Difference-in-Differences with multiple time periods
Difference-in-Differences with multiple time periods
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DOI：
10.1016/j.jeconom.2020.12.001
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发表时间：
2021-10-20
影响因子：
6.3
通讯作者：
Sant'Anna, Pedro H. C.
中科院分区：
文献类型：
--
作者：
Callaway, Brantly;Sant'Anna, Pedro H. C.
在这篇文章中，我们考虑了使用差异法(DID)对治疗效果参数进行识别、估计和推断的程序，其中(I)多个时间段，(Ii)治疗时机的变化，以及(Iii)当“平行趋势假设”仅在观察到的协变量条件下可能成立时。我们表明，在交错的DID设置中识别了一系列因果效应参数，即使观察到的特征的差异在组之间产生了非平行的结果动态。我们的识别结果允许使用结果回归、逆概率加权或双稳健估计。我们还提出了不同的聚集方案，可以用来突出不同维度的治疗效果异质性，以及总结参与治疗的整体效果。我们建立了所提出的估计量的渐近性质，并证明了计算上方便的Bootstrap过程进行渐近有效的同时(而不是逐点)推断的有效性。最后，我们通过分析2001-2007年最低工资对青少年就业的影响来说明我们提出的工具的相关性。开放源码软件可用于实施所提议的方法。(C)2020爱思唯尔B.V.保留所有权利。
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 prob-ability 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. (c) 2020 Elsevier B.V. All rights reserved.