The Effects of COVID-19 on U.S. Small Businesses (NBER Working Paper 27833)
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NBER Working Paper No. 27833, The Effects of COVID-19 on U.S. Small Businesses: Evidence from Owners, Managers, and Employees, dated September 2020, by Georgij Alekseev, Safaa Amer, Manasa Gopal, Theresa Kuchler, JW Schneider, Johannes Stroebel and Nils C. Wernerfelt. It analyzes a survey fielded in late April 2020 among Facebook business page administrators, frequent Marketplace sellers and the general Facebook user population, with more than 66,000 responses to 136 questions. The paper reports that 67.1% of the sample was operational, that 61.7% of businesses had increased their online presence, and that about 55.9% expected to survive if late-April conditions continued longer than 6 months. Many were struggling to pay bills (31.3%), rent (24.9%) and wages (24.1%). It notes that Amer, Schneider and Wernerfelt are Facebook employees.
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NBER WORKING PAPER SERIES
THE EFFECTS OF COVID-19 ON U.S. SMALL BUSINESSES:
EVIDENCE FROM OWNERS, MANAGERS, AND EMPLOYEES
Georgij Alekseev
Safaa Amer
Manasa Gopal
Theresa Kuchler
JW Schneider
Johannes Stroebel
Nils C. Wernerfelt
Working Paper 27833
http://www.nber.org/papers/w27833
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
September 2020
Amer, Schneider, and Wernerfelt are employees at Facebook. Kuchler and Stroebel have a
research consulting relationship with Facebook. 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 They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies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 Georgij Alekseev, Safaa Amer, Manasa Gopal, Theresa Kuchler, JW Schneider,
Johannes Stroebel, and Nils C. Wernerfelt. 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.
The Effects of COVID-19 on U.S. Small Businesses: Evidence from Owners, Managers, and
Employees
Georgij Alekseev, Safaa Amer, Manasa Gopal, Theresa Kuchler, JW Schneider, Johannes
Stroebel, and Nils C. Wernerfelt
NBER Working Paper No. 27833
September 2020
JEL No. G0,M1
ABSTRACT
We analyze a large-scale survey of owners, managers, and employees of small businesses in the
United States to understand the effects of the early stages of the COVID-19 pandemic on those
businesses. The survey was fielded in late April 2020 among Facebook business page
administrators, frequent sellers on Facebook's e-commerce platform Marketplace, and the general
Facebook user population. We observe more than 66,000 responses covering most sectors of the
economy, including many businesses that had stopped operating due to the pandemic. The survey
asks 136 questions covering topics such as changes in business operations and employment,
changes in financing patterns, and the interaction of household and business responsibilities. We
characterize the adjustments implemented to survive the pandemic and explore the key challenges
to continue operating or to re-open. We show how these patterns differ across industry, firm size,
owner gender, and other firm characteristics.
Georgij Alekseev JW Schneider
Stern School of Business Facebook
New York University 1 Hacker Way
44 West 4th Street Menlo Park
New York, NY 10012 jdub@fb.com
galeksee@stern.nyu.edu
Johannes Stroebel
Safaa Amer Stern School of Business
Facebook New York University
1 Hacker Way 44 West 4th Street
Menlo Park New York, NY 10012
safy@fb.com and NBER
johannes.stroebel@nyu.edu
Manasa Gopal
Scheller College of Business Nils C. Wernerfelt
Georgia Institute of Technology Facebook
800 W Peachtree St NW 1 Hacker Way
Atlanta, GA 30308 Menlo Park
manasa.gopal@scheller.gatech.edu nilsw@fb.com
Theresa Kuchler
Stern School of Business
New York University
44 West 4th Street
New York, NY 10012
and NBER
tkuchler@stern.nyu.edu
I I NTRODUCTION
The COVID-19 pandemic and associated public health interventions have led to substantial changes
in business and work environments. Government orders forced businesses to close suddenly, in-
fection risks led to an accelerated adaptation of modern sales and communication technologies,
and school closures placed increased childcare duties on many working parents. These forces have
put particular pressure on owners, managers, and employees of small businesses, where central
responsibilities are often only shared between a small number of individuals. However, while
these small businesses are a key driver of economic growth and employment in the U.S., as well as
a central focus of many policy efforts, they are often underrepresented in traditional data sources
(see Buffington, Dennis, Dinlersoz, Foster, and Klimek, 2020). This presents important challenges
for researchers and policymakers hoping to understand the aggregate and distributional effects of
large economic shocks such as COVID-19 on the performance of small businesses.
In this paper, we report findings from a large and comprehensive survey on the impact of the
early stages of the COVID-19 pandemic on small businesses in the United States. The survey was
conducted through the Facebook platform between April 20th and April 28th , 2020. It was fielded
among Facebook business page administrators, frequent sellers on its e-commerce platform Mar-
ketplace, and the general user population. This sampling frame allowed the survey to reach own-
ers, managers, and employees of employer firms, as well as self-employed individuals. We were
also able to survey a large number of businesses that had ceased operations during the survey
period, which is central to understanding the challenges to small business survival. The survey
contained 136 questions and obtained more than 66,000 responses — 46,669 from business owners
or managers, 4,163 from operators of personal enterprises, and 15,435 from business employees
— making it one of the largest undertakings to date to describe the effects of the COVID-19 crisis
on U.S. small businesses. While no survey sample is fully representative, due to selection both in
the sample that can be reached as well as in who responds, we find that our respondents broadly
match the characteristics of small businesses in the U.S. as described by the Census Bureau.
The survey first asked respondents if they were an employee or whether they own, man-
age, or operate a business. Owners/managers were then asked whether their business was cur-
rently operational. These questions were used to direct respondents to different sections of the
survey. Owners/managers of closed firms and business employees were asked to respond to a
specific set of questions, while owners/managers of operational businesses were randomized into
five thematic sets of questions covering business operations, business finances, the interaction
of business and household responsibilities, business employees, and survival strategies. All own-
ers/managers also answered a core set of demographic and business questions. A flowchart of the
logical sequence of questions is provided in Figure 1, and full details on the survey methodology
are provided in Section II. We next preview some central findings from the survey.
Open Businesses. First, we focus on businesses that were operational as of the survey date (67.1%
of our sample). Older businesses, larger businesses, and businesses with more male employees
were more likely to be open. Among operational businesses, we explore several themes.
1
Business Operations. We first document how business operations were affected by the pandemic.
We find that most businesses (60.9%) had seen their workload fall; those firms lost, on average,
over half their business. We also document changes to standard business operations and virus
containment measures. For example, we find that 61.7% of businesses had increased their on-
line presence in response to the pandemic. About 55.9% of businesses expected to survive if the
conditions in late April were to continue longer than 6 months. Older businesses, majority-male
businesses, and businesses that could operate remotely had more optimistic survival expectations.
Business Finances. We then provide a detailed exploration of small business finances during the
pandemic, a feature that has not been the focus of other contemporaneous surveys, and is useful
beyond the COVID-19 crisis. Many businesses were struggling to pay bills (31.3%), rent (24.9%),
wages (24.1%), and debt obligations (23.0%). About 42% of businesses reported having more
outflows than inflows in the past month, and 78.2% of businesses were concerned about cash flows
over the next 3 months. Only a quarter of the businesses had access to formal sources of financing
through a loan or line of credit from a financial institution, and most businesses were reliant on
personal savings and informal sources of financing. We highlight heterogeneities in the sources
of financing, especially across employer/non-employer firms and across business sectors. Finally,
we show that deteriorating financial conditions, through lack of access to capital or negative cash
flows, lead to a general decline in product prices.
Business and Household Responsibilities. A novel feature of our survey is the ability to ex-
plore household duties during the pandemic as well as their interaction with business work.
56.6% of managers and owners said that household responsibilities had impeded their business
work; 55.0% reported that business responsibilities had affected their household duties. Own-
ers/managers increased both their time spent on business work (57.1%) and their housework
(48.6%). Specifically, they had increased their time spent on child education, daycare, and taking
care of dependent adults. We look at how these effects vary by household characteristics and find
that women and parents affected by school closures struggled the most.
Small Business Employees. Next, we explore employment patterns at small businesses from the
employers’ perspective. 44.5% of businesses reduced the number of active employees, using a
combination of furloughs and layoffs; we find that firms with fewer in-person interactions were
least likely to lay off workers. 29.3% of businesses could let all their employees work from home,
while 37.1% could not let any of their employees do so. A majority of businesses reported provid-
ing their employees with protective equipment to reduce their exposure to the virus. Employers
reported that female employees were more affected by the crisis than male employees.
Business Survival Strategies. We also explore the adjustments that businesses made to survive
the pandemic. 52.5% of businesses responded to the crisis by providing online services, 35.1% ex-
panded digital payments, 25.7% used delivery services, and 24.4% used curbside pickup. On the
other hand, 36.1% of firms found it difficult to change the delivery of goods or services in response
to the pandemic. Many businesses obtained their information about the pandemic through inter-
net news sources (55.6%) or social media (51.3%). 25.4% of firms reported that they did not have
all the information needed to make business decisions.
2
Closed Businesses. We then analyze the 32.9% of firms that had ceased operations by the time of
the survey in late April 2020. We examine their reasons for closure, their re-opening plans, their
future employment and product plans, and the most important actions needed for re-opening.
Among closed businesses, 57.5% reported that they had shut down in response to government
and health authority orders; 27.3% were unsure of whether they would re-open in the future.
Closed businesses planned to use their personal savings to re-open (41.2%) and often planned to
make some changes to the products and services they offered prior to the closure (23.3%).
Business Support. The final set of business questions was answered by both operational and
closed businesses, and explores the biggest challenges faced by firms during the crisis as well
as the policies that firms reported would be most helpful to survive the COVID-19 pandemic.
The biggest challenge for 38.9% of businesses was accessing capital. Furthermore, many busi-
ness owners and managers were feeling burnt out from taking care of both their business and
their household. They also reported difficulties with processing information from contradictory
sources. The most popular policy responses that businesses thought would support them through
the crisis were salary subsidies, access to loan and credit guarantees, and tax deferrals.
Employees. Lastly, we surveyed employees about the impact of COVID-19 on their work and
personal lives; this module includes responses from employees at businesses with more than 500
employees. 37.1% of workers reported having to take care of children due to school closures.
Women were more likely to voluntarily quit their jobs in response to school closures and were
more likely to report that household work was affecting their ability to focus on their jobs. The
main challenges in working remotely were multi-tasking between work and household responsi-
bilities (28.9%), the nature of the work (16.1%), and difficulty in collaborating remotely (11.5%).
Related Literature. Our findings expand upon a growing body of research studying the eco-
nomic implications of the COVID-19 pandemic, including several contemporaneous efforts to use
surveys to better understand the performance of U.S. small businesses during this period.1 For ex-
ample, Bartik, Bertrand, Cullen, Glaeser, Luca, and Stanton (2020); Fairlie (2020), and Humphries,
Neilson, and Ulyssea (2020) document business closures and mass layoffs early in the pandemic
(see also Bartlett, 2020; Campello, Kankanhalli, and Muthukrishnan, 2020). In addition to ex-
ploring the pandemic’s effect on general business performance, our paper focuses on business re-
sponses affecting customers and employees, as well as changes in the interaction of household and
work responsibilities. We also provide the perspectives of both small business owners/managers
and their employees. We document important heterogeneities across affected firms, with a par-
ticular focus on exploring differences across owner gender and the ability to operate remotely.
Women reported being more affected than men along a number of dimensions: the implemen-
tation of work from home might have a considerable impact on the distribution of household
work, and school closures cause additional household burden (see also Alon, Doepke, Olmstead-
Rumsey, and Tertilt, 2020a,b; Carlson, Petts, and Pepin, 2020).
1 Other research papers in the large emerging literature studying the economic effects of COVID-19 include Cox,
Ganong, Noel, Vavra, Wong, Farrell, and Greig (2020); Giglio, Maggiori, Stroebel, and Utkus (2020); Kuchler, Russel,
and Stroebel (2020b), and Coibion, Gorodnichenko, and Weber (2020).
3
II S URVEY M ETHODOLOGY
The survey was conducted alongside Facebook’s ongoing data collection efforts with the World
Bank and OECD on the Future of Business, and in partnership with the Small Business Roundtable.
In this section, we describe the structure of the survey and the sampling methodology.
Sampling and Screening. The survey was fielded on the Facebook platform between April 20
and April 28, 2020, to a stratified probability-based random sample of Facebook users.2 Every
monthly active U.S. Facebook account was eligible for the survey,3 though we oversampled ac-
counts of Facebook business page administrators and active sellers on Facebook Marketplace.4
This sampling frame led to a high chance of identifying individuals who own or manage small
businesses, many of which have a Facebook presence. Respondents from the general Facebook
population were more likely to be employees, both at small and large enterprises.
Sampled users received an invitation to participate in a survey at the top of their newsfeed.
This invitation was shown for three successive logins. After accepting the invitation, users were
shown an introductory text and screening questions to understand their roles within the business
(see Appendix Figure A.1 for more details). The introductory text described that participation in
the survey was voluntary, that responses would be kept confidential, and that aggregated results
from the survey might be shared publicly. There was no compensation of any kind.
The survey invitation was sent to about 1.9 million Facebook users, and 66,297 eligible indi-
viduals completed at least part of the survey: 46,669 business owners and managers, 4,163 opera-
tors of personal enterprises, and 15,435 business employees. There was no screening on firm size,
though the sampling frame ensured that most respondents were associated with small businesses.
Indeed, as we describe in detail in the following section, 68% of owner/manager respondents
were associated with businesses with fewer than 10 employees (and 93% were associated with
businesses with fewer than 500 employees); on the other hand, about 25% of the employee sample
worked at businesses with more than 500 employees. We verify that our findings are robust to
excluding such firms and individuals.
2 Facebook was created in 2004, and, by June 2020, had 2.7 billion active users around the world, and 256 million
active users in the United States and Canada. An independent survey of Facebook users from 2019 found that more
than 69% of the U.S. adult population used Facebook (Perrin and Anderson, 2019). That same survey shows that
Facebook usage rates among U.S.-based online adults were relatively constant across income groups, education levels,
and race, and among urban, rural, and suburban residents; usage rates were slightly declining in age (from 79% of
individuals aged 18 to 29, to 46% of individuals aged 65 and older). See Allen, Peng, and Shan (2020); Bailey, Cao,
Kuchler, and Stroebel (2018); Bailey, Cao, Kuchler, Stroebel, and Wong (2018); Bailey, Dávila, Kuchler, and Stroebel
(2019); Bailey, Farrell, Kuchler, and Stroebel (2020); Bailey, Gupta, Hillenbrand, Kuchler, Richmond, and Stroebel (2020);
Bailey, Johnston, Kuchler, Russel, State, and Stroebel (2020); Bali, Hirshleifer, Peng, and Tang (2018); Kuchler, Russel,
and Stroebel (2020a); Kuchler, Peng, Stroebel, Li, and Zhou (2020); Wilson (2019) and Rehbein and Rother (2020) for
other economics and finance research using data from Facebook.
3 Facebook generally does not allow accounts to receive multiple surveys in a short span of time. Since some of
these surveys followed different sampling regimes (e.g., simple random or potentially targeted sampling), the total
pool for our survey was not drawn completely at random from the overall Facebook population. In practice, reweight-
ing for sampling (and non-response) moves the point estimates minimally, and the observable characteristics of our
respondents align well with those from nationwide, offline estimates (see Tables 1 and 2).
4 Facebook pages are profiles on Facebook specifically for businesses, brands, communities, or public figures. Each
page must have an account tied to it as an administrator, and we oversampled those that were specifically from business
pages. A business page is required for small businesses to advertise on Facebook. Facebook Marketplace is an e-
commerce platform where users can buy and sell different products.
4
Survey Instrument. The questionnaire was designed with a complex flow to reduce the burden
on respondents while addressing a wide range of social and economic issues. A respondent could
choose to skip any question in the survey with no prompts to answer, and the questionnaire flow
would take them to the next logical question. The survey started with screening questions, fol-
lowed by special modules and a core set of questions. Modules were assigned to respondents in
a semi-random fashion based on their business role and whether their business was operational.
The survey consisted of 136 possible questions but each respondent received a maximum of 30
questions for the longest path; since respondents could skip questions, the number of responses
differs somewhat across questions. A flowchart detailing the different modules and possible paths
through the survey is provided in Figure 1. We next present a brief overview of the survey path.
1. Respondents were asked a preliminary set of questions to classify their employment status,
role in the firm, and operational status of the firm.
2. Based on work status, respondents were then classified as one of: business owners and man-
agers, operators of personal businesses, or employees (employed or recently unemployed).5
Individuals not fitting any of these categories were not asked further questions.
3. Respondents who were categorized as business owners, business managers, or operators of
personal businesses that were operational, were randomly assigned to one of five thematic
modules.
4. Respondents who were categorized as business owners, business managers, or operators of
personal businesses that were closed, were assigned their own question block.
5. Respondents who were categorized as employees (employed or unemployed) were given
their own question block.
6. Finally, all respondents (both owners/managers and employees) answered questions about
their demographics and firm characteristics.
III C HARACTERISTICS OF S AMPLED B USINESSES
A module on core business characteristics was answered by all respondents who identified as
business owner or manager of a business, independent of whether the business was open or closed
at the time of the survey.6 We start by presenting summary statistics of the sampled businesses.
Respondents were first asked about firm age, previous year revenue, sector, and location.
Figure 2 presents these characteristics. Panel A shows that 9.9% of firms were less than a year
old, 10.5% between one and two years, 17.3% between two and five years, and 62.2% were older
5 “Personal" businesses were defined as respondents who reported that they were “Self-employed providing goods
or services" or that they “Produce goods sold for personal income" but did not otherwise self-identify as an owner or
manager of a business. While there is no standard term for this category of businesses, they overlap a great deal with
what are commonly called sole-proprietor or micro businesses.
6 Specifically, this section was answered by individuals who chose “Manage day-to-day operations of a business”,
“Self-employed providing goods or services”, “Produce goods sold for personal income”, “Promote or sell goods or
services”, “Control business finances with authority to sign loans, leases, and contracts”, “Own a business” or “Other
business management or leadership activity” when asked about their activities during the past 3 months.
5
than five years. Panel B shows the distribution of 2019 revenue for our sample. About 19.3% of
businesses had sales of less than $9,999 in 2019, 29.6% between $10,000–$99,999, 26.8% between
$100,000–$999,999 and 19.9% had sales of over $1,000,000. Finally, 4.2% of the firms surveyed were
not operational in 2019. Panel C shows the sectors our firms operate in. These are services (33.1%),
retail and wholesale trade (16.9%), hotels, cafes, and restaurants (7.3%), information and commu-
nication (6.2%), construction (5.3%), manufacturing (2.8%), transportation (2.6%), and agriculture
and mining (2.5%). 23.1% of the surveyed firms did not self-classify into any of the above sectors.
Finally, Panel D shows that our survey covers firms in urban (31.5%), rural (23.6%), and suburban
areas (44.9%). Table 1 compares our sample characteristics with the universe of businesses in the
U.S. according to the U.S. Census Bureau’s Business Dynamics Statistics (BDS).
We next describe the baseline operating conditions of the firms in our sample. Figure 3 ex-
plores employment characteristics before the start of the pandemic. Panel A shows that 29.6% of
firms were sole-proprietorships, while 25.8% of firms had fewer than 5 employees. The remain-
ing firms employed between 5–49 (28.3%), 50–249 (7.1%), and 250–499 (2.3%) workers. Finally,
6.9% of firms were large businesses with over 500 employees.7 Our sample includes more female-
owned/managed businesses (56.9%, Panel B), and Panel C shows that 31.6% of the businesses
employed more women than men, 24.6% had an equal share of men and women, and 17.9% of
firms had majority-male employment.8
Panel A of Figure 4 shows that 57.3% of firms turned a profit in the last quarter of 2019, 22.1%
broke even, and 16.7% made losses. To understand the impact of lockdown orders, we asked
businesses about the extent to which business operations could be conducted remotely. Panel B
documents that 32.4% of firms needed to conduct all of their interactions in-person, 17.2% more
than half their interactions, and 10.3% half their interactions. On the other hand, 23.4% did not
have any in-person employee-client interactions, and for an additional 16.7% of firms, in-person
interactions constituted less than half of their normal operations.
Panel A of Figure 5 shows that 67.1% of firms were operational at the time of the survey. We
present results for operational firms in Section IV and results for closed firms in Section V. In Pan-
els B to F of Figure 5, we present crosstabs of businesses’ operational status by firm characteristics.
Older businesses were more likely to be open (Panel B): 67.6% of firms older than 5 years were
operating in late April, and 55.6% of firms younger than a year. Similarly, consistent with Fairlie
(2020), larger firms (measured by 2019 sales) were more likely to be operating: 83.5% of firms with
sales over $3 million in 2019 were operational, and 55% of firms with sales below $100,000 (Panel
C). Manufacturing firms had coped the best, with 79.5% of firms operational, while businesses in
the hotels, cafes, and restaurants sector fared the worst, with only 55.6% of firms operational in
late April 2020 (Panel D). Businesses with a higher share of women have suffered more: 63.7% of
majority-female firms were operational, and 74.8% of majority-male firms (Panel E). Finally, 52.5%
of firms that conduct all of their operations in-person were closed, and 26.6% of firms that did not
require any in-person operations (Panel F).
Table 3 explores these relationships in a multivariate regression framework. Columns 1 to
7 Information on the total number of employees before the pandemic was only surveyed for operational businesses.
8 The remaining 25.9% stated “Not applicable”, consistent with our sample’s share of sole-proprietorships.
6
4 show regressions with the businesses’ operational status as dependent variable and controls
only for firm age, sector, size, and share of in-person operations, respectively. They confirm the
previous descriptive statistics. Column 5 includes all controls jointly. The effects of industry, firm
sales, and remote work continue to impact businesses’ operational status. However, the coefficient
on male-owned/managed businesses drops by two-thirds and the impact of firm age disappears
(in part because firm size and firm age are strongly correlated).
We also asked firms about their optimism for the future. Panel A of Figure 6 shows that, in
late April 2020, 21.2% of businesses were pessimistic, 19.6% had a neutral outlook, and 59.2% were
optimistic about the future. Hotels, cafes, and restaurants had the worst outlook, with 51.6% re-
porting being optimistic (Panel B), while information and communication, and construction firms
had the most positive outlook, with 63.0% and 61.8% feeling optimistic, respectively. The youngest
firms (≤1 year) were the most optimistic about the future (62.7%, Panel C). Panel D shows a U-
shaped relationship between optimism and firm size: the smallest and largest firms by sales were
more optimistic than medium-sized firms. Panel E suggests no meaningful differences in opti-
mism by employee gender ratio. Finally, firms with less than half their operations in-person were
more optimistic about the future (63.7%) than firms with more than half their operations in-person
(56.5%, Panel F). Table 4 presents a multivariate regression of business optimism on firm char-
acteristics. The dependent variable is equal to one if the respondent reported being optimistic.
Columns 1 to 6 include controls for the firm’s operational status, age, sector, sales, share of in-
person interactions, and gender of the owner/manager, respectively. They confirm the previous
descriptive statistics. Column 7 includes all regressors jointly; operational status, age, industry,
firm size and remote-work ability continue to have a significant effect on business optimism.
IV O PEN B USINESSES
We next explore various aspects about the performance and adjustments of businesses that were
operating in late April 2020. All operational businesses were first asked about the biggest chal-
lenge they expected to face in the next few months. Figure 7 shows that their biggest concerns
were cash flow (28.3% of respondents), lack of demand (21.7%), government or health authority
orders (11.9%), logistics (5.5%), finding supplies (5.0%), lack of staff (4.3%), and inventory (3.6%).
Table 5 looks at how firm characteristics affect the biggest perceived challenges of the busi-
nesses in our sample. The dependent variable takes a value of one in each column for each of
the biggest challenges reported by the business. Cash flow problems were more likely to be the
biggest challenge for medium-sized businesses as well as information, communication, hotel, cafe
and restaurant businesses. Firms with a low share of in-person interactions and manufacturing
firms often reported lack of demand. Government orders were most often stated by male-owned
or managed businesses and businesses with a high share of in-person interactions. Finally, lack of
staff was more likely to be the biggest problem among larger businesses.
After this question, respondents were randomly assigned to one of five modules to under-
stand their business operations, finances, the interaction between work and household responsi-
bilities, the impact on their employees, and their crisis survival strategy. We next review responses
to each of these modules.
7
IV.A Business Operations
We first explore how firms’ workloads changed during the pandemic. Panel A of Figure 8 shows
that 60.9% of firms experienced a drop in workload since the start of the pandemic. Of these firms,
48.0% said the drop was due to decreased demand or other customer-related reasons, and 33.6%
said it was due to government or health-authority orders (Panel B). On the flip side, 18.3% of firms
saw increased workloads during the pandemic. These firms responded by extending operating
hours (39.7%), increasing supplies (19.7%), and hiring more employees (18.8%, Panel C).
Changes in workload vary by firm characteristics. Panel A of Figure 9 shows that larger firms
were more likely to see increased workloads than smaller firms. Panel B shows that businesses
in agriculture or mining were most likely to report increased workloads (30.0% of firms in that
sector), followed by businesses in the retail and wholesale trade (26.6%). Sectors with the largest
declines in workload were hotels, cafes, restaurants, as well as transportation and logistics. Panel
C shows that firm age was not correlated with changes in workload. However, there is an inverse
U-shaped relationship in firm size (Panel D): 59.5% of firms with sales between $100k–$250k in
2019 saw reduced workload, compared with 43.3% of firms with sales ≤$5k and 46.5% of firms
with sales ≥$3 million. Majority-female firms were marginally more likely to see drops in work-
load than majority-male firms (54.9% compared to 50.2%, Panel E). Notably, they were also more
likely to see an increase in workload (24.2% compared to 19.5%). Finally, businesses with more
in-person interactions saw drops in workload more often (Panel F): 59.7% of firms with all their
interactions in-person and 40.8% of firms with no in-person interactions reported declining work-
loads. Column 1 of Table 6 shows related estimates from a multivariate regression; the relations
are similar, though the effects of firm size (sales) and gender balance become insignificant.
We also asked open businesses how the pandemic had affected their sales. Panel A of Figure
10 shows that 54.2% of firms said sales decreased in comparison to 2019. On the other hand,
sales remained steady for 13.7% of firms and increased for 16.3% of firms. Panel B shows that
businesses with a drop in sales, on average, had seen sales fall by more than 50%, and 11.6% of
firms had seen a drop of over 90%. On the other extreme, 17.5% of firms with increased sales
saw their sales more than double, while 37.1% of the firms saw growth between 10-30%. The
heterogeneities in change in sales resemble the workload heterogeneities closely (see Appendix
Section A.I, Appendix Figure A.3, and Appendix Table A.1), though 9% of firms with increased
sales had a lower workload, and 10% of firms with lower sales had a higher than normal workload.
Business Survival. Next, we look at how long operational businesses expected to survive if the
conditions in April 2020 were to continue: 22.1% expected to survive less than 6 months while
55.9% expected to survive for more than 6 months under prevailing conditions (Figure 11, Panel
A). The remaining 22.1% of firms were unsure about their survival expectations. Figure 12 shows
heterogeneity in business survival expectations. Larger businesses, older businesses and busi-
nesses with fewer in-person interactions had more optimistic survival expectations. Businesses
in agriculture/mining had the highest survival expectations, while hotels, cafes, and restaurants
were most pessimistic. Column 2 of Table 6 corroborates these observations in a multivariate
framework, though the effects of firm age on survival expectations are statistically insignificant.
8
Related to business survival, we asked businesses what support would be the most helpful
in adapting to the pandemic (Panel B of Figure 11). Firms stated that the most helpful support
would be zero-interest loans/financial assistance (46.9%), equipment/software for remote work
and customer engagement (14.7%), or more information to facilitate business decisions (14.7%).
Business Challenges and Responses. The following paragraphs provide more details on busi-
nesses’ main challenges as well as their responses during the pandemic. Panel A of Figure 13
shows that 38.7% of businesses faced supply challenges. Panel B breaks up these supply chal-
lenges, and shows that firms were struggling with limited product supply (64.4%), delayed supply
shipments (54.8%), and obtaining cleaning products (36.4%).
Panel A of Figure 14 looks at changes to the business operations intended to mitigate the spread of
the virus. The most common changes were to shift more business activities online (59.2% of firms),
more frequent cleaning (46.4%), provision of PPE to employees (36.8%), and increased work from
home (35.8%). Table 7 explores how firm characteristics were related to the implementation of
virus response measures. Column 1 shows that increasing the online presence was more common
for firms in the information and communication sector, larger firms, female-managed firms, and
firms that rely less on in-person interactions. Hotels, cafes and restaurants, larger firms, firms with
more in-person interactions, and female-managed firms responded by cleaning their work-spaces
more frequently (Column 2). Hotels, cafes and restaurants, larger firms, and firms with more in-
person interactions were also more likely to provide their employees with protective equipment
(Column 3) and cleaning products for their clients (Column 4). Businesses in information and
communication, larger businesses, and businesses that require less in-person interactions were
more likely to shift to working from home (Column 5). Finally, larger businesses and female-
managed businesses were more likely to have closed their physical locations (Column 6).
Panels B and C of Figure 14 explore changes to the standard business operations due to the
pandemic. Overall, 75.7% of firms implemented changes. These businesses changed their client
interaction and communication methods (65.3%), online tools (46.4%), operating hours (43.6%),
and employee location (39.7%). Table 8 explores how changes in business operations vary by firm
characteristics. The dependent variables take a value of one if the business made a particular
change. Client communication changed most for the largest firms and female-owned/managed
firms (Column 1). Larger firms were also more likely to change their operating hours, as were
restaurants and businesses that rely on more in-person interactions (Column 2). Firms that rely
less on in-person interactions and female-owned/managed firms made increased use of online
tools (Column 3) while also changing employees’ work locations (Column 4). Larger and older
businesses were also more likely to have changed their employees’ locations.
IV.B Business Finances
This section explores the financial situation and financing sources of small businesses, both during
the COVID-19 pandemic and more generally. Our paper is thus the first to focus on the financial
conditions and financial challenges of small businesses during the COVID-19 crisis.9
9 While Bartik, Bertrand, Cullen, Glaeser, Luca, and Stanton (2020) look at firms’ cash at-hand and whether firms
would take up schemes similar to the CARES act, our survey focuses on sources of existing capital at-hand, cash flow
9
Payment Difficulties. First, we look at whether businesses were struggling to pay their expenses
during the crisis. Panel A of Figure 15 shows that many firms were struggling to pay bills (31.3% of
firms), rent (24.9%), employee wages (24.1%), and debt commitments (23.0%). On the other hand,
49.1% of firms reported no challenges in making any of these payments. Table 9 looks at how the
business’s struggles vary with firm characteristics. In each column, the dependent variable takes
a value of one if the business was struggling to pay a particular type of obligation. Mid-sized
firms and firms relying on in-person interactions to operate had the most difficulties paying their
bills (Column 1). The largest firms and firms requiring in-person interactions were most worried
about debt repayments (Column 2). Hotels, cafes and restaurants, and firms that cannot operate
remotely struggled with paying wages and their taxes (Columns 3 and 5). The same firms, along
with smaller firms, and firms in trade and services, were struggling to pay their rent (Column 4).
Formal and Informal Financing. Next, we explore the businesses’ access to formal and informal
sources of funding. Panel A of Figure 16 shows that 26.3% of firms had an outstanding loan or a
credit line from a financial institution. However, Panels B and C highlight that access to financing
varied significantly across employer and non-employer firms: 34.6% of employer firms had an
outstanding loan, compared with only 11.7% of non-employer firms. The multivariate regression
in Column 1 of Table 10 confirms that larger firms were more likely to have an outstanding bank
loan; retail and service firms were less likely to have a loan.
We also explore whether firms applied for formal sources of funding during the pandemic.
Panel A of Figure 17 shows that relatively few firms applied for government loans (31.4%) and
government grants (21.8%). Only 14.4% of firms approached banks for financing during the pan-
demic, and 54.1% did not make any financing applications at all. Corroborating the differences in
funding, employer firms were more likely to apply for any sources of capital than non-employer
firms (57.4% compared with 26.0%). Columns 2 to 6 of Table 10 show that hotels, cafes, and
restaurants were most likely to apply for funding from the government or private grants. Larger
businesses were more likely to apply for funding from banks and government grants/loans.
Similarly, we looked at the prevalence of informal sources of funding. Figure 18 shows that
the most common informal sources of funding for small businesses were personal savings (46.3%),
funds from friends and family (12.5%), and unemployment benefits (9.7%). However, 40.3%
of businesses did not have access to any private funds. Patterns were relatively similar across
employer and non-employer firms, except for the more frequent occurrence of personal savings
among non-employer firms.10 The multivariate analysis in Column 1 of Table 13 shows that busi-
nesses in construction, manufacturing, and transportation industries were least likely to have ac-
cess to informal sources of financing. The largest firms (sales) were less likely to rely on personal
trends, financial struggles, and firms’ responses through product pricing. A parallel paper by Campello, Kankanhalli,
and Muthukrishnan (2020) studies the response through corporate hiring. However, their financial data is limited to
large, public firms, while our paper is focused on studying small businesses.
10 In Table 11, we look at the prevalence of internal vs. external financing by industry. We use the presence of bank
loans or lines of credit as a measure of access to external financing (Sufi, 2007). We measure access to internal financing
as access to personal savings, funds from family and friends, or retirement funds. The sectors with the largest share of
firms with access to external financing are manufacturing (42%), transportation (38%), and construction (36%). Retail
firms are least likely to have access to external financing (21%). The sectors with the largest share of firms with access
to what we classify as internal financing are agriculture or mining (64%), and information and communication (59%).
10
savings (Column 2) and family/friends (Column 3), but more likely to access unemployment ben-
efits (Column 4). Male-owned firms were more likely to have access to personal savings. Finally,
firms in the construction sector were less likely to receive community donations (Column 6).
Liquidity and Price Responses. Cash outflows were higher than inflows for 41.8% of businesses
(Figure 19), especially for firms relying on in-person interactions (Table 14, Column 1). Most
businesses (78.2%) were concerned about their cash flow situation over the next 3 months (Figure
19, Panel B), especially hotels, cafes, restaurants, transportation firms, and firms relying on in-
person interactions (Table 14, Column 2). Concerns were driven by reduced revenue (76.3%), the
inability to secure funds (22.9%), and challenges in paying suppliers (17.9%, Figure 19, Panel C).
23.5% of firms reduced prices during the pandemic (Figure 20, Panel A); especially smaller
firms (Table 14, Column 3). Panels B to E present crosstabs between firms’ financial conditions and
the response in product prices. Businesses with more outflows than inflows reduced their product
prices more often (29.2%) than firms with inflows greater than outflows (21.0%). Similarly, firms
that were very concerned about their cash flow situation over the next 3 months reduced prices
more often (33.2%) than firms that were not (11.0%). Among the various cash flow concerns,
businesses who were unable to secure funds were most likely to reduce prices (33.0%), as were
firms facing challenges in paying their suppliers (30.3%). Furthermore, price decreases were more
likely among firms that accessed crowdsourced capital (32.8%) or applied for bank loans (27.3%),
than among firms that had not filed any financing applications (21.6%).
Summarizing, our results persistently show that negative financial conditions, originating
either through lack of access to capital or negative cash flows, have led firms to lower their product
prices. These results are in line with Kim (2020) who shows that businesses facing a credit crunch
temporarily decrease their prices.
Contribution. The insights from this survey are useful beyond understanding the immediate im-
pact of the COVID-19 pandemic. In particular, information on the financial conditions of small
private businesses has been largely unavailable in the United States. The best existing sources are
the Survey of Small Business Finance (SSBF) and the FED Small Business Credit Survey. However,
the SSBF has not been conducted since 2003.11 Meanwhile, in the last two decades, the financing
sources of small businesses have significantly changed, with a decline in bank lending and a rise
in nonbank sources of financing such as finance companies and FinTech lenders (Gopal and Schn-
abl, 2020). Our survey contributes by providing a timely snapshot of small business finances. We
highlight the importance of informal sources of financing, thereby revealing information on busi-
nesses with no credit that are typically ignored in prior studies.12 The FED Small Business Credit
Survey has been conducted annually since 2016. However, unlike our paper, they do not provide
information on how financing conditions and business challenges vary by business and owner
characteristics. Beyond that, our survey provides detailed information on access to financing, and
responses of non-employer firms.13
11 See Mach (2007) for detailed information on the coverage and usage of the SSBF.
12 Conventional wisdom suggests that SMEs largely rely on internal financing and that the sources of financing vary
with firm size and age (Berger and Udell, 1998). Our results corroborate this view. However, lack of detailed microdata
on small business finances has made it hard to identify how access to external financing varies by industry.
13 While the Small Business Credit Survey does provide reports on non-employer firms, the breadth of questions on
11
IV.C Business & Household Responsibilities
A special feature of the COVID-19 recession is that it came alongside a dramatically increased
household burden for many individuals, due to a combination of school closures, requirements
to quarantine, and the unavailability of hired domestic help. The third module of our survey,
therefore, explores the interaction between business and household responsibilities. For reference,
most respondents to our survey live with two to five other people (63.5%), while 23.5% live with
one other person (see Figure 21 for details).
Interaction of Business and Household. We first explore how the pandemic affected the ease
of both business and household work, and the interaction between the two. Panel A of Figure
22 shows that 31.5% of business owners/managers stated that their household responsibilities
affected their ability to focus on work during the crisis a lot. Interestingly, Panel B shows that
respondents who could sometimes work remotely were the worst affected, as opposed to those
who could work remotely all the time or never. Similarly, 28.9% of business owners/managers
reported that business responsibilities had made it very difficult to take care of their household
(Panel C), again with respondents who could sometimes work remotely doing worst (Panel D).
Table 15 explores how firm characteristics affect the owner/manager’s ability to focus on their
businesses and their households. The dependent variable takes a value of one if the manager re-
ported struggling a lot or more in conducting their business or household work. Female own-
ers/managers were most likely to struggle with focusing on their business due to household re-
sponsibilities; they were also more likely to report that their business responsibilities affected their
ability to take care of their family members. In addition, managers of businesses that were more
affected by the pandemic (hotels, cafes, and restaurants, larger firms, firms that require in-person
interactions) were also finding it hard to focus on their household. This finding highlights that the
financial struggles of small businesses during the COVID-19 pandemic had direct effects on the
households of business owners and managers.
Household Activities. We further look at what activities respondents spent more time on since
the start of the crisis. Panel A of Figure 23 shows that respondents were spending more time on
business work (57.1%), housework (48.6%), education of children (31.0%), care of self-isolating
household members (17.8%), and daycare (17.2%). Overall, 54.8% of respondents spent less than
3 hours a day on domestic activities, though 18.9% spent at least 5 hours a day (Panel B).
Table 16 explores heterogeneities in these responses across firm characteristics. Male-managed
and female-managed firms reported similar degrees of additional business work requirements
(Column 1). However, female-managed firms reported a disproportional increase in the need to
spend additional time on housework (Column 2), daycare (Column 3), and education for children
(Column 4) in response to the crisis. In Column 5, we explore the number of hours spent on house-
work (rather than the incremental burden imposed by the pandemic). Female managers/owners
and owners of smaller businesses reported spending more hours on household work.
Panel A of Figure 24 explores which home activities had the largest negative effect on the
ability of owners to focus on their businesses. Respondents who spend more time caring for
sources of financing in our survey is significantly greater.
12
household members have seen their ability to focus on the business being affected most strongly,
e.g. due to child daycare (58.3%), education for children (50.7%), or caring for a dependent/self-
isolating adult (49.6%/48.0%). Panel B shows that respondents who spent more hours a day on
household work had a harder time focusing on their business responsibilities.
Household Expenses and Income. We also asked respondents whether they struggled to pay their
usual household expenses in the past month. Panel A of Figure 25 shows that 35.4% of respon-
dents said it was either difficult or very difficult. The Appendix looks at heterogeneities in these
responses (Table A.2, Column 1) and finds that owners/managers struggling with making house-
hold expenses were more likely to work in firms most affected by the pandemic (i.e., restaurants,
smaller businesses, and businesses that rely on in-person interactions). This highlights the close
linkage between personal and business finances for owners and managers of small businesses.
Similarly, we asked business owners/managers what sources of money they had access to.
Panel B of Figure 25 shows that 57.9% reported that they were earning a salary, 55.1% had access
to personal savings and 14.4% had access to income from a second business. In the Appendix, we
verify that access to these additional income sources correlates with whether or not respondents
had difficulties in paying usual household expenses (Table A.2, Column 2).
IV.D Business Employees
This section explores how business employees have been affected by the pandemic; it comple-
ments the information elicited directly from employees of small businesses, which we discuss in
Section VII. Naturally, our sample for this module was restricted to employer businesses.
Layoffs, Furloughs, and Hires. Figure 26 presents information on business employment changes.
44.5% of employers reduced the number of employees as a result of the pandemic, while 4.0% of
employers increased their workforce (Panel A). This finding is in agreement with Bartik, Bertrand,
Cullen, Glaeser, Luca, and Stanton (2020), who highlight that many workforce reductions at small
businesses had already occurred by late April. Among firms that decreased employment, 27.5%
laid off their workers, 30.0% furloughed workers, and the rest did a combination of the two (Panel
B). Firms reported that these employment changes affected their full-time workers (59.7%), part-
time workers (45.3%), and contractors/outside consultants (27.2%, Panel C). Finally, most firms
stated that women and men were equally affected (53.4%), though 13.3% said women were more
affected, compared to 9.2% stating men were more affected (Panel D).
Figure 27 explores heterogeneities in the change of business employees by firm characteris-
tics. Medium-sized firms were most likely to lay off workers, with 55.2% of 10-49 employee firms
reducing their workforce (Panel A). Furthermore, the likelihood of increasing the workforce in-
creased with firm size: 11.8% of 250-499 employee firms hired employees while only 1.7% of the
smallest firms did. This finding is corroborated by Campello, Kankanhalli, and Muthukrishnan
(2020), who argue that small firms nearly halted their hiring. Panel C confirms the stronger im-
pact on female employment during the pandemic; 47.4% of majority-female firms decreased their
workforce, compared to 40.6% of majority-male firms. Finally, being able to work from home was
associated with fewer layoffs: 22.4% of firms where all employees could be working from home
reduced their workforce, compared with 51.5% of firms where no employees could do so.
13
Working from Home. Panel A of Figure 28 shows that at 29.3% of businesses all employees were
able to work from home for more than a few days, while 37.1% of firms could not facilitate any
remote work at all. Among firms that permitted work from home, 88.7% provided at least some
of their workers with equipment/resources to support their remote work (Panel B). Column 1
of Table 17 shows that firms in information and communication industries and firms with fewer
in-person interactions were more likely to allow employees to work from home. This finding sup-
ports the evidence in favor of remote work being more widespread among industries with more
educated workers (Bartik, Cullen, Glaeser, Luca, and Stanton, 2020).14 Female-managed firms
were also more likely to allow employees to work from home (Table 17, Column 1) and manufac-
turing firms were least likely to provide workers remote work support (Column 2). Crosstabs are
shown in the Appendix Figure A.2.
Worker Safety. We also asked firms how they were ensuring the safety of their workers. Panel A
of Figure 30 shows that 45.3% of firms said that the COVID-19 exposure of their employees while
working was low; 23.3% and 14.2% rated the risk as medium and high, respectively. Column 3
of Table 17 shows that male-owned firms and firms with fewer in-person interactions were less
likely to report a high risk of exposure to the virus. Panel B of Figure 30 shows that businesses
with low exposure risk were more likely to be optimistic about the future than businesses with
high exposure risk (66.5% compared with 60% saying they were optimistic).
Panel C of Figure 30 shows that firms reduced their employees’ exposure to the virus by pro-
viding hand sanitizer (63.0%), alcohol or disinfectant wipes (54.0%), disposable gloves (51.4%),
and protective masks (50.5%). They also provided monetary compensation by giving paid-time-
off (36.4%), health insurance (35.0%), and sick leave (32.9%, Panel D). Column 4 of Table 17
explores firm characteristics associated with providing employee compensation. Larger firms
and firms with fewer in-person interactions were more likely to provide compensation, whereas
restaurants, cafes, and hotels were less likely to do so.
Finally, we look at how employee risk correlates with business employment changes, protec-
tive measures, and pay. Panel A of Figure 31 shows that firms that reported a higher infection risk
at work reduced their workforce more often. Panel B does not reveal an increased use of a partic-
ular type of protective measure among high-risk firms. Lastly, Panel C shows that high-risk firms
were more likely to compensate employees with hazard pay: 26.8% of firms providing hazard pay
were high risk compared to 12.9% of firms that did not provide any additional support.
IV.E Business Survival Strategy
The last of the five randomized modules only shown to operational businesses asked business
owners/managers about their strategies to adjust to and survive the pandemic.
Client Adjustments. Panel A of Figure 32 shows that 80.6% of firms had to make changes to
accommodate their clients’ needs during the pandemic. Panel B shows that these changes include
providing online services (52.5% of firms), expanding digital payments (35.1%), offering delivery
14 Moreover, we compared the self-reported share of employees that could be working from home and the estimated
share by Dingel and Neiman (2020) by industry (see Figure 29 and Table 12). We assumed a share of 100%, 75%, 50%,
25% and 0% if the respondent stated that “all”, “more than half”, “about half”, “less than half”, and “none” could work
remotely, respectively. The statistics match very well with a Pearson correlation coefficient of 80.5%.
14
services (25.7%), and offering curbside pickup (24.4%). Table 18 explores how firm characteristics
affected the changes undertaken. Hotels, cafes, and restaurants, and retail firms were most likely
to provide delivery services and curbside pickup. Firms that rely on in-person interactions were
more likely to provide delivery and curbside pickup. Service firms were more likely to shift to
online services and less likely to provide curbside pickup.
Business Model Adjustments. Figure 33 looks at adjustments to the firms’ business models. Panel
A shows that 30.9% of firms made all of their sales online in the prior 30 days and another 27.8%
of firms made more than half their sales online. Panel B shows that firms used many tools to con-
tinue operations, in particular their website (53.9%), online advertising (51.9%), customer com-
munication tools (47.1%), and digital payments (43.9%). Panel C explores different approaches to
ensuring the availability of needed supplies during the pandemic: 23.5% of firms adjusted their
order schedules, 21.0% changed their delivery procedures, and 19.9% delayed orders. At the same
time, Panel D shows that 36.1% of firms reported that it has been difficult to change the delivery
of goods or services as a result of the pandemic.
Table 19 shows that hotels, cafes, and restaurants, and retail businesses were most likely to
use online advertising. The transportation industry and old firms were least likely to use online
advertising or digital payment tools (Columns 1 and 2). A high share of in-person interactions
was also associated with a lower likelihood of using digital payment tools. Order schedules and
delivery processes were most often adjusted by large businesses, and least often by information
and communication businesses (Columns 3 and 4). Finally, small businesses, businesses with few
in-person interactions, and businesses in information and communication had the least difficulties
in changing the delivery of their goods and services (Column 5).
Information Sources. Panel A of Figure 34 shows that 65.5% of managers/owners believed they
had sufficient information to make business decisions during the pandemic, though 22.8% re-
ported not having all the information needed. Most firms received their information about how
they could get support during the pandemic from government sources (57.5%), news on the inter-
net (55.6%), social media (51.3%), and TV news (44.2%, Panel B). Panels C to F of Figure 34 explore
heterogeneities in information availability by firm characteristics. Column 6 of Table 19 explores
these relationships in a regression framework. Firms with a high share of in-person interactions
and female-owned/managed firms were more likely to report insufficient information.
Finally, in Appendix Section A.II and Figure A.4, we explore how business tools, client accom-
modations, and information sources vary with the share of revenues coming from online sales.
V C LOSED B USINESSES
Up until now, our results have focused on the set of businesses that continued to operate by the
time of our survey in late April 2020. In this section, we focus on the set of businesses that ceased
operations. In particular, 16,147 respondents (32.9% of firms in our sample) reported closing their
businesses during the pandemic. Of these, 19.1% closed in February, 59.5% closed in March, and
11.3% shut after April 1st (Figure 35, Panel A). Panel B explores how owners/managers spent their
time during business closure. Common activities included planning for the re-opening (56.1%),
15
setting up an online website (26.9%), taking business-related courses or training (21.6%), securing
funding (20.3%), and looking for a job (19.7%).
Reasons for Closing. Panel C of Figure 35 shows that most businesses reported closing due to
government and health authority orders (61.7%), financial challenges (7.7%), and customer-related
reasons (6.9%). Table 20 explores how the reasons for firm closure varied by firm characteristics.
Larger firms (sales) and firms relying on in-person interactions were more likely to have closed
due to government orders (Column 1). Younger and smaller firms were more likely to have shut
due to financial challenges (Column 2). Firms relying less on in-person interactions were more
likely to report client-related reasons for closure (Column 3).
Plans for Re-Opening. Panel A of Figure 36 shows that most businesses (67.9%) planned to re-
open in the future, while 27.3% were unsure, and 4.9% reported that they did not plan to re-open.
The reasons for closure were related to firms’ expectations of re-opening. Panel B documents that
businesses that closed due to government orders, customer-related reasons, or because they were
facing logistical challenges were most likely to plan to re-open, with 70.2%, 68.6% and 68.9%,
respectively, responding in the affirmative. On the other hand, only 48.3% of businesses facing
financial challenges said they would re-open in the future. Panel C of Figure 36 shows that the
main reasons given for not re-opening were being unable to pay bills or rent (31.4%), personal
reasons (15.6%), and debt payments (10.0%).
We asked businesses that planned to re-open or that were unsure about their future re-opening
about the most important actions required for the business to re-open. Panel D of Figure 36 shows
the results. The most common required actions were government permission to operate (55.5%),
securing funding (21.5%), changing the business activities (4.9%), better understanding the market
(4.0%), and hiring skilled workers (1.1%). Table 21 looks at how firm characteristics affect firms’
most important reason for re-opening. Larger firms and firms in the service sector were more
likely to report requiring government permission to open (Column 1). Younger firms, smaller
firms, and male-managed firms were more likely to report the need to secure funds (Column 2).
To finance their re-opening, 42.4% of businesses planned to use their personal savings (Figure
36, Panel E). Other owners/managers were planning to borrow on their credit card (15.7%) or
make use of their firm assets (15.0%). However, a large fraction of respondents, 42.4%, did not
know how they were going to fund their re-opening.
Lastly, we asked respondents about their plans for the re-opened businesses. The products
will stay the same for 69.0% of firms, while 23.3% of firms planned to make some changes to the
products and services they offered prior to the closure (Figure 37, Panel A). Column 3 of Table
21 shows that businesses with fewer in-person interactions were more likely to plan changes in
offered products; construction firms were least likely to do so. 41.8% of firms planned to re-hire the
same employees or workers as before for the re-opening and 14.9% were retaining their employees
and paying them at least a part of their wages during the closure (Figure 37, Panel B).
Appendix Section A.III and Figure A.5 further explore how the most important action for
re-opening and future products differ by closing reason.
16
VI B USINESS S UPPORT N EEDS
The final section answered by business owners/managers further explores the challenges they
were facing during the pandemic and asks them about the policies that would optimally help
them going forward. These questions were shown to all open and closed businesses we surveyed.
Biggest Challenges. Panel A of Figure 38 explores firms’ biggest challenges during the pandemic.
Access to capital/funding was the biggest challenge for 38.9% of businesses, especially for restau-
rants, cafes, and hotels, younger firms, and male-owned/managed firms (Table 22, Column 1). Of
these, 46.9% were afraid of borrowing money because they could not pay it back, and 25.3% did
not know where to get the money they required to keep their business open (Figure 39, Panel A).
Worries brought on by their employees were the main challenge for 15.2% of firms. This
concern was particularly common among larger firms, older firms, and firms with more in-person
interactions (Table 22, Column 2). Among firms reporting this to be their primary challenge, 33.5%
were concerned about not doing the type of work that allowed employees to work from home, and
27.2% were worried about the exposure of their employees to the virus (Figure 39, Panel B).
Government interventions and regulations were the biggest worries for 14.5% of businesses.
These businesses were more likely to be large, to require more in-person interactions, and to be
male-owned/managed (Table 22, Column 3). Panel C of Figure 39 shows that this concern was
driven by government-imposed regulations that were required to keep the business open (30.5%),
or because their business had been designated as non-essential and had to be closed (18.1%).
A further 10.8% of respondents stated that taking care of household members was their biggest
challenge. This was true especially for respondents from female-owned/managed firms (Table 22,
Column 4). Panel D of Figure 39 shows that these respondents felt burnt out taking care of both
their firm and household (47.6%), were afraid of bringing the virus home (27.0%), and were con-
cerned about their inability to find someone to take care of their children’s education (15.0%).
Using online tools was the biggest challenge for 8.7% of respondents. These tended to be own-
ers or managers from smaller firms, from businesses that relied on fewer in-person interactions,
or from female-managed firms (Table 22, Column 5). Of these respondents, 28.0% were over-
whelmed by the amount of resources available online, and 22.3% did not know about the tools to
support businesses available online (Figure 39, Panel E).
Finally, 3.8% of respondents stated that gathering information was their biggest challenge,
especially owners/managers of smaller firms (Table 22, Column 6). For these individuals, the
dominant issue (53.2%) was the high amount of contradictory information (Figure 39, Panel F).
Support Policies. We then asked businesses which three policies would help them the most in
surviving the pandemic. Panel B of Figure 38 shows that the most commonly requested support
policies were salary subsidies (42.7%), access to loan guarantees (41.1%), tax deferral (33.7%), loan
repayment deferral (29.3%), rent deferral (29.1%), utility subsidies (26.0%), and support in taking
care of household members (25.1%). Table 23 explores how suggested policies varied by firm
characteristics. The dependent variable takes a value of one if the respondent mentioned a policy
as one of the three main policies that would be beneficial in supporting the business. Larger
firms, female-owned/managed firms, and firms that relied on more in-person interactions were
17
more likely to request salary subsidies (Column 1). Male-owned/managed firms and younger
firms were more likely to request access to loan guarantees (Column 2). Tax deferrals were most
popular among mid-sized, male-owned/managed, and construction firms (Column 3).
VII E MPLOYEES
While the previous sections focused on the perspective of business owners and managers, in the
final section, we focus on the responses of 15,435 self-reported employees. The results in this
section are based on all employee respondents in our survey, including the approximately 25% of
employees working for firms with more than 500 employees. For completeness, Appendix Section
A.VI reproduces all graphs in this section while excluding employees working for firms with more
than 500 employees. We conclude that they do not affect any of the patterns we highlight below.
Employee Demographics. Appendix Figures A.6, A.7, A.8 and A.9 provide information on the
employees’ ages, educational qualifications, locations, sectors of employment, and the type of
workers covered by our sample. Table 2 shows that, on average, our sample corresponds relatively
closely to the characteristics of U.S. employees. A few noticeable differences include the larger
share of female workers in our sample (63.3% vs. 58.2%), as well as more employees from the
information/communication sector (8.5% vs. 1.8%) and the services sector (17.4% vs. 12.4%).
Recently Unemployed. We first explore the responses of individuals that had worked at some
point during the last three months but were unemployed as of late April 2020. This applies to
26.3% of individuals in our employee sample.15
Panel A of Figure 40 shows that the primary reasons for unemployment were business clo-
sures (40.4%) or layoffs by the former employer (24.4%) due to the pandemic. 7.6% stated their
unemployment was due to a reason other than the pandemic. 4.1% of respondents reported that
they could not work because they had to educate their children at home. Panel B of Figure 40
shows that this reason was given by 4.6% of the unemployed women and only 2.3% of the unem-
ployed men, confirming the impact of school closures on gender inequalities (see Alon, Doepke,
Olmstead-Rumsey, and Tertilt, 2020b).
Next, we look at the demographics of unemployed individuals (Figure 41). Self-reported
unemployment was more common among less educated, younger, and older respondents; it was
lowest within the age group of 36-45 years. Moreover, respondents that worked at smaller firms
were more likely to be unemployed: 29.5% of former sole-proprietorships compared to 11.9% of
workers that worked at large businesses (≥500 employees) were unemployed.
Figure 42 explores what individuals were doing while being unemployed. 17.8% of workers
reported doing nothing, 17.7% were homeschooling children, 13.6% were looking for a job in
another sector, and 4.2% were taking business-related training.
Panel A of Figure 43 shows that 32.3% of the unemployed were receiving government assis-
tance, with the most common being unemployment benefits (65.8%, Panel B). However, 58.3%
of the unemployed had not applied for any government/NGO assistance by late April 2020. Of
15 Note that this measure is not comparable to the official unemployment rate in the U.S. – we do not include respon-
dents that have been unemployed for longer than three months, account for job search, job availability or active duty
forces, or restrict age. For reference, see: https://www.bls.gov/cps/cps_htgm.htm.
18
those, Panel C shows, many mentioned being ineligible (24.7%) or not knowing the application
procedures (24.8%). 28.5% of respondents stated they did not need to apply for assistance.
Employees & Household Responsibilities. Similar to our analysis of small business owners and
managers, Figure 44 shows that many households faced increased care burdens during the COVID-
19 crisis. School closures increased the burden of care for school-aged children: 27.5% of respon-
dents served as primary caregivers and 9.6% as non-primary caregivers (Panel A). Similarly, Panel
B shows that for 10.8% of respondents, the pandemic also necessitated additional care of depen-
dent adults. Care duties were not distributed equally across the population – 33.4% of women
were primary caregivers of school-age children and 6.2% were non-primary caregivers, compared
to 19.7% and 15.1% of men, respectively.
Household activities affected the ability of employees to focus on their job a lot or more for
24.2% of workers (Figure 45, Panel A). That share was 15.6% for workers without children, and in-
creases to 42.5% for primary caregivers and 27.2% for non-primary caregivers of children affected
by school closures (Panel B). Similarly, women were more likely than men to report that house-
hold responsibilities were significantly affecting their ability to focus on work (26.2% compared to
19.2%, Panel C); the most affected age group were between 36-45 years (30.1%, Panel D).
Perhaps surprisingly, employees that work remotely some of the time reported most often
(32.3%) to have their jobs affected, compared to employees that could not work remotely at all
(23.0%) and employees that could work remotely all the time (20.3%, Figure 45, Panel E). This is
consistent with patterns found among business owners and managers (see Figure 22).
Column 1 of Table 24 looks at these heterogeneities in a multivariate regression framework.
Respondents that were female, young to middle-aged, lived in urban areas, worked remotely
some of the time, and, particularly, primary caregivers of school-age children had the greatest
difficulties in fulfilling their job due to household responsibilities.
In addition to household responsibilities affecting the employees’ ability to conduct work,
work responsibilities also affected household duties. 18.2% of respondents said their work had
affected their ability to take care of their households a lot or more (Figure 46), compared to 28.8%
of firm owners/managers stating the same. On the other hand, 37.7% of respondents stated that
their job was not at all affecting their ability to take care of the household. Female, young to
middle-aged workers, and workers taking care of children due to school closures experienced the
most difficulties in caring for their household due to job responsibilities (Table 24, Column 2);
workers that could work completely remotely had fewer difficulties.
We explore the amount of time spent on domestic/care activities, and how it varied with
household characteristics, in the Appendix Section A.IV, Figure A.10, and Table A.3.
Employee Income Sources, Benefits, and Concerns. Information on levels and heterogeneities in
employee income sources, benefits, applications for government/NGO assistance, and top con-
cerns can be found in Appendix Section A.IV and Figures A.11, A.12 and A.13.
Working from Home. One of the most significant changes during the current pandemic is the
widespread emergence of remote work. Figure 47 shows that 31.9% of employees reported being
able to work remotely all the time, 23.1% could do so sometimes, and 45.0% were unable to work
19
remotely. Among those who could work remotely at least partially, the most common challenges
reported were multi-tasking between work and household responsibilities (28.9%), the nature of
the work (16.1%), difficulties in collaborating with others (11.5%), a lack of resources (9.4%), and
household responsibilities (3.6%).
Figure 48 shows heterogeneities in the main challenge of working from home. Balancing work
and home responsibilities was the dominant concern for primary caregivers of children affected
by school closures (51.4%, Panel A) and for respondents that had to take care of a dependent
adult (Panel B). Difficulty in collaborating with others was more prevalent among highly educated
workers (Panel C), with 15.2% of employees with a master’s degree stating this reason, and 2.4%
of respondents with less than high school education. Women were more likely than men to state
multi-tasking between work and household responsibilities was the main concern, and less likely
than men to emphasize the nature of their work (Panel D). Similar to previous trends, the age
group from 36 to 45 years was particularly occupied with their household duties (Panel E). Finally,
limited communication tools were most often mentioned as a concern in rural areas (Panel F).
Employee Information and Outlook. Figure 49 shows that most employees received information
about support opportunities during the pandemic from internet news (54.1%), TV (51.7%), and
social media (49.5%), which were ranked above government or health authority (40.2%).
Finally, we asked workers about their optimism for future employment; 33.6% of respondents
were very optimistic, 29.4% were somewhat optimistic, 11.8% stated that they were somewhat
pessimistic, and 4.1% were very pessimistic (Figure 50). Optimism was positively correlated with
education; 23.0% of respondents with no formal education were pessimistic about their future em-
ployment compared to only 13.0% with at least a master’s degree. Moreover, young respondents
and respondents living in urban areas were less optimistic in our sample.
VIII C ONCLUSION
Small businesses are a key contributor to economic activity in the United States. Yet, we know very
little about how these small businesses were affected by the current crisis. This paper presents
results from a large-scale survey on the impact of COVID-19 on small business owners, managers,
and employees. Our survey was fielded on the Facebook platform in late April 2020 and obtained
over 66k responses to 136 questions. Our survey covers respondents across closed and operational
businesses, employees and the self-employed, and themes ranging from financial conditions to
household responsibilities of businesses owners, managers, and employees.
Our findings highlight the struggles faced by small businesses and their employees, and un-
derscore the need for policy interventions. Going forward, we plan to expand our study to busi-
nesses outside the United States. Such a study would help us compare how differences in gov-
ernment responses and orders impacted small businesses in various countries, and provide better
evidence for effective policy interventions. We also hope to explore a follow-up survey among
U.S. businesses to study how business struggles, expectations, and needs have changed since the
beginning of the crisis. Variation in state-level responses within the U.S. could further highlight
the trade-off between health and economic outcomes.
20
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22
F IGURES
Figure 1: Flowchart of Core Survey Paths. This figure provides a broad overview of the
path users could take through the survey. Numbers in parentheses refer to the number
of people who responded to either the first or last question in the corresponding section.
For example, 97,152 people answered the business role question, 15,435 responded in a
way that indicates they were employees, and 9,837 people answered the last question of
the employee module. Finally, the Business Employment section was only given to firms
with more than one employee, since its aim was to ask about employment outcomes,
causing the lower number. The drop off rate is large but within the bounds of what is
expected given the survey length and platform.
23
Figure 2: Basic Business Information
(A) Firm Age Distribution (B) Firm Sales Distribution
(C) Firm Sectoral Distribution (D) Firm Location
24
Figure 3: Business Employees
(A) Firm Employment
(B) Firm Owner Gender (C) Firm Gender Balance
Figure 4: Business Operations
(A) Firm Profits (B) Firm In-Person Operations
25
Figure 5: Business Closure
(A) Firm Currently Operational (B) Business Closure, by Firm Age
(C) Business Closure, by Firm Sales (D) Business Closure, by Firm Sector
(E) Business Closure, by Firm Gender Balance (F) Business Closure, by In-person Operations
26
Figure 6: Future Expectations
(A) Future Expectations (B) Future Expectations, by Firm Sector
(C) Future Expectations, by Firm Age (D) Future Expectations, by Firm Sales
(F) Future Expectations, by Firm In-Person
(E) Future Expectations, by Firm Gender Balance Operations
27
Figure 7: Business Challenges
Figure 8: Business Workload During COVID-19
(A) Business Workload
(B) Reason for Lower Workload (C) Accommodation for Higher Workload
28
Figure 9: Workload Crosstabs
(A) Workload, by Business Employment (B) Workload, by Business Sector
(C) Workload, by Business Age (D) Workload, by Business Sales
(E) Workload, by Business Gender Balance (F) Workload, by Business In-Person Operations
29
Figure 10: Change in Business Sales During COVID-19
(A) Business Sales
(B) Business Sales Drop (C) Business Sales Growth
Figure 11: Business Survival
(A) Business Persistence (B) Business Requirements
30
Figure 12: Survival Expectations Crosstabs
(A) Business Survival, by Business Employment (B) Business Survival, by Business Sector
(C) Business Survival, by Business Age (D) Business Survival, by Business Sales
(E) Business Survival, by Business Gender (F) Business Survival, by Business In-person
Balance Operations
31
Figure 13: Business Supply Challenges
(A) Business Supply Challenges (B) Types of Business Supply Challenges
Figure 14: Virus Containment and Operational Responses
(A) Business Actions
(B) Business Changes (C) Types of Changes
32
Figure 15: Business Financial Struggles
Figure 16: Business Bank Loan
(A) Business Bank Loans
(C) Business Bank Loans, Non-Employer Firms
(B) Business Bank Loans, Employer Firms Only Only
33
Figure 17: Formal Sources of Business Capital
(A) Formal Sources of Business Capital
(B) Formal Sources of Business Capital, (C) Formal Sources of Business Capital,
Employer Firms Only Non-Employer Firms Only
34
Figure 18: Informal Sources of Business Capital
(A) Informal Sources of Business Capital
(B) Informal Sources of Business Capital, (C) Informal Sources of Business Capital,
Employer Firms Only Non-Employer Firms Only
35
Figure 19: Business Cash Flow
(A) Past Cash Flows
(B) Future Cash Flows (C) Cash Flow Concerns
36
Figure 20: Product Pricing
(A) Product Pricing
(B) Product Pricing, by Past Cash Flows (C) Product Pricing, by Future Cash Flows
(D) Product Pricing, by Cash Flow Concerns (E) Product Pricing, by Business Applications
37
Figure 21: Household Size
Figure 22: Ease of Work
(B) Impact of Household on Business, by
(A) Impact of Household on Business Remote Work
(D) Impact of Business on Household, by
(C) Impact of Business on Household Remote Work
38
Figure 23: Time Use
(A) Household Activities (B) Time Spent on Household Activities
Figure 24: Time Use Crosstabs
(A) Impact of Household on Business, by (B) Impact of Household on Business, by Time
Household Activities Spent on Household Activities
Figure 25: Household Income and Expenses
(A) Household Expenses (B) Personal Finances
39
Figure 26: Business Employment Changes
(A) Employment Change (B) Type of Change
(C) Type of Workers (D) Effect by Gender
40
Figure 27: Business Employment Changes Crosstabs
(A) Employment Change, by Firm Size (B) Employment Change, by Sales
(D) Employment Change, by Work From Home
(C) Employment Change, by Gender Balance Abilities
41
Figure 28: Business Employees - Work From Home
(A) Work From Home Access (B) Work From Home Support
Figure 29: Share of employees reported to be able to work from home in comparison to
the Dingel and Neiman (2020) measure of remote work feasibility
We assumed a share of 100%, 75%, 50%, 25% and 0% if the respondent stated that “all”, “more
than half”, “about half”, “less than half”, and “none” of their employees could work remotely,
respectively. The Dingel and Neiman (2020) measure is based on pre-crisis surveys describing the
typical experience of U.S. workers for each industry. The statistics match very well with a Pearson
correlation coefficient of 80.5%.
42
Figure 30: Business Employee Risk & Protection
(B) Employee Risk and Optimism about
(A) Employee Risk Business Future
(C) Employee Protection (D) Employee Support Pay
43
Figure 31: Employee Risk Crosstabs
(A) Employment Change, by Employee Risk (B) Employee Risk, by Employment Protection
(C) Employee Risk, by Employee Pay
Figure 32: Client Accommodation
(A) Accommodation for Clients (B) Types of Adjustments for Clients
44
Figure 33: Business Adjustments
(A) Online Sales (B) Business Tools
(C) Supply Changes (D) Ease of Change
45
Figure 34: Business Information
(A) Access to Information (B) Information Sources
(C) Access to Information, by Sales (D) Access to Information, by Sector
(F) Access to Information, by Male
(E) Access to Information, by Firm Age owned/managed
46
Figure 35: Business Closure
(A) Date of Closing (B) Activities during Business Closure
(C) Reason for Closing
47
Figure 36: Business Re-opening
(B) Re-opening Expectations, by Closure
(A) Plans to Re-open Reason
(C) Reason for Not Opening (D) Actions for Opening
(E) Cash Sources for Opening
48
Figure 37: Business Future
(A) Future Products (B) Future Employment
Figure 38: Business Challenges and Support
(A) Business Challenges (B) Business Support
49
Figure 39: Business Challenges
(A) Funding Challenges (B) Employee Challenges
(C) Government Challenges (D) Household Support Challenges
(E) Online Tool Challenges (F) Information Challenges
50
Figure 40: Employee/Worker Self-Reported Main Reason for Unemployment
(A) Main Reason (B) Main Reason, by Gender
51
Figure 41: Employee/Worker Unemployment Demographics
(B) Fraction Unemployed, by Educational
(A) Fraction Unemployed, by Age Background
(C) Fraction Unemployed, by Size of Most
Recent Company (D) Fraction Unemployed, by Gender
(E) Fraction Unemployed, by Most Regular
Employment
52
Figure 42: Employee/Activities during Unemployment
Figure 43: Government/NGO Assistance for Unemployed
(A) Government/NGO Assistance Applications,
by Unemployment Status
(B) Types of Government Support Receiving, (C) Reasons for not Applying for Government
by Unemployment Status Assistance, by Unemployment Category
53
Figure 44: Household Care Responsibilities
(A) Childcare due to School Closures (B) Care of a Dependent Adult
(C) Childcare due to School Closures, by Gender (D) Care of a Dependent Adult, by Gender
54
Figure 45: Employee/Worker: Household Impact on Job
(B) Household Impact on Job, by Childcare due
(A) Household Impact on Job to School Closures
(C) Household Impact on Job, by Gender (D) Household Impact on Job, by Age
(E) Household Impact on Job, by Work From
Home
55
Figure 46: Employee/Worker: Job Impact on Household
Figure 47: Employee/Worker: Remote Work
(A) Remote Work (B) Main Challenge in Working Remotely
56
Figure 48: Employee/Worker Remote Work Challenges
(A) Main Challenge in Working Remotely, by (B) Main Challenge in Working Remotely, by
Childcare due to School Closures Care for a Dependent Adult
(C) Main Challenge in Working Remotely, by (D) Main Challenge in Working Remotely, by
Education Gender
(E) Main Challenge in Working Remotely, by (F) Main Challenge in Working Remotely, by
Age Area
57
Figure 49: Employee/Worker: News Sources during the Pandemic
58
Figure 50: Employee/Worker: Optimism about Future Employment
(B) Optimism about Future Employment, by
(A) Optimism about Future Employment Education
(C) Optimism about Future Employment, by (D) Optimism about Future Employment, by
Gender Age
(E) Optimism about Future Employment, by (F) Optimism about Future Employment, by
Area Childcare due to School Closures
59
TABLES
Table 1: Business Demographics vs. Nationwide
(1) (2)
Facebook Sample US
Firm Size (Employees)1
1 to 4 people 55.4% 55.8%
5 to 9 people 13.0% 20.0%
10 to 49 people 15.3% 19.7%
50 to 249 people 7.1% 3.7%
250 to 499 people 2.3% 0.4%
500 people or more 6.9% 0.4%
Firm Location1
Urban/Metropolitan 31.5% 84.6%
Suburban 44.9% N.A.
Rural/Non-Metropolitan 23.6% 15.4%
Firm Age1
Less than 1 year 9.9% 9.0%
Between 1 and 2 years 10.5% 6.7%
Between 2 and 5 years 17.3% 15.6%
5 years or more 62.2% 68.7%
Sector1, 2
Agriculture, farming, forestry, or mining 2.5% 2.9%
Construction 5.3% 7.4%
Manufacturing 2.8% 4.5%
Retail and wholesale trade 24.2% 23.1%
Services 39.3% 49.0%
Transportation and logistics 2.8% 3.9%
Other 23.1% N.A.
1 Data available at https://www.census.gov/programs-surveys/bds/data/data-tables.html.
2 Due to differences between our survey and SIC classification, we aggregated “Hotels, cafes, and restaurants” within
“Retail and wholesale trade” and “Information and communication” within “Services”.
60
Table 2: Employee/Worker Demographics vs. Nationwide
(1) (2)
Facebook Sample US
Basic Demographics1, 2
Female 63.3% 58.2%
Urban 71.5% 80.7%
Full vs. Part Time3
Full Time 81.5% 85.7%
Part Time 14.0% 14.3%
Highest Educational Level4
No formal education 0.2% 0.3%
Less than high school graduate 2.3% 9.6%
High school graduate or GED 18.2% 28.1%
Some college, no degree 23.0% 15.7%
Associate’s degree 11.9% 5.9%
Bachelor’s degree 24.0% 22.5%
Technical, trade, or vocational school 7.5% 4.4%
Master, doctorate, or professional degree 12.8% 13.5%
Age5
≤25 (16-24) 12.0% 12.3%
26-35 (25-34) 28.0% 22.8%
36-45 (35-44) 23.9% 21.0%
46-55 (45-54) 20.8% 20.3%
56-65 (55-64) 12.3% 17.1%
66 and older (65 and older) 3.0% 6.6%
Sector6
Agriculture, farming, forestry, or mining 1.5% 2.0%
Construction 4.4% 7.2%
Hotels, cafes, and restaurants 6.8% 9.3%
Information and communication 8.5% 1.8%
Manufacturing 8.6% 10%
Retail and wholesale trade 9.3% 12.5%
Services 17.4% 12.4%
Transportation and logistics 6.0% 5.7%
Other 37.4% 39.0%
1 Fraction of civilian labor force that is female and 16 or older: https://www.census.gov/quickfacts/
fact/table/US/LFE046218. 2 2010 Census: https://www.census.gov/programs-surveys/geography/
guidance/geo-areas/urban-rural/2010-urban-rural.html. Note this is the overall population, not
the population of the labor force. 3 Employee CPS April 2020: https://www.bls.gov/web/empsit/
cpseea06.htm. The Facebook numbers do not sum to 100% since there were other options with
low response rates (e.g. “Paid day laborer"). 4 Nationwide data from the CPS Annual Social and
Economic Supplement, 2012: https://www.census.gov/content/census/en/data/tables/2019/demo/
educational-attainment/cps-detailed-tables.html. We restrict to respondents aged 26 and older to en-
sure comparatibility. 5 CPS: https://www.bls.gov/cps/cpsaat18b.htm. The CPS age buckets are listed in
parentheses, due to slight differences. 6 CPS: https://www.bls.gov/cps/cpsaat18b.htm.
61
Table 3: Open Businesses
This table looks at how businesses’ operational status varies by firm and owner characteristics.
The dependent variable takes a value of one if the respondent replied yes to “Is this business cur-
rently operational or engaging in any revenue-generating activities?". Age is the time since the busi-
ness opened, industry is the self-reported industry of the business, sales is the total revenues of
the business in 2019, in-person interactions captures the share of the business’s interactions be-
tween customers and employees that need to be conducted in the same physical location, and
male-owned or managed takes a value of one if the gender of the business owner or manager
(respondent) is male. The omitted base groups are age - less than 1 year, industry - agriculture
or mining, sales - business not operational in 2019, in-person interactions - more than half of the
interactions are in-person, and gender - female-owned or managed.
Business Operational
(1) (2) (3) (4) (5) (6)
Age - Between 1 and 2 years 0.024* −0.006
(0.013) (0.014)
Age - Between 2 and 5 years 0.065*** −0.000
(0.011) (0.013)
Age - 5 years or more 0.120*** 0.007
(0.010) (0.012)
Construction −0.030 −0.108***
(0.019) (0.020)
Hotels, cafes, and restaurants −0.201*** −0.193***
(0.019) (0.020)
Information and communication 0.034* −0.057***
(0.018) (0.019)
Manufacturing 0.038* −0.068***
(0.021) (0.022)
Other −0.150*** −0.179***
(0.017) (0.018)
Retail and wholesale trade −0.043** −0.055***
(0.017) (0.018)
Services −0.173*** −0.180***
(0.016) (0.017)
Transportation and logistics −0.067*** −0.127***
(0.022) (0.023)
Sales less than $50,000 0.008 0.020
(0.015) (0.018)
Sales between $50,000 - $999,999 0.136*** 0.174***
(0.015) (0.019)
Sales $1,000,000 or more 0.280*** 0.308***
(0.016) (0.019)
In-person interactions - Half 0.214*** 0.229***
or less (0.005) (0.006)
Male-owned or managed 0.064*** 0.021***
(0.006) (0.006)
Obs. 30,511 31,056 27,552 31,072 28,198 25,705
R2 0.008 0.025 0.045 0.050 0.004 0.124
62
Table 4: Business Optimism
This table looks at how business optimism varies by firm and owner characteristics. The depen-
dent variable takes a value of one if the respondent replied “Extremely Optimistic” or “Somewhat
Optimistic” to “How do you feel about the future of this business?". Open takes a value of one if the
business is operational as of the time of the survey. The omitted base groups are age - less than 1
year, industry - agriculture or mining, sales - business not operational in 2019, in-person interac-
tions - more than half of the interactions are in-person, and gender - female-owned or managed.
Other variables are as defined in Table 3.
Optimistic
(1) (2) (3) (4) (5) (6) (7)
Open 0.085*** 0.071***
(0.006) (0.007)
Age - Between 1 and 2 years −0.019 −0.022
(0.013) (0.014)
Age - Between 2 and 5 years −0.039*** −0.045***
(0.011) (0.014)
Age - 5 years or more −0.041*** −0.069***
(0.010) (0.013)
Construction 0.034 0.041*
(0.022) (0.024)
Hotels, cafes, and restaurants −0.069*** −0.046**
(0.021) (0.023)
Information and communication 0.045** 0.040*
(0.022) (0.023)
Manufacturing 0.001 −0.030
(0.025) (0.027)
Other 0.020 0.032
(0.019) (0.020)
Retail and wholesale trade −0.012 −0.011
(0.020) (0.021)
Services 0.019 0.041**
(0.019) (0.020)
Transportation and logistics −0.029 −0.022
(0.025) (0.027)
Sales less than $50,000 −0.053*** −0.024
(0.015) (0.018)
Sales between $50,000 - $999,999 −0.068*** −0.025
(0.015) (0.019)
Sales $1,000,000 or more 0.008 0.057***
(0.016) (0.020)
In-person interactions - Half 0.065*** 0.041***
or less (0.006) (0.007)
Male-owned or managed −0.007 −0.010*
(0.006) (0.006)
Obs. 29,069 29,048 28,926 27,081 28,943 28,116 25,623
R2 0.007 0.001 0.003 0.004 0.004 0.000 0.017
63
Table 5: Biggest Challenge
This table looks at how business challenges vary by firm and owner characteristics. Businesses
were asked “What do you expect this business’s biggest challenge to be in the next few months?". The de-
pendent variable takes a value of one if the response is “Cash flow” (Column 1), “Lack of demand”
(Column 2), “Government or health authority orders” (Column 3), “Logistics (e.g. shipping, deliv-
ering services or goods)” (Column 4), “Finding supplies” (Column 5), or “Lack of staff” (Column
6). The omitted base groups are age - less than 1 year, industry - agriculture or mining, sales -
business not operational in 2019, in-person interactions - more than half of the interactions are
in-person, and gender - female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3) (4) (5) (6)
Lack of Government Finding Lack of
Cash flow demand orders Logistics supplies staff
Age - Between 1 and 2 years 0.004 −0.022 0.012 0.005 −0.010 −0.005
(0.018) (0.017) (0.011) (0.010) (0.009) (0.006)
Age - Between 2 and 5 years 0.006 −0.005 0.003 −0.003 −0.012 −0.005
(0.017) (0.016) (0.010) (0.009) (0.009) (0.006)
Age - 5 years or more −0.004 −0.025* 0.023** −0.007 −0.006 0.001
(0.016) (0.015) (0.010) (0.008) (0.008) (0.005)
Construction 0.016 0.076*** 0.026 −0.046*** 0.003 0.003
(0.024) (0.022) (0.018) (0.015) (0.013) (0.011)
Hotels, cafes, and restaurants 0.086*** 0.015 0.045** −0.057*** −0.009 0.023**
(0.024) (0.021) (0.018) (0.014) (0.012) (0.011)
Information and communication 0.122*** 0.042** −0.009 −0.048*** −0.038*** 0.004
(0.024) (0.021) (0.016) (0.015) (0.011) (0.010)
Manufacturing −0.010 0.107*** −0.026 −0.017 0.044*** −0.002
(0.026) (0.026) (0.018) (0.018) (0.016) (0.012)
Other 0.051** 0.010 0.040*** −0.060*** −0.010 −0.014
(0.021) (0.019) (0.015) (0.014) (0.011) (0.009)
Retail and wholesale trade 0.028 0.021 −0.004 −0.008 0.011 0.004
(0.021) (0.019) (0.015) (0.014) (0.011) (0.009)
Services 0.085*** 0.046** 0.023 −0.064*** −0.019* 0.000
(0.021) (0.019) (0.014) (0.014) (0.011) (0.009)
Transportation and logistics −0.002 0.056** 0.002 0.011 0.003 −0.008
(0.027) (0.026) (0.020) (0.019) (0.014) (0.012)
Sales less than $50,000 0.049** 0.033 −0.013 0.005 −0.006 −0.003
(0.022) (0.022) (0.014) (0.012) (0.013) (0.007)
Sales between $50,000 - $999,999 0.104*** −0.013 0.018 −0.007 −0.025* 0.018**
(0.024) (0.023) (0.015) (0.013) (0.013) (0.008)
Sales $1,000,000 or more 0.007 −0.018 0.030* 0.018 −0.017 0.052***
(0.024) (0.023) (0.015) (0.013) (0.014) (0.009)
In-person interactions - Half −0.026*** 0.041*** −0.047*** 0.018*** −0.004 −0.010***
or less (0.008) (0.007) (0.006) (0.004) (0.004) (0.003)
Male-owned or managed −0.011 −0.001 0.040*** −0.003 −0.021*** −0.002
(0.007) (0.007) (0.005) (0.004) (0.003) (0.003)
Obs. 16,573 16,573 16,573 16,573 16,573 16,573
R2 0.016 0.011 0.022 0.016 0.011 0.017
64
Table 6: Business Operations
This table looks at how business operations vary by firm and owner characteristics. In Column 1,
businesses were asked “Since the start of the coronavirus (COVID-19) pandemic, which of the following
reflects the work load of this business?". The dependent variable takes a value of one if the response
was “Lower than normal”. In Column 2, businesses were asked “How long do you expect this busi-
ness to stay open if the current circumstances persist?". The dependent variable takes a value of one
if the response was “More than 6 months”. The omitted base groups are age - less than 1 year,
industry - agriculture or mining, sales - business not operational in 2019, in-person interactions -
more than half of the interactions are in-person, and gender - female-owned or managed. Other
variables are as defined in Table 3.
(1) (2)
Lower work-load Survival ≥ 6 months
Age - Between 1 and 2 years 0.039 0.023
(0.046) (0.046)
Age - Between 2 and 5 years 0.051 0.044
(0.042) (0.043)
Age - 5 years or more 0.026 0.062
(0.040) (0.040)
Construction 0.304*** −0.061
(0.060) (0.064)
Hotels, cafes, and restaurants 0.465*** −0.227***
(0.056) (0.064)
Information and communication 0.240*** 0.032
(0.054) (0.060)
Manufacturing 0.396*** −0.218***
(0.067) (0.072)
Other 0.292*** −0.048
(0.048) (0.055)
Retail and wholesale trade 0.315*** −0.098*
(0.049) (0.056)
Services 0.355*** −0.059
(0.047) (0.054)
Transportation and logistics 0.429*** −0.079
(0.068) (0.071)
Sales less than $50,000 0.030 −0.057
(0.056) (0.056)
Sales between $50,000 - $999,999 0.061 −0.066
(0.059) (0.059)
Sales $1,000,000 or more −0.040 0.142**
(0.061) (0.061)
In-person interactions - Half −0.118*** 0.069***
or less (0.019) (0.019)
Male-owned or managed 0.030 −0.013
(0.018) (0.018)
Obs. 3,025 3,025
R2 0.050 0.052
65
Table 7: Business Actions
This table looks at how business actions in response to COVID-19 vary by firm and owner char-
acteristics. Businesses were asked “What has this business done so far to help reduce the spread of
the coronavirus (COVID-19)?". The dependent variable takes a value of one if the response is “In-
creased online interaction with clients or customers” (Column 1), “More frequent cleaning of office
or workspaces” (Column 2), “Provided gloves, masks, hand sanitizer, or other protective equip-
ment to employees/workers” (Column 3), “Provided hand sanitizer or cleaning wipes for client
or customer use” (Column 4), “Requested employees/workers work from home” (Column 5), or
“Closed physical location(s)” (Column 6). Respondents could select multiple options. The omit-
ted base groups are age - less than 1 year, industry - agriculture or mining, sales - business not
operational in 2019, in-person interactions - more than half of the interactions are in-person, and
gender - female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3) (4) (5) (6)
More Cleaning
Increased
online frequent PPE to products WFH for Closed
presence cleaning employees for clients employees locations
Age - Between 1 and 2 years −0.028 0.009 −0.075* −0.036 −0.027 0.026
(0.046) (0.042) (0.041) (0.038) (0.036) (0.040)
Age - Between 2 and 5 years 0.040 0.035 −0.021 0.023 0.013 0.057
(0.043) (0.040) (0.040) (0.037) (0.035) (0.037)
Age - 5 years or more −0.012 0.050 −0.010 0.031 0.042 0.082**
(0.041) (0.037) (0.037) (0.034) (0.033) (0.034)
Construction −0.003 −0.119** 0.098 −0.040 0.052 0.020
(0.062) (0.060) (0.064) (0.061) (0.050) (0.053)
Hotels, cafes, and restaurants 0.139** 0.160*** 0.214*** 0.150** −0.049 0.098*
(0.062) (0.059) (0.061) (0.060) (0.047) (0.055)
Information and communication 0.253*** −0.141** −0.148*** −0.127** 0.377*** 0.175***
(0.057) (0.057) (0.057) (0.053) (0.049) (0.051)
Manufacturing 0.105 0.043 0.108 0.059 0.299*** −0.014
(0.072) (0.068) (0.067) (0.064) (0.060) (0.059)
Other 0.182*** −0.032 −0.044 0.031 0.255*** 0.212***
(0.053) (0.052) (0.054) (0.051) (0.043) (0.046)
Retail and wholesale trade 0.183*** 0.044 −0.003 0.024 0.059 0.139***
(0.054) (0.052) (0.054) (0.051) (0.042) (0.046)
Services 0.195*** −0.058 −0.051 0.019 0.244*** 0.207***
(0.052) (0.051) (0.053) (0.050) (0.042) (0.045)
Transportation and logistics 0.021 0.013 0.121* 0.040 0.150** −0.033
(0.072) (0.070) (0.071) (0.069) (0.061) (0.061)
Sales less than $50,000 0.047 −0.082 −0.089* −0.026 −0.012 −0.009
(0.055) (0.052) (0.052) (0.046) (0.043) (0.045)
Sales between $50,000 - $999,999 0.126** 0.089 0.064 0.143*** 0.176*** 0.126***
(0.059) (0.055) (0.055) (0.050) (0.046) (0.049)
Sales $1,000,000 or more 0.168*** 0.246*** 0.253*** 0.274*** 0.419*** 0.194***
(0.060) (0.056) (0.057) (0.052) (0.048) (0.051)
In-person interactions - Half 0.110*** −0.150*** −0.170*** −0.177*** 0.105*** 0.004
or less (0.018) (0.018) (0.018) (0.018) (0.017) (0.018)
Male-owned or managed −0.085*** −0.048*** −0.001 −0.002 −0.006 −0.055***
(0.018) (0.017) (0.017) (0.017) (0.017) (0.017)
Obs. 3,029 3,029 3,029 3,029 3,029 3,029
R2 0.047 0.134 0.168 0.139 0.199 0.062
66
Table 8: Business Changes
This table looks at how changes implemented by the business vary by firm and owner character-
istics. Businesses that responded yes to “Did this business have to change anything compared to
standard business work/operations due to the coronavirus (COVID-19) pandemic?” were asked
“What did this business have to change?". The dependent variable takes a value of one if the response
is “Client communication or interaction” (Column 1), “Operating hours” (Column 2), “Use of on-
line tools” (Column 3), or “Employee/worker location” (Column 4). Respondents could select
multiple options. The omitted base groups are age - less than 1 year, industry - agriculture or
mining, sales - business not operational in 2019, in-person interactions - more than half of the in-
teractions are in-person, and gender - female-owned or managed. Other variables are as defined
in Table 3.
(1) (2) (3) (4)
Client
communication Operating Use of Employee
or interaction hours online tools location
Age - Between 1 and 2 years 0.012 −0.015 0.026 0.028
(0.056) (0.056) (0.058) (0.046)
Age - Between 2 and 5 years −0.008 −0.014 0.096* 0.054
(0.052) (0.052) (0.054) (0.044)
Age - 5 years or more −0.028 −0.011 0.051 0.095**
(0.050) (0.049) (0.051) (0.041)
Construction −0.017 −0.052 0.004 0.168**
(0.076) (0.077) (0.075) (0.069)
Hotels, cafes, and restaurants −0.054 0.227*** 0.076 −0.014
(0.074) (0.074) (0.074) (0.064)
Information and communication 0.045 0.004 0.162** 0.366***
(0.072) (0.073) (0.072) (0.064)
Manufacturing −0.187** 0.151* 0.070 0.238***
(0.087) (0.086) (0.088) (0.079)
Other 0.051 0.021 0.207*** 0.230***
(0.066) (0.067) (0.066) (0.058)
Retail and wholesale trade −0.048 0.173** 0.164** 0.091
(0.067) (0.068) (0.067) (0.059)
Services 0.086 0.027 0.170*** 0.218***
(0.065) (0.066) (0.065) (0.057)
Transportation and logistics −0.091 0.062 −0.039 0.035
(0.087) (0.088) (0.083) (0.075)
Sales less than $50,000 −0.000 0.097 −0.073 −0.006
(0.068) (0.066) (0.070) (0.053)
Sales between $50,000 - $999,999 0.107 0.186*** −0.029 0.148***
(0.071) (0.070) (0.073) (0.057)
Sales $1,000,000 or more 0.147** 0.199*** 0.074 0.364***
(0.073) (0.071) (0.075) (0.059)
In-person interactions - Half −0.014 −0.118*** 0.067*** 0.084***
or less (0.020) (0.021) (0.021) (0.020)
Male-owned or managed −0.077*** −0.005 −0.055*** −0.045**
(0.020) (0.021) (0.021) (0.019)
Obs. 2,316 2,316 2,316 2,316
R2 0.036 0.057 0.036 0.150
67
Table 9: Business Struggles
This table looks at how business struggles vary by firm and owner characteristics. Businesses were
asked “Is this business struggling to pay any of the following?". The dependent variable takes a value
of one if the response is “Bills or accounts payable” (Column 1), “Debt or loans” (Column 2), “Em-
ployee/worker salaries and wages” (Column 3), “Rent or lease” (Column 4), or “Taxes” (Column
5). Respondents could select multiple options. The omitted base groups are age - less than 1 year,
industry - agriculture or mining, sales - business not operational in 2019, in-person interactions -
more than half of the interactions are in-person, and gender - female-owned or managed. Other
variables are as defined in Table 3.
(1) (2) (3) (4) (5)
Bills or
accounts Debt or Employee Rent or
payable loans wages lease Taxes
Age - Between 1 and 2 years 0.016 0.035 −0.044 −0.018 0.007
(0.040) (0.036) (0.035) (0.037) (0.032)
Age - Between 2 and 5 years −0.003 0.053 0.020 −0.004 0.059*
(0.037) (0.034) (0.033) (0.035) (0.031)
Age - 5 years or more −0.008 0.015 −0.019 −0.023 0.017
(0.035) (0.031) (0.031) (0.033) (0.029)
Construction −0.054 −0.017 −0.028 −0.007 0.046
(0.064) (0.059) (0.058) (0.050) (0.050)
Hotels, cafes, and restaurants 0.089 0.104* 0.163*** 0.303*** 0.286***
(0.064) (0.061) (0.060) (0.055) (0.054)
Information and communication −0.033 −0.072 0.086 0.045 0.001
(0.061) (0.055) (0.056) (0.048) (0.046)
Manufacturing −0.024 −0.059 0.071 0.098* −0.061
(0.071) (0.062) (0.069) (0.058) (0.048)
Other −0.093 −0.056 0.034 0.072 0.000
(0.057) (0.052) (0.052) (0.045) (0.043)
Retail and wholesale trade −0.026 −0.029 −0.019 0.137*** 0.061
(0.057) (0.052) (0.051) (0.045) (0.043)
Services −0.021 −0.007 0.068 0.120*** 0.047
(0.056) (0.051) (0.051) (0.044) (0.042)
Transportation and logistics 0.035 0.021 0.020 0.110* 0.069
(0.072) (0.066) (0.068) (0.060) (0.058)
Sales less than $50,000 0.081* 0.004 −0.040 −0.013 0.030
(0.049) (0.044) (0.045) (0.048) (0.038)
Sales between $50,000 - $999,999 0.162*** 0.073 0.153*** 0.077 0.131***
(0.052) (0.047) (0.048) (0.051) (0.040)
Sales $1,000,000 or more −0.014 −0.081* 0.101** −0.093* −0.009
(0.054) (0.048) (0.051) (0.052) (0.042)
In-person interactions - Half −0.103*** −0.050*** −0.081*** −0.105*** −0.046***
or less (0.017) (0.016) (0.017) (0.016) (0.015)
Male-owned or managed −0.005 0.018 −0.015 0.029* 0.023
(0.017) (0.015) (0.016) (0.015) (0.014)
Obs. 3,473 3,473 3,473 3,473 3,473
R2 0.043 0.035 0.061 0.063 0.062
68
Table 10: Sources of Formal Financing
This table looks at how sources of formal financing vary by firm and owner characteristics. In
Column 1, the dependent variable takes a value of one if businesses responded yes to “At this
time, does this business have a line of credit or a loan from a financial institution or bank?". In Columns
2 to 6, businesses were asked “In the past 30 days, which of the following sources of capital did this
business apply for?". The dependent variable takes a value of one if the response is “Bank loans”
(Column 2), “Government loans” (Column 3), “Grants from the government” (Column 4), “Grants
from private-sector companies or agencies” (Column 5), or “Crowdsourced capital” (Column 6).
Respondents could select multiple options. The omitted base groups are age - less than 1 year,
industry - agriculture or mining, sales - business not operational in 2019, in-person interactions -
more than half of the interactions are in-person, and gender - female-owned or managed. Other
variables are as defined in Table 3.
(1) (2) (3) (4) (5) (6)
Bank Loan Bank Loan Loans from Grants from Private Crowdsourced
Outstanding Application Government Government Grants Capital
Age - Between 1 and 2 years −0.008 −0.021 0.038 −0.006 0.026 0.000
(0.030) (0.025) (0.032) (0.033) (0.021) (0.011)
Age - Between 2 and 5 years −0.021 −0.014 0.045 0.046 0.055*** 0.010
(0.028) (0.025) (0.030) (0.031) (0.020) (0.012)
Age - 5 years or more 0.024 −0.036 0.017 −0.015 0.015 −0.007
(0.027) (0.023) (0.028) (0.029) (0.018) (0.010)
Construction −0.041 0.002 0.064 0.116** −0.030 −0.037*
(0.057) (0.046) (0.053) (0.052) (0.030) (0.021)
Hotels, cafes, and restaurants −0.074 0.013 0.218*** 0.206*** 0.078** −0.016
(0.057) (0.046) (0.053) (0.052) (0.036) (0.023)
Information and communication −0.066 −0.012 0.096** 0.070 0.027 0.009
(0.053) (0.042) (0.048) (0.047) (0.031) (0.024)
Manufacturing 0.023 −0.008 0.076 0.062 −0.013 −0.003
(0.065) (0.053) (0.063) (0.059) (0.034) (0.027)
Other −0.117** −0.022 0.090** 0.102** 0.057** −0.004
(0.049) (0.039) (0.044) (0.043) (0.029) (0.022)
Retail and wholesale trade −0.094* −0.029 0.087** 0.080* 0.001 −0.021
(0.049) (0.039) (0.044) (0.042) (0.028) (0.021)
Services −0.094** −0.031 0.112*** 0.133*** 0.015 −0.014
(0.048) (0.038) (0.043) (0.042) (0.027) (0.021)
Transportation and logistics −0.051 −0.015 0.117* 0.044 −0.001 −0.027
(0.067) (0.052) (0.062) (0.058) (0.036) (0.023)
Sales less than $50,000 0.009 0.022 −0.021 0.007 0.014 −0.012
(0.035) (0.030) (0.038) (0.038) (0.022) (0.017)
Sales between $50,000 - $999,999 0.184*** 0.120*** 0.233*** 0.209*** 0.040 −0.006
(0.038) (0.032) (0.041) (0.042) (0.025) (0.018)
Sales $1,000,000 or more 0.358*** 0.156*** 0.231*** 0.176*** 0.040 −0.004
(0.042) (0.035) (0.044) (0.044) (0.027) (0.019)
In-person interactions - Half −0.057*** −0.032*** −0.049*** −0.079*** −0.009 −0.009
or less (0.016) (0.012) (0.016) (0.016) (0.010) (0.006)
Male-owned or managed 0.022 0.016 −0.004 −0.019 −0.021** 0.002
(0.015) (0.012) (0.016) (0.015) (0.010) (0.006)
Obs. 3,476 3,476 3,476 3,476 3,476 3,476
R2 0.121 0.033 0.091 0.069 0.019 0.007
69
Table 11: Internal vs. External Financing Share by Industry
This table presents information on the share of firms with access to sources of external and internal
financing by industry. Firms are coded as having access to forms of internal financing if they have
access to “Personal Savings”, “Funds from family or friends”, or “Loan from retirement funds”.
Firms are coded as having access to external financing if they have a loan or line of credit from a
financial institution.
(1) (2)
Industry Internal Financing External Financing
Agriculture or mining .635 .302
Construction .465 .363
Hotels, cafes, and restaurants .479 .320
Information and communication .587 .261
Manufacturing .313 .419
Retail and wholesale trade .543 .214
Services .556 .249
Transportation and logistics .402 .376
Table 12: Remote-Work by Industry
This table presents information on the self-reported share of employees that could be working
from home by industry. We compare it to the estimated share by Dingel and Neiman (2020). We
have assumed a share of 100%, 75%, 50%, 25% and 0% if the respondent stated that “all”, “more
than half”, “about half”, “less than half” and “none” could work remotely, respectively.
(1) (2)
Industry Remote-Work Reported Dingel & Neiman (2020)
Information and communication .812 .717
Real estate activities .764 .418
Professional services .650 .762
Education and childcare services .569 .826
Performing arts and entertainment .563 .297
Manufacturing .328 .225
Retail and wholesale trade .326 .330
Agriculture, farming, forestry, or mining .264 .165
Transportation and logistics .239 .186
Construction .215 .186
Hotels, cafes and restaurants .089 .035
70
Table 13: Sources of Informal Financing
This table looks at how sources of informal financing vary by firm and owner characteristics. Busi-
nesses were asked “Which of the following sources of private capital or funds does this business currently
have access to?". The dependent variable takes a value of one if the respondent picks any of the
options (Column 1), “Personal Savings” (Column 2), “Funds from family or friends” (Column
3), “Unemployment Benefits” (Column 4), “Loan from retirement funds” (Column 5), or “Com-
munity Donations” (Column 6). Respondents could select multiple options. The omitted base
groups are age - less than 1 year, industry - agriculture or mining, sales - business not operational
in 2019, in-person interactions - more than half of the interactions are in-person, and gender -
female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3) (4) (5) (6)
Informal Personal Family and Unemployment Retirement Community
Financing Savings Friends Benefits Funds Donations
Age - Between 1 and 2 years −0.010 −0.030 −0.021 0.000 −0.005 0.005
(0.041) (0.042) (0.032) (0.021) (0.019) (0.018)
Age - Between 2 and 5 years 0.001 0.008 −0.010 −0.000 0.002 0.011
(0.038) (0.039) (0.030) (0.020) (0.018) (0.017)
Age - 5 years or more 0.020 −0.013 −0.003 0.002 0.016 0.013
(0.036) (0.037) (0.029) (0.020) (0.017) (0.015)
Construction −0.136** −0.098 −0.022 −0.005 −0.059 −0.085***
(0.061) (0.064) (0.046) (0.035) (0.039) (0.030)
Hotels, cafes, and restaurants −0.080 −0.101 0.006 −0.013 −0.038 −0.042
(0.061) (0.064) (0.048) (0.035) (0.039) (0.035)
Information and communication −0.010 −0.004 0.030 0.004 −0.042 0.027
(0.057) (0.060) (0.046) (0.031) (0.038) (0.035)
Manufacturing −0.197*** −0.228*** −0.034 −0.035 −0.108*** −0.060*
(0.071) (0.071) (0.050) (0.040) (0.038) (0.034)
Other −0.056 −0.090 −0.027 0.004 −0.043 0.036
(0.053) (0.056) (0.042) (0.029) (0.035) (0.033)
Retail and wholesale trade −0.077 −0.056 −0.013 −0.013 −0.048 −0.038
(0.053) (0.056) (0.043) (0.029) (0.035) (0.031)
Services −0.052 −0.043 −0.015 0.002 −0.027 −0.021
(0.052) (0.055) (0.042) (0.028) (0.035) (0.031)
Transportation and logistics −0.209*** −0.192*** −0.022 −0.003 −0.074* −0.057
(0.070) (0.070) (0.052) (0.042) (0.041) (0.035)
Sales less than $50,000 0.008 0.039 −0.015 −0.010 −0.003 −0.016
(0.052) (0.055) (0.043) (0.028) (0.024) (0.025)
Sales between $50,000 - $999,999 −0.008 0.006 −0.079* 0.037 0.018 −0.009
(0.054) (0.058) (0.045) (0.030) (0.026) (0.026)
Sales $1,000,000 or more −0.089 −0.166*** −0.126*** 0.129*** 0.019 0.010
(0.057) (0.060) (0.047) (0.034) (0.028) (0.028)
In-person interactions - Half −0.020 0.002 −0.022* −0.011 0.020** −0.030***
or less (0.018) (0.018) (0.012) (0.011) (0.010) (0.009)
Male-owned or managed 0.030* 0.081*** 0.015 −0.019* 0.015 −0.013
(0.017) (0.017) (0.012) (0.010) (0.009) (0.009)
Obs. 3,476 3,476 3,476 3,476 3,476 3,476
R2 0.015 0.039 0.017 0.033 0.009 0.025
71
Table 14: Business Financial Condition
This table looks at how the financial condition of businesses vary by firm and owner characteris-
tics. In Column 1, businesses were asked “In the past 30 days, which of the following best describes the
cash flow situation for this business?". The dependent variable takes a value of one if the response
was “Outflow is greater than inflow”. In Column 2, businesses were asked “How do you feel about
the cash situation for this business over the next 3 months?". The dependent variable takes a value
of one if the response was “I am very concerned”. In Column 3, businesses were asked “How, if
at all, did this business change the average prices it charges for its goods or services during the past 30
days?". The dependent variable takes a value of one if the response was “Decreased prices”. The
omitted base groups are age - less than 1 year, industry - agriculture or mining, sales - business
not operational in 2019, in-person interactions - more than half of the interactions are in-person,
and gender - female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3)
Very concerned Decreased
Outflow > Inflow future cashflow product price
Age - Between 1 and 2 years 0.026 0.000 0.033
(0.043) (0.041) (0.039)
Age - Between 2 and 5 years −0.009 0.034 −0.013
(0.039) (0.038) (0.036)
Age - 5 years or more −0.023 −0.003 −0.048
(0.037) (0.036) (0.033)
Construction 0.095 0.038 0.029
(0.066) (0.063) (0.055)
Hotels, cafes, and restaurants 0.072 0.188*** 0.072
(0.066) (0.063) (0.056)
Information and communication −0.005 0.005 0.048
(0.062) (0.059) (0.053)
Manufacturing −0.018 0.095 −0.011
(0.074) (0.071) (0.061)
Other 0.012 0.064 −0.012
(0.058) (0.056) (0.049)
Retail and wholesale trade −0.023 0.047 0.037
(0.058) (0.056) (0.049)
Services 0.024 0.066 0.051
(0.057) (0.054) (0.048)
Transportation and logistics 0.063 0.144** 0.148**
(0.075) (0.072) (0.066)
Sales less than $50,000 0.012 −0.002 0.110**
(0.055) (0.054) (0.044)
Sales between $50,000 - $999,999 0.041 0.043 0.099**
(0.057) (0.056) (0.047)
Sales $1,000,000 or more −0.028 −0.105* 0.054
(0.060) (0.059) (0.049)
In-person interactions - Half −0.096*** −0.121*** 0.011
or less (0.018) (0.018) (0.016)
Male-owned or managed −0.002 −0.010 0.001
(0.018) (0.017) (0.015)
Obs. 3,450 3,468 3,461
R2 0.017 0.037 0.015
72
Table 15: Business and Household Struggles
This table looks at how ease of conducting business and household activities vary by firm and
owner characteristics. In Column 1, the dependent variable takes a value of one if the respondent
replied “A lot” or “A great deal” to “How much have your household responsibilities affected your ability
to focus on this business during the coronavirus (COVID-19) pandemic?". In Column 2, the dependent
variable takes a value of one if the respondent replied “A lot” or “A great deal” to “How much have
your business responsibilities affected your ability to take care of your household during the coronavirus
(COVID-19) pandemic?". Other variables are as defined in Table 3.
(1) (2)
Difficulty in Difficulty in
conducting business caring for household
Age - Between 1 and 2 years 0.009 −0.068*
(0.041) (0.038)
Age - Between 2 and 5 years 0.004 −0.054
(0.038) (0.036)
Age - 5 years or more −0.064* −0.099***
(0.036) (0.034)
Construction 0.020 0.094*
(0.051) (0.049)
Hotels, cafes, and restaurants 0.113** 0.251***
(0.052) (0.051)
Information and communication 0.076 0.085*
(0.050) (0.046)
Manufacturing 0.055 0.073
(0.055) (0.053)
Other 0.036 0.085**
(0.044) (0.041)
Retail and wholesale trade 0.039 0.096**
(0.044) (0.041)
Services 0.069 0.108***
(0.043) (0.040)
Transportation and logistics 0.106* 0.143**
(0.062) (0.061)
Sales less than $50,000 −0.006 0.099**
(0.051) (0.046)
Sales between $50,000 - $999,999 −0.014 0.151***
(0.054) (0.049)
Sales $1,000,000 or more −0.067 0.152***
(0.055) (0.050)
In-person interactions - Half −0.016 −0.043**
or less (0.018) (0.017)
Male-owned or managed −0.046*** −0.029*
(0.016) (0.016)
Remote work - Sometimes 0.052*** 0.017
(0.019) (0.018)
Remote work - Not at all −0.033 0.018
(0.024) (0.024)
Obs. 3,568 3,567
R2 0.023 0.021
73
Table 16: Time Spent on Business and Household Activities
This table looks at how time spent on activities by business owners and managers vary by firm and
owner characteristics. Businesses were asked “Which of the following have you personally had to spend
more time on since the beginning of the coronavirus (COVID-19) pandemic?". The dependent variable
takes a value of one if the response is “Business work” (Column 1), “Housework or homemaking”
(Column 2), “Daycare for children in your household” (Column 3), or “Education for school-aged
children in your household” (Column 4). Respondents could select multiple options. In Column
5, the dependent variable is the response in number of hours to the question “On average, how many
hours per day are you spending on domestic or household care activities?”. The omitted base groups are
age - less than 1 year, industry - agriculture or mining, sales - business not operational in 2019, in-
person interactions - more than half of the interactions are in-person, and gender - female-owned
or managed. Other variables are as defined in Table 3.
(1) (2) (3) (4) (5)
Business Household Daycare Education Hours spent on
work work for children for children Household work
Age - Between 1 and 2 years −0.005 0.043 0.017 −0.026 −0.080
(0.039) (0.039) (0.031) (0.037) (0.245)
Age - Between 2 and 5 years 0.012 0.006 −0.020 −0.004 0.039
(0.037) (0.036) (0.029) (0.034) (0.233)
Age - 5 years or more −0.017 −0.041 −0.079*** −0.068** −0.256
(0.035) (0.034) (0.026) (0.032) (0.223)
Construction 0.031 0.082 −0.036 0.099** −0.342
(0.056) (0.054) (0.040) (0.050) (0.337)
Hotels, cafes, and restaurants 0.198*** 0.010 −0.027 0.042 −0.468
(0.054) (0.053) (0.039) (0.048) (0.344)
Information and communication 0.148*** 0.078 0.012 0.051 −0.221
(0.052) (0.052) (0.039) (0.047) (0.334)
Manufacturing 0.148** 0.029 −0.063 0.063 −0.224
(0.059) (0.059) (0.041) (0.054) (0.374)
Other 0.147*** 0.126*** 0.006 0.092** −0.218
(0.047) (0.046) (0.035) (0.041) (0.309)
Retail and wholesale trade 0.104** 0.049 0.003 0.064 −0.197
(0.047) (0.046) (0.035) (0.042) (0.312)
Services 0.104** 0.091** 0.023 0.085** −0.128
(0.046) (0.045) (0.034) (0.041) (0.305)
Transportation and logistics 0.069 0.070 0.037 0.103* 0.370
(0.065) (0.064) (0.051) (0.059) (0.416)
Sales less than $50,000 −0.043 0.009 −0.024 −0.092** −0.436
(0.049) (0.049) (0.038) (0.047) (0.338)
Sales between $50,000 - $999,999 0.075 −0.010 0.033 −0.020 −0.587*
(0.051) (0.051) (0.040) (0.050) (0.356)
Sales $1,000,000 or more 0.081 −0.007 0.066 0.011 −0.860**
(0.053) (0.053) (0.041) (0.051) (0.364)
In-person interactions - Half 0.013 0.053*** 0.027** 0.026 −0.372***
or less (0.017) (0.017) (0.013) (0.016) (0.101)
Male-owned or managed 0.006 −0.164*** −0.024* −0.107*** −0.402***
(0.016) (0.016) (0.012) (0.015) (0.092)
Obs. 3,931 3,931 3,931 3,931 3,954
R2 0.020 0.044 0.014 0.022 0.020
74
Table 17: Business Employees
This table looks at how business and owner characteristics affect the employees of the business.
In Column 1, the dependent variable takes a value of one if the respondent replied “More than
half” or “All” to “During the coronavirus (COVID-19) pandemic, how many of this business’s employ-
ees/workers could work from home for more than a few days?”. In Column 2, the dependent variable
takes a value of one if the respondent replied “Yes, for all employees/workers” to “Does this busi-
ness allow employees/workers to take resources or equipment home with them to support working from
home?”. In Column 3, the dependent variable takes a value of one if the respondent replied “High”
to “How would you rate the potential for exposure of the employees/workers of this business to coronavirus
(COVID-19) infection while working?”. In Column 4, the dependent variable takes a value of one if
the respondent replied in the affirmative to any of the options to “Does the business pay any of the
following totally or partially to its employees/workers during the current circumstances?”. The omitted
base groups are age - less than 1 year, industry - agriculture or mining, sales - business not opera-
tional in 2019, in-person interactions - more than half of the interactions are in-person, and gender
- female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3) (4)
Support for High exposure Employee
Work from home work from home risk compensation
Age - Between 1 and 2 years 0.022 0.039 −0.007 −0.013
(0.056) (0.088) (0.043) (0.053)
Age - Between 2 and 5 years 0.057 0.032 −0.017 −0.011
(0.055) (0.083) (0.040) (0.052)
Age - 5 years or more −0.049 −0.063 0.003 0.120**
(0.052) (0.080) (0.039) (0.049)
Construction 0.017 −0.128 −0.060 −0.032
(0.056) (0.104) (0.040) (0.053)
Hotels, cafes, and restaurants 0.025 −0.266** −0.003 −0.129**
(0.052) (0.108) (0.044) (0.052)
Information and communication 0.557*** 0.062 −0.016 0.093*
(0.056) (0.095) (0.039) (0.055)
Manufacturing −0.004 −0.328*** −0.074* 0.040
(0.063) (0.099) (0.042) (0.061)
Other 0.363*** 0.017 0.050 0.080*
(0.052) (0.091) (0.038) (0.047)
Retail and wholesale trade 0.139*** −0.116 0.023 0.016
(0.053) (0.093) (0.039) (0.048)
Services 0.344*** 0.040 −0.010 0.043
(0.051) (0.090) (0.037) (0.046)
Transportation and logistics 0.039 −0.253** 0.053 −0.026
(0.060) (0.106) (0.054) (0.062)
Sales less than $50,000 0.065 −0.004 −0.003 −0.005
(0.082) (0.110) (0.056) (0.062)
Sales between $50,000 - $999,999 −0.002 −0.061 0.004 0.221***
(0.084) (0.114) (0.058) (0.067)
Sales $1,000,000 or more 0.031 −0.137 0.071 0.566***
(0.085) (0.115) (0.060) (0.068)
In-person interactions - Half 0.326*** 0.238*** −0.162*** 0.065***
or less (0.018) (0.027) (0.015) (0.019)
Male-owned or managed −0.076*** 0.032 −0.031** 0.002
(0.017) (0.025) (0.014) (0.018)
Obs. 2,435 1,513 2,438 2,423
R2 0.297 0.141 0.085 0.244
75
Table 18: Client Accommodation
This table looks at changes implemented by businesses to accommodate their customers. Busi-
nesses were asked “Did this business have to change anything to accommodate its clients or customers
during the coronavirus (COVID-19) pandemic?". The dependent variable takes a value of one if the
response is “Provide online services” (Column 1), “Expand use of digital payments” (Column
2), “Provide delivery of servides or goods at home” (Column 3), or “Provide curbside delivery
at business location” (Column 4). Respondents could select multiple options. The omitted base
groups are age - less than 1 year, industry - agriculture or mining, sales - business not operational
in 2019, in-person interactions - more than half of the interactions are in-person, and gender -
female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3) (4)
Provide Expanded use of Provide Provide
online services digital payment delivery services curbside pickup
Age - Between 1 and 2 years 0.005 −0.019 −0.019 −0.052
(0.047) (0.047) (0.042) (0.036)
Age - Between 2 and 5 years −0.020 −0.098** −0.040 −0.046
(0.044) (0.043) (0.040) (0.034)
Age - 5 years or more −0.031 −0.088** −0.036 −0.029
(0.041) (0.041) (0.038) (0.032)
Construction −0.077 −0.104 −0.108* −0.200***
(0.065) (0.069) (0.061) (0.060)
Hotels, cafes, and restaurants 0.017 0.046 0.152** 0.439***
(0.063) (0.067) (0.063) (0.061)
Information and communication 0.329*** −0.106 −0.093 −0.210***
(0.065) (0.068) (0.061) (0.058)
Manufacturing 0.068 −0.256*** −0.128* −0.079
(0.080) (0.075) (0.069) (0.074)
Other 0.229*** −0.143** −0.085 −0.166***
(0.058) (0.060) (0.055) (0.056)
Retail and wholesale trade 0.163*** −0.039 0.156*** 0.137**
(0.059) (0.062) (0.058) (0.059)
Services 0.235*** −0.116* −0.064 −0.149***
(0.057) (0.060) (0.055) (0.056)
Transportation and logistics 0.003 −0.128 −0.035 −0.116*
(0.077) (0.078) (0.072) (0.070)
Sales less than $50,000 0.012 0.021 −0.045 0.014
(0.064) (0.062) (0.059) (0.050)
Sales between $50,000 - $999,999 0.016 0.054 −0.057 0.025
(0.067) (0.064) (0.061) (0.052)
Sales $1,000,000 or more 0.029 0.045 −0.026 0.071
(0.069) (0.066) (0.063) (0.053)
In-person interactions - Half 0.020 −0.028 −0.051*** −0.118***
or less (0.019) (0.018) (0.017) (0.015)
Male-owned or managed −0.058*** −0.011 0.001 −0.020
(0.019) (0.018) (0.017) (0.014)
Obs. 2,926 2,926 2,926 2,926
R2 0.050 0.022 0.062 0.229
76
Table 19: Business Model Changes
This table looks at changes businesses made to their business model and difficulties in doing so.
The first two columns relate to the question "Which of the following has this business used in the past 30
days to continue operating during the pandemic?". The dependent variable takes a value of one if the
respondent stated “Online advertising” (Column 1) or “Digital payment tools” (Column 2). The
following two columns relate to the question "Which of the following changes has this business made
to assure needed supplies are available during the pandemic?" and take a value of one if the respondent
stated “Adjusted order schedules” (Column 3) or “Changed delivery processes” (Column 4). Col-
umn 5 takes a value of one if the respondent stated “Difficult” or “Very difficult” as a response
to "How easy or difficult is it for this business to change the delivery of goods or services as a result of
the pandemic?". Column 6 takes a value of one if the respondent stated “Yes, I have sufficient in-
formation” as a response to "Do you feel that you have access to enough information to make decisions
about this business during pandemic?". The omitted base groups are age - less than 1 year, industry -
agriculture or mining, sales - business not operational in 2019, in-person interactions - more than
half of the interactions are in-person, and gender - female-owned or managed. Other variables are
as defined in Table 3.
(1) (2) (3) (4) (5) (6)
Online Digital Adjusted Changed Difficult to change Sufficient
advertising payment tools order schedules delivery processes delivery processes information
Age - Between 1 and 2 years −0.015 −0.014 −0.006 −0.024 −0.046 0.100***
(0.038) (0.040) (0.005) (0.032) (0.037) (0.038)
Age - Between 2 and 5 years −0.055 −0.050 −0.005 −0.060** −0.015 0.021
(0.036) (0.037) (0.005) (0.029) (0.035) (0.037)
Age - 5 years or more −0.148*** −0.075** −0.006 −0.025 −0.007 0.082**
(0.034) (0.035) (0.004) (0.028) (0.033) (0.034)
Construction −0.101* −0.111** −0.009 −0.071 0.046 0.078
(0.058) (0.056) (0.010) (0.047) (0.057) (0.057)
Hotels, cafes, and restaurants 0.237*** 0.097* 0.010 0.105** 0.054 0.014
(0.057) (0.057) (0.010) (0.051) (0.056) (0.057)
Information and communication 0.018 −0.012 −0.018** −0.079* −0.120** 0.077
(0.057) (0.056) (0.009) (0.046) (0.053) (0.055)
Manufacturing −0.055 −0.089 0.016 −0.012 0.010 0.087
(0.067) (0.065) (0.012) (0.057) (0.064) (0.063)
Other 0.060 −0.011 −0.019** −0.094** 0.012 0.042
(0.051) (0.050) (0.008) (0.042) (0.049) (0.049)
Retail and wholesale trade 0.184*** 0.074 0.003 0.060 −0.025 0.030
(0.051) (0.050) (0.009) (0.044) (0.049) (0.050)
Services 0.062 0.038 −0.021** −0.067 0.012 0.053
(0.050) (0.049) (0.008) (0.042) (0.048) (0.049)
Transportation and logistics −0.152** −0.162** −0.002 0.086 0.040 0.034
(0.067) (0.064) (0.011) (0.061) (0.066) (0.066)
Sales less than $50,000 −0.038 0.020 0.012** 0.019 0.119*** −0.008
(0.049) (0.051) (0.006) (0.042) (0.043) (0.052)
Sales between $50,000 - $999,999 0.024 0.035 0.019*** 0.044 0.192*** −0.073
(0.051) (0.054) (0.006) (0.044) (0.046) (0.054)
Sales $1,000,000 or more −0.015 −0.038 0.039*** 0.090** 0.165*** −0.023
(0.054) (0.056) (0.007) (0.046) (0.049) (0.056)
In-person interactions - Half 0.023 0.086*** 0.007*** −0.015 −0.175*** 0.044***
or less (0.017) (0.017) (0.002) (0.014) (0.017) (0.017)
Male-owned or managed −0.013 −0.018 0.005** 0.014 0.016 −0.042***
(0.017) (0.017) (0.002) (0.013) (0.016) (0.016)
Obs. 3,704 3,704 25,803 3,702 3,685 3,689
R2 0.053 0.035 0.009 0.038 0.059 0.013
77
Table 20: Reasons for Business Closure
This table looks at how reasons for business closure vary by firm and owner characteristics. Busi-
nesses were asked “What was the main reason this business closed?”. The dependent variable takes
a value of one if the response is “Government or health-authority orders” (Column 1), “Financial
challenges” (Column 2), or “Client or customer-related reasons” (Column 3). The omitted base
groups are age - less than 1 year, industry - agriculture or mining, sales - business not operational
in 2019, in-person interactions - more than half of the interactions are in-person, and gender -
female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3)
Government or health Financial Client or customer
authority orders challenges related reasons
Age - Between 1 and 2 years −0.041** 0.018 −0.010
(0.021) (0.014) (0.012)
Age - Between 2 and 5 years −0.028 0.002 −0.012
(0.019) (0.013) (0.011)
Age - 5 years or more 0.018 −0.030*** −0.011
(0.018) (0.011) (0.010)
Construction 0.127*** −0.034 0.059**
(0.047) (0.032) (0.025)
Hotels, cafes, and restaurants 0.197*** −0.040 0.009
(0.043) (0.029) (0.020)
Information and communication −0.013 0.018 0.103***
(0.047) (0.034) (0.027)
Manufacturing 0.088 −0.015 0.046
(0.058) (0.039) (0.031)
Other 0.215*** −0.060** 0.004
(0.041) (0.028) (0.019)
Retail and wholesale trade 0.138*** −0.004 0.007
(0.042) (0.029) (0.019)
Services 0.245*** −0.064** 0.027
(0.040) (0.028) (0.019)
Transportation and logistics 0.026 −0.021 0.060**
(0.052) (0.035) (0.028)
Sales less than $50,000 0.080*** −0.031* 0.049***
(0.025) (0.018) (0.012)
Sales between $50,000 - $999,999 0.208*** −0.040** 0.021*
(0.026) (0.018) (0.013)
Sales $1,000,000 or more 0.194*** −0.052*** 0.025*
(0.029) (0.020) (0.014)
In-person interactions - Half −0.183*** 0.053*** 0.035***
or less (0.011) (0.006) (0.006)
Male-owned or managed −0.013 0.012** 0.010*
(0.010) (0.006) (0.006)
Obs. 9,144 9,144 9,144
R2 0.101 0.032 0.019
78
Table 21: Business Re-opening
This table looks at how actions for business re-opening vary by firm and owner characteristics.
Businesses were asked ‘What would be the most important action for this business to re-open in the
future?”. The dependent variable takes a value of one if the response was “Government allowing
business operations” (Column 1), or “Securing funds” (Column 2). In Column 3, the dependent
variable takes a value of one if the business responds “Totally different products or services”
or “Some different products or services in addition to the products or services offered before”
to “Compared to before this business closed, what type of products or services will be offered when it re-
opens?”. The omitted base groups are age - less than 1 year, industry - agriculture or mining, sales
- business not operational in 2019, in-person interactions - more than half of the interactions are
in-person, and gender - female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3)
Government allowing Securing Provide new
business operations funds products and services
Age - Between 1 and 2 years −0.016 −0.033 0.031
(0.022) (0.021) (0.021)
Age - Between 2 and 5 years −0.001 −0.054*** 0.018
(0.021) (0.019) (0.019)
Age - 5 years or more 0.040** −0.091*** −0.029
(0.019) (0.017) (0.018)
Construction 0.085* 0.037 −0.206***
(0.049) (0.044) (0.044)
Hotels, cafes, and restaurants 0.101** −0.008 −0.051
(0.045) (0.040) (0.043)
Information and communication −0.008 0.036 0.028
(0.049) (0.045) (0.048)
Manufacturing 0.002 0.003 −0.069
(0.061) (0.054) (0.058)
Other 0.133*** −0.083** −0.066
(0.042) (0.038) (0.041)
Retail and wholesale trade 0.095** −0.013 −0.055
(0.043) (0.039) (0.042)
Services 0.183*** −0.065* −0.086**
(0.042) (0.038) (0.041)
Transportation and logistics 0.009 0.020 −0.194***
(0.053) (0.049) (0.047)
Sales less than $50,000 0.069** 0.007 −0.033
(0.027) (0.025) (0.026)
Sales between $50,000 - $999,999 0.127*** 0.042 −0.020
(0.028) (0.027) (0.027)
Sales $1,000,000 or more 0.194*** −0.058** −0.038
(0.031) (0.028) (0.030)
In-person interactions - Half −0.130*** 0.042*** 0.056***
or less (0.011) (0.010) (0.010)
Male-owned or managed −0.015 0.033*** 0.011
(0.011) (0.009) (0.010)
Obs. 8,716 8,716 8,621
R2 0.048 0.026 0.016
79
Table 22: Business Challenges and Support Needs
This table looks at how business challenges and support needs vary by firm and owner character-
istics. Businesses were asked “In which of the following is this business facing the most challenges and
would need support with to survive the current situation?”. The dependent variable takes a value of
one if the response was “Access to capital or funding” (Column 1), “Employees/Workers” (Col-
umn 2), “Government intervention or regulation changes” (Column 3), “Support taking care of
household members” (Column 4), “Online tools and resources” (Column 5) or “Information re-
sources” (Column 6). The omitted base groups are age - less than 1 year, industry - agriculture
or mining, sales - business not operational in 2019, in-person interactions - more than half of the
interactions are in-person, and gender - female-owned or managed. Other variables are as defined
in Table 3.
(1) (2) (3) (4) (5) (6)
Government Care of Online
Capital Employees regulation HH member tools Information
Age - Between 1 and 2 years 0.018 −0.020* −0.004 −0.008 0.031** −0.008
(0.020) (0.011) (0.012) (0.014) (0.013) (0.008)
Age - Between 2 and 5 years 0.025 −0.021** −0.003 −0.012 0.017 −0.011
(0.019) (0.010) (0.012) (0.013) (0.012) (0.008)
Age - 5 years or more −0.043** 0.015 0.014 −0.020 0.016 −0.005
(0.018) (0.010) (0.011) (0.013) (0.011) (0.007)
Construction 0.082*** 0.028 −0.038* −0.022 −0.021 −0.008
(0.027) (0.022) (0.021) (0.018) (0.014) (0.012)
Hotels, cafes, and restaurants 0.131*** 0.004 0.028 −0.049*** −0.026* −0.023**
(0.027) (0.021) (0.022) (0.017) (0.013) (0.011)
Information and communication 0.119*** −0.018 −0.050** 0.005 0.013 0.003
(0.026) (0.020) (0.020) (0.018) (0.015) (0.012)
Manufacturing 0.057* 0.032 −0.026 −0.009 −0.013 −0.005
(0.030) (0.025) (0.024) (0.021) (0.016) (0.013)
Other 0.093*** −0.061*** −0.014 −0.020 0.013 −0.014
(0.024) (0.018) (0.019) (0.016) (0.013) (0.011)
Retail and wholesale trade 0.096*** −0.016 −0.055*** −0.002 0.039*** −0.019*
(0.024) (0.018) (0.018) (0.017) (0.014) (0.011)
Services 0.114*** −0.037** −0.023 0.002 0.006 −0.012
(0.023) (0.018) (0.018) (0.016) (0.013) (0.011)
Transportation and logistics 0.044 −0.003 −0.000 −0.011 −0.017 0.008
(0.031) (0.025) (0.025) (0.020) (0.015) (0.014)
Sales less than $50,000 0.008 0.006 −0.011 0.014 −0.013 −0.000
(0.026) (0.012) (0.016) (0.019) (0.018) (0.011)
Sales between $50,000 - $999,999 0.094*** 0.066*** 0.014 −0.042** −0.076*** −0.018
(0.027) (0.014) (0.017) (0.019) (0.018) (0.012)
Sales $1,000,000 or more −0.084*** 0.198*** 0.050*** −0.023 −0.083*** −0.020*
(0.028) (0.015) (0.018) (0.020) (0.018) (0.012)
In-person interactions - Half 0.011 −0.040*** −0.032*** 0.025*** 0.026*** −0.000
or less (0.008) (0.006) (0.006) (0.005) (0.004) (0.003)
Male-owned or managed 0.033*** −0.001 0.026*** −0.044*** −0.023*** 0.000
(0.008) (0.006) (0.006) (0.005) (0.004) (0.003)
Obs. 15,567 15,567 15,567 15,567 15,567 15,567
R2 0.031 0.072 0.018 0.020 0.030 0.004
80
Table 23: Business Support Policies
This table looks at how policies to support businesses vary by firm and owner characteristics.
Businesses were asked “What would be the three most needed policies to support this business over the
COVID-19 crisis?”. The dependent variable takes a value of one if the response was “Salary subsi-
dies” (Column 1), “Access to loan and credit guarantees” (Column 2), or “Tax deferral” (Column
3). The omitted base groups are age - less than 1 year, industry - agriculture or mining, sales -
business not operational in 2019, in-person interactions - more than half of the interactions are
in-person, and gender - female-owned or managed. Other variables are as defined in Table 3.
(1) (2) (3)
Access to loan/
Salary subsidies credit guarantees Tax deferral
Age - Between 1 and 2 years −0.010 −0.007 −0.018
(0.018) (0.020) (0.018)
Age - Between 2 and 5 years −0.006 0.015 −0.009
(0.017) (0.018) (0.017)
Age - 5 years or more 0.021 −0.062*** −0.005
(0.016) (0.017) (0.016)
Construction 0.084*** 0.070*** 0.080***
(0.026) (0.026) (0.026)
Hotels, cafes, and restaurants 0.131*** 0.090*** 0.053**
(0.026) (0.026) (0.025)
Information and communication 0.145*** 0.049* 0.035
(0.025) (0.025) (0.024)
Manufacturing 0.089*** 0.015 0.023
(0.029) (0.030) (0.029)
Other 0.115*** 0.045** −0.012
(0.022) (0.023) (0.022)
Retail and wholesale trade 0.069*** 0.059*** 0.029
(0.022) (0.023) (0.022)
Services 0.125*** 0.072*** 0.027
(0.021) (0.022) (0.021)
Transportation and logistics 0.078*** 0.057* 0.075**
(0.030) (0.031) (0.030)
Sales less than $50,000 0.032 0.007 0.016
(0.022) (0.025) (0.023)
Sales between $50,000 - $999,999 0.176*** 0.125*** 0.102***
(0.023) (0.026) (0.024)
Sales $1,000,000 or more 0.246*** 0.037 0.074***
(0.024) (0.027) (0.025)
In-person interactions - Half −0.032*** −0.005 0.003
or less (0.008) (0.008) (0.008)
Male-owned or managed −0.025*** 0.048*** 0.040***
(0.008) (0.008) (0.007)
Obs. 16,615 16,615 16,615
R2 0.043 0.019 0.014
81
Table 24: Employee Perspective: Business and Household Struggles
This table looks at how the difficulty in conducting business due to household distractions, and
how the difficulty in doing household work due to job distractions, varies by individual charac-
teristics. Households were asked "How much have your business responsibilities affected your ability to
take care of your household during the coronavirus (COVID-19) pandemic?" and "How much have your
household responsibilities affected your ability to focus on this business during the coronavirus (COVID-
19) pandemic?". The dependent variable takes a value of one if the response was either “A lot” or
“A great deal”, and zero otherwise. The regressors are gender, age, area, education, firm sector,
remote-work ability and having to take care of school-age children due to school closures. The
coefficients for education and sector are omitted from the table to increase legibility. The omit-
ted default values are female, older than 66, rural, remote work - not at all and children - no,
respectively.
(1) (2)
Difficulty in Difficulty in
conducting business caring for household
Male −0.047*** −0.053***
(0.010) (0.010)
Age - 56 to 65 0.011 0.023
(0.027) (0.023)
Age - 46 to 55 0.009 0.039*
(0.027) (0.023)
Age - 36 to 45 0.066** 0.081***
(0.027) (0.023)
Age - 26 to 35 0.083*** 0.096***
(0.027) (0.022)
Age - 18 to 25 0.088*** 0.144***
(0.029) (0.026)
Age - Younger than 18 0.016 0.125
(0.084) (0.081)
Suburban 0.001 −0.007
(0.012) (0.011)
Urban 0.034** 0.023*
(0.014) (0.013)
Remote work - Yes, some of the 0.065*** −0.005
time (0.014) (0.013)
Remote work - Yes, all of the −0.021* −0.091***
time (0.012) (0.011)
Children - Yes, but I am not the 0.118*** 0.077***
primary caregiver (0.018) (0.016)
Children - Yes, and I am the 0.256*** 0.155***
primary caregiver (0.013) (0.012)
Obs. 7,182 7,184
R2 0.101 0.065
82
T HE E FFECTS OF COVID-19 ON U.S. S MALL B USINESSES :
E VIDENCE FROM O WNERS , M ANAGERS , AND E MPLOYEES *
A PPENDIX
G EORGIJ A LEKSEEV† S AFAA A MER‡ M ANASA G OPAL § T HERESA K UCHLER¶
JW S CHNEIDERk J OHANNES S TROEBEL ** N ILS W ERNERFELT††
September 2020
Contents
A.I Business Operations 1
A.II Business Survival Strategy 1
A.III Closed Businesses 1
A.IV Employees 2
A.V Figures A.3
A.VI Figures - Small Business Employees Only A.13
A.VII Tables A.22
∗ Amer, Schneider, and Wernerfelt are employees at Facebook. Kuchler and Stroebel have a research consulting
relationship with Facebook.
† NYU Stern. Email: galeksee@stern.nyu.edu.
‡ Facebook. Email: safy@fb.com.
§ Scheller College of Business, Georgia Tech. Email: manasa.gopal@scheller.gatech.edu.
¶ NYU Stern. NBER, CEPR. Email: theresa.kuchler@nyu.edu.
k Facebook. Email: jdub@fb.com.
∗∗ NYU Stern. NBER, CEPR. Email: johannes.stroebel@nyu.edu.
†† Facebook. Email: nilsw@fb.com.
A.I B USINESS O PERATIONS
Businesses with a drop in sales, on average, have seen sales falling by more than 50%, and 11.6%
of firms have seen a drop of over 90%. On the other extreme, 17.5% of firms with increased sales
saw their sales more than double, while 37.1% of the firms saw growth between 10–30%.
On average, larger businesses (employment) have seen more sales growth (Figure A.3, Panel
A), as have businesses in agriculture or mining (Panel B), and younger firms (Panel C).1 Panel
D suggests a U-shaped relationship between sales and change in sales during the pandemic –
34.2% of firms with sales ≤$5,000 in 2019 saw reduced sales, compared to 68.7% of firms with
sales between $100,000–$250,000 and 52.8% of firms with sales ≥$3 million. Panel E shows that
61.1% of majority-female businesses have had lower sales, compared to 54.3% of majority-male
businesses. Finally, we show that firms with fewer in-person interactions have seen a drop in
sales less often – 59.9% of firms with all their interactions in-person compared to 43.6% of firms
with none of their interactions in-person (Panel F).
Table A.1 presents these correlations between firm characteristics and sales in a regression
table. The results corroborate the previous observations and all coefficients are significant.
A.II B USINESS S URVIVAL S TRATEGY
We look at heterogeneities in the share of revenue coming from online sales against business strate-
gies. Many firms that got all their revenues from online sales were the ones that got their infor-
mation from social media (33.3%), online groups (36.0%), or news on the internet (32.2%) – in
comparison to firms that relied on radio (26.1%) or print (26.7%, Figure A.4, Panel A). Similarly,
firms that adopted online tools were more likely to get their revenues from online sales – firms
that often get all their revenues from online sales had a phone app (33.1%), digital ordering tools
(34.6%), digital payment tools (34.6%), or a website (33.4%, Panel B). Finally, the share of online
revenues also varied with client accommodations. Firms providing online services (30.2%) or free
cancellation (30.7%) often made all their sales online – unlike firms offering curbside pickup, of
which 15.4% made all their sales online.
A.III C LOSED B USINESSES
Figure A.5 Panel A shows that the closure reason also affects the actions businesses think are
important for re-opening. Firms that closed due to government orders thought government per-
mission would be the most important requirement with 69.5% mentioning it as the top reason. For
businesses facing financial challenges, 54.9% stated they need to secure funds to re-open.
Plans for future product offerings differed by closure reason. Panel B of Figure A.5 shows that
firms facing financial challenges were more likely to plan new products (37.0%), whereas firms
that were closed due to government orders were less likely to consider altering their offering.
1 Excluding firms that are younger than one year, since they lack comparability.
1
A.IV E MPLOYEES
Employees & Household Responsibilities. We asked workers about the amount of time spent on
domestic or household care activities (Figure A.10 Panel A). The modal answer was between 1-2
hours (25.8%), though 19.7% spent more than 5 hours per day. There were heterogeneities in the
responses – 30.3% of primary caregivers were spending 5 or more hours per day on household
activities, compared to 11.0% of respondents that did not have to take care of children affected by
school closures (Panel B). We looked at the heterogeneities in a multivariate regression controlling
for gender, age, sector, education, area, remote-work and having to take care of children due
to school closures (Table A.3). Men aged 46–65 spent the least amount of time on household
duties, whereas primary caregivers and respondents sometimes able to work remotely spent the
most. We included an interaction term between remote-work and gender, which turned out to be
insignificant.
Employee Income Sources, Benefits and Concerns. The most common income sources among em-
ployed respondents were their job income (75.3%), their partner’s income (30.1%), personal sav-
ings (19.6%), and social security benefits (12.4%, Figure A.11, Panel A). Income sources varied
by household characteristics (Panel B and C) – women and primary caregivers affected by school
closures relied more often on their partner’s income.
Benefits received by employees during the crisis included health insurance (54.1%), paid time
off (38.0%), paid sick leave (33.0%), and unemployment benefits (20.0%, Figure A.12, Panel A).
Only 11.8% of workers stated they had applied for any kind of government/NGO assistance in
response to the pandemic (Panel B). Respondents that took care of children due to school closures
applied more often for assistance – primary and non-primary caregivers alike.
Among those who had not applied for government/NGO assistance, 24.6% did not satisfy the
eligibility criteria and 11.9% did not know the application procedures. Among those who applied,
56.1% received unemployment benefits, though 24.9% received nothing (Panel E).
Finally, we asked respondents about their top three concerns during the pandemic (Figure
A.13, Panel A). The most common concerns were having enough money to sustain the household
(48.7%), having enough food and basic supplies (44.7%), and losing their current job (37.7%). Re-
spondents that had to take care of children were substantially more worried about having enough
food, supplies, and money for their family than they were about losing their current job (Panel B).
2
A.V F IGURES
Figure A.1: Screenshots of Survey Invitation and First Question
(A) Invitation to take the survey, as it would appear to one of the coauthors
(B) First question, shown after accepting the survey invitation
A.3
Figure A.2: Business Employees - Work from Home Crosstabs
(A) Work From Home, by Sales (B) Work From Home, by Employees
(C) Work From Home, by Sector (D) Work From Home, by Area
(E) Work From Home, by Firm Age (F) Work From Home, by Gender Balance
A.4
Figure A.3: Change in Business Sales Crosstabs
(A) Change in Business Sales, by Business
Employment (B) Change in Business Sales, by Business Sector
(C) Change in Business Sales, by Business Age (D) Change in Business Sales, by Business Sales
(E) Change in Business Sales, by Business (F) Change in Business Sales, by Business
Gender Balance In-Person Operations
A.5
Figure A.4: Online Sales Crosstabs
(A) Online Sales, by Information Sources (B) Online Sales, by Business Tools
(C) Online Sales, by Client Accommodation
A.6
Figure A.5: Reason for Closure Crosstabs
(A) Actions to Open, by Closure Reason (B) Future Services, by Closure Reason
Figure A.6: Age and Educational Demographics of Employees/Workers
(A) Age Distribution (B) Educational Distribution
Figure A.7: Sectors of Employees/Workers
A.7
Figure A.8: Employment Types of Respondents who currently have a Job
Figure A.9: Geographical Distribution of Employee/Worker Respondents
A.8
Figure A.10: Time Spent on Household Activities
(B) Time Spend on Household Activities, by
(A) Time Spend on Household Activities Parental Status
(C) Time Spend on Household Activities, by (D) Time Spend on Household Activities, by
Gender Age
A.9
Figure A.11: Employee/Worker Income
(A) Income Sources, Respondent is Employed
(B) Income Sources, by having Children (C) Income Sources, by Gender
A.10
Figure A.12: Employee/Worker Benefits
(A) Employee/Worker Benefits
(B) Applications for Government/NGO (C) Applications for Government/NGO
Assistance Assistance, by having Children
(E) Government/NGO Assistance for
(D) Reasons for Not Applying Applicants
A.11
Figure A.13: Employee/Worker Concerns
(B) Top Self-reported Concerns, by having
(A) Top Self-reported Concerns Children
(C) Top Self-reported Concerns, by Gender
A.12
A.VI F IGURES - S MALL B USINESS E MPLOYEES O NLY
Figure A.14: Employee/Worker Self-reported Main Reason for Unemployment
(A) Main Reason (B) Main Reason, by Gender
A.13
Figure A.15: Employee/Worker Unemployment Demographics
(B) Fraction Unemployed, by Educational
(A) Fraction Unemployed, by Age Background
(C) Fraction Unemployed, by Size of Most
Recent Company (D) Fraction Unemployed, by Gender
(E) Fraction Unemployed, by Most Regular
Employment
A.14
Figure A.16: Employee/Worker Activities during Unemployment
Figure A.17: Government/NGO Assistance for Unemployed
(A) Government/NGO Assistance Applications,
by Unemployment Status
(B) Types of Government Support Receiving, (C) Reasons for not Applying for Government
by Unemployment Status Assistance, by Unemployment Category
A.15
Figure A.18: Household Care Responsibilities
(A) Childcare due to School Closures (B) Care of a Dependent Adult
(C) Childcare due to School Closures, by Gender (D) Care of a Dependent Adult, by Gender
A.16
Figure A.19: Employee/Worker: Household Impact on Job
(B) Household Impact on Job, by Childcare due
(A) Household Impact on Job to School Closures
(C) Household Impact on Job, by Gender (D) Household Impact on Job, by Age
(E) Household Impact on Job, by Work From
Home
A.17
Figure A.20: Employee/Worker: Job Impact on Household
Figure A.21: Employee/Worker: Remote Work
(A) Remote Work (B) Main Challenge in Working Remotely
A.18
Figure A.22: Employee/Worker Remote Work Challenges
(A) Main Challenge in Working Remotely, by (B) Main Challenge in Working Remotely, by
Childcare due to School Closures Care for a Dependent Adult
(C) Main Challenge in Working Remotely, by (D) Main Challenge in Working Remotely, by
Education Gender
(E) Main Challenge in Working Remotely, by (F) Main Challenge in Working Remotely, by
Age Area
A.19
Figure A.23: Employee/Worker: News Sources during the Pandemic
A.20
Figure A.24: Employee/Worker: Optimism about Future Employment
(B) Optimism about Future Employment, by
(A) Optimism about Future Employment Education
(C) Optimism about Future Employment, by (D) Optimism about Future Employment, by
Gender Age
(E) Optimism about Future Employment, by (F) Optimism about Future Employment, by
Area Childcare due to School Closures
A.21
A.VII TABLES
Table A.1: Business Operations
This table looks at how business sales vary by firm and owner characteristics. Businesses were
asked “Comparing this business sales for the last 30 days (in 2020) with the same month last year (in
2019), are this business sales?". The dependent variable takes a value of one if the response was
“Lower than last year”. The omitted base groups are age - less than 1 year, industry - agriculture
or mining, sales - business not operational in 2019, in-person interactions - more than half of the
interactions are in-person, and gender - female-owned or managed.
Lower sales
Age - Between 1 and 2 years 0.371***
(0.040)
Age - Between 2 and 5 years 0.423***
(0.036)
Age - 5 years or more 0.442***
(0.033)
Construction 0.170***
(0.062)
Hotels, cafes, and restaurants 0.355***
(0.058)
Information and communication 0.175***
(0.058)
Manufacturing 0.216***
(0.070)
Other 0.216***
(0.052)
Retail and wholesale trade 0.199***
(0.053)
Services 0.258***
(0.051)
Transportation and logistics 0.297***
(0.069)
Sales less than $50,000 0.118***
(0.035)
Sales between $50,000 - $999,999 0.190***
(0.040)
Sales $1,000,000 or more 0.072*
(0.043)
In-person interactions - Half −0.083***
or less (0.018)
Male-owned or managed 0.000
(0.018)
Obs. 3,023
R2 0.132
A.22
Table A.2: Household Expenses
This table looks at factors that affect payment of household expenses. The dependent variable
takes a value of one if the respondent says “Difficult” or “Very difficult” when asked “In the past
30 days, how easy or difficult has it been to pay your household’s usual expenses?”. In Column 1, we focus
on how business and owner characteristics affect ease of payments. In Column 2, we study how
variation in personal financing affects the household. Business owners/ managers were asked
“Which of the following sources of money do you personally have access to during the coronavirus (COVID-
19) pandemic?”. Responses are coded as independent variables in Column 2. The omitted base
groups are age - less than 1 year, industry - agriculture or mining, sales - business not operational
in 2019, in-person interactions - more than half of the interactions are in-person, and gender -
female-owned or managed.
Difficulty in Difficulty in
caring for household caring for household
Age - Between 1 and 2 years −0.000 Income from another business −0.053***
(0.039) (0.016)
Age - Between 2 and 5 years 0.013 Social Security −0.040
(0.036) (0.025)
Age - 5 years or more −0.028 Unemployment insurance 0.076***
(0.034)
(0.027)
Construction −0.011
Pension or retirement −0.085***
(0.052)
(0.020)
Hotels, cafes, and restaurants 0.144***
(0.053) Real estate rent −0.081***
(0.020)
Information and communication −0.028
(0.050) Return from investments −0.081***
(0.020)
Manufacturing −0.040
(0.055) Personal savings −0.093***
(0.013)
Other −0.002
(0.045) None of the above 0.157***
(0.024)
Retail and wholesale trade 0.015
(0.045) Other −0.043
(0.029)
Services 0.041
(0.044) Job wages −0.220***
(0.014)
Transportation and logistics 0.179***
(0.063) Obs. 6,043
R2 0.124
Sales less than $50,000 −0.026
(0.048)
Sales between $50,000 - $999,999 −0.067
(0.051)
Sales $1,000,000 or more −0.227***
(0.052)
In-person interactions - Half −0.101***
or less (0.016)
Male-owned or managed 0.008
(0.016)
Obs. 3,944
R2 0.050
A.23
Table A.3: Time Spent on Household Activities
This table looks at how much time employees/workers spend on household activities. We asked
"How many hours per day do you spend on domestic or household care activities?" and coded their an-
swers
0 – “Less than 1 hour”, 1 – “Between 1 and 2 hours”, 2 – “Between 2 and 3 hours”,
3 – “Between 3 and 4 hours”, 4 – “Between 4 and 5 hours”, 5 – “Between 5 and 6 hours”,
8 – “Between 6 and 10 hours”, 12 – “Between 10 and 14 hours” and 14 – “More than 14 hours”.
The regressors are gender, remote-work, gender X remote-work, age, area, education, firm sector,
and having to take care of school-age children due to school closures. The coefficients for edu-
cation and sector are omitted from the table to increase legibility. The omitted default values are
female, remote work - not at all, older than 66, rural, and children - no, respectively.
Time spent on household activities
(1) (2) (3) (4) (5) (6)
Male −0.396*** −0.273***
(0.070) (0.104)
Remote work - Yes, some of the 0.467*** 0.233**
time (0.091) (0.113)
Remote work - Yes, all of the 0.037 −0.106
time (0.082) (0.099)
Male × Remote work −0.008 0.115
- Yes, some of the time (0.149) (0.182)
Male × Remote work −0.343*** 0.066
- Yes, all of the time (0.131) (0.155)
Age - 56 to 65 −0.654*** −0.729***
(0.202) (0.230)
Age - 46 to 55 −0.304 −0.713***
(0.198) (0.229)
Age - 36 to 45 0.338* −0.466**
(0.200) (0.232)
Age - 26 to 35 0.105 −0.469**
(0.198) (0.227)
Age - 18 to 25 −0.308 −0.490**
(0.209) (0.238)
Age - Younger than 18 −1.003* −0.273
(0.593) (0.791)
Suburban −0.199*** −0.008
(0.077) (0.080)
Urban −0.182** 0.098
(0.090) (0.095)
Children - Yes, but I am not the 0.891***
primary caregiver (0.121)
Children - Yes, and I am the 1.805***
primary caregiver (0.088)
Obs. 9,157 8,215 7,404 9,922 9,648 7,167
R2 0.012 0.009 0.010 0.019 0.009 0.092
A.24
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