MARK GRIFFITHS, Principal, MG2 Investments LLC
MIKE MAULDIN, Director of the Excellence In Banking Program, Texas Tech University
DREW WINTERS, Pickering Chair in Finance, Texas Tech University

Phase 1 of the Paycheck Protection Program (PPP) has been completed with roughly $349 billion distributed to businesses across the country. The program was established as part of the $3 trillion coronavirus relief bill passed in March 2020. A second round of an additional $310 billion commenced in April 2020 with an original closing date of June 30. The closing date was extended through August 8, yet the second round closed with $130 billion of unused funds. A third round is currently under consideration at the time of this writing, but there appears to be no consensus on the size or timing of any additional support.

The PPP is administered by the Small Business Administration (SBA), a division of the U.S. Department of the Treasury. The program was designed for any business (for-profit, nonprofit, veterans organization or tribal concern) with 500 or fewer employees to be eligible for a government-backed loan equal to eight weeks of its prior average payroll, plus an additional amount to cover various other non-payroll operating expenses such as interest rent and utilities. The payroll amount was capped at a maximum of $100,000 (annualized) for each employee unless the total amounted to more than $10 million, the maximum for any individual firm. The loan was structured as having a two-year term and a 1 percent interest rate.^[1] We detailed the qualification criteria in our earlier paper. ^[2]

Demand greatly exceeded supply in the first round of PPP, to the point where the program ran out of money in 13 days. Phase 2 of the PPP has been completed with several modifications. One reason for the modifications was that some large successful companies received funds from phase 1 of PPP. To respond to these problems, the House Select Subcommittee on the Coronavirus Crisis launched an investigation into the implementation of the PPP in June to determine whether larger companies were receiving loans intended for small businesses. Among other problems related to oversight, the analysis identified 10,856 loans totaling more than $1 billion granted to borrowers who received more than one loan and 600-plus loans totaling more than $96 million that went to companies forbidden from doing business with the federal government.^[3] In particular, the report highlights the fact that there was no evidence that the SBA issued guidance to lenders to prioritize borrowers in underserved and rural markets. These borrowers, including rural, minority and women-owned businesses, may not have received the loans as intended.^[4]

Accordingly, we ask the question: did the second round of PPP funds get to small businesses as intended? Given the above-noted problems, we wanted to know whether the later round of PPP was better administered than the first round. Unfortunately, the data do not exist to answer this question directly. So, as in our initial paper, we take an indirect approach and examine the participation of community banks because of their commitment to reinvest local dollars back into the community and help create local jobs. In particular, we examine the loans approved for amounts under $150,000 to ensure that we are examining funds that should be going to small businesses. Specifically, we examine the changes between the first and second rounds in the distribution of PPP funds for the state of Texas. We chose Texas for comparability purposes with our initial paper.

Data Description

We begin with the lists of Texas banks participating in both rounds of PPP. We merge this list with an FDIC list of community banks to identify Texas community banks participating in the program. Then, we hand code the other Texas first-round PPP lenders into the following categories: stress-tested banks^[5], credit unions, non-bank lenders and other banks. The category of “other” banks are the Texas PPP lenders that are not identified for our specific categories.

The first round of PPP comprises 147,461 loans after deleting any loan in which the size was less than $5,000.^[6] 95,311 of the 147,461 are for amounts under $150,000. The second round of PPP dataset comprises 207,401 under $150,000, after adjusting for dubious entries.^[7]

We note that the SBA does not make any representations about the accuracy or completeness of any information that borrowers provided to their lenders. Not all borrowers provided all information. For example, more than 80 percent of all PPP2 loans did not include any demographic information because that information was not provided by the borrowers or recorded by the lenders. The inspector general found that the SBA failed to provide a demographic questionnaire with the PPP loan application, undermining the SBA’s ability to determine whether lenders appropriately prioritized loans to underserved communities.^[8]

Results Related to Lenders

We commence by examining the differences in the number and amount of the loans under $150,000 approved under the two rounds of PPP in Texas. Despite there being less total funds available in the second round, we find more loans for more dollars in Texas. Specifically, PPP1 had 95,311 loans totaling $4,725,437,983, while PPP2 had 207,401 loans totaling $6,555,311,074. The mean (median) loan sizes were $49,579 ($38,511) in PPP1, but only $31,607 ($20,800) under PPP2. The result is smaller loans under PPP2 in Texas, which is consistent with reaching the under-served businesses targeted by the program.

In PPP1, Texas community banks made 66,192 loans (69.4 percent of all loans in the state), totaling $3,259,617,655. In PPP2, Texas community banks made 77,259 loans (37.3 percent of all loans in the state) totaling $2,477,118,424. There are several additional matters of interest in these data. First, stress-tested banks increased the number of their loans from 2,885 in PPP1 to 59,940 loans in PPP2. Second, “other banks” more than doubled the loans made in the initial round of PPP, credit unions increased their loans more than three-fold while “other lenders” increased nearly five-fold. We present these data in tables 1A and 1B.

We speculate that the increase in activity was related to the fact that PPP1 funds were exhausted in 13 days and that it took some time for the larger banks, credit unions and other lenders to organize in anticipation of PPP2. To support this claim indirectly, we present in Table 1C the number of new lenders in PPP2, along with the amounts approved. Approximately 80 percent of the new lenders are “other lenders,” the most notable of which was Kabbage Inc., an online financial technology company based in Atlanta, Georgia.

Table 1A: PPP1 Loans Under $150,000 by Lender

Table 1B: PPP2 Loans Under $150,000 by Lender

Table 1C: PPP2 Loans Under $150,000 by Lender Type—New Lenders Only

* We used the following list of stress-tested major banks: JP Morgan Chase & Co., Citigroup, Bank of America Corp., Wells Fargo & Co., Goldman Sachs Group, Morgan Stanley, PNC Financial Services Group, US Bancorp, Bank of NY Mellon Corp., SunTrust Banks Inc., State Street Corp., Capital One Financial Corp., BB&T Corp., Regions Financial Corp., American Express Co., Fifth Third Bancorp and Keycorp. We define other bank types as non-stress-tested banks that are not defined as community banks.

Next, we provide an analysis of the major lenders with a breakdown of the lenders with 1,000 or more PPP loans by round. Table 2A provides the list of lenders for PPP1 loans under $150,000 and Table 2B provides the list of lenders for PPP2 loans. As reported earlier, in the initial period the under $150,000 category contains 15 lenders. The largest lender is Frost Bank (categorized as an “other” bank) that made 6,698 loans. Frost is followed by eight community banks, one non-bank lender, one stress-tested bank^[9] (JP Morgan Chase) and four additional “other” banks.

Table 2A: PPP1 Lenders with 1,000 or More Loans Under $150,000

Table 2B: PPP2 Lenders with 1,000 or More Loans Under $150,000

 * We used the following list of stress-tested major banks: JP Morgan Chase & Co., Citigroup, Bank of America Corp., Wells Fargo & Co., Goldman Sachs Group, Morgan Stanley, PNC Financial Services Group, US Bancorp, Bank of NY Mellon Corp., SunTrust Banks Inc., State Street Corp., Capital One Financial Corp., BB&T Corp., Regions Financial Corp., American Express Co., Fifth Third Bancorp and Keycorp. We define other bank types as non-stress tested banks that are not defined as community banks.

In the second period, there are 22 lenders with 1,000 or more loans. Here, the leading lender is JPMorgan Chase, a stress-tested bank with 23,566 loans, followed by two additional stress-tested banks: Bank of America (20,562 loans) and Wells Fargo (13,182 loans). These three are followed by eight “other” banks, two non-bank lenders led by Kabbage Inc. (7,485 loans) and eight community banks and an additional stress-tested bank (Regions Bank with 1,445 loans). We provide the details in Tables 2A and 2B.

The purpose of the PPP program was to assist small businesses by targeting any business with 500 or fewer employees. The government-backed loan was equal to eight weeks of the business’ prior average payroll, plus an additional amount (up to 40 percent in PPP2) to cover various other non-payroll operating expenses, such as interest rent and utilities. We pose the question: how many people were helped by this program? One of the fields (not mandatory) recorded in the SBA database was the number of jobs retained.

In PPP1 (Table 3A), $4,725,437,983 in loans were made with a view to saving 731,538 jobs—an average of $6,460 per position. With the program being eight weeks long, the per-job average equates to $808 per week per job, which can equate to as little as $646 per job per week with the allowance for up to 25 percent of the loan for overhead.^[10] Community banks were responsible for distributing roughly 69 percent of the funds to save 66 percent of the jobs. In PPP2 (Table 3B), $6,555,311,074 in loans were made to help retain 981,990 jobs—an average of $6,676 per position. Using the same metric as in PPP1, this equates to as little as $668 per week per job after allowance for overhead. Community banks distributed roughly 38 percent of all the funds to save roughly 36 percent of all of the positions retained in the state through this program. This apparent reduction in community support is more directly a result of the increased role of the stress-tested banks, which helped retain 21,268 jobs under PPP1, but 246,317 positions in PPP2.

Table 3A: PPP1 Jobs Retained with Loans Under $150,000 by Lender

 Table 3B: PPP2 Jobs Retained with Loans Under $150,000 by Lender

Our next concern centered on which industries were served by the community banks. That is, did community banks concentrate on only a relatively small number of industries and did the community banks participate at a lower rate than other financial institutions? We perform this analysis by examining the NAICS codes assigned to the borrowers.^[11] In the PPP1 loan category, as we reported in our earlier paper, community banks loaned on a comparable basis to other banks and stress-tested banks in all categories except professional and technical services and health care and social assistance for which they provide roughly 50 percent fewer loans in these code groups relative to other banks and stress-tested banks. We pose the question: did anything change with PPP2?

In the PPP2 category (Table 3C), community banks are again comparable in all but two code groups. Again, they did not make as many loans to professional and technical services and health care and social assistance, but they led the way in lending to the construction trades. One potential source of concern was the role of non-bank lenders. Did the participation of these lenders cause any shifts in the industries served? Accordingly, we provide relevant data in Table 3D. Generally, we find that non-bank lenders made loans across the same industries as other lenders and to much the same degree, although they participated marginally more in finance and insurance and marginally less in transportation and warehousing.

Table 3C: Jobs Retained with PPP2 Loans Under $150,000 by Lender
Industry frequency (5 percent for more or loans for any group of banks)

Table 3D: New Lenders in PPP2
Industry frequency (5 percent for more or loans for any group of banks)

Results Related to Gender and Race

Congress specifically encouraged the administration to issue guidance “to ensure that the processing and disbursement of covered loans prioritize small-business concerns and entities in underserved and rural markets,” including businesses owned by veterans, members of the military, socially and economically disadvantaged individuals and women. Shortly after all the funds for the initial PPP loans were distributed, the media began reporting discrimination against minority businesses in the distribution of the funds. Accordingly, this section of our analysis examines the data from PPP in Texas for women and minority-owned businesses.

The PPP loan data are for loans approved. It must be stated that this is an incomplete measure of the process, as it only provides a measure of the outcome. There are no data on initial PPP requests and/or denials, which are the data needed to provide an informed analysis of discrimination. In addition, as stated earlier, the borrower was not required to report demographic data and the lender was not required to collect it. We note that the loan practices by banks prohibit the collection of gender and race because in most cases collecting this data is against the law, so not collecting this data under PPP should not be considered as an attempt by the banks to hide something. The result is that the demographic data on the borrowers are mostly missing, so the available data cannot be considered to be a representative sample of all of the PPP borrowers. Thus, the reader should not draw any conclusions from these data. We only present the analysis to provide an informed response to media reports of discrimination.   

Academic research on the relationship between gender and capital access^[12] finds limited access for women-owned and minority-owned businesses^[13]. It is believed that this contributes to reduced opportunities for business growth^[14]. Coleman and Carsky (1996) report that women-owned firms, in general, are less likely to rely on bank financing for their businesses^[15], perhaps due to perceptions of potential discrimination in the lending process^[16]. In a 2018 study by the Women’s Business Enterprise National Council, 25 percent of women business owners sought business financing, compared to 33 percent of male business owners. However, Shepherd (2020)^[17] notes that 57.4 percent of the SBA microloan program’s loans went to women-owned or women-led businesses. Shepherd also reports that women entrepreneurs apply for approximately $35,000 less in business financing than male entrepreneurs, and men receive an average loan size of $43,916, while women receive an average loan size of $38,942.^[18]

Our initial findings reveal substantial differences between the amount and number loans made to men and women. In PPP1 (Table 4A), women received 23.26 percent of the loans and men received 76.74 percent of the loans in which gender is indicated. More than 65 percent of all loans do not indicate the gender of the owner. In PPP2 (Table 4B), women received 25.1 percent of the loans and men received 74.9 percent of the loans. Again, caution is necessary in interpreting these statistics—in approximately 81 percent of the cases, gender was not identified.

Table 4A: Distribution of PPP1 Loans Under $150,000 by Gender 

Note: As noted in the text, the high level of missing observations suggests that the reader should not draw any conclusions from these data. We only present the analysis to provide an informed response to media reports of discrimination.

Table 4B: Distribution of PPP2 Loans Under $150,000 by Gender 

As noted in the text, the high level of missing observations suggests that the reader should not draw any conclusions from these data. We only present the analysis to provide an informed response to media reports of discrimination.

The average minority-owned business in the U.S. operates with much less capital, even after controlling for factors influencing financing amounts. Further, according to Prakash (2020), these businesses tend to report less revenue and have less access to capital than non-minority-owned firms^[19], and a second report by Otiz-Walters and Gius (2012) indicates that some minority-owned firms tend to be less profitable^[20]. Performance gaps can be attributed to factors that include obtaining sufficient financial capital to buffer losses, the achievement of efficient scale and exploitation of business opportunities^[21]. Businesses that operate in the category of having less capital also do not have, as a general rule, the structured financial statement data that are required to successfully be approved for a loan. As we reported in our initial paper, the Kauffmann Foundation reports that Black-owned and Hispanic-owned businesses experience less favorable loan application outcomes than do White-owned and Asian-owned businesses, even after controlling for firm- and owner-specific characteristics. The Kauffmann report concludes that Black and Hispanic entrepreneurs enter industries with low capital requirements and high failure rates, which weaken their firms’ abilities to buffer losses and financial growth if they survive in the early stages.

In PPP1 (Table 5A), approximately 82 percent of the loans do not document the borrower’s race. Where race is stated, it appears that 0.36 percent were American Indian/Alaskan Native, 17.48 percent were Asian, 1.8 percent were Black or African American, 15.77 percent were Hispanic and 64.59 percent were White.   

In PPP2 (Table 5B), approximately 92 percent of the loans do not document the borrower’s race. Where race is stated, it appears that 0.6 percent were American Indian/Alaskan Native, 19.3 percent were Asian, 4.1 percent were Black or African American, 25.9 percent were Hispanic and 50 percent were White. Due to the large amount of missing data, we are unable to state whether any differences with respect to the PPP1 figures are significant.

Table 5A: Distribution of PPP1 Loans Under $150,000 by Race/Ethnicity

As noted in the text, the high level of missing observations suggests that the reader should not draw any conclusions from these data. We only present the analysis to provide an informed response to media reports of discrimination.

Table 5B: Distribution of PPP2 Loans Under $150,000 by Race/Ethnicity

As noted in the text, the high level of missing observations suggests that the reader should not draw any conclusions from these data. We only present the analysis to provide an informed response to media reports of discrimination.

Our findings could be interpreted as support for the argument that women and minority-owned businesses did not receive the appropriate levels of support in PPP. However, we note that the 2018 Annual Business Survey conducted by the U.S. Census Bureau reported that Black- or African American-owned businesses accounted for 2.2 percent of business in the U.S. and female-owned businesses account for 19 percent of businesses^[22]. These percentages of ownership are consistent with the rates of participation documented from the limited responses under PPP1 and PPP2. We again caution the reader against drawing conclusions on discrimination due to the large amount of missing demographic data.

Results from Non-Bank Lenders: An Analysis of Kabbage Inc. in PPP2

A report from the House Select Subcommittee on the Coronavirus Crisis concluded that the U.S. Treasury Department privately encouraged lenders to prioritize existing customers when issuing loans. The Treasury Department, which helped run the program along with the Small Business Administration, denied the allegation^[23]. The House report shows continued concern that it is harder for certain groups to access the program, including minorities and business owners in rural areas.

We have shown in our previous paper that community banks got PPP1 loans to rural areas of Texas. Nothing in the PPP data allows us to directly test if the PPP loans prioritized existing customers, although we would be surprised if existing customers were not the first to ask for PPP access from a bank and then get served first according to the program rules of first come, first served. In addition, under the current regulatory environment, existing customers would have the Customer Identification Program (CIP) and beneficial interest information required to secure a loan. Many would also have current financial statement information in their file. This would allow banks to process PPP requests more quickly for current customers^[24]. This could appear as disadvantaging minority-owned businesses as they are less likely to have existing banking relationships^[25]. We do not have access to the banks’ existing customers, but an analysis of Kabbage Inc. might provide some insights.

Kabbage Inc. is an online financial technology company based in Atlanta, Georgia, so it is unlikely to have a large established customer base in Texas. In addition, the Federal Reserve Bank of New York reports that Black-owned businesses are uncomfortable with loan requests at banks and perceive a higher probability of getting a loan from an online lender, such as Kabbage. ^[26].

We begin our analysis of Kabbage across all of Texas. It made 7,485 PPP2 loans for a total amount of $146 million. The loans hope to retain 17,145 jobs at a loan cost of $8,533 per job saved. This is a higher loan per job saved than any of the groups of lenders reported in Table 3B. Kabbage lends into the same industries as the other groups of lenders and at similar percentages. Kabbage documentation has less than a 5 percent response rate on gender and less than a 2 percent response rate on race, so we cannot say anything reliable on serving these groups.

The Federal Reserve Bank of New York reports that Harris County, Texas, (the Houston area) is in the top 10 counties for Black business receipts. A heat map (Figure 1) of Kabbage’s PPP2 loans in Texas shows clusters in Houston, Dallas, San Antonio and Austin. With Harris County having substantial Black-owned business activity, we analyze Kabbage’s PPP2 activity in that county.

Kabbage Inc. made 2,539 PPP2 loans in Harris County for a total loan amount of $50,982,634—about a third of all PPP2 dollars loaned by Kabbage in Texas. These loans hope to save 6,267 jobs, which is a loan amount of $8,135 per job saved. These loans are distributed across industries in a manner similar to that reported in Table 3D. We hoped to say something about gender and race from Harris County, but only 135 loans reported the gender of the owner and only 33 reported the race of the owner.

Kabbage provides for an analysis of a non-bank, non-Texas lender. The results from Kabbage, both across Texas and in Harris County, are similar to the other groups of lenders. These results suggest that even if PPP borrowers with existing banking relationships got PPP loans first, the ordering did not distort the distribution of PPP funds across industries.  

Conclusions

PPP is intended to provide short-term support to small businesses that were temporarily closed due to the COVID-19 pandemic and subject to the insufficient level of funding. It appears to have worked as intended and community bankers are significant contributors throughout the two phases of the program to date.

It is not surprising to us that the Texas-based community banks led the way in making the most loans in both phases of the PPP program. The need to provide liquidity quickly to businesses not only illustrates the value of community banking, but also the value of the banking system with its diversity of size and location. Our results suggest that community banks in Texas were instrumental in distributing PPP loans to rural markets. Our findings also suggest that community banks can react more quickly to changing conditions in their neighborhoods, as evidenced by the later entrance of stress-tested banks and other non-bank lenders.

The media has reported that initial PPP loans did not get to traditionally underserved business owners. The PPP data lean in the direction of the media reports. However, the media reports do not discuss the demographics of actual business owners, which appears to be consistent with the limited data on the mix of gender and race reported in PPP. The omission of complete data on the owners’ demographics in the SBA database limits any conclusions.

Congress is concerned that banks prioritized existing customers in distributing PPP funds. If it is that simple, we would expect to see the largest banks—labeled “stress-tested” in this study—to lead the way in distributing PPP funds because they have the most customers. They were not major players distributing funds until PPP2. Also, if banks were cherry-picking existing customers, then non-bank lenders, such as Kabbage Inc., could have different demographics in their distribution of PPP funds. Kabbage distributed funds across the same industries in similar percentages as banks. These results suggest to us that what some call prioritizing existing customers is simply community banks taking care of their communities.

^[1] See Paycheck Protection Program (PPP) Information Sheet: Borrowers.

^[2] M. Griffiths, M. Mauldin and D. Winters, “Did the Paycheck Protection Program (PPP) Funds Get to Small Businesses? A Study of the Role of Community Banks in Texas,” Bankers Digest, September 14, 2020.

^[3] See Memorandum on Preliminary Analysis of Paycheck Protection Program Data, September 1, 2020.

^[4] Small Business Administration, Office of Inspector General, Flash Report: Small Business Administration's Implementation of the Paycheck Protection Program Requirements, May 8, 2020.

^[5] We use the set of large banks identified for the initial stress testing under Dodd-Frank Act. We collected this list from Allen, Cyree, Whitledge and Winters (Journal of Economics and Business, 2018, vol. 98, pp. 19–31).

^[6] The purpose of this restriction was to eliminate potential errors in the data. For example, one loan was for $585, which supposedly retained 115 jobs. Similarly, a second loan of $4,400 was to support 158 positions. These small amounts could simply be “placeholders.” There were 1,069 such eliminations, all of which took place on either April 15 or 16, 2020. The placeholder argument may have some merit, given 788 of the eliminations were for no jobs or only one job retained.

^[7] There were 33,456 such entries. Additional details with respect to these loans can be found in our earlier paper.

^[8] See Memorandum on Preliminary Analysis of Paycheck Protection Program Data, September 1, 2020.

^[9] We used the following list of stress-tested major banks: JPMorgan Chase & Co., Citigroup, Bank of America Corp., Wells Fargo & Co., Goldman Sachs Group, Morgan Stanley, PNC Financial Services Group, US Bancorp, Bank of NY Mellon Corp., SunTrust Banks Inc., State Street Corp., Capital One Financial Corp., BB&T Corp., Regions Financial Corp., American Express Co., Fifth Third Bancorp and Keycorp.

^[10] A 2018 Pew Research Center report reveals that 29 percent of Americans live in the “lower class,” with a median income of $25,624 in 2016. Our weekly loan per job for PPP1 is $646, which annualizes over 50 weeks to $32,300. This suggests that PPP1 reached the intended workers.

^[11] A NAICS code is a classification within the North American Industry Classification System. The NAICS System was developed for use by federal statistical agencies for the collection, analysis and publication of statistical data related to the U.S. economy. NAICS is a self-assigned system; no one assigns you an NAICS code. A company selects the code that best depicts its primary business activity and then uses it when asked for the code.

^[12] S. Carter, E. Shaw, W. Lam and F. Wilson, “Gender, Entrepreneurship and Bank Lending: The Criteria and Processes Used by Bank Loan Officers in Assessing Applications,” Entrepreneurship Theory and Practice 31(3), 2020, pp. 427–444.

^[13] B. Orser, S. Hogarth-Scott and A Riding, “Performance, Firm Size and Management Problem-Solving,” Journal of Small Business Management 38(4), 2000, pp. 42–58.

^[14] R. Carpenter and B. Petersen, “Is the Growth of Small Firms Constrained by Internal Finance?,” Review of Economics and Statistics 84(2), 2002, pp. 298–309. G.H. Haines, B.J. Orser and A.L. Riding, A.L., “Myths and Realities: An Empirical Study of Banks and the Gender of Small-Business Clients.” Canadian Journal of Administrative Sciences, 16(4), 1999, pp. 291–307.

^[15] S. Coleman and M. Carsky, “Understanding the Market of Women-Owned Small Businesses,” Journal of Retail Banking Services 18(2), 1996, pp. 47– 49.

^[16] S. Coleman, “Access to Capital and Terms of Credit: A Comparison of Men- and Women-Owned Small Businesses,” Journal of Small Business Management, 38(3), 2000, pp. 37–52.

^[17] M. Shepherd, Women-Owned Businesses: Statistics and Overview. Fundera, 2020.

^[18] M. Shepherd, Women-Owned Businesses: Statistics and Overview. Fundera, 2020.

^[19] P. Prakash, Top Small-Business Loans for Minorities, 2020 (accessed July 25, 2020).

^[20] R. Ortiz-Walters and M. Gius, “Performance of Newly Formed Micro Firms: The Role of Capital Financing in Minority-Owned Enterprises,” Journal of Developmental Entrepreneurship 17(3), 2012.

^[21] Kauffmann Compilation: Research on Race and Entrepreneurship, Ewing Marion Kauffmann Foundation, 2016.

^[22] “A Higher Share of Black-Owned Businesses are Women-Owned than Non-Black Businesses,” USAFacts.org

^[23] A. Omeokwe and R. Tracy, “Treasury Department Encouraged Banks to Prioritize Existing Customers for PPP Loans, House Panel Says,” The Wall Street Journal, October 16, 2020.

^[24] An example of a PPP documentation checklist can be found here.

^[25] The Federal Reserve Bank of New York reports that among financial sound businesses, 54 percent White-owned businesses have existing bank relationships while only 33 percent of Black-owned businesses have existing banking relationships.

^[26] C.K. Mills, “Double Jeopardy: COVID-19’s Concentrated Health and Wealth Effects in Black Communities,” Federal Reserve Bank of New York, August 2020.