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Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities

Author

Listed:
  • Guido de Blasio

    (Structural Economic Analysis Directorate, Bank of Italy)

  • Alessio D'Ignazio

    (Structural Economic Analysis Directorate, Bank of Italy)

  • Marco Letta

Abstract

Using police archives, we apply machine learning algorithms to predict corruption crimes in Italian municipalities during the period 2012-2014. We correctly identify over 70% (slightly less than 80%) of the municipalities that will experience corruption episodes (an increase in corruption crimes). We show that algorithmic predictions could strengthen the ability of the 2012 Italy’s anti-corruption law to fight white-collar delinquencies.

Suggested Citation

  • Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    • Handle: RePEc:saq:wpaper:16/20
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    File URL: http://www.diss.uniroma1.it/sites/default/files/allegati/DiSSE_deBlasioetal_wp16_2020.pdf
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    References listed on IDEAS

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    1. Gallego, Jorge & Prem, Mounu & Vargas, Juan F., 2022. "Predicting Politicians' Misconduct: Evidence from Colombia," SocArXiv 5dp8t, Center for Open Science.

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    More about this item

    Keywords

    crime prediction; white-collar crimes; machine learning; classification trees; policy targeting;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
    • K10 - Law and Economics - - Basic Areas of Law - - - General (Constitutional Law)

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