Lender Automation and Racial Disparities in Credit Access (The Journal of Finance, April 2024)
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Journal article Lender Automation and Racial Disparities in Credit Access by Sabrina T. Howell, Theresa Kuchler, David Snitkof, Johannes Stroebel and Jun Wong, in The Journal of Finance, Vol. LXXIX, No. 2, April 2024. The abstract says process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. The authors use SBA data on 11.8 million PPP loans made between April 3, 2020, and May 31, 2021, and report that 26.5% of PPP loans from fintech lenders went to Black-owned businesses, against 3.3% at small banks. The authors also report that after traditional banks automated loan processing procedures, PPP lending to Black-owned firms increased.
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THE JOURNAL OF FINANCE • VOL. LXXIX, NO. 2 • APRIL 2024
Lender Automation and Racial Disparities
in Credit Access
SABRINA T. HOWELL, THERESA KUCHLER, DAVID SNITKOF,
JOHANNES STROEBEL, and JUN WONG*
ABSTRACT
Process automation reduces racial disparities in credit access by enabling smaller
loans, broadening banks’ geographic reach, and removing human biases from deci-
sion making. We document these findings in the context of the Paycheck Protection
Program (PPP), where private lenders faced no credit risk but decided which firms
to serve. Black-owned firms obtained PPP loans primarily from automated fintech
lenders, especially in areas with high racial animus. After traditional banks auto-
mated their loan processing procedures, their PPP lending to Black-owned firms in-
creased. Our findings cannot be fully explained by racial differences in loan applica-
tion behaviors, preexisting banking relationships, firm performance, or fraud rates.
RESEARCHERS AND POLICY MAKERS IN THE United States and elsewhere
have long been concerned about racial disparities in access to financial services
(Crutsinger (2021), Abrams (2021), Crowell (2021)). The recent emergence of
fintech lenders has raised important questions about how their technologies—
notably, algorithmic underwriting and process automation—might affect racial
* Sabrina T. Howell, Theresa Kuchler, and Johannes Stroebel are with NYU Stern and NBER.
David Snitkof is with Ocrolus. Jun Wong is with the University of Chicago. This paper subsumes
results in an earlier note from December 2020 titled “Which lenders had the highest minority
share among their Payment Protection Program (PPP) loans?” We are grateful to Georgij Alekseev,
Sara Gong, and Cangyuan Li for superb research assistance, and to Enigma, Middesk, Lendio,
Biz2Credit, and Ocrolus for sharing part of their data. In particular, we thank Scott Monaco at
Ocrolus for his insights and analytical work. We are also grateful for help from Sriya Anbil, Ro-
hit Arora, Venkatesh Bala, Brock Blake, Katherine Chandler, Christine Dobridge, Kyle Mack,
Karen Mills, David Musto, Hicham Oudghiri, Madeline Ross, Kurt Ruppel, and Sam Taussig. We
thank seminar and conference participants at the NBER, the AFA Meetings, Cornell, Chicago
Booth, Princeton, Maryland Smith, Arizona State University, the Government Accountability Of-
fice, Northeastern University, the Boston Fed, the Virtual Corporate Finance Seminar, NYU, the
NY State Department of Financial Services, and University of Georgia as well as Michael Faulk-
ender and the reviewing team at The Journal of Finance (Editor Amit Seru, as well as an associate
editor and multiple anonymous referees) for their helpful comments. Howell’s work on this project
is funded by the Kauffman Foundation. Snitkof is an employee and Howell an unpaid contrac-
tor at Ocrolus, which provided some data for the project. We have read The Journal of Finance’s
disclosure policy and have no further conflicts of interest to disclose.
Correspondence: Sabrina Howell, Stern School of Business, New York University, Kaufman
Management Center, 44 West Fourth Street, New York, NY 10012; e-mail: sabrina.howell@nyu.
edu.
DOI: 10.1111/jofi.13303
© 2023 the American Finance Association.
1457
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1458 The Journal of Finance®
disparities in lending. Much of this literature focuses on the effects of algo-
rithmic underwriting practices on racial disparities in credit access (Blattner,
Nelson, and Spiess (2021), Bartlett et al. (2022), Fuster et al. (2022)). In this
paper, we study the role of process automation and show that automating pro-
cesses such as income and payroll verification can substantially reduce racial
disparities in small business lending.
We explore three possible channels for this finding. First, since automated
lenders have lower fixed costs per loan, they can serve smaller businesses,
which are more likely to be minority owned. Second, automation often goes
hand-in-hand with online loan origination procedures, which allows automated
lenders to more easily serve customers in regions with higher minority shares
that are traditionally underserved by the branch networks of less automated
lenders. Third, automation reduces human influence on decisions such as the
order in which to process loans when facing capacity constraints, and thus
can mitigate racial discrimination—whether of the taste based or statistical
variety—in lending. Using an array of novel data sets, we show that automa-
tion appears to reduce racial disparities in small business lending through all
three channels, including through an economically important reduction in dis-
crimination.
One challenge to understanding the determinants of racial disparities in
credit markets is disentangling the independent role of race from other factors,
such as differences in credit risk, that might be correlated with race while also
directly influencing credit outcomes. To overcome these challenges, we study
small business lending in a setting with no role for confounding factors such
as credit risk and selection on contract terms. Our setting is the Paycheck Pro-
tection Program (PPP), which was established by the Coronavirus Aid, Relief,
and Economic Security (CARES) Act in March 2020 to help small businesses
struggling during the COVID-19 pandemic. With more than $800 billion in
loans, it is one of the largest public finance programs in U.S. history.
Three features make the PPP a promising setting to study racial disparities
in small business lending. First, PPP loan size was set as a fixed percentage
of payroll, and there was no variation in other contract terms. Second, PPP
loan distribution occurred via private lenders, which were compensated with
a fixed share of the loan amount. Most lenders originated PPP loans using
processes designed for preexisting small business lending volume. When con-
fronted with 100 or 1,000 times the normal application volumes, lenders faced
severe capacity constraints, forcing them to prioritize among applicants (Morel
et al. (2021), Zhou (2020), Flitter and Cowley (2020)). The Small Business Ad-
ministration (SBA) did not issue specific guidance on loan distribution, leaving
private lenders to independently determine which businesses to serve given
the fee structure and other factors such as lender cost structure or processing
capacity. Third, PPP loans were 100% guaranteed by the federal government,
so the originating lenders had no financial exposure to the borrower’s perfor-
mance. Consequently, any observed racial disparities in lending should not
reflect differences in expected interest revenues or loan losses. Studying a set-
ting with no credit risk also allows us to isolate the role of process automation
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Lender Automation and Racial Disparities in Credit Access 1459
(e.g., automating application intake and payroll verification) from the role of al-
gorithmic underwriting (e.g., predicting default risk using machine learning),
which has been a focus of much of the related research in financial economics.
We work with public administrative data from the SBA on 11.8 million PPP
loans made between April 3, 2020, and May 31, 2021. We restrict the sam-
ple to “first draw” loans made before February 24, 2021, when program rules
were changed to explicitly prioritize lending to small firms and minority-owned
businesses. In a first step, we build on a well-established literature to predict
the race or ethnicity of the owners of PPP-funded businesses based on the own-
ers’ names and locations (Imai and Khanna (2016), Humphries et al. (2019),
Tzioumis (2018)). After collecting owner names from business registrations in
collaboration with the data analytics firm Middesk, we assign race and eth-
nicity to business owners for 4.2 million PPP loans. Predicted race based on
signals provided by name and location is particularly relevant in our context,
since loan officers typically observe these characteristics but do not know bor-
rowers’ actual race. Our results are robust to using only the subsample of PPP
borrowers with information on self-identified race.
To establish the effect of automation on racial disparities in small busi-
ness lending, we use two approaches. First, we study the rates of lending to
minority-owned businesses across lender types with different degrees of au-
tomation, where fintech firms have the most automated lending systems and
small banks have the least automated systems. (We use data on branch-level
software spending to show that automation generally increases in bank size.)
Second, we analyze within-lender changes in PPP lending to minority-owned
businesses in a subset of traditional lenders who automated their loan origina-
tion processes during the PPP period. Both sets of analyses show that lenders
with more-automated loan processing systems were more likely to extend PPP
loans to minority-owned firms. Our evidence suggests that this result is due
in part to lenders with more automated systems making smaller loans and
loans in higher minority areas, and in part to automated loan processing re-
ducing the scope for human biases to lead to discrimination in lending. Data
from a subset of PPP loan applications provide additional evidence that racial
differences in loan approval rates at conventional lenders (but not at fintechs)
contribute to the observed cross-lender patterns.
We first document variation across types of financial institutions in the un-
conditional propensity to extend PPP loans to businesses owned by different
races. Among fintech lenders with the most automated lending systems, 26.5%
of their PPP loans were issued to Black-owned businesses. Among traditional
banks, PPP loan shares to Black-owned businesses were increasing with bank
size, ranging from 3.3% at small banks to 6.2% at the four largest banks (Wells
Fargo, Bank of America, JPMorgan Chase, and Citibank). We also show that
bank branches with more automation, as measured by software spending, lend
more to Black-owned firms. Overall, fintech lenders were responsible for 53.6%
of PPP loans to Black-owned businesses, while accounting for only 17.4% of
all PPP loans. There are also some smaller differences across lenders in the
propensity to lend to White-, Asian-, and Hispanic-owned firms. However, the
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1460 The Journal of Finance®
differences in lending to Black-owned businesses across lender types are much
more dramatic, and thus we focus here on understanding this disparity.
To directly test whether automating the loan origination process increases
PPP lending to Black-owned businesses, we analyze lending at a number
of smaller banks that automated during the PPP period by outsourcing the
back-end processing of their PPP loan applications to a third-party software
provider. In an event study differences-in-differences analysis, we find that
relative to other comparable banks that did not automate their processes, au-
tomation increased the automating banks’ shares of PPP loans to Black-owned
businesses by 6 percentage points, relative to a pre-automation share of 4.4%.
We next explore why automation might increase lending to Black-owned
businesses. We first focus on automated lenders’ ability to make smaller loans
and originate loans without relying on a branch network. The compensation
structure for originating PPP loans included payments to banks that were in-
creasing in loan size (and thus mechanically also increasing in firm size). The
lower cost structure and higher processing capacity of automated lenders could
thus allow them to originate smaller PPP loans, which were disproportion-
ately granted to Black-owned businesses. Second, traditional lenders might be
more likely to serve borrowers in areas where the lenders have a physical pres-
ence, leaving fintech lenders to disproportionately serve firms in underbanked
areas—firms that are more likely to be Black owned.
We assess the role of these factors by including tight controls for loan size
and firm location (as well as other firm characteristics such as industry, em-
ployer status, and business form). Before controlling for firm characteristics,
Black-owned firms are 40 percentage points more likely to obtain a fintech PPP
loan than firms owned by individuals of another race, compared to a baseline
probability across all firms of getting a fintech loan of 17.4%. Including controls
for firm characteristics reduces this difference to 12.1 percentage points. Sim-
ilarly, Black-owned firms are unconditionally 31 percentage points less likely
to obtain their PPP loan from a small bank (relative to a baseline probabil-
ity of getting a PPP loan from such a lender of 49.8%), a gap that falls to
8.2 percentage points after controlling for firm characteristics. In addition to
this cross-lender analysis, we show that firm characteristics also explain part
of the causal within-bank effect of automation. However, even with granular
controls for loan and firm characteristics, the effect of automating on making
a PPP loan to a Black-owned firm is 4.3 percentage points, which represents
nearly a doubling of the pre-automation Black-owned share among the rele-
vant lenders.
Overall, our findings indicate that a substantial part of the unconditional
racial disparities in PPP lender identity is driven by more automated lenders,
and in particular fintech lenders, making smaller PPP loans and serving bor-
rowers in geographic areas underserved by traditional lenders. However, even
after conditioning on firm characteristics and focusing on an environment like
the PPP with no credit risk, lending rates to Black-owned firms are substan-
tially higher at more automated lenders, and increase within lender after au-
tomating the loan origination process. These findings raise the possibility that
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Lender Automation and Racial Disparities in Credit Access 1461
automation also reduces racial disparities in small business lending by miti-
gating racial discrimination. In credit markets, such discrimination can gener-
ally take two forms. Statistical discrimination describes decision makers using
race as a proxy for unobserved credit risk. Preference-based discrimination
arises when decision makers systematically dislike (and thus disadvantage)
members of a certain race. While both forms of discrimination are illegal in
the United States, preference-based discrimination has particular ethical and
regulatory implications. The PPP setting offers a unique setting in which pri-
vate lenders face no credit risk, implying no reason for statistical discrimina-
tion in either human or algorithmic lending decisions. If evidence for racial
discrimination can be found, it is thus most likely preference based.
To explore this possibility, we test whether the conditional disparity in lend-
ing to Black-owned businesses across lenders with different levels of automa-
tion is larger in areas with more racial animus. Using six measures of anti-
Black racial animus, including racially biased Google searches, implicit and
explicit bias tests, and measures of local housing segregation, we find that
the tendency of Black-owned businesses to borrow from fintech lenders is con-
sistently higher in areas with more racial animus, even after controlling for
firm and loan characteristics. (We rule out the possibility that racial animus
in these specifications is simply proxying for other relevant local characteris-
tics.) Similarly, in areas with high racial animus, Black-owned businesses are
particularly unlikely to obtain their PPP loans from the smallest traditional
lenders. Furthermore, we find that the positive effect of bank automation on
the lending rates to Black-owned businesses is larger in locations with higher
racial animus. These findings support the view that automation can reduce
taste-based racial discrimination in the loan origination process.
Our results so far highlight that process automation is associated with
higher rates of lending to Black-owned businesses. This is true both within
lenders who automate their loan origination process over time, as well as
across lender types, where the most automated fintech lenders make the
largest share of loans to Black-owned businesses, while the least automated
small lenders make the smallest. This in part reflects automated lenders be-
ing able to profitably make smaller loans that are particularly common for
minority-owned businesses, and in part reflects automation reducing the po-
tential for discrimination by humans faced with decisions such as which loan
to prioritize in the face of capacity constraints.
However, while fintech firms, large banks, and small banks differ in the ex-
tent to which they have automated their loan origination processes, and hence
in the extent to which human biases affect prioritization and approval deci-
sions, they also differ on other characteristics that likely contribute to the
observed cross-lender heterogeneity in lending to Black-owned firms. Thus,
in the remainder of the paper, we explore whether the cross-lender patterns
can be fully explained by such differences, including racial differences in
firms’ PPP application behaviors or preexisting banking relationships. (Im-
portantly, none of those differences could plausibly confound our most cleanly
identified within-bank analysis of the causal effects of bank automation on
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1462 The Journal of Finance®
lending to Black-owned firms). While we cannot confidently determine the de-
gree to which these other factors contribute to the overall cross-bank condi-
tional racial disparities in PPP lending to Black-owned firms, we can conclude
that racial disparities in approvals of otherwise similar completed PPP ap-
plications at banks (but not at fintechs) contribute meaningfully to the ob-
served patterns.
A first possible alternative explanation for the observed cross-lender pat-
terns is that Black-owned firms are more likely to borrow from fintech lenders
not because they are more likely to be rejected by conventional lenders, but be-
cause they are more likely to apply to fintech lenders. Barkley and Schweitzer
(2020, 2022) document such racial differences in small business loan applica-
tion behaviors using data from the Federal Reserve’s Small Business Credit
Survey. Since the public SBA data only contain information on granted PPP
loans but not applications, we assess this possibility using data on PPP loan ap-
plications from the marketplace lending platform Lendio. These data contain
information on approximately 280,000 completed PPP loan applications that
Lendio routed quasi-randomly to both conventional banks and fintech lenders,
with applicants having no control over where their application was routed.
Among applications routed to fintech lenders, we observe no racial dispar-
ities in the likelihood of getting a PPP loan from that lender. In contrast,
among PPP applications routed to conventional lenders, Black-owned firms
were 3.9 percentage points (12.3%) less likely to obtain a PPP loan from that
lender. Furthermore, Black-owned firms were 5.8 percentage points (15.9%)
more likely to get no PPP loan at all—through Lendio or otherwise—when
their application was routed to a conventional lender. These racial disparities
are even stronger when the application was routed to a small bank. Therefore,
the least automated lenders appear most likely to reject PPP loan applications
from Black-owned firms, relative to otherwise similar firms with firm owners of
other races. This finding shows that lower rates of originating PPP loan appli-
cations from Black-owned firms at banks (but not at fintechs) contributes to the
observed cross-lender disparities in lending to Black-owned firms. This finding
also highlights important real effects of automation: Automation impacts not
only the identity of the final PPP lender, but also the ability of Black-owned
firms to obtain any PPP loan at all.
Next, we explore whether the across-bank findings largely reflect conven-
tional lenders preferentially serving their own clients. Such a mechanism could
help explain the racial disparity in PPP lending if Black-owned businesses did
not bank with active PPP lenders. We test this hypothesis using bank state-
ment data from Ocrolus, a firm that digitizes and analyzes financial documents
for financial institutions. These bank statements include information on bank
and credit relationships as well as cash flows. Within the matched sample of
about 170,000 PPP borrowers, which selects on having a checking account and
a prior fintech loan application, Black ownership is associated with a 5.5 per-
centage point higher probability of obtaining a PPP loan through a fintech
lender, conditional on controls. Although we show that banks did preferentially
serve their own customers, this fact is orthogonal to the observed racial dispar-
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Lender Automation and Racial Disparities in Credit Access 1463
ity, in large part because there were no large racial differences in the patterns
of credit or banking relationships, at least within this sample. Instead, the
racial disparity across lenders is driven by the 72.6% of firms that obtained
PPP loans from banks other than their checking account bank and therefore
had to establish a new banking relationship. Among these firms, Black-owned
businesses were much more likely to obtain their PPP loan from a nonrelation-
ship fintech lender, and much less likely to obtain it from a nonrelationship
small bank.
Finally, we examine whether differences in firm performance or fraud can
explain our results. First, while there was no credit risk from originating the
federally guaranteed PPP loans, conventional lenders may still have priori-
tized firms that appeared to be more profitable future customers. We find,
however, that the observed racial disparities are unaffected by controls for
monthly credit and debit card revenues or bank statement cash flows. This
result suggests that conditional on our baseline controls, there was no sub-
stantial differential performance of Black-owned firms that correlates with
the identity of their PPP lender. Second, we assess whether higher rates of
fraudulent PPP applications from Black-owned businesses combined with sys-
tematically tighter compliance standards at small banks in particular could
explain the across-lender patterns. We find no evidence that differential fraud
rates drive the results.
Several contemporaneous papers offer results consistent with ours. Erel and
Liebersohn (2020) find that fintech lenders made more PPP loans in areas with
higher minority population shares. Fairlie and Fossen (2021) also find that to-
tal PPP loan flows to an area were negatively correlated with the minority
share of the population. Relative to these papers, we show that even within a
given geography, fintech lenders disproportionately lent to Black-owned firms,
so bank branch location cannot fully explain the observed patterns. In work
complementary to ours, Chernenko and Scharfstein (2022) use rich data on
restaurants to study PPP take-up. They show that minority-owned businesses
are less likely to get a PPP loan because of the lower take-up of PPP loans from
banks, which is only partly offset by greater take-up of PPP loans from fintechs.
Our analysis establishes the degree of automation in the lending process as a
key factor explaining variation in PPP lending to minority-owned firms across
banks and over time, in part by reducing racial biases. We also rule out differ-
ential application behaviors and other factors as alternative explanations.1
Our work contributes to an extensive literature studying bias against Black
people across a wide variety of settings (Arnold, Dobbie, and Yang (2018),
Bertrand and Mullainathan (2004), Knowles, Persico, and Todd (2001), Anwar
1 Other researchers have examined whether firm size or preexisting banking relationships can
explain access to PPP loans (Humphries, Neilson, and Ulyssea (2020), Li and Strahan (2020)).
We also contribute to a literature exploring how the COVID-19 pandemic and associated policy
responses affected small businesses (Alekseev et al. (2020), Bartik et al. (2020), Bartik et al. (2020),
Fairlie (2020), Kim, Parker, and Schoar (2020), Hubbard and Strain (2020), Faulkender, Jackman,
and Miran (2020), Granja et al. (2020), Autor et al. (2020), Bartlett and Morse (2020)).
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1464 The Journal of Finance®
and Fang (2006), Charles and Guryan (2008), Price and Wolfers (2010)), includ-
ing racial disparities in access to financial services (see, for example, Tootell
(1996), Bayer, Ferreira, and Ross (2018), Bhutta and Hizmo (2021), Ambrose,
Conklin, and Lopez (2020), Giacoletti, Heimer, and Yu (2021), Begley and
Purnanandam (2021), Blattner and Nelson (2021)). Most directly relevant
is work on the role of race in small business lending (Blanchflower, Levine,
and Zimmerman (2003), Robb and Robinson (2018), Fairlie and Robb (2007),
Asiedu, Freeman, and Nti-Addae (2012), Bellucci, Borisov, and Zazzaro (2013),
Fairlie, Robb, and Robinson (2020)).
More broadly, our findings contribute to the current debate about fintech
lenders’ role in the financial system (Seru (2019), Philippon (2019), Federal Re-
serve (2020), Ranson (2020), Gopal and Schnabl (2020), Ben-David, Johnson,
and Stulz (2021)). Most closely related to this paper is a literature that explores
the role of fintech lenders in extending credit to traditionally underserved mi-
norities (Buchak et al. (2018), Tang (2019), Fuster et al. (2019), Balyuk, Berger,
and Hackney (2020), Berg et al. (2020), D’Acunto et al. (2022), Bartlett et al.
(2022), Atkins, Cook, and Seamans (2022)). We contribute by focusing on the
role of automation, which enables lenders to profitably make smaller loans and
largely eliminates the role of human bias. Through this channel, automation
at fintech lenders and traditional banks can help reduce racial disparities in
credit outcomes.
I. The PPP: Setting and Data
The PPP was established as part of the CARES Act, passed on March 27,
2020. The PPP provided federally guaranteed loans to firms that certified
their businesses were “substantially affected by COVID-19.” To facilitate the
speedy disbursal of PPP funds, the federal government outsourced the origi-
nation of PPP loans to private lenders. While the SBA approved lenders and
individual loans, this primarily involved a duplication check to avoid granting
multiple loans to a single entity. Although Section 1102 of the CARES Act
specifies that the program should prioritize “small business concerns owned
and controlled by socially and economically disadvantaged individuals,” this
was a nonbinding “Sense of the Senate” portion of the legislation. In practice,
it was largely left up to the private lenders to determine which PPP applica-
tions to prioritize, and media reports early in the PPP raised concerns that
banks facing capacity constraints were turning away large numbers of PPP
applications from minority-owned businesses (Simon and Rudegeair (2020),
Zhou (2020), Beer (2020)).
The initial CARES Act authorized $349 billion in loan guarantees for the
PPP, and issuance began on April 3, 2020. Demand for PPP loans vastly ex-
ceeded expectations, and funding for the initial program ran out on April 16,
2020. Congress approved a second PPP tranche of $310 billion on April 24,
2020, and its distribution began on April 27, 2020. A third tranche of $284.5
billion was approved on December 27, 2020. In this round, firms were eligi-
ble to receive a “second draw” loan if they met certain conditions. By the time
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Lender Automation and Racial Disparities in Credit Access 1465
the program closed permanently at the end of May 2021, 11.8 million loans,
administered by 5,310 lenders and totaling $799 billion, had been approved.
PPP Terms: PPP loans were government-guaranteed and uncollateralized.
The loan amount was fixed at 2.5 times a firm’s monthly pre-COVID payroll.
A PPP loan was forgivable—turning into a grant—if the business used it for
eligible expenses within six months of receiving it; 60% of the amount had to
be spent on payroll, and the rest could be spent on items such as rent, utilities,
and mortgage interest. As of January 9, 2022, 81% of loans and 85% of loan
value had already been forgiven. In the event that a loan was not forgiven, re-
payment would begin six months after the loan had to be used plus a 10-month
grace period. At that point, loan maturity was two years, and the interest rate
was set at 1%. The SBA compensated lenders for originating and servicing PPP
loans according to the following upfront fee schedule:
r 5% of the loan amount for loans of not more than $350,000;
r 3% of the loan amount for loans of more than $350,000 and less than
$2,000,000; and
r 1% of the loan amount for loans of at least $2,000,000.
As a result of their preexisting loan infrastructures, conventional lenders
were widely reported to face capacity constraints in processing the large vol-
ume of PPP applications (e.g., Buchanan (2020)). Lenders participated volun-
tarily in the PPP, and they entered and left the program over time. Fintechs
tended to enter somewhat later for several reasons. Some required special
approval because they were not regulated insured depository institutions or
preapproved SBA lenders. Others did not have large enough balance sheets to
originate many PPP loans and needed to wait for the Federal Reserve’s PPP
Liquidity Facility to come online, which occurred several weeks into the pro-
gram. This facility enabled banks, and later fintechs, to post PPP loans as
collateral for new funds to originate loans. Fintechs also participated by part-
nering with originating charter banks, such as Celtic. In our analysis below,
we control for the week of PPP loan approval to ensure that our results are not
affected by these time-series patterns of lender participation.
Lender Obligations and Risks: In originating PPP loans and processing for-
giveness applications, lenders faced lower compliance burdens than when mak-
ing conventional loans.2 This reflected the high priority that Congress and the
Executive branch placed on getting funds out quickly. Specifically, the program
required lenders to accomplish only the following tasks: “Each lender shall:
(1) Confirm receipt of borrower certifications contained in Paycheck Protec-
tion Program Application form issued by the Administration;
2 The CARES Act explicitly held lenders “harmless” from any enforcement action related to loan
forgiveness: “The lender does not need to independently verify the borrower’s reported information
if the borrower submits documentation supporting its request for loan forgiveness and attests that
it accurately verified the payments for eligible costs” (86 FR 8283).
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1466 The Journal of Finance®
(2) Confirm receipt of information demonstrating that a borrower had em-
ployees for whom the borrower paid salaries and payroll taxes on or
around February 15, 2020;
(3) Confirm the dollar amount of average monthly payroll costs for the pre-
ceding calendar year by reviewing the payroll documentation submitted
with the borrower’s application;
(4) Follow applicable BSA requirements (85 FR 20811 III.3.b).”
Here, “BSA” refers to the Bank Secrecy Act, which requires baseline anti-
money laundering and know-your-customer measures. Although there was
some uncertainty about the precise policy early in the program—which is one
reason we ensure the results are robust to both excluding or restricting atten-
tion to the first few weeks of PPP loan approvals—legal experts indicated that
lenders faced minimal enforcement risk.3 This benefited smaller banks, which
typically have less robust and less automated compliance infrastructure4
(Duren (2020)).
Since minimal lender risk in the PPP context is important for our conclu-
sions, we summarize the lender’s risks via a series of questions and answers.
First, what happens if the borrower does not use the loan as intended? The
loan is not forgiven, and the borrower enters a repayment plan. Second, what
happens if the borrower defaults? The loan is 100% government-guaranteed,
so the lender recoups the loan amount. Third, what happens if the borrower is
found ex post to have committed fraud? The lender’s fee is subject to potential
clawback, but “SBA’s determination of borrower eligibility will have no effect
on SBA’s guaranty of the loan” (85 FR 33010 3). In sum, unlike for other credit
decisions, lenders faced de minimis risk in PPP lending.
A. PPP Data
We obtain information on all PPP loans as of August 15, 2021, directly from
the SBA. These data were released following a court order and include the
business name and address for all PPP loan recipients, as well as information
about the business type, loan size, self-reported number of jobs saved, the loan
originator, and the loan servicer. Hereafter, we refer to the loan originator as
the “PPP lender.” To construct our data set, we retain only a firm’s first loan,
so that each firm appears once. Specifically, we begin with a raw data set from
the SBA, which has 11.8 million loan observations. Of these, 2.9 million are
3 Reginald Harris, partner at Bryan Cave Leighton Paisner LLP, said that “The Bank Secrecy
Act puts some responsibility on banks to report suspected fraud to authorities. But the coronavirus
relief act that created the PPP made it so that banks would be ‘held harmless’ for borrowers’ failure
to comply with program criteria” (Duren (2020)).
4 David Rybicki, a partner at K&L Gates LLP, said under the CARES Act, “the lender was able
to rely on data from borrowers…Compliance is often burdensome for small banks that do not have
the resources of their larger counterparts. A lot of smaller lenders are participating in part because
of the fact that there aren’t significant added compliance burdens”
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Lender Automation and Racial Disparities in Credit Access 1467
tagged as second draws (where a firm legally obtained a second PPP loan).
After dropping these observations, we are left with 8.8 million first PPP loans.
Since our goal is to understand lending behavior in a relatively representa-
tive population of both lenders and borrowers, we also drop loans made after
February 23, 2021. The Biden Administration made drastic changes to the
PPP at this time, which included first prioritizing loans to small firms with
less than 20 employees, and then permitting only Community Development
Financial Institutions (CDFIs) to use PPP funds. This leaves us with 5.7 mil-
lion PPP loans for our baseline analysis. Our results are very similar, however,
when we use the full time period and when we include second-draw loans.
B. Lender Classification
In some of our analyses, we explore differences in PPP lending to minority-
owned businesses across lenders with different degrees of automation. We clas-
sify PPP lenders into the following mutually exclusive groups:
(i) Top 4 banks by assets (JP Morgan Chase, Bank of America, Wells
Fargo, and Citibank);
(ii) Large banks: banks with more than $100 billion in assets, excluding
the top 4 banks;
(iii) Medium-sized banks: banks with more than $2.2 billion in assets (but
below $100 billion);
(iv) Small banks: banks with less than $2.2 billion in assets;5
(v) Credit unions: based on the lender name (i.e., “credit union” or “CU” at
the end of the name);
(vi) CDFIs and nonprofits;
(vii) Minority Depository Institutions (MDIs): as classified by the FDIC;
(viii) Fintech lenders: All lenders officially designated as such by the SBA.
We further include online lenders who originate primarily for or via
fintech partners or platforms, online lenders founded since 2005, and
online lenders who received venture capital (VC) investment.6
5 We include the roughly 6,000 loans by Business Development Corporations (BDCs) in the
Small Bank category, since these loans behave similarly in terms of the variables we study as the
Small Bank loans.
6 Internet Appendix Table IA.I lists all lenders classified as “fintech.” In some cases, the origina-
tor listed in the table made loans primarily through fintech partners. For example, all of PayPal’s
fintech loans were originated by WebBank, and Square’s loans by Celtic Bank. The only fintech
lender with a branch is Cross River. However, Cross River originated an overwhelming quantity of
loans for fintech partners such as Kabbage, was founded in 2008, and has received VC funding, so
we consider it a fintech lender for our purposes. In the data, we do not observe loan referrals from
traditional banks to other lenders, so loans referred to fintechs by other lenders would be classi-
fied as fintech loans. We also do not observe back-end processors that do not show up as lenders
or servicers, including Finastra, Ocrolus, and Customers Bank (which played this role for other
lenders even as it was also processing its own PPP loans). So some loans processed by fintech firms
but originated by other lenders would be classified according to their ultimate lender. The Internet
Appendix is available in the online version of the article on The Journal of Finance website.
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1468 The Journal of Finance®
Table I shows how PPP loan origination varied across these lender types
for the full sample (Panel A) and the analysis sample with predicted race
(Panel B). Focusing on Panel B, traditional banks originated 75% of PPP loans,
with non–top 4 banks responsible for 59% of all loans. Fintech lenders origi-
nated 17.4% of all PPP loans, while credit unions originated about 4%, and CD-
FIs and MDIs between 1% and 2%. Fintech and other nonbank lenders made
substantially smaller loans. The average (median) PPP loan amount for fin-
tech lenders is $31,228 ($15,338) compared to, for example, $88,083 ($20,833)
for small banks.
Cross-Lender Variation in Automation: The degree of process automation dif-
fers widely across the different types of lenders. At one end of the spectrum are
fintech firms, which achieve substantial cost savings by fully automating their
loan origination processes. Automation also varies substantially among tra-
ditional lenders, where it is widely believed to increase in bank size, with one
industry observer noting that “Large banks have avidly adopted robotic process
automation…It’s tougher for smaller banks to follow suit” (Crosman (2020)). In-
deed, while the largest banks have invested extensive resources in augment-
ing human loan officers with substantially automated processes,7 manual pro-
cesses persist at smaller banks where individual employees have considerable
leeway in decision making. For example, one industry article profiled the PPP
strategy of a small SBA-preferred bank in Georgia: “While The Piedmont Bank
considered some automated, online solutions, they ultimately decide to process
the applications manually…Everyone who works there is preparing to put in
long hours and a lot of elbow grease. They know they’re going up against big
banks and their automated systems” (Smith (2020)).8
We confirm these perceptions of differences in the degree of automation
across and within conventional lenders using data on bank branch-level
7 For example, JPMorgan Chase noted that it processed four years worth of small business
loan applications in 23 days for the PPP, which it attributed to a “strategic decision to use a com-
bination of digital plus human capacity” (Roberts (2020)). Similarly, Sharon Miller, the head of
small business at Bank of America explains BofA’s success in extending a large number of PPP
loans as follows: “the investments that we’ve made for digital capabilities, have really helped set us
apart from the rest because we were able to quickly get up and running…In 45 days, we processed
18 years’ worth of loans” (Bhattacharyya (2020)). Despite this substantial process automation, and
unlike at fintech lenders, humans also remained actively involved in the loan origination process
at BofA: “We’re digital first but we still have that human element, the combination of high tech and
high touch.”
8 As a result of the largely manual loan processing, humans with all their potential biases play a
larger role in the loan origination process at smaller banks. Cross (2021) explains: “In community
banking, when you’re closing a loan, you’re probably closing it with a lady or gent you went to
high school with, maybe on the hood of a Cadillac at a Friday night football game or Sunday after
church. Those things are nice, but they don’t scale.” Providing a specific example, she continues: “In
the initial round of the Paycheck Protection Program, First Bank in Hamilton, N.J., leaned on its
bankers rather than technology to help small businesses stay afloat. That manual labor ironically
turned out to be a good thing, because we had people helping small businesses through the process,
and they had a number and name to talk to,’ said Patrick Ryan, president and CEO of the $2.3
billion-asset bank.”
Table I
Summary Statistics by Lender Type
This table reports summary statistics for PPP loans by originating lender type. Panel A describes all PPP loans, while Panel B focuses on our analysis
sample, which is composed of first-draw PPP loans between April 3, 2020, and February 23, 2021, for which we can predict the borrower’s race. All
subsequent statistics and analyses are drawn from subsamples of the data included in Panel B. Panel C reports statistics for banking and credit
relationships as well as financial performance for borrowers in the analysis sample that we can match to bank statement data from Ocrolus. The
column “SME has Checking Account with PPP Lender” captures whether the borrower’s business checking account bank is the same institution that
originated the PPP loan. The remaining variables are derived from transactions on the borrowers’ most recent monthly bank statement. Internet
Appendix Table IA.II repeats Panels B and C for the subset without predicted race.
Panel A: All PPP loans
PPP Loan Amount ($)
Number Number Total Mean P10 P50 P90
Lenders Loans Amt ($ bn)
All 5,310 11,768,450 798.7 67,867 4,199 20,678 124,500
Top 4 4 1,258,063 94.6 75,165 5,181 21,578 140,241
Large Banks 18 898,175 108.8 121,127 6,000 28,133 230,895
Medium Banks 420 2,135,084 271.1 126,977 6,095 30,500 266,410
Small Banks 3,548 2,526,637 191.9 75,950 4,165 20,832 153,000
Credit Union 958 378,673 15.7 41,479 3,602 15,882 82,422
CDFI/Nonprofit 221 1,516,834 34.4 22,711 4,400 20,000 20,833
Minority Dep Inst 115 288,072 16.5 57,363 3,575 17,755 105,600
Fintech 29 2,766,912 65.6 23,726 3,083 17,208 29,166
(Continued)
Lender Automation and Racial Disparities in Credit Access
1469
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1470
Table I—Continued
Panel B: Analysis sample
PPP Loan Amount ($)
Number Number Total Amt Mean P10 P50 P90 Share Share
Lenders Loans ($ bn) Black Borr White Borr
All 5,266 4,183,623 391.9 93,666 4,462 20,833 176,900 8.6% 75.0%
Top 4 4 663,471 53.0 79,901 5,000 21,744 145,497 6.2% 67.0%
Large Banks 18 393,884 57.7 146,505 6,250 32,100 279,297 5.3% 81.4%
Medium Banks 420 1,017,781 145.2 142,616 6,200 33,600 295,500 4.1% 84.0%
Small Banks 3,538 1,064,306 93.7 88,083 4,552 20,833 175,848 3.3% 88.9%
Credit Union 938 173,368 7.9 45,412 3,550 16,000 89,567 9.7% 78.1%
CDFI/Nonprofit 209 82,276 5.9 71,564 3,904 20,800 145,833 10.6% 74.4%
The Journal of Finance®
Minority Dep Inst 114 61,653 5.8 93,838 4,600 23,234 180,800 4.0% 27.2%
Fintech 28 726,884 22.7 31,228 2,853 15,338 54,465 26.5% 49.2%
(Continued)
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Table I—Continued
Panel C: Bank and credit relationships sample (Ocrolus)
SME Has
Mean Checking Monthly Net Cash
Loan Acct with Credit With Any: Inflow ($)
N Amount PPP Lender Fintech Non-Fintech Mean P50 Share Share
Black Borr White Borr
All 168,360 80,897 27.4% 14.2% 80.0% 9,016 1,332 15.3% 63.5%
Top 4 29,709 72,738 67.2% 15.6% 87.3% 11,939 2,773 8.4% 63.6%
Large Banks 15,179 104,023 48.8% 14.7% 82.3% 12,226 2,491 8.1% 74.6%
Medium Banks 28,430 133,374 38.4% 14.5% 75.5% 9,371 1,482 5.9% 78.3%
Small Banks 22,818 118,334 23.3% 14.5% 75.5% 7,730 1,248 6.1% 79.9%
Credit Union 5,644 56,194 34.5% 13.0% 72.0% 6,794 825 14.2% 69.5%
CDFI/Nonprofit 3,046 66,732 8.4% 13.6% 79.1% 8,141 1,178 17.8% 61.4%
Minority Dep Inst 2,479 112,982 14.3% 13.3% 75.2% 3,302 1,069 5.7% 28.4%
Fintech 61,055 42,379 0.0% 13.3% 80.7% 7,592 932 28.7% 48.6%
Lender Automation and Racial Disparities in Credit Access
1471
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1472 The Journal of Finance®
IT spending for the years 2017 to 2019, provided by Spiceworks Ziff Davis
(SWZD).9 We observe spending data from 108,099 branches of 3,440 unique
conventional PPP lenders. Following He et al. (2021), we use software spend-
ing to proxy for investment in automation, and construct average annual
branch-level software spending between 2017 and 2019.10 Panel A of Internet
Appendix Figure IA.1 shows the distribution of branch-level software spend-
ing for different bank types (Internet Appendix Table IA.VII provides further
summary statistics). Median annual spending is over $58,000 for branches of
top 4 banks but less than $30,000 for branches of small banks. While there
are some challenges with interpreting these data—notably they measure
flow spending over a relatively short time horizon rather than the stock of
automation investment—these data confirm that automation is increasing in
bank size. This conclusion is also consistent with the longer term analysis in
He et al. (2021), who show that there has been little growth over time in IT
spending at small banks, in contrast to substantial growth at large banks.
C. Identifying Borrower Race and Ethnicity
A key element of our analysis is identifying the race and ethnicity of the
owners of the firms participating in the PPP. The SBA data contain details on
owner race for a subset of PPP borrowers who chose to self-report this infor-
mation in their loan application, and for which the lender also chose to report
this information to the SBA. To construct a signal of race and ethnicity for a
larger set of PPP borrowers, we build on a well-established literature and pre-
dict race from a business owner’s name, and the firm’s location, industry, and
employer status.
We first identify a borrower firm’s individual owner or most senior executive.
Our primary source for this information is data on current firm officers as of
July 2021 drawn from Secretary of State registrations, provided to us by the
analytics firm Middesk.11 For nonemployer firms, we rely on the fact that the
“business name” reported in the PPP data usually corresponds to the owner’s
name. Finally, we obtain applicant names for a sample of PPP applicants from
Lendio. We combine these data with public SBA information on the firm’s ad-
dress, industry, and employer status. Based on these data, we use a machine
9 These data, formerly known as the Harte Hanks Market Intelligence Computer Intelligence
Technology database, are sold as market intelligence to technology firms and have been used by
Forman, Goldfarb, and Greenstein (2012), Bloom et al. (2014), and He et al. (2021). He et al. (2021)
show that the data cover more than 80% of the U.S. commercial banking market.
10 He et al. (2021) write that “The specialty of these software products lies in automatically pro-
cessing information from loan applicants’ paper document packets through specialized program-
ming and AI technologies, which would otherwise be done manually by loan officers. By greatly
enhancing the efficiency in document assembly, digitization, and information classification, these
software improve accuracy and shorten processing speed.” As discussed in He et al. (2021), when
a bank’s headquarters incurs the expenditure, but the IT is used by the branches, the spending is
distributed to the branches rather than appearing only at the headquarters level.
11 The owner is identified as the first individual listed as owner or principal under “business
contacts” in Secretary of State filings.
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Lender Automation and Racial Disparities in Credit Access 1473
learning approach to estimate the conditional probability of a business owner
being Asian, Black, Hispanic, or White.
Our process involves two steps. In the first step, we follow the methodology in
Imai and Khanna (2016) and combine the Census list of last names (Word et al.
(2008)) with the census tracts of business locations to estimate the conditional
probability that an individual belongs to a certain racial group given their last
name and location (see Section I.A of the Internet Appendix for details). In
the second step, we combine the resulting Bayesian posterior probability with
the racial distribution of common first names and industries by employer sta-
tus as features in a random forest model with 1,000 trees (see Tzioumis (2018),
United States Census Bureau (2012)). We train and validate the random forest
model on the more than 800,000 PPP loans with self-reported race, a success-
fully geolocated address, an identified owner name, and information on firm
industry and employer status. The model estimates the probability that a bor-
rower belongs to a certain race given first and last name, location, industry,
and employer status. For our baseline analysis, we identify the borrower as
having the race with the largest probability across the set of racial groups R.12
In total, we can predict the race of 4.18 million unique PPP borrowers. For
the remaining firms, we do not observe the owner name or the geolocation fails.
We also exclude about 30,000 loans for which the algorithm predicts the owner
race to be “Other.” To assess the out-of-sample quality of the prediction, we
randomly set aside a “hold-out” subsample of borrowers who self-identify their
race but whom we exclude from the training of the random forest model. Table
IA.III shows that in the hold-out sample, 84% of those business owners who
we predict to be Black self-identified as Black.
We show below that our main results on the effects of both cross-lender and
within-lender variation in automation are robust to using the subsample of
borrowers for whom the SBA data include information on self-identified race.
However, our signal of predicted race is likely to be more relevant in our set-
ting because loan officers typically observe applicants’ names and locations,
but not their self-identified race. Therefore, they are likely to respond to the
race or ethnicity most associated with a given name, rather than to the bor-
rower’s actual race or ethnicity. For example, two of the prediction algorithm’s
“errors” in the holdout sample are individuals whose last names are Huang
and Rodriguez, and who self-identify as Black but are predicted to be Asian
and Hispanic, respectively. It is plausible that loan officers observing only ap-
plicants’ names might also infer an incorrect race for these individuals, and
our algorithmically assigned race may correspond more closely to the race in-
ferred (and potentially acted upon) by a loan officer. Such behavior would be
highly consistent with findings from audit studies such as Bertrand and Mul-
12 The probability distributions for each race as predicted by the algorithm are summarized in
Internet Appendix Table IA.III and Figure IA.2. For example, among people predicted to be Black,
the mean the probability of being Black according to the algorithm is 76%, with a median of 80%.
All results in the paper are robust to only considering individuals for which this probability is
larger than 90%, or when we use this probability directly instead of a race dummy.
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1474 The Journal of Finance®
lainathan (2004), who document discrimination against job applicants with
“African American-sounding” names.
We call the sample for which we can identify race the “analysis sample.”
Panel B of Table I and Panel A of Internet Appendix Table IA.IV show that,
within this sample, 8.6% of business owners are Black, 7.5% are Hispanic, 8.9%
are Asian, and 75.0% are White. The distributions of originating lender and
firm characteristics such as loan amount and business type are similar across
the full first-draw sample and the analysis sample (Table IA.V). For example,
the average PPP loan amount is $93,784 in the full sample and $93,666 in the
analysis sample. Thus, the sample for which we can predict race is broadly
representative of the overall PPP population, which in turn is relatively rep-
resentative of privately owned U.S. businesses on industry and geography (see
SBA May 2021 Program Report).13
Other Data: Below we describe other sources of data—including business
checking account data from Ocrolus, PPP loan applications from Lendio,
credit and debit card transactions from Enigma, and bank automation data
from Biz2Credit—used to explore the mechanisms behind the observed racial
disparities.
II. Automation and Lending to Minority-Owned Businesses
Lenders can automate many aspects of the loan origination process, allowing
them to remove humans from processes such as application intake, information
transfer to internal software systems, payroll verification, and fraud checks.
Such automation can increase lending to minority-owned firms through sev-
eral mechanisms:
(i) Automated loan processing systems reduce the fixed cost of originating
each PPP loan. They also expand the capacity of total loans that can be
processed. Through both channels, more automated lenders would be
able to serve smaller businesses with demand for lower loan amounts,
which are more likely to be minority owned (see Table II).
(ii) Automation generally goes hand-in-hand with an online loan origina-
tion process. This allows automated lenders to more easily serve cus-
tomers in regions with higher minority shares that are traditionally un-
derserved by existing bank branch networks (Wang and Zhang (2020)).
(iii) Automation reduces the role of human decision making in the lending
process and can thus reduce racial discrimination—whether of the taste
based or statistical variety—in lending.
13 The racial composition of our PPP analysis sample is also similar to that of the population
of U.S. small business owners. For example, Internet Appendix Table IA.VI shows that 2.8% of
employer businesses in the PPP analysis sample are Black owned, compared to 2.1% of the pop-
ulation of comparable small business owners in the 2012 U.S. Census Bureau Small Business
Owners survey.
Table II
Summary Statistics by Predicted Race
This table reports summary statistics by race/ethnicity. Panel A contains loan and firm characteristics for the analysis sample. Panel B summarizes
information from the bank statement-matched data.
Panel A: Analysis sample
All Asian-Owned Black-Owned Hispanic-Owned White-Owned
Loan Amount
Mean Loan Amount ($) 93,666 53,492 24,315 54,287 110,317
Median Loan Amount ($) 20,833 20,071 14,886 18,218 23,700
Share Total Loans Made by Bank Types
Top 4 15.9% 27.4% 11.4% 24.1% 14.2%
Large Banks 9.4% 8.1% 5.8% 6.9% 10.2%
Medium Banks 24.3% 18.1% 11.5% 17.1% 27.3%
Small Banks 25.4% 12.4% 9.8% 11.7% 30.2%
Credit Union 4.1% 2.4% 4.7% 3.8% 4.3%
CDFI/Nonprofit 2.0% 1.7% 2.4% 1.9% 2.0%
Minority Dep Inst 1.5% 8.0% 0.7% 4.0% 0.5%
Fintech 17.4% 21.7% 53.6% 30.5% 11.4%
Business Types
Corporation 27.8% 40.3% 11.2% 28.7% 28.1%
LLC 26.8% 24.4% 19.9% 23.9% 28.1%
Other 11.9% 8.3% 26.2% 15.8% 10.3%
Sole Proprietorship 20.4% 14.9% 38.4% 21.5% 18.8%
Subchapter S Corporation 13.1% 12.0% 4.3% 10.1% 14.6%
Employer Institution 63.4% 73.8% 21.0% 57.8% 67.6%
Industries (3 Digit NAICS)
Professional/Technical Services 12.7% 7.8% 10.6% 10.8% 13.7%
Ambulatory Health Care Services 7.5% 10.3% 8.2% 6.3% 7.2%
Food and Drinking Services 5.9% 14.7% 3.5% 8.7% 4.8%
Lender Automation and Racial Disparities in Credit Access
Personal and Laundry Services 6.0% 12.0% 15.6% 7.5% 4.0%
Specialty Trade Contractors 5.4% 0.9% 2.2% 6.3% 6.2%
Other 62.6% 54.3% 60.0% 60.4% 64.1%
Observations 4,183,623 372,993 359,366 313,389 3,137,875
1475
(Continued)
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1476
Table II—Continued
Panel B: Bank and credit relationships sample (Ocrolus)
All Asian-Owned Black-Owned Hispanic-Owned White-Owned
Banking Relationships
Share Checking Account from Top 4 Banks 47.8% 57.5% 47.1% 61.9% 44.1%
Share Checking Account from Large Banks 15.8% 13.6% 19.1% 11.6% 16.0%
Share Checking Account from Small/Medium-Sized Banks 27.5% 20.3% 16.5% 19.9% 32.7%
Share Checking Account from Others 8.9% 8.7% 17.2% 6.7% 7.2%
Credit Relationships
Share Credit Relationship with Fintech 14.2% 13.2% 7.9% 12.5% 16.2%
Share Credit Relationship with Non-Fintech 80.0% 78.9% 81.4% 85.8% 78.9%
Share Credit with checking account bank 17.6% 18.4% 14.4% 19.5% 17.9%
Cash In/Outflows
Mean Monthly Cash Inflow ($) 231,390 223,615 81,706 201,382 273,948
The Journal of Finance®
Median Monthly Cash Inflow ($) 65,232 70,141 11,636 58,644 87,949
Mean Monthly Cash Outflow ($) 218,738 213,055 72,270 189,775 260,004
Median Monthly Cash Outflow ($) 60,890 67,033 8,960 52,466 82,450
Mean Monthly Net Cash Inflow ($) 9,016 7,426 6,332 8,681 9,977
Median Monthly Net Cash Inflow ($) 1,332 1,232 703 1,698 1,676
Observations 168,360 17,166 25,782 18,531 106,881
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Lender Automation and Racial Disparities in Credit Access 1477
There are also forces through which process automation may reduce lending
to minority-owned firms. For example, if more automated lenders provided less
personalized help to borrowers to complete their application materials, and if
such help was particularly valuable for minority-owned firms, more-automated
lending systems might reduce lending to minority-owned firms. The overall
effect of automation on lending to minority-owned firms is thus an empiri-
cal question.
In this section, we begin by exploring the empirical relationship between the
degree of automation in the loan origination process and rates of lending to
minority-owned firms. We find that automation is associated with higher rates
of PPP lending to minority-owned firms, both within and across lenders. In the
following sections, we provide evidence that each of the three factors described
above contributes to this pattern.
A. Cross-Lender Variation
While about 8.6% of PPP loans in our analysis sample went to Black-owned
firms, there is wide heterogeneity across lenders. We begin by exploring the re-
lationship between cross-lender variation in extending PPP loans to minority-
owned business and the extent of automation across lenders.
Panel A of Figure 1 shows the share of PPP loans to Black-owned firms by
lender type (see also Panel B of Table I). At the lower end of the distribution,
3.3% of PPP loans originated by small banks went to Black-owned firms. At
large banks, Black-owned firms represent 5.3% of originated PPP loans, while
top 4 banks issued 6.2% of their PPP loans to Black-owned firms. At the top
end of the distribution, CDFIs made 10.6% and fintech lenders made 26.5% of
their PPP loans to Black-owned firms (CDFIs, unlike fintechs, provide finan-
cial services specifically to economically disadvantaged and underserved com-
munities). Overall, fintech lenders were responsible for 53.6% of PPP loans to
Black-owned firms in our sample (Panel B of Figure 1, Panel A of Table II).
Internet Appendix Table IA.I shows that while there is some variation across
fintech lenders in the share of PPP loans to Black-owned firms, this variation
is not driven by a few lenders.
We present the corresponding figures for other racial and ethnic groups as
well as by gender in Internet Appendix Figures IA.4 and IA.5. Lending to
Black-owned firms exhibits the most striking variation across lender types,
motivating our focus on better understanding the determinants of PPP lend-
ing to these firms in particular. Fintechs are somewhat more likely to lend to
Asian- and Hispanic-owned firms relative to small- and medium-sized banks.
Motivated by evidence that women, like minorities, face challenges in access-
ing financing and career opportunities (Ewens and Townsend (2020), Egan,
Matvos, and Seru (2022), Howell and Nanda (2022)), we also consider gender.
While the disparities are smaller, the general patterns are similar: The share
of loans to female-owned firms is largest for fintech lenders and smallest for
small and medium-sized banks.
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1478 The Journal of Finance®
Figure 1. Black-owned business PPP lending by lender type. Panel A shows,
for each lender type, the share of PPP loans that went to Black-owned businesses
(P(Black-owned|Originating Lender Type)). Panel B shows the share of all PPP loans to Black-
owned businesses made by each lender type (P(Originating Lender Type|Black-owned)).
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Lender Automation and Racial Disparities in Credit Access 1479
In Panel B of Figure IA.1, we group conventional lenders into quintiles of
their average branch-level software spending and show the average share
of PPP loans to Black-owned firms in each of these quintiles. Lending to
minority-owned firms among conventional lenders is increasing in this mea-
sure of the extent of automation. Overall, we consistently find that lenders
with larger investments in automating their processes are more likely to
extend PPP loans to minority-owned firms.
B. Event Study of Bank Automation
While the prior analysis shows a strong correlation between the extent of
automation and PPP lending to Black-owned firms, one might naturally be
concerned about omitted variables that could be driving the observed relation-
ships: Banks and fintech lenders may differ along characteristics other than
automation that can affect both the propensity of minority-owned firms to
apply and the propensity of lenders to approve completed loans from minority-
owned firms. To identify a causal effect of automation on PPP lending to
Black-owned firms, we exploit the adoption of automation during the PPP by a
number of small and medium-sized banks. We study how these banks’ rates of
lending to Black-owned firms changed around the automation event compared
to the lending patterns at otherwise similar banks that did not automate.
B.1. Data
Our first source of bank-level automation dates is the fintech firm
Biz2Credit, which offers a white-label SaaS product called Biz2X that banks
can license to outsource and automate their loan processing and underwrit-
ing. During the PPP, some banks—motivated by the influx of PPP loan
applications—hired firms such as Biz2Credit to automate their lending pro-
cesses. Once a bank automates using Biz2X, loan application materials are au-
tomatically redirected from the bank’s website to Biz2Credit. For PPP loans,
Biz2Credit then automatically processes documents such as tax filings and
proof of business documentation, conducts fraud checks, ensures compliance
with PPP eligibility rules, makes a decision, and forwards the required mate-
rials back to the bank to originate the loan. Importantly, the “front-end” bank
website that the customer faces did not change around automation. Biz2Credit
provided us with the launch dates of their service for their clients during the
PPP. We also manually searched newspaper articles to identify additional au-
tomating banks.14
We obtain automation dates for 20 small and medium-sized banks that au-
tomated during the sample period. Those banks account for about 75,000 PPP
14 These banks often automated via other fintech service providers, including Customers Ban-
corp, Numerated, and Fountainhead. For some of these manually identified automation events, we
only have a rough date of automation, potentially creating some noise in our estimation. We find
similar results when restricting our analysis only to banks that automated through Biz2Credit.
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1480 The Journal of Finance®
Table III
Summary Statistics on Automation during PPP among Small and
Medium-Sized Banks
This table contains summary statistics for the banks used in the automation analysis. Columns
present unweighted summary statistics across the sample of automating and nonautomating
banks. For nonautomating banks, the rows “Before Automation” and “After Automation” both show
full sample statistics.
Automating Banks Nonautomating Banks
N = 20 N = 3,941
Mean P50 SD Mean P50 SD
Number of Loans 3,748 1,250 6,062 511 187 1,634
Assets (Million $) 8,673 1,235 15,078 1,243 269 4,753
Loan Amount ($) 131,297 129,011 61,355 77,820 59,044 73,927
Share Loans After Automation 29.9% 17.6% 31.3%
Asian-Owned Share Before Automation 5.4% 5.1% 3.5% 3.5% 1.8% 5.8%
Asian-Owned Share After Automation 7.3% 5.4% 10.4% 3.5% 1.8% 5.8%
Black-Owned Share Before Automation 4.4% 3.3% 5.0% 2.7% 1.0% 4.8%
Black-Owned Share After Automation 12.0% 11.4% 11.6% 2.7% 1.0% 4.8%
Hispanic-Owned Share Before Automation 5.3% 3.6% 6.8% 3.0% 1.1% 7.4%
Hispanic-Owned Share After Automation 6.4% 5.5% 5.9% 3.0% 1.1% 7.4%
White-Owned Share Before Automation 85.0% 87.7% 13.4% 90.8% 94.3% 11.5%
White-Owned Share After Automation 74.4% 72.6% 21.5% 90.8% 94.3% 11.5%
loans in our analysis sample, or 3.6% of all PPP loans originated by small and
medium-sized banks. Table III presents summary statistics for the automat-
ing banks and the control group of otherwise similar nonautomating banks.
Among automating banks, about 30% of their PPP loans occur after automa-
tion. Automating banks are somewhat larger than banks in the control group.
B.2. The Effect of Automation
Table III shows that, on average, automating banks’ share of loans to Black-
owned businesses increased after automation, from 4.4% to 12% (there are also
smaller increases in the share of loans to Hispanic-owned and Asian-owned
firms, and corresponding declines in the share of PPP loans to White-owned
firms). However, these average increases could reflect a broader increase in
loans to Black-owned businesses over time among all banks, or a specific sec-
ular trend in lending to Black-owned businesses among automating banks. To
isolate the effect of automation separately from such potential time trends,
we estimate the following dynamic differences-in-differences specification us-
ing all PPP loans originated by small and medium-sized banks in our analysis
sample:
1(BlackOwnedibt ) = βk 1(t − Ab = k) + αb + αt + εibt , (1)
k=−1
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Lender Automation and Racial Disparities in Credit Access 1481
where 1(BlackOwnedibt ) indicates whether loan i originated by bank b in
month t went to a Black-owned business, Ab corresponds to the month in which
bank b automates, and 1(t − Ab = k) is an indicator for being k months away
from that automation date. The coefficients are relative to the omitted period,
k = −1, which represents the month prior to automation. The model also in-
cludes fixed effects for bank and origination period.
Panel A of Figure 2 plots the βk coefficients from the dynamic differences-in-
differences model in equation (1). We do not report more than three months of
pre-automation data because the set of automation dates mean that we rarely
observe PPP loans four months before an automation event (automation dates
are mostly in late spring 2020, and then after a period in which the PPP was
inactive, in late fall 2020). We observe no differential pre-trends in the rates of
lending to Black-owned businesses among automating banks prior to automa-
tion. In contrast, following automation, automating banks have a persistent
increase in the rate of lending to Black-owned businesses relative to other,
nonautomating banks. This finding is consistent with a causal effect of au-
tomation on banks’ rates of extending PPP loans to Black-owned firms. It is
also consistent with the cross-lender patterns of lending to Black-owned firms
presented above.
We conduct two robustness checks. First, we find similar results in the
smaller and potentially selected sample of individuals who self-report race
(Figure IA.8). Second, when we estimate equation (1) at the weekly level for
banks that originate loans over the six weeks on both sides of the automation
date, we find no pretrends and a clear discontinuity in the weeks following au-
tomation, even though for some of the banks our data do not include the exact
week of automation (Figure IA.9).
In the following sections, we explore the importance of the various mecha-
nisms described above in driving the observed relationship between automa-
tion and lending to Black-owned firms. We also rule out a variety of other
factors, such as racial differences in loan application behavior or banking rela-
tionships, as the only determinants of the observed cross-lender differences in
lending to minority-owned firms.
III. Mechanisms: The Role of Loan Size and Firm Characteristics
As discussed above, a key mechanism through which automation can in-
crease lending to Black-owned firms is by reducing the fixed cost of lending
and increasing processing capacity. Both of these factors would allow lenders
to originate more PPP loans with smaller loan amounts—precisely the types
of loans that the relatively smaller Black-owned firms are disproportionately
eligible for. In addition, more-automated lenders (and fintech firms in particu-
lar) generally acquire and originate their loans online, allowing them to serve
borrowers independent of their locations. In contrast, traditional banks dis-
proportionately acquire customers through their branch networks, which have
less presence in minority neighborhoods.
To assess whether firm characteristics such as firm size and location
can explain the striking unconditional variation across lenders in serving
1482
The Journal of Finance®
Figure 2. Share of loans to Black-owned businesses before and after small-bank automation. This figure reports dynamic differences-in-
differences estimates at the monthly level (see equation (1)). Period 0 following the dashed vertical line corresponds to the automation month. Panel
A includes fixed effects for the bank and week of loan approval. Panel B adds the vector of controls included in Table V, Panel A, column (2). We do
not include more than three months before automation because the automation dates (in late Spring 2020 and in late Fall 2020 following a large gap
in the PPP program) mean that we observe essentially no loans made four months prior to automation dates. Standard errors are clustered by zip
code. The gray bars represent 95% confidence intervals.
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Lender Automation and Racial Disparities in Credit Access 1483
Black-owned businesses, we use the regression framework in equation (2):
1(LenderTypei ) = β1(BlackOwnedi ) + Xi δ + εi . (2)
The dependent variable, LenderTypei , is an indicator for whether PPP bor-
rower i’s loan comes from a certain type of lender. The key explanatory variable
is an indicator for whether a firm is Black-owned, defined as in the previous
section. For example, when LenderTypei = Fintechi , β measures the higher
propensity (in percentage points) of Black-owned firms to get their PPP loan
from a fintech lender, relative to all other racial and ethnic groups. The vector
Xi represents a vector of control variables.
A. Lending to Black-Owned Firms by Fintech Lenders
Panel A of Table IV reports results from regression (2) with LenderTypei =
Fintechi . Column (1) shows that, consistent with Section II.A, Black-owned
businesses have a 39.7 percentage point higher unconditional probability of
obtaining their PPP loan through a fintech lender, a large difference given that
only 17.4% of all firms obtained their PPP loans through fintechs.
We first consider whether the timing of PPP applications explains some of
the unconditional racial disparity in lender identity. As shown in Figure IA.10,
the share of PPP loans to Black-owned firms and the share of PPP loans made
by fintechs both increased over time (see also Table IA.VIII). While this rela-
tionship might be causal—with Black-owned firms successfully obtaining PPP
loans only after fintech lenders entered the program—it could alternatively re-
flect coincidental timing, with Black-owned firms only applying for PPP loans
later in the PPP. Since we cannot separate these two explanations, we focus
on understanding differences among loans originated at the same time. Those
differences are unlikely to be confounded by timing factors unrelated to au-
tomation, and present a lower bound on the total variation that is determined
by lender characteristics. Column (2) of Table IV shows that even after con-
trolling for week-of-loan-approval fixed effects, Black-owned businesses are 26
percentage points more likely to obtain their PPP loan from a fintech lender.
We next consider the effect of loan size. Black-owned firms receive the small-
est PPP loans, with a mean amount of $24,315, compared to about $54,000
for Hispanic- and Asian-owned firms, and $110,317 for White-owned firms
(Panel A of Table II). Under the PPP program, lenders were compensated
for originating loans with a fixed fraction of the loan amount. If automation
reduces origination costs or increases loan processing capacity, fintechs could
profitably make more PPP loans to the small-loan segments disproportionately
comprised of Black-owned firms. Consistent with such a view, Table I shows
that the average PPP loan size for fintech-originated loans is about one-third
of the average loan size in the overall PPP. In column (3) of Table IV, Panel A,
we add fixed effects for each percentile of the loan size distribution. Controlling
for loan amount explains some of the variation, consistent with the ability to
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1484 The Journal of Finance®
Table IV
Business Owner Race and PPP Lender Type
This table reports estimates of equation (2). The dependent variable in Panel A is an indicator for
whether the originating lender is a fintech firm. Panel B repeats the specifications in columns (1)
and (7) of Panel A, using as dependent variables indicators for whether the originating lender is
a top 4 bank (columns (1) and (2)), a large bank (columns (3) and (4)), and a small/medium-sized
bank (columns (5) and (6)). Panel C is estimated on the sample of PPP loans matched to bank
branch-level software spending data. The dependent variable, the log of “Bank Branch Software
Spend,” is a measure of automation for the bank branches of the PPP lender within the PPP bor-
rower’s zip code (or county, if there is no zip code match). Control variables generally pertain to
the borrower firm and their particular PPP loan. Loan Amount FE are 100 indicator variables for
each percentile of the loan size distribution. Zip Code and Census Tract FE are indicators for each
zip code and census tract. Approval Week FE are indicators for the week in which the PPP loan
was approved by the SBA. Industry FE are 104 indicators for NAICS three-digit classifications
that appear in the data. Business type FE are seven indicators for the firm’s business type. Em-
ployer status is an indicator for whether the firm has at least one employee. Additional controls
in Panel C are indicators for each percentile of lender assets (“Bank Size”) and branch-level rev-
enue (“Branch Size”). Standard errors are clustered by borrower zip code. ∗∗∗ , ∗∗ , and ∗ indicate
statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Fintech PPP loan
1(Fintech)
Dependent Variable: (1) (2) (3) (4) (5) (6) (7)
1(Black-Owned) 0.397*** 0.260*** 0.232*** 0.207*** 0.219*** 0.224*** 0.121***
(0.005) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002)
Approval Week FE No Yes Yes Yes Yes Yes Yes
Loan Amount FE No No Yes No No No Yes
Zip Code FE No No No Yes No No Yes
Industry FE No No No No Yes No Yes
Business Type FE No No No No No Yes Yes
Employer Status FE No No No No No Yes Yes
Dep Var Mean 0.174 0.174 0.174 0.174 0.174 0.174 0.174
R2 0.086 0.227 0.240 0.276 0.265 0.272 0.356
Observations 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623
Panel B: Bank PPP loan
1(Top 4 Bank) 1(Large Bank) 1(Small/Med Bank)
Dependent Variable: (1) (2) (3) (4) (5) (6)
1(Black-Owned) −0.049*** −0.008*** −0.039*** −0.025*** −0.311*** −0.082***
(0.002) (0.001) (0.001) (0.001) (0.004) (0.001)
Approval Week FE No Yes No Yes No Yes
Loan Amount FE No Yes No Yes No Yes
Zip Code FE No Yes No Yes No Yes
Industry FE No Yes No Yes No Yes
Business Type FE No Yes No Yes No Yes
Employer Status FE No Yes No Yes No Yes
Dep Var Mean 0.159 0.159 0.094 0.094 0.498 0.498
R2 0.001 0.317 0.001 0.131 0.030 0.396
Observations 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623
(Continued)
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Lender Automation and Racial Disparities in Credit Access 1485
Table IV—Continued
Panel C: Branch software spending and PPP loans to Black-owned businesses
Log(Branch Software Spending)
Dependent Variable: (1) (2) (3) (4) (5) (6)
1(Black-owned) 0.071*** 0.032*** 0.025*** 0.025*** 0.015*** 0.006**
(0.006) (0.004) (0.004) (0.004) (0.003) (0.003)
Approval Week FE No Yes Yes Yes Yes Yes
Borrower County FE No Yes Yes Yes No No
Loan Amount FE No Yes Yes Yes Yes Yes
Industry FE No Yes Yes Yes Yes Yes
Business Type FE No Yes Yes Yes Yes Yes
Employer Status FE No Yes Yes Yes Yes Yes
Branch Size FE No No Yes Yes Yes Yes
Bank Size FE No No No Yes No Yes
Bank FE No No No No Yes No
Borrower Zip FE No No No No No Yes
Dep Var Mean 10.799 10.799 10.799 10.799 10.799 10.799
Observations 2,928,221 2,928,221 2,928,221 2,928,221 2,928,221 2,928,221
serve smaller loans contributing to fintech lenders serving more Black-owned
businesses. However, Black-owned firms remain 23.2 percentage points more
likely to receive fintech loans even after controlling for loan size.
Next, we turn to the role of firm location. Fintech lenders may have been
more accessible to businesses in areas underserved by bank branch networks
because fintech PPP applications were generally completed entirely online.
Therefore, in column (4) of Table IV, Panel A, we add zipcode fixed effects to
the specification from column (2). Fintech lenders’ ability to reach firms across
all geographies indeed appears to account for some of their higher share of
loans to Black-owned firms: Controlling for firm location reduces the dispro-
portionate probability of Black-owned firms borrowing from a fintech lender
from 26 percentage points to 20.7 percentage points (see also Erel and Lieber-
sohn (2020)).15
Finally, we explore the role of industry. Small businesses have notoriously
heterogeneous business models across industries, making them difficult for
banks to assess (Mills (2018)). In addition, banks may prefer working with
small firms from certain sectors, for example, those with more formalized
accounting practices. In contrast, fintechs have developed automated tech-
nologies to lend to traditionally underserved industries. For example, they
have invested in computer-reading technology and automatically processing
diverse documents, including handwritten payroll slips, creating an advan-
tage in sectors with less formal accounting systems, which have higher shares
15 In Table IA.IX, we verify that all businesses located in areas with high minority ownership—
even businesses in areas that are owned by White individuals—were somewhat more likely to
obtain their PPP loans through fintech lenders.
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1486 The Journal of Finance®
of Black-owned firms.16 We include industry fixed effects in column (5) of
Table IV, Panel A, to control for such industry-specific differences.17 Even
within an industry, Black-owned firms are 21.9 percentage points more likely
to obtain their PPP loans from fintech lenders.
Jointly controlling for all firm characteristics explains just over half of the
unconditional difference in the probability of obtaining a PPP loan from a
fintech lender between Black-owned and non–Black-owned firms obtaining
their PPP loans at the same time (column (7) versus column (2) of Table IV,
Panel A). This finding suggests that fintech lenders are indeed more likely to
lend to Black-owned firms by making smaller loans, by operating in otherwise
underserved locations, and by lending to types of businesses less likely to be
served by conventional lenders. However, even after we include this rich set
of controls, Black-owned businesses remain 12.1 percentage points more likely
to obtain their PPP loans from a fintech lender than otherwise identical busi-
nesses owned by individuals of a different race or ethnicity.
B. Lending to Black-Owned Firms across Conventional Lenders
As discussed above, conventional lenders differ substantially in how they
process loan applications. Large banks have more automated and standardized
lending processes than smaller banks, though large banks’ processes are not
nearly as automated as those of fintech lenders. Section II.A highlights that
the variation in the degree of automation across conventional lenders aligns
closely with their unconditional rates of lending to Black-owned firms.
In Panel B of Table IV, we explore the extent to which this unconditional
variation in lending to Black-owned firms across conventional lenders can
be explained by observable differences across firms. To do so, we replace
LenderTypei in equation (2) with an indicator for obtaining a PPP loan from
a top 4 bank (columns (1) and (2)), a large bank (columns (3) and (4)), or a
small/medium bank (columns (5) and (6)). Unconditionally, Black-owned firms
are less likely to get their loans from all of these types of banks, consistent
with the findings described above. After including the full set of controls, this
relationship is close to zero for the top 4 banks. In contrast, the majority of the
12.1 percentage point fintech differential in column (7) of Panel A of Table IV is
accounted for by lower rates of PPP lending to Black-owned firms by small and
medium-sized banks—those banks with the lowest degree of automation in the
loan origination process.18 These small and medium-sized banks were instead
16 Based on conversations with executives at Kabbage and the New England Regional SBA
Office senior leadership.
17 Borrower industry is captured with NAICS three-digit industry fixed effects. Examples of in-
dustries in this classification scheme are “Health and Personal Care Stores,” “Truck Transporta-
tion,” and “Food Services and Drinking Places.” Table II, Panel A, shows that the industry dis-
tribution differs by owner race. For instance, businesses in the “Personal and Laundry Services”
sector are more likely to be Black-owned than firms in the “Specialty Trade Contractors” sector.
18 Figure 3 depicts the conditional cross-lender patterns by comparing all types of lending in-
stitutions simultaneously. Here, we show the degree to which the lender types were statistically
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Lender Automation and Racial Disparities in Credit Access 1487
disproportionately likely to lend to White-owned firms (see Table IA.X). Sec-
tion II of the Internet Appendix presents robustness tests of these cross-lender
findings. For example, we show that the results are similar in the smaller and
potentially selected sample of individuals who self-reported race. Other tests
document persistent effects across periods within the PPP as well as when
separately considering, for example, employer versus nonemployer firms.
We also use the previously described data on branch-level software spend-
ing at conventional lenders to see whether this proxy for automation is as-
sociated with more PPP lending to Black-owned firms. Specifically, we match
each firm obtaining a PPP loan from a conventional lender to the software
spending at her lender’s local branches.19 In Panel C of Table IV, we present
results from equation (2) using the log of branch-level software spending be-
tween 2017 to 2019 as the dependent variable. The results are consistent
with the cross-lender-type analysis. Unconditionally, Black-owned firms obtain
their PPP loans from branches with about 7.1% higher spending on software
in 2017 to 2019. Conditioning on firm location and firm characteristics reduces
but does not eliminate this disparity. Importantly, even within the same bank,
Black-owned businesses are more likely to get their PPP loans from a branch
with higher software spending than from a similarly sized branch of the same
bank with lower software spending.
The Causal Effect of Automation at Small and Medium-Sized Banks: We next
explore whether part of the reason why automation caused banks to increase
their loan shares to Black-owned firms during the PPP period is that it allowed
banks to make smaller loans (see Section II.B). Such a mechanism would be
consistent with evidence that automation substantially increased banks’ lend-
ing capacities during the PPP, allowing them to process more of the lower-
amount (and thus lower-fee) loan applications from Black-owned firms.20 To
identify how much of the treatment effect of automation can be explained by
different from one another in their propensity to lend to each of the four racial and ethnic groups,
conditional on our controls. The fraction of fintechs’ loans to Black-owned firms was over 5 percent-
age points higher than the fraction for other lender types. MDIs made a disproportionate share of
their loans to Asian-owned firms. Note that the reversal for MDIs in Hispanic loans relative to the
summary statistics reflects the location control, in particular, a very large MDI in Puerto Rico.
19 To do so, we first construct branch-level software spending at the zip code level for each bank,
excluding the bank headquarters site. We then match PPP borrowers to software spending at the
bank zip code level. For example, a PPP borrower in zip code 10012 who got a PPP loan from
Citibank would be matched to the average software spending for Citibank branches in 10012.
When the bank has multiple branches but none in the borrower’s zip code, we use the average of
all branches in the borrower’s county. When there is no county match, we use the bank’s nationwide
average. All results are robust to only using PPP loans that we can match at the zip code level.
Overall we can match about 2.9 million PPP borrowers to bank branch software spending.
20 One bank official attested that “Compared to 10 days of manual lending, with every bank
resource that we had, in terms of volume of new loans generated, we were able to do it in 2 days
with [Numerated].” Cross (2021) similarly notes that “When HV Bancorp in Doylestown, Penn-
sylvania, first went live with the Paycheck Protection Program last April, ‘we just had bodies in
front of keyboards using the Small Business Administration’s E-Tran system and entering appli-
cations,’ said Hugh Connelly, chief lending officer in the business banking division of Huntingdon
Valley Bank…The urgency of the Paycheck Protection Program propelled community banks to find
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1488 The Journal of Finance®
Figure 3. Conditional share of PPP loans to each race by institution type. This
figure shows shares of PPP loans made to Black-owned businesses by originating lender type.
Each graph presents β coefficients from variations of the following regression: Black-ownedi =
βLender Typei + γ Xi + i , where Xi is a vector of fixed effects for borrower zip code, loan amount
percentile (in 100 bins), approval week, three-digit NAICS industry, business type, and employer
status. Standard errors are clustered by zip code. The mean of the omitted category, small banks,
is added back to each panel. In each panel, we change the dependent variable to be an indicator for
whether a borrower is a Black-owned (Panel A), White-owned (Panel B), Hispanic-owned (Panel
C), or Asian-owned (Panel D) business, multiplied by 100 to facilitate interpretation of coefficients.
compositional changes in loan characteristics on nonrace dimensions, we use a
standard differences-in-differences model,
1(BlackOwnedi,b,t ) = αb + αt + β1(PostAutob,t ) + Xi δ + εibt . (3)
The dependent variable is an indicator for whether PPP loan i made by bank
b in week t is to a Black-owned firm, 1(PostAutob,t ) is an indicator for bank
b having automated (i.e., having started service with Biz2Credit or another
a speedier way to disburse loans to small businesses than relying on phone and email. Many turned
to software to originate loans, automate the underwriting process, collect documents and transmit
the information to the SBA’s processing system.”
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Lender Automation and Racial Disparities in Credit Access 1489
white-label fintech) as of week t, αb is a bank fixed effect that controls for any
baseline differences in lending to Black-owned firms, αt is a fixed effect for the
week of loan approval that removes any general time trends in the share of
loans by small banks to Black-owned firms, and Xi represents firm and loan
controls that we add sequentially. The coefficient of interest β captures the
effect of automation.
We report results from estimating equation (3) in the sample of all PPP loans
originated by small and medium-sized banks in Panel A of Table V. The base-
line differences-in-differences model controlling only for bank and time fixed
effects is presented in column (1). The coefficient implies that the share of
loans to Black-owned firms increased by 6 percentage points after automation,
relative to a preautomation share of 4.4%. In column (2), we add the same
set of controls as in Table IV, Panel A, column (7). The magnitude of the β
coefficient declines by about one-third, but remains at 4.3 percentage points.
At 98% of the pre-automation share of Black-owned borrowers (4.4%), this is
economically large.21
Panel B of Figure 2 shows the coefficients from the dynamic differences-in-
differences regression (1) after adding the full vector of controls (as in Table V,
Panel A, column (2)). As before, prior to automation, the trends in lending
to Black-owned businesses among automating banks are the same as those
at nonautomating banks. Following automation, banks increase their rates of
lending to Black-owned businesses, though the magnitude of the increase is
somewhat smaller than in the unconditional specification shown in Panel A.
IV. Mechanisms: The Role of Discrimination
Above we documented that the automation of lending processes is associated
with a higher probability of lending to Black-owned firms. This is true both
across lenders, where fintech lenders and (to a somewhat lesser extent) the
largest banks with more automated lending processes tend to grant more PPP
loans to Black-owned businesses, and within lenders, where we find increased
lending to Black-owned firms after banks automate their loan origination pro-
cesses. Controlling for loan size, firm location, and other firm characteristics
reduces the racial disparities associated with automation by between one- and
two-thirds. But even when comparing loans to otherwise similar firms in a set-
ting with no credit risk, more automated lenders are substantially more likely
to lend to Black-owned firms.
Beyond the mechanisms explored above, automation could also reduce racial
disparities by removing human biases from any decision making during the
manual review and processing of PPP applications. Loan officers may become
aware of applicant race through the applicant’s name, which we have shown
21 In columns (3) to (5) of Table V, Panel A, we consider how lending to non–Black-owned firms
was affected. Following automation, we see a small increase in the rate of lending to Hispanic-
and Asian-owned firms. All of the increase in lending to Black-owned firms following automation
comes at the expense of White-owned firms.
1490
Table V
Effect of Automation during PPP on Lending to Black-Owned Small Businesses
This table reports estimates of equation (3), estimated on the sample of PPP loans extended by small and medium banks. Columns (1) and (2) of Panel
A show the effect of automation on the probability that a loan is extended to a Black-owned business. Columns (3) to (5) consider effects on lending
to Hispanic-, Asian-, and White-Owned businesses, respectively, using the fully controlled model from column (2). Panel B interacts the automation
indicator with measures of local racial animus (see Table VI), using the fully controlled model from Panel A, column (2). All racial animus measures
are standardized at their respective levels of geography, weighted by the number of PPP loans. Controls are as described in Table IV. Standard errors
are clustered by zip code. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 1%, 5%, and 10% level, respectively.
Panel A: Bank automation on loan share by race and ethnicity
1(Black-Owned) 1(Owned by:)
Hispanic Asian White
Dependent Variable: (1) (2) (3) (4) (5)
1(After Automation) 0.060*** 0.043*** 0.008*** 0.009*** −0.060***
(0.003) (0.003) (0.002) (0.003) (0.004)
Bank FE Yes Yes Yes Yes Yes
Approval Week FE Yes Yes Yes Yes Yes
The Journal of Finance®
Loan Amount FE No Yes Yes Yes Yes
Business Type FE No Yes Yes Yes Yes
Zip Code FE No Yes Yes Yes Yes
Industry FE No Yes Yes Yes Yes
Employer Status FE No Yes Yes Yes Yes
Dep Var Mean 0.037 0.037 0.044 0.055 0.865
Observations 2,090,197 2,090,197 2,090,197 2,090,197 2,090,197
(Continued)
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Table V—Continued
Panel B: Bank automation on loan share by race and ethnicity and racial animus
1(Black-Owned)
Dependent Variable: (1) (2) (3) (4) (5) (6)
1(After Automation) 0.041*** 0.042*** 0.045*** 0.042*** 0.037*** 0.041***
(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
1(After Automation) × Racial Animus 0.009*** 0.009*** 0.004* 0.005 0.025*** 0.001
(0.003) (0.002) (0.002) (0.003) (0.002) (0.002)
Racial Animus Measure IAT (Implicit) IAT (Explicit) Stephens- Nationscape Segregation Segregation
Davidowitz (Isolation) (Dissimilarity)
Bank FE Yes Yes Yes Yes Yes Yes
Approval Week FE Yes Yes Yes Yes Yes Yes
Loan Amount FE Yes Yes Yes Yes Yes Yes
Zip Code FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
Business Type FE Yes Yes Yes Yes Yes Yes
Employer Status FE Yes Yes Yes Yes Yes Yes
Dep Var Mean 0.037 0.037 0.037 0.037 0.037 0.037
Observations 2,090,197 2,090,197 2,090,197 2,090,197 2,090,197 2,090,197
Lender Automation and Racial Disparities in Credit Access
1491
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1492 The Journal of Finance®
to be highly predictive of race,22 or visually through manual review of appli-
cants’ drivers licenses, which were required in color for all PPP applicants. If
preference-based discrimination contributed to the observed higher probability
of otherwise similar Black-owned firms obtaining a PPP loan from automated
lenders, the difference should be larger in regions with higher racial animus.
We next explore this hypothesis by studying the interaction between the de-
gree of automation and geographic variation in racial animus.
Racial Animus Data: We collect six geographic measures of anti-Black racial
animus. The first measure is the share of an area’s Google searches that
contain racially charged words from Stephens-Davidowitz (2014). The second
measure follows Bursztyn et al. (2021) and is based on how favorably White
respondents rate Black Americans as a group in the Nationscape survey (Tau-
sanovitch and Vavreck (2020)). The third measure comes from the Implicit
Association Test (IAT), which assesses implicit bias against Black individuals.
The fourth measure is from a survey question that explicitly asks individuals
who just took the IAT for their feelings toward Black Americans (Xu, Nosek,
and Greenwald (2014)). The last two measures of racial animus are based
on the extent of local residential segregation (Massey and Denton (1988)).
The dissimilarity index captures differences in the distributions of White and
Black residents across city tracts. The isolation index measures the probability
of a Black resident sharing the same census tract with another Black resident.
Section I.C of the Internet Appendix describes the six measures of racial
animus in more detail, and examines their geographic variation as well
as the degree to which they are correlated with one another. Importantly,
Figure IA.14 shows that the places where racial animus is high differ
substantially across our measures, indicating that they offer somewhat
independent signals of animus.
Cross-Lender Racial Disparities by Racial Animus: Table VI estimates
whether, for a Black-owned firm, the probability of obtaining a PPP loan from
different lenders varies with the degree of racial animus in the firm’s location.
In each panel, column (1) includes the same right-hand side variables as in
column (7) of Table IV, Panel A. In columns (2) to (7), we additionally interact
the indicator for being Black-owned with each of the proxies for racial animus.
The location fixed effects absorb any direct effect of racial animus on the prob-
ability of borrowing from fintech lenders that is constant across all borrowers.
Each racial animus measure is standardized to have a mean of zero and a
standard deviation of one, so that the coefficients can be interpreted as the
effect, in percentage points, of a one-standard-deviation increase in the racial
animus measure on the probability of Black-owned firms obtaining their PPP
loan from a specific lender type.
22 Most Americans can infer race for a large fraction of names, perhaps not with the accuracy
of our algorithm, but well enough to lead to systematic bias (Bertrand and Mullainathan (2004),
Milkman, Akinola, and Chugh (2012), Bartoš et al. (2016)).
Table VI
Black Business Ownership and Lender Identity: The Effect of Racial Animus
This table reports estimates of a modified equation (2), focusing on the interaction between the indicator for Black-owned business and a standardized
measure of racial animus in the borrower location. The dependent variable differs across the three panels. In Panel A it is an indicator for a fintech
PPP loan, in Panel B it is an indicator for a non–top 4 bank PPP loan, and in Panel C it is an indicator for a top 4 bank PPP loan. In each panel,
column (1) repeats the specification in Table IV, Panel A, column (7). The racial animus measures are as follows: columns (2) to (3) use the implicit
and explicit score from the Implicit Association Test (IAT) aggregated to the county level; column (4) uses the number of racially charged searches in a
designated media market (DMA); column (5) uses responses to the question on favorability toward Black people in the Nationscape survey aggregated
to the congressional district level; and columns (6) to (7) use the dissimilarity and isolation index at the metropolitan statistical area (MSA) level. All
racial animus measures are standardized at their respective levels of geography, weighted by the number of PPP loans. Controls are as described in
Table IV. Standard errors are clustered by zip code. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 1%, 5%, and 10% level, respectively.
Panel A: Fintech PPP loans as dependent variable
1(Fintech)
Dependent Variable: (1) (2) (3) (4) (5) (6) (7)
1(Black-Owned) 0.121*** 0.120*** 0.121*** 0.124*** 0.121*** 0.117*** 0.107***
(0.002) (0.001) (0.001) (0.001) (0.002) (0.001) (0.001)
1(Black-Owned) × Racial Animus 0.013*** 0.011*** 0.004** 0.014*** 0.016*** 0.029***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Dep Var Mean 0.174 0.174 0.174 0.174 0.174 0.174 0.174
Panel B: Non–Top 4 bank PPP loan as dependent variable
1(Non-Top 4 Bank)
Dependent Variable: (1) (2) (3) (4) (5) (6) (7)
1(Black-Owned) −0.107*** −0.105*** −0.107*** −0.108*** −0.107*** −0.104*** −0.094***
(0.002) (0.001) (0.001) (0.001) (0.001) (0.002) (0.001)
Lender Automation and Racial Disparities in Credit Access
1(Black-Owned) × Racial Animus −0.022*** −0.014*** −0.013*** −0.012*** −0.012*** −0.028***
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)
Dep Var Mean 0.592 0.592 0.592 0.592 0.592 0.592 0.592
(Continued) 1493
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1494
Table VI—Continued
Panel C: Top 4 bank PPP loans as dependent variable
1(Top 4)
Dependent Variable: (1) (2) (3) (4) (5) (6) (7)
1(Black-Owned) −0.008*** −0.009*** −0.008*** −0.009*** −0.008*** −0.008*** −0.009***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
1(Black-Owned) × Racial Animus 0.008*** 0.004*** 0.010*** −0.001 −0.004*** 0.004***
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Racial Animus Measure IAT (Implicit) IAT (Explicit) Stephens- Nationscape Segregation Segregation
Davidowitz (Dissimilar- (Isolation)
ity)
Approval Week FE Yes Yes Yes Yes Yes Yes Yes
Loan Amount FE Yes Yes Yes Yes Yes Yes Yes
Zip Code FE Yes Yes Yes Yes Yes Yes Yes
The Journal of Finance®
Industry FE Yes Yes Yes Yes Yes Yes Yes
Business Type FE Yes Yes Yes Yes Yes Yes Yes
Employer Status FE Yes Yes Yes Yes Yes Yes Yes
Dep Var Mean 0.159 0.159 0.159 0.159 0.159 0.159 0.159
Observations 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623 4,183,623
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Lender Automation and Racial Disparities in Credit Access 1495
In Panel A of Table VI, we consider the effects of increased racial animus
on the probability of a Black-owned firm obtaining a PPP loan from a fintech
lender. We find a robust positive interaction between the various racial animus
measures and Black firm ownership. The coefficient magnitudes vary from 0.4
to 2.9 percentage points across the various measures. This implies that, rela-
tive to the mean probability of a fintech loan of 17.4%, a one-standard-deviation
increase in racial animus is associated with a 2.3% to 16.7% increase in the
probability that a Black-owned firm obtains its PPP loan from a fintech lender.
With the implicit bias (IAT) measure—which is probably the most widely used
in the academic literature—the coefficient estimate of 1.3 percentage points
implies a 7.5% increase. In sum, we find robust evidence that in areas with
higher racial animus, Black-owned firms are particularly likely to obtain their
PPP loans from fintech lenders.
Prior analyses show that Black-owned firms’ substitution toward fintechs
came primarily from smaller banks. If racial discrimination at small banks ex-
plains some of our findings, the pattern in Panel A should reverse when we
consider the probability of obtaining PPP loans from smaller banks. Panel B
finds precisely this relationship. For example, the coefficient using the implicit
bias (IAT) measure implies that a one-standard-deviation higher racial ani-
mus score is associated with a 2.2 percentage point decrease in the likelihood
that Black-owned firms get their PPP loans from a non–top 4 bank. Consistent
with prior findings, Panel C shows that the propensity of top 4 banks to lend to
Black-owned firms does not systematically vary with the degree of racial ani-
mus.
In Table IA.XVII, we show that our measures of racial animus in these
specifications do not simply proxy for local levels of education and income
among Black people, and their possible effects on the probability of Black-
owned firms to obtain PPP loans from different lender types. Specifically,
we interact the indicator for Black-owned with county-level measures of the
percent of Black people with at least a bachelor’s degree and the median Black
household income, both from the Census 2019 ACS. If anything, the interac-
tion between local racial animus and Black-owned is somewhat stronger with
these additional interactions.
The Effect of Bank Automation by Racial Animus: In Panel B of Table V, we
examine whether the effect of automation on the share of loans to Black-owned
businesses is larger in areas with higher racial animus. There are positive and
significant interactions for four of the six measures, with the remaining two
being positive but marginally insignificant at conventional levels. In terms of
magnitude, the interaction coefficient for the implicit bias test implies that au-
tomation increased the share of PPP lending to Black-owned firms by about
20% more in areas with one-standard-deviation higher racial animus. Table
IA.XII, Panel B repeats the analysis on the subset of loans with self-reported
race and ethnicity and finds similar results. This finding provides additional
evidence that one of the mechanisms through which automation increases PPP
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1496 The Journal of Finance®
lending to Black-owned firms is by reducing the effect of preference-based dis-
crimination.
V. Mechanisms: Loan Applications and Rejections
Our analysis so far studies racial differences in PPP lender identity among
firms that ultimately received PPP loans. Our interpretation of the observed
racial differences involves less automated conventional lenders—and in par-
ticular smaller banks—having a higher tendency to not process or to reject the
PPP applications of Black-owned firms, because Black-owned firms are gen-
erally smaller and thus eligible for smaller and less profitable PPP loans and
because of racial discrimination.23
We next use PPP application data to address possible concerns that our
across-bank results might instead reflect Black-owned firms applying more
frequently to fintech lenders (as documented in other settings by Barkley and
Schweitzer (2020), Barkley and Schweitzer (2022)), perhaps because of a par-
ticular fintech affinity in this population or because they anticipate less dis-
crimination. While such differences in application behavior would not confound
our within-lender analysis, we next show that differential application behavior
also cannot fully explain our cross-bank findings (though our data do not allow
us to rule out that they contribute to the differences).24
A. Lendio Loan Application Data
We obtain data on PPP applications to the marketplace platform Lendio
through November 2020. Firms could submit PPP applications through the
Lendio website, which were then forwarded to one or two of around 300 part-
ner lenders that include both fintech firms and conventional banks.
Our conversations with Lendio executives, including CEO Brock Blake, in-
dicate that the routing of applications to lenders was random conditional on
loan size, geography, and capacity criteria set by the lender partners. Lenders
then decided whether to approve the application, complete the SBA approval
(duplicate check) process, and finally fund successful applicants. As the appli-
cation through Lendio included all necessary components and was screened for
completeness, the lender typically did not have further interactions with the
borrower. Importantly, applicants did not know which bank their applications
would be forwarded to when applying through Lendio, and they had no control
23 Investigative reporting (Morel et al. (2021), Zhou (2020)) and survey data (Small Business
Majority survey) suggest widespread rejections of PPP loan applications. For example, in a survey
of around 10,000 employer firms, the Federal Reserve found that approval rates varied between
about 75% and 90% depending on the lender type (Fed Small Business Survey Data). One of the
largest lenders was reported to reject more than 90% of applications (Flitter and Cowley (2020)).
24 To the extent that Black-owned firms are less likely to apply to conventional lenders because
they correctly anticipate discriminating treatment, the main thrust of our findings remains un-
changed. Indeed, substitution away from lenders who discriminate may partially explain demand
for fintech products more generally.
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Lender Automation and Racial Disparities in Credit Access 1497
over the application routing. These data, therefore, permit us to largely hold
fixed firms’ application behavior.
We observe 278,404 applications that Lendio forwarded to at least one
lender. The average application was routed to 1.5 lenders, composed of 0.9 fin-
techs and 0.6 conventional lenders (Table IA.XVIII). Among the firms whose
PPP applications were forwarded by Lendio to at least one lender, just over
60% ultimately received a PPP loan, while the remaining 40% did not end up
receiving any PPP loan at all. Among firms that got a PPP loan, about 25% re-
ceived the loan from one of the lenders to which its application was forwarded
by Lendio, while the rest got the loan from a different lender.
Statistics on the Lendio sample and its demographic breakdown are sum-
marized in Panel C of Table IA.IV. The loan amount is the actual PPP loan
amount except for the “No PPP Loan” category, in which case it is the amount
sought as reported by Lendio. The average loan amount for borrowers sent only
to fintechs is less than half the amount for borrowers sent only to conventional
lenders, likely due to fintechs specifying lower target loan amounts at Lendio
because of lower fixed costs per loan.
B. Analysis of Lendio Loan Applications
In Table VII, we analyze PPP loan outcomes for applicants through the
Lendio platform. We consider two outcomes: (i) whether the firm ultimately
receives a PPP loan from a lender to which Lendio sent the firm’s application,
and (ii) whether the firm fails to obtain any PPP loan.
When pooling across all PPP applications in our sample, we find that Black-
owned applicants are less likely to get a PPP loan from one of the lenders to
which their application was forwarded by Lendio, even after controlling for a
wide range of firm characteristics (column (1)). The regression specification
includes fixed effects for the identity of the lenders the application was sent
to. Our findings thus imply that, conditional on an application to a given
lender, Black-owned firms are less likely to obtain a PPP loan from that
lender. Column (2) shows that, in addition, Black-owned firms are less likely
to get any PPP loan at all compared to otherwise similar firms with non-Black
ownership forwarded to the same lender.
In columns (3) and (4) of Table VII, we restrict the sample to PPP applica-
tions that were sent only to fintech lenders (recall that, conditional on loan
characteristics, which lenders an application gets forwarded to is random).
Among PPP applications that are routed to fintechs lenders, Black-owned firms
face no differential chance of getting a PPP loan from that lender (column (3)).
This is consistent with Black-owned firms facing no disparate treatment at
fintech lenders. Black-owned firms routed to fintech lenders did face a slightly
higher chance of getting no PPP loan at all (column (4)). The difference between
columns (3) and (4) is driven by racial differences in PPP outcomes among the
sample of firms that were unable to obtain their eventual PPP loan through
Lendio. While column (3) shows that, conditional on controls, this sample is
1498
Table VII
Lending to Black-Owned Firms Conditional on Applications
This table reports estimates of a modified version of equation (2) within the sample of PPP loan application to Lendio. Columns (1) and (2) include
the full sample, columns (3) and (4) restrict attention to the subsample of applications that Lendio quasi-randomly forwarded only to fintech lenders,
columns (5) and (6) restrict attention to the subsample of loans that were forwarded only to conventional lenders, and columns (7) and (8) restrict
attention to the subsample of loans that were forwarded only to small banks. In each sample, we consider two dependent variables: an indicator for
whether the application eventually resulted in a PPP loan from one of the lenders who Lendio forwarded the application to (columns (1), (3), (5),
and (7)), and an indicator variable for whether the loan applicant received no PPP loan at all (through a Lendio lender or an alternative lender). We
include fixed effects for each combination of lenders who the application was routed to. Other controls are as described in Table IV, with the exception
that we include application week fixed effects instead of origination week fixed effects. Standard errors are clustered by zip code. ∗∗∗ , ∗∗ , and ∗ indicate
statistical significance at the 1%, 5%, and 10% level, respectively.
Sent to Lenders: Any Only Fintech Only Conventional Only Small Banks
1(PPP Loan 1(No PPP 1(PPP Loan 1(No PPP 1(PPP Loan 1(No PPP 1(PPP Loan 1(No PPP
from Lendio Loan) from Lendio Loan) from Lendio Loan) from Lendio Loan)
Lender) Lender) Lender) Lender)
Dependent Variable: (1) (2) (3) (4) (5) (6) (7) (8)
1(Black-owned) −0.011*** 0.021*** −0.000 0.006** −0.039*** 0.058*** −0.056*** 0.068***
The Journal of Finance®
(0.003) (0.002) (0.004) (0.003) (0.005) (0.005) (0.010) (0.009)
Application Week FE Yes Yes Yes Yes Yes Yes Yes Yes
Loan Amount FE Yes Yes Yes Yes Yes Yes Yes Yes
Zip Code FE Yes Yes Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes Yes Yes
Business Type FE Yes Yes Yes Yes Yes Yes Yes Yes
Sent Lenders FE Yes Yes Yes Yes Yes Yes Yes Yes
Dep Var Mean 0.250 0.396 0.239 0.399 0.316 0.364 0.382 0.375
Observations 278,404 278,404 153,434 153,434 83,328 83,328 36,276 36,276
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Lender Automation and Racial Disparities in Credit Access 1499
not selected on race, some of these firms subsequently applied to conventional
lenders where they would face the same disparities described in Section IV.
In columns (5) and (6) of Table VII, we restrict the sample to PPP applica-
tions that were forwarded only to conventional lenders. Among those applica-
tions, Black-owned firms are 3.9 percentage points less likely to get a PPP loan
from a Lendio lender and 5.8 percentage points less likely to get a PPP loan
at all. Note that the differences between columns (3) and (5) do not reflect fin-
techs being more permissive in general; the average rate of originating Lendio
loans is, in fact, somewhat lower at fintechs than at conventional lenders. In-
stead, the result in column (6) represents a real effect of higher rejection rates
for Black-owned firms at conventional lenders. It indicates that Black-owned
firms are 5.8 percentage points, or 15.9% of the mean, more likely to obtain
no PPP loan at all when their application is forwarded only to conventional
lenders. In columns (7) and (8), we further restrict the sample to loans that
Lendio forwarded to small banks, those banks with the least automated pro-
cessing systems on average. Consistent with our prior findings, racial differ-
ences in obtaining PPP loans are largest among applications forwarded to this
group of lenders.
In sum, racial differences in the propensity to apply to different lenders
cannot fully explain the main cross-lender differences (in addition to not ex-
plaining our within-lender findings). Instead, differences in lender decisions
to process or approve completed PPP applications at least partly explains why
Black-owned firms are more likely to receive fintech loans, and less likely to re-
ceive small bank loans. This behavior has important real effects. Black-owned
firms whose application is quasi-randomly assigned to be processed by conven-
tional lenders are less likely to get a PPP loan at all, in addition to being less
likely to get a PPP loan from that lender. There is no comparable racial dis-
parity among firms whose applications are assigned to be processed by fintech
lenders. This finding indicates that automation not only affects which lender
Black-owned firms get their PPP loan from, but also whether they obtain a PPP
loan at all. Thus, whatever the determinants of the racial disparities identified
in this paper, they have important real effects.
VI. Mechanisms: Bank and Credit Relationships
There is substantial evidence that many banks tended to first serve their
own clients’ PPP loan applications, for example, because processing these ap-
plications was lower cost or because PPP loans might enable clients to repay
preexisting loans to the bank (Granja et al. (2020), Flitter and Cowley (2020)).
If banks prioritized their own clients in distributing PPP loans, and if Black-
owned businesses were less likely to bank with active PPP lenders, this could
explain some of the observed differences in their propensity to eventually bor-
row from other lenders such as fintech firms (though again, it should not con-
found the within-lender analysis). We directly assess this hypothesis using a
sample of PPP borrowers matched to bank statement data.
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1500 The Journal of Finance®
A. Bank Statement Data
We employ data from Ocrolus on firms’ bank statements through July 2021.
Ocrolus digitizes documents for fintech companies, including business check-
ing account statements used in the underwriting process, and thus has a large
repository of such statements. We match around 168,000 unique PPP borrow-
ers in our analysis sample to Ocrolus’ database using information on the busi-
ness name and address. If several bank statements are available for a firm (the
average firm has three bank statements, mostly from 2019 and 2020), we focus
on the most recent statement prior to the issuance of the PPP loan.
Panel C of Table I shows that the bank statement sample and the full anal-
ysis sample are broadly similar along dimensions such as loan amount. Firms
with bank statements are somewhat more likely to be minority owned. The
main dimension of selection is that firms with matched bank statements have
higher rates of fintech PPP loans—36.3%, compared to 17.4% in the analysis
sample. This result reflects the fact that Ocrolus processes loan applications
for many fintech clients, thus selecting on firms with fintech affinity or experi-
ence.
We define a firm’s checking account bank as the bank that issued the state-
ment. In addition, text descriptions of transactions in the bank statements
permit us to identify credit relationships. Specifically, we use the existence
of a transaction to or from a lender to indicate a credit relationship—loan,
credit line, or credit card—with this lender. Since these relationships include
business credit cards, they are much broader than other sources of data, such
as UCC filings for secured debt. Among all borrowers, 14.2% had a credit re-
lationship with a fintech firm, while 80.0% had a credit relationship with a
traditional bank (Table II, Panel B). The share of firms with access to exter-
nal financing in the Ocrolus sample is relatively high because Ocrolus obtains
bank statements for firms actively seeking external credit. There are no large
differences by PPP lender type in the propensity of firms to have prior credit
relationships with a fintech or a traditional lender (Table I, Panel C). We also
use the bank statement data to calculate monthly cash inflows and outflows
as a measure of firm financial performance. Panel B of Table II shows that
the mean net monthly cash inflow across all firms is $9,016, while it is $6,332
among Black-owned businesses.
B. Banking Relationship Analysis
Consistent with media reports and previous literature, we find that con-
ventional banks’ PPP clients were also often their business checking account
clients. Panel C of Table I shows that 27.4% of PPP borrowers had a checking
account at their PPP lender. About two-thirds of all PPP loans originated by
the top 4 banks went to checking account clients of those banks. For other large
banks this number is 48.8%, and for medium and small banks it is 38.4% and
23.3%, respectively. For fintech lenders, which do not usually offer checking
accounts, this number was essentially zero.
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Lender Automation and Racial Disparities in Credit Access 1501
Although conventional lenders served their own clients at higher rates, we
show in Table VIII that this fact does not explain the higher rate of fintech
PPP loans for Black-owned firms. First, in column (1), we estimate the fully
controlled model from Table IV, Panel A, column (7), in the bank statement-
matched sample. In this sample, Black-owned firms are 5.5 percentage points
more likely to obtain their PPP loan from a fintech lender. In column (2), we
add fixed effects for the identity of the bank where the firm has a checking
account. In this model, we are comparing, for example, the origination of PPP
loans to Black-owned and other firms with a checking account at JPMorgan
Chase. The inclusion of these fixed effects has essentially no effect on the
differential probability of Black-owned firms to obtain their PPP loans through
fintech lenders.
In Panel B of Table VIII, we find that, as in the full sample, Black-owned
firms in the Ocrolus matched sample have no differential chance of a top 4
bank PPP loan (column (1)), but are substantially less likely to get a small
bank PPP loan (column (5)). As with fintech loans in Panel A, these relation-
ships do not attenuate much with the inclusion of checking account bank fixed
effects. Therefore, the racial disparity in this data set does not reflect Black-
owned firms holding their checking accounts at banks that were less active as
PPP lenders.
We next assess whether there are racial differences in the propensity of a
firm to obtain its PPP loan from its checking account bank. In Table IX, we
split the sample of checking account holders by the identity of the checking ac-
count bank. Among firms with checking accounts at top 4 banks, Black-owned
firms have the same chance as other firms of getting their PPP loan from their
checking account bank (column (1) of Table IX, Panel A). Black-owned busi-
nesses are slightly less likely than other groups to obtain their PPP loans from
their checking account banks if they bank with non–top 4 banks (column (1) of
Table IX, Panels B and C).25
In column (2) of Table IX, we show that Black-owned firms’ excess probability
of getting a fintech loan is similarly large regardless of where they have their
checking account, and holds even for borrowers with checking accounts at top 4
banks. The subsequent columns show that this result reflects variation in PPP
lender types among firms that do not get their PPP loan from their checking
account bank.
25 Many newspaper articles offer examples of Black-owned businesses failing to obtain PPP
loans through their checking account banks. For example, the Associated Press interviewed Lisa
Marsh, the Black owner of MsPsGFree, a Chicago-based baking business (Rosenberg and Myers
(2020)): “Lisa Marsh tried in vain to get banks to process her application. She first applied in June
but she couldn’t get answers on her status from her bank, a subsidiary of a big national bank.
She also got nowhere with smaller community banks… [Marsh] finally applied through an online
lender in late July and got her loan a few days before the PPP ended. ‘I was very frustrated and
almost gave up,’ she says.” In a similar story, The New York Times described Black auto dealership
owner Jenell Ross who, “sought a Paycheck Protection Program loan, [but] her longtime bank told
her to look elsewhere” (Cowley (2021)).
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1502 The Journal of Finance®
Table VIII
Black Business Ownership and PPP Lender Type with Bank and
Credit Relationship Controls
This table reports estimates of a modified equation (2), focusing on the role of bank and credit
relationships. The sample is restricted to bank statement-matched data. We include only informa-
tion from a firm’s latest statement prior to the loan approval. The dependent variable in Panel A
is an indicator for whether a PPP loan is originated by a fintech lender. The dependent variables
in Panel B are indicators for whether the originating lender is a top 4 bank (columns (1) and (2)),
a large bank (columns(3) and (4)), or a small/medium-sized bank (columns (5) and (6)). We report
coefficients on indicators for whether the borrower has previous credit relationships with fintech
and nonfintech lenders. Checking Acct Bank FE are indicators for the bank where the borrower
has its main business checking account, so that we compare borrowers who bank with the same
institution. Monthly Net Cash Inflow FE and Monthly Cash Inflow FE are each a set of 100 per-
centile indicators for monthly net cash inflow and total cash inflow, respectively. Other controls are
as described in Table IV. Standard errors are clustered by borrower zip code. ∗∗∗ , ∗∗ , and ∗ indicate
statistical significance at the 1%, 5%, and 10% level, respectively.
Panel A: Fintech PPP loan
1(Fintech)
Dependent Variable: (1) (2) (3) (4)
1(Black-Owned) 0.055*** 0.055*** 0.056*** 0.055***
(0.004) (0.004) (0.004) (0.004)
1(Credit from Fintech) 0.075*** 0.078***
(0.003) (0.003)
1(Credit from Conv.) −0.012*** −0.011***
(0.003) (0.003)
Approval Week FE Yes Yes Yes Yes
Loan Amount FE Yes Yes Yes Yes
Zip Code FE Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes
Business Type FE Yes Yes Yes Yes
Employer Status FE Yes Yes Yes Yes
Months Since Statement FE No Yes Yes Yes
Checking Acct Bank FE No Yes Yes Yes
Monthly Cash Inflow FE No No No Yes
Monthly Net Cash Inflow FE No No No Yes
Dep Var Mean 0.363 0.363 0.363 0.363
Observations 168,360 168,360 168,360 168,360
(Continued)
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Lender Automation and Racial Disparities in Credit Access 1503
Table VIII—Continued
Panel B: Bank PPP loan
1(Top 4 Bank) 1(Large Bank) 1(Small/Med Bank)
Dependent Variable: (1) (2) (3) (4) (5) (6)
1(Black-Owned) 0.005 −0.000 −0.017*** −0.019*** −0.047*** −0.037***
(0.003) (0.003) (0.003) (0.002) (0.004) (0.003)
1(Credit from Fintech) −0.025*** −0.011*** −0.035***
(0.003) (0.002) (0.003)
1(Credit from Conv.) 0.001 0.005** 0.001
(0.002) (0.002) (0.003)
Approval Week FE Yes Yes Yes Yes Yes Yes
Loan Amount FE Yes Yes Yes Yes Yes Yes
Zip Code FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
Business Type FE Yes Yes Yes Yes Yes Yes
Employer Status FE Yes Yes Yes Yes Yes Yes
Months Since Statement FE No Yes No Yes No Yes
Checking Acct Bank FE No Yes No Yes No Yes
Monthly Cash Inflow FE No Yes No Yes No Yes
Monthly Net Cash Inflow FE No Yes No Yes No Yes
Bank Statement Sample Latest Latest Latest Latest Latest Latest
Dep Var Mean 0.177 0.177 0.090 0.090 0.304 0.304
Observations 168,360 168,360 168,360 168,360 168,360 168,360
These results highlight two channels that contribute to the higher rate of
fintech loans among Black-owned firms. First, Black-owned firms with check-
ing accounts at non–top 4 banks were somewhat less likely to obtain their
PPP loans from their checking account bank. Second, among firms whose PPP
lenders were not their checking account banks, Black-owned firms were much
less likely to obtain loans from non–top 4 banks, and much more likely to ob-
tain them from fintech lenders. Quantitatively, this second channel, which cap-
tures racial differences in the rates of establishing new banking relationships
with different types of lenders, explains the majority of the observed disparity.
Consistent with the earlier findings, the difference in this sample is largest
among new clients at small banks—those banks with the least-automated ap-
plication systems—with no evidence of substantial disparate treatment at top
4 banks.
We next explore whether prior credit relationships explain PPP lending pat-
terns. In column (3) of Table VIII, Panel A, we include indicators for whether
a PPP borrower has credit relationships with any fintech and conventional
lenders. Unsurprisingly, a prior credit relationship with a fintech lender is as-
sociated with a significantly higher chance of obtaining a PPP loan from a
fintech lender. Similarly, having previously received credit from a nonfintech
lender reduces the likelihood of getting a fintech PPP loan and increases the
probability of a nonfintech PPP loan. The preferential treatment of firms with
prior credit relationships, however, does not account for the disproportionate
1504
Table IX
Black Business Ownership and PPP Lender Type by Checking Account Bank Type
This table reports estimates of a modified equation (2), focusing on various samples of firms with different checking account bank and credit relation-
ships. Panels A, B, and C limit the sample to PPP borrowers with checking accounts at top 4 banks, non–top 4 large banks, and small/medium banks,
respectively. Panel D limits the sample to PPP borrowers who have previous credit relationships with fintech lenders. Across all panels, the depen-
dent variable in column (1) is an indicator for whether a PPP loan is originated by the borrower’s checking account bank. The dependent variables in
columns (2)–(5) are indicators for whether a PPP loan is originated by a fintech lender, top 4 bank, non–top 4 large bank, and small/medium bank,
respectively. Controls are as described in Table VIII. Standard errors are clustered by borrower zip code. ∗∗∗ , ∗∗ , and ∗ indicate statistical significance
at the 1%, 5%, and 10% level, respectively.
Banks:
1(Lender is Bank) 1(Fintech) 1(Top 4) 1(Large) 1(Small/Medium)
Dep Var: (1) (2) (3) (4) (5)
Panel A: Sample of borrowers with checking accounts at top 4 banks
1(Black-Owned) −0.006 0.051*** −0.007 −0.010*** −0.034***
(0.005) (0.006) (0.005) (0.003) (0.004)
Observations 80,560 80,560 80,560 80,560 80,560
The Journal of Finance®
Dep Var Mean 0.248 0.402 0.310 0.048 0.188
Panel B: Sample of borrowers with checking accounts at non-top 4 large banks
1(Black-Owned) −0.024** 0.044*** 0.009 −0.033*** −0.041***
(0.010) (0.011) (0.006) (0.010) (0.009)
Observations 26,539 26,539 26,539 26,539 26,539
Dep Var Mean 0.261 0.364 0.062 0.309 0.216
(Continued)
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Table IX—Continued
Panel C: Sample of borrowers with checking accounts at small/medium-sized banks
1(Black-Owned) −0.015 0.056*** −0.001 −0.008 −0.044***
(0.010) (0.010) (0.005) (0.005) (0.010)
Observations 46,352 46,352 46,352 46,352 46,352
Dep Var Mean 0.355 0.256 0.051 0.053 0.591
Panel D: Sample of borrowers with fintech credit relationship
1(Black-Owned) 0.019 0.032** 0.016 −0.009 −0.040***
(0.014) (0.014) (0.012) (0.009) (0.011)
Observations 23,890 23,890 23,890 23,890 23,890
Dep Var Mean 0.298 0.339 0.194 0.093 0.311
Approval Week FE Yes Yes Yes Yes Yes
Loan Amount FE Yes Yes Yes Yes Yes
Zip Code FE Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes
Business Type FE Yes Yes Yes Yes Yes
Employer Status FE Yes Yes Yes Yes Yes
Months Since Statement FE Yes Yes Yes Yes Yes
Checking Acct Bank FE Yes Yes Yes Yes Yes
Monthly Cash Inflow FE Yes Yes Yes Yes Yes
Monthly Net Cash Inflow FE Yes Yes Yes Yes Yes
Credit Rel. Controls Yes Yes Yes Yes Yes
Lender Automation and Racial Disparities in Credit Access
1505
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1506 The Journal of Finance®
lending to Black-owned businesses by fintech lenders in the PPP: Black-owned
firms are 5.6 percentage points more likely to get their PPP loan from a fintech
lender compared to other PPP borrowers, even after conditioning on the iden-
tity of the checking account bank and the presence of credit relationships with
both fintech and nonfintech lenders (Table VIII, Panel A, column (3)).
In Section V, we showed that differential propensities of Black-owned firms
to apply to fintech lenders are unlikely to explain the observed racial dispar-
ities in PPP lender identity. We further explored a specific such mechanism,
which suggests that one reason Black-owned firms may have been more likely
to apply to fintechs is that they may have been more tech-savvy or have had
higher fintech affinity. To further test this hypothesis, we condition on firms in
the bank statement–matched data that we observe having a preexisting credit
relationship with fintech firms. Within this sample of firms, all of which show
a certain degree of past fintech affinity, we continue to find an economically im-
portant substitution of PPP borrowing of Black-owned businesses from small
and medium banks toward fintech lenders (Panel D of Table IX). This find-
ing implies that the main results are unlikely to be driven by higher fintech
affinity among Black-owned firms.
VII. Other Possible Explanations: Performance and Fraud
The previous results suggest that automation explains an important part
of the variation across lender types in PPP lending to Black-owned firms. In
Section II of the Internet Appendix, we consider two final mechanisms that
may contribute to the observed cross-bank effects (although they could not ex-
plain the within-bank effects): contemporaneous firm performance and fraud.
Here, we briefly summarize the tests and their results.
First, one might be concerned that lenders treated Black-owned firms differ-
ently because those firms experienced particularly negative pandemic shocks.
To assess this concern, we obtain data from Enigma, which observes at least
60% of all U.S. debit and credit card transactions, on overall monthly credit
card revenues. We can match more than 800,000 PPP borrowers to the Enigma
data. Although this sample consists of firms that are on average larger and
more sophisticated, Black-owned firms are still 16% more likely to get a fin-
tech PPP loan. Adding controls for card revenue has no effect on this disparity.
Therefore, racial differences in the real-time firm performance do not explain
the results.
A final possibility is that the cross-lender variation in lending to Black-
owned firms could result from differential statistical discrimination by lenders
based on their differential fraud rates. In particular, if Black business own-
ers were much more likely to submit fraudulent PPP applications and fintechs
had much lower compliance standards, in particular relative to small banks,
this channel could contribute to the large observed racial disparities in lender
identity. Section II of the Internet Appendix presents a variety of evidence sug-
gesting that this hypothesis is unlikely to explain our findings.
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Lender Automation and Racial Disparities in Credit Access 1507
VIII. Conclusion and Discussion
The original legislation authorizing the PPP included an explicit mandate to
prioritize socioeconomically disadvantaged businesses. Yet, in practice, many
conventional banks did not serve Black-owned firms in proportion to their
share in the PPP borrower population. Instead, it was fintech lenders who
originated a disproportionate share of loans to Black-owned firms, accounting
for over half of the PPP loans to Black-owned businesses. Among conventional
lenders, small banks had a particularly low rate of lending to Black-owned
firms. What explains these observations, given that PPP loans were 100% guar-
anteed by the federal government? This question is the focus of our paper.
We argue that varying degrees of automation across lender types help ex-
plain these patterns. First, we find that racial differences in loan shares
across lenders align with differences in automation rates, with the most au-
tomated lenders (fintechs) issuing the largest share of loans to Black-owned
firms, while the least automated lenders (small banks) contributed the small-
est share. Second, we show that after conventional lenders automated their
lending processes, their rates of lending to Black-owned businesses increased
substantially. Borrower characteristics—including location, loan amount, loan
approval date, industry, and business form—can explain some but not all of
the unconditional disparity between fintechs and other lenders. Some of these
characteristics are related to the channels through which automated lending
can increase credit access for Black-owned firms. For example, automation al-
lowed fintechs to make smaller loans. Since loan size under PPP was tied to
payroll and Black-owned firms tend to be smaller, this disproportionately ben-
efited Black-owned firms.
However, even with a rich array of controls, Black-owned businesses re-
main about 12 percentage points more likely than other firms to receive
their PPP loans from a fintech lender. Moreover, we show that differential
preexisting bank relationships, firm application behavior, real-time revenue,
fintech affinity, and fraud rates cannot fully explain this gap. Instead, we
find suggestive evidence that preference-based discrimination helps explain
lower rates of lending to Black-owned businesses among smaller conventional
lenders. Since many of the variables we condition on partially reflect historical
discrimination patterns (e.g., location controls to capture the distribution of
bank branch networks), the substantial differences in our controlled models
represent a lower bound on the overall effect of discrimination on small
business lending patterns.
Our results relate to the ongoing conversation about the equity effects of new
technologies in the provision of financial services. While there are legitimate
concerns that the use of algorithms may lead to discriminatory effects, for ex-
ample, because the algorithms are trained on biased data, our results suggest
that there may be substantial equity benefits from automation. Specifically, by
eliminating the manual review conducted by potentially biased humans, au-
tomation could reduce the incidence of taste-based discrimination. A promising
area for future research is whether there are similar equity benefits from other
financial activities such as securitization that increase the weight placed on
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1508 The Journal of Finance®
hard information in lending decisions, reducing the scope for taste-based dis-
crimination.
The PPP setting has many advantages to help shed light on the effects of
automation on racial disparities in access to credit, most notably by removing
the need for banks to evaluate credit risk. However, while we believe that the
broad findings in this work are likely to generalize to other settings, there are
reasons to expect the overall magnitude of the effect of automation on racial
disparities to not translate directly. For example, during the early COVID pe-
riod, the economic cost to conventional lenders from any taste-based racial dis-
crimination was relatively small: PPP loan applications dramatically exceeded
banks’ processing capacities, and any decision not to process a loan application
from a Black-owned firm could typically be substituted with the processing
of an equally profitable application from a White-owned firm. One would ex-
pect that in other settings with less binding capacity constraints, economic
forces might push more strongly against substantial taste-based discrimina-
tion, leading to lower observed racial disparities. On the other hand, during
the COVID period, even small banks dramatically reduced in-person service
and typically accepted PPP applications online (see Figures IA.6 and IA.7). In
our setting, most of the effects of differential automation therefore occur only
after the application arrives at the financial institution, in processes such as
payroll verification. As a result, one might expect racial disparities caused by
automation to be larger in a normal lending market in which small banks do
more of their business in person (Arnold, Dobbie, and Yang (2018), Knowles,
Persico, and Todd (2001), Price and Wolfers (2010)). Better understanding the
magnitudes of racial disparities across other lending markets is thus an im-
portant avenue for future research.
Initial submission: February 4, 2022; Accepted: July 3, 2022
Editors: Stefan Nagel, Philip Bond, Amit Seru, and Wei Xiong.
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Supporting Information
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article at the publisher’s website:
Appendix S1: Internet Appendix.
Replication Code.
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