Brantly Callaway, Pedro H. C. Sant'Anna
arXiv 23 Mar 2018 · Econometrics · publishedJournal of Econometrics (2020) · 1,413 citations (OpenAlex)
arXiv:1803.09015 · PDF · DOI · OpenAlex · Extracted main text
In this article, we consider identification, estimation, and inference procedures for treatment effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods, (ii) variation in treatment timing, and (iii) when the "parallel trends assumption" holds potentially only after conditioning on observed covariates. We show that a family of causal effect parameters are identified in staggered DiD setups, even if differences in observed characteristics create non-parallel outcome dynamics between groups. Our identification results allow one to use outcome regression, inverse probability weighting, or doubly-robust estimands. We also propose different aggregation schemes that can be used to highlight treatment effect heterogeneity across different dimensions as well as to summarize the overall effect of participating in the treatment. We establish the asymptotic properties of the proposed estimators and prove the validity of a computationally convenient bootstrap procedure to conduct asymptotically valid simultaneous (instead of pointwise) inference. Finally, we illustrate the relevance of our proposed tools by analyzing the effect of the minimum wage on teen employment from 2001--2007. Open-source software is available for implementing the proposed methods.
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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 | Sun \ Abraham (2020) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 1.000 | 13 | 3 | 100% |
| 2 | de Chaisemartin \ D'Haultfuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 1.000 | 11 | 4 | 100% |
| 3 | Athey \ Imbens (2018) Design-based analysis in difference-in-differences settings with staggered adoption | 1.000 | 8 | 3 | 100% |
| 4 | Goodman-Bacon (2019) Difference-in-differences with variation in treatment timing | 1.000 | 6 | 3 | 100% |
| 5 | Heckman, Ichimura \ Todd (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 0.961 | 9 | 3 | 89% |
| 6 | Abadie (2005) Semiparametric difference-in-difference estimators | 0.928 | 10 | 4 | 80% |
| 7 | Borusyak \ Jaravel (2017) Revisiting event study designs | 0.928 | 4 | 3 | 100% |
| 8 | Sant’Anna \ Zhao (2020) Doubly robust difference-in-differences estimators | 0.916 | 13 | 5 | 77% |
| 9 | Heckman, Ichimura, Smith \ Todd (1998) Characterizing selection bias using experimental data | 0.874 | 7 | 2 | 100% |
| 10 | Dube, Lester \ Reich (2010) Minimum wage effects across state borders: Estimates using contiguous counties | 0.874 | 6 | 2 | 100% |
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