A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages
Abstract
We develop a finite-mixture framework for nonparametric difference-indifference analysis with unobserved heterogeneity correlating treatment and outcome. Our framework includes an instrumental variable for the treatment, and we demonstrate that this allows us to relax the common-trend assumption. Outcomes can be modeled as first-order Markovian, provided at least 2 post-treatment observations of the outcome are available. We provide a nonparametric identification proof. We apply our framework to evaluate the effect of on-the-job training on wages, using novel French linked employee-employer data. Estimating our model using an EM-algorithm, we find small ATEs and ATTs on hourly wages, around 1%.
Fichier principal
2022_cassagneau_francis_gary_bobo_pernaudet_robin_a_nonparametric_finite_mixture_approach_to_difference_in_difference_estimation_with_an_application_to_on_the_job_training_and_wages.pdf (2.09 Mo)
Télécharger le fichier
Origin : Files produced by the author(s)