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Inference in Differences-in-Differences: How Much Should We Trust in Independent Clusters?

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  • Ferman, Bruno
Abstract
We analyze the conditions in which ignoring spatial correlation is problematic for inference in differences-in-differences (DID) models. Assuming that the spatial correlation structure follows a linear factor model, we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups, and do not exhibit significant spatial correlation. We present simulation results with real datasets that corroborate these conclusions. Our results provide important guidelines on how to minimize inference problems due to spatial correlation in DID applications.

Suggested Citation

  • Ferman, Bruno, 2019. "Inference in Differences-in-Differences: How Much Should We Trust in Independent Clusters?," MPRA Paper 93746, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:93746
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    Cited by:

    1. Luis Alvarez & Bruno Ferman, 2020. "Inference in Difference-in-Differences with Few Treated Units and Spatial Correlation," Papers 2006.16997, arXiv.org, revised Apr 2023.
    2. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    3. Xiqian Cai & JunJie Wu & Wenchao Xu & Jialiang Zhu, 2024. "Negative emotions increase unhealthy eating: Evidence from the Wuhan lockdown during COVID‐19," Health Economics, John Wiley & Sons, Ltd., vol. 33(4), pages 604-635, April.
    4. Bruno Ferman, 2023. "Inference in difference‐in‐differences: How much should we trust in independent clusters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 358-369, April.

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    More about this item

    Keywords

    inference; differences-in-differences; spatial correlation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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