5 Uganda 18 28 92.0 .. 13 227 4.6 38 535 44.9 Zambia 6 33 30.5 2.2 6 70 9.6 35 471 38.7 Zimbabwe 10 96 676.2 54.6 4 30 25.0 38 410 32.0 NORTH AFRICA 9.3 14 15.4 35.8 8 83 4.9 42 705 23.8 Algeria 14 24 13.2 45.2 14 51 7.5 47 630 21.9 Egypt, Arab Rep. 7 9 28.6 12.9 7 193 1.0 42 1,010 26.2 Libya .. .. .. .. .. .. .. .. .. .. Morocco 6 12 11.5 59.8 8 47 4.9 40 615 25.2 Tunisia 10 11 8.3 25.3 4 39 6.1 39 565 21.8 a. Indexes run from 0 (least desirable) to 10 (most desirable). b. Average of the disclosure, director liability and shareholder suits indexes. c. This index is the average of three subindexes: a difficulty of hiring index, a rigidity of hours index, and a difficulty of firing index. 76 Part III. Development outcomes PRIVATE SECTOR DEVELOPMENT Dealing with construction permits Protecting investorsa Employing workers Firing Rigidity of Cost (% Director Shareholder Investor Rigidity Difficulty Difficulty costs employment index Number of Time of income Disclosure liability suits protection of hours of hiring of firing (weeks of (0 least rigid to procedures (days) per capita) index index index indexb index index index wages) 100 most rigid)c 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 2008 18 261 2,676.6 5 3 5 4.2 43 41 41 68 42 14 337 1,109.7 5 6 6 5.7 60 78 70 58 69 15 332 316.6 6 1 3 3.3 40 39 40 36 40 24 167 322.3 8 2 3 4.3 20 0 40 90 20 32 226 701.2 6 1 4 3.7 40 83 30 34 51 20 384 9,939.0 4 1 5 3.3 60 0 30 26 30 15 426 1,202.9 6 1 6 4.3 40 28 70 33 46 18 120 718.3 1 5 6 4.0 40 33 60 91 44 21 239 288.3 6 1 5 4.0 60 72 50 22 61 9 181 1,063.8 6 1 5 4.0 60 39 40 36 46 18 164 77.8 6 1 5 4.0 60 39 40 100 46 14 322 2,112.6 3 3 4 3.3 80 72 70 31 74 14 169 565.9 6 1 3 3.3 60 78 70 33 69 21 628 247.7 6 1 3 3.3 60 33 20 49 38 14 195 1,010.6 5 2 0 2.3 40 67 30 56 46 18 201 239.9 6 1 4 3.7 60 67 70 133 66 .. .. .. 4 5 5 4.7 40 0 20 69 20 12 128 1,094.4 4 4 5 4.3 40 33 30 40 34 16 210 49.8 6 1 3 3.3 60 17 80 43 52 17 146 363.7 2 1 5 2.7 40 0 40 9 27 18 220 1,498.3 7 5 6 6.0 40 22 50 178 37 32 255 237.7 6 1 1 2.7 60 33 40 26 44 15 167 2,607.0 6 1 5 4.0 60 67 70 87 66 10 100 58.8 3 2 10 5.0 0 33 30 47 21 15 601 805.3 2 1 8 3.7 40 22 0 44 21 25 398 65,845.6 4 1 6 3.7 20 33 40 84 31 16 268 880.0 5 6 6 5.7 60 89 40 30 63 21 213 1,978.0 4 7 5 5.3 0 56 20 84 25 14 208 1,320.7 6 1 3 3.3 40 33 40 31 38 25 201 565.5 5 3 3 3.7 40 56 40 31 45 18 107 43.3 6 8 9 7.7 20 0 50 35 23 17 361 705.0 5 4 9 6.0 60 83 20 143 54 12 139 188.3 5 5 6 5.3 40 0 20 24 20 17 265 2,822.5 6 1 3 3.3 60 100 50 35 70 18 350 1,016.0 5 7 5 5.7 0 0 20 50 7 16 227 822.1 2 5 1 2.7 40 56 30 26 42 13 255 825.9 3 1 6 3.3 80 50 60 91 63 16 220 570.8 6 1 2 3.0 60 72 50 38 61 19 144 46.5 4 8 5 5.7 20 33 50 39 34 49 235 581.4 3 6 8 5.7 60 44 50 189 51 .. .. .. .. .. .. .. .. .. .. .. .. 17 174 30.4 8 8 8 8.0 40 56 30 24 42 19 271 296.0 0 6 4 3.3 20 39 50 118 36 13 93 94.0 0 1 5 2.0 20 11 20 53 17 21 308 2,365.5 3 4 8 5.0 40 89 50 18 60 15 277 1,366.3 6 1 4 3.7 60 61 40 36 54 16 143 811.8 2 5 5 4.0 0 0 10 13 3 17 254 1,518.0 3 6 7 5.3 60 33 20 178 38 19 952 11,799.0 8 1 4 4.3 40 0 60 446 33 22 186 447.4 5 4 4 4.2 40 43 58 63 47 22 240 57.8 6 6 4 5.3 60 44 40 17 48 28 249 474.9 7 3 5 5.0 20 0 60 132 27 .. .. .. .. .. .. .. .. .. .. .. .. 19 163 334.7 6 2 1 3.0 40 100 50 85 63 20 93 922.1 0 4 6 3.3 40 28 80 17 49 PRIVATE SECTOR DEVELOPMENT Part III. Development outcomes 77 Drivers of growth ableT5.2 Investment climate Viewed by firms as a major constraint (% of firms) Domestic Court Trade Net foreign credit to system Crime, identifying Private direct private is fair, theft customs & investment investment sector impartial and and Labor Labor Trans- trade (% of GDP) ($ millions) (% of GDP) Corruption uncorrupted disorder Tax rates Finance Electricity regulations skills portation regulations 2006a 2006 2006 2006­07b 2006­07b 2006­07b 2006­07b 2006­07b 2006­07b 2006­07b 2006­07b 2006­07b 2006­07b SUB-SAHARAN AFRICA 12.5 65.1 Angola 2.4 ­228.3 7.5 36.1 31.9 36.6 23.0 55.3 45.8 12.3 21.2 27.3 21.4 Benin .. .. 17.2 .. .. .. .. .. .. .. .. .. .. Botswana 9.6 536.1 18.8 22.6 69.6 24.1 24.6 41.4 6.8 9.0 19.5 13.4 10.9 Burkina Faso .. .. 17.9 54.0 39.1 18.0 75.2 79.9 48.9 12.2 13.0 55.8 31.7 Burundi .. 0.0 24.6 19.7 40.7 19.7 36.1 50.9 72.3 3.9 11.8 21.1 20.9 Cameroon 14.3 .. 9.2 52.1 25.6 33.1 75.6 68.0 61.1 9.9 8.1 33.1 34.8 Cape Verde 29.4 122.6 48.4 16.3 61.8 27.6 50.0 38.8 65.3 15.3 16.3 18.6 24.5 Central African Republic 5.6 .. 6.6 .. .. .. .. .. .. .. .. .. .. Chad 13.2 .. 2.6 .. .. .. .. .. .. .. .. .. .. Comoros 4.9 .. 7.9 .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. .. .. 2.9 20.0 19.8 22.6 52.4 60.4 70.3 9.0 13.1 30.0 15.1 Congo, Rep. 13.8 .. 2.1 .. .. .. .. .. .. .. .. .. .. Côte d'Ivoire 6.8 318.9 14.3 .. .. .. .. .. .. .. .. .. .. Djibouti 22.0 108.3 20.2 .. .. .. .. .. .. .. .. .. .. Equatorial Guinea 26.6 .. 2.8 .. .. .. .. .. .. .. .. .. .. Eritrea 4.2 .. 29.0 .. .. .. .. .. .. .. .. .. .. Ethiopia 7.6 545.3 23.8 23.1 24.2 11.6 40.0 44.2 21.5 4.0 23.0 11.8 17.0 Gabon 18.3 .. 9.3 .. .. .. .. .. .. .. .. .. .. Gambia, The 14.9 82.1 15.6 9.8 62.8 12.3 30.7 40.3 78.1 3.5 11.7 11.1 12.8 Ghana 18.3 434.5 17.8 9.9 59.8 11.4 30.6 66.2 86.2 1.7 4.6 17.6 9.8 Guinea 10.2 .. .. 47.7 25.7 30.4 39.4 58.3 83.6 2.5 11.7 51.5 12.4 Guinea-Bissau 6.4 .. 3.9 44.0 12.1 29.6 44.0 71.6 74.1 3.5 12.3 24.8 25.6 Kenya 15.2 26.7 25.8 .. .. .. .. .. .. .. .. .. .. Lesotho 26.1 77.8 8.9 .. .. .. .. .. .. .. .. .. .. Liberia .. .. 8.6 .. .. .. .. .. .. .. .. .. .. Madagascar 14.5 .. 10.2 .. .. .. .. .. .. .. .. .. .. Malawi 14.4 .. 8.7 46.8 59.2 47.2 56.3 42.8 60.4 12.6 49.7 39.0 24.2 Mali 14.3 82.1 17.2 15.7 49.6 4.7 54.0 60.4 55.7 1.9 8.0 20.1 8.2 Mauritania 17.7 .. .. 17.1 48.5 1.4 35.2 43.6 28.9 2.8 23.0 16.2 25.9 Mauritius 15.9 97.2 78.0 .. .. .. .. .. .. .. .. .. .. Mozambique 7.1 153.3 13.8 .. .. .. .. .. .. .. .. .. .. Namibia 20.0 342.4 61.7 19.1 66.1 27.6 20.4 18.4 6.5 6.8 19.6 7.9 7.1 Niger .. .. 8.5 58.5 35.7 6.4 69.4 55.7 21.6 6.4 18.4 40.0 31.2 Nigeria .. .. 13.2 .. .. .. .. .. .. .. .. .. .. Rwanda 12.8 25.6 .. 4.4 67.1 4.1 44.7 36.0 55.0 2.8 11.7 27.4 13.5 São Tomé and Principe .. 34.4 33.1 .. .. .. .. .. .. .. .. .. .. Senegal 19.1 .. 22.9 23.8 55.4 11.6 40.5 49.2 57.7 4.8 9.5 27.4 15.1 Seychelles 22.6 137.8 36.9 .. .. .. .. .. .. .. .. .. .. Sierra Leone 10.4 58.6 4.5 .. .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. .. .. .. .. South Africa 15.8 ­6,719.3 160.8 .. .. .. .. .. .. .. .. .. .. Sudan 14.5 3,534.1 0.1 .. .. .. .. .. .. .. .. .. .. Swaziland 7.5 34.0 22.5 24.9 40.3 34.4 28.5 32.9 12.4 9.9 12.7 14.2 16.5 Tanzania 9.9 474.5 11.0 19.7 46.7 16.4 36.7 40.6 88.4 4.8 19.7 14.1 11.6 Togo .. .. 16.8 .. .. .. .. .. .. .. .. .. .. Uganda 18.2 393.2 7.9 23.6 43.5 13.4 62.7 47.8 84.2 1.3 10.2 22.2 9.8 Zambia 18.8 615.8 9.6 12.6 54.7 10.1 25.6 20.8 11.9 5.8 7.9 10.6 9.8 Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. .. NORTH AFRICA 17,079.9 37.7 Algeria .. .. 12.4 64.3 .. 20.7 46.7 50.1 48.1 13.8 36.8 24.7 36.1 Egypt, Arab Rep. 11.4 9,894.4 55.3 .. .. .. .. .. .. .. .. .. .. Libya .. 1,590.0 15.7 .. .. .. .. .. .. .. .. .. .. Morocco 24.9 2,355.6 58.1 27.3 43.5 3.4 55.7 31.6 37.0 15.8 31.0 8.2 14.3 Tunisia .. 3,239.9 63.7 .. .. .. .. .. .. .. .. .. .. a. Provisional b. Data are for the most recent year available during the period specified. 78 Part III. Development outcomes PRIVATE SECTOR DEVELOPMENT Regulation and tax administration Highest Time dealing Average time Average time Market Time to, marginal with to clear to clear Interest rate capitalization Turnover Number prepare, Total tax rate, officials (% of direct exports imports through spread (lending Listed of listed ratio of tax file and pay tax rate corporate management through customs rate minus domestic companies for traded payments taxes (hours) (% of profit) rate (%) time) customs (days) (days) deposit rate) companies (% of GDP) stocks (%) 2007 2007 2007 2006 2006­07b 2006­07b 2006­07b 2006 2006­07b 2006a 2006­07b 38 325 67.3 7.8 7.8 9.6 31 272 53.2 .. 7.1 16.5 28.3 15.0 .. .. .. 58 270 75.8 38.0 .. 6.3 12.2 .. .. .. .. 19 140 17.2 15.0 5.0 1.4 3.1 7.6 18.0 35.9 2.2 45 270 47.6 .. 9.5 2.8 5.3 .. .. .. .. 32 140 278.7 .. 5.7 .. 10.8 .. .. .. .. 41 1,400 51.9 .. 12.8 4.3 11.7 11.0 .. .. .. 57 100 54.0 .. 12.2 .. 10.6 .. .. .. .. 54 504 203.8 .. .. .. .. 11.0 .. .. .. 54 122 63.7 .. .. .. .. 11.0 .. .. .. 20 100 48.8 .. .. .. .. 8.0 .. .. .. 32 308 228.1 40.0 6.3 3.6 13.1 .. .. .. .. 61 606 65.4 .. .. .. .. 11.0 .. .. .. 66 270 48.4 35.0 .. .. .. .. 38.0 24.1 2.5 35 114 38.7 .. .. .. .. .. .. .. .. 46 296 59.5 .. .. .. .. 11.0 .. .. .. 18 216 84.5 .. .. .. .. .. .. .. .. 20 212 31.1 30.0 3.8 4.3 14.1 3.4 .. .. .. 26 272 44.7 .. .. .. .. 11.0 .. .. .. 50 376 292.4 .. 7.3 5.0 3.0 17.1 .. .. .. 37 304 36.1 25.0 4.0 7.8 6.8 .. 32.0 25.4 3.9 56 416 49.9 .. 2.7 4.3 10.4 .. .. .. .. 46 208 45.9 .. 2.9 5.6 11.0 .. .. .. .. 42 432 51.4 .. .. 4.7 8.9 8.5 51.0 49.9 10.6 21 564 26.2 .. .. 2.3 3.5 7.6 .. .. .. 32 158 35.8 .. .. .. .. .. .. .. .. 26 304 46.5 .. .. 3.5 7.0 7.2 .. .. .. 19 370 32.6 .. 5.8 3.5 6.4 21.3 9.0 18.6 3.5 58 270 51.4 .. 2.4 8.1 9.1 .. .. .. .. 38 696 107.5 .. 5.8 3.9 6.8 .. 40.0 .. .. 7 161 23.4 25.0 .. 4.4 5.1 11.5 90.0 56.7 8.0 37 230 34.3 32.0 .. .. .. 8.2 .. .. .. 37 375 26.5 35.0 2.9 1.5 3.3 4.9 9.0 8.3 3.7 41 270 42.3 .. 11.5 7.4 6.9 .. .. .. .. 35 1,120 32.2 .. .. .. .. 7.2 212.0 22.3 28.2 34 168 37.2 .. 5.9 6.7 12.7 .. .. .. .. 42 424 48.7 .. .. .. .. .. .. .. .. 59 696 46.0 .. 2.9 6.6 8.9 .. .. .. .. 16 76 48.4 .. .. .. .. 7.4 .. .. .. 28 399 270.4 .. .. .. .. 13.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 12 350 37.6 29.0 .. 4.5 6.5 4.0 422.0 280.4 55.0 42 180 31.6 .. .. .. .. .. .. .. .. 33 104 36.6 30.0 4.4 4.0 2.2 6.2 6.0 7.2 0.0 47 172 43.8 30.0 4.0 5.7 14.9 8.8 7.0 3.8 2.1 53 270 48.0 .. .. .. .. .. .. .. .. 33 237 35.8 30.0 5.2 4.7 7.4 9.6 5.0 1.2 5.2 37 132 16.5 .. 4.6 2.3 6.6 12.8 15.0 10.9 4.1 53 216 33.6 .. .. .. .. 293.1 82.0 .. 5.1 34 418 57.6 .. 3.5 10.3 6.3 46 451 76.9 .. 25.1 8.6 16.8 6.3 .. .. .. 41 596 49.1 .. .. 4.8 10.0 6.6 435.0 87.0 45.6 .. .. .. .. .. .. .. 3.8 .. .. .. 28 358 43.4 .. 11.4 2.2 3.8 .. 74.0 75.5 42.1 22 268 61.0 .. .. .. .. .. 50.0 14.4 13.3 PRIVATESECTOR DEVELOPMENT Part III. Development outcomes 79 Drivers of growth ableT6.1 International trade and tariff barriers Trade Merchandise Terms of trade Exports Imports Exports Imports Annual growth (%) trade index (% of GDP) ($ millions) ($ millions) (% of GDP) (% of GDP) Exports Imports (2000=100) 2006a 2006a 2006a 2006a 2006a 2006a 2006a 2006a SUB-SAHARAN AFRICA 71.7 276,758 257,218 37.2 34.5 .. 12.3 Angola 111.7 33,317 17,129 73.8 37.9 .. .. .. Benin .. .. .. .. .. .. .. .. Botswana 78.9 5,581 3,109 50.7 28.2 13.2 1.0 98.3 Burkina Faso 38.3 665 1,547 11.5 26.8 24.4 1.1 96.1 Burundi 58.7 99 432 10.9 47.8 .. .. .. Cameroon 44 4,130 3,762 23 21 1.3 1.0 108.0 Cape Verde 72.2 229 624 19.4 52.8 32.8 1.2 58.6 Central African Republic 36 207 324 14 21.9 14.4 1.0 72.0 Chad 101 3,852 2,509 61.1 39.8 11.2 1.0 100.0 Comoros 47.3 47 143 11.7 35.5 ­4.9 1.0 100.0 Congo, Dem. Rep. 70.4 2,517 3,499 29.5 41 2.4 1.1 139.6 Congo, Rep. 130.8 6,717 3,398 86.9 43.9 .. .. .. Côte d'Ivoire 93.8 9,004 7,189 52.1 41.6 ­1.6 1.0 82.1 Djibouti 97.2 307 441 39.9 57.3 2.8 1.2 100.0 Equatorial Guinea 144.7 8,096 4,295 94.5 50.1 ­11.0 1.1 167.8 Eritrea 58.1 87 543 8 50 ­1.1 1.0 86.0 Ethiopia 50.4 2,097 5,539 13.8 36.5 ­0.2 1.2 79.3 Gabon 89.1 6,238 2,267 65.4 23.7 ­9.7 1.1 150.4 Gambia, The .. .. .. .. .. .. .. .. Ghana 104.6 5,063 8,234 39.8 64.8 10.3 1.1 103.0 Guinea 69.8 1,073 1,163 33.5 36.3 ­1.2 1.0 92.9 Guinea-Bissau 94.5 129 162 41.8 52.7 5.1 1.1 96.9 Kenya 62.6 5,720 8,534 25.1 37.5 9.2 1.1 87.0 Lesotho 149 755 1,472 50.5 98.5 7.9 1.0 116.4 Liberia 102.4 175 453 28.6 73.8 .. .. .. Madagascar 70.7 1,635 2,252 29.7 40.9 23.8 1.0 129.2 Malawi 46.4 537 932 17 29.4 ­11.8 0.9 184.9 Mali 72.3 1,884 2,360 32.1 40.2 11.3 1.0 96.3 Mauritania 113.7 1,453 1,573 54.6 59.1 117.9 .. .. Mauritius 127.1 3,809 4,257 60 67.1 8.0 1.1 87.9 Mozambique 88.9 2,831 3,245 41.4 47.5 8.0 1.1 81.1 Namibia 110 3,577 3,644 54.5 55.5 14.1 1.1 104.2 Niger .. .. .. .. .. .. .. .. Nigeria 71.4 63,391 41,518 43.2 28.3 .. .. .. Rwanda 37.8 296 787 10.3 27.4 23.6 1.3 74.5 São Tomé and Principe .. .. .. .. .. .. .. .. Senegal 69.2 2,351 4,062 25.4 43.8 ­8.6 1.0 94.9 Seychelles 244.5 860 1,034 111 133.5 17.8 1.1 100.0 Sierra Leone 60.7 333 529 23.4 37.2 .. .. .. Somalia .. .. .. .. .. .. .. .. South Africa 63.1 76,106 84,692 29.8 33.2 5.5 1.2 113.4 Sudan 44 6,014 9,995 16.5 27.5 0.4 1.1 100.0 Swaziland 135.1 1,774 1,988 63.7 71.4 4.6 1.1 96.4 Tanzania 49.7 3,106 3,941 21.9 27.8 ­0.2 1.0 78.0 Togo .. .. .. .. .. .. .. .. Uganda 44.8 1,403 2,854 14.8 30.1 4.0 1.2 127.0 Zambia 67.4 4,120 3,221 37.8 29.6 21.0 1.1 140.8 Zimbabwe .. .. .. .. .. .. .. .. NORTH AFRICA .. .. 115,394 .. 31.2 .. 13.7 Algeria .. .. .. .. .. .. .. .. Egypt, Arab Rep. 61.5 32,191 33,931 29.9 31.6 21.3 1.2 112.3 Libya .. .. .. .. .. .. .. .. Morocco 71.4 21,592 25,125 33 38.4 10.5 1.1 104.4 Tunisia 106.3 16,477 16,449 53.2 53.1 3.9 1.0 100.8 a. Provisional b. Data are for most recent year available during the period specified. 80 Part III. Development outcomes TRADE Structure of merchandise exports (% of total) Structure of merchandise imports (% of total) Export Agricultural Agricultural diversification raw Ores and raw Ores and index (0 low Food materials Fuel metals Manufactures Food materials Fuel metals Manufactures to 100 high) 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2006 15.5 5.8 35.6 9.9 33.3 10.0 1.2 14.6 2.8 65.4 .. .. .. .. .. .. .. .. .. .. 1.1 25.8 64.3 0.2 0.5 9.1 29.8 4.2 20.4 0.9 43.9 5.3 2.4 0.1 0.0 10.7 86.4 13.9 0.8 4.4 1.1 75.1 1.6 16.4 72.3 2.8 0.6 8.0 12.0 0.6 24.4 0.6 62.5 1.5 86.8 4.2 0.1 2.5 6.2 6.5 1.4 8.5 0.8 82.3 3.0 12.0 16.3 61.6 4.9 3.0 18.0 1.7 30.8 1.0 48.4 2.9 62.1 0.0 .. 0.3 37.6 29.2 1.4 8.9 0.6 59.9 8.6 0.8 41.2 0.4 16.9 36.1 17.1 27.2 16.9 1.5 36.7 3.9 .. .. .. .. .. .. .. .. .. .. 1.2 88.7 0.0 .. 0.0 8.2 21.9 0.4 4.1 0.2 72.5 5.5 .. .. .. .. .. .. .. .. .. .. 6.3 .. .. .. .. .. .. .. .. .. .. 1.3 35.2 8.0 37.0 0.2 15.2 17.3 0.6 31.8 1.1 43.2 6.9 .. .. .. .. .. .. .. .. .. .. 22.9 .. .. .. .. .. .. .. .. .. .. 1.2 42.0 26.0 0.0 1.8 30.3 45.6 0.9 0.8 0.9 51.7 17.1 62.0 25.9 0.0 0.7 11.4 21.5 0.7 12.0 1.5 64.0 4.6 0.8 6.7 85.6 3.1 3.7 16.6 0.4 4.0 1.1 77.4 1.9 81.1 3.9 0.8 0.9 14.1 31.2 2.1 17.4 0.6 48.7 4.1 61.0 4.0 0.7 3.1 30.9 13.4 1.2 13.8 1.3 70.0 4.3 2.0 0.8 0.1 71.6 25.3 23.1 1.2 21.7 0.8 53.0 4.3 .. .. .. .. .. .. .. .. .. .. 1.3 52.0 15.9 0.9 5.5 25.7 10.4 2.1 24.3 1.6 61.3 18.5 7.1 5.1 0.0 0.1 87.4 23.1 0.8 7.4 0.7 62.8 7.5 .. .. .. .. .. .. .. .. .. .. 3.2 34.7 4.4 5.5 3.6 41.2 14.5 1.0 18.7 0.7 64.6 19.3 82.9 3.3 0.1 0.1 12.9 15.1 1.0 11.4 0.7 71.1 2.7 13.9 73.5 0.8 0.1 10.4 13.7 0.6 21.1 0.6 63.7 1.7 24.8 0.0 .. 68.6 0.0 25.0 0.6 26.9 0.3 47.1 3.9 28.6 0.5 0.1 0.8 68.8 16.5 2.1 16.8 1.0 63.5 12.1 15.8 3.5 14.7 59.9 5.0 13.9 1.0 16.9 0.4 48.2 2.3 25.9 0.7 0.5 26.0 46.5 16.2 0.6 3.2 0.9 78.2 5.0 23.9 3.8 1.9 54.3 14.5 34.2 4.0 14.7 1.4 45.6 1.4 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 3.0 94.5 0.6 0.0 0.1 4.9 30.6 0.8 20.2 1.4 46.9 4.4 43.8 5.3 0.0 6.9 44.0 23.4 1.6 25.9 1.0 48.1 26.0 93.2 0.0 .. 0.0 6.8 24.0 1.3 26.7 0.5 40.5 3.6 91.6 0.8 .. 0.1 7.5 22.5 7.6 39.7 0.8 29.3 4.7 .. .. .. .. .. .. .. .. .. .. 8.6 7.1 1.8 9.4 28.7 53.0 4.4 1.0 18.4 2.4 66.2 38.4 6.8 4.8 87.3 0.4 0.1 13.0 0.7 1.2 1.0 83.2 1.3 17.2 7.7 0.6 0.5 73.8 18.0 1.1 11.5 0.7 65.8 19.5 53.2 10.7 0.2 17.5 18.4 12.2 0.8 24.2 1.2 61.5 26.4 21.5 8.9 1.2 10.3 58.1 15.5 0.8 29.0 2.1 52.6 7.7 62.3 8.9 5.0 2.4 21.3 13.6 1.4 21.1 1.1 62.5 5.6 6.0 2.8 0.6 84.8 5.8 7.6 0.8 15.1 2.5 74.0 2.2 30.1 8.2 0.3 23.2 38.1 9.6 2.4 15.1 40.4 32.4 14.9 6.6 0.7 62.3 2.4 26.7 15.8 3.1 14.1 3.0 58.3 0.2 0.0 97.9 0.7 1.2 19.2 2.1 1.1 1.8 75.8 2.3 6.6 1.5 56.4 2.3 21.2 18.9 3.9 16.3 3.3 43.3 13.2 .. .. .. .. .. 16.8 0.6 0.7 0.9 81.1 1.3 19.3 1.7 1.9 9.3 67.8 9.3 2.9 21.7 3.5 62.7 69.3 10.4 0.6 13.0 1.2 74.9 8.5 2.6 13.7 3.1 72.0 43.0 TRADE Part III. Development outcomes 81 Drivers of growth ableT6.1 International trade and tariff barriers (continued) Competitiveness Indicator (%) Tariff barriers, all products (%) Share of Share of lines with lines with Binding Simple mean Simple mean Weighted mean international specific Sectoral effect Global effect coverage bound rate tariff tariff peaks rates 2002­06 2002­06 2006 2006 2006 2006 2006 2006 SUB-SAHARAN AFRICA Angola 15.4 35.7 100.0 59.2 7.6 6.5 10.4 0.8 Benin ­10.5 ­1.2 39.1 28.6 13.4 11.3 53.4 .. Botswana ­19.6 14.9 96.3 19.0 8.7 10.5 20.9 1.3 Burkina Faso 13.5 12.4 38.9 42.2 12.2 9.8 43.5 .. Burundi ­6.4 ­5.2 21.2 66.7 14.7 13.5 27.9 .. Cameroon 4.2 5.6 .. .. .. .. .. .. Cape Verde ­13.4 24.9 .. .. .. .. .. .. Central African Republic ­21.4 ­13.1 .. .. .. .. .. .. Chad ­14.7 919.7 .. .. .. .. .. .. Comoros ­49.4 20.3 .. .. .. .. .. .. Congo, Dem. Rep. ­7.6 ­16.1 100.0 96.2 13.1 11.4 43.3 0.2 Congo, Rep. 14.8 41.5 .. .. .. .. .. .. Côte d'Ivoire ­9.2 ­8.4 33.0 11.1 13.5 7.3 49.9 .. Djibouti 1.3 30.0 100.0 41.0 30.2 29.1 87.9 6.3 Equatorial Guinea 4.2 54.8 .. .. .. .. .. .. Eritrea ­14.4 18.2 .. .. .. .. .. .. Ethiopia ­6.1 27.5 .. .. 16.4 10.7 49.2 0.1 Gabon 7.3 ­18.4 .. .. .. .. .. .. Gambia, The ­20.3 ­0.1 .. .. .. .. .. .. Ghana ­9.4 ­0.6 .. .. .. .. .. .. Guinea ­2.2 ­5.1 .. .. .. .. .. .. Guinea-Bissau ­22.1 ­11.4 97.7 48.7 12.7 9.1 50.1 .. Kenya ­2.7 ­3.5 14.0 95.1 11.9 6.6 36.2 0.4 Lesotho ­21.0 2.9 100.0 78.4 9.9 16.5 24.2 1.9 Liberia 158.2 ­193.1 .. .. .. .. .. .. Madagascar ­21.4 5.8 29.7 27.4 13.3 8.7 43.5 .. Malawi ­20.6 1.9 30.2 74.9 12.9 8.1 40.3 .. Mali ­9.2 ­1.6 40.7 29.3 12.6 8.5 46.3 .. Mauritania 2.1 20.2 39.4 19.6 11.6 7.2 44.3 3.9 Mauritius ­6.0 ­14.9 18.0 94.0 4.2 1.6 8.5 8.1 Mozambique ­7.8 20.1 12.9 97.4 12.7 8.3 38.2 .. Namibia ­5.3 45.8 96.3 19.4 5.8 0.8 15.7 2.6 Niger 7.1 15.7 96.8 44.6 13.1 9.8 50.3 .. Nigeria 23.5 21.1 18.1 118.5 11.7 11.6 41.5 .. Rwanda ­0.5 ­25.3 100.0 89.4 19.7 14.4 52.2 0.1 São Tomé and Principe 18.8 9.9 .. .. .. .. .. .. Senegal ­10.0 ­18.3 100.0 30.0 13.5 9.4 51.3 .. Seychelles ­12.9 ­7.3 .. .. 6.3 30.7 12.2 1.6 Sierra Leone ­6.9 0.1 .. .. .. .. .. .. Somalia ­11.3 5.7 .. .. .. .. .. .. South Africa 2.1 ­6.8 96.3 19.4 8.3 5.1 19.3 2.1 Sudan 8.2 14.6 .. .. 17.1 15.3 38.1 .. Swaziland ­6.1 ­0.9 96.3 19.4 10.3 9.2 25.0 2.4 Tanzania ­0.3 0.1 13.4 120.0 12.5 7.2 37.6 0.4 Togo ­0.7 ­15.3 13.2 80.0 14.0 9.7 52.3 .. Uganda ­7.7 ­7.0 14.9 73.5 12.0 7.4 37.1 0.5 Zambia 23.1 23.2 .. .. .. .. .. .. Zimbabwe ­5.7 ­12.6 .. .. .. .. .. .. NORTH AFRICA Algeria 24.2 ­3.4 .. .. 15.8 10.7 38.7 .. Egypt, Arab Rep. 5.2 32.0 .. .. .. .. .. .. Libya 17.6 20.9 .. .. .. .. .. .. Morocco ­5.3 ­3.7 100.0 41.3 15.5 11.0 45.3 2.0 Tunisia ­7.9 2.4 57.9 57.7 22.9 18.5 55.5 .. a. Provisional b. Data are for most recent year available during the period specified. 82 Part III. Development outcomes TRADE Tariff barriers, Average cost to ship 20 ft Average time Tariff barriers, primary products (%) manufactured products (%) container from port to final destination ($) to clear customs (days) Simple mean Weighted mean Simple mean Weighted mean Direct tariff tariff tariff tariff Export Import exports Imports 2006 2006 2006 2006 2006 2006 2006 2006 1,750 2,181 5.0 9.6 11.5 13.1 6.9 5.0 1,850 2,325 16.5 28.3 13.2 10.9 13.4 11.7 1,167 1,202 6.3 12.2 3.6 0.8 9.2 12.4 2,328 2,595 1.4 3.1 11.3 7.8 12.4 11.0 2,096 3,522 2.8 5.3 15.1 11.7 14.6 13.8 2,147 3,705 .. 10.8 .. .. .. .. 524 1,360 4.3 11.7 .. .. .. .. .. .. .. 10.6 .. .. .. .. 4,581 4,534 .. .. .. .. .. .. 4,867 5,520 .. .. .. .. .. .. .. .. .. .. 14.2 11.3 12.8 11.5 3,120 3,308 3.6 13.1 .. .. .. .. 2,201 2,201 .. .. 15.4 4.2 13.1 9.5 1,653 2,457 .. .. 23.1 23.2 31.3 31.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 935 1,185 .. .. 18.1 12.6 16.3 10.4 1,617 2,793 4.3 14.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. 5.0 3.0 .. .. .. .. 822 842 .. .. .. .. .. .. 570 995 4.3 10.4 14.3 9.0 12.4 9.2 .. .. 5.6 11.0 14.8 6.4 11.6 6.6 1,980 2,325 4.7 8.9 7.5 3.2 10.0 17.3 1,188 1,210 2.3 3.5 .. .. .. .. .. .. .. .. 14.2 3.0 13.2 12.2 982 1,282 3.5 7.0 12.8 6.1 12.9 8.9 1,623 2,500 3.5 6.4 11.5 8.6 12.7 8.5 1,752 2,680 8.1 10.0 11.5 9.3 11.6 6.6 3,733 3,733 3.9 6.8 6.1 1.5 3.9 1.7 683 683 4.4 5.1 15.4 8.9 12.3 8.0 1,155 1,185 .. .. 3.5 0.6 6.2 0.9 1,539 1,550 1.5 3.3 13.1 10.0 13.1 9.7 2,945 2,946 7.4 6.9 14.8 15.1 11.4 10.2 798 1,460 .. .. 17.4 14.0 20.0 14.5 3,840 4,080 6.7 12.7 .. .. .. .. 690 577 .. .. 14.4 8.5 13.4 10.3 828 1,720 6.6 7.0 12.8 49.6 4.9 6.7 .. .. .. .. .. .. .. .. 1,282 1,242 .. .. .. .. .. .. .. .. .. .. 5.5 1.7 8.6 6.4 1,087 1,195 4.5 6.5 22.9 19.7 16.6 14.6 1,870 1,970 .. .. 8.0 3.8 10.5 9.6 .. .. 4.0 2.2 16.9 7.7 12.0 7.0 822 917 5.7 14.9 13.9 8.7 14.0 10.7 463 695 .. .. 14.6 7.0 11.7 7.6 1,050 2,945 4.7 7.4 .. .. .. .. 2,098 2,840 2.3 4.8 .. .. .. .. 1,879 2,420 .. .. 1,023 1,259 3.5 6.4 15.5 9.3 15.8 11.1 1,606 1,886 8.6 23.2 .. .. .. .. 1,014 1,049 4.8 10.0 .. 15.1 .. .. .. .. .. .. 21.9 11.7 14.9 10.6 700 1,500 2.2 2.8 33.1 14.7 22.0 20.0 770 600 .. .. TRADE Part III. Development outcomes 83 Drivers of growth ableT6.2 Top three exports and share in total exports, 2006 First Share of total Product exports (%) SUB-SAHARAN AFRICA Angola Petroleum oils and oils obtained from bituminous minerals, crude 96.0 Benin Cotton, not carded, combed 35.1 Botswana Diamonds non-industrial unworked or simply sawn, cleaved or bruted 78.6 Burkina Faso Cotton, not carded, combed 82.7 Burundi Coffee, not roasted, not decaffeinated 56.3 Cameroon Petroleum oils and oils obtained from bituminous minerals, crude 57.0 Cape Verde Tunas, yellowfin, frozen excluding heading No 03.04, livers and roes 24.6 Central African Republic Logs, tropical hardwoods not elsewhere specified 37.1 Chad Petroleum oils and oils obtained from bituminous minerals, crude 90.9 Comoros Electrostatic photo-copying apparatus, indirect process type 25.4 Congo, Dem. Rep. Diamonds non-industrial unworked or simply sawn, cleaved or bruted 31.2 Congo, Rep. Petroleum oils and oils obtained from bituminous minerals, crude 88.7 Côte d'Ivoire Cocoa beans, whole or broken, raw or roasted 31.4 Djibouti Coffee, not roasted, not decaffeinated 13.1 Equatorial Guinea Petroleum oils and oils obtained from bituminous minerals, crude 91.7 Eritrea Medicaments not elsewhere specified, in dosage 12.8 Ethiopia Coffee, not roasted, not decaffeinated 42.2 Gabon Petroleum oils and oils obtained from bituminous minerals, crude 72.3 Gambia, The Cashew nuts, in shell, fresh or dried 46.7 Ghana Cocoa beans, whole or broken, raw or roasted 46.9 Guinea Aluminium ores and concentrates 40.0 Guinea-Bissau Cashew nuts, in shell, fresh or dried 86.9 Kenya Black tea (fermented) and partly fermented tea in packages exceeding 3 kg 14.5 Lesotho Pullovers, cardigans and similar articles of cotton, knitted 22.2 Liberia Cargo vessles not elsewhere specified and other vessels for the transport of both persons and goods 31.4 Madagascar Pullovers, cardigans and similar articles of wool or fine animal hair, knitted 12.5 Malawi Tobacco, unmanufactured, partly or wholly stemmed or stripped 59.1 Mali Cotton, not carded or combed 56.2 Mauritania Petroleum oils and oils obtained from bituminous minerals, crude 35.8 Mauritius Raw sugar, cane 17.9 Mozambique Aluminium unwrought, not alloyed 65.9 Namibia Diamonds non-industrial unworked or simply sawn, cleaved or bruted 39.5 Niger Natural uranium & its compounds. Mixtures containing natural uranium & its compounds 58.9 Nigeria Petroleum oils and oils obtained from bituminous minerals, crude 89.6 Rwanda Coffee, not roasted, not decaffeinated 53.7 São Tomé and Principe Cocoa beans, whole or broken, raw or roasted 39.5 Senegal Ground-nut oil, crude 8.8 Seychelles Tunas, skipjack & Atl bonito, prepared/preserved, whole/in pieces, excluding minced 49.2 Sierra Leone Diamonds non-industrial unworked or simply sawn, cleaved or bruted 42.9 Somalia Goats, live 27.3 South Africa Platinium unwrought or in powder form 9.4 Sudan Petroleum oils and oils obtained from bituminous minerals, crude 89.3 Swaziland Raw sugar, cane 14.3 Tanzania Tobacco, unmanufactured, partly or wholly stemmed or stripped 7.6 Togo Cocoa beans, whole or broken, raw or roasted 29.6 Uganda Coffee, not roasted, not decaffeinated 33.3 Zambia Copper cathodes and sections of cathodes unwrought 66.2 Zimbabwe Nickel unwrought, not alloyed 16.8 NORTH AFRICA Algeria Petroleum oils and oils obtained from bituminous minerals, crude 64.7 Egypt, Arab Rep. Natural gas, liquefied 19.7 Libya Petroleum oils and oils obtained from bituminous minerals, crude 87.2 Morocco Phosphoric acid and polyphosphoric acids 5.4 Tunisia Petroleum oils and oils obtained from bituminous minerals, crude 8.7 AFRICAa Petroleum oils and oils obtained from bituminous minerals, crude 51.9 [19.3] 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. 84 Part III. Development outcomes TRADE Second Share of total Product exports (%) Waste and scrap, copper or copper alloy 18.0 Nickel mattes 11.4 Black tea (fermented) and partly fermented tea in packages exceeding 3 kg 10.5 Lumber, tropical hardwood not elsewhere specified, sawn lengthwise >6mm 7.4 Skipjack or stripe-bellid bonito, frozen excluding heading No 03.04, livers and roes 13.7 Diamonds unsorted whether or not worked 32.3 Cloves (whole fruit, cloves and stems) 22.7 Cobalt ores and concentrates 18.2 Petroleum oils and oils obtained from bituminous minerals, crude 17.4 Raw sugar, cane 8.3 Parts of boring or sinking machinery, whether or not self-propelled 11.8 Sesanum seeds, whether or not broken 18.6 Manganese ores and concentrates and the like 8.0 Ground-nut oil, crude 11.8 Manganese ores and concentrates and the like 4.7 Petroleum oils and oils obtained from bituminous minerals, crude 21.9 Cut flowers and flower buds for bouquets or ornamemtal purposes, fresh 14.0 Mens/boys trousers and shorts, of cotton, not knitted 18.2 Tankers 26.4 Shrimps and prawns, frozen, in shell or not, including boiled in shell 12.3 Raw sugar, cane 8.2 Tankers 13.9 Iron ores & concentrates, other than roasted iron pyrites, nonagglomerated 34.0 T-shirts, singlets and other vests, of cotton, knitted 17.7 Aluminium unwrought, alloyed 5.1 Zinc not alloyed unwrought containing by weight 99.99% or more of zinc 15.3 Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 30.9 Natural gas, liquefied 5.0 Niobium, tantalum and vanadium ores and concentrates 18.2 Motorcycles with reciprocating piston engine displacg > 50 cc to 250 cc 20.0 Phosphoric acid and polyphosphoric acids (8.8%) 8.8 Tunas, yellowfin, frozen excluding heading No 03.04, livers and roes 12.2 Aluminium ores and concentrates 11.6 Bovine, live, pure-bred breeding 12.9 Diamonds non-industrial unworked or simply sawn, cleaved or bruted 5.9 Gas turbines not elsewhere specified of a power exceeding 5000 KW 9.2 Fish fillets and other fish meat, minced or not, fresh or chilled 6.9 Natural calcium phosphates, aluminium calcium phosphates and the like ground 11.6 Fish fillets and other fish meat, minced or not, fresh or chilled 23.4 Copper ores and concentrates 5.8 Tobacco, unmanufactured, partly or wholly stemmed or stripped 10.0 Natural gas, liquefied 11.0 Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 13.2 Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 8.1 Mens/boys trousers and shorts, of cotton, not knitted 6.2 Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 4.1 [4.5] TRADE Part III. Development outcomes 85 Drivers of growth ableT6.2 Top three exports and share in total exports, 2006 (continued) Third Number of exports Share of accounting total exports for 75% Product (%) of total exports SUB-SAHARAN AFRICA Angola 1 Benin Cashew nuts, in shell, fresh or dried 14.5 4 Botswana Diamonds industrial unworked or simply sawn, cleaved or bruted 4.1 1 Burkina Faso 1 Burundi Filtering or purifying machinery and apparatus for liquids not elsewhere specified 4.3 4 Cameroon Bananas including plantains, fresh or dried 6.1 5 Cape Verde Turbo-jets of a thrust exceeding 25 KN 10.9 7 Central African Republic Lumber, tropical hardwood not elsewhere specified, sawn lengthwise >6mm 8.7 3 Chad Comoros Tugs and pusher craft 19.2 4 Congo, Dem. Rep. Copper ores and concentrates 9.7 5 Congo, Rep. 1 Côte d'Ivoire Cocoa paste not defatted 6.1 8 Djibouti Dump trucks designed for off-highway use 6.1 24 Equatorial Guinea 1 Eritrea Bovine leather, otherwise pre-tanned, not elsehwere specified 9.1 14 Ethiopia 8 Gabon Logs, tropical hardwoods not elsewhere specified 7.6 2 Gambia, The Ground-nuts shelled, whether or not broken, not roast or otherwise cooked 8.8 5 Ghana Aluminium unwrought, not alloyed 3.6 10 Guinea Aluminium oxide not elsewhere specified 11.6 4 Guinea-Bissau 1 Kenya Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 5.5 37 Lesotho Diamonds non-industrial unworked or simply sawn, cleaved or bruted 18.0 6 Liberia Petroleum oils and oils obtained from bituminous minerals, crude 13.6 4 Madagascar Womens/girls trousers and shorts, of cotton, not knitted 80.0 25 Malawi Black tea (fermented) & partly fermented tea in packages exceeding 3 kg 7.5 4 Mali Cargo vessels nes&oth vessels for the transport of both persons and goods 13.5 7 Mauritania Octopus, frozen, dried, salted or in brine 9.6 3 Mauritius Tunas,skipjack & Atl bonito, prepared/preserved,whole/in pieces, excluding minced 9.7 19 Mozambique Tobacco, unmanufactured, partly or wholly stemmed or stripped 4.6 3 Namibia Natural uranium and its compounds mixtures containing natural uranium/its compounds 9.8 5 Niger 2 Nigeria 1 Rwanda Black tea (fermented) & partly fermented tea in packages exceeding 3 kg 9.2 3 São Tomé and Principe Motorcycles with reciprocatg piston engine displacg > 500 cc to 800 cc 13.1 4 Senegal Fish not elsewhere specififed, fresh or chilled excluding heading No 03.04, livers and roes 7.4 23 Seychelles Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 11.0 4 Sierra Leone Cocoa beans, whole or broken, raw or roasted 8.0 6 Somalia Wood charcoal (including shell or nut charcoal) 9.3 9 South Africa Gold in unwrought forms non-monetary 5.7 74 Sudan 1 Swaziland Mixtures of odoriferous substances for the food or drink industries 8.6 25 Tanzania Cotton, not carded or combed 6.7 21 Togo Cement climkers 9.8 8 Uganda Fish fillets frozen 5.8 6 Zambia Wire of refined copper of which the maximum cross sectional dimension >6mm 4.7 3 Zimbabwe Ferro-chronium containing by weight more than 4% of carbon 9.1 17 NORTH AFRICA Algeria Petroleum oils and oils obtained from bituminous minerals, other than crude and the like 9.3 2 Egypt, Arab Rep. Petroleum oils and oils obtained from bituminous minerals, crude 13.0 48 Libya 1 Morocco 71 Tunisia Olive oil, virgin 5.4 77 AFRICAa Natural gas, liquefied 3.6 [35.2] 24 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. 86 Part III. Development outcomes TRADE Drivers of growth ableT6.3 Regional integration, trade blocs Year of entry into force of the Type of the Year most recent most recent Merchandise exports within bloc ($ millions) established agreement agreementa 1990 1995 2000 2003 2004 2005 2006 Economic and Monetary Community 1994 1999 CU 139 127 96 146 174 198 245 of Central African States (CEMAC ) Common Market for Eastern and 1994 1994 FTA 1,164 1,390 1,448 2,041 2,427 2,869 3,546 Southern Africa (COMESA) East African Community (EAC) 1996 2000 CU 230 530 595 706 750 857 1059 Economic Community of Central 1983 2004b NNA 163 163 191 198 240 271 334 African States (ECCAS) Economic Community of West African 1975 1993 PS 1,532 1,875 2,715 3,037 4,366 5,497 5,957 States (ECOWAS) Indian Ocean Commission (IOC) 1984 2005b NNA 73 127 106 179 155 159 172 Southern African Development 1992 2000 FTA 677 1,015 4,383 5,609 6,590 7,668 8,571 Community (SADC) West African Economic and Monetary 1994 2000 CU 621 560 741 1,076 1,233 1,390 1,545 Union (UEMOA) Merchandise exports within bloc (% of total bloc exports) Economic and Monetary Community 1994 1999 CU 2.3 2.1 1.0 1.4 1.2 0.9 0.9 of Central African States (CEMAC ) Common Market for Eastern and 1994 1994 FTA 4.2 5.4 3.7 4.4 4.1 3.4 3.2 Southern Africa (COMESA) East African Community (EAC) 1996 2000 CU 13.4 17.4 20.5 18.3 16.7 15.1 16.5 Economic Community of Central 1983 2004b NNA 1.4 1.5 1.1 1.0 0.9 0.6 0.6 African States (ECCAS) Economic Community of West 1975 1993 PS 8.0 9.0 7.6 8.5 9.3 9.3 8.3 African States (ECOWAS) Indian Ocean Commission (IOC) 1984 2005b NNA 4.1 6.0 4.4 6.2 4.3 4.6 4.7 Southern African Development 1992 2000 FTA 6.8 9.2 9.4 10.1 9.7 9.2 9.1 Community (SADC) West African Economic and Monetary 1994 2000 CU 13.0 10.3 13.1 13.3 12.9 13.4 13.1 Union (UEMOA) a. FTA is free trade agreement, CU is customs union, EIA is economic integration agreement, PS is partial scope agreement and NNA is not notified agreement, which refers to prefentrial tarde agreements established among member ocuntries that are not notified to the World Trade Organization (these agreements may be functionally equivalent to any of th eother agreements). b. Years of the most recent agreement are collected from official trade bloc website 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; 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; 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 Sey- chelles; 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; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d'Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo. TRADE Part III. Development outcomes 87 Drivers of growth ableT7.1 Water and sanitation Access, supply side Access, demand side Internal freshwater Population with Population with resources sustainable access to sustainable access to per capita improved water source (%) improved sanitation (%) (cubic meters) Total Urban Rural Total Urban Rural 2000­06a 2006 2006 2006 2006 2006 2006 SUB-SAHARAN AFRICA 5,088 58 81 46 31 42 24 Angola 9,195 51 62 39 50 79 16 Benin 1,213 65 78 57 30 59 11 Botswana 1,307 96 100 90 47 60 30 Burkina Faso 897 72 97 66 13 41 6 Burundi 1,285 71 84 70 41 44 41 Cameroon 15,341 70 88 47 51 58 42 Cape Verde 592 .. .. .. .. .. .. Central African Republic 33,640 66 90 51 31 40 25 Chad 1,479 48 71 40 9 23 4 Comoros 1,998 85 91 81 35 49 26 Congo, Dem. Rep. 15,322 46 82 29 31 42 25 Congo, Rep. 61,498 71 95 35 20 19 21 Côte d'Ivoire 4,132 81 98 66 24 38 12 Djibouti 373 92 98 54 67 76 11 Equatorial Guinea 53,708 43 45 42 51 60 46 Eritrea 619 60 74 57 5 14 3 Ethiopia 1,623 42 96 31 11 27 8 Gabon 127,064 87 95 47 36 37 30 Gambia, The 1,855 86 91 81 52 50 55 Ghana 1,345 80 90 71 10 15 6 Guinea 25,104 70 91 59 19 33 12 Guinea-Bissau 10,019 57 82 47 33 48 26 Kenya 582 57 85 49 42 19 48 Lesotho 2,625 78 93 74 36 43 34 Liberia 58,109 64 72 52 32 49 7 Madagascar 18,077 47 76 36 12 18 10 Malawi 1,217 76 96 72 60 51 62 Mali 5,168 60 86 48 45 59 39 Mauritania 135 60 70 54 24 44 10 Mauritius 2,252 100 100 100 94 95 94 Mozambique 4,885 42 71 26 31 53 19 Namibia 3,070 93 99 90 35 66 18 Niger 264 42 91 32 7 27 3 Nigeria 1,563 47 65 30 30 35 25 Rwanda 1,029 65 82 61 23 34 20 São Tomé and Principe 14,415 86 88 83 24 29 18 Senegal 2,192 77 93 65 28 54 9 Seychelles .. .. 100 .. .. .. 100 Sierra Leone 28,641 53 83 32 11 20 5 Somalia 732 29 63 10 23 51 7 South Africa 955 93 100 82 59 66 49 Sudan 813 70 78 64 35 50 24 Swaziland 2,299 60 87 51 50 64 46 Tanzania 2,183 55 81 46 33 31 34 Togo 1,843 59 86 40 12 24 3 Uganda 1,347 64 90 60 33 29 34 Zambia 6,987 58 90 41 52 55 51 Zimbabwe 938 81 98 72 46 63 37 NORTH AFRICA 308 92 96 87 76 90 60 Algeria 341 85 87 81 94 98 87 Egypt, Arab Rep. 25 98 99 98 66 85 52 Libya 101 .. .. .. 97 97 96 Morocco 962 83 100 58 72 85 54 Tunisia 419 94 99 84 85 96 64 a. Data are for most recent year available during the period specified. 88 Part III. Development outcomes INFRASTRUCTURE Quality of supply Financing Committed nominal ODA gross aid Water supply investment in disbursements for failure for firms water projects with water supply and receiving water private participation sanitation sector (average days per year) ($ millions) ($ millions) 2000­06a 2000­06a 2006 739.2 83.5 .. 5.4 19.2 .. 32.3 0.0 .. 0.1 11.8 .. 40.7 94.1 .. 2.7 6.8 .. 2.5 12.8 .. 2.0 .. .. 0.9 .. .. 13.8 .. .. 0.5 81.8 .. 0.0 .. 0.0 9.3 .. .. 1.0 .. .. 0.0 .. .. 2.9 79.2 .. 6.9 0.0 .. 30.2 .. .. 0.0 .. .. 2.9 .. 0.0 62.0 .. .. 14.4 43.2 .. 1.3 85.2 .. 30.3 19.2 .. 3.9 .. .. 0.5 5.2 .. 4.6 21.3 .. 4.8 2.1 .. 27.1 92.5 .. 6.3 16.7 .. 27.8 .. .. 24.4 10.2 0.0 3.8 0.1 3.4 19.8 .. .. 15.4 .. .. 18.7 .. .. 0.3 5.6 0.0 20.5 .. .. .. .. .. 0.9 .. .. 3.6 4.8 0.0 81.0 .. .. 16.8 18.1 .. 0.6 105.0 8.5 43.4 .. .. 1.0 2.7 0.0 47.6 13.6 0.0 35.6 .. .. 1.7 345.0 31.0 510.0 4.9 5.2 .. 55.6 .. .. 0.0 1.3 .. 170.4 .. .. 107.9 INFRASTRUCTURE Part III. Development outcomes 89 Drivers of growth ableT7.2 Transportation Access, supply side Access, demand side Road density Ratio to arable land Ratio to total land Rural access (% of rural Road network Rail lines (road km/1000 ha (road km/1000 sq. population within 2 km (km) (km) arable land) km of land area) of an all-season road) 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a SUB-SAHARAN AFRICA Angola 51,429 2,761 17.1 4.1 .. Benin 19,000 578 6.9 17.2 32.0 Botswana 24,455 888 64.9 4.3 .. Burkina Faso 92,495 622 19.1 33.8 25.0 Burundi 12,322 .. 12.6 48.0 .. Cameroon 50,000 1,016 8.4 10.7 22.0 Cape Verde 1,350 .. 30.7 33.5 .. Central African Republic 24,307 .. 12.6 3.9 .. Chad 33,400 .. 9.5 2.7 .. Comoros 880 .. 11.0 47.3 .. Congo, Dem. Rep. 153,497 3,641 22.9 6.8 26.0 Congo, Rep. 17,289 795 34.9 5.1 .. Côte d'Ivoire 80,000 639 22.9 25.2 .. Djibouti 3,065 781 3,065.0 13.2 .. Equatorial Guinea 2,880 .. 22.2 10.3 .. Eritrea 4,010 306 7.2 4.0 .. Ethiopia 39,477 781 2.8 3.9 30.2 Gabon 9,170 810 28.2 3.6 .. Gambia, The 3,742 .. 10.7 37.4 .. Ghana 57,613 977 13.8 25.3 .. Guinea 44,348 1,115 40.3 18.0 37.0 Guinea-Bissau 3,455 .. 11.5 12.3 .. Kenya 63,265 1,917 12.0 11.1 .. Lesotho 5,940 .. 18.0 19.6 .. Liberia 10,600 490 27.9 11.0 .. Madagascar 49,827 732 17.2 8.6 .. Malawi 15,451 710 6.3 16.4 .. Mali 18,709 733 3.9 1.5 .. Mauritania 7,660 717 15.7 0.7 .. Mauritius 2,015 .. 20.2 99.3 .. Mozambique 30,400 3,070 7.8 3.9 11.0 Namibia 42,237 .. 51.8 5.1 .. Niger 18,423 .. 1.3 1.5 17.0 Nigeria 193,200 3,528 6.2 21.2 .. Rwanda 14,008 .. 11.7 56.8 .. São Tomé and Principe 320 .. 53.3 33.3 .. Senegal 13,576 906 5.5 7.1 .. Seychelles 458 .. 458.0 99.6 .. Sierra Leone 11,300 .. 21.1 15.8 22.0 Somalia 22,100 .. 21.2 3.5 .. South Africa 364,131 20,247 24.7 30.0 .. Sudan 11,900 5,478 0.7 0.5 .. Swaziland 3,594 301 20.2 20.9 .. Tanzania 78,891 4,582 8.6 8.9 16.0 Togo 7,520 568 3.0 13.8 .. Uganda 70,746 259 13.6 35.9 .. Zambia 91,440 1,273 17.4 12.3 51.0 Zimbabwe 97,267 .. 30.2 25.1 .. NORTH AFRICA Algeria 108,302 3,572 14.5 4.5 .. Egypt, Arab Rep. 92,370 5,150 31.2 9.3 .. Libya 83,200 2,757 45.8 4.7 .. Morocco 57,626 1,907 6.8 12.9 .. Tunisia 19,232 1,909 6.9 12.4 .. a. Data are for most recent year available during the period specified. 90 Part III. Development outcomes INFRASTRUCTURE Access, demand side Quality Pricing Financing Vehicles fleet (per 1000 people) Roads Committed nominal ODA gross aid Road network Ratio Price investment in transport disbursements Commercial Passenger in good or fair of paved to of diesel fuel Price of gasoline projects with private for transport and vehicles vehicles condition (%) total roads (%) (US$ per liter) (US$ per liter) participation ($ millions) storage ($ millions) 2000­06a 2000­06a 2000­06a 2000­06a 2006 2006 2000­06a 2006 0.98 1.03 976.9 .. 8.0 .. 10.4 0.36 0.50 .. 2.2 .. 13.0 82.9 9.5 0.81 0.81 .. 13.1 113.0 47.0 .. 33.2 0.74 0.78 .. .. 7.0 5.0 69.3 4.2 1.12 1.15 .. 39.6 .. 1.0 .. 10.4 1.22 1.20 .. 2.2 11.0 11.0 52.0 10.0 1.07 1.14 .. 39.9 .. .. .. 69.0 0.39 0.59 .. 26.0 .. 1.0 .. .. 1.27 1.37 .. 5.2 .. .. 72.8 0.8 1.20 1.31 .. 20.9 1.0 1.0 .. 76.5 .. .. 0.5 1.4 .. .. 23.2 1.8 1.00 0.94 .. 7.1 .. 8.0 .. 5.0 0.67 0.96 .. 25.9 .. 7.0 .. 8.1 1.06 1.20 .. 0.1 .. .. .. 45.0 0.54 0.98 300.0 0.5 .. .. .. .. .. .. 72.0 .. .. .. .. 21.8 0.81 1.90 .. 3.6 2.0 1.0 69.0 12.7 0.62 0.93 .. 134.7 .. .. .. 10.2 0.39 0.64 91.8 10.0 7.0 5.0 94.6 19.3 1.01 1.08 .. 2.1 21.0 5.0 73.0 17.9 0.84 0.86 .. 78.4 14.0 8.0 44.2 9.8 0.82 0.79 .. 5.7 1.0 .. .. 27.9 .. .. .. 18.4 18.0 9.0 67.2 14.1 0.98 1.12 404.0 46.5 .. .. .. 18.3 0.88 0.89 .. 2.2 .. 6.0 .. 6.2 0.85 0.79 .. 0.0 .. .. .. 11.6 1.00 1.15 12.5 82.4 .. .. .. 45.0 1.12 1.17 .. 6.5 .. .. 62.0 18.0 1.04 1.22 55.4 45.3 .. .. .. 11.3 0.84 0.97 .. 10.0 130.0 96.0 .. 100.0 0.56 0.74 .. 0.1 .. .. 64.0 18.7 1.06 1.15 186.9 80.8 85.0 42.0 .. 12.8 0.87 0.87 .. 4.7 5.0 4.0 63.1 20.6 1.11 1.14 .. 6.2 .. 17.0 .. 15.0 0.66 0.51 262.1 0.1 3.0 1.0 .. 19.0 1.08 1.11 .. 12.7 .. .. .. 68.1 0.71 0.90 .. 4.4 14.0 10.0 .. 29.3 1.09 1.31 55.4 32.7 121.0 74.0 .. 96.0 .. .. .. 0.1 4.0 2.0 .. 8.0 0.98 0.98 .. 12.0 .. .. .. 11.8 0.67 0.74 .. 0.3 143.0 98.0 .. 17.3 0.84 0.85 3,483.0 0.0 .. .. .. 36.3 0.49 0.72 30.0 9.3 84.0 40.0 .. 30.0 0.85 0.80 .. 9.6 .. 1.0 .. 8.6 0.99 1.04 27.7 80.6 .. 10.0 .. 31.6 1.01 1.03 .. 5.7 5.0 2.0 31.0 23.0 1.01 1.17 404.0 20.7 .. .. 37.0 22.0 1.22 1.31 15.6 59.8 .. 45.0 60.0 19.0 0.65 0.61 .. 0.0 0.19 0.32 213.7 91.0 58.0 .. 70.2 0.19 0.32 .. 42.3 .. 27.0 .. 81.0 0.12 0.30 86.2 5.8 257.0 232.0 .. 57.2 0.13 0.13 .. .. 59.0 46.0 .. 61.9 0.87 1.22 140.0 98.3 95.0 83.0 .. 65.8 0.57 0.83 .. 62.9 INFRASTRUCTURE Part III. Development outcomes 91 Drivers of growth ableT7.3 Information and communication technology Access, supply side Access, demand side Quality Average delay for firm Telephone subscribers (per 100 people) Households with own telephone of obtaining a Duration Telephone Total Urban Rural mainline phone Internet of phone faults Mainline Mobile (% of (% of urban (% of rural connection users (per outages (per 100 Total telephone phone households) households) households) (days) 100 people) (hours) mainlines) 2006a 2006a 2006a 2000­06a 2000­06a 2000­06a 2006a 2006a 2000­06a 2000­06a SUB-SAHARAN AFRICA 19.4 1.6 17.5 3.4 Angola 14.3 0.6 13.7 .. .. .. 41.8 0.6 .. .. Benin 12.9 0.9 12.1 4.4 10.3 1.0 .. 1.4 6.1 7.0 Botswana 51.4 7.1 44.3 .. .. .. 22.8 4.3 .. .. Burkina Faso 7.7 0.7 7.1 3.7 19.8 0.3 44.8 0.6 .. 18.4 Burundi 2.9 0.4 2.5 .. .. .. 36.6 0.7 .. 6.0 Cameroon 18.0 0.7 17.3 2.3 4.8 0.0 105.2 2.0 .. .. Cape Verde 34.8 13.8 21.0 .. .. .. 8.4 6.4 .. 3.0 Central African Republic 2.9 0.3 2.6 .. .. .. .. 0.3 .. 56.0 Chad 4.6 0.1 4.5 0.9 4.3 0.0 .. 0.6 .. 60.8 Comoros 9.1 3.1 6.0 .. .. .. .. 3.4 .. 55.8 Congo, Dem. Rep. 7.3 0.0 7.3 .. .. .. 29.2 0.3 .. .. Congo, Rep. .. .. 21.7 1.3 2.2 0.2 .. 1.9 .. .. Cote d'Ivoire 22.9 1.4 21.5 .. .. .. .. 1.6 .. 81.0 Djibouti .. .. 5.4 .. .. .. .. 1.3 .. 136.0 Equatorial Guinea .. .. 28.3 .. .. .. .. 1.6 .. .. Eritrea 2.1 0.8 1.3 .. .. .. .. 2.1 .. 63.8 Ethiopia 2.1 0.9 1.1 4.4 35.4 0.2 58.5 0.3 .. 100.0 Gabon 71.3 2.8 68.5 15.3 20.0 1.8 .. 6.2 .. 13.4 Gambia, The 27.1 2.8 24.3 .. .. .. 24.8 5.0 .. .. Ghana 24.2 1.6 22.6 7.5 17.0 0.7 .. 2.7 .. 3.2 Guinea .. .. .. 7.2 23.7 0.3 59.2 0.5 .. 1.6 Guinea-Bissau 10.0 0.4 9.6 .. .. .. 27.6 2.3 .. 70.5 Kenya 20.9 0.8 20.1 12.3 37.4 6.0 .. 7.6 27.2 70.1 Lesotho 20.6 2.7 17.9 16.9 45.8 10.6 .. 3.0 26.4 60.0 Liberia .. .. 7.8 .. .. .. .. 0.0 .. .. Madagascar 6.1 0.7 5.5 4.9 11.9 3.0 .. 0.6 21.3 36.0 Malawi 6.1 1.0 5.2 6.0 26.7 2.1 107.7 0.4 28.0 .. Mali 13.3 0.7 12.6 3.5 12.8 0.1 .. 0.7 10.3 177.6 Mauritania 36.0 1.2 34.8 3.6 8.0 0.2 14.5 1.0 .. 5.5 Mauritius 90.1 28.5 61.6 .. .. .. .. 25.5 5.3 23.0 Mozambique 11.5 0.3 11.2 2.1 6.1 0.1 .. 0.9 .. 46.0 Namibia 36.4 6.7 29.8 17.4 43.5 4.5 7.3 4.4 .. 35.0 Niger .. .. 3.5 .. .. .. 60.1 0.3 .. 71.4 Nigeria 23.5 1.2 22.3 5.1 11.7 1.8 .. 5.5 .. 20.6 Rwanda 3.5 0.2 3.3 1.1 6.1 0.2 61.7 1.1 .. 18.2 Sao Tome and Principe 16.8 4.9 11.9 .. .. .. .. 14.2 .. 14.0 Senegal 27.1 2.3 24.7 19.8 35.9 7.5 .. 5.4 11.4 2.0 Seychelles 107.6 24.4 83.1 .. .. .. .. 34.3 .. 6.0 Sierra Leone .. .. .. .. .. .. .. 0.2 .. .. Somalia 7.7 1.2 6.5 .. .. .. .. 1.1 .. .. South Africa 93.6 9.9 83.7 .. .. .. .. 7.8 3.9 48.2 Sudan 13.4 2.0 11.4 .. .. .. .. 8.5 .. 5.0 Swaziland 25.8 3.9 22.0 .. .. .. 36.9 3.7 .. 0.7 Tanzania 15.0 0.4 14.6 9.7 31.4 3.0 23.3 1.0 10.8 26.0 Togo 12.3 1.3 11.0 .. .. .. .. 5.0 .. 6.2 Uganda 7.1 0.4 6.7 3.1 18.5 0.9 12.8 5.0 16.9 .. Zambia 15.0 0.8 14.2 4.3 11.2 0.6 .. 4.3 11.7 108.0 Zimbabwe 9.0 2.5 6.4 .. .. .. .. 9.2 .. 57.0 NORTH AFRICA 53.0 10.9 43.0 .. .. .. .. 10.5 Algeria 71.5 8.5 63.0 .. .. .. .. 7.4 .. 0.8 Egypt, Arab Rep. 38.8 14.6 24.3 .. .. .. .. 8.1 .. 0.1 Libya .. .. 65.0 .. .. .. .. 4.3 .. .. Morocco 56.6 4.2 52.5 .. .. .. .. 20.0 15.0 25.0 Tunisia 85.0 12.5 72.5 .. .. .. .. 12.8 .. 20.0 a. Data are for most recent year available during the period specified. 92 Part III. Development outcomes INFRASTRUCTURE Pricing Financing Price of 3-minute telephone Connection Annual investment Committed ODA gross aid calls during peak hours charge ($ millions) nominal investment disbursements Price basket Fixed Cellular International Residential Mobile in telecommunication for for Internet telephone local call to telephone cellular Telephone Mobile Telecom- projects with private communication ($ per month) local call ($) call ($) US ($) ($) ($) service communication munications participation ($ millions) ($ millions) 2006 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2006 42.1 0.14 0.77 0.13 45.0 18.3 65.6 29.6 0.09 0.74 0.09 46.0 49.2 .. .. 157.0 250.0 0.8 20.9 0.16 0.96 0.11 112.6 9.6 .. 3.6 3.9 17.0 0.8 18.2 0.13 1.06 0.13 39.4 3.4 .. .. 404.0 18.0 0.3 91.4 0.19 0.77 0.19 47.4 28.4 .. 66.6 202.6 290.0 0.1 52.0 0.07 0.58 0.07 9.3 9.3 .. .. .. .. 1.4 44.0 0.11 1.32 0.29 75.8 9.6 .. 110.4 211.4 63.0 0.6 40.7 0.05 1.18 0.05 33.8 45.6 12.4 1.8 15.9 .. 1.5 144.6 0.57 0.57 0.57 67.1 37.9 .. .. 0.1 .. .. 105.0 0.14 1.05 0.14 100.7 7.7 .. .. .. 26.4 .. 38.1 0.19 0.70 0.13 102.4 63.8 .. .. 4.2 .. .. 109.5 0.15 .. 0.15 118.6 .. .. .. .. 74.0 0.2 82.7 .. .. .. .. .. .. .. .. 10.0 0.3 67.7 0.29 2.26 0.29 19.1 19.1 32.2 266.2 382.5 13.0 0.2 41.0 0.08 0.51 0.08 56.3 56.3 .. .. 12.4 .. 0.1 32.2 .. .. .. .. .. .. .. .. .. .. 28.6 0.04 0.33 0.04 65.1 91.1 .. 7.3 16.5 40.0 0.0 22.8 0.02 0.25 0.02 35.1 52.9 14.5 5.2 60.3 .. 1.8 39.2 0.28 0.56 0.29 103.2 23.7 .. .. 18.2 .. 0.1 17.8 0.03 0.67 0.03 41.2 15.1 .. .. 3.7 .. 0.0 22.6 0.17 0.20 0.16 101.4 8.3 .. .. 59.4 215.0 0.5 17.8 0.07 0.54 0.07 98.0 33.3 .. .. 0.8 48.0 0.1 75.0 .. .. .. 67.4 .. .. .. .. .. 0.1 79.2 0.11 0.64 0.13 30.4 33.1 .. 511.8 792.9 619.0 0.9 36.2 0.26 0.46 0.25 53.0 6.3 .. 1.5 1.7 5.5 .. .. .. .. .. .. .. .. .. .. 10.5 .. 43.3 0.20 0.45 0.20 23.2 4.7 .. .. 50.8 .. 0.1 39.0 0.10 0.60 0.09 10.1 4.2 .. .. .. 30.5 0.7 28.7 0.10 0.85 0.10 38.0 56.8 .. .. 93.9 .. 1.7 52.9 0.11 0.51 0.11 18.8 7.5 .. .. 30.2 .. 0.0 16.2 0.07 0.11 0.06 33.9 17.7 .. 18.4 36.4 .. 0.1 29.0 0.15 0.38 0.13 19.3 0.3 .. 4.5 21.4 15.6 9.5 45.3 0.06 1.18 0.06 39.4 14.4 .. .. 20.4 8.8 0.5 102.4 0.14 0.92 0.14 28.7 40.9 .. .. .. .. 0.1 50.3 0.14 0.89 0.14 68.6 3.8 .. .. 386.9 2,535.1 0.3 29.4 0.08 0.79 0.18 25.6 2.2 .. .. .. 10.0 0.3 62.6 0.14 .. 0.12 44.0 .. .. .. 1.1 .. 0.1 25.8 0.22 0.57 0.23 44.4 40.2 .. 100.5 183.6 212.0 9.1 31.9 0.13 1.63 0.13 95.3 9.1 .. .. 12.8 .. .. 10.7 0.03 0.90 0.03 46.7 13.4 .. .. .. 40.0 0.1 .. 0.10 0.04 0.10 .. .. .. .. .. .. .. 59.4 0.19 1.27 0.19 39.6 23.4 .. 360.3 870.6 1,357.0 2.2 70.9 0.06 0.26 0.06 20.5 82.1 .. 229.4 128.5 706.3 0.4 48.7 0.09 1.23 0.08 33.5 10.2 .. .. 27.6 .. 0.1 93.6 0.16 0.69 0.14 35.4 18.1 .. .. 9.4 70.0 2.1 45.1 0.22 0.72 0.23 111.9 17.2 26.4 26.3 67.4 .. 0.1 95.8 0.28 0.67 0.28 65.5 11.2 .. .. 69.0 .. 0.8 81.2 0.13 0.62 0.17 11.2 3.1 .. 36.9 42.5 238.0 0.6 24.6 0.00 7.62 0.05 0.0 166.7 .. 0.7 115.9 20.0 0.8 12.0 0.05 0.34 0.02 38.1 3.8 .. 48.2 9.3 0.08 0.22 0.08 41.3 16.5 .. .. 96.5 702.0 3.5 5.0 0.01 0.16 0.02 86.5 4.3 .. 543.5 2,669.8 3,751.0 29.8 22.1 .. 0.34 .. 38.1 3.8 .. 212.4 .. .. .. 26.9 0.15 1.14 0.17 67.7 3.4 .. .. 463.5 575.6 3.9 12.0 0.02 0.34 0.02 15.0 7.7 .. 216.4 311.8 2,343.0 3.3 INFRASTRUCTURE Part III. Development outcomes 93 Drivers of growth ableT7.4 Energy Access, demand side Access to electricity Solid fuels use GDP per unit of Electric power energy use (constant Total Urban Rural Total Urban Rural consumption 2000 PPP $ per kg (% of total (% of urban (% of rural (% of total (% of urban (% of rural (kWh per capita) of oil equivalent) population) population) population) population) population) population) 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a SUB-SAHARAN AFRICA Angola 141.0 6.1 .. .. .. .. .. .. Benin 69.4 4.0 22.0 50.9 5.6 95.6 89.5 99.1 Botswana 1,405.8 11.7 .. .. .. .. .. .. Burkina Faso .. .. 10.2 53.5 0.8 97.5 88.5 99.4 Burundi .. .. .. .. .. .. .. .. Cameroon 196.1 5.0 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. 91.1 0.9 .. .. .. .. .. .. Congo, Rep. 159.6 10.0 34.9 51.3 16.4 83.2 71.3 96.5 Côte d'Ivoire 170.3 3.8 .. .. .. .. .. .. Djibouti .. .. .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. Ethiopia 34.4 2.2 12.0 85.9 2.0 89.0 69.7 91.6 Gabon 999.5 10.4 75.2 90.6 31.4 34.1 16.4 84.4 Gambia, The .. .. .. .. .. .. .. .. Ghana 265.8 2.9 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 138.4 2.8 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 450.2 1.4 11.0 29.8 1.5 96.9 91.9 99.5 Namibia 1,428.0 6.7 31.7 74.6 10.4 65.9 18.7 89.3 Niger .. .. .. .. .. .. .. .. Nigeria 126.6 2.4 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 151.0 6.0 46.4 82.1 19.0 58.7 24.3 85.2 Seychelles .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. South Africa 4,847.2 3.1 .. .. .. .. .. .. Sudan 94.3 3.4 .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. Tanzania 61.2 2.0 10.6 38.9 1.8 98.1 93.4 99.5 Togo 94.1 2.4 .. .. .. .. .. .. Uganda .. .. 8.4 47.5 2.6 97.4 87.8 98.8 Zambia 721.2 1.9 20.1 50.0 3.5 83.6 58.5 97.6 Zimbabwe 953.1 .. .. .. .. .. 5.1 .. NORTH AFRICA .. .. .. .. .. .. Algeria 898.6 7.0 .. .. .. .. .. .. Egypt, Arab Rep. 1,245.4 5.4 .. .. .. .. .. .. Libya 3,299.5 4.0 .. .. .. .. .. .. Morocco 643.5 7.8 .. .. .. .. .. .. Tunisia 1,193.9 7.6 .. .. .. .. .. .. a. Data are for most recent year available during the period specified. 94 Part III. Development outcomes INFRASTRUCTURE Quality Financing Firms identifying Committed nominal ODA gross Average delay Electric power electricity as investment in aid for firm in transmission Electric power Firms that major or very severe energy projects with disbursements obtaining electrical and distribution outages in a typical share or own their obstacle to business private participation for energy connection (days) losses (% of output) month (number) own generator (%) operation and growth (%) ($ millions) ($ millions) 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2006 156.4 60.2 14.5 7.8 67.7 45.8 9.4 0.3 71.7 .. .. 26.9 69.2 590.0 0.0 25.5 14.8 1.7 15.7 6.8 .. 1.2 19.6 .. 10.1 24.0 48.9 .. 13.8 24.1 .. 12.0 41.9 72.3 .. 0.1 78.9 15.8 12.7 61.3 61.1 440.0 0.1 7.8 .. 12.5 34.0 65.3 .. 0.0 .. .. .. .. .. .. 0.0 .. .. .. .. .. .. 0.9 .. .. .. .. .. .. 0.1 20.5 3.7 17.8 41.0 70.3 .. 0.0 .. 55.6 .. .. .. .. 0.2 .. 18.1 .. .. .. .. 0.1 .. .. .. .. .. .. 0.0 .. .. .. .. .. .. 0.2 .. .. .. .. .. .. 0.6 44.2 10.0 5.1 25.6 21.5 .. 0.6 .. 17.8 .. .. .. .. 0.0 63.9 .. 23.8 63.9 78.1 .. 0.2 .. 14.4 .. .. .. 590.0 1.7 16.1 .. 33.9 59.9 83.6 .. 0.0 20.5 .. 9.2 68.4 74.1 .. 0.0 51.3 17.8 .. 70.9 48.2 116.7 20.9 51.4 .. .. 26.1 35.6 .. 0.1 .. .. .. .. .. .. 0.0 58.0 .. .. 21.5 41.3 .. 2.9 98.5 .. 76.9 49.1 60.4 .. 0.2 35.6 .. .. 45.3 24.2 .. 4.1 7.5 .. 3.7 28.6 28.9 .. 0.4 23.2 .. .. 39.5 12.7 .. 0.0 .. 12.3 .. .. .. 5.8 35.4 9.2 18.4 1.7 12.8 6.5 1.0 1.0 20.6 .. 20.7 24.8 21.6 .. 1.6 .. 24.0 .. .. .. 1129.0 2.6 18.2 .. 13.7 58.0 55.0 1.6 0.2 .. .. .. .. .. 50.0 0.1 13.2 30.1 .. 62.5 30.7 93.3 2.7 .. .. .. .. .. .. .. .. .. .. .. .. .. 0.4 .. .. .. .. .. .. 0.1 7.3 6.3 .. 9.5 9.0 9.9 11.3 .. 15.7 .. .. .. .. 0.5 16.9 .. 2.5 36.8 12.4 .. 0.0 44.3 26.9 12.0 45.7 88.4 28.4 13.3 .. 46.0 .. .. .. 590.0 0.0 33.0 .. 11.0 28.9 84.2 11.8 10.3 142.4 4.7 .. 38.2 39.6 3.0 2.3 .. 7.2 .. .. .. .. 0.2 .. 306.1 124.9 13.2 .. 29.5 11.5 2320.0 0.3 167.4 15.8 .. 26.7 27.4 678.0 181.4 .. 13.2 .. .. .. .. .. 7.5 17.9 .. 18.1 9.3 360.0 121.7 .. 12.3 .. .. .. 30.0 0.9 INFRASTRUCTURE Part III. Development outcomes 95 Drivers of growth ableT7.5 Financial sector infrastructure Macroeconomy International Gross Money and Foreign currency sovereign ratings national savings quasi money Long-term Short-term (% of GDP) (M2) (% of GDP) Real interest rate (%) 2008 2008 2006a 2006a 2006a SUB-SAHARAN AFRICA 9.0 35.6 Angola .. .. 37.0 13.9 4.2 Benin B B .. 28.2 .. Botswana .. .. 53.3 30.5 0.7 Burkina Faso .. .. 6.3 19.4 .. Burundi .. .. .. 33.6 14.1 Cameroon B B 18.9 17.0 11.0 Cape Verde B+ B 26.8 76.0 4.4 Central African Republic .. .. 11.7 16.3 10.6 Chad .. .. 20.4 9.5 8.6 Comoros .. .. 4.9 20.8 8.3 Congo, Dem. Rep. .. .. 8.9 8.7 .. Congo, Rep. .. .. 43.0 13.9 -2.7 Côte d'Ivoire .. .. 14.7 24.2 .. Djibouti .. .. 20.7 75.5 .. Equatorial Guinea .. .. 46.2 6.7 -3.4 Eritrea .. .. 8.7 134.6 .. Ethiopia .. .. 15.1 39.2 -4.1 Gabon BB­ B 41.3 18.2 6.9 Gambia, The .. .. 10.0 48.5 27.1 Ghana B+ B 27.4 29.2 .. Guinea BB­ B 11.8 .. .. Guinea-Bissau .. .. 22.7 33.6 .. Kenya .. .. 12.5 36.3 3.9 Lesotho B­ B 27.3 30.1 7.7 Liberia B­ B .. 21.0 5.8 Madagascar .. .. 16.0 19.5 16.4 Malawi .. .. 15.5 13.6 12.1 Mali B B 13.0 28.3 .. Mauritania BBB­ F3 28.7 .. -4.5 Mauritius .. .. 19.0 100.3 16.3 Mozambique BB­ B 3.1 27.0 11.9 Namibia B­ B 42.2 44.8 1.9 Niger .. .. .. 14.2 .. Nigeria .. .. .. 16.9 -2.2 Rwanda .. .. 13.8 .. 2.6 São Tomé and Principe .. .. .. 45.5 7.9 Senegal .. .. 18.3 34.0 .. Seychelles BBB+ F2 9.4 116.1 7.7 Sierra Leone .. .. 9.5 19.1 11.1 Somalia .. .. .. .. .. South Africa .. .. 13.9 60.0 4.0 Sudan .. .. 10.3 20.0 .. Swaziland B B 14.5 20.7 0.7 Tanzania .. .. 10.7 24.2 10.8 Togo .. .. .. 30.1 .. Uganda .. .. 12.9 19.7 9.2 Zambia .. .. 23.3 17.9 9.4 Zimbabwe BB+ B .. .. .. NORTH AFRICA 14.6 66.3 Algeria BBB­ F3 .. 49.5 -2.5 Egypt, Arab Rep. .. .. 22.0 91.0 4.9 Libya .. .. .. 27.0 -6.5 Morocco .. .. 34.5 89.5 .. Tunisia .. .. 25.4 57.5 .. a. Data are consolidated for regional security markets where they exist. 96 Part III. Development outcomes INFRASTRUCTURE Intermediation Capital marketsa Domestic credit Interest rate spread Ratio of bank non- Market capitalization to private sector (lending rate perfoming loans to Listed domestic of listed companies Turnover ratio for (% of GDP) minus deposit rate) total gross loans (%) companies, total (% of GDP) traded stocks (%) 2006a 2006a 2006a 2006a 2006a 2006a 65.1 7.5 15.0 .. .. .. .. 17.2 .. .. .. .. .. 18.8 7.6 .. 18.0 35.9 2.3 17.9 .. .. .. .. .. 24.6 .. .. .. .. .. 9.2 11.0 .. .. .. .. 48.4 .. .. .. .. .. 6.6 11.0 .. .. .. .. 2.6 11.0 .. .. .. .. 7.9 8.0 .. .. .. .. 2.9 .. .. .. .. .. 2.1 11.0 .. .. .. .. 14.3 .. .. 40.0 24.1 3.3 20.2 .. .. .. .. .. 2.8 11.0 .. .. .. .. 29.0 .. .. .. .. .. 23.8 3.4 .. .. .. .. 9.3 11.0 11.1 .. .. .. 15.6 17.1 .. .. .. .. 17.8 .. 7.9 32.0 25.4 2.1 .. .. .. .. .. .. 3.9 .. .. .. .. .. 25.8 8.5 .. 51.0 49.9 14.6 8.9 7.6 1.0 .. .. .. 8.6 .. .. .. .. .. 10.2 7.2 .. .. .. .. 8.7 21.3 .. 10.0 18.6 3.5 17.2 .. .. .. .. .. .. .. .. .. .. .. 78.0 11.5 .. 41.0 56.7 4.4 13.8 8.2 3.7 .. .. .. 61.7 4.9 2.9 9.0 8.3 3.8 8.5 .. .. .. .. .. 13.2 7.2 .. 202.0 22.3 13.6 .. .. .. .. .. .. 33.1 .. .. .. .. .. 22.9 .. 16.0 .. .. .. 36.9 7.4 .. .. .. .. 4.5 13.6 .. .. .. .. .. .. .. .. .. .. 160.8 4.0 1.2 401.0 280.4 48.8 0.1 .. .. .. .. .. 22.5 6.2 2.0 6.0 7.2 0.0 11.0 8.8 .. 6.0 3.8 2.1 16.8 .. .. .. .. .. 7.9 9.6 2.8 5.0 1.2 5.2 9.6 12.8 .. 14.0 10.9 2.1 .. 293.1 .. 80.0 .. 6.2 37.7 12.4 6.3 .. .. .. .. 55.3 6.6 24.7 603.0 87.0 54.8 15.7 3.8 .. .. .. .. 58.1 .. 10.9 65.0 75.5 35.3 63.7 .. 19.2 48.0 14.4 14.3 INFRASTRUCTURE Part III. Development outcomes 97 Participating in growth ableT8.1 Education Literacy rate % Primary education Gross enrollment ratio Net enrollment ratio Youth (ages 15­24) Adult (ages 15 and older) (% of relevant age group) (% of relevant age group) Student- Total Male Female Total Male Female Total Male Female Total Male Female teacher ratio 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2000­06a 2006 2006 2006 2006 2006 2006 2006 SUB-SAHARAN AFRICA Angola 72 84 63 67 83 54 .. .. .. .. .. .. .. Benin 45 59 33 35 48 23 96 105 87 80 87 73 44 Botswana 94 92 96 81 80 82 .. .. .. .. .. .. .. Burkina Faso 33 40 26 24 31 17 60 66 54 47 52 42 46 Burundi 73 77 70 59 67 52 103 108 98 75 76 73 54 Cameroon .. .. .. 68 77 60 107 117 98 .. .. .. 45 Cape Verde 96 96 97 81 88 76 106 108 103 88 88 87 25 Central African Republic 59 70 47 49 65 33 61 72 49 46 53 38 .. Chad 38 56 23 26 41 13 .. .. .. .. .. .. .. Comoros .. .. .. .. .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. 70 78 63 67 81 54 .. .. .. .. .. .. .. Congo, Rep. 97 98 97 85 91 79 108 113 102 55 58 52 55 Côte d'Ivoire 61 71 52 49 61 39 71 79 62 .. .. .. 46 Djibouti .. .. .. .. .. .. 44 49 39 38 42 34 34 Equatorial Guinea 95 95 95 87 93 80 .. .. .. .. .. .. .. Eritrea .. .. .. .. .. .. 62 69 56 47 50 43 47 Ethiopia 50 62 39 36 50 23 83 90 77 65 68 62 59 Gabon 96 97 95 84 88 80 .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. 74 71 77 62 59 64 35 Ghana 71 76 65 58 66 50 92 92 91 64 63 64 35 Guinea 47 59 34 29 43 18 88 96 81 72 77 66 44 Guinea-Bissau .. .. .. .. .. .. .. .. .. .. .. .. .. Kenya 80 80 81 74 78 70 106 107 104 75 75 76 .. Lesotho .. .. .. 82 74 90 114 115 114 72 71 74 40 Liberia 67 65 69 52 58 46 91 96 87 39 40 39 19 Madagascar 70 73 68 71 77 65 139 142 137 96 96 96 48 Malawi .. .. .. .. .. .. 119 117 121 91 88 94 .. Mali .. .. .. 24 33 16 80 90 71 61 67 54 56 Mauritania 61 68 55 51 60 43 102 99 104 79 78 82 41 Mauritius 95 94 95 84 88 81 102 102 102 95 94 96 22 Mozambique .. .. .. .. .. .. 105 113 97 76 79 73 67 Namibia 92 91 93 85 87 83 107 107 107 76 74 79 31 Niger 37 52 23 29 43 15 51 58 43 43 50 37 40 Nigeria 84 87 81 69 78 60 .. .. .. .. .. .. .. Rwanda 78 79 77 65 71 60 140 137 142 .. .. .. 66 São Tomé and Principe 95 96 95 85 92 78 128 130 126 96 97 95 31 Senegal 49 58 41 39 51 29 80 81 79 71 71 70 39 Seychelles 99 99 99 92 91 92 .. .. .. .. .. .. .. Sierra Leone 48 60 37 35 47 24 .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. 23 .. .. .. South Africa .. .. .. .. .. .. .. .. .. .. .. .. .. Sudan 77 85 71 61 71 52 66 71 61 54 .. .. 34 Swaziland 88 87 90 80 81 78 .. .. .. .. .. .. .. Tanzania 78 81 76 69 78 62 110 112 109 98 98 97 52 Togo 74 84 64 53 69 38 102 110 95 80 86 75 38 Uganda 77 83 71 67 77 58 117 116 117 .. .. .. 49 Zambia .. .. .. .. .. .. 117 118 116 92 90 94 51 Zimbabwe 98 97 98 89 93 86 101 102 101 88 87 88 38 NORTH AFRICA Algeria 90 94 86 70 80 60 110 114 106 95 96 94 24 Egypt, Arab Rep. 85 90 79 71 83 59 103 107 100 94 96 92 26 Libya 98 100 96 84 93 75 110 113 108 .. .. .. .. Morocco 70 81 60 52 66 40 106 112 100 88 91 85 27 Tunisia 94 96 92 74 83 65 108 110 107 96 96 97 19 a. Data are for most recent year available during the period specified. 98 Part III. Development outcomes HUMAN DEVELOPMENT Secondary education Tertiary education Public spending on Gross enrollment ratio Net enrollment ratio Gross enrollment ratio education (%) (% of relevant age group) (% of relevant age group) (% of relevant age group) Share of Student- government Total Male Female Total Male Female teacher ratio Total Male Female expenditure Share of GDP 2006 2006 2006 2006 2006 2006 2006 2005 2005 2005 2000­06a 2000­06a .. .. .. .. .. .. .. .. .. .. .. 3.0 .. .. .. .. .. .. .. 5 .. .. 17.0 3.0 .. .. .. .. .. .. .. .. .. .. 22.0 11.0 15 17 12 12 14 10 30 2 3 1 15.0 5.0 14 16 12 .. .. .. .. 2 3 1 18.0 5.0 24 26 21 .. .. .. 16 7 8 6 17.0 2.0 80 75 86 59 56 63 19 8 8 8 16.0 7.0 .. .. .. .. .. .. .. 1 2 0 .. .. .. .. .. .. .. .. .. .. .. .. 10.0 2.0 .. .. .. .. .. .. .. .. .. .. 24.0 4.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 8.0 2.0 .. .. .. .. .. .. .. .. .. .. 22.0 5.0 22 27 18 .. .. .. 31 2 3 2 22.0 8.0 .. .. .. .. .. .. .. .. .. .. 4.0 1.0 31 39 23 25 30 20 54 .. .. .. .. 5.0 27 34 21 24 29 19 .. 2 4 1 18.0 6.0 .. .. .. .. .. .. .. .. .. .. .. 4.0 45 47 43 38 40 37 24 .. .. .. 9.0 2.0 46 50 42 38 39 36 20 5 6 3 .. 5.0 35 45 24 28 35 20 .. 5 8 2 26.0 2.0 .. .. .. .. .. .. .. .. .. .. .. .. 50 52 49 42 43 42 .. .. .. .. 18.0 7.0 37 33 41 24 19 29 25 4 3 4 30.0 13.0 .. .. .. .. .. .. .. .. .. .. .. .. 24 24 23 17 17 18 24 3 3 3 25.0 3.0 29 32 27 24 25 23 .. .. .. .. .. 6.0 28 35 21 .. .. .. .. .. .. .. 17.0 4.0 25 27 23 16 16 15 26 4 5 2 10.0 2.0 .. .. .. .. .. .. .. 17 16 18 13.0 4.0 16 18 13 4 4 4 36 .. .. .. 23.0 4.0 57 53 61 35 30 40 25 6 6 5 21.0 7.0 11 14 9 9 12 7 30 1 2 1 18.0 2.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 12.0 4.0 46 44 47 .. .. .. 22 .. .. .. .. .. 24 27 20 20 23 18 .. .. .. .. 26.0 5.0 .. .. .. .. .. .. .. .. .. .. 13.0 5.0 .. .. .. .. .. .. .. .. .. .. .. 5.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 15 14 17 18.0 5.0 34 34 33 19 .. .. 22 .. .. .. .. .. .. .. .. .. .. .. .. 4 4 4 .. 6.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 14.0 3.0 .. .. .. 16 17 15 .. .. .. .. 18.0 5.0 .. .. .. .. .. .. .. .. .. .. 15.0 2.0 40 41 38 37 38 36 .. .. .. .. .. 5.0 .. .. .. .. .. .. .. 22 19 24 .. .. .. .. .. .. .. .. .. .. .. .. 12.0 .. 94 86 101 .. .. .. .. .. .. .. .. .. 52 .. .. .. .. .. .. 12 13 11 27.0 7.0 85 81 89 .. .. .. 19 31 26 37 21.0 7.0 HUMAN DEVELOPMENT Part III. Development outcomes 99 Participating in growth ableT8.2 Health Mortality Diseases Maternal mortality Infant ratio, modeled Prevalence Incidence of Deaths due Life expectancy Under-five mortality rate estimate of HIV tuberculosis to malaria at birth (years) mortality rate (per 1,000) (per 1,000 (per 100,000 (% of (per 100,000 (per 100,000 Total Male Female Total Male Female live births) live births) ages 15­49) people) people) 2006 2006 2006 2000­06b 2000­06b 2000­06b 2006 2000­06b 2007 2006 2000­06b SUB-SAHARAN AFRICA 50.5 49.5 51.5 157 94 902 5.0 368 Angola 42.4 40.8 44.0 260 276 243 154 1,400 2.1 285 354 Benin 56.2 55.1 57.4 148 152 153 88 840 1.2 90 177 Botswana 49.8 49.5 50.0 124 123 109 90 380 23.9 551 15 Burkina Faso 51.9 50.4 53.5 204 193 191 122 700 1.6 249 292 Burundi 49.0 47.7 50.5 181 196 184 109 1,100 2.0 367 143 Cameroon 50.3 49.9 50.7 149 156 143 87 1,000 5.1 192 108 Cape Verde 71.0 68.0 74.2 34 38 35 25 210 .. 168 22 Central African Republic 44.4 43.0 45.8 175 201 185 115 980 6.3 345 137 Chad 50.6 49.3 52.0 209 212 188 124 1,500 3.5 299 207 Comoros 63.2 62.2 64.4 68 76 64 51 400 <0.1 44 80 Congo, Dem. Rep. 46.1 44.8 47.5 205 217 192 129 1,100 .. 392 224 Congo, Rep. 54.8 53.5 56.1 127 113 103 80 740 3.5 403 78 Côte d'Ivoire 48.1 47.2 49.0 127 225 162 90 810 3.9 420 76 Djibouti 54.5 53.3 55.7 130 131 120 86 650 3.1 809 .. Equatorial Guinea 51.1 49.9 52.4 206 213 195 124 680 3.4 256 152 Eritrea 57.3 55.0 59.8 74 89 75 48 450 1.3 94 74 Ethiopia 52.5 51.2 53.8 123 175 158 78 720 2.1 378 198 Gabon 56.7 56.3 57.2 91 102 80 60 520 5.9 354 80 Gambia, The 59.1 58.2 60.1 113 129 115 84 690 0.9 257 52 Ghana 59.7 59.3 60.1 120 113 111 76 560 1.9 203 70 Guinea 55.5 54.0 57.2 161 160 150 98 910 1.6 265 200 Guinea-Bissau 46.2 44.7 47.8 200 212 194 119 1,100 1.8 219 150 Kenya 53.4 52.4 54.6 121 129 110 79 560 .. 385 63 Lesotho 42.9 42.9 42.9 132 87 76 102 960 23.2 635 84 Liberia 45.3 44.4 46.2 235 249 220 157 1,200 1.7 331 201 Madagascar 59.0 57.3 60.8 115 128 117 72 510 0.1 248 184 Malawi 47.6 47.4 47.9 120 179 172 76 1,100 11.9 377 275 Mali 53.8 51.6 56.1 217 230 208 119 970 1.5 280 454 Mauritania 63.7 62.0 65.6 125 134 115 78 820 0.8 316 108 Mauritius 73.2 69.9 76.6 14 17 14 13 15 1.7 23 .. Mozambique 42.5 41.9 43.0 138 154 150 96 520 12.5 443 232 Namibia 52.5 52.0 53.0 61 70 57 45 210 15.3 767 52 Niger 56.4 57.3 55.5 253 256 262 148 1,800 0.8 174 469 Nigeria 46.8 46.3 47.3 191 198 195 99 1,100 3.1 311 141 Rwanda 45.6 44.0 47.3 160 211 195 98 1,300 2.8 397 200 São Tomé and Principe 65.2 63.4 67.1 96 122 114 63 .. .. 103 80 Senegal 62.8 60.8 64.8 116 141 132 60 980 1.0 270 72 Seychelles 72.2 68.9 75.7 13 14 13 12 .. .. 33 .. Sierra Leone 42.2 40.7 43.9 270 296 269 159 2,100 1.7 517 312 Somalia 47.7 46.5 48.9 146 222 228 90 1,400 0.5 218 81 South Africa 50.7 49.0 52.5 69 72 62 56 400 18.1 940 .. Sudan 58.1 56.7 59.6 89 98 84 61 450 1.4 242 70 Swaziland 40.8 41.6 39.9 164 163 150 112 390 26.1 1,155 .. Tanzania 51.9 50.8 53.0 118 134 117 74 950 6.2 312 130 Togo 58.2 56.5 60.0 108 151 128 69 510 3.3 389 47 Uganda 50.7 50.1 51.4 134 144 132 78 550 5.4 355 152 Zambia 41.7 41.5 41.9 182 190 173 102 830 15.2 553 141 Zimbabwe 42.7 43.3 42.1 105 136 121 68 880 15.3 557 1 NORTH AFRICA 71.5 69.5 73.5 35 30 159 44 Algeria 72.0 70.6 73.4 38 41 39 33 180 0.1 56 .. Egypt, Arab Rep. 71.0 68.8 73.3 35 36 36 29 130 .. 24 .. Libya 74.0 71.5 76.6 18 20 19 17 97 .. 18 .. Morocco 70.7 68.6 72.9 37 47 38 34 240 0.1 93 .. Tunisia 73.6 71.7 75.6 23 29 22 19 100 0.1 25 .. a. Diptheria, pertusis, and tetanus toxoid. b. Data are for most recent year available during the period specified. 100 Part III. Development outcomes HUMAN DEVELOPMENT Preventation and treatment Births Children sleeping Tuberculosis Tuberculosis Children under Child immunization attended Contraceptive under insecticide- cases detected treatment age 5 with fever rate (% of children Malnutrition (% of by skilled prevalence rate treated bednets under DOTS success rate receiving any ages 12­23 months) children under-five) health staff (% of women (% of children (% of estimated (% of registered antimalarial drugs Measles DPTa Stunting Underweight (% of total) ages 15­49) under age 5) cases) cases) within 24 hrs (%) 2006 2006 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2006 2000­06b 2000­06b 72 73 48 44 50.8 27.5 44.7 6.2 2.3 75.8 72.2 63.0 89 93 39.1 21.5 78.7 17.2 20.1 85.9 86.7 54.0 90 97 29.1 10.7 94.2 44.4 .. 80.2 69.9 .. 88 95 43.1 35.2 53.5 17.4 9.6 17.2 71.5 48.0 75 74 63.1 38.9 33.6 9.1 8.3 23.5 78.5 30.0 73 81 35.4 15.1 63.0 29.2 13.1 91.2 73.7 57.8 65 72 .. .. .. .. .. 33.3 64.4 .. 35 40 44.6 21.8 53.4 19.0 15.1 68.6 64.9 57.0 23 20 44.8 33.9 14.4 2.8 0.6 .. 69.0 44.0 66 69 46.9 25.0 61.8 25.7 9.3 41.6 91.4 62.7 73 77 44.4 33.6 60.7 31.4 0.7 60.6 84.9 52.0 66 79 31.2 11.8 86.2 44.3 .. 51.2 28.1 48.0 73 77 34.0 20.2 56.8 12.9 5.9 37.1 75.0 36.0 67 72 38.8 25.6 92.9 17.8 1.3 39.6 79.6 9.5 51 33 42.6 15.7 64.6 .. 0.7 .. 51.3 48.6 95 97 43.7 34.5 28.3 8.0 4.2 34.7 88.1 3.6 63 72 50.7 34.6 5.7 14.7 1.5 27.0 78.0 3.0 55 38 26.3 8.8 85.5 32.7 .. 57.5 46.4 .. 95 95 24.1 15.4 56.8 17.5 49.0 63.9 86.7 62.6 85 84 35.6 18.8 49.7 16.7 21.8 37.6 72.6 60.8 67 71 39.3 22.5 38.1 9.1 0.3 54.6 72.1 43.5 60 77 36.1 21.9 38.8 10.3 39.0 64.3 69.2 45.7 77 80 35.8 16.5 41.6 39.3 4.6 70.0 82.4 26.5 85 83 45.2 16.6 55.4 37.3 .. 79.1 72.9 .. 94 88 45.3 22.8 50.9 10.0 2.6 55.1 76.0 .. 59 61 52.8 36.8 51.3 27.1 0.2 73.2 74.5 34.2 85 99 52.5 18.4 53.6 41.7 23.0 42.0 73.2 23.9 86 85 42.7 30.1 40.6 8.1 8.4 25.5 75.3 38.0 62 68 39.5 30.4 56.9 8.0 2.1 34.5 54.6 33.4 99 97 .. .. 99.2 75.9 .. 67.1 86.4 .. 77 72 47.0 21.2 47.7 16.5 .. 46.9 79.4 15.0 63 74 29.5 20.3 75.5 43.7 3.4 82.9 74.6 14.4 47 39 54.8 39.9 17.7 11.2 7.4 49.5 74.0 33.0 62 54 43.0 27.2 36.3 12.6 1.2 20.2 74.9 33.9 95 99 51.7 18.0 38.6 17.4 5.0 27.4 82.9 12.3 85 99 35.2 10.1 80.7 30.3 41.7 .. .. 24.7 80 89 20.1 14.5 51.9 11.8 7.1 .. 74.4 26.8 99 99 .. .. .. .. .. .. 92.3 .. 67 64 38.4 24.7 43.2 5.3 5.3 35.0 85.6 51.9 35 35 42.1 32.8 33.0 14.6 9.2 83.0 88.6 7.9 85 99 .. .. 92.0 60.3 .. 71.2 71.4 .. 73 78 47.6 38.4 49.2 7.6 27.6 30.0 82.2 54.2 57 68 36.6 9.1 74.0 48.1 0.1 48.9 42.5 25.5 93 90 44.4 16.7 43.4 26.4 16.0 46.4 82.2 58.2 83 87 .. .. 62.4 16.8 38.4 19.4 70.8 47.7 89 80 44.8 19.0 42.1 23.7 9.7 44.3 73.2 61.8 84 80 52.5 23.3 43.4 34.2 22.8 52.5 83.9 57.9 90 90 35.8 14.0 79.7 60.2 2.9 42.4 67.8 4.7 96 97 .. .. 91 95 21.6 10.2 95.2 61.4 .. 101.6 86.9 .. 98 98 23.8 5.4 74.2 59.2 .. 59.3 78.5 .. 98 98 .. .. .. .. .. 156.3 68.6 .. 95 97 23.1 9.9 62.6 63.0 .. 94.9 81.3 .. 98 99 .. .. 89.9 62.6 .. 81.4 90.0 .. HUMAN DEVELOPMENT Part III. Development outcomes 101 Participating in growth ableT8.2 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) Nurses and Community Total Urban Rural Total Urban Rural Physicians midwives workers 2006 2006 2006 2006 2006 2006 2005 2005 2005 SUB-SAHARAN AFRICA 58 81 46 31 42 24 Angola 51 62 39 50 79 16 0.1 1.4 .. Benin 65 78 57 30 59 11 0.0 0.8 0.0 Botswana 96 100 90 47 60 30 0.4 2.7 .. Burkina Faso 72 97 66 13 41 6 0.1 0.5 0.1 Burundi 71 84 70 41 44 41 0.0 0.2 0.1 Cameroon 70 88 47 51 58 42 0.2 1.6 .. Cape Verde .. .. .. .. .. .. 0.5 0.9 0.1 Central African Republic 66 90 51 31 40 25 0.1 0.4 0.1 Chad 48 71 40 9 23 4 0.0 0.3 0.0 Comoros 85 91 81 35 49 26 0.2 0.7 0.1 Congo, Dem. Rep. 46 82 29 31 42 25 0.1 0.5 .. Congo, Rep. 71 95 35 20 19 21 0.2 1.0 0.0 Côte d'Ivoire 81 98 66 24 38 12 0.1 0.6 .. Djibouti 92 98 54 67 76 11 0.2 0.4 0.0 Equatorial Guinea 43 45 42 51 60 46 0.3 0.5 2.5 Eritrea 60 74 57 5 14 3 0.1 0.6 .. Ethiopia 42 96 31 11 27 8 0.0 0.2 0.3 Gabon 87 95 47 36 37 30 0.3 5.0 .. Gambia, The 86 91 81 52 50 55 0.1 1.3 0.7 Ghana 80 90 71 10 15 6 0.2 0.9 .. Guinea 70 91 59 19 33 12 0.1 0.5 0.0 Guinea-Bissau 57 82 47 33 48 26 0.1 0.7 2.9 Kenya 57 85 49 42 19 48 0.1 1.2 .. Lesotho 78 93 74 36 43 34 0.1 0.6 .. Liberia 64 72 52 32 49 7 0.0 0.3 0.0 Madagascar 47 76 36 12 18 10 0.3 0.3 0.0 Malawi 76 96 72 60 51 62 0.0 0.6 .. Mali 60 86 48 45 59 39 0.1 0.6 0.0 Mauritania 60 70 54 24 44 10 0.1 0.6 0.1 Mauritius 100 100 100 94 95 94 1.1 3.7 0.2 Mozambique 42 71 26 31 53 19 0.0 0.3 .. Namibia 93 99 90 35 66 18 0.3 3.1 .. Niger 42 91 32 7 27 3 0.0 0.2 .. Nigeria 47 65 30 30 35 25 0.3 1.7 0.9 Rwanda 65 82 61 23 34 20 0.1 0.4 1.4 São Tomé and Principe 86 88 83 24 29 18 0.5 1.9 2.3 Senegal 77 93 65 28 54 9 0.1 0.3 .. Seychelles .. 100 .. .. .. 100 1.5 7.9 .. Sierra Leone 53 83 32 11 20 5 0.0 0.5 0.1 Somalia 29 63 10 23 51 7 .. .. .. South Africa 93 100 82 59 66 49 0.8 4.1 0.2 Sudan 70 78 64 35 50 24 0.3 0.9 0.2 Swaziland 60 87 51 50 64 46 0.2 6.3 4.3 Tanzania 55 81 46 33 31 34 0.0 0.4 .. Togo 59 86 40 12 24 3 0.0 0.4 0.1 Uganda 64 90 60 33 29 34 0.1 0.7 .. Zambia 58 90 41 52 55 51 0.1 2.0 .. Zimbabwe 81 98 72 46 63 37 0.2 0.7 0.0 NORTH AFRICA 92 96 87 76 90 60 Algeria 85 87 81 94 98 87 1.1 2.2 0.0 Egypt, Arab Rep. 98 99 98 66 85 52 2.4 3.4 .. Libya .. .. .. 97 97 96 1.3 4.8 .. Morocco 83 100 58 72 85 54 0.5 0.8 .. Tunisia 94 99 84 85 96 64 1.3 2.9 .. a. Diptheria, pertusis, and tetanus toxoid. b. Data are for most recent year available during the period specified. 102 Part III. Development outcomes HUMAN DEVELOPMENT Expenditure on health Share of GDP (%) Share of total health expenditure (%) Out-of-pocket (% of private Health expenditure Total Public Private Public Private expenditure on health) per capita ($) 2005 2005 2005 2005 2005 2005 2005 6.0 2.6 3.4 43.1 56.8 46.6 49.4 1.8 1.5 0.3 81.5 18.5 100.0 36.0 5.4 3.0 2.4 55.6 44.4 99.9 28.0 8.3 4.5 2.5 78.4 21.6 27.7 431.1 6.7 4.0 2.7 59.5 40.5 94.2 27.0 3.4 1.0 2.4 28.6 71.4 100.0 3.0 5.2 1.5 3.7 28.0 72.0 94.6 49.0 5.6 4.6 1.0 81.8 18.2 99.7 114.0 4.0 1.5 2.5 37.5 62.5 95.3 13.0 3.7 1.5 2.2 39.8 60.2 96.2 22.0 3.0 1.6 1.4 53.3 46.7 100.0 14.0 4.2 1.5 2.7 34.6 65.4 100.0 5.0 1.9 0.9 1.0 47.1 52.9 100.0 31.0 3.9 0.8 3.1 21.5 78.5 87.8 34.0 6.9 4.5 1.7 75.8 24.2 98.4 61.2 1.7 1.3 0.4 78.9 21.1 73.6 211.0 3.7 1.7 2.0 44.9 55.1 100.0 8.0 4.9 3.0 1.9 61.0 39.0 80.6 6.0 4.1 3.0 1.1 74.0 26.0 100.0 276.0 5.2 3.4 1.8 65.4 34.6 70.3 14.8 6.2 2.1 4.1 34.1 65.9 79.1 30.0 5.6 0.7 4.9 11.9 88.1 99.5 21.0 5.2 1.7 3.5 31.9 68.1 85.7 10.0 4.5 2.1 2.4 46.6 53.4 80.0 24.0 5.5 8.5 0.9 56.1 43.9 68.9 40.8 6.4 4.4 2.0 68.2 31.8 98.7 10.0 3.2 2.0 1.2 62.5 37.5 52.6 9.0 12.2 8.7 3.5 71.3 28.7 30.6 19.0 5.8 2.9 2.9 50.6 49.4 99.5 28.0 2.7 1.7 1.0 63.2 36.8 100.0 17.0 4.3 2.2 2.1 51.5 48.5 81.4 218.0 4.3 2.7 1.6 63.6 36.4 40.5 14.0 5.3 3.5 1.8 65.2 34.8 15.5 165.0 3.8 1.9 1.9 50.5 49.5 85.2 9.0 3.9 1.2 2.7 30.9 69.1 90.4 27.0 7.2 4.1 3.1 56.9 43.1 36.9 18.6 9.8 8.3 1.5 84.8 15.2 100.0 49.0 5.4 1.7 3.7 31.7 68.3 90.3 38.0 6.8 4.9 1.9 72.2 27.8 62.5 557.0 3.7 1.9 1.8 51.5 48.5 100.0 8.0 .. .. .. .. .. .. .. 8.7 3.6 5.1 41.7 58.3 17.4 437.0 3.8 1.4 2.4 37.6 62.4 98.3 29.0 6.3 4.0 2.3 64.1 35.9 41.7 146.0 5.1 2.9 2.2 56.9 43.1 83.4 17.0 5.3 1.4 3.9 25.5 74.5 84.7 18.0 7.0 2.0 5.0 28.6 71.4 51.8 22.0 5.6 2.7 2.9 49.0 51.0 71.5 36.0 8.1 3.6 4.5 44.8 55.2 52.0 21.0 4.7 2.4 2.4 50.0 50.0 88.7 97.7 3.5 2.6 0.9 75.3 24.7 94.6 108.0 6.1 2.3 3.8 38.0 62.0 94.9 78.0 3.2 2.2 1.0 69.5 30.5 100.0 223.0 5.3 1.9 3.4 36.6 63.4 76.0 89.0 5.5 2.4 3.1 44.3 55.7 82.2 158.5 HUMANDEVELOPMENT Part III. Development outcomes 103 Participating in growth ableT9.1 Rural development Rural population (%) Rural population below the national Rural population density poverty line (%) Share of total Annual (rural population per Surveys Surveys population growth sq. km of arable land) 1990­1999 2000­2006 2006 2006 2000­06a Yeara Percent Yeara Percent SUB-SAHARAN AFRICA 64.5 1.7 Angola 45.1 0.9 224.4 .. .. .. .. Benin 59.6 2.5 185.2 1999 33.0 .. .. Botswana 41.9 ­0.6 207.9 .. .. .. .. Burkina Faso 81.3 2.5 235.2 1998 61.1 2003 52.4 Burundi 90.2 3.6 732.5 1998 64.6 .. .. Cameroon 44.9 0.3 136.4 1996 59.6 2001 49.9 Cape Verde 41.9 0.5 469.3 .. .. .. .. Central African Republic 61.7 1.5 134.4 .. .. .. .. Chad 74.2 2.5 180.4 1996 67.0 .. .. Comoros 72.0 2.1 541.2 .. .. .. .. Congo, Dem. Rep. 67.3 2.3 595.3 .. .. .. .. Congo, Rep. 39.4 1.2 290.2 .. .. 2005 64.8 Côte d'Ivoire 52.5 0.5 282.5 .. .. .. .. Djibouti 13.5 ­1.2 11,178.5 .. .. .. .. Equatorial Guinea 60.9 2.1 227.5 .. .. .. .. Eritrea 80.2 3.0 572.8 .. .. .. .. Ethiopia 83.6 2.2 480.9 1996 47.0 2000 45.0 Gabon 15.9 ­1.4 65.1 .. .. .. .. Gambia, The 45.3 1.0 213.0 1998 61.0 2003 63.0 Ghana 51.5 0.7 281.1 1997 49.6 2005 39.2 Guinea 66.5 1.2 502.6 .. .. .. .. Guinea-Bissau 70.3 2.9 374.7 .. .. .. .. Kenya 79.0 2.3 536.3 1997 52.9 2005 49.1 Lesotho 76.0 ­0.2 460.4 1993 53.9 .. .. Liberia 41.2 2.3 377.5 .. .. .. .. Madagascar 71.2 2.3 451.8 1999 76.7 .. .. Malawi 82.2 2.0 420.7 1998 66.5 .. .. Mali 68.9 2.2 168.1 1998 75.9 .. .. Mauritania 59.4 2.3 353.2 1996 65.5 2000 61.2 Mauritius 57.6 0.7 717.4 .. .. .. .. Mozambique 64.7 0.9 305.7 1996 71.3 2002 55.3 Namibia 64.3 0.4 160.8 .. .. .. .. Niger 83.6 3.4 76.7 1993 66.0 .. .. Nigeria 53.1 1.0 237.7 1992 36.4 2003 63.8 Rwanda 82.2 2.1 634.8 .. .. 2000 65.7 São Tomé and Principe 41.1 ­0.3 710.5 .. .. .. .. Senegal 58.1 2.1 269.6 1992 40.4 .. .. Seychelles 46.6 1.0 3,904.6 .. .. .. .. Sierra Leone 62.9 2.3 588.4 .. .. 2003 78.5 Somalia 64.4 2.3 393.4 .. .. .. .. South Africa 40.2 ­0.1 129.4 .. .. .. .. Sudan 58.3 0.7 112.4 .. .. .. .. Swaziland 75.6 0.2 482.3 .. .. 2001 75.0 Tanzania 75.4 1.9 317.0 1991 40.8 2001 38.7 Togo 59.4 1.5 150.6 .. .. .. .. Uganda 87.3 3.1 469.1 1999 37.4 2005 34.2 Zambia 64.9 1.7 141.8 1998 83.1 2004 78.0 Zimbabwe 63.6 0.1 261.2 1996 48.0 .. .. NORTH AFRICA 47.4 1.1 Algeria 36.1 ­0.3 161.8 1995 30.3 .. .. Egypt, Arab Rep. 57.4 1.7 1,393.9 1996 23.3 .. .. Libya 22.8 1.2 77.8 .. .. .. .. Morocco 44.7 0.4 160.0 1999 27.2 .. .. Tunisia 34.3 ­0.2 127.5 1995 13.9 .. .. a. Data are for most recent year available during the period specified. 104 Part III. Development outcomes AGRICULTURE,RURAL DEVELOPMENT AND ENVIRONMENT , Share of rural population with sustainable access (%) To transportation To an improved To an improved To electricity (within 2 km of To water source sanitation facilities (%) an all-season road) landline telephone 2006a 2006a 2000­06a 2000­06a 2000­06a 46 24 39 16 .. .. .. 57 11 5.6 32.0 1.0 90 30 .. .. .. 66 6 0.8 25.0 0.3 70 41 .. .. .. 47 42 16.3 20.0 0.0 73 19 .. .. .. 51 25 .. .. .. 40 4 0.3 5.0 0.0 81 26 .. .. .. 29 25 .. 26.0 .. 35 21 16.4 .. 0.2 66 12 .. .. .. 54 11 .. .. .. 42 46 .. .. .. 57 3 .. .. .. 31 8 2.0 17.0 0.2 47 30 31.4 .. 1.8 81 55 .. .. .. 71 6 20.9 .. 0.7 59 12 3.2 .. 0.3 47 26 .. .. .. 49 48 3.6 .. 6.0 74 34 0.8 .. 10.6 52 7 .. .. .. 36 10 9.7 .. 3.0 72 62 2.5 .. 2.1 48 39 2.7 .. 0.1 54 10 2.7 .. 0.2 100 94 .. .. .. 26 19 1.5 .. 0.1 90 18 10.4 .. 4.5 32 3 .. 37.0 .. 30 25 34.6 47.0 1.8 61 20 1.5 .. 0.2 83 18 .. .. .. 65 9 19.0 .. 7.5 75 100 .. .. .. 32 5 .. .. .. 10 7 .. .. .. 82 49 .. .. .. 64 24 .. .. .. 51 46 .. .. .. 46 34 1.8 38.0 3.0 40 3 .. .. .. 60 34 2.6 .. 0.9 41 51 3.5 .. 0.6 72 37 .. .. .. 87 60 .. .. .. 81 87 .. .. .. 98 52 .. .. .. 68 96 .. .. .. 58 54 .. .. .. 84 64 .. .. .. AGRICULTURE,RURAL DEVELOPMENT AND ENVIRONMENT , Part III. Development outcomes 105 Participating in growth ableT9.2 Agriculture Production Index 1999­2001=100 Cereal Trade Agriculture Production Exports Imports Agricultural value added (thousand of (thousand of (thousand of Exports Imports (% of GDP) Crop Food Livestock metric tons) metric tons) metric tons) ($ millions) ($ millions) 2006 2004­06a 2004­06a 2004­06a 2006a 2005 2005 2005 2005 SUB-SAHARAN AFRICA 14.5 Angola 8.9 119.2 152.5 100.0 724 1 639 2 1,018 Benin .. 133.9 112.4 116.2 934 15 398 262 262 Botswana 1.7 113.1 103.3 102.4 45 2 33 48 96 Burkina Faso 32.8 130.0 130.0 110.3 3,681 14 288 274 258 Burundi .. 104.2 101.2 100.2 269 .. 64 54 34 Cameroon 19.3 104.9 112.2 103.2 1,407 0 767 604 453 Cape Verde 8.8 85.4 91.8 102.1 12 0 76 1 133 Central African Republic 53.4 97.7 108.1 114.7 172 .. 38 16 33 Chad 20.9 115.7 122.1 107.6 1,913 .. 131 105 90 Comoros 45.2 105.9 98.7 95.9 21 3 49 14 41 Congo, Dem. Rep. 43.3 96.7 95.6 100.4 1,524 0 493 34 406 Congo, Rep. 4 105.5 112.4 121.1 9 2 242 54 285 Côte d'Ivoire 23.1 97.4 105.3 111.0 1,400 21 1,177 3,021 672 Djibouti 3.1 114.6 105.3 108.5 0 1 238 11 151 Equatorial Guinea 2.7 93.8 93.4 101.9 .. .. 19 3 57 Eritrea 16 71.5 86.3 99.5 199 1 510 2 139 Ethiopia 44.4 110.5 134.5 115.8 13,393 .. .. 1,643 951 Gabon 4.9 102.3 103.3 101.5 32 0 134 43 269 Gambia, The .. 65.6 95.9 103.1 215 0 171 17 168 Ghana 38 121.2 131.5 111.8 1,919 0 927 1,165 1,052 Guinea 12.7 110.4 124.4 115.3 2,445 2 319 74 276 Guinea-Bissau 60.3 109.8 114.6 109.1 225 .. 71 87 47 Kenya 24 101.6 128.5 108.7 3,955 20 1,617 1,545 689 Lesotho 14.4 111.2 106.0 100.0 89 0 27 4 64 Liberia .. 99.3 101.8 110.0 107 3 229 105 171 Madagascar 25.1 108.8 114.7 104.4 3,991 0 417 134 255 Malawi 30.5 91.8 91.8 101.9 2,752 3 170 445 142 Mali 34.1 111.2 109.6 117.9 3,693 10 165 240 225 Mauritania 12.1 100.5 108.8 110.0 175 .. 403 17 148 Mauritius 4.9 103.5 107.0 113.6 0 42 309 397 417 Mozambique 25.6 107.4 144.3 101.1 2,107 3 919 158 404 Namibia 9.9 110.8 92.9 113.9 139 7 42 156 240 Niger .. 122.1 137.7 104.6 4,030 1 404 69 258 Nigeria 31.7 105.9 130.0 108.8 28,884 20 4,966 655 2,436 Rwanda 41.3 113.1 122.3 109.9 366 0 38 51 60 São Tomé and Principe .. 109.3 110.3 107.7 3 .. 11 4 5 Senegal 13.6 76.8 83.9 101.1 988 16 1,313 149 19 Seychelles 3 93.8 104.4 91.1 .. .. 18 2 881 Sierra Leone 45.1 115.0 195.0 105.2 1,157 .. 123 17 77 Somalia .. .. .. .. 261 0 360 72 104 South Africa 2.4 102.6 107.2 108.6 9,454 2,209 2,279 3,925 254 Sudan 31.3 109.7 116.1 107.2 6,742 3 2,184 504 2,679 Swaziland 6.1 100.8 101.0 111.1 68 14 182 254 798 Tanzania 37.9 106.8 107.4 109.6 5,793 128 596 531 285 Togo .. 110.9 122.7 109.2 889 36 184 95 347 Uganda 28.7 108.7 105.5 110.3 2,557 76 555 416 1,171 Zambia 19.9 108.2 107.0 98.9 1,604 69 177 321 365 Zimbabwe .. 66.1 89.1 99.0 1,849 1 235 449 183 NORTH AFRICA 10.3 118.6 Algeria .. 128.4 143.1 104.8 4,018 14 8,263 59 3,922 Egypt, Arab Rep. 13.2 105.5 114.8 122.3 22,991 1,154 10,893 1,169 3,948 Libya .. 99.8 104.7 100.9 210 1 2,457 7 1,267 Morocco 14.0 148.6 142.1 99.8 9,239 93 5,029 1,353 2,303 Tunisia 11.1 101.7 115.9 98.8 1,646 113 2,454 963 85 a. Data are for most recent year available during the period specified. 106 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Fertilizer Agricultural Trade Share of land area (%) consumption machinery Agricultural Agriculture Food (100 grams Tractors per 100 employment value added Cereal yield Exports Imports Permanent Cereal Irrigated land per hectare of hectares of (% of total per worker (kilograms ($ millions) ($ millions) cropland cropland (% of cropland) arable land) arable land employment) (2000 US$) per hectare) 2005 2005 2005 2006 2002­05a 2006a 2005 2000­06a 2003­05a 2006 105.7 13.4 1,262 1 806 0.2 1.2 2.2 22.6 31.2 .. 196 485 67 241 2.4 7.5 0.4 .. 0.7 .. 536 1,125 45 68 0.0 0.1 0.3 122.0 159.2 21.2 367 342 58 189 0.2 12.5 0.5 126.3 4.1 .. 179 1,127 1 32 14.2 8.1 1.5 34.9 1.8 .. 64 1,330 346 428 2.6 2.3 0.4 78.7 0.8 60.6 666 1,408 0 107 0.7 8.3 6.1 47.8 12.4 .. 1,510 355 15 26 0.1 0.3 0.1 3.1 0.2 .. 384 1,074 55 73 0.0 2.0 0.8 48.6 0.4 .. 225 750 13 38 28.5 8.4 .. 37.5 0.6 .. 436 1,338 10 370 0.5 0.9 0.1 15.7 3.6 .. 149 785 34 244 0.1 0.0 0.4 94.4 14.1 .. .. 790 2,452 566 11.3 2.5 1.1 142.2 26.5 .. 817 1,777 10 123 .. 0.0 .. .. 80.0 .. 65 1,500 2 23 3.2 .. .. .. 16.2 .. 1,198 .. 1 138 0.0 3.7 3.5 22.0 7.3 .. 63 406 681 839 0.8 8.4 2.5 26.0 2.3 80.2 177 1,590 9 221 0.7 0.1 1.4 26.9 28.6 .. 1,663 1,540 16 139 0.5 21.0 0.6 25.4 2.9 .. 243 1,223 1,101 940 9.7 6.3 0.5 75.4 8.6 .. 332 1,335 46 204 2.7 6.9 5.4 28.2 46.3 .. 193 1,436 86 34 8.9 4.9 4.5 80.0 0.7 .. 246 1,625 400 587 0.8 4.1 1.8 177.9 25.5 .. 343 1,675 1 51 0.1 6.1 0.9 343.7 60.6 .. 412 654 9 150 2.3 .. 0.5 .. 8.5 .. .. .. 103 216 1.0 2.6 30.6 53.7 1.9 78.0 175 2,511 68 87 1.5 16.4 2.2 352.8 5.5 .. 109 1,107 49 187 0.0 2.6 4.9 89.4 5.4 41.5 244 1,068 16 125 0.0 0.2 9.8 59.4 7.6 .. 356 782 371 335 3.0 0.0 20.8 2,574.7 53.5 10.0 5,338 7,793 81 347 0.3 2.6 2.6 16.2 14.5 .. 157 902 139 167 0.0 0.4 1.0 19.2 38.7 31.1 1,134 434 63 228 0.0 5.3 0.5 3.2 0.1 .. 157 605 567 2,249 3.3 21.1 0.8 67.2 9.4 .. .. 1,464 0 49 11.1 13.3 0.6 137.1 0.5 .. 185 1,118 4 15 49.0 1.1 18.2 .. 138.9 27.9 .. 2,455 100 797 0.2 5.8 4.8 254.4 2.7 .. 227 879 1 67 10.9 .. .. 170.0 400.0 .. 433 .. 14 81 1.1 10.8 4.7 5.6 1.5 .. .. 1,485 70 248 0.0 1.1 15.7 4.2 10.2 .. .. 589 2,666 1,731 0.8 2.5 9.5 451.4 42.7 10.3 2,636 3,143 295 733 0.1 3.9 10.2 25.8 9.3 .. 666 718 225 220 0.8 2.9 26.0 393.3 221.9 .. 1,344 547 145 289 1.3 3.9 1.8 103.7 23.4 82.1 306 1,514 50 64 2.6 14.4 0.3 81.8 0.3 .. 353 1,135 78 323 11.2 8.5 0.1 10.7 8.7 69.1 235 1,523 158 154 0.0 0.8 2.9 69.2 11.4 .. 204 1,837 131 117 0.3 4.3 5.2 338.7 74.5 .. 205 714 1,264.8 139.3 2,906 50 3,455 0.4 1.1 6.9 144.2 134.5 21.1 2,219 1,503 899 3,355 0.5 3.0 100.4 7,330.7 325.3 29.9 2,128 7,499 1 1,113 0.2 0.2 21.9 671.3 227.1 .. .. 611 1,167 1,774 2.1 12.6 15.4 425.7 58.5 44.6 1,657 1,622 782 861 13.9 8.5 7.4 644.2 143.2 .. 2,687 1,245 AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Part III. Development outcomes 107 Participating in growth ableT9.3 Environment Renewable internal Annual Forests freshwater resources freshwater withdrawals Total (billion Per capita Total (billions of Share of internal Forest area (% of land area) cubic meters) (cubic meters) cubic meters) resources (%) 1990 2005 2005 2005 2002 2002 SUB-SAHARAN AFRICA Angola 48.9 47.4 148 9,195 0.4 0.2 Benin 30.0 21.3 10 1,213 0.1 1.3 Botswana 24.2 21.1 2 1,307 0.2 8.1 Burkina Faso 26.1 24.8 13 897 0.8 6.4 Burundi 11.3 5.9 10 1,285 0.3 2.9 Cameroon 52.7 45.6 273 15,341 1.0 0.4 Cape Verde 14.3 20.7 0 592 0.0 7.3 Central African Republic 37.2 36.5 141 33,640 0.0 0.0 Chad 10.4 9.5 15 1,479 0.2 1.5 Comoros 6.4 3.0 1 1,998 0.0 0.8 Congo, Dem. Rep. 62.0 58.9 900 15,322 0.4 0.0 Congo, Rep. 66.5 65.8 222 61,498 0.0 0.0 Côte d'Ivoire 32.1 32.7 77 4,132 0.9 1.2 Djibouti 0.2 0.2 0 373 0.0 6.3 Equatorial Guinea 66.3 58.2 26 53,708 0.1 0.4 Eritrea .. 15.4 3 619 0.3 10.7 Ethiopia 15.2 13.0 122 1,623 5.6 4.6 Gabon 85.1 84.5 164 127,064 0.1 0.1 Gambia, The 44.2 47.1 3 1,855 0.0 1.0 Ghana 32.7 24.2 30 1,345 1.0 3.2 Guinea 30.1 27.4 226 25,104 1.5 0.7 Guinea-Bissau 78.8 73.7 16 10,019 0.2 1.1 Kenya 6.5 6.2 21 582 1.6 7.6 Lesotho 0.2 0.3 5 2,625 0.1 1.0 Liberia 42.1 32.7 200 58,109 0.1 0.1 Madagascar 23.5 22.1 337 18,077 15.0 4.4 Malawi 41.4 36.2 16 1,217 1.0 6.3 Mali 11.5 10.3 60 5,168 6.5 10.9 Mauritania 0.4 0.3 0 135 1.7 425.0 Mauritius 19.2 18.2 3 2,252 0.6 21.8 Mozambique 25.4 24.5 100 4,885 0.6 0.6 Namibia 10.6 9.3 6 3,070 0.3 4.8 Niger 1.5 1.0 4 264 2.2 62.3 Nigeria 18.9 12.2 221 1,563 8.0 3.6 Rwanda 12.9 19.5 10 1,029 0.2 1.6 São Tomé and Principe 28.5 28.5 2 14,415 .. .. Senegal 48.6 45.0 26 2,192 2.2 8.6 Seychelles 87.0 87.0 .. .. .. .. Sierra Leone 42.5 38.5 160 28,641 0.4 0.2 Somalia 13.2 11.4 6 732 3.3 54.8 South Africa 7.6 7.6 45 955 12.5 27.9 Sudan 32.1 28.4 30 813 37.3 124.4 Swaziland 27.4 31.5 3 2,299 1.0 40.1 Tanzania 46.8 39.8 84 2,183 5.2 6.2 Togo 12.6 7.1 12 1,843 0.2 1.5 Uganda 25.0 18.4 39 1,347 0.3 0.8 Zambia 66.1 57.1 80 6,987 1.7 2.2 Zimbabwe 57.5 45.3 12 938 4.2 34.2 NORTH AFRICA Algeria 0.8 1.0 11 341 6.1 54.2 Egypt, Arab Rep. 0.0 0.1 2 25 68.3 3794.4 Libya 0.1 0.1 1 101 4.3 711.3 Morocco 9.6 9.8 29 962 12.6 43.4 Tunisia 4.1 6.8 4 419 2.6 62.9 a. Data are for most recent year available during the period specified. 108 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Water productivity (2000 $ per cubic meter of freshwater withdrawal) Energy Emissions of organic water Energy production Energy use (kilotons Combustible renewables and Total Agriculture Industry pollutants (kilograms per day) (kilotons of oil equivalent) of oil equivalent) waste (% oftotal energy use) 2001­04a 2001­04a 2001­04a 1990 2000­04a 1990 2005 1990 2005 1990 2005` 30.8 3.3 130.8 .. .. 28,652 70,700 6,285 9,898 68.8 63.8 19.0 15.4 12.1 .. .. 1,774 1,672 1,678 2,584 93.2 64.7 35.4 1.8 105.9 4,509 3,299 910 1,051 1,272 1,895 33.1 24.1 3.6 1.2 105.3 .. .. .. .. .. .. .. .. 2.6 1.2 6.1 1,570 .. .. .. .. .. .. .. 11.1 3.0 44.9 13,989 10,032 12,090 11,942 5,032 6,978 75.9 78.6 26.2 3.0 269.6 103 .. .. .. .. .. .. .. 38.3 502.7 35.3 998 .. .. .. .. .. .. .. 7.3 3.3 .. .. .. .. .. .. .. .. .. 21.7 23.2 50.4 .. .. .. .. .. .. .. .. 12.1 18.7 15.2 .. .. 12,019 17,391 11,903 16,967 84.0 92.5 76.1 .. .. .. .. 9,005 13,677 1,056 1,199 69.4 56.3 11.0 4.1 21.8 .. .. 3,382 8,196 4,408 7,843 72.1 58.3 30.4 6.0 .. .. .. .. .. .. .. .. .. 22.7 131.9 129.8 61 .. .. .. .. .. .. .. 2.3 0.3 .. .. .. .. .. .. .. .. .. 1.6 0.8 51.2 18,593 22,085 14,158 19,855 15,151 21,633 92.8 90.6 43.0 6.2 291.3 .. .. 14,630 12,116 1,243 1,721 59.8 58.7 14.1 5.2 15.7 .. .. .. .. .. .. .. .. 5.5 2.9 14.9 .. .. 4,392 6,356 5,337 8,937 73.1 66.0 2.2 0.5 35.0 .. .. .. .. .. .. .. .. 1.1 0.8 3.9 .. .. .. .. .. .. .. .. 8.4 3.9 20.5 42,588 56,102 10,272 13,888 12,479 17,246 78.4 74.6 17.9 11.8 17.6 2,958 .. .. .. .. .. .. .. 5.4 .. .. .. .. .. .. .. .. .. .. 0.2 0.1 1.9 .. 67,154 .. .. .. .. .. .. 1.6 0.7 4.5 10,024 .. .. .. .. .. .. .. 0.4 0.2 11.8 .. .. .. .. .. .. .. .. 0.7 0.2 5.8 .. .. .. .. .. .. .. .. 7.9 .. .. 17,813 .. .. .. .. .. .. .. 8.2 2.0 120.3 20,414 10,231 6,846 11,742 7,203 10,207 94.4 85.4 12.4 1.6 69.7 .. .. .. 328 .. 1,379 .. 13.5 0.9 0.4 33.7 .. 386 .. .. .. .. .. .. 6.0 .. .. .. .. 150,453 231,775 70,905 103,785 79.8 78.0 13.9 7.9 23.3 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.2 0.3 18.4 10,309 6,603 1,363 1,265 2,238 3,041 60.6 39.2 .. .. .. .. .. .. .. .. .. .. .. 2.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 11.3 0.5 53.1 261,618 181,691 114,534 158,590 91,229 127,637 11.4 10.5 0.4 0.2 10.5 .. 38,583 8,775 31,127 10,642 18,398 81.7 79.5 1.3 0.1 34.5 6,586 .. .. .. .. .. .. .. 2.0 0.9 61.7 31,125 .. 9,063 19,099 9,808 20,404 91.0 92.1 8.2 6.5 63.8 .. .. 1,203 1,591 1,447 1,995 82.6 79.4 22.1 18.3 25.2 .. .. .. .. .. .. .. .. 2.0 0.5 6.7 15,880 .. 4,923 6,513 5,470 7,124 73.4 78.7 1.6 0.3 4.3 37,149 .. 8,550 8,860 9,384 9,723 50.4 61.9 9.7 1.3 39.5 106,978 .. 104,439 175,070 23,858 34,768 0.1 0.2 1.6 0.3 8.2 211,531 186,059 54,869 76,039 31,895 61,301 3.3 2.3 8.7 .. .. .. .. 73,173 94,966 11,541 19,047 1.1 0.8 3.3 0.6 28.7 41,710 90,990 773 983 6,725 13,813 4.7 3.3 7.9 1.0 54.7 .. 55,775 6,127 6,681 5,536 8,451 18.7 13.3 AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Part III. Development outcomes 109 Participating in growth ableT9.3 Environment (continued) Greenhouse gas emissions Carbon dioxide emissions, Methane Agricultural Industrial industrial (thousands emissions (kt of methane emissions methane emissions of metric tons) CO2 equivalent) (% of total) (% of total) 1990 2004 1990 2005 1990 2005 1990 2005 SUB-SAHARAN AFRICA Angola 4,645 7,890 13,630 37,020 65.7 39.1 21.6 11.6 Benin 714 2,385 2,730 4,840 60.1 47.5 16.1 8.9 Botswana 2,169 4,297 130 4,480 84.6 71.9 7.7 17.9 Burkina Faso 993 1,095 .. .. .. .. .. .. Burundi 194 220 .. .. .. .. .. .. Cameroon 1,604 3,835 10,500 15,110 57.0 56.0 20.2 17.9 Cape Verde 88 275 .. .. .. .. .. .. Central African Republic 198 253 .. .. .. .. .. .. Chad 143 125 .. .. .. .. .. .. Comoros 66 88 .. .. .. .. .. .. Congo, Dem. Rep. 3,971 2,103 2,670 5,750 20.2 11.8 47.9 49.6 Congo, Rep. 1,172 3,539 27,720 50,320 42.9 26.3 10.4 7.7 Côte d'Ivoire 5,385 5,158 5,410 15,320 49.9 20.6 18.9 11.2 Djibouti 352 366 .. .. .. .. .. .. Equatorial Guinea 117 5,421 .. .. .. .. .. .. Eritrea .. 755 2,090 2,410 75.6 77.6 11.0 7.5 Ethiopia 2,963 7,974 39,110 47,740 78.4 77.2 9.3 10.0 Gabon 5,989 1,370 3,120 2,040 6.7 4.4 46.5 79.9 Gambia, The 191 286 .. .. .. .. .. .. Ghana 3,766 7,183 5,310 8,630 42.7 49.6 13.7 10.7 Guinea 1,011 1,337 .. .. .. .. .. .. Guinea-Bissau 209 271 .. .. .. .. .. .. Kenya 5,821 10,579 19,410 20,310 71.4 65.0 15.7 18.0 Lesotho .. .. .. .. .. .. .. .. Liberia 465 469 .. .. .. .. .. .. Madagascar 941 2,729 .. .. .. .. .. .. Malawi 601 1,044 .. .. .. .. .. .. Mali 421 564 .. .. .. .. .. .. Mauritania 2,634 2,553 .. .. .. .. .. .. Mauritius 1,462 3,194 .. .. .. .. .. .. Mozambique 996 2,165 9,430 11,680 61.9 64.3 17.5 16.9 Namibia 7 2,469 4,320 4,260 90.7 89.9 3.7 4.7 Niger 1,048 1,213 .. .. .. .. .. .. Nigeria 45,326 113,923 59,690 78,290 33.9 33.7 47.3 45.5 Rwanda 528 571 .. .. .. .. .. .. São Tomé and Principe 66 92 .. .. .. .. .. .. Senegal 3,132 4,989 5,550 6,340 76.2 75.9 4.5 4.7 Seychelles 114 546 .. .. .. .. .. .. Sierra Leone 333 993 .. .. .. .. .. .. Somalia 18 .. .. .. .. .. .. .. South Africa 331,743 436,641 52,260 59,200 31.2 23.8 52.4 54.3 Sudan 5,381 10,363 39,760 67,310 69.1 73.3 21.4 21.5 Swaziland 425 956 .. .. .. .. .. .. Tanzania 2,333 4,348 26,860 39,460 66.4 63.5 21.3 20.3 Togo 751 2,308 1,790 2,840 56.4 48.6 18.4 14.8 Uganda 813 1,824 .. .. .. .. .. .. Zambia 2,443 2,286 9,820 16,770 72.5 68.6 8.1 5.7 Zimbabwe 16,641 10,549 10,850 10,400 65.4 60.4 22.2 24.8 NORTH AFRICA Algeria 76,971 193,828 18,570 24,310 19.3 15.3 61.2 66.3 Egypt, Arab Rep. 75,414 158,095 23,250 32,960 39.0 44.2 33.4 31.2 Libya 37,762 59,861 8,750 8,540 11.8 8.9 79.1 77.6 Morocco 23,480 41,132 9,070 13,240 57.6 41.6 6.2 2.6 Tunisia 13,256 22,864 3,740 4,390 42.2 34.2 26.2 32.1 a. Data are for most recent year available during the period specified. 110 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Greenhouse gas emissions ODA gross Nitrous oxide Other greenhouse ODA gross aid aid disbursement emissions Agricultural nitrous Industrial nitrous gas emissions, HFC, disbursement for general (metric tons of oxide emissions oxide emissions PFC and SF6 (thousand for forestry environment protection CO2 equivalent) (% of total) (% of total) metric tons of CO2 equivalent) ($ millions) ($ millions) 1990 2005 1990 2005 1990 2005 1990 2005 2006 2006 5,110 28,350 80.4 35.9 .. .. .. .. 71.3 327.5 2,120 4,660 90.6 68.0 .. .. .. .. 0.0 0.5 .. 2,460 .. 96.3 .. .. .. .. 0.0 6.5 .. .. .. .. .. .. .. .. 0.1 4.2 .. .. .. .. .. .. .. .. 1.9 10.1 8,290 14,540 85.5 85.0 .. .. 810.0 890.0 0.0 3.6 .. .. .. .. .. .. .. .. 5.2 11.9 .. .. .. .. .. .. .. .. 0.0 9.3 .. .. .. .. .. .. .. .. 20.2 2.3 .. .. .. .. .. .. .. .. 0.6 2.0 820 2,250 23.2 15.6 .. .. .. .. .. 0.0 19,390 38,680 43.7 23.2 .. .. .. .. .. 2.0 2,460 12,350 82.1 25.0 .. .. .. .. 2.3 6.0 .. .. .. .. .. .. .. .. 0.0 13.4 .. .. .. .. .. .. .. .. .. .. 1,340 2,350 97.0 99.1 .. .. .. .. .. .. 50,730 63,130 97.9 98.6 .. .. .. .. 0.4 0.2 1,850 420 13.0 57.1 .. .. .. .. 2.1 7.9 .. .. .. .. .. .. .. .. 0.6 4.6 4,540 10,520 84.1 88.6 .. .. 190.0 170.0 0.4 0.1 .. .. .. .. .. .. .. .. 5.5 5.9 .. .. .. .. .. .. .. .. 2.0 6.7 21,830 19,060 97.6 96.4 .. .. .. .. .. 0.6 .. .. .. .. .. .. .. .. 1.3 13.5 .. .. .. .. .. .. .. .. 0.1 0.0 .. .. .. .. .. .. .. .. 0.7 0.0 .. .. .. .. .. .. .. .. 0.1 19.5 .. .. .. .. .. .. .. .. 1.9 2.0 .. .. .. .. .. .. .. .. 0.4 8.3 .. .. .. .. .. .. .. .. 0.0 4.0 2,950 9,930 72.5 99.7 .. .. .. .. .. 0.0 4,240 4,620 97.2 99.1 .. .. .. .. 0.0 14.7 .. .. .. .. .. .. .. .. 1.2 1.9 28,050 39,030 87.9 87.1 .. .. 120.0 80.0 0.1 8.1 .. .. .. .. .. .. .. .. .. 0.6 .. .. .. .. .. .. .. .. 0.1 1.3 6,220 10,250 95.8 99.0 .. .. .. 10.0 0.0 0.2 .. .. .. .. .. .. .. .. 4.3 46.4 .. .. .. .. .. .. .. .. .. 0.1 .. .. .. .. .. .. .. .. .. 0.1 26,460 29,250 88.0 82.7 3.6 7.3 1,450.0 2,600.0 .. .. 39,400 59,750 94.1 96.2 .. .. .. .. 0.1 23.5 .. .. .. .. .. .. .. .. 0.1 0.2 23,300 31,690 91.3 84.3 .. .. .. .. .. .. 1,990 5,470 93.5 88.8 .. .. .. .. 3.9 20.3 .. .. .. .. .. .. .. .. .. 0.1 4,800 11,410 72.5 65.1 .. 3.7 .. .. 4.6 4.0 8,970 10,160 89.4 97.1 5.9 .. .. 20.0 0.2 6.3 8,780 10,330 90.9 89.1 4.4 7.2 230.0 110.0 6.4 64.7 16,980 27,810 88.6 85.6 8.2 11.5 2,250.0 1,820.0 0.2 3.1 2,860 2,050 96.5 91.7 .. .. 100.0 290.0 0.0 35.2 14,380 15,510 98.5 75.2 .. .. .. .. .. 0.0 4,260 7,230 87.1 94.2 10.6 4.1 .. 30.0 1.3 9.9 AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Part III. Development outcomes 111 Participating in growth ableT9.4 Climate change* Temperature (degrees Celsius) Precipitation (mm) Minimum Maximum Minimum Maximum Annual monthly monthly Annual monthly monthly Dec. ­ Feb. Mar. ­ May Jun. ­ Aug. Sept. ­ Nov. average avearge average precipitation avearge average (DJF) (MAM) (JJA) (SON) 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 SUB-SAHARAN AFRICA Angola 21.87 18.57 24.11 1,003.83 0.67 176.69 484.29 303.44 5.08 211.02 Benin 27.58 25.31 30.67 931.83 0.21 186.97 3.20 169.36 503.62 255.64 Botswana 21.41 14.18 25.72 543.07 0.17 141.36 313.99 126.04 5.14 97.90 Burkina Faso 28.57 25.35 33.42 705.71 0.03 198.55 1.71 116.90 438.76 148.35 Burundi 21.03 19.82 21.74 833.58 1.65 182.50 284.17 240.15 22.05 287.21 Cameroon 24.85 23.40 27.25 1,724.76 12.20 380.35 73.16 363.75 733.58 554.26 Cape Verde 22.86 20.76 25.25 383.49 11.59 84.49 50.42 42.04 138.75 152.29 Central African Republic 25.07 23.79 27.66 1,256.66 3.05 223.09 23.51 241.70 605.51 385.95 Chad 26.67 21.08 31.12 313.39 0.15 96.83 1.88 40.66 231.87 38.98 Comoros 25.38 23.07 26.70 1,729.80 24.77 308.57 632.10 485.70 437.90 174.10 Congo, Dem. Rep. 24.19 22.98 25.17 1,374.39 62.90 183.98 369.11 368.92 226.87 409.49 Congo, Rep. 24.70 23.04 25.82 1,691.66 33.24 202.17 507.46 421.73 197.19 565.28 Cote d'Ivoire 26.37 24.46 28.74 1,443.77 11.48 275.53 50.22 420.53 627.43 345.59 Djibouti 28.55 23.76 33.54 201.06 2.24 41.22 11.30 56.31 73.10 60.34 Equatorial Guinea 24.76 23.43 25.74 2,426.82 12.83 364.66 426.51 634.94 444.61 920.77 Eritrea 27.47 23.05 30.90 212.60 0.50 52.10 12.20 35.45 103.38 61.58 Ethiopia 23.19 21.40 25.32 734.32 1.35 118.39 13.67 193.86 289.64 237.15 Gabon 25.20 23.12 26.38 1,990.99 4.17 290.09 641.25 535.92 96.49 717.33 Gambia, The 27.98 24.87 31.05 1,043.04 0.00 317.32 0.32 0.52 655.49 386.71 Ghana 27.29 25.16 29.61 1,120.16 5.76 158.05 21.87 431.96 412.06 254.28 Guinea 26.10 24.06 29.51 1,757.30 2.01 459.68 12.38 151.67 1,067.13 526.12 Guinea-Bissau 27.23 24.56 31.01 1,826.07 - 506.87 0.39 10.80 1,144.95 669.92 Kenya 24.62 23.28 26.67 509.03 1.95 109.19 63.80 181.68 77.86 185.69 Lesotho 12.84 6.40 18.13 886.46 1.40 158.46 398.99 264.83 13.04 209.61 Liberia 25.60 24.01 26.95 2,270.66 21.85 383.20 127.69 492.16 922.71 728.11 Madagascar 23.00 19.79 25.10 1,407.48 21.96 271.10 742.48 254.61 154.09 256.30 Malawi 22.20 18.26 24.98 1,150.51 1.05 259.78 705.13 276.92 17.05 151.42 Mali 28.99 22.64 34.57 313.95 0.04 99.90 0.40 25.70 222.98 64.87 Mauritania 28.23 20.47 33.54 89.89 0.26 35.84 1.54 0.96 67.23 20.16 Mauritius 22.91 20.15 25.40 1,643.20 36.80 434.40 758.00 434.35 267.50 183.35 Mozambique 23.87 19.61 26.05 1,171.68 3.82 241.48 660.05 318.57 51.52 141.55 Namibia 20.07 15.07 23.90 331.96 1.05 81.81 187.77 98.16 3.68 42.35 Niger 27.42 19.26 33.47 147.28 0.00 51.24 0.80 4.81 118.99 22.68 Nigeria 26.85 24.12 30.48 1,090.92 0.78 311.58 7.15 152.39 608.80 322.58 Rwanda 19.29 18.16 20.03 797.58 7.89 154.79 190.73 230.49 60.35 316.01 Sao Tome & Principe 23.30 21.70 24.55 2,163.70 24.00 386.75 411.70 486.65 508.50 756.85 Senegal 28.38 25.15 31.58 791.21 0.00 243.25 0.43 2.39 520.17 268.21 Seychelles .. .. .. .. .. .. .. .. .. .. Sierra Leone 26.46 24.70 28.80 2,518.66 4.76 605.49 30.35 274.17 1,366.26 847.88 Somalia 27.03 24.85 28.94 267.18 3.28 50.21 16.30 109.54 46.56 94.78 South Africa 17.81 11.36 22.92 616.87 6.91 110.03 283.73 175.94 27.19 130.00 Sudan 27.47 22.96 31.08 434.54 0.54 110.13 2.60 74.90 251.63 105.42 Swaziland 20.01 15.23 23.90 1,273.77 4.63 293.91 693.30 325.09 18.72 236.66 Tanzania 22.87 20.95 24.08 887.45 6.34 200.06 354.18 336.43 29.59 167.25 Togo 27.27 24.94 29.92 1,028.10 0.13 167.40 7.83 302.59 443.79 273.88 Uganda 23.25 22.36 24.45 732.87 10.25 108.31 54.72 153.74 208.44 315.97 Zambia 21.91 17.37 25.60 1,105.30 0.12 249.46 708.99 250.64 2.80 142.87 Zimbabwe 21.07 15.29 24.66 1,034.10 0.37 254.31 650.34 231.18 35.94 116.64 NORTH AFRICA Algeria .. .. .. .. .. .. .. .. .. .. Egypt, Arab Rep. .. .. .. .. .. .. .. .. .. .. Libya .. .. .. .. .. .. .. .. .. .. Morocco .. .. .. .. .. .. .. .. .. .. Tunisia .. .. .. .. .. .. .. .. .. .. a. Data are for the most recent year available during the specified period. * For a discussion about climate change, see Box 8 in the technical notes. 112 Part III. Development outcomes AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Total aggregate affected Emissions population by disaster Malaria CO2 emissions per Total greenhouse Emissions from land Number of capita (metric gas emissions use change & Agriculture clinical Reported tons of carbon (includes land use change) forestry as percentage of value added Irrigated land malaria cases malaria dioxide per capita) million metric tons CO2 greenhouse gas emissions (% of GDP) (% of crop land) Drought Flood reported deaths 2004 2000 2000 2006 2002­03a 2000­07 2000­07 1999­01a 1999­01a 0.5 30.8 57.8 8.9 2.2 25,000 563,328 1,385,597 9,255 0.3 37.8 95.8 .. 0.4 0 10,000 779,041 670 2.4 23.9 82.4 1.7 0.3 0 111,736 28,221 14 0.1 1.8 33.3 32.8 0.5 0 108,580 880,014 3,479 0.0 7.5 97.3 .. 1.5 2,800,000 17,695 2,855,868 1,289 0.2 84.1 91.7 19.3 0.4 0 500 .. .. 0.6 0.2 .. 8.8 6.1 .. .. 29 .. 0.1 9.2 97.8 53.4 0.1 .. .. 127,964 484 0.0 3.6 97.2 20.9 0.8 800,000 348,763 386,197 1,001 0.1 0.1 .. 45.2 .. .. .. 3,718 16 0.0 319.4 99.3 43.3 0.1 .. .. 46138* 218* 1.0 12.9 76.7 4.0 0.4 0 25,000 .. 0.3 98.0 93.0 23.1 1.1 .. .. 400,402 432 0.5 0.4 0.0 3.1 .. 292,750 98,500 .. .. 11.5 6.5 67.7 2.7 .. .. .. .. .. 0.2 0.6 .. 16.0 3.5 2,300,000 7,000 125,746 129 0.1 12.1 69.4 44.4 2.5 19,700,000 1,105,354 150,715 .. 1.1 8.2 45.1 4.9 1.4 .. .. .. .. 0.2 .. .. .. 0.6 .. .. 127,899 .. 0.3 34.4 81.1 38.0 0.5 0 473,800 3,383,025 3,726 0.2 11.9 88.2 12.7 5.4 0 241,885 899,089 441 0.2 1.4 78.6 60.3 4.5 ... .. 202,379 631 0.3 22.4 53.1 24.0 1.8 28,802,000 1,129,050 132,590 .. .. 0.2 0.0 14.4 0.9 975,000 0 .. .. 0.1 39.8 99.0 .. 0.5 0 17,000 .. .. 0.2 62.5 96.3 25.1 30.6 845,290 111,488 .. .. 0.1 27.8 96.0 30.5 2.2 8,449,435 1,142,896 2,955,627 59,414 0.1 8.6 93.0 34.1 4.9 1,025,000 68,681 86,512 182 0.9 2.5 .. 12.1 9.8 1,000,000 89,120 243,942 337 2.6 2.8 .. 4.9 20.8 .. .. .. .. 0.1 10.7 86.9 25.6 2.6 2,739,500 6,288,151 3,172,106 4,700 1.2 4.1 56.1 9.9 1.0 345,000 89,300 733,509 99 0.1 1.9 36.8 .. 0.5 6,584,558 111,420 606,802 987 0.8 270.2 72.1 31.7 0.8 0 382,865 31,685 58 0.1 8.1 91.4 41.3 0.6 1,894,545 27,500 915,916 2,678 0.6 0.1 0.0 .. 18.2 ... ... .. .. 0.4 7.8 46.2 13.6 4.8 284,000 235,577 1,120,094 1,337 6.6 .. .. 3.0 .. .. .. .. .. 0.2 13.9 95.7 45.1 4.7 0 19,500 .. .. .. .. .. .. 15.7 1,400,000 659,800 .. .. 9.4 362.6 0.5 2.4 9.5 15,000,000 97,816 26,506 119 0.3 36.4 83.8 31.3 10.2 2,000,000 1,204,000 .. .. 0.9 .. .. 6.1 26.0 1,380,000 274,500 12,296 .. 0.1 17.3 83.8 37.9 1.8 .. .. 673,366 .. 0.4 10.1 85.1 .. 0.3 0 129,880 431,826 791 0.1 41.0 95.9 28.7 0.1 1,955,000 455,710 5,622,934 .. 0.2 237.4 99.2 19.9 2.9 1,200,000 2,383,816 2,010,185 5,763 0.8 60.8 78.0 .. 5.2 8,100,000 265,000 680,900 412 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. AGRICULTURE, RURAL DEVELOPMENT AND ENVIRONMENT , Part III. Development outcomes 113 Participating in growth ableT10.1 Labor force participation Labor force ages 15 and older Total (in thousands) Male (% of total labor force) Female (% of total labor force) 2000 2006 2000 2006 2000 2006 SUB-SAHARAN AFRICA 276,539 323,324 57.5 57.8 42.5 42.2 Angola 6,135 7,329 54.1 54.2 45.9 45.8 Benin 2,810 3,431 60.7 61.7 39.3 38.3 Botswana 628 695 57.2 59.7 42.8 40.3 Burkina Faso 5,268 6,468 52.6 52.9 47.4 47.1 Burundi 3,207 4,188 47.2 48.6 52.8 51.4 Cameroon 6,145 6,989 60.3 60.4 39.7 39.6 Cape Verde 144 169 65.4 66.2 34.6 33.8 Central African Republic 1,760 1,953 53.6 54.0 46.4 46.0 Chad 3,318 4,031 54.1 53.2 45.9 46.8 Comoros 222 259 59.5 60.0 40.5 40.0 Congo, Dem. Rep. 20,234 24,213 58.6 58.7 41.4 41.3 Congo, Rep. 1,423 1,544 59.4 59.9 40.6 40.1 Côte d'Ivoire 6,289 7,139 70.8 70.7 29.2 29.3 Djibouti 294 345 60.1 60.8 39.9 39.2 Equatorial Guinea 175 202 63.6 63.1 36.4 36.9 Eritrea 1,490 1,972 58.6 59.0 41.4 41.0 Ethiopia 28,989 34,427 55.1 55.1 44.9 44.9 Gabon 526 614 56.8 57.3 43.2 42.7 Gambia, The 586 711 58.7 59.2 41.3 40.8 Ghana 8,806 10,285 51.6 52.2 48.4 47.8 Guinea 3,883 4,356 52.7 52.5 47.3 47.5 Guinea-Bissau 556 660 59.2 59.2 40.8 40.8 Kenya 13,959 16,650 55.6 55.8 44.4 44.2 Lesotho 674 697 55.3 56.5 44.7 43.5 Liberia 1,136 1,307 60.5 60.3 39.5 39.7 Madagascar 7,337 8,921 51.4 51.7 48.6 48.3 Malawi 5,450 6,289 50.3 50.0 49.7 50.0 Mali 4,097 4,820 52.3 50.8 47.7 49.2 Mauritania 1,035 1,264 60.3 60.8 39.7 39.2 Mauritius 529 577 65.9 64.3 34.1 35.7 Mozambique 8,797 9,787 46.5 46.6 53.5 53.4 Namibia 613 687 55.3 56.2 44.7 43.8 Niger 4,842 5,931 57.6 57.6 42.4 42.4 Nigeria 45,013 52,668 64.1 64.7 35.9 35.3 Rwanda 3,625 4,385 47.3 48.6 52.7 51.4 São Tomé and Principe 42 47 70.7 70.9 29.3 29.1 Senegal 4,149 4,775 59.3 58.8 40.7 41.2 Seychelles .. .. .. .. .. .. Sierra Leone 1,939 2,452 61.5 61.5 38.5 38.5 Somalia 3,033 3,612 60.7 60.8 39.3 39.2 South Africa 18,695 19,996 61.2 62.1 38.8 37.9 Sudan 9,076 10,664 74.9 75.1 25.1 24.9 Swaziland 318 360 66.2 67.7 33.8 32.3 Tanzania 16,744 19,317 50.2 50.3 49.8 49.7 Togo 2,130 2,549 62.9 63.3 37.1 36.7 Uganda 10,492 12,609 51.9 51.6 48.1 48.4 Zambia 4,478 4,985 57.0 57.4 43.0 42.6 Zimbabwe 5,447 5,995 55.2 56.4 44.8 43.6 NORTH AFRICA 46,221 54,750 75.6 74.3 24.4 25.7 Algeria 11,101 13,887 72.1 69.0 27.9 31.0 Egypt, Arab Rep. 19,780 23,111 78.2 78.3 21.8 21.7 Libya 1,980 2,498 76.5 72.2 23.5 27.8 Morocco 10,049 11,315 74.2 73.9 25.8 26.1 Tunisia 3,311 3,939 74.6 72.1 25.4 27.9 114 Part III. Development outcomes LABOR, MIGRATION AND POPULATION , Participation rate (%) 15­24 Participation rate (%) 15­64 years Total Male Female Total Male Female 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 2000 2006 73.6 72.9 86.2 85.4 73.6 72.9 75.0 74.3 87.1 86.4 63.2 62.5 82.9 82.4 92.0 91.5 74.3 73.7 84.3 83.7 92.7 92.2 76.2 75.6 71.4 69.9 87.6 86.1 55.5 53.6 72.1 70.7 87.9 86.4 56.6 54.7 58.9 57.6 69.0 69.6 49.3 45.9 60.1 59.0 69.6 70.5 50.9 47.5 83.9 83.4 90.4 89.4 77.8 77.6 85.6 85.0 91.2 90.2 80.1 79.8 92.4 92.7 92.6 93.3 92.2 92.0 93.1 93.3 93.0 93.7 93.2 93.0 67.9 65.7 82.7 79.9 53.4 51.8 69.6 67.5 83.9 81.0 55.6 54.0 55.8 53.5 79.6 75.9 35.7 34.0 58.3 56.0 81.4 77.6 38.3 36.5 79.7 79.6 89.3 89.4 70.8 70.5 80.1 80.1 89.6 89.7 71.3 71.0 72.8 71.6 80.3 77.5 65.7 65.9 73.0 71.7 80.0 77.2 66.2 66.3 72.2 72.4 86.3 87.3 58.2 57.6 73.1 73.4 86.4 87.4 59.8 59.4 75.3 75.7 90.5 90.6 60.9 61.4 76.7 77.1 91.1 91.3 62.7 63.2 76.7 72.0 93.2 87.7 61.0 56.8 77.0 71.9 93.5 87.6 61.1 56.6 64.9 64.4 88.5 88.7 39.5 38.8 65.8 65.3 89.2 89.3 40.6 40.1 68.5 67.9 83.1 83.0 54.2 53.0 69.9 69.3 84.0 83.9 55.9 54.8 70.3 70.6 91.5 91.0 50.1 51.1 72.6 72.9 94.2 93.7 51.7 52.6 73.9 73.6 90.4 90.3 58.7 58.2 75.2 74.8 90.9 90.7 60.5 59.9 80.9 79.9 90.5 89.2 71.6 70.9 82.9 82.0 91.8 90.6 74.2 73.5 72.6 72.5 83.6 83.3 62.0 61.8 75.0 74.6 85.3 84.8 64.8 64.4 72.9 72.5 86.0 86.1 60.0 59.0 73.7 73.4 86.6 86.7 60.9 60.2 74.3 72.8 76.3 75.3 72.3 70.3 75.3 73.9 76.9 76.0 73.7 71.8 84.5 83.6 88.9 87.4 80.0 79.7 86.4 85.5 89.8 88.3 83.0 82.7 76.0 76.5 92.0 92.6 60.7 61.1 77.5 77.9 92.7 93.1 62.9 63.1 79.6 79.4 89.5 89.5 69.8 69.6 80.9 80.7 90.1 90.0 71.9 71.6 61.6 58.4 77.1 73.5 49.4 46.1 63.9 60.7 78.5 74.9 52.2 49.0 69.4 68.9 84.4 83.4 54.5 54.5 70.2 69.8 84.8 83.8 55.8 55.7 81.9 82.5 85.1 86.2 78.9 78.9 82.6 83.1 85.4 86.4 79.8 79.9 86.9 87.5 89.5 89.5 84.4 85.6 87.6 88.1 89.8 89.8 85.4 86.4 79.4 76.9 87.7 82.3 72.1 72.0 81.8 79.3 89.1 84.0 75.2 74.9 69.2 69.3 84.1 84.2 54.6 54.4 71.0 71.1 85.3 85.4 56.8 56.7 60.0 60.6 79.9 78.8 40.6 42.8 64.6 65.5 84.6 83.7 44.4 47.2 85.8 83.8 85.8 83.0 85.9 84.6 85.9 83.9 85.5 82.8 86.3 84.9 56.6 54.4 64.7 62.8 49.1 46.5 58.4 56.2 66.5 64.4 50.8 48.2 83.2 83.0 95.3 95.1 71.0 70.9 84.5 84.4 96.0 95.8 73.0 73.1 66.0 65.1 85.8 85.2 46.8 45.5 67.0 66.2 86.6 85.9 47.9 46.7 84.7 81.4 86.4 83.6 83.3 79.5 86.2 83.0 87.4 84.5 85.3 81.6 51.5 51.5 74.5 74.7 29.6 29.3 53.9 53.9 77.0 77.2 31.7 31.4 71.2 68.1 84.8 81.0 57.8 55.6 73.5 70.7 86.7 83.4 60.4 58.3 .. .. .. .. .. .. .. .. .. .. .. .. 74.7 74.7 94.1 94.2 56.2 56.1 76.2 76.2 94.5 94.6 58.6 58.5 76.8 76.6 94.9 94.8 59.4 59.1 78.0 77.8 95.2 95.1 61.3 61.0 63.6 62.0 80.1 79.2 48.1 45.8 66.7 65.5 82.8 82.2 51.2 49.3 47.0 47.4 70.6 71.2 23.6 23.6 47.5 48.0 70.9 71.8 24.0 24.1 53.4 52.1 75.8 74.8 33.8 31.9 55.2 54.0 77.7 76.8 35.4 33.5 89.1 88.0 91.1 90.1 87.2 86.1 90.5 89.5 91.7 90.7 89.3 88.4 70.9 69.8 90.7 89.9 51.7 50.3 71.9 70.8 91.3 90.5 53.1 51.6 84.3 83.1 88.6 86.3 80.1 80.0 85.7 84.3 89.5 87.1 81.9 81.5 78.5 78.4 91.2 91.0 66.3 66.0 80.0 80.0 92.0 91.8 68.4 68.3 74.0 74.3 83.5 84.7 65.0 64.1 74.9 75.2 84.8 85.9 65.4 64.7 50.3 51.4 76.3 76.8 50.3 51.3 52.8 54.2 79.5 80.3 26.1 28.2 55.2 58.6 79.3 80.4 30.9 36.6 57.9 61.6 82.6 83.7 32.8 38.9 46.4 46.5 73.0 73.4 20.1 20.1 48.9 49.4 76.2 77.2 21.5 21.6 54.6 59.3 79.4 82.0 27.1 34.5 56.2 61.4 81.3 84.3 28.3 36.3 53.2 52.8 80.9 80.3 26.9 26.8 55.7 55.4 84.0 83.5 28.6 28.7 49.7 52.1 74.2 75.0 25.3 29.2 52.6 55.2 77.7 78.3 27.4 31.9 LABOR, MIGRATION AND POPULATION , Part III. Development outcomes 115 Participating in growth ableT10.2 Labor force composition Sectora Agriculture Industry Services Male Female Male Female Male Female (% of male (% of female (% of male (% of female (% of male (% of female employment) employment) employment) employment) employment) employment) 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b SUB-SAHARAN AFRICAc Angola .. .. .. .. .. .. Benin .. .. .. .. .. .. Botswana 28.6 12.9 28.0 16.5 43.3 70.6 Burkina Faso .. .. .. .. .. .. Burundi .. .. .. .. .. .. Cameroon 53.1 68.4 14.1 3.9 25.5 22.5 Cape Verde .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. Chad .. .. .. .. .. .. Comoros .. .. .. .. .. .. Congo, Dem. Rep. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. Côte d'Ivoire .. .. .. .. .. .. Djibouti .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. Eritrea .. .. .. .. .. .. Ethiopia 84.3 75.5 5.2 8.4 10.3 16.0 Gabon .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. Ghana .. .. .. .. .. .. Guinea .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. Kenya .. .. .. .. .. .. Lesotho .. .. .. .. .. .. Liberia .. .. .. .. .. .. Madagascar 76.7 79.3 7.4 6.0 16.0 14.6 Malawi .. .. .. .. .. .. Mali 49.8 29.9 17.8 14.7 32.4 55.3 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 30.6 22.8 26.3 5.9 42.6 70.7 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 28.8 17.8 Zambia .. .. .. .. .. .. Zimbabwe .. .. .. .. .. .. NORTH AFRICA Algeria 22.8 11.0 23.8 25.2 53.4 63.7 Egypt, Arab Rep. 27.7 39.0 22.9 6.2 49.3 54.7 Libya .. .. .. .. .. .. Morocco 37.6 62.7 22.1 14.0 40.2 23.2 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. 116 Part III. Development outcomes LABOR, 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) 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 73.2 74.4 72.0 12.2 8.1 16.8 2.2 2.3 2.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 19.2 29.3 8.7 59.3 57.0 61.7 18.2 9.5 27.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 7.9 9.3 6.2 41.5 55.8 25.1 50.3 34.6 68.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 15.0 17.8 12.0 43.7 51.6 35.4 40.6 29.7 51.9 .. .. .. .. .. .. .. .. .. 13.6 15.2 11.4 71.4 66.4 78.4 15.0 18.4 10.2 .. .. .. .. .. .. .. .. .. 80.4 78.6 84.1 17.3 20.4 11.1 2.1 0.9 4.7 .. .. .. .. .. .. .. .. .. 61.5 66.7 54.9 16.0 15.0 17.2 16.9 12.8 22.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 80.9 80.2 80.0 18.3 17.9 18.8 0.7 0.4 1.1 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 6.9 .. .. 8.4 .. .. 3.8 .. .. .. .. .. .. .. .. .. .. .. 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 51.0 23.1 50.4 38.6 63.2 11.9 10.4 13.6 64.8 64.6 65.8 27.8 27.9 26.6 7.2 7.2 7.2 56.5 58.3 49.3 29.5 32.3 18.6 14.0 9.4 32.2 .. .. .. .. .. .. .. .. .. 37.4 39.5 31.6 30.9 37.6 12.5 31.7 22.8 55.7 64.3 .. .. 26.8 .. .. 8.7 .. .. LABOR,MIGRATION AND POPULATION , Part III. Development outcomes 117 Participating in growth ableT10.3 Unemployment* Unemployment (15 and above) Youth unemployment (15­24 years) Long term unemployment Total Male Female Total Male Female Total Male Female 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b SUB-SAHARAN AFRICA Angola .. .. .. .. .. .. .. .. .. Benin .. .. .. .. .. .. .. .. .. Botswana 23.8 21.4 26.3 39.6 33.9 46.1 .. .. .. Burkina Faso .. .. .. .. .. .. .. .. .. Burundi .. .. .. .. .. .. .. .. .. Cameroon 7.5 8.2 6.7 .. .. .. .. .. .. Cape Verde .. .. .. .. .. .. .. .. .. Central African Republic .. .. .. .. .. .. .. .. .. Chad .. .. .. .. .. .. .. .. .. Comoros .. .. .. .. .. .. .. .. .. Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. Congo, Rep. .. .. .. .. .. .. .. .. .. Côte d'Ivoire .. .. .. .. .. .. .. .. .. Djibouti .. .. .. .. .. .. .. .. .. Equatorial Guinea .. .. .. .. .. .. .. .. .. Eritrea .. .. .. .. .. .. .. .. .. Ethiopia 5.4 2.7 8.2 7.7 4.1 11.2 24.4 24.3 24.4 Gabon .. .. .. .. .. .. .. .. .. Gambia, The .. .. .. .. .. .. .. .. .. Ghana .. .. .. .. .. .. .. .. .. Guinea .. .. .. .. .. .. .. .. .. Guinea-Bissau .. .. .. .. .. .. .. .. .. Kenya .. .. .. .. .. .. .. .. .. Lesotho .. .. .. .. .. .. .. .. .. Liberia .. .. .. .. .. .. .. .. .. Madagascar 5.0 3.8 6.2 7.0 6.7 7.3 .. .. .. Malawi .. .. .. .. .. .. .. .. .. Mali 8.8 7.2 10.9 .. .. .. .. .. .. Mauritania .. .. .. .. .. .. .. .. .. Mauritius 9.6 5.8 16.5 25.9 20.5 34.3 .. .. .. Mozambique .. .. .. .. .. .. .. .. .. Namibia 31.1 26.8 35.9 44.8 40.4 49.3 .. .. .. Niger .. .. .. .. .. .. .. .. .. Nigeria .. .. .. .. .. .. .. .. .. Rwanda .. .. .. .. .. .. .. .. .. São Tomé and Principe 14.4 12.5 17.8 .. .. .. .. .. .. Senegal .. .. .. .. .. .. .. .. .. Seychelles .. .. .. .. .. .. .. .. .. Sierra Leone .. .. .. .. .. .. .. .. .. Somalia .. .. .. .. .. .. .. .. .. South Africa 26.7 26.8 26.6 60.1 55.8 64.7 .. .. .. Sudan .. .. .. .. .. .. .. .. .. Swaziland .. .. .. .. .. .. .. .. .. Tanzania 5.1 4.4 5.8 .. .. .. .. .. .. Togo .. .. .. .. .. .. .. .. .. Uganda 3.2 2.5 3.9 .. .. .. .. .. .. Zambia .. .. .. .. .. .. .. .. .. Zimbabwe 8.2 10.4 6.1 24.9 28.2 21.4 .. .. .. NORTH AFRICA Algeria 15.3 14.9 17.5 43.4 42.8 46.3 .. .. .. Egypt, Arab Rep. 10.7 6.8 24.4 27.1 21.4 40.0 .. .. .. Libya .. .. .. .. .. .. .. .. .. Morocco 9.7 9.7 9.7 16.6 17.5 14.1 .. .. .. Tunisia 14.2 13.1 17.3 30.7 31.4 29.3 .. .. .. 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. * For a discussion on umemployment inn Africa, see Box 9 in technical notes. 118 Part III. Development outcomes LABOR, MIGRATION AND POPULATION , Unemployment by education level Primary Secodnary Tertiary Total Male Female Total Male Female Total Male Female 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b 2000­06b .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 65.5 64.4 66.3 27.3 23.9 30.2 .. .. .. 46.8 44.5 56.9 19.3 16.3 32.4 5.6 5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 35.9 50.6 30.8 13.3 19 11.3 3.2 5.7 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 61.5 65.6 58.9 18.8 19.9 18.1 6.1 7.8 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 48.6 49.3 48.1 44.9 44.3 45.3 5.4 5.9 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 60.7 62.8 59.4 24.1 23 24.9 5.9 0.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 50.2 53.2 47.4 41 38.5 43.3 5.1 4.2 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 59.3 65.2 32.5 23 21.4 30.4 11.4 6.6 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 51.1 57.7 36.6 22.4 21.7 23.9 21.6 16.2 .. 79.1 83.3 70.4 .. .. .. 13.6 9 .. LABOR,MIGRATION AND POPULATION , Part III. Development outcomes 119 Participating in growth ableT10.4 Migration and population* International migration Population Stock Population dynamics Share of Workers remittances, Annual Fertility population Net received Total Male Female growth rate (births (%) Total migration ($ millions) (millions) (% of total) (% of total) rate (%) per woman) 2006 2006 2006 2006 2006 2006 2006 2006 2006 SUB-SAHARAN AFRICA 2.1 15,726,612 ­1,070,433 782.5 49.8 50.2 2.5 5.2 Angola 0.4 56,351 175,000 .. 16.6 49.3 50.7 2.9 6.5 Benin 2.1 174,726 98,831 .. 8.8 50.4 49.6 3.2 5.5 Botswana 4.4 80,064 20,000 78.7 1.9 49.7 50.3 1.2 3.0 Burkina Faso 5.5 772,817 100,000 .. 14.4 50.1 49.9 3.1 6.1 Burundi 1.3 100,189 191,600 0.0 8.2 48.9 51.1 4.0 6.8 Cameroon 0.8 136,909 6,254 .. 18.2 50.0 50.0 2.1 4.4 Cape Verde 2.2 11,183 ­5,000 135.8 0.5 48.1 51.9 2.3 3.4 Central African Republic 1.8 76,484 ­45,000 .. 4.3 48.8 51.2 1.8 4.7 Chad 4.3 437,049 218,966 .. 10.5 49.7 50.3 3.2 6.3 Comoros 11.2 67,185 ­10,000 .. 0.6 50.2 49.8 2.2 4.0 Congo, Dem. Rep. 0.9 538,838 ­236,676 .. 60.6 49.5 50.5 3.2 6.3 Congo, Rep. 8 287,603 ­10,000 .. 3.7 49.6 50.4 2.2 4.6 Côte d'Ivoire 12.8 2,371,277 ­338,732 2.1 18.9 50.8 49.2 1.8 4.6 Djibouti 2.5 20,272 .. 3.7 0.8 50.0 50.0 1.8 4.1 Equatorial Guinea 1.2 5,800 .. .. 0.5 49.5 50.5 2.4 5.4 Eritrea 0.3 14,612 229,376 .. 4.7 49.1 50.9 3.7 5.1 Ethiopia 0.7 555,054 ­140,460 169.2 77.2 49.8 50.2 2.6 5.3 Gabon 18.9 244,550 9,566 .. 1.3 50.1 49.9 1.6 3.1 Gambia, The 14.3 231,739 31,127 62.9 1.7 50.2 49.8 2.8 4.8 Ghana 7.4 1,669,267 11,690 105.3 23.0 50.7 49.3 2.1 3.9 Guinea 4.5 405,772 ­425,000 .. 9.2 50.5 49.5 2.0 5.5 Guinea-Bissau 1.2 19,171 1,181 .. 1.6 49.5 50.5 3.0 7.1 Kenya 1 344,857 25,144 570.5 36.6 49.9 50.1 2.7 5.0 Lesotho 0.3 5,886 ­36,000 4.5 2.0 47.1 52.9 0.7 3.5 Liberia 1.5 50,172 ­118,767 685.0 3.6 50.0 50.0 4.0 6.8 Madagascar 0.3 62,787 ­5,000 .. 19.2 49.8 50.2 2.8 4.9 Malawi 2.1 278,793 ­30,000 .. 13.6 49.7 50.3 2.6 5.7 Mali 0.4 46,318 ­134,204 192.7 12.0 48.8 51.2 3.1 6.6 Mauritania 2.2 65,889 30,000 .. 3.0 50.7 49.3 2.7 4.5 Mauritius 1.7 20,725 .. .. 1.3 49.8 50.2 0.8 2.0 Mozambique 2 405,904 ­20,000 15.8 21.0 48.5 51.5 2.1 5.2 Namibia 7.1 143,275 ­1,000 6.5 2.1 49.4 50.6 1.3 3.3 Niger 0.9 123,687 ­28,497 .. 13.7 50.8 49.2 3.6 7.0 Nigeria 0.7 971,450 ­170,000 .. 144.7 50.0 50.0 2.4 5.4 Rwanda 1.3 121,183 42,943 17.2 9.5 48.2 51.8 2.5 5.9 São Tomé and Principe 4.9 7,499 ­7,000 1.6 0.2 49.6 50.4 1.6 4.0 Senegal 2.8 325,940 ­100,000 .. 12.1 50.0 50.0 2.6 5.3 Seychelles 5.9 4,932 .. 13.8 0.1 .. .. 2.1 2.0 Sierra Leone 2.1 119,162 472,289 29.6 5.7 49.3 50.7 2.8 6.5 Somalia 3.4 281,702 100,000 .. 8.4 49.7 50.3 3.0 6.1 South Africa 2.4 1,106,214 75,000 .. 47.4 49.2 50.8 1.1 2.7 Sudan 1.7 638,596 ­531,781 1,154.5 37.7 50.4 49.6 2.2 4.3 Swaziland 4 45,459 ­6,000 1.3 1.1 48.4 51.6 0.6 3.5 Tanzania 2.1 792,328 ­345,000 8.5 39.5 49.8 50.2 2.5 5.3 Togo 2.9 183,304 ­3,570 .. 6.4 49.5 50.5 2.8 4.9 Uganda 1.8 518,158 ­5,000 665.2 29.9 50.1 49.9 3.3 6.7 Zambia 2.4 274,842 ­81,713 57.7 11.7 49.9 50.1 1.9 5.3 Zimbabwe 3.9 510,637 ­75,000 .. 13.2 49.8 50.2 0.8 3.8 NORTH AFRICA 0.8 1,195,541 ­1,234,000 154.2 50.2 49.8 1.6 2.6 Algeria 0.7 242,446 ­140,000 .. 33.4 50.6 49.4 1.5 2.4 Egypt, Arab Rep. 0.2 166,047 ­525,000 5,329.5 74.2 50.2 49.8 1.8 2.9 Libya 10.4 617,536 10,000 6.0 6.0 51.9 48.1 2.0 2.8 Morocco 0.4 131,654 ­550,000 5,454.3 30.5 49.3 50.7 1.2 2.4 Tunisia 0.4 37,858 ­29,000 1,510.0 10.1 50.5 49.5 1.0 2.0 * For a discussion on demographic transition, see Box 10 in the technical notes. 120 Part III. Development outcomes LABOR, 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 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 43.3 21.8 21.4 53.6 26.6 27.0 3.1 1.4 1.7 0.90 64.5 35.5 1.7 3.9 46.3 23.1 23.2 51.3 25.2 26.1 2.4 1.1 1.4 0.90 45.1 54.9 0.9 4.5 44.0 22.4 21.6 53.3 26.9 26.4 2.7 1.1 1.6 0.90 59.6 40.4 2.5 4.1 35.1 17.7 17.4 61.5 30.7 30.8 3.4 1.3 2.1 0.60 41.9 58.1 ­0.6 2.5 46.0 23.5 22.5 51.0 25.4 25.6 3.1 1.3 1.8 1.00 81.3 18.7 2.5 5.3 44.7 22.5 22.4 52.7 25.4 27.1 2.6 1.0 1.6 0.90 90.2 9.8 3.6 7.0 41.5 20.9 20.5 55.0 27.5 27.5 3.5 1.6 1.9 0.80 44.9 55.1 0.3 3.6 39.0 19.6 19.4 56.8 27.0 29.7 4.2 1.5 2.7 0.80 41.9 58.1 0.5 3.6 42.5 21.1 21.3 53.7 26.1 27.6 3.9 1.6 2.3 0.90 61.7 38.3 1.5 2.2 46.2 23.2 22.9 50.9 25.2 25.7 2.9 1.3 1.6 1.00 74.2 25.8 2.5 4.9 41.8 21.2 20.5 55.5 27.8 27.7 2.7 1.2 1.5 0.80 72.0 28.0 2.1 2.4 47.3 23.7 23.6 50.1 24.8 25.4 2.6 1.1 1.5 1.00 67.3 32.7 2.3 5.1 41.9 21.0 20.8 54.9 27.3 27.7 3.2 1.4 1.8 0.80 39.4 60.6 1.2 2.8 41.4 20.7 20.7 55.4 28.5 26.9 3.2 1.6 1.6 0.80 52.5 47.5 0.5 3.2 37.9 19.1 18.8 59.1 29.5 29.5 3.0 1.4 1.7 0.70 13.5 86.5 ­1.2 2.2 42.3 21.2 21.0 53.6 26.5 27.2 4.1 1.8 2.3 0.90 60.9 39.1 2.1 2.8 42.9 21.7 21.3 54.8 26.5 28.1 2.3 0.9 1.4 0.80 80.2 19.8 3.0 5.8 44.2 22.2 21.9 52.9 26.3 26.7 2.9 1.3 1.6 0.90 83.6 16.4 2.2 4.4 35.4 17.9 17.5 60.0 30.1 29.8 4.6 2.1 2.6 0.70 15.9 84.1 ­1.4 2.1 41.0 20.7 20.2 55.2 27.6 27.6 3.8 1.8 2.0 0.80 45.3 54.7 1.0 4.4 38.6 19.8 18.8 57.7 29.2 28.5 3.7 1.7 1.9 0.70 51.5 48.5 0.7 3.6 43.3 22.0 21.2 53.7 27.1 26.5 3.1 1.3 1.7 0.90 66.5 33.5 1.2 3.4 47.6 23.8 23.7 49.4 24.3 25.1 3.0 1.3 1.7 1.00 70.3 29.7 2.9 3.3 42.6 21.5 21.2 54.7 27.2 27.5 2.7 1.2 1.5 0.80 79.0 21.0 2.3 4.1 40.1 20.2 19.9 55.1 25.0 30.2 4.7 1.9 2.8 0.80 76.0 24.0 ­0.2 3.8 47.0 23.6 23.4 50.8 25.4 25.4 2.2 1.0 1.2 1.00 41.2 58.8 2.3 5.1 43.6 21.8 21.7 53.3 26.5 26.8 3.2 1.4 1.7 0.90 71.2 28.8 2.3 3.9 47.0 23.7 23.2 49.9 24.7 25.4 3.0 1.3 1.7 1.00 82.2 17.8 2.0 5.4 47.6 24.0 23.7 48.8 23.3 25.5 3.6 1.5 2.0 1.00 68.9 31.1 2.2 4.9 40.1 20.6 19.4 56.3 28.4 27.9 3.6 1.6 2.0 0.80 59.4 40.6 2.3 3.2 24.0 12.2 11.7 69.3 34.8 34.5 6.7 2.8 3.9 0.40 57.6 42.4 0.7 1.0 44.3 22.2 22.0 52.5 25.0 27.6 3.2 1.3 1.9 0.90 64.7 35.3 0.9 4.4 38.3 19.3 19.1 58.2 28.6 29.5 3.5 1.5 2.0 0.70 64.3 35.7 0.4 3.0 48.0 24.6 23.3 48.8 24.5 24.4 3.2 1.7 1.4 1.00 83.6 16.4 3.4 4.0 44.1 22.3 21.7 53.0 26.4 26.6 2.9 1.3 1.6 0.90 53.1 46.9 1.0 3.9 43.1 21.5 21.8 54.5 25.7 28.5 2.5 1.0 1.5 0.80 82.2 17.8 2.1 4.1 41.4 20.9 20.4 54.3 26.8 27.6 4.3 1.9 2.3 0.80 41.1 58.9 ­0.3 3.0 41.9 21.2 20.7 53.8 26.7 27.2 4.3 2.1 2.2 0.90 58.1 41.9 2.1 3.2 .. .. .. .. .. .. .. .. .. .. 46.6 53.4 1.0 2.9 42.8 21.4 21.4 53.9 26.4 27.4 3.3 1.5 1.9 0.90 62.9 37.1 2.3 3.6 44.2 22.2 21.9 53.2 26.3 27.0 2.6 1.2 1.4 0.90 64.4 35.6 2.3 4.2 31.9 16.1 15.8 63.7 31.4 32.3 4.4 1.7 2.7 0.60 40.2 59.8 ­0.1 1.9 40.3 20.5 19.7 56.1 28.2 27.9 3.6 1.6 1.9 0.80 58.3 41.7 0.7 4.3 39.2 19.8 19.6 57.5 27.2 30.2 3.3 1.4 1.9 0.70 75.6 24.4 0.2 1.8 44.4 22.3 22.0 52.6 26.1 26.6 3.0 1.3 1.7 0.90 75.4 24.6 1.9 4.3 43.0 21.5 21.5 53.9 26.7 27.3 3.1 1.3 1.7 0.90 59.4 40.6 1.5 4.5 49.3 24.8 24.4 48.3 24.1 24.2 2.5 1.1 1.4 1.10 87.3 12.7 3.1 4.5 45.6 22.9 22.6 51.4 25.7 25.8 2.9 1.2 1.7 0.90 64.9 35.1 1.7 2.3 39.0 19.6 19.4 57.5 28.7 28.8 3.5 1.5 2.0 0.70 63.6 36.4 0.1 2.2 30.9 15.8 15.1 64.2 32.1 32.0 4.9 2.2 2.7 0.60 47.4 52.6 1.1 2.0 28.9 14.9 14.2 66.5 33.6 32.7 4.6 2.1 2.5 0.50 36.1 63.9 ­0.3 2.5 33.0 16.9 16.2 62.1 31.1 31.0 4.9 2.2 2.7 0.60 57.4 42.6 1.7 1.9 30.2 15.5 14.8 65.9 34.4 31.4 3.9 1.9 2.0 0.50 22.8 77.2 1.2 2.2 29.7 15.2 14.6 65.0 31.7 33.2 5.3 2.4 2.9 0.50 44.7 55.3 0.4 1.8 25.4 13.2 12.3 68.3 34.4 33.8 6.3 2.9 3.4 0.50 34.3 65.7 ­0.2 1.6 LABOR, MIGRATION AND POPULATION , Part III. Development outcomes 121 Participating in growth ableT11.1 HIV/AIDS* Estimated number of people living with HIV/AIDS (thousands) Estimated prevalence rate (%) Adults (ages Women (ages Children Adults (ages 15­49) Total 15 and older) 15 and older) (ages 0­14) Point estimate Low estimate High estimate 2007 2007 2007 2007 2007 2007 2007 SUB-SAHARAN AFRICA 22,000 20,300 12,000 1,800 5.0 4.6 5.4 Angola 190 180 110 17 2.1 1.7 2.5 Benin 64 59 37 5 1.2 1.1 1.4 Botswana 300 280 170 15 23.9 22.5 24.9 Burkina Faso 130 120 61 10 1.6 1.4 1.9 Burundi 110 90 53 15 2.0 1.3 2.5 Cameroon 540 500 300 45 5.1 3.9 6.2 Cape Verde .. .. .. .. .. .. .. Central African Republic 160 140 91 14 6.3 5.9 6.7 Chad 200 180 110 19 3.5 2.4 4.3 Comoros <0.2 <0.2 <0.1 .. <0.1 0.1 0.1 Congo, Dem. Rep. .. .. .. .. .. 1.2 1.5 Congo, Rep. 79 73 43 7 3.5 2.8 4.2 Côte d'Ivoire 480 420 250 52 3.9 3.2 4.5 Djibouti 16 15 9 1 3.1 2.3 3.8 Equatorial Guinea 11 10 6 <1 3.4 2.6 4.6 Eritrea 38 35 21 3 1.3 0.8 2.0 Ethiopia 980 890 530 92 2.1 1.8 2.2 Gabon 49 46 27 2 5.9 4.4 8.3 Gambia, The 8 8 5 <1 0.9 0.4 1.3 Ghana 260 250 150 17 1.9 1.7 2.2 Guinea 87 81 48 6 1.6 1.3 2.2 Guinea-Bissau 16 15 9 2 1.8 1.3 2.6 Kenya .. .. .. .. .. 7.1 8.5 Lesotho 270 260 150 12 23.2 21.9 24.5 Liberia 35 32 19 3 1.7 1.4 2.0 Madagascar 14 13 3 <0.5 0.1 <0.1 0.2 Malawi 930 840 490 91 11.9 11.0 12.9 Mali 100 93 56 9 1.5 1.2 1.8 Mauritania 14 14 4 <0.5 0.8 0.5 1.5 Mauritius 13 13 4 <0.1 1.7 1.0 3.6 Mozambique 1,500 1,400 810 100 12.5 10.9 14.7 Namibia 200 180 110 14 15.3 12.4 18.1 Niger 60 56 17 3 0.8 0.6 1.1 Nigeria 2,600 2,400 1,400 220 3.1 2.3 3.8 Rwanda 150 130 78 19 2.8 2.4 3.2 São Tomé and Principe .. .. .. .. .. .. .. Senegal 67 64 38 3 1.0 0.7 1.4 Seychelles .. .. .. .. .. .. .. Sierra Leone 55 51 30 4 1.7 1.3 2.4 Somalia 24 24 7 <1 0.5 0.3 1.0 South Africa 5,700 5,400 3,200 280 18.1 15.4 20.9 Sudan 320 290 170 25 1.4 1.0 2.0 Swaziland 190 170 100 15 26.1 25.1 27.1 Tanzania 1,400 1,300 760 140 6.2 5.8 6.6 Togo 130 120 69 10 3.3 2.7 4.1 Uganda 940 810 480 130 5.4 5.0 6.1 Zambia 1,100 980 560 95 15.2 14.3 16.4 Zimbabwe 1,300 1,200 680 120 15.3 14.6 16.1 NORTH AFRICA Algeria 21 21 6 .. 0.1 <0.1 0.2 Egypt, Arab Rep. 9 9 3 .. .. <0.1 <0.1 Libya .. .. .. .. .. <0.2 <0.2 Morocco 21 21 6 .. 0.1 <0.1 0.2 Tunisia 4 4 1 .. 0.1 <0.1 0.2 * For a discussion on HIV prevalance and incidence, see Box 11 in the technical notes. 122 Part III. Development outcomes HIV/AIDS Estimated prevalence rate (%) Deaths of adults AIDS orphans Young men (ages 15­24) Young women (ages 15­24) and children due to (ages 0­17, Point estimate Low estimate High estimate Point estimate Low estimate High estimate HIV/AIDS (thousands) thousands) 2007 2007 2007 2007 2007 2007 2007 2007 1.1 0.8 1.4 3.2 2.6 3.8 1,500 11,600 0.2 0.1 0.4 0.3 0.1 0.5 11 50 0.3 0.1 0.5 0.9 0.6 1.2 3 29 5.1 2.1 7.9 15.3 10.0 20.8 11 95 0.5 0.2 0.8 0.9 0.5 1.3 9 100 0.4 0.2 0.7 1.3 0.6 2.0 11 120 1.2 0.5 2.2 4.3 1.0 5.9 39 300 .. .. .. .. .. .. .. .. 1.1 0.5 1.5 5.5 4.1 7.0 11 72 2.0 0.9 2.9 2.8 1.3 4.1 14 85 0.1 <0.1 0.2 <0.1 0.1 0.1 .. <0.1 .. 0.1 0.4 .. 0.7 1.2 .. .. 0.8 0.3 1.1 2.3 1.3 3.3 6 69 0.8 0.3 1.3 2.4 1.0 3.4 38 420 0.7 0.3 1.1 2.1 1.4 3.0 1 5 0.8 0.4 1.4 2.5 1.7 3.7 .. 5 0.3 0.1 0.6 0.9 0.4 1.6 3 18 0.5 0.2 0.7 1.5 1.1 1.9 67.0 650.0 1.3 0.6 2.4 3.9 2.0 6.3 2 18 0.2 0.1 0.4 0.6 0.3 1.0 <1 3 0.4 0.2 0.6 1.3 0.9 1.7 21 160 0.4 0.2 0.6 1.2 0.9 1.8 5 25 0.4 0.2 0.8 1.2 0.3 2.5 1 6 .. 0.8 2.5 .. 4.6 8.4 .. .. 5.9 2.5 9.6 14.9 10.6 18.4 18 110 0.4 0.2 0.6 1.3 0.8 1.7 2.3 15.0 0.2 0.1 0.3 0.1 <0.1 0.2 <1 3 2.4 0.9 3.8 8.4 6.7 10.4 68 550 0.4 0.2 0.5 1.1 0.7 1.5 6 44 0.9 0.4 1.9 0.5 0.2 1.0 <1 3 1.8 0.8 4.5 1.0 0.5 2.2 <0.5 <0.5 2.9 1.2 4.2 8.5 5.9 11.1 81 400 3.4 1.4 5.3 10.3 6.2 14.5 5 66 0.9 0.4 1.5 0.5 0.3 0.8 4 25 0.8 0.3 1.2 2.3 1.2 3.3 170 1,200 0.5 0.3 0.7 1.4 0.9 1.9 8 220 .. .. .. .. .. .. .. .. 0.3 0.1 0.5 0.8 0.5 1.2 2 8 .. .. .. .. .. .. .. .. 0.4 0.2 0.7 1.3 0.7 1.9 3 16 0.6 0.3 1.4 0.3 0.1 0.6 2 9 4.0 1.7 6.0 12.7 9.1 17.0 350 1,400 0.3 0.2 0.5 1.0 0.6 1.5 25 .. 5.8 2.2 9.3 22.6 17.7 27.2 10 56 0.5 0.4 0.7 0.9 0.5 1.3 96 970 0.8 0.4 1.2 2.4 1.4 3.3 9 68 1.3 0.6 1.9 3.9 2.7 5.2 77 1,200 3.6 1.6 5.2 11.3 8.5 14.2 56 600 2.9 1.2 4.4 7.7 3.8 11.7 140 1,000 0.1 <0.1 0.3 0.1 <0.1 0.2 <1 .. .. <0.1 <0.1 .. <0.1 <0.1 <1 .. .. .. .. .. .. .. .. .. 0.1 <0.1 0.2 0.1 <0.1 0.2 <1 .. 0.1 <0.1 0.2 <0.1 0.1 0.1 <0.2 .. HIV/AIDS Part III. Development outcomes 123 Participating in growth ableT12.1 Malaria Deaths due to malaria Risk of malaria (% of population) Population (per 100,000 (millions) Endemic Epidemic Negligible population) 2006 2000­2006a 2000­2006a 2000­2006a 2000­2006a SUB-SAHARAN AFRICA 782.5 Angola 16.6 90.5 8.4 1.2 354 Benin 8.8 100.0 .. .. 177 Botswana 1.9 .. 31.5 68.5 15 Burkina Faso 14.4 100.0 .. .. 292 Burundi 8.2 67.6 17.3 15.2 143 Cameroon 18.2 93.6 4.4 2.0 108 Cape Verde 0.5 .. .. .. 22 Central African Republic 4.3 100.0 .. .. 137 Chad 10.5 96.5 3.5 0.0 207 Comoros 0.6 .. 100.0 .. 80 Congo, Dem. Rep. 60.6 91.6 2.6 5.8 224 Congo, Rep. 3.7 100.0 .. .. 78 Côte d'Ivoire 18.9 100.0 .. 0.0 76 Djibouti 0.8 1.7 98.3 .. .. Equatorial Guinea 0.5 98.0 1.5 0.5 152 Eritrea 4.7 92.2 6.9 1.0 74 Ethiopia 77.2 39.7 23.9 36.4 198 Gabon 1.3 96.5 .. 3.5 80 Gambia, The 1.7 100.0 .. .. 52 Ghana 23.0 100.0 .. .. 70 Guinea 9.2 100.0 0.0 .. 200 Guinea-Bissau 1.6 99.5 .. 0.5 150 Kenya 36.6 53.4 24.4 22.2 63 Lesotho 2.0 .. .. .. 84 Liberia 3.6 100.0 .. .. 201 Madagascar 19.2 89.1 7.1 3.8 184 Malawi 13.6 96.7 2.5 0.7 275 Mali 12.0 99.1 0.9 0.0 454 Mauritania 3.0 65.3 34.5 0.2 108 Mauritius 1.3 .. .. .. .. Mozambique 21.0 99.5 0.3 0.2 232 Namibia 2.0 .. 40.8 59.2 52 Niger 13.7 97.1 2.8 0.1 469 Nigeria 144.7 100.0 0.0 0.0 141 Rwanda 9.5 53.0 13.6 33.4 200 São Tomé and Principe 0.2 .. 100.0 .. 80 Senegal 12.1 100.0 .. .. 72 Seychelles 0.1 .. .. .. .. Sierra Leone 5.7 100.0 0.0 0.0 312 Somalia 8.4 19.9 79.1 1.1 81 South Africa 47.4 .. 19.8 80.2 .. Sudan 37.7 74.1 24.7 1.3 70 Swaziland 1.1 .. 76.6 23.4 .. Tanzania 39.5 93.1 3.0 3.9 130 Togo 6.4 100.0 .. .. 47 Uganda 29.9 90.2 2.9 6.9 152 Zambia 11.7 96.1 3.0 0.9 141 Zimbabwe 13.2 .. 84.2 15.8 1 NORTH AFRICA 154.2 .. .. .. .. Algeria 33.4 .. .. .. .. Egypt, Arab Rep. 74.2 .. .. .. .. Libya 6.0 .. .. .. .. Morocco 30.5 .. .. .. .. Tunisia 10.1 .. .. .. .. a. Data are for the most recent year available during the period specified. 124 Part III. Development outcomes MALARIA Children Children with fever receiving antimalarial sleeping under treatment within 24 hrs Children Under-five insecticide- (% of children under 5 with fever) under age 5 with Pregnant mortality treated bednets Effective Any fever receiving any women receiving two rate (% ofchidlren antimalarial antimalarial antimalarial drugs doses of intermittent (per 1,000) under age 5) treatment treatment within 24 hrs (%) preventive treatment (%) 2000­2006a 2000­2006a 2000­2006a 2000­2006a 2000­2006a 2000­2006a 157 260 2.3 20.0 .. 63.0 .. 148 20.1 18.5 24.7 54.0 2.5 124 .. .. .. .. .. 204 9.6 44.9 41.0 48.0 1.3 181 8.3 .. 19.1 30.0 .. 149 13.1 26.7 38.2 57.8 5.8 34 .. .. .. .. .. 175 15.1 .. 41.6 57.0 8.7 209 0.6 .. .. 44.0 .. 68 9.3 .. .. 62.7 .. 205 0.7 .. .. 52.0 .. 127 .. 8.6 22.1 48.0 .. 127 5.9 .. 25.9 36.0 8.3 130 1.3 2.9 9.5 9.5 .. 206 0.7 .. .. 48.6 .. 74 4.2 7.5 1.8 3.6 .. 123 1.5 0.7 0.7 3.0 .. 91 .. .. .. .. .. 113 49.0 .. 52.4 62.6 32.5 120 21.8 44.2 48.3 60.8 27.3 161 0.3 13.9 13.9 43.5 2.9 200 39.0 .. 27.2 45.7 7.4 121 4.6 10.8 11.1 26.5 3.9 132 .. .. .. .. .. 235 2.6 .. .. .. .. 115 0.2 .. .. 34.2 .. 120 23.0 22.7 19.9 23.9 44.5 217 8.4 .. .. 38.0 .. 125 2.1 11.8 11.8 33.4 .. 14 .. .. .. .. .. 138 .. 8.3 8.3 15.0 .. 61 3.4 .. .. 14.4 .. 253 7.4 24.9 24.9 33.0 0.3 191 1.2 24.9 24.6 33.9 0.8 160 5.0 .. 2.5 12.3 0.3 96 41.7 .. 17.0 24.7 .. 116 7.1 12.2 12.2 26.8 9.2 13 .. .. .. .. .. 270 5.3 45.0 45.0 51.9 1.8 146 9.2 .. 2.9 7.9 0.9 69 .. .. .. .. .. 89 27.6 .. .. 54.2 .. 164 0.1 .. .. 25.5 .. 118 16.0 49.3 51.0 58.2 21.7 108 38.4 .. 37.5 47.7 18.1 134 9.7 28.9 29.0 61.8 16.6 182 22.8 8.7 37.0 57.9 61.2 105 2.9 3.4 3.4 4.7 6.3 35 .. .. .. .. .. 38 .. .. .. .. .. 35 .. .. .. .. .. 18 .. .. .. .. .. 37 .. .. .. .. .. 23 .. .. .. .. .. MALARIA Part III. Development outcomes 125 Capable states and partnership ableT13.1 Aid and debt relief Net ODA aid (2005 $ millions) Net private aid (millions) From From From From all From DAC non-DAC multilateral From all From DAC non-DAC donors donors donors donors donors donors donors 2006 2006 2006 2006 2006 2006 2006 SUB-SAHARAN AFRICA 37,975 27,476 312 10,187 523 347 176 Angola 171 ­55 102 124 ­5 ­5 0 Benin 375 228 ­0 147 ­4 ­10 7 Botswana 65 36 ­2 30 11 11 .. Burkina Faso 871 386 10 475 99 99 .. Burundi 415 222 0 192 ­12 ­12 .. Cameroon 1,684 1,505 6 173 84 84 .. Cape Verde 138 99 2 38 57 57 .. Central African Republic 134 65 .. 69 3 3 .. Chad 284 153 3 128 25 25 .. Comoros 30 20 1 10 0 0 .. Congo, Dem. Rep. 2,056 1,500 ­1 556 ­189 ­189 .. Congo, Rep. 254 169 1 84 396 396 .. Côte d'Ivoire 251 199 0 52 316 315 1 Djibouti 117 89 2 26 53 53 .. Equatorial Guinea 27 19 ­0 8 914 914 .. Eritrea 129 63 ­2 67 7 7 .. Ethiopia 1,947 1,024 25 898 24 20 4 Gabon 31 32 1 ­1 227 227 .. Gambia, The 74 25 6 43 ­2 ­2 .. Ghana 1,176 595 1 580 554 553 1 Guinea 164 103 3 58 19 19 .. Guinea-Bissau 82 39 0 43 ­1 ­1 .. Kenya 943 761 16 166 ­177 ­177 0 Lesotho 72 38 ­1 34 ­3 ­3 .. Liberia 269 187 0 81 43 43 .. Madagascar 754 266 4 485 142 113 30 Malawi 669 398 12 259 33 33 .. Mali 825 398 9 418 14 14 .. Mauritania 188 94 1 93 ­9 ­9 .. Mauritius 19 9 ­2 12 784 784 .. Mozambique 1,611 938 3 669 ­6 ­6 .. Namibia 145 106 2 38 53 53 .. Niger 401 235 0 166 ­924 ­924 .. Nigeria 11,434 10,820 2 613 ­8,433 ­8,557 123 Rwanda 585 321 0 263 ­62 ­62 .. São Tomé and Principe 22 18 0 3 4 4 .. Senegal 825 509 12 304 11 11 .. Seychelles 14 7 ­1 7 ­33 ­33 .. Sierra Leone 364 199 0 164 91 91 .. Somalia 392 263 3 125 8 8 .. South Africa 718 561 1 157 7,598 7,592 6 Sudan 2,058 1,518 87 453 62 59 3 Swaziland 35 12 ­1 23 ­6 ­6 .. Tanzania 1,825 992 1 832 127 127 0 Togo 79 55 0 24 88 88 .. Uganda 1,551 938 3 609 23 23 .. Zambia 1,425 1,115 2 308 ­81 ­81 0 Zimbabwe 280 200 0 80 ­202 ­202 .. NORTH AFRICA 2,596 1,628 196 803 5,930 5,889 41 Algeria 209 205 7 ­4 297 291 6 Egypt, Arab Rep. 873 537 50 287 4,174 4,176 ­2 Libya 37 33 1 3 761 724 37 Morocco 1,046 567 116 362 323 323 .. Tunisia 432 287 21 154 ­65 ­65 .. 126 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP Heavily indebted Poor Net ODA aid Country (HIPC) Debt Initiative Share of Share of imports Share of central Cereal food Debt service Share of gross capital of goods and government aid shipments Decision Completion relief committed GDP (%) Per capita ($) formation (%) services (%) expenditures (%) (thousand of tonnes) point point ($ millions) 2006 2006 2006 2006 2006 2006 2006 2006 2006 5.1 48.5 27.0 7.0 28.3 2,204 52,315 0.4 10.3 2.8 0.3 .. 7 .. .. .. 8.1 42.8 .. .. .. 6 Jul. 2000 Mar. 2003 460 0.6 35 2.0 0.7 3.1 .. .. .. .. 15.1 60.6 83.3 39.4 68.6 24 Jul. 2000 Apr. 2002 930 45.9 50.8 275.7 78.3 157.0 38 Oct. 2000 .. 1,472 9.4 92.7 55.8 21.3 97.7 5 Oct.2000 Apr. 2006 4,917 11.7 266.6 30.7 16.2 56.5 16 Sep. 2008 .. .. 9.1 31.4 101.3 25.2 85.4 8 .. .. .. 4.5 27.1 20.2 4.5 76.8 61 May 2001 .. 260 7.5 49.5 76.7 16.0 59.9 .. .. .. .. 24.1 33.9 148.8 34.2 331.1 80 Jul. 2003 Floating 10,389 3.3 69 14.4 2.5 24.9 5 Mar. 2006 Floating 2,881 1.5 13.3 14.7 1.5 17.3 13 Mar. 1998 .. .. 15.2 143.2 51.6 15.7 54.5 8 .. .. .. 0.3 54.1 0.8 0.2 10.9 .. .. .. .. 11.9 27.5 63.6 20.5 28.1 10 .. .. .. 12.8 25.2 53.0 25.5 106.2 504 Nov. 2001 Apr. 2004 3,275 0.3 23.7 1.4 0.4 3.9 .. .. .. .. 14.5 44.5 59.7 .. .. 6 Dec. 2000 Dec. 2008 90 9.2 51.1 28.1 8.8 68.9 21 Feb. 2002 Jul. 2004 3,500 5.1 17.8 38.3 7.3 90.7 19 Dec. 2000 Floating 800 26.8 50 155.6 28.3 151.6 7 Dec. 2000 Floating 790 4.1 25.8 19.0 6.6 25.5 245 .. .. .. 4.8 36 14.5 3.2 26.6 7 .. .. .. 43.8 75.1 .. 42.7 .. 42 .. .. .. 13.7 39.4 55.3 19.4 155.7 37 Dec. 2000 Oct. 2004 1,900 21.1 49.3 89.1 45.5 179.6 52 Dec. 2000 Aug.2006 1,000 14.1 69 61.5 19.5 141.7 49 Sep. 2000 Mar.2003 895 7 61.6 30.3 6.2 35.4 38 Feb. 2000 Jun. 2002 1,100 0.3 14.8 1.2 0.2 2.0 .. .. .. .. 23.6 76.8 122.0 26.5 212.4 81 Apr. 2000 Sep. 2001 4,300 2.2 71 7.5 2.0 9.3 0 .. .. .. 11.2 29.2 .. .. .. 93 Dec. 2000 Apr. 2004 1,190 7.8 79 .. 10.9 .. .. .. .. .. 20.4 61.8 100.4 54.0 173.8 19 Dec. 2000 Apr. 2005 1,316 17.5 138.9 .. .. .. 0 Dec. 2000 Mar. 2008 200 8.9 68.3 30.8 12.9 92.6 12 Jun. 2000 Apr. 2004 850 1.8 164.9 5.5 0.7 7.3 .. .. .. .. 25.6 63.4 165.5 42.2 196.1 19 Mar. 2002 Dec. 2006 950 .. 46.4 .. .. .. 115 .. .. .. 0.3 15.1 1.4 0.4 1.4 .. .. .. .. 5.7 54.6 22.3 12.9 33.8 425 .. .. .. 1.2 30.3 7.8 0.9 6.3 4 .. .. .. 12.9 46.3 77.0 25.9 78.8 7 Apr. 2000 Nov. 2001 3,000 3.5 12.3 .. .. .. 0 .. .. .. 16.3 51.9 69.9 36.4 111.1 36 Feb. 2000 May. 2000 1,950 13.1 121.8 57.9 19.4 130.5 19 Dec. 2000 Apr. 2005 3,900 .. 21.2 .. .. .. 68 .. .. .. 0.7 16.8 .. 1.6 8.0 35 .. 0.2 6.3 .. .. .. 10 .. .. .. 0.8 11.8 4.3 1.3 6.6 24 .. .. .. 0.1 6.2 .. .. .. .. .. .. .. 1.6 34.3 5.1 2.2 8.7 .. .. .. .. 1.4 42.7 5.9 1.3 10.4 .. .. .. .. CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 127 Capable states and partnership ableT13.2 Capable states Viewed by firms as a major contraint (% of firms) Enforcing contracts Court system is fair, impartial and Crime, theft Number of Time required Cost uncorrupted and disorder procedures (days) (% of debt) 2006­07c 2006­07c 2008 2008 2008 SUB-SAHARAN AFRICA 39 673 48.6 Angola 31.9 36.6 46 1,011 44.4 Benin .. .. 42 825 64.7 Botswana 69.6 24.1 29 987 28.1 Burkina Faso 39.1 18.0 37 446 107.4 Burundi 40.7 19.7 44 558 38.6 Cameroon 25.6 33.1 43 800 46.6 Cape Verde 61.8 27.6 37 425 21.8 Central African Republic .. .. 43 660 82.0 Chad .. .. 41 743 77.4 Comoros .. .. 43 506 89.4 Congo, Dem. Rep. 19.8 22.6 43 685 151.8 Congo, Rep. .. .. 44 560 53.2 Côte d'Ivoire .. .. 33 770 41.7 Djibouti .. .. 40 1,225 34.0 Equatorial Guinea .. .. 40 553 18.5 Eritrea .. .. 39 405 22.6 Ethiopia 24.2 11.6 39 690 15.2 Gabon .. .. 38 1,070 34.3 Gambia, The 62.8 12.3 32 434 37.9 Ghana 59.8 11.4 36 487 23.0 Guinea 25.7 30.4 50 276 45.0 Guinea-Bissau 12.1 29.6 41 1,140 25.0 Kenya .. .. 44 465 26.7 Lesotho .. .. 41 695 19.5 Liberia .. .. 41 1,280 35.0 Madagascar .. .. 38 871 42.4 Malawi 59.2 47.2 42 432 142.4 Mali 49.6 4.7 39 860 52.0 Mauritania 48.5 1.4 46 400 23.2 Mauritius .. .. 37 750 17.4 Mozambique .. .. 31 1,010 142.5 Namibia 66.1 27.6 33 270 29.9 Niger 35.7 6.4 39 545 59.6 Nigeria .. .. 39 457 32.0 Rwanda 67.1 4.1 24 310 78.7 São Tomé and Principe .. .. 43 1,185 34.8 Senegal 55.4 11.6 44 780 26.5 Seychelles .. .. 38 720 14.3 Sierra Leone .. .. 40 515 149.5 Somalia .. .. .. .. .. South Africa .. .. 30 600 33.2 Sudan .. .. 53 810 19.8 Swaziland 40.3 34.4 40 972 23.1 Tanzania 46.7 16.4 38 462 14.3 Togo .. .. 41 588 47.5 Uganda 43.5 13.4 38 535 44.9 Zambia 54.7 10.1 35 471 38.7 Zimbabwe .. .. 38 410 32.0 NORTH AFRICA 42 705 23.8 Algeria .. 20.7 47 630 21.9 Egypt, Arab Rep. .. .. 42 1,010 26.2 Libya .. .. .. .. .. Morocco 43.5 3.4 40 615 25.2 Tunisia .. .. 39 565 21.8 a. Indexes run from 0 (least desirable) to 10 (most desirable). b. Average of the disclosure, director liability and shareholder suits indexes. c. Data are for the most recent year available during the period specified. 128 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP Extractive Industries Protecting investorsa Regulation and tax administration Transparency Initiative Director Shareholder Investor Number Time to prepare, Total tax Disclosure Liability Suits Protection of tax file, and pay rate Report index Index Index Indexb payments taxes (hours) (% of profit) Endorsed produced 2008 2008 2008 2008 2008 2008 2008 2007 2007 5 3 5 4.2 38 320 66.6 5 6 6 5.7 31 272 53.2 No No 6 1 3 3.3 55 270 73.3 No No 8 2 3 4.3 19 140 17.2 No No 6 1 4 3.7 45 270 47.6 No No 4 1 5 3.3 32 140 278.7 No No 6 1 6 4.3 41 1400 51.9 Yes Yes 1 5 6 4.0 57 100 54.0 No No 6 1 5 4.0 54 504 203.8 No No 6 1 5 4.0 54 122 63.7 Yes No 6 1 5 4.0 20 100 48.8 No No 3 3 4 3.3 32 308 229.8 Yes No 6 1 3 3.3 61 606 65.4 Yes No 6 1 3 3.3 66 270 45.4 Yes No 5 2 0 2.3 35 114 38.7 .. .. 6 1 4 3.7 46 296 59.5 Yes No 4 5 5 4.7 18 216 84.5 No No 4 4 5 4.3 20 198 31.1 No No 6 1 3 3.3 26 272 44.7 Yes Yes 2 1 5 2.7 50 376 292.4 No No 7 5 6 6.0 33 304 32.9 Yes Yes 6 1 1 2.7 56 416 49.9 Yes Yes 6 1 5 4.0 46 208 45.9 No No 3 2 10 5.0 41 432 50.9 No No 2 1 8 3.7 21 342 20.4 No No 4 1 6 3.7 32 158 35.8 Yes No 5 6 6 5.7 26 238 46.5 Yes No 4 7 5 5.3 19 370 32.2 No No 6 1 3 3.3 58 270 51.4 Yes No 5 3 3 3.7 38 696 107.5 Yes No 6 8 9 7.7 7 161 21.7 No No 5 4 9 6.0 37 230 34.3 No No 5 5 6 5.3 37 375 26.5 No No 6 1 3 3.3 42 270 42.4 Yes No 5 7 5 5.7 35 1120 32.2 Yes Yes 2 5 1 2.7 34 168 33.8 No No 3 1 6 3.3 42 424 48.7 Yes No 6 1 2 3.0 59 696 46.0 No No 4 8 5 5.7 16 76 48.4 No No 3 6 8 5.7 28 399 233.5 Yes No .. .. .. .. .. .. .. No No 8 8 8 8.0 11 350 37.1 No No 0 6 4 3.3 42 180 31.6 No No 0 1 5 2.0 33 104 36.6 No No 3 4 8 5.0 48 172 44.3 No No 6 1 4 3.7 53 270 48.2 No No 2 5 5 4.0 33 237 37.4 No No 3 6 7 5.3 37 132 16.1 No No 8 1 4 4.3 52 256 53.0 No No 5 4 4 4.2 30 447 56.9 6 6 4 5.3 34 451 74.2 .. .. 7 3 5 5.0 36 711 47.9 .. .. .. .. .. .. .. .. .. .. .. 6 2 1 3.0 28 358 44.6 .. .. 0 4 6 3.3 22 268 61.0 .. .. CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 129 Capable states and partnership ableT13.3 Governance and anticorruption indicators Voice and accountability Political stability and absence of violence Government effectiveness 1996 2006 2007 1996 2006 2007 1996 2006 2007 SUB-SAHARAN AFRICA Angola ­1.5 ­1.2 ­1.1 ­2.3 ­0.4 ­0.5 ­1.4 ­1.3 ­1.2 Benin 0.7 0.3 0.3 1.1 0.4 0.4 0.0 ­0.5 ­0.6 Botswana 0.8 0.5 0.5 0.7 1.0 0.8 0.2 0.6 0.7 Burkina Faso ­0.2 ­0.3 ­0.3 0.0 ­0.1 0.1 ­0.7 ­0.8 ­0.8 Burundi ­1.5 ­1.1 ­0.8 ­2.0 ­1.4 ­1.4 ­1.0 ­1.3 ­1.3 Cameroon ­1.2 ­1.0 ­0.9 ­1.4 ­0.3 ­0.4 ­1.2 ­0.8 ­0.9 Cape Verde 0.8 0.8 0.9 1.1 1.0 1.0 ­0.1 0.2 0.4 Central African Republic ­0.5 ­1.0 ­0.9 ­0.2 ­1.8 ­1.8 ­0.9 ­1.4 ­1.4 Chad ­0.9 ­1.4 ­1.4 ­0.7 ­1.9 ­2.0 ­0.7 ­1.3 ­1.5 Comoros 0.0 ­0.2 ­0.5 1.1 ­0.2 ­0.4 ­0.7 ­1.7 ­1.8 Congo, Dem. Rep. ­1.6 ­1.6 ­1.5 ­1.9 ­2.4 ­2.3 ­1.7 ­1.7 ­1.7 Congo, Rep. ­0.5 ­1.1 ­1.1 ­0.8 ­1.0 ­0.8 ­1.2 ­1.3 ­1.3 Côte d'Ivoire ­0.8 ­1.3 ­1.3 ­0.1 ­2.2 ­2.1 0.1 ­1.4 ­1.4 Djibouti ­0.7 ­1.0 ­1.1 0.2 ­0.2 ­0.1 ­1.0 ­1.0 ­1.0 Equatorial Guinea ­1.6 ­1.8 ­1.9 ­0.4 ­0.1 ­0.2 ­1.5 ­1.3 ­1.4 Eritrea ­1.1 ­2.0 ­2.2 0.3 ­0.9 ­1.0 ­0.4 ­1.3 ­1.3 Ethiopia ­0.9 ­1.2 ­1.2 ­1.2 ­1.7 ­1.7 ­1.0 ­0.6 ­0.5 Gabon ­0.4 ­0.8 ­0.8 ­0.3 0.1 0.2 ­1.0 ­0.7 ­0.7 Gambia, The ­1.3 ­0.9 ­1.0 0.1 0.0 ­0.1 ­0.4 ­0.8 ­0.7 Ghana ­0.3 0.5 0.5 ­0.2 0.3 0.2 ­0.4 0.0 0.0 Guinea ­1.1 ­1.2 ­1.2 ­1.4 ­1.8 ­2.0 ­1.1 ­1.4 ­1.5 Guinea-Bissau ­0.3 ­0.4 ­0.5 ­0.6 ­0.4 ­0.4 ­0.6 ­1.2 ­1.2 Kenya ­0.8 ­0.1 ­0.1 ­0.7 ­1.0 ­1.1 ­0.3 ­0.7 ­0.6 Lesotho ­0.2 0.2 0.1 0.6 0.2 0.0 0.1 ­0.4 ­0.4 Liberia ­1.4 ­0.6 ­0.4 ­2.6 ­1.3 ­1.2 ­1.8 ­1.2 ­1.2 Madagascar 0.4 ­0.1 0.0 0.1 0.1 ­0.1 ­1.0 ­0.3 ­0.3 Malawi 0.0 ­0.3 ­0.3 ­0.3 .. 0.0 ­0.7 ­0.9 ­0.6 Mali 0.7 0.3 0.3 0.6 0.0 ­0.1 ­0.7 ­0.5 ­0.6 Mauritania ­1.0 ­0.8 ­0.8 0.6 ­0.1 ­0.3 0.2 ­0.8 ­0.7 Mauritius 0.9 0.8 0.9 0.7 0.7 0.8 0.5 0.6 0.6 Mozambique 0.1 ­0.1 ­0.1 ­0.8 0.5 0.4 ­0.3 ­0.4 ­0.4 Namibia 0.6 0.5 0.6 0.5 0.8 0.9 0.5 0.2 0.2 Niger ­1.0 ­0.3 ­0.4 0.0 ­0.3 ­0.6 ­1.1 ­0.9 ­0.9 Nigeria ­1.8 ­0.5 ­0.5 ­1.6 ­2.1 ­2.1 ­1.4 ­0.9 ­0.9 Rwanda ­1.3 ­1.2 ­1.2 ­2.0 ­0.5 ­0.2 ­1.2 ­0.4 ­0.4 São Tomé and Principe 0.5 0.4 0.4 1.1 0.4 0.3 ­0.7 ­0.9 ­0.8 Senegal ­0.1 0.1 0.0 ­0.3 ­0.3 ­0.2 ­0.2 ­0.2 ­0.3 Seychelles 0.0 0.0 ­0.1 1.1 1.1 1.0 ­0.6 0.0 0.0 Sierra Leone ­0.9 ­0.4 ­0.3 ­2.3 ­0.5 ­0.3 ­0.6 ­1.1 ­1.1 Somalia ­1.9 ­1.8 ­1.9 ­2.3 ­2.8 ­3.0 ­1.8 ­2.2 ­2.4 South Africa 0.8 0.8 0.7 ­1.2 0.1 0.2 0.4 0.8 0.7 Sudan ­2.0 ­1.7 ­1.7 ­2.6 ­2.1 ­2.3 ­1.5 ­1.1 ­1.2 Swaziland ­1.1 ­1.1 ­1.1 0.0 ­0.1 0.1 ­0.3 ­0.7 ­0.7 Tanzania ­0.6 ­0.2 ­0.2 ­0.3 ­0.1 ­0.1 ­0.8 ­0.4 ­0.4 Togo ­1.0 ­1.3 ­1.2 ­0.5 ­0.7 ­0.5 ­0.7 ­1.6 ­1.5 Uganda ­0.5 ­0.5 ­0.5 ­1.3 ­1.3 ­1.2 ­0.6 ­0.4 ­0.4 Zambia ­0.6 ­0.3 ­0.3 ­0.5 0.3 0.2 ­0.6 ­0.7 ­0.6 Zimbabwe ­0.6 ­1.5 ­1.5 ­0.6 ­1.1 ­1.3 ­0.4 ­1.4 ­1.5 NORTH AFRICA Algeria ­1.4 ­0.9 ­1.0 ­2.4 ­1.0 ­1.2 ­0.4 ­0.4 ­0.5 Egypt, Arab Rep. ­1.0 ­1.3 ­1.2 ­1.1 ­0.9 ­0.8 0.0 ­0.5 ­0.4 Libya ­1.8 ­2.0 ­1.9 ­1.8 0.3 0.5 ­1.0 ­0.8 ­1.1 Morocco ­0.6 ­0.6 ­0.6 ­0.6 ­0.3 ­0.5 ­0.1 ­0.1 ­0.1 Tunisia ­0.9 ­1.2 ­1.2 0.2 0.3 0.1 0.5 0.5 0.5 Note: The rating scale for each criterion varies from ­2.5 (weak performance) to 2.5 (high performance). 130 Part III. Development outcomes CAPABLESTATES AND PARTNERSHIP Corruption Perceptions Index Regulatory quality Rule of law Control of corruption (mean score, 0 low to 10 high) 1996 2006 2007 1996 2006 2007 1996 2006 2007 2006 2007 ­1.4 ­1.1 ­1.0 ­1.5 ­1.3 ­1.4 ­1.1 ­1.2 ­1.1 2.2 2.2 0.2 ­0.4 ­0.4 ­0.3 ­0.6 ­0.6 .. ­0.6 ­0.5 2.5 2.7 0.7 0.5 0.5 0.6 0.6 0.7 0.4 0.9 0.9 5.6 5.4 ­0.1 ­0.4 ­0.3 ­0.3 ­0.5 ­0.5 ­0.3 ­0.4 ­0.4 3.2 2.9 ­1.6 ­1.2 ­1.2 ­0.9 ­1.0 ­1.2 .. ­1.1 ­1.1 2.4 2.5 ­0.8 ­0.7 ­0.7 ­1.5 ­1.0 ­1.1 ­1.2 ­1.0 ­0.9 2.3 2.4 ­0.8 ­0.2 ­0.2 0.5 0.6 0.6 .. 0.6 0.8 .. 4.9 ­0.3 ­1.3 ­1.2 ­0.3 ­1.5 ­1.5 .. ­1.0 ­0.9 2.4 2.0 ­0.9 ­1.1 ­1.2 ­0.9 ­1.4 ­1.4 .. ­1.2 ­1.2 2.0 1.8 ­0.8 ­1.5 ­1.4 .. ­0.9 ­0.9 .. ­0.7 ­0.7 .. 2.6 ­2.6 ­1.4 ­1.4 ­2.1 ­1.7 ­1.7 ­2.1 ­1.4 ­1.3 2.0 1.9 ­0.9 ­1.1 ­1.2 ­1.4 ­1.2 ­1.3 ­0.9 ­1.1 ­1.0 2.2 2.1 0.0 ­0.9 ­1.0 ­0.7 ­1.5 ­1.5 0.4 ­1.2 ­1.1 2.1 2.1 0.2 ­0.9 ­0.8 ­0.2 ­0.6 ­0.5 .. ­0.6 ­0.5 .. .. ­1.0 ­1.3 ­1.4 ­1.2 ­1.2 ­1.2 ­1.1 ­1.5 ­1.4 2.1 1.9 .. ­1.9 ­2.0 ­0.3 ­1.0 ­1.1 .. ­0.3 ­0.6 2.9 2.8 ­1.8 ­0.9 ­0.9 ­0.9 ­0.6 ­0.5 ­1.1 ­0.7 ­0.7 2.4 2.4 0.0 ­0.5 ­0.5 ­0.9 ­0.6 ­0.6 ­1.3 ­0.9 ­0.9 3.0 3.3 ­1.8 ­0.4 ­0.4 0.4 ­0.3 ­0.2 0.4 ­0.7 ­0.8 2.5 2.3 0.1 0.0 .. ­0.4 ­0.1 ­0.1 ­0.5 ­0.1 ­0.2 3.3 3.7 0.2 ­1.0 ­1.1 ­1.4 ­1.4 ­1.5 0.4 ­1.0 ­1.3 1.9 1.9 0.1 ­1.0 ­1.1 ­1.7 ­1.3 ­1.4 ­1.0 ­1.0 ­1.1 .. 2.2 ­0.4 ­0.3 ­0.2 ­1.1 ­0.9 ­1.0 ­1.1 ­0.9 ­0.9 2.2 2.1 ­0.6 ­0.6 ­0.7 ­0.3 ­0.3 ­0.4 .. ­0.1 ­0.2 3.2 3.3 ­3.1 ­1.4 ­1.2 ­2.3 ­1.0 ­1.1 ­1.7 ­0.7 ­0.4 .. 2.1 ­0.5 ­0.2 ­0.2 ­1.0 ­0.4 ­0.4 0.4 ­0.2 ­0.2 3.1 3.2 ­0.2 ­0.7 ­0.5 ­0.6 ­0.4 ­0.4 ­0.5 ­0.7 ­0.7 2.7 2.7 0.0 ­0.4 ­0.3 ­0.6 ­0.4 ­0.4 ­0.3 ­0.4 ­0.4 2.8 2.7 ­0.9 ­0.3 ­0.4 ­0.9 ­0.6 ­0.6 .. ­0.6 ­0.5 3.1 2.6 0.1 0.5 0.6 0.8 0.7 0.8 0.5 0.4 0.4 5.1 4.7 ­1.0 ­0.5 ­0.5 ­0.9 ­0.6 ­0.7 ­0.4 ­0.7 ­0.6 2.8 2.8 0.1 0.1 0.0 0.3 0.2 0.1 0.7 0.1 0.2 4.1 4.5 ­1.2 ­0.6 ­0.6 ­0.9 ­0.8 ­0.9 ­0.3 ­1.0 ­0.9 2.3 2.6 ­1.1 ­1.0 ­0.9 ­1.4 ­1.2 ­1.2 ­1.3 ­1.1 ­1.0 2.2 2.2 ­1.8 ­0.5 ­0.6 ­1.5 ­0.7 ­0.7 .. ­0.1 ­0.1 2.5 2.8 ­0.3 ­0.7 ­0.8 .. ­0.5 ­0.4 .. ­0.5 ­0.5 .. 2.7 ­0.4 ­0.3 ­0.4 ­0.4 ­0.4 ­0.4 ­0.4 ­0.5 ­0.5 3.3 3.6 ­1.4 ­0.6 ­0.6 .. 0.2 0.2 .. 0.1 0.0 3.6 4.5 ­0.9 ­1.1 ­1.0 ­1.3 ­1.2 ­1.1 ­1.7 ­1.1 ­1.0 2.2 2.1 ­2.9 ­2.7 ­2.7 ­2.1 ­2.6 ­2.6 ­1.7 ­1.8 ­1.9 .. 1.4 0.0 0.6 0.5 0.3 0.2 0.2 0.6 0.4 0.3 4.6 5.1 ­1.9 ­1.2 ­1.3 ­1.6 ­1.3 ­1.5 ­1.1 ­1.2 ­1.3 2.0 1.8 0.1 ­0.6 ­0.7 0.8 ­0.7 ­0.8 .. ­0.4 ­0.5 2.5 3.3 ­0.1 ­0.4 ­0.4 ­0.4 ­0.5 ­0.5 ­1.1 ­0.4 ­0.5 2.9 3.2 0.6 ­1.0 ­1.0 ­1.4 ­1.0 ­0.9 ­1.0 ­1.1 ­1.0 2.4 2.3 0.3 ­0.2 ­0.2 ­0.6 ­0.5 ­0.5 ­0.6 ­0.7 ­0.8 2.7 2.8 0.3 ­0.6 ­0.5 ­0.6 ­0.7 ­0.6 ­1.0 ­0.7 ­0.6 2.6 2.6 ­0.8 ­2.1 ­2.2 ­0.7 ­1.6 ­1.7 ­0.2 ­1.3 ­1.3 2.4 2.1 ­0.9 ­0.7 ­0.7 ­1.2 ­0.6 ­0.7 ­0.4 ­0.5 ­0.5 .. .. 0.2 ­0.5 ­0.3 0.1 ­0.1 ­0.1 0.1 ­0.5 ­0.6 .. .. ­2.1 ­1.3 ­1.0 ­1.3 ­0.7 ­0.6 ­1.0 ­0.9 ­0.8 .. .. 0.2 ­0.2 ­0.1 0.1 ­0.1 ­0.2 0.2 ­0.3 ­0.2 .. .. 0.6 0.1 0.2 ­0.2 0.3 0.3 ­0.1 0.0 0.1 .. .. CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 131 Capable states and partnership ableT13.4 Country policy and institutional assessment ratings Economic Management Structural Policies CPIA Overall rating Business (IDA resource Macro-economic Financial regulatory allocation index)a Averageb management Fiscal policy Debt policy Averageb Trade sector environment 2006 2007 2007 2007 2007 2007 2007 2007 2007 2007 SUB-SAHARAN AFRICA Angola 2.7 2.7 3.0 3.0 3.0 3.0 2.8 4.0 2.5 2.0 Benin 3.6 3.6 4.0 4.5 4.0 3.5 3.7 4.0 3.5 3.5 Botswanac .. .. .. .. .. .. .. .. .. .. Burkina Faso 3.7 3.7 4.3 4.5 4.5 4.0 3.3 4.0 3.0 3.0 Burundi 3.0 3.0 3.2 3.5 3.5 2.5 3.0 3.5 3.0 2.5 Cameroon 3.2 3.2 3.7 4.0 4.0 3.0 3.2 3.5 3.0 3.0 Cape Verde 4.1 4.2 4.5 4.5 4.5 4.5 3.8 4.0 4.0 3.5 Central African Republic 2.4 2.5 2.8 3.5 3.0 2.0 2.7 3.5 2.5 2.0 Chad 2.8 2.6 2.7 3.0 2.5 2.5 2.8 3.0 3.0 2.5 Comoros 2.4 2.4 2.0 2.5 1.5 2.0 2.7 3.0 2.5 2.5 Congo, Dem. Rep. 2.8 2.8 3.2 3.5 3.5 2.5 3.0 4.0 2.0 3.0 Congo, Rep. 2.8 2.7 2.5 3.0 2.0 2.5 2.8 3.5 2.5 2.5 Côte d'Ivoire 2.5 2.6 2.3 3.0 2.5 1.5 3.2 3.5 3.0 3.0 Djibouti 3.1 3.1 2.8 3.5 2.5 2.5 3.7 4.0 3.5 3.5 Equatorial Guineac .. .. .. .. .. .. .. .. .. .. Eritrea 2.5 2.4 2.2 2.0 2.0 2.5 1.8 1.5 2.0 2.0 Ethiopia 3.4 3.4 3.5 3.0 4.0 3.5 3.2 3.0 3.0 3.5 Gabonc .. .. .. .. .. .. .. .. .. .. Gambia, The 3.1 3.2 3.3 4.0 3.5 2.5 3.5 4.0 3.0 3.5 Ghana 3.9 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 Guinea 2.9 3.0 3.0 3.0 3.5 2.5 3.3 4.0 3.0 3.0 Guinea-Bissau 2.6 2.6 2.0 2.0 2.5 1.5 3.2 4.0 3.0 2.5 Kenya 3.7 3.6 4.2 4.5 4.0 4.0 3.8 4.0 3.5 4.0 Lesotho 3.5 3.5 4.0 4.0 4.0 4.0 3.3 3.5 3.5 3.0 Liberiad .. .. .. .. .. .. .. .. .. .. Madagascar 3.6 3.7 3.7 4.0 3.0 4.0 3.8 4.0 3.5 4.0 Malawi 3.4 3.4 3.3 3.5 3.5 3.0 3.5 4.0 3.0 3.5 Mali 3.7 3.7 4.3 4.5 4.0 4.5 3.5 4.0 3.0 3.5 Mauritania 3.3 3.4 3.5 3.5 3.0 4.0 3.5 4.5 2.5 3.5 Mauritiusc .. .. .. .. .. .. .. .. .. .. Mozambique 3.5 3.6 4.2 4.0 4.0 4.5 3.7 4.5 3.5 3.0 Namibiac .. .. .. .. .. .. .. .. .. .. Niger 3.3 3.3 3.7 4.0 3.5 3.5 3.3 4.0 3.0 3.0 Nigeria 3.2 3.4 4.3 4.0 4.5 4.5 3.2 3.0 3.5 3.0 Rwanda 3.6 3.7 3.8 4.0 4.0 3.5 3.5 3.5 3.5 3.5 São Tomé and Principe 3.0 3.0 2.8 3.0 3.0 2.5 3.2 4.0 2.5 3.0 Senegal 3.7 3.7 4.2 4.5 4.0 4.0 3.8 4.0 3.5 4.0 Seychelles .. .. .. .. .. .. .. .. .. .. Sierra Leone 3.1 3.1 3.7 4.0 3.5 3.5 3.0 3.5 3.0 2.5 Somaliad .. .. .. .. .. .. .. .. .. .. South Africac .. .. .. .. .. .. .. .. .. .. Sudan 2.5 2.5 2.7 3.5 3.0 1.5 2.7 2.5 2.5 3.0 Swazilandc .. .. .. .. .. .. .. .. .. .. Tanzania 3.9 3.9 4.3 4.5 4.5 4.0 3.7 4.0 3.5 3.5 Togo 2.5 2.5 2.2 2.5 2.5 1.5 3.2 4.0 2.5 3.0 Uganda 3.9 3.9 4.5 4.5 4.5 4.5 3.8 4.0 3.5 4.0 Zambia 3.4 3.5 3.7 4.0 3.5 3.5 3.7 4.0 3.5 3.5 Zimbabwe 1.8 1.7 1.0 1.0 1.0 1.0 2.0 2.0 2.5 1.5 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 International Development Association (IDA) resource allocation index. 132 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP Policies for Social Inclusion/Equity Public Sector Management and Institutions Policies & Property Quality of Transparency, Equity of Building Social institutions for rights & budgetary Efficiency Quality of accountability Gender public human protection environmental rule-based & financial of revenue public & corruption in Averageb equality resource use resources and labor sustainability Averageb governance management mobilization administration public sector 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2.7 3.0 2.5 2.5 2.5 3.0 2.4 2.0 2.5 2.5 2.5 2.5 3.3 3.5 3.0 3.5 3.0 3.5 3.3 3.0 3.5 3.5 3.0 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 3.3 4.0 3.5 3.0 3.0 3.0 2.6 2.5 3.0 3.0 2.5 2.0 3.1 3.0 3.0 3.5 3.0 3.0 3.0 2.5 3.5 3.5 3.0 2.5 4.3 4.5 4.5 4.5 4.5 3.5 4.0 4.0 4.0 3.5 4.0 4.5 2.2 2.5 2.0 2.0 2.0 2.5 2.3 2.0 2.0 2.5 2.0 2.5 2.6 2.5 3.0 2.5 2.5 2.5 2.2 2.0 2.0 2.5 3.0 2.0 2.7 3.0 3.0 3.0 2.5 2.0 2.2 2.5 1.5 2.5 2.0 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 2.6 2.5 2.5 3.0 2.5 2.5 2.3 2.5 1.5 2.5 2.5 2.5 2.4 2.0 2.0 4.0 2.0 2.0 3.0 2.5 3.0 3.5 3.0 3.0 2.8 2.5 3.0 3.5 2.5 2.5 .. .. .. .. .. .. .. .. .. .. 3.0 3.5 3.0 3.5 3.0 2.0 2.7 2.5 2.5 3.5 3.0 2.0 3.7 3.0 4.5 4.0 3.5 3.5 3.3 3.0 4.0 4.0 3.0 2.5 .. .. .. .. .. .. .. .. .. .. 3.1 3.5 3.0 3.5 2.5 3.0 3.0 3.5 3.0 3.5 3.0 2.0 3.9 4.0 4.0 4.5 3.5 3.5 3.9 3.5 4.0 4.5 3.5 4.0 3.0 3.5 3.0 3.0 3.0 2.5 2.7 2.0 3.0 3.0 3.0 2.5 2.6 2.5 3.0 2.5 2.5 2.5 2.6 2.5 2.5 3.0 2.5 2.5 3.2 3.0 3.0 3.5 3.0 3.5 3.3 2.5 3.5 4.0 3.5 3.0 3.4 4.0 3.0 3.5 3.0 3.5 3.4 3.5 3.0 4.0 3.0 3.5 .. .. .. .. .. .. .. .. .. .. 3.7 3.5 4.0 3.5 3.5 4.0 3.5 3.5 3.5 3.5 3.5 3.5 3.4 3.5 3.5 3.0 3.5 3.5 3.4 3.5 3.0 4.0 3.5 3.0 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 3.5 4.0 3.0 3.5 3.5 4.0 3.5 3.5 3.0 3.5 3.0 3.0 2.5 3.5 3.0 3.0 .. .. .. .. .. .. .. .. .. .. 3.3 3.5 3.5 3.5 3.0 3.0 3.3 3.0 3.5 4.0 2.5 3.0 .. .. .. .. .. .. .. .. .. .. 3.0 2.5 3.5 3.0 3.0 3.0 3.2 3.0 3.5 3.5 3.0 3.0 3.2 3.0 3.5 3.0 3.5 3.0 2.9 2.5 3.0 3.0 2.5 3.0 3.8 3.5 4.5 4.5 3.5 3.0 3.5 3.0 4.0 3.5 3.5 3.5 2.8 3.0 3.0 3.0 2.5 2.5 3.1 2.5 3.0 3.5 3.0 3.5 3.4 3.5 3.5 3.5 3.0 3.5 3.5 3.5 3.5 4.0 3.5 3.0 .. .. .. .. .. .. .. .. .. .. 2.9 3.0 3.0 3.5 3.0 2.0 2.8 2.5 3.5 2.5 3.0 2.5 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. 2.4 2.0 2.5 2.5 2.5 2.5 2.3 2.0 2.0 3.0 2.5 2.0 .. .. .. .. .. .. .. .. .. .. 3.8 4.0 4.0 4.0 3.5 3.5 3.7 3.5 4.0 4.0 3.5 3.5 2.6 3.0 2.0 3.0 2.5 2.5 2.2 2.5 2.0 2.5 2.0 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 3.5 3.0 3.0 1.8 2.5 1.5 1.5 1.0 2.5 1.8 1.0 2.0 3.5 2.0 1.0 .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 133 Capable states and partnership ableT13.5 Polity indicators Polity scorea Institutionalized democracyb Institutionalized autocracyb 1995 2000 2006 1995 2000 2006 1995 2000 2006 SUB-SAHARAN AFRICA Angola .. ­3.0 ­2.0 .. 1.0 2.0 .. 4.0 4.0 Benin 6.0 6.0 7.0 6.0 6.0 7.0 .. .. .. Botswana 8.0 9.0 9.0 8.0 9.0 9.0 .. .. .. Burkina Faso ­5.0 ­3.0 .. .. .. 2.0 5.0 3.0 2.0 Burundi .. ­1.0 6.0 .. 1.0 7.0 .. 2.0 1.0 Cameroon ­4.0 ­4.0 ­4.0 1.0 1.0 1.0 5.0 5.0 5.0 Cape Verde .. .. .. .. .. .. .. .. .. Central African Republic 5.0 5.0 ­1.0 5.0 5.0 1.0 .. .. 2.0 Chad ­4.0 ­2.0 ­2.0 .. 1.0 1.0 4.0 3.0 3.0 Comoros .. ­1.0 9.0 .. 1.0 9.0 .. 2.0 .. Congo, Dem. Rep. .. .. 5.0 .. .. 6.0 .. .. 1.0 Congo, Rep. 5.0 ­6.0 ­4.0 6.0 .. .. 1.0 6.0 4.0 Côte d'Ivoire ­6.0 4.0 .. .. 5.0 .. 6.0 1.0 .. Djibouti ­7.0 2.0 2.0 .. 3.0 3.0 7.0 1.0 1.0 Equatorial Guinea ­5.0 ­5.0 ­5.0 .. .. .. 5.0 5.0 5.0 Eritrea ­6.0 ­6.0 ­7.0 .. .. .. 6.0 6.0 7.0 Ethiopia 1.0 1.0 1.0 3.0 3.0 3.0 2.0 2.0 2.0 Gabon ­4.0 ­4.0 ­4.0 .. .. .. 4.0 4.0 4.0 Gambia, The ­7.0 ­5.0 ­5.0 .. .. .. 7.0 5.0 5.0 Ghana ­1.0 2.0 8.0 1.0 3.0 8.0 2.0 1.0 .. Guinea ­1.0 ­1.0 ­1.0 1.0 1.0 1.0 2.0 2.0 2.0 Guinea-Bissau 5.0 5.0 6.0 5.0 5.0 6.0 .. .. .. Kenya ­5.0 ­2.0 8.0 .. 2.0 8.0 5.0 4.0 .. Lesotho 8.0 .. 8.0 8.0 .. 8.0 .. .. .. Liberia .. .. 6.0 .. 3.0 7.0 .. 3.0 1.0 Madagascar 9.0 7.0 7.0 9.0 7.0 7.0 .. .. .. Malawi 6.0 6.0 6.0 6.0 6.0 6.0 .. .. .. Mali 7.0 6.0 6.0 7.0 6.0 6.0 .. .. .. Mauritania ­6.0 ­6.0 ­3.0 .. .. .. 6.0 6.0 3.0 Mauritius 10.0 10.0 10.0 10.0 10.0 10.0 .. .. .. Mozambique 6.0 6.0 6.0 6.0 6.0 6.0 .. .. .. Namibia 6.0 6.0 6.0 6.0 6.0 6.0 .. .. .. Niger 8.0 5.0 6.0 8.0 6.0 7.0 .. 1.0 1.0 Nigeria ­6.0 4.0 4.0 .. 4.0 4.0 6.0 .. .. Rwanda ­6.0 ­4.0 ­3.0 .. .. .. 6.0 4.0 3.0 São Tomé and Principe .. .. .. .. .. .. .. .. .. Senegal ­1.0 8.0 8.0 2.0 8.0 8.0 3.0 .. .. Seychelles .. .. .. .. .. .. .. .. .. Sierra Leone ­7.0 .. 5.0 .. .. 5.0 7.0 .. .. Somalia .. .. .. .. .. .. .. .. .. South Africa 9.0 9.0 9.0 9.0 9.0 9.0 .. .. .. Sudan ­7.0 ­7.0 ­4.0 .. .. .. 7.0 7.0 4.0 Swaziland ­9.0 ­9.0 ­9.0 .. .. .. 9.0 9.0 9.0 Tanzania ­1.0 1.0 1.0 2.0 3.0 3.0 3.0 2.0 2.0 Togo ­2.0 ­2.0 ­4.0 1.0 1.0 1.0 3.0 3.0 5.0 Uganda ­4.0 ­4.0 ­1.0 .. .. 1.0 4.0 4.0 2.0 Zambia 6.0 1.0 5.0 6.0 3.0 5.0 .. 2.0 .. Zimbabwe ­6.0 ­3.0 ­4.0 .. 1.0 1.0 6.0 4.0 5.0 NORTH AFRICA Algeria ­3.0 ­3.0 2.0 1.0 1.0 3.0 4.0 4.0 1.0 Egypt, Arab Rep. ­6.0 ­6.0 ­3.0 .. .. 1.0 6.0 6.0 4.0 Libya ­7.0 ­7.0 ­7.0 .. .. .. 7.0 7.0 7.0 Morocco ­7.0 ­6.0 ­6.0 .. .. .. 7.0 6.0 6.0 Tunisia ­3.0 ­3.0 ­4.0 1.0 1.0 1.0 4.0 4.0 5.0 a The polity rating scale ranges from +10 (strongly democratic) to ­10 (strongly autocratic). b. The institutionalized democracy and autocracy indicator are each an additive eleven-point scale (0­10) * For a discussion on conflict, fragility and democracy, see Box 12 in the technical notes. 134 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP ableT14.1 Burkina Faso household survey, 2003 Expenditure Quintile National Rural Urban 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: eradicate 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) .. .. .. .. .. .. .. .. .. .. .. .. .. Total 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 5 km away (% of population) 65 65 64 66 59 66 67 98 94 97 99 99 99 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 Other .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km away (% of population) 90 88 88 90 90 88 85 98 98 97 97 97 98 Market less than 5 km 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 HOUSEHOLD WELFARE Part IV. Household welfare 135 ableT14.2 Cameroon household survey, 2001 Expenditure Quintile National Rural Urban 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: eradicate 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 .. .. .. .. .. .. .. .. .. .. .. .. Total 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 5 km 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 Missionary or nongovernmental organization 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 25 13 15 21 29 35 75 58 68 75 79 84 Water source less than 5 km away (% of population) 68 75 71 80 73 74 76 56 56 59 61 57 50 Market less than 5 km 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 136 Part IV. Household welfare HOUSEHOLD WELFARE ableT14.3 Ethiopia household survey, 1999/00 Expenditure Quintile National Rural Urban 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: eradicate 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) Total .. .. .. .. .. .. .. .. .. .. .. .. .. 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) .. .. .. .. .. .. .. .. .. .. .. .. .. 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 Missionary or nongovernmental organization .. .. .. .. .. .. .. .. .. .. .. .. .. 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) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. 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 HOUSEHOLD WELFARE Part IV. Household welfare 137 ableT14.4 Liberia household survey, 2007 Expenditure Quintile National Rural Urban Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic Indicators Sample size (households) 3,595 2,204 382 386 439 462 535 1,391 283 260 251 270 327 Total Population (thousands) 2,736 1,900 380 379 379 380 379 836 167 167 166 167 167 Age dependency ratio 0.8 0.9 0.9 1.0 0.8 0.8 0.7 0.7 0.9 0.7 0.8 0.6 0.5 Average household size 5 6 6 6 6 5 5 5 6 6 6 5 4 Marital Status of head of household (%) Monogamous male 51 55 60 59 54 57 49 43 45 46 46 47 34 Polygamous male 4 6 8 5 6 5 5 1 2 2 1 1 1 Single male 18 14 11 10 13 12 21 26 20 22 23 21 37 De facto female 9 9 6 12 12 8 9 9 13 9 8 8 7 De jure female 18 16 16 14 16 17 16 21 20 21 22 23 21 MDG 1: eradicate extreme poverty and hunger Mean monthly expenditure (Liberian dollar) 10,571 10,062 4,128 6,731 8,270 10,224 18,120 11,688 4,774 8,354 10,488 13,340 17,493 Mean monthly share on food (%) 59 59 54 61 61 60 59 59 62 61 61 57 55 Mean monthly share on health (%) 3 3 3 3 3 3 3 2 2 2 2 2 2 Mean monthly share on education (%) 4 3 4 4 3 3 2 5 5 5 5 5 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 36 30 25 25 36 28 37 51 27 39 51 58 79 Male 37 31 31 32 33 21 40 51 32 38 42 66 80 Female 36 29 19 18 40 35 34 51 23 39 59 51 79 Net secondary enrollment rate (% of relevant age group) Total 15 8 6 6 7 7 15 27 16 25 25 36 33 Male 18 10 9 6 10 10 16 30 18 26 27 41 36 Female 13 6 4 6 5 3 14 24 13 24 23 31 29 Tertiary enrollment rate (per 10,000) Total .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 59 50 43 47 50 54 57 76 61 72 77 86 84 Male 72 65 56 60 67 71 71 86 73 84 86 94 90 Female 45 35 30 33 34 38 42 66 48 59 68 77 78 Youth literacy rate (% ages 15­24) Total 78 73 63 76 72 78 75 89 82 86 90 94 93 Male 86 83 73 82 86 86 87 92 88 92 92 96 94 Female 71 63 54 70 57 69 64 86 76 79 87 92 92 MDGs 4 and 5: child mortality; maternal health Health Center less than 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 43 46 41 46 46 48 49 36 33 36 38 38 36 Health care provider consulted when sick (%) 92 92 89 91 90 93 94 92 86 89 93 93 96 Type of health care provider consulted (% of total) Public 55 58 66 59 56 59 50 48 71 54 44 39 40 Private, modern medicine 29 22 16 20 22 24 27 48 23 44 50 58 58 Private, traditional healers 7 8 9 9 7 6 9 2 5 1 2 1 1 Missionary or nongovernmental organization 9 12 9 12 14 12 13 2 1 1 4 2 1 Other .. .. .. .. .. .. .. .. .. .. .. .. .. Child survival and malnutrition (%) Birth assisted by trained staff 63 54 48 53 54 56 60 83 76 81 80 93 99 Immunization coverage, 1-year-olds 68 66 65 65 65 67 68 73 68 72 74 75 84 Measles immunization coverage, 1-year-olds 79 77 77 75 75 79 81 83 78 84 85 84 94 Stunting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6­59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 40 32 21 33 36 30 38 60 46 54 61 73 68 Water source less than 5 km away (% of population) 98 98 96 99 98 99 99 99 99 99 99 99 99 Market less than 5 km away (% of population) 55 39 33 35 37 42 47 92 89 93 94 90 92 Access to improved water source (% of population) Totala 51 49 54 50 49 52 39 57 62 61 56 54 51 Own tap 4 3 1 5 3 2 3 6 4 6 4 6 7 Other piped 31 29 27 28 34 34 25 33 31 32 37 34 33 Well, protected 17 17 26 17 12 16 11 18 27 23 15 13 10 Traditional fuel use (%) Totala 99 99 96 99 100 99 100 100 100 100 100 100 99 Firewood 67 90 94 95 91 86 82 15 34 17 11 10 4 Charcoal 32 9 2 4 9 13 17 84 66 83 89 90 94 a. Components may not sum to total because of rounding 138 Part IV. Household welfare HOUSEHOLD WELFARE ableT14.5 Malawi household survey, 2003/04 Expenditure Quintile National Rural Urban 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: eradicate extreme poverty and hunger Mean monthly expenditure (kwacha) 6,835 5,909 2,721 3,823 4,723 6,086 9,829 13,064 4,238 6,547 8,760 11,133 27,244 Mean monthly share on food (%) 73 75 76 77 77 75 73 61 70 66 63 59 52 Mean monthly share on health (%) 3 3 3 3 3 3 3 2 2 2 2 2 3 Mean monthly share on education (%) 1 1 1 1 1 1 1 2 1 1 1 2 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 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) Total .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 63 60 50 56 57 63 69 83 69 80 85 90 89 Male 74 72 64 69 71 75 77 89 82 86 92 91 90 Female 52 49 39 44 45 52 60 77 56 73 76 89 88 Youth literacy rate (% ages 15­24) Total 75 73 68 72 71 75 76 86 83 86 87 90 85 Male 79 77 74 76 77 80 77 87 87 86 91 87 86 Female 71 69 62 68 66 70 75 85 78 86 84 92 85 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 a. Components may not sum to total because of rounding HOUSEHOLD WELFARE Part IV. Household welfare 139 ableT14.6 Niger household survey, 2005 Expenditure Quintile National Rural Urban Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic Indicators Sample size (households) 6,690 4,670 670 787 863 1,024 1,326 2,020 273 308 381 449 609 Total Population (thousands) 12,627 10,510 2,100 2,106 2,099 2,103 2,100 2,116 423 422 423 423 423 Age dependency ratio 1.1 1.2 1.4 1.3 1.3 1.1 0.9 0.9 1.2 1.1 0.9 0.8 0.7 Average household size 6.4 6.4 8.4 7.4 6.8 6.0 4.7 6.3 7.8 7.2 6.9 6.1 4.5 Marital Status of head of household (%) Monogamous male 68 69 62 68 69 72 70 64 60 63 59 66 67 Polygamous male 22 22 32 26 23 20 16 18 22 19 24 18 12 Single male 3 3 1 2 2 2 6 4 1 2 2 3 9 De facto female 1 0 0 0 0 1 1 1 2 1 .. 1 1 De jure female 7 5 4 4 6 5 7 13 15 14 15 12 11 MUG 1: eradicate extreme poverty and hunger Mean monthly expenditure (CFA franc) 61,173 53,499 20,610 31,335 40,516 50,124 97,417 98,719 34,161 55,544 76,782 100,276 176,847 Mean monthly share on food (%) 81 83 78 82 83 84 85 71 76 76 75 69 62 Mean monthly share on health (%) 3 3 3 2 2 3 3 3 2 2 3 4 5 Mean monthly share on education (%) 0 0 0 0 0 0 0 1 1 1 1 2 2 Mugs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. .. Net primary enrollment rate (% of relevant age group) Total 32 28 26 26 29 30 30 54 40 47 63 63 67 Male 35 32 29 30 33 35 33 55 40 49 67 63 62 Female 29 24 21 20 25 25 27 54 40 46 58 63 71 Net secondary enrollment rate (% of relevant age group) Total 7 3 3 2 4 4 3 22 9 16 21 32 34 Male 9 5 5 3 7 7 4 22 8 12 24 33 39 Female 6 1 0 2 2 1 2 22 10 20 19 31 30 Tertiary enrolment rate (per 10,000) Total .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 29 24 20 22 23 25 27 52 35 43 49 63 66 Male 43 38 34 37 37 39 43 64 47 53 61 76 76 Female 15 9 6 8 8 11 12 41 22 33 37 50 56 Youth literacy rate (% ages 15­24) Total 38 31 26 29 32 31 36 66 48 58 68 78 73 Male 52 47 41 44 50 46 53 72 56 62 75 86 81 Female 23 15 10 13 15 16 19 59 40 54 60 69 65 Mugs 4 and 5: child mortality; maternal health Health Center less than 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 10 10 9 10 9 10 12 7 6 7 8 7 10 Health care provider consulted when sick (%) 8 8 7 8 7 8 10 6 4 6 7 6 9 Type of health care provider consulted (% of total) Public 64 63 58 63 69 67 61 67 65 72 69 68 62 Private, modern medicine 21 20 24 23 14 16 23 27 28 17 25 26 34 Private, traditional healers 15 16 18 14 16 17 16 6 7 10 6 5 4 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) .. .. .. .. .. .. .. .. .. .. .. .. .. MUG 7: environmental sustainability Access to sanitation facilities (% of population) 21 10 8 8 11 12 12 75 56 71 75 83 92 Water source less than 5 km away (% of population) 86 83 83 83 82 84 85 99 99 99 98 99 99 Market less than 5 km away (% of population) 51 42 39 37 46 46 43 97 96 97 98 96 99 Access to improved water source (% of population) Totala 51 44 42 40 48 45 45 84 86 87 79 84 86 Own tap 8 3 4 3 3 3 4 32 13 17 28 44 55 Other piped 26 21 22 21 22 22 19 51 70 68 49 38 28 Well, protected 17 19 16 16 23 20 23 2 3 2 2 2 2 Traditional fuel use (%) Totala 97 97 96 96 97 97 98 96 95 99 98 94 95 Firewood 96 96 95 96 97 97 97 95 95 98 96 93 92 Charcoal 1 1 1 0 1 0 1 1 0 1 2 1 3 a. Components may not sum to total because of rounding 140 Part IV. Household welfare HOUSEHOLD WELFARE ableT14.7 Nigeria household survey, 2003/04 Expenditure Quintile National Rural Urban 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 1,265 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: eradicate extreme poverty and hunger Mean monthly expenditure (Nigeria 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 enrolment rate (per 10,000) Total .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km 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 Missionary or nongovernmental organization .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 5 km 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 a. Components may not sum to total because of rounding HOUSEHOLD WELFARE Part IV. Household welfare 141 ableT14.8 São Tomé and Principe household survey, 2000/01 Expenditure Quintile National Rural Urban 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: eradicate 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) Total .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km 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 Missionary or nongovernmental organization .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km away (% of population) 88 93 93 94 93 95 92 84 82 80 87 86 85 Market less than 5 km 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 142 Part IV. Household welfare HOUSEHOLD WELFARE ableT14.9 Sierra Leone household survey, 2002/03 Expenditure Quintile National Rural Urban 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: eradicate 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 8 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) Total .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km 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 .. Missionary or nongovernmental organization 8 7 11 14 8 9 5 8 25 11 12 10 2 Other .. .. .. .. .. .. .. .. .. .. .. .. .. 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 5 km away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 5 km 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 a. Components may not sum to total because of rounding. HOUSEHOLD WELFARE Part IV. Household welfare 143 ableT14.10 Tanzania household survey, 2000/01 Expenditure Quintile National Rural Urban Indicator total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic Indicators Sample size (households) 22,178 7,627 2,022 1,436 1,378 1,390 1,401 14,551 4,797 3,063 2,528 2,137 2,026 Total Population (thousands) 31,897 25,723 5,146 5,142 5,144 5,151 5,148 6,168 1,233 1,234 1,234 1,236 1,234 Age dependency ratio 0.9 1.0 1.3 1.1 1.0 0.9 0.7 0.7 1.0 0.9 0.7 0.6 0.4 Average household size 4.9 5.1 6.7 5.9 5.5 4.9 3.6 4.4 6.1 5.5 4.7 4.1 3.1 Marital Status of head of household (%) Monogamous male 65 66 69 70 69 68 60 62 60 68 67 64 54 Polygamous male 3 4 3 5 4 5 3 1 3 2 1 1 0 Single male 8 8 4 6 3 5 15 12 6 5 7 9 23 De facto female 7 7 7 6 8 9 7 6 7 6 7 5 5 De jure female 16 15 16 14 16 14 15 20 23 19 18 21 18 MUG 1: eradicate extreme poverty and hunger Mean monthly expenditure (Tanzania Shilling) 52,827 42,975 20,255 29,886 37,024 44,600 64,738 88,541 34,912 55,335 69,467 87,064 145,942 Mean monthly share on food (%) 76 79 78 80 81 79 76 68 76 74 71 67 60 Mean monthly share on health (%) 2 2 2 2 2 2 3 3 3 2 3 3 2 Mean monthly share on education (%) 2 1 2 1 1 1 1 2 2 2 2 2 3 Mugs 2 and 3: education and literacy; gender equality Primary school within 30 minutes (% of households) Net primary enrollment rate (% of relevant age group) Total 60 58 50 53 58 62 70 71 60 73 78 77 69 Male 59 56 46 51 60 59 66 72 60 72 79 79 76 Female 62 60 53 55 56 65 76 71 61 75 77 75 63 Net secondary enrollment rate (% of relevant age group) Total 5 2 0 2 2 2 5 14 6 9 17 16 19 Male 4 2 0 2 1 2 5 14 5 10 16 19 21 Female 5 3 0 3 3 2 5 14 7 9 18 13 18 Tertiary enrolment rate (per 10,000) Total .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 71 67 56 63 66 69 76 88 77 84 87 91 96 Male 80 76 66 71 76 79 83 93 87 91 91 95 97 Female 64 59 48 56 57 60 70 83 70 78 84 88 94 Youth literacy rate (% ages 15­24) Total 82 78 73 72 78 82 85 94 88 92 96 96 97 Male 84 81 74 75 82 84 87 95 89 93 97 97 97 Female 80 76 71 70 74 80 84 94 88 92 96 95 97 Mugs 4 and 5: child mortality; maternal health Health Center less than 5 km away (% of population) Morbidity (% of population) 27 28 27 28 28 28 31 22 24 23 22 24 20 Health care provider consulted when sick (%) 100 100 100 100 100 100 100 100 100 100 100 100 100 Type of health care provider consulted (% of total) Public 56 57 54 50 62 59 61 49 54 55 52 44 43 Private, modern medicine 26 23 21 28 23 20 25 36 31 29 33 42 47 Private, traditional healers 14 16 20 20 13 16 12 4 7 5 2 4 2 Missionary or nongovernmental organization 3 2 1 1 1 3 2 9 8 9 12 10 9 Other 2 2 4 2 1 2 1 1 1 1 1 1 0 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) .. .. .. .. .. .. .. .. .. .. .. .. .. MUG 7: environmental sustainability Access to sanitation facilities (% of population) 96 93 89 93 93 95 97 98 97 98 98 98 98 Water source less than 5 km away (% of population) 92 91 91 91 92 90 90 97 95 98 98 99 98 Market less than 5 km away (% of population) 80 76 74 72 73 77 84 99 97 99 99 99 100 Access to improved water source (% of population) Totala 54 46 44 49 43 43 51 88 79 85 91 93 93 Own tap 4 1 1 0 0 1 2 16 7 11 15 16 32 Other piped 34 28 23 30 26 27 32 61 55 61 65 69 56 Well, protected 16 17 19 19 17 15 17 11 17 12 10 8 6 Traditional fuel use (%) Totala 95 98 98 98 97 98 97 82 96 92 87 76 60 Firewood 82 94 97 97 96 94 87 30 60 40 27 15 7 Charcoal 13 3 1 2 1 3 10 52 36 52 60 61 53 a. Components may not sum to total because of rounding 144 Part IV. Household welfare HOUSEHOLD WELFARE ableT14.11 Uganda household survey, 2005/06 Expenditure Quintile National Rural Urban Indicators total All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5 Demographic Indicators Sample size (households) 7420 5722 984 1039 1104 1189 1406 1698 310 324 329 311 424 Total Population (thousands) 27,157 22,987 4,597 4,595 4,599 4,597 4,597 4,169 833 834 828 840 832 Age dependency ratio 1.2 1.2 1.6 1.4 1.3 1.2 0.8 0.8 1.3 1.0 1.0 0.7 0.5 Average household size 5 5 6 6 6 5 4 5 6 5 5 4 3 Marital Status of head of household (%) Monogamous male 3 4 1 2 3 4 6 3 0 1 2 6 3 Polygamous male 5 4 0 1 1 2 10 9 0 3 6 11 17 Single male 74 76 78 82 80 77 67 65 78 76 68 63 52 De facto female 8 7 6 6 6 8 9 13 7 8 12 10 20 De jure female 10 10 15 9 10 8 8 11 14 13 11 11 7 MDG 1: eradicate extreme poverty and hunger Mean monthly expenditure (Uganda shillings) 210,511 81,422 81,422 121,094 149,974 196,944 321,952 319,608 119,278 173,209 237,305 322,609 573,926 Mean monthly share on food (%) 56 58 60 60 61 59 52 46 53 50 47 45 38 Mean monthly share on health (%) 6 7 4 6 6 7 9 5 4 4 6 4 5 Mean monthly share on education (%) 5 4 2 3 3 4 7 8 5 6 7 8 12 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) .. Total .. .. .. .. .. .. .. .. .. .. .. .. .. Adult literacy rate (%) Total 60 58 47 52 59 60 66 69 58 71 71 70 73 Male 62 60 51 53 62 62 70 72 61 72 75 74 75 Female 51 48 37 49 48 53 53 61 52 67 61 58 64 Youth literacy rate (% ages 15­24) Total 58 58 57 53 62 61 59 55 57 63 61 50 50 Male 59 59 59 51 61 61 61 57 58 63 64 53 50 Female 56 57 53 63 63 59 50 52 53 61 53 44 50 MDGs 4 and 5: child mortality; maternal health Health Center less than I hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Morbidity (% of population) 40 42 39 40 44 44 42 33 36 32 37 30 30 Health care provider consulted when sick (%) 88 87 83 86 86 90 91 89 84 90 90 90 92 Type of health care provider consulted (% of total) Public 28 29 40 31 27 26 24 23 35 29 21 13 14 Private, modern Medicine 59 58 45 54 61 62 64 66 51 62 71 75 74 Private, traditional Healers 1 1 1 1 1 1 1 1 2 2 1 1 1 Missionary or nongovernmental organization 6 6 8 7 5 6 6 7 7 5 5 8 8 Other 5 5 5 6 5 5 4 3 5 3 2 4 3 Child survival and malnutrition (%) Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. .. Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. .. Stunting (6-59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Wasting (6-59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. Underweight (6-59 months) .. .. .. .. .. .. .. .. .. .. .. .. .. MDG 7: environmental sustainability Access to sanitation facilities (% of population) 78 76 61 69 76 83 90 94 82 93 96 99 99 Water source less than I hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Market less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. .. Access to improved water source (% of population) Totala 99 99 100 99 99 98 97 99 100 100 100 98 100 Own tap 6 2 1 1 1 1 7 19 3 3 9 30 46 Other piped 14 7 5 5 6 9 12 43 28 51 60 40 38 Well, protected 79 89 93 93 92 88 79 38 69 45 31 28 16 Traditional fuel use (%) Totala 99 99 99 100 99 99 98 96 100 100 98 96 86 Firewood 83 93 98 98 94 94 81 29 56 34 24 22 11 Charcoal 15 6 1 2 5 6 17 66 44 65 74 74 75 Note: Data are provisional. Mean monthly expenditure is deflated by regional price deflators and not by Consumer Price Index. Consumer Price Index allows for time period comparison. a. Components may not sum to total because of rounding. HOUSEHOLD WELFARE Part IV. Household welfare 145 Users Guide Tables be assured, and care must be taken in inter- e tables are numbered by section and preting indicators. Many factors affect data display the identifying icon of the section. availability, comparability, and reliability. Countries are listed alphabetically. Indica- Data coverage may not be complete because tors are shown for the most recent year or of special circumstances affecting the collec- period for which data are available and, in tion and reporting of data, such as conflicts. most tables, for an earlier year or period Except where otherwise stated, growth rates (usually 1980 or 1990 or 1995 in this edi- are in real terms. tion). Time-series data are available on the Africa Development Indictors CD-ROM and Classification of economies ADI online. Known deviations from standard For operational and analytical purposes the definitions or breaks in comparability over World Bank's main criterion for classifying time or across countries are footnoted in the economies is gross national income (GNI) tables. When available data are deemed to be per capita (calculated by the World Bank too weak to provide reliable measures of lev- Atlas method. See Box 1). Every economy els and trends or do not adequately adhere is classified as low income, middle income to international standards, the data are not (subdivided into lower middle and upper shown. middle) or high income. Low- and middle- income economies are sometimes referred Aggregate measure for region to as developing economies. e term is and sub-classifications used for convenience; it is not intended to e aggregate measures cover only low- and imply that all economies in the group are middle-income economies. experiencing similar development or that other economies have reached a preferred or final stage of development. Note the classifi- Statistics cation by income does not necessarily reflect Data are shown for economies as they were development status. Because GNI per capita constituted in 2006, and historical data are changes over time, the country composition revised to reflect current political arrange- of income groups may change from one edi- ments. Exceptions are noted throughout tion of the Africa Development Indicators to the tables. Data will, however, be provided the next. Once the classification is fixed for for some macro indicators as well as doing an edition, based on GNI per capita in the business, investment climate, governance most recent year for which data are avail- and anticorruption indicators, and Country able (2006 in this edition), all historical data Policy and Institutional Assessment ratings presented are based on the same country (CPIA) for later years (2007­08). grouping. Low-income economies are those with Data consistency, reliability and a GNI per capita of $905 or less in 2006. comparability Middle-income economies are those with a Considerable effort has been made to harmo- GNI per capita of more than $905 but less nize the data, but full comparability cannot than $11,116. Lower middle-income and up- Users Guide 147 Classification of Economies per middle-income economies are separated at a GNI per capita of $3,595. High-income Low income Lower middle income Upper middle income economies are those with a GNI per capita of Benin Algeria Botswana $11,116 or more. Burkina Faso Angola Equatorial Guinea Gabon Burundi Cameroon Libya Central Africa Republic Cape Verde Mauritius Alternative conversion factors Chad Congo, Rep. Seychelles e World Bank systematically assesses the Comoros appropriateness of official exchange rates as Djibouti South Africa conversionfactors.Analternativeconversion Congo, Dem. Rep. Egypt, Arab Rep. factor is used when the official exchange rate Côte d'Ivoire Lesotho is judged to diverge by an exceptionally large Eritrea Morocco margin from the rate effectively applied to Ethiopia Namibia domestic transactions of foreign currencies Gambia, The Swaziland and traded products. is applies to only a Ghana Tunisia small number of countries. Alternative con- Guinea version factors are used in the Atlas meth- Guinea-Bissau odology and elsewhere in Africa Development Kenya Indicators as single-year conversion factors. Liberia Madagascar Malawi Symbols Mali .. means that data are not available or that aggregates cannot be calculated because Mauritania of missing data in the years shown Mozambique $ means U.S. dollars Niger 0 or 0.0 means zero or small enough that the Nigeria number would round to zero at the dis- Rwanda played number of decimal places. São Tomé and Principe Dash or hyphen in dates, as in 2004­06, Senegal means that the period of time straddles Sierra Leone between those years. Somalia Sudan Tanzania Data presentation conventions Togo A blank means not applicable or, for an ag- Uganda gregate, not analytically meaningful. Zambia A billion is 1,000 million. Zimbabwe Source: World Bank. 148 Africa Development Indicators 2008/09 Box 1 The World Bank Atlas method for converting gross national income to a common denominator In calculating GNI and GNI per capita in U.S. dollars for certain operational purposes, the World Bank uses the Atlas conversion factor. The purpose of the Atlas conversion factor is to reduce the impact of exchange rate fluctuations in the cross-country comparison of national incomes. The Atlas conversion factor for any year is the average of the official exchange rate or alternative conversion factor for that year and for the two preceding years, adjusted for difference between the rate of inflation in the country and that in Japan, the United Kingdom, the United States, and the euro area. A country's inflation rate is measured by the change in its GDP deflator. The inflation rate for Japan, the United Kingdom, the United States, and the euro area, representing international inflation, is measured by the change in the "SDR deflator." The SDR (Special drawing rights or SDRs are the International Monetary Fund's unit of account) is cal- culated as a weighted average of these countries GDP deflators in SDR terms, the weights being the amount of each country's currency in one SDR unit. Weights vary over time because both the composition of the SDR and the relative exchange rates for each currency change. The SDR deflator is calculated in SDR terms first and then converted to U.S. dollars using the SDR to dollar Atlas conversion factor. The Atlas conversion factor is then applied to a country's GNI. The resulting GNI in U.S. dollars is divided by the midyear population for the latest of the three years to derive GNI per capita. When official exchange rate are deemed to be unreliable or unrepresentative of the effective exchange rate during a period, an alterna- tive estimate of the exchange rate is used in the Atlas formula below. The following formulas describe the procedures for computing the conversion factor for year t: and for calculating per capita GNI in U.S. dollars for year t: where e * is the Atlas conversion factor (national currency to the U.S. dollar) for year t, e is the average annual exchange rate (national cur- t t rency to the U.S. dollar) for year t, p is the GDP deflator for year t, p S$ is the SDR deflator in U.S. dollar terms for year t, Y $ is current GNI t t t per capita in U.S. dollars in year t, Yt is current GNI (local currency) for year t, N is midyear population for year t. t Users Guide 149 Technical notes 1. Basic indicators tality rates. e probability is expressed as a rate per 1,000. Table .. Basic Indicators Gini index is the most commonly used Population is total population is based on measure of inequality. e coefficient ranges the de facto definition of population, which from 0, which reflects complete equality, to counts all residents regardless of legal status 100, which indicates complete inequality or citizenship, except for refugees not per- (one person has all the income or consump- manently settled in the country of asylum, tion, all others have none). Graphically, the who are generally considered part of the Gini index can be easily represented by the population of their country of origin. e area between the Lorenz curve and the line values shown are midyear estimates. of equality. Land area is the land surface area of a Adult literacy rate is the percentage of country, excluding inland waters, national adults ages 15 and older who can, with un- claims to continental shelf, and exclusive derstanding, read and write a short, simple economic zones. statement on their everyday life. Gross domestic product (GDP) per capita Net ODA aid per capita is calculated by is gross domestic product divided by mid- dividing the nominal total net aid (net dis- year population. GDP is the sum of gross bursements of loans and grants from all value added by all resident producers in the official sources on concessional financial economy plus any product taxes and minus terms) by midyear population. ese ratios any subsidies not included in the value of the offer some indication of the importance of products. It is calculated without making de- aid flows in sustaining per capita income and ductions for depreciation of fabricated assets consumption levels, although exchange rate or for depletion and degradation of natural fluctuations, the actual rise of aid flows, and resources. Data are in constant U.S. dollars. other factors vary across countries and over Growth rates are shown in real terms. ey time. have been calculated by the least-squares Regional aggregates for GNI per capita, method using constant 2000 (See Box 2). life expectancy at birth, and adult literacy Life expectancy at birth is the number of rates are weighted by population. years a newborn infant would live if prevail- ing patterns of mortality at the time of its Source: Data on population, land area, birth were to remain the same throughout GDP per capita, life expectancy at birth, un- its life. Data are World Bank estimates based der-five mortality, Gini coefficient, and adult on data from the United Nations Population literacy are from the World Bank's World Division, the United Nations Statistics Divi- Development Indicators database. Data sion, and national statistical offices. on aid flows are from the Organization for Under-five mortality rate is the probability Economic Co-operation and Development's that a newborn baby will die before reaching Geographic Distribution of Aid Flows to De- age 5, if subject to current age-specific mor- veloping Countries database. Technical Notes 151 2. National accounts certain items, such as imputed bank charges, are added in total GDP. Growth rates have Table .. Gross Domestic Product, been calculated by the least-squares growth Nominal rate formula (See Box 2). Gross domestic product (GDP), nominal, is the sum of gross value added by all resident Source: World Bank country desk data. producers in the economy plus any product taxes and minus any subsidies not included Table .. Gross Domestic Product, in the value of the products. It is calculated Real without making deductions for depreciation Gross domestic product (GDP), real, is ob- of fabricated assets or for depletion and deg- tained by converting national currency GDP radation of natural resources. GDP figures series to U.S. dollars using constant (2000) are shown at market prices (also known as exchange rates. For countries where the offi- purchaser values) and converted from do- cial exchange rate does not effectively reflect mestic currencies using single year official the rate applied to actual foreign exchange exchange rates. For a few countries where transactions, an alternative currency conver- the official exchange rate does not reflect the sion factor has been used. Growth rates are rate effectively applied to actual foreign ex- shown in real terms. ey have been calcu- change transactions, an alternative conver- lated by the least-squares method using con- sion factor is used. stant 2000 (See Box 2). e sum of the components of GDP by industrial origin (presented here as value Source: World Bank country desk data. added) will not normally equal total GDP for several reasons. First, components of GDP Table .. Gross Domestic Product by expenditure are individually rescaled and Growth summed to provide a partially rebased series Gross domestic product (GDP) growth is the av- for total GDP. Second, total GDP is shown erage annual growth rate of real GDP (Table at purchaser value, while value added com- 2.2) at market prices based on constant local ponents are conventionally reported at pro- currency. Aggregates are based on constant ducer prices. As explained above, purchaser 2000 U.S. dollars. values exclude net indirect taxes, while pro- ducer prices include indirect taxes. ird, Source: World Bank country desk data. Box 2 Growth rates Growth rates are calculated as annual averages and represented as percentages. Except where noted, growth rates of values are computed from constant price series. Rates of change from one period to the next are calculated as proportional changes from the earlier period. Least-squares growth rate. Least squares growth rates are used wherever there is a sufficiently long time series to permit a reliable calcu- lation. No growth rate is calculated if more than half the observations in a period are missing. The least squares growth rate, r, is estimated by fitting a linear regression trend line to the logarithmic annual values of the variable in the relevant period. The regression equation takes the form ln X = a + bt t which is equivalent to the logarithmic transformation of the compound growth equation, X = X (1 + r)2 t o In this equation X is the variable, t is time, and a = lnX and b = ln(1 + r) are parameters to be estimated. If b* is the least squares estimate of o b, then the average annual growth rate, r, is obtained as [exp(b*) ­ 1] and is multiplied by 100 for expression as a percentage. The calculated growth rate is an average rate that is representative of the available observations over the entire period. It does not necessarily match the actual growth rate between any two periods. 152 Africa Development Indicators 2008/09 Table .. Gross Domestic Product Per per capita in current prices, except that the Capita, Real use of three-year averages of exchange rates Gross domestic product (GDP) per capita, smoothes out sharp fluctuations from year real, is calculated by dividing real GDP (Table to year. 2.2) by corresponding midyear population. Source: World Bank country desk data. Source: World Bank country desk data. Table .. Gross Domestic Product Table .. Gross Domestic Product Per Deflator (Local Currency Series) Capita Growth Gross domestic product (GDP) deflator (local Gross domestic product (GDP) per capita growth currency series) is nominal GDP in current is the average annual growth rate of real GDP local currency divided by real GDP in con- per capita (Table 2.4). stant 2000 local currency, expressed as an index with base year 2000. It is the total Source: World Bank country desk data. domestic and foreign value added claimed by residents, which comprises gross do- Table .. Gross National Income, mestic product plus net factor income from Nominal abroad (the income residents receive from Gross national income, nominal, is the sum of abroad for factor services including labor value added by all resident producers plus and capital) less similar payments made to any product taxes (less subsidies) not in- nonresidents who contribute to the domes- cluded in the valuation of output plus net tic economy, divided by midyear population. receipts of primary income (compensation It is calculated using the World Bank Atlas of employees and property income) from method (See Box 1) with constant 2000 ex- abroad. Data are converted from national change rates. currency in current prices to U.S. dollars at official annual exchange rates. Growth rates Source: World Bank country desk data. are calculated by the least-squares method growth rate formula (See Box 2). Table .. Gross Domestic Product Deflator (U.S. Dollar Series) Source: World Bank and Organisation for Gross domestic product (GDP) deflator (U.S. Economic Co-operation and Development dollar series) is nominal GDP in current U.S. (OECD) national accounts data. dollars (Table 2.1) divided by real GDP in constant 2000 U.S. dollars (Table 2.2), ex- TABLE .. GROSS NATIONAL INCOME, pressed as an index with base year 2000. e ATLAS METHOD series shows the effects of domestic price Gross national income (GNI) is calculated us- changes and exchange rate variations. ing the World Bank Atlas method (See Box 1). It is similar in concept to GNI in current Source: World Bank country desk data. prices, except that the use of three-year av- erages of exchange rates smoothes out sharp Table .. Inflation, Consumer fluctuations from year to year. Growth rates Price Index are shown in real terms. ey have been cal- Inflation as measured by the consumer price culated by the least-squares method using index reflects the annual percentage change constant 2000 (See Box 2). in the cost to the average consumer of ac- quiring a basket of goods and services that Source: World Bank country desk data. may be fixed or changed at specified inter- vals, such as yearly. e Laspeyres formula is Table .. Gross National Income generally used. Per Capita Gross national income (GNI) per capita is cal- Source: International Monetary Fund, culated using the World Bank Atlas method International Financial Statistics and data (See Box 1). It is similar in concept to GNI files. Technical Notes 153 Box 3 Using Data to Inform Policy: Impact of the Food Price Crisis in Africa and Policy Responses What do higher food prices mean for poverty in Africa? A series of recent papers -- based for the most part on recent household survey data -- finds that rising food prices generate higher poverty because the adverse impact on households that are net food consumers outweighs benefits to net food producers. The series also uses the survey data to examine common policy responses in order to determine which are likely to have the largest benefit for the poor. Data from a dozen countries are used to simulate the poverty impact of higher food prices. The measurements obtained when consider- ing only the effect on consumers are considered as upper-range estimates. Those also factoring in producer gains are considered as lower- range estimates, because producers may not reap the full benefit of price increases (market intermediaries may keep part of the higher food prices to boost their margins or pay for higher transport costs, while producers face higher operating costs that limit their profits). With a 50% increase in selected food prices, upper-range increases in poverty measures range from 1.8 percentage points in Ghana to 9.6 points in Senegal. The average impact, considering both upper- and lower-range estimates, is around 3.5 percentage points. For Africa as a whole, this would mean 30 million more poor people. Poverty mapping techniques show that the degree of impact varies within countries. This poses a dilemma in focusing policy responses, since the hardest-hit areas in a country often are not the poorest. As a first step in dealing with the crisis, governments have reduced food taxes. But such tax cuts have large fiscal costs and are poorly targeted. For example, the share of rice consumed by the poorest 40% of the population ranges from 11% in Mali to 32% in Sierra Leone. Therefore, on average only about 20 cents out of every dollar of tax cut might benefit this group. In addition, if markets are dominated by a few traders, tax cuts may not be fully passed on to consumers. And lowering import tariffs may hurt domestic producers if prices of locally produced foods adjust to international prices. Expanding social protection programs shows more promise. In Burundi and Liberia household survey data suggest that the poor are roughly as likely as the non-poor to benefit from food aid. This does not constitute good targeting, but it is better than tax cuts. Simulations suggest that geographic targeting is required to avoid high leakage in labor-intensive public programs since most countries have large populations working without pay or at very low pay. Thus, even among the non-poor, participation in public programs could be high even if wages are low. In addition, part of the wages paid through public works may not reduce poverty because of substitution effects (participants typically have to give up some sources of income to enroll in the programs). The most promising interventions are those boosting agricultural productivity. Mali's rice initiative aims to increase production by 50%. Using a dynamic computable general equilibrium model for Mali, analysis shows that a 15% increase in productivity could generate a large increase in rice production that would ultimately reduce poverty despite the increase in international rice prices. By contrast, the model suggests that import tax cuts would not reduce poverty by much. Another finding is that the general equilibrium effect of the increase in international rice prices is about half the impact predicted using household surveys. This suggests that without policy interventions, behav- ioral changes following price increases could help offset part, but certainly not all, of the adverse impact on the poor. Another general equilibrium finding relates to the relative way in which households are affected by oil and food price increases. Using social accounting matrices, analysis shows that in some countries the indirect multiplier effect of higher oil prices may be more severe than that of higher food prices. This suggests that even though the food price crisis has recently attracted more attention, the effects of the oil price crisis must also be dealt with. Source: Wodon (2008). Table .. Price Indices Imports price index is derived by dividing Inflation, GDP deflator is measured by the an- the national accounts exports of goods and nual growth rate of the GDP implicit defla- services in current U.S. dollars by imports tor and shows the rate of price change in the of goods and services in constant 2000 U.S. economy as a whole. dollars, with 2000 equaling 100. Consumer price index is a change in the cost to the average consumer of acquiring Source:WorldBanknationalaccountsdata, a basket of goods and services that may be and OECD National Accounts data files. fixed or changed at specified intervals, such as yearly. Table .. Gross Domestic Savings Exports price index is derived by dividing Gross domestic savings is calculated by de- the national accounts exports of goods and ducting total consumption (Table 2.17) from services in current U.S. dollars by exports nominal gross domestic product (Table 2.1). of goods and services in constant 2000 U.S. dollars, with 2000 equaling 100. Source: World Bank country desk data. 154 Africa Development Indicators 2008/09 Box 4 Why Public Perception About Inflation may be Different From Government Reported Inflation: A Case Study of Ethiopia1 There is general consensus in Ethiopia that the country is experi- Weights used for computing Ethiopia's CPI encing high inflation, though there is considerable confusion about the inflation number itself. Such a problem however is not unique to Ethiopia since inflation numbers tend to be scrutinized more closely during episodes of high inflation. This technical box identifies some of the underlying reasons why the public perception about inflation may be different from the government reported inflation number. I. Variations in the Consumption Basket Inflation is defined as the change in the general price level. The latter, technically captured by the Consumer Price Index (CPI), is measured by tracking the prices of a fixed basket of goods and ser- vices that is consumed by the representative consumer (household) in the country. The CPI is usually based on household expenditure weights of the goods and services in the basket and their current market prices. In case of Ethiopia, the weights used for computing the CPI are shown in Figure 1. The consumption basket (i.e., the goods and services consumed by the household) is likely to vary considerably across households. A relatively poor household is likely to spend a much larger share of its expenditure on food than a relatively rich household and vice- versa for luxury goods and services, for example, recreation and entertainment. As shown in Figure 2, the inflation rate facing differ- ent households could vary considerably. In Ethiopia, since a large part of the general inflation is due to food price inflation and the poor households spend a larger share of their expenditure on food, they have experienced a twelve month inflation rate of 66% at the end of June 2008. The corresponding number for the rich house- holds is found to be 33%--almost half the rate facing the poorer households. The government, based on the consumption basket of the representative household, reported the national inflation rate to be 55%. It is therefore easy to see why the inflation experienced by one household may vary considerably from its neighbors. II. Regional Variation in Inflation Inflation rates could also vary substantially across regions, espe- cially in large countries and/or if the regional markets are not well integrated with one another. For example, in Ethiopia the difference in inflation between the highest and lowest inflation region is found to be as high as 29 percentage points--households in Afar expe- rienced an inflation rate of 36% compared to 66% for households in SNNP. III. Average vs. End of Period Inflation In some cases, the difference between perception and reality could be explained by the method the government uses to estimate and report the annual inflation rate. For example, in Ethiopia, the twelve month end of period inflation rate was 55% in June 2008 (i.e., the CPI increased by 55% between June 2007 and June 2008). On the other hand, the twelve month annual average inflation rate was only 25%, which is calculated by averaging the end of period inflation rate of the past twelve months. Since inflation rate has accelerated in recent months, the difference between the two estimates has widened by as much as 30 percentage points. In Ethiopia the newspaper articles tend to quote the end of period inflation (55%), while the Government of Ethiopia uses the average inflation rate (25%). Understandably, most citizens feel confused. It is, however, important to note that despite the huge variation between (continued on next page) Technical Notes 155 Box 4 Why Public Perception About Inflation may be Different From Government Reported Inflation: A Case Study of Ethiopia1 (continued) the above two inflation estimates, they are both correct and they capture different aspects of the inflation rate. In periods of rising inflation, the end of period inflation rate tends to exceed the average inflation rate and vice versa in periods of falling inflation rate. Table 1 Average vs. End of period Inflation, in percent 2005/06 2006/07 2007/08 Overall inflation Average 11% 17% 25% End of period 12% 15% 55% Difference ­1% 1% ­30% Food inflation Average 13% 18% 35% End of period 11% 18% 78% Difference 1% 0% ­43% Source: CSA 1Prepared by Deepak Mishra (Lead Economist, Ethiopia and Sudan) and Mesfin Girma Bezawagaw (Research Assistant, Ethiopia). Table .. Gross National Savings durable products (such as cars, washing ma- Gross national savings is the sum of gross chines, and home computers), purchased by domestic savings (Table 2.13), net factor in- households. It excludes purchases of dwell- come from abroad, and net private transfers ings but includes imputed rent for owner-oc- from abroad. e estimate here also includes cupied dwellings. It also includes payments net public transfers from abroad. and fees to governments to obtain permits and licenses. Here, household consumption Source: World Bank country desk data. expenditure includes the expenditures of nonprofit institutions serving households, Table .. General Government Final even when reported separately by the coun- Consumption Expenditure try General government consumption is all current expenditure for purchases of goods and ser- Source: World Bank national accounts vices by all levels of government, including data, and OECD National Accounts data capital expenditure on national defense and files security. Other capital expenditure by gov- ernment is included in capital formation. Table .. Final Consumption Expenditure Plus Discrepancy Source: World Bank country desk data. Final consumption expenditure (formerly to- tal consumption) is the sum of household Table .. Household Final final consumption expenditure (Table 2.16) Consumption Expenditure and general government final consumption Household final consumption expenditure (for- expenditure (Table 2.15) shown as a share merly private consumption) is the market of gross domestic product. is estimate value of all goods and services, including includes any statistical discrepancy in the 156 Africa Development Indicators 2008/09 use of resources relative to the supply of re- of value added is determined by the Inter- sources. Private consumption, not separate- national Standard Industrial Classification ly shown here, is the value of all goods and (ISIC), revision 3. Note: For VAB countries, services purchased or received as income in gross value added at factor cost is used as the kind by households and nonprofit institu- denominator. tions. It excludes purchases of dwellings, but includes imputed rent for owner-occupied Source: World Bank national accounts data, dwellings. In practice, it includes any statis- and OECD National Accounts data files. tical discrepancy in the use of resources. Table .. Services Plus Discrepancy Source: World Bank country desk data. Value Added Services correspond to ISIC divisions 50­99 Table .. Final Consumption Expendi- and include value added in wholesale and re- ture Plus Discrepancy Per Capita tail trade (including hotels and restaurants), Final consumption expenditure per capita is transport, and government, financial, pro- final consumption expenditure in current fessional, and personal services such as edu- U.S. dollars (Table 2.17) divided by midyear cation, health care, and real estate services. population. Also included are imputed bank service charges, import duties, and any statistical Source: World Bank country desk data. discrepancies noted by national compilers as well as discrepancies arising from rescal- Table .. Agriculture Value Added ing. Value added is the net output of a sector Agriculture corresponds to ISIC divisions 1­5 after adding up all outputs and subtracting and includes forestry, hunting, and fishing, intermediate inputs. It is calculated without as well as cultivation of crops and livestock making deductions for depreciation of fab- production. Value added is the net output ricated assets or depletion and degradation of a sector after adding up all outputs and of natural resources. e industrial origin subtracting intermediate inputs. It is calcu- of value added is determined by the Inter- lated without making deductions for depre- national Standard Industrial Classification ciation of fabricated assets or depletion and (ISIC), revision 3. Note: For VAB countries, degradation of natural resources. e origin gross value added at factor cost is used as the of value added is determined by the Inter- denominator. national Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, Source: World Bank national accounts gross value added at factor cost is used as the data, and OECD National Accounts data denominator. files. Source: World Bank national accounts Table .. Gross Fixed Capital data, and OECD National Accounts data Formation files. Gross fixed capital formation consists of gross domestic fixed capital formation plus net Table .. Industry Value Added changes in the level of inventories. Gross Industry corresponds to ISIC divisions 10­45 capital formation consists of outlays by the and includes manufacturing (ISIC divisions public sector (Table 2.23) and the private 15­37). It comprises value added in mining, sector (Table 2.24). Examples include im- manufacturing (also reported as a separate provements in land, dwellings, machinery, subgroup), construction, electricity, water, and other equipment. For some countries and gas. Value added is the net output of a the sum of gross private investment and sector after adding up all outputs and sub- gross public investment does not total gross tracting intermediate inputs. It is calculated domestic investment due to statistical dis- without making deductions for depreciation crepancies. of fabricated assets or depletion and deg- radation of natural resources. e origin Source: World Bank country desk data. Technical Notes 157 Table .. General Government Fixed Imports of goods and services is the value of Capital Formation all goods and other market services received General government fixed capital formation is from the rest of the world. gross domestic fixed capital formation (see Net income is the receipts and payments Table 2.22) for the public sector. of employee compensation paid to nonresi- dent workers and investment income (re- Source: World Bank country desk data. ceipts and payments on direct investment, portfolio investment, other investments, Table .. Private Sector Fixed and receipts on reserve assets). Capital Formation Net current transfers are recorded in the Private sector fixed capital formation is gross balance of payments whenever an economy domestic fixed capital formation (see Table provides or receives goods, services, income, 2.22) for the private sector. or financial items without a quid pro quo. Current account balance is the sum of net Source: World Bank country desk data. exports of goods, services, net income, and net current transfers. Table .. Resource Balance Total reserves is the holdings of monetary (Exports Minus Imports) gold, special drawing rights, reserves of IMF Resource balance is the difference between free members held by the IMF, and holdings of on board exports (Table 2.26) and cost, insur- foreign exchange under the control of mon- ance, and freight imports (Table 2.27) of goods etary authorities. and services (or the difference between gross domestic savings and gross capital formation). Source: Data on current account balance, e resource balance is shown as a share of net income, net transfers and total reserves nominal gross domestic product (Table 2.1). are from the International Monetary Fund, International Financial Statistics and data Source: World Bank country desk data. files. Data on exports and imports of goods and services are from the World Bank na- Tables . and .. Exports and Im- tional accounts data, and OECD National ports of Goods and Services, Nominal Accounts data files Exports and imports of goods and services, nominal, comprise all transactions between Table .. Structure of Demand residents of an economy and the rest of the Household final consumption expenditure (for- world involving a change in ownership of merly private consumption) is the market general merchandise, goods sent for process- value of all goods and services, including ing and repairs, nonmonetary gold, and ser- durable products (such as cars, washing ma- vices expressed in current U.S dollars. chines, and home computers), purchased by households. Source: World Bank country desk data. General government final consumption expenditure (formerly general government Tables . and .. Exports and consumption) is all government current ex- Imports of Goods and Services penditures for purchases of goods and ser- ( of GDP) vices. Exports and imports of goods and services are Gross capital formation (formerly gross defined as in Tables 2.24 and 2.25, but ex- domestic investment) consists of outlays pressed as a proportion of GDP. on additions to the fixed assets of the economy plus net changes in the level of Source: World Bank country desk data. inventories. Exports of goods and services is the value of Table .. Balance of Payment and all goods and other market services provided Current Account to the rest of the world. Exports of goods and services is the value of all Imports of goods and services is the value of goods and other market services provided to all goods and other market services received the rest of the world. from the rest of the world. 158 Africa Development Indicators 2008/09 Box 5 Africa at Purchasing Power Parity The International Comparison Project (ICP) round of 2005 marks a significant departure from the previous one in 1993--for Sub Sa- haran Africa (SSA) in particular--for two major reasons. The first one is the increased coverage of SSA countries. While in 1993 only 19 countries actually participated through their statistical offices to the ICP round, there were 44 participants out of a potential 53 in 2005, covering 98% of the region's population. The second one is the likely improvement in data quality. Statistical capacity in par- ticipating countries greatly improved since 1993, notably when it comes to data collection, validation and processing. Furthermore, the ICP 2005 put particular efforts in linking regions across the globe in order to minimize the inherent tension between compa- rability and representativeness. Goods and services should have similar characteristics (comparable) and be consumed everywhere (representative). To compensate for non comparability of repre- sentative products, the ICP conducted for the first time in 2005 two parallel programs: selecting items at the regional level, where consumption patterns are broadly similar across countries, and se- lecting items for global comparison among a few countries from each region. The results of this second program were used to link the results of the first into a single set of global Purchasing Power Parities (PPPs).1 Thus, the new ICP round provides a more accurate view of the Africa Region in 2005, and unsurprisingly, a different picture of its relative size and structure. Measured at PPPs, in 2005 the SSA region represented 2.3% of World GDP, against 1.4% measured at market exchange rates. The difference from previous estimates is minimal, as the SSA region was believed to represent 2.4% of GDP based on ICP 1993 data. But this comparison conceals great revisions in countries' GDP at PPP between the two ICP rounds. Indeed, SSA countries' GDP were on average revised upwards or downwards by 35%. Largest revisions concern the Republic of Congo and Gabon, which see their GDP at PPP revised upwards by 133% and 96% respectively. But many countries record lower GDP at PPP than previously estimated. Among the largest 20 revi- sions (Figure 1) 16 countries did not participate to the ICP round of 1993. With new PPPs, poverty in SSA is being revised upwards. Re- vised PPPs in SSA entail--as for most other developing countries, a downward revision of GDP levels, since prices in these countries are now generally believed to be higher than previously thought. In turn, poverty lines and rates were revised upwards accordingly. While the number of poor at US$1 a day was estimated at 298 Source: World Bank staff estimates millions in 2004 using old PPPs, it is now estimated at 299 millions in 2005 using new PPPs, an insignificant change overall.2 But the availability of new PPPs also prompted the revision of poverty lines to reflect the average poverty lines of the poorest countries as of 2005.3 Accordingly, the international poverty line for extreme poverty is now set at US$1.25 a day. Using this figure, the number of poor in SSA was close to 384 millions in 2005, or 50% of the population. But if higher today than previously thought, past figures of poverty were also revised upwards. This leaves basically unchanged the recent evolution of poverty rates, a five-percentage-point decline since 1990, from 55% to 50%. Thus akin to conclusions reached using old PPPs, the SSA region is at current trends off-track to meet the Millennium Development Goal of halving poverty between 1990 and 2015. (continued on next page) Technical Notes 159 Box 5 Africa at Purchasing Power Parity (continued) High inequalities make the goal of halving poverty in SSA particularly challenging. One important reason behind the slow decline in poverty rates is the high degree of inequality in SSA, compared with other regions (Figure 2). Using PPPs, inequalities within and between countries can be combined to create regional income distributions, gathering all individuals from the same re- gion. Such calculations reveal that inequalities between individu- als are particularly high in Sub-Saharan Africa. Besides, half of Sub-Saharan Africa's inequalities can be attributed to differences in average per capita incomes across countries, reflecting the re- gion's low economic integration (Figure 3). Its average per capita income is the lowest of all regions, but there are large differences across countries. In the last decade, inequalities between African countries did not narrow. Indeed, there is no evidence of income convergence, and countries with lowest initial per capita incomes did not grow significantly faster than richer ones. This confirms previous in-depth analysis on the distribution of incomes and growth across Sub-Saharan countries based on PPPs derived from the ICP round 1993.4 1For more details see World Bank (2008A). 2Poverty figures at the country level might be more affected given the significant PPP revisions at the country level and the use of more recent household surveys. 3See Chen and Ravallion (2008). 4See Arbache and Page (2007). Gross National Savings, growth is the gross bundle of goods that make up gross domes- national income less total consumption, plus tic product (GDP) across countries. net transfers. Real effective exchange rate is the nominal effective exchange rate (a measure of the Source: World Bank national accounts value of a currency against a weighted aver- data, and OECD National Accounts data age of several foreign currencies) divided by files. a price deflator or index of costs. GDP, PPP is gross domestic product con- Table .. Exchange Rates and Pur- verted to international dollars using pur- chasing Power Parity chasing power parity rates. An international Official exchange rate is the exchange rate de- dollar has the same purchasing power over termined by national authorities or to the GDP as the U.S. dollar has in the United rate determined in the legally sanctioned ex- States. GDP is the sum of gross value added change market. by all resident producers in the economy Purchasing power parity (PPP) conversion plus any product taxes and minus any sub- factor is the number of units of a country's sidies not included in the value of the prod- currency required to buy the same amount ucts. It is calculated without making deduc- of goods and services in the domestic mar- tions for depreciation of fabricated assets ket as a U.S. dollar would buy in the United or for depletion and degradation of natural States. resources. Data are in current international Ratio of PPP conversion factor to market dollars. exchange rate is the a national price level, GDP per capita, PPP is GDP per capita making it possible to compare the cost of the based on purchasing power parity (PPP). 160 Africa Development Indicators 2008/09 Box 6 The Franc Zone One of the most important economic and monetary unions in sub-Saharan Africa is the franc zone. Consisting of two separate regional groupings in Central and West Africa, the CFA zone consists of fourteen Francophone countries ­ six in Central Africa (Congo, Chad, Central African Republic, Gabon, Cameroon, and Equatorial Guinea) and eight in West Africa (Benin, Burkina Faso, Ivory Coast, Guinea-Bissau, Mali, Niger, Senegal and Togo). Created in March, 1994, the Communauté Economique et Monétaire d'Afrique Centrale (CEMAC), previously known as UDEAC (Union Douanière et Economique de l'Afrique Centrale), is a customs and monetary union among the former French Cen- tral African countries, while the Union Economique et Monétaire Ouest-africaine (UEMOA) is the equivalent organization in West Africa. Since the mid-1990s, in both Francophone West and Central Africa, macroeconomic policy convergence, trade liberalization and a resulting common external tariff, and fiscal reforms have led to greater economic integration. The overall goal of the two unions, modeled in many ways on the European Union, has been to strengthen regional integration and cooperation and improve economic space through reduced barriers to trade and the flow of goods and services between these countries. Moreover, the zone is based on the premise that through economic union, these countries can be locked into policy reform. A product of French colonial history, the groups consist of two customs and monetary unions united by a common currency and a fixed exchange rate regime pegged to the euro (the French franc before 1999). The rationale for the fixed exchange rate regime was to introduce greater stability to the economies of the zone and prevent risks that result from a more volatile flexible exchange rate. Furthermore, since under the prevailing institutional arrangement, the goal of monetary policy is to support the fixed exchange peg, the exchange rate arrange- ment is meant to lead to greater fiscal discipline. Countries relinquish their monetary policy to a regional central bank in exchange for price stability, and there is a clear output-inflation tradeoff as the desire for stability can offset policies which are growth-enhancing. Reserves are deposited in an Operations Account with the French Treasury, and the fixed parity has helped guarantee the convertibility of the CFA franc. However, this arrangement has had costs, especially from the perspective of the poor economies in the zone. The predominance of agricultural products and natural resource exports makes these economies especially vulnerable to terms of trade shocks. As a result, the adjustment to these shocks has been difficult, leading to balance of payments difficulties and 50% devaluation in 1994. Nevertheless, in a world of increasingly floating exchange rates, the CFA fixed regime has been one of the few exchange rate arrangements which have not been altered over the years. However, the recent sustained appreciation of the dollar against the euro, especially since 2001, has put some pressure on the exchange rate arrangement and led to an appreciation of the trade-weighted real effective exchange rate of both CFA zones.1 From May, 2000 to May, 2008, there has been an appreciation in real terms of more than twenty percent in CEMAC and fifteen percent in UEMOA, as the dollar has slid against the euro by more than 30 percent. The REER is the geometric product of the bilateral effective exchange rates that are adjusted for inflationary differences between countries. Although there has been an appreciation, different methodologies yield diverse estimates of overvaluation. The PPP exchange rate, which is defined as that rate which equalizes the cost of a market basket of goods between two countries, is useful as a first approximation although it does not hold in the long-run. Under PPP, there should be no changes in REERs over time if the currencies are in equilibrium. A single-equation estimation is potentially very fruitful, as it assesses an exchange rate's departure from a vector of fundamentals, although there are various estimates depending on methodology and econometric technique. While some work finds an overvaluation, others find no major deviation between the current REER and its long-run fundamentals in the franc zone. Overall, the peg to the rising euro has led to a loss in competitiveness for the franc zone's exports, although the impact has been felt asymmetrically. Moreover, the precise nature of the impact depends on each country's economic structure and on the policy regime in place. For many CFA countries, especially cotton producers in West Africa such as Burkina, Benin, and Niger, the appreciation of the euro has translated into an increase in production costs; since world-prices for most of these commodities are dollar-based and farmers are paid in CFA, the recent dollar decline has hindered these countries' competitiveness. As a result, the cotton sector in these countries is facing financial dif- ficulties and questions are emerging about their long-term future. For other CFA countries whose exports are petroleum, such as Republic of Congo and Equatorial Guinea, the effect on the REERs have been significant, although other factors have offset the loss of competitiveness. Although the oil windfalls have contributed to an increase in domestic inflation rates, a percentage of oil production costs are dollar-based and off-shore, and the high oil prices have allowed these countries to reap significant windfalls. Finally, the current accounts of each of the CFA nations differ depending on economic structure, terms of trade shocks, and debt and aid flows and hence, measurements of underlying "sustainable" current accounts vary. As a result, the changes in exchange rates that are needed to yield a more balanced current account are difficult to determine for the region as a whole. Overall, the rise in the euro/dollar ex- change has raised concerns about the efficacy and the sustainability of the CFA fixed parity to the euro and led to increasing concerns of loss of competitiveness. A persistently overvalued REER will hinder the export diversification of the franc zone. 1See Zafar (2005). Technical Notes 161 PPP GDP is gross domestic product convert- ondary sources. Efforts have been made to ed to international dollars using purchasing harmonize these data series with those pub- power parity rates. An international dollar lished on the United Nations Millennium has the same purchasing power over GDP Development Goals website (www.un.org/ as the U.S. dollar has in the United States. millenniumgoals), but some differences in GDP at purchaser's prices is the sum of gross timing, sources, and definitions remain. value added by all resident producers in the Data on child malnutrition and popula- economy plus any product taxes and minus tion below minimum dietary energy con- any subsidies not included in the value of sumption are from the Food and Agricul- the products. It is calculated without mak- ture Organization (see www.fao.org/faostat/ ing deductions for depreciation of fabricated foodsecurity/index_en.htm). assets or for depletion and degradation of natural resources. Data are in current inter- Table .. Millennium Development national dollars. Goal : Achieve Universal Primary Education Source: International Monetary Fund, In- Primary education provides children with ba- ternational Financial Statistics sic reading, writing, and mathematics skills along with an elementary understanding of 3. Millennium Development Goals such subjects as history, geography, natural science, social science, art, and music. Table .. Millennium Development Net primary enrollment ratio is the ratio of Goal : Eradicate Extreme Poverty children of official primary school age based and Hunger on the International Standard Classification Share of population below national poverty line of Education 1997 who are enrolled in pri- (poverty headcount ratio) is the percentage mary school to the population of the corre- of the population living below the national sponding official primary school age. poverty line. National estimates are based Primary completion rate is the percent- on population-weighted subgroup estimates age of students completing the last year of from household surveys. primary school. It is calculated as the total Share of poorest quintile in national con- number of students in the last grade of pri- sumption or income is the share of consump- mary school minus the number of repeaters tion, or in some cases income, that accrues in that grade divided by the total number of to the poorest 20 percent of the population. children of official graduation age. Prevalence of child malnutrition, under- Share of cohort reaching grade 5 is the per- weight, is the percentage of children under centage of children enrolled in grade 1 of pri- age 5 whose weight for age is more than two mary school who eventually reach grade 5. standard deviations below the median for the e estimate is based on the reconstructed international reference population ages 0­59 cohort method. months. e reference population, adopted Youth literacy rate is the percentage of by the World Health Organization in 1983, people ages 15­24 who can, with under- is based on children from the United States, standing, both read and write a short, simple who are assumed to be well nourished. statement about their everyday life. Population below minimum dietary energy consumption (also referred to as prevalence of Source: Data are from the United Nations undernourishment) is the population whose Educational, Scientific, and Cultural Orga- food intake is insufficient to meet dietary nization Institute for Statistics. Data have energy requirements continuously. been compiled by World Bank staff from primary and secondary sources. Efforts have Source: Data on poverty measures are been made to harmonize these data series prepared by the World Bank's Development with those published on the United Nations Research Group. e national poverty lines Millennium Development Goals website are based on the World Bank's country pov- (www.un.org/millenniumgoals), but some erty assessments. Data have been compiled differences in timing, sources, and defini- by World Bank staff from primary and sec- tions remain. 162 Africa Development Indicators 2008/09 Box 7 Improving Data on the Role of Faith-Inspired Organizations in Service Delivery in Africa It is often argued that faith-inspired organizations (FIOs) provide a substantial share of education, health and other social services in African countries, and that the services provided by FIOs tend to be better targeted to the poor and possibly more cost effective than is the case for other private service providers and even for public providers. These assertions, if correct, could have implications for policy, since donors (and possibly governments) might then be more inclined to support FIOs in their service delivery activities. It is also important to consider the substantial and growing service delivery partnerships that do exist between governments, donors, and FIOs. In addition, governments might expand their efforts to benefit from the expertise acquired by FIOs in reaching the poor, so that government agencies might learn from FIOs (and vice versa). Unfortunately, good data necessary to document such assertions and hypotheses about the role of FIOs in service delivery are often missing. At least two different types of data can be used to assess the role played by FIOs in service delivery. Administrative data kept by FIOs as well as government agencies could provide information on the market share of FIOs in terms of students served, patients re- ceived in health care facilities and the like. In addition, household surveys (as well as other types of surveys dealing with the beneficiaries of social services) may also provide useful data, to the extent that the surveys include questions on the types of service providers used by households and assuming that the users of services know whether the service provider they use is faith-based or not. If household survey data were available to document the role of FIOs, they would have an advantage over administrative data because of the ability to link the uptake of services to many other household and individual characteristics, including the poverty status of the household and the degree of satisfaction of the household with the services received. It is therefore useful to assess to what extent information can be extracted from household surveys on the role of FIOs. Table 1 provides preliminary estimates of the market share of FIOs in the delivery of health and education services in twenty West and Central African countries according to the data available in the main multi-purpose household surveys implemented in the various countries. Two main observations can be made. First, for half of the countries in the case of health, and for one fourth of the countries in the case of education, the survey questionnaires do not include any information on whether the education and health service providers are faith-based or not. Second, when data are available, the market shares of FIOs appear to be low, with the exception of Sierra Leone in the case of Table 1 Preliminary Data on service delivery by faith-inspireded organizations in African household surveys Identification of FIOs in survey questionnaire Preliminary estimate of market share of FIOs Country and household survey Education Health Education Health Benin (QUIBB 2003) Yes No <5% NA Burkina Faso (QUIBB 2003) Yes No 5%­10% NA Burundi (QUIBB 2006) Yes Yes <5% 10%­20% Cameroon (ECAM 2007) Yes Yes 10%­20% <5% Cape Verde (CWIQ 2007) No No <5% NA Chad (ECOSIT2 2003/04) No Yes NA 10%­20% Cote d'Ivoire (ENV 2002) No No NA NA DRC (Enquête 1­2­3 2004/05) No No NA NA Gabon (CWIQ 2005) Yes No 5%­10% NA Ghana (GLSS 2005/2006) Yes Yes 5%­10% 5­10% Guinea (ELEP 2007) Yes No <5% NA Liberia (CWIQ 2007) Yes No <5% NA Mali (CWIQ 2006) Yes Yes <5% 5%­10% Niger (ENBC 2007) Yes Yes <5% <5% Nigeria (LMS 2003/2004) Yes Yes <5% <5% ROC (ECOM­CWIQ 2005) Yes Yes <5% <5% Rwanda (EICV 1997/1998) No No NA NA Senegal (ESPS 2005) Yes Yes 5%­10% <5% Sierra Leone (QUIBB 2003) Yes Yes >50% <5% Togo (QUIBB 2006) Yes No 5%­10% NA Source: Wodon et al. (2008). NA means not applicable. (continued on next page) Technical Notes 163 Box 7 Improving Data on the Role of Faith-Inspired Organizations in Service Delivery in Africa (continued) education, as well as Burundi and Chad for health. In a large majority of cases, less than 5% of the students enrolled in school (all levels included) or patients seeking health care would do so through faith-based service providers according to those data. These data are preliminary, but they do suggest a lower market share of FIOs than commonly assumed, at least as measured through the household surveys. At the same time, it is likely that in many countries, household surveys underestimate the market share of FIOs in service delivery. Consider Ghana for which the GLSS5 household survey suggests that 44% of health care visits are made to public organi- zations, versus 7% to private religious organizations and 49% to private non-religious facilities. The market share for religious organizations appears low, given that it is often argued that CHAG (the Christian Health Association of Ghana) provides about a third of health services in Ghana, and that administrative data available from the District Health Information System on the number of facilities operated by CHAG in comparisons to other private and public providers also suggests a higher market share than 7%. Various factors could lead household surveys to underestimate the market share of FIOs in service delivery when the survey question- naire does include questions as to whether the providers are public, private religious, or private non-religious. One possibility is that some households may simply not know that their service provider is faith-based. Another possibility could be that the way the questions on service providers are asked in the surveys is too generic and not adapted enough to the particularities of the education and health systems in various countries, so that specific faith-based groups that may operate large networks of providers such as CHAG are not well identified in the questionnaires. In contrast to evidence here derived from household surveys, alternative methods like community-level participatory mapping, employed by the African Religious Health Assets Program (ARHAP) and the World Health Organization (WHO) to measure FIO service delivery in Lesotho and Zambia, suggest that as high as 70% of health services are provided by faith organizations. The magnitude of the discrepancy in findings from household surveys and the recent ARHAP/WHO study further suggest that more efforts are needed to better capture the scope of FIO service delivery on the ground and to reconcile findings from various sources of data. Changes that would need to be made to household survey questionnaires to better identify FIOs as service providers are relatively minor and probably not too difficult or costly to implement. In the case of health for example, many surveys today only seek information as to whether households seek care in hospitals, clinics, or other health facilities, without information on who runs the facilities. It should be feasible to provide additional modalities for the responses that households can provide to the questions asked in those surveys. This was done for example in Burundi's 2006 QUIBB survey. That survey identified ten different types of providers, namely hospitals, missionary hospitals, private hospitals, public health centers, missionary health centers, private health centers, pharmacies, private doctors, private "sage-femme," and traditional healers. With these additional options provided in the questionnaire, it is feasible not only to compare the market shares of FIOs and other service providers, but also to analyze which segments of the population the various providers are reach- ing and to what extent the population groups served by the various providers are satisfied with the services provided to them. This type of analysis can provide useful information for the policy dialogue on alternative service provision schemes. Table .. Millennium Development Institute for Statistics. Data on women in Goal : Promote Gender Equity and national parliaments are from the Inter-Par- Empower Women liamentary Union. Data on women's employ- Ratio of girls to boys in primary and secondary ment are from the International Labor Orga- school is the ratio of female to male gross nization's Key Indicators of the Labor Market, enrollment rate in primary and secondary fourth edition. school. Ratio of young literate women to men is the Table .. Millennium Development ratio of the female to male youth literacy Goal : Reduce Child Mortality rate. Under-five mortality rate is the probability Women in national parliament are the per- that a newborn baby will die before reaching centage of parliamentary seats in a single or age 5, if subject to current age-specific mor- lower chamber occupied by women. tality rates. e probability is expressed as a Share of women employed in the nonagri- rate per 1,000. cultural sector is women wage employees in Infant mortality rate is the number of in- the nonagricultural sector as a share of total fants dying before reaching one year of age, nonagricultural employment. per 1,000 live births. Child immunization rate, measles, is the Source: Data on net enrollment and lit- percentage of children ages 12­23 months eracy are from the United Nations Educa- who received vaccinations for measles before tional, Scientific, and Cultural Organization 12 months or at any time before the survey. 164 Africa Development Indicators 2008/09 A child is considered adequately immunized Contraceptive prevalence rate is the per- against measles after receiving one dose of centage of women who are practicing, or vaccine. whose sexual partners are practicing, any form of contraception. It is usually measured Source: Data on under-five and infant for married women ages 15­49 only. mortality are the harmonized estimates of Deaths due to malaria is the number of the World Health Organization, United Na- malaria deaths per 100,000 people. tions Children's Fund (UNICEF), and the Children sleeping under insecticide-treated World Bank, based mainly on household sur- bednets is the percentage of children under veys, censuses, and vital registration, supple- age 5 with access to an insecticide-treated mented by the World Bank's estimates based bednet to prevent malaria. on household surveys and vital registration. Incidence of tuberculosis is the estimated Other estimates are compiled and produced number of new tuberculosis cases (pulmo- by the World Bank's Human Development nary, smear positive, and extrapulmonary), Network and Development Data Group in per 100,000 people. consultation with its operational staff and Tuberculosis cases detected under DOTS is country offices. Data on child immunization the percentage of estimated new infectious are from the World Health Organization and tuberculosis cases detected under DOTS, the UNICEF estimates of national immuniza- internationally recommended tuberculosis tion coverage. control strategy. Table .. Millennium Development Source: Data on HIV prevalence are from Goal : Improve Maternal Health the Joint United Nations Programme on Maternal mortality ratio, modeled estimate is HIV/AIDS and the World Health Organiza- the number of women who die from preg- tion's (WHO) 2006 Report on the Global AIDS nancy-related causes during pregnancy and Epidemic. Data on contraceptive prevalence childbirth, per 100,000 live births. e data are from household surveys, including De- are estimated with a regression model using mographic and Health Surveys by Macro information on fertility, birth attendants, International and Multiple Indicator Cluster and HIV prevalence. Surveys by the United Nations Children's Maternal mortality ratio, national estimate is Fund (UNICEF). Data on deaths due to ma- the number of women who die during preg- laria are from the WHO. Data on insecticide- nancy and childbirth, per 100,000 live births. treated bednet use are from UNICEF's Stateof Births attended by skilled health staff is the the World's Children 2006 and Childinfo, and percentage of deliveries attended by person- Demographic and Health Surveys by Macro nel who are trained to give the necessary International. Data on tuberculosis are from supervision, care, and advice to women dur- the WHO's Global Tuberculosis Control Report ing pregnancy, labor, and the postpartum 2006. period; conduct deliveries on their own; and care for newborns. Table .. Millennium Development Goal : Ensure Environmental Source: Data on maternal mortality are Sustainability from AbouZahr and Wardlaw (2003). Data Forest area is land under natural or planted on births attended by skilled health staff are stands of trees, whether productive or not. from the United Nations Children's Fund's Nationally protected areas are totally or State of the World's Children 2006 and Child- partially protected areas of at least 1,000 info, and Demographic and Health Surveys hectares that are designated as scientific re- by Macro International. serves with limited public access, national parks, natural monuments, nature reserves Table .. Millennium Development or wildlife sanctuaries, and protected land- Goal : Combat Hiv/Aids, Malaria, and scapes. Marine areas, unclassified areas, and Other Diseases litoral (intertidal) areas are not included. e Prevalence of HIV is the percentage of people data also do not include sites protected un- ages 15­49 who are infected with HIV. der local or provincial law. Technical Notes 165 Gross domestic product (GDP) per unit of on energy use are from electronic files of the energy use is the GDP in purchasing power International Energy Agency. Data on carbon parity (PPP) U.S. dollars per kilogram of oil dioxide emissions are from the Carbon Di- equivalent of energy use. PPP GDP is gross oxide Information Analysis Center, Environ- domestic product converted to 2000 con- mental Sciences Division, Oak Ridge National stant international dollars using purchasing Laboratory, in the U.S. state of Tennessee. power parity rates. An international dollar Data on solid fuel use are from household has the same purchasing power over GDP as survey data, supplemented by World Bank a U.S. dollar has in the United States. estimates. Data on access to water and sanita- Carbon dioxide emissions are those stem- tion are from the World Health Organization ming from the burning of fossil fuels and the and United Nations Children's Fund's Meeting manufacture of cement. ey include carbon the MDG Drinking Water and Sanitation Target dioxide produced during consumption of (www.unicef.org/wes/mdgreport). solid, liquid and gas fuels, and gas flaring. Solid fuels use is the percentage of the Table .. Millennium Development population using solid fuels as opposed to Goal : Develop a Global Partnership modern fuels. Solid fuels are defined to in- for Development clude fuel wood, straw, dung, coal, and char- Heavily Indebted Poor Country (HIPC) Debt coal. Modern fuels are defined to include Initiative decision point is the date at which electricity, liquefied petroleum gas, natural a HIPC with an established track record of gas, kerosene, and gasoline. good performance under adjustment pro- Population with sustainable access to im- grams supported by the International Mon- proved water source is the percentage of the etary Fund (IMF) and the World Bank com- population with reasonable access to an ad- mits to undertake additional reforms and to equate amount of water from an improved develop and implement a poverty reduction source, such as a household connection, strategy. public standpipe, borehole, protected well or HIPC completion point is the date at which spring, or rainwater collection. Unimproved the country successfully completes the key sources include vendors, tanker trucks, and structural reforms agreed on at the deci- unprotected wells and springs. Reasonable sion point, including developing and imple- access is defined as the availability of at least menting its poverty reduction strategy. e 20 liters a person a day from a source within country then receives the bulk of debt relief 1 kilometer of the dwelling. under the HIPC Initiative without further Population with sustainable access to im- policy conditions. proved sanitation is the percentage of the Debt service relief committed is the amount population with at least adequate access to of debt service relief, calculated at the En- excreta disposal facilities that can effectively hanced HIPC Initiative decision point that prevent human, animal, and insect contact will allow the country to achieve debt sus- with excreta. Improved facilities range from tainability at the completion point. simple but protected pit latrines to flush toi- Public and publicly guaranteed debt service lets with a sewerage connection. e excreta is the sum of principal repayments and in- disposal system is considered adequate if it terest actually paid on total long-term debt is private or shared (but not public) and if (public and publicly guaranteed and private it hygienically separates human excreta from nonguaranteed), use of IMF credit, and in- human contact. To be effective, facilities terest on short-term debt. must be correctly constructed and properly Youth unemployment rate is the percent- maintained. age of the labor force ages 15­24 without work but available for and seeking employ- Source: Data on forest area are from the ment. Definitions of labor force and unem- Food and Agricultural Organization's Global ployment may differ by country. Forest Resources Assessment. Data on na- Fixed-line and mobile phone subscribers are tionally protected areas are from the United subscribers to a fixed-line telephone service, Nations Environment Programme and the which connects a customer's equipment to World Conservation Monitoring Centre. Data the public switched telephone network, or 166 Africa Development Indicators 2008/09 to a public mobile telephone service, which which a country has an operational devel- uses cellular technology. opment strategy to guide the aid coordina- Personal computers are self-contained tion effort and the country's overall devel- computers designed for use by a single indi- opment. e score is based on the World vidual. Bank's 2005 Comprehensive Development Internet users are people with access to Framework Progress Report. An operational the worldwide web. strategy calls for a coherent long-term strat- egy derived from it; specific targets serving a Source: Data on HIPC countries are from holistic, balanced and well sequenced devel- the IMF's March 2006 "HIPC Status Re- opment strategy; and capacity and resources ports." Data on external debt are mainly for its implementation. from reports to the World Bank through PDI-2a Reliable public financial manage- its Debtor Reporting System from member ment is the World Bank's annual Country countries that have received International Policy and Institutional Assessment rating Bank for Reconstruction and Development for the quality of public financial manage- loans or International Development Associa- ment. Measured on a scale of 1 (worst) to tion credits, as well as World Bank and IMF 5 (best), its focus is on how much existing files. Data on youth unemployment are from systems adhere to broadly accepted good the International Labor Organization's Key practices and whether a reform program is Indicators of the Labor Market, fourth edition. in place to promote improved practices. Data on phone subscribers, personal com- PDI-2b Reliable country procurement sys- puters, and Internet users are from the Inter- tems. Donors use national procurement pro- national Telecommunication Union's (ITU) cedures when the funds they provide for the World Telecommunication Development Re- implementationofprojectsandprogramsare port database and World Bank estimates. managed according to the national procure- ment procedures as they were established in 4. Paris Declaration indicators the general legislation and implemented by government. e use of national procure- Table .. Status of Paris Declaration ment procedures means that donors do not Indicators make additional, or special, requirements on e Paris Declaration is the outcome of the governments for the procurement of works, 2005 Paris High-Level Forum on Aid Ef- goods and services. (Where weaknesses in fectiveness. In the Declaration, 60 partner national procurement systems have been countries, 30 donor countries, and 30 de- identified, donors may work with partner velopment agencies committed to specific countries in order to improve the efficiency, actions to further country ownership, har- economy, and transparency of their imple- monization, alignment, managing for de- mentation). e objective of this indicator is velopment results, and mutual accountabil- to measure and encourage improvements in ity for the use of aid. Participants agreed on developing countries' procurement systems. 12 indicators. ese indicators include good PDI-3 Government budget estimates com- national development strategies, reliable prehensive and realistic. e objective of this country systems for procurement and pub- indicator is to improve transparency and lic financial management, the development accountability by encouraging partner coun- and use of results frameworks, and mutual tries and donors to accurately record aid as assessment of progress. Qualitative desk much as possible in the national budget, reviews by the Organization for Economic thereby allowing scrutiny by parliaments. Co-operation and Development's Develop- PDI-4 Technical assistance aligned and co- ment Assistance Committee and the World ordinated with country programs. Coordinated Bank and a survey questionnaire for gov- technical co-operation means free standing ernments and donors are used to calculate and embedded technical co-operation that the indicators. Table 4.1 includes these in- respects the following principles: (i) Owner- dicators. ship ­ partner countries exercise effective PDI-1 Operational national development leadership over their capacity development strategies are the degree to the extent to programs; (ii) Alignment ­ technical co-oper- Technical Notes 167 ation in support of capacity development is PDI-8 Bilateral Aid that is untied. Tied aid aligned with countries' development objec- is aid provided on the condition that the re- tives and strategies; and (iii) Harmonization cipient uses it to purchase goods and servic- ­ where more than one donor is involved es from suppliers based in the donor coun- in supporting partner-led capacity develop- try. e target for this indicator is to increase ment, donors co-ordinate their activities and untied aid over time. contributions. PDI-9 Aid provided in the framework of PDI-5a and 5b Aid for government sectors program-based approaches (PBAs) are a way uses of country public financial management of engaging in development co-operation and procurement systems. e objective is based on the principles of coordinated sup- to encourage donors to increasingly use port for a locally owned program of devel- country, rather than donor, systems for opment, such as a national development managing. strategy, a sector program, a thematic pro- PDI-6 Parallel project implementation units gram or a program of a specific organiza- (PIUs)isthenumberofparallelprojectimple- tion. Program-based approaches share the mentation units. "Parallel" indicates that the following features: (i) leadership by the host units were created outside existing country country or organization; (ii) a single com- institutional structures. e survey guidance prehensive program and budget framework; distinguishes between PIUs and executing (iii) a formalized process for donor co-ordina- agencies and describes three typical fea- tion and harmonization of donor procedures tures of parallel PIUs: they are accountable for reporting, budgeting, financial manage- to external funding agencies rather than to ment and procurement; and (iv) efforts to in- country implementing agencies (ministries, crease the use of local systems for programme departments, agencies, and the like), most of design and implementation, financial man- the professional staff is appointed by the do- agement, monitoring and evaluation. nor, and the personnel salaries often exceed PDI-10a Donor co-ordinated missions fo- those of civil service personnel. Interpreta- cuses only on the proportion of (i) missions tion of the Paris Declaration survey question undertaken jointly by two or more donors, on this subject was controversial in a num- or (ii) missions undertaken by one donor on ber of countries. It is unclear whether within behalf of another (delegated co-operation). countries all donors applied the same criteria PDI-10b Country analysis coordinated (i) with the same degree of rigor or that across Country analytic work undertaken by one countries the same standards were used. or more donors jointly; (ii) Country analytic In several cases the descriptive part of the work undertaken by one donor on behalf of survey results indicates that some donors another donor (including work undertaken applied a legalistic criterion of accountabil- by one and/or used by another when it is co- ity to the formal executing agency, whereas financed and formally acknowledged in of- the national coordinator and other donors ficial documentation); (iii) Country analytic would have preferred greater recognition of work undertaken with substantive involve- the substantive reality of accountability to ment from government. the donor. Some respondents may have con- PDI-11 Existence of a monitorable perfor- fused the definitional question (Is the unit mance assessment frameworks measure the "parallel?") with the aid management ques- extent to which the country has realized its tion (Is the parallelism justified in terms of commitment to establishing performance the developmental benefits and costs?). frameworks. e indicator relies on the scor- PDI-7 Aid disbursements on schedule and ings of the 2005 CDF Progress Report and recorded by government. e objective is two- considers three criteria: the quality of devel- fold. First and foremost, it is to encourage opment information, stakeholder access to disbursements of funds within the year development information, and coordinated they are scheduled. Second, it is to encour- country-level monitoring and evaluation. age accurate recording of disbursements by e assessments therefore reflect both the partner authorities. Both objectives require extent to which sound data on development strong cooperation between donors and outputs, outcomes and impacts are collected, partner authorities. and various aspects of the way information 168 Africa Development Indicators 2008/09 is used, disseminated among stakeholders, capita. Only official costs required by law and fed back into policy. are recorded, including fees, transfer taxes, PDI-12 Existence of a mutual accountability stamp duties and any other payment to the indicates whether there is a mechanism for property registry, notaries, public agencies mutual review of progress on aid effective- or lawyers. Other taxes, such as capital gains ness commitments. is is an important in- tax or value added tax, are excluded from the novation of the Paris Declaration because it cost measure. Both costs borne by the buyer develops the idea that aid is more effective and those borne by the seller are included. when both donors and partner governments If cost estimates differ among sources, the are accountable to their constituents for the median reported value is used. use of resources to achieve development re- Number of procedures to enforce a contract sults and when they are accountable to each is the number of independent actions, man- other. e specific focus is mutual account- dated by law or courts that demand interac- ability for the implementation of the part- tion between the parties of a contract or be- nership commitments included in the Paris tween them and the judge or court officer. Declaration and any local agreements on en- Time required to enforce a contract is the hancing aid effectiveness. number of calendar days from the filing of the lawsuit in court until the final determi- Source: Overview of the Results 2007 nation and, in appropriate cases, payment. Survey on Monitoring the Paris Declaration Cost to enforce a contract is court and at- and World Bank data. torney fees, where the use of attorneys is mandatory or common, or the cost of an 5. Private sector development administrative debt recovery procedure, ex- pressed as a percentage of the debt value. Number of startup procedures to register a busi- Number of procedures dealing with con- ness is the number of procedures required struction permits is the number of procedures to start a business, including interactions to required to obtain construction-related per- obtain necessary permits and licenses and to mits. complete all inscriptions, verifications, and Time to deal with construction permits is notifications to start operations. the average wait, in days, experienced to ob- Time to start a business is the number of cal- tain construction-related permit from the endar days needed to complete the procedures day the establishment applied for it to the to legally operate a business. If a procedure can day it was granted. be speeded up at additional cost, the fastest Cost (% of income per capita) is recorded procedure, independent of cost, is chosen. as a percentage of the country's income per Cost to start a business is normalized by capita. Only official costs are recorded. All the presenting it as a percentage of gross nation- fees associated with completing the proce- al income (GNI) per capita. dures to legally build a warehouse are record- Minimum capital (% of income per capita) is ed, including those associated with obtaining paid-in minimum capital requirement and re- land use approvals and preconstruction de- flects the amount that the entrepreneur needs sign clearances; receiving inspections before, to deposit in a bank or with a notary before during and after construction; getting utility registration and up to 3 months following in- connections; and registering the warehouse corporation and is recorded as a percentage property. Nonrecurring taxes required for the of the country's income per capita. completion of the warehouse project also are Number of procedures to register property recorded. ebuildingcode,informationfrom is the number of procedures required for a local experts and specific regulations and fee business to secure rights to property. schedules are used as sources for costs. If sev- Time to register property is the number of eral local partners provide different estimates, calendar days needed for a business to se- the median reported value is used. cure rights to property. Protecting investors disclosure index mea- Cost (% of property value) is recorded as a sures the degree to which investors are pro- percentage of the property value, assumed tected through disclosure of ownership and to be equivalent to 50 times income per financial information. Technical Notes 169 Director liability index measures a plain- for reemployment. For the first question an tiff's ability to hold directors of firms liable answer of yes for workers of any income level for damages to the company). gives a score of 10 and means that the rest of Shareholder suits index measures share- the questions do not apply. An answer of yes holders' ability to sue officers and directors to question (iv) gives a score of 2. For every for misconduct. other question, if the answer is yes, a score of Investor protection index measures the 1 is assigned; otherwise a score of 0 is given. degree to which investors are protected Questions (i) and (iv), as the most restrictive through disclosure of ownership and finan- regulations, have greater weight in the con- cial information regulations. struction of the index. Rigidity of hours index has 5 components: Firing cost is the notice requirements, (i) whether night work is unrestricted; (ii) severance payments and penalties due when whether weekend work is unrestricted; (iii) terminating a redundant worker, expressed whether the work week can consist of 5.5 in weeks of salary. days; (iv) whether the workweek can extend Rigidity of employment index measures the to 50 hours or more (including overtime) for regulation of employment, specifically the 2 months a year to respond to a seasonal in- hiring and firing of workers and the rigidity crease in production; and (v) whether paid of working hours. is index is the average of annual vacation is 21 working days or fewer. three subindexes: a difficulty of hiring index, For each of these questions, if the answer is a rigidity of hours index, and a difficulty of no, the economy is assigned a score of 1; oth- firing index. erwise a score of 0 is assigned. Difficulty of hiring index is the applicabil- Source: Data are from the World Bank's ity and maximum duration of fixed-term Doing Business project (http://rru.world- contracts and minimum wage for trainee or bank.org/DoingBusiness/). first-time employee. It measures (i) whether fixed term contracts are prohibited for per- Table .. Investment Climate manent tasks; (ii) the maximum cumulative Private investment is private sector fixed capi- duration of fixed term contracts; and (iii) tal formation (Table 2.24) divided by nomi- the ratio of the minimum wage for a trainee nal gross domestic product (Table 2.1). or first time employee to the average value Net foreign direct investment is invest- added per worker. ment by residents of the Organization for Difficulty of firing index is the notifica- Economic Co-operation and Development's tion and approval requirements for termi- (OECD) Development Assistance Commit- nation of a redundant worker or a group of tee (DAC) member countries to acquire a redundant workers, obligation to reassign lasting management interest (at least 10 per- or retrain and priority rules for redundancy cent of voting stock) in an enterprise operat- and reemployment. It has 8 components: (i) ing in the recipient country. e data reflect whether redundancy is disallowed as a ba- changes in the net worth of subsidiaries in sis for terminating workers; (ii) whether the recipient countries whose parent company is employer needs to notify a third party (such in the DAC source country. as a government agency) to terminate 1 re- Domestic credit to private sector is finan- dundant worker; (iii) whether the employer cial resources provided to the private sector, needs to notify a third party to terminate a such as through loans, purchases of non- group of 25 redundant workers; (iv) whether equity securities, and trade credits and other the employer needs approval from a third accounts receivable that establish a claim for party to terminate 1 redundant worker; (v) repayment. For some countries these claims whether the employer needs approval from a include credit to public enterprises. third party to terminate a group of 25 redun- Corruption, is the percentage of firms dant workers; (vi) whether the law requires identifying corruption as a major constraint. the employer to reassign or retrain a worker e computation of the indicator is based before making the worker redundant; (vii) on the rating of the obstacle as a potential whether priority rules apply for redundan- constraint to the current operations of the cies; and (viii) whether priority rules apply establishment. 170 Africa Development Indicators 2008/09 Court system is fair, impartial and uncor- obstacle as a potential constraint to the cur- rupted, is the percentage of firms believing rent operations of the establishment. the court system is fair, impartial and uncor- Number of tax payments is the number of rupted as a major constraint. e computa- taxes paid by businesses, including electronic tion of the indicator is based on the rating of filing. e tax is counted as paid once a year the obstacle as a potential constraint to the even if payments are more frequent. current operations of the establishment. Time to prepare, file, and pay taxes is the Crime, theft and disorder is the percentage number of hours it takes to prepare, file, and of firms who ranked crime, theft, and disor- pay (or withhold) three major types of taxes: der as a major constraint. e computation the corporate income tax, the value added or of the indicator is based on the rating of the sales tax, and labor taxes, including payroll obstacle as a potential constraint to the cur- taxes and social security contributions. rent operations of the establishment. Total tax rate is the total amount of taxes Tax rates are the percentage of firms who payable by the business (except for labor ranked tax rates as a major constraint. e taxes) after accounting for deductions and computation of the indicator is based on exemptions as a percentage of profit. the rating of the obstacle as a potential con- Highest marginal tax rate, corporate, is the straint to the current operations of the es- highest rate shown on the schedule of tax tablishment. rates applied to the taxable income of cor- Finance is the percentage of firms who porations. ranked access to finance or cost of finance as Time dealing with officials is the average a major constraint. e computation of the percentage of senior management's time indicator is based on the rating of the ob- that is spent in a typical week dealing with stacle as a potential constraint to the current requirements imposed by government regu- operations of the establishment. lations (for example, taxes, customs, labor Electricity is the percentage of firms who regulations, licensing, and registration), in- ranked electricity as a major constraint. cluding dealings with officials, completing e computation of the indicator is based forms, and the like. on the rating of the obstacle as a potential Average time to clear direct exports through constraint to the current operations of the customs is the number of days to clear direct establishment. exports through customs. Labor regulations is the percentage of firms Average time to clear imports through cus- who ranked labor regulations as a major con- toms (days) is the average number of days to straint. e computation of the indicator is clear imports through customs. For survey based on the rating of the obstacle as a po- data collected in 2006 and 2007, this indica- tential constraint to the current operations tor is computed for the manufacturing mod- of the establishment. ule only. Labor skills are the percentage of firms who Interest rate spread is the interest rate ranked skills of available workers as a major charged by banks on loans to prime custom- constraint. e computation of the indicator ers minus the interest rate paid by commer- is based on the rating of the obstacle as a po- cial or similar banks for demand, time, or tential constraint to the current operations savings deposits. of the establishment. Listed domestic companies are domestical- Transportation is the percentage of firms ly incorporated companies listed on a coun- who ranked transportation as a major con- try's stock exchanges at the end of the year. straint. e computation of the indicator is ey exclude investment companies, mutual based on the rating of the obstacle as a po- funds, and other collective investment ve- tential constraint to the current operations hicles. of the establishment. Market capitalization of listed companies, Trade identifying customs & trade regula- also known as market value, is the share tions is the percentage of firms who ranked price of a listed domestic company's stock trade identifying customs and trade regula- times the number of shares outstanding. tions as a major constraint. e computation Turnover ratio for traded stocks is the to- of the indicator is based on the rating of the tal value of shares traded during the period Technical Notes 171 divided by the average market capitalization the rest of the world involving a change in for the period. Average market capitalization ownership of general merchandise, goods is calculated as the average of the end-of- sent for processing and repairs, and non- period values for the current period and the monetary gold. Data are shown in current previous period. U.S. dollars. Exports and imports as a share of gross domestic product (GDP) are calcu- Source: Data on private investment are lated as merchandise exports and imports from the World Bank's World Development divided by nominal GDP. Annual growth of Indicators database. Data on net foreign di- exports and imports is calculated using the rect investment are from the World Bank's real imports and exports. World Development Indicators database. Terms of trade index measures the relative Data on domestic credit to the private sector movement of export and import prices. is are from the International Monetary Fund's series is calculated as the ratio of a country's International Financial Statistics database export unit values or prices to its import unit and data files, World Bank and OECD gross values or prices shows changes over a base domestic product (GDP) estimates, and the year (2000) in the level of export unit values World Bank's World Development Indica- as a percentage of import unit values. tors database. Data on investment climate Structure of merchandise exports and im- constraints to firms are based on enterprise ports components may not sum to 100 per- surveys conducted by the World Bank and cent because of unclassified trade. its partners during 2001­07 (http://rru. Food comprises the commodities in worldbank.org/EnterpriseSurveys). Data on Standard International Trade Classification regulation and tax administration and high- (SITC) sections 0 (food and live animals), 1 est marginal corporate tax rates are from the (beverages and tobacco), and 4 (animal and World Bank's Doing Business project (http:// vegetable oils and fats) and SITC division 22 rru.worldbank.org/DoingBusiness). Data on (oil seeds, oil nuts, and oil kernels). time dealing with officials and average time Agricultural raw materials comprise the to clear customs are from World Bank En- commodities in SITC section 2 (crude ma- terprise Surveys (http://rru.worldbank.org/ terials except fuels), excluding divisions 22, EnterpriseSurveys/). Data on interest rate 27 (crude fertilizers and minerals excluding spreads are from the IMF's International Fi- coal, petroleum, and precious stones), and nancial Statistics database and data files and 28 (metalliferous ores and scrap). the World Bank's World Development Indica- Fuel comprise SITC section 3 (mineral tors database. Data on listed domestic com- fuels). panies and turnover ratios for traded stocks Ores and metals comprise the commodi- are from Standard & Poor's Emerging Stock ties in SITC sections 27, 28, and 68 (nonfer- Markets Factbook and supplemental data and rous metals). the World Bank's World Development Indi- Manufactures comprise the commodi- cators database. Data on market capitaliza- ties in SITC sections 5 (chemicals), 6 (basic tion of listed companies are from Standard manufactures), 7 (machinery and transport & Poor's Emerging Stock Markets Factbook and equipment), and 8 (miscellaneous manufac- supplemental data, World Bank and OECD tured goods), excluding division 68. estimates of GDP, and the World Bank's Export diversification index measures the World Development Indicators database. extent to which exports are diversified. It is constructed as the inverse of a Herfindahl 6. Trade index, using disaggregated exports at four digits (following the SITC3). A higher index Table .. International Trade indicates more export diversification. and Tariff Barriers Competitiveness Indicator has two aspects: Merchandise trade is the sum of imports and Sectoral effect and Global competitiveness ef- exports of divided by nominal gross domes- fect. To calculate both indicators, growth of tic product. exports is decomposed into three compo- Exports and imports comprise all transac- nents: the growth rate of total international tions between residents of an economy and trade over the reference period (2002­2006); 172 Africa Development Indicators 2008/09 the sectoral effect, which measures the contri- World Integrated Trade Solution system. bution to a country's export growth of the Data on the export diversification index and dynamics of the sectoral markets where the the competitiveness indicator are from the country sells its products, assuming that Organization for Economic Co-operation sectoral market shares are constant; and the and Development. Data on tariffs are from competitiveness effect, which measures the the United Nations Conference on Trade contribution of changes in sectoral market and Development and the World Trade Or- shares to a country's export growth. ganization. Data on global imports are from Tariff barriers are a form of duty based on the United Nations Statistics Division's the value of the import. COMTRADE database. Data on merchan- Binding coverage is the percentage of prod- dise exports and imports are from World uct lines with an agreed bound rate. Bank country desks. Data on shipping costs Simple mean bound rate is the unweighted are from the World Bank's Sub-Saharan Af- average of all the lines in the tariff schedule rica Transport Policy Program (SSATP). Data in which bound rates have been set. on average time to clear customs are from Simple mean tariff is the unweighted av- World Bank Enterprise Surveys (http://rru. erage of effectively applied rates or most fa- worldbank.org/EnterpriseSurveys/). vored nation rates for all products subject to tariffs calculated for all traded goods. Table . Top Three Exports and Share Weighted mean tariff is the average of ef- in Total Exports, fectively applied rates or most favored nation Top exports and share of total exports are based rates weighted by the product import shares on exports disaggregated at the four-digit corresponding to each partner country. level (following the Standard International Share of lines with international peaks is Trade Classification Revision 3). the share of lines in the tariff schedule with Number of exports accounting for 75 per- tariff rates that exceed 15 percent. cent of total exports is number of exports in Share of lines with specific rates is the share a country that account for 75 percent of the of lines in the tariff schedule that are set on country's exports. a per unit basis or that combine ad valorem and per unit rates. Source: All indicators in the table are from Primary products are commodities clas- the Organisation for Economic Co-operation sified in SITC revision 2 sections 0­4 plus and Development. division 68. Manufactured products are commodities Table . Regional Integration, classified in SITC revision 2 sections 5­8 ex- Trade Blocs cluding division 68. Merchandise exports within bloc are the sum Average cost to ship 20 ft container from of merchandise exports by members of a port to destination is the cost of all operations trade bloc to other members of the bloc. associated with moving a container from ey are shown both in U.S. dollars and as a onboard a ship to the considered economic percentage of total merchandise exports by center, weighted based on container traffic the bloc. for each corridor. Average time to clear direct exports through Source: Data on merchandise trade flows customs is the number of days to clear direct are published in the International Mon- exports through customs. etary Fund's (IMF) Direction of Trade Statis- Average time to clear imports through cus- tics Yearbook and Direction of Trade Statistics toms (days) is the average number of days to Quarterly. e data in the table were calcu- clear imports through customs. For survey lated using the IMF's Direction of Trade da- data collected in 2006 and 2007, this indica- tabase. e United Nations Conference on tor is computed for the manufacturing mod- Trade and Development publishes data on ule only. intraregional trade in its Handbook of Inter- national Trade and Development Statistics. e Source: All indicators in the table were information on trade bloc membership is calculated by World Bank staff using the from World Bank. Technical Notes 173 7. Infrastructure Source: Data on fresh water resources are from the World Bank's World Development Table .. Water and Sanitation Indicators database. Data on access to water Internal fresh water resources per capita is the and sanitation are from the World Health sum of total renewable resources, which in- Organization and United Nations Children's clude internal flows of rivers and ground- Fund's Meeting the MDG Drinking Water water from rainfall in the country, and river and Sanitation Target (www.unicef.org/wes/ flows from other countries. mdgreport). Data on water supply failure are Population with sustainable access to an from World Bank Investment Climate Sur- improved water source is the percentage of veys. Data on committed nominal invest- population with reasonable access to an ad- ment in potable water projects with private equate amount of water from an improved participation are from the World Bank's source, such as a household connection, Private Participation in Infrastructure data- public standpipe, borehole, protected well or base. Data on ODA disbursements are from spring, or rainwater collection. Unimproved the Organization for Economic Co-operation sources include vendors, tanker trucks, and and Development. unprotected wells and springs. Reasonable access is defined as the availability of at least Table .. Transportation 20 liters a person a day from a source within Road network is the length of motorways, 1 kilometer of the user's dwelling. highways, main or national roads, secondary Population with sustainable access to im- or regional roads, and other roads. proved sanitation is the percentage of the Rail lines are the length of railway route population with at least adequate access to available for train service, irrespective of the excreta disposal facilities that can effectively number of parallel tracks. prevent human, animal, and insect contact Road density, ratio to arable land is the with excreta. Improved facilities range from total length of national road network per simple but protected pit latrines to flush toi- 1,000 square kilometers of arable land area. lets with a sewerage connection. e excreta e use of arable land area in the denomina- disposal system is considered adequate if it tor focuses on inhabited sectors of total land is private or shared (but not public) and if area by excluding wilderness areas. it hygienically separates human excreta from Road density, ratio to total land is the total human contact. To be effective, facilities length of national road network per 1,000 must be correctly constructed and properly square kilometers of total land area. maintained. Rural access is the percentage of the rural Water supply failure for firms receiving wa- population who live within 2 kilometers of ter is the average number of days per year an all-season passable road as a share of the that firms experienced insufficient water total rural population. supply for production. Vehicle fleet is motor vehicles, including Committed nominal investment in water cars, buses, and freight vehicles but not two- projects with private participation is annual wheelers. committed investment in water projects with Commercial vehicles are the number of private investment, including projects for po- commercial vehicles that use at least 24 li- table water generation and distribution and ters of diesel fuel per 100 kilometers. sewerage collection and treatment projects. Passenger vehicles are road motor vehicles, Official development assistance (ODA) gross other than two-wheelers, intended for the aid disbursements for water supply and sanita- carriage of passengers and designed to seat tion are disbursements for water supply and no more than nine people (including the sanitation by bilateral, multilateral, and driver). other donors. e release of funds to, or the Road network in good or fair condition is purchase of goods or services for a recipient; the length of the national road network, by extension, the amount thus spent. Dis- including the interurban classified network bursements record the actual international without the urban and rural network, that is transfer of financial resources, or of goods or in good or fair condition, as defined by each services valued at the cost of the donor. country's road agency. 174 Africa Development Indicators 2008/09 Ratio of paved to total roads is the length of ODA disbursements are from the Organiza- paved roads--which are those surfaced with tion for Economic Co-operation and Devel- crushed stone (macadam) and hydrocarbon opment. binder or bituminized agents, with concrete, or with cobblestones--as a percentage of all Table .. Information and the country's roads. Communication Technology Price of diesel fuel and gasoline is the price Telephone subscribers are subscribers to a as posted at filling stations in a country's main telephone line service, which con- capital city. When several fuel prices for nects a customer's equipment to the public major cities were available, the unweighted switched telephone network, or to a cellular average is used. Since super gasoline (95 telephone service, which uses cellular tech- octane/A95/premium) is not available ev- nology. erywhere, it is sometime replaced by regu- Households with own telephone is the per- lar gasoline (92 octane/A92), premium plus centage of households possessing a tele- gasoline (98 octane/A98), or an average of phone. the two. Average delay for firm in obtaining a tele- Committed nominal investment in trans- phone connection is the average actual delay port projects with private participation is an- in days that firms experience when obtain- nual committed investment in transport ing a telephone connection, measured from projects with private investment, including the day the establishment applied to the day projects for airport runways and terminals, it received the service or approval. railways (including fixed assets, freight, in- Internet users are people with access to tercity passenger, and local passenger), toll the worldwide network. roads, bridges, and tunnels. Duration of telephone outages is the aver- Official development assistance (ODA) age duration in hours of instances of tele- gross aid disbursements for transportation and phone unavailability related to production. storage are disbursements for transportation Telephone faults are the total number of and storage by bilateral, multilateral, and reported faults for the year divided by the other donors. e release of funds to, or the total number of mainlines in operation mul- purchase of goods or services for a recipient; tiplied by 100. e definition of fault can by extension, the amount thus spent. Dis- vary. Some countries include faulty custom- bursements record the actual international er equipment; others distinguish between transfer of financial resources, or of goods or reported and actual found faults. ere is services valued at the cost of the donor. also sometimes a distinction between resi- dential and business lines. Another consid- Source: Data on length of road network eration is the time period: some countries and size of vehicle fleet are from the Inter- report this indicator on a monthly basis; in national Road Federation's World Road Sta- these cases data are converted to yearly es- tistics. Data on rail lines and ratio of paved to timates. total roads are from the World Bank's World Price basket for Internet is calculated based Development Indicators database. Data on on the cheapest available tariff for accessing road density and rural access to roads are the Internet 20 hours a month (10 hours from the World Bank's Sub-Saharan Af- peak and 10 hours off-peak). e basket does rica Transport Policy Program (SSATP) and not include telephone line rental but does in- World Development Indicators database. clude telephone usage charges if applicable. Data on length of national network in good Data are compiled in the national currency or fair condition and average time and costs and converted to U.S. dollars using the an- are from the World Bank's SSATP. Data on nual average exchange rate. fuel and gasoline prices are from the German Cost of 3 minute local phone call during peak Agency for Technical Cooperation (GTZ). hours is the cost of a three-minute local call Data on committed nominal investment in during peak hours. Local call refers to a call transport projects with private participation within the same exchange area using the are from the World Bank's Private Partici- subscriber's own terminal (that is, not from pation in Infrastructure database. Data on a public telephone). Technical Notes 175 Cost of 3 minute cellular local call during Source: Data on telephone subscribers, off-peak hours is the cost of a three-minute reported phone faults, and cost of local and cellular local call during off-peak hours. cellular calls are from the International Tele- Cost of 3 minute phone call to the U.S, dur- communications Union. Data on households ing peak hours is the cost of a three-minute with own telephone are from Demographic call to the United States during peak hours. and Health Surveys. Data on delays for firms Residential telephone connection charge re- in obtaining a telephone connection and du- fers to the one time charge involved in apply- ration of telephone outages are from World ing for basic telephone service for business Bank Investment Climate Assessments. Data purposes. Where there are different charges on Internet users and pricing are from the for different exchange areas, the charge is International Telecommunication Union, World generally for the largest urban area unless Telecommunication Development Report and otherwise noted. is indicator is expressed database, and World Bank estimates. Data on in US dollars. cost of a call to the United States are from the Mobile cellular connection charge is the World Bank's Global Development Finance initial, one-time charge for a new subscrip- and World Development Indicator databases. tion. Refundable deposits are not counted. Data on committed nominal investment are e price of the SIM card is included in the from the World Bank's Private Participation connection charge. A note indicates whether in Infrastructure database. Data on ODA dis- taxes are included (preferred) or not. It is also bursements are from the Organization for noted if free minutes are included in the plan. Economic Co-operation and Development. is indicator is expressed in US dollars. Annual investment in telephone service Table .. Energy is the annual investment in equipment for Electric power consumption is the production fixed telephone service. of power plants and combined heat and Annual investment in mobile communica- power plants, less distribution losses and tion is the capital investment on equipment own use by heat and power plants. for mobile communication networks. GDP per unit of energy use is nominal GDP Annual investment in telecommunications is in purchasing power parity (PPP) U.S. dollars the expenditure associated with acquiring the divided by apparent consumption, which is ownership of telecommunication equipment equal to indigenous production plus imports infrastructure (including supporting land and stock changes minus exports and fuels and buildings and intellectual and non-tan- supplied to ships and aircraft engaged in in- gible property such as computer software). It ternational transport. includes expenditure on initial installations Access to electricity is the percentage of and on additions to existing installations. the population living in households with ac- Committed nominal investment in telecom- cess to electricity. munication projects with private participation Solid fuels use is the percentage of the is annual committed investment in telecom- population using solid fuels as opposed to munication projects with private invest- modern fuels. Solid fuels include fuel wood, ment, including projects for fixed or mobile straw, dung, coal, and charcoal. Modern fu- local telephony, domestic long-distance tele- els include electricity, liquefied petroleum phony, and international long-distance tele- gas, natural gas, kerosene, and gasoline. phony. Average delay for firm in obtaining electri- Official development assistance (ODA) gross cal connection is the average actual delay in aid disbursements for communication are dis- days that firms experience when obtaining bursements for communication by bilateral, an electrical connection, measured from the multilateral, and other donors. e release of day the establishment applied to the day it funds to, or the purchase of goods or servic- received the service or approval. es for a recipient; by extension, the amount Electric power transmission and distribution thus spent. Disbursements record the actual losses are technical and nontechnical losses, international transfer of financial resources, including electricity losses due to operation or of goods or services valued at the cost of of the system and the delivery of electricity the donor. as well as those caused by unmetered supply. 176 Africa Development Indicators 2008/09 is comprises all losses due to transport and base, supplemented by World Bank Project distribution of electrical energy and heat. Appraisal Documents. Data on committed Electrical outages of firms are the average nominal investment are from the World number of days per year that establishments Bank's Private Participation in Infrastruc- experienced power outages or surges from ture database. Data on ODA disbursements the public grid. are from the Organization for Economic Co- Firms that share or own their own genera- operation and Development. tor is the percentage of firms that responded "Yes" to the following question: "Does your Table .. Financial Sector establishment own or share a generator?" Infrastructure Firms identifying electricity as major or very Sovereign ratings are long- and short-term severe obstacle to business operation and growth foreign currency ratings. is the percentage of firms that responded International Long-Term Credit Ratings "major" or "very severe" obstacle to the fol- (LTCR) may also be referred to as Long-Term lowing question: "Please tell us if any of the Ratings. When assigned to most issuers, it is following issues are a problem for the opera- used as a benchmark measure of probability tion and growth of your business. If an issue of default and is formally described as an Is- (infrastructure, regulation, and permits) pos- suer Default Rating (IDR). e major excep- es a problem, please judge its severity as an tion is within Public Finance, where IDRs will obstacle on a five-point scale that ranges from not be assigned as market convention has 0 = no obstacle to 5 = very severe obstacle." always focused on timeliness and does not Committed nominal investment in energy draw analytical distinctions between issuers projects with private participation is annual and their underlying obligations. When ap- committed investment in energy projects plied to issues or securities, the LTCR may be with private investment, including projects higher or lower than the issuer rating (IDR) for electricity generation, transmission, and to reflect relative differences in recovery ex- distribution as well as natural gas transmis- pectations. sion and distribution. A Short-term rating has a time horizon Official development assistance (ODA) gross of less than 13 months for mostobligations, aid disbursements for energy are disburse- or up to three years for US public finance, ments for energy by bilateral, multilateral, in line with industrystandards, to reflect and other donors. e release of funds to, or unique risk characteristics of bond, tax, and the purchase of goods or services for a recipi- revenueanticipation notes that are common- ent; by extension, the amount thus spent. ly issued with terms up to three years.Short- Disbursements record the actual internation- term ratings thus place greater emphasis al transfer of financial resources, or of goods on the liquidity necessary to meet financial or services valued at the cost of the donor. commitments in a timely manner. Gross national savings are the sum of Source: Data on electric power consump- gross domestic savings (Table 2.13) and net tion and PPP GDP per unit of energy use are factor income and net private transfers from from the World Bank's World Development abroad. e estimate here also includes net Indicators database. Data on access to elec- public transfers from abroad. tricity and solid fuels use are from house- Money and quasi money (M2) are the sum hold survey data, supplemented by World of currency outside banks, demand deposits Bank Project Appraisal Documents. Data other than those of the central government, on delays for firms in obtaining an electri- and the time, savings, and foreign curren- cal connection, electrical outages of firms, cy deposits of resident sectors other than firms that share or own their own generator, the central government. is definition of and firms identifying electricity as a major money supply is frequently called M2 and or very severe obstacle to business operation corresponds to lines 34 and 35 in the IMF's and growth are from World Bank Investment International Financial Statistics. Climate Assessments. Data on transmission Real interest rate is the lending interest and distribution losses are from the World rate adjusted for inflation as measured by Bank's World Development Indicators data- the gross domestic product (GDP) deflator. Technical Notes 177 Domestic credit to private sector is finan- and the World Bank's World Development cial resources provided to the private sector, Indicators database. Data on ratios of bank such as through loans, purchases of non- nonperforming loans to total are from the equity securities, and trade credits and other IMF's Global Financial Stability Report and accounts receivable, that establish a claim for the World Bank's World Development In- repayment. For some countries these claims dicators database. Data on bank branches include credit to public enterprises. are from surveys of banking and regulatory Interest rate spread is the interest rate institutions by the World Bank's Research charged by banks on loans to prime custom- Department and Financial Sector and Op- ers minus the interest rate paid by commer- erations Policy Department and the World cial or similar banks for demand, time, or Development Indicators database. Data on savings deposits. listed domestic companies and turnover ra- Ratio of bank nonperforming loans to total tios for traded stocks are from Standard & gross loans is the value of nonperforming Poor's Emerging Stock Markets Factbook and loans divided by the total value of the loan supplemental data and the World Bank's portfolio (including nonperforming loans World Development Indicators database. before the deduction of specific loan-loss Data on market capitalization of listed com- provisions). e loan amount recorded as panies are from Standard & Poor's Emerging nonperforming should be the gross value of Stock Markets Factbook and supplemental the loan as recorded on the balance sheet, data, World Bank and OECD estimates of not just the amount that is overdue. GDP, and the World Bank's World Develop- Listed domestic companies are domestically ment Indicators database. incorporated companies listed on a country's stock exchanges at the end of the year. ey 8. Human development excludeinvestmentcompanies,mutualfunds, 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 ages 15­24 who can, with understanding, price of a listed domestic company's stock both read and write a short, simple state- times the number of shares outstanding. ment about their everyday life. Turnover ratio for traded stocks is the to- Adult literacy rate is the proportion of tal value of shares traded during the period adults ages 15 and older who can, with un- divided by the average market capitalization derstanding, read and write a short, simple for the period. Average market capitalization statement on their everyday life. is calculated as the average of the end-of- Primary education provides children with period values for the current period and the basic reading, writing, and mathematics skills previous period. along with an elementary understanding of such subjects as history, geography, natural Source: Data on sovereign ratings are from science, social science, art, and music. Fitch Ratings. Data on gross national savings Secondary education completes the provi- are from World Bank country desks. Data on sion of basic education that began at the pri- money and quasi money and domestic credit mary level and aims to lay the foundations to the private sector are from the IMF's In- for lifelong learning and human development ternational Financial Statistics database and by offering more subject- or skill-oriented in- data files, World Bank and OECD estimates struction using more specialized teachers. of GDP, and the World Bank's World Devel- Tertiary education, whether or not at an opment Indicators database. Data on real in- advanced research qualification, normally terest rates are from the IMF's International requires, as a minimum condition of admis- Financial Statistics database and data files sion, the successful completion of education using World Bank data on the GDP defla- at the secondary level. tor and the World Bank's World Development Gross enrollment ratio is the ratio of total Indicators database. Data on interest rate enrollment, regardless of age, to the popu- spreads are from the IMF's International lation of the age group that officially corre- Financial Statistics database and data files sponds to the level of education shown. 178 Africa Development Indicators 2008/09 Net enrollment ratio is the ratio of chil- "point prevalence." Estimates include cases dren of official school age based on the In- of tuberculosis among people with HIV. ternational Standard Classification of Edu- Deaths due to malaria is the number of cation 1997 who are enrolled in school to malaria deaths per 100,000 people. the population of the corresponding official Child immunization rate is the percentage school age. of children ages 12­23 months who received Student-teacher ratio is the number of stu- vaccinations before 12 months or at any time dents enrolled in school divided by the num- before the survey for four diseases--measles ber of teachers, regardless of their teaching and diphtheria, pertussis (whooping cough), assignment. and tetanus (DPT). A child is considered ad- Public spending on education is current and equately immunized against measles after capital public expenditure on education plus receiving one dose of vaccine and against subsidies to private education at the prima- DPT after receiving three doses. ry, secondary, and tertiary levels by local, re- Stunting (height for age) is the percent- gional, and national government, including age of children under 5 whose height for municipalities. It excludes household contri- age is more than two standard deviations butions. below the median for the international ref- erence population ages 0 to 59 months. Source: United Nations Educational, Sci- For children up to two years of age, height entific, and Cultural Organization Institute is measured by recumbent length. For older for Statistics. children, height is measured by stature while standing. e reference population adopted Table .. Health by the WHO in 1983 is based on children Life expectancy at birth is the number of from the United States, who are assumed to years a newborn infant would live if pre- be well-nourished. vailing patterns of mortality at the time of Underweight (weight for age) is the per- its birth were to remain the same through- centage of children under 5 whose weight out its life. Data are World Bank estimates for age is more than two standard deviations based on data from the United Nations below the median reference standard for Population Division, the United Nations their age as established by the World Health Statistics Division, and national statistical Organization, the U.S. Centers for Disease offices. Control and Prevention, and the U.S. Na- Under-five mortality rate is the probability tional Center for Health Statistics. Figures that a newborn baby will die before reaching are based on children under age 3, 4, and 5 age 5, if subject to current age-specific mor- years of age, depending on the country. tality rates. e probability is expressed as a Births attended by skilled health staff are rate per 1,000. the percentage of deliveries attended by per- Infant mortality rate is the number of in- sonnel trained to give the necessary supervi- fants dying before reaching one year of age, sion, care, and advice to women during preg- per 1,000 live births. nancy, labor, and the postpartum period; to Maternal mortality ratio, modeled estimate conduct deliveries on their own; and to care is the number of women who die from preg- for newborns. nancy-related causes during pregnancy and Contraceptive prevalence rate is the per- childbirth, per 100,000 live births. e data centage of women who are practicing, or are estimated with a regression model using whose sexual partners are practicing, any information on fertility, birth attendants, form of contraception. It is usually measured and HIV prevalence. for married women ages 15­49 only. Prevalence of HIV is the percentage of peo- Children sleeping under insecticide-treated ple ages 15­49 who are infected with HIV. bednets is the percentage of the children un- Incidence of tuberculosis is the number of der 5 with access to an insecticide-treated tuberculosis cases (pulmonary, smear posi- bednets to prevent malaria. tive, and extrapulmonary) in a population Tuberculosis cases detected under DOTS is at a given point in time, per 100,000 people. the percentage of estimated new infectious is indicator is sometimes referred to as tuberculosis cases detected under DOTS, the Technical Notes 179 internationally recommended tuberculosis Public health expenditure consists of recur- control strategy. rent and capital spending from government Tuberculosis treatment success rate is the (central and local) budgets, external borrow- percentage of new smear-positive tubercu- ings and grants (including donations from losis cases registered under DOTS in a given international agencies and nongovernmen- year that successfully completed treatment, tal organizations), and social (or compulso- whether with bacteriologic evidence of success ry) health insurance funds. is is expressed ("cured") or without ("treatment completed"). as a proportion of GDP. Children under age 5 with fever receiving Private health expenditure includes direct antimalarial drugs within 24 hrs are the per- household (out-of-pocket) spending, private in- centage of children under age 5 in malaria- surance, charitable donations, and direct service risk areas with fever being treated with any payments by private corporations. is is ex- antimalarial drugs. pressed as a proportion of GDP. Population with sustainable access to an im- Public health expenditure consists of recur- proved water source is the percentage of the rent and capital spending from government population with reasonable access to an ad- (central and local) budgets, external borrow- equate amount of water from an improved ings and grants (including donations from source, such as a household connection, international agencies and nongovernmen- public standpipe, borehole, protected well or tal organizations), and social (or compulso- spring, or rainwater collection. Unimproved ry) health insurance funds. is is expressed sources include vendors, tanker trucks, and as a proportion of total health expenditure unprotected wells and springs. Reasonable Private health expenditure includes direct access is defined as the availability of at least household (out-of-pocket) spending, private 20 liters a person a day from a source within insurance, charitable donations, and direct 1 kilometer of the dwelling. service payments by private corporations. Population with sustainable access to im- is is expressed as a proportion of total proved sanitation is the percentage of the pop- health expenditure. ulation with at least adequate access to excreta Out-of-pocketexpenditureisanydirectout- disposal facilities that can effectively prevent lay by households, including gratuities and human, animal, and insect contact with ex- in-kind payments, to health practitioners creta. Improved facilities range from simple and suppliers of pharmaceuticals, therapeu- but protected pit latrines to flush toilets with tic appliances, and other goods and services a sewerage connection. e excreta disposal whose primary intent is to contribute to the system is considered adequate if it is private restoration or enhancement of the health or shared (but not public) and if it hygieni- status of individuals or population groups. It cally separates human excreta from human is a part of private health expenditure. contact. To be effective, facilities must be cor- Totalgovernmentexpenditureincludescon- rectly constructed and properly maintained. solidated direct outlays and indirect outlays, Physicians are the number of physicians, including capital of all levels of government, including generalists and specialists. social security institutions, autonomous Nurses and midwives are professional bodies, and other extrabudgetary funds. nurses, auxiliary nurses, enrolled nurses, Health expenditure per capita is the total and other nurses, such as dental nurses health expenditure. It is the sum of public and primary care nurses, and professional and private health expenditures as a ratio midwives, auxiliary midwives, and enrolled of total population. It covers the provision midwives. of health services (preventive and curative), Total health expenditure is the sum of pub- family planning activities, nutrition activi- lic and private health expenditure. It covers ties, and emergency aid designated for health the provision of health services (preven- but does not include provision of water and tive and curative), family planning activi- sanitation. Data are in current U.S. dollars. ties, nutrition activities, and emergency aid designated for health but does not include Source: Data are from the latest Core provision of water and sanitation. is is ex- Health Indicators from World Health Or- pressed as a proportion of GDP. ganization sources, including World Health 180 Africa Development Indicators 2008/09 Statistics 2006 and World Health Report 2006 system is considered adequate if it is private (http://www3.who.int/whosis/core/core_se- or shared (but not public) and if it hygieni- lect.cfm?path=whosis,core&language=englis cally separates human excreta from human h). Data on health expenditure are from the contact. To be effective, facilities must be World Health Organization's World Health correctly constructed and properly main- Report and updates and from the OECD tained. for its member countries, supplemented by Share of rural population with access to elec- World Bank poverty assessments and coun- tricity is the percentage of the rural popula- try and sector studies, and household sur- tion living in households with access to elec- veys conducted by governments or by statis- tricity. tical or international organizations. Share of rural population with access to transportation is the percentage of the rural population who live within 2 kilometers of 9. Agriculture, rural development, and an all-season passable road as a share of the environment total rural population. Share of rural households with access to a Table .. Rural Development landline telephone is the percentage of rural Rural population is the difference between the households possessing a telephone. total population and the urban population. Rural population density is the rural popu- Source: Data on rural population are calcu- lation divided by the arable land area. Arable lated from urban population shares from the land includes land defined by the Food and United Nations Population Division's World Agriculture Organization (FAO) as land un- Urbanization Prospects and from total popu- der temporary crops (double-cropped areas lation figures from the World Bank. Data on are counted once), temporary meadows for rural population density are from the FAO mowing or for pasture, land under market and World Bank population estimates. Data or kitchen gardens, and land temporarily fal- on rural population below the poverty line low. Land abandoned as a result of shifting are national estimates based on population- cultivation is excluded. weighted subgroup estimates from household Rural population below the national poverty surveys. Data on rural population with access line is the percentage of the rural population to water and rural population with access to living below the national poverty line. sanitation are from World Health Organiza- Share of rural population with sustainable tion and United Nations Children's Fund's access to an improved water source is the per- Meeting the MDG Water and Sanitation Target centage of the rural population with reason- (www.unicef.org/wes/mdgreport). Data on able access to an adequate amount of water rural population with access to electricity are from an improved source, such as a house- from household survey data, supplemented hold connection, public standpipe, borehole, by World Bank Project Appraisal Documents. protected well or spring, or rainwater collec- Data on rural population with access to trans- tion. Unimproved sources include vendors, port are from the World Bank's Sub-Saharan tanker trucks, and unprotected wells and Africa Transport Policy Program (SSATP). springs. Reasonable access is defined as the Data on rural households with own telephone availability of at least 20 liters a person a are from Demographic and Health Surveys. day from a source within 1 kilometer of the dwelling. Table .. Agriculture Share of rural population with sustainable Agriculture value added is shown at fac- access to improved sanitation facilities is the tor cost in current U.S. dollars divided by percentage of the rural population with at nominal gross domestic product. Agricul- least adequate access to excreta disposal fa- ture corresponds to ISIC divisions 1­5 and cilities that can effectively prevent human, includes forestry, hunting, and fishing, as animal, and insect contact with excreta. well as cultivation of crops and livestock Improved facilities range from simple but production. Value added is the net output protected pit latrines to flush toilets with a of a sector after adding up all outputs and sewerage connection. e excreta disposal subtracting intermediate inputs. It is calcu- Technical Notes 181 lated without making deductions for depre- Cereal cropland refers to harvested area, ciation of fabricated assets or depletion and although some countries report only sown degradation of natural resources. e origin or cultivated area. of value added is determined by the Inter- Irrigated land is areas equipped to provide national Standard Industrial Classification water to the crops, including areas equipped (ISIC), revision 3. Note: For VAB countries, for full and partial control irrigation, spate gross value added at factor cost is used as irrigation areas, and equipped wetland or in- the denominator. land valley bottoms. Crop production index shows agricultural Fertilizer consumption is the aggregate of production for each year relative to the base nitrogenous, phosphate, and potash fertil- period 1999­2001. It includes all crops ex- izers. cept fodder crops. Regional and income Agricultural machinery refers to the num- group aggregates for the Food and Agricul- ber of wheel and crawler tractors (excluding ture Organization's (FAO) production index- garden tractors) in use in agriculture at the es are calculated from the underlying values end of the calendar year specified or during in international dollars, normalized to the the first quarter of the following year. Arable base period 1999­2001. landincludeslanddefinedbytheFAOasland Food production index covers food crops under temporary crops (double-cropped ar- that are considered edible and that contain eas are counted once), temporary meadows nutrients. Coffee and tea are excluded be- for mowing or for pasture, land under mar- cause, although edible, they have no nutri- ket or kitchen gardens, and land temporarily tive value. fallow. Land abandoned as a result of shift- Livestock production index includes meat ing cultivation is excluded. and milk from all sources, dairy products Agricultural employment includes people such as cheese, and eggs, honey, raw silk, who work for a public or private employer wool, and hides and skins. and who receive remuneration in wages, sal- Cereal production is crops harvested for ary, commission, tips, piece rates, or pay in dry grain only. Cereals include wheat, rice, kind. Agriculture corresponds to division 1 maize, barley, oats, rye, millet, sorghum, (International Standard Industrial Classifi- buckwheat, and mixed grains. Cereal crops cation, ISIC, revision 2) or tabulation catego- harvested for hay or harvested green for ries A and B (ISIC revision 3) and includes food, feed, or silage and those used for graz- hunting, forestry, and fishing. ing are excluded. Agriculture value added per worker is the Cereals (exports and imports) quantities output of the agricultural sector (ISIC divi- and include wheat, rice, maize, barley, oats, rye, sions 1­5) less the value of intermediate millet, sorghum, buckwheat, and mixed grains. inputs. Agriculture comprises value added Agricultural exports and imports are ex- from forestry, hunting, and fishing as well pressed in current U.S. dollars at free on as cultivation of crops and livestock pro- board (fob) prices for exports and cost-in- duction. Data are in constant 2000 U.S. surance freight (cif) prices for imports. e dollars. term agriculture in trade refers to both food Cereal yield is dry grain only and includes and agriculture and does not include forestry wheat, rice, maize, barley, oats, rye, millet, and fishery products. sorghum, buckwheat, and mixed grains. Pro- Food exports and imports are expressed in duction data on cereals relate to crops har- current U.S. dollars at free on board prices vested for dry grain only. Cereal crops har- (fob) prices for exports and cost-insurance vested for hay or harvested green for food, freight (cif) prices for imports. feed, or silage and those used for grazing are Permanent cropland is land cultivated excluded. with crops that occupy the land for long pe- riods and need not be replanted after each Source: Data on agriculture value added harvest, such as cocoa, coffee, and rubber. It are from World Bank country desks. Data on includes land under flowering shrubs, fruit crop, food, livestock, and cereal production, trees, nut trees, and vines, but excludes land cereal exports and imports, agricultural ex- under trees grown for wood or timber. ports and imports, permanent cropland, 182 Africa Development Indicators 2008/09 cereal cropland, and agricultural machinery gas liquids, and oil from nonconventional are from the FAO. Data on irrigated land sources), natural gas, solid fuels (coal, lignite, are from the FAO's Production Yearbook and and other derived fuels), and combustible re- data files. Data on fertilizer consumption newables and waste--and primary electric- are from the FAO database for the Fertilizer ity, all converted into oil equivalents. Yearbook. Data on agricultural employment Energy use refers to use of primary energy are from the International Labor Organiza- before transformation to other end-use fu- tion. Data on incidence of drought are from els, which is equal to indigenous production the Southern Africa Flood and Drought Net- plus imports and stock changes, minus ex- work and East Africa Drought (CE). Data on ports and fuels supplied to ships and aircraft agriculture value added per worker are from engaged in international transport. World Bank national accounts files and the Combustible renewables and waste com- FAO's Production Yearbook and data files. prise solid biomass, liquid biomass, biogas, industrial waste, and municipal waste, mea- Table .. Environment sured as a percentage of total energy use. Forest area is land under natural or planted Carbon dioxide emissions are those stem- stands of trees, whether productive or not. ming from the burning of fossil fuels and the Renewable internal freshwater resources re- manufacture of cement. ey include carbon fer to internal renewable resources (internal dioxide produced during consumption of river flows and groundwater from rainfall) in solid, liquid, and gas fuels and gas flaring. the country. Industrial methane emissions (% of total) are Annual freshwater withdrawals refer to to- emissions from the handling, transmission, tal water withdrawals, not counting evapora- and combustion of fossil fuels and biofuels. tion losses from storage basins. Withdrawals Agricultural methane emissions (% of total) also include water from desalination plants are emissions from animals, animal waste, in countries where they are a significant rice production, agricultural waste burning source. Withdrawals can exceed 100 percent (nonenergy, on-site), and savannah burning.. of total renewable resources where extrac- Agricultural nitrous oxide emissions (% of tion from nonrenewable aquifers or desalina- total) are emissions produced through fertil- tion plants is considerable or where there is izer use (synthetic and animal manure), ani- significant water reuse. Withdrawals for ag- mal waste management, agricultural waste riculture and industry are total withdrawals burning (nonenergy, on-site), and savannah for irrigation and livestock production and burning. for direct industrial use (including withdraw- Industrial nitrous oxide emissions (% of to- als for cooling thermoelectric plants). With- tal) are emissions produced during the man- drawals for domestic uses include drinking ufacturing of adipic acid and nitric acid. water, municipal use or supply, and use for Nitrous oxide emissions (metric tons of CO2 public services, commercial establishments, equivalent) are emissions from agricultural and homes. biomass burning, industrial activities, and Water productivity is calculated as gross livestock management domestic product in constant prices divided Other greenhouse gas emissions, HFC, PFC by annual total water withdrawal. Sectoral and SF6 (thousand metric tons of CO2 equiva- water productivity is calculated as annual lent) are by-product emissions of hydro- value added in agriculture or industry divid- fluorocarbons, perfluorocarbons, and sulfur ed by water withdrawal in each sector. hexafluoride. Emissions of organic water pollutants are Methane emissions (kt of CO2 equivalent) measured in terms of biochemical oxygen are those stemming from human activi- demand, which refers to the amount of ties such as agriculture and from industrial oxygen that bacteria in water will consume methane production. in breaking down waste. is is a standard Official development assistance (ODA) dis- water-treatment test for the presence of or- bursements for forestry are disbursements for ganic pollutants. forestry by bilateral, multilateral, and other Energy production refers to forms of pri- donors. e release of funds to, or the pur- mary energy--petroleum (crude oil, natural chase of goods or services for a recipient; Technical Notes 183 by extension, the amount thus spent. Dis- MAM is the sum of the precipitation in the bursements record the actual international quarter: March, April, May (millimeters). transfer of financial resources, or of goods or JJA is the sum of the precipitation in the services valued at the cost of the donor. quarter: June, July, August (millimeters). Official development assistance (ODA) dis- SON is the sum of the precipitation in bursements for general environment protection the quarter: September, October, November are disbursements for general environment (millimeters). protection by bilateral, multilateral, and Carbon dioxide emissions per capita are other donors. e release of funds to or the those stemming from the burning of fossil purchase of goods or services for a recipient; fuels and the manufacture of cement. ey by extension, the amount thus spent. Dis- include carbon dioxide produced during con- bursements record the actual international sumption of solid, liquid, and gas fuels and transfer of financial resources, or of goods or gas flaring. services valued at the cost of the donor. Total greenhouse gas emissions is the com- bination of atmospheric gases, primarily Source: Data on forest area and deforesta- carbon dioxide, methane, and nitrous oxide, tion are from the Food and Agriculture Or- restricting some heat energy from escaping ganization's (FAO) Global Forest Resources from the earth's atmosphere directly back Assessment 2005. Data on freshwater re- into space. sources and withdrawals are from the World Emissions from land-use change and for- Resources Institute, supplemented by the estry include the following types of land- FAO's AQUASTAT data. Data on emissions use change and management activities: of organic water pollutants are from the (a) clearing of natural ecosystems for per- World Bank. Data on energy production and manent croplands (cultivation) (b) clear- use and combustible renewables and waste ing of natural ecosystems for permanent are from the International Energy Agency. pastures (no cultivation) (c) abandonment Data on carbon dioxide emissions are from of croplands and pastures with subsequent Carbon Dioxide Information Analysis Cen- recovery of carbon stocks to those of the ter, Environmental Sciences Division, Oak original ecosystem (d) shifting cultivation Ridge National Laboratory, in the U.S. state (swidden agriculture, repeated clearing, of Tennessee. Data on disbursements are abandonment, and reclearing of forests in from the Organization for Economic Coop- many tropical regions) (d) wood harvest eration and Development (OECD) (industrial wood as well as fuel wood). It is important to note that these estimates in- Table .. Climate Change clude the emissions of carbon from wood Annual average is the average annual of tem- products (burned, stored in longterm pools, perature (degree Celsius). decayed over time). Minimum monthly average is the mini- Agriculture value added is shown at fac- mum of the monthly averages of tempera- tor cost in current U.S. dollars divided by ture (degree Celsius). nominal gross domestic product. Agricul- Maximum monthly average is the maxi- ture corresponds to ISIC divisions 1­5 and mum of the monthly averages of tempera- includes forestry, hunting, and fishing, as ture (degree Celsius). well as cultivation of crops and livestock Annual precipitation is the average annual production. Value added is the net output of precipitation (millimeters). of a sector after adding up all outputs and Minimum monthly average is the mini- subtracting intermediate inputs. It is calcu- mum of the monthly averages of precipita- lated without making deductions for depre- tion in year 2000 (millimeters). ciation of fabricated assets or depletion and Maximum monthly average is the maxi- degradation of natural resources. e origin mum of the monthly averages of precipita- of value added is determined by the Inter- tion in year 2000 (millimeters). national Standard Industrial Classification DJF is the sum of the precipitation in the (ISIC), revision 3. Note: For VAB countries, quarter: December, January, February (mil- gross value added at factor cost is used as limeters). the denominator. 184 Africa Development Indicators 2008/09 Irrigated land refers to areas purposely 10. Labor, migration, and population provided with water, including land irrigated by controlled flooding. Cropland refers to ar- Table .. Labor Force Participation able land and permanent cropland. Labor force is people ages 15 and older who Flood is a significant rise of water level a meet the International Labor Organization stream, lake, reservoir or coastal region (ILO) definition of the economically active Drought is a long lasting event; triggered population. It includes both the employed by lack of precipitation. Drought is an ex- and the unemployed. While national prac- tended period of time characteristics by a tices vary in the treatment of such groups as deficiency in a region's water supply that is the armed forces and seasonal or part-time the supply that is the result of constantly be- workers, the labor force generally includes low average precipitation. Drought can lead the armed forces, the unemployed, and first- to losses to agriculture, affect inland naviga- time job seekers, but excludes homemakers tion and hydropower plants, and cause lack and other unpaid caregivers and workers in of drinking water and famine. the informal sector. Number of clinical malaria cases reported Participation rate is the percentage of the are the sum of cases confirmed by slide population ages 15­64 that is economically examination or RDT and probable and un- active, that is, all people who supply labor for confirmed cases (cases that were not test- the production of goods and services during ed but treated as malaria). NMCPs often a specified period. collect data on the number of suspected cases, those tested, and those confirmed. Source: International Labor Organization, Probable or unconfirmed cases are calcu- Global Employment Trends Model 2006, lated by subtracting the number tested Employment Trends Team. from the number suspected. Not all cases reported as malaria are true malaria cases Table .. Labor Force Composition since most health facilities lack appropriate Agriculture corresponds to division 1 (Inter- diagnostic services. e misdiagnosis may national Standard Industrial Classification, have led to under- or over-reporting ma- ISIC, revision 2) or tabulation categories A laria cases and missing diagnosis of other and B (ISIC revision 3) and includes hunting, treatable diseases. forestry, and fishing. Reported malaria deaths include all deaths Industry corresponds to divisions 2­5 in health facilities that are attributed to ma- (ISIC revision 2) or tabulation categories C­F laria, whether or not confirmed by micros- (ISIC revision 3) and includes mining and copy or by RDT. quarrying (including oil production), manu- facturing, construction, and public utilities Source: Data on temperatures and rain- (electricity, gas, and water). fall are from IRI (2008) based on CRU data- Services correspond to divisions 6­9 (ISIC sets. Data on carbon dioxide emissions are revision 2) or tabulation categories G­P (ISIC from Carbon Dioxide Information Analysis revision 3) and include wholesale and retail Center, Environmental Sciences Division, trade and restaurants and hotels; transport, Oak Ridge National Laboratory, in the U.S. storage, and communications; financing, in- state of Tennessee. Data on Agriculture val- surance, real estate, and business services; ue added are from the World Bank country and community, social, and personal services. desks. Data on irrigated land are from the Wageandsalariedworkersareworkerswho Food and Agriculture Organization, Produc- hold the type of jobs defined as paid employ- tion Yearbook and data files. Data on malaria ment jobs, where incumbents hold explicit are from World Health Organization Global (written or oral) or implicit employment Malaria Programme. Data on drought and contracts that give them a basic remunera- floods are from e International Emergen- tion that is not directly dependent on the cy Disasters Database or www.em-dat.net. revenue of the unit for which they work. Data on greenhouse gas emissions are from Self-employed workers are self-employed World Resources Institute and International workers with employees (employers), self- Energy Agency employed workers with without employees Technical Notes 185 Box 8 Climate Variability and Change in Sub-Saharan Africa In Africa, the number of weather-related disasters, droughts, and floods, has doubled over the last 25 years, and Africa has higher mortality rates from droughts than any other region. According to the IPCC (TAR 2001a, b, c and FAR 2007a, b, c, d), temperatures are rising and rainfall is becoming more unpredictable in Africa. Potential future climate changes in Africa include: an increase in global mean tempera- tures between 1.4°C and 5.8°C by 2100; temperature warming across the continent ranging from 0.2°C per decade to more than 0.5°C per decade, with warming expected to be greatest over semi-arid regions of the Sahara and central and South Africa; varying precipitation (southern Africa will become hotter and drier, while central Africa is expected to become hotter and wetter; some of the drylands may get higher rainfall, but in the form of heavier torrential rains); an increasing probability of the occurrence of extreme weather events: droughts, floods, and typhoons; and a projected rise in sea levels of 15­95 cm by 2100, with projections suggesting that the number of people at risk from coastal flooding could increase from 1 million in 1990 to 70 million in 2080, forcing major population movements. Along with rising temperatures, there is also likely to be an increase in rainfall variability, leading to more extreme precipitations and growing water stress. Crop-growing seasons will be affected. There are likely to be more intense and unpredictable weather events in coun- tries such as Kenya, Ethiopia, Malawi, Mozambique, and Madagascar. Despite the fact that Africa accounts for only 4% of global CO emissions (two-thirds of which are from land-use changes), Africa's 2 vulnerability to climate change is compounded by the fact that two-thirds of the continent is fragile desert or dryland. Agriculture, which contributes some 30% of GDP and employs 70% of the population, is mainly rainfed and highly sensitive to droughts and floods. Water storage capacity is the lowest in the world; only one in four people have access to electricity; and malaria, which is already the biggest killer in Africa, is spreading to higher elevations. Moreover, Africa's rapidly urbanizing population are vulnerable due to poorly defined property rights, weak land use planning, and informal settlements, frequently on land subject to erosion or flood plains. Finally, armed conflict, terms of trade shocks, and aid dependence add to the continent's vulnerability to climate change. Countries Most Affected by Climate-Related Threats Droughts Floods Storms Sea Level rise (1m) Agriculture Malawi Bangladesh Philippines All low-lying Island States Sudan Ethiopia China Bangladesh Vietnam Senegal Zimbabwe India Madagascar Egypt Zimbabwe India Cambodia Vietnam Tunisia Mali Mozambique Mozambique Moldova Indonesia Zambia Niger Laos Mongolia Mauritania Morocco Mauritania Pakistan Haiti China Niger Eritrea Sri Lanka Samoa Mexico India Sudan Thailand Tonga Myanmar Malawi Chad Vietnam China Bangladesh Algeria Kenya Benin Honduras Senegal Ethiopia Iran Rwanda Fiji Libya Pakistan Source: World Bank 2008b. Note: The typology is based on both absolute effects (i.e., total number of people affected) and relative effects (i.e., number affected as a share of GDP) (own-account workers), and members of pro- the two statuses, since the socioeconomic ducer cooperatives. Although the contribut- implications associated with each status can ing family workers category is technically be significantly varied. is practice follows part of the self-employed according to the that of the ILO's Key Indicators of the Labor classification used by the International La- Market. bor Organization (ILO), and could therefore Contributing family workers (unpaid work- be combined with the other self-employed ers) are workers who hold self-employment categories to derive the total self-employed, jobs as own-account workers in a market- they are reported here as a separate category oriented establishment operated by a related in order to emphasize the difference between person living in the same household. 186 Africa Development Indicators 2008/09 Source: Data are from the ILO's Key Indi- level of educational attainment, as a percent- cators of the Labor Market, fourth edition. age of the unemployed. Unemployment with secondary education, Table .. Unemployment female (% of female unemployment) is the un- Unemployment, total (% of total labor force ages employed by level of educational attainment, 15 and over) is the share of the labor force as a percentage of the unemployed.. ages 15 and over without work but available Unemployment with secondary education, for and seeking employment. male (% of male unemployment) is the unem- Unemployment, male (% of male labor ployed by level of educational attainment, as force ages 15 and over) is the share of the a percentage of the unemployed. labor force ages 15 and over without work Unemployment with secondary education but available for and seeking employ- (% of total unemployment) is the unemployed ment. by level of educational attainment, as a per- Unemployment, female (% of female labor centage of the unemployed force ages 15 and over) is the share of the la- Unemployment with tertiary education, fe- bor force ages 15 and over without work but male (% of female unemployment) is the unem- available for and seeking employment. ployed by level of educational attainment, as Unemployment, youth male (% of male la- a percentage of the unemployed. bor force ages 15­24) is the share of the labor Unemployment with tertiary education, force ages 15­24 without work but available male (% of male unemployment) is the unem- for and seeking employment. ployed by level of educational attainment, as Unemployment, youth total (% of total la- a percentage of the unemployed. bor force ages 15­24) is the share of the labor Unemployment with tertiary education (% force ages 15­24 without work but available of total unemployment) is the unemployed by for and seeking employment. level of educational attainment, as a percent- Unemployment, youth female (% of female age of the unemployed. labor force ages 15­24) is the share of the la- bor force ages 15­24 without work but avail- Source: International Labor Organiza- able for and seeking employment. tion, Key Indicators of the Labour Market Long-term unemployment (% of total un- database. employment) is the number of people with continuous periods of unemployment ex- Table .. Migration and Population tending for a year or longer, expressed as a Stock is the number of people born in a coun- percentage of the total unemployed. try other than that in which they live. It in- Long-term unemployment, female (% of fe- cludes refugees. male unemployment) is the number of people Net migration is the net average annual with continuous periods of unemployment number of migrants during the period, that extending for a year or longer, expressed as a is, the annual number of immigrants less the percentage of the total unemployed. annual number of emigrants, including both Long-term unemployment, male (% of male citizens and noncitizens. Data are five-year unemployment) is the number of people with estimates. continuous periods of unemployment ex- Workers remittances received comprise cur- tending for a year or longer, expressed as a rent transfers by migrant workers and wages percentage of the total unemployed. and salaries by nonresident workers. Unemployment with primary education, fe- Population is World Bank estimates, usu- male (% of female unemployment) is the unem- ally projected from the most recent popu- ployed by level of educational attainment, as lation censuses or surveys (mostly from a percentage of the unemployed. 1980­2004). Refugees not permanently Unemployment with primary education, settled in the country of asylum are gener- male (% of male unemployment) is the unem- ally considered to be part of the population ployed by level of educational attainment, as of their country of origin. a percentage of the unemployed. Fertility rate is the number of children Unemployment with primary education (% that would be born to a woman if she were of total unemployment) is the unemployed by to live to the end of her childbearing years Technical Notes 187 Box 9 Unemployment and the Process of Development Growing unemployment is a major concern for policy makers as it is often seen as a symptom of poor economic performance. This view reflects the experience of the developed world where growing unemployment is mostly associated with poor GDP performance and higher poverty incidence. However, the relationship appears to be reversed in the developing world, where richer countries experi- ence comparatively higher unemployment rates. This is evident in the figure below, which plots unemployment and GDP per capita for 64 developing countries for all available years. The positive long-run relationship--with an elasticity of 0.46--can be explained by the occurrence of two related phenomena: urbanization and ris- ing incomes.1 During the initial stages of development, economic growth ac- celerates the pace at which the rural population is drawn into urban areas in search of waged work. But employment is frequently hard to come by in an often demand-constrained, rationed waged sec- tor. Nevertheless, in low income settings with scarce safety nets, unemployment is a luxury that few urban labor workers can afford, so that most are forced to earn a meager living in low-productivity, self-employment activities. As growth raises incomes and wealth, unemployment becomes a viable option for a larger proportion of the labor force, informality decreases and unemployment rises. Therefore, using unemployment as an indicator of poor economic performance in poor developing countries is often misleading. The main concern of policy makers in developing countries should be with the low productivity/earnings of existing jobs rather than with the number of unemployed. This is not to say that the growing unemployment that is associated with urbanization should not be also ad- dressed. However, in doing so it is essential to remember that the priority of policies should lie with creating better employment opportuni- ties for the working poor. 1 e relation is statistically significant, and holds for different samples of countries. is relationship holds in the long run (several years). In the short and medium-run within countries, unemployment is countercyclical: it shrinks during economic booms and increases during reces- sion. and bear children in accordance with current 11. HIV/AIDS age-specific fertility rates. Age composition refers to the percentage Table .. HIV/AIDS of the total population that is in specific age Estimated number of people living with HIV/ groups. AIDS is the number of people in the relevant Dependency ratio is the ratio of depen- age group living with HIV. dents--people younger than 15 or older Estimated prevalence rate is the percentage than 64--to the working-age population-- of the population of the relevant age group those ages 15­64. who are infected with HIV. Depending on the Rural area population is calculated as the reliability of the data available, there may be difference between the total population and more or less uncertainty surrounding each the urban population. estimate. erefore, plausible bounds have Urban area population is midyear popu- been presented for each age-range rate (low lation of areas defined as urban in each and high estimate). country. Deaths due to HIV/AIDS are the estimat- ed number of adults and children that have Source: World Bank's World Development died in a specific year based on the modeling Indicators database. of HIV surveillance data using standard and appropriate tools. 188 Africa Development Indicators 2008/09 Box 10 The Demographic Transition in Sub-Saharan Africa The demographic transition, which is also referred to as the de- mographic revolution, is defined as the shift from a traditional demographic regime with a high semi-equilibrium (high mortality and high fertility) to a modern regime with a low semi-equilibrium (low mortality and low fertility). This shift, which is accompanied by transformations in the socio-economic context and by increas- ing urbanization, results in profound changes. The most important is the decline of mortality (the initial phase of the demographic transition), which usually occurs first because of improvements in survival conditions, especially during early childhood. These im- provements are most often exogenous, as exemplified by immu- nization campaigns and programs to control diarrhea and malaria. The ensuing reduction in mortality triggers rapid and currently ac- celerating rates of population growth. The onset of fertility decline, which often occurs with a time lag, marks the second phase of the demographic transition. Most regions of the world have undergone the demographic transition. This process started in Britain and France at the be- ginning of the eighteenth century, and then spread to the rest of Europe and the territories of European settlement and, finally, to the other parts of the world. In the 1960s and 1970s, population programs were implemented in Asia and Latin America, with the specific purpose of accelerating the fertility decline. Along with other socio- economic changes, the rate of population growth fell in these two regions from about 2.5% per year in the 1960s to less than 1.5% today. By contrast, the population growth rate in sub-Saharan Africa has remained at 2.5% per year over the past half century, except in southern Africa. The second phase of the demographic transition has started in the region, but it appears to be slower than in other parts of the world. The large number of young Africans (almost 2 out of 3 people are under 25), and the current high fertility levels (above 5 children per woman on average) imply that population growth will continue despite the HIV/AIDS epidemic. In mid-2008, Sub-Saharan Africa had an estimated 800 million people ­ 12.1% of the world's population. This share will increase to 18.2% in 2050, or about 1.8 billion people. This assumes an average of 2.5 children per African woman by 2050, according to the medium variant of the United Nations 2006 population projections. The medium variant implies a sharp reduction in fertility, but also substantial improvements in the expectancy of life at birth. However, this rapid decline in fertility levels has not yet occurred, except in southern Africa. As such, higher 2050 population figures, poten- tially reaching 2 billion or more, are plausible if fertility declines more slowly. The sustained population growth of the past 50 years in Sub-Saharan Africa has resulted in a very young age structure, as illustrated by the population pyramid (UN 2006 medium variant). Slower population growth in the region, thereby reducing the youth bulge, may help im- prove Africa's human capital formation (e.g., education and health), enhance youth employment opportunities, and ease poverty reduction efforts. As the East Asia experience has shown, a slower rate of population growth leads to more favorable dependency ratios -- limiting the number of child dependents on a comparatively larger, productive workforce. A slower population growth would also help reduce the pressures countries face regarding food security, land tenure, and environmental degradation. However, the question of how to accelerate fertility decline in sub-Saharan Africa, particularly in rural areas, remains difficult in a context of low education attainments, gender inequality, and logistical difficulties to set up effective programs. AIDS orphans are the estimated number Organization's 2008 Report on the Global of children who have lost their mother or AIDS Epidemic. both parents to AIDS before age 17 since the epidemic began in 1990. Some of the or- 12. Malaria phaned children included in this cumulative total are no longer alive; others are no longer Table .. Malaria under age 17. Population is the total population based on the de facto definition of population, which Source: e Joint United Nations Pro- counts all residents regardless of legal status gramme on HIV/AIDS and the World Health or citizenship, except for refugees not per- Technical Notes 189 Box 11 HIV Prevalence and Incidence In order to understand reductions in HIV prevalence, it is important to distinguish between HIV prevalence and HIV incidence. HIV preva- lence includes all HIV infections, new and old. HIV incidence is limited to new HIV infections, acquired in the last year. Because of the long duration between HIV infection and death, HIV prevalence trends lag HIV incidence trends by several years, as shown below. U.S. Bureau of the Census models suggest that HIV incidence began to fall in several countries in Eastern and Southern Africa in the late 1980s and early 1990s, as shown below. Incidence is a better measure of HIV trends and program effec- tiveness, because it measures new infections. However, it is much harder to measure. There are three major approaches, each with limitations. First, we can establish a cohort, follow HIV-negative people over time and as they seroconvert, establish the HIV incidence. However, cohorts are expensive to establish and usually require a major research study. Moreover, the results are limited to the spe- cific cohort in question. Second, we can use techniques to identify recent infections in cross-sectional data, such as the detuned elisa or the BED test. However, these are not considered accurate enough to establish incidence rates. Third, we can model incidence from several years of preva- lence data. If we have comprehensive, regular prevalence data, this is an attractive option. In summary, incidence is scientifically a better measure, but is rarely available. Figure 2 Modeled HIV Incidence Trends in Africa 12 Botswana:Tot Cameroon:Tot 10 Congo (Kinshasa):Tot Cote d'Ivoire:Tot 8 Ethiopia:Tot Kenya:Tot rate Lesotho:Tot 6 Mozambique:Tot incidence Nigeria:Tot 4 Rwanda:Tot South Africa:Tot 2 Swaziland:Tot Tanzania:Tot Uganda:Tot 0 Zambia:Tot 1980 1983 1986 1989 1992 1995 1998 2001 Zimbabwe:Tot Year Incidence Prevalence Source: US Bureau of Census 190 Africa Development Indicators 2008/09 manently settled in the country of asylum, based on World Health Organization (WHO) who are generally considered part of the estimates. Data on under-five mortality are population of their country of origin. e harmonized estimates of the WHO, United values shown are midyear estimates. Nations Children's Fund, and the World Endemic risk of malaria is the percentage Bank, based mainly on household surveys, of the population living in areas with signifi- censuses, and vital registration, supplement- cant annual transmission of malaria, be it ed by World Bank estimates based on house- seasonal or perennial. hold surveys and vital registration. Data on Epidemic risk of malaria is the percentage insecticide-treated bednet use are from De- of the population living in areas prone to mographic and Health Surveys and Multiple distinct interannual variation, with no trans- Indicator Cluster Surveys. mission taking place at all in some years. Negligible risk of malaria is the percentage 13. Capable states and partnership of the population living in areas where ma- laria is ordinarily not present and where the Table .. Aid and Debt Relief risk of malaria outbreaks is negligible. Net aid from all donors is net aid from the Deaths due to malaria are the number of Organization for Economic Co-operation malaria deaths per 100,000 people. and Development's (OECD), Development Under-five mortality rate is the probability Assistance Committee (DAC), non-DAC bi- that a newborn baby will die before reaching lateral (Organization of Petroleum Export- age 5, if subject to current age-specific mor- ing Countries [OPEC], the former Council tality rates. e probability is expressed as a for Mutual Economic Assistance [CMEA] rate per 1,000. countries, and China [OECD data]), and Children sleeping under insecticide-treated multilateral donors. OPEC countries are bednets is the percentage of the children un- Algeria, Iran, Iraq, Kuwait, Libya, Nigeria, der 5 with access to an insecticide-treated Qatar, Saudi Arabia, the United Arab Emir- bednet to prevent malaria. ates, and Venezuela. e former CMEA Children with fever receiving any antimalar- countries are Bulgaria, Czechoslovakia, the ial drugs are the percentage of children under former German Democratic Republic, Hun- age 5 in malaria-risk areas with fever being gary, Poland, Romania, and the former So- treated with antimalarial drugs. viet Union). Children with fever receiving effective antima- Net aid from DAC donors is net aid from larial drugs are the percentage of children un- OECD's DAC donors, which include Austra- der age 5 in malaria-risk areas with fever being lia, Austria, Belgium, Canada, Denmark, Fin- treated with effective antimalarial drugs. land, France, Germany, Greece, Ireland, Italy, Pregnant women receiving two doses of Japan, Luxembourg, the Netherlands, New intermittent preventive treatment are the Zealand, Norway, Portugal, Spain, Sweden, number of pregnant women who receive at Switzerland, the United Kingdom, and the least two preventive treatment doses of an United States. effective antimalarial drug during routine Net aid from non-DAC donors is net aid antenatal clinic visits. is approach has from OECD's non-DAC donors, which in- been shown to be safe, inexpensive, and ef- clude Czech Republic, Hungary, Iceland, Is- fective. rael, Korea Republic, Kuwait, Poland, Saudi Arabia, Slovak Republic, Taiwan China, ai- Source: Data on population are from the land, Turkey and United Arab Emirates and World Bank's World Development Indicators other donors. database. Data on risk of malaria, children Net aid from multilateral donors is net aid with fever receiving antimalarial drugs, and from multilateral sources, such as the Afri- pregnant women receiving two doses of in- can Development Fund, the European De- termittent preventive treatment are from velopment Fund for the Commission of the Demographic Health Surveys, Multiple Indi- European Communities, the International cator Cluster Surveys, and national statisti- Development Association, the International cal offices. Data on deaths due to malaria are Fund for Agricultural Development, Arab from the United Nations Statistics Division and OPEC financed multilateral agencies, Technical Notes 191 and UN programs and agencies. Aid flows Net ODA aid per capita is calculated by from the International Monetary Fund's dividing the nominal total net aid (net dis- (IMF) Trust Fund and Structural Adjustment bursements of loans and grants from all Facility are also included. UN programs and official sources on concessional financial agencies include the United Nations Techni- terms) by midyear population. ese ratios cal Assistance Programme, the United Na- offer some indication of the importance of tions Development Programme, the United aid flows in sustaining per capita income and Nations Office of the High Commissioner consumption levels, although exchange rate for Refugees, the United Nations Children's fluctuations, the actual rise of aid flows, and Fund, and the World Food Programme. Arab other factors vary across countries and over and OPEC financed multilateral agencies in- time. clude the Arab Bank for Economic Develop- Net aid as a share of gross capital formation ment in Africa, the Arab Fund for Economic is calculated by dividing the nominal total and Social Development, the Islamic Devel- net aid by gross capital formation. ese opment Bank, the OPEC Fund for Interna- data highlight the relative importance of tional Development, the Arab Authority for the indicated aid flows in maintaining and Agricultural Investment and Development, increasing investment in these economies. the Arab Fund for Technical Assistance to e same caveats mentioned above apply to African and Arab Countries, and the Islamic their interpretation. Furthermore, aid flows Solidarity Fund. do not exclusively finance investment (for Net private aid is private transactions bro- example, food aid finances consumption), ken down into direct investment, portfolio and the share of aid going to investment var- investment and export credits (net). Private ies across countries. transactions are those undertaken by firms Net aid as a share of imports of goods and and individuals resident in the reporting services is calculated by dividing nominal to- country. Portfolio investment corresponds tal net aid by imports of goods and services. to bonds and equities. Inflows into emerg- Net aid as a share of central government ing countries' stocks markets, are, however, expenditure is calculated by dividing nominal heavily understated. Accordingly, the cover- total net aid by central government expen- age of portfolio investment differs in these diture. regards from the coverage of bank claims, Heavily Indebted Poor Country (HIPC) which include indistinguishably export cred- Debt Initiative decision point is the date at it lending by banks. e bank claims data which a HIPC with an established track re- represent the net change in banks' claims cord of good performance under adjustment after adjustment to eliminate the effect of programs supported by the International changes in exchange rates. ey are there- Monetary Fund and the World Bank com- fore a proxy for net flow data, but are not mits to undertake additional reforms and to themselves a net flow figure. ey differ in develop and implement a poverty reduction two further regards from other OECD data. strategy. First, they relate to loans by banks resident Cereal food aid shipments represent a in countries which report quarterly to the transfer of food commodities from donor Bank for International Settlements (BIS). to recipient countries on a total-grant basis. Secondly, no adjustment has been made to Processed and blended cereals are converted exclude short-term claims. into their grain equivalent by applying the Net aid as a share of gross domestic product conversion factors included in the Rule of (GDP) is calculated by dividing the nominal Procedures under the 1999 Food Aid Con- total net aid from all donors by nominal vention to facilitate comparisons between GDP. For a given level of aid flows, devalu- deliveries of different commodities. For ce- ation of a recipient's currency may inflate reals, the period refers to July/June, begin- the ratios shown in the table. us, trends ning in the year shown. for a given country and comparisons across HIPC Debt Initiative completion point is countries that have implemented different the date at which the country successfully exchange rate policies should be interpreted completes the key structural reforms agreed carefully. on at the decision point, including develop- 192 Africa Development Indicators 2008/09 ing and implementing its poverty reduction tected through disclosure of ownership and strategy. e country then receives the bulk financial information. of debt relief under the HIPC Initiative with- Director liability index measures a plain- out further policy conditions. tiff's ability to hold directors of firms liable Debt service relief committed is the amount for damages to the company). of debt service relief, calculated at the deci- Shareholder suits index measures share- sion point, that will allow the country to holders' ability to sue officers and directors achieve debt sustainability at the completion for misconduct. point. Investor protection index measures the degree to which investors are protected Source: Net ODA data are OECD and through disclosure of ownership and finan- World Bank data. Data on food aid ship- cial information regulations. ments from 1970/71 to 1990/91 was com- Number of tax payments is the number of piled by FAO from the information provid- taxes paid by businesses, including electronic ed by donor countries, and complemented filing. e tax is counted as paid once a year by data provided by the FAO Consultative even if payments are more frequent. Sub-Committee on Surplus Disposal, the Time to prepare, file, and pay taxes is the World Food Programme (WFP), the Inter- number of hours it takes to prepare, file, and national Wheat Council, OECD, and other pay (or withhold) three major types of taxes: international organizations. From 1990/91 the corporate income tax, the value added or to date, the information on food aid ship- sales tax, and labor taxes, including payroll ments has been provided to FAO exclusive- taxes and social security contributions. ly by WFP. Total tax payable is the total amount of taxes payable by the business (except for Table .. Capable States labor taxes) after accounting for deductions Court system is the percentage of firms believ- and exemptions as a percentage of gross ing the court system is fair, impartial and un- profit. For further details on the method corrupted as a major constraint. e compu- used for assessing the total tax payable, see tation of the indicator is based on the rating the World Bank's Doing Business 2006. of the obstacle as a potential constraint to Extractive Industries Transparency Initia- the current operations of the establishment. tive(EITI)Endorsedindicateswhetheracoun- Crime is the percentage of firms who try has implemented or endorsed the EITI, ranked crime, theft, and disorder as a major a multi-stakeholder approach to increasing constraint. e computation of the indicator governance and transparency in extractive is based on the rating of the obstacle as a po- industries. It includes civil society, the pri- tential constraint to the current operations vate sector, and government and requires a of the establishment. work plan with timeline and budget to en- Number of procedures to enforce a con- sure sustainability, independent audit of tract is the number of independent actions, payments and disclosure of revenues, publi- mandated by law or courts that demand in- cation of results in a publicly accessible man- teraction between the parties of a contract ner, and an approach that covers all compa- or between them and the judge or court nies and government agencies. EITI supports officer. improved governance in resource-rich countries Time required to enforce a contract is the through the verification and full publication of number of calendar days from the filing of company payments and government revenues the lawsuit in court until the final determi- from oil, gas, and mining. EITI is a global ini- nation and, in appropriate cases, payment. tiative and the EITI Secretariat has developed Cost to enforce a contract is court and at- an EITI Source Book that provides guidance for torney fees, where the use of attorneys is countries and companies wishing to implement mandatory or common, or the cost of an the initiative (http://www.eitransparency.org/ administrative debt recovery procedure, ex- section/abouteiti). pressed as a percentage of the debt value. EITI report produced indicates which the Protecting investors disclosure index mea- country has publicly released an EITI report. sures the degree to which investors are pro- is appears only for those in which a public Technical Notes 193 report is released. Generally, the production Corruption Perceptions Index transparency of a report is subsequent to the adoption of index is the annual Transparency Interna- the EITI principles. tional corruption perceptions index, which ranks more than 150 countries in terms of Source: Data on investment climate con- perceived levels of corruption, as determined straints to firms are based on enterprise sur- by expert assessments and opinion surveys. veys conducted by the World Bank and its partners during 2001­05 (http://rru.world- Source: Data are from the World Bank In- bank.org/EnterpriseSurveys). Data on en- stitute's Worldwide Governance Indicators forcing contracts, protecting investors, and database, which relies on 33 sources, includ- regulation and tax administration are from ing surveys of enterprises and citizens, and the World Bank's Doing Business project expert polls, gathered from 30 organizations (http://rru.worldbank.org/DoingBusiness/). around the world. Data on the EITI are from the EITI website, www.eitransparency.org. Data on corruption Table .. Country Policy and perceptions index are from Transparency In- Institutional Assessment Ratings ternational (www.transparency.org/policy_ e Country Policy and Institutional Assess- research/surveys_indices/cpi). ment (CPIA) assess the quality of a country's present policy and institutional framework. Table .. Governance and "Quality" means how conducive that frame- Anticorruption Indicators work is to fostering sustainable, poverty- Voice and accountability measures the extent reducing growth and the effective use of de- to which a country's citizens are able to par- velopment assistance. e CPIA is conducted ticipate in selecting their government and to annually for all International Bank for Recon- enjoy freedom of expression, freedom of as- struction and Development and International sociation, and a free media. Development Association borrowers and has Political stability and absence of violence evolved into a set of criteria grouped into four measures the perceptions of the likelihood clusters with 16 criteria that reflect a balance that the government will be destabilized or between ensuring that all key factors that fos- overthrown by unconstitutional or violent ter pro-poor growth and poverty alleviation means, including domestic violence or ter- are captured, without overly burdening the rorism. evaluation process. Government effectiveness measures the · Economic management quality of public services, the quality and · Macroeconomic management assess- degree of independence from political pres- es the quality of the monetary, ex- sures of the civil service, the quality of policy change rate, and aggregate demand formulation and implementation, and the policy framework. credibility of the government's commitment · Fiscal policy assesses the short- and to such policies. medium-term sustainability of fis- Regulatory quality measures the ability of cal policy (taking into account mon- the government to formulate and implement etary and exchange rate policy and sound policies and regulations that permit the sustainability of the public debt) and promote private sector development. and its impact on growth. Rule of law measures the extent to which · Debt policy assesses whether the agents have confidence in and abide by the debt management strategy is con- rules of society, in particular the quality of ducive to the minimization of bud- contract enforcement, the police, and the getary risks and ensures long-term courts, as well as the likelihood of crime and debt sustainability violence. · Structural policies Control of corruption measures the extent · Trade assesses how the policy to which public power is exercised for private framework fosters trade in goods. gain, including petty and grand forms of cor- It covers two areas: trade regime ruption, as well as "capture" of the state by restrictiveness--which focuses on elites and private interests. the height of tariffs barriers, the 194 Africa Development Indicators 2008/09 extent to which nontariff barri- vulnerable, or have unequal access ers are used, and the transparency to services and opportunities are and predictability of the trade identified; a national development regime; and customs and trade strategy with explicit interventions facilitation--which includes the to assist those individuals, groups, extent to which the customs ser- and localities has been adopted; and vice is free of corruption, relies on the composition and incidence of risk management, processes duty public expenditures are tracked sys- collections and refunds promptly, tematicallyandtheirresultsfedback and operates transparently. into subsequent resource allocation · Financial sector assesses the struc- decisions. e assessment of the ture of the financial sector and the revenue collection dimension takes policies and regulations that affect into account the incidence of major it. It covers three dimensions: fi- taxes--for example, whether they nancial stability; the sector's ef- are progressive or regressive--and ficiency, depth, and resource mo- their alignment with the poverty bilization strength; and access to reduction priorities. When relevant, financial services. expenditure and revenue collection · Business regulatory environment as- trends at the national and sub- sesses the extent to which the legal, national levels should be consid- regulatory, and policy environment ered. e expenditure component helps or hinders private business in receives two-thirds of the weight in investing,creatingjobs,andbecom- computing the overall rating. ing more productive. e emphasis · Building human resources assesses is on direct regulations of business the national policies and public activity and regulation of goods and private sector service delivery and factor markets. It measures that affect access to and quality of three subcomponents: regulations health and nutrition services, in- affecting entry, exit, and competi- cluding: population and reproduc- tion; regulations of ongoing busi- tive health; education, early child- ness operations; and regulations of hood development, and training factor markets (labor and land). and literacy programs; and preven- · Policies for social inclusion and equity tion and treatment of HIV/AIDS, · Gender equality assesses the extent tuberculosis, and malaria. to which the country has enacted · Social protection and labor assess and put in place institutions and government policies in the area of programs to enforce laws and poli- social protection and labor market cies that promote equal access for regulation, which reduce the risk men and women to human capital of becoming poor, assist those who development, and to productive are poor to better manage further and economic resources and that risks, and ensure a minimal level give men and women equal status of welfare to all people. Interven- and protection under the law. tions include social safety net pro- · Equity of public resource use assesses grams, pension and old age savings the extent to which the pattern of programs, protection of basic labor public expenditures and revenue standards, regulations to reduce collection affects the poor and is segmentation and inequity in la- consistent with national poverty re- bor markets, active labor market duction priorities. e assessment programs (such as public works or of the consistency of government job training), and community driv- spending with the poverty reduc- en initiatives. In interpreting the tion priorities takes into account guidelines it is important to take the extent to which individuals, into account the size of the econo- groups, or localities that are poor, my and its level of development. Technical Notes 195 · Policies and institutions for envi- istrative departments, including ronmental sustainability assess autonomous agencies. It excludes the extent to which environmen- the armed forces, state-owned en- tal policies foster the protection terprises, and sub national govern- and sustainable use of natural ment. resources and the management · Efficiency of revenue mobilization of pollution. Assessment of envi- assesses the overall pattern of rev- ronmental sustainability requires enue mobilization--not only the multidimensional criteria (that is, tax structure as it exists on paper, for air, water, waste, conservation but revenue from all sources as management, coastal zones man- they are actually collected. agement, and natural resources · Transparency, accountability, and management). corruption in the public sector assess · Public sector management and the extent to which the executive institutions branch can be held accountable · Property rights and rule-based gover- for its use of funds and the results nance assess the extent to which pri- of its actions by the electorate and vate economic activity is facilitated by the legislature and judiciary, by an effective legal system and and the extent to which public rule-based governance structure in employees within the executive which property and contract rights are required to account for the are reliably respected and enforced. use of resources, administrative ree dimensions are rated sepa- decisions, and results obtained. rately: legal basis for secure property Both levels of accountability are and contract rights; predictability, enhanced by transparency in transparency, and impartiality of decision-making, public audit in- laws and regulations affecting eco- stitutions, access to relevant and nomic activity, and their enforce- timely information, and public ment by the legal and judicial sys- and media scrutiny. tem; and crime and violence as an impediment to economic activity. Source: World Bank's Country Policy and · Quality of budgetary and financial Institutional Assessment 2005. management assesses the extent to which there is a comprehen- Table .. Polity Indicators sive and credible budget, linked to Polity score is computed by subtracting the policy priorities; effective financial Institutionalized autocracy score from the In- management systems to ensure stitutionalized democracy score; the resulting that the budget is implemented unified polity scale ranges from +10 (strong- as intended in a controlled and ly democratic) to ­10 (strongly autocratic). predictable way; and timely and Institutionalized democracy is conceived accurate accounting and fiscal re- as three essential, interdependent ele- porting, including timely and au- ments. One is the presence of institutions dited public accounts and effective and procedures through which citizens can arrangements for follow-up. express effective preferences about alter- · Quality of public administration as- native policies and leaders. Second is the sesses the extent to which civilian existence of institutionalized constraints central government staffs (includ- on the exercise of power by the executive. ing teachers, health workers, and ird is the guarantee of civil liberties to police) are structured to design and all citizens in their daily lives and in acts implement government policy and of political participation. Other aspects of deliver services effectively. Civilian plural democracy, such as the rule of law, central government staffs include systems of checks and balances, freedom of the central executive together with the press, and so on are means to, or spe- all other ministries and admin- cific manifestations of, these general prin- 196 Africa Development Indicators 2008/09 ciples. We do not include coded data on civ- Age dependency ratio is the ratio of de- il liberties. is is an additive eleven-point pendents--people younger than 15 or older scale (0­10). e operational indicator of than 64--to the working-age population-- democracy is derived from codings of the those ages 15­64. competitiveness of political participation Average household size is the average num- using some weights. ber of people in a household. Institutionalized autocracy is a pejorative Monogamous male is a household head- term for some very diverse kinds of politi- ed by man who has no more than one spouse cal systems whose common properties are (wife). a lack of regularized political competition Polygamous male is a household headed by and concern for political freedoms. e a man who has more than one spouse (wife). term Autocracy is used and defined opera- Single male is a household headed by a tionally in terms of the presence of a dis- man who is widowed or divorced or who has tinctive set of political characteristics. In never married. mature form, autocracies sharply restrict De facto female refers to a household with- or suppress competitive political participa- out a resident male head or where the male tion. eir chief executives are chosen in head is not present and the wife is the head a regularized process of selection within by default and serves as the main decision the political elite, and once in office they maker in his absence or a household where exercise power with few institutional con- the resident male head has lost most of his straints. Most modern autocracies also ex- functions as the economic provider due to ercise a high degree of directiveness over infirmity, inability to work, or the like. social and economic activity, but we regard De jure female refers to a household head- this as a function of political ideology and ed by a woman who is widowed, separated, choice, not a defining property of autocra- or divorced or who has never been married. cy. Social democracies also exercise relative- Mean monthly expenditure is the average ly high degrees of directiveness. We prefer monthly expenditure on both food and non- to leave open for empirical investigation food items. the question of how Autocracy, Democracy, Mean monthly share on food is total and directiveness (performance) have cova- monthly food expenditure and food own ried over time. consumption as a share of total household expenditure. Source: Polity IV Project Political Regime Mean monthly share on health is total Charateristics and Transistions, 1800­2006, health expenditure (consultation, medical Center for Systemic Peace www.systemic- procedure, among other) as a share of total peace.org/polity household expenditure. Health expenditure excludes hospitalization. 14. Household welfare Mean monthly share on education is total education expenditure (tuition, transport, e questions asked in household surveys and the like) as a share of total household vary by country. Quintiles are derived by expenditure ranking weighted sample population by area Primary school within 30 minutes is the of residence (rural and urban) and per capita share of households that live within 30 min- expenditure. Two sets of quintiles are calcu- utes of a primary school. lated, one for rural and one for urban. Each Net primary enrollment rate is the ratio of quintile contains an equal number of people children of a country's official primary school rather than households. e definitions of age who are enrolled in primary school to rural and urban also vary by country. the total population of the corresponding of- Sample size is the number of households ficial primary school age. Primary education surveyed in the country. provides children with basic reading, writ- Total population is the weighted estimate ing, and mathematics skills along with an of all the surveyed population in the coun- elementary understanding of such subjects try based on the survey--that is, it is the as history, geography, natural science, social weighted sample population. science, art, and music. Technical Notes 197 Box 12 Conflict, Fragility and Democracy Countries that have experienced conflict or have weak institutions face unique challenges to development. Conflicts often result in tremendous loss of life, destroyed infrastructure, and losses in hu- man capital due to interrupted education and displacement that can slow or reverse the progress of developing countries. Such setbacks may increase the likelihood of conflict relapse, resulting in a "conflict trap" wherein violence begets poverty and visa versa, perpetuating a cycle of conflict. Likewise, the effects of conflict can weaken or destroy institutions and contribute to what could be called a "fragility trap." While it is difficult to separate out the causes of conflict and fragility as they reflect systemic collapses of govern- ments and social order, analysis demonstrates that these afflictions are related. It is also possible that one way out of these traps may be through representative government reflected by democracy. Conflict While definitions of conflict may vary, measures of conflict usually involve the number of casualties or battle deaths resulting from violence. The common academic definition of a major conflict is violence between at least two organized groups that results in more than 1000 battle deaths in a calendar year, with more than 5% of the battle deaths from each side. Minor conflict is defined as violence with more than 25 battle deaths per annum. Persistent minor conflict is violence with more than 25 battle deaths per annum and more than 1000 battle deaths over the length of the conflict. Generally, countries are defined as "conflict-affected" if they have had a major or persistent minor conflict in the last ten years. Fragility The term "fragile state" is intended to capture both the instability and the delicate nature of the limited capacity, unstable gover- nance, and fledgling institutions often found in these environments. Measures of fragility reflect the quality of institutions in these states; therefore the World Bank defines low-income countries as "fragile" if they have a Country Policy and Institutional Assessment (CPIA) score of 3.2 or below. Although a threshold is used to define fragility, it should be noted that the CPIA is an index, an average of twenty different subjective measures and therefore an imperfect measure of state capacity. Thus the term "fragile" is intended only as a guideline for iden- tifying those countries that may have special needs due to particularly low institutional capacity and a low IDA allocation out of the entire spectrum of developing countries that are the Bank's clients. Democracy Researchers of political systems use many sources to measure the level and quality of democratic institutions. The Polity Index measures the constraints on the executive branch of government and the quality of competition for elected positions yielding a score on a scale of ­10 to 10. A score of ­10 on the Polity Index suggests a very autocratic state and a score of 10 on the Polity index reflects a very democratic state. Typically, countries with a Polity score over 3 are considered "democratic" while those with a polity score under ­3 are considered "autocratic." The relationship between institutional capacity (measured by CPIA) and democracy is strong. Countries with highly representative and freely and fairly elected governments reflected by high Polity scores often have better quality institutions and improved capacity for service delivery reflected by higher CPIA scores. This relationship is demonstrated by the trendline in the figure below for African states. Additionally, the figure demonstrates the interrelationship between conflict and fragility traps that can contribute to persistent poverty. Of the 22 low-income African states with a CPIA score 3.2 or below in 2006, 14 (64%) were conflict-affected.1 Additionally, of these same 22 countries, only seven (32%) had democratized (had a Polity Score>3). All six low-income countries with low CPIA and Polity greater than 3: (Burundi, DRC, Guinea-Bissau, Liberia (not shown in the figure), Sierra Leone and Nigeria) were conflict-affected in the last decade. Source: World Bank and Polity IV Project Political Regime. 1Four countries are not shown in the figure because they are missing CPIA or Polity data or both (Somalia, Cote d'Ivoire, Liberia, São Tomé and Principe). 198 Africa Development Indicators 2008/09 Net secondary enrollment rate is the ratio range of local organizations in countries that of children of a country's official secondary are recipients of local and foreign assistance. school age who are enrolled in secondary It is a voluntary non-profit grouping of in- school to the total population of the cor- dividuals with a purpose of enhancing the responding official secondary school age. legitimate economic, social and/or cultural Secondary education completes the provi- development organization. sion of basic education that began at the Other is other types of health providers primary level and aims to lay the founda- that cannot be classified by the categories tions for lifelong learning and human de- described above. velopment by offering more subject- or Birth assisted by trained staff are the per- skill-oriented instruction using more spe- centage of deliveries attended by personnel cialized teachers. trained to give the necessary supervision, Tertiary enrolment rate is the number of care, and advice to women during pregnan- students currently in tertiary education per cy, labor, and the postpartum period; to con- 10,000 people. Tertiary education, whether duct deliveries on their own; and to care for or not to an advanced research qualification, newborns. normally requires, as a minimum condition Immunization coverage, 1-year-olds, is the of admission, the successful completion of percentage of children ages 12­23 months at education at the secondary level. the time of survey who received one dose of Adult literacy rate is the percentage of Bacille Calmette Guerin vaccine, three doses adults ages 15 and older who can both read of polio vaccine, three doses of diphtheria, and write a simple sentence in any language. pertussis, and tetanus vaccine, and one does Youth literacy rate is the percentage of of measles vaccine. youth ages 15­24 who can both read and Measles immunization coverage, 1-year- write a simple sentence in any language. olds, is the percentage of children ages 12­23 Health center less than 1 hour away is the months at the time of survey who received percentage of the population living less than a dose of measles vaccine. A child is consid- 1 hour away from a health center. ered adequately immunized against measles Health center less than 5 km away is the after receiving one dose of vaccine. percentage of the population living less than Stunting is the percentage of children un- 5 kilometers away from a health center der age 5 whose height for age is more than Morbidity is the percentage of the popula- two standard deviations below the median tion who were sick or injured within a given for the international reference population number of weeks before the survey. ages 6­59 months. e reference popula- Health care provider consulted when sick is tion, adopted by the World Health Organiza- the percentage of sick people who took any tion in 1983, is based on children from the remedial action when sick. United States, who are assumed to be well Type of health care provider consulted is the nourished. type of facility visited by a sick household Wasting is the percentage of children member. Public includes fully government- under age 5 whose weight for height is owned as well as semi-public health facilities. more than two standard deviations below Private, modern medicine, is facilities set the median for the international reference up with profit as their main focus and in- population ages 6­59 months. e reference cludes private doctors. Private, traditional population, adopted by the World Health healers refer to health care providers whose Organization in 1983, is based on children knowledge, skills, and practices are based on from the United States, who are assumed to the experiences indigenous to different cul- be well nourished. tures and whose services are directed toward Underweight is the percentage of chil- the maintenance of health, as well as the dren under age 5 whose weight for age is prevention, diagnosis, and improvement of more than two standard deviations below physical and mental illness. the median for the international reference Missionary is one managed and support- population ages 6­59 months. e reference ed by a religious organization. A Non-Govern- population, adopted by the World Health mental Organization (NGO) includes a wide Organization in 1983, is based on children Technical Notes 199 from the United States, who are assumed to World Bank, the African Development Bank, be well nourished. and the United Nations through the United Access to sanitation facilities is the percent- Nations Development Program. Sample size age of the population with at least adequate selected about 8,500 households. access to excreta disposal facilities that can effectively prevent human, animal, and in- Table .. Cameroon Household Sur- sect contact with excreta. Improved facilities vey, range from simple but protected pit latrines Household is people who live under the same to flush toilets with a sewerage connection. roof, take their meals together or in little e excreta disposal system is considered ad- groups, and put some or all of their incomes equate if it is private or shared (but not pub- together for the group's spending purposes, lic) and if it hygienically separates human at the head of household's discretion. excreta from human contact. To be effective, facilities must be correctly constructed and Source:Cameroon'sBureauCentraldesRe- properly maintained. censements et des Enquêtes of the Direction Water source less than 5 km away is the de la Statistique et de la Comptabilité carried percentage of the population living less than out the Enquête Camerounaise auprès des 5 kilometers away from a water source. Ménages in 2001. Data collection between Market less than 5 km away is the percent- October 2001 and December 2001. Sample age of the population living less than 5 kilo- size selected about 12,000 households. meters away from a market. Access to improved water source refers to Table .. Ethiopia Household Survey, the percentage of the population with rea- / sonable access to an adequate amount of Household is a person or a group of people water from an improved source, such as a who live under the same roof, share the household connection, public standpipe, same meals, and recognize one person as the borehole, protected well or spring, or rainwa- head. ter collection. Unimproved sources include vendors, tanker trucks, and unprotected Source: e 1999/2000 Household In- wells and springs. Own tap is a household come, Consumption, and Expenditure Sur- water connection. Other piped is a public wa- vey was carried out by the Central Statistical ter connection. Well, protected, is a ground Office. e data collection process was car- water source. ried out from June 1999 to February 2000. Traditional fuel use is the percentage of Sample size selected was about 26,000 for the population using traditional fuels such the Income and Expenditure survey. as firewood and charcoal as the main source of cooking fuel. Table .. Liberia Household Survey, Table .. Burkina Faso Household Household is a person living alone or a group Survey, of people, either related or unrelated, who Household is the basic socioeconomic unit live together as a single unit in the sense that in which the different members--related or they have common housekeeping arrange- living in the same house or property--put ments (that is, share or are supported by a together their resources and jointly meet common budget). Someone who did not live their basic needs, including food, under the with the household during the survey period authority of one person who is recognized as was not counted as a current member of the the head. household. Source: Burkina Faso's Institut National Source: Liberia Core Welfare Indicators de la Statistique et de la Démographie carried Questionnaire (CWIQ) Survey. Sample size out the Enquête Prioritaire II sur les Condi- selected about 3,600 households. tions de Vie des Ménages au Burkina. Data were collected in 2003. e project was fund- Table .. Malawi Household Survey, ed by the government of Burkina Faso, the / 200 Africa Development Indicators 2008/09 Household is a person living alone or a group Literacy measures the number of people of people, either related or unrelated, who with the ability to read and write either in live together as a single unit in the sense that English or any of the local languages. they have common housekeeping arrange- ments (that is, share or are supported by a Source: e Federal Office of Statistics, common budget). Someone who did not live Abuja, of Nigeria carried out the Nigeria with the household during the survey period Living Standards Survey, an integrated sur- was not counted as a current member of the vey. Data were collected between September household. 2003 and August 2004. Sample size selected Literacy measures the ability to read and was about 22,000 households. write a simple sentence for those who had not attended school in the past two months Table .. São Tomé and Principe and was defined based on education attain- Household Survey, / ment for those who had attended school in Household is the set of people, related or not, the past two months. who live together under the same roof, put their resources together, and address as a Source: e Malawi National Statistics unit their primary needs, under the author- Office carried out the Integrated Household ity of one person whom they recognize as Survey in 2004/5. Sample size selected about the head of the household. 11,280 households. Literacy measures the number of people with the ability to read and write a simple Table .. Niger Household Survey, sentence. Household is the set of people who partly Source: e Instituto Nacional de Estatis- or totally shared their expenditures, had tica of the Ministério de Planomento, Finan- not been absent for more than 6 of the 12 ças e Cooperaçao carried out the Enquête sur months preceding the survey, and were not les Conditions de Vie des Ménages in 2000. domestic help. In the case of polygamous e project was financed by the government households, each wife and her children were of São Tomé and Principe with assistance considered to be a separate household. from the African Development Bank and the Literacy measures the number of people United Nations Development Programme. with ability to read and write in Portuguese. Technical assistance was provided by the International Labour Organization. Data Source: Direction de la Statistique et des collected between November 2000 and Feb- comptes nationaux carried out the Enquete ruary 2001 and sample size selected about Nationale sur les Conditions de vie des Me- 5,200 households. nages from April 14 to July 11, 2005. Sample size selected about 6,690 households. Table .. Sierra Leone Household Survey, / Table .. Nigeria Household Survey, Household is a group of people who nor- / mally cook, eat, and live together. Number Household is a group of persons who nor- of months sharing in these activities was mally cook, eat, and live together. Number another criterion used to qualify as a house- of months sharing in these activities was hold member (minimum three months). another criterion used to qualify as a house- However, all heads of households irrespec- hold member (minimum of three months). tive of number of months living elsewhere However, all heads of households irrespec- were included as household members. ese tive of number of months living elsewhere people may or may not be related by blood, were included as household members. ese but make common provision for food or people may or may not be related by blood, other essentials for living, and they have one but make common provision for food or person whom they all regarded as the head other essentials for living, and they have one of the household. person whom they all regard as the head of Literacy measures the number of people the household. with the ability to read and write a simple Technical Notes 201 sentence in either English or the local lan- Source: Tanzania Bureau of Statistics. e guages. Tanzanian Household Budget Survey (HBS), conducted in 2000/01 by the National Bu- Source: e Sierra Leone Central Statistical reau of Statistics (NBS), is the largest-ever Office carried out the Living Conditions Mon- household budget survey in Tanzania. Data itoring Survey. Data were collected between collection between May 2000 and June November 2002 and January 2003. Sample 2001. Sample size selected covered 22,178 size selected was about 3,720 households. households. Table .. Tanzania Household Table .. Uganda Household Survey, Survey, / / Household is a group of people who nor- Household is individuals who normally eat mally cook, eat, and live together. Number and live together. of months sharing in these activities was Literacy measures the number of people another criterion used to qualify as a house- who responded that they could both read hold member (minimum three months). and write. e level of education was also However, all heads of households irrespec- used to determine literacy. tive of number of months living elsewhere were included as household members. ese Source: e Uganda Bureau of Statistics people may or may not be related by blood, carried out the National Household Survey. but make common provision for food or Data collection occurred between May 2005 other essentials for living, and they have one and April 2006. Sample size selected covered person whom they all regarded as the head about 7,400 households. of the household. 202 Africa Development Indicators 2008/09 Technical notes references AbouZahr, Carla and Tessa Wardlaw (2003), IPCC (Intergovernmental Panel on Climate "Maternal Mortality in 2000. Estimates Change) (2001c): Climate Change 2001: Developed by WHO, UNICEF, and UN- Mitigation. Contribution of Working FPA" World Health Organization, Ge- Group III to the ird Assessment Re- neva. port of the Intergovernmental Panel on Arbache, Jorge Saba and John Page (2007), Climate Change, B. Metz, O. Davidson, "Patterns of Long Term Growth in Sub- R. Swart and J. Pan, Eds., Cambridge Saharan Africa". Policy Research Work- University Press, Cambridge. ing Paper No. 4398, e World Bank, IPCC (Intergovernmental Panel on Climate Washington D.C. Change) (2007a): Climate Change 2007: Central Statistical Agency, Government of e Physical Science Basis. Contribu- Ethiopia. tion of Working Group I to the Fourth Chen, Shaohua, and Martin Ravallion (2008), Assessment Report of the Intergovern- " e Developing World Is Poorer an mental Panel on Climate Change, S. Sol- We ought, But no Less Successful in omon, D. Qin, M. Manning, Z. Chen, M. e Fight Against Poverty". Policy Re- Marquis, K. B. Averyt, M. Tignor and H. search Working Paper No. 4703, e L. Miller, Eds., Cambridge University World Bank, Washington D.C. Press, Cambridge. ILO ( International Labor Organization) IPCC (Intergovernmental Panel on Climate Various years. Key Indicators of the Labor Change)(2007b):ClimateChange2007: Market. Geneva. Impacts, Adaptation and Vulnerability. IPCC (Intergovernmental Panel on Climate Contribution of Working Group II to Change) (2001a): Climate Change the Fourth Assessment Report of the 2001: e Scientific Basis. Contribution Intergovernmental Panel on Climate of Working Group I to the ird Assess- Change, M.L. Parry, O.F. Canziani, J.P. ment Report of the Intergovernmental Palutikof, P.J. van der Linden and C.E. Panel on Climate Change, J.T. Hough- Hanson, Eds., Cambridge University ton, Y. Ding, D.J. Griggs, M. Noguer, P.J. Press, Cambridge. van der Linden, X. Dai, K. Maskell and IPCC (Intergovernmental Panel on Climate C.A. Johnson, Eds., Cambridge Univer- Change) (2007c): Climate Change 2007: sity Press, Cambridge. Mitigation. Contribution of Working IPCC (Intergovernmental Panel on Climate Group III to the Fourth Assessment Change) (2001b): Climate Change Report of the Intergovernmental Panel 2001: Impacts, Adaptation, and Vul- on Climate Change, B. Metz, O. David- nerability. Contribution of Working son, P.Bosch, R. Dave and L. Meyer, Group II to the ird Assessment Re- Eds., Cambridge University Press, Cam- port of the Intergovernmental Panel bridge. on Climate Change, J.J. McCarthy, O.F. IPCC (Intergovernmental Panel on Climate Canziani, N.A. Leary, D.J. Dokken and Change) (2007d): Climate Change K.S. White, Eds., Cambridge University 2007: Synthesis Report. Contribution Press, Cambridge. of Working Groups I, II and III to the Fourth Assessment Report of the Inter- Technical notes references 203 governmental Panel on Climate Change, Wodon, Q. (2008), Using Data to Inform Core Writing Team, R.K Pachauri and A. Policy: Impact of the Food Price Crisis Reisinger, Eds., IPCC, Geneva. in Africa and Policy Responses, Devel- U.S. Census Bureau. HIV/AIDS surveillance opment Dialogue on Values and Ethics data base. September, 2004: http:// Note, World Bank, Washington, DC. www.census.gov/ipc/www/hivaidsd. Wodon, Q., P. Backiny-Yetna and C. Tsimpo html (accessed March 23, 2006). (2008), e Role of Faith-Based Organi- World Bank. 2008a. Global Purchasing Power zationsinServiceDeliveryforEducation Parities and Real Expenditures: 2005 In- and Health: Estimates from Household ternational Comparison Program. Wash- Surveys in West and Central Africa, De- ington D.C. velopment Dialogue on Values and Eth- World Bank 2008b. IDA 15 Background Pa- ics Note, World Bank, Washington, DC. per. Toward a Strategic Framework on Zafar, Ali (2005) " e Impact of the Strong Climate Change and Development for the Euro on the Real Effective Exchange World Bank Group. Washington D.C. Rates of the Two Francophone African World Bank. 2000. Trade Blocs. New York: Zones". World Bank Policy Research Oxford University Press. Working Paper Series No.3751. World Bank. Various years. "World Develop- ment Indicators". Washington, D.C. 204 Africa Development Indicators 2008/09 User's Guide Africa Development Indicators 2008/09 CD-ROM Introduction Operation Creating your own country or indicator This CD-ROM is part of the Africa To start the CD-ROM, go to the WB list. You can create your own group of Development Indicators suite of products. Development Data program group and countries, series, or periods by saving It was produced by the Office of the Chief click on the Africa Development Indicators your query on the appropriate screen. You Economist for the Africa Region and 2008/09 icon. can also save all elements of the query on the Operational Quality and Knowledge the Query screen. You can reload a saved Services Group in collaboration with Note that standard WindowsTM controls query in a future session. the Development Data Group of the are used for most functions. For detailed Development Economics Vice Presidency. instructions, refer to the on-screen Help To save a query: It uses the latest version of the World menu or tool tips (on-screen explanations 1. Highlight items on any of the Bank's *STARS* data retrieval system, of buttons that are displayed when the Countries, Series, or Periods (or any Win*STARS version 5.0. cursor rolls over them). two or all three) selection screens and The CD-ROM contains about 1,400 click on Select to place them in the macroeconomic, sectoral, and social Features and instructions Selected box. indicators, covering 53 African countries. Win*STARS has four main functions-- 2. Click on the Save Query icon and Time series include data from 1965 Home, Query, Result, and Map. Move follow the naming prompts. to 2006. A few macro indicators have among them at any time by clicking on provisional data for 2007 while others the respective tabs. To load a query: indicators have data for 2007­2008. 1. Go to the selection screen in which Win*STARS 5.0 features mapping and Home your query is saved. For example, if charting and several data export formats you have saved a set of countries, go On the Home screen you can access (AccessTM, ASCII, dBASETM, ExcelTM, to the Countries selection screen. each element of the Africa Development and SASTM). We invite you to explore it. Indicators 2008/09 CD-ROM. Use the 2. Click on the Load Query icon, select browser controls to link to the Africa the query you want, and click on OK. A note about the data Development Indicators tables, The Users should note that the data for the Little Data Book on Africa 2008/09, time To modify a saved query: Africa Development Indicators suite series database, maps, and other related 1. Load the query. of products are drawn from the same information. 2. In the Selected box, highlight the database.The general cutoff date for data items to be removed and click on the is September 2008. Query Remove icon. 1. Click on the Query button to start your 3. Add new items if necessary. Help time series selection. 4. Resave the query. This guide explains how to use the main 2. Click on each of the Country, Series, functions of the CD-ROM. For details and Periods buttons and make your Result about additional features, click Help on selections on each screen. There are On the Result screen, data are presented the menu bar or the Help icon; or call one many ways to make a selection--see in a three-dimensional spreadsheet and, of the hotline numbers listed in the Help below, or use the Help menu. initially, in scientific notation. Data for menu and on the copyright page of this 3. Highlight the items you want. the third dimension are presented on booklet. 4. Click on the Select button to move separate screens. You can change the them into the Selected box. selection displayed by clicking on the Installation 5. Deselect items at any time by third dimension scroll box. You can also As is usual for WindowsTM products, you highlighting them and clicking on the change the scale and the number of should make sure that other applications Remove icon. digits after the decimal. If the column is are closed while you install the CD-ROM. 6. When selection is complete, click too narrow to present all the digits, they To install the single-user version: on OK to return to the main Query will appear as a series of ######. Double Insert the CD-ROM into your CD drive. screen. click on the column's guideline to widen 2. Click on Start and select Run. Type it, or choose a larger scale (millions, for 7. If you want to, you can display D:\SETUP.EXE (where D: is your example). To scale series individually, information on data availability by CD-ROM drive letter), click OK and click Options and check Enable Series- clicking on the Availability icon. You follow the instructions. For Windows Level Scaling. Click the far right scroll box can choose to count time series or VistaTM, click the Computer icon on to view the percentage change over each total observations. your desktop, navigate to your CD- selected period or to index the data. 8. Click on View Data to see the data on ROM drive, and launch the Setup the Result screen. application. Changing the orientation. You can view 3. Win*STARS 5.0 requires Microsoft the result in six different orientations Making selections. Countries: You can Internet ExplorerTM 4.0 or higher. If (countries down/periods across, series select countries from an alphabetical you do not have Internet Explorer, it down/countries across, etc.). To change list, by Classification (region, income may be downloaded at no charge from the orientation, click on the Orientation group, or lending category), by www.microsoft.com. It does not need scroll box. Criteria (up to two can be specified), to be your default browser. If you do or by Group (aggregates have been not wish to use Internet Explorer, you Charting and mapping data. On the calculated only when there were have the option to install Win*STARS Result screen, you can chart or map adequate data). Series: You can choose 4.2. the data displayed. Highlight a set of from an alphabetical list or by Category. 4. You can delete this program at any cells for charting or a particular cell for When selecting series by category, the time by clicking on Start, Settings, mapping. Click on the Chart or Map icon subcategory buttons change with each Control Panel,Add/Remove Programs. on the toolbar accordingly. The charting category. Periods: Select time periods To reinstall it, reboot your computer function has many features. After you from the Periods list box. first. have displayed a chart, right click on the User's Guide 205 chart to open the Chart Wizard for more the WORLD BANK (the "Bank") hereby to you, as evidenced by a copy of options. Mapping is described below. grants you a nonexclusive license to your receipt. The Bank's entire liability From this screen you can choose to use the enclosed data and Win*STARS and your exclusive remedy shall be map all countries or only your selected retrieval program (collectively, the the replacement of any CD-ROMs countries. "Program") subject to the terms and that do not meet the Bank's limited conditions set forth in this license warranty. Defective CD-ROMs should Cutting, pasting, printing, and saving. You agreement. be returned within the warranty period, can cut, paste, and print the result, or with a copy of your receipt, to the you can save the spreadsheet in another 2. OWNERSHIP. As a licensee you own address specified in section 9 below. format. Click on the appropriate icon on the physical media on which the EXCEPT AS SPECIFIED ABOVE, THE the toolbar and follow the prompts. Click Program is originally or subsequently PRODUCT IS PROVIDED "AS IS" on Help for more details. recorded. The Bank, however, retains WITHOUT WARRANTY OF ANY KIND, the title and ownership of the program EITHER EXPRESSED OR IMPLIED, Map recorded on the original CD-ROMs and INCLUDING, BUT NOT LIMITED TO, On the Map screen, you can select all subsequent copies of the Program. THE IMPLIED WARRANTIES OF a country and view a set of tables This license is not considered to be MERCHANTABILITY AND FITNESS describing it, or you can map a series for a sale of the Program or any copy FOR A PARTICULAR PURPOSE. THE all countries. In the upper left corner of thereof. BANK DOES NOT WARRANT THAT the screen, the country name will appear THE FUNCTIONS CONTAINED IN as the cursor rolls slowly over the map. To 3. COPY RESTRICTIONS. The Program THE PROGRAM WILL MEET YOUR zoom in for a closer look at the map, click and accompanying written materials REQUIREMENTS OR THAT THE on the Zoom icon. are copyrighted. You may make one OPERATION OF THE PROGRAM WILL copy of the Program solely for backup BE UNINTERRUPTED OR ERROR-FREE. Selecting a country or viewing country purposes. Unauthorized copying of the IN NO EVENT WILL THE BANK BE tables. To highlight a country and view any Program or of the written materials is LIABLE TO YOU FOR ANY DAMAGES of its tables, click on the country on the expressly forbidden. ARISING OUT OF THE USE OF OR THE map or select it in the Locate a Country INABILITY TO USE THE PROGRAM. scroll box in the upper right corner. 4. USE. You may not modify, adapt, THE ABOVE WARRANTY GIVES YOU translate, reverse-engineer, SPECIFIC LEGAL RIGHTS IN THE Mapping a series. On the Map screen, decompile, or disassemble the UNITED STATES THAT MAY VARY click on the Series icon. A list of key Program. You may not modify, adapt, FROM STATE TO STATE. BECAUSE indicators will be displayed. (To show all translate, or create derivative works SOME STATES DO NOT ALLOW THE available indicators, click on the box by based on any written materials without EXCLUSION OF IMPLIED WARRANTIES Show default series to remove the X.) the prior written consent of the Bank. OR LIMITATION OF EXCLUSION Highlight a series, select a period from If you have purchased the single-user OF LIABILITY FOR INCIDENTAL OR the Available Periods list box (the default version of this product, you may use CONSEQUENTIAL DAMAGES, PARTS is the latest available) and click on Paint the Program only on a single laptop/ OF THE ABOVE LIMITATIONS AND Map.The map will be colored according to desktop computer. You may not EXCLUSIONS MAY NOT APPLY TO the legend settings, any of which you can distribute copies of the Program or YOU. change. Note that as the cursor moves accompanying written materials to across the map, the series value is now others. You may not use the Program 7. TERMINATION. This license is also displayed in the upper left corner. on any network, including an Intranet effective from the date you open the or the Internet, without obtaining prior package until the license is terminated. Changing the map legend and colors. The written permission from the Bank. If You may terminate it by destroying default interval range is an equal number you have purchased the multiple-user the Program and its documentation of countries. To set an equal interval version of this product, your license and any backup copy thereof or by range or to map multiple periods, click is valid only up to 15 users. Should returning these materials to the Bank. on the Recalculate icon. Set your own you need to add additional users, If any of the terms or conditions of intervals by editing the legend. To change please send a request, indicating this license are broken, the Bank may map colors, double click on the legend the number of users you would like terminate the license and demand color boxes. Press the Remap icon to see to add, to: World Bank Publications, that you return the Program. your changes. Rights and Permission, 1818 H Street, N.W., Washington, D.C. 20433, fax: 8. GOVERNING LAW. This license shall Printing and saving. Click on the 202-522-2422, email: pubrights@ be governed by the laws of the District appropriate icon to print the map or save worldbank.org. of Columbia, without reference to it as a bitmap or metafile. conflicts of law thereof. 5. TRANSFER RESTRICTIONS. This License agreement Program is licensed only to you, the 9. GENERAL. If you have any questions You must read and agree to the terms licensee, and may not be transferred concerning this product, you may of this License Agreement prior to using to anyone without prior written contact the Bank by writing to World this CDROM product. Use of the software consent of the Bank. Bank Publications, CD-ROM Inquiries, and data contained on the CD-ROM is The World Bank, 1818 H Street, N.W., governed by the terms of this License Washington, D.C. 20433, email: data@ Agreement. If you do not agree with 6. LIMITED WARRANTY AND worldbank.org. All queries on rights these terms, you may return the product LIMITATIONS OF REMEDIES. The and licenses should be addressed unused to the World Bank for a full refund Bank warrants that under normal use to World Bank Publications, Rights of the purchase price. the CDROMs on which the Program and Permission, 1818 H Street, is furnished are free from defects N.W., Washington, D.C. 20433, fax: 1. LICENSE. In consideration of your in materials and workmanship for a 202-522-2422, email: pubrights@ payment of the required license fee, period of ninety (90) days from delivery worldbank.org. 206 Africa Development Indicators 2008/09 e African Continent TUNISIA MOROCCO ALGERIA LIBYA FORMER ARAB REP. SPANISH OF EGYPT SAHARA CAPE VERDE MAURITANIA MALI NIGER ERITREA SENEGAL CHAD THE GAMBIA BURKINA SUDAN GUINEA-BISSAU FASO DJIBOUTI GUINEA BENIN NIGERIA SIERRA LEONE CÔTE ETHIOPIA D'IVOIRE GHANA CENTRAL LIBERIA AFRICAN REPUBLIC TOGO CAMEROON SOMALIA EQUATORIAL GUINEA UGANDA SÃO TOMÉ AND PRÍNCIPE GABON CONGO KENYA DEM. REP. OF RWANDA CONGO BURUNDI TANZANIA SEYCHELLES COMOROS ANGOLA Atlantic ZAMBIA MALAWI Ocean ZIMBABWE MOZAMBIQUE MADAGASCAR MAURITIUS NAMIBIA BOTSWANA SWAZILAND SOUTH LESOTHO Indian This map was produced by the Map Design Unit of The World Bank. AFRICA The boundaries, colors, denominations and any other information shown on Ocean this map do not imply, on the part of The World Bank Group, any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries. Africa Development Indicators 2008/09 provides the most detailed collection of data on Africa available in one volume. It contains more than 450 macroeconomic, sectoral, and social indicators, covering 53 African countries. Additional data may be found on the companion CD-ROM, covering about 1,400 indicators from 1965 to 2006. A few macro indicators have provisional data for 2007 while others indicators have data for 2007­2008. Africa Development Indicators includes data on the following fields: · Basic indicators · National accounts · Balance of payments · Inflation · Millennium Development Goals · Paris Declaration indicators · Private sector development · Trade · Infrastructure · Human development · Rural development and agriculture · Environment and climate change · Labor, migration and population · HIV/AIDS · Malaria · Capable states and partnerships · Governance and polity · Household welfare Designed to provide all those interested in Africa with a focused and convenient set of data to monitor development programs and aid flows in the region, this is an invaluable reference tool for analysis and policy makers who want a better understanding of the economic and social developments occurring in Africa. ISBN 978-0-8213-7787-1 SKU 17787