← ireadpaper · 顶刊中的公共政策研究

配对实验和小规模分层实验应在哪个层面对标准误进行聚类?

At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?
American Economic Journal: Applied Economics · 2023 · Clément de Chaisemartin、Jaime Ramírez

中文摘要

在配对匹配实验中,每对簇中有一个簇被分配到处理组。为估计处理效应,研究者通常将结果变量对处理指示变量和配对固定效应进行回归,并在随机化单位层面对标准误进行聚类。我们表明,即使处理没有效应,基于这一回归、显著性水平为5%的t检验仍会错误地得出存在处理效应的结论,其发生概率最高可达16.5%。为解决这一问题,研究者应改为在配对层面对标准误进行聚类。通过模拟,我们表明,类似结果也适用于采用小规模分层的整群实验。

Abstract

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 percent–level t-test based on this regression will wrongly conclude that the treatment has an effect up to 16.5 percent 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. (JEL C21, C90, G21, O16, O18)
在 ireadpaper 查看全部 →