Predicting politicians' misconduct: Evidence from Colombia. (14th November 2022)

• Record Type: Journal Article
• Authors: Gallego, Jorge; Prem, Mounu; Vargas, Juan F.
• Is Part Of: Data & policy. Volume 4(2022)
• Publication Date: 2022-11-14
• Subjects: Colombia -- corruption -- machine learning -- prediction; Policy sciences -- Periodicals; Policy sciences -- Statistical methods -- Periodicals; Policy sciences -- Data processing -- Periodicals; Decision making -- Data processing -- Periodicals; 320.60727
• DOI: 10.1017/dap.2022.35
• Languages: English
• ISSNs: 2632-3249

Abstract

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 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.