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Design-based analysis in Difference-In-Differences settings with staggered adoption

Susan Athey and Guido Imbens

Journal of Econometrics, 2022, vol. 226, issue 1, 62-79

Abstract: In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the staggered adoption setting where units, e.g, individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences (DID) estimator is an unbiased estimator of a particular weighted average causal effect. We characterize the exact finite sample properties of this estimand, and show that the standard variance estimator is conservative.

Keywords: Staggered adoption design; Difference-In-Differences; Fixed effects; Randomization distribution (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (135)

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Related works:
Working Paper: Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption (2018) Downloads
Working Paper: Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption (2018) Downloads
Working Paper: Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption (2018) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:226:y:2022:i:1:p:62-79

DOI: 10.1016/j.jeconom.2020.10.012

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