WORKING PAPER SERIES

Small Bank Financing and Funding Hesitancy in a 

Crisis: Evidence from the Paycheck Protection Program

Tetyana Balyuk

Emory University

Nagpurnanand Prabhala

The Johns Hopkins University

Manju Puri

Duke University, NBER, and FDIC

November 2020

Last Updated: September 2021

Previously circulated as “Indirect Costs of Government Aid and Intermediary Supply Effects: 

Lessons from the Paycheck Protection Program”

FDIC CFR WP 2021-01

fdic.gov/cfr

The Center for Financial Research (CFR) Working Paper Series allows CFR staff and their coauthors to circulate 

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suggestions are welcome and should be directed to the authors. References should cite this research as a “FDIC CFR 

Working Paper” and should note that findings and conclusions in working papers may be preliminary and subject to 

revision.
Small Bank Financing and Funding Hesitancy in a Crisis:

Evidence from the Paycheck Protection Program

September 6, 2021

ABSTRACT

We study the delivery of subsidized financing to small firms through the Paycheck Protection Program (PPP). Smaller firms are less likely to gain early PPP

access, an effect attenuated in small banks and firms with prior lending relationships. Their more even treatment offers a new rationale, beyond traditional soft

information arguments, for why small businesses pair with small banks. We also

detect a “funding hesitancy” in PPP uptake by small businesses, partly reflecting

their wariness of the extensive, subjective government powers to investigate PPP

recipients. We discuss the implications of the results for research and policies on

small business financing.

Keywords: Bank Relationships, Covid-19, Coronavirus, Paycheck Protection Program, PPP, SBA, Small Business Lending

JEL classification: G32, G38, H81, E61
1. Introduction

The Covid-19 pandemic has been a black swan event. It has resulted in the loss of over 3.5

million lives. It has also severely impacted livelihoods due to a widespread, speedy, and deep

economic contraction. For instance, U.S. GDP contracted by a third and unemployment

jumped from 4% to 15% within a quarter. Small businesses have been hit particularly

hard (Chetty, Friedman, Hendren, and Stepner, 2020). Moreover, they are key sources of

employment and growth, so helping them has been a policy priority.1

Fiscal stimulus has been the key instrument for helping small businesses. In the U.S.,

its centerpiece is the $669 billion Paycheck Protection Program (PPP), which provides extraordinarily inexpensive financing for small firms. Loans are disbursed through a simple

application process. Proceeds used in eligible ways are forgiven while the rest converts to

a 5-year 1% loan – attractive terms for any firm. Not surprisingly, small businesses rushed

to get PPP funding. The initial PPP funding of $349 billion was quickly exhausted. The

program closed, reopening after 10 days after a fresh appropriation of $320 billion.

We exploit the fact that the initial days of the PPP were a large-scale shock for both

the banks used to pipe PPP funding and the small businesses the PPP targets. We study

two related issues. To motivate the first question, observe that banks were overwhelmed in

the initial PPP stages, both due to the aggregate shortage of PPP funds and the surge in

applications for this inexpensive funding. Banks were forced to prioritize clients. This setting

provides a rare - and clear - window into banks’ allocation priorities when facing resource

constraints - a subject of importance, given that crisis tend to recur, and banks periodically

face resource constraints. Our particular focus is on the differences between small and big

banks.

We find that there are significant differences between how big and small banks prioritize

small clients: big banks are more likely to prioritize larger clients early. We show that this

effect is attenuated and sometimes reversed for small banks, who extend more even treatment

to their smaller clients.2 We further investigate this pattern using a large database of UCC

filings to identify bank-firm lending relationships. We find that such relationships also help

firms gain early PPP access. We find little evidence that relationships with big banks help

more – if anything, small bank relationships help firms get early PPP access, especially with

1Small businesses account for 60.6 million jobs, or 47.1% of the total U.S. employment in 2020. See “2020

Small Business Profile at https://cdn.advocacy.sba.gov/wp-content/uploads/2020/06/04144224/202

0-Small-Business-Economic-Profile-US.pdf.

2For anecdotal evidence of “concierge treatment,” see, e.g., “Banks Gave Richest Clients ‘Concierge

Treatment’ for Pandemic Aid,” The New York Times, April 22, 2020 at https://www.nytimes.com/2020

/04/22/business/sba-loans-ppp-coronavirus.html. Our study presents formal evidence and shows its

variation across big and small banks, an issue that has attracted less attention.

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small bank lenders. The more even treatment of small firms vis-a-vis large firms by small

banks offers a new rationale for why small businesses tend to borrow from small banks.

This rationale is different from and adds to the more traditional explanations based on soft

information, which, as we discuss later, has little role in PPP lending.

The second part of our analysis focuses on the small businesses targeted by the PPP.

As these small firms are highly constrained even in normal times, they should welcome

the PPP, which delivers inexpensive funding, does so promptly, and as we show later, has

positive valuation effects. However, we find a “funding hesitancy” among PPP recipients,

most starkly manifest in the decisions of several firms to return PPP funds without using

them and the positive valuation effects associated with PPP return. Further empirical tests

suggest that funding hesitancy is partly due to wariness of government powers to investigate

PPP recipients, in turn likely due to subjectivity in its standards and scope as well as the

absence of safe harbors. We discuss the policy implications of these results for the design of

government aid programs.

We construct four datasets. The first is the December 2020 SBA PPP data release

comprising 5.2 million PPP borrowers. The second dataset includes publicly listed PPP

borrowers identified through algorithmic searches of 8-K and 10-K filings. The public PPP

sample has two sets of benefits. One, we have firm-specific information for controls and return

data to compute program valuation effects. As importantly, this sample lets us identify PPP

returners and address issues relating to funding hesitancy. The SBA PPP dataset does not

identify returners. In both samples, we define early borrowers as those approved for PPP

before April 17, 2020, the initial program PPP closure date.

The third and fourth datasets identify bank relationships. Our initial source is the

DealScan database, which, however has limited coverage of small firms. The other dataset

comprises Uniform Commercial Code (UCC) security interest filings of banks through December 2020. These data include 32.5 million observations and significantly expand the

coverage of small firm bank relationships (Gopal and Schnabl, 2020).

We turn to the results on early access. From the viewpoint of demand for PPP by firms,

smaller firms should access PPP funds sooner if they are more constrained, weaker, and

less resilient to the Covid-19 shock. However, we find that larger firms tend to gain early

PPP access. For example, in the public PPP sample, early borrowers are larger, with mean

(median) book value of assets of $120.3 ($39.2) million compared to $99.8 ($23.3) million for

late borrowers. We find similar results when we use the PPP loan amount as a size proxy,

in the SBA PPP sample, and in regressions with controls. Large borrower prioritization is a

robust feature of the data.

We next analyze the variation in large firm PPP access across small and big banks. We

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define big banks as the top 10 banks according to asset size. Other banks are small.Big banks

tend to grant early access for larger firms, an effect attenuated in small banks. For example, in

big banks, early PPP borrowers are 2-3 times larger than late borrowers: the mean (median)

book values equal $144.2 ($55.11) million versus $61.36 ($18.7) million, respectively. For

small banks, the early and late borrowers are closer in size with mean (median) book values

of $82.4 ($36.4) million and $86.7 ($26.9) million, respectively. Regressions with controls

confirm the results for the public and the SBA PPP samples.

We turn to the role of prior bank relationships. Identifying relationships is straightforward

in the DealScan data and for public firms using COMPUSTAT identifiers. The process is

less simple with the UCC filings and for the SBA PPP dataset as the linking identifiers are

firm and bank names. Besides variations in spelling, we must contend with bank mergers

in mapping the legacy UCC bank names to those in the PPP data. We use fuzzy matching

followed by extensive manual interventions to cross-walk the 33 million observations in the

UCC dataset with the 5.2 million observations in the SBA PPP dataset.

We find that bank relationships, particularly with small banks, matter. Firms applying

for PPP with small banks tend to gain early access but a prior small bank relationship has

its own beneficial effect. For example, firms that have no UCC or DealScan relationship,

early PPP borrowers are twice as large as late borrowers, with median (mean) book values of

$64 ($35) million versus $38 ($15) million, respectively; these figures are $248 ($161) million

and $151 ($69) million for firms with big bank relationships. The patterns reverse sharply

when borrowers have small bank relationships. Here, early firms are smaller than the later

firms: $119 ($43) million versus $178 ($94) million, respectively. Thus, a prior small bank

relationship helps mitigate the pattern of greater early access to PPP for larger firms. The

results carry over to the larger SBA PPP sample.

The results have interesting implications for banking research. A distinct pattern in the

data is that small firms tend to pair with small banks. This is seen in the Federal Reserve’s

Survey of the Terms of Bank Lending to Businesses (STBL), small business lending data

in Call Reports, and most recently in the 2018 FDIC Survey of Small Business Lending.

Related research includes Berger, Saunders, Scalise, and Udell (1998) and Berger, Miller,

Petersen, Rajan, and Stein (2005). Stein (2002) provides a theory for small bank – small

firm pairing. He argues that soft information, which is critical in small business lending, is

hard to transmit up bank hierarchies typical of large banks. Thus, in small business lending,

it is optimal to colocate decision making and information gathering, as in small banks.

Our PPP evidence offers an alternative explanation for why small businesses tend to bank

with small banks. In the initial PPP period, a surge in PPP demand coupled with aggregate

shortage of PPP funding forced banks to prioritize clients. We find that in this period,

3
small banks tend to be more responsive to small businesses. The finding suggests that in

big banks, small businesses experience a “small fish in big pond” effect that is mitigated in

smaller banks for whom small business lending is a mainstay of their lending portfolio. This

explanation does not rely on soft information considerations, which has little role here given

government guarantees of PPP loans as we discuss in detail later.

The second issue we study concerns PPP uptake. As motivation, we note that the firms

targeted by PPP, small businesses, face severe financial constraints even in normal times

(Petersen and Rajan, 1994; Whited and Wu, 2006; Hadlock and Pierce, 2010; Banerjee and

Duflo, 2014). PPP should be welcome news for such firms. It is immediately available and

it is extraordinarily inexpensive, with costs range from -100% (with full forgiveness) to a

maximum of 1%. Moreover, markets reward firms that take PPP. Average uptake valuation

effects exceed 1% and the treatment effects are positive after econometric adjustments for

heterogeneity in pandemic period returns and partial anticipation of PPP uptake.

Yet, we find some reluctance by firms to take PPP funds, which we call “funding hesitancy.” Perhaps the sharpest evidence of hesitancy is our finding that over 100 public firms

that get PPP funds simply return the funds without using them. Moreover, while one might

expect returning cheap funding should decrease share prices, reflecting lost financing subsidies, returners experience positive valuation effects averaging 3%. Markets appear to reward

firms that return PPP!

What might explain this aversion to PPP funding? Direct costs of PPP funds are clearly

implausible as these funds are far cheaper than alternatives from banks or the market. The

direct costs from PPP restrictions (e.g., on layoffs) are also implausible. For firms that find it

difficult to comply, the worst case scenario is that PPP forgiveness is lost and PPP becomes

a 1% loan, still far less expensive than alternatives. Perhaps then, rather than direct costs,

there are some indirect costs that prompt PPP return. What could their nature be?

A significant indirect cost of PPP comes from the threat of ex-post government investigations of PPP recipients. Three features make these costs salient. One, investigation

windows are very long – at least 6 years from PPP disbursement. Two, the standards for

initiating investigations, their scope, and the process are highly subjective, leaving a lot of

discretion in government hands.3 Finally, the program provides no safe harbors that shield

PPP recipients from ex-post investigations. Given this lack of clarity and the fungibility

of funds, it is easy to attribute any use of funds to PPP funding, making recipients fodder

for sensationalist media coverage.4 Should these ambiguities concerning investigations mat3An example of the ambiguity is the need to certify that “[PPP is] ... necessary taking into account

current activity... and access to other sources of liquidity in a manner not significantly detrimental to the

business.” These are clearly subjective standards.

4Consider the story in Washington Post on September 26, 2020 “Publicly traded firms paid dividends,

4
ter to firms? Research certainly suggests so: Simply being an investigation target triggers

shareholder wealth losses. Capital raising in its shadow is difficult and, moreover, senior

executives and directors face negative consequences in the labor market.5

We conduct tests to assess to explore these indirect costs. One set of tests looks at

the decision to return PPP, using the public PPP sample in which returners are identified.

If larger loans are more likely to be investigated by the government, PPP returns should

be more likely for larger loans. Relatedly, SBA rules indicate that PPP loans exceeding $2

million and outstanding after May 18, 2020 are subject to audit. Two implications fall out of

this observation. One, PPP returns may be more likely above the $2 million cutoff, which we

can examine for the public PPP sample. Two, loan application probabilities should exhibit

a discontinuity around $2 million. This test can be done for the larger SBA sample. A

further implication is that the discontinuity should be asymmetric before and after May 18,

2020, when the $2 million cutoff became salient. The results broadly support the view that

government investigation threats are at least partially responsible for funding hesitancy.

The rest of the paper is organized as follows. Section 2 discusses background information

and relevant literature. Section 3 describes the datasets used in the study. Section 4 analyzes

early PPP access. Sections 5 and 6 discuss the uptake of PPP, relevant treatment effects

estimation, and funding hesitancy. Section 7 concludes.

2. Background

2.1. The PPP Program Architecture

Figure 1 gives the timeline leading up to the passage of the $669 Paycheck Protection

Program (PPP). This original “CARES” Act included a $349 appropriation for PPP. It was

proposed on January 24, 2020, passed by the legislature on March 25, 2020 and March 26,

2020, and signed into law by the U.S. President on March 27, 2020. The initial PPP allocation

of $349 billion was exhausted quickly by April 16, 2020, when the program stopped accepting

new applications. The PPP resumed accepting applications on April 27, 2020 after an

additional $320 billion appropriation under the Paycheck Protection Program and Health

Care Enhancement Act of 2020 signed on April 24, 2020.

The next significant milestone was May 18, 2020. As announced in late April–early May,

firms returning PPP before this date would not be subject to audit. The next milestone was

bought their own stock after receiving PPP loans to pay employees” at https://www.washingtonpost.com

/business/2020/09/24/dividends-buybacks-ppp-loans.

5Jarrell and Peltzman (1985), Alexander (1999), Haslem, Hutton, and Smith (2017), Karpoff, Lee, and

Martin (2008), Fich and Shivdasani (2007), Murphy, Shrieves, and Tibbs (2009), Lin and Paravisini (2011).

5
June 2, 2020, when the PPP Flexibility Act (PPPFA) was passed, which altered program

rules significantly. 96% of PPP proceeds were disbursed prior to this date within a relatively

homogenous regulatory regime. Our sample focuses on this period. The program expired on

August 8, 2020. It disbursed $525 billion to 5.21 million borrowers.

Application Process: The PPP is a forgivable, collateral-free loan program administered

through the Small Business Administration (SBA), a federal agency that guarantees the

PPP loans. Borrowers apply through an approved financial institution, provide documentation relating to eligible expenses such as payroll and allowed overhead, and make necessary

representations and certifications. After SBA approval, funds are disbursed by the banks.

Eligibility: Eligible borrowers are small firms as defined in the CARES Act. Standards

include a principal place of residence in the U.S., being in operation before February 15,

2020, having fewer than 500 employees, or other standards, e.g., as under Small Business

Act, 15 U.S.C 632. PPP eligibility is not conditioned on the credit risk of borrowers.

Amount and Terms: The maximum PPP loan is $10 million per eligible entity. Forgiveness applies to amounts spent as per the program rules. The principal not forgiven turns

into a loan with a 5-year maturity if made on or after June 5, 2020 and a 2-year maturity

otherwise unless both the lender and borrower agree to a 5-year term.

Spending PPP funds: The PPP incentivizes early spending of proceeds. Banks must

make loans within 10 days of approval. Forgiveness applies to eligible payroll spent within

time periods laid out in the Act, initially 8 weeks and extended later to 24 weeks under the

June 2, 2020 PPPFA. Forgiveness is reduced if there are layoffs.

Certification: Borrowers receiving PPP funds must certify that “... the current economic

uncertainty makes the loan necessary to support ongoing operations” and as per PPP Rule 31

issued on June 25, 2020 “... taking into account ... their ability to access other sources of

liquidity” in a manner that is not significantly detrimental to the business.

Lender Responsibilities and Fees: Lenders submit applications on behalf of borrowers and

fund the loans. The rules are quite explicit that lenders should rely on borrower certifications

and representations to submit applications. Thus, the traditional soft information production

role of banks is moot under PPP. Moreover, fraud becomes a concern only when banks

undertake willful actions to actively create and abet it. Lender fees equal 5% for loans up

to $350,000, 3% for loans between $350,000 and $2,000,000, and 1% for loans of at least

$2,000,000. An April 17, 2020 ruling lets lenders sell PPP loans.

Powers to Investigate: The SBA retains the right to review all loans for eligibility. It

clarified that loans over $2 million would be audited but smaller loans may not be. If a firm

is found ineligible in an audit, it needs to return principal plus interest and is subject to any

other remedies that the SBA may seek. Firms that repay PPP loans in full by May 7, 2020

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– later extended to May 18, 2020 – are not subject to investigations. These and related rules

were clarified by the SBA at different times and attracted attention. While the clarifications

help clarify SBA rules, the absence of formal safe harbors and the repeated pronouncements

on the topic can raise the investigative threat more salient for PPP recipients.

2.2. Related Covid-19 Research

The Covid-19 pandemic has attracted a vast literature. For a curated collection of several

hundred Covid Economics papers, see the website maintained by Center for Economic Policy

and Research.6 An extended review of the banking research related to Covid-19 is in Berger

and Demirguc-Kunt (2021). We briefly review some of the work relevant to our study.

Several studies examine the impact of Covid-19 on firm outcomes. Granja, Makridis,

Yannelis, and Zwick (2020) show in an ongoing real time analysis that the PPP has had

limited impact on labor market outcomes. Kim (2020) argues that the impact is greater if

one accounts for lender constraints. Cororaton and Rosen (2020) analyze the characteristics

of 424 public PPP applicants, and in later versions, follow our approach and verify a subset

of our results on PPP announcement and returns in Sections 5 and 6. Li and Strahan

(2020) show that the PPP supply alleviates Covid-related unemployment shocks, using an

identification strategy at a geographic level based on the structure of the local banking sector.

Bartik, Cullen, Glaeser, Luca, Stanton, and Sunderam (2020) and Bartlett and Morse (2020)

find positive impacts on very small businesses. While assessing PPP impact is not our main

focus, we provide some evidence based on stock market data.

Theoretical work on PPP funding includes Hanson, Stein, Sunderam, and Zwick (2020),

who discuss the government as a venture capitalist of last resort. Other literature addresses

financing concerns and stock market patterns in the pandemic. Acharya and Steffen (2020)

and Acharya, Engle, and Steffen (2020) highlight a Covid period “dash for cash.” Over 95%

of our sample includes unrated firms, who do not display this dash for cash. The role of

FinTech lenders in PPP supply is addressed by Erel and Liebersohn (2020). The unusual

nature of Covid-19 period stock returns is the focus of Acharya et al. (2020), Fahlenbrach,

Rageth, and Stulz (2020), Baker, Bloom, Davis, Kost, Sammon, and Viratyosin (2020). We

account for this issue in estimating valuation effects.

Relevant to our work on funding hesitancy is the literature on the penalties suffered by

firms due to government investigations of their conduct. Prominent examples include Jarrell

and Peltzman (1985), who study FTC investigations into false or misleading advertising and

Alexander (1999) who analyzes penalties in a range of violations in government contracts.

6See https://cepr.org/content/covid-economics-vetted-and-real-time-papers-0.

7
See Haslem et al. (2017) for a recent review of this line of research. The implications of

such investigations for firms, their managers, and directors are the subject of Karpoff et al.

(2008), Fich and Shivdasani (2007)), Murphy et al. (2009), and Slutzky (2020). Of special

interest is Armour, Mayer, and Polo (2017). Using UK data from 2001 to 2011, it finds that

stock market penalties are 9 times the actual fines imposed on companies for violations. The

threat of government investigations thus goes beyond possible fines, which can explain firms’

aversion to such investigations and the positive valuation effects from its release.

3. Data

We use multiple datasets for our study. One is the SBA PPP release in December

2020, which covers 5,156,850 PPP recipients. Borrowers and lenders are identified by their

names as recorded in the PPP application. Our second and third datasets identify bank-firm

relationships. We start with the DealScan database. However, given its limited coverage of

smaller and private firms, we turn to the data on bank relationships identified via security

interest filings under the Uniform Commercial Code (UCC). See Gopal and Schnabl (2020).

We obtain a version of the UCC data updated to December 2020.

The UCC data provider also matched PPP recipients to its proprietary dataset on all

small firms. This list includes 1,469,167 PPP recipients. We call this intersection the “SBA

PPP” dataset. This dataset is useful in tests involving bank relationships. It seems to be a

more conservative choice for the overall universe of firms used for assessing bank relationship

effects. That said, the results are similar with the overall SBA PPP dataset, so the ultimate

choice on what to present and what to keep for robustness is immaterial.

The next dataset includes publicly listed PPP recipients. We obtain this dataset by

hand-collecting data from firms’ SEC filings – rather than the SBA PPP release – for three

reasons. One, the SBA PPP release does not cover or identify PPP returners. Thus, the

funding hesitancy tests are infeasible with the SBA release. Two, the tests on stock market

effects require dates on which the markets learn of PPP uptake or return, which are available

in the SEC filings. Finally, we have balance sheet and income statement data for public firms,

which are not available in the private PPP recipient dataset released by the SBA.

3.1. SBA PPP Dataset

The SBA has three releases of PPP borrowers between June and December 2020. The

earlier releases did not identify exact loan amounts. The December 2020 release does so.

We use it in our tests. Inspecting the sample reveals that there a very large number of very

8
small loans; we test and verify that our results are not artifacts of these observations. The

SBA provides data on recipient types, which could be corporations (29.1%), limited liability

companies (28.2%), sole proprietorships (15.9%), Subchapter S corporations (13.6%), nonprofit organizations (3.5%), and other business types (9.8%). We exclude 99,460 financial

firms and REITs. We identify and focus on the subsample of 1,498,552 firms that are

incorporated as corporations. This makes the sample homogeneous and eliminates biases

from the late entry of some firms (e.g., non-employers, proprietarships) that became eligible

later. Including them would mechanically show that small firms have late PPP access.

Table 1 reports the overall statistics for this sample (Panel A). It shows that the SBA

overall disbursed $523 billion in PPP funding to 5.2 million borrowers, about 78.2% of the

PPP appropriation of $669 billion.7 Of this, $320 billion (61%) is disbursed to 1.6 million

(31%) of the borrowers during the initial rush period from April 3, 2020 to April 16, 2020.

Thus, the initial PPP disbursements are to larger borrowers.

3.2. Public PPP Applicants

We download all company 8-K, 10-K, and 10-Q filings with the Securities and Exchange

Commission (SEC) filed between April 1, 2020 and September 30, 2020. To identify PPP

recipients, we employ a combination of computer code and a manual procedure. The code

searches for keywords such as “PPP,” “Paycheck Protection,” and “CARES” in the filings.

We read through the candidate documents to identify PPP recipients and collect other data

such as loan amounts and filing dates and cross-verify some through news releases. We follow

the same procedure for firms that returned PPP loans. 48.2% of companies report their PPP

loans in 8-Ks followed by 40.1% in 10-Qs. Most 8-K filers (63.6%) report PPP loans under

“Item 1.01: Entry into a Material Definitive Agreement.”8

Our initial sample includes 1,233 PPP loans provided to 1,020 public PPP borrowers

(Table 2). After matching to COMPUSTAT, excluding financial firms and firms that apply

after the PPPFA, the usable sample for most cross-sectional tests is about 682 public PPP

borrowers. Many (75.2%) of these firms have nominal share prices of less than $5. As

stock returns for these firms may be unreliable, especially at high frequency, we interact

return-based explanatory variables (e.g., volatility) with a penny stock dummy variable.9

7The often cited official number, $525 billion, differs due to variations across SBA releases.

8About 21.5% of firms use “Item 2.03: Creation of a Direct Financial Obligation or an Obligation under

an Off-Balance Sheet Arrangement of a Registrant.” Other items include “Item 8.01: Other Events” (7.2%),

“Item 9.01: Financial Statements and Exhibits” (3.9%), “Item 2.02: Results of Operations and Financial

Condition” (2.3%), and “Item 7.01: Regulation FD Disclosure” (1.3%). In 10-Q’s and 10-K’s, disclosures

are mostly in the “Subsequent Events” section (52.8%).

9As due diligence, we try to match our public data set to the SBA PPP release. We match 72% of the firms

9
To assess the locus of public PPP applicants within the universe of listed small firms, we

construct a pool of small firms who did not apply for PPP funds. These are COMPUSTAT

firms with fewer than 500 disclosed employees as of the fiscal 2019 or the prior year if

missing, excluding financials or SPACs. The final non-applicant sample includes 1,452 small

firms in COMPUSTAT that did not apply for PPP.10 Financial characteristics are from

COMPUSTAT and returns are based on adjusted stock prices downloaded from Yahoo!

Finance. Credit ratings data are from Standard & Poor’s or Mergent and distance-to-default

data are from NUS RMI Credit Research Initiative. We match PPP borrowers and their

subsidiaries to DealScan and UCC. The data on subsidiaries are from Nexis Uni.

3.3. Identifying Firms Returning PPP Funds

We identify 123 public PPP borrowers that return PPP funds without using them. 58.1%

of these firms disclose returns in 8-Ks and 34.4% do so in 10-Qs. In most cases, the information pertaining to PPP loan repayments is contained in “Item 9.01: Financial Statements

and Exhibits” of 8-K reports (48.9% of 8-K filings).11 In 10-Q reports, PPP loan repayments

are most frequently in the “Subsequent Events” section (59.6%) and otherwise spread across

various other portions dealing with liquidity, debt, and related issues. Excluding financials

and those without financial data or stock prices slightly reduces the sample to 117 firms.

3.4. Bank Relationship Data

DealScan is a common source for data on bank-firm relationships in academic research

(e.g., Drucker and Puri, 2005). We supplement DealScan data with data from filings of

banks taking collateral interests under the Uniform Commercial Code (UCC) rules, which

potentially offer better coverage of smaller firms (Gopal and Schnabl, 2020) that tend to

take PPP loans. We obtain the data as of December 2020.

We find about 270,000 lenders in the UCC data. We identify bank lenders as those with a

non-missing DUNS number and a name that includes “bank,” its common derivatives (e.g.,

“bankcorp”), abbreviations (e.g., “bnk,” “bk”), and related misspellings (e.g., “bnak”). We

by name and 92.5% after using fuzzy matching algorithms. The small residual likely reflects name variations

due to issues such as borrowings recorded by parents versus subsidiaries or franchiser versus franchisees. Our

results are not sensitive to dropping the unmatched firms.

10Alternative specifications such as excluding companies that operate in an industry with 2-digit NAICS

equal to 72 give similar results.

11Others include “Item 2.02: Results of Operations and Financial Condition” (21.3%), “Item 8.01: Other

Events” (9.6%), “Item 1.02: Termination of a Material Definitive Agreement” (8.5%), “Item 1.01: Entry

into a Material Definitive Agreement” (5.3%), “Item 2.03: Creation of a Direct Financial Obligation or an

Obligation under an Off-Balance Sheet Arrangement of a Registrant” (5.3%), and “Item 7.01: Regulation

FD Disclosure” (1.1%).

10
identify big banks using bank names, again filtering for variations and misspellings in names,

and correct for mergers using a manual process. We then assign these classifications to UCC

lenders with missing DUNS numbers using lender name. We define a firm as having a prior

bank relationship if the UCC filing date is recorded in the 5 years before December 31, 2019,

well before the PPP.

We also obtain from the UCC data provider the subsample of PPP recipients they could

match with the universe of all firms their data hold. We could reliably match the names

with the SBA PPP data. We call this dataset the UCC–SBA PPP data. Panel B of Table 1

reports the statistics for this sample. The sample covers 1.5 million borrowers (28.5% of

the SBA PPP data) that obtained $298 billion in PPP loan proceeds (57.1% of the SBA

PPP data). Restricting the data to corporations results in 617,749 observations, of which

we eliminate 29,449 financial firms. After filtering for whether the PPP loan occurs before

PPPFA, the UCC–SBA data contains 560,829 observations.

We next match the UCC bank relationship data with the public PPP dataset using

the company names. The match involves 1,021 current names, approximately 1,131 former

names, and over 7,300 subsidiary names of public PPP borrowers in our dataset. We find

valid matches for 231 pre-PPPFA public borrowers in DealScan, 301 public borrowers in

UCC, and 405 in the union. Thus, using UCC data appears to significantly expand coverage

of banking relationships even in the public PPP sample. To further understand the coverage

of DealScan and UCC, we examine characteristics of firms matched to both datasets, as well

as of those that remain unmatched (Internet Appendix, Table IA.1). The data show that

DealScan-matched firms are larger, so bringing in the UCC data incorporates many more

smaller firms.

4. Early Applicants and Lender Effects

Our first analysis examines which firms gain early PPP access. From the viewpoint of

firm demand, smaller and weaker firms are more distressed and constrained and they should

want to access PPP funds earlier. Option to wait arguments produce similar results. Because

forgiveness eligibility requires use of proceeds quickly, companies may be better off waiting

for demand to recover, when PPP proceeds would be more productive. However, this option

is viable only for stronger and less constrained firms, so relatively smaller and weaker firms

should demand PPP proceeds sooner.

Intermediary supply effects produce the opposite predictions. Bank capacity was strained

by the initial surge in PPP demand, compounding which was an aggregate shortage of PPP

funds. These forces forced lenders to prioritize the clients they would push for early access.

11
Intermediaries had two types of incentives. One, larger and better quality firms produce

greater revenues from banking relationships. Additionally, PPP fees increase in loan size

while processing costs are relatively fixed, pushing banks to prioritize larger loans.12

The upshot is that if intermediary supply effects matter, we should find that larger firms

(and loans) should be more likely to gain early access. If lender priorities vary across small

and big banks, the probability of early access for larger firms should vary by bank size. Our

tests are organized along these lines.

4.1. Size and Early PPP Access

We start by examining early access for public PPP recipients. Figure 2 shows the time

series patterns in PPP applications. We see an initial wave before April 17, 2020, part of the

early surge that depleted the initial PPP corpus of $349 billion. The next wave begins on

April 27, 2020 after the PPP reopens. A brief pause occurs around May 8, 2020, the initial

no-fault deadline to return funds. Loans resume thereafter and gradually taper off.

Table 3, Panel A reports data for the final sample of the 447 early and 235 late public

PPP borrowers. The PPP loan amounts are greater for early applicants with mean (median)

of $2.671 ($1.482) million compared to $1.982 ($0.723) million for later applicants. Larger

loans are prioritized sooner, consistent with intermediary supply effects shaping the delivery

of PPP funds. We note similar patterns in the larger SBA PPP dataset in Table 3, Panel B

and in Figure 3, which depicts the results visually. We also see a decline in the density just

above a loan amount of $2 million in PPP Round 2, consistent with investigation aversion

of firms.

Table 3 also reports data on a number of financial characteristics of early versus late

public PPP recipients. Early applicants are larger whether size is measured in terms of book

value of assets, the market value of assets, sales, or employee counts. Early applicants also

exhibit traits of better quality firms, as they are less likely to have negative book value, are

more likely to pay dividends, and have greater current ratios. Once again, the findings are

less consistent with firms’ demand driving early access. The multivariate regressions reported

in Table 4 show similar patterns.13 Firm size is positive and significant in all specifications.

It is possible that larger firms gain early access because they can better handle PPP

paperwork. Here, the evidence within public PPP firms is relevant. While it is plausible

that small private businesses such as tiny corner stores find the PPP paperwork daunting,

12Larger loans are more profitable within buckets but there is some non-monotonicity at certain points

where fees decline. For instance, simple computations show that a PPP loan of $580,000 produces less fees

than one of $350,000 – although neither generates more fees than a $5 million loan.

13The coefficients in linear probability models are interpretable as marginal effects.

12
this is less plausible for publicly listed firms, who file far more complex documents to comply

with periodic disclosure and audit requirements. The results for public firms indicate that

intermediary supply effects shape PPP funds delivery and more generally that intermediary

supply effects matter (see also Cherry, Jiang, Matvos, Piskorski, and Seru, 2021).

4.2. Size and Early PPP Access: Small versus Big Banks

We next examine whether large firm prioritization varies across small and big banks.

Differences in prioritization – or the lack thereof – can speak to the client priorities of small

versus big banks. The hypothesis that such prioritization can exist come from the view

that the value added by big banks is access to one-stop shopping and networks, which are

more relevant to larger firms. Small banks specialize in relationship-oriented lending. Recent

evidence that emphasizes this viewpoint is the 2018 FDIC Small Business Lending Survey.14

In the PPP setting, the loans are SBA guaranteed. Further, the rules are quite explicit that

lenders should rely on borrower certifications and representations to submit applications.

Hence soft information held by banks about borrower credit quality, one reason for housing

small firm relationships in small banks (Stein, 2002) is not a consideration. Do we still see

a big bank tilt towards larger firms?

Our tests need a definition of a “big bank.” Our classification is based on whether a bank

belongs to the top 10 banks by asset size in the U.S. The classification produces reasonable

variation in bank type. The big-10 banks provide $480 million in PPP funding in our public

PPP sample while $1.1 billion comes from smaller (non-big-10) lenders. The smaller banks

provide $802.2 million (or 71.5%) in funding in PPP Round 1 and $280.3 million (63.6%) in

funding in PPP Round 2. Relatedly, small banks seem more likely to offer early PPP access

compared to big banks, a pattern seen across many specifications, suggesting that they are

quicker to serve small businesses in times of need compared to big banks.

Table 5 reports the firm and loan size data for early versus late PPP borrowers crossclassified by bank type. Panel A shows that there are significant differences in both size

measures between the two PPP rounds for big banks. For big banks, average early and late

loan sizes are $3.4 million and $1.8 million, respectively, versus $2.5 million and $2.2 million

for small banks. The early-late differences are muted for small banks. The mean asset size

of big-10 bank clients is $144.2 million for early PPP borrowers versus $61.4 million for late

PPP borrowers. For the smaller banks, the early-late size differences are muted and even

reversed using this metric for size.

14See https://www.fdic.gov/bank/historical/sbls/full-survey.pdf. Other work on relationship

banking includes Petersen and Rajan (1994); Berger and Udell (1995), Drucker and Puri (2005) and Bharath,

Dahiya, Saunders, and Srinivasan (2007).

13
We present visual evidence of the size distributions for big and small banks. A leftward

shift in the density of PPP customers in the later period would indicate a propensity to shift

smaller clients later. Figure 4 depicts the densities of firm size for small and big banks for

the early and late periods. We observe a more significant shift in the density for big banks.

For big banks, the Kolmogorov-Smirnov D-statistic testing for the equality of cumulative

distribution functions between early and late PPP borrowers is 0.3272 (p-value = 0.001).

The differences are significant. For small banks, the statistic halves to 0.1527 (p = 0.085).

We see similar patterns when looking at loan (rather than firm) size in Figure 5.

Regression results are in Table 6. We interact the natural logarithm (log) of firm assets

with a bank size indicator in Panel A and log loan size in Panel B. The interaction coefficients

are negative, indicating that small banks mitigate the impact of early access for large firms.

Internet Appendix Table IA.2 shows that multinomial logit estimates show similar results.

Small banks mitigate the propensity for large firms to get early PPP funding.

4.3. Bank Relationships

We consider the role played by bank relationships in large borrower prioritization in PPP.

The bank relationship dummy variable is best interpreted as an indicator for firms that have

economically meaningful bank relationships captured in the DealScan or UCC databases.

Let us start with all firms that have a recorded bank relationship with big banks. Early

PPP access firms at big banks have a slightly greater mean book value, $247.6 million

compared to $151.1 million for late borrowers (Table 5, Panel B). The differences are not

significant. The results for PPP loan amount in Panel B are similar. Directionally, large

firms go sooner but the size differences are not significant. We have one piece of evidence

that relationships help alleviate earlier PPP access for large firms.

In contrast, Table 5, Panel C shows a strikingly different pattern for firms with no

recorded bank relationship: early and late access firms provided with PPP funds by big

banks have mean size of $80.0 million and $33.6 million, respectively. The difference is

significant (p = 0.003). We find similar patterns for no-relationship borrowers at small banks,

with mean firm size of $64.3 million and $37.7 million, for early and late PPP borrowers,

respectively. The results on PPP loan amount in Panel C are similar. The sharp contrast

between the no-relationship and relationship samples indicate that prior relationships help

mitigate early access for large borrowers. Figure 6 and 7 show supporting evidence in

graphs. The distributions of the book value of assets tilt less towards larger firms receiving

PPP early when firms have prior bank relationships. The patterns are somewhat clearer in

Figure 8, which plots histograms rather than densities of the probability of early PPP access.

14
We turn to regressions for the econometric evidence. Examine Columns 3–4 of Table 6

first, which pertain to firms with a prior bank relationship. As in Columns 1–2, the coefficients of the two size measures, firm assets (Panel A) and PPP loan amount (Panel B)

in Columns 3–4 are positive but the size coefficient is significant in only one of four specifications. The more interesting evidence is based on the size-small bank interaction term.

Here, the interaction coefficient is negative and significant. Thus, within firms with a prior

banking relationship, a small bank relationship helps small firms gain early PPP access.

Let us now examine the no-relationship sample in columns 5–6 of Table 6. Large firms

tend to go early, as indicated by the significant and positive size coefficient. The coefficients

for the size-small bank interaction term in Columns 5–6 are now economically small and

three of four coefficients are insignificant. That is, early PPP access for large firms is no

longer reversed. Thus, the “concierge” treatment of larger firms is predominantly when firms

do not have prior banking relationships, but it is mitigated when PPP applicants have prior

banking relationships with small banks. The results are robust to using multinomial logit

models (Internet Appendix, Tables IA.3 and IA.4).

4.4. The SBA PPP Dataset

We round off the analysis of relationships with evidence from the SBA PPP dataset. We

note that financial characteristics such as book value are unavailable in the sample, so we

use loan amount as a proxy for size. A second issue is the presence of a very large number of

tiny firms. A concern this raises that they have undue influence on the results. We note this

point and will present analysis attentive to this issue. The main question again is whether

large firms gain early access and whether there is an attenuating effect for small banks.

It is convenient to present the visual evidence using histograms based on SBA-defined

bins. We first check whether unconditionally, large firms get early access in this sample.

They do. See Figure 9 for the UCC–SBA PPP sample, which, we recollect, is the universe

of PPP borrowers matching with the master database of all firms maintained by the UCC

database provider.15 The next question is whether the large firm preferences vary across

big and small banks. Figure 10 shows that for small banks, the histogram is relatively flat

for loan amounts above the smallest bin of $0–$150,000 – and decreases for large loan sizes.

However, the histogram slopes upward for the big banks. Thus, even in the SBA PPP

sample, big, but not small banks, appear to prioritize big firms for early access.16 Panels C

through F report the data classified by bank relationships. We do not find it easy to tell any

15The results for the full SBA sample in Internet Appendix Figure IA.1 are similar.

16The results are robust to shrinking small bank capacity in a manner that creates equivalence between

the proportion of PPP clients that big and small banks serve early. There results are available upon request.

15
differences between big and small banks in the figures. We turn to the statistical evidence.

For the basic descriptive statistics, we revert to Panel B, Table 3. We have 274,764 early

and 286,065 late PPP applicants in the UCC–SBA sample. Size is again positively correlated

with early access. For example, in this sample, the mean (median) PPP loan size for early

applicants is $277,680 ($109,913), whereas it is $128,396 ($49,877) for late applicants. The

results are similar if we use the number of jobs supported by the PPP, which we tread here

with caution given press reports that there may be errors in jobs reporting.17

We turn to regressions in Table 7 using a structure parallel to that in Table 6. Early

access is the dependent variable. Of interest are the coefficients for PPP loan amount

and its interaction with the big-small bank indicator. Controls in the SBA PPP dataset

include granular NAICS-6 industry and zip code level geography fixed effects, the fields

made available in the release.

The results have an interesting feature that we address after discussing the main results.

In all specifications in Table 7, the coefficient for the size is positive. Thus, size is positively

related to early access and consistent with intermediary supply effects shaping PPP supply.

Turn to specifications (2) and (3) in Table 7. Specification (2) shows that the coefficient for

the size-small bank interactive term is negative. This is the by-now familiar result that small

banks help undo the large firm prioritization, now obtained for the broad PPP sample. In

the no-relationship sample, the coefficient is now positive and significant. Thus, absent prior

banking relationships, small banks preference larger loan amounts even more. The results

based on multinomial logit models are qualitatively similar (Internet Appendix, Table IA.5).

An interesting feature of the results in Table 7 is the positive coefficient for the small

bank-firm size interaction in specification (1) that does not condition on bank relationships.

This sign, which indicates that small banks prioritize large firms more than big banks, is

exactly the opposite of what we find for the public sample in Table 6. In a statistical sense,

any sign is possible because the overall specification should average the positive and negative

coefficients in specifications (2) and (3). However, the economics are interesting and reflect

the point made in opening this section: the preponderant presence of very tiny firms. We

investigate this issue formally.

In the aggregate PPP dataset, loans in the lowest SBA bins of below $150,000 and between

$0 and $350,000 account for 69.7% and 86.1% of the observations, respectively. There is a

very large number of tiny “microloans” in the SBA data set. To what extent do they drive

the results, especially the positive firm size-small bank interaction coefficient in the first

17E.g., see “Faulty data collection raises questions about Trump’s claims on PPP program,” Washington

Post, July 14, 2020 at https://www.washingtonpost.com/business/2020/07/14/ppp-job-claims-sba.

Note also that the SBA reporting may vary because of PPP requirements (e.g., foreign employees) or because

not all firm’s affiliates are able to apply.

16
specification in Table 7? As one test, we redo the regressions replacing loan amounts with

bin indicators based on SBA loan buckets. We find that the anomalous positive interaction

term is only in the smallest bucket. Another approach drawn from the asset pricing literature

is to report “value weighted” results based on weighted least squares that assigns greater

weights for larger loans. The coefficient for the size-small bank interaction reverses from

0.0262 to −0.0410 (p-value <0.001), the familiar result that small banks help undo the

prioritization of large firms for early PPP access. These results are available upon request.18

Finally, we examine whether, within the universe of bank relationships, applying through

relationship banks helps early access. Table 8 reports the evidence. Specification (1) shows

the baseline result. The coefficient for applying through a relationship bank is positive,

so applying through a relationship bank enables early access. Specification (2) examines

the role of a prior small bank relationship. Once again, having a small bank relationship

appears to help with early PPP access. Finally, in specification (3), we interact the small

bank relationship with applying through a relationship bank. The positive sign indicates

that the effects of applying through a relationship bank are more pronounced when the bank

is small and the borrower has a prior relationship with the small bank.

To summarize the results, the PPP is an interesting natural experiment in which banks

face a sudden and unexpected shock in which they must prioritize their clients. Given

government guarantees of PPP loan repayment, traditional soft information considerations

are not relevant in this setting. Larger clients appear to gain beneficial access to PPP funds

sooner, an effect that is attenuated in small banks. The more even treatment of small clients

is perhaps one reason why small firms bank with small banks, as banking with larger banks

could lead the small firms into facing a “small fish in a big pond” effect. The relative

prioritization of small businesses offers a different rationale for why small firms domicile

their bank relationships with small banks worthy of more attention by academicians and

regulators alike.

5. PPP Uptake

Besides the intermediaries involved in piping PPP supply to borrowers, a second force

that drives PPP uptake comes from the concerns of small firms that are the program’s

targets. We open with an analysis of PPP uptake and show that it has positive average

valuation effects, a metric of the program treatment effects. We then uncover a curious PPP

18The results are consistent with the view that small banks push small firms when the stake is meaningful,

perhaps because of the greater option value from better chances of firms surviving and becoming economically

consequential in the longer term.

17
“funding hesitancy,” characterize related valuation effects, and explore the possible sources

of this hesitancy exploiting the structural features of the program. This analysis focuses on

public PPP recipients, as we observe the event of PPP return only for this sample. Control

firms are small non-applicants identified in Section 3.

5.1. Characteristics of PPP Applicants

To understand PPP uptake, we begin by describing the types of listed firms that apply

for PPP. Table 9 shows that the median (mean) PPP loan amount equals $1.1 ($2.4) million,

representing 4.41% (7.30%) of the book value of assets and 3.52% (10.98%) of the market

value of equity, respectively. The median (mean) ratio of the loan amount to cash and

equivalents winsorized at 100% equals 26.9% (43.2%). Thus, PPP loans are economically

meaningful relative to the liquidity on hand for our sample firms.

5.1.1. Industry

The SBA uses NAICS industry classifications in its lending operations. We drop firms

with NAICS-2 equal to 99, special purpose acquisition firms, 79 PPP applicants from the

financial sector, combining those with fewer than 20 PPP applicants into one bucket.19

Figure 11, Panel A shows that the percentage of applicants varies substantially across sectors,

ranging from lows of 4% to 5% in NAICS-2 = 22 and 48 to upwards of 50% in NAICS-2 =

33, 49, 54, and 56. The probability peaks at 70% for NAICS-2 = 72, the “accommodation

and food services” sector that includes chain restaurants such as Denny’s and Shake Shack

relying on franchising as a business model. We include these firms in the main analysis, but

excluding them does not alter our main conclusions.

For the regressions, we construct a suite of industry fixed effects with two considerations

in mind. One, we attempt to construct industry variables of economic interest. The second is

the familiar practical consideration, creating industry clusters that are populated sufficiently

to result in estimable regressions. Internet Appendix Section IA.B describes the four industry

clusters and shows that they capture substantial variation in application probabilities (see

Figure 11, Panel B).

5.1.2. Financial Characteristics of PPP Applicants

Table 9 reports characteristics of PPP loan applicants as well as those of control nonapplicants. The p-values are based on Wilcoxon rank-sum tests as many variables are skewed.

19The consolidated sectors have 67 firms from NAICS-2 = 11, 23, 44, 45, 52, 61, 71, and 81.

18
Panel A of Table 9 shows that PPP applicants are small. The mean (median) market

value of equity of applicants, $110.9 million ($35.9 million), places the firms in the smallest

Fama-French size decile. Applicants are smaller than non-applicants, whose mean (median)

market value equals $484.7 million ($118.7 million). Other size metrics such as book value,

sales, and the number of employees show the same pattern.20

PPP applicants appear to have less liquidity and less financing access relative to the

small firm pool. Their cash and current ratios are lower and are highly unlikely to have

credit ratings, which signals credit access (Faulkender and Petersen, 2006). Over 70% of the

PPP applicants fall into the top quartile of constrained firms according to the Whited and

Wu (2006) (WW) or the Hadlock and Pierce (2010) (SA) financial constraints indexes. PPP

applicants also have greater distress risk. Applicants have low interest coverage ratios: more

than three-quarters of them cannot cover interest 2.5 times, a benchmark for large investment

grade companies. Over 68% of PPP applicants have Altman (1968) Z-scores below 1.81, a

benchmark for high default risk. PPP applicants have lower distance to default (Bharath

and Shumway, 2008; Duan, Wang, et al., 2012), lower nominal share prices, and 75.2% of

them are penny stocks, an indicator of mortality.21

We note that the patterns are prior to the pandemic – and not caused by it. Thus, PPP

applicants are smaller, weaker, more constrained, and have less liquidity than typical small

firms before the pandemic. Moreover, these firms do not seem to have done better in the

pandemic. Figure 12 shows that PPP applicants have lower average cumulative buy-andhold abnormal returns (BHARs) from January 1, 2020 to March 23, 2020, the day before the

passage of the CARES Act. Applicants do not do better in terms of returns from February 3,

2020 to March 23, 2020, the Covid period defined in Fahlenbrach et al. (2020) or in returns

on March 24, 2020, when there was news on the imminent passage of the CARES Act.

Table 10 reports estimates of several regressions that explain the characteristics of PPP

applicants relative to control firms. The return variables are interacted with a penny stock

dummy variable to account for noise in price data in the latter. The regression results

indicate that applicants are smaller, older, slower growing, and have less liquidity. The data

paint a clear picture of the types of firms that apply for PPP. The applicants are typical of

small firms in some respects and where there are differences, the PPP applicants appear to

be weaker: smaller, less liquid, more constrained, and with greater distress risk. Additional

tables employing indicators of constraints and distress, not reported here, give similar results.

20We note here that the results are conservative. The COMPUSTAT-disclosed number of employees for

PPP firms is sometimes more than 500. For example, PPP eligibility for hotels is based on employees in an

establishment, but COMPUSTAT reports the total employment count.

21See Seguin and Smoller (1997) and the SEC guidance on penny stocks, available at https://www.sec.

gov/fast-answers/answerspennyhtm.html, which argues that these stocks are risky.

19
5.2. PPP Application Announcement Effects

We next examine the stock market valuation effects related to PPP. Figure 13 shows

CARs (cumulative abnormal returns) for [-5, 5] where [0] denotes the filing date. The

average CAR is over 4% in the time interval [-5, 1], much of it in [-2, 1]. From Panel B, the

results are similar for both market adjusted or market model abnormal returns.

Table 11 uses a narrow [-1, +1] window around the filing date to estimate PPP uptake

valuation effects. The estimates regress daily abnormal returns (using market-adjusted or

market model returns) from January 1, 2020 until the event date plus one day. The independent variables of interest are the three dummy variables representing the filing date plus

or minus one day. Using a regression sample within the pandemic period addresses the point

in prior research that pandemic period returns are unusual. We include firm fixed effects,

which absorb firm-level heterogeneity within the pandemic period. Finally, we account for

the possible bunching of PPP applications in waves (Figure 2) by clustering standard errors

by calendar dates.

We report three estimates, one for all PPP applicants, another one for those reporting

PPP in 8-Ks, and finally, a sample that excludes penny stocks. For the full sample, Panel A

of Table 11 shows that the PPP abnormal returns are about 1.1% and accrue on day [-1]. The

estimates increase to about 1.6% for firms announcing PPP through 8-Ks. This increase is

not surprising given that 10-Qs or 10-Ks contain other information and are thus less precise

in picking up PPP information. See also Heitz, Narayanamoorthy, and Zekhnini (2020).

Excluding penny stocks produces somewhat greater coefficients with no material impact on

standard errors (Panel C). The announcement effects are somewhat greater for the subset of

larger applicants. Overall, there is little evidence that taking PPP harms shareholders. In

fact, PPP uptake appears to increase share prices.

5.3. Partial Anticipation and Treatment Effects

We discuss unadjusted treatment effects first. Using announcement effects and the preannouncement value of PPP applicants, we find that the average change in the dollar market

value of a PPP applicant is $1.57 million (after 1% winsorization to account for outliers).

Across all the 555 PPP borrowers for which we have the relevant data, the total change in

value is $871.61 million. As the total PPP loan amount for these firms equals $1.590 billion,

the PPP bang for the buck equals $871.61 million ÷ $1.590 billion = 0.548. The 417 penny

stocks in our sample take $937.94 million in PPP loans and experience an increase in market

value of $342.44 million, giving a bang for the buck of just 0.365. The non-penny stocks, the

stronger firms, gain $522.95 million and take $651.84 million, giving a bang for the buck of

20
about 0.802. Thus, the bang for the PPP buck is greater for the larger and stronger firms.

We adjust for partial anticipation next. Valuation changes on the event date are less than

the actual event effect if an event is partially anticipated, as anticipations bake potential

treatment effects into the pre-announcement share prices. We adjust for anticipation by

scaling up the announcement returns by a factor related to the probability of PPP application

(Acharya, 1988; Eckbo, Maksimovic, and Williams, 1990; Prabhala, 1997).

Suppose qf is the probability assessed by the market that firm f applies for a PPP loan.

Let the benefit of the PPP be B. On the date the market learns that firm f has applied

for PPP funds, the change in its value, say D, equals (1 − qf ) B. Thus, the actual benefit

B equals D

1−qf

and the aggregate across all firms is P

f

D

1−qf

, where 0 ≤ qf ≤ 1. We obtain

estimates of qf , the ex-ante probability of applying for PPP, from Table 10.

For the 533 PPP borrowers with the relevant data, the anticipation-corrected treatment

effect of the PPP program equals $1.411 billion, 91.2% of the aggregate PPP loan amount

of $1.548 billion. The bang for the buck is thus 0.912.22 For penny stocks, the bang for

the buck is 0.817, reflecting gains of $742.20 million versus loan amounts of $908.27 million.

For non-penny stocks, it is 1.046, with shareholder gains and PPP loan amounts of $669.08

million and $639.40 million. As before, the larger, stronger PPP applicants produce a greater

bang for the PPP buck.

5.4. Economics of the Estimates

How useful are the stock market data in understanding the PPP program impact? While

impact assessment is not our focus, we comment briefly on this issue. In our view, the stock

market valuation effects are useful as we have reasonably accurate times (news release dates)

when share prices impound the PPP impact. On the other hand, share prices may not be

the primary impact metric, which may be outcomes such as jobs or wages. But shareholder

value is not entirely without use. The PPP was designed to keep enterprises running and

is more impactful when business owners experience its positive effects. Additionally, the

number of public PPP firms is small. Thus, any analysis of share prices, however accurately

they pin down impact, does not subsume the need for alternative impact assessment studies.

Our study simply adds one more data point and a different lens for this assessment.

Some other points are worth mentioning in this context. The PPP is announced alongside

other aid packages as part of the $2 trillion CARES Act bill. The other pieces of the bill

– and other local pandemic and economic mitigation measures – can be confounders in

22We note that one large borrower, AutoNation, takes $77 million in PPP loans and has about 10 times

the amount as shareholder gain. We thus winsorize changes in value at the 1st and 99th percentiles to ensure

that outliers like AutoNation do not drive the average treatment effects.

21
interpreting PPP effects. Our experiment design mitigates such concerns. In our study,

stock prices reflect impact on event dates, which are staggered across firms and spaced

away from the CARES Act announcement. Moreover, the totality of the CARES package

is perhaps reflected in (and thus contaminates) the aggregate stock market return but the

market return is stripped out when we compute abnormal returns.23

A final point is related to real-time economic tracking, a focus of the economics literature

particularly in the wake of the pandemic (see Chetty et al., 2020), but also in other areas

such as nowcasting macroeconomic data. The stock market responses are in fact a real-time

assessment of the program effects that incorporate information known to market about the

PPP application and its assessment of PPP effects. We do not advocate extrapolating our

estimates of treatment effects to the entire $525 billion as the non-public firms have simply

no analog in the public space, e.g., private schools or not-for-profit organizations. In our

view, the takeaway is that even in public firms, the valuation effects are positive but the

bang for the buck is small and concentrates in the larger and stronger subset of firms.

6. Funding Hesitancy

We turn to tests of funding hesitancy. As discussed in Section 1, several firms that

obtained PPP loans returned the funds within a short period of time before using the funds.

We characterize the decision to return PPP funding, examine related valuation effects, and

assess evidence for the role played by government investigation threats in the decision to

return PPP funds.

6.1. Returners versus Retainers

Table 12 reports the characteristics of 117 public firms that returned PPP loans – henceforth “returners.” Alongside, we report the characteristics of the 565 public PPP recipients

firms that did not return PPP funds – henceforth “retainers.” The mean (median) loan

amount for PPP returners equals $4.43 ($3.33) million compared to $2.02 ($0.96) million for

retainers. Firms that return PPP money have applied for larger loan amounts. As we will

see shortly, returners also appear to be larger by other metrics and somewhat financially

stronger.

The mean (median) book value of assets for the returners equals $288.4 ($86.0) million

compared to $71.4 ($27.7) million for the retainers. Table 12 shows that the difference is

23The broad market-wide effects of PPP are perhaps best reflected in the market’s positive reaction

(+9.38%) to the PPP on March 24, 2020, but this is not the impact of PPP alone as the program is

embedded in the larger CARES package.

22
significant. We see similar differences in other size characteristics including market capitalization, sales, and the number of employees. Returners are less likely to have negative book

value, have higher Tobin’s Q but lower sales growth, and are more likely to pay dividends

(24.3% versus 12.4% for retainers). Returners have better liquidity with higher current ratios, cash balances, and are more likely to be rated (6.0% versus 1.9% for retainers) although

access to public debt is overall low, as typical for small firms. Returners are also less financially constrained per the Whited-Wu and the Hadlock-Pierce SA indexes, have greater

distances to default, higher Altman Z scores, and are less likely to be penny stocks compared

to retainers. Returners are clearly stronger than retainers.

We also examine industry patterns (Figure 14). Around 22.5% of health sector firms return PPP funds compared to 15.1% of non-health firms. In contrast, only 10.7% of high-tech

firms return PPP funds compared to 18.4% of firms in non-high-tech industries. While both

industries faced better prospects in the pandemic, the health firms have greater regulatory

interactions, which may explain why these firms are less likely to keep PPP funds and face

the prospect of a negative investigation. Only 11.4% of the firms in industries impacted by

Covid return PPP funds compared to 18.3% in non-Covid industries. Likewise, only 12.5%

of machinery industry firms return PPP funds compared to 17.3% of firms that are not in

the machinery sector. Thus, firms with greater demand for funds and those more prone to

distress from illiquidity are less likely to return funds.

We do not find evidence that returners benefit less from PPP stimulus (Figure 15 and

Table 12). The mean (median) stimulus day return of PPP returners equals 7.0% (6.5%)

compared to 5.2% (3.6%) for retainers. The data show that the Covid period returns and

the PPP loan announcement effects are similar for the two samples. Returners have a higher

PPP application bang for the buck than do the retainers. Little in these patterns suggests

that firms that return PPP funds are the ones that have benefitted less from PPP.

6.2. Regression Evidence

Table 13 reports estimates of regressions that model the decision to return PPP loans.

In the sample are 543 PPP firms with sufficient data. The role of firm size is notable. Large

firms are more likely to return PPP funds. A one standard deviation increase in the natural

logarithm of assets increases the probability of returning the PPP loan by at least 9.05 pp,

or by 52.74% relative to the unconditional mean. This result also holds for firms with at

least $50 million in market capitalization. Penny stock firms are less likely to return PPP

funds. As discussed earlier, research (e.g., Seguin and Smoller, 1997) suggests that penny

stocks face greater failure hazard, and given their classification as speculative investments,

23
face greater hurdles in capital raising. We find that these firms are more likely to retain PPP

funds.

Specification 3 includes stock return data. We find that firms with better Covid period

returns tend to return PPP funds. In Internet Appendix Tables IA.6 and IA.7, we find that

less constrained and low bankruptcy firms are more likely to return PPP funds. Collectively,

these findings add to the basic point that returners are more likely to be the better quality

applicants for PPP – even before the pandemic – concentrating PPP uptake among the

weaker firms.

6.3. PPP Return Announcement Effects

We display the cumulative abnormal returns (CARs) associated with PPP return starting

from date t = −5, where t = 0 denotes the filing date of the disclosing 8-K. Figure 16 shows

that the announcement effects are positive for both the market-adjusted and the market

model abnormal returns. In Figure 17, we find that PPP return dates spike in early May

2020, when the SBA clarified that firms returning funds early would not be audited.

Table 14 reports the estimates of a fixed effects regression of the abnormal return on date

dummy variables that are non-zero in [-1, +1] where [0] is the filing date. The sample includes

returns in [-60, +1]. We cluster standard errors by calendar date. The announcement effect

is about +3% regardless of the specification.24 We consider treatment effects akin to those

for announcement effects. The average change in value is $11.74 million, with the total of

$1.256 billion across all 107 borrowers for which we can compute this statistic.25 The amount

of the loan returned is $538.87 million, producing a bang for the buck estimate of about 2.

If we add the loan amount returned, $538.87 million, to the dollar announcement effect,

essentially assuming that the loan amount was a wealth transfer, wealth effect of returning

PPP is $1.795 billion.

As before, we can correct announcement effects for partial anticipation by scaling them

for the portion impounded in the pre-announcement share price, which is (1 − q) where q is

the probability that the funds are returned. Doing so using the probability model estimated

in Table 13 shows that anticipation-corrected net gain is $2.148 billion across 105 PPP

returners. Returning $530.64 million should have resulted in losses of $530.64 million to the

returners but instead this turns into a gain of $2.148 million, or about 10.5% of firm value

(before PPP return announcement).

24The results are robust to excluding a small number of firms (about a half-dozen) who indicate in their

8-K disclosures that their performance has improved.

25We winsorize changes in value at the 1st and 99th percentiles to account for outliers such as AutoNation.

24
6.4. More Evidence on Indirect Costs of PPP

The fact that firms return PPP although it is bargain financing – and that markets react

positively to it – suggests that there are indirect costs of taking PPP. The release from these

costs is realized when PPP is returned and is valued by markets. We next consider tests

that can speak to the nature of these costs, specifically those arising out of government

investigation. Before these tests, we consider some other explanations for PPP return.

One possibility is that returners have alternative sources of funds. Equity financing is

one possible source but equity issuance traditionally sends a negative signal to the market.

A better candidate is bank lending, whose announcements trigger positive announcement

effects (James, 1987; Lummer and McConnell, 1989; Billett, Flannery, and Garfinkel, 1995;

Chen, Ho, and Liu, 2019). This interpretation, if correct, reinforces our point concerning

indirect costs. If firms prefer more expensive bank capital over far cheaper PPP funding, it

suggests that they see positive value in being subject to scrutiny by private capital providers

but the same scrutiny by the government has negative value.

Another explanation is signaling. According to this hypothesis, firms return PPP funds

to signal improvements in firm fundamentals otherwise unobserved by the market. We have

reservations about this explanation. One issue is that the $2 million discontinuity, which we

will turn to shortly, is not relevant for signaling. Another is the evidence on announcement

effects by firm size. Signaling should be stronger when less anticipated, that is, for smaller

and worse-quality firms. We do not find this in the data. We leave more tests of signaling

for future work, not necessarily mutually exclusive from the ones we examine.

6.4.1. Loan Amount and PPP Return

If government investigations are more likely for large PPP loans, the probability of returning PPP funds should increase in PPP loan size. We note that audit pronouncements

were released at various points of time in late April to May.26 Thus, investigation concerns

are less likely to have informed applications that occurred before but could drive the decision

to return PPP.

Figure 18 shows that the probability of returning PPP funds is greater for larger loan

amounts. We confirm the positive relation between PPP loan return and loan size in regression analysis in Table 15 (Column 1). We find that a one standard deviation increase in the

26E.g., see “Joint Statement by Secretary Steven T. Mnuchin and Administrator Jovita Carranza on the

Review Procedure for Paycheck Protection Program Loans,” SBA, April 28, 2020 at https://www.sba.go

v/article/2020/apr/28/joint-statement-secretary-steven-t-mnuchin-administrator-jovita-ca

rranza-review-procedure-paycheck and “PPP Changes Trip Up Small Businesses,” Wall Street Journal,

May 12, 2020 at https://www.wsj.com/articles/paycheck-protection-program-changes-trip-up-s

mall-businesses-11589288403.

25
natural logarithm of PPP loan amount increases the probability of returning the loan by

10.36 pp, or by 60.37% relative to the unconditional mean. We examine other tests next.

6.4.2. $2 Million Discontinuity: PPP Return

A second test concerns a $2 million loan amount threshold for loan audits. The interim

final rule issued by SBA in the Federal Register on June 1, 2020 says that all loans are subject

to audit. However, both the Treasury and the SBA have indicated that firms with loans less

than $2 million will be presumed to have applied in good faith.27 If this is relevant, firms

just above the $2 million threshold experience more threats relating to PPP investigation

than those below.

We first examine univariate statistics around the $2 million threshold. The sample odds

of returning PPP funds are 9% for loan amounts of $1.5–$2 million but more than double to

24% for loan amounts between $2–$2.5 million (Figure 18). Relatedly, 9.50% of the 442 firms

with loan amounts less than $2 million returned PPP funds, while 31.2% of the 237 firms

with loan amounts more than $2 million did so. The discontinuity in PPP return probability

is clearly visible in Figure 19 that superposes the histogram for applying for PPP on the

histogram for returning PPP. The discontinuity in PPP returns is quite stark here.

We turn to discontinuity designs around the $2 million loan amount threshold. One

issue in this type of analysis is statistical power. Regression discontinuity designs fit high

order polynomials to the running treatment variable and then check for discontinuities.

Our public PPP sample is relatively small to filter out the non-linear curvatures present

under a hypothetical null, with the additional difficulty of having a 1/0 outcome variable

for returns and in local designs that use even smaller subsamples centered around the $2

million threshold. We choose to include all observations in a global design. This approach

brings in more observations but ones that are more distant from the threshold are possibly

less informative of considerations around the $2 million threshold. We have no easy answer

to this tradeoff but present the evidence for what it is worth and turn to other evidence in

larger samples thereafter.

Table 15 gives the regression results. The coefficient for the threshold indicator is the

marginal effect of borrowing above the discontinuity threshold beyond the effect of PPP loan

size. In the baseline model, this threshold indicator coefficient is positive and significant.

The probability of returning the loan increases by 8.85% when firms borrow $2 million and

above (Column 2). The coefficient remains positive but with reduced significance when we

27E.g., see “SBA Clarifies Certification Requirements Today For Paycheck Protection Program Borrowers,”

Forbes, May 13, 2020 at https://www.forbes.com/sites/juliejason/2020/05/13/sba-clarifies-cer

tification-requirements-today-for--paycheck-protection-program-borrowers.

26
incorporate the full set of controls (Column 3).

6.4.3. $2 Million Discontinuity: Loan Application

We exploit another PPP design feature that creates a different type of discontinuity

around the $2 million cutoff but speaks to the government investigation concerns in the

larger SBA PPP sample. The SBA announced that PPP funds returned before May 18,

2020 would be presumed to have been made in good faith and thus not subject to audit.28

Thus, if investigation threats are of concern, we should see that PPP applications after

May 18, 2020 should tail off just above the $2 million threshold relative to just below.29

Figure 20 shows the histogram for SBA loan application amounts before and after May 18,

2020. A discontinuity appears in the PPP application density just above the $2 million cutoff

after the PPP return deadline. Before May 18, 2020, applications for loan amounts between

$1.5–$2.5 million comprise 66.56% of applications for $1.5–$2 million. After May 18, 2020,

90.48% of all applications for loan amounts between $1.5–$2.5 million are applications for

$1.5–$2 million. This result suggests that investigation aversion is a part of the PPP funding

hesitancy we find. The threat of audit appears to have deterred some firms from applying

for PPP – or pushed them to apply for amounts below $2 million.30

6.4.4. Indicative UK Evidence

We next consider some evidence from the UK. Here, the government program to aid

businesses in the wake of Covid took the form of a business rates relief program, equivalent

to relief on property taxes. This program also saw “PPP-like” returns of funds. An important

difference is that in contrast to the U.S., firms in the U.K. returned government aid due to

public pressure that was not accompanied by any explicit or implicit threat of government

investigation.31 Thus, share price responses to returns should be free of concerns about

government investigations and reflect, e.g., losses from access to Covid-19 related relief.

We gather data on U.K. business rates relief returners from press reports. The subset we

identified from press reports includes large retailers: Tesco, Sainsbury’s, Morrison’s, Asda,

28E.g., see “PPP Loan Rules Relaxed By SBA For Loans Under $2,000,000 – Uncertainty Still Abounds

For Many,” Forbes, May 14, 2020 at https://www.forbes.com/sites/alangassman/2020/05/14/ppp-lo

an-rules-relaxed-by-sba-for-loans-under-2000000uncertainty-still-abounds-for-many.

29The before-after design mitigates fee discontinuity related explanations – the fees did not change but

the application behavior did. Relatedly, we don’t see a discontinuity for the $350,000 loan amount cutoff

that also presents a fee discontinuity.

30The public sample is rather small for a before-after May 18, 2020 design. However, Figure 3 shows a

decline in the density of PPP applications by public firms above the $2 million cutoff in PPP Round 2.

31See, e.g., £1.8Bn-Plus in Covid Rates Relief to Be Handed Back as B&M Joins List, The Guardian, December 3, 2020 and B&Q Owner Kingfisher to Repay £130M of Covid Business Rates Relief, The Guardian,

December 7, 2020.

27
B&M, Pets at Home, and Whole Foods. The announcement effects for the subset with returns

is negative, and range from -2.05% to -11.08%. One interesting case is that of the retailer

Marks and Spencer, which announced that it would not shy away from the business rates

relief. Upon this announcement, the firm experienced +9.02% abnormal return. The UK

evidence suggests that returning subsidies unrelated to scrutiny avoidance results in negative

announcement effects. In the U.S., we see positive announcement effects when firms return

subsidized funding, supporting the investigation-avoidance interpretation of our U.S. PPP

return results. As additional evidence, Internet Appendix Section IA.C gives extracts from

company reports showing concerns about scrutiny motivating PPP returns.

7. Conclusion

The Paycheck Protection Program (PPP), a fiscal stimulus program in the U.S., aims to

help small businesses suffering from the economic fallout of the Covid-19 pandemic. At $669

billion in fiscal commitments, the PPP is a significant portion of the $2 trillion CARES Act

package intended to help the country through the deep, sudden, and widespread economic

contraction and job losses from the Covid-19 pandemic.

The PPP is a significant shock in the supply of financing to small businesses. It provides extraordinarily cheap financing to small businesses that are highly constrained even

in normal times. We use this shock to examine two questions. One is about using financial

intermediaries to pipe the funding supply, which highlights the supply decisions of small

and big banks under situations of extreme constraints. The other concerns the “funding

hesitancy,” or the apparent reluctance on the part of small businesses to receive government

funding.

The first question concerns PPP funding delivery through the banking system. Banks are

the dominant types of financial institutions in most countries and have widespread branch

networks with extensive reach. Thus, they are logical entities to use to deliver funding to

important sectors.32The need to get credit to important sectors is especially relevant in difficult times. But banks have their own incentives which can shape the delivery system. Our

findings suggest that there are intermediary supply effects, that is, bank financial intermediaries shape the supply of PPP financing. Our evidence suggests that larger firms are likely

to gain early access but the pro-large firm effects are attenuated in small banks and when

firms have prior banking relationships.

32For example, in Germany, a bank-based system, German banks tried to develop the German VC market

(Da Rin, Hellmann, and Puri, 2013). In India, banks are given quotas for priority sector lending, in the hope

of getting credit to underserved, critical sectors and jumpstarting their growth (Banerjee and Duflo, 2014).

28
The finding is interesting in light of the large literature in banking suggesting that small

firms benefit from pairing up with small banks. The literature lends empirical support to

this proposition, and theoretical justification. Stein (2002) lays out a theory that explains

this matching based on soft information. A number of papers have examined Stein (2002)’s

theory, using different experiment designs, starting with Berger et al. (2005) who finds supportive evidence of small banks being better able to collect and act on soft information than

large banks. But the literature has not converged on other additional reasons for small firmsmall bank pairing. Our evidence suggests a new rationale for small firm-small bank pairing

through evidence in which there is a large shock but credit risk – thus private information

and soft information – is not at play given repayment guarantees.

We find that absent relationships both small and large banks prioritize large firms. This

is consistent with underlying incentives since, if capacity constraints bind so that banks can

process only a fixed amount of loan applications, banks make more fees by processing larger

loans. Interestingly however, this prioritization flips when prior banking relationships are

taken into account. Small banks prioritize small firms with prior bank relationships but we

do not see similar prioritization by large banks. In times of shock and capacity constraints,

small banks are more likely to prioritize small firm relationships. Given that PPP loans

are guaranteed by the government, and there is no private or soft information involved our

results suggest a new rationale for the benefits of pairing of small firms with small banks.

With its extraordinarily inexpensive financing terms, the PPP is also a very positive

shock in financing supply to small businesses. Taking PPP has positive announcement

effects, consistent with beneficial program impact for our small universe of public firms.

Nevertheless, we detect a “funding hesitancy,” some reluctance on the part of firms to take

up PPP. Perhaps the most stark evidence of this hesitancy is that several public firms that

bring up the bottom in terms of financial constraints indexes, manage to qualify for, apply

for, and take PPP funds – yet return the money without using it as the program unfolds

and the threat of government investigation begins to loom large. Interestingly, returning the

PPP funds results in positive announcement effects, indicating that both firms – by revealed

preference, giving up cheap funding – and markets value the release from PPP.

The findings suggest that taking PPP funds imposes significant indirect costs on participants. The costs appear to be related to the possibility of ex-post investigations of PPP

recipients. Of concern are the subjectivity in the audit process, the broad powers of the

government to seek remedies, and especially an openly adversarial stance towards public

firms articulated in its pronouncements. It does not seem surprising that firms choose to

turn away from PPP funding. Firms that are stronger before the pandemic tend to do so,

concentrating PPP funds among the financially weaker applicants. From a policy viewpoint,

29
our findings suggest that when taxpayer funding is involved, policymakers should focus on

both objective standards for program eligibility and also specify with similar objectivity the

conduct of the ex-post audits concerning funds use. For instance, delineating safe harbors

to circumscribe litigation, a standard practice in securities law since the 1930s, may be an

appropriate tool in designing government aid programs.

Understanding the intermediary supply effects, market responses to PPP funding, and

funding hesitancy can help design better policies for small businesses. We hope that our

findings also shed light on and help move forward research on the nature of firm-bank relationships, especially for small firms and small banks.

30
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34
Figure 1. PPP timeline. This figure illustrates the timeline and key milestones of the Paycheck

Protection Program (PPP).

Figure 2. Density of PPP loan grant announcement dates. This figure plots the density

of announcement dates for PPP loan grant announcements by public PPP borrowers.

Figure 3. PPP loan size by PPP Round. This figure plots the density of PPP loan size for

PPP Round 1 (before April 17, 2020) versus PPP Round 2 (after April 26, 2020) for public PPP

borrowers. We set PPP loan size to $10M for loans above $10M, for the ease of exposition.

35
Figure 4. Early PPP access and firm assets: small versus big banks. This figure plots

kernel densities of the natural logarithm of firm’s assets for early versus late public PPP borrowers.

Panel A reports the results for smaller (non-big-10) banks. Panel B reports the results for big-10

banks. Log (Assets) is winsorized at the 1st and the 99th percentiles.

Figure 5. Early PPP access and PPP loan size: small versus big banks. This figure

plots kernel densities of the natural logarithm of PPP loan amount for early versus late public PPP

borrowers. Panel A reports the results for smaller (non-big-10) banks. Panel B reports the results

for big-10 banks. Log (PPP Loan Amount) is winsorized at the 1st and the 99th percentiles.

36
Bank relationship

No bank relationship

Figure 6. Early PPP access and firm assets by prior bank relationship. This figure plots

kernel densities of the natural logarithm of firm’s assets for early versus late public PPP borrowers,

for firms with and without prior bank relationships (i.e., before the PPP). Panel A reports the

results for smaller (non-big-10) banks and firms with bank relationships. Panel B reports the

results for big-10 banks and firms with bank relationships. Panel C reports the results for smaller

(non-big-10) banks and firms without bank relationships. Panel D reports the results for big-10

banks and firms without bank relationships. Log (Assets) is winsorized at the 1st and the 99th

percentiles.

37
Bank relationship

No bank relationship

Figure 7. Early PPP access and PPP loan size by prior bank relationship. This figure

plots kernel densities of the natural logarithm of PPP loan amount for early versus late public

PPP borrowers, for firms with and without prior bank relationships (i.e., before the PPP). Panel A

reports the results for smaller (non-big-10) banks and firms with bank relationships. Panel B

reports the results for big-10 banks and firms with bank relationships. Panel C reports the results

for smaller (non-big-10) banks and firms without bank relationships. Panel D reports the results

for big-10 banks and firms without bank relationships. Log (PPP Loan Amount) is winsorized at

the 1st and the 99th percentiles.

38
All firms

Bank relationship

No bank relationship

Figure 8. Probability of early PPP borrowing and PPP loan size by prior bank relationship. This figure plots the probability of borrowing early (i.e., in PPP Round 1) versus borrowing late (i.e., in PPP Round 2) for public PPP borrowers, by PPP loan size bin. Panel A reports

the results for smaller (non-big-10) banks. Panel B reports the results for big-10 banks. Panel C

reports the results for smaller (non-big-10) banks and firms with bank relationships. Panel D reports the results for big-10 banks and firms with bank relationships. Panel E reports the results

for big-10 banks and firms without bank relationships. Panel F reports the results for smaller

(non-big-10) banks and firms without bank relationships.

39
Figure 9. PPP loan size by PPP Round: SBA PPP data. This figure plots the density of

PPP loan size for PPP Round 1 (before April 17, 2020) versus PPP Round 2 (after April 26, 2020).

The sample is based on UCC–SBA PPP data and includes all PPP borrowers that are non-financial

corporations. The results are similar if we include other types of businesses.

40
All firms

Bank relationship

No bank relationship

Figure 10. Probability of early PPP borrowing by prior bank relationship: SBA PPP

data. This figure plots the probability of borrowing early (i.e., in PPP Round 1) versus borrowing

late (i.e., in PPP Round 2) for PPP borrowers from the UCC–SBA PPP data, which we restrict

to non-financial corporations, for comparability, by PPP loan size bin. Panel A reports the results

for smaller (non-big-10) banks, with this category including non-bank lenders. Excluding non-bank

lenders does not affect the results. Panel B reports the results for big-10 banks. Panel C reports

the results for smaller (non-big-10) banks and firms with bank relationships. Panel D reports the

results for big-10 banks and firms with bank relationships. Panel E reports the results for smaller

(non-big-10) banks and firms without bank relationships. Panel F reports the results for big-10

banks and firms without bank relationships.
Figure 11. Public PPP borrowers by industry. This figure plots the share of PPP-eligible

U.S. public companies that were granted a PPP loan, by NAICS (Panel A) and industry type

(Panel B). We measure industry using 2-digit NAICS as follows: 21 = Mining, Quarrying, and

Oil and Gas Extraction; 22 = Utilities; 31–33 = Manufacturing; 42 = Wholesale Trade; 48 =

Transportation and Warehousing; 51 = Information; 53 = Real Estate and Rental and Leasing;

54 = Professional, Scientific, and Technical Services; 56 = Administrative and Support and Waste

Management and Remediation Services; 62 = Health Care and Social Assistance; 71 = Arts,

Entertainment, and Recreation; 72 = Accommodation and Food Services; and other = Other

(except Public Administration).

Figure 12. Evolution of PPP borrower returns: PPP applicants and control firms.

This figure plots daily buy-and-hold average returns (BHRs) for PPP-eligible small public firms

versus the S&P 500 index (Panel A) and buy-and-hold average abnormal returns (BHARs) for

PPP borrowers versus PPP-eligible non-borrowers (Panel B), from January to August 2020. We

define the abnormal return as the stock return over the S&P 500 return. We exclude penny stocks,

which we define as stocks with an average share price of less than $5 in December 2019, because of

illiquidity. BHAR is set to zero for the first trading day of the year, which is January 2, 2020.

42
Figure 13. Cumulative abnormal returns (CARs) around PPP loan grant announcement dates. This figure plots daily CARs for PPP loan grant announcements. The abnormal

return in Panel A is the stock return minus the S&P 500 return, where the abnormal return is set

to zero for Day -5. The abnormal return in Panel B is calculated using the market model based on

S&P 500 return, where the abnormal return is set to zero for Day -5. We exclude penny stocks,

which we define as stocks with an average share price of less than $5 in December 2019.

Figure 14. Firms that return PPP loans by industry. This figure plots the share of

PPP borrowers that returned the PPP loans to the SBA, by NAICS (Panel A) and industry type

(Panel B). See Fig. 11 for NAICS definitions.

43
Figure 15. Evolution of PPP borrower stock returns: PPP returners and retainers.

This figure plots daily buy-and-hold average abnormal returns (BHARs) for PPP returners versus

retainers from January to August 2020, where the abnormal return is the stock return over the

S&P 500 return. We exclude penny stocks, which we define as stocks with an average share price

of less than $5 in December 2019, because of illiquidity. BHAR is set to zero for the first trading

day of the year, which is January 2, 2020.

Figure 16. Cumulative abnormal returns (CARs) around PPP loan return announcement dates. This figure plots daily CARs for PPP loan return announcements. The abnormal

return in Panel A is the stock return minus the S&P 500 return, where the abnormal return is set

to zero for Day -5. The abnormal return in Panel B is calculated using the market model based on

S&P 500 return, where the abnormal return is set to zero for Day -5. We exclude penny stocks,

which we define as stocks with an average share price of less than $5 in December 2019.

44
Figure 17. Density of PPP loan return announcement dates. This figure plots the density

of announcement dates for returning PPP loans. We omit two announcement dates in August 2020,

for the ease of exposition.

Figure 18. PPP loan return probability by loan size. This figure plots the probability of

U.S. public borrowers returning PPP loans to the SBA, by PPP loan size bin. The vertical line is

the loan amount of $2 million. We set PPP loan size equal to $8M for loans above $8M, for the

ease of exposition.

45
Figure 19. PPP loan return probability around the $2M loan size cutoff. This figure

plots the probability of U.S. public borrowers returning PPP loans to the SBA, in the vicinity of

the $2M loan size cutoff, above which the loans are subject to audit. We also plot the application

probability of these firms around the cutoff. The vertical line is the loan amount of $2 million.

Figure 20. PPP applications around the $2M loan size cutoff: SBA PPP data. This

figure plots densities of the PPP loan amount in the vicinity of the $2M loan size cutoff, above

which the loans are subject to audit. The graph is based on the UCC–SBA PPP data, which we

restrict to non-financial corporations, for comparability. Panel A reports the results for the period

before the PPP loan return deadline on May 18, 2020. Panel B reports the results for the period

after the PPP loan return deadline. The vertical line is the loan amount of $2 million.

46
Table 1

Full SBA PPP sample and UCC–SBA PPP sample

This table summarizes the number and the amount of PPP loans to all PPP recipients, by recipient

type. Panel A reports statistics for the SBA’s PPP data release in December 2020. Panel B reports

statistics for the UCC–SBA PPP data.

Number Amount ($B) Median Mean

Recipient Type (1) (2) (3) (4)

Panel A: Full SBA data

Corporation 1,498,552 207.97 41,120 138,782

LLC 1,455,353 135.25 25,000 92,935

Subchapter S 701,332 98.06 40,000 139,825

Non-Profit Organization 178,533 36.85 41,600 206,393

Sole Proprietorship 817,826 16.94 11,400 20,719

LLP 36,448 6.07 46,904 166,446

Independent Contractor 144,472 1.64 8,976 11,383

Other 324,334 20.16 15,200 62,153

Total 5,156,850 522.95 22,880 101,409

Panel B: UCC–SBA data

Corporation 617,749 130.25 69,000 210,843

LLC 406,595 66.41 47,777 163,326

Subchapter S 247,101 60.19 80,352 243,565

Non-Profit Organization 45,705 22.90 152,500 501,119

Sole Proprietorship 84,297 4.22 20,000 50,066

LLP 14,509 3.68 76,500 253,362

Independent Contractor 2,692 0.08 13,945 30,415

Other 50,519 10.62 47,881 210,242

Total 1,469,167 298.34 59,920 203,070

Table 2

Public PPP borrower sample

Column (1) reports the number of observations at each stage of PPP borrower sample construction.

Column (2) reports the respective numbers for firms that subsequently returned PPP loans to SBA.

All PPP Borrowers PPP Loan Returners

(1) (2)

1. Initial PPP borrower sample 739 123

2. Exclusions based on economic considerations: 57 6

Reason 1: Financial firm 57 6

Reason 2: Special purpose acquisition company (SPAC) 0 0

3. Final sample, including: 682 117

Matched to Compustat 586 113

Matched to Yahoo! Finance 663 117

Matched to SBA’s PPP loan disclosures (Jul, Aug, or Dec) 538 17

Matched to Lender RSSD IDs (e.g., FDIC, FFIEC, NCUA) 623 92

Matched to DealScan & UCC 405 82

4. PPP announcements, including: 682 117

PPP announcements from 8-K filings 439 68

PPP announcements from 10-Qs, 10-Ks, and other sources 243 49

5. PPP borrowing disclosed after PPPFA 258 8

47
Table 3

Public and private PPP borrowers by PPP round

Columns (1) to (3) report the mean, median, and standard deviation of several financial characteristics of firms that obtained PPP loans in PPP Round 1. Columns (4) to (6) report the same

statistics for firms that obtained PPP loans in PPP Round 2. Column (7) reports the number of

observations and Column (8) reports p-values from a Wilcoxon rank sum test comparing early borrowers with late borrowers. In the case of discrete variables, the statistics are proportions and the

p-values are for tests of the difference in proportions. Variables are defined in Internet Appendix

Section IA.D. Continuous variables are winsorized at the 1st and the 99th percentiles.

Early PPP borrowers Late PPP borrowers Difference tests

Mean Median SD Mean Median SD N p-value

(1) (2) (3) (4) (5) (6) (7) (8)

Panel A: Public PPP borrower sample

Firm size

PPP Loan Amount ($ million) 2.671 1.482 3.084 1.982 0.723 3.098 679 0.000

Book Value of Assets ($ million) 120.3 39.2 520.2 99.8 23.3 367.7 569 0.000

Market Cap ($ million) 115.4 41.6 282.6 101.4 24.6 297.1 561 0.000

Sales ($ million) 77.8 25.8 160.9 86.8 11.1 229.8 569 0.000

# Employees (’000) 0.254 0.109 0.380 0.217 0.061 0.359 561 0.000

Other financial characteristics

Firm Age (years) 15.694 12.000 12.958 15.315 10.500 13.104 569 0.199

Book Equity <0 (1/0) 17.7% — — 32.1% — — 568 0.000

Tobin’s Q 1.773 1.201 2.181 1.516 0.972 2.188 569 0.001

Sales Growth 0.617 0.032 3.484 0.468 0.002 2.536 511 0.571

Dividend Payer (1/0) 15.3% — — 13.6% — — 569 0.172

Current Ratio 2.651 1.876 2.872 2.551 1.407 4.150 568 0.002

Cash/Non-Cash Assets 1.086 0.237 3.353 1.086 0.215 2.730 569 0.587

Free Cash Flow/Assets 0.144 0.032 0.872 0.327 0.047 2.106 477 0.235

Financial constraints

Has Credit Rating (1/0) 2.7% — — 2.6% — — 682 0.531

WW Index ≥ p75 (1/0) 72.4% — — 71.0% — — 406 0.857

SA Index ≥ p75 (1/0) 76.1% — — 79.5% — — 527 0.641

Leverage and distress

Zero Debt (1/0) 8.6% — — 9.2% — — 569 0.410

Market Leverage 0.234 0.165 0.224 0.303 0.212 0.295 512 0.297

Interest Coverage <1.5 (1/0) 77.3% — — 80.1% — — 436 0.725

Altman Z-score <1.81 (1/0) 67.8% — — 71.2% — — 479 0.616

Distance-to-Default 2.743 2.368 2.075 2.534 1.960 2.030 409 0.087

Penny Stock (1/0) 72.4% — — 81.1% — — 561 0.222

Stock returns

Covid Period Return -0.322 -0.388 0.383 -0.309 -0.387 0.379 638 0.800

Stimulus Day Return 0.059 0.049 0.109 0.047 0.030 0.127 644 0.022

Panel B: Full SBA PPP sample and UCC–SBA PPP sample

Full SBA data

PPP Loan Size ($ million) 0.209 0.080 0.349 0.084 0.032 0.183 1,297,214 0.000

# Jobs (’000) 0.040 0.018 0.062 0.018 0.008 0.036 1,211,239 0.000

UCC–SBA data

PPP Loan Size ($ million) 0.278 0.110 0.452 0.128 0.050 0.269 560,829 0.000

# Jobs (’000) 0.050 0.022 0.077 0.024 0.010 0.048 526,684 0.000

48
Table 4

Early PPP borrower propensity

This table reports the results from a linear regression in which the dependent variable is an indicator

for public company receiving a PPP loan before April 17, 2020 when the PPP funds ran out and

independent variables are company characteristics. Continuous variables are winsorized at the 1st

and the 99th percentiles. Standard errors are clustered at the industry level, defined as 2-digit

NAICS, where we combine industries with few PPP-eligible companies into one bucket. ***, **,

and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. t-statistics are

presented in parentheses.

Dependent variable = Early PPP Borrower (1/0)

(1) (2) (3)

Log (Assets) 0.0338** 0.0325** 0.0370**

(2.48) (2.20) (2.74)

Log (Age) 0.00373 0.00412 0.00353

(0.12) (0.13) (0.11)

Book Equity <0 (1/0) -0.141*** -0.132** -0.127**

(-3.05) (-2.73) (-2.54)

Tobin’s Q -0.00162 -0.000765 0.00448

(-0.08) (-0.04) (0.24)

Current Ratio -0.00537 -0.00510 -0.00611

(-1.00) (-0.93) (-1.14)

Cash/Non-Cash Assets 0.00417 0.00385 0.00429

(1.51) (1.38) (1.57)

Penny Stock (1/0) -0.0414 -0.0432 -0.0115

(-0.71) (-0.73) (-0.21)

Covid Industry (1/0) 0.00389 0.0112

(0.06) (0.14)

Machinery Industry (1/0) 0.140* 0.152*

(1.80) (2.07)

Health Industry (1/0) -0.00694 -0.0196

(-0.19) (-0.49)

High-Tech Industry (1/0) -0.0494 -0.0513

(-1.11) (-1.22)

Covid Period Return -0.161

(-1.61)

Covid Period Return × Penny Stock (1/0) 0.191*

(2.08)

Stimulus Day Return -0.590

(-1.49)

Stimulus Day Return × Penny Stock (1/0) 0.570

(1.59)

# obs. 543 543 535

Adjusted R2 0.0321 0.0291 0.0256

49
Table 5

Early versus late PPP access, small versus big banks,

and bank relationships

This table compares the size of early versus late PPP borrowers that obtained a PPP loan through

one of big-10 banks (Big-10 Bank) versus other banks (Smaller Bank). Panel A reports the results

for all firms. Panel B reports the results for firms with prior bank relationships. Panel C reports

the results for firms without prior bank relationships. We classify the following banks as big-10

banks: JPMorgan Chase Bank, Wells Fargo Bank, Bank of America, Citibank, U.S. Bank, Truist

Bank, PNC Bank, Bank of New York, State Street Corporation, and TD Bank. Columns (1)

to (2) report means and medians of the size variable for firms that obtained PPP loans in PPP

Round 1 (i.e., early PPP borrowers). Columns (3) to (4) report the respective means and medians

for PPP Round 2 (i.e., late PPP borrowers). Column (5) reports the number of observations and

Column (6) reports p-values from a Wilcoxon rank sum test comparing early borrowers with late

borrowers. Continuous variables are winsorized at the 1st and the 99th percentiles.

Early PPP borrowers Late PPP borrowers Difference tests

mean median mean median N p-value

(1) (2) (3) (4) (5) (6)

Panel A: All firms

Smaller banks

Book Value of Assets 82.402 36.385 86.682 26.979 372 0.093

PPP Loan Amount 2.468 1.404 2.216 0.862 440 0.004

Big-10 banks

Book Value of Assets 144.158 55.110 61.366 18.625 153 0.000

PPP Loan Amount 3.428 2.220 1.848 0.588 179 0.000

Panel B: Bank relationship

Smaller banks

Book Value of Assets 118.763 43.079 178.308 94.652 125 0.119

PPP Loan Amount 3.577 2.124 5.267 3.558 137 0.138

Big-10 banks

Book Value of Assets 247.562 161.465 151.072 68.755 48 0.158

PPP Loan Amount 6.069 5.533 4.428 1.639 53 0.150

Panel C: No bank relationship

Smaller banks

Book Value of Assets 64.317 34.813 37.709 14.878 247 0.000

PPP Loan Amount 1.932 1.054 1.091 0.681 303 0.000

Big-10 banks

Book Value of Assets 80.047 37.745 33.638 11.947 105 0.003

PPP Loan Amount 1.880 1.087 1.127 0.464 126 0.001

50
Table 6

Early PPP access, lender effects, and bank relationships

This table reports the results from a linear regression in which the dependent variable is an indicator

for an early public PPP borrower. An early borrower is a company receiving a PPP loan before

April 17, 2020. The size variable is Book Value of Assets in Panel A and PPP Loan Amount in

Panel B. Smaller Bank (1/0) is a dummy for PPP lender being a non-big-10 bank. We classify

the following banks as big-10 banks: JPMorgan Chase Bank, Wells Fargo Bank, Bank of America,

Citibank, U.S. Bank, Truist Bank, PNC Bank, Bank of New York, State Street Corporation, and

TD Bank. Continuous variables are winsorized at the 1st and the 99th percentiles. Standard errors

are clustered at the industry level, defined as 2-digit NAICS, where we combine industries with few

PPP-eligible companies into one bucket. ***, **, and * denote statistical significance at the 1%,

5%, and 10% levels, respectively. t-statistics are presented in parentheses. Constants are omitted

for brevity. Continuous variables are winsorized at the 1st and the 99th percentiles.

Dependent variable = Early PPP Borrower (1/0)

All firms Bank relationship No bank relationship

(1) (2) (3) (4) (5) (6)

Panel A: Firm size

Log (Assets) × Smaller Bank (1/0) -0.0547** -0.0583** -0.0960* -0.120** -0.0224* -0.0209

(-2.22) (-2.63) (-2.07) (-2.38) (-1.80) (-1.65)

Log (Assets) 0.0875*** 0.0692*** 0.0752** 0.0540 0.0880*** 0.0795***

(5.22) (3.04) (2.51) (1.18) (7.52) (4.15)

Smaller Bank (1/0) 0.421*** 0.429*** 0.538** 0.651** 0.338*** 0.319***

(4.10) (4.70) (2.68) (2.61) (4.25) (4.57)

Firm Characteristics No Yes No Yes No Yes

Penny Stock Dummy No Yes No Yes No Yes

Industry Dummies No Yes No Yes No Yes

Returns Controls No Yes No Yes No Yes

# obs. 525 493 173 167 352 326

Adjusted R2 0.0884 0.0705 0.0131 0.0415 0.132 0.0941

Panel B: PPP loan size

Log (PPP Loan Size) × Smaller Bank (1/0) -0.00813 -0.0297* -0.0438 -0.103** 0.0231 0.0126

(-0.38) (-1.77) (-0.80) (-2.98) (1.39) (0.55)

Log (PPP Loan Size) 0.0664** 0.0602** 0.0360 0.0518 0.0750*** 0.0763***

(2.54) (2.43) (0.68) (0.92) (3.22) (3.02)

Smaller Bank (1/0) 0.260** 0.333*** 0.303 0.581** 0.199** 0.221**

(2.58) (4.22) (1.16) (2.87) (2.66) (2.90)

Firm Characteristics No Yes No Yes No Yes

Penny Stock Dummy No Yes No Yes No Yes

Industry Dummies No Yes No Yes No Yes

Returns Controls No Yes No Yes No Yes

# obs. 525 493 173 167 352 326

Adjusted R2 0.0760 0.0672 0.00146 0.0408 0.127 0.0956

51
Table 7

Early PPP access and bank relationships:

SBA PPP data

This table reports the results from a linear regression in which the dependent variable is an indicator

for an early PPP borrower. The sample is the UCC–SBA PPP dataset. An early borrower is a

company receiving a PPP loan before April 17, 2020. Column 1 estimated the regression based

on all firms in the dataset. Columns 2 ( 3) use the subsample of firms with (without) a bank

relationship before the PPP. A firm has a bank relationship if it is recorded as having a security

interest filing under UCC between 2015 and 2019. Log (PPP Loan Size) is the natural logarithm

of the PPP loan size from the SBA PPP data, which we restrict to non-financial corporations, for

comparability. Smaller Bank (1/0) is a dummy for PPP lender being a non-big-10 bank. We classify

the following banks as big-10 banks: JPMorgan Chase Bank, Wells Fargo Bank, Bank of America,

Citibank, U.S. Bank, Truist Bank, PNC Bank, Bank of New York, State Street Corporation, and

TD Bank. Continuous variables are winsorized at the 1st and the 99th percentiles. Standard errors

are clustered at the industry level, defined as 2-digit NAICS, where we combine industries with few

PPP-eligible companies into one bucket. ***, **, and * denote statistical significance at the 1%,

5%, and 10% levels, respectively. t-statistics are presented in parentheses. Constants are omitted

for brevity. Continuous variables are winsorized at the 1st and the 99th percentiles.

Dependent variable = Early PPP Borrower (1/0)

All Bank No bank

firms relationship relationship

(1) (2) (3)

Log (PPP Loan Size) × Smaller Bank (1/0) 0.0262*** -0.00510 0.0488***

(6.73) (-1.58) (12.85)

Log (PPP Loan Size) 0.0784*** 0.0976*** 0.0543***

(22.10) (33.60) (12.99)

Smaller Bank (1/0) 0.357*** 0.303*** 0.403***

(34.71) (39.89) (37.63)

NAICS-6 FEs Yes Yes Yes

ZIP-5 FEs Yes Yes Yes

# obs. 549,390 298,842 244,967

Adjusted R2 0.284 0.254 0.266

52
Table 8

Early PPP access and Applying Through Relationship Bank

SBA PPP data

This table reports the results from linear regression in which the dependent variable is an indicator

for an early PPP borrower. The sample includes all firms in the UCC–SBA PPP data that have

a bank relationships before the PPP. A firm has a bank relationship if it is recorded as having a

security interest filing under UCC between 2015 and 2019. An early borrower is a company receiving

a PPP loan before April 17, 2020. Relationship Bank PPP is a dummy variable for whether the firm

applies through its relationship bank. Small Bank Relationship is a dummy variable for whether

the firm had a relationship with a small bank. We classify the following banks as big-10 banks:

JPMorgan Chase Bank, Wells Fargo Bank, Bank of America, Citibank, U.S. Bank, Truist Bank,

PNC Bank, Bank of New York, State Street Corporation, and TD Bank. Standard errors are

clustered at the industry level, defined as 2-digit NAICS, where we combine industries with few

PPP-eligible companies into one bucket. ***, **, and * denote statistical significance at the 1%,

5%, and 10% levels, respectively. t-statistics are presented in parentheses. Constants are omitted

for brevity. Continuous variables are winsorized at the 1st and the 99th percentiles.

Dependent variable = Early PPP Borrower (1/0)

(1) (2) (3)

Relationship Bank PPP (1/0) 0.0950*** -0.102***

(17.38) (-13.97)

Small Bank Relationship (1/0) 0.213*** 0.0971***

(32.29) (16.14)

Relationship Bank PPP (1/0) × Small Bank Relationship (1/0) 0.242***

(47.74)

NAICS-6 FEs Yes Yes Yes

ZIP-5 FEs Yes Yes Yes

# obs. 298,842 298,842 298,842

Adjusted R2 0.150 0.169 0.186

53
Table 9

Publicly listed PPP applicants and control firms

Columns (1) to (3) report the mean, median, and standard deviation of several financial characteristics of firms that applied for PPP loans. Columns (4) to (6) report the same statistics for the

control group of firms with fewer than 500 employees reported in COMPUSTAT that did not apply

for the PPP. Column (7) reports the number of observations and Column (8) reports p-values from

a Wilcoxon rank sum test comparing applicants with non-applicants. In the case of discrete variables, the statistics are proportions and the p-values are for tests of the difference in proportions.

Continuous variables are winsorized at the 1st and the 99th percentiles.

PPP borrowers Non-applicants Difference

(N=682) (N=1,452) tests

Mean Median SD Mean Median SD N p-value

(1) (2) (3) (4) (5) (6) (7) (8)

Firm size

PPP Loan Amount ($ million) 2.434 1.139 3.104 — — — 679 —

Book Value of Assets ($ million) 113.7 34.8 476.0 526.3 84.7 1,503.0 2,021 0.000

Market Cap ($ million) 110.9 35.9 287.1 487.8 118.7 951.6 1,947 0.000

Sales ($ million) 80.7 22.9 185.8 120.8 13.0 299.2 2,021 0.000

# Employees (’000) 0.242 0.090 0.373 0.116 0.059 0.132 2,013 0.000

Other financial characteristics

Firm Age (years) 15.571 12.000 12.995 9.873 7.000 9.740 2,021 0.000

Book Equity <0 (1/0) 22.4% — — 15.9% — — 2,019 0.000

Tobin’s Q 1.690 1.140 2.185 2.592 1.333 3.875 2,021 0.000

Sales Growth 0.570 0.024 3.216 0.672 0.037 3.176 1,545 0.845

Dividend Payer (1/0) 14.8% — — 14.3% — — 2,021 0.546

Current Ratio 2.619 1.692 3.337 5.017 2.451 7.207 2,004 0.000

Cash/Non-Cash Assets 1.086 0.233 3.162 3.501 0.400 7.733 2,007 0.000

Free Cash Flow/Assets 0.203 0.033 1.392 0.392 0.011 2.750 1,682 0.000

Financial constraints

Has Credit Rating (1/0) 2.6% — — 5.7% — — 2,134 0.000

WW Index ≥ p75 (1/0) 71.9% — — 46.5% — — 1,262 0.000

SA Index ≥ p75 (1/0) 77.2% — — 72.7% — — 1,872 0.248

Leverage and distress

Zero Debt (1/0) 8.8% — — 15.5% — — 2,018 0.000

Market Leverage 0.256 0.178 0.251 0.228 0.100 0.270 1,682 0.000

Interest Coverage <1.5 (1/0) 78.2% — — 76.8% — — 1,457 0.749

Altman Z-score <1.81 (1/0) 68.9% — — 61.0% — — 1,623 0.030

Distance-to-Default 2.685 2.254 2.062 3.713 3.137 2.486 961 0.000

Penny Stock (1/0) 75.2% — — 59.5% — — 1,948 0.000

Stock returns

Covid Period Return -0.318 -0.387 0.381 -0.327 -0.361 0.310 2,028 0.136

Stimulus Day Return 0.055 0.042 0.116 0.058 0.049 0.103 2,042 0.078

54
Table 10

PPP borrowing propensity

This table reports the results from a linear probability (OLS) model where the dependent variable

is an indicator for public company receiving a PPP loan and independent variables are company

characteristics. Continuous variables are winsorized at the 1st and the 99th percentiles. Standard

errors are clustered at the industry level, defined as 2-digit NAICS, where we combine industries

with few PPP-eligible companies into one bucket. ***, **, and * denote statistical significance at

the 1%, 5%, and 10% levels, respectively. t-statistics are presented in parentheses.

Dependent variable = PPP Borrower (1/0)

(1) (2) (3)

Log (Assets) -0.0350*** -0.0268*** -0.0255**

(-3.81) (-4.10) (-2.86)

Log (Age) 0.0635*** 0.0639*** 0.0646**

(3.45) (3.09) (2.88)

Book Equity <0 (1/0) -0.0602 -0.0606 -0.0635

(-1.53) (-1.45) (-1.48)

Tobin’s Q -0.0134*** -0.0126*** -0.0128***

(-4.70) (-4.19) (-3.74)

Current Ratio -0.00725*** -0.00642*** -0.00622***

(-3.78) (-3.37) (-3.28)

Cash/Non-Cash Assets -0.00558*** -0.00578*** -0.00556**

(-3.67) (-3.05) (-2.77)

Penny Stock (1/0) 0.00162 0.0114 0.0728

(0.03) (0.30) (1.66)

Covid Industry (1/0) 0.331** 0.334**

(2.82) (2.88)

Machinery Industry (1/0) 0.127 0.134

(1.07) (1.03)

Health Industry (1/0) 0.0510 0.0502

(0.46) (0.44)

High-Tech Industry (1/0) 0.134 0.144

(1.13) (1.23)

Covid Period Return -0.129

(-1.56)

Covid Period Return × Penny Stock (1/0) 0.157*

(2.06)

Stimulus Day Return 0.158

(0.39)

Stimulus Day Return × Penny Stock (1/0) 0.124

(0.33)

# obs. 1,797 1,797 1,762

Adjusted R2 0.0798 0.111 0.114

55
Table 11

PPP loan grant announcement effects

This table reports the results from a event study analysis where the dependent variable is the

company’s stock return measured as stock return minus S&P 500 return (Panel A) or abnormal

stock return calculated using the market model based on S&P 500 return (Panel B). The key

independent variable Day is an indicator for the treading day relative to the PPP loan grant

announcement date, Day 0 (e.g., 8-K filing, press release). The time period is from Day -60 to

Day +1. The estimation window for the market model is Day -270 to Day -61. The day count

excludes non-trading days (e.g., weekends, holidays). Continuous variables are winsorized at the

1

st and the 99th percentiles. Standard errors are clustered at the trading day level. ***, **, and *

denote statistical significance at the 1%, 5%, and 10% levels, respectively. t-statistics are presented

in parentheses.

Panel A: Market-adjusted Panel B: Market model

Day -1 Day 0 Day +1 Day -1 Day 0 Day +1

(1) (2) (3) (4) (5) (6)

Panel A: All observations

Across firms

Abnormal return 0.0112** 0.00307 0.00194 0.0114*** 0.00533 0.00530

t-statistic (2.43) (0.64) (0.39) (2.88) (1.15) (1.28)

Within firms

Abnormal return 0.0112** 0.00309 0.00195 0.0114*** 0.00534 0.00531

t-statistic (2.42) (0.63) (0.38) (2.88) (1.12) (1.28)

Panel B: Only 8-K filings

Across firms

Abnormal return 0.0159*** 0.00431 0.00676 0.0157*** 0.00652 0.00850

t-statistic (3.68) (0.74) (1.26) (3.97) (1.14) (1.66)

Within firms

Abnormal return 0.0159*** 0.00435 0.00680 0.0158*** 0.00656 0.00853*

t-statistic (3.70) (0.74) (1.28) (3.98) (1.13) (1.70)

Panel C: Only 8-K filings & non-penny stocks

Across firms

Abnormal return 0.0175*** 0.00741 0.0106 0.0165*** 0.0108 0.0122

t-statistic (3.22) (0.97) (1.43) (2.88) (1.25) (1.61)

Within firms

Abnormal return 0.0175*** 0.00742 0.0106 0.0165*** 0.0108 0.0122

t-statistic (3.23) (0.98) (1.39) (2.95) (1.24) (1.57)

56
Table 12

Public PPP returners versus retainers

Columns (1) to (3) report the mean, median, and standard deviation of several financial characteristics of firms that returned PPP loans to SBA. Columns (4) to (6) report the same statistics for

firms that retained PPP loans. Column (7) reports the number of observations and Column (8)

reports p-values from a Wilcoxon rank sum test comparing returners with retainers. In the case of

discrete variables, the statistics are proportions and the p-values are for tests of the difference in

proportions. Continuous variables are winsorized at the 1st and the 99th percentiles.

Returned PPP loan Retained PPP loan Difference

(N=117) (N=565) tests

Mean Median SD Mean Median SD N p-value

(1) (2) (3) (4) (5) (6) (7) (8)

Firm size

PPP Loan Amount ($ million) 4.430 3.330 4.105 2.023 0.956 2.681 679 0.000

Book Value of Assets ($ million) 288.4 86.0 1,032.0 71.4 27.7 126.7 569 0.000

Market Cap ($ million) 276.1 118.0 565.1 71.0 28.2 133.0 561 0.000

Sales ($ million) 159.1 49.6 321.5 61.7 19.2 127.2 569 0.000

# Employees (’000) 0.390 0.215 0.467 0.206 0.077 0.337 561 0.000

Other financial characteristics

Firm Age (years) 17.441 13.000 14.627 15.118 11.000 12.544 569 0.327

Book Equity <0 (1/0) 11.7% — — 24.9% — — 568 0.000

Tobin’s Q 2.085 1.432 2.693 1.594 1.063 2.035 569 0.000

Sales Growth 0.464 0.070 2.695 0.597 0.011 3.337 511 0.018

Dividend Payer (1/0) 24.3% — — 12.4% — — 569 0.000

Current Ratio 3.845 2.548 4.814 2.321 1.504 2.793 568 0.000

Cash/Non-Cash Assets 1.131 0.405 2.050 1.075 0.211 3.379 569 0.003

Free Cash Flow/Assets 0.137 0.017 1.115 0.218 0.043 1.449 477 0.152

Financial constraints

Has Credit Rating (1/0) 6.0% — — 1.9% — — 682 0.000

WW Index ≥ p75 (1/0) 51.3% — — 76.7% — — 406 0.001

SA Index ≥ p75 (1/0) 62.7% — — 80.7% — — 527 0.015

Leverage and distress

Zero Debt (1/0) 12.6% — — 7.9% — — 569 0.000

Market Leverage 0.187 0.118 0.199 0.272 0.199 0.259 512 0.003

Interest Coverage <1.5 (1/0) 61.5% — — 81.8% — — 436 0.014

Altman Z-score <1.81 (1/0) 48.3% — — 73.6% — — 479 0.000

Distance-to-Default 3.807 3.043 2.349 2.336 1.953 1.834 409 0.000

Penny Stock (1/0) 53.2% — — 80.5% — — 561 0.000

Stock returns

Covid Period Return -0.332 -0.384 0.362 -0.315 -0.388 0.386 638 0.823

Stimulus Day Return 0.070 0.065 0.091 0.052 0.036 0.120 644 0.020

PPP Grant Abnormal Return 0.031 0.017 0.108 0.021 0.001 0.127 646 0.151

57
Table 13

PPP loan return propensity

This table reports the results from a linear probability (OLS) model where the dependent variable

is an indicator for public company returning a PPP loan to the SBA and independent variables are

company characteristics. Continuous variables are winsorized at the 1st and the 99th percentiles.

Standard errors are clustered at the industry level, defined as 2-digit NAICS, where we combine

industries with few PPP-eligible companies into one bucket. ***, **, and * denote statistical

significance at the 1%, 5%, and 10% levels, respectively. t-statistics are presented in parentheses.

Dependent variable = PPP Loan Returner (1/0)

(1) (2) (3)

Log (Assets) 0.0560*** 0.0540*** 0.0563***

(4.44) (4.36) (3.99)

Log (Age) -0.00962 -0.00134 -0.00225

(-0.78) (-0.10) (-0.18)

Book Equity <0 (1/0) 0.0311 0.0226 0.0206

(0.56) (0.45) (0.45)

Tobin’s Q 0.0121*** 0.00826** 0.00696*

(3.68) (2.47) (1.83)

Current Ratio 0.0168*** 0.0158*** 0.0163***

(3.50) (3.11) (3.25)

Cash/Non-Cash Assets 0.000876 -0.000548 0.000252

(0.21) (-0.12) (0.05)

Penny Stock (1/0) -0.112** -0.122** -0.214*

(-2.22) (-2.74) (-2.09)

Covid Industry (1/0) -0.0309 -0.0318

(-1.35) (-1.24)

Machinery Industry (1/0) -0.0695** -0.0813**

(-2.56) (-2.38)

Health Industry (1/0) 0.0814** 0.0836**

(2.30) (2.65)

High-Tech Industry (1/0) -0.0367 -0.0362

(-1.48) (-1.25)

Covid Period Return 0.297**

(2.65)

Covid Period Return × Penny Stock (1/0) -0.239

(-1.70)

Stimulus Day Return 0.140

(0.44)

Stimulus Day Return × Penny Stock (1/0) -0.157

(-0.60)

PPP Grant Abnormal Return 0.158

(0.36)

PPP Grant Abnormal Return × Penny Stock (1/0) 0.0424

(0.08)

# obs. 543 543 535

Adjusted R2 0.110 0.116 0.118

58
Table 14

PPP loan return announcement effects

This table reports the results from a event study analysis where the dependent variable is the

company’s stock return measured as stock return minus S&P 500 return (Panel A) or abnormal

stock return calculated using the market model based on S&P 500 return (Panel B). The key

independent variable Day is an indicator for the treading day relative to the PPP loan return

announcement date, Day 0 (e.g., 8-K filing, press release). The time period is from Day -60 to Day

1. The estimation window for the market model is Day -270 to Day -61. The day count excludes

non-trading days (e.g., weekends, holidays). Continuous variables are winsorized at the 1st and

the 99th percentiles. Standard errors are clustered at the trading day level. ***, **, and * denote

statistical significance at the 1%, 5%, and 10% levels, respectively. t-statistics are presented in

parentheses.

Panel A: Market-adjusted Panel B: Market model

Day -1 Day 0 Day +1 Day -1 Day 0 Day +1

(1) (2) (3) (4) (5) (6)

Panel A: All observations

Across firms

Abnormal return -0.00662 0.0256*** 0.00395 -0.00742 0.0263*** 0.00409

t-statistic (-1.08) (3.23) (0.56) (-1.15) (3.23) (0.56)

Within firms

Abnormal return -0.00659 0.0256*** 0.00386 -0.00740 0.0263*** 0.00410

t-statistic (-1.05) (3.32) (0.54) (-1.12) (3.35) (0.56)

Panel B: Only 8-K filings

Across firms

Abnormal return -0.00983 0.0294*** 0.00273 -0.0116 0.0308*** 0.00162

t-statistic (-1.29) (2.97) (0.34) (-1.46) (3.04) (0.18)

Within firms

Abnormal return -0.00983 0.0294*** 0.00273 -0.0116 0.0308*** 0.00162

t-statistic (-1.25) (3.12) (0.33) (-1.42) (3.22) (0.18)

Panel C: Only 8-K filings & non-penny stocks

Across firms

Abnormal return -0.0108 0.0281*** 0.00455 -0.0132 0.0300*** 0.00384

t-statistic (-1.35) (2.67) (0.35) (-1.48) (2.68) (0.28)

Within firms

Abnormal return -0.0108 0.0281*** 0.00455 -0.0132 0.0300*** 0.00384

t-statistic (-1.28) (2.72) (0.35) (-1.43) (2.71) (0.29)

59
Table 15

PPP loan return, loan size, and $2M loan size cutoff

This table reports the results from a linear probability (OLS) model where the dependent variable

is an indicator for public company returning a PPP loan to the SBA and independent variables

are the natural logarithm of PPP loan amount, an indicator for loan amount above $2 million, and

company characteristics. Continuous variables are winsorized at the 1st and the 99th percentiles.

Standard errors are clustered at the industry level, defined as 2-digit NAICS, where we combine

industries with few PPP-eligible companies into one bucket. ***, **, and * denote statistical

significance at the 1%, 5%, and 10% levels, respectively. t-statistics are presented in parentheses.

Dependent variable = PPP Loan Returner (1/0)

(1) (2) (3)

Log (PPP Loan Amount) 0.0751*** 0.0489** 0.0449

(7.22) (2.74) (1.63)

Above $2M (1/0) 0.0885* 0.00473

(1.82) (0.08)

Log (PPP Loan Amount)2 0.0208**

(2.76)

Firm Characteristics No No Yes

Penny Stock Dummy No No Yes

Industry Dummies No No Yes

Returns Controls No No Yes

# obs. 568 568 534

Adjusted R2 0.0600 0.0628 0.127

60
Internet Appendix to

“Small Bank Financing and Funding Hesitancy in a Crisis:

Evidence from the Paycheck Protection Program”

NOT FOR PUBLICATION

Section IA.A: Supplementary Graphs and Tables

Figure IA.1. Probability of early PPP borrowing: small versus big banks – Full SBA

data. This figure plots the probability of borrowing early (i.e., in PPP Round 1) versus borrowing

late (i.e., in PPP Round 2) for PPP borrowers from the SBA’s PPP data release in December 2020,

which we restrict to non-financial corporations, for comparability, by PPP loan size bin. Panel A

reports the results for smaller (non-big-10) banks, with this category including non-bank lenders.

Excluding non-bank lenders does not affect the results. Panel B reports the results for big-10 banks.

1
Table IA.1

Matching statistics: DealScan and UCC

Columns (1) to (3) report the mean, median, and standard deviation of several financial characteristics of firms that we were able to match to DealScan. Columns (4) to (6) report the same statistics

for firms that we were able to match to UCC and Columns (7) to (9) report the same statistics

for unmatched firms. In the case of discrete variables, the statistics are proportions. Continuous

variables are winsorized at the 1st and the 99th percentiles.

Matched to DealScan Matched to UCC Unmatched

(N=231) (N=301) (N=277)

Mean Median SD Mean Median SD Mean Median SD

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Panel A: Firm size

PPP Loan Amount ($ million) 4.106 2.651 3.834 3.278 1.900 3.527 1.233 0.564 1.844

Book Value of Assets ($ million) 154.5 62.1 209.7 116.0 48.9 182.8 45.8 17.6 78.1

Market Cap ($ million) 119.1 41.9 206.1 126.1 39.7 220.9 65.4 31.5 98.6

Sales ($ million) 136.8 65.4 183.9 103.8 34.5 170.5 29.5 6.2 82.4

# Employees (’000) 0.629 0.230 1.178 0.456 0.150 0.977 0.097 0.042 0.159

Panel B: Other financial characteristics

Firm Age (years) 22.846 23.000 13.631 16.936 13.000 13.791 10.681 7.500 10.105

Book Equity <0 (1/0) 15.0% — — 20.7% — — 27.6% — —

Tobin’s Q 1.252 1.066 1.204 1.614 1.128 1.847 1.847 1.177 2.311

Sales Growth 0.154 -0.011 1.710 0.316 0.026 1.779 1.289 0.036 5.477

Dividend Payer (1/0) 22.0% — — 14.2% — — 10.0% — —

Current Ratio 2.330 1.683 2.078 2.381 1.662 2.279 2.761 1.712 3.134

Cash/Non-Cash Assets 0.484 0.104 1.555 0.833 0.163 2.161 1.360 0.383 2.338

Free Cash Flow/Assets 0.098 0.029 0.442 0.099 0.033 0.608 0.259 0.040 1.086

Panel C: Financial constraints

Has Credit Rating (1/0) 7.4% — — 5.0% — — 0.4% — —

WW Index ≥ p75 (1/0) 60.5% — — 66.5% — — 79.7% — —

SA Index ≥ p75 (1/0) 51.8% — — 70.5% — — 93.5% — —

Panel D: Leverage and distress

Zero Debt (1/0) 9.3% — — 7.5% — — 10.0% — —

Market Leverage 0.326 0.278 0.264 0.304 0.245 0.267 0.186 0.100 0.227

Interest Coverage <1.5 (1/0) 67.6% — — 77.1% — — 86.4% — —

Altman Z-score <1.81 (1/0) 56.4% — — 66.4% — — 78.3% — —

Distance-to-Default 2.676 2.370 1.840 2.632 2.305 2.019 2.703 2.054 1.963

Penny Stock (1/0) 65.4% — — 72.7% — — 83.9% — —

Panel E: Stock returns

Covid Period Return -0.331 -0.408 0.408 -0.326 -0.398 0.391 -0.280 -0.371 0.466

Stimulus Day Return 0.066 0.052 0.110 0.066 0.057 0.121 0.044 0.016 0.131

PPP Grant Abnormal Return 0.016 0.002 0.108 0.022 0.006 0.122 0.028 0.004 0.135

2
Table IA.2

Early PPP access and lender effects

This table reports the results from a multinomial logit model for public PPP recipients where

the dependent variable is a categorical variable for the intersection between a bank type (big-10

bank versus smaller bank) and borrower type (early versus late PPP borrower). The independent

variable is firm size (Panel A) and the PPP loan size (Panel B). The dependent variable Bank

Type–Borrower Type Category (Y) takes the value of 3 for smaller (non-big-10) bank and early PPP

borrower, 2 for smaller bank and late PPP borrower, 1 for big-10 bank and early PPP borrower,

and 0 for big-10 bank and late PPP borrower. We classify the following banks as big-10 banks:

JPMorgan Chase Bank, Wells Fargo Bank, Bank of America, Citibank, U.S. Bank, Truist Bank,

PNC Bank, Bank of New York, State Street Corporation, and TD Bank. An early borrower is a

public company receiving a PPP loan before April 17, 2020. Continuous variables are winsorized

at the 1st and the 99th percentiles. Standard errors are clustered at the industry level, defined as

2-digit NAICS, where we combine industries with few PPP-eligible companies into one bucket. ***,

**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. z -statistics

are presented in parentheses. Constants are omitted for brevity.

Dependent variable = Bank Type–Borrower Type Category (Y)

Predictor Coefficient Relative risk ratio z-statistic

(1) (2) (3) (4)

Panel A: Firm size

Y = 3: Smaller Bank–Early PPP Borrower Log (Assets) -0.196*** 0.82 (-3.08)

Y = 2: Smaller Bank–Late PPP Borrower Log (Assets) -0.364*** 0.69 (-5.63)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (Assets) -0.448*** 0.64 (-4.37)

# obs. 525

Pseudo-R2 0.0185

Log-likelihood -615.6

Panel B: PPP loan size

Y = 3: Smaller Bank–Early PPP Borrower Log (PPP Loan Amount) -0.233** 0.79 (-2.41)

Y = 2: Smaller Bank–Late PPP Borrower Log (PPP Loan Amount) -0.412*** 0.66 (-3.10)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (PPP Loan Amount) -0.554*** 0.57 (-3.75)

# obs. 525

Pseudo-R2 0.0172

Log-likelihood -616.5

3
Table IA.3

Early PPP access and lender effects

Bank relationship

This table reports the results from a multinomial logit model for public PPP recipients where

the dependent variable is a categorical variable for the intersection between a bank type (big-10

bank versus smaller bank) and borrower type (early versus late PPP borrower), for a subsample of

publicly listed firms with prior bank relationships (i.e., before the PPP). The independent variable

is firm size (Panel A) and the PPP loan size (Panel B). The dependent variable Bank Type–

Borrower Type Category (Y) takes the value of 3 for smaller (non-big-10) bank and early PPP

borrower, 2 for smaller bank and late PPP borrower, 1 for big-10 bank and early PPP borrower,

and 0 for big-10 bank and late PPP borrower. We classify the following banks as big-10 banks:

JPMorgan Chase Bank, Wells Fargo Bank, Bank of America, Citibank, U.S. Bank, Truist Bank,

PNC Bank, Bank of New York, State Street Corporation, and TD Bank. An early borrower is a

public company receiving a PPP loan before April 17, 2020. Continuous variables are winsorized

at the 1st and the 99th percentiles. Standard errors are clustered at the industry level, defined as

2-digit NAICS, where we combine industries with few PPP-eligible companies into one bucket. ***,

**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. z -statistics

are presented in parentheses. Constants are omitted for brevity.

Dependent variable = Bank Type–Borrower Type Category (Y)

Predictor Coefficient Relative risk ratio z-statistic

(1) (2) (3) (4)

Panel A: Firm size

Y = 3: Smaller Bank–Early PPP Borrower Log (Assets) -0.497*** 0.61 (-3.07)

Y = 2: Smaller Bank–Late PPP Borrower Log (Assets) -0.384** 0.68 (-1.99)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (Assets) -0.364** 0.69 (-2.20)

# obs. 173

Pseudo-R2 0.0253

Log-likelihood -198.2

Panel B: PPP loan size

Y = 3: Smaller Bank–Early PPP Borrower Log (PPP Loan Amount) -0.750*** 0.47 (-4.36)

Y = 2: Smaller Bank–Late PPP Borrower Log (PPP Loan Amount) -0.588*** 0.56 (-2.98)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (PPP Loan Amount) -0.500** 0.61 (-2.23)

# obs. 173

Pseudo-R2 0.0328

Log-likelihood -196.7

4
Table IA.4

Early PPP access and lender effects

No bank relationship

This table reports the results from a multinomial logit model for public PPP recipients where

the dependent variable is a categorical variable for the intersection between a bank type (big-10

bank versus smaller bank) and borrower type (early versus late PPP borrower), for a subsample

of publicly listed firms without prior bank relationships (i.e., before the PPP). The independent

variable is firm size (Panel A) and the PPP loan size (Panel B). The dependent variable Bank

Type–Borrower Type Category (Y) takes the value of 3 for smaller (non-big-10) bank and early PPP

borrower, 2 for smaller bank and late PPP borrower, 1 for big-10 bank and early PPP borrower,

and 0 for big-10 bank and late PPP borrower. We classify the following banks as big-10 banks:

JPMorgan Chase Bank, Wells Fargo Bank, Bank of America, Citibank, U.S. Bank, Truist Bank,

PNC Bank, Bank of New York, State Street Corporation, and TD Bank. An early borrower is a

public company receiving a PPP loan before April 17, 2020. Continuous variables are winsorized

at the 1st and the 99th percentiles. Standard errors are clustered at the industry level, defined as

2-digit NAICS, where we combine industries with few PPP-eligible companies into one bucket. ***,

**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. z -statistics

are presented in parentheses. Constants are omitted for brevity.

Dependent variable = Bank Type–Borrower Type Category (Y)

Predictor Coefficient Relative risk ratio z-statistic

(1) (2) (3) (4)

Panel A: Firm size

Y = 3: Smaller Bank–Early PPP Borrower Log (Assets) -0.0409 0.96 (-0.52)

Y = 2: Smaller Bank–Late PPP Borrower Log (Assets) -0.393*** 0.68 (-4.79)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (Assets) -0.425*** 0.65 (-6.47)

# obs. 352

Pseudo-R2 0.0289

Log-likelihood -409.6

Panel B: PPP loan size

Y = 3: Smaller Bank–Early PPP Borrower Log (PPP Loan Amount) -0.00414 1.00 (-0.08)

Y = 2: Smaller Bank–Late PPP Borrower Log (PPP Loan Amount) -0.428*** 0.65 (-6.21)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (PPP Loan Amount) -0.526*** 0.59 (-3.80)

# obs. 352

Pseudo-R2 0.0273

Log-likelihood -410.3

5
Table IA.5

Early PPP access, lender effects, and bank relationships:

SBA PPP data

This table reports the results from a multinomial logit model based on SBA PPP data and UCC–

SBA PPP data where the dependent variable is a categorical variable for the intersection between a

bank type (big-10 bank versus smaller bank) and borrower type (early versus late PPP borrower).

The independent variable Log (PPP Loan Size) is the natural logarithm of the PPP loan size from

the SBA’s PPP data release in December 2020, which we restrict to non-financial corporations, for

comparability. The dependent variable Bank Type–Borrower Type Category (Y) takes the value of 3

for smaller (non-big-10) bank and early PPP borrower, 2 for smaller bank and late PPP borrower, 1

for big-10 bank and early PPP borrower, and 0 for big-10 bank and late PPP borrower. We classify

the following banks as big-10 banks: JPMorgan Chase Bank, Wells Fargo Bank, Bank of America,

Citibank, U.S. Bank, Truist Bank, PNC Bank, Bank of New York, State Street Corporation,

and TD Bank. An early borrower is a public company receiving a PPP loan before April 17,

2020. Continuous variables are winsorized at the 1st and the 99th percentiles. Standard errors are

clustered at the industry level, defined as 2-digit NAICS, where we combine industries with few

PPP-eligible companies into one bucket. ***, **, and * denote statistical significance at the 1%,

5%, and 10% levels, respectively. z -statistics are presented in parentheses. Constants are omitted

for brevity.

Dependent variable = Bank Type–Borrower Type Category (Y)

Predictor Coefficient Relative risk ratio z-statistic

(1) (2) (3) (4)

Panel A: All firms

Y = 3: Smaller Bank–Early PPP Borrower Log (PPP Loan Size) -0.167*** 0.85 (-7.77)

Y = 2: Smaller Bank–Late PPP Borrower Log (PPP Loan Size) -0.601*** 0.55 (-19.03)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (PPP Loan Size) -0.635*** 0.53 (-27.00)

# obs. 554,422

Pseudo-R2 0.0390

Log-likelihood -626379

Panel B: Bank relationship

Y = 3: Smaller Bank–Early PPP Borrower Log (PPP Loan Size) -0.256*** 0.77 (-7.89)

Y = 2: Smaller Bank–Late PPP Borrower Log (PPP Loan Size) -0.634*** 0.53 (-16.21)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (PPP Loan Size) -0.600*** 0.55 (-38.48)

# obs. 301,552

Pseudo-R2 0.0294

Log-likelihood -323917

Panel C: No bank relationship

Y = 3: Smaller Bank–Early PPP Borrower Log (PPP Loan Size) -0.103*** 0.90 (-4.33)

Y = 2: Smaller Bank–Late PPP Borrower Log (PPP Loan Size) -0.515*** 0.60 (-14.52)

Y = 1: Big-10 Bank–Early PPP Borrower [Base outcome] — — —

Y = 0: Big-10 Bank–Late PPP Borrower Log (PPP Loan Size) -0.549*** 0.58 (-24.88)

# obs. 252,870

Pseudo-R2 0.0284

Log-likelihood -296605

6
Table IA.6

Financial constraints, solvency, and PPP loan return

This table reports the results from a linear probability (OLS) model where the dependent variable

is an indicator for public company returning a PPP loan to the SBA and independent variables

are financial constraints and solvency indexes. Continuous variables are winsorized at the 1st and

the 99th percentiles. Standard errors are clustered at the industry level, defined as 2-digit NAICS,

where we combine industries with few PPP-eligible companies into one bucket. ***, **, and *

denote statistical significance at the 1%, 5%, and 10% levels, respectively. t-statistics are presented

in parentheses.

Dependent variable = PPP Loan Returner (1/0)

(1) (2) (3) (4) (5) (6) (7) (8) (9)

WW Index -0.142*** -0.138*** -0.137***

≥ p75 (1/0) (-3.72) (-3.85) (-3.38)

SA Index -0.111*** -0.123*** -0.113**

≥ p75 (1/0) (-3.27) (-3.31) (-2.71)

Altman Z-score -0.097*** -0.109*** -0.111***

<1.81 (1/0) (-3.42) (-3.29) (-3.46)

Penny Stock Dummy Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry Dummies — Yes Yes — Yes Yes — Yes Yes

Returns Controls — — Yes — — Yes — — Yes

# obs. 400 400 394 519 519 511 479 479 472

Adjusted R2 0.0716 0.0920 0.0910 0.0587 0.0754 0.0753 0.0720 0.0934 0.0962

7
Table IA.7

PPP loan return propensity and distance to default

This table reports the results from a linear probability (OLS) model where the dependent variable

is an indicator for public company returning a PPP loan to the SBA and independent variables are

Distance-to-Default and other company characteristics. Continuous variables are winsorized at the

1

st and the 99th percentiles. Standard errors are clustered at the industry level, defined as 2-digit

NAICS, where we combine industries with few PPP-eligible companies into one bucket. ***, **,

and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. t-statistics are

presented in parentheses.

Dependent variable = PPP Loan Returner (1/0)

(1) (2) (3)

Distance-to-Default 0.0549*** 0.0617*** 0.0633***

(4.93) (4.62) (4.79)

Log (Assets) 0.0872*** 0.0901*** 0.0970***

(4.21) (3.66) (3.48)

Log (Age) -0.0270 -0.0150 -0.0199

(-1.71) (-1.00) (-1.25)

Book Equity <0 (1/0) 0.0331 0.0104 0.0286

(0.46) (0.21) (0.50)

Tobin’s Q 0.00776 0.00141 0.00313

(0.49) (0.11) (0.31)

Current Ratio 0.00669 0.00361 0.00287

(1.32) (0.73) (0.62)

Cash/Non-Cash Assets 0.00239 -0.00107 -0.000491

(0.20) (-0.09) (-0.05)

Penny Stock (1/0) -0.00524 -0.0101 -0.0893

(-0.10) (-0.19) (-0.73)

Covid Industry (1/0) -0.0362 -0.0408

(-0.99) (-0.99)

Machinery Industry (1/0) -0.0896** -0.0908

(-2.26) (-1.76)

Health Industry (1/0) 0.153*** 0.132***

(3.03) (3.41)

High-Tech Industry (1/0) -0.0211 -0.0186

(-0.67) (-0.48)

Covid Period Return 0.169

(1.13)

Covid Period Return × Penny Stock (1/0) -0.0802

(-0.41)

Stimulus Day Return -0.713**

(-2.71)

Stimulus Day Return × Penny Stock (1/0) 0.615*

(1.98)

PPP Grant Abnormal Return 0.645

(1.28)

PPP Grant Abnormal Return × Penny Stock (1/0) -0.697

(-1.06)

# obs. 383 383 382

Adjusted R2 0.140 0.162 0.166

8
Section IA.B: Industry Clusters for Estimating Application Probability

We create the following four industry clusters for estimating application probability:

• Covid Impacted Industries: These firms are in industries negatively impacted by Covid19 pandemic (Fahlenbrach et al. (2020)) and may be more likely to apply for PPP

funding.

• Health Sector: We identify these as firms in Fama-French 49 industries 11, 12, and 13,

respectively. Firms in these sector may experience growth opportunities that increase

funding demand. On the other hand, availability of private capital may deter PPP

application as it raises the probability of a negative finding of ineligibility. This may

be a concern for these firms as many are supervised by government agencies and apply

for government grants.

• High Tech Sector: We identify these as firms with Fama-French-49 industry codes of

35, 36, or 37. The firms may see growth opportunities from changes in work habits

that result in greater demand for technology. They may also be better able to function

remotely than (say) a meatpacking plant. These factors may push the firms to seek

PPP funding.

• Machinery Sector: This includes “heavy industry” firms with Fama-French-49 industry

code = 21 (usually NAICS-2 = 33). These firms have more rigidity on the real side

and may thus have greater demand for short-term liquidity.

Figure 11, Panel B shows that our industry clusters pick up significant variation in PPP

application patterns. 64.8% of the 176 firms in Covid-19 industries apply for PPP, about

double the full-sample application probability of 31.96%. In the machinery sector, 16 out of

37 firms (43.2%) apply, again higher than the baseline odds. The application probability is

lower in the health sector, where 191 out of 805 firms apply for PPP loans (23.7%). We find

that 112 out of 255 high tech firms (43.9%) apply for PPP funding, again higher than the

baseline of around 39%.

9
Section IA.C: Evidence of PPP Scrutiny Concerns

Quotes from EDGAR filings

• Albert Bourla, Pfizer CEO: “Pfizer made it a point to not accept government funding.

. . . to liberate our scientists from any bureaucracy that could come from accepting

money.”

• Adamis Pharmaceuticals 8-K filing: “. . . Such audit or review could result in the diversion of management’s time and attention and legal and reputational costs.”

• Nature’s Sunshine Products 8-K filing: “. . . civil, criminal, and administrative penalties . . . adverse publicity, damage to reputation . . . consume significant financial and

management resources.”

PPP Loan Necessity Questionnaire (Form #3509)

Subjective questions, assessment based on “. . . totality of circumstances.”1

• . . . Has Borrower voluntarily ceased, reduced, or altered its operations? [Why? in 1000

characters or less]

• Did Borrower begin any new capital improvement projects not due to COVID-19?

[Comments in 1000 characters or less]

• Has borrower paid dividends, prepaid debt, paid any employees $250K, was 20% owned

by public companies . . . ?

1See, e.g., New Uncertainty About the Uncertainty Certifiction: SBA’s Draft Questionnaires for PPP

Loans Over $2 Million, Daily Tax Report, November 4, 2020.

10
Section IA.D: Definitions of Variables

Variable Definition Source Formula

PPP borrowing

Early PPP Borrower (1/0) Dummy for early PPP borrower versus late

borrower, defined based on SBA approval

date, loan grant date, or contract date (in

that order)

EDGAR,

SBA

=1 if approved or borrowed

before April 27, 2020, 0 o/w

PPP Borrower (1/0) Dummy for PPP borrower versus public

PPP-eligible non-borrower

EDGAR =1 if borrower, 0 o/w

PPP Loan Returner (1/0) Dummy for PPP loan returner versus PPP

loan retainer

EDGAR =1 if loan returned, 0 o/w

PPP Loan Amount ($ million) Aggregate PPP loan amount per EDGAR

filer, measured in $ million

EDGAR =ppp size

PPP Loan Size ($ million) PPP loan amount per SBA PPP recipient,

measured in $ million

SBA PPP

data (Dec)

=loanamount

Above $2M (1/0) Dummy for PPP loan size of $2 million or

above

EDGAR =(ppp size≥2)

Big-10 Bank (1/0) Dummy for PPP lender being a big-10 bank,

that is, JPMorgan Chase Bank, Wells Fargo

Bank, Bank of America, Citibank, U.S. Bank,

Truist Bank, PNC Bank, Bank of New York,

State Street Corporation, or TD Bank

Call reports =1 if big bank=1, 0 o/w

Smaller Bank (1/0) Dummy for PPP lender being a non-big-10

bank; see Big-10 Bank (1/0)

Call reports =1 if big bank=0, 0 o/w

Bank Type–Borrower Type

Category

Categorical variable for the intersection

between a bank type (big-10 bank versus

smaller bank) and borrower type (early

versus late PPP borrower)

EDGAR,

Call reports

= 3 if big bank=1 & early=1;

2 if big bank=1 & early=0; 1 if

big bank=0 & early=1; 0 if

big bank=0 & early=0

Bank Relationship (1/0) Dummy for the firm obtaining a secured loan

from a bank in 2015–2019

UCC =1 if securedpartyname is

bank, 0 o/w

Relationship Bank PPP (1/0) Dummy for the firm obtaining a secured loan

from its PPP lender in 2015–2019

UCC =1 if lender=

securedpartyname, 0 o/w

Small Bank Relationship (1/0) Dummy for the firm obtaining a secured loan

from a small (non-big-10) bank in 2015–2019

UCC, Call

reports

=1 if securedpartyname!=

big bank, 0 o/w

Firm size

Book Value of Assets ($ million) Assets, measured in $ million Compustat =at

Market Cap ($ million) Market Capitalization, measured in $ million Compustat =prcc f*csho

Sales ($ million) Sales, measured in $ million Compustat = sale

# Employees (’000) Employees, measured in thousand Compustat =emp, set to 2018 if N/A

Other financial characteristics

Firm Age (years) Years since IPO, capped at 37 years Compustat =fyear-min(fyear) if prcc f!=.,

0 if missing

Book Equity <0 Dummy for negative Book Equity defined as

in Fama-French: BE = book value of

stockholders’ equity, plus balance sheet

deferred taxes and investment tax credit (if

available), minus the book value of preferred

stock

Compustat =(seq OR ceq+pstk OR at-lt,

in that order) +(txditc OR

txdb+itcb, where each is set to

0 if missing OR 0, in that

order) +(pstkrv OR pstkl OR

pstk, in that order)

Tobin’s Q Market Value of Assets/Assets Compustat =(at+prcc f*csho-ceq)/at, set

to 0 if ceq<0 or if missing

Sales Growth Year-on-year sales growth Compustat =(sale-sale[ n-1])/sale[ n-1]

Dividend Payer (1/0) Dummy for dividend payer Compustat =(dv>0) if dv!=.

Current Ratio Current Assets/Current Liabilities Compustat =act/lct

Cash/Non-Cash Assets Cash/(Assets-Cash) Compustat =che/(at-che), where at is in

thousands

Free Cash Flow/Assets (Operating Cash Flow - Extraordinary Items

+ Interest Paid - Interest Expense*((Pretax

Income - Net Income)/Pretax Income) -

CapEx)/Assets

Compustat =(oancf-xidoc+intpn-xint*((pini)/pi)-capx)/at

Financial constraints

Has Credit Rating (1/0) Dummy for company having a long-term S&P

credit rating or a Mergent rating for an issue

with maturity of at least three years

S&P,

Mergent

=1 if rated, 0 o/w

11
Variable Definition

WW Index ≥ p75 (1/0) Dummy for WW (Whited and Wu) Index in

4th (upper) quartile of all public firms, where

WW Index is calculated as -0.737*Ln of

Min(Assets,4500) + 0.043*Squared Ln of

Min(Assets,4500) – 0.040*Min(Years since

IPO,37)

Compustat =(WW quart==4) if

WW quart!=., using xtile,

where

WW=-0.737*log(min(at,4500))

+0.043*(log(min(at,4500)))2

-0.040*min(age,37)

SA Index ≥ p75 (1/0) Dummy for SA (Hadlock and Pierce) Index

in 4th quartile of all public firms, where SA

Index is -0.737*Ln of Min(Assets,4500)

+0.043*Squared Ln of Min(Assets,4500)

-0.040*Min(Years since IPO,37)

Compustat =(SA quart==4) if

SA quart!=., using xtile, where

SA=-0.737*log(min(at,4500))

+0.043*(log(min(at,4500)))2

-0.040*min(age,37)

Leverage and distress

Zero Debt (1/0) Dummy for zero debt Compustat =1 if dlc+dltt=0 or (dltt=. &

dlc=0), 0 o/w

Market Leverage (Short-Term Debt + Long-Term Debt)/(Debt

+ Market Cap)

Compustat =(dlc+dltt)/

(dlc+dltt+prcc f*csho)

Interest Coverage <1.5 (1/0) Dummy for 1 + Pretax Income/Interest

Expense <1.5

Compustat =((1+pi/xint)<1.5) if

(1+pi/xint)!=.

Altman Z-score < 1.81 (1/0) Dummy for Altman Z-score <1.81, wehere

Z-score is calculated as 1.2*Working

Capital/Assets + 1.4*Retained

Earnings/Assets + 3.3*EBIT/Assets +

0.6*Market Cap/Liabilities +

0.999*Sales/Assets

Compustat =1 if Z-score <1.81, 0 o/w,

where Z-score=

1.2*(act-lct)/at +1.4*re/at

+3.3*(pi+xint+dpdp)/at+.6*prcc f*csho/lt

+.999*sale/at

Distance-to-Default Distance-to-default from NUS RMI Credit

Research Initiative

NUS RMI = dtd

Penny Stock (1/0) Dummy for company’s stock price below $5

based on (1) closing stock price as of fiscal

year-end for cross-sectional tests or (2)

average stock price in December 2019 for

event study analysis

Compustat =(prcc f<5) if prcc f!=. OR

mean(p adjclose, Dec 2019)<5

Industry composition

Covid Industry (1/0) Dummy for Covid-19 Affected Industry Compustat =1 if sic corresponds to

Covid-19 industry, 0 o/w

Machinery Industry (1/0) Dummy for Machinery Industry Compustat =1 if sic corresponds to

FF49==21, 0 o/w

Health Industry (1/0) Dummy for Health, Pharma, and Biotech

Industry

Compustat =1 if sic corresponds to

FF49==11,12,13, 0 o/w

High-Tech industry (1/0) Dummy for Business Equipment –

Computers, Software, and Electronic

Equipment Industry

Compustat =1 if sic corresponds to

FF49==35,36,37, 0 o/w

Returns and event study variables

Buy-and-Hold Abnormal Return Cumulative return on the stock over the S&P

500 return from January 1, 2020 to August

15, 2020, adjusted for stock splits and

dividends

Yahoo!

Finance

=cumulative (R adjclose - S&P

500 R adjclose)

Covid Period Return Cumulative stock return from February 2,

2020 to March 23, 2020, adjusted for stock

splits and dividends

Yahoo!

Finance

=cumulative R adjclose, Covid

period

Stimulus Day Return Stock return on March 24, 2020, adjusted for

stock splits and dividends

Yahoo!

Finance

=R adjclose, Mar 24, 2020

Abnormal Return Company’s (1) market-adjusted return (stock

return minus S&P 500 return) or (2) market

model return (based on S&P 500), adjusted

for stock splits and dividends

Yahoo!

Finance

=R adjclose - S&P 500

R adjclose OR Abnormal

R adjclose

Day -1 (1/0) Trading day preceding (1) PPP loan grant

announcement day or (2) PPP loan return

announcement day, as appropriate

EDGAR =(evday==-1) OR

(evday1==-1)

Day 0 (1/0) Trading day of (1) PPP loan grant

announcement or (2) PPP loan return

announcement, as appropriate

EDGAR =(evday==0) OR

(evday1==0)

Day +1 (1/0) Trading day following (1) PPP loan grant

announcement day or (2) PPP loan return

announcement day, as appropriate

EDGAR =(evday==1) OR

(evday1==1)

12