Gender wage gap: A semi-parametric approach with sample selection correction

Sizeable gender differences in employment rates are observed in many countries. Sample selection into the workforce might therefore be a relevant issue when estimating gender wage gaps. We propose a semi-parametric estimator of densities in the presence of covariates which incorporates sample selection. We describe a simulation algorithm to implement counterfactual comparisons of densities. The proposed methodology is used to investigate the gender wage gap in Italy. We find that, when sample selection is taken into account, the gender wage gap widens, especially at the bottom of the wage distribution.

► We develop an estimator of wage distributions with covariates and sample selection.
► We exploit this estimator to investigate the gender wage gap in Italy.
► The gender wage gap widens when we control for gender differences in characteristics.
► When we control for sample selection, the gender wage gap becomes even larger.