Propensity Score in the Tails and Returns to Education in Italy

The propensity score defining the probability of completing a given degree of education—to balance covariates—and the Mincer equation is here estimated at various degrees of higher education. The novelty is in implementing propensity score and regression estimators together in a double-robust approach in order to ensure against misspecification. The model is analyzed not only at the average but also in the tails of both components to gain a detailed analysis of the tail behavior and robustness. Analyzing survey data from the 2010 and 2020 waves, we find a negative impact of southern regions and gender on education. This impact becomes milder at the mean and is not significant in the right tail. The mixing of propensity score and quantile regression shows the irrelevance of education at low wages and, in a few cases, decreasing premia as school years increase. The private sector rewards lower premiums to young workers, and these distributions are more dispersed, i.e., show higher inequality. In the women’s subset, there is a marked pay gap, even wider for those working in the private sector.