Identification and estimation of distributional impacts of interventions using changes in inequality measures

This paper presents estimators of distributional impacts of interventions when selection to the program is based on observable characteristics. Distributional impacts are calculated as differences in inequality measures of the marginal distributions of potential outcomes of receiving and not receiving the treatment. The estimation procedure involves a first non-parametric estimation of the propensity score. In the second step weighted versions of inequality measures are computed using weights based on the estimated propensity score. Consistency, semi-parametric efficiency and validity of inference based on the percentile bootstrap are shown for the estimators. Results from Monte Carlo exercises show its good performance in small samples.