We develop an instrumental variable approach for identification of dynamic treatment effects on survival outcomes in the presence of dynamic selection, noncompliance, and right-censoring. The approach is nonparametric and does not require independence of observed and unobserved characteristics or separability assumptions. We propose estimation procedures and derive asymptotic properties. We apply our approach to evaluate a policy reform in which the pathway of unemployment benefits as a function of the unemployment duration is modified. Those who were unemployed at the reform date could choose between the old and the new regime. We find that the new regime has a positive average causal effect on the job finding rate.

This paper provides a method to infer the presence of treatment spillovers within markets where a fraction of agents is treated. We model individual outcomes as functions of the assigned treatment status and the distribution of assigned treatments in a market. We develop a two-step identification and estimation method, focusing first on the treatment distribution among individuals within markets and then on the treatment distribution across markets. We apply our approach to training programs for unemployed individuals in France using rich administrative data. Our results provide evidence of interactions within local labor markets as potential individual outcomes vary with the proportion of treated individuals.