What
determines earnings in Ghana? The Human Capital model postulates that the log
of earnings of an individual is a function of that individual's productive
characteristics. These individual characteristics help explain the marginal
productivity and the returns to them.[1] In
Mincer (1974) this model was formalized as in equation (1):
In equation
(1), lnYt is the log of
earnings in year t, Educ is years of schooling, Exp is years of cumulative work
experience, and X is a vector of
other variables. We ran this model for Ghana using GLSS data with X including the variables shown in
table 1. We build on Gundersen (2016) in specifying the model used in this
analysis.[2]
We find that, conditional on age and age squared (as a proxy for experience), sex, parents’ education, occupation, public versus private sector employment, and marital status, an additional year of education boosts annual earnings by 5.7 percent. Experience has a statistically positive marginal effect on annual earnings, but this effect dissipates as experience grows. Being female is associated with poorer labor earnings - the estimated marginal effect of being female on one’s annual earnings is negative and can be interpreted as saying that, conditional on the other correlates, females are expected to earn 74 percent of male earnings per year. If we apply model (1) to explain variation in urban, rural, youth and nonyouth annual earnings, we observe a range of returns of 9 to 14 percent (figure 1). Females show higher conditional marginal returns to education than men; non-youth (ages 35 plus) have greater returns to education than youth (ages 15-35); and the rural/urban difference in returns to education is minimal.
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Source: Own estimates using GLSS 7. |
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Note: The conditional marginal estimates of
returns to education are derived from the log-linear model 1, in which we
regress the natural logarithm of annual earnings on years of education, age
and age squared (as a proxies for experience and experience squared),
parents’ education, and marital status. All estimates are statistically significant
at the 1 percent level of significance. |
Other
researchers find that globally (across 131 countries), average rates of return
to education are 10.4 percent per year and that the returns are highest in
Africa, where estimates average 12.8 percent per year.[3]
They also find that in Africa, the highest returns exist at the tertiary level
at 21.9 percent. Our estimates confirm this tertiary premium in Ghana - returns
for those with 12-plus years of education are close to 20 percent per year (see figure 2). High returns at tertiary levels have remained in the
10-year period assessed, reflecting the relative scarcity of human capital with
this level of education.
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* p<0.05, ** p<0.01, *** p<0.001 |
|
Source: Own
estimates based on data from GLSS 7. Note: The conditional marginal estimates of returns to
education at each level are derived from a log linear regression of the
natural logarithm of annual earnings on age and age squared (as a proxies for
experience and experience squared); sex; parents’ education; and marital
status. Education splines are generated with STATA’s ‘mkspline’ command, which
creates knots specified at 0-6, 9-12, and 12-plus years of schooling,
following Gundersen (2016). The average estimates as well as estimates for
those with 12 plus years of education are statistically significant in both
periods. |
[1] Gundersen, S., 2016. "Disappointing returns to education in Ghana: A test of the robustness of OLS estimates using propensity score matching." International Journal of Educational Development Volume 50, September 2016, Pages 74-89 https://www.sciencedirect.com/science/article/pii/S0738059316300608?via%3Dihub
[2] Mincer, J., 1974. “Schooling,
Experience and Earnings.” Columbia University Press
[3]
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