Electoral competition and platform choice: a computational linguistics approach based on Brazilian data

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dc.contributor.advisorFirpo, Sergio Pinheiro
dc.contributor.authorPereira, Leila Albuquerque Rocha
dc.coverage.cidadeSão Paulopt_BR
dc.coverage.paisBrasilpt_BR
dc.creatorPereira, Leila Albuquerque Rocha
dc.date.accessioned2022-08-04T01:42:33Z
dc.date.available2022-08-04T01:42:33Z
dc.date.issued2021
dc.description.otherThis dissertation empirically investigates some valuable aspects of the electoral competition theories by taking advantage of computational linguistic methods that transform texts into data. I start by employing statistical text analysis methods (i.e., LDA model and Wordscores) to build and describe a unique dataset containing the topics discussed and the partisan-scores of Brazilian mayoral candidates' political platforms in 2012 and 2016 elections. Then, I use this dataset to test some theoretical implications of both the Downsian location models and the Salience Theory. From a Downsian perspective, I investigate the relationship between campaign spending and platform choice by testing whether a candidate's platform positioning strategy, compared to her opponent, is infuenced by the amount of money she can spend in an election. Then, from a Salience Theory perspective, I analyze the relationship between reputation and platform choice by estimating the incumbency effect on a candidate's platform issue concentration. In a nutshell, I find that candidates seem to be making strategic decisions concerning their platform's contents and partisan leaning. The data's descriptive analysis shows a systematic correlation between the candidate's characteristics and her platform's contents. Moreover, the empirical evidence supports the theories that connect platform choice to reputation but not the theories that establish a link between platform choice and campaign spending. In particular, I find that opposing candidates' strategies to diferentiate their platforms do not change significantly depending on the size of their campaign expenditures. Furthermore, I show that incumbents tend to produce more concentrated platforms than challengers.pt_BR
dc.description.qualificationlevelDoutoradopt_BR
dc.format.extent198 p.pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/3861
dc.language.isoInglêspt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsElectoral competitionpt_BR
dc.subject.keywordspolitical platformspt_BR
dc.subject.keywordsDownsian Modelspt_BR
dc.subject.keywordsSalience theorypt_BR
dc.subject.keywordsnatural language processingpt_BR
dc.subject.keywordsLatent Dirichlet Allocationpt_BR
dc.subject.keywordswordscorept_BR
dc.subject.keywordsdiscontinuos regressionpt_BR
dc.titleElectoral competition and platform choice: a computational linguistics approach based on Brazilian datapt_BR
dc.typedoctoral thesis
dspace.entity.typePublication
local.contributor.boardmemberLUCAS MARTINS NOVAES
local.contributor.boardmemberCorbi, Raphael Botturapt_BR
local.contributor.boardmemberEstevan, Fernanda Gonçalves de La Fuentept_BR
local.contributor.boardmemberBueno, Natália Salgadopt_BR
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeTesept_BR
relation.isBoardMemberOfPublication128ca8db-42f9-48cb-a0a5-02028ca4e870
relation.isBoardMemberOfPublication.latestForDiscovery128ca8db-42f9-48cb-a0a5-02028ca4e870
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Doutorado em Economia dos Negócios