A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages
Oliver Cassagneau-Francis,
Robert Gary-Bobo (),
Julie Pernaudet () and
Jean-Marc Robin
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Julie Pernaudet: University of Chicago
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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%.
Keywords: Finite Mixtures; Unobserved Heterogeneity; EM Algorithm; Wage Distributions; Training; Matched Employer-Employee Data E24; E32; J63; J64 (search for similar items in EconPapers)
Date: 2022-10-10
New Economics Papers: this item is included in nep-ecm
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-03869547v1
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Related works:
Working Paper: A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages (2022) 
Working Paper: A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages (2022) 
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