What Predicts Corruption?

33 Pages Posted: 13 Feb 2019 Last revised: 24 Dec 2020

See all articles by Emanuele Colonnelli

Emanuele Colonnelli

University of Chicago - Booth School of Business

Jorge A. Gallego

Universidad del Rosario

Mounu Prem

Universidad del Rosario

Date Written: December 23, 2020

Abstract

The ability to predict corruption is crucial to policy. Using rich micro-data from Brazil, we show that multiple machine learning models display high levels of performance in predicting municipality-level corruption in public spending. We then quantify which individual municipality features and groups of similar characteristics have the highest predictive power. We find that measures of private sector activity, financial development, and human capital are the strongest predictors of corruption, while public sector and political features play a secondary role. Our findings have implications for the design and cost-effectiveness of various anti-corruption policies.

Keywords: Corruption, Machine Learning, Prediction, Private Sector

JEL Classification: H5

Suggested Citation

Colonnelli, Emanuele and Gallego, Jorge A. and Prem, Mounu, What Predicts Corruption? (December 23, 2020). Available at SSRN: https://ssrn.com/abstract=3330651 or http://dx.doi.org/10.2139/ssrn.3330651

Emanuele Colonnelli (Contact Author)

University of Chicago - Booth School of Business ( email )

HOME PAGE: http://emanuelecolonnelli.com

Jorge A. Gallego

Universidad del Rosario ( email )

Calle 12 No. 6-25
Bogota, DC
Colombia

Mounu Prem

Universidad del Rosario ( email )

Casa Pedro Fermín
Calle 14 # 4-69
Bogota
Colombia

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