Utility of inequality sensitive measures of the gender wage gap: Evidence from South Africa

We examine the trend in the gender wage gap in South Africa taking into consideration the high level of general inequality in the country. To do this, we utilize the information-theoretic approach of the Generalised Entropy (GE) measures of divergence supplemented by stochastic dominance analysis and tests. Under this approach, male and female wage distributions are summarized by suitable evaluation functions and then the difference between the evaluations is computed. Unlike conventional measures of the gender wage gap based on the mean and quantiles, this approach does not assume rank invariance and makes explicit the evaluation functions used. We find that the rate of convergence of the gender wage gap depends on the measure used to aggregate the gap across the entire distribution. Comparison of the aggregate measures of the gender wage gap (mean, median and the entropy measure) reveals that the convergence of the gender wage gap is faster under the entropy measure. This is because unlike the mean, the entropy measure captures the dispersion of wages across individuals. Stochastic dominance test results reveal that male wages dominate female wages mostly in the second-order sense i.e., only for evaluation functions that are increasing in wages and concave. Finally, decomposition results show that across measures, the persistent gender wage gap is attributable to wage structure effects as opposed to composition effects. Therefore, to effectively address the persistent gender wage gap in the middle of the income distribution, policies should be geared towards the returns to characteristics at the median. The implication of these findings is that measures of the gender wage gap that ignore the overall inequality are likely to skew the estimate of the gender wage gap.