The emerging fertility transition in sub-Saharan Africa

Pergamon World Development Vol. 26, No. 8, pp. 1431-1461, 1998

0 1998 Elsevier Science Ltd All rights reserved. Printed in Great Britain

0305-750)(/98 $19.00+0.00 PII: SO305750X(98)00058-8

The Emerging Fertility Transition in Sub-Saharan

Africa *

BARNEY COHEN National Research Council, Washington, DC, U S.A.

Summary. - This paper summarizes the recent evidence on levels, trends and differentials in achieved fertility, nuptiality, and contraceptive use in sub-Saharan Africa. Drawing from a wide variety of data sources, not all of which have been readily available in the past, an interesting picture of fertility decline emerges, one that is quite at odds with the popular perception of stationary or very limited fertility decline. A fairly widespread decline in fertility is currently underway across Africa. Moderate to large declines in fertility have already taken place in Kenya, Rwanda, Zimbabwe, Botswana, South Africa, and CBte d’Ivoire, with smaller declines observed in Malawi, Tanzania, Zambia, Cameroon, Central African Republic, Burkina Faso, Gambia, Ghana, Mauritania, Senegal, and Sierra Leone. The driving forces behind these changes are later marriage and the greater use of modern contraception. A unique characteristic of African transitions appears to be the extent to which contraceptives are being used to space rather than to limit births. 0 1998 Elsevier Science Ltd. All rights reserved.

Key words - fertility, nuptiality, family planning, sub-Saharan Africa

1. INTRODUCTION

Sub-Saharan Africa is the only major region of the developing world that has not yet undergone a general decline in fertility. Consequently, the question of when fertility is likely to decline is a pressing one. This paper attempts to summarize the recent evidence on levels, trends and differentials in achieved fertility, nuptiality, and contraceptive use in sub-Saharan Africa. Drawing from a wide variety of data sources, not all of which have been readily available in the past, the paper provides estimates of fertility in virtually every country in sub-Saharan Africa. By assembling this data, an interesting picture emerges, one that is quite at odds with the popular perception of stationary fertility or very limited fertility decline (see, for example, recent statements by Mburugu, 1994 and Isiugo- Abanihe, 1994a). In fact, fertility is declining virtually across the board, although some of the observed declines are quite small. It is possible that these small changes are the product of using different strategies to estimate fertility at various points in time or are an artifact of unreliable data. This is, however, unlikely. Inspection of data on trends in nuptial@ and contraceptive

use reveals that both age at marriage and modern contraceptive use are rising, particularly within easily identifiable subgroups. Conse- quently, most of the small observed declines in fertility are probably genuine.

Until recently, there was a general reluctance within the demographic community to accept virtually any signs of a fertility decline in sub- Saharan Africa. Because data from the region have been of extremely variable quality in the past, estimates of the fertility rate for particular countries have fluctuated wildly. Close examination of African data has often revealed gross internal inconsistencies within datasets that could only be the result of age misreporting or the omission or systematic displacement of vital events. Thus, historically, when one was presented with different estimates of vita1 rates for the same African country, one was always left wondering whether the recorded differences reflected a genuine trend or, more likely, methodological difficulties in one or both surveys.’

Early indications that a widespread decline in fertility was occurring in sub-Saharan Africa

*Final revision accepted: February 21, 1998.

1431

1432 WORLD DEVELOPMENT

emerged from the publication of results from the first phase of the Demographic and Health Surveys program (DHS-I) in the late 1980s. DHS-I included national-level surveys in 11 different African countries (Botswana, Burundi, Ghana, Kenya, Liberia, Mali, Senegal, Sudan, Togo, Uganda, and Zimbabwe). Preliminary analyses indicated that statistically significant declines in fertility had occurred in eight of the 11 countries (Freedman and Blanc, 1992). In three of these eight cases (Kenya, Botswana, and Zimbabwe), the declines were accepted as basic- ally valid, although later in-depth analysis revealed that at least part of the declines in Botswana and Zimbabwe were attributable to sampling problems. In the other five cases (Burundi, Mali, Senegal, Northern Sudan, and Togo), the magnitude of the declines were judged to be implausible since they were not accompanied by a drop in fertility preferences or an appreciable rise in age at marriage or modern contraceptive use.

Some of the concerns about data quality and the plausibility of reported fertility declines in these five cases appears to have been well founded. In Mali, for example, both the 1987 DHS survey and preliminary results from the 1987 Census recorded a decline in fertility among women aged 15-34 that began in the early 1980s. Nevertheless, following the publication of the results from the 1995-96 DHS survey of Mali, it appears that the earlier suggested decline was probably spurious, which in the case of the 1987 DHS was probably the result of the omission or systematic displacement of recent births by interviewers in order to avoid answering an extensive series of additional questions on the health and well-being of young children.’

Nevertheless, it also is clear that there has been a genuine under-appreciation of the speed and magnitude with which these changes have occurred. In Kenya, for example, the commence- ment of a decline in fertility in the late 1980s as reported in the 1989 Kenyan DHS appeared to be at odds with nearly all prevailing expert opinion (see, for example, Blacker, 1994). The decline was only commonly accepted as factual after much careful examination and comparison of the DHS data with a sufficient number of other sources that pointed to the same conclusion (Robinson, 1992). In Botswana and Zimbabwe, despite clear evidence from early DHS surveys that fertility had declined somewhat in both countries during the mid-1980s a debate ensued around the true magnitude of the declines. Secondary analysis of

the data revealed that some portion of the reported fertility decline could be attributable to differences in the composition of the sample populations (Thomas and Muvandi, 1994a,b; Blanc and Rutstein, 1994). This debate became redundant with the publication of the 1994 DHS in Zimbabwe that revealed a further dramatic decline in fertility in both urban and rural areas over 1988-94. Similarly, the search for explanations for the surprising stability of fertility among Ghana’s relatively well-educated and urbanized population, will likely lose much momentum following the publication of the latest round of DHS results, which shows a one-child per woman drop in the total fertility rate over the course of the last five years. Finally, for years South Africa was excluded from tables of sub-Saharan Africa for political reasons so that clear evidence of fertility decline in South Africa from as early as the 1970s was ignored.

Uncertainties about the speed and size of the African fertility transition have fueled a related debate about whether the African transition is a “new” transition, in the sense that emerging patterns of fertility decline are inherently different from those that occurred in Asia or Latin America. For example, Caldwell et al. (1992) have argued that there are good reasons why the declines in sub-Saharan Africa are unlikely to resemble those witnessed elsewhere. First, they argue, constraints on premarital and extramarital sexual relations are weaker in Africa than in Asia or Latin America. Consequently, as age at marriage rises, a high prevalence of premarital sexual relations will fuel a strong demand for contraceptives among younger women. Second, to a far greater extent than elsewhere in the world, African women use modern contraceptives to improve child spacing and not to shape total family size. Hence, modern contraceptives are merely replacing traditional spacing practices such as prolonged breastfeeding and postpartum abstinence, often with considerable redundancy.’

The purpose of this paper is to clarify the ongoing changes in fertility, nuptiality, and family planning that are taking place across the region. The plan for the remainder of the paper is as follows: Section 2 presents a brief overview of the sources and quality of demographic data in the region. National and subnational data on levels and trends in African fertility are presented in Section 3. Section 4 provides an exploration of the relationship between trends in fertility and trends in some of the proximate determinants of fertility, particularly age at marriage and use of modern contraception.

FERTILITY TRANSITION IN SUB-SAHARAN AFRICA 1433

Section 5 contains a summary of the major findings and offers some concluding observations.

2. SOURCES AND QUALITY OF DEMOGRAPHIC DATA

Until 1960, virtually no sub-Saharan African country had conducted a complete census. Consequently, knowledge about key demographic variables in the region was very limited, leading to the common perception that fertility rates in Africa were very high and stationary. In the few cases where censuses or demographic surveys were undertaken, they were often unreliable and of limited content. Typically, early censuses did not include questions related to the number of children ever born or childhood mortality. Furthermore, a large percentage of the adult population was unable to report their age accurately, making the calculation of age-specific schedules highly problematic. Finally, vital registration data were virtually nonexistent throughout the region, and when available, were of questionable quality.

Fortunately, demographic data collection in Africa has improved considerably over the last 30 years. Although vital registration is still rare, most countries have conducted one and in many cases several censuses of varying quality. More reliable demographic information has come from surveys, particularly those conducted under the auspices of large multinational data collection efforts such as the World Fertility Survey (WFS) program or the Demographic and Health Surveys (DHS) program. The WFS operated in the mid-1970s and early 1980s and administered surveys in 10 sub-Saharan African countries. The DHS program, which began in the mid-1980s and is on-going, has published standardized demographic reports for 24 sub-Saharan African countries and issued preliminary results for two others. In addition, WFS provided technical assistance for a demographic survey in Rwanda in 1983, and the DHS provided technical assistance for a demographic survey in Guinea in 1992 that are usually not included among their list of accomplishments.

The quality of DHS data has been analyzed periodically by DHS staff. In cases where data problems have been identified, they have been most severe in sub-Saharan Africa (Blanc and Rutenberg, 1990). For example, Arnold (1990) discovered errors in the coverage and timing of births including: a systematic displacement of children’s birth dates forward in time making low parity children appear younger than they really

are; a disproportionate number of women’s ages heaped on digits ending in 0 and 5; and, missing or incomplete information in some birth histories. These problems were determined to be most severe in the first phase of the DHS in Botswana, Burundi, Liberia, Mali, and Togo.

Fortunately, the effect of these problems on fertility levels is relatively minor. For example, Arnold and Blanc (1990) calculate that without any displacement, the total fertility rate in Liberia, where displacement was a serious problem, would have been 6.5 instead of 6.3 births per woman during 1983-88. Nevertheless, it is important to acknowledge that the danger of drawing incorrect conclusions from data collected in areas where vital events go unrec- orded always exists. Consequently, a single point estimate of fertility from Africa should be inter- preted with some caution.

3. LEVELS AND TRENDS IN FERTILITY

3.1. Total fertility

All available estimates of the total fertility rate (TFR)4 for each sub-Saharan African country can be found in Appendix A, For ease of comparison, all available fertility estimates have been converted into point estimates. In reality, a particular figure may be the mid-point of a range of plausible estimates. Doubtless, some of the fluctuations observed in the data reported in Appendix A are the result of differences in data quality so that trends in fertility may appear more erratic than they truly are. For example, in Ethiopia, the data imply a substantial increase in fertility during the 1970s followed by a rapid decline in the 1980s. Both trends are almost certainly exaggerated.

Although incomplete, several important conclusions can be drawn from the data in Appendix A. First, despite recent changes, real or imaginary, most Africans still have large families. Few countries have TFRs below five children per woman, and nowhere is fertility currently less than four births per women, a rate well above that required for simple replacement. Second, beyond this broad generalization, there are large variations in fertility rates across the region. For the 14 East African countries included in this paper, the most recent fertility estimates ranges from 4.3 children per woman in Zimbabwe to 6.9 children per woman in Uganda. Among the 14 West African countries, the estimated TFRs lie within a narrower range of 5.5 births per woman in Ghana to 7.4 births per woman in Niger.


There is some evidence to suggest that fertility rates rose in several African countries during the 1960s and 1970s although it is unclear what proportion of the change is genuine and what proportion is attributable to improvements in data collection. Kenya is the most cited example of a country in which fertility rates may have increased over this period. Historical fertility estimates for Kenya are available from the 1962 Post-Enumeration Sample Census, the 1969 Census, and the 1977 National Demographic Survey. At face value, the data from these sources indicate that fertility rose dramatically from 5.3 births per woman in 1962 to 6.6 in 1969 and to 8.0 in 1977. Extensive manipulation resulted in official estimates of fertility in the earlier periods being revised to 6.8 for 1962 and 7.6 for 1969. It is now apparent, however, that the whole shape of the age-specific fertility distributions derived from both the 1962 and 1969 censuses were almost certainly biased (Blacker et al., 1979). Despite all the official data massaging, TFR estimates for 1962 and 1969 were probably still too low. Fertility probably increased in Kenya during 1962-77, probably due to a reduction in pathological sterility and shorter birth intervals due to declines in breastfeeding and postpartum abstinence, but the true extent of the increase is unknown.

An increase in fertility in the 1970s and 1980s is more definite in Cameroon and certain other Central African countries. This increase has largely been attributed to a reduction in the historically high incidence of pathological sterility in central Africa resulting from the high prevalence of gonorrhea and other STDs (Frank, 1983; Tambashe, 1992). Nevertheless, pockets of infertility still remain, such as among the Zande in the Central African Republic where data from the 1994-95 DHS of Central African Republic indicate that 26% of Zande women aged 40-49 have never given birth.

In most places, however, signs of a fertility decline are beginning to appear, although only in a handful of countries has fertility rates fallen to below five or six children per woman. Recent trends in African fertility can be detected in a number of different ways: For example, within a single survey one can analzye complete birth histories, if available, but this requires reasonably high quality data because of the likely omission or systematic displacement of vital events (Potter, 1977). Another option, if only one dataset is available, is to use information on children ever born as a measure of lifetime fertility experience up to the moment at which the data are collected and to compare it with

information on recent births. A full description of this method (commonly called the “Parity/ Current Fertility” or P/F ratio method) can be found in United Nations (1983)’ Alternatively, if data from multiple surveys are available, one can compare information on just recent fertility over time.

Using the parity/current fertility (P/F) ratio technique, Cleland et al. (1994) carried out a detailed analysis of all major single-round surveys undertaken in Africa between the late 1980s and early 1990s. Five distinct patterns emerged, associated with poor data or with constant, ambiguously falling, unambiguously falling, or in one instance, rising, fertility. Based on this analysis, Cleland et al. (1994) conclude that only three DHS surveys - Botswana in 1988, Kenya in 1989, and N. Sudan in 1989-90 - display the classic pattern of upward P/F ratios usually associated with declining marital fertility. Data from six other surveys - Zimbabwe in 1984 and 1988, Senegal in 1986, Togo in 1988, Nigeria in 1990, and Cameroon in 1990 - and data from one census - Swaziland in 1986 - display a pattern that is consistent with both an underreporting of recent births and a recent fertility decline that has affected all age groups. Out of this latter list, the authors accept only the Zimbabwe data as indicating a genuine fertility decline although it is clear from more detailed analysis that the Nigeria data mask large regional differentials. Fertility declines have occurred in the South-East and South-West regions of the country but not elsewhere (Reinis et al., 1991).

Since the Cleland et al. (1994) review, data have become available from more than 30 additional censuses or surveys, although several of these are still in preliminary form. Inspection of P/F ratios from these recent sources indicates that the list of countries that have experienced a recent decline in marital fertility has grown considerably longer. To the list of four countries identified above (N. Sudan, Kenya, Botswana, and Zimbabwe), one must now add Cote d’Ivoire (on the basis of a survey conducted in 1994), Ghana (1993), Kenya (1993), Lesotho (1986) Namibia (1992) Rwanda (1992), Senegal (1992-93) Tanzania (1991-92 and 1994), Zambia (1992), and Zimbabwe (1994). Note that there is some overlap in the countries included in the most recent round of surveys so that some of the more recent surveys merely serve to confirm an earlier conclusion of fertility decline.

Table 1 provides a visual summary of trends in total fertility for all sub-Saharan African countries for which sufficient data are available.


Table 1. Summary of recent changes in fertili& Sub-Saharan Afica”

Region and Country

Recent Trend in National-Level Fertility Data

Rising No Ambiguous/ Moderate/ Insufficient Change Small Large Data

Decline Decline

Basis of Determinatior?

East Africa Burundi Djibouti Eritrea Ethiopia Kenya Madagascar Malawi Mozambique Rwanda Somalia Tanzania Uganda Zambia Zimbabwe

Central Afica Angola Cameroon C.A.R. Chad Congo D.R. of Congo Equatorial Guinea Gabon

Southern Africa Botswana Lesotho Namibia South Africa Swaziland

West Africa Benin Burkina Faso C&e d’Ivoire The Gambia Ghana Guinea Liberia Mali Mauritania Niger Nigeria Senegal Sierra Leone Togo

0

0

0

0

0 0

a 0

0

0

0

0

0

0

0

0 0

0

0

0

0 0

0

0

0

0

0

0

0 0

0

0 0 0

0

0 0

0

0

0

0

1987s 1990s 199Oc

1995196s 1981s, 1984c, 1990s 1984s, 1989s 1993s 1992s 1997’ 1984s, 1992s 1996s 198Oc, 1997s” 1983s 1992s

1988c, 1991/92s, 1996s 198Oc, 1988s, 1995s 198Oc, 1992s, 1996s 1984s, 1988s, 1994s

1978s, 1991s 1988c, 1994/95s 1993c, 1997s’

1978c, 1984~

1984s, 1988s 1977s, 1986~ 1992s 1987/89s, 1993s 1976c, 1986c, 1988s

1982s, 1996s 1985c, 1991s 1993s 1980/8ls, 1994s 1983c, 1990s 1979180s 1988s, 1993s 1983c, 1992s 1986s 1987s 1995196s 1981s 1990s 1988c, 1992s 1971s, 1981/82s, 1990s 1986s, 1992/93s, 1997s” 1974c, 1985~ 1971c, 198lc, 1988s

“Quality of data: 0 Fair, 0 Poor bc = population census; s = national-level demographic survey ‘Only preliminary results currently available.


The data for this table can be found in Appendix A. The table shows fertility falling in 22 countries across the continent. In most cases, the declines are quite small, less than one birth per woman. Moderate to large declines in fertility, taken here to imply declines of 1.5 children per woman or greater have occurred in 10 countries.

Evidence of recent declines in fertility is strongest in east and southern Africa. In east Africa, Kenya’s precipitous decline has been analyzed in considerable detail by Brass and Jolly (1993). By analyzing multiple surveys and different cohorts within surveys, the authors found that the declines in fertility have cut across almost all socioeconomic and demographic subgroups: Fertility has fallen in both urban and rural areas, across all levels of education, within all age groups, and across all provinces, regardless of level of socioeconomic development. A similarly dramatic recent decline in fertility has been recorded in Zimbabwe, where fertility has fallen by more than two children per woman in under a decade; in Rwanda, where there has been a sizable decline of 1.5 children per woman in a lo-year period, although the current level of fertility is still very high; and, in Ethiopia, although here the data are too unreliable to detect a trend with any measure of certainty. Smaller declines have been recorded in Tanzania, where fertility fell by approximately half a child per woman between the 1991-92 and the 1996 surveys, and in Malawi and Zambia, based on preliminary results from surveys conducted in 1996.

In southern Africa, fertility is declining in all five countries for which data are available. There has been considerable discussion about the true extent of the decline in Botswana, which may be partly attributable to sampling problems. Recent estimates of the TFR in Lesotho and Swaziland indicate that fertility in these countries is about 5.0 children per woman. Fertility is slightly lower in South Africa, where fertility has probably been in decline since the early 1970s (Mostert, 1990).

In the past, Caldwell and others have argued that the cultural uniqueness of sub-Saharan Africa presents special obstacles to fertility decline (see, for example, Caldwell and Caldwell, 1990). In particular, Caldwell and his collabora- tors have suggested that West Africa may be more resistant to fertility change than elsewhere in the region because the core features of social organization and traditional pronatalist attitudes - for example, the idea that having children allows ancestors to be “reborn” - are presumed to be strongest there (Caldwell et al., 1992). Nevertheless, all six West African countries that

participated in the WFS have been resurveyed within the last seven years, and survey results indicate that some fertility declines, weak or strong, have occurred in all of them, although not always for the same reasons. Fertility has fallen furthest in C&e d’Ivoire, from 7.2 births per woman in the 1980-81 WFS to 5.7 births per woman in the 1994 DHS, primarily due to rising contraceptive use and later age at first marriage in urban areas. Fertility has fallen by between one and one and a half children per woman in Senegal and Ghana. In Senegal, the decline has occurred almost entirely among women under age 30 as a result of a trend toward later marriage and later first birth (Pison et al., 1995). In Ghana, there has been virtually no change in marriage patterns in the five years between the 1988 and the 1993 DHS surveys, but noticeable changes have taken place in fertility preferences and the use of modern contraceptives. Smaller declines, of less than one child per woman, have been recorded in Benin, Mauritania, and Nigeria.

3.2. Subnational trends in fertility

Important subgroup variation is often missed if one relies solely on national-level trends. While the publication of the initial results of the World Fertility Survey established the existence of significant differences in total fertility between various socioeconomic groups, it was not until the recent outpouring of demographic data from the Demographic and Health Surveys that one was able to investigate whether total fertility is changing within subgroups. As an illustration, Figure 1 contains four graphs showing various trends in total fertility by urban-rural residence and by level of education. Countries that have experienced a moderate or large decline in fertility are shown separately from countries that have experienced little or no decline in fertility to date (see Table 1). Hence, the panel in the top left-hand corner displays fertility trends by urban-rural residence for countries listed in Table 1 that have experienced moderate or large declines in fertility while the panel in the upper right-hand corner displays the same information but for countries with little or no decline in fertility at the national level, to date. In both cases, trends in urban areas are indicated by solid lines, trends in rural areas by dotted lines.

All surveys report that fertility is lower in urban than rural areas. The conventional explanation is that women in urban areas often have more schooling and are thus more likely to parti-


cipate in the formal labor market. Consequently, they are more likely to appreciate the advantages of having a smaller family. Furthermore, urban women often have better knowledge of and access to modem contraception. Nevertheless, the top left-hand panel makes it clear that fertility is falling in both urban and rural areas in countries where moderate or large recorded declines in fertility have been recorded. In contrast, fertility declines are limited to urban areas for countries where national-level declines in fertility are small or insignificant (see top right-hand panel of Figure 1). In this latter group of countries, urban-rural differences in fertility are widening. For example, in Ghana, fertility has fallen by nearly two children per woman in urban areas (5.8 children in 1979-80 versus only 4.0 children in 1993) but by less than half a child per woman in rural areas (6.8 children in 1979-80 versus 6.4 children in 1993).

A similar comparison of fertility trends by broad education subgroups reveals similar patterns (see lower two panels of Figure l).h In

declines in countries‘ with moderate to large

Trends ountries with noderOWlar9e declines ---_ .\

8 ----.,

.\

national-level fertility, one can observe women of all educational strata having fewer children (bottom left-hand panel of Figure 1). Elsewhere, however, it is only women with secondary school education or higher who have reduced their fertility, so that, in these countries, weak national-level fertility declines are actually made up of strong declines among women with secondary school education or higher and little or no decline among women with no education (see bottom right-hand panel of Figure 1)’

3.3. The shape of the fertility distribution

Construction of age-specific fertility schedules requires reasonably accurate data because the omission or misplacement of vital events can lead to significant errors. Nevertheless, when they are possible to construct, a great deal about the timing and intensity of childbearing can be learned from them. Even simple comparisons with model fertility schedules can be quite revealing.

in fertility by residence OuntPi(lS with small OP no duClinl)S

8

- urban 3 3

lSl30 19k Year

iseo lQb5 19bo iSki 19bo Year

lQb5

Trends in fertility by level of education ountrlBs with l oderats/largn declines

4 Duntries with small 0P no declines

*.

_)

lQb0 19b5 19ao 19Qk 19klo VBW

19&i vear 19bO 19bs

Figure 1. Trends in total fertility, selected sub-Saharan African countries.


Figure 2 illustrates how an age-specific fertility schedule usually changes as fertility comes under increasing control. These schedules represent age patterns of fertility rather than the level of fertility so that the sum of the rates, taken over all reproductive ages is 1.0. The figure is based on model fertility schedules developed by Coale and Trussell (1974), who essentially multiplied a

Age

Figure 2. Model fertility schedules (total fertili& = 1.0)

Kenya

; #;-/--q&.1 t

0-

19-19 ’ I I 20-24 25229 30-‘34 35-39 40-44 45-49

Age

Botswana

p’ 0 is-19 ' 20-24 2d29 30-134 n-39 ' 40-44 4s-i9

Age

model schedule of nuptiality (Coale and McNeil, 1972) with a model schedule of marital fertility to produce model fertility schedules that can be characterized by only three parameters: age at first marriage (a,,), rate of marriage (k), and extent of marital fertility control as measured by the extent of departure from a natural fertility schedule (m). These model fertility schedules have been found to fit historical fertility schedules of different populations extraordinarily well. As Figure 2 demonstrates, as societies depart from “natural” or uncontrolled fertility (i.e. m = 0) towards more controlled fertility (m > 0), the age-specific fertility schedule usually changes in a regular, predictable way, with the greatest declines occurring at older ages. This is because as women age, they are more likely to have achieved their desired family size and thus use efficient contraception pregnancies.’

to prevent further

Inspection of age patterns of fertility from various African demographic surveys suggests, however, an alternative pattern. Figure 3 shows changes in the proportion of age-specific fertility

time in four African countries (Kenya,

Zimbabwe

' -19 20124 25129 30-134 35-M 40-44 Age

45249

.4 - Cote d'Ivoire z z !2 ; .3

[;I/-,,

k o- 15-19 20J24 2G29 30-34 35-39

m 40-44 4d49

Figure 3. Changes over time in the proportion of total fertiliy in each age group, selected sub-Saharan African countries.


Botswana, Zimbabwe, and C&e d’Ivoire).’ All four of these countries are listed in Table 1 as having experienced a moderate to large decline in fertility and all four have conducted at least two independent demographic surveys over the past 20 years that are thought to be relatively reliable. In Kenya, the difference in total fertility across surveys is 2.7 children per woman. In the other three countries, it ranges from 2.2 to 1.5 children per woman. The figure reveals remark- ably similar patterns of childbearing within and between countries, particularly given the many difficulties associated with collecting accurate demographic data in the region. There is virtually no evidence of a stopping pattern in any of the fertility schedules, despite the reported practice of terminal abstinence in some societies. Apparently, in these four countries, fertility has fallen, but not through a disproportional reduction in higher order births. Again, this demon- stration is further evidence that contraceptives are rarely being used to restrict total family size. Rather they are used for child spacing purposes, to guarantee appropriate intervals between births to protect the health of the children and to preserve the future reproductive capacity of the mother.”

3.4. Trends in jirst births

The age at which a woman gives birth for the first time has important implications for the health and well-being of the mother and her child (National Research Council, 1989). Compared to older women, teenage mothers - and especially young teenagers - face greater risks of pregnancy-related and delivery complica- tions, maternal morbidity and mortality, and having premature and low birthweight babies. Significant negative educational and economic consequences also result from having children at a very young age. The most publicized examples of these consequences relate to lost educational opportunities (Bledsoe and Cohen, 1993). Furthermore, under a fertility regime with little or no use of modern contraception, the mother’s age at first birth is also an important determinant of completed family size.

In general, African countries have relatively high rates of adolescent fertility; and the median age of women at first birth in sub-Saharan Africa is approximately two years younger than it is in North Africa, Asia, or Latin America (Arnold and Blanc, 1990). To a large extent, differences in teenage fertility rates are largely attributable to differences in the average age of marriage. For example, teenage fertility is highest in Niger

where the median age of 14.9 at first marriage is among the lowest in Africa (Kourgueni et al., 1993). Similarly, teenage fertility is low in Burundi where the mean age at first marriage is 19.5 years (Segamba et al., 1988).”

Recent survey data have identified a weakening of the link between the age a woman first marries and the age a woman first gives birth. In several countries, age at first marriage is rising but age at first birth is remaining constant, resulting in fewer total adolescent births but disproportionally more births to unmarried adolescents (Bledsoe and Cohen, 1993). In Kenya and Botswana, for example, over 70% of teenagers who give birth are either unmarried or became pregnant before they married (Popula- tion Reference Bureau, 1992).

4. PROXIMATE DETERMINANTS OF FERTILITY

Reproductive change can be accounted for in terms of a set of proximate (biological and behavioral) determinants that directly affect fertility. These proximate determinants can be grouped into three broad categories: factors relating to exposure to intercourse, factors related to fertility in the absence of deliberate efforts to limit births (i.e. fecundability, sterility, postpartum infecundity, and intrauterine mortality), and factors related to deliberate fertility control (contraception and induced abortion). All other social and economic factors affect fertility indirectly through these proximate determinants (Bongaarts and Potter, 1983).

In the past, high fertility in Africa resulted from early and near universal marriage, and extremely low rates of efficient contraception. Fertility was limited (outside of geographic areas of pathological sterility) by social pressures against premarital sex, the practice of postpartum sexual abstinence, and long breastfeeding periods that lead to lengthy periods of lactational amenorrhea (Caldwell and Caldwell, 1977, 1987; Page and Lesthaeghe, 1981). These fertility-reducing practices were enacted princi- pally to ensure exceptionally long birth intervals in order to minimize infant mortality and to relieve maternal physical stress. Recently, however, there are signs that some of these cornerstones of African fertility may be weakening (Page and Lesthaeghe, 1981; Caldwell et al., 1992; Westoff, 1992; Jolly and Gribble, 1993).

One reason it is often difficult to detect the start of a decline in fertility is that certain aspects of socioeconomic development have


competing effects on different proximate determinants, which cancel each other out at low levels of development (Page and Lesthaeghe, 1981). For example, formal education tends to raise the age at marriage and delay childbearing, but small amounts of education also can break down traditional child-spacing practices, such as prolonged breastfeeding and postpartum abstinence, leading to an increase in fertility (United Nations, 1995). Consequently, examining data on trends in various proximate determinants is important to understanding recent changes in African reproductive behavior. The discussion below concentrates on age at first marriage and contraceptive use, the two most dynamic proximate determinants.

4. I. Trends in African marriage and sexual union

In most societies, exposure to sexual intercourse, and consequently, the great majority of childbearing still takes place within the legal confines of marriage.12 Thus, changing marriage patterns have an important bearing on total fertility. For example, much of the early fertility decline observed in Algeria, Egypt, and Tunisia, can be attributed to a shift toward later age at first marriage (Fargues, 1989; National Research Council, 1982). In these north African countries, the initial stage of fertility decline was immediately followed by a second stage when the demand for children fell substantially and there was a corresponding increase in the use of modern contraception among married women.

In sub-Saharan Africa, however, inquiry into marriage patterns is complicated. Numerous forms of union exist, and entry into marriage may be a long ambiguous process rather than a discrete event. To confuse matters further, conjugal relationships appear to be becoming more, rather than less, fluid (Bledsoe, 1990). In Botswana, for example, the institution of marriage has essentially disappeared. Instead, loose associations such as visiting relationships are commonplace; and a considerable percentage of childbearing takes place outside the legal confines of marriage (Lesetedi ef al., 1989).

African marriages are usually marked by a ceremony and the transfer of bridewealth, which can range from symbolic tokens to substantial payments of goods and services spanning several years. But the payment of bridewealth, the ceremony, the cohabitation of spouses, and the consummation of the marriage can occur over several months and not always in the same order (Meekers, 1992).13 Such ambiguities make it

difficult to define what constitutes a premarital birth.

With these ambiguities in mind, we now turn to an examination of trends in nuptiality. As with examining trends in fertility, trends in age at marriage can be discerned either by comparing the experience of older and younger cohorts of women in a single survey, or they can be examined by comparing the experiences of similar cohorts of women across time from different surveys. The first of these two approaches is taken in Table 2. The first two columns of this table show the proportion of women who report that they were married by age 20 for two groups of women: those aged 20-24 and those aged 45-49, at the time of the survey. Although most African women still marry while they are quite young, the proportion of women marrying before age 20 is declining rapidly in some countries. In east Africa, there is a great deal of heterogeneity associated with age at first marriage, but a substantial shift toward later first marriage has occurred in eight of the nine east African countries for which recent data are available. The transformation is greatest in Rwanda, where the shift between older and younger cohorts is so large one suspects that it may be overstated. It is smallest in neighboring Uganda, which shows no such trend. By contrast, in West Africa, where recent survey information is available for 11 countries, only two (Senegal and Guinea) suggest that a change in marriage patterns has occurred between older and younger women.

Splicing together cohorts of women from various demographic surveys and censuses produces similar results. Using data available in 1991, Westoff (1992) found some suggestion, weak or strong, of the emergence of a recent trend toward later age at marriage and age at first birth in Kenya, Uganda, and Zimbabwe in East Africa, and Mauritania, Nigeria, Senegal, Togo and possibly Ghana in West Africa. (Mauritania has been excluded from Table 2 because the data are considerably out of date.) The changes in Nigeria have been confirmed by subsequent detailed analysis (Isiugo-Abanihe, 1994b).

A complete explanation for the changes in the timing of marriage is beyond the scope of this paper, however, they are partly compositional, as indicated by a significant positive relationship to urban residence and educational attainment (Antoine and Nanitelamio, 1991; see also Table 2). In some cases, however, these changes also are occurring among rural women and women who have never been to school, implying that


there are also structural explanations associated with more general and fundamental transformations taking place in African family and social institutions (see, for example, Bledsoe, 1990).

4.2. Trends in contraceptive use

Contraceptive prevalence is one of the most important proximate determinants of fertility. Over the last 40 years, the fertility transitions observed in developing countries have been achieved largely by increased fertility regulation within marriage, through the use of contraception or abortion. Of course, in some countries,

particularly in Asia and in north Africa, rising age at marriage also has made an appreciable contribution (Cleland et al., 1994). Nevertheless, a strong negative linear relationship between fertility levels and contraceptive prevalence in crossnational data has been demonstrated widely (Westoff, 1990; Ross and Frankenberg, 1993).

Trends in current use of modern contraceptives provide the most obvious and widely accepted criterion for judging the success of family planning programs. Figure 4 summarizes the data from nationally representative surveys, showing the percentage of currently married women using a modern method of contraception at the time of the survey. The solid lines repre-

Table 2. Measures of nuptial@

Country and date of survey

Proportion of women married by

age 20

Women Women 20-24 45-49

Median Age at First Marriage (Women 25-49)

Urban Rural Level of Education Total

None Primary Secondary

East Africa Burundi, 1987 Eritrea, 1995-96 Kenya, 1993 Malawi, 1992 Rwanda, 1992 Tanzania, 1996 Uganda, 1995 Zambia, 1996 Zimbabwe, 1994

Central Africa Cameroon, 1991 C.A.R. 1994-95

Southern Africa Botswana, 1988 Namibia, 1992

West Africa Benin, 1996 Burkina Faso, 1992-93 C&e d’Ivoire, 1994 Ghana, 1993 Guinea, 1992 Liberia, 1986 Mali, 1995-96 Niger, 1992” Nigeria, 1990 Senegal, 1992-93” Togo, 1988

44.3 53.7 18.9 19.5 19.4 19.8 69.5 79.4 18.0 16.3 16.2 17.9 46.1 68.9 20.6 18.5 17.0 19.4 76.6 65.9 18.5 17.6 17.4 18.1 35.1 64.0 21.4 20.0 19.4 20.3 60.3 70.1 18.7 18.2 17.0 19.3 74.7 73.5 18.7 17.2 16.6 17.4 64.3 81.6 18.7 17.5 16.8 17.3 51.7 61.7 19.5 18.7 17.5 18.5

22.3 23.9 21.5

23.2 _a

20.5 _d

20.8

19.5 16.7 18.8 17.7h 20.0 18.2 17.4 18.0b 18.9

73.1 85.7 17.4 16.0 15.2 17.6 20.3 16.5 73.5 69.6 16.9 17.6 17.4 17.0 17.1 17.3

92.5 81.5 20.1 23.1

17.3 17.3 16.9 17.4 24.0 22.6 24.8

17.9 17.3h 24.8

65.4 71.6 19.2 18.0 18.0 19.3 85.6 88.4 17.9 17.4 17.5 17.1 58.3 67.4 18.8 17.8 17.7 18.4 59.7 59.8 19.5 18.6 18.5 18.3 77.1 95.0 16.8 15.6 15.6 16.5 64.1 69.5 18.5 16.8 16.8 17.3 82.1 91.2 17.0 15.8 15.9 16.6 90.1 92.9 15.4 15.0 15.0 17.1’ 67.6 71.9 19.0 16.3 15.8 19.1 59.7 82.5 18.2 15.7 15.8 19.3 63.0 66.2 19.7 17.9 17.8 18.8

23.0 21.0 21.4 22.3 20.2 20.5 20.5

_

23.9 23.0 22.5

18.4 17.5 18.1 18.8 15.8 17.5b 16.0 14.9 16.9 16.2 18.4

“Too few cases. ‘Based on women 20-49. ‘In Botswana, childbearing is by no means restricted to unions, hence figures are for age at first sexual intercourse. dRefers to age first marriage was consumated. ‘Primary education and above.


I I

1970 1975 19!80 Year 1985 1990 ,995

Figure 4. Trends in contraceptive prevalence rates.

sent countries exhibiting moderate or large declines in fertility, the dashed lines represent countries showing either a weak decline or no decline in fertility. Once again, this classification is based on information provided in Table 1.

Figure 4 displays the large variation in contraceptive prevalence rates (CPRs) across the region, It is clear that, by and large, those countries with the highest CPRs are the same countries that have experienced moderate or large declines in fertility. Countries with lower CPRs have fewer signs of a fertility transition. It is not true, however, that this latter group of countries has not made any progress raising CPRs. Figure 5, which shows urban-rural differences in CPRs, is restricted to countries where substantial changes in fertility have not taken place at the national level. A quick look is all that one requires to see that even in these

~~~~~

0 _ ,-_-_ - - _ _ ____-_-.-_-_-_-. _ _ _ - - - - - -

I I , I 1990 1985 Year 1990 1995

Figure 5. Urbanlmral differences in contraceptive pram- lence rates.

countries, contraceptive prevalence is rising rapidly in urban areas.

Understanding the motivations for contraceptive use in Africa is important because identifying the characteristics of women who want to use family planning services enables policymakers to evaluate the effectiveness of current programs and to estimate the future market for these services. As already discussed, much of the demand for contraceptives in Africa is to ensure that children are spaced adequately rather than to lower completed family size. The demand for efficient contraceptives among unmarried women who want to delay marriage and childbearing also may be growing (Caldwell et al., 1992). In Ilorin, Nigeria, Oni and McCarty (1986) found that a significant number of women were using contraceptives as a substitute for prolonged periods of postpartum sexual abstinence. Greene et al. (1997) found some substitution of contraceptives for postpartum abstinence in DHS data from Ghana and some substitution of contraceptives for prolonged breastfeeding in DHS data from Ghana, Senegal, and Uganda. Similarly, Sambisa and Curtis (1997) found a widespread and prolonged overlap between contraceptive use and breastfeeding in Zimbabwe. Given that extended breastfeeding delays the return of menses anyway, there may be overlap between contraceptive use and postpartum amenorrhoea, so that a large fraction of contraceptive use may be redundant. A different interpretation might be, however, that this is not a redundant use of contraception at all, since double protection is a rational applica- tion to prevent pregnancies in the face of uncer- tainty regarding the contraceptive effects of breastfeeding (Bledsoe et al., 1994). Regardless of the interpretation, double protection explains a large part of the “gap” between the contraceptive prevalence rate and the actual versus the expected levels of fertility in Zimbabwe (Thomas and Mercer, 1995).

Information about current use of family planning can be combined with answers to questions regarding the desirability and timing of future births to provide some indication of the unmet need for family planning services. Exact definitions of unmet need for family planning vary slightly. The basic idea, however, is that women at risk of becoming pregnant who say that they either do not want additional children or that they want to wait before having any additional children, but who are not using any method of contraception, are considered to have an unmet need for family planning, regardless of whether they consider family planning services


desirable. In contrast, women who say that they want to stop childbearing or want to wait and are currently using some form of contraception are said to have a met need for family planning.

Despite evidence that family planning programs in Africa are beginning to succeed, Table 3 indicates that a very considerable demand for family planning services in Africa is going unmet. In all, the percentage of women appearing to need for some form of family planning, either to limit births or to improve spacing, ranges from just under a quarter of all currently married women in Niger, to over 60% of currently women in Botswana, Kenya, Rwanda, and Zimbabwe. Of those women with an apparent need for family planning services, 65%, or approximately 28% of the total sample, reported unmet need. The remaining 35% of women had their family planning needs met. Note, however, that met need includes need met by either modern or traditional forms of contraception, and some of the latter are virtually ineffective.14

4.3. Other biological and traditional restraints on fert@y

In much of sub-Saharan Africa, women breastfeed new born infants and refrain from sexual relations for extended periods. Both of these practices are perceived as necessary to ensure the proper spacing of births and as a means of minimizing the health risks to their children. Early resumption of sexual relations is viewed as dangerous, not to the mother or new fetus, but to the youngest child (van de Walle and van de Walle, 1991). Two common views are that either intercourse or a new pregnancy - or both - will cause a mother’s milk to turn bad (van de Walle and van de Walle, 1991). At a purely biological level, breastfeeding delays the resumption of the normal menstrual cycle and lengthens a woman’s period of postpartum infecundity. In the absence of contraceptive use and extended postpartum abstinence, the average length of time between births will be determined primarily by the duration and intensity of breastfeeding.

Table 3. h4er and. unmet need for family planning in Africa (base: currently married women)

Country and date of survey

Met Need Unmet Need Total % Met Need

For For Total For For Total Spacing Limiting Spacing Limiting

East Africa Burundi, 1987 Eritrea, 199.5-96 Kenya, 1993 Malawi, 1992 Rwanda, 1992 Tanzania, 1996 Ueanda. 1995 Zimbia; 1996 Zimbabwe, 1994

Central Africa Cameroon, 1991 CAR, 1994-95

Southern Africa Botswana, 1988 Namibia, 1992

West Africa Benin, 1996 Burkina Faso, 1992-93 CBte d’Ivoire, 1994 Ghana, 1993 Guinea. 1992 Liberia; 1986 Mali, 1995-96 Nieer. 1992 Nigeria, 1990 Senegal, 1992-93 Togo, 1988

5.8 2.9 8.7 17.7 7.4 25.1 33.8 25.8 5.7 2.2 8.0 21.4 6.1 27.5 35.4 22.4 9.9 22.9 32.7 21.6 14.8 36.4 69.2 47.3 7.4 5.7 13.0 19.8 16.5 36.3 49.3 26.4

10.2 11.0 21.2 21.0 19.4 40.4 61.6 34.4 10.0 8.4 18.4 15.4 8.5 23.9 42.3 43.5 6.7 6.7 13.4 13.9 8.0 21.9 35.3 37.9

15.8 10.0 25.9 18.7 7.8 26.5 52.4 49.4 27.0 21.1 48.1 9.2 5.6 14.9 65.2 77.2

11.0 5.0 16.1 12.4 9.6 22.0 38.0 42.2 11.9 2.9 14.8 11.6 4.6 16.2 31.0 47.7

17.9 15.1 33.0 19.4 7.4 26.9 61.6 53.6 11.2 17.7 28.9 15.7 7.8 23.5 52.4 55.1

12.6 4.2 16.8 13.9 6.7 20.6 37.4 44.9 5.5 2.5 7.9 20.3 8.8 29.1 37.0 21.4 8.0 3.4 11.4 34.0 9.4 43.4 54.9 20.8

10.5 9.7 20.3 25.3 13.3 38.6 58.8 34.4 0.9 0.9 1.7 18.3 6.4 24.7 26.4 6.5 3.6 2.9 6.4 19.8 13.0 32.8 39.3 16.4 5.7 2.2 7.9 17.9 4.8 22.8 30.7 25.8 3.8 0.7 4.4 14.1 5.1 19.2 23.6 18.8 3.4 2.7 6.0 11.5 9.3 20.8 26.8 22.5 4.3 3.1 7.4 19.3 8.7 27.9 35.4 21.0 8.0 4.1 12.1 28.5 11.7 40.1 52.2 23.2


Bongaarts et al. (1990) estimate that, given current levels of modern contraceptive use, total fertility in Africa would increase by 72% if the fertility inhibiting effects of breastfeeding and postpartum abstinence were removed.”

Table 4 shows the latest available information on these important proximate determinants of fertility. Breastfeeding is essentially universal in sub-Saharan Africa. In every survey, at least 97% of mothers reported breastfeeding their children. The shortest median duration of breastfeeding recorded was 17 months (Liberia) and the longest was 28 months (Rwanda). Most women breastfeed their children between 18 to 24 months. In east Africa, the period of postpartum abstinence is quite short and ends well before weaning so that it has little additional affect on fertility. Elsewhere on the continent, the practice of postpartum abstinence varies widely. In some parts of west Africa, such as in Niger, Nigeria,

and Senegal, the period of postpartum abstinence is rather short, while in others such as Burkina Faso, Benin, Togo, and Guinea, it can be much longer, as long as two years, so that it has a substantial effect on birth intervals and therefore on total fertility.

How have these traditional practices changed over time? Most scholars assume that the length of breastfeeding and postpartum abstinence will decline in the face of modernization. In support of this hypothesis, analyses of breastfeeding and postpartum practices typically reveal that the length of breastfeeding is shorter in urban than in rural areas.16 Small amounts of education also are often held responsible for breaking down traditional postpartum practices, leading to the much documented inverted U-shaped relationship between education and fertility in Africa (see, for example, Jejeebhoy, 1995). In general, however, comparisons between the surveys of the

Table 4. Median duration (months) of postpartum variables for currently mam’ed women, selected DHS countries, latest survey information available

Country and Breast- date of survey feeding

Amenorrheic Abstaining Nonsusceptible Period period

Sample Size (Weighted No.

of Births)

East Ajiica Burundi, 1987 Eritrea, 1995-96 Kenya, 1993 Madagascar, 1992 Malawi, 1992 Rwanda, 1992 Tanzania, 1991-96 Uganda. 1995 Zimbia; 1992 Zimbabwe, 1994

24.3 22.0 21.1 19.4 21.2 27.9 21.6 19.5 18.7 18.5

17.6 14.2

1.2 2.7 3.0 3.6

n.a.d 0.6 6.5 2.2 4.4 3.5

17.8 16.6 12.9 13.4 n.a. 17.1 15.6

2427 2556 3596 3482 2800 3378 5051

10.8 12.5 11.9 16.6 13.3 12.6 11.7 12.9

13.4 4587 13.3 3987 14.1 2331

Central Africa Cameroon, 1991 C.A.R., 1994-95

Southern Africa Botswana, 1988’ Namibia, 1992

West Afn’ca Benin, 1996 Burkina Faso, 1993 CBte d’Ivoire, 1994 Ghana, 1993 Guinea, 1992 Liberia, 1986 Mali. 1995-96 Niger, 1992 Nigeria, 1990 Senegal, 1992-93 Togo, 1988

17.4 10.4 13.3 16.0 2080 20.6 14.1 10.4 16.4 2807

18.8 11.6 12.7 15.6 1990 17.3 8.3 6.0 12.8 2469

22.8 25.2 20.3 21.4 23.5 16.7 21.6 20.9 19.5 20.1 22.6

13.4 14.6 12.3 13.0 12.0

7.7 13.6 15.2 14.6 14.3 14.4

15.8 18.9 11.8 9.0

23.1 10.7

2.8 2.0

10.8 3.5

17.5

18.9 2845 22.2 3678 16.6 3921 16.2 2151 23.6 2996 15.5 3249 14.4 5961 15.8 4219 19.0 4802 16.2 3319 20.3 1928

%.a. = not available. ‘Mean not median.


late 1970s and those of the late 1980s and early 1990s indicate that these proximate determinants are changing only very slowly (Jolly and Gribble, 1993) and so cannot account for the recent changes in fertility, which are more directly related to changing marriage patterns and increasing contraceptive use, as discussed earlier.

5. DISCUSSION

This paper summarizes much of what is known about what has happened to fertility, nuptiality, and family planning in sub-Saharan Africa over the last 30 years. Total fertility rates in Africa remain quite high and there are many reasons for this. In the absence of many financial and legal institutions, children are still prized as positive economic assets that provide labor, wealth, risk insurance, and old-age security to their parents and other relatives. In addition, strong (but changing) cultural forces, particularly in parts of West Africa, tend to sustain high fertility (Caldwell and Caldwell, 1990; Caldwell et al., 1992; Caldwell, 1996). Not surprisingly, therefore, Africans continue to maintain a strong preference for large families. There are, however, signs that things are changing.

A fairly widespread decline in fertility is currently underway across Africa. Moderate to large declines in fertility have already taken place in Kenya, Rwanda, Zimbabwe, Botswana, South Africa, and Cote d’Ivoire, with smaller declines observed in Malawi, Tanzania, Zambia, Cameroon, Central African Republic, Burkina Faso, Gambia, Ghana, Mauritania, Senegal, and Sierra Leone. The list would surely be longer if more up-to-date information were available from other countries. Moreover, even in countries such as Uganda or Mali where there is little evidence so far of a national-level trend toward lower fertility, changes at the subnational level are clearly visible. The driving forces behind these changes are a trend toward later marriage - a characteristic linked to fertility transitions in other developing regions - and the adoption of modern contraceptives for spacing rather than stopping purposes, a characteristic unique to the African transition (Locoh and Makdessi, 1995).

This is quite a different story from the popular perception that a fertility transition is not yet under way in Africa, or that it is restricted to only a handful of countries. Although small changes in fertility across surveys in any one country could be due to measurement error and not the result of a genuine change in reproductive behavior, if one views the continent as a whole, one sees that there is virtually no survey

in the last 10 years which indicates that fertility is increasing and few seem to indicate that it is even stationary. Instead, virtually all recent surveys point to rising age at marriage, rising contraceptive use, and declining fertility, particularly amongst certain well-defined subgroups. Hence, small declines in fertility at the national level are usually the result of a mixture of strong declines in urban areas and among the more educated and weak declines elsewhere.

Despite the increase in information available from sample surveys and censuses, there is still a great deal that has yet to be learned about the causes of the decline or why the decline has progressed furthest in East and Southern Africa. Analysis of fertility transitions in Asia and Latin America suggests that fertility declines in those regions took place under a wide variety of social, economic, and demographic conditions, and no single threshold level of development or child mortality was identifiable (Cleland, 1994). Instead, unique combinations of social, political, economic, cultural, and programmatic factors influenced the pathway that each individual country took. Existing theoretical models of reproductive behavior provide an incomplete explanation as to why the experiences of these countries differed so widely. Consequently, demographers have been largely silent on questions related to the likely timetable for future declines in Africa or whether particular policies and programs are liable to be more effective than others at speeding up the transition. Doubtless the search for causal factors underlying the transition in Africa will need to take account of the central role of mortality decline, the importance of expanded educational opportunities (particularly for women), the diver- sity in governments’ and donors’ (commitment and) capacity to provide family planning services, and the cultural differences between West Africa and East and Southern Africa (Caldwell et al., 1992). Other contributing factors surely include rising female labor force participation, the relative downturn in African economies that has resulted in poorer job prospects for new labor market entrants and rising pressure on family budgets, and the weakening of links to the extended family.

A large body of evidence points to the need to increase the level of family planning services in Africa. All recent surveys agree that certain segments of the population are currently badly underserved, implying that service providers need to find ways to make family planning services more widely available. Probably the most immediate need is to continue to increase the


supply of reversible methods of modern contra- zation or improvements in education and living ception, given that a large fraction of the family standards. In Malaysia in the mid-1970s while planning services in Africa are being used to Chinese and Indian fertility declined steadily, better space pregnancies, not to stop child- fertility decline among Malays effectively stalled bearing altogether. In all likelihood, family at around four children per woman, as a result of planning providers can expect African women to a resurgence in Islamic values, shifts in govern- be increasingly receptive to the idea of using ment policy, and son preference (Leete, 1996). modern family planning methods for spacing This example should serve as a strong warning to purposes, given that the advantages of well- anyone wanting to gamble that African fertility spaced births have been long appreciated will fall smoothly to replacement levels.” throughout Africa. Finally, it is important to note that despite the

It is important to realize that even countries fact that fertility is declining, the population of that have been fairly successful at lowering their sub-Saharan Africa is still expected to double fertility still have total fertility rates in excess of within the next 22-23 years. Even if fertility were four children per woman. Consequently, there is to fall immediately to replacement levels, it still a very long way to go before any country would not alter the fact that past high fertility reaches replacement fertility. Indeed, it is still has produced large numbers of potential unclear whether fertility will fall inexorably prospective parents who are about to enter their toward replacement. If women view family childbearing years. Thus, there is a certain planning as a means to protect and maintain amount of demographic momentum built into their bodily capacity to continue childbearing the young age structure of the population rather than as a means to limit family size as (Keyfitz, 1977). Indeed, it may prove to be a Bledsoe et al. (1998) have suggested, there is considerable challenge for many African govern- little reason to think that fertility will fall much ments to maintain just current levels of family below four or five births per woman. This is very planning coverage, let alone provide the neces- unlikely to be the whole story, but one should sary higher quality of client services needed to not assume that fertility rates will decline to induce additional changes in reproductive replacement as a predictable outcome of urbani- behavior.

NOTES

1. For example, for some unexplained reason, cross- sectional surveys in Africa have tended to undercount births occurring in the most recent years prior to a survey. As a result, cross-sectional data often demon- strate apparent declines in fertility just prior to a survey that frequently are not confirmed in a subsequent investigation (Caldwell et al., 1992).

2. Similarly the recent DHS Survey that was conducted in Mali in 1995-96 also claims that fertility among women aged 15-34 has declined in the recent past.

3. Bledsoe et al. (1998) argue that Gambian women are more concerned about preserving adequate births intervals to protect their own health. By avoiding the adverse consequences of a quick subsequent pregnancy, whether through abstinence or contraception, women can better preserve their bodily capacity to bear future children.

4. The total fertility rate (TFR) is a synthetic measure of fertility that reflects the total number of children a hypothetical woman would have if she survived to the end of her reproductive years and at every age experienced the level and pattern of childbearing that is in effect for women of that age at the time the data are collected. One advantage of using the TFR over other measures of fertility such as the crude birth rate is that

it is independent of the age structure of the population.

5. In actual fact, an inspection of parity/current fertility (P/F) ratios can often provide a crude indicator of data quality because, under a regime of near constant fertility, P/F ratios should be close to 1.0 for all age groups. Thus, systematic deviations from 1.0 can suggest problems. For example, P/F ratios roughly constant above 1.0 are often the result of an undercount of recent births, as can result from an ill-defined reference period, but can also occur when complete birth histories are collected if recent births are displaced backward in time. Similarly, a set of P/F ratios that are close to 1.0 at younger ages and falling below 1.0 in older ages are most usually attributable to older women having poor recall of all their children, often omitting those who have died, moved away, or were born a long time ago. Interpretation of P/F ratios becomes considerably harder if fertility cannot be assumed to be unchanging. The pattern of P/F ratios will depend on the pattern of fertility decline. P/F ratios constant above 1.0 (say 1.1) might indicate a fairly uniform decline in fertility across all age groups. This may occur if contraception is used primarily as a substitute for traditional birth spacing practices, which might result in longer birth intervals and lower fertility without much altering the age profile of childbearing. This is not the usual pattern, but it may be the case in


Africa. More commonly, age-specific fertility rates tend to fall at ages 30 and over as higher parity women decide to restrict their fertility. This translates into a pattern of P/F ratios that are close to 1.0 for women under age 30 rising above 1.0 as older women begin to regulate their fertility in order to achieve lower completed fertility than the previous generation. A third possibility is that decline in fertility is more the result of a delay in age at marriage and age at first birth than an uptake of contraception among older women. This would produce P/F ratios higher than 1.0 at younger ages, and falling over time, a pattern that can be accentuated if older women omit some of their children. Indeed, all of the above patterns can be obscured to some degree by the omission of some children born to women at older ages.

6. To avoid cluttering the graph, women with primary education have been excluded.

7. For the most part, fertility declines among women with primary education (not shown) fall somewhere between these two extremes. In some instances, however, small amounts of education can break down traditional birth-spacing practices such as prolonged breastfeeding or postpartum abstinence without altering fertility desires or increasing age at first marriage. In such cases, small amounts of education are associated with slightly higher as opposed to slightly lower fertility compared to women with no education. (See, for example, the results of WFS surveys from Cameroon, CBte d’Ivoire, Kenya, and Nigeria).

8. Note that Bledsoe et al. (1994) argue that in The Gambia, “natural” fertility is not formed passively but is achieved through careful use of western contraceptives and traditional methods for short periods.

9. Again, these schedules represent age patterns of fertility rather than the level of fertility so that the sum of the rates, taken over all reproductive ages is 1.0. Note that the African fertility schedules can include extramarital fertility, which may be considerable, but which is excluded in the Coale-Trussell models.

10. It is only fair to mention that there are exceptions to this basic pattern. For example, if Senegal were included instead of C&e d’Ivoire among the four countries, the uniformity would be partly broken because fertility decline in Senegal appears to be restricted to women under 30 (Pison et al., 1995).

11. Botswana is an interesting exception to the genera1 rule, where the average age at first marriage is very late and rates of adolescent fertility are also higher than average. Women in Botswana are increasingly having children outside of wedlock. Although the specific reasons for this are beyond the scope of this

study, they relate to changing economic and social conditions.

12. Many African adolescents engage in premarital sexual relationships with either sequential or concurrent partners. Many unmarried African males have a “main” girlfriend whom they expect to marry, and one or more other girlfriends, for whom there are no such expectations (Meekers and Calves, 1997). Some women have similar arrangements. Young women also use premarital sexual relations for economic support, despite the risks involved from the high incidence of HIV infection in the region (Meekers and Calves, 1997).

13. Among the Poular of Senegal, for example, the consummation of marriage may occur several years after a marriage ceremony. Newly married Poular couples undergo a traditional waiting period that is often decided mutually among the parents of the newlyweds and helps explain the usually low levels of fertility in the first years of marriage among the Poular (Pison et al., 1995).

14. Indeed, a recent study of the effect of contraceptive use on birth intervals in five African countries concluded that, surprisingly, use of contraception appears to have little effect on the length of birth intervals. This is partly because of the substitution of contraceptives for abstinence and breastfeeding discussed above, but also partly because contraceptive users are either using ineffective traditional methods or using modern methods ineffectively, or they are self- selected for higher fecundity/exposure (Greene et al., 1997).

15. Other traditional birth spacing techniques are the use of beads or string tied around the waist and the drinking of potions made from local herbs or other medicines. Sometimes these herbal concoctions are inserted in the vagina prior to sex to act as a spermi- tide (e.g.. in Malawi, see Kalioeni and Zulu, 1993). But, these methods are unlikely to be particularly effective forms of contraception except perhaps when local medicines are used as an abortifacient rather than a prophylaxis (Madise and Diamond, 1993). Nonethe- less, the use of beads or local potions signal the potential demand for more efficient forms of contraception.

16. Interestingly, the difference in amenorrhea between urban and rural is usually even greater - suggesting either better nutrition or less intense breastfeeding in urban areas.

17. Chimere-Dan (1997) recently observed that beneath the trend toward a rapidly falling TFR at the national level in South Africa, lies the potential for the rate of decline to slow, due to relatively high fertility among young women.

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Singh, S., Chvusu, J. Y. and Shah, I. H., Eds. (1985) Demographic Patterns in Ghana: Evidence from the Ghana Fertility Survey, 1979-80. World Fertility Survey, London.

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United Nations (1983) Manual X Indirect Techniques for Demograjk Estimation. United Nations, New York.

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US Dept of Commerce (1979) A Compilation of Age-Specific Fertility Rates for Developing Countries. International Research Document No. 7, Bureau of the Census, Washington, DC.

van de Walle, E. and van de Wage, F. (1991) Breast- feeding and popular aetiology in the Sahel. Health Transition Review l(l), 69-81.

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APPENDIX Table Al. Fertility estimates for sub-saharan African countries, 1960-97

Country Date of Estimate”

TFR Data and Methodology’ Reference

East Africa Burundi

Djibouti

Eritrea

Ethiopia

1964-65

1970171

1973 1978 1979 1987

1990 1990 1991

1995-96

1964-67

1968-71

1970

1981

1984

7.1

6.1

7.3 6.5 6.4 7.0

6.7 6.6 7.0

6.1

6.7

5.8

7.2

8.8

7.9

1990 6.6

Kenya 1962

1969

1972-73

1977

6.8

7.6

7.7

8.0

1977-78 8.1

1979 7.9

National Demographic Survey

National Demographic Survey; P/F ratios

Demographic Survey Pilot survey, pre-census Post-Enumeration Survey Demographic and Health

Survey, 1987 Census Demographic Survey Demographic Survey; P/F

ratios Demographic and Health

Survey

National Sample Survey, 1st round; stable population theory

National Sample Survey, 2nd round; P/F ratios

Sub-National Demographic Survey; P/F ratios

Demographic Survey; P/F ratios

Census; P/F ratios

Family and Fertility Survey (preliminary)

Census; authors’ assessment from a range of methods

Census; authors assessment from a range of methods

Sub-National Demographic Baseline Survey

National Demographic Survey (1st round)

World Fertility Survey 1977178

Census; Gompertz model

Rtpublique du Burundi (1966)

Republilue du Burundi (1972)

Thibon (1993) Barampanze (1991) Thibon (1993) Segamba et al. (1988)

Thibon (1993) Thibon (1993) Republique de Djibouti

(1991) National Statistics Office

[Eritrea] and Macro International Inc. (1995)

Central Statistical Office [Ethiopia] (1971)d

Central Statistical Office [Ethiopia] ( 1974)d

Kidane (1990)

Kidane (1990)

Office of the Population and Census Commission [Ethiopia] (1991)d

Central Statistical Authority [Ethiopia] (1991)

Blacker et al. (1979)

Blacker et al. (1979)

US Dept of Commerce (1979)

National Council for Population and Development [Kenya] (1989)

Central Bureau of Statistics [Kenya] (1980)

National Council for Population and Development [Kenya] (1989)’


APPENDIX Table Al. - Continued

Countly Date of Estimatea

TFRb Data and Methodology” Reference

Malawi

1984 7.7

1989 6.7

1993 5.4

Madagascar 1962 6.6

1966 6.6

1975 6.4 1992 6.1

1997 6.0

1971/72 7.9

1977 7.6

1982 7.6

1984 7.5

1987 6.9

1988 7.0

1991 6.7h

Mozambique

1992 6.7

1970 6.2

1980 6.2

1997 5.8

Contraceptive Prevalence Survey, 1984

Demographic and Health Survey, 1988189

Demographic and Health survey, 1993

Rural Household Sample survey


Census Demographic and Health

Survey, 1992 Demographic and Health

Survey (1992) (preliminary)

Population Change Survey; P/F ratios

Census; method of adjustment not stated

National Demographic Survey; average of P/F ratio method, Relational Gompertz model and cumulative parities

Family Formation Survey; P/F ratios

Census; Gompertz relational model fitted to mean parities of younger women

Traditional and Modem Methods of Child Spacing Survey; P/F ratios

Food Security and Nutrition Monitoring Survey; P/F ratios


Census; P/F ratios

Census; stable population theory


Central Bureau of Statistics [Kenya] (1984)



Lopez-Escartin (1991a)

Lopez-Escartin (1991a)

Lopez-E-scat-tin (1991a) Refeno et al. (1994)

Institut National de la Statistique [Madagascar] (1998)

Hill ( 1986)d

National Statistical Office [Malawi] (1980)

National Statistical Office [Malawi] (1987a)

Ministry of Health [Malawi] (1987)

National Statistical Office [Malawi] (1987l~)~

Madise (1993)’

Cohen and House (1996)

National Statistical Office [Malawi] (1994)


Republica de Mocambique (1992)

Instituto National de Estatfstica [Mozambique] (1998)




TFRb Data and Methodology’ Reference

Uganda

Rwanda 1970

1978 1983

1991 1992

1975

7.7 National Demographic Survey; P/F ratios

8.7 8.5

6.9 6.2

7.2

Census National Demographic

Survey Census Demographic and Health

Survey, 1992 Census; P/F ratios

1980181 7.3 National Population Survey; P/F ratios

1983 6.7

1969 7.1

Sample Survey of 5 Cities; CEB women 45-49

Census; P/F ratios

1980 7.2 Census

1988189 7.3 Demographic and Health Surveys, 1988/89

1991 7.1

1995 6.9

Census Ministry of Finance and Economic Planning [Uganda] (1996)

Demographic and Health survey, 1995

United Rep. of Tanzania

1967

1972173

7.9 Census; P/F ratios

6.3g

1978

1988

1991192

1994

6.9

6.5

6.3

5.6g

5.8

National Sample Survey; author’s assessment from a range of methods

Census; P/F ratios

Census


Knowledge, Attitudes, and Practices Survey


Office general des statistiques [Rwanda] (1973)’

May (1996) May (1996)

May (1996) Barr&e et al. (1994)

Ministry of National Planning [Somalia] (1984)’

Ministry of National Planning [Somalia] (n.d.)‘,’

Ministry of Health [Somalia] (1985)

Ministry of Finance, Planning and Economic Development [Uganda] (1973)d

Ministry of Finance and Economic Planning [Uganda] (1996)

Kaijuka et al. (1989)

Ministry of Finance and Economic Planning [Uganda] (1996)

Bureau of Statistics [Tanzania] (n.d.y’

Ewbank (1979)

Bureau of Statistics [Tanzania] (1983)


Ngallaba et al. (1993)

Weinstein et al. (1995)




Country Date of Estimatea


Zambia

Zimbabwe

Central Africa Angola

Cameroon

Central African Republic

19.59160

6.4

5.5 5.8

5.0

1975 5.8 1988 6.1

1994195 5.1

Chad 1960/61 5.4

1974 1980

1992

1996

1969

1982

1984

1987

1988

1992 1994

1970

1984

1960162

1976

1978

1987 1991

1993

6.9

6.7 7.2

6.5

6.1

8.3

7.1

6.5

6.9

5.5

4.7 4.3

Census; P/F ratios

Sample Census; P/F ratios Census; P/F ratios



Census; P/F ratios

Census; P/F ratios

Reproductive Health Survey

Intercensal Demographic survey



survey, 1994

6.7 Census

6.6 Census

4.6 Demographic Survey

6.0 Census; P/F ratios

World Fertility Survey, 1978

Census; P/F ratios Demographic and Health

survey, 1991 National Demographic

Survey

5.6

Census Census Demographic and Health

survey Sub-National Sample; P/F

ratios Census

Central Statistical Office [Zambia] (1985a)

Hill (1985) Central Statistical Office

[Zambia] (1985b) Gaisie et al. (1992)

Central Statistical Office [Zambia] (1997)

Thomas and Muvandi (1994a)

Thomas and Muvandi (1994a)”

Central Statistical Office [Zimbabwe] (1989)



Hill and Marindo (1997) Central Statistical Office

[Zimbabwe] (1995)

United States Bureau of the Census (n.d.)f

Republica Popular de Angola (n.d.)’


Direction de la Statistique et de la Comptabilite National [Cameroon] et Enquete Mondiale sur la FCconditC (1983)’

BalCpa et al. (1992)

Balepa et al. (1992) Balepa et al. (1992)

INSEE (1964)

DGSEE [C.A.R.] (1987) Ndamobissi et al. (1995) Ndamobissi et al. (1995)

US Dept of Commerce (1979)’

Bureau central du recensement [Chad] (1997)



Country Date of Estimatea


Congo

Democratic Republic of Congo

Equatorial Guinea

Gabon

Southern Africa Botswana

Lesotho

Namibia

1997 6.6

1960/61 4.8

1974 5.5

Demographic and Health Survey, 1996-97 (preliminary)

Demographic Survey; P/F ratios

Census

1984 6.3 Census; P/F ratios

1955l57 5.1 National Demographic Survey

1978 6.2 Census; Coale method

1982184 6.9

1984 6.7

1983 5.6

1960/61 4.1

Contraceptive and Prevalence Survey

Census

Census

Census and Demographic Survey

1971 6.5

1981 6.2

1984 6.5

1987 4.5

1988 5.0

1968189 5.7h

Census; P/F ratios

Census

1971173 5.6

1976 4.7

1977 5.8

Contraceptive Prevalence Survey

Inter-Censal Demographic survey


Rural Household Consumption and Expenditure Survey

Multi-round Demographic survey

Census


1986 4.9 Census; P/F ratios 1960 6.8’ Census

1992 5.4 Demographic and Health Survey, 1992

Bureau central du recensement [Chad] (1997)

INSEE (1965a)

Direction des statistiques demographiques et sociales [Congo] (1978)

Bureau central du recensement [Congo] (1987)d

Lopez-Escartin (1992)

Institut national de la statistique [Zaire] (1991)d

Institut national de la statistique [Zaire] (1984)

Institut national de la statistique [Zaire] (1991)

Lopez-Escartin (1991b)

INSEE (1965b)


Rutenberg and Diamond (1993)

Manyeneng et al. (1985)

Rutenberg and Diamond (1993)

Lesetedi et al. (1989)

Timaeus and Balasubramanian (1984)



Ministry of Planning and Statistics [Lesotho] (1981)

Kingdom of Lesotho (1991) US Dept. of Commerce

(1979) Katjiuanjo et al. (1993)



Country Date of TFRb Estimate”

Data and Methodology’ Reference

Western Africa

Benin

Burkina Faso

South Africa

1960

1970 1980

1987189

Census Chimere-Dan (1993)

Swaziland

1993 1994 1966

1976

6.4’

5.8’ 5.4’ 4.6’

($

Census Census South Africa Demographic

and Health Survey Living Standards Survey October Household Survey Census; method of

estimation not stated

1986 1988

1961

1982

7.0

6.6 5.0

Census; method of adjustment not stated

Census Family Health Survey

6.9 Demographic Survey, Dahomey

1992 1996

1960161

1973174

1976

7.1

6.0 6.3

6.2



Survey, 1996 National Demographic

survey

7.28h

6.7

Sub-National Survey

Post-Enumeration Survey

1985 7.2 Census

1991 1993

C&e d’Ivoire

1962164

1978179

7.3 6.9

6.3

6.8

Demographic Survey Demographic and Health

survey, 1993 National Demographic

Survey National Survey; P/F ratios

1980/81 7.2 1988 6.8

1994 5.7

World Fertility Survey Census; method of

adjustment not stated Demographic and Health

survey

Chimere-Dan (1993) Chimere-Dan (1993) Mostert (1990)

Chimere-Dan (1997) Chimere-Dan (1997) US Dept of Commerce

(1979)

Blacker (1994)

Cleland et al. (1994) Warren et al. (1992)

Minis&e du Plan et de la Statistique [Benin] (1988)

Cochrane and Farid (1989)

Kodjogbe et al. (1997) Kodjogbe et al. (1997)

Minis&e du Plan et de la Cooperation [Burkina Faso] (n.d.)’


Minis&e du Plan et de la Cooperation [Burkina Faso] (n.d.)’

Ministere du Plan et de la Cooperation [Burkina Faso] (n.d.)’

Konatt et al. (1994) KonatC et al. (1994)

Roussel(1995)

Ministbre de I’Economie et des Finances [Cote d’Ivoire] (1984)

Sombo et al. (1995) Sombo et al. (1995)

Sombo et al. (1995)




TFRh Data and Methodology”

Ghana

Guinea

Liberia

Mali

Mauritania

Niger

Gambia 1973 6.4 Census; P/F ratios

1983 6.4 Census; P/F ratios

1990 5.9

1960 7.2

1968169 7.1

1971 7.3 1979180 6.5

1988 6.4

1993 5.5

1983 5.8

1992 5.7

1970171 6.1

1974 6.2k

1986 6.6

1960/61 7.4 1987 6.8

1987 6.9

199516 6.7

1964165 5.7

1976177 6.5 1981 6.3

1990 5.4

1959160 6.9

1988 7.5 1992 7.4

Contraceptive Prevalence and Fertility Determinants Survey

Post-Enumeration Survey

National Demographic Survey, 2nd round

Supplementary Inquiry World Fertility Survey,

1979/80 Demographic and Health


Survey, 1993 Census; method of

adjustment not stated Demographic and Health

Survey Liberian Population

Growth Survey Census


Demographic Survey Census; method of

estimation not stated Demographic and Health


Survey, 199516 Demographic Survey

Census World Fertility Survey,

1981 Maternal and Child Health

Survey, 1990 Demographic Survey; P/F

ratios Census; P/F ratios Demographic and Health

survey

Reference

Ministry of Economic Planning and Industrial Development [Gambia] (1976)

Ministry of Economic Planning and Industrial Development [Gambia] (1987)

Pacqut-Margolis et al. (1993)



Singh et al. (1985) Singh et al. (1985)

Ghana Statistical Service (1989)

Ghana Statistical Service (1994)

Keita et al. (1994)

Keita et al. (1994)


Chieh-Johnson et al. (1988)

Chieh-Johnson et al. (1988)

Traore et al. (1989) Traore et al. (1989)

Traore et at. (1989)

Coulibaly et al. (1996)


Ignegongba (1992) Ignegongba (1992)

Ministry of Planning [Mauritania] (1992)


Kourgueni et al. (1993) Kourgueni et al. (1993)





Nigeria

Senegal

1973 1974 1985

Togo 1961

1971

1965166 5.6h

1971173 7.3

1981182 6.3

1990 6.0

1960 5.4

1970/71 6.4

1978 7.1

1986 6.6

1988 6.3

1992193 6.0

1997 5.7

Sierra Leone

1963

1969170

7.5

7.5

6.8 6.5 6.4 7.0

6.6

1981 6.0 1988 6.6

Multi-round survey

National Fertility Survey; P/F ratios

World Fertility Survey, 1981/82

Demographic and Health Survey II, 1990

Demographic Survey




Census

Demographic and Health Survey, 1992/93


Census

Fertility and Family Planning Survey (CEB to women 40-49)

Pilot Census; P/F ratios Census Census Demographic Survey

Census; method of estimation not stated


Survey, 1988

US Dept of Commerce (1979>c



Federal Office of Statistics [Nigeria] (1992)




Ndiaye et al. ( 1988)

Direction de la Statistique [Senegal] (1992)

Ndiaye et al. (1994)

Ministere de l’Economie, des Finances, et du Plan [Senegal] (1998)

Dow and Benjamin (1975)

Dow (1971)

Makannah (1996) Makannah (1996) Makannah (1996) US Dept of Commerce

(1979) Agounke et al. (1989)

Agounke et al. (1989) Agounkt et al. (1989)

NOTES: “The TFR’s have not always been calculated using similar recall periods because in a number of

instances theorectical purity was outweighted by data limitations. bEstimate refers to women 15-49 unless marked otherwise. ‘Where no method is noted, direct estimation was used. dIndirect estimate based on the author’s calculations. ‘Indirect estimate provided in reference. ‘n.d. = no date. gExcludes Zanzibar. “Rural only. IBlacks only. ‘Judged to be quite unreliable by Chimere-Dan (1997). ‘TFR for women 15-44.

The emerging fertility transition in sub-Saharan Africa

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