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Testing for Racial Profiling in Traffic Stops from Behind a Veil of Darkness

Author

Listed:
  • Jeffrey Grogger
  • Greg Ridgeway

Abstract

The key problem in testing for racial profiling in traffic stops is estimating the risk set, or "benchmark," against which to compare the race distribution of stopped drivers. To date, the two most common approaches have been to employ Census-based residential population data or to conduct traffic surveys in which observers tally the race distribution of drivers at a certain location. It is widely recognized that residential population data may provide poor estimates of the population at risk of a traffic stop; at the same time, traffic surveys have limitations and may be too costly to carry out on the ongoing basis required by recent legislation and litigation. In this paper, we propose a test for racial profiling that does not require explicit, external estimates of the risk set. Rather, our approach makes use of what we refer to as the "veil of darkness" hypothesis, which asserts that at night, police cannot determine the race of a motorist until they actually make a stop. The implication is that the race distribution of drivers stopped at night should equal the race distribution of drivers at risk of being stopped at night. If we further assume that racial differences in traffic patterns, driving behavior, and exposure to law enforcement do not vary between day and night, we can test for racial profiling by comparing the race distribution of stops made during daylight to the race distribution of stops made at night. We propose a means of weakening this assumption by restricting the sample to stops made during the evening hours and controlling for clock time while estimating day/night contrasts in the race distribution of stopped drivers. We provide conditions under which our estimates are robust to a substantial non-reporting problem present in our data and in many other studies of racial profiling. We propose an approach to assess the sensitivity of our results to departures from our maintained assumptions. Finally, we apply our method to data from Oakland, California. In this example, the data yield little evidence of racial profiling in traffic stops.

Suggested Citation

  • Jeffrey Grogger & Greg Ridgeway, 2005. "Testing for Racial Profiling in Traffic Stops from Behind a Veil of Darkness," Working Papers 0507, Harris School of Public Policy Studies, University of Chicago.
  • Handle: RePEc:har:wpaper:0507
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    Cited by:

    1. Bhattacharya, Debopam, 2013. "Evaluating treatment protocols using data combination," Journal of Econometrics, Elsevier, vol. 173(2), pages 160-174.
    2. Ritter, Joseph A., 2013. "Racial Bias in Traffic Stops: Tests of a Unified Model of Stops and Searches," Miscellaneous Publications 152496, University of Minnesota, Department of Applied Economics.
    3. Ritter, Joseph A., 2017. "How do police use race in traffic stops and searches? Tests based on observability of race," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 82-98.
    4. Bjerk, David J. & Helland, Eric, 2017. "Using a Ratio Test to Estimate Racial Differences in Wrongful Conviction Rates," IZA Discussion Papers 10631, Institute for the Study of Labor (IZA).
    5. repec:ucp:jlawec:doi:10.1086/693822 is not listed on IDEAS
    6. Gregory DeAngelo & R. Kaj Gittings & Amanda Ross & Annie Walker, 2016. "Police Bias in the Enforcement of Drug Crimes: Evidence from Low Priority Laws," Working Papers 16-01, Department of Economics, West Virginia University.
    7. 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.
    8. Gabbidon, Shaun L. & Craig, Ronald & Okafo, Nonso & Marzette, Lakiesha N. & Peterson, Steven A., 2008. "The consumer racial profiling experiences of Black students at historically Black colleges and universities: An exploratory study," Journal of Criminal Justice, Elsevier, vol. 36(4), pages 354-361, August.
    9. Jesse Kalinowski & Stephen L. Ross & Matthew B. Ross, 2017. "Endogenous Driving Behavior in Veil of Darkness Tests for Racial Profiling," Working papers 2017-03, University of Connecticut, Department of Economics.
    10. Brady P. Horn & Jill J. Mccluskey & Ron C. Mittelhammer, 2014. "Quantifying Bias In Driving-Under-The-Influence Enforcement," Economic Inquiry, Western Economic Association International, vol. 52(1), pages 269-284, January.
    11. Sarah Marx Quintanar, 2009. "Man vs. Machine: An Investigation of Speeding Ticket Disparities Based on Gender and Race," Departmental Working Papers 2009-16, Department of Economics, Louisiana State University.
    12. Dragan Ilić, 2013. "Marginally discriminated: the role of outcome tests in European jurisdiction," European Journal of Law and Economics, Springer, vol. 36(2), pages 271-294, October.
    13. Briggs Depew & Ozkan Eren & Naci Mocan, 2017. "Judges, Juveniles, and In-Group Bias," Journal of Law and Economics, University of Chicago Press, vol. 60(2), pages 209-239.
    14. Miller, Kirk, 2009. "Race, driving, and police organization: Modeling moving and nonmoving traffic stops with citizen self-reports of driving practices," Journal of Criminal Justice, Elsevier, vol. 37(6), pages 564-575, November.
    15. Ritter, Joseph A., 2017. "How do police use race in traffic stops and searches? Tests based on observability of race," Miscellaneous Publications 253354, University of Minnesota, Department of Applied Economics.
    16. Lundman, Richard J., 2010. "Are police-reported driving while Black data a valid indicator of the race and ethnicity of the traffic law violators police stop? A negative answer with minor qualifications," Journal of Criminal Justice, Elsevier, vol. 38(1), pages 77-87, January.
    17. Paul Heaton, 2010. "Understanding the Effects of Antiprofiling Policies," Journal of Law and Economics, University of Chicago Press, vol. 53(1), pages 29-64, February.
    18. O'Flaherty, Brendan & Sethi, Rajiv, 2010. "The racial geography of street vice," Journal of Urban Economics, Elsevier, vol. 67(3), pages 270-286, May.
    19. repec:bla:coecpo:v:35:y:2017:i:4:p:630-657 is not listed on IDEAS
    20. Matt E. Ryan, 2016. "Frisky business: race, gender and police activity during traffic stops," European Journal of Law and Economics, Springer, vol. 41(1), pages 65-83, February.
    21. Brock, William A. & Cooley, Jane & Durlauf, Steven N. & Navarro, Salvador, 2012. "On the observational implications of taste-based discrimination in racial profiling," Journal of Econometrics, Elsevier, vol. 166(1), pages 66-78.

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