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15 septembre 2022
- ISIDORE Id: 10670/1.fb54fc...
- hal: hal-03873897
- doi: 10.2139/ssrn.3520820
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info:eu-repo/semantics/altIdentifier/doi/10.2139/ssrn.3520820
http://creativecommons.org/licenses/by-nc/ , info:eu-repo/semantics/OpenAccess
Mots-clés
clustered standard errors clustering paired experiments stratified experiments randomized experiments RCT C - Mathematical and Quantitative Methods/C.C0 - General/C.C0.C01 - Econometrics C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile Regressions C - Mathematical and Quantitative Methods/C.C9 - Design of ExperimentsSujets proches
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Clément de Chaisemartin et al., « At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments? », HAL SHS (Sciences de l’Homme et de la Société), ID : 10.2139/ssrn.3520820
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In clustered and paired experiments, to estimate treatment effects, researchers often regress their outcome on the treatment and pair fixed effects, clustering standard errors at the unit-ofrandomization level. We show that even if the treatment has no effect, a 5%-level t-test based on this regression will wrongly conclude that the treatment has an effect up to 16.5% of the time, an error rate much larger than the researcher's 5% target. To achieve their targeted error rate, researchers should instead cluster standard errors at the pair level. Using simulations, we show that similar results apply to clustered experiments with small strata.
