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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.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 10634.

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Date of creation: Jul 2004
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Publication status: published as 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, 09.
Handle: RePEc:nbr:nberwo:10634

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  1. 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, 08.
  2. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-39, February.
  3. 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 367-94, October.
  4. Shamena Anwar & Hanming Fang, 2005. "An Alternative Test of Racial Prejudice in Motor Vehicle Searches: Theory and Evidence," NBER Working Papers 11264, National Bureau of Economic Research, Inc.
  5. Dhammika Dharmapala & Stephen L. Ross, 2003. "Racial Bias in Motor Vehicle Searches: Additional Theory and Evidence," Working papers 2003-12, University of Connecticut, Department of Economics, revised Dec 2003.
  6. John Knowles & Nicola Persico & Petra Todd, . "Racial Bias in Motor Vehicle Searches: Theory and Evidence," Penn CARESS Working Papers 5940d5c4875c571776fb29700, Penn Economics Department.
  7. Joseph G. Altonji & Charles R. Pierret, 2001. "Employer Learning And Statistical Discrimination," The Quarterly Journal of Economics, MIT Press, vol. 116(1), pages 313-350, February.
  8. 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, 06.
  9. 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.
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