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Addressing Bias in Politician Characteristic Regression Discontinuity Designs

Author

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  • Torres, Santiago

    (Universidad de los Andes, Facultad de Economía)

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.

Suggested Citation

  • Torres, Santiago, 2023. "Addressing Bias in Politician Characteristic Regression Discontinuity Designs," Documentos CEDE 20304, Universidad de los Andes, Facultad de Economía, CEDE.
    • Handle: RePEc:col:000089:020304
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    More about this item

    Keywords

    Regression discontinuity designs; Close elections; Bias correction; Sensitivity analysis.;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • P00 - Political Economy and Comparative Economic Systems - - General - - - General

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