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Endogenous Driving Behavior in Veil of Darkness Tests for Racial Profiling

Listed author(s):
  • Jesse Kalinowski

    (Quinnipiac University)

  • Stephen L. Ross

    (University of Connecticut)

  • Matthew B. Ross

    (Ohio State University)

Several prominent applications of the Veil of Darkness (VOD) test, where solar variation is used to identify racial profiling in traffic stops, have found reverse discrimination in cities widely purported to disproportionately target minorities. We develop a theoretical model of traffic enforcement and demonstrate that the VOD test for racial profiling cannot distinguish between discrimination and reverse discrimination. In our model, this problem arises because motorists rationally alter their driving behavior when faced with discriminatory policing. For groups that face discrimination, our model implies that motorists who previously did not speed choose to speed in darkness, when demography cannot be observed, thus creating the possibility that the share of stopped minority motorists increases in darkness. We develop a follow-up test for identifying the direction of differential treatment by examining the speed distribution of motorists across daylight and darkness. Using data on traffic stops in Massachusetts made by State and Local Police, we reject the VOD test for equal treatment and demonstrate that driving speeds of stopped African-Americans are higher in darkness consistent with discrimination.

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File URL: http://web2.uconn.edu/economics/working/2017-03.pdf
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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2017-03.

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Length: 81 pages
Date of creation: Feb 2017
Handle: RePEc:uct:uconnp:2017-03
Note: Matthew B. Ross is the corresponding author
Contact details of provider: Postal:
University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063

Phone: (860) 486-4889
Fax: (860) 486-4463
Web page: http://www.econ.uconn.edu/

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  1. Kate Antonovics & Brian G. Knight, 2009. "A New Look at Racial Profiling: Evidence from the Boston Police Department," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 163-177, February.
  2. Shamena Anwar & Hanming Fang, 2004. "An Alternative Test of Racial Prejudice in Motor Vehicle Searches: Theory and Evidence," Cowles Foundation Discussion Papers 1464, Cowles Foundation for Research in Economics, Yale University.
  3. Kowalski, Brian R. & Lundman, Richard J., 2007. "Vehicle stops by police for driving while Black: Common problems and some tentative solutions," Journal of Criminal Justice, Elsevier, vol. 35(2), pages 165-181.
  4. Dharmapala Dhammika & Ross Stephen L, 2004. "Racial Bias in Motor Vehicle Searches: Additional Theory and Evidence," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 3(1), pages 1-23, September.
  5. 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.
  6. Austin C. Smith, 2016. "Spring Forward at Your Own Risk: Daylight Saving Time and Fatal Vehicle Crashes," American Economic Journal: Applied Economics, American Economic Association, vol. 8(2), pages 65-91, April.
  7. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, 05.
  8. Grogger, Jeffrey & Ridgeway, Greg, 2006. "Testing for Racial Profiling in Traffic Stops From Behind a Veil of Darkness," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 878-887, September.
  9. 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.
  10. William C. Horrace & Shawn M. Rohlin, 2016. "How Dark Is Dark? Bright Lights, Big City, Racial Profiling," The Review of Economics and Statistics, MIT Press, vol. 98(2), pages 226-232, May.
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