On terror, drugs and racial profiling

International Review of Law and Economics 28 (2008) 194–203

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International Review of Law and Economics

On terror, drugs and racial profiling Tomer Blumkin, Yoram Margalioth ∗ Ben-Gurion University, Department of Economics, Buchmann Faculty of Law, Tel Aviv University, Tel Aviv, Israel

a r t i c l e

i n f o

JEL classification: K14 K42 Keywords: Racial profiling Statistical discrimination Terror Equity-efficiency trade-off

a b s t r a c t We show that for racial profiling (defined as policy rules that employ statistical discrimination based on racial attributes) to be efficient in fighting ordinary crime, it needs to focus on the racial composition of marginal offenders. Efficiency thus may counter-intuitively call for targeting the group with the lower offending rates. In the context of terror, however, it has to be based primarily on differences in offending rates across racial population groups (group-wise averages). We demonstrate that, assuming correlation between race and crime, racial profiling would nearly always be efficient. Finally, we discuss equity considerations and suggest that if awarding compensation is perceived to be a viable policy option, it should be paid on an ex ante basis. © 2008 Elsevier Inc. All rights reserved.

1. Introduction Although racial profiling is a widely condemned practice, its use has actually increased since the September 11, 2001 terrorist attacks on the United States, as contended in a recent Amnesty report heralding a public campaign against racial profiling (Amnesty International USA, 2004).1 The expanded use of racial profiling in ‘the war on terror,’ also applies to ordinary crime enforcement, most notably in ‘the war on drugs’ (see, e.g. Banks, 2003; Stuntz, 2002). If race has no explanatory power in regressions that estimate search probabilities and take into account other observable characteristics thought to be related to criminal propensity, such as tinted windows, this is taken as evidence of no discrimination (see, e.g. expert witness testimony by John Donohue in Chavez v. Illinois State Police (2000)). When police is found to rely on race, this may be either due to statistical discrimination (Arrow, 1973) or to prejudice (Becker, 1957). The main strand in the law and economics literature on racial profiling debates this issue, starting with the influential work of Knowles, Persico, and Todd (2001) (see, e.g. Antonovics & Knight, 2004; Anwar & Fang, 2004; Dharmapala & Ross, 2004; Dominitz, 2003; Dominitz & Knowles, 2006; Gross & Barnes, 2002; Hernandez-Murillo & Knowles, 2004).

∗ Corresponding author. E-mail address: [email protected] (Y. Margalioth). 1 We define the term ‘racial profiling’ as the law enforcement practice of taking certain ‘sensitive’ traits, such as ethnicity, national origin or race (namely, traits that relate to groups who may suffer from discrimination), into account in deciding whether to initiate the stop, search or investigation of a suspect. 0144-8188/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.irle.2008.06.002

Another strand in the literature, to which this paper belongs, examines the normative aspects of racial profiling. Persico (2002) characterizes a set of conditions under which forcing the police to reduce the extent of profiling (assuming the police are maximizing ‘hit-rates’) would reduce the total amount of crime, thus promoting both efficiency and equity. Risse and Zeckhauser (2004), who assume that racial profiling is efficient, focus their analysis on equity aspects analyzed from a philosophical point of view, characterizing conditions under which profiling could be morally justified. Harcourt (2004) defines a set of conditions that would be necessary for racial profiling to pass constitutional muster. In this article we aim to contribute to the debate on the social desirability of racial profiling, defined as any policy rule, which employs statistical discrimination based on racial/ethnic attributes. We make a novel distinction between the efficient design of racial profiling used to curb ordinary crime (the focus of the existing literature) from that used to fight terror.2 Moreover, we demonstrate that racial profiling would nearly always be efficient, by considering the counter-intuitive option of targeting the group with the lower offending rate, a possibility that is usually overlooked by the literature and in the public discourse. Finally, we argue that in case awarding compensation to the targeted group members is a viable

2 Our analysis is premised on two key assumptions. First, as we analyze the issue from a normative perspective, we assume that police officers are not racially biased. Second, we focus our discussion on racial profiling rules, taking all other means to be exogenous. Clearly, other policy tools could be used to mitigate the adverse effects of crime; tools that are beyond the scope of police work, such as extending legal job opportunities to disadvantaged population groups, and within the control of police, for example, matching the racial composition of police force to that of the supervised population (Donohue & Levitt, 2001).

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policy option, compensation should be awarded on an ex ante basis, contrary to conventional wisdom. Minimizing ordinary crime is mainly achieved by deterrence. Deterrence should concentrate on the marginal offenders. Racial profiling is efficient only if there is a correlation between race and being a member of the marginal offenders group. Being a member of the marginal offenders group is not the same as being a member of a group that, on average, commits more crimes. We demonstrate that, barring knife-edge cases, racial profiling policy would always be efficient, by noting the possibility of targeting the group with the lower offending rate, but with higher representation (relative to its share in the general population) in the group of marginal offenders. We further show that differences in group-size are irrelevant for determining the optimal rule. Although the literature does not distinguish between terror and ordinary crime when discussing the efficiency aspects of racial profiling, we argue that there is an important difference between the two.3 It seems plausible to assume that terrorists, especially suicidal ones, cannot be significantly deterred by increased police enforcement efforts (see Ganor, 2003; Pape, 2005). Thus, unlike ordinary crimes, such as drug trafficking, racial profiling should focus on prevention, that is, incapacitation, rather than on deterrence (for an exposition of the notions of deterrence and incapacitation in the context of criminal law, see Shavell, 2004). Using racial profiling may help primarily to incapacitate potential terrorists, such as those found in security checks in airports. When the effect on deterrence is minor, efficiency considerations call for hit-rate maximization. Therefore, an efficient allocation of police resources calls for targeting individuals who belong to the group with the higher offending rate. This would be true even if the differences in offending rates between the groups are very small and even if the percentage of terrorists in the entire group is minuscule. In an extreme case, where deterrence effect is negligibly small, as can be approximately presumed in the context of suicidal terror, hit-rate equalization, discussed in the literature as describing the racial profiling rule practiced by the police, will not take place because behavioral response is virtually non-existent. Being efficient does not render racial profiling socially desirable because of the grave equity concerns it entails. Awarding compensation to the targeted population group as a means to address equity concerns is controversial (see, e.g. Cooter & Sugarman, 1994, cf. Donohue, 1998). However, if awarding compensation is perceived to be a viable policy option, we posit that compensation should be awarded on an ex ante basis. The paper proceeds as follows. In Section 2.1 we lay down the micro-foundation necessary for understanding how profiling works in the context of ordinary crime. In Section 2.2 we discuss the use of racial profiling in the context of terror. In Section 3 we discuss the optimal rule, taking equity costs into account. In Section 4 we conclude. 2. Efficiency

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the majority by NW . Let N denote the aggregate population. The two groups differ in their group-wise incidence of crime. Individuals decide whether to engage in criminal acts, taking into account their personal attributes, as well as the extent of law-enforcement. Following Becker’s (1968) seminal work on the determinants of crime, suppose that criminal activity yields a certain payoff given by Z > 0. Suppose further, that individuals who commit crimes incur costs that could be either psychic/moral or pecuniary. These costs may also include the opportunity cost associated with forgoing a legal job. We follow Persico (2002) by assuming that costs incurred in illegal activities differ across the two population groups. Naturally, costs may also vary within each population group. To capture this, we assume that the cost incurred by each typical minority-group individual and typical majority-group individual, respectively, is drawn independently from an identical group-wise distribution. We denote by HW (c) and HA (c), correspondingly, the cumulative distribution functions for the majority and minority population groups defined over some common support given by the interval [0, c¯ ]. If, for example, HW (c) reflects the legal job opportunities of the majority-group members, then HW (c) defines the probability that a representative majority-group individual would find a legal job paying an amount that is less than or equal to c dollars.4 For later purposes, denote by hW (c) and hA (c), the (strictly positive) densities associated with the cumulative distribution functions HW (c) and HA (c). Loosely put, the density hW (c) measures the fraction of majority-group individuals that incur the cost c. The same applies to the minority-group. We assume that the distribution HW (c) firstorder stochastically dominates the distribution HA (c); that is, for all c ∈ [0, c¯ ], HA (c) ≥ HW (c), with strict inequality for some value of c. To interpret this property, consider the case where the cost reflects a forgone legal job opportunity. Then the stochastic dominance would simply imply that minority-group members are more likely to wind up in low-paying legal jobs vis-a-vis majority-group members. This assumption implies that the minority population group is, as a whole, more prone to commit crimes, other things being equal. That is, the aggregate demand for criminal activities of the minority population exceeds that of the majority population. It is further assumed that the individual costs are private information and thus unobservable by the police.5 This asymmetry in information raises a screening problem. In such a second-best world, the police could gain from employing differential enforcement measures based on observed racial characteristics—a practice known as racial profiling. Note that, in our context, profiling implies applying different search rates to different racial groups. However, such a policy still implies random sampling within each group.6 The police, when enforcing the law, face a limited budget constraint. For example, the number of investigations conducted is limited to a measure k of suspects, which is plausibly smaller than the size of the entire population. Suppose that the police choose to investigate xA minority-group individuals and xW majority-group individuals, such that:

2.1. Ordinary crime xA + xW = k.

(1)

Consider a society whose population is large and consists of two groups of individuals, a minority, denoted by A, and a majority, denoted by W. Denote the minority-group size by NA and that of

3 It is sometimes posited that profiling in the context of terror entails lower equity costs, because passengers can be prepared for the inconvenience, and are assumed to be more sympathetic to the policy objective of preventing plane hijackings and/or crashes (e.g. Risse & Zeckhauser, 2004).

4 We denote the payment by c to reflect the fact that forgoing a legal job is an opportunity cost for a prospective criminal. Note that c denotes the highest paying job available. 5 The distribution of costs is assumed to be common knowledge. 6 In our framework, race is the only source of observable variation across individuals. In reality, profiling is based on a much wider set of characteristics that include age and marital status.

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We assume that once detected, the criminal is subject to a fine, denoted by F > 0.7 The agents are assumed to be risk neutral, thus a typical minority-group individual incurring cost cA would commit a crime if-and-only-if the following inequality holds: Z−

x  A

NA

· F ≥ cA .

(2)

Interpreting the inequality in Eq. (2) is straightforward. On the right-hand side we find the cost of committing a crime. The expression on the left-hand side captures the net expected payoff from committing the crime, which is the gross benefit, given by Z, minus the expected fine, given by the product of the detection probability, xA /NA , and the fine, F. Similarly, a majority-group individual incurring cost cW would commit a crime if-and-only-if the following inequality holds: Z−

x  W

NW

· F ≥ cW .

(3)

Employing the cumulative distribution functions HA (c) and HW (c), the offending rates for the minority and majority population groups, denoted by oA and oW , respectively, are given by:



oA = HA Z −



x 

oW = HW Z −

A

NA

x  W

NW



·F ,

(4)



·F .

(5)

To interpret Eqs. (4) and (5), note that the offending rate is equal to the fraction of the population that chooses to engage in crime. By virtue of Eqs. (2) and (3), it follows that there exists some cutoff cost level for each population group, such that all individuals who incur a cost that is lower than the cutoff level will engage in crime, while all other individuals (who find criminal activity sufficiently costly) will refrain from criminal activity. These cutoff levels are given by implicit solutions to Eq. (2) (for the minority population) and Eq. (3) (for the majority population), when the two inequalities are satisfied as equalities. The equality implies that an individual who incurs the cutoff cost level is indifferent as to engaging in criminal activity or refraining from it. It follows that the fraction of individuals in each population group who commit a crime is given by the probability of having a cost lower than or equal to the population group cutoff cost level. Note that offending rates are negatively related to the intensity of enforcement measures directed at group members. Further note that by virtue of our assumptions, when suspects are chosen regardless of their racial attribute (i.e. at random), oA > oW , namely, the offending rate is higher for group A. Denote by C the aggregate crime level, where C = CA + CW , with Ci = oi ·Ni ; i = A, W denoting the level of crime for group i, given by the product of the offending rate and the size of the population.8 Hence we denote by SCC (C) the social costs associated with crime. We plausibly assume that SCC (C) increases with respect to crime level C. We suppose that the police employ a random search rule; that is, they search both population groups with the same intensity by setting xA /NA = xW /NW , and address the following question: under

7 Note that F captures monetary fines as well as incarceration and is measured from the individual’s perspective. 8 Note that C measures potential crime, therefore, in order to derive the actual level of crime one should subtract the number of crimes directly prevented by incapacitation. Given the limited scope of police searches, we assume that in the context of ordinary crimes such as drug trafficking, apprehension rates are small such that the incapacitation effect relative to the deterrence effect is of secondary importance. In contrast, see our discussion in Section 2.2 and footnote 8 infra.

what conditions would profiling, namely, a deviation from a random search rule, lower the level of crime?9 Substituting the resource constraint [given by Eq. (1)] and the two incentive constraints for the minority-group and majoritygroup populations [given, respectively, by Eqs. (4) and (5)] into the objective (crime level), one can express the objective as a function of a single control variable, which is the number of minority-group members being searched. Differentiating the objective with respect to this variable, evaluating the derivative at the random search allocation, and re-arranging terms, yields the following sufficient condition (and necessary subject to concavity assumption) for the dominance of profiling over a race-blind enforcement system:



∂SCC (C)  ∂xA 



<> 0 xi =ri ·k

⇔ hW Z −

k N





· F − hA Z −

k N



· F <> 0,

(6)

where ri ≡ Ni /N, i = A, W denotes the relative share in the population of group i. Put into words, condition (6) states that a slight increase in the search intensity of the minority-group members (A), at the expense of majority-group individuals (W), would reduce crime if-and-onlyif the density of members of group A, who are indifferent as to committing a crime and abiding by the law, exceeds the respective density of group-W members. Thus, when hA > hW , targeting groupA members would reduce crime, and vice versa. Only in a knifeedge case, where the two densities are equal, would shifting police resources in the margin (in any direction) have no impact on the crime level; hence there would be no gain from profiling in the margin.10 Prima facie, one would predict that targeting the population group with the higher natural offending rate (in our example, group A) more intensively, would always reduce crime. This prediction, however, is not necessarily correct. It is based on a presumption that the offending rate of minority-group members exceeds that of the majority-group members at the point of random search, namely, HA [Z − (k/N)·F] > HW [Z − (k/N)·F]. It is possible, however, as illustrated in Fig. 1 below, that hW [Z − (k/N)·F] > hA [Z − (k/N)·F]; thus, targeting members of the majority-group would reduce crime. This counter-intuitive observation derives from a fundamental conceptual distinction we offer between marginal and average offenders. Fig. 1 depicts the probability distribution functions (PDF) of the cost of committing crime, c, for the minority-group (the dotted line) and the majority-population group (the solid line). When the police employ a random search rule, individuals of both population groups face the same detection probability (k/N). Thus, the cost threshold above which individuals abide by the law is the

9 Whether the police objective is indeed crime minimization is debatable. For example, Knowles et al. (2001) as well as Persico (2002) assume that police seek to maximize hit-rate; whereas Dominitz and Knowles (2006) assume (as we do) that police objective is to minimize the aggregate level of crime. The latter paper also examines a setting in which police objective is to minimize the level of unpunished crime, which is the level of (undeterred) crime net of the number of apprehended criminals. 10 In such a case, assuming that the second order conditions are satisfied, searching at random would constitute the optimal allocation of police resources. Note that equalizing densities [as shown by Persico, 2002] is a necessary (but not a sufficient) condition for crime minimization. In this paper we do not attempt to characterize the optimal search rule (that minimizes crime), but rather to demonstrate that a deviation from a random search strategy, that is profiling, is (nearly) always efficient. Ruling out the possibility of the existence of other local optima is crucial for the ability to prove that police search decisions are not driven by racial animosity, when densities are equalized, as shown by Dominitz and Knowles (2006). We do not address the issue of identifying police bias in this paper.

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Fig. 1. Cost probability distributions for the two population groups.

same for both population groups as depicted by point cˆ in the figure. Note that the threshold cost attribute is equal to the benefits derived from a criminal activity, net of the expected fine [formally, cˆ = Z − (k/N) · F]. The area under the PDF of each population group (to the left of the cost threshold cˆ ) represents its respective offending rate, under a random search rule. As can be observed from the figure, and consistent with our assumption, the offending rate of the minority population group exceeds that of the majority population group [formally, HA (ˆc ) > HW (ˆc )]. The densities hW (ˆc ) and hA (ˆc ), loosely measure the fraction of marginal offenders in the majority and minority population groups, respectively, under a random search rule. It is straightforward to observe that the fraction of marginal offenders among the majority population group exceeds that of the minority population group [formally, hW (ˆc ) > hA (ˆc )]. Thus, although on average a minority population group member has a higher propensity to commit a crime, enhanced deterrence would call for targeting the majority population group. The intuition for the latter observation is straightforward. Given the prevalence of crime and the limited scope of searches, incapacitation is of secondary importance. Hence, reduction in crime derives, by and large, from enhanced deterrence. This suggests that the police should target the marginal offenders; namely, those individuals who are (nearly) indifferent as to committing a crime and abiding by the law. In a first-best world, where police can directly observe the cost attributes of individuals, it would target marginal offenders, regardless of race. In a second-best world, however, as the police cannot directly observe the marginal offenders in the general population, profiling may be useful for targeting the racial group whose members are over-represented in the marginal offenders group, relative to its share in the general population. This would enhance deterrence, that is, reduce crime, by focusing police enforcement efforts on individuals who are more likely to be marginal offenders. In a random search allocation, the number of marginal offenders in population group i is approximated by the term hi [Z − (k/N)·F]·Ni . Targeting members of population group A would reduce crime if their share in the group of marginal offenders exceeded their share in the general population; formally, hA ·NA /[hA ·NA + hW ·NW ] > NA /[NA + NW ], which holds if-and-only-if hA > hW . Therefore, evaluated at the point where suspects are randomly selected (xi = ri ·k, reflecting the respective population ratio), when the density (fraction) of minority-group individuals who are marginal offenders exceeds that of marginal majority-group offenders, targeting the minority-group members would reduce crime, and vice versa. Our assumption that group A has a higher offending rate or, in other words, is more prone to engage in criminal activity, does not necessarily imply that members of group A are over-represented in the group of marginal offenders, relative to its share in the general

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population. The term ‘offending rate’ reflects the group’s average crime rate. The average crime rate in group A could be higher than the average crime rate in group W, due to a relatively larger number of infra-marginal offenders; namely, individuals whose decision to commit a crime will not be affected by a slight increase in police enforcement efforts, as their benefit from criminal activity strictly exceeds their cost in committing a crime (see the lower tail of the density distributions in Fig. 1, where group A has a larger number of infra-marginal offenders relative to group W). The policy implication would be to shift police enforcement efforts, in the margin, towards the racial group which is overrepresented in the marginal offenders group, relative to its share in the general population, up to the point at which the marginal offenders group mirrors the racial composition of society. This is the point where crime level is minimized, given the police resource constraint. To determine whether profiling may reduce crime and in order to design an efficient profiling rule, one has to identify the racial composition of the group of marginal offenders. In Appendix A, we illustrate how marginal offenders could be identified using equilibrium conditions. An important implication of the densities condition in Eq. (6) is that, contrary to conventional wisdom, difference in population size has no effect on the efficiency of profiling. To see this, suppose that the police consider whether to slightly increase the extent to which they target a certain population group at the expense of searching the other group. Searching an additional member of the targeted group (which is assumed to be the smaller group, namely, the minority) comes at the expense of searching one fewer member of the other group (the majority). This results in two offsetting effects. Searching one fewer majority-group member will decrease the deterrence of a relatively large number of individuals, while searching one additional minority-group member will increase the deterrence of only a relatively small number of individuals. This seems to lead us to conclude that overall deterrence is reduced, and crime level is increased (Harcourt, 2004). However, we would like to point out that searching one more, or one fewer, individual has a relatively greater effect (in absolute value) on minority-group members than on majority-group members, because one minority individual represents a higher percentage of the minority population. The two effects work in opposite directions, and exactly offset each other. Therefore, group-size does not matter. To see this formally, recall that the crime level of group i is given by the product of its offending rate and its group-size, that is: Ci = oi ·Ni . A change in crime level as a response to increased enforcement is thus given by: ∂Ci /∂xi = ∂oi /∂xi ·Ni . By differentiating the incentive constraints in (4) and (5), it can be observed that ∂oi /∂xi is inversely related to population size. This establishes the argument. 2.2. Acts of terror In the context of terror it seems plausible to assume that the deterrence effect of using racial profiling as a law-enforcement tool would be much less pronounced than in the drug interdiction case.11 As noted by Pape (2005) the readiness of terrorists to die in order to kill Americans is what made September 11 most jarring, as

11 The Knowles et al. (2001) model and the literature that followed are premised on the key assumption that racial profiling enhances deterrence; viewing equal hitrates as proof that variation in search rates across population groups was not driven by racial animus. In the absence of deterrence, differences in search intensities across population groups that result in equal hit-rates are indicative of racial animosity.

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it amplified Americans’ sense of vulnerability. “After September 11, Americans know that we must expect that future al-Qaeda or other anti-American terrorists may be equally willing to die, and so not deterred by fear of punishment or of anything else.” There are various explanations as to why people become suicide terrorists, but all of them are consistent with our assumption that for an individual terrorist, once decided to commit an act of terror, deterrence would be fairly limited. Following Emile Durkheim’s study of suicide in nineteenth-century Europe, which has been routinely confirmed by contemporary studies of suicide, individuals’ motives are defined as egoistic, anomic, altruistic or fatalistic (See Durkheim, 1951 [1897]). Egoistic and anomic suicides occur when individuals experience great disappointments or personal traumas and find the future to offer no hope for improvement.12 Altruistic suicide takes place when individual identifies with the collective good of her society to such an extent that she sees it as her duty to sacrifice her life to further society’s goals.13 Fatalistic suicides take place in cult-like situations where isolated groups of people follow their leaders and commit mass suicides. Early literature on suicide terrorism viewed it as driven by egoistic or anomic motivation, namely, carried by people who wished to die for personal reasons (Merari, 1990). Recent literature (Pape, 2005) sees it mostly as an altruistic act, similar to that of soldiers who are willing to take upon themselves suicide missions, such as the kamikaze pilots in World War II. Whether driven by egoistic or altruistic motivation, monetary or non-monetary sanctions are clearly of little relevance in the eyes of a person who is willing to sacrifice his or her life. The suicide terrorist has already accepted to bear the maximum cost. This is not to say that nothing can be done to reduce terrorist activity. First, terror may be reduced if the underlying conflict is resolved or mitigated. In the prevalent case (at least according to Pape, 2005) that suicide terrorists attack due to the occupation (broadly defined to include military, economic or cultural occupations) of what they perceive to be their homeland, getting out of those territories may stop or reduce the terror attacks.14 Second, the terror organizations themselves may be targeted. The people who head them are usually not suicide terrorists. A rare successful example for this strategy is Turkey’s capture of the Kurdish PKK leader that ended PKK’s terror attacks (Pape, 2005). Third, the expected benefit to the suicide terrorist can be significantly reduced if her motive is altruistic and society ceases to support such actions, and does not view her as a martyr. Similarly, if terrorists do it wholly or partly due to a strong belief that the act will provide them with heavenly rewards,15 religious leaders may refute such belief. Lastly, and more controversially, the suicide terrorist clearly does not care about her own life, but she may care about the well being of others. When suicide terrorists act out of altruism, their

12

Egoistic suicide is the outcome of a chronic depression whereas anomic suicide is the result of an abrupt change of circumstances such as the loss of a child or a spouse. Revenge may be a strong motive for suicide terrorism. There is a terrorist group of female suicide attackers, known as the Black Widows, whose members are Chechen women who lost their family members to Russian military action. Another well-known example is the assassination of Rajiv Gandhi by Dhanu, a Tamil Tiger suicide bomber. According to Pape (2005) Dhanu may have been gang-raped by Indian soldiers and her four brothers were killed. 13 Altruistic suicide can also take place in smaller groups, such as in the case of a soldier jumping over a hand-grenade covering it with his body to protect his friends, or a parent sacrificing her life in an effort to save her child. 14 As mentioned by Pape (2005), building a high fence, as was done by Israel, may also have proved efficient. 15 See, inter alia, The Guardian (2002); Andriolo (2002). The final instructions to the 19 terrorists that committed the September 11 terror attacks included the promise: ¨ “Afterwards, we will all meet in the highest heaven(Lincoln, 2003).

families gain significant social and economic rewards.16 This is part of the social mechanism that creates altruistic suicide terror. Taking away those rewards, or even punishing the families of suicide terrorists may provide effective deterrence.17 This may be one of the reasons why suicide terrorism is used only against democracies.18 Non-democracies are likely to use extreme measures that will prevent a society from supporting suicide terrorists. In-depth discussion of these policies is beyond the scope of this article, for the same reason that non-enforcement policies (such as investment in education or the creation of legal job opportunities) that could mitigate ordinary crime were left outside the scope of the vast literature that discussed racial profiling in the context of drug searches. For the same reason we do not discuss the potential diversion effects racial profiling might have. The literature on racial profiling employed in the context of drug searches did not consider the possible effect of racial profiling on drivers’ choice of routes. To be consistent with the framework presented in the previous section, the cost attribute, c, may be interpreted as capturing the degree of moral inhibitions that individuals have when they consider whether to commit an act of terror.19 Without being excessively unrealistic, we assume that the population can be divided into two sub-groups: those who have no such inhibitions and will commit an act of terror regardless of the extent of enforcement (prospective terrorists); and the rest of the population whose inhibitions are so great that they will never commit an act of terror irrespective of enforcement level.20 Crucially note that the extreme assumption on the shape of the cost distribution is made for analytical purposes to put our argument regarding the distinctive nature of profiling in the context of the war on terror relative to that used in the context of ordinary crime in its sharpest relief. The implication is that there will be no marginal offenders and hence no deterrence effect. In the absence of a deterrence effect, racial profiling could serve to promote the goal of minimizing crime only as a means of enhancing the actual prevention of terror acts; that is, incapacitation. Unlike the case of drug trafficking, where targeting marginal offenders reduced crime via enhanced deterrence; crime minimization in the context of terror is tantamount to hit-rate maximization. That is, as cost attributes cannot be directly observed, assuming that the offending rate differs across population groups, law-enforcement agents should, in the optimum, target only the group with the higher offending rate. This corner solution derives from the fact that there is no behavioral response to policing, and as the cost attribute is assumed to be identically and independently distributed within each population group, the probability that an individual member of a certain group is a terrorist is constant, regardless of enforcement. Thus, minimizing crime implies that, in the margin, police will always search the individual

16 Families of Palestinian suicide terrorists received significant monetary awards. See Riemer (1998) and Ganor (2003). 17 Punishing relatives is a highly controversial issue and is only applied in the aftermath of a successful act of terror. Paradoxically, racial profiling aimed at prevention reduces the (possible) deterrence effect of sanctions imposed on relatives, as the expected fine decreases with respect to the probability of detection. This strengthens our argument that, in the context of terror, racial profiling has no deterrence effect. 18 As shown by Pape (2005) the target state of every modern suicide campaign has been a democracy. 19 Differences in costs are equivalent to differences in benefits, as what matters from the point of view of the prospective criminal (terrorist) and varies across individuals, is Z − c. The fine and the detection probability in the absence of profiling are uniform across all individuals. 20 Formally, the distribution of costs will be discrete, with a (small) mass at c = 0 and a large mass at c = c¯ (for all values of c, other than the extreme points, the density will be equal to zero), where Z–F > 0, and Z < c¯ .

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who is more likely to be a terrorist, who is a member of the population group for which the offending rate is (constantly) higher.21 More formally, denote by S the expected number of apprehensions, given by: S ≡ xA · oA + xW · oW .

(7)

The police seek to maximize S subject to the resource constraint in (1), which is reproduced for convenience: xA + xW = k.

(8)

By assumption, there is no deterrence effect; thus, ∂oi /∂xi = 0; i = A, W. It immediately follows from (7) that ∂Si /∂xi = oi ; i = A, W, where oi ; i = A, W are constants that do not depend on the precise allocation of police resources. Assume that offending rates differ across population groups, and with no loss in generality, let oA > oW . Thus ∂S/∂xA > ∂S/∂xW . It follows that any allocation where the police target the majority population group (by setting xW > 0) can be improved by shifting resources in the margin towards the minoritygroup population. Thus we obtain a corner solution, where only minority-group members are being searched. Two important application points are in order.22 First, due to cognitive biases law-enforcement agents may rely too much on race and ethnicity in their assessment of risks compared to other relevant traits, such as nervousness (see also Schauer, 2003). In case these biases are significant, ordering selectors to ignore race and ethnicity may actually increase profiling efficiency. See Appendix B for an illustration of this argument. Second, notwithstanding the previous point, ethnic profiling is likely to be important in identifying potential terrorists. Contrary to popular belief (see, for example, Schauer, 2003) relying on characteristics such as traveling alone, on a one-way ticket, with no suitcases (carry-on only), last minute ticket purchase using cash, and late check-in time, are not helpful in identifying terrorists. All these characteristics make sense. Thus, for example, there is no reason to buy a two-way ticket if you are planning to crash the plane. Moreover, in order to allow law-enforcement authorities as little time as possible to investigate your identity you are better off purchasing the ticket on the last minute using cash. The problem, however, is that terrorists know that these characteristics are considered suspicious. The currently employed Computer-Assisted Passenger Pre-screening System (CAPPS), which was put in service following a White House Commission on Aviation Safety and Security, chaired by Vice-President Al Gore in 1997, uses this set of criteria. This system failed to detect the 19 terrorists that boarded four planes on September 11. The reason it failed, and is likely to fail again, is that it uses characteristics that are within the terrorist’s control. The terrorist can travel with company (there seems to be no shortage in suicide terrorists), buy a two-way ticket long ahead of time, using a credit card and giving a frequent traveler number, arrive early and check-in a suitcase or two. Ethnic origin, on the other hand, is not within the terrorist’s control. Different countries face different terror threats. The threat faced by the United States, for example, is not from Tamil terrorists but mainly from Al-Qaeda and some other Arab or Muslim terror organizations. All 19 terrorists that committed September 11

21 Even when the behavioral response (deterrence effect) is present, it is likely to be small; such that the offending rate for one group will exceed that of the other. Crime minimization may still call for a corner solution, where members of only one population group will be searched. 22 We are grateful to Rafi Ron for pointing them out to us. Rafi Ron was in charge of security at Israel’s main airport. Following September 11 attacks he was invited to Logan airport in Boston to redesign its security system.

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attacks were Muslim Arabs of Middle Eastern ethnicity. This is not a coincidence. The data show that the majority of al-Qaeda suicide terrorists come from Saudi Arabia and other Persian Gulf states, and Pape (2005) estimates that the organization might collapse without this core support from Persian Gulf states. In light of the discussion above, ethnicity seems to be a natural candidate for profiling, being a visible characteristic that is positively correlated with being a terrorist and beyond the terrorist’s control.23 Acknowledging the failures of CAPPS, which allowed the terrorists to board the four planes on September 11, a new system, named CAPPS II, was developed. This system was designed to focus on the type of characteristics that terrorists may find difficult to mask or change. It was designed to perform background checks on each passenger reserving a flight to determine the passenger’s “risk” to airline safety (Homeland Security, 2004). The system was supposed to rely on commercial companies that are in the business of compiling extensive dossiers about the lives of most Americans to assess their credit risk. This information was supposed to be added to a database containing the information available in all government databases. A special algorithm was then supposed to be used to derive every passenger’s flight safety score. Airport security personnel were supposed to search passengers according to their safety scores. The development of the system was stopped because the system was viewed to be posing excessive threat on the freedom and privacy of the people. A new system, code named ‘Secure Flight,’ is now under development (Hall & DeLollis, 2004; Singel, 2004). It is supposed to be a mild version of CAPPS II, but no official knowledge is available at the time of writing this article. Before we turn to discuss equity issues, a final concluding remark is called for. Law-enforcement is never a crude binary choice between deterrence and prevention. In ordinary crimes (like drug trafficking) in as much as in the war against terror, both types of considerations play an important role in the design of enforcement policy, and profiling as a part of which. Our analysis suggests that in types of crime where deterrence is the primary goal, emphasis should be put on targeting marginal offenders, whereas in cases (like suicidal terror) where deterrence is of secondary importance, average offenders should be the prime targets.24 Our analysis that exemplifies the two polar cases should thus not be taken in face value, but rather suggest to the key considerations that should be taken into account in policy design and to the stark differences between different types of crime. 3. Equity In the previous sections we discussed the efficiency rationale for using racial profiling. Even when considered efficient, racial profiling rules are highly controversial due to the major equity concerns they raise (Kennedy, 1997). In this section we will briefly discuss some of these concerns and possible remedies. As it is based on statistical inference, racial profiling violates the notion of horizontal equity, where identical individuals face, ex ante, equal treatment by the law. Thus, individuals who are stopped and searched (and possibly fined or incarcerated if found guilty)

23 Note that in essence potential suicide bombers from other ethnic origins can be hired to avoid the screening process. However, recalling our assumption that sacrificing life requires strong moral convictions and has little to do with monetary rewards renders this conjecture less plausible. 24 The law and economics literature tends to stress deterrence. However, it is important to note that the criminology literature acknowledges the growing importance of incapacitation in ordinary crimes. See, e.g. Feeley and Simon (1992).

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more often than others who are identical to them in all respects other than race, bear a disproportionate share of the cost that society pays for law-enforcement. This applies to the innocent and to criminals alike. The targeted innocent are subject to more frequent searches, with the entailed inconvenience, humiliation and loss of time, compared to the rest of society; while the criminals face a higher probability of getting caught compared to criminals who are not members of the targeted group. Note that while any law-enforcement system is incomplete and thus results in ex post different treatment of ex ante identical individuals, profiling is horizontally in-equitable even when measured from an ex ante perspective. What makes racial profiling exceptionally controversial is the fact that it is based on sensitive traits such as race, ethnicity, religion and gender. Minorities and women are often subject to discrimination on other grounds. Thus the inequity entailed by profiling employed in the context of law-enforcement is added to the inequity associated with underlying discrimination. Profiling reminds the targeted group individuals of their being disadvantaged and exacerbates their feelings of humiliation. As the bulk of the equity cost of profiling stems not from the profiling itself, but from the underlying discrimination, even very limited use of profiling may entail significant marginal equity costs.25 This may allude to the possibility of a corner solution where the use of profiling to any degree would be strictly prohibited by the law (as in the context of labor and employment law). In such a case, the marginal equity cost at (close to) zero profiling is so large as to outweigh any marginal efficiency gain. Note that in the absence of underlying discrimination, for example, targeting young individuals for their higher propensity to commit crimes, profiling is far less controversial, as in general being young is considered to be an advantage. A key issue in resolving the equity-efficiency tradeoff to determine the optimal extent of profiling (possibly a corner solution that prohibits such practice) is assessing the option of awarding compensation to targeted group members.26 While restraining (and possibly ruling out) the use of profiling is a natural remedy for equity concerns, an alternative policy would be to set the profiling rule at its most efficient point and use the tax-and-transfer system to compensate for the inequity costs. Public hostility towards transfers (pecuniary or in-kind) as an appropriate means of addressing racial issues may preclude the use of the tax system to compensate the targeted group. People care about the symbolic value of laws and suffer disutility because of their distaste for what they see as allowing discriminators to purchase the right to discriminate (see Bell, 1992; Donohue, 1998). For example, in the context of terror, people may find it objectionable if police were to search, almost exclusively, Muslims and people of Middle Eastern origin, as efficiency calls for, even if the targeted individuals were eligible for compensation. If awarding compensation is perceived to be a viable option, we posit that it should be awarded on an ex ante basis. Namely, it should be awarded to all members of the targeted group, innocent and (ex post) criminals, as racial profiling puts all targeted group members at greater risk of being searched, interrogated or incar-

25 In analogy, starting from a system where only income tax is present, adding a tax on consumption goods, even if set at very low rates, would entail a large marginal deadweight loss, as it builds on the underlying distortion of the income tax (see Stiglitz, 2000). 26 Another key issue, not discussed in this paper, is setting the benchmark for measuring the degree of racial profiling (see the discussion in Dominitz, 2003).

cerated. This affects their lives, even before a search takes place, and even if no search ever takes place. For example, an innocent groupA individual who plans to drive on a highway needs to consider longer expected commuting time; a group-A criminal weighing the cost and benefits of committing a crime should consider the higher probability of getting caught and punished. Awarding compensation on an ex ante basis is not the prevailing norm (see discussion in the context of tort law in Porat & Stein, 2001; cf. Cooter & Sugarman, 1988). However, in the context of racial profiling, ex ante compensation would address equity concerns while creating less distortion than awarding compensation on an ex post basis. Compensating innocent targeted group individuals on an ex post basis, namely, awarding compensation to each innocent member of the targeted group who was searched, as suggested in racial profiling literature, is inefficient. Awarding such compensation would induce behavioral effects, and therefore, is likely to entail distortions. For example, innocent targeted group members who value the compensation more than the cost of being searched (i.e. waste of time, risk of being charged with an offence with which they did not expect to be charged, etc.) will have an incentive to be searched. As for targeted criminals, the literature does not suggest to offer them compensation at all. As argued above, equity considerations call for compensating them as well, for being discriminated against in comparison to non-targeted group criminals. Awarding criminals ex post compensation may reduce deterrence, while awarding compensation ex ante would have no such effect.27 The ex ante compensation could be implemented via the tax-andtransfer system by a uniform refundable tax credit which would be given to all members of the targeted ethnic group. As mentioned above awarding such compensation may not be feasible politically. In addition, such compensation scheme would require pre-announcement of search probabilities regarding the ethnic attribute in order to ensure that compensation would commensurate with the (expected) extent of racial profiling. This may mitigate the deterrence effect of police searches in the context of ordinary crimes, assuming potential criminals are ambiguityaverse.28 Racial profiling could also serve as an example of a case in which the policymaker should deviate from the most efficient rule to accommodate equity concerns, even when compensation is a viable policy option. This connects our discussion to a central debate in the law and economics literature: whether legal rules should be designed based on efficiency grounds only, or should they be equity informed (see, e.g. Kaplow & Shavell, 1994; Sanchirico, 2000). Awarding compensation to targeted group members and limiting the use of profiling are two alternatives to accommodate equity concerns.29 However, while a small deviation from an efficient

27 The advantage of awarding compensation on an ex post basis derives from the ability to distinguish between criminals and the innocent. On one hand, it seems that criminals are less deserving from a moral point of view. On the other hand, targeted group criminals are exposed to two different layers of discrimination, when compared to criminals in the non-targeted population group, whereas their innocent counterparts are subject to one layer only. The former face both a higher probability of being searched and incarcerated, whereas the latter face only a higher search probability. As the two effects work in opposite directions, assuming that they offset each other warrants uniform compensation. Hence, ex ante compensation would be desirable. 28 On the notion of ambiguity aversion, see the discussion in Gilboa and Schmeidler (1989). 29 Trivially, when compensation is considered illegitimate, modifying the profiling rule towards the random search rule is the only means available to accommodate equity concerns.

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profiling rule entails a negligible efficiency cost, by virtue of its presumed optimality,30 awarding compensation is likely to entail a significant deadweight loss, due to the pre-existing distortions entailed by the tax system.31 Thus, some deviation from the most efficient profiling rule is warranted even when compensation is viable. Finally, note that the distinction between ordinary crimes and terrorism is not confined to the efficiency dimension, that is, the extent to which deterrence effects differ across the two types of crime. It also bears on the equity-efficiency trade-off. The greater severity of the consequences of successful acts of terror may tilt the balance towards greater reliance on efficiency enhancing measures such as racial profiling in the context of terror.32 Hence, the distinction between the use of racial profiling in the context of ordinary crimes and terror is not limited to the type of profiling strategy being used (average versus marginal offenders), but also to the extent to which profiling is being used at all.

4. Conclusion This paper is a theoretical contribution to the popular and academic debates regarding the social desirability of using racial profiling in criminal law-enforcement. Discussing efficiency aspects of racial profiling rules, the main contribution of the paper is in making a novel distinction between ordinary crime (e.g. the war on drugs) and terror. Whereas in the context of ordinary crime, law-enforcement focuses on deterrence, hence as we explain, racial profiling should target marginal offenders; in the context of terror, incapacitation prevails, justifying ‘hit-rate’ maximization, focusing on ‘average’ offenders. We prove that, barring knife-edge cases, profiling is always efficient, helping the police to mitigate the screening problem when criminal activity varies across racial groups. Notably, in the context of ordinary crime (but not in the context of terror), efficient racial profiling policy may counter-intuitively call for targeting the group with the lower offending rates. Moreover, we show that contrary to conventional wisdom, group-size does not matter. Although desirable in a second-best world with asymmetries of information, using racial profiling raises significant equity concerns as evidenced by the intense scholarly and public debate. Briefly discussing the equity aspects of racial profiling, we argue that racial profiling may be prohibited entirely as is the case in the labor market context where statistical discrimination is banned. This will depend on the pre-existence of discrimination against the population being targeted by the profiling rule. Assuming that using compensation to targeted group members as a means to alleviate equity costs is a viable policy option, we make the novel argument that compensation should be paid on an ex ante basis due to efficiency considerations.

30 In the context of ordinary crime, assuming an interior solution, a first-order approximation to the effect of an arbitrarily small shift in police search allocation on the crime level is zero. 31 The distortion associated with raising the marginal dollar to finance the transfer to the targeted population is added to the already existing distortions entailed by the tax and transfer system. 32 The fact that the public distinguishes between ordinary crime and terror based on the greater severity of the consequences of terror is reflected by the public’s willingness to trade-off civil liberties for additional prevention (see, e.g. the USA Patriot Act).

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Appendix A. Identifying marginal offenders from the equilibrium conditions Recall that being a marginal offender is not an attribute that can be directly observed by the police. Nonetheless, as we illustrate below, given a set of identifying assumptions,33 the police can infer the racial composition of the group of marginal offenders from the equilibrium condition and determine whether racial profiling is efficient. First, we assume that the cost incurred in committing a crime may be decomposed into a pecuniary cost associated with the forgone legal job opportunity and psychic/moral costs. Second, we assume that the psychic costs are distributed identically and independently across the general population (thus, psychic costs will bear no impact on the issue of the desirability of using profiling). Specifically, we assume that for each wage rate (denoted by c), a proportion 0 < ˛ < 1 of the population (regardless of racial origin) is honest (that is, incurring a large enough psychic cost that renders crime non-profitable) and the remainder of the population (a proportion of 1 − ˛) incurs zero psychic costs.34 We further assume that the distribution of legal job opportunities for each population group is continuous, with strictly positive densities throughout its entire support.35 Finally, we assume that individuals who abide by the law report their legal income truthfully, whereas those who choose to commit crimes report zero income. In order to determine whether deviation from a random search rule would reduce crime, we need to check whether members of a certain racial population group are over-represented in the group of marginal offenders, relative to their share in the general population [see condition (6) in the body of the text]. In such a case, searching the members of that racial group slightly more intensively will reduce crime. Thus, we need to compare the densities of the two population groups at the point cˆ ≡ Z − k/N · F, where both population groups face the same probability of detection (k/N). For this purpose, we need to identify the (unobserved) parameters Z and F; that is, the benefit and the fine, as perceived by would-be criminals, so as to infer the value of cˆ and then evaluate the densities at the point of random search hA (ˆc ) and hW (ˆc ). We turn next to showing how, given the assumptions stipulated above, this can be done. ˜ i (c) denote population group i’s reported legal wage disLet H tribution under a regime where the police search xi individuals of population group i, i = A, W. In particular, the police may use profiling (possibly, but not necessarily, maximizing hit-rate). We denote the corresponding density by h˜ i (c). We further denote by cA and cW , the legal wage rate thresholds for the minority and majority population groups, respectively, above which individuals abide by the law and below which individuals with zero psychic costs choose to engage in criminal activity. Although these thresholds are not directly observed by the police, they may be inferred from the reported wage distribution. As the distribution of legal job opportunities (in contrast to the reported wage distribution) is assumed to be continuous, the cost thresholds may be recovered from the discontinuous jumps in the reported wage distributions (by virtue of the discrete nature of the distribution of the psychic costs).36

33 Similar assumptions are used by Persico (2002). Clearly, this set of identifying assumptions is restrictive and used for illustrative purposes only. By no means do we argue that, in general, one can identify the propensity to commit crime (the marginal offenders) based on observed wage-distribution alone. 34 Note that the parameter ˛ is unknown to the police, but as shown below may be inferred from the observed data. 35 Below, we discuss alternative identifying assumptions if the continuity assumption is relaxed. 36 When ˛ = 0; that is, no psychic costs are entailed by engaging in criminal activity, the thresholds are simply given by the minimal wage rates in the legal sector.

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Parameter ˛ may be recovered in a similar way. It follows immediately that the probability distribution function (PDF) of the legal job opportunities for population group i is given by:

Table 1 Proportion of terrorists within population sub-groups

h˜ i (c) ˛ hi (c) = h˜ i (c)

Parent Non-parent

hi (c) =

for c < ci

(A1)

x 

cW = Z −

A

NA

· F,

x  W

NW

· F.

(A2) (A3)

Relaxing the assumption regarding the continuity of the distribution of legal job opportunities, one can still identify the densities and the parameters Z and F, using alternative identifying assumptions. To see this, note that by using the variation in police enforcement over time (assuming that the distributions of legal job opportunities are fixed over time) one can recover parameter ˛ by comparing the reported wage distributions across time. Integrating the densities (summing the probabilities) to obtain the cumulative distribution function for each population group, and assuming that police maximize hit-rate (denoting by  the common hit-rate for both population groups), one can find the cost thresholds, ci , i = A, W, by solving the following equation (for each population group): =

Non-Muslim

0.1 0.3

0 0.2

for c ≥ ci

Given the two thresholds, cA , cW , one can find Z and F by solving the following two equations for the two unknowns: cA = Z −

Muslim

(1 − ˛) ˜ i [ci ], i = A, W. ·H ˛

(A4)

One can use Eqs. (A2) and (A3) to solve for Z and F. Employing (A1) one can then calculate the densities hA (ˆc ) and hW (ˆc ). Appendix B. Selectors’ cognitive bias Profiling strategy is not confined to racial attributes. Thus, airport selectors sort out potential terrorists by resorting to additional observable characteristics that are known to be correlated with the propensity to commit an act of terror.37 The optimal profiling rule is based on a hybrid of the various attributes. Thus, for example, an efficient profiling rule may not subject a parent boarding the airplane with her kids to a closer scrutiny, even if she is of Muslim ethnic origin. In our model we assumed that the only observable attribute that is being used by the law-enforcement agents is the ethnic origin for tractability purposes. When several attributes are being used there is a risk that the racial attributes will be utilized more than their actual predictive contribution would justify, due to selectors cognitive biases. To illustrate the point suppose that the daily passenger population at a certain airport differs in two attributes: (i) ethnic origin (individuals are either Muslim or Non-Muslim); (ii) parenthood (individuals are either parents joined by their kids or traveling alone). The following table presents the proportion of terrorists within each one of the four population sub-groups. For simplicity it is assumed that all sub-groups are of equal size (aggregate population is normalized to unity, with no loss in generality). Suppose that due to limited resources, the airport security authority is only capable of searching 60% of the daily passengers. It is assumed that the objective of the security authority is to maximize expected incapacitation (prevention). Consider first the case in which racial profiling is prohibited. In such a case, profiling based on the parenthood attribute could still enhance

37 Such characteristics usually include gender (male) and demeanor (nervous). See, e.g. Schauer (2003) and Raines (2006).

efficiency relative to a random search. To see this note that with a random search, given the fact that each sub-group is of equal size, the proportion of terrorists in the aggregate population is equal to 0.15 = (0.3 + 0.2 + 0.1 + 0)/4. Thus, given the fact that only 60 percent of the population is being searched, the expected incapacitation (prevention) is given by the product of the fraction of the population being searched and the proportion of terrorists in the general population: 0.09 = 0.6·0.15. If, alternatively, individuals would be searched according to the parenthood attribute, the optimal profiling rule would be to subject all non-parents to extensive search, then, to search at random from the rest of the population. From Table 1, one can observe that the proportion of terrorists amongst non-parents is given by 0.25 = (0.3 + 0.2)/2, whereas the corresponding proportion amongst the parents is given by 0.05 = (0.1 + 0)/2. The expected incapacitation would then be: 0.13 = 0.5·0.25 + 0.1·0.05. This constitutes an improvement relative to a random search strategy. Consider now the optimal profiling rule (that is, maximizing incapacitation) when profiling based on the racial attribute is allowed for. In such a case, the optimum would call for searching the entire non-parent population and then searching at random the parent-Muslim sub-group. The expected incapacitation would then be: 0.135 = 0.5·0.25 + 0.1·0.1. Finally, suppose that due to cognitive bias, the selectors prefer selecting according to race to selecting according to parenthood (when allowed to engage in racial profiling). In such a case, the selectors would search the entire Muslim population, and then search at random the non-parent (non-Muslim) population. As the proportion of terrorists amongst the Muslim population is given by 0.2 = (0.3 + 0.1)/2, the expected incapacitation, under the assumption of cognitive bias, would be 0.12 = 0.5·0.2 + 0.1·0.2, which is lower than the expected incapacitation under the optimal search rule, when racial profiling is ruled out (0.13, as calculated above). We conclude that in the presence of cognitive biases, banning racial profiling may be socially desirable. References Amnesty International USA, 2004. “Threat and Humiliation, Racial Profiling, Domestic Security, and Human Rights in the United States” (http://www.amnestyusa.org/racial profiling/report/index.html). Andriolo, Karin. (2002). Murder by suicide: Episodes from Muslim history. American Anthropologist, 104(3), 736–742. Antonovics, Kate L. & Knight, Brian G. (2004). A New Look at Racial Profiling: Evidence from the Boston Police Department. NBER Working Papers: 10634. Anwar, Shamena, & Hanming Fang. 2004. “An alternative test of racial prejudice in motor vehicle searches: Theory and evidence, Cowles Foundation Discussion Paper No. 1464.” (http://ssrn.com/abstract=567766). Arrow, Kenneth. 1973. The theory of discrimination, in Orley Ashenfelter and Albert Rees, eds., Discrimination in Labor Markets. Princeton: Princeton University Press. Banks, Richard. (2003). Beyond profiling: Race, policing, and the drug war. Stanford Law Review, 56, 571–603. Becker, Gary S. (1957). The economics of discrimination. 2d ed. 1957, 1971 Series. (ERS) Economic Research Studies. Becker, Gary S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76, 169–217. Bell, Derrick. (1992). Faces at the bottom of the well. The Permanence of Racism, 47–64. Basic Books Cooter, Robert D., & Sugarman, Stephen D. (1988). A regulated market in unmatured tort claims: Tort reform by contract. In New direction in liability law. W. Academy of Political Science., p. 174–185. Cooter, Robert D., & Sugarman, Stephen D. (1994). Market affirmative action. San Diego Law Review, 31, 133–168.

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