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mystery shopper tests show continuing racial discrimination in small business lending at banks, made worse by a dash of socioeconomic bias

The customer experience was worst for Black borrowers perceived to be less educated

Racial discrimination in lending, housing and other spheres is illegal. For instance, race is one of a list of “protected classes” in anti-discrimination laws such as the Fair Housing Act and the Equal Credit Opportunity Act, along with gender, race, age and disability.

But what about class itself? Defining socioeconomic status (SES) is complicated. Understanding SES’s role in accessing credit is even more complicated. 

The American Psychological Association defines SES as the social standing or class of an individual or group and is often measured as a combination of education, income and occupation. 

To better understand the interconnectedness between race and SES within the small business lending arena, the National Community Reinvestment Coalition (NCRC), in conjunction with our academic partners – Dr. Sterling Bone (Utah State University), Dr. Maura Scott (Florida State University) and Dr. Glenn Chrisitinsan (Brigham Young University) – conducted matched-pair testing at financial institutions in the Washington, DC, metropolitan area between November and December 2020. These matched-pair tests focused on both race and SES through the manipulation of perceived education. 

We tested 34 different bank branches from 22 different financial institutions. We divided testers into four categories: High SES Black, High SES White, Low SES Black and Low SES White. We conducted both a quantitative statistical analysis of the marketplace as a whole and a qualitative fair lending analysis on each of the matched-pair tests conducted at the different bank branches.  

The statistical analysis of the marketplace revealed that the Low SES Black testers received significantly worse customer service treatment by financial institutions than the Black High SES testers. 

We also found a statistically significant difference in warmth behaviors experienced by testers. Warmth behaviors are the behaviors demonstrated by loan officers, like willingness to be friendly, respectful, helpful, and caring, all indicators of the financial institution’s willingness to engage with that potential applicant. The Low SES Black testers were shown fewer warmth behaviors than all the other testers (High SES Black, Low SES White, High SES White).

In the fair lending review, SES differences compounded previously observed differences in treatment based on race. In 38% of the tests, there was a difference in treatment between the High SES White and the High SES Black tester, and 44% of tests revealed a difference in treatment between the Low SES White tester and the Low SES Black tester. These differences in treatment were fair lending violations of the protected class status of race under ECOA. 

Forty-four percent of the tests revealed a difference in treatment based on perceived SES between the White testers, and 41% of the tests revealed a difference in treatment based on perceived SES between the Black testers. These differences uncovered the implicit bias that bank employees have as the Low SES tester had a better financial profile than the High SES tester.

In one test, there was a difference in treatment through the information that the testers received depending on the race and SES of the tester. The bank employee emailed three out of the four testers (the White High SES tester, the Whilte Low SES tester and the Black High SES tester) about documents and information they would need to submit when applying for a small business loan. Each email provided a list of different documents that needed to be submitted. However, the White High SES tester was provided the most robust list. The White Low SES tester received a list with a few documents omitted. The Black High SES tester received a list containing the minimum documents listed on the financial institution’s website. The Black Low SES tester did not receive an email follow-up. This was concerning behavior because not every tester was given the same information, and would not have comparable applications if they were to submit. 

To combat the implicit bias and discrimination that we found, we suggest:

  • All lenders should conduct matched-pair tests as a component of their fair lending compliance
  • Implicit bias training should be added to fair lending training
  • Lenders should be consistent in the information they provide to potential borrowers, including emails 
  • Regulators should strengthen their supervision of lenders
  • Additional research on the intersectionality of SES and race in the lending arena 

Elimination of implicit bias and discrimination makes the financial services marketplace more fair and more equitable. Fair lending compliance provides an economic benefit to the financial institution as these institutions are not discouraging applicants from applying because of their employee’s implicit bias or outright discrimination.

To read the full report: https://ncrc.org/does-perceived-socio-economic-status-impact-access-to-credit-in-the-small-business-arena

Anneliese Lederer is NCRC’s Director of Fair Lending.

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Redlining and Neighborhood Health

Before the pandemic devastated minority communities, banks and government officials starved them of capital.

Lower-income and minority neighborhoods that were intentionally cut off from lending and investment decades ago today suffer not only from reduced wealth and greater poverty, but from lower life expectancy and higher prevalence of chronic diseases that are risk factors for poor outcomes from COVID-19, a new study shows.

The new study, from the National Community Reinvestment Coalition (NCRC) with researchers from the University of Wisconsin–Milwaukee Joseph J. Zilber School of Public Health and the University of Richmond’s Digital Scholarship Lab, compared 1930’s maps of government-sanctioned lending discrimination zones with current census and public health data.

Table of Content

  • Executive Summary
  • Introduction
  • Redlining, the HOLC Maps and Segregation
  • Segregation, Public Health and COVID-19
  • Methods
  • Results
  • Discussion
  • Conclusion and Policy Recommendations
  • Citations
  • Appendix

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