Rates of Return on Educational Investment from Micro Studies: The Effects of Human Capital on Economic Development
Rates of Return on Educational Investment from Micro Studies: The Effects of Human Capital on Economic Development
based largely on surveys of rate of return studies by Psacharopoulos. In the most recent of these
surveys, (Psacharopoulos, 1994), the social rate of return on primary education in sub-Saharan Africa
was estimated to be 24 per cent, for secondary education it was 18 and for higher education 11 per
cent. Private returns were higher at 41%, 27% and 28% respectively. However, this view has been
persuasively challenged by Bennell (1996). Bennell makes two points. First, the original sources do
not support Psacharopoulos’s estimates. Second, that in so far as it ever was true, “the conventional
rate of return on education patterns almost certainly do not prevail in sub-Saharan Africa under
current labour market conditions” (p.195). That this second objection is possibly correct is suggested
by the survey of the Mincerian returns to education in sub-Saharan Africa in Appleton, Hoddinott and
Mackinnon (1996) reproduced as Table 6. The average (private) returns to education suggested by
their survey are substantially below those presented in Psacharopoulos (1994). This is true for both
primary and post-primary schooling, although the latter still appears to have substantial returns.
More recent evidence is available. Data has been collected over three years for a panel of firms
within the manufacturing sectors of the Cameroon, Ghana, Kenya, Zambia and Zimbabwe over the period 1992-1995, Bigsten et al (1997). The sectors within the manufacturing sector were chosen so
as to be as similar as possible across the countries. At the same time as the firms were surveyed a
parallel interview was carried out for a representative sample of the workers in the enterprises.
In Table 7 the data is presented for the earnings for each educational category by country in US
dollars. At the university level there is a fairly narrow range for three of the countries, Cameroon,
Zambia and Zimbabwe, which widens steadily as the education level falls. For all educational levels
wages in Ghana are substantially below the other countries. A university completer in Ghana earns less
than a primary school completer in Cameroon. In broad terms we observe a wide spectrum of earnings
with workers in Ghana at the bottom. In Ghana an employee who has completed primary school earns
US$47, which is well below wage levels for rural workers in Chinese enterprises, Knight, Song and
Huaibin (1997). The differential across countries is not identical for all the educational categories. The
table also presents Mincerian rates of return to education (Mincer, 1974). The assumption which underlies
the Mincerian interpretation is that, for each educational level, the only costs are opportunity costs: the
wages forgone whilst acquiring education. The measure omits pecuniary costs (including those born by
the government) and so will tend to over-estimate the returns, particularly at the university level. However,
at the primary level, the assumption that wages are forgone by attending school may be incorrect and
cause the returns to be under-estimated. As with the estimates in Table 6, the rates of return to education
rise with the level of education. The rates of return on university education are very much larger than
those at lower levels. As Figure 3 shows the increments in earnings from completing either primary or
secondary are dwarfed by the rises that accrue to those with university education. These high earnings
may partly reflect the ability of those with higher qualifications to work abroad, a problem which has
been identified as a brain drain, see Box 3.
There are several reasons why the returns to education presented in Table 7 may be based on
coefficients that are biased. The years of schooling are measured assuming no repetition (see
Behrman and Deolalikar, 1991). Biases may also arise as we have not allowed for selectivity.
Those who work in the manufacturing sector are highly atypical. Secondly, such educational
measures cannot distinguish between signalling and credentionalism as alternatives to the human capital
interpretation. The signalling explanation for the findings suggests that education is associated with
higher incomes because those with higher education have more ability. Education signals ability. The
credentalist view is that institutional wage structures reward those with qualifications: it is qualifications
that are rewarded rather than the skills the qualifications are meant to reflect. Thirdly, it is known that
parental background can play an important role in educational choice. The sample is limited to those in
manufacturing, and it was not possible to use variables measuring ability or information on parental
background. To see if these factors indicate that the rates of return presented in Table 7 are too high it
is necessary to assess the importance of these problems.
A recent study examining some of these issues for Ghana is Glewwe (1996). The question that
needs to be posed is as follows: if no controls are included for cognitive skills or parental background,
is there evidence of significant bias in the education variable? Glewwe provides evidence that there may
be some upward bias. If selectivity is allowed for in the private sector earnings function then the coefficient
on years of schooling becomes insignificant. Glewwe then calculates the rate of return on education
based on the measures of cognitive skills available for his data set. He finds a figures of 4 per cent, for
an individual aged 25, which compares with a rate of return of 7 per cent from the earning function. If
a years of education measure is used in the earnings function for the data in Table 7 the return to
education for Ghana is 6 per cent. Thus the evidence from Glewwe (1996) suggests there may be some
bias in an upward direction.
Four studies which have information on parental background are Behrman and Wolfe (1983),
Lam and Schoeni (1993), Heckman and Hotz (1986) and Kingdon (1997). The conclusion, which is
uniform across the studies is that the inclusion of parental background reduces the returns to schooling
by about 20 per cent. Again this is evidence that the estimates presented in Table 7 may be upwardly
biased. A recent study which uses a panel data set of twins to estimate the returns to school quality,
Behrman, Rosenzweig and Taubman (1996) finds that controlling for family background does affect
the assessment of the returns from school quality but has only very marginal effects on the returns to
schooling coefficient. A study which has very detailed information on cognitive skills and parental
background is that of Knight and Sabot (1990). Their study uses comparative data drawn from workers
in the manufacturing sectors of Kenya and Tanzania. They argue that the returns of education variable
is picking up human capital formation. While signalling may play some role, it is not the primary reason
years of education determines earnings.
The conclusion we would draw is that the evidence suggests that the education variable may
overstate the returns to human capital, but not by very much, and that the major influence of years of
education on earnings is through its effects of cognitive skills and not, as the signalling explanation would imply, indirectly through signalling ability. Even if the biases are more significant that the empirical evidence
currently suggests, it is not clear that they would explain, or mitigate, the non-linearity in the returns to
education. The conclusion, that the returns to education are modest, is one common to other studies.
Freeman (1986, p.377) notes that “every study also finds that, by itself, years of schooling explains a
relatively small part of the variance of log earnings, say 3-5 percent at most”.
In Table 7 the pattern is similar across all the countries, the rate of return falls with the level of
education. What might account for this pattern? The returns to primary and secondary school completers
obtain mainly through gaining employment in the formal sector. In the context of Africa such jobs have
declined in the last decade. This decline has occurred in the context of the rapid expansion of education
and very low growth rates of physical capital. In such a context low rates of return on education might
be expected.
Human Capital and Economic Development
Simon Appleton and Francis Teal
Rates of Return on Educational Investment from Micro Studies The Effects of Human Capital on Economic Development - To learn more about this author, visit African Development Bank's Website.
Like this article? Share it with your friends
The conventional wisdom is that there is a high rate of return on primary schooling. This view is
based largely on surveys of rate of return studies by Psacharopoulos. In the most recent of these
surveys, (Psacharopoulos, 1994), the social rate of return on primary education in sub-Saharan Africa
was estimated to be 24 per cent, for secondary education it was 18 and for higher education 11 per
cent. Private returns were higher at 41%, 27% and 28% respectively. However, this view has been
persuasively challenged by Bennell (1996). Bennell makes two points. First, the original sources do
not support Psacharopoulos’s estimates. Second, that in so far as it ever was true, “the conventional
rate of return on education patterns almost certainly do not prevail in sub-Saharan Africa under
current labour market conditions” (p.195). That this second objection is possibly correct is suggested
by the survey of the Mincerian returns to education in sub-Saharan Africa in Appleton, Hoddinott and
Mackinnon (1996) reproduced as Table 6. The average (private) returns to education suggested by
their survey are substantially below those presented in Psacharopoulos (1994). This is true for both
primary and post-primary schooling, although the latter still appears to have substantial returns.
More recent evidence is available. Data has been collected over three years for a panel of firms
within the manufacturing sectors of the Cameroon, Ghana, Kenya, Zambia and Zimbabwe over the period 1992-1995, Bigsten et al (1997). The sectors within the manufacturing sector were chosen so
as to be as similar as possible across the countries. At the same time as the firms were surveyed a
parallel interview was carried out for a representative sample of the workers in the enterprises.
In Table 7 the data is presented for the earnings for each educational category by country in US
dollars. At the university level there is a fairly narrow range for three of the countries, Cameroon,
Zambia and Zimbabwe, which widens steadily as the education level falls. For all educational levels
wages in Ghana are substantially below the other countries. A university completer in Ghana earns less
than a primary school completer in Cameroon. In broad terms we observe a wide spectrum of earnings
with workers in Ghana at the bottom. In Ghana an employee who has completed primary school earns
US$47, which is well below wage levels for rural workers in Chinese enterprises, Knight, Song and
Huaibin (1997). The differential across countries is not identical for all the educational categories. The
table also presents Mincerian rates of return to education (Mincer, 1974). The assumption which underlies
the Mincerian interpretation is that, for each educational level, the only costs are opportunity costs: the
wages forgone whilst acquiring education. The measure omits pecuniary costs (including those born by
the government) and so will tend to over-estimate the returns, particularly at the university level. However,
at the primary level, the assumption that wages are forgone by attending school may be incorrect and
cause the returns to be under-estimated. As with the estimates in Table 6, the rates of return to education
rise with the level of education. The rates of return on university education are very much larger than
those at lower levels. As Figure 3 shows the increments in earnings from completing either primary or
secondary are dwarfed by the rises that accrue to those with university education. These high earnings
may partly reflect the ability of those with higher qualifications to work abroad, a problem which has
been identified as a brain drain, see Box 3.
There are several reasons why the returns to education presented in Table 7 may be based on
coefficients that are biased. The years of schooling are measured assuming no repetition (see
Behrman and Deolalikar, 1991). Biases may also arise as we have not allowed for selectivity.
Those who work in the manufacturing sector are highly atypical. Secondly, such educational
measures cannot distinguish between signalling and credentionalism as alternatives to the human capital
interpretation. The signalling explanation for the findings suggests that education is associated with
higher incomes because those with higher education have more ability. Education signals ability. The
credentalist view is that institutional wage structures reward those with qualifications: it is qualifications
that are rewarded rather than the skills the qualifications are meant to reflect. Thirdly, it is known that
parental background can play an important role in educational choice. The sample is limited to those in
manufacturing, and it was not possible to use variables measuring ability or information on parental
background. To see if these factors indicate that the rates of return presented in Table 7 are too high it
is necessary to assess the importance of these problems.
A recent study examining some of these issues for Ghana is Glewwe (1996). The question that
needs to be posed is as follows: if no controls are included for cognitive skills or parental background,
is there evidence of significant bias in the education variable? Glewwe provides evidence that there may
be some upward bias. If selectivity is allowed for in the private sector earnings function then the coefficient
on years of schooling becomes insignificant. Glewwe then calculates the rate of return on education
based on the measures of cognitive skills available for his data set. He finds a figures of 4 per cent, for
an individual aged 25, which compares with a rate of return of 7 per cent from the earning function. If
a years of education measure is used in the earnings function for the data in Table 7 the return to
education for Ghana is 6 per cent. Thus the evidence from Glewwe (1996) suggests there may be some
bias in an upward direction.
Four studies which have information on parental background are Behrman and Wolfe (1983),
Lam and Schoeni (1993), Heckman and Hotz (1986) and Kingdon (1997). The conclusion, which is
uniform across the studies is that the inclusion of parental background reduces the returns to schooling
by about 20 per cent. Again this is evidence that the estimates presented in Table 7 may be upwardly
biased. A recent study which uses a panel data set of twins to estimate the returns to school quality,
Behrman, Rosenzweig and Taubman (1996) finds that controlling for family background does affect
the assessment of the returns from school quality but has only very marginal effects on the returns to
schooling coefficient. A study which has very detailed information on cognitive skills and parental
background is that of Knight and Sabot (1990). Their study uses comparative data drawn from workers
in the manufacturing sectors of Kenya and Tanzania. They argue that the returns of education variable
is picking up human capital formation. While signalling may play some role, it is not the primary reason
years of education determines earnings.
The conclusion we would draw is that the evidence suggests that the education variable may
overstate the returns to human capital, but not by very much, and that the major influence of years of
education on earnings is through its effects of cognitive skills and not, as the signalling explanation would imply, indirectly through signalling ability. Even if the biases are more significant that the empirical evidence
currently suggests, it is not clear that they would explain, or mitigate, the non-linearity in the returns to
education. The conclusion, that the returns to education are modest, is one common to other studies.
Freeman (1986, p.377) notes that “every study also finds that, by itself, years of schooling explains a
relatively small part of the variance of log earnings, say 3-5 percent at most”.
In Table 7 the pattern is similar across all the countries, the rate of return falls with the level of
education. What might account for this pattern? The returns to primary and secondary school completers
obtain mainly through gaining employment in the formal sector. In the context of Africa such jobs have
declined in the last decade. This decline has occurred in the context of the rapid expansion of education
and very low growth rates of physical capital. In such a context low rates of return on education might
be expected.
Human Capital and Economic Development
Simon Appleton and Francis Teal
Rates of Return on Educational Investment from Micro Studies The Effects of Human Capital on Economic Development - To learn more about this author, visit African Development Bank's Website.
Like this article? Share it with your friends
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