Human Capital Externalitiesand Vertical Labor Mobility in South Africa

Pierre-Carl Michaud and Désiré Vencatachellum

December 1, 2000

Abstract

A person's human capital has an external effect on the productivity of others. InSouth Africa, where apartheid led to important differences in the average human capitalof each racial group, these externalities may differ across and within races. The impactof human capital externalities on wages, and vertical labor mobility, are examined usingthe 1993 South Africa Project for Statistics on Living Standards and Development. Weestimate a wage equation which accounts for both individual and aggregate humancapital and controls for parental assets. We find that: (1) the race-specific human capitalof Blacks and Colored affects same-race wages positively, (2) Blacks’ aggregate humancapital has a positive impact on white workers' wages, (3) Whites’ human capital doesnot affect black workers' wages, and (4) Blacks and Colored have the lowest verticallabor mobility . Our estimates indicate that policies which curtailed the education ofBlacks had the unexpected effect of depressing Whites’ wages. Similarly, any negativeshock which depletes the human capital of Blacks is likely to have adverse effects on thewages of both black and white workers.

Keywords : Human capital externalities, wage determinants, parents' background, South Africa

JEL Classification : J31, H52

We would like to thank the World Bank for the data used here. Financial support was provided by the Fonds pour laFormation de Chercheurs et l'Aide à la Recherche (FCAR). Bernard Fortin, Pascal François, Pierre Thomas Léger,Germano Mwabu, Simon van Norden, and François Vaillancourt provided helpful comments. Authors’ affiliation : Institutd’economie appliquee, École des HEC, Montreal, Quebec, Canada. Corresponding author : D. Vencatachellum, Institutd’economie appliquee, École des HEC, 3000, chemin de la Cote-Sainte-Catherine, Office 4.155, Montréal (Québec)Canada H3T 2A7. Email : p141@hec.ca Fax : (514) 340-6469. Web site : http://des.dyndns.org.

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1. INTRODUCTION

In 1953, the Government of South Africa implemented the Bantu Act which

restricted the quality of education available to non-Whites.1 This act, as well as

apartheid, likely had persistent effects on wages and private returns to education. Knight

and McGrath (1977) document the considerable wage gap between Blacks and Whites

in apartheid South Africa, and find wage discrimination against Blacks in both the public

and private sectors. Using multivariate analysis, more recent empirical studies have

investigated similar issues with a special emphasis on the returns to education. For

instance, Moll (1998) finds positive returns to higher education in South Africa. Mwabu

and Schultz (1996) investigate how the returns to education vary across races. They

report that, among the four races in South Africa, Blacks have the highest marginal

returns to education. Mwabu and Schultz (2000) push the analysis further by

investigating the determinants of marginal returns to education in South Africa. They

estimate these returns by race, sex and age groups, finding them to be negatively

correlated with a measure of the educated cohort. Mwabu and Schultz (2000) explain

their result by an increase in the supply of skilled workers.

In addition to the effect of the size of the educated cohort on the marginal

returns to education, it is worth investigating, and this is the goal of this paper, how

changes in the proportion of an educated cohort in South Africa affect labor market

equilibrium and wages. Commenting on the returns to education, Griliches (in Krueger

and Taylor, 2000, [p.183]) notes: "The interesting question is, having expanded

education, one might have thought the average return would go down rather than go up.

It didn’t." One possible explanation for this phenomenon is that the educated generate a

positive externality on the productivity of all workers. Indeed, the external effect of

human capital is at the heart of the endogenous growth literature pioneered by Lucas

(1988).

The workings of human capital externalities are two-fold. First, interaction with

and among educated individuals can increase all workers’ productivity through a learning

effect. As mentioned by Lucas (1988) "there are group interactions that are central to

individual productivity". Someone who works, or studies, in an environment where most

1 In 1975, the government spent 15 times more per white student than per black student (Thomas, 1996). This ratiobegan to fall in the 1980’s and was about 5:1 in 1992 (Moll, 1996).

2

colleagues are well trained is more likely to obtain answers to her questions and

therefore increase her productivity.2 Second, in developing countries a larger share of

educated individuals may trigger the adoption of high-productivity technologies as firms

anticipate there will be enough skilled labor to work with these technologies. In other

words, human capital externalities are akin to a productivity effect. Therefore, an

increase in the aggregate human capital in a competitive economy where workers are

paid their marginal product induces an increase in the demand for labor. We call this

shift in the demand for labor arising from human capital externalities the "demand

effect". Note that the demand for labor is also affected by institutional considerations,

these being particularly relevant in apartheid South Africa. For example, as mentioned

by Moll (1996), in 1972, the Chamber of Mines decided, for political reasons, to reduce

the share of foreign mine workers by increasing its recruitment within South-Africa. This

policy led to an increase in the wages of unskilled labor. This paper uses data collected

in 1993, at the end of apartheid, and focuses on the economic reasons for a shift in labor

demand.

Moreover, when a larger share of the population is educated, the supply of skilled

labor increases. This is termed the "supply effect" by Mwabu and Schultz (2000),. The

net effect on wages is positive when human capital externalities are high enough for the

demand effect to dominate the supply effect. Hence, as mentioned by Behrman and

Birdsall (1988), the omission of cohort variables, such as the proportion of the educated

population, in the wage equation is likely to yield misleading estimates. This paper

departs from the empirical literature on wage determinants in South Africa by integrating

measures of aggregate human capital as explanatory variables in the wage equation.

Our primary objective is thus to estimate the direct effect of aggregate human

capital on wages. We allow wages to depend simultaneously on province-wide human

capital, sector and individual characteristics. Our specification includes both individual-

specific human capital and aggregate human capital of the four racial groups as

explanatory variables. We test whether the average education of a racial group has a

direct impact on wages. We thus answer the question whether restricting non-Whites’

education through the Bantu Act, for example, had perverse effects on Whites’ wages.

Moreover, to control respectively for labor market segmentation and a wage-earner's

social background, we also include the sector in which the worker is employed and

2 See Lucas (1988, [p. 35-39]) for a thorough discussion of the external effects of human capital.

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parental assets as explanatory variables. Our second objective is thus to assess vertical

labor mobility across the four racial groups in South Africa.

Schultz (1988, p. 587) discusses the empirical literature which investigates the

role of family background in estimated wage specification. Just as with aggregate human

capital, omitting the parents' characteristics in a wage equation is likely to bias the

estimates. The parents' background may affect wages and employment opportunities

through a network effect. Moreover, labor market segmentation implies that factors

other than those directly related to the agent’s productivity, such as the parent’s social

network, may affect wages. As a consequence, the apartheid system in South Africa,

which segmented the labor market, may have led to such effects. If parental assets

strongly influence wages, vertical labor mobility is likely to be low.3

Our estimates indicate that a racial group’s human capital never has a negative

impact on the wages of same-race workers. Thus the supply effect never dominates the

demand effect within a race. However, the impact of inter-race human capital

externalities on wages differ by race. More specifically, we find that the average human

capital of Blacks has a positive effect on the wages white workers. As Blacks become

more educated, the productivity of white workers increases. As for Whites, their

aggregate human capital is statistically insignificant in explaining black workers’ wages.

In other words, the productivity of black workers is not affected by Whites' average

human capital. We also find that vertical labor mobility is lowest for Blacks and Colored.

The remainder of the paper is organized as follows. In section 2, we discuss the

data and wage specification. We then estimate the wage equation to assess intra- and

inter-race human capital externalities and vertical labor mobility in section 3. Conclusions

are drawn in section 4.

3 We do not test for labor market segmentation in this paper but rather include sector variables to account for itspossibility. Future research could investigate the wage premium due to labor market segmentation using the methodology of Funkhouser (1997).

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2. DATA AND SPECIFICATION

We use the 1993 South Africa Project for Statistics on Living Standards and

Development (PSLSD). Following the definition of Statistics South Africa, we assume

that the labor force consists of those who are above 15 years old, who are not mentally

(223) or physically (139) disabled, who are not enrolled at school (4,427) or retired

(2,535). Of the 18,785 individuals who satisfy these criteria, 5,985 are wage-earners.4

Sample statistics reported in Table 1 differ considerably by racial group, a likely

consequence of apartheid. For example, fewer Blacks are wage-earners (see Sherer

(2000) for a discussion) and males are more likely to be wage-earners than females.

Following Mincer (1974) we specify a log-linear wage equation with individual, group and

sector explanatory variables.5 A thorough discussion of these variables for South Africa

is provided in Mwabu and Schultz (1998). In this section, we pay particular attention to

education, household characteristics, human capital externalities and sector variables.

Physical characteristics Table 2 reports average wages across races and regions.

The average monthly wage in our sample is 1,051 rands. Blacks are at the lower end of

the wage distribution with 626 rands per month, while Whites are at the upper end with

2,706 rands per month. Furthermore, Indians receive slightly less than half of the

average White’s wage (1,250 rands per month) while Colored are not significantly better

off than Blacks (785 rands per month). It is particularly striking that a black worker with

some post-secondary education earns 51 rands which is less than a white employee

with no primary education (55 rands). An examination conducted across regions and

gender (Table 2) reveals that, for all races, women receive lower wages than men and

wages in the urban sector are significantly higher than those in the rural sector. In a

4 The PSLSD is a joint project of the World Bank and the Southern African Labor and Development Research Unit. Moll(1998) provides a description of the survey in which 43,974 people were interviewed in 1993. The employment rate,which can be computed from Table 1, varies with the definition of the labor force. We use the same definition of the laborforce as Statistics South Africa, except that we also include women who report housework as their main occupation.While the sample size of wage earners is the same as in Schultz and Mwabu (2000), these authors restrict their attentionto those who have actively looked for a job during the week preceding the survey or who were working during that week tocalculate the unemployment rate. These definitional issues do not affect our results because we estimate the wageequation on the sample of wage earners. This sample does not depend on the definition of the labor force.

5 See also Becker (1975) and Griliches (1977) for the wage specification.

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nutshell, black females living in rural regions receive the lowest wages, while white

males in urban areas receive the highest wages.

Wage-earners' human capital The specific human capital of workers can be

measured by their experience and education. We define a worker’s potential experience

as his or her age minus five years and the number of years of schooling. As for

education, we use Moll’s (1996, 1998) spline construction.6 Letting x denote the number

of years of schooling, we define the following three education variables :

primary = min [ x, 7 ] , (1)

secondary = min [ max [ x – 7, 0 ] , 5 ] , (2)

post-secondary = max [ x – 12, 0 ]. (3)

Primary education reaches its ceiling at seven years, the time needed to complete this

education cycle in South Africa. Those who do not complete primary school have from 0

to a maximum of six years of primary education and 0 years of secondary and post-

secondary education. The same reasoning applies for the construction of the secondary

and post-secondary variables. Among wage-earners, 31 percent have not completed

primary school, 28 percent completed the secondary education cycle, and 11 percent

have some post-secondary education (Table 3). These figures hide a large variation

across races (Table 3). More than 40 percent of Blacks have less than seven years of

schooling and only 16 percent have at least a secondary school certificate. Indians fare

nearly as well as Whites at the primary school level, with more than 90 percent of Indian

and white wage earners holding a primary school certificate. However, while 72 percent

of Whites have twelve years of schooling, this is true for only 53 percent of Indians.

Aggregate human capital In addition to individual-specific human capital, a wage-

earner may benefit from the aggregate human capital in the economy. For reasons

discussed in the introduction, human capital yields a positive externality on wages. For

instance, Berhrman and Birdsall (1988) find that in Brazil such cohort variables affect

wages. However, in South Africa, racial segregation may have acted as a barrier to such

human capital externalities across the different races. We use the average number of

6 Moll (1998) provides a description of the South Africa education system. This author also notes that the spline variablesassume constant marginal returns to one additional year of schooling within each cycle. Future research could test thisassumption as there may be a wage premium for graduating.

6

years of schooling of the labor force, for a given race in a given province, as a proxy for

race-specific human capital. The average number of years of schooling for the entire

labor force is 7.4 years; Blacks have 6.7 years, and Whites 11.0 years (see Table 5).

Indians and Colored fare better than Blacks but slightly worse than Whites.

These racial differences are compounded by geographical ones (Table 5). The

Pretoria Witwatersrand Verreniging province enjoys the highest average human capital.

The North West, Northern and Eastern Transvaal provinces have the least. These

differences across regions and races, as well as the fact that apartheid prevented the

different races from fully socializing with each other, may have led to asymmetric human

capital externalities. We thus allow the impact of human capital externalities on wages to

differ by using, as explanatory variables, black, white and colored province-specific

average human capital in our wage equation. We say that human capital externalities

are constrained by racial segregation if these externalities differ across races. Note that

we do not include Indians' average human capital as an explanatory variable because

most Indians (87 percent) reside in Kwazulu-Natal (Table 4).

Household’s assets Apart from economy-wide variables, an individual’s wage

opportunities may be affected by her family’s wealth. For instance, the number of years

spent at school does not take into account the quality of education. Private schooling of

higher quality may provide its graduates with a wage premium. In such cases, wealthier

parents may wish to purchase private education in order to obtain such a premium for

their children. To examine the link between wealth and wages, we proxy wealth by the

sum of TV sets, radios and telephones owned by the household. Other wealth measures

do not provide enough variance or are unreported for too many individuals in order to be

considered.7 Our wealth measure, as expected, indicates that Whites are significantly

richer than other races, while Blacks are the least wealthy. According to this measure,

the average white household is three times richer than its black counterpart (Table 1).

In addition to wealth, the household heads' education may also affect their

offspring’s labor market opportunities through a network effect. Parents who have

completed secondary school may socialize with one another, creating a network of

relatively educated individuals which in turn may allow their children to have get to better

7 For instance, a very small percentage of wage-earners report owning their place of residence.

7

jobs. As reported in Table 1, the average white household head is twice as educated as

the black one, with Indians and Colored falling in between.8

Sectors During apartheid, non-Whites could neither occupy certain positions nor

move freely in the country. Although these legal constraints no longer exist, occupational

differences still persist as reported in Table 1. The share of professionals is highest

among white workers (30 percent) and lowest among black workers (15 percent); 1 in 3

Blacks work in the primary sector while a bit less than 1 in 2 Indians work in the

manufacturing sector. Moll (1993) finds that industry dummies are statistically significant

in explaining earnings in South Africa. In our analysis, we condition wages on the

workers' sector of employment since sample size considerations prevent us from using

industry dummies as explanatory variables.9 Geographical differences are also likely to

have an influence on wages. Most black workers reside in rural regions, while more than

90 percent of each of the three other races reside in urban areas (Table 1).

We next specify a Mincerian wage function with the explanatory variables

discussed above, and where the dependent variable is the logarithm of hourly wages.

The average of the dependent variable for each race is reported at the bottom of Table

6. However, individuals who participate in the labor market may either report positive

wages, in which case they are wage-earners, or report no wages. As shown by

Heckman (1979), estimating the wage equation without accounting for sample selection

may lead to biased estimates. The likelihood that a person who participates in the labor

market is a wage-earner is assumed to depend on three groups of explanatory variables:

first, human capital, as measured by a person's age (rather than by experience for

identification purposes) and education; second household characteristics by wealth and

number of children below 15; third gender and race. We first test for possible sample

selection bias arising from a wage-earner's status by estimating the wage equation using

Heckman’s two-step selection correction procedure. We then compare those estimates

with the ordinary least square (OLS) estimates and justify the use of the latter to test our

main hypotheses .

8 We acknowledge that the parents’ education may affect their children’s education and, consequently, wages, throughhome education. In this case, both the household head's and the children's education are correlated. The inclusion ofboth the worker’s and the parent’s education in the wage equation may lead to a decrease in the precision of theestimates due to collinearity. When most parameter estimates in the wage equation are significant, as reported in Table 6and discussed in section 3, this is not a concern.

9 Moll (1993) uses census data, and his sample consists of 37,944 Africans and 46,638 Whites. Evidence that theindustry in which one works is a determinant of wages is also provided in Smit (1999).

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3. RESULTS

The probit estimates of the wage-earner status reported in Table 7 indicate that

the specification fits the data well, with a pseudo R-square greater than 0.45 for any

racial sub-sample. The specification also correctly predicts the wage-earner status more

than two out of three times. Both identifying variables, age and the number of children

below 15, are always jointly statistically significant which implies that the selection model

is robust. When the determinants of the wage-earner status are investigated using the

entire labor force, we find that, ceteris paribus, the likelihood of being a wage-earner is

highest for white males. Estimating the wage-earner probit equation separately for each

race reveals that Colored and Whites with only primary education have a lower

likelihood of being wage-earners than Blacks or Indians with similar characteristics. This

result may arise because only low-ability Whites and Colored dropout of primary school.

We will come back to this point when discussing the estimates of the returns to

education below. Finally, the number of children below 15 in a household has a negative

incidence on the likelihood of being a wage earner and reflects child rearing constraints.

The Heckman estimates of the wage equation in Table 8 indicate that the inverse

of Mills’ ratio (IMR) is statistically significant and negative for Blacks and Colored at the 1

percent level, significant for Whites at the 5 percent level, and insignificant for Indians.

The negative correlation between the disturbance in the wage equation and the selection

criterion means that individuals who have unexplained higher wage opportunities are

less likely to be wage-earners. This result arises because agents with higher

(unexplained) wage opportunities also have higher reservation wages. The fact that the

inverse of Mill's ratio is statistically significant for Blacks is consistent with Mwabu and

Schultz (2000), who find that sample selection matters for African men. However, in our

model, sample selection has strong statistical significance for Colored and weak

significance for Whites. This may be due to our definition of the labor force (see section

2 and footnote 3) and the specification of the wage-earner status equation. However, the

fact that both identifying variables are jointly statistically significant, contrary to Mwabu

and Schultz (2000), and the high goodness of fit measures, lends support to our

estimates.

9

Turning to the OLS estimates of the wage equation for the entire sample and

each racial group, in Table 6, we find the estimates have the same sign as and are close

to those in Table 8. One exception is the potential number of years of experience which

is statistically insignificant for Colored wage-earners in Heckman’s sample selection

correction procedure but is statistically significant in the probit estimate of the wage-

earner status. This would indicate that experience matters for Colored in obtaining

employment, though their wage profile is independent of experience. Given that sample

selection does not substantially bias the OLS estimates, and that the statistical effects of

the two groups of variables in question, namely aggregate human capital and household

assets, are nearly identical in both estimation methods, we focus on the sample of wage-

earners (Table 6) as in Mwabu and Schultz (2000).

The model fits the data well with an adjusted R-square of 0.53 when estimated

over all races, and the coefficients are consistent with the theory. Both gender and race

are statistically significant at 1 percent and have the strongest impact on wages. The

high wage premium for white workers reflects the well documented racial wage

discrimination in South Africa (see, for example, Knight and McGrath (1977)). Similarly,

when controlling for other factors, a worker in the urban sector earns a higher wage than

his rural counterpart. Marginal returns to education increase with the education level for

the entire sample. The negative estimated returns to primary education for Colored

confirm Moll’s (1996) findings.10 In addition, our results indicate that the same result

holds for Whites. These negative returns may be a consequence of type-signaling

whereby only low-ability Colored and Whites quit school during or at the end of the

primary cycle. To the extent that black workers are the only ones with statistically

positive returns to primary education, this implies that, among Blacks, low-ability

students are not the only ones to drop out of primary school. This result reflects the

additional constraints faced by Blacks in acquiring education. Furthermore, white cross

effects are negative and significant for the three education cycles. Hence, ceteris

paribus, Whites enjoy lower marginal returns from one additional year of schooling than

the other races.

The previous estimates of the wage equation are not different from those in the

empirical literature on the determinants of wages in South Africa. Our contribution to the

literature resides in the fact that province-wide human capital and measures of

10

household assets are statistically significant in the wage equation for some races as

discussed hereafter.

3.1 Intra-race Human Capital Externalities

We begin by considering how the race-specific average human capital affects

same-race workers’ wages. Specifically, as to the effects of providing education to more

Blacks, our estimates indicate that the average human capital of the black population

has a positive impact on black workers' wages. Moreover, Blacks’ average human

capital has a higher impact on a black worker’s wages than one year of primary

education acquired by a black worker. Although the returns to primary education are the

lowest of the three education cycles, black workers with low education hence benefit

proportionately more from intra-race human capital externalities. Colored workers benefit

the most from an increase in their own racial group human capital, presumably because

they are geographically concentrated such that human capital spillovers are more easily

absorbed through interaction with each other. As for Whites, they are the only racial

group for whom wages are independent of intra-race human capital externalities, which

indicates that the labor demand and supply effects compensate each other. This result

arises because the average human capital of Whites is already high, and therefore the

marginal benefits of increasing such human capital is rather low.

No racial group's average human capital has a negative effect on the wages of

same-race workers. In other words, if labor markets are (imperfectly) segmented along

race lines, the supply effect on each labor market never dominates the demand effect.

For instance, an increase in the supply of skilled Blacks is accompanied by a high

enough increase in the demand for black workers such that the demand effect exceeds

the supply effect, and black workers’ wages increase. Consequently, increasing the

proportion of educated people in the black population has two effects on the returns to

education. First, appealing to Mwabu and Schultz’s (2000) results, i.e. a fall in the

marginal returns to education as more people are educated, the new returns to

education function is flatter. Second, our estimates indicate that the returns to education

function shifts upwards such that for a given level of education, a black worker’s wages

increase. Our result finds support in Griliches’s statement as well as the aforementioned

10 Moll (1996) uses earnings as the dependent variable and his data spans three years, namely 1980, 1985 and 1990.

11

endogenous growth literature. In this respect, South Africa is no different from other

countries.

3.2 Inter-race Human Capital Externalities

The next result concerns inter-race aggregate human capital measured by the

cross effects of one racial group’s human capital on the wages of workers of another

race. When the model is estimated using the sample of white workers, an increase in the

average education of the black labor force has a positive effect on white workers’ wages.

Indeed whites workers’ wages increase more than the wages of black workers when the

average human capital of the black labor force increases. One rationale for this result is

as follows. It seems reasonable to assume that the labor supply of Whites is unchanged

following an increase in the average human capital of Blacks. Ceteris paribus, if white

workers’ wages subsequently increase, the demand for white workers must have also

increased. The vehicle for the demand effect is black human capital externalities, which

positively affect white workers' productivity. In a company where black workers are more

productive, white management may assign more tasks to black workers, which in turn

allows white workers to focus on specialized activities of higher productivity.

To the extent that apartheid was at work during the period when most workers in

our sample acquired their education, it is noteworthy that Whites do benefit from the

average skills of Blacks. Indeed, our results suggest that the Bantu Act had an

unexpected boomerang effect, namely, that constraining the average education of

blacks reduced the wages of white workers. Public support for investment in the

education of Blacks is thus justified and will likely lead to a Pareto superior situation.

As for the average province-wide human capital of Whites, it has no effect on the

wages of black workers, and has a negative impact on the wages of colored workers. In

other words, an increase in Whites' aggregate human capital does not affect black

workers' productivity. Among the possible causes, one can pinpoint the little socialization

between Whites and Blacks, and the fact that Blacks and Whites hold jobs with

completely different characteristics (unskilled positions for Blacks, skilled ones for

Whites) which preclude any learning-by-doing benefiting Blacks. It is also likely that a

minimum level of education is required for workers to benefit from human capital

12

externalities. For example, reading skills are a prerequisite for an unskilled worker to

learn how to operate a computer from a skilled worker. Hence, the low level of education

of black workers acts as an invisible barrier preventing them from absorbing these

externalities. In addition, the black labor supply is unchanged. Hence a change in the

aggregate human capital of white workers does not affect the wages of black workers.

However, this does not apply to Colored whose wages vary inversely with

Whites’ aggregate human capital. As the number of qualified Whites increases, ceteris

paribus, the demand for colored workers, and therefore their wages, fall. Moll (1992)

reports that, under apartheid, Colored were subject to some, but not all, of the job

reservations that applied to Blacks. Indeed, according to Moll, from 1960 to 1980, the

percentage of colored managers increased and discrimination against Colored fell. Our

estimates can be interpreted in light of this evidence. When there is a shortage of white

skilled workers (low average human capital among Whites), employers hire colored

workers but revert to white workers when this constraint is relaxed.

3.3 Vertical Labor Mobility

Next, the degree of vertical labor market mobility is reflected in the effects of the

household wealth and the education of the household head on wages. Wealth has a

significant and positive effect on a person’s wage for all four races. This effect is

strongest among Blacks and Colored, and smallest among Whites. The other measure

of vertical mobility, the household head’s education, is significant and positive when the

model is estimated over the entire sample. We explore this result a bit further by

investigating whether it is robust across all four races. Interestingly, the number of years

of education of the household head for Whites is never significant. As for Blacks and

Colored, the number of years of primary and post-secondary education of the household

head has a positive impact on wages. Our estimates show that when we control for

other factors which affect wages, a white worker is less likely to face constrained wage

opportunities than for a black or a colored worker when all of them are from a poor

background and with less educated parents.

Hence, the estimates of the statistical effects of the parents’ wealth and

household head’s education on wages indicate that vertical mobility is lowest among

13

Blacks and Colored, relatively high for Indians and high for Whites. This last result may

indicate that socialization is conditioned on race among Whites and on education among

the other races. Low vertical mobility among Blacks and Colored may reflect the

relatively weak business network of these two communities as well as the credit and

institutional constraints they face. When we combine this result with Thomas’ (1996)

finding that intergenerational education persistence is insignificant for all races except

Blacks, it is clear that the latter face the highest hurdle on the labor market.

4. CONCLUSION

Economy-wide human capital exhibits positive externalities on labor productivity.

Consequently, increasing the percentage of the population which is educated has both a

supply and demand effect on the labor market. Intra-race human capital externalities in

South Africa are such that the supply effect never dominates the demand effect.

However, our estimates indicate that a consequence of apartheid is asymmetric inter-

race human capital externalities. Paradoxically, Whites enjoy positive externalities when

Blacks have a higher average human capital. As for Whites, a change in their aggregate

human capital does not affect the wages of black workers. Finally, our results show that

Blacks and Colored face the highest barriers for vertical mobility in the labor market.

Our results indicate that policies aimed at increasing the education of the Black

population are likely to benefit both Blacks and Whites, and do not penalize colored or

Indian workers. Consequently, Whites should favor education policies targeting Blacks.

Moreover, any negative shock which depletes the human capital of Blacks is likely to

affect the wages of both black and white workers. This is especially relevant in light of

the HIV-Aids epidemic in South Africa. It is noteworthy that, although private returns to

primary education are smaller than the returns to secondary or post-secondary

education, it may be better, from both a social and private point of view, to target primary

education if an increase in the quality of secondary education occurs at the expense of

primary education (Lloyd-Ellis, 2000). This is especially true in South Africa where the

quality of primary education is already low for Blacks (Moll, 1996).

14

5. REFERENCES

Becker, G. S. (1975) "Human Capital : a Theoretical and Empirical Analysis withSpecial Reference to Education", National Bureau of Economic Research, 2nd edition,New York.

Behrman, J. R., and N. Birdsall (1988) "The Reward for Good Timing : Cohort Effectsand Earnings Functions for Brazilian Males", The Review of Economics and Statistics,LXX(1), 129-135.

Funkhouser, E. (1997) "Mobility and Labor Market Segmentation : The Urban LaborMarket in El Salvador", Economic Development and Cultural Change, 18(1), 121-153.

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15

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All races Black White Colored Indian

Labor force (1) 18 785 14 348 2 342 1 551 544

Number of wage-earners 6 022 4 015 1 105 657 245

Percentage of males among wage-earners 59% 61% 55% 56% 61%

Average age, in years, of wage-earners 37 38 36 35 35

Percentage of wage-earners who live in the urban sector 62% 47% 93% 90% 98%

Percentage of wage-earners who work in the following sector

Primary 28% 33% 19% 22% 7%

Manufacturing 23% 20% 23% 31% 44%

Tertiaty 23% 26% 14% 20% 17%

Professional 19% 15% 30% 18% 22%

Military 2% 2% 3% 2% 3%

Wealth of the household (2) 1.73 1.32 3.94 1.87 2.59

Average education of the household head in years 7.5 6.0 12.0 8.2 10.2(1) See section 2 for a definition of the labor force(2) Wealh of the household equals to the sum of TV sets, radios and telephones

Gender Sectors All races Black White Colored Indian

Male Urban 1 543 769 3 396 920 1 387

Rural 667 605 2 867 319 2 033

Urban and rural 1 185 677 3 359 856 1 400

Female Urban 1 017 594 1 908 757 1 012

Rural 538 498 1 678 139 ..

Urban and rural 854 547 1 892 695 1 017

Male and female Urban 1 316 694 2 735 847 1 240

Rural 620 567 2 325 242 1 900

Urban and rural 1 051 626 2 706 785 1 250

Percentage with less than the following number of years of schooling All races Black White Colored Indian

7 years 31 41 9 22 6

12 years 72 84 28 81 47

13 years (no post-secondary education) 89 94 65 95 86

Table 2 Monthly wage in rands

Table 3 Distribution of wage-earners's education by race

Table 1 Sample Statisitcs for Wage-earners

All races Black White Colored Indian

Western Cape 708 132 162 412 2

Northern Cape 69 21 .. 48 ..

Eastern Cape 381 308 28 45 ..

Kwazulu-Natal 1108 736 92 68 212

Orange Free State 580 493 67 20 ..

Eastern Transvaal 631 555 71 1 4

Northern Transvaal 379 315 64 .. ..

North West 621 550 68 2 1

Pretoria Witwatersrand Vereening 1540 902 553 59 26

South Africa 6017 4012 1105 655 245

Race Western Northern Eastern Kwazulu Orange Eastern Northern North PWV South Africa

Cape Cape Cape Natal Free State Transvaal Transvaal West

Black 7.5 7.5 6.5 6.7 6.3 6.3 6.5 6.4 7.9 6.7

White 11.3 .. 10.1 11.4 11.5 10.4 10.1 10.3 11.1 11.0

Colored 8.3 6.9 8.2 9.6 8.2 .. .. 7.0 9.7 8.3

Indian 9.5 .. .. 10.0 .. 10.6 .. .. 9.6 9.9

All races 8.8 7.0 6.7 7.3 6.8 6.7 6.7 6.7 9.1 7.4

Note .. Indicates less than 5 individuals

Table 4 Distribution of wage-earners by province of residence and by race

Provinces

Table 5 Average years of schooling of the labor force by race and province

Province

(1)

(2)

All races Black White Colored Indian (3)

Constant -1.4383 -1.4259 0.5473 -3.1131 0.3449(7.72) (5.82) (0.47) (3.28) (0.77)

Gender equals 1 if male 0.4010 0.4210 0.3461 0.2887 0.3136(16.22) (13.37) (7.22) (3.86) (2.79)

Race equals 1 if white 1.6736 .. .. .. ..(11.50)

0.0317 0.0426 -0.0427 -0.0726 -0.0230(3.80) (4.66) (1.78) (2.88) (0.35)

0.1712 0.1387 0.0783 0.2152 0.0737(15.35) (10.79) (2.60) (6.41) (1.31)

0.2786 0.2021 0.1281 0.1371 0.3379(12.39) (6.16) (5.89) (1.66) (6.53)

0.0447 0.0438 0.0524 0.0357 0.0674(10.43) (8.84) (7.60) (3.22) (4.16)

-0.0548 -0.0539 -0.0779 -0.0445 -0.1319(divided by 100) (7.60) (6.54) (5.33) (2.43) (3.96)

Average number of years of schooling of Blacks in the province 0.0645 0.0803 0.1183 -0.0480 ..(2.91) (2.91) (2.43) (0.53)

Average number of years of schooling of Whites in the province 0.0088 -0.0005 -0.0243 -0.0342 ..(1.00) (0.03) (0.24) (1.88)

Average number of years of schooling of Colored in the province -0.0244 -0.0312 0.0055 0.3073 ..(5.27) (5.83) (0.34) (3.42)

Wealth of the household 0.0700 0.092 0.030 0.1033 0.066(7.74) (6.37) (2.52) (2.90) (1.85)

Number of years of primary education of household head 0.0228 0.0241 -0.0018 0.0512 -0.0454(3.57) (3.34) (0.07) (2.86) (1.44)

Number of years of secondary education of household head 0.0277 0.0160 0.0089 0.0426 0.1247(2.61) (1.22) (0.29) (1.28) (3.00)

Number of years of postsecondary education of household head -0.0046 0.0709 0.0029 0.1522 -0.1046(0.23) (1.68) (0.14) (1.78) (1.74)

Sectors (4)

Dummy equals 1 if the wage-earner is in the manufacturing sector -0.1131 -0.1616 0.0067 -0.1223 -0.4709(3.47) (3.93) (0.11) (1.14) (2.82)

Dummy equals 1 if the wage-earner is in the tertiary sector 0.0711 0.0493 0.0830 0.0544 0.0692(2.14) (1.22) (1.15) (0.45) (0.39)

Dummy equals 1 if the wage-earner is in the professional sector 0.6000 0.7692 0.2401 0.5474 0.3143(16.93) (16.22) (4.16) (4.54) (1.84)

Dummy equals 1 if the wage-earner resides in an urban area 0.2667 0.2509 0.0631 .. ..(8.60) (6.95) (0.72)

White and potential experience 0.0054 .. .. .. ..(0.65)

White and number of years of potential labor market experience squared -0.0199 .. .. .. ..(divided by 100) (1.19)

White and number of years of primary education -0.0735 .. .. .. ..(3.20)

White and number of years of secondary education -0.1060 .. .. .. ..(3.64)

White and number of years of postsecondary education -0.1661 .. .. .. ..(6.28)

Adjusted R-squared 0.533 0.381 0.230 0.475 0.411Sample size 5 853 3 907 1 060 644 243Mean of dependent variable 1.132 0.770 2.471 0.989 1.505

(1)

(2)

(4) Geographical concentration of Indians prevents us from including province-wide average education and region of residence as explanatory variables.(4) The primary sector is the reference

Physical characteristics

Number of years of primary education

Number of years of secondary education

Aggregate human capital

Household assets

Hourly wages equal to monthly wages divided by the number of hours worked in that month.

TABLE 6: OLS estimates of the wage equation in South Africa with human capital externalitiesDependent variable : logarithm of hourly wages in Rands

Wage-earner human capital

Ordinary least squares estimates. Absolute T-ratio corrected for heteroscedasticity are in parentheses under the point estimate. At a 1%, 5% and 10% level, the critical T is 2.58, 1.96 and 1.64 respectively.

Explanatory Variables

Number of years of post-secondary education

Number of years of potential labor market experience

Number of years of potential labor market experience squared

White cross effects

(1)

(2)

Explanatory variables All races Black White Colored Indian

Constant -2.683 -2.727 -2.580 -2.369 -2.542

(28.52) (24.91) (8.78) (7.63) (3.79)

Human capital

Age 0.116 0.114 0.140 0.130 0.117

(24.59) (20.74) (9.75) (8.41) (3.37)

Age squared (divided by 100) -0.123 -0.117 -0.167 -0.148 -0.146

(20.98) (17.15) (9.25) (7.53) (3.20)

Number of years of primary education -0.001 0.008 -0.108 -0.056 -0.034

(0.21) (1.54) (5.25) (2.66) (0.78)

Number of years of secondary education 0.060 0.012 0.272 0.105 0.162

(9.10) (1.51) (11.59) (4.50) (3.64)

Number of years of post-secondary education 0.183 0.252 0.071 0.044 0.136

(9.31) (7.72) (2.47) (0.48) (1.49)

Household characteristics

Wealth of the household 0.072 0.094 -0.011 0.105 0.045

(8.32) (7.79) (0.68) (3.18) (1.00)

Number of children under 15 years old in the household -0.121 -0.121 -0.093 -0.068 -0.104

(26.68) (24.95) (3.46) (3.31) (2.39)

Physical characteristics

Gender equals 1 if male 0.435 0.358 0.808 0.547 1.156

(22.13) (15.90) (12.58) (7.98) (9.09)

Race equals 1 if white 0.216 .. .. .. ..

(5.54) .. .. .. ..

Sample size 18 785 14 348 2 342 1 551 544

Pseudo R-squared 0.488 0.458 0.5019 0.452 0.525

Percentage of right predictions 69 68 77 66 75

Log-likelihood -11 089 -8 476 -1 107 -931 -284

(1)

(2) The identifying variables are age and number of children under 15 years old in the household

Absolute T-ratio is in parentheses under the point estimate. The critical T equals 2.58, 1.96 and 1.64 at 1%, 5% and 10% levels respectively

Table 7: Probit estimates of the wage-earner equationDependent variable equals 1 if individual is a wage-earner, 0 otherwise

(1)

(2)

All races Black White Colored Indian (3)

Constant -0.6903 -0.6948 1.1274 -0.9837 0.5500

(3.13) (2.48) (1.01) (0.88) (0.65)

Sex equals 1 if male 0.2905 0.3254 0.1789 0.0213 0.2293

(9.37) (8.71) (1.82) (0.16) (0.78)

Race equals 1 if white 1.4557 .. .. .. ..

(9.65) .. .. .. ..

0.0326 0.0405 -0.0086 -0.0107 -0.0204

(3.91) (4.43) (0.33) (0.36) (0.31)

0.1446 0.1255 -0.0035 0.1463 0.0610

(11.94) (9.58) (0.07) (2.84) (0.84)

0.2313 0.1388 0.1125 0.0892 0.3284

(9.78) (4.04) (4.66) (1.05) (5.46)

0.0285 0.0283 0.0402 -0.0065 0.0634

(5.62) (4.90) (4.17) (0.31) (2.94)

Number of years of potential labor market experience squared -0.0336 -0.0349 -0.0552 0.0253 -0.1236

(divided by 100) (4.15) (3.85) (2.87) (0.76) (2.82)

Average number of years of schooling of Blacks in the province 0.0594 0.0714 0.1196 -0.0887 ..

(2.68) (2.59) (2.47) (0.97) ..

Average number of years of schooling of Whites in the province 0.0087 0.0009 -0.0304 -0.0441 ..

(0.99) (0.06) (0.31) (2.23) ..

Average number of years of schooling of Colored in the province -0.0248 -0.0316 0.0055 0.3173 ..

(5.36) (5.90) (0.35) (3.19) ..

Wealth of the household 0.0612 0.0824 0.0338 0.0686 0.0626

(6.74) (5.71) (2.76) (1.74) (1.63)

Number of years of primary education of household head 0.0187 0.0198 -0.0094 0.0426 -0.0464

(2.91) (2.73) (0.42) (2.20) (1.46)

Number of years of secondary education of household head 0.0297 0.0165 0.0218 0.0566 0.1265

(2.79) (1.26) (0.75) (1.64) (3.02)

Number of years of postsecondary education of household head -0.0006 0.0764 0.0055 0.1569 -0.1027

(0.03) (1.86) (0.26) (1.83) (1.72)

Sectors (4)

Dummy equals 1 if the wage-earner is in manufacturing sector -0.0992 -0.1433 0.0016 0.1393 -0.4717

(3.05) (3.48) (0.03) (1.40) (2.82)

Dummy equals 1 if the wage-earner is in the tertiary sector 0.0806 0.0552 0.0862 0.3438 0.0686

(2.44) (1.37) (1.21) (2.95) (0.39)

Dummy equals 1 if the wage-earner is in the professional sector 0.6022 0.7748 0.2320 0.7867 0.3129

(17.03) (16.41) (4.03) (6.59) (1.83)

Dummy equals 1 if the wage-earner resides in an urban area 0.2507 0.2386 0.0631 .. ..

(8.08) (6.60) (0.72) .. ..

White and potential experience 0.0137 .. .. .. ..

(1.62) .. .. .. ..

White and number of years of potential labor market experience squared -0.0309 .. .. .. ..

(divided by 100) (1.84) .. .. .. ..

White and number of years of primary education -0.0728 .. .. .. ..

(3.17) .. .. .. ..

White and number of years of secondary education -0.0984 .. .. .. ..

(3.38) .. .. .. ..

White and number of years of postsecondary education -0.1511 .. .. .. ..

(5.74) .. .. .. ..

Sample selectionInverse of Mills's ratio -0.3953 -0.3595 -0.5124 -1.1112 -0.1522

(5.85) (4.94) (1.81) (2.78) (0.29)

Adjusted R-squared 0.536 0.385 0.227 0.442 0.409

Correlation of disturbance in the wage equation and selection criterion -0.442 -0.426 -0.670 -0.915 -0.183

Sample size 5 853 3 907 1 060 644 243

Mean of dependent variable 1.132 0.770 2.471 0.989 1.505

(1)

(2)

(3)

(4) The primary sector is the reference.

Physical characteristics

Number of years of primary education

Number of years of secondary education

TABLE 8 Heckman's two-step estimates of the wage equation in South Africa

Hourly wages equal to monthly wages divided by the number of hours worked in that month.

Number of years of post-secondary education

Number of years of potential labor market experience

White cross effects

Wage earner human capital

Dependent variable : logarithm of hourly wages in rands

Explanatory Variables

Geographical concentration of Indians prevents us from including province-wide average education and region of residence as explanatory variables.

Ordinary least squares estimates. Absolute T-ratio corrected for heteroscedasticity are in parentheses under the point estimate. At a 1%, 5% and 10% level the critical T is 2.58, 1.96 and 1.64 respectively.

Aggregate human capital

Household assets