arXiv 31 Oct 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2210.16991 · PDF · DOI · OpenAlex · Extracted main text
The conventional cluster-robust (CR) standard errors may not be robust. They are vulnerable to data that contain a small number of large clusters. When a researcher uses the 51 states in the U.S. as clusters, the largest cluster (California) consists of about 10% of the total sample. Such a case in fact violates the assumptions under which the widely used CR methods are guaranteed to work. We formally show that the conventional CR methods fail if the distribution of cluster sizes follows a power law with exponent less than two. Besides the example of 51 state clusters, some examples are drawn from a list of recent original research articles published in a top journal. In light of these negative results about the existing CR methods, we propose a weighted CR (WCR) method as a simple fix. Simulation studies support our arguments that the WCR method is robust while the conventional CR methods are not.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bugni, F., I. Canay, A. Shaikh, and M. Tabord-Meehan (2022) Inference for cluster randomized experiments with non-ignorable cluster sizes | 1.000 | 7 | 3 | 100% |
| 2 | Djogbenou, A. A., J. G. MacKinnon, and M. . Nielsen (2019) Asymptotic theory and wild bootstrap inference with clustered errors | 0.794 | 6 | 4 | 50% |
| 3 | MacKinnon, J. G., M. . Nielsen, and M. D. Webb (2022) b): Fast and reliable jackknife and bootstrap methods for cluster-robust inference | 0.737 | 3 | 3 | 67% |
| 4 | Alfonsi, L., O. Bandiera, V. Bassi, R. Burgess, I. Rasul, M. Sulaima… (2020) Tackling youth unemployment: Evidence from a labor market experiment in Uganda | 0.644 | 4 | 1 | 100% |
| 5 | Burstein, A., G. Hanson, L. Tian, and J. Vogel (2020) Tradability and the labor-market impact of immigration: theory and evidence from the United States | 0.644 | 4 | 1 | 100% |
| 6 | Enikolopov, R., A. Makarin, and M. Petrova (2020) Social media and protest participation: Evidence from Russia | 0.644 | 4 | 1 | 100% |
| 7 | Arellano, M (1987) Computing robust standard errors for within-groups estimators | 0.644 | 2 | 2 | 100% |
| 8 | Hansen, B. E (2022) a): Jackknife standard errors for clustered regression | 0.644 | 2 | 2 | 100% |
| 9 | Hansen, B. E. and S. Lee (2019) Asymptotic theory for clustered samples | 0.644 | 2 | 2 | 100% |
| 10 | Hersch, J (1998) Compensating differentials for gender-specific job injury risks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes | 0.405 | 1 | 1 |
| 2 | Inference in Cluster Randomized Trials with Matched Pairs | 0.405 | 1 | 1 |
| 3 | Estimation and exclusion restrictions in clustered linear models | 0.405 | 1 | 1 |