The impact of COVID-19 on small business outcomes
and expectations
Alexander W. Bartika, Marianne Bertrandb, Zoe Cullenc, Edward L. Glaeserd, Michael Lucac,1 ,
and Christopher Stantonc
aDepartment of Economics, University of Illinois at Urbana-Champaign, Urbana, IL 61801; bBooth School of Business, The University of Chicago, Chicago, IL
60637; cHarvard Business School, Boston, MA 02163; and dDepartment of Economics, Harvard University, Cambridge, MA 02138
Edited by Jose A. Scheinkman, Columbia University, New York, NY, and approved June 23, 2020 (received for review April 13, 2020)
To explore the impact of coronavirus disease 2019 (COVID-19)
on small businesses, we conducted a survey of more than 5,800
small businesses between March 28 and April 4, 2020. Several
themes emerged. First, mass layoffs and closures had already
occurred—just a few weeks into the crisis. Second, the risk of clo-
sure was negatively associated with the expected length of the
crisis. Moreover, businesses had widely varying beliefs about the
likely duration of COVID-related disruptions. Third, many small
businesses are financially fragile: The median business with more
than $10,000 in monthly expenses had only about 2 wk of cash on
hand at the time of the survey. Fourth, the majority of businesses
planned to seek funding through the Coronavirus Aid, Relief, and
Economic Security (CARES) Act. However, many anticipated prob-
lems with accessing the program, such as bureaucratic hassles and
difficulties establishing eligibility. Using experimental variation,
we also assess take-up rates and business resilience effects for
loans relative to grants-based programs.
COVID-19 | small businesses | CARES Act
In addition to its impact on public health, coronavirus disease2019 (COVID-19) has caused a major economic shock. In
this paper, we explore the impact of COVID-19 on the small
business landscape in the United States, focusing on three ques-
tions. First, how did small businesses adjust to the economic
disruptions resulting from COVID-19? Second, how long did
businesses expect the crisis to last, and how do expectations affect
their decisions? Third, how might alternative policy proposals
impact business and employment resilience?
To explore, we surveyed more than 5,800 small businesses that
are members of Alignable, a network of 4.6 million small busi-
nesses. The survey was conducted between March 28 and April
4, 2020. The timing of the survey allows us to understand expecta-
tions of business owners at a critical point in time when both the
progression of COVID-19 and the government’s response were
quite uncertain.
The results suggest that the pandemic had already caused
massive dislocation among small businesses just several weeks
after its onset and prior to the availability of government aid
through the Coronavirus Aid, Relief, and Economic Security
(CARES) Act. Across the full sample, 43% of businesses had
temporarily closed, and nearly all of these closures were due
to COVID-19. Respondents that had temporarily closed largely
pointed to reductions in demand and employee health concerns
as the reasons for closure, with disruptions in the supply chain
being less of a factor. On average, the businesses reported hav-
ing reduced their active employment by 39% since January.
The decline was particularly sharp in the Mid-Atlantic region
(which includes New York City), where 54% of firms were closed
and employment was down by 47%. Impacts also varied across
industries, with retail, arts and entertainment, personal services,
food services, and hospitality businesses all reporting employ-
ment declines exceeding 50%; in contrast, finance, professional
services, and real estate-related businesses experienced less dis-
ruption, as these industries were better able to move to remote
production.
Our results also highlight the financial fragility of many busi-
nesses. The median firm with monthly expenses over $10,000 had
only enough cash on hand to last roughly 2 wk. Three-quarters
of respondents only had enough cash on hand to last 2 mo or
less.∗ Not surprisingly, firms with more cash on hand were more
optimistic that they would remain open by the end of the year.
Our survey also elicited businesses’ beliefs about the evolu-
tion of the crisis, allowing us to study the role of beliefs and
expectations in decisions. The median business owner expected
the dislocation to last well into midsummer, as 50% of respon-
dents believed that the crisis would last at least until the middle
of June. However, beliefs about the likely duration of the cri-
sis varied widely. This raises the possibility that some firms were
making mistakes in their forecasts of how long the crisis will
last.†
The crisis duration plays a central role in the total poten-
tial impact. For a crisis lasting 4 mo instead of 1 mo, only
47% of businesses expected to be open in December compared
to 72% under the shorter duration. There is also considerable
heterogeneity in how sensitive businesses are to the crisis. In-
person industries like personal services or retail reported worse
prospects for riding out the pandemic than professional services
or other sectors with minimal need for face-to-face contact.
Lastly, our analysis explores variants of stimulus packages
that were being discussed at the time of the survey. The results
show that over 70% of respondents anticipated taking advantage
of aid when asked about a program that resembles the Pay-
check Protection Program (PPP) that is part of the CARES Act.
Moreover, they expected this funding to influence other busi-
ness decisions—including layoff decisions and staying in business
Significance
Drawing on a survey of more than 5,800 small businesses,
this paper provides insight into the economic impact of coro-
navirus 2019 (COVID-19) on small businesses. The results shed
light on both the financial fragility of many small businesses,
and the significant impact COVID-19 had on these businesses
in the weeks after the COVID-19–related disruptions began.
The results also provide evidence on businesses’ expectations
about the longer-term impact of COVID-19, as well as their
perceptions of relief programs offered by the government.
Author contributions: A.W.B., M.B., Z.C., E.L.G., M.L., and C.S. designed research,
performed research, analyzed data, and wrote the paper.y
The authors declare no competing interest.y
This article is a PNAS Direct Submission.y
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDerivatives License 4.0 (CC BY-NC-ND).y
1 To whom correspondence may be addressed. Email: mluca@hbs.edu.y
This article contains supporting information online at https://www.pnas.org/lookup/suppl/
doi:10.1073/pnas.2006991117/-/DCSupplemental.y
First published July 10, 2020.
*See refs. 1 and 2 for discussions of cash holdings of small businesses.
†See refs. 3–5 for related literature on the behavioral economics of firm decisions.
17656–17666 | PNAS | July 28, 2020 | vol. 117 | no. 30 www.pnas.org/cgi/doi/10.1073/pnas.2006991117
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Fig. 1. Firm size in the survey and Census. This figure plots the share of
firms in each employment category for the 2017 Census of US Businesses and
the survey respondents. The sample size for the survey is 4,873 responses,
omitting 959 responses with missing employment data.
altogether. At the same time, many businesses were reluctant to
apply for funding through the CARES Act because of concerns
about administrative complexity and eligibility. A large number
of respondents also anticipated problems with accessing the aid,
citing potential issues such as bureaucratic hassles and difficulties
establishing eligibility.
Our survey was constructed to allow for a counterfactual eval-
uation of a straight loan policy, which is a stylized representation
of traditional Small Business Administration disaster relief pro-
grams. While the more generous PPP program does improve take-
up and business outcomes, traditional loans with speedy delivery
and sufficient liquidity are also found to meaningfully shift busi-
ness owners’ expectations about survival. When compared to a
straight loan without forgiveness provisions, the CARES Act had
modestly greater take-up, but at much higher cost to the gov-
ernment. Because the majority of business owners would have
taken up aid in the form of less generous loans, our results suggest
that liquidity provision was paramount for these owners.
Overall, our paper contributes to our understanding of the
economic impact of COVID-19 on the small business ecosys-
tem. The fate of the 48% of American workers who work in
small businesses is closely tied to the resilience of the small busi-
ness ecosystem to the massive economic disruption caused by the
pandemic. Our survey was conducted during a period of substan-
tial policy uncertainty and before any federal response had been
enacted. Our results provide a unique snapshot into business
decisions and expectations at that time, while offering insight
for policy designed to aid the recovery. Our results highlight the
role the length of the crisis will play in determining its ultimate
impact, which policy makers should consider as they contemplate
the scale of the required interventions. We estimate that closures
alone might lead to 32.7 million job losses if the crisis lasts for 4
mo and 35.1 million job losses if the crisis lasts for 6 mo. While
some of these workers will surely find new jobs, these projec-
tions suggest that the scale of job dislocation could be larger than
anything America has experienced since the Great Depression
and larger than the impact of the 1918 influenza epidemic (6–8).
Another important take-away of our work is that, during liquidity
crunches with significant cash flow disruptions, the form of cash
injection (e.g., grant vs. loan) may be less important than mak-
ing sure that funding is rapidly available with little administrative
complexity.‡
‡This echoes a growing literature that suggests that reducing, simplifying, or providing
assistance in the process of signing up for programs can increase take-up. For examples,
see refs. 9 and 10.
The rest of the paper proceeds as follows. Survey Design
and Details discusses the survey design. Firm Characteristics
and Representativeness discusses the characteristics of the firms
that responded to the survey and their representativeness. In
Responses to the COVID-19 Pandemic and Lockdown, we explore
the current and expected impacts of COVID-19 on these busi-
nesses. In Anticipated Response to CARES Act Programs, we
present results from a module of the survey that experimen-
tally varies policy proposals, allowing us to explore responses to
policies such as the recently passed CARES Act as well as alter-
native policies. Industry Differences in Response to Crisis Duration
considers survival rate differences across industries, and how
survival depends on the duration of the crisis. We conclude in
Conclusion.
Survey Design and Details
Our survey was sent out in partnership with Alignable, a
network-based platform focused on the small business ecosys-
tem. Alignable enables businesses to share knowledge and inter-
act with one another, and currently has a network of 4.6 million
small businesses across North America. Much of the network
growth has been organic, with little outside marketing.
Alignable also regularly sends out polls (which they call “pulse
surveys”) to users. At the end of a regular pulse poll, par-
ticipants who took that poll received an email inviting them
to participate in a more comprehensive survey being con-
ducted by researchers at Harvard Business School. Partici-
pants were shown a disclosure statement and consent proto-
col. No payments were offered; participation was completely
voluntary. The survey was approved by the Harvard University
Institutional Review Board.
We received 7,511 responses between March 27 and April
4; 5,843 of these can be traced back to US-based businesses,
which is the relevant sample for understanding policy. While
the 7,511 responses represent a small fraction (0.017%) of
Alignable’s total membership, they represent a much larger share
of Alignable’s membership that has engaged with their weekly
pulse surveys on COVID-19. Alignable estimates that 50,000 to
70,000 members are taking these pulse surveys weekly, which
suggests a 10 to 15% conversion rate of these more active
respondents.
Fig. 2. Average per capita payroll ($1,000s) in the survey and Census. This
figure plots per-employee payroll in thousands of dollars by firm size for
the 2017 Census of US Businesses aggregates and the survey respondents.
The Census data only report annual payroll for W2 workers and the num-
ber of firms in an employment size category. To calculate payroll for the
survey firms, we take the midpoint of categorical answers for monthly
expenses, multiply by the fraction of expenses going toward payroll, and
divide by total employees (we cannot distinguish between W2 employees
and contractors).
Bartik et al. PNAS | July 28, 2020 | vol. 117 | no. 30 | 17657
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Table 1. Summary measures across regions
Expected to close Weeks COVID Current/January
Closed at time by December will last employment
Mean SD Mean SD Mean SD Mean SD
E. North Central 0.45 0.50 0.35 0.48 14.7 10.2 0.68 0.38
E. South Central 0.41 0.49 0.36 0.48 16.3 11.5 0.57 0.49
Mid Atlantic 0.54 0.50 0.37 0.48 14.5 10.0 0.53 0.45
Mountain 0.39 0.49 0.35 0.48 16.0 11.3 0.68 0.38
New England 0.47 0.50 0.33 0.47 16.6 10.2 0.55 0.49
Pacific 0.46 0.50 0.37 0.48 15.4 10.7 0.55 0.48
South Atlantic 0.41 0.49 0.38 0.48 15.5 10.3 0.63 0.45
W. North Central 0.43 0.50 0.35 0.48 15.7 10.8 0.66 0.41
W. South Central 0.40 0.49 0.39 0.49 15.2 11.1 0.68 0.43
Total 0.45 0.50 0.37 0.48 15.4 10.6 0.61 0.45
n 4,976 . 4,059 . 4,162 . 4,365 .
This table reports breakdowns by regions. Totals include 12 observations with unknown region. Note that
the Closed at time column includes both temporary and permanent closures. The measure Expected to close
by December comes from a question asking about the likelihood of being open in December, where answers
were given on a five-point scale. Closure is coded as a binary indicator for those marking “Extremely Unlikely,”
“Somewhat Unlikely,” or “Somewhat Likely” to be open in December. The ratios of current employment versus
January employment are weighted by January employment.
Our sample, therefore, is selected in three ways: 1) They are
firms that have chosen to join Alignable, 2) they are Alignable
firms that have chosen to stay actively engaged taking surveys,
and 3) they are the set of firms that are active within Alignable
that chose to answer our survey. Consequently, there are many
reasons to be cautious when extrapolating to the entire universe
of America’s small businesses. We will discuss their representa-
tiveness based on observable attributes in the next section of this
report.
The survey included a total of 43 questions, with basic informa-
tion about firm characteristics (including firm size and industry),
questions about the current response to the COVID-19 crisis,
and beliefs about the future course of the crisis. Some questions
were only displayed based on skip logic, so most participants
responded to fewer questions. The survey also includes an exper-
imental module that randomized scenarios between respondents
to understand how different federal policies might impact these
firms’ behavior and survival as the crisis unfolds. Specifically,
we experimentally varied some of the descriptions of poten-
tial policies across the sample to shed light on the potential
impact of policy initiatives that, at the time, were very uncer-
tain. We will discuss that module more thoroughly in Anticipated
Fig. 3. Coverage by state. This figure plots shares of survey responses across
different states.
Response to CARES Act Programs. A further experimental mod-
ule included between-respondent randomization which explored
decisions under different hypothetical durations of the crisis.
Firm Characteristics and Representativeness
The survey contains three baseline questions which enable us
to assess the representativeness of the sample along observable
dimensions: number of employees, typical expenses (as of Jan-
uary 31, 2020), and share of expenses that go toward payroll. We
are also able to get rough information about geolocation to assess
representativeness by state.
We compare our data with data on businesses from the
2017 Census of US Businesses, using the publicly available
statistics published by the US Census Bureau. The underlying
data are drawn from the County Business Patterns sampling
frame and cover establishments with paid employees, including
sole proprietorships if the owner receives a W2. The Census
Fig. 4. Firm locations in the Census, downstream survey, and upstream
presurvey Alignable poll. This figure plots the share of firms in each state for
the 2017 Census of US Businesses, the survey respondents, and the respon-
dents who took the upstream Alignable poll. Users who took the survey did
so after taking the Alignable poll. They were then redirected to the Har-
vard Business School Qualtrics web link. Note that the upstream poll did
not ask questions about firm size or payroll, so prior figures cannot check
compositional differences based on firm size or pay.
17658 | www.pnas.org/cgi/doi/10.1073/pnas.2006991117 Bartik et al.
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Table 2. Summary measures by firm size
Expected to close Weeks COVID Current/January
Number of Closed at time by December will last employment
Employees Mean SD Mean SD Mean SD Mean SD
Under 5 0.46 0.50 0.36 0.48 15.8 10.9 0.66 0.49
5 to 9 0.47 0.50 0.39 0.49 14.7 10.2 0.52 0.44
10 to 19 0.41 0.49 0.42 0.49 14.7 10.1 0.55 0.47
20 to 99 0.36 0.48 0.30 0.46 14.1 9.5 0.58 0.42
100 to 499 0.26 0.44 0.22 0.42 16.2 10.8 0.72 0.44
Unknown 0.41 0.49 0.49 0.50 16.7 10.5 . .
Total 0.45 0.50 0.37 0.48 15.4 10.6 0.61 0.45
n 4,976 . 4,059 . 4,162 . 4,365 .
This table reports breakdowns by firm size. There are 103 firms in this sample with unknown employment as
of January. All measures are coded according to Table 1 legend.
data capture large and small businesses alike, but, for our
comparisons, we will look only at businesses with fewer than 500
employees.
The Alignable network allows users to share customer leads,
which could potentially skew our sample toward retail and ser-
vice businesses that interact directly with consumers. Since retail
businesses are particularly vulnerable to COVID-19 disruptions,
our sample could overstate the aggregate dislocation created by
the crisis. Naturally, industries dominated by large firms, such
as manufacturing, are underrepresented. However, as we discuss
later, our data on the industry mix of responses suggest that the
sample represents a wide swath of America’s smaller businesses.
Fig. 1 shows the size distribution of our sample and the size
distribution of businesses with fewer than 500 employees in the
Economic Census. The match of employment sizes is reassur-
ing. About 64% of the businesses in our sample have fewer
than five employees, while about 60% of the firms in the Eco-
nomic Census are that small. About 18% of businesses in both
Table 3. Summary measures by industry
Expected to close Weeks COVID Current/January
Closed at time by December will last employment
Mean SD Mean SD Mean SD Mean SD
Raw data
Retailers, except grocery 0.53 0.50 0.45 0.50 14.1 9.5 0.49 0.42
Arts and entertainment 0.70 0.46 0.42 0.49 17.5 11.3 0.40 0.46
Banking/finance 0.19 0.39 0.25 0.43 16.1 10.9 0.81 0.33
Construction 0.32 0.47 0.38 0.49 14.3 10.3 0.66 0.40
Health care 0.45 0.50 0.29 0.45 15.1 10.4 0.69 0.37
Other 0.39 0.49 0.35 0.48 16.6 11.2 0.70 0.41
Personal services 0.86 0.34 0.39 0.49 11.8 8.3 0.35 0.40
Professional services 0.21 0.41 0.29 0.45 15.7 10.6 0.80 0.41
Real estate 0.37 0.48 0.30 0.46 15.8 11.4 0.70 0.41
Restaurant/bar/catering 0.56 0.50 0.52 0.50 13.1 8.7 0.24 0.37
Tourism/lodging 0.61 0.49 0.45 0.50 16.2 10.0 0.30 0.35
Total 0.45 0.50 0.37 0.48 15.5 10.6 0.58 0.44
n 4413 . 3953 . 4000 . 3935 .
Reweighted to census by size and region
Retailers, except grocery 0.53 0.50 0.44 0.50 14.3 9.8 0.51 0.42
Arts and entertainment 0.70 0.46 0.41 0.49 17.1 11.4 0.43 0.47
Banking/finance 0.20 0.40 0.25 0.43 16.3 11.1 0.84 0.30
Construction 0.33 0.47 0.38 0.49 14.4 10.3 0.71 0.38
Health care 0.43 0.50 0.28 0.45 14.5 10.1 0.72 0.35
Other 0.39 0.49 0.34 0.47 16.4 11.2 0.74 0.38
Personal services 0.86 0.34 0.39 0.49 11.9 8.4 0.37 0.40
Professional services 0.21 0.41 0.30 0.46 15.6 10.7 0.80 0.41
Real estate 0.37 0.48 0.31 0.47 15.6 11.0 0.74 0.39
Restaurant/bar/catering 0.58 0.49 0.49 0.50 13.4 9.0 0.23 0.36
Tourism/lodging 0.60 0.49 0.43 0.50 16.1 9.9 0.31 0.35
Total 0.45 0.50 0.36 0.48 15.4 10.6 0.61 0.43
n 4,326 . 3,877 . 3,921 . 3,935 .
This table reports breakdowns by industry. The top section contains raw data, and the bottom section
contains data reweighted to match the Census share of firms by size and region bucket. Missing industry infor-
mation explains differences in observations between raw data and prior analysis. Differences in observations
between raw data and reweighted data arise from firms with unknown January employment or region. All
measures are coded according to Table 1 legend.
Bartik et al. PNAS | July 28, 2020 | vol. 117 | no. 30 | 17659
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Table 4. Breakdown of issues affecting businesses
n (total) n (answering) Supply chain Employee health Demand/orders
Raw data
Currently open 2,759 2,196 30.2 49.8 66.2
Temporarily closed 2,116 1,774 35.5 59.7 83.4
Permanently closed 100 85 37.9 60.8 86.1
Total 4,975 4,055 34.6 56.8 78.6
Reweighted to Census by size and region
Currently open 2,698 2,149 30.4 50.4 65.9
Temporarily closed 2,080 1,744 35.7 60.2 83.7
Permanently closed 94 80 39.2 63.6 85.8
Total 4,872 3,973 35.1 58.1 78.5
This table reports respondents’ reactions to the importance of issues affecting their business, broken down
by the status of the business at the time of taking the survey.
samples have between five and nine employees. The survey
becomes less precisely matched to the Census among the larger
employment groupings, and we believe that our survey will
capture the experience of larger employers with less accuracy.
While our survey does not allow for a direct comparison of
payroll expenses with Census data, we constructed a rough com-
parison by approximating payroll expenses for the Alignable
firms from categorical questions about monthly expenses and
the share of these expenses going toward payroll. The Census
provides annual payroll expenses for W2 employees. To get a
sense of the match, we compared our estimated monthly pay-
roll expenses in our sample with one-twelfth of annual expenses
in the US Census. To facilitate comparison, we divide by an
estimate of total employment.§ Fig. 2 shows the size distribu-
tion of monthly estimated payroll expenses in our sample and a
comparable breakdown for the Census using a per capita adjust-
ment. The match is imperfect, especially for larger firms. The
discrepancy might reflect the underrepresentation of manufac-
turing or professional services firms in our sample, which are
among the highest paying of all two-digit North American Indus-
try Classification System sectors in the Census data. SI Appendix,
Table S1 provides further detail on the industry match to the
Census.
Fig. 3 shows the geographic scope of our sample. The
Alignable sample draws particularly from California, the New
York region, Florida, and Texas. The sample is sparse in Amer-
ica’s western heartland, which matches the location distribution
of smaller businesses in the Economic Census.
Fig. 4 shows the share of our sample coming from the 10 most
populous states. The figure also includes the share of small busi-
nesses in the Economic Census that are within each state. For
example, California has 14.4% of our Alignable survey sample,
12.5% of small businesses in the Census data, and 11.52% of
total US population. Our sample does overrepresent the coasts
and underrepresents Illinois. Alignable shared the geographic
distribution of their weekly pulse survey takers, and the final
set of columns within each grouping allows us to assess selec-
tion differences between respondents to the shorter pulse poll
and our downstream survey. There are some minor sampling
differences across states, but the Alignable pulse poll sample
§This comparison is very likely to include a different definition of “headcount” as we do
not disambiguate between W2 and 1099 employment in the survey whereas the Census
data only include W2 employees, who are more likely to be full-year, full-time employ-
ees. Although we cannot disambiguate part-time W2 employees who would show up
in the Census versus contractors who would not, 32% of the January employment cap-
tured in the survey falls into the category. According to Current Population Survey data
for 2019, about 17% of the broader labor force was part-time; recent figures on the
number of contract workers suggests they made up about 12% of the labor force in
2016, but they would not have been captured in the Census (11).
and those taking our broader survey have quite good geographic
coverage.
To shed further light on our sample, we conducted a follow-up
phone screen of 400 businesses—a randomly selected set of 200
businesses that responded to our survey and 200 businesses from
the broader active Alignable membership (i.e., that filled out
their previous pulse poll), but who did not respond to our survey.
During the phone screen, we asked each business whether they
were still open for business. For businesses that did not answer
the phone on a first attempt, we made a second attempt to call.
Out of the businesses who responded to our survey, roughly 42%
reported being open when we called them. Out of the businesses
that are active on Alignable but did not respond to our survey,
roughly 56% reported being open.
Overall, while the sample captured by the survey may be an
imperfect snapshot for certain pockets of America’s small busi-
nesses, it also allows for important insight into the overall small
business ecosystem. The sample is large and includes firms from
most major industry groups, states, and firm size categories.
Responses to the COVID-19 Pandemic and Lockdown
We now turn to our main results, which we group into three
categories. First, we describe the impact of COVID-19 on busi-
ness operations and employment toward the beginning of the
crisis. Second, we report our results on the financial fragility
of those businesses, as captured by their cash on hand and
ongoing expenses. Third, we turn to their expectations about the
Fig. 5. Months of cash. This figure plots firms’ months of cash available as
a multiple of January 2020 expenses. We compute this measure by taking
the midpoint of categorical responses for the amount of cash on hand and
dividing by the midpoint of the categorical response for typical monthly
expenses prior to the crisis. The sample size is 4,176.
17660 | www.pnas.org/cgi/doi/10.1073/pnas.2006991117 Bartik et al.
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Fig. 6. Mean and median months of cash split by monthly expenses
($1,000s). This figure plots means and medians of the months of cash
available measure across the distribution of typical monthly expenses.
duration of the crisis and their own economic survival, as mea-
sured at a particularly sensitive point for understanding the
impact of future policies.
Temporary Closings and Employment. The initial survey ques-
tion asked owners, “is this business currently operational?” We
allowed owners to respond that the business was operational,
temporarily closed, or permanently closed. We also allowed them
to report whether the business was closed because of COVID-19
or another reason.¶
Across the sample, 41.3% of businesses reported that they
were temporarily closed because of COVID-19. A far smaller
number—1.8%—reported that they were permanently closed
because of the pandemic. By contrast, only 1.3% reported that
they were temporarily closed for other reasons; 55.5% reported
that they were still operational.
We also asked the business owners to fill in a matrix that con-
tained the number of full-time and part-time employees that
were employed by the firm “now,” as of the survey date, and on
January 31, 2020. Over the entire sample, the number of full-time
employees had fallen by 32% between January 31 and late March
2020. The number of part-time employees was 57% lower than
at the end of January. Overall employment declined significantly,
totaling a 39% reduction from January headcount. These results
include businesses that had temporarily closed. If we look only
at businesses that were still operating, we find that the number
of total full-time employees had fallen by 17.3%. The number
of part-time employees declined by 34%. These estimates can
also be compared to other emerging data points. The Atlanta
Fed conducted a similar survey (14), drawn from Dun & Brad-
street listings, and found smaller employment effects (roughly
10% decrease in employment). Whereas their survey includes
larger firms as well, our focus is on smaller businesses. Fur-
ther, their survey undersamples newer firms, which may have
larger employment changes. We can also compare our results
to publicly released aggregated payroll data from Automatic
Data Processing, Inc. (ADP), a provider of human resources
management software (which may have different issues of rep-
resentativeness). In those data, paid employment at firms with
less than 500 employees declined by about 18% between January
¶We did not attempt to assess the quality of firm management, as in ref. 12. We hope
that future surveys will test when quality of management helps protect firms against
closure during this crisis. This crisis also presents an opportunity for understanding
managerial decision-making under stress, as discussed by ref. 13.
Fig. 7. Cumulative distribution function of expected COVID end date. This
figure plots the distribution function across respondents for the expected
end date of COVID-related disruptions. The y axis represents the share of
respondents who believe that COVID disruptions will end on or before the
date given on the x axis.
and April.‖ These data, however, treat anyone receiving pay in
April as employed even if they were laid off during or before
the interval. Looking at higher-frequency data on paychecks in
the ADP microdata, concurrent but independent work by Cajner
et al. (15) finds that employment declined, on average, 27% for
firms with less than 500 employees and about 28% for firms with
less than 50 employees between mid-February and mid-April.∗∗
These numbers are somewhat smaller than the 39% decline in
employment for small businesses that we find but higher than
the estimates of the Atlanta Fed survey.
We then expand to look at geographic variation of the effects.
Table 1 shows our results across the 11 Census divisions and dis-
plays the share of businesses that had temporarily closed because
of COVID-19 and the reduction in total employment between
January 31 and the survey date. The results are not meaningfully
different if we separate out full-time or part-time employees.
While there is regional heterogeneity, the disruptions are severe
almost everywhere.
The Mid-Atlantic division had the sharpest decreases in
employment and the largest share of firms that had temporarily
suspended operations. Fifty-four percent of firms in that region
were closed in late March/early April, and employment had
fallen by an average of 47%. The Mountain region was the least
affected, but, even there, 39% of firms had temporarily closed,
and employment had declined by 32%.
Tables 2 and 3 display the same breakdown by firm size and
industry. Smaller firms with fewer than 20 employees in January
were more likely to be closed. Firms with between 66 and 19
‖Data were accessed from https://adpemploymentreport.com/2020/April/SBR/SBR-April-
2020.aspx on May 21, 2020. We aggregate the estimates across firm size bins to estimate
job losses for firms with 1 to 499 employees using firm weights. The corresponding esti-
mates using employment weights are also 18%. The weights come from the Bureau of
Labor Statistics’ Business Employment Dynamics figures of the distribution of private
sector employment (table F) and firm size (table G) for the first-quarter of 2019 (not sea-
sonally adjusted). Data were accessed from https://www.bls.gov/bdm/bdmfirmsize.htm
on May 22, 2020.
**These figures were computed using the estimates in figure 10 from the May 6, 2020
version of ref. 15. We aggregate the estimates across firm size bins to estimate job
losses using firm weights. The corresponding estimates using employment weights
are 23% job losses for firms with 1 to 499 employees and 27% job losses for firms
with 1 to 49 employees. The weights come from the Bureau of Labor Statistics’
Business Employment Dynamics figures of the distribution of private sector employ-
ment (table F) and firm size (table G) for the first quarter of 2019 (not seasonally
adjusted). Data were accessed from https://www.bls.gov/bdm/bdmfirmsize.htm on May
22, 2020.
Bartik et al. PNAS | July 28, 2020 | vol. 117 | no. 30 | 17661
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Fig. 8. Likelihood of remaining open or reopening by December. This figure displays the frequency of answers to a question about the likelihood of being
open in December 2020. Responses are plotted based on whether the firm has more than the median number of months of cash on hand given their
pre-COVID expenses.
employees in January had the largest employment reductions.
Across industries, in-person retail and service businesses had
declined precipitously. Although hard hit, the impact was not
as extreme for professional services firms—banking and finance,
real estate, or construction. Table 3 also allows a comparison of
how our results might change if we reweight to the region and
firm size cells in the Census data and then cut by industry (a
dimension that is not targeted in the reweighting). The results
change little across industries in the reweighted data compared
to the raw data.
Table 4 shows the problems that firms reported facing, split
by their operational status at the time of the survey. We asked
owners to rate, on a 1 to 100 scale, the problems they were
experiencing with employee illness, supply chains, and customer
demand. The scale had numerical values and also a text label
that went from “Not a concern” at one end to “Extremely dis-
ruptive” at the other end. We differentiate between firms that
are open, temporarily closed, and permanently closed, and we
show the share of firms in each category that indicate significant
difficulties in each of these areas.
On average, firms rated the disruptions resulting from supply
chain challenges to be 35 on the 100-point scale (which is in the
“slightly disruptive” part of the scale). Concerns about employee
health were more prominent, with firms rating it as 57 out of 100
(which maps to “somewhat disruptive”). Reductions in demand
were even more disruptive, with firms rating the importance of
this to be 79 out of 100 (extremely disruptive). While closed
firms noted worse disruptions due to demand, the basic ranking
of the different disruptions was consistent across different types
of firms. These findings suggest, thus far, that supply chain prob-
lems have been less pronounced, relative to disruptions resulting
from demand shocks and concerns about employee health.
Altogether, these results suggest that a vast number of enter-
prises had temporarily shut down and laid off workers over
the first several weeks of the crisis. The impact on business
disruptions in the coming months will depend both on the
length of the crisis and on the financially fragility of firms. The
central role of the demand shock highlights the challenges in
adjusting to the financial shock caused by COVID-19–related
disruptions. We now directly explore financial fragility, and the
extent to which firms’ resources might allow them to weather the
crisis.
Financial Fragility. To measure financial fragility, we asked the
respondents “roughly how much cash (e.g. in savings, checking)
do you have access to without seeking further loans or money
from family or friends to pay for your business?” We then divided
this amount by their January 31 monthly expenses to understand
how long they could maintain operations without seeking extra
credit or outside assistance.††
Fig. 5 shows a histogram of cash available as a multiple of Jan-
uary 31, 2020 monthly expenses. Approximately one-fourth of
firms had cash on hand totaling less than 1 mo of expenses. About
one-half of firms had enough cash on hand to cover between 1 mo
and 2 mo of expenses.
Fig. 6 sorts firms by January 31, 2020 monthly expenses and
then tabulates the mean and median cash on hand relative to pre-
crisis expenses. The median firm with under $10,000 in monthly
expenses had 1 mo of cash on hand. For all firms with greater
than $10,000 in monthly expenses, the median firm typically had
less than 15 d of cash on hand, based on their precrisis expense
levels. These firms did not have cash on hand to meet their
regular expenses.
These limited levels of cash on hand help to shed light on why
layoffs and shutdowns were so prevalent. Absent these actions, it
is hard to understand how these firms could have met payroll.
Predicting the Path of the Crisis. Finally, we ask the firms to predict
how long the COVID-19 crisis will last and whether they believe
they will be open again at the end of 2020. To predict the end
of the crisis, we asked the survey respondents “the most likely
date” when the crisis would be over. We also asked them their
confidence about this belief on a 1 to 10 scale.
Fig. 7 shows the distribution of expected end dates. The fig-
ure shows that roughly 20% of respondents believed that the
crisis would be over by the end of May. Thirty percent of respon-
dents believed that the crisis would end between the end of May
and the start of July. Just over one half of the firms answered
that they thought that the crisis would still be going at the start
of July.
††We did not collect information about access to lines of credit or outside borrowing,
but, given the severity of the contraction in demand, those credit facilities may be
unlikely to remain accessible without a government guarantee.
17662 | www.pnas.org/cgi/doi/10.1073/pnas.2006991117 Bartik et al.
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However, the firms were not particularly confident about their
answers. Fifty percent of respondents reported their confidence
level as 5 or less on the 1 to 10 scale. Sixteen percent gave their
confidence a 2 or less. Their uncertainty highlights the broader
uncertainty that was present throughout the world at the time.
Fig. 8 shows the histogram of responses about whether firms
will be open on December 31, 2020. Overall, more than 90%
thought it is at least somewhat likely that they would be open.
More than 63% reported that it is very or extremely likely
that they would be open—which we later use as a measure of
the probability of being open. A growing literature has found
entrepreneurs to be overoptimistic about their prospects (see,
for example, ref. 13). This suggests that true survival rates may
be even lower than predicted by businesses.
The firms with more cash on hand were more confident about
their future, as evidenced by the split based on whether the
firm had more or less cash on hand (relative to usual monthly
expenses) than the median in our sample. Fifty percent of those
firms with more than the median cash on hand thought it was
extremely likely that they would be open at the end of the year.
Thirty-one percent of firms with less cash on hand, relative to
the median, thought that they would be open at the end of the
year. One interpretation of these findings is that liquidity gen-
erated confidence in the ability to survive this crisis. Among
firms with at least 20 employees, 71% expressed that they were
either very likely or extremely likely to survive, which may indi-
cate greater access to outside resources despite having a higher
expense base.
Fig. 9 shows that the share of firms that think that they are
“very likely” or “extremely likely” to be open varies based on
their belief about the duration of the crisis. The firms that
thought that the crisis will be short also believed that they are
more likely to survive. Those who believed in a longer crisis were
more pessimistic.
Anticipated Responses to CARES Act Programs
In this section, we discuss the survey’s questions about take-
up of the CARES Act PPP loans and their expected impact
on employment. One important aspect of the CARES pro-
gram is that “loans will be fully forgiven when used for payroll
costs, interest on mortgages, rent, and utilities,” as long as 75%
of the forgiven amount is spent on payroll and the employer
either maintains or quickly rehires workers and maintains salary
Fig. 9. Likelihood of remaining open or reopening by December 2020 as a
function of beliefs about COVID end date. This figure plots the likelihood
of being open in December, 2020 as a function of respondents’ expected
COVID end date. Averages are plotted, and the shaded region is the CI. The
opening likelihood is computed as the share of respondents who answered
“Extremely likely” or “Very likely.”
Fig. 10. Differences in policy take-up across loans versus CARES Act PPP split
by hypothetical limits on borrowing amount. This figure displays policy take-
up rates for loans versus the stylized PPP policy using a between-subjects
design. The borrowing base was also randomized between subjects as a
multiple of typical monthly expenses prior to the crisis. The text displayed
for the PPP program was, “Imagine a policy where the government allows
you to borrow up to [borrowing base] times your typical monthly expenses
without posting any collateral. You could use this money to cover any of
your business expenses. The loan will be forgiven by the amount spent on
payroll, lease, rent, mortgage, and utility payments in the 8 weeks after
origination (you can consider this amount to be a grant). The remainder
of the loan (that is not spent on these items) will have deferred payments
for 1 year. After that, the loan would have an annual interest rate of 4%
(deferred for 1 year) and you would have up to 10 years to repay the loan.
For example, if you borrow $50,000 and you have no qualifying expenses to
offset the loan, the required monthly payment starting 1 year from today
would be $506 per month for 10 years. If you borrow $50,000 and spend
$40,000 to pay your employees during the first 8 weeks, you will have 10
years to pay the remaining $10,000 with monthly payments of $102.” Sub-
jects in the loan condition saw the text, “Imagine the government offers
a loan allowing you to borrow up to [borrowing base] times your typical
monthly expenses without posting any collateral. You could use this money
to cover any of your business expenses. The loan would have an annual
interest rate equivalent of 4% and principal and interest payments would
be deferred for 1 year. You would have up to 10 years to repay the loan. For
example, if you borrow $50,000, the required monthly payment starting 1
year from today would be $506 per month for 10 years.” Pooled means for
the loan and CARES Act responses are 0.59 and 0.72, respectively. The sam-
ple size is 2,610, and the pooled t-statistic on the difference between policies
is 6.97.
levels (https://home.treasury.gov/system/files/136/PPP%20–%20
Overview.pdf). Consequently, a significant portion of the “loans”
can be seen as a grant rather than traditional debt.
The high level of loan forgiveness means that this represents a
large potential transfer to small businesses. We assess the impor-
tance of the grant component of the CARES loans relative to
a pure (and far less expensive) loan program. One-third of the
survey respondents were randomly asked about their interest in
a CARES-like program, which was describe as a loan program
which “will be forgiven by the amount spent on payroll, lease,
rent, mortgage, and utility payments in the 8 weeks after origina-
tion.” One-third of the respondents were randomly asked about
their interest in a loan program that was otherwise identical, but
without prompting any possibility of forgiveness.‡‡ As part of
the display, the amount of liquidity was varied, with the caveat
to respondents that these policies may not be the actual poli-
cies currently available to them. This was designed to measure
‡‡Because there was significant policy uncertainty at the time of the survey, one-third of
respondents were also asked about a potential policy that focused on aid that could
only be used for payroll. This policy became less relevant after the details of the CARES
Act emerged.
Bartik et al. PNAS | July 28, 2020 | vol. 117 | no. 30 | 17663
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Fig. 11. Differences in policy effects on the propensity to remain open in
December of 2020, split by hypothetical limits on borrowing amount. This
figure plots differences in the propensity to remain open under different
policies. The measure is computed using a follow-up question after pol-
icy information displayed, using the fraction that chose “Very likely” or
“Extremely likely” to be open in December of 2020. See Fig. 10 legend for
additional detail about the policy display. Pooled means for the loan and
CARES Act are 0.805 and 0.848, respectively. The sample size is 2,550, and
the pooled t-statistic on the difference between policies is 2.76.
how program generosity affects take-up and perceived business
resilience.§§
Fig. 10 shows the expected take-up of the two programs (the
exact details of the question wording is contained in the fig-
ure legend). Seventy-two percent of respondents who were told
about the loans with forgiveness said that they would like to
take them up. Fifty-nine percent of respondents were inter-
ested in taking up the loan program without forgiveness. While
there was substantial interest in a pure-loan program, there was
significantly more interest in the loan program with forgiveness.
A primary reason to forgive loans is that such a subsidy might
do more to maintain employment and keep businesses open in
the long term.¶¶ We therefore reasked businesses to project
their likelihood of being open and their expectations about
employment after we told them about the loan programs. Figs. 11
and 12 show the expected probability of being open and the
expected employment (relative to January 2020 employment) for
the two groups of respondents.
Before they were told about the policies, both groups had
experienced similar employment declines since January, and
both groups expected their employment in December 2020 to be
about 40% less relative to January 2020 (that is, assignment was
balanced). After the respondents were told about the CARES-
like loans, they projected their employment would decline by
only 6% by December 2020. The respondents who were told
about loans without forgiveness predicted their employment lev-
els would fall by 14%. (Because we randomize the policy and
the generosity, this analysis is equally weighted across firms.) We
are unable to distinguish precisely whether it is the conditional
§§A few program features differed between what was displayed to respondents and
the actual program. The most relevant is that the interest rate displayed was 4%,
which was higher than the interest rate under the program for the nonforgiven por-
tions of the PPP loans. This reflected the maximum interest rate in the legislative
text of the CARES Act. The actual implemented interest rates ended up below this
maximum.
¶¶An exact welfare analysis is beyond the scope of this paper. Hamilton (16) suggests
that the median person in self-employment might be realizing nonpecuniary benefits
because earnings differences may not justify the risk of running a business, but those
who persist in self-employment over the long run likely have a comparative advantage
in running their own business relative to their other options (17).
Fig. 12. Differences in policy effects on relative employment between
December and January. This figure plots differences in the ratio of relative
employment between December 2020 and January 2020 under different
policies. The December 2020 employment measure is computed using a
follow-up question after policy information displayed. See Fig. 10 legend
for additional detail about the policy display. Pooled means for the loan
and CARES act responses are 0.86 and 0.94, respectively. The sample size is
2,341, and the pooled t-statistic on the difference between policies is 2.42.
nature of the PPP program or the more favorable credit terms
that drive these differences.
When asked about their expectation of remaining in business
in December 2020, businesses responded similarly. Before being
told about the loans, the businesses thought that they had a 62
to 63% chance of being open in December 2020. The probabil-
ity rose to 81% among those who were told about the standard
loans. The projected chance of survival increased to 85% for
the businesses who were informed about the PPP loans that
came with forgiveness. Again, the flow of credit seems important,
but forgiveness did have a statistically significant impact on the
expectation of staying in business.
Why would businesses not take the aid that comes with such
generous forgiveness terms? Fig. 13 asks the 28% of firms that
said that they would not take a CARES-like loan why they would
turn down such a generous offer. The most common response,
given by 35% of refusers, was that they did not need the cash,
which suggests that one-tenth of our sample truly feels confident
with their financial security.∗∗∗
A significant number of those who said that they wouldn’t take
the CARES assistance cited other concerns. Thirty percent of
these respondents said that they didn’t think that they would
qualify. Nearly 20% said that they didn’t trust the government
to forgive the debt. Over one-tenth thought that it would be
too much of a hassle. These results suggest that clarity about
the program and a streamlined process are important policy
considerations to ensure a high take-up rate.
We also randomly informed survey recipients about the
changes in unemployment insurance under the CARES act. We
found that informing employers about the increased generosity
of unemployment insurance was associated with lower employ-
ment projection in December 2020, among those businesses
that were told about the CARES-like loans. Information about
unemployment insurance had no impact on the expected prob-
ability of remaining open. More work is needed to understand
how interactions between programs may influence economic
outcomes.
***Those who report their intention not to take up the program due to having sufficient
cash have a median of 3.5 mo of cash on hand. Those who express other reasons for
lack of take-up have a median of 1 mo of cash.
17664 | www.pnas.org/cgi/doi/10.1073/pnas.2006991117 Bartik et al.
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Fig. 13. Reasons for not using the resources in the CARES Act. This figure
contains the frequency of responses for reasons that respondents would not
take up aid under the CARES Act policy condition; 383 respondents indicated
they would not use the policy, and 382 answered this question. Respondents
could select more than one option, so percentages need not sum to 100.
Fifty percent of respondents selected an additional reason not displayed or
filled in the free text entry for other.
Industry Differences in Response to Crisis Duration
COVID-19 disruptions do not affect all businesses equally. Some
are deemed essential and remained open, while others were
required to close. Some businesses could shift employees to
remote work, while others were ill equipped for the transition. In
this section, we explore the cross-industry variation in its effects.
Our results suggest that disparities will be larger if the pandemic
ends up lasting for several months. Specifically, we asked busi-
nesses the following: “We want to understand how the duration
of the COVID-19 disruptions might change your answers. Sup-
pose that most COVID-19 disruptions continue for X months,
what is the likelihood of your business remaining operational by
Dec. 31, 2020? Please provide your best guess.” We randomize
the duration (X) to be 1 mo, 4 mo, or 6 mo, and offer respondents
a five-point scale ranging from extremely unlikely to extremely
likely. As before, we transform this answer into a binary outcome
of likely or unlikely to remain open for ease of exposition.
Table 5 displays the responses to this question by industry.
When firms are told to expect a 1-mo crisis, the expectation of
remaining open by the end of the 2020 hovers above 68% across
all industries, with the exception of arts and entertainment, per-
sonal services, and tourism and lodging. In those industries, the
expectation of remaining open drops to 66%, 57%, and 63%,
respectively. When firms are told to expect a 6-mo crisis, the
average expectation of remaining open falls to 39%, and there is
significant heterogeneity between sectors. The expected survival
probability for firms in Arts and Entertainment drops precipi-
tously to 45% under a 4-mo crisis, and 35% if the crisis lasts 6 mo.
The expected probability of being open for Personal Services
firms falls to 19% if the crisis lasts 6 mo.
The restaurant industry also seems particularly vulnerable to
a long crisis. Restaurateurs believed that they had a 74% chance
of survival if the crisis lasts 1 mo, but if the crisis lasts 4 mo, they
gave themselves a 29% chance of survival. Under a 6-mo crisis,
they expected to survive with only a 19% probability. Likewise,
the chance of survival for firms in tourism and lodging drops to
25% by the 6-mo mark. Meanwhile, banking and finance, real
estate, and professional services reported they will be able to
weather extended disruptions far better than these more exposed
sectors.
In Table 6, using the results in Table 5 around closure proba-
bilities as a function of crisis duration, we examine how employ-
ment separations might evolve due to firm closure. Building on
our estimates of the impact of crisis duration on job loss, Table 6
estimates the impact of COVID-19 on aggregate job loss from
small business closures and how businesses expected this to vary
with crisis duration. Specifically, we begin with the number of
workers who are projected to lose their jobs from small firm
closures. We then multiply the initial employment level (based
on the 2017 Economic Census), at the employment size level,
by the survey-based estimate of the share of firms that will be
closed in December depending on the length of the crisis. The
first row shows that there were 5.9 million workers in firms with
fewer than five employees in 2017. In our survey, 43% of those
smaller firms expected to be closed in December even if the crisis
lasted for only 1 mo. Next, we multiplied 0.43 times 5.9 million
workers to project 1.6 million separations due to firm closings (in
the absence of additional aid beyond what was expected at the
time of the survey). These smaller firms are extremely fragile,
but, since they represent a relatively small share of employment,
their closures add only modestly to overall job losses. Firms with
over 50 employees are more optimistic about their survival, even
if the crisis lasts for several months. Yet, even among these
firms, 54% expected to be closed in December if the crisis lasts
at least 4 mo. Those closures would create 14.6 million sepa-
rations. This figure may be an overestimate, because this firm
size category is large, and the closure rates may be lower for
larger firms.
Taken altogether, the closures are projected to create 32.7 mil-
lion job losses if the crisis lasts for 4 mo and 35.1 million job losses
if the crisis lasts for 6 mo. Moreover, these job losses look only
at business closures and do not account for the reduction in the
number of workers by firms that remain open or job losses among
workers who are employed by larger firms.
Table 5. Reported likelihood of remaining open by industry and
hypothetical crisis duration
Industry n 1 mo 4 mo 6 mo
Raw data
Retailers, except grocery 490 0.68 0.35 0.34
Arts and entertainment 281 0.66 0.45 0.35
Banking/finance 148 0.78 0.61 0.60
Construction 383 0.72 0.43 0.45
Health care 395 0.78 0.47 0.35
Other 1,384 0.76 0.48 0.38
Personal services 168 0.57 0.40 0.19
Professional services 201 0.79 0.64 0.55
Real estate 93 0.74 0.57 0.58
Restaurant/bar/catering 163 0.74 0.29 0.19
Tourism/lodging 145 0.63 0.50 0.25
Total 3,851 0.72 0.47 0.39
Reweighted to Census by size and region
Retailers, except grocery 485 0.69 0.35 0.34
Arts and entertainment 271 0.66 0.45 0.36
Banking/finance 144 0.78 0.64 0.62
Construction 372 0.72 0.42 0.46
Health care 386 0.77 0.47 0.37
Other 1,361 0.76 0.48 0.39
Personal services 167 0.56 0.37 0.19
Professional services 197 0.79 0.62 0.56
Real estate 93 0.72 0.55 0.59
Restaurant/bar/catering 160 0.75 0.35 0.19
Tourism/lodging 143 0.65 0.52 0.23
Total 3,779 0.71 0.47 0.39
This table reports results of expectations about remaining open in
December under different hypothetical durations of the COVID crisis. This
question was asked at the end of the survey, after policy questions were
conducted. The randomization is between subjects.
Bartik et al. PNAS | July 28, 2020 | vol. 117 | no. 30 | 17665
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Table 6. Extensive margin separations arising from firm closures over different hypothetical
crisis durations (all numbers in millions)
End-of-2020 separations
2017 Census if crisis lasts
January employees Firms Employees 1 mo 4 mo 6 mo
Under 5 3.7 5.9 1.6 3.1 3.6
6 to 24 1.8 18.1 4.8 10.5 12.1
25 to 49 0.3 9.5 1.6 4.4 6.9
50+ 0.3 26.8 5.8 14.6 12.5
Total 6.1 60.4 13.7 32.7 35.1
This table reports results of end-of-2020 employment separations based on extensive margin business clo-
sures using the between-subjects survey design that asked about the ability to remain open under different
crisis durations in Table 5. We take the fraction of businesses closing by December 2020 as the complement
of the numbers in Table 5 and then multiply by the number of 2017 employees (in millions) from the Census,
yielding the implied level of employment separations in each cell.
These results suggest that the damage to our economy and its
network of small businesses will be far larger if the crisis lasts
for many months. This suggests large potential economic bene-
fits for policies that can safely lead to reopening the economy
quickly.
Conclusion
Small businesses employ almost 50% of American workers. Yet,
our results underscore the financial fragility of many small busi-
nesses, and how deeply affected they are by the current crisis. In
our sample, which is skewed toward the retail sector, we found
that 43% of businesses were temporarily closed and that employ-
ment had fallen by 40%. This represents a shock to America’s
small firms that has little parallel since the Great Depression of
the 1930s. Our results suggest that many of these firms had lit-
tle cash on hand toward the beginning of the pandemic, which
means that they will either have to dramatically cut expenses,
take on additional debt, or declare bankruptcy. This highlights
the ways in which the immediacy of new funding might impact
medium term outcomes.
Small businesses’ responses to our survey suggest that many
are likely to fail absent financial assistance. As of the last week
of March 2020, 38% of businesses viewed it as unlikely or only
somewhat likely that they would be open as of the end of 2020.
While optimism increased when they were informed about the
CARES loan program, it is unclear whether the CARES act
will enable most of America’s small businesses to survive—or
whether beliefs about its impact are overly optimistic.
The results also highlight the importance of well-designed and
sustained economic and public health policy measures. Three
policy-relevant results of our survey stand out. First, more than
13% of respondents say that they do not expect to take out
CARES Act PPP loans because of the application hassle, dis-
trust that the federal government will forgive the loans, or worry
about complicated eligibility rules. Therefore, streamlining the
application process and clarifying the eligibility criterion and
loan forgiveness rules might increase the take-up rate for loans.
Second, firms in particularly exposed industries—such as restau-
rants, tourism, and personal services—project that they will find
it extremely difficult to stay in business if the crisis lasts for
longer than 4 mo. These findings suggest large economic benefits
from any policies that can safely shorten the economic shutdown
(e.g., through stronger short-term containment policies). Third,
if we extrapolate the 72% of businesses who indicate they would
take up the CARES PPP loans to all US small businesses, the
total volume of loans would be approximately $410 billion. (This
assumes that all businesses take out the maximum loan size [2.5
mo of expenses].) When we allow for different take-up rates by
employer size and multiply by the 2017 Census payroll amounts
in each firm size category, we estimate total loan demand of $436
billion, in excess of the $349 billion allocated in the first tranche
of the CARES act.††† Total demand for such aid may ultimately
be even higher under an extended crisis.
ACKNOWLEDGMENTS. We thank Karen Mills for connecting us to Alignable
and to Eric Groves, Venkat Krishnamurthy, and Geoff Cramer for help
in facilitating survey distribution. Dylan Balla-Elliott, Manal Saleh, and
Pratyush Tiwari provided excellent research assistance.
†††Monthly payroll in the 2017 Census data for businesses under 500 employees totaled
226 billion dollars. Our estimates do not account for increases in total payroll in this
sector since 2017. Our estimates also do not account for the fact that the PPP
guidelines allowed some firms with more than 500 employees to access aid.
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