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Distribution Regression Difference-In-Differences

Iván Fernández-Val, Jonas Meier, Aico van Vuuren, Francis Vella

arXiv 3 Sep 2024 · Econometrics

arXiv:2409.02311 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We provide a simple distribution regression estimator for treatment effects in the difference-in-differences (DiD) design. Our procedure is particularly useful when the treatment effect differs across the distribution of the outcome variable. Our proposed estimator easily incorporates covariates and, importantly, can be extended to settings where the treatment potentially affects the joint distribution of multiple outcomes. Our key identifying restriction is that the counterfactual distribution of the treated in the untreated state has no interaction effect between treatment and time. This assumption results in a parallel trend assumption on a transformation of the distribution. We highlight the relationship between our procedure and assumptions with the changes-in-changes approach of Athey and Imbens (2006). We also reexamine the Card and Krueger (1994) study of the impact of minimum wages on employment to illustrate the utility of our approach.

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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Bivariate distribution regression; theory, estimation and an application to intergenerational mobility0.51121
2Difference-in-Differences Designs: A Practitioner's Guide0.40511
3On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator0.40511