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

No 305, "Marco Fanno" Working Papers from Dipartimento di Scienze Economiche "Marco Fanno"

Abstract: We discuss the potentially severe bias in the commonly-used Difference-in-Difference estimators under compositional changes and propose a Double Inverse Probability Weighting estimator for repeated cross-sections based on both the probability of being treated and of belonging to the post-treatment period, deriving also its doubly-robust version. Through Monte Carlo simulations, we compare its performance with several methods suggested by the literature. Results show that our proposed estimator outperforms all alternatives in the most realistic scenarios. We provide an empirical application estimating the effect of tariff reduction on bribing behavior on trades data between South Africa and Mozambique during the period 2006–2014.

Keywords: Difference-in-Difference; Monte-Carlo simulations; Semi-parametric; Machine-learning. (search for similar items in EconPapers)
Pages: 57 pages
Date: 2023-11
New Economics Papers: this item is included in nep-ecm and nep-mfd
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Persistent link: https://EconPapers.repec.org/RePEc:pad:wpaper:0305

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