Analysing the contribution of health to the gender wage gap in Germany using a Quantile regression and Oaxaca-Blinder decomposition technique

Analysing the contribution of health to the gender wage gap in Germany using a Quantile Regression and Oaxaca-Blinder decomposition technique The gender wage gap is a topic that has been discussed and studied extensively. Only a few studies have included health factors as explanatory variables for the wage gap, whilst it has been shown that health does affect income. This study uses the GSOEP dataset to research the contribution of health to the wage gap in Germany using 20 waves of the period 1993-2013. Actual health impairment indicators and self-assessed health values are used as health variables and they are amongst others, regressed on the logarithm of hourly wage. Quantile regression is first applied to investigate the contribution of health to wages separately for males and females. Subsequently, the Oaxaca-Blinder decomposition technique is applied for decomposing the wage differential of this sample into detailed information of the contribution of each explanatory variable individually. This is subdivided into the explained differential, the wage gap due to differences in characteristic distributions between genders, and an unexplained differential. The latter constitutes differences in returns to characteristics, and is named as the actual wage gap. This is the gap which might expose discrimination. The results show that the females in this sample on average have a lower self-assessed health than males. For both genders self-assessed health positively contributes to wages, where this is stronger for males than females. This effect is found being the strongest in the lower income levels. The health impairment variables negatively contribute to wages, but only significantly in the higher income levels. Decomposition of the wage differential shows that the health impairment variables have no contribution to the unexplained wage gap. Self-assessed health does have a significant contribution to this gap, however it only entails 6.4% of the total unexplained gap.