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A New Look at Racial Profiling: Evidence from the Boston Police Department

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  • Kate L. Antonovics
  • Brian G. Knight

Abstract

This paper provides new evidence on the role of preference-based versus statistical discrimination in racial profiling using a unique data set that includes the race of both the driver and the officer. We first generalize the model presented in Knowles, Persico and Todd (2001) and show that the fundamental insight that allows them to distinguish between statistical discrimination and preference-based discrimination depends on the specialized shapes of the best response functions in their model. Thus, the test that they employ is not robust to a range of alternative modeling assumptions. However, we also show that if statistical discrimination alone explains differences in the rate at which the vehicles of drivers of different races are searched, then search decisions should be independent of officer race. We then test this prediction using data from the Boston Police Department. Consistent with preference-based discrimination, our baseline results demonstrate that officers are more likely to conduct a search if the race of the officer differs from the race of the driver. We then investigate and rule out two alternative explanations for our findings: race-based informational asymmetries between officers and the assignment of officers to neighborhoods.

Suggested Citation

  • Kate L. Antonovics & Brian G. Knight, 2004. "A New Look at Racial Profiling: Evidence from the Boston Police Department," NBER Working Papers 10634, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:10634
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    References listed on IDEAS

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    1. Joseph G. Altonji & Charles R. Pierret, "undated". "Employer Learning and Statistical Discrimination," IPR working papers 97-18, Institute for Policy Resarch at Northwestern University.
    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. 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.
    4. Rubén Hernández-Murillo & John Knowles, 2004. "Racial Profiling Or Racist Policing? Bounds Tests In Aggregate Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(3), pages 959-989, August.
    5. 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.
    6. Donohue, John J, III & Levitt, Steven D, 2001. "The Impact of Race on Policing and Arrests," Journal of Law and Economics, University of Chicago Press, vol. 44(2), pages 367-394, October.
    7. David Bjerk, 2007. "Racial Profiling, Statistical Discrimination, and the Effect of a Colorblind Policy on the Crime Rate," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 9(3), pages 521-545, June.
    8. 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.
    9. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    10. Kenneth Arrow, 1971. "The Theory of Discrimination," Working Papers 403, Princeton University, Department of Economics, Industrial Relations Section..
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    JEL classification:

    • K0 - Law and Economics - - General
    • H0 - Public Economics - - General

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