Factor-augmented Bayesian treatment effects models for panel outcomes

Abstract

A new, flexible model for inference of the effect of a binary treatment on a continuous outcome observed over subsequent time periods is proposed. The model allows to separate the associations due to endogeneity under treatment selection and additional longitudinal association of the outcomes, thus yielding unbiased estimates of dynamic treatment effects if both sources of association are present. The performance of the proposed method is investigated on simulated data and employed to re-analyze data on the longitudinal effects of a long maternity leave on mothers’ earnings after their return to the labour market.