How Much Should We Trust Regression Discontinuity Design Estimates? Evidence from Experimental Benchmarks of the Incumbency Advantage

Leandro De Magalhães University of Bristol
Dominik Hangartner London School of Economics and Political Science & Swiss Federal Institute of Technology in Zurich
Salomo Hirvonen University of Bristol
Jaakko Meriläinen Instituto Tecnológico Autónomo de México
Nelson Ruiz University of Oxford
Janne Tukiainen University of Turku


Regression discontinuity designs (RDD) are widely used in the social sciences to estimate causal effects from observational data. Scholars can choose from a range of methods that implement different RDD estimators, but there is a paucity of research on the performance of these different estimators in recovering experimental benchmarks. Leveraging exact ties in local elections in Colombia and Finland, which are resolved by random coin toss, we find that RDD estimation using bias-correction and robust inference (CCT) performs better in replicating experimental estimates of the individual incumbency advantage than local linear regression with conventional inference (LLR). We assess the generalizability of our results by estimating incumbency effects across different subsamples and in other countries. We find that CCT consistently comes closer to the experimental benchmark, produces smaller estimates than LLR, and that incumbency effects are highly heterogeneous, both in magnitude and sign, across countries with similar open-list PR systems.


Thumbnail image of rdd_manuscript_20200722.pdf


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