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Search Profiling With Partial Knowledge of Deterrence

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  • Charles F. Manski

Abstract

Consider the choice of a profiling policy where decisions to search for evidence of crime may vary with observable covariates of the persons at risk of search. I pose a planning problem whose objective is to minimise the social cost of crime and search. The consequences of a search rule depend on the extent to which search deters crime. I study the planning problem when the planner has partial knowledge of deterrence. I show how the planner can eliminate dominated search rules and how he can use the minimax or minimax-regret criterion to choose an undominated rule. Copyright 2006 The Author(s). Journal compilation Royal Economic Society 2006.

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Economic Journal.

Volume (Year): 116 (2006)
Issue (Month): 515 (November)
Pages: F385-F401

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Handle: RePEc:ecj:econjl:v:116:y:2006:i:515:p:f385-f401

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References

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  1. A. Mitchell Polinsky & Steven Shavell, 1999. "The Economic Theory of Public Enforcement of Law," NBER Working Papers 6993, National Bureau of Economic Research, Inc.
  2. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
  3. Brock,W.A., 2004. "Profiling problems with partially identified structure," Working papers 21, Wisconsin Madison - Social Systems.
  4. John Knowles & Nicola Persico & Petra Todd, . ""Racial Bias in Motor Vehicle Searches: Theory and Evidence''," CARESS Working Papres 99-06, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
  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. Steven N. Durlauf, 2006. "Assessing Racial Profiling," Economic Journal, Royal Economic Society, vol. 116(515), pages F402-F426, November.
  7. 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.
  8. Manski, Charles F., 2000. "Identification problems and decisions under ambiguity: Empirical analysis of treatment response and normative analysis of treatment choice," Journal of Econometrics, Elsevier, vol. 95(2), pages 415-442, April.
  9. Mirrlees, James A, 1971. "An Exploration in the Theory of Optimum Income Taxation," Review of Economic Studies, Wiley Blackwell, vol. 38(114), pages 175-208, April.
  10. Nicola Persico & Petra Todd, 2005. "Using Hit Rates to Test for Racial Bias in Law Enforcement: Vehicle Searches in Wichita," PIER Working Paper Archive 05-004, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  11. Nicola Persico, 2002. "Racial Profiling, Fairness, and Effectiveness of Policing," American Economic Review, American Economic Association, vol. 92(5), pages 1472-1497, December.
  12. Charles Manski, 2003. "Statistical treatment rules for heterogeneous populations," CeMMAP working papers CWP03/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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Citations

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Cited by:
  1. Stefanie Behncke & Markus Frölich & Michael Lechner, 2007. "Targeting Labour Market Programmes - Results from a Randomized Experiment," University of St. Gallen Department of Economics working paper series 2007 2007-37, Department of Economics, University of St. Gallen.
  2. Charles F. Manski, 2012. "Choosing Size of Government Under Ambiguity: Infrastructure Spending and Income Taxation," NBER Working Papers 18204, National Bureau of Economic Research, Inc.
  3. Nicola Persico & Petra Todd, 2005. "Using Hit Rates to Test for Racial Bias in Law Enforcement: Vehicle Searches in Wichita," PIER Working Paper Archive 05-004, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  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. William A. Brock & Charles F. Manski, 2008. "Competitive Lending with Partial Knowledge of Loan Repayment," NBER Working Papers 14378, National Bureau of Economic Research, Inc.
  6. 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.
  7. Stoye, Jörg, 2012. "Minimax regret treatment choice with covariates or with limited validity of experiments," Journal of Econometrics, Elsevier, vol. 166(1), pages 138-156.
  8. William A. Brock & Steven N. Durlauf & James M. Nason & Giacomo Rondina, 2007. "Simple versus optimal rules as guides to policy," Working Paper 2007-07, Federal Reserve Bank of Atlanta.
  9. Charles F. Manski, 2008. "Partial Prescriptions For Decisions With Partial Knowledge," NBER Working Papers 14396, National Bureau of Economic Research, Inc.
  10. Jeff Dominitz & John Knowles, 2005. "Crime Minimization and Racial Bias: What Can We Learn From Police Search Data?," PIER Working Paper Archive 05-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  11. Charles F. Manski, 2005. "Optimal Search Profiling with Linear Deterrence," American Economic Review, American Economic Association, vol. 95(2), pages 122-126, May.
  12. Charles Manski, 2008. "Adaptive partial policy innovation: coping with ambiguity through diversification," CeMMAP working papers CWP10/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  13. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.

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