Search Profiling with Partial Knowledge of Deterrence
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.
|Date of creation:||Dec 2005|
|Date of revision:|
|Publication status:||published as Manski, Charles F. "Optimal Search Profiling With Linear Deterrence," American Economic Review, 2005, v95(2,May), 12-126.|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
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- William A. Brock, 2006.
"Profiling Problems With Partially Identified Structure,"
Royal Economic Society, vol. 116(515), pages F427-F440, November.
- Brock,W.A., 2004. "Profiling problems with partially identified structure," Working papers 21, Wisconsin Madison - Social Systems.
- Charles F. Manski, 2003.
"Statistical treatment rules for heterogeneous populations,"
CeMMAP working papers
CWP03/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, 07.
- J. A. Mirrlees, 1971. "An Exploration in the Theory of Optimum Income Taxation," Review of Economic Studies, Oxford University Press, vol. 38(2), pages 175-208.
- John Knowles & Nicola Persico & Petra Todd, .
"Racial Bias in Motor Vehicle Searches: Theory and Evidence,"
Penn CARESS Working Papers
5940d5c4875c571776fb29700, Penn Economics Department.
- 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.
- 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.
- John Knowles & Nicola Persico & Petra Todd, 1999. "Racial Bias in Motor Vehicle Searches: Theory and Evidence," NBER Working Papers 7449, National Bureau of Economic Research, Inc.
- Charles F. Manski, 1997.
"Monotone Treatment Response,"
Econometric Society, vol. 65(6), pages 1311-1334, November.
- Nicola Persico, 2002. "Racial Profiling, Fairness, and Effectiveness of Policing," American Economic Review, American Economic Association, vol. 92(5), pages 1472-1497, December.
- A. Mitchell Polinsky & Steven Shavell, 1999.
"The Economic Theory of Public Enforcement of Law,"
NBER Working Papers
6993, National Bureau of Economic Research, Inc.
- 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.
- 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.
- Charles F. Manski, 2005. "Optimal Search Profiling with Linear Deterrence," American Economic Review, American Economic Association, vol. 95(2), pages 122-126, May.
- Durlauf,S.N., 2005.
"Assessing racial profiling,"
2, Wisconsin Madison - Social Systems.
- 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.
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