A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages - Sciences Po Access content directly
Preprints, Working Papers, ... (Working Paper) Year : 2022

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%.
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Dates and versions

hal-03869547 , version 1 (24-11-2022)

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Attribution - NonCommercial - NoDerivatives

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  • HAL Id : hal-03869547 , version 1

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Oliver Cassagneau-Francis, Robert Gary-Bobo, Julie Pernaudet, Jean-Marc Robin. A Nonparametric Finite Mixture Approach to Difference-in-Difference Estimation, with an Application to On-the-job Training and Wages. 2022. ⟨hal-03869547⟩
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