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How does technology‐based monitoring affect street‐level bureaucrats' behavior? An analysis of body‐worn cameras and police actions

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  • Inkyu Kang

Abstract

Body‐worn cameras may produce varying effects on police behavior, depending on the agency‐specific accountability context in which the technology adoption is embedded. The cameras may encourage coercive police actions when acquired to incentivize performance, such as by protecting officers from false complaints. By contrast, when acquired to enhance procedural accountability, such as by enabling closer scrutiny of officer misconduct, the cameras may discourage coercive police actions. Based on this framework, this study examined the case of the New Orleans Police Department, an agency that implemented a body‐worn camera program to enhance both performance and procedural accountability. Results of Bayesian structural time‐series modeling with synthetic control show that the program increased the number of investigatory stops and follow‐up measures (i.e., frisk, search, citation, arrest) while decreasing the ratio of more‐to‐less coercive measures during stops (i.e., arrest/citation‐to‐warning ratio and search‐to‐frisk ratio). However, the program had a null effect on the minority‐to‐White suspect ratio, despite the agency's bias‐free policing initiative. The percentage of frisks and searches detecting drugs or weapons also declined. A broader implication of the findings is that technology‐based monitoring mechanisms are important, but not a silver bullet for improving the behavior of street‐level bureaucrats.

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  • Inkyu Kang, 2023. "How does technology‐based monitoring affect street‐level bureaucrats' behavior? An analysis of body‐worn cameras and police actions," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(4), pages 971-991, September.
    • Handle: RePEc:wly:jpamgt:v:42:y:2023:i:4:p:971-991
    DOI: 10.1002/pam.22493
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    1. Cynthia Lum & Christopher S. Koper & David B. Wilson & Megan Stoltz & Michael Goodier & Elizabeth Eggins & Angela Higginson & Lorraine Mazerolle, 2020. "Body‐worn cameras’ effects on police officers and citizen behavior: A systematic review," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(3), September.
    2. Fiammetta Menchetti & Fabrizio Cipollini & Fabrizia Mealli, 2023. "Combining counterfactual outcomes and ARIMA models for policy evaluation," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-24.
    3. Commandeur, Jacques J.F. & Koopman, Siem Jan, 2007. "An Introduction to State Space Time Series Analysis," OUP Catalogue, Oxford University Press, number 9780199228874.
    4. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    5. Varano, Sean P. & Schafer, Joseph A. & Cancino, Jeffrey M. & Decker, Scott H. & Greene, Jack R., 2010. "A tale of three cities: Crime and displacement after Hurricane Katrina," Journal of Criminal Justice, Elsevier, vol. 38(1), pages 42-50, January.
    6. Sounman Hong, 2016. "Representative Bureaucracy, Organizational Integrity, and Citizen Coproduction: Does an Increase in Police Ethnic Representativeness Reduce Crime?," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(1), pages 11-33, January.
    7. Headley, Andrea M. & Guerette, Rob T. & Shariati, Auzeen, 2017. "A field experiment of the impact of body-worn cameras (BWCs) on police officer behavior and perceptions," Journal of Criminal Justice, Elsevier, vol. 53(C), pages 102-109.
    8. Cook, Brian J. & Wood, B. Dan, 1989. "Principal-Agent Models of Political Control of Bureaucracy," American Political Science Review, Cambridge University Press, vol. 83(3), pages 965-978, September.
    9. Terry M. Moe, 2012. "Delegation, Control, and the Study of Public Bureaucracy [The Handbook of Organizational Economics]," Introductory Chapters,, Princeton University Press.
    10. Emiel Kerpershoek & Martijn Groenleer & Hans de Bruijn, 2016. "Unintended responses to performance management in dutch hospital care: Bringing together the managerial and professional perspectives," Public Management Review, Taylor & Francis Journals, vol. 18(3), pages 417-436, March.
    11. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    12. McCarty, William P. & Meng, Zhe & Buslik, Marc S. & Moreno, Rafael, 2021. "Body-worn cameras and arrest: Zooming in on disaggregated metrics and possible unintended consequences," Journal of Criminal Justice, Elsevier, vol. 75(C).
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