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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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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: Statistics@afdb.orgWebsite: http://www.afdb.org
Copyright © 2008 African Development Bank
Design & Production:Phoenix Design Aid / www.phoenixdesignaid.dk
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
April 2008
2
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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-
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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).
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Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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.
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
34
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
5D
iff.
ICP
‘05
WD
I ‘0
5D
iff.
ICP
‘05
WD
I ‘0
5D
iff.
An
gola
a 3.
533
2.33
551
% 1
.945
2.
058
-5%
55,0
37,2
48%
30,3
32,8
-8%
Ben
in1.
390
1.13
023
% 5
79
508
14%
10,5
9,5
10%
4,4
4,3
2%
Bo
tsw
ana
12.0
5712
.154
-1%
5.7
12
5.91
8-3
%20
,521
,5-4
%9,
710
,4-7
%
Bu
rkin
a Fa
soa
1.14
01.
249
-9%
433
43
10%
14,6
16,5
-12%
5,5
5,7
-3%
Bu
run
di
a 41
069
9-4
1% 1
30
105
23%
3,1
5,3
-41%
1,0
0,8
23%
Cam
ero
on
1.99
52.
300
-13%
950
1.
034
-8%
35,0
37,5
-7%
16,6
16,9
-1%
Cap
e V
erd
ea
2.83
15.
831
-51%
2.2
15
1.97
212
%1,
43,
0-5
4%1,
11,
06%
Cen
tral
Afr
ican
Rep
ub
lica
675
1.22
4-4
5% 3
38
339
-0%
2,7
4,9
-45%
1,4
1,4
-1%
Ch
ada
1.74
91.
524
15%
690
60
414
%14
,914
,90%
5,9
5,9
-0%
Co
mo
ros
a 1.
063
1.99
3-4
7% 6
11
645
-5%
0,6
1,2
-46%
0,4
0,4
-4%
Co
ngo
, Dem
. Rep
.a
264
716
-63%
120
12
3-3
%15
,741
,2-6
2%7,
17,
10%
Co
ngo
, Rep
.3.
621
1.25
718
8% 1
.845
1.
493
24%
12,0
5,0
139%
6,1
6,0
3%
Cô
te d
’Ivo
ire
1.57
51.
616
-3%
858
88
4-3
%30
,129
,32%
16,4
16,1
2%
Dji
bo
uti
a 1.
964
2.16
0-9
% 9
36
894
5%1,
51,
7-1
4%0,
70,
7-1
%
Eg
yp
t, A
rab
Rep
.5.
049
4.32
117
% 1
.412
1.
259
12%
353,
431
9,9
10%
98,8
93,2
6%
Eq
uat
ori
al G
uin
eaa
11.9
9917
.294
-31%
6.5
38
14.9
36-5
6%12
,28,
740
%6,
67,
5-1
2%
Eth
iop
iaa
591
1.08
4-4
6% 1
54
159
-4%
42,5
77,3
-45%
11,1
11,4
-3%
Gab
on
12.7
426.
585
94%
6.1
90
6.26
2-1
%17
,89,
196
%8,
78,
70%
Gam
bia
, Th
ea
726
1.92
1-6
2% 1
92
304
-37%
1,1
2,9
-64%
0,3
0,5
-39%
Gh
ana
a 1.
225
2.48
0-5
1% 5
02
485
4%26
,154
,8-5
2%10
,710
,7-0
%
Gu
inea
946
2.35
0-6
0% 3
17
370
-14%
8,8
21,2
-59%
2,9
3,3
-12%
Gu
inea
-Bis
sau
a 56
982
7-3
1% 2
34
190
23%
0,8
1,3
-42%
0,3
0,3
3%
Ken
ya1.
359
1.24
010
% 5
31
560
-5%
47,9
42,5
13%
18,7
19,2
-2%
Leso
tho
a 1.
415
3.38
4-5
8% 7
77
812
-4%
2,6
6,1
-57%
1,4
1,5
-0%
Lib
eria
383
.. 1
88
161
17%
1,2
0,6
0,5
15%
Mad
agas
car
988
924
7% 3
20
271
18%
16,8
17,2
-2%
5,5
5,0
8%
Mal
awi
691
669
3% 2
30
161
43%
8,6
8,6
-1%
2,9
2,1
38%
37
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
Mal
i1.
027
1.03
4-1
% 4
68
392
19%
12,1
14,0
-14%
5,5
5,3
3%
Mau
rita
nia
a 1.
691
2.23
4-2
4% 6
31
605
4%4,
86,
9-3
0%1,
81,
9-3
%
Mau
riti
us
10.1
5512
.720
-20%
5.0
53
4.96
42%
12,6
15,8
-20%
6,3
6,2
2%
Mo
rocc
o3.
547
4.60
8-2
3% 1
.952
1.
713
14%
107,
113
8,9
-23%
59,0
51,6
14%
Mo
zam
biq
ue
a 74
31.
226
-39%
347
34
51%
14,4
24,3
-41%
6,7
6,8
-1%
Nam
ibia
a 4.
547
7.63
4-4
0% 3
.049
3.
045
0%9,
315
,5-4
0%6,
26,
21%
Nig
era
613
786
-22%
264
24
38%
7,7
11,0
-29%
3,3
3,4
-2%
Nig
eria
1.89
21.
095
73%
868
68
626
%24
7,3
154,
860
%11
3,5
97,0
17%
Rw
and
a81
31.
206
-33%
271
23
714
%7,
210
,9-3
4%2,
42,
111
%
São
To
mé
and
Pri
nci
pe
1.46
0..
769
71
97%
0,2
..0,
10,
12%
Sen
egal
1.67
61.
780
-6%
800
70
713
%18
,120
,8-1
3%8,
78,
25%
Sie
rra
Leo
ne
790
806
-2%
293
22
033
%4,
04,
5-1
0%1,
51,
223
%
So
uth
Afr
ica
a 8.
477
11.1
87-2
4% 5
.162
5.
162
0%39
7,5
524,
5-2
4%24
2,0
242,
00%
Su
dan
2.24
92.
083
8% 9
94
770
29%
79,6
75,5
5%35
,227
,926
%
Sw
azila
nd
4.38
44.
868
-10%
2.2
70
2.31
0-2
%4,
95,
5-1
0%2,
62,
6-2
%
Tan
zan
ia1.
018
707
44%
360
32
710
%35
,927
,232
%12
,712
,61%
Togo
888
1.48
3-4
0% 4
05
343
18%
4,6
9,1
-49%
2,1
2,1
0%
Tu
nis
ia6.
461
8.37
5-2
3% 2
.896
2.
859
1%64
,884
,0-2
3%29
,028
,71%
Ug
and
aa
991
1.45
4-3
2% 3
45
295
17%
26,3
41,9
-37%
9,1
8,5
7%
Zam
bia
1.17
51.
023
15%
636
62
32%
13,4
11,9
13%
7,3
7,3
0%
Zim
bab
we
538
2.06
5-7
4% 7
96
263
203%
6,2
26,9
-77%
9,2
3,4
168%
(a) C
ou
ntr
y e
stim
ates
for
WD
I 200
5 w
ere
bas
ed o
n r
egre
ssio
n e
stim
ates
for
1993
-96
extr
apo
late
d fo
rwar
d t
o 2
005.
S
ou
rces
: 200
5 IC
P F
inal
Res
ult
s, W
DI d
atab
ase
(Sep
tem
ber
200
7)
38
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
Ann
ex II
: Rea
l and
Nom
inal
GD
P in
Afr
ica
R
eal G
DP
Nom
inal
GD
P R
eal G
DP
Nom
inal
GD
P PL
I
Sha
re
(Afr
ica=
100)
Ran
kS
hare
(A
fric
a=10
0)R
ank
in B
illio
n U
S $
in B
illio
n U
S $
Ind
exR
ank
So
uth
Afr
ica
22,
35
128
,84
139
7,46
242,
011,
354
Eg
yp
t, A
rab
Rep
ub
lic 1
9,88
2
11,7
83
353,
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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
Highlights of the Results of the 2005 Round of the International Comparison Program for Africa
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: Statistics@afdb.orgWeb site: http://www.afdb.org
Copyright © 2008 African Development Bank