IDEAS home Printed from https://ideas.repec.org/p/hka/wpaper/2017-017.html
   My bibliography  Save this paper

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

Author

Listed:
  • Jesse Kalinowski

    (Quinnipiac University)

  • Stephen L. Ross

    (University of Connecticut)

  • Matthew B. Ross

    (Ohio State University)

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 & Stephen L. Ross & Matthew B. Ross, 2017. "Endogenous Driving Behavior in Tests of Racial Profiling in Police Traffic Stops," Working Papers 2017-017, Human Capital and Economic Opportunity Working Group.
  • Handle: RePEc:hka:wpaper:2017-017
    Note: MIP
    as

    Download full text from publisher

    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/Kalinowski_Ross_Ross_2017_driving-veil-darkness.pdf
    File Function: First version, February 2017
    Download Restriction: no

    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/Kalinowski_Ross_Ross_2017_driving-veil-darkness_v2.pdf
    File Function: Third version, March 31, 2020
    Download Restriction: no

    File URL: http://humcap.uchicago.edu/RePEc/hka/wpaper/Kalinowski_Ross_Ross_2017_driving-veil-darkness_v3.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. 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.
    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. 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.
    5. 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.
    6. 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 1-21, September.
    7. 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.
    8. 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.
    9. Nicolai T. Borgen, 2016. "Fixed effects in unconditional quantile regression," Stata Journal, StataCorp LP, vol. 16(2), pages 403-415, June.
    10. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    11. 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.
    12. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Makofske, Matthew, 2020. "Pretextual Traffic Stops and Racial Disparities in their Use," MPRA Paper 121003, University Library of Munich, Germany, revised 29 Jul 2023.
    2. Makofske, Matthew, 2020. "Pretextual Traffic Stops and Racial Disparities in their Use," MPRA Paper 100792, University Library of Munich, Germany.
    3. Ingrid Gould Ellen & Stephen L. Ross, 2018. "Race and the City," Working Papers 2018-022, Human Capital and Economic Opportunity Working Group.
    4. Lieberman, Carl, 2024. "Variation in racial disparities in police use of force," Journal of Urban Economics, Elsevier, vol. 141(C).
    5. 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.
    6. 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.
    7. Jesse Kalinowski & Matthew B. Ross & Stephen L. Ross, 2019. "Addressing Seasonality in Veil of Darkness Tests for Discrimination: An Instrumental Variables Approach," Working papers 2019-07, University of Connecticut, Department of Economics.
    8. Mark Hoekstra & CarlyWill Sloan, 2022. "Does Race Matter for Police Use of Force? Evidence from 911 Calls," American Economic Review, American Economic Association, vol. 112(3), pages 827-860, March.
    9. 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.
    10. Kevin Lang & Ariella Kahn-Lang Spitzer, 2020. "Race Discrimination: An Economic Perspective," Journal of Economic Perspectives, American Economic Association, vol. 34(2), pages 68-89, Spring.
    11. Shi, Ying & Zhu, Maria, 2022. "Equal time for equal crime? Racial bias in school discipline," Economics of Education Review, Elsevier, vol. 88(C).
    12. 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.
    13. Madina Kurmangaliyeva & Matteo Sostero, 2022. "Walking while Black :Racial Gaps in Hit-and-Run Cases," Working Papers ECARES 2022-08, ULB -- Universite Libre de Bruxelles.
    14. 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.
    15. Georgiou, Georgios, 2022. "Do correctional authorities treat all offenders equally? Evaluating the use of a risk assessment instrument," International Review of Law and Economics, Elsevier, vol. 69(C).
    16. 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.
    17. 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.
    18. Dragan Ilić, 2013. "Spatial and Temporal Aggregation in Racial Profiling," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 149(I), pages 27-56, March.
    19. Federico Masera, 2022. "The economics of policing and crimeThe economics of policing and crime," Chapters, in: Paolo Buonanno & Paolo Vanin & Juan Vargas (ed.), A Modern Guide to the Economics of Crime, chapter 2, pages 12-29, Edward Elgar Publishing.
    20. Nicola Persico & Petra Todd, 2004. "Using Hit Rate Tests to Test for Racial Bias in Law Enforcement: Vehicle Searches in Wichita," NBER Working Papers 10947, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    police; crime; racial discrimination; racial profiling; disparate treatment;
    All these keywords.

    JEL classification:

    • 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
    • J7 - Labor and Demographic Economics - - Labor Discrimination
    • H11 - Public Economics - - Structure and Scope of Government - - - Structure and Scope of Government
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hka:wpaper:2017-017. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jennifer Pachon (email available below). General contact details of provider: https://edirc.repec.org/data/mfichus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.