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

被引：319

作者：

Athey, Susan ^{[1
,2
]}

Imbens, Guido W. ^{[1
,2
,3
,4
]}

机构：

[1] Stanford Univ, Grad Sch Business, Stanford, CA 94305 USA

[2] NBER, Cambridge, MA 02138 USA

[3] Stanford Univ, Dept Econ, Stanford, CA 94305 USA

[4] SIEPR, Stanford, CA USA

来源：

JOURNAL OF ECONOMETRICS | 2022年 / 226卷 / 01期

关键词：

Staggered adoption design; Difference-In-Differences; Fixed effects; Randomization distribution; ROBUST STANDARD ERRORS; INFERENCE; IDENTIFICATION; DURATION;

D O I：

10.1016/j.jeconom.2020.10.012

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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. (C) 2021 Elsevier B.V. All rights reserved.

引用

页码：62 / 79

页数：18

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