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The regression discontinuity (RD) design offers identification of causaleffects under weak assumptions, earning it a position as a standard method inmodern political science research. But identification does not necessarilyimply that causal effects can be estimated accurately with limited data. Inthis paper, we highlight that estimation under the RD design involves seriousstatistical challenges and investigate how these challenges manifest themselvesin the empirical literature in political science. We collect all RD-basedfindings published in top political science journals in the period 2009-2018.The distribution of published results exhibits pathological features; estimatestend to bunch just above the conventional level of statistical significance. Areanalysis of all studies with available data suggests that researcherdiscretion is not a major driver of these features. However, researchers tendto use inappropriate methods for inference, rendering standard errorsartificially small. A retrospective power analysis reveals that most of thesestudies were underpowered to detect all but large effects. The issues weuncover, combined with well-documented selection pressures in academicpublishing, cause concern that many published findings using the RD design maybe exaggerated.

On the reliability of published findings using the regression discontinuity design in political science