Publications

Methodology

A Practical Guide to Regression Discontinuity

Regression discontinuity (RD) analysis is a rigorous nonexperimental approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point. Over the last two decades, the regression discontinuity approach has been used to evaluate the impact of a wide variety of social programs (DiNardo and Lee, 2004; Hahn, Todd, and van der Klaauw, 1999; Lemieux and Milligan, 2004; van der Klaauw, 2002; Angrist and Lavy, 1999; Jacob and Lefgren, 2006; McEwan and Shapiro, 2008; Black, Galdo, and Smith, 2007; Gamse, Bloom, Kemple, and Jacob, 2008). Yet, despite the growing popularity of the approach, there is only a limited amount of accessible information to guide researchers in the implementation of an RD design. While the approach is intuitively appealing, the statistical details regarding the implementation of an RD design are more complicated than they might first appear. Most of the guidance that currently exists appears in technical journals that require a high degree of technical sophistication to read. Furthermore, the terminology that is used is not well defined and is often used inconsistently. Finally, while a number of different approaches to the implementation of an RD design are proposed in the literature, they each differ slightly in their details. As such, even researchers with a fairly sophisticated statistical background can find it difficult to access practical guidance for the implementation of an RD design.

To help fill this void, the present paper is intended to serve as a practitioners’ guide to implementing RD designs. It seeks to explain things in easy-to-understand language and to offer best practices and general guidance to those attempting an RD analysis. In addition, the guide illustrates the various techniques available to researchers and explores their strengths and weaknesses using a simulated dataset, which can be accessed here.

The guide provides a general overview of the RD approach and then covers the following topics in detail: (1) graphical presentation in RD analysis, (2) estimation (both parametric and nonparametric), (3) establishing the interval validity of RD impacts, (4) the precision of RD estimates, (5) the generalizability of RD findings, and (6) estimation and precision in the context of a fuzzy RD analysis. Readers will find both a glossary of widely used terms and a checklist of steps to follow when implementing an RD design in the Appendixes.                        

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