The Paycheck Protection Program, though it ended on May 21st, is still making headlines – this time for the racial bias behind the program’s implementation, which has been cited as the driving force behind the disparity identified between minority and white borrowers to find a willing lender.
Background
The Paycheck Protection Program (PPP) – which was passed under the Trump administration as part of a larger package of measures all aimed at boosting the fledgling economy during the pandemic – was aimed at incentivizing small businesses to rehire or keep employees on their payrolls by providing them with forgivable loans. This was the heart of the PPP – it was virtually risk-free given the loans were administered by private lenders but federally guaranteed. Essentially, the Small Business Administration (SBA) would advance small businesses up to $10 million, the actual size of the loan was determined based on the business’ headcount and payroll, and then the government would pay off the loans in full as long as operators followed the rules; even if the borrower defaulted, the government would still repay the lender – which, in theory, would be more than enough to make any lender willing to lend to any qualifying applicants.
The program quickly, however, was met with lots of issues. For one, funding ran out within a month (can you please link to my post from 7/28) for most borrowers. Then came a variety of technological, administrative, and bureaucratic hurdles. This was followed by the realization that the program was highly susceptible to fraud (can you please link to my post from 7/29) – which has led to hundreds of white-collar investigations and charges. Now, deeper trends are being uncovered. Many had already identified that minority owners, especially Black owners, were struggling to find a willing lender much more than white owners – even when the program had just begun. But this is just the tip of the iceberg.
A new study conducted by NYU researchers primarily found that “the majority of Black borrowers who received aid from the $800 billion relief program got their loan from a financial technology company, not a bank.” According to the study, smaller banks were much less likely to lend to lend to Black-owned firms, while the Top-4 banks (JPMorgan Chase, Bank of America, Wells Fargo and Citibank) exhibited little to no disparity after including controls. According to the data, this skew toward so-called fintechs was far sharper among Black borrowers than any other racial group. The researchers clarified that they used “novel data to show that the disparity is not primarily explained by differences in pre-existing bank or credit relationships, firm financial positions, fintech affinity, or borrower application behavior.”
Significantly, the researchers tested other common hypotheses about the PPP’s racial lending disparities – but found little evidence for such. They explain that “even after controlling for variables like the applicant’s ZIP code, industry, recent revenue, affinity for online lenders, and loan size and approval date, the gap persisted.” In contrast, the researchers continue, they “ document that Black-owned businesses’ higher rate of borrowing from fintechs compared to smaller banks is particularly large in places with high racial animus, pointing to a potential role for discrimination in explaining some of the racial disparities in small business lending.”
Why is this the case? According to the researchers, this can be attributed to the fact that “when small banks automate their lending processes, and thus reduce human involvement in the loan origination process, their rate of PPP lending to Black-owned businesses increases.” As such, the study concludes that “automated loan vetting and processing systems used by the fintechs, as well as some of the nation’s biggest banks, significantly improved approval rates for Black borrowers.” In one example used to support this conclusion, the researchers conducted a deep dive into a group of small banks that switched halfway through the PPP to using automated systems from several fintech firms; the outcome: their share of loans to Black-owned firms noticeably increased after the switch – thereby confirming their conclusions.
Significance and Reactions
After her working paper was released, Dr. Sabrina T Howell – the lead author of the study – stated “I was taken aback by the striking disparity — it was a surprising and unexpected fact, and we wanted to figure out why.” According to her, the driving force behind the program’s racial disparity is that “the human brain is a much scarier black box than any machine-learning algorithm. You can constrain an algorithm to meet fair-lending standards, and you can ensure the data it trains on isn’t biased. That may be hard to do, but it’s a clear and objective possibility. Whereas when you have a human loan officer who is in front of someone and making a decision, you can never do that.”
Several other individuals and organizations have supported the findings laid out in this study. In fact, several other studies conducted around the same time confirm the same conclusions. For one, Purdue University Krannert School of Management associate professor of finance, Sergey Chernenko, has stated that Dr. Howell’s research aligns with his own findings on race-based gaps in Paycheck Protection Program lending. In his words, “this fits very well with and complements our finding that minority-owned businesses were less likely to get loans because of racial bias, and to the extent that they do get them, they’re more likely to get them from fintechs than banks.” Another studycomes from the Federal Reserve Bank of New York, which found that “41% of Black businesses had folded amid the pandemic — the highest share among all racial and ethnic groups.” Several other Federal Reserve Banks looked deeper into this trend as well. According to results from a survey conducted by a coalition of these Banks, Black business owners were found to be the most likely to draw from their personal funds to help keep their businesses afloat. Their survey also revealed that 1) Black business owners were 5 times more likely to not receive any of the PPP funding they had requested compared with White-owned businesses, and 2) while 79% of White-owned firms received all of the PPP funding they sought, only 43% of Black-owned firms did. Perhaps the most significant study that confirms Dr. Howell’s findings came from the very Government Accountability Office audit of the PPP, which found that lending to “traditionally underserved” businesses – which they ascribe to those run by women, veterans and minority owners – was disproportionately low throughout the program’s first year.
On the other hand, the study has inevitably encountered lots of pushback as well. According to the Independent Community Bankers of America, its community lenders actually “outperformed the rest of the banking industry in serving minority-owned, women-owned and veteran-owned businesses.” In a more targeted manner, the ICBA criticized Dr. Howell’s study on the basis of the steps it took to determine the rate of applicants. When researching, the NYU team used Census Bureau data to gather information on borrowers’ locations, surnames, and race because collecting data on borrowers’ ethnicity was made optional for lenders. Because of this, the ICBA deemed those methods to turn the research into “an unreliable guessing game.” Another dissenter is the American Bankers Association which, when asked for a comment on small banks’ disparate lending when it comes to Black businesses by the New York Times, answered “banks played a vital role in getting the majority of those (PPP) loans into the hands of borrowers across the country, including millions of small businesses in communities of color.”
With this being said, Dr. Howell has stated that she hopes “her study’s results and the growing body of research on racial bias in lending would spotlight the ways technology may help banks make fairer credit decisions.” This growing body of research has been primarily focused on how algorithmic systems and artificial intelligence can inadvertently perpetuate bias. In her opinion, however, Dr. Howell believes that “there are times where there may be real benefits to removing humans from the process.”
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