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What predicts corruption?

dc.contributor.gruplacFacultad de Economíaspa
dc.creatorColonnelli, Emanuele
dc.creatorGallego Durán, Jorge Andrés
dc.creatorPrem, Mounu
dc.creatorGallego Durán, Jorge Andrés
dc.date.accessioned2019-02-08T14:17:49Z
dc.date.available2019-02-08T14:17:49Z
dc.date.created2019-02-07
dc.date.issued2019
dc.description.abstractUsing rich micro data from Brazil, we show that multiple popular machine learning models display extremely high levels of performance in predicting municipality-level corruption in public spending. Measures of private sector activity, financial development, and human capital are the strongest predictors of corruption, while public sector and political features play a secondary role. Our findings have implications for the design and cost-effectiveness of various anti-corruption policies.eng
dc.format.extent21spa
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_19026
dc.identifier.urihttp://repository.urosario.edu.co/handle/10336/19026
dc.language.isoengspa
dc.relation.citationTitleSerie Documentos de trabajo. Economía
dc.relation.urihttps://ideas.repec.org/p/col/000092/017144.html
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.ddcAdministración públicaspa
dc.subject.jelH5spa
dc.subject.keywordCorruptioneng
dc.subject.keywordMachine learningeng
dc.subject.keywordPredictioneng
dc.subject.lembCorrupción administrativaspa
dc.subject.lembCorrupción políticaspa
dc.subject.lembAuditoría administrativaspa
dc.titleWhat predicts corruption?spa
dc.typeworkingPapereng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de trabajospa
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