African Census Analysis Project (ACAP) UNIVERSITY OF PENNSYLVANIA

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1 African Census Analysis Project (ACAP) UNIVERSITY OF PENNSYLVANIA Population Studies Center 3718 Locust Walk Philadelphia, Pennsylvania (USA) Tele: or or Fax: Contemporary Fertility Levels and Trends in South Africa: Evidence from Reconstructed Census Birth Histories Amson Sibanda and Tukufu Zuberi ACAP Working Paper No 8, April 1999 Prepared for presentation at the Third African Population Conference, Johannesburg, South Africa, on 6-10 December 1999, Union of African Population Studies (UAPS). This research was done as part of the African Census Analysis Project (ACAP), and was supported by grants from the Rockefeller Foundation (RF #21; RF #22), from Andrew W. Mellon Foundation, and from the Fogarty International Center and the National Institute of Child Health and Human Development (TW ). We would like to thank Timothy Cheney for computer programming assistance.

2 Recommended citation: Amson Sibanda and Tukufu Zuberi Contemporary Fertility Levels and Trends in South Africa: Evidence from Reconstructed Census Birth Histories. ACAP Working Paper No 8. April The African Census Analysis Project (ACAP), Population Studies Center, University of Pennsylvania, Philadelphia, Pennsylvania.

3 ABSTRACT This study presents an assessment of fertility levels and trends in South Africa since the early 1980s. The decline of fertility in South Africa prior the end of apartheid is largely unknown. Because of its apartheid policies, the fertility transition in South Africa did not attract the attention of many researchers outside the country. However, since the end of the apartheid era, unraveling the age and parity structure of the country s fertility transition has been of much interest to researchers in and outside of South Africa. Because data on fertility change are somewhat less reliable and largely unavailable, there is still speculation about what happened in South Africa. Despite the lack of credible data, there does seem to be widespread agreement that fertility began to decline in South Africa prior to the end of apartheid among all its major population groups. What is unknown is whether there has been a pervasive decline in fertility across all age groups and parities, a pattern that has been observed in countries such as Botswana, Kenya, and Zimbabwe. This study attempts to fill the gaps that exist in South Africa s demographic history by focusing on contemporary features of South African fertility. The study uses the 1996 South African population census. We estimate fertility levels and trends for South Africa in the period from reconstructed birth histories. We also examine marital and nonmarital fertility. The paper shows that fertility has been declining in South Africa as a whole and among all the major population groups and across all age groups since the 1980s. 2

4 INTRODUCTION Little is known with certainty about the course and structure of demographic phenomena in South Africa. Demographic data of the past were collected under questionable circumstances and there were no official fertility or mortality estimates of Africans in the Republic of South Africa prior to the 1950s (Zuberi and Khalfani 1999a, van de Walle and Page 1969). Analysis of recently available data like the 1996 South African population census gives us new insights into the country s demographic past. This study presents an assessment of contemporary features of South African fertility since the 1980s. Because of its apartheid policies, South Africa s fertility transition did not attract the attention of many researchers outside the country. The World Fertility Survey and the Demographic and Health Survey programs that have provided fascinating snapshots of reproductive change in much of Africa in the last two decades were not carried out in South Africa because of its apartheid policies. However, since the early 1990s, the need to unravel the course and age-parity structure of the country s fertility transition has been of much interest to researchers in and outside of South Africa (see Caldwell and Caldwell 1993; Kaufman 1998; Chimere-Dan 1993, 1997). Because data on fertility change have largely been unavailable and somewhat unreliable, there is still speculation about what happened in South Africa. Despite the lack of credible data, there seems to be widespread agreement that fertility began to decline in South Africa prior to the end of apartheid among all its major population groups. South Africa s total fertility rate dropped from 6.0 in the 1950s to about 4.3 in the 1980s and it is now reported to be about 3.3 (Chimere-Dan 1993; Caldwell and Caldwell 1993; Kaufman 1998; Udjo 1999). However, most of these historical estimates refer to the "old" South Africa- a country that did not include former African homelands like Transkei, 3

5 Bophutatswana, Ciskei and Venda. Although the mere existence of these trends is in itself a significant contribution to knowledge of historical South African fertility trends, there is no doubt that their accuracy is largely unknown. These studies note that fertility has declined among all the major population groups in the country (Africans, Whites, Indians/Asians, and Coloureds). However, what is unknown is whether there has been a pervasive decline in fertility across all age groups and parities, a pattern that has been observed in countries like Botswana, Kenya and Zimbabwe (Sibanda 1998; Caldwell and Caldwell 1993; Cohen 1993). The total fertility rate is not a sensitive measure of fertility that can pick up the behavior of fertility (Luther and Pejaranonda 1991; Feeney 1988). Given the considerable gaps in knowledge about the new South Africa s demographic past and the fact that there is a need to devise with alternative estimates for the purpose of comparison, this study attempts to fill in the gaps that exist in South Africa s demographic history. The country has a long history of census taking; the first modern census was conducted in 1911 (Zuberi and Khalfani, 1999b). Since alternative reliable sources of data are practically nonexistent, these censuses offer us the opportunity to study historical trends in fertility in the country, as they provide a long and continuous series view of demographic processes in the country. We present empirical evidence highlighting the nature of the South African fertility decline and establish the quantum and tempo aspects of the decline. This paper examines contemporary fertility levels and trends in South Africa by exploiting own-children data collected in the 1996 population census of South Africa. This study of fertility trends using own-children data is the first such study for South Africa. In the African context, there are only limited studies of this nature. Cho, Retherford, and Cho (1986) examined fertility levels and trends in Kenya using own-children data 4

6 from Kenyan censuses and the Kenya World Fertility Survey and Zuberi and Sibanda (1998) examined fertility levels and trends in Zambia using the 1990 census data. Own-child studies have been done extensively in Asia and the United States using census data and have been an important contribution to our understanding of fertility transitions in these areas (Coale et al. 1980; Levin and Retherford 1982; Luther and Cho 1988, Retherford and Alam 1985, Jones 1977; Cho, Grabill, and Bogue 1970; Cho, Retherford, and Cho 1986). The fertility levels and trends estimated from the 1996 South African census data will provide a historical picture of fertility trend. The 1996 census data are invaluable for establishing a reliable picture of demographic trends and processes in South Africa. The own-child based results are subject to evaluation once data from DHS-type surveys become available. Contemporary features of South African fertility will be made by comparing the consistency of own-children derived estimates with those from DHS-type surveys. AIMS OF THIS STUDY This paper attempts to study recent trends in fertility in the Republic of South Africa by giving new fertility estimates for the period The objectives of this study are rather simple and limited but important since they give us some urgent answers to questions concerning the levels and trends in fertility and the onset of fertility decline across South Africa s major population groups in the last 15 years. Given the considerable gaps in knowledge that exist about reproductive change in South Africa, we propose to establish a basic picture that should precede any rigorous analyses of fertility dynamics in the country. The quantum and tempo aspects of South African fertility can be properly contextualized only within the simple story that first needs to be established about what has been happening in the 5

7 country. Our work is therefore descriptive and it focuses only on overall national trends and on marital/nonmarital fertility and racial differences. We have four objectives: First, we link mothers and their children in the South African census. We continue our efforts to demonstrates the value of own-children data when reconstructing fertility levels and trends in Africa using census data, something that has rarely been done in the context of sub-saharan Africa (see Zuberi and Sibanda 1998; Cho, Retherford, and Cho 1986). Second, we describe recent fertility levels and trends in South Africa ( ). Third, we identify prominent features of contemporary South African fertility (e.g. its age structure) by race and marital status. We have elected to examine these two differentials because of their importance for future demographic change and public policy, and because race has been a powerful social engineering tool in South Africa that has affected fertility, mortality, and migration patterns in various ways. Almost all studies that have reviewed the historical record in South Africa point out the racial differential in terms of the quantum and tempo aspects of fertility and the timing of fertility decline across the racial divide. Fourth, we ask if post changes in South African fertility are period-driven. That is, in those calendar years when fertility increased, was this increase pervasive at all ages? And when it declined in certain calendar years, did this decline also occur across all age groups? Caldwell and Caldwell (1993) postulated that a new type of fertility transition in sub-saharan Africa will be marked by pervasive fertility declines at all ages and parities. DATA AND METHODS The study utilizes a 10 percent sample of the 1996 South African population census. Fertility levels and trends for South Africa in the period between are estimated from reconstructed birth 6

8 histories. Measures of fertility based on own-children data are presented. General Methodology The own-children method is a reverse-survival technique for estimating age-specific fertility in years prior to a census (usually 15 years). Its main advantage is that it allows us to calculate fertility estimates by woman s age and by single calendar years; ad by constraining the period of observation to 15 years, we minimize problems of memory recall associated with dates of vital events (Rele 1988). An obvious question to consider is: can an adequate analysis of South African fertility be based on census data? Reverse-survival methods have been used extensively in demography to study infant mortality, to reconstruct populations by age and sex, or to re-estimate the size of national populations (see Preston and Palloni 1978; Hueveline 1998; Preston et al. 1998; and Arriaga, Johnson, and Jamison 1994). They have also been used to estimate fertility levels, trends, and differentials (Cho, Grabill, and Bogue 1970; Cho, Retherford, and Cho 1986; Luther and Pejaranonda 1991; Retherford and Cho 1978). The success of reverse-survival methods depends largely on the availability of accurate age distributions (Lee and Lam 1983). The strategies we use to reconstruct birth histories for South Africa were developed using Zambia census data (see Zuberi and Sibanda 1998). In this method, children who were enumerated in maternal homes are first matched to mothers within households by means of questions on relationship to head of household, age of mother, marital status, age of each enumerated child, children ever born, children dead, and children living at home. The strategies used to match women to own children vary according to whether the family under consideration is nuclear or not and if there are female boarders in the household who might be potential mothers of the children 7

9 enumerated in that household. Given an appropriate set of life tables describing age-specific mortality in the selected population, these matched children, classified by own age and mother s age, are then reversesurvived (using Coale, Demeny, and Vaughan s (1983) North Model Life Table level 13) to obtain births by age of mother in previous years using the following equation: Ba x( t x) = Cx, a( l0 / lx), where B a -x denotes the number of own-children aged x of women aged a enumerated in the census, and t denotes time. Women grouped by age are also reverse-survived to obtain the number of women at earlier ages in previous years as follows: f Wa xt x Wa t la x l f ( ) = ( )( / a ), where W a -x denotes women aged a - x at time t - x, reverse-survived using women aged a at time of census (W a (t)). Once corrections are made for mortality using a suitable life table, final adjustments are then made for unmatched children by computing the reciprocal of the proportion of children aged x to x + 1 at the time of the census who were matched to mothers. Age-specific fertility rates are then calculated as follows: fa x( t x) = Ba x( t x)/ Wa x( t x), where f a - x denotes age specific fertility rates for women aged a-x at time t - x. Basic Data Requirements 8

10 Studies based on Asian and United States censuses have concluded that the general order of magnitude of fertility levels, trends and differentials established by own-children data can be accepted if the following conditions are met: Accurate age reporting, particularly of children. This will allow better matching of children to mothers and will also prevent age heaping of children. If age heaping among children is severe, this can inflate or deflate fertility estimates in some calendar years. In addition, the omission of very young children can influence estimation of period fertility. Because of South Africa s high level of development (and literacy), one can safely assume that the quality of age reporting is higher than what one finds in most parts of sub-saharan Africa. An examination of the 10 percent sample and age pyramids produced by Statistics South Africa (1999) using the 1996 census seem to indicate minimal problems of age heaping. In addition, about 99 percent of the 3.6 million people included in the 1996 population sample report their ages, therefore nonreporting of age is not a major concern. Moderately low proportions of fostered or non-own children in the household. In the case of South Africa, the proportions of fostered or other non-own children might vary according to race since living arrangements/household structures among Africans, Whites, Coloureds, and Asians are different. We assume that the mothers of non-own children have the same age structure as the mothers of linked children. Clearly specified kin relationships among household members. The more categories one has on relationship to head of household the better because, in situations where we have many female boarders in the household and many fostered children we can link children more successfully. In other words, sifting out fostered or non-own children is made easier when we have detailed relationship codes. The 1996 South African census collected the following information: 9

11 1) Head/acting head of household, 2) Husband/wife/partner, 3) Son/daughter/stepchild/adopted child, 4) Brother/sister, 5) Father/mother, 6) Grandparent, 7) Grandchild, 8) Other relative (e.g., in-laws), 9) Non-related person. Moderately low levels of mortality during the estimation period (that is, 15 years prior to the date of enumeration). In the case of South Africa, there is evidence that life expectancy at birth has been increasing in recent years for both males and females. For instance, female life expectancy at birth rose from about 58 years in 1970 to about 65 years in 1996 (Udjo 1999). At the national level, life expectancy jumped from 54 years to about 63 years during this period. Infant mortality levels have also been decreasing. Between the and period, infant mortality in South Africa dropped from 76 per 1,000 to 53 per 1,000 (Chimere-Dan 1998). In addition, it is important to point out that AIDS had not yet exerted itself fully on the force of mortality in South Africa prior to 1996 to the extent that it significantly affected proportions of orphaned children or increased the number of women who lost their children to AIDS-related mortality. Low rates of migration during the estimation period. However, this requirement can be waived if there are similar age-specific fertility schedules for migrants and nonmigrants. Although South Africa is at the receiving end of a major Southern African migration system, most of the migrants from neighboring countries are male, and the fertility of the small female migrant population does not have a major impact on overall fertility levels. Meeting these conditions, especially the first requirement, is difficult in the African context. Numerous studies have documented that age data are affected by memory lapses regarding age and of young children or children who have died (Potter 1977). 10

12 Linking Children Figure 1 shows the percentage of own-children who were linked to their mothers by age and race in South Africa. At the national level, our matching strategy managed to link a high of about 68 percent of children less than one year old to their mothers and a low of about 58 percent of the 15-year-olds. This national trend appears to be heavily influenced by slightly lower linkage percentages among African children than among other populations groups. For instance, among children less than one year old, our computer algorithm successfully linked 65 percent of African children to their mothers and among 15-year-olds, we linked about 53 percent. However, linkage percentages for the other three population groups were relatively high. For instance, among Whites and Asians, over 86 percent of children under one year old were linked to their mothers and the percentage linked was still above 80 percent at age 17. Coloureds also show relatively good linkage, from a high of about 77 percent at less than one year of age to a low of about 70 percent at age

13 Fig. 1 Percentage of Own-Children Linked by Age: South Africa Percent linked Age of child at time of census All Blacks Coloured Asian White The percentage of children linked by age to their biological mothers is largely influenced by fertility levels and living arrangements across these population groups. If fertility is low in a country or a particular group, then the estimates one gets by exploiting own-children data should be good simply because there are only a few children to be matched to any given woman. However, when fertility is high, many children may live elsewhere, the population may experience high infant and child mortality, and women are less likely to remember the ages of all their children. The different linkage rates shown in Figure 1 are partly explained by differential fertility by race. African families are likely to have different living arrangement patterns than Whites, Coloureds, and Asians because of different cultural traditions, the impact of apartheid policies, and labor migration (Preston-Whyte 1991, 1978). For instance, since the exploitation of mines and the 12

14 establishment of urban areas, young African males have been forced to create and maintain two homes: one in the rural areas where the wife and children were expected to stay, and one in the urban or mining areas. During the apartheid years ( ), the movement of Africans between their rural homes and towns was also tightly controlled with Pass Laws. As a consequence, traditional living arrangements were hampered. The high labor-force participation rate of African women also contributed to the low linkage percentage for children at very young ages. During the apartheid era, children of African domestic servants homes of whites were not allowed to live in the backyard cottages that were provided as accommodation by the employers (Preston-Whyte 1991, 1978). This practice is still in existence today. For women who are employed by the government or other private employers, maternity-leave arrangements are also not conducive to mothers living with their young children. Hence, for most African women, their young children live with grandparents or with other relatives in townships or rural areas. African populations typically have higher rates of child fosterage (McDaniel and Zulu 1996). Young urban couples or single women might let their young children stay in the rural areas with their grandmothers. On the other hand, some rural families let their school-age children reside with relatives in urban areas so that their children can take advantage of better-equipped urban schools. These processes have not impacted Whites, Coloureds, or Asians in the same way. These other groups have always maintained an urban nuclear family pattern even though some of them might have a farmhouse in the rural areas. Thus, most of their children under the age of 15 are less likely to be living with non-biological parents. The sharp drops we see in Figure 1 around age 18 for Whites, Asians, and Coloureds indicate the onset of adulthood when children either move to college or begin living alone. 13

15 The different linkage percentages by race mean that the adjustment factors for non-own children would be done by race. Appendix Table A1 shows the different adjustment factors for Africans, Coloureds, Asians, and Whites. RESULTS Levels and Trends in Age-Specific Fertility Rates and TFR for South Africa, The few studies that have documented fragmented data on fertility levels and trends in South Africa have all observed that of the four major population groups, Whites have experienced a long and sustained decline in fertility prior to the beginning of the twentieth century. For instance, fertility declined from a high of 7 births per woman (married) in the late nineteenth century to about 4 births per woman at the turn of the twentieth century. By the mid-1950s, the total fertility rate had dropped to about 3.5, and by 1989 some studies indicate that Whites in South Africa had attained belowreplacement fertility, with a TFR of 1.9 (Chimere-Dan 1993). For Asians, it is reported that a steady decline in fertility began in the mid-1950s from a high of about 6.7 births per woman, reaching a low of about 2.5 in the late 1980s. Coloureds also began a steady decline in the 1960s from a TFR of 6.5 and by the late 1980s they had about 3 children per woman on average. With respect to African fertility, it is estimated that fertility fell from a high of 6.8 to a low of about 4.6 between the mid- 1950s and the late 1980s (Chimere-Dan 1993). Prior to the 1960s, van de Walle and Page (1969) contend that there was some decline in the birth rate among Africans. The crude birth rate declined from about 45.2 to 40.8 between 1936 and

16 Fig. 2 Trends in the Total Fertility Rate by race in South Africa; Total fertility rate All women Africans Coloreds Asians Whites Figure 2 shows to what extent these trends and levels are captured by our estimates of fertility between 1981 through As Morgan (1996: 20) simply puts it when describing contemporary features of US fertility: These are the fertility trends to be explained. The graph shows that to a large extent, the racial trends follow the historical patterns noted by du Plessis, Hoffmeyer, and Mostert (1991) although the levels for the 1980s are somewhat different. Africans experience the highest fertility levels, followed by Coloureds and then Asians and Whites. The differences in levels among the four population groups are also much narrower than those produced by du Plessis, Hoffmeyer, and Mostert (1991). However, since the early 1980s and for most of this century, Whites have had the lowest fertility and in the last 15 years, they are the only population group that did not exhibit marked declines in total fertility. Asians seem to have maintained the 15

17 momentum of a steady decline that started at some point prior to In contrast, Africans have maintained the highest fertility levels, but the last 15 years have seen a marked and sustained drop in their fertility. Unlike the estimates produced by du Plessis et al. which showed Coloured fertility at a level much lower than the national level since the 1970s, Figure 2 suggests that contemporary Coloured fertility is slightly above the national trend. It appears this is the only major difference between our estimates and those established elsewhere (see du Plessis, Hoffmeyer, and Mostert 1991). Kauffman (1998: 3) notes that du Plessis s trends are...based on estimates calculated by South African demographers under a number of assumptions. While the levels may not be accurate, the general trends by race are probably acceptable approximations. We should also point out that the national trend exhibited in Figure 2 is the lowest ever observed in sub-saharan Africa, and the African trend is the lowest observed for a sub-saharan African population. Udjo (1999) estimated a TFR of 3.3 for 1996 compared to our estimate of 2.9. He used the Brass P/F ratio technique, a procedure that adjusts fertility estimates upwards (Collumbien, Timaeus, and Acharya 1997). Figure 3 presents the national trends in age-specific fertility rates. The graph clearly shows the interplay of three possible scenarios: delayed age at first birth, spacing effects and stopping effects between various birth cohorts. The general trend seems to be greater fertility among recent birth cohorts (that is, in the more recent past) than among the older cohorts for ages 20-24, 25-29, and Age-specific fertility rates among the year-olds have been declining over the years indicating that recent birth cohorts have not been progressing rapidly to first births. Evidence for a delay (spacing) of births for women aged 20-24, 25-29, and is shown by the declines in agespecific rates between 1981 and 1996 at these ages as the cohorts get younger. Evidence of potential changes in family size are indicated by declining age-specific fertility rates among birth cohorts aged 16

18 35-39, 40-44, and These between-cohort changes in age-specific fertility affected total fertility rates at various periods Fig. 3 Trends in Age-Specific Fertility in South Africa; Births per 1000 women South African age-specific fertility patterns are different from those observed in most of sub- Saharan Africa. For instance, the rate at age has consistently been higher than at and more importantly, the rate at age is now higher than the rate at age In other words, with its mode at age group 25-29, the distribution of fertility rates by age of women is atypical of much of sub-saharan Africa. With the exception of countries like Burundi, Nigeria (Ondo State), Ghana, and 17

19 Namibia where fertility schedules peak at age (see Appendix Table A2), in most sub-saharan African countries, fertility schedules tend to peak at age Changes in age at marriage or first birth, coupled with use of contraception for spacing purposes at younger ages and among women exposed to on-time fertility, are more than likely to produce shifts in the mode of the age-specific fertility schedules. However, it is well known that South Africa has a later age at first birth, higher levels of education, is more urbanized, and, in general, has a highly developed socioeconomic infrastructure. For instance, du Plessis (1996) notes that between , the mean ages at first union and first birth were 19.0 and 20.4 among Africans; 20.1 and 21.1 among Coloureds; 20.6 and 22.2 among Indians (Asians) and 21.4 and 23.4 among Whites. Hence, the age-specific fertility schedules that peak at age instead of are normal in such a society. In other words, the observed fertility schedules in South Africa clearly indicate that women begin childbearing late and seem to either finish childbearing early or slow down the pace considerably at older ages. This phenomenon of a short childbearing span is also becoming evident in Zimbabwe, Kenya and Botswana (Sibanda 1998; Gaisie 1998). Levels and Trends in Age-Specific Fertility Rates and TFR for South Africa by Race, Table 2 (in Appendix) shows the levels and trends in age-specific fertility rates for the four major population groups in South Africa. By showing trends in age-specific fertility rates by race, we can examine race-specific fertility changes in South Africa. Table 2 (see Appendix) shows that since the 1980s, fertility rates have declined across all age groups among South Africa s African population. These trends are of great significance in that any marked demographic changes in South Africa are driven by what happens among the African majority. Africans constitute 75 percent of South 18

20 Africa s total population (Statistics South Africa 1999). Women aged have contributed most to period total fertility among Africans. However, the gap that existed between the and age groups in the 1980s is now disappearing as indicated by the post-1993 convergence of fertility trends. This is probably indicative of the growing importance of late fertility among Africans. On the other hand, the fertility trends for women aged and have also been consistently declining over the years, probably reflecting declining rates of early marriage and childbearing. On the whole, the overall picture among Africans is not very different from the national trends (Figure 3). Age-specific fertility rates among the Coloured population declined across all age groups as well. Over the entire period, the percentage of decline was greatest among women in their twenties and smallest among women in their forties. The practice of birth spacing among women of prime childbearing age has been spreading remarkably possibly as a result of more young women adopting contraceptives for spacing purposes. Among year-olds, it is evident that the practice of early childbearing has also been waning over the years. Between , the fertility rates for all age groups among the Asian population decreased significantly. The biggest percentage drop occurred at ages and Unlike Africans, ontime fertility is still important among Asians; women aged show no signs of moving toward late fertility. From 1981 to 1996, the fertility rates for women aged have also declined, possibly indicating a continued rise in age at first marriage and birth. The table also shows that the least reduction in age-specific fertility occurred in the least fertile age groups, that is among and year-old women. Although a substantial decrease occurred in the fertility rates for Whites at ages and 19

21 25-29, these age groups are still responsible for a substantial number of total births each year. For instance, throughout this period, women aged accounted for most of the period total fertility rate among Whites. It is therefore expected that in the course of a future fertility decline among Whites, the decrease in the fertility rates at these ages will account for the greatest reduction of fertility. Having examined the age-specific patterns of fertility for the four major population groups in South Africa, the next question is: are there any marked differential fertility declines among women of different ages within South Africa s population groups? Among Whites and Coloureds, fertility is concentrated among the and age groups, while for Africans, and to some extent Coloureds, fertility is concentrated at ages 20-24, and Levels in Marital and Nonmarital Fertility for South Africa, One way to investigate the underlying sources for the substantial racial differences in fertility in South Africa is to examine differences in marital and nonmarital fertility by race. The 1996 South African census asked women aged 12 and over whether they were currently single, married, living together, widowed or divorced. The census did not collect information on age at first marriage. Current marital status may be a better measure of nuptiality patterns than age at first marriage (van de Walle 1993). We use reverse-survival methods to estimate the number of women who were married in the past. Of course, the key assumption is that proportions married have been constant in the last 15 years. Thus, in a way, it is possible to ascertain if the observed trends in South African fertility by race or age resulted from changes in marital status or proportions married at any given period between It is possible that fertility rates in South Africa declined at the national 20

22 level and among its major racial groups partly because of increasing marital disruptions. Many researchers have noted that in much of sub-saharan Africa, marriage tends to be an early and universal socio-cultural institution (van de Walle 1993; Westoff 1992). Typically, the proportion ever married among women in their forties is well over 95 percent (van de Walle 1993). Figure 4 shows the proportion of women ever married by current age of woman and population group in South Africa. Among Africans and to some extent Coloureds, marriage is far from being an early and universal social institution. Africans have consistently low marriage prevalence at all ages. In addition, there are substantial racial differences in the proportions ever married that emerge in the early twenties, with the widest gap at age 30. The proportion reported as never married is very high (over 80 percent) below age 20 for all population groups. Among Africans, the proportion never married declines less sharply from about 20 percent below age 20 to less than 80 percent among women in their forties. 21

23 Fig. 4 Proportion Females Ever Married by Race South Africa, Proportion ever married Age Blacks Colored Asian White AGE PATTERNS AND TRENDS OF MARITAL AND NONMARITAL FERTILITY Figure 5 shows trends in marital and nonmarital fertility in South Africa. Marital fertility has been consistently high (over five children per woman) in the early to mid-1980s. A decline that began around 1985 was interrupted by a slight rise in the late 1980s. This rise was then followed by another decline, which appears to have been interrupted again in the mid-1990s. It is possible that a slight decline in marital fertility has occurred in conjunction with an increase in age at first marriage or first birth. Chimere-Dan (1998) has postulated that marital fertility is now of less significance to overall fertility rates in South Africa. 22

24 Fig. 5 Trends in marital and non-marital fertility:sa Total fertility rate non-marital fertility all women current marital fertility However, the levels and trends shown in Figure 5 clearly show that marital fertility is not decreasing in importance at all. With regard to nonmarital fertility, the figure shows that nonmarital fertility has been declining steadily over the years, from a high of about 2.7 births per woman in 1982 to about 1.7 births per woman in Thus, both marital and nonmarital fertility do make significant contributions to the overall fertility levels in South Africa. The levels of nonmarital fertility shown here are the highest ever observed in sub-saharan Africa. Ethnographic research on non-marital fertility among Coloureds, Whites and Africans has consistently shown remarkably high societal tolerance of this practice in South Africa (Rubensztein 1992, Preston-Whyte and Allen 1992, Preston-Whyte and Zondi 1992). Among African families, these births are by no means confined to social drop-outs, or to a few mavericks who willfully flout convention. Some of the most 23

25 successful and respected women in the urban community have not married, but have children...thus the fate of perpetual poverty or ostracism does not present itself as the inevitable result of falling pregnant before marriage" (Preston-Whyte and Zondi 1992:232). To get better insight into what has been happening to fertility we examine the age-specific patterns of marital and nonmarital fertility in Figures 6-8. Among currently married women, there were considerable reductions in marital fertility among women aged 20-24, 25-29, and since the early 1990s. With regard to nonmarital fertility, age-specific fertility is high among women in their twenties and early thirties. Substantial differences also exist among South Africa s four main population groups. Trends in nonmarital fertility by race closely mirror the trends we observed for overall and marital fertility by race. African and Coloured women exhibit the highest nonmarital fertility rates, followed by Asians and Whites. African and Coloured women also exhibit a slow but steady decline while Asian and White women show erratic trends in the early 1980s followed by steady declines as well. Although fewer proportions of African and Coloured women ever get married in South Africa, these two groups still have relatively high levels of non-marital fertility. Non-marital fertility is especially high among Africans because... the value placed upon children is so high for many people that marriage is, in some contexts, quite irrelevant to the bearing of a child (Preston-Whyte and Zondi 1992:233). 24

26 Fig. 6 Trends in age specific current marital fertility: SA Marital births per 1000 women Fig. 7 Trends in age specific non-marital fertility: SA Non-marital births per 1000 women

27 Fig. 8 Trends in non-marital fertility by race:sa Total non-marital fertility rate Africans Coloreds Asians Whites All women Fig. 9 Trends in current marital fertility by race:sa Total marital fertility rate All women African Colored Asian White 26

28 The overall levels of non-marital fertility clearly indicate that in South Africa, fertility outside of marriage is significantly higher than what one usually finds in most parts of sub-saharan Africa. Out of wedlock birth figures for South Africa seem to backup this picture. For instance, the Health Department of the Western Cape reported that in 1988 and 1990, 46.6 and 48.4 per cent of all births recorded within its jurisdiction were illegitimate births. On the other hand, the Cape Town Medical Officer of Health reported that for the period , 45.7 per cent of all births were also recorded as illegitimate. Breaking this figure by race, reveals that among Africans, 69.8 per cent of all recorded births were classified as illegitimate and among Whites, Coloureds and Asians, the percentages were 19.6, 44, 7.3 per cent respectively (Burman 1992). It is important to out that the definition of what constitutes an illegitimate birth account for the high percentages noted for Africans and Coloureds. Many customary law marriages were not recognized by the apartheid State. In addition, polygamous unions and any marriages that were performed by Muslim, Hindu or Christian African religious leaders who had not attained a prescribed level of education, had passed a state examination, and had not sworn to uphold the laws of the apartheid State was not recognized by the state. Hence any births from such a marriage were classified as illegitimate (Burman 1992). Despite this problem of classification, the main criterion that was used to classify births by the health authorities was the name of the father. If surname of the father was different from that of the woman or if the father s name was missing, then that birth was classified as illegitimate (Burman 1992). Obviously, such a classification criterion can inflate the number of children who can be classified as illegitimate. However, as previously indicated, these classification problems are not a major concern in this paper since the non-marital fertility estimates were calculated using never married women only. Thus, the extremely high percentage of illegitimate children among Africans is 27

29 related to this problem. Despite these high percentages of out-of-wedlock births, figure 8 shows that overall non-marital fertility has been declining in South Africa at both the national level and across the major population groups in the country. The declines in non-marital total fertility are more likely to be driven by contraceptive use. In addition, as the HIV/AIDS situation in South Africa worsens, the downward trend can be expected to continue at a much faster pace. Figure 9 shows that there has been a mixture of slow and fast decline in marital fertility since the early 1980s depending on the population group. Although Whites experienced a relatively slow decline in marital fertility, Africans, Coloureds, and Asians experienced fairly rapid declines in marital fertility during this period. The figure also shows that among Africans, Coloureds and to some extent Whites, there was a slight increase in marital fertility in the late 1980s. Among Africans and Coloureds, the faster pace of decline was probably the result of the increasing use of contraceptives and declining proportions of women of childbearing age who married. As expected, marital fertility is higher than overall fertility across all population groups in South Africa. Table 3 (in Appendix) shows that between 1981 and 1996, there were substantial differences in age-specific marital fertility among South Africa s four major population groups in terms of both overall levels and trends. Among African women, marital fertility did not decline much across most age groups prior to the 1980s. However, from the early 1990s, fairly substantial declines occurred across all age groups, with the bigger declines occurring among women in their twenties and thirties. When we compare trends in age-specific marital fertility and overall age specific marital fertility among African women (Tables 2 and 3), it is clear that overall fertility declined more sharply across all age groups when compared to marital fertility. Thus, age specific declines in nonmarital fertility are driving the overall age-specific fertility declines experienced by African women between

30 and Among Coloureds, there were no dramatic declines in age-specific marital fertility comparable to the overall age-specific declines observed in Table 2. The only steady declines in marital fertility occurred at ages 25-29, and Age-specific marital fertility rates are much higher than overall age-specific fertility rates among Coloured women as well. With regard to Asians, the trends in age-specific marital fertility rates are erratic at ages and 25-29, although the overall trend is an impressive decline. There were also marked declines in marital fertility at ages and At the oldest ages (40-49), marital fertility is low (below 30 births per 1,000). In contrast, among Africans, marital fertility rates at these older ages have been over 50 births per 1,000 for most of 1980s and early 1990s. Lastly, among White women, marital fertility declined rather slowly at the young ages (20-34), with very little changing at age 35 and over. At the oldest ages (40-49), marital fertility is very low among White women (below 20 births per 1,000). The differences in marital fertility rates at these older ages among South Africa s four population groups indicates that the timing of stopping differs somewhat among African, Coloured, Asian, and White women. CONCLUSION Proportions ever married are low among Africans because of the entrenched system of labor migration in the country that drives thousands of young men to the gold mines and major cities like Johannesburg. Another reason for the low proportions married among African women is probably the distorted sex ratios in favor of females (a sex ratio of 93). This sex ratio implies shortages of potential marriage partners, hence many women remain single; the unbalanced sex ratio may also 29

31 have encouraged casual sexual contacts. On the other hand, it is also possible that among Africans, the definition of current marital status did not include relatively stable living together relationships as ever married. However, the low ever-married proportions noticed here are not necessarily unique to South Africa. Survey and census data from Botswana have consistently shown low proportions (less than 90 percent) of ever-married women as well (van de Walle 1993). The general conclusion that marriages in Africa are early and universal is not necessarily true. In the case of South Africa, marriage is more universal among Whites and Asians. The proportion ever married for these two racial groups rises sharply between ages 20-30, reaching highs of about 83 percent before leveling off around 97 percent at the older ages. What are the implications of these patterns for fertility in South Africa? It is important to note that in societies that do not use contraception effectively either for spacing or stopping purposes, the level of fertility is influenced by proportions of women married and by marital stability. The more stable marital unions are, and the higher the proportion of women in some form of marital union, the higher the fertility. However, it is widely held that the use of contraception by South African women is the highest in sub-saharan Africa (Caldwell and Caldwell 1993; Potgieter 1996). Hence, the level of fertility is not necessarily influenced by proportions married. Furthermore, traditionally children were born within marriage, but nowadays this tradition has weakened as a result of a myriad of factors. What are the implications of these proportions for differential fertility by population group or race? Although Africans have the lowest proportions ever married at any age in the country, they have the highest fertility rates at any given age. This indicates first that it is possible that married African women tend to have more children than any other racial group and second, nonmarital fertility is a common feature among the various African ethnic groups. In the case of South Africa, 30

32 these two processes occur concurrently. The primary goals of this paper have been to describe recent fertility levels and trends in South Africa, to identify the prominent features of the fertility decline, and to assess if the post-1980 changes in South African fertility have been period-driven. It appears the decline in fertility is being driven by what is happening to the starting, spacing, and stopping components of reproductive change. There is some variation between races and age groups in the amount of decline of both marital and nonmarital fertility. Women aged 20-24, 25-29, and recorded greater declines in fertility. Overall fertility declines among the four major population groups in the country were largest among Africans. Signs of changing patterns of starting, spacing, and stopping are exhibited at all ages and across all the major population groups. The reduction in the total fertility also appears not to be a temporary period effect. Rather, the decline in fertility appears to be driven by changing family size patterns (quantum effects) and spacing effects. In other words, demand to limit family size is well established in South Africa among Africans, Coloureds, Asians, and Whites. Another picture that emerges from this analysis is that the decline of fertility in South Africa that we noted from the early 1980s will continue at both the national level and individually among South Africa s major population groups. Our goal was to establish a reliable demographic picture of contemporary South African fertility by exploiting census data using the own-children technique. The strategy of matching own children to mothers that we employed in this paper will allow us to fully exploit census micro data in the analysis of socioeconomic processes and fertility differentials, fosterage, living arrangements, and family structure in Africa. 31

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