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

72 Pages Posted: 28 May 2020 Last revised: 4 Nov 2022

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

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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