A Machine Learning Approach to Analyze and Support Anticorruption Policy
| Authors |
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| Publication date | 05-2025 |
| Journal | American Economic Journal. Economic Policy |
| Volume | Issue number | 17 | 2 |
| Pages (from-to) | 162-193 |
| Organisations |
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| 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. |
| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1257/pol.20210618 |
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