Addressing Bias in Politician Characteristic Regression Discontinuity Designs

Santiago Torres ()
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Santiago Torres: Universidad de los Andes, Facultad de Economía

No 20304, Documentos CEDE from Universidad de los Andes, Facultad de Economía, CEDE

Abstract: Politician characteristic regression discontinuity (PCRD) designs are a popular strategy when attempting to casually link a specific trait of an elected politician with a given outcome. However, recent research has revealed that this methodology often fails to retrieve the target causal effect¿a problem also known as the PCRD estimation bias. In this paper, I provide a new econometric framework to address this limitation in applied research. First, I propose a covariate-adjusted local polynomial estimator that corrects for the PCRD estimation bias provided all relevant confounders are observed. I then leverage the statistical properties of this estimator to propose several decompositions of the bias term and discuss their potential applications. Next, I devise a strategy to assess the robustness of the new estimator to omitted confounders that could potentially invalidate results. Finally, I illustrate these methods through an application: a PCRD aimed at evaluating the impact of female leadership during the COVID-19 pandemic.

Keywords: Regression discontinuity designs; Close elections; Bias correction; Sensitivity analysis. (search for similar items in EconPapers)
JEL-codes: C18 C51 P00 (search for similar items in EconPapers)
Pages: 87 pages
Date: 2023-08-25
New Economics Papers: this item is included in nep-ecm and nep-inv
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:col:000089:020304

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