Gender wage inequality: new evidence from penalized expectile regression

The Machado-Mata decomposition building on quantile regression has been extensively analyzed in the literature focusing on gender wage inequality. In this study, we generalize the Machado-Mata decomposition to the expectile regression framework, which, to the best of our knowledge, has never been applied in this strand of the literature. In contrast, in recent years, expectiles have gained increasing attention in other contexts as an alternative to traditional quantiles, providing useful statistical and computational properties. We flexibly deal with high-dimensional problems by employing the Least Absolute Shrinkage and Selection Operator. The empirical analysis focuses on the gender pay gap in Germany and Italy. We find that depending on the estimation approach (i.e. expectile or quantile regression) the results substantially differ along some regions of the wage distribution, whereas they are similar for others. From a policy perspective, this finding is important as it affects conclusions about glass ceiling and sticky floors.