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In depth

Data and the model

Kenya and Tanzania data

	Kenya		Tanzania
	Mean	Standard Dev.	Mean	Standard Dev.
Earnings(1)	74.6	117.4	54.7	71.2
Years of Education	9.1	2.9	8.8	3.5
Age	33.9	9.1	35.5	10.0
Years of Tenure	7.9	7.2	8.1	7.3
Male Dummy	0.85		0.80
Works in Capital City	0.64		0.44
Old Age group(2)	0.57		0.63
Observations	4039		2738

(1) Earnings are in US$

Figure 1

Figure 2


Estimating the Earnings Function

• In investigating the shape of the earnings function we follow Belzil and Hansen (2002), and several other authors, in assuming that individual differences in realised returns to schooling are due to the shape of the earnings function being nonlinear.
• We do not wish to impose the precise form of the shape of function and therefore estimate the equation using a semi-parametric approach modelling the earnings-education profile as a piecewise linear spline function.
• Variables included in the controls are years of tenure, age and age squared, a dummy variable for whether the individual is a male or not and a dummy variable for whether the individual lives in the capital city.
• Our data begin in 1993 and span seven years for Kenya and eight years for Tanzania, and central to our concerns is whether the returns to education have changed over this period and whether there are differences across age groups. Therefore we estimate period-age group specific profiles.

The Model

The slope of the earnings function is given by

 

If then the earnings function is linear.

Earnings, Endogeneity and Age

• So far we have focused on the shape of the function implicitly abstracting from the concerns of endogeneity which have been extensively investigated (Card (2001) gives what has become a seminal review).
• Later we will consider the possible role of instruments and biases in the OLS results.
• Throughout the analysis we put in nodes of the earnings-education profile at 7, 10 and 12 years of education.
• Using four segments of the earnings-education profile ensures that there is a reasonable number of observations in each category.
• We divide the data into two age groups only, where an individual is considered ‘young’ if his/her age is less than 30 years and ‘old’ otherwise.
  • This way of dividing up the sample enables us to assess to some extent how changes in the returns to education have affected new entrants into the labour market.

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