Data and Code for: At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?
Principal Investigator(s): View help for Principal Investigator(s) Clément de Chaisemartin, Sciences Po; Jaime Ramirez-Cuellar, Microsoft
Version: View help for Version V1
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| AppendixD4 | 06/01/2023 07:47:PM | ||
| Data | 06/01/2023 07:58:PM | ||
| Figure1 | 06/01/2023 07:58:PM | ||
| Figure2 | 06/01/2023 07:58:PM | ||
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application/pdf | 34 KB | 06/01/2023 03:47:PM |
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Project Citation:
de Chaisemartin, Clément, and Ramirez-Cuellar, Jaime. Data and Code for: At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments? Nashville, TN: American Economic Association [publisher], 2023. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-06-14. https://doi.org/10.3886/E168781V1
Project Description
Summary:
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In matched-pairs experiments in which one cluster per pair of clusters is
assigned to treatment, to estimate treatment effects, researchers often regress their
outcome on a treatment indicator and pair fixed effects, clustering standard errors
at the unit-of-randomization 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. To fix this problem, researchers
should instead cluster standard errors at the pair level. Using simulations, we
show that similar results apply to clustered experiments with small strata.
Scope of Project
JEL Classification:
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C01 Econometrics
C12 Hypothesis Testing: General
C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
C90 Design of Experiments: General
C01 Econometrics
C12 Hypothesis Testing: General
C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
C90 Design of Experiments: General
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