I examine here an aspect of law enforcement that has recently been the subject of debate. This is the choice of a profiling policy wherein decisions to search for evidence of crime may vary with observable covariates of the persons at risk of being searched. Policies that make search rates vary with personal attributes are variously defended as essential to effective law enforcement and denounced as unfair to classes of persons subjected to relatively high search rates. Variation of search rates by race has been particularly controversial; see, for example, Knowles, Persico, and Todd (2001), Persico (2002), and Dominitz (2003). Whereas recent research on profiling has sought to define and detect racial discrimination, my concern is to understand how a social planner might choose a profiling policy. This paper studies optimal profiling in a simple, illustrative setting. In related work (Manski, 2004), I consider how a planner might reasonably behave when he does not possess all of the information needed to determine an optimal policy. Section I poses a utilitarian planning problem whose objective is to minimize the social cost of crime and search. Search is costly per se, and search that reveals a crime entails costs for punishment of offenders. Search is beneficial to the extent that it deters or prevents crime.

Normative judgments embodied in the American legal system mandate that, in certain respects, public policy should treat all members of the population uniformly. Nevertheless, the legal system permits many forms of disparate treatment of the population. The Medicare program provides health-care benefits to persons age 65 and older, but not to younger Americans. The federal welfare-to-work program known as TANF permits states to treat welfare recipients differentially, placing some in job-training and others in basic-skills classes. Judicial sentencing guidelines variously permit or require judges to sentence convicted offenders differentially based on past convictions. Public high schools track students, making class assignments vary with past student achievement. In these and other settings where legal constraints do not preclude disparate treatment, society may choose among many alternative treatment rules. A program could mandate uniform treatment of the population or require that treatment vary in particular ways with observable covariates of the persons treated (e.g., age in the case of Medicare, past convictions in the case of sentencing), or permit agents of society (e.g., judges, welfare case managers, school counselors) to make their own treatment choices, subject to specified constraints. Research on program evaluation can help to inform public policy through efforts to learn the consequences of alternative treatment rules. In particular, evaluation research should seek to characterize how treatment response varies across the population. If we learn that all persons respond to treatment in much the same manner, then the best policy may be one that treats all persons uniformly. However, if we learn that treatment response varies with observable covariates of the persons treated, then society may be able to do better by designing programs in which treatment varies appropriately with these covariates. For example, society may be able to lower recidivism among criminal offenders by sentencing some offenders to prison and others to probation. It may be able to increase the life-cycle earnings of welfare recipients by placing some in job-training and others in basic-skills classes. In these and many other cases, the key to success is to determine which persons should receive which treatments. Regrettably, evaluation research has had little to say about how treatment response varies with observable covariates of the persons treated. A common practice, especially in observational studies, has been to assume that all persons respond to treatment in the same manner. Studies that are sensitive to possible variation in treatment response may report findings by race or gender or age, but they rarely disaggregate the population more finely. As a consequence, policymakers seeking to design programs for heterogeneous populations have to speculate on the consequences of alternative treatment rules. This short article draws on my recent research (Manski, 1997, 2000a, b) to argue that increased attention to observable variation in treatment response would enhance the value of evaluation research.

random processes ; information ; mathematical analysis