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Critical Perspectives on Schooling and Fertility in the Developing World (1999)

Chapter: 6 Fertility, Education, and Resources in South Africa

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Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

6
Fertility, Education, and Resources in South Africa

Duncan Thomas

Introduction

South Africa has emerged from three-quarters of a century of apartheid, which placed comprehensive restrictions on the geographic and economic mobility of its population. As those restrictions are relaxed and the society moves toward integration, one of the greatest challenges facing the people of South Africa will be the absorption of a young and rapidly growing labor force into an economy undergoing dramatic restructuring. While South Africa appears to be ahead of other African countries in its demographic transition, there is tremendous diversity within the country. It seems safe to conjecture, therefore, that population policy will play a key role in influencing the success of social and economic development in the country (Chimere-Dan, 1993; African National Congress, 1994, 1995).

Using recently collected household survey data, this chapter examines an important consideration in the design of that policy: the relationship between fertility and resources, with a focus on the role played by maternal education. A vast number of studies have demonstrated that education and fertility tend to be negatively correlated in a wide array of contexts. In South Africa, education is a key correlate of fertility among all black women, but the association is less clear among women of other races, except, perhaps, those who are better educated. Over and above simply documenting these findings, this chapter examines three potential mechanisms in an attempt to provide insight into what underlies the observed association between education and fertility among South African blacks.

First, education is not randomly assigned within a population; rather, people

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

choose how much they invest in schooling given the constraints and opportunities they face. In the extreme, it might be argued that schooling provides no value-added, but is simply a sorting mechanism: those who are more able spend more time at school in order to signal their ability to their future employers (or spouses). Placing the spotlight on educational attainment around natural exit points in the South African education system, the evidence suggests that self-selection in education does play some role in explaining the effect of education on fertility in a regression context. Thus, a naive interpretation of the effect as being entirely causal would be misleading.

Second, education and household resources tend to be correlated, so a woman's education may simply be a proxy for her, or her family's, income. To the extent that it can be tested with the available data, this interpretation does not appear to tell the entire story in South Africa. While household resources do affect fertility outcomes, even after controlling for spousal characteristics, household income, labor market choices, and community characteristics, female education continues to have a powerful negative association with fertility. Thus one can conclude that a substantial part of the effect of female education on fertility operates independently of resources. Spousal education is also negatively correlated with fertility (see Basu, this volume), and the impacts of male and female education are, in most cases, close in magnitude. In South Africa, men appear to have an important influence on demographic outcomes.

Third, one can attempt to isolate the relationship between skills likely to be learned in school and demographic outcomes. Drawing on a sub-sample of women who completed a short comprehension and quantitative test, one finds that, after controlling for income and education, performance on these tests has an independent impact on fertility. The impact of comprehension skills is particularly large in magnitude, suggesting that the acquisition and assimilation of information may be important in affecting family decision making.

The next section sets the stage for the main analysis of the chapter by placing fertility and educational attainment in South Africa in historical context. This is followed by three sections that use recently collected household survey data to examine in turn the three mechanisms discussed above. The final section presents conclusions.

Fertility And Education In Historical Perspective

Given the central role played by population policy in the apartheid system of South Africa, it is remarkable how little solid evidence exists on the demography of the country. Even today, few of the data sources predating 1993 that contain information on fertility have been placed in the public domain. As a result, one must rely on reports that have not been subjected to the sort of scientific scrutiny they warrant (see Caldwell and Caldwell, 1993, for an excellent discussion). Fortunately, as the government has embraced the concept of openness and access

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

to information, this situation has been remedied. Data collected by the Central Statistical Service and, in many cases, by units that are independent of the government are now routinely placed in the public domain; the Project for Statistics on Living Standards and Development (PSLSD) and the annual October Household Surveys are good examples.

The 1994 October Household Survey estimates South Africa's population at about 40 million, of whom over three-quarters are black; half the rest are white, nearly 9 percent are mixed-race coloreds, and fewer than 3 percent are Asians, mostly of Indian descent. That survey estimates the total fertility rate (TFR) to be 4.1, although the Central Statistical Service views this as a substantial underestimate, particularly among blacks.

Much of the historical data on fertility and family planning in South Africa is described by van Zyl (1994), who also documents the methodologies used for the main surveys conducted by the Human Sciences Research Council that form the basis of these estimates. There are good reasons to be skeptical about the quality of some of those surveys, not least of which is the fact that population policy was an important political issue in South Africa, and it is far from clear that, at least at that time, the Human Sciences Research Council played a role that was entirely divorced from the political system. It is also prudent to treat comparisons across time with considerable caution since the surveys are not always comparable, and several focused on specific subpopulations. For example, in many surveys, the so-called homelands (where many of the poorest South Africans lived) were excluded from the samples, and in some surveys, fertility questions were asked only of married women. Setting aside the fact that the definition of marriage is complex in this society, many women who would not declare themselves as married have borne children, and teenage pregnancy rates are very high. According to the 1994 October Household Survey, about 33 of every 100 women have given birth out of wedlock. (See, for example, Preston-Whyte, 1990, for an insightful discussion.)

While remaining mindful of these caveats about data quality, it is useful to attempt to place South Africa's fertility rate in historical context. TFR estimates for 1950 through 1990 for each of the four main racial groups in South Africa are presented in the upper panel of Figure 6-1.1 According to these estimates, in 1950, the TFRs of blacks, coloreds, and Indians were all high (above 6) and reasonably close to one another, whereas that of whites was much lower (slightly above 3). By 1990, the picture was dramatically different: TFRs among coloreds and Indians had been reduced by more than half and were very close to that of whites. The TFR of blacks, however, remained much higher, around 5.0. While fertility among blacks had declined sharply during the 1970s and early 1980s, it had remained virtually constant during the earlier two decades. It appears that

1  

The data are drawn from Lucas (1992) and van Zyl (1994).

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

FIGURE 6-1 Total fertility rates and education of females, South Africa, 1950-1990.

among blacks, the demographic transition began to accelerate only around a quarter-century ago. In sharp contrast, there were rapid declines in fertility among Indians dating back to the 1950s, and their fertility stabilized around 1980. Coloreds fall between these two groups: their fertility actually rose in the 1950s, so that in the early 1960s, the TFRs of blacks and coloreds were almost identical (around 7), but the TFR of coloreds declined during the following quarter-century to a level that is now very close to that of Indians. The fertility of whites changed very little during the period, having risen slightly during the 1950s and declined slowly since then to below replacement levels.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

If these estimates reflect reality, several questions arise. First, what underlies the differences in the timing of fertility decline among Indians, coloreds, and blacks'? Second, why has the fertility of coloreds and Indians fallen so dramatically, while that of blacks has remained at levels comparable with those of neighboring Botswana and Zimbabwe, whose populations are substantially poorer and less well educated?

A plausible hypothesis centers around differences in community resources, specifically access to family planning services. There is, however, little direct evidence on the relationship between the fertility of individual women in South Africa and their access to services, and the evidence in both Botswana and Zimbabwe suggests that access to services alone cannot explain fertility decline there. Nevertheless, casual inspection of the evidence in South Africa does suggest that service availability may have played a role.

Prior to the mid-1960s, government had little involvement in family planning, and it seems likely that only whites, and possibly Indians, would have had good access to services. Public investments in family planning began in the mid-1960s and were targeted largely toward urban dwellers: this coincides with the decline in fertility among colored women, the majority of whom live in urban areas. In the mid-1970s, the National Family Planning Programme was established, and access to services was massively expanded, with special attempts being made to serve rural women. In 1972, for example, there were fewer than 250 family planning clinics in the country, but by the early 1980s, that number had risen to more than 36,000 (Lucas, 1992). It was also during this period that the decline in black fertility took hold.

Yet even today, access to family planning services is probably not universal in South Africa. Evidence for this conclusion is provided by data from the PSLSD indicating that there is a family planning clinic in the community for over three-quarters of Indian women aged 15 to 49, about half of colored women, but only one-third of black women.2

2  

These discrepancies are likely to be underestimates of differences in service availability for at least two reasons. First, many women in South Africa obtain family planning services from places other than family planning clinics, and blacks are the least likely to use alternative sources. Second. the estimates are based on data collected at the community level, and it is well known that those data are difficult to collect in a survey setting and are therefore prone to serious measurement error. (See Frankenberg and Sudharto, 1995, for a general discussion, and Thomas and Maluccio, 1996, for a specific description of the nature and extent of problems that are evident in a community survey conducted in Zimbabwe in 1990.) Two issues are of special importance. First, it is not entirely clear how to define ''community," particularly in urban and metropolitan areas, and the definition may not mean the same thing to all respondents. Second, the so-called "community informants" who are "prominent members of the community such as school principals, priests and chiefs" (Project for Statistics on Living Standards and Development, 1994:v) may themselves not be very well informed. For example, the PSLSD reports a family planning clinic in two-thirds of urban communities, one-third of rural communities, but only one-quarter of metropolitan communities. Yet it is the metropolitan communities that are likely to be best served of all.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Racial differences in fertility rates may also be explained by differences in household resources. The benefits of growth and development in South Africa have not been distributed evenly across races, and the society is marked by very high levels of inequality. This inequality is reflected, for example, in differences in education as depicted in the lower panel of Figure 6-1, which displays mean educational attainment for women, by birth cohort, as reported in the 1993 PSLSD.3 Among women born in 1935, whites are much better educated than coloreds, Indians, and blacks: the average white woman had completed 9 years of schooling, whereas the average black had completed less than 2. The educational attainment of all groups except whites—especially Indians and, to a lesser extent, blacks—has increased substantially since then. Thus among women born in 1975, Indians are at least as well educated as whites, and blacks are nearly as well educated as coloreds. While the convergence of average educational attainment across races is striking, it is well known that there are substantial differences in the quality of schooling between racial groups, so it is unlikely that a year of education means the same thing to a black and a white.

A comparison of the upper and lower panels of Figure 6-1 does suggest that educational attainment and fertility are negatively correlated. However, it remains difficult to explain the timing of declines in TFRs for each race using an argument based on the average education of women. Digging a little more deeply and examining changes in other parts of the education distribution, a more complex picture emerges.

Thomas (1996) exploits the large sample sizes of the 1991 South African Census to explore rates of growth in educational attainment at each quartile of the education distribution. He reports that among those born between 1920 and 1970, an Indian woman would have spent 1.5 more years in school if she had been born a decade later, and the growth was most rapid among the least educated; for example, growth rates were 1.8 years at the bottom quartile and 1.2 years at the top quartile of the distribution. A similar pattern emerges for coloreds, although their average growth rate was only 0.7 years per decade. In sharp contrast, however, the least-educated blacks were largely excluded from the increase in education, which was, instead, concentrated among the better educated

3  

Completed years of education is defined as I if the woman completed sub-A, sub-B, or Standard 1, with another year being added for each grade completed until Standard 10, when most students take the matriculation (matric) examination. In the survey, post-secondary schooling is not reported in years, but in terms of qualifications. Diplomas, technical degrees, teacher training, nursing training, and completion of some university education are assigned 12 years of schooling. Completion of a university degree is assigned 14 years of schooling. Since only 5 percent of women report attending school beyond Standard 10, the impact of making different reasonable assumptions about years of schooling for these women is likely to be small. Moreover, the specifications for the regressions discussed in detail below are sufficiently flexible to ensure that coefficient estimates are not biased by this assumption.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

of that group. There is a positive note in this dismal picture for the poorest black South Africans since, among those born in the latter part of the period, increases in education have occurred across all black women. This more recent extension of the fruits of development to the poorest may well be related to the tardiness of the fertility decline among blacks.

While they are suggestive, these aggregate data can give only a very incomplete picture of the relationship between fertility and education at the micro level. Therefore, in the following sections, survey data from the PSLSD are used to examine not only the correlation between fertility and education, but also some of the mechanisms that underlie that correlation. Before doing that, however, it is useful to take a slight detour and examine the determinants of educational attainment in South Africa. Ideally, one would like to be able to say something about how much of the increase in educational attainment in South Africa over the last half-century can be attributed to public policies such as investments in school infrastructure, teachers, and teacher training. It is not possible to answer that question directly with the available data, but it is possible to turn the question around and assess the extent to which resources in the home and in the community have affected the educational outcomes of adults and children.

We begin with completed schooling of adults. Respondents in the South African Social Stratification Survey (1991-1994)4 report their own education; a migration history, including information on the type of house and community they lived in at birth; and characteristics, including education and occupation, of the people they viewed as their father- and mother-figure at age 14. Regressions in the upper panel of Table 6-1 report the impact of maternal and paternal years of schooling on the completed years of education of adult children aged 20 to 70 at the time of the survey. Parental education is a powerful predictor of own education: fully 26 percent of the variation in educational attainment among blacks is explained by parental education alone. Intergenerational mobility has risen over time, as indicated by the larger coefficients on education for older than younger blacks; this change is particularly striking for the impact of maternal education. While mobility remains lower among blacks relative to whites, even for whites an additional year of parental schooling is associated with one-fifth of a year more schooling for the respondent.

How much of the impact of parental education can be attributed to the role of household and community resources? It is difficult to imagine any household survey measuring resources in the home 50, 40, or even 20 years prior to the interview with any degree of accuracy. Fortunately, the South African Social

4  

The survey was conducted by Don Treiman in collaboration with the Human Sciences Research Council. One (randomly chosen) adult respondent was interviewed from each of 9,000 households in South Africa. See Treiman (1996).

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Stratification Survey does provide proxies for resources in the home and community when the respondent was a child. Drawing on this information, the regressions were reestimated to include controls for the occupation of the parents when the respondent was age 14, location of birth, type of residence at birth, and language spoken at home. In addition, to capture variation in access to schools and possibly quality of schools, as well as other infrastructure at the community level, the reestimated regressions include fixed effects for town of birth. Results are reported in Panel B of Table 6-1.

Among blacks, over half the variation in educational attainment is explained by the controls, and the effects of parental education, particularly that of fathers, are significantly reduced. This suggests that household and community resources do matter and that paternal education is, in part, proxying for those resources. Further support for this interpretation is provided by the fact that among the covariates, paternal occupation is key. If a respondent's father had a job working in the wage sector or for the government or if the father was self-employed, the respondent is likely to have completed half a year of additional schooling, but a third of a year less if the father was a farm laborer; the excluded category includes informal-sector workers, communal farmers, and those not working at all.5

The additional controls do little to diminish the impact of maternal education, especially among older blacks, suggesting that a woman's education has an independent effect on the human capital of her children over and above the role of resources. Among whites, the additional controls do reduce the impact of parental education, but the differences are relatively small and not significant. This may be because variation in household and community resources is less important among whites or because the measures of these resources are too crude to be informative.

To provide some evidence on that question, one can examine the effects of parental education and resources on the educational attainment of children still resident at home using data from the PSLSD.6 An advantage of these data is that they contain information on household per capita expenditures, a longer-run measure of resources available to the family; the disadvantage is that one is forced to restrict attention to only those children who are still resident in the

5  

The regressions include controls for whether the respondent was born in an urban, peri-urban, or squatter area, along with town-of-birth fixed effects. Paternal occupation is not, therefore, simply proxying for rural-urban differences. The negative effect of occupation if the father was a farm laborer is greater (and significant) among the younger cohort, suggesting that it may partly capture the effect of the Verwoerdian policy of "putting a school on every farm." The quality of those schools has been roundly criticized (see, for example, Department of Education, 1996).

6  

The PSLSD is a nationally representative survey of some 9,000 households conducted in South Africa during October through December 1993, a few months before the first democratic elections in April 1994.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-1 Intergenerational Transmission of Education

ADULTS

BLACKS (by age in years)

WHITES (by age in years)

ALL

20-39

41-70

ALL

20-39

41-70

A. Education only Maternal education

0.296*

0.258*

0.419*

0.201*

0.224*

0.185*

[0.02]

[0.02]

[0.04]

[0.02]

[0.03]

[0.04]

Paternal education

0.264*

0.235*

0.337*

0.178*

0.164*

0.194*

[0.02]

[0.02]

[0.05]

[0.02]

[0.03]

[0.03]

B. Full set of controls Maternal education

0.258*

0.211*

0.397*

0.176*

0.156*

0.194*

[0.02]

[0.03]

[0.05]

[0.03]

[0.04]

[0.05]

Paternal education

0.178*

0.155*

0.215*

0.177*

0.160*

0.187*

[0.03]

[0.03]

[0.08]

[0.03]

[0.04]

[0.04]

R2

0.52

0.46

0.57

0.42

0.43

0.50

Sample size

4,533

2,964

1,569

2,161

1,133

1,028

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

C. CHILDREN

BLACKS (by age in years)

WHITES

 

ALL

10-11

12-13

14-15

16-17

ALL

Maternal education

0.073*

0.038*

0.076*

0.102*

0.115*

0.003

 

[0.01]

[0.01]

[0.01]

[0.02]

[0.02]

[0.01]

Paternal education

0.056*

0.021+

0.046*

0.063*

0.073*

0.015

 

[0.01]

[0.01]

[0.02]

[0.02]

[0.02]

[0.01]

In(per capita expenditure)

0.373*

0.200*

0.289*

0.415*

0.529*

0.066

 

[0.05]

[0.05]

[0.06]

[0.09]

[0.10]

[0.12]

R2

0.56

0.26

0.32

0.35

0.34

0.85

Sample size

6,609

1,706

1,729

1,658

1,516

577

NOTE: Dependent variable is years of completed education. Adult regressions based on respondents aged 20-70 in South African Social Stratification Survey, 1991/94. Adult "education only" regression includes controls for gender and (linear spline in) age of respondent, whether education of parent is missing, and whether father-figure and mother-figure are biological parents. "Full set of controls" adds controls for occupation of father and mother (at respondent's age 14), language spoken in home at respondent's age 14, type of place lived in at birth, type of house lived in at birth, and fixed effect for town of birth. Child regressions based on children aged 10-17 in PSLSD, 1994. Regressions include dummies for gender of child, each year of age of child, and whether urban or rural dweller.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

home. The focus here is on children aged 10 through 17, but recognizing that a substantial fraction of older children are likely to have left the nest, the analyses are stratified into 2-year age bands. Panel C of Table 6-1 presents the regression results.

Educational attainment of black children is significantly correlated with parental education and household per capita expenditures, and all the effects increase with the age of the child. Among whites, there is no evidence that parental education or household resources are correlated with child educational attainment. One can conclude, therefore, that while there has been substantial intergenerational transmission in educational attainment among whites, resource constraints and limited access to schools are unlikely to have had a large impact on this group's educational attainment. Among blacks, however, resources do seem to matter, indicating that there is substantial scope for public interventions designed to raise schooling levels. Corroborating evidence is reported by Case and Deaton (1995), who show that even crude measures of teacher-pupil ratios are correlated with educational attainment among blacks, but not whites. The interpretation has considerable intuitive appeal in view of the history of South Africa, where racially segregated education was a central pillar of the apartheid system, state policies sought to restrict blacks' access to schooling, and a very well-funded system for whites coexisted with a substantially poorer system for blacks. In 1975, for example, the year before the Soweto school riots, public expenditures on education of the average white school-age child were more than 15 times greater than expenditures on the average black child.

Relationship Between Fertility And Education

Having shown that parental education and, for blacks, family and community resources play an important role in determining the educational attainment of the next generation, the discussion now turns to the correlation between education and fertility. The following analyses are based on the PSLSD, in which female respondents aged 15 to 49 reported the number of children ever born (CEB), how many survived to age I and how many to age 5, and how many were alive at the time of the survey. Unfortunately, fertility in the last year (or last 5 years) was not recorded. We must therefore focus on CEB as our sole measure of fertility.7

Summary statistics for the 10,500 female respondents are reported in Table 6-2. Three-quarters of the women in the survey were black and, as indicated in Figure 6-1, they had borne more children than coloreds, Indians, or whites. But

7  

It is also unfortunate that the limited set of information reduces the scope for undertaking internal consistency checks in these data—an issue of considerable import given the concerns raised about data quality above.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-2 Means and [Standard Errors]

Characteristic

Black

Black

Lit. samplea

Colored

Indian

White

No. children ever born

2.106

1.785

2.002

1.604

1.535

 

[0.026]

[0.079]

[0.068]

[0.088]

[0.044]

(among those aged 40-49)

5.000

4.781

4.250

2.949

2.503

 

[0.117]

[0.230]

[0.273]

[0.277]

[0.107]

Woman's characteristics

Years of education

5.654

6.139

6.489

8.023

9.140

 

[0.036]

[0.107]

[0.094]

[0.160]

[0.113]

Fraction completed

Standard 0

0.109

0.066

0.041

0.041

0.074

Standard 1

0.041

0.032

0.022

0.006

0.003

Standard 2

0.048

0.039

0.025

0.006

0.004

Standard 3

0.061

0.058

0.047

0.021

0.006

Standard 4

0.075

0.067

0.074

0.032

0.026

Standard 5

0.114

0.096

0. 115

0.035

0.014

Standard 6

0.127

0.150

0.162

0.120

0.042

Standard 7

0.099

0.140

0.134

0.091

0.04

Standard 8

0.118

0.145

0.165

0.170

0.161

Standard 9

0.088

0.091

0.071

0.123

0.055

Standard 10

0.091

0.091

0.108

0.276

0.324

Diploma/>

Standard 10

0.029

0.025

0.028

0.044

0.135

University degree

0.008

0.035

0.118

Age

28.472

25.887

30.150

30.704

32.544

 

[0.105]

[0.338]

[0.318]

[0.535]

[0.290]

Fraction worked in last year

0.176

0.126

0.335

0.290

0.433

In (wage)

6.140

6.158

6.562

7.000

7.508

 

[0.026]

[0.098]

[0.049]

[0.059]

[0.025]

Monthly Wage (Rs)

705.730

790.271

929.008

1327.217

2102.629

 

[17.226]

[64.935]

[36.406]

[98.8091

[55.561]

Fraction in urban sector

0.173

0.213

0.357

0.531

0.248

Fraction in rural sector

0.651

0.591

0.069

0.003

0.077

Fraction ever married

0.546

0.365

0.567

0.660

0.750

Spouse's characteristics

Fraction dead

0.058

0.000

0.053

0.040

0.016

Fraction absent

0.292

0.327

0.122

0.053

0.049

Fraction present

0.650

0.673

0.825

0.907

0.935

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Characteristic

Black

Black

Lit. samplea

Colored

Indian

White

Years of education

2.975

3.194

5.589

8.049

9.778

[0.053]

[0.206]

[0.160]

[0.264]

[0.146]

Fraction completed

Standard 1

0.051

0.065

0.018

0.004

0.005

Standard 2

0.040

0.049

0.022

0.013

0.001

Standard 3

0.038

0.019

0.045

0.004

0.000

Standard 4

0.059

0.061

0.049

0.004

0.012

Standard 5

0.074

0.107

0.079

0.027

0.004

Standard 6

0.078

0.071

0.152

0.080

0.028

Standard 7

0.034

0.049

0.079

0.049

0.016

Standard 8

0.057

0.049

0.165

0.147

0.118

Standard 9

0.027

0.039

0.067

0.120

0.025

Standard 10

0.047

0.039

0.087

0.298

0.264

Diploma

0.015

0.013

0.033

0.044

0.257

University degree

0.005

0.010

0.010

0.084

0.164

Household income

1143.240

1135.677

2382.135

4483.229

6936.798

[17.251]

[42.084]

[65.842]

[492.055]

[312.383]

In (household income)

6.577

6.611

7.469

7.994

8.545

[0.012]

[0.037]

[0.031]

[0.049]

[0.024]

Sample size

8,142

788

896

341

1,084

a Literacy assessment sub-sample.

in contrast with the TFRs in Figure 6-1, the gap between blacks and coloreds is very small: blacks reported 2.1 children on average, whereas coloreds reported 2.0. Indians and whites reported about .5 child less—1.6 and 1.5, respectively. This clustering of coloreds with blacks and Indians with whites matches the education levels presented in the lower panel of Figure 6-1 and is also reflected in the fertility of women aged 40 to 49. (Because the sample sizes of coloreds and Indians are small, the present analysis uses the fertility of this relatively broad age group as the measure of completed fertility, although it is important to recognize that many women in this age group will continue to have more children.) The 1994 October Household Survey reports a birth history for each female respondent; among those aged 40 to 49, the fertility rates of whites and Indians are remarkably close to those reported in the PSLSD. (For whites, completed fertility is 2.39 in the October Household Survey and 2.50 in the PSLSD. For Indians, it is 2.89 and 2.95, respectively.) However, for coloreds and blacks, reported fertility is much lower in the October Household Survey than in the PSLSD (3.31 versus 4.25 for coloreds and 2.49 versus 5.00 for blacks in the October Household Survey and PSLSD, respectively). The Central Statistical Service argues

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

that the fertility estimates for blacks in the October Household Survey are almost certainly underestimates; the same is probably true for coloreds.

Precisely what underlies the discrepancy between the October Household Survey and PSLSD estimates is not clear, although an important factor may be that the former survey was conducted by the government, whose agencies during apartheid had a history of discouraging fertility among blacks, while the PSLSD was conducted by a consortium of private and quasi-government survey teams.8 Whatever the reasons, the PSLSD estimates cast a shadow of doubt on the reliability of the time series of TFR estimates presented in Figure 6-1 and raise serious questions about what is thought to be known about fertility in South Africa.

Returning to the PSLSD, the correlation between fertility and education is reported in the first line of Table 6-3, which presents the coefficient (and standard error) from a linear regression of CEB on completed years of education, controlling for age (splines), and indicator or dummy variables for urban or rural residence (with metropolitan areas excluded). There is a powerful association between fertility and education among black women: an additional year of education is associated with 0.12 fewer children. The correlation is almost as large for coloreds, half as large for Indians, and relatively small for whites.

While the above is a simple summary of the data, it is not obvious that the relationship between fertility and education should be linear. Figure 6-2, which presents the mean CEB for each year of completed education, indicates that the relationship is, in fact, not linear. Those estimates confound the role of age and education, which we know from Figure 6-1 are negatively correlated because of the tremendous growth in education across cohorts. Thus, Panel C of Table 6-3 reports the analogous regression estimates, controlling for age and location (which are also reported).

Before discussing those results, it is useful to examine the distribution of education reported in Panel B of Table 6-2. Primary school ends at Standard 5; many women exit secondary school at Standard 8 (after completing their Junior Certificate); and Standard 10 is the terminal year of secondary school, at which time women write matric examinations (which are used for entry into universities, tecnicons, and training programs). Looking first at black women, there is clear evidence of stacking of the distribution at the natural exit points, but there are many women who left school at other times. Note in particular that a substantial fraction of women left in the year immediately prior to completing primary school (Standard 4) and in the first year of secondary school (Standard 6). Moreover, fully one-third of black women did not complete primary school, and among those who attended secondary school, only 12 percent completed matric (Stan-

8  

There is, for example, anecdotal evidence of enumerators in the 1980s lecturing respondents about the virtues of small family sizes.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-3 Children Ever Born and Female Education, Women Aged 15-49

Explanatory Variable

Black

Black Indian

Colored

Indian

White

A. Linear

Years of education

-0.117

-0.119

-0.094

-0.056

-0.037

[0.006]

[0.007]

[0.018]

[0.023]

[0.009]

B. Spline

0-5 years

-0.106

-0.103

0.051

0.172

0.185

[0.013]

[0.015]

[0.044]

[0.067]

[0.034]

6-10 years

-0.134

-0.134

-0.168

-0.175

-0.160

[0.012]

[0.016]

[0.033]

[0.045]

[0.027]

> 10 years

-0.059

-0.119

-0.175

-0.046

-0.070

[0.041]

[0.053]

[0.092]

[0.066]

[0.022]

C. Semiparametric

Standard 1

-0.128

-0.156

0.874

0.388

-0.566

[0.102]

[0.119]

[0.398]

[0.882]

[0.638]

Standard 2

-0.079

-0.208

0.236

1.432

0.117

[0.097]

[0.111]

[0.385]

[0.875]

[0.556]

Standard 3

-0.338

-0.383

0.529

0.679

-0.045

[0.090]

[0.107]

[0.322]

[0.538]

[0.460]

Standard 4

-0.423

-0.371

0.731

1.681

0.151

[0.085]

[0.102]

[0.294]

[0.472]

[0.239]

Standard 5

-0.592

-0.650

0.578

1.021

0.429

[0.076]

[0.092]

[0.275]

[0.456]

[0.307]

Standard 6

-0.623

-0.589

0.163

0.849

0.726

[0.075]

[0.093]

[0.266]

[0.360]

[0.203]

Standard 7

-0.699

-0.660

0.252

0.285

0.862

[.081]

[0.102]

[0.271]

[0.383]

[0.210]

Standard 8

-0.921

-0.933

0.015

0.460

0.515

[0.077]

[0.100]

[0.265]

[0.350]

[0.147]

Standard 9

-0.961

-0.969

-0.047

0.329

0.447

[0.083]

[0.106]

[0.299]

[0.365]

[0.188]

Standard 10

-1.274

-1.331

-0.283

0.179

0.009

[0.083]

[0.108]

10.282]

[0.341]

[0.135]

Diploma

-1.401

-1.577

-0.785

-0.009

-0.303

[0.117]

[0.153]

[0.371]

[0.435]

[0.152]

University degree

-0.990

-0.078

-0.223

 

 

[0.592]

[0.460]

[0.156]

dard 10). The shape of the distribution of education for coloreds is very similar (although it is shifted to the right relative to blacks). Indians and whites, however, display different patterns: only about 15 percent had not completed primary school, while more than one-third of Indians and more than half of white women had completed matric. Whereas most whites left school at either Standard 8 or Standard 10, the distribution is much smoother for Indians. The stark differences

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Explanatory Variable

Black

Black Rural

Colored

Indian

White

Age spline

< 30

0.185

0.193

0.161

0.164

0.158

[0.005]

[0.006]

[0.014]

[0.020]

[0.012]

30-39

0.161

0.180

0.162

0.079

0.091

[0.007]

[0.010]

[0.019]

[0.026]

[0.013]

40-49

0.109

0.123

0.068

0.006

0.004

[0.011]

[0.014]

[0.027]

[0.037]

[0.015]

(1) if urban

0.195

-0.159

0.012

0.142

[0.060]

 

[0.105]

[0.130]

[0.078]

(1) if rural

0.498

0.203

-0.456

0.270

[0.049]

 

[0.201]

[1.170]

[0.126]

Intercept

-2.763

-2.511

-2.889

-3.329

-3.242

[0.140]

[0.161]

[0.419]

[0.5621

[0.313]

F(all covariates)

579.87

480.27

57.17

20.59

49.21

R2

0.533

0.560

0.525

0.520

0.440

Sample size

8,142

5,303

896

341

1,084

NOTE: Standard errors below coefficients; p-values below F statistics.

FIGURE 6-2 Children ever born and female education.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

in the shapes of these distributions are, in large part, a reflection of state policies during apartheid.

The semiparametric estimates of the association between education and fertility in Panel C of Table 6-3 impose no restrictions on the shape of the fertility-education function since each year of completed education is represented by an indicator variable. Because the excluded education category is zero years, the coefficients should be interpreted as the impact of that level of schooling relative to not having attended school. For example, black women who completed Standard 5 had 0.59 children fewer than those who reported no schooling; those who had completed Standard 10 had 1.27 fewer children. Because so few blacks had a university degree (less than 0.5 percent), postmatric schooling is combined into a single category.

The semiparametric estimates provide several important new insights beyond those revealed by the linear regressions in Panel A. First, the relationship between fertility and education is not linear for any of the racial groups. Second, focusing on blacks, the negative association between fertility and education is relatively weak at the bottom of the education distribution and is significant only among women with at least 3 years of schooling. Third, the marginal effect of an additional year of schooling is largest at schooling levels that are natural exit points in the schooling system. For example, women who had completed matric (Standard 10) had 0.39 fewer children than those who had completed only Standard 9. This is three times larger than the average effect for each year of schooling, which, from Panel A, is 0.12 fewer children. Similarly, the marginal effect of completing Standard 8 relative to Standard 7 is 0.22 fewer children. If this reflects an increasing marginal impact of education with more years of schooling, women who had completed Standard 9 should have had many fewer children than those who had graduated Standard 8. They did not: the marginal difference is a mere 0.04 child.

A similar pattern emerges around completion of primary school. The difference between women who fell just short of completing primary school (Standard 4) and those who completed Standard 5 is 0.17 child, but another year of schooling is associated with only 0.03 fewer children. Among rural blacks, women who had started secondary school but completed only 1 year (Standard 6) actually had more children than those who had exited at the end of primary school (the effects being -0.59 and -0.65, respectively). While this upward turn in the fertility-education function is not significantly different from a flat, it is striking that the first 2 years of secondary schooling are associated with virtually no decline in fertility among rural women, whereas there is a huge impact associated with completing the third year, Standard 8, when the Junior Certificate examination is taken.

There may be important age and education interactions since younger women are better educated in South Africa, and fertility rises with age. To further isolate the relationship between fertility and education, Table 6-4 repeats the same re-

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-4 Children Ever Born and Female Education, Women Aged 40-49

Explanatory Variable

Black

Black Rural

Colored

Indian

White

A. Linear

Years of education

-0.136*

-0.152*

-0.124*

0.010

-0.054*

[0.023]

[0.031]

[0.062]

[0.053]

[0.020]

B. Spline

0-5 years

-0.150*

-0.175*

0.146

0.172

0.233*

[0.040]

[0.052]

[0.130]

[0.134]

[0.071]

6-10 years

-0.140*

-0.085

-0.362*

-0.145

-0.249*

[0.066]

[0.114]

[0.129]

[0.117]

[0.061]

> 10 years

0.016

-0.265

-0.026

0.154

-0.069

[0.193]

[0.338]

[0.292]

[0.196]

[0.051]

C. Semiparametric

Standard 1

-0.053

-0.010

1.249

-1.381

[0.290]

[0.351]

[1.094]

 

[1.281]

Standard 2

-0.058

-0.106

1.572

-0.546

0.258

[0.300]

[0.347]

[1.226]

[1.706]

[0.921]

Standard 3

-0.505+

-0.668+

1.343

0.731

0.497

[0.280]

[0.350]

[0.886]

[0.880]

[0.772]

Standard 4

-0.659*

-0.680+

2.595*

1.710*

0.397

[0.270]

[0.344]

[0.804]

[0.827]

[0.511]

Standard 5

-0.930*

-1.069*

1.424+

1.188

1.710

[0.260]

[0.345]

[0.782]

[0.850]

[1.285]

Standard 6

-0.677*

-0.716*

0.459

0.565

0.995*

[0.235]

[0.323]

[0.775]

[0.700]

[0.406]

Standard 7

-0.753*

-0.391

0.991

0.017

0.847

[0.361]

[0.662]

[0.834]

[0.959]

[0.567]

Standard 8

-1.140*

-1.222*

0.345

0.089

0.315

[0.306]

[0.604]

[0.788]

[0.781]

[0.289]

Standard 9

-1.367*

-1.703+

0.542

0.945

0.212

[0.465]

[0.925]

[1.038]

[0.830]

[0.415]

Standard 10

-1.649*

-1.592+

0.953

0.318

-0.075

[0.472]

[0.805]

[2.154]

[0.882]

[0.275]

Diploma

-1.424*

-1.782*

0.743

0.829

-0.965*

[0.446]

[0.621]

[1.366]

[1.013]

[0.338]

University degree

-1.047

0.809

-0.263

 

 

[1.604]

[1.125]

[0.334]

gressions for women aged 40 to 49. Among black women, reported in the first two columns, the same pattern emerges. Only 5 percent of women reported more than Standard 8 schooling, so we focus on completion of primary school. There is a very large decline in fertility for women who had completed Standard 4 as compared with those who had completed Standard 5. In terms of completed fertility, those who had stayed at school for 1 more year (until Standard 6) look

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Explanatory Variable

Black

Black Rural

Colored

Indian

White

Age in years

0.104*

0.133*

0.056

0.045

0.063*

[0.024]

[0.032]

[0.056]

[0.075]

[0.025]

( 1) if urban

0.660*

-0.202

-0.297

-0.029

[0.227]

 

[0.335]

[0.371]

[0.172]

(1) if rural

1.192*

1.063

0.241

[0.192]

 

[0.686]

 

[0.276]

Intercept

-0.204

-0.266

0.509

0.424

-0.434

[1.072]

[1.435]

[2.614]

[3.417]

[1.141]

F(all covariates)

11.190

4.200

2.290

0.880

3.080

[0.000]

[0.000]

[0.006]

[0.579]

[0.000]

R2

0.103

0.057

0.164

0.139

0.140

Sample size

1,377

852

191

85

300

much more like the Standard 4 women than do those who had exited at Standard 5, and it is only those who had completed Standard 8 who reported fewer children than the Standard 5 women.

In sum, the fertility-education profile for black women in South Africa is characterized by a generally downward slope that is punctuated by steeper steps at natural exit points and flats (or upward shifts) immediately afterward. A traditional human capital interpretation of this evidence would conclude that particular years of schooling are especially productive (at least in terms of their impact on reproduction), while others are, frankly, of little value. Or put another way, if more women were only to pass their examinations, such as the Junior Certificate in Standard 8 or matric in Standard 10, fertility would decline even more rapidly. This seems a very unlikely scenario.

A more plausible interpretation of the evidence recognizes the fact that education is not randomly distributed across women, but is the outcome of a set of choices made by each woman and her family when she was an adolescent. Those women who did not quite complete an important school hurdle, such as passing the Standard 8 examinations, are likely to be different in both observable and unobservable ways from women who did pass those exams. To attribute all the differences in the observed relationship between education and fertility to productivity effects associated with schooling itself and ignore the role of the unobserved attributes of the women is likely to be very misleading. Following the same logic, women who started secondary school but completed only the first year (Standard 6) before dropping out may be even less able than those who exited at the end of primary school. Thus, the small impact of education for those women does not mean that Standard 6 is unproductive, but that the women who

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

exit at that point are selected from a different part of the distribution of tastes and, possibly, abilities than those who exit at Standard 5.

It is worth emphasizing that schooling decisions are not made in a vacuum, but reflect the choices of individuals and families given the opportunities and constraints they face. The fact that under apartheid South Africa, many government policies seriously curtailed the opportunities open to blacks does nothing to diminish the import of the argument that education is not randomly assigned in the population. On the contrary, it seems reasonable to suppose that self-selection may be particularly germane in this context, where state policies actively sought to limit the education opportunities of blacks (Samuel, 1990).

An alternative explanation for the nonlinearities in the effects of education has been proposed in the labor economics literature. In estimates of wage functions, evidence of steps and flats around completion of particular levels of education (or particular examinations) has been interpreted as ''certification" effects. However, that interpretation has little appeal in a fertility context. Rather, interpreting the steps and flats as reflecting self-selection in educational attainment is a plausible explanation for both the wage- and fertility-education relationships.9 (See Weiss, 1995, for a review and discussion in the context of wage functions.)

To be sure, the evidence in Tables 6-3 and 6-4 cannot say anything conclusive about the productivity of education in terms of its impact on fertility. The powerful negative correlation certainly suggests there may be a causal link, but without taking account of the potential endogeneity of education, one cannot be certain. However, the evidence does indicate that a naive causal interpretation of the magnitude of the association is probably flawed, and that failure to take account of the selection process underlying educational achievement is likely to lead to substantially incorrect inferences.

The same pattern of steps and flats is apparent in the association between contraceptive use and education in neighboring Zimbabwe, as demonstrated by Thomas and Maluccio (1996). They show that Zimbabwean women who have completed primary school are twice as likely to use modern contraceptives as those who have dropped out in the year before completion (a step) and that primary school graduates are more likely to use contraceptives than those who

9  

Evaluating these interpretations of education in the context of fertility rather than wages has an important additional advantage: wages are earned only by those who are working in the labor market, and that choice needs to be taken into account in the estimation. Unfortunately. there are no good instruments for measuring labor market participation in the PSLSD. However, there is some evidence that self-selection in education is important for black women since the probability a woman works is nearly twice as high if she completed Standard 5 (9 percent) than if she completed Standard 4 or Standard 6 (5 percent). Conditional on working, women who complete Standard 8 have wages only 6 percent lower than those who leave school at Standard 9, but their wages are fully 75 percent higher than those of women who exit school at Standard 7. Both female labor force participation and wage functions in South Africa are, therefore, also characterized by steps and flats.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

have attended 1 year of secondary school (a flat). Using data from Brazil, Lam and Duryea (1995) demonstrate a similar pattern for the fertility of women aged 30 to 34. The pattern of steps and flats is, therefore, not unique to South Africa.

Turning to the remaining three columns in Tables 6-3 and 6-4, the fertility of coloreds appears to rise with education until around Standard 4, whereupon it declines rapidly, following the inverted-U shape that has been observed in many other countries and is discussed, for example, by Cochrane (1983). As a simplified summary of the evidence, Panel B of the two tables reports a spline specification with knots at 5 and 10 years of schooling. Among women with more than 5 years of schooling, there is a powerful negative association between education and fertility: each year of education is associated with around 0.17 fewer children, which is larger in magnitude than the decline among similar black women. The fertility of Indian women also increases until around the completion of primary school and then falls dramatically (at about the same rate as that among coloreds) until completion of Standard 10, at which point it seems to stabilize. Thus, Indian women with no schooling have, on average, the same number of children as women with more than 10 years of education. A roughly similar pattern emerges for white women, with the exception that the peak in fertility is observed among women with Standard 7 education, although fewer than 15 percent of white women had not completed at least Standard 8.

Influence Of Household Resources On Demographic Outcomes

It has been argued above that the education effects reported in Tables 6-3 and 6-4 are likely to reflect in part the impact of observable and unobservable characteristics of the woman. It is very difficult to take account of unobservable differences among women in a survey unless the survey incorporates a special experimental design that permits comparisons among otherwise identical women with different levels of education. See, for example, Ashenfelter and Krueger (1994), who compare the wages and educational attainment of monozygotic twins; even that design is not without potential flaws, since identical twin pairs with different levels of education may also be different in unobserved ways. However, it is possible to take into account observable differences among women, and this is the approach taken in this and the next section, which exploit some of the breadth and richness of the PSLSD. In this section, the focus is on a set of proxies for income, wages, and community characteristics; in the following section, attention is turned to alternative measures of human capital accumulation.

By controlling for observed characteristics, one cannot uncover "pure" estimates of the effect of education. In some sense, the reduced-form estimates discussed above are "pure" or "full" estimates of those effects. Rather, by adding controls to the regression, one can better understand the mechanisms through which education affects fertility choices. In so doing, one is trading off the

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

(possible) purity of reduced-form estimates with estimates that include outcomes of choices as regressors that are potentially endogenous. However, in view of the argument above that education itself is a choice, converting to purism now would appear to be somewhat inconsistent.

The first column of Table 6-5A repeats the CEB regression in the first column of Table 6-3 for black women. Additional covariates are included, and their effects are reported in Table 6-5B for the flexible regression with a semiparametric specification for women's education. For reference, the associated linear and spline estimates with the same sets of additional controls are reported in Panels A and B of Table 6-5A. Results for the other three races are discussed below but not shown.

A natural starting point is the inclusion of spousal characteristics. While most women in South Africa do get married, slightly over half of the black women in the survey had ever been married by the time of the survey.10 Among those who were married, only two-thirds were living with their husbands; this finding reflects, in part, massive migration by black South African men, typically to urban areas for employment reasons, leaving their wives and families behind. Fertility was higher among married women and highest among those whose husbands were present, who had 1.5 children more than unmarried women. In contrast, relative to not being married, a woman whose husband was absent had only .25 child more on average. It is unfortunate that the PSLSD provides so little information about these absent husbands and does not even report their education or location. Knowing more about the welfare of these families, including the noncoresident members, as well as understanding the strategies they adopt in times of crisis, would likely yield important behavioral insights that would, in turn, be important for policy design.

Among those black women who were married and whose spouses were in the household at the time of the survey, the correlation between their educational levels is 0.58 (which is considerably higher than among any of the other racial groups).11 Thus the inclusion of spousal characteristics in the fertility regression is associated with a decline of about one-third in the estimated effects of female education, and this decline is essentially constant across the entire education distribution. The estimated spousal education effects in Table 6-5B indicate that around the time of completion of primary school, husband's education has a larger depressing effect on fertility than does wife's education. However, as husband's education increases, the impact on fertility does not increase much, so

10  

This is because the PSLSD is a random sample of women, and many in the survey were too young to have been married by the survey date. The age of the average black female respondent is 28.

11  

The correlations are 0.5, 0.4, and 0.3 for coloreds, Indians, and whites, respectively.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-5A Children Ever Born, Female Education, and Household and Community Resources, Black Women Aged 15-49

 

 

 

Add Household Income

 

 

 

Baseline (1)

Add Spouse Characteristics (2)

OLS (3)

IV (4)

Add Wages and LFP (5)

Include Community Fixed Effects (6)

A. Linear

Years

-0.117*

-0.080*

-0.073*

-0.065*

-0.072*

-0.068*

[0.006]

[0.006]

[0.006]

[0.007]

[0.006]

[0.007]

B. Spline

0-5 years

-0.106*

-0.071*

-0.069*

-0.067*

-0.069*

-0.071*

[0.013]

[0.013]

[0.013]

[0.013]

[0.013]

[0.013]

6-10 years

-0.134*

-0.089*

-0.079*

-0.067*

-0.080*

-0.070*

[0.012]

[0.012]

[0.012]

[0.013]

[0.012]

[0.013]

> 10 years

-0.059

-0.067+

-0.051

-0.027

-0.004

-0.014

[0.041]

[0.040]

[0.041]

[0.042]

[0.041]

[0.042]

C. Semiparametric

Standard 1

-0.128

-0.016

0.001

0.001

-0.013

-0.050

[0.102]

[0.099]

[0.100]

[0.100]

[0.099]

[0.100]

Standard 2

-0.079

0.075

0.120

0.142

0.094

0.044

[0.097]

[0.094]

[0.096]

[0.097]

[0.094]

[0.0961

Standard 3

-0.338*

-0.179*

-0.184*

-0.174*

-0.176*

-0.187*

[0.090]

[0.088]

[0.089]

[0.090]

[0.088]

[0.090]

Standard 4

-0.423*

-0.235*

-0.217*

-0.207*

-0.216*

-0.245*

[0.085]

[0.083]

[0.085]

[0.086]

[0.083]

[0.085]

Standard 5

-0.592*

-0.385*

-0.368*

-0.353*

-0.368*

-0.371*

[0.076]

[0.076]

[0.077]

[0.078]

[0.076]

[0.079]

Standard 6

-0.623*

-0.390*

-0.372*

-0.350*

-0.373*

-0.402*

[0.075]

[0.075]

[0.076]

[0.077]

[0.075]

[0.078]

Standard 7

-0.699*

-0.437*

-0.402*

0.375*

-0.415*

-0.421*

[0.081]

[0.081]

[0.082]

[0.083]

[0.081]

[0.083]

Standard 8

-0.921*

-0.561*

-0.512*

-0.462*

-0.525*

-0.497*

[0.077]

[0.078]

[0.079]

[0.081]

[0.078]

[0.081]

Standard 9

-0.961*

-0.572*

-0.527*

-0.475*

-0.548*

-0.547*

[0.083]

[0.083]

[0.085]

[0.087]

[0.083]

[0.087]

Standard 10

-1.274*

-0.829*

-0.764*

-0.693*

-0.768*

-0.746*

[0.083]

[0.083]

[0.085]

[0.089]

[0.084]

[0.087]

> Standard 10

-1.401*

-0.992*

-0.874*

-0.739*

-0.739*

-0.747*

[0.117]

[0.118]

[0.121]

[0.130]

[0.125]

[0.129]

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

 

 

 

Add Household Income

 

 

 

Baseline (1)

Add Spouse Characteristics (2)

OLS (3)

IV (4)

Add Wages and LFP (5)

Include Community Fixed Effects (6)

D. Test statistics

F(all covariates)

579.87

359.03

336.15

334.40

311.16

35.70

[0.00]

[0.00]

[0.00]

[0.00]

[0.00]

[0.00]

Overidentification

1.09

test

 

 

 

[0.37]

 

 

R2

0.533

0.570

0.571

0.569

0.573

0.599

NOTE: OLS = ordinary least squares; IV = instrumental variables; LFP = labor force participation. In all regressions, 8,142 observations used. Standard errors below coefficients; p-values below F statistics. See Table 6-5B for additional controls. Specifications A, B, and C all include those controls, along with maternal education specification as denoted. Results reported in Table 6-5B correspond to the semiparametric specification, C, as do test statistics, D. Instruments for income used in column 4 are seven indicator variables for ownership of a bicycle, radio, television, telephone, refrigerator, electric stove, and primus (gas) stove. Overidentification test is generalized method of moments (GMM) test for validity of instruments; it is distributed as an F statistic with 7 degrees of freedom.

at the top of the education distribution, it is the wife's education that plays a dominant role. The evidence is succinctly summarized in a linear spline specification: husband's education is associated with 0.087 fewer children for each year of schooling between 1 and 5, whereas wife's education is associated with only 0.071 fewer children. The relative importance of male and female education reverses for those who attended secondary school, with the effect of husband's education being smaller (0.061 per year) and that of wife's education being larger (0.089 per year).12

In many studies conducted in a wide range of contexts, it has been shown that relative to the education of a mother, her husband's schooling has a smaller effect on both the quantity of children and their quality, as indicated by, for

12  

Since the effect of husband's education is relatively constant across the education distribution, the effect of wife's education increases with education, and there is a constant proportionate decline in her education effect. When spouse's education is added to the regression, the correlation between the education of spouses must rise with education. It does: for women who did not complete primary school, the correlation is 0.25; for those who did, the correlation is 0.54.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-5B Children Ever Born, Female Education, Household and Community Resources, Black Women Aged 15-49

 

 

 

Add Household Income

 

 

 

Baseline (1)

Add Spouse Characteristics (2)

OLS (3)

IV (4)

Add Wages and LFP (5)

Include Community Fixed Effects (6)

Spouse's characteristics

Standard 1

-0.112

-0.099

-0.096

-0.103

-0.159

 

[0.121]

[0.123]

[0.124]

[0.121]

[0.122]

Standard 2

-0.073

-0.113

-0.110

-0.080

-0.112

 

[0.134]

[0.138]

[0.138]

[0.133]

[0.135]

Standard 3

-0.212

-0.202

-0.194

-0.222

-0.254+

 

[0.136]

[0.137]

[0.138]

[0.135]

[0.137]

Standard 4

-0.280*

-0.202+

-0.174

-0.286*

-0.253*

 

[0.117]

[0.118]

[0.120]

[0.117]

[0.118]

Standard 5

-0.448*

-0.467*

-0.440* *

-0.451

-0.405*

 

[0.110]

[0.112]

[0.112]

[0.110]

[0.111]

Standard 6

-0.604*

-0.628*

-0.589*

-0.608*

-0.557*

 

[0.108]

[0.110]

[0.111]

[0.108]

[0.110]

Standard 7

-0.231

-0.191

-0.170

-0.258+

-0.260+

 

[0.144]

[0.147]

[0.147]

[0.145]

[0.146]

Standard 8

-0.681*

-0.644*

-0.584*

-0.656*

-0.620*

 

[0.120]

[0.122]

[0.124]

[0.122]

[0.123]

Standard 9

-0.554*

-0.537*

-0.491*

-0.566*

-0.571*

 

[0.158]

[0.161]

[0.163]

[0.160]

[0.161]

Standard 10

-0.785*

-0.732*

-0.647*

-0.765*

-0.719*

 

[0.129]

[0.131]

[0.135]

[0.133]

[0.134]

> Standard 10

-0.532*

-0.461*

-0.316

-0.481*

-0.343+

 

[0.185]

[0.187]

[0.194]

[0.194]

[0.198]

(1) if dead

1.023*

1.014*

0.965*

1.026*

1.002*

 

[0.107]

[0.110]

[0.112]

[0.107]

[0.108]

(1) if absent

0.251*

0.243*

0.237*

0.266*

0.371*

 

[0.053]

[0.054]

[0.054]

[0.053]

[0.058]

(1) if present

1.524*

1.511*

1.480*

1.599*

1.569*

 

[0.081]

[0.083]

[0.083]

[0.086]

[0.088]

example, survival rates and health status. This finding is often attributed to the fact that men tend to bear less of the time burden associated with childrearing. If men spend no time on child care and if women's leisure time is weakly separable from that of their husbands, then male education will have only an income effect on fertility. These are strong assumptions and unlikely to be true, although they are often made in empirical studies of household choices. We can make some progress in testing these assumptions by including income in the fertility regression.

The primary focus here is on the extent to which the association between

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

 

 

 

Add Household Income

 

 

 

Baseline (1)

Add Spouse Charac teristics (2)

OLS (3)

IV (4)

Add Wages and LFP (5)

Include Community Fixed Effects (6)

Income and wages

In(HH income)

-0.069*

-0.186*

 

 

[0.018]

[0.044]

 

 

In(female wage)

 

-0.223*

-0.217*

 

 

 

 

[0.047]

[0.051]

(1) if employed

-0.216*

-0.155*

 

 

 

 

[0.049]

[0.052]

In(spouse's wage)

0.129*

0.076

 

 

 

 

[0.053]

[0.059]

(1) if spouse employed

 

-0.191*

-0.041

 

 

 

 

[0.059]

[0.063]

Woman's age (spline)

<30

0.185*

0.136*

0.136*

0.136*

0.139*

0.139*

[0.005]

[0.005]

[0.005]

[0.005]

[0.005]

[0.006]

30-39

0.161*

0.144*

0.144*

0.145*

0.148*

0.149*

[0.007]

[0.007]

[0.007]

[0.007]

[0.007]

[0.007]

40-49

0.109*

0.102*

0.107*

0.109*

0.101*

0.102*

[0.011]

[0.010]

[0.011]

[0.011]

[0.010]

[0.010]

Location

(1) if urban

0.195*

0.085

0.136*

0.110*

0.121*

[0.060]

[0.076]

[0.059]

[0.059]

[0.058]

 

(1) if rural

0.498*

0.189

0.367*

0.305*

0.352*

[0.049]

[0.122]

[0.049]

[0.054]

[0.048]

 

Intercept

-2.763*

-2.160*

-1.709*

-0.935*

-1.662*

-1.143*

[0.140]

[0.140]

[0.185]

[0.326]

[0.415]

[0.481]

NOTES: See Table 6-5A.

maternal education and fertility is simply capturing the role of household resources. Thus, in column 3 of Tables 6-5A and 6-5B, (the logarithm of) household income is added to the regression. The male and female education effects are only slightly reduced (by less than 10 percent). Among parents with little education, the reduction is trivial, but for those who are better educated, especially men and women with some secondary schooling, the declines are larger, suggesting that part, but only part, of the effect of education does reflect the role of household resources.

The regressions also indicate that current income does have a significant, albeit small, effect on fertility. For example, a threefold increase in income

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

would be associated with a 20 percent decline in fertility. There are, however, at least two problems with the analysis of income effects. First, one can expect income to be measured with error in any household survey. Second, current income is not the appropriate concept in a regression of cumulative fertility. Ideally, one would like to examine the effect of income-and changes in income-on fertility decisions over the life course. This is not possible with a single cross-section in which respondents report only CEB and current income. Under the assumption that there is a good deal of serial correlation in income, one might interpret current income as a proxy for long-run household resources, but since it is at best an error-ridden proxy for resource availability over the life course, the estimated income effect is likely to be downward biased. This raises a question regarding the interpretation of education effects as being net of income since education is likely to be correlated with long-run income.

One strategy for reducing the impact of measurement error is to use predicted income, based on longer-run measures of resources. We use household ownership of seven assets that are likely to be related to wealth and present instrumental variables estimates in the fourth column of Tables 6-5A and 6-5B.13 Consistent with the measurement error hypothesis, the estimated income effect is much larger: if income were to double, fertility would be predicted to fall by about 20 percent. The key point for present purposes is that the estimated maternal education effects remain large and significant. For example, the impact on fertility of doubling income is about the same as the difference between a woman who completed Standard 9 and one who completed Standard 10.

We have examined nonlinearities in the effect of income in some detail. The results of including quadratic and cubic terms in log (income) indicate that a linear effect cannot be rejected. This inference was checked using nonparametric methods to estimate the fertility-income function (both unconditionally and conditional on the woman's age, education, and location, as well as her spouse's education). The relationship is linear in logs for all women except those whose household income is in the top decile, for whom income and fertility are positively associated. There are two reasons for concluding that this result is due to measurement error in household income that is correlated with income. First, nonlinear instrumental variables estimates indicate that the income effects are negative throughout the income distribution, and most negative at the top of the

13  

The instruments are ownership of a bicycle, radio, television, telephone, refrigerator, electric stove, and primus (gas) stove. The first-stage regression explains 30 percent of the variation in (log) household income, and the instruments are significant predictors of income (F35,8106 = 269.55) after controlling for other observables in the regression in column 4. In addition to being good predictors of income, the assets should not be correlated with residuals from the second-stage regression. They are not: the Generalized Method of Moments (GMM) overidentification test reported at the bottom of Table 6-5A is not significant.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

distribution. Second, nonparametric estimates of the relationship between fertility and household expenditures, which may be a better indicator of longer-run household resources than income, suggest a negative correlation at the top of the expenditure distribution. The estimated education effects are essentially unchanged by including nonlinearities in income or replacing income in the regressions with expenditure, so we conclude that the education effects do not reflect purely the role of household resources in fertility.

The estimated effects of income on fertility reflect a combination of two mechanisms. First, better-educated people tend to earn higher wages, which implies a higher market value of their time and, assuming that rearing children is time-intensive, a reduction in the number of children a woman will want to have. Second, higher income implies more resources for the family to spend, which, assuming children are valued, raises the demand for children. The fact that the impact of income is negative suggests that the first of these mechanisms, the substitution effect, dominates.

Following the same argument, increases in the value of time as education rises are likely to contribute to the negative correlation between fertility and education. To investigate this issue, the regression in the fifth column of Tables 6-5A and 6-5B includes the woman's (log) wages, her spouse's (log) wages, and controls for whether they had worked during the year prior to the survey. Not only is working associated with fewer children, but women who earn higher wages tend to have smaller families. Working spouses also tend to be associated with fewer children, but conditional on the spouse's working and his education, higher wages are associated with more children. This association might be interpreted as a pure income effect.14

The coefficients on maternal education are very close to those in the third column of Tables 6-5A and 6-5B. Dropping the woman's education from the regression yields a female wage effect of -0.38 (and a standard error of 0.04), suggesting that changes in the value of the woman's time and in her tastes for children are mutually reinforcing as education increases. Thus, we conclude that a higher value of time does not fully explain the negative correlation between education and fertility. The positive wage effect for males indicates that men whose wages are higher than would be expected, given their education, desire larger families. Dropping the spouse's education from the regression yields a wage effect that is zero (0.02, with a standard error of 0.05), suggesting that, in contrast to females, a higher value of time among better-educated men is offset by a desire for a larger family.

14  

As with income, one must assume there is sufficient serial correlation in wages so that current wages (conditional on age) are informative about wages over the life course.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Causality in this model is not unidirectional. On the one hand, if childrearing costs rise with the number of children, the shadow price of time for women who have borne more children will be higher, and, ceteris paribus, they will be less likely to participate in the labor market. On the other hand, women with higher wages, holding age and education constant, are more productive in the labor market and so are less likely to leave the labor market to bear children. Part of this higher productivity may be a result of more labor market experience, in which case it is the fact the woman has had fewer children that underlies her higher wages and its impact on fertility.

In principle, if there is a set of instruments that predicts labor market choices but has no direct bearing on fertility, an instrumental variables approach will make it possible to disentangle the influences in each direction. At least two classes of instruments have been suggested in the literature: nonlabor or asset income, and indicators of local labor market demand. In the context of a static model, it may be argued that both are valid instruments. By their nature, however, fertility and labor supply choices demand a dynamic modeling framework: current fertility is the cumulation of choices from adolescence, and there is evidence of substantial state dependence and serial correlation in labor market choices. Given the South African data, estimation of a dynamic model of labor supply and fertility is well beyond the scope of the present analysis, but see Hyslop (1996), who demonstrates the empirical importance of taking seriously unobserved heterogeneity and state dependence in a dynamic model of labor supply and fertility choices. In the context of a dynamic model, treating nonlabor income or ownership of assets as predetermined is not very appealing since it is, to a large extent, the culmination of prior savings, and thus reflects previous labor supply and consumption choices.15 Note that this is a substantially different motivation for using instrumental variables than that which applied earlier in the discussion of concerns about measurement error in income.

The second class of instruments is indicators of local labor demand. They are not exogenous if women are mobile, and employment opportunities affect their choice of residential location. In South Africa, where pass laws have restricted family and, particularly, female mobility in response to earning opportunities, treating local labor demand as exogenous appears to be the lesser of two evils. For example, only 6 percent of women in the sample reported having moved during the 5 years prior to the survey.

Implementation of the instrumental variables approach using local labor demand as the instruments involves empirically characterizing the local labor market (which is not straightforward), along with indicators of other local infrastructure (which is virtually impossible with these data; see note 2 above). As an

15  

In addition, that approach does a poor job of predicting each of the labor market factors in the first stage, so that estimates in the second stage are very imprecise, and none is significant.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

alternative approach, in the direction of accounting for unobserved heterogeneity, the regressions were reestimated controlling for community fixed effects, with the communities being defined as sampling cluster units, or Census Enumerator Subdistricts.16 These controls absorb all observable and unobservable local market and community characteristics that are fixed across households. They therefore absorb all possible measures of local labor demand and thus any instruments that might be constructed at the local market level. If the only source of unobserved heterogeneity that affects the choice to work is fixed at the community level, the fixed effects estimates in column 5 of Tables 6-5A and 6-5B will be unbiased. The community fixed effects estimates also remove the influence of all formal and informal local information and infrastructure, which may include, for example, local family planning programs and messages, as well as the role of community-level social networks.

While the impact of female labor force participation on fertility decreases with the inclusion of community fixed effects, the association with female wages is little changed. Both female employment characteristics remain important and significant correlates of fertility choices. Male labor market choices, however, are all but eliminated: the effect of spouse's employment is trivial, and the impact of his wage is almost halved (and is not significant). These results suggest that much of what would be attributed to a male employment effect in column 4 of Tables 6-5A and 6-5B is in fact associated with community resources and infrastructure. Further evidence along these lines is provided by reestimating the regression in column 3, which includes (log) household income, with community fixed effects: the estimated income effect is reduced from -0.069 to -0.019 (with a standard error of 0.019). This result suggests that community services may play a role in affecting fertility outcomes, although identification of the critical services involved is not possible with the available data.

In contrast with income and male employment, the estimated roles of male and female education in the fertility function are remarkably robust to the inclusion of community fixed effects. Both sets of covariates remain significant and, in terms of magnitude, remain very close to one another, as well as the instrumental variables estimates in column 4. Caldwell (1977, 1979) has suggested that the nature of information in the community, as measured by community-level educational attainment, is a more important determinant of demographic outcomes than an individual woman's education. To the extent that the sampling clusters proxy the local community, the fixed effects estimates remove community-level education; thus the results suggest that in South Africa, individual education does remain an important correlate of demographic choices.

To directly investigate the relative importance of community education, the

16  

There are 295 clusters in this sample, and the median cluster contains 28 women aged 15 to 49.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

regressions in columns 3 and 4 of Tables 6-5A and 6-5B were reestimated with the race-specific mean and standard deviation of education of women aged 15 to 49 in the community being included. The male and female education effects in this specification are very close in magnitude and significance to those in the fixed effects specification in column 6. Moreover, the mean educational level in the community has a significant negative association with fertility, while the standard deviation of education in the community is not significant. These results suggest that mean education in the community is a good proxy for all community characteristics, and it may be a sufficient statistic for heterogeneity in the community.

However, that inference is not supported by the evidence on the impact of household income. Recall that when community fixed effects are included in the model, the impact of household income is reduced to zero. When community characteristics are proxied by average education, the impact of income remains virtually unchanged (and significant) in both the ordinary least squares and instrumental variables estimates.

In contrast with community-level education, the first two moments of (log) household income in the community are good at capturing unobserved community-level heterogeneity. Reestimating the model while replacing community-level education with community-level income yields two results. First, the community mean of (log) income has a significant negative impact on fertility that is at least as large as that of the household's own income, suggesting that community resources are important. Second, greater inequality in a community, as measured by the standard deviation of (log) household income, is associated with higher fertility, indicating that community resources do not benefit everyone equally. After controlling for community-level income, household income has no effect on fertility, the estimated education effects are very similar to those in column 6 of Tables 6-5A and 6-5B, and both the mean and standard deviation of education in the community are unrelated to fertility.17

These results raise an important question regarding the ability to separate out the impacts of community characteristics and local income on demographic outcomes. Intuitively, one assumes that communities served by good infrastructure tend to be wealthier, so it is difficult to interpret the effects of community services in models of fertility or child health that fail to control fully for household or at least local income levels. Since income is, at best, sketchy in surveys such as the Demographic and Health Surveys, this is a serious concern for studies based on such data.

17  

The coefficient (and standard error) on the community mean of (log) income is -0.20 (0.04) and --0.15 (0.06) in the ordinary least squares and instrumental variables regressions, respectively. The corresponding coefficients (and standard errors) on the standard deviation of (log) income are 0.23 (0.07) and 0.21 (0.07).

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Restricting attention to black women aged 40 to 49 yields essentially the same results. To summarize the differences, female education effects are slightly larger, and the magnitude of household income effects is also slightly larger (but significant only in the instrumental variables regression). The largest differences emerge in the relationship between fertility and current labor market behavior: while the woman's wages are negatively associated with fertility, neither her labor market participation nor her husband's wage or participation is correlated with family size. These results warrant an especially cautious interpretation since a dynamic framework that simultaneously models labor market and fertility choices over the life course would seem particularly germane among women at the end of their childbearing.

Differences among the four main South African racial groups in the relationships between fertility and education, income, and community resources are striking. First, among colored women with at least 5 years of schooling, the effect of her education is negative and large, and persists even after controlling for income, wages, and community fixed effects. Spousal education effects are smaller, and among labor market choices, only female wages are associated with fertility. Second, the effect of own education on the fertility of Indian women follows an inverse-U shape (peaking toward the end of primary school). The effect is dramatically reduced when spouse's education is controlled, and his education plays a more important role in determining family size than does female education. Among Indians, moreover, income effects and labor market factors are relatively unimportant, suggesting that education, particularly male education, captures differences in tastes rather than the value of time. Third, the education of white women also follows an inverted U-shape, but spousal education, income, and even labor market characteristics do not seem to affect the shape of the relation. Working women and women with higher wages have substantially smaller families, and the results are little affected by the inclusion of community fixed effects. Labor market factors appear to be important correlates of fertility among whites.

In sum, education of both men and women has a powerful association with fertility outcomes in South Africa. Among blacks, household resources, labor market factors, and community characteristics are significant determinants of fertility, but only a relatively small portion of the association between female education and family size can be attributed to those factors. The evidence suggests that the estimated education effects can be attributed in part to the role of unobserved characteristics of women who self-select into particular school grades. However, the magnitude of the education coefficients—and their persistence across the education distribution—suggests there may be some productivity gains associated with education itself. To explore that idea in more depth, the next section examines the relationship between fertility and performance on a set of cognitive tests conducted as part of the PSLSD.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Role Of Other Measures Of Human Capital

As noted above, there were vast differences in public investments in education across racial groups under apartheid in South Africa, and according to data in the PSLSD, these gaps were reinforced by private expenditures on schooling. There are therefore dramatic differences in school quality across races, regions, and socioeconomic groups. Eight years of schooling probably does not mean the same thing to every South African woman. Even the standard examinations, such as matric, had their own race- and region-specific boards, and standards were not uniform across those boards. The Indian board, for example, has a reputation of setting the highest standard.

In an effort to capture some of these differences in the meaning of schooling, a Literacy Assessment Module (LAM) was fielded in conjunction with the PSLSD. The LAM sought to measure three sets of basic skills: reading comprehension (6 questions), listening comprehension and practical mathematics (2 questions), and computational skill (6 questions). The specific questions appear in Appendix A. Fuller et al. (1995) provide an extensive description of the data.

The LAM was intended to be given to a random subsample of 25 percent of all households. Within each household, the enumerator was instructed to choose randomly two household members for the test; one was to be aged 13 to 18 and the other aged 18 to 50. Unfortunately, the sampling scheme that was implemented in the field was not random. The LAM was completed in only 15 percent of households, and because the sample sizes are small, the present analysis focuses on black women. The characteristics of the LAM subsample are reported in the second column of Table 6-2. A regression of the probability that a woman is in the LAM subsample provides a simple way of summarizing the differences between the subsample and the women in the full sample. That regression indicates that enumerators tended to select younger, never-married, urban women. In large part, this presumably reflects the fact that these women were more likely to be at home and that younger (and therefore better-educated) respondents would complete the module more quickly. However, conditional on age, there is no evidence of differences between women in the LAM subsample and the full sample in terms of their education and household income.

The effect of the selection is reflected in the first column of Table 6-6, which reports the results of regressing fertility on education, controlling for age and location, based on data from the 778 black women who completed the LAM. The comparable regression, based on the entire sample of black women, is reported in the first column of Table 6-5A. The correlation between education and fertility is stronger among women in the LAM subsample and, as is apparent from column 2 of Table 6-6, so are income effects.

Within this subsample, the inclusion of raw test scores has a substantial

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

TABLE 6-6 Children Ever Born and Cognitive Test Scores, Black Women Aged 15-49

 

Baseline

HH Income

Test: Total

Test: Separate

Income & Tests

Access Information

All

Years of education

Woman

-0.147*

-0.102*

-0.089*

-0.087*

-0.082*

-0.096*

-0.079*

[0.016]

[0.016]

[0.018]

[0.018]

[0.018]

[0.017]

[0.018]

Spouse

 

-0.057*

-0.066*

-0.066*

-0.056*

-0.057*

-0.056*

 

[0.025]

[0.025]

[0.025]

[0.025]

[0.025]

[0.025]

In(HH income)

-0.147*

 

-0.133*

-0.142*

-0.130*

 

[0.045]

 

 

[0.045]

[0.045]

[0.045]

Test scores

Total

-0.052*

 

 

 

[0.017]

 

 

 

 

Comprehension

-0.076*

-0.068*

-0.067*

 

 

 

[0.033]

[0.033]

[0.033]

Quantitative

 

-0.033

-0.030

-0.028

 

 

 

[0.028]

[0.028]

 

[0.028]

(1) if read paper in last 2 weeks

-0.099

-0.061

 

 

 

 

 

[0.096]

[0.097]

F(all human capital)

28.27

19.09

15.87

20.12

11.99

 

 

[0.00]

[0.00]

[0.00]

[0.00]

[0.00]

F(test scores)

 

 

 

5.04

4.06

 

3.72

 

 

 

[0.01]

[0.02]

 

[0.02]

F(all covs.)

279.14

179.93

179.49

164.54

154.09

165.04

143.00

R2

0.69

0.72

0.72

0.72

0.72

0.72

0.72

NOTES: In each regression, 778 women were included. Sample characteristics are reported in column 2 of Table 6-2. Standard errors in parentheses below coefficients; p-values below F test statistics. Regressions include controls for age (spline): spouse present. absent, or dead; and location of residence.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

impact on the effect of education: the effect is reduced by more than one-third.18 Moreover, the tests themselves are negatively correlated with fertility. Specifically, it is the reading comprehension test that is of particular relevance. A woman with better-developed comprehension skills (who answered all six questions correctly) would, on average, have nearly half a child less than a woman who failed to answer any of the questions correctly.

Since women who score well on such tests are likely to earn more and live in higher-income households, the impact of the test scores may simply reflect the role of household resources. Controlling for income, however, has very little impact on the estimates. More directly, in a regression of (log) wages on age, education, and test scores among black women, it is computational skills that appear to be rewarded in the labor market: an additional correct answer to the computational questions is associated with a 15 percent higher wage (t statistic is 2.5), whereas a correct comprehension answer is associated with only a 7 percent higher wage (t statistic is 1.3). Thus if test scores are simply a proxy for income, computational skills should have a larger impact on fertility than do comprehension skills. In fact, the reverse is true.

Why do comprehension skills affect demographic outcomes, but not wages? Women with better comprehension skills may be better able to access and assimilate information in the community. They may thus be likely to be better informed than their peers and therefore better able to use community services effectively.

In one attempt to examine this hypothesis, test scores were replaced with a control for whether the woman reported that she had read a newspaper in the previous 2 weeks. Slightly fewer than half of the women in the sample had done so. Clearly, reading a newspaper is a choice, so the interpretation of the regression is not ambiguous; however, the covariate is not significantly correlated with fertility, and its inclusion in a regression with the test scores does not affect the estimated impact of comprehension skills.

An alternative strategy for examining the information hypothesis is to include community fixed effects that capture all community-level information and services available to women. If women who score well on the tests live in communities with services that affect fertility, these effects will be absorbed by the fixed effects. While the impact of the total test score is robust to their inclusion (its impact is -0.047 with a standard error of 0.022), the impact of the comprehension score is halved, and it is not significant. (In contrast, the impact of the quantitative score rises.) This result suggests that access to information may play an important role in explaining the link between fertility and test scores, and therefore education.

18  

Put another way, test scores and education are highly correlated in these data. For example, among black women, controlling for age and location, an additional year of schooling is associated with a 0.21 higher score on the quantitative tests and a 0.22 higher score on the comprehension test. Standard errors are 0.02 in both cases.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Conclusions

Female education is a powerful predictor of fertility outcomes among women in South Africa, particularly those who are black or colored. Drawing primarily on survey data from the PSLSD, this chapter has investigated some of the mechanisms that underlie this relationship. The discussion has focused on black women, among whom an additional year of schooling is associated with about 0.12 fewer children ever born.

Part of this effect reflects assortative mating in the marriage market (see Basu, this volume). The education of husbands and wives is positively correlated, and controlling for the education of the spouse, the effect of maternal education on children ever born is reduced by about a third. This reduction is greatest among the least-educated women. The results indicate that with regard to fertility choices, men matter in South Africa, particularly among the poorest groups, suggesting that family planning outreach should be directed toward both men and women.

It is difficult with the available data to determine the precise magnitude of the effect of household resources on fertility with much confidence. There can be no doubt, however, that income significantly depresses childbearing. Higher rates of female labor force participation and higher wages are also associated with lower fertility. Thus, policies designed to increase growth in income, particularly female income, are likely to lead to slower population growth.

There is suggestive evidence from aggregate data on the timing of fertility declines in South Africa that greater access to family planning services may have been associated with lower fertility. Evidence from the PSLSD demonstrates that community characteristics do indeed affect fertility outcomes, although the impact apparently varies within communities. It is, however, not possible, using the PSLSD, to identify the specific factors that are important or who benefits most. This is unfortunate since investments in community infrastructure and services can play a central role in public policy interventions, particularly in the social sector, and in policies that are targeted toward particular subpopulations, such as the poorest.

While household and community resources are important determinants of fertility outcomes, the key finding for this analysis is that they can explain at most half of the correlation between maternal education and fertility, and most of the difference can be attributed to the role of husband's education. Parental education, particularly schooling of the mother, is also a powerful predictor of the educational outcomes of children, after controlling for family and community resources. Thus as maternal education increases, there is both a decline in the quantity of children desired and an increase in their quality. One can conclude, therefore, that maternal education has an important independent association with fertility and investments in the human capital of the next generation.

Part of the influence of maternal education is related to skills that are likely

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

to be learned in school; in particular, comprehension skills appear to play a special role in affecting demographic outcomes (see also Diamond et al., this volume). Thus raising cognitive functioning by improving the quality of schooling of today's students and also, perhaps, by providing adult education programs may yield returns beyond the direct economic benefits through the additional impact on demographic and social outcomes.

However, part of the association between education and fertility appears to have nothing to do with the education production function per se, but is a reflection of the fact that education is not randomly distributed across the population. Specifically, women and their families choose their educational attainment given the opportunities and constraints they face, so the better educated are a self-selected sample of the underlying population. This is an important insight as it suggests that a naive interpretation of the correlation between education and fertility as entirely causal is likely to be misleading and to lead to an overstatement of the potential impact of increased levels of female schooling on population growth.

In sum, this chapter has identified several mechanisms through which the correlation between fertility and maternal education operates. A small part of the correlation can be attributed to the role of family and community resources, a larger part to the role of husband's schooling, and part to the acquisition of cognitive skills. An indeterminate fraction of the correlation is associated with unobserved heterogeneity, reflecting the fact that educational attainment is ultimately a choice made by individuals.

Acknowledgments

I am grateful to SALDRU and The World Bank for providing access to the survey data used here. The comments of Caroline Bledsoe, John Bongaarts, John Casterline, Barney Cohen, Elizabeth Frankenberg, Simon Mpele, Ingrid Woollard, and three anonymous referees have been very helpful.

References

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Ashenfelter, O., and A. Krueger 1994 Estimates of the economic return to schooling from a new sample of twins. American Economic Review 84(5): 1157-1173.


Caldwell, J. 1977 The economic rationality of high fertility: An investigation illustrated with Nigerian survey data. Population Studies 31(1):5-27.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

1979 Education as a factor in mortality decline: An examination of Nigerian data. Population Studies 33(3):395-413.

Caldwell, J., and P. Caldwell 1993 The South African fertility decline. Population and Development Review 19(2):225-261.

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Cochrane, S. 1983 Effects of education and urbanization on fertility. In R. Bulatao and R.D. Lee, eds., Determinants of Fertility in Developing Countries. New York: Academic Press.

Department of Education 1996 The Organisation, Governance and Funding of Schools. Education White Paper 2. Pretoria: Department of Education.


Frankenberg, E., and P. Sudharto 1995 Community-Facility Data Collection in the Indonesian Family Life Survey. Santa Monica, Calif.: RAND. (mimeographed)

Fuller, B., P. Pillay, and N. Sirur 1995 Literacy Trends in South Africa: Expanding Education While Reinforcing Unequal Achievement? Cambridge, Mass.: Harvard University. (mimeographed)


Hyslop, D. 1996 Estimation of a Dynamic Model of Female Labor Force Participation. Industrial Relations Working Paper. Princeton, N.J.: Princeton University.


Lam, D., and S. Duryea 1995 Effects of Schooling on Fertility, Labor Supply and Investments in Children, with Evidence from Brazil. Ann Arbor, Mich.: University of Michigan Population Studies Center. (mimeographed)

Lucas, D. 1992 Fertility and family planning in Southern and Central Africa. Studies in Family Planning 23(3): 145-158.


Preston-Whyte, E. 1990 Qualitative perspectives on fertility trends among African teenagers. In W.P. Mostert and J.M. Lotter, eds., South Africa's Demographic Future: Pretoria: Human Sciences Research Council.

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Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

van Zyl. J. 1994 History, scope and methodology of fertility and family planning surveys in South Africa. Pretoria: Human Sciences Research Council. (mimeographed)


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Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Appendix ASaldru, University Of Cape Town

Project for Living Standards and Development

LITERACY ASSESSMENT MODULE [LAM]

Section A: Listening, Comprehension, and Practical Math (about 5 minutes)

Question 1:

''Imagine that you are taking a trip on the bus to a township that is located 280 kilometres away. The bus driver says that he will drive at a speed of 80 kilometres per hour. How many hours will the trip take to complete?"

Question 2 :

[in Mother Tongue]

"Meshack would like to set up a fruit stand in the town market. The manager of the market says that Meshack must pay him R 100 to set up his stand. He must also pay R 25 per month to the market manager. Over the first year, how much in total must Meshack pay to the manager?"

Section B: Reading Comprehension (15 minutes)

Question 3:

"When Mbaya was a child, he got very excited when his mother, Corfu, asked if he would like to go to the meat market with her. As they walked into the centre of town, the wonderful odours of meat-both fresh and spoiled—could be smelled up to one kilometre away. The hundreds of market stalls formed a row of almost 1 and 1/2 kilometres long. It took almost one hour to walk slowly from one end of the meat market to the other.

"Sometimes Corfu would let Mbaya choose what meat they would buy that morning. The smell of fresh beef was Mbaya's favorite. But sometimes Mbaya would accidentally choose the beef that was not fresh. Corfu would go up close to the big piece of meat hanging from the rack and smell it. Once she was close to it, Corfu could tell immediately that the beef was not fresh. Then she would laugh at Mbaya and tease him for picking spoiled meat. The meat seller would be angry, as Corfu let on to other shoppers that his beef was not fresh. Mbaya would then start looking around for beef that seemed more fresh, no longer trusting that his nose is the best instrument for finding fresh meat."

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Q3a. How long was the row of meat stalls, from one end to the other end'?

a. 1 and 1/2 kilometres long.

b. 1 kilometre long.

c. It was very close from one end to the other end.

d. Hundreds of stalls were lined up.

Q3b. What did Mbaya most like to do inside the meat market?

a. Try to find spoiled meat.

b. Walk from one end to the other end of the market.

c. Have his mother tease him when he found spoiled meat.

d. Find fresh beef for his mother to buy.

Q3c. What did not happen when Mbaya would choose a spoiled piece of meat'?

a. The meat seller would get angry.

b. Mbaya and his mother would leave the market.

c. Corfu would tease Mbaya.

d. They would keep shopping for fresh meat.

Question 4:

"Zenariah was riding to work in her usual combi. The driver and the woman sitting next to him, named Roseline, were arguing over whether it was any use for the woman's son to stay in school. The son, named Philemon, was 16. His secondary school had been closed for many days over the past 6 months. Teachers often did not show up for work. But the woman felt that if he could pass matric, Philemon could eventually find a good job, perhaps as a clerk or office worker. The driver, however, claimed that even university graduates were having difficulty finding jobs as clerks. Zenariah had graduated from the University of the Western Cape, and it had taken her 3 months to find her job as an assistant accountant. She was sympathetic to the woman's position, but also had to agree that until the economy improved, education would not guarantee a good job."

Q4a. When Zenariah goes to work in the morning ...

a. She usually takes the combi.

b. She always sits next to Roseline.

c. She tries to find different drivers and combis.

d. She usually talks about her son, Philemon, in the combi.

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Q4b. What kind of job did Philemon's mother hope he would find?

a. Assistant accountant.

b. Combi driver.

c. Office worker.

d. Teacher.

Q4c. What was the combi driver's position?

a. Schools are of high quality.

b. Philemon should go to the University of the Western Cape.

c. Completing school will lead to a good job.

d. Schooling does not guarantee a good job.

Section C: Computational Problems (15 minutes)

Question 5:103 kg - 37 kg = _____ kg

Question 6: R 35.50 x 7 = R _____

Question 7:25% of R 228 = R _____

Question 8: R 22.25 - R 7.88 = R _____

Question 9:

"According to the doctor, the mother must buy 0.30 litres of cough mixture for her two sick children. She can either buy three bottles, each containing 0.10 litres, for R 9.50 per bottle or she can buy four bottles, each containing 0.08 litres, for R 7.00 per bottle. What is the least amount of Rand she needs to spend to get the 0.30 litres required by the doctor?

Question 10:

"Namane was trying to figure out her transport costs from the township to the city to get to her job. The combi cost R 2.00 each day. If she took a combi, then a taxi for part of the trip, she would have to spend R 3.50 each day. How much more would the taxi plus the combi cost for the week, than just taking a taxi, if she went to work five days during the week?"

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×

Section E: Spoken Languages and General Reading

Question 11: What language do you speak?

a. At home or when talking with friends?

b. At work (if currently working, otherwise go to Q12).

Question 12: During the past two weeks, did you read any parts of the following items?

a. A newspaper?

If yes, in what language?

b. A magazine'?

If yes, in what language?

c. A book of any kind'?

If yes, in what language?

Suggested Citation:"6 Fertility, Education, and Resources in South Africa." National Research Council. 1999. Critical Perspectives on Schooling and Fertility in the Developing World. Washington, DC: The National Academies Press. doi: 10.17226/6272.
×
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This volume assesses the evidence, and possible mechanisms, for the associations between women's education, fertility preferences, and fertility in developing countries, and how these associations vary across regions. It discusses the implications of these associations for policies in the population, health, and education sectors, including implications for research.

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