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Administrative Records Mask Racially Biased Policing

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  • KNOX, DEAN
  • LOWE, WILL
  • MUMMOLO, JONATHAN

Abstract

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this article, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, and many quantities of interest are unidentified—even among investigated individuals—absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely. We conclude by outlining a general and feasible design for future studies that is robust to this inferential snare.

Suggested Citation

  • Knox, Dean & Lowe, Will & Mummolo, Jonathan, 2020. "Administrative Records Mask Racially Biased Policing," American Political Science Review, Cambridge University Press, vol. 114(3), pages 619-637, August.
    • Handle: RePEc:cup:apsrev:v:114:y:2020:i:3:p:619-637_2
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    Cited by:

    1. Pradhi Aggarwal & Alec Brandon & Ariel Goldszmidt & Justin Holz & John List & Ian Muir & Gregory Sun & Thomas Yu, 2022. "High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding," Natural Field Experiments 00764, The Field Experiments Website.
    2. Quinn, Katherine G. & Edwards, Travonne & Johnson, Anthony & Takahashi, Lois & Dakin, Andrea & Bouacha, Nora & Voisin, Dexter, 2023. "Understanding the impact of police brutality on Black sexually minoritized men," Social Science & Medicine, Elsevier, vol. 334(C).
    3. Campedelli, Gian Maria, 2022. "Explainable machine learning for predicting homicide clearance in the United States," Journal of Criminal Justice, Elsevier, vol. 79(C).
    4. Charles Crabtree & John B. Holbein & J. Quin Monson, 2022. "Patient traits shape health-care stakeholders’ choices on how to best allocate life-saving care," Nature Human Behaviour, Nature, vol. 6(2), pages 244-257, February.
    5. Nayoung Rim & Roman Rivera & Andrea Kiss & Bocar Ba, 2020. "The Black-White Recognition Gap in Award Nominations," Working Papers 2020-065, Human Capital and Economic Opportunity Working Group.
    6. Desmond Ang & Panka Bencsik & Jesse Bruhn & Ellora Derenoncourt, 2021. "Police violence reduces civilian cooperation and engagement with law enforcement," Working Papers 2021-16, Princeton University. Economics Department..
    7. Desmond Ang, 2021. "The Effects of Police Violence on Inner-City Students," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(1), pages 115-168.
    8. Brendan O'Flaherty & Rajiv Sethi & Morgan Williams, 2024. "The nature, detection, and avoidance of harmful discrimination in criminal justice," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 43(1), pages 289-320, January.
    9. Desmond Ang & Panka Bencsik & Jesse Bruhn & Ellora Derenoncourt, 2023. "Shots Fired: Crime and Community Engagement with Law Enforcement after High-profile Acts of Police Violence," Working Papers 315, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. Gian Maria Campedelli, 2022. "Explainable Machine Learning for Predicting Homicide Clearance in the United States," Papers 2203.04768, arXiv.org.
    11. Anuli Njoku & Marcelin Joseph & Rochelle Felix, 2021. "Changing the Narrative: Structural Barriers and Racial and Ethnic Inequities in COVID-19 Vaccination," IJERPH, MDPI, vol. 18(18), pages 1-14, September.

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