VAN BROEKHUIZEN, HENDRIK. (2011). Returns to education, school quality, and numeracy in the South African labour market.
Author(s): Hendrik van Broekhuizen
Supervisor: Professor Servaas van der Berg
Institution: Stellenbosch University, Faculty of Economic and Management Sciences, Department of Economics
Download: PDF (12.11 MB)
This study investigates the extent to which educational attainment, school quality and numeric competency influence individuals’ employment and earnings prospects in the South African labour market using data from the 2008 National Income Dynamics Study (NIDS). While NIDS is one of the first datasets to contain concurrent information on individual labour market outcomes, educational attainment levels, numeric proficiency and the quality of schooling received in South Africa, it is also characterised by limited and selective response patterns on its school quality and numeracy measures. To account for any estimation biases that arise from the selective observation of these variables or from endogenous selection into labour force participation and employment, the labour market returns to human capital are estimated using the Heckman Maximum Likelihood (ML) approach. The Heckman ML estimates are then compared to Ordinary Least Squares (OLS) estimates obtained using various sub-samples and model specifications in order to distinguish between the effects that model specification, estimation sample, and estimation procedure have on estimates of the labour market returns to human capital in South Africa.
The findings from the multivariate analysis suggest that labour market returns to educational attainment in South Africa are largely negligible prior to tertiary levels of attainment and that racial differentials in school quality may explain a significant component of the observed racial differentials in South African labour market earnings. Neither numeracy nor school quality appears to influence labour market outcomes or the convex structure of the labour market returns to educational attainment in South Africa significantly once sociodemographic factors and other human capital endowment differentials have been taken into account. Though the regression results vary substantially across model specifications and estimation samples, they are largely unaffected by attempts to correct for instances of endogenous selection using the Heckman ML procedure. These findings suggest that the scope for overcoming data deficiencies by using standard parametric estimation techniques may be limited when the extent of those deficiencies are severe and that some form of sensitivity analysis is warranted whenever data imperfections threaten to undermine the robustness of one’s results.