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

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  • Grogger, Jeffrey
  • Ridgeway, Greg

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 da

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of the American Statistical Association.

Volume (Year): 101 (2006)
Issue (Month): (September)
Pages: 878-887

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Handle: RePEc:bes:jnlasa:v:101:y:2006:p:878-887

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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. Sarah Marx Quintanar, . "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.
  5. O'Flaherty, Brendan & Sethi, Rajiv, 2010. "The racial geography of street vice," Journal of Urban Economics, Elsevier, vol. 67(3), pages 270-286, May.
  6. Debopam Bhattacharya, 2012. "Evaluating Treatment Protocols using Data Combination," Economics Series Working Papers 609, University of Oxford, Department of Economics.
  7. 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.
  8. 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.
  9. 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.
  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.

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