Treatment Effects with Censoring and Endogeneity

Abstract

This paper develops a nonparametric approach to identification and estimation of treatment effects in a setting where observed outcomes are censored and treatment status may be endogenous and have arbitrarily heterogeneous effects. Identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework. The paper also proposes a censored quantile treatment effects estimator, derives its asymptotic distribution, and illustrates its performance using Monte Carlo simulations. An empirical application to a subsidized job training program finds that participation significantly and dramatically reduced the duration of jobless spells, especially at the right tail of the distribution.