FERTILITY CHANGES IN

SUB-SAHARAN AFRICA

DHS COMPARATIVE

REPORTS 18

FERTILITY CHANGES IN SUB-SAHARAN AFRICA DHS COMPARATIVE REPORTS 18

SEPTEMBER 2008

This publication was produced for review by the United States Agency for International Development. It was prepared by M. Michel Garenne.
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DHS Comparative Reports No. 18

Fertility Changes 

in Sub-Saharan Africa 

M. Michel Garenne 

French Institute of Research for Development (IRD) 

Institut Pasteur, Paris 

Macro International Inc. 

Calverton, Maryland, USA 

September 2008 

Author address: M. Michel Garenne, Institut Pasteur, Epidémiologie des Maladies Emergentes, 

25 rue du Docteur Roux, 75724 Paris Cedex 15, France. Email: mgarenne@pasteur.fr. 

Disclaimer: The author’s views expressed in this publication do not necessarily reflect the views 

of the United States Agency for International Development, the United States Government, 

Macro International Inc., or the institutions with which the author is affiliated. 
This publication was made possible through support provided by the United States Agency for 

International Development under Contract No. GPO-C-00-03-00002-00. 

Recommended citation: 

Garenne, M. Michel. 2008. Fertility Changes in Sub-Saharan Africa. DHS Comparative Reports No. 18. 

Calverton, Maryland, USA: Macro International Inc.
iii

Contents 

Tables and Figures ........................................................................................................................................ v 

Preface ........................................................................................................................................................ vii 

Acknowledgments ........................................................................................................................................ ix 

Executive Summary ..................................................................................................................................... xi 

1 Introduction ...................................................................................................................................... 1 

2 Data and Methods ............................................................................................................................ 3 

 2.1 Fertility Data ....................................................................................................................... 3 

 2.2 Methods for Reconstructing Fertility Trends ...................................................................... 3 

 2.3 Trends in Proximate Determinates ...................................................................................... 6 

 2.4 Trends in Socioeconomic Correlates .................................................................................. 7 

3 Results ........................................................................................................................................... 11 

 3.1 Cohort Fertility ................................................................................................................. 11 

 3.2 Period Fertility .................................................................................................................. 18 

 3.3 Urban-rural Divide ............................................................................................................ 24 

 3.4 Date of Onset and Speed of Fertility Transition ............................................................... 25 

 3.5 Recent Periods of Fertility Stall ........................................................................................ 26 

 3.6 Premature Fertility ............................................................................................................ 27 

 

4 Discussion ...................................................................................................................................... 29 

Appendix A ................................................................................................................................................. 33 

Appendix B ................................................................................................................................................. 35 

Appendix C ............................................................................................................................................... 105 

References ................................................................................................................................................. 109 

v

Tables and Figures 

Table 3.1 Net effect of proximate determinants of cohort fertility in linear regressions, 

sub-Saharan Africa, 1930 – 1970 cohorts ................................................................. 16 

Table 3.2 Correlation between proximate determinants and socioeconomic correlates 

of cohort fertility, 31 sub-Saharan African countries, 1930 – 1970 .......................... 17 

Table 3.3 Net effect of socioeconomic correlates of cohort fertility in linear 

regressions, 31 sub Saharan African countries, 1930 – 1970 .................................... 17 

Table 3.4 Net effects of proximate determinants on period fertility, 31 sub-Saharan 

African countries, 1950 – 2000 ................................................................................. 20 

Table 3.5 Correlation coefficients between socioeconomic variables, proximate 

determinants, and period fertility rates, 31 sub-Saharan countries, 1950 – 

2000 ........................................................................................................................... 23 

Table 3.6 Net effects of socioeconomic correlates on period fertility, 31 sub-Saharan 

African countries, 1950 – 2000 ................................................................................. 24 

Table 3.7 Net effect of changes in proximate determinants and socioeconomic 

correlates on changes in period fertility levels, 31 sub-Saharan African 

countries, 1977 – 1999 .............................................................................................. 24 

Table 3.8 Fertility stall, selected sub-Saharan African countries, 1987 – 2005 ........................ 27 

Figure 2.1 Correspondence between cohort and period fertility estimates over time .................. 5 

Figure 3.1 Reconstructed trends in cohort fertility, 31 sub-Saharan African countries, 

1930-1970 ................................................................................................................. 11 

Figure 3.2 Reconstructed trends in cohort estimates of proximate determinants of 

fertility, 31 sub-Saharan African countries, 1930-1980 ............................................ 14 

Figure 3.3 Reconstructed trends in period fertility, 31 sub-Saharan African countries, 

1950-2000 ................................................................................................................. 19 

Figure 3.4 Reconstructed trends in period estimates of proximate determinants of 

fertility, 31 sub-Saharan African countries, 1950-2000 ............................................ 20 

Figure 3.5 Reconstructed trends in period estimates of socioeconomic correlates of 

fertility, 31 sub-Saharan African countries, 1950-2000 ............................................ 22 

Figure 3.6 Reconstructed trends in period fertility, by urban versus rural residence, 

31 sub-Saharan African countries, 1950-2000 .......................................................... 25 

Figure 3.7 Distribution of year of onset of the fertility transition in urban and rural 

areas, 31 sub-Saharan African countries, 1950-2000 ................................................ 26 

Figure 3.8 Trends in premarital fertility, 31 sub-Saharan African countries, 1950-2000 .......... 28 

vii

Preface 

One of the most significant contributions of the MEASURE DHS program is the creation of an 

internationally comparable body of data on the demographic and health characteristics of populations in 

developing countries. The DHS Comparative Reports series examines these data across countries in a 

comparative framework. The DHS Analytical Studies series focuses on specific topics. The principal 

objectives of both series are to provide information for policy formulation at the international level and to 

examine individual country results in an international context. Whereas Comparative Reports are 

primarily descriptive, Analytical Studies have a more analytical approach. 

The Comparative Reports series covers a variable number of countries, depending on the availability of data sets. Where possible, data from previous DHS surveys are used to evaluate trends over 

time. Each report provides detailed tables and graphs organized by region. Survey-related issues such as 

questionnaire comparability, survey procedures, data quality, and methodological approaches are addressed as needed. 

The topics covered in Comparative Reports are selected by MEASURE DHS staff in conjunction 

with the U.S. Agency for International Development. Some reports are updates of previously published 

reports. 

It is anticipated that the availability of comparable information for a large number of developing 

countries will enhance the understanding of important issues in the fields of international population and 

health by analysts and policymakers. 

Ann Way 

Project Director 

ix

Acknowledgments 

The author thanks the MEASURE DHS project and the USAID for their support, and for 

the free access to the original data. A special thanks goes to Vinod Mishra and to the editors of 

the Comparative Reports series. 

xi

Executive Summary 

This report provides an overview of major fertility trends in sub-Saharan Africa in the second half 

of the 20th century. It also presents the proximate determinants (factors that have a direct mechanical 

effect on fertility levels) and the socioeconomic correlates of these trends. 

Cohort and period fertility trends were constructed using World Fertility Survey (WFS) and 

Demographic and Health Surveys (DHS) data sets for 31 countries in sub-Saharan Africa. Cohort trends 

were derived from WFS and DHS data for some of the factors that affected fertility change: infertility, 

age at first marriage, education level, proportion Muslim, proportion Christian, proportion living in a 

polygynous union, and nutritional status. Period trends were derived for urbanization and income per 

capita from other sources. 

Cohort fertility was higher among women born in 1950 than in those born in 1930 but tended to 

decline in women born in later years. Changes in cohort fertility levels were small, on average. The mean 

number of children ever born to a woman by age 40 increased from 5.9 in women born in 1930 to 6.2 in 

women born in 1950 and decreased to 5.6 in women born in 1970. In most cases, the increase in cohort 

fertility was apparently due to a decline in infertility and, to a lesser extent, decreasing age at first 

marriage, which was associated with the spread of monotheist religions in the first half of the 20th century. 

Nutritional status did not have any identifiable impact on cohort fertility. 

Like cohort fertility, period fertility tended to rise from 1950 to 1975 and then fall until 2000 or 

later. On average, for the countries investigated, the total fertility rate at age 40 increased from 5.3 

children per woman in 1950 to 6.2 in 1975, then declined to 4.9 in 2000. The decline in period fertility 

appeared to be due primarily to increasing contraceptive use and, to a lesser extent, rising age at first 

marriage and increasing urbanization. A regression model of the explanatory variables indicated that 37 

percent of the decline was attributable to increased contraceptive use, 24 percent to decreased age at first 

marriage, and 16 percent to increased urbanization. These three variables correlated with level of 

education and, to a lesser extent, income per capita. 

The dynamics of the fertility decline were different in urban and rural areas. On average for the 

countries investigated, the trends in urban and rural areas started to split in approximately 1960. The date 

of onset of the fertility decline varied greatly by region and country, ranging from the early 1960s in the 

first urban areas to the late 1990s in the last rural areas. A few rural communities had not started the 

transition at the time covered by the last available survey. 

The speed of the fertility decline, approximately 1 child per decade, also varied markedly among 

countries, from 1.5 children per decade to less than 0.5 children per decade. In addition, a stall in fertility 

decline occurred in six of the countries investigated (Ghana, Kenya, Madagascar [urban areas], Nigeria, 

Rwanda-rural, Tanzania [rural areas]); in five of these countries, this stall occurred in 1995-2005. 

The pattern of the fertility decline in sub-Saharan Africa did not appear to be very different from 

that of many other countries in the world. However, the fertility decline in sub-Saharan Africa seems to 

have been somewhat more influenced by changing nuptiality patterns than elsewhere, and its relationship 

with socioeconomic correlates was somewhat less influenced by income levels and trends than other 

countries. 

The appendices present a detailed analysis of fertility trends by country, with information on 

trends in urban and rural areas, premarital and marital fertility, and periods of monotonic changes. 
xii

Key Words: Fertility transition, fertility decline, fertility increase, fertility stall, infertility, contraception, 

age at marriage, proximate determinants, socioeconomic correlates, sub-Saharan Africa 
1

1 Introduction 

Most countries in the world underwent a marked fertility decline during the 20th century (United 

Nations, 2007a; United Nations 2007b). This major phenomenon in human history swept across cultures 

and religions and has had—and will have—enormous consequences for the ecological balance between 

humans and the environment. 

The fertility decline is closely associated with the modernization process. However, it also 

occurred in communities with high and low incomes, high and low industrialization levels, and high and 

low urbanization levels. The period of fertility decline was sometimes preceded by a period of fertility 

increase for a variety of reasons; this typically occurred in regions in which the health of young women 

improved before they adopted contraception (Dyson and Murphy, 1985; Dyson and Murphy, 1986). 

Fertility transitions, or steady declines in the fertility rate, are often quite rapid, spanning just two 

generations (50 to 60 years). This was the case in many European countries, where the fertility transition 

occurred between 1880 and 1930. The fertility transition was somewhat more rapid in some East Asian 

countries (Japan, China, Taiwan, and Korea) due to the spread of modern contraception after 1960 and the 

wide use of sterilization in the cases of Thailand and China. The transition was somewhat slower in some 

South Asian countries (India and Pakistan). As in Europe, the fertility transition in Latin America lasted 

approximately 50-60 years. Of course, the date of onset and the speed of the decline differed among 

countries. 

Less research has been conducted on fertility transitions in sub-Saharan Africa. This lack of 

research is primarily due to the lack of routinely collected vital registration data, the main source of data 

used to document a fertility transition. In addition, the fertility decline in this region was more recent and 

less striking than in such countries as Thailand or China. 

Although sub-Saharan Africa lacks vital registration data, other data sources are available to 

describe the fertility transition in these countries. These data sources include censuses and surveys that 

often collect information on fertility (children ever born for cohort fertility and births in the past 12 

months for period fertility) and, more importantly, demographic sample surveys from the large 

international programs: the World Fertility Survey (WFS) and the Demographic and Health Surveys 

(DHS). The WFS and DHS surveys collect data on maternity history that can be used to derive period and 

cohort estimates. 

Several authors have tried to document the fertility decline in sub-Saharan Africa (Caldwell et al., 

1992; Caldwell, 1994; Castro-Martin, 1995; Cleland et al., 1994; Gaisie, 1996; Gould and Brown, 1996; 

Lesthaeghe and Jolly, 1995; McNicoll, 1992; National Academy of Sciences, 1993; Ngom and Fall, 2005; 

United Nations, 2001; van de Walle and Foster, 1990). Several methods have been used to document the 

onset of the fertility decline in sub-Saharan Africa. Cohen (1998) compared cohort (parity) and period 

fertility (total fertility rate [TFR]) to analyze the transition in Botswana, Côte d’Ivoire, Ghana, Kenya, 

Lesotho, Namibia, Sudan, Tanzania, and Zambia. Kirk and Pillet (1998) applied Bongaarts’ model to 

many sub-Saharan African countries to document the impact of contraceptive use on fertility trends. 

However, few of these studies focused on the timing of the transition (the onset), urban-rural differences 

in fertility dynamics, or events that preceded the decline. 

The aim of this study is to systematically describe the fertility changes in sub-Saharan Africa by 

focusing on periods of monotonic changes (fertility increase, fertility decline, or fertility stall) and to 

relate these dynamics to trends in the proximate determinants of fertility and various socioeconomic 

correlates of the modernization process. This research is an update of earlier work on the timing of the 
2

fertility transition (Garenne and Joseph, 2002). This study includes more recent data, a more detailed 

analysis of the earlier data, and a more comprehensive study of the determinants of fertility change. 

In the first part of this report, the main focus is to define the onset of the fertility decline in subSaharan Africa based on the data, without reference to any preconceived idea or theory. The second part 

of the report focuses on determinants and correlates, again as they appear in the data and without any 

reference to a given theory. This agnostic attitude opens the possibility that several factors could explain 

the fertility declines, increases, and stalls. This study is, therefore, primarily descriptive and is not 

intended to be a formal evaluation of health policies, such as family planning program policies. However, 

the study does indirectly provide some information on the effects of such programs on fertility trends. 
3

2 Data and Methods 

2.1 Fertility Data 

The reconstruction of fertility trends was made possible by the availability of maternity history 

data collected by the WFS, which includes 10 surveys in sub-Saharan Africa, and the DHS, which had 

issued data from 74 surveys in sub-Saharan Africa as of the spring of 2007. Data from more surveys are 

issued regularly, so more data that can be used to analyze fertility trends in sub-Saharan Africa will 

become available in the future. The DHS surveys cover 35 countries in sub-Saharan Africa and will cover 

even more countries in the future. 

Several countries were excluded from the final analysis. Specifically, Sudan was excluded 

because its data cover only ever-married women, so the data cannot be used to estimate premarital 

fertility levels and trends. South Africa was also excluded because this country’s fertility trend has 

already been analyzed using numerous censuses and surveys (see reviews by Caldwell and Caldwell, 

1993; Mostert et al., 1988; Moultrie and Timaeus, 2003; Swartz, 2003; as well as local studies by Camlin 

et al., 2004; Garenne et al., 2007). Estimates were included from some of these other studies for South 

Africa in the totals for sub-Saharan Africa. Finally, Mauritania and Eritrea were excluded from the final 

analysis because these data are restricted for use. With the exception of South Africa, the inclusion 

criterion for this study was the availability of WFS or DHS data for all women age 15-49. The list of 

countries and surveys used in this analysis is available in Appendix A and the data on each country are 

summarized in Appendix B. 

To estimate trends before the earliest data from the WFS and DHS surveys, demographic census 

and survey data were used from other sources. These data are also useful for confirming upward trends. 

Details on these sources are provided in Appendix B. 

2.2 Methods for Reconstructing Fertility Trends 

The two main fertility indicators studied were the completed family size (CFS), or the mean 

number of children ever born (CEB) to a woman at the end of her reproductive period (cohort estimate), 

and the total fertility rate (TFR), or the sum of the age-specific fertility rates in a given year (period 

estimate). The terms CFS and CEB are used interchangeably throughout this report. 

The first step in this study was to reconstruct period and cohort fertility trends for each selected 

sub-Saharan African country. In this study, period fertility appears to be more suitable for documenting 

fertility declines, whereas cohort fertility appears more suitable for documenting fertility increases, 

primarily because data on CFS are available for older women and therefore cover earlier periods. 

Calculating Period Fertility 

Age-specific period fertility rates were first calculated for each survey and each year for the 15 

years prior to each survey from all the WFS and DHS surveys available. The cumulated fertility rate at 

age 40, TFR(40), was derived from the age-specific fertility rates using standard formulae with five-year 

age groups and one-year time periods. The main reason for calculating TFR at age 40 was to include 

trends over time because TFR(40) could be calculated for the years prior to the survey (see Garenne and 

Joseph, 2002, for more details). TFR(40) can easily be converted to classic TFR(50) by dividing the 

TFR(40) by 0.90, because 90 percent of total fertility occurs before age 40, a value based on the average 

of all DHS data available for sub-Saharan Africa. Data from the same country and the same year were 

combined by adding the numerators (number of births) and the denominators (number of women). 

Separate calculations were made for urban and rural areas. 
4

Calculating Cohort Fertility 

Cohort fertility, defined as the number of children ever born (CEB) to a woman by age 40, 

CEB(40), was calculated from birth history data and the calendar year of the woman’s birth (cohort). For 

women who were age 40-49 at the time of the survey, CEB(40) was calculated by including only births 

that had occurred before age 40. For women age 35-39, a correction factor was applied to extrapolate 

CEB(40). Cohort fertility was calculated for each country as a whole, and not for urban and rural areas 

separately, primarily because cohort fertility for earlier periods was used when urbanization was very 

limited. In addition, other cohort fertility data from census and surveys were not available for urban and 

rural areas separately. More details on this procedure are provided in Appendix B. 

Identification of Monotonic Periods of Fertility Change in Urban and Rural Areas 

To identify monotonic periods of fertility change in urban and rural areas, time trends in 

TFR(40,t) were fitted by linear regression on year (t) using the ordinary least-square method. For this 

procedure, a TFR(40,t) calculated from the number of births before age 40 at time (t) [B(40,t)] was 

considered to be equivalent to the CFS of a cohort of N=B/TFR women with the same TFR and the same 

number of births. When a slope changed from positive to negative, no slope to negative, or negative to nil 

(fertility stall), the change in slope was tested using standard t-tests. This made it possible to formally 

identify periods of monotonic change (fertility decline, fertility increase, or fertility stall). Trends in 

period fertility were calculated for each country as a whole using the weighted average of urban and rural 

trends and the weights for the urban proportion from the United Nations Population Division database. 

A similar procedure was used to test cohort fertility trends directly with a linear regression model. 

Periods of monotonic cohort fertility trends were identified and tested with the same methods used for 

period fertility, although only for each country as a whole and not for urban and rural areas separately. 

Premarital and Marital Fertility 

The proportion of premarital births, defined as the proportion of births occurring prior to the first 

marriage or to never-married women, was calculated directly from the WFS and DHS survey results by 

country and year. The results from each country were aggregated by cumulating premarital births for each 

year. Time trends were fitted in the proportions of premarital births using a linear-logistic regression 

model and monotonic periods of change in the proportions of premarital births were defined. Lastly, the 

fitted proportion of premarital births was applied to the reconstructed TFR(40) trend to obtain the trends 

in premarital fertility. In this analysis, only trends in period premarital fertility, as opposed to cohort 

premarital fertility, were considered. 

Matching Trends in Period and Cohort Fertility 

The approach to studying fertility trends was to have an agnostic attitude toward the data; in other 

words, no a priori assumptions were made, such as hypothesizing a stable population (constant fertility 

and mortality) before analyzing the data. As shown below, many sub-Saharan countries followed a typical 

pattern of a roughly constant, or modest increase in fertility before 1950; a substantial rise in fertility in 

the 1950s and 1960s; and then a decline that usually started in urban areas before reaching rural areas a 

few years later. The countries studied varied in their baseline fertility level, magnitude of increase, speed 

of decline, and time lag between trends in urban and rural areas. 

Before embarking on the analysis of empirical evidence, the effect of the patterns of increases and 

decreases in the fertility level was studied within a few decades on the two main available indicators, the 

CFS and TFR. To do this, a basic theoretical model was built in which age-specific fertility rates (those of 

Zambia) were constant for 35 years to obtain a baseline rate. The rates in the model then rise for 30 years 

and decline for the next 30 years, so that the TFR increases from four children per women in 1930 to 
5

eight in 1960 and decreases to two in 2005. In this model, the age pattern for fertility remains constant 

over time. Under these hypotheses, CFS increases with time, with a time lag of approximately 15 years 

compared to TFR, reaches a lower maximum of 7.2, then intersects with the TFR before decreasing, again 

with a time lag (Figure 2.1). This type of pattern has already been described for the fertility transition 

(Ryder, 1983). 

The country case studies in Appendix B reveal that the pattern shown in Figure 2.1 is the most 

common pattern throughout sub-Saharan Africa, even though its amplitude varies considerably from 

country to country. The magnitude of the changes should not be underestimated because 50-percent 

increases or declines occurred in many sub-Saharan countries over short periods of time. Some of these 

rapid changes were confirmed by independent information, such as the proportion of infertile women 

(see, for example, the Gabon case study in Appendix B). 

Supplementation with Other Data 

In addition to WFS and DHS microdata available from Macro International Inc., CFS estimates 

were used from census and survey data published in the United Nations Demographic Yearbook (in 

particular the special issues of 1978, 1997, and 2000) and original documents available at the Centre 

Français sur la Population et le Développement (CEPED) library in Paris. Details on the data sources used 

for each country are provided in Appendix B and summarized in Appendix C. Whenever possible, raw 

and uncorrected data were used for CFS but it was not always possible to use uncorrected data for TFR 

because many publications provide few details on how these data were obtained. Furthermore, in many 

cases, the values presented as the “official estimates” are the product of complex adjustment procedures, 

usually based on models (such as the Brass parity/fertility ratio) and a series of hypotheses. The authors of 

Figure 2.1 Correspondence Between Cohort and Period 

Fertility Estimates over Time

0

2

4

6

8

10

1900 1920 1940 1960 1980 2000 2020

Year: Period (t) or Cohort (t-30)

Children per woman

TFR (period)

CFS (cohort)
6

these reports often hypothesized a stable population or at least constant fertility; this assumption was 

clearly disproved in cases of increasing fertility. 

Inconsistencies were often found among published data on CEB. Some of these inconsistencies 

occurred in the census or survey data themselves, while others arose from calculations of the estimates. A 

recurrent problem in many sub-Saharan African censuses is the treatment of missing values, that is, the 

value attributed to women for whom no data on CEB are available (unknown category). Estimates of CFS 

can vary widely depending on whether data on these “unknown” cases are ignored (and therefore 

implicitly assumed to be the average CEB level) or these women are considered infertile (no child ever 

born at the time of the survey). For some age groups, especially younger women, it is likely that most 

women who are coded as “unknown” never had any births, whereas the opposite is true for older women. 

This seems to explain the inconsistencies among some of the Kenyan census results, as well as problems 

in a recent census of South Africa. When inconsistencies were too large, they were simply ignored in the 

final analysis. Only in-depth studies of the censuses could resolve these issues. Such studies are becoming 

possible with the free access to census data offered by the Integrated Public Use Microdata Series–

International (IPUMS International), a project based at the University of Minnesota, aimed at 

disseminating census microdata from countries throughout the world. 

2.3 Trends in Proximate Determinants 

Cohort and period trends were used to analyze the impact of proximate determinants of fertility, 

which were calculated from WFS and DHS survey results. Only the proximate determinants found in 

these surveys were analyzed and other determinants were ignored because of the lack of data, which was 

especially true for induced abortion. In this analysis, time trends and intensity of exposure were more 

valued than levels per se, so the indicators used are sometimes different from some of the more common 

indicators used in demography. 

Contraceptive Use 

The “contraceptive use” indicator refers to the proportion of women who had ever used modern 

contraception by age 40. This measure of lifetime exposure to contraception was used to explain 

cumulated fertility by age 40. Contraceptive use was calculated directly from the WFS and DHS data for 

women age 35-49 years at the time of the survey. The data were tabulated by yearly birth cohort and the 

data were converted into the proportion of women age 40 that had ever used modern contraception using 

a logit-linear relationship. Trends were extrapolated backward to estimate the contraceptive prevalence 

among older cohorts, again assuming a logit-linear pattern. 

No evidence of reversals in trends for contraceptive use was found in any of the countries 

investigated. Once contraceptive prevalence started to increase, it continued to increase until the last time 

point for which data were available. 

The cohort estimates were converted into period estimates by multiplying the cohort estimates by 

a standard age pattern of fertility (the average for sub-Saharan Africa) and applying the results to 30 years 

after the birth cohort; these computations were completed for each country. This procedure furnishes a 

robust measure of exposure to modern contraception and the full trend for contraceptive use by year for 

up to 5 years before the last survey. The period estimates are different from the classic “contraceptive 

prevalence” estimates for women age 15-49 calculated in demographic surveys because the weights used 

were proportional to age-specific fertility rates, not to the population’s age structure. 
7

Age at First Marriage 

The median age at first marriage was calculated by yearly birth cohort for all cohorts for which 

these data were available. The median age was defined as the age at which half of the cohort was ever 

married. Therefore, the definition of median age used in this report differs somewhat from the classic 

“median age at first marriage,” which takes into account only those who ever marry or who marry by age 

50. In theory, it would have been more accurate to use the proportion of women who had never married 

by age 40, but this would have led to a far more restrictive time dimension for the process. Because 

median age at first marriage, which is indeed closely related to the proportion of women who will 

eventually marry, was used, the full range of information available was used for some of the 25 yearly 

birth cohorts (age 25-49 at the time of the survey) for which data were available. Period estimates were 

also derived for median age at first marriage by multiplying the cohort estimates by the standard age 

pattern of fertility. The result was applied to the period corresponding to 30 years after the cohort’s birth. 

Infertility 

Infertility was defined as the probability that a woman had had no live birth by age 40. This 

definition differs from definitions used by other authors (Frank, 1983; Larsen and Raggers, 2001), such as 

the probability that a woman had not had any live birth within seven years of marriage while not using 

contraception (known as marital infertility or marital sterility). Based on this definition, women with 

sterility (the biological incapacity to deliver a live infant) and voluntary infertility (by abstinence or 

contraception) would be considered infertile. However, voluntary infertility appears to be rare in Africa, 

at least compared to Western Europe, where it can be as high as 10 to 25 percent. 

Infertility was calculated by yearly birth cohort for women age 35-49 at the time of the survey. 

Infertility rates by age 40 were calculated directly for women age 40-49 and used a conversion factor for 

women age 35-39. Period estimates were not used in this analysis because the changes in infertility were 

small in the most recent period (the years since 1975). 

Nutritional Status 

The mean height of adult women was used as an indicator of nutritional status because a woman’s 

height does not change after age 20. This made it possible to calculate cohort estimates directly for all 

women age 20-49 at the time of the survey by year of birth. Mean height is not modulated by age effects, 

as is the case with body-mass index. The plain measure of height (in centimeters) is a good indicator of 

past malnutrition during childhood and adolescence and could be related to fertility because poor 

nutritional status reduces fecundability. It should be noted that height also depends on genetic 

characteristics and is likely to be heterogeneous among the many sub-Saharan African ethnic groups. 

However, this should not affect trend estimates, which are the critical factor for this analysis. Data on 

women’s height is available only in recent DHS surveys. 

Because nutritional status was used as an explanatory variable only for fertility increases, no 

attempt was made to convert it into period estimates. 

2.4 Trends in Socioeconomic Correlates 

Cohort and period fertility trends were used to analyze socioeconomic correlates of fertility. Most 

explanatory variables were calculated from WFS and DHS surveys, but the estimates of urbanization and 

income per capita were derived from other sources (see Appendix B for details on the sources used). 
8

Education Level 

The mean number of years of schooling by birth cohort was computed for all women age 15-49. 

For women age 15-19, a small adjustment was made to convert actual years of schooling into mean 

number of years of schooling by age 20. Few changes in number of years of schooling were assumed to 

occur after age 20 because this appears to be true for the female sub-Saharan African population as a 

whole, although it is not always true at the individual level. Period estimates were calculated in the same 

way as for the other variables, by multiplying cohort estimates by a standard age pattern of fertility and 

applying the result to the period corresponding to 30 years after the cohort’s birth. Therefore, the period 

estimates in this report are different from the average number of years of schooling in the population age 

15-49 in censuses and surveys. 

Urban Residence 

Cohort estimates of the proportion of women living in urban areas were calculated directly from 

the WFS and DHS data for all cohorts of women age 15-49. No attempt was made to make any 

corrections by age. The period estimates of urban residence used in this report came from the 2006 United 

Nations Population Division estimates (United Nations, 2007a). The data from these different sources do 

not match because the United Nations data are based on census estimates. However, the levels and trends 

from all of these sources are similar for all of the countries investigated. The correlation of urbanization 

levels by country between both sources was high (r = 0.91). The average levels of urbanization were 

somewhat higher in the cohort estimates (25.4 percent on average for the 1930-1970 cohorts versus 22.8 

percent for the 1960-2000 cohorts). The slopes tended to be somewhat lower in the cohort estimates, 

primarily because women sometimes migrate to urban areas during their reproductive years. 

Religion 

Most of the WFS and DHS surveys provide data on religious affiliation. The proportion of 

women who were Christian or Muslim at the time of the survey was calculated by yearly birth cohort. In 

the regression analysis, the reference category was therefore the “other” category (neither Christian nor 

Muslim), which covers the traditional religions, other religions, and unknown religion. No attempt was 

made to convert the religious affiliation data to period estimates. 

In earlier work on trends in age at marriage, monotheist religions were found to be associated 

with age at first marriage (Garenne, 2004). 

Type of Marriage 

The WFS and DHS surveys include data on type of marriage (i.e., monogamous versus 

polygynous). The proportion of women who were in a polygynous union was tabulated by yearly birth 

cohort. No attempt was made to control for age or to convert these data into period estimates. 

In earlier work on trends in age at marriage, polygyny was found to be associated with age at 

marriage (Garenne, 2004). 

Income per Capita 

The last factor included in this study was income per capita, measured by the gross domestic 

product in purchasing power parity (GDP-PPP) using constant currency (1990 U.S. dollars [USD]). 

Period data are available for all years from 1950 to 2001 for all countries investigated; these data were 

obtained from the Organisation for Economic Co-operation and Development (Maddison, 2003). 
9

Calculating Estimates for Sub-Saharan Africa 

The country estimates were merged to produce estimates for sub-Saharan Africa of fertility trends 

as well as of proximate determinants and socioeconomic correlates. To merge the data on different 

countries, a standard weight (the 2000 population estimated by the United Nations Population Division) 

by country was used. 

Multivariate Analysis 

To identify the most salient factors in the fertility changes, several regression models were tested 

with both period and cohort fertility data. The first type of model links the outcome (cohort or period 

fertility) to the proximate determinants or the socioeconomic correlates on aggregate data by country and 

year, weighted by the corresponding number of women (simple linear regression model). This type of 

model takes into account the heterogeneity of the sub-Saharan African countries with respect to all 

variables and sample sizes. 

The second type of model links the changes in cohort or period fertility to the changes in 

proximate and socioeconomic determinants after fitting the trends in the dependent and independent 

variables. Here again, a simple linear regression model was used. This type of model is more suitable for 

explaining the changes in fertility with the changes in the independent variables. These models are based 

on a typical longitudinal approach; therefore, their results differ markedly from those of cross-sectional 

models focusing on socioeconomic differentials, which are the most widely used in demography. The 

SPSS-11 statistical package was used for all regression analyses. 

11

3 Results 

The results are presented below on cohort fertility, period fertility, urban-rural differences, 

premarital fertility, and fertility stall. 

3.1 Cohort Fertility 

Trends in Cohort Fertility 

The reconstruction of cohort fertility trends from WFS and DHS data indicate an increase in 

cohort fertility followed by a decline. On average, the estimated CFS(40) in the selected countries rose 

from 5.9 children per woman born in 1930 to 6.2 children per woman born in 1950, then declined to 5.6 

children per woman born in 1970 (Figure 3.1). 

Figure 3.1 Reconstructed trends in cohort fertility, 31 subSaharan African countries, 1930 - 1970 

4

5

6

7

1920 1930 1940 1950 1960 1970 1980

Year of birth (cohort)

Children ever born by age 40

The overall changes in cohort fertility level are small but these data hide larger or divergent 

changes in some countries. For example, the fertility increase from the 1930 cohort to the 1950 cohort 

exceeded one child per woman in Benin (increase of 1.23 children per woman), Cameroon (1.19), Chad 

(1.31), and Namibia (1.26). Furthermore, cohorts experienced a fertility decline during this period in 

Ghana (decline of 0.36 children per woman), Kenya (-0.36), Malawi (-0.48), Rwanda (-0.62), and Togo (-

0.68). More details on changes in cohort fertility levels are provided in Appendix B and Appendix C. 

The 1930-1970 cohorts were covered by the WFS and DHS surveys, but no surveys have 

documented fertility trends in sub-Saharan Africa prior to 1930. However, estimates can be made using 

census data on the mean number of CEB classified by birth cohort. In countries with low numbers of CEB 

(these countries typically had high levels of infertility), many of the changes occurred prior to the 1930 

cohort’s birth. Trends in cohort fertility documented in the WFS and DHS survey results were consistent 
12

with trends from census and other survey results in most cases, which cross-validates the results from all 

of these sources. Details by country are provided in Appendix B. 

Major increases in CEB occurred in the infertility belt in central Africa (Central African 

Republic, Congo, and Gabon); in Sahelian areas (Chad and Niger); in coastal West Africa (Liberia); in 

eastern Africa (Mozambique and Zambia); and in the islands (Comoros and Madagascar). In a few 

countries (Burundi, Ghana, Guinea, Lesotho, Malawi, and Rwanda), the increase in cohort fertility was 

very small and hardly significant. Despite some erratic patterns in the census data, no evidence was found 

of any major inconsistency in trends between census and survey data and the WFS and DHS data. 

Trends in Infertility 

Major changes in infertility occurred in sub-Saharan Africa in the first half of the 20th century. 

The reconstruction using WFS and DHS data indicates a drop in the proportion of infertile women from 

8.1 percent among women born in 1930 to 3.1 percent among women born in 1957-1958, followed by a 

small rise for cohorts that had not yet reached age 40 by the time of the last survey in each country 

(Figure 3.1). Despite the overall decline in infertility for the earlier cohorts, differences between countries 

were large. Absolute values dropped substantially in the countries most affected by infertility in the early 

part of the 20th century. The declines were -10.1 percent in Cameroon, -10.9 percent in the Central 

African Republic, -8.1 percent in Congo, -6.3 percent in Gabon, -8.8 percent in Mozambique, -5.5 percent 

in Niger, and -13.7 percent in Tanzania. 

Increases in infertility in the later period (after 1980) occurred in some of the “infertility belt” 

countries (Congo and Mozambique), “late marriage” countries (Lesotho, Namibia, and South Africa) 

where the median age at marriage is above 23 years, and other countries (Ethiopia, Ghana, Mali, Rwanda, 

Senegal, and Tanzania). These recent increases deserve a separate study and are beyond the scope of this 

report. 

For the 31 countries combined, the changes in the infertility level alone could explain the rise in 

cohort fertility from 1930 to 1950. The formula linking these two variables can be derived from basic 

equations, assuming no change in the fertility level for non-sterile women: 

( ) (1 ) Log CFS  Log p0

where p0 designates the proportion that is infertile.

In this analysis, the relative change in CFS was 0.044 and the relative change in the proportion 

fertile was 0.049; these values are highly consistent. However, this comparison would not apply to many 

countries, especially those that experienced an early fertility decline. Indeed, based on WFS and DHS 

data, the decline in infertility could explain the fertility decline in only seven of the countries investigated. 

The complex relationship between the decline in infertility and the rise in cohort fertility should be 

analyzed carefully by country, taking into account confounding factors and, possibly, erratic data. 

Infertility in WFS and DHS Surveys and Censuses 

The proportion of infertile women in the WFS and DHS survey results was compared to the 

census data on CEB, using the proportion of women age 35 and older who had never had any live births, 

classified by birth cohort. This comparison was made for all cohorts included in both the censuses and the 

WFS and DHS surveys in the same country. 

On average for the 15 countries for which the comparison was feasible, the infertility rate (3.3 

percent) measured using the WFS and DHS survey results tended to be about half the rate from the 
13

censuses (6.5 percent). In all 15 countries, the WFS and DHS estimates were lower, with the risk ratio 

(census/survey) for each country ranging from 1.4 to 2.8. 

The estimates from the different sources might have differed for many reasons. For example, 

WFS and DHS surveys typically focus on households in which women live during their fertile years, 

whereas censuses cover the entire population. Even if institutions are not included, infertile women could 

live in different types of households or could avoid answering the relevant questions in the DHS survey. 

Censuses might also overestimate infertility because of poorer responses to the question on number of 

children ever born compared to full-scale maternity histories that are likely to provide a more accurate 

count of births followed by a child’s early death. Finally, some censuses might code women who do not 

answer the question on CEB as being infertile, which is why census data were not used to extrapolate the 

findings on infertility trends to earlier cohorts. 

However, the trends in infertility were similar in the WFS and DHS survey results and the census 

data. The most striking declines in infertility occurred in central Africa (some are shown in Appendix B), 

especially Congo, Cameroon, Central African Republic, Gabon, and Zambia, as well as two countries in 

the Sahel, Burkina Faso and Chad. 

Level of Infertility and Family Size 

A negative correlation was found between the infertility level and family size at baseline (1930 

cohort). When infertility exceeded 8 percent, CFS(40) was lower than 6.2 children per woman; when 

CEB(40) was higher than 6.2 children per woman, the infertility level was lower than 8 percent. However, 

some countries (Namibia, Botswana, South Africa, and Lesotho) had a low average family size (fewer 

than 5 children per woman) despite moderate levels of infertility (less than 6 percent). The fact that all of 

these countries were in southern Africa indicates that other factors, especially late marriage, played a role 

in the low fertility level at baseline. 

Trends in Other Proximate Determinants 

The other proximate determinants of fertility examined using WFS and DHS data were 

contraceptive use, median age at first marriage, and nutritional status. 

Contraceptive use increased steadily in the cohorts investigated, from an estimated 8 percent in 

the 1930 cohort to an estimated 52 percent in the 1970 cohort (Figure 3.2). Because fertility rose between 

1930 and 1950, the increase in contraceptive use could explain the fertility decline that occurred after 

1950 only and probably reduced the fertility increase that occurred before 1950. 

The median age at first marriage changed over time, as did the proportion of women who 

eventually marry. Reconstructed cohort trends from WFS and DHS data show a decline in median age at 

first marriage from 17.6 years in the 1930 birth cohort to 17.2 years in the 1942 birth cohort, followed by 

a rise to 18.5 years in the 1970 birth cohort (Figure 3.2). The increases and decreases were remarkable in 

some of the countries; these changes have been documented elsewhere (Garenne, 2004). The trends in age 

at marriage were consistent with the trends in cohort fertility for sub-Saharan Africa as a whole but not 

with the trends in many of the countries investigated. 
14

Figure 3.2 Reconstructed Trends in Cohort Estimates of 

Proximate Determinants of Fertility, 31 sub-Saharan 

African countries, 1930-1980 

Infertility

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975

Year of birth (cohort)

Proportion infertile at age 40

Contraceptive use

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975

Year of birth (cohort)

Percent ever use by age 40
15

Figure 3.2 – continued 

Age at first marriage

16

17

18

19

20

1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975

Year of birth (cohort)

Median age at marriage (years)

Height of adult women

155

157

159

161

163

1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985

Year of birth (cohort)

Height (cm)
16

Nutritional status has also changed over time. According to DHS survey results, the average 

height of women born in sub-Saharan Africa in 1966, 158.8 cm, was greater than that of those born in 

1940, 157.3 cm, indicating a slight improvement in nutritional status (Figure 3.2). Surprisingly, average 

female height stopped increasing in younger generations and even declined in Benin, Central African 

Republic, Congo, Madagascar, Mozambique, Namibia, Nigeria, Rwanda, Uganda, and Zambia. 

Although improvements in nutritional status could have played a role in the fertility increase in 

sub-Saharan Africa before 1950 (but the DHS survey results do not include the 1930-1940 cohorts), 

nutritional status trends after 1950 were not consistent with fertility trends. The impact of nutritional 

status changes on fertility trend changes would have had an approximately 15-year time lag and the 

magnitude of the nutritional changes measured by changes in height was so small that these changes were 

unlikely to have had much influence on fertility. 

Net Effects of Proximate Determinants 

To evaluate the net effects of proximate determinants on cohort fertility in sub-Saharan Africa, 

two regressions linking proximate determinants with cohort fertility were run. The first regression 

included the three determinants (infertility, age at marriage, and contraceptive use) for which information 

was available for the 1930-1970 cohorts. The second regression included the same three determinants 

plus height; this model included only the 1940-1970 cohorts. The results show that all of these 

determinants have low explanatory power (Table 3.1). 

Table 3.1 Net effect of proximate determinants of cohort fertility in linear regressions, sub-Saharan Africa, 1930-1970 cohorts 

Variable 

Beta 

coefficient 

Standard 

error T-test P-value Significance Net effect 

Model 1 (1930-1970 cohorts) 

Constant 10.8988 0.0873 124.85 0.0000 * 

Proportion infertile -1.7832 0.1332 -13.39 0.0000 * -0.026 

Age at first marriage -0.2428 0.0048 -50.76 0.0000 * -0.099 

Contraceptive use -1.7661 0.0158 -111.86 0.0000 * -0.222 

Model 2 (1940-1970 cohorts) 

Constant 5.9853 0.2658 22.51 0.0000 * 

Proportion infertile -0.5070 0.2166 -2.34 0.0193 * -0.005 

Age at first marriage -0.0674 0.0087 -7.71 0.0000 * -0.021 

Contraceptive use -2.2822 0.0227 -100.46 0.0000 * -0.265 

Adult height +0.0114 0.0012 9.69 0.0000 * +0.021 

* p <0.05 

Even when all variables are significant and have the expected sign, they can account for only a 

small proportion of the fertility changes. In the first model, only contraceptive use appears to have had a 

substantial impact on fertility, with a net effect of -0.222 children for one standard deviation. The role of 

infertility appears to have been underestimated with this procedure, probably because of the high level of 

heterogeneity among the countries included in the analysis. Of course, infertility has had a major impact 

on the low parity in many countries in the “infertility belt.” 

Socioeconomic Correlates of Cohort Fertility 

Similar correlations and regressions were conducted with the socioeconomic correlates of cohort 

fertility. Urbanization level, education level, proportion Muslim or Christian, and prevalence of polygyny 
17

were steady over time in the cohorts investigated. Therefore, these correlates could not explain the 

changing fertility trends. 

Only income, measured using GDP-PPP, rose and fell and could be correlated statistically with 

trends in fertility. GDP-PPP tended to increase in sub-Saharan Africa from 1950 to 1975 (corresponding 

to the 1920-1945 cohorts) and then decrease from 1975 to 2000 (corresponding to the 1945-1970 

cohorts). However, these were period changes that affected the various cohorts at various ages differently. 

Therefore, these changes probably had a weak effect and changes in GDP-PPP were not considered in the 

analysis of cohort trends. 

Table 3.2 Correlation coefficients between proximate determinants and socioeconomic correlates of cohort fertility, 31 subSaharan African countries, 1930-1970 

 Proximate determinants 

Socioeconomic correlates Fertility (CEB) Infertility (%) 

Age at first 

Marriage (years) 

Ever used 

Contraception (%) 

Height 

(cm) 

Urban (%) -0.208 -0.067 +0.028 +0.746 -0.116 

Polygyny (%) -0.040 -0.194 -0.235 +0.331 +0.023 

Muslim (%) -0.042 +0.013 -0.058 +0.217 -0.143 

Christian (%) -0.247 -0.324 +0.176 +0.866 -0.156 

Education (number of years) -0.243 -0.211 +0.210 +0.913 -0.162 

Note: All correlation coefficients significant at p <0.001. 

Correlations with cohort fertility level were weak for urban residence, proportion Christian, and 

education level; correlations were nil for the other socioeconomic correlates. The negative correlations of 

cohort fertility level with urban residence, Christian religion, and education level were primarily 

associated with contraceptive use. Infertility had only weak correlations with socioeconomic correlates; 

these correlations were negative for Christian religion and education level (a higher proportion of 

Christians and a higher education level correlated with lower infertility levels). Age at first marriage also 

had weak correlations with socioeconomic correlates, although these were in the expected direction 

(larger urban population, higher proportion of Christians, and higher education level correlated with 

higher age at first marriage). Surprisingly, height correlated negatively with urban residence and 

education in this sample, probably because of the strong ethnic heterogeneity between west and southern 

Africa. 

Table 3.3 Net effect of socioeconomic correlates of cohort fertility in linear regressions, 31 sub-Saharan African countries, 

1930-1970 

Variable 

Beta 

coefficient 

Standard 

error T-test P-value Significance 

Net 

effect 

Constant +6.3560 0.0248 256.60 0.000 * 5.267 

Proportion urban -0.0238 0.0608 -0.39 0.696 NS -0.001 

Proportion in 

polygynous relationship +1.5780 0.0453 34.82 0.000 * 0.076 

Proportion Muslim -0.6984 0.0609 -11.46 0.000 * -0.023 

Proportion Christian -1.8747 0.0548 -34.23 0.000 * -0.146 

Level of education -0.1737 0.0071 -24.42 0.000 * -0.101 

*p <0.05. 

NS = not significant 
18

Here again, the net effects of socioeconomic correlates were significant and in the expected 

direction, but their magnitude was small. Only changes in education level and proportion Christian had a 

discernible net effect on cohort fertility, and the effect of urbanization disappeared after controlling for 

education and social factors. The effect of monotheist religions should be understood in the context of 

social change and modernization and should be interpreted after controlling for urbanization and 

education. 

None of the trends for any socioeconomic variable could account for the rise in cohort fertility for 

women born between 1930 and 1950. As a result of socioeconomic development, more women lived in 

urban areas, more women adhered to monotheist religions, and women had more education, all variables 

that could explain the fertility decline after 1950 but not the rise before 1950. 

Accounting for the Cohort Fertility Increase 

To account for the cohort fertility increase for women born between 1930 and 1950, the 

reconstructed trends were used to calculate changes in fertility and changes in explanatory variables 

between the 1930 birth cohort and the cohort with peak fertility; the cohort with peak fertility levels 

varied from country to country. Only countries in which cohort fertility increased were included in this 

analysis (see Appendix B for details), and the potential explanatory factors were again the proximate 

determinants and socioeconomic correlates described above. 

A simple linear regression model was used to match the changes in cohort fertility with the 

changes in the explanatory variables. The model revealed that two basic factors explained the rise in 

cohort fertility: the decline in infertility (explaining 47 percent of the fertility increase) and the change in 

religious affiliation, especially the increase in the proportion Christian (20 percent increase) and Muslim 

(22 percent increase). These changes in religious affiliation were apparently associated with earlier 

marriage and therefore higher fertility in the first half of the 20th century. However, the effect of changes 

in religious affiliation subsequently changed and after 1950, affiliation with a monotheist religion was 

more closely associated with increasing urbanization and education levels (see below). 

These averages for all countries combined should be interpreted with caution because they hide 

major differences by country. In some countries, infertility explains all changes in cohort fertility levels 

but in other countries, social factors seem to have played the leading role in these changes. 

The fertility increase before 1950, therefore, appears to have been due primarily to health factors, 

especially primary infertility, that were largely independent of socioeconomic factors. In most cases, high 

levels of sterility were due to infectious and parasitic diseases (especially trypanosomiasis and certain 

sexually transmitted diseases), so that trends in infertility had their own dynamics and were the product of 

health policies and programs that were largely independent of social dynamics (McFalls and McFalls, 

1984). 

3.2 Period Fertility 

Period Fertility Trends 

Reconstructed trends in period fertility for the 31 countries in sub-Saharan Africa indicate a rise 

from a TFR(40) of 5.3 children per woman in 1950 to 6.2 children in 1980, followed by a decline to 4.9 

children in 2000. 

These average rates hide major discrepancies in the rates for different countries. For example, 

period fertility rates in 1950 ranged from higher than 6.0 children per woman in Ghana, Kenya, Malawi, 

Rwanda, and Senegal to less than 4.0 in Gabon, Madagascar, and Mozambique. The peak values were as 
19

high as 7.5 or more children per woman in Kenya, Burkina-Faso, Mali, and Niger or as low as 6.0 or 

fewer in Botswana, Lesotho, Mozambique, Congo, and Central African Republic. After 1990, levels were 

lowest (fewer than 3.5 children per woman) in southern Africa (Botswana, Zimbabwe, South Africa, and 

Lesotho) and Gabon. In contrast, TFR(40) in 2000 was 6.0 or more children per woman in the Sahelian 

countries (Chad, Mali, and Niger) and Uganda. 

The wide ranges in fertility levels in and after 1950 are supported by census data for CEB as well 

as recent DHS publications. 

Figure 3.3 Reconstructed trends in period fertility, 31 subSaharan African countries, 1950-2000

2

3

4

5

6

7

1945 1955 1965 1975 1985 1995 2005

Year (Period)

Total fertility rate by age 40

Proximate Determinants of Period Fertility 

In this analysis, only two proximate determinants of period fertility could be used: contraceptive 

use and median age at first marriage. Average period height could not be computed because data were 

only available for a limited number of cohorts. Infertility could not be estimated for the 1980-2000 period 

because this would have required data on fertility status by age 40 for all women concerned, including the 

cohorts born after 1970. 

Period trends in contraceptive use reflect the cohort trends described above (Figure 3.2). 

Contraceptive prevalence, defined as ever having used a modern method by age 40, has been increasing 

dramatically in sub-Saharan Africa from 3 percent of women in 1950 to 58 percent in 2000. In contrast, 

average median age at first marriage decreased from 17.9 years in 1950 to 17.4 in 1970 before increasing 

to 18.8 in 2000. 

Variations among countries were remarkable. Contraceptive use was universally low in 1950, but 

it ranged from 6 percent to 95 percent in 2000. In Guinea, Chad, and Burundi, less than 30 percent of the 

population was using contraceptives by 2000, while more than 85 percent of the population used 

contraceptives in 2000 in southern African countries (Zimbabwe, Namibia, Lesotho, and South Africa) 

and Gabon. 
20

Variations in age at first marriage were also remarkable. By 2000, average age at first marriage in 

Chad, Guinea, Mali, and Niger was still less than 16.5 years, whereas it was higher than 24 years in 

Botswana, Namibia, and South Africa. 

These marked variations in the leading proximate determinants of period fertility probably had a 

dramatic effect on fertility levels and trends. The trend in contraceptive use was monotonic (increased 

steadily) but that of age at first marriage tended to increase and decrease at times that roughly matched 

those of changing trends in period fertility. 

Net Effects of Proximate Determinants 

A regression model linking period fertility to age at first marriage and contraceptive use by 

country and period (year) was tested. The results indicate that both proximate determinants had similar 

net effects and probably played symmetric roles (Table 3.4). 

Table 3.4 Net effects of proximate determinants on period fertility, 31 sub-Saharan African countries, 1950-2000 

Variable 

Beta 

coefficient 

Standard 

error T-test P-value Significance Net effect 

Constant 7.8511 0.1876 41.852 0.0000 * 

Contraceptive use -1.0434 0.1048 -9.960 0.0000 * -0.242 

Age at first marriage -0.1024 0.0109 -9.359 0.0000 * -0.228 

* p <0.05. 

Figure 3.4 Reconstructed trends in period estimates of 

proximate determinants of fertility,31 sub-Saharan 

African countries, 1950-2000 

Age at first marriage

16

17

18

19

20

1945 1955 1965 1975 1985 1995 2005

Year (Period)

Median age (years)
21

Figure 3.4 – continued 

Contraceptive use

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1945 1955 1965 1975 1985 1995 2005

Year (period)

Percent ever use by age 40

Trends in Socioeconomic Correlates 

Trends in education level reflect the cohort trends in education analyzed above. In general, the 

trends were steady (Figure 3.5) though they tended to stagnate in the last years for which data are 

available in Zambia and Madagascar. Differences in education level between countries were marked; the 

average number of years of school was higher than seven in 2000 in Congo, Gabon, Kenya, Lesotho, 

Namibia, South Africa, and Zimbabwe but lower than two in Burkina-Faso, Chad, Ethiopia, Guinea, 

Mali, and Niger. 

Trends in urbanization were also steady overall (Figure 3.5) and stagnated only in Zambia. The 

contrasts in urbanization level by country were marked. In 1950, only Congo, Senegal, and South Africa 

had more than a quarter of their population living in urban areas, whereas by 2000, a third of the countries 

had populations that were more than 40 percent urban, and more than 50 percent of the population was 

urban in Botswana, Congo, Gabon and South Africa. In contrast, by 2000, less than 16 percent of the 

population was urban in Rwanda, Burundi, Uganda, Malawi, and Ethiopia. 

Trends in income (GDP-PPP) were more complex and, for the selected countries, evolved 

roughly (and surprisingly) in the same direction as trends in fertility. Income increased from 1950 to 1975 

(when fertility was increasing), then decreased from 1975 to 1995 (when fertility was decreasing), and 

started to increase again after 1995. 

Large differences in income levels existed among countries. At baseline in 1950, only Gabon, 

Namibia, and South Africa had an average income that exceeded 2,000 USD and one-third of the 

countries had an average income below 500 USD. By 2000, average income in Botswana, and Congo had 

risen to at least 2,000 USD, but average income in Chad was still less than 500 USD. 
22

The dynamics of changes in income per capita also differed over time. In some countries, such as 

Kenya, growth was steady. In others, such as Senegal, growth stagnated, and yet in others, such as Niger, 

GDP per capita declined over time. 

Figure 3.5 Reconstructed trends in period estimates of 

socioeconomic correlates of fertility, 31 sub-Saharan 

African countries, 1950-2000 

 

Level of education

0

1

2

3

4

5

6

1945 1955 1965 1975 1985 1995 2005

Year (Period )

Mean number of years schooling

Urbanization

0%

10%

20%

30%

40%

50%

60%

70%

80%

1945 1955 1965 1975 1985 1995 2005

Year (period)

Percent urban
23

Figure 3.5 – continued 

Income per capita

500

1000

1500

1945 1955 1965 1975 1985 1995 2005

Year (period)

GDP-PPP (USD)

Correlation among Socioeconomic Variables, Proximate Determinants, and Fertility Levels 

Correlations among socioeconomic variables, proximate determinants, and fertility levels were 

strong and in the expected direction (Table 3.5). Urbanization level, education level, and income per 

capita were all associated with higher contraceptive use rates, higher age at first marriage, and lower 

fertility levels. However, these averages conceal differences among countries that need to be further 

investigated. 

Table 3.5 Correlation coefficients of socioeconomic variables, proximate determinants, and 

period fertility rates, 31 sub-Saharan countries, 1950-2000 

Socioeconomic variable 

Total 

fertility rate 

Contraceptive 

use 

Age at first 

marriage 

Urban residence (%) -0.302 0.648 0.233 

Level of education (years) -0.406 0.849 0.547 

Income per capita (GDP) -0.381 0.427 0.466 

Note: All correlation coefficients significant at p <0.001. 

Net Effects of Socioeconomic Correlates 

A regression model linking period fertility levels to socioeconomic correlates was tested (Table 

3.6). The results show that only level of education had a substantial impact on the fertility decline 

between 1950 and 2000. Urbanization had no effect on period fertility, even though urban fertility 

declined quickly during this period. Based solely on population composition, urbanization should have 
24

had an effect on national period fertility trends. This effect is not reflected in the regression analysis 

because of the strong heterogeneity among countries. 

Table 3.6 Net effects of socioeconomic correlates on period fertility, 31 sub-Saharan African countries, 1950-2000 

Variable 

Beta 

coefficient 

Standard 

error T-test P-value Significance Net effect

Constant 6.25590 0.037 168.53 0.0000 * 

Urban residence -0.00074 0.199 0.00 0.9971 NS 0.000 

Level of education -0.12535 0.013 -9.98 0.0000 * -0.266 

Income per capita -0.00019 0.000 -8.35 0.0000 * -0.218 

* p <0.05 

NS = not significant 

Reasons for the Period Fertility Decline 

A more convincing approach than using plain regression with levels is to study the changes 

between peak fertility and the last point for which data are available for each country selected. Linear 

regressions similar to those used for cohort fertility were run. These regressions linked the changes in 

period fertility to the changes in proximate determinants and socioeconomic correlates. 

These models led to different and more plausible conclusions than the regression model linking 

period fertility to socioeconomic correlates. First, the model had higher explanatory power, explaining 77 

percent of the changes in fertility. Second, the explanatory factors for fertility decline were the two 

proximate determinants (contraceptive use and age at first marriage) and the structural factor 

(urbanization). Income and education had no independent impact because their effects were probably 

mediated by the explanatory factors. The unexplained proportion of the fertility changes could be 

attributed to limitations in the model; factors not accounted for in the model, especially induced abortion; 

and, possibly, minor changes in infertility and nutritional status, as occurred in the cohort analysis. 

Table 3.7 Net effect of changes in proximate determinants and socioeconomic correlates on changes in period 

fertility levels, between 1977 and 1999, 31 sub-Saharan African countries 

Factors 

Year of peak 

fertility (1977) 

Last year for which data 

are available (1999) 

Change between 

1977 and 1999 Net effect 

Percentage 

explained 

Contraceptive use 0.212 0.578 +0.366 -0.647 37 

Age at first marriage 17.94 19.12 +1.18 -0.416 24 

Urban residence 0.214 0.334 +0.120 -0.288 16 

Level of education 1.971 4.066 +2.095 +0.172 

Income per capita 1249 1335 +86 +0.004 

TFR(40) 6.594 4.829 -1.765 -1.176 77 

3.3 Urban-rural Divide 

The main result of the country analysis was the differential dynamics of fertility trends in urban 

and rural areas (see Appendix B and Appendix C for details). Urban areas simultaneously concentrate 

differentials in proximate determinants (less infertility due to biological factors, higher age at first 

marriage, higher contraceptive use, better nutrition) and socioeconomic factors (more education, higher 

income). The differential period dynamics for the 31 countries are displayed in Figure 3.6. 

Trends in urban and rural areas started to split in approximately 1960. The difference between 

rural and urban areas increased over time from 0.3 children in 1960 to 1.9 children in 2000. Urban 
25

fertility began to decline in approximately 1970, when the average TFR(40) was 5.7, and the decline has 

been rapid, at -0.70 children per decade. In contrast, rural fertility started to decline in approximately 

1980, when the average TFR(40) was 6.5, and this decline proceeded at a slower pace than in urban areas, 

at -0.47 children per decade. 

Figure 3.6 Reconstructed trends in period fertility, by urban 

and rural residence, 31 sub-Saharan 

African countries, 1950-2000

2

3

4

5

6

7

1945 1955 1965 1975 1985 1995 2005

Year (Period)

Total fertility rate by age 40

Urban areas

Rural areas

3.4 Date of Onset and Speed of the Fertility Transition 

The onset of the fertility transition can be dated to either the period when rates in urban and rural 

areas started to split or when fertility levels began to decline in urban areas. The results show a wide 

range of possibilities for African countries (Figure 3.7 and Table C-2). The earliest fertility declines in 

urban areas began in the 1950s in South Africa; in the 1960s in Namibia, Kenya, and Ghana; and in the 

1970s or 1980s in the remaining sub-Saharan African countries. The earliest declines in rural areas 

occurred in the early 1970s in Kenya and Zambia, followed by declines in many other countries in the 

1980s and 1990s. As of 2000, fertility rates had not started to decline in rural areas of two countries 

(Congo and Mozambique), and trends were unknown in Burundi and Liberia. With a few exceptions, 

most sub-Saharan African countries underwent a fertility transition within a 50-year period (between the 

1950s and 2000); this is similar to the span of Europe’s fertility decline between 1880 and 1930 (van de 

Walle and Knodel, 1980). 

The speed of the fertility decline also varied markedly among countries, from levels as high as -

1.5 children per decade (Comoros in 1986-1996, Côte d’Ivoire in 1982-1998, Gabon in 1974-2000, and 
26

Mali in 1990-2001) to lower than -0.5 children per decade (Congo in 1968-2005, Ghana in 1950-2003, 

Mozambique in 1988-2003, Nigeria in 1980-2003, and Uganda in 1968-2000). 

The average speed of fertility decline was approximately 1 child per woman per decade (standard 

deviation of 0.5 children), so the typical transition from a peak of 6.5 children per woman to a low of 2.5 

children occurred within approximately 40 years. The transition period was sometimes shorter in urban 

areas and much longer in rural areas. In some countries, the fertility transition was rapid in rural areas. 

For example, the transition in South Africa occurred within approximately 30 years, even in such remote 

regions as Kwazulu-Natal and Mpumlanga (Camlin et al., 2004; Garenne et al., 2007). However, the 

transition will probably take much longer in many remote rural areas of the Sahelian countries, where 

fertility levels are still high and the pace of change has been slow in the past 20 years. 

Figure 3.7 Distribution of year of onset of the fertility transition in 

urban and rural areas, 31 sub-Saharan African countries, 1950-2000

0

1

2

3

4

5

6

7

8

9

< 1960

1960-1964

1965-1969

1970-1974

1975-1979

1980-1984

1985-1989

1990-1994

1995-1999

>= 2000

Period

Number of countries

Urban Rural

3.5 Recent Periods of Fertility Stall 

A few countries experienced periods of fertility stall: Ghana in 1998-2003; Kenya in 1997-2003; 

Madagascar in 1987-1993; Nigeria in 1998-2003; Rwanda in 1998-2005; and Tanzania in 1995-2004. All 

of the changing slopes for these countries were highly significant (p <0.05) (Table 3.8). Researchers have 

studied the fertility stalls in some of these countries (Bongaarts, 2005; Shapiro and Gebreselassie, 2007), 

but the others merit more analysis. 
27

Table 3.8 Fertility stall, selected sub-Saharan African countries, 1987-2005 

Mean TFR(40) during stall P-value of changing slopes 

Country/period Total Urban Rural Total Urban Rural 

Ghana 1998-2003 4.14 2.88 5.13 0.0001 0.0003 0.0011 

Kenya 1997-2003 4.44 3.31 5.09 0.0000 0.0228 0.0000 

Madagascar 1987-1993 5.73 4.29 0.0001 0.0060 

Nigeria 1998-2003 5.24 4.54 5.80 0.0000 0.0003 0.0000 

Rwanda 1998-2005 5.44 0.0109 

Tanzania 1995-2004 5.91 0.0215 

In Ghana, Kenya, Nigeria, Rwanda, and Tanzania, the fertility stall occurred in 1995-2005 and 

was still ongoing at the time of the latest survey. However, the fertility stall in Madagascar was earlier, in 

1987-1993, and ended after seven years. Fertility stalls occurred in both urban and rural areas in many 

countries, but the stall in Rwanda and Tanzania occurred only in rural areas. Agincourt, a rural area of 

South Africa, also experienced a fertility stall; this stall lasted only five years before fertility began 

declining again (Garenne et al., 2007). 

The stalls occurred at different points toward the middle of the transition, with average TFR(40) 

ranging from 2.9 to 4.5 in urban areas and 5.1 to 5.9 in rural areas. More research is needed on the fertility 

stall in each country. 

3.6 Premarital Fertility 

The proportion of premarital births, defined as the proportion of births that occurred prior to the 

mother’s first marriage or to a never-married mother, increased by 50 percent for sub-Saharan Africa, 

from 3.8 percent in 1950 to 5.7 percent in 2000 (Figure 3.8). This small increase hides major differences 

by country, with levels ranging from 0.3 percent in Niger and 0.4 percent in Chad to 44 percent in 

Namibia and 43 percent in Botswana. However, while the fertility decline has become pronounced, the 

premarital fertility rate has been declining since 1985, when the rate was 0.32 children per woman, 

reaching 0.27 children per woman in 2000. In addition to hiding substantial differences by country, from 

0.017 to 2.06 children per woman, these average rates hide even greater differences by ethnic group. 

More details on premarital fertility, especially the role of ethnicity and social factors, in subSaharan Africa can be found elsewhere (Garenne and Zwang, 2006). 
28

Figure 3.8 Trends in Premarital Fertility, 

31 sub-Saharan African countries, 1950-2000 

Proportion of premarital births

0%

2%

4%

6%

8%

10%

1945 1955 1965 1975 1985 1995 2005

Year (Period)

Proportion of all births

Premarital fertility

0.0

0.1

0.2

0.3

0.4

0.5

1945 1955 1965 1975 1985 1995 2005

Year (Period)

Children per woman
29

4 Discussion 

This study highlights the value of the WFS and DHS surveys for documenting fertility changes. 

By recording full maternity histories, they enable one to reconstruct period and cohort fertility trends. 

Furthermore, by providing information on numerous factors, these surveys allow a detailed analysis of 

proximate determinants and socioeconomic correlates of fertility. 

The use of yearly estimates is not conventional because each yearly estimate has a large 

confidence interval due to the small number of births. However, by using trends over monotonic periods, 

researchers can take full advantage of the richness of the information and obtain more precise point 

estimates as well as reliable trends. 

In theory, censuses could provide data comparable to those of WFS and DHS surveys, at least for 

cohort estimates. However, census data are rarely available by year of birth, and this limits their 

usefulness. Only the IPUMS International program currently provides access to census sample microdata 

and, so far, only six samples are available for three sub-Saharan African countries. This seriously limits 

the potential use of census data for comparative analyses of fertility changes. 

A detailed analysis of increases and decreases in fertility levels is necessary to understand the 

dynamics of these changes over a 50-year period. In sub-Saharan Africa, a comparison of average fertility 

levels in 1950 and in 2000 is misleading because it hides the major and rapid changes that occurred in 

virtually all of the countries investigated. Similarly, ignoring the different dynamics in urban and rural 

areas would hide some of the major changes that occurred in these regions. 

The fertility dynamics vary greatly by country, with some experiencing a steady and rapid 

fertility decline, others a steady and slow decline, others a small rise followed by a rapid decline, and still 

others a major rise followed by a slow decline. Identifying a typology would be difficult because the 

number of different scenarios is the same as the number of countries due to the different dynamics and 

determinants in each country. 

Cases of fertility increases have been documented elsewhere (Dyson and Murphy, 1986), but the 

most dramatic increases seem to have occurred in the sub-Saharan African countries affected by high 

levels of infertility in the first half of the 20th century. The rapid reduction in infertility, still present in 

some of the WFS and DHS case studies, appears to have played a major role in many of the fertility 

increases in sub-Saharan Africa; these changes are most clearly evident in cohort fertility rates. The social 

changes that led to lower ages at first marriage in the first half of the 20th century also appear to have 

contributed to fertility increases in some of the countries investigated. These effects have rarely been 

documented for sub-Saharan Africa (Dyson, 1988). 

Fertility began to decline in most sub-Saharan African countries in the second half of the 20th

century, except for some remote rural areas. On average, fertility began to decline within only a few years 

of some of the more advanced developing countries of Asia and Latin America, and roughly only a 

century after Europe. These are small differences from a broad historical perspective, given the complex 

political dynamics in sub-Saharan Africa in the 19th and 20th centuries, especially the late development of 

political independence. 

The speed of the fertility decline in sub-Saharan Africa is comparable to that of many other 

developing countries, such as India and Pakistan, but is lower than that of the countries with the most 

rapid fertility declines, such as China and Thailand. The poorest and least urbanized areas will probably 

take many years to reach replacement fertility levels. The fertility declines in some large African cities, 

such as Addis Ababa, Ethiopia, were as rapid as the fastest declines in other parts of the world (Lindstrom 
30

and Woubalem, 2003). The slow fertility decline in remote rural areas of sub-Saharan Africa is similar to 

the decline in rural France in the 18th century and in the rural United States in the 19th century. The 

diversity of conditions in Africa appears analogous to the diversity of conditions in other parts of the 

world. 

This analysis had limitations due to the small sample sizes and limited years covered by the WFS 

and DHS surveys. Some countries are covered by a WFS survey and several DHS surveys, but others are 

covered by only one or two DHS surveys. Some of the estimated dates for the onset of a fertility change 

are unstable; these values have a margin of error of approximately plus or minus five years. Similarly, the 

distinction between urban and rural areas trends was based on hazardous extrapolations in some cases; 

readers should review these distinctions with caution. Lastly, a small number of inconsistencies was 

found between the WFS and DHS survey data and data from other surveys and censuses. As a result, the 

averages may have been biased. 

Another limitation of the study is the classification of urban and rural residence. Surveys and 

censuses identified the residence status of a woman and all of her children based on the woman’s 

residence at the time of the interview, regardless of where she lived when each child was born. This might 

explain why differences between urban and rural fertility rates were very small early in the fertility 

transition, when urbanization was very recent and many newly urban women had given birth to several of 

their children in rural areas. However, by later stages of the fertility transition, a larger proportion of the 

population had been living in urban areas for several decades. This bias would tend to reduce the 

observed differences in fertility between urban and rural areas, so that the differences documented in this 

study are likely to be underestimates of the true differences between urban and rural fertility. Therefore, 

the increased urbanization of women in these analyses does not invalidate the findings concerning fertility 

trends in urban and rural areas but rather indicates that the differential trends were probably even greater 

than reported here. 

Most of the effects of proximate determinants were straightforward and consistent with those 

found in previous studies, even though these studies used different methods (Harwood-Lejeune, 2000; 

Gobopamang and Letamo, 2001). This investigation revealed the roles of infertility and religion in the 

fertility increase in sub-Saharan Africa, as well as the roles of contraceptive use, age at marriage, and 

urbanization in the fertility decline. 

Modern education appears to have played a different role in this longitudinal investigation than in 

cross-sectional studies. This is not surprising because the two approaches focus on different aspects 

(determinants versus differentials), and modern education is primarily a critical factor of differentials 

rather than of trends, as is also true for mortality studies. This highlights the fact that fertility decline can 

occur in areas with low levels of education and that no minimum education level is necessary for fertility 

decline; this has been shown repeatedly throughout the world (van de Walle and Knodel, 1980). 

The role of urbanization is equally complex. For sub-Saharan Africa as a whole, urbanization 

appears to be a structural factor because social change is more rapid in urban areas than rural areas. Urban 

residence is also associated with higher education and income levels and a variety of other factors that 

have some relation to fertility. Very rural countries, such as Kenya, underwent some of the most striking 

fertility transitions, and the urbanization stall in South Africa from 1960 to 1994 did not hamper its rapid 

fertility decline. In these countries, successful family planning programs and, to a lesser extent, increases 

in age at first marriage could explain their fertility declines. These observations are compatible with a 

detailed analysis of the European fertility transition that also showed virtually no relationship between 

fertility decline and urbanization or socioeconomic indicators (van de Walle and Knodel, 1980). 
31

The relationship between family planning programs and fertility decline is difficult to describe 

because of its mechanical nature (Mbacke, 1994). The statistical relationship between contraceptive use 

and TFRs is straightforward and basically linear; numerous authors have described this relationship 

(Rutenberg, 1991; Leridon and Toulemon, 1996; Garenne and Joseph, 2002). What remains to be 

analyzed is the speed of the fertility decline as a function of the efforts to provide all women with 

efficient, safe, and affordable contraception after controlling for potential confounders, such as age at 

marriage. Such an analysis would require another study and both quantitative and qualitative data on 

family planning programs. Regardless, this study provides confirmation that contraceptive use is the 

leading driver of the fertility decline in sub-Saharan Africa. The fertility decline appears to be primarily a 

consequence of the different reproductive health programs and policies of countries in this region. 

A single, precise definition of the onset of the fertility transition is difficult to develop. If one 

focuses on the split between urban and rural trends, one often finds that the fertility decline began earlier 

than if one considers the steady decline in urban areas, the decline in the country as a whole, or the 

decline in rural areas. Considering the split between period and cohort fertility, which some authors have 

chosen, could also give a different onset date. Using the 10 percent threshold below the peak fertility, as 

is common in historical demography, would be misleading in the case of sub-Saharan Africa. Regardless 

of the definition selected for the onset of the fertility transition, the important issue is the magnitude and 

speed of the fertility changes in sub-Saharan Africa, which have somehow been hidden in broad 

international comparisons of fertility levels and certainly do not match economic development indicators 

and trends. 

Some authors have emphasized the impact of differences among regions (eastern versus western 

or southern Africa) or colonial cultures (Anglophone, Francophone, and Lusophone areas) on fertility 

decline. Some patterns are emerging, but differences between countries are much larger than among 

regions or cultures. For example, the fertility declines in the urban areas of Senegal, Burkina-Faso, Togo, 

and Mozambique were as large as in the countries with the most rapid fertility declines (such as Botswana 

and Zimbabwe). In contrast, the fertility decline in rural Uganda was as slow as in some of the 

Francophone Sahelian countries. Furthermore, the fertility decline in the urban areas of countries that 

were not affected by colonization, such as Ethiopia, was even more rapid than elsewhere. 

The fertility transition, a path from peak fertility to replacement fertility levels, is well advanced 

in sub-Saharan Africa. Approximately 60 percent of the transition was completed in urban areas within 

about 30 to 40 years, and approximately 22 percent of the transition was completed in rural areas within 

approximately 20 to 30 years. These trends are likely to continue in the future, even though minor fertility 

stalls occur periodically. These stalls rarely last very long, at least based on previous experiences in 

Africa or Latin America. Several regions in sub-Saharan Africa, especially urban areas, have almost 

achieved replacement fertility levels; a few rural areas are also approaching replacement fertility levels 

(Garenne et al., 2007; Shemeikka et al., 2005). 

No one knows what future course the transition will take, but it is possible that even lower levels, 

below replacement fertility, could be achieved in the near future. This has already occurred in large cities 

in South Africa and Ethiopia. Whether the fertility decline becomes “endless,” as some authors have 

suggested, remains to be seen (Basu, 2007). 

Many aspects of the fertility decline in sub-Saharan Africa remain to be analyzed, such as 

differences by country, residence area, and possibly region or ethnicity. Even though all sub-Saharan 

African countries appear to be undergoing a fertility decline, the fertility dynamics and their impact are 

often different in different regions. Further research on these differences could shed new light on the 

ultimate determinants of fertility changes in sub-Saharan Africa and the potential future of fertility levels 

in the region. 

33

Appendix A 

Table A.1 List of WFS and DHS surveys analyzed, sub-Saharan Africa 

Country WFS DHS-I DHS-II DHS-III DHS-IV 

Benin 1982 1996 2001 

Botswana 1988 

Burkina Faso 1993 1999 2003 

Burundi 1987 

Cameroon 1978 1991 1998 2004 

Central African Republic 1994 

Chad 1996 2004 

Comoro Islands 1996 

Congo 2005 

Côte d'Ivoire 1980 1994 1999 

Ethiopia 2000 2005 

Gabon 2000 

Ghana 1979 1988 1993 1999 2003 

Guinea 1992 1999 2005 

Kenya 1978 1989 1993 1998 2003 

Lesotho 1977 2004 

Liberia 1986 

Madagascar 1992 1997 2003 

Malawi 1992 2000 2004 

Mali 1987 1996 2001 

Mozambique 1997 2003 

Namibia 1992 2000 

Niger 1992 1997 

Nigeria 1982 1990 1999 2003 

Rwanda 1983 1992 2000 2005 

Senegal 1978 1986 1993 1997 2005 

South Africaa

 1998 

Sudan 1979 1989 

Tanzania 1991 1996 1999 2004 

Togo 1988 1998 

Uganda 1988 1995 2001 

Zambia 1992 1996 2001 

Zimbabwe 1988 1994 1999 

WFS = World Fertility Survey 

DHS = Demographic and Health Surveys. 

a

Data from surveys in South Africa were not used for the country analysis of fertility trends but were integrated into the 

analysis of fertility determinants. 
34

Table A.2 List of censuses and demographic surveys (other than WFS and DHS surveys) analyzed, subSaharan Africa 

Country 

Demographic 

surveys Censuses 

Benin 1960-61 1992 

Botswana 1984 

1991 

1971 

1981 

Burkina Faso 1961 

1991 

1975 

1985 

Burundi 1965 

1970-1971 

Cameroon 1965 

Central African Republic 1959-60 1975 

1988 

Chad 1964 

Comoro Islands 1958 

1966 

1991 

Congo 1961 1984 

Côte d'Ivoire 1962-1964 

1978-1979 

1988 

Ethiopia 1990 

1998 

1984 

1994 

Gabon 1961 1993 

Ghana 1971 1948 

1960 

Guinea 1955 1983 

1996 

Kenya 1977 

1984 

1962 

1969 

1979 

Lesotho 2001 1976 

1986 

1996 

Liberia 1971 1974 

Madagascar 1966 

1980 

1975 

Malawi 1982 

1984 

1996 

1977 

1987 

1998 

Mali 1957 1987 

Mozambique 1950 

1981 

1997 

Namibia 1981 

1991 

2001 

Niger 1960 

1963 

1988 

Nigeria 1965-1966 

1994 

Rwanda 1970 1978 

1990 

Senegal 1957 

1960 

1979 

Tanzania 1967 

1978 

1988 

Togo 1961 1971 

1981 

Uganda 1959 

1969 

1991 

Zambia 1969 

1980 

1990 

Zimbabwe 1969 

1992 
35

Appendix B

This appendix presents reconstructed fertility trends for 31 sub-Saharan African countries over 

the 1950-2000 period. The reconstruction focuses on period fertility and uses cohort fertility as an 

independent check and to reconstruct period fertility trends from 1950 to the first year covered by World 

Fertility Surveys (WFS) and Demographic and Health Surveys (DHS) surveys. 

Throughout this appendix, cohort analyses are based on year of birth 30 years before the 1950-

2000 period, i.e., 1920-1970. When earlier or later data are available, they are also mentioned. 

The relationship between period and cohort fertility trends is explained in the methods section of 

the report, where the somewhat different dynamics of these two indicators are discussed. Because of these 

dynamics, the period estimates are often higher than the cohort estimates during periods of rising fertility 

and, conversely, the period estimates are often lower than the cohort estimates during periods of declining 

fertility. The trend estimates end at the last year for which data are available from the WFS and DHS 

surveys. 

The period and cohort fertility trends are presented for each sub-Saharan African country in the 

analysis as a whole, and separately for urban and rural areas and for premarital and marital fertility. 

Calculating Period Fertility Rates

The total fertility rate (TFR) considered here refers to cumulative fertility by age 40 [TFR(40)]. 

TFR(40) is the sum of age-specific fertility rates [f(a)] below age 40. The age-specific fertility rate is the 

ratio of births by age [B(a)] to the number of person-years lived at that age [N(a)]. Age-specific fertility 

rates from age 12 to 40 were added up from DHS and WFS data for each calendar year. 

   

 

39

12

40

12 ( )

( ) (40) ( )

a a N a

B a TFR f a da

The person-years and births were calculated for the age groups 12-14, 15-19, 20-24, 25-29, 30-34 

and 35-39, and for one-year periods. When several surveys covered the same period, the numerators and 

denominators were added from each survey for the same country. 

For convenience and to minimize random fluctuations, the figures in this appendix display results 

by two-year period (or three-year period when the sample size is small). In this case, the results for 

TFR(40) were calculated by adding the numerators and denominators for all years within a given time 

period. 

The results for TFR(40) were compared with published data from other surveys and censuses on 

period fertility. The censuses and other surveys used usually collected information on period fertility by 

asking respondents about births in the past 12 months. Five-year fertility rates were computed, usually for 

groups age 15-19 to 35-39, and occasionally starting at age 10-14 or 12-14. 

Calculating Cohort Fertility Rates

Cohort fertility estimates were compared to period estimates. 

For demographic surveys (other than DHS or WFS surveys) and censuses that only reported data 

by 5-year age group, the number of children ever born by age 40 [CEB(40)] was calculated as the average 

completed family size in age groups 35-39 and 40-44. For women older than 50, the mean number of 
36

children ever born was converted into CEB(40) by applying a constant coefficient of 0.90 (90 percent of 

total fertility occurs before age 40 in DHS survey results from sub-Saharan Africa). This made it possible 

to use census data on older women, primarily those in the 50-59, 60-69, and 70-79 age groups. These 

values were applied to corresponding periods of 25, 35, and 45 years before the survey. 

Because a woman’s average age at first birth is almost 30 years in sub-Saharan Africa, the cohort 

fertility estimates were matched with period estimates 30 years after the year of the mother’s birth. For 

example, for women born in 1930, the completed family size at age 40 was matched with the period 

fertility rate in 1960. 

The period corresponding to the cohort estimates is referred to as the “reference period” in all of 

the tables in this appendix. 

Fertility Trends in Urban and Rural Areas

TFR(40,t), or the cumulated fertility by age 40 by calendar year (t), was calculated. Time trends 

were fitted using a linear regression on each year by ordinary least square (OLS). For this procedure, the 

TFR(40,t), calculated from B(40,t) births, was considered to be equivalent to the completed family sized 

(CFS) of a cohort of N=B/TFR women with the same TFR and the same number of births. 

To estimate the standard deviations of the estimates, the births were distributed by parity and a 

synthetic cohort of women was constructed. The distribution of births was calculated by parity for each 

country and each year by fitting a logit linear relationship between proportion of birth order and TFR(40) 

using cohort data from birth order 0 to 16+. A small adjustment was made to ensure that the sum of the 

proportions equaled 1, so that the reconstructed mean CFS equaled the observed TFR(40). The main 

advantages of this procedure were that it provided standard errors for estimates, made it possible to test 

differences in slopes using classic T-tests, and made possible direct comparisons with trends in cohort 

fertility. 

Next, the regression line was fitted directly from the reconstructed data using an OLS model: 

TFR(40,t)  A B*t

where TFR(40,t) is the cumulated fertility by age 40 at time (t), t is the calendar year, and A and 

B are two constant coefficients. 

When the slope for TFR(40) changed from positive to negative, from no slope to negative, or 

from negative to nil (fertility stall), the change in slope was tested using the same T-test. Trends between 

1950 and the first year for which survey data were available were derived from the same equation by 

extrapolating the data backward in time. Trends for urban areas were cut at the point at which they 

intersected the national average. Before the intersection point, TFR(40,t) was assumed not to differ 

between urban and rural areas; the data appear to support this assumption in most of the countries for 

which information is available. 

Premarital and Marital Fertility

The proportion of premarital births is defined as the ratio of the number of premarital births 

before age 40 to the total number of births before age 40. 
37







  40

12

40

12

( )

( )

Pr ( )

a

a

prem

prem

B a da

B a da

op t

These trends in premarital and marital fertility were estimated by fitting a logit-linear relationship 

of the proportion of premarital births by calendar year using WFS and DHS survey data. 

Logit op t A B t (Pr prem ( ))   

The proportion of premarital births was applied to the reconstructed trends in total fertility. Total 

fertility is therefore the sum of the premarital and marital fertility rates, which is different from the sum of 

age-specific marital fertility rates used in historical demography to calculate classic estimates of total 

marital fertility. 

TFR (40,t) TFR(40,t) Pr op (t) prem  

prem

and 

TFR(40) TFR (40,t) TFR (40,t)  prem  marital

Throughout this appendix, all references to “premarital fertility level” pertain to TFR(40) in 

never-married women at the time of the child’s birth. 
38

Presentation

Essentially the same types of information are presented for each sub-Saharan African country 

case study: data available from DHS, WFS, and other demographic surveys and censuses; a table with 

estimates of cohort and period fertility levels in different years and age groups based on data from sources 

other than WFS and DHS surveys; a brief comment on trends, with special reference to urban and rural 

areas and marital and premarital fertility; and two figures showing trends for rural versus urban fertility 

and premarital versus marital fertility. 

For each country considered, the first figure displays the reconstructed period fertility trends for 

urban and rural areas separately using WFS and DHS survey data and additional information on fertility 

levels, when available, such as data from censuses and other surveys (surveys other than DHS or WFS) 

on period and cohort fertility. The figures also display the trends estimated by regression for the period 

covered by the surveys and the extrapolation from 1950 to the earliest point for which survey data are 

available, based either on later trends or on cohort estimates of CFS. 

Arrows display the time points at which major changes occurred, such as changes in slope from 

nil or positive to negative for urban and rural areas, or divergent trends between urban and rural areas. 

The arrows have different patterns, defined as follows: 

All data in the first figures for each country come from DHS and WFS survey results, unless 

otherwise specified. When the figures display data from sources other than WFS and DHS surveys, these 

data apply to the entire country and generally represent a midway point between urban and rural areas 

estimates, depending on the proportion of urban residents in the country. The data points labeled 

“published” are published values from DHS reports and are shown separately for urban and rural areas. 

The second figure for each country displays the trends in premarital and marital fertility. 

Premarital births are defined as births occurring before a woman’s first marriage or to women who had 

never been married at the time of the survey. Conversely, marital births are births that occur after the 

mother’s first marriage. 

The case studies are presented in alphabetical order by country name. No attempt was made to 

discuss in depth the quality of the data from sources other than the WFS and DHS. The focus was on the 

main patterns of fertility trends. 

 

Split between urban 

and rural areas 

Decline in urban areas Decline in rural areas 
39

Benin 

Benin conducted a WFS survey in 1982 and DHS surveys in 1996 and 2001. The fertility trends 

were similar in the three surveys. The 1982 WFS survey covered 1967-1982, when fertility rates were 

rising slowly, with an average TFR(40) of 6.48. The 1996 DHS survey covered 1981-1996, which could 

be divided into two parts. In the first part, fertility was somewhat higher than in the 1982 WFS survey, 

and the second part showed declining fertility trends, with peak fertility occurring in 1985-1986. The 

2001 DHS survey covered 1986-2001 and indicated declining fertility trends, with levels and slopes 

compatible with those of the 1996 DHS survey for the overlapping period (1986-1996). 

A demographic sample survey in 1960-1961 (the Dahomey survey) estimated TFR(40) in Benin 

to be 6.30 in 1961. CFS was 5.37 in approximately 1951 (women age 35-44 in 1960-1961 had given birth 

10 years earlier, on average). Estimates of CFS for women age 50-59 (5.18) and 60 years and above 

(5.38) in 1960-1961, which pertained to births that had occurred approximately 15 to 25 years earlier 

(between 1936 and 1926), were basically identical. The similarity of CFS rates for older women indicates 

that fertility was stable, at approximately 5.2, prior to 1951; this rate corresponds to a TFR(50) of 5.7 

(Table B.1). 

Table B.1 Estimated fertility levels, Benin, 1926-1991 

Source (survey name, date) 

Type of fertility data 

(age group) Reference period P(40) P(50) 

Cohort fertility 

Survey, 1960-61 CEB (60+) 1926.5 5.38 5.98 

Survey, 1960-61 CEB (50-59) 1936.5 5.18 5.75 

Survey, 1960-61 CEB (35-44) 1951.5 5.37 5.77 

Census, 1992 CEB (70-79) 1947.1 4.82 5.35 

Census, 1992 CEB (60-69) 1957.1 5.06 5.63 

Census, 1992 CEB (50-59) 1967.1 5.29 5.88 

Census, 1992 CEB (35-44) 1982.1 5.48 5.92 

Period fertility 

Survey, 1960-61 TFR (40) 1961.0 6.30 6.86 

Census, 1992 TFR (40) 1991.6 5.65 6.14 

The combined estimates from the three DHS and WFS surveys showed an increasing fertility 

level from a TFR(40) of 5.88 in 1967 to a peak of 6.72 in 1986, followed by a declining level to 5.05 in 

2001; both slopes were highly significant (p <0.0001). The evidence therefore clearly shows that fertility 

rose in Benin before 1986 and declined rapidly thereafter. 

The trend obtained by extrapolating the WFS and DHS survey data backward to 1951 yielded a 

TFR(40), 5.16, close to that observed, 5.37. What occurred between 1951 and 1967 remains unclear; it is 

possible that fertility rose more rapidly between 1951 and 1961 and less rapidly thereafter. 

Period fertility trends in urban and rural areas differed markedly (Figure B.1.1). TFR(40) in urban 

areas seems to have remained stable, at approximately 5.72, between 1967 and 1986, then declined 

thereafter to reach 3.89 in 2001. In contrast, period fertility increased in rural areas from 5.99 in 1967 to 

7.22 in 1989 before declining to 5.70 in 2001. The split in trends between urban and rural areas seems to 

have occurred in approximately 1960 and became pronounced in the early 1980s. The declining fertility 

trends after 1989 were similar in urban and rural areas (slopes of -0.131 in urban areas and -0.133 in rural 

areas, p = 0.955). 
40

The 1992 census data yielded estimates of TFR(40) that were approximately 10 percent lower 

than estimates based on DHS survey data for the same period (1960-1990), although differences between 

urban and rural areas based on the census and DHS survey data were consistent. However, estimates of 

CFS for earlier periods (prior to 1960) were more consistent with other estimates. 

The increase in fertility rates from 1951 to 1989, which was most obvious in rural areas, can be 

explained by declining infertility rates. For example, the 1992 census shows that, based on primary 

infertility rates (number of women who never had any live birth) by cohort, the proportion of infertile 

women declined from 12.3 percent for women age 75+ in 1992 (born around 1915) to 4.9 percent for 

women age 35-39 in 1992 (born in approximately 1955). 

Premarital fertility had a low prevalence in Benin over the period considered and showed a 

declining trend (slope = -0.024, p <0.001) (Figure B.1.2). 

Figure B.1.1 Trends in Cumulated Fertility by Age 40, 

Benin, 1950-2001

2

3

4

5

6

7

8

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman 

Urban Rural Trends

Census & Survey (CEB) Census & Survey (TFR) Extrapolated

Published
41

 Figure B.1.2 Trends in Marital and Premarital Fertility, 

Benin, 1960-2001

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
42

Botswana 

Botswana has conducted only one DHS survey, in 1988, covering 1973-1988. These data indicate 

that fertility had a rapidly declining trend (slope of -.108, p <0.0001). The survey had a small sample and 

this analysis included only 8,831 births; as a result, yearly estimates fluctuate widely. 

Censuses in 1971, 1981, and 1991 and a demographic survey (Botswana Family Health Survey, 

1984) provide additional evidence on fertility trends in Botswana in the 20th century. Data from the 1981 

and the 1991 censuses and the 1984 demographic survey cover a broad period and their data on fertility 

levels, but not trends, are consistent with the DHS data. Unlike the DHS data, the census data show that 

fertility trends were increasing or erratic during the study period. Data prior to 1950 are consistent and 

indicate that TFR(40) was stable at 4.7 [TFR(50) of 5.3]. Fertility increased in the 1950s and 1960s, 

reaching a level of approximately 6.0 by 1973, the time of the earliest DHS data. However, TFR(40) 

levels from the 1971 census data are lower than the expected levels, suggesting that the data quality is 

poor or the 1971 value was erratic. In contrast, the TFR(40) from the Botswana Family Health Survey is 

higher than expected, suggesting a possible selection bias in the sample. 

Table B.2 Estimated fertility levels, Botswana, 1926-1984 

Source (survey name, date) 

Type of fertility data 

(age group) Reference period P(40) P(50) 

Cohort fertility 

Census, 1971 CEB(70-79) 1926.7 4.65 5.17 

Census, 1971 CEB(60-69) 1936.7 4.60 5.11 

Census, 1971 CEB(50-59) 1946.7 4.72 5.25 

Census, 1971 CEB(40) 1961.7 5.32 5.57 

Census, 1981 CEB(60-69) 1946.6 4.47 4.97 

Census, 1981 CEB(50-59) 1956.6 4.89 5.44 

Census, 1981 CEB(40) 1971.6 5.26 5.66 

Survey, 1984 CEB(40) 1974.5 5.82 6.84 

Census, 1991 CEB(60-69) 1956.6 5.26 5.84 

Census, 1991 CEB(50-59) 1966.6 5.75 6.39 

Census, 1991 CEB(40) 1981.6 5.12 6.09 

Period fertility 

Census, 1971 TFR(40) 1971.2 4.85 5.79 

Census, 1981 TFR(40) 1981.1 5.13 6.22 

Survey, 1984 TFR(40) 1984.0 5.48 6.46 

When the trends estimated from the 1988 DHS survey are extrapolated beyond 1988, the 

predicted TFR(40) is 2.99 in 2001. This figure is almost identical to the value from the 2001 census 

(2.94), a good indication of the accuracy of the reconstruction. 

In their comprehensive analysis of fertility trends in Botswana, Rutenberg and Diamond (1993) 

concluded that fertility had declined rapidly since 1981 and previous fertility levels had been 

overestimated. The reconstruction of fertility trends is essentially consistent with the estimates of 

Rutenberg and Diamond (1993). 

Trends in urban and rural areas were parallel in the DHS survey results, and the difference 

between the two slopes was not significant (p = 0.767) (Figure B.2.1). The precise timing of the onset of 

the fertility transition in Botswana is therefore difficult to delineate. Because TFR(40) has apparently 

never exceeded 6.0, the fertility transition seems to have begun in approximately 1968 in urban areas and 

approximately 1973 in rural areas. 
43

Botswana and its western and southern neighbors have some of the highest rates of premarital 

fertility in Africa. The proportion of premarital births increased from 34 percent in 1963 to 43 percent in 

1988. As a result, the premarital fertility level remained essentially constant over time, at an average of 

approximately 2.0 children per women. In contrast, marital fertility appears to have declined rapidly from 

3.98 in 1973 to 2.52 in 1988 (Figure B.2.2). 

Figure B.2.1 Trends in Cumulated Fertility by Age 40, 

Botswana, 1950-1988

2

3

4

5

6

7

8

1925 1935 1945 1955 1965 1975 1985 1995 2005

Year

Children per woman 

Urban Rural Trends Census & Survey (CEB) Census & Survey (TFR) Published
44

Figure B.2.2 Trends in Marital and Premarital Fertility, 

Botswana, 1960-1988

0

1

2

3

4

5

6

1955 1960 1965 1970 1975 1980 1985 1990

Year

TFR(40)

Premarital

Marital
45

Burkina Faso 

Burkina-Faso conducted DHS surveys in 1993, 1999 and 2003; these surveys covered 1978-2003. 

All three surveys had similar results for fertility levels and showed declining trends in the 15 years 

preceding the survey. 

Burkina-Faso, formerly known as Upper Volta or Haute-Volta, conducted demographic sample 

surveys in 1961 and 1991 and censuses in 1975 and 1985 (Table B.3). The census and survey data 

indicate that TFR(40) was probably very stable, at approximately 4.9, prior to 1950. By 1961, period 

fertility by age 40 was already higher (5.52), and it continued to increase until 1980. 

Table B.3 Estimated fertility levels, Burkina-Faso, 1916-1991 

Source 

(survey name, date) 

Type of fertility data

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1961 CEB(70+) 1916.0 4.85 5.39 

Survey, 1961 CEB(60-69) 1926.0 4.97 5.53 

Survey, 1961 CEB(50-59) 1936.0 4.88 5.43 

Survey, 1961 CEB(40) 1951.0 5.03 5.40 

Census, 1985 CEB(70-79) 1941.0 4.44 4.93 

Census, 1985 CEB(60-69 1951.0 4.78 5.32 

Census, 1985 CEB(50-59) 1961.0 5.32 5.91 

Census, 1985 CEB(40) 1976.0 5.84 6.31 

Survey, 1991 CEB40) 1981.5 6.29 6.87 

Period fertility 

Survey, 1961 TFR(40) 1960.5 5.52 6.05 

Census, 1975 TFR(40) 1975.4 6.03 6.70 

Census, 1985 TFR(40) 1985.5 6.47 7.18 

Survey, 1991 TFR(40) 1991.0 6.48 7.31 

Period fertility trends in urban and rural areas were markedly different. In urban areas, TFR(40) 

declined from 6.97 in 1978, the first year covered by the DHS surveys, to 3.08 in 2003. In rural areas, 

period fertility levels increased from 7.27 in 1978 to 7.40 in 1986, when they started to decline, reaching 

6.08 in 2003. The change in slopes was highly significant (p <0.0001). The intersection between urban 

and rural fertility trends occurred in 1977, the year of onset of the fertility transition in Burkina Faso. 

When the increasing trend was extrapolated backward, the period fertility level was estimated to 

be 5.44 in 1951 and 6.12 in 1961. The resulting trend was consistent with, but somewhat higher than, 

trends from other surveys. Fertility levels were clearly increasing in rural areas from 1951 to 1986 in 

Burkina Faso before decreasing. 

The proportion of premarital births remains small in Burkina Faso, with an average rate of 1.7 

percent, and has had no significant trend (p = 0.155). 
46

Figure B.3.1 Trends in Cumulated Fertility by Age 40, 

Burkina-Faso, 1950-2003

2

3

4

5

6

7

8

9

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Year

Children per woman 

Urban Rural Trends Published Survey and Census (CEB) Census & Survey (TFR)

Figure B.3.2 Trends in Marital and Premarital Fertility, 

Burkina-Faso, 1978-2003

0

1

2

3

4

5

6

7

8

1975 1980 1985 1990 1995 2000 2005

Year

TFR(40)

Premarital

Marital
47

Burundi 

Burundi conducted only one DHS survey, in 1986, that covered 1971-1986. The survey collected 

data on only 9,408 births, including just 326 births in urban areas. 

Two demographic surveys were conducted in Burundi in 1965 and 1970-1971. These surveys 

provide additional evidence on the onset of the country’s fertility transition (Table B.4). According to the 

results of these demographic surveys, cohort fertility levels were probably stable at approximately 5.4 

between 1935 and 1965 and increased slightly between 1960 and 1970, when the rate was 6.2. The period 

fertility rate changed only a little during the years covered by the DHS survey. 

Table B.4 Estimated fertility levels, Burundi, 1935-1970 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1970-71 CEB (60+) 1935.9 5.35 5.94 

Survey, 1970-71 CEB (50-59) 1945.9 5.40 6.00 

Survey, 1970-71 CEB (35-44) 1960.9 5.51 5.92 

Period fertility 

Survey, 1965 TFR (40) 1964.9 5.89 7.43 

Survey, 1970-71 (retrospective) TFR (40) 1969.9 5.08 5.67 

Survey, 1970-71 (prospective) TFR (40) 1970.9 5.25 5.90 

Retrospective and prospective estimated fertility levels from the 1970-1971 survey results are 

lower than the reconstructed levels, whereas the levels for completed family size based on the 1970-1971 

survey results were consistent with the reconstructed levels. The differences in fertility trend estimates 

might be due to defective data or the effects of civil unrest marked by ethnic strife in 1970-1972. 

Trends in urban and rural fertility differed. Period fertility rates reached a plateau at 

approximately 6.24 in rural areas and declined in urban areas to 4.48 in 1987. Urban and rural trends 

began to diverge in 1970, which can be considered the turning point for the fertility changes in Burundi. 

Because the country was primarily rural over the period, national trends were similar to those of rural 

areas. 

As in neighboring Rwanda, the proportion of premarital births had a very low prevalence rate (1 

percent) over the period covered by the DHS survey in Burundi and did not change significantly (p = 

0.239). 
48

Figure B.4.1 Trends in Cumulated Fertility by Age 40, 

Burundi, 1950-1987

2

3

4

5

6

7

8

9

1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)
49

Figure B.4.2 Trends in Marital and Premarital Fertility, 

Burundi, 1972-1987

0

1

2

3

4

5

6

7

1971 1974 1977 1980 1983 1986 1989

Year

TFR(40)

Premarital

Marital
50

Cameroon 

Cameroon conducted a WFS survey in 1978 and DHS surveys in 1991, 1998, and 2004, which 

covered 1963 to 2004. The fertility levels and trends from the results of all four surveys are essentially 

similar, except for some minor discrepancies. For example, the fertility levels were lower in 1995-1997 in 

the 1998 survey results than the 2004 survey. The 1978 WFS survey results indicated that the fertility rate 

had been rising since 1964, the 1991 DHS survey showed a plateau followed by a minor decline after 

1980, and the 1998 and 2004 DHS surveys showed that fertility had been declining during the 15 years 

prior to survey. 

The reconstructed trend was very similar to the trends from the WFS and DHS surveys. The 

reconstructed data indicate a rise in period fertility from 5.06 in 1963 to 6.34 in 1983, followed by a 

steady decline to 4.65 in 2004. 

Several other surveys were conducted in Cameroon’s major geographical areas in the 1960s. 

Table B.5 displays the findings from some of these surveys. 

Table B.5 Estimated fertility levels, Cameroon, 1955-1965 

Source 

(survey name, date) 

Type of fertility data

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Western Cameroon, 1965 CEB(40) 1955 5.14 

Period fertility 

Western Cameroon, 1965 TFR(40) 1965 5.35 5.62 

Available data show that the period fertility level was moderate, around 5 children per women by 

age 40, in Cameroon prior to 1963. The rate tended to rise steadily until 1983 before starting to decline. 

Trends in period fertility in urban and rural areas were remarkably similar prior to 1983, and the 

two slopes were identical (p = 0.619). The difference between fertility levels in urban and rural areas 

remained small, at 0.73, between 1963 and 1983. After 1983, levels changed markedly: urban areas 

underwent a rapid decline from 5.77 in 1983 to 3.54 in 2003, whereas fertility declined more slowly in 

rural areas, from 6.51 in 1983 to 5.92 in 2003. Changes in trends between 1963-1983 and 1983-2003 were 

highly significant in both urban and rural areas (p <0.0001 for both rural and urban areas). The difference 

between the slopes for fertility trends in urban and rural areas after 1983 was also highly significant (p 

<0.0001). 

No major trend was found in premarital fertility rates over the period covered by the DHS and 

WFS surveys. The proportion of premarital births was 6 percent, on average, throughout the period. The 

proportion was slightly higher between 1963 and 1973, slightly lower between 1973 and 1983, and close 

to 6 percent thereafter. The small decline in the proportion of premarital births from 1963 to 1983 was 

probably due to the increase in fertility levels during this period. As a result, the premarital fertility level 

appears to have remained essentially constant over time in Cameroon. 
51

Figure B.5.1 Trends in Cumulated Fertility by Age 40, 

Cameroon, 1950-2004

2

3

4

5

6

7

8

1945 1955 1965 1975 1985 1995 2005 2015

Year

Children per woman 

Urban Rural Trends Published Census & Survey (CEB Census & Survey (TFR)

Figure B.5.2 Trends in Marital and Premarital Fertility, 

Cameroon, 1960-2004

0

1

2

3

4

5

6

7

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

TFR(40)

Premarital

Marital
52

Central African Republic 

The Central African Republic conducted only one DHS survey, in 1994, covering 1980 to 1994. 

The survey indicated a rise in fertility levels from 1980 to 1988, followed by a steady decline. 

A demographic survey was conducted in 1959-1960 in the Central African Republic, covering 

most of the country (about 86 percent of the population), except for the northeastern provinces. In 

addition, two censuses were conducted in 1975 and 1988. Data from these sources indicate that the 

Central African Republic’s period fertility level was low, around 3.8, prior to 1950 and had probably been 

stable since at least 1925. Indeed, the 1959-1960 survey was one of the first to document very high rates 

of infertility in central Africa, where approximately 20 percent of women were sterile. The Central 

African Republic’s infertility rate tended to diminish over time; for example, the 1988 census data 

showed that the proportion of women who never had a live birth was 18 percent for women age 50-59, 

16.5 percent for women age 40-49, and 14 percent for women age 30-39. 

Table B.6 Estimated fertility levels, Central African Republic, 1915-1978 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1959-60 CEB(40) 1950.0 3.64 4.06 

Survey, 1959-60 CEB(50-59) 1935.0 3.74 4.16 

Survey, 1959-60 CEB(60-69) 1925.0 3.87 4.30 

Survey, 1959-60 CEB(70+) 1915.0 3.29 3.66 

Census, 1975 CEB(40) 1965.9 4.53 4.70 

Census, 1988 CEB(40) 1978.9 4.62 4.64 

Period fertility 

Survey, 1959-60 TFR(40) 1959.5 4.74 4.93 

The fertility trends in urban and rural areas in the Central African Republic had several unusual 

features (Figure B.6.1). First, the fertility rates and directions of the trends in urban and rural areas were 

similar. Second, trends in urban and rural fertility appear to have intersected in approximately 1986. 

Fertility levels in rural areas were lower, increased more rapidly, and started to decline slightly later than 

in urban areas. This can be explained by the fact that infertility was more prevalent in rural areas in the 

earlier years and, therefore, the decline in infertility presumably had a greater effect on fertility rates in 

these areas. However, in the results from both the 1960 survey and the 1988 census, fertility levels in 

urban areas were somewhat higher than in rural areas; further research is needed on this issue. 

The proportion of premarital births was low, 3.0 percent, in 1980 but tended to increase rapidly 

thereafter, reaching an estimated prevalence of 8.4 percent in 1994. As a result, premarital fertility 

increased over the period covered by the DHS survey, despite the fertility decline after 1988 (Figure 

B.6.2). 
53

Figure B.6.1 Trends in cumulated fertility by age 40, 

Central African Republic, 1950-1994

2

3

4

5

6

7

8

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

 Figure B.6.2 Trends in Marital and Premarital Fertility, 

Central African Republic, 1960-1995

0

1

2

3

4

5

6

1950 1960 1970 1980 1990 2000

Year

TFR(40)

Premarital

Marital
54

Chad 

Chad conducted DHS surveys in 1997 and 2004 covering 1983 to 2004. The fertility levels and 

trends from the results of both surveys were similar. The 1997 survey indicated an increase in fertility 

followed by a slight decrease. The 2004 survey also showed that fertility increased and then decreased, 

and fertility levels from both surveys were similar in the overlapping period (1989-1997). 

A survey conducted in Chad in 1964 provides detailed information on fertility in the first half of 

the 20th century (Table B.7). The results of this survey indicate that Chad’s TFR(40) was low, around 4.0 

[TFR(50) = 4.4], prior to 1950 and had changed little since 1920. 

Table B.7 Estimated fertility levels, Chad, 1919-1963 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1964 CEB(70+) 1919.3 3.80 4.22 

Survey, 1964 CEB(60-69) 1929.3 3.99 4.43 

Survey, 1964 CEB(50-59) 1939.3 4.12 4.58 

Survey, 1964 CEB(40) 1954.3 4.22 4.73 

Period fertility 

Survey, 1964 TFR(40) 1963.8 4.64 5.13 

The DHS survey results indicate that the period fertility rate was much higher by 1982, and trends 

in urban and rural areas diverged. In urban areas, the fertility rate appears to have declined steadily over 

the period covered by the DHS surveys, whereas fertility seems to have increased first and then decreased 

in rural areas. The slope for fertility in urban areas was significantly negative in 1982-2004 (p = 0.0001) 

and was very different from the rural slope for 1982-1997, which was significantly positive (p = 0.047); 

the difference between the slopes for urban and rural areas was highly significant. The negative slope for 

urban areas in 1997-2004 was also highly significant (p <0.0001), although this slope was based on a 

small number of points. Fertility levels for both urban and rural areas peaked in 1998, which seems to 

have been an unusual year; this peak is highly significant and is not based on any obvious peculiarity. The 

peak levels might have been due to an abnormally high number of births recorded for 1998 by the 2004 

survey (1,505 births were recorded that year, compared with 1,158 in 1997 and 1,059 in 1999). 

Compared with earlier data, the increase in fertility in Chad between 1950 and 1980 appears 

remarkable. Detailed data are lacking for this period, but the pattern is consistent with that of other 

countries and with the increasing trend in rural areas prior to 1997. 

Based on reconstructed trends, the onset of the fertility decline in Chad seems to have occurred in 

approximately 1985 in urban areas and some 12 years later in rural areas. 

The premarital fertility level remained very low in Chad over the period covered by the DHS 

surveys, with the proportion of premarital births decreasing from 0.8 percent in 1982 to 0.4 percent in 

2004. As a result, the national trends primarily reflect changes in marital fertility rates (Figure B.7.2). 
55

Figure B.7.1 Trends in cumulated fertility by age 40, 

Chad, 1950-2005

2

3

4

5

6

7

8

9

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005 2015

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.7.2 Trends in Marital and Premarital Fertility, 

Chad, 1960-2005

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
56

Comoro Islands 

The Comoro Islands have conducted only one DHS survey, in 1996. The survey covered 1981-

1996 and showed two periods of monotonic trends: an increase from 1981 to 1987, followed by a 

decrease to 1996. 

Few detailed data other than the DHS data were available for comparison. The 1996 DHS survey 

report mentions data from the 1991 census but does not provide details on these data. The 1991 census 

results are not consistent with the DHS survey data. Data on CFS and TFR from earlier censuses in 1958 

and 1966 could be used, whereas published data from the 1980 census were already corrected and 

therefore could not be used. In any case, CFS appears to have been well below 5 prior to 1980, and 

probably below 4 prior to 1950 (Table B.8). 

Table B.8 Estimated fertility levels, Comoro Islands, 1958-1991 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1958 CEB(45-49) 1948.5 3.33 3.7 

Census, 1966 CEB(45-49) 1956.5 4.14 4.6 

Period fertility 

Census, 1991 TFR(50) 1991.0 3.42 3.8 

Period fertility trends in urban and rural areas differed in levels and slopes (Figure B.8.1). 

Between 1981 and 1988, fertility was still increasing in rural areas, whereas it was decreasing in urban 

areas. The declining trend in urban areas continued until the last year, 1996, for which data are available. 

Fertility declined at about the same rate in rural areas as urban areas between 1988 and 1996, the 

difference between the two slopes being not significant (p = 0.233). The change in slope in rural areas 

was highly significant (p = 0.004). The split between urban and rural areas seemed to have occurred in 

approximately 1980, the year of onset for the fertility decline in the Comoro Islands. 

When the increasing trend in rural areas was extrapolated backward, the results were consistent 

with census estimates for CFS before 1960. TFR(40) in rural areas was estimated to be approximately 4.0 

in 1950. 

The proportion of premarital births remained very low over the period covered by the DHS 

survey, declining from 1.5 percent in 1981 to 0.7 percent in 1996 (Figure B.8.2) 
57

Figure B.8.1 Trends in cumulated fertility by age 40, 

Comoro Islands, 1950-1996

2

3

4

5

6

7

8

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman

Urban Rural Trends

Published Census & Survey (CFS) Census & Survey (TFR)

Figure B.8.2 Trends in Marital and Premarital Fertility, 

Comoro Islands, 1960-1996

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000

Year

TFR(40)

Premarital

Marital
58

Congo 

The Democratic Republic of the Congo (formerly known as République Populaire du Congo or 

Congo-Brazzaville) conducted a DHS survey in 2005 covering 1990-2005. This survey showed declining 

trends in fertility. 

A demographic survey conducted in 1961 and a 1984 census documented fertility trends in earlier 

years. CFS was low, with an average of 4.0, prior to 1950 and increased thereafter.

Table B.9 Estimated fertility levels, Democratic Republic of the Congo, 1915-1984 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1961 CEB(70+) 1915.9 3.58 3.98 

Survey, 1961 CEB(60-69) 1925.9 3.70 4.11 

Survey, 1961 CEB(50-59) 1935.9 4.02 4.47 

Survey, 1961 CEB(40) 1950.9 3.99 4.32 

Census, 1984 CEB(80+) 1929.5 3.28 3.64 

Census, 1984 CEB(70-79) 1939.5 3.43 3.81 

Census, 1984 CEB(60-69) 1949.5 3.60 4.01 

Census, 1984 CEB(50-59) 1959.5 4.22 4.69 

Census, 1984 CEB(40) 1974.5 5.30 5.21 

Period fertility 

Survey, 1961 TFR(40) 1960.4 4.67 5.22 

Census, 1984 TFR(40) 1984.0 4.29 4.84 

The DHS survey results indicate a major discrepancy between fertility trends in urban and rural 

areas. The fertility decline was marked in urban areas between 1990 and 2005 (p = 0.0005), whereas 

fertility tended to increase in rural areas during this period, although the slope was not significantly higher 

than 0 (p = 0.444). The difference between the urban and rural slopes was highly significant (p = 0.009). 

The divergence between urban and rural areas seems to have started in approximately 1967. 

Due to diverging trends in fertility and a relatively high urbanization rate, overall TFR(40) in 

Congo stayed roughly constant, at approximately 4.7 [TFR(50) = 5.2], between 1960 and 1990 

When the rural trend was extrapolated backward, the resulting CFS was consistent with earlier 

estimates of CFS. Based on this extrapolation, fertility appears to have increased slightly between 1940 

and 1960. 

The proportion of premarital births declined markedly from 17.2 percent in 1990 to 12.5 percent 

in 2005. Extrapolating this trend backward to periods before 1990 is probably hazardous, but the results 

of this exercise suggest that marital fertility rates remained constant over the years because the decreasing 

fertility trend in urban areas was compensated by increasing proportions of marital births. 
59

Figure B.9.1 Trends in cumulated fertility by age 40, 

Congo (Brazza), 1950-2005

2

3

4

5

6

7

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CFS) Census & Survey (TFR)

Figure B.9.2 Trends in Marital and Premarital Fertility, 

Congo (Brazza), 1960-2005

0

1

2

3

4

5

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
60

Côte d’Ivoire

Côte d’Ivoire conducted a WFS survey in 1980 and DHS surveys in 1994 and 1999, covering 

1965 to 1999. The results of all three surveys showed similar fertility levels and trends. The 1980 WFS 

survey indicated that the fertility level was stable in 1965-1980, and the two DHS surveys showed that the 

rate declined between 1981 and 1999. 

Additional data on fertility in Côte d’Ivoire were available from a demographic survey conducted 

in 1962-1964, a multiround survey conducted in 1978-1979, and the 1988 census (Table B.10). These 

data show that CFS increased between 1940 and 1970 and the TFR(40) remained constant between 1960 

and 1990. 

Table B.10 Estimated fertility levels, Côte d’Ivoire, 1943-1987 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1978-79 CEB(60+) 1943.4 4.75 5.28 

Survey, 1978-79 CEB(50-59) 1953.4 5.04 5.60 

Survey, 1978-79 CEB(40) 1968.4 5.82 6.10 

Census, 1988 CEB(50+) 1963.2 5.18 5.75 

Census, 1988 CEB(40) 1978.2 5.76 6.22 

Period fertility 

Survey, 1962-64 TFR(40) 1962.5 5.65 6.38 

Survey, 1978-79 TFR(40) 1977.9 5.75 6.38 

EPR-Prospective TFR(40) 1978.9 5.94 6.54 

Census, 1988 TFR(40) 1987.7 5.58 6.29 

WFS and DHS data indicate divergent trends in urban and rural areas. In urban areas, fertility 

declined slowly between 1966 and 1982, with a non-significant slope (p = 0.254), then declined rapidly 

from 1982 to 1999 (p <0.0001). In rural areas, fertility increased between 1966 and 1987, with a 

significantly positive slope (p = 0.030), then declined rapidly from 1987 to 1999, with a negative slope (p 

<0.0001). The differences in slopes in 1966-1982 between urban and rural areas was significant (p = 

0.050), whereas the speeds were similar in 1982-1999 (p = 0.831). 

Compared with the reconstructed trends, TFR estimates from all 1962-1964 and 1978-1979 

surveys and the 1988 census were more frequently lower than the estimates from the DHS survey results 

(7 percent lower in 1962-1964, 10 percent lower in 1978-1979, and 13 percent lower in 1991). The 

differences between the TFR estimates from the reconstructed trends, the DHS survey estimates, and 

estimates from the census and other surveys are somewhat surprising. These differences could be due to a 

systematic deficiency in the 1962-1964 and 1978-1979 surveys or a selection bias in the DHS survey 

favoring households with more fertile women. 

The proportion of premarital births increased rapidly, from an estimated 5.1 percent in 1966 to 

12.9 percent in 1999. As a result, premarital fertility increased steadily from 1966 to 1987 and declined 

only slightly afterwards. In contrast, marital fertility rates stopped increasing after 1970 and underwent a 

major decline after 1986 (Figure B.10.2). 
61

Figure B.10.1 Trends in Cumulated Fertility by Age 40, 

Côte d'Ivoire, 1950-1999

2

3

4

5

6

7

8

1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Census & Survey (CFS) Census & Survey (TFR) Published

Figure B.10.2 Trends in Marital and Premarital Fertility, 

Cote d'Ivoire, 1960-1999

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
62

Ethiopia 

Ethiopia conducted DHS surveys in 2000 and 2005, covering 1985-2005. The fertility levels and 

trends from the results of the two surveys are similar, although the 2005 survey yielded higher estimates 

for the overlapping period, 1994-1999, despite similar estimates in 1993 and 2000. Both surveys indicate 

declining fertility levels in Ethiopia since the earliest years covered. 

Other data on fertility in Ethiopia come from censuses in 1984 and 1994 and surveys in 1990 and 

1998 (Table B.11). These censuses and surveys do not cover periods prior to 1969, and the baseline level 

in Ethiopia therefore remains unknown. The available data indicate a rise in fertility from 1970 to 1990; 

this is consistent with trends in other sub-Saharan African countries and with the DHS survey results.

 

Table B.11 Estimated fertility levels, Ethiopia, 1969-1990 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1984 CEB(40) 1974.5 4.95 5.30 

Survey, 1990 CEB(40) 1980.5 6.00 6.60 

Census, 1994 CEB(50+) 1969.8 5.02 5.58 

Census, 1994 CEB(40) 1984.8 5.65 6.10 

Survey, 1998 CEB(40) 1988.5 6.05 6.80 

Period fertility 

Survey, 1990 TFR(40) 1990.0 5.58 6.38 

DHS data indicate a rapid period fertility decline in both urban and rural areas, with an earlier 

onset in urban areas. Based on the assumption that the trend prior to 1990 was roughly parallel to the 

estimated CFS trend, the urban fertility decline began in approximately 1980 and the rural fertility decline 

started in approximately 1990. The 1990 survey estimate for period fertility (urban and rural areas 

combined) is within the range of DHS estimates, providing additional evidence for the consistency of the 

Ethiopian data. The level of fertility in urban areas in 2005 [TFR(50)= 2.06] was lower than the net 

reproduction level, which is rare in sub-Saharan African countries other than South Africa. 

The premarital fertility level remained very low in Ethiopia, with a significant decline in the 

proportion of premarital births from 0.9 percent in 1985 to 0.6 percent in 2005 (p = 0.006). As a result, 

the overall changes in fertility in Ethiopia reflect primarily changes in marital fertility (Figure 11.2). 
63

Figure B.11.1 Trends in Cumulated Fertility by Age 40, 

Ethiopia, 1960-2005

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

Children per woman

Urban Rural Trends Published Census & Survey Census & Survey (TFR)

Figure B.11.2 Trends in Marital and Premarital Fertility, 

Ethiopia, 1960-2005

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital

 
64

Gabon 

Gabon conducted only one DHS survey, in 2001. The results of this survey indicate that Gabon 

experienced a steady fertility decline over the period covered (1986-2001). 

Two other data sources complement the DHS survey data: a survey in 1961 and a census in 1993 

(Table B.12). The data from this survey and census show that, unlike other sub-Saharan African 

countries, Gabon had a very low fertility level prior to 1950 and probably had declining fertility levels 

from 1915 to 1950. The very low fertility level in the first half of the 20th century is associated with 

extremely high levels of infertility identified in the 1961 survey report. For example, 36 percent of 

women age 40-44 in 1961 had never had any live birth and this proportion increased over time; only 20 

percent of women age 70 or older in 1961 had been infertile during their childbearing years, compared to 

22 percent of women age 65-69, 26 percent of women age 60-64, 28 percent of women age 55-59, 30 

percent of women age 50-54, and 32 percent of women age 45-49. The infertility level subsequently 

dropped rapidly to 12 percent of women age 40-44 in 1993 and 3 percent in 2001. Women born between 

1915 and 1925 appear to have experienced the highest infertility rates. Since 1925, infertility has declined 

steadily, reaching levels comparable to those of other countries for women born in the 1960s (Figure 

B.12.3). The low fertility level in 1950 closely corresponds to the peak of infertility (1920 cohort). 

Table B.12 Estimated fertility levels, Gabon, 1916-1993 

Source 

(survey name, date) 

Type of fertility data

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1961 CEB(70+) 1916.5 3.13 3.48 

Survey, 1961 CEB(60-69) 1926.5 2.97 3.30 

Survey, 1961 CEB(50-59) 1936.5 2.67 2.97 

Survey, 1961 CEB(40) 1951.5 2.49 2.78 

Census, 1993 CEB(60+) 1958.5 2.57 2.86 

Census, 1993 CEB(50-59) 1968.5 3.75 4.17 

Census, 1993 CEB(40) 1983.5 4.92 4.98 

Period fertility 

Survey, 1961 TFR(40) 1961.0 3.80 4.15 

Census, 1993 TFR(40) 1993.0 3.98 4.36 

Period fertility trends in urban areas during the period covered by the DHS survey, 1986-2001, 

were much more pronounced than in rural areas (Figure B.12.1). Both trends were significantly negative 

(p <0.0001) and their slopes were significantly different from one another (p <0.0001). The intersection 

between the trends occurred in 1984, when the fertility transition in Gabon began. The reconstruction 

suggests a major period fertility increase, from 3.2 to 6.1, between 1950 and 1984; this is one of the most 

substantial increases in sub-Saharan Africa. 

The proportion of premarital births in Gabon was high, with an average level of 20 percent in 

1986-2001. The premarital fertility level increased slightly during this period, but the change was not 

significant (p = 0.49). As a result, the trends in premarital and marital fertility in 1986-2001 were similar 

to the overall fertility trends (Figure B.12.2) 
65

Figure B.12.1 Trends in Cumulated Fertility by Age 40, 

Gabon, 1950-2001

2

3

4

5

6

7

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CFS) Census & Survey (TFR)

Figure B.12.2 Trends in Marital and Premarital Fertility, 

Gabon, 1960-2001

0

1

2

3

4

5

6

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
66

Figure B.12.3 Trends in infertility, 

Gabon, 1890-1965

0%

5%

10%

15%

20%

25%

30%

35%

40%

1880 1890 1900 1910 1920 1930 1940 1950 1960 1970

Year of birth

Percentage with no live birth

Survey, 1961

Census, 1993

DHS, 2001
67

Ghana 

Ghana conducted a WFS survey in 1979 and DHS surveys in 1988, 1993, 1998, and 2003; these 

surveys covered 1964-2003. Overall, the results from these surveys indicate the same fertility levels and 

trends, with a plateau in the first survey and declining trends in the four DHS surveys. 

Ghana conducted censuses in 1948 and 1960 and a survey in 1971 used in this analysis to 

supplement the DHS survey data (Table B.13). Data from these sources indicate that TFR(40) was 

probably stable before 1950 at approximately 5.8 children per woman [CFS(50) = 6.4]; this is consistent 

with the levels found in later surveys. 

This analysis is based on the WFS and DHS surveys, as well as a 1971 survey and the 1948 and 

1960 censuses. More data are apparently available elsewhere but access to these data was not available. 

Table B.13 Estimated fertility levels, Ghana, 1938-1971 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1948 CEB(40) 1938.5 5.94 6.60 

Census, 1960 CEB(40) 1950.2 5.60 6.22 

Survey, 1971 CEB(40) 1961.5 5.85 6.50 

Period fertility 

Survey, 1971 TFR(40) 1971.0 5.24 5.92 

The WFS and DHS survey results indicate divergent trends in urban and rural areas. In urban 

areas, fertility declined slowly from 1965 to 1975 (the slope was not significant), then more rapidly from 

1975 to 1998; the fertility rate stalled from 1998 to 2003. Both changes in slopes were significant (p = 

0.013 for 1965-1975 and p = 0.0003 for 1975-1998). In rural areas, fertility was stable from 1965 to 1985 

before decreasing (p <0.0001); as in urban areas, fertility levels in rural areas stalled between 1998 and 

2003. As with urban areas, changes in slope for rural areas were significant (p <0.0001 for 1965-1985 and 

p = 0.001 for 1985-1998). The fertility stall in Ghana (1998-2003) is similar to that in Kenya during 

approximately the same period. More research is needed on Ghana’s fertility stall, which might simply be 

a small, short-lived historical accident in the fertility transition. 

Premarital fertility had a low prevalence in Ghana, but the proportion of premarital births doubled 

from 1.9 percent 1965 to 3.9 percent in 2003. As a result, the premarital fertility level tended to increase 

over time, whereas marital fertility levels were similar to those of the overall population (Figure 13.2). 
68

Figure B.13.1 Trends in Cumulated Fertility by Age 40, 

Ghana 1950-2003

2

3

4

5

6

7

8

1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Census & Survey (CEB) Census & Survey (TFR) Published

Figure B.13.2 Trends in Marital and Premarital Fertility, 

Ghana 1960-2003

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
69

Guinea 

Guinea (also known as République de Guinée or Guinée-Conakry) conducted three DHS surveys, 

in 1992, 1999, and 2005. Unfortunately, access to the 1992 survey data was not available, so this analysis 

is based only on the results from the 1999 and 2005 DHS surveys, which covered 1984-2005. 

Fertility data are also available from a survey conducted by France’s Institut National de la 

Statistique et des Études Économiques (INSEE) in francophone Africa in 1955 and censuses in 1983 and 

1996 (Table B.14). These data indicate that TFR(40) was probably stable in Guinea before 1950 at 

approximately 4.9 (CFS = 5.5) and it might have been decreasing slightly. Since 1950, fertility increased 

steadily until the mid-1980s. 

Table B.14 Estimated fertility levels, Guinea, 1910-1996 

Source 

(survey name, date) 

Type of fertility data

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

INSEE survey, 1955 CEB(70+) 1910.3 5.08 5.64 

INSEE survey, 1955 CEB(60-69 1920.3 4.98 5.54 

INSEE survey, 1955 CEB(50-59) 1930.3 4.82 5.36 

INSEE survey, 1955 CEB(40) 1945.3 4.75 5.33 

Period fertility 

INSEE survey, 1955 TFR(40) 1954.8 6.58 7.06 

Census, 1983 TFR(40) 1982.6 4.39 4.88 

Census, 1996 TFR(40) 1996.0 5.44 5.96 

DHS data indicate a marked fertility decline in urban areas in 1986-2005; the slope was highly 

significant (p <0.0001). In contrast, fertility increased in rural areas between 1984 and 1995; again, the 

slope was highly significant (p <0.0001). After 1995, fertility started to decline steadily and rapidly in 

rural areas between 1995 and 2005; the slope was equivalent to that for urban areas (p = 0.347). The 

turning point for Guinea’s fertility transition appears to have been 1986, when the fertility decline started 

in urban areas (Figure B.14.1). 

Premarital fertility had a continuously low prevalence in Guinea. However, the proportion of 

premarital births increased from an estimated 1.9 percent in 1984 to 3.4 percent in 2005; this increase was 

highly significant (p <0.0001). Because premarital fertility was so low, the trend for marital fertility was 

similar to the national trend, and premarital fertility tended to increase steadily over this period (Figure 

B.14.2). 
70

Figure B.14.1 Trends in Cumulated Fertility by Age 40, 

Guinea, 1950-2005

2

3

4

5

6

7

8

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends Census & Survey (CEB) Census & Survey (TFR) Published

Figure B.14.2 Trends in Marital and Premarital Fertility, 

Guinea, 1960-2005

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
71

Kenya

Kenya conducted a WFS survey in 1978 and DHS surveys in 1989, 1993, 1998, and 2003; these 

surveys covered 1963-2003. The fertility levels and trends from the results of all five surveys are 

consistent; these data show a decline starting in the mid-1960s, followed by a fertility stall after 1995. 

A wealth of other demographic data is available on Kenya, including censuses in 1962, 1969, 

1979 and surveys in 1977 and 1984 (Table B.15). Data from these censuses and surveys suggest a major 

increase in fertility before 1950, with the CFS rising from 4.0 in 1920 to 6.0 in 1950. A substantial 

decrease started in the 1950s, a time of severe crisis during Kenya’s struggle for independence. After 

1960, fertility apparently increased dramatically, reaching very high levels in the late 1960s. These trends 

had a similar pattern to the 1920-1950 trends. 

Table B.15 Estimated fertility levels, Kenya, 1917-1984 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1962 CEB(70-79) 1917.6 3.94 4.37 

Census, 1962 CEB(60-69) 1927.6 4.66 5.18 

Census, 1962 CEB(50-59) 1937.6 5.20 5.78 

Census, 1962 CEB(40) 1952.6 5.34 5.90 

Census, 1969 CEB(60-69) 1934.6 5.20 5.78 

Census, 1969 CEB(50-59) 1944.6 5.76 6.40 

Census, 1969 CEB(40) 1959.6 4.68 6.69 

Survey, 1977 CEB(40) 1967.5 7.00 7.50 

Census, 1979 CEB(40) 1969.5 6.75 7.20 

Survey, 1984 CEB(40) 1974.5 7.40 8.20 

Period fertility 

Census, 1962 TFR(40) 1962.1 4.40 5.25 

Census, 1969 TFR(40) 1969.1 5.69 6.60 

Survey, 1977 TFR(40) 1977.0 7.04 7.99 

Census, 1979 TFR(40) 1979.0 7.28 7.88 

Survey, 1984 TFR(40) 1984.0 6.82 7.69 

The WFS and DHS survey results show substantial differences between fertility levels and trends 

in urban and rural areas. Fertility levels were already lower in urban areas than in rural areas in 1965 and 

continued to decline steadily until 1995 (p <0.00001). After 1995, fertility in urban areas remained stable 

(p = 0.522). The changes in the slopes for urban areas before and after 1995 were highly significant (p 

<0.00001). In rural areas, fertility remained high from 1963 until 1980, when it decreased slightly but 

significantly (p = 0.016). After 1980, fertility in rural areas decreased rapidly (p <0.00001 ) before 

stalling at approximately the same time as in urban areas (p = 0.160). The changes in slope in 1963-1980 

and 1980-1995 were highly significant (p <0.00001). Based on the assumption that fertility increased 

substantially in the 1960s, the split between urban and rural areas apparently occurred in the mid-1960s, 

when Kenya established its family planning program. 

The proportion of premarital births increased dramatically, from approximately 4.9 percent in 

1963 to 9.6 percent in 1996 before declining to approximately 8 percent in 2003. The decline in the 

premarital fertility level occurred during Kenya’s fertility stall. As a result, premarital fertility increased 

between 1963 and 1981, decreased from 1981 to 1995, and increased again from 1995 to 2003, with an 

average of 0.45 children per woman. In contrast, marital fertility followed the same pattern as the overall 

population: an increase, decrease, and stall (Figure B.15.2). 

This reconstruction is generally consistent with that of Blacker (2007). 
72

Figure B.15.1 Trends in Cumulated Fertility by Age 40, 

Kenya 1950-2002

2

3

4

5

6

7

8

9

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Children per woman 

Urban Rural Trends

Census & Survey (CEB) Census & Survey (TFR) Published

Figure B.15.2 Trends in Marital and Premarital Fertility, 

Kenya, 1960-2003

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
73

Lesotho 

Lesotho conducted a WFS survey in 1977 and a DHS survey in 2004; these surveys covered 

1962-2004. Fertility levels and trends based on results from the two surveys were similar. The 1979 WFS 

survey results showed an increase in TFR(40) from 4.8 in 1962 to 5.9 in 1977. The 2004 DHS survey 

results showed a decrease in TFR(40) from 4.6 in 1989 to 3.0 in 2004. The two trends intersected in 1977, 

the year when fertility in Lesotho was at its peak. 

Several censuses in 1976, 1986, 1996 and a 2001 survey provide additional data on fertility in 

Lesotho; the 2004 DHS survey report includes data from all of these sources. Unfortunately, access to 

original data on CEB from Lesotho’s censuses was not available. TFR(40) in the WFS survey was 4.9, on 

average, in women born between approximately 1925 and 1939. Therefore, period fertility was probably 

stable, at less than 5 children per woman, before 1960. 

Table B.16 Estimates of fertility level, Lesotho 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Period fertility 

1976 Census 1976.5 1976.0 4.75 5.43 

1986 Census 1986.5 1986.0 4.87 5.49 

1996 census 1996.5 1996.0 2.86 3.33 

2001 LDS 2001.5 1999.0 3.76 4.20 

Trends in urban and rural areas were parallel, with an almost steady gap (Figure B.16.1). In 1962-

1976, fertility increased at similar rates in the two areas; the difference in slopes was not significant (p = 

0.737). However, the fertility decline was more rapid in urban areas in 1976-2004, and the slopes for 

urban and rural areas were significantly different (p = 0.014). 

Premarital fertility had a low prevalence in the WFS survey results. However, the proportion of 

premarital births increased rapidly, from an estimated 0.9 percent in 1963 to 12.1 percent in 2004. As a 

result, premarital fertility increased steadily over the period covered by the WFS and DHS surveys, 

reaching 0.4 children per women in 2004. In contrast, marital fertility followed the same pattern as 

fertility for overall population in that it increased and then decreased. 
74

Figure B.16.1 Trends in Cumulated Fertility by Age 40, 

Lesotho, 1950-2004

1

2

3

4

5

6

7

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman

Urban Rural Trends Published Census & Survey (TFR)

Figure B.16.2 Trends in Marital and Premarital Fertility, 

Lesotho, 1960-2005

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
75

Liberia 

Liberia conducted only one DHS survey, in 1986. This survey showed no clear trend over the 

period covered, 1971-1986. 

Data from a 1971 survey and a 1974 census provide additional evidence on fertility in Liberia 

(Table B.17). These data show that the period fertility level prior to 1950 was low, at less than 4.0 

children per women, and was probably increasing slowly. The fertility level seems to have increased 

rapidly in the 1950s and 1960s to reach 6.0 in 1971, according to the DHS survey results. The estimates 

of TFR from the 1971 survey and the 1974 census are consistent with the reconstruction. 

Table B.17 Estimated fertility levels, Liberia, 1936-1974 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1971 CEB(60-69) 1936.5 3.51 3.90 

Survey, 1971 CEB(50-59) 1946.5 3.70 4.11 

Survey, 1971 CEB(40) 1961.5 4.43 4.67 

Period fertility 

Survey, 1971 TFR(40) 1970.0 5.59 6.27 

Survey, 1971 TFR(40) 1971.0 5.35 5.72 

Census, 1974 TFR(40) 1974.0 5.83 6.48 

DHS data show diverging trends in urban and rural areas between 1971 and 1986 (Figure B.17.1). 

The difference in slopes for the rural and urban trends was highly significant (p <0.0001). Liberia’s 

fertility decline apparently started in approximately 1971, the first year covered by the DHS survey, 

although fertility continued to increase in rural areas between 1971 and 1986, continuing the country’s 

previous trend. The changes in urban and rural fertility levels between 1971 and 1986 tended to 

compensate for one another so, as a result, the changes in the national fertility level during this period 

were not significant. Applying the proportion urban (from the United Nations database) to both urban and 

rural trends shows a slightly increasing trend nationwide. 

The prevalence of premarital fertility in Liberia was high, in contrast to neighboring countries, 

and increasing rapidly during the period covered by the DHS survey. The estimated proportion of 

premarital births was 7.7 percent in 1971 and 11.5 percent in 1986. As a result, the premarital fertility 

level tended to increase between 1971 and 1986, reaching 0.7 children per woman in 1986. The fertility 

trend for marital fertility was similar to the trend for the overall population (Figure B.17.2) 
76

Figure B.17.1 Trends in Cumulated Fertility by Age 40, 

Liberia, 1950-1986

2

3

4

5

6

7

8

1935 1945 1955 1965 1975 1985

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.17.2 Trends in Marital and Premarital Fertility, 

Liberia, 1960-1986

0

1

2

3

4

5

6

1955 1965 1975 1985 1995

Year

TFR(40)

Premarital

Marital
77

Madagascar

Madagascar conducted DHS surveys in 1992, 1997, and 2003, covering 1977-2003. Results from 

the three surveys show consistent fertility levels and trends; all three indicate that fertility levels declined 

after 1980. 

In addition, fertility data are available from a 1966 survey, a 1975 census, and a 1980 survey 

(Table B.18). Data from these sources indicate that the fertility level was very low prior to 1950, with an 

average TFR(40) of 2.7 (CFS = 3.0), although the TFR(40) probably increased over time. Estimated 

fertility increased steadily after 1950, reaching a peak in approximately 1975. 

Table B.18 Estimated fertility levels, Madagascar, 1940-1980 

Source 

(survey name, date) 

Type of fertility data

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1966 CEB(40) 1956.0 4.03 4.48 

Census, 1975 CEB(60+) 1940.5 2.40 2.67 

Census, 1975 CEB(50-59) 1950.5 2.97 3.30 

Census, 1975 CEB(40) 1965.5 4.05 3.86 

Period fertility 

Survey, 1966 TFR(40) 1965.5 4.69 5.13 

Census, 1975 TFR(40) 1975.0 5.56 6.39 

Survey, 1980 TFR(40) 1980.0 4.95 5.76 

DHS data show divergent and complex trends in urban and rural areas (Figure B.18.1). In urban 

areas, fertility declined rapidly between 1977 and 1986 (p = 0.005). After a stall for approximately 7 

years (p = 0.217), fertility began to decline again in 1993 and continued to decline until the last year for 

which data are available, 2003. The changes in slope and were highly significant (p = 0.004 for 1977-

1986 and p = 0.006 for 1993-2003). 

In rural areas, fertility decreased slowly between 1977 and 1986 and the slope was not significant 

(p = 0.122). It is possible that the slow decrease in 1977-1980 simply reflected the end of the fertility 

increase that had occurred between 1950 and 1975. Like urban areas, rural areas experienced a fertility 

stall, in 1986-1992; the fertility slope became positive during this period but was not significantly higher 

than 0 (p = 0.243). However, the change in slope from 1988-1992 to 1992-2003 had borderline 

significance (p = 0.058). The fertility decline in rural areas after 1992 was rapid and had a similar slope to 

that of urban areas during this period (p = 0.864). The apparent fertility stall in urban and rural areas 

occurred immediately after a turbulent period during the mid-1980s. It is possible that fertility became 

lower than expected during the mid-1980s before resuming its previous trend, resulting in apparent 

increases and decreases. 

The proportion of premarital births in Madagascar increased from 5.5 percent in 1977 to 7.2 

percent in 2004. As a result, the premarital fertility level remained flat between 1977 and 2004, at about 

0.36 children per woman, whereas marital fertility followed the same trend as the general population’s 

fertility trend. 
78

Figure B.18.1 Trends in Cumulated Fertility by Age 40, 

Madagascar, 1950-2003

2

3

4

5

6

7

8

1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Published Census & Survey, CEB Census & Survey (TFR)

Figure B.18.2 Trends in Marital and Premarital Fertility, 

Madagascar, 1960-2003

0

1

2

3

4

5

6

7

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
79

Malawi 

Malawi conducted DHS surveys in 1992, 2000, and 2004, covering 1977-2004. The results from 

all three surveys showed similar fertility levels and trends, and all indicate that the fertility level declined 

over the period covered. 

Censuses conducted in 1977, 1987, and 1998, as well as surveys conducted in 1982, 1984, and 

1996 provide additional evidence on CEB and TFR (Table B.19). Results from these censuses and 

surveys yield generally similar results. They indicate that the period fertility level before 1950 was high, 

around 6.3 children per woman (CFS of 7.0) and was stable or increased slightly in the 1950s and 1960s 

before reaching a peak at 7.0 in 1977; the 1992 DHS survey also found that TFR(40) was 7.0 in 1977. 

Table B.19 Estimated fertility levels, Malawi, 1942-1994 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1977 CEB(60+) 1942.0 6.30 7.00 

Census, 1977 CEB(50-59) 1952.0 6.35 7.06 

Census, 1977 CEB(40) 1967.0 6.43 7.13 

Survey, 1982 CEB(40) 1972.5 6.75 7.30 

Survey, 1984 CEB(40) 1974.5 6.20 6.80 

Census, 1987 CEB(60+) 1952.7 6.46 7.18 

Census, 1987 CEB(50-59) 1962.7 6.55 7.28 

Census, 1987 CEB(40) 1977.7 6.43 7.18 

DHS, 1992 CEB(40) 1982.8 6.40 7.30 

Survey, 1996 CEB(40) 1986.5 6.25 7.77 

Census, 1998 CEB(70+) 1953.5 6.01 6.67 

Census, 1998 CEB(60-69) 1963.5 6.10 6.77 

Census, 1998 CEB(50-59) 1973.5 6.17 6.85 

Census, 1998 CEB(40) 1988.5 5.88 6.66 

DHS, 2000 CEB(40) 1990.7 6.10 7.00 

DHS, 2004 CEB(40) 1994.9 5.97 6.83 

Period fertility 

Census, 1977 TFR(40) 1976.5 5.64 6.86 

Survey, 1982 TFR(40) 1982.0 5.58 6.63 

Survey, 1984 TFR(40) 1982.5 6.52 7.58 

The DHS survey results indicate that fertility trends in urban and rural were split in 1978, the first 

year covered by the DHS surveys and the year when Malawi’s fertility decline began (Figure B.19.1). The 

negative slopes for both urban and rural areas and the differences between the slopes are highly 

significant (p <0.0001). 

The proportion of premarital births remained low, at 3.0 percent on average, and remained 

remarkably stable over the years. As a consequence, both premarital and marital fertility declined between 

1978 and 2004, reaching a low 0.16 children per unmarried woman in 2004. 
80

Figure B.19.1 Trends in Cumulated Fertility by Age 40, 

Malawi, 1950-2004

2

3

4

5

6

7

8

1935 1945 1955 1965 1975 1985 1995 2005 2015

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.19.2 Trends in Marital and Premarital Fertility, 

Malawi, 1960-2004

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
81

Mali 

Mali conducted DHS surveys in 1987, 1996, and 2001, covering 1972-2001. The results for 

fertility levels and trends from all three surveys were similar and indicate that fertility rose in the early 

1970s and then declined. However, the 1987 survey results showed lower fertility levels than the 1996 

survey results in 1982-1985, a period covered by both surveys. 

Data from a survey conducted in the rural areas in central Malawi in 1957 and the 1987 census 

provide additional evidence on fertility levels. The results from the 1957 survey are not nationally 

representative, but they indicate that the fertility level was approximately 5.2 in the late 1940s and that 

this level increased in the 1950s. 

Table B.20 Estimated fertility levels, Mali, 1947-1986 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Central Niger Delta, 1957 CEB(40) 1947.0 5.17 5.44 

Period fertility 

Central Niger Delta, 1957 TFR(40) 1957.0 6.04 6.68 

Census, 1987 TFR(40) 1986.5 6.15 6.87 

The DHS survey results indicate major discrepancies between fertility levels in urban and rural 

areas (Figure B.20.1). In urban areas, fertility remained constant between 1972 and 1986 (p = 0.833), then 

declined rapidly (p < 0.0001); the change in slope was highly significant (p <00001). In rural areas, 

fertility continued to increase between 1972 and 1990; the slope was strongly positive (p <0.0001). After 

1990, fertility in rural areas began to decline rapidly, at a similar speed to urban areas (p = 0.274). The 

split between urban and rural areas occurred in approximately 1967. When the increasing fertility levels 

in rural areas were extrapolated backward, the resulting levels were similar to the estimates from the 1957 

survey, providing additional evidence for the continuous increase in rural fertility over that period. 

The proportion of premarital births remained very low between 1972 and 2001. However, the 

1987 DHS survey results for premarital fertility are slightly different from the results of the 1996 and 

2001 surveys. The later DHS survey results indicate a significant rise in the proportion of premarital 

births from 1.4 percent in 1972 to 2.8 percent in 2001. As a result, premarital fertility levels continued to 

increase over the period, reaching 0.16 children per woman in 2001, whereas the trend for marital fertility 

was similar to the overall trend in that marital fertility levels declined starting in approximately 1990 

(Figure B.20.2) 
82

Figure B.20.1 Trends in Cumulated Fertility by Age 40, 

Mali, 1947-2001

2

3

4

5

6

7

8

9

1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.20.2 Trends in Marital and Premarital Fertility,

Mali, 1960-2001

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
83

Mozambique 

Mozambique conducted DHS surveys in 1997 and 2003 covering 1982-2003. Results from both 

surveys indicated that fertility was declining during this period, although the 2003 survey results indicate 

a more rapid decline than the 1997 survey. 

The censuses conducted in 1950, 1981, and 1997 provide additional data on fertility in 

Mozambique (Table B.21). The census results indicate that the TFR(40) in Mozambique was low before 

1940, approximately 3.5 (CFS = 3.9), which is consistent with the level measured in 1950 and the 

subsequent rise. The results from the 1950-1997 censuses and the 1997 and 2003 DHS surveys indicate a 

major increase in fertility after 1950. 

Table B.21 Estimated fertility levels, Mozambique, 1925-1987 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1950 CEB(50-59) 1925.7 3.53 3.99 

Census, 1950 CEB(40) 1940.0 3.42 3.80 

Census, 1997 CEB(50-59) 1972.6 5.05 5.62 

Census, 1997 CEB(40) 1987.6 5.34 5.85 

Period fertility 

Census, 1950 TFR(50) 1950.0 3.57 4.02 

Census, 1981 TFR(50) 1981.0 5.84 6.49 

The DHS data cover a period of declining fertility, when trends in urban and rural areas diverged 

(Figure B.21.1). In urban areas, the fertility rate slope was highly negative and highly significant (p 

<0.0001) throughout 1982-2003. In rural areas, fertility continued to increase in 1982-1990 (p = 0.001). 

Rural fertility levels declined slightly between 1990 and 2003, although the change was not significantly 

different from 0 (p = 0.605); the change in slope was highly significant (p = 0.004). Urban fertility levels 

might have been slightly higher than rural fertility levels in the early 1980s, when overall fertility rates in 

Mozambique stopped increasing and started decreasing. 

The proportion of premarital births remained at approximately 5.5 percent over the period 

covered by the DHS surveys and showed no discernible trend (p = 0.262). As a result, both premarital and 

marital fertility trends were similar to the national trend. The number of premarital births per woman was 

0.26 in 2003. 
84

Figure B.21.1 Trends in Cumulated Fertility by Age 40, 

Mozambique, 1950-2003

2

3

4

5

6

7

8

1925 1935 1945 1955 1965 1975 1985 1995 2005 2015

Year

Children par woman 

Urban Rural Trends Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.21.2 Trends in Marital and Premarital Fertility, 

Mozambique, 1960-2003

0

1

2

3

4

5

6

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
85

Namibia 

Namibia conducted DHS surveys in 1992 and 2000, covering 1977-2000. The results from both 

surveys concerning fertility levels and trends are similar and indicate a decline in fertility. 

Other data on fertility are available from censuses conducted in 1981, 1991, and 2001 (Table 

B.22). A longitudinal analysis of data from parish registers from the former Ovamboland (northcentral 

Namibia) provides independent confirmation of the fertility levels and trends from the censuses 

(Shemeikka et al., 2005). The study of parish registers in Ovamboland shows similar fertility levels and 

trends to the DHS survey and the 1981-2001 censuses. According to the parish registers study results, 

fertility increased in the 1950s, reached a plateau in the mid-1970s, and declined rapidly thereafter in 

Ovamboland in a parallel fashion to Namibia’s overall decline (Figure B.22.1). 

Table B.22 Estimated fertility levels, Namibia, 1956-1991 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1991 CEB(60-69) 1956.8 5.34 5.94 

Census, 1991 CEB(50-59) 1966.8 5.72 6.35 

Census, 1991 CEB(40) 1981.8 5.10 6.08 

Census, 2001 CEB(40) 1991.7 3.92 4.87 

Period fertility 

Census, 1981 TFR(40) 1981.0 5.97 6.64 

The results from the 1981 and 2001 censuses suggest that the period fertility level in rural areas 

was approximately 5 in 1950 and increased between 1950 and the mid-1970s. DHS survey results indicate 

a major drop in fertility in rural areas in 1975-2000, with a strongly negative slope (p <0.0001). The 

slopes for urban and rural areas were not significantly different (p = 0.409), and a gap of about 15 years 

occurred between the two fertility declines (Figure B.22.1). The precise timing of the fertility decline 

onset in Namibia is difficult to assess from the limited data available. Assuming that the increase in 

period fertility was parallel to that of cohort fertility, the onset of the country’s fertility decline seems to 

have occurred in the early 1960s in urban areas and in the mid-1970s in rural areas. 

Premarital fertility is highly prevalent in Namibia. The proportion of premarital births has 

increased rapidly, from 30.0 percent in 1977 to 44.2 percent in 2000. The Ovamboland study results 

showed an increase in premarital fertility between 1977 and 2000. As a result of the increase in the 

proportion of premarital births, premarital fertility levels remained stable during this period, at an average 

of 1.7 children per women. In contrast, the marital fertility level decreased dramatically. The premarital 

and marital fertility levels were essentially equivalent by 2000 (Figure B.22.2). 
86

Figure B.22.1 Trends in Cumulated Fertility by Age 40, 

Namibia, 1950-2000

2

3

4

5

6

7

8

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman

Urban Rural Trends Published Census (CEB) Ovamboland Census (TFR)

Figure B.22.2 Trends in Marital and Premarital Fertility, 

Namibia, 1960-2000

0

1

2

3

4

5

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
87

Niger 

Niger conducted DHS surveys in 1992 and 1998, covering 1977-1998. The results of both 

surveys showed similar fertility levels and trends; these levels were initially high and declined slowly. 

A survey was conducted in Niger’s sedentary population in 1960 and in the country’s nomadic 

population in1963. The results of this survey and Niger’s 1988 census provide additional data on fertility 

levels and trends (Table B.23). These data indicate a steady TFR(40) level before 1950, at approximately 

5.0 children per woman for the sedentary population and 4.2 in the nomadic population. The results from 

the surveys and census also indicate a substantial rise in fertility in the 1950s and 1960s. 

Table B.23 Estimated fertility levels, Niger, 1914-1987 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1960 (sedentary population) CEB(70+) 1914.8 4.81 5.34 

Survey, 1960 (sedentary population) CEB(60-69) 1924.8 5.01 5.57 

Survey, 1960 (sedentary population) CEB(50-59) 1934.8 5.08 5.65 

Survey, 1960 (sedentary population) CEB(40) 1949.8 4.94 5.82 

Survey, 1963 (nomadic population) CEB(50+) 1938.6 3.83 4.26 

Survey, 1963 (nomadic population CEB(40) 1953.6 4.58 4.99 

Census, 1988 CEB(40) 1978.4 5.94 6.40 

Period fertility 

Survey, 1960 (sedentary population) TFR(40) 1959.3 6.54 7.27 

Survey, 1963 (nomadic population) TFR(40) 1963.1 5.55 6.44 

Census, 1988 TFR(40) 1987.9 6.57 7.21 

 

The DHS data cover the peak fertility period in Niger, with an estimated TFR(40) as high as 8.2 

[corresponding to TFR(50) of 9.1] in 1975. After reaching this peak, the fertility level decreased 

dramatically in both urban and rural areas (Figure B.23.1). Both slopes were strongly negative between 

1977 and 1998 (p <0.00001 for both slopes); the absolute value of the urban fertility level slope was 

higher than that of the rural fertility slope (p <0.00001). The urban and rural fertility trends intersected in 

1975, when the fertility decline began in both regions. 

The proportion of premarital births in Niger was very low, although it increased from 0.4 percent 

in 1977 to 0.8 percent in 1998. As a result, the pattern of marital fertility was similar to that of the general 

population (Figure B.23.2). 
88

Figure B.23.1 Trends in Cumulated Fertility by Age 40, 

Niger, 1950-1998

2

3

4

5

6

7

8

9

1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.23.2 Trends in Marital and Premarital Fertility, 

Niger, 1960-1998

0

1

2

3

4

5

6

7

8

9

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
89

Nigeria 

Nigeria conducted a WFS survey in 1982 and DHS surveys in 1990, 1999, and 2003, covering 

1967-2003. The results of the four surveys concerning fertility levels and trends were similar and showed 

an increasing level in the 1970s followed by a decline, then a stall in 1999-2003. However, the rates from 

the 1982 WFS survey results for 1980-1981 were lower than the rates from the DHS survey and the 1996-

1997 rates from the 1999 DHS survey were lower than the results from the 2003 DHS survey. 

Data from the 1965-1966 National Rural Demographic Survey and the 1994 National Sentinel 

Survey complement the DHS survey data (Table B.24). Access to the original data was not available but 

information on these data was available from a publication by Feyisetan and Bankole (2002). The 

available evidence suggest that the average period fertility rate in Nigeria in the 1950s was approximately 

5.0 children per woman at age 40 (CFS = 5.6). 

Table B.24 Estimated fertility levels, Nigeria, 1955 and 1993 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

National Rural Demographic 

Survey, 1965-66 CEB(50) 1955 5.04 5.6 

Period fertility 

National Sentinel Survey, 1994 TFR(50) 1993 4.86 5.4 

The WFS and DHS survey results show similar trends in and a similar gap between urban and 

rural fertility levels (Figure B.24.1). In urban areas, the fertility level increased from 1977 to 1980; the 

slope was significantly positive (p = 0.008). The fertility level in urban areas then decreased from 1980 to 

1999, with a highly significant negative slope (p <0.00001). The urban fertility level stalled or increased 

slightly between 1999 and 2003; the slope was positive (p = 0.031). The changes in slope for the turning 

points in 1980 and 1999 were highly significant (P< 0.00001 for 1980 and P= 0.003 for 1999). 

Fertility levels in rural areas increased between 1967 and 1984, decreased from 1984 to 1999, and 

increased again between 1999 and 2003. All slopes were significantly different from 0 (p = 0.018 for 

1967-1984, p <0.00001 for 1984-1999, and p = 0.0004 for 1999-2003), and the changes in slopes at 

turning points (1984 and 1999) were highly significant (p <0.00001 for both changes in slope). 

The trends in urban and rural areas were parallel between 1967 and 2003, and urban fertility 

levels were already lower than rural fertility levels in the 1960s. When the trends from 1967 were 

extrapolated backward to the early 1980s, the results matched the estimates for the early 1950s, providing 

additional evidence for the consistency of the data on urban and rural fertility levels and trends. 

The premarital fertility level in Nigeria remained low throughout the period covered by the DHS 

and WFS surveys (0.14 children per woman on average) and the proportion of premarital births declined 

from 2.7 percent in 1967 to 2.1 percent in 2003. Even when overall fertility rates were rising (1967-1980), 

premarital fertility levels in Nigeria were already declining and these rates did not increase during the 

country’s fertility stall (1999-2003). The marital fertility trend was similar to the trend for the general 

population (Figure B.24.2). 
90

Figure B.24.1 Trends in Cumulated Fertility by Age 40, 

Nigeria, 1950-2003

2

3

4

5

6

7

8

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman

Urban Rural Trends Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.24.2 Trends in Marital and Premarital Fertility,

Nigeria, 1960-2003

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
91

Rwanda 

Rwanda conducted a WFS survey in 1983 and DHS surveys in 1992, 2000, and 2005, covering 

1977-2005. The results of the four surveys showed similar fertility levels and trends. All of the surveys 

indicated a plateau, followed by a decline and then another plateau. 

Data on fertility are also available from a survey conducted in 1970 and censuses conducted in 

1978 and 1990 (Table B.25). These data indicate that TFR(40) was high, approximately 5.9 (CFS = 6.5), 

before 1950 and subsequently increased. 

Table B.25 Estimated fertility levels, Rwanda, 1935-1991 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1970 CEB(60-69) 1935.7 5.90 6.55 

Survey, 1970 CEB(50-59) 1945.7 5.83 6.48 

Survey, 1970 CEB(40) 1960.7 6.40 6.53 

Census, 1978 CEB(40) 1968.6 6.87 7.64 

Period fertility 

Survey, 1970 TFR(40) 1970.2 6.84 7.66 

Census, 1978 TFR(40) 1978.1 7.21 8.66 

Census, 1990 TFR(40) 1991.0 4.72 5.55 

The WFS and DHS survey results show rising fertility levels for the country as a whole, followed 

by divergent trends between urban and rural areas (Figure B.25.1). In urban areas, the fertility level 

decreased steadily after 1968 (significant slope, p <0.00001) and was markedly low in the early 1990s 

according to all three DHS surveys covering this period. In rural areas, the slope increased between 1968 

and 1982 (borderline significance, p = 0.081), then decreased from 1982 to 1999 (p <0.00001); this was 

followed by a fertility stall between 1999 and 2005 (p = 0.324). All changes in slope for rural areas were 

highly significant (p <0.0001 for the change before and after 1982 and p = 0.011 for the change before 

and after 1999). The fertility decline between 1992 and 1999 was more rapid in rural areas than urban 

areas (p <0.0001). 

Rwanda’s civil war (the genocide) in 1994 was not associated with any marked decline in 

fertility. This could be due to selection bias (families who were exterminated could not be counted in later 

censuses and surveys), the effect of sexual abuse and rape, or immigration by former refugees. This issue 

deserves further research. 

Premarital fertility was extremely low in Rwanda, although it increased rapidly. The proportion of 

premarital births increased from 0.4 percent in 1968 to 4.2 percent in 2005, a 10-fold increase. Premarital 

fertility was high during the crisis years (1991-1997) and again in 2001-2005. However, in absolute 

terms, premarital fertility remained low, reaching 0.2 children per women in children per women in 2005. 

Trends in marital fertility were similar to those for the general population (Figure B-25.2). 
92

Figure B.25.1 Trends in Cumulated Fertility by Age 40, 

Rwanda, 1950-2005

2

3

4

5

6

7

8

1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.25.2 Trends in Marital and Premarital Fertility,

Rwanda, 1960-2005

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005 2015

Year

TFR(40)

Premarital

Marital
93

Senegal

Senegal conducted a WFS survey in 1978 and DHS surveys in 1986, 1993, 1997, and 2005; these 

surveys covered 1963-2005. The results from these surveys showed similar fertility levels and trends. 

According to these results, fertility reached a high plateau before declining. 

Surveys conducted in 1960, 1970, and 1979 provide additional data on fertility levels. Data from 

the Senegal River valley survey in 1957 and the Niakhar and Ngayokheme demographic surveillance 

systems surveys, which cover 1963-2005, were also used (Table B.26). These results indicate that 

Senegal’s cohort fertility level was approximately 5.0 prior to 1950 and increased in the 1950s and 1960s. 

Fertility levels in the Senegal River valley, a rural area in the north, rose from 1947 to 1957. The results 

from the Ngayokheme and Niakhar demographic surveillance systems surveys show that fertility levels in 

these regions were higher in the 1960s than in other parts of the country and these levels increased until 

1986 before dropping. However, the fertility level in this region was lower in 1973-1977, when 

Ngayokheme and Niakhar experienced a drought. 

Table B.26 Estimated fertility levels, Senegal, 1947-1998 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1960 CEB(40) 1950.3 4.73 5.41 

Senegal River Valley Survey, 1957 CEB(40) 1947.0 5.17 4.79 

Period fertility 

Survey, 1960 TFR(40) 1959.8 5.00 5.37 

Survey, 1970 TFR(40) 1971.1 5.60 6.46 

Survey 1979 TFR(40) 1979.0 7.26 8.07 

Senegal River Valley Survey, 1957 TFR(40) 1957.0 5.60 6.22 

Demographic surveillance systems 

Ngayokheme, 1963-1967 TFR(40) 1965.5 6.27 7.02 

Ngayokheme, 1968-1972 TFR(40) 1970.5 6.43 7.05 

Ngayokheme, 1973-1977 TFR(40) 1975.5 6.20 6.89 

Ngayokheme, 1978-1982 TFR(40) 1980.5 6.53 7.12 

Niakhar, 1984-1987 TFR(40) 1986.0 7.03 7.87 

Niakhar, 1988-1991 TFR(40) 1990.0 6.89 7.74 

Niakhar, 1992-1995 TFR(40) 1994.0 6.49 7.27 

Niakhar, 1996-1999 TFR(40) 1998.0 6.18 6.85 

The results of the WFS and DHS surveys reveal differences in trends between urban and rural 

areas (Figure B.26.1). In urban areas, fertility declined slowly between 1963 and 1980; the slope was not 

significantly different from 0 (p = 0.311). Thereafter, urban fertility dropped rapidly (p <0.00001); the 

difference in slopes was significant (p = 0.004). In rural areas, fertility levels increased from 1963 to 1982 

(p = 0.0003), then decreased substantially until 2005, the last year for which data are available; the slope 

was highly significant (p <0.00001). 

The proportion of premarital births increased from 1.3 percent in 1963 to 3.5 percent in 1992 

before decreasing to 1.5 percent in 2005. As a result, premarital fertility levels also increased and then 

decreased, whereas the trend for marital fertility was similar to the trend for the general population 

(Figure B.26.2). 
94

Figure B.26.1 Trends in Cumulated Fertility by Age 40, 

Senegal, 1950-2005

2

3

4

5

6

7

8

9

1945 1955 1965 1975 1985 1995 2005

Year

Children per woman 

Urban Rural Trends

Senegal River valley 1957 Niakhar Census & Survey (CEB)

Census & Survey (TFR) Published

Figure B.26.2 Trends in Marital and Premarital Fertility, 

Senegal, 1960-2005

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
95

Tanzania 

Tanzania conducted DHS surveys in 1991, 1996, 1999, and 2004, covering 1976-2004. The 

results for fertility levels and trends from all four surveys were similar. In general, these results indicated 

a declining trend in 1976-2004. 

Data from censuses conducted in 1967, 1978, and 1988 provide additional evidence on fertility 

levels and trends (Table B.27). These data indicate that the TFR(40) was approximately 4.4 prior to 1950 

(CFS = 4.9) and increased thereafter. 

Table B.27 Estimated fertility levels, Tanzania, 1932-1988 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1967 CEB(60+) 1932.6 4.36 4.84 

Census, 1967 CEB(50-59) 1942.6 4.50 5.00 

Census, 1967 CEB(40) 1957.6 4.76 5.05 

Period fertility 

Census, 1978 TFR(40) 1978.0 6.39 7.09 

Census, 1988 TFR(40) 1988.0 5.87 6.48 

The DHS survey results indicate declining fertility levels in both urban and rural areas (Figure 

B.27.1). In urban areas, the fertility level declined more rapidly from 1977 to 1995 (p <0.00001) and 

more slowly from 1995 to 2004, when the slope was only slightly lower than 0, with borderline 

significance (p = 0.075). In rural areas, fertility also declined from 1977 to 1995 (p <0.00001); however, 

the fertility level in rural areas stalled in 1995 to 2004 (p = 0.909). The changes in slope for both urban 

and rural areas were significant (p = 0.001 for urban areas, p = 0.021 for rural areas). The fertility decline 

was more rapid in urban areas than rural areas between 1977 and 1995 (p <0.00001), and the slopes for 

the two areas were not significantly different after 1995 (p = 0.152). 

Premarital fertility is common in Tanzania, with an average proportion of premarital births of 

approximately 6.0 percent over the period studied. This proportion tended to increase from 4.0 percent in 

1977 to 7.0 percent in 1988 and then decreased thereafter. Premarital fertility levels were higher and 

increased later than fertility in the general population, whereas trends in marital fertility were similar to 

trends for the general population (Figure B.27.2). 
96

Figure B.27.1 Trends in Cumulated Fertility by Age 40, 

Tanzania, 1950-2003

2

3

4

5

6

7

8

1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.27.2 Trends in Marital and Premarital Fertility, 

Tanzania, 1960-2003

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000 2010

Year

TFR(40)

Premarital

Marital
97

Togo 

Togo conducted DHS surveys in 1988 and 1998, covering 1973-1998. The results of these 

surveys showed similar fertility levels and trends; both indicate that fertility levels declined over the 

period covered. 

Data from a survey conducted in 1961 and two censuses conducted in 1971 and 1981 provide 

additional evidence on fertility trends in Togo. These data indicate that the TFR(40) in Togo averaged 

approximately 5.4 (CFS = 6.0) prior to 1950 and increased thereafter. 

Table B.28 Estimated fertility levels, Togo, 1936-1981 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Survey, 1961 CEB(50+) 1936.0 5.38 5.98 

Survey, 1961 CEB(40) 1951.0 5.50 5.94 

Census, 1971 CEB(40) 1961.5 5.21 5.68 

Census, 1981 CEB(40) 1971.5 5.51 6.00 

Period fertility 

Survey, 1961 TFR(40) 1960.5 6.28 7.06 

Census, 1971 TFR(40) 1971.0 4.93 5.74 

Census, 1981 TFR(40) 1981.0 5.27 6.02 

The results from the DHS surveys indicate that fertility trends in urban and rural areas diverged 

(Figure B.28.1). In urban areas, fertility declined substantially throughout the period covered by the 

surveys; the slope was highly significant (p <0.00001). In rural areas, fertility continue to increase slowly 

from 1973 to 1980, although the slope was not significantly different from 0 for the few years covered (p 

= 0.648). After 1980, fertility in rural areas declined dramatically; the slope was highly significant (p 

<0.0001) but the decline was not as rapid as in urban areas (p = 0.005). 

The premarital fertility level in Togo remained low and tended to decline over time (Figure B.28.2).
98

Figure B.28.1 Trends in Cumulated Fertility by Age 40, 

Togo, 1950-1998

2

3

4

5

6

7

8

1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends

Published Census & Survey (CEB) Census & Survey (TFR)

Figure B.28.2 Trends in Marital and Premarital Fertility, 

Togo, 1960-1998

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
99

Uganda 

Uganda conducted DHS surveys in 1988, 1995, and 2001, covering 1973-2001. The results from 

all three showed similar fertility levels and trends, although the 2001 survey results showed somewhat 

higher levels in 1991-1994 than the 1995 survey. All of the DHS survey results showed that fertility 

declined slowly during the period covered. 

Data from censuses conducted in 1959, 1969, and 1991 provide additional evidence on fertility 

levels and trends in Uganda (Table B.29). The results from these censuses indicate that the cohort fertility 

level was approximately 5.3 before 1950. However, the data on the fertility level in 1950 from the 1959 

and 1969 census results appear to be inconsistent. 

Table B.29 Estimated fertility levels, Uganda, 1959-1991 

Source 

(survey name, date) 

Type of fertility data

(age group) 

Reference 

period P(40) P(50) 

Cohort fertility 

Census, 1969 CEB(60+) 1934.6 4.23 4.70 

Census, 1969 CEB(50-59) 1944.6 4.29 4.76 

Census, 1969 CEB(40) 1959.6 4.99 5.05 

Census, 1991 CEB(80+) 1936.0 5.28 5.86 

Census, 1991 CEB(70-79) 1946.0 5.31 5.90 

Census, 1991 CEB(60-69) 1956.0 5.63 6.26 

Census, 1991 CEB(50-59) 1966.0 6.18 6.86 

Census, 1991 CEB(40) 1981.0 6.59 7.10 

Period fertility 

Census, 1959 TFR(40) 1959.1 4.95 5.42 

Census, 1969 TFR(40) 1969.1 4.90 5.40 

Census, 1991 TFR(40) 1990.5 6.09 6.66 

The DHS survey results indicate a steady decline in fertility in both urban and rural areas after 

1988 (Figure B.29.1). Both slopes are significantly lower than 0 (p <0.0001 for urban areas and p = 0.001 

for rural areas). The slope for urban areas had a much higher absolute value than the slope for rural areas 

(p <0.00001). 

Premarital fertility remained at a low and steady level (0.3 children per women) in Uganda. 

Premarital fertility played a small role in the overall population’s fertility level, given the high marital 

fertility rate (6.1 in 2000) (Figure B.29.2). 
100

Figure B.29.1 Trends in Cumulated Fertility by Age 40, 

Uganda, 1950-2001

2

3

4

5

6

7

8

9

1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trends Published Census & Survey (CEB)

Figure B.29.2 Trends in Marital and Premarital Fertility, 

Uganda, 1960-2001

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
101

Zambia 

Zambia conducted DHS surveys in 1992, 1996, and 2001, covering 1977-2001. The results from 

the three surveys show similar fertility levels and trends, and all indicate a steady decline over the period 

covered. 

Data from censuses conducted in 1969, 1980, 1990, and 2000 provide additional data on fertility 

levels and trends (Table B.30). The results from these censuses indicate that the cohort fertility level 

might have been as low as 4 prior to 1940 before increasing steadily to reach a peak level in the mid1970s. 

Table B.30 Estimated fertility levels, Zambia, 1969-2000 

Source 

(survey name, date) 

Type of fertility data 

(age group) Reference period P(40) P(50) 

Cohort fertility 

Census, 1969 CEB(60-64) 1939.6 3.86 4.29 

Census, 1969 CEB(50-59) 1944.6 4.16 4.63 

Census, 1969 CEB(40) 1959.6 5.12 4.99 

Census, 1990 CEB(60-69) 1955.6 5.03 5.59 

Census, 1990 CEB(50-59) 1965.6 6.02 6.68 

Census, 1990 CEB(40) 1980.6 6.44 7.15 

Census, 2000 CEB(40) 1990.8 5.90 6.80 

Period fertility 

Census, 1980 TFR(40) 1980.0 4.79 5.68 

The period covered by DHS data began immediately after the fertility level in Zambia had 

already peaked (Figure B.30.1). These data indicate a steady fertility decline in both urban and rural areas 

since 1977; both slopes were highly significant (p <0.00001). The slopes for fertility in urban and rural 

areas intersected in 1973 at a high CEB(40) of 7.4 [TFR(40) = 8.2]. The urban fertility slope rose more 

rapidly than the rural fertility slope (p <0.00001). 

The proportion of premarital births was low in Zambia at the beginning of the period covered by 

the DHS surveys and increased rapidly from 3.5 percent in 1977 to 7.8 percent in 2001 (p <0.00001). As a 

result, the premarital fertility level increased steadily over the period, reaching 0.4 children per woman in 

2001, whereas marital fertility levels and trends were similar to those for the general population (Figure 

B.30.2). 
102

Figure B.30.1 Trends in Cumulated Fertility by Age 40, 

Zambia, 1950-2001

2

3

4

5

6

7

8

1935 1945 1955 1965 1975 1985 1995 2005 2015

Year

Children per woman 

Urban Rural Trends Census & Surveys (CEB) Census & survey (TFR) Published

Figure B.30.2 Trends in Marital and Premarital Fertility, 

Zambia, 1960-2003

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
103

Zimbabwe 

Zimbabwe conducted DHS surveys in 1988, 1994, and 1999, covering 1973-1999. The results 

from all three surveys showed similar fertility levels and trends and indicated a consistent decline over the 

period covered. 

Data from censuses conducted in 1969 and in 1992 provide additional evidence on fertility trends 

(Table B.31). These data indicate a TFR(40) of approximately 5.2 (CFS = 5.7) prior to 1950 that 

subsequently increased steadily. The trend in Zimbabwe is similar to the trend in Zambia, although 

fertility levels were higher in Zimbabwe. 

Table B.31 Estimated fertility levels, Zimbabwe, 1924-1991 

Source 

(survey name, date) 

Type of fertility data 

(age group) 

Reference 

period P(40) P(50) 

Census, 1969 CEB(70+) 1924.3 4.86 5.40 

Census, 1969 CEB(60-69) 1934.3 5.21 5.79 

Census, 1969 CEB(50-59) 1944.3 5.41 6.02 

Census, 1969 CEB(40) 1959.3 5.78 6.24 

Period fertility 

Census, 1992 TFR(40) 1991.5 4.15 4.71 

As in Zambia, the period covered by the DHS survey data for Zimbabwe probably began after 

fertility peaked (Figure B.31.1). The results of the DHS surveys show divergent fertility trends in urban 

and rural areas. In urban areas, fertility declined rapidly and steadily between 1973 and 1999, with a 

highly significant slope (p <0.00001). In rural areas, fertility levels reached a plateau between 1977 and 

1982 (p = 0.800) before declining rapidly (p <0.00001). The slope for rural areas after 1982 was 

significantly steeper than for urban areas (p <0.00001), which is rare in sub-Saharan Africa. 

The proportion of premarital births remained at approximately 6 percent in Zimbabwe and tended 

to increase over time, but premarital fertility levels remained very low compared with marital fertility 

levels (Figure B.31.2). 
104

Figure B.31.1 Trends in Cumulated Fertility by Age 40, 

Zimbabwe, 1950-1999

2

3

4

5

6

7

8

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Year

Children per woman 

Urban Rural Trend (U) Census & Survey (CEB) Census & Survey (TFR)

Figure B.31.2 Trends in Marital and Premarital Fertility, 

Zimbabwe, 1960-1999

0

1

2

3

4

5

6

7

8

1955 1965 1975 1985 1995 2005

Year

TFR(40)

Premarital

Marital
105

Appendix C

Table C.1 Periods of monotonic change in fertility, urban areas, sub-Saharan Africa 

Monotonic period 

Number 

of births 

Trend line 

Country Onset End Intercept Slope 

Standard 

deviation 

Benin 1967 1983 607 -10.78541 0.00835 0.02388 

Benin 1983 2001 1674 266.64689 -0.13131 0.01360 

Botswana 1973 1988 533 213.70975 -0.10567 0.02419 

Burkina Faso 1978 2003 1732 314.90717 -0.15568 0.00980 

Burundi 1972 1981 34 214.52015 -0.10571 0.17040 

Burundi 1981 1987 32 95.86692 -0.04581 0.26246 

Cameroon 1963 1983 926 -119.09992 0.06306 0.01568 

Cameroon 1983 2004 3237 216.47415 -0.10626 0.00777 

Central African 

Republic 1980 1987 412 -85.68666 0.04602 0.05980 

Central African 

Republic 1987 1995 582 401.15064 -0.19897 0.04868 

Chad 1982 2004 1158 119.69011 -0.05688 0.01491 

Comoro Islands 1981 1996 306 283.57871 -0.14034 0.03252 

Congo 1990 2005 1576 89.22526 -0.04283 0.01225 

Côte d’Ivoire 1966 1982 1018 50.61129 -0.02245 0.01970 

Côte d’Ivoire 1982 1999 1537 400.44266 -0.19883 0.01603 

Ethiopia 1985 2005 1782 337.93880 -0.16762 0.00960 

Gabon 1986 2001 1806 362.83338 -0.17977 0.01394 

Ghana 1965 1975 591 42.81579 -0.01896 0.03578 

Ghana 1975 1998 2795 222.35820 -0.10981 0.00659 

Ghana 1998 2003 507 -90.64050 0.04671 0.04287 

Guinea 1984 2005 1722 233.59669 -0.11454 0.01226 

Kenya 1963 1997 2533 249.73159 -0.12361 0.00579 

Kenya 1997 2003 526 57.13855 -0.02693 0.04207 

Lesotho 1962 1980 125 -129.57895 0.06821 0.05332 

Lesotho 1980 2005 657 281.51027 -0.13972 0.01609 

Liberia 1971 1986 862 69.69603 -0.03224 0.02248 

Madagascar 1977 1987 546 220.56107 -0.10886 0.03876 

Madagascar 1987 1993 793 -109.55505 0.05721 0.04638 

Madagascar 1993 2004 922 193.31990 -0.09480 0.02412 

Malawi 1977 2005 1779 250.34720 -0.12308 0.01026 

Mali 1972 1990 1364 -0.85890 0.00371 0.01763 

Mali 1990 2001 1706 249.67967 -0.12242 0.02133 

Mozambique 1982 2004 2496 174.90249 -0.08531 0.00977 

Namibia 1977 2000 2053 130.54348 -0.06383 0.00770 

Niger 1977 1998 1017 271.77427 -0.13344 0.01696 

Nigeria 1967 1983 1244 -92.03305 0.04947 0.01869 

Nigeria 1983 1997 1924 171.63117 -0.08371 0.01478 

Nigeria 1997 2003 679 -214.35561 0.10942 0.05083 

Rwanda 1968 2005 1435 135.26281 -0.06534 0.00947 

Senegal 1963 1976 633 64.05351 -0.02930 0.02893 

Senegal 1976 2005 5430 228.99143 -0.11261 0.00451 

Tanzania 1977 1995 2166 227.43552 -0.11208 0.01145 

Tanzania 1995 2005 1159 77.22335 -0.03686 0.02069 

Togo 1973 1980 169 -177.78447 0.09261 0.09139 

Togo 1980 1998 1463 306.35495 -0.15196 0.01240 

Uganda 1974 2001 1036 215.49946 -0.10575 0.01342 

Zambia 1977 2002 3646 236.85939 -0.11631 0.00770 

Zimbabwe 1974 1999 2193 229.37296 -0.11346 0.00764 
106

Table C.2 Periods of monotonic change in period fertility, rural areas, sub-Saharan Africa 

Monotonic period 

Number 

of births 

Trend line 

Country Begin End Intercept Slope 

Standard 

deviation 

Benin 1967 1990 2306 -104.70736 0.05628 0.00861 

Benin 1990 2001 1881 271.19313 -0.13268 0.02003

Botswana 1973 1988 1209 197.14344 -0.09677 0.01781 

Burkina Faso 1978 1986 1333 -126.36598 0.06756 0.03035 

Burkina Faso 1986 2003 6863 161.04461 -0.07736 0.00717 

Burundi 1972 1987 1455 7.72769 -0.00075 0.01711 

Cameroon 1963 1983 3074 -136.52353 0.07215 0.00934 

Cameroon 1983 2004 3796 62.15259 -0.02806 0.00806 

Central African Republic 1980 1989 824 -244.23871 0.12589 0.03397 

Central African Republic 1989 1995 639 451.95261 -0.22432 0.06205 

Chad 1982 1997 3191 -40.18228 0.02364 0.01193 

Chad 1997 2004 1162 552.48646 -0.27275 0.03564 

Comoro Islands 1981 1987 287 -253.89701 0.13107 0.08241 

Comoro Islands 1987 1996 508 417.43386 -0.20683 0.04529 

Congo 1990 2005 1221 -21.48545 0.01346 0.01758 

Côte d’Ivoire 1966 1987 2400 -33.35731 0.02035 0.00940 

Côte d’Ivoire 1987 1999 1659 414.24432 -0.20485 0.02320 

Ethiopia 1985 2005 9014 199.20539 -0.09660 0.00586 

Gabon 1986 2001 484 78.40048 -0.03644 0.03012 

Ghana 1965 1985 2997 16.36082 -0.00516 0.00911 

Ghana 1985 1998 3046 207.07985 -0.10122 0.01259 

Ghana 1998 2003 605 -204.83691 0.10493 0.06199 

Guinea 1984 1995 2263 -162.88153 0.08510 0.01847 

Guinea 1995 2005 2283 190.49414 -0.09207 0.02051 

Kenya 1963 1980 3891 50.12864 -0.02146 0.00892 

Kenya 1980 1997 7377 360.67798 -0.17827 0.00680 

Kenya 1997 2003 1425 -95.71686 0.05042 0.03584 

Lesotho 1962 1980 1467 -165.69941 0.08690 0.01605 

Lesotho 1980 2005 1892 182.17202 -0.08912 0.01277 

Liberia 1971 1986 1214 -182.48907 0.09525 0.01880 

Madagascar 1977 2004 6443 117.27999 -0.05584 0.00543 

Malawi 1977 2005 9646 108.03345 -0.05108 0.00465 

Mali 1972 1990 3715 -127.81746 0.06824 0.01026 

Mali 1990 2001 4232 306.89394 -0.15016 0.01373 

Mozambique 1982 1992 2469 -110.47129 0.05848 0.01798 

Mozambique 1992 2004 3885 19.65290 -0.00695 0.01346 

Namibia 1977 2000 2600 151.04035 -0.07337 0.00861 

Niger 1977 1998 4561 111.09838 -0.05209 0.00796 

Nigeria 1967 1983 4430 -39.61185 0.02319 0.00981 

Nigeria 1983 1997 4560 131.11275 -0.06284 0.01003 

Nigeria 1997 2003 1371 -321.95594 0.16384 0.03990 

Rwanda 1968 1982 2433 -41.11344 0.02438 0.01395 

Rwanda 1982 1999 6900 233.24329 -0.11404 0.00683 

Rwanda 1999 2005 2034 68.28538 -0.03133 0.03176 

Senegal 1963 1982 2240 -72.29744 0.04017 0.01118 

Senegal 1982 2005 6121 135.30356 -0.06460 0.00557 

Tanzania 1977 1995 6525 88.96062 -0.04165 0.00727 

Tanzania 1995 2005 2863 1.92485 0.00199 0.01750 

Togo 1973 1980 376 -49.92329 0.02885 0.06322 

Togo 1980 1998 2724 169.76016 -0.08210 0.01111 

Uganda 1974 2001 6272 43.78137 -0.01851 0.00564 

Zambia 1977 2002 4532 108.21828 -0.05110 0.00717 

Zimbabwe 1974 1982 838 -13.40051 0.01020 0.04026 

Zimbabwe 1982 1999 3234 375.76176 -0.18616 0.01005 
107

Table C.3 Dates of onset and speed of the fertility decline in selected sub-Saharan African countries 

Year of latest 

survey 

Onset of the fertility decline Split between 

urban and rural 

fertility trends 

Speed of transition 

Country Urban Rural Urban Rural 

Benin 2001 1986 1990 1961 -1.27 -1.27 

Botswana 1988 1968 1976 1969 -1.04 -0.97 

Burkina Faso 2003 1976 1983 1977 -1.50 -0.77 

Burundi 1987 1970 NA 1971 -1.05 NA 

Cameroon 2004 1982 1984 1964 -1.06 -0.28 

Central African Republic 1994 1987 1989 1987 -1.99 -2.87 

Chad 2004 1986 2000 1986 -0.60 -2.98 

Comoro Islands 1996 1980 1988 1981 -1.41 -2.07 

Congo 2005 1967 >2005 1968 -0.44 NA 

Côte d'Ivoire 1999 1968 1989 1967 -1.08 -1.94 

Ethiopia 2005 1979 1991 1980 -1.69 -0.97 

Gabon 2000 1984 1984 1984 -1.80 -0.36 

Ghana 2003 1950 1986 1950 -0.56 -0.55 

Guinea 2005 1986 1995 1987 -1.15 -0.92 

Kenya 2003 1960 1970 1960 -0.95 -0.77 

Lesotho 2004 1977 1977 1960 -1.40 -0.87 

Liberia 1986 1974 NA 1974 -0.32 NA 

Madagascar 2003 1971 1980 1971 -0.80 -0.56 

Malawi 2004 1977 1978 1978 -1.22 -0.51 

Mali 2001 1986 1991 1968 -1.22 -1.43 

Mozambique 2003 1984 >2003 1985 -0.83 NA 

Namibia 2000 1958 1975 1958 -0.64 -0.71 

Niger 1997 1975 1976 1976 -1.33 -0.52 

Nigeria 2003 1980 1986 1960 -0.47 -0.12 

Rwanda 2005 1966 1983 1967 -0.65 -0.77 

Senegal 2005 1963 1983 1963 -0.79 -0.62 

Tanzania 2004 1973 1979 1974 -0.95 -0.24 

Togo 1998 1967 1981 1967 -1.24 -0.82 

Uganda 2001 1968 1970 1970 -1.05 -0.18 

Zambia 2001 1973 1973 1973 -1.16 -0.51 

Zimbabwe 2005 1965 1983 1965 -1.11 -1.87 

NA = not available.

109

References 

Basu, A.M. 2007. On the prospects for endless fertility decline in South Asia. In United Nations (Ed.), 

Completing the fertility transition. New York, New York, USA: United Nations, Population 

Division, pp. 554-561. 

Blacker, J. 2007. Kenya’s fertility transition: How low will it go? In United Nations (Ed.), Completing the 

fertility transition. New York, New York, USA: United Nations Population Division, pp. 457-

468. 

Bongaarts, J. 2005. The causes of stalling fertility. Population Council Working Paper No. 204. 

Caldwell, J.C. 1994. Fertility in sub-Saharan Africa: status and prospects. Population and Development 

Review 20(1): 179-187. 

Caldwell, J.C., and P. Caldwell. 1993. The South African fertility decline. Population and Development 

Review 19(2): 225-262. 

Caldwell, J.C., I.O. Orubuloye, and P. Caldwell. 1992. Fertility decline in Africa: A new type of 

transition? Population and Development Review 18(2): 211-242. 

Camlin, C., M. Garenne, and T. Moultrie. 2004. Fertility trends and patterns in a rural area of South 

Africa in the context of HIV/AIDS. African Journal of Reproductive Health 8(2): 38-54. 

Castro-Martin, T. 1995. Women's education and fertility: Results from 26 Demographic and Health 

Surveys. Studies in Family Planning 26(4): 187-202. 

Cleland, J., N. Onuoha, and I. Timaeus. 1994. Fertility change in sub-Saharan Africa: A review of 

evidence. In T. Locoh and V. Hertrich (Eds.), The onset of fertility transition in sub-Saharan 

Africa. Liège, Belgium: Derouaux Ordina. 

Cohen, B. 1998. The emerging fertility transition in sub-Saharan Africa. World Development 26(8): 

1431-1461. 

Dyson, T. 1988. Decline of traditional fertility restraints: demographic effects in developing countries. 

IPPF Medical Bulletin 22(6): 1-3. 

Dyson, T., and M. Murphy. 1985. The onset of fertility transition. Population and Development Review 

11(3): 399-440. 

Dyson, T., and M. Murphy. 1986. Rising fertility in developing countries. In R. Woods and P. Rees 

(Eds.), Population structures and models: Developments in spatial demography. Boston, 

Massachusetts, USA, and London, England: George Allen and Unwin, pp. 68-94. 

Feysetan, B.J., and A. Bankole. 2002. Fertility transition in Nigeria: trends and prospect. In United 

Nations, Department of Economic and Social Affairs, Population Division (Ed.), Completing the 

fertility transition. New York, New York, USA: United Nations Available at 

http://www.un.org/esa/population/publications/completingfertility/RevisedBANKOLEpaper.PDF. 

Frank, O. 1983. Infertility in sub-Saharan Africa: Estimates and implications. Population and 

Development Review 9(1): 137-144. 

Gaisie, S.K. 1996. Demographic transition: The predicament of sub-Saharan Africa. Health Transition 

Review 6 (Suppl.): 345-369. 

Garenne, M. 2004. Age at marriage and modernization in sub-Saharan Africa. Southern African Journal 

of Demography 9(2): 57-77. 

Garenne, M., and V. Joseph. 2002. The timing of the fertility transition in sub-Saharan Africa. World 

Development, 30(10): 1835-1843. 

Garenne, M., S. Tollman, K. Kahn, and M. Collison. 2007. Fertility trends and net reproduction in 

Agincourt: 1992-2004. Scandinavian Journal of Public Health 35; in press. 

Garenne, M., and J. Zwang. 2006. Premarital fertility and ethnicity in Africa. DHS Comparative Reports 

No 13. 

Gobopamang, L., and H.N. Letamo. 2001. The role of proximate determinants in fertility transition: A 

comparative study of Botswana, Zambia and Zimbabwe. Southern African Journal of 

Demography 8(1): 29-35. 
110

Gould, W.T., and M.S. Brown. 1996. A fertility transition in Sub-Saharan Africa? International Journal of 

Population Geography 2(1): 1-22. 

Harwood-Lejeune, A. 2000. Rising age at marriage and fertility in southern and eastern Africa. European 

Journal of Population 17: 261-280. 

Kirk, D., and B. Pillet. 1998. Fertility levels, trends, and differentials in sub-Saharan Africa in the 1980s 

and 1990s. Studies in Family Planning 29(1): 1-22. 

Larsen, U., and H. Raggers. 2001. Levels and trends in infertility in sub-Saharan Africa. In J. Ties 

Boerma and Z. Mgalla (Eds.), Women and infertility in sub-Saharan Africa: A multi-disciplinary 

perspective. Amsterdam, the Netherlands : Royal Tropical Institute, KIT Publishers, pp. 25-69. 

Leridon, H., and L. Toulemon. 1996. La régulation des naissances se généralise. Les Dossiers du CEPED 

No. 41. 

Lesthaeghe, R., and C. Jolly. 1995. The start of the sub-Saharan fertility transition: Some answers and 

many questions. Journal of International Development 7(1): 25-45. 

Lindstrom, D.P., and Z. Woubalem. 2003. The demographic components of fertility decline in Addis 

Ababa, Ethiopia: A decomposition analysis. Genus 54 (3-4): 147-158. 

Maddison, A. 2003. L’économie mondiale: statistiques historiques. Paris, France: Organisation for 

Economic Co-Operation and Development. 

Mbacke, C. 1994. Family planning programs and fertility transition in sub-Saharan Africa. Population and 

Development Review 20(1): 188-193. 

McFalls, J.A., and M.H. McFalls. 1984. Disease and fertility. New York, New York, USA: Academic 

Press. 

McNicoll, G. 1992. Changing fertility patterns and policies in the third world. Annual Review of 

Sociology 18: 85-108. 

Mostert, W.P., J.L. van Tonder, and B.E. Hofmeyr. 1988. Demographic trends in South Africa. In: H.C. 

Marais (Ed.), South Africa: Perspectives on the future. Pinetown, South Africa: Owen Burgess, 

pp. 59-86. 

Moultrie, T., and I. Timaeus. 2003. The South African fertility decline: Evidence from two censuses and a 

Demographic and Health Survey. Population Studies 57(3): 265-284. 

National Academy of Sciences. 1993. Demographic change in sub-Saharan Africa. Washington, D.C., 

USA: National Academies Press. 

Ngom, P., and S. Fall. 2005. Fertility decline in francophone sub-Saharan Africa: 1980-2010. Nairobi, 

Kenya: African Population and Health Research Center, Inc. Working Paper 26. 

Rutenberg, N. 1991. Knowledge of contraception. DHS Comparative Studies No. 5. Columbia, Maryland, 

USA: IRD-Macro International. 

Rutenberg, N., and I. Diamond. 1993. Fertility in Botswana: The recent decline and future prospects. 

Demography 3(2): 143-157. 

Ryder, N.B. 1983. Cohort and period measures of changing fertility. In: R.A. Bulatao and R.D. Lee 

(Eds.), Determinants of fertility in developing countries. Volume 2. Fertility regulation and 

institutional influences. New York, New York, USA: Academic Press, pp. 737-756. 

Shapiro, D., and T. Gebreselassie. 2007. Fertility transition in sub-Saharan Africa: Falling and stalling. 

Paper presented at the 2007 Annual Meeting of the Population Association of America, New 

York, New York, USA. 

Shemeikka, R., V. Notkola, and H. Siiskonen. 2005. Fertility decline in North-Central Namibia: An 

assessment of fertility in the period 1960-2000 based on parish registers. Demographic Research 

13(4): 83-116. 

Swartz, L. 2003. Fertility transition in South Africa and its implications on the four major population 

groups. In Human Sciences Research Council (Ed.), Fertility: Current South African issues of 

poverty, HIV/AIDS and youth. Cape Town, South Africa: Human Sciences Research Council, pp. 

7-26. 

United Nations. 1978. Demographic yearbook 1978. Special issue: Historical supplement. New York, 

New York, USA: United Nations, Statistical Office. 
111

United Nations. 1997. Demographic yearbook 1997. Historical supplement. New York, New York, USA: 

United Nations, Statistics Division. 

United Nations. 2000. Demographic yearbook 2000. New York, New York, USA: United Nations, 

Statistics Division. 

United Nations. 2001. The state of demographic transition in Africa. Addis Ababa, Ethiopia: United 

Nations Economic Commission for Africa. 

United Nations. 2007a. World population prospects: The 2006 revision. New York, New York, USA: 

United Nations, Population Division. 

United Nations. 2007b. Completing the fertility transition. New York, New York, USA: United Nations, 

Population Division. 

van de Walle, E., and J. Knodel. 1980. Europe's fertility transition: New evidence and lessons for today's 

developing world. Population Bulletin 34(6): 3-44. 

van de Walle, E., and A.D. Foster. 1990. Fertility decline in Africa: Assessment and prospects. World 

Bank Technical Paper No. 125 (Africa Technical Department Series). Washington, D.C., USA: 

World Bank. 



	

	

1. Westoff, Charles F. 2001. Unmet Need at the End of the Century.

2. Westoff, Charles F. and Akinrinola Bankole. 2002. Reproductive Preferences in 

Developing Countries at the Turn of the Century.

3. Rutstein, Shea O. 2002. Fertility Levels, Trends, and Differentials 1995-1999.

4. Mahy, Mary. 2003. Childhood Mortality in the Developing World: A Review of Evidence 

from the Demographic and Health Surveys.

5. Westoff, Charles F. 2003. Trends in Marriage and Early Childbearing in Developing 

Countries. 

6. Rutstein, Shea O. and Kiersten Johnson. 2004. The DHS Wealth Index.

7. Yoder, P. Stanley, Noureddine Abderrahim, and Arlinda Zhuzhuni. 2004. Female Genital 

Cutting in the Demographic and Health Surveys: A Critical and Comparative Analysis. 

8. Stallings, Rebecca. 2004. Child Morbidity and Treatment Patterns. 

9. Rutstein, Shea O. and Iqbal H. Shah. 2004. Infecundity, Infertility, and Childlessness in 

Developing Countries.

10. Mukuria, Altrena, Jeanne Cushing, and Jasbir Sangha. 2005. Nutritional Status of 

Children: Results from the Demographic and Health Surveys, 1994–2001. 

11. Mukuria, Altrena, Casey Aboulafia, and Albert Themme. 2005. The Context of Women’s 

Health: Results from the Demographic and Health Surveys, 1994-2001.

12. Yoder, P. Stanley, Noureddine Abderrahim, and Arlinda Zhuzhini. 2005. L’excision dans 

les Enquêtes Démographiques et de Santé: Une Analyse Comparative. 

13. Garenne, Michel, and Julien Zwang. 2006. Premarital Fertility and Ethnicity in Africa.

14. Westoff, Charles F. 2006. New Estimates of Unmet Need and the Demand for Family 

Planning.

15. Fort, Alfredo L., Monica T. Kothari, and Noureddine Abderrahim. 2006. Postpartum 

Care: Levels and Determinants in Developing Countries.

16. Khan, Shane, Vinod Mishra, Fred Arnold, and Noureddine Abderrahim. 2007. 

Contraceptive Trends in Developing Countries.

17. Johnson, Kiersten, and Yuan Gu. Men’s Reproductive Health, Health-Related Behaviors, 

and Involvement in Family Dynamics. (forthcoming)

18. Garenne, M. Michel. 2008. Fertility Changes in Sub-Saharan Africa.