Graduate employment and the returns to higher education in Africa
Mahdi Barounia and Stijn Broeckeb
a
b
Institute for Research in the Sociology and Economics of Education, University of Bourgogne, France
Research Department, African Development Bank, Tunis, Tunisia
Abstract
In this paper, we estimate the return to higher education for 12 African countries using recent
data and a variety of methods. Importantly, one of our methods adjusts for the effect of
higher education on the rate of joblessness, which is substantial in most African countries, and
particularly for women. Our results confirm that Mincerian coefficients cannot be interpreted
as a true rate of return, and that the latter (even after taking into account the employment
effect) is considerably lower than what has previously been suggested in the literature (less
than half). For Sub-Saharan Africa, we also uncover an interesting relationship between a
country’s level of education and the return to higher education: contrary to expectations, we
find that in countries where a high proportion of the working age population is educated to
tertiary level, the return to higher education is highest.
JEL classification: I21, I23, J31
Key words: graduate unemployment, returns to education, higher education
1.
Introduction
Estimates of the return to higher education are a potentially useful tool for policy-makers. In
England, for example, the increase in fees from September 2012 was largely defended on the
grounds that the private returns to higher education are high: “The lifetime earnings of
graduates are higher than those of non-graduates. The so-called graduate premium, net of tax,
is still worth comfortably over £100,000 in today's money.”1
At the same time, a recent survey of the returns to higher education in Africa found that “an
increasing amount of work needs to be done with respect to returns to higher education in SubSaharan Africa” (Diagne and Diene, 2011). The authors argue that most existing studies focus on
a limited number of countries only, and that the data are frequently more than 15 years old. An
initial objective of the present paper was therefore to fill this gap by looking at the return to
higher education in a range of African countries using recent household and labour force survey
data. This is one contribution this paper makes to the literature.
1
http://www.bis.gov.uk/news/speeches/david-willetts-guardian-he-summit
A second contribution of the paper is that it adjusts these private rates of return to higher
education by the likelihood of finding employment. As argued by Barceinas et al (2000)
“increasing the educational level is profitable not only because this allows the more educated to
obtain higher wages, but also because the more educated have a lower probability to become
unemployed. As a consequence, the returns to education must be computed taking the
unemployment probability into account.” Similarly, Colclough et al (2009) remark that the
returns to education in developing countries are “typically not adjusted for unemployment
among the educated”. Writing in the context of Europe, where unemployment decreases with
education, Barceinas et al (2000) conclude that “unadjusted marginal rates of return to
education present a general underestimation because the differentials in employment
probability […] across levels of education are not taken into account”.2
In many African countries the unemployment rate for graduates is higher than for the working
age population overall (top panel Figure 1). However, because of high rates of inactivity among
people with lower levels of education, this is not true of the level of joblessness (bottom panel
Figure 1), which is almost always lower among graduates. So, unlike in Europe, a complex
relationship exists in Africa between the level of education and labour market outcomes. In
nearly all countries analysed in this paper we find that the returns to higher education increase
once the risk of joblessness is factored in. This effect is particularly strong for women.
The analysis uncovered some other interesting findings. Most importantly, we show that the
return to higher education varies widely with the method used for estimating it. In particular,
we find that estimates of Mincerian coefficients are frequently higher than the true internal rate
of return, and should therefore not be confounded with them. This calls for more caution and
candidness by researchers in describing the methods that they use as well as in explaining the
meaning of their estimates. We follow Heckman, Lochner and Todd (2008) in arguing that
Mincerian coefficients should no longer be blindly (and often wrongly) presented as estimates
of the “return to education”3.
Finally, we find that the rate of return to higher education varies considerably across countries.
Interestingly, we find that in countries where a high proportion of the working age population
has a university qualification, the rate of return to higher education is the highest.
2
Similarly, in the case of Spain, Arrazola and de Hevia (2008) conclude that “an additional year of education not
only means an increase in the average wage of employed individuals but also raises the probability of being
employed”.
3
Similarly, Björklund and Kjellström argue that “the internal rate of return is one such measure [of the return to
investment in schooling], but the wage premium is not”.
Figure 1: Graduate unemployment and joblessness in Africa
Unemployment
45%
University
All
40%
35%
30%
25%
20%
15%
10%
5%
0%
Guinea (1996)
Sudan (2008)
Egypt (2006)
Sierra Leone
(2004)
Rwanda (2002)
Uganda (2002)
Mali (1998)
Tanzania (2002) Senegal (2002)
Kenya (1999)
Ghana (2000)
Malawi (2008)
South Africa
(2007)
Tanzania (2002) Senegal (2002)
Kenya (1999)
Ghana (2000)
Malawi (2008)
South Africa
(2007)
Joblessness
70%
University
All
60%
50%
40%
30%
20%
10%
0%
Guinea (1996)
Sudan (2008)
Egypt (2006)
Sierra Leone
(2004)
Rwanda (2002)
Uganda (2002)
Mali (1998)
Source: Authors’ calculations based on Minnesota Population Center (2011). Integrated Public Use Microdata Series,
International: Version 6.1
The remainder of this paper is structured as follows. Section 2 provides a brief overview of the
literature on the returns to higher education in Africa. Section 3 offers a discussion of the
methodologies used for estimating the returns to education, their drawbacks and advantages.
Section 4 describes the data that we use in our paper, and Section 5 presents the results from
the analysis. Section 6 concludes.
2.
Literature review
Popular wisdom, influenced by the work of Psacharopoulos (1973, 1981, 1985, 1994) and
Psacharopoulos and Patrinos (2004a, 2007), has it that the returns to higher education in Africa
are lower than those at lower levels of education. These findings have very much influenced
donor investments in education across the continent, but have been criticised by a number of
authors, including Bennell (1996) who questioned the quality of the data and analysis used in
these studies, as well as Schultz (2003) who analysed data from six African countries and found
that private returns were actually higher at secondary and post-secondary levels that at primary
level.
Since then, a large number of individual country studies have been published, and these have
recently been reviewed by Diagne and Diene (2011). The studies they review for the period
2000-10 (ten studies for six countries) reveal an average rate of return to higher education of
19.0%. The estimates obtained by Colclough et al for four African countries average 26.0%, and
their literature review suggests an average return of 22.7%. All these estimates are for one year
of university education. Furthermore, Diagne and Diene (2011) conclude that the return to
higher education is higher than for other levels of education (a finding confirmed by Kingdon et
al, 2008; and Colclough et al, 2009), although it has been falling over time.
As Diagne and Diene (2011) and others allude to, one obvious problem with the various
estimates obtained in the literature is that there is very little consistency in the methods used to
obtain them. There are issues about: how comparison groups are constructed; how higher
education is defined; how wages, earnings or income are measured4; what control variables are
included (if any); what type of data is used; etc… In addition to these relatively minor issues,
there are more fundamental methodological concerns with the estimates obtained in the
literature, with the majority of results reported not truly estimates of the “return to education”.
This is the subject of the next section.
3.
Methodology
Although the return to education is a standard concept in the economics of education, there is
little consistency in the approach used for estimating it. A variety of methods/measures exist,
and researchers frequently use them interchangeably without necessarily being clear about
what it is they are estimating, or about the limitations of the method they are applying. This
section starts by setting out some of the methods used in the literature and ends by outlining
the approach used in this paper.
Method 1: “Elaborate” internal rate of return
As with any other investment, the private rate of return to an investment in a given level of
education is estimated by finding the rate of discount that equalises the stream of discounted
benefits to the stream of costs at a given point in time. In the case of a university education
lasting four years (and assuming a working life of 42 years), the formula is:
42
4
𝑡=1
𝑡=1
(𝐸𝑢 − 𝐸𝑠 )𝑡
∑
= ∑(𝐸𝑠 )𝑡 (1 + 𝑟)𝑡
(1 + 𝑟)𝑡
4
(i)
The choice of dependent variable is not innocuous. Wages are the pay per unit of time. Earnings are wages times
the time worked. So earnings include both a wage as well as an employment effect. Because education also has an
employment effect, the return to education using earnings tends to be higher than the return using wages
(Karasiotou 2003). In this paper, where possible, we use earnings as a dependent variable.
Where (Eu-Es) is the earnings differential between a university graduate (subscript u) and a
secondary school graduate (subscript s). The right-hand side represents the opportunity cost of
higher education (i.e. 4 years of foregone earnings at the level of what someone with secondary
education would have earned). The right-hand side could also be augmented with other costs
associated with education such as tuition fees. However, for the sake of simplicity and because
most university systems in Africa do not charge fees, these will be left out in the analysis
presented in this paper. Although this may not be realistic, it should be remembered that
foregone earnings are the largest cost associated with a university education5. It is also worth
pointing out that, although fees are left out of the calculation, so are grants, scholarships and
bursaries which are common in many African countries, as well as any income that students
may earn while studying.
The internal rate of return (IRR) as defined above is calculated by building age-earnings profiles
for both secondary school and university graduates (assuming no earnings for the first four
years in the case of university graduates), differencing the two, and finding the discount rate
that results in a 0 (zero) net present value of this net age-earnings profile. In practice, this
“elaborate method” is very data intensive as sufficient observations are need to populate each
age/qualification cell and construct well-behaved age-earnings profiles. This is rarely the case in
African labour force or household surveys6 and so this method is generally impractical. One way
around this problem is to estimate two simple earnings equations (one for secondary school and
one for university graduates) with earnings as the dependent variable, and age and age squared
as explanatory variables:
log(𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠) = 𝛽 + 𝑎𝑔𝑒 + 𝑎𝑔𝑒 2 + 𝜀
(ii)
Using the coefficients of these regressions, smooth age-earnings profiles can be built for both
secondary school and university graduates7. As before, no earnings are assumed for the first
four years of those investing in a university education. Using these age-earnings profiles we can
then calculate the internal rate of return as before. As in the case of the full/elaborate method,
this method is rarely used in the literature.
Method 2: Simple earnings function/Mincerian method
In practice, many researchers have made use of the earnings function/Mincerian method
(Mincer 1958; 1974) to estimate the returns to education. This is primarily due to its simplicity:
5
Recent estimates of the rates of return to tertiary education across the OECD suggest that that, in the OECD,
direct costs represent around 22% of the overall estimated costs of attending higher education – the remainder
being made up by foregone earnings (OECD, 2012).
6
As shown in Table 1, the number of observations with tertiary education is rarely more than a few hundred.
7
Another option is to estimate the earnings profiles non-parametrically using local linear regressions (as in
Heckman (2005) who use local linear regression).
log(𝑒𝑎𝑟𝑛𝑖𝑛𝑔𝑠) = 𝛽0 + 𝛽1 𝐻𝐸 + 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 + 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 2 + 𝜀
(iii)
In this equation, experience is frequently replaced by age and other explanatory variables (like
sex) are added to the right hand side – although the inclusion of such variables is not innocuous
as many (e.g. sector of employment, marital status, number of children, region) may be
considered endogenous and so should not be included in the regression8. The equation above
would be run on a sub-sample of individuals with secondary and tertiary education only, and so
the coefficient β1 would identify the effect on earnings of having a higher education
qualification9. Frequently, the estimate is then converted into an annual return by dividing by
the number of years of higher education.
Most research using the Mincerian method interprets the coefficient on HE as the return to
higher education, and uses it as a substitute for the IRR calculated through the method
described previously. Heckman, Lochner and Todd (2008) criticise this interpretation and argue
that many strong assumptions are required to claim that estimates of β1 accurately measure the
internal rate of return. One obvious difference with the two methods described previously is
that the Mincerian equation assumes no loss of working life with additional years of schooling,
so that the coefficient β1 is more accurately interpreted as the earnings premium or mark-up
associated with a higher education qualification than as a rate of return. Psacharopoulos (1994)
uses numerous empirical studies to show that earnings’ functions and the elaborate method
yield very similar results – however Heckman, Lochner and Todd (2008) reach a different
conclusion, and so do we in this paper.
Method 3: The “short-cut” method
In addition to the methods described above, Psacharopoulos and Patrinos (2004b) describe a
“short-cut” method for calculating the rate of return which does not rely on the availability of
individual data. The formula used is simply:
𝑝𝑟𝑖𝑣𝑎𝑡𝑒 𝑟𝑒𝑡𝑢𝑟𝑛 =
𝐸𝑢 − 𝐸𝑠
4𝐸𝑠
(iv)
Where 𝐸𝑢 are the average earnings of those with a university qualification, and 𝐸𝑠 the average
earnings of someone with just secondary schooling. The denominator represents the
opportunity cost of an investment in higher education (i.e. four years of earnings at the level of
8
Pereira and Martins (2004) address this issue in detail. They show why considering a number of educationdependent covariates in a wage equation decreases the coefficient of education in that equation and argue for the
use of a simple specification of the Mincer equation for the study of the total returns to education.
9
Halvorsen and Palmquist (1980) and Kennedy (1981) point out that, in semilog models in which discrete variables
are used as regressors, the percentage change in the level of the dependent variable is not equal to the coefficient
of the dummy multiplied by 100, as is the case of continuous variables. Instead, the appropriate measure for the
proportional effect on the outcome variable is exp(β)-1.
a secondary school graduate10). Although simple, the method has clear drawbacks in that it
assumes flat age-earnings profiles and no discounting of earnings that occur later in life.
Accounting for the risk of joblessness
Given that education has both an impact on earnings as well as the likelihood of employment, it
is surprising that very few estimates in the literature have factored in the risk of joblessness in
estimating the returns to higher education. In countries with low rates of joblessness, this is
perhaps understandable, but in Africa, where the level of joblessness frequently ranges
between 20% and 60% of the working age population (Figure 1), this would seem a major
omission. One contribution of this paper is that we adjust the estimates of the return to higher
education by the risk of joblessness. In practice, we calculate the adjusted internal rate of return
by generating age-expected earnings profiles. We predict age-earnings profiles using equation
(ii) above. In addition, however, we simulate age-employment profiles using a logit regression of
the following form:
employment = 𝛽 + 𝑎𝑔𝑒 + 𝑎𝑔𝑒 2 + 𝜀
(v)
At each age, we then weight the predicted earnings by the predicted likelihood of being in
employment at that age in order to derive an age-expected earnings profile. We do this
separately for those with secondary and those with tertiary qualifications.
Ability bias
Much of the recent literature on returns to education has been dedicated to tackling ability bias
in estimating the return to schooling. As early as 1964, Denison (1964) expressed scepticism
about the causal effect of schooling on earnings, arguing that observed differences in earnings
between education groups are more likely to reflect inherent ability differences rather than true
productivity differentials. Generally, it is believed that the correlation between schooling and
earnings obtained through OLS overstates the true causal effect of education. A standard
solution to this problem is instrumental variables estimation and a large literature has
developed using instruments such as the minimum school leaving age, the geographic proximity
of schools, etc… Generally, this literature has found that OLS under- rather than overestimates
the return to schooling. One potential explanation for this finding advanced by Card (2001) is
that these estimates are frequently obtained using structural changes in schooling systems (for
example a raising of the school-leaving age) which affect more marginal students for whom the
return to schooling might be higher.
10
Note that Psacharaopoulos and Patrinos (2004b) divide by the average earnings of those with a university
education. However, it appears more realistic to assume that, without a university qualification, those currently
studying could only command average earnings equal to those with just a secondary school qualification.
In this paper, we do not attempt to tackle the issue of ability bias. Although instrumental
variables methods have proved useful in trying to establish the causal effect of schooling on
earnings, they suffer from the same drawback as outlined above for the Mincer equation:
except under very restrictive assumptions, they do not estimate rates of return to schooling, nor
are they designed to (Heckman, Lochner and Todd, 2005). Given that instrumental variable
estimates have generally been found to be larger than standard OLS estimates, and that the OLS
estimates we obtain are already considerably larger than the ones obtained through our nonparametric IRR method, this omission does not detract from the main arguments made in this
paper.
Summary
Before turning to a description of the data, we briefly summarise the methodologies employed
in this paper. We estimate the return to higher education using four different methods: (1)
simple Mincerian earnings functions as in equation (iii) – with experience replaced by age; (2)
the short-cut method (equation (iv)); (3) the IRR using simple earnings functions as in equation
(ii); and the IRR adjusted for employment probabilities.
Across all countries and estimation methods we attempt to be consistent in the definition of
variables and assumptions used. We define the working age population as those aged 15-64.
We restrict our sample to those with tertiary and, where possible, academic secondary
education only. Only the latter are included as we aim to generate a control group of individuals
who had the potential to go on to university (but this is not always feasible). Tertiary education
includes everything from diplomas and degree through to postgraduate qualifications (masters
and doctorates)11. This was a pragmatic decision in view of the small sample sizes of higher
educated individuals encountered in most African household and labour force surveys.
We ignore selection issues, and do not include any additional explanatory variables in our
regressions (although we do estimate the returns separately for men and for women). We also
ignore taxes, benefits, tuition fees and scholarships. On the other hand, in our IRR calculations,
we assume foregone earnings for a period of four years, equivalent to the average earnings for
those whose highest qualification is upper secondary education. Finally, our measure of
earnings will depend on the survey employed, but we have tried to focus on earnings from the
primary occupation where possible (i.e. excluding benefits). Also note that, in order to avoid
estimation problems induced by outlier values, we trim our dataset by removing the top and
bottom percentile of earnings.12
11
Note that, where possible, we focus on qualifications obtained rather than on years of education. There is
evidence that qualifications, and not years of education, drive the returns to education (Dickson and Smith, 2011).
12
Annex A provides further detail on the exact variables and definitions used.
4.
Data
Table 1 below provides an overview of the data used in this paper. We use a mixture of
household and labour force surveys for 12 African countries. None of the data is older than
2005.13
Table 1 highlights one of the key issues in estimating the returns to higher education in Africa. In
many countries, educational attainment is so low that the number of observations with tertiary
qualifications (or even upper secondary qualifications) is very small14 – apart from in some of
the middle income or larger countries like Egypt, Nigeria and South Africa. The proportion of the
working age population with tertiary education ranges from 0.7% in Rwanda to 13.8% in Tunisia.
This is a problem rarely highlighted in the literature, yet is one of the reasons why estimates of
the return to higher education vary so widely, even within the same country.
Table 1 also confirms some of the analysis presented in Figure 1. In nearly all countries, a higher
education qualification leads to a higher employment probability than for those with upper
secondary education, with the exception of Rwanda, Tanzania and Uganda. The table also
reminds us that, in some African countries, joblessness rates reach alarmingly high levels, and
hence that ignoring the impact of education on the likelihood of finding employment in
estimating the returns to education would be a significant omission.
Finally, the table shows average university graduates earnings in comparison with secondary
school graduate earnings (index=100 for the latter). On average, across all countries, the
average earnings of university graduates are twice as high as those of individuals with upper
secondary qualifications.
13
Annex B provides a list of the exact data sources used.
In fact, in many African countries, nearly all those with upper secondary qualifications go on to higher education.
So in some of the samples looked at, there are more observations with tertiary education than with upper
secondary education.
14
Table 1: Summary overview of data sources
Burundi15
Ghana
Egypt
Mali
Nigeria
Rwanda
Soudan
RSA
Tanzania
Togo
Tunisia
Uganda
2006
2005
2006
2007
2010
2005
2009
2010
2011
2011
2010
2006
Number of observations
6,646
37,128
37,140
16,350
23,212
34,461
33,660
342,470
20,559
29,781
549,015
43,097
Working age population
75.1%
55.4%
63.9%
46.0%
61.7%
54.2%
49.1%
64.0%
52.0%
53.7%
69.3%
45.3%
Upper secondary
322
1,358
7,368
249
3,160
492
210
47,544
482
464
144,893
1,350
% of working age population
6.7%
6.9%
29.9%
2.4%
23.9%
2.4%
1.1%
24.7%
3.4%
3.5%
39.8%
6.7%
Tertiary
455
662
3,046
178
639
211
233
18,747
148
577
41,869
303
% of working age population
9.6%
3.4%
12.2%
1.6%
5.2%
0.7%
0.9%
9.8%
0.8%
4.7%
13.8%
1.6%
Inactive
49.3%
38.5%
36.0%
23.1%
37.6%
18.1%
28.9%
31.4%
35.7%
41.1%
52.2%
28.1%
Unemployed
11.7%
8.2%
7.5%
19.4%
5.8%
10.8%
9.2%
18.3%
3.2%
8.6%
6.6%
3.6%
Employed
39.0%
53.3%
56.5%
57.6%
56.6%
71.1%
61.9%
50.3%
61.1%
50.4%
41.2%
68.3%
Inactive
31.9%
11%
20.5%
26.5%
19.5%
26.0%
18.4%
12.9%
39.4%
37.8%
32.0%
38.8%
Unemployed
10.1%
8.2%
12.1%
12.7%
9.0%
9.4%
8.2%
8.0%
6.4%
9.1%
15.6%
6.5%
Employed
58.0%
80.8%
67.4%
60.9%
71.5%
64.6%
73.4%
79.1%
54.2%
53.1%
52.4%
54.7%
Upper secondary
100
100
100
100
100
100
100
100
100
100
100
100
Tertiary
187
200
187
143
209
277
190
181
268
148
162
265
Country
Year
Population
Education
Employment status, by education
Upper secondary
Tertiary
Earnings, by education (indexed)
15
Urban population only.
5.
Results
Table 2 presents estimates of the return to higher education (in comparison to those with upper
secondary qualifications) for the 12 African countries analysed. Estimates are derived using the
four different methods summarised at the end of Section 3.
Table 2: The return to higher education
Country
Mincer
Short-Cut Method
IRR
IRR (Employment Adjusted)
Burundi
95%
22%
29%
42%
Egypt
32%
22%
10%
15%
Ghana
78%
25%
26%
49%
Mali
44%
11%
9%
13%
Nigeria
96%
27%
13%
16%
Rwanda
158%
44%
33%
29%
South Africa
85%
20%
21%
51%
Sudan
90%
23%
16%
25%
Tanzania
141%
42%
26%
31%
Togo
70%
12%
31%
42%
Tunisia
81%
18%
22%
27%
Uganda
87%
41%
22%
18%
A few interesting conclusions can be drawn from this table. First, estimates vary considerably
depending on the method used. As previously discussed, the Mincer coefficient does not
represent a true rate of return, but merely an earnings premium associated with having a higher
education qualification. The Mincer coefficients we estimate are always (considerably) larger
than the IRR. The Mincer-to-IRR ratio ranges from 2 in Togo to 7 in Nigeria. This suggests that
previous estimates obtained in the literature for the rate of return to higher education using
Mincer coefficients are likely overestimates of the true rate of return to higher education in
Africa.
That said, the IRR almost always increases once the probability of being in employment is taken
into account (except for in Rwanda and Uganda). On average, for the twelve countries
considered, the IRR increases by 9% once adjusted for the rate of joblessness. This confirms our
hypothesis that, in countries where the likelihood of being in employment varies considerably
with the level of education, calculations of the rate of return to education cannot ignore the
effect it has on employment. Interestingly, the employment adjusted IRRs obtained in our
analysis, when converted to a return by year of education, average around 7.5%. This is
considerably below the rates of return obtained elsewhere in the literature (which hover are
20%, which is very close to our average Mincerian coefficient converted to a return by year of
education, i.e. 22%).
No clear relationship emerges when comparing the estimates obtained through the short-cut
method and those obtained with the IRR. These differences are probably driven by differences
in the shape of the earnings functions, with the IRR method providing greater weight to
earnings occurring early on in the graduate’s career.
We also observe that the rate of return to higher education varies considerably across
countries. The employment-adjusted IRR ranges from 13% in Mali to 51% in South Africa. This
begs the question as to why this might be the case. One possibility is that, in countries where
university graduates are scarce, the return to higher education is high. Figure 2 explores this
issue by plotting the employment-adjusted IRR against the proportion of the working age
population which has attained a tertiary qualification. At first, no obvious relationship emerges,
however removing the two North Africa countries and focusing on the Sub-Saharan countries,
we observe the opposite of what we would expect: in countries where a high proportion of the
working age population is educated to tertiary level, the rate of return is high.
Figure 2: Proportion of the working age populated with higher education and the return to
higher education
A number of conceivable explanations for this apparent puzzle exist. One is the possibility of
reverse causality: in countries where the return to higher education is high, people have
invested more in higher education. Another explanation might be that supply creates its own
demand: in countries where the supply of university graduates is high, there may be more
innovation, business start-ups, etc… which in turn would increase the demand for university
graduates and hence the return to education. Either way, this is an issue which merits to be
further investigated.
In Table 3, we break down the analysis by gender. When considering the Mincer coefficients, no
clear pattern emerges: in about half the countries looked at the return to higher education is
higher for females; in the other half it is higher for men. However, once we turn to the
employment adjusted IRR, the return to higher education is higher for women in eight of the
countries considered. This suggests that factoring in the employment effect of education is even
more important when looking at the return for women who, in many African countries, tend to
have worse employment outcomes, particularly at lower levels of education.
Table 3: The return to higher education, by gender
Country
Mincer
Short-Cut Method
IRR
IRR (Employment Adjusted)
Men
Women
Men
Women
Men
Women
Men
Women
Burundi
108%
70%
27%
11%
38%
22%
45%
39%
Egypt
41%
20%
22%
23%
11%
9%
13%
19%
Ghana
67%
108%
21%
36%
26%
27%
45%
58%
Mali
63%
ns
12%
ns
14%
ns
12%
Ns
Nigeria
104%
78%
27%
22%
15%
12%
16%
15%
Rwanda
127%
227%
40%
50%
27%
42%
21%
46%
South Africa
77%
103%
19%
24%
20%
22%
48%
55%
Sudan
88%
107%
21%
42%
16%
18%
22%
34%
Uganda
86%
108%
23%
131%
18%
26%
17%
20%
Tanzania
145%
140%
47%
29%
22%
36%
23%
56%
Togo
76%
38%
14%
5%
34%
22%
36%
69%
Tunisia
80%
84%
18%
19%
20%
23%
19%
11%
Conclusion
In this paper, we have estimated the return to higher education for 12 African countries using
recent data and a variety of methods. Importantly, one of our methods adjusts for the effect of
higher education on the rate of joblessness, which is substantial in most African countries.
Our results confirm that Mincerian coefficients cannot be interpreted as a true rate of return,
and that the true rate of return (even after taking into account the employment effect) is
considerably lower than what has previously been suggested in the literature (less than half).
Our paper highlights the importance of considering the employment effect of education in
countries where the level of joblessness is high, particularly when estimating the rate of return
for women. Further research should refine the methodology by taking into account the direct
costs of education (fees, books, etc…), bursaries and other financial aid, as well as more realistic
estimates of the time taken to complete a university education and considering the tax/benefit
implications of higher earnings.
Finally, we uncover an interesting relationship between a country’s level of education and the
return to higher education: contrary to expectations, we find that in countries where a high
proportion of the working age population is educated to tertiary level, the return to higher
education is the highest. Further research should aim to understand the reasons behind this
seemingly counterintuitive finding.
Acknowledgements
The authors would like to thank Arnaud Chevalier and Paul Schultz for comments on earlier
drafts of this paper. All remaining errors are our own. The views expressed in this paper are
those of the authors, and do not in any way reflect those of the African Development Bank.
References
Arrazola, M. and J. de Hevia (2008) ‘Three measures of returns to education: An illustration for
the case of Spain’, Economics of Education Review, 27(3): 266-275.
Barceinas, F., J. Oliver, J.L. Raymond, J.L. Roig and B. Weber (2000) ‘Unemployment and returns
to education in Europe’ PURE Working Paper.
Bennell, P. (1996) ‘Rates of return to education: Does the conventional pattern prevail in SubSaharan Africa?’ World Development, 24 (1): 183-199.
Björklund, A. and C. Kjellström (2002) ‘Estimating the return to investments in education: how
useful is the standard Mincer equation?’ Economics of Education Review, 21(3): 195-210.
Card (2001) ‘Estimating the return to schooling: Progress on some persistent econometric
problems’ Econometrica, 69 (5): 1127-1160.
Colclough, C., G. Kingdon and H.A. Patrinos (2009) ‘The pattern of returns to education and its
implications’ Research Consortium on Educational Outcomes and Poverty (RECOUP) Policy
Brief 4.
Denison, E.F. (1964) ‘Measuring the contribution of education (and the residual) to economic
growth’, in The Residual Factor and Economic Growth, Study Group in the Economics of
Education. Paris: OECD.
Diagne, A. and B. Diene (2011) ‘Estimating returns to higher education: A survey of models,
methods and empirical evidence’ Journal of African Economies, 20, AERC Supplement 3:
iii80–iii132.
Dickson, M. and S. Smith (2011) ‘What determines the return to education: An extra year or a
hurdle cleared?’ IZA Discussion Paper 5524, Institute for the Study of Labor (IZA).
Halvorsen, R. and R. Palmquist (1980). ‘The interpretation of dummy variables in
semilogarithmic equations’, American Economic Review, 70: 474-75.
Heckman, J.J., L.J. Lochner and P.E. Todd (2005). ‘Earnings functions, rates of return and
treatment effects: The Mincer equation and beyond’, IZA Discussion Paper 1700.
Heckman, J.J., L.J. Lochner and P.E. Todd (2008) ‘Earnings functions and rates of return’,
University of Western Ontario CIBC Working Paper 2008-2.
Karasiotou, P. (2003). ‘Education, unemployment and earnings: Decomposing the returns to
education’ Center for Research in Economics Working Paper 2003/4, Facultés universitaires
Saint-Louis, Brussels.
Kennedy, P. (1981). ’Estimation with correctly interpreted dummy variables in semilogarithmic
equations’ American Economic Review, 71, 801.
Kingdon, G., H.A. Patrinos, C. Sakellariou and M. Söderbom (2008) ‘International pattern of
returns to education’, World Bank Mimeo.
OECD (2012). ‘Education at a glance 2012’, OECD Indicators, OECD Publishing.
Pereira, P.T., and P.S. Martins (2004) ‘Returns to education and wage equations’, Applied
Economics, 36(6): 1-7
Psacharopoulos, G. (1973) ‘Returns to education: An international comparison’, Amsterdam:
Elsevier.
Psacharopoulos, G. (1981) ‘Returns to education: An updated international comparison’,
Comparative Education, 17 (3): 321-341.
Psacharopoulos, G. (1985) ‘Returns to education: A further international update and
implications’, Journal of Human Resources, 20 (4): 583-604.
Psacharopoulos, G. (1994) ‘Returns to investment in education: A global update’, World
Development, 22 (9): 1325-43.
Psacharopoulos, G. and H.A. Patrinos (2004a) ‘Returns to investment in education: A further
update’, Education Economics, 12 (2): 111-134.
Psacharopoulos, G. and H.A. Patrinos (2004b) ‘Human capital and rates of return’, in G. Johnes
and J. Johnes (ed.), International Handbook on the Economics of Education, Cheltenham, UK:
Edward Elgar Publishing Ltd.
Psacharopoulos, G. and H.A. Patrinos (2007) ‘Returns to education: An international update,
World Bank Policy Research Working Paper.
Schultz, T.P. (2003) ‘Evidence of returns to schooling in Africa from household surveys:
Monitoring and restructuring the market for education’, Yale University Economic Growth
Center Discussion Paper 875.
Annex A: Education and earnings definitions and variables used
Country
Earnings variable used
Education variables used
Definition
secondary
Definition
tertiary
Burundi
 Revtotal
 M16: avez-vous été au moins à l'école primaire?
 M20: quel niveau d'enseignement avez-vous atteint?
supérieur
Egypt
 hrwg: hourly wage (primary job)
 educ4: educational attainment (13 categories)
secondaire
général cycle
II
secondary
Ghana
 s4aq8: will you receive payment for this work
 s4aq9a: amount including any bonuses received
 s4aq9b: time unit
 s2aq1: name ever attended school?
 s2aq3: highest qualification attained
gce 'o' level
ssce
gce 'a' level
Mali
 salmp: salaire moyen de l'activité principale
 M11: avez-vous déjà été à l'école ?
 M14: diplôme le plus élevé obtenu
cap
bt
baccalauréat
Nigeria
 s3q21a: how much was your last payment
 s3q21b: time unit
 s2q4: have you ever attended school
 s2q8: highest qualification attained
sss 'o level'
a level
tech/prof cert
tech/prof dip
hnd
bachelor
masters
doctorate
DUTS/BTS/DUT/DEUG
et autre niveau BAC+2
diplôme ens.
supérieur
ba/bsc/hnd
tech/prof
masters
doctorate
Rwanda
 s6eq8: amount received last paycheck after deductions
 s6eq8t: time unit
 s2aq2: ever been to school
 s2aq4: certificate or degree received
humanities
South
Africa
 Q54a_mon: monthly earnings for employees
 Q57a_mon: monthly earnings for employers and selfemployed
 Education: education status
secondary
completed
Sudan
 d8: value of last actual payment or expected payment (SDG)
Tanzania
 hh_e22_1: how much was your last payment?
 hh_e22_2: what period of time did this payment cover?






sec. 4
sec. 5
sec. 6
’O’ level
form 5
form 6
c2: school attendance – ever
c5: current school attendance
c7: highest level of education
hh_c03: did [NAME] ever go to school?
hh_c07: what is the highest grade completed by [NAME]?
hh_c09: what grade is [NAME] currently attending?
university (4 & 5 yrs)
postgraduate
bachelor
professional license
engineer
masters
doctorate
tertiary
post-secondary
diploma
university
U2
U3
U4
Country
Earnings variable used
Education variables used
Definition
secondary
Definition
tertiary
 hh_c10: what grade was [NAME] attending last year?
‘A’+course
Diploma
U5&+
Togo
 E28A: revenu moyen_unite de temps
 E28B: revenu moyen_montant
 C2: fréquentation de l'école
 C3: dernier type d'enseignement suivi
 C4: classe achevée
terminale
enseignement
supérieur 1er, 2ème et
3ème cycle
Tunisia
 salaire: average monthly earnings
 v_240: niveau d'instruction (ENPE)
 level_educ
secondary
tertiary
Uganda
 h8q8a: how much was your last cash payment?
 h8q8c: what period of time did this payment cover?
 h4q2: have you ever attended any formal education?
 h4q4: what was the highest grade that you completed?
 h4q6: what grade are you currently attending?
completed s.4
attending s.5
completed s.5
attending s.6
completed s.6
completed degree and
above
attending degree and
above
Annex B: List of surveys used in the analysis
Country
Survey
Year
Togo
Questionnaire Unifié des Indicateurs de Base du Bien-être (QUIBB)
2011
Egypt
Egypt Labor Market Panel Survey
2006
Mali
Enquête Permanente Emploi Auprès des Ménages
2007
South Africa
Labour Market Dynamics
2010
Nigeria
General Household Survey –Panel (Post Planting‐2010)
2010
Ghana
Living Standars Survey 5
2005
Burundi
Enquête 1-2-3: Phase 1 Enquête Emploi
2006
Rwanda
Enquête Intégrale sur les Conditions de Vie des Ménages
2005
Tanzania
National Panel Survey
2011
Sudan
National Poverty Survey
2009
Uganda
Uganda National Household Survey
2006
Tunisia*
Enquête Nationale sur la Population et l’Emploi (ENPE)
Administrative databases (National social security funds)
2010
* For Tunisia, we use two separate data sources. The employment equation is estimated using the Enquête
Nationale sur la Population et l’Emploi. The wage regression is estimated using administrative data from the
National Social Security Funds.