IDEAS home Printed from https://ideas.repec.org/a/kap/ejlwec/v36y2013i2p271-294.html
   My bibliography  Save this article

Marginally discriminated: the role of outcome tests in European jurisdiction

Author

Listed:
  • Dragan Ilić

Abstract

For decades, racial profiling has been subject of intense debate in US jurisdiction. Recently, outcome tests based on economic models have contributed to the legal discourse. However, it is not readily obvious if and to what extent they also pertain to European jurisdiction, where racial profiling has only as of late stirred up controversy. In a comprehensive examination of their basic building blocks, this paper illustrates why the these tests are not particularly suited for the European case. The models are tailored to identify racial prejudice but are unfit to provide evidence of statistical discrimination, reflecting their adaption to the current US legal approach. A simple alternative test remedies this shortcoming and manages to inform the European jurisdiction. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • 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.
  • Handle: RePEc:kap:ejlwec:v:36:y:2013:i:2:p:271-294
    DOI: 10.1007/s10657-013-9409-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10657-013-9409-9
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10657-013-9409-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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. Nicola Persico, 2009. "Racial Profiling? Detecting Bias Using Statistical Evidence," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 229-254, May.
    3. Gary S. Becker, 1974. "Crime and Punishment: An Economic Approach," NBER Chapters, in: Essays in the Economics of Crime and Punishment, pages 1-54, National Bureau of Economic Research, Inc.
    4. 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.
    5. John Knowles & Nicola Persico & Petra Todd, 2001. "Racial Bias in Motor Vehicle Searches: Theory and Evidence," Journal of Political Economy, University of Chicago Press, vol. 109(1), pages 203-232, February.
    6. John Yinger, 1998. "Evidence on Discrimination in Consumer Markets," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 23-40, Spring.
    7. Alberto Alesina & Eliana La Ferrara, 2014. "A Test of Racial Bias in Capital Sentencing," American Economic Review, American Economic Association, vol. 104(11), pages 3397-3433, November.
    8. 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.
    9. 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.
    10. Nicola Persico & Petra E. Todd, 2005. "Passenger Profiling, Imperfect Screening, and Airport Security," American Economic Review, American Economic Association, vol. 95(2), pages 127-131, May.
    11. 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.
    12. Shamena Anwar & Hanming Fang, 2015. "Testing for Racial Prejudice in the Parole Board Release Process: Theory and Evidence," The Journal of Legal Studies, University of Chicago Press, vol. 44(1), pages 1-37.
    13. James J. Heckman, 1998. "Detecting Discrimination," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 101-116, Spring.
    14. 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.
    15. Altonji, Joseph G. & Blank, Rebecca M., 1999. "Race and gender in the labor market," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 48, pages 3143-3259, Elsevier.
    16. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-661, September.
    17. Coate, Stephen & Loury, Glenn C, 1993. "Will Affirmative-Action Policies Eliminate Negative Stereotypes?," American Economic Review, American Economic Association, vol. 83(5), pages 1220-1240, December.
    18. Close Billy R & Mason Patrick Leon, 2007. "Searching for Efficient Enforcement: Officer Characteristics and Racially Biased Policing," Review of Law & Economics, De Gruyter, vol. 3(2), pages 263-321, September.
    19. Nicola Persico, 2002. "Racial Profiling, Fairness, and Effectiveness of Policing," American Economic Review, American Economic Association, vol. 92(5), pages 1472-1497, December.
    20. Scott Smart & Joel Waldfogel, 1996. "A Citation-Based Test for Discrimination at Economics and Finance Journals," NBER Working Papers 5460, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Christopher Cotton & Cheng Li, 2015. "Profiling, Screening, and Criminal Recruitment," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 17(6), pages 964-985, December.
    4. 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.
    5. 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.
    6. Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2017. "Are University Admissions Academically Fair?," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 449-464, July.
    7. 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.
    8. 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.
    9. Bhattacharya, Debopam, 2013. "Evaluating treatment protocols using data combination," Journal of Econometrics, Elsevier, vol. 173(2), pages 160-174.
    10. Dragan Ilić, 2014. "Replicability and Pitfalls in the Interpretation of Resampled Data: A Correction and a Randomization Test for Anwar and Fang," Econ Journal Watch, Econ Journal Watch, vol. 11(3), pages 250-276, September.
    11. Xiao Lin & Mark J. Browne & Annette Hofmann, 2022. "Race discrimination in the adjudication of claims: Evidence from earthquake insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 553-580, September.
    12. Blumkin, Tomer & Margalioth, Yoram, 2008. "On terror, drugs and racial profiling," International Review of Law and Economics, Elsevier, vol. 28(3), pages 194-203, September.
    13. Ilić, Dragan, 2018. "Prejudice in naturalization decisions: Theory and evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 1-18.
    14. Mujcic, Redzo & Frijters, Paul, 2013. "Still Not Allowed on the Bus: It Matters If You're Black or White!," IZA Discussion Papers 7300, Institute of Labor Economics (IZA).
    15. Ingrid Gould Ellen & Stephen L. Ross, 2018. "Race and the City," Working Papers 2018-022, Human Capital and Economic Opportunity Working Group.
    16. 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.
    17. 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.
    18. Shamena Anwar & Hanming Fang, 2015. "Testing for Racial Prejudice in the Parole Board Release Process: Theory and Evidence," The Journal of Legal Studies, University of Chicago Press, vol. 44(1), pages 1-37.
    19. Curry, Philip A. & Klumpp, Tilman, 2009. "Crime, punishment, and prejudice," Journal of Public Economics, Elsevier, vol. 93(1-2), pages 73-84, February.
    20. Carlsson, Magnus & Fumarco, Luca & Rooth, Dan-Olof, 2013. "Artifactual Evidence of Discrimination in Correspondence Studies? A Replication of the Neumark Method," IZA Discussion Papers 7619, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    Racial profiling; Outcome test; Discrimination; Europe; J71; K42;
    All these keywords.

    JEL classification:

    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

    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:kap:ejlwec:v:36:y:2013:i:2:p:271-294. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.