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Racial Disparities in Consumer Credit

Paper Session

Friday, Jan. 5, 2024 8:00 AM - 10:00 AM (CST)

Convention Center, 225B
Hosted By: American Economic Association
  • Chair: James Weston, Rice University

Reducing Racial Disparities in Consumer Credit: Evidence from Anonymous Loan Applications

Poorya Kabir
,
National University of Singapore
Tianyue Ruan
,
National University of Singapore

Abstract

Using a unique experiment of anonymizing online loan applications, we test whether race-blind loan screening procedures reduce racial disparities in consumer credit. With names on applications, ethnic minority applicants are 10.6% less likely to receive online loan offers and receive worse offer terms than otherwise identical ethnic majority applicants. Anonymizing applications reduces such disparities substantially. High-income minority applicants benefit more than low-income minorities. We show that the racial disparities are not driven by differences in socioeconomic status or credit demand. Overall, anonymous loan applications reduce racial disparities in access to credit by increasing lender reliance on objective credit risk measures.

The Arity of Disparity: Updating Disparate Impact for Modern Fair Lending

Spencer Caro
,
University of Chicago
Scott Nelson
,
University of Chicago

Abstract

Plaintiffs often cannot prevail under a disparate impact (“DI”) claim of discrimination unless they show the defendant failed to implement a less discriminatory alternative (“LDA”) to a practice that yields a disparate output across protected classes. Traditional LDA analysis focuses on a singular notion of fairness: parity, or the equality of screening decisions across protected groups. However, recent scholarship highlights that parity is only one of numerous competing notions of fairness that may seem just as compelling as, but be mutually exclusive with, parity. The arity of disparity is larger than DI has acknowledged.
We propose formalizing the LDA as an explicit constraint on choices over screening models and data inputs. The constraint restricts model-induced disparities in both parity and a competing notion of fairness—accuracy—relative to a “budget” that depends straightforwardly on overall model performance. We also show how this trade-off leads to balancing other notions of fairness, as weighted combinations of parity and accuracy span many other fairness notions in a helpful way.
For concreteness, our legal argument and our applied examples focus on DI under the Equal Credit Opportunity Act (“ECOA”). We address tension between DI’s traditional focus on parity and ECOA’s statutory emphasis on “credit-worthy” consumers and discuss implications for new frontiers in credit underwriting, including the use of machine learning and alternative data sources.

Consumer Bankruptcy Attorneys and Racial Differences in Outcomes

Paul Goldsmith-Pinkham
,
Yale University
Dana Scott
,
Yale University
Jialan Wang
,
University of Illinois-Urbana-Champaign

Abstract

Using administrative data on consumer bankruptcy filings, we study the difference in bankruptcy filing behavior for Black and non-Black Americans, and the role of bankruptcy attorneys in that gap. We first document three facts: 1) Black bankruptcy filers are 27 percent less likely to successfully discharge their debt compared to a non-Black filers, and are more than twice as likely to file for Chapter 13, the more stringent chapter of consumer bankruptcy; 2) Chapter choice is extremely predictable out-of-sample using only balance sheet characteristics at time of filing, but that the prediction loadings do not coincide with things typically associated with Chapter 13, such as homeownership; 3) the Black-White gap in bankruptcy success rate for Chapter 13 is concentrated in bankruptcy dismissals in the first year of repayment plans. Then, using new data on attorney choice and payments, we study the extent to which bankruptcy attorneys affect the Black-White gap in chapter choice and discharge rates, and how attorneys' compensation structure causes this impact.

Algorithmic Underwriting in High Risk Mortgage Markets

Janet Gao
,
Georgetown University
Hanyi (Livia) Yi
,
Boston College
David Zhang
,
Rice University

Abstract

We study the effects of a policy that shifted from pure human underwriting to human-augmented algorithmic underwriting for low-credit-score, high-leverage mortgage borrowers. Estimating the bunching of loans around the policy's debt-to-income threshold, we find a large credit expansion to affected borrowers with little changes in default risks or interest rates among the affected group. Such effects are more pronounced among non-Hispanic White borrowers and higher-income borrowers. Consequently, low-credit-score households are more likely to move to better school districts. We use a structural approach to quantify the welfare implications of the policy change and isolate the credit supply channel. Overall, our results suggest that automated underwriting systems (AUS) can help increase financial inclusion while controlling risk. However, it can also generate disparate impact across racial groups and along the income distribution.

Fraud Litigation and FHA Mortgage Lending

Erik Mayer
,
University of Wisconsin-Madison
Billy Y. Xu
,
University of Rochester
Lawrence Zhao
,
Texas Tech University

Abstract

We study the impact of recent increases in mortgage lenders’ litigation risk on borrowers. In the last decade, the U.S. Department of Justice brought suits against many of the largest lenders in the FHA mortgage market, alleging fraud under the False Claims Act. These suits led to over $5.4 Billion in settlements and caused targeted banks and their peers to precipitously exit the FHA market. A combination of difference-in-differences and triple differences tests exploiting geographic variation in exposure to exiting banks show a 19% reduction in aggregate FHA lending in heavily affected areas. Smaller non-bank lenders with higher historical misconduct rates partially filled the void in the FHA market, highlighting potential unintended consequences of aggressive consumer financial protection litigation. The reduction in the quantity and quality of FHA lending disproportionately affected lower-income and high minority share communities.

Discussant(s)
Sabrina Howell
,
New York University
Brittany Almquist Lewis
,
Washington University-St. Louis
John Mondragon
,
Federal Reserve Bank of San Francisco
Charlotte Haendler
,
Southern Methodist University
JEL Classifications
  • G5 - Household Finance
  • J1 - Demographic Economics