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Crime is Terribly Revealing: Information Technology and Police Productivity

Author

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  • Giovanni Mastrobuoni

Abstract

An increasing number of police departments use information technology (IT) to optimize patrolling strategies, yet little is known about its effectiveness in preventing crime. Based on quasi-random access to “predictive policing,” this study shows that IT improves police productivity as measured by crime clearance rates. Thanks to detailed information on individual incidents and offender-level identifiers it also shows that criminals strategies are predictable. Moreover, the introduction of predictive policing coincides with a large negative trend-discontinuity in crime rates. The benefit–cost ratio of this IT innovation appears to be large.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:restud:v:87:y:2020:i:6:p:2727-2753.
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    File URL: http://hdl.handle.net/10.1093/restud/rdaa009
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    Citations

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    Cited by:

    1. d'Este, Rocco & Yuchtman, Noam, 2023. "Correcting Racial Injustice: Forensic DNA Technology and the Exoneration of the Wrongfully Convicted," IZA Discussion Papers 16076, Institute of Labor Economics (IZA).
    2. Ritika Jain & Shreya Biswas, 2021. "The road to safety- Examining the nexus between road infrastructure and crime in rural India," Papers 2112.07314, arXiv.org.
    3. Do,Quy-Toan & Gomez Parra,Nicolas & Rijkers,Bob, 2021. "Transnational Terrorism and the Internet," Policy Research Working Paper Series 9885, The World Bank.
    4. d'Este, Rocco, 2022. "Scientific Advancements in Illegal Drugs Production and Institutional Responses: New Psychoactive Substances, Self-Harm, and Violence inside Prisons," IZA Discussion Papers 15248, Institute of Labor Economics (IZA).
    5. Elisa Facchetti, 2024. "Police infrastructure, police performance, and crime: Evidence from austerity cuts," IFS Working Papers W24/16, Institute for Fiscal Studies.
    6. Tom Kirchmaier & Ekaterina Oparina, 2024. "Under pressure: Victim withdrawal and police officer workload," CEP Discussion Papers dp1985, Centre for Economic Performance, LSE.
    7. 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.
    8. Weiguang Deng & Xue Li & Zijun Luo, 2023. "A model of police financing through income and consumption taxes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 70(3), pages 217-230, July.
    9. Vitezslav Titl & Deni Mazrekaj & Fritz Schiltz, 2024. "Identifying Politically Connected Firms: A Machine Learning Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 137-155, February.
    10. Do, Quy-Toan & Gomez-Parra, Nicolas & Rijkers, Bob, 2023. "Transnational terrorism and the internet," Journal of Development Economics, Elsevier, vol. 164(C).
    11. Chaudhary, Amit, 2021. "Do workers, managers, and stations matter for effective policing? A decomposition of productivity into three dimensions of unobserved heterogeneity," The Warwick Economics Research Paper Series (TWERPS) 1377, University of Warwick, Department of Economics.
    12. 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).
    13. Federico Masera, 2022. "The economics of policing and crimeThe economics of policing and crime," Chapters, in: Paolo Buonanno & Paolo Vanin & Juan Vargas (ed.), A Modern Guide to the Economics of Crime, chapter 2, pages 12-29, Edward Elgar Publishing.

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