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A Practitioner’s Guide to Cluster-Robust Inference
Abstract
We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. In such settings default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specific fixed efects, few clusters, multi-way clustering, and estimators other than OLS.
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“This type of standard errors ensures valid inference even when certain model assumptions are violated.”
From Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge (2025) · cites this paper as Cameron and Miller, 2015
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“We cluster standard errors by subject and question in the multiple period analysis and use robust standard errors within the one-shot experiment 29,32 .”
Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge
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“To determine whether self-and family-bias estimates are statistically significant, we quantify uncertainty around these estimates by computing 90% Wald (Gaussian) confidence intervals using robust (White) standard errors [Buja et al., 2019a, Cameron and Miller, 2015, Freedman, 2006].”
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Metadata
- Authors
- Colin Cameron, A., & Miller, D. L.
- ORCID
- Miller, Douglas L.
- Venue
- Journal of Human Resources
- Publisher
- University of Wisconsin Press
- Published
- 2015
- Volume
- 50
- Issue
- 2
- Pages
- 317-372
- ISSN
- 0022-166X (ISSN-L)
- 1548-8004
- OpenAlex
- W2118836230
- Cited by
- 5,037
- References
- 83
- Citations by year
- 2026: 992 · 2025: 497 · 2024: 370 · 2023: 385 · 2022: 445 · 2021: 436 · 2020: 425 · 2019: 358 · 2018: 396 · 2017: 344 · 2016: 225 · 2015: 115
- FWCI
- 321.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Human Resources (publishedVersion)
- Project Muse (Johns Hopkins University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- cluster-robust standard errors, clustered data, intra-cluster correlation, regression inference, ordinary least squares, estimator precision
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