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Practical and Effective Approaches to Dealing With Clustered Data

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  • Esarey, Justin
  • Menger, Andrew

Abstract

Cluster-robust standard errors (as implemented by the eponymous cluster option in Stata) can produce misleading inferences when the number of clusters G is small, even if the model is consistent and there are many observations in each cluster. Nevertheless, political scientists commonly employ this method in data sets with few clusters. The contributions of this paper are: (a) developing new and easy-to-use Stata and R packages that implement alternative uncertainty measures robust to small G, and (b) explaining and providing evidence for the advantages of these alternatives, especially cluster-adjusted t-statistics based on Ibragimov and Müller. To illustrate these advantages, we reanalyze recent work where results are based on cluster-robust standard errors.

Suggested Citation

  • Esarey, Justin & Menger, Andrew, 2019. "Practical and Effective Approaches to Dealing With Clustered Data," Political Science Research and Methods, Cambridge University Press, vol. 7(3), pages 541-559, July.
    • Handle: RePEc:cup:pscirm:v:7:y:2019:i:03:p:541-559_00
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    2. Rustam Ibragimov & Jihyun Kim & Anton Skrobotov, 2020. "New robust inference for predictive regressions," Papers 2006.01191, arXiv.org, revised Mar 2023.
    3. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Testing for the appropriate level of clustering in linear regression models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2027-2056.
    4. 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.
    5. Gary Charness & Ramón Cobo-Reyes & Erik Eyster & Gabriel Katz & Ángela Sánchez & Matthias Sutter, 2020. "Improving healthy eating in children: Experimental evidence," ECONtribute Discussion Papers Series 047, University of Bonn and University of Cologne, Germany.
    6. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    7. Charness, Gary & Cobo-Reyes, Ramón & Eyster, Erik & Katz, Gabriel & Sánchez, Ángela & Sutter, Matthias, 2023. "Improving children's food choices: Experimental evidence from the field," European Economic Review, Elsevier, vol. 159(C).
    8. Rustam Ibragimov & Paul Kattuman & Anton Skrobotov, 2021. "Robust Inference on Income Inequality: $t$-Statistic Based Approaches," Papers 2105.05335, arXiv.org, revised Nov 2021.
    9. Ongena, Steven & Antoniou, Fabio & Delis, Manthos & Tsoumas, Christos, 2020. "Pollution permits and financing costs," CEPR Discussion Papers 15517, C.E.P.R. Discussion Papers.
    10. Gianluigi Giustiziero & Aseem Kaul & Brian Wu, 2019. "The Dynamics of Learning and Competition in Schumpeterian Environments," Organization Science, INFORMS, vol. 30(4), pages 668-693, July.
    11. Miltiadis S. Chalikias & Georgios X. Papageorgiou & Dimitrios P. Zarogiannis, 2024. "Estimator Comparison for the Prediction of Election Results," Stats, MDPI, vol. 7(3), pages 1-14, July.
    12. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2024. "Cluster-robust jackknife and bootstrap inference for binary response models," Papers 2406.00650, arXiv.org.
    13. Abdul Quddoos & Klaus Salhofer & Ulrich B. Morawetz, 2023. "Utilising farm‐level panel data to estimate climate change impacts and adaptation potentials," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 75-99, February.
    14. Matthew D. Webb, 2023. "Reworking wild bootstrap‐based inference for clustered errors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(3), pages 839-858, August.
    15. Grieco, Daniela & Bripi, Francesco, 2022. "Participation of charity beneficiaries," Journal of Economic Behavior & Organization, Elsevier, vol. 199(C), pages 1-17.
    16. Huang, Naqun & Ning, Guangjie & Rong, Zhao, 2022. "Destination homeownership and labor force participation: Evidence from rural-to-urban migrants in China," Journal of Housing Economics, Elsevier, vol. 55(C).
    17. Cheung, Ron & Salmon, Timothy C. & Xie, Kuangli, 2022. "Homeowner associations and city cohesion," Regional Science and Urban Economics, Elsevier, vol. 93(C).
    18. Cassandra Handan-Nader & Daniel E. Ho & Becky Elias, 2020. "Feasible Policy Evaluation by Design: A Randomized Synthetic Stepped-Wedge Trial of Mandated Disclosure in King County," Evaluation Review, , vol. 44(1), pages 3-50, February.
    19. Acuff, Christopher, 2022. "Beyond the City-County Divide: Examining Consolidation Referenda Since 2000," SocArXiv pb7ug, Center for Open Science.
    20. Walter Distaso & Rustam Ibragimov & Alexander Semenov & Anton Skrobotov, 2020. "COVID-19: Tail Risk and Predictive Regressions," Papers 2009.02486, arXiv.org, revised Oct 2021.
    21. Vilde Lunnan Djuve & Carl Henrik Knutsen, 2024. "Economic crisis and regime transitions from within," Journal of Peace Research, Peace Research Institute Oslo, vol. 61(3), pages 446-461, May.
    22. Marina Nistotskaya & Michelle D'Arcy, 2021. "No taxation without property rights: Formalization of property rights on land and tax revenues from individuals in sub-Saharan Africa," WIDER Working Paper Series wp-2021-175, World Institute for Development Economic Research (UNU-WIDER).
    23. Li, Shenyu & Popkowsky Leszczyc, Peter T.L. & Qiu, Chun, 2023. "International retailer performance: Disentangling the interplay between rule of law and culture," Journal of Retailing, Elsevier, vol. 99(2), pages 193-209.
    24. Heyes, Anthony & Zhu, Mingying, 2019. "Air pollution as a cause of sleeplessness: Social media evidence from a panel of Chinese cities," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).

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