In this section, I consider the received view about discrimination in law and economics that informs regulation in many countries. Zooming in on business and consumer lending, I show how assessments of credit risk (the risk that borrowers default on their loans) may be clouded by prejudice concerning the credibility and skills of members of disadvantaged social groups. I propose testimonial injustice as a candidate explanation for some of the existing forms of disparity in financial services; however, I also note that testimonial injustice fails to account for disparity that is due to demand-side factors, such as the way prospective borrowers gain information about lenders.
Economics of Discrimination
The criteria for what counts as discriminatory treatment, according to most instances of anti-discrimination legislation, are primarily drawn from economics.Footnote 1 According to Becker’s (1957) influential theory of taste-based discrimination, for disparate treatment to count as discriminatory, the treatment must go against the economic interests of the discriminator. Discriminating agents have a ‘taste’ for discrimination that supports disparate treatment even if that means that discriminators forego income or other economic gains.
It is questionable whether most discrimination is taste-based, though. If Becker were right, non-discriminators should be seen driving discriminators out of the market. In a racist environment, for instance, a non-racist employer would hire black employees at lower costs. Economists such as Arrow (1973) and Phelps (1972) have pointed out, however, that these predictions are not borne out by the facts. That racial discrimination decreased in the US in the 20th century was not so much the result of increased competition as it was due to legislation: the Civil Rights Act of 1964 in particular (Darity and Mason 1998). Arrow and Phelps therefore put forward the alternative concept of statistical discrimination, which is the standard concept in law and economics.
Statistical discrimination takes place when members of a certain social group receive disparate treatment because they are believed to be statistically different from members of other groups in relevant ways. If loan applications from members of group X are rejected more often than those of members of group Y, because loan officers believe that members of group X are, on average, less creditworthy, then this counts as statistical discrimination. Statistical discrimination is illegal (in some but not all jurisdictions) if and only if it involves groups that are defined by particular characteristics singled out by law for special protection. These characteristics typically include gender, skin colour, sexual preference, and marital status (Schwemm 1990).Footnote 2
Using the concept of statistical discrimination, economists have investigated disparity in a number of markets, including the market for second-hand cars, the job market, the market for real estate, and financial markets (mortgages, consumer loans, business loans). There is broad agreement among economists about the existence and scale of racial and gender disparity in financial markets (Ladd 1998). Female borrowers pay higher interest rates on their loans than male borrowers (Cheng et al. 2011); they get loans with less favourable conditions (Carter et al. 2007); their loan applications are more frequently rejected (Gray 2012); and they face less favourable treatment in case of defaulting on a loan (Dymski et al. 2013). Very similar observations hold for people of colour.Footnote 3 Part of the observed disparity may be the result of women (or people of colour, respectively) borrowing money for different types of projects than men (or white people, respectively). The projects may be different in terms of the size of the loan, the loan-to-income ratio, or the borrowers may have different income or assets, etc. However, once these and other relevant control variables are taken into account, most studies still find statistically significant disparity.
Lending and Credit Risk
To understand the contribution epistemic (or testimonial) injustice makes to disparity, it is useful first to examine lending in more detail. Banks and other lenders lend money to businesses to buy new machinery, to explore new markets, to develop new products, etc., and they lend to households to finance the purchase of a house, a piece of land, an education, and a host of other things. Loans have to be repaid, and hence prudent lenders make the conditions of the loan dependent on their estimate of the likelihood that the borrower will repay the loan. This means that ceteris paribus a higher credit risk (the risk that the borrower will not repay the loan) will be associated with a higher interest rate.
An applicant’s creditworthiness depends on exogenous factors (such as the general state of the economy) that the borrower cannot control. It also depends on endogenous characteristics of the borrower and the projects the loan is meant to finance. Endogenous characteristics include the financial position of the borrower (income, assets, etc.), the borrower’s skills and expertise, or, in the case of business loans, the company’s financial position, its revenues and expenses, the skills and experience of its employees, the quality of management, etc. Banks and other lenders request information from prospective borrowers concerning these and other endogenous characteristics. This is part of standard risk management.
When such information is interpreted and processed in biased ways, however, this may lead to testimonial injustice—and disparity may be the result. An illustrative example involves loan applications from black female managers of small businesses (Gray 2012). The prejudice concerns the relevance of college education as an indication of the level of the CEO’s (i.e., the applicant’s) management skills. Generally, higher levels of education will be associated with stronger skills, and, consequently, with higher creditworthiness and lower credit risk. It is therefore common practice among most lenders to request information about the educational level of the CEO.
In the absence of prejudices concerning credibility and skills, we should expect that, whenever a college degree makes a difference to the likelihood of the bank approving a particular loan, this difference is ceteris paribus the same across social groups. Yet this is not what we see in practice. Gray (2012) examined the impact of having a college degree on loan approval rates among small and medium-sized enterprises with white and black female CEOs. The idea of this study was that if having a college degree increases the chances of getting funding, not having a college degree should set black businesspeople back as compared to white businesspeople. Gray found that black applicants are indeed ‘punished’ much more heavily for not having a college degree than white applicants. For white applicants, not having a college degree decreases the probability of the loan being approved by 0.4 percent, which for practical purposes is nil. For black applicants, on the other hand, the decrease is 81 percent.
Gray’s study offers a striking example of how, on the basis of similar evidence (a college degree), loan officers judge the applicant’s level of credibility and skills differently. They judge one applicant as more suitable to turn the loan into a profitable project than another, despite their being the same in all relevant respects. This constitutes testimonial injustice.Footnote 4 The differences between loan approval rates reveal a prejudicial belief about applicants without a college degree. This prejudice comes down to the following: While white applicants without a college degree possess the credibility and skills (required to manage a business well) to an extent sufficient for being a successful CEO, black applicants without a college degree do not possess these skills to the required degree. As a result, prejudiced lenders believe that black applicants need a college degree to prove that they have the requisite skills, whereas white applicants do not need such proof. The prejudice is false, however, because there is no indication that white applicants without a college degree possess more of these skills.Footnote 5 Requiring additional proof, then, harms and wrongs black applicants in a way that reminds us of an oft-quoted statement by Steven Carter (1994), a Yale law professor, to the effect that ‘Our parents’ advice was true: We really do have to work twice as hard to be considered half as good [as whites].’
I have offered testimonial injustice here as a potential explanation of racial disparity. This is a supply-side explanation, as it attributes the disparity to decisions made by the suppliers of the loans (loan officers in particular). Emerging research in economics suggests, however, that a considerable part of gender disparity must be attributed to demand-side factors. A study by Cheng et al. (2011) considers gender disparity in mortgage lending. While the data they collect unequivocally show that women pay higher interest rates on their mortgages than men (even controlled for relevant variables), there is no evidence that this happens as a result of testimonial injustice on the part of loan officers. Rather, the study attributes the disparity to differences in how prospective borrowers gain information about mortgage deals. There is evidence that the search techniques used by women differ from those used by men across two dimensions. The first dimension is search intensity. Women search less intensely than men in that they spend less time and effort looking for the best mortgage deal. The second dimension is search diversity, that is, the variety of sources of information retrieved. The study shows that women use a less diverse or varied range of sources of information. Where men gain information about mortgages through, for instance, advisers and the Internet, women largely opt for mortgage lenders recommended by family members or friends.Footnote 6
Regulators will see disparity arising out of supply-side testimonial injustice as an illegal form of statistical discrimination. Disparity that can be traced back to demand-side factors (such as search behaviour) will, in contrast, not generally be viewed as immoral or unjust. In the next section, I argue against that view. Let us briefly anticipate the argument. At the root of testimonial injustice vis-à-vis members of a disadvantaged social group X lie negative prejudices concerning its members. These prejudices lead members of other groups to devalue the credibility of members of X, that is, to commit testimonial injustice. At the same time, repeated instances contribute to maintaining or propagating the underlying prejudices. So testimonial injustice is caused by and contributes to negative prejudices. My argument is, then, that not only does an unjust decrease in credibility among members of X but also an unjust decrease in their epistemic self-confidence harm and wrong them as knowing subjects. This follows an idea touched upon by Fricker (2007, pp. 47–51) in her initial treatment of epistemic injustice. Using insights from the psychology and sociology of finance, I show that the demand-side factors that account for gender disparity can be explained as resulting from a deflated sense of epistemic self-confidence that is due to persistent negative prejudices concerning women and finance.