Geographic boundaries as regression discontinuities

Luke J. Keele, Rocío Titiunik

Research output: Contribution to journalArticlepeer-review

188 Scopus citations


Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries.

Original languageEnglish (US)
Pages (from-to)127-155
Number of pages29
JournalPolitical Analysis
Issue number1
StatePublished - 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations


Dive into the research topics of 'Geographic boundaries as regression discontinuities'. Together they form a unique fingerprint.

Cite this