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High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding

Author

Listed:
  • Pradhi Aggarwal
  • Alec Brandon
  • Ariel Goldszmidt
  • Justin Holz
  • John List
  • Ian Muir
  • Gregory Sun
  • Thomas Yu

Abstract

Prior research finds that, conditional on an encounter, minority civilians are more likely to be punished by police than white civilians. An open question is whether the actual encounter is related to race. Using high-frequency location data of rideshare drivers operating on the Lyft platform in Florida, we estimate the effect of driver race on traffic stops and fines for speeding. Estimates obtained across traditional and machine learning approaches show that, relative to a white driver traveling the same speed, minorities are 24 to 33 percent more likely to be stopped for speeding and pay 23 to 34 percent more in fines. We find no evidence that these estimates can be explained by racial differences in accident and re-offense rates. Our study provides key insights into the total effect of civilian race on outcomes of interest and highlights the potential value of private sector data to help inform major social challenges.

Suggested Citation

  • 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.
  • Handle: RePEc:feb:natura:00764
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    References listed on IDEAS

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    1. Manudeep Bhuller & Gordon B. Dahl & Katrine V. Løken & Magne Mogstad, 2020. "Incarceration, Recidivism, and Employment," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1269-1324.
    2. M. Keith Chen & Kareem Haggag & Devin G. Pope & Ryne Rohla, 2019. "Racial Disparities in Voting Wait Times: Evidence from Smartphone Data," Papers 1909.00024, arXiv.org, revised Oct 2020.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    4. Knox, Dean & Lowe, Will & Mummolo, Jonathan, 2020. "Administrative Records Mask Racially Biased Policing—CORRIGENDUM," American Political Science Review, Cambridge University Press, vol. 114(4), pages 1394-1394, November.
    5. Ilyana Kuziemko, 2013. "How should inmates be released from prison? An assessment of parole versus fixed-sentence regimes," The Quarterly Journal of Economics, Oxford University Press, vol. 128(1), pages 371-424.
    6. Felipe Goncalves & Steven Mello, 2021. "A Few Bad Apples? Racial Bias in Policing," American Economic Review, American Economic Association, vol. 111(5), pages 1406-1441, May.
    7. Evan K. Rose & Yotam Shem-Tov, 2021. "How Does Incarceration Affect Reoffending? Estimating the Dose-Response Function," Journal of Political Economy, University of Chicago Press, vol. 129(12), pages 3302-3356.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
    9. Emily Owens & Bocar Ba, 2021. "The Economics of Policing and Public Safety," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 3-28, Fall.
    10. Anbarci, Nejat & Lee, Jungmin, 2014. "Detecting racial bias in speed discounting: Evidence from speeding tickets in Boston," International Review of Law and Economics, Elsevier, vol. 38(C), pages 11-24.
    11. Roland G. Fryer Jr., 2019. "An Empirical Analysis of Racial Differences in Police Use of Force," Journal of Political Economy, University of Chicago Press, vol. 127(3), pages 1210-1261.
    12. Steven N. Durlauf & James J. Heckman, 2020. "An Empirical Analysis of Racial Differences in Police Use of Force: A Comment," Journal of Political Economy, University of Chicago Press, vol. 128(10), pages 3998-4002.
    13. Samuel Norris & Matthew Pecenco & Jeffrey Weaver, 2021. "The Effects of Parental and Sibling Incarceration: Evidence from Ohio," American Economic Review, American Economic Association, vol. 111(9), pages 2926-2963, September.
    14. Michael D. Makowsky & Thomas Stratmann, 2011. "More Tickets, Fewer Accidents: How Cash-Strapped Towns Make for Safer Roads," Journal of Law and Economics, University of Chicago Press, vol. 54(4), pages 863-888.
    15. 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.
    16. M. Keith Chen & Devin G. Pope, 2020. "Geographic Mobility in America: Evidence from Cell Phone Data," NBER Working Papers 27072, National Bureau of Economic Research, Inc.
    17. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    18. Tanaya Devi & Roland G. Fryer Jr, 2020. "Policing the Police: The Impact of "Pattern-or-Practice" Investigations on Crime," NBER Working Papers 27324, National Bureau of Economic Research, Inc.
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