Data and Code for: “A Machine Learning Approach to Analyze and Support Anti-Corruption Policy”

Principal Investigator(s):  View help for Principal Investigator(s) Elliott Ash, ETHZ; Sergio Galletta, ETHZ; Tommaso Giommoni, University of Amsterdam

Version:  View help for Version V1

Name File Type Size Last Modified
  replication_pack_aej_policy 04/22/2025 07:39:AM

Project Citation: 

Ash, Elliott, Galletta, Sergio, and Giommoni, Tommaso. Data and Code for: “A Machine Learning Approach to Analyze and Support Anti-Corruption Policy.” Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-05-05. https://doi.org/10.3886/E197821V1

Project Description

Summary:  View help for Summary 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.

Scope of Project

JEL Classification:  View help for JEL Classification
      C53 Forecasting Models; Simulation Methods
      D73 Bureaucracy; Administrative Processes in Public Organizations; Corruption
      H83 Public Administration; Public Sector Accounting and Audits
      K42 Illegal Behavior and the Enforcement of Law
Geographic Coverage:  View help for Geographic Coverage Brazil

Methodology

Geographic Unit:  View help for Geographic Unit municipality

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Published Versions

V1 [2025-05-05]

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DDI 2.5

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