Principal Stratification in sample selection problems with non normal error terms

The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true distribution of the error terms with a mixture of Gaussian. We propose an EM type algorithm for ML estimation. In a simulation study we show that our estimator has lower MSE than the ML and two-step Heckman estimators with any non normal distribution considered for the error terms. Finally we provide an application to the Job Corps training program.