Health Expenditures, Services, and Outcomes in Africa
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Health Expenditures,
Services, and
Outcomes in Africa - extracted text
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HUMAN DEVELOPMENT NETWORK
Health, Nutrition, and Population Series
Health Expenditures,
Services, and
Outcomes in Africa
Basic Data and Cross-National
Comparisons, 1990-1996
David H. Peters,
Kami Kandola,
A. Edward Elmendorf,
and Gnanaraj Chellaraj
NUTRITION, AMD POPUl ATIQM
THE
WORLD
BANK
Health, Nutrition, and Population Series
This series is produced by the Health, Nutrition, and Population
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The series aims to provide a vehicle for publishing material on the
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ial committee, which is coordinated by Alexander S. Preker. The
other members of this committee are A. Edward Elmendorf. Mariam
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Prabhat K. Jha, Jack Langenbrunner, Maureen A. Lewis, Samuel S.
Lieberman, Milla Mclachlan, Judith Snavely Mcguire. Akiko Maeda.
Thomas W. Merrick, Philip Musgrove, David H. Peters, Oscar
Picazo, George Schiebcr, and Michael Walton.
HUMAN DEVELOPMENT NETWORK
Health, Nutrition, and Population Series
Health Expenditures,
Services, and Outcomes
in Africa
Basic Data and Cross-National Comparisons
1990-1996
David H. Peters, Kami Kandola,
A. Edward Elmendorf, and
Gnanaraj Chellaraj
The World Bank
Washington, D.C.
© 1999 The International Bank for Reconstruction
and Development I the world bank
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First printing June 1999
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Cover graphic by Erin Beth Harris, Spot Color Inc.
Library of Congress Cataloging-in-Publication Data
Health expenditures, services, and outcomes on Africa : basic data and
cross-national comparisons, 1990-1996 / David H. Peters ... let al.].
p.
cm.
Includes bibliographical references.
ISBN 0-8213-4438-2
1. Medical care, Cost of—Africa, Sub-Saharan. 2 Public health—
Africa, Sub-Saharan. 3. Medical care—Africa, Sub-Saharan.
1. Peters, David H., .1962- .
RA410.55.A357H43
1999
99-22639
338.4'33621'0967—dc21
CIP
\ 4^344)
Contents
Foreword
v
Acknowledgments
Abstract
vii
1
1 Introduction and Methodological Overview
2
2 Background on Previous Health Expenditure Studies
5
3 Principal Findings from Cross-National and Intertemporal Comparisons
4 Limitations of the Data, Their Uses at the Country Level, and Conclusions
Annex 1: References
27
32
Annex 2: Glossary and Data Sources
Annex 3: Tables
8
35
39
Text Tables*
1
Key Indicators for African Countries, by Country Group, 1990-1996
9
2
Classification of African Countries According to Country Income Group
(Average Annual Per Capita GNP, 1990-1996)
10
3
Macroeconomic Performance by African Country Income Group, 1990-1996
11
4
Average Annual Per Capita Official Development Assistance, by Population Size
and African Country Income Group, 1990-1996
11
5
Key Education, Sanitation, and Water Supply Indicators, by African Country Income Group
11
6
National Policy Indicators, by African Country Income Group
12
7
Government, Donor, and Private Health Expenditures, by African Country Income Group, 1990
13
8
Mean Health Expenditures, by Category and African Country Income Group, 1990-1996
14
9
Proportion of Public Sector Health Expenditures Accruing to the Poorest and Richest Income
Quintiles, Selected African Countries
16
10
Selected Health Sendee Indicators, by African Country Income Group, 1990-1996
16
11
Selected Health Outcome Indicators, by African Country Income Group, 1990-1996
17
12
Changes from 1990 to 1995 tn Selected Health Outcome Indicators, by African Country
Income Group
18
13
Levels of Selected Health Services in African Countries, by Level of Public Sector Health
Expenditures, 1990-1996
23
* Unless indicated otherwise, the data in the tables are population-weighted averages. In some cases, data in the tables do
not add up completely because of rounding.
iii
IV
Health Expenditures, Services, and Outcomes in Africa
14
15
16
17
18
19
20
Multiple Linear Regression Models of Public Sector Health Expenditures and Selected Health
Services in African Countries, 1990-1996
23
Infant Mortality and Childhood Malnutrition in African Countries, by Level of Measles
Immunization Coverage, 1990-1996
24
Infant Mortality and Total Fertility Rates in African Countries, by Level of Contraceptive
Prevalence, 1990-1996
24
Infant Mortality in African Countries, by Level of Supervised Births, 1990-1996
24
Multiple Linear Regression Model of Measles Immunization Coverage and Childhood
Malnutrition in African Countries, 1990-1996
25
Multiple Linear Regression Model of Contraceptive Prevalence Rates and Total Fertility Rates in
African Countries, 1990-1996
25
Estimated Relationships among Infant Mortality Rates, Female Illiteracy, and Selected
Health Services in African Countries, 1990-1996
25
Text Figures
1
Public Sector Health Expenditures (% of GDP) versus Real Per Capita GDP in African Countries,
1990-1996
15
2
Capital and Recurrent Public Sector Health Expenditures in African Countries, 1990-1996
15
3
DPT3 Coverage in African Countries, 1980-1996
18
4
Infant Mortality Rates in African Countries, 1960-1995
19
5
Total Fertility Rales in African Countries, 1990-1995
20
6a Male Life Expectancy at Birth in African Countries, 1990-1995
21
6b Female Life Expectancy at Birth in Afncan Countries, 1990-1995
21
7a Male Adult Mortality in African Countries, 1960-1995
22
7b Female Adult Mortality in African Countries, 1960-1995
22
8
Infant Mortality versus Public Sector Health Expenditures, Sub-Saharan Africa, 1990-1996
24
Text Boxes
1
Health Outcome and Health Sendees Indicators Used in the Study
3
2
Using the Data for Benchmarking: Where Does Cote d’Ivoire Stand in Relation to other
African Countries?
29
Annex Tables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Health Expenditures in Africa, by Country and Source, 1990
39
Public Sector Health Expenditures as a Percentage of GDP in Africa, by Country, 1990-1996
Public and Private Sector Health Expenditures in Africa, by Country', 1990-1995
42
Public Sector Health Expenditures in Africa, by Country'and Category; 1990-1996
44
National Policy Indicators in Africa, by Country, 1990s
46
Selected Mortality Indicators in Africa, by Country, 1990-1995
47
Selected Social and Health Indicators in Africa, by' Country, 1990-1995
48
Child Survival Indicators tn Africa, by Country, 1990-1996
49
Immunization Coverage Rates in Africa, by Country, 1990-1996
50
Reproductive Health Indicators in Africa, by Country, 1990-1996
51
Population and Population Growth in Africa, by Country; 1990-1996
52
GNP Per Capita in Africa, by Country, 1990-1996
53
Real Per Capita GDP in Africa, by Country, 1990-1996
54
Net ODA Per Capita in Africa, by Country, 1990-1996
55
41
Foreword
The following technical paper is a compilation of
recent, available information on African health expen
ditures, services, and outcomes. Focused on the coun
try level, the paper may be useful to policymakers,
managers, aid agency officials, researchers, and mem
bers of the general public who are interested in Africa.
It provides strategic information to improve health sys
tem performance and secure sustainable financing for
health services, particularly with regard to the conti
nent’s poor countries. By documenting how both indi
vidual countries and groups of countries with similar
incomes performed in the early to mid-1990s, this
paper can help identify suitable, feasible policies for
African countries in the long term.
The study uses standard health outcome measures
to document conditions at the country level, including
life expectancy and fertility; infant, child, maternal,
and adult mortality; and anthropometric measures of
malnutrition. The study does not use a composite indi
cator of the burden of morbidity and premature mor
tality, such as disability-adjusted life years (DALYs).
Whereas this choice fails to integrate all variables into
a single indicator, the use of standard outcome mea
sures makes the study more understandable and use
ful to African decision-makers and service providers.
The study shows that the health-related experiences
of African countries are increasingly diverse. Whereas
general declines in fertility and infant mortality have
continued across the continent, the most significant
gains are being made in the middle-income countries.
The study also documents a current increase in adult
mortality rates, following decades of steady decline.
Further investigation and action on the causes of this
trend are needed.
The analysis also suggests that selected health ser
vices and public expenditures on health have some
important payoffs in African countries. Such expendi
tures are associated with higher levels of health ser
vices, notably with regard to measles immunization
and, to a lesser degree, contraceptive use, even after the
effects of income and female literacy are accounted for.
Greater coverage of selected health services is also asso
ciated with better health outcomes. For example,
measles immunization is linked to better childhood
nutrition, the use of contraception with lower fertility
rates, and family planning and supervised deliveries
with lower infant mortality rates.
The study argues that scarcity of information
inhibits governments from making informed choices
about the allocation of public resources for better
health, as well as improvements in the management of
publicly provided and/or financed services. Increased
information is particularly crucial to understanding the
financial resources and sendees for health that are pro
vided by nongovernmental actors, including private
voluntary organizations, the private for-profit subsec
tor, and households.
The nongovernmental sector plays a major role in
the financing and provision of health sendees in Africa,
a role that is likely to become even greater in the future.
Public authorities therefore need to pay increasing
attention to policies that affect the sector.
This study should be read in the larger context of the
World Bank’s work on health, nutrition, and population
Assistance Strategy. In addition, several other regional
strategy' papers are being formulated by the Bank’s
Africa Region. The data and analyses in the present
study represent a key input to the Bank’s ongoing work.
strategies. Relevant publications include the World
Development Report 1993: Investing in Health (WDR93),
the 1994 study Better Health in Africa, and the Bank’s
1997 global Health, Nutrition, and Population Sector
David de Ferranti
Vice President and Head
Human Development Network
Christopher Lovelace
Director
Health, Nutrition,
and Population
Human Development Network
vi
Ok Pannenborg
Sector Leader
Health, Nutrition,
and Population
Africa Region
Acknowledgments
We thank many people for their advice and the
invaluable information they provided during the
writing of this paper, including Jamie Blanchard,
University of Manitoba; Richard Morrow, the Johns
Hopkins School of Hygiene and Public Health; Ok
Pannenborg, Sector Leader for Health, Nutrition, and
Population, Africa Region, the World Bank; and the
late Theophil Sodoganji (deceased), Drug Action
Program, the World Health Organization.
Appreciation is also expressed to several people
from the World Bank: Satish Mannan, PREM Advisory
Group; Deepak Bhattasali, Economic Advisor, Africa
Region; Eduard Bos, Population Specialist; and
Olivier Dupriez. World Bank staff who contributed
data to the study include Anwar Bach-Baouab, Linda
English, Charles Griffin, Kees Kostermans, Sergiu
Luculescu, Julie McLaughlin, Montserrat MeiroLorenzo, Mary Mulusa, and Albert Voetberg. Special
thanks go to the World Bank’s Africa Region Database
Committee.
Helpful comments on a draft of the paper were
received from Luca Barbone, Deepak Bhattasali,
Shiyan Chao, Deon Filmer, Louis Goreux, Jeffrey
Hammer, Barbara Herz, Kees Kostermans, Mead Over,
Ok Pannenborg, Lant Pritchett, and Paul Shaw, all
from the World Bank. David Peters conceived and
revised the paper, supervised the collection of data,
and prepared the regression analyses, while Kami
Kandola collated and screened the data, prepared
most of the tables, and wrote the first draft. A. Edward
Elmendorf served as World Bank task manager for the
paper, provided advice during the data collection and
review stages, and prepared the final text with con
sideration to Bank staff reviews. Finally, Gnanaraj
Chelleraj wrote chapter 2. Any errors in interpretation
or fact are the sole responsibility of the authors.
1 Introduction and Methodological Overview
In the past 30 years, African countries1 have made
remarkable improvements in health conditions and
status. However, they still suffer from some of the worst
health problems in the world. Premature death and
high levels of morbidity, fertility, and malnutrition are
common throughout the continent. In 1990, the medi
an age of death was estimated to be five years (World
Bank 1993). Communicable, maternal, pennatai, and
nutritional causes accounted for 65.9 percent of total
DALYs lost in Africa in 1990, amounting to one-third
of the global total. Projections of the disease profile for
Africa in the year 2020 indicate an epidemiological
transition, with an increase in the percentage of noncommunicable diseases from 19 percent of DALYs lost
in 1990 to 32 percent in 2020 (Murray and Lopez
1996). Similarly, during this period injuries are pro
jected to nearly double from 15 to 28 percent of DALYs
lost. African countries must be prepared to cope with
these changes.
Aside from this overview, the present paper uses tra
ditional rather than composite measures of health out
comes. Hence it focuses not on DALYs, but on factors
such as life expectancy and fertility; infant, child, mater
nal, and adult mortality; and childhood malnutrition.
African countries face enormous difficulties in both
mobilizing and managing resources for improved
health outcomes. Two-thirds of African countries are
classified as low-income, and nearly all have weak
health management systems. Resource mobilization
and management are particularly difficult problems.
Several factors have combined to make the role of
information increasingly important for management
and accountability in the health sector in African coun
tries. Key challenges include a massive burden of mor
bidity due to largely manageable conditions, the emer
gence of new diseases and health problems (such as
HIV and drug resistance to tuberculosis), and changes
in the political climate. Good information about
inputs, processes, and results in the health sector is
vital for governments to make intelligent choices about
health strategies and investments. Yet in much of
Africa, information that would be critical to policy
makers, health system managers, and consumers of
health services is often not available, despite an
increasing emphasis on data collection in many coun
tries. In addition, even the little information that is
available is unfortunately rarely used. As one response
to this situation, this study aims to make national-level
information on health expenditures, service outputs,
and outcomes available in a way that can be of assis
tance for health planning and policy development in
Africa. Country examples are therefore provided
throughout the text.
This study considers all 48 countries officially des
ignated by the World Bank as belonging to the Africa
Region, which comprises all countries located south of
the Sahara Desert as of 1997.2 It outlines broad pat
terns of health spending, service delivery, mortality,
fertility, and malnutrition in Africa in the early to mid1990s. The data are used to document variations and
trends in financing, services, and outcomes. Countries
are both grouped according to income and level of per
formance in these variables and considered individu
ally. By exploring gaps in available information and the
potential uses of health information, this paper intends
to stimulate discussion on how to better monitor
progress and use information in order to achieve
improved health outcomes within and among different
African countries.
Countries were classified into three categories for
purposes of the study: “lowest-income countries,” “lowincome countries,” and “middle-income countries.” The
2
3
Introduction and Methodological Overview
average annual per capita gross national product (GNP)
during the period 1990-1996, calculated according to
the World Bank Atlas method, was used in relation to
this classification. The lowest-income countries were
thereby defined as having an average per capita GNP of
less than US$300, low-income countries as having a per
capita GNP of US$300-$765, and middle-income
countries as having a per capita GNP greater than
US$765. Three countries (Djibouti, Eritrea, and Liberia)
did not have an official GNP recorded in the 1990s.
Djibouti was presumed to belong to the middle-income
category, whereas Eritrea and Liberia were classified
under the lowest-income category, based on previous
macroeconomic assessments by the World Bank.
Data were derived mainly from the World Bank, the
International Monetary Fund (IMF), the World Health
Organization (WHO), the United Nations Fund for
Children (UNICEF), and the United Nations
Population Division. Household surveys sponsored by
the World Bank and the United States Agency for
International Development (USAID), such as
Demographic and Health Surveys, were also used.
Health financing data were derived largely from gov
ernment accounts, World Bank expenditure reviews,
household surveys, the IMF, and other studies. These
findings were then compared to the WDR93 estimates
for 1990. Missing or incomplete information was
updated from the World Bank Africa Region Database.
Where possible, annual estimates of variables were
used for 1990-1995. Data sources and definitions for
individual data series are contained in the glossary in
Annex 2. Definitions were obtained primarily from
established World Bank literature in order to maintain
consistency and comparability with other reports.
Period averages for 1990-1996 were calculated
using information from as many years as possible. Data
were considered insufficient to justify computation of
period averages if they failed to represent at least 60
percent of the population and more than 50 percent of
the countries in any category.
Macroeconomic and sectoral indicators were select
ed based on three criteria relevant to the purposes of
the study: availability of recent data, completeness of
information, and reliability of the data source. The
main health outcome variables used are infant mortal
ity rate, the prevalence of child malnutrition (weight
for age), and TFR. A complete list of health outcomes
and services variables examined is presented in box 1.
Expenditure data were calculated as real per capita
US$ and percentage of GDP In keeping with the
methodology used in WDR93, only health expendi
tures made with the aim of improving health were
examined. Hence expenditures for water and sanitation
projects, road infrastructure, education, and emergency
disaster and relief assistance were not considered. The
expenditure data were initially divided into sources and
uses. Sources were further classified as health expendi
tures from governments, donors, the public sector
(including governments and donors), and the private
sector, as well as total expenditures. Because of the lim
ited availability of annual data, expenditure data were
aggregated to period averages for 1990-1996.
Box 1 Health Outcome and Health Services Indicators Used in the Study
Health Outcome Indicators
Health Services Indicators
Infant mortality rate
Child mortality rate
Life expectancy at birth, by gender
Adult mortality, by gender
Crude birth rate
Crude death rate
Years of potential life lost
Total fertility rate
Adolescent fertility rate
Maternal mortality ratio
Low-birth-weight babies
Childhood underweight (weight for age)
Childhood stunting (height for age)
Childhood wasting (weight for height)
Inpatient beds per 1,000 population
Physicians per 1,000 population
Supervised births
Contraceptive prevalence rate
Immunization coverage (BCG, DPT3, measles, tetanus toxoid
4
The original data estimates were obtained in nomi
nal terms for local currencies. Using the official
exchange rate for a particular year, estimates were con
verted into current USS and subsequently adjusted for
inflation in order to obtain real estimates that were in
keeping with World Bank practice. The index year for
all real estimates was 1987. Spending was also disag
gregated into capital investment and recurrent expen
ditures and expressed as a percentage of public sector
health expenditures. Where possible, recurrent expen
ditures were broken down further into personnel,
pharmaceutical, hospital, and primary care expendi
tures.
Key indicators outside of the health sector that are
of concern in connection with outcomes are also
reported. These include total government expendi
ture, level of expenditure on national defense, level
of corruption, and the quality of the civil service
bureaucracy. The World Bank has begun monitoring
both corruption and the quality of bureaucracy. The
database of the Bank’s Poverty Reduction and
Economic Management (PREM) network contains
comparative ratings of legal and regulatory frame
works in developing countries. These ratings mea
sure the level of corruption on a scale of 1-5, with 1
indicating a high level of corruption, 3 a satisfactory
situation, and 5 an outstanding record. The same
database also contains assessments of bureaucratic
quality on an ascending scale from 0 (low) to 4 (high)
(PREM 1997).
The data covered in the study include major macroeconomic indicators such as real GDP, rate of GDP
growth, inflation rate, and per capita official develop
ment assistance (ODA). Key social indicators are pre
sented, including level of education (as expressed by
the prevalence of adult female illiteracy and gross sec
ondary school enrollment) and access to safe water and
sanitation.
In an exploratory analysis of the data, boxplots3 are
presented with aggregated cross-national data in order
to identify measures of central tendency (means and
medians), variation (range and quartile spread), and
outliers among the countries. Population-weighted
averages and ranges were also determined for each
variable, effectively separating countries according to
Health Expenditures, Services, and Outcomes in Africa
their income group.
Multivariate analyses were performed using the least
squares linear regression method. Natural logarithmic
transformations of independent variables were used to
obtain normal and linear distributions to fit linear
models. A two-tailed benchmark of statistical signifi
cance of p < 0.05 was used, although one-tailed tests
would also have been appropriate (e.g., when examin
ing the relationship between contraceptive prevalence
and total fertility).
Analytical results are derived from the detailed,
country-by-country data that are contained in the
annex tables. This paper is thereby transparent and
invites readers to make additional analyses of their own,
either at the country level or for groups of countries.
The data in the paper, particularly in the tables, are
presented in a value-neutral fashion, from numerical
ly low to numerically high levels, regardless of positive
or negative values or performance connotations. Thus
in Table 1 and subsequent tables, measles immuniza
tion coverage rises from 50 percent in the “lowest quar
tile of countries" (those with the poorest performance)
to 66 percent in the "highest quartile of countries.”
Similarly, infant mortality rate rises from 70 per 1,000
live births in the "lowest quartile of countries” (in this
case, those with the best performance) to 120 in the
“highest quartile of countries.”
Notes
1. The terms Africa and Sub-Saharan Africa are used
synonymously in this study.
2. The French dependencies of La Reunion and
Mayotte are not covered.
3. In a boxplot, the median value is indicated by a
central line. The upper and lower edges of the box
mark the 25th and 75th percentile values, which can
be applicable as benchmarks. The “whiskers” extend
ing from the box edges indicate the most extreme
value within 1.5 times the width of the box (the
interquartile range), which acts as the nonparametric
equivalent of a standard deviation. The “whiskers” are
also useful as a reference for identifying outlying val
ues and experiences.
2 Background on Previous Health
Expenditure Studies
study found that Africa accounted for the largest share
of donor support, both per capita (US$2.45) and as a
percentage of total health aid (38.5 percent). However,
after adjusting for population and income, it was also
found that the region did not receive more health aid
relative to other regions and that the smaller and poor
er countries received relatively more aid than the larg
er and richer countries. Half the aid was directed to
specific disease programs, while other assistance was
given to general hospital and health services.
In another background paper for WDR93, Murray,
Govindaraj, and Chellaraj (1993) subdivided their
analysis into public sector health expenditures (gov
ernment and parastatal) and private expenditures.
Data on private expenditures (including private vol
untary and household spending) were extremely lim
ited, while government health expenditures were often
incomplete, unreliable, and difficult to track in disag
gregated terms. Public sector health expenditures
accounted for 60 percent of the global total, while in
Africa this figure was only around 44 percent.
Meanwhile, capital expenditures in Africa accounted
for about 17 percent of total government health expen
ditures. Salaries accounted for approximately 50 per
cent of total recurrent expenditures, primary health
care 22 percent, drugs and supplies 20 percent, admin
istrative expenditures 7 percent, and population pro
grams 6 percent.
Since the mid-1980s, several attempts have been
made to gather and analyze health-related data among
regions of the developing world, including Latin
America (McGreevey and others 1992; Lee and
Bobadilla 1994; Musgrove 1983), Eastern Europe
(Chellaraj and others 1996; Chellaraj, Heleniak, and
Staines 1998), Asia (Gertler, 1998), and Africa
(Dunlop 1983; Vogel 1989), as well as in a number of
The first major study of health care expenditures was
on six countries from Western Europe and North
America. It was undertaken by Abel-Smith (1963),
who standardized cross-national data by defining cat
egories of health services and sources of finance. A sec
ond study involved 29 countries, including five from
Africa (Abel-Smith 1967). These studies concluded
that increasing levels of national income were associat
ed with improved health status and that the demand
for health care increased in countries with declining
mortality. Abel-Smith’s research laid the foundation for
the development of methodologies for tracking health
expenditures in both the private and public sectors.
These methodologies were later applied in industrial
countries (Getzen and Poullier 1991; Poullier 1989;
Getzen 1990; OECD 1985, 1989). As data collection
methodologies improved, a number of studies were
conducted, primarily in industrialized countries, to
track trends in health expenditures (Schieber and
Poullier 1993, 1991a, 1991b, 1987; Schieber 1990,
1987; Newhouse 1987, 1977; Parkin, McGuire, and
Yule 1987; McGuire and others 1996; Gerdtham and
others 1992; Gerdtham and Jonsson 1991a, 1991b,
1994).
While external assistance constitutes a substantial
share of health expenditures in Africa (more than 10
percent of the total in 1990), information on it is sur
prisingly weak. One of the most comprehensive stud
ies on external assistance for health around the world
was undertaken for the WDR93. In preparation for the
publication, Michaud and Murray (1994) reviewed the
flow of external assistance to the health sector in devel
oping countries from multilateral and bilateral agen
cies and nongovernmental organizations and founda
tions. Total aid disbursement was estimated for each
developing region, including Africa, for 1990. The
5
6
individual countries. The data collected for most of
these studies were rather limited in scope, with a
detailed breakdown of macrolevel health expenditures
presented only for Eastern Europe and the former
Soviet Union. Nonetheless, these studies laid the
foundation for the development of methodologies for
national health accounting. Berman (1997) has done
considerable work in this area to develop and stan
dardize a disaggregated “sources and use” expenditure
profile similar to national health accounts established
in the United States. Similarly, a World Bank study
(1996) proposed a methodology for national health
accounting schemes in developing countries that is
intended to facilitate the collection and analysis of
health-related data.
Among the studies examining health expenditures
across Africa, Vogel’s 1993 study stands out, although
good studies also exist that deal with individual coun
tries (for example, McIntyre and others 1995). One of
the major lessons of these studies is that the lack of
information on private expenditures (including spend
ing by religious bodies) remains a major stumbling
block to research and policy formulation. The Vogel
study examined real pharmaceutical imports during
the 1972-1989 period, health insurance coverage,
health sendees (e.g., immunization coverage), and var
ious health outcomes (e.g., mortality rates), as well as
the cost-effectiveness of certain health interventions.
As a result, it presented a number of health financing
reform options. Better Health in Africa (World Bank
1994) was also significant in collecting a wide variety
of information on health and health expenditures in
Africa, identifying positive experiences and generic
sources of inefficiency and inequity, and outlining
options for reform, including an indicative costing of
basic health services in Africa.
Sahn (1992) and Serageldin, Elmendorf, and ElTigani (1994) found that adjustment programs in
Africa were not associated with a substantial increase
or decrease in real per capita levels of social sector
expenditures. These studies also concluded that in
recent years, elasticities in health and education spend
ing with respect to GDP were greater than unity.1 More
recently, van der Gaag and Barham (1998) focused on
the impact of World Bank structural adjustment oper
ations on health expenditures and outcomes, includ
Health Expenditures, Services, and Outcomes in Africa
ing in much of Africa. These studies found that coun
tries involved in structural adjustment programs
showed increasing real health care expenditures per
capita, whereas nonadjusting countries experienced
real per capita declines in health expenditures in both
the public and private sectors. However, trends m child
mortality indicators showed progress during the past
thirty years, with very little difference among countries
associated with whether they were involved in struc
tural adjustment programs.
Gbesemete and Gerdtham (1992) found that GNP
per capita, percentage of births attended by health staff,
and foreign aid per capita accounted for 78 percent of
the variance in health expenditures in Africa, with per
capita GDP being the most significant factor. Income
elasticity was also close to unity, and aid had a positive
and significant impact on health expenditures.
However, variables such as crude birth rate and the
proportion of the population under 15 years of age
were not significant in explaining variations in health
expenditures.
Stanton (1995) found that the cost of key inputs
for high-mortality populations was relatively low
because the sources of major morbidity and prema
ture mortality were less costly to treat. These condi
tions were frequently due to diarrhea and acute res
pirator}' infections. The cost for low-mortality
populations was substantially higher because of a
change in epidemiological profile, whereby perinatal
and noncommunicable diseases and trauma predom
inated. One implication of these trends is that coun
tries that spend little on health are less equipped to
bear the heavy cost of an epidemiological transition
in disease profile. Korte and others (1992) empha
sized equity in health expenditures and a need to find
an appropriate mix of public and private sector inter
ventions, elements of costsharing for services and
drugs, insurance schemes, and the more efficient use
of available resources.
Several recent studies on Africa have concentrated
specifically on health, nutrition, and population status.
Among these, determinants of nutritional status were
laid out for Botswana (Gobotswang 1998), Malawi
(Madise and Mpoma 1997), Zambia (Ng’andu 1992)
and Southern Africa (Eele 1994; Haddad and Kennedy
1994). A few studies attempted to link nutrition and
7
Background on Previous Health Expenditure Studies
health status with productivity (Audibert 1986; Strauss
1986). Several others concentrated specifically on pop
ulation and child survival in Africa (Robinson 1992;
Larsen 1995).
Other studies attempted to describe the relationship
between health financing and health services and sta
tus among countries in Latin America (Govindaraj,
Chellaraj, and Murry 1997) and Eastern Europe and
Central Asia (Goldstein and others 1996; Klugman and
others 1996; Staines 1998). Filmer and Pritchett
(1997) did not directly study health sendees, but took
a more analytical approach to cross-national health
spending and health outcomes and found that public
spending on health was not a major contributor to dif
ferences in child mortality among countries.
The present paper builds on these previous studies
and attempts to fill some of the information gap that
remains with regard to Africa by updating information
on health expenditures, services, and outcomes and by
demonstrating some of its applications.
Note
1. This indicates that as total GDP rises and countries
develop, the share of social spending in GDP increases.
3 Principal Findings from Cross-National
and Intertemporal Comparisons
In this chapter we present the main findings of this
study for Africa as a whole and by country income
group. The initial focus is on global measures of per
formance, including both key social indicators and the
national policy variables of corruption and quality of
the public service. Health expenditures are disaggre
gated first by source and country income group and
then by category and country income group. Attention
is given to the inputs and outputs of selected health ser
vices. The agenda then shifts to analyze who among the
rich and the poor in Africa benefit from public spend
ing on health.
Thereafter, we discuss the outputs of public spend
ing on health and consider health outcome measures.
Further, the chapter discusses trends in a number of
indicators during the period 1990-1995 and from the
1960s into the 1990s. Finally, to look more closely at
the associations between public sector health expendi
tures and health services, as well as between health ser
vices and health outcomes, the chapter concludes with
a series of cross-tabulations and multiple regression
models.
The data presented confirm the multiple challenges of
poverty and health in Africa. Table 1 summarizes study
results for all of Africa with regard to each of the vari
ables, indicating benchmarks for different levels of per
formance among the countries. The median value is
estimated for each indicator. The values for the 25th
and 75th quartile of countries—a practical measure of
distribution—are also estimated for all of the African
countries.
The hypothetical “median” African country had a
per capita GDP of less than US$400, an adult female
literacy rate of 47 percent, a relatively weak bureau
cracy (1 on a scale of 0-4), and an unsatisfactory, high
level of corruption (2.5 on a scale of 1-5). Il also expe
rienced low public sector expenditures on health
(US$6 per capita), mediocre immunization coverage
(64 percent for DPT3), a contraceptive prevalence rate
of only 15 percent, and only 45 percent of deliveries
supervised by a trained health worker. With regard to
health outcomes, the “median” African country had an
infant mortality of 92 deaths per 1,000 live births dur
ing the period 1990-1996. Life expectancy was only
49 years for males and 52 years for females, the TFR
was 5.7, and 26 percent of children were malnourished
(underweight).
One of the main messages to emerge from this study is
the substantial variation among African countries in their
health experiences. The interquartile range (the differ
ence between the lowest and highest quartile levels of
distribution) for infant mortality was 50 deaths per
thousand, or more than half of the median rate. In
many cases, distribution is markedly skewed. For
example, public sector health spending was US$4 per
capita or less (with the median nearly US$6 per capi
ta) in the lowest quartile of countries, while the top
quartile spent about US$21 per capita or more.
Conditions with regard to other indicators, such as
access to safe water and the proportion of supervised
deliveries, were distributed more evenly.
African countries with different levels of GNP vary
in several aspects that may influence health expendi
tures, services, and health outcomes. Half of the pop
ulation of the middle-income countries studied was
urban, compared to one-third in the low-income coun
tries and about one-quarter in the lowest-income
countries.
Table 2 classifies each country according to per capi
ta GNP and presents basic data on the country-income
groups used for the analyses (other than those in table
1). In the study, there were 10 ’’middle-income" coun8
9
Principal Findings from Cross-National and Intertemporal Comparisons
Tabic 1
Key Indicators for African Countries, by Country Group, 1990-1996
Indicator
Lowest Quartile Countries
(25th percentile)
Median
(50th percentile)
Highest Quartile Countries
(75th percentile)
Socioeconomic Indicators
GN P (per capita), 1990-1996
Real GDP (per capita), 1990-1996
ODA (% of GDP), 1990-1996
Gross Secondary School Enrollment (% of student pop),
1992-1993
Female Adult Illiteracy (% of female pop ), 1995
Access to Safe Sanitation (% of pop.), 1990-95
Access to Safe Water (% of pop.), 1990-95
$244
$256
7%
9%
$387
$393
15%
16%
$652
$771
25%
27%
37%
20%
34%
53%
35%
50%
75%
57%
68%
1
2.5
27%
2.2%
3.5%
2
3.0
40%
3.3%
5.1%
$5.92
1.8%
18%
72%
46%
45%
11%
$20.62
2.7%
35%
92%
61%
56%
17%
1.3
0.09
80%
64%
66%
37%
15%
45%
1.7
0.20
95%
80%
79%
66%
21%
63%
92
156
43
15
49
52
442
373
630
5.7
134
26%
33%
7%
14%
120
192
46
18
55
58
493
406
925
6.4
179
34%
43%
11%
17%
National Policy Indicators
Bureaucratic Quality, 1998
0
Legal & Regulatory Framework (Anticorruption Index), 1997
2.0
Total Government Expenditure (% of GDP), 1990-1996
21%
Military Expenditure (% of GDP), 1990-1994
1.3%
Public Education (% of GDP), 1990-1994
2.6%
Health Expenditure Indicators
$4.02
Annual Public Sector (real per capita USS), 1990-1996
Public Sector Health (% of GDP), 1990-1996
1.2%
Capital Investment (% of public sector health), 1990-1996
8%
Recurrent Expenditure (% of public sector health). 1990-1996 65%
Personnel Costs (% of public sector health), 1990-1996
31%
38%
Hospitals (% of public sector health), 1990-1996
5%
Pharmaceuticals (% of public sector health), 1990-1996
Health Service Indicators
0.7
Inpatient Beds (per 1,000 pop.), 1990-1995
Physicians (per 1000 pop.), 1990-1995
0.05
BCG Coverage (%), 1995
63%
46%
DPT3 Coverage (%), 1995
50%
Measles Coverage (%), 1995
22%
Tetanus Coverage (%), 1995
7%
Contraceptive Prevalence Rate (%), 1990-1996
26%
Supervised Deliveries (%), 1990-1996
Health Outcome Indicators
Infant Mortality Rate (per 1,000 live births), 1995
70
97
Under Five Mortality Rate (per 1,000 live births), 1995
37
Crude Birth Rate (per 1,000 pop.), 1995
Crude Death Rate (per 1,000 pop ), 1995
11
Male Life Expectancy (years), 1995
45
Female Life Expectancy (years), 1995
48
Male Adult Mortality (per 1,000 men, 15-60 yrs.), 1995
356
Female Adult Mortality (per 1,000 women, 15-60 yrs.), 1995
295
Maternal Mortality (per 100,000 live births), 1990-1995
503
Total Fertility Rate, 1995
4.8
Adolescent Fertility Rate (per 1,000 girls, 15-19 yrs), 1995
110
Childhood Underweight (Malnutrition) (%), 1990-1996
20%
Childhood Stunted (%), 1990-1995
24%
Childhood Wasted (%), 1990-1995
5%
Low-Birth-Weight Babies (%), 1990-1996
10%
Source: Annex tables 1-14; data are period averages for the years shown for each indicator.
10
Health Expenditures, Services, and Outcomes in Africa
tries with an average real per capita GNP greater than
US$765 during 1990-1996, which represent only 9
percent of the African population. South Africa alone
made up 81 percent of the entire middle-income pop
ulation. Nineteen countries had an average per capita
GNP between US$300 and $765, representing 28 per
cent of the African population, and are termed “lowincome countries.” Kenya, Sudan, Ghana, Cote
d’Ivoire, Cameroon, and Zimbabwe made up 67 per
cent of the entire low-income population. Nineteen
countries had a per capita GNP of less than US$300,
but represented 63 percent of the African population.
They are termed the “lowest-income countries.”
Nigeria, Ethiopia, Congo Democratic Republic (for
merly Zaire), Tanzania, Uganda, and Mozambique
make up 75 percent of the entire lowest-income pop
ulation.
As shown in table 3, the difference in the average level
of GDP per capita between the lowest-income and lowincome countries was twofold. This difference was almost
14-fold when compared to middle-income countnes.
Nonetheless, per capita GDP growth did not vary remark
ably among income categories. Among the lowest-income
countries, Burkina Faso (with a growth rate of 2.7 per
cent), Ethiopia (3.4 percent), Tanzania (3.4 percent), and
Uganda (6.9 percent) are outliers with relatively high
growth rates. Among the low-income countnes, Benin
(4.2 percent) and Ghana (4.3 percent) stand out.
Total ODA (presented in table 4 by country income
group and population size) as a percentage of GDP
Table 2 Classification of African Countries According to Country Income Group (Average Annual Per Capita
GNP, 1990-1996)
Population
(millions)
(1990-1996)
Total
(percent)
Income Group
Number of
Countries
Annual GNP
per capita
1990-1996
(USS)
Lowest-income
19
<5300
351
63
27
Burkina Faso, Burundi,
Chad, Congo Dem.
Rep., Entrea, Ethiopia,
Guinea-Bissau, Liberia,
Madagascar, Malawi,
Mali, Mozambique,
Niger. Nigeria, Rwanda,
Sierra Leone, Somalia,
Tanzania, Uganda
Low-income
19
5300-5765
155
28
34
Middle-income
10
>5765
49
9
50
Angola, Benin,
Cameroon, CAR,
Comoros, Cdte d'Ivoire,
Equatorial Guinea,
The Gambia, Lesotho,
Ghana, Guinea, Kenya,
Mauritania, Sao Tome
& Principe, Senegal,
Sudan, Togo, Zambia,
Zimbabwe
Botswana, Cape Verde,
Congo, Djibouti,
Gabon, Namibia,
Mauritius, Seychelles,
South Africa, Swaziland
All of Africa
48
5500
555
100
31
Urbanization
(1985-1995)
(percent)
Countries
Principal Findings from Cross-National and Intertemporal Comparisons
11
Table 3 Macroeconomic Performance by African Country Income Group, 1990-1996
Country
Income
Croup
GNP
1990-1996
(annual per
capita USS)
GDP
1990-1996
(annual per
capita USS)
Real Annual Per
Capita GDP Growth
1990-1996
(%)
ODA
1990-1996
(KofGDP)
Lowest
Low
Middle
All of Afnca
197
482
2,682
502
233
558
2,137
497
1.6
2.1
1.2
1.7
15
13
1 5
13
dropped significantly and appropriately from the lowest-income countries (15.3 percent) to the middle
income countries (1.5 percent). However, it was
remarkably similar (15 and 13 percent of GDP, respec
tively) in the lowest- and low-income countries. For
example, Cote d’Ivoire, a low-income country, received
9 percent of GDP as foreign assistance over the period
1990-1996, whereas Burkina Faso, among the lowestincome countries, received 17 percent In contrast,
Mauritius, included among the middle-income coun
tries in this study, received only 1 percent of its GDP as
foreign aid. The data on ODA demonstrate a bias in
favor of countries with small populations, as exempli
fied by the case of Cape Verde with 32 percent of GDP
in ODA, but a total population of only 390,000.
However, as shown in table 4, the small-country bias
did not apply with respect to the lowest-income
African countries.
Table 5 focuses on the determinants of health out
comes outside the health sector. The table reveals a
steady improvement in key education, sanitation, and
water supply indicators as income increased.
Nonetheless, levels of access to safe sanitation and
water remained quite low even in middle-income
countries. The gross secondary school enrollment ratio
showed a progression from 16 percent (lowest-
Table 4 Average Annual Per Capita Official Development Assistance, by Population Size and African Country
Income Group, 1990-1996
Country
Population
Lowest
<5 million
5-15 million
>15 million
Total
44
46
17
25
African Country Income Group
Per Capita ODA in USS, 1990-1996
Low
Middle
74
61
30
49
____________
Total
73
54
18
31
104
—
5
24
— = Not available
Table 5 Key Education, Sanitation, and Water Supply Indicators, by African Country Income Group
Country Income
Group
Female Adult
Illiteracy
1995
(% offemale pop.)
Gross Secondary
School Enrollment
1992-93
(% of student pop.)
Access to
Safe Sanitation
1990-95
(% of pop.)
Access to
Safe Water
1990-95
(% of pop.)
Lowest
Low
Middle
All of Africa
58
51
21
52
16
25
74
24
31
43
46
36
37
53
68
44
12
Health Expenditures, Services, and Outcomes tn Africa
income) io 25 percent (low-income) with a marked
jump to 74 percent (middle-income). A similarly strik
ing decline in female illiteracy distinguished the middle
income from the low- and lowest-income countries.
Exceptions to these patterns included two lowestincome countries, Congo Democratic Republic (with
a relatively low level of female illiteracy of 32 percent
in 1995) and Rwanda (with only 48 percent female
illiteracy in 1995). In addition, Senegal, a low-income
country with a high level of female illiteracy at 77 per
cent in 1995, diverged from these patterns.
Exceptions to general trends in the populations access
to safe sanitation (during 1990-1995) included two of
the lowest-income countries: Burundi, with 51 per
cent access, and Tanzania, with 86 percent access, as
well as Guinea, a low-income country with 70 percent
access.
As shown in table 6, the lowest-income countries
were found to have the highest level of corruption, as
well as the weakest public bureaucracies. Uganda and
Zimbabwe were positive exceptions to this pattern
among the lowest- and low-income countries, with
corruption index values of 2.67 and 3.33, respective
ly, and a bureaucratic quality index of 2 in both cases.
Although faring better on all national policy indica
tors covered by this study, low-income countries had rel
atively poor levels of corruption and bureaucratic quality,
with values much closer to those of the lowest-income coun
tries than to those of the middle-income countries. Lowincome African countries with relatively good perfor
mances (compared to others in the same income
group) included Cote d’Ivoire, with a corruption index
of 3.33 and a bureaucratic quality index of 2). In addi
tion, the military-to-government health expenditure
ratio of the low-income countries (2.41) was only mar
ginally lower than that of the lowest-income countries
(2.90).
As a general rule, with regard to levels of corruption
and bureaucratic quality, the split between the middle
income and the low-income countries was much
sharper than between the low-income and the lowestincome countries. Similarly, the ratio of military-togovemment health expenditures was strikingly differ
ent in the low-income countries (2.41) than in the
middle income countries (0.87). Notable exceptions to
the pattern of improved corruption and bureaucratic
quality variables as income rises were found among the
middle-income countnes. On the negative side is
Gabon, with a corruption index of 2 and a bureaucrat
ic quality value of 2, while on the positive side,
Mauritius had a corruption index of 4.67 and South
Africa had a corruption index of 3.67 and a bureau
cratic quality index of 3.
In light of the worldwide tendency for government
health expenditures to rise along with income, it is not
surprising that the lowest-income countnes had the
smallest proportion of GDP devoted to this aspect.
Furthermore, at 2.90, these countries had the highest
military-to-government health expenditure ratio.
Notable exceptions to this pattern include Madagascar,
with military expenditures of only 1 percent of GDP
and a military-to-govemment health expenditure ratio
of 1.00, and Malawi, with military expenditures of 0.9
percent of GDP and a military-to-government health
expenditure ratio of only 0.39.
Estimates of health expenditures in 1990 (based on
WDR93) are reported in table 7 according to country
income group. Donor financing is excluded in govem-
Table 6 National Policy Indicators, by African Country Income Group
Country
Income
Group
Lowest
Low
Middle
All of Africa
Legal &
Regulatory
Framework
1997
(1-5)
Bureaucratic
Quality
1998
(0-4)
Total
Government
Expenditures
1990-1996
(KofCDP)
Military
Expenditures
1990-1996
1994 (check)
(% of GDP)
Government
Health
Expenditures,
1990
(% of GDP)
Ratio of Military
Expenditures,
1990-1996, to
Government Health
Expenditures, 1990
1.84
2.28
3.53
2.11
0.51
1.41
2.75
0.96
22.8
28.8
33.2
25.5
2.9
3.3
2.7
3.0
1.00
1.37
3.10
1.29
2.90
2.41
0.87
2.32
Principal Findings from Cross-National and Intertemporal Comparisons
13
Tabic 7 Government, Donor, and Private Health Expenditures, by African Country Income Group, 1990
Country
Income
Group
Lowest
Low
Middle
All of Africa
Government
1990
(%ofGDP)
1.00
1.37
3.10
1 29
Donor
Government
1990
1990
(per capita USS) (KofGDP)
2.27
7 30
70.96
10.90
Total
Private
Total
Donor
Private
1990
1990
1990
1990
1990
(per capita USS) (% of GDP) (per capita USS) (% of GDP) (per capita USS)
1.14
0.64
—
0.99
2.17
2.98
—
2.57
1.72
1.92
2.30
1.83
4.01
9.90
52.25
10.77
3.85
3.93
5.50
4.02
8.43
20.18
124.84
24.24
— = Not available
Note: The apparent discrepancies in donor spending in lowest-income countries are due to population weighting.
Source World Bank Development Report 1993
ment spending, but included in public sector spend
ing. The absolute levels of 1990 government per capita
health spending among the lowest-income African countries
(US$2.27) raise serious concerns about the long-term fea
sibility of government financing of a minimum package of
health services that are expected to reach all people in these
countries. In WDR93, the World Bank estimated that
the cost of a basic package of clinical and public health
services would be approximately US$12 per capita in
low-income countries. In Better Health in Africa (1994),
the Bank used a different methodology from the WDR
to estimate the cost of such a package at around US$13
per capita in low-income and $16 per capita in mid
dle-income African countries. The Africawide average
of government health spending at US$10.90 per capi
ta gives the impression that African countries should
be able to finance the basic package, bearing in mind
donor and private spending. However, it can hardly be
expected that the international community and African
households will be able to ensure long-term sustain
ability of resource flows. Such flows are necessary' to
finance a package that costs US$13 per capita per year
in the lowest-income countries, which make up more
than 60 percent of Africa’s population. Thus this study
underscores the importance of countiy-specifc analyses of
the content, cost, and financing of basic health services.
In the lowest-income countries, donor contribu
tions accounted for 53 percent of public sector health
expenditures (defined as including both government
and donor sources). This share dropped to 41 percent
in the low-income countries, which on a real per capi
ta basis received 16 percent more external aid than the
lowest-income group. This suggests that on the
grounds of equal treatment, a case can be made for the
reallocation of official development assistance for
health purposes in Africa to the lowest-income coun
tries. This is true independent of the argument that the
lowest-income countries might, simply on the grounds
of poverty, be justified in receiving a higher share of
development assistance for health.
In reality, of course, ODA is rarely if ever allocated
according to such criteria. Furthermore, it is impossi
ble to consider health assistance alone, without refer
ence to support for other sectors. Yet even when con
sidering total ODA (see table 4), we find a perverse
pattern of per capita assistance. With the exception of
South Africa (which received little assistance), ODA
varied directly with country' income, that is, as income
rose from the lowest-income to the low-income coun
tries, ODA also increased.
The share of the private sector in total health expen
ditures was fairly stable across all three country’
income groups, representing 45 percent, 49 percent,
and 43 percent among the lowest-, low-, and middle
income countries, respectively. Yet private spending
was nearly twice that of government spending in the
lowest-income countries and only one-third higher
than government spending in the low-income coun
tries. The fact that households finance half of health
services in all but the middle-income African coun
tries poses a fundamental challenge to policymakers.
This trend is often viewed, particularly' in resource
scarce environments, as a justification for the intro
duction or increase of fees at public sector health facil
ities.
However, it may be even more appropriate for pub
lic officials to study, in consultation with consumer groups,
how private spending may be attracted away from ineffec
tive and sometimes dangerous remedies to the most costeffective services, regardless of whether these services are
14
Health Expenditures, Services, and Outcomes in Africa
provided by the public or private sector. Al a minimum,
the relatively high level of private spending, combined
with low rates of utilization of public sector health
facilities in many African countries,1 calls for a public
sendee oriented towards clientele. It also underscores
the importance of taking into account the findings of
household surveys when analyzing and financing a
basic package of health services.
Per capita public sector health expenditures in 1990
(see annex table 1) varied greatly, ranging from under
US$2 in eight countries (Congo Democratic Republic,
Ethiopia, Liberia, Madagascar, Mozambique, Rwanda,
Sierra Leone, and Tanzania) to more than US$90 in two
others (Gabon and South Africa). The median per capi
ta public sector health expenditure was US$5.92. As a
percentage of GDP, public sector health expenditures
ranged from less than 1 percent in eight countries
(Burkina Faso, Cameroon, Congo Democratic Repub
lic, Madagascar, Sierra Leone, Sudan, Tanzania, and
Uganda) to 3.2, 3.2, and 3.8 percent, respectively, in
South Africa, Swaziland, and Botswana. Tables 7 and 8
confirm the tendency of health expenditures to
increase in both per capita terms and as a share of GDP
as income rises. The same phenomenon is presented in
a scatterplot in figure 1, which also shows a consider
able variation in country experience, for example with
Gabon once again a striking outlier.
Information separating donor financing from other
public sector health financing was available for only
nine countries. With the exception of Ghana, where
donors contributed less than half of the total of capital
expenditures, external assistance financed nearly all
capital investments in health in the public sector. This
suggests that the donor community holds significant
responsibility for long-term patterns of health services and
recurrent spending on health because of its influence on
public investment in Africa.
As shown in table 8, the breakdown between recur
rent and investment expenditures did not appear to
vary’ much between the low-income and lowestincome country groups, accounting for 80 and 84 per
cent of public sector health expenditures, respectively.
However, figure 2 presents a remarkably different pic
ture with a breakdown by individual countries: Wide
variation among African countries exists with regard to the
breakdown between investment and recurrent expendi
tures. For those countries where data were available,
the investment share ranged from less than 5 percent
(in Zimbabwe, Congo, and Cameroon) to more than
60 percent (in Sao Tome and Guinea-Bissau) of public
sector health expenditures. The median amount of
public sector health expenditures devoted to capital
investment was 19 percent, and to recurrent spending,
71 percent.
In figure 2, two components of recurrent expendi
tures—wages and pharmaceuticals—are presented for
the few countries where such information is available.
Compensation appeared to absorb a large proportion
of public sector health expenditures, representing
nearly 60 percent of recurrent expenditures. However,
distribution is quite skewed. Pharmaceuticals con
sumed a median of 11 percent of total and about 16
percent of recurrent public sector expenditures. With
the exception of Zimbabwe, pharmaceutical expendi
tures did not exceed 20 percent of such expenditures.
Comparable data on public sector health care financ
ing in 1990-1996 by type of medical service were
found for only 14 countries. In these countries, it
appears that nearly half of public sector health expen
ditures went to finance hospitals, with the amounts
Table 8 Mean Health Expenditures by Category and African Country Income Group, 1990-1996
African
Country
Income Croup
Lowest
Low
Middle
All of Africa
— » Not available.
Public Sector
Health
Expenditures
1990-1996
(% of CDP) (per capita USS)
1.4
1.7
3.2
1.6
3.19
9.58
71.99
11.22
Private Sector
Health
Expenditures
1990-1995
(% of CDP) (per capita USS)
—
2.1
4.0
—
—
13.15
90.60
—
Capital
Investment
1990-1996
(% of public sector
health expenditures)
Recurrent
Expenditure
1990-1996
(% of public sector
health expenditures)
17
20
—
18
84
80
—
83
Principal Findings from Cross-National and Intertemporal Comparisons
Figure 1 Public Sector Health Expenditures (% of GDP)
versus Real Per Capita GDP in African Countries, 1990-1996
ranging from 20 percent in The Gambia to 80 percent
in Zimbabwe. (Considerable caution is needed in inter
preting these data, since two major hospitals tn The
Gambia operate outside the formal system and are
financed by the Medical Research Council.)
Data on the financing of primary health care pro
grams were identified in only seven countries (all of
which spent less on primary care than on hospitals),
averaging 26 percent of public sector health expendi
tures. While some differences undoubtedly exist in the
15
definitions of data, this information nonetheless
implies a remarkable disconnect between public and polit
ical rhetoric on the importance of primary care and the
realities of public resource allocation.
In table 9, we turn our attention away from overall
spending patterns towards the critical question of who
benefits from public sector health spending in Africa.
The general pattern is clear: In African countries, the
richest people tend to benefit much more than the poorest.
Furthermore, the poorest people tend to be less healthy
than the richest. For example, in Cote d’Ivoire in 1994,
the under-five mortality rate (U5MR) for the poorest 20
percent of the population was 172, but 121 for the
richest 20 percent. At the same time, 30 percent of chil
dren among the poorest 20 percent of the population
were malnourished, twice the number as among the
richest 20 percent.2 The information on Cote d’Ivoire
and the data in table 9 suggest that the interconnections
among issues of poverty, equity, and health deserve a
stronger position on the health policy agenda of African
countries and their international partners than has been the
case in recent years. The rhetoric of the Alma Ata
Declaration on Primary Health Care needs to give way
Figure 2 Capital and Recurrent Public Sector Health Expenditures in African Countries, 1990-1996
Health Expenditures, Services, and Outcomes in Africa
16
Table 9 Proportion of Public Sector Health Expenditures Accruing to the Poorest and Richest Income Quintiles,
Selected African Countries
Countiy
Cote d'Ivoire (1995)
Ghana (1992)
Guinea (1994)
Kenya(1992)
Madagascar (1993)
South Africa (1994)
Tanzania (1992/1993)
Poorest 20%
Richest 20%
Rich/Poor Ratio
11
12
4
14
12
16
17
32
33
48
24
30
17
29
2.90
2.75
12.00
1 71
2.50
1.06
1 71
Source: Castro-Leal and others 1999.
to a more analysis- and evidence-based dialogue on the
targeting of public spending on health.
Given the patterns of spending on health described
above, what service inputs are being purchased and
what outputs are being produced? Table 10 presents
basic data, disaggregated by country income group.
Inpatient beds and physicians appear to not vary sig
nificantly by income. Exceptions to these patterns
include low-income countries such as Cameroon, with
as many as 2.6 beds per 1,000 population, and Togo,
with 1.5 beds per 1,000. As far as physicians are con
cerned, Ethiopia (among the lowest-income countries)
has only 0.03 physicians per 1,000 population and
both Malawi and Niger currently have 0.02, strikingly
low values for this indicator.
Table 10 also shows that in general, health service
outputs and population coverage with basic services tend to
increase with income level. Contraceptive prevalence and
the proportion of attended births showed steady
increases at each income level, with particularly
marked changes at the middle-income level.
Immunization also increased in stages, with the excep
tion of maternal tetanus coverage, which was at a low
level in all groups.
The data in the annex tables reveal certain positive
exceptions to the patterns among income groups. Two
of the lowest-income countries had high rates of super
vised deliveries, Burkina Faso with 42 percent and
Madagascar with 57 percent. In addition, Cameroon, a
low-income country, had 64 percent of deliveries
supervised. At the other extreme were Ethiopia and
Niger (both lowest-income countries), with only 14
and 15 percent of deliveries supervised, respectively.
In table 11, we turn our attention to key health out
comes, disaggregated by country income group. With
regard to each measurement of mortality, fertility, and
nutritional status, steady improvements occurred as
GDP rose. Infant mortality was at a mean of 102 in the
lowest-income countries and nearly double the rate in
middle-income countries, which had a mean of 55.
Women in the middle-income countries had three
fewer children than women in the lowest-income
Table 10 Selected Health Service Indicators, by Country Income Group, 1990-1996
Health Service Indicators
Health Service Inputs:
Inpatient Beds per 1,000 population, 1990-1995
Physicians per 1,000 population, 1990-1995
Health Service Outputs:
Supervised Births (%), 1990-1996
Contraceptive Prevalence Rate (%), 1990-1996
BCG Coverage (%), 1990-96
DPT3 Coverage (%). 1990-96
Measles Coverage (%), 1990-96
Tetanus Coverage (%), 1990-96
— = Not available.
Lowest
African Country Income Croup
Low
Middle
All of Africa
1.11
0.11
1.25
0.08
—
—
1.19
0.11
30
8
63
47
49
34
50
20
68
56
56
38
81
62
74
76
77
32
41
17
65
52
53
35
17
Principal Findings from Cross-National and Intertemporal Comparisons
Tabic 11 Selected Health Outcome Indicators, by African Country Income Group, 1990-1996
Health Indicator
Lowest
Infant Mortality Rate (per 1,000 live births), 1990-1996
Under-Five Mortality Rate (per 1,000 live births), 1995
Male Life Expectancy (years), 1995
Female Life Expectancy (years), 1995
Male Adult Mortality (per 1,000 men, 15-60 yrs.). 1995
Female Adult Mortality (per 1,000 women, 15-60 yrs.), 1995
Years of Potential Life Lost (per 1,000 population), 1995
Crude Birth Rate (per 1,000 population), 1995
Crude Death Rate (per 1,000 population), 1995
Total Fertility Rate (children), 1990-1996
Maternal Mortality (per 100,000 live births), 1990-1995
Adolescent Fertility Rate (per 1,000 girls, 15-19 yrs.), 1995
Low-Birth-Weight Babies (%), 1990-1996
Childhood Underweight (%), 1990-1996
Childhood Stunting (%), 1990-1995
Childhood Wasting (%), 1990-1995
102
173
48
51
467
389
106
45
16
6.3
1015
153
16
38
44
8
African Country Income Croup
Middle
Low
81
125
52
55
416
357
67
39
12
5.4
606
119
15
26
31
8
55
74
60
65
326
253
40
31.5
9
3.4
277
78
—
11
23
8
All of Africa
92
151
50
54
448
376
89
42
14
5.8
822
137
16
32
39
8
— = Noi available.
countries. The difference in crude birth rate from the
lowest- to middle-income groups was 17 percent,
while the difference in crude death rate was compara
tively much higher at 29 percent. Similarly, the burden
of disease in terms of years of potential life lost (YPLL)
per 1,000 population decreased dramatically, from 106
in the lowest-income countries to 68 in low-income
and 40 in middle-income countries. Children in the
lowest-income countries were three times more likely
to be underweight (low weight for age) and twice as
likely to be stunted (low height for age) when com
pared to middle-income countries, although there was
little difference with regard to wasting (low weight for
height).
The key to a fuller understanding of the data in Table
11 is to examine the differences between the country
income groups and, even more important, to consider
discontinuities in patterns of improvement as income
rises. For example, the fall in U5MR from the lowestto the low-income group and from the low- to the mid
dle-income group was nearly the same in absolute
terms (48 and 51, respectively). However, it was strik
ingly larger in percentage terms from the low- to the
middle-income group (41 percent) than from the low
est- to the low-income group (28 percent).
The performance of several countries with regard to
U5MR (measured in absolute terms) deviated notably
and positively from that of their country income group
as a whole. These included Ghana and Cameroon (two
low-income countries with rates of 116 and 86, respec
tively), Congo Democratic Republic (a lowest-income
country with a rate of 144), and Mauritius (a middle
income country with a rate of 20). Less successful devi
ations from income group patterns include three lowincome countries, Benin (156), The Gambia (213), and
Malawi (225). Two middle-income countries are also
exceptions: Djibouti (181) and Gabon (145). Similar
patterns prevail with regard to infant mortality.
Bearing in mind that achieving health gains may
become more difficult as absolute levels improve, these
data suggest that the fight for child survival in Africa is
far from over. The poorest populations are at risk of
being left behind in the race to improve health out
comes, which makes a continuing, concerted attack on
infant and under-five mortality critical, particularly among
the lowest-income countries.
It was possible to document trends in health services
and outcomes in Africa for only a few variables. Among
health services indicators, only immunization rates
have been collected annually for the majority of African
countries and show similar trends for all childhood
antigens. Boxplots showing annual immunization cov
erage for DPT3 for all African countries from 1980 to
1996 are presented in figure 3, which demonstrates
wide variation in the median coverage from year to year.
A general improvement in immunization coverage took
18
Health Expenditures, Sendees, and Outcomes in Africa
Figure 3 DPT3 Coverage in African Countries, 1980-1996
Source: WHO. EPI Information System Global Summary, August 1997.
place from 1980 to 1990, with decreasing variation among
countries over time. DPT3 immunization coverage levels
in 1990 were much higher than other years (with a
median for the period of 75 percent), since this was the
“crescendo" year for universal childhood immunization
campaigns. The data and the graphs make clear the
negative risk of the campaign strategy: Immunization
coverage declined steadily between 1990 and 1994,
with some recovery occurring in 1995.
Changes in key health outcome indicators between
1990 and 1995 are presented according to country
income group in table 12. Demographic indicators
Table 12 Changes from 1990 to 1995 in Selected Health Outcome Indicators, by African Country Income Group
African Country Income Group
Low
Middle
Health Indicator
Lowest
Infant Mortality Rate (per 1,000 live births), 1990
Infant Mortality Rate (per 1,000 live births), 1995
Percent Change
108
101
-7.0
87
80
-7.7
59
55
-7.6
97
90
-7.2
Male Life Expectancy (years), 1990
Male Life Expectancy (years), 1995
Percent Change
47
48
3.4
52
52
1.3
Female Life Expectancy (years), 1990
Female Life Expectancy (years), 1995
Percent Change
49
51
3.6
55
55
0.7
58
60
2.8
64
65
2.3
49
50
2.3
52
54
2.3
Male Adult Mortality (per 1,000 men, 15-60 yrs.), 1990
Male Adult Mortality (per 1,000 men, 15-60 yrs.), 1995
Percent Change
464
467
0.5
406
416
2.4
444
448
1.0
Female Adult Mortality (per 1,000 women, 15-60 yrs.), 1990
Female Adult Mortality (per 1,000 women, 15-60 yrs.), 1995
Percent Change
386
389
0.8
342
357
4.4
325
326
0.4
247
253
2.4
Total Fertility Rate, 1990
Total Fertility Rate, 1995
Percent Change
6.5
6.2
-5
6.1
5.3
-13
4.4
3.3
-25
All of Africa
370
376
1.8
6.1
5.7
-7
19
Principal Findings from Cross-National and Intertemporal Comparisons
(such as infant mortality, life expectancy, and total fer
tility rates) improved in countries at all income levels,
with one notable exception: adult mortality.
Adult mortality rates for both females and males
between 15 and 60 years of age showed an increase from
1990 to 1995 across all income categories, a disturbing
change m the pattern of steady decline experienced since
the 1960s While it is not the role of the present study
to examine the origin of this change, it is likely that
AIDS, other emerging or reemerging infections such
as tuberculosis and drug-resistant malaria, and wide
spread civil unrest and violence are responsible for
the increase in adult mortality. In 1995, life expectan
cy at birth in middle-income countries was only 11
years higher for males and 14 years higher for females
than in the lowest-income countries. Percentage
based improvements in other mortality indicators dif
fered very little across income levels. However, fertil
ity levels declined rapidly in middle-income
countries (25 percent) and low-income countries (13
percent), even though they were already lower than
in the lowest-income countries. Insufficient data were
available to examine changes in childhood malnutri
tion rates.
Boxplots of infant mortality rates are presented for
all African countries from 1960 to 1995 in figure 4.
These show a consistent sequential decrease in the medi
an rate of infant deaths over the last 35 years, with a
marked slowdown in the current decade. The range of
variation among countries has been fairly consistent,
with an interquartile range (between the 25th and
75th percentiles) of about 50 deaths per 1,000 live
births. The only outlying data point is Mauritius,
which had an exceptionally low infant mortality rate
in 1960.
Boxplots of TFRs in African countries between 1960
and 1995 are shown in figure 5. Overall declines in fer
tility have appeared only during the past 15years, with the
median rate having shifted from 6.5 children in 1980
to 5.7 in 1995. The figure also demonstrates that a
striking increase has occurred with regard to the inter
country variation in fertility. There are also more coun
tries whose fertility rates can be considered outliers in
comparison with the rest of Africa than was the case
with infant mortality. Five of the middle-income coun
tries were low outliers, with Mauritius once again being
the most extreme. The increase in Gabon's fertility rate
from 1960 through 1980 is striking, with evidence of
consistently low outlying values.
In figures 6a and 6b, boxplots of male and female
life expectancy at birth are presented for the period
1960-1995. During this time, substantial increases in
Figure 4 Infant Mortality Rates in African Countries, 1960-1995
44
1960
44
1970
45
1980
47
1990
47
1995
20
Health Expenditures, Services, and Outcomes in Africa
Figure 5 Total Fertility Rates in African Countries, 1960-1995
o Rwanda
o Gabon
o Gabon
o Mauritius
O Mauritius
1960
o Seychelles
o Mauritius
o Seychelles
O Mauritius
43
43
46
48
1970
1980
1990
1995
life expectancy occurred, with stabilization reached in the
1990s. For example, female life expectancy increased
from a median of 42 years in 1980 to 52 years in 1995.
Intercountry variation also seems to be increasing over
time. With the exception of Sao Tome & Principe (in
1995), all countries with high outlying values for life
expectancy were in the middle-income group. Mauritius is,
once again, an outstanding performer.
Male and female adult mortality rates from 1960 to
1995 are presented in figures 7a and 7b, respectively. A
steady sequential decline occurred between 1960 and
1990, with a reversal occurring in 1995 for both male
and female rates. This trend reflects the data in table 12.
The variation in mortality experience among African coun
tries seems to be increasing over time. Guinea-Bissau had
an unusually high adult mortality rate for women in
1995, as did Sierra Leone for men in 1990 and 1995. A
number of countries—again primarily from the middle
income group—had low outlying values.
Table 13 examines more closely the relationships
between public sector expenditures and health services
in African countries. The data demonstrate how three
important aspects of services have improved with
increasing levels of public sector health expenditures.
The differences are substantial. Overall, as per capita
public sector health expenditures increased, measles
coverage, contraceptive prevalence, and the share of
deliveries supervised by trained personnel also
improved. Among countries with high levels of spend
ing, outliers include Seychelles (with a per capita
expenditure of US$157, 93 percent measles coverage,
and 99 percent supervised deliveries 99) and Mauritius
(which spends more than US$50 per capita and has 83
percent measles coverage, 75 percent contraceptive
prevalence, and 97 percent supervised deliveries).
Among low expenditure countries, exceptions are
Eritrea (with public sector spending of less than
US$1.20 per capita annually, 26 percent measles cov
erage, 8 percent contraceptive prevalence, and 21 per
cent supervised deliveries) and Ethiopia (which has
public sector health spending of US$1.79 per capita
annually, 33 percent measles coverage, 4 percent con
traceptive prevalence, and 14 percent supervised deliv
eries).
Principal Findings from Cross-National and Intertemporal Comparisons
21
Figure 6a Male Life Expectancy at Birth in African Countries, 1960-1995
80
2
1960
1970
1980
1990
1995
Figure 6b Female Life Expectancy at Birth in African Countries, 1960-1995
80
q Seychelles
" Mauritius
70
O Seychelles
O Mauritius
O Sao Tome & Pnncipe
O Mauntius
O Mauritius
O Mauritius
60
O Cape Verde
O Cape Verde
2
50
40
30
20 LN»
46
46
46
47
48
1960
1970
1980
1990
1995
22
Health Expenditures, Services, and Outcomes in Africa
Figure 7a Male Adult Mortality in African Countries, 1960-1995
700
E
§
600
L
2
500
400
300
O Mauritius
200
O Sao Tome
& Principe
100
O Sao Tomd
& Principe
0
N =
41
41
40
43
43
1960
1970
1980
1990
1995
Figure 7b Female Adult Mortality in African Countries, 1960-1995
700
600
O Guinea-Bissau
&
B 500
£
8
c
ir>
400
300
200
8 Seychelles
Madagascar
Mauritius
8
O Liberia
O Mauritius
100
O Sao Tome
& Principe
1960
41
40
1970
1980
O Mauritius
O Seychelles
O Sao Tome
& Principe
43
43
1990
1995
23
Principal Findings from Cross-National and Intertemporal Comparisons
Table 13 Levels of Selected Health Services in African Countries, by Level of Public Sector Health Expenditures,
1990-1996
Level of Annual Public Sector
Health Expenditures
Lowest Quartile of Countries
(<US$4.01 per capita)
Middle Two Quartiles of Countries
(US$4.01—$20.62 per capita)
Highest Quartile of Countries
(>US$20 62 per capita)
Measles Coverage
(%)
Contraceptive Prevalence
(%)
Supervised Deliveries
(%)
52
10
32
59
18
43
68
56
70
Table 14 presents the results of using multiple
regression models to assess the relative contributions
of public sector health expenditures on three aspects
of health services, with adjustments made for the
potential confounding variables of per capita GDP and
female illiteracy. These models show that health
expenditures were significantly related to measles
immunization (p = 0.01), did not reach the level of sta
tistical significance for contraceptive prevalence (p =
0.08), and had no statistical association with super
vised deliveries (p = 0.4). Female illiteracy was strong
ly associated with all three aspects, while GDP per
capita was significantly related only to supervised
deliveries.
Further insights into the relationship between infant
mortality and public sector health expenditures can be
gained from the scatterplot in figure 8. The wide dis
parity of country experiences is striking, with Sierra
Leone at one extreme and Mauritius and Seychelles at
the other.
Tables 15, 16, and 17 outline the crude relationships
among measles immunization, contraceptive preva
lence, and supervised deliveries with regard to biolog
ically relevant health outcomes. The results demon
strate that the range of health outcomes is quite large
and that these differences across levels of health ser
vices are important. In each case, the higher the level
of health services, the better the health outcome.
Table 14 Multiple Linear Regression Models of Public Sector Health Expenditures and Selected Health Services in
African Countries, 1990-1996
Measles Coverage (R2 = .32)
Constant
Ln GDP per capita
Female Illiteracy
Ln Public Sector Health Expenditures
Contraceptive Prevalence (R2 = .80)
Constant
Ln GDP per capita
Female Illiteracy
Ln Public Sector Health Expenditures
Supervised Deliveries (R2 = .77)
Constant
Ln GDP per capita
Female Illiteracy
Ln Public Sector Health Expenditures
B
B Standard Error
T
P Value
106.4
-8.9
-0 26
10.6
26.4
4.9
0.13
4.0
1.83
2.03
2.62
0.08
0.05
0.01
8.7
3.6
-0.47
7.9
24.7
5.0
.10
4.4
0.72
4.76
1.81
0.5
0.0001
0.08
-13.5
11.7
-0.34
3.6
20.3
4.1
0.11
4.4
2.85
3.09
0.82
0.008
0.004
0.4
24
Health Expenditures, Sendees, and Outcomes in Africa
Figure 8 Infant Mortality versus Public Sector Health Expenditures, Sub-Saharan Africa, 1990-1996
Sierra Leone
°
Uganda
°
idle d’Ivoire oG=ab0n
Ghana
o
°
Namibia
Kenya
Botswana
South Africa
Congo Democratic Republic
Eritrea
2.5 J------------------1----------------- H
-2
-1
0
Nigeria
Mauritius
o
, „
Seychelles
4
5
1
2
3
Ln Public Sector Health Expenditures
6
Table 15 Infant Mortality and Childhood Malnutrition in African Countries, by Level of Measles Immunization
Coverage, 1990—1996
Measles Immunization Coverage
Lowest Quartile of Countries (<45%)
Middle Two Quartiles of Countries (45%-78%)
Highest Quartile of Countries (>78%)
Infant Mortality Rate
Childhood Malnutrition (%)
104
97
72
34
26
19
Table 16 Infant Mortality and Total Fertility Rates in African Countries, by Level of Contraceptive Prevalence,
1990-1996
Contraceptive Prevalence Rate
Lowest Quartile of Countries (<7.4%)
Middle Quartile of Countries (7.4%-21%)
Highest Quartile of Countries (>21%)
Infant Mortality Rate
Total Fertility Rate
101
93
67
6.3
5.9
4.5
Table 17 Infant Mortality in African Countries, by Level of Supervised Deliveries, 1990-1996
Supervised Deliveries
Lowest Quartile of Countries (<26%)
Middle Two Quartiles of Countries (26%-63%)
Highest Quartile of Countries (>63%)
Infant Mortality Rate
123
99
56
25
Principal Findings from Cross-National and Intertemporal Comparisons
Tables 18, 19, and 20 present the results of using
linear regression models to better understand the rela
tive contribution of health services to health outcomes.
Both measles immunization and GDP per capita were
associated with statistically significant improvements
in childhood nutrition. The relationship between con
traceptive prevalence and total fertility was nearly sta
tistically significant (p = 0.1), female illiteracy was not
statistically significant (p = 0.2), and GDP per capita
was highly significant (p = 0.002). However, one
should not rely heavily on statistical significance in
interpreting the data. The effect of GDP per capita is
likely to manifest itself in the use of contraception, but
it may also have effects on fertility through levels of sex
ual activity and fecundity. The model used here does
not challenge the understanding that contraceptive use
leads to decreased fertility rates.
The examination of health services and infant mor
tality in Africa was more complicated because of the
problem of collinearity among the variables. As shown
in Table 20, contraceptive prevalence and supervised
deliveries are statistically related to lower infant mortal
ity, whereas measles immunization is not (after adjust
ing for the effect of GDP per capita). The direction of all
coefficients suggests a positive effect of selected health
services and female literacy on infant mortality.
Tabic 18 Multiple Linear Regression Model of Measles Immunization Coverage and Childhood Malnutrition in
African Countries, 1990-1996
Childhood Malnutrition (R2 = .56)
Constant
Ln GDP per capita
Female Illiteracy
Measles Immunization
B
B Standard Error
T
P Value
60.2
-4.8
0.13
-0.19
12.9
1.5
0.07
0.08
3.14
1.94
2.32
0.004
0.06
0.03
Table 19 Multiple Linear Regression Model of Contraceptive Prevalence Rates and Total Fertility Rates in African
Countries, 1990-1996
Total Fertility Rate (R2 = .79)
Constant
Ln GDP per capita
Female Illiteracy
Contraceptive Prevalence
B
B Standard Error
T
P Value
10.0
-0.8
0.01
-0.02
1.3
0.2
0.01
0.01
3.54
1 48
1.60
0.002
0.2
0.1
Table 20 Estimated Relationships among Infant Mortality Rates, Female Illiteracy, and Selected Health Services
in African Countries, 1990-1996
Vanable
Measles Immunization
Female Illiteracy
Contraceptive Prevalence
Supervised Deliveries
Sign of Estimated Coefficient
P Value
(-)
(+)
(-)
(-)
>0.1
<0.1
<0.05
<0.001
Note: The equations are estimated according to the formula Infant Mortality = a0 + pjlnGDP + P2var, where var is the variable in question. Each van
able is used once with Ln GDP in a single regression. Taken by itself, GDP per capita has a negative coefficient and is strongly correlated with infant
mortality (p<0.0001). This procedure was used because of muliicollinearity that arose from correlations among the variables. When tw-o or more vari
ables are introduced with LnGDP, coefficients become unstable; when all are used, none (including LnGDP) is statistically significant.
26
Notes
1. See, for example, Cote d’Ivoire, Ministere de la Same
Publique, Rapport Annuel sur la Situation Sanitaire,
1996.
2. World Bank data based on 1994 Demographic and
Health Surveys.
Health Expenditures, Services, and Outcomes in Africa
4 Limitations of the Data, Their Uses at the
Country Level, and Conclusions
This chapter discusses how the data set in the present
study can be applied at the country level. Overall con
clusions of the study are highlighted, and the limita
tions of the data are discussed. Documenting the vari
ation among individual countries and grouping
countries according to income provide a reference
point regarding what changes are currently possible.
Even though one-quarter of the countries studied have
infant mortality rates above 120 per 1,000 live births,
another quarter have already achieved levels below 70.
Among the lowest-income countries, average infant
mortality is 102, whereas in low-income and middle
income countries, it is 81 and 55, respectively. Such
reference points can help determine feasible policy and
financing options for the future and, with the use of
appropriate comparison groups, may serve to identify
issues most relevant to individual countries.
In order to illustrate how the data collected in this
study can be used at the country level, the following
discussion contrasts the experience of three countries
(Ethiopia, Ghana, and Gabon), each of which belongs
to a different country income group.
With 58 million people and a per capita GNP of only
US$110 in 1996, Ethiopia belongs to the lowestincome group. Its social indicators are low even among
the poorest African countries: only 25 percent of adult
females are literate, and only 10 percent of the popu
lation has access to safe sanitation and 27 percent to
safe water. However, its ratings for corruption (2.3)
and government bureaucracy (1) are better than most
other countries in its income group and are about aver
age for Africa as a whole. As a proportion of GDP,
Ethiopia’s public sector spending on health between
1990 and 1996 (1.2 percent) was in the 25th percentile
of African countries, below average for lowest-income
Africa despite steady increases over time.
Nonetheless, this translates into only USS2 per capi
ta in real terms, near the bottom of the Africa table. In
terms of health services, Ethiopia’s latest indicators pre
sent a mixed picture. The country has some of the low
est levels of supervised deliveries in Africa (14 percent,
only Somalia is worse at 2 percent) and a contraceptive
prevalence rate (8 percent) that places it at the 25th
percentile, about average for lowest-income Africa. On
the other hand, Ethiopia’s DPT3 coverage (39 percent
during 1990-96) varies considerably from year to year,
but was close to the African average in 1995. Among
its health outcomes, Ethiopia stands out during this
time by having some of the highest levels of malnutri
tion (48 percent of children were underweight) and
fertility (a TFR of seven) in Africa. Its infant mortality
rate (119) is better than a quarter of African countries,
but still worse than the average of lowest-income
Africa. If the data are reliable, they suggest that con
tinued increases in health expenditures are warranted
and point to program areas where special emphasis is
needed, namely supervised deliveries, family planning,
nutrition, and greater consistency in immunization.
Ghana, a country with a population of 17.5 million
and a GNP per capita of US$390 in 1996, has many
indicators typical of other low-income countries in
Africa. Its levels of female literacy (53 percent) and
access to safe water and sanitation (56 and 32 percent,
respectively) are near average for this group, although
its ratings for corruption and government bureaucracy
are better than average. Among low-income countries,
Ghana provides a lower level of public sector health
expenditures (both in real per capita terms and as a
percentage of GDP) and receives an average amount of
donor assistance for health. One of the few African
countries with sufficient data on types and sources of
health expenditures, Ghana stands out as having a rel27
28
alively high proportion of capital expenditures, and
particularly as having investments funded by the gov
ernment, rather than almost exclusively by donors.
This may indicate less reliance on donors or suggest
that closer examination of capital expenditures may be
warranted.
Nonetheless, Ghana’s health services outputs—in
terms of supervised deliveries (44 percent), DPT3 cov
erage (47 percent), and contraceptive prevalence rates
(20 percent)—were slightly below average both for
low-income Africa and for Africa as a whole. Despite
this, its infant and child mortality rates (74 and 116
per 1,000 live births, respectively) were much better
than average for low-income Africa, its fertility rates
were marginally better, and its levels of childhood
malnutrition were close to average. The data suggest
that higher public expenditures and better levels of
health services are feasible in Ghana.
With a GNP of US$4,290 per capita and a popula
tion of just over one million in 1996, Gabon is one of
the few middle-income countries in Africa.
Nonetheless, its health outcomes are quite poor, with
infant mortality and fertility rates (90 deaths per 1,000
live births and a TFR of five) closer to levels found in
low-income countries. In addition, its fertility levels are
apparently becoming higher. Gabon has relatively good
access to safe water and sanitation (67 and 76 percent,
respectively), but comparatively low levels of female lit
eracy (48 percent) for a middle-income country. As a
proportion of GDP, Gabon spends very little of its pub
lic resources on health (0.6 percent), even though this
translates into more than US$28 per capita.
Although the country has an average level of super
vised deliveries for middle-income Africa (80 percent),
it performs poorly in immunization coverage. Overall,
the data suggest that Gabon substantially underper
forms in terms of efforts and results, compared both to
Africa as a whole and to other middle-income African
countries. Although Gabon could afford to spend more
on public sector health, its primary problem is the rel
ative inefficiency of its current pattern of health spend
ing. Changes in this aspect, as well as in the quality of
public sector health services, may be the most impor
tant steps for the country.
Box 2 demonstrates how to use the data in this study
to establish benchmarks. It illustrates how a single
Health Expenditures, Services, and Outcomes m Africa
country, COte d’Ivoire, compares both to Africa as a
whole and to other countries in its income group.
Even in light of the limitations in the data on health
expenditures presented here, a number of findings
about the use of funds raise concern. The large varia
tion in capital expenditure (1-60 percent of public sec
tor health expenditures) may be due in part to poor
accounting, but is striking nonetheless. Where data are
available, a consistent pattern emerges of heavy depen
dence on external assistance to finance capital expendi
tures. This pattern of donor behavior is unlikely to be sus
tainable in the long term.
Another concern is the inability of the majority of
countries to report the use of public money on health,
either by type or level of medical service (e g., hospital
versus primary care) or by key items of expenditure
(such as wages and drugs). Similarly, the lack of speci
ficity of donor financing is remarkable. This applies,
interestingly, to the World Bank as well. Project
appraisal documents are, in general, fairly specific
regarding planned World Bank spending However,
project monitoring and completion reports rarely pro
vide such information, especially not in forms that per
mit compilation and comparison across countries.
Since these types of data are vital to making informed
choices about resource allocation, the gradually growing
interest in developing disaggregated national health
accounts in African countries merits encouragement and
support.
Changes in the levels of a number of indicators used
in this study are quite significant. Most disturbing is
the rise in adult mortality rates among both males and
females. As reported in table 12, male adult mortality
increased 1.0 percent and female adult mortality 1.8
percent from 1990 to 1995. The increase was highest
in low-income countries. Although reversals in adult
mortality have occurred in other countries in recent
years, notably in Eastern Europe (Goldstein and others
1996), this has not been the case for countries where
mortality rates are already among the highest in Africa.
There is a need to better understand and address the fac
tors behind increased death rates among adults in Africa.
The fairly uniform decline in infant mortality and
life expectancy at birth is only partially reassuring. One
would hope that declines would be faster in countries
with the highest mortality levels, since it is often
Limitations of the Data, Its Uses at the Country Level, and Conclusions
29
Box 2 Using the Data for Benchmarking: Where Does Cdte d’Ivoire Stand in Relation to Other African
Countries?
The table below shows how the data obtained in this study can be used to review the performance of individual African countries
in relation to each other. For each indicator covered, the table shows in which quartile of African countries Cdte d'Ivoire fell in the
early to mid 1990s, gives the value for Cote d’Ivoire during this period, compares Cote d’Ivoire with all African countries, and gives
the average value for the low-income group of African countries to which Cdte d’Ivoire belongs.
A few key lessons have been learned about Cdte d’Ivoire based on the data. First, relative toother African countries. Cdte d’Ivoire
has a relatively sound public service and legal and regulatory environment, spends fairly large sums of public money on health and
relatively little on defense. However, relative to other African countries, Ivorian performance on some measures (e g., physicians
per capita, supervised deliveries, and low birthweight babies) is about average for African countries. At the same time, performance
in public health-oriented service delivery to large populations (such as immunizations) and in health outcomes (such as infant,
child, and maternal mortality) is weaker than in other African countries. One implication of these trends is that Cdte d’Ivoire might
wish to pay relatively greater attention to public health and preventive services and to the intrasectoral allocation of public expen
ditures for health. Other African countries, in contrast, might need to pay relatively greater attention to the intersectoral allocation
of resources for health and to the mobilization of more public funds for the health sector
Rank of
COte d'Ivoire
Indicator
Value
for COte d'Ivoire
Comparison with All
African Countries
Average Value
for the LowIncome African
Country Group
In the Highest (Fourth)
Quartile of African
Countries
Bureaucratic quality, 1998
Legal and regulator}'
framework, 1997
2
3.33
Highest (4th) quartile => 2
4th quartile => 3.0
1.41
2.28
In the Third Quartile of
African Countries
Real per capita public sector
expenditure on health,
1990-1996
$12.70
3rd quartile = S5.92 - $20.62
$9.58
As Median African Country
Physicians per 1,000, 1990-1995
0.09
0.08
Supervised deliveries, 1990-1996
45%
Lowest (1st) quartile =< 0.05,
4th quartile > 0.20
Lowest (1st) quartile =< 26%.
4th > 63%
Lowest (1st) quartile =< 10%,
4th quartile >17%
Lowest (1st) quartile =< 22%,
4th quartile > 66% (1995)
38%
(1990-1996)
1.6%
2nd quartile = 1.2% - 1.8%
1.74%
11.4%
2nd quartile = 7% - 15%
20.1%
86
138
2nd quartile = 70-92
2nd quartile = 97-156
81
125
600
5.3
2nd quartile = 503 - 6.30
2nd quartile = 4.8 - 5.7
606
5.4
24%
24.4%
57%
2nd quartile = 20% - 26%
2nd quartile = 24% - 33%
2nd quartile = 50% - 66%
25.6%
30.6%
56%
Military expenditures as share
of GDP, 1990-1994
BCG coverage, 1995
1.1%
1st quartile =< 1.3%
49%
DPT3 coverage, 1995
41%
1st quartile =< 63%
(unless data are at issue)
1 st quartile =< 46%
( median = 64%)
Low birthweight babies, 1990-1995 14%
Tetanus coverage
27%
(Cdte d'Ivoire as Median in 1995) (1990-1996)
In the Second Quartile of
African Countries
In the Lowest (First)
Quartile of African
Countries
Public sector health expenditures
as share of GDP, 1990-1996
Contraceptive prevalence rate,
1990-1996
Infant mortality rate, per 1,000
live births, 1995
Under five mortality rate, 1995
Maternal mortality, per 100,000
livebirths. 1990-1995
Total fertility rate, 1995
Childhood malnutrition
(underweight), 1990-1996
Children stunted, 1990-1995
Measles coverage, 1995
50%
15%
3.3%
(1990-1996)
68%
56%
30
expected that health gains can be made most easily
under such conditions. A number of factors have, how
ever, stymied more rapid improvements in the lowestincome countries, including violent conflict and a lack
of resources for already weak health programs.
Variations in changes in TFRs are also important.
Much more rapid declines among middle-income
countries, which already have lower fertility and mor
tality rates, suggest rapid demographic transitions,
whereas the lowest-income countries are barely begin
ning this process. Such trends are likely to result in even
greater variation in the health experiences of African coun
tries eluting the next decade.
Given the paucity of comparable data, it is difficult
to discern many trends in health expenditures or ser
vice indicators over the past five years. Nonetheless,
patterns of immunization coverage, which can be
traced for many years, demonstrate some interesting
features. After reaching its peak in 1990, coverage was
lower for a period of six years, although even the low
est rates achieved between 1991 and 94 were still high
er than those experienced during the 1980s.
Nonetheless, enormous variations remain in perfor
mance, making it worthwhile for countries to examine
how they have done compared with their African
neighbors.
The analysis of public expenditures and health services
offers some encouraging news. Even though public sector
health expenditures are quite low and much financing is
inefficiently used, expenditures are still associated with
greater levels of measles immunization and, to a lesser
degree, with higher contraceptive prevalence. Similarly,
although many determinants of health outcomes exist,
it is useful to note that in Africa the key health services
documented in this study (measles immunization, contra
ception, and supervised deliveries) are related to improved
health outcomes. In particular, measles immunization is
associated with lower childhood malnutrition and con
traceptive prevalence and supervised deliveries are
associated with lower infant mortality rates. These find
ings were statistically significant despite the fact that the
study population (in 48 countries) was quite small .
Caution should be used in interpreting the data in
this study. The findings are consistent with biological
ly plausible relationships between measles and malnu
trition, contraceptive use and fertility and infant mor
Health Expenditures, Services, and Outcomes in Africa
tality, and supervised deliveries and infant mortality.
However, the data do not allow a test of the more gen
eral hypothesis that increased public sector health
spending causes improved health outcomes in Africa.
Further, the study is cross-sectional, limiting the abili
ty to assign cause and effect. Longitudinal and indi
vidual-level study designs are needed to provide more
insight about causality.
In addition, a note of caution is necessary when
interpreting associations between grouped data and
events that occur at an individual level (the “ecological
fallacy”), where much health spending, use of health
services, and mortality take place. This study is based
on aggregated national data and does not analyze
individual-level data Much of the underlying intra
country differences are also not captured. Within a
country, there are often urban-rural and regional dif
ferences in public spending, availability of services,
and health outcomes. For example, in examining
Ministry’ of Health spending in 22 African countries,
Vogel (1993) found that in 16 countries, more than half
of national budgets were spent in urban areas, although
populations were predominantly rural.
Despite its limitations, this study is useful in highlight
ing gaps in available information. The most glaring defi
ciency in understanding health expenditures is the lack
of information on private health expenditures. This infor
mation is critical for countries concerned with over
seeing the health of the population, as distinct from
simply operating public sector health facilities. Private
sector expenditures are particularly critical when
examining questions of the equity and effectiveness of
services delivered through different channels and to
different groups. Obviously, much better information
specifying the uses of government and donor financ
ing is also important.
Health service data, which are most relevant to man
aging health services and public accountability, were
also sorely lacking. In particular, utilization data for
both outpatients and inpatients were not routinely
reported at the national level either as a total figure or
according to cause of illness. Since they were insuffi
cient to justify intercountry comparisons, such indi
cators could not be included in this analysis. Attempts
to capture conditions at private and mission facilities
are even more infrequent. Similarly, efficiency indica
Limitations of the Data, Its Uses at the Country Level, and Conclusions
tors, which relate health service costs to outputs, were
notably unavailable. In order to solve problems in the
future, such measures of efficiency should be incor
porated as unit costs, rather than simply as service
outputs, and be examined over time and among dif
ferent areas.
Among health outcomes, it was disappointing not to
have sufficient data to measure trends in childhood malnu
trition, given the importance of this indicator with
regard to the effects of poverty and poor health in a
population. In addition, reliable morbidity data were
generally not available.
Several issues exist concerning the quality of the data.
Although most of the data reported here should be rou
tinely collected, this was hardly the case in reality.
Information systems are weak in Africa, resulting in a
great deal of missing data and reduced confidence in
the estimates that are reported. Expenditure data are
not routinely collected according to the source and use
of funds, and large differences often exist between bud
gets and actual expenditures. In most cases, reporting
on development assistance is worse than on govern
ment spending. The least reliable data are estimates on
private expenditures.
Another general problem is that many of the inter
nationally used estimates of health services and out
comes are not actually based on primary data, but are
extrapolated from previous studies. It is not clear
whether this results in a systematic bias in the results.
Are poorer countries with weak bureaucracies more
likely to under- or overestimate the number of services
and mortality rates than countries with strong bureau
31
cracies? There is little evidence with which to answer
this question, and other types of studies involving pri
mary data collection and extensive cross-checking of
different types of data would be needed. Finally, the
probable main sources of variance are not available for
analysis, namely errors in the estimates of each nation
al data point.
Another limitation of the study relates to the choice
of income strata. Different classifications of African
countries by income group might lead to somewhat dif
ferent results. The mam advantage of the classifications
used in the present study is convenience. The middle
income group of countries, which had an average per
capita GNP greater than USS765 during 1990-1996,
were easily defined because their income status pro
hibited borrowing from the World Bank’s soft loan affil
iate, the International Development Association. The
dividing line of SUS300 easily split the remaining coun
tries equally into low-income and lowest-income cate
gories. However, the African population remained over
whelmingly concentrated in the lowest-income
countries, which held 63 percent of the total.
In conclusion, it is hoped that this study will help
to facilitate the increasing generation and application
of information on health expenditures, services, and
outcomes in Africa both by making better use of avail
able information and by helping to close some of the
gaps in missing data. The wide and increasing range of
African experiences calls for optimism that greater fiscal
efforts and the better management of health sector
resources can lead to improved health outcomes throughout
the continent.
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Annex 2: Glossary and Data Sources
access to safe sanitation: Percentage of the popula
tion with adequate disposal facilities to effectively pre
vent human, animal, and insect contact with excreta.
Source: The World Bank African Regional
Database, World Development Indicators 1997, supple
mented with data collected by WHO and UNICEF.
Africa: All countries in Sub-Saharan Africa, as used by
the World Bank and as shown in the country data
tables in the annex of this study.
anticorruption index: A World Bank rating on the
legal and regulatory framework of a country based on
a descending scale from 1 (high corruption) to 5 (low
corruption), with a score of 3 being satisfactory.
Source: Poverty Reduction and Economic Manage
ment (PREM) Network Database, the World Bank.
access to safe water: Percentage of the population
with reasonable access to an adequate amount of safe
water (including treated surface water or untreated but
uncontaminated water from sources such as springs,
sanitary wells, and protected boreholes). In an urban
area, such a source may be a public fountain or stand
post located not more than 200 meters away. In rural
areas, access implies that members of the household do
not have to spend a disproportionate part of the day
fetching water.
Source: The World Bank African Regional
Database, World Development Indicators 1997, and
supplemented with data collected by WHO and
UNICEF.
bureaucratic quality: An International Country Risk
Guide rating on the bureaucratic quality of a national
government, based on an ascending scale from 0 (low
quality) to 4 (high quality).
Source: PREM Database, the World Bank.
capital expenditures on health: Expenditures on
fixed assets in the health sector that have a lifespan of
more than one year, including the construction of new
and the rehabilitation of old buildings and the pur
chase of equipment and vehicles.
Source: World Bank human development sector,
IMF, and other studies.
adolescent fertility rate: The total fertility rate for
women 15-19 years of age, per 1,000 women.
Source: Estimates and projections from Demo
graphic and Health Surveys, national estimates, and
other sources of age-specific fertility rates. For coun
tries that lack age-specific fertility schedules, figures
have been derived from models.
child malnutrition: Percentage of children under five
whose weight for age is less than minus two standard
deviations from the median of the reference popula
tion (i.e., underweight). Stunting refers to height-for
age that is less than minus two standard deviations
from the median. Wusting refers to weight for height as
less than minus two standard deviations from the
median.
Source: WHO, supplemented with data from
UNICEF and the United Nations Administrative
Coordination Committee/Subcommittee on Nutrition.
adult mortality: The probability of dying between
ages 15-60, based on prevailing mortality rates.
Source: World Bank estimates based on model life
tables. The data should be interpreted as indicative of
the level of adult mortality, rather than as precise cal
culations.
35
36
crude birth rate: Number of live births occurring per
1,000 persons in a given year, based on midyear pop
ulation estimates.
Source: World Development Indicators 1997, the
World Bank.
crude death rate: Number of deaths occurring per
1,000 persons in a given year, based on midyear pop
ulation estimates.
Source: World Development Indicators 1997, the
World Bank.
contraceptive prevalence rate: Percentage of women
who are practicing, or whose husbands are practicing,
any form of contraception, whether modem or tradi
tional (generally measured for married women ages
15-49).
Source: Data are mainly derived from demographic,
health, and contraceptive prevalence surveys.
donor health expenditure: Development assistance
for health, in official U.S. dollar exchange rate, provid
ed in the form of disbursements, including from bilat
eral and multilateral sources and international, non
governmental organizations.
Source: C. Michaud and C. J. L. Murray. 1994. Aid
Flows to the Health Sector in Developing Countries: A
Detailed Analysis 1972-1990. The World Bank
external debt: The sum of public, publicly guaran
teed, and private, nonguaranteed long-term debt; the
use of IMF credit; and short-term debt.
Source: World Bank Debtor Reporting System,
World Bank African Regional Database.
Expanded Program on Immunization (EPI): A WHO
program for immunization against vaccine-preventable
diseases. Indicators covered include percentage of BCG
(Bacille Camette-Guerin) coverage in infants under one
year of age against tuberculosis, percentage of DPT3
(diphtheria, pertussis, tetanus), percentage of measles
coverage for infants between 12 and 23 months, and two
or more tetanus immunizations of pregnant women.
Source: WHO global 1997 summary report on the
Expanded Program on Immunization surveillance sta
tistics.
Health Expenditures, Sendees, and Outcomes in Africa
female illiteracy: Percentage of women age 15 and
older who cannot, with understanding, read and write
a short, simple statement on their everyday life.
Source: Illiteracy estimates and projections prepared
by UNESCO, estimated from self-reported, school
completion, or census data. Because of differences in
methods, comparisons among countries and over time
within countries should be made with caution.
government health expenditure: Health spending
from the government (local and central) budgets, as
well as social health insurance funds. External assis
tance, including donations from international NGOs,
is not included as part of the government expenditure.
Source: World Bank Human Development Sector,
IMF, and other studies.
gross domestic product (GDP): The sum of the gross
value added by all resident and nonresident producers
in the economy, plus any taxes and minus any subsi
dies not included in the value of the products. GDP is
calculated without making any deductions for depre
ciation of fabricated assets or for the depletion and
degradation of natural resources. The growth of GDP
per capita is computed from GDP measured in con
stant 1987 prices using the least-squares growth rate
method.
Source: Collected from national statistical offices and
central banks by World Bank staff.
gross national product (GNP): The sum of the gross
value added by all resident producers, plus any taxes
(less subsidies) that are not included in the valuation
of the output, plus net receipts of primary income
(employee compensation and property income) from
nonresident sources, divided by the mid-year popula
tion. GNP is converted to U.S. dollars using the World
Bank’s Atlas method.
Source: Collected from national statistical offices and
central banks by World Bank staff.
HIV prevalence: Percentage of adults 15 years and
older who are HIV positive.
Source: UNAIDS estimates, based on blood screen
ing of pregnant women, blood donors, and the gener
al population for various years.
Annex 2: Glossary and Data Sources
37
inpatient beds: Number of beds available in public,
private, general, and specialized hospitals and rehabil
itation centers. Hospitals are defined as establishments
that are permanently staffed by at least one physician.
Source: Data from government statistical yearbooks,
the World Bank, and WHO.
the definition of maternal mortality includes causes
due to complications of pregnancy or abortion or
events that occur during the period after childbirth.
Source: Demographic and Health Surveys, national
estimates, and models developed by WHO and
UN1CEE Estimates should be seen as indicative only.
immunization: See Expanded Program on Immuni
zation.
military expenditure: Percentage of GDP spent on the
maintenance of military forces, including for the pur
chase of military supplies and equipment, construc
tion, recruiting, and training, but not for public order
and safety.
Source: World Bank, IMF, and other studies.
infant mortality rate: Number of deaths of infants
under one year of age, per 1,000 live births in a given
year.
Source: Estimates by the UN Population Division
estimates and World Bank sector studies. Indirect esti
mates from demographic surveys where vital registra
tion systems are unreliable or from country statistical
offices, censuses, and surveys.
inflation: The rate of change in the GDP implicit defla
tor, which is calculated by dividing annual GDP at cur
rent prices by the corresponding value of GDP at con
stant prices, both in the relevant national currency.
Source: Estimates of GDP are collected from nation
al statistical offices and central banks by World Bank
staff. The least-squares method is then used to calcu
late the growth rate of the GDP deflator for the period.
life expectancy at birth: The average number of years
that a newborn would live if prevailing mortality rates
at the time of its birth were to stay the same through
out its life.
Source: UN Population Division, national statistical
offices, and World Bank estimates and projections from
surveys and censuses.
official development assistance (ODA): The net dis
bursements of loans and grants made on concessional
financial terms by official agencies to promote eco
nomic development and welfare. These agencies are
members of the Development Assistance Committee
(DAC) of the Organisation for Economic Co-operation
and Development (OECD) and of the Organization of
Petroleum Exporting Countries (OPEC).
Source: OECD.
personnel health expenditure: Recurrent expendi
ture for the salaries, wages, and personnel benefits of
health professionals and other employees within the
health sector.
Source: World Bank, IMF, and other studies.
pharmaceutical health expenditure: Expenditure
devoted to the procurement of drugs and vaccines
within the health sector.
Source: World Bank, IMF, and other studies.
low-birth-weight babies: Infants born weighing less
than 2,500 grams, with the measurement taken with
in the first hours of life before significant postnatal
weight loss has occurred.
Source: World Bank Africa Region database and
World Development Indicators 1997, the World Bank.
physician-population ratio: Number of physicians
per 1,000 population, based on midyear estimates.
Physicians are defined as graduates of any faculty or
school of medicine working in any medical field (e.g.,
practice, teaching, or research).
Source: Governmental statistical yearbooks, the
World Bank, OECD, and WHO.
maternal mortality ratio: The number of female
deaths that occur during pregnancy and childbirth, per
100,000 live births in a given year. In many countries,
population: Midyear estimates and projections of a
total de facto population. Refugees not permanently
settled in the country of asylum are excluded from
38
population estimates. Population growth rates are
average annual rates calculated using midyear esti
mates and projections using an exponential rate of
change. They are expressed as percentages.
Source: Recent censuses or official estimates, UN
Population Division estimates, and the World Bank,
World Population Projections 1994-1995.
private sector health expenditure: The sum of direct
household (out-of pocket), private insurance, direct
service payments by private corporations, and charita
ble donations for health care.
Source: Household surveys and World Bank, IMF,
and other studies.
public sector health expenditure: The sum of recur
rent government expenditures, government capital
expenditures, foreign assistance for health, and reim
bursement of health expenditures by government and
parastatal institutions. Foreign assistance is rarely
available and is usually underestimated.
Source: World Bank, IMF, and other studies.
public hospital expenditure: Health expenditures
within the public sector devoted to establishing and
maintaining hospital services. It includes both capital
and recurrent expenditure.
Source: World Bank, IMF, and other studies.
recurrent health expenditure: Health expenditure on
noncapital items requiring repeated funding from year
to year, including personnel compensation (salaries,
wages, and benefits) and administrative and operating
costs.
Source: World Bank, IMF, and other studies.
secondary school enrollment ratio (gross): The ratio
of total enrollment, regardless of age, to the total pop
ulation of secondary school age youth.
Sources: The World Bank, World Developmental
Indicators 1997, and UNESCO’s 1995 statistical year
book.
supervised deliveries: Percentage of recorded births
during which a recognized health service worker was
in attendance.
Health Expenditures, Services, and Outcomes in Africa
Source: WHO, supplemented by UNICEF data. Data
for this indicator should be treated with caution since
they are not usually comprehensive.
total fertility rate (TFR): The average number of chil
dren born per woman if she were to live to the end of
her childbearing years and bear children in accordance
with prevailing age-specific fertility rates.
Source: Demographic and Health Surveys, World
Fertility Surveys, and other surveys; World Bank staff
estimates and estimates by the UN Population
Division, including observed, interpolated, and pro
jected estimates.
total health expenditures: The sum of public and pri
vate health sector expenditures, including outlays for
the provision of health services (preventive and cura
tive), population and nutrition activities, and emer
gency aid designated for health. Does not include water
and sanitation.
Source: World Bank, IMF, and other studies.
tuberculosis incidence: The estimated number of
new tuberculosis cases per 100,000 people in a given
year.
Source: WHO, 1997 Global Tuberculosis Control
Report.
under-five mortality rate (U5MR): The probability
(based on prevailing mortality rates) that a newborn
will survive to age five, times 1,000, for a given time
period of one year.
Source: World Bank staff estimates based on
UNICEF’s State of the World’s Children 1997 and on pro
jections from surveys, censuses, and vital registration
data.
years of potential life lost (YPLL): The burden of
mortality in absolute terms. YPLL is the sum of the
years lost to premature death, per 1,000 population
during one year. The number of years of life lost at each
age is obtained from a standard schedule of life
expectancy at that age, given an overall life expectancy
of 80 years for males and 82.5 years for females.
Source: World Development Report 1993, the World
Bank.
Annex 3: Tables
Annex Table 1 Health Expenditures in Africa, by Country and Source, 1990
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dem. Rep.
Cote d'Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigena
Rwanda
SAo Tome &r Principe
Senegal
Government
%oj
Real Per
GDP
Capita USS
%of
GDP
Donor
Real Per
Capita USS
%of
GDP
Private
Real Per
Capita USS
10.90
—
4.25
950
2.33
3.04
6 50
9.45
4.71
3.56
12.04
23.87
041
13.18
—
10.08
—
2.05
97.03
8.23
4 80
7.11
5.20
6.33
10.41
0.71
1.88
3.74
3.75
5.79
—
1.2
—
5.55
3.45
1.49
11.78
13.42
1.0
—
1.6
1.0
6.1
03
0.3
3.4
1.5
3.0
1.3
0.5
0.6
0.1
—
3.3
—
0.7
0.3
3.8
0.5
0.8
4.1
10
2.9
5.6
0.6
1.2
1.5
1.1
—
3.1
—
1.7
0.2
1.4
5.0
0.6
2.57
—
6.18
25.00
17.73
0.65
2.79
24.73
6.43
6.29
6 26
6.28
1.24
0 82
—
11.88
—
0.9
13.23
14.89
2.0
3.79
8.2
3.72
9.43
2.49
1.62
2.64
4.33
5.79
—
3.1
—
5.55
0.69
4.16
21.81
4.74
1.8
—
1.7
1.3
1.7
0.6
1.8
1.9
1.6
1.6
1.6
1.5
2.1
1.6
—
1.4
—
1.6
1.5
1.5
1.8
1.5
1.4
1.6
2.2
1.8
1.3
2.1
2.4
1.6
—
1.7
—
1.5
1.7
1.8
1.7
1.5
10.77
—
6.57
32.50
4.94
1.30
16.71
13.82
6.86
3.35
7.70
18.85
4.34
13.18
—
5 04
—
2.05
66.15
5.88
7.20
7.11
2.80
5.95
7.16
0.8
3.50
4.62
6.92
8.42
—
1.7
—
4.90
5.86
5.35
7.41
11.84
1.3
—
1.1
3.8
08
1.4
0.7
1.3
1.1
1.7
2.5
1.9
0.2
1.6
—
28
—
1.6
22
2.1
1.2
1.5
2.6
1.7
3.2
1.6
0.7
.1.7
1.3
1.1
—
1.2
—
1.7
1.0
0.5
2.7
1.7
Total Health Expenditure
Real Per
%qf
GDP
Capita USS
4.0
—
4.4
6.2
8.6
2.3
2.8
6.6
4.2
6.2
5.4
3.9
2.9
3.4
—
7.5
—
3.9
3.9
7.4
3.5
38
8.0
4.3
8.3
9.0
2.6
5.0
5.2
3.8
—
6.0
—
4.9
2.9
3.7
9.4
3.8
24
—
17
155
25
5
26
48
18
13
26
49
6.0
28
45
27
—
5
172
29
14
18
16
16
27
4
7.0
11
15
20
—
6
—
16
10
11
41
30
(Continued on next page.)
39
Health Expenditures, Services, and Outcomes tn Africa
40
Annex Table 1 (continued)
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Government
Reul Per
%oj
Capita USS
GDP
%of
GDP
Donor
Real Per
Capita USS
% of
GDP
—
0.5
—
3.2
0.4
3.2
0.7
1.7
0.5
2.1
2.5
—
1.2
—
—
02
2.5
2.6
0.9
1.2
0.1
0.7
—
2 50
—
—
0.73
24.65
2.6
4 07
2.12
0.44
4 74
—
0.8
—
2.4
2.8
1.5
1.8
1.6
1.8
1.0
3.0
—
1.04
—
90.29
1.45
31.55
0.70
7.69
0.88
9.19
16.94
— = Not available
Note: Data derived from World Development Report, 1993, the World Bank
Private
Real Per
Capita USS
—
1.67
—
67.71
10.18
14.79
1.80
7.24
3.18
4.38
20.32
Total Health Expenditure
Real Per
% of
GDP
Capita USS
—
2.4
—
5.6
3.3
7.1
5.0
4.2
3.4
3.2
6.2
—
5
—
158
12
70
5
19
6
14
42
Annex 3: Tables
41
Annex Table 2 Public Sector Health Expenditures as a Percentage of GDP in Africa, by Country, 1990-1996
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dem Rep.
Cdte d'Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
SSo Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
1990
1991
1992
1993
1994
1995
1996
1990-96
1.5
1.4
0.5
1.5
—
0.8
0.9
—
—
—
—
1.5
0.2
—
—
5.8
—
1.0
—
—
—
1.2
1.1
1.6
2.6
—
—
—
1.6
—
—
4.4
3.3
—
1.0
1.9
—
2.8
3.5
—
—
3.3
—
1.9
—
1.3
—
2.6
—
1.5
4.0
—
1 7
—
—
0.9
3.6
1.0
—
—
—
—
1.6
—
—
—
0.8
0.5
2.0
1.3
1.2
12
1.7
3.9
—
—
—
1.2
l.l
2.1
4.6
3.6
1.5
0.9
—
—
2.6
3.9
1.5
—
—
—
2.4
—
1.2
—
1.5
2.3
1.4
—
—
1.9
2.3
—
1.1
2.8
0.9
—
—
3.2
—
1.7
—
—
0.6
0.9
0.7
1.7
1.5
1.3
0.7
1.6
3.9
—
1.4
—
1.0
1.1
2.3
—
4.4
1.6
l.l
—
—
4.3
4.2
1.6
—
—
—
2.5
—
1.0
—
1.4
2.1
1.6
—
—
—
—
—
1.1
3.4
1.2
3.5
0.9
2.4
—
16
—
—
0.8
1.0
0.8
1.8
1.6
1.1
1.0
1.9
4.0
—
—
—
1.0
1.2
2.3
—
3.9
1.7
0.8
—
3.1
2.4
—
—
—
3.6
—
—
—
0.7
2.3
1.5
—
1.4
—
1.7
—
—
—
1.0
3.5
1.7
2.0
08
1.8
—
1.6
—
—
1.1
1.8
0.5
1.8
1.8
1.2
1.1
—
3.5
—
1.0
—
2.0
1.5
2.2
—
3.7
2.2
0.3
—
2.7
2.5
4.0
—
—
—
—
2.9
2.8
1.7
1.8
1.8
—
1.9
—
—
—
—
0.9
—
—
1.9
3.4
1.0
—
—
1.4
—
—
• —
1.7
0.6
1.9
1.6
—
—
—
3.7
__
1.1
2.3
—
1.8
—
—
—
1.6
—
—
6.2
1.2
1.0
—
—
—
—
3.0
3.0
—
1.6
2.4
2.0
1.8
—
—
—
—
—
—
—
2.0
2.2
—
—
—
—
—
—
—
1.5
—
—
2.9
__
—
—
—
—
1.3
_
—
—
—
—
—
—
—
—
9.9
1.2
—
—
—
—
—
2.5
—
—
1.9
—
—
1.6
—
—
1.7
__
—
1.0
3.3
1.4
2.8
0.9
2.2
—
1.6
—
—
0.8
1.2
0.6
1.8
1.8
1.2
1.0
1.7
3.6
__
1.2
__
1.4
1.3
2.2
—
3.8
1.7
0.8
—
5.5
2.4
3.3
—
—
—
—
2.5
—
1.2
1.9
1.9
2.1
— = Not available.
Note: Figures in italics indicate estimates based on less than 50 percent of the countnes at 60 percent of the population.
Annex Table 3 Public and Private Sector Health Expenditures in Africa, by Country, 1990-1995
1990
1991
Public Sector
(includes government and donor)
Per Capita
%of
GDP
USS
1992
1993 1994 1995 1990-95 1990-95
AFRICA
1.5
Angola
1.4
Benin
0.5
Botswana
1.5
—
Burkina Faso
Burundi
0.8
Cameroon
0.9
Cape Verde
—
Central African
—
Republic
Chad
—
—
Comoros
Congo
1.5
Congo Dem. Rep. 0.2
—
Cfiic d’Ivoire
—
Djibouti
Equatorial Guinea 5.8
—
Eritrea
Ethiopia
1.0
—
Gabon
—
Gambia. The
—
Ghana
Guinea
1.2
Guinea-Bissau
1.1
Kenya
1.6
Lesotho
2.6
—
Liberia
—
Madagascar
—
Malawi
Mali
1.6
—
Mauritania
—
Mauritius
4.4
Mozambique
Namibia
3.3
—
Niger
Nigeria
1.0
1.5
4.0
—
1.7
—
—
0.9
3.6
1.4
—
—
1.9
2.3
—
1.1
2.8
1.6
—
—
—
—
—
1.1
3.4
1.4
—
1.7
—
—
—
1.0
3.5
1.9
—
—
—
—
0.9
—
—
1.7
2.7
1.1
1.7
2.3
0.9
1.0
3.3
1.0
—
—
—
—
1.6
—
—
—
0.8
0.5
2.0
1.3
1.2
1.2
1.7
3.9
—
—
—
1.2
1.1
2.1
4.6
3.6
1.5
0.9
0.9
—
—
3.2
—
1.7
—
—
0.6
0.9
0.7
1.7
1.5
1.3
0.7
1.6
3.9
—
1.4
—
1.0
1.1
2.3
—
44
16
1.1
1.2
3.5
0.9
2.4
—
1.6
—
—
0.8
1.0
0.8
1.8
1.6
1.1
1.0
1.9
4.0
—
—
—
1.0
1.2
2.3
—
3.9
1.7
0.8
1.7
2.0
0.8
1.8
—
1.6
—
—
1.1
1.8
0.5
1.8
1.8
1.2
1.1
—
3.5
—
1.0
—
2.0
1.5
2.2
—
3.7
2.2
0.3
1.9
3.4
1.0
—
—
1.4
_
—
—
1.7
0.6
1.9
1.6
—
—
—
3.7
—
1.1
2.3
—
1.8
—
—
—
1.6
—
Private
Sector
Total
Health Sector
Per Capita
%of
Per Capita
USJ_
GDP
USS
1990—95
1990—95
1990-95 1990-95
1990
1991
%of
GDP
1992
10.83
22.52
4.07
30.12
6.22
1.79
8 07
26.39
1.9
—
—
—
—
—
—
—
2.3
—
—
1.4
1.1
—
—
—
—
—
—
—
3.2
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
1.4
2.2
—
—
—
—
—
—
24.75
5.77
—
—
—
—
—
—
3.1
4.5
—
—
—
—
—
—
54.87
11.98
—
—
—
1.3
3.0
0.9
2.2
0.2
1.6
5.11
4.80
3.97
22.37
0.34
12.70
5.8
0.8
1.2
0.6
1.8
1.6
1.2
1.0
1.7
36
20.82
1.18
1.79
28.03
4.80
5.31
4.90
2.14
6.44
10.54
1.2
2.3
1.4
1.3
2.2
4.5
3.8
1.7
0.8
2.49
3.32
3.49
6.56
52.31
5.08
58.46
4.84
2.64
—
—
—
1.1
—
—
_
1.3
—
—
—
—
—
—
—
—
—
—
—
—
1.3
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
1.5
4.1
1.3
—
1.6
—
—
—
—
—
3.2
—
—
—
—
—
—
—
—
—
—
—
10
—
—
—
—
—
—
13
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
1.7
—
3.7
—
—
—
—
—
—
—
—
—
—
0.9
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
1.0
—
—
—
—
—
20
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
2.2
—
2.0
—
1.3
0.9
—
—
—
—
—
—
1.0
—
—
—
—
1.4
4.1
14
—
2.7
—
1.0
—
—
—
22.55
—
16.18
—
4.64
1.36
—
—
—
—
—
—
3.77
—
—
—
—
3.58
19 40
33 08
—
40.48
—
3.09
—
—
—
4.4
—
3.6
—
7.1
1.7
—
—
—
—
—
—
2.7
—
—
—
—
2.8
5.4
3.7
—
6.4
—
1.8
—
—
—
44.92
—
28.88
—
25.46
2.54
—
—
—
—
—
—
10.20
—
—
—
—
7.07
25.96
85.38
—
98.94
—
5 73
1993
1994
1995
Rwanda
Sao Tomi &
Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
1.9
—
—
—
—
—
1.9
5.98
—
—
—
—
—
—
—
—
—
—
—
2.8
3.5
—
—
3.3
—
1.9
—
1.3
—
2.6
—
—
2.6
3.9
1.5
—
—
—
2.4
—
1.2
—
1.5
2.3
—
4.3
4.2
1.6
—
—
—
2.5
—
1.0
—
1.4
2.1
3.1
2.4
—
—
—
3.6
—
—
—
0.7
2.3
1.5
—
2.7
2.5
4.0
—
—
—
—
2.9
2.8
1.7
1.8
1.8
—
6.2
1.2
1.0
—
—
—
—
3.0
3.0
—
1.6
2.4
2.0
4.0
2.6
3.3
1.6
19 21
1690
156.53
3.03
3.4
76.85
2.5
2.9
1.2
1.9
1.9
2.1
20.08
4.44
4.09
9.81
5.33
12.93
—
—
—
—
—
—
2.7
—
—
—
1.8
0.7
—
—
—
—
2.0
—
—
—
—
—
2.2
—
—
4.2
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
4.3
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
2.2
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
2.0
—
4.3
2.7
—
—
2.2
2.0
0.7
4.2
—
—
—
4.08
—
92.29
18.47
—
_
8.33
9 91
2.31
27.26
—
—
—
3.6
—
7.7
2.7
—
—
3.4
3.9
2.6
6.3
—
—
—
7.12
—
169.14
18.47
—
—
12.42
19.72
7.64
40.19
— = Not available
Note: Figures in italics indicate estimates based on less than 50 percent of the data.
Annex Table 4 Public Sector Health Expenditures in Africa, by Country and Category, 1990-1996
(% of health expenditures in the public sector)
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African
Republic
Chad
Comoros
Congo
Congo Dem. Rep.
COtc d'Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Total Capital
Investment
%
years
Total
Recurrent
years
%
%
years
Pharmaceutical
years
%
18
5.8
—
14.4
27.5
3.4
4.8
—
90-96
90-91
—
91
90-96
92
94
—
83
94.2
—
85.6
72.6
—
95.2
—
90-96
90-91
—
1991
90-96
—
94
—
31
52.4
64.6
61.1
34.3
52.6
40.5
—
90-96
90-91
90
91
90-96
92
94
—
12
15.8
—
4.9
1.6
21.3
—
—
42.4
32.3
—
3.1
—
—
1.1
35.2
—
16.3
37.4
28.8
28.3
53.0
14.8
25.6
35.0
—
47.7
8.9
37.8
40.1
11.5
32.2
9.9
—
8.0
91-96
95
—
92-94
—
—
94
91
—
90-93
91-95
91-95
94-96
91,92,94
94
90-93,95-96
91-95
—
92,94-96
92
90
91-95
92-94
90-91
92
—
94
57.7
67.7
—
96.5
—
—
—
64.6
—
89.7
62.6
71.2
71.7
47.0
—
74.4
65.0
—
52.3
—
62.4
599
88 5
67.8
90.1
—
92.0
91-96
95
—
92-94
—
—
—
91
—
90-93
91-95
91-95
94-96
91,92,94
—
90-93,95-96
91-95
—
92,94-96
—
90
91-95
92-94
90-91
92
—
94
74.3
48.9
60.9
65.6
—
—
73.3
57.6
—
—
49.6
22.0
31.6
40.2
—
57.2
22 0
—
27 7
20.9
—
44 9
—
—
46.3
—
17.2
91-92
95
93
92,94
—
—
94
91
—
—
—
91-95
94-96
91,92,94
—
95,96
91-95
—
92,94-96
—
—
91-95
—
—
92
—
94
9.3
5.2
—
15.2
—
3.0
—
10.3
—
—
17.5
11.7
1.9
20.9
—
17.2
6.7
—
—
19 2
13.6
—
—
—
0.6
—
—
Personnel
%
years
Primary Health Care
years
%
90-96
90-91
—
90
91
92
—
—
44
—
—
43.3
—
—
—
—
90-96
—
—
90
—
—
—
—
28
—
—
13.7
—
—
—
—
90-96
—
—
90
—
—
—
—
91-92,95
95
—
93-94
—
94
—
91
—
—
92,94
91-93,95
95
91-94
—
95,96
92-95
—
—
92
90
—
—
—
90-94
—
—
—
—
—
—
—
—
—
32.1
73.1
—
—
19.9
—
49.4
—
61.6
43.6
—
—
62.7
—
—
—
—
45.1
—
—
—
—
—
—
—
—
—
91
93-94
—
—
91
—
91-94
—
95,96
95
—
—
92
—
—
—
—
90-94
—
—
—
—
—
—
—
—
—
—
—
—
—
16.5
—
28.0
—
—
—
—
—
—
—
—
—
—
36 8. .
—
—
—
—
—
—
—
—
—
—
—
—
—
92-94
—
91-94
—
—
—
—
—
—
—
—
—
—
92
—
—
Hospital
Rwanda
Sao Tome &
Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
— = Not available.
22.8
95
—
—
42.4
95
—
—
—
—
—
—
62.2
36.5
6.8
7.8
—
—
16.6
20.5
5.6
11.6
34.1
12.5
1.4
93-96
92-96
90-92,94-95
91-96
—
—
94
90-92,94-96
95-96
94
93-96
90-95
91,92,95
37.8
63.5
93.2
92.2
—
—
83.4
79.5
94.4
—
65.9
87.5
98.6
93-96
92-96
90-92.94-95
91-96
—
—
94
90-92,94-96
95-96
—
93-96
90-95
91,92,95
18.3
—
62.5
—
—
—
—
40 3
24.5
55.4
—
63.2
42.0
93-96
—
95
—
_
—
—
91
91
90,94
—
92
91,92,95
—
—
—
—
—
—
—
—
17.0
—
—
6.0
283
—
—
—
—
—
—
—
—
95-96
—
—
95-96
92
—
—
47.4
—
—
28.1
—
—
45.4
—
45.0
—
79 9
—
—
90-92,94-95
—
_
93
—
—
—
37.0
—
—
—
—
—
—
—
37.6
—
118
—
—
90-92,94-95
—
—
—
—
__
—
—
93-96
—
91-92,95
95-96
—
93-96
—
92
Health Expenditures, Services, and Outcomes in Africa
46
Annex Table 5 National Policy Indicators in Africa, by Country, 1990s
Legal and
Bureaucratic
Government Expenditure
Regulatory Frameworl.!
Quality
Education
Defense
1 (poor)-5 (good) 0 (poor)-4 (good) (%ofGDP)
(KofGDP)
1997
1998
1990-1994
1990-1994
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dem. Rep
COte d’Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sio Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
2.1
2
3
3.67
3
2
2
3.67
1
2
—
2
1.67
3.33
—
1
3.67
2.33
2
2 67
3.33
2.67
2 67
1 67
2.33
1
1.33
3
3
2.67
4.67
2.67
3
3
1
2
2.33
2.67
2.33
2.33
1
3.67
1
2.67
2
2.33
2.67
3
3.33
1
0
—
2
1
—
1
—
—
—
—
0
0
2
—
—
—
1
2
2
2
2
1
2
—
0
1
2
0
—
—
0
2
1
0
—
—
1
—
0
0
3
1
—
1
0
2
1
2
3.0
10.4
1.5
3.4
3.0
2.3
1 5
—
1.7
38
—
2.2
—
1.1
—
—
—
6.4
2 1
2.2
0.5
1.7
—
2.4
3.1
—
1.0
0.9
2.4
3.2
0.3
8.3
0.8
1.3
0.7
6.0
—
1.6
—
2.2
1.7
2.9
7.0
1.9
3.7
2.5
1.3
1.3
4.6
— - Not available.
Note: Figures in italics indicate estimates based on less than 50 percent of the data.
3.5
—
—
5.3
—
3.3
—
—
—
1.7
3.5
—
—
—
—
—
—
2.5
—
—
—
—
—
5 1
—
—
—
2.8
—
3.3
2.6
2.5
—
—
—
—
—
3.5
6.2
—
—
—
—
5.0
—
3.8
—
8.0
Health
(KofGDP)
1990-1996
Total Government
Expenditure
(% of GDP)
1990-1996
1.7
2.7
1.1
1.7
23
0.9
1.0
3.3
1.4
2.8
0.9
2.2
0.2
1.6
—
5.8
0.8
1.2
0.6
1.8
1.8
1.2
1.0
1.7
3.6
—
1.2
23
1.4
1.3
2.2
4.5
3.8
1.7
0.8
1.9
5.5
2.4
3.3
1.6
—
3.4
—
2.5
2.9
1.2
1.9
1.9
2.1
25.5
58 5
19.8
43.9
21.2
24.7
20 2
50.0
20.7
26.7
31.3
35.6
14 8
29.6
43 3
41 6
58.2
24.7
26 0
27.4
27.2
19.9
40.0
32.5
53.5
—
18.5
31.2
25.4
28.5
24.6
44.2
36.3
17.1
23.0
22.3
68.7
19.6
49.3
19.8
—
32.7
18.2
32.6
17.5
24.9
17.0
31.9
41.8
47
Annex 3; Tables
Annex Table 6 Selected Mortality Indicators in Africa, by Country, 1990-1995
Civile
Crude
Birth
Death
Rate
Rate
per
per
1,000 pop. 1,000 pop
1995
1995
AFRICA
42
Angola
49
Benin
44
Botswana
35
Burkina Faso
46
Burundi
44
Cameroon
41
34
Cape Verde
Central African Repub lie 39
Chad
43
Comoros
43
Congo
47
—
Congo Dem. Rep
37
CSte d'Ivoire
Djibouti
46
Equatorial Guinea
44
Eritrea
43
47
Ethiopia
Gabon
39
Gambia, The
41
Ghana
37
Guinea
48
Guinea-Bissau
45
Kenya
35
Lesotho
33
47
Liberia
42
Madagascar
Malawi
47
50
Mali
Mauritania
39
Mauritius
19
44
Mozambique
37
Namibia
52
Niger
42
Nigeria
41
Rwanda
35
SSo Tome & Principe
40
Senegal
22
Seychelles
48
Sierra Leone
49
Somalia
30
South Africa
35
Sudan
34
Swaziland
43
Tanzania
44
Togo
49
Uganda
45
Zambia
31
Zimbabwe
„ Not available.
14
19
15
11
18
18
11
8
17
18
12
16
—
12
16
17
16
17
15
18
10
20
25
9
11
20
11
20
17
14
7
18
12
19
14
23
8
14
7
30
18
8
12
9
14
15
19
17
9
Annual
Years of Life
Adult Mortality
Lost
Male
Female
Life Expectancy
per
per
per
Male
Female
1.000 pop.
1,000 men
1,000 women
years
years
1990
1990
1995
1990
1995
1990
1995
1990
1995
49
44
49
56
45
45
53
63
46
45
52
49
—
54
46
45
47
45
51
42
55
41
43
57
56
45
54
44
46
49
66
44
55
43
48
39
64
47
67
34
44
59
50
54
50
50
47
48
59
50
45
49
51
45
44
55
65
46
46
54
49
49
53
48
47
49
47
52
44
57
44
42
57
57
45
56
43
48
51
68
45
55
44
51
37
66
49
68
34
46
61
52
56
50
49
44
46
56
52
47
52
59
47
48
56
65
51
48
55
55
—
57
50
49
50
48
54
46
59
44
44
60
58
48
57
45
49
52
73
48
57
47
51
42
69
49
74
37
48
65
52
59
53
53
47
50
63
54
48
52
54
47
47
58
67
51
50
57
54
54
56
51
51
52
50
56
48
61
45
45
60
59
46
59
44
51
54
74
48
57
49
54
40
72
51
76
38
50
67
54
61
53
52
44
47
59
89
—
89
—
114
81
67
—
74
106
—
—
—
50
—
—
—
107
—
—
55
125
—
45
—
—
63
no
108
—
—
141
—
121
98
124
—
99
—
188
—
40
84
—
112
79
107
86
37
444
514
447
218
—
460
—
245
485
487
365
370
—
352
472
488
433
448
402
530
334
544
529
357
384
254
434
479
434
441
241
418
373
515
476
493
153
—
221
601
426
—
464
260
444
389
526
434
305
448
493
472
212
—
481
—
234
505
470
354
405
—
392
452
474
429
442
386
511
320
498
584
362
347
252
445
553
412
467
222
431
356
510
450
542
149
—
203
589
399
—
445
248
485
377
622
534
391
370
420
369
158
—
379
—
218
381
397
307
273
—
294
387
400
347
358
332
432
270
533
495
287
276
198
377
436
351
365
126
321
318
413
401
409
104
—
113
492
337
—
398
196
373
321
461
377
270
376
406
399
153
—
403
—
206
406
385
286
313
—
333
373
392
342
352
322
419
253
497
572
295
258
198
384
487
326
396
116
339
304
401
377
461
84
—
90
470
313
—
378
191
417
311
558
494
393
Health Expenditures, Services, and Outcomes in Africa
48
Annex Table 7 Selected Social and Health Indicators in Africa, by Country, 1990-1995
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dent. Rep
Cdte d'Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
S4o Tome &r Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
Not available.
Access to
Sanitation
% of pop.
1990-1995
Access to
Water
% of pop
1990-1995
36
16
20
55
18
51
40
24
46
21
83
9
9
54
—
54
—
10
76
37
32
70
20
77
6
18
Z
53
31
64
100
21
34
15
36
—
21
58
92
11
—
46
22
36
86
23
57
23
58
44
32
50
70
78
52
41
51
18
24
47
51
27
72
24
95
Gross Secondary
School
Inpatient
Female
Enrollment
Physicians Hospital Beds
(per
(per
Illiteracy % of school-aged
% of pop.
pop.
1,000 pop)
1,000 pop )
1995
1992-1993
1990-1995 1990-1995
53
—
74
40
91
78
48
36
48
65
50
33
32
70
67
32
—
—
27
67
76
56
62
33
53
52
30
29
45
37
76
98
32
57
53
39
75
47
75
47
78
58
30
38
78
—
70
50
97
34
—
58
77
74
21
77
—
93
53
48
77
—
92
—
—
79
50
18
65
24
43
43
49
63
34
43
74
63
50
29
20
25
—
—
57
9
7
27
27
—
8
19
—
—
25
12
—
15
11
—
19
—
12
—
25
26
—
14
5
8
15
59
7
59
7
29
—
—
16
—
—
0.11
0 04
0.06
0.19
—
0.06
0.08
0.23
0.04
—
0.11
0.27
0 07
0.09
0.17
0.28
—
0.03
05
—
—
0.15
—
0.05
0.04
—
0.12
0.02
0.05
0.06
0.86
—
0.23
0.02
0.19
0.04
0.54
0.05
5.97
—
51
—
—
—
0.1
5
23
11
—
44
—
0.09
—
0.09
0.14
77
—
1.2
1.3
02
1.6
0.3
0.7
26
1.6
0.9
07
2.8
3.3
1.4
0.8
2.6
—
—
0.2
33
0.6
1.5
0.6
1.5
1.7
—
—
0.9
1.6
—
0.7
3.1
0.9
5
—
1.7
1.7
4.8
0.7
—
—
0.7
—
1.1
—
0.9
1.5
0.9
—
0.5
Incidence of
HIV
Tuberculosis
Prevalence
(per
(%
100,000 pop.) of adults)
1995
1999
222
225
135
400
289
367
194
100
139
167
150
250
333
196
600
150
155
155
100
166
222
166
220
140
250
100
310
173
289
220
50
189
400
144
222
260
100
166
40
167
222
250
211
200
187
244
300
345
207
4.3
1.0
1.2
18
6.7
27
3.0
*
—
58
2.7
0.1
7.2
3.7
68
3.0
1.1
3.2
2.5
23
2.1
2.3
0.6
3.1
8.3
3.1
1.3
0.1
13.6
1.3
07
0.1
5.8
6.5
1.0
2.2
7.2
—
1.4
—
3.0
0.3
3.2
1.0
3.8
64
8.5
14.5
17.1
17.4
49
Annex 3: Tables
Annex Table 8 Child Survival Indicators in Africa, by Country, 1990-96
Under Five
Mortality Rate
(per
Anthropometric Measures
Infant Mortality Rate
1,000 live
(% relevant populationaffected)
(per 1.000 live births)
births)
Low Birthweight Underweight
Stunting
Wasting
1990-1995 1990-1995
1990-1996
1990-1995
1995
1995
1990-1996
1990
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dem Rep.
Cote d'Ivoire
Djibouti
Equatonal Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe____________
= Not available-
97
131
103
56
106
108
68
54
102
128
103
87
—
92
118
122
135
126
99
137
84
146
140
62
86
171
103
136
135
106
21
122
66
130
86
131
70
72
17
188
132
56
85
81
89
93
106
109
60
90
124
96
56
100
99
57
47
98
118
89
90
—
86
109
112
132
113
90
127
74
129
137
59
77
177
90
133
124
97
16
114
62
120
81
135
61
63
15
182
129
51
78
70
83
89
98
109
55
92
125
95
56
102
101
59
48
98
121
74
89
93
88
113
115
71
116
92
91
76
127
139
59
79
178
93
134
126
99
19
130
63
122
82
135
64
65
17
184
130
52
80
73
90
90
100
109
56
151
209
156
74
164
162
86
68
160
197
143
144
144
138
181
185
196
188
145
213
116
220
233
90
121
239
127
225
192
158
20
212
78
—
176
200
78
97
19
236
218
67
109
96
133
128
160
180
83
16
19
10
8
21
—
12
—
16
—
12.7
16
15
14
9
10.4
—
16
9.7
10
17
21
16.2
16
11
—
10.2
20
17
11
8
20
12
15
16.5
17
3
11
9.6
17
16
—
15
7
14
20
—
13
10
32
35
24
27
33
38
15
19
23
—
—
24
34
24
23
—
41
48
15
17
27
24
23
22
19
20
36
28
31
48
15
47
26
43
39
29
17
22
6
29
39
9
34
10
29
25
26
29
16
36
—
34
—
32
8
—
7
—
13
26
24
28
13
—
—
36
24
—
40
49
64
18
24
26
27
22
33
33
37
50
49
—
57
10
48
28
32
43
48
—
22
—
35
30
23
34
—
43
—
38
50
22
3
6
—
—
—
—
6
—
—
2
18
8
—
7
11
11
5
7
2
3
6
5
—
16
14
—
9
16
9
4
—
9
—
9
—
8
13
—
6
—
—
5
6
50
Health Expenditures, Services, and Outcomes in Afnca
Annex Table 9 Immunization Coverage Rates in Africa, by Country. 1990-1996
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African
Republic
Chad
Comoros
Congo
Congo Dem. Rep
COtc d’Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tomi & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
— • Nol available.
1990
BCG
1995 1990-1996
Immunization Coverage (% population)________________
DPT3
Measles
1990 1995 1990-1996 1990
1995 1990-1996 1990
TT2
1995
1990-1996
64
48
74
50
—
97
52
97
70
74
96
58
78
77
54
80
65
57
88
58
68
81
49
93
56
24
78
56
—
86
36
88
57
42
89
76
47
63
46
73
52
31
80
66
44
70
37
89
54
38
73
55
—
75
36
79
60
65
82
68
55
50
46
66
53
46
73
64
47
62
38
81
43
21
46
43
—
56
12
90
36
28
81
56
47
33
12
4
35
20
58
56
37
46
12
66
93
—
99
86
46
—
95
rere^
70
70
96
98
71
50
90
54
78
—
98
97
82
79
87
59
—
53
80
57
100
91
98
98
—
59
73
96
85
100
100
97
74
76
43
95
50
51
49
76
99
44
65
73
99
67
78
92
92
55
84
83
99
80
93
86
78
94
50
42
89
95
90
100
48
—
95
92
100
90
63
100
98
95
76
39
83
69
42
53
72
91
43
53
78
89
64
64
91
58
60
84
83
97
75
84
86
70
84
44
54
85
90
78
100
74
—
74
77
74
93
75
99
96
84
61
20
94
77
36
—
85
—
49
49
78
92
50
20
61
42
—
■£=—
71
87
42
33
85
46
—
22
56
57
92
66
99
83
—
74
62
89
78
77
77
71
78
45
18
75
47
35
41
63
64
35
57
56
97
51
73
45
84
58
45
70
77
49
50
89
57
76
23
34
90
79
80
98
35
—
75
77
96
82
82
79
82
86
49
17
64
62
30
47
66
63
33
39
58
83
47
51
61
49
59
45
66
87
42
42
88
52
78
21
38
83
76
62
98
58
—
77
66
80
81
78
76
77
79
67
—
87
77
37
—
85
—
38
38
76
86
52
25
53
41
87
—
57
81
43
38
76
59
—
25
48
55
71
57
76
75
—
79
57
86
79
65
74
68
76
72
24
69
42
41
57
58
61
29
57
57
89
54
69
45
73
82
44
62
97
52
53
85
71
69
43
44
88
74
80
97
37
—
76
77
94
78
39
79
85
83
57
25
58
55
34
55
65
60
26
33
57
76
49
51
54
44
86
44
56
88
47
46
83
62
69
29
45
71
65
57
93
60
—
80
65
73
79
56
75
78
78
65
8
77
52
37
—
80
—
16
16
86
73
19
21
31
45
—
—
31
76
37
15
76
21
50
11
56
87
65
37
100
81
—
—
75
76
65
81
31
—
43
15
54
55
55
20
22
37
63
13
22
29
92
20
66
72
40
10
35
21
78
20
28
78
36
25
36
34
88
40
39
100
54
—
26
68
94
31
43
77
85
11
33
29
37
53
25
27
61
58
13
17
37
80
14
46
41
39
9
35
20
71
23
25
74
27
45
34
35
73
50
34
100
66
26
59
80
38
66
67
62
45
Annex 3: Tables
Annex Table 10 Reproductive Health Indicators in Africa, by Country, 1990-1996
1990
AFRICA
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dem. Rep.
Cdte d'Ivoire
Djibouti
Equatonal Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauntius
Mozambique
Namibia
Niger
Nigeria
Rwanda
SSo Tom6 & Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Afnca
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
6.1
7.2
6.4
5.0
7.1
6.8
—
4.5
5.5
5.9
6.3
6.3
—
6.3
6.0
5.9
5.8
7.0
50
5.9
5.9
6.0
6.5
5.8
5.1
6.8
6.3
7.2
7.1
5.6
2.2
6.5
5.4
7.4
6.0
6.8
5.1
6.2
2.8
6.5
7.0
4.2
5.2
5.1
6.1
6.6
7.0
6.2
4.9
Maternal
Adolescent
Mortality
Fertility Rate
Ratio
(per 1,000 girls (per 100,000
live births)
15-19yrs.)
Total Fertility Rate
1990-1995
1995
1990-1996
1995
5.7
6.9
6.0
4.4
6.7
6.5
5.6
3.7
5.1
5.7
4.8
6.0
6.4
5.3
5.6
57
6.0
7.0
5.0
5.4
5.1
5.7
60
4.7
4.6
6.5
5.8
6.6
6.8
5.2
2.2
6.2
5.0
7.4
5.5
6.2
4.8
5.7
2.5
6.5
7.0
3.0
4.8
4.6
5.7
6.3
6.7
5.9
4.0
5.8
7.0
6.2
4.6
6.8
6.6
5.7
3.9
5.2
5.8
5.1
6.1
6.5
5.6
5.7
5.8
62
7.0
5.0
5.5
5.4
5.7
6.0
5.0
4.7
6.6
6.0
6.7
6.8
5.3
2.2
6.3
5.1
7.4
5.7
6.4
4.9
5.9
2.6
6.5
7.0
3.1
4.9
4.8
5.9
6.4
6.8
6.1
4.2
137
218
127
106
149
66
136
26
145
183
131
140
221
136
171
182
125
164
150
167
109
213
186
95
55
211
145
151
190
123
42
122
130
222
120
65
149
118
51
203
191
68
84
111
123
124
193
122
68
Contraceptive
Prevalence Rate
(% acceptance)
1990-1996
Supervised
Deliveries
(% attendance)
1990-1996
17
—
16.8
—
7.7
—
16
—
14.1
41
15
45
78
42
19
64
49
46
15
—
—
—
45
—
—
21
14
80
44
44
31
27
45
40
58
57
55
24
40
97
25
68
15
31
26
63
46
99
25
2
82
69
67
53
54
—
51
69
822
1,500
500
250
930
1,300
500
—
700
900
460
890
—
600
570
820
1,400
1,400
500
1,000
740
880
910
650
610
560
660
620
580
800
112
1,500
220
593
1,000
1,300
—
510
—
1,800
1,600
230
370
560
530
640
550
230
280
t—
21
—
—
11.4
—
—
8
4.3
—
12
20.3
2
—
33
23.2
—
17.3
17.5
7
4
75
—
29
4.4
6
21
—
7.1
—
—
—
63
9.5
—
13.8
—
14.8
20.5
58
= Not available.
\ oc
He
I
G
CPHE - SOCHARA
(
Koramangala
<^\Bangalore - 34
CClIC)
Health Expenditures, Services, and Outcomes in Africa
52
Annex Tabic 11 Population and Population Growth in Africa, by Countrj 1990-1996
1990
1991
1992
Population
(millions)
1993
511.51
AFRICA
474.44
Excl— S. Africa
Excl. S. Africa & Nigeria 378.24
525.54
487.64
388.65
9.52
4.88
1.32
9.27
5.64
11.83
0.35
3.00
5.83
0.44
2.35
38.64
12.40
0.52
0.36
3.22
52.95
0.99
0.96
15.31
5.92
0.98
24.02
1.82
2.48
12.05
8.74
8.71
2.05
1.07
14.39
1.39
7.91
98.98
7.15
0.12
7.62
0.07
4.09
8.05
37.91
24.58
0.81
26.28
3.63
16.89
8.01
10.02
540.80
502.04
400.16
553.14
513 50
408.60
9.82
5.03
1.35
9.53
5 80
12 18
0.36
3.07
5 98
0.46
2 42
39.90
12.81
0.55
0.36
3.31
54.79
1.02
1.00
15.76
6.09
1.00
24.68
1.86
2.54
12.45
8.99
8.96
2.11
1.08
14.69
1.43
8.17
101.88
7.35
0.12
7.84
0 07
4.19
8.30
38.76
25.10
0.83
27.10
3.74
17.46
8.24
10.28
10.13
5.18
1.39
9.80
5.96
12.54
0.36
3.14
6.13
0.47
2.49
41.19
13.21
0 57
0 37
3.39
53.30
1.05
1.04
16.20
6.26
1.02
25.35
1.90
2.60
12.85
9.24
9.23
2.16
1.10
15.08
1.47
8.44
104.89
7.54
0.12
8.05
0.07
4.29
8.62
39.64
25.63
0.86
27.93
3.85
18.03
8.48
10.54
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Congo Dem. Rep.
Cote d'Ivoire
Djibouti
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Liberia
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
Somalia
South Africa
Sudan
Swaziland
Tanzania
Togo
Uganda
Zambia
Zimbabwe
9.23
4.74
1.28
9.02
5 49
11.48
0.34
2.93
5.68
0.43
2.28
37.41
11.97
0.50
0.35
3.14
51.18
0.94
0.92
14.87
5.76
0.97
23.35
1.78
2.44
11.67
8.51
8.46
2.00
1.06
14.18
1.35
7.67
96.20
6.95
0.11
7.40
0.07
4.00
8.62
37.07
24.06
0.79
25.48
3.52
16.33
7.78
9.75
1994
1995
1996
567.50
526.96
418.95
10.45
5.33
1.42
10.09
6 11
12.91
0 37
3 21
6.29
0 48
2.56
42.50
13.60
0.60
0.39
3.48
54.89
1.07
1.08
16 64
6.42
1.05
26 02
1.94
2.66
13.25
9.49
9.50
2 22
1.11
15.57
1.51
8.72
108.01
6.23
0.13
8.26
0.07
4.40
9.01
40.54
26.16
0.88
28.78
3.99
18.60
8.73
10.78
583.70
542.24
430 97
599.93
557 55
443.15
11.09
5.63
1.48
10.66
6.40
13.70
0.39
3.35
6.61
0.50
2.71
45.34
14.32
0.66
0.41
3.67
58.12
1.13
1.14
17.53
6.78
1 09
27.35
2.02
2.82
14.06
10.01
10.09
2.33
1.14
16 58
1.58
9.33
114.40
6.73
0.13
8.70
0.08
4.63
9.79
42.38
27.33
0.92
30.49
4.23
19.72
9.18
11.21
10.77
5.48
1 45
10.38
6.26
13 29
0.38
3.28
6.45
0.49
2.63
43.85
13.98
0.63
0.40
3.57
56.40
1.10
1.11
17.08
6.59
1.07
26.69
1.98
2.73
13 65
9 76
9.79
2.27
1.13
16.17
1.55
9.03
111.27
6.40
0.13
8.47
0.07
4.51
9.49
41.46
26.71
0.90
29.65
4.11
19.17
8.98
11.01
Average Annual
Population Growth (%)
1990-1996
2.7
2.7
2.6
3.1
2.9
2.6
2.8
2.7
3.0
2.1
2.3
2.6
26
3.0
3.3
3.1
4.9
2.5
2.6
2.1
3.3
3.8
2.8
2.8
2.0
2.7
2.2
2.4
3.2
2.8
3.0
2.6
1.3
2.5
2.7
3.3
2.9
-1.0
2.5
2.8
1.6
2.4
2.2
2.3
2.1
2.6
3.1
3.1
3.2
2.8
2.5
53
Annex 3: Tables
Annex Table 12 GNP Per Capita in Africa, by Country, 1990-1996
AFRICA
Excl. S. Africa
Excl. S Africa & Nigeria
1990
1991
1992
518
355
376
497
325
338
590
380
2,620
310
220
900
740
460
200
530
1,010
190
760
—
350
—
130
5,060
350
410
470
240
350
560
—
210
230
270
510
2,610
90
1,860
310
270
310
490
700
5,300
200
—
2,710
360
1,100
160
430
260
400
680
513
327
339
Angola
780
Benin
370
Botswana
2,270
Burkina Faso
290
Burundi
220
Cameroon
920
Cape Verde
660
Central African Republic
470
Chad
180
Comoros
540
Congo
990
Congo Dem Rep.
230
Cote d’Ivoire
800
—
Djibouti
Equatorial Guinea
360
—
Eritrea
Ethiopia
170
Gabon
4,620
Gambia, The
330
Ghana
390
Guinea
450
Guinea-Bissau
230
Kenya
380
Lesotho
560
—
Liberia
Madagascar
230
Malawi
200
280
Mali
490
Mauritania
2,440
Mauritius
90
Mozambique
1,770
Namibia
320
Niger
270
Nigeria
360
Rwanda
490
Sao Tomfi and Principe
720
Senegal
5,100
Seychelles
260
Sierra Leone
110
Somalia
2,600
South Africa
430
Sudan
1,060
Swaziland
150
Tanzania
430
350
450
Zambia
Zimbabwe____________ 710
= Not available-
500
370
2,990
280
210
910
970
470
210
610
1,090
170
770
—
400
—
no
4,940
340
430
490
250
340
580
—
230
210
310
530
2,960
80
1,980
290
280
310
520
650
6,170
160
—
2,910
310
1,190
150
430
200
380
570
US dollars (Atlas method;
1993
1994
495
300
313
—
380
2,830
230
180
760
990
430
190
620
900
130
720
—
440
—
120
4,840
360
410
510
240
270
580
—
240
240
300
500
3,080
90
1,900
240
250
260
490
550
6,530
160
—
2,990
—
1,140
140
350
190
390
540
478
274
285
—
340
2,850
200
160
630
990
370
170
520
690
120
620
—
370
—
110
3,460
340
360
520
240
270
620
—
230
170
250
480
3,200
90
2,030
210
230
150
520
490
6,730
160
—
3,090
—
1,140
130
300
190
380
540
1995
1996
1990-1996
481
273
287
—
360
3,020
210
160
570
1,030
350
160
480
630
110
620
—
380
—
no
3,490
320
350
540
240
280
650
—
230
170
250
460
3,390
80
2,090
200
220
180
420
530
6,770
170
—
3,150
—
1,170
130
300
240
400
540
493
290
302
496
306
320
340
360
—
220
140
610
1,090
310
160
460
660
—
620
—
510
—
no
3.620
—
360
560
250
330
670
—
240
180
240
470
3,690
90
2,080
200
240
190
350
560
6,960
200
—
3,140
—
—
130
300
290
430
620
553
366
2,763
249
184
757
924
409
181
537
853
158
701
—
401
—
123
4,290
340
387
506
241
317
603
—
230
200
271
491
3,053
87
1,959
253
251
251
469
600
6,223
187
no
2,941
367
1,133
141
363
246
404
600
Income
Group
1990-1996
Low
Low’
Middle
Lowest
Lowest
Low
Middle
Low
Lowest
Middle
Middle
Lowest
Low
Middle
Low
Lowest
Lowest
Middle
Low
Low'
Low'
Lowest
Low
Low
Lowest
Lowest
Lowest
Lowest
Low
Middle
Lowest
Middle
Lowest
Lowest
Lowest
Low
Low
Middle
Lowest
Lowest
Middle
Low
Middle
Lowest
Low
Lowest
Low
Low
54
Health Expenditures, Services, and Outcomes in Africa
Annex Table 13 Real Per Capila GDP in Africa, by Country, 1990-1996
1990
1991
Constant 1987 Prices
( per capita USS )
1992
1994
1995
1996
491.62
362.67
378.22
—
393.23
—
264.45
154.28
732 14
841.49
374.13
168.00
418.30
934 06
107.38
843 64
—
—
—
168.46
4,647.56
—
429.42
434.99
221.87
381 40
—
—
202.57
165 38
258.67
513.32
2.656.22
132.28
1,655.64
275.53
305.94
221.32
479.32
643.33
4.394.46
160.03
—
2,197.64
—
790.77
—
327.98
592.44
262.22
610.71
AFRICA
510.17
Excl. S. Africa
366 91
Excl. S. Africa & Nigeria 381.22
500.11
362.67
374.45
486.52
357.30
367.69
479 37
350.33
360 80
484.17
355 18
367.75
Angola
846.33
Benin
359.19
Botswana
1,669.64
Burkina Faso
252.80
Burundi
227.35
Cameroon
909 92
Cape Verde
782.61
Central African Republic 417.71
Chad
170.43
Comoros
477.44
Congo
1,063.69
Congo Dem. Rep.
189.61
COte d’Ivoire
867 99
—
Djibouti
Equatorial Guinea
359.12
—
Eritrea
Ethiopia
153.08
Gabon
4,773.59
Gambia, The
269.17
Ghana
389.43
Guinea
408.88
Guinea-Bissau
202.74
Kenya
395.38
Lesotho
275.27
—
Liberia
Madagascar
244.14
Malawi
153.91
Mali
260.09
Mauritania
472.50
2,122.99
Mauritius
Mozambique
110.98
Namibia
1,456.13
Niger
310.13
310.61
Nigeria
316.34
Rwanda
518.11
Sao Tome and Principe
673.16
Senegal
4,399.77
Seychelles
228.73
Sierra Leone
114.57
Somalia
2,343.38
South Africa
684.11
Sudan
825.19
Swaziland
152.47
Tanzania
393.46
Togo
470.10
Uganda
304.91
Zambia
631.86
Zimbabwe
826.79
365.76
1,762.65
268.78
232.87
850.79
771.02
405.97
178.51
440.88
1,047.01
168.12
838.67
—
345 74
—
141.01
4,872.26
259.28
398.11
407.37
209.59
389.88
281.31
—
220.63
159.20
251.28
474.02
2,192.30
114 72
1,518.42
308.21
316.24
293.69
482.05
649.14
4,518.68
204.31
—
2,268.24
674.01
796.93
154.14
378.82
479.18
294.83
649.07
813.23
369.28
1.836.41
268.14
232.65
801.86
774 04
386.60
187 83
454.26
1,043.21
145.77
810.74
—
393.19
—
131.49
4,569.11
266.34
400.65
407.75
210.00
376.64
274.29
—
216.11
139.30
263.73
468.88
2,306.21
111.48
1,591.82
279.01
316.20
309.39
485.43
648.83
4.830.31
180.66
—
2,161.54
734.63
795.16
153.28
353.47
478.54
286.95
581.04
622.19
376.03
1,812.53
254.12
193.76
715.23
814.58
389.39
165.18
441 66
922.96
113 76
777.13
—
416.11
—
150.97
4,586 05
265.83
412 40
421.17
208.26
368.29
307.52
—
207 06
132.35
248.39
492.41
2,466.90
130.56
1,585.83
275.53
309.43
167.38
462.98
615.12
5,028.88
178.29
—
2,157.56
800.14
790.31
153.31
305.72
516.23
272.90
601.95
679 62
382.47
1,857.14
257.50
181 99
718.99
830 42
390.12
167.48
422 66
918.38
109.59
808.97
—
451.15
—
155.11
4,626.87
241.90
419.55
428.35
214.14
374 87
328.96
—
204.55
146 11
256.43
504.25
2,526.24
127.47
1,607.38
274.58
307.33
208.84
472.24
628.55
4,938.36
156.76
—
2,174.52
—
800.43
154.76
317.84
556.86
257.12
578.99
Not available.
Average Real Annual
Per Capita
GDP (USS)
1990-1996
Average Annual
Per Capita
GDP Growth (%)
1990-1996
490 42
358.23
370.30
1.7
2.2
2.0
730.70
373 70
1,786.35
260.58
205 09
783 34
802.96
390.47
170.25
445 27
990.46
136 62
818.79
—
396.00
—
150.31
4,659.10
262.63
408.29
417.69
211.11
379.21
291.52
—
215.52
150.34
255.51
487.18
2,379.82
122.36
1,563.21
285.27
311.59
255.50
484.41
639.58
4,745.87
183.62
—
2,207.02
733.40
798.26
153.39
336.89
513.63
280.63
604.91
-2.9
4.2
4.8
2.7
-2.6
-1.7
3.3
0.4
1 3
1.0
0.3
-7.0
1.7
—
7.0
—
3.4
2.5
3.0
4.3
3.8
3.4
1.9
5.0
—
0.3
3.0
2.5
3.5
5.2
5.5
4.3
1.3
3.1
-8.7
1.2
1.8
2.9
-3.2
—
0.8
5.3
2.1
3.4
-1.0
6.9
0.0
1.0
Annex 3: Tables
55
Annex Table 14 Net ODA Per Capita in Africa, by Country, 1990-1996
1990
1991
33.79
36.43
45.03
Angola
29.21
Benin
56 79
Botswana
116.29
Burkina Faso
37.17
Burundi
48.39
Cameroon
38.93
Cape Verde
325 42
Central African Republic 85.73
Chad
55 72
Comoros
107.41
Congo
95 83
Congo Dem Rep
24.00
Cote d'Ivoire
57.57
391 82
Djibouti
Equatorial Guinea
175.85
Eritrea
0 00
Ethiopia
19.92
141.60
Gabon
108.34
Gambia. The
Ghana
37.89
Guinea
51.36
Guinea-Bissau
136.79
Kenya
50 84
79.98
Lesotho
46.04
Liberia
Madagascar
34.22
Malawi
59.40
Mali
57.62
Mauritania
119.72
Mauritius
84.11
Mozambique
71.08
Namibia
91.12
Niger
51 97
Nigeria
2.60
Rwanda
42 16
Sao Tome and Pnncipe 495 15
Senegal
111.12
Seychelles
527.94
Sierra Leone
15.70
Somalia
57 34
South Africa
0.00
Sudan
34.35
Swaziland
69.40
Tanzania
46.09
Togo
74.15
Uganda
41.08
Zambia
61.78
Zimbabwe
34.93
32.34
34.85
AFRICA
Excl S. Africa
Excl. S Afnca & Nigeria
— = Not available.
4 3 05
29.37
54.96
103.34
45.71
45.91
43.85
307 89
58 23
45 62
146 50
56.90
12.32
51.04
207 87
176.58
0.00
20.72
144 85
106 46
57 60
64.50
120.22
38.36
69.31
63.62
37.82
60.00
52.56
107 06
63.24
74.53
132 72
47.67
2.65
50.85
451.20
83 82
334.11
25.63
23.13
0.00
35.84
66.20
41.12
55.67
39.47
110.28
39.26
US dollai•s (current prices)
1992
1993
1994
1995
1996
31 99
33.89
42 17
30.69
32.33
40.18
26.10
27.50
34.10
43 36
48.30
61.12
43.22
51 23
56.61
327.21
51 87
34.15
83.33
141.32
5.78
117.19
214 19
78.53
45 46
19.58
169.84
65.69
32.84
5603
169.64
26.00
60.14
23.86
21.84
49.43
46.57
121.41
12.76
79.09
91 62
43.26
1.76
114.70
406.10
77.95
177.46
62.86
59.66
7.27
15.78
63.80
33.65
31.55
40.34
82.36
51.96
39.40
51.43
62 97
46.89
45.96
33.42
293 42
51.18
37.10
8801
47.63
4.45
86.73
167.67
84.50
41.94
15 74
131.79
42 86
38.24
63 09
110.93
27.42
57.98
45 04
22.21
44.46
55.67
101 58
19.50
68.23
121.10
29.93
1.91
111.08
609.30
78 87
176.87
45.81
20.16
9.31
8.83
61.67
29 74
46.20
43.20
226.65
44.41
49 10
52 00
54.60
39.20
31.70
30.20
310.10
49 90
46.10
79.40
158.80
3.70
67.60
148.20
75.70
42.80
14.60
112.40
33.70
37.30
43.60
164.80
22.20
53.00
73.40
25.90
50.00
50.00
117.50
17.20
55.70
119.10
27.70
1.70
100.20
355.50
66.80
253.00
42.20
9.30
8.50
8.40
33.00
29.30
39.20
31.10
40.70
27.60
33.80
36.41
45.03
35.74
53.77
84.03
46.03
53.75
58 79
348.98
57.91
40.30
107.03
46.95
6.74
59.16
206.84
170 29
0 00
21.58
68.11
112.45
38 89
73.93
107.45
35.92
77.77
47.10
29 15
63.80
48 55
95.95
42.09
100.16
100.46
45.34
2.54
48.12
485.05
86.18
275.07
32.19
78.72
0.00
21.89
64.45
49.57
59.81
41.71
125.70
77.07
30.43
32.24
39.83
29.41
55.92
96.19
47 95
36 66
43.50
328.66
55.20
37.11
107 49
49 46
4.33
57.91
233.21
142.20
20.07
20.52
97.99
82.92
38.16
65.48
95 27
35 93
75 26
47.33
28.27
53.88
39.65
151.81
23.61
78.75
105.53
41.13
2.66
47.51
388.52
62.63
273.62
48.61
103 29
6.94
17.87
61.92
34.12
25.32
33.97
102.82
47.47
Average
1990-1996
31.30
33.40
41.30
36.50
53.30
82.60
43.70
44.80
43.60
320.20
58.60
42.30
102.70
85 30
8.80
71.00
224 30
129.10
21.50
19.00
123.80
78.90
40 10
59.70
129.30
33.80
67.60
49.50
28.50
54.40
50.10
116.40
37.50
75.40
108.80
41.00
2.30
73.50
455.80
81.10
288.30
39.00
50.20
4.60
20.40
60.10
37.70
47.40
38.70
107.20
46.10
% of GDP
1990-1996
6.1
10.5
12.3
6.1
14.4
3.0
16.6
25.7
5.3
32.2
14.9
22.9
19.9
8.9
3.8
9.2
27.7
33.6
—
14.3
2.5
24.2
10.8
11.4
55.0
10.6
18.4
—
11.4
28 3
18.7
23.6
1.2
81.4
5.9
16.4
0.9
31.0
111.6
13.8
4.4
19.9
48.4
0.2
7.0
5.3
26.9
13.1
16.6
25.1
7.6
- Media
14366.pdf
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