Does women’s political empowerment matter for income inequality?

This paper analyzes the relationship between women’s political empowerment (WPE) and income inequality in a sample of 142 countries between 1990 and 2019. To identify causal effects, we rely on the use of Random Forests techniques and the exogenous variation on ancestral and traditional cultural norms of gender roles within an instrumental variable panel data modeling approach. These tree-based machine learning statistical techniques help us to predict the spatio-temporal distribution of WPE with high accuracy solely using ancestral societal traits. This predicted variable is then used in the second stage of the IV estimation of a panel specification of income inequality including fixed and time-period fixed effects. Our panel-IV regressions show that (i) WPE reduces income inequality and that (ii) this effect is partly transmitted via redistributive policies. In addition, we employ partial identification methods to ensure that our results are not influenced by unobserved confounding variables. Furthermore, we find that the negative link between WPE is robust to the presence of spatial interdependence and time persistence in inequality outcomes, the presence of outliers and influential observations, and an alternative definition of income inequality. Taken together, our results suggest that the observed negative link between WPE and income inequality is likely to be causal.