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

67 Pages Posted: 28 May 2020 Last revised: 17 Jun 2021

See all articles by Elliott Ash

Elliott Ash

ETH Zürich

Sergio Galletta

University of Bergamo; ETH Zürich

Tommaso Giommoni

ETH Zürich

Multiple version iconThere are 2 versions of this paper

Date Written: June 17, 2021

Abstract

Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted classifier to predict the presence of corruption in held-out test data. The trained model, when applied to new data, provides a prediction-based measure of corruption that can be used for new empirical analysis or to support policy responses. We validate the empirical usefulness of this measure by replicating and extending some previous empirical evidence on corruption issues in Brazil. We then explore how the predictions can be used to support policies toward corruption. Our policy simulations show that, relative to the status quo policy of random audits, a targeted policy guided by the machine predictions could detect almost twice as many corrupt municipalities for the same audit rate. Similar gains can be achieved for a politically neutral targeting policy that equalizes audit rates across political parties.

Keywords: algorithmic decision-making, corruption policy, local public finance

JEL Classification: D73, E62, K14, K42

Suggested Citation

Ash, Elliott and Galletta, Sergio and Giommoni, Tommaso, A Machine Learning Approach to Analyze and Support Anti-Corruption Policy (June 17, 2021). Available at SSRN: https://ssrn.com/abstract=3589545 or http://dx.doi.org/10.2139/ssrn.3589545

Elliott Ash (Contact Author)

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Sergio Galletta

University of Bergamo ( email )

Via Salvecchio, 19
Bergamo, 24129
Italy

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Tommaso Giommoni

ETH Zürich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

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