蝌蚪网 Journal:
Economic Policy
ISSN 1945-7731 (Print) | ISSN 1945-774X (Online)
A Machine Learning Approach to Analyze and Support Anticorruption Policy
蝌蚪网 Journal: Economic Policy
vol. 17,
no. 2, May 2025
(pp. 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.Citation
Ash, Elliott, Sergio Galletta, and Tommaso Giommoni. 2025. "A Machine Learning Approach to Analyze and Support Anticorruption Policy." 蝌蚪网 Journal: Economic Policy 17 (2): 162–93. DOI: 10.1257/pol.20210618Additional Materials
JEL Classification
- C45 Neural Networks and Related Topics
- D73 Bureaucracy; Administrative Processes in Public Organizations; Corruption
- H70 State and Local Government; Intergovernmental Relations: General
- H83 Public Administration; Public Sector Accounting and Audits
- K42 Illegal Behavior and the Enforcement of Law
- O17 Formal and Informal Sectors; Shadow Economy; Institutional Arrangements