Did The Paycheck Protection Program Hit The Target Nber Working Paper No
- Issuer
- Source documents
- Document type
- Did The Paycheck Protection Program Hit The Target Nber Working Paper No
- Case
- Did The Paycheck Protection Program Hit The Target Nber Working Paper No
Full text
NBER WORKING PAPER SERIES
DID THE PAYCHECK PROTECTION PROGRAM HIT THE TARGET?
João Granja
Christos Makridis
Constantine Yannelis
Eric Zwick
Working Paper 27095
http://www.nber.org/papers/w27095
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
May 2020, Revised September 2021
We thank seminar and conference participants as well as Scott Baker, Jean-Noel Barrot
(discussant), Jediphi Cabal, Sylvain Catherine, Raj Chetty, Gabe Chodorow-Reich (discussant),
Mike Faulkender, Sam Hanson, Steve Kaplan, Anil Kashyap, Mike Minnis, Ben Pugsley
(discussant), Raghuram Rajan, Josh Rauh (discussant), Tiago Pinheiro, Larry Schmidt, Adi
Sunderam, and Luigi Zingales for comments. Livia Amato, Laurence O’Brien, Igor Kuznetsov,
and Zirui Song provided excellent research assistance. João Granja gratefully acknowledges
support from the Jane and Basil Vasiliou Faculty Scholarship and from the Booth School of
Business at the University of Chicago. Yannelis and Zwick gratefully acknowledge financial
support from the Booth School of Business at the University of Chicago. Zwick has provided
compensated expert testimony on behalf of a PPP loan servicer. We are grateful to the Small
Business Administration, Homebase, Womply, and Opportunity Insights for data, and to the
CDBA, ACAP, US Treasury, and the House Select Subcommittee on the Coronavirus Crisis for
helping us understand the institutional background. The views expressed herein are those of the
authors and do not necessarily reflect the views of the National Bureau of Economic Research.
At least one co-author has disclosed additional relationships of potential relevance for this
research. Further information is available online at http://www.nber.org/papers/w27095.ack
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2020 by João Granja, Christos Makridis, Constantine Yannelis, and Eric Zwick. All rights
reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit
permission provided that full credit, including © notice, is given to the source.
Did the Paycheck Protection Program Hit the Target?
João Granja, Christos Makridis, Constantine Yannelis, and Eric Zwick
NBER Working Paper No. 27095
May 2020, Revised September 2021
JEL No. E6,E62,G2,G21,G28,G38,H25,H32,H81,I38
ABSTRACT
This paper provides a comprehensive assessment of financial intermediation and the economic
effects of the Paycheck Protection Program (PPP), a large and novel small business support
program that was part of the initial policy response to the COVID-19 pandemic in the US. We
use loan-level microdata for all PPP loans and high-frequency administrative employment data to
present three main findings. First, banks played an important role in mediating program targeting,
which helps explain why some funds initially flowed to regions that were less adversely affected
by the pandemic. Second, we exploit regional heterogeneity in lending relationships and
individual firm-loan matched data to study the role of banks in explaining the employment effects
of the PPP. We find the short- and medium-term employment effects of the program were small
compared to the program’s size. Third, many firms used the loans to make non-payroll fixed
payments and build up savings buffers, which can account for small employment effects and
likely reflects precautionary motives in the face of heightened uncertainty. Limited targeting in
terms of who was eligible likely also led to many inframarginal firms receiving funds and to a
low correlation between regional PPP funding and shock severity. Our findings illustrate how
business liquidity support programs affect firm behavior and local economic activity, and how
policy trans-mission depends on the agents delegated to deploy it.
João Granja Constantine Yannelis
Booth School of Business Booth School of Business
University of Chicago University of Chicago
5807 S. Woodlawn Avenue 5807 S. Woodlawn Avenue
Chicago, IL 60637-1610 Chicago, IL 60637
joaogranja@chicagobooth.edu and NBER
constantine.yannelis@chicagobooth.edu
Christos Makridis
875 10th St NW Eric Zwick
Apt 527 Booth School of Business
Washington, DC 20001 University of Chicago
United States 5807 South Woodlawn Avenue
cmakridi@stanford.edu Chicago, IL 60637
and NBER
ezwick@chicagobooth.edu
1 Introduction
The COVID-19 pandemic triggered an unprecedented economic freeze and a massive immedi-
ate policy response. Among the firms most affected by the freeze were millions of small busi-
nesses without access to public financial markets or other ways to manage short-term costs.
Without an existing system of social insurance to support these firms, policymakers around
the world rushed to develop new programs to contain the damage, including wage subsidies,
small business grants, and guaranteed business loan schemes, often relying on banks to rapidly
deploy funds to firms.1
This paper studies a large and novel business support program that was part of the crisis
response in the US, the Paycheck Protection Program (PPP), and the role of banks in explaining
the employment effects of the PPP. Part of the CARES Act, the PPP offered guaranteed, forgiv-
able loans to provide liquidity to small and mid-sized businesses and prevent job losses. The
PPP deployed more than $500 billion within just four months of passage, making it one of the
largest firm-based fiscal policy programs in US history. The program was administered by the
Small Business Administration (SBA) with the loan application process operated by commer-
cial banks. We document substantial heterogeneity across banks in disbursing PPP funds and
find that this heterogeneity led to meaningful differences across firms and regions in terms of
targeting and employment impacts.
We have three main findings. First, banks played an important role in mediating program
targeting. The extent of bank participation in the initial phase of the program depends intu-
itively on ex ante bank characteristics, including relationships with the SBA, greater reliance
on labor relative to automation, and active enforcement actions against banks. These differ-
ences in bank participation explain spatial differences in the initial distribution of funds and
why some funds initially flowed to regions that were less adversely affected by the pandemic.
Second, the short- and medium-term employment effects of the program were small compared
to the program’s size. Our analysis reveals how bank performance differences in loan deploy-
ment contribute to these employment effects over time. Third, many firms used the loans to
1
For example, the UK, France, Germany, Spain, Italy, and Australia introduced or expanded loan guarantee and
small business grant schemes in response to the pandemic. Hanson, Stein, Sunderam and Zwick (2020b) provide
a theoretical discussion of business credit support programs in the pandemic and a review of key programs in
Europe. Many of these countries also separately implemented temporary wage subsidy programs to provide
incomes to unemployed workers directly through firms (see Hubbard and Strain (2020) for a comprehensive
list). While the program we study combines these features, the larger source of wage support in the US came via
the unemployment insurance system.
2
make non-payroll fixed payments and build up savings buffers, which can account for small
employment effects and likely reflects precautionary motives in the face of heightened uncer-
tainty.2 Limited targeting in terms of who was eligible likely also led to many inframarginal
firms receiving funds and to a low correlation between regional PPP funding and shock severity.
We bring data from two sources to study the PPP. First, we use loan-level microdata from
the SBA for all PPP loans, which includes lender, geography, and borrower- and loan-level in-
formation. The data offer a clear look at which lenders are most active in disbursing loans, how
program participation evolves over time, and the geographic distribution of PPP lending across
the US economy. Additionally, we obtained high-frequency employment data from Homebase,
a software company that provides free scheduling, payroll reporting, and other services to small
businesses, primarily in the retail and hospitality sectors. The granularity of the data, coupled
with the focus on sectors most adversely affected by the pandemic, allows us to trace out the
response of employment, wages, hours worked, and business closures in almost real-time and
evaluate the effects of PPP support. We complement these primary data sources with a num-
ber of other sources, including county-level unemployment insurance claims, the Census Small
Business Pulse survey, small business revenue data from Womply, and employment rates from
the COVID-19 economic tracker (Chetty, Friedman, Hendren and Stepner, 2020).
We consider three dimensions of program targeting. First, did the funds flow to where
the economic shock was greatest? Second, given that the PPP used the banking system as a
conduit to access firms, what role did the banks play in mediating policy targeting? Third, why
did some banks systematically under- or overperform in disbursing PPP loans relative to their
share of the small business loan market?
Preventing unnecessary mass layoffs and firm bankruptcies by injecting liquidity into firms
were central goals of the program and the benefits of PPP were likely greatest in areas with
more pre-policy economic dislocation. However, we find no evidence that funds flowed to areas
that were more adversely affected by the economic effects of the pandemic, which we proxy
using declines in hours worked, employee counts, business shutdowns, and coronavirus infec-
tions and deaths. If anything, we find evidence that funds flowed to areas less hard hit. Over
both rounds of funding, the correlation between pre-policy economic dislocation and program
participation was approximately zero, which likely reflects the program’s broad definition of
2
Almeida, Campello and Weisbach (2004) and Riddick and Whited (2009) show that uncertainty increases
firms’ precautionary motives to hold cash, particularly when external financing is difficult to obtain.
3
eligibility (Barrios, Minnis, Minnis and Sijthoff, 2020).
We find significant heterogeneity across banks in terms of disbursing PPP funds, which re-
flects more than mere differences in underlying loan demand and contributes to the weak cor-
relation between economic declines and PPP lending. Ex ante bank characteristics, including
greater labor capacity to process loans, pre-existing SBA relationships, and active enforcement
actions against banks, predict banks’ relative performance in disbursing PPP loans. The PPP
program required lenders to collect and enter information into a custom application and submit
it via the SBA portal. Thus, reliance on labor rather than automation, as well as pre-existing ac-
cess and familiarity with the SBA portal facilitated disbursement of PPP loans, especially in the
initial phase. Conversely, banks subject to formal enforcement actions were not automatically
approved to make PPP loans, initially leading to lower PPP disbursement for these banks.
Our results on bank participation motivate two complementary research designs to evaluate
the PPP using ZIP-level variation in banks’ propensity to disburse loans. We use these research
designs to study business shutdowns, employment levels, reductions in hours worked, initial
unemployment insurance (UI) claims, and small business revenues. We construct measures
of geographic exposure to bank performance in the PPP using (1) the distribution of bank
branches across geographic regions and (2) geographic exposure to the ex ante bank charac-
teristics that predict PPP disbursement. Both measures exploit the fact that most small busi-
ness lending is local (Brevoort, Holmes and Wolken, 2010; Granja, Leuz and Rajan, 2018): we
compare regions exposed to high-performance banks—whose share of PPP lending exceeded
what would be expected from them given their national share of the small business lending
market—to regions exposed to low-performance banks—whose PPP lending share underper-
formed relative to their national share of the small business lending market. This bank-driven
variation across regions allows us to isolate the effect of the PPP from differences in loan de-
mand or confounding correlations between PPP funding and local economic outcomes. In the
case of predicted PPP, we further attempt to isolate specific elements of bank performance that
we can trace back to bank-supply frictions prevailing prior to the pandemic.
We do not find evidence that the PPP had a substantial effect on local employment out-
comes or business shutdowns during the first round of the program, and find modest effects
on hours worked and employee counts during the second round. We confirm the firm-level
evidence by documenting limited impacts on initial UI claims, small business revenues, and
employment rates in small businesses at the county level. Our confidence intervals on employ-
4
ment outcomes are wide enough to permit modest effects of the program, but precise enough
to reject large effects. Results are qualitatively and quantitatively similar for both measures of
exposure to bank performance. Our estimates suggest that more than 90% of jobs supported by
the PPP were inframarginal. If wages for inframarginal workers did not adjust, then the bulk
of the program’s economic benefits appear to accrue to other stakeholders, including owners,
landlords, lenders, suppliers, customers, and possibly future workers.
Both research designs are akin to Bartik instruments and rely on the assumption that pre-
policy bank branch shares in a given region are not correlated with the outcomes we study.
This assumption likely holds once we condition on key observables, including the relationship
between PPP funding and the initial severity of the crisis. Our preferred specification conditions
on firm- and state-by-time fixed effects, which remove many potential confounding factors from
the analysis. We present further evidence supporting the Bartik assumption from pre-trends
comparisons between high- and low-exposure groups, heterogeneity analyses that show larger
effects in regions with more eligible firms, and diagnostics for unpacking the Bartik instrument
following Goldsmith-Pinkham, Sorkin and Swift (2020).
We complement our aggregate regional designs with a timing design using matched firm-
loan data. We match by name 10,694 firms in Homebase to PPP loans and then compare firms
that received loans earlier versus later. We instrument for the date of PPP receipt using regional
exposure to lenders that disbursed different amounts of PPP funding or predicted PPP funding.
This variation allows us to capture the effect of firms receiving loans during a crisis in earlier
versus later weeks. Results from this research design also show modest effects that fall within
the confidence interval of our bank exposure design.
The fact that the program disbursed significant funds, yet had little effect on employment,
raises the natural question of what firms did with the money. We draw on the Census Small
Business Pulse Survey to show that PPP funds allowed firms to build up liquidity and to meet
loan and other non-payroll spending commitments. For these firms, the PPP may have strength-
ened balance sheets at a time when shelter-in-place orders prevented workers from doing work,
and when UI was more generous than wages for a large share of workers.
This finding is important because it implies that, while employment effects are small in the
short run, they may well be positive in the longer run because firms are less likely to close
permanently. The program also likely had important effects in terms of promoting financial
stability by avoiding corporate loan defaults and business evictions. Consistent with this notion,
5
we find suggestive evidence that exposure to higher-performing banks is associated with fewer
permanent firm shutdowns, defined as the firm being closed for all weeks from the beginning of
the program through the end of August. This result suggests that initial bank-driven distortions
may have had persistent effects on the ability of firms to reopen after the initial shock.
At the same time, because program eligibility was defined broadly, many less-affected firms
received PPP funding and may have continued as they would have in the absence of the funds,
either by spending less out of retained earnings or by borrowing less from other sources. For
these firms, while the statutory incidence of funding falls on labor and creditors, the economic
incidence falls mainly on business owners.
Our work complements several contemporaneous studies that also focus on the employ-
ment effects of the PPP, although with less emphasis on the role of financial institutions. Three
studies (Autor, Cho, Crane, Goldar, Lutz, Montes, Peterman, Ratner, Villar and Yildirmaz, 2020;
Chetty, Friedman, Hendren and Stepner, 2020; Hubbard and Strain, 2020) use the size thresh-
old of 500 employees to study the employment effects of the program. This research design
estimates a different treatment effect, as it uses variation local to larger firms, while most PPP
loans were disbursed to much smaller firms. Approximately 0.4% of PPP loans were disbursed
to firms with more than 250 employees, which account for only 13% of covered employment
among all borrowers. Nevertheless, despite using different primary data sources and a different
research design, these papers tend to find either modest or negligible effects on employment,
consistent with our findings.3
Several other studies use differences in the timing of PPP receipt to examine the program’s
employment effects, while also exploiting differences in timing due to pre-existing variation
in bank lending relationships. Li and Strahan (2020) use variation in the strength of the rela-
tionships between local banks and firms and, similar to us, find modest employment effects of
the program. Faulkender, Jackman and Miran (2020) leverage the faster pace that community
banks approved and disbursed funds relative to their counterparts and find large employment
effects of the program. Bartik, Cullen, Glaeser, Luca, Stanton and Sunderam (2020) find signif-
icantly lower self-reported survival probabilities for firms whose primary lender was a top-four
bank. Doniger and Kay (2021) find that areas with a greater fraction of businesses receiving
PPP funds right before the end of the first round rather than at the start of the second round
3
Hubbard and Strain (2020) present some specifications that yield larger estimates, but the overall takeaway
from their analysis appears in line with these other threshold designs.
6
had higher employment rates, with magnitudes that align with our uninstrumented matched
sample analysis. While the conceptual approach in these papers is similar to ours, a key source
of difference is the extent to which the research design accounts for nonrandom program tar-
geting. Given the role lenders played in allocating funds to areas that were initially less affected
by the pandemic, accounting for targeting differences across areas is crucial for identifying the
employment effects of the PPP. Our paper also contributes by identifying and exploiting ex ante
bank characteristics that affected banks’ ability to deploy funds quickly.4
More broadly, this paper joins a literature focusing on how government interventions fol-
lowing crises impact recovery and the broader economy (Agarwal, Amromin, Ben-David, Chom-
sisengphet, Piskorski and Seru, 2017; Mian and Sufi, 2012; Zwick and Mahon, 2017). Specif-
ically, we offer a comprehensive evaluation of the role that banks played in allocating PPP
funds, and the impact that this force had on program targeting and economic outcomes. We
contribute to an understanding of government responses to crises, including subsidized lend-
ing, tax incentives, and loan guarantees, a widely-used form of government intervention in
credit markets (Smith, 1983; Gale, 1990, 1991; Lucas, 2016; Kelly, Lustig and Van Nieuwer-
burgh, 2016; Atkeson, d’Avernas, Eisfeldt and Weill, 2018). A burgeoning empirical litera-
ture examines the transmission and effects of loan guarantees or tax-based stimulus on credit
supply, employment, and small business outcomes (House and Shapiro, 2008; Lelarge, Sraer
and Thesmar, 2010; Bachas, Kim and Yannelis, 2020; Barrot, Martin, Sauvagnat and Vallee,
2019; Mullins and Toro, 2017; Gonzalez-Uribe and Wang, 2019; Zwick, Forthcoming). Our
paper contributes directly to this literature by showing how policy transmission depends on
the agents delegated to deploy it (e.g., banks). These results are consistent with those of stud-
ies that emphasize the importance of proximity (Granja, Leuz and Rajan, 2018), as well as
emerging evidence from the pandemic that firms with pre-existing borrowing and lending re-
lationships received access to PPP funds faster than their counterparts (Balyuk, Prabhala and
Puri, 2020; Amiram and Rabetti, 2020; Li and Strahan, 2020).
The article is organized as follows. Section 2 describes the PPP. Section 3 discusses the main
data sources used. Section 4 describes how the distribution of relative performance in the PPP
4
Other studies take a more theoretical approach (Elenev, Landvoigt and Van Nieuwerburgh, 2020; Joaquim
and Netto, 2020; Barrios, Minnis, Minnis and Sijthoff, 2020), or study specific aspects of the PPP, such as the role
of Fintechs, bank lending relationships, and firm size in the allocation of funds under the program, the impact
of the program in supporting liquidity for firms of different size, and how the distribution of funds varied with
businesses’ ability to work remotely (Erel and Liebersohn, 2020; Cororaton and Rosen, 2020; Papanikolaou and
Schmidt, 2020; Morse and Bartlett, 2020; Chodorow-Reich, Darmouni, Luck and Plosser, 2020).
7
is correlated with bank and other characteristics, documents how differences across banks in
PPP activity imply geographic differences in PPP exposure, and explores the implications for
PPP targeting to different geographic areas. Section 5 analyzes the effects of the PPP on local
labor market and economic outcomes using our bank exposure and timing research designs.
Section 6 explores mechanisms behind these effects. Section 7 concludes.
2 The Paycheck Protection Program (PPP)
The Paycheck Protection Program (PPP) began on April 3rd, 2020 as part of the CARES Act as a
temporary source of liquidity for small businesses, authorizing $349 billion in forgivable loans
to help small businesses pay their employees and additional fixed expenses during the COVID-
19 pandemic. Firms applied for support through banks and the Small Business Administration
(SBA) was responsible for overseeing the program and processing loan guarantees and forgive-
ness. A motivation for using the banking system (including FinTech) as a conduit for providing
liquidity to firms is that, because nearly all small businesses have pre-existing relationships
with banks, this connection could be used to ensure timely transmission of funds.5
The lending program was generally targeted toward small businesses of 500 or fewer em-
ployees.6 Although the initial round of funding was exhausted on April 16th, a second round
of $320 billion in PPP funding was passed by Congress as part of the fourth COVID-19 aid bill.
Small businesses were eligible as of April 3rd and independent contractors and self-employed
workers were eligible as of April 10th. The initial deadline for firms to apply to the program
was June 30th, but this was eventually extended to August 8th. Our analysis of the program
runs through the end of August.7
5
Many of these relationships are limited to having transaction accounts. Using data from a large survey on
Facebook, Alekseev, Amer, Gopal, Kuchler, Schneider, Stroebel and Wernerfelt (2020) find that half of firms report
not having pre-existing relationships as borrowers with banks, which appears to have led to such firms initially
struggling to access the program and eventually switching lenders in order to receive funds.
6
A notable exception was made for firms operating in NAICS Code 72 (accommodations and food services),
which are eligible to apply insofar as they employ under 500 employees per physical location. Firms whose
maximum tangible net worth is not more than $15 million and average net income after Federal income taxes
(excluding any carry-over losses) of the business for the two full fiscal years before the date of the appli-
cation is not more than $5 million can also apply. See https://www.sba.com/funding-a-business/
government-small-business-loans/ppp/faq/small-business-concerns-eligibility/ for fur-
ther information about the program.
7
The Consolidated Appropriations Act of 2021 included $284 billion in additional forgivable loans for a second
draw of PPP loans for small businesses. This program began in January 2021 and its eligibility criteria were
targeted to small businesses that experienced reductions in revenue. Our analyses do not encompass the effects
8
The terms of the loan were the same for all businesses. The maximum amount of a PPP
loan is the lesser of 2.5 times the average monthly payroll costs or $10 million. The average
monthly payroll is based on prior year’s payroll after subtracting the portion of compensation
to individual employees that exceeds $100,000.8 The interest rate on all loans is 1% and
their maturity is two years. Under SBA’s interpretation of the initial bill, the PPP loans can
be forgiven if two conditions are met. First, proceeds must be used to cover payroll costs,
mortgage interest, rent, and utility costs over the eight-week period following the provision
of the loan, but not more than 25% of the loan forgiveness amount may be attributable to
non-payroll costs. Second, employee counts and compensation levels must be maintained. If
companies cut pay or employment levels, loans may not be forgiven.9 However, if companies
lay off workers or cut compensation between February 15th and April 26th, but subsequently
restore their employment levels and employee compensation, their standing can be restored.
Congress expanded PPP on June 3rd, allowing more flexible terms for loan forgiveness.
The updates to the PPP expanded the duration from eight weeks to twenty-four and extended
the deadline to rehire workers until the end of the year. This change effectively gave small
businesses more time to use program funds and rehire workers. Additionally, the minimum
amount of funds used for payroll while still qualifying for forgiveness was lowered from 75%
to 60%.
An important feature of the program is that the SBA waived its standard “credit elsewhere”
test used to grant regular SBA 7(a) loans. This test determines whether the borrower has
the ability to obtain the requested loan funds from alternative sources and poses a significant
barrier in the access to regular SBA loans. Instead, under PPP rules, applicants were only
required to provide documentation of their payroll and other expenses, together with a simple
two-page application process where they certify that the documents are true and that current
economic uncertainty makes this loan request necessary to support ongoing operations. In
sum, the PPP program was designed to be a “first-come-first-served” program with eligibility
guidelines that allowed it to reach a broad spectrum of small businesses.10
of this second draw of PPP.
8
Payroll costs include wages and salaries but also payments for vacation, family and medical leave, healthcare
coverage, retirement benefits, and state and local taxes.
9
Loan payments on the remainder of the loan can be deferred for six months and interest accrues at 1%.
10
The traditional SBA program responding to disasters is the Economic Injury Disaster Loan (EIDL) program.
Recipients of an EIDL loan can receive a $10,000 loan advance that does not need to be paid back. EIDL loan
advance amounts are deducted from PPP forgiveness. The EIDL loan itself is capped at a maximum of $2 million, is
not forgivable, and the funds can be used flexibly for operating expenses. The EIDL and PPP programs functioned
9
During the first weeks of April, demand for PPP loans outstripped supply, which was limited
by statute. Between April 3rd and 16th all of the initial $349 billion was disbursed, and the
program stopped issuing loans for a period of time. The House and Senate passed a bill to add
an additional $320 billion in funding on April 21st and 23rd, respectively, which was signed
into law on the 24th. The PPP began accepting applications on April 27th for the second round
of funding. While 60% of the second round funds were allocated within two weeks of ini-
tial disbursement, the remaining second round funds were disbursed slowly, with unallocated
PPP funds being available in late June. By early July, more than $130 billion remained avail-
able in PPP funds. Loan disbursement remained low throughout July and August, suggesting
that the second round had sufficient funds to meet demand. The program stopped accepting
applications on August 8th, culminating in $525 billion in total disbursements.
3 Data
Our primary source for data on the PPP comes from microdata made available through the
Small Business Administration (SBA) and the Department of Treasury. We are able to observe
all loans approved under the program. For all loans, the data include borrower and lender
name, the borrower’s self-reported industry, location, corporate form, and workers covered
by the loan. Our targeting analysis and bank exposure research design use data for all loans
aggregated to either the regional or local geography level, while our individual research design
uses a matched sample of loans that we were able to match to the Homebase dataset.
We merge this data set with the Reports of Condition and Income (Call Reports) filed by
all active commercial banks as of the first quarter of 2020. We are able to match 4,370 bank
participants in the PPP program to the Call Reports data set. We did not match 795 commercial
and savings banks that filed a Call Report in the first quarter of 2020. We assume that these
banks did not participate in the PPP program and made no PPP loans. Overall, lenders in the
PPP sample that we match to the Call Reports account for 90.5% of all loans disbursed under
the PPP.
We obtain information about the financial characteristics of each bank from the Call Re-
ports. This data set includes information about the size, capital structure, and portfolio com-
position of all banks operating in the US. Importantly, we obtain information on the number
in tandem, and EIDL loans are further discussed in Online Appendix E.
10
and amount of small business loans outstanding of each commercial and savings bank from the
“Loans to Small Business and Small Farms Schedule” of the Call Reports. Using this informa-
tion, we benchmark the participation of all commercial and savings banks in the PPP program
relative to their share of the small business lending market prior to the program. We also use
Call Report data to compute measures of average capitalization and liquidity of banks serving a
region and to compute some ex ante characteristics that limited banks’ ability to quickly deploy
funds under the program.
To compute measures of exposure of each state, county, and ZIP to PPP lenders, we combine
the matched-PPP-Call-Reports data set with Summary of Deposits data containing the location
of all branches and respective deposit amounts for all depository institutions operating in the
US as of June 30th, 2019. In our bank exposure research design, we take advantage of the
idea that small business lending is mostly local (e.g., Granja, Leuz and Rajan (2018)) and use
the distribution of deposits across geographic regions to create our Bartik-style measure of ex-
posure of these regions to lenders that over- or underperformed. We define performance using
each bank’s national share of PPP lending relative to its national share of the small business
lending market. We use County Business Patterns data to approximate the amount of PPP lend-
ing per establishment and the fraction of establishments receiving PPP loans in the region and
to investigate whether the fraction of establishments receiving PPP loans in a region is affected
by that region’s exposure to the performance of its local banks in the PPP.
To evaluate whether PPP amounts were allocated to areas that were hardest-hit by the
COVID-19 crisis and whether the program improved economic employment and other eco-
nomic outcomes following its passage, we use data from multiple available sources on the
employment, social distancing, and health impact of the crisis. We obtained detailed data on
hours worked among employees of firms that use Homebase software to manage their schedul-
ing and time clock.11 Homebase processes exact hours worked by the employees of a large
number of businesses in the US. We use information obtained from Homebase to track em-
ployment indicators at a weekly frequency at the establishment level. The Homebase dataset
disproportionately covers small firms in food and beverage service and retail; therefore, it is
not representative of aggregate employment. At the same time, the Homebase data are quite
useful for evaluating the employment impacts of the PPP specifically, since many hard-hit firms
are in the industries Homebase covers and much of the early employment losses came from
11
See https://joinhomebase.com/ for more information.
11
these firms. We use the Homebase data in our bank exposure and matched-sample analysis to
measure the impact of PPP funding on employment and business shutdowns.
To broaden this analysis, we supplement the Homebase data with three additional data
sources. First, we obtain county-by-week initial unemployment insurance claims from state
web sites or by contacting state employment offices for data. Second, we obtain small busi-
ness revenue data from Womply, a company that aggregates data from credit card processors.
The Womply data includes aggregate card spending at small businesses at the county level,
defined by the location where a transaction occurred. We complement these data sources with
additional county-level employment data from Opportunity Insights, which are described in
detail in Chetty, Friedman, Hendren and Stepner (2020).12 The employment data come from
Paychex, Earnin, and Intuit.
We additionally obtain counts of COVID-19 cases by county and state from the Center for
Disease Control and use data on the effectiveness of social distancing from Unacast. To un-
derstand the mechanisms underlying our results, we draw on data from the Census Bureau’s
Small Business Pulse Survey (SBPS), a new representative survey that was launched to obtain
real-time information tailored towards small businesses. In Online Appendix A, we provide a
more detailed discussion of each data source and final dataset construction. Finally, we obtain
data from the Bureau of Economic Analysis (BEA) on the median household income at the
county level between 2017 and 2019 to control for differences in economic activity at the local
level that could be related to the evolution of our outcomes of interest during the pandemic.
4 Program Targeting and Bank Performance
4.1 Paycheck Protection Program Exposure
Table 1 shows summary statistics for the 20 largest financial institutions in the US, as measured
by total assets. Columns (2) and (3) report the share of total PPP volume in the first round
and overall, respectively, while columns (7) and (8) report the share of the number of PPP
loans of each bank in the first round and overall. Columns (4) and (9) show the share of the
small business loan (SBL) market as of the fourth quarter of 2019 in terms of total volume and
number of loans, respectively.
12
We also refer readers to Chetty, Friedman, Hendren and Stepner (2020) who provide comparisons between
Homebase and alternative high-frequency measures of aggregate employment.
12
In columns (5)–(6) and (10)–(11), we compute a measure of relative bank performance in
round one and for the whole program, which is measured as
Share PPP − Share SBL
P P P Eb = × 0.5
Share PPP + Share SBL
where Share PPP is the share of PPP for bank b, and Share SBL is the bank’s small business
loan share. In our main analysis, we use the PPPE measure of relative bank performance that
is based on the share of the number of PPP and SBL loans of each bank.13 We prefer the
number-based measure of relative bank performance because larger businesses had prompter
access to PPP loans (Balyuk, Prabhala and Puri, 2020) and the volume-based measure of bank
performance puts greater weight on large loans and less weight on smaller loans to businesses
whose access to the program was more likely constrained by lack of local access to commercial
banks that were quick to deploy loans.
Figure 1 shows the cumulative share of PPP (blue triangles) and small business loans (red
circles) by all banks at the end of the first (Panel A) and second funding rounds (Panel B), with
banks ordered by number-based PPPE.14 Recall that values close to -0.5 indicate little to no
participation in the program relative to a bank’s initial small business lending share.
There are significant dislocations between the share of PPP lending of underperforming
banks and the share of PPP that we would expect had these banks issued PPP loans in propor-
tion to their share of the small business lending market. If there were no heterogeneity in PPP
performance, the PPP and SBL shares would follow similar patterns. This is not the case, and
the S-shaped pattern for PPP indicates that many banks disbursed relatively few PPP loans,
while roughly a third of banks disbursed half of the PPP loans. Panel A shows that commercial
and savings banks representing 20% of the small business lending market simply did not par-
ticipate at all in the first round of the program (P P P E = −0.5). At the end of the first round,
the group of banks whose share of the program was below their share of the small business
lending market (P P P E < 0) made less than 20% of the PPP loans, but account for approxi-
13
Small business loans include outstanding balances on credit cards issued to small businesses, but it is not
possible to ascertain what fraction of these loans are credit card accounts. To the extent that these balances could
represent the most important lending relationship of many small businesses, including these balances could be
useful to capture the share of firms that consider each bank as a banking relationship. Nevertheless, our findings
that large banks underperform their respective share of small business loans are not affected when we consider
only small business loans with a principal amount between $100,000 and $1 million, which are less likely to
include outstanding credit card balances.
14
Although the PPP application window continued into August, we refer to the end of June as the end of the
second round because nearly all funds were disbursed by then.
13
mately two-thirds of the entire small business lending market. The top-4 banks are central to
this fact, as Table 1 shows that these banks accounted for 36% of total pre-policy small business
loans, but disbursed less than 3% of all PPP loans in the first round.
Figure 1, Panel B shows that these dislocations became less pronounced during the second
round, which accounted for 30% of total PPP lending. In the second round, the banks that
underperformed in the first round were able to catch up and partly close the performance gap.
Yet, there remains a wide spread between banks. If most eligible borrowers ultimately received
funding, this pattern suggests considerable reallocation of borrowers across lenders during the
program. Overall, the evidence is consistent with substantial heterogeneity across lenders in
their responses to the program’s rollout.
4.2 Bank Performance Over Time
Figure 2 traces the evolution of PPP lending over time and by bank size using different metrics.
We plot cumulative average PPPE using a number-based approach (Panel A), average PPPE
using a volume-based approach (Panel B), average loan size (Panel C), and the fraction of loans
above $1 million (Panel D). Panels A and B show that banks with total assets below $50 billion
deployed a greater share of PPP loans relative to their respective share of small business loans.
In contrast, large banks underperformed relative to their share of small business lending. The
differences in bank PPPE across categories of bank size were very large throughout the first
round. These differences partly converged at the beginning of the second round.15 In spite
of this partial convergence, large banks still underperformed overall, consistent with press
accounts suggesting that clients were frustrated by large banks’ inability to process PPP loans
and switched to smaller banks and non-banks. As demand for PPP funds waned during May,
the evolution of bank PPPE across size categories stabilized.
Figure 2, Panels C and D suggest that all banks made larger loans in the earliest weeks
of the program. The average size of loans declines significantly over time and jumps down
at the beginning of the second round. Nearly 50% of the loans disbursed by banks whose
15
The differences were noted in the popular press. For example, see the April 6th Wall Street
Journal article, “Big Banks Favor Certain Customers in $350 Billion Small-Business Loan Program”
(https://www.wsj.com/articles/big-banks-favor-certain-customers-in-350-billion-small-business-loan-program-
11586174401) and the July 31rd Wall Street Journal article, “When Their PPP Loans Didn’t Come Through,
These Businesses Broke Up With Their Banks” (https://www.wsj.com/articles/when-their-ppp-loans-didnt-come-
through-these-businesses-broke-up-with-their-banks-11596205736).
14
total assets ranged between $50 billion and $1 trillion were over $1 million as of April 3rd.
That figure falls to roughly 30% by April 8th and 20% by April 13th. By April 18th, loan sizes
across banks of different sizes begin to converge between $200,000 and $450,000. This fact
may be consistent with higher awareness and sophistication by larger borrowers (Humphries,
Neilson and Ulyssea, 2020), or with banks prioritizing certain customers, such as existing loan
customers who tend to be larger (Balyuk, Prabhala and Puri, 2020).16 Interestingly, the top-
4 banks disbursed a relatively smaller fraction of large loans compared to other large banks,
which likely reflects the large number of microbusinesses and small businesses connected to
these banks, especially in urban regions.
Overall, these findings suggest that the banking system did not play a neutral role in medi-
ating the allocation of PPP funds during the program. There were large differences in perfor-
mance across banks, which likely reflect differences in the ability and willingness of banks to
respond to the sudden influx of PPP applications. In the second round, most underperforming
banks were able to improve their performance and ultimately process many PPP applications.
Despite this improvement, differences in first round performance resulted in substantial dif-
ferences in the timing of access to the program because of the first-come-first-served nature of
the program and limited first round PPP budget. In Online Appendix B, we plot the Kaplan-
Meier curve of the fraction of small businesses receiving PPP approval. Only 25% of all PPP
borrowers located in ZIP codes whose banks underperformed obtained PPP approval prior to
the end of the first round. By contrast, approximately 42% of all PPP borrowers in ZIP codes
whose banks overperformed had access to funds in the first round.
4.3 Bank Attributes and Predicted PPPE
A potential concern with our PPPE measure of relative bank performance is that it might reflect
differences in local demand for the program rather than differences in the ability or willing-
ness to process applications. The broad eligibility criteria and generous terms of the program
likely meant that demand for the program was high across most locations and industries. Nev-
ertheless, we address this specific concern by attempting to isolate variation in relative bank
16
See for example, “Biggest banks ’prioritized’ larger clients for small business loans, lawsuits claim,”
(http://www.cbsnews.com/news/paycheck-protection-program-big-banks-loans-larger-clients-over-smaller-
businesses/). It is also the case that sole proprietors, who represent approximately 15% of total PPP loans, were
only allowed to apply with a delay that likely excluded many such firms from accessing funds until the second
round.
15
performance that is explained by differences in banks’ ability to process applications under the
program. Specifically, we focus on three factors that capture differences in pre-existing condi-
tions and capacity constraints at the bank-level, which led some banks to respond more quickly
to the program’s rollout.
The first factor is motivated by the fact that banks had to employ an unprecedented amount
of labor hours in a short amount of time to process the unexpected and sudden influx of PPP
loan applications. Bank staff had to interact with clients to collect and review their loan doc-
umentation and then submit the information in those applications through the SBA portal.17
Moreover, Bank Secrecy Act and Anti-Money Laundering regulations meant that the staff had
to perform customer due diligence for new clients. Thus, banks with greater labor capacity had
a relative advantage in processing PPP loans more quickly. We use Call Report data to measure
how much a bank spends in wages relative to data processing expenses. This measure serves
as a proxy for bank reliance on a lending model that depends relatively more on labor from
loan officers and less on information technology.
Another critical factor in determining banks’ ability to quickly deploy PPP loans during the
first round of PPP was whether they had a pre-existing SBA lending relationship. Lenders
needed valid SBA portal credentials (E-Tran accounts) and access to the SBA’s Capital Access
Financial System (CAFS) to submit PPP applications for their clients. Fintechs and other com-
mercial banks with no previous SBA lending experience had to wait until almost the end of the
first round of PPP to gain access to the SBA portal.18 To measure the role of prior relationships
with the SBA in explaining relative bank performance during the first round, we create an indi-
cator variable that captures whether the bank had any prior experience working with the SBA
in the three years prior to the program. To capture the intensity of the SBA relationship, we
compute the fraction of the number of SBA-guaranteed loans that the bank originated relative
to the average number of small business loans in the bank’s balance sheet over the previous
three years.
Finally, many banks were operating under active formal supervisory enforcement actions
17
Sparks (2020) provides an account of the critical role of staffing limitations in the deployment of the first
round of PPP.
18
For instance, according to Sparks (2020), Carter Bank & Trust of Martinsville, Virginia was forced to wait
for its SBA portal credentials and only got access to the SBA portal for PPP applications 48 hours before round
one funds were exhausted. Another account of these difficulties can be found in Wooten (2020). Furthermore,
even banks that had previously worked with the SBA had difficulties submitting applications either because they
needed additional authorizations or because they “forgot their credentials or their login expired.”
16
related to deficiencies in their commercial lending operations and in their compliance with
the Bank Secrecy Act and Anti-Money Laundering requirements. Lenders subject to formal
enforcement actions related to unsafe or unsound practices were not automatically approved
to make PPP loans according to the April 2nd, Interim Final Rule of the SBA, which provided
information for lenders interested in participating in the program. Accordingly, banks under
a formal enforcement action could not submit PPP loan applications for their clients without
first getting approval from the SBA, which likely delayed their ability to quickly submit those
applications.19
The most important case of a bank whose ability to lend under the PPP was restricted by
a formal enforcement action is that of Wells Fargo. Wells Fargo had been operating under an
asset growth restriction imposed by its primary regulator since the aftermath of the 2016 fake
accounts scandal. Because of this restriction, Wells Fargo could not make PPP loans because
they would risk breaching the asset cap. It was not until April 8th, 2020 that the Federal Re-
serve issued a press release modifying the growth restriction such that the bank could disburse
PPP loans. This delay meant that Wells Fargo could not process PPP loans until the asset cap
restriction was modified. As a result, its share of PPP lending in the first round was just a small
fraction of its share of small business lending.20
We examine how pre-PPP variation in these characteristics across banks affects their rel-
ative performance in deploying the PPP during the first round. We estimate cross sectional
regressions of the form:
P P P E j = α j + ζB j + " j
where P P P E j is PPPE for bank j at the end of the first round of the program, α j are size deciles,
and B j are measures of the bank attributes: wages over wages plus data expenses, pre-existing
SBA lending relationships, and enforcement actions.
The first three columns of Table 2 represent the three factors discussed above. Column
19
For instance, PeopleFirst Bank from Joliet, Illinois was issued a formal supervisory actions in 2019 due to
weaknesses in its Bank Secrecy Act and Anti-Money Laundering controls. Another example is Home Bank of
Arkansas from Portland, Arkansas which was under an active formal supervisory written agreement for deficien-
cies in management and in their internal audit control programs. Both banks did not disburse any PPP loan in the
first round of PPP but processed a number of loans more commensurate with their small business lending share in
the second round of the program suggesting that the enforcement actions limited these banks’ ability to respond
quickly to the program.
20
We highlight the case of Wells Fargo due to the importance of Wells Fargo in the economy and to the fact that
we can point to an external reason that was the subject of public discussion and directly explains the underper-
formance of Wells Fargo during the first round of the program. In Online Appendix C, we repeat our main results
using the exposure of each local area to Wells Fargo branches as our main exposure measure.
17
(1) shows that a measure of labor capacity at the bank correlates positively with bank PPPE,
consistent with our hypothesis that greater capacity to hand-process loan applications allowed
banks to disburse PPP loans at a faster rate. Column (2) shows that the existence and strength
of a prior relationship with the SBA are both positively associated with bank performance in
rolling-out PPP funds.21 Column (3) shows that banks with active enforcement actions as well
as Wells Fargo performed significantly worse, on average, during the first round. Column (4)
shows that the explanatory power of each of these variables is not subsumed when we include
them in a multivariate specification. In columns (5)–(8), we further show that these estimated
coefficients are very similar when we include controls for bank size. Thus, these factors are
not merely capturing differences in performance across banks of different sizes. We compute
the predicted values of the empirical specification in column (8) of Table 2 as a measure of
relative bank performance that is explained by these predetermined supply-side frictions and
likely to be orthogonal to differences in local demand for PPP funds.
4.4 Geographic Exposure to Bank PPP Performance
Significant heterogeneity across lenders in processing PPP loans would not necessarily result
in aggregate differences in PPP lending across regions if small businesses can easily substitute
to lenders that are willing to accept and expedite applications. If many lenders, however,
prioritize their existing business relationships in the processing of PPP applications, firms’ pre-
existing relationships might determine whether and when they are able to access PPP funds.
In this case, the exposure of geographic areas to banks that underperformed as PPP lenders
might significantly determine the aggregate PPP amounts received by small businesses located
in these areas.
To examine if geographic areas that were exposed to underperforming banks received fewer
PPP funds, we construct regional measures of PPPE by distributing bank-level PPPE and pre-
dicted PPPE based on the share of the number of branches of each bank in a region.22 We first
21
In Online Appendix B, we provide a plot of the relation between bank PPPE and the labor intensity of the
bank as well as the existence and strength of the pre-existing SBA relationship.
22
By using the share of number of branches rather than the share of deposits of each bank in a region, we
implicitly downweight branches with significant amounts of brokered and internet-deposit balances that do not
necessarily represent a commensurate share of the local small business relationships. If data were available,
each bank’s respective pre-pandemic share of small business loans in each local area would be the ideal weight-
ing scheme. However, the best-available data, the Community Reinvestment Act (CRA) small business lending
dataset, only includes county-level data and only provides data for large banks whose total assets exceed $1
billion.
18
consider the spatial distribution of PPPE during the first round of funding. Exposure varies
across the United States with western areas exhibiting much lower levels of PPPE and more
rural areas in the Midwest and Northeast showing higher PPPE.23
To further understand the conditional distribution of PPPE and predicted PPPE, Table 3
reports the results of bivariate regressions of ZIP-level PPPE and ZIP-level Predicted PPPE on
ZIP-level observables. The variables are normalized so that coefficients can be interpreted as
the effect of a one-standard-deviation change. The results are quite similar using both PPPE and
predicted PPPE. The table confirms our earlier descriptive evidence—the top-4 banks disbursed
significantly fewer PPP loans relative to their overall market share, while regions served by
smaller banks performed better and were served by banks with fewer constraints in deploying
PPPE. Perhaps surprisingly, ZIP codes with a greater branch density have slightly lower PPPE.
The table suggests that early PPP disbursement may have been targeted towards areas less
affected by the pandemic. More populous areas, areas with higher population density, as well
as areas with higher COVID-19 cases, deaths, and social distancing see lower PPPE. There is
no statistically significant relationship between unemployment and PPPE and areas that saw
a greater revenue drop prior to the start of the program also see higher PPPE. On the other
hand, areas that saw greater employment declines according to the Opportunity Insights data
had higher PPPE. The coefficients from the bivariate regressions using Predicted PPPE have
similar signs but weaker magnitudes than those of the regular PPPE regressions. This pattern
is consistent with the idea that Predicted PPPE captures supply-side frictions that are less likely
to correlate with the economic, demographic, and health factors.
Figure 3 explores the relation between PPPE and PPP lending at the state-level using data
from the Census Small Business Pulse Survey at the end of the first round of the PPP.24 We
plot the relationship between the percent of firms receiving funds and state exposure to bank
performance. Panel A plots the fraction of all small businesses reporting receiving PPP loans
in each state during the first round of lending. There is a strong positive relationship between
PPP lending and PPPE at the state level. States with the highest PPPE saw nearly 40% of small
businesses receiving PPP funding in round one; states with the lowest PPPE saw just 10% of
small businesses receiving funding.
23
Online Appendix B provides further information on the geography of targeting, a national map of county-level
PPPE and the first round distribution of PPP funds, and a map of ZIP-level PPPE for the Chicago and New York
metro areas.
24
Most of our analyses are at the ZIP-level but the Census survey is only available at the state-level.
19
A potential concern with these results is that the causality runs in reverse. That is, banks do
relatively better in deploying PPP in areas where demand for PPP loans is strong. To address
this concern, we compare survey measures on firm applications and PPP receipt. The Small
Business Census survey includes questions on both PPP application and receipt. Figure 3, Panel
B compares PPPE to the percentage of businesses in each state that report having applied for
PPP funds as of the end of round one in each state. Between 65% and 80% of small businesses
in each state report having applied for PPP funds at the end of the first round. Importantly,
the likelihood of PPP application is unrelated with state PPPE. In other words, demand for PPP
funds at the state level does not seem to correlate with our state-level PPPE measure of relative
bank performance.
The bottom panels of Figure 3 repeat the analysis using predicted PPPE measure at the
state level. We see very similar patterns as with our regular PPPE measure. Figure 3, Panel C
shows that there is a strong positive relationship between state exposure to banks with supply-
side constraints and the percentage of small businesses receiving PPP at the end of the first
round. Panel D shows that there is little to no relationship between our predicted PPPE and
PPP applications. This fact supports the idea that our predicted PPPE measure captures supply-
side frictions that affected banks’ ability to process PPP loans and not differences in exposure
to local demand.
Figure 4 explores the relation between exposure to bank PPP performance during the first
round and PPP lending at a finer geographic level. Specifically, we compute the local exposure
to bank performance at the ZIP level by taking the weighted average of bank PPPE or predicted
PPPE for all branches that are either in the ZIP or within ten miles of the center of the respective
ZIP code. We then partition ZIPs in bins based on their PPPE after demeaning using the average
PPPE of their respective state to ensure that the empirical relations hold when we use only
within-state variation. Panel A shows the relationship between zip-level PPPE and the fraction
of businesses receiving PPP, while Panel B shows the same relationship replacing PPPE with
predicted PPPE. Both panels show similar results. A strong positive relation between ZIP PPPE
and ZIP predicted PPPE and the fraction of businesses receiving PPP during the first round
further supports the idea that the initial allocation of funds was shaped by exposure to the
performance of local banks.25
25
In this figure, we measure PPP loans relative to eligible establishments, which equals all establishments in
a ZIP less an estimate of the share of establishments with more than 500 employees (which are not eligible for
PPP) plus an estimate of the number of proprietorships likely to apply for PPP.
20
The strong positive relation between ZIP PPPE or predicted PPPE and the fraction of busi-
nesses receiving PPP during the first round of the program persists over the following weeks
but becomes gradually weaker later in May and into June. This pattern offers further evidence
that the relation between PPPE and the fraction of businesses receiving PPP in the first round
is driven not by differences in demand for PPP loans across regions but rather by their expo-
sure to banks that underperformed. Otherwise, this positive association would not necessarily
disappear over time. The pattern suggests either that underperforming local banks improved
their performance in deploying PPP over time or that small businesses in areas where local
banks underperformed were able to obtain funds from other non-local lenders.
We further probe the relation between local PPPE and the allocation of PPP funds in Table
4. There, we assess the association between ZIP PPPE or predicted PPPE and the fraction of
businesses receiving PPP in each ZIP-by-industry group after conditioning on state-by-industry
fixed effects. In each panel, the top row shows the relationship between PPPE and the frac-
tion of businesses receiving PPP, while the bottom row shows the same relationship replacing
PPPE with predicted PPPE. Again, both panels show broadly similar results. Thus, we evaluate
whether businesses within the same state and industry had different access to PPP loans be-
cause they were located in ZIP codes whose nearest banks performed relatively well compared
to businesses in the same state and industry but in ZIP codes whose banks underperformed.
Column (1) of Table 4, Panel A, further supports the idea that local exposure to banks
that overperformed in the PPP had a positive impact on the ability of businesses to obtain
PPP funds during the first round. Even within a given state and industry, being in the same
ZIP or within 10 miles of banks that overperformed in the first round was associated with a
significantly higher share of businesses receiving PPP during the first round. We find similar
conclusions when we measure local ZIP exposure to banks that were constrained processing
PPP applications using our predicted PPPE measure.26 In column (1) of Panel B, we assess
whether this impact persisted through both rounds of the program. Consistent with the findings
above, local ZIP exposure to banks that over- or underperformed in the first round is no longer
positively associated with the fraction of businesses receiving PPP after both rounds of the
program. If anything, there is a modest negative relationship between round one PPPE and
26
In Online Appendix B, we show that our results are robust to including county-by-industry fixed effects in
our empirical specification. Thus, we find a positive relationship between ZIP PPPE and the fraction of businesses
receiving PPP even when we compare businesses that are located within the same county and industry and thus
are even more likely to be exposed to similar external conditions.
21
total PPP loans per establishment. This relationship is significant using PPPE, and insignificant
at conventional levels using predicted PPPE. This result further suggests that as supply-side
frictions subsided during the second round of the program, the relation between PPPE and the
fraction of businesses receiving PPP flattened, which indicates that differences in demand for
PPP funds were unlikely to explain the positive relation during the first round.
A potential explanation for the gradual weakening of the relation between local PPPE and
the fraction of businesses receiving PPP is that non-local banks and nonbanks stepped in to
substitute for underperforming local banks. To investigate this possibility, we decompose the
total fraction of establishments receiving PPP in each ZIP and industry into the fraction of es-
tablishments receiving loans from local banks (defined as banks with a branch within 10 miles
of a ZIP code centroid), non-local banks (defined as all banks with branches that are farther
than 10 miles from the ZIP), credit unions, Fintechs, and all other nonbanks participants. Fig-
ure 5 shows the average fraction of establishments receiving PPP during round one, round
two, and the entire program by source of PPP funding. On average, approximately 20% of all
establishments in a ZIP were able to obtain funding during the first round, and local banks ac-
counted for most of these loans. Fintech lenders and non-banks participated very little during
the first round. During the second round, local banks still accounted for the majority of dis-
bursed loans, but Fintech lenders and especially non-local banks participated to a much larger
extent. This pattern is consistent with Fintech institutions substituting for local banks in the
area. Over the entire program, local banks accounted for more than two-thirds of all loans,
while Fintechs and other non-banks institutions accounted for five percent of loans.27
Next, we evaluate whether the presence of non-local banks and Fintechs mattered most
in areas that were exposed to local banks that underperformed in the PPP. Unsurprisingly, in
column (2) of Table 4, Panel A, we show that local PPPE is associated with a greater fraction of
establishments receiving loans from local banks in the first round of the program. Columns (3),
(4), and (6) show that ZIP PPPE is unrelated with the fraction of establishments receiving loans
from non-local banks, credit unions, and nonbank lenders in the first round. Column (5) shows
27
Lenders that were not classified as depository institutions were classified between community lenders, credit
unions, and other businesses manually. We classified the following lenders as Fintech companies: Kabbage,
BSD Capital, Lendistry, Flagship, Marketplace, Fund-Ex Solutions, Fundbox, Fountainhead, Intuit, Itria, MBE,
Mountain Bizcapital, Readycap and Newtek. Some of these lenders, including Kabbage, associated with banks
in the first round because they could not yet operate on a standalone basis due to program rules. Prior to the
eligibility of Fintechs, we count these as non-local banks and thus some substitution between local and non-local
banks could come from collaboration with Fintechs.
22
a negative relation between local bank performance and the fraction of local establishments
served by Fintechs, suggesting that these institutions played a greater role in PPP lending in
areas with worse local bank performance. Despite the statistical significance of the effects of
column (5), their economic magnitude is relatively small, indicating these substitute lenders
were unable to offset the dislocations from underperforming local banks during the first round.
In columns (2) through (6) of Table 4, Panel B, we examine if these non-local sources
of funding had an economically larger role in substituting for local banks during the second
round of the PPP program. In column (2), local PPPE remains an important determinant of the
fraction of loans from local banks, though the relationship is somewhat weaker. This weaker
relationship possibly results from improved performance of low-PPPE banks during the second
round. Consistent with the findings in Erel and Liebersohn (2020), we find in columns (3), (4),
(5), and (6) that other financial institutions such as non-local banks and Fintechs substitute
for underperforming local banks. Non-local banks are the most important source of substitute
funds. Fintechs are less important but still quite elastic to the effect of weak local bank per-
formance. By the end of the program, the total effect of substitute lenders is large enough to
fully offset the weak performance of local banks in low PPPE areas.28
4.5 Are PPP Allocations Targeted to the Hardest Hit Regions?
Were PPP funds disbursed to geographic areas that were initially most affected by the pan-
demic? Given that one of the policy goals of the program was to inject liquidity into small
businesses and prevent unnecessary bankruptcies, we examine whether funds flowed to dis-
tressed areas with more pre-policy economic dislocation and disease spread. In addition, we
ask whether the significant heterogeneity in bank performance and exposure to bank perfor-
mance across regions played an important role in the targeting of the program.
Figure 6 partitions the distribution of ZIP codes according to the ratio of PPP loans in the
first round to the number of establishments in the ZIP code. We then compare areas with high
and low PPP allocations in terms of employment outcomes prior to any funds being distributed.
28
In Online Appendix B, we use a proprietary dataset obtained through a member bank of the Community
Development Bankers Association (CDBA) and we find that the PPP loans issued by that member bank to new
clients are late relative to those from existing clients and these new clients come predominantly from regions
served by banks with low-PPPE performance relative to the PPPE of the regions where the bank and its existing
clients were located. These results further indicate that exposure to banks with low PPPE performance forced
small businesses to seek PPP funding elsewhere.
23
In Panel A, we observe a negative relationship between the share of business shutdowns in the
week of March 22nd–March 28th and the share of businesses receiving PPP in round one.29
Consistent with the broad definition of eligibility of the program and with a decline of the
supply-side distortions during the second round, we find that the relationship between the
share of business shutdowns in the week of March 22nd–March 28th and the share of busi-
nesses receiving PPP weakens substantially when we consider the share of businesses receiving
PPP during both rounds. In Panel B, we repeat the analysis using the decline in hours worked
between January and the week of March 22nd–March 28th. An analogous relationship holds,
with regions receiving more PPP funding during the first round displaying smaller shocks in
terms of the initial decline in hours worked and with this relationship becoming weaker or
even nonexistent when we consider PPP funding over the two rounds. In Panel C, we repeat
the analysis using the decline in the number of employees. The results mirror those in Panels
A and B; regions receiving more PPP funding during the first round see a smaller reduction in
the number of employees prior to the PPP. There is little relationship in the second round.
In Online Appendix D, we further confirm our findings using the Homebase data with other
levels of aggregation and using other data sources – we find no consistent relationship between
PPP allocation and bank exposure with UI claims or small business revenues. We also explore
whether funds initially flowed to areas with early pandemic outbreaks. There is a slight neg-
ative correlation between PPP receipt and COVID-19 confirmed cases and deaths at the state
level. There is little correlation between the magnitude of social distancing at the state level
and PPP allocations. The totality of the evidence suggests that there was little targeting of
funds in the first round to geographic areas that were harder hit by the pandemic and, if any-
thing, areas hit harder by the virus and subsequent economic impacts initially received smaller
allocations.
This interpretation remains true when considering both rounds of funding, as the relation-
ship between shock severity and PPP funding turns less negative without turning positive. Our
findings are also consistent with the broad eligibility criteria for PPP loans—most firms be-
low the size threshold could apply for funding—and the absence of conditionality in program
generosity—loan forgiveness did not depend on shock severity. The argument in Barrios, Min-
nis, Minnis and Sijthoff (2020) that firm payroll closely predicted PPP loan receipt accords
29
Following Bartik, Betrand, Lin, Rothstein and Unrath (2020), we define a business shutdown as businesses
that report zero hours worked during a week using the data from HomeBase.
24
with this view. Nevertheless, our bank-level results also point to an important loan supply
factor distorting the distribution of PPP loans, especially during the program’s initial rollout.
5 Employment Impacts and Local Economic Activity
5.1 Research Design
Did banks’ unequal ability and willingness to quickly process PPP applications have any impact
in explaining the employment effects of the PPP? Our results on PPP performance differences
across banks motivate two complementary research designs for evaluating the PPP. The basic
idea is to use differences in local area PPP exposure (PPPE), as well as pre-determined supply-
side variation in PPPE (predicted PPPE), to partition geographies and compare the evolution of
local outcomes for high versus low PPPE regions. By exploiting differential exposure to banks
that performed poorly in distributing PPP funding during the first round of the program, we can
isolate the effect of the PPP from other differences across regions that may drive differences in
PPP loan demand. As described above, we map bank level aggregates for PPP lending from the
SBA data onto local geographies using measures of local bank branch presence. The research
design is akin to a Bartik instrument and therefore relies on the assumption that pre-policy
bank branch shares are not correlated with the various outcomes we study, conditional on
observables.30
We focus our analysis on the time period between the third week of January and the end
of the program in the last week of August to study the short- and medium-term effects of the
PPP in the immediate aftermath of the pandemic when the injection of liquidity was thought
to matter the most for sustaining employment. Starting the sample period in January allows
us to establish a baseline period prior to the pandemic, thereby controlling for time-invariant
determinants of economic activity within the same location.
The PPP began accepting loans on April 3rd and all of the initial funds were exhausted by
April 16th. During this period, banks played a key role in allocating limited funds, creating
30
Online Appendix G evaluates the research design using diagnostic Bartik tests following Goldsmith-Pinkham,
Sorkin and Swift (2020). The diagnostics provide some intuition about the sources of identification. First, our
estimates are not driven by just one or two banks, or even by the top-4 banks alone. Second, influential banks
tend to be either large or mid-sized banks and those with PPPE pointing to substantial over- or underperformance.
Third, more of the identifying variation comes from banks with positive Rotemberg weights, which enables the
Bartik estimator to be interpreted more easily as a LATE. Finally, bank-branch shares are only weakly correlated
with local observables, supporting the key identification assumption.
25
the variation we use to identify the effects of the program. We exploit the fact that firms are
located in regions that vary in their exposure to bank performance, which mediates both the
level of PPP loan disbursement and its timing. With the second round of funds, which began
on April 27th, PPP funding limits were no longer binding and the gap between high and low
PPPE exposure regions mostly closed. Thus, as we move to study the program later in May
and through the end of August, we will interpret the research design as assigning some firms
funding with a delay, instead of as assigning some firms no funding at all.
In our main analysis, we present reduced form regressions of employment and local eco-
nomic outcomes on PPPE and predicted PPPE while allowing for separate treatment effects by
week or month. Given the rapid nature and size of the economic shock, we highlight two im-
portant considerations when analyzing data from this time period. First, our targeting analysis
shows that regions receiving more PPP funding were less hard hit by the initial shock, in part
due to the banking channel we emphasize. Thus, it is important to properly condition on this
non-random assignment of PPP funding. If one does not break out the data finely enough or
condition properly for targeting differences—for example, by treating the last weeks of March
as a pre-period benchmark—then one might detect a spurious effect of the program. This issue
is very clear when we examine week-by-week outcomes around the policy window.
To account for these targeting differences, we estimate the effects of the program by com-
paring weeks in the post-PPP period to the two weeks in the post-lockdown, pre-PPP period.
The pre-lockdown period serves as a baseline for constructing establishment-level employment
shocks in our analysis. We also include time-varying controls and state-by-time-by-industry
fixed effects to estimate treatment effects under weaker versions of the Bartik assumption.
Controls include the social distance index, COVID cases per capita and deaths per capita mea-
sured as of week 9, all interacted with indicator variables for the months of April, May, June,
July, and August. We also include bank controls for the average tier-1 capital and core de-
posit ratios of all banks within a 10 mile radius of the ZIP code. Once we adjust for targeting
differences, including these more restrictive controls has little effect on our estimates.
A second consideration is that research designs that exploit differences in PPP receipt or
application without an instrument for loan supply or eligibility will likely overstate the impact
of the program. Demand for PPP loans is likely correlated with omitted firm-level factors, such
as whether the firm anticipates being able to use the funds during the forgiveness window.
Our PPPE and predicted PPPE instruments attempt to isolate loan supply drivers independent
26
of loan demand.
5.2 Small Business Employment
A significant portion of the policy and media interest in the PPP concerned the program’s po-
tential employment effects. Previous work has shown that credit market disruptions can have
large effects on employment (Chodorow-Reich, 2014), which may have in part motivated the
quick policy response. We examine several employment outcomes, including business shut-
downs (i.e., hours worked reduced to zero during the entire week), declines in hours worked,
and declines in the number of employees.
Figure 7 presents simple difference-in-difference graphs for each of our Homebase employ-
ment outcomes. We divide all firms in the sample based on whether they are located in regions
with above- or below-median PPPE or predicted PPPE. We take advantage of the granularity
in the Homebase data and conduct our analysis at the ZIP level. We use vertical markers to
demarcate the post-lockdown, pre-PPP period; the post-PPP launch; when the first round of
PPP funds are exhausted and when the second round of PPP funding begins. Predicted PPPE
is determined using the supply-side factors in Table 2: wages over wages plus data expenses,
pre-existing SBA lending relationships, and enforcement actions. Panel A shows business shut-
downs, Panel B shows hours worked, and Panel C shows the change in the number of employ-
ees. Results using both PPPE measures point to very similar patterns.
Prior to the initial lockdown orders, employment outcomes in high- and low-PPPE areas
evolve very similarly, even in the absence of controls, suggesting that in normal times these
areas were following similar trends. We then see a dramatic decline in each employment
outcome starting in the week prior to the lockdowns. Consistent with our targeting results, this
decline is modestly larger for regions with low PPPE. The difference in employment declines
is somewhat smaller when we split the sample into high- and low-predicted PPPE ZIPs, which
indicates that the predicted PPPE measure is less correlated with geographic differences in
targeting of the program. Importantly, during the first round of PPP, the gap between high and
low PPPE areas does not widen further, indicating little incremental impact of PPP during this
time. The gap for the ratio of hours worked and for the change in the number of employees
widens gradually during May and June, which suggests intensive-margin employment effects,
while the gap for shutdowns changes little.
27
Figure 8 plots coefficients and standard errors for regressions of differences in employment
outcomes on exposure to PPPE and predicted PPPE. We estimate weekly regressions of the
form:
∆ yi jnt = αsn + β P P P E j + Γ X i jnt + εi jnt
where ∆ yis jn is the difference between the Homebase outcomes yis jn (business shutdown,
hours decline, and employee counts) of a firm i in each week relative to the average value
in the two weeks prior to the PPP launch; P P P E j is either PPPE or predicted PPPE in ZIP j;
αsn are state-by-industry fixed effects; and X i jnt are additional control variables. The plots
in the top panels use our main PPPE measure as the variable of interest, while the bottom
panels estimate the local projections using the predicted PPPE variable as the main variable
of interest. Panel A, B, and C plot estimates where the outcome variable is the difference in
business shutdowns, the decline in hours worked, and the change in the number of employees,
respectively.
The coefficients capture the effect of PPP exposure on the outcome of interest under the
identifying assumption that the firms and areas differentially exposed would have trended
similarly in the absence of the PPP after conditioning on covariates. Given the fast-moving
employment losses and differential state policies, the choice of baseline and fixed effects are
particularly important. We account for differential targeting by using as a baseline the two
weeks prior to PPP funds being disbursed, which is consistent with the aggregate time series
in Figure 7. The state-by-industry-by-week fixed effects imply that we are comparing trajec-
tories for firms within state-by-industry groups and allowing general time trends within these
groups. Focusing on within-state estimates is particularly important because many lockdown
and reopening policy decisions occur at the state level, and there is some evidence that state
shutdown orders partly influenced the decline in economic activity (Goolsbee and Syverson,
2020).
For both PPPE measures, the results align with the raw differences across high and low
PPPE regions in Figure 7. When using predicted PPPE, we see weaker evidence of targeting
as the gap opens following the launch of the PPP. We see little effect on business shutdowns
until the end of the sample period. Beginning in May, there are statistically significant positive
effects on hours worked and the number of employees, which remain stable through August.
Table 5 presents our regression estimates, in which we pool the weekly effects into months.
We estimate the following specification:
28
Eis jnt = αi + δsnt + β1 1[April] × P P P E j (1)
+ β2 1[May] × P P P E j (2)
+ β3 1[June] × P P P E j (3)
+ β4 1[July] × P P P E j (4)
+ β5 1[August] × P P P E j + γX j + "is jnt , (5)
where Eis jnt is an outcome (business shutdowns, the decline in hours worked, or the number of
employees) for firm i in state s, ZIP j, and industry n in week t. The outcome variable for each
establishment is measured in that week relative to the hours worked in that same establishment
during the two weeks prior to the PPP launch. The term αi captures firm fixed effects, δsnt are
state-by-industry-by-week fixed effects, P P P E j is ZIP PPPE or predicted PPPE, and "is jnt is an
error term. We also include interactions between the social distance index, COVID cases and
deaths per capita measured as of week 9, all interacted with indicator variables for April, May,
June, July, and August. These controls capture time-varying effects of the initial severity of
the pandemic at the local level. We further include bank controls for the average tier 1 capital
and core deposit ratios of all banks within a 10 mile radius of the ZIP code where the firm is
located.
The coefficients β1 , β2 , β3 , β4 , and β5 capture the differential effect of PPP exposure on the
outcome of interest in each month relative to the two weeks prior to the launch of PPP. The
coefficient β1 captures the average effect of exposure to better bank PPP performance after
the initial rollout of the PPP, when most regions remained under some form of shelter-in-place
order. The coefficient β2 captures effects in May, as many regions began to lift restrictions. The
coefficients β3 , β4 , and β5 capture medium-term effects in June, July, and August after state
reopenings continued.
In the first two columns of Table 5, the outcome of interest is business shutdowns, in the
following two columns it is the decline in hours worked, and in the final pair of columns it is the
number of employees. For each pair of columns, the first column includes state-by-industry-
by-week fixed effects, while the second column adds firm fixed effects and additional control
variables. The top panel shows estimates of equation (1) using PPPE as the treatment, while
the bottom panel uses predicted PPPE as the treatment.
The table confirms the finding of no statistically or economically significant relationship
between PPP bank exposure and these employment outcomes in April, the initial month of the
29
PPP. Moreover, our least squares estimates are not simply statistically insignificant with large
confidence intervals; rather, they are precise zeros. In May and June, we continue to find pre-
cise zero effects for business shutdowns, and either no or marginally significant positive effects
in later months when using PPPE. Using predicted PPPE, there is a very small relationship with
shutdowns in May and June which fades out by July. For intensive margin employment, the de-
cline in hours worked measure increases for firms with higher PPP exposure in May and June,
and this effect remains significant through August. The effect sizes are small—approximately
two percentage points in May and three percentage points thereafter for a standard-deviation
increase in PPPE—but highly statistically significant. Effects on the number of employees are
quite similar to those for the number of hours worked. The coefficient patterns are also quite
similar between the top and the bottom panel suggesting that both PPE and predicted PPPE
capture similar variation. In other words, PPPE does not appear to be driven by demand to a
great extent relative to predicted PPPE.
As another way of interpreting our magnitudes, consider the following comparison. The
difference between PPPE for top versus bottom quartile ZIP codes is 0.44. This difference
implies an increase in the share of establishments receiving PPP funding of 8.4 percentage
points, which is large relative to the mean level of 22%.31 Using the reduced form estimates
for April in Table 5, column (2), this change in funding implies an increase in the probability
of firm shutdown of 0.6 percentage points (= 0.44 × 0.002 × (1/0.16)), where 0.16 is one
standard deviation of PPPE. The lower bound of the 95% confidence interval is well below a
one percentage point effect. Analogous calculations for the other outcomes give similarly small
effect sizes. The effect sizes are marginally smaller when using predicted PPPE as an instrument
rather than PPPE, though the confidence intervals overlap. Thus, relative to the aggregate
patterns in Figure 7—a 40 percentage point increase in the probability of firm shutdown and
60 percentage point reductions in the ratio of hours worked and the number of employees
relative to January—we can reject modest effect sizes during this period.
As we move into May and June, the results for business shutdowns do not change. However,
the effect sizes for the decline in hours worked increase. In May, the point estimate of 0.020
implies an increase in hours worked of 5.5 percentage points (= 0.44 × 0.020 × (1/0.16)) with
a 95% confidence upper bound of 7.7 percentage points (= 0.44 × (0.020 + 1.96 × 0.004) ×
31
This calculation comes from 0.44 × 0.19, which is the coefficient of PPP per establishment as of the end of
round one on PPPE in a ZIP-level regression with state fixed effects.
30
(1/0.16)). The analogous estimates for June are 9.4 and 12.5 percentage points, respectively,
which stabilize through August. Estimates for the number of employees are nearly identical to
those for the hours worked outcome.
Because the second round of funds did not reach firms until late in May, our research design
can be interpreted as comparing firms that did receive funds to those that did not for April and
May. In June, the research design is better interpreted as reflecting differences between early
and late recipients. Thus, our estimates may be conservative regarding the overall employment
effects of the program by this point in time. On the other hand, if many firms that did not
receive funds early decided to close permanently, then our estimates for June can be more
easily compared to those in April and May.32
Our results are largely consistent with some contemporaneous evidence from other re-
searchers using different data sets and research designs. Autor, Cho, Crane, Goldar, Lutz,
Montes, Peterman, Ratner, Villar and Yildirmaz (2020) (henceforth ACCGLMPRVY) use pay-
roll data from ADP, a large payroll processor, and also use the 500 employee threshold design to
estimate employment effects. They find that the PPP boosted employment at eligible firms by
2–4.5%. Chetty, Friedman, Hendren and Stepner (2020) use high frequency employment data
from several payroll processors for small businesses and study the evolution of employment
outcomes for firms above and below the 500 employee PPP eligibility threshold. They find
statistically insignificant effects on employment with confidence intervals that permit modest
effect sizes. Hubbard and Strain (2020) use Dun & Bradstreet data to implement the threshold
design. They present some specifications that yield larger estimates, but the overall takeaway
from their analysis appears in line with these other threshold designs.
Relative to this approach, our research design has a few benefits. First, it is not local to
firms around the 500 employee threshold; most PPP borrowers are considerably smaller. Sec-
ond, the threshold design requires smaller firms and larger firms to trend similarly around the
reform, which is a strong assumption if smaller firms are more vulnerable to shocks and be-
cause the PPP coincided with other programs operated by the Federal Reserve to help larger
firms. Third, we use our design in the next section to study impacts on aggregate local labor
market and economic outcomes, which is not feasible with the threshold design. Nevertheless,
32
Online Appendix F uses Homebase data to study the relationship between PPPE and a measure of “permanent”
shutdowns, defined as the establishment being closed for all weeks from the beginning of the PPP through the
end of August. The results suggest a non-trivial impact of PPP on firms over the medium run, consistent with the
idea that some firms that did not receive funds early enough decided to close permanently.
31
it is informative that similar results emerge from different data sets and research designs.33
Several other studies use differences in the timing of PPP receipt to examine the program’s
employment effects, while also exploiting differences in timing due to pre-existing variation
in bank lending relationships. Li and Strahan (2020) find modest employment effects of the
program, as we do, while Faulkender, Jackman and Miran (2020) and Doniger and Kay (2021)
find substantially larger employment effects. While the conceptual approach in these papers is
similar to ours, a key source of difference is the extent to which the research design accounts
for nonrandom program targeting, which we show is quantitatively important. Bartik, Cullen,
Glaeser, Luca, Stanton and Sunderam (2020) also find significantly lower self-reported survival
probabilities for firms whose primary banks were in the top four, though the outcome and
sample of very small firms in this study make it difficult to compare their results to ours.
Aggregate Impacts. We consider two approaches to aggregation. Our first approach follows
Mian and Sufi (2012) and Berger, Turner and Zwick (2020). We estimate the total employment
gains caused by the program in its first three months, exploiting only differences in cross-
sectional exposure and using the group receiving the smallest shock as a counterfactual. We
choose the bottom 1% of ZIPs as the counterfactual group and compute the effect of the policy
for other groups relative to this group. By construction, any time-series effect of the policy
shown by the bottom group is set to zero and removed from the effect computed for other
groups.34
Standardized exposure for the bottom group is −2.03 and increases to 2.49 for the highest
group. Thus, for exposure group g, the aggregate increase in employment induced by the
program is
∆Yg = β t × (e g − (−2.03)) × Yg,pr e
where β t is our preferred reduced form estimate, e g is the weighted-average program exposure
33
Another reason we may find smaller effects than ACCGLMPRVY is that our data measure hours worked while
their data measure payroll. If firms partly deploy PPP to compensate furloughed workers who remain function-
ally unemployed, then this difference in measurement could account for some of the gap between our estimates.
Online Appendix F presents results using the Census Household Pulse Survey data that lean against this interpre-
tation. A relatively small share of households report receiving any payment for time not working in the previous
week. Importantly, the share of households reporting receiving no pay is not associated with state PPPE.
34
As is the case for any aggregate estimates that rely on cross-sectional identification net of time fixed effects, we
cannot observe a counterfactual that measures general equilibrium effects. This is another reason why producing
estimates with different assumed counterfactuals can inform the range of plausible aggregate impacts, in addition
to demonstrating the degree of sensitivity of results to different assumptions.
32
where the weights are estimated eligible employment in each ZIP, and Yg,pr e is within-sample
pre-program employment. A less conservative approach aggregates estimates relative to a no-
exposure baseline, which equals −3.28 in standardized exposure. We can then average the
more and less conservative approaches. We choose β t = 0.0238, the mean of the five monthly
coefficients from Table 5, Panel A, column (6).
Following this approach, we estimate the PPP increased employment by 63,000 within
sample during the first five months of the program, or 4.8% of pre-program employment of
1.33 million. Note this is a lower-bound estimate if the lowest exposure ZIP also responds to
the program. When we aggregate relative to a no-exposure baseline, we estimate an increase of
103,000 within sample, or 7.8% of pre-program employment. Averaging the more conservative
and more aggressive estimates yields an estimate of 6.3%.
The second approach to aggregation follows ACCGLMPRVY. We convert the intention-to-
treat estimate from our banking exposure design into an average treatment effect and then
adjust for the mean take-up rate as of the end of the program’s first round. Formally, consider
Total Payroll Effect t = δ t × γ ×N
| {z }
∆t
where ∆ t is the intention-to-treat estimate for period t, δ t is the treatment-on-the-treated
estimate for period t, γ is the take-up rate of PPP, and N is the number of employees at PPP-
eligible firms. ACCGLMPRVY find ∆ t ∈ [2%, 4.5%] in their research design based on the firm
size eligibility cutoff. We follow ACCGLMPRVY and use N = 70M based on Census data on
employment in small establishments.
We estimate an instrumental variables (IV) version of our bank exposure design at the ZIP-
level, with the effect of ZIP-level PPPE on the fraction of eligible establishments receiving PPP
funding as the first stage. The IV estimate for the effect of PPP receipt on the change in hours
worked in May is 0.34 (s.e.=0.09) with a first-stage coefficient of 0.20 (s.e.=0.02). Mean take-
up as of the end of round one in our data is 24%. Thus, our estimate of ∆ t is 8.1% (s.e.=2.2%)
for the month of May. Analogous specifications for April, June, July, and August yield estimates
of 1.3% (s.e.=1.3%), 14.9% (s.e.=2.8%), 15.0% (s.e.=3.7%), and 10.8% (s.e.=3.5%), respec-
tively. Pooling all three months yields an estimate of 10.0% (s.e.=2.4%).35
35
Weighting regressions by pre-program employment results in effect sizes that are slightly larger. The sample
comprises mainly small firms with median pre-program employment of 27 and mean pre-program employment
of 37 and very few firms with more than 100 employees.
33
Taking both aggregation approaches into account, our preferred aggregate response ranges
from 4.4 million to 7.0 million (either 6.3% or 10.0% of 70 million eligible workers). It is im-
portant to keep in mind that these estimates derive from the particular subset of eligible firms
represented in Homebase, so extrapolating within-sample estimates to the broader economy
does require an assumption about representativeness. Relative to ACCGLMPRVY, whose pre-
ferred estimates range from 2% to 4.5%, our estimates are slightly larger. We also estimate
an increasing treatment effect over the course of the program’s first few months, whereas AC-
CGLMPRVY find an immediate response that appears stable over time. While we do not want
to overstate these differences, they may reflect the fact that our estimates feature smaller firms
who may be more responsive to stimulus policy (Zwick and Mahon, 2017) and who may have
been less able to increase employment while shelter-in-place orders remained in force. These
firms are more representative of the overall population of PPP recipients, so our results might
be especially informative about the program’s overall impact during this time.
When considered relative to the scale of the PPP program, the employment effects we es-
timate are fairly modest. The program disbursed $525 billion in total loans, which implies a
cost-per-job-year ranging from $106,000 to $168,000 under the assumption that half of the
induced jobs persist for a year.36 Incorporating the saved funds from lower unemployment
insurance claims (roughly $5–10K per worker) only modestly alters this calculation. Firms
applying for PPP loans reported 51 million jobs in total supported by the program. When com-
bined with our estimates, an implication is that approximately 90% of these supported jobs
were inframarginal. If wages for inframarginal workers did not adjust, then the bulk of the
program’s economic benefits appear to accrue to other stakeholders, including owners, land-
lords, lenders, suppliers, customers, and possibly future workers.
5.3 Local Labor Market and Economic Activity
Figures 9 and 10 and Table 6 present results using broader measures of employment outcomes:
initial unemployment insurance (UI) claims, small business revenue, and employment data
36
These cost-per-job estimates might be misleading if the employment gains are especially short-lived. If all
induced jobs persisted for 12 months, the range would be $75,000 to $119,000; if they all ended in August,
the range would be $180,000 to $286,000. Finally, some of the PPP loans will be reimbursed, which reduces
the overall cost of the program. Given the research design and time frame we study, we leave to future work
to provide more comprehensive cost-per-job-year estimates. See Chodorow-Reich (2019) for a discussion in the
context of estimating fiscal spending multipliers.
34
from Opportunity Insights (OI). We focus on county-level outcomes because that is the finest
level of aggregation for which these data are available.
Figure 9 splits the counties in the sample in two groups based on their PPPE and predicted
PPPE measures and plots the evolution over time of average employment outcomes. The plots
suggest that, prior to lockdown orders, average UI claims are relatively low, and UI claims, small
business revenues, and OI employment rates all trend similarly across both groups during the
period. After the initial lockdown orders, UI claims surge and small business revenues and
OI employment rates decline in both groups. The high-PPPE group sees somewhat lower UI
claims in May, and small business revenues and OI employment rates recover faster for this
group relative to the low-PPPE group from mid-April until the end of May. These differences
subsequently fade. The graphs also point to the importance of targeting differences across
groups, as high-PPPE areas appear to be less hard hit prior to the PPP’s rollout.
Figure 10 provides further graphical evidence of the impact of PPP on these outcomes. The
figure repeats the local projection analysis, replacing the main outcomes with the difference
in UI claims, decline in small business revenue, and OI employment rates. We observe little
discernible impact on UI claims. The small business revenues analysis suggests a positive im-
pact of PPP initially, which levels off in the subsequent months. The OI data suggest a pattern
similar to our Homebase analysis, albeit with slightly smaller magnitudes.
Table 6 repeats the analysis of Table 5 for these outcomes. We find a small statistically
significant effect on UI claims in April and May when using PPPE, which dissipates by June.
Effects are insignificant when using predicted PPPE. Following the calculations above, the 95%
lower bound estimated effect for the month of May is -13.2 basis points (= 0.44 × (−0.158 −
1.96 × 0.073)). The middle columns show the relationship between small business revenue
and PPPE or predicted PPPE. We find a positive relationship between PPP exposure and small
business revenue, which is statistically significant in a few specifications in April and May, but
levels off and even becomes negative in subsequent months. The last two columns present re-
gressions of employment growth in OI on county-level PPPE and predicted PPPE. The estimates
suggest small effects in April that rise modestly in May and June.
The results using predicted PPPE, shown in the bottom panel, point to statistically insignif-
icant effects of the PPP on UI or employment, and small effects on small business revenues
in early months that dissipate by June. However, the point estimates are similar and the con-
fidence intervals are large enough that we cannot rule out the modest effects from the PPPE
35
instrument. The bottom line is that no specification or outcome variable points toward large
differences across counties based on either PPPE measure, despite the large differences in pro-
gram access predicted by these measures.
5.4 Matched Sample Analysis
We complement our regional estimates with a sample of 10,694 firms, for which we are able
to match PPP loan information to payroll information from Homebase. In this analysis, we
have a smaller sample of firms, but we can also directly measure if and when each individual
firm obtained a PPP loan. We use the individual matched data and variation in the timing of
when firms received PPP loans to examine the impact of PPP receipt on firms’ employment
outcomes. We ask whether differences in timing materially affected short-term employment
outcomes of firms that received loans earlier versus later. Because the timing of loan receipt
may reflect differences in loan demand across firms, we also instrument for the timing of receipt
using PPPE and predicted PPPE. This alternative strategy provides a useful way to assess the
robustness of our main results.
Figure 11 shows the evolution of business shutdowns (Panel A), change in hours worked
(Panel B), and the change in employee counts over time (Panel C) for early and late recipients
of PPP funds. Early recipients are defined as those firms that receive a loan in the week ending
on April 11th or earlier and late recipients are firms that receive a loan in the week beginning
May 3rd or later. For all three outcomes, we see a gap open up prior to PPP loan disbursement,
which may reflect a combination of our targeting results and differences in loan demand, con-
sistent with prior results suggesting that early recipients were bigger and less constrained firms
(Balyuk, Prabhala and Puri, 2020; Doniger and Kay, 2021). Following PPP disbursement, the
gap grows over time. The raw data is suggestive of earlier PPP receipt leading to higher em-
ployment and business survival rates.
To address the concern that difference in timing may be driven by demand, we instrument
using PPPE and predicted PPPE. Table 7 presents results from the individual matched sample
exploring the timing of PPP receipt, regressing outcomes on the week in which a firm received
PPP. We focus on outcomes in the week of May 3rd to May 9th, the final week before the second
round of PPP loans was disbursed. Therefore, the regression is measuring employment effects
using first round recipients as a “treatment” group and second round recipients—who have not
36
yet received their loans—as a “control” group.
We estimate the following specification:
∆ yis j = αsn + ζWeekP P Pi + "is
where ∆ yis j is the difference between the outcomes yis j (shutdown, hours worked, number
of employees) of firm i in the week beginning on May 3rd, and the average outcome for the
same firm during the two weeks prior to the launch of PPP. Using the two weeks prior to the
launch of PPP as a benchmark is particularly important in this analysis given the substantial
gap that opens between the average employment outcomes for these groups prior to the PPP
launch. WeekP P Pi is the week in which a firm received a PPP loan, and thus ζ captures the
effect of receiving a PPP loan one week later. The term αsn represents industry- or state-by-
industry fixed effects. The first two columns show OLS estimates, with columns (1) and (2)
including industry and state-by-industry fixed effects, respectively. Columns (3) and (4) show
IV estimates, instrumenting the week in which a firm received PPP with PPPE measured at the
ZIP level. The final two columns show IV estimates, instrumenting the week in which a firm
received PPP with predicted PPPE, again measured at the ZIP level.
The results in Table 7 are largely consistent with our bank exposure results, suggesting
modest short-term effects of the PPP. In the top panel the outcome is business shutdowns, in
the middle panel it is hours worked, and in the bottom panel it is the number of employees. In
all regressions, the magnitudes of the OLS coefficients are smaller than magnitudes of the IV
estimates. One interpretation of this fact is that larger firms that were less credit-constrained
had earlier access to the program, which would underscore the importance of instrumenting
for the timing of PPP receipt. In the case of column (6) in all panels, the inclusion of state-by-
industry fixed effects weakens the first stage of the IV considerably, which might explain the
relatively larger magnitudes of the coefficients and imprecision of those estimates. As a result,
we do not draw strong conclusions from this specification, though the results remain broadly
consistent if noisier.
Panel A suggests marginally significant effects on business shutdowns in the IV specification,
and that obtaining a PPP loan one week earlier leads to a decrease in shutdowns of between 1.4
and 2.6 percentage points. In Panel B, we find that obtaining a PPP loan one week earlier leads
to an increase in hours worked of between 4.6 and 5.9 percentage points for a firm receiving
37
a PPP loan a week earlier. Panel C points to similar effects on the number of employees, with
obtaining a PPP loan one week earlier leading to an increase in the number of employees of
between 4.0 and 4.7 percentage points for a firm receiving a PPP loan a week earlier.37
6 Interpretation and Mechanisms
6.1 Potential Channels
The primary focus of our paper is to evaluate the PPP and the role of banks in driving the
policy response we identify. We find limited evidence that PPP funding has significant effects on
employment or local economic activity during the first month of the program. In the subsequent
months, we find more evidence of employment effects on the intensive margin, but can still
rule out large employment effects of the program. Thus, while differences in bank performance
lead to distortions in access to the program, these differences appear relatively unimportant for
employment, given the small overall employment effects we estimate. If firms did not primarily
maintain or increase employment, what did they do with PPP funds and how might the funds
ultimately affect employment?
There are several non-mutually exclusive channels through which businesses may have ab-
sorbed the funds without immediate employment effects. First, program eligibility was defined
broadly, so many less affected firms likely received funds and continued as they would have in
the absence of the funds. In these cases, the program’s benefits accrue to the firm’s owners.38
Second, firms retained significant flexibility in how they could use the funds over time,
and they may have used funds initially to strengthen balance sheets and for non-employment
related expenses. Financial frictions can amplify precautionary savings motives, which imply
ambiguous impacts on employment. While funds may have gone to distressed firms, they may
37
In Online Appendix F, we show the coefficients of week-by-week regressions that repeat the OLS and IV
specifications of columns (1) and (3) of Table 7 for every week in the sample. Similar to our regional analyses,
the dynamics indicate that gaps in the number of employees and hours work persist until August. Again, we
caution that the OLS estimator will be biased if firms that obtained loans earlier are fundamentally different from
firms that received PPP loans later.
38
Drawing on data from a large survey of business owners on Facebook, Alekseev, Amer, Gopal, Kuchler, Schnei-
der, Stroebel and Wernerfelt (2020) find that 30% to 40% of small businesses did not experience sales declines
in the first month of the crisis. Among the businesses that did experience declines, the severity of the decline
varies widely from declines of 10% to 20% to nearly complete shutdowns. Moreover, only half of firms surveyed
reported struggling to pay obligated expenses (though presumably this share increased over time). Additionally
Griffin, Kruger and Mahajan (2021) find evidence of significant fraud, with many loans going to ineligible or even
non-existent firms. These loans are unlikely to generate large employment effects.
38
still choose to downsize and cut employees in the face of uncertainty. For example, firms were
uncertain about the duration of the pandemic and future revenue streams, and likely wanted
to hold cash to survive a longer duration crisis. Such motives are consistent with Almeida,
Campello and Weisbach (2004) and Riddick and Whited (2009) who find that uncertainty
increases firms’s precautionary motives to hold cash, particularly when external financing is
difficult to obtain.
Third, some firms may have increased employment or called back workers, though they
account for a relatively small share of total recipients. The primary channel through which the
PPP could affect employment is through financial frictions. Firms may temporarily need liquid-
ity during the downturn to cover cash shortfalls, either due to a loss in demand or lockdown
policies. These firms may be unable to access credit, for example, due to classic asymmetric
information effects where lenders are unable to separate firms that will survive from those
that fail (Stiglitz and Weiss, 1981). In this case, credit supply can be inefficiently low. PPP
guarantees would make lenders willing to extend credit, enabling liquidity-constrained firms
to survive, raising employment, and potentially increasing aggregate welfare by shifting credit
supply toward efficient levels.39
Finally, related to the first channel, banks may substitute more generous PPP loans for other
lending that would have happened otherwise (Gale, 1991). Such crowd-out of private financ-
ing is also consistent with small employment effects. Relatedly, business stealing spillovers
between eligible and ineligible employers could account for low employment effects at the
labor market level.
6.2 Fixed Payments and Precautionary Savings
To explore the effects of PPP on non-employment financial outcomes, we use information from
the first phase of the Census Small Business Pulse Survey measuring the effect of changing
business conditions during the Coronavirus pandemic on US small businesses. The first phase
of the survey was conducted weekly from April to June 2020.40 In the top two panels of Table 8,
39
Programs like the PPP can also increase employment through a subsidy channel, by reducing the cost of
capital for firms and possibly attracting excessively risky borrowers. This channel can affect employment even in
the absence of financial frictions. In these cases, the welfare benefits of subsidized credit are less clear.
40
We do not use the second phase of the survey, which began in August 2020 and ended in October 2020,
because it falls outside the sample period of our analysis. Note that the sample size changes across variables, as
the Census does not report for some state-industry observations, likely due to censoring.
39
we examine whether receipt of PPP allowed firms to avoid becoming delinquent on scheduled
payments (either loan or non-loan). We estimate regressions of the relationship between PPP
fund allocation and the percentage of firms reporting missing payments at the state-industry
level.41 In light of our targeting results, these regressions add controls for pre-PPP measures
of crisis severity, including the pre-PPP decline in hours worked from Homebase, the pre-PPP
counts of COVID cases and deaths per capita, and the pre-PPP social distancing index.
In the top panel of Table 8, column (1) indicates that an increase in the share of firms re-
porting receiving PPP is not significantly associated with a decline in the percentage of firms
missing loan payments. This result, however, could indicate that areas and industries with a
lower percentage of businesses receiving PPP had a larger fraction of businesses that were un-
interested or unable to apply for funds. To address this issue, we use state PPPE and predicted
PPPE to capture geographic differences in access to the supply of PPP funds resulting from
differences across regions in their exposure to bank PPP performance. These differences are
plausibly unrelated to demand factors and therefore less likely to be confounded by them. In
columns (2) and (4), we focus on the relation between the percentage of firms receiving PPPE
and state PPPE or predicted PPPE. Both IVs generate similar results. The relationship is strong,
with F-statistics of 115 and 67 when using state PPPE or predicted PPPE as instruments, respec-
tively. In columns (3) and (5), we present results of an IV strategy whereby we instrument for
the percentage of firms receiving PPP using state PPPE and predicted PPPE. Using this strategy,
we find that a ten percentage point increase in firms receiving PPP is associated with a 1.7 to
1.8 percentage point decline in missing loan payments.
In the middle panel of Table 8, we find that a ten percentage point increase in the share
of firms receiving PPP is associated with an even larger effect on missed non-loan payments.
This result reflects the fact that many small businesses do not necessarily have loans. In-
stead, their primary fixed obligations are rent payments, utilities, supplier payments, and fixed
employment-related expenses. Again the two IVs generate very similar results. The results of
the IV strategy in columns (3) and (5) suggest that a ten percentage point increase in firms
receiving PPP is associated with a 4.9 (s.e.=0.7) percentage point decline in the number of
firms reporting missing any type of scheduled payments.
41
Unfortunately, the Pulse survey does not separate non-loan scheduled payments into payroll versus non-
payroll components. However, it does focus on “required” payments, which firms may interpret as referring to
payments for past labor rather than discretionary payments based on retaining workers going forward. Results
for this measure should be interpreted with some uncertainty about respondents’ interpretation of the question.
40
The Census survey data also reveal that the PPP funds increased firms’ cash on hand. This
exercise also offers a useful sanity check of the informativeness of the survey data. Similar
to results on missed payments, the coefficients reported in column (1) of the bottom panel
of Table 8 do not indicate an economically or statistically significant relation between cash-
on-hand and the percentage of firms in that state-by-industry group that reported receiving
PPP. However, when we examine the relation between state PPPE and predicted PPE, which
better isolates access to the supply of PPP funds, access to PPP is economically and significantly
related to the share of firms reporting significant liquidity. In the IV regression, which uses state
PPPE and predicted PPPE to instrument for the share of firms receiving PPP, a ten percentage
point increase in the share of firms receiving PPP is associated with a 3.2 to 3.8 percentage
point increase in the share of firms reporting at least three months of cash to cover business
operations.
Overall, these results are consistent with the idea that the PPP provided firms with an
important liquidity cushion that they used to navigate the initial months of the pandemic.
These results also align with our evidence that the PPP did not immediately induce employment
responses and only modestly increased employment in the months following PPP receipt.42
Many businesses may have retained the PPP funds in bank accounts as precautionary savings
until they were ready to resume activities, perhaps when demand for their goods and services
return to normal or when relaxed shelter-in-place orders permit them to reopen for business.
Generally, the results are not consistent with the idea that the PPP served as a large-scale
alternative to unemployment insurance for delivering funds directly to affected workers.
6.3 Crowd-Out and Business Stealing
One potential mechanism explaining the small employment effects of the program is crowd-out.
The risk of government loan programs crowding out private lending has long been a concern for
loan guarantee programs (e.g., Gale (1991)). In the counterfactual, PPP loans may have been
made under standard commercial loan programs. In the presence of substantial crowd-out,
the program would have little effect on employment and other firm outcomes. While we find
some evidence of crowd-out, the results suggest that magnitudes are small and private lending
42
In Online Appendix F, we provide additional evidence that exposure to PPPE is associated with fewer per-
manent shutdowns in the Homebase sample. This evidence is consistent with the idea that despite modest em-
ployment effects, the program may have prevented firms from closing and this effect could manifest in stronger
employment outcomes in the long- run.
41
would not have fully offset PPP lending. The results are presented in the Online Appendix
E. This finding is plausible because loans to replace lost revenue would be unlikely to pass a
private loan underwriting test.
Another possibility is that eligible firms might expand at the expense of local competitors.
Such business stealing spillovers could account for low employment effects at the labor market
level. Alternatively, the program might have positive local demand effects, for instance, on the
suppliers of treated firms. Given the scale and severity of the labor market disruption due to the
pandemic, traditional measures of labor market tightness are unlikely to be useful. However,
we can ask whether regions with a larger share of employment in PPP-eligible establishments
exhibit different effects relative to those with fewer eligible establishments. Online Appendix
F presents split sample analyses estimating employment effects for regions based on the share
of establishments that would be eligible for funds. Employment effects are generally similar
or greater in regions where a larger share of establishments are eligible for funds, inconsistent
with a business stealing effect and possibly consistent with the presence of some local demand
effects.
6.4 UI Expansion
One possible reason why the observed employment effects were so small is that historically
high levels of UI made it difficult for firms to recall workers. Indeed, many workers saw UI
replacement rates above their usual salaries due to an additional $600 a week in federal ben-
efits (Ganong, Noel and Vavra, 2020). Some commentators and media reports suggested that
this benefit led to difficulties for firms in recalling workers, which could have attenuated the
employment effects of the PPP.43 While recent work such as Altonji, Contractor, Finamor, Hay-
good, Lindenlaub, Meghir, O’Dea, Scott, Wang and Washington (2020) suggest a muted effect
of UI extensions on unemployment levels and the speed of returning to work, we consider this
possibility by exploiting state variation in UI replacement rates.
We explore whether UI generosity attenuated the employment effects of PPP lending by
splitting our sample by the generosity of state UI benefits. In Online Appendix F, we repeat
our analyses of Tables 5 and 6 with the sample divided between states with above- or below-
43
For example, the Wall Street Journal article “Businesses Struggle to Lure Workers Away From
Unemployment” on May 8th (https://www.wsj.com/articles/businesses-struggle-to-lure-workers-away-from-
unemployment-11588930202?mod=flipboard) suggested that “Businesses looking for a quick return to normal
are running into a big hitch: Workers on unemployment benefits are reluctant to give them up.”
42
median UI replacement rates. The results do not support the hypothesis that the responses
are greater in states with less generous UI. For employment, UI filings, and small business
revenues, effect sizes are either similar or greater in high benefit states. It is important to note
that, even in states with less generous UI systems, replacement rates were historically high for
lower income workers and thus we may be unable to capture the effects of a counterfactual
without elevated UI benefits.
7 Conclusion
This paper studies a large and novel small business support program that was part of the
initial crisis response package, the Paycheck Protection Program (PPP). We focus on the role
that banks played in intermediating PPP funds, the impact of bank performance on program
targeting, and the overall short- and medium-term employment and local economic effects of
the program.
We consider three dimensions of program targeting. First, did the funds flow to where the
economic shock was greatest? Second, given the PPP used the banking system as a conduit to
access firms, we ask what role did the banks play in mediating policy targeting? Third, why
did some banks systematically under- or overperform in disbursing PPP loans relative to their
share of the small business loan market? We find little evidence that funds were targeted to-
ward geographic regions more severely affected by the pandemic. If anything, the opposite is
true and funds were targeted toward areas less severely affected by the virus, at least initially.
Bank heterogeneity played an important role in mediating funds, affecting who received funds
and when their applications were ultimately processed. Ex ante bank characteristics, including
greater labor capacity to process loans, pre-existing SBA relationships, and active enforcement
actions against banks, predict banks’ relative performance in disbursing PPP loans. Regions
with higher exposure to banks that performed well saw higher levels of PPP lending and re-
ceived funds more quickly.44 Limited targeting in terms of who was eligible likely also led
to many inframarginal firms receiving funds and to a low correlation between regional PPP
funding and shock severity.
44
The analysis here focuses on ex ante targeting of the PPP, that is, the distribution of funding provided at the
start of the program. Ultimate targeting will depend on the extent of loan forgiveness and defaults, as well as
subsequent changes to the PPP, including conditions for recoupment based on ex post economic hardship and
changes to program eligibility criteria going forward. See Hanson, Stein, Sunderam and Zwick (2020a) for a
discussion of these dynamic policy considerations in the design of business liquidity support during the pandemic.
43
Using a number of data sources and exploiting lender heterogeneity in disbursement of
PPP funds, we find evidence that the PPP had only a small effect on employment in the months
following the initial rollout. Our estimates are precise enough to rule out large employment
effects in the short-term. It appears likely that many relatively healthy firms received funds
and continued with their business as usual. At the same time, the program may have played
an important role in promoting financial stability. Firms with greater exposure to the PPP hold
more cash on hand, and are more likely to make loan and other scheduled payments.
Measuring the relative importance of these responses is critical for evaluating the social
insurance value of the PPP and similar policies, and designing them effectively. Because pol-
icymakers often rely on banks to deploy credit subsidies, it is important to understand what
distortions in policy targeting are caused by the pre-existing structure of banking markets, as
well as whether and how these distortions can undermine policy priorities. These issues are
not just important for the COVID-19 pandemic and PPP, rather they are likely to re-emerge in
the policy response to the next crisis or recession.
44
References
Agarwal, Sumit, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet, Tomasz
Piskorski, and Amit Seru. 2017. “Policy Intervention in Debt Renegotiation: Evidence from
the Home Affordable Modification Program.” Journal of Political Economy, 125(3): 654–712.
Alekseev, Georgij, Safaa Amer, Manasa Gopal, Theresa Kuchler, JW Schneider, Johannes
Stroebel, and Nils C Wernerfelt. 2020. “The Effects of COVID-19 on U.S. Small Businesses:
Evidence from Owners, Managers, and Employees.” National Bureau of Economic Research
Working Paper 27833.
Almeida, Heitor, Murillo Campello, and Michael S. Weisbach. 2004. “The Cash Flow Sensi-
tivity of Cash.” Journal of Finance, 59(4): 1777–1804.
Altonji, Joseph, Zara Contractor, Lucas Finamor, Ryan Haygood, Ilse Lindenlaub, Costas
Meghir, Cormac O’Dea, Dana Scott, Liana Wang, and Ebonya Washington. 2020. “Em-
ployment Effects of Unemployment Insurance Generosity During the Pandemic.”
Amiram, Dan, and Daniel Rabetti. 2020. “The Relevance of Relationship Lending in Times of
Crisis.” SSRN working paper.
Atkeson, Andrew, Adrien d’Avernas, Andrea Eisfeldt, and Pierre-Olivier Weill. 2018. “Gov-
ernment Guarantees and the Valuation of American Banks.” NBER Macroeconomics Annual.
Autor, David, David Cho, Leland Crane, Mita Goldar, Byron Lutz, Joshua Montes,
William B. Peterman, David Ratner, Daniel Villar, and Ahu Yildirmaz. 2020. “An Evalu-
ation of the Paycheck Protection Program Using Administrative Payroll Microdata.” Mimeo.
Bachas, Natalie, Olivia Kim, and Constantine Yannelis. 2020. “Loan Guarantees and Credit
Supply.” Journal of Financial Economics.
Balyuk, Tetyana, Nagpurnanand R. Prabhala, and Manju Puri. 2020. “Indirect Costs of
Government Aid and Intermediary Supply Effects: Lessons From the Paycheck Protection
Program.” NBER Working Paper 28114.
Barrios, John, Michael Minnis, William Minnis, and Joost Sijthoff. 2020. “Assessing the
Payroll Protection Program: A Framework and Preliminary Results.” Working paper.
Barrot, Jean-Noel, Thorsten Martin, Julien Sauvagnat, and Boris Vallee. 2019. “Employ-
ment Effects of Alleviating Financing Frictions: Worker-level Evidence from a Loan Guaran-
tee Program.” Working Paper.
Bartik, Alexander W., Marianne Betrand, Feng Lin, Jesse Rothstein, and Matt Unrath.
2020. “Measuring the labor market at the onset of the COVID-19 crisis.” Brookings Papers on
Economic Activity.
Bartik, Alexander W., Zoe B. Cullen, Edward L. Glaeser, Michael Luca, Christopher T. Stan-
ton, and Adi Sunderam. 2020. “The Targeting and Impact of Paycheck Protection Program
Loans to Small Businesses.” NBER Working Paper No. 27623.
Berger, David, Nicholas Turner, and Eric Zwick. 2020. “Stimulating Housing Markets.” Jour-
nal of Finance, 75(1): 277–321.
Brevoort, Kenneth P., John A. Holmes, and John D. Wolken. 2010. “Distance Still Matters:
45
The Information Revolution in Small Business Lending and the Persistent Role of Location,
1993-2003.” Board of Governors of the Federal Reserve System.
Buffington, Catherine, Carrie Dennis, Emin Dinlersoz, Lucia Foster, and Shawn Klimek.
2020. “Measuring the Effect of COVID-19 on U.S. Small Businesses: The Small Business
Pulse Survey.” Census for Economic Studies, working paper.
Chetty, Raj, John N. Friedman, Nathaniel Hendren, and Michael Stepner. 2020. “How did
COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-time
Economic Tracker Based on Private Sector Data.” NBER working paper.
Chodorow-Reich, Gabriel. 2014. “The Employment Effects of Credit Market Disruptions:
Firm-level Evidence from the 2008–9 Financial Crisis.” The Quarterly Journal of Economics,
129(1): 1–59.
Chodorow-Reich, Gabriel. 2019. “Geographic Cross-Sectional Fiscal Spending Multipliers:
What Have We Learned?” AEJ: Policy, 11(2): 1–34.
Chodorow-Reich, Gabriel, Olivier Darmouni, Stephan Luck, and Matthew C. Plosser. 2020.
“Bank Liquidity Provision across the Firm Size Distribution.”
Cororaton, Anna, and Samuel Rosen. 2020. “Public Firm Borrowers of the US Paycheck Pro-
tection Program.” SSRN working paper.
Doniger, Cynthia, and Benjamin Kay. 2021. “Ten days late and billions of dollars short: The
employment effects of delays in paycheck protection program financing.” Working Paper.
Edgerton, Jesse. 2012. “Credit Supply and Business Investment During the Great Recession:
Evidence from Public Records of Equipment Financing.” SSRN working paper.
Elenev, Vadim, Tim Landvoigt, and Stijn Van Nieuwerburgh. 2020. “Can the Covid Bailouts
Save the Economy?” National Bureau of Economic Research.
Erel, Isil, and Jack Liebersohn. 2020. “Does FinTech Substitute for Banks? Evidence from the
Paycheck Protection Program.” SSRN working paper.
Faulkender, Michael, Robert Jackman, and Stephen I. Miran. 2020. “The Job-Preservation
Effects of the Paycheck Protection Program Loans.” Office of Economic Policy, Department of
Treasury, Working Paper 2020-01.
Gale, William. 1990. “Federal Lending and the Market for Credit.” Journal of Public Economics,
42: 177–93.
Gale, William. 1991. “Economic Effects of Federal Credit Programs.” American Economic Re-
view, 81(1): 133–52.
Ganong, Peter, Pascal J Noel, and Joseph S Vavra. 2020. “US Unemployment Insurance
Replacement Rates During the Pandemic.” National Bureau of Economic Research.
Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift. 2020. “Bartik Instruments: What,
When, Why, and How.” American Economic Review, 110(8): 2586–2624.
Gonzalez-Uribe, Juanita, and Su Wang. 2019. “Dissecting the Effect of Financial Constraints
on Small Firms.” Working Paper.
Goolsbee, Austan, and Chad Syverson. 2020. “Fear, Lockdown, and Diversion: Comparing
46
Drivers of Pandemic Economic Decline 2020.” NBER Working Paper No. 27432.
Granja, João, Christian Leuz, and Raghuram Rajan. 2018. “Going the Extra Mile: Distant
Lending and Credit Cycles.” National Bureau of Economic Research.
Griffin, John M, Samuel Kruger, and Prateek Mahajan. 2021. “Did FinTech Lenders Facilitate
PPP Fraud?” Available at SSRN 3906395.
Hanson, Samuel, Jeremy Stein, Adi Sunderam, and Eric Zwick. 2020a. “Business Conti-
nuity Insurance and Business Continuity Loans: Keeping America’s Lights on During the
Pandemic.” Policy Brief.
Hanson, Samuel, Jeremy Stein, Adi Sunderam, and Eric Zwick. 2020b. “Business Credit
Programs in the Pandemic Era.” Brookings Papers of Economic Activity, forthcoming.
House, Christopher, and Matthew Shapiro. 2008. “Temporary Investment Tax Incentives:
Theory with Evidence from Bonus Depreciation.” American Economic Review, 98(3): 737–
768.
Hubbard, Glenn R., and Michael Strain. 2020. “Has the Paycheck Protection Program Suc-
ceeded?” Brookings Papers of Economic Activity, forthcoming.
Humphries, John Eric, Christopher Neilson, and Gabriel Ulyssea. 2020. “Information Fric-
tions and Access to the Paycheck Protection Program.”
Joaquim, Gustavo, and Felipe Netto. 2020. “Bank Incentives and the Impact of the Paycheck
Protection Program.” Available at SSRN 3704518.
Kelly, Bryan, Hanno Lustig, and Stijn Van Nieuwerburgh. 2016. “Too-Systemic-to-Fail: What
Option Markets Imply about Sector-Wide Government Guarantees.” American Economic Re-
view, 106(6): 1278–1319.
Lelarge, Clair, David Sraer, and David Thesmar. 2010. “Entrepreneurship and Credit Con-
straints: Evidence From a French Loan Guarantee Program.” International Differences in En-
trepreneurship, 243–273.
Li, Lei, and Philip E. Strahan. 2020. “Who Supplies PPP Loans (and Does it Matter)? Banks,
Relationships, and the COVID Crisis.” SSRN working paper.
Lucas, Deborah. 2016. “Credit Policy as Fiscal Policy.” Brookings Papers on Economic Activity,
, (Spring): 1–57.
Mian, Atif, and Amir Sufi. 2012. “The Effects of Fiscal Stimulus: Evidence from the 2009 Cash
for Clunkers Program.” Quarterly Journal of Economics, 127: 1107–1142.
Morse, Adair, and Robert Bartlett. 2020. “Small Business Survival Capabilities and Policy
Effectiveness: Evidence from Oakland.” National Bureau of Economic Research.
Mullins, William, and Patricio Toro. 2017. “Credit Guarantees and New Bank Relationships.”
Policy Research Working Paper No, 8241.
Papanikolaou, Dimitris, and Lawrence DW Schmidt. 2020. “Working Remotely and the
Supply-side Impact of Covid-19.” National Bureau of Economic Research.
Riddick, Leigh A, and Toni M Whited. 2009. “The Corporate Propensity to Save.” The Journal
of Finance, 64(4): 1729–1766.
47
Smith, Bruce. 1983. “Limited Information, Credit Rationing, and Optimal Government Lend-
ing Policy.” American Economic Review, 73(3): 305–18.
Sparks, Evan. 2020. “We Were Economic First Responders.” American Bankers Association. ABA
Banking Journal, 112(4): 22–31.
Stiglitz, Joseph E, and Andrew Weiss. 1981. “Credit Rationing in Markets with Imperfect
Information.” The American Economic Review, 71(3): 393–410.
Wooten, Kylee. 2020. “Is it too late to automate? How to get started with PPP lending.”
Zwick, Eric. Forthcoming. “The Costs of Corporate Tax Complexity.” American Economic Jour-
nal: Economic Policy.
Zwick, Eric, and James Mahon. 2017. “Tax Policy and Heterogeneous Investment Behavior.”
American Economic Review, 107(1): 217–248.
48
Figure 1: PPPE and PPP Allocation
Figure 1 plots the cumulative share of PPP and small business lending by all banks whose PPPE is below x,
where x ∈ (−0.5, 0.5). Panel A plots cumulative amounts using PPP data as of the end of the first round (April
15th, 2020), and Panel B reports cumulative amounts using PPP data as of when the flow of second round funds
approximately ends (June 30th, 2020). Data is obtained from the SBA and commercial bank Call Reports.
Panel A. Cumulative PPP and SBL Lending on April 15th
1
Cumulative Share of Loans
.2 .4 .6 .8
0
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Bank PPPE
PPP Loans Small Business Loans
Panel B. Cumulative PPP and SBL Lending on June 30th
1
Cumulative Share of Loans
.2 .4 .6 .8
0
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Bank PPPE
PPP Loans Small Business Loans
49
Average Loan Amount (000s) Bank PPPE
03 0 250 500 750 1000 1250 03 -.5 -.25 0 .25 .5
ap ap
r20 r20
20 20
08 08
ap ap
r20 r20
20 20
13 13
ap ap
r20 r20
20 20
<1B 18 <1B 18
ap ap
r20 r20
20 20
23 23
ap ap
r20 r20
20 20
28 28
ap ap
r20 r20
03 20 03 20
ma ma
y2 y2
Figure 2 plots the evolution of average PPPE based on the number of PPP loans (Panel A), average PPPE based on
1-10B 02 1-10B 02
08 0 08 0
ma ma
y2 y2
02 02
13 0 13 0
ma ma
y2 y2
02 02
18 0 18 0
ma ma
y2 y2
02 02
23 0 23 0
ma ma
10-50B y2 10-50B y2
02 02
28 0 28 0
ma ma
y2 y2
02 02
02 0 02 0
jun jun
20 20
20 20
07 07
jun jun
20 20
20 20
12 12
jun jun
50B-1T 20 50B-1T 20
20 20
17 17
jun jun
20 20
20 20
22 22
jun jun
20 20
20 20
27 27
jun jun
20 20
the volume of PPP loans (Panel B), the average loan amount (Panel C), and the fraction of loans above $1 million
20 20
Panel A. Number-based PPPE by Size Bin
Top 4 Top 4
Panel C. Average Loan Amount by Size Bin
50
Fraction of PPP Loans above $1M Bank PPPE
03 0 .1 .2 .3 .4 .5 03 -.5 -.25 0 .25 .5
ap ap
r20 r20
20 20
08 08
ap ap
r20 r20
20 20
13 13
ap ap
r20 r20
20 20
<1B 18 <1B 18
ap ap
r20 r20
20 20
23 23
ap ap
r20 r20
20 20
28 28
ap ap
r20 r20
03 20 03 20
ma ma
y2 y2
1-10B 02 1-10B 02
08 0 08 0
(Panel D) by bank size bin. The size bins stratify all commercial banks operating as of the fourth quarter of 2019
ma ma
y2 y2
02 02
13 0 13 0
ma ma
y2 y2
02 02
18 0 18 0
ma ma
y2 y2
02 02
23 0 23 0
ma ma
10-50B y2 10-50B y2
02 02
28 0 28 0
ma ma
y2 y2
Figure 2: Evolution of PPPE and Average Loan Size by Bank Size
02 02
02 0 02 0
jun jun
20 20
20 20
07 07
jun jun
20 20
20 20
12 12
jun jun
50B-1T 20 50B-1T 20
20 20
17 17
jun jun
20 20
20 20
22 22
jun jun
20 20
20 20
27 27
jun jun
20 20
20 20
Panel B. Volume-based PPPE by Size Bin
Top 4 Top 4
based on their total assets. Data is obtained from the SBA and commercial bank Call Reports.
Panel D. Share of Loans > $1M by Size Bin
Figure 3: State Exposure to PPPE and Share of Small businesses requesting and receiving PPP
Figure 3 presents scatter plots comparing state-level exposure to PPPE and Predicted PPPE and Census survey
outcomes from after the first round of funding. Panels A and C plots the percentage of firms receiving PPP at the
end of the first round, and Panels B and D plots the percentage of small businesses reporting having applied to
PPPE funds at the end of the first round of the PPP program. Data come from the Census Bureau Small Business
Pulse Survey, SBA, Call Reports, Summary of Deposits, and County Business Patterns.
Panel A. PPPE and Share Receiving PPP Panel B. PPPE and Share Requesting PPP
100 100
Share of Small Businesses Receiving PPP (Round 1) Share of Small Businesses Requesting PPP (Round 1)
NJ GA LA AR MSOK ME
CA UT MAWI
PA
TXCT OH
MN
VA IL ALTN RI
MO
FL NY
NC MI IN SDND
AZ OR SC
WA
MD DE
CO KY IAKSNH
HI
NV DC ID AK NM WV
NE
AR OK MT VT
ND ME
MO NE
AL MN WI KS
SD WY
IA
MT
MS
ID KY IN
LA NH
UT DE
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90
IL TN WY
PA
AKTX OH
MA
RI WV VT
VA CO MI
SC
NC MD HI
GA CT
WA
NY
OR FL NM
AZ NJ
NV
CA DC
0 0
-.2 0 .2 .4 -.2 0 .2 .4
State PPPE (Round 1) State PPPE (Round 1)
Panel C. Predicted PPPE and Panel D. Predicted PPPE and
Share Receiving PPP Share Requesting PPP
100 100
Share of Small Businesses Receiving PPP (Round 1) Share of Small Businesses Requesting PPP (Round 1)
GA NJ OK
LAAR ME
TX PA
MN
AL CT IL WI IMO
MS
MAOH
VA CAFL
UT TN INMI NH
NC OR MD SD ND
DE
NY
KS
AZ SC CO WA WV IA
KY HI
AK
NV DC NM ID NE
OK
AR MT VT
ND ME
NE MO
MN SD
AL KS
WI WY
MT IA MS
ID KY LA IN NH
UT DE
10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 70 80 90
WY TX PA TNIL OH
AK WV RI MA VT
VA CO MI
NC SC MD HI
GA CT
WA NY
NM FL OR
AZ NJ
NV
DC CA
0 0
.1 .15 .2 .25 .3 .1 .15 .2 .25 .3
Predicted State PPPE Predicted State PPPE
51
Figure 4: ZIP Exposure to First Round PPPE and PPP Coverage over Time
Figure 4 plots binned scatter plots of the average fraction of small business establishments that received a PPP loan
versus ZIP-level PPPE. (Panel A) and Zip-level Predicted PPPE (Panel B). Eligible establishment counts equal all
establishments in a ZIP less an estimate of the share of establishments with more than 500 employees (which are
not eligible for PPP) plus an estimate of the number of proprietorships likely to apply for PPP. Both variables are
demeaned at the state level to present the within-state relationship. Data come from SBA, Call Reports, Summary
of Deposits, and County Business Patterns.
Panel A. PPPE
PPP Loans Relative to
Eligible Establishments (Residualized)
.1 .2 .3 .4 .5 .6 .7 .8
-.4 -.3 -.2 -.1 0 .1 .2 .3 .4
PPPE Round 1 (Residualized)
04/12-04/18 04/26-05/02 05/10-05/16
05/24-05/30 06/07-06/13 06/21-06/27
Panel B. Predicted PPPE
PPP Loans Relative to
Eligible Establishments (Residualized)
.1 .2 .3 .4 .5 .6 .7 .8
-.15 -.1 -.05 0 .05 .1
Predicted PPPE Round 1 (Residualized)
04/12-04/18 04/26-05/02 05/10-05/16
05/24-05/30 06/07-06/13 06/21-06/27
52
Figure 5: Share of Establishments Receiving PPP by Lender Type
Figure 5 shows the number of PPP loans broken down by lender type and funding round, scaled by the total
number of establishments. Data come from the SBA, FDIC Summary of Deposits, and Census.
.6
Share of Total Establishments
Non-Banks
FinTech
Credit Unions
.2 .4
Non-Local Banks (>10mi)
Local Banks (<10mi)
0
Round 1 Round 2 Both Rounds
53
Figure 6: Targeting of PPP Allocation (First Round and Overall)
Figure 6 stratifies all businesses in Homebase in 10 bins based on the fraction of establishments in their ZIP code
receiving PPP during the first round and during both rounds combined. Panel A plots for each bin the share of
Homebase businesses that shut down in the week of March 22nd–March 28th. Panel B plots for each bin the
average decline in hours worked in the week of March 22nd–March 28th relative to a baseline of the average
weekly hours worked in the last two weeks of January. Panel C plots for each bin the average decline in the
number of employees in the week of March 22nd–March 28th relative to a baseline of the average weekly hours
worked in the last two weeks of January. Data are from SBA, Homebase, and County Business Patterns.
Panel A. Business Shutdowns and Fraction of Establishments Receiving PPP
Round 1 Rounds 1 and 2
.4 .4
Share of Businesses Shutdown Share of Businesses Shutdown
during 03/22 to 03/28 (Pre-PPP) during 03/22 to 03/28 (Pre-PPP)
.25 .3 .35 .25 .3 .35
.2 .2
st 2 3 4 5 6 7 8 9 /Es st 2 3 4 5 6 7 8 9 /Es
t t
t PP t PP
st P st P
P/E P/E
PP PP
Lea Mo Lea Mo
s s
Quantile of PPP Loans Relative to All Establishments (Round 1) Quantile of PPP Loans Relative to All Establishments (Both Rounds)
Panel B. Decline in Hours Worked and Fraction of Establishments Receiving PPP
Round 1 Rounds 1 and 2
Ratio of Hours Worked 03/22 to 03/28 Ratio of Hours Worked 03/22 to 03/28
Relative to January (Pre-PPP) Relative to January (Pre-PPP)
.35 .4 .45 .5 .35 .4 .45 .5
.3 .3
t 2 3 4 5 6 7 8 9 t t 2 3 4 5 6 7 8 9 t
/Es /Es /Es /Es
PP PP PP PP
st P st P st P st P
Lea Mo Lea Mo
Quantile of PPP Loans Relative to All Establishments (Round 1) Quantile of PPP Loans Relative to All Establishments (Both Rounds)
Panel C. Decline in Number of Employees and Fraction of Establishments Receiving PPP
Round 1 Rounds 1 and 2
Ratio of Nbr. Employees from 03/22 to 03/28 Ratio of Nbr. Employees from 03/22 to 03/28
Relative to January (Pre-PPP) Relative to January (Pre-PPP)
.35 .4 .45 .5 .55 .35 .4 .45 .5 .55
t 2 3 4 5 6 7 8 9 t t 2 3 4 5 6 7 8 9 t
/Es /Es /Es /Es
PP PP PP PP
st P st P st P st P
Lea Mo Lea Mo
Quantile of PPP Loans Relative to All Establishments (Round 1)
54 Quantile of PPP Loans Relative to All Establishments (Both Rounds)
55
W W
ee ee
k 1 Share of Businesses Shutdown k 1 Share of Businesses Shutdown
W : Jan W : Jan
ee 0 .1 .2 .3 .4 ee 0 .1 .2 .3 .4
k 2 19 - k 2 19 -
W W
ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25
W 6 W 6
ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F
k 5 eb eb k 5 eb eb
W : F 9 - W : F 9 -
ee F 8 ee F 8
k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5
W eb eb W eb eb
e 2 2 e 2 2
W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e
W k 8: ar b29 W k 8: ar b29
ee ee
W k Ma 1-Ma
W k Ma 1-Ma
ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14
ee ee
k ar
22 ar21 k ar
22 ar21
W 11: - W 11: -
ee M Ma ee M Ma
W k 12 ar29 r28 Initial Lockdown Orders W k 12 ar29 r28 Initial Lockdown Orders
ee ee
k 1 : Ap -Ap k 1 : Ap -Ap
W r r W r r
ee 3: A 5-A 4
k p pr PPP Launch ee 3: A 5-A 4
k p pr PPP Launch
W 14: r12- 11 W 14: r12- 11
ee Ap Ap ee Ap Ap
k r r k r r
W 15: 19-A 18
ee Ap 1st Round Exhausted W 15: 19-A 18
ee Ap 1st Round Exhausted
W pr W pr
ee
k1 r 25 ee
k1 r 25
6: 26- 6: 26-
W k 17 Ma May W k 17 Ma May
ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins
Low Predicted PPPE
Low PPPE
k 1 Ma 3-M k 1 Ma 3-M
W y W y
ee 8: M 10- ay9 ee 8: M 10- ay9
k1 ay Ma k1 ay Ma
W 9: M 17- y16 W 9: M 17- y16
ee
k2 a y
Ma ee
k2 a y
Ma
W 0 24 y23
W 0 24 y23
ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun
W n W n
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1
1 1
W 3: J 4-J 3 W 3: J 4-J 3
ee un u ee un u
k 21 n20 k 21 n20
W 24: -J W 24: -J
ee Ju un2 ee Ju un2
W k n 28 7 W k n 28 7
ee 25: - ee 25: -
k 2 Ju Jul4 k 2 Ju Jul4
W l5- W l5-
ee 6: J ee 6: J
k u Jul1
1 k u Jul1
1
W 27: l12- W 27: l12-
J Ju J Ju
High PPPE
ee ee
k ul1 l18 k ul1 l18
W 28: 9- W 28: 9-
ee J Ju ee J Ju
W k 29 ul26 l25 W k 29 ul26 l25
Panel A. Business Shutdowns
ee : -A ee : -A
W k 30 Aug ug1 W k 30 Aug ug1
ee 2 ee 2
k 3 : Au -Au k 3 : Au -Au
W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug
High Predicted PPPE
2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5
g2 g g2 g
3- 22 3- 22
Au Au
g2 g2
9 9
W W
ee ee
k 1 Ratio of Hours Worked Relative to January k 1 Ratio of Hours Worked Relative to January
W : Jan W : Jan
ee 0 .2 .4 .6 .8 1 ee 0 .2 .4 .6 .8 1
k 2 19 - k 2 19 -
W W
ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25
W 6 W 6
ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F
k 5 eb eb k 5 eb eb
W : F 9 - W : F 9 -
ee F 8 ee F 8
k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5
W eb eb W eb eb
e 2 2 e 2 2
Figure 7 shows the ratio of hours worked over time, the percent of businesses shut down, and the ratio of number of employees splitting the sample into
W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e
W k 8: ar b29 W k 8: ar b29
ee ee
W k Ma 1-Ma
W k Ma 1-Ma
ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14
ee ee
k ar
22 ar21 k ar
22 ar21
W 11: - W 11: -
ee M Ma ee M Ma
W k 12 ar29 r28 Initial Lockdown Orders W k 12 ar29 r28 Initial Lockdown Orders
ee ee
k 1 : Ap -Ap k 1 : Ap -Ap
W r r W r r
ee 3: A 5-A 4
k p pr PPP Launch ee 3: A 5-A 4
k p pr PPP Launch
W 14: r12- 11 W 14: r12- 11
ee Ap Ap ee Ap Ap
k r r k r r
W 15: 19-A 18
ee Ap 1st Round Exhausted W 15: 19-A 18
ee Ap 1st Round Exhausted
W pr W pr
ee
k1 r 25 ee
k1 r 25
6: 26- 6: 26-
W k 17 Ma May W k 17 Ma May
ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins
Low Predicted PPPE
Low PPPE
k 1 Ma 3-M k 1 Ma 3-M
W y W y
ee 8: M 10- ay9 ee 8: M 10- ay9
k1 ay Ma k1 ay Ma
W 9: M 17- y16 W 9: M 17- y16
ee
k2 a y
Ma ee
k2 a y
Ma
W 0 24 y23
W 0 24 y23
ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun
W n W n
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1
1 1
W 3: J 4-J 3 W 3: J 4-J 3
ee un u ee un u
k 21 n20 k 21 n20
W 24: -J W 24: -J
ee Ju un2 ee Ju un2
W k n 28 7 W k n 28 7
ee 25: - ee 25: -
k 2 Ju Jul4 k 2 Ju Jul4
W l5- W l5-
ee 6: J ee 6: J
k u Jul1
1 k u Jul1
1
W 27: l12- W 27: l12-
J Ju J Ju
High PPPE
ee u ee u
k l19 l18 k l19 l18
W 28: W 28:
ee J -Ju ee J -Ju
W k 29 ul26 l25 W k 29 ul26 l25
ee : -A ee : -A
W k 30 Aug ug1 W k 30 Aug ug1
ee 2 ee 2
k 3 : Au -Au k 3 : Au -Au
W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8
Panel B. Change in Hours Worked
k 3 ug ug k 3 ug ug
High Predicted PPPE
2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5
g2 g g2 g
3- 22 3- 22
Au Au
g2 g2
9 9
Figure 7: PPPE and Homebase Employment Outcomes
W W
ee ee
k 1 Ratio of Employees Relative to January k 1 Ratio of Employees Relative to January
W : Jan W : Jan
ee 0 .2 .4 .6 .8 1 ee 0 .2 .4 .6 .8 1
k 2 19 - k 2 19 -
W W
ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25
W 6 W 6
ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F
k 5 eb e k 5 eb e
W : F 9- b8
F W : F 9- b8
F
ee ee
k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5
W eb eb W eb eb
e 2 2 e 2 2
W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e
W k 8: ar b29 W k 8: ar b29
ee ee
W k Ma 1-Ma
W k Ma 1-Ma
ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14
ee ee
k ar
22 ar21 k ar
22 ar21
W 11: - W 11: -
ee M Ma ee M Ma
W k 12 ar29 r28 Initial Lockdown Orders W k 12 ar29 r28 Initial Lockdown Orders
ee ee
k 1 : Ap -Ap k 1 : Ap -Ap
W r r W r r
ee 3: A 5-A 4
k p pr PPP Launch ee 3: A 5-A 4
k p pr PPP Launch
W 14: r12- 11 W 14: r12- 11
ee Ap Ap ee Ap Ap
k r r k r r
W 15: 19-A 18
ee Ap 1st Round Exhausted W 15: 19-A 18
ee Ap 1st Round Exhausted
W pr W pr
ee
k1 r 25 ee
k1 r 25
6: 26- 6: 26-
W k 17 Ma May W k 17 Ma May
ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins
Low Predicted PPPE
Low PPPE
k 1 Ma 3-M k 1 Ma 3-M
W y W y
ee 8: M 10- ay9 ee 8: M 10- ay9
k1 ay Ma k1 ay Ma
W 9: M 17- y16 W 9: M 17- y16
ee M ee M
k 2 ay 2 ay2 k 2 ay 2 ay2
W 0 4 3 W 0 4 3
ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun
W n W n
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1
1 1
W 3: J 4-J 3 W 3: J 4-J 3
ee un u ee un u
k 21 n20 k 21 n20
W 24: -J W 24: -J
ee Ju un2 ee Ju un2
W k n 28 7 W k n 28 7
ee 25: - ee 25: -
k 2 Ju Jul4 k 2 Ju Jul4
W l5- W l5-
ee 6: J ee 6: J
k u Jul1
1 k u Jul1
1
W 27: l12- W 27: l12-
J Ju J Ju
High PPPE
ee u ee u
k l19 l18 k l19 l18
W 28: W 28:
ee J -Ju ee J -Ju
W k 29 ul26 l25 W k 29 ul26 l25
ee : -A ee : -A
W k 30 Aug ug1 W k 30 Aug ug1
ee 2 ee 2
regions with above- versus below-median PPPE and above versus below-median predicted PPPE. Data are from SBA, Homebase, County Business Patterns.
k 3 : Au -Au k 3 : Au -Au
W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug
High Predicted PPPE
1 1 1 1
Panel C. Change in Nbr. Employees
2: 2:
Au 6-Au 5 Au 6-Au 5
g2 g g2 g
3- 22 3- 22
Au Au
g2 g2
9 9
56
We We
e k1 Coefficient (Predicted PPPE) ek Coefficient
0: 10
: M
We
Ma
r22 -.02 0 .02 .04 We ar2 -.02 0 .02 .04
e k1 -M ek 2-M
1: ar2 11 ar2
Ma 8 : M 8
We r29 We ar2
ek -A ek 9-A
12 pr4 12 pr4
We : Ap We : Ap
ek r5-
Ap PPP Launch ek r5-
Ap PPP Launch
13 13
:A r11 :A r11
We pr1 We pr1
ek 2-A ek 2-A
14 pr1 14 pr1
:A 8 :A 8
We pr1 We pr1
ek 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
15 pr2 15 pr2
5 5
Figure 8 plots coefficients and standard errors of regressions investigating the impact of exposure to PPPE (top row) and predicted PPPE (bottom row) on
:A :A
We pr2 We pr2
ek 6 -M ek 6 -M
16 ay 16 ay
We :M 2 2nd Round Begins We :M 2 2nd Round Begins
ek ay ek ay
17 3-M 17 3-M
:M ay :M ay
We ay 9 We ay 9
ek 10 ek 10
18 -M 18 -M
:M ay :M ay
We ay 16 We ay 16
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
ek 24 ek 24
20 -M 20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n2 :J n2
un 0 un 0
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25 l4 25 l4
employment and firm outcomes, defined as the difference between these outcomes in each week relative to their average in the two weeks prior to program
We :J We :J
ul5 ul5
ek
26 - Ju ek
26 - Ju
:J l11 :J l11
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
ek 6 -A ek 6 -A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
Panel A. Business Shutdowns
30 ug 30 ug
We :A
ug 8 We :A
ug 8
ek 9-A ek 9-A
31 ug 31 ug
We
:A
ug 1 5 We
:A
ug 1 5
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
ug ug
29 29
launch (weeks 10 and 11). Panel A plots the coefficients β and standard errors of week-by-week regressions of ∆Shut downi jn = αsn + β P P P E j + Γ X i jn + εi jn ,
We We
e k1 Coefficient (Predicted PPPE) ek Coefficient
0: 10
: M
We
Ma
r22 -.02 0 .02 .04 We ar2 -.02 0 .02 .04
e k1 -M ek 2-M
1: ar2 11 ar2
Ma 8 : M 8
We r29 We ar2
ek -A ek 9-A
12 pr4 12 pr4
We : Ap We : Ap
ek r5-
Ap PPP Launch ek r5-
Ap PPP Launch
13 13
:A r11 :A r11
We pr1 We pr1
ek 2-A ek 2-A
14 pr1 14 pr1
:A 8 :A 8
We pr1 We pr1
ek 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
15 pr2 15 pr2
We
:A
pr2 5 We
:A
pr2 5
ek 6 -M ek 6 -M
16 ay 16 ay
We :M 2 2nd Round Begins We :M 2 2nd Round Begins
ek ay ek ay
17 3-M 17 3-M
:M ay :M ay
We ay 9 We ay 9
ek 10 ek 10
18 -M 18 -M
:M ay :M ay
We ay 1 6 We ay 1 6
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
where ∆Shut downi jn is the difference between the shutdown indicator of firm i in each week and the average shutdown indicator for that firm during the
We ay 23 We ay 23
ek 24 ek 24
20 -M 20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n2 :J n2
un 0 un 0
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek
26 - Ju ek
26 - Ju
:J l11 :J l11
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
ek 6 -A ek 6 -A
29 ug 29 ug
two weeks prior to program launch, P P P E j is the average exposure of the ZIP j to bank PPPE, αsn are state-by-industry fixed effects and X i jn are additional
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We :A
ug 8 We :A
ug 8
ek 9-A ek 9-A
31 ug 31 ug
:A
ug 1 5 :A
ug 1 5
Panel B. Change in Hours Worked
We We
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
ug ug
29 29
We We
e k1 Coefficient (Predicted PPPE) ek Coefficient
0: 10
: M
Ma -.02 0 .02 .04 -.02 0 .02 .04
control variables. Panel B plots estimates from similar week-by-week regressions that use the change in the decline in hours worked relative to January as the
We r22 We ar2
e k1 -M ek 2-M
1: ar2 11 ar2
Ma 8 : M 8
We r29 We ar2
ek -A ek 9-A
12 pr4 12 pr4
We : Ap We : Ap
Figure 8: PPPE and Homebase Post-PPP Outcomes (Local Projections)
ek r5-
Ap PPP Launch ek r5-
Ap PPP Launch
13 13
:A r11 :A r11
We pr1 We pr1
ek 2-A ek 2-A
14 pr1 14 pr1
:A 8 :A 8
We pr1 We pr1
ek 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
15 pr2 15 pr2
We
:A
pr2 5 We
:A
pr2 5
ek 6 -M ek 6 -M
16 ay 16 ay
We :M 2 2nd Round Begins We :M 2 2nd Round Begins
ek ay ek ay
17 3-M 17 3-M
:M ay :M ay
We ay 9 We ay 9
ek 10 ek 10
18 -M 18 -M
:M ay :M ay
We ay 1 6 We ay 1 6
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
ek 24 ek 24
20 -M 20 -M
:M ay :M ay
30 30
ay ay
dependent variable. Panel C plots estimates from similar week-by-week regressions that use the change in the decline in number of firm employees relative
We We
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n2 :J n2
un 0 un 0
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek
26 - Ju ek
26 - Ju
:J l11 :J l11
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
ek 6 -A ek 6 -A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We :A
ug 8 We :A
ug 8
to January as the dependent variable. Data are from Call Reports, SBA, Homebase, and County Business Patterns.
ek 9-A ek 9-A
31 ug 31 ug
:A 15 :A 15
We ug We ug
ek 16 ek 16
-A -A
Panel C. Change in Nbr. Employees
32 ug 32 ug
:A 22 :A 22
ug ug
23 23
-A -A
ug ug
29 29
57
W W
ee ee
k 1 Initial UI Claims/January Employment k 1 Initial UI Claims/January Employment
W : Jan W : Jan
ee 0 1% 2% 3% 4% ee 0 1% 2% 3% 4%
k 2 19 - k 2 19 -
W W
ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25
W 6 W 6
ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F
k 5 eb eb k 5 eb eb
W : F 9 - W : F 9 -
ee F 8 ee F 8
k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5
W eb eb W eb eb
e 2 2 e 2 2
W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e
W k 8: ar b29 W k 8: ar b29
ee ee
W k Ma 1-Ma
W k Ma 1-Ma
ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14
ee ee
k ar
22 ar21 k ar
22 ar21
W 11: - W 11: -
ee M Ma ee M Ma
W k 12 ar29 r28 Initial Lockdown Orders W k 12 ar29 r28 Initial Lockdown Orders
ee ee
k 1 : Ap -Ap k 1 : Ap -Ap
W r r W r r
ee 3: A 5-A 4
k p pr PPP Launch ee 3: A 5-A 4
k p pr PPP Launch
W 14: r12- 11 W 14: r12- 11
ee Ap Ap ee Ap Ap
k r r k r r
W 15: 19-A 18
ee Ap 1st Round Exhausted W 15: 19-A 18
ee Ap 1st Round Exhausted
Figure 9 shows the evolution of the ratio of weekly initial unemployment filing claims at the county level and total county employment (Panel A), the change
W pr W pr
ee
k1 r 25 ee
k1 r 25
6: 26- 6: 26-
W k 17 Ma May W k 17 Ma May
ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins
Low PPPE Low PPPE
k 1 Ma 3-M k 1 Ma 3-M
W y W y
ee 8: M 10- ay9 ee 8: M 10- ay9
k1 ay Ma k1 ay Ma
W 9: M 17- y16 W 9: M 17- y16
ee
k2 a y
Ma ee
k2 a y
Ma
W 0 24 y23
W 0 24 y23
ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun
W n W n
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1
1 1
W 3: J 4-J 3 W 3: J 4-J 3
ee un u ee un u
k 21 n20 k 21 n20
W 24: -J W 24: -J
ee Ju un2 ee Ju un2
Panel A. UI Claims
W k n 28 7 W k n 28 7
ee 25: - ee 25: -
k 2 Ju Jul4 k 2 Ju Jul4
W l5- W l5-
ee 6: J ee 6: J
k u Jul1
1 k u Jul1
1
W 27: l12- W 27: l12-
J Ju J Ju
High PPPE High PPPE
ee ee
k ul1 l18 k ul1 l18
W 28: 9- W 28: 9-
ee J Ju ee J Ju
W k 29 ul26 l25 W k 29 ul26 l25
ee : -A ee : -A
W k 30 Aug ug1 W k 30 Aug ug1
ee 2 ee 2
k 3 : Au -Au k 3 : Au -Au
W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug
2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5
g2 g g2 g
3- 22 3- 22
Au Au
g2 g2
9 9
W W
in aggregate small business revenue at the county level relative to January (Panel B), and the ratio of county employment relative to January (Panel C).
ee ee
k 1 Decline Relative to January k 1 Decline Relative to January
W : Jan W : Jan
ee -.6 -.4 -.2 0 .2 ee -.6 -.4 -.2 0
k 2 19 - k 2 19 -
W W
ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25
W 6 W 6
ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F
k 5 eb eb k 5 eb eb
W : F 9 - W : F 9 -
ee F 8 ee F 8
k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5
W eb eb W eb eb
e 2 2 e 2 2
W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e
W k 8: ar b29 W k 8: ar b29
ee ee
W k Ma 1-Ma
W k Ma 1-Ma
ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14
ee ee
k ar
22 ar21 k ar
22 ar21
W 11: - W 11: -
ee M Ma ee M Mar
Lockdown
Initial W k 1 ar2 Orders 28 Initial Lockdown Orders
W k 12 ar29 r28
ee ee 2 9
k 1 : Ap -Ap k 1 : Ap -Ap
W r r W 3: r5 r4
ee 3: A 5-A 4
k p pr PPPeeLaunch
k Ap -Ap PPP Launch
r
W 14: r12- 11 W 14: r12- 11
ee Ap Ap ee A A
k r r k 1 pr1 pr1
W 15: 19-A 18 W
1st Round Exhausted 9- 8 1st Round Exhausted
ee Ap pr ee 5: A Ap
W k1 r 25 W k 1 pr2 r2
5
ee 6: 26- ee 6: 6-
W k 17 Ma May W k 17 Ma May
ee : y 2 Round: Begins
2nd ee y 2 2nd Round Begins
Low Predicted PPPE
Low PPPE
k 1 Ma 3-M k 1 Ma 3-M
W y W y
ee 8: M 10- ay9 ee 8: M 10- ay9
k1 ay Ma k1 ay Ma
W 9: M 17- y16 W 9: M 17- y16
ee
k2 a y
Ma ee
k2 a y
Ma
W 0 24 y23
W 0 24 y23
ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun
W n W n
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1
1 1
We exclude California counties from the time series of Panel A due to a large outlier in UI claims that is likely due to a backlog in UI claims processing in
W 3: J 4-J 3 W 3: J 4-J 3
ee un u ee un u
k 21 n20 k 21 n20
W 24: -J W 24: -J
ee Ju un2 ee Ju un2
W k n 28 7 W k n 28 7
ee 25: - ee 25: -
k 2 Ju Jul4 k 2 Ju Jul4
W l5- W l5-
ee 6: J ee 6: J
k u Jul1
1 k u Jul1
1
Panel B. Small Bus. Rev.
W 27: l12- W 27: l12-
J Ju J Ju
High PPPE
ee u ee u
k l19 l18 k l19 l18
W 28: W 28:
ee J -Ju ee J -Ju
W k 29 ul26 l25 W k 29 ul26 l25
ee : -A ee : -A
W k 30 Aug ug1 W k 30 Aug ug1
ee 2 ee 2
k 3 : Au -Au k 3 : Au -Au
W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug
High Predicted PPPE
2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5
g2 g g2 g
3- 22 3- 22
Au Au
g2 g2
9 9
Figure 9: PPPE and Alternative Outcome Variables
W W
ee ee
k 1 Decline Relative to January k 1 Decline Relative to January
W : Jan W : Jan
ee -.25 -.2 -.15 -.1 -.05 0 ee -.25 -.2 -.15 -.1 -.05 0
k 2 19 - k 2 19 -
W W
ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25
W 6 W 6
ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F
k 5 eb e k 5 eb e
W : F 9- b8
F W : F 9- b8
F
ee ee
k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5
W eb eb W eb eb
e 2 2 e 2 2
that state. In the appendix, we include the plot that includes Californian counties. Data are from Call Reports, SBA, County Business Patterns, State Labor
W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e
W k 8: ar b29 W k 8: ar b29
ee ee
W k Ma 1-Ma
W k Ma 1-Ma
ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14
ee ee
k ar
22 ar21 k ar
22 ar21
W 11: - W 11: -
ee M Mar ee M Ma
rders Initial 28 Lockdown Orders
W k 12 ar2
ee 9 W k 12 ar29 r28
ee
k 1 : Ap -Ap k 1 : Ap -Ap
W r r4 W r r
ee 3: A 5-A PPP
k p pr Launch ee 3: A 5-A 4
k p pr
W 14: r12- 11 W 14: r12- 11
ee Ap Ap ee Ap Ap
k r r1 k r r
sted W 15: 19
ee Ap 1st-ARound
8 Exhausted W 15: 19-A 18
ee Ap
W pr W pr
ee
k1 r 25 ee
k1 r 25
6: 26- 6: 26-
W k 17 Ma May W k 17 Ma May
ins ee : y 2nd 2 Round Begins ee : y 2
Low Predicted PPPE
Low PPPE
k 1 Ma 3-M k 1 Ma 3-M
W y W y
ee 8: M 10- ay9 ee 8: M 10- ay9
k1 ay Ma k1 ay Ma
W 9: M 17- y16 W 9: M 17- y16
ee M ee M
k 2 ay 2 ay2 k 2 ay 2 ay2
W 0 4 3 W 0 4 3
ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun
W n W n
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1
1 1
W 3: J 4-J 3 W 3: J 4-J 3
ee un u ee un u
k 21 n20 k 21 n20
W 24: -J W 24: -J
ee Ju un2 ee Ju un2
W k n 28 7 W k n 28 7
ee 25: - ee 25: -
k 2 Ju Jul4 k 2 Ju Jul4
W l5- W l5-
ee 6: J ee 6: J
k u Jul1
1 k u Jul1
1
W 27: l12- W 27: l12-
Panel C. OI Employment
J Ju J Ju
High PPPE
ee u ee u
k l19 l18 k l19 l18
W 28: W 28:
ee J -Ju ee J -Ju
W k 29 ul26 l25 W k 29 ul26 l25
ee : -A ee : -A
W k 30 Aug ug1 W k 30 Aug ug1
ee 2 ee 2
Departments, Opportunity Insights website, and Womply.
k 3 : Au -Au k 3 : Au -Au
W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug
High Predicted PPPE
2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5
g2 g g2 g
3- 22 3- 22
Au Au
g2 g2
9 9
58
We We
e k1 Coefficient (Predicted PPPE) ek Coefficient
0: 10
: M
We
Ma
r22 -1 -.5 0 .5 1 We ar2 -1 -.5 0 .5
e k1 -M ek 2-M
1: ar2 11 ar2
Ma 8 : M 8
We r29 We ar2
ek -A ek 9-A
12 pr4 12 pr4
We : Ap We : Ap
ek r5-
Ap PPP Launch ek r5-
Ap PPP Launch
13 13
:A r11 :A r11
We pr1 We pr1
ek 2-A ek 2-A
14 pr1 14 pr1
:A 8 :A 8
We pr1 We pr1
ek 9-A PPP Funds Exhausted ek 9-A PPP Funds Exhausted
15 pr2 15 pr2
5 5
Figure 10 plots coefficients and standard errors of regressions investigating the impact of exposure to PPPE (top row) and predicted PPPE (bottom row) on
:A :A
We pr2 We pr2
ek 6 -M ek 6 -M
16 ay 16 ay
We :M 2 PPP 2nd Round Begins We :M 2 PPP 2nd Round Begins
ek ay ek ay
17 3-M 17 3-M
:M ay :M ay
We ay 9 We ay 9
ek 10 ek 10
18 -M 18 -M
:M ay :M ay
We ay 16 We ay 16
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
ek 24 ek 24
20 -M 20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n2 :J n2
un 0 un 0
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25 l4 25 l4
employment and firm outcomes, defined as the difference between these outcomes in each week relative to their average in the two weeks prior to program
We :J We :J
ul5 ul5
ek
26 - Ju ek
26 - Ju
:J l11 :J l11
We ul1 We ul1
Panel A. UI Claims
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
ek 6 -A ek 6 -A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We :A
ug 8 We :A
ug 8
ek 9-A ek 9-A
31 ug 31 ug
We
:A
ug 1 5 We
:A
ug 1 5
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
ug ug
29 29
launch (weeks 10 and 11). Panel A plots the coefficients β and standard errors of week-by-week regressions of ∆U I c = αsn + β P P P Ec + Γ X c + εc , where
We We
e k1 Coefficient (Predicted PPPE) ek Coefficient
0: 10
: M
We
Ma
r22 -.04 -.02 0 .02 .04 We ar2 -.05 0 .05
e k1 -M ek 2-M
1: ar2 11 ar2
Ma 8 : M 8
We r29 We ar2
ek -A ek 9-A
12 pr4 12 pr4
We : Ap We : Ap
ek r5-
Ap ek r5-
Ap PPP Launch
13 13
:A r11 :A r11
We pr1 We pr1
ek 2-A ek 2-A
14 pr1 14 pr1
:A 8 :A 8
We pr1 We pr1
ek 9-A ek 9-A PPP Funds Exhausted
15 pr2 15 pr2
We
:A
pr2 5 We
:A
pr2 5
ek 6 -M ek 6 -M
16 ay 16 ay
We :M 2 We :M 2 PPP 2nd Round Begins
ek ay ek ay
17 3-M 17 3-M
:M ay :M ay
We ay 9 We ay 9
ek 10 ek 10
18 -M 18 -M
:M ay :M ay
We ay 1 6 We ay 1 6
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
∆U I c is the difference between the UI claims of county c in each week of the sample and the average UI claims for that county during the two weeks prior to
We ay 23 We ay 23
ek 24 ek 24
20 -M 20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n2 :J n2
un 0 un 0
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek
26 - Ju ek
26 - Ju
:J l11 :J l11
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
Panel B. Small Bus. Rev.
We ul2 We ul2
ek 6 -A ek 6 -A
29 ug 29 ug
program launch, P P P Ec is the average exposure of the county to bank PPPE, αs are state fixed effects and X c are additional control variables. Panel B plots
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We :A
ug 8 We :A
ug 8
ek 9-A ek 9-A
31 ug 31 ug
We
:A
ug 1 5 We
:A
ug 1 5
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
ug ug
29 29
We We
e k1 Coefficient (Predicted PPPE) ek Coefficient
0: 10
: M
Ma -.01 0 .01 .02 .03 -.02 0 .02 .04
estimates from similar week-by-week regressions that use the change in weekly small business revenue relative to January as the dependent variable. Panel
We r22 We ar2
e k1 -M ek 2-M
1: ar2 11 ar2
Ma 8 : M 8
We r29 We ar2
ek -A ek 9-A
12 pr4 12 pr4
We : Ap W : A
ek r5-
Ap PPP Launch
PPP eek
Launch pr5-A
Figure 10: PPPE and Alternative Outcome Variables (Local Projections)
13 13 pr1
:A r11 :A 1
We pr1 We pr1
ek 2-A ek 2-A
14 pr1 14 pr1
:A 8 :A 8
We pr1 We pr1
ek 9-A PPP Funds
PPP Funds
ExhaustedExhausted
k1 9 - Ap
15 pr2 5:
We
:A
pr2 5 We Ap
r25
ek 6 -M ek
r26
-M
16 ay 16 ay
We :M 2 PPP 2ndPPP
Round
2nd
Begins
WeRound : Ma Begins 2
ek ay
3-M e k1 y3
17
:M 7: -M
ay ay
We ay 9 We Ma
y1 9
ek 10 ek
18 -M 18 0-M
:M ay :M ay
We ay 1 6 We ay 1 6
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
ek 24 ek 24
20 -M 20 -M
:M ay :M ay
30 30
ay ay
C plots estimates from similar week-by-week regressions that use the change in weekly employment outcomes relative to January as the dependent variable.
We We
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n2 :J n2
un 0 un 0
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek
26 - Ju ek
26 - Ju
:J l11 :J l11
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
ul2 ul2
Panel C. OI Employment
We We
ek 6 -A ek 6 -A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We :A
ug 8 We :A
ug 8
Data are from Call Reports, SBA, Womply, and Opportunity Insights.
ek 9-A ek 9-A
31 ug 31 ug
:A 15 :A 15
We ug We ug
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
ug ug
29 29
59
We
ek1
Share of Businesses Shutdown
:
We Jan
ek 19 0 .1 .2 .3 .4
We 2: J - Jan
ek an 25
2
We 3: F 6-Fe
e eb b1
We k 4: F 02-F
ek eb eb
We 5 :F 9 -Fe 8
ek eb
6: 16 b15
-Fe
We Feb
e 23 b22
-Fe
We k 7:
e Ma b2
9
We k 8: r1
e Ma -Ma
r7
We k 9: r8
ek Ma -Ma
1 r1 r14
We 0: M 5-Ma
ek ar2 r21
1 2
We 1: M -Ma
e a r2
We k 12 r29-A 8
ek :A pr4
p
We 13: A r5-A
ek pr1 p r11
We 14: A 2-Ap
ek pr1 r18
We 5: A 1 9 -A
Panel A. Business Shutdowns
pr2
We ek 16 pr26 5
ek : M -Ma
We 17: a y2
ek Ma y3-M
We 18: M y10-M ay9
ek
Received in 03/29 to 04/11
19 ay 17 ay16
We : Ma -M
ek y 2 ay2
2 4 3
We 0: M -Ma
e ay y
We k 21: 31 30
Figure 11 investigates business shutdowns, changes in the ratio of hours worked, and changes in the number of employees for firms in the Homebase sample
ek Ju -Jun
We 2 2: n 7 6
ek Ju -Jun
23 n 14 1 3
We : Ju -Jun
ek n2
24 1-J 20
We :J u
e un n27
28
We k 25
:J -J
ek ul5 ul4
We 26: -Ju
ek Ju
l1 l11
We 27: J 2-Ju
ek ul1 l18
We 28: J 9 -Ju
e u l25
We k 29: l26-A
ek Au ug
We 30 g 1
ek : A 2-Au
u g
Early vs. Late Recipients
We 31: A g9-A 8
ek u u
32 g16 g15
:A -A
ug ug
23 2
-A 2
ug
Received after 05/03
29
We
ek1
Ratio of Hours Worked Relative to January
:
We Jan
ek 19 .4 .6 .8 1
We 2: J - Jan
ek an 25
2
We 3: F 6-Fe
ek eb
We 4: 02 b1
ek Fe -Feb
b 8
We 5: F 9-Fe
ek e b1 b
6: 6-F 15
We Feb e
e 23 b22
-F
We k 7:
ek Ma eb29
We 8: r1
e Ma -Ma
r7
We k 9: r8
ek Ma -Ma
1 r1 r14
We 0: M 5-Ma
ek ar2 r21
1 2
We 1: M -Ma
e a r2
We k 12 r29-A 8
ek :A pr4
p
We 13: A r5-A
ek pr1 pr11
that are name-matched to the PPP data set from SBA. We compare firms that received PPP approval in week 12 or earlier to those that received PPP approval
We 14: A 2-Ap
ek pr1 r18
Panel B. Change in Hours Worked
We 15: A 9-Ap
r25
We ek 16 pr26
ek : M -Ma
We 17: a y2
ek Ma y3-M
We 18: M y10-M ay9
ek
1 a y1 a y1
Received in 03/29 to 04/11
We 9: Ma 7-M 6
ek y 2 ay2
2 4 3
We 0: M -Ma
e ay y
We k 21: 31 30
ek Ju -Jun
We 22: J n7-J 6
ek u un
13
23 n14
We : Ju -Jun
ek n2
2 1-J 20
u
We 4: J
e un n27
28
We k 25
:J -J
ek ul5 ul4
We 26: -Ju
ek Ju
l1 l11
We 27: J 2-Ju
ek ul1 l18
We 28: J 9-Ju
e u l25
We k 29: l26-A
e A ug
We k 30: ug2-A 1
ek Au ug
We 31: A g9-A 8
ek u u
32 g16 g15
:A -A
ug u
23 g22
-A
Early vs. Late Recipients
ug
Received after 05/03
29
We
ek1
Ratio of Employees Relative to January
:
We Jan
ek 19 .4 .6 .8 1
We 2: J - Jan
ek an 25
2
We 3: F 6-Fe
e eb b1
We k 4: F 02-F
ek eb eb
5 :F 9 -F 8
We
ek eb e
6: 16 b15
-Fe
in week 16 or later. Outcome variables are defined as the difference between measured outcomes in each week and their average in the two weeks preceding
We Feb
e 23 b22
-Fe
We k 7:
e Ma b2
9
We k 8: r1
e Ma -Ma
r7
We k 9: r8
ek Ma -Ma
1 r1 r14
Figure 11: PPPE and Post-PPP Outcomes (Matched Sample Analysis)
We 0: M 5-Ma
ek ar2 r21
1 2
We 1: M -Ma
e a r2
We k 12 r29-A 8
ek :A pr4
p
We 13: A r5-A
ek pr1 p r11
We 14: A 2-Ap
ek pr1 r18
1 9 -A
Panel C. Change in Nbr. Employees
We 5: A pr2
We ek 16 pr26 5
ek : M -Ma
We 17: a y2
ek Ma y3-M
We 18: M y10-M ay9
ek
Received in 03/29 to 04/11
19 ay 17 ay16
We : Ma -M
ek y 2 ay2
2 4 3
We 0: M -Ma
e ay y
We k 21: 31 30
ek Ju -Jun
We 2 2: n 7 6
ek Ju -Jun
23 n 14 1 3
We : Ju -Jun
ek n2
24 1-J 20
We :J u
e un n27
28
We k 25
:J -J
ek ul5 ul4
We 26: -Ju
ek Ju
l1 l11
We 27: J 2-Ju
ek ul1 l18
We 2 8: 9 -Ju
e Ju l25
We k 29: l26-A
ek Au ug
We 30 g 1
ek : A 2-Au
u g
We 31: A g9-A 8
ek u u
32 g16 g15
:A -A
ug ug
23 2
-A 2
ug
Received after 05/03
29
Early vs. Late Recipients
program launch. The right-hand-side variable is the week of PPP receipt. Data is from Call Reports, SBA, and Homebase.
Table 1: PPP Performance and PPPE for the Largest 20 Banks
Table 1 reports individual bank statistics and the PPPE index for the 20 largest financial institutions in the United States. Total Assets is computed using
information from fourth quarter 2019 Call Reports. Share of PPP Volume is the total amount disbursed by each financial institution relative to the total
amount disbursed under either the first round or both rounds of the program. Share of SBL Market is the share of the total outstanding amount of small
business loans held by each financial institution relative to the total outstanding amount of small business loans as of the fourth quarter of 2019. PPPE
(Vol.) is the volume-based bank PPPE index. Total assets are in millions of USD. Share of PPP Loans is the total number of loans processed by each financial
institution relative to the total number of loans processed in the either the first round or both rounds of the program. Share of SBL Loans is the share of the
total number of outstanding small business loans held by each financial institution relative to the total outstanding number of small business loans as of the
fourth quarter of 2019. PPPE (Nbr.) is the number-based bank PPPE index.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
Financial Institution Name Total Share of Share of Share of PPPE R1 PPPE Share of Share of Share of PPPE R1 PPPE
Assets PPP PPP SBL (Vol.) R1&2 PPP PPP SBL (Nbr.) R1&2
Volume Volume Market (Vol.) Loans Loans Loans (Nbr.)
R1 R1&2 R1 R1&2
JPMORGAN CHASE BANK, NATIONAL ASSOCIATION 2337707 3.74% 5.84% 6.54% -0.136 -0.028 1.71% 6.16% 10.4% -0.360 -0.130
60 BANK OF AMERICA, NATIONAL ASSOCIATION 1866841 1.13% 5.10% 9.51% -0.393 -0.151 .595% 7.79% 11.8% -0.452 -0.103
WELLS FARGO BANK, NATIONAL ASSOCIATION 1736928 .038% 2.08% 6.50% -0.494 -0.257 .066% 4.14% 4.30% -0.485 -0.009
CITIBANK, N.A. 1453998 .394% .702% 2.12% -0.343 -0.251 .456% .693% 9.72% -0.455 -0.433
U.S. BANK NATIONAL ASSOCIATION 486004 .723% 1.48% 3.32% -0.321 -0.192 1.15% 2.25% 5.64% -0.331 -0.215
TRUIST BANK 461256 2.97% 2.62% 2.01% 0.096 0.066 2.02% 1.77% 1.73% 0.040 0.006
CAPITAL ONE, NATIONAL ASSOCIATION 453626 .022% .243% 2.82% -0.492 -0.421 .012% .335% 10.3% -0.499 -0.469
PNC BANK, NATIONAL ASSOCIATION 397703 2.75% 2.60% 1.12% 0.210 0.199 1.35% 1.70% 1.37% -0.004 0.054
BANK OF NEW YORK MELLON, THE 342225 0% 0% .002% -0.500 -0.500 0% 0% .000% -0.500 -0.500
TD BANK, N.A. 338272 1.83% 1.69% .687% 0.228 0.212 1.70% 1.88% .569% 0.249 0.268
STATE STREET BANK AND TRUST COMPANY 242148 0% .000% 0% 0.000 0.500 0% .000% 4.49% -0.500 0.413
CHARLES SCHWAB BANK 236995 0% 0% .074% -0.500 -0.500 0% 0% .003% -0.500 -0.500
MORGAN STANLEY BANK, N.A. 229681 0% 0% .144% -0.500 -0.500 0% 0% .008% -0.500 -0.500
GOLDMAN SACHS BANK USA 228836 0% 0% .003% -0.500 -0.500 0% 0% .000% -0.500 -0.500
HSBC BANK USA, NATIONAL ASSOCIATION 172888 .129% .240% .084% 0.105 0.240 .067% .093% .014% 0.328 0.369
FIFTH THIRD BANK, NATIONAL ASSOCIATION 167845 1.01% 1.06% .458% 0.188 0.200 .625% .861% .192% 0.265 0.318
ALLY BANK 167492 .213% .145% 2.11% -0.408 -0.436 .055% .021% 1.38% -0.461 -0.485
CITIZENS BANK, NATIONAL ASSOCIATION 165742 1.14% .992% .807% 0.086 0.051 1.60% 1.15% .527% 0.253 0.187
KEYBANK NATIONAL ASSOCIATION 143390 2.19% 1.59% .729% 0.251 0.186 2.14% .932% .274% 0.387 0.273
BMO HARRIS BANK NATIONAL ASSOCIATION 137588 1.20% .919% 1.95% -0.120 -0.181 .683% .489% .541% 0.058 -0.025
ALL OTHER BANKS 6889908 80.4% 72.6% 58.9% -0.042 -0.048 85.7% 69.6% 40.9% 0.215 0.212
Table 2: Bank PPPE and Capacity Constraints
Table 2 examines the relation between bank performance in deploying PPP during the first round of the pro-
gram and bank characteristics. The dependent variable, Bank PPPE is the number-based bank PPPE index.
W ages
(Dat aE x penses+W a g es) is a measure of labor intensity at the bank that we compute as the ratio between bank wages
(RIAD4135) and the sum of wages and data processing expenses (RIADC017). I(SBA Lender=1) is an indicator
variable that takes the value of one if the bank originated at least one SBA 7(a) guaranteed loan between 2017
and 2019. SBA Loans/SBL Loans is the ratio between the number of SBA government-guaranteed loans that a
bank originated between 2017 and 2019 and the number of all small business loans (SBA and non-SBA) that the
bank held on its balance sheet at the end of 2019. Active Enforcement Action is an indicator variable that takes
the value of one if the bank had an active enforcement action when the PPP was launched. I(Wells Fargo=1)
is an indicator variable that takes the value of one for Wells Fargo Bank. Columns (5) – (8) include controls
for size deciles to ensure that the results are not driven by differences in size. Standard errors are presented in
parentheses, and are clustered at the state level. ***, **, and *, represent statistical significance at 1%, 5%, and
10% levels, respectively.
(1) (2) (3) (4) (5) (6) (7) (8)
LHS is Bank PPPE
W ag es
(Dat aE x penses+W ag es)
0.330∗∗∗ 0.251∗∗∗ 0.295∗∗∗ 0.283∗∗∗
(0.072) (0.060) (0.060) (0.055)
I(SBA Lender=1) 0.172∗∗∗ 0.170∗∗∗ 0.149∗∗∗ 0.150∗∗∗
(0.012) (0.011) (0.016) (0.016)
SBA Loans/SBL Loans 0.158∗∗∗ 0.173∗∗∗ 0.162∗∗∗ 0.176∗∗∗
(0.036) (0.036) (0.038) (0.037)
Active Enforcement Action -0.283∗∗∗ -0.280∗∗∗ -0.254∗∗∗ -0.256∗∗∗
(0.054) (0.052) (0.051) (0.050)
I(Wells Fargo=1) -0.418∗∗∗ -0.554∗∗∗ -0.470∗∗∗ -0.522∗∗∗
(0.053) (0.053) (0.056) (0.057)
Observations 5204 5212 5212 5204 5204 5212 5212 5204
Adjusted R2 0.005 0.076 0.009 0.088 0.058 0.103 0.062 0.114
Other Controls Yes Yes Yes Yes Yes Yes Yes Yes
Size Deciles No No No No Yes Yes Yes Yes
61
Table 3: Correlates of PPPE Exposure
Table 3 presents bivariate regressions of PPPE and Predicted PPPE on ZIP-level observables. Both PPPE, Predicted
PPPE, and observables are residualized with respect to state dummies. Variables have been normalized, so the
coefficients can be interpreted as a one-standard deviation change in x produces a β-standard deviation change
in PPPE exposure, where β is the reported coefficient. ***, **, and *, represent statistical significance at 1%, 5%,
and 10% levels, respectively.
LHS is Resid PPPE as of R1 LHS is Pred Resid PPPE as of R1
Coefficient R2 N Coefficient R2 N
Exposure Correlates:
Share of Top 4 Banks -0.703*** 0.3619 35882 -0.603*** 0.2584 35882
(0.006) (0.013)
Number of Branches per Capita -0.020*** 0.0006 29545 -0.010*** 0.0002 29545
(0.002) (0.001)
Share of Small Banks Deposits 0.400*** 0.1592 35830 0.193*** 0.0364 35830
(0.006) (0.006)
Other Correlates:
Log(Population) -0.203*** 0.0476 29545 -0.065*** 0.0046 29545
(0.005) (0.006)
Log(Population Density) -0.336*** 0.1044 29545 -0.116*** 0.0118 29545
(0.006) (0.006)
Social Distancing 0.225*** 0.0412 35549 0.099*** 0.0078 35549
(0.007) (0.008)
Covid Cases per Capita -0.262*** 0.0797 35870 -0.120*** 0.0161 35870
(0.007) (0.003)
Deaths per Capita -0.160*** 0.0344 35870 -0.059*** 0.0045 35870
(0.006) (0.004)
Unemployment Filing Ratios 0.012 0.0001 24576 0.056*** 0.0019 24576
(0.008) (0.008)
Employment Opportunity Insights -0.075*** 0.0071 19525 -0.012* 0.0002 19525
(0.007) (0.007)
Revenue Change of Small Business 0.159*** 0.0333 29715 0.075*** 0.0073 29715
(0.006) (0.006)
62
Table 4: ZIP PPPE in Round 1 and PPP Reallocation across Funding Sources
Table 4 shows the correlation between PPPE and the fraction of establishments receiving PPP loans from different
sources in the first and second rounds of the program. The left-hand-side variable in column (1) is the fraction
of establishments within a ZIP and 2-digit NAICS industry that received PPP in the first round in Panel A and in
both rounds in Panel B. Left-hand-side variables in other columns represent a decomposition of the dependent
variable in column (1) into the fraction of establishments within a ZIP and 2-digit NAICS industry that received
PPP from local banks, non-local banks, credit unions, FinTech companies, and other nonbanks. ZIP PPPE (Round
1) is the weighted average of bank PPPE during the first round at the ZIP level. The weights are defined by
the share of the number of branches of each bank within 10 miles of the center of the respective ZIP. ZIP PPPE is
standardized to permit coefficients to be interpreted as the effect of a one-standard-deviation increase in ZIP PPPE
and observations are weighted by the number of establishment counts in each zip-industry pair. All regressions
include state-by-NAICS fixed effects. Standard errors are presented in parentheses, and are clustered at the state
level. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
Panel A: Allocation in Round 1
(1) (2) (3) (4) (5) (6)
PPP Loans Relative to All Establishments by Lender Source
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Zip PPPE (Round #1) 5.458∗∗∗ 5.401∗∗∗ 0.162 0.152 -0.204∗∗∗ -0.007
(0.736) (0.581) (0.117) (0.109) (0.021) (0.020)
Observations 250078 250678 251344 251468 251488 251435
Adjusted R2 0.408 0.390 0.126 0.156 0.294 0.045
State×Industry FE Yes Yes Yes Yes Yes Yes
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Predicted PPPE 2.837∗∗∗ 2.961∗∗∗ -0.085 0.054 -0.073∗∗∗ 0.003
(0.981) (0.866) (0.149) (0.093) (0.024) (0.017)
Observations 250078 250678 251344 251468 251488 251435
Adjusted R2 0.380 0.357 0.126 0.155 0.290 0.045
State×Industry FE Yes Yes Yes Yes Yes Yes
Panel B: Allocation in Round 1 and 2
(1) (2) (3) (4) (5) (6)
PPP Loans Relative to All Establishments by Lender Source
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Zip PPPE (Round #1) -1.580∗∗∗ 1.224∗∗∗ -1.942∗∗∗ 0.310 -1.480∗∗∗ -0.139∗
(0.345) (0.350) (0.271) (0.336) (0.191) (0.080)
Observations 234128 244928 250263 251342 251377 251387
Adjusted R2 0.359 0.324 0.199 0.222 0.231 0.088
State×Industry FE Yes Yes Yes Yes Yes Yes
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Predicted PPPE -0.660 0.932∗ -1.095∗∗∗ 0.031 -0.751∗∗∗ -0.040
(0.453) (0.516) (0.391) (0.287) (0.262) (0.041)
Observations 234128 244928 250263 251342 251377 251387
Adjusted R2 0.358 0.323 0.193 0.221 0.213 0.087
State×Industry FE Yes Yes Yes Yes Yes Yes
63
Table 5: PPP Exposure and Homebase Employment Outcomes
Table 5 reports the results of OLS regressions examining the relation between exposure to PPPE during the first round and the difference
between a firm’s average employment outcomes in the two weeks prior to the launch of PPP and the firm’s outcomes in each of the following
weeks. The left-hand-side variable in Panel A, ∆ Bus. Shutdown, is the difference between the firm’s shutdown status in a week and its
average shutdown status in weeks 10 and 11, where shutdown status takes a value of one if the business reported zero hours worked over
the entire week. The left-hand-side variable in Panel B, ∆ Hours Worked, is the difference in the ratio of hours worked in each establishment
in a week and the average ratio of hours worked in that establishment in weeks 10 and 11. The ratio of hours worked in each establishment
is measured as the hours worked in that week relative to the hours worked in that same establishment during the last two weeks of January.
∆ Nbr. Employees, is the difference in the ratio of the number of employees in each establishment in a week and the average ratio of number
of employees in that establishment in weeks 10 and 11. The ratio of number of employees in each establishment is measured as the number
of distinct employees that worked in the establishment in that week relative to the number of distinct employees working in that same
establishment during the last two weeks of January. Zip PPPE (Round 1) is the weighted average of bank PPPE during the first round at the
ZIP level. The weights are defined by the share of branches of each bank in the zip code or within 10 miles of the center of the respective ZIP.
Predicted PPPE (Round 1) is the weighted average of predicted bank PPPE during the first round at the ZIP level. The predicted values of bank
PPPE are obtained from estimating the empirical specification of column (8) of table 2. The weights are defined by the share of branches of
each bank in the zip code or within 10 miles of the center of the respective ZIP. I(Month=‘M’), where M = {Apr il, M a y, June, Jul y, August}
are indicator variables for the weeks that span those respective months. Other control variables include interactions between the median
household income, social distance index, COVID cases per capita and deaths per capita measured as of week 9 interacted with the indicator
variables for April, May, June, July, and August and controls for the average tier 1 capital and core deposit ratios of all banks within the zip
code or within a 10 miles radius of the zip code also interacted with the indicator variables for April, May, June, July, and August. Standard
errors are clustered at the state level. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
Zip PPPE (Round #1) × I(Month=April) 0.002 0.002 0.002 0.003 0.001 0.003
(0.004) (0.003) (0.003) (0.002) (0.003) (0.002)
Zip PPPE (Round #1) × I(Month=May) -0.000 -0.003 0.022∗∗∗ 0.020∗∗∗ 0.019∗∗∗ 0.020∗∗∗
(0.004) (0.004) (0.004) (0.004) (0.004) (0.005)
Zip PPPE (Round #1) × I(Month=June) 0.005 -0.003 0.034∗∗∗ 0.033∗∗∗ 0.030∗∗∗ 0.032∗∗∗
(0.005) (0.005) (0.006) (0.006) (0.006) (0.007)
Zip PPPE (Round #1) × I(Month=July) 0.011∗∗ -0.001 0.029∗∗∗ 0.032∗∗∗ 0.026∗∗∗ 0.033∗∗∗
(0.005) (0.006) (0.008) (0.008) (0.008) (0.009)
Zip PPPE (Round #1) × I(Month=August) 0.011∗ -0.001 0.029∗∗∗ 0.031∗∗∗ 0.024∗∗ 0.031∗∗∗
(0.005) (0.006) (0.008) (0.008) (0.009) (0.009)
Observations 819834 819834 819834 819834 819834 819834
Adjusted R2 0.058 0.602 0.134 0.629 0.110 0.571
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables No Yes No Yes No Yes
Firm Fixed Effects No Yes No Yes No Yes
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
Predicted PPPE × I(Month=April) -0.000 0.000 -0.001 -0.001 -0.000 0.000
(0.003) (0.003) (0.002) (0.002) (0.002) (0.002)
Predicted PPPE × I(Month=May) -0.005 -0.006∗∗ 0.012∗∗∗ 0.009∗∗∗ 0.010∗∗∗ 0.008∗∗
(0.003) (0.003) (0.004) (0.003) (0.004) (0.003)
Predicted PPPE × I(Month=June) -0.003 -0.007∗ 0.022∗∗∗ 0.019∗∗∗ 0.019∗∗∗ 0.017∗∗∗
(0.004) (0.004) (0.005) (0.005) (0.006) (0.006)
Predicted PPPE × I(Month=July) 0.000 -0.005 0.019∗∗∗ 0.017∗∗∗ 0.013∗ 0.014∗∗
(0.003) (0.004) (0.005) (0.004) (0.007) (0.007)
Predicted PPPE × I(Month=August) 0.001 -0.005 0.017∗∗∗ 0.016∗∗∗ 0.011 0.012∗
(0.004) (0.004) (0.005) (0.004) (0.007) (0.007)
Observations 819834 819834 819834 819834 819834 819834
Adjusted R2 0.058 0.602 0.133 0.629 0.110 0.571
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables No Yes No Yes No Yes
Firm Fixed Effects No Yes No Yes No Yes
64
Table 6: PPP Exposure and Local Labor Market and Economic Effects
Table 6 reports the results of OLS regressions examining the relation between exposure to PPPE during the first round and county-level
unemployment filings, small business revenue from Womply, and employment growth from Opportunity Insights. ∆ UI Claims is the difference
between the county unemployment filings during a week and the average unemployment filings in the county in weeks 10 and 11. ∆ Small
Business Revenue is the difference between the county aggregate change in small business revenue relative to January and the average change
in small business revenue in weeks 10 and 11 relative to January. Aggregate change in small business revenue is from Womply. ∆ OI Emp.,
is the difference between county employment growth relative to January in a week and the average county employment growth relative to
January in weeks 10 and 11. The county-level employment data come from Opportunity Insights. County PPPE is the weighted county average
of the bank PPPE at the end of the first round, weighted by the share of the number of branches of each bank in each county. Predicted PPPE is
the weighted county average of predicted bank PPPE at the end of the first round. The predicted values of PPPE are obtained from estimating
the empirical specification of column (8) of Table 2. The weights are defined by the share of the number of branches of each bank in the
county. I(Month=‘M’), where M = {Apr il, M a y, June, Jul y, August} are indicator variables for the weeks that span those respective months.
Other control variables include interactions between the median household income, social distance index, COVID cases per capita and deaths
per capita measured as of week 9 interacted with the indicator variables for April, May, June, July, and August and controls for the average
tier 1 capital and core deposit ratios of all banks within the county also interacted with the indicator variables for April, May, June, July, and
August. Appendix Table B.1 shows summary statistics. Standard errors are clustered at the state level. ***, **, and *, represent statistical
significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County PPPE × I(Month=April) -0.111 -0.114∗∗ 0.013∗∗∗ 0.001 -0.001 -0.000
(0.071) (0.056) (0.004) (0.004) (0.002) (0.003)
County PPPE × I(Month=May) -0.151∗ -0.158∗∗ 0.028∗∗∗ 0.007 0.009∗∗∗ 0.007∗∗∗
(0.086) (0.073) (0.005) (0.007) (0.003) (0.003)
County PPPE × I(Month=June) -0.104 -0.119 0.003 -0.011 0.016∗∗ 0.011∗∗∗
(0.082) (0.074) (0.006) (0.008) (0.006) (0.003)
County PPPE × I(Month=July) -0.073 -0.116 -0.012∗∗ -0.022∗∗ 0.020∗∗ 0.014∗∗∗
(0.099) (0.075) (0.005) (0.009) (0.008) (0.003)
County PPPE × I(Month=August) -0.066 -0.084 -0.014∗∗∗ -0.021∗∗∗ 0.020∗∗ 0.013∗∗∗
(0.106) (0.073) (0.005) (0.007) (0.009) (0.004)
Observations 46092 45533 43930 43863 17112 17112
Adjusted R2 0.745 0.950 0.491 0.735 0.697 0.879
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables No Yes No Yes No Yes
County Fixed Effects No Yes No Yes No Yes
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County Predicted PPPE × I(Month=April) -0.084 -0.059 0.011∗∗∗ 0.002 -0.001 -0.000
(0.084) (0.057) (0.003) (0.003) (0.003) (0.003)
County Predicted PPPE × I(Month=May) -0.124 -0.098 0.025∗∗∗ 0.008 0.007 0.002
(0.100) (0.073) (0.008) (0.007) (0.006) (0.004)
County Predicted PPPE × I(Month=June) -0.103 -0.090 0.013 0.004 0.015 0.006
(0.094) (0.075) (0.009) (0.009) (0.010) (0.004)
County Predicted PPPE × I(Month=July) -0.064 -0.097 -0.002 -0.004 0.018 0.008
(0.125) (0.080) (0.006) (0.006) (0.014) (0.005)
County Predicted PPPE × I(Month=August) -0.016 -0.037 -0.001 -0.002 0.019 0.008
(0.142) (0.080) (0.006) (0.005) (0.015) (0.006)
Observations 46092 45533 43886 43863 17112 17112
Adjusted R2 0.744 0.950 0.489 0.734 0.688 0.878
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables No Yes No Yes No Yes
County Fixed Effects No Yes No Yes No Yes
65
Table 7: Homebase Employment and PPP Loan Timing (Matched Sample)
Table 7 presents the results from the individual matched sample. The left-hand-side variable in Panel A, ∆ Shutdown, is the difference between
each matched firm’s shutdown status in week 16 (May 3rd to May 9th) and its average shutdown status in weeks 10 and 11. Shutdown is an
indicator variable that takes the value of one if the business reported zero hours worked over the entire week. ∆ Hours Worked is the change
between the average number of hours worked at each establishment in the last two weeks prior to the launch of PPP and the number of worked
at each establishment in week 16 (May 3rd to May 9th). ∆ Nbr. Employees is the change between the average number of employees working
for each establishment in the last two weeks prior to the launch of PPP and the number of employees working for each establishment in week
16 (May 3rd to May 9th). Week of PPP Loan is a variable representing the week in which the firm received PPP loan approval. Standard errors
are clustered at the state level. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
Panel A. Business Shutdowns
(1) (2) (3) (4) (5) (6)
∆ Shutdown
OLS IV IV
Week of PPP loan 0.007∗∗∗ 0.007∗∗∗ 0.012 0.014 0.026∗∗ 0.061∗
(0.002) (0.002) (0.009) (0.019) (0.012) (0.032)
Observations 10694 10024 10556 9891 10556 9891
F-Stat 186.060 51.073 28.033 4.982
Industry Fixed Effects Yes No Yes No Yes No
State×Ind Fixed Effects No Yes No Yes No Yes
Instrument - - PPPE Zip Predicted PPPE
Panel B. Ratio Hours Worked
(1) (2) (3) (4) (5) (6)
∆ Hours Worked
OLS IV IV
Week of PPP loan -0.012∗∗∗ -0.011∗∗∗ -0.057∗∗∗ -0.059∗∗∗ -0.046∗∗∗ -0.076
(0.001) (0.001) (0.006) (0.018) (0.013) (0.046)
Observations 10694 10024 10556 9891 10556 9891
F-Stat 186.060 51.073 28.033 4.982
Industry Fixed Effects Yes No Yes No Yes No
State×Ind Fixed Effects No Yes No Yes No Yes
Instrument - - PPPE Zip Predicted PPPE
Panel C. Ratio Nbr. Employees
(1) (2) (3) (4) (5) (6)
∆ Nbr. Employees
OLS IV IV
Week of PPP loan -0.011∗∗∗ -0.010∗∗∗ -0.047∗∗∗ -0.047∗∗∗ -0.040∗∗∗ -0.053
(0.001) (0.001) (0.006) (0.017) (0.011) (0.039)
Observations 10694 10024 10556 9891 10556 9891
F-Stat 186.060 51.073 28.033 4.982
Industry Fixed Effects Yes No Yes No Yes No
State×Ind Fixed Effects No Yes No Yes No Yes
Instrument - - 66 PPPE Zip Predicted PPPE
Table 8: PPP Receipt, Missed Payments, and Cash-on-Hand (Census Pulse Survey)
Table 8 reports the results of OLS and IV regressions examining the relation between the geographic allocation of PPP funds during the
first round and outcomes from the Census Small Business Pulse Survey. Survey outcomes cover the six weeks from April 26th through June
27th. The left-hand-side variable in the top panel is the percentage of firms reporting a missed scheduled loan payment. The left-hand-side
variable in the middle panel is the percentage of firms reporting a missed other scheduled payment such as rent, utilities, and payroll. The
left-hand-side variable in the bottom panel is the fraction of businesses with cash on hand to sustain operations for two months or more. %
PPP Received is the percentage of businesses reporting having received PPP funds in a state-by-industry group. State PPPE is the weighted state
average of bank PPPE at the end of the first round, where the weights are given by the share of the number of branches of each bank in each
state. State Predicted PPPE is the weighted state average of predicted bank PPPE at the end of the first round. The predicted values of bank
PPPE are obtained from the empirical specification of column (8) of Table 2. The weights are defined by the share of branches of the number
of branches of each bank in the state. Regressions include controls for: Pre-PPP Decline Hours Worked, which equals the average decline in
hours worked in each state between January and the last week of March; Pre-PPP State Covid-19 Cases (per capita) and Pre-PPP State Covid-19
Deaths (per capita) at the state level; and Pre-PPP State Social Distancing Index, which is the change in average distance travelled in the state
until the end of March using individuals’ GPS signals. All specifications include industry×week fixed effects. Standard errors are clustered at
the state level. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5)
OLS IV 1st Stage IV 2nd Stage IV 1st Stage IV 2nd Stage
LHS Variable % Miss Loan Pmt % PPP Rec. % Miss Loan Pmt % PPP Rec. % Miss Loan Pmt
% PPP Received -0.013 -0.166∗∗∗ -0.184∗∗∗
(0.011) (0.035) (0.039)
State PPPE 31.238∗∗∗
(2.910)
State Predicted PPPE 62.610∗∗∗
(7.630)
Observations 3659 3659 3659 3659 3659
Adjusted R2 0.518 0.614 -0.119 0.601 -0.147
FStat 115.265 67.343
LHS Variable % Miss Schd Pmt % PPP Rec. % Miss Schd Pmt % PPP Rec. % Miss Schd Pmt
% PPP Received -0.082∗∗∗ -0.492∗∗∗ -0.492∗∗∗
(0.018) (0.066) (0.063)
State PPPE 31.014∗∗∗
(2.880)
State Predicted PPPE 62.154∗∗∗
(7.447)
Observations 3612 3612 3612 3612 3612
Adjusted R2 0.646 0.619 -0.128 0.606 -0.128
FStat 115.934 69.656
LHS Variable % Cash 3 mths % PPP Rec. % Cash 3 mths % PPP Rec. % Cash 3 mths
∗∗
% PPP Received 0.009 0.380 0.316∗∗
(0.030) (0.143) (0.149)
State PPPE 26.992∗∗∗
(2.961)
State Predicted PPPE 58.403∗∗∗
(6.815)
Observations 1445 1445 1445 1445 1445
Adjusted R2 0.603 0.774 -0.270 0.767 -0.200
FStat 83.103 73.435
Controls Yes Yes Yes Yes Yes
Industry×Week Fixed Effects Yes Yes Yes Yes Yes
67
A Data Appendix
We draw upon microdata made available through the Small Business Administration (SBA)
and the Department of Treasury containing all PPP loans, allowing us to observe all loans
approved under the program.45 For all loans, the data include loan amount, lender name,
the borrower’s self-reported industry and corporate form, workers covered by the loan, and
some demographic data on firm owners, including borrower name. Our targeting analysis and
bank exposure research design use data for all loans aggregated to either the regional or local
geography level.
We merge this data set with the Reports of Condition and Income (Call Reports) filed by
all active commercial banks as of 2020:Q1. Specifically, we use a bigram string comparator
for the lender name to match the lender names in the PPP data set to commercial and savings
banks in the Call Reports data set. The main challenge in this process is that many lender
names are matched to multiple distinct banks with the same legal name. For instance, there
are fifteen distinct banks whose legal name is “Community State Bank” filing a call report in
the first quarter of 2020. We address this issue by assigning each loan made by these distinct
banks with similar legal names to the similarly-named bank with the branch that is closest to
the zip code where the loan was made.46 We are able to match 4,370 bank participants in
the PPP program to the Call Reports data set. We did not match 795 commercial and savings
banks that filed a Call Report in the first quarter of 2020. We assume that these banks did not
participate in the PPP program and made no PPP loans. Overall, lenders in the PPP sample
that we matched to the Call Report account for 90.5% of all loans disbursed under the PPP.
We classified 926 PPP program participants as credit unions and 45 participants as agricul-
tural credit associations. We also classified the remaining 123 participants as non-bank PPP
lenders. This group is very heterogenous and comprises small community development funds
(e.g. Montana Community Development Corporation), as well as finance companies and Fin-
tech lenders. After careful investigation of companies websites, we classified thirteen non-bank
lenders as Fintech lenders. Interestingly, Fintech lenders account for 4.2% of the total number
of loans in the program and a single Fintech lender, Kabbage Inc., accounts for more than half
of the loans made by Fintech lenders.
We obtain financial characteristics of all banks from the Call Reports, which provide detailed
45
An earlier version of this paper used data from a Freedom of Information Act request on the number of
approved PPP loans and approved PPP amounts during the first round of the program.
46
Most of these banks with similar legal names are small and operate in different states. Given the proximity
between lenders and PPP borrowers across the entire sample, we are confident that our allocation process assigns
most loans to their correct lender.
68
data on the size, capital structure, and asset composition of each commercial and savings bank
operating in the United States. Importantly, we obtain information on the number and amount
of small business loans outstanding of each commercial and savings bank from the “Loans to
Small Business and Small Farms Schedule” of the Call Reports. Using this information, we
benchmark the participation of all commercial and savings banks in the PPP program relative
to their share of the small business lending market prior to the program.
As noted in the main text, we use the matched-PPP-Call-Reports data and Summary of De-
posits data containing the location of all branches and respective deposit amounts for all de-
pository institutions operating in the United States as of June 30th, 2019. A significant number
of depository institutions merged in the second half of 2019, which means that some branches
are assigned to commercial and savings banks that no longer exist as stand-alone institutions.
Notably, SunTrust Banks, Inc. merged with Branch Banking and Trust Company (BB&T) to
create the sixth largest financial institution in the United States. We use the bank mergers file
from the National Information Center to adjust the branch network of merged institutions and
account for these mergers. We use data from the County Business Patterns dataset to approxi-
mate the amount of PPP lending per establishment and the fraction of establishments receiving
PPP loans in the region. It is important to note that the County Business Patterns data include
establishments for all firms, including those too large to qualify for the PPP. We use these data
to examine how the use of the banking system to deploy the PPP funds affected their distri-
bution. The maintained assumption is that the unobservable share of establishments that are
not eligible to receive PPP funds is not systematically associated with the exposure to the PPP
performance of local banks in the region.
To evaluate whether PPP amounts were allocated to areas that were hardest-hit by the
COVID-19 crisis and whether the program improved economic employment and other eco-
nomic outcomes following its passage, we use data from multiple available sources on the
employment, social distancing, and health impact of the crisis. We obtained detailed data on
hours worked among employees of firms that use Homebase to manage their scheduling and
time clock. Homebase processes exact hours worked by the employees of a large number of
businesses in the United States. We use information obtained from Homebase to track em-
ployment indicators at a weekly frequency at the establishment level. The Homebase data set
disproportionately covers small firms in food and beverage service and retail; therefore, it is
not representative of aggregate employment. At the same time, the Homebase data are quite
useful for evaluating the employment impacts of the PPP specifically, since many hard-hit firms
are in the industries Homebase covers and much of the early employment losses came from
69
these firms. To broaden our targeting analysis, we complement the Homebase data set with
official weekly state unemployment insurance filings from the Department of Labor.
We use the Homebase data in our bank exposure and matched sample analysis to measure
the impact of PPP funding on employment and business shutdowns. To broaden this analy-
sis, we supplement the Homebase data with three additional data sources. First, we obtain
county-by-week initial unemployment insurance claims from state web sites or by contacting
state employment offices for data. We use initial unemployment insurance claims as a mea-
sure of flows into unemployment. Second, we supplement the Homebase data with data from
Womply, a company that aggregates data from credit card processors. The Womply data in-
cludes aggregate card spending at small businesses at the county-industry level, defined by the
location where a transaction occurred. We aggregate the Womply dataset at the county level
to harmonize the level of aggregation we use across different analyses. We find similar results
when we use the Womply dataset at the county-industry level. Small businesses are defined
as businesses with revenues below SBA thresholds. We complement these data sources with
additional county-level employment data from Opportunity Insights, which are described in
detail in Chetty, Friedman, Hendren and Stepner (2020).47 The employment rates are based
on employment data that Opportunity Insights obtained from Paychex, Earnin, Intuit, and Kro-
nos. The data are at the county/week level and span the period from January 2020 to the end
of August.
We obtain counts of COVID-19 cases by county and state from the Center for Disease Con-
trol and use data on the effectiveness of social distancing from Unacast. Unacast provides a
social distancing scoreboard that describes daily changes in average physical mobility. Unacast
measures the change in average distance travelled using individuals’ GPS signals. The data are
available on a daily basis at the county level. We obtain information on the effective dates of
statewide shelter-in-place orders from the New York Times.48
To understand the mechanisms underlying our results, we draw on data from the Census
Bureau’s Small Business Pulse Survey (SBPS), launched within seven weeks of the national
emergency declaration in March (Buffington, Dennis, Dinlersoz, Foster and Klimek, 2020). To
obtain real-time information tailored towards small businesses, the SBPS was run weekly from
April 26th to June 27th with businesses contacted via email based on the Census Bureau’s
Business Register, which is populated using responses to the Economic Census across the 50
47
We also refer readers to Chetty, Friedman, Hendren and Stepner (2020) who provide comparisons between
HomeBase and aggregate employment, showing that it provides an overall good glimpse of employment dynamics.
48
See https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.
html.
70
states (and D.C. and Puerto Rico).49 Furthermore, the SBPS focuses on businesses with receipts
that are greater than or equal to $1,000 but retain 500 employees or fewer. This sampling frame
closely fits the target population for the PPP. Each week, the sample weights are adjusted to
maintain representativeness.
Our goal in assembling these diverse data is to conduct a comprehensive assessment of
how business liquidity support affects firm behavior. We observe both intensive and extensive
margin employment and operating responses by targeted firms and in their local labor markets.
We link this behavior to data on the performance and geographic footprint of banks, the agents
used to transmit funds to eligible firms as quickly as possible. With the Census data, we draw
upon responses to questions about small business liquidity, loans, defaults, and applications for
various forms of private and public government assistance, including specific questions about
the PPP and EIDL programs.
49
We focus on the first phase of the survey that spans the period from April 26th to June 27th because the
second phase of the survey only started mid-August and ended in October after the PPP ended.
71
B PPPE Statistics and Heterogeneous Demand for PPP Loans
This Appendix provides further details about some statistical properties of our PPPE measure
of local exposure to banks that over- or underperformed in the deployment of PPP funds and
further investigates the impact that local differences in demand for PPP funds might have on
the empirical analyses.
Figure B.1 is a histogram of the distribution of bank PPPE at the end of the first round and
during the second round of the program. Both histograms show a wide dispersion of relative
performance. The second round histogram shows a shift in PPPE, with some banks that barely
participated in the first round considerably improving their performance subsequently.
Figure B.2 provides a description of the spatial distribution of PPPE. Panel A plots the spa-
tial distribution of county-level PPPE across the United States and Panels B and C describe the
spatial distribution of ZIP-level PPPE in Chicago and New York City, respectively. Panel A indi-
cates that the Midwest and New England regions were exposed to banks that performed well
in delivering PPP during the first round whereas Western areas were less exposed to banks that
performed well in deploying PPP. Panels B and C show that, even within cities, there could be
relevant differences in local exposure to banks that performed well. The less affluent areas of
the city of Chicago, such as the South Side of the city, were exposed to banks that performed
well, whereas firms in the downtown area were served by banks that did not do so well in
deploying PPP funds. On the other hand, in New York City, less affluent and less densely pop-
ulated areas were served by banks that performed worse in the first round relative to banks
elsewhere in the city. These differences in exposure between Chicago and New York City prob-
ably reflect the heterogenous role of exposure to bank PPP performance.
Figure B.3 is a survival function of the time to receive PPP funds after partitioning firms in
the sample based on their local exposure to ZIP-PPPE. The figure shows that exposure to un-
derperforming PPPE banks is associated with meaningful differences in when borrowers could
access funds. Only 25% of all PPP borrowers located in ZIP codes whose banks underperformed
obtained PPP approval prior to the end of the first round. By contrast, approximately 42% of
all PPP borrowers in ZIP codes whose banks overperformed had access to funds in the first
round.
Figure B.4 shows that we find a similar relationship between our state exposure to PPPE
and the share of firms that report receiving and requesting PPP funds at the end of the first
round when we measure bank PPP performance based on the share of the total amount of PPP
and total amount of small business loans disbursed by each bank, rather than the share of the
number of PPP and small business loans by each bank.
72
Table B.1 shows summary statistics of the PPPE variable and other outcome variables in the
paper.
We believe the differences in PPPE mostly capture initial variation in banks’ abilities to
process PPP loans. Here, we present some supplementary evidence to support this conjecture.
In Appendix Table B.2, we show that the ability of banks to include information about the
program in their websites and to receive online applications is positively associated with bank
performance.
In Table B.3, we repeat the empirical analysis of Table 4 but include more stringent county×industry
fixed effects rather than state×industry fixed effects. By including county×industry fixed ef-
fects, we ensure that our results compare firms that are exposed to banks with weak perfor-
mance during the first round with firms in the same county and in the same industry that were
close to banks that were quick to disburse PPP loans and had a strong performance during the
first round. This empirical strategy further assuages concerns that PPPE captures differences
in demand, since it is comparing firms that are exposed to very similar economic and health
conditions (same county and same industry) but that happened to be located in areas close to
banks that were swift in processing PPP applications.
To further allay concerns about the possibility that PPPE captures differences in demand for
PPP loans across regions, we created a predicted PPPE measure that uses variation in supply-
side restrictions that limit banks’ ability to process PPP loans. Panel A of Figure B.5 suggests
that banks that had greater labor intensity ratios performed relatively better in deploying PPP
loans during the first round. Panel B of Figure B.5 shows that banks with no prior relationship
over the past three years performed worse, on average, than banks that originated at least one
SBA guaranteed loan over the past three years. Moreover, the intensity of the SBA relationship
is also associated with improved bank performance. Banks with a greater fraction of their small
business lending in the form of SBA-guaranteed loans performed relatively better than banks
that only sporadically interacted with the SBA.
The generous terms of the PPP and the broad eligibility criteria likely meant that demand for
the program was very high almost everywhere. Our evidence in Figure 3 suggests that, even
at the end of the first round, approximately 75% of establishments surveyed in the Census
Pulse Survey had applied for PPP funds. Our conjecture is that differences in access to banks
that quickly processed PPP were the main factor explaining differences in timing of receipt
of PPP funds. Because of capacity constraints and difficulties in processing the sudden influx
of PPP applications, banks prioritized their existing relationships. Small businesses that had
relationships with banks that were swift in deploying PPP obtained access to PPP funds quickly.
73
Other businesses that were close to or were in relationships with banks that were unable or
unwilling to process their applications either waited until their bank was able to process their
application or found another bank that was willing and able to process their application.
To provide further evidence supporting this conjecture, we obtained access to a proprietary
dataset from a Chicago community bank. This bank performed well in delivering PPP to their
clients, i.e., it had a high bank PPPE. In this proprietary dataset, we are able to observe the
date in which each PPP loan was fully funded and, more importantly, we also learn about the
location of each small business that received a PPP loan from that bank and whether it is a
new or old client of the bank. In Figure B.6, we show that this bank delivered funds to its
existing clients much quicker than it did to small businesses that opened an account with the
bank after the announcement of the program. Consistent with the idea that new clients were
likely in relationships with other banks that were not able to process their applications quickly,
in Table B.4 we find that the new clients that were served by this community banks came from
areas with lower PPPE, lower predicted PPPE, and greater exposure to Wells Fargo. We also
find that these new clients came from farther away and were smaller than existing clients of
the community bank. This pattern suggests that these new clients likely were in relationships
with other banks that were not processing PPP applications quickly. Alternatively, their banks
did not prioritize theses applications, and these firms had to search for other, more distant
banks to obtain access to PPP funds.
74
Figure B.1: Histogram of Bank Paycheck Protection Program Exposure (PPPE)
Figure B.1 plots the distribution of bank PPPE measured at the end of the first round (April 15th, 2020) and
when the flow of second round funds approximately ends (June 30th, 2020). We compute this measure as:
P P P E b,N br = Share Nbr. PPP−Share Nbr. SBL
Share Nbr. PPP+Share Nbr. SBL × 0.5. We weigh each bank observation by its size measured as total assets
as of the end of 2019. Data are from the SBA and commercial bank Call Reports.
3
Density (Asset-Weighted)
1 2
0
-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5
Bank PPPE
As of 2020-04-15 As of 2020-06-30
75
Figure B.2: Map of Exposure to PPPE
Figure B.2 plots the average exposure of each county to the PPPE. County exposure to PPPE is computed as the average of the PPPE of each
bank with a branch presence in the county. The PPPE of each bank is weighted by the share of the number of branches of the bank in the
county as of June 30th, 2019. Data is from the SBA, Call Reports, and FDIC’s Summary of Deposits.
Panel A: County PPPE
0.320 − 0.500
0.218 − 0.320
0.091 − 0.218
-0.500 − 0.091
No data
Panel B: ZIP PPPE in Chicago Panel C: Zip PPPE in New York City
-0.123 − -0.045
-0.129 − -0.123
-0.142 − -0.129
-0.191 − -0.142
-0.009 − 0.079
-0.034 − -0.009
-0.055 − -0.034
-0.125 − -0.055
76
Figure B.3: Kaplan-Meier Survival Functions
Figure B.3 plots Kaplan-Meier survival functions. The blue line represents the survival function for the group of firms located in zip codes
exposed to banks with low PPPE and the red line plots the survival function for the group of firms located in zip codes exposed to banks with
high PPPE. Data is obtained from the SBA and call reports.
1.00 Low PPPE Zip
High PPPE Zip
Survival Function (Kaplan-Meier)
0.75
0.50
0.25
0.00
20 0 20 20 0 20 20 20 20 0 0 0 0 20 0 0 0 0 0
20 02 20 20 02 20 20 20 20 ,2 t, 2 ,2 t, 2 20 20
2
20
2
20
2
20
2
20
2
,2 t, 2 02 02 02 02
l1 6th th, 16 26 st, st, y1 6th 6th ne th, th, th, th, th,
st, th, 21 th, 1th 1s 1s
l 11 s y1 y6
y1 y2 y2 y3 5th 10 15 20 25 30
Ap ril
Ma Ma , ,
ri Ap Ap Ap Ap Ap Ma Ma Ma Ma Ma Ju ne ne ne ne ne
ri ril ril ril Ju Ju Ju Ju Ju
Date PPP Received
77
Figure B.4: State Exposure to PPPE and PPP per Establishment
Figure B.4 are scatterplots of state exposure to the volume-based PPPE at the end of Round #1 of the PPP and the
percentage of firms reporting receiving PPP funds at the end of the first round (Panel A) and of state exposure to
the volume-based PPPE at the end of Round #1 and the percentage of firms in each state reporting requesting PPP
funds (Panel B). Data comes from the Census Bureau Small Business Pulse Survey, SBA, Call Reports, Summary
of Deposits, and County Business Patterns.
Panel A: State Exposure to Volume-based PPPE and Share Receiving PPP
100
Share of Small Businesses Requesting PPP (Round 1) AR OK ME
ND
NE MO
MN SD
AL MTWIKS
IA MS
ID KY
LA IN NH
UT DE
10 20 30 40 50 60 70 80 90
ILWY TNTX AK PA
OH VT
VA CO WV MA RI MI
SC NCCT MD HI
GA
WA
NY
OR FL NM
AZ NJ
NV
CA DC
0
-.2 -.1 0 .1 .2
State Vol. PPPE (Round 1)
Panel B: State Exposure to Volume-Based PPPE and Share Requesting PPP
100
Share of Small Businesses Requesting PPP (Round 1)
GA AR OK ME
MN ILNJ
LACTTXALMO WI MA PA
MSOHRI
CA FLVATN
NY UT NC D IN MI
OR SD MD KSDE NH
AZ WA IA
SC CO KY HI
NV DC ID
NE NM AK WV
MT VT
WY
10 20 30 40 50 60 70 80 90
0
-.2 -.1 0 .1 .2
State Vol. PPPE (Round 1)
78
Figure B.5: Bank PPPE and Supply Side Factors
Figure B.5 plots the average bank PPPE of each bank decile based on partitioning banks on their ratio of wage expenses to data processing
expenses (Panel A) and on the ratio between the number of SBA loans and small business loans originated in the past three years (Panel B).
Data is obtained from the SBA and call reports
.35
.3
Bank PPPE (Number of Loans)
.05 .1 .15 .2 .25
0
-.05
-.1
1 2 3 4 5 6 7 8 9 10
Labor Intensity Bin (Decile)
.5
.45
Bank PPPE (Number of Loans)
.2 .25 .3 .35 .4
.15
.1
.05
No SBA Loan 1 2 3 4 5 6 7 8 9 10
SBA Relationship Intensity Bin (Decile)
79
Figure B.6: Kaplan-Meier Survival Functions by Bank Relationship
Figure B.6 plots Kaplan-Meier survival functions of the time that took the PPP applicants of a small community bank to receive PPP funds.
The blue line represents the survival function for the group of firms that represented a new relationship for the bank and the red line plots the
survival function for the group of firms that had a banking relationship with the bank that had begun prior to March 2020. Data is obtained
from the SBA, call reports, and from a proprietary dataset obtained from a small community bank.
1.00 New Relationship
Survival Function (Kaplan-Meier)
Existing Relationship
0.75
0.50
0.25
0.00
Ap ril 11 th, 02
20 200
0
02 Ap
ApMa
Ma
ril
ril 16
21
26
y1
th,
st,
th,
st,
20
20
20
20
20
20
20
t, 2 Ma
Ma
Ma,2y1
y1
y2
y6 st,
1th 20
,2
6th 20
1s ,22
t, 200
02
20
0
6th
Ma y2 6th 02 0
1s Ma
Ju
Ju
Ju ne
ne
y3
ne 1s
5th
10
15
,2
t, 2
th,,2 02
0
2002
20
2
0
0
0
ril Ju ne 20 th, 20 20
rilJu
Ju ne
ne
ril 25
30 th,
th,
th, 20
20 20
20
Ap Ap
Ju
Ju
Ju
Ju ly
Ap
ly
ly
ly 5th 20
10
15
20 th,
th,
th,
,220
02
20
20 20
200
AuJu
Ju ly
ly 25
30 th,
th, 20
20 20
20
Au
Au gu
gu
gu st
st 4th 20
,2
9th 20
,220
0
st 14 th, 02
20 200
Date PPP Received
80
Table B.1: Summary Statistics for Bank Exposure Analysis
Table B.1 reports summary statistics for the analyses in Tables 5 and 6. See the notes to those tables for variable
definitions.
Mean 10th Median 90th N
Outcomes
∆ Bus. Shutdown (ZIP/Week) -0.08 -1.00 0.00 0.00 819834
∆ Hours Worked (ZIP/Week) 0.19 -0.13 0.06 0.73 819834
∆ Nbr. Employees 0.22 -0.14 0.06 0.78 819834
∆ UI Claims (Cnty/Week) -1.78 -4.05 -1.55 0.01 46092
∆ Small Bus. Rev. (Cnty) 0.19 -0.05 0.18 0.47 43930
∆ OI Emp. (Cnty/Week) -0.01 -0.11 -0.00 0.09 17112
Exposure
PPPE in Round 1 (ZIP) 0.02 -0.18 0.01 0.24 35645
PPPE in Round 1 (Cnty) 0.05 -1.28 0.20 1.20 2111
Cross Sectional Characteristics
Pre-PPP Soc. Dist. Index (ZIP) -0.01 -1.03 -0.12 1.21 35645
Pre-PPP Cases per cap ×103 (ZIP) 0.05 0.00 0.02 0.09 35645
Pre-PPP Deaths per cap ×106 (ZIP) 0.99 0.00 0.00 1.66 35645
Pre-PPP Soc. Dist. Index (Cnty) -0.30 -0.47 -0.34 -0.09 2081
Pre-PPP Cases per cap ×103 (Cnty) 0.02 0.00 0.00 0.04 2110
Pre-PPP Deaths per cap ×106 (Cnty) 0.37 0.00 0.00 0.00 2110
81
Table B.2: PPP in Bank Websites and PPP Performance
Table B.2 reports results of OLS regressions examining the relation between bank PPPE and the availability of
information about applications to the PPP program in each bank’s internet websites as of April 10th, 2020. The
dependent variables are PPP info, Receiving PPP applications, and Online Application. PPP info is an indicator
variables that takes the value of one if the bank provides any information about the PPP program in its internet
website. Receiving PPP applications is an indicator variables that takes the value of one if the bank states in its
website that is receiving applications to the PPP program as of April 10th. Online Application is an indicator
variables that takes the value of one if the bank receives online applications through its internet website. Bank
PPPE (Round #1) is the bank PPPE measured as of the end of round one of the PPP. Data about the PPP offerings
in bank’s websites was hand-collected from banks’ websites during April 9th and April 10th, 2020. Standard
errors are presented in parentheses, and are clustered at the level of the state. ***, **, and *, represent statistical
significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
PPP info Receiving PPP applications Online Application
Bank PPPE (Round #1) 0.186∗∗∗ 0.170∗∗∗ 0.177∗∗∗ 0.162∗∗∗ 0.047∗∗ 0.036∗
(0.028) (0.029) (0.027) (0.027) (0.020) (0.020)
ln(Assets) 0.121∗∗∗ 0.118∗∗∗ 0.103∗∗∗ 0.101∗∗∗ 0.056∗∗∗ 0.056∗∗∗
(0.007) (0.008) (0.007) (0.007) (0.004) (0.005)
Observations 4857 4856 4857 4856 4857 4856
Adjusted R2 0.167 0.182 0.137 0.147 0.059 0.066
State Fixed Effects No Yes No Yes No Yes
82
Table B.3: ZIP PPPE in Round 1 and PPP Reallocation (County×Year Fixed Effects)
Table B.3 shows the correlation between PPPE and the fraction of establishments receiving PPP loans from differ-
ent sources in the first and second rounds of the program. The left-hand-side variable in column (1) is the fraction
of establishments within a ZIP and 2-digit NAICS industry that received PPP in the first round in Panel A and in
both rounds in Panel B. Left-hand-side variables in other columns represent a decomposition of the dependent
variable in column (1) into the fraction of establishments within a ZIP and 2-digit NAICS industry that received
PPP from local banks, non-local banks, credit unions, FinTech companies, and other nonbanks. ZIP PPPE (Round
1) is the weighted average of bank PPPE during the first round at the ZIP level. The weights are defined by the
share of the number of branches of each bank within 10 miles of the center of the respective ZIP. ZIP PPPE is stan-
dardized to permit coefficients to be interpreted as the effect of a one-standard-deviation increase in ZIP PPPE
and observations are weighted by the number of establishment counts in each zip-industry pair. All regressions
include county-by-NAICS fixed effects. Standard errors are presented in parentheses, and are clustered at the
state level. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
Panel A: Allocation in Round 1
(1) (2) (3) (4) (5) (6)
PPP Loans Relative to All Establishments by Lender Source
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Zip PPPE (Round #1) 2.237∗∗∗ 2.714∗∗∗ -0.245∗∗ -0.090∗∗∗ -0.110∗∗∗ -0.032
(0.229) (0.239) (0.115) (0.026) (0.016) (0.019)
Observations 239411 239411 239411 239411 239411 239411
Adjusted R2 0.477 0.470 0.225 0.315 0.374 0.192
State×Industry FE Yes Yes Yes Yes Yes Yes
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Predicted PPPE 0.775∗∗∗ 0.786∗∗∗ 0.034 -0.008 -0.037∗∗ -0.002
(0.185) (0.250) (0.139) (0.034) (0.014) (0.004)
Observations 239411 239411 239411 239411 239411 239411
Adjusted R2 0.475 0.466 0.224 0.315 0.373 0.192
County×Industry FE Yes Yes Yes Yes Yes Yes
Panel B: Allocation in Round 1 and 2
(1) (2) (3) (4) (5) (6)
PPP Loans Relative to All Establishments by Lender Source
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Zip PPPE (Round #1) 0.321 2.075∗∗∗ -0.744∗∗ -0.264∗∗∗ -0.612∗∗∗ -0.133∗∗∗
(0.418) (0.468) (0.335) (0.064) (0.127) (0.048)
Observations 239411 239411 239411 239411 239411 239411
Adjusted R2 0.466 0.483 0.274 0.413 0.258 0.215
State×Industry FE Yes Yes Yes Yes Yes Yes
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
Predicted PPPE 0.150 0.345 0.104 -0.030 -0.249∗∗ -0.021∗
(0.291) (0.443) (0.323) (0.092) (0.105) (0.012)
Observations 239411 239411 239411 239411 239411 239411
Adjusted R2 0.466 0.482 0.273 0.412 0.257 0.215
County×Industry FE Yes Yes Yes Yes Yes Yes
83
Table B.4: Differences in Characteristics of PPP Applicants With and Without Previous Bank
Relationship
Table B.4 uses a proprietary dataset obtained from a small community bank to examine the characteristics of firms
that received PPP funds from that bank. We examine the differences in characteristics of PPP applicants with and
without a previous bank relationship. We define a new bank relationship as a firm that did not have any account
with the bank prior to the start of the pandemic. Data is from a proprietary dataset of a small community bank,
SBA, and Call Reports.
New Bank Relationship Existing Bank Relationship
Mean Std. Dev. N Mean Std. Dev. N Diff t-stat
Zip PPPE (Round #1) -0.0116 0.0884 239 0.0280 0.0944 584 -0.0396 -5.568
Predicted PPPE (Round #1) 0.237 0.0245 239 0.248 0.0264 585 -0.0115 -5.776
Zip Exposure to Wells Fargo 0.00750 0.0275 239 0.00390 0.0183 584 0.00360 2.210
Distance to Bank 43.07 210.8 239 15.10 93.18 585 27.97 2.641
PPP Loan Amount 59670 116015 239 192471 418803 585 -132801 -4.825
Number of Employees 7.864 14.17 236 19.58 42.51 569 -11.72 -4.140
84
C Wells Fargo Appendix
This appendix repeats the main empirical analyses of the paper using the local exposure to
Wells Fargo as our main variable of interest. Wells Fargo did not accept PPP applications out of
concerns that it might breach the asset cap restriction imposed by the Office of the Comptroller
of the Currency in the aftermath of the 2016 fake accounts scandal. On April 8th, the Federal
Reserve issued a press release exempting PPP loans from counting toward the total assets for-
mula used to determine compliance with the asset cap restriction. Given that this restriction
was externally imposed and Wells Fargo is a very large depository institution in the United
States, we use each local area exposure to Wells Fargo’s branches as an alternative measure of
exposure to supply-side difficulties in accessing the PPP. Table C.1 and Figure C.1 show that lo-
cal areas that were more exposed to Wells Fargo branches received fewer loans during the first
round. Figure C.2 and Table C.2 use local differences in exposure to Wells Fargo to examine the
effect of the program on employment outcomes using Homebase data. Figure C.3 and Table
C.3 use local differences in exposure to Wells Fargo to examine the effect of the program on
employment outcomes using county unemployment filings, Womply, and Opportunity Insights
Tracker data. Overall, the estimated results when we use this alternative empirical strategy are
quantitively and qualitatively similar to those presented in the paper.
85
Figure C.1: Exposure to Wells Fargo and PPP Coverage over Time
Figure C.1 plots binned scatter plots of the average fraction of small business establishments that received a PPP
loan versus exposure to Wells Fargo branches at the Zip level. Eligible establishment counts equal all establish-
ments in a ZIP less an estimate of the share of establishments with more than 500 employees (which are not
eligible for PPP) plus an estimate of the number of proprietorships likely to apply for PPP. Both variables are de-
meaned at the state level to present the within-state relationship. Data come from SBA, Call Reports, Summary
of Deposits, and County Business Patterns.
PPP Loans Relative to
Eligible Establishments (Residualized)
.1 .2 .3 .4 .5 .6 .7 .8
-.4 -.2 0 .2 .4
Wells Fargo Exposure (Residualized)
04/12-04/18 04/26-05/02 05/10-05/16
05/24-05/30 06/07-06/13 06/21-06/27
86
W W W
ee ee ee
k1 Ratio of Employees Relative to January k1 Ratio of Hours Worked Relative to January k 1 Share of Businesses Shutdown
W : Jan W : Jan W : Jan
ee 0 .2 .4 .6 .8 1 ee 0 .2 .4 .6 .8 1 ee 0 .1 .2 .3 .4
k 2 19 - k 2 19 - k 2 19 -
W W W
ee : Ja Jan ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25 k 3 n2 25
W 6 W 6 W 6
ee : Fe -Fe ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F ee 4: F 2-F
k 5 eb e k 5 eb e k 5 eb e
W : F
9- b8
F W : F
9- b8
F W : F
9- b8
F
ee ee ee
k 6 eb1 eb1 k 6 eb1 eb1 k 6 eb1 eb1
: F 6-F 5 : F 6-F 5 : F 6-F 5
W eb eb W eb eb W eb eb
e 2 2 e 2 2 e 2 2
W ek 7 3-F 2 W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e ee :M e
ar b29 ar b29 ar b29
W k 8:
ee 1 W k 8:
ee 1 W k 8:
ee 1
W k Ma -Ma
W k Ma -Ma
W k Ma -Ma
ee 9: M r8-M r7 ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar k1 ar ar
The panels on the left of Figure C.2 shows the ratio of hours worked over time, the percent of businesses shut down,
W 0: M 15-M 14 W 0: M 15-M 14 W 0: M 15-M 14
ee a ee a ee a
k ar
22 r21 k ar
22 r21 k ar
22 r21
W 11: - W 11: - W 11: -
ee M Ma ee M Ma ee M Ma
W k 12 ar29 r28 Initial Lockdown Orders W k 12 ar29 r28 Initial Lockdown Orders W k 12 ar29 r28 Initial Lockdown Orders
ee ee ee
k 1 : Ap -Apr k 1 : Ap -Apr k 1 : Ap -Apr
W r W r W r
ee 3: A 5-A 4
k pr pr PPP Launch ee 3: A 5-A 4
k pr pr PPP Launch ee 3: A 5-A 4
k pr pr PPP Launch
W 14: 12- 11 W 14: 12- 11 W 14: 12- 11
ee Ap Ap ee Ap Ap ee Ap Ap
k r r k r r k r r
W 15: 19-A 18
ee Ap 1st Round Exhausted W 15: 19-A 18
ee Ap 1st Round Exhausted W 15: 19-A 18
ee Ap 1st Round Exhausted
W pr W pr W pr
k1 r2 25 k1 r2 25 k1 r2 25
ee 6: 6 - ee 6: 6 - ee 6: 6 -
W k 17 Ma May W k 17 Ma May W k 17 Ma May
ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins
k 1 Ma 3-M k 1 Ma 3-M k 1 Ma 3-M
W y a W y a W y a
Low Wells Fargo Exposure Low Wells Fargo Exposure Low Wells Fargo Exposure
ee 8: M 10-M y9 ee 8: M 10-M y9 ee 8: M 10-M y9
k1 ay a k1 ay a k1 ay a
W 9: M 17- y16 W 9: M 17- y16 W 9: M 17- y16
ee
k2 a y
Ma ee
k2 a y
Ma ee
k2 a y
Ma
W 0 24 y23
W 0 24 y23
W 0 24 y23
ee : Ma -Ma ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun k 2 : Ju -Jun
W n W n W n
Time Series
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1 k 2 un n1
1 1 1
W 3: J 4-Ju 3 W 3: J 4-Ju 3 W 3: J 4-Ju 3
ee un ee un ee un
k 21 n20 k 21 n20 k 21 n20
W 24: -J W 24: -J W 24: -J
ee Ju un2 ee Ju un2 ee Ju un2
W k 25 n28 7 W k 25 n28 7 W k 25 n28 7
ee - ee - ee -
k 2 : Ju Jul4 k 2 : Ju Jul4 k 2 : Ju Jul4
W l5- W l5- W l5-
and the ratio of number of employees splitting the sample into firms with above- and below-median exposure to
ee 6: J ee 6: J ee 6: J
k u Jul1 k u Jul1 k u Jul1
W 27: l12-J 1 W 27: l12-J 1 W 27: l12-J 1
ee
k J ul1 ul1 ee
k J ul1 ul1 ee
k J ul1 ul1
W 28: 9 8 W 28: 9 8 W 28: 9 8
ee Ju -Ju ee Ju -Ju ee Ju -Ju
W k 29 l26 l25 W k 29 l26 l25 W k 29 l26 l25
ee : A -Au ee : A -Au ee : A -Au
W k ug g1 W k ug g1 W k ug g1
ee 30: 2 - ee 30: 2 - ee 30: 2 -
k 3 Au Au k 3 Au Au k 3 Au Au
W g g W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug k 3 ug ug
2: 1 1 2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5 Au 6-Au 5
g2 g g2 g g2 g
3- 22 3- 22 3- 22
Au Au Au
g2 g2 g2
High Wells Fargo Exposure High Wells Fargo Exposure High Wells Fargo Exposure
9 9 9
Panel C. Change in Nbr. Employees Panel B. Change in Hours Worked
87
We We We
e k1 Coefficient (Wells Fargo Exposure) e k1 Coefficient (Wells Fargo Exposure) ek Coefficient (Wells Fargo Exposure)
0: 0: 10
: M
We
Ma
r22 -.02 0 .02 .04 We
Ma
r22 -.02 0 .02 .04 We ar2 -.02 0 .02 .04
e k1 -M e -M k1 ek 2-M
1: a r28 ar2 1: 11 ar2
Ma Ma 8 : M 8
We r29 We r29 We ar2
e k1 -A ek -A ek 9-A
12 12
Wells Fargo branches. The panels on the right of Figure C.2 plot coefficients and standard errors of regressions
2: pr4 : pr4 : pr4
We Ap We Ap We Ap
e k1 r5-
A PPP Launch ek r5-
Ap PPP Launch ek r5-
Ap PPP Launch
3: pr1 13 13
Ap 1 :A r11 :A r11
We r12 We pr1 We pr1
e k1 -A ek 2-A ek 2-A
4: pr1 14 pr1 14 pr1
Ap 8 :A 8 :A 8
We r19 We pr1 We pr1
e k1 -A 1st Round Exhausted ek 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
5: pr2 15 pr2 15 pr2
We Ap 5 We
:A
pr2 5 We
:A
pr2 5
r26
e k1 -M ek 6-M ek 6-M
a 16 a y 16 a y
Panel A. Business Shutdowns
6: y2 :M 2 :M 2
We
e Ma 2nd Round Begins We
ek ay 2nd Round Begins We
ek ay 2nd Round Begins
k1 y3
7: -M 17
:
3-M 17
:
3-M
ay M a M a
We Ma 9 We ay y9 We ay y9
e k1 y1
0-M ek 10 ek 10
8: 18 -M 18 -M
Ma ay :M ay :M ay
We 16 We ay 16 We ay 16
e k1
y1
7-M ek 17 ek 17
9: 19 -M 19 -M
Ma ay :M ay :M ay
We y2 23 We ay 23 We ay 23
e 4-M ek 24 ek 24
k2 -M -M
ay 0: 20
:M a y3 20
:M a y3
30 Ma 0 0
We y3 We ay We ay
e 1-J k2 ek 31
- J ek 31
- J
un 1: 21 un 21 un
We 6 Ju We :J
un 6 We :J
un 6
k2e n7
-Ju ek 7-J ek 7-J
2: n1 22
:J u n1 22
:J u n1
We Ju
n1 3 We un 3 We un 3
ek 4-J ek 14 ek 14
23 un 23 -Ju 23 -Ju
:J 20 :J n2 :J n2
un un 0 un 0
We 21 We 21 We 21
ek -Ju ek -Ju ek -Ju
investigating the impact of exposure to Wells Fargo on employment and firm outcomes. The regressions are similar
24 n2 24 n2 24 n2
:J 7 :J 7 :J 7
un un un
We
ek 2 8
We
ek 2 8
We
ek 2 8
-Ju -Ju -Ju
25
:J l4 25
:J l4 25
:J l4
We ul5 We ul5 We ul5
ek -Ju ek -Ju ek -Ju
Time Series Local Projection Time Series Local Projection
Local Projection
26 l11 26 l11 26 l11
:J :J :J
We ul1 We ul1 We ul1
ek 2-J ek 2-J ek 2-J
27 ul1 27 ul1 27 ul1
:J 8 :J 8 :J 8
We ul1 We ul1 We ul1
ek 9 -Ju ek 9 -Ju ek 9 -Ju
28 l25 28 l25 28 l25
:J :J :J
We ul2 We ul2 We ul2
ek 6-A ek 6-A ek 6-A
29 ug 29 ug 29 ug
We :A 1 We :A 1 We :A 1
ek ug ek ug ek ug
2-A 2-A 2-A
30 ug 30 ug 30 ug
We :A
ug 8 We :A
ug 8 We :A
ug 8
ek 9-A ek 9-A ek 9-A
31 ug 31 ug 31 ug
:A 15 :A 15 :A 15
We ug We ug We ug
ek 16 ek 16 ek 16
32 -A 32 -A 32 -A
:A ug :A ug :A ug
ug 22 ug 22 ug 22
23 23 23
-A -A -A
ug ug ug
29 29 29
Figure C.2: Wells Fargo Exposure and Evolution of Homebase Employment Outcomes
to those of Figure 8 in the main paper. Data are from SBA, Homebase, County Business Patterns.
W W W
ee ee ee
k1 Decline Relative to January k1 Decline Relative to January 1 k Initial UI Claims/January Employment
W : Jan W : Jan W : Jan
ee -.25 -.2 -.15 -.1 -.05 0 ee -.6 -.4 -.2 0 .2 ee 0 1% 2% 3% 4%
k 2 19 - k 2 19 - k 2 19 -
W W W
ee : Ja Jan ee : Ja Jan ee : Ja Jan
k 3 n2 25 k 3 n2 25 k 3 n2 25
W 6 W 6 W 6
ee : Fe -Fe ee : Fe -Fe ee : Fe -Fe
W k b0 b1 W k b0 b1 W k b0 b1
ee 4: F 2-F ee 4: F 2-F ee 4: F 2-F
k 5 eb e k 5 eb e k 5 eb e
W : F
9- b8
F W : F
9- b8
F W : F
9- b8
F
ee ee ee
k 6 eb1 eb1 k 6 eb1 eb1 k 6 eb1 eb1
The panels on the left of Figure C.3 show the evolution of the ratio of weekly initial unemployment filing claims
: F 6-F 5 : F 6-F 5 : F 6-F 5
W eb eb W eb eb W eb eb
e 2 2 e 2 2 e 2 2
W ek 7 3-F 2 W ek 7 3-F 2 W ek 7 3-F 2
ee :M e ee :M e ee :M e
ar b29 ar b29 ar b29
W k 8:
ee 1 W k 8:
ee 1 W k 8:
ee 1
W k Ma -Ma
W k Ma -Ma
W k Ma -Ma
ee 9: M r8-M r7 ee 9: M r8-M r7 ee 9: M r8-M r7
k1 ar ar k1 ar ar k1 ar ar
W 0: M 15-M 14 W 0: M 15-M 14 W 0: M 15-M 14
ee a ee a ee a
k ar
22 r21 k ar
22 r21 k ar
22 r21
W 11: - W 11: - W 11: -
ee M Ma ee M Ma e M Ma
Initial Lockdown Orders W ek 1 ar2 r28 Initial Lockdown Orders
W k 12 ar29 r28
ee
W k 12 ar29 r28
ee ee 2 9
k 1 : Ap -Apr k 1 : Ap -Apr k 1 : Ap -Apr
W r W r W r
ee 3: A 5-A 4
k pr pr
ee 3: A 5-A 4
k pr pr PPP Launch ee 3: A 5-A 4
k pr pr PPP Launch
W 14: 12- 11 W 14: 12- 11 W 14: 12- 11
ee Ap Ap ee Ap Ap ee A A
k r r k r r k 1 pr1 pr1
W 15: 19-A 18
ee Ap
W 15: 19-A 18
ee Ap 1st Round Exhausted We 5: A 9-A 8 1st Round Exhausted
W pr W pr
k1 r2 25 k1 r2 25 W ek 1 pr2 pr25
ee 6: 6 - ee 6: 6 - ee 6: 6 -
W k 17 Ma May W k 17 Ma May W k 17 Ma May
ee : y 2 ee : y 2 2nd Round Begins ee : y 2 2nd Round Begins
k 1 Ma 3-M k 1 Ma 3-M k 1 Ma 3-M
W y a W y a W y a
Low Wells Fargo Exposure Low Wells Fargo Exposure Low Wells Fargo Exposure at the county level and total county employment (Panel A), the change in aggregate small business revenue at
ee 8: M 10-M y9 ee 8: M 10-M y9 ee 8: M 10-M y9
k1 ay a k1 ay a k1
9
ay ay
W 9: M 17- y16 W 9: M 17- y16 W : 1 1
ee
k2 a y
Ma ee
k2 a y
Ma ee Ma 7-Ma 6
k2 y
W 0 24 y23
W 0 24 y23
W 0 24 y23
ee : Ma -Ma ee : Ma -Ma ee : Ma -Ma
W k 21 y 3 y30 W k 21 y 3 y30 W k 21 y 3 y30
ee 1 ee 1 ee 1
k 2 : Ju -Jun k 2 : Ju -Jun k 2 : Ju -Jun
W n W n W n
Time Series Time Series
ee 2: J 7-Ju 6 ee 2: J 7-Ju 6 ee 2: J 7-Ju 6
k 2 un n1 k 2 un n1 k 2 un n1
1 1 1
W 3: J 4-Ju 3 W 3: J 4-Ju 3 W 3: J 4-Ju 3
ee un ee un ee un
k 21 n20 k 21 n20 k 21 n20
W 24: -J W 24: -J W 24: -J
ee Ju un2 ee Ju un2 ee Ju un2
W k 25 n28 7 W k 25 n28 7 W k 25 n28 7
ee - ee - ee -
k 2 : Ju Jul4 k 2 : Ju Jul4 k 2 : Ju Jul4
W l5- W l5- W l5-
ee 6: J ee 6: J ee 6: J
k u Jul1 k u Jul1 k u Jul1
W 27: l12-J 1 W 27: l12-J 1 W 27: l12-J 1
ee
k J ul1 ul1 ee
k J ul1 ul1 ee
k J ul1 ul1
W 28: 9 8 W 28: 9 8 W 28: 9 8
ee Ju -Ju ee Ju -Ju ee Ju -Ju
W k 29 l26 l25 W k 29 l26 l25 W k 29 l26 l25
ee ee ee
the county level relative to January (Panel B), and the ratio of county employment relative to January (Panel C)
k : A -Au k : A -Au k : A -Au
W ug g1 W ug g1 W ug g1
ee 30: 2 - ee 30: 2 - ee 30: 2 -
k 3 Au Au k 3 Au Au k 3 Au Au
W g g W g g W g g
ee 1: A 9-A 8 ee 1: A 9-A 8 ee 1: A 9-A 8
k 3 ug ug k 3 ug ug k 3 ug ug
2: 1 1 2: 1 1 2: 1 1
Au 6-Au 5 Au 6-Au 5 Au 6-Au 5
g2 g g2 g g2 g
3- 22 3- 22 3- 22
Au Au Au
g2 g2 g2
High Wells Fargo Exposure High Wells Fargo Exposure High Wells Fargo Exposure
9 9 9
Panel B. Small Business Revenues
88
We We We
e k1 Coefficient (Wells Fargo Exposure) e k1 Coefficient (Wells Fargo Exposure) ek Coefficient (Wells Fargo Exposure)
0: 0: 10
: M
We
Ma
r22 -.01 0 .01 .02 .03 We
Ma
r22 -.04 -.02 0 .02 .04 We ar2 -.5 0 .5
e -M e -M ek 2-M
splitting the sample into firms with above- and below-median exposure to Wells Fargo branches. The panels on
k1 a k1 a 11 ar2
1: r28 1: r28
Ma Ma : M 8
We r29 We r29 We ar2
e k1 -A e k1 -A ek 9-A
2: pr4 2: pr4 12 pr4
We Ap We Ap We : Ap
e k1 r5-
A PPP Launch PPP Launch e k1 r5-
A ek r5-
Ap PPP Launch
Panel A. UI Claims
3: pr1 3: pr1 13
Ap 1 Ap 1 :A r11
We r12 We r12 We pr1
e k1 -A e k1 -A ek 2-A
4: pr1 4: pr1 14 pr1
Ap 8 Ap 8 :A 8
We r19 We r19 We pr1
e k1 -A PPP Funds Exhausted PPP Funds Exhausted e k1 -A ek 9-A PPP Funds Exhausted
5: pr2 5: pr2 15 pr2
We Ap 5 We Ap 5 We
:A
pr2 5
r26 r26
e k1 -M e k1 -M ek 6-M
6: a y2 6: a y2 16 a y 2
Panel C. County Employment
We Ma PPP 2nd Round Begins Ma We :M PPP 2nd Round Begins
e k1 y3
PPP 2nd Round BeginsWee
k1 y3 ek ay
7: -M 7: -M 17
:
3-M
ay ay M a
We Ma 9 We Ma 9 We ay y9
e k1 y1
0-M e k1 y1
0-M ek 10
8: 8: 18 -M
Ma ay Ma ay :M ay
We 16 We 16 We ay 16
e k1
y1
7-M e k1
y1
7-M ek 17
9: 9: 19 -M
ay ay :M ay
the right of Figure C.3 plot coefficients and standard errors of regressions investigating the impact of exposure to
Ma 23 Ma 23 ay 23
We y2 We y2 We 24
e k2 4-M e k2 4-M ek -M
0: ay ay 0: 20
:M a y3
Ma 30 30 Ma 0
We y3 We y3 We ay
e k2 1-J e 1-J k2 ek 31
- J
1: un un 1: 21 un
We Ju 6 We 6 Ju We :J
un 6
e k2
n7
-Ju k2e n7
-Ju ek 7-J
2: n1 2: n1 22
:J u n1
We Ju
n 3 We Ju
n1 3 We un 3
e k2
14
-Ju ek 4-J ek 14
-Ju
3: n2 23 un 23 n2
Ju 0 :J 20 :J 0
We n 21 We un
21 We un
21
e k2 -Ju ek -Ju ek -Ju
n2 24 n2 24 n2
4:
Ju 7 :J 7 :J 7
We n 28 We un
2 We un
2
e k2 -Ju ek 8 -Ju ek 8 -Ju
5: l4 25
:J l4 25
:J l4
We Ju We We
e k2 l5 -Ju ek ul5
-Ju ek ul5
-Ju
Local Projection Local Projection
Time Series Local Projection
6: l11 26 l11 26 l11
Ju :J :J
We l12 We ul1 We ul1
e k2 -Ju ek 2-J ek 2-J
7: l18 27 ul1 27 ul1
Ju :J 8 :J 8
Wells Fargo on the same outcomes. The regressions are similar to those of Figure 10 in the main paper. Data are
We l19 We ul1 We ul1
e k2 -Ju ek 9 -Ju ek 9 -Ju
8: l25 28 l25 28 l25
Ju :J :J
We l26 We ul2 We ul2
e -A ek 6-A ek 6-A
k2 u g1 29 ug 29 ug
We
9:
Au We :A 1 We :A 1
e g2 ek ug ek ug
k3 -A 30
2-A
30
2-A
0: ug ug ug
We Au 8 We :A
ug 8 We :A
ug 8
e k3 g9
-A ek 9-A ek 9-A
1: ug 31 ug 31 ug
Au 15 :A 15 :A 15
We g1 We ug We ug
e k3 6-A ek 16
-A ek 16
-A
2: ug 32 ug 32 ug
Au 22 :A 22 :A 22
g2 ug ug
3-A 23 23
-A -A
ug ug ug
29 29 29
Figure C.3: Wells Fargo Exposure and Other Local Labor Market and Economic Effects
from SBA, Homebase, County Business Patterns.
Table C.1: Wells Exposure in Round 1 and PPP Reallocation across Funding Sources
Table C.1 shows the correlation between local exposure to Wells Fargo branches and the fraction of establishments
receiving PPP loans from different sources in the first and second rounds of the program. The left-hand-side
variable in column (1) is the fraction of establishments within a ZIP and 2-digit NAICS industry that received PPP
in the first round in Panel A and in both rounds in Panel B. Left-hand-side variables in other columns represent
a decomposition of the dependent variable in column (1) into the fraction of establishments within a ZIP and
2-digit NAICS industry that received PPP from local banks, non-local banks, credit unions, FinTech companies,
and other nonbanks. % Wells (Zip 10 miles) is the share of the number of branches in the zip code or within 10
miles of the center of the respective zip code that belong to Wells Fargo. % Wells (Zip 10 miles) is standardized to
permit coefficients to be interpreted as the effect of a one-standard-deviation increase in exposure to Wells Fargo
and observations are weighted by the number of establishment counts in each zip-industry pair. All regressions
include state-by-NAICS fixed effects. Standard errors are presented in parentheses, and are clustered at the state
level. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
Panel A: Allocation in Round 1
(1) (2) (3) (4) (5) (6)
PPP Loans Relative to All Establishments by Lender Source
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
% Wells (Zip 10 miles) -0.017∗∗∗ -0.014∗∗∗ -0.004∗∗∗ 0.000 0.001∗∗∗ 0.000
(0.004) (0.004) (0.001) (0.001) (0.000) (0.000)
Observations 251511 251511 251511 251511 251511 251511
Adjusted R2 0.356 0.329 0.126 0.166 0.285 0.043
State×Industry FE Yes Yes Yes Yes Yes Yes
Panel B: Allocation in Round 1 and 2
(1) (2) (3) (4) (5) (6)
PPP Loans Relative to All Establishments by Lender Source
PPP/Est (%) Local Banks Non-Local Banks Credit Unions FinTech Nonbanks
% Wells (Zip 10 miles) 0.008∗ 0.002 -0.002 0.002 0.006∗∗∗ 0.001∗∗∗
(0.005) (0.004) (0.003) (0.002) (0.002) (0.000)
Observations 251511 251511 251511 251511 251511 251511
Adjusted R2 0.399 0.392 0.187 0.219 0.194 0.090
State×Industry FE Yes Yes Yes Yes Yes Yes
89
Table C.2: Wells Exposure and Homebase Employment Outcomes
Table C.2 reports the results of OLS regressions examining the relation between exposure to Wells Fargo and the difference between a firm’s
average employment outcomes in the two weeks prior to the launch of PPP and the firm’s outcomes in each of the following months. The
left-hand-side variable in Panel A, ∆ Bus. Shutdown, is the difference between the firm’s shutdown status in a week and its average shutdown
status in weeks 10 and 11, where shutdown status takes a value of one if the business reported zero hours worked over the entire week. The
left-hand-side variable in Panel B, ∆ Hours Worked, is the difference in the ratio of hours worked in each establishment in a week and the
average ratio of hours worked in that establishment in weeks 10 and 11. The ratio of hours worked in each establishment is measured as the
hours worked in that week relative to the hours worked in that same establishment during the last two weeks of January. ∆ Nbr. Employees, is
the difference in the ratio of the number of employees in each establishment in a week and the average ratio of number of employees in that
establishment in weeks 10 and 11. The ratio of number of employees in each establishment is measured as the number of distinct employees
that worked in the establishment in that week relative to the number of distinct employees working in that same establishment during the last
two weeks of January. % Wells is the share of the number of branches in the zip code or within 10 miles of the center of the ZIP that belongs
to Wells Fargo. I(Month=‘M’), where M = {Apr il, M a y, June, Jul y, August} are indicator variables for the weeks that span those respective
months. Other control variables include interactions between the median household income, social distance index, COVID cases per capita
and deaths per capita measured as of week 9 interacted with the indicator variables for April, May, June, July, and August and controls for
the average tier 1 capital and core deposit ratios of all banks within the zip code or within a 10 miles radius of the zip code also interacted
with the indicator variables for April, May, June, July, and August. Standard errors are clustered at the state level. ***, **, and *, represent
statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
% Wells × I(Month=April) -0.004∗ -0.004∗ 0.002 0.003 0.002 0.001
(0.003) (0.003) (0.002) (0.002) (0.002) (0.002)
% Wells × I(Month=May) 0.002 0.004 -0.012∗∗ -0.009∗∗ -0.011∗∗ -0.009∗∗
(0.003) (0.003) (0.005) (0.004) (0.004) (0.004)
% Wells × I(Month=June) -0.002 0.003 -0.021∗∗∗ -0.018∗∗∗ -0.017∗∗ -0.016∗∗
(0.004) (0.004) (0.007) (0.007) (0.007) (0.007)
% Wells × I(Month=July) -0.005 0.001 -0.015∗∗ -0.015∗∗∗ -0.010 -0.013∗
(0.003) (0.003) (0.006) (0.005) (0.007) (0.007)
% Wells × I(Month=August) -0.006 0.000 -0.014∗∗ -0.013∗∗ -0.007 -0.010
(0.004) (0.004) (0.006) (0.005) (0.007) (0.007)
Observations 819834 819834 819834 819834 819834 819834
Adjusted R2 0.058 0.602 0.133 0.629 0.110 0.571
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables No Yes No Yes No Yes
Firm Fixed Effects No Yes No Yes No Yes
90
Table C.3: Wells Fargo Exposure and Local Labor Market and Economic Effects
Table C.3 reports the results of OLS regressions examining the relation between exposure to Wells Fargo and county-level unemployment
filings, small business revenue from Womply, and employment growth from Opportunity Insights. ∆ UI Claims is the difference between
the county unemployment filings during a week and the average unemployment filings in the county in weeks 10 and 11. ∆ Small Business
Revenue is the difference between the county aggregate change in small business revenue relative to January and the average change in small
business revenue in weeks 10 and 11 relative to January. Aggregate change in small business revenue is from Womply. ∆ OI Emp., is the
difference between county employment growth relative to January in a week and the average county employment growth relative to January
in weeks 10 and 11. The county-level employment data come from Opportunity Insights. County % Wells is the share of the number of Wells
Fargo branches in the county. I(Month=‘M’), where M = {Apr il, M a y, June, Jul y, August} are indicator variables for the weeks that span
those respective months. Other control variables include interactions between the median household income, social distance index, COVID
cases per capita and deaths per capita measured as of week 9 interacted with the indicator variables for April, May, June, July, and August
and controls for the average tier 1 capital and core deposit ratios of all banks within the county also interacted with the indicator variables
for April, May, June, July, and August. Standard errors are clustered at the state level. ***, **, and *, represent statistical significance at 1%,
5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County % Wells × I(Month=April) -0.020 -0.003 -0.014∗∗∗ -0.002 0.002 0.001
(0.072) (0.053) (0.003) (0.003) (0.004) (0.004)
County % Wells × I(Month=May) -0.006 0.013 -0.023∗∗ -0.005 -0.001 0.002
(0.074) (0.058) (0.009) (0.008) (0.004) (0.004)
County % Wells × I(Month=June) -0.006 0.025 -0.011 -0.006 -0.004 0.001
(0.071) (0.058) (0.010) (0.010) (0.006) (0.003)
County % Wells × I(Month=July) -0.094 0.016 -0.003 -0.001 -0.004 0.001
(0.075) (0.059) (0.006) (0.006) (0.007) (0.003)
County % Wells × I(Month=August) -0.147∗ -0.047 0.001 -0.001 -0.003 0.003
(0.080) (0.062) (0.007) (0.007) (0.008) (0.004)
Observations 46092 45533 43886 43863 17112 17112
Adjusted R2 0.744 0.949 0.489 0.734 0.682 0.878
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables No Yes No Yes No Yes
County Fixed Effects No Yes No Yes No Yes
91
D Pre-Targeting Appendix
This Appendix expands the pre-targeting analysis of section 5.5 using the Homebase data and
other data sources. The purpose of this section is to show that the pre-targeting results pre-
sented in the paper generalize to other outcomes and other levels of aggregation. Figure D.1
shows that the Homebase pre-PPP employment outcomes at the state level are worse in states
that received a smaller PPP allocation in the first round. Figure D.2 shows that similar patterns
hold when we examine other state-level outcomes such as pre-PPP initial unemployment insur-
ance filings, pre-PPP change in small business revenue from Womply, and pre-PPP change in
employment from the Opportunity Insights tracker. Figure D.3 shows that the pre-PPP disease
spread was greater in states that received a smaller allocation of the PPP during the first round
and Figure D.4 shows that states receiving greater PPP allocation in the first round had less
pre-PPP social distancing and issued shelter-in-place orders later. Figures D.5 and D.6 show
similar relations between pre-PPP employment outcomes and PPP allocation during the first
round when we implement the analysis at the county level. Figure D.7 shows that the pre-PPP
evolution of Homebase employment outcomes did not vary with the exposure of firms to banks
with supply-side constraints. When we partition firms based on deciles of predicted PPPE, we
see similar pre-PPP levels of business shutdown, declines in hours worked, and declines in
number of workers.
92
Figure D.1: Pre-PPP Homebase Employment Outcomes and PPP Allocation by State (Round 1)
Figure D.1 presents scatterplots of the share of businesses in each state that shutdown in the week of March 22nd
to March 28th (Panel A), of the decline in hours worked in each state relative to a January Baseline (Panel B),
and of the decline in the number of employee counts in each state relative to a January baseline (Panel C). The
figures on the left plot the pre-PPP state-level employment outcomes from Homebase against the number of PPP
loans received by small businesses in each state during the first round of the program divided by the total number
of small businesses in the state. The figures on the right plot the same pre-PPP employment outcomes and the
state-level PPPE measure. Data is from Homebase, SBA, and Call Reports.
Panel A. Business Shutdowns
Fraction Receiving PPP State PPPE
.5 .5
Share of Businesses Shutdown during 03/22 to 03/28 (Pre-PPP) Share of Businesses Shutdown during 03/22 to 03/28 (Pre-PPP)
NY NY
PA PA
.4 ME .4 ME
IL IL
DC
NV NV DC
NJ WV RI NJ RI WV
CA CT CA CT
OR OR MA
MA NH NH
MI KY MI
KY
MD VT .3 MD LA VT
.3 OHLA OH
DE WI
MN WA DE MN WI
WA
IN HI IN HI
FL FL
TX
NC NM GA TX CO IA NC GA CO NM IA
MO SC VA MO
VASC AL AL
.2 AZ
.2 AZ AR
AR MS
TN MS
MT TN MT
KS KS
OK OK
WY WY
ID NE ID NE
AK AK
UT UT
.1 ND .1 ND
SD SD
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
Panel B. Decline in Hours Worked
Fraction Receiving PPP State PPPE
.8 SD .8 SD
Ratio of Hours Worked relative to January (Pre-PPP) Ratio of Hours Worked relative to January (Pre-PPP)
.7 .7
UT UT
NE ND NE ND
.6 OK .6 OK
ID ID
MS MS
KSMT KS
MT
IA IA
AL AK AL MO
AZ AK MO .5 AZ
.5 SC AR SC GA TN KY AR
GA TN KY
NC MN NC TX MN
WY
VA TX WY VA
FL FL CO
CO DE IN
MD DE IN MD
WI
WA NM WI WA
.4 OR NM
.4 OR LA
NV OHLA RI NH NV OHRI NH
CA CA
NJ CT NJ CT
WV
MI IL HI ME MI WV
HI ME
MA IL MA VT
VT
.3 NY .3 NY
PA DC PA
DC
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
Panel C. Decline in Number of Employees
Fraction Receiving PPP State PPPE
SD SD
.8 .8
Ratio of Employees relative to January (Pre-PPP) Ratio of Employees relative to January (Pre-PPP)
UT .7 UT
.7
NE ND NE ND
ID ID OK
.6 OK .6
MS MS
KS KS
MT MT
AZ AK AL MO IA AZ AKAL MO IA
GA
SC TN AR SC GA TN AR
.5 KY .5 NC TX KY
NC VA TX MN FL
VA MN
WY
FL WY CO
CO
MD DE MD DE
WA NM IN WI WA LANM WI IN
OR LA OR
CANV OH RI
.4 NV CA OHRI NH
.4 NH
NJ CT NJ CT
IL HI IL MI HI
MI WV MA ME MA WV ME
VT
VT
DCNY DC NY
PA .3 PA
.3
.1 .2 .3
Share of Small Businesses Receiving PPP (Round 1)
.4 .5
93 -.2 0
State PPPE (Round 1)
.2 .4
Figure D.2: Other Labor and Economic Outcomes and PPP Allocation by State (Round 1)
Figure D.2 presents scatterplots of the average pre-PPP UI claims as a percentage of employment (Panel A), of the
average decline in small business revenues at the state level from Womply (Panel B), and of the average decline
in employment at the state level from OI (Panel C). The figures on the left plot the pre-PPP outcomes against the
number of PPP loans received by small businesses in each state during the first round of the program divided by
the total number of small businesses in the state. The figures on the right plot the same pre-PPP outcomes and
the state-level PPPE measure. Data is from State Labor Departments, Womply, Opportunity Insights, SBA, and
Call Reports.
Panel A. UI Claims
Fraction Receiving PPP State PPPE
.2 .2
RI RI
Pre-PPP State Unemployment Claims as % of Employment Pre-PPP State Unemployment Claims as % of Employment
MI MI
PA PA
NV HI NV HI
.15 KY
NH
.15 KY
NH
WA LA WA LA
NJ NJ
OH MA OHMA
CA ME
MT CA MT ME
GA GA
AK MN VT AK MN VT
DE DE IN
IN ID IA ID IA
AL .1 AL
.1
NC MD WI KS NC MD WI KS
NM SC AZ SC NM
AZ MO NY MO
NY VA IL AR OK VA IL AR OK
ND ND
OR TN MS OR TN MS
NE NE
TX WY TX WY
FL CT UT FL UT CT
.05 WV CO .05 CO WV
SD SD
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
Panel B. ∆ Small Business Revenues
Fraction Receiving PPP State PPPE
Total Consumer Spending 03/22 to 03/28 relative to January (Pre-PPP) Total Consumer Spending 03/22 to 03/28 relative to January (Pre-PPP)
-.2 -.2
NE NE
AL AL SD
SD
ID MO WY ND ID WYMO AR KSND
NC KY AR KSMT NC TX MNKY MT OK
SC TX IN
GA
MN MS OK SC GA IN MS
AZ TN AZ OR TN
OR NM
NMVA VA
-.4 UT
NH -.4 UT
MI NH
MI IA FL IA
FL
CT OHLA WI LA CT OH WI
WA IL WA IL
CA WV CO CA CO WV
MD DE MD DE
NY AK ME
NV NY AK VT ME NV VT
PA PA
RI RI
MA
NJ MA NJ
-.6 -.6
DC DC
HI HI
-.8 -.8
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
Panel C. ∆ OI Employment
Fraction Receiving PPP State PPPE
Decline in OI Employment 03/22 to 03/28 relative to January (Pre-PPP) Decline in OI Employment 03/22 to 03/28 relative to January (Pre-PPP)
0 0
IA IA
SD SD
MO MO
AZ OK AZ OK
-.05 DC WY -.05 DC WY
FL SC MN SC
FLWA MN
WA MS NE LA IN NE MS
NV NC VAGA TX INLA UT WI KS NV UT GA VA
NC TX WI KS
NM WV CO ID AR ID CO NM ARWV
CA MD IL AL CA MD IL AL
DE TN KY DE TN KY
OR OH OR OH
PA PA
-.1 ME
-.1 AK ME
MI AK NJ MI ND
NJ ND
CT CT
NH NH
HI HI
MA MA
NY NY
-.15 MT -.15 MT
RI RI
VT VT
-.2 -.2
.1 .2 .3
Share of Small Businesses Receiving PPP (Round 1)
.4 .5
94 -.2 0
State PPPE (Round 1)
.2 .4
Figure D.3: Pre-PPP Health Outcomes and PPP Allocation by State
Figure D.3 presents scatterplots of the relation between the cumulative number of pre-PPP COVID-19 cases and
deaths per thousand in each state as of April, 3rd 2020 and state-level measures of PPP allocation. Data comes
from the Center for Disease Control, SBA, Call Reports, and FDIC Summary of Deposits.
Panel A. Covid Cases per Capita
Fraction Receiving PPP State PPPE
3 3
Cumulative Covid-19 Cases per Thousand until 04/03 (Pre-PPP) Cumulative Covid-19 Cases per Thousand until 04/03 (Pre-PPP)
NJ NJ
NY NY
LA LA
2 2
MA MA
CT CT
MI MI
1 1
WA WA
IL PA CO IL
CO PA
VT VT
GA IN RI ID NV GAID RI IN
NV FL MD DE MS FL MD DE TN MS
TN UT NH UT NH
CA SC OH AL WI
MO ME CA SC AL OHWYWIMO ME
WY OK AZ VA AKTX
NM MT
AR HI OK
AZ ORNC NMVA TXAK AR HI MT
KSIA SD ND OR NC KSND
IA
SD
WV KY MN NE MNKY NE
WV
0 0
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
Panel B. Covid Deaths per Capita
Fraction Receiving PPP State PPPE
.08 .08
Cumulative Covid-19 Deaths per Thousand until 04/03 (Pre-PPP) Cumulative Covid-19 Deaths per Thousand until 04/03 (Pre-PPP)
LA LA
NJ NJ
.06 .06
MI MI
.04 WA
.04 WA
CT CT
MA MA
NY NY
.02 CO .02 GA CO
GA IL
IL IN
NV DE IN NV DE
VT VT
MS OK WI MSOK
WI FL SC PA
AL OH
CA FL MD SC PAOH KY AL KS ME CA
IDMD RIKY KS ME
AZ OR NMVA TN RI ID NH
MO MT AZ OR NM TNMN MO AR IA
VA AKTX MTNH
TX
AK MN AR IA NE ND NC NESDND
NC WV UT HI SD 0 UT HI
WV
0 WY WY
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
95
Figure D.4: Pre-PPP Social Distancing and Public Health Interventions and PPP Allocation by
State
Figure D.4 presents scatterplots of the timing of statewide shelter-in-place orders and measures of PPP allocation
at the state level and scatterplots with measures of pre-PPP social distancing and measures of PPP allocation
across states. Data comes from the New York Times, SBA, Call Reports, and FDIC Summary of Deposits.
Panel A. Social Distancing Index
Fraction Receiving PPP State PPPE
WY WY
-.2 -.2
Social Distancing Index during 03/22 to 03/28 (Pre-PPP) Social Distancing Index during 03/22 to 03/28 (Pre-PPP)
-.3 -.3
NM SC AL SC NM
AL
AZ MT OK AZ MT OK
UT ID
WV TN UT ID AR MS TN ARWV
MS
NC GA NC GA
-.4 OR
KY
ME -.4 OR
KY
ME
ND ND
VA MO VA MO
LA SD
NE LA NESD
TX KSIA TX IAKS
CA RI CA RI
AK NH
-.5 FL
WA MD
AK IN
OH
NH -.5 FLWA MD CO
IL OH IN
NV DE IL CO NV DE CT
CT WI PA WI
PA MA MA
VT VT
NJ NJ
NY
-.6 NY
MI -.6 MI
MN MN
HI HI
DC DC
-.7 -.7
.1 .2 .3 .4 .5 -.2 0 .2 .4
Share of Small Businesses Receiving PPP (Round 1) State PPPE (Round 1)
Panel B. Shelter in Place Orders
Fraction Receiving PPP State PPPE
.5 .4
VT ME
Share of Small Businesses Receiving PPP (Round 1)
ND
NE NH OK
ND
SD MT KS MS SD
WV HI IA
NE
IN AR
OK .2 WI KY MO
State PPPE (Round 1)
ME WY MI
MA WY
MT
KS MS IA
OH MN RI PA
HI TN
VT AR CT NM AL
LA TX
WI MN
NH MO IL DE CO AK
ID
.2 .3 .4
AL VA
MD
RI ID
MA KY UT GA
LA IN CO TN NY NJ NC SC UT
OH 0 WA
AK PA TX FL
IL WV
CT DE SC GA
MI VA OR
NM MD
NC
WA
OR FL DC
NY NJ
DC
AZ NV CA AZ
CA
.1 -.2 NV
20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 r 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 r
de de
r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 Or r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 r20 Or
ma ma ma ma ma ma ma ma ma ma ma ma ma ma ap ap ap ap ap ap ap te ma ma ma ma ma ma ma ma ma ma ma ma ma ma ap ap ap ap ap ap ap te
18 19 20 21 22 23 24 25 26 27 28 29 30 31 01 02 03 04 05 06 07 Sta 18 19 20 21 22 23 24 25 26 27 28 29 30 31 01 02 03 04 05 06 07 Sta
No No
Date Shelter Order Date Shelter Order
96
Figure D.5: Pre-PPP Homebase Employment Outcomes and PPP Allocation by County (Round
1)
Figure D.5 presents scatterplots of the share of businesses in each county that shutdown in the week of March
22nd to March 28th (Panel A), of the decline in hours worked in each county relative to a January Baseline (Panel
B), and of the decline in the number of employee counts in each county relative to a January baseline (Panel C).
The figures on the left plot the pre-PPP county-level employment outcomes from Homebase against the average
number of PPP loans received by small businesses in each county during the first round of the program divided
by the total number of small businesses in the county. The figures on the right plot the same pre-PPP employment
outcomes and the average county-level PPPE measure. Data is from Homebase, SBA, and Call Reports.
Panel A. Business Shutdowns
Fraction Receiving PPP County PPPE
.8 .8
Share of Business Shutdowns during 03/22 to 03/28 (Pre-PPP) Share of Business Shutdowns during 03/22 to 03/28 (Pre-PPP)
.6 .6
.4 .4
.2 .2
0 0
-2 -1 0 1 2 3 -.5 -.3 -.1 .1 .3 .5
Share of Businesses receiving PPP (Round #1) County PPPE (Round #1)
Panel B. Decline in Hours Worked
Fraction Receiving PPP County PPPE
Ratio of Hours Worked 03/22 to 03/28 relative to January (Pre-PPP) Ratio of Hours Worked 03/22 to 03/28 relative to January (Pre-PPP)
1 1
.75 .75
.5 .5
.25 .25
0 0
-2 -1 0 1 2 3 -.5 -.3 -.1 .1 .3 .5
Share of Businesses receiving PPP (Round #1) County PPPE (Round #1)
Panel C. Decline in Number of Employees
Fraction Receiving PPP County PPPE
Ratio of Nbr. Employees 03/22 to 03/28 relative to January (Pre-PPP) Ratio of Nbr. Employees 03/22 to 03/28 relative to January (Pre-PPP)
1 1
.75 .75
.5 .5
.25 .25
0
97 0
-2 -1 0 1 2 3 -.5 -.3 -.1 .1 .3 .5
Share of Businesses receiving PPP (Round #1) County PPPE (Round #1)
Figure D.6: Pre-PPP Other Employment and Economic Outcomes and PPP Allocation by County
(Round 1)
Figure D.6 presents scatterplots of the average county pre-PPP UI claims as a percentage of employment (Panel
A), of the average decline in small business revenues at the county level from Womply (Panel B), and of the
average decline in employment at the county level from OI (Panel C). The figures on the left plot the pre-PPP
county-level employment outcomes against the average number of PPP loans received by small businesses in each
county during the first round of the program divided by the total number of small businesses in the county. The
figures on the right plot the same pre-PPP employment outcomes and the average county-level PPPE measure.
Data is from Homebase, SBA, and Call Reports.
Panel A. UI Claims
Fraction Receiving PPP County PPPE
15 15
Pre-PPP County Unemployment Claims as a % of Employment Pre-PPP County Unemployment Claims as a % of Employment
10 10
5 5
0 0
0 .2 .4 .6 .8 1 -.5 -.3 -.1 .1 .3 .5
Share of County Businesses receiving PPP (Round #1) County PPPE (Round #1)
Panel B. ∆ Small Business Revenues
Fraction Receiving PPP County PPPE
1 1
Decline in County Small Buz Rev 03/22 to 03/28 relative to Jan Decline in County Small Buz Rev 03/22 to 03/28 relative to Jan
.5 .5
0 0
-.5 -.5
-1 -1
0 .2 .4 .6 .8 1 -.5 -.3 -.1 .1 .3 .5
Share of County Businesses receiving PPP (Round #1) County PPPE (Round #1)
Panel C. ∆ OI Employment
Fraction Receiving PPP County PPPE
Decline in County OI Employment 03/22 to 03/28 relative to January Decline in County OI Employment 03/22 to 03/28 relative to January
.1 .1
0 0
-.1 -.1
-.2 -.2
-.3 98 -.3
0 .2 .4 .6 .8 1 -.5 -.3 -.1 .1 .3 .5
Share of Businesses receiving PPP (Round #1) County PPPE (Round #1)
Figure D.7: Targeting of PPP Allocation across Quantiles of PPPE and Predicted PPPE (First
Round)
Figure D.7 stratifies all businesses in Homebase in 10 bins based on the PPPE (left panels) and Predicted PPPE
(right panels) of the their ZIP codes during the first round. Panel A plots for each bin the share of Homebase
businesses that shut down in the week of March 22nd–March 28th. Panel B plots for each bin the average decline
in hours worked in the week of March 22nd–March 28th relative to a baseline of the average weekly hours worked
in the last two weeks of January. Panel C plots for each bin the average decline in the number of employees in
the week of March 22nd–March 28th relative to a baseline of the average weekly hours worked in the last two
weeks of January. Data are from SBA, Homebase, and County Business Patterns.
Panel A. Business Shutdowns and Fraction of Establishments Receiving PPP
PPPE (Round 1) Predicted PPPE (Round 1)
.4 .4
Share of Businesses Shutdown Share of Businesses Shutdown
during 03/22 to 03/28 (Pre-PPP) during 03/22 to 03/28 (Pre-PPP)
.25 .3 .35 .25 .3 .35
.2 .2
st 2 3 4 5 6 7 8 9 /Es st 2 3 4 5 6 7 8 9 /Es
t t
t PP t PP
st P st P
P/E P/E
PP PP
Lea Mo Lea Mo
s s
PPPE Quantile (Round 1) Predicted PPPE Quantile (Round 1)
Panel B. Decline in Hours Worked and Fraction of Establishments Receiving PPP
PPPE (Round 1) Predicted PPPE (Round 1)
Ratio of Hours Worked 03/22 to 03/28 Ratio of Hours Worked 03/22 to 03/28
Relative to January (Pre-PPP) Relative to January (Pre-PPP)
.35 .4 .45 .5 .35 .4 .45 .5
.3 .3
t 2 3 4 5 6 7 8 9 t t 2 3 4 5 6 7 8 9 t
/Es /Es /Es /Es
PP PP PP PP
st P st P st P st P
Lea Mo Lea Mo
PPPE Quantile (Round 1) Predicted PPPE Quantile (Round 1)
Panel C. Decline in Number of Employees and Fraction of Establishments Receiving PPP
PPPE (Round 1) Predicted PPPE (Round 1)
Ratio of Nbr. Employees from 03/22 to 03/28 Ratio of Nbr. Employees from 03/22 to 03/28
Relative to January (Pre-PPP) Relative to January (Pre-PPP)
.35 .4 .45 .5 .55 .35 .4 .45 .5 .55
t 2 3 4 5 6 7 8 9 t t 2 3 4 5 6 7 8 9 t
/Es /Es /Es /Es
st P
PP
st P
PP
99 st P
PP
st P
PP
Lea Mo Lea Mo
PPPE Quantile (Round 1) Predicted PPPE Quantile (Round 1)
E Crowd-out Appendix
This appendix presents evidence that suggests that the PPP crowded out some private loans.
We find some evidence of modest crowd-out, but the results suggest that magnitudes are small
and private lending would not have fully offset PPP lending.
Appendix Figure E.1 shows suggestive evidence of crowd-out from California Uniform Com-
mercial Code (UCC) filings.1 The figure shows a significant spike in UCC filings in May, follow-
ing the exhaustion of PPP funds, which is consistent with the program crowding-out private
lending. On the other hand, the time series could simply reflect bureaucratic delays in filing or
the recovery. Appendix Figure E.2 shows scatterplots of the ratio of UCC filings per establish-
ment and county-level PPPE under different time horizons. The relationship is relatively flat,
suggesting little relationship between the availability of PPP loans and commercial lending.2
While suggestive of some crowd-out, we use the Call Reports to explore this pattern more
broadly and formally. The Call Reports are quarterly at the bank level, and the first two annual
quarters (January-March, April-Jun) almost perfectly align with the disbursement of PPP loans,
which began on April 3. The number of loans in the second quarter is given by C&I LoansQ2 =
C&I LoansQ1 +P P P +N L−P, where C&I LoansQi are commercial and industrial loans in quarter
i, P P P refers to PPP loans, N L are other new non-PPP commercial loans, and P are loans that
are paid or charged-off. We rearrange the equation in terms of quarterly loan growth and write:
C&I LoansQ2 P P P Loans
=1+γ + ζ. (1)
C&I LoansQ1 C&I LoansQ1
The coefficient γ captures crowd-out. If there is no crowd-out, an additional PPP loan leads
to one additional total loan, and γ = 1. Under full crowd-out, an additional PPP loan is offset
by a reduction in another commercial loan, and γ = 0. Appendix Figure E.3 plots the ratio of
Commercial and Industrial Loans in Q2 2020 and Commercial and Industrial Loans in Q1 2020
and the ratio of PPP loans and Commercial and Industrial Loans in Q1 2020. Appendix Table
E.1 reports OLS and IV regressions examining the relation between the ratio of Commercial and
C&I Loans
Industrial Loans in Q2 2020 and Commercial and Industrial Loans in Q1 2020 ( C&I LoansQ2
Q1
) and
P P P Loans
the ratio of PPP loans and Commercial and Industrial Loans in Q1 2020 ( C&I LoansQ1 ). Column
1
We obtained data from California on all UCC filings, which are required for all secured business loans to protect
creditor claims. These UCC laws are set at the state level, although the National Conference of Commissioners
has sought to make them fairly uniform across states. We are able to observe the names and addresses of the
debtor, which we use to subsequently match with the SBA data. We refer readers to Edgerton (2012) for further
details about the UCC data and its features.
2
Note that PPP loans are unsecured and hence are not included in UCC filings.
100
(2) instruments using lender PPPE. In column (1), the coefficient γ is 0.558 and statistically
significant at the one-percent level. This is suggestive of some crowd-out, but not full crowd-
LoansQ1 and ζ are correlated, so in column (2) we
P P P Loans
out. The OLS estimates may be biased if C&I
instrument using lender PPPE. The IV estimate in column (2) provides an estimate of γ = 0.991,
and we cannot reject no crowd out. The confidence interval of the IV estimate allows us to
reject full crowd-out.
Though we find limited evidence of private sector crowd-out, the PPP may have crowded
out other federal loan programs, namely, the Economic Injury Disaster Loan (EIDL) program.
If firms could obtain other federally guaranteed loans in the absence of PPP, this would also
lead to the program having muted effects. The COVID-19 EIDL program is an SBA program
that provides economic relief to small businesses that experience a temporary loss of revenue
due to the coronavirus. The program offers advantageous terms for regular businesses with
interest rates set at 3.75% and maturity of 30 years with no prepayment penalties. Given these
terms, it is possible that the second-best option of firms that did not obtain access to PPP was
to apply for a loan under the EIDL program.
Appendix Figure E.4 shows cumulative PPP and EIDL lending over time. Similar to UCC
filings, we see an uptick in EIDL loans after PPP funds level off in May. The SBA was slower
to open up the expanded EIDL provisions of the CARES Act, which may also account for this
lagged increase. The fact that EIDL loans only start rising in late May implies that crowd-out
of EIDL is unlikely to explain the modest effects we estimate for April and May.
Appendix Figure E.5 shows scatterplots of the average fraction of small business establish-
ments that received an EIDL loan in each percentile bin based on state PPPE in the weeks of
May 3-9 and June 28-July 4. The figure shows a weak relationship in the early period with
very few firms receiving EIDL loans. In the later period, there is a strong negative relationship,
consistent with crowd-out and indicating that higher PPP exposure is associated with fewer
EIDL loans. If firms that were unable to access PPP were more likely to apply for and receive
an EIDL loan, and if EIDL loans were sufficiently good substitutes for PPP loans, this fact could
help account for modest estimated effects of PPP in June.
101
We
ek Number of UCC filings (California)
1:
Ja 0 20000 40000 60000
We n1
ek 9-
2: Ja
Ja n2
We
ek n2 5
3: 6 -Fe
We Fe
b0 b 1
ek 2-F
We 4: eb
Fe 8
ek b9
5: -Fe
We Fe
b1 b 1
ek 6-F 5
6: eb
Fe
We b 23 22
ek -Fe
We 7 :M b 29
ek ar1
8: -M
We M a r7
ek ar8
We 9: -M
Ma ar1
ek
10 r15 4
: M -M
We a ar2
ek r22 1
11 -
: M Mar2
We
ek a r29 8
12 -A
We :A pr4
Figure E.1 shows the number of UCC filings between January and July 2020. Data comes from California UCC
ek pr5
13
: -A
We Ap pr11
ek r12
14
: -A
We Ap p r18
ek r19
102 1 -A
pr2
We 5: Ap
ek r26 5
We 16 -M
:M ay
Week ek
17 ay 2
We :M 3-M
ek ay ay
18 10 9
We :M -M
ay
ek ay
17 1 6
19 -M
:M ay
We ay 23
ek 24
20 -M
: Ma ay
Figure E.1: UCC Filings Over Time
We
y3 30
ek
21 1-J
We :J un
ek un 6
22 7-J
We :J un
ek un 13
23 14
:J -Ju
un n2
We
ek 21 0
24 -Ju
: n 2
We Ju
n2 7
ek 8-J
We 25 ul4
:J
ek ul5
26 -Ju
:J l11
We
ek u l12
27 -Ju
We :J
ul1 l 18
ek 9-J
28 ul2
:J 5
ul2
6-J
ul3
1
filings.
Figure E.2: UCC Filings and PPPE
Figure E.2 are scatterplots of the the ratio of UCC filings per establishment in each county and the county exposure
to the number-based PPPE during the first round of PPP. Data comes from SBA, Call Reports, Summary of Deposits,
County Business Patterns and California UCC filings.
.4
Ratio UCC filings/Establishments
.3
.2
.1
0
-.5 0 .5
PPPE Round 1
03/22-03/28 04/19-04/25 May17-May23 Jun14-Jun 20 Jul12-Jul18
103
Figure E.3: PPP Lending and Commercial & Industrial Loans
Figure E.3 are scatterplots of the ratio of Commercial and Industrial Loans in Q2 2020 and Commercial and
C&I Loans
Industrial Loans in Q1 2020 ( C&I LoansQ2
Q1
) and the ratio of PPP loans and Commercial and Industrial Loans in Q1
P P P Loans
2020 C&I LoansQ1 . Data comes from Federal Reserve Call Reports and SBA.
1.08
1.06
C&I Loans Q2 / C&I Loans Q1
1.04
1.02
1
0 .02 .04 .06 .08 .1
PPP Loans / C&I Loans Q1
104
Table E.1: PPP Lending and Crowd-Out
Table E.1 reports the results of ordinary least squares (OLS) and instrumental variables (IV) regressions examining
the relation between the ratio of Commercial and Industrial Loans in Q2 2020 and Commercial and Industrial
C&I Loans
Loans in Q1 2020 ( C&I LoansQ2 Q1
) and the ratio of PPP loans and Commercial and Industrial Loans in Q1 2020
P P P Loans
C&I LoansQ1 . Column (2) instruments using lender PPPE. Data comes from Federal Reserve Call Reports and SBA.
Robust standard errors are presented in parentheses. ***, **, and *, represent statistical significance at 1%, 5%,
and 10% levels, respectively.
(1) (2)
OLS IV
C&I LoansQ2 C&I LoansQ2
Dep. Variable: C&I LoansQ1 C&I LoansQ1
P P P Loans
C&I LoansQ1 0.558∗∗∗ 0.991∗∗∗
(0.162) (0.278)
Constant 1.017∗∗∗ 1.011∗∗∗
(0.00255) (0.00457)
Observations 4845 4845
Adjusted R2 0.010 0.004
105
Cummulative Loan Count
04 0 1000000 2000000 3000000 4000000
/01
/20
04
/08
/20
04
/15
/20
04
/22
/20
04
/29
/20
05
/06
/20
05
/13
106
PPP Loans /20
05
/20
/20
05
/27
Figure E.4: EIDL and PPP
/20
06
/03
/20
06
EIDL Loans /10
/20
06
/17
/20
06
/24
/20
07
/01
Figure E.4 shows cumulative PPP and EIDL lending between April and July, 2020. Data comes from the SBA.
/20
Figure E.5: EIDL and PPPE
Figure E.5 are scatterplots of the fraction of small business establishment that received an EIDL loan in each state
and the respective State PPPE during the first round. Data comes from SBA, Call Reports, Summary of Deposits,
and County Business Patterns.
.02
NV
LA
Fraction of Small Businesses
CA CT
RI
DC WA GA NH
receiving EIDL as of Week 16
FL
ME
NJ CO
NC MD
VA OHMA ND
AZ SC
UT VT
IL TXAL MN MI HI
OR NY NM PA SD OK
IA
KS
.005 .01 .015
KY IN
DE TN WI NEMT
WY MS
AR
ID MO
WV
AK
0
-.2 0 .2 .4
State PPPE (Round #1)
HI
.4 FL
CA GA
LA
NV
Fraction of Small Businesses
NJ
NY TX
receiving EIDL as of Week 24
MS
AZ AK
SC MD CT
IL AL
DC OR NC
WA VA CO NM TN RI
.2 .3
MI
MA OK VT
DE NH
UT SD
PA AR MT
ID OH
MN ME
WY
KYWI NE
MO KSND
IN
WV
IA
.1
-.2 0 .2 .4
State PPPE (Round #1)
107
F Supplemental Analysis
This appendix presents additional robustness checks and additional empirical analyses explor-
ing possible mechanisms that can account for the effect of the program on employment out-
comes.
Figure F.1 examines the possibility that the program allowed firms to maintain their em-
ployees at home without pay. The plot suggests that conditional on households reporting not
working in each week of the survey, more than 85% reported receiving no pay. Moreover, the
share that report receiving no pay for staying at home does not have a strong relationship with
State PPPE. This suggests that the weak relationship between access to the program and num-
ber of hours worked that we documented in Table 5 is likely not explained by the possibility
that PPP recipients were keeping their workers at home with pay.
In Table F.1, we explore whether the expansion of unemployment insurance could have led
to difficulties for firms in recalling workers and, in turn, attenuated the employment effects
of the PPP. Following the work of Ganong, Noel and Vavra (2020), we partition the sample
between state with above- and below-median state-level UI replacement rates. The results
of Table F.1 do not suggest that the PPP was more effective in states with low UI replacement
rates. In Table F.2 we examine the impact of PPP on changes in initial unemployment insurance
claims, small business revenues, and Opportunity Insights employment after partitioning the
sample into two subsamples based on the median state UI replacement rates. Again, we do
not see striking differences between the measured impact of the program in both subsamples.
In fact, with the exception of the empirical specifications using OI employment data as the
outcome variable, greater access to PPP is more effective in states with relatively more generous
employment benefits.
Figure F.2 uses data from Homebase to plot binned scatter plots of the share of small busi-
ness establishments that “permanently” closed versus ZIP-level PPPE. Both variables are resid-
ualized with respect to the controls in Table 5, an additional control for the average number
of shutdowns in a ZIP code in the two weeks prior to PPP, and demeaned at the state level to
present the within-state relationship. Permanent shutdowns are defined as the establishment
being closed for all weeks from the beginning of the PPP through the end of August. The
graph reports regression coefficients with and without the Table 5 controls and standard errors
clustered at the state level.
The graph suggests a non-trivial impact of PPP on firms over the medium run, as a one-
standard-deviation change in PPPE is associated with a 0.97 percentage point reduction in
permanent shutdowns. This effect is reasonably large compared to the overall permanent
108
shutdown rate of 12%. The result is consistent with our interpretation that, by helping firms
bolster their balance sheets during the crisis, receiving PPP funds enabled these firms to reopen
at higher rates than if they had not received funds. Such impacts are also consistent with
modest short-term employment impacts providing an incomplete overall picture of the impact
of the program.
Figure F.3 uses the Homebase-PPP matched sample and shows the coefficients of week-by-
week regressions that repeat the OLS and IV specifications of columns (1) and (3) of Table 7
for every week in the sample. We obtain results that are qualitatively similar to those of our
regional analyses. Both the OLS and IV specifications show a small impact of receiving PPP
earlier on business shutdowns. This impact is small and fades away over time. The coefficients
of Panels B and C indicate firms receiving PPP earlier have better employment outcomes in
terms of number of hours and number of employees employed and that these gaps in the
number of employees and hours worked persist until August. Again, we caution that the OLS
estimator may be biased if firms that obtained loans earlier are fundamentally different from
firms that received PPP loans later. We note, however, that the IV coefficients show larger
standard errors likely due to a smaller sample size relative to our regional analyses. These
larger standard errors limit our ability to draw strong conclusions from this analysis.
Tables F.3 and F.4 partition the sample into two subsamples based on the median share of
establishments in the county that are eligible for participation in the PPP. The goal is to inves-
tigate whether eligible firms might expand at the expense of local competitors. Such business
stealing spillovers could account for low employment effects at the labor market level. Alter-
natively, the program might have positive local demand effects, for instance on the suppliers
of treated firms. Given the scale and severity of the labor market disruption due to the pan-
demic, traditional measures of labor market tightness are unlikely to be useful. However, we
can ask whether regions with a larger share of employment in PPP-eligible establishments ex-
hibit different effects relative to those with fewer eligible establishments. Employment effects
are generally similar or greater in regions where a larger share of establishments are eligible
for funds, inconsistent with a business stealing effect and possibly consistent with the presence
of some local demand effects. The results are also useful as a robustness check, since omitted
variables would have to account for stronger effects in regions with a larger share of firms that
are eligible for the program.
109
Figure F.1: Share of Households reporting receiving no pay and Exposure to State PPPE
Figure F.1 are scatterplots of state exposure to the state-level PPPE in Round 1 and the percentage of households and the share of households
in the Census Household Pulse Survey that report not receiving any payment for time not working in the previous week. The plots represent
the evolution of the relation between Round 1 State PPPE and the share of respondent reporting receiving no pay over the first six weeks of
the survey. Data comes from the Census Bureau and SBA.
Week 15: Apr23-May5 Week 16: May7-May12
100 100
VT
Share of Households reporting receiving no pay for time not working Share of Households reporting receiving no pay for time not working
MO MT
ME
NC
NY IL AR SD
OR PA WI NM WV
AR HI OR FL AK
DE TN
NC ID IL WY WV
ND VT AZ CO TX
ID
OHPA
MI
90 UT CO 90 WAUT AL KY HI
CA FLWA
SC IA NH ME ND
NY AK AL OH VA MN WI
AZ TN RI
MI MO CA DC OK
MD TX SD
MS
MT MD IN
LA CT
MN NEKS SC DE CT
NV KY WY KS
NJ VA MA NJ GA IA NH
IN RI
DC NM NV LA
GA MA NE
OK
80 80 MS
70 70
-.2 0 .2 .4 -.2 0 .2 .4
State PPPE (Round 1) State PPPE (Round 1)
Week 17: May14-May19 Week 18: May 21-May26
100 100
Share of Households reporting receiving no pay for time not working Share of Households reporting receiving no pay for time not working
NH
OR AK OR WY
CT ME ID
AZ AZ VT
FL VT MD
NC MN ME
NJ OH WV NH NE
ID NM
TX MI MT
ND FL NC IAKS
UT GA MD CO TN WY INARNEIA
KS
SD
SC
NY IL
90 SC IL 90 WA
UT CO MT
WI VA AK NM CTTN MA
AL
KYWI
MO
RI MO NJ RI
MN
DC VA PA OK CA SD OK
NV GA TX
PA IN WV ND
CA WA KY LA MI
NY AL NV AR MS
MA DC HI
HI
MS
LA DE
80 80
OH
DE
70 70
-.2 0 .2 .4 -.2 0 .2 .4
State PPPE (Round 1) State PPPE (Round 1)
Week 19: May 28-Jun2 Week 20: Jun4-Jun9
100 100
Share of Households reporting receiving no pay for time not working Share of Households reporting receiving no pay for time not working
ID
OR
ID AK FL ME
WA MTNH VT
ME
CO TNMN IA ND
ARNE AK PA NESD
MT
AZ OR FL DELA WY HI CA NC
SC ND
OK VT
MO KYWI
90 DC NY MD IL
NM 90 NV CO NM TN AR KS
UT VA NY
WA
NJ AL OHWY
CT IA NH
NJ KYWI MS AZ DE
ILLA MI
MA IN
NV CA NC MI KS OK
SD MD HIMS
PA WV MN
SC AL
CT IN TX WV
MA
RI RI MO
GA
OH UT GA
TX VA
DC
80 80
70 70
-.2 0 .2 .4 -.2 0 .2 .4
State PPPE (Round 1) State PPPE (Round 1)
110
Figure F.2: PPP Exposure and Permanent Shutdowns
Figure F.2 plots binned scatter plots of the share of small business establishments that permanently closed versus ZIP-level PPPE. Both variables
are residualized with respect to the controls in Table 5, an additional control for the average number of shutdowns in a ZIP code in the
two weeks prior to PPP, and demeaned at the state level to present the within-state relationship. Permanent shutdowns are defined as the
establishment being closed for all weeks from the beginning of the PPP through the end of August. The graph reports regression coefficients
with and without the Table 5 controls and standard errors clustered at the state level. Outcome data come from Homebase.
16
No Controls: β = −0.72(0.32)
Controls: β = −0.97(0.37)
Permanent Shutdown (Residualized, %)
10 12 14
8
-2 -1 0 1 2
Exposure (Residualized and Standardized)
111
112
We We
ek Coefficient e Coefficient
1 0:
k1
0:
We
Ma
r2 -.02 0 .02 .04 We
Ma
r22 -.02 0 .02 .04
e k1 2-M ek -M
1: ar2 11 ar2
Ma 8 : M 8
We
e r2 9-A
We
ek
ar2
9-A
k1 pr4 12 pr4
2: :A
We Ap We pr5
e k1 r5-A PPP Launch ek -A PPP Launch
3: pr1 13 pr1
We A pr1 1 We
:A
pr1 1
e k1 2-A ek 2-A
4: pr1 14 pr1
We A pr1 8 We
:A
pr1 8
e k1 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
5: pr2 15 pr2
We Ap 5 We
:A
p 5
r26 r26
e k1 -M ek -M
ay 16 ay
Figure F.3 investigates business shutdowns, changes in the ratio of hours worked, and changes in the number of employees for firms in the Homebase sample
6: 2 2
We
e M
ay 2nd Round Begins We
ek
:M
ay 2nd Round Begins
k1 3-M 17 3-M
7: ay : ay
We Ma
y1 9 We Ma
y1 9
ek 0-M ek 0-M
18 18
:M ay :M ay
We ay 16 We ay 16
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
24 24
ek
20 -M ek
20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -Ju 21 -Ju
We :J n6 We :J n6
un un
ek 7-J ek 7-J
22 un 22 un
:J 13 :J 13
We un We un
ek 14 ek 14
23 -J un 23 -J un
:J 20 :J 20
un un
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek
26 -J ul1
ek
26 -J ul1
:J 1 :J 1
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
that are name-matched to the PPP data set from SBA. In the upper panels, we plot OLS estimates from week-by-week regressions examining the impact of
ek 6-A ek 6-A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We : Au 8 We : Au 8
ek g9 ek g9
Week of PPP Projections (IV)
-A -A
Panel A. Business Shutdowns
31 ug 31 ug
:A 1 5 :A 1 5
Week of PPP Projections (OLS)
We ug We ug
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
ug ug
29 29
We We
ek Coefficient e Coefficient
1 0:
k1
0:
We
Ma
r2 -.15 -.1 -.05 0 .05 We
Ma
r22 -.02 0 .02 .04
e k1 2-M ek -M
1: ar2 11 ar2
Ma 8 : M 8
We
e r2 9-A
We
ek
ar2
9-A
k1 pr4 12 pr4
2: :A
We Ap We p
e k1 r5-A PPP Launch ek r5-A PPP Launch
3: pr1 13 p
We A pr1 1 We
:A
pr1
r11
e
delays in receiving PPP on differences in employment and firm outcomes. The OLS regressions repeat the specifications of column (1) of Table 7 for every
k1 2-A ek 2-A
4: pr1 14 pr1
We A pr1 8 We
:A
pr1 8
e k1 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
5: pr2 15 pr2
We Ap 5 We
:A
pr2 5
r26
e k1 -M ek 6-M
6: ay 16 a y
We M 2 2nd Round Begins We :M 2 2nd Round Begins
e k1 ay
3 ek ay
7: -M 17
:M
3-M
ay ay
We Ma
y1 9 We ay 9
ek 0-M ek 10
18 18 -M
:M ay :M ay
We ay 16 We ay 16
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
24 24
ek
20 -M ek
20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -J un 21 -J un
We :J
un 6 We :J
un 6
ek 7-J ek 7-J
22
:J u n1 22
:J u n1
We un 3 We un 3
ek 14 ek 14
23 -Ju 23 -Ju
:J n 20 :J n 20
un un
We 21 We 21
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek -Ju ek -Ju
26 l1 1
26 l1 1
week in the sample. In the bottom panels, we plot IV estimates from week-by-week regressions examining the impact of delays in receiving PPP on differences
:J :J
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
ek 6-A ek 6-A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We :A
u 8 We :A
u 8
ek g9 ek g9
Week of PPP Projections (IV)
-A -A
31
:A u g1 31
:A u g1
5 5
Week of PPP Projections (OLS)
We ug We ug
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
22 22
Panel B. Change in Hours Worked
ug ug
23 23
-A -A
ug ug
29 29
We We
ek Coefficient e Coefficient
1 0:
k1
0:
We
Ma
r2 -.15 -.1 -.05 0 .05 We
Ma
r22 -.02 0 .02 .04
e k1 2-M ek -M
1: ar2 11 ar2
8 8
in employment and firm outcomes. The IV regressions repeat the specifications of column (3) of Table 7 for every week in the sample. Outcome variables
Ma : M
We
e r2 9-A
We
ek
ar2
9-A
k1 pr4 12 pr4
2: :
We Ap We Ap
e k1 r5-A PPP Launch ek r5-A PPP Launch
3: pr1 13 pr1
We A pr1 1 We
:A
pr1 1
e k1 2-A ek 2-A
4: pr1 14 pr1
We A pr1 8 We
:A
pr1 8
e k1 9-A 1st Round Exhausted ek 9-A 1st Round Exhausted
5: pr2 15 pr2
We Ap 5 We
:A
p 5
r26 r26
e k1 -M ek -M
6: ay 16 ay
We M 2 2nd Round Begins We :M 2 2nd Round Begins
e k1 ay
3-M ek ay
3-M
7: 17
ay : ay
We Ma
y1 9 We Ma
y1 9
ek 0-M ek 0-M
18 18
:M ay :M ay
We ay 16 We ay 16
ek 17 ek 17
19 -M 19 -M
:M ay :M ay
We ay 23 We ay 23
24 24
ek
20 -M ek
20 -M
:M ay :M ay
30 30
We ay We ay
ek 31 ek 31
21 -Ju 21 -Ju
We : Ju n6 We : Ju n6
n7 n7
Figure F.3: PPPE and Post-PPP Outcomes in the Matched Sample (Dynamic Analysis)
ek -Ju ek -Ju
22 n1 22 n1
:J 3 :J 3
We un We un
ek 14 ek 14
23 -J un 23 -J un
:J 20 :J 20
un un
We 21 We 21
are defined as the difference between measured outcomes in each week and their average in the two weeks preceding program launch. The right-hand-side
ek -Ju ek -Ju
24 n2 24 n2
:J 7 :J 7
We un We un
ek 28 ek 28
-Ju -Ju
25
:J l4 25
:J l4
We ul5 We ul5
ek
26 -J ul1
ek
26 -J ul1
:J 1 :J 1
We ul1 We ul1
ek 2-J ek 2-J
27 ul1 27 ul1
:J 8 :J 8
We ul1 We ul1
ek 9-J ek 9-J
28 ul2 28 ul2
:J 5 :J 5
We ul2 We ul2
ek 6-A ek 6-A
29 ug 29 ug
We :A 1 We :A 1
ek ug ek ug
2-A 2-A
30 ug 30 ug
We : Au 8 We : Au 8
ek g9 ek g9
Week of PPP Projections (IV)
31 -A 31 -A
ug ug
:A 1 5 :A 1 5
Week of PPP Projections (OLS)
We ug We ug
ek 16 ek 16
32 -A 32 -A
:A ug :A ug
ug 22 ug 22
23 23
-A -A
Panel C. Change in Nbr. Employees
ug ug
29 29
variable is the week of PPP receipt. Data is from Call Reports, SBA, and Homebase.
Table F.1: PPP Exposure and Homebase Employment Outcomes: Partition by State UI Replace-
ment Rates
Table F.1 reports the results of OLS regressions repeating the analysis of Table 5 after partitioning the sample into two subsamples based on
the median state UI replacement rates following the work of Ganong, Noel and Vavra (2020). Standard errors are clustered at the state level.
***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
Zip PPPE (Round #1) × I(Month=April) 0.007 -0.003 0.001 0.006∗∗ 0.001 0.005∗∗
(0.004) (0.005) (0.003) (0.002) (0.003) (0.002)
Zip PPPE (Round #1) × I(Month=May) -0.000 -0.007 0.021∗∗∗ 0.020∗∗∗ 0.022∗∗∗ 0.019∗∗
(0.007) (0.004) (0.005) (0.007) (0.006) (0.007)
Zip PPPE (Round #1) × I(Month=June) 0.003 -0.009∗∗ 0.034∗∗∗ 0.035∗∗∗ 0.035∗∗∗ 0.031∗∗∗
(0.007) (0.003) (0.009) (0.008) (0.011) (0.007)
Zip PPPE (Round #1) × I(Month=July) 0.007 -0.007 0.030∗∗ 0.035∗∗∗ 0.033∗∗ 0.033∗∗∗
(0.008) (0.006) (0.012) (0.009) (0.014) (0.009)
Zip PPPE (Round #1) × I(Month=August) 0.007 -0.008∗ 0.029∗∗ 0.035∗∗∗ 0.028∗∗ 0.033∗∗∗
(0.009) (0.004) (0.012) (0.010) (0.014) (0.009)
Observations 446476 373358 446476 373358 446476 373358
Adjusted R2 0.606 0.597 0.635 0.619 0.556 0.595
Sample High Rep Low Rep High Rep Low Rep High Rep Low Rep
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
Predicted PPPE × I(Month=April) 0.005 -0.006 -0.003 0.002 -0.002 0.003
(0.003) (0.004) (0.002) (0.004) (0.002) (0.004)
Predicted PPPE × I(Month=May) -0.004 -0.009 0.009∗∗ 0.008 0.009∗ 0.007
(0.004) (0.005) (0.004) (0.006) (0.004) (0.006)
Predicted PPPE × I(Month=June) -0.007 -0.005 0.022∗∗∗ 0.014∗ 0.020∗∗ 0.013∗
(0.005) (0.005) (0.007) (0.007) (0.009) (0.008)
Predicted PPPE × I(Month=July) -0.003 -0.007 0.016∗∗∗ 0.019∗∗ 0.011 0.019∗
(0.004) (0.006) (0.005) (0.008) (0.009) (0.010)
Predicted PPPE × I(Month=August) -0.002 -0.006 0.014∗∗∗ 0.018∗∗ 0.007 0.018∗
(0.005) (0.006) (0.004) (0.009) (0.009) (0.010)
Observations 446476 373358 446476 373358 446476 373358
Adjusted R2 0.606 0.597 0.635 0.619 0.556 0.595
Sample High Rep Low Rep High Rep Low Rep High Rep Low Rep
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
113
Table F.2: PPP Exposure and Local Labor Market and Economic Effects: Partition by State UI
Replacement Rates
Table F.2 reports the results of OLS regressions repeating the analysis of Table 6 after partitioning the sample into two subsamples based on
the median state UI replacement rates following the work of Ganong, Noel and Vavra (2020). Standard errors are clustered at the state level.
***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County PPPE × I(Month=April) -0.210∗∗ -0.101 0.001 -0.001 -0.000 -0.001
(0.084) (0.062) (0.006) (0.007) (0.007) (0.002)
County PPPE × I(Month=May) -0.282∗∗ -0.143 0.010 0.014 0.006 0.009∗∗
(0.104) (0.086) (0.011) (0.010) (0.005) (0.004)
County PPPE × I(Month=June) -0.266∗∗ -0.092 -0.006 -0.007 0.012 0.014∗∗∗
(0.115) (0.105) (0.015) (0.012) (0.011) (0.004)
County PPPE × I(Month=July) -0.258∗∗ -0.093 -0.017 -0.027∗ 0.008 0.018∗∗∗
(0.108) (0.102) (0.013) (0.014) (0.009) (0.006)
County PPPE × I(Month=August) -0.256∗∗ -0.051 -0.020 -0.013 0.013 0.019∗∗
(0.112) (0.086) (0.013) (0.009) (0.010) (0.007)
Observations 23261 22272 13419 14974 4422 5801
Adjusted R2 0.948 0.945 0.701 0.725 0.868 0.865
Sample High Rep Low Rep High Rep Low Rep High Rep Low Rep
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
County Fixed Effects Yes Yes Yes Yes Yes Yes
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County Predicted PPPE × I(Month=April) -0.081 -0.068 0.001 0.001 -0.004∗ 0.000
(0.061) (0.080) (0.005) (0.007) (0.002) (0.002)
County Predicted PPPE × I(Month=May) -0.081 -0.155 0.021 0.003 -0.007 0.010∗∗
(0.073) (0.130) (0.015) (0.007) (0.004) (0.004)
County Predicted PPPE × I(Month=June) -0.067 -0.160 0.023 -0.003 0.003 0.012
(0.072) (0.148) (0.020) (0.010) (0.008) (0.008)
County Predicted PPPE × I(Month=July) -0.064 -0.165 0.003 -0.013 -0.009 0.018
(0.080) (0.141) (0.013) (0.010) (0.017) (0.012)
County Predicted PPPE × I(Month=August) -0.076 -0.089 0.007 0.002 -0.009 0.023
(0.082) (0.126) (0.015) (0.010) (0.017) (0.014)
Observations 23261 22272 13419 14974 4422 5801
Adjusted R2 0.947 0.945 0.701 0.723 0.868 0.864
Sample High Rep Low Rep High Rep Low Rep High Rep Low Rep
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
County Fixed Effects Yes Yes Yes Yes Yes Yes
114
Table F.3: PPP Exposure and Homebase Employment Outcomes: Partition by Share Eligible for
PPP in County
Table F.3 reports the results of OLS regressions repeating the analysis of Table 5 . Standard errors are clustered at the state level. ***, **, and
*, represent statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
Zip PPPE (Round #1) × I(Month=April) 0.004 0.003 0.003 0.001 0.003 0.001
(0.004) (0.006) (0.002) (0.004) (0.002) (0.004)
Zip PPPE (Round #1) × I(Month=May) -0.005 0.001 0.017∗∗∗ 0.020∗∗∗ 0.016∗∗∗ 0.021∗∗∗
(0.005) (0.008) (0.005) (0.007) (0.005) (0.007)
Zip PPPE (Round #1) × I(Month=June) -0.008 0.003 0.033∗∗∗ 0.030∗∗∗ 0.029∗∗∗ 0.030∗∗
(0.006) (0.006) (0.006) (0.010) (0.008) (0.011)
Zip PPPE (Round #1) × I(Month=July) -0.010 0.012 0.038∗∗∗ 0.019 0.039∗∗∗ 0.020
(0.008) (0.007) (0.008) (0.013) (0.011) (0.015)
Zip PPPE (Round #1) × I(Month=August) -0.014∗ 0.018∗∗ 0.038∗∗∗ 0.018 0.039∗∗∗ 0.016
(0.007) (0.008) (0.008) (0.013) (0.011) (0.016)
Observations 409565 409078 409565 409078 409565 409078
Adjusted R2 0.601 0.603 0.634 0.628 0.606 0.551
Sample Hi Elig Low Elig Hi Elig Low Elig Hi Elig Low Elig
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
∆ Bus. Shutdown ∆ Hours Worked ∆ Nbr. Employees
Predicted PPPE × I(Month=April) 0.003 -0.003 -0.001 -0.001 -0.000 -0.001
(0.003) (0.005) (0.002) (0.004) (0.002) (0.004)
Predicted PPPE × I(Month=May) -0.002 -0.011∗ 0.002 0.014∗∗ 0.001 0.013∗∗
(0.003) (0.006) (0.004) (0.006) (0.005) (0.006)
Predicted PPPE × I(Month=June) -0.003 -0.012∗ 0.006 0.027∗∗∗ 0.002 0.025∗∗
(0.004) (0.007) (0.005) (0.009) (0.009) (0.010)
Predicted PPPE × I(Month=July) -0.004 -0.006 0.006 0.022∗∗∗ 0.006 0.013
(0.005) (0.007) (0.006) (0.006) (0.012) (0.010)
Predicted PPPE × I(Month=August) -0.004 -0.004 0.006 0.022∗∗∗ 0.005 0.013
(0.005) (0.007) (0.006) (0.006) (0.013) (0.010)
Observations 409565 409078 409565 409078 409565 409078
Adjusted R2 0.601 0.603 0.633 0.628 0.606 0.551
Sample Hi Elig Low Elig Hi Elig Low Elig Hi Elig Low Elig
State×Industry×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
Firm Fixed Effects Yes Yes Yes Yes Yes Yes
115
Table F.4: PPP Exposure and Local Labor Market and Economic Effects: Partition by Share
Eligible for PPP in County
Table F.4 reports the results of OLS regressions repeating the analysis of Table 6 after partitioning the sample into two subsamples based on
the median share of establishments in the county that are eligible for participation in the PPP. Standard errors are clustered at the state level.
***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.
(1) (2) (3) (4) (5) (6)
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County PPPE × I(Month=April) -0.113∗∗∗ -0.057 -0.002 0.004 0.005 -0.004
(0.035) (0.074) (0.006) (0.003) (0.004) (0.006)
County PPPE × I(Month=May) -0.141∗∗ -0.119 -0.001 0.017∗ 0.009∗∗ 0.008
(0.057) (0.094) (0.012) (0.008) (0.004) (0.005)
County PPPE × I(Month=June) -0.103 -0.111 -0.027∗∗ 0.004 0.013∗∗∗ 0.010∗
(0.066) (0.091) (0.011) (0.009) (0.005) (0.005)
County PPPE × I(Month=July) -0.107 -0.098 -0.037∗∗ -0.011 0.017∗∗∗ 0.009∗
(0.065) (0.095) (0.016) (0.007) (0.006) (0.005)
County PPPE × I(Month=August) -0.089 -0.061 -0.031∗∗∗ -0.012 0.015∗∗ 0.009
(0.067) (0.096) (0.011) (0.008) (0.007) (0.006)
Observations 22426 22774 20973 21487 7728 8050
Adjusted R2 0.942 0.952 0.694 0.753 0.894 0.862
Sample Hi Elig Low Elig Hi Elig Low Elig Hi Elig Low Elig
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
County Fixed Effects Yes Yes Yes Yes Yes Yes
∆ UI claims ∆ Small Bus. Rev. ∆ OI Emp.
County Predicted PPPE × I(Month=April) -0.061 -0.028 -0.005 0.006 -0.004 0.000
(0.053) (0.091) (0.006) (0.004) (0.005) (0.003)
County Predicted PPPE × I(Month=May) -0.082 -0.064 -0.004 0.014 0.008 -0.003
(0.070) (0.101) (0.009) (0.009) (0.005) (0.005)
County Predicted PPPE × I(Month=June) -0.070 -0.069 -0.006 0.011 0.013∗∗ 0.001
(0.065) (0.108) (0.008) (0.012) (0.006) (0.007)
County Predicted PPPE × I(Month=July) -0.072 -0.071 -0.018∗∗ 0.001 0.012 0.002
(0.067) (0.122) (0.009) (0.009) (0.009) (0.007)
County Predicted PPPE × I(Month=August) -0.063 0.002 -0.016 -0.001 0.013 0.002
(0.069) (0.115) (0.010) (0.009) (0.010) (0.007)
Observations 22426 22774 20973 21487 7728 8050
Adjusted R2 0.942 0.952 0.692 0.752 0.894 0.862
Sample Hi Elig Low Elig Hi Elig Low Elig Hi Elig Low Elig
State×Week Fixed Effects Yes Yes Yes Yes Yes Yes
Other Control Variables Yes Yes Yes Yes Yes Yes
County Fixed Effects Yes Yes Yes Yes Yes Yes
116
G Bartik Diagnostics Appendix
This appendix presents diagnostic analysis of our PPP Exposure research design following the
tests suggested in Goldsmith-Pinkham, Sorkin and Swift (2020) for “unpacking the black box”
of Bartik-style research designs. In Goldsmith-Pinkham, Sorkin and Swift (2020), the Bartik
estimator is shown to be a weighted average of just-identified, group-specific IV estimates βk ,
where k denotes a group. The weights αk are called Rotemberg weights. They combine the
group-specific shock g k with the covariance of group-region shares zl k and the endogenous
variable X to be instrumented. The estimand of interest is the effect of X on some outcome Y.
(See Proposition 3, p.2600.)
In our setting, k refers to a bank, g k is bank-level PPPE, and zl k are bank-ZIP-level shares
of the number of branches. In the main text, we focus on the reduced form relation between
our Bartik instrument and various outcomes. Here, we use a two-stage setup where Y is the
mean ZIP-level change in hours worked between the base period and week 18 (May 17–May
23) and X is the ZIP-level number of PPP loans received at the end of round one relative to the
number of eligible establishments.
For reference, in this setup, the Bartik estimator βBar t ik estimated by 2SLS with standard
errors clustered at the state-level is 0.41 (s.e.=0.099) with an F statistic of 1150. The regression
in this setup conditions on the controls in our main regression in Table 5.
Table G.1 lists the ten banks with the most positive and most negative Rotemberg weights
in the Bartik IV implementation of our PPPE research design. These weights reflect the banks’
respective influence in the Bartik instrument, which combines bank-ZIP-level shares of the
number of branches with bank-level PPPE. First, the table suggests that influential banks tend
to be either large or mid-sized banks and those banks with PPPE that points to substantial
overperformance or underperformance. For example, JP Morgan, Citibank, and Wells Fargo
all have large Rotemberg weights. Second, Wells Fargo’s weight is the only negative weight
with absolute magnitude over 0.01. This fact indicates that more of the identifying variation
comes from banks with positive weights, which enables the Bartik estimator to be interpreted
as a LATE. Finally, the table suggests that the Bartik estimator reflects contributions from a
large number of banks, as the top ten banks in terms of Rotemberg weight only contribute
approximately 20% of total weight to the Bartik estimator. This result suggests our approach
is not driven just by one or two banks, or even by the top-4 banks alone.
Table G.2 reports statistics about the Rotemberg weights in the Bartik IV implementation of
our PPPE research design (following Table 1 in Goldsmith-Pinkham, Sorkin and Swift (2020)).
These weights reflect the banks’ respective influence in the Bartik instrument, which combines
117
bank-ZIP-level shares of the number of branches with bank-level PPPE. Panel A shows that
banks with positive Rotemberg weights account for two-thirds of observations while banks
with negative weights account for one-third of observations. However, Panel C shows that
negative weights contribute relatively little to the overall estimator.
We also considered several of the Bartik diagnostics advocated by Goldsmith-Pinkham,
Sorkin and Swift (2020). Table G.3 presents bivariate regressions of bank branch shares (zlk )
on ZIP-level observables. Both branch shares and observables are residualized with respect to
state dummies. Variables have been normalized, so the coefficients can be interpreted as a
one-standard deviation change in x produces a β-standard deviation change in branch shares,
where β is the reported coefficient. While bank-branch shares are modestly correlated with
local observables—for example, branch concentration is higher in low income and less dense
areas—all correlations are below 0.1 in absolute value, supporting our identification assump-
tion of approximate independence of bank branch shares from the outcomes of interest.
Goldsmith-Pinkham, Sorkin and Swift (2020) also advocate plotting pre-trends for research
designs where regions are sorted based on the level of the Bartik instrument. We show in
Figures 7 and 9 that pre-COVID, pre-PPP trends are very similar for high and low PPPE regions
for a variety of outcomes. Our local projections in Figures 8 and 10 shows parallel trends in the
short window after COVID lockdowns and prior to the PPP, once we condition on differences
in targeting.
118
Table G.1: Unpacking PPP Exposure: Influential Banks
Table G.1 lists the ten banks with the most positive and most negative Rotemberg weights in the Bartik IV im-
plementation of our PPPE research design. These weights reflect the banks’ respective influence in the Bartik
instrument, which combines bank-ZIP-level shares of the number of branches with bank-level PPPE. αk refers to
Rotemberg weight for bank k. g k is bank-level PPPE. βk is the just-identified coefficient. See Goldsmith-Pinkham,
Sorkin and Swift (2020) for additional discussion of these diagnostics.
Financial Institution Name αk gk βk
Top 10 Positive αk
KEYBANK NATIONAL ASSOCIATION 0.0667 0.3868 -0.1196
MANUFACTURERS AND TRADERS TRUST COMPANY 0.0585 0.3964 -0.2267
JPMORGAN CHASE BANK, NATIONAL ASSOCIATION 0.0264 -0.3596 -0.5714
CITIZENS BANK, NATIONAL ASSOCIATION 0.0244 0.2529 -0.2744
HUNTINGTON NATIONAL BANK, THE 0.0146 0.4204 1.4877
NBT BANK, NATIONAL ASSOCIATION 0.0143 0.3351 -0.2642
BANCORPSOUTH BANK 0.0123 0.3998 0.0079
CITIBANK, N.A. 0.0115 -0.4552 0.6701
CAPITAL ONE, NATIONAL ASSOCIATION 0.0113 -0.4988 0.2671
UNITED COMMUNITY BANK 0.0111 0.4436 0.6872
Bottom 10 Negative αk
WELLS FARGO BANK, NATIONAL ASSOCIATION -0.0353 -0.4849 0.8005
TIMBERWOOD BANK -0.0055 -0.5000 -0.8560
BANK OZK -0.0053 0.4067 -2.0285
COMERICA BANK -0.0048 0.2157 0.3192
BANK OF HAWAII -0.0045 0.4407 0.5173
SOUTH STATE BANK -0.0042 -0.5000 -1.7268
PEDESTAL BANK -0.0041 -0.5000 -1.7775
BOKF, NATIONAL ASSOCIATION -0.0041 0.4705 1.0714
TRI CITY NATIONAL BANK -0.0040 0.4678 0.2811
WESBANCO BANK, INC. -0.0040 0.3507 -1.9260
119
Table G.2: Unpacking PPP Exposure: Summary of Rotemberg Weights
Table G.2 reports statistics about the Rotemberg weights in the Bartik IV implementation of our PPPE research
design (following Table 1 in Goldsmith-Pinkham, Sorkin and Swift (2020)). These weights reflect the banks’
respective influence in the Bartik instrument, which combines bank-ZIP-level shares of the number of branches
with bank-level PPPE. αk refers to Rotemberg weight for bank k. g k is bank-level PPPE. βk is the just-identified
coefficient. V(zk ) refers to the variation in bank-ZIP-level shares within a bank across ZIPs. See Goldsmith-
Pinkham, Sorkin and Swift (2020) for additional discussion of these diagnostics.
Sum Mean Share (N)
Panel A. Negative and positive weights
Negative -0.6202 -0.0004 0.3348 (1,716)
Positive 1.6202 0.0005 0.6652 (3,409)
αk gk βk V(zk )
Panel B. Correlations
αk 1.0000
gk 0.0405 1.0000
βk 0.0013 -0.0175 1.0000
V(zk ) 0.0475 -0.0296 -0.0026 1.0000
α-weighted Share of
sum overall β Mean
Panel C. Estimates of βk for positive and negative weights
Negative -0.0034 -0.0096 3.7104
Positive 0.3578 1.0096 -5.7637
120
Table G.3: Correlates of Bank Branch Shares
Table G.3 presents bivariate regressions of bank branch shares (zl k ) on ZIP-level observables. Both branch shares
and observables are residualized with respect to state dummies. Variables have been normalized, so the coef-
ficients can be interpreted as a one-standard deviation change in x produces a β-standard deviation change in
branch shares, where β is the reported coefficient. ***, **, and *, represent statistical significance at 1%, 5%,
and 10% levels, respectively, with standard errors clustered at the state level. See Goldsmith-Pinkham, Sorkin
and Swift (2020) for additional discussion of these diagnostics.
LHS is Bank Deposit Shares
Coefficient R2 N
Log(Population) -0.032*** 0.0009 668583
(0.004)
Log(Population Density) -0.068*** 0.0034 668583
(0.007)
Social Distancing 0.068*** 0.0023 669985
(0.010)
Covid Cases per Capita -0.071*** 0.0013 670219
(0.008)
Deaths per Capita -0.030*** 0.0005 670219
(0.003)
Unemployment Filing Ratios 0.023 0.0001 444375
(0.016)
Revenue Change of Small Business -0.048*** 0.0012 670219
(0.012)
Median Household Income -0.055*** 0.0019 670204
(0.009)
121
File and source
- File
- did-the-paycheck-protection-program-hit-the-target-nber-working-paper-no.pdf
- Size
- 1,977,214 bytes
- SHA-256
- 560e780e6bc089813ada34b606082e45ec43c0ccfb5e16e639816ab5c616a9c4
- Original
- www.nber.org