سوگیری نژادی در جستجوهای و توقف های نیروهای پلیس: تجزیه و تحلیل اقتصادی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|28212||2001||21 صفحه PDF||سفارش دهید||9456 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : European Journal of Political Economy, Volume 17, Issue 1, March 2001, Pages 17–37
The purpose of this paper is to provide an economic analysis of racial bias in police stops and searches. It develops a model of policing behaviour, which is used to define discrimination, clarify its nature and identify its sources. This paper identifies two sources of discrimination—bigotry and business necessity—and suggests how they might be identified in terms of the available data. Bigotry is always inefficient but discrimination based on business necessity makes for efficient policing. However, discrimination based on business necessity may be unacceptable on equity grounds and the paper explores the tension between efficient and equitable policing.
“Zero-tolerance” policing—under which no offence, however trivial, is allowed to go unpunished—is increasingly viewed as the most effective method of reducing crime rates Farrington et al., 1986, Goldstein, 1990, Wilson and Petersilia, 1995, Kelling and Colis, 1996 and Bratton, 1998. This policing model, which has won admirers all over the world,1 has dramatically altered the way that the police go about their business. A major casualty has been the concept of “community policing”: under zero-tolerance, as Massing (1998) notes, the New York Police Department, which pioneered the use of this concept of policing, was resolved, in direct contrast to the restraint usually counseled by community policing, to adopt a more aggressive stance in the community. A major instrument of aggression was the large scale stopping and searching of suspected offenders, with young black men being particular targets. In consequence, an unfortunate, but perhaps inevitable, consequence of zero-tolerance policing has been a rupture in relations between the police and the black community in New York. In England and Wales (E&W) too, the use of stop and search methods by the police ruffles racial sensitivities. Police officers in E&W, using their powers under the Police and Evidence Act of 1984 to stop and search suspected offenders (hereafter, abbreviated to “stops”) carried out over 1 million stops in 1998. Judging by Home Office data,2 the likelihood of being stopped was much greater for black and Asian persons than for persons who were white. In 1998, for example, 145 blacks and 45 Asians, but only 19 whites, in E&W were stopped per 1000 of their respective population (Home Office, 1998). Therefore, there can be little doubt that there was a racial bias to these stops with the police in E&W discriminating against blacks and Asians in favour of whites. The fact of discrimination, nevertheless, leaves open the question of why such discrimination should arise. Without knowing its sources, one cannot address the problem of eradicating discrimination. The first purpose of this paper, which derives from this observation, is to develop, in Section 2, a model of policing behaviour which can be used to define discrimination, clarify its nature and identify its sources. This model is an adaptation of Longhofer and Peters' (1998) model of discriminatory behaviour by mortgage lenders. Implicit in this is a parallel between the behaviour of lenders and that of the police. Both lenders and the police have to decide on whether to detain a “client” or to allow him/her to proceed. For lenders, clients are loan applicants and detaining means refusing a loan; for the police, clients are persons who are out on the street and detaining means stopping and searching. In both cases, the decision to detain a person is based on the fear that if the person was allowed to proceed, an adverse outcome would follow: a loan would not be repaid or a crime would be committed. The decision on whether or not to detain a client is based on inferring, from certain observed characteristics of the client, the likelihood of that client “offending”. Lenders study the financial history and circumstances of their applicants while police officers observe a person's age, sex, demeanour, behaviour and circumstances. In both cases, action is triggered if the likelihood of offending exceeds some threshold value: the lender rejects a loan application while the police stop a person. Discrimination arises if different groups are assigned different threshold values for triggering action. The sources of discrimination are essentially two: a lower “action-triggering” threshold may be set for, say, black persons because the responsible authority—the police or lenders—dislikes blacks. In this case, discrimination is based on bigotry. Alternatively, discrimination may be based upon the belief, whether justified or not, that black persons are, on average, more likely to offend, whether by defaulting on loans or by committing crimes, than persons from other groups. In this case, discrimination against blacks is “statistical” or, equivalently, based on “business interest”. Needless to say, any given act of discrimination may contain elements of both bigotry and business interest, and indeed, the perception that there is a business interest to discrimination may itself stem from bigotry. Putting this last point to one side, Section 2 shows that statistical discrimination, untainted by bigotry, is optimal from a policing perspective because it maximises the number of arrests consequent upon a given number of persons stopped. However, statistical discrimination—carrying as it does the implication that “race matters” in determining the likelihood of a person being stopped—may be reprehensible to society. Instead, society may prefer its police to implement a “colour-blind” policy. This then leads to the second purpose of the paper (Section 3) which is to define a socially optimum distribution of stops between different groups and then to derive from this a measure of the degree to which the existing distribution is (socially) sub-optimal. This analysis draws upon the method of Atkinson (1970) to develop an inequality measure for the distribution of stops which perfectly reflects the level of social welfare associated with that distribution. Section 4 explores, using Home Office (1998) data, the extent to which the police, in carrying out stops, discriminate against blacks and Asians and attempts to allocate the totality of discrimination into a “bigotry” and a “business interest” component. Section 5 concludes the paper.
نتیجه گیری انگلیسی
The issue of racial discrimination in police stops poses a puzzle not unlike that observed in the area of mortgage lending. Longhofer and Peters (1998) remarked that the puzzle about mortgage lending is why high rates of rejection of minority loan applicants co-existed with high rates of default by minority borrowers. In the area of police stops the puzzle, at least in England, is why the high likelihood of blacks, relative to whites, being stopped by the police co-exists with black arrest rates, which are no lower than that of whites. The answer in both cases is the same: discrimination on grounds of business necessity. If the likelihood of being stopped was the same for blacks and whites, then the likelihood of being arrested after a stop would be substantially higher for blacks. That is because the evidence suggests that—with all its flaws—the average arrest-likelihood of blacks is greater than that of whites. The police in England exploit this fact to stop a greater proportion of the black, than of the white, population. A corollary of such practice is that inequality in black–white stop rates co-exists with relative equality in black–white arrest rates. It is possible, none the less, that discrimination on grounds of business necessity could be supplemented with bigotry. The relevant question is how much of the observed discrimination against blacks by police stops is based on business necessity and how much is the result of bigotry? The conclusion of this paper was that the implementation of police stops in England, while undoubtedly discriminating against blacks, was largely free of bigotry. This is not to deny that racism exists among the police forces in England (McPherson, 1999); but it would be wishful thinking to explain away the large imbalance in black–white stop rates, which exists in every police area in England, in terms of police racism. Rather, it is more accurate to view the high stop rate for blacks as the consequence of the police in England targeting their resources to achieve the maximum effect in terms of arrests. In turn, this observation raises the further question as to whether such targeting can be justified? If, adopting a civil libertarian perspective, one treats the disparate treatment of individuals, on the basis of their belonging to particular groups, as wrong then clearly statistical discrimination cannot be justified. Under such discrimination, the treatment of an individual is conditional upon the average behaviour of the group to which he/she belongs. The fact that the sins of the group visit themselves upon every member of the group may, to many persons, be distasteful. On the other hand, if one is prepared to tolerate disparate treatment on grounds of business necessity, then such discrimination may be defended on the grounds that it contributes to police efficiency. As Wilson (1989) observed, the essence of the stopping (and then discharging or arresting) process is exercising judgement about who is likely to have committed a crime. In this process, the guiding principle is the unequal treatment of persons. As this paper has argued, society might not object to persons being treated unequally if some broader principle of fairness was adhered to. This broader principle might be, for example, that unequal treatment was untainted by racism and that it contributed positively to the efficiency of the business. The paper's central contribution has been to show that, with respect to the treatment of blacks vis-a-vis whites, such an assurance can be plausibly given for police stops and searches in England.