Ítem
Acceso Abierto
Predicting Politicians’ Misconduct: Evidence from Colombia
| dc.contributor.gruplac | Grupo de investigaciones. Facultad de Economía. Universidad del Rosario | es |
| dc.creator | Gallego Durán, Jorge Andrés | |
| dc.creator | Prem, Mounu | |
| dc.creator | Vargas Duque, Juan Fernando | |
| dc.date.accessioned | 2022-10-18T21:15:27Z | |
| dc.date.available | 2022-10-18T21:15:27Z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2022-10 | |
| dc.description | La corrupción tiene efectos generalizados en el desarrollo económico y el bienestar de la población. A pesar de ser crucial y necesario, combatir la corrupción no es una tarea fácil porque es un fenómeno difícil de medir y detectar. Sin embargo, los avances recientes en el campo de la inteligencia artificial pueden ayudar en esta búsqueda. En este artículo, proponemos el uso de modelos de aprendizaje automático para predecir la corrupción a nivel municipal en un país en desarrollo. Usando datos de procesos disciplinarios llevados a cabo por una agencia anticorrupción en Colombia, entrenamos cuatro modelos canónicos (Random Forests, Gradient Boosting Machine, Lasso y Neural Networks) y ensamblamos sus predicciones para predecir si un alcalde cometerá o no actos. de corrupción Nuestros modelos logran niveles aceptables de desempeño, basados en métricas como la precisión y el área bajo la curva ROC, lo que demuestra que estas herramientas son útiles para predecir dónde es más probable que ocurra un mal comportamiento. Además, nuestro análisis de la importancia de las características nos muestra qué grupos de variables son más importantes para predecir la corrupción. | es |
| dc.description.abstract | Corruption has pervasive effects on economic development and the well-being of the population. Despite being crucial and necessary, fighting corruption is not an easy task because it is a difficult phenomenon to measure and detect. However, recent advances in the field of artificial intelligence may help in this quest. In this article, we propose the use of machine learning models to predict municipality-level corruption in a developing country. Using data from disciplinary prosecutions conducted by an anti-corruption agency in Colombia, we trained four canonical models (Random Forests, Gradient Boosting Machine, Lasso, and Neural Networks), and ensemble their predictions, to predict whether or not a mayor will commit acts of corruption. Our models achieve acceptable levels of performance, based on metrics such as the precision and the area under the ROC curve, demonstrating that these tools are useful in predicting where misbehavior is most likely to occur. Moreover, our feature-importance analysis shows us which groups of variables are most important upon predicting corruption. | es |
| dc.format.extent | 21 pp | es |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/36744 | |
| dc.language.iso | spa | es |
| dc.publisher | Universidad del Rosario | |
| dc.publisher.department | Facultad de Economía | |
| dc.relation.uri | https://ideas.repec.org/p/col/000092/020504.html | |
| dc.rights.accesRights | info:eu-repo/semantics/openAccess | es |
| dc.rights.acceso | Abierto (Texto Completo) | es |
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| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | |
| dc.subject | Predicción en Colombia | |
| dc.subject | Corrupción en Colombia | |
| dc.subject | Desarrollo económico | |
| dc.subject.ddc | Economía | es |
| dc.subject.keyword | Prediction in Colombia | es |
| dc.subject.keyword | Corruption in Colombia | es |
| dc.subject.keyword | Machine learning | es |
| dc.subject.keyword | Economic development | es |
| dc.title | Predicting Politicians’ Misconduct: Evidence from Colombia | es |
| dc.type | workingPaper | es |
| dc.type.hasVersion | info:eu-repo/semantics/draft | |
| dc.type.spa | Documento de trabajo | es |
| local.department.report | Facultad de Economía | es |
