A Machine Learning Approach to Analyze and Support Anticorruption Policy

• 成果类型: Article
• 署名作者: Ash, Elliott; Galletta, Sergio; Giommoni, Tommaso
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich; Centre for Economic Policy Research - UK; University of Amsterdam
• 刊物名称: AMERICAN ECONOMIC JOURNAL-ECONOMIC POLICY
• ISSN/ISSBN: 1945-7731
• DOI: 10.1257/pol.20210618
• 发表日期: 2025
• 页码: 162-193
• 关键词: field experiment; CORRUPTION; INFORMATION; prediction; allocation; IMPACT; audits; state

摘要

Can machine learning support better governance? This study uses a tree-based, gradient-boosted classifier to predict corruption in Brazilian municipalities using budget data as predictors. The trained model offers a predictive measure of corruption, which we validate through replication and extension of previous corruption studies. Our policy simulations show that machine learning can significantly enhance corruption detection: Compared to random audits, a machine-guided targeted policy could detect almost twice as many corrupt municipalities for the same audit rate. (JEL C45, D73, H70, H83, K42, O17)