A large-scale analysis of racial disparities in police stops across the United States

Abstract

We assessed racial disparities in policing in the United States by compiling and analysing a dataset detailing nearly 100 million traffic stops conducted across the country. We found that black drivers were less likely to be stopped after sunset, when a ‘veil of darkness’ masks one’s race, suggesting bias in stop decisions. Furthermore, by examining the rate at which stopped drivers were searched and the likelihood that searches turned up contraband, we found evidence that the bar for searching black and Hispanic drivers was lower than that for searching white drivers. Finally, we found that legalization of recreational marijuana reduced the number of searches of white, black and Hispanic drivers—but the bar for searching black and Hispanic drivers was still lower than that for white drivers post-legalization. Our results indicate that police stops and search decisions suffer from persistent racial bias and point to the value of policy interventions to mitigate these disparities.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Geographic coverage of compiled traffic stop data.
Fig. 2: An illustration of the veil-of-darkness test for stops occurring in three short time windows in a single state, Texas.
Fig. 3: Hit rates and inferred search thresholds by race and location.
Fig. 4: Percentage of stops that resulted in a search before and after recreational marijuana was legalized in Colorado and Washington at the end of 2012.
Fig. 5: Percentage of stops that resulted in a search in 12 states in which recreational marijuana was not legalized.

Data availability

All data to reproduce the findings of this study are available at https://openpolicing.stanford.edu.

Code availability

All code to reproduce the findings of this study is available at https://openpolicing.stanford.edu.

References

  1. 1.

    Davis, E., Whyde, A.& Langton, L. Contacts between Police and the Public, 2015 (Bureau of Justice Statistics, 2018); http://www.bjs.gov/index.cfm?ty=pbdetail&iid=6406

  2. 2.

    Langton, L. & Durose, M. Police Behavior during Traffic and Street Stops, 2011 https://www.bjs.gov/content/pub/pdf/pbtss11.pdf (US Department of Justice, 2013).

  3. 3.

    Baumgartner, F.R., Epp, D.A. & Shoub, K. Suspect Citizens: What 20 Million Traffic Stops Tell Us about Policing and Race (Cambridge Univ. Press, 2018).

  4. 4.

    Glaser, J. Suspect Race: Causes and Consequences of Racial Profiling (Oxford Univ. Press, 2015).

  5. 5.

    Epp, C., Maynard-Moody, S. & Haider-Markel, D. Pulled Over: How Police Stops Define Race and Citizenship (Univ. of Chicago Press, 2014).

  6. 6.

    Antonovics, K. & Knight, B. A new look at racial profiling: evidence from the Boston Police Department. Rev. Econ. Stat. 91, 163–177 (2009).

  7. 7.

    Simoiu, C., Corbett-Davies, S. & Goel, S. The problem of infra-marginality in outcome tests for discrimination. Ann. Appl. Stat. 11, 1193–1216 (2017).

  8. 8.

    Anwar, S. & Fang, H. An alternative test of racial prejudice in motor vehicle searches: theory and evidence. Am. Econ. Rev. 96, 127–151 (2006).

  9. 9.

    Ridgeway, G. & MacDonald, J. Doubly robust internal benchmarking and false discovery rates for detecting racial bias in police stops. J. Am. Stat. Assoc. 104, 661–668 (2009).

  10. 10.

    Ridgeway, G. Assessing the effect of race bias in post-traffic stop outcomes using propensity scores. J. Quant. Criminol. 22, 1–29 (2006).

  11. 11.

    Ryan, M. Frisky business: race, gender and police activity during traffic stops. Eur. J. Law Econ. 41, 65–83 (2016).

  12. 12.

    Rojek, J., Rosenfeld, R. & Decker, S. The influence of driver’s race on traffic stops in Missouri. Police Q. 7, 126–147 (2004).

  13. 13.

    Smith, M. & Petrocelli, M. Racial profiling? A multivariate analysis of police traffic stop data. Police Q. 4, 4–27 (2001).

  14. 14.

    Warren, P., Tomaskovic-Devey, D., Smith, W., Zingraff, M. & Mason, M. Driving while black: bias processes and racial disparity in police stops. Criminology 44, 709–738 (2006).

  15. 15.

    Hetey, R., Monin, B., Maitreyi, A. & Eberhardt, J. Data for Change: A Statistical Analysis of Police Stops, Searches, Handcuffings, and Arrests in Oakland, Calif., 2013–2014 (Stanford Univ., SPARQ, 2016); https://sparq.stanford.edu/data-for-change

  16. 16.

    Voigt, R. et al. Language from police body camera footage shows racial disparities in officer respect. Proc. Natl Acad. Sci. USA 114, 6521–6526 (2017).

  17. 17.

    Chohlas-Wood, A., Goel, S., Shoemaker, A. & Shroff, R. An Analysis of the Metropolitan Nashville Police Department’s Traffic Stop Practices https://policylab.stanford.edu/media/nashville-traffic-stops.pdf (Stanford Computational Policy Lab, 2018).

  18. 18.

    Gelman, A., Fagan, J. & Kiss, A. An analysis of the New York City Police Department’s ‘stop-and-frisk’ policy in the context of claims of racial bias. J. Am. Stat. Assoc. 102, 813–823 (2007).

  19. 19.

    Baumgartner, F. R., Epp, D. A., Shoub, K. & Love, B. Targeting young men of color for search and arrest during traffic stops: evidence from North Carolina, 2002–2013. Polit. Groups Identities 5, 107–131 (2017).

  20. 20.

    Legewie, J. Racial profiling and use of force in police stops: how local events trigger periods of increased discrimination. Am. J. Sociol. 122, 379–424 (2016).

  21. 21.

    Grogger, J. & Ridgeway, G. Testing for racial profiling in traffic stops from behind a veil of darkness. J. Am. Stat. Assoc. 101, 878–887 (2006).

  22. 22.

    Pierson, E., Corbett-Davies, S. & Goel, S. In International Conference on Artificial Intelligence and Statistics (eds Storkey, A. & Fernando Perez-Cruz, F.) 96–105 (PMLR, 2018).

  23. 23.

    Becker, G. Nobel Lecture: the economic way of looking at behavior. J. Polit. Econ. 101, 385–409 (1993).

  24. 24.

    Becker, G. The Economics of Discrimination (Univ. of Chicago Press, 1957).

  25. 25.

    Ridgeway, G. Cincinnati Police Department Traffic Stops: Applying RAND’s Framework to Analyze Racial Disparities (RAND Corporation, 2009); https://www.rand.org/pubs/monographs/MG914.html

  26. 26.

    Ayres, I. Outcome tests of racial disparities in police practices. Justice Res. Policy 4, 131–142 (2002).

  27. 27.

    Knowles, J., Persico, N. & Todd, P. Racial bias in motor vehicle searches: theory and evidence. J. Polit. Econ. 109, 203–229 (2001).

  28. 28.

    Engel, R. S. & Tillyer, R. Searching for equilibrium: the tenuous nature of the outcome test. Justice Q. 25, 54–71 (2008).

  29. 29.

    Mitchell, O. & Caudy, M. Examining racial disparities in drug arrests. Justice Q. 32, 288–313 (2015).

  30. 30.

    Angrist, J.D. & Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton Univ. Press, 2008).

  31. 31.

    Goel, S., Rao, J. & Shroff, R. Precinct or prejudice? Understanding racial disparities in New York City’s stop-and-frisk policy. Ann. Appl. Stat. 10, 365–394 (2016).

  32. 32.

    Mummolo, J. Modern police tactics, police–citizen interactions, and the prospects for reform. J. Polit. 80, 1–15 (2018).

  33. 33.

    Goel, S., Rao, J. & Shroff, R. Personalized risk assessments in the criminal justice system. Am. Econ. Rev. 106, 119–123 (2016).

  34. 34.

    Jung, J., Concannon, C., Shroff, R., Goel, S. & Goldstein, D.G. Simple rules to guide expert classifications. J. R. Stat. Soc. Ser. A (in the press).

  35. 35.

    Corbett-Davies, S., Pierson, E., Feller, A., Goel, S. & Huq, A. Algorithmic decision making and the cost of fairness. In Proc. 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017).

  36. 36.

    Friberg, B. et al. Texas Troopers Ticketing Hispanic Drivers as White (KXAN, 2015); https://www.kxan.com/investigations/texas-troopers-ticketing-hispanic-drivers-as-white/

  37. 37.

    Word, D., Coleman, C., Nunziata, R. & Kominski, R. Demographic Aspects of Surnames from Census 2000 http://www2.census.gov/topics/genealogy/2000surnames/surnames.pdf (US Census Bureau, 2008).

  38. 38.

    Word, D. & Perkins, C. Building a Spanish Surname List for the 1990s: A New Approach to an Old Problem (Population Division, US Census Bureau, 1996).

  39. 39.

    Melendres v Arpaio (2009). 598 F. Supp. 2d 1025 (D. Ariz., 2009).

  40. 40.

    Arrow, K. in Discrimination in Labor Markets (eds Ashenfelter, O. & Rees, A.) 3–33 (Princeton Univ. Press, 1973).

  41. 41.

    Phelps, E. The statistical theory of racism and sexism. Am. Econ. Rev. 62, 659–661 (1972).

  42. 42.

    Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. Bayesian Data Analysis 2nd edn (Taylor & Francis, 2014).

  43. 43.

    Gelman, A., Meng, X.-L. & Stern, H. Posterior predictive assessment of model fitness via realized discrepancies. Stat. Sin. 6, 733–760 (1996).

Download references

Acknowledgements

We thank B. Bonilla, W. Kim, J. Nudell, S. Robertson and E. Sagara for their assistance throughout this project; we also thank A. Chohlas-Wood and A. Feller for their helpful feedback. This work was supported in part by the John S. and James L. Knight Foundation and by the Hellman Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

E.P., C.S., J.O., S.C.-D., D.J., A.S., V.R., P.B., C.P., R.S. and S.G. designed research, performed research, analyzed data, and wrote the paper.

Corresponding author

Correspondence to Sharad Goel.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary handling editor: Aisha Bradshaw

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Supplementary Tables 1 and 2.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pierson, E., Simoiu, C., Overgoor, J. et al. A large-scale analysis of racial disparities in police stops across the United States. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-020-0858-1

Download citation