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What predicts corruption?

In: A Modern Guide to the Economics of Crime

Author

Listed:
  • Emanuele Colonnelli
  • Jorge Gallego
  • Mounu Prem

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.

Suggested Citation

  • Emanuele Colonnelli & Jorge Gallego & Mounu Prem, 2022. "What predicts corruption?," Chapters, in: Paolo Buonanno & Paolo Vanin & Juan Vargas (ed.), A Modern Guide to the Economics of Crime, chapter 16, pages 345-373, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:19378_16
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    Cited by:

    1. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    2. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2020. "Corruption in the Times of Pandemia," Working papers 43, Red Investigadores de Economía.
    3. Jhorland Ayala-García & Jaime Bonet-Morón & Gerson Javier Pérez-Valbuena & Eduardo José Heilbron-Fernández & Jéssica Dayana Suret-Leguizamón, 2022. "La corrupción en Colombia: un análisis integral," Documentos de Trabajo Sobre Economía Regional y Urbana 20080, Banco de la República, Economía Regional.
    4. Gustavo J. Bobonis & Luis Raúl Cámara Fuertes & Harold J. Toro & Julie Wilson, 2021. "Development and Decay: Political Organization, Economic Conditions, and Municipal Corruption in Puerto Rico, 1952-2015," Working Papers tecipa-687, University of Toronto, Department of Economics.
    5. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.

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