Predicting Police Misconduct

92 Pages Posted: 15 May 2024

See all articles by Greg Stoddard

Greg Stoddard

University of Chicago Crime Lab

Dylan Fitzpatrick

University of Chicago Crime Lab

Jens Ludwig

Georgetown University - Public Policy Institute (GPPI); National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

Multiple version iconThere are 2 versions of this paper

Date Written: May 15, 2024

Abstract

Whether police misconduct can be prevented depends partly on whether it can be predicted. We show police misconduct is partially predictable and that estimated misconduct risk is not simply an artifact of measurement error or a proxy for officer activity. We also show many officers at risk of on-duty misconduct have elevated off-duty risk too, suggesting a potential link between accountability and officer wellness. We show that targeting preventive interventions even with a simple prediction model – number of past complaints, which is not as predictive as machine learning but lower-cost to deploy – has marginal value of public funds of infinity.

JEL Classification: C0, K0

Suggested Citation

Stoddard, Greg and Fitzpatrick, Dylan and Ludwig, Jens, Predicting Police Misconduct (May 15, 2024). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2024-62, Available at SSRN: https://ssrn.com/abstract=4829604 or http://dx.doi.org/10.2139/ssrn.4829604

Greg Stoddard

University of Chicago Crime Lab ( email )

33 North LaSalle Street
Suite 1600
Chicago, IL 60602
United States

Dylan Fitzpatrick

University of Chicago Crime Lab ( email )

33 North LaSalle Street
Suite 1600
Chicago, IL 60602
United States

Jens Ludwig (Contact Author)

Georgetown University - Public Policy Institute (GPPI) ( email )

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Washington, DC 20057
United States

National Bureau of Economic Research (NBER)

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IZA Institute of Labor Economics

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Germany

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