Working Paper SeriesNumber 71

Southern Africa Labour and Development Research Unit

byJustine Burns and Malcolm Keswell

Inheriting the Future: Intergenerational Persistence of Educational status in KwaZulu-Natal, South Africa

About the Author(s) and Acknowledgments

Justine Burns: School of Economics, University of Cape Town, Rondebosch 7700, Cape Town (email:justine.burns@uct.ac.za)Malcolm Keswell: Southern Africa Labour and Development Research Unit &School of Economics, University of Cape Town, Rondebosch 7700, Cape Town (email: mal-colm.keswell@uct.ac.za).

We thank two anonymous referees for very helpful comments.

Recommended citation

Burns, B. and Keswell, M. (2011). Inheriting the Future: Intergenerational Persistence of Educational status in KwaZulu-Natal, South Africa. A Southern Africa Labour and Development Research Unit Working Paper Number 71. Cape Town: SALDRU, University of Cape Town

ISBN: 978-1-920517-12-0

© Southern Africa Labour and Development Research Unit, UCT, 2011

Working Papers can be downloaded in Adobe Acrobat format from www.saldru.uct.ac.za.Printed copies of Working Papers are available for R15.00 each plus vat and postage charges.

Orders may be directed to:The Administrative Officer, SALDRU, University of Cape Town, Private Bag, Rondebosch, 7701,Tel: (021) 650 5696, Fax: (021) 650 5697, Email: brenda.adams@uct.ac.za

Inheriting the Future:

Intergenerational Persistence of Educational status

in KwaZulu-Natal, South Africa

Justine Burns and Malcolm Keswell∗

November 14, 2011

Abstract

This paper examines the changes in the educational attainment of threesuccessive generations in South Africa: grandparents, parents and children.Many of the results accord with widely known facts, such as the educationalpenalty faced by individuals who are African or who live in rural areas or infemale-headed households. Similarly, the larger impact of mothers educationon child outcomes relative to fathers education accords with previous work,although it is interesting that this gender difference is only sizeable and signif-icant for relationships between the second and third generation. Key findingsin this paper include the fact that persistence has increased with subsequentgenerations.

JEL Keywords: Education and Inequality; Intergenerational Mobility

JEL Codes: I24, J62

∗Burns: School of Economics, University of Cape Town, Rondebosch 7700, Cape Town (email :justine.burns@uct.ac.za); Keswell: Southern Africa Labour and Development Research Unit &School of Economics, University of Cape Town, Rondebosch 7700, Cape Town (email : mal-colm.keswell@uct.ac.za). We thank two anonymous referees for very helpful comments.

1 Introduction

Economists increasingly acknowledge that history matters. This idea is prevalent in

the convergence literature, where initial conditions affect a country’s long run growth

rate and convergence to the steady state (Barro and Martin, 1992), as well as in

the literature concerning lock-in and path dependence in technological development

(Arthur, 1989; David, 1985). But history in the form of inherited status also matters

for individual outcomes. For example, genetic endowments are transmitted across

generations, and affect health and cognitive outcomes for subsequent generations.

Similarly, a large body of international literature documents the intergenerational

correlation between parents and their offspring in the domain of earnings, education

and occupation (Solon, 1992; Featherman and Hauser, 1978; Mulligan, 1999; Corak

and Heisz, 1998).

Very few South African studies have examined the intergenerational transmission

of status, be it income status, educational attainment or even occupational choice

between parents and their children. In part, this is owing to data limitations as

few South African data sets exist that allow one to make meaningful comparisons

of parental and child attributes.1 Arguably, understanding the nature and extent of

inherited status in outcomes is vital in devising programmes to redress previously

legislated inequalities. Indeed, analysing access to opportunities by measuring the

nature and extent of social and economic mobility may provide a better measure

of changing opportunities than the more traditional measures of inequality. (Bird-

sall and Graham, 2000). If inequality reflects discrimination against certain groups

or results from linguistic, cultural or historical handicaps that ensure the intergen-

erational transmission of poverty, then mobility, measured over lifetimes and even

generations, will be constrained. Inequality may also reflect persistent differences

in the capacity of individuals and households to exploit markets or to achieve equal

access to education, employment or property rights.

From a policy perspective, inheritance of status is an important dimension. Poli-

cies that are to successfully deal with adverse outcomes need an understanding of

the underlying processes by which they are generated. Recent policy shifts in South

Africa reflect a growing belief in the supremacy of the market to achieve optimal out-

comes, and to redress past inequalities. Equal access to employment and education

1A recent exception is the National Income Dynamics Study data. As additional waves areadded to this panel data set, analysts will have a better basis of exploring these intergenerationalcorrelations.

2

are deemed sufficient to erase the painful past. And yet, there is growing discontent

with the slow pace of change, despite the dramatic shifts to equalise spending on

education across race groups. In part, this is because policy makers have failed to

realise that market-oriented policies on their own are insufficient to overcome the

handicaps imposed (or benefits afforded) by inherited status. To the extent that in-

herited status is important in explaining outcomes in successive generations, policies

to account for inherited inequalities are vital.

Using data from the KwaZulu Income Dynamics Study (KIDS), we proceed by

examining changes in the educational attainment of three successive generations

in South Africa: grandparents, parents and children. The sample is limited to

Indians and Africans, and it is apparent that successive generations have made

significant advances in the number of years of schooling attained, although these

gains are more significant for the second generation than the third. We find that

the intergenerational correlation in education status is higher between the second

and third generation than between the first and second generation. Furthermore,

when the education of both parents is included in a regression, there are differences

in the magnitude of the estimated coefficient by the gender of the parent. However,

this gender difference is only sizable and significant for relationships between the

second and third generation (mothers and fathers to daughters and sons) and not

for relationships between the first and second generation.

2 Measuring Educational Mobility

Much of the literature on intergenerational mobility has focused on the transmission

of economic status, as measured by income, between parents and their children. A

standard Galton regression is estimated as follows:

lnwc = β1 + β2 lnwp + β3agec + β4age2c + β5agep + β6age

2p + ε

where w denotes the logged welfare indicator of interest, and the c and p sub-

scripts refer to child and parent respectively. In this specification, the standardised

coefficient provides an estimate of the intergenerational correlation coefficient for

the welfare indicator. A larger value of β2 indicates a stronger correlation between

the outcomes of parent and child, suggesting high persistence in status across the

two generations. Under the (implausible) assumption that parental education is

exogenous in this regression, a policy interpretation would be that giving parent’s

3

more education leads to higher educational outcomes for their children. Both in-

terpretations would imply that child outcomes are strongly conditioned on parental

attributes. For present purposes, we remain agnostic about the precise interpreta-

tion given to β2 and wish only to document the extent of the correlation between

parent’s educational status and that of their children, leaving aside the question of

the various channels through which transmission might take place.2

While most economic mobility studies have focussed on intergenerational in-

come coefficients, and the role played by education in income mobility, there are

good reasons to focus on education as an outcome in its own right. First, educa-

tional opportunity is likely to be major mechanism through which intergenerational

social mobility is affected. Not only is education likely to have a positive impact on

the chances of upward occupational mobility, but it also raises the opportunity for

upward income mobility. Key studies by Blau and Duncan (1967) and Featherman

and Hauser (1978) argue that the educational attainment of American men is the

main known determinant of their occupational status, and that the educational and

occupational status of fathers affects their sons occupational attainment primarily

via the sons education. Even in studies where intellectual ability has been included

to control for unmeasured heritable traits (ability bias ) and the influence of assorta-

tive mating, the influence of fathers education on sons education has maintained its

relative position as the most important of parental-background influences (Sewell,

1980).3

Secondly, to the extent that one is concerned with income mobility, intergen-

erational schooling correlations may provide an upper bound to the true earnings

correlation since parental tastes and wealth influence childs schooling much more

than their adult earning capacities (Behrman et al, 1980, Haveman, 1995).

Thirdly, a focus on educational mobility as an outcome in its own right may be

especially important in South Africa, given the evidence of a strong negative corre-

lation between the level of education and poverty status. Moreover, since current

2A key limitation of the KIDS data is that it lacks credible options for controlling for the endo-geneity of parental schooling. Current work coming out of the NIDS project (not yet published)is beginning to addresses the causal question. Our concern here is to learn something about theconditioning role of parental education at a key moment in the history of South Africa: the pe-riod immediately following the post Apartheid period, using a sample of individuals that arguablywould not have been exposed to many of the innovations in school reform that were beginning tobe introduced in the late 1990’s.

3While concerns over biased estimates arising from unobserved ability bias are important,Bowles and Gintis (Bowles, 2001) argue that the genetic inheritance of traits contributing tothe cognitive skills measured on IQ and related tests explain less than one twentieth of the inter-generational transmission of economic status.

4

data limitations rule out the possibility of examining the intergenerational persis-

tence of income status, we have to look elsewhere in making inferences about the

importance of income mobility. 4 It is standard in the literature to focus on inter-

mediate outcomes (like schooling) in the absence of better information on income.

Since educational attainment tends to have a finite time horizon, with most indi-

viduals completing their education by their early twenties, it is substantially easier

to estimate intergenerational correlations in education status for parents and their

children, since information on educational attainment is routinely collected in cross-

sectional household surveys.5

Numerous international studies document the association between family back-

ground, parental schooling and the schooling of children. (Behrman (1997) and

Behrman and Knowles (1999) are good survey articles). Mulligan (1999), for ex-

ample, finds that across eight sets of estimates , the intergenerational correlation

coefficient on education attainment ranges from 0.14-0.45, averaging at 0.29.6 This

suggests fairly high mobility (or low persistence) in educational status across gen-

erations, with subsequent generations making large gains in educational attainment

relative to their parents. In addition, these studies almost always find a significant

positive association between child’s schooling and parental education, with mother’s

education being about 10% more important than father’s education at the median

of estimates that include both (Haveman, 1995; Hill, 1987; Schultz, 1993; Case,

1991).7

3 Data

The data used in this analysis comes from the first two waves of the KwaZulu Income

Dynamics Study (KIDS), a panel data set covering approximately 1100 households,

and 11400 individuals. The first round of data was collected in 1993 under the aus-

pices of the Project for Statistics on Living Standards and Development (PSLSD),

which was the first ever nationally representative demographic and socio-economic

survey to be conducted in South Africa. In 1998, a resurvey (which excluded white

4To document intergenerational income correlations between parents and their children requiresincome data for parents and children at the same stage in their respective earnings lifecycles, andto date, this type of data doesnt exist in South Africa.

5Of course, this argument only applies to co-resident household members and this is an impor-tant caveat that must be borne in mind.

6These estimates are based on data for the US, Germany, Malaysia, and Kalamazoo, Michigan.7A notable exception is the study by Behrman and Taubman (1985) that estimated that in the

USA, the impact of fathers education on offspring education is larger than mothers education.

5

and coloured households) was conducted in the province of KwaZulu-Natal only,

providing panel data for Africans and Indians in this province.

There are a number of possible data problems that arise when using cross-

sectional data, most notably measurement error. The effect of measurement error

is to exaggerate the dynamics, since not all of the observed intertemporal variation

in the welfare indicator is due to mobility. In estimation, this is the problem of

errors in variables (Greene, 1997; Solon, 1992.) One way to minimise this error is

to exploit the presence of panel data and average the welfare indicator (in this case,

educational attainment and age) across time periods, which is the approach adopted

here.

The sample used in this analysis is limited to Africans and Indians, aged 21

years and above, who had completed their schooling. We examine the educational

attainment of three successive generations in South Africa: grandparents, parents

and children. We denote grandparents as the first generation, parents (mother or

father) as the second generation and children (daughter or son) as the third genera-

tion. Grandparents, on average, are between the age of 64 and 66, putting them at

school in the 1940s, while second generation adults are, on average, between the age

of 41 and 45, putting them at school in the 1960s, a time period where educational

access for non-whites was rapidly expanded. Third generation individuals, who are

30 years old on average, would have attended school in the late 1970s and early

1980s (Table 1).

4 Empirical Results

4.1 Descriptive Statistics

Successive generations in South Africa have obtained, on average, more years of

education than previous generations. However, the gain in the number of years

of education made by parents relative to grandparents is far larger than the gains

made by children relative to their parents (Table 1 and Figure 1). This difference

in educational attainment may be at least partly attributable to the time period in

which the individual attended school, with second generation individuals attending

school during the time of rapid expansion of educational access to non-whites in

the 1960s. There do not appear to be significant gender differences in terms of

the average years of education obtained, and in this regard, South Africa stands in

contrast to many other countries in Africa (Thomas, 1996).

6

These features confirm previous work by Thomas (1996) on South Africa, and

Peil (1990) on trends in Africa more broadly. Thomas (1996) argues that among

black South Africans, educational attainment of those born in the 1950s and 1960s

increased significantly more rapidly than those born earlier. However, the distri-

bution of this growth has not been uniform, with the least educated having been

excluded from the rise in schooling over the last five decades. Thomas (1996) argues

that black South Africans at the top of the education distribution have benefited

most. Black men in the top quartile of the education distribution completed seven

more years of education over these five decades, while education of those in the

bottom quartile rose by only 1.5 years. Peil (1990), in her analysis of educational

systems in Africa, argues that expanding educational systems in Africa have in-

creasingly allowed offspring to attain higher education than their fathers, with this

tendency being higher amongst older offspring. Many younger offspring are found

to have no more education than their fathers, and in part, this is attributed to

the selective targeting of household resources for education towards some offspring

rather than others.

4.2 Intergenerational persistence in education status

Table 2 presents simple intergenerational correlation coefficients in educational at-

tainment between individual parents and their children, providing a measure of the

extent of educational persistence across generations. Following the standard Galton

approach, a series of regressions were run for each parent-child pair, each taking the

form:

lnEc = β1 + β2 lnEp + β3agec + β4age2c + β5agep + β6age

2p + ε

where E denotes logged education and c and p denote child and parent respec-

tively.8 To begin, only one parent is included in any one regression.

In each regression, the coefficient on parents education was highly significant.

8The actual regression tables are provided in the Appendix for reference purposes. As is stan-dard in the literature, theses types of models are often estimated in log-linear form so that β2

can be interpreted as an elasticity. The log-transformation of course, is only defined for positivevalues. As a large number of individuals reported zero years of education, the education variablewas transformed by adding one to the years of education reported for each individual before logtransforming the variable. In principle, several other transformations are possible that would ap-proximate the log-transformation without recording the zero values to ones, such as the inversehyperbolic sine transformation. But these alternatives often do not amount to major qualitativedifferences in the empirical estimates, so we limit our approach to the log-transformation we whichconsider to be the more standard approach.

7

From the standardised coefficients in these regressions, it is evident that educational

persistence is higher between second generation parents (mothers and fathers) and

their children (sons and daughters) than between first generation parents (grand-

parents) and second generation parents.9 This accords with the evidence presented

earlier that the gain in the number of years of education made by parents relative to

grandparents is far larger than the gains made by third generation children relative

to their parents.10

Furthermore, there appears to be very little difference in the magnitude of the

correlation coefficient by gender of the parent, that is, the correlation between a

mother or fathers education status and their sons (or daughters) are similar. How-

ever, the degree of association between parents and their daughters is higher than the

degree of association between parents and their sons, suggesting lower educational

mobility for third generation girls than boys.

In short, these results suggest that inherited status in education has become

increasingly important over time; that is, the extent of educational mobility is lower

for third generation children. In part, this has to do with the opening up of the

education system to Africans, albeit to poor quality education, in the early 1950s

with the advent of Bantu education, which allowed second generation individuals

far greater educational opportunity than grandparents. Moreover, the higher persis-

tence in educational status between second and third generation individuals suggests

that the reforms of the late 1970s were largely inadequate at substantially increas-

ing the attainment levels of subsequent generations of Indians and Africans. These

results also suggest that inherited status is more important for daughters than sons,

given that third generation girls experiencing lower educational mobility than boys.

The simple correlation coefficients of Table 2 do not control for possible interac-

tions between paternal and maternal education, nor do they control for the influence

of factors such as household income or location. Table 3 summarises the intergener-

ational correlation coefficient on educational status from a series of regressions that

included the education status of both parents , along with additional controls for

race and location.11

9We base our comparisons on the standardised coefficients in order to take account of the dif-ferences in inequality (as proxied by differences in the standard deviation) in educational outcomesacross the three generations.

10This would imply lower persistence i.e. because second generation individuals made relativelylarge gains in educational attainment relative to their first generation parents, one would expect alower correlation between their final educational outcomes and their parents. This translates intohigher educational mobility for second generation individuals.

11The reason for including the education of both parents in the regression is to examine whetherthe education status of one parent has a differential effect on the status of their child, controlling for

8

The magnitude of the correlation coefficients between first and second genera-

tion parents and children is smaller than those between second and third generation

parent-child pairs, again confirming the relatively higher educational mobility for

second generation individuals during the 1960s. Moreover, when the education of

both parents is included in the regression, there are differences in the magnitude of

the correlation coefficient by the gender of the parent, but these are only significant

for the relationships between second and third generation individuals. Gender dif-

ferences in the impact of parental education on child educational outcomes are small

and insignificant between the first and second generation. In stark contrast, gender

differences in the impact of parental education on child outcomes between the sec-

ond and third generation are large and significant at the 10% level.12 Controlling for

parental and offspring age but no other variables, the correlation coefficient between

mothers and their sons is 10 points higher than between fathers and sons, while the

correlation coefficient between mothers and their daughters is almost double that of

fathers and their daughters. These differences remain (although the size of the cor-

relation coefficients decreases marginally) once additional controls for race, location

and gender of the child are added to the regression. This suggests the differential

importance of mother’s education status for that of her children between the sec-

ond and third generation.13 This might arguably be attributable to the gradual

relaxation of influx control laws and the intensification of the migrant labour sys-

tem, which facilitated the employment of African men on the mines during the time

period when third generation children would have been attending school. These

external changes left women to see to the education and nurturing of children in

the household, and thus, it is perhaps not surprising that their educational status

becomes increasingly important in influencing child attainment levels.

their spouses educational attainment. Moreover, this strategy has the added benefit of conditioningout the role played by assortative mating. See section 5 for more on this point.

12We conduct indirect t-tests of the hypothesis that the coefficient on paternal schooling isthe same as the coefficient on maternal schooling. The differences in these coefficients in theregressions between paternal grandparents and fathers, as well as the regressions between maternalgrandparents and mothers are not statistically significant. However, in the regressions betweenparents and daughters, the computed t-statistic of 1.74 exceeds its critical value of 1.65 for the10% level of significance. In the regressions between parents and children, the computed t-statisticof 2.13 exceeds its critical value of 1.96 for the 5% level of significance. The tables reported inappendix A show the corresponding restricted and unrestricted models estimated in each case.

13These results run contrary to the findings of Behrman and Taubman (1985) who estimatethat in the USA, the impact of fathers education on offspring education is larger than motherseducation. However, the results do accord with their finding that whereas the fathers effect isevenly spread between the sexes, the mothers effect is tilted in favour of daughters. Lillard andWillis (1994) report similar results.

9

It is again evident in Table 3 that educational mobility for third generation girls

is substantially lower than for boys. A necessary next step would be to interrogate

the causal implications of these findings in order to be able to determine whether

policies directed towards improving educational outcomes of women are likely to

have a greater effect on child outcomes that those targeted to men. We leave this

question to future research.

Finally, these simple OLS estimates assume a constant mobility coefficient across

the entire educational distribution. This implies that the strength of persistence in

educational status is uniform irrespective of whether ones parents have relatively

low or high educational attainments. Yet, this need not be the case, and to the

extent that persistence in educational attainment status is higher in the tails of

the distribution, this might be suggestive of an educational poverty trap. Stronger

persistence in the tails of the distribution would imply that children of parents with

low educational attainment are themselves more likely to experience low educational

attainment, while the converse holds true for children with highly educated parents.

The quantile regression results in Table 4 suggest that this is indeed the case when

examining persistence in outcomes between second and third generation parent-child

pairs.

5 Discussion and Conclusion

This paper has examined the changes in educational attainment across three gener-

ations of South Africans. Successive generations of South Africans have experienced

increases in average attainment, although the rate of increase in attainment appears

to have slowed for third generation children. Over time, the educational status of

mothers has come to play a larger role in affecting child educational outcomes rela-

tive to fathers, possibly as a result of influx control and the migrant labour system,

although this gender difference is only sizeable and significant for relationships be-

tween the second and third generation. These results also suggest that educational

mobility is lower for daughters than sons. While these results provide an historical

snapshot of the evolution of educational attainment over time in South Africa, there

are a number of caveats worth making.

10

5.1 Concerns about Sample Selection and Attrition

The analysis uses the first two waves of KIDS data. In both waves, we observe

only the educational attainment of co-resident grandparents, parents and children.

There is of course a selection issue here: non co-resident members of any three

generation dynasty are not in our sample, but this is the best that can be done with

this particular survey.

As should be clear from Table 1, we use the panel structure of the KIDS data

to correct for measurement error in reported schooling, by averaging the reported

schooling for the sub-sample of individuals with completed schooling across the

two waves. This practice is fairly standard in the literature (see for example Solon,

1992). In the event that the data were missing in either of the two waves, we use the

information that was provided in the remaining wave. Of course, for members of the

household that were not co-resident in at least one wave, no information would have

been provided. This could account for why the sample of mothers is much larger

(1886 observations) compared to the sample of fathers (1014 observations). The

fact that fathers might be absent could be due, in part, to the effects of the migrant

labour system, but it could also be because children are borne out of wedlock and

are thus raised ostensibly by the mothers. However, the potential bias is likely to

run in the same direction, as the father is absent in both instances. Ultimately, we

have no satisfactory method of dealing with this problem. The best that can be

done with the given data is to estimate the models with and without controlling for

father’s schooling. These estimates are shown in Tables 2 and 3. Since we make no

claim that our results generalizes to the larger population of fathers, this approach

is justifiable.

With that caveat in mind, our preferred estimates are those where both chan-

nels of intergenerational transmission are controlled for. This is common practice

in the literature because that part of the intergenerational correlation in schooling

attainment explained by unobserved ability that operates through assortative mat-

ing is fully accounted for with this approach. Standard practice seems to favour

controlling for this source of endogeneity rather than worrying about selection bias

in the sample (again, under the proviso that the interpretation is restricted to the

sample of co-resident parents and children). These estimates, shown in Table 3, are

consistent with our hypothesis that assortative mating causes an upward bias in the

rate of mobility, since the estimates of mobility in this instance are much larger than

those shown in Table 2.

11

In terms of attrition, as explained above, as long as we observe the educational

attainment in at least one of the two waves, attrition is of no consequence, as the

sample of households for which there is missing data on both parents in both waves

is vanishingly small. Moreover, as Maluccio (2000) reports, educational attainment

is uncorrelated with whether a household is likely to drop out of the sample in 1998.

5.2 Interpreting the effect of mother’s education

It is not straightforward to interpret the finding that the effect of mothers’ education

is stronger for girls than for boys. From the descriptive statistics, it is clear that

there are no major gender differences in educational attainment, though here we are

limited to univariate comparisons. However, in the regressions run to produce the

coefficients presented in Table 3, where additional controls for gender are included,

there is no statistical association between gender and the log of child’s education

(i.e., the coefficient on Child is female is not statistically significant). So if there

is no significant difference in the educational attainment of girls versus boys, what

accounts for the higher persistence from parents to daughters as compared with

parents to sons?

One possibility is that part of the differential is driven by omitted parental

ability operating through assortative mating. However, in the regressions where

we control for the educational attainment of both parents, the results of which are

summarised in table 3, the extent of persistence drops across the board, and the gap

in persistence between daughters and sons (the final two columns in table 3) widens.

So while assortative mating operates directly on persistence, the differential effect

by gender cannot be explained by this channel.

Another possibility is that the unobserved ability of both parents that do not

operate in concert (e.g., genes that girls inherit solely from mothers) confounds the

estimated effects of parental schooling. Unfortunately, the KIDS data does not con-

tain any plausible options for dealing with this problem. The usual approaches (dif-

ferencing the schooling attained by twin parents; restricting the sample to adopted

children; constructing IVs through schooling reforms) are simply not viable with

this data.14 Having said that, it is not all that clear that adequately controlling for

14The sample of twin parents living in the same household would be vanishingly small in a dataset like KIDS so this approach would not work for practical reasons. The idea behind only lookingat educational outcomes of adopted children would not be plausible in South Africa, where it isvery likely that the majority of “adopted” children in a given household would be under the careof a genetically related individual – most often a grandmother. Finally, while there might besome possibilities to exploit educational policy innovations under Apartheid, the largest increases

12

the endogeneity of parental schooling will eliminate the gender differentiated rates

of intergenerational persistence we observe. This certainly seems to be the case for

the small but growing literature on the USA and Europe that have attempted to

make these corrections, though in these studies, the effect of paternal education

is stronger than that of maternal education. The interpretation of these findings

is that policies that give fathers more education have a larger positive prediction

for offspring education than policies that foster improved maternal education. In

our context, notwithstanding the caveat of remaining endogeneity, we hypothesise

that policies that foster greater schooling attainment among women are likely to

have a greater impact on the educational outcomes of daughters than sons. This is

consistent with the work of Thomas (1996) and Duflo (2003). However, a proper

test of this hypothesis would require better controls for the endogeneity of parental

education, which is beyond the scope of this paper and must therefore be left for

future research.

in educational attainment happened in the 1990’s which also coincided with the most significantpolicy shifts that took place over the last generation. The cohort of school leavers in the KIDSdata would not have been exposed to these policy innovations. This would also be true for all butthe very youngest cohort of school leavers in every other household survey that could be used toinvestigate intergenerational issues.

13

Table 1: Educational attainment across generations (all individuals age 21 years orolder)

Table 1. Educational attainment across generations (all individuals age 21 years or older)

Female Years of education and age Mean N Male years of education and age Mean N Paternal grandmother education 1.1

(1.91) 776 Paternal grandfather education 1.26

(2.23) 743

Paternal grandmother age 67.94 (12.08)

789 Paternal grandfather age 65.66 (12.77)

761

Maternal grandmother education 1.25 (2.01)

1206 Maternal grandfather education 1.38 (2.36)

1170

Maternal grandmother age 66.23 (12.28)

1221 Maternal grandfather age 64.25 (12.65)

1191

Mother education 4.63 (3.53)

1886 Father education 4.83 (3.64)

1014

Mother age 41.90 (15.42)

2177 Father age 44.89 (13.56)

1124

Daughter education 6.75 (3.30)

986 Son education 6.11 (3.34)

1111

Daughter age 30.08 (7.70)

1171 Son age 31.46 (8.23)

1282

Note: Standard deviations in brackets. Figure 1: Average years of education by generation and gender

Note: Standard deviations in brackets.

14

Table 2: Persistence in education status between parents and children

Table 2: Persistence in education status between parents and children Parent child pair Standardised

Coefficient Adjusted

R-squared df

Maternal grandmother to mother 0.37a 0.35 1109

Maternal grandfather to mother 0.38b 0.36 1074

Paternal grandmother to father 0.30c 0.27 722

Paternal grandfather to father 0.33d 0.29 693

Mothers to sons 0.37e 0.24 867

Fathers to sons 0.39f 0.22 409

Mothers to daughters 0.49g 0.28 800

Fathers to daughters 0.46h 0.21 415

Mothers to child 0.43i 0.26 1673

Father's to child 0.42j 0.22 830

Notes:

Each line of the table represents a different regression. In each separate regression, the dependent variable is the ln of scaled education of the child in the parent-child pair. Each regression includes age controls for the parent and child, and education of the parent. For example, for the results for maternal grandmother to mother, the regression includes controls for ln education of maternal grandmother, the age of the mother, age squared of the mother, age of the maternal grandmother, and age squared of the maternal grandmother. a. See Appendix 2, Table B, Model 1, !4. b. See Appendix 2, Table B, Model 2, !7; c . See Appendix 2, Table A, Model 1, !4.; d. See Appendix 2, Table A, Model 2, !7.; e. See Appendix 2, Table D, Model 1, !4.; f. See Appendix 2, Table D, Model 1, !7.; g. See Appendix 2, Table C, Model 1, !4.; h. See Appendix 2, Table B, Model 2, !7.; i. See Appendix 2, Table E, Model 1, !4.; j. See Appendix 2, Table E, Model 1, !7.

Furthermore, there appears to be very little difference in the magnitude of the correlation co-efficient by gender of the parent, that is, the correlation between a mother or father’s education status and their sons (or daughters) are similar. However, the degree of association between parents and their daughters is higher than the degree of association between parents and their sons, suggesting lower educational mobility for third generation girls than boys. In short, these results suggest that inherited status in education has become increasingly important over time, that is, the extent of educational mobility is lower for third generation children. In part, this has to do with the opening up of the education system to Africans, albeit to poor quality education, in the early 1950s with the advent of Bantu education, which allowed second generation individuals far greater educational opportunity than grandparents. Moreover, the higher persistence in educational status between second and third generation individuals suggests that the reforms of the late 1970s were largely inadequate at substantially increasing the attainment levels of subsequent generations of Indians and Africans. These results also suggest that inherited status is more important for daughters than sons, given that third generation girls experiencing lower educational mobility than boys.

Notes: Each line of the table represents a different regression. In each separate regression,the dependent variable is the ln of scaled education of the child in the parent-child pair.Each regression includes age controls for the parent and child, and education of theparent. For example, for the results for maternal grandmother to mother, the regressionincludes controls for ln education of maternal grandmother, the age of the mother, agesquared of the mother, age of the maternal grandmother, and age squared of the maternalgrandmother.

a. See Appendix A, Table A.6, Model 1

b. See Appendix A, Table A.6, Model 2

c. See Appendix A, Table A.5, Model 1

d. See Appendix A, Table A.5, Model 2

e. See Appendix A, Table A.8, Model 1

f. See Appendix A, Table A.8, Model 1

g. See Appendix A, Table A.7, Model 1

h. See Appendix A, Table A.6, Model 2

i. See Appendix A, Table A.9, Model 1

j. See Appendix A, Table A.9, Model 1

15

Tab

le3:

Per

sist

ence

ined

uca

tion

stat

us

bet

wee

npar

ents

and

childre

n,

contr

olling

for

the

educa

tion

ofb

oth

par

ents

.

10

Tab

le 3

: Per

sist

ence

in e

duca

tion

stat

us b

etw

een

pare

nts

and

child

ren,

con

trol

ling

for

the

educ

atio

n of

bot

h pa

rent

s.

Firs

t gen

erat

ion

to s

econ

d ge

nera

tion

Seco

nd g

ener

atio

n to

third

gen

erat

ion

Dep

. Var

iabl

e: L

n ed

ucat

ion

Pare

nt

Mat

erna

l gr

andp

aren

ts to

m

othe

rs

Pate

rnal

gra

ndpa

rent

s to

fa

ther

s Pa

rent

s to

chi

ld

Pare

nts

to d

augh

ter

Pare

nts

to s

ons

Add

ition

al C

ontr

ols

in

regr

essi

on

B

etaa

Adj

. R-s

q (d

f)

Bet

ab A

dj. R

-sq

(df)

B

etac

Adj

. R-s

q (d

f)

Bet

ad A

dj. R

-sq

(df)

B

etae

Adj

. R-s

q (d

f)

Age

M

othe

r 0.

23

0.39

0.

13

0.31

0.

34

0.32

0.

37

0.29

0.

31

0.33

Fath

er

0.26

10

51

0.27

67

2 0.

21

697

0.21

35

3 0.

20

335

Age

and

chi

ld is

Afr

ican

M

othe

r 0.

24

0.40

0.

19

0.38

0.

34

0.32

0.

36

0.29

0.

31

0.35

Fath

er

0.21

10

50

0.12

67

1 0.

18

696

0.21

35

2 0.

14

334

Age

and

chi

ld l

ives

in

Rur

al

area

M

othe

r 0.

22

0.42

0.

14

0.37

0.

33

0.33

0.

35

0.31

0.

30

0.35

Fa

ther

0.

21

1050

0.

16

671

0.17

69

6 0.

18

352

0.14

33

4

A

ge a

nd C

hild

is fe

mal

e M

othe

r

0.

34

0.32

Fath

er

0.21

69

6

A

ge,

child

is

Afr

ican

and

in

rura

l are

a M

othe

r 0.

22

0.42

0.

17

0.39

0.

33

0.33

0.

36

0.31

0.

31

0.36

Fa

ther

0.

20

1049

0.

10

670

0.17

69

5 0.

19

351

0.13

33

3

A

ge,

child

is

fem

ale,

Afr

ican

an

d liv

es in

rura

l are

a M

othe

r

0.

33

0.33

Fa

ther

0.

17

694

Not

es:.

In e

ach

regr

essi

on, t

he d

epen

dent

var

iabl

e is

the

ln o

f sca

led

educ

atio

n of

the

child

in th

e pa

rent

-chi

ld p

airin

g.

Thes

e co

-eff

icie

nts

corr

espo

nd to

the

regr

essi

ons

in A

ppen

dix

2, T

able

B, M

odel

s 3-

6; b

. The

se c

o-ef

ficie

nts

corr

espo

nd to

the

regr

essi

ons

in A

ppen

dix

2, T

able

A

, Mod

els

3-6;

c. T

hese

co-

effic

ient

s co

rres

pond

to th

e re

gres

sion

s in

App

endi

x 2,

Tab

le E

, Mod

els

3-8;

d. T

hese

co-

effic

ient

s co

rres

pond

to th

e re

gres

sion

s in

A

ppen

dix

2, T

able

C, M

odel

s 3-

6; e

. The

se c

o-ef

ficie

nts

corr

espo

nd to

the

regr

essi

ons

in A

ppen

dix

2, T

able

D, M

odel

s 3-

6.

Not

es:.

Inea

chre

gres

sion

,th

ede

pend

ent

vari

able

isth

eln

ofsc

aled

educ

atio

nof

the

child

inth

epa

rent

-chi

ldpa

irin

g.

aT

hese

coeffi

cien

tsco

rres

pond

toth

ere

gres

sion

sin

App

endi

x2,

Tab

leB

,M

odel

s3-

6.

bT

hese

co-e

ffici

ents

corr

espo

ndto

the

regr

essi

ons

inA

ppen

dix

2,T

able

A,

Mod

els

3-6.

cT

hese

co-e

ffici

ents

corr

espo

ndto

the

regr

essi

ons

inA

ppen

dix

2,T

able

E,

Mod

els

3-8.

dT

hese

co-e

ffici

ents

corr

espo

ndto

the

regr

essi

ons

inA

ppen

dix

2,T

able

C,

Mod

els

3-6.

eT

hese

co-e

ffici

ents

corr

espo

ndto

the

regr

essi

ons

inA

ppen

dix

2,T

able

D,

Mod

els

3-6.

16

Table 4: Quantile Regressions of intergenerational transmission of education status

12

Table 4: Quantile regression analysis of intergenerational transmission of education status Dep var: Ln of child's education Full sample First

quartile Second quartile

Third quartile

Top quartile

Child is African -0.11 ** -0.06 -0.09 ** -0.08 ** -0.65 *

0.04 0.08 0.04 0.03 0.05 Child lives in urban areas 0.14 * 0.11 0.05 0.07 ** 0.13 *

0.05 0.07 0.04 0.03 0.05 Child is female 0.03 0.06 0.06 ** 0.04 ** -0.02

0.04 0.05 0.02 0.02 0.07 Average age of child 0.03 0.06 ** 0.10 * 0.04 * -0.12 *

0.03 0.03 0.02 0.01 0.03 Average age of child squared 0.00 0.00 * 0.00 * 0.00 * 0.00 *

0.00 0.00 0.00 0.00 0.00 Age of mother -0.04 -0.05 -0.03 *** -0.01 0.06 ***

0.04 0.04 0.02 0.02 0.04 Average age of mother squared 0.00 0.00 0.00 ** 0.00 0.00

0.00 0.00 0.00 0.00 0.00 Ln of mother's education 0.28 * 0.35 * 0.16 * 0.09 * 0.37 *

0.04 0.04 0.02 0.02 0.03 Father's age 0.02 0.03 0.02 0.01 -0.09 *

0.04 0.03 0.02 0.01 0.03 Average father's age squared 0.00 0.00 0.00 0.00 0.00 ***

0.00 0.00 0.00 0.00 0.00 Ln of father's education 0.13 * 0.17 * 0.08 * 0.04 ** 0.34 *

0.03 0.04 0.02 0.02 0.03 Constant 1.84 ** 0.76 1.03 *** 1.72 * 3.29 *

0.86 1.08 0.54 0.44 1.23

Adjusted/Pseudo R-squared 0.33 0.26 0.15 0.09 0.29 N 706 *=Significant at 1% level; **=Significant at 5% level; ***=Significant at 10% level 5. Conclusion This paper has examined the changes in educational attainment across three generations of South Africans. Successive generations of South Africans have experienced increases in average attainment, although the rate of increase in attainment appears to have slowed for third generation children. Over time, the educational status of mothers has come to play a larger role in affecting child educational outcomes relative to fathers, possibly as a result of influx control and migrant labour system. Educational mobility higher for boys than girls. Target resources towards education, females in particular. Data can tell us that history matters – inherited status is important, and need active interventions to minimse role of inherited inequalities. Non-linearities. .

∗ = Significant at 1% level; ∗∗ =Significant at 5% level; ∗∗∗ = Significant at10% level

17

Bibliography Arthur, W.B. (1989) “Competing Technologies, Increasing Returns and Lock-in by Historical Events”, The Economic Journal, Vol. 99(394), March. Barro, R.J. and Martin, S.i (1992) “Convergence”, Journal of Political Economy, Vol . 100(20), April. Behrman, J (1999) “Labour markets in developing countries”, Handbook of Labour economics, Vol 3, eds. Ashenfelter, O and Card, D; Elsevier Science. Behrman, J and Taubman, P (1985) “Intergenerational earnings mobility in the United States: Some estimates and a test of Becker’s Intergenerational Endowments Model” , The Review of Economics and Statistics, Vol 67, 1, Feb, pg 144-151 Behrman, J, Zdneck, H, Taubman, P and Wales, T (1980) Socioeconomic Success: A study of the effects of genetic endowments, family environment and schooling; Amsterdam: North-Holland Publishing Co) Behrman, J (1997) “Mother’s schooling and child education: A survey”, Mimeo. UPenn. Birdsall, N and Graham, C. (2000) New Markets, New Opportunities: Economic and social mobility in a changing world, The Brookings Institute, Washington DC. Blau, P and Duncan, D.D. (1967) The American Occupational Structure. New York: Wiley. Bowles, S. and Gintis, H. (2001). "The Inheritance of Economic Status: Education, Class and Genetics," M. Feldman, Genetics, Behaviour and Society. Oxford: Elsevier. Case, A.; and Katz, L. (1991). "The Company You Keep: The Effects of Family and Neighbourhood on Disadvantaged Youths," National Bureau of Economic Research. Corak, M and Heisz, A (1998) “The Intergenerational Earnings and Income Mobility of Canadian Men: Evidence from Longitudinal Income Tax data”, Journal of Human Resources, XXXIV, No. 3. David, P.A. (1985) “Clio and the Economics of QWERTY”, American Economic Review Papers and Proceedings, Vol. 75(2), May. Featherman, D and Hauser, R. (1978). Opportunity and Change. New York: Academic Press. Greene, W.H. (1997) Econometric Analysis. New Jersey: Prentice Hall.

18

Haveman, R.; and Wolfe, B. (1995) "The Determinants of Children's Attainments: A Review of Methods and Findings." Journal of Economic Literature, XXXIII(December), pp. 1829-78. Hill, M and Duncan, G (1987) “Parental Family Income and the Socio-economic Attainment of children”, Social Science Res., 16(1), pp 39-73 Maluccio, J. (2000) “Attrition in the KwaZulu Natal Income Dynamics Study, 1993-1998”, FCND Discussion Paper No. 95, IFPRI, Washington D.C. Mulligan, C.B. (1999). "Galton Versus the Human Capital Approach to Inheritance." Journal of Political Economy, 107(6), pp. S184-S223. Peil, M (1990) “Intergenerational mobility through education: Nigeria, Sierra Leone and Zimbabwe”, International Journal of Educational Development, 10, no.4, 311-25 Sewell, W.H., Hauser, R.M. and Wolf, W.C. (1980). "Sex, Schooling and Occupational Status." American Journal of Sociology, 86, pp. 551-83. Schultz, P.T. (1993). "Returns to Women's Education," E. M. a. H. King, M.A., Women's Education in Developing Countries: Barriers, Benefits, and Policies. Baltimore and London: John Hopkins University Press, 51-99. Solon, G (1992) “Intergenerational Income Mobility in the United States”, The American Economic Review, Vol 82, Issue 3 (June 1992, pg 393-408). Thomas, D (1996) “Education across generations in South Africa”, American Economic Review Papers and Proceedings, May.

19

Appendices

A Complete Regression Tables

20

Tab

leA

.5:

Pat

ernal

Gra

ndpar

ent

toF

ather

(Dep

enden

tV

aria

ble

:L

nof

fath

er’s

educa

tion

)

13

App

endi

x 2

Tabl

e A

: Pat

erna

l Gra

ndpa

rent

to F

athe

r (D

epen

dent

Var

iabl

e: L

n of

fath

er's

educ

atio

n)

Var

iabl

e M

odel

1

Mod

el 2

M

odel

3

Mod

el 4

M

odel

5

Mod

el 6

! "

!

"

! "

!

"

! "

!

"

C

onst

ant

1.75

6

* 1.

329

**

1.

674

**

2.

906

*

1.83

2

* 2.

655

*

(0

.624

)

(0

.597

)

(0

.759

)

(0

.730

)

(0

.724

)

(0

.723

)

Fa

ther

’s A

ge

0.02

0 0.

292

0.

025

0.36

5

0.02

7 0.

398

***

0.00

9 0.

133

0.

017

0.25

7

0.00

8 0.

124

(0.0

16)

(0.0

16)

(0.0

17)

(0.0

16)

(0.0

16)

(0.0

16)

Squa

re o

f Fat

her’

s A

ge

0.00

0 -0

.646

*

0.00

0 -0

.711

*

0.00

0 -0

.730

*

0.00

0 -0

.467

**

0.

000

-0.5

82

* 0.

000

-0.4

53

**

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

Ln

of P

ater

nal G

rand

mot

her's

Edu

catio

n

0.36

1 0.

298

*

0.16

6 0.

137

* 0.

225

0.18

5 *

0.17

3 0.

142

* 0.

212

0.17

4 *

(0

.041

)

(0.0

52)

(0.0

49)

(0.0

49)

(0.0

49)

Pate

rnal

Gra

ndm

othe

r's A

ge

-0.0

12

-0.1

77

-0.0

19

-0.2

66

-0

.025

-0

.348

-0.0

07

-0.0

98

-0

.016

-0

.225

(0

.015

)

(0.0

16)

(0.0

15)

(0.0

16)

(0.0

15)

Squa

re o

f Pat

erna

l Gra

ndm

othe

r's A

ge

0.00

0 0.

184

0.00

0 0.

303

0.

000

0.40

0 **

* 0.

000

0.14

7

0.00

0 0.

281

(0.0

00)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

Ln

of P

ater

nal G

rand

fath

er's

Educ

atio

n

0.

374

0.32

6 *

0.29

4 0.

258

* 0.

135

0.11

8 *

0.18

0 0.

158

* 0.

112

0.09

8 **

(0

.039

)

(0

.048

)

(0

.049

)

(0

.048

)

(0

.048

)

Pa

tern

al G

rand

fath

er's

Age

-0.0

05

-0.0

70

-0

.002

-0

.024

-0.0

02

-0.0

37

-0

.002

-0

.024

-0.0

02

-0.0

33

(0

.013

)

(0

.014

)

(0

.013

)

(0

.013

)

(0

.013

)

Sq

uare

of P

ater

nal G

rand

fath

er's

Age

0.00

0 0.

076

0.

000

0.02

7

0.00

0 0.

041

0.

000

0.03

1

0.00

0 0.

040

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

A

fric

an

-0

.679

-0

.301

*

-0

.488

-0

.217

*

(0.0

75)

(0

.086

)

R

ural

-0

.480

-0

.273

*

-0.2

85

-0.1

62

*

(0.0

58)

(0.0

67)

R 2

0.27

0.

29

0.30

0.

38

0.36

0.

39

Df

722

693

672

671

671

670

Not

es:

Age

of

pate

rnal

gra

ndpa

rent

s in

clud

es d

ecea

sed

indi

vidu

als.

! ‘

s ar

e re

gres

sion

coe

ffic

ient

s w

hen

all

varia

bles

are

nor

mal

ised

. A

ll va

riabl

es a

re a

vera

ged

to

acco

unt

for

mea

sure

men

t er

ror

(Sol

on,

1992

). S

tand

ard

erro

rs a

re i

n pa

rent

hese

s. *

Rep

rese

nts

stat

istic

al s

igni

fican

ce a

t 1%

lev

el,

** r

epre

sent

s st

atis

tical

si

gnifi

canc

e at

5%

, and

***

repr

esen

ts s

tatis

tical

sig

nific

ance

at 1

0% le

vel.

“Afr

ican

” is

a ra

ce d

umm

y, a

nd “

Rur

al”

is a

loca

tion

dum

my.

Ta

ble

B. M

ater

nal G

rand

pare

nt to

Mot

her (

Dep

ende

nt V

aria

ble:

Ln

of m

othe

r's e

duca

tion)

Age

ofpa

tern

algr

andp

aren

tsin

clud

esde

ceas

edin

divi

dual

s.γ

’sar

ere

gres

sion

coeffi

cien

tsw

hen

all

vari

able

sar

eno

r-m

alis

ed.

All

vari

able

sar

eav

erag

edto

acco

unt

for

mea

sure

men

ter

ror

(Sol

on,1

992)

.St

anda

rder

rors

are

inpa

rent

hese

s.“A

fric

an”

isa

race

dum

my,

and

Rur

alis

alo

cati

ondu

mm

y.∗

=Si

gnifi

cant

at1%

leve

l;∗∗

=Si

gnifi

cant

at5%

leve

l;∗∗∗

=Si

gnifi

cant

at10

%le

vel.

21

Tab

leA

.6:

Mat

ernal

Gra

ndpar

ent

toM

other

(Dep

enden

tV

aria

ble

:L

nof

mot

her

’sed

uca

tion

)

14

M

odel

1

Mod

el 2

M

odel

3

Mod

el 4

M

odel

5

Mod

el 6

! "

!

"

! "

!

"

! "

!

"

C

onst

ant

1.74

5

* 2.

056

*

1.74

0

* 1.

978

*

1.89

7

* 1.

951

*

(0

.399

)

(0

.410

)

(0

.479

)

(0

.480

)

(0

.470

)

(0

.474

)

M

othe

r’s

Age

0.

000

0.00

2

0.00

0 0.

007

0.

006

0.09

6

0.00

4 0.

064

0.

003

0.04

4

0.00

2 0.

039

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

(0.0

10)

Squa

re o

f Mot

her’

s A

ge

0.00

0 -0

.362

**

0.

000

-0.3

81

**

0.00

0 -0

.426

*

0.00

0 -0

.393

**

0.

000

-0.3

74

**

0.00

0 -0

.369

**

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Ln o

f Mat

erna

l Gra

ndm

othe

r's E

duca

tion

0.

423

0.37

4 *

0.

258

0.22

7 *

0.27

1 0.

238

* 0.

250

0.22

0 *

0.25

4 0.

224

*

(0.0

29)

(0

.034

)

(0

.034

)

(0

.034

)

(0

.034

)

M

ater

nal G

rand

mot

her's

Age

-0

.003

-0

.043

0.

003

0.04

2

0.00

4 0.

058

0.

006

0.09

3

0.00

6 0.

093

(0.0

10)

(0

.010

)

(0

.010

)

(0

.010

)

(0

.010

)

Sq

uare

of M

ater

nal G

rand

mot

her’

s A

ge

0.00

0 0.

033

0.00

0 -0

.046

0.00

0 -0

.055

0.00

0 -0

.091

0.00

0 -0

.090

(0

.000

)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Ln o

f Mat

erna

l Gra

ndfa

ther

's Ed

ucat

ion

0.40

3 0.

383

* 0.

278

0.26

3 *

0.22

6 0.

214

* 0.

219

0.20

7 *

0.20

9 0.

198

*

(0

.027

)

(0

.031

)

(0

.034

)

(0

.032

)

(0

.034

)

M

ater

nal G

rand

fath

er's

Age

-0.0

12

-0.1

97

-0

.014

-0

.214

-0.0

14

-0.2

15

-0

.012

-0

.183

-0.0

12

-0.1

86

(0

.010

)

(0

.010

)

(0

.010

)

(0

.010

)

(0

.010

)

Sq

uare

of M

ater

nal G

rand

fath

er’s

Age

0.00

0 0.

185

0.

000

0.20

8

0.00

0 0.

209

0.

000

0.16

9

0.00

0 0.

173

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

A

fric

an

-0

.251

-0

.103

*

-0

.071

-0

.029

(0.0

64)

(0

.072

)

R

ural

-0

.296

-0

.165

*

-0.2

72

-0.1

52

*

(0.0

45)

(0.0

51)

R 2

0.35

0.

360

0.39

0.

40

0.42

0.

42

Df

1109

10

74

1051

10

50

1050

10

49

Not

es:

Age

of

pate

rnal

gra

ndpa

rent

s in

clud

es d

ecea

sed

indi

vidu

als.

! ‘

s ar

e re

gres

sion

coe

ffic

ient

s w

hen

all

varia

bles

are

nor

mal

ised

. A

ll va

riabl

es a

re a

vera

ged

to

acco

unt

for

mea

sure

men

t er

ror

(Sol

on,

1992

). S

tand

ard

erro

rs a

re i

n pa

rent

hese

s. *

Rep

rese

nts

stat

istic

al s

igni

fican

ce a

t 1%

lev

el,

** r

epre

sent

s st

atis

tical

si

gnifi

canc

e at

5%

, and

***

repr

esen

ts s

tatis

tical

sig

nific

ance

at 1

0% le

vel.

“Afr

ican

” is

a ra

ce d

umm

y, a

nd “

Rur

al”

is a

loca

tion

dum

my.

Age

ofpa

tern

algr

andp

aren

tsin

clud

esde

ceas

edin

divi

dual

s.γ

’sar

ere

gres

sion

coeffi

cien

tsw

hen

all

vari

able

sar

eno

r-m

alis

ed.

All

vari

able

sar

eav

erag

edto

acco

unt

for

mea

sure

men

ter

ror

(Sol

on,1

992)

.St

anda

rder

rors

are

inpa

rent

hese

s.“A

fric

an”

isa

race

dum

my,

and

Rur

alis

alo

cati

ondu

mm

y.∗

=Si

gnifi

cant

at1%

leve

l;∗∗

=Si

gnifi

cant

at5%

leve

l;∗∗∗

=Si

gnifi

cant

at10

%le

vel.

22

Tab

leA

.7:

Par

ent

toD

augh

ter

(Dep

enden

tV

aria

ble

:L

nof

child’s

educa

tion

)

15

Tabl

e C

. Par

ent t

o D

augh

ter (

Dep

ende

nt V

aria

ble:

Ln

of c

hild

's ed

ucat

ion)

V

aria

ble

Mod

el 1

M

odel

2

Mod

el 3

M

odel

4

Mod

el 5

M

odel

6

!

"

!

"

!

"

!

"

!

"

!

"

C

onst

ant

1.37

7

* 2.

078

**

1.

808

1.82

2

2.

142

**

* 2.

127

**

*

(0.5

13)

(1.0

40)

(1.3

12)

(1.3

15)

(1.3

02)

(1.3

00)

Dau

ghte

r’s

Age

0.

018

0.20

2

-0.0

08

-0.0

72

-0

.039

-0

.314

-0.0

38

-0.3

05

-0

.035

-0

.278

-0.0

42

-0.3

40

(0.0

19)

(0.0

45)

(0.0

50)

(0.0

50)

(0.0

49)

(0.0

49)

Squa

re o

f Dau

ghte

r’s

Age

-0

.001

-0

.460

**

0.

000

-0.0

17

0.

000

0.21

1

0.00

0 0.

203

0.

000

0.17

5

0.00

0 0.

224

(0.0

00)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

Ln o

f Mot

her's

Edu

catio

n

0.42

8 0.

489

*

0.30

7 0.

366

* 0.

306

0.36

5 *

0.29

5 0.

352

* 0.

299

0.35

6 *

(0

.028

)

(0.0

52)

(0.0

52)

(0.0

51)

(0.0

51)

Mot

her’

s A

ge

-0.0

06

-0.0

93

0.00

7 0.

095

0.

007

0.08

6

-0.0

01

-0.0

08

0.

002

0.02

9

(0

.018

)

(0.0

44)

(0.0

44)

(0.0

43)

(0.0

43)

Squa

re o

f Mot

her’

s A

ge

0.00

0 0.

218

0.00

0 0.

129

0.

000

0.13

7

0.00

0 0.

227

0.

000

0.19

6

(0

.000

)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Ln o

f Fat

her's

Edu

catio

n

0.

332

0.45

8 *

0.15

2 0.

209

* 0.

149

0.20

6 *

0.12

8 0.

176

* 0.

139

0.19

1 *

(0.0

34)

(0.0

46)

(0.0

48)

(0.0

47)

(0.0

47)

Fath

er’s

Age

-0.0

08

-0.1

28

0.

001

0.01

3

0.00

1 0.

017

0.

002

0.03

0

0.00

1 0.

009

(0

.033

)

(0

.041

)

(0

.041

)

(0

.040

)

(0

.040

)

Sq

uare

of F

athe

r’s

Age

0.00

0 0.

157

0.

000

-0.1

68

0.

000

-0.1

70

0.

000

-0.1

87

0.

000

-0.1

72

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

A

fric

an

-0

.021

-0

.011

0.

152

0.08

0

(0.0

96)

(0

.108

)

R

ural

-0

.202

-0

.139

*

-0.2

55

-0.1

75

*

(0.0

67)

(0.0

76)

R 2

0.28

0.

21

0.29

0.

29

0.31

0.

31

Df

800

415

353

352

352

351

Not

es:

Age

of

pate

rnal

gra

ndpa

rent

s in

clud

es d

ecea

sed

indi

vidu

als.

! ‘

s ar

e re

gres

sion

coe

ffic

ient

s w

hen

all

varia

bles

are

nor

mal

ised

. A

ll va

riabl

es a

re a

vera

ged

to

acco

unt

for

mea

sure

men

t er

ror

(Sol

on,

1992

). S

tand

ard

erro

rs a

re i

n pa

rent

hese

s. *

Rep

rese

nts

stat

istic

al s

igni

fican

ce a

t 1%

lev

el,

** r

epre

sent

s st

atis

tical

si

gnifi

canc

e at

5%

, and

***

repr

esen

ts s

tatis

tical

sig

nific

ance

at 1

0% le

vel.

“Afr

ican

” is

a ra

ce d

umm

y, a

nd “

Rur

al”

is a

loca

tion

dum

my.

Age

ofpa

tern

algr

andp

aren

tsin

clud

esde

ceas

edin

divi

dual

s.γ

’sar

ere

gres

sion

coeffi

cien

tsw

hen

all

vari

able

sar

eno

r-m

alis

ed.

All

vari

able

sar

eav

erag

edto

acco

unt

for

mea

sure

men

ter

ror

(Sol

on,1

992)

.St

anda

rder

rors

are

inpa

rent

hese

s.“A

fric

an”

isa

race

dum

my,

and

Rur

alis

alo

cati

ondu

mm

y.∗

=Si

gnifi

cant

at1%

leve

l;∗∗

=Si

gnifi

cant

at5%

leve

l;∗∗∗

=Si

gnifi

cant

at10

%le

vel.

23

Tab

leA

.8:

Par

ent

toSon

(Dep

enden

tV

aria

ble

:L

nof

child’s

educa

tion

)

16

Tabl

e D

. Par

ent t

o So

n (D

epen

dent

Var

iabl

e: L

n of

chi

ld's

educ

atio

n)

Varia

ble

Mod

el 1

M

odel

2

Mod

el 3

M

odel

4

Mod

el 5

M

odel

6

!

"

! "

!

"

! "

!

"

! "

C

onst

ant

1.94

4

* 1.

575

2.55

5

**

3.06

6

* 2.

811

**

3.

037

*

(0

.529

)

(0

.999

)

(1

.209

)

(1

.203

)

(1

.194

)

(1

.199

)

So

n’s

Age

0.

024

0.27

4

0.00

6 0.

054

0.

031

0.28

2

0.03

6 0.

326

0.

039

0.35

1

0.03

9 0.

354

(0.0

17)

(0.0

39)

(0.0

39)

(0.0

39)

(0.0

39)

(0.0

39)

Squa

re o

f Son

’s A

ge

-0.0

01

-0.5

84

* -0

.001

-0

.302

-0.0

01

-0.5

30

-0

.001

-0

.565

-0.0

01

-0.6

00

***

-0.0

01

-0.5

98

***

(0

.000

)

(0

.001

)

(0

.001

)

(0

.001

)

(0

.001

)

(0

.001

)

Ln

of M

othe

r's E

duca

tion

0.

334

0.37

4 *

0.

255

0.31

0 *

0.25

9 0.

314

* 0.

251

0.30

5 *

0.25

5 0.

309

*

(0.0

29)

(0

.049

)

(0

.048

)

(0

.048

)

(0

.048

)

M

othe

r’s

Age

-0

.024

-0

.365

-0

.083

-1

.071

**

* -0

.099

-1

.274

**

-0

.080

-1

.028

-0.0

91

-1.1

68

***

(0

.019

)

(0.0

51)

(0.0

50)

(0.0

50)

(0.0

50)

Mot

her’

s A

ge

0.00

0 0.

481

***

0.

001

1.27

7 **

0.

001

1.46

8 **

0.

001

1.22

0 **

0.

001

1.35

7 **

(0.0

00)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

Ln

of f

athe

r's E

duca

tion

0.

308

0.39

4 *

0.15

0 0.

200

* 0.

107

0.14

2 **

0.

106

0.14

2 **

0.

095

0.12

6 **

(0

.038

)

(0

.044

)

(0

.046

)

(0

.045

)

(0

.046

)

Fa

ther

’s A

ge

0.

004

0.05

1

0.02

8 0.

412

0.

033

0.48

2

0.02

1 0.

305

0.

026

0.38

5

(0.0

35)

(0.0

44)

(0.0

44)

(0.0

44)

(0.0

44)

Squa

re o

f Fat

her’

s A

ge

0.

000

0.00

6

0.00

0 -0

.548

0.00

0 -0

.612

0.00

0 -0

.438

0.00

0 -0

.515

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Afr

ican

-0.2

98

-0.1

52

*

-0.1

83

-0.0

94

***

(0.0

92)

(0

.112

)

R

ural

-0

.225

-0

.156

*

-0.1

48

-0.1

03

***

(0

.068

)

(0

.082

)

R

2 0.

24

0.22

0.

33

0.35

0.

35

0.36

D

f 86

7

40

9

33

5

33

4

33

4

33

3

Not

es:

Age

of

pate

rnal

gra

ndpa

rent

s in

clud

es d

ecea

sed

indi

vidu

als.

! ‘

s ar

e re

gres

sion

coe

ffic

ient

s w

hen

all

varia

bles

are

nor

mal

ised

. A

ll va

riabl

es a

re a

vera

ged

to

acco

unt

for

mea

sure

men

t er

ror

(Sol

on,

1992

). S

tand

ard

erro

rs a

re i

n pa

rent

hese

s. *

Rep

rese

nts

stat

istic

al s

igni

fican

ce a

t 1%

lev

el,

** r

epre

sent

s st

atis

tical

si

gnifi

canc

e at

5%

, and

***

repr

esen

ts s

tatis

tical

sig

nific

ance

at 1

0% le

vel.

“Afr

ican

” is

a ra

ce d

umm

y, a

nd “

Rur

al”

is a

loca

tion

dum

my.

Age

ofpa

tern

algr

andp

aren

tsin

clud

esde

ceas

edin

divi

dual

s.γ

’sar

ere

gres

sion

coeffi

cien

tsw

hen

all

vari

able

sar

eno

r-m

alis

ed.

All

vari

able

sar

eav

erag

edto

acco

unt

for

mea

sure

men

ter

ror

(Sol

on,1

992)

.St

anda

rder

rors

are

inpa

rent

hese

s.“A

fric

an”

isa

race

dum

my,

and

Rur

alis

alo

cati

ondu

mm

y.∗

=Si

gnifi

cant

at1%

leve

l;∗∗

=Si

gnifi

cant

at5%

leve

l;∗∗∗

=Si

gnifi

cant

at10

%le

vel.

24

Tab

leA

.9:

Par

ent

toC

hild

(Dep

enden

tV

aria

ble

:L

nof

child’s

educa

tion

)

17

Tabl

e E.

Par

ent t

o C

hild

(Dep

ende

nt V

aria

ble:

Ln

of c

hild

's ed

ucat

ion)

V

aria

bles

M

odel

1

Mod

el 2

M

odel

3

Mod

el 4

M

odel

5

Mod

el 6

M

odel

7

Mod

el 8

! "

!

"

! "

!

"

! "

!

"

! "

!

"

C

onst

ant

1.61

2

* 1.

565

**

1.

776

**

1.

964

**

2.

081

**

1.

777

**

2.

084

**

2.

087

**

(0.3

68)

(0.7

08)

(0.8

70)

(0.8

71)

(0.8

61)

(0.8

70)

(0.8

63)

(0.8

63)

Chi

ld’s

Age

0.

022

0.24

2 **

* 0.

011

0.10

4

0.02

0 0.

173

0.

026

0.22

1

0.02

4 0.

205

0.

020

0.17

2

0.02

4 0.

206

0.

024

0.20

5

(0

.013

)

(0

.029

)

(0

.029

)

(0

.029

)

(0

.029

)

(0

.029

)

(0

.029

)

(0

.029

)

Sq

uare

of C

hild

’s A

ge

-0.0

01

-0.5

24 *

0.

000

-0.2

86

-0.0

01

-0.3

54

-0.0

01

-0.3

94

-0.0

01

-0.3

85

-0.0

01

-0.3

50

-0.0

01

-0.3

86

-0.0

01

-0.3

82

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

Ln

of M

othe

r's E

duca

tion

0.

382

0.43

2 *

0.

284

0.34

2 *

0.28

2 0.

340

* 0.

276

0.33

3 *

0.28

1 0.

339

* 0.

276

0.33

3 *

0.27

2 0.

328

*

(0.0

20)

(0

.035

)

(0

.035

)

(0

.034

)

(0

.035

)

(0

.034

)

(0

.035

)

M

othe

r’s

Age

-0

.014

-0

.208

-0.0

35

-0.4

45

-0.0

41

-0.5

31

-0.0

38

-0.4

86

-0.0

36

-0.4

55

-0.0

38

-0.4

88

-0.0

39

-0.5

01

(0

.013

)

(0.0

32)

(0.0

32)

(0.0

32)

(0.0

32)

(0.0

32)

(0.0

32)

Squa

re o

f Mot

her’

s A

ge

0.00

0 0.

327

***

0.

000

0.66

8 **

* 0.

001

0.74

7 **

* 0.

001

0.70

2 **

* 0.

000

0.67

7 **

* 0.

001

0.70

3 **

* 0.

001

0.71

4 **

*

(0.0

00)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

(0

.000

)

Ln

of F

athe

r's E

duca

tion

0.32

1 0.

424

* 0.

157

0.21

2 *

0.13

6 0.

184

* 0.

123

0.16

6 *

0.15

8 0.

214

* 0.

123

0.16

6 *

0.12

4 0.

168

*

(0

.025

)

(0

.032

)

(0

.033

)

(0

.032

)

(0

.032

)

(0

.033

)

(0

.033

)

Fa

ther

’s A

ge

0.

000

0.00

6

0.01

4 0.

202

0.

016

0.23

3

0.01

2 0.

178

0.

014

0.20

9

0.01

2 0.

179

0.

013

0.18

6

(0.0

24)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

(0.0

29)

Squa

re o

f Fat

her’

s A

ge

0.

000

0.04

1

0.00

0 -0

.348

0.

000

-0.3

74

0.00

0 -0

.326

0.

000

-0.3

56

0.00

0 -0

.326

0.

000

-0.3

36

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

Afr

ican

-0.1

57

-0.0

81 *

*

-0

.004

-0

.002

-0

.003

-0

.002

(0

.066

)

(0

.077

)

(0

.077

)

R

ural

-0

.214

-0

.147

*

-0

.212

-0

.147

*

-0.2

14

-0.1

48 *

(0.0

47)

(0

.056

)

(0

.056

)

C

hild

is F

emal

e

0.02

8 0.

023

0.03

4 0.

028

(0

.039

)

(0.0

39)

R 2

0.26

0

0.

220

0.32

0

0.

320

0.33

0

0.

320

0.33

0

0.

330

Df

1673

83

0

69

7

69

6

69

6

69

6

69

5

69

4

Notes

:Age

of p

ater

nal g

rand

pare

nts

incl

udes

dec

ease

d in

divi

dual

s. !

‘s a

re re

gres

sion

coe

ffic

ient

s w

hen

all v

aria

bles

are

nor

mal

ised

. All

varia

bles

are

ave

rage

d to

ac

coun

t fo

r m

easu

rem

ent

erro

r (S

olon

, 19

92).

Sta

ndar

d er

rors

are

in

pare

nthe

ses.

* R

epre

sent

s st

atis

tical

sig

nific

ance

at

1% l

evel

, **

rep

rese

nts

stat

istic

al

sign

ifica

nce

at 5

%, a

nd *

** re

pres

ents

sta

tistic

al s

igni

fican

ce a

t 10%

leve

l. “A

fric

an”

is a

race

dum

my,

and

“R

ural

” is

a lo

catio

n du

mm

y.

Age

ofpa

tern

algr

andp

aren

tsin

clud

esde

ceas

edin

divi

dual

s.γ

’sar

ere

gres

sion

coeffi

cien

tsw

hen

all

vari

able

sar

eno

r-m

alis

ed.

All

vari

able

sar

eav

erag

edto

acco

unt

for

mea

sure

men

ter

ror

(Sol

on,1

992)

.St

anda

rder

rors

are

inpa

rent

hese

s.“A

fric

an”

isa

race

dum

my,

and

Rur

alis

alo

cati

ondu

mm

y.∗

=Si

gnifi

cant

at1%

leve

l;∗∗

=Si

gnifi

cant

at5%

leve

l;∗∗∗

=Si

gnifi

cant

at10

%le

vel.

25

The Southern Africa Labour and Development Research Unit (SALDRU) conducts research directed at improving the well-being of South Africa’s poor. It was established in 1975. Over the next two decades the unit’s research played a central role in documenting the human costs of apartheid. Key projects from this period included the Farm Labour Conference (1976), the Economics of Health Care Conference (1978), and the Second Carnegie Enquiry into Poverty and Development in South Africa (1983-86). At the urging of the African National Congress, from 1992-1994 SALDRU and the World Bank coordinated the Project for Statistics on Living Standards and Development (PSLSD). This project provide baseline data for the implementation of post-apartheid socio-economic policies through South Africa’s first non-racial national sample survey. In the post-apartheid period, SALDRU has continued to gather data and conduct research directed at informing and assessing anti-poverty policy. In line with its historical contribution, SALDRU’s researchers continue to conduct research detailing changing patterns of well-being in South Africa and assessing the impact of government policy on the poor. Current research work falls into the following research themes: post-apartheid poverty; employment and migration dynamics; family support structures in an era of rapid social change; public works and public infrastructure programmes, financial strategies of the poor; common property resources and the poor. Key survey projects include the Langeberg Integrated Family Survey (1999), the Khayelitsha/Mitchell’s Plain Survey (2000), the ongoing Cape Area Panel Study (2001-) and the Financial Diaries Project.

www.saldru.uct.ac.zaLevel 3, School of Economics Building, Middle Campus, University of Cape Town

Private Bag, Rondebosch 7701, Cape Town, South AfricaTel: +27 (0)21 650 5696

Fax: +27 (0) 21 650 5797Web: www.saldru.uct.ac.za

southern africa labour and development research unit