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Endogenous Driving Behavior in Tests of Racial Profiling in Police Traffic Stops

Author

Listed:
  • Jesse Kalinowski

    (Quinnipiac University)

  • Matthew B. Ross

    (New York University and Claremont Graduate University)

  • Stephen L. Ross

    (University of Connecticut)

Abstract

African-American motorists may adjust their driving in response to increased scrutiny by law enforcement. We develop a model of police stop and motorist driving behavior and demonstrate that this behavior biases conventional tests of discrimination. We empirically document that minority motorists are the only group less likely to have fatal motor vehicle accidents in daylight when race is more easily observed by police, especially within states with high rates of police shootings of African-Americans. Using data from Massachusetts and Tennessee, we also find that African-Americans are the only group of stopped motorists whose speed relative to the speed limit slows in daylight. Consistent with the model prediction, these shifts in the speed distribution are concentrated at higher percentiles of the distribution. A calibration of our model indicates substantial bias in conventional tests of discrimination that rely on changes in the odds that a stopped motorist is a minority.

Suggested Citation

  • Jesse Kalinowski & Matthew B. Ross & Stephen L. Ross, 2017. "Endogenous Driving Behavior in Tests of Racial Profiling in Police Traffic Stops," Working papers 2017-03, University of Connecticut, Department of Economics, revised Mar 2020.
  • Handle: RePEc:uct:uconnp:2017-03
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    References listed on IDEAS

    as
    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. 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.
    3. Nicola Persico & Petra Todd, 2006. "Generalising the Hit Rates Test for Racial Bias in Law Enforcement, With an Application to Vehicle Searches in Wichita," Economic Journal, Royal Economic Society, vol. 116(515), pages 351-367, November.
    4. 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.
    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. 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.
    8. 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.
    9. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    10. 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.
    11. Shamena Anwar & Hanming Fang, 2006. "An Alternative Test of Racial Prejudice in Motor Vehicle Searches: Theory and Evidence," American Economic Review, American Economic Association, vol. 96(1), pages 127-151, March.
    12. Nicolai T. Borgen, 2016. "Fixed effects in unconditional quantile regression," Stata Journal, StataCorp LP, vol. 16(2), pages 403-415, June.
    13. 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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    Cited by:

    1. Jesse Kalinowski & Matthew Ross & Stephen L. Ross, 2019. "Addressing Seasonality in Veil of Darkness Tests for Discrimination: An Instrumental Variables Approach," Working Papers 2019-028, Human Capital and Economic Opportunity Working Group.
    2. Jesse J. Kalinowski & Matthew B. Ross & Stephen L. Ross, 2019. "Now You See Me, Now You Don't: The Geography of Police Stops," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 143-147, May.
    3. Cho, Sungwoo & Gonçalves, Felipe & Weisburst, Emily, 2021. "Do Police Make Too Many Arrests? The Effect of Enforcement Pullbacks on Crime," IZA Discussion Papers 14907, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Police; Crime; Discrimination; Racial Profiling; Disparate Treatment; Traffic Stops;
    All these keywords.

    JEL classification:

    • H1 - Public Economics - - Structure and Scope of Government
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • J7 - Labor and Demographic Economics - - Labor Discrimination
    • K14 - Law and Economics - - Basic Areas of Law - - - Criminal Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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