A Machine Learning Approach to Analyze and Support Anticorruption Policy

Elliott Ash, Sergio Galletta and Tommaso Giommoni

American Economic Journal: Economic Policy, 2025, vol. 17, issue 2, 162-93

Abstract: 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-codes: C45 D73 H70 H83 K42 O17 (search for similar items in EconPapers)
Date: 2025
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Working Paper: A Machine Learning Approach to Analyze and Support Anti-Corruption Policy (2021)
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DOI: 10.1257/pol.20210618

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