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Search Profiling with Partial Knowledge of Deterrence

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  • Charles F. Manski

Abstract

Economists studying public policy have generally assumed that the relevant social planner knows how policy affects population behavior. Planners typically do not possess all of this knowledge, so there is reason to consider policy formation with partial knowledge of policy impacts. Here I 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 being searched. To begin I pose a planning problem whose objective is to minimize the utilitarian social cost of crime and search. The consequences of candidate search rules depends on the extent to which search deters crime. Deterrence is expressed through the offense function, which describes how the offense rate of persons with given covariates varies with the search rate applied to these persons. I study the planning problem when the planner has partial knowledge of the offense function. To demonstrate general ideas, I suppose that the planner observes the offense rates of a study population whose search rule has previously been chosen. He knows that the offense rate weakly decreases as the search rate increases, but he does not know the magnitude of the deterrent effect of search. In this setting, I first show how the planner can eliminate dominated search rules and then how he can use the minimax or minimax-regret criterion to choose an undominated search rule.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 11848.

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Date of creation: Dec 2005
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Handle: RePEc:nbr:nberwo:11848

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  1. Charles F. Manski, 1997. "Monotone Treatment Response," Econometrica, Econometric Society, vol. 65(6), pages 1311-1334, November.
  2. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, 07.
  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. 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. Steven N. Durlauf, 2006. "Assessing Racial Profiling," Economic Journal, Royal Economic Society, vol. 116(515), pages F402-F426, November.
  6. Charles F. Manski, 2005. "Optimal Search Profiling with Linear Deterrence," American Economic Review, American Economic Association, vol. 95(2), pages 122-126, May.
  7. 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.
  8. Steven Shavell & A. Mitchell Polinsky, 2000. "The Economic Theory of Public Enforcement of Law," Journal of Economic Literature, American Economic Association, vol. 38(1), pages 45-76, March.
  9. William A. Brock, 2006. "Profiling Problems With Partially Identified Structure," Economic Journal, Royal Economic Society, vol. 116(515), pages F427-F440, November.
  10. 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.
  11. 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.
  12. Nicola Persico, 2002. "Racial Profiling, Fairness, and Effectiveness of Policing," American Economic Review, American Economic Association, vol. 92(5), pages 1472-1497, December.
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Cited by:
  1. 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.
  2. 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.
  3. Stefanie Behncke & Markus Frölich & Michael Lechner, 2009. "Targeting Labour Market Programmes - Results from a Randomized Experiment," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 145(III), pages 221-268, September.
  4. 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.
  5. 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.
  6. Charles F. Manski, 2005. "Optimal Search Profiling with Linear Deterrence," American Economic Review, American Economic Association, vol. 95(2), pages 122-126, May.
  7. Jeff Dominitz & John Knowles, 2006. "Crime minimisation and racial bias: what can we learn from police search data?," Economic Journal, Royal Economic Society, vol. 116(515), pages F368-F384, November.
  8. 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.
  9. Charles F. Manski, 2008. "Partial Prescriptions For Decisions With Partial Knowledge," NBER Working Papers 14396, National Bureau of Economic Research, Inc.
  10. 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.
  11. 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.
  12. 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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