Heterogeneous economic returns to higher education: evidence from Italy

Abstract

This paper uses official Italian micro data and different methods to estimate, in the framework of potential outcomes, the marginal return to college education allowing for heterogeneous returns and for self-selection into higher education. Specifically, the paper is focused on the estimation of heterogeneity of average treatment effect (ATE) on a cohort of college and high school graduates using the 2008 survey on household, income and wealth of the Bank of Italy. Methodologically, this study was carried out by using both propensity-score-based (PS-based) methods and a new approach based on marginal treatment effects (MTE), recently proposed by Heckman and his associates as a useful strategy when the ignorability assumption may be violated. In the PS-based approach, heterogeneous treatment effects are estimated in three different manners: the traditional stratification approach (propensity score strata), the regression adjustment within propensity score strata and, finally, a non-parametric smoothing approach. In the MTE approach, the treatment effect heterogeneity across individuals is estimated in a parametric as well as a semi-parametric strategy. Our empirical analysis shows that the estimated heterogeneity is substantial: following MTE based results (quite representative of other methods) the return to college graduation for a randomly selected individual varies from as high as 20 % (for persons who would add one fifth of wage from graduating college) to as low as −22 % (for persons who would lose from college graduation), suggesting that returns are higher for individuals more likely to attend college. Furthermore, the results of different methods show very low (point) estimates of ATE: average college returns vary from 3.5 % by the PS-smoothing method to 1.8 % by the parametric MTE method, which also leads a greater treatment effect on treated (5.5 %), a moderate, but significant sorting gain and a negligible selection bias.