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Pitfalls When Estimating Treatment Effects Using Clustered Data

Author

Listed:
  • James G. MacKinnon

    () (Queen's University)

  • Matthew D. Webb

    () (Carleton University)

Abstract

Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models anddifference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard errors can be much too small if the number of treated (or control) clusters is small. Standard errors also tend to be too small when cluster sizes vary a lot, resulting in too many false positives. Bootstrap methods generally perform better than t-tests, but they can also yield very misleading inferences in some cases.

Suggested Citation

  • James G. MacKinnon & Matthew D. Webb, 2017. "Pitfalls When Estimating Treatment Effects Using Clustered Data," Working Paper 1387, Economics Department, Queen's University.
  • Handle: RePEc:qed:wpaper:1387
    as

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    File URL: https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1387.pdf
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    References listed on IDEAS

    as
    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.
    2. Guido W. Imbens & Michal Kolesár, 2016. "Robust Standard Errors in Small Samples: Some Practical Advice," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 701-712, October.
    3. repec:clg:wpaper:2013-20 is not listed on IDEAS
    4. James G. MacKinnon & Matthew D. Webb, 2018. "The wild bootstrap for few (treated) clusters," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 114-135, June.
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    14. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    15. James G. MacKinnon & Matthew D. Webb, 2017. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 233-254, March.
    16. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, Oxford University Press, vol. 119(1), pages 249-275.
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    20. Bester, C. Alan & Conley, Timothy G. & Hansen, Christian B., 2011. "Inference with dependent data using cluster covariance estimators," Journal of Econometrics, Elsevier, vol. 165(2), pages 137-151.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    3. Cho, Sungtaek & Kwak, Do Won & Lee, Hongshik, 2020. "Participation in the Kaesong Industrial Complex and its impact on productivity: South Korean textile firms’ experiences," Japan and the World Economy, Elsevier, vol. 53(C).
    4. Jason M. Lindo & Mayra Pineda-Torres, 2019. "New Evidence on the Effects of Mandatory Waiting Periods for Abortion," NBER Working Papers 26228, National Bureau of Economic Research, Inc.
    5. Biewen, Martin & Schwerter, Jakob, 2019. "Does More Math in High School Increase the Share of Female STEM Workers? Evidence from a Curriculum Reform," IZA Discussion Papers 12236, Institute of Labor Economics (IZA).
    6. Ozturk, Orgul D. & Frongillo, Edward A. & Blake, Christine E. & McInnes, Melayne M. & Turner-McGrievy, Gabrielle, 2020. "Before the lunch line: Effectiveness of behavioral economic interventions for pre-commitment on elementary school children's food choices," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 597-618.
    7. Djogbenou, Antoine A. & MacKinnon, James G. & Nielsen, Morten Ørregaard, 2019. "Asymptotic theory and wild bootstrap inference with clustered errors," Journal of Econometrics, Elsevier, vol. 212(2), pages 393-412.
    8. Ban, Radu & Gilligan, Michael J. & Rieger, Matthias, 2020. "Self-help groups, savings and social capital: Evidence from a field experiment in Cambodia," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 174-200.

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    More about this item

    Keywords

    CRVE; grouped data; clustered data; panel data; wild cluster bootstrap; difference-in-differences; DiD regression;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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

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