Ash, E., Galletta, S. and Giommoni, T. (2025). A Machine Learning Approach to Analyze and Support Anticorruption Policy American Economic Journal: Economic Policy, 17(2):162--193.
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Affiliated author
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Publication year2025
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JournalAmerican Economic Journal: Economic Policy
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.