This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that the duration framework in discrete time provides a fertile ground for effect evaluations. We suggest easy-to-use nonparametric survival function...

We examine the direct impact of idiosyncratic match quality on entry wages and job mobility using unique data on worker talents matched to job-indicators and individual wages. Tenured workers are clustered in jobs with high job-specific returns to their types of talents. We therefore measure...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that, without additional assumptions, it is not possible to estimate the average treatment effect and treatment on the treated. It is, however,...

We examine the direct impact of idiosyncratic match quality on entry wages and job mobility using unique data on worker talents matched to job-indicators and individual wages. Tenured workers are clustered in jobs with high job-specific returns to their types of talents. We therefore measure...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that, without additional assumptions, it is not possible to estimate the average treatment effect and treatment on the treated. It is, however,...

This paper discusses the evaluation problem using observational data when the timing of treatment is an outcome of a stochastic process. We show that, without additional assumptions, it is not possible to estimate the average treatment effect and treatment on the treated. It is, however,...

We examine the direct impact of idiosyncratic match quality on entry wages and job mobility using unique data on worker talents matched to job-indicators and individual wages. Tenured workers are clustered in jobs with high job-specific returns to their types of talents. We therefore measure...