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Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity

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  • Ferman, Bruno
  • Pinto, Cristine

Abstract

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, inference in DID models when there are few treated groups is still an open question. We show that usual inference methods used in DID models might not perform well when there are few treated groups and residuals are heteroskedastic. In particular, when there is variation in the number of observations per group, we show that inference methods designed to work when there are few treated groups would tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups would have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) dataset to show that this problem is relevant even in datasets with large number of observations per group. Then we derive alternative inference methods that provide accurate hypothesis testing in situations of few treated groups and many control groups in the presence of heteroskedasticity (including the case of only one treated group). The main assumption is that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. Finally, we also show that an inference method for the Synthetic Control Estimator proposed by Abadie et al. (2010) can correct for the heteroskedasticity problem, and derive conditions under which this inference method provides accurate hypothesis testing.

Suggested Citation

  • Ferman, Bruno & Pinto, Cristine, 2015. "Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity," MPRA Paper 67665, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:67665
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    References listed on IDEAS

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    1. MacKinnon, James G. & Webb, Matthew D., 2020. "Randomization inference for difference-in-differences with few treated clusters," Journal of Econometrics, Elsevier, vol. 218(2), pages 435-450.
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    More about this item

    Keywords

    differences-in-differences; inference; heteroskedasticity; clustering; few clusters; bootstrap;
    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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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