Michelle Marcus, Pedro H. C. Sant'Anna
arXiv 3 Sep 2020 · Econometrics · publishedJournal of the Association of Environmental and Resource Economists (2020) · 25 citations (OpenAlex)
arXiv:2009.01963 · PDF · DOI · OpenAlex · Extracted main text
Difference-in-Differences (DID) research designs usually rely on variation of treatment timing such that, after making an appropriate parallel trends assumption, one can identify, estimate, and make inference about causal effects. In practice, however, different DID procedures rely on different parallel trends assumptions (PTA), and recover different causal parameters. In this paper, we focus on staggered DID (also referred as event-studies) and discuss the role played by the PTA in terms of identification and estimation of causal parameters. We document a “robustness” vs. “efficiency” trade-off in terms of the strength of the underlying PTA, and argue that practitioners should be explicit about these trade-offs whenever using DID procedures. We propose new DID estimators that reflect these trade-offs and derived their large sample properties. We illustrate the practical relevance of these results by assessing whether the transition from federal to state management of the Clean Water Act affects compliance rates.
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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 | Borusyak and Jaravel (2017) Revisiting Event Study Designs | 0.928 | 4 | 3 | 100% |
| 2 | Grooms (2015) Enforcing the Clean Water Act: The effect of state-level corruption on compliance | 0.874 | 19 | 2 | 100% |
| 3 | Goodman-Bacon (2019) Difference-in-Differences with Variation in Treatment Timing | 0.811 | 4 | 2 | 100% |
| Chernozhukov2013 | unmatched citation key Chernozhukov2013 | 0.644 | 2 | 2 | 100% |
| Gibbons2018 | unmatched citation key Gibbons2018 | 0.644 | 2 | 2 | 100% |
| 6 | Laporte and Windmeijer (2005) Estimation of panel data models with binary indicators when treatment effects are not constant over time | 0.644 | 2 | 2 | 100% |
| 7 | Wooldridge (2005) Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models | 0.644 | 2 | 2 | 100% |
| 8 | Callaway and Sant'Anna (2020) Difference-in-Differences with Multiple Time Periods | 0.511 | 2 | 1 | 100% |
| 9 | Cunningham (2018) | 0.511 | 2 | 1 | 100% |
| 10 | Hansen (1982) Large Sample Properties of Generalized Method of Moments Estimators | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 28 scored citations. 2 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.