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Gotham city. Predicting ‘corrupted’ municipalities with machine learning

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  • de Blasio, Guido
  • D'Ignazio, Alessio
  • Letta, Marco

Abstract

The economic costs of white-collar crimes, such as corruption, bribery, embezzlement, abuse of authority, and fraud, are substantial. How to eradicate them is a mounting task in many countries. Using police archives, we apply machine learning algorithms to predict corruption crimes in Italian municipalities. Drawing on input data from 2011, our classification trees correctly forecast over 70 % (about 80 %) of the municipalities that will experience corruption episodes (an increase in corruption crimes) over the period 2012–2014. We show that algorithmic predictions could strengthen the ability of the 2012 Italy's anti-corruption law to fight white-collar delinquencies and prevent the occurrence of such crimes while preserving transparency and accountability of the policymaker.

Suggested Citation

  • de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:tefoso:v:184:y:2022:i:c:s0040162522005376
    DOI: 10.1016/j.techfore.2022.122016
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    1. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    2. 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.
    3. Aaron Chalfin & Oren Danieli & Andrew Hillis & Zubin Jelveh & Michael Luca & Jens Ludwig & Sendhil Mullainathan, 2016. "Productivity and Selection of Human Capital with Machine Learning," American Economic Review, American Economic Association, vol. 106(5), pages 124-127, May.
    4. Giovanni Mastrobuoni, 2020. "Crime is Terribly Revealing: Information Technology and Police Productivity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(6), pages 2727-2753.
    5. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    6. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    7. Ilaria Angelis & Guido Blasio & Lucia Rizzica, 2020. "Lost in Corruption. Evidence from EU Funding to Southern Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(2), pages 355-377, July.
    8. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    9. Ceren Baysan & Marshall Burke & Felipe González & Solomon Hsiang & Edward Miguel, 2018. "Economic and Non-Economic Factors in Violence: Evidence from Organized Crime, Suicides and Climate in Mexico," NBER Working Papers 24897, National Bureau of Economic Research, Inc.
    10. Baysan, Ceren & Burke, Marshall & González, Felipe & Hsiang, Solomon & Miguel, Edward, 2019. "Non-economic factors in violence: Evidence from organized crime, suicides and climate in Mexico," Journal of Economic Behavior & Organization, Elsevier, vol. 168(C), pages 434-452.
    11. Félix J. López-Iturriaga & Iván Pastor Sanz, 2018. "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 975-998, December.
    12. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    13. Lentz, E.C. & Michelson, H. & Baylis, K. & Zhou, Y., 2019. "A data-driven approach improves food insecurity crisis prediction," World Development, Elsevier, vol. 122(C), pages 399-409.
    14. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    15. James Horrocks & Andrea Kutinova Menclova, 2011. "The effects of weather on crime," New Zealand Economic Papers, Taylor & Francis Journals, vol. 45(3), pages 231-254, September.
    16. Knippenberg, Erwin & Jensen, Nathaniel & Constas, Mark, 2019. "Quantifying household resilience with high frequency data: Temporal dynamics and methodological options," World Development, Elsevier, vol. 121(C), pages 1-15.
    17. Gorr, Wilpen & Harries, Richard, 2003. "Introduction to crime forecasting," International Journal of Forecasting, Elsevier, vol. 19(4), pages 551-555.
    18. Ranson, Matthew, 2014. "Crime, weather, and climate change," Journal of Environmental Economics and Management, Elsevier, vol. 67(3), pages 274-302.
    19. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    20. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    21. Hossain, Marup & Mullally, Conner & Asadullah, M. Niaz, 2019. "Alternatives to calorie-based indicators of food security: An application of machine learning methods," Food Policy, Elsevier, vol. 84(C), pages 77-91.
    22. Mohler, George, 2014. "Marked point process hotspot maps for homicide and gun crime prediction in Chicago," International Journal of Forecasting, Elsevier, vol. 30(3), pages 491-497.
    23. Jonah E. Rockoff & Brian A. Jacob & Thomas J. Kane & Douglas O. Staiger, 2011. "Can You Recognize an Effective Teacher When You Recruit One?," Education Finance and Policy, MIT Press, vol. 6(1), pages 43-74, January.
    24. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
    25. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    26. Vincenzo Carrieri & Raffele Lagravinese & Giuliano Resce, 2021. "Predicting vaccine hesitancy from area‐level indicators: A machine learning approach," Health Economics, John Wiley & Sons, Ltd., vol. 30(12), pages 3248-3256, December.
    27. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    28. Linden McBride & Austin Nichols, 2018. "Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning," The World Bank Economic Review, World Bank, vol. 32(3), pages 531-550.
    29. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    30. Ballestar, María Teresa & Doncel, Luis Miguel & Sainz, Jorge & Ortigosa-Blanch, Arturo, 2019. "A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
    31. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
    32. Garbero, Alessandra & Carneiro, Bia & Resce, Giuliano, 2021. "Harnessing the power of machine learning analytics to understand food systems dynamics across development projects," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    33. Dana Chandler & Steven D. Levitt & John A. List, 2011. "Predicting and Preventing Shootings among At-Risk Youth," American Economic Review, American Economic Association, vol. 101(3), pages 288-292, May.
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    More about this item

    Keywords

    Crime forecasting; 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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