NBER Working Paper 29732 — The 2021 Paycheck Protection Program Reboot (Fairlie and Fossen, February 2022)

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NBER Working Paper No. 29732, dated February 2022, by Robert W. Fairlie and Frank Fossen, on loan disbursement to employer and nonemployer businesses in minority communities in the 2021 Paycheck Protection Program reboot. The abstract asks whether the $278 billion reboot of the PPP in early 2021, a program of $800 billion, was disbursed equitably to minority communities. It states that, using administrative microdata on the universe of PPP loans, the authors find a strong positive relationship between PPP flows and the minority share in the third round, and a negative relationship in the first round of 2020. The paper includes an appendix table of PPP loan amounts by round.

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NBER WORKING PAPER SERIES

THE 2021 PAYCHECK PROTECTION PROGRAM REBOOT:
LOAN DISBURSEMENT TO EMPLOYER AND
NONEMPLOYER BUSINESSES IN MINORITY COMMUNITIES

Robert W. Fairlie
Frank Fossen

Working Paper 29732
http://www.nber.org/papers/w29732

NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
February 2022

We would like to thank Karen Mills, Javier Miranda, and participants at the AEA Meetings,
NBER Entrepreneurship Workshop, the PPIC California labor market workshop, the Kauffman
Foundation Entrepreneurship Issue Forum, Axios/Google Small Business Matters Expert Panel,
and JPMorgan Chase Institute Data Dialogue meetings for comments and suggestions. The
research project has also benefited from numerous conversations with the press and
policymakers. An initial analysis of the third (2021) round of the PPP was prepared for the U.S.
Senate Committee on Small Business and Entrepreneurship. Special thanks go to Zach Mider,
Jason Grotto and Cedric Sam for discussions of the data and analysis. We also thank Gagandeep
Sachdeva for research assistance. The authors did not receive funding for this research project
and have no disclosures. The views expressed herein are those of the authors and do not
necessarily reflect the views of the National Bureau of Economic Research.

NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.

© 2022 by Robert W. Fairlie and Frank Fossen. All rights reserved. Short sections of text, not to
exceed two paragraphs, may be quoted without explicit permission provided that full credit,
including © notice, is given to the source.
The 2021 Paycheck Protection Program Reboot: Loan Disbursement to Employer and Nonemployer
Businesses in Minority Communities
Robert W. Fairlie and Frank Fossen
NBER Working Paper No. 29732
February 2022
JEL No. J15,L26

ABSTRACT

Was the $278 billion reboot of the $800 billion Paycheck Protection Program (PPP) in early 2021
disbursed equitably to minority communities? This paper provides the first analysis of how PPP
funds were disbursed to minority communities in the third and final round of the program, which
was specifically targeted to underserved and disadvantaged communities. Using administrative
microdata on the universe of PPP loans, we find a strong positive relationship between PPP
flows, as measured by the number of loans per employer business or loan amounts per employee,
and the minority share of the population or businesses in the third round. In contrast, the
relationship was negative in the first round of 2020 and less positive in the second round of 2020.
We find a stronger positive relationship between minority share and loan numbers or amounts to
employer businesses for first draw loans than second draw loans in 2021 (capturing some
persistence in inequities). The patterns are similar for loan numbers and amounts to nonemployer
businesses but with a strong positive relationship with minority share for both first draw and
second draw loans. The rebooted PPP that ran from January to May 2021 appears to have been
disbursed to minority communities as intended.

Robert W. Fairlie
Department of Economics
Engineering 2 Building
University of California at Santa Cruz
Santa Cruz, CA 95064
and NBER
rfairlie@ucsc.edu

Frank Fossen
1664 N. Virginia Street
Department of Economics
College of Business
Reno, NV 89557-0030
United States
ffossen@unr.edu
1. Introduction

The economic impacts of the pandemic have been especially severe for small businesses, workers,

and communities of color (Couch et al. 2020; Cowan 2020; Fairlie 2020; Montenovo et al. 2020).

Anticipating these potential losses, one of the stated goals in the $2.2 trillion CARES Act passed

into law on March 27, 2020 was to prioritize serving “underserved markets” and businesses owned

by “socially and economically disadvantaged individuals” (U.S. Congress 2020). In the beginning

of the pandemic, however, minority businesses and communities were generally delayed in

obtaining financial assistance through a key component of the CARES Act, the Paycheck

Protection Program (PPP) (Grotto, et al. 2020). The initial Paycheck Protection Program (PPP)

was designed to provide loans to small businesses to keep them afloat and retain their employees.

Loan amounts were intended to equal 2.5 months of average payroll costs, and could be forgiven

if the business retained its employees on the payroll. The first round of the PPP provided $342

billion through 1.7 million loans with disproportionately less going to minority communities

(Grotto et al. 2020; Fairlie and Fossen 2021).

As the pandemic continued and the negative economic impacts became increasingly clear,

Congress appropriated additional funds and the PPP started up again on April 27, 2020. In this

second round of PPP funding, as shown in Table 1, fintech lenders such as Cross River Bank,

Customers Bank, BSD Capital, Kabbage, Itria Ventures, Celtic Bank and WebBank were more

involved in making loans, and disbursement to minority businesses and communities improved

(Grotto et al. 2020; Fei and Yang, 2021). 1 The second round which ran until August 8, 2020

provided $189 billion through 2.6 million loans to small businesses.

1
See Fairlie and Fossen (2021) for an analysis of disbursement of Economic Injury Disaster Loan Program (EIDL)
funds to minority communities. The EIDL program, which is also administered by the SBA, is designed to provide
either loans or advances to small businesses that are losing revenues and sales due to COVID-19. Nearly 3.6 EIDL

1
A few months after the PPP ended in summer 2020, COVID cases began to rise rapidly

and social-distancing restrictions returned. New COVID cases rose from 1.2 million in September

2020 to 6.4 million in December 2020 and 6.1 million in January 2021 (Centers for Disease Control

and Prevention 2021). Given these concerns the PPP restarted in January 2021 with a strong

emphasis on helping eligible borrowers in underserved and disadvantaged communities. To

promote access for smaller lenders and their customers, the SBA restricted the program which

included a head start for applications through Community Development Financial Institutions

(CDFI) before more broadly opening up the program, and a two-week exclusivity application

period for businesses with fewer than 20 employees in February 2021. Part of the funds were set

aside for low- or moderate-income businesses, and some exclusionary restrictions were eliminated

(U.S. Small Business Administration 2021). Another major change was that the PPP opened up to

allow small businesses that continued to struggle in the pandemic to obtain a second loan. During

this third round of the PPP from January to May 2021, a total of $278 billion through 6.7 million

loans was provided of which $209 billion and 2.9 million loans were second draw loans. At this

stage of the pandemic, PPP loans were focused more on sole proprietors, self-employed

individuals, and independent contractors without employees than employer businesses with an

emphasis on helping to preserve the owner’s job.

Did PPP funds in the rebooted program in 2021 get disproportionately disbursed to

minority communities as intended or did the program struggle with equitable loan disbursement?

How did the disbursement of second draw loans compare to first loan draws? Demand for second

draw loans is likely to be higher in disadvantaged communities as small business continued to

struggle, and major structural changes to the program are likely to have increased the supply of

loans for $200 billion and nearly 5.8 million EIDL advances for $20 billion were provided to small businesses in
2020.

2
loans to those same communities, but second draw loan receipt is conditional on receiving a loan

in one of the first two rounds. Did disbursement to minority communities differ between loans to

small businesses with employees and loans to businesses without employees (nonemployers),

which were increasingly targeted in 2021? Equitable disbursement of employer business loans has

implications for community impacts on not only owners but also jobs and broader economic

activity, while equitable loans to nonemployer businesses also has important implications for the

owner. Finally, how does first loan disbursement across minority communities in the third round

compare with disbursement in the earlier first loan draw rounds in the pandemic? This paper

provides the first exploration of these important unanswered questions. We use administrative

microdata covering the universe of PPP loans to small businesses. We also use newly available

information on first vs second draw loans and newly available information on exact loan amounts

for larger loans.

Our analysis reveals a strong positive relationship between PPP flows, as measured by the

number of loans per employer business or loan amounts per employee, and the minority share of

the population or businesses in the third round. We find a stronger positive relationship between

minority share and loan numbers or amounts to employer businesses for first draw loans than

second draw loans in 2021 (capturing some persistence in inequities). The patterns are similar for

loan numbers and amounts to nonemployer businesses but with a strong positive relationship with

minority share for both first draw and second draw loans. In contrast to these findings, we find a

negative relationship in the first round of 2020 and a less positive relationship in the second round

of 2020 for both employer and nonemployer businesses. The rebooted PPP that ran from January

to May 2021 and provided $278 billion in funds to small businesses appears to have been disbursed

to minority communities as intended.

3
2. Data

We use loan-level PPP microdata provided by the U.S. Small Business Administration (SBA). The

PPP data cover the universe of loans provided through the program, which was from April 3, 2020

to June 30, 2021. Over the entire program, 11.8 million loans were approved for a total of $800

billion.

The loan microdata include information on the amount of the loan. In the first release of

data for larger loans, only ranges were reported ($150,000-350,000, $350,000-1 million, $1-2

million, $2-5 million, and $5-10 million). But, this restriction was later lifted and exact loan

amounts are now available for all PPP loans. Exact addresses, including the zip code, and even the

name of the business is provided for all PPP loans. The data also include information on industry,

business type, jobs retained self-reported by the business, and name of the lender. The top lender

providing PPP loans was Prestamos, a nonprofit CDFI which was especially active in 2021 (see

Table 1).

Information on the race, ethnicity, gender and veteran status of the owner is incomplete.

Some versions of the application form include questions to optionally self-report demographic

information on the owners 2 while others 3 relied on banks to report the information. The result is

that 70 percent of loans provide neither race nor ethnicity information, and those that have

information are heavily correlated with zip code and owner characteristics indicating that

missingness is likely to be non-random. 4 Recently, a couple of alternative approaches have been

2
https://www.sba.gov/sites/default/files/2021-03/BorrowerApplication2483ARPrevisions%20%28final%203-18-
21%29-508.pdf.
3
https://www.sba.gov/sites/default/files/2020-07/PPP-Borrower-Application-Form-508.pdf.
4
Noting these concerns Atkins, Cook and Seamans (2021) use different comparison groups with and without
missing race information and find that Black-owned businesses received loans that were approximately 50 percent
lower than white-owned businesses after controlling for other factors. Darity and Garcia (2022) find evidence of
benefits from not reporting race by black-owned businesses on the PPP loan applications.

4
taken to identify the race of the business owner on the loan. Howell et al. (2021), for example,

predict a business owner’s race and ethnicity using information such as the owner’s name and

location. Owner names are obtained from business registrations in collaboration with a data

analytics firm. A random forest model trained by using the subset of PPP loans with owner’s race

information is used to improve prediction accuracy. They note that the assigned race should be

viewed as being highly correlated with self-reported race and that it contains important

socioeconomic content, consistent with previous research showing discrimination against job

applicants with “African American-sounding” names. Fei and Yang (2021) focus on PPP

recipients in the Food Services and Drinking Places sector for which they can find a Yelp listing.

They proxy for minority-owned businesses based on the food type from yelp.com. Chernenko and

Scharfstein (2021) merge race and ethnicity information for owners of restaurants in Florida from

voter registration data.

We distinguish between loans to employer businesses and loans to nonemployer

businesses. The distinction is relevant because the percent of PPP loans going to independent

contractors, self-employment individuals and sole proprietors with no employees increased

substantially in 2021. We classify all self-employed individuals and independent contractors as

nonemployer businesses as well as sole proprietorships that report only one person working in the

business. 5 All other businesses are classified as employer businesses. We exclude nonprofit

businesses (2.3 percent) and businesses with a nonclassifiable industry (0.7 percent) throughout

our analysis.

To normalize the number of PPP loans to employer businesses by zip code we calculate

loans per employer business. We use data from County Business Patterns (CBP) on business

5
A very small number of sole proprietorships report zero persons, we also classify these as nonemployer businesses.

5
establishments with employees. The data are provided by the U.S. Census Bureau at the zip code

level as well as other geographical levels. The CBP data on employer establishments do not include

counts of farms and nonprofits. We acquire farm data by zip code from the U.S. Department of

Agriculture’s National Agricultural Statistics Service (NASS).

To normalize loan amounts to employer businesses we calculate average loan amounts per

business employee in each zip code. CBP data also includes employment levels for employer

business establishments down to the zip code level. The normalization adjusts for loan amount

differences due to differences in employment size by location. However, no information on the

number of employees from agricultural businesses is available, so we exclude these businesses

from the analysis of loan amounts.

Data on the number of nonemployer businesses are unavailable at the zip code level, we

therefore analyze loans to nonemployer businesses at the county level. To normalize the number

of PPP loans and loan amounts to nonemployer businesses we use the 2018 Nonemployer Statistics

(NES) provided by the U.S. Census Bureau. A caveat with this normalization is that the available

Census data include all business entities that have annual receipts of at least $1,000, file IRS tax

forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations

(the Form 1120 series), and have no paid employees. These data include all consultants, side

businesses, hobby businesses, part-time contract or gig work, and other small-scale business

activity as long as it produces $1,000 or more in annual revenues which are generally ineligible

for PPP loans. Based on this very inclusive definition there are roughly 26.8 million nonemployer

business entities in the United States.

We compare these measures of loan receipt and loan amounts to data from the Census of

Population on the minority share of the population across communities. We measure minority

6
share of the population by zip code for employer businesses and by county for nonemployer

businesses. In addition to analyzing the relationship between PPP loan receipt per employer

business by the minority share of the population we examine the relationship by minority share of

employer businesses. We use data from the Annual Business Survey (ABS) on employer

businesses at the county level to calculate the minority share of employer businesses in each

location. We exclude PPP loans to agricultural businesses here due to a lack of data on the minority

status of farmers. Data from the ABS are not available at the zip code level.

Table 2 provides mean values for the main variables of interest, weighted by population in

the zip code or county. Across counties, the number of PPP loans to nonemployer businesses per

nonemployer business increased substantially from the first round in early 2020 to the third round

in 2021 (first draw loans). The weighted average loan amount to nonemployer businesses per

nonemployer business (unconditional on receiving a loan) also increased dramatically over this

period. In contrast, the number of PPP loans to employer businesses per employer business in zip

codes and the unconditional PPP loan amount to employer businesses per employee in zip codes

decreased from the first round to the third round. Consistent with second draw loans in Round 3

originating with businesses receiving loans in Rounds 1 or 2, loan numbers and amounts were

higher for employer businesses for second draw loans than first draw loans but lower for

nonemployer businesses. The minority share of the population across zip codes or counties has a

weighted mean of 0.389 and the minority share of employer businesses has a lower weighted mean

of 0.180 reflecting substantially lower business ownership rates among minorities.

The distribution of PPP loan amounts appears in Table 3, separately for employer and

nonemployer businesses. 6 For employer businesses loans in the first round in early 2020 were

6
Appendix Table A1 reports the combined distribution of all PPP loans.

7
generally substantially larger than in later rounds consistent with large banks providing loans to

their experienced small business clients in the first round (Table 1). The pattern across rounds is

different for nonemployer businesses. For nonemployer businesses, median loans were largest in

the third round in 2021 when the PPP was rebooted with a different emphasis.

3. Results

In this section we analyze how PPP loan receipt was distributed across minority communities. We

present results for employer businesses first and then turn to nonemployer businesses, in each case

analyzing both loan numbers and loan amounts per business in the zip code or county. We always

start with the most recent data for the third round in 2021, which has not been analyzed before,

and then compare with results for the first two rounds in 2020.

The following column charts show mean normalized numbers of loans or loan amounts in

zip codes or counties in bins of minority shares of 5 percentage points, weighted by the population

in the zip code or county. 7 The figures also include plotted weighted quadratic regression curves

to help show the relationship. The double arrow on each Y-axis indicates the range of half a

standard deviation from the median across zip codes or counties to provide a sense of relative scale

of the relationships.

Before discussing the results, two important points are noted. First, we do not report

confidence intervals (i.e. “whiskers”) because we use the universe of PPP loans and administrative

data based on the Census Business Register. Second, we focus on the raw relationship between

PPP loan receipt and minority share of the population without controlling for other factors because

we are trying to capture the influences of these neighborhood characteristics. For example, if

7
Unweighted charts show the same patterns and are available from the authors on request.

8
minority communities have higher poverty rates and that is correlated with receipt of PPP loans

then we want to include that in our measurement (not control for it). Even if the driver of loan

receipt is income it is reflected in race and that is what we are trying to capture.

3.1 Employer Businesses

We begin with PPP loan receipt per employer business by minority share of the population at the

zip code level. Figure 1 displays the relationship in the third round of the PPP in 2021 for the first

draw of a PPP loan (Panel A) and the second draw for businesses that had received a PPP loan

before (Panel B). Loan receipt per business can be interpreted as the percentage of employer

businesses receiving PPP loans in each zip code, but it is important to note that not all employer

businesses qualify for loans nor necessarily want loans, and thus we do not expect the measure to

ever reach 1. Both figures exhibit a positive relationship between loan receipt per employer

business and the minority share of the population across zip codes in the United States. 8 For first

draw loans, moving from the first quartile in minority share of the population (16 percent minority)

to the third quartile (59 percent minority), loan receipt increases from 0.055 to 0.088 PPP loans to

employer businesses per employer business in the zip code. Figure 2 shows that there was already

a positive, but weaker relationship in round 2 (Panel B). However, the first round shows the

opposite pattern (Panel A): There is a strong negative relationship between loan receipt and

minority population share. Moving from the first quartile to the third quartile in minority share is

associated with a decrease from 0.17 to 0.13 PPP loans per employer business.

8
In all regressions shown in figures in this paper as dashed lines, the coefficients of the linear and squared terms of
the minority share are jointly significantly different from zero at the 95% confidence level. This also holds when we
run unweighted regressions.

9
The disbursement of PPP funds across communities by minority share might differ when

measured by loan amounts instead of number of loans. Figure 3 display average loan amounts to

employer businesses per business employee by minority share in the population at the zip code

level in the third round (first and second draw). Loan amounts per employee can be interpreted as

the unconditional average PPP loan amount per employee in a zip code but not all employer

businesses qualify and not all employees qualify for loans, and thus we do not expect the measure

to reach the maximum amount per employee. We find an upward relationship of PPP loan amounts

per employee with minority share for first draw loans and a flat association for second draw loans.

For first draw loans, moving from the first to the third quartile in minority share is associated with

an increase from $349 to $775 in average loan amount per employee. Figure 4 shows that there

was already a positive, but less pronounced relationship in the second round, but a negative

relationship in the first round, similar to what we found for loan numbers.

The minority share of businesses in a community may be different from the minority share

of the population. Therefore, we turn to analyzing the relationship between PPP loans per employer

business and the minority share of businesses in counties. The relationship for the third PPP round

(first and second draw) is displayed in Figure 5 and for the first two rounds in Figure 6. We

combine counties with minority shares of businesses equal to or larger than 45% into one column

because the number of counties in 5% bins with higher minority shares of businesses drops below

5. Similar to our findings using the minority share of the population, we find positive relationships

between loan receipt and minority business share in the third round in 2021 (first and second draw)

as well as the second round in 2020, but a negative relationship in the first round 2020.

3.2 Nonemployer Businesses

10
The 2021 reboot of the PPP (third round) emphasized supporting sole proprietorships, self-

employed individuals and independent contractors with no employees. Therefore, we separately

analyze nonemployer businesses next (at the county level due to data availability). Figure 7 reveals

that the number of PPP loans to nonemployer businesses per nonemployer business in the county

has a strong positive relationship with minority share of the population, both for the first and

second draw. Different from what we find for employer businesses the positive relationship is also

very strong for second draw loans, which might reflect more continuing struggles to find work

among minority independent contractors and small sole proprietors in the pandemic in comparison

to minority-owned stores, restaurants and other employer businesses. The column for the 75-80%

bin of minority share of the population is an outlier. There are 20 counties in this bin, but the

weighted average is dominated by Hinds County, Mississippi, which is by far the most populous

county in this bin with 240,000 inhabitants. In general, bins in the right half of the county-level

charts only contain comparably small numbers of counties (minimum 5), so conclusions should be

drawn from the dashed weighted regression curves rather than from individual columns at the right

side of the chart.

Figure 8 shows that the association between nonemployer loans per nonemployer business

in the county was essentially flat in the second round, but clearly negative in the first round. This

pattern of results is similar to what we found for employer businesses. 9 The PPP evolved over time

not just for employer businesses but also for much smaller nonemployer businesses. The column

corresponding to the 80%-85% of the minority share is again an outlier: its 19 counties are

dominated by Honolulu, Hawaii, which is by far the most populous county in this bin.

9
Tables A1 and A2 in the Appendix combine employer and nonemployer businesses and present the total number of
loans to all businesses per business at the county level. The pattern of relationships with minority share in the
different rounds of the PPP is the same.

11
Finally, we analyze PPP loan amounts to nonemployer businesses per nonemployer

business in the county. In this case, we view the owner’s job as the sole worker for normalization,

and as noted above the use of all nonemployer business entities in the county is likely to be over

inclusive. The results appear in Figures 9 and 10. We find a similar pattern: The relationship

between loan amounts to nonemployer businesses with minority share is strongly positive in 2021

(first and second draw), weaker positive in the second round in 2020, and strongly negative in the

first round in 2020.

4. Discussion

As COVID-19 cases began to rise sharply in the fall of 2020, the PPP was rebooted, and rebooted

with an emphasis on helping eligible borrowers in underserved and disadvantaged communities.

The program ran from January 2021 to May 2021 handing out 6.7 million loans for a total of $278

billion. Analyzing administrative microdata on the universe of PPP loans for this third and final

round, we find substantial evidence that the program disbursed funds to minority communities in

2021. The relationship between loan receipt or amounts and minority share of the population is

generally strong and positive across all measures used. The various cuts of the data reveal

interesting and important findings. The positive relationship is stronger for first loan draws than

for second loan draws for employer businesses. For both first and second draw loans, demand for

PPP loans is likely to be higher in disadvantaged communities as small business continued to

struggle, and the major structural changes to the program are likely to have increased the supply

of loans to those same communities. But, eligibility for a second draw loan depended on receipt

of a loan in 2020 when relatively fewer businesses in communities with higher minority shares

12
were served. The lack of a strong positive relationship in second draw loans generates some

concerns over the persistence of inequities in the program from the early stages of the PPP rollout.

The shift in PPP loan provision from employer businesses to nonemployer businesses in

2021 and differences between the two types of businesses are important. Equitable disbursement

of employer business loans has implications for community impacts on not only owners but also

jobs and broader economic activity but equitable loans to nonemployer businesses also have

important implications for the owner. We find strong positive relationships between loan receipt

or amounts and minority share for first draw loans to both types of businesses, but concerning

second draw loans, there is a strong positive relationship only for nonemployer businesses.

Overall, our findings suggest that PPP funds in the third round in 2021 were distributed, as

intended, to minority owners of businesses of all sizes and their employees.

There is a clear evolution of PPP funding to minority communities across subsequent

rounds of the PPP from a negative relationship in the first round in 2020 to the strong positive

relationship in the rebooted program (i.e. third round) in 2021. Why did the slope of the

relationship change over time? Applying for PPP loans very early in the pandemic favored having

long established relationships with banks which minority businesses were less likely to have (Mills

et al. 2020). Minority-owned businesses also tend to be smaller than non-minority-owned

businesses (U.S. Census Bureau 2016; Fairlie and Robb 2008), and smaller businesses often took

longer to complete required paperwork due to a lack of resources and experience. 10 Importantly,

fintech lenders became increasingly involved over time, and these lenders often served minority

businesses (Liu and Parilla 2020, Fei and Yang 2021, Howell et al., 2021). For example, BSD

10
A large share of early funds flowed through smaller community banks which were often in rural areas because
these banks were nimbler at accessing the aid (Grotto et al. 2020). In the second round larger banks with more urban
and racially diverse customer bases caught up.

13
Capital and Itria Ventures were almost absent in the first round in 2020 but moved up to the top

10 lenders by number of PPP loans in 2021 (Table 1). In 2021, the SBA also explicitly promoted

involvement of Community Development Financial Institutions (CDFIs), for example by

providing a head start for applications through CDFIs at the beginning of the third round in January

2021. The non-profit CDFI Prestamos started at rank 4274 of lenders by PPP loans in the first

round in 2020, moved up to rank 325 in the second round and became the number one lender in

the third round and thereby also overall (Table 1). Prestamos especially targeted Latinx business

owners. Provisions such as the 14-day exclusivity application period for businesses with fewer

than 20 employees in February 2021 may also have helped to bring PPP loans to disadvantaged

small businesses. More research is needed to assess the relative importance of these potential

explanations for the change in the disbursement of PPP loans to minority communities over time.

It is also unclear how severe the short-term and long-term consequences of the delay in receiving

loans were for minority businesses and communities.

Our analysis of administrative data on PPP loans suggests a few additional areas for further

research. First, the patterns that we find for loan receipt by minority share of communities are

mostly consistent with recent studies that use proxies for business owner race and ethnicity such

as Yelp listed restaurants, and names and locations (e.g. Fei and Yang 2021; Howell et al. 2021).

More research linking data with information on the race and ethnicity of owners to loan data would

be useful. Second, the PPP loan microdata could add additional information such as which

application form was used, which would allow a cleaner separation of owner jobs from employees.

The SBA could also provide information on their distinctions by bank type used in their reports

which would be useful for analyzing the data more carefully by type (e.g. fintechs, traditional

banks, or CDFIs). Third, using linked longitudinal data it would be interesting to examine which

14
businesses and circumstances led to second draw loans in 2021. Many minority businesses might

have been reluctant to apply for PPP loans because of uncertainty over future revenues due to

entering the pandemic in a weakened position (Mills and Battisto 2020). More research is needed

on the dynamics of PPP receipt over the three rounds of funding.

15
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Small-Business Virus Relief,” Bloomberg.
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Equitable Access to SBA Relief,” https://www.sba.gov/funding-programs/loans/covid-19-relief-
options/paycheck-protection-program/ppp-lender-information.

17
Tables
Table 1: Main PPP Lenders

All Rounds Round 1 (2020) Round 2 (2020) Round 3, 1st Draw Round 3, 2nd Draw
Loans Loans Amt. Amt. Rank Loans Amt. Rank Loans Amt. Rank Loans Amt. Rank Loans Amt. Rank
Lender Name Number % Mil. $ % % % % % % % % %
Prestamos 495,468 4.37 76,957 1.04 1 0.00 0.00 4274 0.03 0.01 325 12.88 11.47 1 0.66 0.17 27
Cross River Bank 472,780 4.17 124,865 1.68 2 0.86 0.33 12 5.28 2.76 4 5.45 6.13 5 2.99 1.27 6
Capital Plus Financial 463,563 4.09 74,178 1.00 3 0.00 0.00 NA 0.00 0.00 NA 10.92 10.35 2 2.13 0.41 9
Bank of America 462,981 4.08 321,076 4.33 4 0.58 1.18 24 9.37 10.87 1 0.36 0.80 22 4.68 4.08 2
Harvest Small Bus. Fin. 432,778 3.82 85,318 1.15 5 0.01 0.03 2000 0.15 0.58 65 7.76 7.33 3 5.02 1.36 1
JPMorgan Chase Bank 414,413 3.65 392,196 5.29 6 1.66 3.87 6 7.04 8.70 2 0.95 1.83 11 4.32 5.35 3
Benworth Capital 331,258 2.92 45,607 0.61 7 0.00 0.00 NA 0.02 0.01 516 7.15 5.84 4 2.36 0.41 8
Customers Bank 284,436 2.51 69,500 0.94 8 0.07 0.12 219 2.00 1.17 9 3.56 3.24 9 3.06 1.19 5
Fountainhead SBF 272,561 2.40 41,183 0.56 9 0.00 0.01 2390 0.08 0.18 125 5.18 3.95 6 2.79 0.62 7
Wells Fargo Bank 266,601 2.35 127,974 1.73 10 0.06 0.03 251 5.44 5.08 3 0.82 1.10 13 1.98 1.34 10
BSD Capital 249,089 2.20 47,913 0.65 11 0.00 0.00 2536 0.10 0.09 96 4.27 4.54 7 3.12 0.86 4
Kabbage 178,128 1.57 32,498 0.44 12 0.00 0.00 NA 4.84 1.62 5 0.00 0.00 NA 0.66 0.13 28
Itria Ventures 176,747 1.56 48,850 0.66 13 0.00 0.00 NA 0.09 0.07 113 3.58 3.98 8 1.48 1.11 13
U.S. Bank 167,288 1.48 99,863 1.35 14 1.12 0.73 10 2.56 2.61 7 0.58 0.78 15 1.57 1.27 12
Celtic Bank 164,423 1.45 43,788 0.59 15 0.07 0.04 243 4.36 1.96 6 0.00 0.00 NA 0.71 0.33 25
TD Bank 128,541 1.13 109,735 1.48 16 1.62 1.74 7 1.75 1.35 10 0.25 0.43 29 1.30 1.56 14
WebBank 117,481 1.04 30,903 0.42 17 0.24 0.06 64 2.15 1.03 8 0.46 0.36 17 0.92 0.39 20
Truist Bank 111,747 0.99 147,686 1.99 18 1.89 2.75 3 1.42 1.81 11 0.24 0.41 30 0.96 1.54 19
Readycap Lending 110,794 0.98 48,910 0.66 19 2.22 0.85 1 0.00 0.01 2132 1.16 1.42 10 1.20 0.74 15
PNC Bank 109,859 0.97 156,177 2.11 20 1.27 2.73 9 1.42 1.96 12 0.37 0.63 21 1.07 1.79 16
Notes: The ranks refer to the number of loans made in each round. Only the 20 lenders providing the largest numbers of loans overall are shown. PPP loans to
nonprofit businesses and businesses with nonclassifiable industry are excluded.

18
Table 2: Descriptive Statistics at the Local Level
Round 1 (2020) Round 2 (2020) Round 3 (2021) Round 3 (2021),
1st Draw 2nd Draw
Mean N Mean N Mean N Mean N
PPP number of loans to employer businesses per employer
business in zip code 0.143 31952 0.279 31952 0.077 31952 0.193 31952
PPP unconditional average loan amount to employer
businesses per employee in zip code 2406 30356 1669 30356 257 30356 1659 30356
PPP number of loans to nonemployer businesses per
nonemployer business in county 0.0027 3137 0.0298 3137 0.1178 3137 0.0367 3137
PPP unconditional average loan amount to nonemployer
businesses per nonemployer business in county 39 3137 327 3137 1660 3137 506 3137
PPP number of loans to all businesses per business in county 0.043 3137 0.089 3137 0.100 3137 0.076 3137
Minority share of the population (zip code) 0.389 32670 0.389 32670 0.389 32670 0.389 32670
Minority share of the population (county) 0.389 3142 0.389 3142 0.389 3142 0.389 3142
Min. share of employer businesses (county) 0.180 1031 0.180 1031 0.180 1031 0.180 1031
Notes: Means are weighted by population. The PPP average loan amount to employer businesses per employee and the minority share of employer
businesses exclude agricultural businesses.

19
Table 3: Distribution of PPP Loan Amounts to Employer and Nonemployer Businesses

Round 1 (2020) Round 2 (2020) Round 3 (2021)
1st Draw 2nd Draw
PPP Loan Amounts to Employer Businesses:
Mean 200,511 70,263 33,289 102,133
Std. dev. 518,568 239,892 162,035 210,260
st
1 quartile 25,000 11,100 7,386 17,710
Median 61,200 23,008 16,036 37,813
rd
3 quartile 161,100 57,500 21,285 95,020
Number of loans 1,465,974 2,492,018 598,387 1,810,632
PPP Loan Amounts to Nonemployer Businesses:
Mean 14,400 11,194 14,186 13,736
Std. dev. 45,099 14,603 7,325 8,513
st
1 quartile 5,800 4,100 7,082 6,426
Median 10,500 8,723 17,708 14,747
rd
3 quartile 20,000 18,500 20,832 20,833
Number of loans 67,241 804,083 3,095,717 1,006,372
Notes: PPP loans to nonprofit businesses and businesses with nonclassifiable industry are excluded.

20
Figure 1: PPP Number of Loans to Employer Businesses per Employer Business in Zip Code by
Minority Share in 2021
Panel A: First Draw Panel B: Second Draw

Notes: Mean numbers of first and second draw PPP loans to employer businesses in 2021 (Round 3) per employer
business in zip codes by minority share of the population, weighted by population. The dashed lines are from quadratic
regressions at the county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.

Figure 2: PPP Number of Loans to Employer Businesses per Employer Business in Zip Code by
Minority Share in 2020
Panel A: First Round Panel B: Second Round

Notes: Mean numbers of PPP loans to employer businesses in 2020 (Rounds 1 and 2) per employer business in zip
codes by minority share of the population, weighted by population. The dashed lines are from quadratic regressions
at the county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.

21
Figure 3: PPP Loan Amounts to Employer Businesses per Employee in Zip Code by Minority
Share in 2021
Panel A: First Draw Panel B: Second Draw

Notes: Unconditional mean amounts of first and second draw PPP loans to employer businesses in 2021 (Round 3)
per employee in zip codes by minority share of the population, weighted by population. The dashed lines are from
quadratic regressions at the county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.
Excluding agricultural businesses.

Figure 4: PPP Loan Amounts to Employer Businesses per Employee in Zip Code by Minority
Share in 2020
Panel A: First Round Panel B: Second Round

Notes: Unconditional mean amounts of PPP loans to employer businesses in 2020 (Rounds 1 and 2) per employee in
zip codes by minority share of the population, weighted by population. The dashed lines are from quadratic regressions
at the county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation. Excluding
agricultural businesses.

22
Figure 5: PPP Number of Loans to Employer Businesses per Employer Business in County by
Minority Share of Businesses in 2021
Panel A: First Draw Panel B: Second Draw

Notes: Mean numbers of first and second draw PPP loans to employer businesses in 2021 (Round 3) per employer
business in counties by minority share of businesses, weighted by population. The dashed lines are from quadratic
regressions at the county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.

Figure 6: PPP Number of Loans to Employer Businesses per Employer Business in County by
Minority Share of Businesses in 2020
Panel A: First Round Panel B: Second Round

Notes: Mean numbers of PPP loans to employer businesses in 2020 (Rounds 1 and 2) per employer business in counties
by minority share of businesses, weighted by population. The dashed lines are from quadratic regressions at the county
level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.

23
Figure 7: PPP Number of Loans to Nonemployer Businesses per Nonemployer Business in
County by Minority Share in 2021
Panel A: First Draw Panel B: Second Draw

Notes: Mean numbers of first and second draw PPP loans to nonemployer businesses in 2021 (Round 3) per
nonemployer business (owner job) in counties by minority share of the population, weighted by population. The
dashed lines are from quadratic regressions at the county level. The double arrow on the Y-axis indicates the median
± ½ standard deviation. Excluding agricultural businesses.

Figure 8: PPP Number of Loans to Nonemployer Businesses per Nonemployer Business in
County by Minority Share in 2020
Panel A: First Round Panel B: Second Round

Notes: Mean numbers of PPP loans to nonemployer businesses in 2020 (Rounds 1 and 2) per nonemployer business
(owner job) in counties by minority share of the population, weighted by population. The dashed lines are from
quadratic regressions at the county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.

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Figure 9: PPP Loan Amounts to Nonemployer Businesses per Nonemployer Business in County
by Minority Share in 2021
Panel A: First Draw Panel B: Second Draw

Notes: Unconditional mean amounts of first and second draw PPP loans to nonemployer businesses in 2021 (Round
3) per nonemployer business (owner job) in counties by minority share of the population, weighted by population.
The dashed lines are from quadratic regressions at the county level. The double arrow on the Y-axis indicates the
median ± ½ standard deviation. Excluding agricultural businesses.

Figure 10: PPP Loan Amounts to Nonemployer Businesses per Nonemployer Business in County
by Minority Share in 2020
Panel A: First Round Panel B: Second Round

Notes: Unconditional mean amounts of PPP loans to nonemployer businesses in 2020 (Rounds 1 and 2) per
nonemployer business (owner job) in counties by minority share of the population, weighted by population. The
dashed lines are from quadratic regressions at the county level. The double arrow on the Y-axis indicates the median
± ½ standard deviation. Excluding agricultural businesses.

25
Appendix
Table A1: Distribution of PPP Loan Amounts to All Businesses
Round 1 (2020) Round 2 (2020) Round 3 (2021)
1st Draw 2nd Draw
Mean 192,349 55,853 17,280 70,553
Std. dev. 508,587 210,249 65,935 173,884
1st quartile 21,870 8,292 7,141 11,666
Median 56,600 19,300 17,500 20,833
3rd quartile 152,500 41,668 20,832 55,252
Number of loans 1,533,215 3,296,101 3,694,104 2,817,004
Notes: PPP loans to nonprofit businesses and businesses with nonclassifiable industry are excluded.

Figure A1: PPP Number of Loans to All Businesses per Business in County by Minority Share in
2021
Panel A: First Draw Panel B: Second Draw

Notes: Mean numbers of first and second draw PPP loans to all businesses in 2021 (Round 3) per business in counties
by minority share of the population, weighted by population. The dashed lines are from quadratic regressions at the
county level. The double arrow on the Y-axis indicates the median ± ½ standard deviation.

26
Figure A2: PPP Number of Loans to All Businesses per Business in County by Minority Share in
2020
Panel A: First Round Panel B: Second Round

Notes: Mean numbers of PPP loans to all businesses in 2020 (Rounds 1 and 2) per business in counties by minority
share of the population, weighted by population. The dashed lines are from quadratic regressions at the county level.
The double arrow on the Y-axis indicates the median ± ½ standard deviation.

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