RMM Vol. 5, 2014, 13–29 http://www.rmm-journal.de/ Vanessa Scholes You Are Not Worth the Risk: Lawful Discrimination in Hiring* Abstract: Increasing empirical research on productivity supports the use of statistical or 'rational' discrimination in hiring. The practice is legal for features of job applicants not covered by human rights discrimination laws, such as being a smoker, residing in a particular neighbourhood or being a particular height. The practice appears largely morally innocuous under existing philosophical accounts of wrongful discrimination. This paper argues that lawful statistical discrimination treats job applicants in a way that may be considered degrading, and is likely to constrain people's freedoms in relation to employment, thus giving us reason for moral concern. Keywords: discrimination, ethics, employment, hiring, statistical discrimination. 1. Introduction An employer has sorted through application forms and resumes from 50 applicants for a particular vacancy and has come up with a list of twelve candidates, all of whom seem good prospects in terms of qualifications, experience and suitable ambitions expressed for the position. This group must be whittled down to a short list of five applicants for interviews. The employer then sees that two of the twelve are smokers. The employer has read some research suggesting that non-smokers are, on average, more productive employees than smokers (see, e.g., Chadwick 2006; Lecker 2009). A particular smoker, of course, might happen to be much more productive than your average non-smoker. Nonetheless, the employer now believes that smokers, as a group, are less productive employees than non-smokers. What, if anything, is morally concerning about the employer dismissing the applications of the two smokers simply because of the statistic that the group of people who smoke is less productive than the group of non-smokers? All employers 'discriminate' in a broad sense whenever they have to choose one or more job applicants over others. Usually we judge such choices by whether we think there is a fair basis for the choice; for example, whether it is based on * Thanks to Simon Keller, Tony Burton and especially Ramon Das for helpful discussion; to Philip Catton for pointing me to residential demographics as a basis for statistical discrimination, and to an anonymous reviewer for this journal for useful critical comment. 14 Vanessa Scholes job-relevant criteria, such as having higher qualifications. 'Taste'-discrimination-choosing between applicants on the basis of a personal taste or prejudice for or against a particular group feature-is generally held to be unfair. Statistical discrimination-taking a group statistic and using it as a proxy for information about an individual-is distinguished from taste-discrimination in hiring by its motive and its rationale. In the previous smoking example, the employer's motive for discriminating need not involve any personal distaste for smokers. Instead, the motive is to increase the productivity of the workforce, and thereby increase profits for the firm. Similarly, the employer's rationale need not be based in any personal prejudice. The employer is not holding out that it is somehow socially appropriate that smokers be afforded less employment opportunity in society. Instead, the employer's rationale could be to carry out a fiduciary responsibility to stockholders to increase profits through legally permissible actions; and it is not illegal to discriminate against smokers. So the sorts of motives and rationales that we might find ethically objectionable for taste-discrimination need not apply in the case of statistical discrimination. Statistical discrimination in hiring occurs when an employer takes a group characteristic reliably correlated with higher or lower employee performance at the group level, and uses it as a proxy for performance information about individual applicants, to deselect applicants who belong to a group with increased risk of lower performance. Lawful statistical discrimination in hiring could include, for example, screening for smoking; residential demographics (good / bad neighborhood) (see, e.g., Nunn et al. 2010; Truth and Justice Commission 2011, 225; Cass and Garde 1984); or height (see, e.g., Schick and Steckel 2010); amongst others. The practice is not restricted to employment; statistical discrimination can crop up in any decision where there are benefits from taking a feature characterizing a group and using it as a proxy for information about individuals belonging to that group. This is already occurring in contexts such as admissions to tertiary study, where institutions may try to discourage or prevent the enrolment of students who have characteristics that are correlated with a lower chance of passing courses (see, e.g., Simpson 2009). The mere fact that a practice involves statistical discrimination does not show it is morally corrupt or that we should avoid treating individual persons on the basis of a group proxy. Economists have theorized about statistical discrimination in hiring since the 1960s, but rarely examine the ethics of the practice. Philosophers who have analysed the practice substantively have not suggested we ought to be much concerned by it. Deborah Hellman (2008) does not support the idea that it is a case of wrongful discrimination. Frederick Schauer (2003) argues forcefully in favour of the use of group generalisations for some law, public policy and public sector decisions. And Kasper Lippert-Rasmussen (2007; 2011) argues statistical discrimination is not intrinsically morally objectionable; that any failure to treat persons as individuals does not automatically indicate a moral problem (2007, 403); and that there may be a case for it in some contexts-such as trying to efficiently hire the best qualified (2007, 386). In general, the economics and philosophical literature on the topic gives the imYou Are Not Worth the Risk: Lawful Discrimination in Hiring 15 pression that how lawful statistical discrimination in hiring treats job seekers does not really warrant moral concern. This paper argues it does. The first section of the paper places lawful statistical discrimination in the context of some philosophical accounts of wrongful discrimination, noting its incongruity, but also drawing categories of evaluation from these accounts that will be used to assess the practice. The second section explains the practice of statistical discrimination to exclude applicants, illustrating how and why it can be carried out. The third section compares the practice with traditional methods of hiring, countering the objection that traditional methods make similar use of statistical assumptions. The fourth section of the paper raises some non-consequentialist concerns, contrasting statistical discrimination in hiring with its use in insurance to argue it may be considered degrading treatment of job seekers. The final section explores some consequentialist concerns with the practice, illustrating the invidious constraints it can place on job seekers. The aim of the paper is to argue that at least some forms of lawful statistical discrimination in hiring present a moral concern, and to indicate the nature of those concerns. The discussion that follows focusses on the perspective of the moral patients-job applicants-rather than the moral agents-employers. A full normative account of the ethics of statistical discrimination in hiring would require in addition substantial analysis of the employer's perspective and the application of ethical theory. In particular, such an account would include detailed consideration of all viable options for hiring methods available to the employer. This is beyond the scope of the present paper. Instead, this paper argues the claim that contrary to what is indicated in some philosophical accounts of wrongful discrimination, we should not dismiss the practice of lawful statistical discrimination in hiring as morally innocuous. 2. Philosophical Accounts of Wrongful Discrimination There is no philosophical consensus on how to specify the moral wrong(s) of discrimination. Many widely cited contributions from philosophers focus primarily on defining the wrong of 'taste'-based discrimination. Larry Alexander (1992), for example, sees this form of discrimination as failing to correctly acknowledge a person's moral equality or moral worth in a way that harms others; Richard Arneson (2006, 779) argues wrongful discrimination is defective conduct based on "unjustified hostile attitudes toward people perceived to be of a certain kind or faulty beliefs about the characteristics of people of that type", and Deborah Hellman (2008) conceives of it as treating someone differently in a way that demeans them. These accounts of discrimination, while distinct, cluster around the concept of differential treatment of individuals based in the holding or expressing of morally offensive attitudes or beliefs about group characteristics. Statistical discrimination, by contrast, has nothing to do with attitudes or beliefs about the moral worth of groups. Because it is distinct from taste-based 16 Vanessa Scholes discrimination, it is perhaps not surprising that statistical discrimination does not connect with these definitions. Philosophical accounts cover a wider conception of discrimination by including consideration of 'indirect' discrimination, also known as 'disparate impact': when policies that do not seem to be based in intentional disrespect or prejudice nevertheless impact significantly more negatively on a socially disadvantaged group, despite this being avoidable. Human rights legislations commonly prohibit employment discrimination against members of some socially disadvantaged groups, including discrimination on the grounds of sex, race, religious beliefs, ethnicity and national origin, amongst others. If any of these grounds are the proxies for statistical discrimination in hiring, then the discrimination will be unlawful. For example, if research showed that women between 25 and 45 years were less productive employees, as a group, than other applicants, it would still be illegal to discriminate against any particular woman in that age range applying for a job. There has been considerable discussion of ethical and legal matters concerning discriminating against applicants (or employees) on prohibited grounds (see, e.g., Lippert-Rasmussen 2012; Hellman 2008; Alexander 2006; Narveson 2002). However, there has been far less discussion of the application of statistical discrimination on non-prohibited grounds; in other words, lawful application of statistical discrimination,1 which is a key reason it is focused on in this paper. Alexander (1992, 193) explicitly addresses discrimination on the basis of proxies, suggesting that as long as the proxies are not merely a front for hostile bias, the practice is intrinsically benign. Arneson also rejects the idea that there is anything intrinsically wrong with indirect discrimination. Arneson (2006, 793) proposes that if we would consider a policy justifiable were we blind to whether it had a disparate impact on some groups, then there is nothing intrinsically morally wrong with it for turning out to have such an impact. He argues that the reason that hostile attitudes or prejudice regarding certain social groups makes 'taste' discrimination wrong is that there is nothing about those groups, as categories, that is relevant to moral classification. So there is no reason to single out, say, women or Asian people for different treatment; unlike, say, serial killers or saints. The converse of this, according to Arneson (2006, 794), is that a policy based on moral principles that do not rely on any morally irrelevant categorization is not intrinsically wrong simply for resulting in disparate impacts for some groups-because the policy does not prescribe anything for those groups. Arneson's view suggests that 'indirect' discrimination ought not to form part of a concept of wrongful discrimination because the people affected would be disadvantaged by it as individual people, first and foremost, not as a member of a group experiencing prejudicial treatment. Statistical discrimination, however, involves actively prescribing different treatment for people based on their membership in different groups. A question for this paper then becomes, 1 But see Schauer 2003 and Baumle and Fossett 2005. See also Stephen Maitzen 1991 and LippertRasmussen 2011 and 2007. However, only Schauer and Baumle and Fossett have an in-depth treatment of employment; in both cases drawn from the North American legal context. You Are Not Worth the Risk: Lawful Discrimination in Hiring 17 how does statistical discrimination in hiring categorize people, relative to other forms of hiring? Is the hiring more akin to (wrongfully) singling out women and Asian people or (rightfully) singling out serial killers and saints? Hellman's (2008) account of discrimination as a form of demeaning suggests the moral wrong of discrimination lies in what the discriminatory act expresses. She claims that neither the intention of the agent nor the consequences of the act are necessary to defining what the discriminatory act expresses. Instead, the expressive nature of the discriminatory act is a function of the social context giving its denigratory meaning coupled with the agent being in a position of power relative to the person subject to the discrimination. 'Demeaning' acts or policies express a disregard for the moral equality of those being discriminated against. Hellman's definition covers 'taste' and 'indirect' discrimination; the moral wrongness of a particular instance of either form of discrimination depends on whether we appropriately understand that instance to be demeaning. Her definition will presumably only count lawful statistical discrimination morally wrong if it somehow brings into question the moral equality of those subject to it. This seems unlikely; as noted earlier, this discrimination does not appear to pass judgment on moral worth. Nevertheless, pace Hellman's approach we can still ask the general question: what does lawful statistical discrimination in hiring seem to express about people, and is this morally concerning? Sophia Moreau (2010) presents a consequentialist approach to what makes discrimination wrong, defining it as differential treatment that impacts on deliberative freedoms, which are freedoms to access core life opportunities without the concern that one's 'normatively extraneous' traits will be counted as costs against oneself. 'Normatively extraneous' traits are those that we think people should not be evaluated on in the distribution of opportunities to access some goods and services-traits such as gender or skin color or religious tradition. On Moreau's account, indirect discrimination is easily included under the main definition of wrongful discrimination, and is already illegal for many traits covered by this definition. Moreau's definition might cover lawful statistical discrimination for some characteristics, such as height and residential demographics, but not others (such as smoking, perhaps)-dependent on which characteristics we think fit her idea of 'normatively extraneous'. Even if some characteristics are not covered, we can consider lawful statistical discrimination in hiring's impact (if any) on people's freedoms around accessing employment opportunities. Finally, theorists whose accounts or arguments support a more expansive definition have also argued for, or cited, a lessened significance of 'taste'-discrimination, or greater significance of other forms of discrimination. Oran Doyle (2007) argues that indirect discrimination ought not to be considered secondary or subordinate to 'taste'-discrimination; Moreau says disparate impact is now a more important focus in discrimination law than 'taste'-discrimination; and H. E. Baber (2006) argues that statistical discrimination is a greater issue than traditional discrimination for employment inequality, in particular. After explaining the practice of lawful statistical discrimination in hiring, I will first compare how it categorizes applicants relative to other hiring practices; then 18 Vanessa Scholes consider what it expresses about applicants; and finally look at its impact on people's freedom to access a core life opportunity. 3. Statistical Discrimination in Hiring: Why and How Discussion of statistical discrimination in employment has occurred predominantly in the economics literature and has tended to focus on the likely difference in wages between a group that suffers from statistical discrimination and a group that benefits from it.2 Typically theorists propose a scenario with applicant groups A and B. Members of Group A are rated by employers as having lower average productivity than Group B, either because Group A has lower average productivity, or because the employer finds it harder to estimate the productivity of Group A members. Using mathematical models, economists found that the hypothesized statistical discrimination of a lower wage offer to Group A members would occur, and would persist over time. This finding was of significant interest, as previous discussion of employment discrimination in the economics literature focused on 'taste'-discrimination and argued that it was not supported by a competitive market system, so market forces would penalize employers for this and work toward eliminating it (see, e.g., Becker 1971[1957]). With statistical discrimination's potential to benefit firms, however, the unequal treatment and economic discrimination are actually generated by a competitive market system (Sattinger 1998, 229). Statistical discrimination in hiring requires a belief that a particular feature correlates with higher or lower performance across the group of persons that has it. If the relevance or impact of a group feature is believed to be unclear, decision-makers are faced with an uncertainty that stymies the use of the characteristic. But if research can assign a probability to the relation between the group feature and the benefit, this creates a rational basis for statistical discrimination. Assigning a probability effectively turns an uncertainty into a situation of quantifiable risk-thus creating a measurable incentive for engaging in statistical discrimination. Gathering empirical data on the impact of statistical discrimination in employment is difficult (Moro and Norman 2003; see, however, Ewens et al. 2011). Evidence on widespread group discrimination in hiring has been presented (see, e.g. Bertrand and Mullainathan 2004), but determining whether it is statistical discrimination or taste discrimination that is operating-or perhaps, how much of the discrimination is due to taste and how much due to using a perceived proxy for productivity-is considerably harder (Harford 2008). Also, in the economics literature, it is common to model the effect that statistical discrimination has on the level of wage an employer will offer an applicant. On the 2 Seminal papers by Edmund Phelps (1972) and Kenneth Arrow (1973) are credited with originating discussion of statistical discrimination. Dennis Aigner and Glen Cain (1977) also produced an early influential treatment. For more, see Hanming Fang and Andrea Moro 2011. You Are Not Worth the Risk: Lawful Discrimination in Hiring 19 whole, there has been much less discussion of employers engaging in statistical discrimination to make a decision whether or not to screen applicants out of consideration (see, however, Harford 2008; Berk 2001). Yet this screening decision is both more typical, and more worrying if discrimination occurs (Sattinger 1998, 229). While it is bad to be offered a lower wage for a job due to statistical discrimination, it is worse not to be offered a job at all-or to be excluded from further consideration for a job. Hence an initial screening decision that excludes applicants is the focus of this paper. There are several ways in which differences between groups can make it potentially beneficial for an employer to screen using statistical discrimination when hiring. Probably the most common basis for discrimination is where there is (believed to be) a difference in the average productivity of one group compared with another group. Say Bryan is expanding his data-entry business and needs to hire staff. He knows there will be a large pool of applicants as the work is not highly skilled, and the job offers a comfortable environment with flexible working hours. Bryan has read research suggesting the performance of the group of people who take some types of mind-altering drugs is more impaired than the performance of the group of non-users. He decides to require applicants to take a drug-test. Applicants who test positive or refuse the drug test will be screened out. A second basis for statistical discrimination is where the average productivity for two groups is the same but there is a higher variance in the productivity of members across one group compared with another. Imagine a city with a spread of suburbs, many of which have relatively homogenous sub-populations. Suburb X, however, has two distinct sub-populations. Three quarters of Suburb X comprises well-resourced families whose adult members are more work-ready and supported for employment than populations in surrounding suburbs in the city. The remaining quarter of Suburb X is on the wrong side of the tracks; it comprises gang families whose adult members are far less work-ready and supported for employment than the populations in surrounding suburbs. The productivity average for Suburb X as a whole is no different from any other suburb; but there is a wider variance in the productivity of suburb members. Ex hypothesi, an employer who selects an applicant from Suburb X rather than another suburb has a higher chance of getting a higher productivity employee, but also a higher chance of getting a significantly lower productivity employee. Employers tend to be risk-averse (Albert and Cabrillo 2000, 6), and a risk-averse employer would have an incentive to choose a member from another suburb over a member of Suburb X. A third basis for statistical discrimination involves variability in the results for groups due to the test or selection process, rather than due to significant variability in the groups themselves (Albert and Cabrillo 2000, 8). This is where the test-or tester-for predicting performance produces more accurate predictions for one group than for another.3 For instance, say an employer gets a high 3 For a detailed discussion of this issue, see Schmidt and Hunter 1976. 20 Vanessa Scholes volume of applications for a couple of skilled jobs. She decides to do a quick initial sort of the resumes looking at the college / university the applicant attended. She is familiar with a reasonable number of colleges; she recognizes the names of familiar colleges as 'high quality' or 'low quality' colleges. But quite a few of the colleges mentioned by applicants are from other countries and are unfamiliar to her. The employer does her quick initial sort of resumes. When the recognized colleges are sorted, some go in the 'Yes' pile, and some in the 'No' pile (those recognized as 'high quality' or 'low quality' respectively).4 But for the 'unfamiliar' colleges, all go in the 'No' pile. Here the benefit to the employer from the discrimination is not due to any difference in average productivity from graduates of familiar versus unfamiliar colleges, but from the avoidance of the uncertainty associated with the latter. In summary, statistical discrimination in hiring can occur when an employer takes into account information about i) a characteristic that correlates with higher or lower productivity across the group of persons that has it; or ii) a high variance in the distribution of a productivity-correlated characteristic in one group; or iii) an uncertainty or pressure regarding measurement for a productivity-correlated characteristic for one group. The information is taken into account when considering employees from one group compared with those from other groups, in an attempt to reduce the risk of lower productivity. Employers thus use a group characteristic as a proxy for productivity information on applicants. 4. Comparison with traditional Lawful Discrimination in Hiring Traditionally, common methods used to gain information from applicants in the hiring process include application forms and/or CVs, interviews and reference checks (see, e.g., Levashina and Campion 2009, 236–239; Taylor et al. 2002, 9– 10). Huffcut and Youngcourt (2007, 181) suggest that application blanks and interviews are the most widely-used selection methods. These methods typically produce information about qualifications; past work experience; skills gained from other activities; endorsements from previous employers (referees); communication and social skills (interview); and job applicants' stated ambitions and expectations of the position. But consider: there is very probably a strong statistical correlation showing applicants with, say, an engineering qualifica4 Bertrand, Chugh and Mullainathan 2005, 96, look at what they term "implicit" discrimination, where cognitive factors affecting decision makers influence them to rely on their perceptions of a group characteristic. These factors might include being rushed, having multiple obligations competing for attention, and performing a task with a nonverbal automated response as its output- such as a manager considering resumes and putting them in a 'Yes' pile or a 'No' pile. The results of the researchers' exploratory experiments to test for this, if extrapolated to real hiring situations, would fit the profile of statistical discrimination-if making quicker decisions using the group characteristic was more profitable under these circumstances. You Are Not Worth the Risk: Lawful Discrimination in Hiring 21 tion performing better in an engineering job than applicants without that qualification. If employers assume this statistical correlation when they seek this information, on the face of it, are they not engaging in the same practice of statistical discrimination as outlined earlier? If so, perhaps we should be no more morally concerned with hiring processes that exclude short people, or smokers, or people from certain neighborhoods than we are with these more typical hiring processes. There is a key difference with the use of this sort of statistical correlation, however: the information given speaks to that individual's work skills. The information that an applicant has an engineering degree is not a proxy for productivity information, but instead gives a direct indication of the individual's capabilities. So while it is possible that employers ask about factors such as qualifications-or for that matter, work history or work experience-on the basis of their statistical correlation with productivity in the job, the information provided says something concrete about that individual in relation to work. By contrast, the information that feature X is somehow correlated with productivity across the group with X does not tell us what a particular applicant with feature X has done, or can do. This is the crux of the issue. Information about a group of which an individual is a member, bears only a statistical link to the individual. With statistical discrimination, applicants are judged on a feature that relates to productivity at the group level, but has no necessary connection to the productivity of the particular individual applicant. Baumle and Fossett (2005, 1255) suggest that hiring decisions using academic qualifications could still involve statistical discrimination, however. They give the example of an employer who screened applicants for a job, not on the basis of having a particular qualification such as engineering or law, but simply on the basis of whether the applicants had a college degree. If there are some applicants who are good candidates for the job but do not have a degree, these applicants will miss out on being considered for the job. This instance of statistical discrimination seems closer to the earlier definition of a group level feature used to judge an individual applicant. Even this instance, though, differs from the previously-mentioned bases for statistical discrimination such as smoking, residential demographics or height, because the feature being used (college degree) provides evidence for work attributes. These attributes might include passing a certain level of literacy, persisting with a major undertaking, managing one's personal life sufficiently to allow the achievement of the undertaking, and being willing to invest time and money in one's future. Like information about a person's work history or experience in a line of work different from the particular job being applied for, knowing that a person has a significant tertiary qualification provides direct evidence for work-related attributes. Distinguishing applicants on the basis of direct evidence for work-related attributes does not seem a ground for strong moral concerns as far as employment discrimination goes. Just to be clear, not all screening of job applicants based on college degrees involves statistical discrimination. Say an employer is looking to hire someone 22 Vanessa Scholes to fill a position as a lawyer, and having a law degree is necessary to practice law in the position. If the employer screens out applicants who do not have a law degree, this does not count as statistical discrimination against those applicants. By definition, no applicant without the qualification could be a good candidate; having the degree is an essential attribute for being able to do that job. Here the employer's discrimination is not based on a statistical correlation with productivity across a group, but rather a feature that identifies a minimum threshold for being able to do the job. In conclusion, this section has considered an objection that traditional hiring grounds can involve statistical discrimination in the same way as the earlier examples raised. It counters that, while using traditional grounds may sometimes rely on statistical correlations, this use is not judging individuals simply on group-level information that may not apply to the individual; instead, this use connects directly to work-related information about the individual applicant. Harking back to Arneson's point about morally relevant categories, using traditional hiring grounds in the employment process is more akin to (rightfully) singling out saints or serial killers than (wrongfully) singling out women or Asian people. We can extrapolate from this by saying it is not (morally concerning) statistical discrimination if a hiring process identifies a minimum threshold for being able to perform the job, or seeks information about factors that indicate that individual's productivity with regard to the job requirements. Hiring processes that, say, explicitly exclude people who live in certain neighborhoods are different in nature from processes that exclude applicants without an academic qualification. By implication, we should reject the claim that if the latter is morally unproblematic, we should expect the former to be so too. This does not yet show that a hiring process that does exemplify statistical discrimination-in other words, uses group-level proxy information at the expense of individual consideration, and does not identify a minimum threshold required for performing the job-is thereby morally concerning. It is the work of the next two sections to explain and illustrate the moral concerns. 5. Comparison with Statistical Discrimination in Insurance Kasper Lippert-Rasmussen argues that the use of statistical discrimination will not necessarily fail to treat persons as individuals. He suggests part of treating someone as an individual could involve using information that categorizes them as part of a group-if we also have some care to take into account other information on the same factor that is particular to that individual: "X treats Y as an individual if, and only if, X's treatment of Y is informed by all relevant information, statistical or non-statistical, reasonably available to X." (LippertRasmussen 2011, 54) For example, an employer could consider the 'statistical' fact that an applicant belongs to a group with lower-than-average productivYou Are Not Worth the Risk: Lawful Discrimination in Hiring 23 ity; but also the 'non-statistical' information that the applicant's work history, showing continued promotion within their last place of employment, suggests higher-than-average productivity. This dual consideration means the applicant's individuality is still taken into account. While Lippert-Rasmussen's definition of individual treatment is reasonable as a generic claim, more depends on 'relevant' than is at first obvious. In the example just given, for instance, once the employer has the 'non-statistical' information, the 'statistical' fact seems of no further relevance, and something that ought to play no part in judging the applicant. In any case, on Lippert-Rasmussen's definition, the use of statistical discrimination in hiring to screen out applicants likely counts as failing to treat applicants as individuals-as no account is taken of any 'non-statistical' information the applicant makes available. This raises the question of whether pure statistical discrimination, using only 'statistical' information, is inherently morally faulty. Lippert-Rasmussen (2011, 53) allows there are morally poor ways of considering information from generalizations, that can usefully be described as "failing to treat people as individuals". But he rejects the claim that using some information gained from a general categorization while ignoring other relevant information will necessarily be morally faulty. He gives an example of how this could be done in a way that aims simply to benefit the 'discriminatee' (2007, 401). However, it must be admitted that the evaluative term 'discrimination' is not typically used to cover situations where the person discriminating is doing so out of goodwill aiming to benefit the individuals concerned. Lippert-Rasmussen's claim that there is nothing necessarily morally wrong about people being judged purely on group categorizations would be better supported, for our purposes, with an example that parallels more closely the commercial statistical discrimination in which we are interested here. Fortunately, we have a ready-made example: insurance. The insurance industry is set up to work specifically on the principle of discriminating between people on the basis of statistical correlations for groups to which they belong. Consumers from groups which are statistically more likely to require a payout are charged higher premiums. Customers know that the information they provide when applying for insurance is mapped against group statistics for particular characteristics to form an overall risk profile that is used to determine whether they will be offered an insurance policy, and at what price. Having an insurance industry allows customers the opportunity to accept to be treated as an average of some collective statistics and pay premiums in order to trade off the financial risk posed by potential future adversities. As insurance customers, we want to be able to protect ourselves in case of the sudden, huge costs that can be associated with accident, illness, business loss or other future misfortune, and we explicitly trade off our individuality for this protection; that is what it is to get insurance. The insurance context, by treating customers simply as bundles of risk, certainly seems to fail to treat persons as individuals. But given that it is a necessary part of the industry, and that we know and accept this when we apply for insurance, this failure is not a reason to think the whole practice of insuring treats customers morally poorly. 24 Vanessa Scholes I have argued we should agree with Lippert-Rasmussen's conclusion (2011, 58) that statistical discrimination may not pose moral concerns even where it patently fails to treat people as individuals. However, the insurance context differs from the hiring context. Baumle and Fossett (2005, 1268–1269), drawing on legal rulings and policies of some North American states, conclude that considerations of fairness in insurance have focused on fairness at the group level. Say that the group 'male drivers under 25 years old' is involved in more car crashes than any other group; as long as the insurance premiums accurately reflect the average crash risk for that group, those premiums have been considered fair. Baumle and Fossett note that the legal system's approach to statistical discrimination tends to differ depending on whether the discrimination occurs in the insurance context or the employment context. They suggest that perhaps using group proxies in the pursuit of economic efficiency is more acceptable in insurance than in employment, as insurance is less fundamental to our lives than is our work.5 I would like to draw another perspective on this. Employment plays a fundamental role in the formation of a person's identity (Fadjukoff 2007, 32). An important focus (although it is to be hoped, not the sole focus) of schools and colleges when educating people and influencing their identity, is to develop competencies for work activities. One of your earliest inquiries of a stranger at a party would probably encourage them to say where they work or have worked, and something about their lines of work. When people apply for a job, it seems reasonable that it is the outcomes of their work-relevant activity that they put forward and expect to be judged on, rather than to be treated simply as a bundle of risks. For any person, 'group risk' is an extremely negative lens through which to be viewed. Not only does it strip us of our individualized features, it presents us in the aspect of a threat to be mitigated. From the perspective of a person subject to it, this is surely somewhat degrading. As noted, it is necessary to the functioning of the insurance industry, and fortunately, being an insurance customer is not likely to form a major part of a person's identity. But the same cannot be said for the employment context. Viewing applicants simply as bundles of risk is not a necessary part of hiring; and employment is a key aspect of a person's identity. Hence I submit that statistical discrimination that treats individual applicants simply as bundles of risks can reasonably count as acceptable in insurance, but yet degrading in hiring. An especially important concern with statistical discrimination in employment is its potential to create a vicious circle, becoming a self-fulfilling prophecy for some groups subject to it. Consider this real-life historical example from a company operating in West Africa. British managers of the company were encouraged to promote African staff. However, the managers had low expectations of how well these newly-promoted staff would perform. After they promoted 5 As evidence of this, Baumle and Fossett (2005, 1270) note that in states in the US where car insurance is mandatory-so not having insurance impacts more fundamentally on people's lives, preventing them from being able to run a car-some states have prohibited the usual practice in the automobile industry of statistical discrimination based on sex. You Are Not Worth the Risk: Lawful Discrimination in Hiring 25 African staff, the British managers withdrew responsibilities from the posts to which the staff were promoted, based on their low performance expectations of these staff. But this thereby undermined any opportunities for African staff to show good performance and develop in the posts (Decker 2010, 799). This meant the discriminatory practice of the British managers stimulated and perpetuated the problem, turning their expectations into a self-fulfilling prophecy. Even Lippert-Rasmussen (2007, 400–403), who does not seem to think we should be concerned about accurate use of statistical discrimination, makes an exception with regard to its potential for creating self-fulfilling prophecies of this sort. Finally, applicants for employment may not be aware that some of the personal information they provide may form the basis for statistical discrimination. Applicants for insurance, by contrast, are aware of this, and are, in a sense, choosing the discriminatory practices in order to gain the insurance benefits. In the hiring context, applicants simply have the practice imposed on them. The lack of awareness, and hence reduced freedom to consent or otherwise act on the matter, is another factor making statistical discrimination morally concerning in employment. Applicants for insurance, in actively choosing the statistical discrimination that is an open and necessary part of a non-compulsory insurance industry, are able to act as agents in the insurance application process. In hiring situations involving unacknowledged statistical discrimination, where applicants lack the freedom to consent, the applicants lose their individual agency in the process. 6. Impact on People Seeking Work A scenario will help illustrate the nature of the moral concerns with statistical discrimination from an individual applicant's perspective. Imagine once more our employer who has read studies showing a statistical correlation between smoking cigarettes and lower productivity in employees. The employer makes sure that whenever she advertises a vacancy, the application form asks whether the applicant smokes. When the forms come in, she screens out all applicants who failed to answer 'No' to the question about smoking. Say a smoker applying for the job realizes such discrimination could occur. What could the applicant do? The applicant could give up smoking, then truthfully answer 'No' about smoking and remain in the running for the job. Or he could remain a smoker, and lie about it, again answering 'No' to the question about smoking. Or the applicant could remain a smoker and truthfully answer 'Yes' to the question, or he could simply refuse to answer the question; either way getting struck off the list. In exercising his liberty over providing this private information, the applicant is forced to choose between: 1) stopping his private activity to maintain the same chance of getting the job; 2) lying about his private activity to maintain the same chance of getting the job; 3) telling the truth and probably losing the 26 Vanessa Scholes chance to get this job; or 4) refusing to answer and probably losing the chance to get this job. Every option in this situation appears unpalatable. The first option imposes a serious restriction on the applicant's liberty. The second requires the applicant to tell an outright lie; this would be uncomfortable for many applicants, and leaves them vulnerable in their job if the lie is later exposed. The third and fourth options both have the outcome that the applicant will no longer be considered for the job. In this scenario, the practice of statistical discrimination is morally concerning due to the constraints it places on applicants, imposing a choice between withdrawing from a private activity, lying about it, or revealing it to their detriment. There is a significant and growing body of empirical research discussing how to hire to improve productivity (see, e.g., Sackett and Lievens 2008; Le et al. 2007; Schmidt and Hunter 1998) that includes consideration of what features of employees are associated with higher and lower job performance. It is to be expected that the very pursuit of empirical research into the characteristics of groups will prompt increasing use of statistical discrimination in the future (Gandy 2010). If we add widespread usage of statistical discrimination to the given scenario, the employment market appears to assume inordinate control over a jobseeker's private life. The employment market already exerts pressures on jobseekers' private lives; for example, to engage in extra activity to maintain or further one's professional competence, to maintain a general level of fitness such that one is able to work, and to secure living conditions that allow one to maintain a reasonable level of presentation at work. These pressures, though, are at least directly work-related (of course, they may also exist in some form in society generally, whether or not people happen to be seeking work). The pressure exerted by statistical discrimination is different. It creates a situation where jobseekers are pressured on not just work-related aspects of their private lives, but non-work, personal aspects of their private lives. The practice thus raises moral concerns due to the additional constraints on applicants' liberty- on their freedom with regard to their personal lives. Statistical discrimination may also raise a privacy issue, depending on the feature used. Say, for example, employers have read some research indicating that the level of 'online social networkedness' is statistically correlated with productivity. Employers decide to ask applicants to supply a password for their private social networking page, such as a Facebook page, and will ignore applications from those who refuse to comply. Here, job applicants who wish to protect the privacy of their personal lives (including applicants who do not have private social networking pages) will be excluded. This scenario requires applicants to give a stranger largely unrestricted access to considerable amounts of personal information, just to have a chance at consideration for a job. Overall, the practice of lawful statistical discrimination in hiring raises moral concerns for liberty, and perhaps privacy, through its potential to make significant demands on non-work, personal aspects of an applicant's life. You Are Not Worth the Risk: Lawful Discrimination in Hiring 27 7. Conclusion Increasing empirical research on productivity supports the conditions for lawful statistical discrimination in hiring. In this paper I have compared how this practice categorizes applicants relative to other hiring practices, arguing it offers a substantially different way of considering applicants that does not connect with work-relevant information about any given applicant. I then discussed what the practice expresses about applicants, comparing it with the insurance context to argue that it may be considered degrading. Finally, the paper outlines the implications of lawful statistical discrimination for job seekers, illustrating how it is likely to constrain people's freedoms in relation to employment. None of this analysis pretends to ground the strong claim that the practice is morally wrong; instead my claim is that-contrary to what is indicated by some major philosophical approaches to wrongful discrimination-the practice does warrant moral concern. A full moral evaluation of statistical discrimination in hiring requires in addition a detailed analysis of the employer's perspective, followed by application of a normative approach such as a particular ethical theory. It may be that such an evaluation could find the practice to be justified. By arguing that the practice of lawful statistical discrimination in hiring offers grounds for moral concerns, this paper provides a motivation to undertake such an analysis. References Aigner, D. J. and G. C. Cain (1977), "Statistical Theories of Discrimination in Labor Markets", Industrial and Labor Relations Review 30, 175–187. Albert, R. and F. Cabrillo (2000), "Sex Discrimination: Is the Labour Market a Market for Lemons?", URL: http://www.ucm.es/info/iudem/cabrillo.DT00-4.pdf [15.09.2005]. Alexander, L. (2006) (ed.), "Editors Symposium on The Rights and Wrongs of Discrimination", San Diego Law Review 43, 733–1069. - (1992), "What Makes Wrongful Discrimination Wrong? Biases, Preferences, Stereotypes and Proxies", University of Pennsylvania Law Review 141(1), 149–219. Arneson, R. (2006), "What is Wrongful Discrimination?", San Diego Law Review 43, 775–808. Arrow, K. J. (1973), "The Theory of Discrimination", in: Ashenfelter, O. and A. Rees (eds.), Discrimination in Labor Markets, Princeton: Princeton University Press. Baber, H. E. (2006), "Left Libertarianism: What's in It for Me?", San Diego Law Review 4(3), 995–1016. Baumle, A. K. and M. Fossett (2005), "Statistical Discrimination in Employment: Its Practice, Conceptualization, and Implications for Public Policy", The American Behavioral Scientist 49, 1250–1274. Becker, G. S. (1971[1957]), The Economics of Discrimination, 2nd edition, Chicago: University of Chicago Press. 28 Vanessa Scholes Berk, J. B. (2001), "Statistical Discrimination in a Labor Market with Job Selection", URL: http://faculty.haas.berkeley.edu/berk/papers/disc.pdf [26.09.2007]. Bertrand, M., D. Chugh and S. Mullainathan (2005), "Implicit Discrimination", American Economic Review 95, 94–98. - and S. Mullainathan (2004), "Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination", American Economic Review 94, 991–1013. Cass, B. and P. Garde (1984), "Unemployment in the Western Region of Sydney: Job Seeking in a Local Labour Market", SWRC Reports and Proceedings 47, 3–43. Chadwick, K. L. (2006), "Is Leisure-time Smoking a Valid Employment Consideration?", Albany Law Review 70, 117–141. Decker, S. (2010), "Postcolonial Transitions in Africa: Decolonization in West Africa and Present Day South Africa", Journal of Management Studies 47, 791–813. Doyle, O. (2007), "Direct Discrimination, Indirect Discrimination and Autonomy", Oxford Journal of Legal Studies 27(3), 537–553, DOI: http://dx.doi.org/10.1093/ojls/ gqm008. Ewens, M., B. Tomlin and L. C. Wang (2011), "Statistical Discrimination or Prejudice? A Large Sample Field Experiment", URL: http://ewens.tepper.cmu.edu/wp-content/ uploads/2011/04/statistical-discrimination2011.pdf [30.06.2011]. Fadjukoff, P. (2007), Identity Formation in Adulthood, Jyväskylä: Jyväskylä University Printing House. Fang, H. and A. Moro (2011), "Theories of Statistical Discrimination and Affirmative Action: A Survey", in: Benhabib, J., M. O. Jackson and A. Bisin (eds.), Handbook of Social Economics 1A, The Netherlands: Elsevier, 133–200. Gandy, O. H. (2010), "Engaging Rational Discrimination: Exploring Reasons for Placing Regulatory Constraints on Decision Support Systems", Ethics and Information Technology 12(1), 29–42. Harford, T. (2008), "The Dangers of Rational Racism", in: The Logic of Life, London: Abacus, 145–165. Hellman, D. (2008), When Is Discrimination Wrong?, Cambridge/MA: Harvard University Press. Huffcut, A. I. and S. S. Youngcourt (2007), "Employment Interviews", in: Whetzel, D. L. and G. R. Wheatson (eds.), Applied Measurement: Industrial Psychology in Human Resource Management, Mahwah: Lawrence Erlbaum. Le H., I.-S. Oh, J. Shaffer and F. Schmidt (2007), "Implications of Methodological Advances for the Practice of Personnel Selection: How Practitioners Benefit from Metaanalysis", Academy of Management Perspectives 21(3), 6–15. Lecker, M. J. (2009), "The Smoking Penalty: Distributive Justice or Smokism?", Journal of Business Ethics 84, 47–64. Levashina, J. and M. A. Campion (2009), "Expected Practices in Background Checking: Review of the Human Resource Management Literature", Employee Responsibilities and Rights Journal 21, 231–49. Lippert-Rasmussen, K. (2012), "Intentions and Discrimination in Hiring", The Journal of Moral Philosophy 9(1), 55–74. You Are Not Worth the Risk: Lawful Discrimination in Hiring 29 - (2011), "'We Are All Different': Statistical Discrimination and the Right to Be Treated as an Individual", The Journal of Ethics 15, 47–59. - (2007), "Nothing Personal: On Statistical Discrimination", The Journal of Political Philosophy 15, 385–403. Maitzen, S. (1991), "The Ethics of Statistical Discrimination", Social Theory and Practice 17, 23–45. Moreau, S. (2010), "Discrimination as Negligence", Canadian Journal of Philosophy 40(1), 123–149. Moro, A. and P. Norman (2003), "Empirical Implications of Statistical Discrimination on the Returns to Measures of Skills", Annales d'Economie et de Statistique 71–72, 399– 417. Phelps, E. S. (1972), "The Statistical Theory of Racism and Sexism", American Economic Review 62, 659–61. Narveson, J. (2002), "Have We a Right to Non-discrimination?", in: Respecting Persons in Theory and Practice: Essays on Moral and Political Philosophy, Maryland: Rowman and Littlefield. Nunn, A., T. Bickerstaff, T. Hogarth, D. Bosworth, A. E. Green and D. Owen (2010), "Postcode Selection? Employers' Use of Areaand Address-based Information Shortcuts in Recruitment Decision", Department for Work and Pensions Research Report 664. Sackett, P. R. and F. Lievens (2008), "Personnel Selection", Annual Review of Psychology 59, 419–50. Sattinger, M. (1998), "Statistical Discrimination with Employment Criteria", International Economic Review 39, 205–230. Schauer, F. (2003), Profiles, Probabilities and Stereotypes, Cambridge/MA: Harvard University Press. Schick, A. and R. H. Steckel (2010), "Height as a Proxy for Cognitive and Non-cognitive Ability", NBER Working Paper No. 16570. Schmidt, F. L. and J. E. Hunter (1976), "Critical Analysis of the Statistical and Ethical Implications of Test Bias", Psychological Bulletin 83(6), 1053–1071. - and - (1998), "The Validity and Utility of Selection Methods in Personnel Psychology: Practical and Theoretical Implications of 85 Years of Research Findings", Psychological Bulletin 124, 262–74. Simpson, O. (2009), "Open to People, Open with People: Ethical Issues in Open Learning", in: Demiray, U. and R. C. Sharma (eds.), Ethical Practices and Implications in Distance Learning, IGI Global. Taylor, P., Y. Keelty and B. McDonnell (2002), "Evolving Personnel Selection Practices in New Zealand Organisations and Recruitment Firms", New Zealand Journal of Psychology 31, 8–18. Truth and Justice Commission (2011), "Volume One Report of the Truth and Justice Commission", URL: http://www-previous.usip.org/files/ROL/TJC_Vol1.pdf [29.07. 2013].