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African Development Bank

Comparative Output, Incomes and Price Levels in African Countries

Highlights of the Results of the 2005 Round of the International Comparison Program for Africa

April 2008

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This document was prepared by the Statistical Ca-pacity Building Division of the Statistics Department of the African Development Bank Group. Designa-tions used in this publication do not imply the ex-pression of any opinion on the part of the African De-velopment Bank concerning the legal status of any country or territory or the delimitation of its frontiers. While every effort has been made to present reliable information, the African Development Bank accepts no responsibility whatsoever for any consequences of its use.

Statistical Capacity Building Division Statistics DepartmentChief Economist ComplexAfrican Development BankTemporary Relocation Agency (TRA)BP 323, 1002 Tunis, BelvédèreTunisia

Tel.: (216) 71 10 36 54Fax: (216) 71 10 37 43

E-mail: [email protected]: http://www.afdb.org

Copyright © 2008 African Development Bank

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Highlights of the Results of the 2005 Round of the International Comparison Program for AfricaAfrican Development Bank

Comparative Output, Incomes and Price Levels in African Countries


April 2008

2



AcknowledgmentsThis publication was prepared by a team led by Michel Mouyelo-Katoula. The

core team included Abdoulaye Adam, Luc Mbong Mbong, Adalbert Nshimyu-

muremyi, Besa Muwele, Stephen Bahemuka, Marianne Kurzweil, Mathieu Bi-

okou Djayeola, and Marc Koffi Kouakou.

The validation of country data was carried out by the participating 48 African

countries under the supervision of the AfDB’s statistics team. The multilateral

review of input data and the generation of results was led by Yuri Dikhanov

from the World Bank, who also provided valuable input on aggregation meth-

ods. Comprehensive methodological support was provided by the ICP Global

Office, led by Frederic Vogel.

The program also benefited from support provided by the ICP-Africa coordi-

nation teams in the four participating subregional organizations, led by Mar-

tin Balepa (AFRISTAT), Themba Munalula (COMESA), Ackim Jere (SADC), and

Joseph Ilboudo (ECOWAS).

African government representatives, private sector partners, and civil soci-

ety members provided valuable input and comments during this project. Sev-

eral institutions also contributed to the project at various stages: the United

Kingdom Office for National Statistics, U.K. Department of International Devel-

opment (DFID), Japanese Trust Fund, African Capacity Building Foundation,

French Institut National de la Statistique et des Études Économiques (INSEE),

Indian Trust Fund, and ICP Global Office Management and Directorate at the

World Bank.

The AFDB Statistics Department team responsible for desktop publishing was

led by Koua Louis Kouakou.

The project also benefited from valuable input from staff of the national statis-

tical offices of the 48 participating African countries.

This publication was prepared under the direction of Charles Leyeka Lufumpa,

Director of the AfDB Statistics Department, and the overall guidance of AfDB

Chief Economist Louis Kasekende.


PrefaceThis report presents the highlights of the final results of the extensive monthly

price surveys and gross domestic product (GDP) expenditure data compila-

tion undertaken in 48 African countries during the past two years under the

International Comparison Program for Africa (ICP-Africa) managed by the

AfDB. A more detailed report will follow later and will provide information on

the methodological approaches used to generate the results.

The ICP is a global program involving about 150 participating countries world-

wide, 48 of which are in Africa. By using purchasing power parities, the pro-

gram aims to provide a reliable basis for comparing GDP and related economic

aggregates across countries. It allows comparisons of the real value of pro-

duction for each country using a standardized benchmark free of price and

exchange rate distortions. The AfDB is responsible for managing ICP-Africa,

the African component of the global program. The AfDB’s involvement marked

the first time since the inception of ICP nearly 40 years ago that an African

institution has taken the lead role in implementing ICP activities in the region.

In addition to preparing ICP estimates for Africa, the Bank also aims to develop

the statistical capacity of participating countries, including the enhancement

of the skills of national statisticians.

To be successful, the program required a major team effort. On behalf of the

AfDB, I wish to thank those who have contributed to making this ICP-Africa

round a great success. National statistical offices have done an outstanding

job, often under very difficult circumstances, to prepare the data needed for

this endeavor. Without their strong commitment, this project would not have

been possible. Because of the number and diversity of the countries of Africa,

the work was coordinated by four subregional organizations under the techni-


cal guidance of the staff in the AfDB Statistics Department. I was delighted by

both the result and the high level of cooperation and commitment exercised by

everyone involved in the program.

I offer my congratulations to everyone involved for a job well done and recom-

mend this publication to all AfDB clients.

Louis Kasekende

Chief Economist

African Development Bank Group


Abbreviations and AcronymsACBF : African Capacity Building FoundationAFCE : Actual Final Consumption ExpenditureAfDB : African Development Bank AFRISTAT : Observatoire Economique et Statistique d’Afrique SubsaharienneCIS : Commonwealth of Independent StatesCOMESA : Common Market for Eastern and Southern AfricaCPD : Country Product DummyCPI : Consumer Price IndexECOWAS : Economic Community of West African StatesEKS : Elteto-Köves-SzulcEMCCA : Economic and Monetary Community of Central AfricaGDP : Gross Domestic ProductGEKS : Generalized Elteto-Köves-Szulc GFCF : Gross Fixed Capital FormationGK : Geary-KhamisHDI : Human Development Index HFCE : Household Final Consumption ExpenditureICP : International Comparison ProgramIMF : International Monetary FundINSEE : Institut National de la Statistique et des Etudes EconomiquesITF : Indian Trust FundNGO : Non-Governmental OrganizationNORAD : Norwegian Agency for Development CooperationONS-UK : Office for National Statistics - United KingdomOECD : Organization for Economic Cooperation and Development PLI : Price Level IndexPPP : Purchasing Power ParitySADC : Southern African Development CommunitySNA : System of National AccountsSPD : Structured Product DescriptionTRA : Temporary Relocation AgencyUK-DFID : United Kingdom - Department for International DevelopmentUN : United NationsUNDP : United Nations Development ProgramUNESCO : United Nations Educational, Scientific and Cultural OrganizationUNICEF : United Nations Children’s FundUS$ : United States DollarsWAEMU : West African Economic and Monetary UnionWDI : World Development Indicators

WHO : World Health Organization


Table of Contents 1. Introduction 8

2. What are Purchasing Power Parities? 11

3. Why Use Purchasing Power Parities rather than Exchange Rates? 13

4. Uses and Applications of Purchasing Power Parity Data 15

5. Limitations of using Purchasing Power Parities 17

6. Compilation Methodology 19

7. Highlights of the Results 21

A. Which are the largest or smallest economies? 21

B. Which are the richest or poorest countries? 23

C. Which countries have the highest or lowest living standards? 25

D. Which are the most or least expensive economies? 27

E. Which countries have the highest or lowest relative investment expenditures? 28

8. Conclusion and the Way Forward 30

Appendices

Annex I. Comparison of ICP 2005 Global Results with WDI 2005 33

Annex II. Real and Nominal GDP in Africa 38

Annex III. Real and Nominal Per Capita GDP in Africa 40

Annex IV. Real and Nominal Per Capita Actual Final Consumption Expenditure

and Price Level Indices in Africa 41

Annex V. Real and Nominal Per Capita Gross Fixed Capital Formation and

Price Level Indices in Africa 42

List of Figures

Figure 1. GDP Distribution in Africa 22

Figure 2. Richest and Poorest Countries in Africa 24

Figure 3. African Countries with the Highest or Lowest Living Standards 25

Figure 4. Most or Least Expensive Countries in Africa 26

Figure 5. African Countries with the Highest or

Lowest Per Capita Investment Expenditures 27

Figure 6. African Countries with the Highest or

Lowest Price Levels for Investment 28

Figure 7. Distribution of Real Gross Fixed Capital Formation in Africa 29

8


1. Introduction1.1 The United Nations International Comparison Project was started in 1968

with the aim of conducting global comparisons. Comparisons were made every

five years commencing in 1970. Initially ten countries were involved, including

one from Africa. By 1993, the program included 118 countries, including 22

countries from Africa. Substantial changes to the program were implemented

following a major review of the 1993 round of comparisons. For the current

International Comparison Program (ICP) round (the 2005 round), about 150

countries are participating, an ICP record.

1.2 Major changes have been made to the scope of the ICP program, and new

governance arrangements have overcome some of the limitations of earlier

rounds. Overall ICP coordination was achieved through a Global Executive

Board, comprising representatives of the main stakeholders, including inter-

national organizations, regional agencies, and national statistical offices. The

African Development Bank (AfDB) and two prominent African statisticians

represent the Africa Region on the Board. The Board was responsible for set-

ting goals and objectives as well as the strategic framework for the global

ICP, taking into consideration the statistical needs of regional agencies and

countries.

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1.3 A secretariat housed at the World Bank was responsible for day-to-day

management of the global program. An independent technical advisory group,

comprising world-renowned eminent scholars and statisticians, provided

guidance on technical issues and monitored the use of appropriate methodol-

ogy.

1.4 Regional implementing agencies were responsible for designing, imple-

menting, and managing the regional programs, including providing technical

guidance and coordinating activities in the participating countries. The AfDB

was responsible for managing the African program. The regional agencies are

as follows:

Africa: African Development Bank (48 participating countries)•

Asia: Asian Development Bank (23 participating countries)•

Commonwealth of Independent States (CIS): Russia Federal State Statistics •

Service and Statistics Committee of the CIS (10 participating countries)

European Union and OECD countries: Eurostat and OECD (45 participating •

countries)

Latin America and Caribbean: U.N. Economic Commission for Latin Ameri-•

ca and Caribbean and Statistics Canada (10 participating countries)

Western Asia and Middle East: U.N. Economic and Social Commission for •

Western Asia (11 participating countries)

1.5 ICP-Africa was launched in 2002 by the AfDB. As the coordinator, the

AfDB was supported by four subregional organizations (AFRISTAT, COMESA,

ECOWAS, and SADC) that helped supervise administrative activities as well

as coordinate some field activities at the subregional level. The United King-

dom Office for National Statistics and the French INSEE (Institut National de

la Statistique et des Études Économiques) provided technical assistance on a

needs basis and in line with the AfDB’s technical requirements. The AfDB intro-

duced fundamental changes to the program to allow for greater participation

10


by African countries. As a result, virtually all African countries (a total of 48)

have been part of the ICP comparison, and together they comprise the largest

single regional participating group and one-third of the countries in the global

comparison. Unlike programs in other regions, the Africa program also serves

as a platform for improving the national statistical systems of participating

African countries. This broad-based, capacity-building effort involves African

and international partnerships.

1.6 The African region is one of the most diverse in the world. The already

complex task of conducting a large-scale project like ICP-Africa, covering 48

countries, was further complicated by the countries’ geographic dispersion

and by the large variations in their size, structure, and standard of living. The

huge variety in the types of goods and services produced and consumed in

different parts of the region presented the AfDB with some difficulties during

the process of developing a common list of products to be priced across the

region. These difficulties were further compounded by the fact that several

countries in the region are at very low levels of statistical development.

1.7 The differences among the participating countries in size, geography, and

statistical capacity posed a big challenge for sound economic comparison.

Most challenges were largely overcome through mechanisms that were put

in place at various stages of program implementation. All participating coun-

tries worked closely under AfDB technical supervision to generate price and

national accounts data that are now more comparable and meet international

standards. As a result, the estimates of purchasing power parities (PPPs) in this

round are far more robust than previous estimates due to improved methodol-

ogy coupled with better data collection, editing, and processing procedures.

1.8 The highlights in this report provide an overview of key findings of the

2005 ICP-Africa round that will be presented in the main report to be pub-

lished in June 2008.


11


2. What Are Purchasing Power Parities?

2.1 A purchasing power parity is a form of exchange rate that takes into ac-

count the differences in price levels across countries. One can think of a PPP

as a rate at which one country’s currency would have to be exchanged to buy

the same quantity of goods and services in another country. A PPP between

two countries, A and B, is, therefore, the ratio of the number of units of country

A’s currency needed to purchase in country A the same quantity of a specific

good or service as one unit of country B’s currency would purchase in coun-

try B. PPPs can be expressed in terms of the currency of either country.

2.2 Using PPPs allows for the comparison of real values of goods and serv-

ices produced in various economies, adjusted through a common set of in-

ternational (or regional) average prices. The PPPs can, therefore, be seen as

the average price ratios in participating countries. This process allows for the

removal of distortions caused by different price levels and market exchange

rates observed between countries for similar goods and services.


12


2.3 PPPs generated for a country can then be used to facilitate real compari-

son of various economic aggregates across countries or across regions within

the same countries. Section 4 provides an overview of some of the key uses

of PPP statistics.

13


3. Why Use Purchasing Power Parities Rather than Exchange Rates?

3.1 Using observed market exchange rates to convert aggregates into local

currency units can be misleading because exchange rates do not reflect rela-

tive domestic price levels and are inherently biased for several reasons: (i)

they do not measure differences in the price levels of commodities in the dif-

ferent countries; (ii) in some countries they are fixed by policy decrees and

do not necessarily reflect the true value of the currency; (iii) they are subject

to fluctuations from currency speculation and short-term capital movements;

(iv) they do not indicate differences in price levels in the various sectors of

the economy; and (v) fluctuations can result in some arbitrary changes in the

wealth of countries sometimes overnight, as has been the case in the euro

zone following the weakening of the dollar during the past few years. In this

sense, PPPs provide a much better comparative measure of economic aggre-

gates across countries at a given time.

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3.2 Human development has many dimensions and is measured using vari-

ous factors, which may include per capita incomes, economic growth, health,

education, social progress, globalization, or poverty reduction or a combina-

tion of these factors. In each case, having internationally comparable, high-

quality statistical measures is vital to making reliable inter-country compari-

sons, monitoring progress, and assisting in evidence-based decision making.

Comparing economic and social data (such as poverty statistics) is complex

because economic aggregates are typically expressed in national currencies.

The use of exchange rates is a common method to convert economic data from

a national currency to a numeraire currency such as the United States dollar.

3.3 This simplistic approach is not appropriate, however, for comparisons of

real income or output and for comparisons of productivity and standards of

living. Using exchange rates to convert aggregates in national currency units

can be misleading because exchange rates do not reflect relative domestic

price levels and are influenced by extraneous factors such as financial flows.

Exchange rates are often subject to large, short-term swings of a speculative

nature that can wrongly imply corresponding shifts in relative living stand-

ards. In assessing relative standards of living, it is necessary to compare the

volumes of goods and services (value aggregates in real terms or at constant

prices) actually available to residents of different countries in their own coun-

tries, taking into account the relative price levels of each of the countries.

3.4 PPPs directly take into account differences in the relative price levels be-

tween countries. For example, products in low-income countries are normally

cheaper than those in high-income countries largely because services are

usually cheaper in low-income countries. Many services are produced and

consumed within a country and cannot be exported or imported directly (e.g.,

haircuts, dry cleaning). The price charged for these services is based largely

on the wages paid to those providing the service. As a result, in lower-income

countries, the prices paid for such services are cheap because wages are

low, and vice versa for high-income countries. Such services do not affect a

country’s exchange rate, but they have a marked impact on PPPs, which are

obtained by directly comparing the prices paid for such services in differ-

ent countries. Using PPPs rather than exchange rates to convert values into

a common currency generally has the effect of (proportionally) narrowing the

gap observed between high-income and low-income countries.

15


4. Uses and Applications of Purchasing Power Parity Data

4.1 As the benefits of PPPs and PPP-converted data have become more appar-

ent, the range and types of users have increased. International organizations,

universities, economic analysts, private sector businesses, and policy makers

use PPP-based data for analyzing levels of economic activity, productivity, in-

come, investment, and inequality in the distribution of incomes between coun-

tries and for compiling statistics on regional and global poverty.

4.2 ICP data also make it possible to analyze the structural characteristics

of the economy using international prices. For example, economic and price

structures of countries at different stages of development could be examined

in relation to a comparator country. A country could also take measures to im-

prove its competitiveness based on analysis of its price structure in relation to

regional price levels. Such analysis may point to the need to improve transport

and storage facilities, packaging, and marketing practices to reduce transac-

tion costs and thus attract investment.

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4.3 Multinational corporations also increasingly use ICP data for monitoring

and assessing exchange rate developments because their investment deci-

sions are based on the real values of the return on their investment. ICP data

are also used for evaluating cross-country investment costs, including unit

labor and material costs, and determining project viability, market size, and

asset allocation. The assessment of industry growth potential and associated

investment risks across countries is another important potential use of ICP

data in the private sector. Some specialized firms also use ICP data to deter-

mine PPP-adjusted cost-of-living allowances across countries on a monthly

basis to meet the needs of multinational corporations, major nongovernmental

organizations, and international development agencies.

4.4 At the international level, PPP data are used, among other things, for es-

tablishing the international poverty threshold (World Bank), constructing the

Human Development Index (U.N. Development Programme), comparing health

expenditures per capita (World Health Organization), assessing per capi-

ta expenditures in education (UNESCO), monitoring the welfare of children

(UNICEF), comparing the relative sizes of economies and estimating weighted

averages of regional growth rates (International Monetary Fund and AfDB),

and adjusting salaries and expatriate allowances to compensate for cost-of-

living differentials (donors). The international community uses the interna-

tional poverty line of $1 a day measured in PPPs to monitor progress toward

reducing the number of people in absolute poverty.

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5. Limitations of Using Purchasing Power Parities

5.1 While the use of PPPs provides a more robust method for spatial com-

parison of various economic aggregates across and within countries and re-

gions, caution must be exercised in using PPPs to draw conclusions about

the appropriate exchange rates for any country. First, PPPs do not necessarily

provide an indication of what the exchange rate “should be.” This would be

the case only if PPPs covered only tradable goods. The PPPs generated in the

ICP exercise, however, cover not only tradable products but also non-tradable

ones, such as housing and personal and government services. Exchange rates

are determined by the total demand for a particular currency, and financing

foreign trade is only one component of this demand. Therefore, PPPs should

not be used to determine a country’s “correct” exchange rate. This is more ap-

propriately determined by international currency markets.

5.2 Second, PPPs are statistical estimates and, therefore, subject to estima-

tion and sampling errors. The same can be said about the national accounts

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statistics that are used as weights for generating PPPs at basic heading lev-

els. When PPPs and national accounts are combined into total or per capita

GDP (in PPP terms), the resulting real GDP or per capita figures should not

necessarily be used to establish rigid rankings among countries, particularly

in situations where differences between countries are relatively small. This is

because the reliability of PPPs and volume measures depend to a large extent

on the level of detail. At a more aggregated level, PPPs are likely to be more re-

liable. For example, PPPs for food and nonalcoholic beverages would be more

reliable than PPPs for food alone, and PPPs for bread and cereals are likely to

be more reliable than PPPs for just rice. This has been an important considera-

tion in determining the optimal level of data disaggregation presented in this

publication.

5.3 In the same vein, caution should be used when comparing countries by

their GDPs or in per capita measures. Because errors may occur in the calcu-

lation of GDP and population sizes as well as in the estimation of PPPs, small

differences should not be considered significant. Caution should also be ex-

ercised about making comparisons of price levels or per capita expenditures

at low levels of aggregation, where small errors may lead to large discrepan-

cies.

5.4 Finally, time series of different benchmark estimates of real GDP (in PPP

terms) are not directly comparable over time. Real GDP provides a snapshot of

the relative real GDP levels among participating countries for a given bench-

mark year. When benchmark PPP estimates for different benchmarks are

placed side by side, these snapshots may appear to provide a moving picture

of relative real GDP levels over the years. This apparent time series of real

GDP, however, is actually similar to a current price time series showing the

combined effect of changes in relative price levels and changes in relative real

GDP levels. Within each year, the indexes are at a uniform price level, but the

uniform price level changes from one reference year to the next1.

1 Asian Development Bank, Purchasing Power Parity and Real Expenditure Highlights, December 2007.

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6. Compilation Methodology6.1 Participation in ICP-Africa included two basic data requirements. First,

each participating country had to provide estimates of its GDP according to

the framework described in the 1993 System of National Accounts (SNA93)

following the expenditure approach.

6.2 Second, for a country’s data to be used in the ICP-Africa program, the

AfDB required that it be compiled using the expenditure approach, with its

components allocated to 155 basic headings. In this regard, several clas-

sifications as used in the SNA93 were required. For ICP purposes, the most

important classifications are those relating to expenditure. In particular, the

Classification of Individual Consumption by Purpose provides a good frame-

work for dividing individual consumption expenditure by households into

110 basic headings.

6.3 Similarly, the Classification of the Functions of Government provides the

framework for delineating government expenditure, individual and collective.

The other large component of GDP, gross fixed capital formation (GFCF), is clas-

sified by type of asset on which expenditures were incurred, such as construc-

tion and equipment.

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6.4 In several participating countries, the national accounts were compiled us-

ing the production approach, meaning that the expenditure estimates required

for ICP purposes were not available. In such cases, the basic headings were

computed using alternate data sources (household surveys, supply and use

tables, or commodity flows).

6.5 Participating countries were also required to provide annual average ex-

change rates and the midyear resident population for the reference year. The

prices that countries were required to collect were national annual z prices

charged to consumers. Countries were not required to price inventories, valu-

ables, and exports and imports.

6.6 Obtaining PPPs for the 48 participating countries in ICP-Africa involved

three broad aggregation processes:

1. Averaging the individual price observations to form a national annual av-

erage price for each product in each economy;

2. Calculating PPPs at the basic heading level; and

3. Calculating PPPs for GDP and its major aggregates in the 48 countries

within the region for which GDP estimates were available.

21


7. Highlights of the Results7.1 This section provides an overview of the key findings of the ICP-Africa

data collection exercise conducted during the period 2005 to 2007. It provides

information on the size and relative rankings of African countries, compari-

son of living standards across countries, relative price levels observed in the

countries, as well as comparative investment expenditure levels.

A) Which are the largest or smallest economies?

7.2 GDP is the most commonly used measure of the size of a country’s econo-

my. A country’s GDP is the sum of the product of prices of goods and services

consumed during a year and their respective quantities. ICP-Africa gives an

opportunity to compare the size of heterogeneous economies on the basis

of their purchasing power and rank countries’ contributions to the region’s

output.

7.3 Annex II shows GDP figures of African countries at PPPs and exchange

rates using results from the current ICP-Africa round.2 The figures reveal that

2 The current results reveal some differences between previous estimates on GDP and GDP per capita published in the World Bank’s World Development Indicators (WDI) and the results of the current ICP-Africa round. For an explanation of the differences, refer to Annex I.

22


when Africa’s GDP is measured at PPPs, its size more than doubles in com-

parison to GDP size at market exchange rates. This is because exchange rates

often tend to understate the purchasing power of the currencies of developing

countries, particularly for non-tradable goods and services.

7.4 Figure 1 provides summary information on the distribution of Africa’s GDP

at current PPPs and official exchange rates. Using either of these measures

shows that the top five countries account for nearly two-thirds of the region’s

GDP when measured in real terms (i.e., PPP-adjusted): South Africa (22 per-

cent), Egypt (20 percent), Nigeria (14 percent), Morocco (6 percent), and Su-

dan (4 percent).3 Three (Egypt, Nigeria, and Sudan) are oil-producing coun-

tries, and one (Nigeria) is the most populated country in Africa. Thirty-three

African countries individually account for less than 1 percent of the region’s

output and collectively account for less than 15 percent of the region’s total

GDP. Some dynamic changes in the relative size and shares of these top five

African economies appear, however, when using PPPs or market exchange

rates to measure output.

Figure 1. GDP Distribution in Africa

Share of Africa’s GDP (%)

Country At PPPs At Market Exchange Rates

South Africa 22.35 28.84

Egypt 19.88 11.78

Nigeria 13.91 13.52

Morocco 6.03 7.03

Sudan 4.48 4.19

Tunisia 3.64 3.46

Angola 3.00 3.61

Kenya 2.70 2.23

Ethiopia 2.39 1.32

Tanzania 2.02 1.51

Rest of African Countries 19.60 22.51

Note: For country details, refer to Annex II.

3 Algeria would likely have been among these top five countries, but it did not participate in the 2005 ICP-Africa round.

23


7.5 In particular, the size and share of the Egyptian economy increase signifi-

cantly using PPPs. Egypt’s economy, which is less than one-half the size of the

South African economy when using market exchange rates, more than trebled

when using PPPs. Egypt’s share of the region’s aggregate GDP also increases

to about 20 percent when using PPPs, compared with 12 percent when us-

ing market exchange rates. Measuring the economy of Egypt in U.S. dollars at

market exchange rates, therefore, underestimates its relative weight and size.

On the other hand, South Africa’s share in the region’s GDP falls from 29 to 22

percent when its output is measured using PPPs rather than market exchange

rates. This is a reflection of relatively low price levels in Egypt compared to

South Africa.

B) Which are the richest or poorest countries?

7.6 Real GDP per capita is typically used to distinguish between rich and

poor countries. Deflating GDP by population removes the distortion created

by population size and allows a comparison of the standard of living across

countries. Real GDP per capita measures the flow of goods and services that

is available to countries to contribute to their economic well-being. Figure 2

illustrates the distribution of per capita income in PPP and in nominal terms

(US$) by country.

7.7 Measured by real GDP per capita, the five richest countries are Gabon (US$

12,742), Botswana (US$ 12,057), Equatorial Guinea (US$ 11,999), Mauritius

(US$ 10,155), and South Africa (US$ 8,477). Four of these five countries have

a small population—between 1 and 1.7 million—and their share in real terms

of the regional output varies from 0.68 to 1.2 percent. The region’s average real

GDP per capita is US$ 2,223 in PPP terms. Thirty-four countries have a real GDP

per capita of less than US$ 2,500; half have a real GDP per capita of less than

US$ 1,400; and a quarter have a real GDP per capita of less than US$ 800. As

shown in Figure 2, the latter category includes the five poorest countries: Ethi-

opia (US$ 591), Guinea-Bissau (US$ 569), Zimbabwe (US$ 538), Liberia (US$

383), and Democratic Republic of Congo (US$ 264).

24


7.8 On one hand, adjusted PPP estimates of GDP result in a drop in the wealth of

some countries. The largest changes in ranking are for Central African Republic

and Comoros. Central African Republic is ranked the 35th richest economy when

the market exchange rate is used but drops to 41st when the PPP exchange rate

is used (see Annex III). Comoros drops from 25th to 29th place. On the other hand,

some countries are found to be richer when real GDP per capita is used instead

of the market-based exchange rate converted GDP. For example, Egypt moves up

from 13th to 7th place, Mauritania from 24th to 19th, Chad from 22nd to 18th, and The

Gambia from 43rd to 39th place.

7.9 Egypt posts the biggest rank gain when PPP-adjusted GDP is used. Indeed,

although Egypt is a middle-income country, it is the cheapest country in the

sample (as is discussed in section 7.4), with the lowest price level index, and

is tied with Ethiopia, which is much poorer. The difference in Egypt’s ranking

provides an illustration of the extent to which PPP rather than market exchange

rates is regarded as a better measure of the relative cost of living.

Botsw

ana

Gabon

0

2000

4000

6000

8000

10000

12000

14000

Equ. Guin

ea

Mau

ritiu

s

South

Afri

ca

Africa

Ave

rage

Ethio

pia

Guinea

-Biss

au

Zim

babwe

Liber

ia

Congo

, Dem

. Rep

.

Figure 2: Richest and Poorest Countries in Africa (Real GDP Per Capita in US$)

Note: For country details, refer to Annex III.

25


C) Which countries have the highest or lowest living standards?

7.10 While the full range of goods and services that enter GDP serve to meas-

ure countries’ general economic development, to compare living standards

in different countries, PPPs based on what households consume should be

used. A more appropriate measure of the economic well-being of the popula-

tion is obtained by comparing per capita actual final consumption expenditure

(AFCE). Figure 3 shows real per capita AFCE for the highest and lowest coun-

tries.

7.11 Although the same group of countries that were at the top of the list on the

basis of real per capita GDP continue to dominate the top rankings when the

comparison is based on per capita real AFCE, their rankings change. Gabon,

Botswana, and Equatorial Guinea each drop by four and five positions, moving

respectively from first, second and third to fifth, seventh and eighth. Mauritius,

South Africa, Tunisia, and Egypt, each move up by three positions, from fourth,

South

Afri

ca

Mau

ritiu

s

450

400

350

300

250

200

150

100

50

0

500

Tunisi

a

Egypt

Gabon

Africa

Ave

rage

Niger

Guinea

-Biss

au

Zim

babwe

Liber

ia

Congo

, Dem

. Rep

.

Figure 3: African Countries with the Highest or Lowest Living Standards (Real Per Capita AFCE Index: Africa=100)

Note: For country details, refer to Annex IV.

26


fifth, sixth, and seventh to first, second, third, and fourth, respectively.

7.12 When real household expenditures per capita is used rather than real

GDP per capita, the largest drop in the rankings of countries is observed for

Angola, which moves from the 12th position to the 35th, the Republic of Congo

from 10th to 23rd, and Chad from 18th to 25th. These countries have the small-

est share of real consumption expenditures of households in GDP (19 percent

for Angola, 25 percent for the Republic of Congo, and 46 percent for Chad).

Because these are oil-producing countries, the small shares in consumption

expenditures indicate the domination of oil resources in their economies. Oil

producing countries4 have more spending power and this is reflected in their

4 Oil producing countries in the region include Angola, Cameroon, Chad, Congo, Democratic Repub-lic, Cote d’Ivoire, Egypt, Arab Republic, Equatorial Guinea, Gabon, Mauritania, Nigeria, Republic of Congo, Sudan, and Tunisia. Note that Algeria and Libya, two major oil producers in Africa, did not participate in the 2005 ICP round. Libya, however, participated in some of the AfDB’s capacity-building activities.

0

0,5

1.0

1.5

2.0

2.5

3.0

3.5

Cape V

erde

Zim

babwe

Namib

ia

South

Afri

ca

Comor

os

Africa

Ave

rage

Mad

agas

car

Burundi

Gambia,

The

Egypt

Ethio

pia

Figure 4: Most or Least Expensive Countries in Africa (Price Level Indices: Africa =1.0)

Note: For country details, refer to Annex II.

27


per capita expenditure.

7.13 For other countries the ranking in terms of real household expenditures

per capita instead of real GDP per capita causes Lesotho to move up from the

23rd to 13th, Togo from 35th to 26th, and Sao-Tome and Principe from 22nd to 15th.

D) Which are the most or least expensive countries?

7.14 The price level index (PLI) is the ratio of a country’s PPP to the exchange

rate of its currency to the U.S. dollar. PLIs provide a comparison of the coun-

tries’ overall price levels with respect to Africa’s average. A PLI greater than 1

means that prices are higher than the region’s average, and a PLI less than 1

means that prices are relatively lower than the region’s average. Hence, PLIs

allow the identification of the most and the least expensive countries, as indi-

cated in Figure 4.

7.15 While on average, PLIs are higher in richer countries than in poorer ones,

0

100

200

300

400

500

600

700

800

Botsw

ana

Gabon

Equitoria

l Guin

ea

Mau

ritiu

s

South

Afri

ca

Africa

Ave

rage

Gambia,

The

Sierra

Leo

ne

Cote

d'Ivoi

re

Congo

, Dem

. Rep

.

Centra

l Afri

can R

ep.

Figure 5: African Countries with the Highest or Lowest Per Capita Investment Expenditures (Real Per Capita GFCF Index: Africa =100)

Note: For country details, refer to Annex V.

28


some notable exceptions appear. Zimbabwe has the highest PLI, followed by

Cape Verde, Namibia, South Africa and Comoros. PLIs are lowest in Ethiopia,

Egypt, The Gambia, and Burundi. The fact that Zimbabwe has the highest PLI

is a reflection of the hyper inflation situation prevailing in the country. Indeed,

prices in Zimbabwe are close to twice as high as the second most expensive

country in the region.

E) Which countries have the highest or lowest relative investment expenditures?

7.16 Gross fixed capital formation measures a country’s investment expendi-

tures and consists primarily of purchases of machinery and equipment goods

and construction services. Gross fixed capital formation accounts on average

for around 22 percent of the regional GDP. Figure 5 presents real per capita

investment expenditures for the countries that have the highest or lowest ex-

0

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

Republic

of C

ongo

Cote

d'Ivoi

re

Equitoria

l Guin

ea

Leso

tho

Zim

babwe

Africa

Ave

rage

Burundi

Congo

, Dem

. Rep

.

Egypt

Ethio

pia

Mala

wi

Figure 6: African Countries with the Highest or Lowest Price Levels For Investment (Africa Index=1.00)

Note: For country details, refer to Annex V.

29


penditure levels.

7.17 The same group of countries that were at the top of the list on the basis of

real GDP per capita continues to dominate the top rankings when the compari-

son is based on real gross fixed capital formation per capita: Gabon, Botswana,

Equatorial Guinea, Mauritius, and South Africa. At the level of gross fixed capital

formation, price level indices provide a measure of the differences in investment

prices between countries. On one hand, Cote d’Ivoire and Republic of Congo

(both 1.92) are almost twice as expensive as the average for the region. On the

other hand, Malawi has the lowest PLI (0.46) for investment, representing the

cheapest investment destination on the continent (see Figure 6).

7.18 South Africa has the highest share of fixed capital formation per capita

in the region regardless of whether it is measured at the market exchange

rate (25.8 percent) or by PPP (22.4 percent) (see Figure 7). Note, however, that

South Africa’s share is smaller when the PPP measure is used. While very few

changes occur in the share of fixed capital formation per capita when meas-

ured at the market exchange rate or at PPP, a few countries exhibit significant

changes, which lead to a change in ranking (see Annex V). For example, Egypt,

which is ranked third when using the exchange rate measure, moves up to

the second, thereby moving Morocco to third place. At the other end of the

spectrum, Côte d’Ivoire drops nine places, while Equatorial Guinea and the

Republic of Congo each drop eight. As indicated earlier, this reflects the high

cost of investments in those countries.

Rest of Africa

Angola

Nigeria

Morocco

Egypt, Arab Rep.

South Africa

Figure 7: Distribution of Real Gross Fixed Capital Formation in Africa

Note: For country details, refer to Annex V. Percentages may not total 100 due to rounding.

Rest of Africa 37

Angola 5

Nigeria 8

Morocco 10

South Africa 22

Egypt, Arab Rep. 16

30


8. Conclusion and the Way Forward

8.1 The ICP-Africa 2005 activities represent a key milestone because this was

the first time an African institution, the AfDB, coordinated the program from

the beginning to the end. It was also the first time that almost all countries on

the African continent participated in the comparison. The involvement of sub-

regional organizations and the ICP Global Office in the day-to-day administra-

tive and technical aspects of the program ensured sound methodologies and

orderly data collection procedures. Consequently, the estimates provide a firm

basis for meaningful inter-country comparisons.

8.2 The results generated from this ICP-Africa round resulted in improved

data to assess the relative standing of the countries in the region. Country

GDPs can now be compared using PPPs, which provide a more robust set of

comparisons than was previously the case when only exchange rates were

used. Additionally, ICP-Africa provided an opportunity to strengthen human

resource skills in the region.

31


8.3 The ICP results comprise a critical input in the policy-management and

decision-making processes at national and international levels. Besides the

usefulness of the data for facilitating cross-country comparison of GDP and

related aggregates, the results are useful for comparing regional poverty inci-

dences and analyzing poverty across countries. They can also be used in the

investment and employment decisions of various economic agents.

8.4 In view of the importance of ICP data for development policy management,

the AfDB and African countries must sustain ICP activities beyond the cur-

rent round. In particular, countries must make ICP activities an integral part

of their regular activities with a specified resource envelope. Some countries

have committed resources for ICP activities, and the heads of national statisti-

cal offices made a commitment in the Accra Declaration of December 2007 to

integrate the core ICP-Africa activities into their routine statistical activities.

8.5 Most countries participating in ICP-Africa also collected data in 2006 and

2007. This data will be published in 2009. Before the next ICP-Africa round in

2011, the AfDB is expected to take advantage of the synergy created between

the ICP-Africa and the Consumer Price Index data collection activities in par-

ticipating countries to publish ICP-Africa results for the years 2008 to 2010.

In addition, the AfDB plans to increase its statistical capacity-building initia-

tives in partnership with all key stakeholders and in line with the principles

espoused in the Regional Reference Strategic Framework (RRSF) for Statistical

Capacity Building in Africa and the National Strategies for the Development of

Statistics (NSDS), which clearly delineate country priorities for results meas-

urement.

32


Appendices

33

Annex I: Comparison of ICP 2005 Global Results with WDI 2005 This annex explains why the new PPPs differ from the previous estimates for

2005, which were based on extrapolations from the previous benchmark sur-

veys as published by the World Bank in the World Development Indicators

(WDI). The annex is adapted from the World Bank’s “ICP Global Final Results”

and focuses only on the data concerning African countries.

The previous PPPs for African countries were based on the 1993 benchmark

ICP exercise in which 22 African countries participated. The results for 2005

were obtained through extrapolation of the 1993 benchmark results, using GDP

deflators. If a country did not take part in the 1993 ICP round (non-benchmark

countries), estimates were computed using a regression model based on in-

formation obtained from participating countries. These extrapolated estimates

are referred to as the WDI 2005 estimates because they appeared in the WDI

2007 and in the WDI database.

Once the estimations are obtained for the benchmark year, PPPs and the as-

sociated PPP-adjusted GDP per capita estimates for both benchmark and non-

benchmark countries are extrapolated backward and forward to create time

series. For PPPs, the local rate of inflation (measured by the GDP deflator) rela-

tive to the United States is used, while real GDP and real GDP per capita are

extrapolated using growth rates derived from constant price national data.

When PPP estimates of one benchmark year are extrapolated by rates of infla-

tion in an economy relative to the base country, they will not necessarily be

consistent with the estimates obtained for a new benchmark year for several

reasons:

The treatment of problematic areas such as housing and non-market serv-•

ices may be different in successive ICP rounds. In general, we can assume

that better methods are introduced in each successive round. For exam-

ple, productivity adjustments were made to government salaries in some

countries.


34


The extrapolation is done at the macro or GDP level instead of at the indi-•

vidual product or basic heading level. This also assumes that each coun-

try has a similar economic structure to that of the numeraire country and

that the economies of both are evolving in a similar way.

The product baskets in successive rounds of the ICP may be different, and •

the ICP baskets will also be different from national baskets used in calcu-

lating national rates of inflation.

The magnitude of sampling and non-sampling errors in the two surveys •

may be different.

Different aggregation methods may have been used.•

The number of countries participating in the ICP rounds is different. For •

example, the 1993 comparison in Africa included 22 countries, and the

2005 Africa comparison added 26 more countries, including South Africa.

The PPPs are the result of a multilateral estimating process, which means

that the relationship between any two countries are affected by indirect

parities with all other countries in the region.

Ad hoc methods were used in ICP 1993 to link Africa to the OECD, with sim-•

ilar problems experienced using Japan to link Asia-Pacific to the OECD.

SNA93 was the basis for the 2005 expenditures and weights while the •

SNA68 was the basis for the previous round.

Even if the general methodologies and aggregation procedures in the two sur-

veys were the same, the extrapolated values will not necessarily equal new

benchmark values. This is because ICP surveys work with current year esti-

mates so that successive benchmark estimates reflect changes from one year

to another not only in quantities but also in prices. Extrapolating one bench-

mark year value to another benchmark year by relative rates of inflation will

yield changes in quantity only and fail to capture any changes in the compo-

sition of the quantity. This may result from changes in relative prices and the

interplay of supply and demand of complementary and substitute products.

35


For countries with large external trade volumes, extrapolations are more prob-

lematic because of changes in the terms of trade. For example, if the physical

quantity of a country’s exports remains the same but the price decreases, ex-

trapolated exports will be unchanged but output measured in current prices

will decrease. A similar effect will occur if import prices increase: Extrapolated

GDP will exceed actual GDP. The opposite will occur for increases in export

prices and decreases in import prices, everything else being the same.

The results reported in this publication are based on actual 2005 benchmark

data submitted by the countries to the AfDB. These data sometimes differ from

those in the WDI. Table A1 provides a summary by country of the data from

the new benchmark compared with what was estimated from earlier data. The

footnote indicates countries not included in the 1993 comparison. For these

countries, estimates were imputed using the regression model described

above. The table shows total GDP and GDP per capita in PPP and U.S. dollars for

the ICP 2005 and WDI 2005 sources. The differences for exporting countries

are mostly positive. The last two columns show the GDP for African countries

as used in the 2005 ICP compared to the WDI database.

The table shows that the realignment of PPPs brought about by the new bench-

mark results have led to adjustments of GDP estimates of six of the largest Afri-

can countries, with the biggest reductions recorded for Ethiopia (-45 percent),

Morocco (-23 percent), and South Africa (-24 percent). Five of the ten largest

countries show significant increases, ranging from 60 percent for Nigeria, 32

percent for Tanzania, 48 percent for Angola, 13 percent for Kenya, and 10 per-

cent for Egypt. In particular, as a result of the PPP realignments, the economies

of Egypt and Nigeria, which were previously recorded at 60 and 30 percent,

respectively, of South Africa’s GDP are now measured at about 90 and 60 per-

cent.

The 2005 estimates are based on more robust benchmark ICP survey data,

while the previous ones were based on an extrapolation exercise of outdat-

ed and limited PPP data. The real outputs of the region’s countries have not

changed—only the way we measure them has.

36


Fig

ure

A1:

Com

par

ison

of I

CP

2005

Glo

bal

Res

ults

wit

h W

DI 2

005

GD

P p

er c

apit

a, P

PP

GD

P p

er c

apit

a, U

S$

GD

P, P

PP

(b

ln)

GD

P, U

S$

(b

ln)

ICP

‘05

WD

I ‘0

5D

iff.

ICP

‘05

WD

I ‘0

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iff.

ICP

‘05

WD

I ‘0

5D

iff.

ICP

‘05

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I ‘0

5D

iff.

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gola

a 3.

533

2.33

551

% 1

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55,0

37,2

48%

30,3

32,8

-8%

Ben

in1.

390

1.13

023

% 5

79

508

14%

10,5

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4,4

4,3

2%

Bo

tsw

ana

12.0

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5.7

12

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%

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rkin

a Fa

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Cam

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1.25

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8% 1

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ya1.

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eria

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.. 1

88

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Mad

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924

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271

18%

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8%

Mal

awi

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669

3% 2

30

161

43%

8,6

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-1%

2,9

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38%

37


Mal

i1.

027

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% 4

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(a) C

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38


Ann

ex II

: Rea

l and

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R

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Nom

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4198

,83

0,58

47

Nig

eria

13,

91

313

,52

224

7,28

113,

461,

0124

Mo

rocc

o 6

,03

47,

034

107,

1458

,96

1,22

7

Su

dan

4,4

8 5

4,19

579

,59

35,1

80,

9829

Tu

nis

ia 3

,64

63,

467

64,7

929

,04

0,99

28

An

gola

37

3,61

654

,97

30,2

71,

226

Ken

ya 2

,70

82,

238

47,9

318

,73

0,86

35

Eth

iop

ia 2

,39

91,

3212

42,5

511

,07

0,58

48

Tan

zan

ia 2

,02

101,

5111

35,9

412

,70

0,78

39

Cam

ero

on

1,9

7 11

1,98

934

,98

16,6

51,

0521

Co

te d

’Ivo

ire

1,6

9 12

1,95

1030

,07

16,3

91,

219

Ug

and

a 1

,48

131,

0915

26,2

59,

140,

7740

Gh

ana

1,4

7 14

1,28

1326

,14

10,7

20,

9133

Bo

tsw

ana

1,1

5 15

1,16

1420

,50

9,71

1,05

22

Sen

egal

1,0

2 16

1,03

1718

,13

8,65

1,06

19

Gab

on

1,0

0 17

1,03

1617

,84

8,67

1,07

18

Mad

agas

car

0,9

5 18

0,65

2816

,84

5,45

0,72

44

Co

ngo

, Dem

ocr

atic

Rep

ub

lic 0

,89

190,

8519

15,7

47,

121,

0027

Ch

ad 0

,84

200,

7025

14,8

95,

870,

8734

Bu

rkin

a Fa

so 0

,82

210,

6626

14,5

95,

540,

8436

Mo

zam

biq

ue

0,8

1 22

0,80

2014

,42

6,75

1,03

23

Zam

bia

0,7

6 23

0,87

1813

,44

7,27

1,20

11

Mau

riti

us

0,7

1 24

0,75

2212

,63

6,28

1,10

16

39


Eq

uit

ori

al G

uin

ea 0

,68

250,

7921

12,1

86,

641,

2110

Mal

i 0

,68

260,

6527

12,0

55,

491,

0126

Rep

ub

lic o

f C

on

go 0

,68

270,

7324

12,0

36,

131,

1314

Ben

in 0

,59

280,

5229

10,4

74,

360,

9231

Nam

ibia

0,5

2 29

0,74

239,

296,

231,

483

Gu

inea

0,4

9 30

0,35

318,

782,

940,

7441

Mal

awi

0,4

8 31

0,34

328,

572,

850,

7443

Nig

er 0

,44

320,

4030

7,74

3,33

0,95

30

Rw

and

a 0

,40

330,

2834

7,15

2,39

0,74

42

Zim

bab

we

0,3

5 34

…..

6,20

…..

3,27

1

Sw

azila

nd

0,2

8 35

0,30

334,

942,

561,

1513

Mau

rita

nia

0,2

7 36

0,21

364,

811,

790,

8337

Togo

0,2

6 37

0,25

354,

632,

111,

0125

Sie

rra

Leo

ne

0,2

3 38

0,18

374,

031,

490,

8238

Cen

tral

Afr

ican

Rep

ub

lic 0

,15

390,

1639

2,70

1,35

1,11

15

Leso

tho

0,1

5 40

0,17

382,

641,

451,

218

Dji

bo

uti

0,0

8 41

0,08

411,

470,

701,

0520

Cap

e V

erd

e 0

,08

420,

1340

1,35

1,06

1,73

2

Lib

eria

0,0

7 43

0,07

421,

230,

611,

0917

Gam

bia

, Th

e 0

,06

440,

0345

1,06

0,28

0,59

46

Gu

inea

-Bis

sau

0,0

4 45

0,04

440,

750,

310,

9132

Co

mo

ros

0,0

4 46

0,04

430,

650,

371,

275

Sao

To

me

and

Pri

nci

pe

0,0

1 47

0,01

460,

220,

111,

1612

Bu

run

di

…

…..

…..

…..

0,70

45

Afr

ica

Reg

ion

100

,00

10

0,00

1.83

5.58

9,00

839.

156,

001,

00

40


Annex III: Real and Nominal Per Capita GDP in Africa

Real GDP/capita Nominal GDP/capita

in US $ Rank in US $ RankGabon 12.742 1 6.190 2

Botswana 12.057 2 5.712 3

Equatorial Guinea 11.999 3 6.538 1

Mauritius 10.155 4 5.053 5

South Africa 8.477 5 5.162 4

Tunisia 6.461 6 2.896 7

Egypt 5.049 7 1.412 13

Namibia 4.547 8 3.049 6

Swaziland 4.384 9 2.270 8

Republic of Congo 3.621 10 1.845 12

Morocco 3.547 11 1.952 10

Angola 3.533 12 1.945 11

Cape Verde 2.831 13 2.215 9

Sudan 2.249 14 994 14

Cameroon 1.995 15 950 15

Djibouti 1.964 16 936 16

Nigeria 1.892 17 868 17

Chad 1.749 18 690 22

Mauritania 1.691 19 631 24

Senegal 1.676 20 800 19

Côte d’Ivoire 1.575 21 858 18

Sao Tome and Principe 1.460 22 769 21

Lesotho 1.415 23 777 20

Benin 1.390 24 579 26

Kenya 1.359 25 531 27

Ghana 1.225 26 502 28

Zambia 1.175 27 636 23

Burkina Faso 1.140 28 433 30

Comoros 1.063 29 611 25

Mali 1.027 30 468 29

Tanzania 1.018 31 360 32

Uganda 991 32 345 34

Madagascar 988 33 320 36

Guinea 946 34 317 37

Togo 888 35 405 31

Rwanda 813 36 271 39

Sierra Leone 790 37 293 38

Mozambique 743 38 347 33

Gambia, The 726 39 192 43

Malawi 691 40 230 42

Central African Republic 675 41 338 35

Niger 613 42 264 40

Ethiopia 591 43 154 45

Guinea-Bissau 569 44 234 41

Zimbabwe 538 45 …

Liberia 383 46 188 44

Congo, Democratic Republic 264 47 120 46

Burundi … …

Africa Average 2.223 1.016

41


Annex IV: Real and Nominal Per Capita Actual Final Consumption Expenditure and Price Level Indices in Africa

Real AFCE/capita Nominal AFCE/capita PLI

Index Rank Index Rank Index RankMauritius 488 1 527 1 1,07 18

South Africa 377 2 516 2 1,36 7

Tunisia 280 3 283 4 1,01 23

Egypt, Arab Republic 254 4 151 11 0,59 47

Gabon 214 5 314 3 1,46 6

Swaziland 203 6 221 9 1,08 16

Botswana 200 7 256 8 1,27 9

Equitorial Guinea 186 8 275 5 1,47 5

Namibia 175 9 265 7 1,50 4

Cape Verde 166 10 269 6 1,61 2

Morocco 144 11 176 10 1,22 10

Sudan 120 12 111 13 0,92 32

Lesotho 115 13 115 12 1,00 24

Cameroon 97 14 99 15 1,01 22

Sao Tome and Principe 95 15 103 14 1,08 17

Senegal 86 16 90 16 1,03 21

Nigeria 81 17 87 17 1,06 19

Djibouti 78 18 86 19 1,09 15

Kenya 77 19 64 26 0,82 39

Mauritania 77 20 66 23 0,85 37

Cote d’Ivoire 76 21 86 18 1,13 14

Benin 68 22 65 25 0,94 29

Republic of Congo 66 23 80 20 1,21 11

Ghana 62 24 56 29 0,90 34

Chad 60 25 58 27 0,96 28

Togo 59 26 57 28 0,96 27

Comoros 58 27 79 21 1,35 8

Zambia 57 28 66 24 1,13 13

Burkina Faso 54 29 45 31 0,83 38

Uganda 52 30 40 35 0,75 42

Tanzania 52 31 41 33 0,78 40

Madagascar 49 32 34 37 0,68 45

Mali 49 33 49 30 0,99 25

Sierra Leone 47 34 40 34 0,86 36

Angola 46 35 73 22 1,59 3

Guinea 42 36 31 38 0,73 44

Central African Republic 41 37 43 32 1,05 20

Rwanda 40 38 30 39 0,76 41

Gambia, The 39 39 26 43 0,66 46

Mozambique 39 40 35 36 0,90 33

Malawi 33 41 29 40 0,87 35

Ethiopia 31 42 18 44 0,58 48

Niger 30 43 28 41 0,92 31

Guinea-Bissau 28 44 27 42 0,97 26

Zimbabwe 28 45 … 3,57 1

Liberia 17 46 16 45 0,93 30

Congo, Democratic Republic

10 47 12 46 1,20 12

Burundi … … 0,73 43

Africa Average 100 100 1,00

42


Ann

ex V

: Rea

l and

Nom

inal

Per

Cap

ita

Gro

ss

Fixe

d C

apit

al F

orm

atio

n an

d P

rice

Lev

el In

dic

es in

Afr

ica

R

eal G

FCF/

cap

ita

Nom

inal

GFC

F/ca

pit

aR

eal G

FCF

Nom

inal

GFC

FPL

I

Ind

exR

ank

Ind

exR

ank

Sha

re

(Afr

ica=

100)

Ran

k S

hare

(A

fric

a=10

0)R

ank

Ind

exR

ank

Gab

on

745

173

12

1,23

151,

2415

0,97

26

Bo

tsw

ana

712

258

13

1,43

131,

1918

0,80

42

Eq

uit

ori

al G

uin

ea67

23

1.09

21

0,81

211,

3413

1,60

3

Mau

riti

us

507

455

84

0,75

250,

8423

1,08

20

So

uth

Afr

ica

404

545

55

22,4

31

25,8

01

1,11

19

Tu

nis

ia38

26

332

94,

546

4,03

70,

8634

Cap

e V

erd

e33

07

379

70,

1937

0,22

371,

1318

Nam

ibia

322

838

66

0,78

220,

9520

1,18

12

An

gola

292

934

18

5,39

56,

435

1,15

15

Mo

rocc

o28

210

287

1010

,10

310

,48

21,

0024

Sw

azila

nd

226

1127

411

0,30

350,

3733

1,19

11

Mau

rita

nia

224

1219

512

0,76

230,

6728

0,85

35

Eg

yp

t, A

rab

Rep

ub

lic17

113

123

1516

,37

210

,38

30,

7146

Leso

tho

9714

141

140,

2136

0,32

351,

434

Sen

egal

9515

9217

1,21

161,

2117

0,96

27

Rep

ub

lic o

f C

on

go91

1617

813

0,36

340,

7226

1,92

2

Gh

ana

8917

8120

2,25

82,

109

0,90

30

Su

dan

8918

106

163,

737

4,53

61,

1714

Dji

bo

uti

8619

7821

0,08

400,

0741

0,89

31

Cam

ero

on

7120

8619

1,47

121,

8310

1,20

10

Zam

bia

7021

8918

0,95

181,

2316

1,25

9

Ben

in69

2258

230,

6128

0,53

300,

8338

Ch

ad66

2372

220,

6626

0,74

251,

0821

Nig

eria

5424

5424

8,37

48,

454

0,97

25

Gu

inea

5425

4527

0,59

290,

5131

0,82

40

43


Bu

rkin

a Fa

so49

2644

280,

7524

0,69

270,

8932

Ken

ya49

2751

252,

0410

2,17

81,

0322

Tan

zan

ia48

2841

292,

0111

1,74

110,

8337

Mad

agas

car

4329

4031

0,87

190,

8124

0,90

29

Mal

awi

4330

2037

0,63

270,

3036

0,46

48

Ug

and

a42

3140

301,

3114

1,29

140,

9528

Rw

and

a38

3232

350,

4033

0,34

340,

8239

Mo

zam

biq

ue

3733

5126

0,84

201,

1819

1,35

6

Mal

i32

3437

330,

4531

0,53

291,

1317

Togo

3035

3534

0,18

380,

2238

1,13

16

Nig

er28

3628

360,

4232

0,44

321,

0123

Eth

iop

ia24

3716

422,

079

1,45

120,

6747

Gu

inea

-Bis

sau

2338

1939

0,04

430,

0343

0,81

41

Gam

bia

, Th

e23

3920

380,

0442

0,04

420,

8833

Sie

rra

Leo

ne

2340

1840

0,14

390,

1139

0,78

43

Co

te d

’Ivo

ire

2041

4032

0,46

300,

9221

1,92

1

Co

ngo

, Dem

ocr

atic

R

epu

blic

1742

1243

1,19

170,

8922

0,72

45

Cen

tral

Afr

ican

R

epu

blic

1243

1741

0,06

410,

0840

1,35

7

Zim

bab

we

……

……

1,39

5

Sao

To

me

and

P

rin

cip

e…

……

…1,

298

Lib

eria

……

……

1,17

13

Co

mo

ros

……

……

0,85

36

Bu

run

di

……

……

0,75

44

Afr

ica

Ave

rag

e10

010

010

0,00

100,

001,

00

45


I

Statistical Capacity Building Division Statistics Department

Chief Economist ComplexAfrican Development Bank

Temporary Relocation Agency (TRA)BP 323, 1002 Tunis, Belvédère

Tunis, Tunisia

Tel.: (216) 71 10 36 54Fax: (216) 71 10 37 43

E-mail: [email protected] site: http://www.afdb.org

Copyright © 2008 African Development Bank

Comparative Output, Incomes and Price Levels in African Countries · Comparative Output, Incomes...

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