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THE WORLD BANK

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Copyright © 2006 by the International Bankfor Reconstruction and Development/! e World Bank1818 H Street, N.W.Washington, D.C. 20433, U.S.A.

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Photo credits: front cover, Curt Carnemark; top row, left to right, Trevor Samson/World Bank; Curt Carnemark/World Bank; Scott Wallace/World Bank; Eric Miller/World Bank; Curt Carnemark/World Bank; back cover, small top inset, Eric Miller/World Bank; large top inset, Curt Carnemark/World Bank; bottom, left to right: Scott Wallace/World Bank; Eric Miller/World Bank; Curt Car-nemark/World Bank; Ray Witlin/World Bank; Eric Miller/World Bank.

ISBN-10: 0-8213-6537-1ISBN-13: 978-0-8213-6537-3eISBN: 0-8213-6538-XDOI: 10.1596/978-0-8213-6537-3

Contents iii

Foreword viAcknowledgments vii

Moving from the Year of Africa to the Decade of Africa—From Promises to Results 1Notes 20References 21

Indicator tables 23Part I. Basic indicators and national accounts 1. Basic indicators1.1 Basic indicators 25

2. National accounts 2.1 Gross domestic product, real 262.2 Gross domestic product per capita, real 272.3 Agriculture value added 282.4 Industry value added 292.5 Services value added 302.6 Gross domestic product, nominal 312.7 Total consumption 322.8 General government consumption 332.9 Gross fi xed capital formation 342.10 General government fi xed capital formation 352.11 Private sector fi xed capital formation 362.12 Gross domestic savings 372.13 Gross national savings 382.14 Resource balance (exports minus imports) 392.15 Exports of goods and services, nominal 402.16 Imports of goods and services, nominal 412.17 Exports of goods and services, real 422.18 Imports of goods and services, real 432.19 Gross domestic product growth 442.20 Gross domestic product per capita growth 452.21 Gross national income per capita 462.22 Total consumption per capita 47

Contents

iv Africa Development Indicators 2006

Part II. Millennium Development Goals 3. Millennium Development Goals3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger 483.2 Millennium Development Goal 2: achieve universal primary education 503.3 Millennium Development Goal 3: promote gender equality and empower women 513.4 Millennium Development Goal 4: reduce child mortality 523.5 Millennium Development Goal 5: improve maternal health 533.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases 543.7 Millennium Development Goal 7: ensure environmental sustainability 553.8 Millennium Development Goal 8: develop a global partnership for development 56

Part III. Development outcomes

Results framework

4.1 Status of Paris Declaration indicators 58

Drivers of growth 5. Private sector development5.1 Business environment 595.2 Investment climate 60

6. Trade6.1 International trade and tariff barriers 626.2 Regional integration, trade blocs 65

7. Infrastructure7.1 Water and sanitation 667.2 Transportation 687.3 Information and communication technology 707.4 Energy 727.5 Financial sector infrastructure 74

Participating in growth 8. Human development8.1 Education 768.2 Health 78

9. Agriculture and rural development9.1 Rural development 819.2 Agriculture 82

10. Labor, migration, and population10.1 Labor 8410.2 Migration and population 86

Contents v

11. HIV/AIDS11.1 HIV/AIDS 87

12. Malaria12.1 Malaria 88

13. Capable states and partnership13.1 Aid and debt relief 8913.2 Capable states 9013.3 Governance and anticorruption indicators 9213.4 Country Policy and Institutional Assessment ratings, 2005 94

Part IV: Household Welfare 14. Household welfare14.1 Burkina Faso household survey, 2003 9514.2 Burundi household survey, 1998 9614.3 Cameroon household survey, 2001 9714.4 Côte d’Ivoire household survey, 1998 9814.5 Ethiopia household survey, 2000 9914.6 Gambia household survey, 1998 10014.7 Ghana household survey, 1998/99 10114.8 Kenya household survey, 1997 10214.9 Madagascar household survey, 1999 10314.10 Malawi household survey, 1997/98 10414.11 Mozambique household survey, 1996 10514.12 Nigeria household survey, 2004 10614.13 São Tomé and Principe household survey, 2000 10714.14 Sierra Leone household survey, 2002/03 10814.15 Uganda household survey, 2002/03 10914.16 Zambia household survey, 1998 110

Technical notes 111

Statistical references 147

Users guide: World Bank Africa Database 2006 andAfrica Development Indicators CD-ROM 151

vi Africa Development Indicators 2006

Making headway against African poverty and meeting the Millennium Development Goals are raising diffi cult challenges in most African countries. Despite pockets of success, nearly half the region’s population still lives in ex-treme poverty, and Africa still houses about three-fourths of the world’s poorest countries. But Africa is today a continent on the move. Average economic growth remains strong, ex-ports are increasing, and many countries are making tangible progress on delivering better health and education outcomes. Africa Development Indicators 2006 is the latest annual report from the World Bank on social and economic conditions across the continent. It was revamped this year to better report and monitor the challenges and transformations in Africa. Africa Devel-opment Indicators has evolved from a single data book, and this year consists of three in-dependent but complementary products: this book, which brings together an essay and key outcome indicators for Africa, � e Little Data Book on Africa 2006, and the World Bank Af-rica Database (CD-ROM).

Each year’s essay will discuss a topic of rel-evance for Africa. Starting the new series is an essay about the Year and the Decade of Af-rica. ! e essay takes stock of how countries and donors performed during 2005, which was marked by meetings of the UN Millen-nium Task Force, the U.K. Commission for Africa, and the Group of Eight Summit at Gleneagles, Scotland, and culminated in the UN Millennium+5 Summit in New York. ! e essay refl ects both the magnitude of the chal-lenges and the strength of the response and also sounds a clear warning on the need to move from promises to results to meet the Millennium Development Goals by 2015.

Data in the book have been assembled from a variety of sources to present a broad

picture of development across Africa. ! e book is designed to provide a set of key indi-cators to monitor development outcomes in the region. It is an important reference tool for those who want a better understanding of the economic and social developments occur-ring in Africa. ! e tables comprise a selection of key development outcome indicators from 1980 to 2004. ! ey off er detailed informa-tion in areas such as the Millennium Devel-opment Goals, private sector development, trade, agriculture and rural development, HIV/AIDS and malaria, infrastructure, the Paris Declaration, governance, and aid. � e Little Data Book on Africa 2006 is a pocket edition of Africa Development Indica-tors and is intended as a quick reference on the most recent key indicators for users of Africa Development Indicators 2006.

! e World Bank Africa Database 2006 of-fers the most comprehensive database on Africa, covering about 1,200 indicators of macroeconomic, sectoral, and human devel-opment variables, with time series of many indicators going back to 1965. ! e CD-ROM also off ers country at-a-glance tables for all African countries and map tools for Africa.

Despite the rich data refl ected in this fam-ily of products, the lack of data for Africa is still a major impediment to monitoring de-velopment progress. As users will notice, many variables have few country observa-tions and underline the need to improve data collection across the continent. I hope that this new series will contribute to the way countries, development partners, analysts, academics, and others understand and design development policies in Africa.

John PageChief Economist, Africa Region

Foreword

Africa Development Indicators 2006 was pro-duced by the Offi ce of the Chief Economist and the Operational Quality and Knowledge Services Group of the Africa Region. ! e De-velopment Data Group of the Development Economics Vice Presidency collaborated in the production of � e Little Data Book on Afri-ca 2006 and the World Bank Africa Database 2006. ! is book and its companions, � e Little Data Book on Africa 2006 and World Bank Af-rica Database 2006, were prepared by a team led by Jorge Arbache and Vildan Verbeek-Demiraydin, comprising Zena Angesom, Francoise Genouille, Rose Mungai, Joan Pandit, and Christophe Rockmore. Mehdi Akhlaghi provided technical support for � e Little Data Book on Africa 2006, and William Prince provided technical support for the World Bank Africa Database CD-ROM. John Page, Chief Economist, Africa Region, pro-vided overall guidance and supervision. ! e essay and Africa Development Indi-cator tables benefi ted from contributions from a large number of people. Ivar Ander-sen, Jorge Araujo, Demba Ba, ! orsten Beck, Misha Belkindas, Harry Broadman, Karen Brooks, Derek Byerlee, Michael Fuchs, Mad-hur Gautam, Linda Van Gelder, Delfi n Go,

Catalina Gutierrez, Katie Heller, Lawrence E. Hinkle, Stefan Hochhuth, James Keough, Mohamed Khatouri, Jean Michel Marchat, Sergio Margulis, Celestin Monga, Anna Mu-ganba, Jonathan Munemo, Francois Nanko-bogo, Benno Nludu, Essama Nssah, Sonia Plaza, Francesca Recanatini, Ivan Rossignol, Francis Rowe, Pieter Serneels, Sudhir Shetty, Stephanie H. Tam, Mark Roland ! omas, Robert Townsend, Dileep Wagle, Naoko Watanabe, Elizabeth White, and Yutaka Yo-shino provided inputs in the form of com-ments, suggestions, background notes, and boxes. Xiao Ye helped the team in preparing tables and charts for the essay and provided a box. Cecilia Briceno-Garmendia, Arnaud Des-marchelier, Vivien Foster, Sudeshna Ghosh Banerjee, and Kavita Sethi contributed to the infrastructure indicators and a box. Communications Development Incorpo-rated provided overall design direction, ed-iting, and layout, led by Bruce Ross-Larson, Meta de Coquereaumont, and Christopher Trott. Richard Fix supported the concept and production of � e Little Data Book on Africa 2006. Staff from the World Bank Offi ce of the Publisher oversaw publication and dissemi-nation of the book and its companions.

Acknowledgments

Acknowledgments vii

Moving from the Year of Africa to the Decade of Africa 1

Moving from the Year of Africa to the Decade of Africa—from promises to results

In 2005, in what came to be called the Year of Africa, the region’s leaders reaffi rmed their commitment to achieve the Millennium De-velopment Goals by accelerating growth, cre-ating jobs, delivering human services, and fi ghting poverty. ! ey also challenged the international community to support their ef-forts.

Why the Year of Africa? In the view of many Africans and their international part-ners, 2005 marked a turning point in the region’s evolution.

Africa is indeed on the move. In contrast with the 1990s, confl icts in Africa have de-clined, economic performance has improved, and some clear “high performers” are be-ginning to emerge. Since the late 1990s, 14 African countries have had average growth rates of above 5 percent.1 Across the conti-nent countries are advancing in all spheres of transformation.• Growth for all Sub-Saharan Africa, which

averaged 2.4 percent in the 1990s, rose to 4.0 percent in 2000–04 (indicator ta-ble 2.19) and came in at an estimated 4.3 percent in 2005.

• Macroeconomic indicators have im-proved, with infl ation down to historic lows, exchange rate distortions mostly eliminated, and fi scal defi cits dropping.

• Progress toward the Millennium De-velopment Goals (MDGs), while slow, improved in some countries (indicator tables 3.1–3.8). Cape Verde, Comoros, Eritrea, Guinea, and Mozambique have recorded signifi cant reductions in child (under-fi ve) mortality. In education, Mauritania has made progress in increas-ing the primary enrollment of girls.

• ! ere is a growing awareness by govern-ments of the need to be accountable to their people. ! e African Union has an-

nounced that it will not recognize gov-ernments that come to power through unconstitutional means.

Many countries have increased exports by more than 8 percent a year since the late 1990s (indicator table 2.17), despite falling prices in some of their primary commodities. ! ese export successes—in cut fl owers, vege-tables, and clothing—show an Africa capable of both diversifying and building a construc-tive relationship with global markets.

Progress is also being made in human development. With political support, ! e Gambia is advancing toward the Millennium Development Goal nutrition targets, and in fi ve years, Niger has gone from being one of the worst performers in expanding primary education to one of the best.2

Some of the fastest growing countries have also done well in reducing poverty (in-dicator table 3.1). Since the second half of the 1990s, many low-income African coun-tries, including Burkina Faso, Cameroon, Cape Verde, Ghana, Mozambique, Senegal, and Uganda, have lifted signifi cant percent-ages of their citizens above the poverty line. While in some countries growth and poverty reduction have recently declined moderately, many countries are still on course to meet the income poverty MDG target of halving poverty by 2015.3

Despite this progress, a lot more has to be done, especially in areas such as governance, civil society, private sector development, and human development, to make economic growth more sustainable, less exposed, and more resilient to shocks.

Drawing on the indicator tables, this anal-ysis takes stock of how countries and devel-opment partners performed during the Year of Africa. It then spells out what countries, with the support of their partners, are doing

2 Africa Development Indicators 2006

to transform their eff orts into the Decade of Africa. ! e main message: Africans and their development partners need to increase their focus on supporting the drivers of growth, sharing participation in and the benefi ts of growth, and building capable states. For shared growth to become a reality, the De-cade of Africa must be about results not promises.

T Y A

! e year 2005 was marked by the UN Mil-lennium Project, the UK Commission for Africa, and the G-8 Summit at Gleneagles, Scotland, all culminating in the UN Mil-lennium+5 Summit in New York. African governments committed to improving their economic and political institutions, accel-erating economic growth, and achieving real progress toward the MDGs. Developed countries pledged to increase aid to Africa by $25 billion a year by 2010, more than doubling the assistance to the region, to open their markets to African products, and to forgive the debts of 25 of the continent’s poorest countries.4 ! e Multilateral Debt Relief Initiative for Africa would reduce debt to the International Development As-sociation (IDA), the International Monetary Fund, and the African Development Fund by $42.1 billion.

African governments are taking more control of their destinyAfrican leaders are increasing ownership of their development strategies in new ways, helping to end confl icts through the African Union, and pushing for accountability in eco-nomic and political governance through the Peer Review Mechanism of the New Partner-ship for Africa’s Development. After a peak in 2002 the number of African confl icts has de-clined from 16 to 5.5 ! e transitions to peace include some of the longest running civil wars, such as the 21-year confl ict between North and South Sudan and the protracted wars in Angola and Liberia.

African governments are also taking ac-tion to improve the region’s reputation by certifying good practices in governance for a critical mass of African countries under the Africa Peer Review Mechanism. ! ey are in-creasing regional connectivity to improve

the capacity to trade—within the region and with the rest of the world. ! ey are also en-hancing the capacity of a rationalized system of regional bodies to provide regional public goods—such as cross-country transporta-tion and power sharing, coordinating the management of pandemics, and protecting such regional commons as the Nile and the Great Lakes. And African enterprises, too long marginalized, have begun to show that they can connect constructively to world markets, with pockets of export successes outside commodities.

More African states are choosing demo-cratic arrangements, with elected offi cials in charge in many countries formerly ruled by military regimes or authoritarian gov-ernments. In addition, new arrangements and new spaces are being created to enable citizens to express their views and articulate their demands. Some African civil society leaders, for example, are studying Asian ex-periences with citizen report cards to assess the quality of service delivery. An invigorated media and civil society are making themselves heard, often leading the drive for greater ac-countability.

! e average scores on the World Bank’s Country Policy and Institutional Assessment have been rising, as has the number of Af-rican countries with scores above the good performance threshold of 3.5 (indicator table 13.4).6

Recent economic progress continued during the past yearIn 2005 higher oil prices did not strongly aff ect economic growth, and real GDP is ex-pected to have grown by 4.3 percent, com-pared with 5.1 percent in 2004 (indicator table 2.19). Oil-importing countries did well, with their economies growing by 4.5 per-cent, above the 4.2 percent seen in 2004.7

Economic growth in the main oil-exporting countries slowed somewhat to 4.7 percent from 8.4 percent in 2004 and 6.2 percent in 2003, mainly because of constraints on oil supply in Chad, Equatorial Guinea, and Nige-ria (indicator table 2.19).

Also in 2005 the recent gains in reducing infl ation were maintained, suggesting pru-dent macroeconomic management during external shocks. As expected, there was a pass-through of high oil prices to consumer


prices in both oil-importing and oil-export-ing countries. So infl ation crept up slightly to 10.8 percent, from 9.8 percent in 2004.8

! e continent remained vulnerable to terms of trade shocks and the negative eff ects of exchange rate volatility. Because of the oil price increases in the fi rst half of 2005, there was an equivalent, cumulative loss of around 3.5 percent of GDP in Sub-Saharan Africa’s net oil-importing countries.9 And nonoil commodity prices, while strong, were subject to volatility and could be an area of signifi -cant vulnerability in the future.

Private investors—both domestic and for-eign—were still reluctant to invest, except in minerals and some services. World Bank Doing Business surveys show that the con-tinent continues to trail most other develop-ing regions in starting a business, registering property, trading across borders, and access-ing credit.

Fast and slow growth countries remained on divergent paths! e development pattern in Sub-Saharan Af-rica has become increasingly diverse over the last decade. Since the mid-1990s, 16 coun-tries have had annual GDP growth rates in

excess of 4.5 percent (table 1). For several of them—including Ghana, Mozambique, Sen-egal, Tanzania, and Uganda—higher growth has been accompanied by diversifi cation of their economies and exports. Excluding the oil-rich countries, the fastest growing group of African countries has had an aver-age growth rate of 5.5 percent. ! ese coun-tries host 35 percent of the region’s people. ! e 13 slowest growing economies, by con-trast, have seen an average growth of only 1.3 percent, with some having near zero or negative growth. ! ese countries—many either engaged in confl ict or having recently emerged from confl ict—host 20 percent of the region’s people.

Increasing macroeconomic divergence is also observed in other dimensions. ! e stan-dard deviation of the average annual growth rate in Sub-Saharan Africa has jumped from 2 percent in the 1980s to 4 percent in 2000–04. ! e standard deviation across countries has also risen signifi cantly over time for ex-ports and imports, value added of industry, agriculture, and services, and other variables, thus reinforcing that Africa is becoming in-creasingly diverse.

Average annual GDP growth, 1996–2005 (%)

Little or no growth countries

Average: 1.3 percent

20 percent of

African population

Slow growth countries


16 percent of population

Sustained growth

countries



Oil-exporting countries



Swaziland 2.8 Nambia 4.0 Mozambique 8.4 Equitorial Guinea 20.9

Kenya 2.8 Zambia 3.6 Rwuanda 7.5 Angola 7.9

Lesotho 2.7 Guinea 3.6 Cape Verde 6.5 Chad 7.8

Eritrea 2.2 Niger 3.5 Uganda 6.1 Sudan 6.4

Comoros 2.0 Togo 3.3 Mali 5.7 Nigeria 4.0

Seychelles 2.0 Madagascar 3.3 Botswana 5.7 Congo, Rep. 3.5

Côte d’lvoire 1.5 Malawi 3.2 Ethiopia 5.5 Gabon 1.7

Burundi 1.2 South Africa 3.1 Tanzania 5.4

Sierra Leone 1.1 São Tomé and Principe 3.1 Mauritius 4.9

Central African Republic 0.9 Mauritania 4.9

Guinea-Bissau 0.6 Benin 4.8

Congo, Dem. Rep. 0.0 Ghana 4.7

Zimbabwe –2.4 Senegal 4.6

Burkina Faso 4.6

Gambia, The 4.5

Cameroon 4.5

Note : Data on growth rates are not presented for Liberia and Somalia, but they are included in the denominator in the calculation of population shares.

Table 1 Divergent growth paths


< e international community had mixed results in living up to its fi nancial commitmentsBecause of their performance, 14 countries in Africa are already eligible for the 100 per-cent debt relief initiative agreed at the G-8 Summit in Gleneagles. ! is number will grow to 25 as countries reach their comple-tion points under the Heavily Indebted Poor Country (HIPC) Debt Relief Initiative (table 2

and indicator table 13.1). As of March 2006, the debt service relief committed in nominal terms was $50.3 billion.

! e debt relief initiative will have impor-tant consequences for African countries and development partners, allowing treasuries to retain general budget resources that would otherwise have gone to debt repayment—and placing greater responsibility on African governments to build honest, effi cient, and results-focused public expenditure systems.

While there is still room for optimism that the Gleneagles pledges can be achieved by 2010, the Organisation for Economic Co-operation and Development’s Develop-ment Assistance Committee and the Strate-gic Partnership with Africa10 both estimate that much of the increase in development assistance to Africa between 2006 and 2008 will be more apparent than real, con-sisting mainly of debt relief and emergency food aid.

Progress by the rich countries in bringing the Doha Round of trade negotiations to a successful conclusion has been disappointing. Trade barriers need to be dismantled to level the playing fi eld. With the end of the Multifi -bre Arrangement, World Trade Organization members have yet to defi ne the nature and extent of preferences to be extended to the least developed countries and the role of “aid for trade” in the multilateral system (box 1).

$ millions

Countries that have

reached HIPC completion

point

Estimated total

nominal debt service

relief

Countries between HIPC

decision point and

completion point

Estimated total

nominal debt service

relief

Benin 460 Burundi 1,472

Burkina Faso 930 Cameroon 2,800

Ethiopia 3,275 Chad 260

Ghana 3,500 Congo, Dem. Rep. 10,389

Madagascar 1,900 Congo, Rep. 2,881

Mali 895 Gambia, The 90

Mauritania 1,100 Guinea 800

Mozambique 4,300 Guinea-Bissau 790

Niger 1,190 Malawi 1,000

Rwanda 1,400 São Tomé and Prince 200

Senegal 850 Sierra Leone 950

Tanzania 3,000

Uganda 1,950

Zambia 3,900

Source : Indicator table 13.1.

Table 2 Debt service relief under the Heavily Indebted Poor Country Debt Relief Initiative

Sub-Saharan Africa would see a $4.8 billion

increase in real income thanks to deep, mul-

tilateral reform of global merchandise trade,

according to new World Bank estimates. Al-

though small in absolute terms, this increase

is signifi cant relative to initial incomes and

trade fl ows. Two-thirds of the gains would

come from reforms by other countries (includ-

ing other countries in Sub-Saharan Africa) and

one-third from countries’ own liberalization.

Reform in the agricultural sector would ac-

count for an estimated 78 percent of the total

gain for Sub-Saharan Africa, of which 12 per-

cent (if Southern Africa is excluded) would

come from cotton. Although reform in cotton

trade would contribute a negligible amount

(0.5 percent) to the global gains, the sector is

very important to Africa.

While Sub-Saharan Africa as a whole would

gain from multilateral trade reform, some

countries on the continent would lose from

preference erosion or, in the case of net food

importers, from higher food prices. Losses

from preferences, as with benefi ts, would be

less than expected due to strict rules of ori-

gin and because they are highly concentrated

among relatively few countries and products

(mostly those with quota rents, such as sugar).

But even relatively small losses can pose sig-

nifi cant adjustment problems for some African

countries, underlining the need for increased

assistance for domestic implementation of re-

forms—“aid for trade”—to support and com-

plement trade reform.

Source: World Bank 2006b.

Box 1 Africa and trade reform


! e suspension of the World Trade Organiza-tion talks in Geneva will hit poor Sub-Saha-ran African countries hardest—and the talks may well not start up again until sometime in 2007 or even 2008. Now entering the last decade to achieve the MDGs, Africa and its development partners have to move from promises to results—from the Year of Africa to the Decade of Africa.

M D A

! e Year of Africa focused global attention on Africa. But rebuilding infrastructure, tackling the disease burdens posed by HIV/AIDS and malaria, addressing the potential impact of avian fl u, and building competitive export-oriented businesses are all medium- to long-

term challenges. Realistically, the work will be done over a decade or more. And as in any region, the progress of economic devel-opment among African countries is bound to move at diff erent speeds, depending on countries’ initial conditions, their historical legacies, and the impact of weather and other natural shocks. In contrast with past decades, economic, political, and social outcomes are more varied, with more positive and negative aspects sometimes coinciding, sometimes in opposition.

Both Africans and their development part-ners need to focus on ways to address the key challenges to progress by:• Supporting the drivers of growth.• Participating in and sharing the benefi ts

of growth.

The World Bank Group’s Africa Action Plan,

launched in September 2005, is a results-

driven partnership strategy with 25 priority

initiatives designed to support African coun-

tries in implementing their national strategies

to accelerate growth and reduce poverty. The

plan is the core of the World Bank’s commit-

ment to supporting improved development

outcomes in Africa.

The new replenishment for the International

Development Association (IDA-14) is projected

to increase resources in 2006–08 by an aver-

age of just under $1 billion a year over IDA13 in

2003–05. Under the Africa Action Plan, most

of the increase in IDA will target strengthening

Africa’s development by:

• Ensuring that development support is

implemented, monitored, and evaluated

within a country-led results-oriented

framework.

• Increasing the rate of shared growth by

developing infrastructure, fostering re-

gional integration to boost export com-

petitiveness, and improving health and

human development, so that all citizens,

including the poor and women, partici-

pate in and benefi t from greater economic

development.

• Building capable states that practice

sound governance, transparency, and

accountability.

• Leveraging the IDA-14 partnership through

more effective collaboration and harmoni-

zation among development partners.

The World Bank is supporting shared

growth by enlarging investment in African

infrastructure from $600 million in 2000 to a

projected $2.4 billion in 2008. It is working as

part of the Africa Infrastructure Consortium in

bilateral and multilateral partnerships. It is also

signifi cantly increasing support for regional in-

tegration and regional approaches to support

national development strategies. And the Ma-

laria Booster Program is to be expanded by

150 percent in 17 countries by the end of 2008,

meaning that a total commitment of $500 mil-

lion to $1 billion may be required over the next

fi ve years.

The Africa Catalytic Growth Fund, estab-

lished in March 2006 with a grant from the

United Kingdom, is a key element for lever-

aging the IDA-14 partnership. The fund allows

the World Bank to increase support to invest-

ments that:

• Ease the constraints to growth in well per-

forming economies.

• Lay the groundwork for sustained growth

in transforming economies, where dedi-

cated leadership can strengthen eco-

nomic reforms.

• Support regional programs, where prob-

lems of collective action and donor prac-

tices result in too little investment.

Source: www.worldbank.org/afr/aap.

Box 2 The World Bank Group’s Africa Action Plan


• Providing the underpinnings for shared growth by building capable states and improving governance.

Supporting the drivers of growthOne major international development objec-tive is for African countries to increase their growth to the 7 percent a year needed to

substantially reduce income poverty. Africa’s slow growth is due mainly to much slower productivity growth (box 3). Accelerating and sustaining high growth rates in Africa are major challenges, however, and there is no obvious or unique recipe for success.

! e region’s economic activity is highly concentrated. Of Sub-Saharan Africa’s nom-inal GDP of $526 billion in 2004, South Af-rica accounted for the largest share ($215 billion), followed by Nigeria ($72 billion), Sudan ($21 billion), Angola ($20 billion), and Kenya ($16 billion) (fi gure 1 and indi-cator table 2.6). Of industry value added of $116 billion in 2004, Nigeria accounted for $21 billion and South Africa $42 billion (indicator table 2.4). And of service value added of $182 billion in 2004, South Africa accounted for $92 billion (indicator table 2.5). Because most African economies are fairly small, a regional approach is required to achieve sustainable growth.

$ billions, 2004

Figure 1 Nigeria and South Africa account for more than half of Africa’s GDP

Source: Indicator table 2.6

Nigeria,72

SouthAfrica,215

Rest of Africa,234

Africa is the slowest growing region in the

world. Average per capita output growth in

the 1990s was negative due mainly to very

low growth rates. But what explains such a

performance from an international perspec-

tive? Growth decomposition is a widely used

tool to assess the contributions of changes in

factor inputs (human and physical capital) and

total factor productivity to economic growth.

(Total factor productivity measures changes in

economic effi ciency in the use of inputs and

changes in technology for observed output

growth.)

Growth accounting shows that growth in

physical capital per worker in Africa has been

less than 0.5 percent a year since 1960, far

slower than the world average of 1 percent,

and was negative between 1990 and 2003,

suggesting low capital investment in the re-

gion. The contribution of human capital to

growth kept pace with the rest of the world and

has increased lately, mainly as a result of rising

average years of schooling. But the main con-

tributor to Africa’s disappointing growth is total

factor productivity, negative since the 1960s

and –0.4 percent between 1990 and 2003.

Box 3 Sources of economic growth in Africa—an international perspective

Region

Growth in output

(%)

Growth in output per worker

(%)

Growth in physical capital

per worker(%)

Education per worker

(%)

Total factor productivity

(%)

World 3.09 1.88 0.93 0.27 0.67

Africa 2.48 –0.09 –0.05 0.40 –0.44

Industrial countries 2.31 1.55 0.84 0.22 0.49

China 9.70 8.51 3.32 0.29 4.72

East Asia less China 5.24 3.12 2.05 0.47 0.58

Latin America 2.61 0.33 0.14 0.34 –0.16

South Asia 5.34 3.10 1.29 0.40 1.38

Middle East 3.64 0.61 0.20 0.51 –0.11

Source : Bosworth and Collins 2006.

Sources of growth—a decomposition analysis, 1990–2003


Developing the private sector What has been missing in most of Africa is a buoyant private sector. Building the African private sector is crucial for growth and for fostering a national consensus for growth-oriented policies. It is also critical for reassur-ing the African public that a growth process led by the private sector is one that everyone can participate in and benefi t from.

Central to this eff ort is improving the in-vestment climate and enhancing the capacity of African and foreign entrepreneurs to invest. ! ere is considerable room for improvement: net foreign direct investment in Africa was a mere $10.1 billion in 2004, 1.6 percent of glob-al fl ows, with more than half going to Nigeria and Sudan (fi gure 2 and indicator table 5.2). Improving the business environment. Ac-cording to Doing Business 2006: Creating Jobs (World Bank 2006a), 6 of the 10 countries judged as having the most diffi cult environ-ment for starting a business are in Africa. It takes an average of 64 days to start a busi-ness, ranging from 14 in the Central African Republic to 155 in the Democratic Republic of Congo (fi gure 3 and indicator table 5.1). Enforcing a contract is no less onerous, run-ning an average of 439 days, ranging from 154 in Botswana to 1,011 in Angola. Invest-ment Climate Assessments in more than a dozen countries point to specifi c changes that governments can eff ect to encourage higher levels of investment and faster job growth.

! e payoff s can be signifi cant. In Madagas-car a garment exporter estimated that if port clearance were reduced to one day, it would cut total costs by a sum equal to as much as 30 percent of the wage bill.

Africa has not attracted many foreign investors because the prevailing business environment and the market conditions failed to attract even domestic investors. Fundamental obstacles remain throughout much of the region—economic and policy barriers to new business entry, poor gover-nance, limited property rights protection, weak market institutions, undeveloped in-frastructure, low technical capacity of fi rms, low skill of management and workers, and size of markets.

$ million, 2004

Source: Indicator table 5.2.

Nigeria,$4,409

Sudan,$1,481

Rest of Africa,$4,227

More than half of Africa’s foreign direct investment goes to Nigeria and Sudan

Figure 2

Central Africa Republic

Rwanda

Sierra Leone

Benin

Ethiopia

Botswana

Angola

Mozambique

Congo, Dem. Rep.

São Tomé and Principe

0 50 100 150 200

Time required to start a business (days)

Source: World Bank 2006a.

Fast starters—and slow startersFigure 3


! e private sector’s participation in policy matters is increasing. Investor councils in Ghana, Senegal, and Tanzania are showing the benefi ts of a constructive, problem-solving in-teraction between government and business in tackling ineffi cient customs and inconsis-tent tax enforcement. New councils started in Mali and Uganda will add to the experience. A more engaged private sector in Africa should be able to push for regulatory and infrastruc-ture changes that will make Africa less of a high-cost, high-risk place to do business.

Strengthening fi nancial systems. Improving the performance of Africa’s fi nancial systems is also high on the agenda for enterprise de-velopment. Despite numerous reforms over several decades, most Sub-Saharan fi nancial systems remain weak, with Kenya, Mauri-tius, and South Africa among the exceptions. Little savings are mobilized from domestic or foreign sources. Credit to the private sector is limited and costly. Many national fi nancial sectors are dominated by a few banks provid-ing an uncompetitive, small range of services. ! e assets of banks exceed $10 billion only in Nigeria ($22 billion) and South Africa ($219 billion) and are less than $1 billion in 24 Sub-Saharan countries (indicator table 7.5).

Financial reform programs anticipated an initial increase and then a reduction in the spread between lending and deposit interest rates, but the spread remains large in many countries. And since liberalization, many fi nancial systems have seen high real inter-est rates. Financial deepening has also been lacking.

Although Africa has about a dozen stock markets—several opened in the 1990s—they have been inconsequential for economic growth and investment. Simply put, there are too few opportunities for sharing risk, trad-ing shares, and providing liquidity. Except for South Africa, the stock markets are by far the smallest of any region, both in the number of listed companies and in market capitaliza-tion. ! ey are also highly illiquid, seriously constraining their ability to contribute to economic growth.

Tackling these fi nancial development chal-lenges will require enlarging access to fi nan-cial services—particularly savings facilities. ! is will require strengthening links between formal and informal fi nancial systems—be-cause most Africans do not have access to the

formal fi nancial sector. And given the small size of many African economies, a regional approach to fi nancial sector development is needed to increase competition, cut costs, and lower risks.

Closing the infrastructure gaps. Africa has a major infrastructure defi cit, slowing econom-ic growth, reducing trade and international competitiveness, and retarding poverty re-duction (indicator tables 7.1–7.4). Nearly 40 percent of Africa’s people live in landlocked countries with high transport costs and poor trade links.11 Transport costs for intra-Africa trade (including trans-shipment) are unusu-ally high, estimated at nearly twice the levels in other developing regions. GDP per square kilometer in Africa (excluding South Africa) is one-tenth the level in Latin America and one-twentieth that in India.12

Improving infrastructure is thus central to the growth and the development of the private sector, especially of nontraditional, noncommodity sectors that require more in-tensive inputs of infrastructure and logistics. African enterprises can be competitive in factory fl oor direct costs for the production of manufactures, but their indirect costs, es-pecially the high costs of poor infrastructure services, are a major bottleneck for fi rms.13

Surveys of African fi rms indicate that in-adequate roads, ineffi cient ports, and power outages hobble African enterprises in their push to secure a place in global markets. For example, clothing exports from Uganda are an estimated 80 percent more expensive be-cause of transport costs. Rwandan farmers, who are making great strides in exporting coff ee, receive only 20 percent of the price of their coff ee as it is loaded onto ships in Mombassa. ! e other 80 percent disappears into transport costs—a combination of poor roads and administrative hurdles—between Rwanda and Kenya. Kenya loses the equiva-lent of 9 percent of its output to power out-ages—compared with 2 percent in China.14

To achieve the 7 percent growth rates need-ed to halve income poverty, Africa will need to invest 5 percent of GDP in infrastructure and an additional 4 percent of GDP to cover operation and maintenance requirements. To reach this goal, Africa’s infrastructure invest-ment needs will amount to around $20 bil-lion a year, twice what the region has been in-vesting.15 ! e road sector alone accounts for


about 40 percent of total investment needs, with the energy and water sectors accounting for 20 percent each.

While private capital has contributed to infrastructure fi nance in Africa, it is con-centrated in a few countries (South Africa accounted for half of total investment) and sectors (70 percent went to telecommunica-tions). Furthermore, private capital fl ows have declined steadily since 2001, refl ecting a global trend toward disengagement from emerging markets. Total annual offi cial de-velopment assistance for infrastructure in Africa has also been on the decline, account-ing for an average of about $826 million in 2000–04 (indicator tables 7.1–7.4).

Because of its magnitude, fi nancing infra-structure requires concerted eff ort from all funding sources, both public and private. ! e Gleneagles G-8 summit emphasized investing in Africa’s infrastructure. It agreed to support an Africa Infrastructure Consortium—led by the African Union and the New Partnership for Africa’s Development and including the African Development Bank and the World Bank—to mobilize infrastructure resources to both national and regional projects.

Creating an export push and fostering regional integrationGlobal trade has increased at unprecedented levels over the last three decades, and the world marketplace has become ever more competitive. Yet Africa lost market share in traditional exports and made little progress in diversifi cation of exports. Of the region’s exports of $128 billion in exports (in 2000 prices), only Angola, Nigeria, and South Africa had exports of more than $5 billion

(indicator table 2.17). Imports that year to-taled $144 billion.

Expanding and diversifying exports. Enter-ing new markets and diversifying into non-traditional exports can spur productivity, innovation, and competitiveness in Africa. Asia—particularly China and India—with its rising incomes and consumer demand, off ers new prospects for growth in exports by Afri-can producers in markets for tea, coff ee, and cocoa. Global competition in mass manufac-turing is fi erce in some of Africa’s existing export markets, such as textiles and cloth-ing, where competition increased substan-tially with the expiration of the Multifi bre Arrangement in 2005, and some footloose investors left Africa. On the other hand, exports of services—including tourism and back-offi ce support—off er new opportuni-ties. Indeed, new World Bank research on Africa’s trade and investment relations with China and India suggests that countries such as Ghana, Kenya, and Tanzania, among oth-ers, have the ability to compete eff ectively in certain global services markets.16

To be sure, the prospects for boosting and diversifying Africa’s export fl ows depend sig-nifi cantly on improved market access and reduced subsidies in world markets. ! e benefi ts of preferential access provisions cur-rently enjoyed by African countries would be far greater if they were more certain, not subject to burdensome rules of origin, and applied more uniformly across the continent. To this end, reforms are called for in the U.S. African Growth and Opportunity Act and the EU’s Everything But Arms initiative.

Improvements could also come from fur-ther multilateral liberalization that opens

Ghana suffered from a very weak investment

climate in the telecom sector in early 2004

due to international disputes in telecommuni-

cations, a lack of formal licenses for mobile

operators, and an outdated regulatory frame-

work. The result was a low fi xed and mobile

telephone penetration rate of 5 percent in early

2004. Today, there is a 15 percent penetration

rate, and all telecom operators are investing

heavily in the sector.

How was this accomplished? In partnership

with the U.S. Agency for International Develop-

ment, the World Bank fi nanced the formulation

of the National Telecommunication Policy and

has been deeply engaged in policy advice on

current reforms related to new legislation. The

Bank’s Africa Aid Program helped accelerate

the preparation of an “eGhana” project aimed

at creating jobs, diversifying global informa-

tion and communication technology services,

and providing more effi cient government ser-

vices delivery through the use of information

and communication technology applications

and public-private partnerships.

Box 4 Information and communication technology improvements in Ghana


developed country and other developing country markets for specifi c crops, such as cotton, sugar, and groundnuts, and for pro-cessed agricultural products. Improvements would also arise from a reduction of barriers in nonagricultural sectors, especially in other developing country markets. For example, some countries in Latin America heavily pro-tect their own garment manufacturers and other labor-intensive manufactures, reduc-ing the opportunity for African products to penetrate those markets.

But even if there were signifi cant reforms in market access, reductions in tariff s and subsidies, and elimination of nontariff bar-riers in overseas markets, for African fi rms to be able to eff ectively exploit the export opportunities created, signifi cant changes must take place “behind the border”—that is, within African countries themselves.17 One such precondition is speeding the clearance of goods through customs. Moving goods to and from African countries takes the longest of any region—60 days on average to import, 48 to export. Regulations in the typical Afri-can country require 30 signatures to import, 19 to export.

Integrating regional markets. Equally im-portant to African trade and enterprise is a framework for regional integration that will foster competitiveness and promote economic effi ciencies. But promoting eco-

nomic integration in Africa is not an easy task because there is limited economic com-plementarity among countries, and they are potential competitors in resource-intensive products. But as African economies are too small and too fragmented, regional inte-gration can promote internal and external economies of scale, encourage product dif-ferentiation and diversifi cation, intra-in-dustry trade, and a learning-by-exporting process, which would help make local fi rms more competitive in international markets. Eff ective regional integration is particu-larly important for landlocked countries. Currently, intraregional trade generally amounts to only a single-digit fi gure in most trade blocs of Africa (fi gure 4 and indicator table 6.2).

It is African leaders who will build and sustain the regional economic alliances that African economies require to compete internationally. ! e New Partnership for Africa’s Development has regional integra-tion as a core objective. It is encouraging the continent’s multiple and often overlapping regional organizations to defi ne their roles and responsibilities and to focus on “open regionalism,” regional (rather than national) trading agreements, and greater competitive-ness in global markets. Similarly, the Europe-an Union is supporting the rationalization of regional bodies to spur progress toward more competitive subregional economic group-ings. Most countries are now giving more at-tention to regional integration as a means to sharpen competitiveness, expand trade, and accelerate growth.

Successful regional integration will require better regional customs arrangements, lower tariff s, and much improved infrastructure and logistics to speed the movement and reduce costs of inputs, goods, and services within subregions to facilitate trade outside the region.

Participating in and sharing the benefi ts of growth World Development Report 2006: Equity and Development (World Bank 2005c) showed that inequalities in income—and thus in opportunities—waste much productive po-tential and lead to ineffi cient resource allo-cations. Such inequalities impair the devel-opment of institutions that might lift the

60

30

0

90

120

Exports to Africa

Exports to the rest of the world

150

1990 1992 1994 1996 1997 1998 2000 2002 20031991 1993 1995 1999 2001 2004

$ billions

Source: International Monitary Fund’s Direction of Trade Statistics.

Intraregional trade is small but increasingFigure 4


constraints on people’s economic pursuits and improve the way markets work. And it is precisely those weak institutions that per-petuate the unequal distribution of income. ! ey are also bad for the investment and risk-taking that underpin long-term growth and poverty reduction.

! e poor have been ill equipped to par-ticipate in and benefi t from the growth that has taken place in the region. Women in particular have suff ered from the inability to own assets and from discrimination in economic activity. In short, growth alone will not be enough to achieve the MDGs in Africa.

What is needed in addition?• Making agriculture more productive and

sustainable.• Connecting poor people to markets.• Enhancing human development.• Getting services to poor rural popula-

tions, especially to women.• Using natural resource rents well.

Making agriculture more productive and sustainable With poverty largely a rural phenomenon in Sub-Saharan Africa, agricultural and ru-

ral development are essential for the broad growth needed to achieve the MDGs (indi-cator tables 9.1 and 9.2). Agriculture still dominates the economies of most African countries, accounting for about 17 percent of the region’s GDP, 40 percent of exports, and a substantial share of employment (in-dicators tables 2.3, 6.1, and 10.1). In addi-tion, agricultural and rural development can reduce poverty more than in other regions. A 1 percent increase in crop yield in Sub-Sa-haran Africa translates into a 0.7 percent re-duction in the number of poor people, more than the 0.5 percent reduction in East and South Asia and seven times the 0.1 percent reduction in Latin America.18 Agricultural growth, important to all the MDGs, has the greatest impact on poverty and hunger, and empirical evidence for poor countries show that it can be an engine of growth.19 Find-ings from countries with signifi cant poverty reduction achievements suggest that agri-cultural productivity growth may be respon-sible for as much as 40–70 percent of those reductions.20 ! e challenge for Sub-Saharan Africa is to commit to policy and investment improvements that will have explicit produc-tivity-enhancing outcomes.

Like other developing regions, most countries

in Sub-Saharan Africa either have negative

growth rates and increasing poverty or posi-

tive growth rates and declining poverty. Sub-

Saharan countries have a median per capita

growth rate of 0.8 percent a year, substantially

lower than the overall median of 2.1 percent,

and most are above the regression line, indi-

cating lower poverty reduction performance

than for a typical developing country with simi-

lar growth performance.

There are important differences across

countries. For example, Ghana’s annual

growth rate over 1987–99 was 1 percent and

Uganda’s was 3 percent over 1989–96, but

their annual rate of change in poverty ranged

from about –8 percent to 2 percent. Differ-

ences in the Gini coeffi cient and differences

in the change of income distribution over time

may account for these results.

Sub-Saharan countries tend to have a low

sensitivity of poverty to growth, and the con-

tribution of changes in inequality to changes

in poverty in the region is similar to that in

the developing world as a whole. Together

these fi ndings suggest that poverty reduction

in Sub-Saharan Africa has been disappoint-

ing primarily because of its slow growth and

low sensitivity of poverty to growth (holding

constant the distribution of income). This low

sensitivity can be traced to the region’s low

incomes and high inequality (Sub-Saharan

Africa and Latin America are the world’s most

unequal regions).

What are the implications for policy? At a

basic level, growth remains crucial for reduc-

ing poverty in Africa—all the more so given

that the region’s low incomes imply a relatively

low sensitivity of poverty to growth. More-

over, the importance of growth as the driver of

changes in poverty seems to be even clearer

over longer periods, suggesting that growth

remains critical for sustained reductions in

poverty, though it is not the only factor.

Source: Adapted from World Bank 2005d.

Box 5 Growth is central to sustained poverty reduction


Productivity growth in agriculture is linked to higher income, increased employ-ment, and improved well-being. Growth in productivity increases production and low-ers food prices, which in turn holds down urban wages and encourages industrializa-tion. Increased production also creates more jobs for the poor both on and off the farm. Agricultural production growth of 1 percent typically leads to agricultural employment growth of 0.3–0.6 percent and nonagricul-tural employment growth of around 0.9 percent.21 ! ese growth and employment eff ects have proven most powerful when agricultural growth is driven by broad-based productivity increases in rural economies dominated by small farms.

One reason that African agricultural pro-ductivity is far below that of other regions is that the percentage of irrigated area is very small—less than 4 percent of the total cropped area (indicator table 9.2). Govern-ments will be expanding their eff orts in water management, using lessons from successful irrigation programs, both small (the fadama projects in Nigeria) and large (rehabilita-tion of the Offi ce du Niger in Mali). African farming can also be improved by employing environmentally sustainable farming meth-ods, including appropriate use of fertilizer and improved seeds, better soil conservation practices, and longer fallows and rotations.

Connecting poor people to markets Interventions to connect the rural poor to the modern economy through investments in rural roads, electrifi cation, and commu-nications are central elements of the shared growth strategy. ! e share of the rural popu-lation within two kilometers of an all-sea-son road ranges from 5 percent in Chad and Uganda to more than 50 percent in Madagas-car, Mali, Niger, and Zambia (fi gure 5 and in-dicator table 7.2). Rural access to electricity is also low, ranging from 0.2 percent in Burkina Faso and Niger to more than 20 percent in Cameroon, Côte d’Ivoire, Ghana, and Nigeria (fi gure 6 and indicator table 7.4). Infrastruc-ture is important to integrate markets and therefore essential for poor people to be able to develop their businesses, get contracts, en-hance the value of their assets, have access to new technologies, and above all, benefi t from the growth of the economy.

Lack of access to credit, education, raw ma-terials, goods, and public services also isolate poor people and expose them to emergencies, poor harvests, health problems, and econom-ic crisis. If isolation raises the costs of obtain-ing education, for instance, the accumulation of human capital becomes economically in-feasible. One consequence of low integration of the poor to markets is that poor people, especially those in rural areas, usually end up hostages of intermediaries who sell them

Nigeria

Tanzania

Niger

Benin

Congo, Dem. Rep.

Burkina Faso

Cameroon

Ethiopia

Chad

0 10 20 30 40 50

Share of rural population within 2 kilometers of an all-season road (%)


Rural access to transportation networks varies widely...Figure 5


provisions, seeds, and domestic utensils dur-ing the planting season or during the produc-tion of some agricultural or other product and who then buy their entire output for a very low price. ! e same happens with mon-eylenders who take advantage of the poor in urban areas and take possession of their in-come and goods.

Building the assets of women and connect-ing them to markets are essential for shared growth. Much of Africa’s economic activity is in the hands of women. Data from Uganda suggest that women contribute about 50 per-cent to GDP and that women and men are not equally distributed across productive sectors. Time allocation studies throughout Sub-Sa-haran Africa confi rm women’s preponderant role in agriculture.

Because men and women diff er in their ac-cess to, and control over, productive and oth-er assets, economic capacities and incentives are gender-diff erentiated in ways that aff ect resource allocation within the household, labor productivity, and welfare. ! ese dif-

ferences have implications for the fl exibility, responsiveness, and dynamism of the econ-omy. A study in Burkina Faso showed that shifting existing resources between men’s and women’s plots in the same household could increase agricultural output by 10–20 percent. Another study in Kenya concluded that giving female farmers the same level of agricultural inputs and education as male farmers could increase their yields by more than 20 percent.

Enhancing human development Half the population in Sub-Saharan Africa is between the ages of 5 and 24, indicating that the population will continue to rise well into the twenty-fi rst century. ! ere is a substan-tial need for future scaling up of human de-velopment, notably in health and education, which can only be met by high and sustain-able economic growth rates.22 If this large co-hort requires massive resources, the increas-ing working-age population can potentially contribute to boost economic growth.

Nigeria

Côte d’lvoire

Cameroon

Ghana

Comoros

Burundi

Central African Republic

Niger

Burkina Faso

Chad

0 5 10 15 20 25 30

Share of rural population with access to electricity (%)


... as does rural access to electricityFigure 6

In 1991 only 30 percent of Ethiopian children

attended school. Addressing this challenge

has involved mobilizing communities, training

teachers, and obtaining fi nancing for the ef-

fort. Development partners are supporting a

sectorwide education reform program. One of

the results has been a rise in the gross enroll-

ment rate to 77 percent in 2004 thanks to an-

nual reviews and a good monitoring system.

But improving quality remains a challenge.

Box 6 Scaling up support for education in Ethiopia has large payoffs


Africa’s human development indicators have traditionally been among the lowest in the world, but they are on the rise in many areas. Gross primary enrollment rates as a share of the relevant age group shot up to 93 percent in 2004 from 72 percent in 1990, holding the promise that many more Afri-cans will contribute to and benefi t from an expanding economy (indicator table 8.1). Indeed, the increase appears to have already contributed to a rise in literacy rates from 50 percent in 1997 to 65 percent in 2002. A fur-ther challenge now is to improve quality of primary education.

In most countries, however, retention to the end of the primary cycle remains a problem, and the result is to undermine at-tainment of the education MDGs (universal primary school completion and gender par-ity in education—indicator table 3.3). In 2004 primary school completion rates in African countries averaged no more than 62 percent, up from 51 percent in 1991, and in only a few countries did the rate ex-ceed 90 percent. Success will require, among other measures, improving the quality and relevance of schooling services and encour-aging parents to let their children, particu-larly girls, complete the full cycle of primary schooling.

With progress toward universal primary school completion, the number of students aspiring to post-primary education has been growing rapidly, and the pace can be expected to pick up in the coming years. ! e magni-tude of the expected increase poses major policy challenges regarding the resources required to expand capacity without dilut-ing the quality of services and the reforms needed to manage costs and ensure equitable

distribution of the burden of fi nancing. Yet in too many African countries, these issues have received inadequate attention at best. In the meantime, conditions in post-prima-ry education have deteriorated. Enrollments have grown, public budgets have stagnated and private sources of funding remain un-tapped. Moreover, post-primary education tends to be poorly diversifi ed and typically leaves students unprepared for most employ-ment opportunities.

Many African national health care systems are under stress due to weakening human resource capacity and fi nancing constraints. Solid evidence and best-practice examples show how sectorwide approaches are among the best options for ensuring that strength-ened public and private health care systems are mutually reinforcing with disease-specifi c programs. It is necessary to scale up policies to address the systemic constraints in human resources for health, which include the dra-matic brain drain of physicians and nurses from Africa.23

Despite the recent progress on treatment and prevention, HIV/AIDS remains a major development challenge in the region, par-ticularly in Southern Africa, the epicenter of the epidemic. It continues to reverse life ex-pectancy gains, consume savings, dilute pov-erty eff orts, erode productivity, and threaten economic growth.24

Malaria, a completely preventable and treatable disease, has taken the heaviest toll on Africa (indicator table 12.1). Each year, it kills more than 1 million people around the world—90 percent of them in Africa. Every 30 seconds, an African child dies of malaria, making it the largest single killer of African children.25

The Booster Program for Malaria Control sup-

ports country efforts to deliver concrete and

measurable results. Currently targeting 19

countries, the program has a 10-year horizon,

with the initial three-year “Intensive Phase” al-

ready well under way. Four projects have been

approved—for Eritrea, Democratic Republic

of Congo, Niger, and Zambia—and prepa-

rations are proceeding rapidly in nine more

countries and on a regional project. Strong

collaboration and partnership with key agen-

cies and countries are moving the work pro-

gram forward rapidly by focusing on results

and strengthening donor harmonization and

partnerships, including the U.S. Presidential

Initiative for Malaria Control, the Bill & Melinda

Gates Foundation, ExxonMobil, the United

Nations Children’s Fund, the World Health

Organization’s Regional Offi ce for Africa, and

the Global Fund to Fight AIDS, Tuberculosis,

and Malaria.

Box 7 Responding aggressively to malaria in Sub-Saharan Africa


! e disease takes a high toll on house-holds and health care systems and increases poverty by signifi cantly reducing productiv-ity and social stability. ! e annual economic burden in Sub-Saharan Africa is estimated to be about $12 billion. Although the disease is preventable and curable with available tech-nology, coverage with eff ective interventions has remained low, particularly among poor and rural populations.

In Sub-Saharan Africa 38 of 47 countries fall short of the World Health Organization’s standard of at least 20 physicians per 100,000 people.26 ! e March 2005 report of the Com-mission for Africa called for a tripling of the healthcare workforce in Africa. Africa will need to train an additional 1 million workers over a decade in the health sector and provide an improved workplace environment (salary, equipment, supplies) to make that happen.

Getting services to poor rural populations, especially womenSince 1999, when the Poverty Reduction Strategy approach became the key policy framework for development partners, many African governments have been consciously investing more in pro-poor service delivery, especially in health, education, HIV/AIDS, rural development (roads), agriculture, and water.

According to World Development Report 2004: Making Services Work for Poor People (World Bank 2003), the biggest payoff s to service delivery are likely to come from a few key actions: spending smartly and predict-ably in line with priorities and coordinated across sectors; managing decentralization to reap the benefi ts of being closer to clients; developing and deploying administrative ca-pacity to take sound decisions at the top and to implement them well; curtailing corrup-tion; and learning from success and failure. Reforming basic incentives that strengthen

accountability and that raise performance closer to formal standards is the place to start. As incentives become better aligned and internalized and as administrative ca-pacity grows, more advanced reforms can be deployed to support deeper institutional change and scaling up.

! e rural-urban divide can be enormous. In Kenya the share of the population with access to an improved water source is 39 per-cent in rural areas and 86 percent in urban; the share with a health center less than an hour away is 65 percent in rural areas and 98 percent in urban (indicator table 14.8). ! e male-female divide can also be wide. ! e diff erent roles of men and women in the market economy are coupled with their equally diff erent—and unbalanced—roles in the household. In addition to their promi-nence in agriculture, women take on most domestic tasks: processing food crops, pro-viding water and fi rewood, and caring for the elderly and the sick. Providing women in particular with better services could enable them to be much more productive, and im-prove quality of life.

Using natural resource rents wellResource-based rents are widespread and growing due to new discoveries and favor-able prices. During the 1990s, 65 percent of all foreign direct investment was concen-trated in oil, gas, and mining, and 45 per-cent of exports came from the oil and min-ing sectors. ! ose fi gures have substantially increased in recent years. Between 2000 and 2010, $200 billion in oil revenue will accrue to African governments. Estimates of the value of the oil price windfall to Af-rican oil- producers in 2004 range from 9 percent of government revenue in Gabon to 56 percent in Equatorial Guinea.27 Min-eral-dependent countries in Africa tend to have higher poverty rates, greater income

Water supply and sanitation investments have

important implications for health, especially

child health. Nearby water and school latrines

can dramatically increase mothers’ ability to

care for their children and can increase girls’

school attendance. Better roads improve

school attendance and the use of health fa-

cilities. And health and education investments

have clear complementary effects: mothers’

education is strongly correlated with child

survival, and school attainment is directly af-

fected by family illness, especially HIV/AIDS.

Source: World Bank 2006b.

Box 8 Investing smartly across sectors


inequality, less spending on health care, higher prevalence of child malnutrition, and lower literacy and school enrollments than other countries at the same income level.

But mineral-exporting economies can share growth. Chile, Indonesia, and Malaysia have all used natural resource wealth to pro-vide a basis for a more diversifi ed economy in which poor people can participate in and contribute to the process of growth.

! e Extractive Industries Transparency Initiative (EITI) aims to increase transpar-ency in transactions between governments and companies within extractive industries. It shows what African governments, devel-opment partners, and private business can achieve when they act together to improve the transparency and accountability of natural resource fl ows. Revenues from oil, gas, and mining companies—in the form of taxes, royalties, signature bonuses, and other payments—can be an important engine for economic growth and social development.

To date, 14 African countries have com-mitted to implement the EITI, twice as many as a year ago (indicator table 13.2).28 In many of those countries EITI programs are for the fi rst time involving local civil soci-ety groups in the direct oversight of natural resource revenues. ! e resource fl ows that could be overseen by the programs are stag-gering. Nigeria’s fi rst EITI report, released in early 2006, reported an average of more than $15 billion in annual payments to gov-ernment by oil and gas companies. Compare that with $4.7 billion in IDA commitments for all Africa in 2006.

Providing the underpinning for shared growth by building capable states and improving governanceWithout institutions that can deliver basic services, the rule of law, predictable admin-istration, and responsive regulation, Africa’s plans and ambitions are not likely to gain traction. Support to the drivers of growth and a focus on strategies for shared growth must be complemented by a stronger focus on governance. ! is will require a strong civil society presence in order to make gov-ernments more accountable and to allow them to actively participate in building their country’s destiny. Furthermore, countries must identify their greatest shortcomings and challenges in order to fi nd entry points where reforms can make the biggest diff er-ence. A country that has already dealt with electoral reforms might focus on tax system or state procurement. And the private sector, an independent media, and civil society or-ganizations can help build demand for good governance.

Developing institutional capacityCapacity development is a regionwide chal-lenge for Africa, although countries are at various points on the spectrum from weak to strong capacity and from low to high ac-countability (indicators tables 13.2–13.4). To varying degrees, African countries all face the task of mitigating the brain drain of highly skilled workers with incentives for capacity retention and utilization. ! ey have to re-vive failing service delivery systems to help achieve the MDGs, promote a competitive environment for private sector–led growth,

Nigeria has long suffered the effects of mas-

sive corruption and weak governance, making

it diffi cult for investors to do business in Nige-

ria, wasting public resources, and impoverish-

ing the average Nigerian. Today, all of Nigeria’s

governance and corruption indicators show an

improvement.

How has this been accomplished? The

government has embarked on a comprehen-

sive effort to fi ght corruption and improve

governance:

• Its targeted anticorruption efforts have

yielded about 40 convictions, including

of high-ranking offi cials, and forfeiture of

about $3 billion in cash and assets to the

public treasury.

• It has introduced a new Virtual Poverty

Fund to help ensure that the federal gov-

ernment’s $750 million in annual savings

on debt service payments go to the right

uses and are tracked and monitored.

• It has moved to open and competitive pro-

curement systems.

It is conducting independent audits of the

oil accounts conducted under the Extractive

Industries Transparency Initiative.

Box 9 Governance is being tackled in Nigeria


safeguard their people from civil strife and crime by strengthening the rule of law, and use open decisionmaking processes to in-clude the voices of civil society and citizens.

Capacity development eff orts need clear objectives and should not rely on fragment-ed, project-centered approaches. ! ey need time to be institutionalized, require follow-through by political and technical leaders, and must be supported by appropriate incentives. To be sure, training, equipment, and techni-cal assistance are needed, but they should be provided in response to clear and eff ective demand so that they can be eff ectively ab-sorbed. ! e successful cases—such as com-munity-driven development in Malawi, per-formance management in Tanzania, public fi nancial management in Burkina Faso and Madagascar, and decentralization in Uganda and Ethiopia—rely on strong leadership, use fl exible and programmatic approaches to fi -nancing, and match existing capacities with global and local knowledge.

Strengthening public expenditure management and fi nancial accountabilitySound public fi nancial management is fun-damental for eff ective use of domestic re-sources and of external aid. In most African countries expenditure and revenue account-ability remains a challenge. Past eff orts have addressed regulatory changes, the rehabilita-tion of existing systems, the establishment of internal and external audits, and procure-ment reforms. ! ere has been progress in the development of an indicator-based approach to public fi nancial management system as-sessment, evolving from the approach used

in assessing progress in the HIPC Debt Re-lief Initiative during 2001–04 and further refi ned by the Public Expenditure and Finan-cial Accountability initiative.29

Reforming legal and judicial systems Early reforms focused on adjusting legal and regulatory frameworks to facilitate economic development, such as protecting property. Regional economic integration triggered reforms of the regulatory frameworks for commercial and business laws. An important issue is the relationship between the formal judicial system and informal confl ict resolu-tion mechanisms. Many African countries have a well functioning system of village or community courts where decisions are made by laypersons in accordance with traditional rules. Judicial reforms must appreciate the role and importance of these traditional sys-tems for African societies and their relevance for the judiciary.

Improving public sector management! e public sector is critical for good gover-nance and development. In many countries civil services are overstaff ed, overly bureau-cratic, and ineffi cient, with limited motiva-tion and user-orientation. Well functioning administrative systems and a motivated, ap-propriately sized civil service are important for effi cient service delivery and optimal use of public resources. To address existing dys-functions in the civil service, countries are reforming civil service with diff ering empha-ses—in Nigeria and Zambia, improving the operational effi ciency and rationalizing com-plex bureaucratic structures; and in Mozam-

The World Bank Task Force on Capacity De-

velopment in Africa’s September 2005 report,

Building Effective States, Forging Engaged

Societies (World Bank 2005a) highlighted the

need for African countries to implement home-

grown, fully owned strategies for capacity de-

velopment as an explicit part of their medium-

term development programs. They should

take the lead in determining their capacity

needs and set priorities for implementation

in line with their poverty reduction strategies

and other national development strategies.

The focus should be on strengthening the ca-

pabilities of the state. This means strengthen-

ing the skills and professional profi les of the

public sector, increasing organizational effec-

tiveness, and developing institutions capable

of supporting change management. Many Af-

rican countries are still far from taking strong

leadership in programming and implementa-

tion, but the experiences of Botswana, Mauri-

tius, and South Africa, for example, show that

this can be done successfully.

Source: World Bank 2005a.

Box 10 A vision for capacity development


bique, Nigeria, Sierra Leone, and Tanzania, pursuing bottom-up administrative reforms by building demand for change and capacity to implement development programs and to absorb aid. ! is also includes cross-cutting institutional reforms aimed at comprehen-sive decentralization and capacity develop-ment in the context of far-reaching political reforms, as in Ethiopia.

Strengthening local governments Recognizing that decentralization is a politi-cal choice made by many African countries, governments are addressing the political, reg-ulatory, administrative, and fi nancial impli-cations of a decentralized state. In Ethiopia, Lesotho, Madagascar, Sierra Leone, Tanza-nia, and Uganda governments are developing or reforming the system of intergovernmen-tal fi scal relations. Multisectoral public sector operations complement community-driven activities, especially where linked to local governance development.

Community-driven development has proved an eff ective means for rebuilding soci-eties emerging from confl ict. It addresses ur-gent local needs and restores collapsed trust and relationships. By engaging people in addressing problems at the local level, com-munity-driven development can yield an in-valuable peace dividend. Rwanda created the Community Reintegration and Development Project, which largely emphasized decentral-ization and encouraging local government. To involve local people in decisionmaking, community development committees give an active role to local governments and voice and infl uence to communities. Citizens have more decisionmaking power through an ap-proach that links community participation with local management of resources, making both citizens and politicians accountable for results.

Managing the impact of shocks Low-income countries in Africa are dispro-portionately aff ected by outside economic shocks, in part due to continued heavy reli-ance on primary commodity exports or large swings in world prices of important inputs, such as the signifi cant rise in oil prices and other minerals, which has had adverse eff ects on other sectors of the economy thanks to the exchange rate appreciation. ! ese coun-

tries have been buff eted by droughts, locusts, and other natural disasters while frequently lacking economic cushions, such as large for-eign exchange reserves or insurance markets to protect themselves. Terms of trade shocks, for example, which tend to take several years to dissipate if triggered by export price de-clines, have very signifi cant negative impacts on growth rates. Weak capacity to manage terms of trade volatility has led to fi scal vola-tility as country authorities fi nd it diffi cult to shield public expenditures (especially public consumption) from shocks.

Negative shocks very often lead to in-creases in poverty. In the absence of stabiliz-ing mechanisms, an “input shock,” such as a drought, very quickly translates into an “out-put shock,” leading to a fall in consumption. Since adequate social safety nets are usually lacking, poor people are hurt disproportion-ately, often being forced to sell their meager assets to try to maintain consumption.

National strategies for the development of statisticsWithout baseline data and good information on trends, it’s not easy to put a target-driven development strategy into gear. Yet, with the MDG time horizon of 2015 rapidly ap-proaching, many countries still do not collect the statistics they need to track progress. In many Sub-Saharan countries, policymakers do not know how many people are living in poverty or whether the number is growing or shrinking.

Building a sustainable statistical system that can produce and disseminate statistics to manage development results has been a focus of many countries struggling to meet demand for data. Recognizing the impor-tance of strategic planning to guide the im-provement of statistical systems, the Mar-rakech Action Plan for Statistics endorsed by developing countries and development agencies recommended that all low-income countries prepare a national strategy for the development of statistics by the end of 2006. ! is recommendation was further sup-ported by the Reference Regional Strategic Framework for Statistical Capacity Building in Africa, which aims to help African coun-tries meet the data challenges of the results agenda by 2015.


M

For shared growth to become a reality, the Decade of Africa must be about results not promises. In the fi nal push to achieve as many of the MDGs in every African country, the ability to track progress is of enormous value. For example, the share of the popula-tion lacking access to safe water has dropped to 42 percent, from 51 percent in 1990, but an accelerated eff ort is needed to reach the goal of lowering this to 26 percent by 2015.

! e basis for realistic optimism is based primarily on changes in Africa and in new approaches among the continent’s global partners. First, the African leadership—in government, the private sector, and civil society—is taking ownership for develop-ment on the continent. Complementing this initiative, we see a new international commitment to increase assistance and make it more eff ective, and to open markets

to African products. Meanwhile, the signifi -cant growth in Asia, particularly in China and India, opens new opportunities for in-creased trade and investment.

It is critical that the global conversation about the challenge of Africa’s economic development continue. But it must be more than a discussion about how much aid or market access wealthy developed countries will off er—important as these considerations are. It must also focus on the quality of de-velopment assistance and on the resourceful-ness that Africans can bring to the challenges of building honest and capable states, and encouraging competitive companies that can generate jobs in agriculture, manufacturing, and services. Without Africans’ own invest-ment and leadership, outside eff orts—how-ever generous—will falter or worse, create new problems. In short, Africa’s future will be most signifi cantly determined by what Africans do.

1 Throughout this essay Africa and African refer to Sub-Saharan

Africa. The 14 countries are Angola (6.4 percent), Botswana

(5.8 percent), Cape Verde (5.9 percent), Chad (9.9 percent),

Equatorial Guinea (15.5 percent), Liberia (8 percent), Mali (6

percent), Mauritania (5.3 percent), Mozambique (8.4 percent),

Rwanda (6.2 percent), Sierra Leone (6.7 percent), Sudan (6.2

percent), Tanzania (5.7 percent), and Uganda (5.7 percent).2 World Bank 2006b.3 World Bank Africa Region Poverty Studies.4 World Bank 2006b.5 Tcheyan 2006.6 The Country Policy and Institutional Assessment (CPIA)

assesses the quality of a country’s present policy and

institutional framework. “Quality” means how conducive that

framework is to fostering sustainable, poverty-reducing growth

and the effective use of development assistance. The CPIA is

conducted annually for all International Bank for Reconstruction

and Development and International Development Association

borrowers and has evolved into a set of criteria grouped into four

clusters: economic management, structural policies, policies for

social inclusion and equity, and public sector management and

institutions.7 Data on GDP growth for 2005 is preliminary.8 These fi gures include Zimbabwe, which experienced infl ation

rates of 350 percent in 2004 and 237 percent in 2005.

Removing Zimbabwe from the calculation substantially reduces

the average infl ation to about half those rates ( IMF 2006).9 Bacon and Mattar 2005.10 Established in 1987, the Strategic Partnership for Africa is an

informal association of donors and African partners that works

to improve the quality and increase the quantity of assistance to

Africa. For more information, see www.spa-psa.org.11 This fi gure includes the Democratic Republic of Congo, a coastal

country whose population is virtually landlocked.12 Eifert, Gelb, and Ramachandran 2005.13 Eifert, Gelb, and Ramachandran 2005.14 Tcheyan 2006.15 World Bank 2005b.16 Broadman 2006.17 Broadman 2006.18 Thirtle, Piesse, and Lin 2003.19 Tiffi n and Irz 2006.20 Thirtle, Piesse, and Lin 2003.21 Mellor 2001.22 World Bank 2006c.23 High-Level Forum on the Health MDGs 2004.24 Haacker 2004.25 WHO and UNICEF 2005.26 Page and Plaza 2005.27 Page forthcoming.28 Angola, Cameroon, Chad, Democratic Republic of Congo,

Republic of Congo, Equatorial Guinea, Gabon, Ghana, Guinea,

Mauritania, Niger, Nigeria, São Tomé and Principe, and Sierra

Leone.

29 The Public Expenditure and Financial Accountability initiative,

started in December 2001, is jointly fi nanced by the World

Bank’s Development Grant Facilities, the European Commission,

the U.K. Department for International Development, the Swiss

State Secretariat for Economic Affairs, the Royal Norwegian

Ministry of Foreign Affairs, and the French Ministry of Foreign

Affairs. The International Monetary Fund and the Strategic

Partnership with Africa are also partners. A steering committee,

comprising members of these agencies, manages the initiative,

and a secretariat has been set up at the World Bank in

Washington, D.C.

Notes


References 21

Bacon, Robert, and Adib Mattar. 2005. “The Vulnerability of

African Countries to Oil Price Shocks: Major Factors and Policy

Options.” World Bank, Washington, D.C.

Bosworth, B., and S. Collins. 2006. “Economics of Developing

Countries.” Brookings Institutions, Washington, D.C. [www.

brookings.edu/es/research/projects/develop/develop.htm].

Broadman, Harry G. 2006. “Africa’s Silk Road: China and India’s

New Economic Frontier.” World Bank, Washington, D.C.

Eifert, Benn, Alan Gelb, and Vijaya Ramachandran. 2005.“Business Environment and Comparative Advantage in Africa:

Evidence from the Investment Climate Data.” Working Paper 52.

Center for Global Development, Washington, D.C.

Haacker, Markus. 2004. “HIV/AIDS: The Impact on the Social

Fabric and the Economy.” In The Macroeconomics of HIV/AIDS.

Washington, D.C.: International Monetary Fund.

High-Level Forum on the Health MDGs. 2004. “Addressing

Africa’s Health Workforce Crisis: An Avenue for Action.”

Background paper for the Second High-Level Forum on

the Health MDGs, December 2–3, Abuja, Nigeria. [www.

hlfhealthmdgs.org/Documents/AfricasWorkforce-Final.pdf]

IMF (International Monetary Fund). 2006. Regional Economic

Outlook: Sub-Saharan Africa. May. Washington, D.C.

———. Various years. Directory of Trade Statistics.

Washington, D.C.

Mellor, J. 2001. “Reducing Poverty, Buffering Economic Shocks—

Agriculture and the Non-Tradable Economy.” Background paper

prepared for Experts’ Meeting, Roles of Agriculture Project, Food

and Agricultural Organization, March 19–21, Rome.

Page, John. Forthcoming. “Strategies for Pro-Poor Growth: Pro-

Poor, Pro-Growth or Both?” Journal of African Economies.

Page, John, and Sonia Plaza. 2005. “Migration, Remittances and

Development: A Review of Global Evidence.” Paper presented

at the Plenary Session of the African Economic Research

Consortium, May 29, Nairobi.

Tcheyan, Nils. 2006. “Revisioning Africa outside the Old

Categories.” Paper presented at the University of California at

Berkeley, April 8.

Thirtle, C., J. Piesse, and L. Lin. 2003. “The Impact of Research-

Led Productivity Growth on Poverty in Africa, Asia and Latin

America.” World Development 31 (12): 1959–75.

Tiffi n, Richard, and Xavier Irz. 2006. “Is Agriculture the Engine of

Growth?” Agricultural Economics 35 (1): 79–89.

References

WHO (World Health Organization) and UNICEF (United Nations Children’s Fund). 2005. World Malaria Report. Geneva.

World Bank. 2003. World Development Report 2004: Making

Services Work for Poor People. Washington, D.C.

———. 2005a. “Building Effective States, Forging Engaged

Societies.” Task Force on Capacity Development in Africa. World

Bank, Washington, D.C.

———. 2005b. “Infrastructure in Sub-Saharan Africa: The Role of

the World Bank and the Donor Community.” World Bank, Africa

Region, Financial, Private Sector and Infrastructure Department,

Washington, D.C.

———. 2005c. World Development Report 2006: Equity and

Development. Washington, D.C.

———. 2005d. Global Monitoring Report. Washington, D.C.

———. 2006a. Doing Business 2006: Creating Jobs.

Washington, D.C.

———. 2006b. Global Monitoring Report. Washington, D.C.

———. 2006c. World Development Indicators 2006.

Washington, D.C.

Part I. Basic indicators and national accounts1. Basic indicators1.1 Basic indicators 25

2. National accounts 2.1 Gross domestic product, real 262.2 Gross domestic product per capita, real 272.3 Agriculture value added 282.4 Industry value added 292.5 Services value added 302.6 Gross domestic product, nominal 312.7 Total consumption 322.8 General government consumption 332.9 Gross fi xed capital formation 342.10 General government fi xed capital formation 352.11 Private sector fi xed capital formation 362.12 Gross domestic savings 372.13 Gross national savings 382.14 Resource balance (exports minus imports) 392.15 Exports of goods and services, nominal 402.16 Imports of goods and services, nominal 412.17 Exports of goods and services, real 422.18 Imports of goods and services, real 432.19 Gross domestic product growth 442.20 Gross domestic product per capita growth 452.21 Gross national income per capita 462.22 Total consumption per capita 47

Part II. Millennium Development Goals3. Millennium Development Goals3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger 483.2 Millennium Development Goal 2: achieve universal primary education 503.3 Millennium Development Goal 3: promote gender equality and empower women 513.4 Millennium Development Goal 4: reduce child mortality 523.5 Millennium Development Goal 5: improve maternal health 533.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases 543.7 Millennium Development Goal 7: ensure environmental sustainability 553.8 Millennium Development Goal 8: develop a global partnership for development 56

Part III. Development outcomes4. Results framework4.1 Status of Paris Declaration indicators 58

Drivers of growth5. Private sector development

Indicator tables

Contents 23


5.1 Business environment 595.2 Investment climate 60

6. Trade6.1 International trade and tariff barriers 626.2 Regional integration, trade blocs 65

7. Infrastructure7.1 Water and sanitation 667.2 Transportation 687.3 Information and communication technology 707.4 Energy 727.5 Financial sector infrastructure 74

Participating in growth8. Human development8.1 Education 768.2 Health 78

9. Agriculture and rural development9.1 Rural development 819.2 Agriculture 82

10. Labor, migration, and population10.1 Labor 8410.2 Migration and population 86

11. HIV/AIDS11.1 HIV/AIDS 87

12. Malaria12.1 Malaria 88

13. Capable states and partnership13.1 Aid and debt relief 8913.2 Capable states 9013.3 Governance and anticorruption indicators 9213.4 Country Policy and Institutional Assessment ratings, 2005 94

Part IV: Household Welfare14. Household welfare14.1 Burkina Faso household survey, 2003 9514.2 Burundi household survey, 1998 9614.3 Cameroon household survey, 2001 9714.4 Côte d’Ivoire household survey, 1998 9814.5 Ethiopia household survey, 2000 9914.6 Gambia household survey, 1998 10014.7 Ghana household survey, 1998/99 10114.8 Kenya household survey, 1997 10214.9 Madagascar household survey, 1999 10314.10 Malawi household survey, 1997/98 10414.11 Mozambique household survey, 1996 10514.12 Nigeria household survey, 2004 10614.13 São Tomé and Principe household survey, 2000 10714.14 Sierra Leone household survey, 2002/03 10814.15 Uganda household survey, 2002/03 10914.16 Zambia household survey, 1998 110

BASIC INDICATORS Part I. Basic indicators and national accounts 25

Tab

le1.1 Basic indicators

Participating in growth

GNI per capita

Under-fi ve mortality rate

(per 1,000)

Total net offi cial

development assistance per

capita(current $)

Population(millions)

Land area(thousands of sq km)

Life expectancy

at birth(years)

Adult literacy rate(% ages 15 and older)

Dollarsa

Average annual growth

(%)Gini

coeffi cient Male Female

2004 2004 2004 2000–04 2004 1996–2004b 1996–2004b 2000–04b 2000–04b 2004

SUB–SAHARAN AFRICA 726.4 23,619 600 1.7 46 168.2 .. .. .. 33excluding South Africa 680.9 22,405 397 2.0 46 172.2 .. .. .. 34excl. S. Africa & Nigeria 552.2 21,494 390 1.9 47 166.4 .. .. .. 41Angola 15.5 1,247 930 4.6 41 260.0 .. 83 54 74Benin 8.2 111 450 1.2 55 152.0 36.5 48 23 46Botswana 1.8 567 4,360 5.7 35 116.0 .. 80 82 22Burkina Faso 12.8 274 350 0.3 48 192.0 39.5 29 15 48Burundi 7.3 26 90 0.0 44 190.0 42.4 67 52 48Cameroon 16.0 465 810 2.7 46 149.4 44.6 77 60 47Cape Verde 0.5 4 1,720 40.0 70 36.4 .. .. .. 282Central African Republic 4.0 623 310 0.3 39 193.0 .. 65 33 26Chad 9.4 1,259 250 3.6 44 200.0 .. 41 13 34Comoros 0.6 2 560 –0.1 63 70.0 .. .. .. 42Congo, Dem. Rep. 55.9 2,267 110 0.0 44 205.0 .. 81 54 32Congo, Rep. 3.9 342 760 –0.5 52 108.0 .. .. .. 30Côte d’Ivoire 17.9 318 760 –2.4 46 193.6 44.6 61 39 9Djibouti 0.8 23 950 0.0 53 125.6 38.6 .. .. 82Equatorial Guinea 0.5 28 .. 0.0 43 204.0 .. 93 80 60Eritrea 4.2 101 190 –3.4 54 82.0 .. .. .. 61Ethiopia 70.0 1,000 110 1.3 42 166.4 30.0 .. .. 26Gabon 1.4 258 4,080 0.3 54 91.0 .. .. .. 28Gambia, The 1.5 10 280 0.8 56 122.0 50.2 .. .. 43Ghana 21.7 228 380 2.4 57 112.0 40.8 66 50 63Guinea 9.2 246 410 1.0 54 155.0 .. 43 18 30Guinea-Bissau 1.5 28 160 3.8 45 203.0 .. .. .. 50Kenya 33.5 569 480 0.3 48 119.5 42.5 78 70 19Lesotho 1.8 30 730 1.9 36 112.2 .. 74 90 57Liberia 3.2 96 120 –2.8 42 235.0 .. .. .. 65Madagascar 18.1 582 290 –1.5 56 122.6 47.5 77 65 68Malawi 12.6 94 160 –0.3 40 175.2 50.3 75 54 38Mali 13.1 1,220 330 2.3 48 219.0 .. 27 12 43Mauritania 3.0 1,025 530 4.0 53 125.0 39.0 60 43 60Mauritius 1.2 2 4,640 2.9 73 15.2 .. 88 81 31Mozambique 19.4 784 270 6.2 42 151.6 39.6 .. .. 63Namibia 2.0 823 2,380 3.2 47 63.4 .. 87 83 89Niger 13.5 1,267 210 0.0 45 258.8 .. 43 15 40Nigeria 128.7 911 430 2.7 44 196.6 43.7 .. .. 4Rwanda 8.9 25 210 0.3 44 203.0 .. 71 60 53São Tomé and Principe 0.2 1 390 2.3 63 118.0 .. .. .. 218Senegal 11.4 193 630 1.6 56 136.6 .. 51 29 92Seychelles 0.1 0 8,190 –2.3 .. 13.5 .. 91 92 124Sierra Leone 5.3 72 210 5.3 41 282.8 .. 47 24 67Somalia 8.0 627 .. 0.0 47 225.0 .. .. .. 24South Africa 45.5 1,214 3,630 2.2 45 67.0 57.8 84 81 14Sudan 35.5 2,376 530 7.5 57 91.4 .. 71 52 25Swaziland 1.1 17 1,660 –0.7 42 156.4 .. 81 78 104Tanzania 37.6 884 320 4.6 46 126.0 34.6 78 62 46Togo 6.0 54 310 –0.7 55 139.6 .. 69 38 10Uganda 27.8 197 250 1.8 49 137.8 43.0 77 58 42Zambia 11.5 743 400 0.3 38 182.0 42.1 76 60 94Zimbabwe 12.9 387 620 –6.2 37 129.0 .. .. .. 14NORTH AFRICA 150.5 5,738 1,784 0.0 71 32.8 32.0 .. .. 19Algeria 32.4 2,382 2,270 3.0 71 40.0 34.4 80 60 10Egypt, Arab Rep. 72.6 995 1,250 0.0 70 36.0 .. .. .. 20Libya 5.7 1,760 4,400 0.0 74 20.0 39.5 .. .. ..Morocco 29.8 446 1,570 3.0 70 43.0 39.8 66 40 24Tunisia 9.9 155 2,650 3.4 73 25.0 .. 83 65 33ALL AFRICA 876.9 29,358 803 1.9 50 137.6 .. .. .. 31a. Calculated by the World Bank Atlas method.b. Data are for most recent year available during the period specifi ed.

Tab

le

26 Part I. Basic indicators and national accounts NATIONAL ACCOUNTS

Constant prices(2000 $ millions)

Average annual growth(%)

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

2.1 Gross domestic product, real

SUB–SAHARAN AFRICA 222,703 269,422 317,026 324,192 334,895 346,453 358,273 372,971 391,961 1.8 2.4 4.0excluding South Africa 127,255 158,478 192,437 196,664 202,061 209,994 216,776 227,280 239,766 2.2 2.7 4.3excl. S. Africa & Nigeria 97,330 126,079 152,452 156,242 159,937 166,568 172,685 178,445 187,999 2.6 2.8 4.0Angola 6,746 8,464 8,584 8,862 9,129 9,416 10,768 11,139 12,378 3.5 1.0 8.1Benin 1,084 1,412 2,036 2,131 2,255 2,368 2,474 2,571 2,650 2.7 4.7 4.1Botswana 1,130 3,175 4,631 4,881 5,251 5,526 5,804 6,193 6,494 10.9 4.7 5.5Burkina Faso 1,263 1,750 2,399 2,560 2,601 2,754 2,875 3,062 3,182 3.9 4.1 5.2Burundi 559 865 722 715 709 724 756 747 783 4.5 –3.2 2.3Cameroon 6,339 8,793 9,262 9,669 10,075 10,530 10,952 11,393 11,815 4.5 1.3 4.1Cape Verde .. 303 459 498 531 552 577 613 640 6.3 5.9 4.9Central African Republic 730 809 899 931 953 967 959 886 898 1.6 1.8 –2.0Chad 661 1,099 1,398 1,389 1,383 1,527 1,655 1,902 2,463 6.7 2.3 14.7Comoros 136 181 195 200 204 209 213 218 222 2.9 1.2 2.2Congo, Dem. Rep. 7,025 7,670 4,838 4,625 4,306 4,215 4,363 4,612 4,925 2.1 –5.0 3.7Congo, Rep. 1,727 2,765 3,068 2,976 3,220 3,342 3,496 3,524 3,651 3.8 0.9 3.1Côte d’Ivoire 7,706 8,274 10,618 10,786 10,425 10,436 10,266 10,095 10,261 0.7 3.5 –0.6Djibouti .. 608 537 549 553 .. .. .. .. .. –1.7 ..Equatorial Guinea .. 248 934 1,322 1,341 1,361 1,600 1,835 2,019 .. 20.7 11.8Eritrea .. .. 729 729 634 692 697 724 738 .. 7.9 3.6Ethiopia .. 6,241 6,986 6,161 6,528 7,104 7,239 6,972 7,904 2.1 4.0 3.7Gabon 3,265 3,904 5,154 4,835 4,932 5,055 5,055 5,187 5,259 0.5 3.2 1.6Gambia, The 213 305 375 399 421 445 431 460 484 3.5 2.7 3.2Ghana 2,640 3,267 4,597 4,800 4,978 5,187 5,420 5,675 5,959 2.6 4.3 4.6Guinea .. 2,113 2,920 3,054 3,112 3,237 3,373 3,413 3,505 .. 4.5 3.0Guinea-Bissau 115 186 186 200 215 216 201 202 206 3.8 1.4 –1.5Kenya 7,087 10,557 12,348 12,630 12,705 13,262 13,314 13,683 14,276 4.1 2.2 2.7Lesotho 400 614 846 848 859 887 918 946 976 4.1 4.2 3.2Liberia 1,391 433 363 446 561 577 599 411 422 –3.3 0.2 –8.7Madagascar 3,099 3,266 3,537 3,701 3,878 4,111 3,590 3,941 4,149 0.8 1.7 0.9Malawi 1,000 1,243 1,666 1,716 1,744 1,657 1,704 1,808 1,936 2.4 3.8 3.0Mali 1,536 1,630 2,199 2,347 2,422 2,716 2,828 3,039 3,105 0.5 3.9 6.3Mauritania 582 686 940 1,013 1,081 1,120 1,146 1,219 1,303 1.9 4.5 4.7Mauritius 1,517 2,676 4,056 4,292 4,465 4,713 4,851 4,992 5,212 5.9 5.3 3.7Mozambique 2,157 2,189 3,446 3,706 3,778 4,273 4,621 4,986 5,360 –0.9 6.3 8.9Namibia 2,002 2,263 3,191 3,298 3,414 3,495 3,729 3,858 4,088 1.1 4.0 4.7Niger 1,523 1,507 1,835 1,824 1,798 1,926 1,984 2,090 2,090 –0.4 2.4 3.9Nigeria 29,112 32,376 39,942 40,382 42,078 43,382 44,054 48,766 51,692 0.8 2.4 5.4Rwanda 1,457 1,782 1,588 1,709 1,811 1,933 2,114 2,133 2,218 2.5 –1.6 5.2São Tomé and Principe .. 38 44 45 46 48 50 52 54 .. 1.7 4.0Senegal 2,417 3,281 4,010 4,257 4,385 4,591 4,642 4,946 5,251 3.2 3.0 4.4Seychelles 290 393 576 587 615 601 609 571 559 3.1 4.5 –2.4Sierra Leone 754 824 607 594 634 588 754 828 908 0.5 –3.7 11.2Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 95,503 110,945 124,638 127,577 132,878 136,512 141,549 145,761 152,276 1.4 2.0 3.4Sudan 5,538 7,079 10,887 11,577 12,330 13,082 13,867 14,699 15,581 2.4 5.3 6.0Swaziland 554 1,024 1,315 1,361 1,389 1,414 1,455 1,490 1,521 6.5 3.3 2.4Tanzania .. 6,801 8,344 8,639 9,079 9,646 10,345 11,081 11,822 .. 2.7 6.9Togo 964 1,071 1,307 1,340 1,329 1,327 1,382 1,419 1,461 1.5 3.6 2.6Uganda .. 3,077 5,192 5,610 5,926 6,219 6,622 6,912 7,300 2.3 7.2 5.4Zambia 2,730 3,028 3,058 3,126 3,238 3,396 3,508 3,688 3,887 1.0 0.2 4.6Zimbabwe 4,376 6,734 8,334 8,034 7,399 7,199 6,883 6,167 5,908 3.3 2.7 –5.9NORTH AFRICA 114,040 172,850 222,130 228,009 236,346 246,041 254,992 266,091 279,378 4.2 3.3 4.2Algeria 34,268 45,148 50,583 52,057 53,306 55,232 57,138 59,356 62,064 2.9 1.7 3.8Egypt, Arab Rep. 37,658 65,727 91,404 93,967 98,782 102,042 105,103 109,832 115,873 5.5 4.4 4.0Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 18,307 26,717 33,045 33,056 33,344 35,512 37,074 38,335 39,715 4.2 2.4 4.4Tunisia 8,630 12,255 17,538 18,586 19,462 20,513 21,744 23,157 24,547 3.2 4.6 6.0ALL AFRICA 339,883 444,006 539,713 552,702 571,694 592,841 613,581 639,340 671,651 2.6 2.7 4.1a. Preliminary.

Tab

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NATIONAL ACCOUNTS Part I. Basic indicators and national accounts 27

2.2 Gross domestic product per capita, real

SUB–SAHARAN AFRICA 581 523 502 503 507 512 517 526 541 –1.1 –0.2 1.6excluding South Africa 359 330 326 327 328 333 335 343 354 –0.8 0.1 1.8excl. S. Africa & Nigeria 341 324 319 322 321 327 330 333 343 –0.4 0.2 1.5Angola 861 804 651 656 660 662 737 740 799 0.5 –1.8 5.1Benin 292 273 300 305 313 319 323 325 324 –0.7 1.3 0.9Botswana 1,077 2,222 2,706 2,812 2,994 3,130 3,277 3,496 3,671 7.5 2.4 5.3Burkina Faso 192 205 225 233 230 237 239 247 248 1.3 1.2 1.9Burundi 135 153 115 112 109 109 111 106 107 1.1 –4.4 –0.6Cameroon 724 755 650 664 678 695 709 723 737 1.6 –1.2 2.1Cape Verde .. 852 1,067 1,132 1,179 1,196 1,221 1,267 1,292 4.1 3.4 2.4Central African Republic 314 270 247 251 252 252 247 225 225 –1.0 –0.6 –3.3Chad 143 182 182 175 168 180 188 208 261 3.9 –0.8 10.8Comoros 405 416 376 378 377 378 379 379 378 0.3 –1.0 0.0Congo, Dem. Rep. 251 203 101 94 86 82 83 85 88 –0.8 –7.7 0.8Congo, Rep. 958 1,113 952 894 937 942 956 935 940 0.6 –2.3 0.0Côte d’Ivoire 924 654 664 658 623 612 592 573 574 –3.5 0.6 –2.3Djibouti .. 1,089 802 792 774 .. .. .. .. .. –3.8 ..Equatorial Guinea .. 703 2,183 3,015 2,988 2,961 3,403 3,815 4,101 .. 17.8 9.3Eritrea .. .. 220 213 178 187 180 179 174 .. 6.2 –0.9Ethiopia .. 122 114 119 122 129 126 120 132 –1.1 1.1 0.8Gabon 4,689 4,078 4,243 3,885 3,877 3,897 3,830 3,867 3,860 –2.7 0.2 –0.2Gambia, The 327 325 304 313 320 328 308 320 327 –0.2 –0.8 0.2Ghana 234 211 242 247 251 255 261 268 275 –0.6 1.7 2.4Guinea .. 340 361 370 369 376 383 379 381 .. 1.2 0.7Guinea-Bissau 144 183 144 151 158 154 138 135 134 1.4 –1.6 –4.5Kenya 435 451 421 421 414 423 416 418 427 0.3 –0.6 0.5Lesotho 310 386 482 478 481 494 510 526 543 1.8 3.0 3.1Liberia 744 203 134 153 183 183 187 128 130 –4.9 –3.3 –9.9Madagascar 342 271 232 235 239 247 209 224 229 –2.0 –1.3 –1.8Malawi 162 131 153 153 151 140 141 146 154 –1.9 2.0 0.7Mali 220 183 200 207 208 226 229 239 237 –1.9 1.2 3.2Mauritania 362 338 376 395 409 411 408 422 437 –0.5 1.8 1.6Mauritius 1,570 2,532 3,497 3,655 3,762 3,927 4,009 4,085 4,223 4.9 4.0 2.7Mozambique 179 163 201 211 211 234 247 262 276 –1.9 3.0 6.7Namibia 2,029 1,619 1,767 1,780 1,802 1,811 1,902 1,943 2,035 –2.3 0.8 3.2Niger 246 178 167 160 153 158 157 160 155 –3.4 –0.9 0.4Nigeria 425 358 356 352 358 360 358 387 402 –2.0 –0.3 3.1Rwanda 280 251 232 228 226 231 245 244 250 –1.2 –1.7 2.6São Tomé and Principe .. 330 327 329 332 338 344 349 354 .. –0.1 1.6Senegal 406 411 407 422 424 433 428 445 461 0.2 0.4 2.0Seychelles 4,507 5,614 7,302 7,294 7,579 7,405 7,277 6,893 6,688 2.3 2.9 –3.2Sierra Leone 233 202 142 136 141 126 154 162 170 –1.9 –4.3 6.5Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 3,463 3,152 2,975 2,972 3,020 3,046 3,122 3,181 3,346 –1.2 –0.3 2.5Sudan 277 272 346 359 375 390 405 422 439 –0.4 2.8 4.0Swaziland 981 1,330 1,327 1,335 1,329 1,324 1,337 1,347 1,358 3.3 0.1 0.6Tanzania .. 259 251 254 261 272 286 300 314 .. –0.2 4.8Togo 346 270 261 258 248 240 243 243 244 –2.1 0.5 –0.2Uganda .. 173 227 238 244 248 255 257 262 –1.3 3.9 1.9Zambia 451 361 298 298 303 311 316 327 339 –2.3 –2.2 2.8Zimbabwe 599 637 675 644 587 567 538 479 457 –0.5 0.8 –6.5NORTH AFRICA 1,261 1,473 1,633 1,676 1,712 1,749 1,778 1,834 1,879 1.6 1.5 2.4Algeria 1,827 1,785 1,708 1,738 1,755 1,774 1,819 1,923 1,992 –0.1 –0.3 3.4Egypt, Arab Rep. 880 1,181 1,411 1,469 1,519 1,543 1,562 1,579 1,615 2.9 2.5 1.5Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 950 1,117 1,215 1,200 1,197 1,258 1,284 1,339 1,349 2.0 0.8 3.0Tunisia 1,353 1,503 1,879 1,967 2,036 2,110 2,122 2,228 2,341 0.6 2.9 3.4ALL AFRICA 718 702 703 709 717 727 735 751 771 –0.2 0.3 1.8a. Preliminary.

Constant prices(2000 $)


1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

2.3 Agriculture value added

SUB–SAHARAN AFRICA 36,019 44,387 55,544 57,763 58,916 61,159 62,702 64,769 67,503 2.3 3.2 3.3excluding South Africa 32,933 40,677 51,989 53,986 54,959 57,335 58,626 60,782 63,593 2.2 3.4 3.6excl. S. Africa & Nigeria 25,571 32,242 41,163 42,596 43,236 45,168 45,936 47,263 49,184 2.1 3.5 3.1Angola .. 686 467 473 517 610 691 772 886 .. –2.9 14.0Benin 316 467 744 774 824 849 911 932 985 5.2 5.8 4.6Botswana 133 155 151 139 139 144 143 145 149 1.7 –1.0 1.5Burkina Faso 410 531 819 850 881 848 919 919 1,086 3.6 4.3 5.1Burundi 218 300 272 269 255 247 257 248 247 3.1 –1.9 –0.6Cameroon 1,877 2,380 3,388 3,619 3,782 3,923 4,067 4,209 4,393 2.5 5.3 3.8Cape Verde 23 50 65 59 64 64 60 63 67 15.3 4.2 0.9Central African Republic 298 332 433 450 478 498 517 536 533 1.7 3.6 3.0Chad 306 321 597 578 563 621 617 648 614 3.1 5.5 2.2Comoros 43 59 69 72 83 88 90 93 95 4.0 2.3 3.3Congo, Dem. Rep. 1,565 2,011 2,353 2,407 2,126 2,043 2,053 2,078 2,090 2.5 2.2 –0.2Congo, Rep. 118 166 174 164 171 180 188 200 212 3.3 1.0 5.5Côte d’Ivoire 1,628 1,756 2,265 2,216 2,400 2,407 2,351 2,379 2,474 –0.4 3.2 0.5Djibouti .. 18 18 18 18 .. .. .. .. .. 0.7 ..Equatorial Guinea .. 54 78 86 94 100 104 92 .. .. 7.2 –0.1Eritrea .. .. 170 157 89 115 80 89 95 .. 5.4 –1.2Ethiopia .. 2,901 3,282 2,806 2,868 3,197 3,124 2,730 3,247 0.1 2.0 0.9Gabon 320 342 293 306 318 331 348 366 388 1.5 –1.9 5.1Gambia, The 87 89 93 120 133 145 104 124 142 1.2 2.2 –0.2Ghana 1,227 1,269 1,653 1,717 1,757 1,822 1,896 1,984 2,075 0.9 3.3 4.3Guinea .. 457 647 699 697 740 778 801 834 .. 4.7 4.5Guinea-Bissau 45 79 100 108 112 114 113 121 128 5.0 4.3 3.3Kenya 2,192 3,138 3,452 3,696 3,649 4,012 3,870 3,967 4,024 3.2 1.8 1.9Lesotho 104 137 135 141 145 145 139 137 137 2.1 1.7 –1.7Liberia .. .. .. .. .. .. .. .. .. .. .. ..Madagascar 690 852 972 1,005 1,013 1,054 1,041 1,054 1,086 2.4 1.8 1.4Malawi 263 302 536 590 622 585 600 636 653 2.1 8.5 1.8Mali 656 792 958 1,046 938 1,043 1,005 1,183 1,128 2.6 3.1 5.1Mauritania 107 127 186 195 193 187 182 193 188 2.0 4.8 –0.2Mauritius 224 274 298 300 230 304 318 269 278 3.1 0.7 2.6Mozambique .. 707 947 1,000 887 971 1,076 1,173 1,271 7.3 5.3 9.5Namibia 179 223 294 312 338 304 334 347 336 1.3 3.5 1.2Niger 479 537 782 743 680 770 785 832 .. 1.8 3.6 6.4Nigeria 7,011 8,447 10,839 11,399 11,730 12,176 12,692 13,513 14,392 2.9 3.3 5.3Rwanda 535 558 633 687 750 812 930 901 901 0.8 1.5 4.8São Tomé and Principe .. 6 9 9 9 10 10 10 10 .. 3.6 3.0Senegal 495 654 720 818 850 879 700 836 872 2.8 2.5 0.0Seychelles 18 17 15 17 17 17 17 15 15 –1.7 –0.5 –3.1Sierra Leone 370 489 305 323 349 218 284 305 323 3.5 –2.9 1.9Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 3,073 3,692 3,557 3,777 3,956 3,827 4,077 3,991 3,923 2.9 0.7 0.3Sudan 1,713 2,144 4,244 4,653 4,963 5,239 5,633 6,006 5,943 2.3 9.1 5.1Swaziland 126 144 144 156 150 136 138 142 144 2.0 1.0 –0.3Tanzania .. 2,767 3,505 3,650 3,773 3,980 4,178 4,346 4,604 .. 3.2 5.0Togo 220 342 453 477 455 461 494 489 505 5.7 4.5 2.7Uganda .. 1,401 1,803 1,908 2,014 2,111 2,193 2,243 2,361 1.5 3.6 3.9Zambia 371 471 576 634 644 627 616 647 675 4.1 4.3 1.3Zimbabwe 599 858 1,091 1,137 1,174 1,128 872 863 838 2.8 4.2 –9.0NORTH AFRICA 17,093 23,548 29,317 28,745 28,100 .. .. .. .. 3.9 2.4 ..Algeria 2,300 3,112 4,521 4,529 4,302 4,604 4,788 4,979 5,178 4.3 4.0 4.6Egypt, Arab Rep. 8,668 11,478 14,496 14,909 15,418 .. .. .. .. 2.7 3.1 ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 4,023 5,925 6,567 5,378 4,509 5,546 5,934 5,993 6,053 7.0 0.1 6.9Tunisia 1,201 1,792 2,189 2,415 2,390 2,426 2,584 2,752 2,931 2.0 2.1 5.5ALL AFRICA 53,056 67,896 84,817 86,505 87,042 91,549 94,379 97,466 101,399 2.8 2.9 3.7a. Preliminary.

Tab

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1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

2.4 Industry value added

SUB–SAHARAN AFRICA 74,683 83,702 92,831 94,133 97,639 100,381 102,596 109,853 116,040 1.0 1.7 4.5excluding South Africa 41,975 48,249 56,253 57,621 59,247 61,247 62,416 69,354 73,740 1.3 2.2 5.8excl. S. Africa & Nigeria 23,829 33,018 39,103 40,912 41,516 43,050 45,690 48,874 52,341 3.6 2.8 6.1Angola .. 4,861 5,973 6,359 6,584 6,853 7,841 8,138 9,127 .. 3.9 8.6Benin 115 203 281 287 313 341 362 372 370 3.3 4.1 4.3Botswana 550 1,682 2,113 2,223 2,480 2,608 2,694 2,887 3,032 11.8 3.0 5.2Burkina Faso 252 333 375 398 422 457 460 460 .. 4.2 2.2 2.7Burundi 98 159 121 127 119 111 104 98 92 4.6 –4.7 –6.2Cameroon 1,029 1,930 1,564 1,663 1,746 1,764 1,777 1,787 1,783 7.5 –2.0 0.6Cape Verde 23 59 84 97 95 95 108 114 124 14.6 5.1 7.4Central African Republic 146 157 152 159 173 180 187 200 205 1.6 0.2 4.6Chad 62 170 167 158 155 177 220 344 780 7.9 0.5 47.7Comoros 20 16 24 24 24 26 27 28 29 –2.3 4.6 4.1Congo, Dem. Rep. 2,497 2,288 802 1,061 863 840 909 1,025 1,162 2.3 –8.9 8.2Congo, Rep. 958 1,735 2,191 2,226 2,323 2,253 2,336 2,292 2,469 5.5 3.0 1.4Côte d’Ivoire 1,227 1,575 2,532 2,573 2,288 2,214 2,117 1,960 2,034 5.1 6.6 –3.5Djibouti .. 107 69 69 70 .. .. .. .. .. –6.2 ..Equatorial Guinea .. 32 746 1,202 1,178 1,156 1,420 1,782 .. .. 50.0 15.6Eritrea .. .. 141 145 135 144 155 158 158 .. 18.3 4.1Ethiopia .. 812 825 559 569 597 632 661 707 3.9 3.5 5.5Gabon 1,743 2,260 2,545 2,581 2,624 2,676 2,749 2,828 2,913 0.4 2.6 2.7Gambia, The 28 42 45 46 48 51 56 60 64 4.3 0.7 7.2Ghana 866 962 1,151 1,208 1,265 1,325 1,409 1,510 .. 2.6 2.3 6.1Guinea .. 712 974 1,026 1,075 1,133 1,186 1,191 1,225 .. 4.6 3.2Guinea-Bissau 24 34 24 25 26 28 31 30 30 1.3 –2.5 4.0Kenya 1,189 1,752 1,990 1,943 1,908 1,946 1,996 2,108 2,181 3.7 1.5 3.5Lesotho 117 186 301 303 320 334 357 373 384 4.1 5.4 4.9Liberia .. .. .. .. .. .. .. .. .. .. .. ..Madagascar 461 398 458 478 504 543 430 492 525 0.7 2.0 –0.2Malawi 175 236 276 285 282 252 248 261 288 2.4 2.0 0.8Mali 166 248 433 443 464 561 661 599 597 4.7 6.4 5.9Mauritania 193 271 351 359 380 366 394 416 440 5.4 3.5 4.3Mauritius 319 704 1,088 1,158 1,208 1,294 1,328 1,335 1,356 9.0 5.5 2.7Mozambique .. 308 698 817 906 1,095 1,203 1,324 1,392 –4.5 11.9 11.1Namibia 689 670 856 861 873 905 975 1,021 1,170 –0.3 2.5 7.3Niger 306 275 306 313 319 327 337 350 .. –2.0 1.8 3.1Nigeria 16,961 15,230 17,150 16,713 17,732 18,199 16,741 20,484 21,434 –2.1 0.9 5.1Rwanda 459 566 340 358 371 399 431 450 460 2.8 –5.5 5.7São Tomé and Principe .. 7 8 8 8 8 9 9 .. .. 1.4 4.3Senegal 399 630 841 864 901 940 1,032 1,084 1,165 4.4 4.0 6.8Seychelles 44 65 170 158 178 177 187 165 171 3.8 11.9 –1.5Sierra Leone 231 303 189 161 170 153 193 219 263 0.2 –5.1 13.1Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 32,864 35,440 36,574 36,511 38,387 39,132 40,177 40,507 42,338 0.5 0.9 2.3Sudan 1,210 1,431 2,358 2,558 2,523 2,904 3,122 4,528 5,044 0.9 6.0 20.1Swaziland 107 313 418 427 432 444 451 460 468 11.8 3.9 2.0Tanzania .. 967 1,166 1,232 1,319 1,410 1,542 1,702 1,873 .. 2.5 9.3Togo 216 212 221 228 237 230 255 291 312 0.7 1.7 8.2Uganda .. 381 929 1,040 1,095 1,168 1,260 1,351 1,426 4.4 12.6 7.0Zambia 886 1,018 756 709 729 796 873 949 1,050 0.7 –4.4 9.5Zimbabwe 1,235 1,714 1,832 1,770 1,586 1,456 1,296 1,108 1,069 2.9 1.1 –10.1NORTH AFRICA 48,101 64,746 83,581 86,894 93,288 .. .. .. .. 2.9 3.0 ..Algeria 19,763 25,325 27,559 28,968 30,221 30,452 31,483 32,888 34,735 2.9 1.4 3.6Egypt, Arab Rep. 12,108 17,338 27,341 28,019 31,577 .. .. .. .. 3.2 4.5 ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 5,929 8,041 10,075 10,461 10,727 11,048 11,435 11,835 12,297 2.7 3.1 3.5Tunisia 2,488 3,523 5,028 5,330 5,609 5,900 6,219 6,555 6,909 2.9 4.5 5.4ALL AFRICA 124,438 149,895 176,978 181,412 191,046 195,797 201,034 213,482 225,199 1.7 2.2 4.2a. Preliminary.

Tab

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1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

2.5 Services value added

SUB–SAHARAN AFRICA 95,542 123,949 146,380 150,717 155,862 162,027 168,823 175,089 182,315 2.5 2.4 4.0excluding South Africa 47,353 62,710 73,858 75,339 77,419 80,740 84,135 86,760 89,825 2.6 2.3 3.8excl. S. Africa & Nigeria 41,590 54,454 63,114 64,522 66,170 69,115 71,741 73,505 75,629 2.6 2.1 3.3Angola .. 2,556 2,103 2,035 2,028 1,935 2,219 2,194 2,335 .. –2.8 4.2Benin 679 752 1,011 1,073 1,118 1,178 1,195 1,266 1,292 1.1 4.0 3.7Botswana 292 1,091 1,992 2,118 2,233 2,377 2,483 2,567 2,673 15.2 8.0 4.5Burkina Faso 598 877 1,206 1,311 1,298 1,439 1,490 1,671 2,111 3.9 4.6 11.9Burundi 163 298 243 242 258 304 338 346 396 5.5 –3.4 10.4Cameroon 3,695 4,443 4,169 4,160 4,306 4,715 5,091 5,504 5,873 3.5 0.0 8.1Cape Verde 92 196 312 342 373 393 407 433 447 8.5 6.4 4.7Central African Republic 236 254 228 233 215 204 173 112 121 1.3 –0.3 –16.0Chad 308 528 577 602 612 670 735 797 859 7.4 0.6 8.9Comoros 78 114 106 107 96 93 94 95 96 3.3 –0.4 0.2Congo, Dem. Rep. 3,931 4,607 1,656 1,328 1,265 1,249 1,294 1,376 1,497 2.3 –13.0 4.4Congo, Rep. 458 690 687 638 726 804 848 865 859 3.0 –1.1 4.2Côte d’Ivoire 4,857 4,958 5,854 6,043 5,738 5,811 5,796 5,741 5,729 –0.1 2.5 –0.2Djibouti .. 410 390 396 403 .. .. .. .. .. –0.8 ..Equatorial Guinea .. 25 54 60 66 74 83 80 .. .. 9.7 6.9Eritrea .. .. 356 364 365 382 383 408 414 .. 6.9 3.2Ethiopia .. 2,219 2,429 2,362 2,586 2,708 2,832 2,897 3,092 4.5 6.1 4.3Gabon 1,226 1,364 2,262 1,949 1,990 2,045 1,977 2,015 1,995 0.3 4.5 –0.1Gambia, The 100 136 187 185 190 203 221 227 238 2.5 3.9 5.9Ghana 673 1,117 1,785 1,869 1,956 2,044 2,133 2,220 .. 5.3 5.9 4.3Guinea .. 901 1,168 1,192 1,224 1,249 1,273 1,293 1,313 .. 3.6 1.8Guinea-Bissau 38 59 50 54 61 60 67 63 63 3.1 –0.7 1.2Kenya 2,625 4,235 5,498 5,613 5,718 5,899 6,030 6,171 6,497 4.9 3.4 3.1Lesotho 149 218 317 317 315 322 329 341 356 3.9 4.8 3.2Liberia .. .. .. .. .. .. .. .. .. .. .. ..Madagascar 1,612 1,609 1,769 1,866 1,967 2,086 1,756 1,940 2,056 –0.2 2.0 0.2Malawi 445 592 695 685 669 671 691 701 738 3.2 1.9 2.4Mali 531 636 771 819 854 905 911 994 1,084 2.1 2.8 5.9Mauritania 236 244 333 382 431 473 484 516 570 –0.1 4.9 6.7Mauritius 778 1,316 2,157 2,285 2,429 2,583 2,726 2,870 3,025 4.8 6.4 5.6Mozambique .. 970 1,497 1,522 1,611 1,832 1,940 2,030 2,209 7.3 4.7 7.6Namibia 775 1,185 1,733 1,797 1,869 1,942 2,064 2,135 2,223 3.7 4.6 4.5Niger 738 695 746 768 798 828 862 907 .. –1.3 1.6 4.3Nigeria 5,636 8,267 10,746 10,820 11,251 11,628 12,393 13,248 14,167 2.7 3.1 6.1Rwanda 500 712 621 666 690 721 748 783 860 3.9 –2.5 5.4São Tomé and Principe .. 27 28 28 29 30 32 33 .. .. 0.7 4.6Senegal 1,518 1,995 2,455 2,575 2,633 2,772 2,929 3,038 3,228 2.9 2.9 5.1Seychelles 228 310 391 412 419 408 405 390 373 3.3 2.6 –2.7Sierra Leone 81 82 77 75 79 83 106 117 129 –1.1 –2.6 14.1Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 48,244 61,244 72,527 75,379 78,441 81,288 84,690 88,325 92,466 2.4 2.6 4.2Sudan 2,684 3,072 3,655 4,024 4,377 4,530 4,713 4,576 4,944 0.7 2.2 2.6Swaziland 165 264 351 367 383 396 416 428 439 4.7 3.5 3.6Tanzania .. 2,444 2,947 3,100 3,286 3,467 3,682 3,887 4,120 .. 2.4 5.8Togo 539 516 634 633 637 635 627 631 634 –0.5 3.7 –0.2Uganda .. 1,065 2,004 2,147 2,285 2,474 2,672 2,841 3,022 2.1 8.3 7.2Zambia 1,250 1,159 1,364 1,449 1,512 1,585 1,647 1,726 1,763 0.0 2.0 4.0Zimbabwe 1,993 2,893 3,780 3,856 3,588 3,610 3,877 3,293 2,541 2.8 3.4 –7.5NORTH AFRICA 43,930 75,732 95,710 101,743 104,152 .. .. .. .. 5.6 3.7 ..Algeria 9,623 13,481 15,267 15,379 15,634 16,102 16,990 18,074 19,298 3.3 1.8 5.5Egypt, Arab Rep. 15,159 32,594 41,259 45,328 45,827 .. .. .. .. 8.2 4.5 ..Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 8,690 13,209 16,810 17,393 18,109 19,037 19,851 20,627 21,461 4.2 2.7 4.3Tunisia 4,924 6,908 10,317 10,827 11,462 12,198 12,954 13,869 14,727 3.7 5.3 6.5ALL AFRICA 140,764 200,440 242,506 252,642 260,285 271,028 283,009 294,719 307,862 3.5 2.9 4.3a. Preliminary.

Tab

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2.6 Gross domestic product, nominal

SUB–SAHARAN AFRICA 261,288 301,381 328,394 327,604 334,895 334,599 356,494 434,165 526,485 250,845 313,764 397,327excluding South Africa 181,358 189,495 194,115 194,442 202,061 216,299 245,948 268,110 311,836 160,131 179,741 248,851excl. S. Africa & Nigeria 116,062 161,088 161,994 159,668 159,937 168,215 199,234 209,726 239,628 124,270 149,758 195,348Angola .. 10,260 6,445 6,154 9,129 8,936 10,835 13,825 19,564 3,780 7,042 12,458Benin 1,405 1,845 2,335 2,387 2,255 2,372 2,807 3,558 4,047 1,318 2,005 3,008Botswana 1,061 3,792 4,932 5,025 5,251 5,184 5,423 7,737 8,974 1,576 4,511 6,514Burkina Faso 1,929 3,120 2,794 2,811 2,601 2,814 3,203 4,182 4,824 2,002 2,629 3,525Burundi 920 1,132 894 808 709 662 628 595 664 1,065 979 652Cameroon 6,741 11,152 8,703 10,904 10,075 9,598 10,880 13,672 15,775 9,159 10,052 12,000Cape Verde .. 339 540 583 531 550 616 797 948 187 448 689Central African Republic 797 1,488 1,047 1,051 953 967 1,046 1,195 1,307 929 1,177 1,094Chad 1,033 1,739 1,745 1,537 1,383 1,702 1,982 2,671 4,307 1,068 1,602 2,409Comoros 124 263 216 223 204 220 247 318 369 144 238 272Congo, Dem. Rep. 14,395 9,350 6,218 4,711 4,306 4,690 5,547 5,671 6,632 10,028 7,161 5,369Congo, Rep. 1,706 2,799 1,949 2,354 3,220 2,788 3,017 3,564 4,343 2,106 2,343 3,386Côte d’Ivoire 10,175 10,796 12,783 12,556 10,425 10,554 11,482 13,734 15,475 8,609 11,200 12,334Djibouti .. 418 514 536 553 .. .. .. .. 380 485 553Equatorial Guinea .. 132 456 872 1,341 1,702 2,118 2,915 3,235 107 294 2,262Eritrea .. .. 746 689 634 671 631 747 928 .. 609 722Ethiopia .. 12,083 7,724 6,498 6,528 6,510 6,058 6,651 8,075 6,594 7,115 6,764Gabon 4,279 5,952 4,619 4,352 4,932 4,334 4,971 6,055 7,229 3,676 5,062 5,504Gambia, The 241 317 417 432 421 418 370 367 401 225 374 395Ghana 4,445 5,886 7,474 7,710 4,978 5,309 6,160 7,521 8,620 4,692 6,576 6,517Guinea .. 2,818 3,588 3,461 3,112 3,042 3,208 3,638 3,775 2,230 3,376 3,355Guinea-Bissau 111 244 206 224 215 199 201 235 270 156 242 224Kenya 7,265 8,591 14,093 12,896 12,705 13,059 13,192 15,036 16,088 7,069 9,906 14,016Lesotho 431 615 890 911 859 763 699 1,065 1,367 412 847 951Liberia 954 384 360 442 561 543 559 435 492 935 264 518Madagascar 4,042 3,081 3,739 3,717 3,878 4,529 4,397 5,474 4,364 3,124 3,326 4,528Malawi 1,238 1,881 1,751 1,776 1,744 1,717 1,935 1,764 1,903 1,255 1,900 1,812Mali 1,787 2,421 2,597 2,570 2,422 2,630 3,343 4,362 4,882 1,609 2,486 3,528Mauritania 709 1,020 1,133 1,126 1,081 1,098 1,116 1,340 1,534 806 1,085 1,234Mauritius 1,153 2,383 4,146 4,258 4,465 4,534 4,555 5,237 6,038 1,387 3,563 4,966Mozambique 3,526 2,463 3,874 3,985 3,778 3,697 4,092 4,786 5,912 3,375 2,766 4,453Namibia 2,169 2,350 3,399 3,386 3,414 3,216 3,122 4,473 5,712 1,859 3,119 3,987Niger 2,509 2,481 2,077 2,018 1,798 1,945 2,170 2,731 3,053 2,000 2,013 2,340Nigeria 64,202 28,472 32,144 34,776 42,078 48,000 46,711 58,294 72,053 35,577 30,007 53,427Rwanda 1,163 2,584 1,989 1,931 1,811 1,703 1,732 1,684 1,835 1,761 1,771 1,753São Tomé and Principe 47 58 41 47 46 48 54 60 57 52 48 53Senegal 2,987 5,699 4,678 4,757 4,385 4,564 4,998 7,501 7,775 3,341 4,922 5,845Seychelles 147 369 608 623 615 619 698 703 703 197 494 668Sierra Leone 1,101 650 672 664 634 814 946 999 1,085 963 779 895Somalia 604 917 .. .. .. .. .. .. .. 855 917 ..South Africa 80,710 112,014 134,296 133,184 132,878 118,479 110,882 166,169 214,663 90,894 134,007 148,614Sudan 7,617 13,167 11,086 10,702 12,330 13,345 15,054 17,550 21,463 12,478 9,644 15,948Swaziland 543 882 1,346 1,377 1,389 1,260 1,192 1,906 2,517 552 1,186 1,653Tanzania .. 4,259 8,383 8,638 9,079 9,441 9,772 10,291 11,311 5,009 5,904 9,979Togo 1,136 1,628 1,587 1,576 1,329 1,328 1,476 1,759 2,061 1,021 1,458 1,591Uganda 1,245 4,304 6,585 5,999 5,926 5,681 5,848 6,255 6,822 3,611 4,835 6,107Zambia 3,884 3,288 3,237 3,131 3,238 3,637 3,697 4,327 5,423 3,171 3,349 4,064Zimbabwe 6,679 8,784 6,066 5,964 7,399 12,879 30,853 7,913 4,696 7,204 7,375 12,748NORTH AFRICA 126,527 165,240 216,647 222,680 236,346 240,489 240,231 254,856 269,150 135,859 179,698 248,214Algeria 42,345 62,045 47,357 47,592 53,306 56,689 57,990 64,097 75,068 53,750 47,968 61,430Egypt, Arab Rep. 22,912 43,130 84,829 89,207 98,782 97,545 90,650 84,395 75,744 31,646 59,638 89,423Libya 35,545 .. .. .. .. .. .. .. .. 26,527 .. ..Morocco 18,821 25,784 35,817 35,277 33,344 34,219 37,555 45,817 52,528 16,987 31,339 40,693Tunisia 8,743 12,291 19,813 20,970 19,462 20,032 22,067 26,632 29,993 8,923 16,839 23,637ALL AFRICA 392,413 470,822 546,688 551,406 571,694 575,198 598,100 693,664 804,404 391,471 497,210 648,612a. Preliminary.

Current prices($ millions)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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2.7 Total consumption

SUB–SAHARAN AFRICA 72.4 80.1 84.7 83.8 80.9 81.7 82.5 80.0 78.1 78.0 83.4 80.6excluding South Africa 76.7 82.6 86.7 85.8 80.8 82.1 83.8 81.0 78.0 81.7 85.6 81.2excl. S. Africa & Nigeria 83.3 84.9 88.0 86.9 84.6 86.1 86.1 84.7 83.3 82.7 87.5 85.0Angolab .. 70.3 80.7 79.3 60.5 84.9 74.8 80.6 75.5 76.0 77.5 75.3Benin 106.3 97.8 93.4 95.2 94.0 93.5 96.3 94.0 94.5 102.4 96.2 94.5Botswana 73.3 57.4 60.2 61.3 58.3 59.5 61.1 61.4 61.8 64.7 60.7 60.4Burkina Faso 107.2 94.8 88.4 91.9 93.5 95.0 95.3 96.1 94.4 102.7 92.4 94.9Burundi 100.6 105.4 104.7 102.5 107.3 109.0 111.4 108.2 108.4 96.9 105.2 108.9Cameroon 78.3 79.3 80.8 68.8 79.7 81.0 81.0 81.4 81.5 75.8 79.9 80.9Cape Verde .. 108.1 115.3 117.5 114.2 115.1 115.7 115.8 112.2 101.8 105.6 114.6Central African Republic 108.9 100.6 94.7 89.0 92.2 88.9 89.7 85.6 86.8 101.1 96.3 88.6Chad .. 107.7 94.1 100.2 94.5 94.7 102.5 81.5 69.5 108.1 100.5 88.6Comoros 110.1 103.0 104.7 105.7 98.9 100.9 98.2 98.7 101.5 104.5 104.4 99.6Congo, Dem. Rep. 89.9 90.7 101.0 90.9 95.5 96.6 96.0 95.0 96.1 89.1 91.2 95.8Congo, Rep. 64.3 76.2 69.7 59.0 42.3 46.9 50.0 52.6 48.6 68.1 71.2 48.1Côte d’Ivoire 79.6 88.7 80.3 78.7 82.1 81.0 73.7 79.4 79.5 80.4 82.2 79.2Djibouti .. .. 100.8 105.5 105.3 .. .. .. .. .. 105.4 105.3Equatorial Guinea .. 120.1 80.1 .. .. .. .. .. .. .. 86.3 ..Eritrea .. .. 132.9 141.2 134.7 127.1 133.7 160.4 162.9 .. 130.9 143.7Ethiopia .. 90.4 86.9 97.9 99.1 96.9 97.5 99.0 99.5 93.1 94.7 98.4Gabon 39.4 63.1 61.0 65.2 71.7 49.7 51.6 54.7 53.6 55.7 58.3 56.2Gambia, The 94.2 89.3 88.2 89.0 91.5 88.0 87.1 88.9 89.5 93.5 92.6 89.0Ghana 95.1 94.5 89.7 96.5 94.4 93.0 92.6 90.9 89.7 95.2 92.5 92.1Guinea .. 82.3 82.7 82.7 83.2 84.3 90.9 92.5 93.5 83.6 84.2 88.9Guinea-Bissau 101.0 97.2 110.1 101.2 108.5 119.3 112.1 98.8 101.7 100.9 98.5 108.1Kenya 81.9 81.5 89.7 89.3 90.6 88.7 87.2 87.0 87.4 82.1 84.4 88.2Lesotho 151.9 152.9 133.5 122.4 120.4 116.3 118.0 112.2 107.7 166.4 138.1 114.9Liberia 85.2 .. .. .. .. 103.4 103.3 103.0 100.6 97.8 .. 102.6Madagascar 101.4 94.5 93.0 92.8 92.3 84.7 92.3 91.1 92.2 97.1 95.8 90.5Malawi 89.2 86.6 91.9 100.6 96.2 96.2 110.1 110.7 109.1 87.3 96.6 104.5Mali 98.9 93.6 89.6 90.5 88.0 86.0 88.7 86.8 89.3 100.4 92.4 87.8Mauritania 103.5 95.1 95.6 95.0 86.8 101.4 104.9 117.6 118.8 96.9 92.6 105.9Mauritius 85.5 76.5 75.1 76.7 76.1 74.0 74.9 75.0 76.4 80.0 75.9 75.3Mozambique 108.9 105.8 89.2 86.3 88.4 92.0 89.0 89.9 87.7 106.2 99.0 89.4Namibia 61.6 81.8 86.3 87.5 86.0 83.0 82.2 73.8 73.3 89.2 87.3 79.7Niger 85.4 98.8 97.3 96.3 96.5 95.6 94.7 95.0 94.2 92.7 97.3 95.2Nigeriac 68.6 70.6 80.4 80.9 66.6 68.3 74.2 67.6 60.4 82.5 76.0 67.4Rwanda 95.8 93.8 102.8 100.0 98.7 97.4 100.0 100.8 97.6 95.0 105.5 98.9São Tomé and Principe 111.6 142.4 107.0 110.3 104.1 122.9 117.2 122.1 129.0 116.2 120.0 119.1Senegal 105.0 91.1 88.1 88.7 89.1 90.5 94.4 92.3 90.2 99.9 89.9 91.3Seychelles 72.9 79.7 81.3 74.4 77.7 80.1 76.7 80.4 78.6 75.9 78.3 78.7Sierra Leone 99.1 91.3 97.5 110.3 113.3 111.5 109.3 107.4 104.9 90.9 97.1 109.3Somalia 112.9 112.5 .. .. .. .. .. .. .. 106.3 112.5 ..South Africa 62.1 76.8 81.9 81.0 81.1 80.8 79.8 78.3 78.3 71.5 80.6 79.7Sudan 97.9 .. 94.2 92.3 84.1 90.2 86.7 84.3 81.3 95.0 94.4 85.3Swaziland 98.8 90.8 98.6 99.7 95.7 96.9 80.5 82.4 85.9 96.3 97.7 88.3Tanzania .. 98.7 100.8 97.2 90.7 91.4 88.4 90.5 91.5 89.3 98.6 90.5Togo 76.8 85.3 97.2 96.8 102.2 99.0 99.4 94.7 95.5 87.7 93.3 98.2Uganda 100.4 99.4 94.4 91.9 90.8 93.7 94.5 93.5 90.9 97.7 94.2 92.7Zambia 80.7 83.4 96.1 101.1 91.7 82.7 82.3 81.3 81.8 86.0 92.9 84.0Zimbabwe 86.2 82.5 81.0 84.0 86.7 89.8 94.0 94.1 95.2 83.5 83.1 91.9NORTH AFRICA 59.5 77.7 81.8 78.2 74.9 75.1 75.6 75.2 74.4 71.6 79.2 75.0Algeria 56.9 72.9 72.9 68.6 55.8 59.1 62.0 63.6 64.3 68.5 69.9 61.0Egypt, Arab Rep. 84.8 83.9 88.0 83.1 82.7 82.3 82.2 81.5 81.0 84.5 86.0 81.9Libya 43.1 .. .. .. .. .. .. .. .. 53.1 .. ..Morocco 86.3 80.7 81.9 80.4 81.8 80.7 80.9 80.2 79.9 84.1 83.0 80.7Tunisia 76.0 74.5 76.4 75.4 76.1 75.5 75.1 74.4 73.3 77.3 76.0 74.9ALL AFRICA 68.4 79.3 83.7 81.7 78.7 79.1 80.0 78.3 77.0 75.7 82.0 78.6a. Preliminary.b. The variability in the data are due to weaknesses in national account statistics.c. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.8 General government consumption

SUB–SAHARAN AFRICA 14.1 16.8 15.2 16.0 17.0 17.3 16.9 17.9 18.0 15.5 16.7 17.4excluding South Africa 13.9 15.2 12.6 14.3 16.2 16.8 16.2 17.0 16.8 14.4 14.7 16.6excl. S. Africa & Nigeria .. 15.2 13.2 14.5 14.4 13.9 14.2 15.1 15.1 14.5 15.1 14.5Angola .. 34.5 30.0 59.7 42.5 34.9 36.9 34.0 29.3 31.5 43.9 35.5Benin 8.6 11.0 9.4 10.0 11.6 11.6 12.5 13.3 13.6 12.7 10.5 12.5Botswana 21.3 24.1 28.9 30.4 30.4 31.9 33.1 33.8 34.3 24.3 27.3 32.7Burkina Faso 9.2 13.2 13.2 12.5 12.6 12.2 13.1 12.8 13.0 12.2 13.6 12.7Burundi 9.2 10.8 17.1 18.2 17.6 19.9 19.1 22.7 25.8 9.3 17.0 21.0Cameroon 9.7 12.8 9.2 8.4 9.5 10.2 10.2 10.1 10.2 10.0 10.5 10.0Cape Verde .. 14.7 15.0 19.4 21.3 11.3 11.7 14.7 14.8 10.8 17.0 14.8Central African Republic 15.1 14.9 11.6 11.5 11.3 11.4 11.8 10.5 11.6 15.6 13.9 11.3Chad .. 10.0 5.6 6.9 7.7 7.5 7.8 7.8 5.0 11.3 8.1 7.2Comoros 30.9 23.4 15.7 14.6 13.3 16.3 15.9 13.9 13.2 28.6 19.8 14.5Congo, Dem. Rep. 8.4 11.5 8.1 6.0 7.5 6.0 5.5 6.3 8.2 9.0 9.9 6.7Congo, Rep. 17.6 13.8 24.2 15.1 11.6 14.1 18.4 17.0 16.0 17.7 18.0 15.4Côte d’Ivoire 16.9 16.8 6.5 6.5 7.2 7.5 7.8 8.2 8.3 16.5 11.9 7.8Djibouti .. .. 23.5 26.4 25.2 .. .. .. .. .. 27.6 25.2Equatorial Guinea .. 39.7 21.0 .. .. .. .. .. .. 27.4 25.1 ..Eritrea .. .. 52.3 69.5 63.8 51.5 44.0 51.9 53.5 .. 39.7 52.9Ethiopia .. 13.2 12.0 18.8 22.7 16.8 19.3 23.8 22.4 15.7 13.5 21.0Gabon 13.2 13.4 16.9 16.6 10.0 .. .. .. .. 18.3 14.5 10.0Gambia, The 31.2 13.7 12.7 13.0 13.7 14.4 12.9 11.0 11.1 29.1 13.8 12.6Ghana 11.2 9.3 10.3 10.8 10.2 9.7 9.9 8.9 8.4 9.0 11.7 9.4Guinea .. 8.9 6.1 5.9 3.8 6.8 7.5 7.5 5.7 8.9 7.1 6.3Guinea-Bissau 27.6 10.3 9.3 10.8 14.0 12.6 13.0 12.8 14.5 18.9 8.4 13.4Kenya 19.8 18.6 16.2 15.8 15.1 15.6 16.7 17.8 17.0 18.3 15.8 16.4Lesotho 21.4 14.1 20.8 18.6 19.1 17.8 15.6 15.4 14.2 19.4 16.7 16.4Liberia 19.1 .. .. .. .. 14.4 13.7 8.0 9.7 22.0 .. 11.4Madagascar 12.1 8.0 7.8 7.2 6.8 8.3 8.2 9.1 9.6 9.8 7.9 8.4Malawi 19.3 15.1 14.7 13.4 14.6 15.8 14.7 16.3 16.9 17.5 16.6 15.7Mali 11.6 13.8 14.7 15.9 8.6 9.2 8.7 8.4 10.0 12.3 12.7 9.0Mauritania 45.3 25.9 12.8 12.9 15.0 13.7 16.4 15.7 14.9 30.6 16.6 15.1Mauritius 14.4 12.8 12.9 12.7 13.1 12.9 12.8 14.1 13.2 13.5 13.0 13.2Mozambique 12.2 13.5 8.4 9.5 10.1 10.0 9.7 10.3 10.4 13.8 11.0 10.1Namibia 17.4 30.6 29.6 30.3 28.8 28.4 26.4 26.5 24.5 27.9 31.0 26.9Niger 10.4 15.0 13.1 14.9 13.0 12.4 12.2 11.3 12.5 11.9 14.6 12.3Nigeriab 12.1 15.1 9.7 13.4 22.8 26.5 24.7 23.7 22.1 13.9 12.9 24.0Rwanda 12.5 10.1 10.0 11.0 10.5 11.7 11.8 15.1 12.9 13.0 11.5 12.4São Tomé and Principe 34.5 27.6 25.6 29.8 50.9 64.2 66.7 36.4 33.7 36.6 30.3 50.4Senegal 20.3 14.7 11.8 12.7 14.0 12.6 13.3 13.4 13.9 17.3 13.2 13.4Seychelles 28.7 27.7 31.2 26.9 24.2 24.8 22.7 25.6 25.2 33.1 29.0 24.5Sierra Leone 8.4 7.8 9.1 10.5 13.2 17.4 16.2 15.2 13.4 7.7 10.2 15.1Somalia 15.6 .. .. .. .. .. .. .. .. 17.6 .. ..South Africa 14.3 19.7 18.8 18.4 18.1 18.3 18.4 19.3 19.7 17.4 19.4 18.8Sudan 16.0 .. 6.3 6.5 7.6 8.6 8.4 10.9 11.9 12.1 6.3 9.5Swaziland 27.0 18.1 22.2 24.6 24.1 17.7 18.6 18.6 21.0 21.5 22.6 20.0Tanzania .. 17.8 7.8 7.9 8.5 9.3 9.6 11.4 12.8 14.1 14.0 10.3Togo 22.4 14.2 10.9 9.7 10.2 10.0 8.4 9.8 9.7 16.9 12.8 9.6Uganda .. 7.5 12.9 12.9 13.7 13.8 15.2 14.8 14.5 9.9 11.1 14.4Zambia 25.5 19.0 15.8 12.9 9.5 12.8 13.0 13.5 12.7 23.0 17.7 12.3Zimbabwe 18.5 19.4 15.8 13.6 13.9 14.6 13.4 16.7 21.1 20.1 17.2 15.9NORTH AFRICA 12.5 14.6 14.8 14.0 13.0 12.9 12.6 12.7 12.8 15.6 14.4 12.8Algeria 15.2 16.1 18.1 17.1 14.1 14.2 14.8 15.3 15.5 17.2 16.7 14.8Egypt, Arab Rep. 15.7 11.3 11.3 10.1 9.7 10.3 9.5 9.1 8.7 16.2 10.5 9.4Libya 21.8 .. .. .. .. .. .. .. .. 30.0 .. ..Morocco 18.3 15.5 18.0 19.2 19.1 17.9 15.8 15.1 14.5 16.6 17.2 16.5Tunisia 14.5 16.4 15.6 15.5 15.7 13.6 13.7 13.7 13.7 16.5 16.1 14.1ALL AFRICA 13.3 16.1 15.0 15.3 15.4 15.6 15.3 16.1 16.4 15.4 15.9 15.8a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.9 Gross fi xed capital formation

SUB–SAHARAN AFRICA 22.0 16.4 19.0 18.2 17.1 17.8 17.7 18.8 18.9 18.5 17.3 18.1excluding South Africa 18.6 15.7 20.4 19.4 17.8 19.2 18.5 20.0 19.9 15.9 17.8 19.1excl. S. Africa & Nigeria 17.1 15.8 19.6 18.6 17.9 18.1 16.6 18.9 19.1 15.6 17.4 18.1Angola .. 11.7 35.5 27.1 12.7 13.4 13.3 12.8 9.2 14.8 20.7 12.3Benin 15.2 14.2 17.0 17.5 18.9 19.2 17.7 18.8 18.2 15.1 16.3 18.6Botswana 40.1 37.4 33.5 28.0 20.0 23.0 27.8 29.6 30.6 30.0 29.5 26.2Burkina Faso 15.1 18.2 27.2 23.8 22.7 18.9 17.8 18.7 19.1 17.4 21.7 19.4Burundi 13.9 14.5 6.8 5.9 6.1 6.2 6.4 11.3 13.7 16.6 9.1 8.8Cameroon 21.0 17.8 17.5 15.7 16.7 20.3 19.8 18.3 18.9 23.8 16.0 18.8Cape Verde .. 22.9 19.8 20.9 19.7 18.3 20.9 18.7 20.4 40.5 29.6 19.6Central African Republic 7.0 12.3 13.5 14.4 10.8 14.0 14.8 16.6 17.5 10.9 11.4 14.8Chad .. 6.8 16.9 13.7 23.3 40.5 60.5 53.5 24.9 6.3 13.1 40.5Comoros 33.2 18.8 17.9 14.9 13.1 11.8 12.2 11.6 10.2 28.8 17.7 11.8Congo, Dem. Rep. 10.0 9.1 2.1 3.1 3.5 5.4 8.9 12.2 12.8 11.7 7.6 8.6Congo, Rep. 35.8 15.9 26.7 27.8 21.0 26.4 23.3 22.9 24.2 32.5 25.9 23.6Côte d’Ivoire 26.5 6.7 13.3 13.1 10.8 11.2 10.1 10.1 10.8 16.5 11.3 10.6Djibouti .. .. 15.3 8.9 12.9 .. .. .. .. .. 10.2 12.9Equatorial Guinea .. 17.4 91.6 .. .. .. .. .. .. .. 59.5 ..Eritrea .. .. 32.4 36.0 31.9 28.7 26.0 25.4 22.8 .. 25.0 27.0Ethiopia .. 12.9 21.1 16.9 15.9 17.8 20.5 20.5 19.8 14.3 14.5 18.9Gabon 27.5 21.7 37.3 28.0 26.1 30.5 28.4 23.9 25.4 34.6 25.7 26.9Gambia, The 26.7 22.3 18.4 17.8 17.4 17.4 21.2 20.3 28.1 19.7 20.1 20.9Ghana 5.6 14.4 23.1 21.0 24.0 26.6 19.7 23.0 24.2 7.8 19.9 23.5Guinea .. 17.5 21.0 22.1 22.0 17.6 13.1 9.9 10.8 16.1 19.5 14.7Guinea-Bissau 28.2 29.9 11.3 16.8 11.3 15.0 9.6 12.6 13.2 32.0 25.9 12.3Kenya 24.5 24.2 16.7 15.5 17.4 19.2 16.3 17.4 18.3 22.7 18.3 17.7Lesotho 37.0 52.7 47.1 48.6 42.2 40.2 42.3 44.8 41.1 39.9 56.3 42.1Liberia .. .. .. .. .. 4.9 4.7 8.8 12.3 .. .. 7.7Madagascar 15.0 17.0 14.8 14.9 15.0 18.5 14.3 17.9 24.3 10.6 12.4 18.0Malawi 24.7 23.0 13.5 14.7 13.6 14.9 11.4 11.8 15.3 19.4 17.7 13.4Mali 15.5 23.0 20.9 21.2 24.6 31.0 18.6 24.2 19.0 17.2 22.5 23.5Mauritania 26.3 20.0 16.7 16.2 26.5 14.5 14.8 19.5 21.5 27.5 18.7 19.4Mauritius 25.4 30.7 27.6 25.5 25.9 23.3 21.4 22.8 24.2 23.5 28.4 23.5Mozambique 7.6 22.1 24.2 36.7 33.5 25.9 29.8 25.9 20.7 12.2 25.2 27.2Namibia 30.6 33.7 25.8 23.3 19.5 23.4 19.7 29.8 25.5 18.4 22.6 23.6Niger 28.1 8.1 11.3 10.2 11.4 12.1 14.2 14.2 15.8 15.3 8.9 13.5Nigeriab 21.3 14.7 24.1 23.4 17.7 22.8 26.1 23.8 22.4 16.5 19.8 22.5Rwanda 16.1 14.6 14.8 17.2 17.5 18.4 16.9 18.4 20.5 15.3 14.5 18.4São Tomé and Principe 16.8 15.6 35.8 39.0 43.6 35.8 32.8 30.4 34.5 15.8 41.6 35.4Senegal 11.7 13.8 18.6 18.5 20.9 19.2 16.7 20.7 23.4 12.1 16.1 20.2Seychelles 38.3 24.6 34.6 43.3 25.2 40.3 25.6 10.1 14.7 26.4 30.3 23.2Sierra Leone 16.2 10.0 5.3 5.4 8.0 6.6 10.0 13.8 10.5 12.2 7.4 9.8Somalia 42.4 15.5 .. .. .. .. .. .. .. 28.8 15.5 ..South Africa 29.9 17.7 17.0 16.4 15.9 15.3 16.1 16.9 17.5 23.4 16.7 16.3Sudan 14.7 .. 18.0 16.8 18.3 17.6 19.4 20.0 22.5 14.4 15.7 19.6Swaziland 40.7 19.1 22.4 18.7 19.9 18.4 19.8 18.0 17.4 27.2 21.1 18.7Tanzania .. 26.1 13.8 15.5 17.6 17.0 19.1 18.6 18.4 16.8 21.0 18.2Togo 28.4 26.6 16.4 13.3 17.8 20.4 18.5 18.9 18.0 19.5 16.3 18.7Uganda 6.2 12.7 16.4 19.5 20.0 18.6 19.3 20.5 22.5 8.5 16.1 20.2Zambia 23.3 17.3 16.4 17.6 18.7 20.0 23.0 26.1 26.0 16.1 14.1 22.8Zimbabwe 16.9 17.4 20.8 14.4 13.6 9.3 7.1 10.6 12.8 17.3 19.5 10.7NORTH AFRICA 28.2 28.4 23.6 25.8 24.3 24.4 25.4 26.5 27.3 29.0 24.5 25.6Algeria 39.1 28.6 27.0 27.8 23.8 25.8 28.4 30.0 30.9 33.9 28.9 27.8Egypt, Arab Rep. 27.5 28.8 21.5 25.5 23.9 22.7 23.0 23.5 23.9 28.6 20.8 23.4Libya 22.1 .. .. .. .. .. .. .. .. 26.5 .. ..Morocco 24.2 25.3 22.2 23.4 24.4 24.6 25.0 25.3 25.4 24.4 22.2 24.9Tunisia 29.4 32.5 26.9 26.8 27.4 28.1 28.6 29.1 29.9 28.8 28.3 28.6ALL AFRICA 23.5 20.3 20.7 21.0 19.8 20.3 20.6 21.4 21.5 21.8 19.7 20.7a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.10 General government fi xed capital formation

SUB–SAHARAN AFRICA .. 4.0 4.8 4.7 4.4 5.3 4.7 4.7 4.6 3.6 3.8 4.7excluding South Africa .. 6.5 6.9 6.9 6.4 7.2 6.3 6.8 6.9 .. 6.4 6.7excl. S. Africa & Nigeria .. 5.7 6.0 6.2 5.7 5.8 5.4 6.1 6.3 5.1 6.0 5.8Angola .. 5.1 5.9 12.8 6.1 6.4 7.1 7.7 4.9 5.0 6.4 6.4Benin .. 7.4 5.8 6.3 7.6 7.8 6.6 6.1 5.4 9.1 7.5 6.7Botswana 0.0 12.7 13.2 13.2 12.6 10.8 11.4 11.9 11.4 0.0 12.6 11.6Burkina Faso .. 3.9 12.4 14.9 12.1 8.2 7.2 7.5 8.3 6.7 9.5 8.7Burundi 12.8 12.5 5.5 5.4 5.4 3.7 4.6 8.3 10.7 13.8 9.3 6.5Cameroon 4.4 5.5 2.0 2.0 2.1 2.2 2.3 2.3 2.6 6.9 2.2 2.3Cape Verde .. 10.3 21.3 6.5 12.5 10.8 13.0 9.8 8.8 19.3 20.3 11.0Central African Republic 3.7 4.7 7.1 6.8 7.1 7.4 7.6 2.1 2.0 5.5 6.2 5.2Chad .. 10.0 7.6 9.6 10.5 8.9 10.2 12.8 8.0 3.8 7.6 10.1Comoros 23.2 5.0 7.7 5.4 3.8 4.4 5.9 5.5 4.3 18.7 6.8 4.8Congo, Dem. Rep. 5.1 4.0 0.1 1.1 0.4 0.1 1.0 2.7 2.8 4.4 1.7 1.4Congo, Rep. .. 5.6 4.7 6.1 7.0 10.1 8.6 6.5 7.0 11.1 6.4 7.8Côte d’Ivoire 11.4 3.6 6.0 4.2 2.8 1.9 3.2 2.7 2.8 7.1 5.6 2.7Djibouti .. .. 6.6 .. .. .. .. .. .. .. 4.6 ..Equatorial Guinea .. 10.5 7.5 .. .. .. .. .. .. .. 6.9 ..Eritrea .. .. 26.2 30.3 26.8 23.5 21.7 17.8 17.5 .. 16.4 21.5Ethiopia .. 4.0 6.2 8.1 5.2 8.5 11.5 10.5 10.8 6.9 6.4 9.3Gabon 5.3 3.9 13.4 7.3 4.8 5.6 5.4 3.7 5.5 6.7 6.6 5.0Gambia, The .. 7.4 5.2 4.7 4.6 11.2 7.9 5.7 10.9 10.4 7.8 8.0Ghana .. 7.5 11.3 9.8 10.4 10.4 9.6 7.8 7.2 6.3 11.1 9.1Guinea .. 9.2 5.6 7.2 7.1 7.1 4.0 3.7 3.7 7.4 7.0 5.1Guinea-Bissau .. 27.4 6.2 10.8 10.3 14.8 9.0 13.1 19.9 33.3 20.2 13.4Kenya 0.0 9.7 4.9 4.5 4.6 4.4 4.3 4.2 4.3 0.8 7.0 4.3Lesotho 0.0 23.0 12.0 8.7 8.0 10.3 11.0 8.5 7.1 4.6 16.1 9.0Liberia .. .. .. .. .. 0.0 0.0 0.0 0.0 .. .. 0.0Madagascar .. 7.9 7.9 6.9 6.7 7.3 4.8 7.8 12.5 6.9 6.9 7.8Malawi 17.5 7.7 8.8 10.3 10.0 10.3 7.7 9.3 12.6 9.5 9.2 10.0Mali .. 10.5 9.5 9.4 8.6 7.0 7.0 6.9 7.5 10.2 10.1 7.4Mauritania .. 6.2 4.9 6.2 17.5 10.6 10.4 14.7 15.5 7.6 4.9 13.7Mauritius 9.1 4.6 3.1 3.4 3.7 6.7 7.0 7.8 7.7 6.0 3.7 6.6Mozambique 7.6 12.0 9.8 11.6 10.4 15.4 12.5 11.7 9.4 9.5 11.7 11.9Namibia 15.7 8.2 7.7 11.0 6.1 8.7 6.6 8.1 7.2 10.7 8.2 7.4Niger 20.4 7.4 6.4 6.4 6.6 7.1 8.8 8.3 9.0 11.2 5.6 7.9Nigeriab .. 10.8 11.3 10.4 9.3 12.1 10.2 9.7 9.1 .. 8.9 10.1Rwanda 12.2 5.9 6.8 6.3 6.0 6.6 4.9 5.6 8.5 12.1 7.2 6.3São Tomé and Principe .. .. 19.8 26.0 26.1 21.6 14.8 16.4 19.4 .. 25.3 19.7Senegal 5.5 4.1 7.0 8.1 5.8 6.9 7.9 9.1 10.2 4.2 5.4 8.0Seychelles .. 8.2 2.9 1.4 13.8 25.2 9.4 2.3 3.7 12.0 9.9 10.9Sierra Leone 5.3 3.9 4.9 2.4 6.3 4.4 4.3 4.8 4.6 4.0 3.3 4.9Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 0.0 0.0 1.8 1.5 1.4 1.9 1.3 1.2 1.2 0.0 0.3 1.4Sudan 6.9 .. 0.7 1.2 2.3 2.3 3.0 3.0 5.0 4.3 0.6 3.1Swaziland 11.9 5.7 6.0 6.7 6.2 8.0 7.4 5.7 7.7 8.0 6.6 7.0Tanzania .. 10.5 3.3 3.1 6.0 5.6 7.6 7.4 7.3 3.5 5.8 6.8Togo 20.2 7.3 3.8 3.1 3.0 2.3 1.4 3.7 5.3 11.2 3.7 3.1Uganda .. 6.2 4.7 5.4 6.4 5.8 5.3 4.7 5.2 4.4 5.6 5.5Zambia .. 6.2 11.3 10.6 10.0 11.9 11.8 11.5 9.2 .. 6.8 10.9Zimbabwe 1.8 3.4 1.7 1.7 0.7 1.3 1.0 1.6 2.1 2.9 2.9 1.3NORTH AFRICA .. 9.8 10.3 8.9 8.3 .. .. .. .. 11.7 8.9 8.3Algeria 11.0 8.2 7.6 5.9 8.0 .. .. .. .. 13.8 7.3 8.0Egypt, Arab Rep. .. 14.7 14.1 9.8 7.5 6.4 6.1 5.7 5.3 12.6 8.4 6.2Libya 19.4 .. .. .. .. .. .. .. .. 19.4 .. ..Morocco 9.8 4.8 4.2 .. .. .. .. .. .. 10.3 8.8 ..Tunisia 15.0 11.0 11.6 12.4 12.5 .. .. .. .. 14.1 12.1 12.5ALL AFRICA .. 5.9 6.8 6.1 5.7 6.2 5.5 5.3 5.2 6.1 5.4 5.6a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.11 Private sector fi xed capital formation

SUB–SAHARAN AFRICA 12.7 12.6 13.9 13.0 12.0 12.1 12.5 13.4 13.3 13.5 13.2 12.7excluding South Africa .. 8.6 12.9 12.3 10.9 11.6 12.0 12.6 12.2 8.3 11.3 11.8excl. S. Africa & Nigeria .. 9.5 13.0 12.2 11.5 11.9 11.1 12.1 11.8 9.3 11.4 11.7Angola .. 1.7 29.6 16.0 6.6 7.1 6.1 5.1 4.3 9.2 17.0 5.8Benin .. 6.0 11.2 11.2 11.3 11.4 11.6 12.0 12.1 4.5 8.3 11.7Botswana 34.5 19.7 14.2 14.8 12.9 13.4 12.6 12.0 13.2 29.0 15.0 12.8Burkina Faso .. 13.6 14.8 8.9 10.6 10.8 10.6 11.2 10.8 11.5 12.2 10.8Burundi 1.1 2.7 0.4 0.4 0.8 2.5 1.5 3.0 2.7 2.3 –0.3 2.1Cameroon 15.6 11.9 15.5 13.8 13.9 18.1 17.5 15.7 15.7 14.2 13.8 16.2Cape Verde .. 12.6 -1.5 14.4 7.2 7.5 7.9 8.9 11.6 7.4 9.3 8.6Central African Republic 3.2 6.7 6.5 7.7 3.7 6.6 7.2 3.9 4.1 4.7 5.0 5.1Chad .. 1.3 6.5 7.4 10.5 27.9 49.9 37.1 15.3 0.6 4.0 28.1Comoros 5.3 6.4 7.0 6.5 6.5 4.9 4.4 4.3 4.2 5.5 7.5 4.9Congo, Dem. Rep. 3.7 8.9 2.0 2.0 3.0 5.3 8.0 9.5 10.0 7.1 6.3 7.2Congo, Rep. .. 11.6 19.6 20.4 12.3 16.3 13.8 16.4 16.6 11.4 18.5 15.1Côte d’Ivoire 13.0 4.9 9.9 10.2 7.5 10.6 6.0 7.8 7.1 8.7 7.3 7.8Djibouti .. .. 8.6 .. .. .. .. .. .. .. 5.9 ..Equatorial Guinea .. 6.9 84.1 .. .. .. .. .. .. .. 52.6 ..Eritrea .. .. 6.2 5.6 5.1 5.2 4.3 7.6 5.3 .. 8.6 5.5Ethiopia .. 8.9 14.8 8.9 10.7 9.3 9.0 10.0 9.0 9.1 8.1 9.6Gabon 21.4 17.6 23.9 20.8 21.3 24.9 23.0 20.2 19.9 27.2 18.8 21.9Gambia, The .. 14.9 13.2 13.1 12.8 6.2 13.3 13.5 13.9 8.6 12.3 11.9Ghana .. 6.9 11.1 10.7 12.7 16.7 9.2 14.4 16.2 3.8 8.6 13.8Guinea .. 8.3 14.0 14.0 14.0 9.5 8.9 6.1 7.1 8.7 11.8 9.1Guinea-Bissau .. 8.4 5.2 6.0 1.0 0.2 0.6 1.5 2.1 10.0 7.7 1.1Kenya 8.2 10.9 8.5 7.9 7.5 7.6 7.7 7.8 7.5 10.7 9.8 7.6Lesotho 35.6 29.7 37.0 38.9 36.6 32.5 33.1 35.7 33.9 34.9 40.6 34.4Liberia .. .. .. .. .. 2.0 2.2 4.5 3.9 .. .. 3.2Madagascar .. 6.9 6.9 8.0 8.3 11.2 9.5 10.1 11.8 3.6 5.5 10.2Malawi 4.7 12.4 2.3 2.4 2.3 3.5 2.7 1.5 1.8 6.3 6.0 2.4Mali .. 12.4 11.4 11.8 15.9 24.0 11.6 17.3 11.5 9.9 12.4 16.1Mauritania .. 13.7 11.8 10.0 9.1 3.9 4.4 4.8 6.0 19.0 13.8 5.6Mauritius 15.1 23.7 21.7 21.6 21.6 16.4 15.3 14.4 14.5 15.1 23.4 16.5Mozambique 0.0 10.1 14.5 25.1 23.2 10.5 17.3 14.2 11.3 2.7 13.6 15.3Namibia 11.4 13.0 15.3 12.0 12.7 13.2 14.6 21.1 17.9 7.8 12.8 15.9Niger 5.1 4.0 4.6 3.6 4.6 4.8 5.2 5.7 6.8 3.0 3.4 5.4Nigeriab .. 3.8 12.8 13.0 8.4 10.7 15.9 14.1 13.2 5.9 10.9 12.5Rwanda .. 8.7 8.0 10.9 11.6 11.8 12.0 12.8 12.0 7.8 7.2 12.0São Tomé and Principe .. .. 16.0 14.0 17.6 14.2 18.0 14.0 15.1 .. 19.3 15.8Senegal 7.7 8.8 10.5 11.3 11.1 11.9 8.4 11.3 12.9 8.4 9.9 11.1Seychelles .. 14.8 31.1 40.1 11.4 15.1 16.2 7.8 10.9 10.1 19.3 12.3Sierra Leone 9.5 5.7 0.4 3.0 1.7 2.2 5.7 9.0 5.9 7.3 3.5 4.9Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 25.9 19.1 15.2 13.9 13.8 13.2 13.7 14.6 14.9 23.1 16.0 14.0Sudan 3.8 .. 17.3 15.6 16.0 15.3 16.4 17.0 17.5 8.9 15.1 16.5Swaziland 23.1 12.7 16.4 12.1 13.7 10.4 12.3 12.3 9.7 17.3 13.8 11.7Tanzania .. 15.3 10.3 12.3 11.4 11.2 11.4 11.1 11.0 10.4 15.0 11.2Togo 8.0 18.0 12.5 10.3 14.8 19.0 17.4 17.2 15.9 7.8 11.8 16.9Uganda .. 6.5 11.3 13.8 13.3 12.4 13.7 15.4 16.9 5.4 10.3 14.3Zambia .. 7.2 3.5 5.4 7.2 6.8 9.8 13.3 15.4 4.9 5.8 10.5Zimbabwe 12.3 14.8 18.9 11.6 11.1 9.7 8.0 11.2 13.4 13.1 17.2 10.7NORTH AFRICA .. 16.8 12.7 16.2 15.0 19.2 18.8 19.9 20.8 14.4 15.8 18.7Algeria 22.8 18.8 19.0 20.1 13.5 24.6 19.2 20.1 20.7 17.7 19.2 19.6Egypt, Arab Rep. .. 12.3 7.2 13.0 14.1 14.4 16.1 17.1 17.9 11.0 11.9 15.9Libya 1.8 .. .. .. .. .. .. .. .. 1.8 .. ..Morocco 16.7 19.3 17.8 20.7 20.7 21.2 21.7 22.1 22.5 18.0 18.5 21.6Tunisia 13.3 19.7 13.3 12.9 13.8 23.7 24.2 24.7 25.5 13.5 14.9 22.4ALL AFRICA .. 14.0 13.5 14.2 13.2 14.9 14.9 15.6 15.6 13.7 14.1 14.8a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.12 Gross domestic savings

SUB–SAHARAN AFRICA 27.6 19.9 15.3 16.2 19.1 18.3 17.5 20.0 21.9 22.0 16.6 19.4excluding South Africa 23.3 17.4 13.3 14.2 19.2 17.9 16.2 19.0 22.0 18.3 14.4 18.8excl. S. Africa & Nigeria 16.7 15.1 12.0 13.1 15.4 13.9 13.9 15.3 16.7 17.3 12.5 15.0Angola .. 29.7 19.3 20.7 39.5 15.1 25.2 19.4 24.5 24.0 22.5 24.7Benin –6.3 2.2 6.6 4.8 6.0 6.5 3.7 6.0 5.5 –2.4 3.8 5.5Botswana 26.7 42.6 39.8 38.7 41.7 40.5 38.9 38.6 38.2 35.3 39.3 39.6Burkina Faso –7.2 5.2 11.6 8.1 6.5 5.0 4.7 3.9 4.8 –2.7 7.6 5.0Burundi –0.6 –5.4 –4.7 –2.5 –7.3 –9.0 –11.4 –8.2 –8.4 3.1 –5.2 –8.9Cameroon 21.7 20.7 19.2 31.2 20.3 19.0 19.0 18.6 18.5 24.2 20.1 19.1Cape Verde .. –8.1 –15.3 –17.5 –14.2 –15.1 –15.7 –15.8 –12.2 –1.8 –5.6 –14.6Central African Republic –8.9 –0.6 5.3 11.0 7.8 11.1 10.3 14.4 13.2 –1.1 3.7 11.4Chad .. –7.7 5.9 –0.2 5.5 5.3 –1.3 18.5 30.5 –8.1 –0.5 11.7Comoros –10.1 –3.0 –4.7 –5.7 1.1 –0.9 1.8 1.3 –1.5 –4.5 –4.4 0.4Congo, Dem. Rep. 10.1 9.3 –1.0 9.1 4.5 3.4 4.0 5.0 3.9 10.9 8.8 4.2Congo, Rep. 35.7 23.8 30.3 41.0 57.7 53.1 50.0 47.4 51.4 31.9 28.8 51.9Côte d’Ivoire 20.4 11.3 19.7 21.3 17.9 19.0 26.3 20.6 20.5 19.6 17.8 20.8Djibouti .. .. –0.8 –5.5 –5.3 .. .. .. .. .. –5.4 –5.3Equatorial Guinea .. –20.1 19.9 .. .. .. .. .. .. .. 13.7 ..Eritrea .. .. –32.9 –41.2 –34.7 –27.1 –33.7 –60.4 –62.9 .. –30.9 –43.7Ethiopia .. 9.6 13.1 2.1 0.9 3.1 2.5 1.0 0.5 6.9 5.3 1.6Gabon 60.6 36.9 39.0 34.8 28.3 50.3 48.4 45.3 46.4 44.3 41.7 43.8Gambia, The 5.8 10.7 11.8 11.0 8.5 12.0 12.9 11.1 10.5 6.5 7.4 11.0Ghana .. .. .. .. .. .. .. 9.1 11.4 .. .. 10.2Guinea .. 17.7 17.3 17.3 16.8 15.7 9.1 7.5 6.5 16.4 15.8 11.1Guinea-Bissau –1.0 2.8 –10.1 –1.2 –8.5 –19.3 –12.1 1.2 –1.7 –0.9 1.5 –8.1Kenya 18.1 18.5 10.3 10.7 9.4 11.3 12.8 13.0 12.6 17.9 15.6 11.8Lesotho –51.9 –52.9 –33.5 –22.4 –20.4 –16.3 –18.0 –12.2 –7.7 –66.4 –38.1 –14.9Liberia 14.8 .. .. .. .. –3.4 –3.3 –3.0 –0.6 2.2 .. –2.6Madagascar –1.4 5.5 7.0 7.2 7.7 15.3 7.7 8.9 7.8 2.9 4.2 9.5Malawi 10.8 13.4 8.1 –0.6 3.8 3.8 –10.1 –10.7 –9.1 12.7 3.4 –4.5Mali 1.1 6.4 10.4 9.5 12.0 14.0 11.3 13.2 10.7 –0.4 7.6 12.2Mauritania –3.5 4.9 4.4 5.0 13.2 –1.4 –4.9 –17.6 –18.8 3.1 7.4 –5.9Mauritius 14.5 23.5 24.9 23.3 23.9 26.0 25.1 25.0 23.6 20.0 24.1 24.7Mozambique –8.9 –5.8 10.8 13.7 11.6 8.0 11.0 10.1 12.3 –6.2 1.0 10.6Namibia 38.4 18.2 13.7 12.5 14.0 17.0 17.8 26.2 26.7 10.8 12.7 20.3Niger 14.6 1.2 2.7 3.7 3.5 4.4 5.3 5.0 5.8 7.3 2.7 4.8Nigeriab 31.4 29.4 19.6 19.1 33.4 31.7 25.8 32.4 39.6 17.5 24.0 32.6Rwanda 4.2 6.2 –2.8 0.0 1.3 2.6 0.0 –0.8 2.4 5.0 –5.5 1.1São Tomé and Principe –11.6 –42.4 –7.0 –10.3 –4.1 –22.9 –17.2 –22.1 –29.0 –16.2 –20.0 –19.1Senegal –5.0 8.9 11.9 11.3 10.9 9.5 5.6 7.7 9.8 0.1 10.1 8.7Seychelles 27.1 20.3 18.7 25.6 22.3 19.9 23.3 19.6 21.4 24.1 21.7 21.3Sierra Leone 0.9 8.7 2.5 –10.3 –13.3 –11.5 –9.3 –7.4 –4.9 9.1 2.9 –9.3Somalia –12.9 –12.5 .. .. .. .. .. .. .. –6.3 –12.5 ..South Africa 37.9 23.2 18.1 19.0 18.9 19.2 20.2 21.7 21.7 28.5 19.4 20.3Sudan 2.1 .. 5.8 7.7 15.9 9.8 13.3 15.7 18.7 5.0 5.6 14.7Swaziland 1.2 9.2 1.4 0.3 4.3 3.1 19.5 17.6 14.1 3.7 2.3 11.7Tanzania .. 1.3 –0.8 2.8 9.3 8.6 11.6 9.5 8.5 8.8 1.4 9.5Togo 23.2 14.7 2.8 3.2 –2.2 1.0 0.6 5.3 4.5 12.3 6.7 1.8Uganda –0.4 0.6 5.6 8.1 9.2 6.3 5.5 6.5 9.1 2.3 5.8 7.3Zambia 19.3 16.6 3.9 –1.1 8.3 17.3 17.7 18.7 18.2 14.0 7.1 16.0Zimbabwe 13.8 17.5 19.0 16.0 13.3 10.2 6.0 5.9 4.8 16.5 16.9 8.1NORTH AFRICA 40.5 22.3 18.2 21.8 25.1 24.9 24.4 24.8 25.6 28.4 20.8 25.0Algeria 43.1 27.1 27.1 31.4 44.2 40.9 38.0 36.4 35.7 31.5 30.1 39.0Egypt, Arab Rep. 15.2 16.1 12.0 16.9 17.3 17.7 17.8 18.5 19.0 15.5 14.0 18.1Libya 56.9 .. .. .. .. .. .. .. .. 46.9 .. ..Morocco 13.7 19.3 18.1 19.6 18.2 19.3 19.1 19.8 20.1 15.9 17.0 19.3Tunisia 24.0 25.5 23.6 .. 25.3 24.5 24.9 25.6 26.7 20.5 21.2 25.4ALL AFRICA 31.6 20.7 16.3 18.3 21.3 20.9 20.0 21.7 23.0 24.3 18.0 21.4a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.13 Gross national savings

SUB–SAHARAN AFRICA 27.2 16.0 13.3 14.0 15.7 15.1 14.0 16.4 18.3 20.7 13.9 15.9excluding South Africa 23.7 13.4 12.0 12.7 15.6 14.9 12.4 15.1 17.8 18.1 11.8 15.2excl. S. Africa & Nigeria 18.8 12.1 11.3 11.2 12.6 11.9 11.8 13.3 15.0 18.3 10.3 12.9Angola .. 5.5 5.1 –1.7 21.4 –1.4 10.4 7.6 13.4 19.9 12.1 10.3Benin 1.1 5.8 11.3 9.9 10.9 12.5 7.3 9.4 8.9 2.1 7.3 9.8Botswana 28.7 43.3 47.1 38.5 39.1 41.7 30.0 38.8 41.8 33.7 41.3 38.3Burkina Faso .. 13.4 15.1 6.6 5.6 6.9 6.8 7.7 10.5 .. 14.6 7.5Burundi .. .. –0.2 –0.1 –0.1 0.4 1.3 6.5 7.9 .. 1.4 3.2Cameroon 5.1 16.1 15.0 28.0 15.4 13.5 15.0 16.3 16.9 19.3 14.9 15.4Cape Verde .. 17.6 8.4 8.1 9.1 8.0 9.4 9.2 14.2 21.8 21.2 10.0Central African Republic 1.6 –0.4 5.9 12.0 8.0 12.3 7.6 14.7 13.2 5.6 3.9 11.2Chad .. –2.7 8.7 2.4 7.9 6.6 –3.0 5.1 18.3 –3.3 3.5 7.0Comoros –0.4 9.7 8.3 2.5 14.8 14.8 11.6 7.3 7.6 15.0 6.6 11.2Congo, Dem. Rep. 7.9 0.8 –6.8 1.1 –1.3 0.3 5.3 10.3 6.9 5.9 1.1 4.3Congo, Rep. .. 6.6 7.4 11.0 28.5 23.3 23.0 22.8 26.6 18.0 5.0 24.8Côte d’Ivoire .. –4.3 10.7 11.7 8.0 10.1 16.3 11.9 13.6 8.6 6.2 12.0Djibouti .. .. .. .. .. .. .. .. .. .. .. ..Equatorial Guinea .. –22.0 9.7 .. .. .. .. .. .. .. 6.0 ..Eritrea .. .. 8.3 4.9 20.4 28.7 26.3 –6.4 –20.8 .. 14.2 9.6Ethiopia .. 11.9 19.7 8.5 10.5 13.0 12.6 13.7 14.3 8.8 12.2 12.8Gabon .. 25.4 23.9 18.9 11.5 33.7 29.2 28.2 29.5 23.5 26.6 26.4Gambia, The .. .. 16.4 14.2 13.6 14.8 18.2 18.6 14.3 .. 16.2 15.9Ghana .. .. .. .. .. .. .. 21.5 22.8 .. .. 22.2Guinea .. 10.6 14.3 14.8 14.7 14.3 8.8 6.5 4.6 8.7 11.6 9.8Guinea-Bissau –6.3 15.3 –7.8 –3.3 –2.7 –15.7 –8.0 5.1 9.8 –0.3 5.5 –2.3Kenya 15.4 18.6 12.8 13.7 15.2 15.6 16.3 17.1 16.9 15.8 16.1 16.2Lesotho 34.6 39.0 14.3 22.4 22.6 26.2 25.6 29.3 35.8 32.9 29.4 27.9Liberia .. .. .. .. .. –21.4 –11.1 –10.1 28.7 .. .. –3.5Madagascar –2.4 9.2 7.3 9.5 9.4 17.2 8.3 13.0 13.5 2.1 4.9 12.3Malawi 6.6 15.4 4.3 –2.5 2.2 2.4 –12.0 –5.1 –5.3 11.1 2.5 –3.6Mali 1.9 15.1 12.9 11.9 16.0 12.9 8.5 14.5 11.0 3.5 14.4 12.6Mauritania 3.9 17.6 14.3 18.7 24.0 7.6 15.6 –3.8 –5.4 17.1 14.0 7.6Mauritius 14.0 26.3 26.7 24.9 25.3 27.6 26.5 26.5 24.0 19.7 26.5 26.0Mozambique –6.9 2.1 9.8 14.7 15.4 6.4 11.1 11.3 12.5 –3.8 4.0 11.4Namibia 26.9 34.8 28.2 24.3 27.7 27.7 27.7 41.6 39.7 18.5 27.3 32.9Niger 13.0 –1.2 1.6 2.8 2.8 4.4 4.7 5.5 6.3 5.3 1.0 4.8Nigeriab 26.1 19.4 15.4 13.9 27.2 25.4 15.1 21.5 27.1 13.4 17.0 23.3Rwanda .. .. –4.0 –1.3 –0.5 0.2 –2.3 –3.8 –0.6 .. –9.2 –1.4São Tomé and Principe –5.5 –51.1 –18.1 –19.0 –11.4 –29.6 –20.7 –24.4 –33.0 –17.6 –28.9 –23.8Senegal –7.4 6.0 14.4 12.7 14.6 14.5 10.8 14.2 16.7 –4.2 10.1 14.2Seychelles .. 21.7 16.9 23.2 15.7 15.5 15.2 14.8 18.5 .. 21.5 15.9Sierra Leone 0.5 2.6 2.7 –5.6 –7.9 –3.1 5.2 6.2 5.6 7.2 0.1 1.2Somalia –5.8 –21.9 .. .. .. .. .. .. .. 3.2 –21.9 ..South Africa 33.9 19.1 15.2 15.9 15.8 15.4 17.2 18.4 19.0 24.3 16.6 17.2Sudan 4.0 .. 0.4 0.0 3.4 2.8 8.1 10.0 16.5 6.5 –0.8 8.2Swaziland 16.7 27.4 15.5 16.2 14.5 13.9 24.7 19.9 19.0 20.2 19.9 18.4Tanzania .. 5.9 –1.9 0.9 7.5 7.5 10.8 9.5 8.0 18.1 2.9 8.7Togo 26.4 17.8 5.8 3.8 0.4 3.1 4.1 7.3 6.5 13.3 8.2 4.3Uganda –0.9 0.6 6.0 8.2 9.6 6.9 6.7 7.3 10.4 2.6 8.0 8.2Zambia 7.3 6.7 –3.6 –6.6 2.9 12.1 13.7 15.2 10.0 2.2 –1.2 10.8Zimbabwe .. 15.7 16.4 16.1 9.6 9.3 5.7 4.8 3.1 17.3 16.0 6.5NORTH AFRICA 37.1 19.8 20.8 20.1 23.3 26.7 26.2 26.4 26.8 24.6 18.2 25.9Algeria .. .. .. .. 40.6 38.1 35.9 34.7 34.3 .. .. 36.7Egypt, Arab Rep. .. .. 18.9 .. 23.0 22.7 22.5 22.9 23.1 .. .. 22.8Libya 53.5 .. .. .. .. .. .. .. .. 40.5 .. ..Morocco .. .. 21.8 22.5 22.8 23.8 23.2 24.1 24.2 .. 21.9 23.6Tunisia 25.0 .. 23.4 .. 24.7 23.8 24.2 25.0 26.1 20.9 20.7 24.8ALL AFRICA 30.2 18.5 16.1 17.7 19.9 19.6 18.5 19.8 20.9 22.7 16.9 19.8a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le


2.14 Resource balance (exports minus imports)

SUB–SAHARAN AFRICA 3.1 1.7 –3.7 –2.7 1.6 0.2 –0.7 0.6 2.5 –0.1 –1.0 0.8excluding South Africa 0.8 –0.7 –7.0 –6.3 0.7 –1.9 –2.9 –1.9 1.3 –3.1 –3.8 –0.9excl. S. Africa & Nigeria –5.1 –3.3 –7.5 –6.8 –3.2 –4.9 –3.5 –4.8 –3.5 –4.4 –5.3 –4.0Angola .. 18.0 –16.3 –6.4 26.8 1.7 12.0 6.6 15.3 9.1 1.8 12.5Benin –21.5 –12.0 –10.4 –12.7 –12.9 –12.7 –13.9 –12.8 –12.7 –17.5 –12.5 –13.0Botswana –13.4 5.3 6.3 10.7 21.7 17.5 11.1 8.9 7.5 5.3 9.8 13.4Burkina Faso –22.3 –13.0 –15.6 –15.7 –16.2 –13.9 –13.1 –14.8 –14.3 –20.2 –14.1 –14.5Burundi –14.5 –19.9 –11.4 –8.4 –13.5 –15.2 –17.8 –19.5 –22.0 –13.5 –14.4 –17.6Cameroon 0.8 2.9 1.7 –0.2 3.6 –1.3 –0.8 0.4 –0.4 0.4 2.5 0.3Cape Verde .. –31.0 –35.1 –38.4 –33.9 –33.4 –36.6 –34.5 –32.6 –42.3 –35.2 –34.2Central African Republic –15.9 –12.9 –8.2 –3.4 –3.1 –2.9 –4.5 –2.2 –4.4 –12.1 –7.7 –3.4Chad –11.9 –14.4 –11.1 –13.9 –17.8 –35.2 –60.7 –35.0 5.6 –13.5 –13.6 –28.6Comoros –43.2 –21.8 –22.6 –20.7 –12.0 –12.6 –10.4 –10.3 –11.6 –33.3 –22.2 –11.4Congo, Dem. Rep. 0.1 0.3 –3.1 6.0 1.0 –2.0 –4.9 –7.2 –8.9 –0.8 1.2 –4.4Congo, Rep. –0.1 7.9 3.7 13.2 36.7 26.6 26.7 24.5 27.2 –0.5 2.9 28.3Côte d’Ivoire –6.2 4.6 6.4 8.2 7.1 7.8 16.2 10.4 9.7 3.2 6.5 10.3Djibouti .. .. –16.1 –14.4 –18.2 .. .. .. .. .. –15.7 –18.2Equatorial Guinea .. –37.4 –71.7 .. .. .. .. .. .. –28.6 –45.8 ..Eritrea .. .. –65.3 –77.2 –66.6 –55.8 –59.7 –85.8 –85.7 .. –55.8 –70.7Ethiopia .. –3.3 –8.0 –14.8 –15.0 –14.7 –18.0 –19.4 –19.4 –7.4 –9.2 –17.3Gabon 33.1 15.2 1.7 6.8 2.2 19.8 20.0 21.4 21.0 9.7 16.0 16.9Gambia, The –20.9 –11.7 –6.6 –6.8 –8.9 –5.4 –8.3 –9.2 –17.6 –13.2 –12.6 –9.9Ghana –0.7 –9.0 –12.9 –17.5 –18.4 –19.6 –12.3 –13.9 –13.9 –3.1 –12.4 –15.6Guinea .. 0.2 –3.7 –4.8 –5.1 –1.9 –4.0 –2.3 –4.3 0.2 –3.7 –3.5Guinea-Bissau –29.2 –27.1 –21.4 –18.0 –19.8 –34.3 –21.7 –11.4 –14.9 –32.9 –24.5 –20.4Kenya –6.4 –5.6 –6.3 –4.9 –8.0 –7.9 –3.5 –4.4 –5.6 –4.9 –2.7 –5.9Lesotho –89.0 –105.6 –80.6 –71.0 –62.7 –56.5 –60.4 –57.0 –48.8 –106.3 –94.4 –57.1Liberia –0.1 .. .. .. .. –8.4 –8.1 –11.8 –13.0 2.9 .. –10.3Madagascar –16.4 –11.4 –7.8 –7.7 –7.3 –3.2 –6.6 –9.0 –16.5 –7.7 –8.2 –8.5Malawi –14.0 –9.6 –5.3 –15.3 –9.7 –11.1 –21.4 –22.5 –24.4 –6.7 –14.3 –17.8Mali –14.4 –16.6 –10.5 –11.7 –12.6 –17.0 –7.3 –10.9 –8.3 –17.6 –14.9 –11.2Mauritania –29.8 –15.1 –12.3 –11.2 –13.3 –16.0 –19.7 –37.1 –40.3 –24.4 –11.3 –25.3Mauritius –10.9 –7.2 –2.7 –2.2 –1.9 2.7 3.8 2.1 –0.6 –3.5 –4.3 1.2Mozambique –16.5 –27.9 –13.5 –22.9 –21.9 –17.9 –18.8 –15.8 –8.3 –18.4 –24.2 –16.5Namibia 7.8 –15.5 –12.0 –10.8 –5.5 –6.4 –2.0 –3.6 1.2 –7.6 –10.0 –3.3Niger –13.5 –6.9 –8.6 –6.5 –7.9 –7.7 –8.9 –9.2 –10.0 –8.0 –6.2 –8.7Nigeriab 10.2 14.6 –4.5 –4.2 15.7 8.9 –0.2 8.6 17.2 1.1 4.1 10.0Rwanda –11.9 –8.5 –17.6 –17.3 –16.2 –15.8 –16.9 –19.3 –18.1 –10.3 –19.9 –17.3São Tomé and Principe –28.4 –58.0 –42.8 –49.3 –47.7 –58.7 –50.0 –52.5 –63.5 –32.0 –61.7 –54.5Senegal –16.7 –4.9 –6.7 –7.2 –10.0 –9.7 –11.2 –13.0 –13.6 –12.0 –5.9 –11.5Seychelles –11.2 –4.3 –15.9 –17.6 –2.9 –20.4 –2.3 9.6 6.7 –2.3 –8.6 –1.9Sierra Leone –15.4 –1.3 –2.8 –15.7 –21.3 –18.1 –19.3 –21.1 –15.4 –3.1 –4.5 –19.0Somalia –55.3 –28.0 .. .. .. .. .. .. .. –35.1 –28.0 ..South Africa 8.0 5.5 1.1 2.6 3.0 3.9 4.2 4.7 4.2 5.1 2.8 4.0Sudan –12.6 .. –12.2 –9.1 –2.4 –7.8 –6.2 –4.3 –3.9 –9.4 –10.2 –4.9Swaziland –39.4 –12.5 –21.0 –18.4 –15.6 –15.4 –0.3 –0.3 –3.3 –23.5 –19.1 –7.0Tanzania .. –24.8 –14.7 –12.7 –8.3 –8.4 –7.5 –9.1 –9.9 –12.3 –19.6 –8.7Togo –5.3 –11.9 –13.6 –10.1 –20.0 –19.4 –18.0 –13.6 –13.5 –7.2 –9.6 –16.9Uganda –6.6 –12.1 –10.8 –12.0 –11.9 –12.1 –14.7 –14.1 –13.9 –6.2 –11.7 –13.3Zambia –4.0 –0.7 –12.5 –18.7 –10.4 –2.8 –5.3 –7.4 –7.7 –2.1 –7.0 –6.7Zimbabwe –3.2 0.1 –1.7 1.6 –0.3 0.8 –1.1 –4.7 –8.0 –0.8 –2.6 –2.6NORTH AFRICA 4.7 –3.4 –2.8 –1.4 3.5 3.3 1.8 1.3 1.4 –3.1 –0.9 2.3Algeria 4.0 –1.5 0.1 3.5 20.4 15.0 9.6 6.3 4.8 1.5 1.3 11.2Egypt, Arab Rep. –12.4 –12.7 –9.5 –8.6 –6.6 –5.0 –5.2 –5.0 –4.9 –13.2 –6.7 –5.3Libya 34.8 .. .. .. .. .. .. .. .. 20.4 .. ..Morocco –10.5 –6.0 –4.1 –3.8 –6.1 –5.3 –5.9 –5.5 –5.3 –8.5 –5.2 –5.6Tunisia –5.4 –7.0 –3.4 –2.2 –3.5 –3.6 –3.7 –3.5 –3.2 –6.1 –4.3 –3.5ALL AFRICA 3.6 0.1 –3.2 –2.1 2.4 1.4 0.3 1.0 2.4 –1.0 –0.8 1.5a. Preliminary.b. Since 1994 Nigeria’s ratios have been distorted because the offi cial exchange rate used by the government for oil exports and oil value added is signifi cantly overvalued.

Share of GDP(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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2.15 Exports of goods and services, nominal

SUB–SAHARAN AFRICA 79,589 77,334 90,678 93,010 110,599 107,305 110,298 140,988 177,280 62,624 85,417 129,294excluding South Africa 51,112 50,175 56,228 59,268 73,564 71,811 74,034 94,618 120,252 36,479 53,877 86,856excl. S. Africa & Nigeria 31,308 37,841 45,451 46,437 51,149 51,042 55,002 65,625 80,879 28,593 41,344 60,739Angola .. 3,993 3,666 5,311 8,182 6,847 8,406 9,709 13,722 2,613 4,223 9,373Benin 222 264 399 385 342 360 380 487 539 214 327 422Botswana 563 2,087 2,537 2,743 3,222 2,835 2,504 3,232 3,570 999 2,350 3,073Burkina Faso 173 352 362 287 237 260 272 357 416 184 287 308Burundi 81 89 71 61 55 45 39 55 59 111 89 51Cameroon 1,880 2,251 2,306 2,241 2,343 2,104 2,169 2,762 3,061 2,240 2,179 2,488Cape Verde 19 43 109 113 146 167 194 253 295 37 79 211Central African Republic 201 220 178 117 126 121 126 138 150 181 185 132Chad 175 234 323 282 234 251 253 675 2,274 153 254 738Comoros 11 36 26 29 31 35 39 58 60 22 40 44Congo, Dem. Rep. 2,372 2,759 1,852 1,109 964 875 1,174 1,482 2,023 2,016 1,595 1,304Congo, Rep. 1,024 1,502 1,487 1,702 2,586 2,232 2,434 2,766 3,669 1,092 1,393 2,737Côte d’Ivoire 3,561 3,421 5,038 5,067 4,211 4,357 5,695 6,280 7,445 3,142 4,129 5,598Djibouti .. .. 244 250 247 .. .. .. .. .. 222 247Equatorial Guinea .. 42 464 .. .. .. .. .. .. 32 160 ..Eritrea .. .. 111 66 96 133 128 103 122 .. 132 116Ethiopia .. 672 1,038 918 984 980 983 1,139 1,369 607 727 1,091Gabon 2,770 2,740 2,119 1,964 1,825 2,619 2,957 3,781 4,420 1,964 2,621 3,120Gambia, The 103 190 213 199 202 150 157 158 184 108 195 170Ghana 376 993 2,532 2,473 2,429 2,401 2,625 3,066 3,349 554 1,684 2,774Guinea .. 870 770 759 735 822 785 807 824 660 740 794Guinea-Bissau 14 24 30 56 68 57 61 77 98 15 32 72Kenya 2,144 2,207 2,843 2,687 2,743 2,968 3,281 3,590 4,207 1,805 2,594 3,358Lesotho 86 104 239 216 256 319 390 520 763 66 187 450Liberia 613 .. .. .. .. 126 111 133 171 519 .. 135Madagascar 539 512 805 909 1,190 1,317 704 1,264 1,425 414 673 1,180Malawi 307 447 574 498 446 480 471 480 511 295 465 478Mali 263 415 644 680 649 876 1,066 1,153 1,341 255 514 1,017Mauritania 261 465 399 370 379 379 382 359 451 387 448 390Mauritius 539 1,529 2,653 2,716 2,801 2,978 2,757 3,099 3,356 764 2,191 2,998Mozambique 383 201 519 586 744 1,004 1,188 1,353 1,828 215 373 1,223Namibia 1,712 1,220 1,562 1,563 1,558 1,446 1,548 2,300 2,644 1,139 1,543 1,899Niger 617 372 369 321 320 329 330 438 491 420 325 382Nigeria 18,859 12,366 10,776 12,832 22,416 20,774 19,044 28,997 39,372 7,725 12,563 26,120Rwanda 168 145 111 114 151 157 132 139 189 173 107 154São Tomé and Principe 10 8 12 16 15 16 20 22 25 12 11 20Senegal 803 1,450 1,419 1,466 1,310 1,402 1,527 2,137 2,165 924 1,376 1,708Seychelles 100 230 370 422 464 490 555 648 663 123 298 564Sierra Leone 252 146 109 84 115 129 153 197 240 187 155 167Somalia 200 90 .. .. .. .. .. .. .. 119 90 ..South Africa 28,555 27,149 34,451 33,742 37,034 35,495 36,266 46,372 57,032 26,088 31,523 42,440Sudan 806 .. 607 828 1,891 1,711 1,996 2,613 3,822 875 682 2,407Swaziland 405 658 1,059 1,006 1,133 1,156 1,131 1,641 2,357 394 886 1,484Tanzania .. 538 1,144 1,166 1,307 1,537 1,667 1,881 2,016 478 913 1,681Togo 580 545 471 455 409 421 498 595 691 464 441 523Uganda 242 312 635 735 663 690 697 778 933 371 500 752Zambia 1,608 1,180 865 701 682 980 875 891 1,059 1,060 1,083 897Zimbabwe 1,561 2,009 2,632 2,767 2,660 2,357 2,012 1,855 1,694 1,530 2,469 2,116NORTH AFRICA 41,644 42,750 52,125 55,873 69,479 70,139 69,781 76,628 83,995 32,437 49,017 74,004Algeria 14,541 14,546 10,880 13,259 22,579 21,701 20,578 21,804 25,103 12,221 12,508 22,353Egypt, Arab Rep. 6,992 8,647 13,754 13,500 15,940 17,072 16,332 16,009 14,997 6,654 12,493 16,070Libya 23,523 .. .. .. .. .. .. .. .. 17,320 .. ..Morocco 3,273 6,830 9,970 10,624 10,409 10,405 11,152 13,886 16,128 3,790 8,399 12,396Tunisia 3,518 5,353 8,529 8,852 8,566 8,863 9,682 11,711 13,279 3,312 7,161 10,420ALL AFRICA 119,851 120,832 143,447 149,272 180,196 177,254 180,144 219,111 264,541 95,467 135,057 204,249a. Preliminary.


Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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2.16 Imports of goods and services, nominal

SUB–SAHARAN AFRICA 71,605 72,339 102,796 101,854 105,173 106,736 112,819 138,198 164,342 62,943 88,497 125,453excluding South Africa 49,598 51,408 69,879 71,567 72,066 75,860 81,220 99,694 116,289 41,489 60,580 89,026excl. S. Africa & Nigeria 37,269 43,207 57,643 57,263 56,271 59,350 62,065 75,690 89,320 34,079 49,338 68,539Angola .. 2,147 4,714 5,705 5,736 6,697 7,110 8,801 10,728 1,895 4,019 7,814Benin 524 486 642 688 634 662 772 944 1,055 447 579 813Botswana 705 1,888 2,229 2,204 2,080 1,930 1,900 2,542 2,893 842 1,896 2,269Burkina Faso 603 758 798 729 658 650 693 977 1,104 579 659 816Burundi 214 314 174 129 151 146 151 171 205 254 234 165Cameroon 1,829 1,931 2,159 2,268 1,981 2,228 2,254 2,712 3,128 2,219 1,926 2,461Cape Verde 100 148 298 337 326 351 419 529 604 104 237 446Central African Republic 327 411 264 153 155 149 174 164 207 292 282 170Chad 298 485 516 495 480 850 1,457 1,611 2,034 305 469 1,286Comoros 64 93 75 75 55 63 65 91 103 67 93 75Congo, Dem. Rep. 2,354 2,731 2,045 827 920 971 1,447 1,891 2,610 2,107 1,537 1,568Congo, Rep. 1,026 1,282 1,416 1,391 1,404 1,490 1,629 1,891 2,488 1,093 1,309 1,780Côte d’Ivoire 4,190 2,927 4,224 4,041 3,471 3,529 3,837 4,848 5,939 2,906 3,406 4,325Djibouti .. .. 327 327 347 .. .. .. .. .. 302 347Equatorial Guinea .. 92 791 .. .. .. .. .. .. 61 270 ..Eritrea .. .. 597 597 518 507 505 744 917 .. 482 638Ethiopia .. 1,069 1,655 1,882 1,961 1,938 2,073 2,430 2,934 1,096 1,351 2,267Gabon 1,354 1,837 2,041 1,669 1,718 1,761 1,962 2,484 2,902 1,586 1,797 2,165Gambia, The 153 227 240 228 239 173 188 192 255 137 242 209Ghana 407 1,522 3,492 3,826 3,347 3,441 3,380 4,113 4,550 709 2,509 3,766Guinea .. 864 903 924 894 880 913 892 986 658 867 913Guinea-Bissau 46 90 74 96 111 125 105 104 138 67 91 117Kenya 2,608 2,691 3,737 3,312 3,757 4,002 3,741 4,257 5,114 2,154 2,942 4,174Lesotho 470 753 956 864 794 750 812 1,127 1,430 503 977 983Liberia 614 .. .. .. .. 171 156 184 235 491 .. 187Madagascar 1,202 864 1,095 1,197 1,474 1,463 993 1,756 2,146 668 942 1,566Malawi 480 629 667 769 616 672 886 878 974 384 716 805Mali 520 817 915 982 954 1,322 1,311 1,630 1,747 536 882 1,393Mauritania 473 619 538 495 523 554 601 856 1,069 576 571 721Mauritius 665 1,701 2,767 2,808 2,888 2,854 2,584 2,988 3,393 809 2,334 2,941Mozambique 965 888 1,041 1,500 1,571 1,665 1,958 2,108 2,320 773 1,001 1,925Namibia 1,542 1,584 1,972 1,927 1,746 1,652 1,610 2,461 2,573 1,284 1,844 2,008Niger 957 545 547 452 462 479 523 688 795 583 448 589Nigeria 12,324 8,203 12,236 14,304 15,794 16,511 19,151 23,997 26,965 7,362 11,214 20,484Rwanda 307 364 462 448 445 427 425 464 521 354 405 456São Tomé and Principe 24 42 30 40 38 44 46 54 61 28 41 49Senegal 1,302 1,728 1,733 1,806 1,746 1,844 2,085 3,110 3,223 1,283 1,664 2,401Seychelles 117 246 467 531 482 616 572 581 616 123 344 573Sierra Leone 421 154 128 188 250 276 336 408 407 225 191 335Somalia 534 346 .. .. .. .. .. .. .. 403 346 ..South Africa 22,073 21,016 32,917 30,287 33,107 30,889 31,615 38,525 48,071 21,441 27,961 36,442Sudan 1,763 .. 1,955 1,802 2,189 2,756 2,924 3,367 4,650 1,853 1,750 3,177Swaziland 619 768 1,342 1,260 1,349 1,350 1,134 1,648 2,441 515 1,116 1,584Tanzania .. 1,595 2,375 2,263 2,064 2,333 2,403 2,816 3,138 1,060 1,977 2,551Togo 640 738 687 615 674 678 763 833 969 542 586 784Uganda 324 834 1,344 1,455 1,366 1,378 1,554 1,662 1,879 619 1,042 1,568Zambia 1,764 1,203 1,268 1,287 1,018 1,080 1,072 1,212 1,478 1,148 1,313 1,172Zimbabwe 1,771 2,002 2,737 2,670 2,680 2,249 2,341 2,225 2,068 1,598 2,661 2,312NORTH AFRICA 35,735 48,320 58,282 59,057 61,124 62,285 65,481 73,361 80,245 36,910 50,712 68,499Algeria 12,847 15,472 10,850 11,571 11,709 13,177 14,998 17,742 21,472 13,875 11,907 15,820Egypt, Arab Rep. 9,822 14,109 21,812 21,144 22,457 21,963 21,010 20,264 18,706 10,787 16,635 20,880Libya 11,167 .. .. .. .. .. .. .. .. 10,722 .. ..Morocco 5,247 8,374 11,425 11,959 12,458 12,220 13,362 16,426 18,932 5,136 9,979 14,680Tunisia 3,987 6,220 9,194 9,317 9,257 9,583 10,494 12,636 14,251 3,834 7,840 11,244ALL AFRICA 105,915 120,427 161,198 160,957 166,337 169,047 178,347 211,993 245,485 99,277 139,313 194,242a. Preliminary.


Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

2.17 Exports of goods and services, real

SUB–SAHARAN AFRICA 66,392 72,535 105,566 105,105 110,601 112,092 112,871 120,689 127,834 0.0 5.0 3.7excluding South Africa 46,987 49,929 71,807 70,917 73,566 74,403 74,981 82,709 88,916 –0.3 4.8 4.9excl. S. Africa & Nigeria 24,776 33,884 46,903 49,120 51,151 52,877 55,871 57,458 62,879 0.0 4.7 5.0Angola 3,618 4,804 8,223 8,499 8,182 7,951 9,753 9,443 10,632 1.6 7.2 7.2Benin 391 247 351 344 342 359 359 376 378 –4.5 2.0 2.5Botswana 691 1,993 2,509 2,680 3,222 3,063 2,736 2,603 2,595 13.8 4.0 –5.8Burkina Faso 226 253 297 262 237 244 273 300 307 –1.7 –0.1 7.5Burundi 10 18 30 41 55 69 66 116 89 3.8 5.2 16.0Cameroon 1,054 1,870 2,184 2,466 2,343 2,387 2,363 2,453 2,495 6.5 2.7 1.5Cape Verde 31 42 93 96 146 167 182 208 226 0.3 13.9 11.5Central African Republic .. .. .. .. .. .. .. .. .. –1.3 .. –8.4Chad 159 214 259 254 234 225 211 481 1,301 7.4 2.8 52.1Comoros 9 28 28 32 31 27 28 24 32 11.0 –2.2 –0.2Congo, Dem. Rep. 667 1,224 669 1,000 964 983 1,062 1,065 1,279 11.2 –2.5 6.7Congo, Rep. 1,118 2,024 3,032 2,553 2,586 2,635 2,917 2,800 3,027 4.9 5.6 3.8Côte d’Ivoire 3,048 4,084 4,203 4,337 4,211 4,152 4,372 4,263 4,997 1.2 1.5 3.8Djibouti .. .. .. .. .. .. .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. .. .. .. .. .. .. ..Eritrea .. .. 111 67 96 131 127 86 80 .. –1.4 –7.4Ethiopia .. 579 704 761 984 1,032 1,172 1,351 1,842 3.6 5.9 14.6Gabon 1,118 1,647 2,027 1,893 1,825 1,857 1,915 1,976 2,041 1.8 2.3 2.9Gambia, The 112 182 191 184 202 160 183 200 211 0.5 –1.1 3.2Ghana 853 1,005 2,139 2,409 2,429 2,430 2,389 2,453 2,547 1.4 10.5 1.0Guinea .. 501 691 713 735 791 781 739 765 .. 4.4 0.1Guinea-Bissau 22 17 33 55 68 71 71 76 79 –3.9 14.2 3.7Kenya 1,479 2,374 2,482 2,712 2,743 2,878 3,134 3,340 4,002 3.3 1.2 9.5Lesotho 58 83 178 204 256 339 430 395 447 4.7 10.7 13.5Liberia .. .. .. .. .. .. .. .. .. .. .. ..Madagascar 976 756 893 1,031 1,190 1,304 706 993 1,008 –1.8 3.3 –5.9Malawi 315 354 574 479 446 491 499 480 465 2.1 4.8 0.6Mali 175 266 559 657 649 811 1,067 907 905 4.7 10.1 8.1Mauritania 338 412 363 377 379 365 338 306 332 3.5 –1.5 –4.3Mauritius 732 1,739 2,726 2,859 2,801 3,101 3,394 3,632 3,570 10.1 5.8 6.6Mozambique 436 237 572 564 744 1,127 1,310 1,485 1,840 –8.9 10.5 23.2Namibia 1,096 954 1,504 1,571 1,558 1,525 1,739 2,139 1,994 1.4 4.3 8.7Niger 257 214 291 261 320 298 286 282 287 –3.2 3.1 –2.7Nigeria 21,726 16,042 24,883 21,797 22,416 21,536 19,138 25,252 26,045 –1.5 5.1 4.7Rwanda 171 210 116 138 151 264 279 274 304 4.2 –6.4 15.5São Tomé and Principe .. 9 10 14 15 22 26 28 34 .. 1.5 19.6Senegal 520 807 1,203 1,364 1,310 1,424 1,470 1,472 1,526 3.7 6.2 3.4Seychelles .. 252 360 378 464 503 533 613 638 .. 4.9 8.7Sierra Leone 199 144 96 95 115 128 128 232 261 –1.1 –7.8 25.0Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 19,504 22,613 33,766 34,192 37,034 37,687 37,888 37,991 38,937 1.6 5.6 1.1Sudan 764 334 462 845 1,891 1,736 1,790 2,135 2,434 –5.0 8.8 7.4Swaziland 424 778 1,058 1,006 1,133 1,318 1,345 1,264 1,278 7.5 3.5 2.0Tanzania .. 685 1,200 1,120 1,307 1,500 1,559 1,649 1,534 .. 7.8 4.2Togo 499 414 432 426 409 460 476 508 523 0.4 1.4 6.1Uganda .. 229 566 715 663 757 844 911 968 1.4 16.0 9.9Zambia 812 559 759 797 682 880 939 1,034 1,164 –3.0 3.5 13.1Zimbabwe 638 1,011 2,377 2,502 2,660 2,521 2,210 1,942 1,981 4.3 10.8 –8.2NORTH AFRICA 28,647 46,965 61,588 65,859 69,829 72,299 72,627 76,518 84,537 4.9 3.7 4.3Algeria 10,759 16,228 19,562 20,735 22,560 21,951 22,983 24,959 25,808 4.2 2.9 2.7Egypt, Arab Rep. 6,893 11,050 14,282 15,584 16,175 16,707 15,406 17,226 21,981 4.4 3.3 6.0Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 2,848 6,337 9,495 10,196 10,452 11,529 12,276 12,083 12,647 7.6 5.0 2.8Tunisia 3,205 5,248 7,625 7,985 8,597 9,641 9,434 9,050 9,520 5.1 5.1 6.9ALL AFRICA 96,405 119,419 167,789 171,093 180,508 184,300 185,426 197,256 212,036 0.9 4.6 3.9a. Preliminary.

Tab

le




1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

2.18 Imports of goods and services, real

SUB–SAHARAN AFRICA 31,471 63,935 101,955 102,576 105,176 111,306 120,963 129,714 143,743 6.6 6.1 8.1excluding South Africa 18,973 45,474 67,641 71,152 72,071 78,138 86,122 91,787 100,476 8.4 5.2 8.6excl. S. Africa & Nigeria 9,032 35,618 55,134 56,769 56,278 60,712 66,628 70,018 78,220 14.9 5.6 8.3Angola 3,008 2,461 4,871 5,410 5,736 6,833 7,885 10,188 11,166 –1.8 9.3 18.9Benin 797 484 560 615 634 660 668 670 678 –7.4 1.8 1.5Botswana 715 1,729 2,159 2,172 2,080 2,064 2,048 2,012 2,067 8.2 3.7 –0.4Burkina Faso 517 650 816 752 658 718 762 1,000 1,035 2.6 1.7 13.2Burundi 51 66 93 110 151 175 204 289 309 1.8 1.8 21.4Cameroon 795 1,261 1,658 1,706 1,981 2,213 2,416 2,353 2,450 4.9 4.1 5.0Cape Verde 122 145 254 288 326 351 393 435 448 –1.9 8.2 8.9Central African Republic 110 99,316 .. 149 155 135 72 110 101 96.5 .. –10.0Chad 318 535 413 447 480 762 3,550 1,147 1,163 11.5 –2.8 24.4Comoros 83 84 69 75 55 65 63 73 74 0.3 –0.9 7.4Congo, Dem. Rep. 566 1,063 492 785 920 1,012 1,387 1,767 2,233 13.1 –6.5 26.2Congo, Rep. 1,031 1,044 1,135 1,344 1,404 1,493 1,854 2,178 3,531 –4.7 2.5 24.9Côte d’Ivoire 3,345 2,313 4,398 4,316 3,471 3,624 3,554 3,984 4,486 –2.2 8.9 6.3Djibouti .. .. .. .. .. .. .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. .. .. .. .. .. .. ..Eritrea .. .. 596 607 518 520 502 508 434 .. 9.9 –3.7Ethiopia .. 1,116 1,679 1,967 1,961 1,952 2,148 2,515 3,028 4.6 5.3 11.9Gabon 1,662 1,729 2,009 1,679 1,718 1,757 1,806 1,865 1,936 –2.1 0.9 3.0Gambia, The 303 218 216 211 239 184 219 242 260 –7.5 –1.6 4.5Ghana 1,888 1,541 3,673 4,082 3,347 3,606 3,449 3,713 4,134 –0.5 11.8 4.6Guinea .. 780 834 864 894 921 1,004 881 912 .. 1.0 0.0Guinea-Bissau 79 84 64 82 111 107 92 84 91 1.1 –2.5 –6.3Kenya 1,913 1,854 3,742 3,687 3,757 4,489 4,132 4,240 4,889 0.9 10.3 4.8Lesotho 520 761 951 846 794 850 989 966 1,009 3.4 2.1 6.3Liberia .. .. .. .. .. .. .. .. .. .. .. ..Madagascar 1,827 1,027 1,139 1,209 1,474 1,647 1,235 1,638 2,045 –7.7 3.3 6.7Malawi 803 743 688 787 616 704 907 806 800 –1.5 –0.3 6.8Mali 392 683 869 938 954 1,238 1,110 1,260 1,225 6.8 3.1 5.3Mauritania 518 625 531 483 523 612 685 754 828 0.7 –0.8 12.0Mauritius 829 1,793 2,691 2,853 2,888 2,955 3,108 3,233 3,230 9.5 5.2 3.2Mozambique 1,232 851 1,146 1,610 1,571 1,246 1,756 1,985 2,067 –4.4 5.5 10.7Namibia 1,107 1,151 1,817 1,842 1,746 1,918 1,983 2,096 1,868 0.5 6.0 2.3Niger 1,017 569 468 378 462 504 526 533 558 –6.7 –2.1 4.4Nigeria 25,771 9,462 12,527 14,392 15,794 17,425 19,492 21,761 22,257 –15.4 3.9 9.5Rwanda 177 210 532 496 445 447 422 445 464 4.3 6.1 0.8São Tomé and Principe .. 41 30 38 38 63 62 67 83 .. –2.3 17.9Senegal 1,040 1,295 1,548 1,702 1,746 1,965 2,094 2,180 2,310 1.7 2.9 6.9Seychelles .. 209 496 489 482 632 548 549 593 .. 11.1 2.8Sierra Leone 381 176 178 158 250 380 512 553 487 –5.3 –1.7 18.7Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 19,907 18,795 34,297 31,429 33,107 33,179 34,859 37,943 43,278 –1.0 7.9 6.9Sudan 1,742 1,083 2,739 2,064 2,189 2,182 2,346 2,470 4,643 –7.5 9.6 17.7Swaziland 580 770 1,313 1,260 1,349 1,480 1,477 1,418 1,437 2.6 4.7 0.8Tanzania .. 1,829 2,047 2,062 2,064 2,049 2,129 2,472 2,527 .. 0.1 6.1Togo 708 803 776 710 674 681 716 736 758 3.0 1.0 3.2Uganda .. 687 1,283 1,397 1,366 1,408 1,623 1,640 1,723 4.7 10.7 6.4Zambia 1,977 1,180 1,253 1,274 1,018 1,295 1,219 1,264 1,401 –1.7 2.6 6.3Zimbabwe 618 984 2,414 2,470 2,680 2,434 2,556 2,256 2,248 2.7 9.8 –4.2NORTH AFRICA 48,971 50,959 57,810 57,821 61,124 63,329 67,352 70,614 74,372 –2.0 2.5 5.1Algeria 16,346 14,285 11,799 10,943 11,709 13,722 14,970 15,771 16,501 –4.0 –1.4 8.6Egypt, Arab Rep. 19,678 18,158 21,571 21,914 22,457 21,597 22,571 23,501 24,613 –2.2 3.0 2.7Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 4,291 7,787 11,284 11,560 12,458 12,743 13,601 14,184 15,073 2.4 5.7 5.0Tunisia 4,456 6,357 8,196 8,444 9,257 9,835 10,432 11,100 11,805 0.6 3.5 6.3ALL AFRICA 66,055 112,065 159,877 160,531 166,339 174,748 188,518 200,632 218,660 4.2 4.8 7.1a. Preliminary.

Tab

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2.19 Gross domestic product growth

SUB–SAHARAN AFRICA 4.2 1.1 2.3 2.6 3.3 3.5 3.4 4.1 5.1 1.8 2.4 4.0excluding South Africa 2.0 2.1 3.5 2.8 2.7 3.9 3.2 4.8 5.5 2.2 2.7 4.3excl. S. Africa & Nigeria 1.1 0.6 4.0 3.3 2.4 4.1 3.7 3.3 5.4 2.6 2.8 4.0Angola .. –0.3 6.8 3.2 3.0 3.1 14.4 3.4 11.1 3.5 1.0 8.1Benin 6.8 3.2 4.5 4.7 5.8 5.0 4.5 3.9 3.1 2.7 4.7 4.1Botswana 12.0 6.8 6.0 5.4 7.6 5.2 5.0 6.7 4.9 10.9 4.7 5.5Burkina Faso 0.8 –1.5 1.0 6.7 1.6 5.9 4.4 6.5 3.9 3.9 4.1 5.2Burundi 1.0 3.5 4.8 –1.0 –0.9 2.1 4.4 –1.2 4.8 4.5 –3.2 2.3Cameroon –2.0 –6.1 5.0 4.4 4.2 4.5 4.0 4.0 3.7 4.5 1.3 4.1Cape Verde .. 0.7 7.4 8.6 6.6 3.8 4.6 6.2 4.4 6.3 5.9 4.9Central African Republic –4.5 –2.1 4.7 3.6 2.3 1.5 –0.8 –7.6 1.3 1.6 1.8 –2.0Chad –6.0 –4.2 7.0 –0.7 –0.4 10.4 8.4 14.9 29.5 6.7 2.3 14.7Comoros .. 5.1 0.9 2.9 1.9 2.3 2.3 2.1 1.9 2.9 1.2 2.2Congo, Dem. Rep. 2.2 –6.6 –1.6 –4.4 –6.9 –2.1 3.5 5.7 6.8 2.1 –5.0 3.7Congo, Rep. 17.6 1.0 3.7 –3.0 8.2 3.8 4.6 0.8 3.6 3.8 0.9 3.1Côte d’Ivoire –11.0 –1.1 4.8 1.6 –3.3 0.1 –1.6 –1.7 1.6 0.7 3.5 –0.6Djibouti .. –1.9 0.5 2.2 0.7 .. .. .. .. .. –1.7 ..Equatorial Guinea .. 3.3 21.9 41.4 1.5 1.5 17.6 14.7 10.0 .. 20.7 11.8Eritrea .. .. 1.8 0.0 –13.1 9.2 0.7 3.9 2.0 .. 7.9 3.6Ethiopia .. 2.1 –4.3 5.4 6.0 8.8 1.9 –3.7 13.4 2.1 4.0 3.7Gabon 2.6 5.2 2.1 –6.2 2.0 2.5 0.0 2.6 1.4 0.5 3.2 1.6Gambia, The 6.3 3.6 3.5 6.4 5.5 5.8 –3.2 6.9 5.1 3.5 2.7 3.2Ghana 0.5 3.3 4.7 4.4 3.7 4.2 4.5 4.7 5.0 2.6 4.3 4.6Guinea .. 4.4 4.8 4.6 1.9 4.0 4.2 1.2 2.7 .. 4.5 3.0Guinea-Bissau –16.0 6.1 –28.1 7.8 7.5 0.2 –7.1 0.6 2.2 3.8 1.4 –1.5Kenya 5.6 4.2 3.3 2.3 0.6 4.4 0.4 2.8 4.3 4.1 2.2 2.7Lesotho –2.7 6.4 –4.6 0.2 1.3 3.2 3.5 3.1 3.1 4.1 4.2 3.2Liberia –4.1 –51.0 29.7 22.9 25.7 2.9 3.7 –31.3 2.6 –3.3 0.2 –8.7Madagascar 0.8 3.1 3.9 4.7 4.8 6.0 –12.7 9.8 5.3 0.8 1.7 0.9Malawi 0.4 5.7 3.9 3.0 1.6 –5.0 2.9 6.1 7.1 2.4 3.8 3.0Mali –4.3 –1.9 6.0 6.7 3.2 12.1 4.2 7.4 2.2 0.5 3.9 6.3Mauritania 3.4 –1.8 3.5 7.8 6.7 3.6 2.3 6.4 6.9 1.9 4.5 4.7Mauritius .. 5.8 6.0 5.8 4.0 5.6 2.9 2.9 4.4 5.9 5.3 3.7Mozambique .. 1.0 12.6 7.5 1.9 13.1 8.2 7.9 7.5 –0.9 6.3 8.9Namibia .. 2.5 3.3 3.4 3.5 2.4 6.7 3.5 6.0 1.1 4.0 4.7Niger –2.2 –1.3 10.4 –0.6 –1.4 7.1 3.0 5.3 0.0 –0.4 2.4 3.9Nigeria 4.2 8.2 1.9 1.1 4.2 3.1 1.5 10.7 6.0 0.8 2.4 5.4Rwanda 9.0 –2.4 8.9 7.6 6.0 6.7 9.4 0.9 4.0 2.5 –1.6 5.2São Tomé and Principe .. 1.8 2.5 2.5 3.0 4.0 4.1 4.0 3.8 .. 1.7 4.0Senegal –3.3 3.9 4.5 6.2 3.0 4.7 1.1 6.5 6.2 3.2 3.0 4.4Seychelles –4.2 7.0 8.4 1.9 4.8 –2.2 1.3 –6.3 –2.0 3.1 4.5 –2.4Sierra Leone 4.8 3.3 1.7 –2.1 6.7 .. 28.2 9.8 9.6 0.5 –3.7 11.2Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 6.6 –0.3 0.5 2.4 4.2 2.7 3.7 3.0 4.5 1.4 2.0 3.4Sudan 1.5 –5.5 6.4 6.3 6.5 6.1 6.0 6.0 6.0 2.4 5.3 6.0Swaziland 12.4 8.6 3.3 3.5 2.0 1.8 2.9 2.4 2.1 6.5 3.3 2.4Tanzania .. 7.0 3.7 3.5 5.1 6.2 7.2 7.1 6.7 .. 2.7 6.9Togo 14.6 –0.2 –2.3 2.5 –0.8 –0.2 4.1 2.7 3.0 1.5 3.6 2.6Uganda .. 6.5 4.9 8.1 5.6 4.9 6.5 4.4 5.6 2.3 7.2 5.4Zambia 3.0 –0.5 –1.9 2.2 3.6 4.9 3.3 5.1 5.4 1.0 0.2 4.6Zimbabwe 14.4 7.0 2.9 –3.6 –7.9 –2.7 –4.4 –10.4 –4.2 3.3 2.7 –5.9NORTH AFRICA 5.3 4.1 6.0 4.2 3.7 4.1 3.6 4.4 5.0 4.2 3.3 4.2Algeria 0.8 0.8 5.1 3.2 2.4 3.6 3.4 3.9 4.6 2.9 1.7 3.8Egypt, Arab Rep. 10.0 5.7 6.2 6.0 5.1 3.3 3.0 4.5 5.5 5.5 4.4 4.0Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 3.6 4.0 7.7 0.0 0.9 6.5 4.4 3.4 3.6 4.2 2.4 4.4Tunisia 7.4 8.0 4.8 6.1 4.7 5.4 6.0 6.5 6.0 3.2 4.6 6.0ALL AFRICA 4.6 2.1 3.7 3.2 3.4 3.7 3.5 4.2 5.1 2.6 2.7 4.1a. Preliminary.

Annual growth(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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2.20 Gross domestic product per capita growth

SUB–SAHARAN AFRICA 1.1 –1.8 –0.2 0.1 0.8 1.0 1.0 1.8 2.9 –0.8 –0.6 1.5excluding South Africa –1.1 –0.9 0.9 0.3 0.3 1.4 0.7 2.4 3.1 –0.9 –0.2 1.6excl. S. Africa & Nigeria –2.1 –2.3 1.3 0.8 –0.1 1.6 1.1 0.9 2.9 –0.5 –0.3 1.3Angola .. –3.0 4.4 0.9 0.5 0.4 11.2 0.5 7.9 –0.3 –1.7 4.1Benin 3.5 –0.3 1.6 1.7 2.7 1.8 1.2 0.6 –0.2 –0.2 1.1 1.2Botswana 8.2 3.9 4.2 3.9 6.5 4.6 4.7 6.7 5.0 8.0 2.8 5.5Burkina Faso –1.3 –4.4 –1.7 3.8 –1.3 2.7 1.1 3.1 0.6 1.1 0.9 1.2Burundi –1.9 1.0 3.9 –2.1 –2.5 –0.2 1.6 –4.3 1.3 1.0 –2.8 –0.8Cameroon –4.8 –8.8 2.8 2.2 2.1 2.4 2.0 2.1 1.8 1.1 –2.1 2.1Cape Verde .. –1.6 5.0 6.1 4.1 1.4 2.2 3.7 2.0 4.2 2.8 2.7Central African Republic –7.0 –4.4 2.6 1.7 0.6 0.0 –2.1 –8.8 0.0 –1.6 –1.1 –2.1Chad –8.0 –7.0 3.7 –3.8 –3.7 6.7 4.6 10.9 25.2 2.7 –0.8 8.7Comoros .. 2.4 –1.2 0.7 –0.3 0.2 0.2 0.0 –0.2 0.1 –0.6 0.0Congo, Dem. Rep. –0.9 –9.7 –3.5 –6.3 –8.9 –4.5 0.8 2.7 3.7 –1.2 –8.2 –1.2Congo, Rep. 14.0 –2.2 0.3 –6.1 4.8 0.6 1.4 –2.2 0.6 3.5 –2.3 1.0Côte d’Ivoire –15.1 –4.4 2.1 –0.8 –5.4 –1.8 –3.2 –3.2 0.1 –4.4 –0.3 –2.7Djibouti .. –6.4 –3.0 –1.2 –2.3 .. .. .. .. –6.8 –3.8 –2.3Equatorial Guinea .. 1.4 19.0 38.1 –0.9 –0.9 14.9 12.1 7.5 –1.6 17.4 6.5Eritrea .. .. –1.1 –3.3 –16.3 4.8 –3.7 –0.7 –2.3 .. 6.4 –3.7Ethiopia .. –1.6 –6.7 4.0 2.9 5.5 –2.2 –5.1 10.1 –0.9 –0.1 2.3Gabon –0.5 1.8 –0.6 –8.4 –0.2 0.5 –1.7 1.0 –0.2 –1.3 –0.2 –0.1Gambia, The 2.9 –0.3 0.1 3.0 2.2 2.6 –6.1 3.9 2.2 0.3 –0.4 1.0Ghana –2.0 0.5 2.4 2.1 1.4 1.9 2.2 2.5 2.8 –1.1 1.6 2.2Guinea .. 0.9 2.6 2.5 –0.2 1.8 2.0 –1.0 0.5 1.6 0.9 0.6Guinea-Bissau –18.8 3.1 –30.0 4.9 4.5 –2.7 –9.8 –2.4 –0.8 0.4 –1.0 –2.3Kenya 1.7 0.8 0.9 0.0 –1.6 2.1 –1.7 0.6 2.0 0.5 –0.6 0.3Lesotho –5.2 5.0 –5.8 –0.8 0.6 2.7 3.3 3.1 3.3 1.3 2.7 2.6Liberia –7.2 –50.5 19.0 14.2 19.3 –0.2 2.2 –31.6 2.0 –6.2 –3.2 –1.7Madagascar –2.0 0.2 0.8 1.6 1.7 3.0 –15.1 6.8 2.4 –2.4 –1.3 –0.2Malawi –2.6 1.7 1.0 0.2 –1.1 –7.3 0.5 3.8 4.8 –2.4 2.0 0.2Mali –6.5 –4.3 3.2 3.8 0.3 8.9 1.1 4.3 –0.8 –1.8 0.9 2.7Mauritania 0.9 –4.0 0.7 4.8 3.6 0.6 –0.7 3.2 3.7 –0.2 1.2 2.1Mauritius .. 5.0 4.9 4.5 2.9 4.4 2.1 1.9 3.4 4.9 4.2 2.9Mozambique .. –0.3 10.0 5.2 –0.2 10.7 6.0 5.8 5.4 –0.6 2.8 5.5Namibia .. –1.8 0.4 0.8 1.2 0.5 5.0 2.1 4.7 –2.3 0.8 2.7Niger –5.2 –4.3 6.7 –3.9 –4.7 3.5 –0.5 1.8 –3.3 –3.0 –1.4 –0.6Nigeria 1.2 5.1 –0.6 –1.3 1.8 0.7 –0.7 8.3 3.7 –1.9 0.4 2.7Rwanda 5.5 –2.1 –1.7 –1.8 –1.0 2.2 6.5 –0.8 2.5 –0.3 1.2 1.9São Tomé and Principe .. –0.4 0.7 0.6 0.9 1.8 1.8 1.6 1.4 –0.6 –0.1 1.5Senegal –5.8 0.9 1.9 3.5 0.5 2.2 –1.3 4.0 3.7 –0.4 0.4 1.8Seychelles –5.4 6.1 6.3 –0.1 3.9 –2.3 –1.7 –5.3 –3.0 1.2 3.3 –1.7Sierra Leone 2.9 1.6 0.1 –4.3 3.5 .. 22.7 4.9 5.2 –1.2 –3.5 5.1Somalia .. .. .. .. .. .. .. .. .. .. .. ..South Africa 4.2 –2.3 –1.8 –0.1 1.6 0.9 2.5 1.9 5.2 –0.3 –0.8 2.4Sudan –1.7 –7.5 4.0 4.0 4.3 4.0 4.0 4.0 4.0 0.6 2.1 4.1Swaziland 9.0 5.2 0.0 0.6 –0.4 –0.4 1.0 0.8 0.8 3.6 0.6 0.3Tanzania .. 3.5 1.4 1.3 2.9 4.1 5.1 5.0 4.7 0.4 0.2 4.4Togo 11.1 –3.1 –5.8 –1.1 –4.0 –3.1 1.3 0.0 0.4 –0.9 –0.4 –1.1Uganda .. 2.7 1.8 4.8 2.4 1.6 3.0 0.9 2.0 –0.6 3.5 2.0Zambia –0.3 –3.4 –4.1 0.0 1.5 2.9 1.5 3.4 3.7 –1.8 –2.1 2.6Zimbabwe 10.5 3.8 1.6 –4.7 –8.8 –3.5 –5.1 –10.9 –4.7 1.4 0.6 –6.6NORTH AFRICA 2.5 1.8 4.4 2.6 2.2 2.2 1.6 3.1 2.5 1.7 1.5 2.3Algeria –2.5 –1.7 3.6 1.8 1.0 1.1 2.5 5.7 3.6 –0.3 –0.4 2.8Egypt, Arab Rep. 7.5 3.5 4.3 4.1 3.4 1.5 1.2 1.1 2.2 3.4 2.6 1.9Libya .. .. .. .. .. .. .. .. .. .. .. ..Morocco 1.1 1.9 6.4 –1.2 –0.2 5.1 2.0 4.3 0.7 1.6 1.1 2.4Tunisia 4.6 5.4 3.5 4.7 3.5 3.7 0.5 5.0 5.1 1.0 3.3 3.6ALL AFRICA 1.5 –0.6 1.3 0.9 1.1 1.3 1.1 2.2 2.7 0.0 0.1 1.7a. Preliminary.

Annual growth(%)

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

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2.21 Gross national income per capita

SUB–SAHARAN AFRICA 650 577 514 498 482 473 464 510 600 576 534 506excluding South Africa 519 372 317 309 300 306 313 349 397 412 322 333excl. S. Africa & Nigeria 434 393 325 315 304 303 311 343 390 386 335 330Angola .. 730 460 390 430 470 610 690 930 740 452 626Benin 390 330 340 340 340 330 330 380 450 319 329 366Botswana 960 2,450 3,100 2,860 2,870 3,000 2,700 3,500 4,360 1,202 2,876 3,286Burkina Faso 310 350 260 260 250 240 250 290 350 284 287 276Burundi 220 210 140 130 110 100 100 90 90 231 166 98Cameroon 620 960 610 600 580 560 570 650 810 883 749 634Cape Verde .. 940 1,240 1,290 1,280 1,240 1,210 1,400 1,720 910 1,122 1,370Central African Republic 340 460 290 280 270 260 250 260 310 345 365 270Chad 230 260 210 200 180 190 200 230 250 217 236 210Comoros .. 550 420 410 400 400 400 460 560 383 512 444Congo, Dem. Rep. 600 220 110 100 80 80 90 100 110 347 154 92Congo, Rep. 820 880 530 450 520 570 620 640 760 990 658 622Côte d’Ivoire 1,120 730 740 710 650 600 570 630 760 811 712 642Djibouti .. .. 750 760 780 780 790 870 950 .. 790 834Equatorial Guinea .. 350 1,070 830 650 710 .. .. .. 343 546 680Eritrea .. .. 220 210 180 180 170 170 190 .. 208 178Ethiopia .. 170 100 100 110 110 100 90 110 156 131 104Gabon 4,790 4,780 3,820 3,180 3,090 3,080 2,990 3,340 4,080 4,403 4,232 3,316Gambia, The 370 310 320 320 320 310 270 270 280 304 333 290Ghana 410 380 370 380 330 290 270 310 380 370 379 316Guinea .. 430 460 440 400 370 360 380 410 415 460 384Guinea-Bissau 150 220 140 150 160 140 130 140 160 178 202 146Kenya 460 380 440 440 430 420 400 430 480 381 348 432Lesotho 490 640 680 650 630 590 540 590 730 522 724 616Liberia 530 .. 130 120 130 130 140 100 120 399 120 124Madagascar 440 230 240 240 240 250 220 280 290 320 232 256Malawi 190 180 200 180 150 140 140 150 160 168 187 148Mali 250 260 240 230 220 220 220 270 330 204 253 252Mauritania 450 540 440 440 460 410 450 460 530 458 480 462Mauritius .. 2,300 3,760 3,710 3,690 3,850 3,850 4,100 4,640 1,390 3,199 4,026Mozambique .. 170 200 220 210 210 210 230 270 251 167 226Namibia .. 1,740 2,020 1,890 1,870 1,770 1,740 1,990 2,380 1,466 1,991 1,950Niger 390 280 190 170 160 160 160 180 210 289 213 174Nigeria 810 280 280 280 280 320 320 380 430 496 270 346Rwanda 250 360 270 270 250 230 210 200 210 289 282 220São Tomé and Principe .. 420 280 290 300 310 320 350 390 485 355 334Senegal 500 660 470 460 450 440 420 510 630 474 560 490Seychelles 2,080 5,020 7,320 7,290 7,320 7,220 6,800 7,430 8,190 2,764 6,420 7,392Sierra Leone 380 200 160 150 140 150 160 190 210 280 173 170Somalia 100 140 .. .. .. .. .. .. .. 127 140 ..South Africa 2,510 3,390 3,280 3,150 3,050 2,830 2,630 2,850 3,630 2,797 3,471 2,998Sudan 450 550 300 310 310 340 390 440 530 528 326 402Swaziland 960 1,200 1,460 1,470 1,370 1,380 1,190 1,310 1,660 934 1,404 1,382Tanzania .. 200 250 270 280 290 300 310 320 .. 205 300Togo 410 380 290 280 270 240 240 260 310 306 324 264Uganda .. 320 280 280 260 240 230 230 250 285 245 242Zambia 600 420 310 310 290 300 310 350 400 432 346 330Zimbabwe 930 850 570 500 460 580 890 920 620 858 659 694NORTH AFRICA 1,276 1,375 1,534 1,578 1,641 1,674 1,634 1,670 1,784 1,305 1,380 1,681Algeria 2,060 2,420 1,560 1,540 1,570 1,660 1,710 1,930 2,270 2,462 1,755 1,828Egypt, Arab Rep. 500 760 1,240 1,350 1,460 1,480 1,390 1,310 1,250 631 956 1,378Libya 10,460 .. .. .. .. .. 4,760 4,350 4,400 7,826 .. 4,503Morocco 970 1,030 1,290 1,240 1,220 1,230 1,220 1,370 1,570 806 1,170 1,322Tunisia 1,360 1,430 2,050 2,080 2,080 2,060 1,990 2,260 2,650 1,264 1,808 2,208ALL AFRICA 769 732 697 689 685 681 666 709 803 716 692 709a. Calculated by the World Bank Atlas method.b. Preliminary.

Dollarsa

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004b 1980–89 1990–99 2000–04

Tab

le


2.22 Total consumption per capita

SUB–SAHARAN AFRICA 491 469 440 424 408 402 423 488 566 445 452 458excluding South Africa 389 326 285 276 264 280 317 326 357 321 285 309excl. S. Africa & Nigeria 334 352 298 283 270 282 326 329 361 309 300 314Angola .. 685 394 361 399 534 554 741 953 586 460 636Benin 403 349 321 325 295 299 353 422 468 313 317 367Botswana 742 1,523 1,736 1,774 1,746 1,747 1,870 2,684 3,136 780 1,714 2,236Burkina Faso 314 347 232 236 215 229 254 324 355 277 252 276Burundi 224 210 148 130 117 109 103 91 99 215 171 104Cameroon 603 759 494 515 540 513 570 706 802 691 618 626Cape Verde 458 1,030 1,446 1,558 1,347 1,373 1,509 1,910 2,148 550 1,189 1,657Central African Republic 373 499 272 252 233 224 241 260 285 352 344 249Chad .. 309 213 194 159 190 231 238 317 221 235 227Comoros 406 622 436 445 373 402 430 544 637 394 519 477Congo, Dem. Rep. 462 224 131 88 82 88 101 99 114 288 154 97Congo, Rep. 609 859 421 417 396 369 413 497 543 681 592 444Côte d’Ivoire 971 757 642 603 512 501 488 620 688 681 637 562Djibouti .. .. 774 815 815 .. .. .. .. .. 828 815Equatorial Guinea .. 450 853 .. .. .. .. .. .. .. 463 ..Eritrea .. .. 299 284 240 230 218 296 357 .. 251 268Ethiopia .. 213 110 101 101 96 88 96 115 140 121 99Gabon 2,420 3,926 2,320 2,280 2,779 1,659 1,942 2,468 2,844 2,501 2,713 2,339Gambia, The 348 303 298 302 293 271 231 227 243 276 315 253Ghana 374 359 353 383 237 243 275 322 357 340 348 287Guinea .. 373 367 346 307 298 331 374 384 324 388 339Guinea-Bissau 141 233 176 171 171 169 156 155 178 177 205 166Kenya 365 299 431 384 375 369 359 400 420 301 312 385Lesotho 507 591 676 628 579 494 459 664 819 475 690 603Liberia 435 .. .. .. .. 178 180 139 153 446 .. 162Madagascar 452 242 228 219 221 230 237 283 222 301 231 239Malawi 179 172 148 159 146 140 176 158 165 150 180 157Mali 254 255 211 206 183 188 240 297 332 206 230 248Mauritania 456 478 434 416 355 409 417 545 611 433 442 467Mauritius 1,020 1,725 2,686 2,780 2,861 2,796 2,818 3,215 3,736 1,078 2,413 3,085Mozambique 319 194 202 196 186 186 195 226 267 277 173 212Namibia 1,354 1,376 1,624 1,598 1,549 1,383 1,310 1,662 2,083 1,474 1,666 1,597Niger 345 289 184 171 147 152 163 199 213 259 202 175Nigeria 643 222 231 245 238 273 281 313 338 385 221 289Rwanda 214 342 299 258 223 198 201 194 202 272 283 203São Tomé and Principe 555 702 325 378 345 411 430 487 482 579 462 431Senegal 526 651 419 418 378 390 435 623 616 480 498 488Seychelles 1,667 4,196 6,270 5,762 5,891 6,101 6,398 6,823 6,609 2,170 5,142 6,364Sierra Leone 337 145 153 167 159 194 211 210 213 250 181 197Somalia 105 155 .. .. .. .. .. .. .. 136 155 ..South Africa 1,818 2,444 2,624 2,514 2,450 2,137 1,950 2,840 3,694 2,094 2,782 2,614Sudan 373 .. 332 307 315 359 382 424 491 488 319 394Swaziland 949 1,041 1,339 1,347 1,271 1,144 881 1,421 1,932 812 1,297 1,330Tanzania .. 160 254 247 237 243 239 252 275 204 189 249Togo 314 351 308 294 253 238 258 285 329 270 303 273Uganda 99 241 272 234 221 212 213 218 223 231 217 217Zambia 518 328 303 302 277 276 274 311 386 397 330 305Zimbabwe 788 686 398 401 509 911 2,267 579 345 700 531 922NORTH AFRICA 824 1,094 1,303 1,259 1,261 1,265 1,251 1,300 1,330 944 1,107 1,282Algeria 1,281 1,789 1,166 1,088 976 1,084 1,145 1,280 1,491 1,697 1,212 1,195Egypt, Arab Rep. 443 650 1,152 1,122 1,214 1,171 1,065 965 844 540 832 1,052Libya 5,040 .. .. .. .. .. .. .. .. 5,044 .. ..Morocco 843 870 1,078 1,030 979 981 1,066 1,276 1,408 667 1,001 1,142Tunisia 1,041 1,124 1,623 1,672 1,550 1,563 1,694 2,013 2,215 961 1,440 1,807ALL AFRICA 563 590 596 573 559 553 569 633 706 545 576 604a. Preliminary.

Dollars

Annual average

1980 1990 1998 1999 2000 2001 2002 2003 2004a 1980–89 1990–99 2000–04

Tab

le

48 Part II. Millennium Development Goals MILLENNIUM DEVELOPMENT GOALS

3.1 Millennium Development Goal 1: eradicate extreme poverty and hunger

International poverty lineShare of population below

national poverty linea (poverty headcount ratio)

Share of population below PPP $1 a daya

Poverty gap ratio at $1 a daya (incidence x depth of poverty)

Surveyyear Percentage



SUB–SAHARAN AFRICAAngola .. .. .. .. .. ..Benin 1999 29.0 2003 30.9 2003 8.2Botswana .. .. 1993 23.5 1993 7.7Burkina Faso 2003 46.4 2003 27.2 2003 7.3Burundi 1990 36.4 1998 54.6 1998 22.7Cameroon 2001 40.2 2001 17.1 2001 4.1Cape Verde .. .. .. .. .. ..Central African Republic .. .. 1993 66.6 1993 38.1Chad 1996 64.0 .. .. .. ..Comoros .. .. .. .. .. ..Congo, Dem. Rep. .. .. .. .. .. ..Congo, Rep. .. .. .. .. .. ..Côte d’Ivoire .. .. 2002 14.8 2002 4.1Djibouti .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. ..Eritrea 1994 53.0 .. .. .. ..Ethiopia 2000 44.2 2000 23.0 2000 4.8Gabon .. .. .. .. .. ..Gambia, The 1998 57.6 1998 59.3 1998 28.8Ghana 1998–99 39.5 1998–99 44.8 1998–99 17.3Guinea 1994 40.0 .. .. .. ..Guinea-Bissau .. .. .. .. .. ..Kenya 1997 52.0 1997 22.8 1997 5.9Lesotho .. .. 1995 36.4 1995 19.0Liberia .. .. .. .. .. ..Madagascar 1999 71.3 2001 61.0 2001 27.9Malawi 1998 65.3 1998 41.7 1998 14.8Mali 1998 63.8 1994 72.3 1994 37.4Mauritania 2000 46.3 2000 25.9 2000 7.6Mauritius .. .. .. .. .. ..Mozambique 1996–97 69.4 1996–97 37.8 1996–97 12.0Namibia .. .. 1993 34.9 1993 14.0Niger 1993 63.0 1995 60.6 1995 34.0Nigeria 1992 34.1 2003 70.8 2003 34.5Rwanda 1999 60.3 2000 51.7 2000 20.0São Tomé and Principe .. .. .. .. .. ..Senegal 1992 33.4 1995 22.3 1995 5.7Seychelles .. .. .. .. .. ..Sierra Leone 2003 70.2 .. .. .. ..Somalia .. .. .. .. .. ..South Africa .. .. 2000 10.7 2000 1.7Sudan .. .. .. .. .. ..Swaziland .. .. .. .. .. ..Tanzania 2000–01 35.7 2000–01 57.8 2000–01 20.7Togo .. .. .. .. .. ..Uganda 2003 37.7 .. .. .. ..Zambia 1998 72.9 2002–03 75.8 2002–03 36.4Zimbabwe 1996 34.9 1995 56.1 1995 24.2NORTH AFRICAAlgeria 1995 22.6 1995 2.0 1995 0.5Egypt, Arab Rep. 1999–2000 16.7 1999–2000 3.1 1999–2000 0.5Libya .. .. .. .. .. ..Morocco 1998–99 19.0 1998–99 2.0 1998–99 0.5Tunisia 1995 7.6 2000 2.0 2000 0.5

a. Data are based on expenditure shares, except for Namibia and Swaziland, where data are based on income sharesb. Data are for most recent year available during the period specifi ed.

MILLENNIUM DEVELOPMENT GOALS Part II. Millennium Development Goals 49

.. .. 20.0 30.5 382003 7.4 .. 22.9 141993 2.2 .. 12.5 302003 6.9 32.7 37.7 171998 5.1 .. 45.1 672001 5.6 15.1 18.1 25

.. .. 13.5 .. ..1993 2.0 .. 24.3 45

.. .. .. 36.7 33

.. .. 18.5 25.4 62

.. .. .. 31.0 72

.. .. .. .. 342002 5.2 23.8 17.2 14

.. .. 22.9 .. 26

.. .. .. 18.6 ..

.. .. 41.0 39.6 732000 9.1 47.7 47.2 46

.. .. .. 11.9 51998 4.8 .. 17.2 27

1998–99 5.6 27.3 22.1 121994 6.4 26.8 32.7 241993 5.2 .. 25.0 371997 6.0 22.5 19.9 311995 1.5 21.4 18.0 12

.. .. .. 26.5 492001 4.9 45.2 41.9 381997 4.9 27.6 21.9 341994 4.6 .. 33.2 282000 6.2 47.6 31.8 10

.. .. .. .. 61996–97 6.5 .. 23.7 45

1993 1.4 26.2 24.0 231995 2.6 42.6 40.1 322003 5.0 39.1 28.7 9

.. .. 29.4 24.3 36

.. .. .. 12.9 121995 6.4 22.2 22.7 23

.. .. .. .. 9

.. .. 28.7 27.2 50

.. .. .. 25.8 ..2000 3.5 .. .. ..

.. .. 33.9 40.7 271994 2.7 .. 10.3 19

2000–01 7.3 28.9 .. 44.. .. .. .. 25

1999 5.9 23.0 22.9 192002–03 6.1 25.2 23.0 47

1995 4.6 15.5 .. 45

1995 7.0 9.2 10.4 51999–2000 8.6 9.9 8.6 3

.. .. .. .. 31998–99 6.5 9.5 10.2 6

2000 6.0 .. 4.0 3

Share of poorest quintile in national consumption or incomea

Prevalence of child malnutrition, underweight (% of children under age 5)

Share of population below minimum dietary energy consumption

(%)

Surveyyear Percentage 1989–94b 2000–04b 2003

Tab

le


3.2 Millennium Development Goal 2: achieve universal primary education

Net primary enrollment ratio (% of relevant age group)

Primary completion rate(% of relevant age group)

Share of cohort reaching grade 5(% of grade 1 students)

Youth literacy rate (% ages 15–24)

1991 2004a 1991 2004a 1991 2003 1990 2004

SUB–SAHARAN AFRICAAngola 50 .. 35 .. .. .. .. 72Benin 41 83 21 49 55 69 40 45Botswana 83 82 79 92 84 .. 83 94Burkina Faso 29 40 21 29 70 76 .. 31Burundi 53 57 46 33 62 63 52 73Cameroon 74 .. 56 63 .. .. 81 ..Cape Verde 91 92 .. 95 .. 91 81 ..Central African Republic 52 .. 27 .. 23 .. 52 59Chad 35 57 18 29 51 46 48 38Comoros 57 55 .. 50 .. 63 57 ..Congo, Dem. Rep. 54 .. 46 .. 55 .. 69 70Congo, Rep. 79 .. 54 66 60 .. 93 ..Côte d’Ivoire 45 56 43 .. 73 .. 53 61Djibouti 29 33 27 29 87 .. 73 ..Equatorial Guinea 91 59 .. .. .. .. 93 95Eritrea 16 48 .. 44 .. 80 61 ..Ethiopia 22 46 .. 51 18 .. 43 ..Gabon 85 77 .. .. .. .. .. ..Gambia, The 48 73 .. .. .. .. 42 ..Ghana 54 58 63 65 80 .. 82 71Guinea 27 64 17 48 59 82 44 47Guinea-Bissau 38 45 .. .. .. .. .. ..Kenya .. 76 .. 89 77 75 90 80Lesotho 71 86 58 71 66 63 87 ..Liberia .. 66 .. .. .. .. 57 ..Madagascar 64 89 33 45 21 57 72 70Malawi 48 95 31 58 64 .. 63 76Mali 21 46 11 44 70 79 .. 24Mauritania 35 74 33 43 75 82 46 61Mauritius 91 95 102 100 97 99 91 95Mozambique 43 71 26 29 34 .. 49 ..Namibia .. 74 .. .. 62 .. 87 92Niger 22 39 17 25 62 74 17 37Nigeria .. 88 .. 75 89 35 74 ..Rwanda 66 73 47 37 60 46 73 78São Tomé and Principe .. .. .. 75 .. .. .. ..Senegal 43 66 .. 45 85 78 40 49Seychelles .. 100 .. 106 93 .. .. 99Sierra Leone 43 .. .. .. .. .. .. 48Somalia 9 .. .. .. .. .. .. ..South Africa 90 89 75 .. .. .. 88 94Sudan 40 43 40 49 94 92 65 77Swaziland 75 77 62 .. 77 .. 85 88Tanzania 49 86 61 57 81 88 83 78Togo 64 79 35 66 48 76 63 74Uganda .. 98 .. 57 36 .. 70 77Zambia .. 80 .. 66 .. .. 81 69Zimbabwe .. 82 91 .. 76 .. 94 ..NORTH AFRICAAlgeria 89 97 79 94 95 96 77 90Egypt, Arab Rep. 84 94 .. 95 .. .. 61 ..Libya 96 .. .. .. .. .. 91 ..Morocco 56 87 46 75 75 .. 55 70Tunisia 94 97 74 97 86 .. 84 94

a. Provisional.

Tab

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3.3 Millennium Development Goal 3: promote gender equality and empower women

Ratio of girls to boys in primary and secondary school

(%)

Ratio of young literate women to men (% ages 15–24)

Women in national parliaments

(% of total seats)

Share of women in wage employment in the

nonagricultural sector(%)

1991 2004a 1990 2004 1990 2006 2000 2003

SUB–SAHARAN AFRICAAngola .. .. .. 75 15 15 .. ..Benin 49 71 44 56 3 7 .. ..Botswana 109 .. 110 104 5 11 45 47Burkina Faso 62 76 .. 65 .. 12 15 15Burundi 82 82 77 92 .. 31 .. ..Cameroon 83 87 88 .. 14 9 .. ..Cape Verde .. 100 87 .. 12 .. .. ..Central African Republic 60 .. 60 67 4 11 .. ..Chad 41 58 65 42 .. 7 .. ..Comoros 71 84 78 .. 0 .. .. ..Congo, Dem. Rep. .. .. 72 81 5 12 .. ..Congo, Rep. 85 .. 95 .. 14 9 .. ..Côte d’Ivoire 65 .. 62 74 6 9 21 20Djibouti 70 75 78 .. 0 .. .. ..Equatorial Guinea .. .. 92 100 13 .. .. ..Eritrea .. 73 68 .. .. 22 34 35Ethiopia 68 73 66 .. .. 21 .. ..Gabon .. .. .. .. 13 9 .. ..Gambia, The 64 .. 68 .. 8 13 .. ..Ghana 79 91 86 86 .. 19 .. ..Guinea 46 72 43 57 .. .. .. ..Guinea-Bissau .. .. .. .. 20 14 .. ..Kenya 94 94 93 101 1 7 34 39Lesotho 124 104 126 .. .. 12 .. ..Liberia .. .. 51 .. .. 13 .. ..Madagascar 98 .. 86 94 7 7 .. ..Malawi 81 98 68 86 10 14 12 13Mali 59 74 .. 52 .. 10 .. ..Mauritania 67 96 65 82 .. .. .. ..Mauritius 102 103 100 102 7 17 39 35b

Mozambique 72 82 48 .. 16 35 .. ..Namibia 108 .. 104 103 7 27 49 51Niger 57 71 37 44 5 12 .. ..Nigeria 79 84 82 .. .. 6 .. ..Rwanda 96 100 86 98 17 49 .. ..São Tomé and Principe .. .. .. .. 12 .. .. ..Senegal 69 90 60 70 13 19 .. ..Seychelles .. .. .. 101 16 .. .. ..Sierra Leone 67 .. .. 63 .. 15 .. ..Somalia .. .. .. .. 4 8 .. ..South Africa 104 .. 100 101 3 33 .. ..Sudan 78 88 71 84 .. 15 20 19Swaziland 98 .. 101 103 4 11 32 31Tanzania 97 .. 87 94 .. 30 .. ..Togo 59 73 60 76 5 7 .. ..Uganda 82 97 76 86 12 24 .. ..Zambia .. 93 88 91 7 13 .. ..Zimbabwe 92 .. 95 .. 11 16 20 22NORTH AFRICAAlgeria 83 99 79 92 2 6 12 16Egypt, Arab Rep. 81 .. 72 .. 4 2 19 22Libya .. .. 84 .. .. 5 .. ..Morocco 70 88 62 75 0 11 26 26Tunisia 86 .. 81 96 4 23 25 25

a. Provisional.b. Data are for 2004.

Tab

le


3.4 Millennium Development Goal 4: reduce child mortality

Under-fi ve mortality rate (per 1,000)

Infant mortality rate (per 1,000 live births)

Child immunization rate, measles (% of children ages 12–23 months)

1990 2004 1990 2004 1990 2004

SUB–SAHARAN AFRICAAngola 260 260 154 154 38 64Benin 185 152 111 90 79 85Botswana 58 116 45 84 87 90Burkina Faso 210 192 113 97 79 78Burundi 190 190 114 114 74 75Cameroon 139 149 85 87 56 64Cape Verde 60 36 45 27 79 69Central African Republic 168 193 102 115 83 35Chad 203 200 117 117 32 56Comoros 120 70 88 52 87 73Congo, Dem. Rep. 205 205 129 129 38 64Congo, Rep. 110 108 83 81 75 65Côte d’Ivoire 157 194 103 117 56 49Djibouti 163 126 122 101 85 60Equatorial Guinea 170 204 103 122 88 51Eritrea 147 82 88 52 .. 84Ethiopia 204 166 131 110 38 71Gabon 92 91 60 60 76 55Gambia, The 154 122 103 89 86 90Ghana 122 112 75 68 61 83Guinea 240 155 145 101 35 73Guinea-Bissau 253 203 153 126 53 80Kenya 97 120 64 79 78 73Lesotho 104 112 74 80 80 70Liberia 235 235 157 157 .. 42Madagascar 168 123 103 76 47 59Malawi 241 175 146 110 81 80Mali 250 219 140 121 43 75Mauritania 133 125 85 78 38 64Mauritius 23 15 20 14 76 98Mozambique 235 152 158 104 59 77Namibia 86 63 60 47 57 70Niger 320 259 191 152 25 74Nigeria 230 197 120 101 54 35Rwanda 173 203 103 118 83 84São Tomé and Principe 118 118 75 75 71 91Senegal 148 137 90 78 51 57Seychelles 19 14 17 12 86 99Sierra Leone 302 283 175 165 .. 64Somalia 225 225 133 133 30 40South Africa 60 67 45 54 79 81Sudan 120 91 74 63 57 59Swaziland 110 156 78 108 85 70Tanzania 161 126 102 78 80 94Togo 152 140 88 78 73 70Uganda 160 138 93 80 52 91Zambia 180 182 101 102 90 84Zimbabwe 80 129 53 79 87 80NORTH AFRICAAlgeria 69 40 54 35 83 81Egypt, Arab Rep. 104 36 76 26 86 97Libya 41 20 35 18 89 99Morocco 89 43 69 38 80 95Tunisia 52 25 41 21 93 95

Tab

le


3.5 Millennium Development Goal 5: improve maternal health

Maternal mortality ratio, modeled estimate

(per 100,000 live births)

Births attended by skilled health staff

(% of total)

2000 1989–94a 2000–04a

SUB–SAHARAN AFRICAAngola 1,700 .. 45Benin 850 .. 66Botswana 100 .. 94Burkina Faso 1,000 42 38Burundi 1,000 .. 25Cameroon 730 58 62Cape Verde 150 .. ..Central African Republic 1,100 .. 44Chad 1,100 .. 14Comoros 480 .. 62Congo, Dem. Rep. 990 .. 61Congo, Rep. 510 .. ..Côte d’Ivoire 690 45 68Djibouti 730 .. 61Equatorial Guinea 880 5 65Eritrea 630 .. 28Ethiopia 850 .. 6Gabon 420 .. 86Gambia, The 540 44 55Ghana 540 44 47Guinea 740 31 56Guinea-Bissau 1,100 .. 35Kenya 1,000 45 42Lesotho 550 50 60Liberia 760 .. 51Madagascar 550 57 51Malawi 1,800 55 61Mali 1,200 .. 41Mauritania 1,000 40 57Mauritius 24 97 99Mozambique 1,000 .. 48Namibia 300 68 76Niger 1,600 15 16Nigeria 800 31 35Rwanda 1,400 26 31São Tomé and Principe .. .. 76Senegal 690 47 58Seychelles .. .. ..Sierra Leone 2,000 .. 42Somalia 1,100 .. 25South Africa 230 .. ..Sudan 590 86 87Swaziland 370 56 74Tanzania 1,500 44 46Togo 570 .. 61Uganda 880 38 39Zambia 750 51 43Zimbabwe 1,100 69 ..NORTH AFRICAAlgeria 140 77 96Egypt, Arab Rep. 84 41 69Libya 97 .. ..Morocco 220 31 63Tunisia 120 .. 90

a. Data are for most recent year available during the period specifi ed.

Tab

le


3.6 Millennium Development Goal 6: combat HIV/AIDS, malaria, and other diseases

Contraceptive prevalence rate Share of women

ages 15–49

Malaria preventionChildren sleeping under

insecticide-treated bednets (% of children under age 5)

Prevalence of HIV

(% ages 15–49)

Deaths due to malaria

(per 100,000 people)

Incidence of tuberculosis


Tuberculosis cases detected under DOTS (% of estimated cases)

2005 Survey year Percentage 1997–2004a 2000 2001–04a 1990 2004 1995 2004

SUB–SAHARAN AFRICA 6.1 .. .. .. .. .. .. .. 32 45Angola 3.7 2001 6 354 .. 2.3 214 259 .. 94Benin 1.8 2001 19 177 .. 7.4 79 87 81 82Botswana 24.1 2000 48 .. .. .. 236 670 72 67Burkina Faso 2.0 2003 14 292 .. 2.0 158 191 12 18Burundi 3.3 2000 16 .. 1.3 .. 125 343 20 29Cameroon 5.4 2004 26 .. 1.3 .. 65 179 .. 91Cape Verde .. 1998 53 .. .. .. 158 172 .. ..Central African Republic 10.7 2000 28 .. 1.5 .. 117 322 .. 4Chad 3.5 2004 3 .. 0.6 .. 101 279 37 16Comoros <0.1 2000 26 .. 9.3 .. 88 46 54 39Congo, Dem. Rep. 3.2 2001 31 224 .. 0.7 133 366 42 70Congo, Rep. 5.3 .. .. .. .. .. 137 377 67 65Côte d’Ivoire 7.1 1998 15 .. 1.1 4.0 143 393 52 38Djibouti 3.1 .. .. .. .. .. 608 734 .. 43Equatorial Guinea 3.2 .. .. .. 0.7 .. 87 239 83 82Eritrea 2.4 2002 8 74 .. 4.2 225 271 .. 14Ethiopia .. 2000 8 198 .. .. 128 353 15 36Gabon 7.9 2000 33 .. .. .. 144 280 .. 81Gambia, The 2.4 2001 18 .. 14.7 .. 193 233 75 66Ghana 2.3 2003 25 70 .. 4.0 223 206 16 37Guinea 1.5 2003 7 .. .. 4.0 122 240 43 52Guinea-Bissau 3.8 2000 8 .. 7.4 .. 165 199 .. 75Kenya 6.1 2003 39 63 2.9 5.0 108 619 56 46Lesotho 23.2 2000 30 .. .. .. 179 696 63 86Liberia .. 2000 10 201 .. .. 112 310 .. 58Madagascar 0.5 2004 27 .. 0.2 .. 192 218 51 74Malawi 14.1 2000 31 275 2.9 .. 257 413 38 40Mali 1.7 2001 8 454 .. 8.4 304 281 14 19Mauritania 0.7 2001 8 .. .. .. 238 287 .. 43Mauritius 0.6 2002 76 .. .. .. 68 64 34 33Mozambique 16.1 2003 17 232 .. .. 167 460 54 46Namibia 19.6 2000 44 .. 3.4 .. 260 717 22 88Niger 1.1 2000 14 469 5.8 .. 131 157 .. 46Nigeria 3.9 2003 13 141 .. 1.0 105 290 11 21Rwanda 3.1 2000 13 200 5.0 .. 135 371 34 29São Tomé and Principe .. 2000 29 .. 22.8 .. 135 107 .. ..Senegal 0.9 2000 11 72 1.7 .. 203 245 61 52Seychelles .. .. .. .. .. .. 43 34 .. 106Sierra Leone 1.6 2000 4 .. 1.5 .. 224 443 28 36Somalia 0.9 .. .. .. .. .. 341 411 .. 44South Africa 18.8 1998 56 .. .. .. 268 718 .. 83Sudan 1.6 2000 7 70 0.4 .. 182 220 .. 35Swaziland 33.4 2002 48 .. 0.1 .. 263 1,226 .. 38Tanzania 6.5 1999 25 130 .. 10.0 179 347 56 47Togo 3.2 2000 26 .. 2.0 .. 351 355 13 17Uganda 6.7 2001 23 152 .. 0.2 161 402 .. 43Zambia 17.0 2002 34 141 .. 6.5 297 680 .. 54Zimbabwe 20.1 1999 54 .. .. .. 135 674 .. 42NORTH AFRICA 0.2 .. .. .. .. .. .. .. 58 80Algeria 0.1 2002 57 .. .. .. 37 54 .. 105Egypt, Arab Rep. <0.1 2003 60 .. .. .. 42 27 43 61Libya .. .. .. .. .. .. 31 20 .. 169Morocco 0.1 2004 63 .. .. .. 135 110 93 80Tunisia 0.1 2000 66 .. .. .. 33 22 .. 95


Tab

le


3.7 Millennium Development Goal 7: ensure environmental sustainability

Forest area (% of total land area)

Nationally protected areas as share of total

land area (%)

GDP per unit of energy use

(constant 2000 PPP $ per kg of oil equivalent)

Carbon dioxide emissions

(metric tons per capita)

Solid fuel use

(% of population)

Population with sustainable access

to an improved water source

(%)

Population with sustainable access to improved sanitation

(%)

1990 2005 2004 1990 2003 1990 2002 2000–04a 1990 2002 1990 2002

SUB–SAHARAN AFRICAAngola 49 47 6.6 3.7 3.1 0.4 0.5 95 32 50 30 30Benin 30 21 11.4 2.6 3.5 0.1 0.3 95 60 68 11 32Botswana 24 21 18.5 .. .. 1.5 2.3 .. 93 95 38 41Burkina Faso 26 25 11.5 .. .. 0.1 0.1 95 39 51 13 12Burundi 11 6 5.7 .. .. 0.0 0.0 100 69 79 44 36Cameroon 53 46 4.5 4.7 4.6 0.1 0.2 83 50 63 21 48Cape Verde 14 21 .. .. .. 0.2 0.3 .. .. 80 .. 42Central African Republic 37 37 8.7 .. .. 0.1 0.1 .. 48 75 23 27Chad 10 9 9.1 .. .. 0.0 0.0 95 20 34 6 8Comoros 5 2 .. .. .. 0.2 0.1 .. 89 94 23 23Congo, Dem. Rep. 62 59 5.0 5.0 2.1 0.1 0.0 .. 43 46 18 29Congo, Rep. 67 66 6.5 2.3 3.3 0.5 0.6 84 .. 46 .. 9Côte d’Ivoire 32 33 6.0 5.2 3.8 0.4 0.4 74 69 84 31 40Djibouti 0 0 .. .. .. 0.6 0.5 78 80 48 50Equatorial Guinea 66 58 .. .. .. 0.3 0.4 .. .. 44 .. 53Eritrea .. 15 4.3 .. .. .. 0.2 80 40 57 8 9Ethiopia .. 13 16.9 2.1 2.1 0.1 0.1 95 25 22 4 6Gabon 85 85 0.7 4.8 4.9 6.3 2.6 .. .. 87 .. 36Gambia, The 44 47 2.3 .. .. 0.2 0.2 .. .. 82 .. 53Ghana 33 24 5.6 4.6 5.0 0.2 0.4 88 54 79 43 58Guinea 30 27 0.7 .. .. 0.2 0.1 .. 42 51 17 13Guinea-Bissau 79 74 .. .. .. 0.2 0.2 .. .. 59 .. 34Kenya 7 6 8.0 2.2 2.1 0.2 0.2 82 45 62 42 48Lesotho 0 0 0.2 .. .. .. .. 83 .. 76 37 37Liberia 42 33 1.7 .. .. 0.2 0.1 .. 56 62 38 26Madagascar 24 22 4.3 .. .. 0.1 0.1 99 40 45 12 33Malawi 41 36 11.2 .. .. 0.1 0.1 95 41 67 36 46Mali 12 10 3.7 .. .. 0.0 0.0 98 34 48 36 45Mauritania 0 0 1.7 .. .. 1.3 1.1 .. 41 56 28 42Mauritius 19 18 .. .. .. 1.4 2.6 .. 100 100 99 99Mozambique 26 25 8.4 1.3 2.5 0.1 0.1 .. .. 42 .. 27Namibia 11 9 13.6 .. 9.9 0.0 1.1 63 58 80 24 30Niger 2 1 7.7 .. .. 0.1 0.1 98 40 46 7 12Nigeria 19 12 3.3 1.1 1.3 0.5 0.4 86 49 60 39 38Rwanda 13 19 6.2 .. .. 0.1 0.1 100 58 73 37 41São Tomé and Principe 28 28 .. .. .. 0.6 0.6 .. .. 79 .. 24Senegal 49 45 11.6 5.0 5.2 0.4 0.4 41 66 72 35 52Seychelles 87 87 .. .. .. 1.6 6.4 .. .. 87 .. ..Sierra Leone 43 38 2.1 .. .. 0.1 0.1 .. .. 57 .. 39Somalia 13 11 0.8 .. .. 0.0 .. .. .. 29 .. 25South Africa 8 8 5.5 3.8 3.9 8.1 7.6 .. 83 87 63 67Sudan 32 28 5.2 2.7 3.7 0.2 0.3 .. 64 69 33 34Swaziland 27 31 .. .. .. 0.6 0.9 .. .. 52 .. 52Tanzania 47 40 29.8 1.4 1.3 0.1 0.1 .. 38 73 47 46Togo 13 7 7.9 4.3 3.2 0.2 0.3 .. 49 51 37 34Uganda 25 18 24.6 .. .. 0.0 0.1 97 44 56 43 41Zambia 66 57 31.9 1.5 1.4 0.3 0.2 85 50 55 41 45Zimbabwe 57 45 12.1 3.0 2.6 1.6 1.0 73 77 83 49 57NORTH AFRICA 5Algeria 1 1 5.0 5.7 5.6 3.0 2.9 5 95 87 88 92Egypt, Arab Rep. 0 0 9.7 5.1 5.1 1.4 2.1 5 94 98 54 68Libya 0 0 0.1 .. .. 8.7 9.1 5 71 72 97 97Morocco 10 10 0.7 11.9 10.2 1.0 1.5 5 75 80 57 61Tunisia 4 7 0.3 6.7 8.1 1.6 2.3 5 77 82 75 80


Tab

le


3.8 Millennium Development Goal 8: develop a global partnership for development

Debt sustainability

Heavily Indebted Poor Country (HIPC)

Debt Initiative

Estimated total nominal debt service relief committed under

Enhanced HIPC Initiative($ millions)

Debt service (% of exports)

Decision point Completion point as of Mar. 2006 1990 2004

SUB–SAHARAN AFRICA 13.5 7.9Angola 8.1 14.8Benin Jul. 2000 Mar. 2003 460 8.2 ..Botswana 4.3 ..Burkina Faso Jul. 2000 Apr. 2002 930 6.8 ..Burundi Jul. 2005 Floating 1,472 43.4 ..Cameroon Oct. 2000 Floating 2,800 20.5 ..Cape Verde 4.8 ..Central African Republic 13.2 ..Chad May 2001 Floating 260 4.4 ..Comoros 2.3 ..Congo, Dem. Rep. Jul. 2003 Floating 10,389 .. ..Congo, Rep. Mar. 2006 2,881 35.3 ..Côte d’Ivoire 35.4 6.9Djibouti .. ..Equatorial Guinea 12.1 ..Eritrea .. ..Ethiopia Nov. 2001 Apr. 2004 3,275 39.0 5.3Gabon 6.4 ..Gambia, The Dec. 2000 Floating 90 22.2 ..Ghana Feb. 2002 Jul. 2004 3,500 38.1 6.6Guinea Dec. 2000 Floating 800 20.0 19.9Guinea-Bissau Dec. 2000 Floating 790 31.0 ..Kenya 35.4 8.6Lesotho 4.2 4.5Liberia .. ..Madagascar Dec. 2000 Oct. 2004 1,900 45.5 ..Malawi Dec. 2000 Floating 1,000 29.3 ..Mali Sep. 2000 Mar. 2003 895 12.3 ..Mauritania Feb. 2000 Jun. 2002 1,100 29.8 ..Mauritius 8.8 7.4Mozambique Apr. 2000 Sep. 2001 4,300 26.2 4.5Namibia .. ..Niger Dec. 2000 Apr. 2004 1,190 17.4 ..Nigeria 22.6 8.2Rwanda Dec. 2000 Apr. 2005 1,400 14.2 11.2São Tomé and Principe Dec. 2000 Floating 200 34.0 ..Senegal Jun. 2000 Apr. 2004 850 20.0 ..Seychelles 9.0 8.1Sierra Leone Mar. 2002 Floating 950 10.1 10.9Somalia .. ..South Africa .. 6.4Sudan 8.7 6.0Swaziland 5.7 1.7Tanzania Apr. 2000 Nov. 2001 3,000 32.9 5.3Togo 11.9 ..Uganda Feb. 2000 May. 2000 1,950 81.4 6.9Zambia Dec. 2000 Apr. 2005 3,900 14.7 ..Zimbabwe 23.1 ..NORTH AFRICA .. ..Algeria 63.4 ..Egypt, Arab Rep. 20.4 7.6Libya .. ..Morocco 21.5 14.0Tunisia 24.5 13.7


.. .. .. .. .. .. 10 84 .. .. .. ..

.. .. .. .. .. .. 7 54 .. 3 0 11

.. .. .. .. .. .. 3 .. 1 4 0 122001 40 2001 34 2001 46 18 396 12 45 2 34

.. .. .. .. .. .. 2 37 0 2 0 4

.. .. .. .. .. .. 1 .. .. 5 0 3

.. .. .. .. .. .. 3 103 2 10 .. 10

.. .. .. .. .. .. 23 281 .. 97 .. 50

.. .. .. .. .. .. 2 18 .. 3 0 2

.. .. .. .. .. .. 1 14 .. 2 .. 6

.. .. .. .. .. .. 8 .. .. 9 .. 14

.. .. .. .. .. .. 1 37 .. .. 0 ..

.. .. .. .. .. .. 6 102 .. 4 0 9

.. .. .. .. .. .. 6 98 1 15 0 17

.. .. .. .. .. .. 10 .. 7 27 0 12

.. .. .. .. .. .. 4 .. .. 14 .. 10

.. .. .. .. .. .. .. 14 .. 4 0 12

.. .. .. .. .. .. 2 .. .. 3 0 2

.. .. .. .. .. .. 22 388 6 29 0 29

.. .. .. .. .. .. 7 .. 2 16 0 332000 16 2000 13 2000 19 3 93 1 5 0 17

.. .. .. .. .. .. 2 .. 2 5 0 5

.. .. .. .. .. .. 6 .. .. .. .. 17

.. .. .. .. .. .. 7 85 2 13 0 45

.. .. .. .. .. .. 8 109 .. .. 0 24

.. .. .. .. .. .. 4 .. .. .. .. ..

.. .. .. .. .. .. 3 .. .. 5 0 5

.. .. .. .. .. .. 3 25 .. 2 .. 4

.. .. .. .. .. .. 1 36 0 3 0 4

.. .. .. .. .. .. 3 .. 6 14 .. 5

.. .. .. .. .. .. 55 700 53 279 2 146

.. .. .. .. .. .. 4 .. 1 6 0 72001 45 2001 40 2001 49 38 206 12 109 0 37

.. .. .. .. .. .. 1 13 .. 1 0 2

.. .. .. .. .. .. 3 79 5 7 0 14

.. .. .. .. .. .. 1 18 .. .. 0 4

.. .. .. .. .. .. 19 .. .. .. .. 131

.. .. .. .. .. .. 6 .. 9 21 0 42

.. .. .. .. .. .. 124 842 .. 179 7 239

.. .. .. .. .. .. 3 27 .. .. 0 2

.. .. .. .. .. .. 2 88 .. 6 0 252003 60 2003 56 2003 65 94 .. 36 82 9 78

.. .. .. .. .. .. 2 58 1 17 0 32

.. .. .. .. .. .. 18 .. .. 32 1 32

.. .. .. .. .. .. 3 .. .. 7 0 9

.. .. .. .. .. .. 3 .. 4 29 0 37

.. .. .. .. .. .. 2 44 1 4 0 7

.. .. .. .. .. .. 8 34 .. 10 0 202002 25 2002 28 2002 21 12 55 7 77 0 63

.. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. 32 215 5 9 0 262001 28 2001 19 2001 51 29 235 6 32 1 54

.. .. .. .. .. .. 51 .. .. .. .. 362003 17 2003 17 2003 16 17 357 4 21 0 117

.. .. .. .. .. .. 37 480 .. 48 0 84

Youth unemployment rate Information and communications

Total Share of total labor force ages 15–24

MenShare of male labor force ages 15–24

WomenShare of female labor

force ages 15–24

Fixed-line and mobile phone subscribers (per 1,000 people)

Personal computers (per 1,000 people)

Internet users (per 1,000 people)

Year Percentage Year Percentage Year Percentage 1990 2004 1996 2004 1996 2004

Tab

le

58 Part III. Development outcomes PARIS DECLARATION INDICATORS

Results framework

Angola Yes NoBenin C 8 Yes NoBotswana NoBurkina Faso C 9 55.0 Yes NoBurundi C Yes NoCameroon C 7 40.3 Yes 1 NoCape Verde C Yes NoCentral African Republic D No NoChad C 7 Yes NoComoros No NoCongo, Dem. Rep. C 3 Yes NoCongo, Rep. C No NoCôte d’Ivoire D Yes NoDjibouti C No NoEquatorial Guinea No NoEritrea Yes NoEthiopia C 7 Yes 107 YesGabon No NoGambia, The D Yes 7 NoGhana C 7 Yes 21 YesGuinea C 5 YesGuinea-Bissau E 0 YesKenya C Yes 3 NoLesotho Yes NoLiberia Yes NoMadagascar C 4 Yes NoMalawi C 5 Yes 22 NoMali C 11 Yes 22 NoMauritania B Yes 35 NoMauritius Yes NoMozambique C 4 Yes 19 YesNamibia No NoNiger C 5 Yes 46 NoNigeria Yes 54 NoRwanda B 8 Yes NoSão Tomé and Principe D 4 No NoSenegal C 7 Yes 72 NoSeychelles No NoSierra Leone D 7 Yes NoSomalia No NoSouth Africa Yes NoSudan No NoSwaziland No NoTanzania B 11 Yes 25 YesTogo Yes NoUganda B 8 Yes 39 YesZambia C 3 Yes 24 NoZimbabwe No No

Note: See technical notes for further details. PDI is Paris Declaration Indicator.a. Comprehensive Development Framework Ratings from A to E, where A means the development strategy substantially achieves good practices; B means it is largely developed toward achieving good practices; C means it refl ects action taken toward achieving good practices; D means it incorporates some elements of good practices; and E means it refl ects little action toward achieving good practices.b. Public expenditure and fi nancial accountability benchmark rating of public expenditure management systems ranges from 0 to 15 and indicates the total number of the 15 required standard benchmarks that a country has met. The higher the number the less system upgrading is required.c. The Benchmark Indicator System ranges from 0 to 100 and assesses a country’s procurement system based on 4 pillars and 12 indicators. It does not provide information on the performance of the system but rather on the available procurement framework. Indicators on performance are being developed. The procurement system has not been measured yet for most countries.d. The AFR Observatory was put in place in 2000 to monitor procurement reform efforts. These efforts are measured against an action plan prepared in partnership with the country.

4.1 Status of Paris Declaration indicators

PDI-1. Operational development strategies

PDI-2a. Reliable public fi nancial management

PDI-2b. Reliable country procurement systems

PDI-6. Avoidance of parallel project implementation

units (PIUs)

PDI-12. Mutual accountability

Comprehensive Development

Framework composite rating of national

development strategya

Benchmark rating of public expenditure management (PEM)

systems(0–15)b

System adheres to broadly accepted goodBenchmark Indicator

System (BIS)(0–100)c

Reform program in placeAFR Observatory Indexd

Independent assessment

framework in placeNumber of

parallel PIUs

Tab

le

PRIVATE SECTOR DEVELOPMENT Part III. Development outcomes 59

Drivers of growth

5.1 Business environment

Number of startup

procedures for new

business

Time required to start

a business(days)

Cost to start a business(% of GNI per capita)

Number of procedures to register a property

Time required to register a property

(days)

Number of procedures to enforce a contract

Time required to enforce a contract

(days)

Protecting investors

disclosure index

(0 low to 10 high)

Time to resolve

insolvency(years)

Rigidity of employment

index(0 least

rigid to 100 most rigid)

2005 2005 2005 2005 2005 2005 2005 2005 2005 2005

SUB–SAHARAN AFRICA 11 64 215 7 118 36 439 5 3 53Angola 14 146 643 7 334 47 1,011 5 6 64Benin 8 32 191 3 50 49 570 5 3 53Botswana 11 108 11 6 69 26 154 8 2 30Burkina Faso 12 45 150 8 107 41 446 6 4 84Burundi 11 43 201 5 94 47 433 1 4 69Cameroon 12 37 173 5 93 58 585 8 3 56Cape Verde .. .. .. .. .. .. .. .. .. ..Central African Republic 10 14 212 3 69 45 660 .. 5 76Chad 19 75 361 6 44 52 526 3 10 72Comoros .. .. .. .. .. .. .. .. .. ..Congo, Dem. Rep. 13 155 503 8 106 51 909 3 5 90Congo, Rep. 8 67 289 6 103 47 560 4 3 80Côte d’Ivoire 11 45 134 7 369 25 525 6 2 45Djibouti .. .. .. .. .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. .. .. .. .. ..Eritrea 13 91 129 6 91 27 385 4 2 27Ethiopia 7 32 65 15 56 30 420 1 2 41Gabon .. .. .. .. .. .. .. .. .. ..Gambia, The .. .. .. .. .. .. .. .. .. ..Ghana 12 81 79 7 382 23 200 7 2 34Guinea 13 49 179 6 104 44 306 5 4 48Guinea-Bissau .. .. .. .. .. .. .. .. .. ..Kenya 13 54 48 8 73 25 360 4 5 28Lesotho 9 92 56 6 101 49 285 2 3 42Liberia .. .. .. .. .. .. .. .. .. ..Madagascar 11 38 54 8 134 29 280 5 .. 59Malawi 10 35 140 6 118 16 277 4 3 21Mali 13 42 191 5 44 28 340 6 4 66Mauritania 11 82 144 4 49 28 410 .. 8 73Mauritius 6 46 9 5 210 17 367 6 2 37Mozambique 14 153 95 8 42 38 580 2 5 61Namibia 10 95 19 9 28 31 270 8 1 27Niger 13 35 465 5 49 33 330 6 5 90Nigeria 9 43 74 21 274 23 730 6 2 38Rwanda 9 21 280 5 371 27 310 .. .. 59São Tomé and Principe 9 192 97 6 51 67 405 6 .. 60Senegal 9 57 109 6 114 33 485 7 3 64Seychelles .. .. .. .. .. .. .. .. .. ..Sierra Leone 9 26 835 8 58 58 305 3 3 80Somalia .. .. .. .. .. .. .. .. .. ..South Africa 9 38 9 6 23 26 277 8 2 52Sudan 10 38 68 .. .. 67 915 .. .. 43Swaziland .. .. .. .. .. .. .. .. .. ..Tanzania 13 35 161 12 61 21 242 3 3 69Togo 13 53 218 6 212 37 535 4 3 79Uganda 17 36 118 8 48 15 209 7 2 13Zambia .. .. .. .. .. .. .. 10 .. ..Zimbabwe .. .. .. .. .. .. .. 8 .. ..NORTH AFRICA 11 28 37 9 112 41 333 .. 3 57Algeria 14 26 25 16 52 49 407 8 4 51Egypt, Arab Rep. 10 34 105 7 193 55 410 5 4 53Libya .. .. .. .. .. .. .. .. .. ..Morocco 5 11 12 3 82 17 240 6 2 60Tunisia 9 14 10 5 57 14 27 0 1 54

Tab

le

60 Part III. Development outcomes PRIVATE SECTOR DEVELOPMENT

Drivers of growth

SUB–SAHARAN AFRICA .. 10,117 .. .. .. .. .. .. .. .. .. .. ..Angola 4.3 853 5.4 .. .. .. .. .. .. .. .. .. ..

Benin 12.1 47 14.5 .. .. .. .. .. .. .. .. .. ..Botswana 13.2 58 19.0 .. .. .. .. .. .. .. .. .. ..Burkina Faso 10.8 21 14.9 .. .. .. .. .. .. .. .. .. ..Burundi 2.7 12 22.6 .. .. .. .. .. .. .. .. .. ..Cameroon 15.7 242 9.9 .. .. .. .. .. .. .. .. .. ..Cape Verde 11.6 39 37.3 .. .. .. .. .. .. .. .. .. ..Central African Republic 4.1 6 7.2 .. .. .. .. .. .. .. .. .. ..Chad 15.3 479 3.3 .. .. .. .. .. .. .. .. .. ..Comoros 4.2 1 9.0 .. .. .. .. .. .. .. .. .. ..Congo, Dem. Rep. 10.0 435 1.5 .. .. .. .. .. .. .. .. .. ..Congo, Rep. 16.6 551 3.2 .. .. .. .. .. .. .. .. .. ..Côte d’Ivoire 7.1 175 14.4 .. .. .. .. .. .. .. .. .. ..Djibouti .. 9 .. .. .. .. .. .. .. .. .. .. ..Equatorial Guinea .. –159 3.6 .. .. .. .. .. .. .. .. .. ..Eritrea 5.3 10 32.8 31.5 2.7 .. .. 1.3 31.1 53.7 38.2 5.2 41.0Ethiopia 9.0 101 24.3 39.3 39.0 .. .. 9.5 73.6 40.2 42.5 4.6 17.9Gabon 19.9 –51 8.6 .. .. .. .. .. .. .. .. .. ..Gambia, The 13.9 50 11.7 .. .. .. .. .. .. .. .. .. ..Ghana 16.2 70 13.1 .. .. .. .. .. .. .. .. .. ..Guinea 7.1 98 3.6 .. .. .. .. .. .. .. .. .. ..Guinea-Bissau 2.1 .. 1.5 .. .. .. .. .. .. .. .. .. ..Kenya 7.5 108 26.8 51.5 73.8 .. 51.3 69.8 68.2 58.3 48.1 22.5 27.6Lesotho 33.9 53 6.5 .. .. .. .. .. .. .. .. .. ..Liberia 3.9 0 6.1 .. .. .. .. .. .. .. .. .. ..Madagascar 11.8 53 10.0 41.5 46.6 34.8 44.6 37.7 44.9 62.9 41.3 14.8 30.5Malawi 1.8 44 8.4 .. .. .. .. .. .. .. .. .. ..Mali 11.5 62 20.1 21.9 48.7 16.9 33.1 22.1 36.6 57.0 24.2 3.9 20.8Mauritania 6.0 7 25.9 .. .. .. .. .. .. .. .. .. ..Mauritius 14.5 35 59.5 .. .. .. .. .. .. .. .. .. ..Mozambique 11.3 245 2.1 .. .. .. .. .. .. .. .. .. ..Namibia 17.9 246 50.4 .. .. .. .. .. .. .. .. .. ..Niger 6.8 18 6.2 .. .. .. .. .. .. .. .. .. ..Nigeria 13.2 4,409 15.6 .. .. .. .. 36.3 .. .. 97.4 .. ..Rwanda 12.0 4 10.7 .. .. .. .. .. .. .. .. .. ..São Tomé and Principe 15.1 9 33.1 .. .. .. .. .. .. .. .. .. ..Senegal 12.9 109 21.2 31.3 39.9 13.3 40.5 15.4 50.8 60.3 30.7 16.3 18.5Seychelles 10.9 30 37.5 .. .. .. .. .. .. .. .. .. ..Sierra Leone 5.9 .. 4.7 .. .. .. .. .. .. .. .. .. ..Somalia .. .. .. .. .. .. .. .. .. .. .. .. ..South Africa 14.9 –1,021 141.3 17.9 16.1 8.8 20.8 29.0 18.6 14.5 9.0 32.9 35.5Sudan 17.5 1,481 7.7 .. .. .. .. .. .. .. .. .. ..Swaziland 9.7 65 19.5 .. .. .. .. .. .. .. .. .. ..Tanzania 11.0 478 9.0 31.5 51.1 20.0 55.1 25.5 73.4 53.0 58.9 12.1 25.0Togo 15.9 125 16.0 .. .. .. .. .. .. .. .. .. ..Uganda 16.9 202 6.8 27.6 38.2 .. 30.1 26.8 48.3 52.8 44.5 10.8 30.8Zambia 15.4 239 8.0 57.0 46.4 38.6 .. 48.8 .. 67.7 39.6 16.9 35.7Zimbabwe 13.4 9 .. .. .. .. .. .. .. .. .. .. ..NORTH AFRICA .. 1,442 .. .. .. .. .. .. .. .. .. .. ..Algeria 20.7 0 11.0 .. 35.2 .. 27.3 .. 44.8 51.3 11.5 12.9 25.5

Egypt, Arab Rep. 17.9 0 54.5 65.8 51.3 27.4 .. .. 81.8 39.0 26.5 28.1 29.8Libya .. .. 16.9 .. .. .. .. .. .. .. .. .. ..Morocco 22.5 822 56.7 .. .. .. .. .. .. .. .. .. ..Tunisia 25.5 620 65.2 .. .. .. .. .. .. .. .. .. ..

a. Provisional.b. Data are for most recent year available during the period specifi ed.

5.2 Investment climate

Viewed by fi rms as a major constraint (% of fi rms)

Private investment(% of GDP)

Net foreign direct

investment ($ millions)

Domestic credit to private sector

(% of GDP)Policy

uncertainty Corruption Courts

Lack of confi dence courts will

uphold property rights Crime Tax rates Finance Electricity

Labor regulation

Labor skills

2004a 2004 2004 2000–05b 2000–05b 2000–05b 2000–05b 2000–05b 2000–05b 2000–05b 2000–05b 2000–05b 2000–05b

PRIVATE SECTOR DEVELOPMENT Part III. Development outcomes 61

41 394 58.1 .. .. .. .. 12.5 904 129.6 27.630 656 32.5 .. .. .. .. 66.9 .. .. ..

75 270 53.1 .. .. .. .. .. .. .. ..24 140 52.9 15.0 .. .. 3.8 5.9 18 28.4 2.040 270 48.3 .. .. .. .. .. .. .. ..41 140 173.5 .. .. .. .. .. .. .. ..51 1,300 47.6 38.5 .. .. .. 13.0 .. .. .... .. .. .. .. .. .. 9.2 .. .. ..

66 504 60.9 .. .. .. .. 13.0 .. .. ..65 122 51.3 .. .. .. .. 13.0 .. .. ..

.. .. .. .. .. .. .. 7.5 .. .. ..34 312 134.7 40.0 .. .. .. .. .. .. ..94 576 66.9 .. .. .. .. 13.0 .. .. ..71 270 46.9 35.0 .. .. .. .. 39 13.5 1.5.. .. .. .. .. .. .. 10.4 .. .. .... .. .. .. .. .. .. 13.0 .. .. ..

18 216 66.3 .. 5.9 9.1 .. .. .. .. ..20 52 43.6 .. 5.7 13.5 0.4 3.6 .. .. ..

.. .. .. 35.0 .. .. .. 13.0 .. .. ..

.. .. .. .. .. .. .. 14.5 .. .. ..35 304 45.3 32.5 .. .. 1.6 .. 30 29.8 3.255 416 51.2 .. .. .. .. 11.9 .. .. ..

.. .. .. .. .. .. .. .. .. .. ..17 372 68.2 30.0 13.8 8.9 1.4 10.1 47 24.2 9.619 564 37.7 .. .. .. .. 8.1 .. .. .... .. .. .. .. .. .. 14.3 .. .. ..

29 400 58.9 .. 25.4 7.0 0.7 10.3 .. .. ..33 782 56.5 38.0 .. .. .. 23.1 8 9.1 13.860 270 44.0 .. 10.8 10.0 .. .. .. .. ..61 696 75.8 .. .. .. .. 13.0 40 99.3 ..7 158 38.2 25.0 .. .. 11.9 12.9 42 39.4 6.1

35 230 50.9 32.0 .. .. .. 12.2 .. .. ..23 50 43.9 35.0 .. .. 4.5 5.0 13 7.7 1.644 270 49.4 .. .. .. .. .. .. .. ..36 1,120 27.1 30.0 .. 17.8 1.6 5.5 214 20.1 11.542 168 53.9 .. .. .. .. .. .. .. ..29 1,008 27.4 .. .. .. .. 18.3 .. .. ..59 696 45.0 35.0 13.8 7.0 .. .. .. .. ..

.. .. .. .. .. .. .. 6.6 .. .. ..20 399 163.9 .. .. .. .. 11.9 .. .. ..

.. .. .. .. .. .. .. .. .. .. ..32 350 43.8 30.0 10.7 6.5 6.0 4.7 388 214.1 41.6

.. .. .. .. .. .. .. .. .. .. ..

.. .. .. 30.0 .. .. .. 6.7 6 9.4 0.048 248 51.3 30.0 16.2 17.5 0.6 9.7 6 6.2 1.951 270 50.9 .. .. .. .. .. .. .. ..31 237 42.9 30.0 5.0 .. 0.5 12.9 5 1.4 ..36 132 38.6 35.0 .. .. 1.5 19.2 11 8.0 22.5

59 216 48.6 30.0 .. .. 3.3 175.7 79 41.3 6.4.. .. .. .. .. .. .. .. .. .. ..

63 504 58.5 .. .. 21.6 .. 5.5 .. .. ..

39 504 32.1 40.0 .. 9.9 3.6 5.7 744 48.9 42.4.. .. .. .. .. .. .. 4.0 .. .. ..

28 690 54.8 35.0 .. 2.7 6.6 7.9 56 50.1 16.431 112 52.7 .. .. .. .. .. 46 9.4 16.8

Regulation and tax administration

Number of tax payments

Time to prepare and pay taxes

(hours)

Total tax payable

(% of gross profi ts)

Highest marginal tax

rate, corporate (%)

Time dealing with offi cials

(% of management

time)

Average time to clear customs

(days)

Bank branches


Interest rate spread (lending

rate minus deposit rate)

Listed domestic

companies

Market capitalization

of listed companies (% of GDP)

Turnover ratio for traded stocks

(%)2005 2005 2005 2000–05b 2000–05b 2000–05b 2004 2004 2000–05b 2000–05b 2000–05b

Tab

le

62 Part III. Development outcomes TRADE

Drivers of growth

6.1 International trade and tariff barriers

Trade

Merchandise trade

(% of GDP) Exports

($ millions)Imports

($ millions)Exports

(% of GDP)Imports

(% of GDP)

Annual growth(%) Terms of

trade index 2000=100Exports Imports

2004a 2004a 2004a 2004a 2004a 2003–04a 2003–04a 2004a

SUB–SAHARAN AFRICA 65 177,280 164,342 33.7 31.2 .. 10.8 ..Angola 125 13,722 10,728 70.1 54.8 12.6 9.6 134.3Benin 39 539 1,055 13.3 26.1 0.4 1.3 91.8Botswana 72 3,570 2,893 39.8 32.2 –0.3 2.7 98.3Burkina Faso 32 416 1,104 8.6 22.9 2.4 3.5 126.9Burundi 40 59 205 8.9 30.9 –24.0 6.8 ..Cameroon 39 3,061 3,128 19.4 19.8 1.7 4.1 96.1Cape Verde 95 295 604 31.2 63.7 8.4 3.0 97.2Central African Republic 27 150 207 11.5 15.8 9.6 –8.2 77.7Chad 100 2,274 2,034 52.8 47.2 170.4 1.4 ..Comoros 44 60 103 16.1 27.8 33.2 1.1 133.4Congo, Dem. Rep. 70 2,023 2,610 30.5 39.4 20.1 26.4 135.3Congo, Rep. 142 3,669 2,488 84.5 57.3 8.1 62.1 172.0Côte d’Ivoire 86 7,445 5,939 48.1 38.4 17.2 12.6 112.5Djibouti .. .. .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. .. .. ..Eritrea 112 122 917 13.2 98.8 –7.4 –14.6 72.2Ethiopia 44 1,369 2,934 17.0 36.3 17.3 20.4 84.4Gabon 101 4,420 2,902 61.1 40.1 3.3 3.8 144.4Gambia, The 110 184 255 46.0 63.7 5.4 7.2 89.0Ghana 92 3,349 4,550 38.9 52.8 3.8 11.3 119.5Guinea 48 824 986 21.8 26.1 3.6 3.5 99.6Guinea-Bissau 87 98 138 36.2 51.1 4.1 7.2 80.8Kenya 58 4,207 5,114 26.2 31.8 19.8 15.3 100.5Lesotho 160 763 1,430 55.8 104.6 13.0 4.5 120.6Liberia 83 171 235 34.8 47.8 .. .. ..Madagascar 82 1,425 2,146 32.6 49.2 1.5 24.8 134.7Malawi 78 511 974 26.8 51.2 –3.0 –0.7 90.0Mali 63 1,341 1,747 27.5 35.8 –0.2 –2.8 103.8Mauritania 99 451 1,069 29.4 69.7 8.5 9.8 105.2Mauritius 112 3,356 3,393 55.6 56.2 –1.7 –0.1 89.5Mozambique 70 1,828 2,320 30.9 39.2 23.9 4.2 88.5Namibia 91 2,644 2,573 46.3 45.0 –6.8 –10.9 96.2Niger 42 491 795 16.1 26.0 1.6 4.8 120.3Nigeria 92 39,372 26,965 54.6 37.4 3.1 2.3 124.8Rwanda 39 189 521 10.3 28.4 11.2 4.2 55.3São Tomé and Principe 151 25 61 43.9 107.5 20.7 23.5 ..Senegal 69 2,165 3,223 27.8 41.5 3.6 5.9 101.7Seychelles 182 663 616 94.3 87.6 4.2 8.0 ..Sierra Leone 60 240 407 22.1 37.5 12.6 –11.9 110.0Somalia .. .. .. .. .. .. .. ..South Africa 49 57,032 48,071 26.6 22.4 2.5 14.1 131.9Sudan 39 3,822 4,650 17.8 21.7 14.0 88.0 156.7Swaziland 191 2,357 2,441 93.6 97.0 1.1 1.3 108.5Tanzania 46 2,016 3,138 17.8 27.7 –7.0 2.2 105.8Togo 81 691 969 33.5 47.0 3.0 3.0 103.5Uganda 41 933 1,879 13.7 27.5 6.2 5.1 88.4Zambia 47 1,059 1,478 19.5 27.3 12.6 10.9 86.2Zimbabwe 80 1,694 2,068 36.1 44.0 2.0 –0.4 93.0NORTH AFRICA 59 83,995 80,245 31.2 .. .. 5.3 94.5Algeria 55 25,103 21,472 33.4 28.6 6.1 4.6 76.9Egypt, Arab Rep. 43 14,997 18,706 19.8 24.7 6.4 4.7 97.7Libya .. .. .. .. .. .. .. ..Morocco 70 16,128 18,932 30.7 36.0 5.2 6.3 110.0Tunisia 98 13,279 14,251 44.3 47.5 7.1 6.3 98.4

TRADE Part III. Development outcomes 63

.. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. 1.140.8 49.4 0.4 0.1 9.3 23.9 5.3 17.4 0.8 52.6 2.1

3.1 0.5 0.1 5.5 90.6 13.9 0.8 6.5 2.0 71.8 ..16.4 72.3 2.8 0.6 8.0 12.0 0.6 24.4 0.6 62.5 2.292.2 1.2 0.0 1.5 5.1 9.0 1.1 16.5 1.1 72.2 1.619.3 23.7 46.7 5.1 5.2 18.3 1.7 17.8 1.3 60.9 4.46.4 5.9 48.5 0.0 87.7 30.9 2.5 13.1 0.6 52.9 9.21.5 25.2 0.1 36.1 37.1 23.4 5.4 11.0 4.2 55.9 3.4

.. .. .. .. .. .. .. .. .. .. 2.688.7 0.0 .. 0.0 8.2 21.9 0.4 4.1 0.2 72.5 1.3

.. .. .. .. .. .. .. .. .. .. 3.0

.. .. .. .. .. .. .. .. .. .. 1.655.8 9.2 12.8 0.2 20.0 21.7 0.6 17.1 1.2 48.5 ..

.. .. .. .. .. .. .. .. .. .. 5.9

.. .. .. .. .. .. .. .. .. .. 1.2

.. .. .. .. .. .. .. .. .. .. 5.262.0 25.9 0.0 0.7 11.4 21.5 0.7 12.0 1.5 64.0 4.0

1.2 9.8 76.2 5.5 7.0 24.2 0.6 3.2 1.2 69.7 1.663.3 7.3 0.8 1.6 27.0 37.6 2.2 10.6 0.9 48.7 5.271.6 9.9 0.1 3.9 14.4 20.8 1.3 1.6 2.1 74.2 4.02.0 0.8 0.1 71.6 25.3 23.1 1.2 21.7 0.8 53.0 4.2

.. .. .. .. .. .. .. .. .. .. 4.839.7 12.0 23.0 4.2 21.1 10.4 2.1 24.3 1.6 61.3 16.0

.. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. 2.060.7 6.2 4.4 5.1 22.5 13.5 0.4 23.3 0.4 61.8 8.177.8 5.3 0.2 0.3 16.4 12.7 1.2 2.7 1.2 82.2 3.09.6 22.3 11.3 0.3 54.6 16.2 0.7 21.9 0.7 60.3 1.3

.. .. .. .. .. .. .. .. .. .. 3.827.0 0.5 0.1 0.4 70.9 17.5 2.3 13.2 1.0 65.9 11.719.1 5.6 16.4 54.8 3.1 10.6 0.7 11.7 0.2 43.3 2.048.3 1.3 1.0 7.3 40.9 14.9 0.7 10.4 3.6 69.4 ..30.4 3.6 1.6 54.9 7.9 33.5 4.3 16.9 1.2 44.0 1.9

0.0 0.0 97.9 0.0 2.1 15.5 0.6 16.0 1.6 66.3 1.352.3 7.3 6.8 23.3 10.3 11.7 4.0 15.6 2.0 66.7 2.4

.. .. .. .. .. .. .. .. .. .. 1.535.0 2.8 19.4 3.9 38.5 28.3 2.0 18.3 2.5 48.9 12.263.3 0.0 30.0 0.0 6.4 28.3 0.7 26.3 0.4 44.2 2.791.6 0.8 .. 0.1 7.5 22.5 7.6 39.7 0.8 29.3 3.8

.. .. .. .. .. .. .. .. .. .. 6.18.8 2.3 9.1 22.2 57.6 5.0 1.3 14.5 2.0 68.8 ..

10.2 6.1 81.2 0.4 1.6 16.4 0.9 3.1 0.8 78.2 1.614.6 7.8 0.7 0.2 76.4 18.2 2.2 12.6 1.0 64.4 ..52.7 12.9 2.2 12.0 20.2 15.0 1.6 16.5 1.1 65.8 21.724.0 15.6 0.4 12.7 47.3 18.4 0.8 23.0 2.2 55.5 5.364.3 15.5 4.6 0.4 15.3 16.8 1.9 10.0 1.5 69.5 7.315.5 10.3 1.7 62.4 10.0 6.5 1.1 11.2 2.6 78.5 5.030.9 15.7 1.6 23.2 28.5 18.7 1.8 13.7 9.8 54.2 8.1

.. .. .. .. .. .. .. .. .. .. ..0.2 0.0 97.4 0.4 2.0 21.9 1.9 0.9 1.3 73.9 3.39.8 7.0 43.1 3.7 30.6 22.2 5.1 8.3 3.5 49.8 20.4

.. .. .. .. .. 16.8 0.6 0.7 0.9 81.1 1.419.0 1.7 2.0 8.2 69.1 10.9 3.0 16.7 2.9 66.5 38.311.1 0.7 9.6 1.1 77.6 8.6 2.8 10.3 2.7 75.6 31.2

Structure of merchandise exports (% of total)

Structure of merchandise imports (% of total)

Export diversifi cation

index(0 low to 100 high)Food

Agricultural raw materials Fuel

Ores and metals Manufactures Food

Agricultural raw materials Fuel

Ores and metals Manufactures

2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2003

(continues)

Tab

le

64 Part III. Development outcomes TRADE

Drivers of growth

6.1 International trade and tariff barriers (continued)

Tariff barriers, all products (%)

Tariff barriers, primary products

(%)

Tariff barriers, manufactured products

(%)

Shipping 20 ft container from port to fi nal destination

Average time to clear

customs(days)

Binding coverage

Simple mean bound

rate

Simple mean tariff

Weighted mean tariff

Share of lines with

international peaks

Share of lines with specifi c

rates

Simple mean tariff


Simple mean tariff


Average cost($)

Average time

(days)2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–04b 2000–05b

SUB–SAHARAN AFRICA .. .. .. .. .. .. .. .. .. .. .. .. ..Angola .. .. 8.1 8.5 15.2 1.6 11.6 14.7 7.5 5.9 .. .. ..Benin 39.1 28.6 14.0 12.7 54.1 0.0 15.5 12.9 13.7 12.5 .. .. ..Botswana 89.0 17.3 5.0 1.0 16.0 1.4 2.1 0.3 5.4 1.1 .. .. ..Burkina Faso 39.3 41.9 13.2 11.4 49.5 0.0 13.7 11.3 13.1 11.5 1,310 17.1 ..Burundi 20.9 67.6 20.0 14.7 32.6 0.6 22.4 10.6 19.6 16.9 .. .. ..Cameroon 31.0 79.9 18.1 15.0 49.5 .. 21.1 16.5 17.7 14.4 .. 17.1 ..Cape Verde .. .. .. .. .. .. .. .. .. .. .. .. ..Central African Republic .. .. .. .. .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. .. .. .. .. .. .. ..Comoros .. .. .. .. .. .. .. .. .. .. .. .. ..Congo, Dem. Rep. .. .. .. .. .. .. .. .. .. .. 2,400 7.0 ..Congo, Rep. .. .. .. .. .. .. .. .. .. .. .. .. ..Côte d’Ivoire .. .. .. .. .. .. .. .. .. .. .. 3.0 ..Djibouti 100.0 40.9 30.9 26.8 92.3 2.3 21.9 19.7 32.6 32.3 .. .. ..Equatorial Guinea .. .. .. .. .. .. .. .. .. .. .. .. ..Eritrea .. .. .. .. .. .. .. .. .. .. 771 10.0 9Ethiopia .. .. 19.4 13.5 52.0 0.2 22.0 6.7 19.1 15.7 451 3.0 14Gabon 100.0 21.4 18.6 14.7 52.3 .. 23.2 19.7 17.9 13.5 .. .. ..Gambia, The 13.6 100.6 .. .. .. .. .. .. .. .. .. 15.0 ..Ghana 14.3 92.1 13.1 11.0 45.0 0.2 17.6 17.1 12.3 8.8 .. 5.0 ..Guinea 39.0 20.1 .. .. .. .. .. .. .. .. .. .. ..Guinea-Bissau .. .. 13.9 13.6 56.0 0.0 16.6 14.5 13.4 12.9 .. .. ..Kenya 14.0 95.1 16.2 10.3 39.1 0.1 19.5 10.1 15.8 10.2 .. .. 9Lesotho .. .. 10.8 17.8 42.1 2.9 16.0 9.2 10.5 17.8 .. .. ..Liberia .. .. .. .. .. .. .. .. .. .. .. .. ..Madagascar 29.7 27.4 5.2 3.6 4.4 0.0 5.5 1.7 5.1 4.6 .. .. 7Malawi 30.2 75.0 12.9 10.2 40.4 0.0 12.6 9.0 12.9 10.7 .. .. ..Mali 40.7 28.8 12.8 10.7 45.9 0.0 15.4 11.5 12.4 10.4 .. .. 10Mauritania 39.4 19.6 12.8 9.3 51.5 0.0 12.6 7.9 12.8 10.0 .. .. ..Mauritius 18.0 94.0 23.5 13.0 40.0 0.1 19.6 9.9 23.8 14.4 .. .. ..Mozambique .. .. 12.7 9.9 36.8 0.0 16.0 9.9 12.1 9.9 .. .. ..Namibia 88.9 17.3 4.5 0.5 13.8 2.4 3.5 0.4 4.6 0.6 .. .. ..Niger 96.8 44.3 12.8 13.8 48.3 0.0 15.6 15.9 12.4 13.0 .. .. ..Nigeria 19.3 118.0 24.8 18.5 51.8 1.0 36.8 26.7 23.2 15.7 .. .. 18Rwanda 100.0 89.1 8.3 6.6 10.7 0.0 11.8 6.4 7.9 6.6 .. .. ..São Tomé and Principe .. .. .. .. .. .. .. .. .. .. .. .. ..Senegal 100.0 30.0 13.4 9.2 50.4 0.0 14.7 8.1 13.2 10.5 1,310 14.0 7Seychelles .. .. 27.2 23.4 57.6 0.3 38.8 46.6 25.4 18.5 .. .. ..Sierra Leone 100.0 47.4 .. .. .. .. .. .. .. .. .. .. ..Somalia .. .. .. .. .. .. .. .. .. .. .. .. ..South Africa 88.9 17.3 8.5 5.6 30.9 2.0 6.7 3.9 8.6 5.8 .. .. 7Sudan .. .. 21.1 19.6 43.8 0.0 28.2 24.0 20.5 18.9 .. .. ..Swaziland 88.9 17.3 1.8 0.6 7.0 0.8 0.9 0.1 2.0 0.9 .. .. ..Tanzania 13.4 120.0 14.1 8.2 37.4 0.0 15.2 7.4 14.0 8.6 .. 12.7 18Togo 13.2 80.0 14.4 10.8 55.7 0.0 15.5 10.1 14.1 11.3 .. .. ..Uganda 14.9 73.5 6.7 5.4 0.0 0.0 9.5 6.4 6.4 5.0 .. .. ..Zambia 15.9 105.6 13.2 9.6 29.5 0.0 13.4 11.4 13.1 9.0 .. .. 5Zimbabwe 20.8 91.3 15.9 18.7 36.3 5.7 19.2 27.2 15.4 15.7 .. .. ..NORTH AFRICA .. .. .. .. .. .. .. .. .. .. .. .. ..Algeria .. .. 17.9 12.0 39.4 0.0 18.1 10.5 17.8 12.5 .. .. ..Egypt, Arab Rep. 99.0 37.2 18.9 13.9 46.2 6.8 18.1 7.9 19.0 16.9 .. .. 10Libya .. .. 20.2 25.2 46.6 2.1 19.2 15.1 20.1 28.5 .. .. ..Morocco 100.0 41.3 28.3 24.9 75.1 0.0 33.5 25.4 27.8 24.6 .. .. 3Tunisia 57.8 57.7 25.3 22.3 65.6 0.0 36.8 18.4 24.2 23.5 .. .. ..

a. Preliminary.b. Data are for most recent year available during the period specifi ed.

Tab

le

TRADE Part III. Development outcomes 65

Drivers of growth

Economic and Monetary Community of Central African States (CEMAC ) 1994 139 120 97 176 2.3 2.1 1.1 1.3 0.2 0.1 0.1 0.2Economic Community of the Countries of the Great Lakes (CEPGL) 1976 7 8 10 19 0.5 0.5 0.8 1.2 0.0 0.0 0.0 0.0Common Market for Eastern and Southern Africa (COMESA) 1994 963 1,386 1,536 2,848 6.6 7.7 5.7 6.7 0.4 0.4 0.4 0.5Cross-Border Initiative (CBI) 1992 613 1,002 1,058 1,700 10.3 11.9 10.6 13.2 0.2 0.2 0.2 0.1East African Community (EAC) 1996 230 530 485 753 13.4 17.4 16.1 14.6 0.1 0.1 0.1 0.1Economic Community of Central African States (ECCAS) 1983 163 163 191 238 1.4 1.5 1.1 0.9 0.3 0.2 0.3 0.3Economic Community of West African States (ECOWAS) 1975 1,557 1,936 2,835 3,973 7.9 9.0 7.9 8.5 0.6 0.4 0.6 0.5Indian Ocean Commission (IOC) 1984 73 127 106 155 4.1 6.0 4.4 4.3 0.1 0.0 0.0 0.0Mano River Union (MRU) 1973 0 1 5 6 0.0 0.1 0.4 0.3 0.1 0.0 0.0 0.0Southern African Development Community (SADC) 1992 1,630 3,373 4,282 6,384 4.8 8.7 9.3 9.5 1.0 0.8 0.7 0.7Central African Customs and Economic Union (UDEAC) 1964 139 120 96 174 2.3 2.1 1.0 1.2 0.2 0.1 0.1 0.2

West African Economic and Monetary Union (WAEMU/UEMOA) 1994 621 560 741 1,283 13.0 10.3 13.1 13.9 0.1 0.1 0.1 0.1

Note: Regional bloc memberships are as follows: Economic and Monetary Community of Central Africa (CEMAC), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, Gabon, and São Tomé and Principe; Economic Community of the Countries of the Great Lakes (CEPGL), Burundi, the Democratic Republic of Congo, and Rwanda; Common Market for Eastern and Southern Africa (COMESA), Angola, Burundi, Comoros, the Democratic Republic of Congo, Djibouti, the Arab Republic of Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; Cross Border Initiative (CBI), Burundi, Comoros, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Swaziland, Tanzania, Uganda, Zambia, and Zimbabwe; East African Community (EAC), Kenya, Tanzania, and Uganda; Economic Community of Central African States (ECCAS), Angola, Burundi, Cameroon, the Central African Republic, Chad, the Democratic Republic of Congo, the Republic of Congo, Equatorial Guinea, Gabon, Rwanda, and São Tomé and Principe; Economic Community of West African States (ECOWAS), Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; Indian Ocean Commission (IOC), Comoros, Madagascar, Mauritius, Réunion, and Seychelles; Mano River Union (MRU), Guinea, Liberia, and Sierra Leone; Southern African Development Community (SADC; formerly Southern African Development Coordination Conference), Angola, Botswana, the Democratic Republic of Congo, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia, and Zimbabwe; Central African Customs and Economic Union (UDEAC; formerly Union Douanière et Economique de l’Afrique Centrale), Cameroon, the Central African Republic, Chad, the Republic of Congo, Equatorial Guinea, and Gabon; West African Economic and Monetary Union (UEMOA), Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.

Value of trade($ millions)

Share of total bloc exports(%)

Share of world exports(%)

Year established 1990 1995 2000 2004 1990 1995 2000 2004 1990 1995 2000 2004

6.2 Regional integration, trade blocs

Tab

le

66 Part III. Development outcomes INFRASTRUCTURE

Drivers of growth

7.1 Water and sanitation

Access, supply side Access, demand side

Internal fresh water resources per capita

(cubic meters)

Population with sustainable access to an improved water source (% of population with access)

Population with sustainable access to improved sanitation

(% of population with access)

Total Urban Rural Total Urban Rural

2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a

SUB–SAHARAN AFRICAAngola 13,607 50 70 40 30 56 16Benin 1,488 68 79 60 32 58 12Botswana 1,742 95 100 90 41 57 25Burkina Faso 1,074 51 82 44 12 45 5Burundi 555 79 90 78 36 47 35Cameroon 16,970 63 84 41 48 63 33Cape Verde .. 80 86 73 42 61 19Central African Republic 36,332 75 93 61 27 47 12Chad 1,748 34 40 32 8 30 0Comoros .. 94 90 96 23 38 15Congo, Dem. Rep. 16,932 46 83 29 29 43 23Congo, Rep. 59,086 46 72 17 9 14 2Côte d’Ivoire 4,574 84 98 74 40 61 23Djibouti .. 80 82 67 50 55 27Equatorial Guinea 52,632 44 45 42 53 60 46Eritrea 683 57 72 54 9 34 3Ethiopia 1,603 22 81 11 6 19 4Gabon 121,984 87 95 47 36 37 30Gambia, The 2,111 82 95 77 53 72 46Ghana 1,451 79 93 68 58 74 46Guinea 28,575 51 78 38 13 25 6Guinea-Bissau 10,744 59 79 49 34 57 23Kenya 627 62 89 46 48 56 43Lesotho 2,789 76 88 74 37 61 32Liberia 59,285 62 72 52 26 49 7Madagascar 19,948 45 75 34 33 49 27Malawi 1,460 67 96 62 46 66 42Mali 5,150 48 76 35 45 59 38Mauritania .. 56 63 45 42 64 9Mauritius .. 100 100 100 99 100 99Mozambique 5,268 42 76 24 27 51 14Namibia 2,978 80 98 72 30 66 14Niger 340 46 80 36 12 43 4Nigeria 1,620 60 72 49 38 48 30Rwanda 596 73 92 69 41 56 38São Tomé and Principe .. 79 89 73 24 32 20Senegal 2,539 72 90 54 52 70 34Seychelles .. 87 100 75 .. .. 100Sierra Leone .. 57 75 46 39 53 30Somalia 623 29 32 27 25 47 14South Africa 982 87 98 73 67 86 44Sudan 894 69 78 64 34 50 24Swaziland .. 52 87 42 52 78 44Tanzania 2,285 73 92 62 46 54 41Togo 2,468 51 80 36 34 71 15Uganda 1,543 56 87 52 41 53 39Zambia 7,690 55 90 36 45 68 32Zimbabwe 1,069 83 100 74 57 69 51NORTH AFRICAAlgeria 440 87 92 80 92 99 82Egypt, Arab Rep. 30 98 100 97 68 84 56Libya 180 72 72 68 97 97 96Morocco 963 80 99 56 61 83 31Tunisia 404 82 94 60 80 90 62


INFRASTRUCTURE Part III. Development outcomes 67

.. .. .. .. 3.4

.. .. .. .. 7.9

.. .. 0.4 .. 2.521 .. 0.8 .. 14.944 .. 0.2 .. 0.834 .. 0.6 .. 3.1

.. .. .. .. 1.8

.. .. .. .. 1.1

.. .. .. 0.0 8.1

.. .. .. .. 0.2

.. .. 0.7 .. 2.2

.. .. .. .. 0.220 .. 0.6 .. 4.2

.. .. .. .. 2.8

.. .. .. .. 1.1

.. 79.2 .. .. 1.340 .. 0.4 .. 12.116 .. 0.5 .. 0.0.. .. .. .. 0.5.. .. .. 0.0 21.7.. .. .. .. 8.7.. .. .. .. 1.1

50 85.2 0.3 .. 14.830 .. 0.7 .. 2.8

.. .. .. .. 0.1

.. 5.2 .. .. 3.730 .. 0.3 .. 5.227 2.1 0.5 73.2 9.4

.. .. .. .. 5.3

.. .. .. .. 7.160 .. 0.5 .. 15.7

.. .. .. 0.0 2.817 .. 0.5 3.4 6.4.. .. 0.4 .. 3.6.. .. .. 0.0 2.5.. .. .. .. 0.8

20 5.6 .. 9.9.. .. .. .. 0.0.. .. .. .. 1.1.. .. .. .. 1.2.. 4.8 6.3 8.1.. .. .. .. 2.1.. .. .. .. 1.0

40 105.0 0.4 2.1 16.5.. .. .. 0.0 3.6.. 2.7 .. .. 18.8

55 13.6 0.6 .. 15.5

.. .. .. .. 4.1

.. 41.8 0.2 182.0 7.7

.. 8.5 0.9 .. 48.4

.. .. .. ..31 .. 1.0 172.3 43.614 .. .. .. 34.5

FinancingQuality of supply Pricing Committed nominal investment

in potable water projects with private participation

(2000 $ millions)

Average annual ODA disbursements

for water and sanitation($ millions)

Average nonrevenue water

(% of total water supplied)

Water supply failure for fi rms receiving water(average days per year)

Average household tariffs for water services, largest city

($ per cubic meter)

2000–04a 2000–04a 2000–04a 2000–04a 2000–04

Tab

le


Drivers of growth

7.2 Transportation


Road network (km)Road density Energy

consumption for transportation (thousand tons of oil equivalent)

Rural access (rural population

within 2 km of an all-season road as % of total rural population)

Vehicle fl eetRail lines

(total route km)

Ratio to arable land

(road km/thousand sq km arable land)

Ratio to total land

(road km/thousand sq km of land area)Total length

Total length of national network

Commercial vehicles

Passenger cars

2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a

SUB–SAHARAN AFRICAAngola 51,429 .. 2,761 .. .. 884 .. .. ..Benin .. .. 438 .. .. 371 32 13,850 44,324Botswana 25,233 .. 888 .. .. .. .. .. ..Burkina Faso .. 15,272 .. 316 56 .. 25 25,749 44,805Burundi .. 4,478 .. 452 174 .. .. .. ..Cameroon 80,932 21,083 988 354 45 769 20 80,178 149,934Cape Verde .. .. .. .. .. .. .. .. ..Central African Republic .. .. .. .. .. .. .. .. ..Chad .. .. .. .. .. .. 5 21,082 13,050Comoros .. .. .. .. .. .. .. .. ..Congo, Dem. Rep. .. 41,453 4,499 619 18 285 26 .. ..Congo, Rep. .. .. .. .. .. 220 .. .. ..Côte d’Ivoire .. 15,413 639 467 49 .. .. 92,826 415,013Djibouti .. .. .. .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. .. .. .. ..Eritrea .. 18,540 306 3,299 184 102 .. 385 6,774Ethiopia .. 8,755 .. 79 9 893 17 52,169 67,614Gabon .. 1,159 731 357 5 198 .. 18,170 28,005Gambia, The .. 11,177 .. 3,548 1,118 .. .. .. ..Ghana .. 7,254 977 173 32 1,168 .. 48,158 102,494Guinea .. .. 837 .. .. .. .. 24,080 16,520Guinea-Bissau .. .. .. .. .. .. .. .. ..Kenya 63,942 .. 2,634 .. .. 1,372 .. 96,726 244,836Lesotho .. .. .. .. .. .. .. .. ..Liberia .. .. 490 .. .. .. .. .. ..Madagascar .. .. .. .. .. .. .. .. ..Malawi .. .. .. .. .. .. .. 33,045 31,152Mali .. 13,013 733 279 11 .. .. 22,000 30,547Mauritania .. .. 717 .. .. .. .. 13,068 22,153Mauritius 2,015 .. .. .. .. .. .. 33,615 92,969Mozambique .. 12,155 .. 279 16 309 .. 28,021 28,951Namibia 42,237 .. .. .. .. 529 .. 81,002 82,580Niger .. 9,677 .. 67 8 .. 37 17,248 41,206Nigeria .. .. 3,557 .. .. .. 47 573,273 976,376Rwanda .. 5,408 .. 451 219 .. .. 19,648 15,035São Tomé and Principe .. .. .. .. .. .. .. .. ..Senegal 13,576 10,261 906 417 53 607 .. 35,753 98,260Seychelles 11,300 .. .. .. .. .. .. .. ..Sierra Leone .. .. .. .. .. .. .. 7,615 11,353Somalia .. .. .. .. .. .. .. .. ..South Africa 362,099 .. 20,041 .. .. 14,834 .. 2,386,968 4,162,933Sudan .. .. 4,578 .. .. 1,469 .. .. ..Swaziland .. .. 301 .. .. .. .. 38,201 37,272Tanzania 78,891 67,718 2,600 1,693 77 .. 38 46,799 28,701Togo .. .. 568 .. .. 284 .. .. ..Uganda 70,746 10,500 259 202 53 .. .. .. ..Zambia 91,440 .. 1,273 .. .. 328 .. .. ..Zimbabwe 97,267 .. .. .. .. 644 .. .. ..NORTH AFRICAAlgeria .. .. .. .. .. ..Egypt, Arab Rep. .. 5,637 .. .. .. .. ..Libya .. .. .. .. .. ..Morocco .. 58,051 1,919 .. 129 .. .. .. ..Tunisia .. 19,505 1,939 .. 122 .. .. .. ..



.. 10.4 .. .. .. 0.3 0.4 13.8 3.0 Yes

.. .. .. .. .. 0.7 0.8 0.0 25.5 Yes

.. 35.1 .. .. .. 0.6 0.7 0.0 0.0 ..52.7 .. 17.1 .. 1,310 0.9 1.2 0.0 16.4 No24.1 .. .. .. .. 1.1 1.0 0.0 0.8 Yes8.9 .. 17.1 .. .. 0.8 0.9 0.0 32.7 Yes

.. .. .. .. .. 0.8 1.4 0.0 2.2 Yes

.. .. .. .. .. 1.1 1.3 0.0 8.6 Yes

.. .. .. .. .. 1.0 1.2 0.0 13.2 Yes

.. .. .. .. .. .. .. 0.1 0.1 ..1.2 .. 7.0 .. 2,400 0.8 0.9 0.0 2.1 No

.. .. .. .. .. 0.6 0.9 0.0 0.7 ..44.4 .. 3.0 .. .. 0.9 1.1 35.0 2.0 Yes

.. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. 5.8 0.5 ..37.0 .. 10.0 11.7 771 0.4 0.8 0.0 0.2 Yes24.1 12.9 3.0 15.0 451 0.4 0.6 0.0 36.2 Yes28.0 3.7 .. .. .. 0.7 0.9 21.4 6.4 Yes42.4 19.3 15.0 .. .. 0.7 0.8 0.0 0.1 Yes27.4 17.9 5.0 .. .. 0.4 0.5 1.3 27.4 Yes

.. 9.8 .. .. .. 0.7 0.8 0.0 8.8 ..

.. .. .. .. .. .. .. 0.0 4.7 ..

.. 12.1 .. 37.4 .. 0.8 0.9 0.0 12.5 Yes

.. .. .. .. .. 0.7 0.7 0.0 9.5 Yes

.. .. .. .. .. 0.8 0.8 0.0 0.0 ..

.. .. .. 16.1 .. 0.8 1.1 5.1 10.7 Yes

.. .. .. .. .. 0.9 1.0 0.0 16.7 Yes18.5 .. .. 20.1 .. 0.9 1.2 0.0 16.7 Yes

.. .. .. .. .. 0.6 0.8 0.0 5.7 ..

.. 100.0 .. .. .. 0.6 0.7 0.0 0.2 ..50.0 .. .. .. .. 0.8 0.9 158.1 28.3 Yes

.. 12.8 .. .. .. 0.6 0.7 112.5 8.3 ..65.2 .. .. .. .. 0.9 1.0 0.0 8.6 Yes

.. .. .. 10.4 .. 0.5 0.9 5.7 0.7 No

.. .. .. .. .. 1.0 1.0 0.0 2.4 Yes

.. .. .. .. .. .. .. 0.0 0.4 ..44.3 29.3 14.0 36.0 1,310 0.9 0.9 0.0 7.9 No

.. .. .. .. .. .. .. 0.0 0.0 ..

.. 8.0 .. .. .. 0.9 0.8 0.0 1.6 ..

.. .. .. .. .. 0.9 1.4 0.0 .. ..

.. 20.3 .. 10.1 .. 0.8 0.8 63.1 0.5 ..

.. .. .. .. .. 0.3 0.5 0.0 0.0 ..

.. .. .. .. .. 0.7 0.8 0.0 1.7 ..43.6 8.6 12.7 22.9 .. 0.9 0.9 1.6 33.7 Yes

.. .. .. .. .. 0.8 0.8 0.0 0.3 Yes30.1 23.0 .. 22.9 .. 0.9 1.0 0.0 12.5 No

.. 22.0 .. 30.4 .. 1.0 1.1 0.0 26.6 Yes

.. 19.0 .. .. .. 0.7 0.6 0.0 3.8 Yes

.. .. .. .. .. .. 0.8 ..

.. .. 4.6 .. 0.1 .. 367.0 5.1 ..

.. .. .. 0.1 .. .. .. ..

.. 56.4 .. 4.6 .. 0.7 .. .. 13.9 ..

.. 65.4 .. 4.9 .. 0.4 .. .. 17.4 ..

Quality Pricing FinancingRoads Average time to

ship 20 ft container from port to fi nal

destination (days)

Firms identifying transport as major or very severe

obstacle to business operation and growth

(%)

Average cost to ship 20 ft container

from port to fi nal destination

($)

Price of diesel fuel

($/liter)

Price of super gasoline ($/liter)

Committed nominal investment in transport

projects with private participation

(2000 $ millions)

Average annual ODA disbursements

for transportation and storage ($ millions)

Existence of road fund

Length of national network in good

condition (km)

Ratio of paved to

total roads (%)

2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04 2000–04a

Tab

le


Drivers of growth

7.3 Information and communication technology


Telephone subscribers (per 1,000 people)

Households with own telephoneDelay for fi rm in obtaining a

telephone connection (average number

of days)

Unmet demand(% of main

telephone lines in operation)

Total (% of total

households)

Urban (% of urban households)

Rural (% of rural

households)TotalMain telephone

line Cellular

telephone 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a

SUB–SAHARAN AFRICA 84.4 10.3 74.1Angola 54.0 6.2 47.8 .. .. .. .. ..Benin 38.2 8.9 29.8 44.5 3.7 8.7 0.7 ..Botswana 395.8 77.1 318.7 .. .. .. .. ..Burkina Faso 37.4 6.3 31.0 20.1 3.8 17.6 0.3 ..Burundi 12.5 3.4 9.1 22.8 .. .. .. ..Cameroon 102.7 6.9 95.8 .. 1.8 3.5 0.1 ..Cape Verde 281.1 148.3 132.8 1.1 .. .. .. ..Central African Republic 17.6 2.5 15.1 13.3 .. .. .. ..Chad 14.4 1.4 13.0 .. .. .. .. ..Comoros 26.5 23.0 3.5 32.9 .. .. .. ..Congo, Dem. Rep. 37.0 0.2 36.8 .. .. .. .. ..Congo, Rep. 102.4 3.6 98.8 .. .. .. .. ..Côte d’Ivoire 98.3 12.6 85.7 1.4 .. .. .. ..Djibouti 43.4 14.3 30.1 .. .. .. .. ..Equatorial Guinea 106.2 20.0 112.8 .. .. .. .. ..Eritrea 14.0 9.3 4.7 121.4 4.4 11.3 0.1 256.3Ethiopia 7.8 6.3 2.5 33.6 1.3 7.9 0.0 130.8Gabon 387.6 28.4 359.2 10.8 12.8 17.2 1.1 ..Gambia, The 99.0 27.4 118.4 27.7 .. .. .. ..Ghana 92.7 14.5 78.2 63.9 6.9 14.3 0.7 ..Guinea 15.3 2.9 12.4 5.6 .. .. .. ..Guinea-Bissau 7.9 7.1 0.9 45.8 .. .. .. ..Kenya 85.0 8.9 76.1 34.2 12.8 32.7 6.2 80.6Lesotho 109.1 20.7 88.4 72.9 .. .. .. ..Liberia 2.8 2.2 14.7 .. .. .. .. ..Madagascar 19.5 3.4 18.4 3.1 .. .. .. 54.1Malawi 25.0 7.4 17.6 23.8 .. .. .. ..Mali 36.2 5.7 30.5 .. 2.4 9.3 0.1 51.0Mauritania 134.5 13.2 175.3 .. 2.9 6.8 0.2 ..Mauritius 699.9 286.7 413.2 4.1 .. .. .. ..Mozambique 26.9 4.1 36.4 15.1 1.6 5.5 0.1 ..Namibia 206.1 63.7 142.4 2.1 .. .. .. ..Niger 12.8 1.8 11.0 .. .. .. .. ..Nigeria 79.1 8.0 71.1 0.0 5.5 11.8 1.9 ..Rwanda 18.2 2.6 15.6 .. 1.1 7.8 0.1 ..São Tomé and Principe 78.9 46.6 32.2 10.4 .. .. .. ..Senegal 72.4 20.6 90.3 4.1 .. .. .. 10.8Seychelles 841.9 253.4 588.6 9.0 .. .. .. ..Sierra Leone 27.2 4.9 22.3 .. .. .. .. ..Somalia 87.9 25.1 62.8 625.0 .. .. .. ..South Africa 473.1 105.2 428.5 1.0 .. .. .. 6.6Sudan 58.5 29.0 29.5 99.1 .. .. .. ..Swaziland 118.7 41.8 100.9 49.0 .. .. .. ..Tanzania 32.2 4.0 43.6 4.9 .. .. .. 18.3Togo 48.1 10.4 37.7 53.7 .. .. .. ..Uganda 44.4 2.6 41.9 .. 2.7 14.5 0.6 25.4Zambia 33.7 7.6 26.1 13.3 3.8 10.2 0.4 54.8Zimbabwe 55.2 24.5 30.7 43.5 .. .. .. ..NORTH AFRICA .. .. ..Algeria 215.4 70.7 144.7 .. .. .. .. ..Egypt, Arab Rep. 235.5 130.3 105.2 1.1 27.8 44.7 11.0 ..Libya 155.8 133.2 22.6 .. .. .. .. ..Morocco 356.9 43.9 313.1 0.4 51.8 65.8 29.0 ..Tunisia 479.9 121.2 358.7 11.3 .. .. .. ..



.. .. .. 8.0 9.0 134.0 .. 69.7 5.1

.. 6.0 .. 59.4 11.4 576.2 .. 21.4 0.2

.. .. .. 33.3 2.2 .. .. 17.0 0.1

.. 19.7 .. 103.2 10.2 258.0 .. 8.4 1.4

.. .. .. 67.9 6.7 371.0 .. 10.7 0.3

.. .. .. 103.2 5.7 .. .. 73.1 2.6

.. 43.0 .. 89.5 4.6 .. 0.0 0.0 0.1

.. .. .. 51.6 43.0 1,359.0 .. .. 0.2

.. 60.8 .. .. 10.8 910.6 .. 2.8 0.5

.. 55.8 .. 73.4 17.2 .. .. .. 0.0

.. .. .. .. .. .. .. 86.3 0.2

.. .. .. .. .. .. .. 12.4 0.0

.. 81.0 .. 61.9 9.1 638.0 0.0 23.0 0.1

.. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. 0.0 ..

.. 51.1 9.0 0.0 2.9 355.0 .. 40.0 0.3

.. 100.0 29.4 10.5 2.3 705.0 .. .. 0.9

.. 0.5 .. 64.5 25.8 1,088.0 .. 5.3 0.5

.. .. .. 47.8 2.8 346.0 .. 3.3 0.0

.. 67.4 .. 70.9 2.5 113.0 11.7 20.2 0.4

.. 1.6 .. 45.6 7.6 461.4 6.0 4.5 0.2

.. 70.5 .. 0.0 .. .. 5.0 6.3 0.420.2 149.0 44.1 57.1 9.4 436.0 .. 157.4 0.3

.. 75.0 .. 25.6 22.2 230.7 18.7 17.1 0.0

.. .. .. 0.0 .. .. .. 25.0 0.014.3 42.5 16.4 58.0 7.3 741.0 12.6 0.0 0.7

.. .. .. 64.5 5.9 6.0 .. 2.3 0.410.3 177.6 14.3 98.2 7.0 1,227.8 .. 27.5 2.0

.. .. .. .. 11.4 .. 24.0 29.9 0.0

.. 41.5 .. 10.0 4.3 250.0 101.5 101.1 0.2

.. 70.0 .. 34.7 7.9 .. 54.5 27.3 3.3

.. 40.4 .. 29.9 4.4 427.5 .. 8.8 0.6

.. 104.6 .. 22.5 10.2 876.6 25.8 20.0 0.2

.. 20.6 59.4 89.6 10.0 .. 409.2 1,036.8 0.8

.. .. .. 36.8 8.8 .. .. 7.9 0.9

.. .. .. 0.0 16.5 495.0 2.4 .. 0.29.5 17.3 3.5 42.8 20.3 180.9 65.8 68.5 0.3

.. 6.0 .. 48.6 16.2 .. .. 7.2 0.0

.. .. .. 48.0 2.9 .. .. 9.7 0.6

.. .. .. 1.0 5.0 .. 0.5 0.5 0.2

.. 48.2 3.0 24.8 14.7 58.3 1,115.3 713.5 1.8

.. .. .. 25.3 3.4 391.8 62.0 136.2 0.1

.. 70.0 .. 33.7 5.2 242.0 .. 4.9 0.149.6 24.0 11.8 40.8 12.1 528.0 43.6 78.2 5.6

.. 6.2 .. 71.0 10.2 215.0 .. 0.0 0.117.8 .. 3.5 42.8 20.9 351.0 37.4 48.5 0.540.0 90.8 32.9 76.7 9.1 645.0 .. 16.3 0.4

.. .. .. 32.7 1.3 436.3 .. 14.8 11.2

.. .. .. .. .. .. .. .. 0.7

.. 0.5 .. 12.8 1.5 257.0 .. .. 0.4

.. .. .. .. .. .. .. .. 0.0

.. 24.8 .. 32.7 16.9 163.0 .. .. 1.1

.. 28.0 .. 40.7 2.3 228.0 .. .. 18.1

Quality Pricing Financing

Duration of telephone outages

(hours)

Reported phone faults

(per 100 mainlines)

Firms identifying telecommunications

as major or very severe obstacle to business operation and growth

(%)

Cost of cellular local call

(US cents per 3 off-peak min)

Cost of local phone call

(US cents per 3 peak min)

Cost of phone call to the

United States (US cents per 3 min)

Committed nominal investment in fi xed-

line and long distance projects with private

participation(2000 $ millions)

Committed nominal investment in mobile phone projects with

private participation(2000 $ millions)

Average annual ODA

disbursements for communications

($ millions)2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04

Tab

le


Drivers of growth

Access, supply side

Access, demand sideEnergy use per

PPP GDP (kg of oil

equivalent per 2000 PPP $, thousands)

Access to electricity Solid fuels use

Installed capacity

(MW)

Electric power consumption

(kWh per capita)

Total (% of total population)

Urban access (% of urban population)

Rural access (% of rural population)

Total (% of

total population)

Urban (% of urban population)

Rural (% of rural population)

2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a

SUB–SAHARAN AFRICAAngola 618.0 103.5 312.9 12.0 .. .. 95.0 .. ..Benin 120.6 12.2 329.7 22.0 49.6 5.5 95.0 87.5 98.7Botswana 132.0 538.5 .. 22.0 .. .. .. .. ..Burkina Faso 121.0 36.6 .. 13.0 39.7 0.2 95.0 .. ..Burundi 43.0 18.0 .. 5.0 45.1 0.4 99.8 98.1 99.9Cameroon 880.0 212.2 212.6 20.0 84.6 21.0 83.0 62.2 98.2Cape Verde 7.0 87.3 .. .. .. .. .. .. ..Central African Republic 39.9 .. .. 5.0 8.0 0.3 .. .. ..Chad 29.0 10.9 .. 3.0 9.4 0.1 95.0 .. ..Comoros 5.0 34.2 .. 51.8 19.6 .. .. ..Congo, Dem. Rep. 2,515.5 109.9 463.4 6.7 .. .. .. .. ..Congo, Rep. 121.0 77.3 268.0 20.9 .. .. 84.0 .. ..Côte d’Ivoire 915.0 219.4 267.4 38.5 85.9 22.5 74.0 .. ..Djibouti .. ..Equatorial Guinea 12.0 45.4 .. .. .. .. .. .. ..Eritrea .. 44.4 .. 17.0 80.6 2.1 79.7 30.4 97.4Ethiopia 533.8 25.7 417.3 4.7 76.2 0.4 95.0 72.9 99.9Gabon 403.3 957.3 196.3 31.0 .. .. .. .. ..Gambia, The 29.0 162.2 .. 5.0 .. .. .. .. ..Ghana 1,227.5 334.1 201.0 50.0 82.4 20.9 88.0 88.0 99.4Guinea 261.3 97.4 .. 5.0 53.8 1.5 .. .. ..Guinea-Bissau 21.0 63.9 .. 7.9 .. .. .. .. ..Kenya 1,084.3 119.7 498.1 7.9 47.5 4.3 81.8 33.8 94.7Lesotho 75.8 173.6 .. 5.0 .. .. 83.0 .. ..Liberia 330.0 112.2 .. .. .. .. .. .. ..Madagascar 285.0 48.5 .. 8.0 47.8 5.2 98.6 96.2 98.9Malawi 272.5 68.8 .. 5.0 28.7 1.0 95.0 83.0 99.6Mali 208.5 46.7 .. 7.6 37.0 2.2 97.9 98.4 99.8Mauritania 114.5 65.4 .. 50.0 49.7 2.5 .. .. ..Mauritius 582.6 1,487.6 .. 100.0 .. .. .. .. ..Mozambique 2,378.0 462.6 433.1 7.2 25.8 2.1 .. .. ..Namibia .. 695.4 98.5 34.0 .. .. 63.0 .. ..Niger 105.0 26.7 .. 7.9 36.5 0.2 97.8 94.8 98.4Nigeria 5,888.0 140.2 788.9 40.0 84.3 27.9 85.7 57.4 94.2Rwanda 34.3 19.9 .. 5.0 38.9 0.9 99.8 98.1 99.9São Tomé and Principe 10.1 105.9 .. .. .. .. .. .. ..Senegal 237.5 166.3 208.3 30.1 68.9 6.0 41.0 .. ..Seychelles 28.0 2,573.9 .. .. .. .. .. .. ..Sierra Leone 124.0 33.8 .. 5.0 .. .. .. .. ..Somalia 79.5 24.2 .. .. .. .. .. .. ..South Africa 41,365.5 4,559.5 256.6 66.1 .. .. .. .. ..Sudan 727.3 71.3 278.2 30.0 .. .. .. .. ..Swaziland 127.5 359.0 .. 20.0 .. .. .. .. ..Tanzania 847.3 67.8 728.8 10.5 27.3 1.1 .. .. ..Togo 35.5 9.1 203.0 9.0 41.2 2.4 .. .. ..Uganda 291.5 68.5 .. 3.7 43.9 2.4 96.8 85.0 98.7Zambia 1,786.0 739.4 792.4 12.0 45.1 2.9 85.0 62.4 98.1Zimbabwe 1,942.0 532.0 333.3 39.7 87.4 8.3 73.0 4.7 93.6NORTH AFRICAAlgeria 6,400.0 913.5 98.0 .. .. 5.0 .. ..Egypt, Arab Rep. .. 1,337.4 215.2 93.8 .. .. 5.0 .. ..Libya 4,700.0 3,338.5 99.8 .. .. 5.0 .. ..Morocco 17,600.0 592.1 99.0 71.1 .. .. 5.0 .. ..Tunisia 2,900.0 1,239.6 130.0 94.6 .. .. 5.0 .. ..


7.4 Energy


Quality Pricing Financing

Delay for fi rm in obtaining electrical

connection (average number

of days)

Electric power transmission and distribution losses

(% of output)

Electrical outages of fi rms

(average number of days per year)

Firms that share or own their own

generator (% of total)

Firms identifying electricity as major

or very severe obstacle to business operation

and growth (%)

Electricity end-user prices

(US cents per kWh)

Committed nominal investment in

energy projects with private participation

(2000 $ millions)

Average annual ODA

disbursements for energy ($ millions)Households Industry

2000–05a 2000–04a 2000–05a 2000–05a 2000–05a 2000–04a 2000–04a 2000–04a 2000–04

.. .. .. .. .. .. .. 42.1 4.4

.. 17.4 .. .. .. .. .. .. 0.9

.. .. .. .. .. 4.0 2.9 .. 0.2

.. 8.0 .. .. .. .. .. .. 1.9

.. .. .. .. .. .. .. .. 0.2.. .. .. .. .. .. 44.5 2.3

.. 15.0 .. .. .. .. .. 143.4 0.1

.. .. .. .. .. .. .. .. 0.6

.. 30.0 .. .. .. .. .. .. 1.4

.. .. .. .. .. .. .. .. 0.4.. .. 0.1.. .. 0.0

.. .. .. .. .. .. .. 3.2

.. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. 0.165.4 18.0 93.9 43.0 38.2 .. .. .. 4.6

105.5 23.0 .. 17.1 42.5 .. .. 280.6 1.1.. .. .. .. .. .. .. .. .... .. .. .. .. .. .. .. 0.1.. .. .. .. .. .. 167.7 8.6.. .. .. .. .. .. .. .. 1.7.. .. .. .. .. .. .. .. 0.2

43.7 18.7 83.6 70.9 48.1 8.2 6.7 .. 21.3.. .. .. .. .. 8.0 2.6 .. 0.2.. .. .. .. .. .. .. .. ..

49.5 .. 78.0 21.5 41.3 .. .. .. 0.6.. 4.4 4.7 .. 4.4

32.0 .. 10.5 45.3 24.2 .. .. 697.3 3.4.. 26.0 .. .. .. .. .. .. 1.1.. .. .. .. .. 14.3 10.0 .. 0.1.. 21.0 .. .. .. 3.1 2.2 1,122.5 25.9.. .. .. .. .. 3.6 2.4 1.0 1.7.. .. .. .. .. .. .. .. 0.4.. 34.0 .. .. 97.4 7.0 .. 227.1 6.4.. 22.0 .. .. .. .. .. .. 0.7.. .. .. .. .. .. .. .. 0.6

10.3 17.5 26.1 62.5 30.7 .. .. .. 4.5.. .. .. .. .. .. .. .. .... 33.0 .. .. .. .. .. .. 0.2.. .. .. .. .. .. .. .. 0.1

5.3 .. 5.5 9.5 9.0 3.1 2.0 388.7 1.4.. .. .. .. .. .. .. .. 0.0.. .. .. .. .. 3.9 3.1 .. 0.0

44.0 24.0 60.6 55.4 58.9 5.1 8.6 330.8 10.0.. 67.7 0.1

25.6 19.6 70.8 36.0 44.5 8.6 6.6 16.9 3.3142.4 .. 30.0 38.2 39.6 2.4 2.6 12.4 1.4

.. 3.6 .. .. .. 5.7 10.3 .. 0.9

.. .. .. 11.5 .. .. 0.9

.. 21.0 .. 26.5 .. .. 659.6 44.1

.. .. .. .. .. .. 0.0

.. 6.0 .. 16.7 .. .. .. 336.8 25.2

.. .. .. .. .. .. 0.2

Tab

le


Drivers of growth

7.5 Financial sector infrastructure

Macroeconomy Size

Claims on governments and

other public entities (% of GDP)

Bank assetsSovereign ratings(long-term

foreign currency)

Gross national savings

(% of GDP)

Money and quasi money (M2) (% of GDP)

Real interest rate

(%)Value

($ millions)Share of GDP

(%)

2006 2004 2004 2004 2004 2004 2004

SUB–SAHARAN AFRICAAngola .. 13 –1 13 28 3,825 19.0Benin B 9 –5 25 .. 1,144 28.1Botswana .. 42 –22 28 10 2,801 32.3Burkina Faso .. 10 –1 24 .. 1,162 24.1Burundi 8 16 26 11 201 30.6Cameroon B– 17 5 18 18 2,839 19.3Cape Verde B+ 14 31 69 10 753 79.4Central African Republic .. 13 10 15 20 134 10.2Chad .. 18 5 8 4 311 7.3Comoros 8 1 23 8 64 17.4Congo, Dem. Rep. .. 7 0 7 .. 436 6.6Congo, Rep. 27 9 13 10 384 8.8Côte d’Ivoire .. 14 4 23 .. 3,468 22.7Djibouti .. .. 6 70 8 536 80.9Equatorial Guinea .. .. –19 11 29 428 13.2Eritrea .. –21 109 147 .. 1,313 142.0Ethiopia .. 14 35 53 –2 4,722 58.5Gabon .. 29 3 16 10 1,478 20.4Gambia, The CCC 14 8 40 19 163 39.2Ghana B+ 23 17 29 .. 3,121 36.2Guinea .. 5 12 15 .. 529 15.1Guinea-Bissau .. 10 7 25 .. 27 9.6Kenya .. 17 10 37 5 7,096 45.5Lesotho BB– 36 –8 28 10 396 28.8Liberia .. 29 213 15 16 123 27.5Madagascar .. 14 5 22 10 975 22.3Malawi CCC –5 11 20 23 348 19.2Mali B– 11 –2 30 .. 1,496 30.8Mauritania .. –5 .. .. .. 396b 25.8b

Mauritius .. 24 23 85 14 6,132 84.3Mozambique B 12 3 25 8 1,680 30.3Namibia BBB– 40 .. .. 8 2,507 45.9Niger .. 6 5 13 .. 343 11.1Nigeria BB– 27 –3 22 –1 22,153 30.7Rwanda .. –1 .. .. .. 330 17.9São Tomé and Principe .. –33 8 48 20 38c 61.4c

Senegal .. 17 1 34 .. 2,737 35.7Seychelles .. 18 103 110 6 941 133.7Sierra Leone .. 6 25 17 5 160 14.9Somalia .. .. .. .. .. .. ..South Africa BBB+ 19 3 62 5 219,303 103.1Sudan .. 17 4 16 .. 2,332 11.9Swaziland .. 19 –4 20 6 633 26.2Tanzania .. 8 0 22 10 2,196 20.2Togo .. 6 1 26 .. 594 28.8Uganda .. 10 4 19 14 1,738 25.4Zambia .. 10 27 19 9 1,392 25.8Zimbabwe .. 3 25 25 –16 5,979 33.7NORTH AFRICAAlgeria .. 34 14 57 –2 42,860 50.6Egypt, Arab Rep. BB+ 23 50 89 2 95,367 121.0Libya .. .. –20 31 –11 13,162 45.2Morocco .. 24 17 90 10 47,828 95.6Tunisia BBB 26 6 55 .. 20,409 72.4

a. Data are consolidated for regional security markets where they exist.b. Data are for 2003.c. Calculated using 2003 exchange rate.


Intermediation Capital marketsa

Domestic credit to private sector

(% of GDP)

Interest rate spread (lending rate minus

deposit rate)

Ratio of bank nonperfoming loans

to total (%)

Bank branches


Listed domestic

companies

Market capitalization of listed companies

(% of GDP)

Turnover ratio for traded stocks

(%)

2004 2004 2004 2004 2004 2004 2004

5 67 13.3 .. .. .. ..14 .. .. .. .. .. ..19 6 2.8 3.8 18 28.4 215 .. .. .. .. .. ..23 .. .. .. .. .. ..10 13 .. .. .. .. ..37 9 .. .. .. .. ..7 13 .. .. .. .. ..3 13 .. .. .. .. ..9 8 .. .. .. .. ..2 .. .. .. .. .. ..3 13 .. .. .. .. ..

14 .. .. .. 39 13.5 321 10 .. .. .. .. ..4 13 .. .. .. .. ..

33 .. .. .. .. .. ..24 4 .. 0.4 .. .. ..9 13 15.8 .. .. .. ..

12 15 .. .. .. .. ..13 .. 16.1 1.6 29 29.8 34 .. .. .. .. .. ..2 .. .. .. .. .. ..

27 10 22.9 1.4 47 24.2 87 8 .. .. .. .. ..6 14 .. .. .. .. ..

10 10 11.4 0.7 .. .. ..8 23 .. .. .. .. ..

20 .. .. .. .. .. .... .. .. .. .. .. ..

59 13 .. 11.9 41 39.4 42 12 6.4 .. .. .. .... 5 .. 4.5 13 7.7 56 .. .. .. .. .. ..

16 5 21.6 1.6 207 20.1 14.. .. .. .. .. .. ..

33 18 .. .. .. .. ..21 .. 14.2 .. .. .. ..37 7 .. .. .. .. ..5 12 14.8 .. .. .. .... .. .. .. .. .. ..

141 5 1.8 6.0 403 214.1 478 .. .. .. .. .. ..

20 7 .. .. 6 9.4 ..9 10 .. 0.6 6 6.2 ..

16 .. .. .. .. .. ..7 13 2.2 0.5 5 1.4 ..8 19 7.6 1.5 11 8.0 ..

22 176 .. 3.3 79 41.3 9

11 6 .. .. .. .. ..55 6 24.2 3.6 792 48.9 1717 4 .. .. .. .. ..57 8 19.4 6.6 52 50.1 965 .. 23.7 .. 44 9.4 9

Tab

le

76 Part III. Development outcomes HUMAN DEVELOPMENT


Literacy rate (%)

Primary education

Youth AdultGross enrollment ratio

(% of relevant age group)Net enrollment ratio

(% of relevant age group)Student-teacher

ratioTotal Male Female Total Male Female Total Male Female Total Male Female2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2004 2004 2004 2004 2004 2004 2004

SUB–SAHARAN AFRICA .. .. .. .. .. .. 93 98 87 64 68 60 49Angola 72 84 63 67 83 54 .. .. .. .. .. .. ..Benin 45 59 33 35 48 23 99 111 86 83 93 72 52Botswana 94 92 96 81 80 82 104 105 104 82 80 84 26Burkina Faso 31 38 25 22 29 15 53 59 47 40 46 35 49Burundi 73 77 70 59 67 52 80 87 73 57 60 54 51Cameroon .. .. .. 68 77 60 114 123 105 .. .. .. 53Cape Verde .. .. .. .. .. .. 111 113 108 92 92 91 27Central African Republic 59 70 47 49 65 33 64 76 52 .. .. .. ..Chad 38 56 23 26 41 13 71 86 56 57 68 46 69Comoros .. .. .. .. .. .. 85 91 80 55 60 50 35Congo, Dem. Rep. 70 78 63 67 81 54 64 .. .. .. .. .. ..Congo, Rep. .. .. .. .. .. .. 89 92 85 .. .. .. 83Côte d’Ivoire 61 71 52 49 61 39 72 80 63 56 62 50 42Djibouti .. .. .. .. .. .. 39 44 35 33 36 29 34Equatorial Guinea 95 95 95 87 93 80 99 102 96 59 61 58 30Eritrea .. .. .. .. .. .. 66 74 59 48 52 44 47Ethiopia .. .. .. .. .. .. 77 85 69 46 49 44 65Gabon .. .. .. .. .. .. 130 130 129 77 77 77 36Gambia, The .. .. .. .. .. .. 79 82 75 73 76 70 37Ghana 71 76 65 58 66 50 81 84 79 58 62 62 32Guinea 47 59 34 29 43 18 79 87 71 64 69 58 45Guinea-Bissau .. .. .. .. .. .. 70 84 56 45 53 37 44Kenya 80 80 81 74 78 70 111 114 108 76 76 77 40Lesotho .. .. .. 82 74 90 131 131 131 86 83 88 44Liberia .. .. .. .. .. .. 99 115 83 66 74 58 38Madagascar 70 73 68 71 77 65 134 136 131 89 89 89 52Malawi 76 82 71 64 75 54 125 123 126 95 93 98 ..Mali 24 32 17 19 27 12 64 71 56 46 50 43 52Mauritania 61 68 55 51 60 43 94 95 93 74 75 74 45Mauritius 95 94 95 84 88 81 103 103 103 95 94 96 22Mozambique .. .. .. .. .. .. 95 104 86 71 75 67 65Namibia 92 91 93 85 87 83 101 100 102 74 71 76 28Niger 37 52 23 29 43 15 45 52 37 39 46 32 44Nigeria .. .. .. .. .. .. 99 107 91 88 95 81 36Rwanda 78 79 77 65 71 60 119 118 120 73 72 75 62São Tomé and Principe .. .. .. .. .. .. 131 134 129 .. .. .. 33Senegal 49 58 41 39 51 29 76 78 74 66 68 64 43Seychelles 99 99 99 92 91 92 113 116 110 100 100 99 14Sierra Leone 48 59 37 35 47 24 79 93 65 .. .. .. 37Somalia .. .. .. .. .. .. .. .. .. .. .. .. ..South Africa 94 93 94 82 84 81 105 107 103 89 88 89 34Sudan 77 85 71 61 71 52 60 64 56 43 47 39 29Swaziland 88 87 90 80 81 78 101 103 98 77 76 77 31Tanzania 78 81 76 69 78 62 101 103 99 86 87 85 58Togo 74 84 64 53 69 38 101 110 92 79 85 72 44Uganda 77 83 71 67 77 58 125 126 125 98 97 99 50Zambia 69 73 66 68 76 60 99 101 97 80 80 80 49Zimbabwe .. .. .. .. .. .. 96 97 95 82 81 82 39NORTH AFRICA .. .. .. .. .. .. .. .. .. .. .. .. ..Algeria 90 94 86 70 80 60 112 116 107 97 98 96 27Egypt, Arab Rep. .. .. .. .. .. .. 100 103 98 94 96 93 22Libya .. .. .. .. .. .. 112 113 112 .. .. .. ..Morocco 70 81 60 52 66 40 106 111 100 87 89 84 28Tunisia 94 96 92 74 83 65 111 113 109 97 97 97 22

a. Data are for most recent year during the period specifi ed.

8.1 Education

HUMAN DEVELOPMENT Part III. Development outcomes 77

Secondary education Tertiary education Public spending on education(%)Gross enrollment ratio

(% of relevant age group)Gross enrollment ratio

(% of relevant age group)Share of government

expenditureShare of

GDPTotal Male Female Total Male Female

2004 2004 2004 2001 2001 2001 2000–05a 2002

30 33 26 5 .. .. .. 3.417 19 15 1 1 1 .. 3.026 34 18 3 5 1 .. 3.374 71 76 6 7 6 25.6 2.212 14 10 1 2 1 .. ..12 14 10 2 3 1 13.0 5.244 46 42 5 6 4 17.2 3.866 63 69 6 5 6 20.7 7.312 .. .. 2 3 1 .. ..15 23 7 1 1 0 .. ..35 40 30 2 3 2 24.1 3.923 24 12 .. .. .. .. ..32 37 27 4 6 1 12.6 3.225 32 18 .. .. .. 21.5 4.622 25 18 2 2 1 20.5 6.130 38 22 3 4 2 1.6 0.628 35 21 1 2 0 .. 3.828 34 21 2 4 1 20.4 6.150 49 42 .. .. .. .. 3.934 40 28 1 2 0 8.9 1.942 45 38 3 4 2 .. ..26 34 17 2 4 1 25.6 1.818 23 13 0 1 0 .. ..48 50 46 .. .. .. 29.2 7.036 32 41 3 2 3 18.5 9.032 37 27 16 18 13 .. ..

.. .. .. 3 3 2 18.2 3.329 32 26 0 1 0 .. 6.022 28 17 2 3 1 .. ..20 22 18 3 5 2 .. 3.480 78 82 17 14 20 15.7 4.711 13 9 1 2 1 .. ..58 54 62 6 6 7 21.0 7.2

8 9 6 1 1 0 .. 2.335 38 31 10 13 7 .. ..14 15 14 3 3 2 .. 2.839 42 36 1 1 1 .. ..19 22 16 5 .. .. .. 4.0

112 113 111 .. .. .. .. 5.426 31 22 2 3 1 .. 3.7

.. .. .. .. .. .. .. ..90 87 94 15 14 17 18.1 5.433 34 32 6 6 6 .. ..42 42 42 4 4 5 .. 6.2

.. .. .. 1 2 1 .. ..39 52 26 4 6 1 13.6 2.619 21 17 3 4 3 18.3 5.226 29 23 2 3 1 14.8 2.836 38 35 4 5 3 .. 4.7.. .. .. .. .. .. .. ..

81 78 84 20 19 20 .. ..87 90 84 29 .. .. .. ..

104 101 107 56 54 59 .. ..47 51 43 11 11 10 27.8 6.377 74 80 26 23 29 18.2 6.4

Tab

le



Mortality Diseases

Infant mortality rate

(per 1,000 live births)

Maternal mortality ratio(per 100,000 live births)

Deaths due to

HIV/AIDS (thousands)

Prevalence of HIV

(% ages 15–49)

Incidence of tuberculosis (per 100,000

people)


(per 100,000people)

Life expectancy at birth(years)

Under-fi ve mortality rate(per,1 000)

Male Female Male Female

2004 2004 2004 2004 2004 2000 2005 2005 2004 1997–2004b

SUB–SAHARAN AFRICAAngola 38 42 276 243 154 1,700 30 3.7 259 354Benin 52 53 152 153 90 850 10 1.8 87 177Botswana 40 40 123 109 84 100 18 24.1 670 ..Burkina Faso 47 48 193 191 97 1,000 12 2.0 191 292Burundi 42 47 196 184 114 1,000 13 3.3 343 ..Cameroon 50 51 156 143 87 730 46 5.4 179 ..Cape Verde 67 71 38 35 27 150 .. .. 172 ..Central African Republic 40 41 201 185 115 1,100 24 10.7 322 ..Chad 45 48 212 188 117 1,100 11 3.5 279 ..Comoros 62 67 76 64 52 480 <0.1 <0.1 46 ..Congo, Dem. Rep. 42 47 217 192 129 990 90 3.2 366 224Congo, Rep. 53 55 113 103 81 510 11 5.3 377 ..Côte d’Ivoire 41 47 225 162 117 690 65 7.1 393 ..Djibouti 54 57 131 120 101 730 1 3.1 734 ..Equatorial Guinea 42 44 213 195 122 880 <1 3.2 239 ..Eritrea 58 62 89 75 52 630 6 2.4 271 74Ethiopia 49 51 175 158 110 850 .. .. 353 198Gabon 55 59 102 80 60 420 5 7.9 280 ..Gambia, The 55 59 129 115 89 540 1 2.4 233 ..Ghana 56 58 113 111 68 540 29 2.3 206 70Guinea 52 55 160 150 101 740 7 1.5 240 ..Guinea-Bissau 45 48 212 194 126 1,100 3 3.8 199 ..Kenya 51 50 129 110 79 1,000 140 6.1 619 63Lesotho 39 44 87 76 80 550 23 23.2 696 ..Liberia 39 44 249 220 157 760 .. .. 310 201Madagascar 55 59 128 117 76 550 3 0.5 218 ..Malawi 41 41 179 172 110 1,800 78 14.1 413 275Mali 44 47 230 208 121 1,200 11 1.7 281 454Mauritania 55 60 134 115 78 1,000 <1 0.7 287 ..Mauritius 69 75 17 14 14 24 <0.1 0.6 64 ..Mozambique 44 46 154 150 104 1,000 140 16.1 460 232Namibia 52 55 70 57 47 300 17 19.6 717 ..Niger 42 41 256 262 152 1,600 8 1.1 157 469Nigeria 45 46 198 195 101 800 220 3.9 290 141Rwanda 44 47 211 195 118 1,400 21 3.1 371 200São Tomé and Principe 57 60 122 114 75 .. .. .. 107 ..Senegal 54 57 141 132 78 690 5 0.9 245 72Seychelles 67 78 14 13 12 .. .. .. 34 ..Sierra Leone 37 40 296 269 165 2,000 5 1.6 443 ..Somalia .. .. .. .. 133 1,100 4 0.9 411 ..South Africa 47 49 72 62 54 230 320 18.8 718 ..Sudan .. .. .. .. 63 590 34 1.6 220 70Swaziland 36 39 163 150 108 370 16 33.4 1,226 ..Tanzania 47 49 134 117 78 1,500 140 6.5 347 130Togo 52 56 151 128 78 570 9 3.2 355 ..Uganda 48 51 144 132 80 880 91 6.7 402 152Zambia 40 40 190 173 102 750 98 17.0 680 141Zimbabwe .. .. .. .. 79 1,100 180 20.1 674 ..NORTH AFRICAAlgeria 69 72 41 39 35 140 <0.5 0.1 54 ..Egypt, Arab Rep. 66 70 36 36 26 84 <0.5 <0.1 27 ..Libya 70 75 20 19 18 97 .. .. 20 ..Morocco 69 73 47 38 38 220 1 0.1 110 ..Tunisia 70 74 29 22 21 120 <0.1 0.1 22 ..

8.2 Health

HUMAN DEVELOPMENT Part III. Development outcomes 79

64 59 45 6 2.3 6 94 68 6385 83 66 19 7.4 33 82 81 6090 97 94 48 .. 85 67 77 ..78 88 38 14 1.6 24 18 66 5075 74 25 16 1.3 14 29 79 3164 73 62 26 0.9 36 91 .. 5369 75 .. 53 .. .. .. .. ..35 40 44 28 1.5 .. 4 59 6956 50 14 3 0.6 3 16 78 5673 76 62 26 9.3 43 39 .. 6364 64 61 31 0.7 4 70 83 4565 67 .. .. .. .. 65 69 ..49 50 68 15 1.1 80 38 72 5860 64 61 .. .. 16 43 73 ..51 33 65 .. 0.7 0 82 51 4984 83 28 8 4.2 5 14 85 471 80 6 8 .. 7 36 70 355 38 86 33 .. 23 81 34 ..90 92 55 18 14.7 9 66 75 5583 80 47 25 3.5 7 37 66 6373 69 56 7 .. 9 52 75 ..80 80 35 8 7.4 1 75 80 5873 73 42 39 4.6 24 46 80 2770 78 60 30 .. 14 86 70 ..42 31 51 10 .. 3 58 73 ..59 61 51 27 0.2 0 74 71 4180 89 61 31 35.5 20 40 73 3275 76 41 8 .. 31 19 65 3864 70 57 8 2.1 40 43 58 3398 98 99 76 .. .. 33 87 ..77 72 48 17 .. 9 46 76 ..70 81 76 44 .. 71 88 63 1474 62 16 14 1.0 5 46 70 4835 25 35 13 1.2 6 21 59 3484 89 31 13 5.0 39 29 67 1391 99 76 29 22.8 .. .. .. 6157 87 58 11 1.7 47 52 70 3699 99 .. .. .. .. 106 100 ..64 61 42 4 1.5 2 36 83 6140 .. 25 .. .. .. 44 .. ..81 93 .. 56 .. 21 83 67 ..59 .. 87 7 .. .. 35 .. ..70 83 74 48 0.1 31 38 42 2694 95 46 25 2.1 7 47 81 5370 71 61 26 2.0 27 17 63 6091 87 39 23 0.2 51 43 68 ..84 80 43 34 6.5 26 54 75 5280 .. .. 54 .. .. 42 .. ..

81 86 96 57 .. 39 105 90 ..97 97 69 60 .. 12 61 80 ..99 97 .. .. .. 35 169 62 ..95 97 63 63 .. 48 80 86 ..95 97 90 66 .. 34 95 91 ..

Prevention and treatment

Child immunization rate(% of children ages 12–23 months)

Births attended by skilled health staff

(%)

Contraceptive prevalance

rate (%)

Children sleeping under insecticide-treated bednets(% of children under age 5)

People with advanced HIV infection

receiving antiretroviral combination therapy

(%)

Tuberculosis cases detected

under DOTS(% of estimated

cases)

DOTS treatment success rate

(% of registered cases)

Children with fever receiving

antimalarial drugs (% of children

under age 5 with fever)Measles DPT3a

2004 2004 2000–04b 1997–2004b 2000–04b 2005 2004 2003 2001

(continues)

Tab

le



8.2 Health (continued)

Water and sanitation Human resources Expenditure on health


to improved water source (%)


to improved sanitation (%)

Health workers(per 1,000 people)

General government expenditure on health

(%)

Out-of-pocketexpenditure

(% of general government expenditure on health)

Per capita government expenditure

on health at average

exchange rate

Share of total expenditure

on health

Share of total government expenditureTotal Urban Rural Total Urban Rural Physicians Nurses Midwives

2002 2002 2002 2002 2002 2002 1997 1997 1997 2003 2003 2003 2003

SUB–SAHARAN AFRICAAngola 50 70 40 30 56 16 0.1 1.1 0.0 84 5 100 22Benin 68 79 60 32 58 12 0.0 0.8 .. 43 10 90 9Botswana 95 100 90 41 57 25 0.4 2.6 .. 58 8 29 135Burkina Faso 51 82 44 12 45 5 0.1 0.4 0.1 47 13 98 9Burundi 79 90 78 36 47 35 0.0 0.2 .. 23 2 100 1Cameroon 63 84 41 48 63 33 0.2 1.6 .. 29 8 98 11Cape Verde 80 86 73 42 61 19 0.5 0.9 .. 73 11 100 57Central African Republic 75 93 61 27 47 12 0.1 0.3 0.1 39 12 95 5Chad 34 40 32 8 30 0 0.0 0.3 0.0 40 11 96 7Comoros 94 90 96 23 38 15 0.1 0.7 .. 54 6 100 6Congo, Dem. Rep. 46 83 29 29 43 23 0.1 0.5 .. 18 5 100 1Congo, Rep. 46 72 17 9 14 2 0.2 1.0 .. 64 4 100 12Côte d’Ivoire 84 98 74 40 61 23 0.1 0.6 .. 28 5 91 8Djibouti 80 82 67 50 55 27 0.2 0.4 0.1 67 11 100 31Equatorial Guinea 44 45 42 53 60 46 0.3 0.4 0.1 68 7 81 65Eritrea 57 72 54 9 34 3 0.1 0.6 .. 46 4 100 4Ethiopia .. 81 11 .. 19 4 0.0 0.2 0.0 58 10 79 3Gabon 87 95 47 36 37 30 0.3 5.2 .. 67 13 100 130Gambia, The 82 95 77 53 72 46 0.1 1.2 0.1 40 14 67 8Ghana 79 93 68 58 74 46 0.2 0.9 .. 32 5 100 5Guinea 51 78 38 13 25 6 0.1 0.6 0.0 17 5 99 4Guinea-Bissau 59 79 49 34 57 23 0.1 0.7 0.0 46 7 80 4Kenya 62 89 46 48 56 43 0.1 1.1 .. 39 7 83 8Lesotho 76 88 74 37 61 32 0.0 0.6 .. 80 10 18 25Liberia 62 72 52 26 49 7 0.0 0.2 0.1 57 18 99 4Madagascar 45 75 34 33 49 27 0.3 0.3 .. 63 9 92 5Malawi 67 96 62 46 66 42 0.0 0.6 .. 35 9 43 5Mali 48 76 35 45 59 38 0.1 0.5 0.0 57 9 89 9Mauritania 56 63 45 42 64 9 0.1 0.6 .. 77 14 100 13Mauritius 100 100 100 99 100 99 1.1 3.7 0.0 61 9 100 105Mozambique 42 76 24 27 51 14 0.0 0.2 0.1 62 11 39 7Namibia 80 98 72 30 66 14 0.3 3.1 .. 70 12 19 101Niger 46 80 36 12 43 4 0.0 0.2 0.0 53 12 89 5Nigeria 60 72 49 38 48 30 0.3 1.7 .. 26 3 91 6Rwanda 73 92 69 41 56 38 0.0 0.4 0.0 44 7 42 3São Tomé and Principe 79 89 73 24 32 20 0.5 1.6 0.3 84 11 100 29Senegal 72 90 54 52 70 34 0.1 0.3 .. 42 9 95 12Seychelles 87 100 75 .. .. 100 1.5 7.9 .. 73 10 63 382Sierra Leone 57 75 46 39 53 30 0.0 0.4 .. 58 8 100 4Somalia .. .. .. .. .. .. .. .. .. .. .. .. ..South Africa 87 98 73 67 86 44 0.8 4.1 .. 39 10 17 114Sudan .. .. .. .. .. .. .. .. .. .. .. .. ..Swaziland 52 87 42 52 78 44 0.2 6.3 .. 57 11 42 61Tanzania 73 92 62 46 54 41 0.0 0.4 .. 55 13 81 7Togo 51 80 36 34 71 15 0.0 0.4 0.0 25 9 88 4Uganda 56 87 52 41 53 39 0.1 0.6 0.1 30 11 53 5Zambia 55 90 36 45 68 32 0.1 1.7 0.3 51 12 68 11Zimbabwe .. .. .. .. .. .. .. .. .. .. .. .. ..NORTH AFRICAAlgeria 87 92 80 92 99 82 1.1 2.2 0.0 81 10 95 71Egypt, Arab Rep. 98 100 97 68 84 56 0.5 2.0 .. 43 8 93 24Libya 72 72 68 97 97 96 1.3 3.6 .. 63 6 100 108Morocco 80 99 56 61 83 31 0.5 0.8 .. 33 6 76 24Tunisia 82 94 60 80 90 62 1.3 2.9 .. 46 7 83 63

a. Diphtheria, pertussis, and tetanus toxoid.b. Data are for most recent year available during the period specifi ed.

Tab

le

AGRICULTURE AND RURAL DEVELOPMENT Part III. Development outcomes 81


9.1 Rural development

Rural population(%) Rural population

density(rural population

per sq km of arable land)

Share of rural population with sustainable access(%) Share of rural

households with own telephone

(%)

Share of total

populationAnnualgrowth

Rural population below the poverty line

To an improved water source

To improved sanitation

To electricity

To transportation (within 2 km of an all-season road)

2004 2004 2004 Survey year Percentage 2004 2004 2000–04a 2000–04a 2000–04a

SUB–SAHARAN AFRICA 64 1.2 355 .. 45 26 6.1 .. 0.8Angola 64 1.7 293 .. 40 16 .. .. ..Benin 55 1.8 166 .. 60 12 5.5 32 0.7Botswana 48 –1.1 228 .. 90 25 .. .. ..Burkina Faso 82 2.8 211 2003 52 44 5 0.2 25 0.3Burundi 90 3.1 640 .. 78 35 0.4 .. ..Cameroon 48 0.2 129 2001 50 41 33 21.0 20 0.1Cape Verde 43 0.4 464 .. 73 19 .. .. ..Central African Republic 57 0.4 117 .. 61 12 0.3 .. ..Chad 75 2.9 190 .. 32 0 0.1 5 ..Comoros 64 1.2 467 .. 96 15 19.6 .. ..Congo, Dem. Rep. 68 2.2 552 .. 29 23 .. 26 ..Congo, Rep. 46 2.0 354 .. 17 2 .. .. ..Côte d’Ivoire 55 0.7 294 .. 74 23 22.5 .. ..Djibouti 16 –1.0 12,513 .. 67 27 .. ..Equatorial Guinea 51 0.4 192 .. 42 46 .. .. ..Eritrea 80 3.8 577 .. 54 3 2.1 .. 0.1Ethiopia 84 1.6 523 2000 45 11 4 0.4 17 0.0Gabon 16 –3.1 67 .. 47 30 .. .. 1.1Gambia, The 74 2.8 337 .. 77 46 .. .. ..Ghana 54 1.2 277 .. 68 46 20.9 .. 0.7Guinea 64 1.0 533 .. 38 6 1.5 .. ..Guinea-Bissau 65 1.8 329 .. 49 23 .. .. ..Kenya 60 0.3 427 .. 46 43 4.3 .. 6.2Lesotho 82 –0.3 447 .. 74 32 .. .. ..Liberia 53 –0.6 450 .. 52 7 .. .. ..Madagascar 73 2.5 438 .. 34 27 5.2 .. ..Malawi 83 1.7 421 .. 62 42 1.0 .. ..Mali 67 2.0 185 .. 35 38 2.2 .. 0.1Mauritania 37 –0.5 227 2000 61 45 9 2.5 .. 0.2Mauritius 56 0.6 693 .. 100 99 .. .. ..Mozambique 63 0.1 282 .. 24 14 2.1 .. 0.1Namibia 67 0.4 165 .. 72 14 .. .. ..Niger 77 2.7 70 .. 36 4 0.2 37 ..Nigeria 53 0.6 220 .. 49 30 27.9 47 1.9Rwanda 80 –0.6 595 .. 69 38 0.9 .. 0.1São Tomé and Principe 62 2.2 1,162 .. 73 20 .. .. ..Senegal 50 0.9 228 .. 54 34 6.0 .. ..Seychelles 50 0.8 4,138 .. 75 100 .. .. ..Sierra Leone 60 3.0 550 2003 79 46 30 .. .. ..Somalia 65 2.5 480 .. 27 14 .. .. ..South Africa 43 –1.8 134 .. 73 44 .. .. ..Sudan 60 0.3 125 .. 64 24 .. .. ..Swaziland 76 1.1 475 .. 42 44 .. .. ..Tanzania 64 0.3 596 2000–01 39 62 41 1.1 38 ..Togo 64 1.7 151 .. 36 15 2.4 .. ..Uganda 88 3.4 453 2003 42 52 39 2.4 .. 0.6Zambia 64 1.2 137 .. 36 32 2.9 .. 0.4Zimbabwe 65 –0.1 260 .. 74 51 8.3 .. ..NORTH AFRICA .. .. .. .. .. .. .. ..Algeria 41 0.2 174 .. 80 82 .. .. ..Egypt, Arab Rep. 58 1.9 1,409 .. 97 56 .. .. ..Libya 13 –0.5 43 .. 68 96 .. .. ..Morocco 42 1.9 145 .. 56 31 .. .. ..Tunisia 36 0.0 128 .. 60 62 .. .. ..


Tab

le

82 Part III. Development outcomes AGRICULTURE AND RURAL DEVELOPMENT


9.2 Agriculture

Agriculture value added (% of GDP)

Crop production

index (1999–

2001=100)

Food production

index (1999–

2001=100)

Nonfood production

index (1999–

2001=100)

Cereal production

(thousands of metric tons)

Livestock production

index (1999–

2001=100)

Food production per capita

index (1999–

2001=100)

Agricultural exports

($ millions)

Agricultural imports

($ millions)2004 2004 2004 2004 2004 2004 2004 2004 2004

SUB–SAHARAN AFRICAAngola 8.5 119 113 89 626 100 100 2 983Benin 32.1 134 137 108 1,102 116 124 228 381Botswana 2.3 113 104 91 45 102 100 52 155Burkina Faso 30.8 130 115 183 3,063 110 103 314 152Burundi 36.1 104 104 94 280 100 93 23 42Cameroon 40.0 105 105 102 1,412 103 97 654 401Cape Verde 6.8 85 92 100 4 102 85 0 113Central African Republic 57.0 98 108 31 202 115 103 1 29Chad 23.5 116 112 121 1,394 108 100 123 74Comoros 36.2 106 105 103 21 96 93 26 34Congo, Dem. Rep. 47.4 97 98 84 1,570 100 87 39 317Congo, Rep. 6.0 106 109 102 9 121 98 40 261Côte d’Ivoire 22.1 97 101 81 2,205 111 95 3,093 711Djibouti .. 115 110 .. 0 109 102 11 163Equatorial Guinea .. 94 93 100 .. 102 84 7 57Eritrea 12.6 72 86 98 102 100 74 3 130Ethiopia 41.4 111 112 114 9,280 116 102 380 423Gabon 8.1 102 102 100 32 102 95 17 226Gambia, The 30.0 66 69 180 162 103 62 22 163Ghana 35.3 121 121 74 1,943 112 111 1,212 1,038Guinea 24.3 110 114 71 1,142 115 107 51 216Guinea-Bissau 63.4 110 110 115 193 109 98 62 41Kenya 23.9 102 104 109 2,709 109 98 1,296 483Lesotho 15.5 111 106 103 248 100 105 6 65Liberia 54.9 99 97 110 110 110 82 96 118Madagascar 26.2 109 108 100 3,391 104 96 118 90Malawi 33.7 92 96 86 1,847 102 88 392 58Mali 33.4 111 110 133 2,728 118 97 336 148Mauritania 17.0 101 109 .. 153 110 97 18 345Mauritius 5.4 104 106 92 0 114 102 407 408Mozambique 21.2 107 104 129 1,813 101 97 124 342Namibia 9.0 111 114 118 98 114 107 237 280Niger 0.0 122 118 25 3,169 105 103 27 136Nigeria 16.3 106 106 103 22,783 109 96 487 2,264Rwanda 41.2 113 113 103 319 110 102 34 60São Tomé and Principe 18.5 109 109 61 3 108 99 4 20Senegal 17.0 77 82 230 1,200 101 74 182 847Seychelles 2.6 94 92 95 .. 91 88 6 49Sierra Leone 43.2 115 114 113 309 105 97 14 153Somalia .. .. .. .. .. .. .. .. ..South Africa 2.7 103 106 87 12,225 109 103 3,421 2,650Sudan 33.2 110 108 129 3,792 107 99 395 638Swaziland 6.5 101 106 44 71 111 102 269 71Tanzania 42.3 107 106 137 4,458 110 98 481 406Togo 41.2 111 104 137 787 109 95 131 98Uganda 29.5 109 109 111 2,625 110 96 359 281Zambia 18.8 108 108 73 1,364 99 103 201 97Zimbabwe 14.2 66 86 56 1,227 99 85 846 465NORTH AFRICAAlgeria 8.6 128 117 104 3,994 105 109 55 4,050Egypt, Arab Rep. .. 106 111 111 20,261 122 102 1,314 3,014Libya .. 100 104 109 213 101 96 12 1,148Morocco 15.2 149 132 98 8,591 100 124 964 2,058Tunisia 11.9 102 102 96 2,155 99 97 974 1,181


AGRICULTURE AND RURAL DEVELOPMENT Part III. Development outcomes 83

290 1,388 2.2 0.0 0.3 .. No 175 597267 968 0.4 187.6 0.0 .. No 610 1,147

3 188 0.3 122.0 1.6 12 Yes 412 24160 3,085 0.5 3.6 0.0 .. No 164 941

365 211 1.5 25.8 0.0 .. Yes 101 1,3291,200 881 0.4 58.6 0.0 .. No 1,215 1,727

3 26 6.1 47.8 0.0 .. No 1,666 15694 185 0.1 3.1 0.0 .. No 423 1,04230 1,807 0.8 48.6 0.0 .. No 199 67152 16 .. 37.5 0.0 .. No 353 1,338

1,100 2,048 0.1 15.7 0.0 .. No 197 76752 11 0.4 4.8 0.1 .. No 347 806

3,600 1,747 1.1 330.3 0.1 .. No 763 1,262.. 0 .. 0.0 0.6 .. Yes 70 1,500

100 .. .. 0.0 0.1 .. No 654 ..3 364 3.7 65.4 0.1 .. Yes 57 405

713 7,405 2.5 151.0 0.0 .. Yes 109 1,244170 20 1.4 9.2 0.5 .. No 1,805 1,641

5 189 0.6 25.4 0.0 .. No 220 1,1232,200 1,333 0.5 74.2 0.1 55 No 346 1,458

650 778 5.4 30.5 0.1 .. No 231 1,468250 141 4.5 80.0 0.0 .. No 229 1,220562 2,085 2.0 310.3 0.3 .. Yes 319 1,322

4 265 0.9 342.4 0.6 .. No 491 936220 120 0.5 0.0 0.1 .. No .. 917600 1,424 30.6 30.9 0.1 78 Yes 173 2,380140 1,683 2.2 839.2 0.1 .. Yes 134 1,09740 3,391 5.0 90.1 0.1 .. No 245 83912 148 9.8 59.4 0.1 .. No 287 1,4486 0 20.8 2,500.0 0.4 9 No 4,659 3,455

230 2,132 2.6 59.3 0.1 .. Yes 147 9595 244 1.0 3.7 0.4 31 No 1,122 447

17 6,739 0.5 3.4 0.0 .. No 174 3942,900 21,563 0.8 55.0 0.1 .. No 890 1,057

270 324 0.6 137.1 0.0 .. Yes 224 1,01647 1 18.2 .. 1.6 .. No 226 2,45547 1,081 4.8 136.1 0.0 .. No 254 9756 .. .. 170.0 4.0 .. No 554 ..

75 253 4.7 5.6 0.0 .. No .. 1,22326 .. 18.7 4.8 0.2 .. Yes .. ..

959 4,330 9.5 654.2 0.4 10 No 2,372 3,330420 9,105 10.7 42.8 0.1 .. No 688 398

14 61 26.0 393.3 2.2 .. No 1,180 1,1601,100 3,410 3.6 17.9 0.2 82 Yes 290 1,472

120 744 0.3 67.9 0.0 .. No 405 1,0582,150 1,549 0.1 18.2 0.1 69 Yes 231 1,695

29 855 2.9 123.9 0.1 .. Yes 210 1,595130 1,501 5.2 341.6 0.7 .. Yes 241 717

670 2,726 6.9 129.9 1.3 21 No 2,113 1,466502 2,822 99.9 4,342.2 3.1 28 No 2,048 7,516335 341 21.9 341.0 2.2 .. No .. 627892 5,708 15.4 475.2 0.6 44 No 1,711 814

2,140 1,590 8.0 368.1 1.3 .. No 2,639 1,450

Permanent cropland

(thousands of hectares)

Land under cereal production

(thousands of hectares)

Irrigated land (% of cropland)

Fertilizer consumption

(hundred grams per hectare of arable land)

Agricultural machinery

(tractors per 100 hectares of

arable land)

Agricultural employment (% of total

employment)Incidence of

drought

Agricultural productivity (agriculture

value added per worker 2000 $)

Cereal yield (kilograms per

hectare)

2003 2004 2003 2002 2003 2000–03a 2005 2003 2005

Tab

le


84 Part III. Development outcomes LABOR, MIGRATION, AND POPULATION

10.1 Labor

SUB–SAHARAN AFRICA 298.5 .. .. .. .. .. .. .. ..Angola 6.8 92 76 .. .. .. .. .. ..Benin 3.2 87 55 .. .. .. .. .. ..Botswana 0.6 69 47 16.8 5.9 31.1 17.4 51.4 75.6Burkina Faso 5.6 90 80 .. .. .. .. .. ..Burundi 3.7 93 93 .. .. .. .. .. ..Cameroon 6.2 82 54 .. .. .. .. .. ..Cape Verde 0.2 79 37 .. .. .. .. .. ..Central African Republic 1.8 89 71 .. .. .. .. .. ..Chad 3.6 77 66 .. .. .. .. .. ..Comoros 0.2 87 60 .. .. .. .. .. ..Congo, Dem. Rep. 22.3 91 63 .. .. .. .. .. ..Congo, Rep. 1.5 87 56 .. .. .. .. .. ..Côte d’Ivoire 6.7 89 40 .. .. .. .. .. ..Djibouti 0.3 84 55 .. .. .. .. .. ..Equatorial Guinea 0.2 .. .. .. .. .. .. .. ..Eritrea 1.7 94 52 .. .. .. .. .. ..Ethiopia 30.9 91 60 .. .. .. .. .. ..Gabon 0.6 91 74 .. .. .. .. .. ..Gambia, The 0.6 84 64 .. .. .. .. .. ..Ghana 9.6 87 60 59.8 50.3 13.5 14.5 26.5 35.6Guinea 4.3 76 72 .. .. .. .. .. ..Guinea-Bissau 0.6 89 83 .. .. .. .. .. ..Kenya 15.1 93 63 .. .. .. .. .. ..Lesotho 0.6 90 71 .. .. .. .. .. ..Liberia 1.2 74 49 .. .. .. .. .. ..Madagascar 8.3 84 56 76.7 79.3 7.4 6.0 16.0 14.6Malawi 5.8 86 80 .. .. .. .. .. ..Mali 5.3 90 86 .. .. .. .. .. ..Mauritania 1.2 86 75 .. .. .. .. .. ..Mauritius 0.6 85 56 11.0 6.1 35.7 39.6 53.2 54.2Mozambique 9.1 84 46 .. .. .. .. .. ..Namibia 0.6 32.8 29.1 17.2 6.7 49.4 63.3Niger 5.7 83 85 .. .. .. .. .. ..Nigeria 46.7 65 49 .. .. .. .. .. ..Rwanda 4.1 96 73 .. .. .. .. .. ..São Tomé and Principe 0.0 86 47 .. .. .. .. .. ..Senegal 4.5 85 82 .. .. .. .. .. ..Seychelles .. 77 32 .. .. .. .. .. ..Sierra Leone 2.3 84 59 .. .. .. .. .. ..Somalia 3.4 .. .. .. .. .. .. .. ..South Africa 19.1 94 58 12.6 7.4 33.3 13.6 53.9 78.9Sudan 10.3 95 61 .. .. .. .. .. ..Swaziland 0.3 82 50 .. .. .. .. .. ..Tanzania 18.9 73 24 80.2 84.0 4.0 1.2 15.7 14.8Togo 2.4 75 33 .. .. .. .. .. ..Uganda 11.5 91 88 60.1 77.3 10.7 4.8 28.8 17.8Zambia 4.9 90 52 .. .. .. .. .. ..Zimbabwe 5.7 88 81 .. .. .. .. .. ..NORTH AFRICA .. 91 68 .. .. .. .. .. ..Algeria 12.9 85 65 .. .. .. .. .. ..Egypt, Arab Rep. 22.3 .. .. 27.5 27.6 22.9 10.4 49.6 61.9Libya 2.2 83 37 .. .. .. .. .. ..Morocco 10.9 77 22 39.2 56.8 20.7 18.6 40.0 24.5Tunisia 3.7 82 32 .. .. .. .. .. ..

a. Data are for most recent year available during the period specifi ed.b. Components may not sum to 100 percent because of unclassifi ed data.

Labor force Employment

Participation rate(% ages 15–64)

Agriculture Industry Services

Total(millions)

Male(% of male

employment)

Female(% of female employment)

Male(% of male

employment)

Female(% of female employment)

Male(% of male

employment)

Female(% of female employment)Male Female

2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a 2000–04a

LABOR, MIGRATION, AND POPULATION Part III. Development outcomes 85

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..82.7 83.2 81.9 15.9 15.3 16.8 1.3 1.4 1.2 3.6 4.2 2.9 12.3 11.2 13.9 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..19.2 29.3 8.7 59.3 57.0 61.7 18.2 9.5 27.2 1.6 2.1 1.1 57.7 54.9 60.6 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..13.8 23.0 6.1 68.8 66.0 70.5 17.2 10.7 23.2 .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..33.4 46.4 19.3 23.8 23.1 24.6 39.6 26.9 53.2 .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..15.0 17.8 12.0 43.7 51.6 35.4 40.6 29.7 51.9 2.1 2.5 1.7 41.6 49.2 33.7 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. 5.8 6.8 4.5 10.2 8.3 12.7 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..81.8 82.4 81.1 17.4 17.1 17.7 0.8 0.5 1.1 15.1 14.8 15.4 2.3 2.3 2.3 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..6.9 9.8 4.0 89.3 87.2 91.4 3.8 3.0 4.6 .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..14.5 22.2 7.5 59.4 67.5 52.1 26.1 10.3 40.5 0.3 0.4 0.2 59.1 67.1 51.9 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..37.7 51.0 23.1 50.4 38.6 63.2 11.9 10.4 13.6 0.5 0.6 0.3 49.9 38.0 62.9 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. 17.4 20.1 5.7 11.9 13.1 6.8 .. .. ..

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

.. .. .. .. .. .. .. .. .. 2.5 3.3 0.5 26.1 31.2 12.1 2.5 3.1 0.9

.. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. ..

EmploymentStatus in employmentb (%) Composition of self-employmentb (%)

Wage and salaried workers (employees)

Self-employed workers

Contributing family workers (unpaid workers) Employers

Own-account workers

Members of producer cooperatives

Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female Total Male Female1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

1999–2003a

Tab

le


86 Part III. Development outcomes LABOR, MIGRATION, AND POPULATION

10.2 Migration and population

International migration Population Stock

Population dynamicsAge composition

(%)Geographic distributionShare

of population

(%) TotalNet

migration

Workers remittances

received ($ millions)

Dependency ratio

Total (millions)

Growth rate (%)

Fertility rate (births

per woman)Ages 0–14

Ages 15–64

Ages 65+

Rural areas

(% of total)

Urban areas

(% of total)2000–04a 2000–04a 2000–04b 2004 2004 2004 2004 2000–04a 2000–04a 2000–04a 2000–04a 2004 2004

SUB–SAHARAN AFRICA .. .. .. 7,339 725.8 2.1 5.3 .. .. .. .. 63.6 36.4Angola 0.3 46,108 –120,090 .. 15.5 2.9 6.6 47 51 2 1.0 63.5 36.5Benin 1.9 133,730 –29,290 55 1.8 0.1 3.1 44 53 3 0.9 54.7 45.3Botswana 3.2 55,890 –7,000 55 8.2 3.2 5.7 38 59 3 0.7 48.0 52.0Burkina Faso 5.1 572,829 –120,590 50 12.8 3.2 6.5 47 50 3 1.0 81.8 18.2Burundi 1.2 76,990 –400,000 .. 7.3 3.4 6.8 46 52 3 0.9 89.7 10.3Cameroon 1.0 147,573 –250 11 16.0 1.8 4.8 42 55 4 0.8 47.8 52.2Cape Verde 2.3 10,375 –5,000 92 0.5 2.3 3.6 40 56 4 0.8 43.3 56.7Central African Republic 1.9 71,612 11,290 .. 4.0 1.2 4.8 43 53 4 0.9 56.8 43.3Chad 1.3 104,825 99,440 .. 9.4 3.4 6.4 47 50 3 1.0 74.6 25.4Comoros 11.3 60,792 –6,000 12 0.6 2.1 4.1 42 55 3 0.8 64.3 35.7Congo, Dem. Rep. 1.4 717,082 –1,410,320 .. 55.9 2.9 6.7 47 50 3 1.0 67.7 32.3Congo, Rep. 6.4 220,493 41,550 1 3.9 3.0 6.3 47 50 3 1.0 46.0 54.0Côte d’Ivoire 14.0 2,336,362 150,000 148 17.9 1.5 4.8 42 55 3 0.8 54.6 45.4Djibouti 4.0 28,481 20,280 .. 0.8 1.8 4.9 42 55 3 0.8 15.9 84.1Equatorial Guinea 1.0 4,517 0 .. 0.5 2.3 5.9 44 52 4 0.9 51.0 49.0Eritrea 0.4 12,952 –8,890 3 4.2 4.3 5.3 45 53 2 0.9 79.6 20.4Ethiopia 1.0 662,444 –76,910 133 70.0 1.9 5.4 45 52 3 0.9 84.1 15.9Gabon 16.5 210,486 14,370 6 1.4 1.6 3.8 40 55 4 0.8 15.6 84.4Gambia, The 14.1 185,116 45,000 8 1.5 2.7 4.5 40 56 4 0.8 73.8 26.1Ghana 7.6 1,504,715 –51,310 82 21.7 2.1 4.2 39 57 4 0.8 54.2 45.8Guinea 8.7 735,231 –227,000 42 9.2 2.2 5.8 44 53 4 0.9 64.3 35.7Guinea-Bissau 1.4 19,233 –10,700 23 1.5 3.0 7.1 47 50 3 1.0 65.2 34.8Kenya 1.1 327,446 –21,390 494 33.5 2.2 5.0 43 54 3 0.8 59.5 40.5Lesotho 0.3 5,462 –36,000 355 1.8 –0.1 3.5 39 56 5 0.8 81.9 18.1Liberia 5.2 159,586 555,110 .. 3.2 0.6 6.8 47 51 2 1.0 52.7 47.3Madagascar 0.4 61,237 –3,000 16 18.1 2.7 5.1 44 53 3 0.9 73.2 26.8Malawi 2.4 280,096 –50,000 1 12.6 2.2 5.9 47 50 3 1.0 83.3 16.7Mali 0.4 48,093 –283,900 154 13.1 3.0 6.8 48 49 3 1.0 67.0 33.0Mauritania 2.4 62,514 9,900 2 3.0 3.0 5.7 43 54 3 0.9 37.0 63.0Mauritius 1.3 15,539 –2,000 215 1.2 1.0 2.0 25 69 7 0.5 56.5 43.5Mozambique 2.0 366,694 75,000 58 19.4 1.9 5.4 44 53 3 0.9 63.2 36.8Namibia 7.5 142,638 20,100 15 2.0 1.2 3.8 42 54 3 0.8 67.0 33.0Niger 1.0 120,494 –5,940 26 13.5 3.4 7.7 49 49 2 1.0 77.3 22.7Nigeria 0.6 751,126 –95,030 2,273 128.7 2.2 5.6 45 52 3 0.9 52.5 47.5Rwanda 1.1 89,003 1,977,020 10 8.9 1.4 5.5 44 53 2 0.9 79.9 20.1São Tomé and Principe 5.2 7,314 –5,000 1 0.2 2.3 3.9 40 56 4 0.8 62.1 37.9Senegal 2.9 297,216 –100,000 511 11.4 2.4 4.8 43 54 3 0.9 49.7 50.3Seychelles 5.6 4,527 .. 2 0.1 1.0 2.1 .. .. .. .. 49.9 50.1Sierra Leone 1.0 46,662 –110,120 25 5.3 4.2 6.5 43 54 3 0.9 60.5 39.5Somalia 0.3 21,937 –214,310 .. 8.0 3.3 6.3 44 53 3 0.9 64.6 35.4South Africa 2.3 1,022,374 364,290 521 45.5 –0.7 2.7 33 63 4 0.6 42.6 57.4Sudan 2.6 853,867 –206,850 1,403 35.5 1.9 4.2 40 57 4 0.8 60.2 39.8Swaziland 4.0 41,504 –12,000 89 1.1 1.3 4.0 42 55 3 0.8 76.3 23.7Tanzania 2.6 892,553 –205,880 11 37.6 1.9 4.8 43 54 3 0.9 63.5 36.5Togo 3.3 176,117 127,710 149 6.0 2.6 5.1 44 53 3 0.9 64.3 35.8Uganda 2.2 528,562 –66,000 306 27.8 3.5 7.1 50 47 2 1.1 87.7 12.4Zambia 3.3 357,469 86,210 .. 11.5 1.6 5.5 46 51 3 1.0 63.8 36.2Zimbabwe 5.2 655,671 –125,000 .. 12.9 0.6 3.4 40 56 4 0.8 64.6 35.4NORTH AFRICA .. .. .. .. .. .. .. .. .. .. .. .. ..Algeria 0.8 250,110 –184,880 2,460 32.4 1.5 2.5 30 65 5 0.5 40.6 59.4Egypt, Arab Rep. 0.3 169,147 –500,000 3,341 72.6 1.9 3.2 34 61 5 0.6 57.8 42.2Libya 10.5 558,770 10,000 10 5.7 2.0 2.9 30 66 4 0.5 13.4 86.6Morocco 0.4 116,715 –300,000 4,221 29.8 3.4 2.5 31 64 5 0.6 41.9 58.1Tunisia 0.4 37,901 –20,000 1,432 9.9 0.9 2.0 27 67 6 0.5 35.9 64.1

a. Data are for most recent year available during the period specifi ed.b. Data are fi ve-year estimates.

Tab

le

HIV/AIDS Part III. Development outcomes 87


11.1 HIV/AIDS

Estimated number of people living with HIV/AIDS (thousands)

Estimated prevalence rate(%)

Deaths due to HIV/AIDS (thousands)

AIDS orphans(ages 0–17, thousands)

Adults(ages 15–49)

Women(ages 15–49)

Children(ages 0–14)

Adults(ages 15–49)

Young women(ages 15–24)

Young men (ages 15–24)Total

2005 2005 2005 2005 2005 2005 2005 2005 2005

SUB–SAHARAN AFRICA 24,500 22,400 13,200 2,000 6.1 4.3 1.5 2,000 12,000excluding South Africa 19,000 17,100 10,100 1,760 .. .. .. 1,680 10,800excl. S. Africa & Nigeria 16,100 14,500 8,500 1,520 .. .. .. 1,460 9,870Angola 320 280 170 35 3.7 2.5 0.9 30 160Benin 87 77 45 10 1.8 1.1 0.4 10 62Botswana 270 260 140 14 24.1 15.3 5.7 18 120Burkina Faso 150 140 80 17 2.0 1.4 0.5 12 120Burundi 150 130 79 20 3.3 2.3 0.8 13 120Cameroon 510 470 290 43 5.4 4.9 1.4 46 240Cape Verde .. .. .. .. .. .. .. .. ..Central African Republic 250 230 130 24 10.7 7.3 2.5 24 140Chad 180 160 90 16 3.5 2.2 0.9 11 57Comoros <0.5 <0.5 <0.1 <0.1 <0.1 <0.1 <0.1 <0.1 ..Congo, Dem. Rep. 1,000 890 520 120 3.2 2.2 0.8 90 680Congo, Rep. 120 100 61 15 5.3 3.7 1.2 11 110Côte d’Ivoire 750 680 400 74 7.1 5.1 1.7 65 450Djibouti 15 14 8 1 3.1 2.1 0.7 1 6Equatorial Guinea 9 8 5 <1 3.2 2.3 0.7 <1 5Eritrea 59 53 31 7 2.4 1.6 0.6 6 36Ethiopia .. .. .. .. .. .. .. .. ..Gabon 60 56 33 4 7.9 5.4 1.8 5 20Gambia, The 20 19 11 1 2.4 1.7 0.6 1 4Ghana 320 300 180 25 2.3 1.3 0.2 29 170Guinea 85 78 53 7 1.5 1.4 0.5 7 28Guinea-Bissau 32 29 17 3 3.8 2.5 0.9 3 11Kenya 1,300 1,200 740 150 6.1 5.2 1.0 140 1,100Lesotho 270 250 150 18 23.2 14.1 5.9 23 97Liberia .. .. .. .. .. .. .. .. ..Madagascar 49 47 13 2 0.5 0.3 0.6 3 13Malawi 940 850 500 91 14.1 9.6 3.4 78 550Mali 130 110 66 16 1.7 1.2 0.4 11 94Mauritania 12 11 6 1 0.7 0.5 0.2 <1 7Mauritius 4 4 <1 .. 0.6 .. .. <0.1 ..Mozambique 1,800 1,600 960 140 16.1 10.7 3.6 140 510Namibia 230 210 130 17 19.6 13.4 4.4 17 85Niger 79 71 42 9 1.1 0.8 0.2 8 46Nigeria 2,900 2,600 1,600 240 3.9 2.7 0.9 220 930Rwanda 190 160 91 27 3.1 1.9 0.8 21 210São Tomé and Principe .. .. .. .. .. .. .. .. ..Senegal 61 56 33 5 0.9 0.6 0.2 5 25Seychelles .. .. .. .. .. .. .. .. ..Sierra Leone 48 43 26 5 1.6 1.1 0.4 5 31Somalia 44 40 23 5 0.9 0.6 0.2 4 23South Africa 5,500 5,300 3,100 240 18.8 14.8 4.5 320 1,200Sudan 350 320 180 30 1.6 .. .. 34 ..Swaziland 220 210 120 15 33.4 22.7 7.7 16 63Tanzania 1,400 1,300 710 110 6.5 3.8 2.8 140 1,100Togo 110 100 61 10 3.2 2.2 0.8 9 88Uganda 1,000 900 520 110 6.7 5.0 2.3 91 1,000Zambia 1,100 1,000 570 130 17.0 12.7 3.8 98 710Zimbabwe 1,700 1,500 890 160 20.1 14.7 4.4 180 1,100NORTH AFRICA 440 400 190 31 0.22 0.20 0.10 37Algeria 19 19 4 .. 0.1 .. .. <0.5 ..Egypt, Arab Rep. 5 5 <1 .. <0.1 .. .. <0.5 ..Libya .. .. .. .. .. .. .. .. ..Morocco 19 19 4 .. 0.1 .. .. 1 ..Tunisia 9 9 2 .. 0.1 .. .. <0.1 ..ALL AFRICA 24,940 22,800 13,390 2,031 .. .. .. 2,037 12,000

Tab

le

88 Part III. Development outcomes MALARIA


12.1 Malaria

Population(millions)

Risk of malaria (% of population)



Under-fi ve mortality rate

(per 1,000)

Children sleeping under

insecticide-treated bednets (% of children under age 5)

Children with fever receiving antimalarial drugs

(% of children under age 5 with fever)

Pregnant women receiving two doses

of intermittent preventive treatment

(%)Any antimalarial

drugsEffective

antimalarial drugsEndemic Epidemic Negligible1997–2004a 1997–2004a 1997–2004a 1997–2004a 1997–2004a 1997–2004a 2000–04a 1997–2004a 1997–2004a 1997–2004a

SUB–SAHARAN AFRICAAngola 15.5 90 8 1 354 260 2.3 63 20 ..Benin 8.2 100 0 0 177 152 7.4 60 19 ..Botswana 1.8 .. .. .. .. 116 .. .. .. ..Burkina Faso 12.8 100 0 0 292 192 1.6 50 45 ..Burundi 7.3 68 17 15 .. 190 1.3 .. .. ..Cameroon 16.0 94 4 2 .. 149 0.9 .. .. ..Cape Verde 0.5 .. .. .. .. 36 .. .. .. ..Central African Republic 4.0 100 0 0 .. 193 1.5 .. .. ..Chad 9.4 96 4 0 .. 200 0.6 .. .. ..Comoros 0.6 .. .. .. .. 70 9.3 .. .. ..Congo, Dem. Rep. 55.9 92 3 6 224 205 0.7 45 .. ..Congo, Rep. 3.9 100 0 0 .. 108 .. .. .. ..Côte d’Ivoire 17.9 100 0 0 .. 194 1.1 .. .. ..Djibouti 0.8 .. .. .. .. 126 .. .. .. ..Equatorial Guinea 0.5 98 1 1 .. 204 0.7 .. .. ..Eritrea 4.2 92 7 1 74 82 4.2 4 7 ..Ethiopia 70.0 40 24 36 198 166 .. 3 .. ..Gabon 1.4 96 0 4 .. 91 .. .. .. ..Gambia, The 1.5 100 0 0 .. 122 14.7 .. .. ..Ghana 21.7 100 0 0 70 112 3.5 63 44 1Guinea 9.2 100 0 0 .. 155 .. .. .. ..Guinea-Bissau 1.5 100 0 0 .. 203 7.4 .. .. ..Kenya 33.5 53 24 22 63 120 4.6 27 11 4Lesotho 1.8 .. .. .. .. 112 .. .. .. ..Liberia 3.2 100 0 0 201 235 .. .. .. ..Madagascar 18.1 89 7 4 .. 123 0.2 .. .. ..Malawi 12.6 97 3 1 275 175 35.5 27 .. 47Mali 13.1 99 1 0 454 219 .. 38 .. ..Mauritania 3.0 65 35 0 .. 125 2.1 .. .. ..Mauritius 1.2 .. .. .. .. 15 .. .. .. ..Mozambique 19.4 100 0 0 232 152 .. .. .. ..Namibia 2.0 .. .. .. .. 63 .. .. .. ..Niger 13.5 97 3 0 469 259 1.0 48 .. ..Nigeria 128.7 100 0 0 141 197 1.2 34 25 1Rwanda 8.9 53 14 33 200 203 5.0 13 .. ..São Tomé and Principe 0.2 0 0 100 .. 118 22.8 .. .. ..Senegal 11.4 100 0 0 72 137 1.7 36 .. ..Seychelles 0.1 .. .. .. .. 14 .. .. .. ..Sierra Leone 5.3 100 0 0 .. 283 1.5 .. .. ..Somalia 8.0 20 79 1 .. 225 .. .. .. ..South Africa 45.5 .. .. .. .. 67 .. .. .. ..Sudan 35.5 74 25 1 70 91 .. 61 .. ..Swaziland 1.1 .. .. .. .. 156 0.1 .. .. ..Tanzania 37.6 93 3 4 130 126 2.1 53 11 ..Togo 6.0 100 0 0 .. 140 2.0 .. .. ..Uganda 27.8 90 3 7 152 138 0.2 .. 7 33Zambia 11.5 96 3 1 141 182 6.5 52 50 ..Zimbabwe 12.9 .. .. .. .. 129 .. .. .. ..NORTH AFRICAAlgeria 32.4 .. .. .. .. 40 .. .. .. ..Egypt, Arab Rep. 72.6 .. .. .. .. 36 .. .. .. ..Libya 5.7 .. .. .. .. 20 .. .. .. ..Morocco 29.8 .. .. .. .. 43 .. .. .. ..Tunisia 9.9 .. .. .. .. 25 .. .. .. ..


Tab

le

CAPABLE STATES AND PARTNERSHIP Part III. Development outcomes 89

Capable states and partnership

SUB–SAHARAN AFRICA 21,740 13,961 8,446 4.5 32.9 23.9 17.8 .. 50,282Angola 1,036 919 131 5.8 73.9 63.5 18.0 15.8Benin 343 191 168 9.3 46.2 51.2 47.9 46.5 Jul. 2000 Mar.2003 460Botswana 343 191 168 0.4 22.1 51.2 47.9 46.5Burkina Faso 553 299 278 12.6 47.6 66.2 77.0 55.9 Jul. 2000 Apr. 2002 930Burundi 320 169 166 52.8 48.2 386.6 245.6 132.6 Jul. 2005 Floating 1,472Cameroon 688 515 189 4.8 47.5 25.5 28.9 30.1 Oct. 2000 Floating 2,800Cape Verde 126 82 49 14.7 282.4 72.3 35.8 49.4Central African Republic 95 50 50 8.0 26.2 45.6 105.4 58.9Chad 292 149 154 7.4 33.8 29.7 43.2 50.4 May 2001 Floating 260Comoros 22 12 11 6.6 41.7 65.1 37.6 33.7Congo, Dem. Rep. 1,645 1,053 650 27.4 32.5 213.7 88.3 178.1 Jul. 2003 Floating 10,389Congo, Rep. 105 42 68 2.7 29.9 11.0 11.0 9.3 Mar. 2006 2,881Côte d’Ivoire 138 177 –43 1.0 8.6 9.2 3.8 4.9 ..Djibouti 59 36 27 .. 82.3 .. .. ..Equatorial Guinea 26 20 7 0.9 60.3 .. .. ..Eritrea 242 168 86 28.0 61.3 122.6 56.4 34.9Ethiopia 1,682 952 760 22.6 26.1 88.2 70.5 74.7 Nov. 2001 Apr. 2004 3,275Gabon 34 21 14 0.5 27.7 2.1 3.4 2.4Gambia, The 58 11 51 15.7 42.5 55.8 26.7 50.3 Dec. 2000 Floating 90Ghana 1,234 812 451 15.7 62.7 65.1 .. 57.7 Feb. 2002 Jul. 2004 3,500Guinea 256 165 101 7.4 30.3 68.6 39.4 42.5 Dec. 2000 Floating 800Guinea-Bissau 69 26 48 28.2 49.5 213.9 78.9 57.1 Dec. 2000 Floating 790Kenya 586 436 166 3.9 19.0 21.6 14.7 17.1Lesotho 93 31 68 7.5 56.8 18.2 8.3 17.9Liberia 197 153 48 42.8 65.0 346.8 78.5 307.5Madagascar 1,119 616 552 28.3 68.2 116.6 85.6 112.9 Dec. 2000 Oct. 2004 1,900Malawi 432 280 166 25.0 37.8 163.8 55.0 57.0 Dec. 2000 Floating 1,000Mali 516 299 241 11.6 43.2 61.1 49.6 51.4 Sep. 2000 Mar.2003 895Mauritania 163 75 96 11.7 60.3 54.5 19.4 36.8 Feb. 2000 Jun. 2002 1,100Mauritius 34 13 25 0.6 30.7 2.6 1.6 2.4Mozambique 1,117 661 497 20.8 63.2 100.6 60.4 84.3 Apr. 2000 Sep. 2001 4,300Namibia 164 119 34 3.1 89.1 12.3 8.5 9.1Niger 485 276 230 17.6 39.7 111.2 96.0 89.2 Dec. 2000 Apr. 2004 1,190Nigeria 525 289 260 0.8 4.5 3.6 3.0 2.2Rwanda 426 198 250 25.5 52.6 124.5 169.4 97.7 Dec. 2000 Apr. 2005 1,400São Tomé and Principe 30 20 12 58.5 218.5 169.5 91.8 65.5 Dec. 2000 Floating 200Senegal 953 682 296 13.5 92.4 57.9 42.4 55.2 Jun. 2000 Apr. 2004 850Seychelles 9 6 3 1.5 123.6 10.0 2.5 2.9Sierra Leone 326 147 196 33.2 67.4 316.8 .. 128.4 Mar. 2002 Floating 950Somalia 174 127 51 .. 24.0 .. .. ..South Africa 560 420 156 0.3 13.6 1.6 1.3 1.1Sudan 821 696 112 4.1 24.8 18.2 33.5 22.3Swaziland 105 95 12 4.6 104.1 26.6 6.1 14.3Tanzania 1,583 929 714 15.4 46.4 83.8 73.8 75.7 Apr. 2000 Nov. 2001 3,000Togo 55 47 9 3.0 10.3 16.5 7.3 20.4Uganda 1,062 627 475 17.0 41.7 75.5 87.7 72.6 Feb. 2000 May. 2000 1,950Zambia 974 669 333 19.9 94.2 76.8 62.6 74.7 Dec. 2000 Apr. 2005 3,900Zimbabwe 169 150 21 4.0 14.4 31.0 9.4 9.7NORTH AFRICA 2,578 1,887 677 1.2 19.4 12.5 9.1 ..Algeria 279 209 78 0.4 9.7 .. .. ..Egypt, Arab Rep. 1,368 1,115 261 1.9 20.1 .. .. ..Libya .. .. .. .. .. .. .. ..Morocco 636 354 242 1.3 23.7 5.6 4.3 4.7Tunisia 295 208 96 1.1 33.0 4.7 2.7 4.4

13.1 Aid and debt relief

Net aid(2003 $ millions) Aid

Net aid(%)

Heavily Indebted Poor Country (HIPC)

Debt Initiative

Estimated total nominal debt service relief committed

under Enhanced HIPC Initiative

($ millions)From all donors

From DAC donors

From multilateral

donors

Share of GDP(%)

Per capita

($)

Share of gross capital

formation

Share of imports of goods and services

Share of central

government expenditure Decision point Completion point

2004 2004 2004 2004 2004 2004 2004 2004 as of Mar. 2006 as of Mar. 2006 as of Mar. 2006

Tab

le

90 Part III. Development outcomes CAPABLE STATES AND PARTNERSHIP


13.2 Capable states

Public fi nancial management

(number of HIPC benchmarks met)

Investment climate(viewed by fi rms as

major constraint, %)

Enforcing contracts Protecting investorsa

Number of procedures

Time required(days)

Cost (% of debt)

Disclosure index

Director liability index

Shareholder suits index

Investor protection

indexbCourts Crimeas of Mar 2006 2005 2005 2005 2005 2005 2005 2005 2005 2005

SUB–SAHARAN AFRICAAngola .. .. .. 47 1,011 11.2 5 6 6 5.7Benin 8 .. .. 49 570 29.6 5 8 4 5.7Botswana .. .. .. 26 154 24.8 8 2 3 4.3Burkina Faso 9 .. .. 41 446 95.4 6 5 3 4.7Burundi .. .. .. 47 433 32.5 1 .. .. ..Cameroon 7 .. .. 58 585 36.4 8 3 6 5.7Cape Verde .. .. .. .. .. .. .. .. .. ..Central African Republic .. .. .. 45 660 72.2 .. .. .. ..Chad 7 .. .. 52 526 54.9 3 6 5 4.7Comoros .. .. .. .. .. .. .. .. .. ..Congo, Dem. Rep. 3 .. .. 51 909 256.8 3 4 5 4.0Congo, Rep. .. .. .. 47 560 43.0 4 5 6 5.0Côte d’Ivoire .. .. .. 25 525 47.6 6 5 3 4.7Djibouti .. .. .. .. .. .. .. .. .. ..Equatorial Guinea .. .. .. .. .. .. .. .. .. ..Eritrea .. .. 1.3 27 385 19.9 4 5 5 4.7Ethiopia 7 .. 9.5 30 420 14.8 1 4 3 2.7Gabon .. .. .. .. .. .. .. .. .. ..Gambia, The 3 .. .. .. .. .. .. .. .. ..Ghana 7 .. .. 23 200 14.4 7 7 4 6.0Guinea 5 .. .. 44 306 27.6 5 6 3 4.7Guinea-Bissau 0 .. .. .. .. .. .. .. .. ..Kenya .. .. 69.8 25 360 41.3 4 2 10 5.3Lesotho .. .. .. 49 285 23.9 2 2 8 4.0Liberia .. .. .. .. .. .. .. .. .. ..Madagascar 4 34.8 37.7 29 280 22.8 5 6 6 5.7Malawi 5 .. .. 16 277 136.5 4 7 5 5.3Mali 11 16.9 22.1 28 340 34.6 6 5 3 4.7Mauritania .. .. .. 28 410 29.3 .. .. .. ..Mauritius .. .. .. 17 367 8.6 6 8 9 7.7Mozambique 4 .. .. 38 580 16.0 2 .. .. ..Namibia .. .. .. 31 270 28.3 8 5 7 6.7Niger 5 .. .. 33 330 42.0 6 5 3 4.7Nigeria .. .. 36.3 23 730 37.2 6 7 4 5.7Rwanda 8 .. .. 27 310 43.2 .. .. .. ..São Tomé and Principe 4 .. .. 67 405 69.5 6 1 6 4.3Senegal 7 13.3 15.4 33 485 23.8 7 1 3 3.7Seychelles .. .. .. .. .. .. .. .. .. ..Sierra Leone 7 .. .. 58 305 31.0 3 6 5 4.7Somalia .. .. .. .. .. .. .. .. .. ..South Africa .. 8.8 29.0 26 277 11.5 8 8 8 8.0Sudan .. .. .. 67 915 30.0 .. .. .. ..Swaziland .. .. .. .. .. .. .. .. .. ..Tanzania 11 20.0 25.5 21 242 35.3 3 3 .. 2.0Togo .. .. .. 37 535 24.3 4 3 5 4.0Uganda 8 26.8 15 209 22.3 7 4 4 5.0Zambia 3 38.6 48.8 16 274 28.7 10 4 8 7.3Zimbabwe .. .. .. 33 350 19.1 8 1 4 4.3NORTH AFRICAAlgeria .. .. .. 49 407 28.7 8 5 1 4.7Egypt, Arab Rep. .. 27.4 .. 55 410 18.4 5 2 5 4.0Libya .. .. .. .. .. .. .. .. .. ..Morocco .. .. .. 17 240 17.7 6 5 1 4.0Tunisia .. .. .. 14 27 12.0 0 3 6 3.0

a. Indexes run from 0 least desirable to 10 most desirable.b. Average of the disclosure, director liability, and shareholder suits indexes.


30 656 32.5 Endorsed 2.0 75 270 53.1 .. 2.9 24 140 52.9 .. 5.9 40 270 48.3 .. 3.4 41 140 173.5 .. 2.3 51 1,300 47.6 Endorsed 2.2 .. .. .. .. 3.4

66 504 60.9 .. 2.4 65 122 51.3 Endorsed 1.7

.. .. .. .. 2.634 312 134.7 Endorsed 2.194 576 66.9 Implementing 2.371 270 46.9 .. 1.9 .. .. .. .. 2.6 .. .. .. Endorsed 1.9

18 216 66.3 .. 2.6 20 52 43.6 .. 2.2

.. .. .. Endorsed 2.9 .. .. .. .. 2.7

35 304 45.3 Implementing 3.5 55 416 51.2 Endorsed 1.7

.. .. .. .. 4.4 17 372 68.2 .. 2.1 19 564 37.7 .. 3.4 .. .. .. .. 2.2

29 400 58.9 .. 2.8 33 782 56.5 .. 2.8 60 270 44.0 .. 2.9 61 696 75.8 Endorsed 4.9 7 158 38.2 .. 4.2

35 230 50.9 .. 2.8 23 50 43.9 .. 4.3 44 270 49.4 Endorsed 2.4 36 1,120 27.1 Implementing 1.9 42 168 53.9 .. 3.1 29 1,008 27.4 Implementing 2.6 59 696 45.0 .. 3.2

.. .. .. .. 4.0 20 399 163.9 Endorsed 2.4

.. .. .. .. 2.1 32 350 43.8 .. 4.5

.. .. .. .. 2.1 .. .. .. .. 2.7

48 248 51.3 .. 2.9 51 270 50.9 .. 2.7 31 237 42.9 .. 2.5 36 132 38.6 .. 2.6 59 216 48.6 .. 2.6

63 504 58.5 .. 2.839 504 32.1 .. 3.4 .. .. .. .. 2.5

28 690 54.8 .. 3.2 31 112 52.7 .. 4.9

Regulation and tax administrationExtractive Industries

Transparency Initiative implementing or endorsed

Corruption Perceptions Index transparency indexa

(mean score)Number of

tax payments

Time to prepare and pay taxes

(hours)Total tax payable

(% of gross profi ts)2005 2005 2005 since 2003 2005

Tab

le



13.3 Governance and anticorruption indicators

Composite aggregate indicator Bottom quintile 2nd quintile 3rd quintile 4th quintile Top quintile

Voice and accountability <–1.09 –1.04 to –0.34 0.14 to 0.39 0.47 to 0.94

Burundi, Cameroon, Central African Republic, Chad, Côte d’Ivoire, Congo, Dem. Rep., Equatorial Guinea, Eritrea, Ethiopia, Guinea, Liberia, Mauritania, Rwanda, Somalia, Sudan, Swaziland, Togo, Tunisia, Zimbabwe

Algeria, Angola, Burkina Faso, Djibouti, Egypt, Gabon, Gambia, Guinea-Bissau, Kenya, Malawi, Morocco, Nigeria, Sierra Leone, Tanzania, Uganda, Zambia

Benin, Comoros, Congo, Rep., Ghana, Lesotho, Madagascar, Mali, Mozambique, Niger, Senegal, Seychelles

Botswana, Cape Verde, Mauritius, Namibia, São Tomé and Principe, South Africa

Political stability <–0.91 –1.04 to –0.34 –0.21 to 0.38 0.46 to 0.91

Algeria, Angola, Burundi, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Côte d’Ivoire, Ethiopia, Guinea, Kenya, Liberia, Nigeria, Rwanda, Somalia, Sudan, Uganda, Zimbabwe

Benin, Burkina Faso, Cameroon, Djibouti, Equatorial Guinea, Guinea-Bissau, Malawi, Niger, Sierra Leone, South Africa, Tanzania, Togo

Comoros, Eritrea, Gabon, Gambia, Ghana, Lesotho, Libya, Madagascar, Mali, Mauritania, Mozambique, São Tomé and Principe, Senegal, Swaziland, Tunisia, Zambia

Botswana, Cape Verde, Mauritius, Namibia, Senegal

Government effectiveness –0.87 –0.84 to –0.39 –0.37 to –0.03 0.22 to 0.83

Angola, Burundi, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Côte d’Ivoire, Equatorial Guinea, Eritrea, Ethiopia, Guinea, Guinea-Bissau, Liberia, Niger, Nigeria, São Tomé and Principe, Sierra Leone, Somalia, Sudan, Togo

Algeria, Benin, Burkina Faso, Cameroon, Djibouti, Gabon, Gambia, Kenya, Libya, Madagascar, Malawi, Mozambique, Rwanda, Swaziland, Uganda, Zambia, Zimbwabwe

Cape Verde, Egypt, Ghana, Lesotho, Mali, Morocco, Senegal, Seychelles, Tanzania, Tunisia

Botswana, Mauritania, Mauritius, Namibia, South Africa

Note: Within each quintile, countries are listed in alphabetical order.


Composite aggregate indicator Bottom quintile 2nd quintile 3rd quintile 4th quintile Top quintile

Regulatory quality <–0.83 –0.78 to –0.36 0.31 to 0.10 0.27 to 0.96

Algeria, Angola, Burundi, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Côte d’Ivoire, Eritrea, Ethiopia, Guinea, Guinea-Bissau, Liberia, Libya, Nigeria, Seychelles, Sierra Leone, Somalia, Sudan

Benin, Cameroon, Djibouti, Egypt, Equatorial Guinea, Gabon, Kenya, Lesotho, Malawi, Niger, Rwanda, São Tomé and Principe, Swaziland, Tanzania, Togo, Zambia, Zimbwabwe

Burkina Faso, Gambia, Ghana, Madagascar, Mali, Mauritania, Morocco, Mozambique, Senegal, Tunisia, Uganda

Botswana, Cape Verde, Mauritius, Namibia, South Africa

Rule of law <–0.95 –0.92 to –0.49 –0.47 to 0.26 0.32 to 0.84

Angola, Burundi, Cameroon, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Côte d’Ivoire, Equatorial Guinea, Ethiopia, Guinea, Guinea-Bissau, Kenya, Liberia, Nigeria, Sierra Leone, Somalia, Sudan, Swaziland, Togo, Zimbabwe

Algeria, Burkina Faso, Djibouti, Eritrea, Gabon, Libya, Mauritania, Niger, Rwanda, São Tomé and Principe, Tanzania, Uganda, Zambia

Benin, Cape Verde, Egypt, Gambia, Ghana, Madagascar, Malawi, Mali, Morocco, Namibia, Senegal, Seychelles, Tunisia

Botswana, Mauritius, South Africa

Control of corruption <–0.88 –0.87 to –0.52 –0.49 to 0.02 0.18 to 0.48 >0.80

Angola, Burundi, Cameroon, Central African Republic, Chad, Comoros, Congo, Dem. Rep., Congo, Rep., Côte d’Ivoire, Djibouti, Equatorial Guinea, Kenya, Libya, Nigeria, Sierra Leone, Somalia, Sudan, Swaziland, Togo, Zimbabwe

Cameroon, Eritrea, Ethiopia, Gabon, Gambia, Guinea, Guinea-Bissau, Liberia, Malawi, Mali, Mozambique, Niger, São Tomé and Principe, Tanzania, Uganda, Zambia

Algeria, Benin, Burkina Faso, Egypt, Ghana, Lesotho, Madagascar, Mauritania, Morocco, Rwanda, Senegal, Seychelles

Cape Verde, Mauritius, Namibia, South Africa, Tunisia

Botswana

Tab

le



13.4 Country Policy and Institutional Assessment ratings, 2005

ClusterCriterion

indicators <2.6 2.6 to 2.9 3.0 to 3.2 3.3 to 3.5 >3.5

Overall rating Economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions

Central African Republic, Comoros, Côte d’Ivoire, Eritrea, Togo, Zimbabwe

Angola, Chad, Congo, Dem. Rep., Congo, Rep., Guinea-Bissau, Sudan

Burundi, Djibouti, Gambia, Guinea, Mauritania, Nigeria, São Tomé and Principe, Sierra Leone

Cameroon, Ethiopia, Lesotho, Madagascar, Malawi, Mozambique, Niger, Rwanda, Zambia

Benin, Burkina Faso, Cape Verde, Ghana Kenya, Mali, Senegal, Tanzania, Uganda

Economic management

Macroeconomic management, fi scal policy, and debt policy

Angola, Central African Republic, Côte d’Ivoire, Eritrea, Guinea-Bissau, Togo, Zimbabwe

Guinea, Mauritania, São Tomé and Principe, Sudan

Congo, Dem. Rep., Congo, Rep., Djibouti, Gambia, Malawi

Burundi, Cameroon, Chad, Madagascar, Niger, Rwanda, Zambia

Benin, Burkina Faso, Cape Verde, Ethiopia, Ghana, Kenya, Lesotho, Mali, Mozambique, Nigeria, Senegal, Sierra Leone, Tanzania, Uganda

Structual policies

Trade, fi nancial sector and business regulatory environment

Comoros, Eritrea, Zimbabwe

Angola, Burundi, Central African Republic, Congo, Rep., Nigeria, Sudan

Chad, Congo, Dem. Rep., Côte d’Ivoire, Ethiopia, Guinea-Bissau, Mozambique, São Tomé and Principe, Sierra Leone, Togo

Burkina Faso, Cameroon, Djibouti, Gambia, Guinea, Lesotho, Malawi, Mali, Mauritania, Nigeria, Rwanda, Zambia

Benin, Cape Verde, Ghana, Kenya, Madagascar, Senegal, Tanzania, Uganda

Policies for social inclusion and equity

Gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability

Cape Verde, Côte d’Ivoire, Sudan, Zimbabwe

Angola, Chad, Comoros, Congo, Dem. Rep., Guinea-Bissau, São Tomé and Principe, Sierra Leone, Togo

Benin, Burundi, Djibouti, Eritrea, Gambia, Guinea, Kenya, Niger, Nigeria

Cameroon, Lesotho, Malawi, Mali, Mauritania, Mozambique, Senegal, Zambia

Burkina Faso, Cape Verde, Ethiopia, Ghana, Madagascar, Rwanda, Tanzania, Uganda

Public sector management and institutions

Property rights and rule-based governance, quality of budgetary and fi nancial management, quality of public administration and transparency accountability and corruption in public sector

Central African Republic, Chad, Comoros, Congo, Dem. Rep., Côte d’Ivoire, Sudan, Togo, Zimbabwe

Burundi, Congo, Rep., Djibouti, Eritrea, Gambia, Guinea, Guinea-Bissau, Mauritania, Nigeria, Sierra Leone

Cameroon, Ethiopia, Mozambique, Nigeria, São Tomé and Principe, Zambia

Benin, Kenya, Lesotho, Madagascar, Malawi, Rwanda, Uganda

Burkina Faso, Cape Verde, Ghana, Mali, Senegal, Tanzania

Note: Within each range of ratings, countries are listed in alphabetical order. Only members of the International Development Association are included. Ratings for each indicator are averaged to calculate the rating for each cluster; the ratings for each cluster are averaged to calculate the overall rating.

HOUSEHOLD WELFARE Part IV. Household welfare 95

Tab

le14.1 Burkina Faso household survey, 2003

Demographic indicatorsSample 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 12MDG 1: extreme poverty and hunger

Mean monthly expenditure (CFA francs) 75,614 65,140 36,960 46,013 58,598 71,470 112,679 129,090 55,311 81,398 106,453 146,524 256,278

Mean monthly share on food (%) 58 65 72 70 69 65 57 42 54 51 48 44 34

Mean monthly share on health (%) 5 5 2 3 3 3 9 6 3 2 6 7 8

Mean monthly share on education (%) 3 1 2 1 2 1 1 8 4 8 8 7 8 MDGs 2 and 3: education and literacy; gender equality

Primary school within 30 minutes (% of households) 63 55 56 58 58 54 53 91 87 86 89 93 93

Net primary enrollment rate (% of relevant age group)

Total 93 91 87 90 92 91 93 96 95 95 94 97 97

Male 93 91 88 90 94 90 93 96 95 93 96 96 98

Female 92 91 84 90 90 92 94 95 94 97 93 97 95

Net secondary enrollment rate (% of relevant age group)

Total 34 21 16 20 17 23 27 48 24 36 42 52 68

Male 32 21 19 18 14 26 29 47 26 34 41 51 70

Female 36 21 9 24 24 19 23 48 23 38 43 53 66

Tertiary enrollment rate (per 10,000)

Adult literacy rate (%)

Total 22 13 9 11 10 12 17 56 34 43 49 57 76

Male 29 19 14 18 17 17 23 66 44 54 58 67 83

Female 15 7 4 5 5 7 11 47 25 33 39 49 69


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 72MDGs 4 and 5: child mortality; maternal health

Health center less than 1 hour away (% of population) 65 57 56 55 55 57 59 95 91 89 94 95 97

Morbidity (% of population) 6 6 3 4 6 6 8 7 5 4 6 7 10

Health care provider consulted when sick (%) 64 62 44 49 56 65 71 71 55 54 72 77 77

Type of health care provider consulted (% of total)

Public 70 72 57 62 67 70 79 62 66 67 57 64 61

Private, modern medicine 7 2 1 4 2 2 2 25 8 13 27 25 31

Private, traditional healers 17 20 39 28 25 18 14 8 22 14 12 6 3

Missionary or nongovernmental organization .. .. .. .. .. .. .. .. .. .. .. .. ..

Child survival and malnutrition (%)

Birth assisted by trained staff 52 43 32 42 43 46 50 94 86 94 93 96 98

Immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. ..

Measles immunization coverage, 1-year-olds .. .. .. .. .. .. .. .. .. .. .. .. ..

Stunting (6–59 months) 43 46 45 46 47 44 47 33 34 29 36 36 31

Wasting (6–59 months) 31 32 35 32 33 32 30 28 24 33 33 28 24

Underweight (6–59 months) 47 50 52 51 51 49 48 35 31 38 43 38 28MDG 7: environmental sustainability

Access to sanitation facilities (% of population) 35 20 12 16 18 20 25 91 70 85 92 95 97

Water source less than 1 hour away (% of population) 90 88 88 90 90 88 85 98 98 97 97 97 98

Market less than 1 hour away (% of population) 83 80 80 80 80 81 79 97 94 96 96 96 98

Access to improved water source (% of population)

Totala 27 15 14 16 15 15 16 72 52 63 75 76 77

Own tap 19 5 4 5 4 5 6 70 44 59 71 74 76

Other piped .. .. .. .. .. .. .. .. .. .. .. .. ..

Well, protected 9 10 10 11 11 11 10 3 8 4 5 2 1

Traditional fuel use (%)

Totala 95 98 99 99 99 99 96 85 99 99 98 93 67

Firewood 91 96 97 98 98 97 94 73 99 94 93 82 47

Charcoal 4 2 2 1 1 2 3 12 1 5 5 11 21

a. Components may not sum to total because of rounding.

Expenditure quintile

Nationaltotal

Rural Urban

Indicator All Q1 Q2 Q3 Q4 Q5 All Q1 Q2 Q3 Q4 Q5

96 Part IV. Household welfare HOUSEHOLD WELFARE

Tab

le14.2 Burundi household survey, 1998


Nationaltotal

Rural Urban


Demographic indicatorsSample size (households) 6,668 3,908 740 649 723 817 979 2,760 473 494 562 541 690

Total population (thousands) 6,026 5,717 1,143 1,153 1,135 1,143 1,144 309 62 62 62 62 62




Monogamous male 68 68 57 67 71 71 74 66 62 72 68 68 61

Polygamous male 0 0 0 0 0 0 0 0 0 0 .. .. ..

Single male 5 5 4 4 4 6 6 14 5 5 10 14 25

De facto female 3 3 2 1 5 3 4 3 4 3 4 4 3


Mean monthly expenditure (Burundi francs) 6,574 5,438 1,438 2,925 4,221 5,910 10,765 27,314 4,454 9,137 15,444 26,007 58,137



Mean monthly share on education (%) 1 1 2 1 1 1 1 3 4 4 3 4 2MDGs 2 and 3: education and literacy; gender equality



Total 56 55 40 50 59 63 66 83 67 84 90 93 94

Male 60 59 44 56 62 69 67 83 67 84 89 93 94

Female 51 50 36 44 55 57 65 83 66 83 91 93 94


Total 9 7 2 4 7 13 11 48 14 34 56 74 74

Male 10 8 3 6 6 15 11 48 16 33 56 71 73

Female 9 7 1 3 8 12 11 48 13 35 56 76 74

Tertiary enrollment rate (per 10,000) 11 .. .. .. .. .. .. .. .. .. .. .. ..


Total 49 47 33 45 47 52 54 84 58 76 87 95 97

Male 58 56 43 55 55 61 62 90 70 85 90 97 98

Female 42 39 26 37 40 44 47 79 50 68 83 93 97


Total 71 70 55 71 66 77 77 90 72 88 91 98 99

Male 72 71 56 72 66 78 79 92 75 91 93 99 98






Public .. .. .. .. .. .. .. .. .. .. .. .. ..

Private, modern medicine .. .. .. .. .. .. .. .. .. .. .. .. ..

Private, traditional healers .. .. .. .. .. .. .. .. .. .. .. .. ..

Other .. .. .. .. .. .. .. .. .. .. .. .. ..


Birth assisted by trained staff .. .. .. .. .. .. .. .. .. .. .. .. ..



Stunting (6–59 months) 56 59 62 64 56 59 52 30 47 31 23 22 23

Wasting (6–59 months) 8 8 8 8 9 9 9 7 5 6 6 5 11


Access to sanitation facilities (% of population) .. .. .. .. .. .. .. .. .. .. .. .. ..




Totala 85 84 80 83 85 84 87 98 94 96 98 99 100

Own tap 4 1 1 1 0 1 2 57 11 28 50 76 88

Other piped 16 14 14 17 14 12 14 40 80 66 48 23 11

Well, protected 66 69 65 66 71 71 71 1 3 2 1 0 1


Totala 99 99 100 100 98 100 98 97 99 100 99 98 93

Firewood 94 98 99 100 98 100 95 15 54 23 11 4 2

Charcoal 5 1 0 .. 0 0 4 82 46 77 88 94 91



Tab

le14.3 Cameroon household survey, 2001

Demographic indicatorsSample 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




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








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


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



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


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




Health care provider consulted when sick (%) .. .. .. .. .. .. .. .. .. .. .. .. ..


Public 53 55 53 53 53 59 58 48 44 49 51 49 48



Other 2 3 2 3 4 3 4 1 1 0 1 1 1





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Underweight (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..MDG 7: environmental sustainability





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


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



Nationaltotal

Rural Urban



Tab

le14.4 Côte d’Ivoire household survey, 1998


Total population (thousands) 16,816 9,225 2,724 2,173 1,880 1,470 978 7,590 578 1,143 1,471 1,889 2,509




Monogamous male 58 58 49 63 62 57 59 58 51 52 65 66 55

Polygamous male 13 18 30 18 18 19 7 7 22 14 9 5 2

Single male 14 12 7 8 9 12 20 16 6 10 9 11 25

De facto female 3 2 1 2 3 3 2 5 7 5 5 4 4






Primary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. ..


Total 52 43 32 43 45 50 59 64 55 56 61 65 79

Male 55 47 37 45 51 52 65 67 58 59 66 68 84

Female 49 40 27 42 40 48 54 61 52 52 54 64 75


Total 21 10 5 8 13 16 13 31 15 20 34 36 39

Male 26 14 7 10 18 21 20 40 20 23 45 48 51

Female 16 6 2 7 7 10 7 24 10 18 23 25 31



Total 48 33 24 31 34 38 43 64 44 56 61 64 76

Male 61 46 36 45 48 52 54 77 57 69 76 79 86

Female 35 21 14 18 21 24 31 51 32 44 45 52 65


Total 61 47 38 46 50 51 55 73 60 70 75 71 79

Male 72 59 48 59 61 64 65 86 71 79 88 89 91


Health center less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. ..




Public .. .. .. .. .. .. .. .. .. .. .. .. ..



Other .. .. .. .. .. .. .. .. .. .. .. .. ..





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..




Market less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. ..


Totala 78 82 84 83 82 77 84 72 79 72 72 69 74

Own tap 19 6 4 4 4 8 11 35 18 24 30 34 44

Other piped 7 2 1 2 3 2 3 13 3 11 9 14 16

Well, protected 51 73 79 77 76 68 69 25 58 37 33 22 14


Totala 89 93 96 98 96 93 86 83 94 92 94 90 69

Firewood 64 90 96 97 95 88 79 32 75 57 44 29 13

Charcoal 25 3 0 1 2 4 7 51 19 35 50 60 56



Nationaltotal

Rural Urban



Tab

le14.5 Ethiopia household survey, 2000

Demographic indicatorsSample 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





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


Mean monthly expenditure (birr) 103 93 42 60 75 95 161 162 49 76 103 147 346






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


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



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


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


Health center less than 5 km away (% of population) 47 38 37 39 40 37 37 98 97 98 99 99 98




Public 45 44 44 49 45 42 41 52 56 59 52 49 43



Other 6 7 6 7 5 9 7 4 4 3 4 3 4



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 (6–59 months) 59 61 64 60 61 61 55 47 56 51 49 43 29

Wasting (6–59 months) 11 11 12 11 11 9 11 7 8 9 6 4 7



Water source less than 5 km away (% of population) 90 99 90 89 88 90 87 98 97 98 98 98 99

Market less than 5 km away (% of population) 58 52 54 52 52 52 50 98 98 98 99 99 97


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


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



Nationaltotal

Rural Urban



Tab

le14.6 Gambia household survey, 1998

Demographic indicatorsSample size (households) 1,958 933 138 158 166 191 280 1,025 119 148 177 230 351

Total population (thousands) 1,699 942 190 189 188 187 188 757 151 152 151 151 153




Monogamous male 50 49 36 39 46 57 58 51 43 48 57 55 48

Polygamous male 27 38 55 48 42 31 28 17 38 27 17 14 8

Single male 7 4 4 4 2 2 6 10 6 3 6 8 18

De facto female 9 5 5 5 6 6 4 12 9 12 13 12 11


Mean monthly expenditure (dalasi) 393 203 51 85 119 166 415 560 121 196 284 423 1,111






Total 49 44 30 49 44 50 48 57 44 49 56 64 75

Male 51 47 37 50 45 50 53 59 46 58 57 62 75

Female 46 41 23 47 42 50 44 54 42 42 54 67 75


Total 18 8 1 8 10 10 14 30 16 20 31 35 54

Male 22 11 1 11 15 12 18 36 21 28 34 40 57

Female 14 5 .. 4 3 7 9 24 12 12 27 30 50



Total .. .. .. .. .. .. .. .. .. .. .. .. ..

Male .. .. .. .. .. .. .. .. .. .. .. .. ..

Female .. .. .. .. .. .. .. .. .. .. .. .. ..


Total .. .. .. .. .. .. .. .. .. .. .. .. ..

Male .. .. .. .. .. .. .. .. .. .. .. .. ..

Female .. .. .. .. .. .. .. .. .. .. .. .. ..MDGs 4 and 5: child mortality; maternal health





Public 82 82 84 80 82 80 87 82 77 88 90 78 75



Other .. .. .. .. .. .. .. .. .. .. .. .. ..





Stunting (6–59 months) 24 28 34 21 27 29 31 17 27 21 15 10 12

Wasting (6–59 months) 7 8 8 6 9 7 8 7 3 7 5 10 13



Water source less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. ..



Totala 79 66 74 74 73 56 63 90 78 85 89 92 96

Own tap 21 2 1 .. 1 2 3 39 15 16 26 39 64

Other piped 38 25 19 23 32 24 25 50 60 65 63 52 31

Well, protected 20 40 54 51 40 31 34 2 4 4 1 1 1


Totala .. .. .. .. .. .. .. .. .. .. .. .. ..

Firewood .. .. .. .. .. .. .. .. .. .. .. .. ..

Charcoal .. .. .. .. .. .. .. .. .. .. .. .. ..



Nationaltotal

Rural Urban



Tab

le14.7 Ghana household survey, 1998/99

Demographic indicatorsSample size (households) 5,998 3,799 568 625 665 776 1,165 2,199 310 343 403 435 708





Monogamous male 53 56 63 57 63 57 48 48 49 54 54 57 37

Polygamous male 3 4 7 5 3 4 2 1 4 2 1 1 0

Single male 12 10 5 6 4 8 17 15 6 7 7 11 28

De facto female 12 10 8 10 13 9 10 14 20 12 15 14 12


Mean monthly expenditure (cedi) 119,877 91,524 23,107 39,132 55,065 77,592 176,149 161,067 38,436 66,819 96,992 136,526 286,033






Total 61 54 39 48 53 68 68 74 67 70 73 81 79

Male 61 53 38 50 50 70 64 77 68 76 73 87 79

Female 61 56 40 46 58 67 71 71 65 65 74 75 80


Total 10 7 3 6 6 8 12 16 7 12 19 21 19

Male 11 8 3 6 10 10 12 16 5 10 22 21 23

Female 10 6 3 6 3 7 12 15 9 13 17 21 16



Total 53 43 24 35 44 50 55 67 45 59 64 75 82

Male 67 59 36 50 63 66 72 79 60 75 76 84 91

Female 40 29 13 23 28 36 39 57 34 47 54 67 73


Total 66 57 42 48 56 66 68 78 67 74 79 80 86

Male 73 65 47 56 69 77 77 85 74 80 86 87 91






Public 47 47 47 39 59 47 45 46 50 49 38 52 45



Other 4 4 2 3 4 4 6 4 6 7 4 3 2





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..






Totala 61 48 44 43 47 48 54 79 62 74 77 81 86

Own tap 4 1 .. .. 0 1 2 8 0 2 4 8 14

Other piped 40 19 7 15 15 19 29 70 60 70 71 72 72

Well, protected 17 28 38 28 32 29 22 1 2 3 2 2 1


Totala 92 97 95 98 99 99 96 85 99 98 89 83 74

Firewood 57 81 93 91 89 83 65 23 64 37 24 17 6

Charcoal 35 16 2 7 10 16 31 62 35 61 65 66 68



Nationaltotal

Rural Urban



Tab

le14.8 Kenya household survey, 1997

Demographic indicatorsSample size (households) 10,874 8,963 1,339 1,521 1,646 1,813 2,644 1,911 273 262 327 514 535





Monogamous male 58 56 57 55 57 59 53 66 76 63 58 64 68

Polygamous male 7 8 11 12 7 8 5 4 6 6 4 2 2

Single male 7 6 2 3 4 4 11 10 2 2 12 12 16

De facto female 12 14 16 14 14 15 11 4 6 3 5 4 2


Mean monthly expenditure (Kenyan shillings) 2,244 1,716 455 711 998 1,431 3,569 4,299 1,048 1,637 2,255 3,541 9,396






Total 73 73 65 73 72 77 82 78 69 81 83 82 80

Male 72 71 64 71 69 77 82 77 66 87 81 78 74

Female 75 74 66 74 74 78 81 78 72 74 85 85 86


Total 12 10 4 6 9 13 18 29 10 14 22 38 62

Male 12 9 3 5 8 13 19 36 18 13 34 42 73

Female 11 10 4 7 10 13 17 22 3 14 14 33 49



Total 78 75 67 72 74 78 79 92 87 91 94 93 95

Male 86 83 77 80 82 86 88 96 92 93 98 96 98

Female 71 67 59 64 67 71 71 89 83 89 89 90 91


Total 94 93 91 91 94 94 95 95 92 98 95 95 96

Male 95 94 92 93 96 95 96 98 95 99 99 98 98






Public 32 31 34 30 38 28 27 36 54 35 31 39 28



Other 7 8 7 12 11 7 6 1 0 1 2 2 1


Birth assisted by trained staff 47 42 30 37 40 50 61 75 62 71 76 88 94



Stunting (6–59 months) 38 40 50 40 34 35 35 32 44 31 26 28 21

Wasting (6–59 months) 6 6 7 5 8 6 6 5 5 7 8 2 2






Totala 49 39 29 39 34 41 46 86 81 82 83 91 90

Own tap 17 10 3 6 8 9 17 44 32 33 38 45 59

Other piped 19 14 10 15 10 14 17 40 42 46 44 45 30

Well, protected 13 16 16 18 17 18 12 2 6 3 1 1 1


Totala 82 96 100 99 99 97 89 26 48 26 28 26 13

Firewood 73 91 99 97 97 93 78 5 16 5 2 4 2

Charcoal 8 5 1 2 2 4 11 21 32 21 26 22 12

Note: Due to logisitcal and fi nancial constraints, the survey did not adequately cover the northeastern provinces and parts of the eastern provinces.a. Components may not sum to total because of rounding.


Nationaltotal

Rural Urban



Tab

le14.9 Madagascar household survey, 1999






Monogamous male 76 77 77 81 81 80 69 72 74 73 73 73 67

Polygamous male .. .. .. .. .. .. .. .. .. .. .. .. ..

Single male 7 7 5 4 4 6 12 6 3 5 3 7 12

De facto female 8 8 10 7 8 7 8 8 10 11 7 8 5


Mean monthly expenditure (Malagasy francs) 67,747 54,210 15,780 26,142 36,507 51,866 108,173 112,001 26,008 47,759 72,121 107,535 226,090






Total 67 64 52 66 64 64 77 82 67 78 84 94 97

Male 67 63 55 63 65 63 74 82 66 74 91 97 95

Female 68 64 50 68 64 65 81 82 68 81 79 92 98


Total 10 6 2 4 4 6 16 24 6 18 24 34 46

Male 9 6 1 3 3 8 14 23 5 20 18 30 53

Female 11 6 2 4 5 4 19 25 8 16 28 37 40



Total 67 61 44 58 58 66 74 84 66 81 85 90 94

Male 71 66 49 64 62 71 77 87 68 86 87 93 95

Female 63 57 40 52 54 61 70 82 63 76 83 87 93


Total 70 64 47 60 63 72 78 87 69 89 91 91 95

Male 70 65 47 62 64 74 76 87 66 92 89 92 96






Public 59 61 76 69 68 59 47 53 62 70 56 39 39



Other 2 3 1 2 2 3 4 1 1 2 1 1 1





Stunting (6–59 months) 49 50 50 48 50 52 49 44 48 51 44 42 32

Wasting (6–59 months) 14 15 16 15 16 13 10 14 17 17 11 15 11






Totala 24 10 3 6 6 10 19 70 43 57 71 76 85

Own tap 4 1 .. .. .. 1 2 15 1 3 7 16 32

Other piped 19 8 3 5 6 8 15 52 40 51 60 54 51

Well, protected 1 1 0 1 0 1 2 3 2 3 4 6 2


Totala 98 99 99 99 99 98 98 95 98 98 97 96 89

Firewood 80 92 99 98 96 92 80 40 84 63 39 28 14

Charcoal 18 7 0 1 3 6 18 55 14 35 59 69 75



Nationaltotal

Rural Urban



Tab

le14.10 Malawi household survey, 1997/98

Demographic indicatorsSample size (households) 6,586 5,657 902 1,024 1,106 1,198 1,427 929 200 184 174 194 177





Monogamous male 68 68 65 68 71 70 66 70 78 80 75 65 56

Polygamous male 1 1 0 1 1 1 1 .. .. .. .. .. ..

Single male 6 5 2 3 3 6 11 14 4 4 9 21 24

De facto female 7 8 10 8 8 6 7 3 5 3 2 2 4


Mean monthly expenditure (Malawi kwacha) 431 308 91 155 218 310 637 1,456 298 510 759 1,212 3,820






Total 62 61 60 58 59 61 68 70 70 63 76 75 68

Male 61 60 58 59 55 59 70 70 62 66 80 76 71

Female 63 62 63 57 62 63 66 70 80 60 72 73 65


Total 5 3 1 3 2 3 6 20 11 13 21 20 34

Male 5 3 1 4 2 3 7 21 12 11 25 20 36

Female 5 3 .. 3 2 3 5 19 9 15 16 19 33



Total 51 47 35 44 44 50 58 85 74 80 93 91 86

Male 62 58 47 55 55 61 68 89 83 88 96 90 86

Female 41 37 26 33 35 40 48 82 65 73 90 92 87


Total 63 59 50 57 57 63 66 88 79 84 96 94 87

Male 69 66 58 62 64 68 73 90 85 88 98 93 87




Health care provider consulted when sick (%) .. .. .. .. .. .. .. .. .. .. .. .. ..


Public .. .. .. .. .. .. .. .. .. .. .. .. ..



Other .. .. .. .. .. .. .. .. .. .. .. .. ..





Stunting (6–59 months) 56 56 58 56 57 58 51 57 54 47 67 68 55

Wasting (6–59 months) 11 11 11 12 11 9 11 10 14 17 7 4 ..


Access to sanitation facilities (% of population) .. .. .. .. .. .. .. .. .. .. .. .. ..




Totala 50 45 47 46 43 43 46 89 79 83 92 96 93

Own tap 6 1 0 0 1 1 3 43 13 22 44 49 72

Other piped 21 19 20 21 18 18 20 39 52 51 45 35 19

Well, protected 23 25 27 24 25 25 24 8 14 11 3 11 2


Totala 94 98 99 99 97 97 96 67 94 88 78 59 29

Firewood 92 97 99 99 97 97 96 49 76 68 56 40 18

Charcoal 2 0 0 0 .. 1 1 18 18 20 22 18 12



Nationaltotal

Rural Urban



Tab

le14.11 Mozambique household survey, 1996

Demographic indicatorsSample size (households) 8,250 5,811 987 948 1,059 1,215 1,602 2,439 337 383 463 519 737





Monogamous male 65 65 66 68 67 66 60 66 63 71 67 65 65

Polygamous male 8 9 15 10 10 8 7 3 2 5 5 4 2

Single male 5 5 2 2 4 4 9 8 2 6 6 10 14

De facto female 6 6 5 9 6 6 7 5 7 4 5 5 6


Mean monthly expenditure (meticais) 192,481 182,572 53,118 86,604 119,027 164,442 350,674 237,861 52,180 93,179 132,226 203,191 592,973






Total 36 32 25 30 33 36 39 54 34 48 60 68 71

Male 38 35 27 32 36 39 42 54 34 53 63 70 66

Female 33 29 22 27 31 33 35 53 34 43 57 67 76


Total 10 5 3 3 6 7 7 26 11 15 23 34 44

Male 10 6 4 3 7 8 9 26 14 19 22 37 40

Female 9 4 2 3 4 5 5 25 8 11 24 31 47



Total 41 33 28 33 35 33 35 72 52 62 73 79 86

Male 58 51 44 51 53 52 53 84 70 80 84 88 94

Female 25 17 14 16 19 16 20 60 36 45 63 70 78


Total 51 42 35 42 47 39 45 82 71 75 83 86 90

Male 64 56 42 56 61 57 63 88 79 87 89 91 93






Public 78 74 68 76 73 82 72 88 90 83 94 85 89



Other 2 2 6 2 2 1 1 0 .. 0 0 0 0





Stunting (6–59 months) 55 61 61 62 62 59 58 34 49 36 30 27 24

Wasting (6–59 months) 9 9 11 7 7 11 8 9 13 11 6 6 7






Totala 18 11 9 8 10 10 14 51 42 40 48 53 65

Own tap 2 0 0 0 0 0 1 9 0 2 5 9 25

Other piped 16 11 9 8 10 10 14 41 41 38 43 44 40

Well, protected .. .. .. .. .. .. .. .. .. .. .. .. ..


Totala 97 99 99 99 99 98 99 87 97 96 94 86 70

Firewood 90 99 99 99 99 98 98 50 70 66 51 41 31

Charcoal 7 0 0 0 0 0 1 37 27 30 44 45 38



Nationaltotal

Rural Urban



Tab

le14.12 Nigeria household survey, 2004

Demographic indicatorsSample size (households) 19,158 14,512 2,321 2,446 2,717 3,120 3,908 4,646 783 779 834 988 1,262





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 14MDG 1: extreme poverty and hungerMean monthly expenditure (Nigerian naira) 11,635 9,924 3,922 6,391 8,008 9,939 16,272 13,705 4,548 8,809 11,580 14,279 22,892



Mean monthly share on education (%) 5 3 4 3 3 3 3 8 11 7 8 7 7MDGs 2 and 3: education and literacy; gender equalityPrimary school within 30 minutes (% of households) .. .. .. .. .. .. .. .. .. .. .. .. ..

Net primary enrollment rate (% of relevant age group) .. .. .. .. .. .. .. .. .. .. .. .. ..

Total .. .. .. .. .. .. .. .. .. .. .. .. ..

Male .. .. .. .. .. .. .. .. .. .. .. .. ..

Female .. .. .. .. .. .. .. .. .. .. .. .. ..


Total .. .. .. .. .. .. .. .. .. .. .. .. ..

Male .. .. .. .. .. .. .. .. .. .. .. .. ..

Female .. .. .. .. .. .. .. .. .. .. .. .. ..

Tertiary enrollment rate (per 10,000) .. .. .. .. .. .. .. .. .. .. .. .. ..


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


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 87MDGs 4 and 5: child mortality; maternal healthHealth center less than 1 hour away (% of population) .. .. .. .. .. .. .. .. .. .. .. .. ..




Public 38 37 27 26 31 32 47 40 36 41 41 39 40


Private, traditional healers 2 2 1 1 2 1 2 1 2 0 1 2

Other 3 3 3 4 4 3 3 4 6 4 3 4 4





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Underweight (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..MDG 7: environmental sustainabilityAccess to sanitation facilities (% of population) 60 50 47 48 50 50 52 72 73 71 71 72 75




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


Totala 65 88 92 93 91 89 79 38 44 52 43 36 24

Firewood 64 87 92 93 90 89 79 37 42 51 42 35 23

Charcoal 1 0 0 0 1 0 1 1 2 1 1 1 2

Note: Data are provisional.a. Components may not sum to total because of rounding.


Nationaltotal

Rural Urban



Tab

le14.13 São Tomé and Principe household survey, 2000


Total population (thousands) 128 57 11 11 11 11 11 71 14 14 14 14 14




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


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






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


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



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


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






Public 70 81 94 88 78 83 68 64 80 78 68 62 53


Private, traditional healers 3 2 .. 3 .. 3 4 4 5 1 3 6 2

Other 1 2 2 .. 6 3 1 1 .. 3 .. .. 2





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..






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


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



Nationaltotal

Rural Urban



Tab

le14.14 Sierra Leone household survey, 2002/03


Total population (thousands) 5,337 3,440 688 689 688 688 688 1,897 379 379 380 379 380




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


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




Primary school within 30 minutes (% of households)


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


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



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


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






Public 53 55 50 39 53 51 61 51 51 52 49 55 51


Private, traditional healers 9 11 23 16 12 8 9 4 6 5 12 5

Other 8 7 11 14 8 9 5 8 25 11 12 10 2





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..






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


Totala 97 99 99 99 99 99 98 95 99 98 98 95 86

Firewood 93 98 98 98 98 98 97 83 98 96 91 83 55

Charcoal 5 1 1 1 1 0 1 12 1 2 7 12 32

Note: Data are provisional.a. Components may not sum to total because of rounding.


Nationaltotal

Rural Urban



Tab

le14.15 Uganda household survey, 2002/03

Demographic indicatorsSample size (households) 9,710 5,648 937 1,019 1,036 1,182 1,474 4,062 894 877 766 701 824





Monogamous male 54 56 56 61 60 57 50 45 52 53 45 45 37

Polygamous male 12 13 13 12 14 14 12 7 9 8 7 8 3

Single male 8 7 3 3 4 6 15 12 5 6 13 14 18

De facto female 8 8 10 9 8 8 7 9 8 8 6 7 12


Mean monthly expenditure (Ugandan shillings) 1,523 1,322 593 854 1,121 1,393 2,175 2,499 864 1,208 1,689 2,281 4,926






Total 63 62 50 59 63 67 73 73 64 69 80 75 82

Male 62 61 51 58 63 65 72 71 63 65 77 75 78

Female 64 63 50 60 64 68 74 75 64 73 82 75 86


Total 13 11 2 5 10 15 22 26 15 19 27 30 40

Male 13 11 1 4 7 19 21 26 13 23 26 29 39

Female 14 12 2 5 14 11 23 27 17 16 28 31 41



Total 70 66 49 61 66 71 79 87 72 83 86 92 95

Male 80 77 66 75 75 81 85 91 80 91 90 93 95

Female 61 56 34 48 58 62 73 84 65 77 82 92 94


Total 80 78 62 73 79 81 86 90 78 89 88 94 95

Male 85 83 72 84 83 86 88 91 80 92 88 93 96






Public 30 32 44 36 29 25 26 18 28 23 18 15 10



Missionary or nongovernmental organization 5 5 4 3 5 6 7 5 4 3 5 7 4

Other 0 0 0 1 0 0 1 1 0 0 0 .. 2





Stunting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..

Wasting (6–59 months) .. .. .. .. .. .. .. .. .. .. .. .. ..






Totala 60 56 57 55 55 56 57 81 77 79 81 82 85

Own tap .. .. .. .. .. .. .. .. .. .. .. .. ..

Other piped 9 2 1 1 1 2 5 48 22 34 46 58 65

Well, protected 51 54 56 54 54 54 52 34 55 45 35 24 20


Totala 97 98 99 99 99 99 97 89 99 97 93 93 73

Firewood 79 90 97 96 97 92 76 22 54 33 19 12 7

Charcoal 18 8 1 3 3 8 20 67 45 64 74 81 66

Note: The survey did not collect data in the Kitgum, Gulu, Kasese, and Bundibugio districts.a. Components may not sum to total because of rounding.


Nationaltotal

Rural Urban



Tab

le14.16 Zambia household survey, 1998

Demographic indicatorsSample size (households) 16,422 8,317 1,414 1,461 1,561 1,655 2,226 8,105 1,298 1,384 1,518 1,752 2,153





Monogamous male 70 70 66 69 75 73 67 70 68 74 76 72 62

Polygamous male .. .. .. .. .. .. .. .. .. .. .. .. ..

Single male 7 6 3 5 4 7 11 10 6 6 7 9 17

De facto female 3 4 4 5 4 3 4 2 2 2 2 3 2


Mean monthly expenditure (Zambian kwacha) 46,798 32,948 5,268 11,113 17,218 27,740 85,708 71,512 13,061 25,361 37,361 56,295 174,264






Total 66 59 46 55 60 67 70 77 65 75 78 84 86

Male 65 59 49 56 59 66 70 77 64 77 77 82 87

Female 66 59 44 55 60 68 69 77 65 73 79 84 86


Total 23 14 8 10 14 18 21 38 21 31 36 45 55

Male 25 16 10 11 16 20 24 40 21 33 38 52 58

Female 22 12 5 8 12 17 18 36 21 28 34 40 53



Total .. .. .. .. .. .. .. .. .. .. .. .. ..

Male .. .. .. .. .. .. .. .. .. .. .. .. ..

Female .. .. .. .. .. .. .. .. .. .. .. .. ..


Total .. .. .. .. .. .. .. .. .. .. .. .. ..

Male .. .. .. .. .. .. .. .. .. .. .. .. ..

Female .. .. .. .. .. .. .. .. .. .. .. .. ..MDGs 4 and 5: child mortality; maternal health

Health center less than 5 km away (% of population) 67 50 47 49 51 50 52 97 96 97 97 98 98




Public .. .. .. .. .. .. .. .. .. .. .. .. ..



Other .. .. .. .. .. .. .. .. .. .. .. .. ..





Stunting (6–59 months) 62 66 70 65 65 62 65 57 64 64 55 54 45

Wasting (6–59 months) 6 6 8 5 6 5 5 5 6 5 5 5 5




Market less than 5 km away (% of population) 60 38 35 36 34 40 43 99 97 99 99 99 99


Totala 56 37 33 35 38 36 42 89 79 87 91 91 93

Own tap 16 1 0 0 1 1 4 41 27 35 39 42 55

Other piped 17 4 3 3 3 4 5 40 41 42 43 43 32

Well, protected 23 32 29 31 34 31 33 8 11 9 8 7 6


Totala 85 99 100 100 99 99 97 60 85 74 65 54 37

Firewood 62 90 95 93 91 89 84 12 29 11 9 6 8

Charcoal 23 9 5 7 9 9 13 48 57 63 56 47 29



Nationaltotal

Rural Urban


Technical notes 111

General notes.. means that data are not available or

that aggregates cannot be calculated because of missing data in the years shown

$ means U.S. dollarsA blank means not applicable or, for an ag-

gregate, not analytically meaningful.A billion is 1,000 million.

1. Basic indicators

T .. B Population is World Bank estimates, usually projected from the most recent population censuses or surveys (mostly from 1980–2004). Refugees not permanently settled in the country of asylum are generally con-sidered to be part of the population of their country of origin.

Land area is the land surface area of a coun-try, excluding inland waters.

Gross national income (GNI) per capita is the total domestic and foreign value added claimed by residents, which comprises gross domestic

Technical notes

product plus net factor income from abroad (the income residents receive from abroad for factor services including labor and capital) less similar payments made to nonresidents who contribute to the domestic economy, divided by midyear population. It is calculated by the World Bank Atlas method (box 1). Growth rates are shown in real terms. / ey have been calculated by the least-squares method using constant (2000) GNI per capita series in na-tional currency (see also table 2.21).

Life expectancy at birth is the number of years a newborn infant would live if prevail-ing patterns of mortality at the time of its birth were to remain the same throughout its life. Data are World Bank estimates based on data from the United Nations Population Division, the United Nations Statistics Divi-sion, and national statistical offi ces.

Under-fi ve mortality rate is the probability that a newborn baby will die before reaching age 5, if subject to current age-specifi c mor-tality rates. / e probability is expressed as a rate per 1,000.

The World Bank Atlas method uses a three-year

average of conversion factors to convert gross

national income (GNI) data, expressed in differ-

ent national currencies, to a common denomina-

tion, conventionally U.S. dollars. The Atlas con-

version factor for any year is the average of the

offi cial exchange rate or alternative conversion

factor for that year and for the two preceding

years, after adjusting them for differences in rela-

tive infl ation between that country and the United

States. This three-year average smoothes fl uc-

tuations in prices and exchange rates for each

country. The resulting GNI in U.S. dollars is di-

vided by the midyear population for the latest of

the three years to derive GNI per capita.

The following formulas describe the pro-

cedures for computing the conversion factor

for year t:

and for calculating per capita GNI in U.S. dol-

lars for year t:

where Yt = current GNI (local currency) for year

t, Pt = GNI defl ator for year t,

Nt midyear population for year t, and P$t = U.S.

GNI defl ator for year t.

Box 1 The World Bank Atlas method


Gini coeffi cient is the most commonly used measure of inequality. / e coeffi cient varies between 0, which refl ects complete equality, and 1, which indicates complete inequality (one person has all the income or consump-tion, all others have none). Graphically, the Gini coeffi cient can be easily represented by the area between the Lorenz curve and the line of equality.

Adult literacy rate is the percentage of adults ages 15 and older who can, with un-derstanding, read and write a short, simple statement on their everyday life.

Total net offi cial development assistance per capita is net disbursements of loans and grants from all offi cial sources on conces-sional fi nancial terms divided by the midyear population for the corresponding year

Regional aggregates for GNI per capita, life expectancy at birth, and adult literacy rates are weighted by population.

Source: Data on population, land area, GNI per capita, life expectancy at birth, under-fi ve mortality, Gini coeffi cient, and adult literacy are from the World Bank’s World Develop-ment Indicators database. Data on aid fl ows are from the Organisation for Economic Co-operation and Development’s Geographic Distribution of Aid Flows to Developing Countries database.

2. National accounts

T .. G , Gross domestic product (GDP), real, is the to-tal output of goods and services for fi nal use produced by residents and nonresidents, regardless of the allocation to domestic and foreign claims. It is calculated without mak-ing deductions for depreciation of fabricated capital assets or depletion and degradation of natural resources. GDP fi gures are shown at market prices (also known as purchaser val-ues) and have been converted to U.S. dollars using constant (2000) exchange rates. For countries where the offi cial exchange rate does not eff ectively refl ect the rate applied to actual foreign exchange transactions, an alternative currency conversion factor has been used.

/ e sum of the components of GDP by industrial origin (presented here as value

added) will not normally equal total GDP for several reasons. First, components of GDP by expenditure are individually rescaled and summed to provide a partially rebased series for total GDP. Second, total GDP is shown at purchaser value, while value added com-ponents are conventionally reported at pro-ducer prices. As explained above, purchaser values exclude net indirect taxes, while pro-ducer prices include indirect taxes. / ird, cer-tain items, such as imputed bank charges, are added in total GDP.

Source: World Bank country desk data.

T .. G , Gross domestic product (GDP) per capita, real, is calculated by dividing GDP (see table 2.1) by corresponding midyear population.


T .. A Agriculture value added is shown at factor cost. It comprises the gross output of for-estry, hunting, and fi shing less the value of their intermediate inputs. However, for Botswana, Cameroon, Chad, Democratic Re-public of Congo, Republic of Congo, Gabon, Guinea, Madagascar, Mali, Morocco, Niger, Rwanda, Senegal, Togo, and Zambia, it is shown at market prices, that is, including intermediate inputs.


T .. I Industry value added is shown at factor cost. It comprises the gross output of mining, manu-facturing, construction, electricity, water, and gas, less the value of their intermediate inputs. However, for Botswana, Cameroon, Chad, Democratic Republic of Congo, Repub-lic of Congo, Gabon, Guinea, Madagascar, Mali, Morocco, Niger, Rwanda, Senegal, Togo, and Zambia, it is shown at market prices, that is, including intermediate inputs.


T .. S Services value added is shown at factor cost and consists of the gross output of all other

Technical notes 113

branches of economic activity, including government, less the value of their inter-mediate inputs. However, for Botswana, Cameroon, Chad, Democratic Republic of Congo, Republic of Congo, Gabon, Guinea, Madagascar, Mali, Morocco, Niger, Rwanda, Senegal, Togo, and Zambia, it is shown at market prices, that is, including intermedi-ate inputs. Other items, such as imputed bank service charges (which are diffi cult to assess in the same fashion for all countries) and any corrections for statistical discrepan-cies, are not included.


T .. G , Gross domestic product (GDP), nominal is ob-tained by converting national currency GDP series in current prices to U.S. dollars at of-fi cial annual exchange rates.


T .. Total consumptionTotal consumption is the sum of private con-sumption and general government consump-tion (table 2.8). Private consumption, not separately shown here, is the value of all goods and services purchased or received as income in kind by households and nonprofi t institu-tions. It excludes purchases of dwellings, but includes imputed rent for owneroccupied dwellings. In practice, it includes any statisti-cal discrepancy in the use of resources.


T .. G -General government consumption includes all current expenditure for purchases of goods and services by all levels of government, including capital expenditure on national defense and security. Other capital expenditure by govern-ment is included in capital formation.


T .. G Gross fi xed capital formation consists of gross domestic fi xed capital formation plus net changes in the level of inventories. Gross

capital formation comprises outlays by the public sector (table 2.10) and the private sec-tor (table 2.11). Examples include improve-ments in land, dwellings, machinery, and other equipment. For some countries the sum of gross private investment and gross public investment does not total gross domestic in-vestment due to statistical discrepancies.


T .. G General government fi xed capital formation is gross domestic fi xed capital formation (see table 2.9) for the public sector.


T .. P Private sector fi xed capital formation is gross domestic fi xed capital formation (see table 2.9) for the private sector.


T .. G Gross domestic savings is calculated by deduct-ing total consumption (table 2.7) from nomi-nal gross domestic product (table 2.6).


T .. G Gross national savings is the sum of gross do-mestic savings (table 2.12), net factor income from abroad, and net private transfers from abroad. / e estimate here also includes net public transfers from abroad.


T .. R ( )Resource balance is the diff erence between free on board exports (table 2.15) and cost, insur-ance, and freight imports (table 2.16) of goods and services (or the diff erence between gross domestic savings and gross capital formation). / e resource balance is shown as a share of nominal gross domestic product (table 2.6).



Three major databases supply information on

gross domestic product (GDP) growth rates:

the World Bank Africa Region Live Database

(AFRLDB), the World Bank World Develop-

ment Indicators (WDI) database, and the Inter-

national Monetary Fund (IMF) World Economic

Outlook (WEO) database. The GDP growth

rates presented in the World Bank databases

and the IMF database often differ, sometimes

substantially. For example, the 2004 GDP re-

gional growth rate was cited as 4.3 percent

by the AFRLDB and 5.2 percent by the WEO

(see fi gure).

The minor difference between the two

World Bank databases is due to different fre-

quencies of data updates. The sometimes sub-

stantial differences between the World Bank

and the IMF databases are due to three main

reasons: data source, aggregation methodol-

ogy, and selection of the weighting variable.

For data sources, the two World Bank

databases use the GDP estimates reported

by the Bank’s country economists, while the

IMF database uses the estimates reported by

IMF country economists. Although the World

Bank and the IMF country economists coor-

dinate their GDP estimates, some differences

remain.

To calculate the growth of regional ag-

gregates, the two World Bank databases use

the ratio of the sum method. This method con-

verts individual country GDP in current local

currency into base year U.S. dollars using the

base year exchange rate. The aggregate GDP

growth rate is then calculated as the total re-

gional GDP in base year U.S. dollars (the ratio

of the sum). The implicit weighting variable of

this method is the country GDP in constant

U.S. dollars.

The IMF database uses the sum of the ra-

tios method. This method fi rst calculates the

GDP growth rates of each individual country

using the country GDP in constant local cur-

rency. The aggregate GDP growth rate is then

calculated using the country growth rates and

the country GDP in current purchasing power

parity U.S. dollars as the weighting variable

(the sum of ratios).

The implications of these differences are

twofold. If the difference is small, it causes con-

fusion. If the difference is large, it can cause

serious miscalculation in economic planning

or in policymaking. For further details visit the

Africa Region Offi ce of the Chief Economist’s

website at www.worldbank.org/afr/ce.

Box 2 Discrepancies in gross domestic product growth estimates for Sub-Saharan Africa

–2

–1

0

1

2

3

4

5

6

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Percentage

Source: World Bank Africa Region Live Database, World Bank World Development Indicators database, and International Monetary Fund World Economic Outlook database.

WDI AFRLDB IMF

Sub-Saharan Africa annual GDP growth reported by different databases

Technical notes 115

T . .. E - , Exports and imports of goods and services, nominal, comprise all transactions between residents of an economy and the rest of the world involving a change in ownership of general merchandise, goods sent for process-ing and repairs, nonmonetary gold, and ser-vices expressed in current U.S dollars.


T . .. E - , Exports and imports of goods and services, real, are defi ned as in tables 2.15 and 2.16, but ex-pressed in constant 2000 U.S. dollars.


T .. G Gross domestic product (GDP) growth is the av-erage annual growth rates of real GDP (table 2.1). See box 2 for information on discrepan-cies in GDP growth estimates for Sub-Saha-ran Africa.


T .. G Gross domestic product (GDP) per capita growth is the average annual growth rate of real GDP per capita (table 2.2).


T .. G Gross national income (GNI) per capita is calculated using the World Bank Atlas meth-od (see box 1). It similar in concept to GNI per capita in current prices, except that the use of threeyear averages of exchange rates smoothes out sharp fl uctuations from year to year.


T .. T Total consumption per capita is obtained by dividing total consumption at current U.S.

dollars (see table 2.7) by the corresponding midyear population.


3. Millennium Development Goals

T .. M D G : Survey year is the year in which the underly-ing data were collected.

Share of population below national poverty line (poverty headcount ratio) is the percentage of the population living below the national poverty line. National estimates are based on population-weighted subgroup estimates from household surveys.

Share of population below purchasing power parity (PPP) $1 a day is the percentage of the

Table

Method

1

Method

2

Method

3

Method

4

Method

5

2.1 Gross domestic product, real X X

2.2 Gross domestic product per capita, real X X

2.3 Agriculture value added X X

2.4 Industry value added X X

2.5 Services value added X X

2.6 Gross domestic product, nominal X X

2.7 Total consumption X X

2.8 General government consumption X X

2.9 Gross fi xed capital formation X X

2.10 General government fi xed capita formation X X

2.11 Private sector fi xed capital formation X X

2.12 Gross domestic savings X X

2.13 Gross national savings X X

2.14 Resource balance (exports minus imports) X X

2.15 Exports of goods and services, nominal X X

2.16 Imports of goods and services, nominal X X

2.17 Exports of goods and services, real X X

2.18 Imports of goods and services, real X X

2.19 Gross domestic product growth X X

2.20 Gross domestic product per capita growth X X

2.21 Gross national income per capita X X

2.22 Total consumption per capita X X

Note : Method 1 is the simple total of the gap-fi lled indicator; method 2 is the simple total of the gap-fi lled main indicator divided by the simple total of the gap-fi lled secondary indicator; method 3 is the simple total of the fi rst gap-fi lled main indicator minus the simple total of the second gap-fi lled main indicator, divided by the simple total of the secondary indicator; method 4 is the arithmetic mean (using the same series as shown in the table; that is, ratio if the rest of the table is shown as ratio, level if the rest of the table is shown as level, growth rate if the rest is shown as growth rate, and so on); method 5 is the least-squares growth rate (using the main indicator).

Table 1 Method used to calculate regional aggregates and period averages in section 2


population living on less than $1.08 a day at 1993 international prices. As a result of re-visions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier edi-tions.

Poverty gap ratio at $1 a day is the mean shortfall from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. / is mea-sure refl ects the depth of poverty as well as its prevalence.

Share of poorest quintile in national consump-tion or income is the share of consumption, or in some cases income, that accrues to the poorest 20 percent of the population.

Prevalence of child malnutrition, under-weight, is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0–59 months. / e reference population, adopted by the World Health Organization in 1983, is based on children from the United States, who are assumed to be well nourished.

Share of population below minimum dietary energy consumption (also referred to as preva-lence of undernourishment) is the percent-age of the population whose food intake is insuffi cient to meet dietary energy require-ments continuously. A value of 2.5 percent signifi es a prevalence of undernourishment of 2.5 percent or less.

Source: Data on poverty measures are prepared by the World Bank’s Development Research Group. / e national poverty lines are based on the World Bank’s country pov-erty assessments. / e international poverty lines are based on nationally representative primary household surveys conducted by national statistical offi ces or by private agen-cies under the supervision of government or international agencies and obtained from government statistical offi ces and World Bank country departments. / e World Bank has prepared an annual review of its poverty work since 1993. For details on data sourc-es and methods used in deriving the World Bank’s latest estimates, see Chen and Raval-lion (2004).

Data have been compiled by World Bank staff from primary and secondary sources. Eff orts have been made to harmonize these

data series with those published on the Unit-ed Nations Millennium Development Goals website (www.un.org/millenniumgoals), but some diff erences in timing, sources, and defi -nitions remain.

Data on child malnutrition and share of population below minimum dietary energy consumption are from the Food and Agricul-ture Organization (see www.fao.org/faostat/foodsecurity/index_en.htm).

T .. M D G : -

Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.

Net primary enrollment ratio is the ratio of children of offi cial primary school age based on the International Standard Classifi cation of Education 1997 who are enrolled in pri-mary school to the population of the corre-sponding offi cial primary school age.

Primary completion rate is the percentage of students completing the last year of primary school. It is calculated as the total number of students in the last grade of primary school minus the number of repeaters in that grade divided by the total number of children of of-fi cial graduation age.

Share of cohort reaching grade 5 is the per-centage of children enrolled in grade 1 of primary school who eventually reach grade 5. / e estimate is based on the reconstructed cohort method.

Youth literacy rate is the percentage of peo-ple ages 15–24 who can, with understanding, both read and write a short, simple statement about their everyday life.

Source: Data are from the United Nations Educational, Scientifi c, and Cultural Organi-zation Institute for Statistics. Data have been compiled by World Bank staff from primary and secondary sources. Eff orts have been made to harmonize these data series with those published on the United Nations Mil-lennium Development Goals website (www.un.org/millenniumgoals), but some diff erenc-es in timing, sources, and defi nitions remain.

Technical notes 117

T .. M D G : Ratio of girls to boys in primary and secondary school is the ratio of female to male gross enrollment rate in primary and secondary school.

Ratio of young literate women to men is the ratio of the female to male youth literacy rate.

Women in national parliaments are the per-centage of parliamentary seats in a single or lower chamber occupied by women.

Share of women in wage employment in the nonagricultural sector are women wage em-ployees in the nonagricultural sector as a share of total nonagricultural employment.

Source: Data on net enrollment and lit-eracy are from the United Nations Educa-tional, Scientifi c, and Cultural Organization Institute for Statistics. Data on women in national parliaments are from the Inter-Parliamentary Union. Data on women’s em-ployment are from the International Labour Organization’s Key Indicators of the Labour Market, fourth edition.

T .. M D G : Under-fi ve mortality rate is the probability that a newborn baby will die before reaching age 5, if subject to current age-specifi c mor-tality rates. / e probability is expressed as a rate per 1,000.

Infant mortality rate is the number of in-fants dying before reaching one year of age, per 1,000 live births.

Child immunization rate, measles, is the per-centage of children ages 12–23 months who received vaccinations for measles before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine.

Source: Data on under-fi ve and infant mor-tality are the harmonized estimates of the World Health Organization, United Nations Children’s Fund (UNICEF), and the World Bank, based mainly on household surveys, censuses, and vital registration, supplement-ed by the World Bank’s estimates based on household surveys and vital registration. Other estimates are compiled and produced

by the World Bank’s Human Development Network and Development Data Group in consultation with its operational staff and country offi ces. Data on child immunization are from the World Health Organization and UNICEF estimates of national immunization coverage.

T .. M D G : Maternal mortality ratio, modeled estimate, is the number of women who die from preg-nancy-related causes during pregnancy and childbirth, per 100,000 live births.

Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliv-eries on their own; and to care for newborns.

Source: Data on maternal mortality are from AbouZahr and Wardlaw (2003). Data on births attended by skilled health staff are from the United Nations Children’s Fund’s State of the World’s Children 2006 and Child-info, and Demographic and Health Surveys by Macro International.

T .. M D G : HIV/AIDS, , Prevalence of HIV is the percentage of people ages 15–49 who are infected with HIV.

Contraceptive prevalence rate is the percent-age of women who are practicing, or whose sexual partners are practicing, any form of contraception. It is usually measured for married women ages 15–49 only.

Deaths due to malaria is the number of ma-laria deaths per 100,000 people.

Children sleeping under insecticide-treated bednets are the percentage of children under age 5 who slept under an insecticide-treated bednet to prevent malaria.

Incidence of tuberculosis is the estimated number of new tuberculosis cases (pulmo-nary, smear positive, and extrapulmonary), per 100,000 people.

Tuberculosis cases detected under DOTS is the percentage of estimated new infectious tuberculosis cases detected under DOTS, the internationally recommended tuberculosis control strategy.


Source: Data on HIV prevalence are from the Joint United Nations Programme on HIV/AIDS and the World Health Organiza-tion’s (WHO) 2006 Report on the Global AIDS Epidemic. Data on contraceptive prevalence are from household surveys, including De-mographic and Health Surveys by Macro International and Multiple Indicator Cluster Surveys by the United Nations Children’s Fund (UNICEF). Data on deaths due to ma-laria are from the WHO. Data on children sleeping under insecticide-treated bednets are from UNICEF’s State of the World’s Chil-dren 2006 and Childinfo, and Demographic and Health Surveys by Macro International. Data on tuberculosis are from the WHO’s Global Tuberculosis Control Report 2006.

T .. M D- G : Forest area is land under natural or planted stands of trees, whether productive or not.

Nationally protected areas as share of total land area are totally or partially protected ar-eas of at least 1,000 hectares that are desig-nated as scientifi c reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, and protected landscapes. Marine areas, unclassi-fi ed areas, and litoral (intertidal) areas are not included. / e data also do not include sites protected under local or provincial law.

Gross domestic product (GDP) per unit of energy use is the GDP in purchasing power parity (PPP) U.S. dollars per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to 2000 con-stant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.

Carbon dioxide emissions are those stem-ming from the burning of fossil fuels and the manufacture of cement. / ey include carbon dioxide produced during consumption of sol-id, liquid, and gas fuels and gas fl aring.

Solid fuel use is the percentage of the popu-lation using solid fuels as opposed to modern fuels. Solid fuels are defi ned to include fuel wood, straw, dung, coal, and charcoal. Mod-ern fuels are defi ned to include electricity, liq-uefi ed petroleum gas, natural gas, kerosene, and gasoline.

Population with sustainable access to an im-proved water source is the percentage of the population with reasonable access to an ad-equate amount of water from an improved source, such as a household connection, public standpipe, borehole, protected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Reasonable access is defi ned as the availability of at least 20 liters a person a day from a source within 1 kilometer of the dwelling.

Population with sustainable access to improved sanitation is the percentage of the population with at least adequate access to excreta dis-posal facilities that can eff ectively prevent human, animal, and insect contact with ex-creta. Improved facilities range from simple but protected pit latrines to fl ush toilets with a sewerage connection. / e excreta disposal system is considered adequate if it is private or shared (but not public) and if it hygieni-cally separates human excreta from human contact. To be eff ective, facilities must be cor-rectly constructed and properly maintained.

Source: Data on forest area are from the Food and Agricultural Organization’s Global Forest Resources Assessment. Data on na-tionally protected areas are from the United Nations Environment Programme and the World Conservation Monitoring Centre. Data on energy use are from electronic fi les of the International Energy Agency. Data on carbon dioxide emissions are from the Carbon Dioxide Information Analysis Cen-ter, Environmental Sciences Division, Oak Ridge National Laboratory, in the U.S. state of Tennessee. Data on solid fuel use are from household survey data, supplemented by World Bank estimates. Data on access to wa-ter and sanitation are from the World Health Organization and United Nations Children’s Fund’s Meeting the MDG Drinking Water and Sanitation Target (www.unicef.org/wes/mdgreport).

T .. M D G : Heavily Indebted Poor Country (HIPC) Debt Initiative decision point is the date at which a HIPC with an established track record of good performance under adjustment programs

Technical notes 119

supported by the International Monetary Fund (IMF) and the World Bank commits to undertake additional reforms and to develop and implement a poverty reduction strategy.

HIPC completion point is the date at which the country successfully completes the key structural reforms agreed on at the decision point, including developing and implement-ing its poverty reduction strategy. / e coun-try then receives the bulk of debt relief under the HIPC Initiative without further policy conditions.

Estimated total nominal debt service relief committed under Enhanced HIPC Initiative is the amount of debt service relief, calculat-ed at the decision point, that will allow the country to achieve debt sustainability at the completion point.

Debt service is the sum of principal repay-ments and interest actually paid on total long-term debt (public and publicly guaran-teed and private nonguaranteed), use of IMF credit, and interest on short-term debt.

Youth unemployment rate is the percentage of the labor force ages 15–24 without work but available for and seeking employment. Defi nitions of labor force and unemployment may diff er by country.

Fixed-line and mobile phone subscribers are subscribers to a fi xed-line telephone service, which connects a customer’s equipment to the public switched telephone network, or to a public mobile telephone service, which uses cellular technology.

Personal computers are self-contained com-puters designed for use by a single individual.

Internet users are people with access to the worldwide network.

Source: Data on HIPC countries are from the IMF’s March 2006 “HIPC Status Re-ports.” Data on external debt are mainly from reports to the World Bank through its Debtor Reporting System from member countries that have received International Bank for Re-construction and Development loans or In-ternational Development Association cred-its, as well as World Bank and IMF fi les. Data on youth unemployment are from the Inter-national Labour Organization’s Key Indicators of the Labour Market, fourth edition. Data on phone subscribers, personal computers, and Internet users are from the International Telecommunication Union’s (ITU) World

Telecommunication Development Report database and World Bank estimates.

4. Paris Declaration indicators

T .. S P D

Source: World Bank data (see box 3).

5. Private sector development

T .. B Number of startup procedures for new business is the number of procedures required to start a business, including interactions to obtain necessary permits and licenses and to com-plete all inscriptions, verifi cations, and noti-fi cations to start operations.

Time required to start a business is the num-ber of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen.

Cost to start a business is normalized by presenting it as a percentage of gross nation-al income (GNI) per capita.

Number of procedures to register a property is the number of procedures required for a busi-ness to secure rights to property.

Time required to register a property is the number of calendar days needed for a busi-ness to secure rights to property.

Number of procedures to enforce a contract is the number of independent actions, mandat-ed by law or courts, that demand interaction between the parties of a contract or between them and the judge or court offi cer.

Time required to enforce a contract is the number of calendar days from the fi ling of the lawsuit in court until the fi nal determina-tion and, in appropriate cases, payment.

Protecting investors disclosure index mea-sures the degree to which investors are pro-tected through disclosure of ownership and fi nancial information.

Time to resolve insolvency is the number of years from the fi ling for insolvency in court until the resolution of distressed assets.

Rigidity of employment index measures the regulation of employment, specifi cally the hiring and fi ring of workers and the rigidity of working hours. / is index is the average


of three subindexes: a diffi culty of hiring index, a rigidity of hours index, and a dif-fi culty of fi ring index.

Source: Data are from the World Bank’s Doing Business project (http://rru.world-bank.org/DoingBusiness/).

In the Paris Declaration, partner countries and

donors agreed on an international monitoring

process to track and encourage progress

in implementing the Paris Declaration at the

country level. As much as possible, surveys

will use local processes to build a common

understanding of how more effective aid con-

tributes to achieving development results, to

provide an accurate picture of how aid is man-

aged, and to support broad-based dialogue

at the country level on how to make aid more

effective. The multidonor Working Party on Aid

Effectiveness has created a subgroup to de-

velop technical guidance, survey instruments,

and a methodology for collecting and report-

ing on country-level data for most of the Paris

Declaration indicators.

Twelve indicators were agreed on at Paris.

They are measured through qualitative desk

reviews undertaken by the Organisation for

Economic Co-operation and Development’s

Development Assistance Committee (indicator

2b), the World Bank (indicators 1, 2a, and 11),

and a questionnaire for governments and do-

nors (remaining eight indicators; see table).

The assessment criteria for indicators 1

and 11 are based on analysis by the World

Bank for the 2005 Comprehensive Develop-

ment Framework Progress Report “Enabling

Country Capacity to Achieve Results.” The

World Bank is now conducting an Aid Effec-

tiveness Review that will build on this earlier

work and will provide information on these two

qualitative indicators.

The survey-based tool was launched in

May 2006. As of June 2006, 26 countries had

confi rmed their participation in the 2006 Sur-

vey of Monitoring the Paris Declaration. An

international report on this fi rst round is tar-

geted for publication at the end of 2006. Other

monitoring rounds will be undertaken in early

2008 and 2010. To reduce the level of detail

and complexity, the defi nitions and guidance

are principle based and objective oriented.

Detailed guidance on each of the eight indica-

tors is provided.

Box 3 About the Paris Declaration

Number Indicator Source of assessment

1Ownership; operational poverty

reduction strategies

Qualitative review, Comprehensive Development

Framework method: desk review (World Bank)

2aQuality of public fi nancial

management system

World Bank Country Policy and Institutional

Assessment desk review

2b Quality procurement systemOrganisation for Economic Co-operation and Develop-

ment’s Development Assistance Committee desk review

3 Aid reported on budget Questionaire

4 Coordinated capacity building Questionaire

5aUse of country public fi nancial

managment systemsQuestionaire

5b Use of country procurement systems Questionaire

6 Parallel project implementation units Questionaire

7 In year predicitability Questionaire

8 Untied aid Questionaire

9 Use of program-based approaches Questionaire

10Joint missions and

country analytical workQuestionaire

11 Sound performance assessment frameworkQualitative review, Comprehensive Development

Framwork method: desk review (World Bank)

12 Review of mutual accountability Questionaire

Status of Paris Declaration indicators

Technical notes 121

T .. I Private investment is private sector fi xed capi-tal formation (table 2.11) divided by nominal gross domestic product (table 2.6).

Net foreign direct investment is investment by residents of the Organisation for Econom-ic Co-operation and Development’s (OECD) Development Assistance Committee (DAC) member countries to acquire a lasting man-agement interest (at least 10 percent of vot-ing stock) in an enterprise operating in the recipient country. / e data refl ect changes in the net worth of subsidiaries in recipient countries whose parent company is in the DAC source country.

Domestic credit to private sector is fi nancial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other ac-counts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.

Policy uncertainty is the share of senior managers who ranked economic and regu-latory policy uncertainty as a major or very severe constraint.

Corruption is the share of senior managers who ranked corruption as a major or very se-vere constraint.

Courts are the share of senior managers who ranked courts and dispute resolution systems as a major or very severe constraint.

Lack of confi dence courts will uphold property rights is the share of senior managers who do not agree with the statement: “I am confi -dent that the judicial system will enforce my contractual and property rights in business disputes.”

Crime is the share of senior managers who ranked crime, theft, and disorder as a major or very severe constraint.

Tax rates are the share of senior managers who ranked tax rates as a major or very se-vere constraint.

Finance is the share of senior managers who ranked access to fi nance or cost of fi -nance as a major or very severe constraint.

Electricity is the share of senior managers who ranked electricity as a major or severe constraint.

Labor regulation is the share of senior man-agers who ranked labor regulations as a ma-jor or severe constraint.

Labor skills are the share of senior manag-

ers who ranked skills of available workers as a major or severe constraint.

Number of tax payments is the number of taxes paid by businesses, including electronic fi ling. / e tax is counted as paid once a year even if payments are more frequent.

Time to prepare and pay taxes is the number of hours it takes to prepare, fi le, and pay (or withhold) three major types of taxes: the cor-porate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions.

Total tax payable is the total amount of tax-es payable by the business (except for labor taxes) after accounting for deductions and exemptions as a percentage of gross profi t. For further details on the method used for assessing the total tax payable.

Highest marginal tax rate, corporate, is the highest rate shown on the schedule of tax rates applied to the taxable income of corpo-rations.

Time dealing with offi cials is the average per-centage of senior management’s time that is spent in a typical week dealing with require-ments imposed by government regulations (for example, taxes, customs, labor regula-tions, licensing, and registration), including dealings with offi cials, completing forms, and the like.

Average time to clear customs is the number of days to clear an imported good through customs.

Bank branches are deposit money bank branches.

Interest rate spread is the interest rate charged by banks on loans to prime custom-ers minus the interest rate paid by commer-cial or similar banks for demand, time, or savings deposits.

Listed domestic companies are domestically incorporated companies listed on a coun-try’s stock exchanges at the end of the year. / ey exclude investment companies, mutual funds, and other collective investment ve-hicles.

Market capitalization of listed companies, also known as market value, is the share price of a listed domestic company’s stock times the number of shares outstanding.

Turnover ratio for traded stocks is the total value of shares traded during the period di-vided by the average market capitalization for the period. Average market capitalization


is calculated as the average of the end-of-pe-riod values for the current period and the previous period.

Source: Data on private investment are from the World Bank’s World Development Indicators database. Data on net foreign di-rect investment are from the World Bank’s World Development Indicators database. Data on domestic credit to the private sector are from the International Monetary Fund’s International Financial Statistics database and data fi les, World Bank and OECD gross domestic product (GDP) estimates, and the

World Bank’s World Development Indica-tors database. Data on investment climate constraints to fi rms are based on enterprise surveys conducted by the World Bank and its partners during 2001–05 (http://rru.worldbank.org/EnterpriseSurveys) (box 4). Data on regulation and tax administration and highest marginal corporate tax rates are from the World Bank’s Doing Business proj-ect (http://rru.worldbank.org/DoingBusiness). Data on time dealing with offi cials and average time to clear customs are from World Bank Enterprise Surveys (http://rru.worldbank.org/EnterpriseSurveys/).

Investment climate surveys or Enterprise surveys have been under-

taken by the World Bank on an Africawide basis since 2003. En-

terprise surveys measure business perceptions of the investment

climate and can be used to analyze the link to job creation and pro-

ductivity growth. Surveys are undertaken on the basis of a standard-

ized survey instrument, identical for each country. The data gathered

during these surveys fall into two categories: objective data, such as

accounting or labor data, and perception data, where interviewees

are asked to provide their own assessment of specifi c issues. Infer-

ences from the use of survey data and of perception information

must be done carefully, as sampling and contextual issues may af-

fect the numbers reported.

Sector coverage in samples. Until 2005 enterprise surveys

mostly surveyed manufacturing fi rms across countries, which

implies that the reported data will not cover the entire economy.

Hence, some of the reported issues may be sector specifi c.

Inference. The data reported are unweighted numbers from

random stratifi ed samples, which implies that population esti-

mates of the same variable may differ somewhat. Although expe-

rience shows that it does not often change the ranking of the most

important constraints, reported numbers may vary slightly from

population estimates (see fi gure for a hypothetical example).

Context for perception data. The social environment in which a

survey takes place may affect the nature of the replies. The sense

of responsibility of managers and the perceived legitimacy of the

survey itself (and of its questions) will affect the replies: for exam-

ple, a survey taking place in a country with no tradition in survey

data gathering—and thus with little perceived legitimacy for a sur-

vey—will yield more uncertain answers. Some topics, by their sen-

sitive nature—for example, questions on corruption—may yield

unexpected results. In addition, the psychology of the respondent

and the overall socioeconomic context of the interview may affect

the answers. For example, assume a country

where a survey takes place between period t–1

and t+1. In period t, a major dispute erupts be-

tween manufacturers and the authorities about

tax issues. It is most likely that replies provided

in t+1 will be much more negative when as-

sessing tax issues than the ones provided in

t–1. In this case, samplewide computations

may be misleading, and it is better for analyti-

cal purposes to separate the answers into be-

fore and after the event.

These issues have two major implications.

First, it is often better to also refer to the In-

vestment Climate Assessment reports for each

country to know the sampling methodology

used in order to assess how generalizations can

be made from the data. Second, these replies,

because of their contextual nature, should be

correlated with other sources of information,

which can be found in Investment Climate As-

sessment reports or other analytical works.

Box 4 Investment climate survey data and perception of constraints by fi rms

Items perceived as a severe constraint (percent)

10

20

30

40

50

60

70

0Access

toFinance

Tax rates

CorruptionCourts Policyundertainty

Electricity Labor skills

Labor regulation

Crime

Source: World Bank Investment Climate Surveys.

Sample numbers Weighted population estimates

Technical notes 123

Data on bank branches are from surveys of banking and regulatory institutions by the World Bank’s Research Department and Fi-nancial Sector and Operations Policy Depart-ment and the World Development Indicators database. Data on interest rate spreads are from the IMF’s International Financial Sta-tistics database and data fi les and the World Bank’s World Development Indicators da-tabase. Data on listed domestic companies and turnover ratios for traded stocks are from Standard & Poor’s Emerging Stock Mar-kets Factbook and supplemental data and the World Bank’s World Development Indicators database. Data on market capitalization of listed companies are from Standard & Poor’s Emerging Stock Markets Factbook and supple-mental data, World Bank and OECD esti-mates of GDP, and the World Bank’s World Development Indicators database.

6. Trade

T .. I Merchandise trade is the sum of imports and exports of divided by nominal gross domes-tic product.

Exports and imports comprise all transac-tions between residents of an economy and the rest of the world involving a change in ownership of general merchandise, goods sent for processing and repairs, and non-monetary gold. Data are shown in current U.S. dollars. Exports and imports as a share of gross domestic product (GDP) are calcu-lated as merchandise exports and imports divided by nominal GDP. Annual growth of exports and imports is calculated using the real imports and exports series in tables 2.17 and 2.18.

Terms of trade index measures the relative movement of export and import prices. / is series is calculated as the ratio of a country’s export unit values or prices to its import unit values or prices shows changes over a base year (2000) in the level of export unit values as a percentage of import unit values.

Structure of merchandise exports and imports components may not sum to 100 percent be-cause of unclassifi ed trade.

Food comprises the commodities in Stan-dard International Trade Classifi cation (SITC) sections 0 (food and live animals), 1

(beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts, and oil kernels).

Agricultural raw materials comprise the commodities in SITC section 2 (crude ma-terials except fuels), excluding divisions 22, 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap).

Fuels comprise SITC section 3 (mineral fuels).

Ores and metals comprise the commodities in SITC sections 27, 28, and 68 (nonferrous metals).

Manufactures comprise the commodities in SITC sections 5 (chemicals), 6 (basic manu-factures), 7 (machinery and transport equip-ment), and 8 (miscellaneous manufactured goods), excluding division 68.

Export diversifi cation index measures the extent to which exports are diversifi ed. It is constructed as the inverse of a Herfi ndahl index, using disaggregated exports at four digits (following the SITC). A higher index indicates more export diversifi cation.

Binding coverage is the percentage of prod-uct lines with an agreed bound rate.

Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set.

Simple mean tariff is the unweighted aver-age of eff ectively applied rates or most fa-vored nation rates for all products subject to tariff s calculated for all traded goods.

Weighted mean tariff is the average of ef-fectively applied rates or most favored nation rates weighted by the product import shares corresponding to each partner country.

Share of lines with international peaks is the share of lines in the tariff schedule with tariff rates that exceed 15 percent.

Share of lines with specifi c rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates.

Primary products are commodities classi-fi ed in SITC revision 2 sections 0ñ4 plus divi-sion 68.

Manufactured products are commodities classifi ed in SITC revision 2 sections 5ñ8 ex-cluding division 68.

Tariff barriers are a form of duty based on the value of the import.

Average cost to ship 20 ft container from port


to fi nal destination is the cost of all operations associated with bringing a container from onboard a ship to the considered economic center, weighted based on container traffi c for each corridor.

Average time to ship 20 ft container from port to fi nal destination is the time in days from when the ship is available for unloading (be it moored at the berth or off shore) until the content of the container is made available to the fi nal customer at the destination in the considered economic center, weighted based on container traffi c for each corridor.

Average time to clear customs is the number of days to clear an imported good through customs.

Source: All indicators in the table were cal-culated by World Bank staff using the World Integrated Trade Solution system. Data on the export diversifi cation index are from the Organisation for Economic Co-operation and Development. Data on tariff s are from the United Nations Conference on Trade and De-velopment and the World Trade Organization. Data on global imports are from the United Nations Statistics Division’s COMTRADE database. Data on merchandise exports and imports are from World Bank country desks. Data on shipping time and costs are from the World Bank’s Sub-Saharan Africa Transport Policy Program (SSATP). Data on average time to clear customs are from World Bank Enterprise Surveys (http://rru.worldbank.org/EnterpriseSurveys/).

T . R , Value of trade is the sum of merchandise ex-ports by members of a trade bloc .

Share of total bloc exports is the sum of mer-chandise exports by members of a trade bloc as a percentage of total merchandise exports by the bloc.

Share of world exports is the ratio of the bloc’s total merchandise exports (within the bloc and to the rest of the world) to total merchandise exports by all economies in the world.

Source: Data on merchandise trade fl ows are published in the International Monetary Fund’s (IMF) Direction of Trade Statistics Year-book and Direction of Trade Statistics Quar-terly. / e data in the table were calculated

using the IMF’s Direction of Trade database. / e United Nations Conference on Trade and Development publishes data on intraregional trade in its Handbook of International Trade and Development Statistics. / e information on trade bloc membership is from World Bank (2000), the World Bank’s Global Eco-nomic Prospects 2005, and the World Bank’s International Trade Unit.

7. Infrastructure

T .. W Internal fresh water resources per capita is the sum of total renewable resources, which in-clude internal fl ows of rivers and ground-water from rainfall in the country, and river fl ows from other countries.

Population with sustainable access to an im-proved water source is the percentage of popu-lation with reasonable access to an adequate amount of water from an improved source, such as a household connection, public standpipe, borehole, protected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks, and unpro-tected wells and springs. Reasonable access is defi ned as the availability of at least 20 liters a person a day from a source within 1 kilome-ter of the user’s dwelling.

Population with sustainable access to im-proved sanitation is the percentage of the population with at least adequate access to excreta disposal facilities that can eff ectively prevent human, animal, and insect contact with excreta. Improved facilities range from simple but protected pit latrines to fl ush toi-lets with a sewerage connection. / e excreta disposal system is considered adequate if it is private or shared (but not public) and if it hygienically separates human excreta from human contact. To be eff ective, facilities must be correctly constructed and properly maintained.

Average nonrevenue water is the diff erence between water supplied and water sold (that is, the volume of water “lost”) expressed as a percentage of net water supplied.

Water supply failure for fi rms receiving water is the average number of days per year that fi rms experienced insuffi cient water supply for production.

Average household tariff s for water services, largest city are the average tariff s for wastewa-

Technical notes 125

ter services for household consumers in the largest city.

Committed nominal investment in potable water projects with private participation is an-nual committed investment in potable water projects with private investment.

Average annual offi cial development assis-tance (ODA) disbursements for water and sani-tation are average annual ODA for water and sanitation, including bilateral, multilateral, and other donors.

Source: Data on fresh water resources are from the World Bank’s World Development Indicators database. Data on access to water and sanitation are from the World Health Organization and United Nations Children’s Fund’s Meeting the MDG Drinking Water and Sanitation Target (www.unicef.org/wes/mdgreport). Data on average nonrevenue water and average household tariff s for wa-ter services in the largest city are from the International Benchmarking Network for Water and Sanitation Utilities (IBNET). Data on water supply failure are from World Bank Investment Climate Surveys. Data on com-mitted nominal investment in potable water projects with private participation are from the World Bank’s Private Participation in Infrastructure database. Data on ODA dis-bursements are from the Organisation for Economic Co-operation and Development.

T .. TTotal length of road network is the length of motorways, highways, main or national roads, secondary or regional roads, and other roads.

Total length of national road network is the length of the interurban classifi ed network without the urban and rural network.

Rail lines are the length of railway route available for train service, irrespective of the number of parallel tracks.

Road density, ratio to arable land is the total length of national road network per 1,000 square kilometers of arable land area. / e use of arable land area in the denominator focus-es on inhabited sectors of total land area by excluding wilderness areas.

Road density, ratio to total land is the total length of national road network per 1,000 square kilometers of total land area.

Energy consumption for transportation is the

transportation sector’s contribution to total fi nal energy consumption.

Rural access is the percentage of the rural population who live within 2 kilometers of an all-season passable road as a share of the total rural population.

Commercial vehicles are the number of com-mercial vehicles, which use at least 24 liters of diesel fuel per 100 kilometers.

Passenger cars are the number of passenger vehicles, which use at least 9 liters of gasoline fuel per 100 kilometers.

Length of national network in good condition is the length of the national road network, including the interurban classifi ed network without the urban and rural network, that is in good condition, as defi ned by each coun-try’s road agency.

Ratio of paved to total roads is the length of paved roadsówhich are those surfaced with crushed stone (macadam) and hydrocarbon binder or bituminized agents, with concrete, or with cobblestonesóas a percentage of all the country’s roads.

Average time to ship 20 ft container from port to fi nal destination is the time in days from when the ship is available for unloading (be it moored at the berth or off shore) until the content of the container is made available to the fi nal customer at the destination in the considered economic center, weighted based on container traffi c for each corridor.

Firms identifying transport as major or very severe obstacle to business operation and growth is the percentage of fi rms that responded “major” or “very severe” obstacle to the fol-lowing question: “Please tell us if any of the following issues are a problem for the opera-tion and growth of your business. If an is-sue (infrastructure, regulation, and permits) poses a problem, please judge its severity as an obstacle on a fi ve-point scale that rang-es from 0 = no obstacle to 5 = very severe obstacle.”

Average cost to ship 20 ft container from port to fi nal destination is the costs of all op-erations associated with bringing a container from onboard a ship to the considered eco-nomic center, weighted based on container traffi c for each corridor.

Price of diesel fuel and super gasoline is the price as posted at fi lling stations in a coun-try’s capital city. When several fuel prices for major cities were available, the unweighted


average is used. Since super gasoline (95 oc-tane/A95/premium) is not available every-where, it is sometime replaced by regular gas-oline (92 octane/A92), premium plus gasoline (98 octane/A98), or an average of the two.

Committed nominal investment in transport projects with private participation is annual committed investment in transport (includ-ing roads, airport, seaport, toll road, and rail-road) projects with private investment.

Average annual offi cial development assis-tance (ODA) disbursements for transportation and storage are average annual ODA for trans-portation and storage, including bilateral, multilateral, and other donors.

Existence of road fund includes fi rst genera-tion (funded primarily by earmarked taxes) and second generation (funded primarily by user charges) road funds.

Source: Data on total length of road network are from the International Road Federation’s World Road Statistics. Data on total length of national network, length of national network in good condition, average time and cost to ship 20 ft container, and existence of a road fund are from the World Bank’s Sub-Saharan Africa Transport Policy Program (SSATP). Data on rail lines and ratio of paved to total roads are from the World Bank’s World De-velopment Indicators database. Data on road density and rural access to roads are from the World Bank’s SSATP and World Devel-opment Indicators database. Data on energy consumption for transportation are from the International Energy Agency. Data on vehicle fl eet and fuel and gasoline prices are from the German Society for Technical Cooperation (GTZ). Data on fi rms identifying transport as a major or very severe constraint to busi-ness operation and growth are from World Bank Investment Climate Surveys. Data on committed nominal investment in transport projects with private participation are from the World Bank’s Private Participation in Infrastructure database. Data on ODA dis-bursements are from the Organisation for Economic Co-operation and Development.

T .. I - Telephone subscribers are subscribers to a main telephone line service, which connects a customer’s equipment to the public switched

telephone network, or to a cellular telephone service, which uses cellular technology.

Unmet demand is unmet applications for connection to the public switched telephone network that have had to be held over owing to a lack of technical facilities (equipment, lines, and the like) divided by the number of main telephone lines in operation.

Households with own telephone is the per-centage of households possessing a tele-phone.

Delay for fi rm in obtaining a telephone con-nection is the average actual delay in days that fi rms experience when obtaining a telephone connection, measured from the day the es-tablishment applied to the day it received the service or approval.

Duration of telephone outages is the average duration in hours of instances of telephone unavailability related to production.

Reported phone faults is the total number of reported faults for the year divided by the total number of mainlines in operation multiplied by 100. / e defi nition of fault can vary. Some countries include faulty customer equipment; others distinguish between re-ported and actual found faults. / ere is also sometimes a distinction between residential and business lines. Another consideration is the time period: some countries report this indicator on a monthly basis; in these cases data are converted to yearly estimates.

Firms identifying telecommunications as ma-jor or very severe obstacle to business operation and growth is the percentage of fi rms that re-sponded “major” or “very severe” obstacle to the following question: “Please tell us if any of the following issues are a problem for the operation and growth of your business. If an issue (infrastructure, regulation, and permits) poses a problem, please judge its severity as an obstacle on a fi ve-point scale that ranges from 0 = no obstacle to 5 = very severe obstacle.”

Cost of a cellular local call is the cost of a three-minute cellular local call during off -peak hours.

Cost of a local phone call is the cost of a three-minute local call during peak hours. Local call refers to a call within the same exchange area using the subscriber’s own terminal (that is, not from a public telephone).

Cost of phone call to the United States is the cost of a three-minute call to the United States during peak hours.

Technical notes 127

Committed nominal investment in fi xed-line and long distance projects with private partici-pation is annual committed investment in fi xed-line and long distance projects with pri-vate investment.

Committed nominal investment in mobile projects with private participation is annual committed investment in mobile projects with private investment.

Average annual offi cial development assistance (ODA) disbursements for communications are av-erage annual ODA for communications, includ-ing bilateral, multilateral, and other donors.

Source: Data on telephone subscribers, re-ported phone faults, and cost of local and cel-lular calls are from the International Telecom-munications Union. Data on unmet demand are from the World Bank’s Infrastructure Vice Presidency. Data on households with own telephone are from Demographic and Health Surveys. Data on delays for fi rms in obtaining a telephone connection, duration of telephone outages, and fi rms identifying telecommunications as a major or very severe obstacle to business operation and growth are from World Bank Investment Climate As-sessments. Data on cost of a call to the Unit-ed States are from the World Bank’s Global Development Finance and World Develop-ment Indicator databases. Data on commit-ted nominal investment are from the World Bank’s Private Participation in Infrastructure database. Data on ODA disbursements are from the Organisation for Economic Co-op-eration and Development.

T .. EInstalled capacity is the sum of conventional thermal electricity installed capacity; hydro-electricity installed capacity; nuclear electric-ity installed capacity; and geothermal, solar, wind, wood, and waste electricity installed capacity.

Electric power consumption is the produc-tion of power plants and combined heat and power plants, less distribution losses and own use by heat and power plants.

Energy use per purchasing power parity (PPP) gross domestic product (GDP) is apparent con-sumption, which is equal to indigenous pro-duction plus imports and stock changes mi-nus exports and fuels supplied to ships and aircraft engaged in international transport,

divided by nominal GDP in PPP U.S. dollars. Access to electricity is the percentage of the

population living in households with access to electricity.

Solid fuels use is the percentage of the population using solid fuels as opposed to modern fuels. Solid fuels include fuel wood, straw, dung, coal, and charcoal. Modern fuels include electricity, liquefi ed petroleum gas, natural gas, kerosene, and gasoline.

Delay for fi rm in obtaining electrical connec-tion is the average actual delay in days that fi rms experience when obtaining an electri-cal connection, measured from the day the establishment applied to the day it received the service or approval.

Electric power transmission and distribution losses are technical and nontechnical losses, including electricity losses due to operation of the system and the delivery of electricity as well as those caused by unmetered supply. / is comprises all losses due to transport and distribution of electrical energy and heat.

Electrical outages of fi rms are the average number of days per year that establishments experienced power outages or surges from the public grid.

Firms that share or own their own genera-tor is the percentage of fi rms that responded “Yes”to the following question: “Does your establishment own or share a generator?” Firms identifying electricity as major or very severe obstacle to business operation and growth is the percentage of fi rms that responded “major” or “very severe” obstacle to the following question: “Please tell us if any of the following issues are a problem for the operation and growth of your business. If an issue (infrastructure, regulation, and permits) poses a problem, please judge its severity as an obstacle on a fi ve-point scale that ranges from 0 = no obstacle to 5 = very severe obstacle.”

Electricity end-user prices are prices actually paid, net of rebates and including transport costs to the consumer and taxes that have to be paid as part of the transaction and that are not refundable (this excludes value added tax).

Committed nominal investment in energy projects with private participation is annual committed investment in energy projects with private investment.

Average annual offi cial development assis-tance (ODA) disbursements for energy are aver-


age annual overseas ODA for energy, includ-ing bilateral, multilateral, and other donors).

Source: Data on installed capacity are from the U.S. Department of Energy’s Inter-national Energy Annual 2004. Data on elec-tric power consumption and energy use per PPP GDP are from the World Bank’s World Development Indicators database. Data on access to electricity and solid fuels use are from household survey data, supple-mented by World Bank Project Appraisal Documents. Data on delays for fi rms in ob-taining an electrical connection, electrical outages of fi rms, fi rms that share or own their own generator, and fi rms identifying electricity as a major or very severe obstacle to business operation and growth are from World Bank Investment Climate Assess-ments. Data on transmission and distribu-tion losses are from the World Bank’s World Development Indicators database, supple-mented by World Bank Project Appraisal Documents. Data on end-user prices are from South African Development through Electricity (SAD-ELEC). Data on commit-ted nominal investment are from the World Bank’s Private Participation in Infrastruc-ture database. Data on ODA disbursements are from the Organisation for Economic Co-operation and Development.

T .. F -Sovereign ratings are long-term foreign cur-rency ratings.

Gross national savings are the sum of gross domestic savings (table 2.12) and net fac-tor income and net private transfers from abroad. / e estimate here also includes net public transfers from abroad.

Claims on governments and other public en-tities correspond to the sum of lines 32an, 32b, 32bx, and 32c in the International Monetary Fund’s (IMF) International Finan-cial Statistics and usually comprise direct credit for specifi c purposes such as fi nanc-ing of the government budget defi cit or loans to state enterprises, advances against future credit authorizations, and purchases of treasury bills and bonds, net of deposits by the public sector. Public sector deposits with the banking system also include sinking funds for the service of debt and temporary

deposits of government revenues. Data are in current local currency.

Money and quasi money (M2) are the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. / is defi nition of mon-ey supply is frequently called M2 and corre-sponds to lines 34 and 35 in the IMF’s Inter-national Financial Statistics.

Real interest rate is the lending interest rate adjusted for infl ation as measured by the gross domestic product (GDP) defl ator.

Bank assets include deposit money bank reserves (line 20 of the IMF’s International Financial Statistics), claims on monetary au-thorities, securities (line 20c), other claims on monetary authorities (line 20n), foreign assets (line 21), and claims on other resident sectors (lines 22). Calculated using period av-erage exchange rate (line rf).

Domestic credit to private sector is fi nancial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other ac-counts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises.

Interest rate spread is the interest rate charged by banks on loans to prime custom-ers minus the interest rate paid by commer-cial or similar banks for demand, time, or savings deposits.

Ratio of bank nonperforming loans to total is the value of nonperforming loans divided by the total value of the loan portfolio (includ-ing nonperforming loans before the deduc-tion of specifi c loan-loss provisions). / e loan amount recorded as nonperforming should be the gross value of the loan as recorded on the balance sheet, not just the amount that is overdue.

Bank branches are deposit money bank branches.

Listed domestic companies are domestically incorporated companies listed on a country’s stock exchanges at the end of the year. / ey exclude investment companies, mutual funds, and other collective investment vehicles.

Market capitalization of listed companies, also known as market value, is the share price of a listed domestic company’s stock times the number of shares outstanding.

Technical notes 129

The availability of performance indicators for the infrastructure

sector is unfortunately rather sparse. One reason for the diffi -

culties with data collection is the institutional fragmentation of

the sectors, with information dispersed across a wide range of

institutions including ministries, (often subnational) state-owned

enterprises, roads funds, and regulatory entities.

Efforts have been made across all sectors to organize the lim-

ited data available according to a common conceptual framework

that shed lights on some of the key policy issues affecting the sec-

tor, namely access, quality of service, pricing, and fi nancing. The

access indicators are further broken down between the supply

side, which refl ects the situation from the service provider’s per-

spective, and the demand side, which refl ects the situation from

the user’s perspective. The disconnect that sometimes exists be-

tween these two sets of indicators itself reveals some of the chal-

lenges inherent in providing effective infrastructure services.

Water and sanitation

There has been a great deal of international effort to improve mea-

surement of access to water and sanitation, since increasing ac-

cess is one of the Millennium Development Goals. The latest fi g-

ures suggest that an average of 64 percent of the population has

access to an improved water source and 37 percent to improved

sanitation. Less than half of those with access to an improved

water source enjoy a piped connection to the household.

Transportation

Road density for the region remains low by international stan-

dards. Due to the existence of relatively large sparsely populated

areas in many countries, road density is less dramatic when

measured relative to total arable land rather than total land area.

Nevertheless, only about 33 percent of the rural population lives

within 2 kilometers of an all-season road. Road quality is also a

major concern, with only around 30 percent of the national net-

work classifi ed as being in reasonable condition. One area where

signifi cant progress has been made is the creation of road funds,

which aim to provide a stable source of fi nance for road main-

tenance. More than 80 percent of countries for which data are

available report having such a fund.

Information and communication technology

The information and communication technology sector is by far

the best documented. The data testify to the huge expansion of

cellular telephone coverage across Africa during the last decade,

with the average number of mobile subscribers at the country

level rising from 13 per 1,000 people in the late 1990s to 73 in

the early 2000s. Nevertheless, unmet demand for fi xed telephone

lines remains high at 46 percent of existing capacity, and fi rms re-

port average wait times of 69 days for a new connection. The cost

of international telephone calls has declined signifi cantly since the

late 1990s but remains very high in absolute terms, with an aver-

age rate of $4.85 for a three minute call to the United States.

Energy

Access and use of modern energy remains extremely limited in

the region. In countries for which data are available around 27

percent of the population has access to electricity and 13 percent

to modern cooking fuels. Investment Climate Surveys indicate

that unreliable power supply is a major constraint for fi rms in the

region, with around 40 percent of enterprises identifying defi -

cient power supply as a major constraint to doing business and

as many as 50 percent maintaining their own generation facilities

to insulate themselves from unreliable public supplies. Notwith-

standing the major shortfalls in access, offi cial development as-

sistance fl ows to the sector are very low, at no more than a few

million dollars per country per year.

Financial sector infrastructure

Macroeconomics. Low domestic savings and limited access

to foreign savings through international borrowing constrain the

supply of funding for viable investments. Domestic savings to

gross domestic product (GDP) amounts to a low 15.5 percent on

average for all of Africa and 15.2 percent for Sub-Saharan Africa.

Low savings coupled with ineffi cient intermediation capacity of

the fi nancial sector result in high cost of fi nance. Interest rates

average above 10 percent for the region, in some countries inter-

est rates reach 20 percent and higher.

Size. Although fi gures vary drastically between countries, on

average the size of the fi nancial sector remains small. For a bank-

dominated fi nancial system, the economies exhibit a very low

penetration of bank assets at 39 percent of GDP. The data also

show the stark contrast between the average amount of bank as-

sets in Sub-Saharan Africa and North Africa. While Sub-Saharan

Africa bank assets stand at 35 percent of GDP, with assets stand-

ing as low as 7 percent in some countries, North Africa registers a

deep banking system with assets averaging 77 percent of GDP.

Intermediation. With very few exceptions, fi nancial systems in

the region fulfi ll their intermediation function poorly. Cross-coun-

try, industry-level, fi rm-level, and country case studies confi rm

that countries with higher levels of fi nancial development experi-

ence better resource allocation, higher GDP per capita growth,

and faster rates of poverty reduction. Financial intermediation

contributes to economic growth through mobilizing savings and

identifying, assessing, and monitoring productive investments in

an economy. However, capacity for intermediation in many Afri-

can countries is strained by ineffi ciency, high costs, and lack of

outreach. Typically, banks have very high liquidity but fail to de-

ploy funds into productive investments, and lending to the private

sector is constrained. Domestic credit to the private sector aver-

ages less than 20 percent of GDP in Africa. Lack of competition

and high overhead costs contribute to high interest rate spreads,

which average 18 percent. Access to fi nance and outreach of

the banking system are limited with many countries having less

then 3 bank branches per 100,000 people. Financial systems are

dominated by banks and lack product and institutional diversity.

The share of nonbank fi nancial institutions in the fi nancial sys-

tem, including pension funds and insurance companies, which

have an important role in promoting long-term savings, remains

negligible. At the same time, ongoing reforms in the pension and

insurance sector in several African countries set the basis for in-

Box 5 Infrastructure indicators


Turnover ratio for traded stocks is the total value of shares traded during the period di-vided by the average market capitalization for the period. Average market capitalization is calculated as the average of the end-of-pe-riod values for the current period and the previous period.

Source: Data on sovereign ratings are from Fitch Ratings. Data on gross national savings are from World Bank country desks. Data on claims on governments and other public en-tities are from the IMF’s International Finan-cial Statistics database and data fi les and the World Bank’s World Development Indicators database. Data on money and quasi money and domestic credit to the private sector are from the IMF’s International Financial Sta-tistics database and data fi les, World Bank and OECD estimates of GDP, and the World Bank’s World Development Indicators data-base. Data on real interest rates are from the IMF’s International Financial Statistics data-base and data fi les using World Bank data on the GDP defl ator and the World Bank’s World Development Indicators database. Data on bank assets are from the IMF’s International Financial Statistics database. Data on inter-est rate spreads are from the IMF’s Interna-tional Financial Statistics database and data fi les and the World Bank’s World Develop-ment Indicators database. Data on ratios of bank nonperforming loans to total are from the IMF’s Global Financial Stability Report and the World Bank’s World Development Indicators database. Data on bank branches are from surveys of banking and regulatory institutions by the World Bank’s Research Department and Financial Sector and Opera-tions Policy Department and the World De-velopment Indicators database. Data on list-ed domestic companies and turnover ratios

for traded stocks are from Standard & Poor’s Emerging Stock Markets Factbook and supple-mental data and the World Bank’s World Development Indicators database. Data on market capitalization of listed companies are from Standard & Poor’s Emerging Stock Mar-kets Factbook and supplemental data, World Bank and OECD estimates of GDP, and the World Bank’s World Development Indicators database.

8. Human development

T .. EYouth literacy rate is the percentage of people ages 15–24 who can, with understanding, both read and write a short, simple state-ment about their everyday life.

Adult literacy rate is the proportion of adults ages 15 and older who can, with un-derstanding, read and write a short, simple statement on their everyday life.

Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.

Secondary education completes the provi-sion of basic education that began at the pri-mary level and aims to lay the foundations for lifelong learning and human development by off ering more subject- or skill-oriented in-struction using more specialized teachers.

Tertiary education, whether or not to an advanced research qualifi cation, normally requires, as a minimum condition of admis-sion, the successful completion of education at the secondary level.

Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the popu-lation of the age group that offi cially corre-sponds to the level of education shown.

creased long-term savings which, subject to effi cient intermedia-

tion, can be channeled to long-term investments in the future.

Capital markets. Capital markets act as a natural source of

long-term fi nance in developed fi nancial systems. Although capi-

tal markets are growing in Africa, most markets still play a limited

role in funding investments. Security markets are small, underde-

veloped, and illiquid both by global and emerging market stan-

dards, although 12 countries have set up stock exchanges in Sub-

Saharan Africa in addition to one regional exchange, the Bourse

des Valeurs d’Abidjan. The markets suffer from poor liquidity and

limited secondary market activity. Only 4 exchanges listed more

than 50 domestic companies in 2004, and turnover averaged less

than 10 percent of their market capitalization. While limited trad-

ing discourages further listings on the exchanges, additional list-

ings can also contribute to improved scale and liquidity.

Box 5 Infrastructure indicators

Technical notes 131

Net enrollment ratio is the ratio of children of offi cial school age based on the Internation-al Standard Classifi cation of Education 1997 who are enrolled in school to the population of the corresponding offi cial school age.

Student-teacher ratio is the number of stu-dents enrolled in school divided by the num-ber of teachers, regardless of their teaching assignment.

Public spending on education is current and capital public expenditure on education plus subsidies to private education at the primary, secondary, and tertiary levels.

Source: United Nations Educational, Scien-tifi c, and Cultural Organization Institute for Statistics.

T .. HLife expectancy at birth is the number of years a newborn infant would live if prevailing pat-terns of mortality at the time of its birth were to remain the same throughout its life. Data are World Bank estimates based on data from the United Nations Population Division, the United Nations Statistics Division, and na-tional statistical offi ces.


Infant mortality rate is the number of in-fants dying before reaching one year of age, per 1,000 live births.

Maternal mortality ratio is the number of women who die from pregnancy-related causes during pregnancy and childbirth, per 100,000 live births.

Deaths due to HIV/AIDS are the estimated number of adults and children that have died in a specifi c year based in the modeling of HIV surveillance data using standard and ap-propriate tools.

Prevalence of HIV is the percentage of peo-ple ages 15–49 who are infected with HIV.

Prevalence of tuberculosis is the number of tuberculosis cases (pulmonary, smear positive, and extrapulmonary) in a popula-tion at a given point in time, per 100,000 people. / is indicator is sometimes referred to as “point prevalence.” Estimates include cases of tuberculosis among people with HIV.

Deaths due to malaria is the number of ma-laria deaths per 100,000 people.

Child immunization rate is the percentage of children ages 12–23 months who received vaccinations before 12 months or at any time before the survey for four diseasesómeasles and diphtheria, pertussis (whooping cough), and tetanus (DPT). A child is considered ad-equately immunized against measles after re-ceiving one dose of vaccine and against DPT after receiving three doses.

Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliv-eries on their own; and to care for newborns.

Contraceptive prevalence rate is the percent-age of women who are practicing, or whose sexual partners are practicing, any form of contraception. It is usually measured for married women ages 15–49 only.

Children sleeping under insecticide-treated bednets are the percentage of children under age 5 who slept under an insecticide-treated bednet to prevent malaria.

People with advanced HIV infection receiv-ing antiretroviral combination therapy are the percentage of people with advanced HIV infection receiving antiretroviral therapy ac-cording to nationally approved treatment protocol (or World Health Organization/Joint United Nations Programme on HIV/AIDS standards).

Tuberculosis cases detected under DOTS are the percentage of estimated new infectious tuberculosis cases detected under DOTS, the internationally recommended tuberculosis control strategy.

DOTS treatment success rate is the percent-age of new smear-positive tuberculosis cases registered under DOTS in a given year that successfully completed treatment, wheth-er with bacteriologic evidence of success (“cured”) or without (“treatment completed”).

Children with fever receiving antimalarial drugs are the percentage of children under age 5 in malaria-risk areas with fever being treated with eff ective antimalarial drugs.

Population with sustainable access to an im-proved water source is the percentage of the population with reasonable access to an ad-equate amount of water from an improved source, such as a household connection,


public standpipe, borehole, protected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Reasonable access is defi ned as the availability of at least 20 liters a person a day from a source within 1 kilometer of the dwelling.

Population with sustainable access to im-proved sanitation is the percentage of the population with at least adequate access to excreta disposal facilities that can eff ectively prevent human, animal, and insect contact with excreta. Improved facilities range from simple but protected pit latrines to fl ush toi-lets with a sewerage connection. / e excreta disposal system is considered adequate if it is private or shared (but not public) and if it hygienically separates human excreta from human contact. To be eff ective, facilities must be correctly constructed and properly maintained.

Physicians are the number of physicians, including generalists and specialists.

Nurses are the number of nurses, includ-ing professional nurses, auxiliary nurses, en-rolled nurses, and other nurses, such as den-tal nurses and primary care nurses.

Midwives are the number of midwives, including professional midwives, auxiliary midwives, and enrolled midwives. Tradition-al birth attendants, who are counted as com-munity health workers, are not included.

General government expenditure on health is the direct outlays earmarked for the enhance-ment of the health status of the population and for the distribution of medical goods and services among the population. / ey can be fi nanced through domestic funds or through external resources.

Out-of-pocket expenditure is any direct out-lay by households, including gratuities and in-kind payments, to health practitioners and suppliers of pharmaceuticals, therapeutic ap-pliances, and other goods and services whose primary intent is to contribute to the restora-tion or enhancement of the health status of individuals or population groups. It is a part of private health expenditure.

Total government expenditure includes con-solidated direct outlays and indirect outlays, including capital of all levels of government, social security institutions, autonomous bod-ies, and other extrabudgetary funds

Source: Data are from the latest Core Health

Indicators from World Health Organization sources, including World Health Statistics 2006 and World Health Report 2006 (http://www3.who.int/whosis/core/core_select.cfm?path=whosis,core&language=english).

9. Agriculture

T .. R Rural population is the diff erence between the total population and the urban population.

Rural population density is the rural popula-tion divided by the arable land area. Arable land includes land defi ned by the Food and Agriculture Organization (FAO) as land un-der temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting culti-vation is excluded.

Rural population below the poverty line is the percentage of the rural population living be-low the national poverty line.

Share of rural population with sustainable ac-cess to an improved water source is the percent-age of the rural population with reasonable access to an adequate amount of water from an improved source, such as a household connection, public standpipe, borehole, pro-tected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Reasonable access is defi ned as the availabil-ity of at least 20 liters a person a day from a source within 1 kilometer of the dwelling.

Share of rural population with sustainable access to improved sanitation is the percentage of the rural population with at least adequate access to excreta disposal facilities that can eff ectively prevent human, animal, and insect contact with excreta. Improved facilities range from simple but protected pit latrines to fl ush toilets with a sewerage connection. / e excreta disposal system is considered adequate if it is private or shared (but not public) and if it hygienically separates human excreta from human contact. To be eff ective, facilities must be correctly con-structed and properly maintained.

Share of rural population with access to elec-tricity is the percentage of the rural population living in households with access to electricity.

Share of rural population with access to transportation is the percentage of the rural

Technical notes 133

population who live within 2 kilometers of an all-season passable road as a share of the total rural population.

Share of rural households with own telephone is the percentage of rural households pos-sessing a telephone.

Source: Data on rural population are calcu-lated from urban population shares from the United Nations Population Division’s World Urbanization Prospects and from total popu-lation fi gures from the World Bank. Data on rural population density are from the FAO and World Bank population estimates. Data on rural population below the poverty line are national estimates based on population-weighted subgroup estimates from house-hold surveys. Data on rural population with access to water and rural population with access to sanitation are from World Health Organization and United Nations Children’s Fund’s Meeting the MDG Water and Sanita-tion Target (www.unicef.org/wes/mdgre-port). Data on rural population with access to electricity are from household survey data, supplemented by World Bank Project Ap-praisal Documents. Data on rural population with access to transport are from the World Bank’s Sub-Saharan Africa Transport Policy Program (SSATP). Data on rural households with own telephone are from Demographic and Health Surveys.

T .. A Agriculture value added is shown at factor cost in current U.S. dollars divided by nominal gross domestic product. Value added in agri-culture comprises the gross output of forestry, hunting, and fi shing less the value of their intermediate inputs. However, for Botswana, Cameroon, Chad, Democratic Republic of Con-go, Republic of Congo, Gabon, Guinea, Mada-gascar, Mali, Morocco, Niger, Rwanda, Senegal, Togo, and Zambia, it is shown at market prices, that is, including intermediate inputs.

Crop production index shows agricultural production for each year relative to the base period 1999–2001. It includes all crops ex-cept fodder crops. Regional and income group aggregates for the Food and Agricul-ture Organization’s (FAO) production index-es are calculated from the underlying values in international dollars, normalized to the base period 1999–2001.

Food production index covers food crops that are considered edible and that contain nutri-ents. Coff ee and tea are excluded because, al-though edible, they have no nutritive value.

Nonfood production index includes 29 products that are not considered nutritious, including coff ee, rubber, cotton lint, and to-bacco leaves.

Cereal production is crops harvested for dry grain only. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Cereal crops harvested for hay or harvested green for food, feed, or si-lage and those used for grazing are excluded.

Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins.

Food production index per capita is the ratio of food production index to the estimated to-tal midyear population fi gures.

Agricultural exports and imports are ex-pressed in current U.S. dollars at free on board prices.

Permanent cropland is land under permanent crops as defi ned and reported by the FAO.

Land under cereal production refers to har-vested area, although some countries report only sown or cultivated area.

Irrigated land is areas equipped to provide water to the crops, including areas equipped for full and partial control irrigation, spate irrigation areas, and equipped wetland or in-land valley bottoms.

Fertilizer consumption is the aggregate of ni-trogenous, phosphate, and potash fertilizers.

Agricultural machinery refers to the num-ber of wheel and crawler tractors (excluding garden tractors) in use in agriculture at the end of the calendar year specifi ed or during the fi rst quarter of the following year. Arable land includes land defi ned by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fal-low. Land abandoned as a result of shifting cultivation is excluded.

Agricultural employment includes people who work for a public or private employer and who receive remuneration in wages, sal-ary, commission, tips, piece rates, or pay in kind. Agriculture corresponds to division 1 (ISIC revision 2) or tabulation categories A


and B (ISIC revision 3) and includes hunting, forestry, and fi shing.

Incidence of drought shows whether a country experienced a signifi cant shortage of rain that unfavorably aff ected agricultural production.

Agricultural productivity measures agricul-ture value added per worker. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1ñ5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunt-ing, and fi shing as well as cultivation of crops and livestock production. Data are in con-stant 2000 U.S. dollars.

Cereal crop yield is includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded..

Source: Data on agriculture value added are from World Bank country desks. Data on crop, food, nonfood, cereal, and livestock production, agricultural exports and imports, permanent cropland, land under cereal pro-duction, and agricultural machinery are from the FAO. Data on irrigated land are from the FAO’s Production Yearbook and data fi les. Data on fertilizer consumption are from the FAO database for the Fertilizer Yearbook. Data on agricultural employment are from the In-ternational Labour Organization. Data on incidence of drought are from the Southern Africa Flood and Drought Network and East Africa Drought (CE). Data on agricultural productivity are from World Bank national accounts fi les and the FAO’s Production Year-book and data fi les.

10. Labor, migration, and population

T .. LLabor force is people ages 15 and older who meet the International Labour Organization (ILO) defi nition of the economically active population. It includes both the employed and the unemployed. While national prac-tices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, the labor force generally includes the armed forces, the unemployed, and fi rst-time job-seekers, but excludes homemakers

and other unpaid caregivers and workers in the informal sector.

Participation rate is the percentage of the population ages 15–64 that is economically active: all people who supply labor for the production of goods and services during a specifi ed period.

Agriculture corresponds to division 1 (ISIC revision 2) or tabulation categories A and B (ISIC revision 3) and includes hunting, for-estry, and fi shing.

Industry corresponds to divisions 2–5 (ISIC revision 2) or tabulation categories C–F (ISIC revision 3) and includes mining and quarrying (including oil production), manu-facturing, construction, and public utilities (electricity, gas, and water).

Services correspond to divisions 6–9 (ISIC revision 2) or tabulation categories G–P (ISIC revision 3) and include wholesale and retail trade and restaurants and hotels; transport, storage, and communications; fi nancing, in-surance, real estate, and business services; and community, social, and personal services.

Wage and salaried workers (employees) are workers who hold the type of jobs defi ned as paid employment jobs, where incumbents hold explicit (written or oral) or implicit em-ployment contracts that give them a basic re-muneration that is not directly dependent on the revenue of the unit for which they work.

Self-employed workers are self-employed workers with employees (employers), self-em-ployed workers with without employees (own-account workers), and members of producer cooperatives. Although the contributing fam-ily workers category is technically part of the self-employed according to the classifi cation used by the ILO, and could therefore be com-bined with the other self-employed categories to derive the total self-employed, they are re-ported here as a separate category in order to emphasize the diff erence between the two sta-tuses, since the socioeconomic implications associated with each status can be signifi cant-ly varied. / is practice follows that of the ILO’s Key Indicators of the Labour Market.

Contributing family workers (unpaid work-ers) are workers who hold self-employment jobs as own-account workers in a market-oriented establishment operated by a related person living in the same household.

Employers are workers who, working on their own account or with one or a few

Technical notes 135

Data availability. The main source for labor

market indicators is the International Labour

Organization. It has two main data reposito-

ries: LABORSTA and Key Indicators of the

Labour Market. LABORSTA has information

on labor force, employment, unemployment,

wages, and hours worked. Key Indicators of

the Labour Market includes these indicators

as well as others on status in employment,

time-related underemployment, long-term

unemployed, part-time workers, youth un-

employment, and employment in the informal

economy) It also contains some estimated and

predicted indicators, which are an attempt to

remedy data unavailability (labor force par-

ticipation rates, inactivity rates, working poor,

labor productivity and labor cost, and em-

ployment elasticities) as well as indicators

indirectly related to labor markets that come

from other sources (inequality, poverty, and

educational attainment). However, in both

sources coverage of Sub-Saharan Africa is

low. For example, the most available indicator

in LABORSTA is unemployment, with only 40

percent of the countries having at least one

data point between 1985 and 2005. The most

available indicators in Key Indicators of the La-

bour Market are the ones that are estimated or

predicted, as well as those indirectly related

to the labor market. Unemployment follows,

with 55 percent of the countries having at least

one data point between 1985 and 2005. In-

formation on informal employment, which can

account for more than 60 percent of employ-

ment in some countries, is scarce. The most

available indicator, employment in the informal

economy according to national defi nitions, is

available for only 11 countries, with coverage

varying from national to only urban population

to capital cities to excluding agriculture.

Data comparability. The lack of compara-

bility has to do with differences in measure-

ment. Although there are standard defi nitions,

in practice, the indicators are constructed from

different sources of information. The data on

which they rely vary widely by country: from

administrative records, such as social secu-

rity registries and employment offi ces, to labor

force surveys, household budget surveys,

population censuses, establishment surveys

or censuses, and economic censuses. Coun-

tries also vary in whether they include specifi c

professional categories such as family work-

ers, particularly women, who assist in fam-

ily enterprises, people in the military, clergy,

seasonal workers, part-time workers, sex-

workers, and the like. There is also variation

in the age brackets used to construct some

indicators.

Data measurement. The underlying con-

cepts are less appropriate and more diffi cult to

measure in low-income countries. For exam-

ple, unemployment, which is the most avail-

able and used indicator, is typically different

in nature in low-income countries, where it is

often concentrated among middle class work-

ers. The prevalence of unemployment among

poor people is relatively low, since they cannot

afford to be unemployed. The current defi ni-

tion relies very much on whether a person has

actively searched for a job, which seems less

appropriate when labor markets are less de-

veloped and mechanisms for looking for jobs

are less institutionalized. Both labor supply

and income are more diffi cult to measure for

informal activities, the self-employed, and un-

paid family workers, which often account for

more than half of employment. Multiple job-

holdings are common. Current data provide

underestimates of labor supply and earnings

when they do not take second or third jobs

into account. Agriculture accounts for a large

share of the labor force, and its inherent sea-

sonality makes labor market indicators more

sensitive to the chosen reference period.

Box 6 Labor market indicators for Sub-Saharan Africa: availability, comparability, and conceptual relevance

partners, hold the type of jobs defined as a self-employment jobs (that is, jobs where the remuneration is directly dependent on the profits derived from the goods and services produced), and, in this capac-ity, have engaged, on a continuous basis, one or more persons to work for them as employees.

Own-account workers are workers who, work-ing on their own-account or with one or more partners, hold the type of jobs defi ned as a self-employment jobs, as have engaged on a contin-uous basis any employees to work for them.

Members of producer cooperatives are work-ers who hold self-employment jobs in a coop-erative producing goods and services.


Source: / e ILO publishes estimates of the economically active population in its Yearbook of Labour Statistics. Labor force numbers were calculated by World Bank staff , applying labor force participation rates from the ILO data-base to population estimates. / e labor force participation rates are from the ILO’s Esti-mates and Projections of the Economically Ac-tive Population, 1980–2020, fi fth edition. / e World Bank’s population estimates are com-piled and produced by its Human Develop-ment Network and Development Data Group in consultation with its operational staff and country offi ces. Important inputs to the World Bank’s demographic work come from the United Nations Population Division’s World Population Prospects: H e 2004 Revision; census reports and other statistical publica-tions from national statistical offi ces; house-hold surveys conducted by national agencies, Macro International, and the U.S. Centers for Disease Control and Prevention; Eurostat, Demographic Statistics (various years); Cen-tro Latinoamericano de Demografía, Boletín Demográfi co (various years); and U.S. Bureau of the Census, International Database. Data on status in employment and composition of self-employment are from the ILO’s Key Indi-cators of the Labour Market, fourth edition.

T .. M Stock is the number of people born in a coun-try other than that in which they live. It in-cludes refugees.

Net migration is the net average annual num-ber of migrants during the period, that is, the annual number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are fi ve-year estimates.

Workers remittances received comprise cur-rent transfers by migrant workers and wages and salaries by nonresident workers.

Population is World Bank estimates, usu-ally projected from the most recent popula-tion censuses or surveys (mostly from 1980–2004). Refugees not permanently settled in the country of asylum are generally con-sidered to be part of the population of their country of origin.

Fertility rate is the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-spe-cifi c fertility rates.

Age composition refers to the percentage of the total population that is in specifi c age groups.

Dependency ratio is the ratio of depen-dents—people younger than 15 or older than 64—to the working-age population—those ages 15–64.

Rural area population is calculated as the diff erence between the total population and the urban population.

Urban area population is midyear population of areas defi ned as urban in each country.

Source: World Bank’s World Development Indicators database.

11. HIV/AIDS

T .. HIV/AIDS Estimated number of people with HIV/AIDS is the number of people in the relevant age group living with HIV. Depending on the re-liability of the data available, there may be more or less uncertainty surrounding each estimate. / erefore, plausible bounds have been presented for adult rate.

Estimated prevalence rate is the percentage of the population of the relevant age group who are infected with HIV.

Deaths due to HIV/AIDS are the estimated number of adults and children that have died in a specifi c year based in the modeling of HIV surveillance data using standard and ap-propriate tools.

AIDS orphans are the estimated number of children who have lost their mother or both parents to AIDS before age 17 since the epi-demic began in 1990. Some of the orphaned children included in this cumulative total are no longer alive; others are no longer under age 17.

Source: / e Joint United Nations Pro-gramme on HIV/AIDS and the World Health Organization’s 2006 Report on the Global AIDS Epidemic.

12. Malaria

T .. MPopulation is World Bank estimates, usually projected from the most recent population censuses or surveys (mostly from 1980–2004). Refugees not permanently settled in the country of asylum are generally con-

Technical notes 137

sidered to be part of the population of their country of origin.

Endemic risk of malaria is the percentage of the population living in areas with signifi cant annual transmission of malaria, be it season-al or perennial.

Epidemic risk of malaria is the percentage of the population living in areas prone to distinct interannual variation, with no trans-mission taking place at all in some years.

Negligible risk of malaria is the per-centage of the population living in areas where malaria is ordinarily not present and where the risk of malaria outbreaks is negligible.

Deaths due to malaria are the number of malaria deaths per 100,000 people.


Children sleeping under insecticide-treated bednets are the percentage of children under

age 5 who slept under an insecticide-treated bednet to prevent malaria.

Children with fever receiving antimalarial drugs are the percentage of children under age 5 in malaria-risk areas with fever being treated with antimalarial drugs.

Pregnant women receiving two doses of inter-mittent preventive treatment are the number of pregnant women who receive at least two preventive treatment doses of an eff ective antimalarial drug during routine antenatal clinic visits. / is approach has been shown to be safe, inexpensive, and eff ective.

Source: Data on population are from the World Bank’s Development Data Platform. Data on risk of malaria, children with fever receiving antimalarial drugs, and pregnanet women receiving two doses of intermittent preventive treatment are from Demographic Health Surveys, Multiple Indicator Cluster Surveys, and national statistical offi ces. Data on deaths due to malaria are from the United Nations Statistics Division based on World

Two factors are causing the apparent stabiliza-

tion or leveling off of HIV prevalence rates ob-

served in much of Sub-Saharan Africa: AIDS

mortality rates and HIV incidence. High and, in

some countries, rising rates of AIDS mortality

and continuing high HIV incidence offsetting

this mortality are the cause of this appearance

of leveling off. In Zambia, for example, national

HIV prevalence appears to have stayed rela-

tively stable for the past 8–10 years. Since it

is estimated that close to 100,000 people liv-

ing in Zambia have been newly infected annu-

ally over that period, overall prevalence has

remained level because AIDS has killed about

the same number of people each year. HIV

prevalence might therefore appear stable, but

it hides a persistently high number of annual,

new HIV infections and an equally high num-

ber of AIDS deaths.

On the other hand, as antiretroviral ther-

apy coverage increases, it is possible that

prevalence levels will increase as more people

infected with HIV live longer. But we are not,

unfortunately, witnessing a regionwide decline

in the epidemic. In the absence of effective

interventions, the epidemic will continue to

wreak havoc in these countries.

It is important to remember that there is

not one typical African AIDS epidemic. In 10

countries adult HIV prevalence is below 2 per-

cent, while in 5 other countries it is over 20 per-

cent. These extreme differences in prevalence

rates fall roughly into geographically sepa-

rate areas. Eight countries of Southern Africa

(Botswana, Lesotho, Mozambique, Namibia,

South Africa, Swaziland, Zambia, and Zimba-

bwe) have prevalence rates above 15 percent,

with Swaziland over 30 percent. In West Africa

HIV prevalence is much lower, with no country

having a prevalence rate above 10 percent and

most having prevalence between 1 percent

and 5 percent. Adult prevalence in countries

in Central and East Africa falls somewhere be-

tween these two groups.

The countries of the region also differ in the

time course of their epidemics, with epidemics

starting earlier in East and Central Africa and

much later in Southern Africa. Uganda is an

example where adult HIV prevalence peaked

in the early 1990.

Box 7 What is causing the apparent stabilization of HIV prevalence in Sub-Saharan Africa?

Source: UNAIDS 2006.


Health Organization (WHO) estimates. Data on under-fi ve mortality are harmonized esti-mates of the WHO, United Nations Children’s Fund, and the World Bank, based mainly on household surveys, censuses, and vital reg-istration, supplemented by World Bank esti-mates based on household surveys and vital registration. Data on children sleeping under insecticide-treated bednets are from Demo-graphic and Health Surveys and Multiple In-dicator Cluster Surveys.

13. Capable States and partnership

T .. A Net aid from all donors is net aid from DAC, non-DAC bilateral (Organization of Petro-leum Exporting Countries (OPEC), the for-mer Council for Mutual Economic Assistance (CMEA) countries, and China (OECD data). OPEC countries are Algeria, Iran, Iraq, Ku-wait, Libya, Nigeria, Qatar, Saudi Arabia, the United Arab Emirates, and Venezuela. / e former CMEA countries are Bulgaria, Czechoslovakia, the former German Demo-cratic Republic, Hungary, Poland, Romania, and the former Soviet Union) and multilat-eral donors.

Net aid from DAC donors is net aid from OECD’s DAC donors, which include Aus-tralia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Japan, the Netherlands, Norway, Sweden, Switzerland, the United Kingdom, and the United States. Ireland and New Zealand have been excluded in this compilation because their aid to Africa is negligible.

Net aid from multilateral donors is net aid from multilateral sources, such as the Afri-can Development Fund, the European De-velopment Fund for the Commission of the European Communities, the International Development Association, the International Fund for Agricultural Development, Arab and OPEC fi nanced multilateral agencies, and UN programs and agencies. Aid fl ows from the International Monetary Fund’s (IMF) Trust Fund and Structural Adjustment Facility are also included. UN programs and agencies include the United Nations Techni-cal Assistance Programme, the United Na-tions Development Programme, the United Nations Offi ce of the High Commissioner for Refugees, the United Nations Children’s

Fund, and the World Food Programme. Arab and OPEC fi nanced multilateral agencies in-clude the Arab Bank for Economic Develop-ment in Africa, the Arab Fund for Economic and Social Development, the Islamic Devel-opment Bank, the OPEC Fund for Interna-tional Development, the Arab Authority for Agricultural Investment and Development, the Arab Fund for Technical Assistance to African and Arab Countries, and the Islamic Solidarity Fund.

Aid as a share of gross domestic product (GDP) is calculated by dividing the nomi-nal total net aid from all donors by nominal GDP. For a given level of aid fl ows, devalu-ation of a recipient’s currency may infl ate the ratios shown in the table. / us, trends for a given country and comparisons across countries that have implemented diff erent exchange rate policies should be interpreted carefully.

Aid per capita is calculated by dividing the nominal total net aid by midyear population. / ese ratios off er some indication of the im-portance of aid fl ows in sustaining per capita income and consumption levels, although ex-change rate fl uctuations, the actual rise of aid fl ows, and other factors vary across countries and over time.

Net aid as a share of gross capital formation is calculated by dividing the nominal total net aid by gross capital formation. / ese data high-light the relative importance of the indicated aid fl ows in maintaining and increasing invest-ment in these economies. / e same caveats mentioned above apply to their interpreta-tion. Furthermore, aid fl ows do not exclusively fi nance investment (for example, food aid fi -nances consumption), and the share of aid go-ing to investment varies across countries.

Total net aid as a share of imports of goods and services is calculated by dividing nominal total net aid by imports of goods and services.

Total net aid as a share of central government expenditure is calculated by dividing nominal to-tal net aid by central government expenditure.

Heavily Indebted Poor Country (HIPC) Debt Initiative decision point is the date at which a HIPC with an established track record of good performance under adjustment programs supported by the International Monetary Fund (IMF) and the World Bank commits to undertake additional reforms and to develop and implement a poverty reduction strategy.

Technical notes 139

HIPC completion point is the date at which the country successfully completes the key structural reforms agreed on at the decision point, including developing and implement-ing its poverty reduction strategy. / e coun-try then receives the bulk of debt relief under the HIPC Initiative without further policy conditions.

Estimated total nominal debt service relief committed under Enhanced HIPC Initiative is the amount of debt service relief, calculat-ed at the decision point, that will allow the country to achieve debt sustainability at the completion point.

Source: OECD and World Bank data.

The Fourteenth Replenishment of the Interna-

tional Development Association (IDA14) in fi s-

cal 2006–08 provides for approximately $33

billion in development assistance, about $18

billion of which is from 40 donor countries. As

in IDA13, IDA14 places special emphasis on

meeting the exceptional development chal-

lenges faced by Africa by directing half of its

resources to the region, subject to perfor-

mance. This translates into an approximately

25 percent increase over the amount of IDA fi -

nancing available for Africa in IDA13. These re-

sources account for 13 percent of all external

aid to Africa (Development Committee 2006).

While this is a large share for one institution,

it is clear that IDA alone cannot address the

analytical and operational challenges to ac-

celerate growth and progress toward the Mil-

lennium Development Goals in every African

country. IDA therefore strives to work effec-

tively with other donors and with governments

to maximize the impact of all external aid.

A key focus during IDA14 is the debt

sustainability of African countries. To this end,

fi nancial support from IDA to poor countries

now takes systematic account of countries’

risk of debt distress. The countries facing the

greatest risk of debt distress—most of which

are in Sub-Saharan Africa—will receive all of

their support in the form of grants, while less

debt-burdened countries will receive credits

or a 50-50 mix of grants and credits. As a re-

sult, grant-fi nanced operations will account

for a substantial share of total IDA14 commit-

ments (initial estimates point to 30 percent).

Over 70 percent of total grant resources

would likely go to Africa. Africa will also ben-

efi t greatly from the ongoing Heavily Indebted

Poor Country Debt Relief Initiative and from

the recently approved Multilateral Debt Relief

Initiative (MDRI).

To maximize the impact of increased con-

cessionality and debt reduction, these initia-

tives must be complemented by increased

development assistance. Donors therefore

agreed to offset the fi nancial impact of grants

on IDA’s fi nances, and to compensate dol-

lar-for-dollar IDA’s forgone credit refl ows that

result from the MDRI debt cancellation. The

agreements on fi nancial compensation to off-

set the cost of debt relief will ensure that IDA’s

fi nancial integrity is preserved and additional

resources are available to help poor countries

achieve the Millennium Development Goals

and support their development efforts. To en-

courage effective use of these additional re-

sources, they will be allocated on the basis

of policy performance to IDA-only countries,

which in turns means that a large share will

go to Africa.

Africa also receives the lion’s share of

the resources allocated under IDA’s alloca-

tion system for post-confl ict countries. These

special post-confl ict allocations are provided

for a limited period of time to support the ex-

ceptional needs of countries emerging from

confl ict. In these diffi cult situations, it is criti-

cal that IDA work closely with other partners—

particularly the United Nations—to ensure that

countries receive timely and appropriate sup-

port throughout the recovery period, while

maintaining a strong focus on performance.

During IDA14 it is envisaged that around

$450 million will be committed each year for

regional projects, $300 million of which will

come from a special provision, with the rest

contributed from each participating country’s

allocation. This regional program is particu-

larly important for Africa, where there is signif-

icant potential for countries to work together

on a regional basis to achieve greater econ-

omies of scale. During IDA13, 88 percent of

regional projects were in Africa, and in July

2004 the Bank established a regional integra-

tion department for the Africa region that will

fund regional pilot projects worth about $500

million by fi scal 2007.

Box 8 Leveraging IDA14 for greater impact


T .. C Public fi nancial management is the number of Heavily Indebted Poor Country (HIPC) Debt Initiative benchmarks on managing and tracking public spending that have been met, as reported to the executive boards of the World Bank and the International Mon-etary Fund

Courts are the share of senior managers who ranked courts and dispute resolution systems as a major or very severe constraint.

Crime is the share of senior managers who ranked crime, theft, and disorder as a major or very severe constraint.

Number of procedures to enforce a contract is the number of independent actions, mandat-ed by law or courts, that demand interaction between the parties of a contract or between them and the judge or court offi cer.

Time required to enforce a contract is the number of calendar days from the fi ling of the lawsuit in court until the fi nal determina-tion and, in appropriate cases, payment.

Cost to enforce a contract is court and attor-ney fees, where the use of attorneys is man-datory or common, or the cost of an adminis-trative debt recovery procedure, expressed as a percentage of the debt value.

Protecting investors disclosure index mea-sures the degree to which investors are pro-tected through disclosure of ownership and fi nancial information.

Director liability index measures a plaintiff ’s ability to hold directors of fi rms liabile for damages to the company).

Shareholder suits index measures share-holders’ ability to sue offi cers and directors for misconduct.

Investor protection index measures the de-gree to which investors are protected through disclosure of ownership and fi nancial infor-mation regulations.

Number of tax payments is the number of taxes paid by businesses, including electronic fi ling. / e tax is counted as paid once a year even if payments are more frequent.

Time to prepare and pay taxes is the number of hours it takes to prepare, fi le, and pay (or withhold) three major types of taxes: the cor-porate income tax, the value added or sales tax, and labor taxes, including payroll taxes and social security contributions.

Total tax payable is the total amount of tax-es payable by the business (except for labor

taxes) after accounting for deductions and exemptions as a percentage of gross profi t.

For further details on the method used for assessing the total tax payable, see the World Bank’s Doing Business 2006.

Extractive Industries Transparency Initia-tive (EITI) implementing or endorsed indicates whether a country has implemented or en-dorsed the EITI, a multistakeholder approach to increasing governance and transparency in extractive industries. / e EITI includes civil society, the private sector, and government and requires a work plan with timeline and budget to ensure sustainability, independent audit of payments and disclosure of revenues, publication of results in a publicly accessible manner, and an approach that covers all com-panies and government agencies.

Corruption perceptions index transparency index is the annual Transparency Interna-tional corruption perceptions index, which ranks more than 150 countries in terms of perceived levels of corruption, as determined by expert assessments and opinion surveys.

Source: Data on public fi nancial manage-ment are from the World Bank and the IMF. Data on investment climate constraints to fi rms are based on enterprise surveys con-ducted by the World Bank and its partners during 2001–05 (http://rru.worldbank.org/EnterpriseSurveys) (see box 4). Data on en-forcing contracts, protecting investors, and regulation and tax administration are from the World Bank’s Doing Business project (http://rru.worldbank.org/DoingBusiness/). Data on the EITI are from the EITI website, www.eitransparency.org. Data on corruption perceptions index are from Transparency In-ternational (www.transparency.org/policy_research/surveys_indices/cpi).

T .. G -

Source: World Bank Institute (see box 9).

T .. C P I- A / e Country Policy and Institutional Assess-ment (CPIA) assess the quality of a country’s present policy and institutional framework. “Quality” means how conducive that frame-work is to fostering sustainable, poverty-re-ducing growth and the eff ective use of devel-

Technical notes 141

opment assistance. / e CPIA is conducted annually for all International Bank for Re-construction and Development and Interna-tional Development Association borrowers and has evolved into a set of criteria grouped into four clusters:• Economic management.• Structural policies.• Policies for social inclusion and equity.• Public sector management and institu-

tions.

Within these clusters are 16 criteria that refl ect a balance between ensuring that all key factors that foster pro-poor growth and poverty alleviation are captured, without overly burdening the evaluation process. Country scores refl ect a variety of indicators and judgments that are based on country’s knowledge; analytic work, policy dialogue, or work done by partners; and relevant publicly available indicators. / e rating scale for each criterion varies from 1 (low) to 6 (high). A

Governance is a key determinant for development and poverty

alleviation. To improve governance, however, it is essential to be

able to measure it. Skeptical views abound among practitioners:

• The composite and subjective nature of governance makes it

diffi cult to measure it correctly.

• Where aspects of governance have been measured, the

tendency has been to provide a single aggregate rating per

country that has little value for policy at the country level.

• Measures of governance are also subject to such large er-

rors because they are based on unreliable perceptions, and,

even if governance could be measured with some accuracy,

the measures would merely confi rm what is widely known by

country observers.

But researchers have increasingly challenged these views,

used to criticize existing measures, leading to a growth in gover-

nance indicators in recent years. This box focuses on a few se-

lected indicators developed by the World Bank Institute on quality

of governance and presents some recent empirical fi ndings for

Sub-Saharan Africa (Kaufman, Mraay, and Mastruzzi 2005).

The fi rst group of indicators, the Kaufmann-Kraay indicators,

uses different sources, as well as a new aggregation technique, to

trace six areas of governance from 1996 to the present in almost

200 countries:

• Voice and accountability.

• Political stability.

• Government effectiveness.

• Regulatory quality.

• Rule of law.

• Control of corruption.

These indicators show, on average, a marginal deteriora-

tion of governance in Sub-Saharan Africa since 2000. A closer

look at each indicator reveals, however, signifi cant differences

in term of quality of governance across countries. Consider, for

example, control of graft. Burkina Faso and Madagascar’s scores

signifi cantly improved, while Namibia’s signifi cantly deteriorated.

Overall, these indicators can help countries identify general areas

of governance weakness. However, the authors caution against

using this data to run “horse races” among countries with similar

ratings. While the methodology used reduces the margins of error,

the margins of error can still be large enough to make precise

rankings of similarly rated countries impossible.

The second group of indicators attempts to provide opera-

tional guidance either to policymakers or to donors on specifi c

governance challenges. Such more comprehensive unbundling

of governance can be achieved through country-level diagnos-

tic surveys. These tools attempt to measure governance through

surveys of three different respondents—public offi cials, fi rms,

and households—and focus on state-fi rm and state-citizen inter-

actions, as well as on the institutional arrangements in the public

sector that drive good or bad performance. These surveys are

calibrated to the country context and collect a core of detail data

that can be used to benchmark performance across agencies

and over time. The wealth of information collected allows also

policymakers to prioritize governance challenges and design ap-

propriate policy responses.

The availability of such data has deepened the understanding

of the costs of poor governance on users of public services. Poor

accountability systems and limited transparency can allow public

offi cials to set different prices for the same public service and to

limit access. Thus, the cost and the access of publicly provided

services may differ across citizens because of bribery. In Sub-

Saharan Africa the data offer ample evidence on how bribery can

limit access to specifi c public services by discouraging users

who need that service. These tools collect information also from

public offi cials, allowing an examination of the quality of gover-

nance by public agency. The cross-evidence available suggests

that governance has an important within-country variation that

needs to be explained. In the presence of such diverse patterns

across and within countries, the key question becomes whether

any general conclusion can be drawn on specifi c measures that

might help these countries curb corruption. Most recently, Re-

canatini, Prati, and Tabellini (2005) analyzed the data from

diagnostic surveys in eight countries and found that specifi c

agency-level institutions—such as auditing mechanisms, publicly

announced budget and personnel decisions, and merit-based

personnel management—are associated with lower corruption

in public agencies.

Box 9 Governance in Sub-Saharan Africa: selected fi ndings and issues

Source: World Bank Institute.


rating of 1 indicates a very weak performance and a rating of 6 very strong performance. However, intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may be given to a country. / e ratings derived are the main determinant of present aid eff ectiveness prospects. Improve-ments in policies and institutions during the course of the year are refl ected in the subse-quent year’s performance ratings.

Source: World Bank’s Country Policy and Institutional Assessment 2005.

. H / e questions asked in household surveys vary by country. Quintiles are derived by ranking weighted sample population by area of residence (rural and urban) and per capita expenditure. Two sets of quintiles are calcu-lated, one for rural and one for urban. Each quintile contains an equal number of people rather than households. / e defi nition of ru-ral and urban also vary by country.

Sample size is the number of households surveyed in the country.

Total population is the weighted estimate of all the surveyed population in the country based on the survey—that is, it is the weight-ed sample population.

Age dependency ratio is the ratio of depen-dents—people younger than 15 or older than 64—to the working-age population—those ages 15–64.

Average household size is the average num-ber of people in a household.

Monogamous male is a household headed by man who has no more than one spouse (wife).

Polygamous male is a household headed by a man who has more than one spouse (wife).

Single male is a household headed by a man who is widowed or divorced or who has never married.

De facto female refers to a household with-out a resident male head or where the male head is not present and the wife is the head by default and serves as the main decision-maker in his absence or a household where the resident male head has lost most of his functions as the economic provider due to infi rmity, inability to work, or the like.

De jure female refers to a household headed by a woman who is widowed, separated, or divorced or who has never been married.

Mean monthly expenditure is the average

monthly expenditure on both food and non-food items.

Mean monthly share on food is total monthly food expenditure and food own consumption as a share of total household expenditure.

Mean monthly share on health is total health expenditure (consultation, medical procedure, among other) as a share of total household expenditure. Health expenditure excludes hospitalization.

Mean monthly share on education is total education expenditure (tuition, transport, and the like) as a share of total household expenditure

Primary school within 30 minutes is the share of households that live within 30 min-utes of a primary school.

Net primary enrollment rate is the ratio of children of a country’s offi cial primary school age who are enrolled in primary school to the total population of the corresponding of-fi cial primary school age. Primary education provides children with basic reading, writ-ing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.

Net secondary enrollment rate is the ratio of children of a country’s offi cial secondary school age who are enrolled in secondary school to the total population of the corre-sponding offi cial secondary school age. Sec-ondary education completes the provision of basic education that began at the primary level and aims to lay the foundations for life-long learning and human development by of-fering more subject- or skill-oriented instruc-tion using more specialized teachers.

Tertiary enrolment rate is the number of students currently in tertiary education per 10,000 people. Tertiary education, whether or not to an advanced research qualifi cation, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.

Adult literacy rate is the percentage of adults ages 15 and older who can both read and write a simple sentence in any language.

Youth literacy rate is the percentage of youth ages 15–24 who can both read and write a simple sentence in any language.

Health center less than 1 hour away is the percentage of the population living less than 1 hour away from a health center.

Technical notes 143

Health center less than 5 km away is the per-centage of the population living less than 5 kilometers away from a health center

Morbidity is the percentage of the popula-tion who were sick or injured within a given number of weeks before the survey.

Health care provider consulted when sick is the percentage of sick people who took any remedial action when sick.

Type of health care provider consulted is the type of facility visited by a sick household member. Public includes fully government-owned as well as semi-public health facilities. Private, modern medicine, is facilities set up with profi t as their main focus and includes private doctors. Private, traditional healers refer to health care providers whose knowl-edge, skills, and practices are based on the experiences indigenous to diff erent cultures and whose services are directed toward the maintenance of health, as well as the preven-tion, diagnosis, and improvement of physical and mental illness. Other is other types of health providers that cannot be classifi ed by the categories described above.

Birth assisted by trained staff are the per-centage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for new-borns.

Immunization coverage, 1-year-olds, is the percentage of children ages 12–23 months at the time of survey who received one dose of Bacille Calmette Guerin vaccine, three doses of polio vaccine, three doses of diphtheria, pertussis, and tetanus vaccine, and one does of measles vaccine.

Measles immunization coverage, 1-year-olds, is the percentage of children ages 12–23 months at the time of survey who received a dose of measles vaccine. A child is considered adequately immunized against measles after receiving one dose of vaccine.

Stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 6–59 months. / e reference population, adopted by the World Health Organization in 1983, is based on children from the United States, who are assumed to be well nourished.

Wasting is the percentage of children under

age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 6–59 months. / e reference population, adopted by the World Health Organization in 1983, is based on children from the United States, who are assumed to be well nour-ished.

Underweight is the percentage of chil-dren under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 6–59 months. / e reference population, adopted by the World Health Or-ganization in 1983, is based on children from the United States, who are assumed to be well nourished.

Water source less than 1 hour away is the percentage of the population living less than 1 hour away from a water source.

Water source less than 5 km away is the per-centage of the population living less than 5 kilometers away from a water source.

Market less than 1 hour away is the percent-age of the population living less than 1 hour away from a market.

Market less than 5 km away is the percent-age of the population living less than 5 kilo-meters away from a market.

Access to improved water source refers to the percentage of the population with reasonable access to an adequate amount of water from an improved source, such as a household connection, public standpipe, borehole, pro-tected well or spring, or rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Own tap is a household water connection. Other piped is a public water connection. Well, protected, is a ground water source.

Traditional fuel use is the percentage of the population using traditional fuels such as fi rewood and charcoal as the main source of cooking fuel

T .. B F , Household is the basic socioeconomic unit in which the diff erent members—related or living in the same house or property—put together their resources and jointly meet their basic needs, including food, under the authority of one person who is recognized as the head.


Source: Burkina Faso’s Institut National de la Statistique et de la Démographie carried out the Enquête Prioritaire II sur les Condi-tions de Vie des Ménages au Burkina. Data were collected in 2003. / e project was fund-ed by the government of Burkina Faso, the World Bank, the African Development Bank, and the United Nations through the United Nations Development Programme.

T .. B , Household is people who habitually live and eat their meals in the same compound. One member is recognized as the head.

Source: / e Institut de Statistiques et d’Études économiques of Burundi, in con-junction with the United Nations Develop-ment Programme, carried out the Enquête Prioritaire—Étude nationale sur les condi-tions de vie des populations. Data were col-lected in 1998.

T .. C , Household is people who live under the same roof, take their meals together or in little groups, and put some or all of their incomes together for the group’s spending purposes, at the head of household’s discretion.

Source: Cameroon’s Bureau Central des Re-censements et des Enquêtes of the Direction de la Statistique et de la Comptabilité carried out the Enquête Camerounaise auprès des Ménages in 2001.

T .. C ’I , Household is a person or a group of people who live under the same roof, share the same meals, and recognize one person as the head.

Source: / e Institut National de la Statis-tique carried out the Enquête Niveau de Vie, a priority survey, in 1998.

T .. E , Household is a person or a group of people who live under the same roof, share the same meals, and recognize one person as the head.

Source: / e 1999/2000 Household Income, Consumption, and Expenditure Survey was carried out by the Central Statistical Offi ce. / e data collection process was carried out from June 1999 to February 2000.

T .. G , Household is the set of people who normally ate and lived together for at least 6 of the 12 months preceding the survey.

Information on literacy is not reliably available from this survey; less than one in 1,100 who could have responded did so.

Source: / e Central Department of Statis-tics of the Ministry of Economic Planning and Industrial Development of Ghana car-ried out the 1998 National Household Pov-erty Survey, an integrated survey. Data were collected between April 1998 and May 1998.

T .. G , /Household is a group of people who have usu-ally slept in the same dwelling and have taken their meals together for at least 9 of the 12 months preceding the interview.

Source: / e Ghana Statistical Service car-ried out the Ghana Living Standards Survey IV, an integrated survey. Data were collected between April 1998 and March 1999.

T .. K , Household is the set of people who normally ate and lived together for 6 of the 12 months preceding the survey.

Source: / e Kenya Central Bureau of Sta-tistics collected data for the 1997 Welfare Monitoring Survey III, a priority survey, in March and April 1997.

T .. M , Household is the set of people who may but do not necessarily have family links between them, who live and sleep in the same housing, and who take their meals together regularly. / e housing may be an individual house, an apartment, or one or more rooms in a larger building. / e household is headed by one in-

Technical notes 145

dividual whose authority is recognized by all in the household.

Literacy measures the number of house-hold members ages 5 and older who com-pleted at least four years of primary school or who could read and write.

Source: / e Institut National de la Statis-tiques carried out the Enquête Prioritaire auprès des ménages in 1999. / e project was funded by the government of Madagascar and the World Bank (International Develop-ment Association).

T .. M , /Household is a person living alone or a group of people, either related or unrelated, who live together as a single unit in the sense that they have common housekeeping arrange-ments (that is, share or are supported by a common budget). Someone who did not live with the household during the survey period was not counted as a current member of the household.

Literacy measures the ability to read and write a simple sentence for those who had not attended school in the past two months and was defi ned based on education attain-ment for those who had attended school in the past two months.

Source: / e Malawi National Statistics Of-fi ce carried out the Integrated Household Survey from November 1997 to October 1998.

T .. M , Household is the set of people who partly or totally shared their expenditures, had not been absent for more than 6 of the 12 months preceding the survey, and were not domestic help. In the case of polygamous households, each wife and her children were considered to be a separate household.

Literacy measures the number of people with ability to read and write in Portuguese.

Source: / e Instituto Nacional de Estatis-tiqua carried out the Inquérito Nacional aos Agregados Familiares sobre as Condições de Vida, an integrated survey undertaken in 1996.

T .. N , Household is a group of persons who nor-mally cook, eat, and live together. Number of months sharing in these activities was another criterion used to qualify as a house-hold a member (minimum of three months). However, all heads of households irrespec-tive of number of months living elsewhere were included as household members. / ese people may or may not be related by blood, but make common provision for food or other essentials for living, and they have one person whom they all regard as the head of the household.

Literacy measures the number of people with the ability to read and write either in English or any of the local languages.

Source: / e Federal Offi ce of Statistics, Abuja, of Nigeria carried out the Nigeria Liv-ing Standards Survey, an integrated survey. Data were collected between September 2003 and August 2004.

T .. S T P , Household is the set of people, related or not, who live together under the same roof, put their resources together, and address as a unit their primary needs, under the author-ity of one person whom they recognize as the head of the household.

Literacy measures the number of people with the ability to read and write a simple sentence.

Source: / e Instituto Nacional de Estatis-tica of the Ministério de Planomento, Finan-ças e Cooperaçao carried out the Enquête sur les Conditions de Vie des Ménages in 2000. / e project was fi nanced by the government of São Tomé and Principe with assistance from the African Development Bank and the United Nations Development Programme. Technical assistance was provided by the In-ternational Labour Organization.

T .. S L , /Household is a group of people who normal-ly cook, eat, and live together. Number of months sharing in these activities was anoth-er criterion used to qualify as a household a

member (minimum three months). However, all heads of households irrespective of num-ber of months living elsewhere were included as household members. / ese people may or may not be related by blood, but make com-mon provision for food or other essentials for living, and they have one person whom they all regarded as the head of the household.

Literacy measures the number of people with the ability to read and write a simple sentence in either English or the local lan-guages.

Source: / e Sierra Leone Central Statisti-cal Offi ce carried out the Living Conditions Monitoring Survey. Data were collected be-tween 2002 and 2003.

T .. U , /Household is individuals who normally eat and live together.

Literacy measures the number of people who responded that they could both read and

write. / e level of education was also used to determine literacy.

Source: / e Uganda Bureau of Statistics carried out the National Household Survey. Data collection occurred between May 2002 and April 2003. / e project was funded by the government of Uganda and the World Bank. Statistics Denmark and the World Bank pro-vided consultants for technical support.

T .. Z , Household is a group of persons who normally cooked, ate, and lived together. / ese people may or may not be related by blood, but make common provision for food or other essen-tials for living, and they have one person whom they all regarded as the head of the household.

Source: / e Zambian Central Statistical Of-fi ce carried out the Living Conditions Moni-toring Survey II. Data were collected between November 1998 and December 1998.


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IntroductionThis CD-ROM is part of the World Bank

Africa Database family of products. It was

produced by the Operational Quality and

Knowledge Services and the Offi ce of the

Chief Economist for the Africa Region, in

collaboration with the Development Data

Group of the Development Economics Vice

Presidency. It uses the latest version of the

World Bank’s *STARS* data retrieval system,

Win*STARS version 5.0.

The CD-ROM contains about 1,200

macroeconomic, sectoral, and social

indicators, covering 53 African countries. Time

series include data from 1965 to 2004. The

CD-ROM includes two sets of tables: Country-

at-a-Glance and Africa Development Indicators.

It also contains a query tool that allows the user

to display about 1,200 time series.

Win*STARS 5.0 features mapping and

charting and several data export formats

(Access™, ASCII, dBASE™, Excel™, and

SAS™). We invite you to explore it.

A note about the dataUsers should note that the data for Africa

Development Indicators, The Little Data

Book on Africa, and the World Bank Africa

Database CD-ROM are drawn from the same

database. The general cutoff date for data is

May 2006.

HelpThis guide explains how to use the main

functions of the CD-ROM. For details about

additional features, click Help on the menu

bar or the Help icon; or call one of the hotline

numbers listed in the Help menu and on the

copyright page of this booklet.

InstallationAs is usual for Windows™ products, you

should make sure that other applications are

closed while you install the CD-ROM.

To install the single-user version:

1. Insert the CD-ROM into your CD drive.

2. Click on Start and select Run. Type

D:\SETUP.EXE (where D: is your CD-ROM

drive letter), click OK and follow the

instructions.

3. Win*STARS 5.0 requires Microsoft

Internet Explorer™ 4.0 or higher. If you

do not have Internet Explorer, it may be

downloaded at no charge from www.

microsoft.com. It does not need to be

your default browser. If you do not wish to

use Internet Explorer, you have the option

to install Win*STARS 4.2.

You can delete this program at any time

by clicking on Start, Settings, Control

Panel, Add/Remove Programs. To reinstall

it, reboot your computer fi rst.

OperationTo start the CD-ROM, go to the WB

Development Data program group and click on

the WB Africa 2006 Database CD-ROM icon.

Note that standard Windows™ controls

are used for most functions. For detailed

instructions, refer to the on-screen Help

menu or tool tips (on-screen explanations of

buttons that are displayed when the cursor

rolls over them).

Features and instructionsWin*STARS has four main functions—Home,

Query, Result, and Map. Move among them at

any time by clicking on the respective tabs.

HomeOn the Home screen you can access each

element of the World Bank Africa Database

CD-ROM. Use the browser controls to link

to the Africa Development Indicator tables,

Country-at-a-Glance tables, time series

database, world map, and other related

information.

Query1. Click on the Query button to start your

time series selection.

2. Click on each of the Country, Series, and

Periods buttons and make your selections

on each screen. There are many ways to

make a selection—see below, or use the

Help menu.

3. Highlight the items you want.

4. Click on the Select button to move them

into the Selected box.

5. Deselect items at any time by highlighting

them and clicking on the Remove icon.

6. When selection is complete, click on OK

to return to the main Query screen.

7. If you want to, you can display information

on data availability by clicking on the

Availability icon. You can choose to count

time series or total observations.

8. Click on View Data to see the data on the

Result screen.

Making selections. Countries: You can

select countries from an alphabetical list,

by Classifi cation (region, income group, or

indebtedness), by Criteria (up to two can

be specifi ed), or by Group (aggregates

have been calculated only when there were

adequate data). Series: You can choose from

an alphabetical list or by Category. When

selecting series by category, the subcategory

buttons change with each category. Periods:

Select time periods from the Periods list box.

Creating your own country or indicator list.

You can create your own group of countries,

series, or periods by saving your query on

the appropriate screen. You can also save all

elements of the query on the Query screen.

You can reload a saved query in a future

session.

To save a query:

1. Highlight items on any of the Countries,

Series, or Periods (or any two or all three)

selection screens and click on Select to

place them in the Selected box.

2. Click on the Save Query icon and follow

the naming prompts.

To load a query:

1. Go to the selection screen in which

your query is saved. For example, if you

have saved a set of countries, go to the

Countries selection screen.

2. Click on the Load Query icon, select the

query you want, and click on OK.

To modify a saved query:

1. Load the query.

2. In the Selected box, highlight the items to

be removed and click on the Remove icon.

3. Add new items if necessary.

4. Resave the query.

ResultOn the Result screen, data are presented

in a three-dimensional spreadsheet and,

initially, in scientifi c notation. Data for the

third dimension are presented on separate

screens. You can change the selection

displayed by clicking on the third dimension

scroll box. You can also change the scale and

the number of digits after the decimal. If the

column is too narrow to present all the digits,

they will appear as a series of ######.

Double click on the column’s guideline to

widen it, or choose a larger scale (millions,

for example). To scale series individually,

click Options and check Enable Series-Level

Scaling. Click the far right scroll box to view

the percentage change over each selected

period or to index the data.

Changing the orientation. You can view the

result in six different orientations (countries

down/periods across, series down/countries

across, etc.). To change the orientation, click

on the Orientation scroll box.

Charting and mapping data. On the Result

screen, you can chart or map the data

displayed. Highlight a set of cells for charting

or a particular cell for mapping. Click on the

Chart or Map icon on the toolbar accordingly.

The charting function has many features.

Users GuideWorld Bank Africa Database 2006 and Africa Development Indicators CD-ROM

User Guide 151

After you have displayed a chart, right click

on the chart to open the Chart Wizard for

more options. Mapping is described on page

8. From this screen you can choose to map

all countries or only your selected countries.

Cutting, pasting, printing, and saving. You

can cut, paste, and print the result, or you

can save the spreadsheet in another format.

Click on the appropriate icon on the toolbar

and follow the prompts. Click on Help for

more details.

MapOn the Map screen, you can select a country

and view a set of tables describing it, or you

can map a series for all countries. In the

upper left corner of the screen, the country

name will appear as the cursor rolls slowly

over the map. To zoom in for a closer look at

the map, click on the Zoom icon.

Selecting a country or viewing country tables.

To highlight a country and view any of its

tables, click on the country on the map or

select it in the Locate a Country scroll box in

the upper right corner.

Mapping a series. On the Map screen, click

on the Series icon. A list of key indicators

will be displayed. (To show all available

indicators, click on the box by Show default

series to remove the X.) Highlight a series,

select a period from the Available Periods

list box (the default is the latest available)

and click on Paint Map. The map will be

colored according to the legend settings,

any of which you can change. Note that as

the cursor moves across the map, the series

value is now also displayed in the upper left

corner.

Changing the map legend and colors. The

default interval range is an equal number

of countries. To set an equal interval range

or to map multiple periods, click on the

Recalculate icon. Set your own intervals by

editing the legend. To change map colors,

double click on the legend color boxes. Press

the Remap icon to see your changes.

Printing and saving. Click on the appropriate

icon to print the map or save it as a bitmap

or metafi le.

License agreementYou must read and agree to the terms of this

License Agreement prior to using this CD-

ROM product. Use of the software and data

contained on the CD-ROM is governed by the

terms of this License Agreement. If you do

not agree with these terms, you may return

the product unused to the World Bank for a

full refund of the purchase price.

1. LICENSE. In consideration of your

payment of the required license fee, the

WORLD BANK (the “Bank” ) hereby grants

you a nonexclusive license to use the

enclosed data and Win*STARS retrieval

program (collectively, the “Program” )

subject to the terms and conditions set

forth in this license agreement.

2. OWNERSHIP. As a licensee you own the

physical media on which the Program

is originally or subsequently recorded.

The Bank, however, retains the title and

ownership of the program recorded on

the original CD-ROMs and all subsequent

copies of the Program. This license is not

considered to be a sale of the Program or

any copy thereof.

3. COPY RESTRICTIONS. The Program

and accompanying written materials are

copyrighted. You may make one copy of

the Program solely for backup purposes.

Unauthorized copying of the Program

or of the written materials is expressly

forbidden.

4. USE. You may not modify, adapt,

translate, reverse-engineer, decompile,

or disassemble the Program. You may

not modify, adapt, translate, or create

derivative works based on any written

materials without the prior written consent

of the Bank. If you have purchased the

single-user version of this product, you

may use the Program only on a single

laptop/desktop computer. You may

not distribute copies of the Program

or accompanying written materials to

others. You may not use the Program on

any network, including an Intranet or the

Internet, without obtaining prior written

permission from the Bank. If you have

purchased the multiple-user version of this

product, your license is valid only up to 15

users. Should you need to add additional

users, please send a request, indicating

the number of users you would like to add,

to: World Bank Publications, Marketing and

Rights, 1818 H Street, N.W., Washington,

D.C. 20433, fax: 202-522-2422, email:

[email protected].

5. TRANSFER RESTRICTIONS. This

Program is licensed only to you, the

licensee, and may not be transferred to

anyone without prior written consent of

the Bank.

6. LIMITED WARRANTY AND LIMITATIONS OF REMEDIES. The Bank

warrants that under normal use the CD-

ROMs on which the Program is furnished

are free from defects in materials and

workmanship for a period of ninety (90)

days from delivery to you, as evidenced

by a copy of your receipt. The Bank’s

entire liability and your exclusive remedy

shall be the replacement of any CD-ROMs

that do not meet the Bank’s limited

warranty. Defective CD-ROMs should be

returned within the warranty period, with

a copy of your receipt, to the address

specifi ed in section 9 below.

EXCEPT AS SPECIFIED ABOVE, THE

PRODUCT IS PROVIDED “AS IS”

WITHOUT WARRANTY OF ANY KIND,

EITHER EXPRESSED OR IMPLIED,

INCLUDING, BUT NOT LIMITED TO,

THE IMPLIED WARRANTIES OF

MERCHANTABILITY AND FITNESS

FOR A PARTICULAR PURPOSE. THE

BANK DOES NOT WARRANT THAT

THE FUNCTIONS CONTAINED IN

THE PROGRAM WILL MEET YOUR

REQUIREMENTS OR THAT THE

OPERATION OF THE PROGRAM WILL BE

UNINTERRUPTED OR ERROR-FREE.

IN NO EVENT WILL THE BANK BE LIABLE

TO YOU FOR ANY DAMAGES ARISING

OUT OF THE USE OF OR THE INABILITY

TO USE THE PROGRAM.

THE ABOVE WARRANTY GIVES YOU

SPECIFIC LEGAL RIGHTS IN THE UNITED

STATES THAT MAY VARY FROM STATE

TO STATE. BECAUSE SOME STATES

DO NOT ALLOW THE EXCLUSION OF

IMPLIED WARRANTIES OR LIMITATION

OF EXCLUSION OF LIABILITY FOR

INCIDENTAL OR CONSEQUENTIAL

DAMAGES, PARTS OF THE ABOVE

LIMITATIONS AND EXCLUSIONS MAY

NOT APPLY TO YOU.

7. TERMINATION. This license is effective

from the date you open the package

until the license is terminated. You may

terminate it by destroying the Program

and its documentation and any backup

copy thereof or by returning these

materials to the Bank. If any of the terms

or conditions of this license are broken,

the Bank may terminate the license and

demand that you return the Program.

8. GOVERNING LAW. This license shall be

governed by the laws of the District of

Columbia, without reference to confl icts

of law thereof.

9. GENERAL. If you have any questions

concerning this product, you may contact

the Bank by writing to World Bank

Publications, CD-ROM Inquiries, The World

Bank, 1818 H Street, N.W., Washington,

D.C. 20433, fax: 202-522-1498, email:

[email protected]. All queries on rights

and licenses should be addressed to

World Bank Publications, Marketing and

Rights, 1818 H Street, N.W., Washington,

D.C. 20433, fax: 202-522-2422, email:

[email protected].


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