Dynamic treatment assignment and evaluation of active labor market policies

Abstract

This paper considers treatment evaluation in a discrete time setting in which treatment can start at any point in time. We consider evaluation under unconfoundedness and propose a dynamic inverse probability weighting estimator. A typical application is an active labor market program that can start after any elapsed unemployment duration. The identification and estimation results concern both cases with one single treatment as well as sequences of programs. The new estimator is applied to Swedish data on participants in a training program and a work practice program. The work practice program increases re-employment rates. Most sequences of the two programs are inefficient when compared to one single program episode.