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What Predicts Corruption?

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  • Colonnelli, Emanuele
  • Gallego, Jorge
  • Prem, Mounu

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.

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  • Colonnelli, Emanuele & Gallego, Jorge & Prem, Mounu, 2020. "What Predicts Corruption?," SocArXiv fq2xb_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:fq2xb_v1
    DOI: 10.31219/osf.io/fq2xb_v1
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    1. Colonnelli, Emanuele & Lagaras, Spyridon & Ponticelli, Jacopo & Prem, Mounu & Tsoutsoura, Margarita, 2022. "Revealing corruption: Firm and worker level evidence from Brazil," Journal of Financial Economics, Elsevier, vol. 143(3), pages 1097-1119.
    2. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2020. "Corruption in the Times of Pandemia," Working papers 43, Red Investigadores de Economía.
    3. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2022. "Predicting Politicians' Misconduct: Evidence from Colombia," SocArXiv 5dp8t, Center for Open Science.

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