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Difference-In-Difference Design With Repeated Cross-Sections Under Compositional Changes: a Monte-Carlo Evaluation of Alternative Approaches

Author

Listed:
  • Tommaso Manfè

    (University of Chicago)

  • Luca Nunziata

    (University of Padova and IZA)

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.

Suggested Citation

  • Tommaso Manfè & Luca Nunziata, 2023. "Difference-In-Difference Design With Repeated Cross-Sections Under Compositional Changes: a Monte-Carlo Evaluation of Alternative Approaches," "Marco Fanno" Working Papers 0305, Dipartimento di Scienze Economiche "Marco Fanno".
    • Handle: RePEc:pad:wpaper:0305
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    References listed on IDEAS

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    Keywords

    Difference-in-Difference; Monte-Carlo simulations; Semi-parametric; Machine-learning.;
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