Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects
Liyang Sun,Sarah Abraham +1 more
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TLDR
In this article, the authors proposed an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.About:
This article is published in Journal of Econometrics.The article was published on 2021-12-01 and is currently open access. It has received 570 citations till now.read more
Citations
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Difference-in-Differences with Variation in Treatment Timing
TL;DR: This paper showed that the two-way fixed effects estimator equals a weighted average of all possible two-group/two-period DD estimators in the data and decompose the difference between two specifications, and provide a new analysis of models that include time-varying controls.
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Difference-in-Differences with multiple time periods
TL;DR: It is shown 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, and the asymptotic properties of the proposed estimators are established.
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How Much Should We Trust Staggered Difference-In-Differences Estimates?
TL;DR: The authors found that correcting for the bias induced by the staggered nature of policy adoption frequently impacts the estimated effect from standard difference-in-difference studies, in many cases, the reported effects in prior research become indistinguishable from zero.
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How much should we trust staggered difference-in-differences estimates?
TL;DR: In this paper , the authors explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased, and present three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs.
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When do shelter-in-place orders fight COVID-19 best? Policy heterogeneity across states and adoption time
TL;DR: In this paper, the authors explored the impact of SIPO adoption on health, with particular attention to heterogeneity in their impacts, using daily state-level social distancing data from SafeGraph and a difference-in-differences approach.
References
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Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme
TL;DR: This paper decompose the conventional measure of evaluation bias into several components and find that bias due to selection on unobservables, commonly called selection bias in econometrics, is empirically less important than other components, although it is still a sizeable fraction of the estimated programme impact.
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Characterizing selection bias using experimental data
TL;DR: In this article, a semiparametric method is developed to estimate the bias that arises from using nonexperimental comparison groups to evaluate social programs and to test the identifying assumptions that justify matching, selection models, and the method of difference-in-differences.
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Semiparametric Difference-in-Differences Estimators
TL;DR: The difference-in-differences estimator is based on the simple idea that simple comparisons of pre-treatment and post-treatment outcomes for those individuals exposed to a treatment are likely to be contaminated by temporal trends in the outcome variable or by the effect of events, other than the treatment, that occurred between both periods as mentioned in this paper.
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Estimating the Effect of Training Programs on Earnings.
TL;DR: In this article, the authors focus on the first three months of training under the Manpower Development and Training Act (MDTA) in the U.S. in order to measure the full inter-temporal impact of training.
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An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s
TL;DR: In this article, the authors investigate the presence of short and long-term effects from joining a Swedish labor market program vis-a-vis more intense job search in open unemployment.