Despite gaping holes in government data, tests show PPP borrowers faced discrimination

The government’s Paycheck Protection Program data is so flawed it is virtually useless to assess if there was any bias in how the money was distributed, or how much money went to specific communities.

But a new study that used testers who talked directly with banks about loans to help their small businesses stay open during the coronavirus pandemic found that White applicants were treated better than Black applicants, a pattern that was well documented in small business lending before COVID-19.

The study, conducted by the National Community Reinvestment Coalition (www.ncrc.org), found that Black and White matched-pair testers experienced different levels of encouragement to apply for loans, different products offered and different information provided by bank representatives. The tests were conducted over the telephone between April 27 and May 29 with 17 banks in the Washington, DC, metro area.

Matched-pair testing is a method used to detect discrimination by using a pair of testers with different races (or other protected class) but similar profiles as a way to determine differences in treatment.

Before the 2020 coronavirus pandemic, matched-pair “mystery shopper” tests conducted by NCRC in 2017, 2019 and earlier in 2020 found similar disparities in the experiences of Black and White testers.

The tests during the pandemic offer unique evidence of disparities in the PPP program. Along with the test results, NCRC released a scathing critique of the PPP data released by the government earlier this month. The data included far less detail about borrowers than what other government small business lending programs have long provided.

“The tests show that old patterns of systemic discrimination in lending didn’t magically disappear when banks made PPP loans,” said Jesse Van Tol, CEO of NCRC. “Banks still have a long way to go to root out discrimination, and clearly they need better training for their employees and more testing to create internal checks and internal pressure to drive out racist practices.

“”The tests are even more critical right now for the PPP program because the government did a terrible job of collecting data on PPP applications and loans. The SBA and Treasury departments know how to collect loan data, they’ve done it for years, banks are used to it, and yet somehow the government found a way to leave gaps in the PPP data it released to the public. The data has so many holes it’s nearly useless as a tool to evaluate patterns of socioeconomic or geographic bias.

“So far, the government’s data reporting on PPP is scandalously, inexcusably incomplete. But it’s also a problem that could be solved quickly if the government acts quickly. Most PPP borrowers will apply for loan forgiveness soon. The government needs to collect and report the missing demographic data through those applications for forgiveness. Then we’ll have much better insight into who got loans, who didn’t, and other details. Our tests showed bias, there’s no question about that. More data will tell us much more about what happened with this program.”

Read the full report.

Read the full analysis of PPP data.

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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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