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  5. A Machine Learning Approach to Analyze and Support Anti-corruption Policy

American Economic Journal:
Economic Policy

ISSN 1945-7731 (Print) | ISSN 1945-774X (Online)

A Machine Learning Approach to Analyze and Support Anti-corruption Policy

  • Elliott Ash
  • Sergio Galletta
  • Tommaso Giommoni
American Economic Journal: Economic Policy (Forthcoming)

Abstract

Can machine learning support better governance? This study uses a tree-based gradientboosted 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.

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