Leveraging Covariates in Regression Discontinuity Designs

TL;DR

This paper explores how incorporating covariates in Regression Discontinuity designs can improve efficiency, reveal heterogeneity, and alter the estimated parameters, with practical guidance for empirical analysis.

Contribution

It provides a comprehensive discussion and empirical illustration of effective covariate use in RD designs, clarifying its multiple roles and implications.

Findings

Covariate adjustment can improve estimator efficiency.

Covariates help identify heterogeneous effects.

Adjusting covariates can change the RD parameter of interest.

Abstract

It is common practice to incorporate additional covariates in empirical economics. In the context of Regression Discontinuity (RD) designs, covariate adjustment plays multiple roles, making it essential to understand its impact on analysis and conclusions. Typically implemented via local least squares regressions, covariate adjustment can serve three main distinct purposes: (i) improving the efficiency of RD average causal effect estimators, (ii) learning about heterogeneous RD policy effects, and (iii) changing the RD parameter of interest. This article discusses and illustrates empirically how to leverage covariates effectively in RD designs.