Clément de Chaisemartin, Xavier D'Haultfœuille
arXiv 18 Dec 2020 · Econometrics · publishedJournal of Econometrics (2023) · 167 citations (OpenAlex)
arXiv:2012.10077 · PDF · DOI · OpenAlex · Extracted main text
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-in-differences 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | V Joseph Hotz \ Mo Xiao (2011) The impact of regulations on the supply and quality of care in child care markets | 0.693 | 9 | 1 | 100% |
| 2 | Kirill Borusyak \ Xavier Jaravel (2017) Revisiting event study designs | 0.693 | 6 | 1 | 100% |
| 3 | Clement de Chaisemartin \ Xavier D'Haultfuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.654 | 31 | 1 | 84% |
| 4 | Liyang Sun \ Sarah Abraham (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.606 | 9 | 1 | 67% |
| 5 | Andrew Goodman-Bacon (2021) Difference-in-differences with variation in treatment timing | 0.585 | 3 | 1 | 100% |
| 6 | Marianne Bertrand, Esther Duflo \ Sendhil Mullainathan (2004) How much should we trust differences-in-differences estimates? | 0.511 | 2 | 1 | 100% |
| 7 | James Robins (1986) A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy w… | 0.405 | 2 | 1 | 50% |
| 8 | Alberto Abadie (2005) Semiparametric Difference-in-Differences Estimators | 0.405 | 1 | 1 | 100% |
| 9 | Clément de Chaisemartin \ Xavier d'Haultfoeuille (2021) Two-way fixed effects regressions with several treatments | 0.405 | 1 | 1 | 100% |
| 10 | Orley Ashenfelter (1978) Estimating the effect of training programs on earnings | 0.405 | 1 | 1 | 100% |
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