arXiv 13 Jul 2022 · Econometrics · 306 citations (OpenAlex)
arXiv:2207.05943 · PDF · DOI · OpenAlex · Extracted main text
A recent literature has shown that when adoption of a treatment is staggered and average treatment effects vary across groups and over time, difference-in-differences regression does not identify an easily interpretable measure of the typical effect of the treatment. In this paper, I extend this literature in two ways. First, I provide some simple underlying intuition for why difference-in-differences regression does not identify a group$\times$period average treatment effect. Second, I propose an alternative two-stage estimation framework, motivated by this intuition. In this framework, group and period effects are identified in a first stage from the sample of untreated observations, and average treatment effects are identified in a second stage by comparing treated and untreated outcomes, after removing these group and period effects. The two-stage approach is robust to treatment-effect heterogeneity under staggered adoption, and can be used to identify a host of different average treatment effect measures. It is also simple, intuitive, and easy to implement. I establish the theoretical properties of the two-stage approach and demonstrate its effectiveness and applicability using Monte-Carlo evidence and an example from the literature.
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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, Liyang and Sarah Abraham (2020) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.961 | 9 | 5 | 89% |
| 2 | Borusyak, Kirill and Xavier Jaravel (2017) Revisiting event study designs, with an application to the estimation of the marginal propensity to consume | 0.874 | 7 | 2 | 100% |
| 3 | Callaway, Brantly, and Pedro Sant'Anna (2020) “Difference-in-differences with multiple time periods and an application on the minimum wage and employment." Journal of Econome… | 0.874 | 5 | 2 | 100% |
| 4 | de Chaisemartin, Clément and Xavier D'Haultf uille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.874 | 5 | 2 | 100% |
| 5 | Goodman-Bacon, Andrew (2018) Difference-in-differences with variation in treatment timing | 0.811 | 4 | 2 | 100% |
| 6 | Athey, Susan and Guido W. Imbens (2018) Design based analysis in difference-in-differences settings with staggered adoption | 0.644 | 2 | 2 | 100% |
| 7 | Imai, Kosuke and In Song Kim (2020) On the use of two-way fixed effects regression models for causal inference with panel data | 0.644 | 2 | 2 | 100% |
| 8 | Autor, David (2003) Outsourcing at will | 0.585 | 3 | 1 | 100% |
| 9 | Gibbons, Charles E., Suárez Serrato, Juan Carlos, and Michael B. Urb… (2017) Broken or fixed effects | 0.511 | 2 | 1 | 100% |
| 10 | Angrist, Joshua D., and Jörn-Steffen Pischke (2009) Mostly Harmless Econometrics: An Empiricist's Companion | 0.405 | 1 | 1 | 100% |
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