📝 SummarXiv

Abstract

Conventional cluster-robust inference can be inconsistent when data contain clusters of unignorably large size. We formalize this issue by deriving a necessary and sufficient condition for the consistency, and show that this condition is frequently violated in practice: 77% of empirical research articles published in the American Economic Review and Econometrica during 2020-2021 appear not to meet it. To address this limitation, we propose a genuinely robust inference procedure based on a novel cluster score bootstrap. We establish its validity and size control across broad classes of data-generating processes where conventional methods break down. Simulation studies corroborate our theoretical findings, and empirical applications illustrate that employing the proposed method can substantially alter conventional statistical conclusions.