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- ISIDORE Id: 10670/1.218162...
- hal: hal-04187969
- doi: 10.1016/j.jeconom.2023.105480
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Mots-clés
differences-in-differences two-way-fixed-effects regressions multiple treatments heterogeneous treatment effects C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C23 - Panel Data Models • Spatio-temporal ModelsSujets proches
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Clément de Chaisemartin et al., « Two-way fixed effects and differences-in-differences estimators with several treatments », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.1016/j.jeconom.2023.105480
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Résumé
We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment's effect, with possibly negative weights, plus a weighted sum of the effects of the other treatments. Thus, those estimators are not robust to heterogeneous effects and may be contaminated by other treatments' effects. We further show that omitting a treatment from the regression can actually reduce the estimator's bias, unlike what would happen under constant treatment effects. We propose an alternative difference-indifferences estimator, robust to heterogeneous effects and immune to the contamination problem. In the application we consider, the TWFE regression identifies a highly non-convex combination of effects, with large contamination weights, and one of its coefficients significantly differs from our heterogeneity-robust estimator.
