Difference-In-Difference Design With Repeated Cross-Sections Under Compositional Changes: a Monte-Carlo Evaluation of Alternative Approaches
Tommaso Manfè () and
Luca Nunziata ()
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Tommaso Manfè: University of Chicago
Luca Nunziata: University of Padova and IZA
No 305, "Marco Fanno" Working Papers from Dipartimento di Scienze Economiche "Marco Fanno"
We discuss the potentially severe bias in the Difference-in-Difference (DiD) design of commonly-used methods, including the regression specification known as Two-Way-Fixed-Effects (TWFE), when researchers must invoke the conditional trend assumption but the distribution of the covariates changes over time. Building on Abadie (2005), we propose a Double Inverse Probability Weighting (DIPW) estimator for repeated cross-sections based on both the probability of being treated and of belonging to the post-treatment period and derive its doubly-robust version (DR-DIPW), similarly as in Sant’Anna and Zhao (2020). Through Monte Carlo simulations, we compare its performance with a number of methods suggested by the literature, which span from the basic TWFE (and our proposed correction) to semi-parametric estimators, including those using machine-learning first-stage estimates, following Chernozhukov et al. (2018). Results show that DR-DIPW outperforms the other estimators in most realistic scenarios, even if TWFE corrections provide substantial benefits. Following Sequeira (2016), we estimate the effect of tariff reduction on bribing behavior by analyzing trades between South Africa and Mozambique during the period 2006–2014. Contrarily to the replication by Chang (2020), our findings show that the effect is lower in magnitude than the one presented in the original paper.
Keywords: Difference-in-Difference; Monte-Carlo simulations; Semi-parametric; Machine-learning. (search for similar items in EconPapers)
Pages: 56 pages
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Persistent link: https://EconPapers.repec.org/RePEc:pad:wpaper:0305
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