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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. 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.
    6. 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.
    7. 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.
    8. Do, Quy-Toan & Gomez-Parra, Nicolas & Rijkers, Bob, 2023. "Transnational terrorism and the internet," Journal of Development Economics, Elsevier, vol. 164(C).
    9. 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.
    10. 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).

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