2014/10/31 by Luke Keele, Luke J. Keele, Rocío Titiunik · 7 citations
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Electoral Systems and Political Participation #School Choice and Performance
paper · pdf · doi:10.1093/pan/mpu014
openalex publication_date 2014/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
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.