Predicting politicians' misconduct: Evidence from Colombia

Jorge Gallego, Mounu Prem, Juan F. Vargas

Producción científica: Contribución a una revistaArtículo de Investigaciónrevisión exhaustiva

2 Citas (Scopus)


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 receiver-operating characteristic 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 in predicting corruption.

Idioma originalInglés estadounidense
Número de artículoe41
PublicaciónData and Policy
EstadoPublicada - nov. 14 2022

Áreas temáticas de ASJC Scopus

  • Informática (miscelánea)
  • Inteligencia artificial
  • Ciencias sociales (miscelánea)
  • Administración pública


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