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What predicts corruption?

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  • Colonnelli, E
  • Gallego, J.A.
  • Prem, M

Abstract

Using rich micro data from Brazil, we show that multiple popular machine learning models display extremely high levels of performance in predicting municipality-level corruption in public spending. 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

  • Colonnelli, E & Gallego, J.A. & Prem, M, 2019. "What predicts corruption?," Documentos de Trabajo 17144, Universidad del Rosario.
    • Handle: RePEc:col:000092:017144
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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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