Self-Selection and Discrimination in Credit Markets
Longhofer, Stanley D., Peters, Stephen R., Real Estate Economics
In this article we make two contributions toward a better understanding of the causes and consequences of discrimination in credit markets. First, we develop an explicit theoretical model of loan underwriting in which lenders use a simple Bayesian updating process to evaluate applicant creditworthiness. Using a signal correlated with an applicant's true creditworthiness and their prior beliefs about the distribution of credit risk in the applicant pool, lenders are able to evaluate an applicant's expected or "inferred" creditworthiness to determine which loans to approve and which to deny. Second, we explicitly model the self-selection behavior of individuals. Because these decisions shape lenders' prior beliefs about the distribution of credit risk, they also affect the Bayesian posterior from which lenders compute an applicant's inferred creditworthiness, implying that statistical discrimination can arise endogenously. As an example, we show that in a market in which only some lenders have Beckerian tastes for discrimination there are conditions under which lenders without racial animus will also discriminate. Our model's flexibility makes it ideal for analyzing a wide variety of empirical and policy questions.
Ever since the initial release of the so-called "Boston Fed Study" on mortgage discrimination (Munnell et al. 1992), academics, bankers, activists and policymakers have struggled to agree on how best to rectify discriminatory practices in consumer credit markets. At the same time, no clear consensus has been reached on whether or not lenders actually do discriminate. At the heart of this conundrum is the difficulty in establishing what discrimination looks like and how it might be detected.
In the intervening years, virtually all research on lending discrimination has been empirical in nature, much of it focusing on the validity of the results of Munnell et al. (1992) and on finding ways to analyze and detect discrimination in mortgage lending data. (1) In contrast, there has been little theoretical work to help explain discriminatory behavior in credit markets or provide a framework for studying the loan underwriting process. (2) This lack of economic theory has not only forestalled policy debates, but has also hindered the design of appropriate empirical tests for lending discrimination.
This article makes two contributions toward a better understanding of the causes and consequences of discrimination in credit markets, yielding interesting insights into the behavior of both lenders and applicants. First, we develop an explicit theoretical model of the loan underwriting process that accounts for lenders' efforts to ascertain applicant creditworthiness in the presence of imperfect information. In our model, lenders use a simple Bayesian updating process to underwrite loans, using both the information provided on the loan application and the lender's own prior underwriting experiences to determine which loans to approve and which to deny. This structural model allows us to define discrimination with respect to observable variables, making it more useful to both empiricists and policy makers. It can also be used to design empirical tests to uncover any discrimination that may exist, as well as to reveal the underlying motivation that gave rise to this discrimination.
The second contribution lies in our focus on the self-selection behavior of individual applicants. We illustrate how the choices that individuals make regarding whether or not to apply for loans--and to which lenders they apply--can lead to a correlation between the creditworthiness of a lender's applicant pool and race. Profit- and utility-maximizing lenders have an incentive to use the information this correlation reveals in order to more accurately assess credit risk. In other words, applicant self-selection behavior can lead to endogenous differences in the average creditworthiness of different racial groups at any given lender. …