Information Frictions and Access to the Paycheck Protection Program (NBER Working Paper 27624)
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NBER Working Paper No. 27624, Information Frictions and Access to the Paycheck Protection Program, by Christopher Neilson, John Eric Humphries and Gabriel Ulyssea, dated July 2020. The abstract states that the PPP extended 669 billion dollars of forgivable loans and that information frictions and the first-come, first-served design skewed resources towards larger firms and may have permanently reduced its effectiveness. It states that using daily survey data on small businesses the authors show that the smallest businesses were less aware of the PPP, applied later, faced longer processing times and were less likely to have applications approved. The introduction states that the sample includes data from more than 14,000 small business owners, with follow-up surveys for almost 3,000.
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NBER WORKING PAPER SERIES
INFORMATION FRICTIONS AND ACCESS TO THE PAYCHECK PROTECTION
PROGRAM
Christopher Neilson
John Eric Humphries
Gabriel Ulyssea
Working Paper 27624
http://www.nber.org/papers/w27624
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
July 2020
We would like to thank Princeton University's Industrial Relations Section and Yale University's
Tobin Center for Economic Policy for financially supporting this work. We would also like to
thank Mariel Bedoya, Michael Borger, Iliana Cabral, Franco Calle, Alvaro Carril, Alejadrina
Correa, Richard De Thorpe, Deniz Dutz, Maria Elena Guerrero, Isabel Jacas, Katherine Kwok,
Manuel Martinez, Cecilia Moreira, Naomi Shimberg, Ignacio Riveros, Eduardo Vargas and
Yagmur Yuksel for helpful research assistance on this project. The views expressed herein are
those of the authors and do not necessarily reflect the views of the National Bureau of Economic
Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2020 by Christopher Neilson, John Eric Humphries, and Gabriel Ulyssea. All rights reserved.
Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission
provided that full credit, including © notice, is given to the source.
Information Frictions and Access to the Paycheck Protection Program
Christopher Neilson, John Eric Humphries, and Gabriel Ulyssea
NBER Working Paper No. 27624
July 2020
JEL No. H0,J01,J08
ABSTRACT
The Paycheck Protection Program (PPP) extended 669 billion dollars of forgivable loans in an
unprecedented effort to support small businesses affected by the COVID-19 crisis. This paper
provides evidence that information frictions and the “first-come, first-served” design of the PPP
program skewed its resources towards larger firms and may have permanently reduced its
effectiveness. Using new daily survey data on small businesses in the U.S., we show that the
smallest businesses were less aware of the PPP and less likely to apply. If they did apply, the
smallest businesses applied later, faced longer processing times, and were less likely to have their
application approved. These frictions may have mattered, as businesses that received aid report
fewer layoffs, higher employment, and improved expectations about the future.
Christopher Neilson Gabriel Ulyssea
School of Public and International Affairs University of Oxford
Princeton University Department of Economics
Firestone Library, Room A2H Manor Road Building
Princeton, NJ 08544 Manor Road
and NBER Oxford, OX1 3UQ
cneilson@princeton.edu United Kingdom
gabriel.ulyssea@economics.ox.ac.uk
John Eric Humphries
Department of Economics
Yale University
37 Hillhouse Ave
New Haven, CT 06511
johneric.humphries@yale.edu
A Project Website is available at https://covid19sbs.org/
1 Introduction
The coronavirus epidemic has had a broad impact on public health in the United States, with 3.7
million cases and more than 139 thousand deaths reported by July 19th, 2020 (Center for Disease
Control, 2020). As a response, most states have introduced strong social distancing measures,
including stay-at-home orders and closure of non-essential businesses. These measures are likely
to have severe effects on small and medium enterprises, as they tend to be more concentrated
in sectors directly affected (e.g. retail and services) and are typically more credit constrained
than larger businesses.1 As a response to the crisis, on March 27th the U.S. Congress passed
The Coronavirus Aid, Relief, and Economic Security (CARES) Act, which included 349 billion
dollars (later expanded to 669 billion dollars) to fund the Paycheck Protection Program (PPP).
The PPP was designed to support small businesses by extending government-backed loans that
can be partially or fully forgiven if certain conditions are met.
This paper provides evidence that, despite the unprecedented amount of aid, the combination
of information frictions and PPP’s “first-come, first-served” design disadvantaged the smallest
businesses. This disadvantage in getting aid may have large implications. While we cannot
claim causality, we show that businesses that received aid report fewer layoffs and improved
expectations about their businesses survival and recovery, with smaller firms showing the largest
improvements. However, our results also show that the smallest businesses were less aware of the
PPP and less likely to apply. Among businesses that applied for the PPP, smaller businesses
applied later, faced longer processing times, and were less likely to have their applications
approved. Taken together, this evidence suggests that information frictions, as well as other
frictions in the application process, may have disadvantaged small businesses.2
Our data come from daily surveys that began on March 28th, the day after the CARES Act
was passed, and continued through May 16th. Early respondents were also asked to complete
follow-up surveys that began on April 19th, the day that the PPP exhausted its initial funding,
and continued through May 16th. Survey participants were recruited via social media ads
targeted at small business owners across the United States that had been affected by COVID-
19. The sample includes data from more than 14,000 small business owners in the U.S., with
follow-up surveys for almost 3,000 small business owners. The surveys collected information on
initial firm size, layoffs, beliefs about businesses’ future prospects, and their awareness of existing
1
These businesses make up the majority of companies in the U.S., and are responsible for a substantial fraction of
employment. Firms with fewer than 20 employees account for 89% of firms and 16.4% of employment (2017 Census
of US Businesses).
2
By information frictions, we mean impediments to awareness of the program, including awareness of how the
program and forgiveness of the loans work, who is eligible, and how to apply.
2
government relief programs that could help their firms. In addition, we collected information
about their experiences seeking aid, the current status of their applications, and difficulties they
faced during the application process.
We document four key facts. First, respondents’ expectations about the future are generally
negative and deteriorated over the three to four weeks following the passing of the CARES
Act. In the first week of the survey, 64% of respondents believed their businesses would recover
within two years, but this number steadily decreases until late April, when it levels out or
slightly reverses. The latter movement corresponds to the period of time when it was clear that
the PPP would continue to be funded. However, after a short period, most outcomes continue
to deteriorate. In the last week of the survey, only 48% of firms report that they expect their
businesses to recover within two years. We observe the same patterns for the proportion of
respondents who think their businesses will ever recover. These results hold when controlling
for a rich set of covariates and when using changes over time within firm.
Second, the smallest businesses were slower to become aware of government programs.3 The
day after the CARES Act was passed (March 28th), businesses with fewer than 10 full-time
equivalent (FTE) employees were much less likely to know about any government programs
designed to support small businesses when compared to larger firms with 10 to 50 employees.
Despite large initial information differences, the proportion of businesses with 5 to 9.5 FTE
employees that knew about government programs rapidly increased, reaching levels similar to
larger firms (above 90%) a day or two after the program started accepting applications. In
contrast, around 68% of businesses with fewer than five employees reported knowing about
government programs that could help their business when PPP applications were open, and
this share remained below 80% through April 16th, when the PPP exhausted its initial funding.
Given the “first-come, first-served” nature of the program, information asymmetries early on
may have resulted in the smaller firms missing out on the first round of PPP loans.
Third, the smallest firms were less likely to apply for the PPP and, conditional on applying,
they applied later, waited longer for their application to be approved, and were less likely to get
approval. Firms with 0 to 4.5 FTE employees were 23 percentage points less likely to apply for
PPP loans compared to firms with 10 to 50 employees. Conditional on applying, businesses with
fewer than five employees applied two days later, and were 27 percentage points less likely to
have received approval. Conditional on receiving a PPP loan, businesses with 0 to 4.5 employees
waited around two days longer for their application to be approved. These patterns, in particular
3
Throughout this paper, we divide firms into three size bins based on the number of full-time equivalent employees:
(1) 0-4.5 FTE employees, (2) 5-9.5 FTE employees, (3) 10-50 FTE employees.
3
longer wait times, are also consistent with other frictions in the application process, such as
differential access or differing existing relationships with banks that made a larger number of
PPP loans (see Granja et al. (2020)).
Fourth, businesses that received a PPP loan report more employees, lower probabilities of
closure or bankruptcy, and higher probabilities of recovering in the next two years. While it is
difficult to establish causality, this finding holds after controlling for the characteristics of the
business, the owner, and location dummies. Conditional on applying for a PPP loan, those that
are approved are, on average, 12 percentage points more likely to report that they will recover
in the next two years, and reported probabilities of bankruptcy or closure that are 8 percentage
points lower. They are also 9 percentage points less likely to report having fewer workers than in
January. All of these effects are stronger for smaller businesses (with fewer than 10 employees).
Taken together, the four facts indicate that information frictions hindered the ability of
small businesses to access PPP resources. These frictions may have been driven by differential
awareness of the PPP program early on, but uncertainty about eligibility criteria or the forgivable
aspect of the loan likely also contributed. Even if only present during the beginning of the PPP,
these frictions may have had real negative consequences for small businesses. Firms’ expectations
about the future fell sharply in the first month of the survey, which may have led firms to lay
off the majority of their workers or shut down completely within this short time frame. These
actions may be costly to reverse, especially since the CARES Act also increased unemployment
insurance benefits by $600 per week. Since full loan forgiveness requires firms to maintain
payroll at pre-crisis levels during the eight weeks following initiation of the loan, firms that
failed to apply for the PPP early on may no longer benefit from it. Moreover, since payroll is
the main forgivable component of the loan, this could have further reduced the attractiveness
of the subsequent 2nd round of PPP funding that is set to close in early August.
Related literature
This paper contributes to a small but rapidly growing literature on the economic impacts of
COVID-19 on small businesses. Closely related work by Bartik et al. (2020) surveys 5,800 U.S.
small businesses through Alignable, a network-based platform for small businesses. Their survey
reached somewhat larger businesses and found that the majority planned to seek funds through
the CARES Act, which is consistent with our findings for the larger small businesses (over 5
employees) in our sample. Fairlie (2020b) and Fairlie (2020a) similarly provides evidence on the
impact of COVID-19 on small businesses using the April and May Consumer Population Surveys
4
– a population-representative data set and shows that the number of active small business owners
fell by 22 percent from February to April 2020, with a partial rebound in May.
Another set of recent papers specifically studies the impact of the PPP. Granja et al. (2020)
use administrative data on the distribution of PPP loans and high-frequency micro-level em-
ployment data to evaluate how well the CARES Act targeted businesses in need. They conclude
that funds flowed to areas that were less impacted by the economic crisis in terms of declines
in hours worked or business shutdowns. Their results also indicate that access to banks that
participated heavily in PPP lending may be an important determinant of access to the program.
While we argue that our results imply that information frictions play a central role, some of our
facts are also consistent with smaller businesses facing larger frictions in the application process
that are related to access to banks (such as lower application rates, and longer processing times).
Chetty et al. (2020) studies the impacts of a number of stabilization policies using data from
private companies and finds little evidence that PPP loans affected employment. However, they
analyze firms much larger than those in our sample, focusing on firms around the 500 employee
cutoff for PPP eligibility. In contrast, less than 1% of firms in our sample have more than
50 employees. Compared to these larger firms, those in our sample may have fewer sources of
liquidity, smaller reserves, and less extensive existing relationships with banks. They are also
less likely to have dedicated human resources or accounting staff who may help determine the
requirements of PPP loans and how to apply.4 Autor et al. (2020) uses a similar design to
study the impacts of the PPP. Using payroll processing data from ADP, the paper studies the
impacts of the PPP using high-frequency employment data and estimates the PPP increased
employment by 2.3 million.
Other work has focused on the effects of COVID-19 on employment. Adams-Prassl et al.
(2020) uses real-time survey evidence to analyze the impacts of the COVID-19 containment
measures on workers in the U.S. and the UK. Most relevant for the results discussed in this
paper, they document substantial negative effects on workers in the U.S. in their first wave of
data (collected on March 24-25, 2020), which is consistent with the strong effects on lay-offs that
we document here.5 Kahn et al. (2020) show that job vacancies fell more than 30% compared to
the beginning of 2020 and align closely with the number of new UI claims filed across the U.S.
4
Cororaton and Rosen (2020) also studies the PPP, but focus on utilization of PPP loans by publicly traded
companies. The paper studies the 273 public firms that received almost one billion dollars in PPP loans in April.
The paper finds that, among publicly traded firms, those that received funds tended to be smaller and in counties
with more COVID-19 cases.
5
As part of their main analysis, the authors document substantial inequality in the observed effects, as workers
without a college degree and women are more severely affected. Beland et al. (2020) finds similar results for the US
using data from the Current Population Survey (CPS).
5
Cajner et al. (2020) uses payroll processing data to show that aggregate payroll fell 21 percent
by late April with a small recovery through late May, with the largest impacts among lower-paid
workers. These results are consistent with Coibion et al. (2020), who use surveys of households
in the Nielsen Homescan data to document that over 20 million people lost their jobs by early
April and that many were not seeking new employment (with a 7 percentage point drop in labor
force participation).6
The paper is also related to a large literature studying how broadly defined information
frictions, behavioral biases, and transaction costs can affect the take-up and targeting of social
programs. Theoretical and empirical evidence suggest that ordeal mechanisms or transaction
costs associated with applying for aid can improve targeting of social programs (Besley and
Coate, 1992). Yet, there is also ample evidence that even individuals who would greatly benefit
from government assistance programs may not take advantage of them due to a lack of informa-
tion, sophistication, or ability to optimize. For example, Bettinger et al. (2012) provide evidence
that low income students are more likely to access financial aid and attend college when they re-
ceive limited assistance with filling out FASFA applications. Bhargava and Manoli (2015) shows
that low awareness and understanding of EITC benefits lead to lower take-up. Importantly,
Bhargava and Manoli (2015) highlights that information frictions also include knowledge of how
EITC works and who is eligible, and not just knowledge of the program’s existence. Finkelstein
and Notowidigdo (2019) similarly shows that improving information and reducing transaction
costs can lead to higher take-up in the case of food stamps, but also lead to reduced target-
ing. While not focused on information frictions, this paper is also related to Zwick and Mahon
(2017), which studies barriers to program take up for firms. Studying a temporary policy which
allows for bonus depreciation of capital, the paper finds evidence of financial frictions or fixed
costs affecting firms’ utilization of the policy.
2 Institutional Background and Data
2.1 The Paycheck Protection Program (PPP)
The Coronavirus Aid, Relief, and Economic Security (CARES) Act was passed by Congress and
signed into law on March 27th, 2020. This large relief package (over USD 2 trillion) established
the Paycheck Protection Program (PPP), which was aimed to provide small businesses with a
temporary source of liquidity in the form of forgivable loans. These loans are designed to help
6
See also Barrero et al. (2020), which discusses the reallocation effects of the shock, and specifically discusses the
impacts of increased unemployment insurance benefits.
6
cover payroll costs and additional fixed expenses during the COVID-19 pandemic.
The maximum loan amount in the program is the minimum between 2.5 times the average
monthly payroll costs and ten million dollars. The interest rate on all loans is 1% and their
maturity is two years. The loans are forgivable when used to cover payroll costs, mortgage
interest, rent, or utilities, with the additional requirement that 75% of the total forgiven amount
must be payroll.7 The loans do not require collateral or personal guarantees, and can be deferred
for six months. To get access to these funds, firms must apply through an authorized Small
Business Administration (SBA) lender.
A key aspect of the PPP is that the loans are forgivable only if the employer maintains
the number of employees and salary levels at pre-COVID-19 levels, or if those are restored by
June 30, 2020. If the number of employees or salary levels are reduced, the amount forgivable
is also reduced.8 The PPP started accepting applications on April 3rd, and the initial funding
of $349 billion was exhausted by April 16th. New legislation was signed on April 24th, which
included an additional $320 billion dollars in funding for the PPP after the program reopened
to applications on April 27th. Appendix A provides additional details on the PPP.
2.2 The Small Business Survey Data
We collect new survey data on small businesses in the U.S. to understand the impacts of the
COVID-19 crisis, and the challenges it created. We recruited the sample of participants via
social media ads targeted at small and medium sized business owners in the United States who
had been affected by the COVID-19 crisis.9 Recruitment began on March 28th, one day after
the CARES Act was passed, and we continuously received baseline responses through May 16th.
The responses are distributed relatively uniformly throughout the sampling period, though there
are fewer respondents in the first four days and last week of the survey. Appendix Table 3 reports
the number of survey responses by day.
The core of the baseline survey contains a set of questions about firm characteristics, in-
cluding size (as measured by the number of full- and part-time employees) and the number of
laid-off employees since January. The survey also asks small business owners to report their
beliefs about the future. More specifically, we ask owners how many employees they expect
to lay off within the next two months, if they believe their businesses will recover in the next
7
The Paycheck Protection Program Flexibility Act, signed into law on June 5th, changed the requirement from
75% to 60%. Similarly, it changed the maturity date from two to five years.
8
More detailed information is available at https://home.treasury.gov/policy-issues/cares/
assistance-for-small-businesses.
9
The Appendix Section B provides more details about the survey and contains the survey instrument.
7
two years, if they think their businesses will ever recover, and the probability that they will
shutdown or go bankrupt within the next six months. We also measure awareness of existing
state and federal programs available to help small businesses cope with the COVID-19 crisis.10
On April 27th, the baseline survey was expanded to include a broader set of questions, with a
particular focus on respondent’s access to the PPP. We ask if and when the respondent applied
and about the outcome of their application. The overall baseline sample consists of 14,208 adult
small business owners in the United States who completed the survey by May 16th, 2020.11 Of
those, 11,104 completed the short baseline survey and 3,104 completed the expanded baseline
survey.
A follow-up survey was launched on April 19th targeting those who completed the short
baseline survey. It repeated questions about employment and expectations, and included the
more comprehensive set of questions used in the extended baseline survey about the PPP. The
follow-up survey was completed by 2,768 of the respondents.
While we did not construct the survey to be representative of the population of firms in the
U.S., the size distribution in the data is similar to the firm size distribution in the 2017 Census
of U.S. Businesses, as shown in Figure 5 in the Appendix. The survey includes responses from
all 50 states and the District of Columbia. The states with the most responses were New York,
California, Pennsylvania, Michigan, Illinois, Florida, and Texas, but there are over 30 responses
from each state (including Alaska and Hawaii).12 New England, the upper mid-west, and the
Northwest are somewhat over-represented in the survey relative to the number of firms with fewer
than 500 employees in each state, while California and the south are somewhat under-represented
(See Appendix Figure 6). Using the US Census Small Business Pulse Survey, Appendix Figures
7 and 8 also show that trends in obtaining PPP loans and beliefs about recovery by firm size
are similar in our survey and the Census’s nationally-representative sample, though firms in our
survey have slightly lower PPP acquisition rates and worse beliefs about recovery.
For the analysis in this paper, we restrict our sample to respondents who report having fifty
or fewer FTE employees in January 2020 and who completed at least the employment portion of
the survey (firms larger than 50 employees represent only 1% of respondents). Appendix Table
4 summarizes the baseline survey responses. The table shows that, on average, respondents had
4.88 FTE employees in January, though the number of employees is right skewed, with a median
10
Specifically, the survey asks “Are you aware of any federal or state programs that could help your business during
this crisis?” and, thus, likely captures both awareness of the program and comprehension of how the program works
and who is eligible.
11
We include responses where the respondent consented to the survey and completed at least the initial questions
regarding employment in January, layoffs to date, and planned layoffs.
12
Appendix Figure 4 maps the distribution of survey responses by state.
8
of 2.5. 79% of the sample expects to recover eventually, with 57% expecting to recover in the
next two years. Finally, on average, awareness of government programs to help businesses was
high (79%), but lower for programs specifically designed to help business cover wages of their
employees (68%). The bottom panel of the table summarizes the additional outcomes from the
follow-up and expanded baseline surveys. Using this subsample, 53% applied for a PPP loan
and 37% of those who applied were approved.
3 Results
This section outlines four sets of results. First, we document how layoffs and expectations of
small businesses evolved from March 28th to May 16th. Second, we provide evidence that the
smallest businesses were substantially less informed about available government programs that
could help their business, and that this gap remains large throughout the sampling period.
Third, smaller businesses were less likely to apply for the PPP and, conditional on applying,
they applied later, were less likely to get approval, and waited longer for their application to
be approved. Fourth, we document that receiving a PPP loan is associated with a notable
improvement in expectations about the future and a small increase in current employment.
3.1 Evolution of layoffs and expectations
The first set of results investigates the evolution of responses for the 50 days after the the CARES
Act was passed. We chart the responses over time, which provide a repeated cross section of
respondents. This provides insights into how businesses have adjusted to the disruption and
how their expectations about the future have evolved. Overall, we see that employment fell
sharply in the first two weeks after the passage of the CARES Act, and has largely leveled off
or slightly improved since mid-April. While employment has improved somewhat, expectations
for the future have not, with a continued decline over the length of the survey.
Figure 1 summarizes the trends over time using daily survey responses. The top panel
provides evidence on how employment decisions have changed for small businesses. The top
left plot shows the time trend for whether businesses have laid off any workers since January
while the top right figure shows the proportion of workers from January currently employed at
the firm. The black line is loess regression fit to the data with the grey region showing the
95% confidence interval. The blue line plots a moving average over 250 responses. The light
red vertical lines show key dates: when the CARES Act was signed, when PPP applications
opened to most businesses, when PPP applications opened to the self-employed and independent
9
contractors, when the initial funding for the PPP was exhausted, and when legislation was signed
to replenish PPP funds. In the first three weeks, there was an upward trend in the proportion of
small businesses that have had to lay off employees – increasing by approximately 10 percentage
points. The top right figure provides similar evidence by plotting the proportion of employees
from January who are still employed. This figure shows a decline of more than 10 percentage
points from late March through mid April. In the last three weeks, the proportion of firms
that have laid off employees levels off, while the proportion of workers from January currently
working changes sign and moderately increases.
While layoffs were concentrated in the first three weeks, expectations about the future de-
clined over the entire fifty days. The bottom row of Figure 1 shows the trends for the proportion
of firms that expect to recover in the next two years (left), and the proportion that expect to
ever recover (right). Both expectations sharply declined by more than 10 percentage points
through late April. Expectations temporarily leveled off before then declining further in the last
two weeks of the survey.
One concern with the visual evidence presented above is that the sample of respondents may
change over time. To address this concern, we provide three pieces of evidence in Appendix D.
First, the composition of firm size – based on employment in January – has remained consistent
across the survey. Second, we show that these trends hold when controlling for state dummies,
cubic polynomials for full-time and part-time employment in January, and the day of the week
on which the survey was taken.13 Third, we use the follow-up survey to directly measure changes
within firms and to control for additional firm characteristics. Appendix Table 6 uses the follow-
up survey to regress within-firm change on the elapsed number of days between the baseline
survey and the follow-up. The regression controls for full-time and part-time employment in
January, state dummies, day of the week when the baseline survey was taken, owner’s years
of education, sex, age group, dummies for the type of firm, dummies for sector, and dummies
for the week the baseline survey was taken. The table shows that for each elapsed day the
probability of recovering in the next two years falls by 0.004, the probability of ever recovering
falls by 0.002. The probability of past layoffs shows a large initial increase in layoffs (0.076 for
the week of April 1st), which then levels off or slightly decreases in later weeks – similar to what
is shown in Figure 1. These results thus confirm that the trends in Figure 1 are not likely driven
by changes in sample composition over time.
13
Barrios and Hochberg (2020) show that some states were less likely to implement or comply with preventative
health measures such as social distancing. They show that these behaviors are correlated with perceived risk, and
show that places with more support for President Trump were less likely to follow preventative health measures.
State fixed-effects provide proxies for persistent differences in behaviors and perceived risks across states.
10
3.2 Information frictions
In contrast to the declining expectations of respondents over the first three to four weeks of the
survey, small business owners rapidly became aware of programs that could help them. Yet, the
levels and trends in awareness differ substantially by the initial size of the business. The survey
question asks specifically about awareness of aid that could help the respondent’s business, and
thus it captures a combination of awareness as well as comprehension of existing programs.
The top panel of Figure 2 shows the time trends in survey respondents’ awareness of any
federal state programs that could help their businesses. Awareness increases substantially over
the first three weeks, with over 70% of businesses reporting that they were aware of programs
when PPP applications opened, increasing to over 80% on April 16th when the PPP exhausted
its initial funding. In late April, we see a slight downturn in awareness, which may be related to
the fact that the awareness question specifically asks if individuals are aware of programs that
“could help your business”. Thus, this slight downturn could be reflecting businesses realizing
the PPP may not help them if they had already laid off their employees.14
The lower panel of Figure 2 breaks out the trends by business size, showing the trends for
businesses with fewer than five FTE employees, five to 9.5 FTE employees, and ten to fifty FTE
employees. There are substantial gaps in awareness across firm size bins from the onset, as
well as marked differences in their evolution over the first three weeks. Businesses with 10–50
FTE employees were highly aware of programs that could help their business throughout the
sample. In comparison, businesses with 0 to 4.5 and 5 to 9.5 employees were much less likely
to be aware of programs immediately after the CARES Act was passed. Yet, these two groups
had very different trends in awareness over time. Businesses with 5 to 9.5 employees rapidly
became more aware of programs, reaching similar levels as those of larger businesses around the
time the PPP opened for applications. In contrast, businesses with fewer than five employees
learned about programs much more slowly, with a large gap persisting through when the PPP
exhausted its initial funding and never closing completely.
In late April, we see awareness of programs falling for the smallest businesses. One expla-
nation for this decline is that, as details of how forgiveness of PPP loans worked became more
widely known, small businesses realized that the program would not (or no longer) be helpful
to them. Specifically, two characteristics of the PPP are key: (i) 75% of the forgiven amount of
the loan was required to be payroll; and (ii) employment levels were required to be at pre-crisis
14
Appendix D.1 provides similar results specifically for programs that provide subsidized loans and programs that
help cover payroll.
11
levels.15 If small businesses had already laid off their employees, qualifying for forgiveness would
be more challenging.16
Overall, the patterns are consistent with smaller businesses facing larger information fric-
tions, where we define information frictions as impediments to awareness of the program, in-
cluding awareness of how the program and forgiveness of the loans work, who is eligible, and
how to apply. Although the above patterns may seem consistent with smaller firms having less
need for assistance and thus remaining less informed, our data does not support this conclusion.
Compared to firms with 10 or more employees in the baseline survey, firms with fewer than 5
employees were only 1 percentage point less likely to report that they expected to ever recover,
and reported probabilities of bankruptcy or permanent closure 2 percentage points higher.
3.3 Frictions in getting PPP loans
Consistent with the differential rates of awareness, we also find that larger businesses were much
more likely to apply for PPP loans, and to get approved conditional on applying. Using the
follow-up survey and the extended baseline survey described in Section 2.2, we collect informa-
tion on whether the business applied for a PPP loan, when the application was submitted, the
outcome of the application, and how long it took for the application to be approved. Table
1 shows how these outcomes are predicted by firm characteristics. The first column reports
results from regressing an indicator for getting a PPP loan on firm characteristics.17 Businesses
with fewer than 5 employees were 23 percentage points less likely to apply than businesses with
10 to 50 employees, while firms with 5 to 9.5 employees applied at approximately the same
rate as these larger businesses. Those with more education were more likely to apply, and –
compared to omitted category of C-Corps and S-Corps – the self-employed, sole proprietorships,
and partnerships were substantially less likely to apply.
The first column shows that firm size and sophistication were important predictors of getting
a PPP loan, which combines the decision to apply and the outcome of the application. The
second and third columns report the same regression for the decision to apply, and receiving a
PPP loan conditional on applying, respectively. Both the decision to apply and getting approved
conditional on applying are positively correlated with firm size, years of education, and the type
15
See Appendix A for details on the PPP and additional changes to the rules introduced in June.
16
While we cannot provide direct quantitative evidence on these mechanisms, they are consistent with several of
the open-ended responses collected at the end of survey.
17
All regressions in this table control for firm size, firm type, firm sector, the respondent’s years of education, an
indicator for being female, age bins, state dummies, and the date the survey was taken. The final column controls
for the date the application was submitted and the week the survey was taken.
12
of firm. While we do not have exogenous variation in the decision to apply, it is informative
that larger firms were more likely to have their PPP loans approved even after conditioning on
those who applied and controlling for a rich set of firm characteristics.18
Consistent with information frictions differentially affecting smaller businesses, we also find
that smaller firms applied later. The fourth column of Table 1 runs the same regression, but
on how many days it took the firm to apply (conditional on applying). The results show that
firms with fewer than five employees applied an average of 1.8 days later. Finally, the fifth
column of the table regresses the average wait time for approval on firm characteristics among
the firms that were approved. The smallest firms waited, on average 1.8 days longer for approval,
while firms with 5 to 9.5 employees waited 0.6 days longer, though this second coefficient is not
statistically significant. The final column also shows that the self-employed waited substantially
longer for approval.
While our results are largely consistent with information frictions playing a central role,
lower rates of getting a PPP loan conditional on applying, and longer delays when waiting for
approval are also consistent with other mechanisms. For example, Granja et al. (2020) shows
that access to banks which participated heavily in PPP lending may have been an important
determinant in getting a PPP loan, which would be consistent with some of the facts we doc-
ument. Nevertheless, Appendix Table 7 provides some evidence that geographic proximity to
financial institutions offering PPP loans are not confounding the estimates on firm size in Table
1.19 Another possibility is that the self-employed reported being unaware of programs until the
PPP program opened to self-employed workers on April 10th. Figure 2 shows that awareness
of programs was still substantially lower on April 10th for the smallest firms. Appendix Table
9 additionally breaks out firms with 0 employees (which are more likely to be sole-proprietors
or self-employed), and we show that firms with 0.5 to 4.5 employees remain much less likely to
apply for PPP loans compared to larger firms.
3.4 The impact of getting the PPP on expectations and employment
We find that getting a PPP loan was associated with substantially better expectations about
the future and having moderately more employees. Table 2 regresses outcomes on an indicator
18
Given that the survey ran through May 16th, some of the differences in approval rates (conditional on applying)
are likely due to loans having not yet been approved, rather than applications being rejected. This is consistent with
column 4 of Table 1, which shows smaller firms applied later.
19
Along with local demographic information and state case and death counts, Appendix Table 7 additionally
controls for the minimum distance from the centroid of the respondent’s ZIP code to the closest SBA approved
lender, the number of SBA approved lenders within 10 kilometers. Adding these controls have little effect on the
coefficients.
13
for receiving a PPP loan and a rich set of controls. For each outcome, the first row includes the
whole sample of individuals who were asked about PPP loans, while the second row restricts to
those who applied for PPP loans. Columns show results for the full sample, as well as regressions
conditional on firm size. Overall, getting a PPP loan is associated with notable improvements in
expectations: the probability of recovery within two years increases by 0.08, and the probability
of closure or bankruptcy in the next six months decreases by 0.08. When conditioning only on
those who applied for a PPP loan, the results are larger, with a 0.12 increase in the probability
of recovering within two years and a 0.11 reduction in the probability of closure or bankruptcy
in the next six months. These effects are largely similar across firm size bins, though they are
larger and have smaller standard errors for firms with fewer than five employees.
The last two panels report the same regressions with current employment and an indicator
for if the firm has any reduction in employment since January as the outcome variables. Those
who received a PPP loan reported slightly more employees. In addition, they are 9 percentage
points less likely to have fewer employees than in January.
Establishing causality is difficult in this setting, as we cannot fully control for self-selection
(though our main specification includes many business-specific controls). Firms may have been
more likely to apply for and get a PPP loan if they were better-off to begin with, or who
were in locations less effected by COVID-19. In Appendix E, we show that our results are
largely unchanged when controlling for additional local controls. These include the number
of COVID-19 cases and deaths per capita in the state at the time the respondent took the
survey, the minimum distance from the centroid of the respondent’s ZIP code to the closest
SBA approved lender, the number of SBA approved lenders within 10 kilometers, and ZIP code
level demographic controls. The appendix also repeats the above analysis, but controlling for
baseline expectations about the future in the sub-sample of respondents that completed both
the baseline and follow-up surveys. This is a smaller sample and estimates are less precise, but
the table finds similar though smaller coefficients on PPP receipt.
3.5 Discussion and conclusion
Our results suggest that information frictions played an important role in determining differen-
tial access to PPP resources between smaller and larger businesses. These frictions might be
associated with, for example, uncertainty about the eligibility criteria or the forgivable aspect of
the loan. We argue that these frictions are more binding for small businesses for at least three
reasons. First, firm sophistication (measured by years of education of the owner and firm type)
14
is positively correlated with firm size, and lower sophistication could imply greater difficulties
in accessing and processing information. Second, larger firms typically have more and better
human resources (e.g. accountants or human resources departments), which also contributes to
reducing the cost of acquiring information, and applying to the program. Third, there are fixed
costs implied by the application process (e.g. finding a bank that will accept the application
and acquiring appropriate documentation of payroll), which are more likely to be binding for
smaller business. The “first-come, first-served” nature of the program magnifies the potential
impacts of these frictions since a timely application was integral to quickly receiving a PPP
loan.
The results also show that lower application rates, longer processing times, and ultimately
less access to the programs may have had negative consequences for small firms. To the extent
that small businesses struggle to re-hire laid-off workers (potentially due to increased unem-
ployment benefits introduced by the CARES Act), the layoffs that have already occurred might
be costly to reverse in the short- to mid-run.20 This by itself also potentially reduces the at-
tractiveness of loans from the later stages of PPP (which are not covered in our survey), as
payroll determines the size of the loan and how much of the loan can be forgiven. Moreover,
uncertainty about what can be forgiven, and how firms will apply for forgiveness may have
caused firms to not apply. When the PPP was initially launched, there were few details on how
forgiveness worked, and the detailed rules were not posted until May 22nd, almost two months
after the CARES Act was signed into law (the rules were then further amended in early June).
The initial uncertainty, combined with changing guidance, may have distorted small business
owners’ beliefs about their eligibility and what the program actually provides.
Overall, our results show unequal access to program resources across firm size. Even though
we cannot rule out all competing explanations, the results strongly indicate that information
frictions, combined with the “first-come, first-served” nature of the PPP, played a central role in
this inequality of access. Moreover, our results indicate that lack of access to PPP is associated
with substantially worse outcomes for small businesses, such as greater layoffs and perceived
probability of bankruptcy, and overall worse expectations about their businesses’ recovery.
20
While many layoffs may have been intended to be temporary (Alstadsæter et al., 2020), the increased UI benefits
introduced as part of the CARES Act, may create additional challenges in re-hiring workers (Barrero et al., 2020).
15
References
Adams-Prassl, Abi, Teodora Boneva, Marta Golin, and Christopher Rauh, “Inequal-
ity in the impact of the coronavirus shock: Evidence from real time surveys,” 2020.
Alstadsæter, Annette, Bernt Bratsberg, Gaute Eielsen, Wojciech Kopczuk, Siem
Markussen, Oddbjorn Raaum, and Knut Røed, “The First Weeks of the Coronavirus
Crisis: Who Got Hit, When and Why? Evidence from Norway,” NBER Working Paper, 2020,
27131.
Autor, David, David Cho, Leland D. Crane, Mita Goldar, Byron Lutz, Joshua
Montes, William B. Peterman, David Ratner, Daniel Villar, and Ahu Yildirmaz,
“An Evaluation of the Paycheck Protection Program Using Administrative Payroll Micro-
data,” Working Paper, MIT July 2020.
Barrero, Jose Maria, Nick Bloom, and Steven J. Davis, “Covid-19 is Also a Reallocation
Shock,” Becker Friedman Institute Working Paper, 2020, 2020-59.
Barrios, John M. and Yael Hochberg, “Risk Perception Through The Lens of Politics in
the Time of the COVID-19 Pandemic,” Working Paper 27008, National Bureau of Economic
Research 2020.
Bartik, Alexander W., Marianne Bertrand, Zoe Cullen, Edward L. Glaeser, Michael
Luca, and Christopher Stanton, “How are small businesses adjusting to COVID-19?
Early Evidence from a Survey,” NBER working paper, 2020, 26989.
Beland, Louis-Philippe, Abel Brodeur, and Taylor Wright, “The Short-Term Economic
Consequences of COVID-19: Exposure to Disease, Remote Work and Government Response,”
IZA DP No.13159, 2020.
Besley, Timothy and Stephen Coate, “Workfare versus welfare: Incentive arguments for
work requirements in poverty-alleviation programs,” The American Economic Review, 1992,
82 (1), 249–261.
Bettinger, Eric P., Bridget Terry Long, Philip Oreopoulos, and Lisa Sanbonmatsu,
“The Role of Application Assistance and Information in College Decisions: Results from the
H&R Block FAFSA Experiment,” The Quarterly Journal of Economics, 07 2012, 127 (3),
1205–1242.
16
Bhargava, Saurabh and Dayanand Manoli, “Psychological frictions and the incomplete
take-up of social benefits: Evidence from an IRS field experiment,” American Economic
Review, 2015, 105 (11), 3489–3529.
Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-
Puertolas, Erik Hurst, Christopher Kurz, and Ahu Yildirmaz, “The U.S. Labor
Market During the Beginning of the Pandemic Recession,” Working Paper 2020-58, Becker
Friedman Institute 2020.
Center for Disease Control, “Cases of Coronavirus Disease (COVID-19) in the U.S.,” https:
//www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html 2020. Ac-
cessed: 2020-07-20.
Chetty, Raj, John N. Friedman, Nathaniel Hendren, and Michael Stepner, “How Did
COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time
Economic Tracker Based on Private Sector Data,” Working Paper 27431, National Bureau of
Economic Research 2020.
Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber, “Labor Markets Dur-
ing the COVID-19 Crisis: A Preliminary View,” Working Paper 27017, National Bureau of
Economic Research April 2020.
Cororaton, Anna and Samuel Rosen, “Public Firm Borrowers of the US Paycheck Protec-
tion Program,” Working Paper, SSRN 2020.
Fairlie, Robert W, “The Impact of COVID-19 on Small Business Owners: Continued Losses
and the Partial Rebound in May 2020,” Working Paper 27462, National Bureau of Economic
Research July 2020.
, “The Impact of Covid-19 on Small Business Owners: Evidence of Early-Stage Losses from the
April 2020 Current Population Survey,” Working Paper 27309, National Bureau of Economic
Research June 2020.
Finkelstein, Amy and Matthew J Notowidigdo, “Take-up and targeting: Experimental
evidence from SNAP,” The Quarterly Journal of Economics, 2019, 134 (3), 1505–1556.
Granja, João, Christos Makridis, Constantine Yannelis, and Eric Zwick, “Did the
Paycheck Protection Program Hit the Target?,” Working Paper 2020-52, Becker Friedman
Institute 2020.
17
Kahn, Lisa B., Fabian Lange, and David G. Wiczer, “Labor Demand in the Time of
COVID-19: Evidence from Vacancy Postings and UI Claims,” NBER Working Paper, 2020,
27061.
Zwick, Eric and James Mahon, “Tax Policy and Heterogeneous Investment Behavior,”
American Economic Review, 2017, 107 (1), 217–248.
18
Table 1: PPP timing by firm characteristics
P .) al
P nd ly rov
toP (co app app
P PP li ed PPP to to
ot pp ot ys ys
G A G Da Da
∗∗∗ ∗∗∗
Jan. FTE Emp 0-4.5 −0.266 −0.229 −0.266∗∗∗ 1.808∗∗∗ 1.886∗
(0.020) (0.020) (0.025) (0.556) (1.024)
Jan. FTE Emp 5-9.5 −0.116∗∗∗ −0.011 −0.141∗∗∗ 0.156 0.599
(0.024) (0.022) (0.027) (0.572) (1.074)
Years of schooling 0.010∗∗∗ 0.017∗∗∗ 0.009∗∗ −0.064 0.228
(0.003) (0.003) (0.004) (0.109) (0.201)
Type: LLC −0.079∗∗∗ −0.082∗∗∗ −0.063∗∗∗ 0.964 2.711∗∗
(0.018) (0.020) (0.024) (0.601) (1.115)
Type: Other −0.013 −0.148∗∗ 0.090 3.405∗ −3.306
(0.055) (0.059) (0.078) (1.911) (2.694)
Type: Self-employed −0.192∗∗∗ −0.286∗∗∗ −0.214∗∗∗ 1.905 4.596
(0.022) (0.038) (0.050) (1.620) (2.825)
Type: Sole prop or part −0.159∗∗∗ −0.228∗∗∗ −0.169∗∗∗ 1.220∗ 2.781∗∗
(0.016) (0.020) (0.024) (0.644) (1.229)
N 4,996 4,996 2,662 1,338 525
R2 0.165 0.131 0.212 0.114 0.408
Note: ∗∗∗ Significant at the 1 percent level, ∗∗ significant at the 5 percent level, and ∗ significant at the 10 percent
level. The first four specifications control for the date the survey was taken. The final regression controls for the
day the first PPP loan application was submitted and the week of the survey. All regressions also include controls
for industry, age groups, and gender, but coefficients were largely not statistically significant nor large and are not
displayed as they largely did not predict the outcomes. All regressions additionally control for state of residence.
The omitted category for firm type is C-Corps and S-Corps. The omitted category for firm-size bins is firms with
10 to 50 FTE employees. Columns 4 and 5 have fewer observations as many respondents did not provide the dates
of their application and approval. Alternative specifications of these results are included in Appendix E. These
include a table replicating these results controlling for for additional state and ZIP code level characteristics, results
controlling for baseline beliefs of the firms, and results separating firms with zero employees from firms with 0.5-4.5
employees.
19
Figure 1: Responses over time
Note: The figure shows survey responses to key questions from March 28th, 2020 to May 16th, 2020. The
black line is fit using locally weighted smoothing regression, with the grey region showing the 95% confidence
interval. The blue line shows a centered moving average over 250 responses. The vertical red lines reference
the dates of key events related to the Paycheck Protection Program.
20
Figure 2: Are you aware of any federal or state programs that could help your business?
Note: In the top panel, the black line is fit using locally weighted smoothing regression, with the grey region
showing the 95% confidence interval. The blue line shows a centered moving average over 250 responses. The
bottom panel shows loess regression lines over time by business size bins based on the number of FTE employees
in January, 2020. Estimates are based on the response to the question “Are you aware of any federal or state
programs that could help your business during this crisis?”and thus may capture a combination of awareness
of the program as well as comprehension of how the program works, who is eligible, and how to apply.
21
Table 2: PPP loans and business outcomes
Recover in next 2 years
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗ ∗∗
PPP 0.080 0.114 0.086 −0.010
(0.019) (0.027) (0.039) (0.044)
PPP (cond) 0.117∗∗∗ 0.135∗∗∗ 0.157∗∗∗ −0.008
(0.022) (0.032) (0.047) (0.057)
N 4,949 2,662 3,357 1,493 916 660 676 509
Prob of closure or bankruptcy
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗ ∗∗∗
PPP −0.084 −0.085 −0.091 −0.078∗∗∗
(0.009) (0.014) (0.019) (0.021)
PPP (cond) −0.109∗∗∗ −0.107∗∗∗ −0.118∗∗∗ −0.104∗∗∗
(0.011) (0.016) (0.023) (0.026)
N 4,949 2,662 3,357 1,493 916 660 676 509
Current employment
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗ ∗∗∗
PPP 0.554 0.233 0.806 0.882
(0.168) (0.070) (0.216) (0.717)
PPP (cond) 0.548∗∗∗ 0.255∗∗∗ 0.856∗∗∗ 0.848
(0.185) (0.077) (0.252) (0.880)
N 4,949 2,662 3,357 1,493 916 660 676 509
Past layoffs
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗
PPP −0.086 −0.115 −0.057 −0.068∗
(0.018) (0.027) (0.038) (0.037)
PPP (cond) −0.112∗∗∗ −0.137∗∗∗ −0.085∗ −0.059
(0.021) (0.029) (0.044) (0.045)
N 4,949 2,662 3,357 1,493 916 660 676 509
∗∗∗ ∗∗ ∗
Note: Significant at the 1 percent level, significant at the 5 percent level, and significant at the 10 percent
level. “PPP” is an indicator for if the business received a PPP loan while “PPP (cond)” is the same regression,
but restricted to those who applied to the PPP program. The first two columns show results for the full sample,
while the remaining columns show results conditional on firm size bins. All regressions control for a third order
polynomial in the number of full-time employees in January, a third order polynomial in the number of part-time
employees in January, the day of the week the survey was completed, state dummies, years of education dummies,
gender, age group dummies, firm type, firm sector, and the date the survey was completed. See Appendix E for
additional robustness results. These include a table replicating this analysis controlling for additional state and
ZIP code level characteristics and a table replicating this analysis controlling for baseline beliefs.
22
A Appendix: Details on the Paycheck Protection Pro-
gram
The Paycheck Protection Program (PPP) was established under the CARES Act to provide loans
for small businesses impacted by COVID-19. These loans differ from previous Small Business
Administration (SBA) loans because they do not require collateral or a credit check. Moreover,
the loans may be fully forgiven if the money is spent on payroll, rent, utilities, or interest
payments on business mortgages for an 8-week period (with a minimum of 75% to payroll).
The first round of aid was available April 3 for small businesses and sole proprietorships. On
April 10, independent contractors and self-employed individuals were also able to apply. The
initial $350 billion allocated for the program was exhausted as of April 16. The Paycheck
Protection Program received a second round of funding from Congress and the Small Business
Administration resumed accepting applicants on April 27. The program expired with funds
remaining on June 30th, but was then extended, reopening to applications on July 7th and is
scheduled to remain open through August 8th.
The SBA posted its first Interim Final Rule that announced the implementation of the Coro-
navirus Aid, Relief, and CARES Act on April 2nd. Since then, the SBA has posted additional
Interim Final Rules that supplement or change those previously posted. Updates have included
additional guidance on on eligibility for: certain pledges of loans, certain electric cooperatives,
student and seasonal workers, and other specific types of businesses. The Interim Final Rules
also provides additional guidance on loan forgiveness. On June 5th, the Paycheck Protection
Program Flexibility Act was signed into law that changed some aspects of the program. Under
the new rules, 60% of forgivable expenses were required to be payroll. Borrowers were also given
24 weeks for which qualified expenses could be forgiven rather than 8. The term of the loan was
increased from two to five years, and exceptions were introduced that could allow some loans to
be forgiven even if payroll was not fully restored.
Eligibility
In order to be eligible for the Paycheck Protection Program, the applicant or business impacted
by COVID-19 must meet entity-specific criteria. Eligible applicants include: sole proprietors,
independent contractors, and self-employed persons. Self-employed individuals must have been
in operation no later than February 15, 2020, been an individual with self-employed income,
have a principal place of residence in the U.S., and have filed or intends to file a 2019 Form 1040
23
Schedule C.
Loan eligibility extends to any small business that meets the SBA’s size standards or business
with a NAICS Code that begins with 72 (as long as the location employs less than 500 per
location). Additional eligible businesses that have 500 or more employees or that meet the
SBA’s industry size standard (if more than 500) include: non-profit organizations, veteran
organizations, or Tribal business.
Applicants are not eligible for the PPP if: (1) they engage in any illegal activities under
federal, state, or local law; (2) If an owner with 20% equity or more is incarcerated, on probation,
on parole, or currently subject to any criminal charges; or has been convicted of a felony within
the last five years; or (3) if the applicant, the applicant’s co-owners, or any of the applicant’s
businesses received or guaranteed a loan from the SBA or other Federal agency, and that loan
is currently delinquent within the last 7 years.
Application Process
In order to apply for the PPP, business owners must submit the application form with supple-
mentary materials to existing SBA approved lenders or other FDIC insured lenders. On the
application form, business owners are asked about whether they received any Economic Injury
Disaster Loans, their number of employees, their average monthly payroll, and information on
any co-owners holding at least 20% equity of the business. In addition to the form, applicants
are asked to provide documentation on the number of full-time equivalent employees on their
payroll and other costs, such as mortgage interest payments and rent. Business owners are not
required to provide any personal guarantee or collateral, or prove that they were not able to
receive credit elsewhere in order to apply for the PPP.
Conditions of the Program
The PPP offers loans to small business owners with a fixed interest rate of 1%. All payments
are deferred for the first six months, though borrowers are not penalized for prepayment. In the
case that business owners default on their loans, the administration is barred from laying any
claims to theirs or their shareholders’ assets. As aforementioned, the PPP allows borrowers to
cover payroll costs, which includes salaries, retirement benefits, group health care benefits, rent,
utilities, any mortgage or debt obligations, payments for vacation, parental, family, or medical
leave, and allowances for dismissals.
While the PPP’s coverage of payroll costs is broad, there are a few limitations. In particular,
24
workers whose primary places of residence are outside of the U.S. cannot be included in the
calculation of payroll costs, nor can independent contractors (who can instead apply for a PPP
loan directly). Wages are also capped at $100,000 for each employee. In addition, the PPP can
only cover family and sick leave wages that are not eligible for credit under the Families First
Coronavirus Response Act (FFCRA).21
The maximum PPP loan amount depends on three main factors: whether the business hires
employees seasonally, whether it is new, and whether it has previously received an Economic
Injury Disaster Loan. The first two factors impact the reference period for calculating payroll
costs. Generally, the loan amount is capped at 2.5 times the average, monthly payroll costs
during the last year. However, seasonal employees can choose to calculate their average, monthly
payroll costs between February 15 and June 30, 2019. If a business is new (defined as not
operating between February 15 and June 30, 2019), the reference time period for average,
monthly payroll costs is between January 1 and February 29, 2020. If an applicant received
an Emergency Injury Disaster Loan (EIDL) between January 31 and April 3, 2020, then any
outstanding payments for the EIDL should be added to the loan amount. Any advance EIDL
loan should be excluded from the calculation, as the advance does not need to be repaid. The
total PPP loan amount is capped at $10 million.
Forgiveness
Generally, all payroll costs covered by the loan may be forgiven, if wages are capped at $100,000
per employee. For other purposes, such as rent, utilities, and mortgage interest payments, the
maximum amount forgivable is 25% of payroll costs. However, the loan may not be forgiven
if an owner reduced their full-time equivalent (FTE) workforce since February 15 or lowered
wages originally below $100,000 by more than 25% compared to the most recent quarter. With
that said, borrowers who made such changes between February 15 and April 26, 2020, may be
exempt from the reduction if they restore their employee numbers and/or wages by June 30,
2020.
B Appendix: Survey details
Participants were recruited via facebook ads targeted at business owners in the United States.
Ads were targeted at people living in the US aged 25 or older, and targeted people who matched:
21
The FFCRA provides fully refundable tax credits for sick and family leave wages if an employee is personally
impacted by COVID-19.
25
• Behaviors: Small business owners
• Employers: Business Owner
• Interests: Small Business
• Job title: Owner and Founder
Figure 3 provides a photo of the ad used for recruiting for the survey.
Figure 3: Facebook Ad used for recruitment
B.1 Survey questions
This subsection provides the three survey instruments used in this study:
• The baseline survey, which was used from March 28th through April 26th, 2020
• An extended baseline survey sued from April 27th through May 16th, 2020
• A follow-up survey which began on April 19th, 2020 for those completing the original
baseline survey
26
Baseline Survey:
Q1.1: The 2020 Small Business Survey has been explained to me. I know that I may
refuse to participate or to stop the interview at any time without repercussions of any
kind. I consent to participate:
Yes
No
Q2.1: How many employees worked at the business you own in January 2020?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Q2.2: Please enter the ZIP code of your business: ___________
Q3.1: As of today, have you been required to lay off any employees in response to the
current health crisis?
Yes
No
Q3.2: How many employees have you been required to lay off?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Q4.1: Do you think you will need to lay off any employees in the next 60 days?
Yes
No
Q4.2: How many employees do you think you will need to lay off in the next 60 days?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Q5.1: Do you think your business will fully recover within the next two years?
Yes
No
Q5.2: How many months do you think this will take? ___________
Q5.3: Do you think that your business will ever recover?
Yes
No
Q6.1: On a scale of 0-100, what do you think the probability is that your business will
file for bankruptcy OR go out of business in the next 6 months? ___________
27
Q7.1: Are you aware of any federal or state programs that could help your business
during this crisis?
Yes
No
Q7.2: Are you aware of any federal or state programs that could help your business
with the following:
Programs That Cover Yes No
Your Employees’ Wages
Business Loans with Yes No
Subsidized Interest Rates
Programs That Allow You Yes No
to Defer Rent, Mortgage,
or Loan Payments
Programs That Cover Yes No
Your Rent
Q8.1: State and federal governments are developed new policies to help businesses
like yours. Would you like to be contacted by a nonprofit organization with
information about federal problems that can help businesses like yours?
Yes
No
Q9.1: Researchers are still learning about how the COVID-19 crisis is affecting small
businesses. Would you be willing to answer another survey in the future?
Yes
No
Q10.1: To send you a follow-up survey, please enter your email: ___________
Q11.1: To send you more information about federal programs that can help
businesses like yours, please enter your contact information:
28
Email: ___________
Phone: ___________
Q11.2: What is your preferred method of contact?
Email
Phone
Q12.1: Many small businesses in the United States have been affected by the COVID-
19 Pandemic. Some types have been affected more than others. Can you share in your
own words, what type of business you run, and how have you and your business been
affected?
Q12.2: There has been a lot of discussion as to what small businesses need during this
crisis. What can the state and federal government do to help your business weather
the health and economic crisis over the next 6 months?
29
Extended Baseline Survey:
Q1.1: The 2020 Small Business Survey has been explained to me. I know that I may
refuse to participate or to stop the interview at any time without repercussions of any
kind. I consent to participate:
Yes
No
Introduction
Q2.1: How many employees worked at the business you own in January 2020?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Q2.2: Please enter the ZIP code of your business: ___________
Q3.1: As of today, have you been required to lay off any employees in response to the
current health crisis?
Yes
No
Q3.2: How many employees have you been required to lay off?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Q4.1: What describes best the current state of your business?
My business is open
My business is closed, but I plan to reopen
My business is permanently closed
Q4.2: If your business is open, is your business:
Open at full capacity
Open, but at reduced capacity (reduced hours or services)
Q4.3: If your business is closed and you plan to reopen, what best describes the
reason for temporary closure?
Lack of business
Closed by government order
Other; please specify: ___________
Q4.4: If your business is permanently closed, are you currently:
I am employed at a new job
30
I am unemployed
Other; please specify: ___________
Q4.5: If your business is permanently closed and you are unemployed, are you actively
looking for work?
Yes
No
Q4.6: If you are unemployed, are you receiving government assistance? (e.g.
unemployment insurance)
Yes
No
Q4.7: If you are employed at a new job, what type of employment is your new job?
Full-time
Part-time
Other; please specify: ___________
Layoff Plans
Q4.8: Do you think you will need to lay off any employees in the next 60 days?
Yes
No
Q4.9: How many employees do you think you will need to lay off in the next 60 days?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Program Access for Closed Programs
Q5.1: If your business is closed, are there any federal, state, or municipal programs
that could have helped your business to survive?
Yes
No
Q5.2: If your business is closed and you believe there were no federal, state, or
municipal programs that could have helped your business survive, please select the
option that best applies:
I was not aware of any programs available to help small businesses
My business was not eligible for the existing programs
The available programs were not what I needed
31
Other; please specify: ___________
Q5.3: If your business is closed and you believe there were federal, state, or municipal
program that could have helped your business, please select the option that best
applies:
I did not know how to apply
I applied, but my application was rejected
I applied, but the aid not arrive in time
Other; please specify: ___________
Q6.1: Please enter the amount of your total sales in March 2020 (or in your last month
of operation): ___________
Recovery
Q6.2: If your business is not closed, do you think your business will fully recover
within the next two years?
Yes
No
Q6.2: If yes, how many months do you think this will take? ___________
Q6.3: If not, do you think that your business will ever recover?
Yes
No
Bankruptcy and Closure
Q7.1: On a scale of 0-100, what do you think the probability is that your business will
file for bankruptcy OR go out of business in the next 6 months? ___________
Q8.1: Which bank, credit union or financial institution do you use the most for your
business? ___________
Relief Access for Open Programs
Q9.1: Are you aware of any federal or state programs that could help your business
during this crisis?
Yes
No
32
Q9.2: Are you aware of any federal or state programs that could help your business
with the following:
Programs That Cover Yes No
Your Employees’ Wages
Business Loans with Yes No
Subsidized Interest Rates
Programs That Allow You Yes No
to Defer Rent, Mortgage,
or Loan Payments
Programs That Cover Yes No
Your Rent
Other Yes No
Q9.3: If other, please specify what other federal or state program could help your
business: ___________
Q9.4: If you are not aware of an federal, state, or municipal programs that could help
your business, are you unware of programs that could help your business because:
I am not aware of any programs available to help small businesses
My business does not qualify for the programs I am aware of
Other; please specify: ___________
Q10.1: Have you taken any steps towards searching or applying for any federal, state
or municipal programs that could help your business?
Yes
No
Q10.2: If you have taken steps toward searching or applying for any federal, state, or
municipal programs, what best describes the steps you have taken to apply to
programs that could help your small business:
33
I have looked into programs, but haven’t decided to apply
I have looked into programs and expect to begin the application soon
I have tried to apply for a program but have not been able to complete an
application
I have completed an application
Other; please specify: ___________
Q10.3: If you have tried to apply for a program but did not complete an application,
what best describes your experience:
Not sure how to provide all the requested information
Couldn’t get ahold of the bank or other institution
Other; please specify: ___________
Q10.4: If you have completed an application, what best describes your experience:
I have not heard back
My application was rejected
I was notified that funds are no longer available
My application was accepted and approved
Other; please specify: ___________
Q10.5: If you have begun or completed an application, was your application to any of
the following programs?
The Paycheck Protection Program (PPP)
The Economic Injury Disaster Loan Program (EIDL)
Other; please specify: ___________
Q11.1: If you applied to the Paycheck Protection Program (PPP), when did you first
try to apply to the Paycheck Protection Program (PPP) that opened applications for
small businesses on April 3, 2020? ____________
Q11.2: If you applied to the PPP, did you apply again after?
Yes
No
Q11.3: If so, when did you last apply to the Paycheck Protection Program (PPP)?
(mm/dd/yyyy): ____________
Q11.4: If your application for the Paycheck Protection Program (PPP) was accepted
and approved, what was the date? (mm/dd/yyyy): ____________
34
Q12.1: If you applied to the Economic Injury Disaster Loan Program (EIDL), when
did you first try to apply to the Economic Injury Disaster Loan Program (EIDL)?
(mm/dd/yyyy): ____________
Q12.2: If your application for the Paycheck Protection Program (PPP) was accepted
and approved, what was the date? (mm/dd/yyyy): ____________
Q13.1: If you applied for a program, was your application through your primary bank,
credit union, or financial institution?
Yes
No
Q13.2: If you applied for a program, did you also try to apply through a different
bank, credit union, or financial institution other than your primary one?
Yes
No
Q14.1: Which banks, credit unions, or financial institutions did you try to apply
through other than your primary? Please write each one: ____________
Q14.2: If you applied, please tell us in your own words about your experience with
applying: ____________
Q15.1. If you have not applied, what best describes your current situation:
I expect to apply in the future
I am still unsure if I will apply or not
I do not plan on applying in the future
Q15.2: If you are not planning to apply in the future, why is that?
I don’t need assistance
The types of programs available are not what my business needs
I am not eligible
I don’t think I will get support even if I apply
Other; if other, please explain: ____________
35
Q16.1: Have government support policies in response to the COVID-19 crisis helped
your business:
Not lay off workers Yes No
Maintain your income Yes No
Maintain the income of Yes No
your workers
Q17.1: Think of all the revenue your business could make in the six months between
March and September 2020 if it stays in business. On a scale of 0-100, what is your
best guess for the percent of revenue that will be lost to your business in the six
months between March and September 2020 because of COVID-19? ___________
Contact
Q18.1: State and federal governments are developed new policies to help businesses
like yours. Would you like to be contacted by a nonprofit organization with
information about federal problems that can help businesses like yours?
Yes
No
Q18.2: If yes, to send you more information about federal programs that can help
businesses like yours, please enter your contact information:
Email: ___________
Phone: ___________
Q18.3: What is your preferred method of contact?
Email
Phone
Q19.1: Researchers are still learning about how the COVID-19 crisis is affecting small
businesses. Would you be willing to answer another survey in the future?
Yes
No
Q19.2: To send you a follow-up survey, please enter your email: ___________
36
Information About You
We would like to know a little bit more about you, if you are willing to answer.
Q20.1: What is the highest educational level you have achieved?
Less than high school
High school graduate
Some college
2 year college degree
4 year college degree
Professional degree
Master’s Degree
Doctorate
Q20.2: What is your sex?
Male
Female
Q20.3: What is your age group?
18-24
25-34
34-49
50+
Information About Your Business
We would like to know a little bit more about your current or former business, if you
are willing to answer.
Q21.1: Which sector best describes your current or previous activity?
Retail
Manufacturing
Services
Agriculture
Construction
Other; if other, please explain: ____________
Q21.2: What year did you first open your business? ____________
Q21.3: Which type of small business do you or did you own?
37
Sole proprietor
Independent contractor
Partnership
C-Corp
S-Corp
LLC
Self-employed individual
Non-profit
Other; if other, please explain: ____________
I am not sure
For the study, it is helpful to track the businesses that answer the survey. For this
purpose, it would be helpful to know a few things about the business. These answers
are not required to be a useful participant in the study and will be treated as strictly
confidential. These responses will be only used for the purpose of studying how the
COVID-19 crisis is affecting small businesses.
Q22.1 Name of business: ____________
Q22.2 Business address: ____________
38
Government Responses
Q23.1 Do you think that the government’s response to the COVID-19 crisis is
appropriate regarding:
Social distancing measures Yes No
(e.g. non-essential
business closures)
Economic policies to help Yes No
workers
Economic policies to help Yes No
small businesses
Economic policies to help Yes No
large businesses
Q23.2: Are there currently any mandated business closures in your area?
Yes
No
Q23.3: If yes, when do you think the government should lift mandated business
closures?
Within two weeks
Within a month
Within two months
Once the crisis is over
Q23.4: If there are no mandated business closures, were there in the past?
Yes, but they were lifted too soon
Yes, but there were lifted at the right time
No, but there should have been
No, because it was not needed
39
Q24.1: Do you think the reaction of your country’s government to the current
coronavirus outbreak is appropriate, too extreme, or not sufficient?
The reaction is much too extreme
The reaction is somewhat too extreme
The reaction is appropriate
The reaction is somewhat insufficient
The reaction is not at all sufficient
Q25.1: Many small businesses in the United States have been affected by the COVID-
19 Pandemic. Some types have been affected more than others. Can you share in your
own words, what type of business you run, and how have you and your business been
affected?
Q25.2: There has been a lot of discussion as to what small businesses need during this
crisis. What can the state and federal government do to help your business weather
the health and economic crisis over the next 6 months?
40
Follow-up Survey:
Q1.1: The 2020 Small Business Survey has been explained to me. I know that I may
refuse to participate or to stop the interview at any time without repercussions of any
kind. I consent to participate:
Yes
No
Introduction
Q2.1: What describes best the current state of your business?
My business is open
My business is closed but I plan to re-open
My business is permanently closed
Q2.2: Is your business:
Open at full-capacity
Open but at reduced capacity (reduced hours or services)
Q2.3: What best describes the reason for temporary closure?
Lack of business
Closed by government order
Other; if other, please explain: ____________
Q2.4: Are you currently:
I am employed at a new job
I am unemployed
Other; if other, please explain: ____________
Q2.5: Are you actively looking for work?
Yes
No
Q3.1: Are you receiving government assistance? (e.g. unemployment insurance)
Yes
No
Q4.1: What type of employment is your new job?
Full-time
41
Part-time
Other; if other, please explain: ____________
Q4.2: Is this new job:
Formal
Informal
Size
Q5.1: How many people are currently employed at your business?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Q5.2: How many of these employees are informal?
Number of Informal Full-time Employees: ___________
Number of Informal Part-time Employees: ___________
Contract/Informal Workers
Q5.3: How many independent contractors currently work for your business?
Number of Full-time Contractors: ___________
Number of Part-time Contractors: ___________
Q5.4: How many independent contractors worked for your business in January 2020?
Number of Full-time Contractors: ___________
Number of Part-time Contractors: ___________
Q5.5: How many informal workers worked for your business in January 2020?
Number of Informal Full-time Employees: ___________
Number of Informal Part-time Employees: ___________
Layoff Plans
Q6.1: Do you think you will need to lay off any employees in the next 60 days?
Yes
No
Q6.2: How many employees do you think you will need to lay off in the next 60 days?
Number of Full-time Employees: ___________
Number of Part-time Employees: ___________
Relief Program Access Shutdown
Q7.1: Are there any federal, state, or municipal programs that could have helped your
business to survive?
Yes
No
Q7.2: Please select the option that best applies:
42
I was not aware of any programs available to help small businesses
My business was not eligible for the existing programs
The available programs were not what I needed
Other; if other, please explain: ____________
Q7.3: If you were familiar with a program, why did you not apply?
I did not know how to apply
I applied, but my application was rejected
I applied, but the aid did not arrive in time
Other; if other, please explain: ____________
Q8.1: Please enter the amount of your total sales in March 2020 (or in your last month
of operation): ___________
Recovery
Q9.1: Do you think that your business will recover within the next two years?
Yes
No
Q9.2: If so, how many months do you think this will take: ___________
Q9.3: Do you think that your business will ever recover?
Yes
No
Bankruptcy and Closure
Q10.1: On a scale of 0-100, what do you think the probability is that your business
will file for bankruptcy OR go out of business in the next 6 months? ___________
Q11.1: Please write the name of the bank, credit union, or financial institution you use
most for your business: ___________
Relief Program Access Open
Q12.1: Are you aware of any federal or state programs that could help your business
during this crisis?
Yes
No
Q12.2: Are you aware of any federal or state programs that could help your business
with the following:
43
Programs That Cover Yes No
Your Employees’ Wages
Business Loans with Yes No
Subsidized Interest Rates
Programs That Allow You Yes No
to Defer Rent, Mortgage,
or Loan Payments
Programs That Cover Yes No
Your Rent
Other Yes No
Q12.3: If you know, please specify what other federal or state program could help
your business: ___________
Q12.4: If you are unaware of programs that could help your business, is it because:
I am not aware of any programs available to help small businesses
My business does not qualify for the programs I am aware of
Other; if other, please explain: ____________
Q13.1: Have you taken any steps towards searching or applying for any federal, state,
or municipal programs that could help your business?
Yes
No
Q13.2: If applicable, what best describes the steps you have taken to apply to
programs that could help your small business:
I have looked into programs but haven’t decided to apply
I have looked into programs and expect to begin the application soon
I have tried to apply for a program but have not been able to complete an
application
I have completed an application
44
Other; if other, please explain: ____________
Q13.3: If applicable, what best describes your experience with applying:
Not sure how to provide all the requested information
Couldn’t get ahold of the bank or other institution
Other; if other, please explain: ____________
Q13.4: If applicable, what best describes the outcome of your application?
I have not heard back
My application was rejected
I was notified that funds are no longer available
My application was accepted and approved
Other; if other, please explain: ____________
Q13.5: If applicable, was your application to any of the following programs:
The Paycheck Protection Program (PPP)
The Economy Injury Disaster Loan Program (EIDL)
Other; if other, please explain: ____________
Q14.1: When did you first try to apply to the program (YYYY-MM-DD):
____________ [Note: question added on 4/26/2020]
Q14.2: When did you hear back from your application (YYYY-MM-DD):
____________ [Note: question added on 4/26/2020]
Q14.3: Was your application through your primary bank, credit union, or financial
institution?
Yes
No
Q14.4: Did you also try to apply through a different bank, credit union, or financial
institution other than your primary one?
Yes
No
Q14.5: If so, which banks, credit unions, or financial institutions did you try to apply
through other than your primary? Please specify each one: ____________
Q14.6: Please tell us in your own words about your experience with applying:
Q14.7: What best describes your current situation:
I expect to apply in the future
45
I am still unsure if I will apply or not
I do not plan on applying in the future
Q14.8: If applicable, why are you not planning to apply in the future?
I don’t need assistance
The types of programs available are not what my business needs
I am not eligible
I don’t think I will get support even if I apply
Other; if other, please explain: ____________
Q15.1: Have government support policies in response to the COVID-19 crisis helped
your business:
Not lay off workers Yes No
Maintain your income Yes No
Maintain the income of Yes No
your workers
Q16.1: On a scale of 0-100, what is your best guess for the percent of revenue that
will be lost to your business in the six months between March and September 2020
because of COVID-19? ___________
Information About You
We would like to know a little bit more about you, if you are willing to answer.
Q17.1: What is the highest educational level you have achieved?
Less than high school
High school graduate
Some college
2 year college degree
4 year college degree
Professional degree
46
Master’s Degree
Doctorate
Q17.2: What is your sex?
Male
Female
Q17.3: What is your age group?
18-24
25-34
34-49
50+
Information About Your Business
We would like to know a little bit more about your business, if you are willing to
answer.
Q18.1: Which sector best describes your current or previous activity?
Retail
Manufacturing
Services
Agriculture
Construction
Other; if other, please explain: ____________
Q18.2: What year did you first open your business? ____________
Q18.3: Which type of small business do you own or did you previously operate?
Sole proprietor
Independent contractor
Partnership
C-Corp
S-Corp
LLC
Self-employed individual
Non-profit
Other; if other, please explain: ____________
47
I am not sure
Q18.4: Does your business have:
Tax registration number Yes No
Municipal and/or state Yes No
license
Other Yes No
Q18.5: Is your business informal?
Yes
No
Other; if other, please explain: ____________
For the study, it is helpful to track the businesses that answer the survey. For this
purpose, it would be helpful to know a few things about the business. These answers
are not required to be a useful participant in the study and will be treated as strictly
confidential. These responses will be only used for the purpose of studying how the
COVID-19 crisis is affecting small businesses.
Q19.1: Name of business: ____________
Q19.2: Business address: ____________
Q19.3: Business ID number: ____________
48
Government Response Questions
Q20.1: Do you think that the government’s response to the COVID-19 crisis is
appropriate regarding:
Social distancing measures Yes No
(e.g. non-essential
business closures)
Economic policies to help Yes No
workers
Economic policies to help Yes No
small businesses
Economic policies to help Yes No
large businesses
Q21.1: Are there currently any mandated business closures in your area?
Yes
No
Q21.2: If yes, when do you think the government should lift mandated business
closures?
Within two weeks
Within a month
Within two months
Once the crisis is over
Q21.3: If there are no mandated business closures, were there in the past?
Yes, but they were lifted too soon
Yes, but there were lifted at the right time
No, but there should have been
No, because it was not needed
49
Q21.4: Do you think the reaction of your country’s government to the current
coronavirus outbreak is appropriate, too extreme, or not sufficient?
The reaction is much too extreme
The reaction is somewhat too extreme
The reaction is appropriate
The reaction is somewhat insufficient
The reaction is not at all sufficient
Q22.1: Can you share in your own words, how have you and your business been
affected since the initial survey date?
Q22.2: In your opinion, which government programs have helped, or are likely to
help, your business handle this health and economic crisis over the next 6 months?
50
C Appendix: Details on survey respondents
This section first provides details on the number of survey respondents over time, the geographic
distribution of respondents across the United States, and summary statistics for key variables.
Second, this section compares the distribution of size and state of respondents to the dis-
tribution of firms in the 2017 Census of US Businesses. The Census of US Businesses data
is restricted to firms with fewer than 500 employees. Figure 5 compares the distribution of
respondent by size bin in our sample compared to firms in the 2017 Census of US Businesses.
Figure 6 shows the relative distribution of responses compared to firms with fewer than 500
employees in each state. New England, the upper mid-west, and the Northwest are somewhat
over-represented in the survey relative to the number of firms with fewer than 500 employees in
each state, and California and the south are somewhat under-represented.
Third, this section compares two comparable questions from our survey to the Census Small
Business Pulse Survey. The Pulse survey was a weekly survey of small businesses started on
April 26th and run for nine week. The target population for the Census Pulse Survey was all
nonfarm, single-location employer businesses 499 or less employees and receipts of $1,000 or
more in the 50 states, District of Columbia, and Puerto Rico and were drawn from the Business
Register. Using this data, Figure 7 compares the proportion of respondents in our survey and
the Pulse Survey who reported getting a PPP loan by firm size and week. Overall, the trends
appear similar, though our survey reports slightly lower rates. This could be due to differential
selection into our sample, or that the Pulse survey is drawn from the Business Registry and will
miss non-employer establishments, such as the self-employed.
Figure 8 repeats the comparison above, but studying on if the businesses expect to ever
recover. The question used from the Census Pulse Survey was either “I do not believe this
business will return to its usual level of operations.” or “I do not believe this business will
return to its normal level of operations relative to one year ago”, then subtracted from one to
align with the question in our surveys. Here, the gaps between the survey are somewhat larger,
though all show similar downward trends in May. The gap may indicate that we are reaching
a more badly hit portion of the population, but may also come from (1) different constructions
of the questions or (2) that the Pulse survey is drawn from the Business Registry and will miss
non-employer establishments, such as the self-employed.
51
Table 3: Valid survey responses by day
Date N Date N
2020-03-28 147 2020-04-22 352
2020-03-29 173 2020-04-23 251
2020-03-30 161 2020-04-24 52
2020-03-31 167 2020-04-25 243
2020-04-01 639 2020-04-26 197
2020-04-02 758 2020-04-27 376
2020-04-03 530 2020-04-28 198
2020-04-04 425 2020-04-29 268
2020-04-05 193 2020-04-30 324
2020-04-06 396 2020-05-01 261
2020-04-07 451 2020-05-02 265
2020-04-08 462 2020-05-03 200
2020-04-09 459 2020-05-04 189
2020-04-10 360 2020-05-05 222
2020-04-11 418 2020-05-06 203
2020-04-12 368 2020-05-07 156
2020-04-13 322 2020-05-08 97
2020-04-14 282 2020-05-09 118
2020-04-15 362 2020-05-10 28
2020-04-16 462 2020-05-11 88
2020-04-17 472 2020-05-12 103
2020-04-18 501 2020-05-13 94
2020-04-19 440 2020-05-14 59
2020-04-20 418 2020-05-15 61
2020-04-21 389 2020-05-16 44
Note: Figure shows number of valid survey responses by day.
52
Figure 4: Distribution of surveys across the United States
Note: Figure shows number of valid survey responses by state.
53
Table 4: Survey summary statistics
Mean Var p5 p25 p50 p75 p95 N missing
Employees in Jan 4.88 41.88 0 1 2.50 5.50 17 14, 208 0
Already laid off workers 0.64 0.23 0 0 1 1 1 14, 208 0
Expect to lay off workers 0.31 0.21 0 0 0 1 1 14, 208 0
Recover in next two years 0.57 0.25 0 0 1 1 1 13, 842 366
Ever recover 0.79 0.16 0 1 1 1 1 13, 759 449
Prob of closure or bankruptcy 0.34 0.08 0 0.10 0.30 0.50 0.90 13, 380 828
Aware of programs to help 0.79 0.17 0 1 1 1 1 12, 911 1, 297
Aware of subsidized loans 0.66 0.22 0 0 1 1 1 12, 570 1, 638
54 Aware of programs to cover wages 0.68 0.22 0 0 1 1 1 12, 570 1, 638
Diff in recover in next 2 years 0.04 0.21 -1 0 0 0 1 2, 607 3, 246
Diff in ever recover 0.03 0.13 -1 0 0 0 1 2, 596 3, 257
Diff in prob of closure or bankruptcy -0.06 0.04 -0.41 -0.16 -0.02 0.04 0.25 2, 587 3, 266
Diff in awareness of programs -0.04 0.16 -1 0 0 0 1 2, 622 3, 231
Apply for PPP 0.53 0.25 0 0 1 1 1 5, 014 839
Got PPP (cond on applying) 0.37 0.23 0 0 0 1 1 2, 673 3, 180
Note: Table shows the summary statistics for data collected between March 28th and April 20th. Two restrictions are imposed
on the data. First, we restrict the analysis to small businesses with fewer than 50 FTE employees, and second, we only include
respondents who complete at least the first module of questions regarding employment. Not all respondents complete the entire
survey. The bottom half of the table shows numbers relying on follow-up surveys or the extended intake survey started on 2020-
04-27. The four rows corresponding to differences use only the follow-up survey, while the final two columns on applying for and
getting the PPP use both the follow-up survey and the extended baseline survey.
Figure 5: Firm size distribution: survey vs. Census
Note: Figure shows the share of firms with fewer than 500 employees in each employment
category in the 2017 Census of US Businesses (in blue) and the survey respondents (in red).
Figure 6: Firm state distribution: survey vs. Census
Note: The map shows the the the proportion of survey respondents from each state divided
by the proportion of firms with fewer than 500 employees from each state (using data from
the 2017 Census of US Businesses). States with values above 1 are over-represented in the
survey, while states with values below 1 are under-represented.
55
Figure 7: Comparing proportion of firms who received PPP loans (data vs. Census Pulse Survey)
Note: Figure shows the weekly proportion of respondents from the survey who expected to ever recover
(blue line) compared to weekly statistics from the Census Small Business Pulse Survey (red line). The
three bins represent firm-size bins based on number of employees. The target population for the Census
Pulse Survey was all nonfarm, single-location employer businesses 499 or less employees and receipts of
$1,000 or more in the 50 states, District of Columbia, and Puerto Rico and were drawn from the Business
Register.
Figure 8: Comparing proportion that expect to ever recover (data vs. Census Pulse Survey)
Note: Figure shows the weekly proportion of respondents from the survey who received a PPP loan (blue
line) compared to weekly statistics from the Census Small Business Pulse Survey (red line). The three
bins represent firm-size bins based on number of employees. The question used from the Census Pulse
Survey was either “I do not believe this business will return to its usual level of operations.” or “I do not
believe this business will return to its normal level of operations relative to one year ago”, then subtracted
from one to align with the question in our surveys. The target population for the Census Pulse Survey
was all nonfarm, single-location employer businesses 499 or less employees and receipts of $1,000 or more
in the 50 states, District of Columbia, and Puerto Rico and were drawn from the Business Register.
56
D Appendix: Robustness of trends over time
This section provides additional results to show that the trends shown in Figure 1 in the main
paper are robust to potential changes in the composition of the sample over time. Table 5
provides regressions on days since 2020-03-27, or regressions with weekly dummy variables plus
controls for the number of part-time and full-time employees in January, the day of the week
the survey was taken, and state dummies. The top panel shows these regressions for if the firm
expects to recover in the next two years. The first column regresses the outcome on days since
2020-03-27 while the second column uses week dummies. The third through fifth column repeats
the second column, but conditions on the three firm-size bins used in the paper.
Table 6 uses respondents from the initial baseline survey who also completed the follow-up
survey to study within-firm changes. Each panel shows the results of a regression of the within-
firm change in one of the outcomes on the days elapsed between the follow-up and baseline
survey. The first column shows results for all firms, while columns 2 through 4 show results
by firm size bins. All regressions control for cubic polynomials for full-time employment and
part-time employment in January, the day of the week the baseline was take, state dummies,
years of education, sex, age group dummies, firm type, firm sector, and dummies for the week
the baseline survey was taken.
Figure 9 shows the size distribution of sample respondents by day. The top panel shows the
proportion of respondents in each of the three size bins used in the paper. The bottom panel
shows the average within those bins over time.
57
Table 5: Trends in outcomes and expectations over time
Recover in next 2 years
All 0–4.5 FTE 5–9.5 FTE 10–50 FTE
Days since 2020-03-27 −0.003∗∗∗
(0.0004)
Week of 2020-04-01 −0.049∗∗ −0.045∗ −0.078 −0.047
(0.023) (0.027) (0.057) (0.073)
Week of 2020-04-08 −0.099∗∗∗ −0.084∗∗∗ −0.189∗∗∗ −0.090
(0.024) (0.028) (0.058) (0.075)
Week of 2020-04-15 −0.146∗∗∗ −0.130∗∗∗ −0.234∗∗∗ −0.141∗
(0.023) (0.027) (0.057) (0.073)
Week of 2020-04-22 −0.136∗∗∗ −0.122∗∗∗ −0.226∗∗∗ −0.111
(0.025) (0.029) (0.061) (0.077)
Week of 2020-04-29 −0.112∗∗∗ −0.110∗∗∗ −0.181∗∗∗ −0.067
(0.025) (0.029) (0.060) (0.076)
Week of 2020-05-06 −0.155∗∗∗ −0.140∗∗∗ −0.238∗∗∗ −0.168∗∗
(0.028) (0.033) (0.071) (0.085)
Week of 2020-05-13 −0.223∗∗∗ −0.180∗∗∗ −0.383∗∗∗ −0.230∗∗
(0.038) (0.046) (0.086) (0.109)
N 13,741 13,741 9,486 2,507 1,748
Ever Recover
All 0–4.5 FTE 5–9.5 FTE 10–50 FTE
Days since 2020-03-27 −0.002∗∗∗
(0.0003)
Week of 2020-04-01 −0.050∗∗∗ −0.040∗ −0.079∗∗ −0.081
(0.017) (0.021) (0.038) (0.050)
Week of 2020-04-08 −0.075∗∗∗ −0.070∗∗∗ −0.116∗∗∗ −0.071
(0.018) (0.022) (0.040) (0.051)
Week of 2020-04-15 −0.099∗∗∗ −0.087∗∗∗ −0.155∗∗∗ −0.118∗∗
(0.018) (0.022) (0.039) (0.050)
Week of 2020-04-22 −0.081∗∗∗ −0.079∗∗∗ −0.109∗∗∗ −0.078
(0.019) (0.023) (0.042) (0.053)
Week of 2020-04-29 −0.079∗∗∗ −0.071∗∗∗ −0.145∗∗∗ −0.057
(0.019) (0.023) (0.042) (0.053)
Week of 2020-05-06 −0.118∗∗∗ −0.103∗∗∗ −0.140∗∗∗ −0.186∗∗∗
(0.023) (0.027) (0.052) (0.066)
Week of 2020-05-13 −0.133∗∗∗ −0.129∗∗∗ −0.163∗∗ −0.142
(0.032) (0.040) (0.067) (0.090)
N 13,673 13,673 9,436 2,495 1,742
Any layoffs
All 0–4.5 FTE 5–9.5 FTE 10–50 FTE
Days since 2020-03-27 −0.0004
(0.0003)
Week of 2020-04-01 0.076∗∗∗ 0.081∗∗∗ 0.084 0.127∗∗
(0.022) (0.026) (0.053) (0.062)
Week of 2020-04-08 0.099∗∗∗ 0.108∗∗∗ 0.085 0.144∗∗
(0.023) (0.026) (0.053) (0.064)
Week of 2020-04-15 0.092∗∗∗ 0.098∗∗∗ 0.093∗ 0.111∗
(0.022) (0.026) (0.052) (0.063)
Week of 2020-04-22 0.061∗∗∗ 0.058∗∗ 0.053 0.131∗∗
(0.024) (0.028) (0.055) (0.065)
Week of 2020-04-29 0.046∗ 0.053∗ 0.051 0.089
(0.024) (0.028) (0.055) (0.066)
Week of 2020-05-06 0.076∗∗∗ 0.087∗∗∗ 0.063 0.081
(0.027) (0.031) (0.063) (0.072)
Week of 2020-05-13 0.037 −0.004 0.096 0.181∗∗
(0.035) (0.043) (0.075) (0.079)
N 14,103 14,103 9,743 2,574 1,786
∗∗∗
Note: Significant at the 1 percent level, ∗∗ significant at the 5 percent level, and ∗ significant at the 10
percent level. Column one shows the outcome regressed on days since 2020-03-28 while column two shows
the outcome regressed on week dummies. The third and fourth columns repeat these results, but additionally
control for number of part-time employees in January, number of full-time employees in January, the day of the
week the survey was taken, and the state indicators.
58
Table 6: Within-firm changes
Change in recover in next two years:
All 0-4.5 5-9.5 10-50
Elapsed days −0.004∗∗ −0.004∗∗ −0.002 −0.006
(0.002) (0.002) (0.004) (0.006)
N 2,605 1,804 478 323
Change in ever recover:
All 0-4.5 5-9.5 10-50
Elapsed days −0.003∗∗ −0.003∗ −0.003 −0.005
(0.001) (0.002) (0.003) (0.005)
N 2,594 1,795 476 323
Change in past layoffs
All 0-4.5 5-9.5 10-50
Elapsed days 0.006∗∗∗ 0.005∗∗ 0.018∗∗∗ 0.005
(0.002) (0.002) (0.006) (0.007)
N 2,672 1,847 491 334
Change in awareness of programs
All 0-4.5 5-9.5 10-50
Elapsed days −0.002 −0.002 −0.001 −0.003
(0.001) (0.002) (0.003) (0.004)
N 2,620 1,815 480 325
∗∗∗
Note: Significant at the 1 percent level, ∗∗ significant at the 5 percent level, and ∗ significant at the 10 percent
level. Table shows the change in outcomes for firms who completed follow-up surveys. The first four columns show
results for those who completed their follow-up before 2020-04-27, while the second four columns show results for
all follow-ups. For each set of four columns, the table shows results for all firms, and then by firm size bin. All
regressions control for cubic polynomials for full-time employment and part-time employment in January, the day
of the week the baseline was take, state dummies, years of education, sex, age group dummies, firm type, firm
sector, and dummies for the week the baseline survey was taken.
59
Figure 9: Firm size across days of the survey
Note: The top panel shows the proportion of daily respondents in three size bins based on their FTE
employment in January (0-4.5 employees, 5-9.5 employees, 10-50 employees). The bottom panel shows a
loess regression by size bin size with 95% confidence intervals shown in grey.
60
D.1 Trends in awareness of programs over time
Figure 10: Awareness of government programs over time (programs that help cover payroll)
Note: In the left panel, the black line is fit using locally weighted smoothing regression, with the grey region
showing the 95% confidence interval. The blue line shows a centered moving average over 250 responses.
The right panel shows loess regression lines over time by business size bin.
Figure 11: Awareness of government programs over time (subsidized loans)
Note: In the left panel, the black line is fit using locally weighted smoothing regression, with the grey region
showing the 95% confidence interval. The blue line shows a centered moving average over 250 responses.
The right panel shows loess regression lines over time by business size bin.
61
E Appendix: Additional results related to the Paycheck
Protection Program
This section provides additional results to supplement the analysis of who applies for a PPP
loan and the benefits associated with getting a loan from the main paper. Tables 7, 8 and
9 supplement Table 1 from the paper. The first repeats the analysis in the main paper with
additional controls for the number of cases and deaths in the state at the time of the study, as
well as zip-code level demographic characteristics, and measures of distance to SBA approved
lenders. The second restricts the sample to those who completed the baseline and follow-up
survey and controls for baseline beliefs. The fourth repeats the analysis, but with four size-bins
for firms, separating firms with no employees in January from the 0 to 4.5 group.
Tables 10 and 11 provides robustness for Table 2 from the paper. Table 10 extends Table
2 by adding a number of location-based controls, including the number of cases and deaths in
the state at the time of the study, as well as zip-code level demographic characteristics, and
measures of distance to SBA approved lenders. Table 11 restricts the analysis to the sub-sample
who completed the original baseline survey and then completed the follow-up survey and adds
additional controls for the baseline responses for the probability of bankruptcy or closure in the
next six months, if they expected to recover in the next two years, and if they ever expected to
recover.
62
Table 7: PPP timing by firm characteristics (additional controls)
) l
P d. va
PP on ly ro
to (c ap
p
ap
p
P PP ied PPP to to
l ys ys
G ot A pp Got Da Da
Jan. FTE Emp 0-4.5 −0.264∗∗∗ −0.220∗∗∗ −0.270∗∗∗ 1.623∗∗∗ 1.907∗
(0.020) (0.020) (0.025) (0.563) (1.050)
Jan. FTE Emp 5-9.5 −0.112∗∗∗ −0.005 −0.143∗∗∗ −0.038 0.721
(0.024) (0.022) (0.027) (0.589) (1.085)
Years of schooling 0.008∗∗∗ 0.014∗∗∗ 0.009∗∗ −0.014 0.239
(0.003) (0.003) (0.004) (0.112) (0.207)
Type: LLC −0.080∗∗∗ −0.081∗∗∗ −0.066∗∗∗ 0.917 2.724∗∗
(0.018) (0.020) (0.024) (0.604) (1.143)
Type: Other −0.012 −0.138∗∗ 0.077 3.445∗ −3.782
(0.055) (0.059) (0.077) (1.943) (2.883)
Type: Self-employed −0.187∗∗∗ −0.268∗∗∗ −0.224∗∗∗ 1.721 5.224∗
(0.022) (0.038) (0.049) (1.608) (2.860)
Type: Sole prop or part −0.156∗∗∗ −0.218∗∗∗ −0.174∗∗∗ 0.954 2.494∗
(0.016) (0.020) (0.025) (0.661) (1.281)
Cases per capita −0.0002 0.0001 −0.0005 0.001 0.039∗
(0.0002) (0.0003) (0.0004) (0.014) (0.022)
Deaths per capita 0.003 −0.002 0.008∗∗ 0.088 −0.194
(0.002) (0.003) (0.004) (0.133) (0.216)
Median HH income −0.0002 0.001 −0.001 −0.017 −0.001
(0.0004) (0.0004) (0.001) (0.015) (0.025)
Suburban −0.020 0.019 −0.051∗∗ −0.543 1.312
(0.016) (0.020) (0.025) (0.623) (1.189)
Urban −0.007 0.065∗∗∗ −0.077∗∗ 0.092 −0.295
(0.020) (0.025) (0.032) (0.851) (1.553)
Min distance to SBA lender 0.00002 −0.002 −0.0003 0.033 −0.151
(0.001) (0.001) (0.003) (0.062) (0.141)
Num of approved lenders within 10km −0.0001 0.0001 −0.0001 0.001 0.003
(0.0001) (0.0001) (0.0001) (0.002) (0.004)
Prop. Black 0.107∗ −0.001 0.239∗∗ −1.054 1.970
(0.062) (0.079) (0.100) (2.611) (4.615)
Prop. Asian 0.115 0.154 0.176 −0.714 10.437
(0.112) (0.145) (0.165) (4.724) (7.188)
Prop. Hispanic −0.081 −0.128∗ −0.096 0.433 0.549
(0.051) (0.067) (0.090) (2.316) (3.742)
Prop. HS graduates −0.232 −0.303 −0.135 6.057 −28.126∗
(0.177) (0.235) (0.306) (8.686) (15.356)
Prop. 4-year coll grads 0.177∗∗ 0.184∗ 0.132 −3.776 9.353
(0.078) (0.100) (0.135) (3.620) (6.260)
Prop. Unemployed −0.489∗ −0.352 −0.938∗ −11.122 −16.868
(0.252) (0.341) (0.492) (13.138) (24.381)
N 4,927 4,927 2,619 1,321 517
R2 0.169 0.144 0.221 0.112 0.442
Note: ∗∗∗ Significant at the 1 percent level, ∗∗ significant at the 5 percent level, and ∗ significant at the 10 percent
level. The first four specifications control for the date the survey was taken. The final regression controls for the
day the first PPP loan application was submitted and the week the survey was taken. All regressions also include
controls for industry, age groups, and gender, but coefficients were largely not statistically significant nor large
and are not displayed as they largely did not predict the outcomes. All regressions additionally control for state
of residence. The omitted category for firm type is C-Corps and S-Corps. The omitted category for firm-size bins
is firms with 10 to 50 FTE employees. Columns 4 and 5 have fewer observations as many respondents did not
provide the dates of their application and approval. Cases and deaths are per 100 thousand in the state by the
day of the survey. “Min dist to SBA lender” is the minimum distance from the ZIP code centroid to the closest
SBA lender listed on sba.gov in kilometers. Similarly, “Num of approved lenders within 10km” is the number of
approved SBA lenders listed on sba.gov within 10 kilometers of the zip code centroid. All other new outcomes are
constructed from the 5-year 2018 ACS and are ZIP code level (ZCTA) outcomes. Median household income is in
tens of thousands of dollars.
63
Table 8: PPP timing by firm characteristics (controlling for baseline expectations)
P .)
P nd
toP (co
PP P
P lied PP
G ot A pp Go
t
∗∗∗
Jan. FTE Emp 0-4.5 −0.246 −0.233∗∗∗ −0.225∗∗∗
(0.028) (0.028) (0.035)
Jan. FTE Emp 5-9.5 −0.165∗∗∗ −0.041 −0.180∗∗∗
(0.032) (0.031) (0.039)
Years of schooling 0.009∗∗∗ 0.016∗∗∗ 0.011∗∗
(0.003) (0.004) (0.005)
Type: LLC −0.069∗∗∗ −0.062∗∗ −0.064∗∗
(0.023) (0.025) (0.031)
Type: Other 0.051 −0.163∗ 0.246∗∗
(0.085) (0.086) (0.114)
Type: Self-employed −0.192∗∗∗ −0.313∗∗∗ −0.215∗∗∗
(0.029) (0.056) (0.075)
Type: Sole prop or part −0.170∗∗∗ −0.227∗∗∗ −0.194∗∗∗
(0.021) (0.027) (0.031)
Baseline prob. of bankruptcy or closure −0.055∗ 0.032 −0.145∗∗∗
(0.030) (0.041) (0.053)
Baseline recover next two years 0.032∗ −0.032 0.056∗∗
(0.017) (0.024) (0.028)
Baseline ever recover −0.001 0.068∗∗ −0.036
(0.022) (0.031) (0.038)
N 2,593 2,593 1,374
R2 0.194 0.176 0.263
Note: ∗∗∗ Significant at the 1 percent level, ∗∗ significant at the 5 percent level, and ∗ significant at the 10 percent
level. Sample is restricted to those taking both the baseline and follow-up surveys. All specifications control for
the date the survey was taken, firm industry, age groups, gender, and state of residence. The omitted category for
firm type is C-Corps and S-Corps. The omitted category for firm-size bins is firms with 10 to 50 FTE employees.
Columns 4 and 5 have fewer observations as many respondents did not provide the dates of their application and
approval. Alternative specifications of these results are included in Appendix E. These include a table replicating
these results controlling for additional state and ZIP code level characteristics, results controlling for baseline beliefs
of the firms, and results separating firms with zero employees into their own group.
64
Table 9: PPP outcomes by firm characteristics (4 firm size bins)
P .) al
P nd ly rov
toP (co app app
P PP li ed PPP to to
ot pp ot ys ys
G A G Da Da
∗∗∗ ∗∗∗
Jan. FTE Emp 0 −0.320 −0.387 −0.331∗∗∗ 4.824∗∗ −3.052
(0.024) (0.035) (0.057) (2.137) (2.984)
Jan. FTE Emp 0.5-4.5 −0.263∗∗∗ −0.220∗∗∗ −0.264∗∗∗ 1.735∗∗∗ 1.913∗
(0.020) (0.020) (0.025) (0.556) (1.025)
Jan. FTE Emp 5-9.5 −0.117∗∗∗ −0.012 −0.141∗∗∗ 0.164 0.608
(0.024) (0.022) (0.027) (0.572) (1.075)
Years of schooling 0.009∗∗∗ 0.017∗∗∗ 0.009∗∗ −0.065 0.228
(0.003) (0.003) (0.004) (0.108) (0.201)
Type: LLC −0.078∗∗∗ −0.079∗∗∗ −0.063∗∗∗ 0.945 2.682∗∗
(0.018) (0.020) (0.024) (0.600) (1.119)
Type: Other −0.009 −0.138∗∗ 0.091 3.338∗ −3.334
(0.055) (0.058) (0.078) (1.854) (2.686)
Type: Self-employed −0.190∗∗∗ −0.280∗∗∗ −0.210∗∗∗ 1.577 4.629
(0.022) (0.038) (0.050) (1.677) (2.843)
Type: Sole prop or part −0.157∗∗∗ −0.221∗∗∗ −0.167∗∗∗ 1.122∗ 2.878∗∗
(0.016) (0.020) (0.024) (0.645) (1.234)
N 4,996 4,996 2,662 1,338 525
R2 0.166 0.135 0.212 0.117 0.409
Note: ∗∗∗ Significant at the 1 percent level, ∗∗ significant at the 5 percent level, and ∗ significant at the 10 percent
level. The first four specifications control for the date the survey was taken. The final regression controls for the
day the first PPP loan application was submitted and the week the survey was taken. All regressions also include
controls for industry, age groups, and gender, but coefficients were largely not statistically significant nor large and
are not displayed as they largely did not predict the outcomes. All regressions additionally control for state of
residence. The omitted category for firm type is C-Corps and S-Corps. The omitted category for firm-size bins is
firms with 10 to 50 FTE employees.Columns 4 and 5 have fewer observations as many respondents did not provide
the dates of their application and approval.
65
Table 10: PPP loans and business outcomes (additional controls)
Recover in next 2 years
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
PPP 0.083∗∗∗ 0.120∗∗∗ 0.080∗∗ −0.012
(0.019) (0.027) (0.041) (0.045)
PPP (cond) 0.122∗∗∗ 0.143∗∗∗ 0.156∗∗∗ −0.016
(0.022) (0.032) (0.049) (0.059)
N 4,883 2,619 3,308 1,463 904 652 671 504
Prob of closure or bankruptcy
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗ ∗∗∗
PPP −0.082 −0.083 −0.087 −0.079∗∗∗
(0.009) (0.014) (0.020) (0.021)
PPP (cond) −0.107∗∗∗ −0.103∗∗∗ −0.115∗∗∗ −0.103∗∗∗
(0.011) (0.016) (0.024) (0.027)
N 4,883 2,619 3,308 1,463 904 652 671 504
Current employment
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗ ∗∗∗
PPP 0.548 0.226 0.783 0.883
(0.170) (0.070) (0.224) (0.726)
PPP (cond) 0.528∗∗∗ 0.244∗∗∗ 0.877∗∗∗ 0.884
(0.185) (0.077) (0.261) (0.904)
N 4,883 2,619 3,308 1,463 904 652 671 504
Past layoffs
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗
PPP −0.083 −0.113 −0.053 −0.073∗
(0.018) (0.027) (0.039) (0.038)
PPP (cond) −0.110∗∗∗ −0.132∗∗∗ −0.089∗∗ −0.059
(0.021) (0.030) (0.045) (0.048)
N 4,883 2,619 3,308 1,463 904 652 671 504
∗∗∗ ∗∗ ∗
Note: Significant at the 1 percent level, significant at the 5 percent level, and significant at the 10 percent
level. “PPP” is an indicator for if the business received a PPP loan while “PPP (cond)” is the same regression,
but restricted to those who applied to the PPP program. The first two columns show results for the full sample,
while the remaining columns show results conditional on firm size bins. All regressions control for a third order
polynomial in the number of full-time employees in January, a third order polynomial in the number of part-time
employees in January, the day of the week the survey was completed, state dummies, years of education dummies,
gender, age group dummies, firm type, firm sector, and the date the survey was completed. Compared to Table 2,
this table additionally controls for total cases and deaths per capita in the state as of the day of the survey, median
household income for the ZIP code, indicators for urban/rural/suburban for the ZIP code, minimum distance to
SBA approved lender from ZIP centroid, number of SBA approved lenders within 10 kilometers of the ZIP centroid,
proportions of the population in the zip code that is Hispanic, Asian, and Black; the proportion of residents with
with a high school degree or more, the proportion of residents with a 4-year degree or more, and the unemployment
rate in the zip code.
66
Table 11: PPP loans and business outcomes (controlling for baseline expectations)
Recover in next 2 years
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗
PPP 0.010 0.054 −0.001 −0.080
(0.023) (0.030) (0.053) (0.057)
PPP (cond) 0.031 0.086∗∗ 0.076 −0.167∗∗
(0.026) (0.036) (0.064) (0.072)
N 2,547 1,374 1,763 790 468 338 316 246
Prob of closure or bankruptcy
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗∗ ∗∗∗ ∗∗
PPP −0.033 −0.034 −0.058 −0.022
(0.010) (0.013) (0.023) (0.025)
PPP (cond) −0.049∗∗∗ −0.045∗∗∗ −0.077∗∗∗ −0.016
(0.011) (0.015) (0.029) (0.032)
N 2,547 1,374 1,763 790 468 338 316 246
Current employment
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
∗∗
PPP 0.460 0.082 0.137 1.434
(0.225) (0.085) (0.314) (1.172)
PPP (cond) 0.209 −0.006 0.034 −0.234
(0.258) (0.094) (0.385) (1.526)
N 2,547 1,374 1,763 790 468 338 316 246
Past layoffs
All 0–4.5 FTE 5–9.5 FTE 10-50 FTE
PPP −0.014 −0.047 0.062 −0.012
(0.027) (0.036) (0.065) (0.069)
PPP (cond) −0.025 −0.034 0.035 −0.002
(0.031) (0.041) (0.079) (0.089)
N 2,547 1,374 1,763 790 468 338 316 246
∗∗∗ ∗∗ ∗
Note: Significant at the 1 percent level, significant at the 5 percent level, and significant at the 10 percent
level. Sample is restricted to those who completed both the baseline and follow up survey. “PPP” is an indicator
for if the business received a PPP loan while “PPP (cond)” is the same regression, but restricted to those who
applied to the PPP program. Compared to Table 2, these results additionally control for the baseline probability
of bankruptcy or closure, the baseline belief on if the firm would recover in the next two years, and the baseline
belief on if the firm would ever recover. The first two columns show results for the full sample, while the remaining
columns show results conditional on firm size bins. All regressions control for a third order polynomial in the
number of full-time employees in January, a third order polynomial in the number of part-time employees in
January, the day of the week the survey was completed, state dummies, years of education dummies, gender, age
group dummies, firm type, firm sector, and the date the survey was completed.
67
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