David Glancy, Bank Relationships and the Geography of PPP Lending (2023)

Summary

Federal Reserve Board Finance and Economics Discussion Series paper 2023-014 by David Glancy, dated January 19, 2023, on how bank relationships shaped the timing and geography of Paycheck Protection Program lending. Using geocoded branch and borrower locations, it finds that about half of bank PPP loans, and two-thirds of loans from relationship-oriented banks, went to borrowers within 2 miles of a branch. Firms near less active lenders shifted to fintechs and other distant lenders, which delayed credit but only slightly lowered loan volumes. A structural model estimates that banks served relationship borrowers 5 to 9 days before other borrowers. The paper notes that the PPP provided over $500 billion in forgivable loans.

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“David Glancy, Bank Relationships and the Geography of PPP Lending (2023).” Original at www.federalreserve.gov. Archived at Pandemic Darlings, https://pandemicdarlings.com/source-documents/david-glancy-bank-relationships-and-the-geography-of-ppp-lending-2023-c3fdec1783/, SHA-256 2fa2701456746526d9d53312fc1c94ddd8c42b2dbb576b2ba95e4f7dd073af8b.

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Finance and Economics Discussion Series

Federal Reserve Board, Washington, D.C.
ISSN 1936-2854 (Print)
ISSN 2767-3898 (Online)

Bank Relationships and the Geography of PPP Lending

David Glancy

2023-014

Please cite this paper as:
Glancy, David (2023). “Bank Relationships and the Geography of PPP Lending,” Finance
and Economics Discussion Series 2023-014. Washington: Board of Governors of the Federal
Reserve System, https://doi.org/10.17016/FEDS.2023.014.

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary
materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth
are those of the authors and do not indicate concurrence by other members of the research staff or the
Board of Governors. References in publications to the Finance and Economics Discussion Series (other than
acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.
Bank Relationships and the Geography of PPP Lending∗

David Glancy†

January 19, 2023

Abstract

I study how bank relationships affected the timing and geographic distribution of

Paycheck Protection Program (PPP) lending. Half of banks’ PPP loans went to bor-

rowers within 2 miles of a branch, mostly driven by relationship lending. Firms near

less active lenders shifted to fintechs and other distant lenders, resulting in delays re-

ceiving credit but only slightly lower loan volumes. I estimate a structural model to fit

the observed relationship between branch distance, bank PPP activity, and origination

timing. I find that banks served relationship borrowers 5 to 9 days before other bor-

rowers, an effect in line with reduced-form estimates using a sample of PPP borrowers

with previous SBA lending relationships.

∗
The analysis and conclusions in this paper are those of the author and should not be interpreted as
reflecting the views of the Board of Governors or the Federal Reserve System.
†
Federal Reserve Board of Governors. Email: David.P.Glancy@frb.gov.

1
1 Introduction

As part of the CARES Act, the Paycheck Protection Program (PPP) provided small busi-
nesses with over $500 billion in forgivable loans during the early stages of the COVID-19
pandemic. The size and speed of the rollout were unprecedented; most of the credit was
extended during the first month of the program despite numerous hurdles, including last-
minute policy changes, operational difficulties related to the effects of the pandemic, and the
rapid exhaustion of the initial funding allocation. To manage these hurdles, banks reportedly
focused on providing credit to existing customers, for whom less due diligence is required
before lending. This behavior raises the question: What happened when relationship credit
was less available?
In this paper, I use geocoded data on the locations of bank branches and PPP borrowers
to study the spatial distribution of PPP loan originations and how it relates to origination
timing. As the majority of banks’ small business lending is concentrated within a short
distance of their branches, the focus on preexisting relationships caused PPP lending to
predominantly fall within banks’ branch footprints. Studying the location of PPP lending—
and what happens when credit is less available from local lenders—is thus informative as to
the role bank relationships played in supporting lending.
I present three findings pertaining to the relationship between borrower proximity, bank
PPP activity, and origination timing. First, PPP lending was highly localized, particu-
larly for relationship-oriented banks.1 About half of bank loans overall, and two-thirds of
loans from relationship-oriented banks, went to borrowers within 2 miles of a branch. Less
relationship-oriented banks did more nonlocal lending. This result suggests that PPP lending
was localized because prepandemic small business lending was. Between government guar-
antees removing the information-based advantages of local banks, and pandemic restrictions
to on-site activities limiting the benefit of branch access, there was little reason to choose
1
Relationship-oriented banks are banks where PPP lending was less than prepandemic small business
lending. Banks for which this is not the case were more likely to have expanded significantly beyond
relationship borrowers.

2
a nearby lender for a PPP loan absent an existing relationship. Consequently, the different
geographic distribution of relationship and nonrelationship PPP lending can be exploited to
examine the benefit of bank relationships.
Second, there is a U-shaped relationship between a bank’s PPP activity and the average
time a borrower needs to wait to receive a loan. The banks providing the shortest wait times
are those whose total volume of PPP lending was comparable to the stock of prepandemic
small business loans. Borrowers tended to face longer delays when their bank was less active
in PPP (indicating bank-specific lending frictions) or very highly active (meaning a high
share of borrowers were not existing clients).
Third, borrowers who obtained PPP loans from nearby lenders received credit earlier,
particularly if their lender was more active in the program. Active banks predominantly
served nearby borrowers early in the program before moving on to other borrowers at later
stages. Banks with more modest participation concentrated their lending around branches
for the entirety of the program, suggesting these banks did not expand beyond existing
clients. Ultimately, the results suggest that there is a benefit to having a relationship with
an active PPP lender.
I rationalize these three findings in a model where banks are heterogeneous in their PPP
lending technology, borrowers face time costs to switching banks, and the spatial distribution
of lending differs for relationship and nonrelationship loans. Banks with better processing
technology originate loans faster and retain more of their relationship clients. However,
once relationship clients are served, better technology then facilitates more nonrelationship
lending, generating the U-shaped relationship between PPP intensity and average origination
time. Nearby borrowers from active lenders receive credit earlier, on average, reflecting these
loans disproportionately being relationship loans. When I estimate the model relating origi-
nation timing to the lending bank’s proximity and PPP intensity, I find that nonrelationship
borrowers had to wait an extra 5 to 9 days to receive credit, on average.
Finally, I present two additional pieces of evidence to validate the findings of the model.

3
First, I study the benefits of relationships for a sample of loans where preexisting relationships
are observable. Specifically, I match borrowers who previously received SBA 7(a) loans with
borrowers in the PPP data. For this sample, I can observe whether borrowers took out
loans from their previous SBA lenders, and can thus assess the delays that come from
switching lenders. The results are generally consistent with the model-estimated switching
costs, where the likelihood of being a relationship lender is inferred by geographic location
instead of directly observed.
Second, I show that geographic differences in average origination timing are consistent
with the hypothesized benefits of relationship lending. If there is a benefit to having a
relationship with an active lender, and small business relationships are overwhelmingly with
banks with nearby branches, then the benefits of a bank being active in the program should
be localized around its branches. I show that census tracts near less active banks faced delays
in receiving loans. In these tracts, firms shifted borrowing towards fintechs or other distant
lenders, consistent with Erel and Liebersohn (2020). However, there is only a small effect on
loan volumes, indicating that borrowers ultimately were able to receive credit, albeit with a
delay.
Taken together, these findings highlight the importance of bank relationships in receiving
PPP loans. Most borrowers got credit from nearby banks, suggesting they borrowed from
banks with whom they had an existing relationship. Borrowers faced delays when credit
was less available from local lenders, pointing towards frictions in substituting away from
relationship lenders. These results demonstrate an important cost to intermediating aid
through the bank sector. While banks were able to distribute funds rapidly, they did so by
prioritizing existing clients, thus disadvantaging firms with weaker banking ties. As such
firms may have fewer alternatives for offsetting revenue shortfalls—for example credit line
draws—frictions in accessing PPP loans were likely highest for the firms most in need of aid.

4
1.1 Background and Related Literature

The COVID-19 pandemic caused an unprecedented increase in unemployment and tempo-
rary business closures starting in March of 2020 (Bartik et al., 2020). In response, Congress
passed the Coronavirus Aid, Relief and Economic Security (CARES) Act on March 27th,
a $2.2 trillion stimulus bill. One of the the largest components of the bill was the Pay-
check Protection Program, which provided low-interest loans to businesses with 500 or fewer
employees. Firms were able to request credit for up to 2.5 times monthly payroll, up to
a maximum value of $10 million. Loans were forgivable so long as businesses maintained
employment levels and payrolls.
The program launched on April 3rd, a mere week after the signing of the CARES act.
The grant-like nature of the program resulted in very high demand, causing the initial $349
billion allocated to the program to be exhausted by April 16th. An additional $320 billion
in funding was provided, enabling the Small Business Administration (SBA) to begin taking
applications again on April 27th. After another rapid period of origination activity in the
following week, the pace of originations slowed. The program stopped taking applications
on August 8th, at which time the program had disbursed $525 billion in credit from 5,460
different lenders.2
The size of the program and the speed with which it was rolled out caused difficulties
upon the initial launch. Final guidance on the program wasn’t released until 12 hours before
PPP went live, and “know your customer” requirements hindered the onboarding of new
clients (Merker et al., 2020). At the same time, many banks were contending with the effects
of branch closures or the shift to work-from-home, causing further operational complications.
Against this backdrop, banks reportedly prioritized their own clients in extending PPP
loans. For these borrowers, less additional due diligence was needed to originate a loan.
Furthermore, prioritizing existing clients reduced the risk of alienating clients and losing
2
The PPP reopened on January 11, 2021 after a third round of funding was provided. However, this
paper focuses on lending done in the first two rounds.

5
valuable relationships (Joaquim and Netto, 2020). However, banks’ chosen allocation of
credit may have had undesirable effects, as credit flowed to many borrowers less in need of
aid, possibly at the expense of borrowers with weaker banking ties. Indeed, the early rush of
PPP lending disproportionately went to less distressed firms (Bartik et al., 2020) and areas
less affected by the pandemic (Granja et al., 2022).
While bank relationships appear to be important in receiving PPP credit, studying this
topic is complicated by data limitations. Little information is available about lending rela-
tionships for the small, private firms that account for the vast majority of PPP loans. To
overcome this difficulty, some papers either use data from surveys (Bartik et al., 2020) or
public firms (Amiram and Rabetti, 2020; Duchin et al., 2022) to analyze subsets of PPP
borrowers for which information on relationships are available.3 Other papers use county
or ZIP code bank branch locations to proxy for relationships, allowing authors to study the
full universe of PPP lending, at the cost of using less direct relationship measures (Erel and
Liebersohn, 2020; Li and Strahan, 2021; Granja et al., 2022).4
I add to this literature by studying the effects of banks’ PPP involvement at a fine
spatial scale. Proxying for preexisting relationships based on branch proximity allows me
to analyze the full universe of PPP lending, instead of relying on a small subsample of
loans for which relationships are observable (though I also validate the estimates with such
a subsample). Additionally, the fine spatial scale also allows me to more narrowly identify
probable relationship lenders than other papers that analyze geographic differences in supply
conditions.5 Finally, the structural model allows me to use these spatial patterns to quantify
3
Bartik et al. (2020) use survey data to show that banks are more likely to approve loans to relationship
borrowers. Amiram and Rabetti (2020); Duchin et al. (2022) use data from public firms in the PPP to
document that relationship borrowers are more likely to get credit and get credit faster.
4
Erel and Liebersohn (2020) show that fintech lenders have a higher market share in ZIP codes with fewer
branches or a larger minority share of the population. Li and Strahan (2021) show that banks allocate more
PPP loans to counties where they did more small business lending before COVID. Granja et al. (2022) show
that ZIP codes with more active banks had higher levels of PPP lending, though employment effects were
modest.
5
I estimate that lenders only a few miles away are significantly less likely to be relationship lenders than
those within a mile, indicating that there is a benefit to conducting analysis at a level finer than ZIP code
or county.

6
the benefits of relationship lending and magnitude of switching costs.

2 Data and Summary Statistics

2.1 Data Sources

PPP loan data Loan level data on PPP originations are provided by the Small Business
Administration (SBA). For each loan, the data include the name of the lender, various
borrower characteristics (address, NAICS code, number of jobs reported) and various loan
characteristics (loan amount, origination date). To analyze the geography of originations,
I use a geocoded version from geocod.io which contains the latitude and longitude of the
borrower addresses in the SBA data.6

Distance to lending bank I combine the data on borrower locations with data on bank
branch locations from the FDIC’s Summary of Deposits (SOD). The SOD includes the
latitude and longitude of US bank branches as well as branch-level deposits as of June 2020.
A key object of interest in the loan level data is the distance between the borrower and
lender. I use the following process to calculate this distance. First, I conduct a fuzzy-
name match between originator names in the PPP data and institutions in the National
Information Center’s (NIC) Institution Directory to find the RSSD ID of the originating
lender.7 I use this unique identifier to match PPP loans to the branch locations of the
lending bank in the SOD and compute the distance to the closest branch of that bank.8 89%
of PPP loans match to the SOD data, with nearly all of the unmatched loans coming from
nonbanks.
6
This data is available here: https://www.geocod.io/geocoded-ppp-loan-data/.
7
Some lender names in the PPP data can match to multiple banks. I disambiguate by selecting fuzzy
name matches that also match on ZIP code, then state, then finally selecting the prospective match with
the highest PPP lending volume.
8
Distance is calculated using the haversine formula. The minimum distance is found using the BallTree
module from scikit-learn.

7
Bank PPP intensity The extent of a bank’s PPP activity is measured by the ratio of PPP
lending to prepandemic small business lending relationships. In the model in Section 4, banks
with superior lending technology are able to process loans more quickly and retain/attract
more borrowers relative to their stock of existing relationships. To reflect this, I measure PPP
intensity as the ratio of 2020 PPP lending for bank b to a weighted average of outstanding
small business loans and outstanding C&I loans in the 2019:Q4 Call Reports.9 As the former
likely excludes many eligible borrowers, while the latter includes loans to many firms that
were too large to participate in PPP, my proxy for the relationship stock is an average of
the two loan balance measures. I place a 90% weight on small business loans to equate the
aggregate for the relationship stock proxy with total PPP lending.10 Namely, the measure
of PPP activity is:

PPPb
PPP Intensityb =
.9 × SB Loansb,19:Q4 + .1 × C&I Loansb,19:Q4

Tract level data The loan level analysis uses controls for tract level labor market and
small business loan market conditions from Longitudinal Employer-Household Dynamics
Origin-Destination Employment Statistics Workplace Area Characteristics (LODES) and
Community Reinvestment Act (CRA) data, respectively. LODES provides data on employ-
ment for 2010 census blocks, which I aggregate to the tract level.11 The key variables of
interest are the share of employment that is nonwhite, the earnings distribution of local
employees, and total employment in firms with 500 or fewer employees (generally reflecting
employment in PPP-eligible firms).12 CRA includes information on the volume of loans to
9
Small business loans are C&I loans with an original balance under $1 million dollars.
10
This weighting simplifies the interpretation of some results, as it centers ln(PPP Intensity) around 0.
However, this decision regarding the supply measure matters little; estimates are similar when measuring
supply by PPP originations to total assets, PPP originations to small business loans, or using the PPP
exposure measure of Granja et al. (2022).
11
I use the data on private jobs in 2017, as this is the most recent year that reports employment disaggre-
gated by firm size. Alaska is missing for 2017, so I instead use 2016 data there.
12
Earnings variables include the share of jobs in the tract earning $1250 or less per month, the share
of jobs earning over $3333 per month, and the share of jobs by workers with at least a bachelor’s degree.
Since PPP loan sizes are pinned to monthly payroll, these variables along with the number of employees in

8
firms with less than $1 million in revenue by census tract.13
Additionally, for tract level analysis, I aggregate ln(PPP Intensityb,−j ) to compute the
average characteristics of banks within 2 miles of census tract j.14 Using the geocoded
branch locations and the locations of tract centroids from 2010 Census Gazetteer files, I find
the set of bank branches located within 2 miles of a tract centroid. The tract level bank
supply measure is the average of ln(PPP Intensityb,−j ) for nearby branches, weighting by
the deposits in those branches. As prepandemic small business lending was highly localized,
this variable measures the likely availability of credit from relationship lenders in an area,
and enables the analysis of how geographic differences in credit availability affected PPP
outcomes at a fine spatial scale.15

2.2 Summary Statistics

Table 1 presents summary statistics of the main variables of interest, with loan level statistics
in Panel 1 and tract level statistics in Panel 2.
The loan level data show that most lending is local, with about half of bank loans coming
from lenders with a branch within 2 miles. However, there is a mean distance of 175 miles,
reflecting a non-negligible share of loans being made independent of geography.
Regarding the supply measure, the median PPP intensity of the lending bank was almost
1.5 (PPP lending that is 50% greater than the stock of relationship loans). However, this
reflects high-intensity banks appearing as lenders more frequently; the median is slightly
above 1 at the bank level.
While a significant share of lending occurred in the early weeks of the program, this was
due in part to larger loan sizes during that time. The median bank borrower had to wait 26
PPP-eligible firms are important controls in specifications predicting PPP lending.
13
I use data from 2019 in order to reflect prepandemic small business lending relationships. The data
is only reported by banks with at least $1.3 billion in assets, and thus does not reflect the extent of small
business lending relationships with the smallest banks.
14
PPP Intensityb,−j is the ratio of PPP lending outside of tract j to prepandemic small business lending.
15
Outcomes considered include: PPP loan volumes, average days until origination, and the shares of PPP
lending accounted for by fintech lenders or local lenders.

9
days after the start of the program to receive credit and only about 31% of bank borrowers
got loans before the first round of funding ran out.

10
Table 1: Summary Statistics
Variable Description Mean sd Percentile N
25 50 75
Panel 1: Loan level Statistics

Distancei,b Miles to nearest branch of lending bank 174.78 519.33 0.80 2.03 8.44 4,548,796
Local Branchi,b 1 if Distancei,b <2 miles 0.50 0.50 0.00 0.00 1.00 4,548,796
PPP Intensityb P P Pb /(.9SBLb + .1CIb ) 18.59 76.40 0.84 1.46 2.67 4,549,275
ln(PPP Intensityb ) Logarithm of PPP Intensityb 0.64 1.54 -0.17 0.38 0.98 4,549,275
Top 4b 1 if lender is JPM, WFC, C or BAC 0.19 0.39 0.00 0.00 0.00 4,549,275
Days to Originationi,b Days between origination date and April 3rd 29.32 25.65 11.00 26.00 28.00 4,549,275
Round 1 Indicatori,b 1 if originated by April 16th 0.31 0.46 0.00 0.00 1.00 4,549,275
ln(Loan Amount)i,b Logarithm of PPP loan balance 10.32 1.46 9.34 10.13 11.23 4,549,274
Small Firmi,b 1 if business has fewer than 5 employees 0.54 0.50 0.00 1.00 1.00 4,549,275
Urbani,b 1 if business has urban location 0.80 0.40 1.00 1.00 1.00 4,549,275
11
Panel 2: Tract level Statistics

ln(PPP Intensity)j PPP intensity (exlcuding j) of local branches -0.10 0.49 -0.41 -0.11 0.19 54,292
Top 4 Sharej Top 4 deposit share within 2 miles 0.35 0.31 0.00 0.32 0.58 54,297
Days to Originationj Average days between origination date and April 3 35.36 12.38 26.90 33.28 41.18 53,889
Round 1 Sharej Percent of loans originated by April 16 26.32 16.94 12.90 24.03 37.93 53,889
Local PPP Sharej Percent of PPP Lending by banks within 2 miles 48.87 25.83 28.03 49.13 69.62 53,145
Fintech PPP Sharej Percent of PPP Lending by fintechs 8.01 12.16 1.34 3.72 9.10 50,629
ln(Total PPP)j Logarithm of PPP lending in tract 15.08 1.36 14.26 15.16 15.98 53,889
ln(Branches)j Logarithm of branches within 2 miles 2.03 1.06 1.39 2.08 2.71 54,297
ln(SB Lending)j Logarithm of 2019 small business Lending 6.52 1.27 5.80 6.69 7.40 50,089
ln(SB Emp)j Logarithm of small business employment 6.25 1.14 5.58 6.32 7.01 53,604
Nonwhitej Nonwhite share of employment 0.24 0.17 0.12 0.21 0.32 53,620
Earnings > $3333j Share jobs earning > $3333 month 0.32 0.15 0.22 0.30 0.41 53,620
Earnings < $1250j Share jobs earning < $1250 month 0.30 0.12 0.22 0.30 0.37 53,620
Collegej Share workers w. college degree 0.25 0.08 0.19 0.24 0.30 53,617
3 Distance, PPP Intensity, and Origination Timing

This section documents three findings pertaining to the relationship between distance, the
PPP intensity of the lending bank, and the time until origination. First, I show that PPP
lending was highly localized, particularly for relationship-oriented banks. Second, I doc-
ument a U-shaped relationship between PPP intensity and time-to-origination. Third, I
demonstrate that borrowers received credit earlier if they are close to a branch of the lend-
ing bank, particularly if it has a high PPP intensity.

3.1 Relationships and Distance

Figure 1 plots histograms of the distance between PPP borrowers and the nearest branch of
the lending bank. The left panel shows results combined across all bank loans. The density
falls rapidly as distance rises and starts to level off at around two or three miles. About half
of loans are from banks within 2 miles of the borrower. However, there is a long right tail,
with about 23% of loans being made by banks that do not have a branch within 10 miles.
39% of these distant loans were provided by three banks—Celtic Bank, Cross River Bank
and WebBank—all of whom utilized fintech partnerships to provide loans. Overall, PPP
loans have a similar spatial distribution to prepandemic small business lending, with a bit
more mass in the right tail driven by these online lenders.16
That PPP lending remained highly localized is interesting as many of the advantages of
being a local lender do not apply to PPP loans. Normally, lending is hypothesized as being
local because proximity either provides an information advantage in evaluating loans (Agar-
wal and Hauswald, 2010) or reduces transportation costs involved in originating/monitoring
loans (Degryse and Ongena, 2005). PPP loans are fully guaranteed by the U.S. government,
removing advantages related to local information or monitoring. Moreover, restrictions on
branch operations due to the pandemic weakened the benefit of easy physical access to a bank
16
Brevoort and Wolken (2009) document that the median distance between small firms and the bank
servicing them is about 3 miles, with nearly 90% of the lenders being within 30 miles.

12
Figure 1: Distribution of Lender Distances

0.40 0.50
Low Intensity Bank
High Intensity Bank

0.40
0.30

0.30

Density Density
0.20

0.20

0.10
0.10

0.00 0.00
1 3 5 7 9 10+ 1 3 5 7 9 10+
Distance (miles) Distance (miles)

(a) Distribution of Distances (b) Distribution by Bank PPP Intensity
Notes: This figure plots histograms of the distance between a PPP borrower and the address of the nearest
branch of the lending bank. The area to the right of the dotted line gives the mass of loans made by lenders
more than 10 miles away. The left panel pools all bank PPP loans, while the right separates banks with a
high and low PPP intensity (the blue and red bars, respectively).

branch. That PPP lending remained strongly tied to banks’ branches despite the advantages
of proximity evaporating indicates that relationship lending drove PPP originations. That
is, banks continued to lend locally because that is where existing clients were, even though
the reasons for favoring nearby firms were no longer relevant.
The right panel of Figure 1 provides more direct evidence for local lending being relation-
ship driven. While the relationship status of a given loan is hard to ascertain, if a bank has
a high ratio of PPP lending to prepandemic small business lending, then their PPP portfolio
likely contains a higher share of nonrelationship loans. The histogram plots the distance dis-
tribution separately for loans from banks with a PPP intensity under 1 (which presumably
predominantly served existing clients), and those with an intensity above 1 (whose portfolios
would contain more nonrelationship loans).
The results are consistent with relationship loans being more localized. Low-intensity
banks have a much higher share of loans going to borrowers within two miles (68%, compared
to only 40% for high-intensity banks). High-intensity banks are slightly more likely to
originate loans that are between 3 and 10 miles away, and much more likely to originate

13
loans that are over 10 miles away. These results are not due to regional differences, for
instance due to density, as results are similar using the residual distance after accounting for
loan controls and census tract fixed effects (see Appendix Figure A1).

3.2 PPP Intensity and Origination Timing

How does PPP intensity relate to origination timing? There are several mechanisms at
play, working in different directions. On one hand, as discussed in the previous subsection,
high-intensity banks likely did a higher share of their lending to nonrelationship customers.
As relationship loans are quicker to originate—search costs have been paid and initial due
diligence already conducted—banks that just focused on existing relationships may be able
to process loans faster. On the other hand, banks with superior processing technology may
originate loans faster and thus attract more clients, resulting in wait times that decrease in
PPP intensity.
Figure 2 presents the results of binscatter regressions of time-to-origination on the log-
arithm of the lending bank’s PPP intensity.17 The left panel is unweighted, while the right
weights by the inverse of the number of loans from the lending bank in order to reduce
the influence the largest lenders have on the estimates. The figures show a U-shaped re-
lationship between a bank’s PPP intensity and the time borrowers have to wait to receive
a loan. Banks with an ln(PPP Intensity) slightly above 0—meaning PPP lending just ex-
ceeded prepandemic small business lending—generally provided the shortest average wait
times. Banks with PPP lending well above or below this level tended to have much longer
average wait times.
This pattern is consistent with the aforementioned competing mechanisms. Banks who
did less PPP lending than prepandemic small business lending likely predominantly served
relationship clients, meaning that marginal differences in PPP activity provides little in-
17
Binscatters are constructed using the Binsreg command in Stata (Cattaneo et al., 2019), residualizing on
loan size, a small firm indicator, census tract characteristics (an urban indicator, the share of employees that
are nonwhite, and the logarithms of prepandemic small business employment and small business lending),
and county fixed effects.

14
Figure 2: Wait Times by PPP Intensity

50 30

45 28

Time to Origination Time to Origination
26

35
24

30
22

25
20

20
-2 0 2 4 6 -3 -2 -1 0 1 2 3
ln(PPP intensity) ln(PPP intensity)

(a) Unweighted (b) Inverse Count Weighted
Notes: This figure plots binscatter regressions of time-to-origination on the log PPP intensity of the lending
bank for 2020 bank PPP loan originations. Controls include the size of the loan, an indicator for whether the
firm has fewer than 5 employees, census tract characteristics (an urban indicator, the share of employees that
are nonwhite, and the logarithms of prepandemic small business employment and small business lending),
and county fixed effects. The left panel is unweighted, while the right panel weights by the inverse of the
number of PPP loans from the lending bank.

formation about relationship status. If most loans are relationship loans, we would expect
a downward sloping relationship between PPP intensity and time-to-origination, as faster
lenders are able to retain more of their clients. However, for banks with a PPP intensity
above 1, increases in PPP intensity indicate more nonrelationship loans, and thus delays in
the average time of loan receipt. This explanation for the U-shaped relationship is formalized
in the model in Section 4.

3.3 Distance and Origination Timing

The final evidence motivating the model pertains to the relationship between distance and
origination timing. Section 3.1 showed that relationship-oriented PPP lenders concentrated
lending within two miles of their branches. Section 3.2 showed that the relationship between
PPP intensity and origination timing was non-monotonic, likely due to a higher share of
nonrelationship loans at the largest PPP lenders. Now I analyze the interaction of distance
and proximity; if the delays receiving loans at high-intensity banks were driven by nonre-

15
lationship loans, such effects should be moderated by proximity (as such nearby loans are
more likely to be relationship loans.)
Figure 3 repeats the binscatter regressions from Figure 2, but disaggregating by whether
loans are to borrowers within 2 miles of a lender. The figures demonstrate that the upward
sloping relationship between wait times and PPP intensity are generally driven by non-local
lending. Wait times are essentially monotonically decreasing in PPP intensity for nearby
borrowers, but U-shaped for more distant borrowers. For banks with a low PPP intensity,
there is not much of a difference in origination timing for local and non-local borrowers. This
result is consistent with timing differences across originators reflecting relationship lending;
if a banks seems to not have expanded lending beyond relationship clients, proximity is
less informative about relationship status. However, banks with PPP lending similar to
or exceeding estimates for their relationship stock are more likely to have a notable share
of nonrelationship loans, making proximity more informative. For banks with a log PPP
intensity around 1 (meaning PPP lending that is almost triple the relationship stock), nearby
borrowers receive loans about 4 days before distant borrowers.
These results are also apparent in regressions. Appendix Table A1 presents estimates
from regressing wait times on ln(PPP intensity), an indicator for whether a borrower is
within 2 miles, and interactions of ln(PPP intensity) with itself and the local dummy.18
Overall, borrowers from nearby banks get credit about a day earlier than other borrowers,
with the benefit of using a local bank increasing in the PPP intensity of that bank. The
quadratic term corroborates that there is a U-shaped relationship between PPP intensity
and origination timing. Expected wait times are minimized at a PPP intensity around 2.3
for distant borrowers and 4.0 for nearby borrowers (1.4 and 4.8 in unweighted regressions).
Patterns are similar if origination timing is measured by an indicator for whether the loan
was originated before the first round of funding was exhausted (Appendix Table A2).
18
All specifications include county and 3-digit NAICS fixed effects, and control loan size, whether the
business has fewer than 5 employees, and tract characteristics (an urban indicator, the share of employees that
are nonwhite, and the logarithms of prepandemic small business employment and small business lending).
Regressions are either unweighted, or weight by the inverse of the number of PPP originations at the bank.

16
Figure 3: Wait Times by Bank Proximity and PPP Intensity

50 30
Distant Bank Distant Bank
Local Bank Local Bank
45 28

Time to Origination Time to Origination
26

35
24

30
22

25
20

20
-2 0 2 4 6 -3 -2 -1 0 1 2 3
ln(PPP intensity) ln(PPP intensity)

3.4 Discussion

To summarize the results so far: (1) nonrelationship PPP lending was more geographical
dispersed than relationship lending, making proximity a useful proxy for a loan’s relationship
status; (2) banks with a very high amount of PPP lending relative to prepandemic small
business lending had longer wait times, consistent with there being delays associated with
switching lenders; and (3) wait times decrease in PPP intensity for nearby borrowers, sug-
gesting that there is a benefit to having a relationship with an active lender, even if there
isn’t a clear benefit from simply borrowing from one.
This set of results is consistent with banks prioritizing existing relationships first, and
the more active PPP lenders then moving on to new clients later. Figure 4 presents evidence
that this is the case. The figure plots the share of loans made to local borrowers by bank
PPP intensity (defined by whether or not PPP intensity is above 1), as well as the market
share of the low-intensity banks, over time. Banks that were less active in PPP (in red)
predominantly served nearby clients throughout the program, with the share of loans to

17
Figure 4: Share of Local Lending over Time

0.80

Share of Loans to Local Borrowers
0.60

0.40

0.20

0.00
01apr2020 01may2020 01jun2020 01jul2020 01aug2020
Date

High PPP Intensity Low PPP Intensity
Market share of Low Intensity Banks

Notes: This figure shows the share of loans by date that is made by banks with branches within 2 miles of
the borrower. Red crosses plot results for low-intensity banks (PPP intensity under 1), while blue circles plot
results for high-intensity banks. The grey area shows the share of loans made by low-intensity banks. Loans
originated within 2 days of the date listed on the x-axis are pooled in order to reduces noise, for example,
due to low volumes during weekends.

borrowers within 2 miles typically staying in the 60 to 80 percent range. However, these
banks were comparatively slower to extend credit; their market share remained below 20
percent for most of the first funding round, and picked-up later. Lending by high-intensity
banks (in blue) started out being fairly localized, with about half of loans going to nearby
borrowers, however this local share dropped in the early stages of the second round and
typically remained around 25 percent during the later stages of the program.
Overall this figure paints a similar picture to other results. Namely, existing clients of
major PPP lenders receive credit earlier, as evidenced by these lenders’ high market share
and focus on nearby borrowers early in the program. Those borrowing from less active banks
faced delays, as evidenced by the low market share of such banks in the first round. Finally,
those borrowing from nonrelationship banks faced delays, as evidenced by the higher rates
of nonlocal lending later in the program.

18
4 Theory

This section presents a model to explain the patterns documented in Section 3. I estimate
the model to fit observed differences in the timing and spatial distribution of PPP loan
originations across banks. I then use these estimates to quantify the costs of switching away
from relationship lenders.

4.1 Model

Supply of PPP Loans Banks had little discretion over the terms of PPP loans; loan
terms were set by the program and were essentially uniform across borrowers, besides loan
size, which was mechanically determined by a firm’s 2019 payroll. Consequently, I model
banks as competing on how quickly loans are processed.
Suppose that bank b takes on average Tb (L) = τb ( RLb )γ days to process their first L
PPP loans, where τb measures the time that it would take to process their Rb relationship
clients and γ reflects congestion effects. γ = 0 means that all loans are processed in τb days
regardless of where they are in the queue, while γ = 1 corresponds to loans being processed
Rb
sequentially at a rate of 2τ b
.
Banks serve existing relationship clients first, before moving on to nonrelationship clients,
and do not distinguish between borrowers within these pools. As a result, relationship and
nonrelationship clients will have different expected wait times, denoted TbR and TbN , respec-
tively. In equilibrium, since banks prioritize existing clients, any bank that does nonrela-
tionship lending will also be able to retain all of their existing clients.19 Consequently, the
amount of relationship PPP lending will be min{Lb , Rb }, where Lb is a bank’s total PPP
lending. The lending technology then implies that the expected processing time for a rela-
b ,Rb } γ
tionship loan will be TbR = τb ( min{L
Rb
) . TbN will be such that the average processing time
for L loans is the average for relationship and nonrelationship borrowers, weighted by their
19
Since borrowers choose the nonrelationship lender with the best processing time, TbN is equalized for all
banks that do nonrelationship lending. Relationship prioritization results in TbR < TbN , so borrowers would
never switch from a bank that does nonrelationship lending.

19
Lb
respective shares. Defining ρb ≡ Rb
as the ratio of PPP loans to relationship borrowers, TbN
must satisfy:
τb ργb = ρ−1 −1 N
b τb + (1 − ρb )Tb if ρb > 1. (1)

where τb and TbN are the processing times for relationship and nonrelationship loans, respec-
tively, and ρ−1
b is the share of loans to relationship borrowers.

Demand for PPP Loans We now derive the number of loans made by a bank as a func-
tion of their processing times. If a firms decides to leave their relationship bank, they choose
the lender with the lowest expected processing time. In equilibrium, all nonrelationship loans
will have the same expected processing time, denoted T .
Demand from relationship borrowers is determined by borrowers’ decision to accept ei-
ther the time offered by their relationship bank or the processing time of T available to
nonrelationship borrowers. Borrowers face a heterogeneous switching cost, which creates a
markup µi ≥ 1 over T in the time to receive a nonrelationship loan, reflecting switching and
on-boarding time. Let µi be pareto distributed with shape parameter α (i.e. µi has a cdf
α
1 − µ1 for µ ≥ 1). If τb > T , the bank cannot process all of their relationship borrowers
in a timely enough manor to induce them all to stay with the bank. In this case, the volume
of lending will be such that:

T α
Lb = Rb × P r(TbR < µi T ) = Rb ( ) if ρb < 1. (2)
TbR

Equilibrium Equilibrium loan volumes and processing times are determined by the inter-
section of the supply curves (determining the time to process a given number of loans) and
the demand curves (determining the number of borrowers who choose to take out a PPP
loan given a particular processing time).
If τb > T , the bank operates on the downward sloping portion of the demand curve, as
some relationship clients with low switching costs leave the bank. Inverting equation (2), we

20
−1
get that the time to process relationship loans is TbR = ρbα T where ρb < 1.
If τb < T , all relationship borrowers stick with their bank and receive an expected time
to origination τb . For these banks, the number of loans is determined by how many loans the
bank is capable of making while offering a processing time of T for nonrelationship borrowers.
ρb −1
Substituting T in for TbN in equation (1), we can solve for τb = TbR = ρ1+γ −1
T .20
b

These expressions all derive the expected time that it takes to receive a relationship loan
as a function of ρb : 
 −1
ρ α if ρ < 1

b b
TbR = T × (3)
ρb −1


 1+γ
ρ −1
if ρb > 1
b

The expected time to receive a nonrelationship loan is µ̄T , where µ̄ is the expected switching
cost for borrowers that switch lenders and T is the equilibrium time to process nonrelation-
ship loans.

Distance None of the expressions for time to origination by bank PPP intensity and
relationship status explicitly depend on distance. Instead, where distance comes in to play is
as a signal that a particular borrower is a relationship client. Motivated by Figure 1, I assume
that relationship and nonrelationship loans have different geographic distributions. Let f R (d)
and f N (d) denote the distribution of distances between banks and their relationship and
nonrelationship clients, which are assumed to be independent of ρb and satisfy the monotone
(d) N
likelihood ratio property (MLRP) λ0 (d) > 0 for λ(d) ≡ ff R (d) . In words, borrowers that are
further from a bank are more likely to be nonrelationship borrowers.
We are interested in solving for the probability that a loan is to a relationship borrower
based on the distance to the lending bank (di,b ) and the share of PPP loans going to re-
lationship borrowers (min{ρ−1
b , 1}). Letting Ri,b denote the event that i has a relationship
20 T
TbR is indeterminate if ρb = 1, taking values in the range [ 1+γ , T ], reflecting the range of τb that is low
enough to serve all relationship customers, but high enough to not attract nonrelationship customers after.
Since there are no observations in the data with this intensity, it does not matter for the estimation.

21
with b, we get the following probability for Ri,b :

f (di,b |Ri,b , ρb )P (Ri,b |ρb )
P (Ri,b = 1|di,j , ρb ) =
f (di,b |ρb )
−1 (4)
= 1 + (ρb − 1)λ(di,b ) if ρb > 1

P (Ri,b = 1|di,j , ρb ) = 1 if ρb < 1 since the bank only does relationship loans.
Combining equations (3) and (4), we get the following expression for the expected time
to loan receipt as a function of a bank’s PPP intensity and the distance to the lending bank:

 −1


 ρbα if ρb < 1


E(Ti,b |di,b , ρb ) = T × µ̄ − 1 + (ρb − 1)λ(di,b )
−1 ρb − 1 (5)
µ̄ − if ρb > 1



 | {z }| ρ1+γ
b −1
 P (Ri,b |di,b ,ρb )
{z }
Benefit of Relationship

Relationship to Empirical Results Equation (5), combined with the MLRP assumption
for λ(d), delivers the three empirical results presented in Section 3. First, PPP lending is
more localized for relationship-oriented banks as MLRP implies that the distance distribution
for nonrelationship borrowers, f N (d), first-order stochastically dominates the distribution for
relationship borrowers, f R (d). This assumption thus implies more localized lending for the
banks that do not extend beyond relationship clients (banks with a ρb < 1).
Second, Equation (5) produces U-shaped relationship between between a bank’s PPP
intensity (ρb ) and the average timing of loan origination. As PPP intensity rises to 1, the
average time to origination falls to T . This pattern is driven by banks with better lending
technology offering faster processing times, losing fewer clients to nonrelationship lenders,
and thus doing more lending. However, as the intensity rises from 1, the average time
T
rises from 1+γ and asymptotes to µ̄T . The eventual reversal reflects a greater share of
nonrelationship loans, for which origination times include switching costs.
Third, there is a benefit to geographic proximity to active lenders. Differentiating equa-

22
tion (5) with respect to di,b . We get that if ρb > 1 then:

∂ E(Ti,b |di,b , ρb ) ρb − 1 −2
= T µ̄ − 1+γ 1 + (ρb − 1)λ(di,b ) (ρb − 1)λ0 (di,b ) > 0
∂di,b ρb − 1 | {z }
| {z } ∂P (R |d ,ρ )
i,b i,b b
Benefit of Relationship − ∂di,b

Namely, the expected time to origination rises with distance because distant lenders are less
likely to be relationship lenders. There is no such effect for banks with ρb < 1 because they
only serve relationship clients.

4.2 Estimation

Estimation of P (Ri,b |di,b , ρb ) From equation (4), the probability that loan is from a re-
lationship lender is a function of ρb and λ(di,b ). ρb is the ratio of 2020 PPP lending to
prepandemic small business lending in the Call Reports for bank b (as discussed in Section
2). I estimate λ(d) in order to match the distributions of loan distance for banks with ρb < 1
and ρb > 1. Since banks with ρb < 1 only make relationship loans, the kernel density es-
timate of the distance distribution provides an estimate of f R (d). Banks with a ρb > 1,
make both relationship and nonrelationship loans, thus their f (d|ρb > 1) is a mixture of the
relationship and nonrelationship distributions, with a weight of Ei (ρ−1
b |ρb > 1) = 0.502 on

f R (d). Consequently, f N (d) is estimated as:

fˆ(d|ρb > 1) − 0.502fˆR (d)
fˆN (d) =
1 − 0.502

where fˆ(d|ρb > 1) and fˆR (d) are the kernel density estimates of distance for high- and low-
intensity lenders, respectively. Taking the ratio of fˆN (d) and fˆR (d) gives an estimate of
λ(d).
The left panel of Figure 5 plots these kernel density estimates. The red-line shows the
distribution for low-intensity lenders. The density is maximized around a distance of 1 mile,
but falls sharply for distances over 2 miles. The distribution for high-intensity lenders is

23
Figure 5: Distance and Relationship Lending

.4 1
Low Intensity (Relationship) Relationship Lender Probability
High Intensity Histogram: Low Intensity
Non-relationship (imputed) Histogram: High Intensity
.8
.3

.1
.2

0 0
.01 .05 .25 1 3 10 75 500 .01 .05 .25 1 3 10 75 500
Distance (Miles, Log Scale) Distance (Miles, Log Scale)

(a) Distances Distribution By Lender Type (b) Relationship Lender Probability
Notes: The left chart plots kernel density estimates of the distribution of loan distances for banks with
a high- and low-PPP intensity (dashed blue and red lines, respectively). The grey line shows the implied
distribution for nonrelationship loans. The line in the right figure plots the estimated probability that a loan
is from a relationship lender by distance.

shifted to the right, and has notably less mass under 2 miles (the typical distance range
for a small business client) and more mass in the right tail. The implied distribution for
nonrelationship loans, shown in grey, is shifted further to the right, reflecting the fact that
some loans made by high-intensity banks are relationship loans and assumed to follow a
similar geographic distribution as those from low-intensity lenders.
The right panel plots the probability that a lender is a relationship lender by distance,
determined by the ratio of the estimated PDFs for nonrelationship and relationship loans.
The probability hovers around 90% for a distance of up to a mile, then falls to 40% around
a distance of 10 miles, and declines further to below 20% for loans over 500 miles away. The
sharp decline around 2 miles makes that a useful reference point; about 88% of bank PPP
loans within that distance are estimated to be relationship loans, compared to only 44% of
loans beyond that distance.

Parameter Estimation Equation (5) defines the expected time to origination as a func-
tion of ρb , λ(di,b ), and parameters (µ, T , γ, and α). These parameters are estimated by

24
nonlinear least squares:

Time-to-Originationi,b = E(Ti,b |di,b , ρb ) + (Xi0 β)+ + εi,b

where Time-to-Originationi,b is the number of days to origination for borrower i, E(Ti,b |di,b , ρb )
comes from substituting ρb and λ̂(di,b ) in to equation (5), and Xi is a vector of controls meant
to capture borrower-specific factors that may affect origination timing beyond differences in
bank processing times and borrower switching costs. As the controls are meant to account
for differences in the time to application, whereas E(Ti,b |di,b , ρb ) is meant to capture differ-
ences in processing times after application, I restrict Xi0 β to be weakly positive.21 Since
the parameters of interest reflect bank-specific origination timing, I weight by the inverse of
the number of PPP loans originated by b to reduce idiosyncratic noise from prominent PPP
lenders. The sample of loans excludes firms with fewer than 5 employees, as such firms were
less likely to be immediately aware of the program, and thus variation in the timing of loan
receipt is less likely to reflect differences in bank processing times.22

4.3 Results

Estimates from this specification are in Table 2. The first column presents estimates from
the baseline specification, while the next three columns, relative to the baseline specification,
omit the application time control (column 2), remove the restriction that application time
is weakly positive (column 3), and remove the inverse loan count weights (column 4).
The primary object of interest is the benefit of borrowing from a relationship bank,
defined as the difference in the average time-to-origination between a bank’s nonrelationship
and relationship borrowers. From equation 5, we can see that this depends on three variables.
21
X includes loan size and the following tract controls: an urban indicator, the share of employees that
are nonwhite, and the logarithms of prepandemic small business lending and employment.
22
Over 20% of businesses with fewer than 5 employees were unaware of the program as of the time the
initial funding was exhausted, whereas the vast majority of larger businesses were aware of it within a couple
days of opening (Humphries et al., 2020).

25
Table 2: Parameter Estimates
T 9.768 13.625 13.930 15.078
µ̄ 1.426 1.317 1.311 1.048
γ 0.088 0.046 0.045 0.694
α 3.778 4.287 4.765 4.691

Weighted X X X
Application Time (Xi0 β)+ 0 (Xi0 β) (Xi0 β)+
Relationship Benefit (days) 5.0 - 9.1 4.9 - 8.5 4.9 - 8.6 6.9 - 15.7

Notes: This table presents estimates from the equation:

Time-to-Originationi,b = E(Ti,b |di,b , ρj ) + (Xi0 β)+ + εi,b

where Time-to-Originationi,b is the number of days until origination for loan i, E(Ti,b |di,b , ρb ) is the expected
processing time from equation (5) given the bank-borrower distance and bank PPP intensity, and (Xi0 β)+
is a control for application time, which is restricted to be weakly positive in the baseline specification. The
X vector includes the log loan amount, and the following tract level controls: the nonwhite employment
share, the logarithm of prepandemic tract small business employment, the logarithm of prepandemic small
business lending, and an urban indicator. The specification is weighted by the inverse of the number of PPP
originations at bank b. Columns 2-4 make the following alterations to this baseline specification: Column
2 omits the controls, Column 3 removes the restriction that time to application is positive, Column 4 is
unweighted.

The benefit of relationship borrowing scales with T , reflecting general processing times. The
benefit is also increasing in µ̄, as higher average switching costs raise the cost of leaving
the relationship bank, and γ, as higher congestion increases the cost of being deprioritized
relative to relationship borrowers. The bottom row of Table 2 plots the range of benefits,
where the lower bound is the benefit of a relationship with a bank that is on the margin of
engaging in nonrelationship lending (the right limit as ρ → 1), while the upper bound is the
benefit at the bank with the highest PPP intensity in the sample.
While parameter estimates vary a fair amount, the ultimate estimate of the benefit of
relationship borrowing is fairly stable. In the baseline specification, the estimates imply that
banks serve relationship clients between 5 and 9 days earlier than nonrelationship clients.
In columns 2 and 3, which exclude controls for application time or don’t restrict that time
to be positive, estimates for T rise, but this is offset by lower estimated switching costs and
congestion costs. Ultimately, the range of benefits from relationship borrowing are little

26
changed. In column 4, where the results are unweighted, estimated relationship benefits rise
to be from 7 to 16 days. In this specification, delays are attributed more to congestion costs
than switching costs, in order to better hit the slow origination times at banks with the
highest PPP intensities. While the unweighted specification implies more notable relation-
ship benefits at the top of the PPP intensity distribution, the estimated effect for the more
typical PPP intensities is only a couple of days higher than those in the unweighted results.

5 Relationships for SBA Borrowers

The previous results estimate the benefit of relationship borrowing by inferring the proba-
bility of a preexisting relationship based on the lending bank’s proximity and PPP intensity.
To increase confidence in these estimates, I now use name-matched SBA 7(a) loan data to
study the benefits of relationship lending in a sample of PPP loans for which a preexisting
bank relationship is known.

5.1 Data and Name Matching

In order to identify whether borrowers are using relationship banks, I use microdata from
the SBA’s 7(a) program to identify PPP borrowers with previous SBA loans.23 I use data
on SBA loans originated from 2010 to 2019, and name match by firm name, blocking on the
firm’s ZIP code. This process identifies about 139,00 SBA borrowers in the 2020 PPP data,
99% of which have lender names that match to an originator in the PPP data, and 91% of
which match a bank PPP originator. Roughly half of these successfully-matched PPP loans
have the same PPP and SBA originator. These loans are identified as relationship loans.
While the SBA data is useful for proving a sample of small businesses for which prepan-
demic relationships are known, there are a couple of limitations involved. First, it may be
unrepresentative of other small business relationships. SBA loans are partially guaranteed by
23
SBA data is available here: https://data.sba.gov/dataset/7-a-504-foia.

27
the government, possibly moderating screening/monitoring incentives, and facilitating more
distant lending. For example, the largest SBA lender by volume is Live Oak, a branchless
bank that specializes in remote lending (Di and Pattison, 2020). Additionally, SBA relation-
ships may differ from other relationships as lenders necessarily have access to and familiarity
with the SBA’s application portal, facilitating the provisioning of PPP loans (Granja et al.,
2022). Second, firms may have multiple relationship, and while SBA relationships can be
observed, other relationships cannot. Thus, “nonrelationship” loans in this analysis may still
be relationship loans even if the bank differs from the one making the SBA loan. Nonethe-
less, this data provides a useful complement to the other analysis, as bank relationships can
be directly observed rather than inferred by borrower location.

5.2 SBA Relationships and Origination Timing

Before analyzing differences by PPP intensity, Figure 6 shows how PPP outcomes differed for
borrowers that previously took out an SBA loan from one of the top 4 U.S. banks by assets.
These banks were less likely to approve PPP applications early in the program (Bartik et al.,
2020) and generally did less PPP lending relative to their overall small business lending than
other banks (Granja et al., 2022).
The left panel plots the shares of borrowers who took out a PPP loan from their re-
lationship lender. The top 4 banks retained 38% of their SBA clients, compared to 50%
retention for other SBA lenders. The right panel shows that relationship borrowers from
the top 4 banks had to wait longer to receive credit than other borrowers, thus providing
an explanation for why clients of the top 4 banks disproportionately sought PPP loans from
other lenders. Relationship loans at the top 4 banks had an average time to origination of
35 days, compared to only 21 days for other relationship lenders. Clients of the top 4 banks
also faced worse delays when they switched to new lenders, although the difference was not
as pronounced. Among the borrowers that switched to a PPP lender that was different from
their SBA lender, clients of the top 4 banks had to wait about 30 days, while clients of other

28
Figure 6: PPP Outcomes by Previous SBA Lender Type

.6 SBA Lender SBA Lender
Top 4 Bank Other Lender Top 4 Bank Other Lender
0.50 40

Share Staying with Relationship Lender
.5
34.7

29.8

Days to Origination
.4 0.38 30 28.2

.3 20.9
20

10
.1

0 0
Switchers Relationship Loans

(a) Share of Relationships Retained (b) Origination Time
Notes: These charts plot the share of borrowers who take out a PPP loan from their previous SBA lender
(left) and the average time-to-origination by whether or not borrowers use their previous SBA lender (right).
Red bars show results for PPP borrowers that took out an SBA loan from a top 4 bank, while blue bars
show results for borrowers from other SBA lenders.

lenders had to wait about 28 days.24
Table 3 similarly documents the interplay between bank relationships, bank PPP inten-
sity, and PPP origination timing, but with regressions of the type described in Section 3.3.
The regressions predict the number of days until a loan is originated as a function of the
lending bank’s PPP intensity, indicators for whether the PPP loan is from a nearby bank or
a relationship SBA lender, and pairwise interactions of these variables.
The most consistent result in the table is that relationship borrowers receive credit earlier,
particularly if their bank is an active PPP lender. The coefficient of -3.75 on Relationshipi,b
means that a non-local borrower from a bank with a PPP Intensity of 1 is expected to wait
3.75 days less on average if their PPP lender is their previous SBA lender. The benefit to
relationship clients is more pronounced for more active lenders, as can be seen by the nega-
tive coefficient on the interaction between the relationship indicator and the PPP intensity
measure. For a bank with a ln(PPP Intensity)b = 2 (around the 95th percentile of lenders)
relationship clients are expected to receive credit 11.35 days earlier.
24
This result is consistent with the model; clients at lower intensity banks are willing to switch lenders at
higher switching costs, resulting in higher average wait times.

29
Columns 2 and 4 additionally add PPP lender fixed effects, and columns 3 and 4 weight
by the inverse of the number of loans from the lender (within the matched sample). These
specifications continue to predict notable benefits to relationship clients, particularly those
of active PPP lenders, though the range of benefits is less expansive. Across these 3 specifi-
cations, relationship clients are expected to receive credit 3.2 to 5.9 days earlier if their bank
has a ln(PPP Intensity)b = 0, and 7.7 to 8.6 days earlier if ln(PPP Intensity)b = 2. These
results, in particular the weighted results in the last two columns, are quite similar to the
range of benefits implied by the baseline model estimates (benefits from 5.0 to 9.3 days).25
The estimates indicate that proximity still relates to origination timing, but the results are
not as robust. In three of the four specifications, borrowers using a local bank receive credit
slightly more than a day earlier than distant borrowers (assuming ln(PPP Intensity)b = 0
and the lender is not the SBA lender).26 Again, using a nearby bank is more beneficial if that
bank is a more active PPP lender. These results are consistent with banks having multiple
relationships, so even those who “switch away” from their relationship SBA lender benefit
from proximity, as those banks are more likely to be another relationship lender. However,
the benefit of using a local branch only accrues to non-SBA relationship borrowing. If a
borrower uses their SBA lender, there is no marginal benefit to it being a local lender,
consistent the effects of proximity being driven by relationships as opposed to proximity
itself.

6 Tract level effects

The results thus far indicate that borrowers are typically close to their relationship banks and
get credit earlier if that relationship bank is a high-intensity PPP lender. Now I investigate
25
I consider the upper range here to reflect the 95th percentile of PPP intensity, whereas in Table 2 the
upper range is determined by the highest PPP intensity in the sample. I do this because the predictions from
the regression specification assume effects are linear in ln(PPP Intensity), making extrapolation to extreme
values more problematic than in the model where effects are concave in the right tail.
26
The positive coefficient in the unweighted specification without the lending bank fixed effect is driven
by the top 4 banks, which predominantly served nearby clients and were slow to extend credit.

30
Table 3: SBA Relationships and Origination Speed
Days to Origination
(1) (2) (3) (4)
Relationshipi,b -3.67** -3.13** -5.78** -4.76**
(0.78) (0.40) (0.44) (0.48)
x ln(PPP Intensityb ) -3.75** -2.40** -1.32** -1.10*
(0.65) (0.56) (0.46) (0.44)
Local Branchi,b 2.04** -1.13** -1.40* -1.13*
(0.58) (0.25) (0.63) (0.52)
x ln(PPP Intensityb ) -5.00** -0.22 -1.17 -0.60
(0.51) (0.30) (0.72) (0.42)
x Relationshipi,b 2.09** 1.24** 2.67** 1.63**
(0.66) (0.22) (0.60) (0.54)
ln(PPP Intensityb ) 1.77** 0.06
(0.56) (0.34)

R2 0.206 0.290 0.330 0.431
Obs. 117,487 116,950 117,487 116,950
Controls? X X X X
County FE X X X X
Lending Bank FE? X X
Weighted X X
Relationship Benefit (days) 3.7 - 11.2 3.1 - 7.9 5.8 - 8.4 4.8 - 6.9

Notes: This table presents estimates from regressing the number of days until origination on log PPP intensity
of the lending bank, an indicator for whether the lending bank has a branch within 2 miles of the borrower,
an indicator for whether the lending bank previously provided an SBA loan to the borrower, as well as all
pairwise interactions of these variables. The sample covers bank PPP loans to borrowers that previously
took out an SBA loan. Columns 2 and 4 additionally include lending bank fixed effects, and columns 3
and 4 weight by the inverse of the number of loans in the sample by the lending bank. All specifications
control for the size of the loan, whether the business has fewer than 5 employees and tract characteristics
(an urban indicator, the share of employees that are nonwhite, and the logarithms of prepandemic small
business lending and employment). Standards errors, in parentheses, are clustered by bank. + ,∗ ,∗∗ indicate
significance at 10%, 5% and 1%, respectively. The bottom row reports the range of the benefits of borrowing
from a relationship bank for banks with an ln(PPP Intensity) between 0 and 2 (assuming a nonlocal bank).

31
the implications of these facts for geographic differences in credit availability. First, I show
that a higher average PPP intensity for banks with branches within 2 miles of a tract
is associated with a smaller delays in receiving PPP loans and fewer borrowers switching
to fintechs or distant banks. Second, I show that these effects decay quickly as distance
increases.

6.1 Effects of PPP Intensity Within 2 Miles

Table 4 regresses tract level PPP outcomes on the average ln(PPP intensity) for the branches
within 2 miles of a census tract, weighted by branch deposits. PPP intensity here is calculated
as the ratio of a bank’s PPP lending in other tracts to prepandemic small business lending
in order to avoid reverse causality from strong PPP lending in a particular tract causing
local banks to have a high PPP intensity.27 I additionally include controls for prepandemic
tract small business lending and the number of branches within two miles of the tract to
account for the likely availability of relationship lenders. I include the logarithm of small
business employment and variables pertaining to earnings for local workers to account for
the amount of PPP credit firms were eligible for (which was tied to small firms’ payrolls).28
Odd columns present OLS estimates of how the PPP intensity of local banks affects lending
outcomes, while even columns present results weighting by tract small business employment.
All specifications include county fixed effects.
In the first two columns, the dependent variable is the average number of days to origi-
nation for PPP loans in a census tract. A unit increase in the average ln(PPP intensity) of
27
Additionally, Appendix Table A3 presents similar estimates, but measuring local supply conditions with
the share of deposits in nearby branches that are from the top 4 banks. Given the geographic extent of these
banks’ operations, there is less concern about reverse causality for this supply measure.
28
The PPP program essentially provided credit for up to 2.5 times monthly payroll for businesses with
500 or fewer employees. This means that the logarithm of total PPP lending in an area will roughly be the
sum of: i. ln(Small Business Employment), ii. ln(2.5 × Average Monthly Payroll of Eligible Firms) and iii.
ln(Share of Eligible Payroll Funded). I control for the logarithm of employment in firms with fewer than 500
employees (to account for i.) and the following variables pertaining to average earnings (to account for ii.):
the share of jobs earning under $1250 per month, the share of jobs earning over $3333 per month, and the
share of workers with a college degree. Effects of other variables can thus be more reasonably attributed to
bank supply conditions.

32
local banks reduces the average time to origination by roughly 1 day. This elasticity is in
the range of effects found in the loan level data.29
In columns 3 and 4, the dependent variable is the share of credit from banks with nearby
branches. A unit increase in the average ln(PPP Intensity) of nearby branches increases the
market share of nearby banks by roughly 4 percentage points. Roughly 15% of this effect is
accounted for by a lower market share for fintech lenders, as shown by columns 5 and 6.30
The rest reflects a decline by further-off banks or other nonbank lenders (not shown).
In the last two columns, the dependent variable is the logarithm of total PPP lending.
While the activity of local banks significantly affects the timing of loan receipt and the
composition of who does the lending, it appears to have little affect on loan volumes. A unit
increase in the average ln(PPP intensity) only increases expected loan volumes by 1 or 2
percent, and is insignificant in the weighted specification.
Regarding estimates for the other variables, the results also show that the racial compo-
sition of the workforce relates strongly to the speed with which PPP loans are extended. A
one standard deviation increase in the share of employees that are nonwhite (0.17) results
in about 4 day delay in receiving credit, on average. This result is consistent with other
work presenting evidence of racial discrimination in the allocation of PPP credit (Howell
et al., 2021; Chernenko and Scharfstein, 2022). However, this result could also indicative of
minority-owned firms being less likely to have bank relationships (Mills and Battisto, 2020),
resulting in less credit availability controlling for local supply conditions.31 Indeed, the lower
market share of local banks in tracts with higher minority employment is consistent with
relationship loans being less available in these tracts. However, it is unclear the extent to
29
In Appendix Table A1, the coefficient on the interaction between ln(PPP Intensity)b and Local Branchi,b
is -2.5 in the unweighted specification and -0.6 in the weighted specification.
30
The fintech share is the share of loans made by the following lenders: Celtic Bank, Cross River Bank,
Intuit, Fundbox, Kabbage, Readycap and WebBank. This includes both nonbanks, and some prominent
banks with significant lending through fintech partnerships. Fintechs make up about a quarter of the decline
in market share of local banks when market shares are measured by counts rather that volumes, as they
tended to originate smaller loans.
31
While the SBA collected data on the race of borrowers, that field is rarely populated. Consequently
it is only feasible to analyze results based on the racial composition of the local workforce instead of the
demographics of the owners themselves.

33
Table 4: Tract Level Origination Speed
Days to Local Bank Fintech ln(PPP Lending)
Origination Share (pp) Share (pp)
(1) (2) (3) (4) (5) (6) (7) (8)
ln(PPP Intensity)j -1.17** -1.02** 4.03** 4.71** -0.70** -0.56** 0.02* 0.01
(0.20) (0.16) (0.34) (0.36) (0.18) (0.10) (0.01) (0.01)
ln(Branches)j -0.81** -0.54** 14.73** 14.87** -0.46** -0.27** 0.00 0.00
(0.12) (0.07) (0.28) (0.28) (0.10) (0.05) (0.00) (0.00)
ln(SB Lending)j -0.30** 0.03 -0.56** 0.13 0.22** 0.41** 0.17** 0.16**
(0.10) (0.07) (0.17) (0.20) (0.08) (0.06) (0.01) (0.01)
Nonwhitej 23.61** 20.75** -13.00** -9.92** 18.13** 9.83** -0.08 -0.43**
(2.41) (2.19) (2.03) (2.28) (2.87) (1.75) (0.09) (0.06)
ln(SB Emp)j -3.87** -3.33** 1.45** 0.43+ -4.04** -1.98** 0.87** 0.94**
(0.12) (0.09) (0.21) (0.25) (0.26) (0.12) (0.01) (0.01)
Earnings > $3333j -0.02 -0.84 -9.34** -15.20** 0.52 -1.61* 0.46** 0.39**
(0.77) (0.84) (1.81) (1.89) (1.04) (0.72) (0.08) (0.06)
Earnings < $1250j 1.85+ 4.25** 4.44* 1.51 5.10** 4.26** -0.82** -0.89**
(1.11) (1.05) (1.92) (2.11) (1.15) (0.77) (0.10) (0.07)
Collegej -8.61** -1.50 21.61** 25.88** -4.00** 0.56 0.35** 0.03
(1.38) (1.20) (2.37) (2.61) (1.48) (0.78) (0.11) (0.11)

R2 0.645 0.678 0.407 0.519 0.397 0.344 0.822 0.909
Obs. 48,897 48,897 48,301 48,301 46,226 46,226 48,897 48,897
County FE? X X X X X X X X
Employment Weighted? X X X X

Notes: This table presents estimates from the equation:

yj = αc(j) + βln(PPP Intensity)j + γ 0 Xj + εj

where yj is a PPP outcome for a tract j. ln(PPP Intensity)j is the average of the logarithm of the ratio of
PPP lending in other tracts to prepandemic small business lending for banks with branches within 2 miles
of the tract centroid, weighted by the deposits in those branches. Xj is a vector of tract-level controls,
including the the logarithms of 2017 small business employment (≤500 employees) and 2019 small business
lending in the tract; the logarithm of the number of branches within 2 miles of the tract; and the shares of
jobs in the tract that earn over $3333 per month, earn under $1250 per month, are held by a worker with a
college degree, or are held by nonwhite employees. αc(j) is a county fixed effect. yj is the average number of
days until origination for the loans in the tract in columns 1 & 2, the percent of credit provided by banks
within two miles in columns 3 & 4, the percent of credit provided by fintech lenders in columns 5 & 6 and the
logarithm of 2020 total PPP lending in the tract in columns 7 & 8. Even-numbered specifications weight by
tract small business employment. Standards errors, in parentheses, are clustered by county. + ,∗ ,∗∗ indicate
significance at 10%, 5% and 1%, respectively.

34
which this result reflects a lack of relationships, or a lack of credit from the relationships
that do exist.32
Additionally, census tracts with more branches within 2 miles, more college educated
employees, or more small business employment are able to receive credit sooner, have a
higher share of PPP lending accounted for by nearby banks, and have a lower share of credit
accounted for by fintechs. Variation in the volume of PPP lending is mostly accounted for
by the amount of small business employment, and variables relating to local earnings, as
would be expected given that PPP amounts were tied to business payrolls.
Overall, the results show that when local banks are less active in PPP, borrowers turn to
other lenders for loans. This switching appears to be costly, as borrowers receive loans later
on average. Although low-intensity banks provide less credit to the tract than other banks
would, the ultimate effect on PPP loan volumes is relatively modest. While it seems that
firms in these tracts were usually able to get funding eventually, the inability to channel credit
to firms in the earliest stage of the program—when unemployment was at its peak—may
have limited its effectiveness (Doniger and Kay, 2020).

6.2 Effects of PPP Intensity by Distance

Table 4 showed that the PPP intensity of banks within 2 miles of a tract is important in
determining how quickly borrowers can access credit. Now I investigate whether this is the
appropriate distance to consider, or whether the activity of more distant banks matters as
well.
Figure 7 presents estimates of {βd } from the equation:

X
yj = αc(j) + βd ln(PPP Intensity)(d)j + γ 0 Xj + εj
d

32
The effect of racial composition on loan volumes is unclear. One specification shows notable effects for
the nonwhite share and the other notable effects for the college share. Assessing differences in the ability to
get PPP loans is complicated by the racial wage gap: lower PPP lending could reflect lower average earnings
among minority employees (reducing loan sizes) or greater frictions in accessing PPP credit (reducing loan
counts).

35
where ln(PPP Intensity)(d)j is the deposit weighted average ln(PPP intensity) of banks with
branches between d − 2 and d miles from tract j. yj is one of the previously discussed tract
level PPP outcomes: the average number of days until origination (Panel 7a), the share of
loans from banks with nearby branches (Panel 7b), the share of loans from fintechs (Panel
7c) or the logarithm of PPP loan volume (Panel 7d).33
In each panel, the coefficient on ln(PPP Intensity)(2)j is similar to the corresponding
estimate in Table 4, meaning that the controls for PPP intensity at more distant banks does
not affect the estimated effects for banks within 2 miles. The effects then decline towards 0
as distance rises (with the sign reversing at greater distances when total loan volumes are
the dependent variable). The effects of PPP intensity on the average number of days to
origination are the slowest to decay with distance. However, even there, the PPP intensity
of banks within 2 miles affects origination timing about 50 percent more than for banks 2–6
miles away, and about 3 times as much as for banks 6–10 miles away. For the fintech market
share, the PPP intensity within 2 miles matters about twice as much as for banks 2–4 miles
away. For the market share of local banks or total PPP lending, effects either go to zero or
reverse sign for banks 2–4 miles away. In short, the effects of PPP intensity on the timing
or composition of tract lending are predominantly driven by banks within 2 miles.
Overall, these results show that variation in supply conditions have very localized effects.
These effects are unlikely to be driven by bank preferences for nearby borrowers as loan
guarantees remove the benefits of local information or easier monitoring. Likewise, these
effects are unlikely to be driven by borrower preferences for nearby banks, as transit costs
of going to a bank just a few miles away are unlikely to be prohibitive. However, most
preexisting small business relationships were with banks within a few miles (Brevoort and
Wolken, 2009), thus the narrow geographic scope of supply shocks is consistent with frictions
in switching from relationship lenders driving the results.
33
Xj is as in Table 4, but with additional dummy variables indicating whether there are no banks within
a particular distance band. To maintain the same sample as in the previous analysis, ln(PPP intensity) is
recoded to 0 when there are no banks within a band and tracts are dropped if there are no banks within 2
miles.

36
Figure 7: Effects of PPP Intensity by Bank Distance

0.00 6.00

Coeffcient on PPP Intensity(d) Coeffcient on PPP Intensity(d)
4.00
-0.50

2.00

-1.00

0.00

-1.50
-2.00
0-2 2-4 4-6 6-8 8-10 0-2 2-4 4-6 6-8 8-10
Distance (miles) Distance (miles)

(a) Days Until Origination (b) Local Bank Share

0.04
0.00

0.02

Coeffcient on PPP Intensity(d) Coeffcient on PPP Intensity(d)
-0.20

-0.40
0.00

-0.60

-0.02
-0.80

-1.00 -0.04

0-2 2-4 4-6 6-8 8-10 0-2 2-4 4-6 6-8 8-10
Distance (miles) Distance (miles)

(c) Fintech Share (d) ln(PPP Lending)
Notes: This figure plots estimates and 95% confidence intervals for {βd } from the specification:
X
yj = αc(j) + βd ln(PPP Intensity)(d)j + γ 0 Xj + εj
d

where ln(PPP Intensity)(d)j is the deposit weighted average ln(PPP intensity) for branches between d − 2
and d miles from a census tract. yj is a PPP outcome for tract j: the average number of days between the
start of PPP and the approval date (panel a), the share of lending by nearby banks (panel b), the share of
lending by fintechs (panel c), and the natural logarithm of the value of PPP approvals (panel d). Xj is a
vector of tract-level controls, including the logarithms of 2017 small business employment (≤500 employees)
and 2019 small business lending in the tract; the logarithm of the number of branches within 2 miles of
the tract; the shares of jobs in the tract that earn over $3333 per month, earn under $1250 per month, are
held by a worker with a college degree, or are held by nonwhite employees; and dummy variables indicating
whether there are no banks within a distance band. ln(PPP Intensity)(d)j is recoded to 0 when there are
no banks within a band so as to keep a consistent sample with Table 4. Each specification includes county
fixed-effects. Standards errors are clustered by county.

37
7 Conclusion

I use geocoded data on PPP borrowers and bank branches to study the spatial distribution of
PPP originations. I show that about half of bank PPP loans go to borrowers within 2 miles
of a branch. Borrowers receive loans sooner if they get credit from a nearby bank, especially
if that bank is a more active PPP lender. Borrowers where nearby banks are less active in
PPP switch to fintechs or other distant lenders, resulting in delays receiving credit. When I
estimate a model to match the observed relationships between distance, PPP intensity, and
origination timing, I find that relationship borrowers were able to receive credit 5 to 9 days
earlier than other borrowers from the same bank. Analysis of a subsample of PPP loans to
previous SBA 7(a) borrowers produces a similar range of benefits to relationship borrowing.
Overall, the findings demonstrate the importance of bank relationships for prompt access
to credit. Though increased lending from distant lenders counteracted declines in areas where
local banks were less active, there were material delays involved. As such delays have been
shown to be associated with worse job losses (Doniger and Kay, 2020), the results suggest
that intermediation frictions likely reduced the program’s effectiveness. Furthermore, these
findings highlight a cost to intermediating aid through the banking sector; if firms without
banking relationships are more financially constrained, funding dispersed by banks is less
likely to flow to where it is most needed.

38
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40
A Appendix

A.1 Figures

Figure A1: Residualized Distance by Bank PPP Intensity

.4
High Intensity Bank
Low Intensity Bank

Density
.2

0
-10 -5 0 5 10
Residual Log Distance

Notes: This chart plots kernel density estimates of the logarithm of the distance to the lending bank,
residualizing on log loan amount, a small firm indicator, and tract fixed effects. The blue line shows the
distribution for banks with a PPP intensity above 1, and the red line the distribution for those below 1.

41
A.2 Tables

Table A1: Bank Proximity and Origination Speed
Days to Origination
(1) (2) (3) (4) (5) (6)
ln(PPP Intensityb ) -2.41** -1.62 -0.65 -1.17** -1.19** -1.00**
(0.75) (1.10) (1.19) (0.18) (0.18) (0.20)
ln(PPP Intensityb )2 1.30** 1.12** 1.01** 0.61** 0.60** 0.59**
(0.14) (0.13) (0.13) (0.09) (0.09) (0.09)
Local Branchi,b -0.87+ -1.11* -0.39 -1.35** -1.35** -1.29**
(0.47) (0.52) (0.45) (0.11) (0.11) (0.11)
x ln(PPP Intensityb ) -2.49** -0.63**
(0.89) (0.18)
ln(Loan Amount)i,b -4.19** -4.07** -4.04** -4.64** -4.63** -4.63**
(0.34) (0.36) (0.35) (0.05) (0.05) (0.05)
Small Firmi,b 4.43** 4.46** 4.47** 4.09** 4.09** 4.10**
(0.78) (0.78) (0.78) (0.12) (0.12) (0.12)
Internet Bankb 2.69 1.14 12.24** 11.60**
(4.11) (4.33) (3.64) (3.56)
Top 4b 4.24* 3.47+ 4.58* 4.28*
(1.90) (1.91) (1.94) (1.97)

R2 0.265 0.267 0.268 0.182 0.182 0.182
Obs. 4,077,112 4,077,112 4,077,112 4,077,112 4,077,112 4,077,112
Tract Controls? X X X X X X
County FE? X X X X X X
3-Digit NAICS FE? X X X X X X
Weighted? X X X

Notes: This table presents estimates from regressing the number of days until origination on log PPP
intensity of the lending bank, log PPP intensity squared and an indicator for whether the bank has a branch
within 2 miles of the borrower. Specifications in columns 2 & 5 add controls for whether the bank is one of
the four largest lenders or an internet bank, and the specifications in columns 3 & 6 add an interaction of log
PPP intensity with the local lender dummy. All specifications control for the size of the loan, whether the
business has fewer than 5 employees and tract characteristics (nonwhite employment share, log small business
employment, log prepandemic small business lending, and an urban indicator), and include county and 3-
digit NAICS fixed effects. Estimates for tract controls are not displayed. Standards errors, in parentheses,
are clustered by county. + ,∗ ,∗∗ indicate significance at 10%, 5% and 1%, respectively.

42
Table A2: Bank Proximity and Round 1 Funding
Round 1 Indicator×100
(1) (2) (3) (4) (5) (6)
ln(PPP Intensityb ) 7.86** 4.33** 2.52 3.63** 3.62** 3.30**
(2.00) (1.62) (1.81) (0.29) (0.29) (0.30)
ln(PPP Intensityb )2 -1.82** -0.70** -0.50* -1.22** -1.21** -1.20**
(0.35) (0.21) (0.22) (0.12) (0.12) (0.12)
Local Branchi,b 1.15 2.14* 0.79 2.53** 2.56** 2.45**
(1.19) (0.88) (0.82) (0.23) (0.23) (0.23)
x ln(PPP Intensityb ) 4.64** 1.07**
(1.47) (0.30)
ln(Loan Amount)i,b 9.95** 9.17** 9.13** 10.79** 10.78** 10.79**
(0.52) (0.68) (0.68) (0.09) (0.09) (0.09)
Small Firmi,b -5.46** -5.63** -5.64** -10.03** -10.03** -10.04**
(0.91) (0.84) (0.85) (0.26) (0.26) (0.26)
Internet Bankb -22.82** -19.94** -9.67 -8.59
(5.85) (5.98) (6.15) (6.36)
Top 4b -21.71** -20.28** -16.32** -15.81**
(3.04) (3.14) (5.91) (5.99)

R2 0.259 0.283 0.285 0.226 0.226 0.226
Obs. 4,077,112 4,077,112 4,077,112 4,077,112 4,077,112 4,077,112
Tract Controls? X X X X X X
County FE? X X X X X X
3-Digit NAICS FE? X X X X X X
Weighted? X X X

Notes: This table presents estimates from regressing an indicator for whether a loan was originated during
the first round of PPP funding on log PPP intensity of the lending bank, log PPP intensity squared and an
an indicator for whether the bank has a branch within 2 miles of the borrower. The round 1 indicator is
multiplied by 100, so estimates are in terms of the percentage point change in the probability of being funded
in round 1. Specifications in columns 2 & 5 add controls for whether the bank is one of the four largest
lenders or an internet bank, and the specifications in columns 3 & 6 add an interaction of log PPP intensity
with the local lender dummy. All specifications control for the size of the loan, whether the business has fewer
than 5 employees and tract characteristics (nonwhite employment share, log small business employment, log
prepandemic small business lending, and an urban indicator), and include county and 3-digit NAICS fixed
effects. Estimates for tract controls are not displayed. Standards errors, in parentheses, are clustered by
county. + ,∗ ,∗∗ indicate significance at 10%, 5% and 1%, respectively.

43
Table A3: Tract Level Origination Speed
Days to Local Bank Fintech ln(PPP Lending)
Origination Share (pp) Share (pp)
(1) (2) (3) (4) (5) (6) (7) (8)
Top 4 Sharej 2.27** 1.86** -4.86** -7.06** 1.84** 1.11** -0.04** -0.02
(0.39) (0.37) (0.77) (0.74) (0.39) (0.24) (0.02) (0.01)
ln(Branches)j -0.82** -0.53** 14.72** 14.85** -0.47** -0.27** 0.00 0.00
(0.12) (0.07) (0.29) (0.29) (0.11) (0.05) (0.00) (0.00)
ln(SB Lending)j -0.30** 0.03 -0.55** 0.14 0.22** 0.41** 0.17** 0.16**
(0.10) (0.07) (0.17) (0.20) (0.08) (0.06) (0.01) (0.01)
Nonwhitej 23.60** 20.79** -13.26** -10.25** 18.07** 9.85** -0.08 -0.43**
(2.46) (2.18) (2.09) (2.28) (2.89) (1.75) (0.09) (0.06)
ln(SB Emp)j -3.86** -3.31** 1.44** 0.38 -4.03** -1.97** 0.87** 0.94**
(0.12) (0.09) (0.21) (0.25) (0.26) (0.12) (0.01) (0.01)
Earnings > $3333j 0.02 -0.80 -9.40** -15.36** 0.55 -1.60* 0.46** 0.39**
(0.78) (0.85) (1.84) (1.96) (1.05) (0.74) (0.08) (0.06)
Earnings < $1250j 1.81+ 4.21** 4.49* 1.58 5.06** 4.23** -0.82** -0.89**
(1.07) (1.02) (1.96) (2.19) (1.15) (0.77) (0.10) (0.07)
Collegej -8.69** -1.57 21.75** 26.10** -4.08** 0.52 0.35** 0.03
(1.38) (1.20) (2.44) (2.62) (1.47) (0.78) (0.11) (0.11)

R2 0.645 0.678 0.405 0.517 0.397 0.344 0.822 0.909
Obs. 48,902 48,902 48,306 48,306 46,231 46,231 48,902 48,902
County FE? X X X X X X X X
Employment Weighted? X X X X

Notes: This table presents estimates from the equation:

yj = αc(j) + βTop 4 sharej + γ 0 Xj + εj

where yj is a PPP outcome for a tract j. Top 4 sharej is the share of deposits in branches within 2 miles of
the tract that belong to one of the four largest U.S. banks. Xj is a vector of tract-level controls, including
the logarithms of 2017 small business employment (≤500 employees) and 2019 small business lending; the
logarithm of the number of branches within 2 miles of the tract; and the shares of jobs in the tract that
earn over $3333 per month, earn under $1250 per month, are held by a worker with a college degree or are
held by nonwhite employees. αc(j) is a county fixed effect. yj is the average number of days until origination
for the loans in the tract in columns 1 & 2, the percent of loans originated by banks within two miles in
columns 3 & 4, the percent of loans originated by fintech lenders in columns 5 & 6, and the logarithm of 2020
PPP lending in the tract in columns 7 & 8. Even-numbered specifications weight by tract small business
employment. Standards errors, in parentheses, are clustered by county. + ,∗ ,∗∗ indicate significance at 10%,
5% and 1%, respectively.

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