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How Do the Laws of Probability Constrain Legislative and Judicial Efforts to Stop Racial Profiling?

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  • Jeff Dominitz

Abstract

Faced with pending legislation and litigation, numerous jurisdictions have begun programs to monitor a range of traffic stop outcomes, focusing on variation by race or ethnicity. Existing programs, however, ignore the unequal outcomes that motivate opposition to racial profiling. Statistical relationships limit the ability of public policy to equalize the various outcomes, even if officers do not engage in racial profiling to "any extent or degree." This article demonstrates relationships among five outcomes that are or should be considered when policy on racial profiling is formulated: search rates, find rates, thoroughness of search, rates of detention of the innocent, and rates of apprehension of the guilty. Once decisions are made as to how to balance desires for equality of each of these outcomes, problems remain that are common to statistical assessments of pattern- or practice-of-discrimination claims. Copyright 2003, Oxford University Press.

Suggested Citation

  • Jeff Dominitz, 2003. "How Do the Laws of Probability Constrain Legislative and Judicial Efforts to Stop Racial Profiling?," American Law and Economics Review, Oxford University Press, vol. 5(2), pages 412-432, August.
  • Handle: RePEc:oup:amlawe:v:5:y:2003:i:2:p:412-432
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    Cited by:

    1. Steven N. Durlauf, 2005. "Racial Profiling as a Public Policy Question: Efficiency, Equity, and Ambiguity," American Economic Review, American Economic Association, vol. 95(2), pages 132-136, May.
    2. Shanti Chakravarty, 2009. "Efficient Legal Procedure And Statistical Discrimination," Working Papers 09002, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    3. 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.
    4. 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.
    5. Charles F. Manski, 2005. "Optimal Search Profiling with Linear Deterrence," American Economic Review, American Economic Association, vol. 95(2), pages 122-126, May.
    6. 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.
    7. Charles F. Manski, 2006. "Search Profiling With Partial Knowledge of Deterrence," Economic Journal, Royal Economic Society, vol. 116(515), pages 385-401, November.
    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. Franklin, Travis W., 2010. "Community influence on prosecutorial dismissals: A multilevel analysis of case- and county-level factors," Journal of Criminal Justice, Elsevier, vol. 38(4), pages 693-701, July.

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