Cluster-Robust Inference: A Guide to Empirical Practice

TL;DR

This paper offers a practical guide for applying cluster-robust inference methods in empirical research, grounded in recent theoretical developments and simulation evidence, illustrated through a labor economics case study.

Contribution

It bridges recent econometric theory with practical guidance for empirical researchers on cluster-robust inference methods.

Findings

Guidance on best practices for cluster-robust inference

Empirical analysis of minimum wage effects on teenage labor supply

Integration of theory, simulation, and real data application

Abstract

Methods for cluster-robust inference are routinely used in economics and many other disciplines. However, it is only recently that theoretical foundations for the use of these methods in many empirically relevant situations have been developed. In this paper, we use these theoretical results to provide a guide to empirical practice. We do not attempt to present a comprehensive survey of the (very large) literature. Instead, we bridge theory and practice by providing a thorough guide on what to do and why, based on recently available econometric theory and simulation evidence. To practice what we preach, we include an empirical analysis of the effects of the minimum wage on labor supply of teenagers using individual data.