At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?

Clément de Chaisemartin and Jaime Ramirez-Cuellar

No 27609, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: In clustered paired experiments, randomization units, say villages, are matched into pairs, and one unit of each pair is randomly assigned to treatment. To estimate the treatment effect, researchers often regress their outcome on the treatment and pair fixed effects, clustering standard errors at the unit-of-randomization level. We show that the variance estimator in this regression may be severely downward biased: under constant treatment effect, its expectation equals 1/2 of the true variance. Instead, researchers should cluster at the pair level. Using simulations, we show that those results extend to clustered stratified experiments with few units per strata.

JEL-codes: C01 C21 (search for similar items in EconPapers)
Date: 2020-07
Note: DEV ED EH LS TWP
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Citations: View citations in EconPapers (16)

Published as Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, vol 16(1), pages 193-212.

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Related works:
Journal Article: At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments? (2024)
Working Paper: At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments? (2023)
Working Paper: At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments? (2022)
Working Paper: At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments? (2022)
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