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Spatial and Temporal Aggregation in Racial Profiling

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  • Dragan Ilić

Abstract

In the last decade, models of rational choice have chimed into the discussion on racial profiling, the use of race in stop and search decisions of the police. The models describe the behavior of motorists and the police and provide empirical tests to assess the question whether the police exhibit racial animus. However, existing studies have neglected the effect of spatial and temporal aggregation of the data on the application of the tests. Using data from the Florida Highway Patrol, this paper shows that regional subsets disclose policing behavior which deviates substantially from the aggregate. Broad conclusions on the absence or presence of racial prejudice are thus at risk of being unfounded. In addition, the disaggregated analysis suggests that the empirical tests implied by the rational choice models are not applicable to all observed regions. The results call for a cautious application of the tests and the interpretation of their conclusions.

Suggested Citation

  • 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.
  • Handle: RePEc:ses:arsjes:2013-i-2
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    References listed on IDEAS

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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. 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.
    3. 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.
    4. 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.
    5. 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.
    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 1-23, September.
    7. 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.
    8. Phelps, Edmund S, 1972. "The Statistical Theory of Racism and Sexism," American Economic Review, American Economic Association, vol. 62(4), pages 659-661, September.
    9. 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.
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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. 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.

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    More about this item

    Keywords

    Racial Profiling; Crime; Police; Rational Choice; Outcome Test; Aggregation;
    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

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