Addressing bias in politician characteristic regression discontinuity designs

dc.contributorFergusson Talero, Leopoldo
dc.contributorFernández Sierra, Manuel
dc.contributorCamacho González, Adriana
dc.contributorMarshall, John
dc.creatorTorres Paz, Santiago
dc.date.accessioned2023-08-02T19:40:04Z
dc.date.accessioned2023-09-07T02:12:08Z
dc.date.available2023-08-02T19:40:04Z
dc.date.available2023-09-07T02:12:08Z
dc.date.created2023-08-02T19:40:04Z
dc.date.issued2023-08-01
dc.identifierhttp://hdl.handle.net/1992/69091
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8729053
dc.description.abstractPolitician 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.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherMaestría en Economía
dc.publisherFacultad de Economía
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dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleAddressing bias in politician characteristic regression discontinuity designs
dc.typeTrabajo de grado - Maestría


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