Robert P. Bartlett and Adair Morse, Small-Business Survival Capabilities and Fiscal Programs: Evidence from Oakland (2021)

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An article by Robert P. Bartlett III (University of California, Berkeley) and Adair Morse (U.S. Department of the Treasury and Berkeley) in the Journal of Financial and Quantitative Analysis, Vol. 56, No. 7, Nov. 2021, pp. 2500-2544 (doi:10.1017/S0022109021000478). Using City of Oakland data during COVID-19, the authors show that survival capabilities vary by firm size: nonemployer businesses rely on low-cost structures, microbusinesses (1-5 employees) depend on 14% greater revenue resiliency, and enterprises (6-50 employees) face 10%-20% higher residual closure risk from committed costs. The Paycheck Protection Program raised medium-run survival probability by 20.5% for microbusinesses. The paper counts 25.7 million nonemployer firms, 5.8 million microbusinesses and 2.8 million larger small-business enterprises, together 44% of U.S. employment and 99% of firms.

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https://doi.org/10.1017/S0022109021000478
JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 56, No. 7, Nov. 2021, pp. 2500–2544
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Michael G. Foster
School of Business, University of Washington. This is an Open Access article, distributed under the terms
of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
doi:10.1017/S0022109021000478

Small-Business Survival Capabilities and
Fiscal Programs: Evidence from Oakland

Robert P. Bartlett III
University of California, Berkeley
rbartlett@berkeley.edu

Adair Morse
U.S. Department of the Treasury and University of California, Berkeley
adair@berkeley.edu (corresponding author)

Abstract
Using City of Oakland data during COVID-19, we document that small-business components
of survival capabilities (i.e., revenue resiliency, labor flexibility, and committed costs) vary
by firm size. Nonemployer businesses rely on low-cost structures to survive. Microbusi-
nesses (1–5 employees) depend on 14% greater revenue resiliency. Enterprises (6–50
employees) use labor flexibility to survive but face 10%–20% higher residual closure
risk from committed costs. The evidence argues for size targeting of financial support
programs, including committed costs and revenue-based lending programs. Supporting
the capabilities mapping, we find that the Paycheck Protection Program (PPP) increased
medium-run survival probability by 20.5% specifically for microbusinesses.

I. Introduction
In the United States, small businesses encompass 25.7 million nonemployer
firms, 5.8 million microbusinesses (1–5 employees), and 2.8 million larger
small-business enterprises (6–100 workers), together accounting for 44% of

The views and analyses in this article are those of the authors and do not represent the policies or agenda
of the U.S. Department of the Treasury. We extend our thanks to the staff of the City of Oakland, whose
foresight in implementing the survey used in this article and countless hours of work on it are a testament to
the desire to support the people and small businesses of Oakland. Particular thanks go to Marisa Raya. We
also thank the SafeGraph and HomeBase companies, which generously shared their data for research related
to COVID-19’s impact on U.S. businesses. For helpful comments and suggestions, we thank Raj Chetty, Ran
Duchin, Michael Faulkender, Jarrad Harford, Peter Molk, Danny Sokol, Philip Strahan, Annette Vissing-
Jørgensen, and Andrew Winden, as well as seminar participants at the University of California, Berkeley; the
University of Florida School of Law; the University of Illinois; the Ohio State University; the University of
St. Gallen; the Swiss Finance Institute; the Bank of Mexico; Cornell University; the New York Federal
Reserve Bank; the University of Frankfurt; Vanderbilt Law School; and participants at the 2021 Allied Social
Sciences Association (ASSA) Annual Meeting, the Columbia School of International and Public Affairs
(SIPA)/Bank Policy Institute (BPI) February 2021 Conference on Bank Regulation, the NBER Fall 2020
Entrepreneurship Working Group, the 2021 Journal of Financial and Quantitative Analysis (JFQA) Sym-
posium on COVID-19, and the 2021 Societies for Financial Studies (SFS) Cavalcade North America.

2500
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Bartlett and Morse 2501

U.S. employment and 99% of firms (2015 and 2017 U.S. Census data, https://
www.census.gov/programs-surveys/susb/technical-documentation/methodology.html).
These 34 million businesses are heterogeneous in their toolkits to adapt to
business-cycle fluctuations in a very simple way, by firm size. Such heterogeneity
implies that a one-size-fits-all policy approach in a time of crisis is likely suboptimal.
In this article, we analyze the role of the ex ante employment size of a small
business in survival, working through mechanisms of revenue resiliency, labor
flexibility, and committed costs. These results build on the emerging literature
examining the economic consequences of the COVID-19 pandemic on the small-
business sector. For example, Bartik, Bertrand, Cullen, Glaeser, Luca, and Stanton
(BBCGLS) (2020), Humphries, Neilson, and Ulyssea (2020), Fairlie (2020), Alek-
seev, Amer, Gopal, Kuchler, Schneider, Stroebel, and Wernerfelt (2020), Kim,
Parker, and Schoar (2020), and Adams, Boneva, Golin, and Rauh (2020) document
many of the patterns of distress within the sector during the pandemic, including the
high incidence of both temporary closures and mass layoffs, speaking to subjects
we also study in the setting of Oakland, California. Our evidence, however, changes
the perspective to survival mechanisms and adds to this body of work by analyzing
the capabilities of survival by firm size.
We then examine the compatibility of these different survival capabilities with
alternative small-business fiscal support programs, such as working capital loans,
labor-cost subsidies, and lease/debt payment-restructuring programs. Our final
analysis tests for the effectiveness of the Paycheck Protection Program (PPP) and
Pandemic Unemployment Assistance (PUA) for small businesses and business
owners with respect to short- and medium-run survival. In this regard, we build
on the PPP employment outcome evidence in BBCGLS (2020), Chetty, Friedman,
Hendren, Stepner, and the Opportunity Insights Team (2020), and Granja, Makridis,
Yannelis, and Zwick (2020); the PPP short-term firm-survival analysis in Bartik,
Cullen, Glaeser, Luca, Stanton, and Sunderam (BCGLSS) (2020) and Granja et al.
(2020); and the PUA household-outcome evidence in Bhutta, Blair, Dettling, and
Moo (2020) and Iverson, Kluender, Wang, and Yang (2020).
Our empirical contributions are cast in a novel framework whereby small
businesses facing an adverse macro shock face survival as a function of their endow-
ment of i) revenue resiliency, ii) labor-cost flexibility, and iii) committed costs (e.g.,
lease and loan payments). We illustrate here the importance of these dimensions in
our main findings using a stylized, previewing example involving three restaurants.
The first is a nonemployer caterer where the owner does everything. The second is a
taqueria with a total employee base of 4 people, each able to handle all core functions.
The third is a growing pizza restaurant employing 5 cooks and 20 waitstaff.
In this stylized illustration, the macro shock causes large revenue reductions for
the high-volume pizza restaurant, but the owner can easily scale back employees
because only a few employees (the cooks) provide the core function of pizza pro-
duction.1 This labor flexibility does not put the owner at ease, however, because large

1
U.S. workers are typically “at-will” employees and can be terminated at any time, regardless of
employee performance. Nor do federal or state laws requiring advance notice of layoffs apply to businesses
having only a few dozen or fewer employees. For instance, the federal Worker Adjustment and Retraining
Notification (WARN) Act requires all U.S. employers to provide at least 60 days’ advance notice before a
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committed costs (e.g., a large commercial lease and capital loans) loom. For instance,
BCGLSS (2020) find that among survey respondents, the median business had
expenses of over $10,000 per month but only enough cash on hand to last for 2 weeks.
At the other end of firm size stands the caterer, who bears no labor cost other
than her own sustenance. Like most nonemployers, the caterer has little growth
expectations and low operating margins, implying that she has low committed
costs. She also relies only on herself, which makes revenue pivots potentially
challenging. Often, whether or not the caterer can withstand the shock depends on
her personal savings and personal utility.2
The taqueria, unlike the pizza restaurant, has very constrained labor flexibility
because the decision to lay off a cook (at the core of the production) would be
tantamount to closing. On committed costs, the taqueria owner is likely to be similar
to the nonemployer in having kept committed costs low because its small (but vital)
labor force implies that it, too, has low growth expectations. Yet the strain of paying
employees forces the taqueria to look for ways to maintain sufficient revenue to
continue in operation, a tough setting that requires innovation aided only by having
more than one core employee to assist in this endeavor. In the context of the
COVID-19 pandemic, this decision to pursue revenue resiliency has been captured
within the media as the small-business “pivot” (“For Small Businesses, It’s All
About the Pivot,” New York Times, June 26, 2020), such as transitioning to pick-up
only, providing remote services, moving to outdoor venues, and similar efforts to
adjust business operations as a result of the public health crisis.
Our empirical contributions emerge out of analyses across 4 data sets. First
are 2 unique survey data sets obtained from approximately 1,000 small businesses
located in Oakland, California, containing data on precrisis employee counts, crisis
reduction in revenues and employment, and medium-run closure risk. Second, we
hand-collected a data set of Oakland small-business responses to the shelter-in-
place order, codifying the details of the industry and the essential, sidewalk-facing,
and open–closed interim status of the businesses. Third, we use foot-traffic data
from SafeGraph, covering consumer visitation data from mobile devices. Finally,
we use labor data for small businesses obtained from HomeBase.
In our empirical analyses, we estimate the effect of the logarithm of the
number of precrisis workers on the 3 survival-capability mechanisms. Two gen-
eral comments are in order about these analyses. First, we run the results against
the continuous variable of workers, but we cast the economic magnitude inference
in terms of nonemployers, microbusinesses (businesses defined to have 1–5
employees before the crisis), and enterprises (those with ex ante 6–50 employees).
The differences between microbusiness and enterprises (although not using this

mass layoff, a plant closure, or a major relocation, but it applies only to employers having more than
100 employees. Similar small-business exemptions apply under state laws imposing analogous notice
requirements to employers operating within a state. For instance, the California WARN Act exempts
businesses that have employed fewer than 75 California employees in the past 12 months.
2
Many nonemployers operate at a loss (Hurst and Pugsley (2011); see also Internal Revenue Service
(IRS) 2017 data at https://www.irs.gov/statistics/soi-tax-stats-nonfarm-sole-proprietorship-statistics).
These proprietors who operate at a loss may not view their businesses as failing. Moskowitz and
Vissing-Jørgensen (2002) and Hurst and Pugsley (2011) show that nonemployers exhibit behavior
consistent with the consumption of nonmonetary utility in the running of their businesses.
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labeling) have been previously noted in terms of growth rates in Haltiwanger, Jarmin,
and Miranda (2013) and Decker, Haltiwanger, Jarmin, and Miranda (2014). As we
will show graphically, the line of dividing a 5- versus 6-worker firm is not critical, yet
the notion that worker-stable “Main Street” microbusinesses are different from firms
oriented toward growth is important in the macroeconomic literature. Thus, we use
terminology to make these categories distinct for mental accounting purposes.
The second general point is that we do not claim that our estimations are
causally identified empirically, rather, just consistent with our model framework.
We also hope to invoke personal intuition in analyzing this new topic of survival
capabilities by using our caterer–taqueria–pizza restaurant story frame. We hope
future research will build on our intuitive framings in accounting and storytelling to
explore additional causal estimation of these insights.
Our first analyses test revenue resiliency by ex ante (log of) firm size. Within
the Oakland survey, nearly 70% of businesses reported a drop of more than 40% in
year-over-year revenues for Mar. 2020. We find statistically relevant, but small,
differences based on firm size, after controlling for revenue-relevant character-
istics, such as location, whether the business was essential under the county’s
shelter-in-place order, industry fixed effects, and year-over-year revenues for the
prior month. Overall, revenue resiliency is low for nonemployers, then somewhat
higher for microbusinesses, and thereafter lower again as ex ante employee counts
increase. The differences are meaningful but economically small. Microbusi-
nesses experience a revenue decline that is roughly 14% lower than that of the
larger small businesses.
We obtain remarkably similar results when we use SafeGraph foot-traffic data
as a measure of revenue-generating patrons. In a difference-in-difference estimation
with firm fixed effects, we estimate that relative to January levels, enterprises and
nonemployers experienced a 78.7% and 78.3% drop, respectively, in foot traffic in
the weeks following the shelter-in-place order. Microbusinesses fared slightly
better, experiencing a 74% decline, suggesting that microbusinesses were able to
avoid roughly 6% of the drop felt by enterprises.
What these results on revenue resiliency do not explain is the mechanism of
why revenue resiliency varies by firm size. We posit possible explanations. It is
possible that microbusinesses either have a greater capacity to pivot revenue gener-
ation or have greater loyalty of workers. We offer further explanation of this possi-
bility in the text, but we note that such intuition is consistent only with the result.
Our second series of analyses tests whether labor flexibility differs by firm
size. Focusing first on the Oakland survey data, we find that layoffs of full-time
workers and part-time workers had elasticities to firm size (based on precrisis
employee headcount) of 0.127 and 0.172, respectively, controlling for location
effects, essential status, industry fixed effects, and Mar. 2020 revenue losses.
Putting these estimates into the context of our growth categories of businesses,
they indicate that enterprises laid off approximately 38% of full-time workers and
50% of part-time workers. In contrast, microbusinesses exhibited roughly half the
labor flexibility of enterprises, laying off approximately 18% and 24% of their full-
and part-time workers, respectively.
We supplement these survey estimates of labor flexibility using HomeBase
data. In a differences-in-differences estimation with firm and week fixed effects,
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we estimate the elasticity of postcrisis employee counts and payrolls to firms’
precrisis employee headcount, controlling for revenue declines. We find elasticities
of postcrisis labor to precrisis worker counts ranging between –0.26 and –0.28,
nationally and in Oakland. Translating these findings into our classification of
firms, we find that during the pandemic, enterprises were able to cut back payrolls
49.5%, whereas microbusinesses cut back only 30.0%, or 60% of the reduction of
enterprises.
Overall, our intuition that labor flexibility is a central survival tactic for
enterprises is more transparent in the data than was the case for our intuition
regarding revenue resilience. Microbusiness employees are all core employees;
thus, for a business to stay open and staffed during working hours, it is simply
harder to scale down workers.
Third, we investigate the role of committed costs in a firm’s survival across
different-sized firms. We base our estimates on respondent’s self-reported proba-
bility of having to close permanently, taking residual closure risk variation as a
proxy for committed costs once we level firms on revenue resiliency and labor
flexibility. We find that closure risk is increasing in worker counts. In particular,
relative to microbusinesses and nonemployers, enterprises face a respective 10%
and 20% higher closure risk as a result of committed costs. These results are
consistent with larger small businesses incurring greater committed costs as they
expand operations, but unlike labor, these costs are less flexible, making them a
primary source of closure risk for growth-oriented small businesses. These findings
are broadly consistent with the survey data from Alekseev et al. (2020), who report
that in addition to reporting difficulties paying salary (24.1%), survey respondents
reported struggling to pay rent (24.9%) and service debt commitments (23.0%).
We next map these findings to the design of small-business disaster assis-
tance, both in the context of COVID-19 and in other periods of local and national
macroeconomic distress. Our mapping puts a framing on a set of simple intuitions.
Working capital loan programs to small businesses (e.g., conventional disaster
loans offered by the Small Business Administration (SBA)) require revenue-
resilience mechanisms to be at work, which, at least in the short term, would
be most effective for microbusinesses and then nonemployers. Conversely, pro-
grams offering subsidies to restructure debt, leases, or other committed costs
would be most effective in improving survival odds for larger enterprises. Lastly,
labor-cost-oriented grant and subsidy programs would be most effective for
microbusinesses and nonemployers, who cannot depend on labor flexibility for
survival because the labor force consists of core employees.
Consider, for example, the landmark PPP implemented as part of the Coro-
navirus Aid, Relief, and Economic Security (CARES) Act in Mar. 2020. The
PPP authorized the expenditure of nearly $610 billion in small-business loans.
As suggested by its name, PPP loans were intended to subsidize labor, and the
original terms of the program provided that the loans could be forgiven entirely if
a business spent at least 75% of loan proceeds to maintain precrisis payrolls in
the first 8 weeks following loan disbursement. But given our findings regarding
labor flexibility, this subsidy sits uncomfortably with the survival capabilities of
larger enterprises, which may have little need for a labor subsidy given their ability
to scale back labor to match reduced revenues. Consistent with this observation,
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Chetty et al. (2020) find that the PPP failed to spur employment among businesses
receiving a PPP loan. Their sample, however, did not include microbusinesses, for
which a labor subsidy may have been more useful under our framework.
To explore this possibility, we turn to the second Oakland survey, a follow-up
survey conducted in June 2020 that focused specifically on the aid that Oakland
small businesses had pursued, as well as their short- and medium-term projections
for survival. Of particular interest is the utilization of PPP loans and the PUA
authorized by the CARES Act. Both programs represent a labor subsidy insofar
that they were intended to cover labor costs for employer and nonemployer busi-
nesses. As such, they should be especially useful for those businesses that continue
operations but are limited in their ability to scale down labor costs.
Our setup to study the effects of the PPP and PUA on survival is subject
to concerns about selection. However, with regard to the PPP, we are aided by
the design of the Oakland survey and the evidence and insights in Granja et al.
(2020) and BCGLSS (2020). The Oakland survey asked whether an owner applied
for each program, allowing the answer to be “no”; “yes, successfully”; or “yes,
unsuccessfully.” Because there were little to no financial or economic eligibility
criteria for the PPP (other than providing attestations), Granja et al.’s (2020) first
stage test shows convincingly that the lack of success largely results from bank
frictions. BCGLSS (2020) similarly demonstrate that for the first wave of PPP
applications, an applicant’s likelihood of approval was largely due to the size of the
bank through which the applicant applied. Thus, we can use the applying-for-the-
PPP variables to control for selection and study the effect of the PPP under the idea
that the Granja et al. (2020) insight holds in our sample.
We find results consistent with the intuition in our framework and the labor-
flexibility results. Relative to firms that were unsuccessful in applying for PPP
funding, those that successfully applied reported a 20.5% greater probability of being
able to survive beyond 6 months. This result, however, is confined to microbusi-
nesses, consistent with these firms’ inflexible labor structures. Because of the impor-
tant role of these businesses within the national economy, this finding suggests that
the PPP has been beneficial for stakeholders in the vast majority of small businesses in
the country. For instance, according to 2015 census data, 92.9% of businesses had
fewer than 20 employees, and 17% of employment was in businesses with under
20 employees. Nonemployer owners who utilized the PUA also reported a lower
likelihood of having to close their businesses in the short term but not the medium
term, consistent with nonemployers using this short-term labor subsidy (which was
originally set to expire in July 2020) to avoid searching for alternative sources of
personal income, thus averting an immediate closure of their establishments.
We contribute to the literature by examining how firm size can induce small
businesses to pursue heterogeneous survival tactics when confronted with an
adverse macroeconomic shock. In this regard, we build off the growing literature
examining the role of firm size (measured by employment) in the growth and risk of
small businesses (Davis, Haltiwanger, and Schuh (1996), Davis, Haltiwanger,
Jarmin, Krizan, Miranda, Nucci, and Sandusky (2009), Haltiwanger et al. (2013),
Decker et al. (2014), and Mayer, Siegel, and Wright (2018)). By focusing on small
businesses faced with a macroeconomic shock, however, we illustrate the unique
ways in which firm size also matters to a perspective on firm survival and policy.
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Both our framing and our empirical findings are also consistent with a
growing body of research exploring the effect of COVID-19 on small businesses
and their heterogeneous survival responses. In addition to the literature noted
previously documenting patterns of distress within the sector, Kim et al. (2020)
find that the smallest businesses began recovering revenues faster than the larger
small businesses in their sample. Using Norwegian data on small businesses,
Alstadsæter, Bjørkheim, Kopczuk, and Økland (2020) provide simulations that
underscore the first-order importance of labor flexibility for firm survival for most
small businesses, as well as differentials across small businesses in the utility of
this tactic in surviving the pandemic. Alekseev et al. (2020) examine differences
in firms’ ability to reopen, finding heterogeneities in prospects of profitability and
the ability to service fixed costs, as well as differences across owner gender and
the ability to work remotely. BCGLSS (2020) explore differentials in the impact
of the PPP on firms’ perception of survival and find it to be most effective for firms
with low fixed expenditures, a finding that echoes our conclusion that a labor
subsidy is insufficient for aiding a growing business that has incurred significant
committed costs. Our empirical frame and findings are thus consistent with the
findings of this growing literature and underscore the importance of firm size in
explaining why firms of different sizes should be expected to experience different
financial challenges and, relatedly, why they would be drawn to different support
policies.3
Our findings have critical implications for the design of small business assis-
tance programs, especially when combined with the established welfare effects of
different-sized small businesses. Decker et al. (2014) show that among small
businesses, only those with more than 10 employees create future job growth past
the formative years. That the PPP was most effective as a survival program for
nonemployers and microbusinesses may accordingly concern those who saw the
program as an aid to future job growth.
Conversely, the PPP might be assessed more favorably among those view-
ing the program through a welfare lens that prioritizes the needs of community
economic stakeholders. The evidence in Hurst and Pugsley (2011) and Austin,
Glaeser, and Summers (2018) documents how nonemployers and microbusi-
nesses critically support local communities through the maintenance of vibrancy
in commercial districts. Community vibrancy directly maps to welfare through
spatial spillovers to governments and other community stakeholders through
commercial and residential property tax bases (Alm, Buschman, and Sjoquist
(2014), Shoag and Veuger (2018), and Tsivanidis and Gechter (2020)) and
through support for females and minority entrepreneurship (SBA (2018)). Thus,
taken in this light, the proportion of PPP funds tapped by microbusinesses and
nonemployers might have induced large welfare gains. Such a finding would

3
A separate line of research additionally examines how firm survival during COVID-19 is also
associated with the race and ethnicity of an establishment’s proprietor. For instance, Fairlie (2020) finds
that African American business owners experienced a drop of 26% in business activity from pre–
COVID-19 levels compared with only a 11% drop for White business owners by May 2020. Aside
from the effectiveness of support policies on different-sized establishments, this literature raises impor-
tant policy questions relating to the equitable distribution of small-business aid, which are also consid-
ered in Erel and Liebersohn (2020).
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Bartlett and Morse 2507

provide a counterpoint to prominent arguments for letting existing unemploy-
ment insurance nets and creative destruction work in the market in an effort to
save funds for the revival of growing enterprises, rather than subsidizing the
survival of all small businesses (Rajan (2020)). In short, designing small-
business assistance with a single-minded focus on job creation risks creating
zombie Main Streets and a greater geographic concentration of commerce and
wealth (Austin et al. (2018)).
To make more progress on these welfare trade-offs is beyond the scope
herein. Yet sound small-business policy points to the need to account for the
divergent welfare effects of supporting different sizes of small businesses through
periods of macro distress. For the same reasons, policy must also consider how the
heterogeneities in small-business survival capabilities map to the design of spe-
cific small-business assistance programs.4 Our contribution is to provide that
mapping.
The rest of the article is organized as follows: In Section II, we present
our framework for small-business survival capabilities. We describe our data in
Section III and provide summary statistics for each of the 4 data sets that comprise
it. Our empirical results are reported in Section IV. Section V examines the policy
implications of our findings with respect to the design of small-business assistance
programs. Section VI reports on whether the PPP and PUA affect medium-term
survival in Oakland, building off the policy frame. Section VII concludes.

II. Frame
We lay out a simple frame of the components of firm-level cash flows to fix
ideas. We define cash flows π as net revenues (rÞ minus labor costs (l) and
committed other costs (c):
(1) Π ¼ r l c,

where net revenues (r) is revenues minus the inventory costs of goods sold. We
consider a negative macro shock R to the economy, which imposes a loss of a unit of
net revenue on average for small businesses but with variance across firms. We are
interested in the survival of a firm, defined as the maintenance of positive cash flows
from the existing cash-flow position of the firm following the macro shock, or:
dπ
(2) survival∶ ¼ π þ > 0:
dR

4
In the context of the PPP, for instance, the need to consider the heterogeneities in small-business
survival capabilities was made evident shortly following the launch of the program. In particular, a
chorus of small-business owners argued that the PPP forgiveness requirement that 75% of the loan
amount be spent on payroll was ill-suited for their survival plans, causing them to pass on the program
(Freedman (2020)). In response, Congress amended the program to decrease the required payroll spend
to 60% of loan proceeds and to allow it to be accomplished over a 24-week period rather than an 8-week
period.
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Taking the derivative and allowing for labor to scale with revenues or to be
directly affected by the shock, we have the survival condition as follows:

dr ∂l dr dl dc
(3) rþ lþ þ c þ > 0:
dR ∂r dR dR dR
|ﬄﬄﬄﬄﬄﬄﬄ {zﬄﬄﬄﬄﬄﬄﬄ } |ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ} |ﬄﬄﬄﬄﬄﬄﬄ {zﬄﬄﬄﬄﬄﬄﬄ }
revenue grit labor flexibility committed costs

Survival is a function, first, of the ability of firms to exhibit revenue resiliency,
preserving as much of ex ante revenue as possible, such as by restructuring
operations (e.g., working remotely, pivoting to pick-up or delivery services, Face-
book campaigns to alert customers of continued operations, etc.). Next, survival is
a function of labor-cost flexibility, which incorporates how elastic a firm’s labor
cost is to revenue as well as direct labor effects from the macro shock. Finally,
survival is a function of the size of the committed costs and the ability to restructure
costs following the shock R .5
In our empirical analysis, we assume that these 3 survival tactics are capabil-
ities of firms in the sense that firms will optimally do whatever they can to adjust to
the macro shock. We estimate these survival capabilities with the lens of looking at
how they vary by firm size, focusing on our 3 categories of nonemployers, micro-
businesses, and enterprises, all within the industry sector.
We then map the results to inferences concerning policies aimed at supporting
small businesses during periods of macro distress. In particular, we consider 3 sets
of program features in terms of how they relate to our frame:
Subsidized Working Capital Loans. Programs such as Economic Injury Disas-
ter Loans (EIDLs) offered through the SBA provide subsidized loans to businesses
struggling with natural disasters. These programs impose conditions on recipients
to ensure that loan proceeds are used to support working capital in rebuilding
revenues. Recipients of EIDLs, for instance, are prohibited from using loan pro-
ceeds to refinance long-term debt or expand operations. As summarized by the
SBA, EIDLs are for entities that are “ready to ‘restart’ their operations once
circumstances allow” (SBA (2020)). These subsidized working capital loans should
therefore be most useful for those firms that have revenue resiliency among their
survival capabilities.
Labor-Cost Grants and Subsidies. Programs such as the PPP and PUA
(as used by nonemployers as income substitution) provide a subsidy to labor costs,
conditional on labor remaining in place. Payroll tax holidays (e.g., on the employer
match for Social Security payroll taxes, as was done by President Carter in the Jobs
Tax Credit in the Tax Reduction and Simplification Act of 1977 and more recently
in the CARES Act) would also subsidize continuing employment. These policies
will be effective when most workers are core-function employees but may be less

5
Importantly, this framework omits a reliance on savings, based on empirical findings that small
businesses maintain very little working capital or cash reserves to weather an adverse shock to revenue.
For instance, during the COVID-19 crisis, estimates from the U.S. Census Small Business Pulse Survey
show that only 25% of firms had enough cash on hand to cover 3 months of operations at the end of May
2020 (U.S. Census (2020); see also BBCGLS (2020)).
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Bartlett and Morse 2509

efficient and attractive for small businesses endowed with high labor flexibility that
they use to survive business-cycle downturns.
Lease or Debt Payment-Restructuring Subsidies. Governments might
choose to implement policies aimed at reducing the committed cost burdens on
small businesses akin to the Home Affordable Modification Program (HAMP)
applied to households and lenders during the Great Recession, whereby the
government subsidizes the lender (or leaseholder) to restructure the obligation.
In the context of small businesses, these programs may take the form of providing
government grants that can be used to offset commercial lease costs. Facilitating
small-business bankruptcy reorganizations would also have the effect of provid-
ing small businesses with leverage to restructure large committed costs. We
provide examples of both forms of these programs in Section V. Either form of
program will be especially relevant for small businesses whose survival will
depend on their ability to restructure large committed costs incurred prior to
the macro shock.

III. Data and Summary Statistics
A primary challenge confronting research about small businesses concerns the
unavailability of firm performance data. We address this challenge through a multi-
step data-collection process that exploits our ability to collect real-time data as small
businesses began to experience the impact of the COVID-19 economic shutdown.
These real-time data come from the following 4 sources.

A. City of Oakland Small-Business Survey
In early Mar. 2020, well before Alameda County (the county where Oakland
sits) imposed its shelter-in-place order, the City of Oakland constructed a survey
to elicit information from its small-business community about resiliency during
the COVID-19 pandemic. The city’s survey went live 3 days prior to the Mar.
16 announcement of the county’s shelter-in-place order. Our core analyses focus on
responses submitted between Mar. 13, 2020, and Apr. 1, 2020. Our sample starts
with 1,088 surveys. After filtering out 37 businesses with more than 50 employees,
19 purely online businesses, and 18 nonprofits, we have a sample of 1,014 firms.
Based on census data, we estimate that the survey captured 11%–15% of the city’s
small businesses.6
Table 1 reports the summary statistics from the City of Oakland survey.
Panel A reports the sample statistics regarding employment, covering the following
variables:

6
According to the 2017 County Business Patterns from the U.S. Census Bureau, there were nearly
14,000 private and government establishments in Oakland’s zip codes. Using this number directly
would imply that our sample captures 7.8% of Oakland’s businesses, but this is too conservative
because the census total i) includes businesses with up to 500 employees (and our survey is for small
businesses); ii) assumes that small businesses have only one establishment; iii) includes non–revenue-
generating registered businesses; and iv) includes schools, government offices, and other nonbusiness
organizations.
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NONEMPLOYER: An indicator for the firm reporting no employees.
MICROBUSINESS: An indicator for the firm reporting 1–5 employees.
ENTERPRISE : An indicator for the firm reporting 6–50 employees.
PRECRISIS_EMPLOYEES : Full- and part-time employees prior to Mar.
2020.
PERCENTAGE_CHANGE_JOB_LOSSES: Full- and part-time positions
lost early in the shutdown, relative to reported precrisis full- and part-
time positions.
A quarter of the sample of Oakland small businesses are nonemployers, 43%
are microbusinesses, and 32% are enterprises. The mean (median) employee count
is 6.5 (2) for all small businesses and 8.7 (4) for businesses excluding the non-
employers. Of these jobs, 17.7% were already lost on average in the first weeks of
the shelter-in-place order.
Panel B of Table 1 reports statistics on reported revenues, covering the
following variables:
DECLINING: Whether or not the business was ex ante declining, defined as
year-over-year (YoY) decline in gross receipts as of Feb. 2020.
PERCENTAGE_CHANGE_RECEIPTS : Reported percentage change in
YoY gross receipts as of Mar. 2020 based on a firm’s selection of 1 of
6 ranges.
Approximately half of all respondents indicated a decline in YoY revenue as
of Feb. 2020, the month prior to the shelter-in-place order. We use this indicator
variable as our proxy for indicating whether a firm’s financial distress predated the
U.S. COVID-19 crisis. The fact that half of small businesses might have negative
growth is consistent with the results in Decker et al. (2014) and Hurst and Pugsley
(2011) concerning the nongrowth nature of most small businesses. Also in Panel
B of Table 1, we report the distribution of gross receipts YoY as of Mar. 2020. By
early March, businesses were severely affected, first by self-imposed staying out
of public spaces and then, on Mar. 16, by the county ordinance. This shows up in
the distribution in Panel B, where 69% of respondents report a YoY decline of
over 40% for March.
In Panel C of Table 1, we report the distribution to the response on closure risk,
defined as follows:
CLOSURE_RISK: Whether the small-business owner responded that he or she
was “very concerned” about the risk of closure, “somewhat concerned,” or
“not concerned.”
Overall, 73% of respondents were very concerned about closure, and only 4%
were not concerned.
As part of this survey, the City of Oakland subsequently conducted a follow-up
survey completed by nearly 300 of the small businesses. We examine this follow-up
survey in Section V.
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TABLE 1
City of Oakland COVID-19 Survey of Small Businesses: Summary Statistics

All data in Table 1 are from the City of Oakland COVID-19 survey of small businesses. Our sample is from Mar. 13, 2020, to Apr.
1, 2020. As reported in Panel A, the NONEMPLOYER, MICROBUSINESS, and ENTERPRISE classifications are indicator
variables dividing the sample into those with no employees, those with 1–5 employees, and those with 6–50 employees,
respectively. PRECRISIS_EMPLOYEES is the number of employees reported in early March (precrisis). The smaller sample in
Panel A, 761 firms, includes those with positive employment. For this sample, we know the existing base of part- and full-time
employees and the jobs lost. Panel B presents two pictures of gross receipts. First is whether the business is declining in gross
receipts (DECLINING), defined as reporting the year-over-year (YoY) receipts as of Feb. 2020 as being in decline relative to
2019. A smaller sample responded to the question as to the declines in March at the initial shelter-in-place order. The survey
asked owners to report their gross receipts in March YoY from Mar. 2019 (PERCENTAGE_CHANGE_RECEIPTS). Panel C
presents closure-risk responses. In particular, the survey respondents were asked how concerned they were about closing
the business, responding in the categories listed.
Panel A. Employment

Variables No. of Obs. Mean Std. Dev. P0 P25 P50 P75 P100

NONEMPLOYER 1,014 0.250 0.433 0 0 0 0 1
MICROBUSINESS 1,014 0.430 0.495 0 0 0 1 1
ENTERPRISE 1,014 0.321 0.467 0 0 0 1 1
PRECRISIS_EMPLOYEES 1,014 6.52 9.81 0 1 2 8 50
If MICROBUSINESS or ENTERPRISE
PRECRISIS_EMPLOYEES 761 8.68 10.46 1 2 4 11 50
FULL_TIME_EMPLOYEES 761 4.46 6.70 0 1 2 5 48
PART_TIME_EMPLOYEES 761 4.22 6.75 0 0 2 5 40
PERCENTAGE_CHANGE_JOB_LOSSES 761 0.177 0.256 0 0 0 0.421 1
Panel B. Gross Receipts

Variables No. of Obs. Percentage Cumulative Percentage

DECLINING 1,014 0.523
PERCENTAGE_CHANGE_RECEIPTS (YoY March):
<2% 6 0.007 0.007
2–5% 7 0.008 0.015
5–10% 30 0.035 0.050
10–20% 53 0.061 0.111
20–40% 168 0.194 0.306
>40% 600 0.694 1.000
Observations with revenue-decline data 864 1.000
Panel C. Business-Closure Risk

CLOSURE_RISK (How concerned are
you about your business closing?) No. of Obs. Percentage Cumulative Percentage

Not concerned 40 0.039 0.039
Somewhat concerned 233 0.230 0.269
Very concerned 741 0.731 1.000
1,014 1.000

B. Hand-Collected Information on Small-Business Operations

We obtain additional operating data on these businesses from manual firm-by-
firm Internet searches conducted between Apr. 24, 2020, and May 3, 2020—the day
on which Alameda County permitted certain outdoor businesses to recommence
operations on a limited basis. We began these manual searches on Google Maps.
Companies, particularly street-facing companies, had a large incentive to keep their
Google Maps status updated, and businesses informed us that Google aggressively
solicited each establishment for this information. We additionally searched for
the company website and other Internet sources of information to determine the
operating status for each survey company.
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For each business, we coded the following variables:
INDUSTRY: Narrowly defined industry.
MAIN_STREET: An indicator for a business being on a street-facing location
(on “Main Street”).
ESSENTIAL: Whether the business constitutes an “essential business” under
Alameda County’s shelter-in-place order (e.g., grocery stores, lumber
and repair, pharmacies, and physician offices).
Panel A of Table 2 breaks down the distribution of industries and provides
detailed examples of the type of businesses in each category. Panel B reports that two-
thirds of the sample consists of street-facing businesses, and a third provide their
goods and services at a home or at an office that is not street-facing. We coded 11% of
respondents to be deemed essential businesses under the shelter-in-place rules.

TABLE 2
Oakland Manual Data Collection: Summary Statistics

Table 2 presents the manual lookup data for the sample of businesses in the City of Oakland COVID-19 survey. We manually
coded all fields (industry, location, whether the business is essential under Alameda County’s shelter-in-place order, and
outcome status) using a 2-step process. First, we looked up the establishment in Google Maps. Second, we went to the
business’s website (always) and other Internet sites where information might be available (if necessary), including Yelp and
local blogs listing closures. In Panels A, B, and C, we show the distribution by industry categories (Panel A), location and
whether the business is an essential business that is permitted to remain open per state regulations (Panel B), and the interim
status of the business (Panel C). The interim outcome is the status of the business during the last week of Apr. 2020,
immediately before reopening began in the county. The sample size is reduced because we are unable to categorize
many professional services (e.g., lawyers and advertising agencies) that lacked updates on any information sites.

Panel A. Industry Classification

Industry No. of Obs. Percentage Examples

BUSINESS_SERVICES 82 8.1% Catering, industrial cleaning, printing, photography, technology
CONSTRUCTION/ 95 9.4% Construction, entertainment venues, event spaces, parking lots,
MANUFACTURING/VENUE housing, manufacturing, wholesale trade
FITNESS/GYM/WELLNESS 86 8.5% Fitness centers, gyms, massage, acupuncture
MEDICAL_OFFICES 38 3.7% Chiropractic, dentist, optical, physical therapy, psychology
PERSONAL_SERVICES_HOME 28 2.8% Home repair, landscape, pet walking, realty
PERSONAL_SERVICES_SHOP 75 7.4% Auto repair, car wash, child care, education, laundry, tattoo
PROFESSIONAL_SERVICES 206 20.3% Architects, consultants, designers, engineers, lawyers
RESTAURANT 156 15.4% Restaurants
RETAIL 144 14.2% Retail shops
SALON 104 10.3% Salons, barbers
1,014 100%
Panel B. Commerce Location and Essential Designation

Variables No. of Obs. Percentage

MAIN_STREET 674 66%
NON_MAIN_STREET (at a venue, home, or offsite) 340 34%
1,014 100%
ESSENTIAL 112 11%
NONESSENTIAL 902 89%
1,014 100%
Cumulative
Panel C. Interim Outcome No. of Obs. Percentage Percentage

STATUS
PERMANENTLY_CLOSED (or lacking ongoing concern signal) 159 0.192 0.192
TEMPORARILY_CLOSED 211 0.255 0.447
TRYING 172 0.208 0.655
OPEN 285 0.345 1.000
827 1.000
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Panel C of Table 2 reports the operating status of the survey respondents,
defined as follows:
STATUS: The hand-coded operating status of the business at the time of the
search, coded among the following: PERMANENTLY_CLOSED
(or lacking ongoing concern signal), TEMPORARILY_CLOSED,
TRYING, and OPEN.
Firms classified as TRYING (21%) indicate those firms that were not permitted
to operate under the shelter-in-place order but nevertheless conducted operations
under alternative arrangements or reduced-revenue models. For example, this class
of businesses might include a restaurant that operated on a limited take-out/delivery
basis or a yoga studio that operated remotely through video conferencing. The
TEMPORARILY_CLOSED status, which was actively pursued by Google Maps
to provide its customers with accurate data on the availability of businesses,
accounted for approximately 26% of the sample. We overrode Google Map’s
TEMPORARILY_CLOSED to be TRYING if the business website indicated
that it was operating in some form to generate revenues; most TRYING businesses
were marked “Temporarily Closed” by Google, except for restaurants, which were
marked “Take-Out Only” or “Delivery Only” (or both). The PERMANENTLY_
CLOSED (or lacking ongoing concern signal) businesses (19%) were a combina-
tion of those businesses that were explicitly marked as permanently closed by
Google Maps and those businesses that showed no sign of any ongoing business
on their Web pages and did not indicate that they were temporarily closed.
In Figure 1, we plot the status by industry in pie charts to underscore
the economic challenge to survey respondents posed by the COVID-19 pan-
demic. Temporary closures were especially high within the retail, fitness/salon/
wellness, and construction sectors. Only in the medical, professional services,
and personal services sectors was the OPEN category the dominant classifica-
tion. In contrast, the TRYING classification was prevalent among restaurants,
fitness, and retail.

C. HomeBase

Our third set of data comes from HomeBase, a company handling workplace
scheduling and payroll management that caters primarily to small businesses.7
The HomeBase data include anonymized data for an establishment’s weekly
employee headcount and, for some firms, reported weekly wages paid to some
or all employees. We filter the HomeBase data to all business establishments that
have a U.S. zip code and a disclosed industry and that have 50 or fewer average
employees between Jan. 1, 2020, and Feb. 15, 2020.
In Panel A of Table 3, we report summary statistics for this sample of Home-
Base establishments. Our primary interest is in the following weekly measures:

7
These data have been made available by HomeBase for researchers examining the labor-market
impact of COVID-19 and have been a primary source of data for examining overall employment trends
within the small-business sector during the COVID-19 economic crisis (see, e.g., Bartik, Bertrand, Lin,
Rothstein, and Unrath (2020)).
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FIGURE 1
Interim Outcome Status by Industry
Figure 1 depicts the interim outcomes via the manual search data for the City of Oakland firms as of the last week of Apr. 2020.
“Trying” indicates that the business is working on a reduced- or alternative-revenue model and is otherwise temporarily
closed. “Permanently Closed” indicates that the business is either marked permanently closed on Google Maps, the company
website, or Yelp or the business shows no information as to closure being a going concern.

business services construct/manuf/venue fitness/wellness medical offices

personal services home personal services shop professional services restaurant

retail salon

1. Permanently Closed 3. Trying

2. Temporarily Closed 4. Open

HEADCOUNT: Weekly full- and part-time employees (regardless of whether
wages are disclosed) per location.
PAYROLL: Weekly wages paid to employees for whom wages are disclosed.

Columns 1–4 indicate that the HomeBase firms within our sample had a mean
(median) employee headcount of 7.6 (5.5) during the period from Jan. 1, 2020, to
Feb. 15, 2020. Not surprisingly, employee headcount varied by industry, ranging
from a low of 4.34 (3) among establishments in the beauty and personal care
industry to a high of 9.21 (7.3) within the food and drink industry. In columns
5–8, we similarly present summary data concerning total weekly wages paid for
those establishments that reported the weekly wages paid to one or more employees
over the same period. Among these firms, mean (median) precrisis wages paid per
week were roughly $2,400 ($1,500). Across industries, beauty and personal care
establishments were the lowest paying within the sample, whereas transportation
establishments paid the most, presumably reflecting the higher hourly wage rates
paid by these latter firms.
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TABLE 3
HomeBase Employee Data and SafeGraph Foot-Traffic Data: Summary Statistics

Panel A of Table 3 presents summary statistics for the sample of small businesses provided by HomeBase, tabulated
separately by HomeBase industry. Columns 1–4 present mean and median employee headcount during the period from
Jan. 1, 2020, to Feb. 15, 2020. Columns 5–8 present statistics for total weekly wages during this same time period for those
firms within HomeBase that reported wages for some or all employees. Panel B presents summary statistics by coded industry
for the variable FOOT_TRAFFIC for 268 small businesses in the Oakland survey that could be matched to a point of interest
(POI) within the SafeGraph data set. FOOT_TRAFFIC is defined as reported visits per day to a POI between Jan. 1, 2020, and
Mar. 15, 2020, scaled by the number of devices observed by SafeGraph in Alameda County per 100,000 residents. This is a
standard scaling recommended per SafeGraph documentation.
1 2 3 4 5 6 7 8

Panel A. HomeBase Employee Headcounts

HEADCOUNT PAYROLL

No. of Std. No. of Std.
HomeBase industry Obs. Mean Dev. P50 Obs. Mean Dev. P50

BEAUTY_&_PERSONAL CARE 417 4.34 4.29 3.0 235 $1,093 $1,344 $692
CHARITIES_EDUCATION_&_MEMBERSHIP 1,430 8.55 8.16 6.0 1,013 $2,084 $3,298 $1,168
FOOD_DRINK 23,159 9.21 7.41 7.3 16,761 $2,491 $2,742 $1,731
HEALTH_CARE_AND_FITNESS 3,934 5.57 5.44 4.0 2,520 $2,086 $2,782 $1,189
HOME_AND_REPAIR 579 5.10 5.82 3.2 409 $2,574 $3,290 $1,753
LEISURE_AND_ENTERTAINMENT 936 7.91 7.95 5.3 735 $2,038 $2,523 $1,175
OTHER 4,189 7.09 7.48 4.5 2,972 $2,725 $4,522 $1,468
PROFESSIONAL_SERVICES 1,986 5.07 5.88 3.2 1,333 $2,470 $4,817 $1,183
RETAIL 8,649 5.46 5.13 4.0 5,962 $2,397 $3,464 $1,354
TRANSPORTATION 373 6.79 7.43 4.2 261 $3,577 $4,741 $1,859
UNKNOWN 4,797 7.19 6.46 5.2 3,262 $2,157 $2,797 $1,282
All 50,449 7.60 6.98 5.5 35,463 $2,413 $3,197 $1,510
Panel B. SafeGraph Foot Traffic

FOOT_TRAFFIC

No. of Std.
INDUSTRY Obs. Mean Dev. P0 P25 P50 P75 P100

BUSINESS _SERVICES 5 3.89 2.32 1.31 2.55 3.42 4.82 7.35
CONSTRUCTION/MANUFACTURING/ 19 6.54 5.54 0.41 1.31 5.01 11.64 16.15
VENUE
FITNESS/GYM/WELLNESS 28 4.83 4.65 0.35 1.23 3.37 7.48 18.25
MEDICAL_OFFICES 4 0.85 0.43 0.46 0.56 0.74 1.14 1.46
PERSONAL_SERVICES_HOME 14 5.73 5.50 0.69 1.96 4.85 6.63 21.47
PERSONAL_SERVICES_SHOP 7 5.12 7.51 0.56 0.68 2.54 4.20 21.85
RESTAURANT 114 7.55 8.31 0.10 3.31 5.37 8.46 59.68
RETAIL 67 4.90 4.45 0.28 1.23 4.00 6.53 17.38
SALON 10 2.52 3.00 0.40 0.89 1.46 3.19 10.50
Total 268 6.02 6.63 0.10 1.83 4.17 7.48 59.68

In Figure 2, we plot the distribution of our sample of HomeBase establish-
ments by state. California, Texas, and Florida claim roughly 16%, 10%, and 9% of
establishments, respectively.

D. SafeGraph Foot-Traffic Data
Our final data set is data on foot traffic in establishments from SafeGraph for
Jan. 1, 2020, to Apr. 30, 2020. SafeGraph covers mobile locations for over 30 mil-
lion individuals using cell-phone tracking information that these individuals have
consented to share pursuant to one or more applications installed on their mobile
devices. SafeGraph overlays this tracking data to 5 million U.S. establishments or
“points of interest” (POIs) based on the actual location and shape of the POI (i.e., its
polygon) rather than its address, thus allowing SafeGraph to identify each instance
when an individual visits a POI.
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FIGURE 2
Distribution of HomeBase Establishments by State
Figure 2 reports the percentage of establishments by state across all establishments within our sample of HomeBase
establishments. The reported state of location is provided by HomeBase.

Percent of Establishments
10

0 California
Texas
Florida
Georgia
New York
North Carolina
Illinois
WashingtonOhio
Pennsylvania
Virginia
Colorado
Michigan
Arizona
Oregon
Tennessee
New Jersey
Missouri
South Carolina
Mary land
Louisiana
Indiana
Wisconsin
Puerto Rico
Alabama
Nevada
Utah
Minnesota
Massachusetts
Oklahoma
Kansas
Nebraska
Connecticut
Kentucky
Arkansas
Idaho
Mississippi
New MexicoIowa
Hawaii
Montana
District of Columbia
New Hampshire
Alaska
West Virginia
Maine
South Dakota
North Dakota
Delaware
Rhode Island
Wyoming
Vermont

Figure 3 maps the mean number of devices during this period for all counties in
the continental United States and within California. SafeGraph tracked over 1,000
devices in over half of all counties. In Alameda County, the focus of our study,
SafeGraph tracked over 50,000 devices, allowing us to use these data as a proxy for
the revenue of small businesses, particularly for “Main Street” businesses, whose
cash flow is likely to depend on foot traffic.
We cannot use the data to infer dollars of revenue per se because different store
types have different conversion rates of customers to revenues. Yet we can use foot
traffic to infer the revenue shock, using firm fixed effects, following the approach of
investigative journalists and policy makers examining the impact of the COVID-19
crisis on consumers.8 Figure 4 illustrates the feasibility of this approach, focusing
on select industries within Alameda County: restaurants, electronics and appliance
stores, fitness/sports centers, and grocery stores. We standardize visits by observed
daily devices and calculate the moving average over the preceding 7-day period.
Plotted is the moving average over time relative to that for Jan. 8, 2020. All
businesses suffered a significant drop in foot traffic following the shelter-in-place
order, which is represented by the dashed vertical line on Mar. 16. The primary

8
See, for example, Megan Cerullo, “Phone Data Show Consumers Avoiding Stores, Restaurants
as COVID Surges,” CBS News, July 2, 2020, available at https://www.cbsnews.com/news/cell-phone-
data-show-consumers-avoiding-stores-as-covid-19-cases-surge/.
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FIGURE 3
Distribution of SafeGraph Devices Nationally and Within California
Figure 3 presents, by county, the mean number of mobile devices observed by SafeGraph between Jan. 1, 2020, and Apr.
30, 2020. Graph A presents national data, and Graph B provides an enlarged figure for California (Alameda County is
shown in black).

Graph A. SafeGraph Devices in the United States
(total devices by county)

50,000 - 500,000
10,000 - 50,000
5,000 - 10,000
2,500 - 5,000
1,000 - 2,500
15 - 1,000

Graph B. SafeGraph Devices in California (total devices by county)

50,000 - 500,000
10,000 - 50,000
5,000 - 10,000
2,500 - 5,000
1,000 - 2,500
15 - 1,000

exception relates to the surge in grocery store foot traffic in the days immediately
following the announcement of the order as residents flooded grocery stores.
We match the Oakland small-business survey respondents to the SafeGraph POI
data set by business name, enabling us to assess directly the extent to which a
business’s foot traffic was affected by Alameda County’s shutdown in economic
activity. These foot-traffic data span from Jan. 1, 2020, to Apr. 30, 2020; however, we
drop the period of closing down in late March (Mar. 16–Mar. 31) to ensure that our
foot-traffic data reflect commercial activity as opposed to visits related to the closing
down of a business. We find exact matches for 268 small businesses, mostly for street-
facing businesses. (Service companies without storefronts, such as construction,
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FIGURE 4
Impact of Shelter-in-Place Order on Select Alameda County Businesses
Figure 4 illustrates the mean change in foot traffic for Alameda County businesses relative to foot traffic for a business on Jan.
8, 2020. Foot-traffic data are from SafeGraph and reflect observed daily visits to a SafeGraph point of interest (POI) located
within Alameda County. Because of variation in daily foot-traffic visits, visits per location are measured as the moving average
over the preceding 7-day period. The industries depicted are chosen not to be comprehensive but as illustrative.

1.5

Relative Foot Traffic
1

0.5

Jan-2020 Feb-2020 Mar-2020 Apr-2020 May-2020
Date
Restaurants Electronics and Appliance Stores
Grocery Stores Fitness and Recreational Sports Centers

consulting, realty, and so forth, are often not covered in SafeGraph, nor would foot
traffic represent a meaningful concept of commerce for these businesses.)
In Panel B of Table 3, we report summary statistics for our primary metric
of interest, average daily foot traffic, for each location by coded industry. As in
Figure 4, the reported figures account for variation in the number of observed devices
by scaling the daily visits to an observed POI by the number of devices observed by
SafeGraph in Alameda County per 100,000 residents. Data represent the overall
mean number of standardized daily visits between Jan. 1, 2020, and Mar. 15, 2020.
Across industries, this standardized measure of daily visits ranged from a mean
(median) of 0.85 (0.74) for medical offices to 7.55 (5.37) for restaurants.

IV. Results
We test whether the facets of small-business survival (revenue resiliency, labor
flexibility, and committed cost) vary by ex ante firm size during the economic crisis
caused by the COVID-19 shelter-in-place order. In our estimations, we consider
the role of firm size using 2 independent variables: the natural logarithm of the
precrisis count of workers (where “workers” equals employees + 1, the owner) and
an indicator for a precrisis nonemployer firm. The nonemployer indicator is used
to pick up any unique attributes of nonemployers that a continuous variable might
miss. We then group predictions into 3 size buckets (following our caterer, taqueria,
and pizza restaurant examples) to depict the patterns of the predicted effects by size
type, always absorbing industry or firm fixed effects, to capture patterns orthogonal
to these systematic influences.
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A. Revenue-Resiliency Results

1. Oakland Survey Revenue-Resiliency Results
Table 4 presents results from 2 revenue-resiliency analyses. In columns 1–4,
we report estimates within the Oakland survey data as follows, denoting the small
business by i:

(4) log ð%△RECEIPTS_DECLINE_MARCHÞi
¼ β0 þ β1 LOG_WORKERSPre
i þ β2 NONEMPLOYERi
þβ3 log ð%△RECEIPTS_DECLINE_FEBÞi
þμMAIN_STREET þ μESSENTIAL þ μINDUSTRY þ εi :

The dependent variable is the log of 1 plus the percentage-change decline
in YoY gross receipts for Mar. 2020.9 The dependent variable is increasing in
the decline in revenues. All columns include the variable log ð%Δ RECEIPTS_
DECLINE_FEBÞ, which is the same variable as the dependent variable except YoY
revenue is reported as of Feb. 2020, thus allowing us to parse out the precrisis
situation of the firm and focus on the effect of the pandemic stress. In addition, all
columns include indicators for the business being located on Main Street
(μMAIN_STREET Þ and whether or not the business is essential (μESSENTIAL Þ. All
columns have our main independent variable, precrisis log of workers. Columns
2 and 4 also include the nonemployer indicator to allow for any unique attributes of
this type of business. Columns 3 and 4 include industry fixed effects (μINDUSTRY Þ.
Turning to the results, we first look to the conditioning variable
log ð%Δ RECEIPTS_DECLINE_FEBÞ. The March decline in receipts has a very
tightly estimated elasticity of 0.220 to the February decline in receipts. This
elasticity is well below 1, reflecting the large change in the setting in March.
Nevertheless, this variable is the most important in terms of partial R2 in the
estimation, even much more so than the industry effects, which are surprisingly
insignificant with other variables included. The variables reflecting Main Street and
essential business (which sometimes compete with the industry effect for power)
are also weak in explanatory power. In short, it appears that the shelter-in-place
order affected businesses across the board, hitting those already in decline 22%
more but with much idiosyncratic impact.
Our main variables of interest, LOG_WORKERSPre i and precrisis
NONEMPLOYERi , are also statistically important. We find a gross-receipts elas-
ticity of 0.027 to the number of precrisis workers in the firm once we allow non-
employer firms to exhibit their own pattern (columns 2 and 4). Larger firms and, to a
lesser degree, nonemployers exhibit a greater percentage-revenue decline.
In Graph A of Figure 5, we plot the marginal effect of the log of workers and
nonemployer status on the percentage decline in gross receipts for March at the
mean value of all other variables (using the specification shown in column 4).

9
Note that the variables for gross-receipt decline are reported in the survey in buckets, as depicted
in Table 1, not as a continuous variable. We make a continuous variable by taking the midpoint of
the bucket as the value. An ordinal logit estimation fits a similar pattern as what we report but with
more noise.
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2520 Journal of Financial and Quantitative Analysis

TABLE 4
Revenue Resiliency

Table 4 presents 2 sets of revenue resiliency results for the City of Oakland. Columns 1–4 report OLS estimations from the City
of Oakland COVID-19 small-business survey. The dependent variable is the reported revenue percentage-change decline,
calculated as the natural logarithm of 1 + percentage-change decline reported year over year (YoY) for Mar. 2020. We control
for the same variable for February to focus on the March crisis impact. The main independent variables are the (preperiod) log
of workers (employees + 1) and a nonemployer indicator, per the Oakland survey data. The employee survey question asks
the business for these worker counts as of before Mar. 2020. In columns 1 and 2, we include an indicator for the business being
street-facing (“Main Street”) and one for essential businesses, both from manual coding. Columns 3 and 4 add in industry fixed
effects. Robust standard errors in columns 1–4 are in parentheses. Columns 5 and 6 report difference-in-differences estimates
of the foot traffic for the same businesses in Oakland that could be matched to a SafeGraph point of interest (POI), including
firm and day fixed effects. We limit to the Oakland survey sample to be able to include the precrisis log worker information.
Standard errors in columns 5 and 6 are clustered by firm (in parentheses). *, **, and *** indicate statistical significance at the
10%, 5%, and 1% levels, respectively.
Dependent Variable

log(%△RECEIPTS_DECLINE_MARCH) log(FOOT_TRAFFIC)

1 2 3 4 5 6

LOG_WORKERS (precrisis) 0.0126** 0.0274*** 0.0099 0.0271***
(0.00616) (0.00895) (0.0071) (0.0103)
NONEMPLOYER 0.0538** 0.0593**
(0.0246) (0.0261)
POST LOG_WORKERS (precrisis) 0.0673 0.126**
(0.0435) (0.0543)
POST NONEMPLOYER 0.334*
(0.192)
log(%△RECEIPTS_DECLINE_FEB) 0.217*** 0.225*** 0.219*** 0.231***
(0.0402) (0.0403) (0.0408) (0.0412)
MAIN_STREET 0.0299* 0.0274 0.0544 0.0651
(0.0166) (0.0167) (0.0799) (0.0732)
ESSENTIAL 0.0107 0.00738 0.00233 0.00327
(0.0207) (0.0201) (0.0265) (0.0266)
BUSINESS _SERVICES 0.0673 0.0842
(0.0889) (0.0835)
CONSTRUCTION/ 0.0059 0.00359
MANUFACTURING/VENUE (0.0326) (0.0336)
FITNESS/GYM/WELLNESS 0.0148 0.0302
(0.0868) (0.0812)
MEDICAL_OFFICES 0.00796 0.0187
(0.0986) (0.0923)
PERSONAL_SERVICES_HOME 0.0495 0.0728
(0.0473) (0.0499)
PERSONAL_SERVICES_SHOP 0.0641 0.0805
(0.0924) (0.0865)
RESTAURANT 0.0118 0.0367
(0.0851) (0.0798)
RETAIL 0.0348 0.0477
(0.0823) (0.0763)
SALON 0.0162 0.0387
(0.0847) (0.0794)
Firm fixed effects Yes Yes
Day fixed effects Yes Yes
No. of obs. 349 349 349 349 23,292 23,292
R2 0.107 0.120 0.125 0.139 0.796 0.797

We scatter-plot this margin using buckets of 5 workers for purposes of calculating
the standard error, but note that because we are plotting the marginal effects by these
size-based buckets, the confidence intervals are naturally larger than is the precision
in the table reflecting the estimated slope, which is significant at the 1% confidence
level. Having said that, interpreting the economic magnitude is important. We infer
that the difference in revenue resiliency across firm size is significant but small
relative to the overall revenue decline across the board.
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Bartlett and Morse 2521

FIGURE 5
Revenue Resiliency by Small-Business Type
Graph A of Figure 5 plots the marginal effect of the precrisis number of workers on the Oakland survey decline in year-over-
year (YoY) revenue as of Mar. 2020, taking all other covariates at the mean, based on the model in column 4 of Table 4. The
marginal effect is calculated by buckets of 5 employees, thus creating larger standard errors than in the continuous single
statistic in the table. The sample in Graph B is Oakland businesses matched to SafeGraph points of interest (POIs). Plotted is
the predicted natural logarithm of foot traffic over time by small-business type resulting from the difference-in-difference
estimate of column 6 of Table 4, including firm and time fixed effects.

Graph A. Revenue Percentage Change Decline
YoY for Mar. 2020 Graph B. Foot Traffic Proxy for Revenue Over Time
0.6
1

0.25 0.5 0.75

Reported Decline in March YoY Revenue
0.55

Predicted Foot Traffic
0.5

–0.75 –0.5 –0.25 0
0.45

0.4

Jan 8, 2020 Jan 18, 2020 Jan 28, 2020 Feb 7, 2020 Feb 17, 2020 Feb 27, 2020 Mar 8, 2020 Mar 18, 2020 Mar 28, 2020
0.35 Apr 7, 2020 Apr 17, 2020 Apr 27, 2020
0 1–5 6–10 11–15 16–20 21–25 26–30
Pre-Crisis Employee Count
Nonemployer 95% CI
Nonemployer Microbusiness Microbusiness 95% CI
Enterprise 95% CI Enterprise 95% CI

Despite considering the marginal effect differences small, the figure suggests
meaningful differences in terms of our 3 small-business types, nonemployers,
microbusiness, and enterprises. Averaged in their range, microbusinesses face a
YoY revenue percentage decline for March of –0.408, a large number but better than
enterprises, which face a revenue decline of –0.476 as a percentage of the prior
year’s gross receipts. Interpreting the result in the context of our setup, micro-
businesses seem to be endowed with the ability to ward off 14% of the shock
relative to enterprises. In the context of real-world events captured by our hypo-
thetical businesses, the taqueria, because it is small, is able to more nimbly keep a
larger proportion of precrisis revenues.
Offering proof of the mechanism is beyond our data capacity and scope here,
but we offer a few possibilities for future research consideration. First, our
intuition in talking with many people and business owners over the course of this
research is that nonemployers survive the crisis by managing their personal
savings and opportunities for government support. For these business owners,
pivoting operations can involve costs out of wealth and can be difficult, given the
lack of employees who can develop and implement ideas. Microbusinesses, in
contrast, have the incentive and employees to implement pivots. Another hypoth-
esis is the possibility that loyalty to a business owner is higher in microbusinesses
than in larger enterprises (in some cases, perhaps because the owner and
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2522 Journal of Financial and Quantitative Analysis

employees are related by family.) As a result, worker participation in saving the
business may be higher. Casting these hypotheses in our restaurant examples, it
may be that the few workers at the taqueria are all core employees and are able to
think and act upon creativity with the holistic company in mind. Larger enter-
prises may have more specialized workers (e.g., waitstaff) who potentially would
not have such vision or loyalty. Again, we want to emphasize the caveat that we do
not have the data to prove these assertions and offer them only to guide future
research in this area.
2. SafeGraph Revenue-Resiliency Results
In the last 2 columns of Table 4, we turn to the SafeGraph data of foot traffic
as a revenue proxy, again focusing on Oakland, where we know employee counts.
These data are a panel, allowing us to estimate a model to absorb firm heterogeneity
and time, akin to a difference-in-differences model but against the continuous
variable LOG_WORKERSPre i :

(5) logðFOOT_TRAFFICit Þ ¼ β1 LOG_WORKERSPre
i POSTt
þ β2 NONEMPLOYERi POSTt þ γi þ δt þ ɛit :

The variables γi and δt denote firm and day fixed effects, respectively. POSTt
represents Mar. 30–Apr. 30, 2020.
As shown in column 6 of Table 4, foot traffic fell dramatically across firms
during the POST period and was especially pronounced for nonemployers and
larger businesses. We again turn to a graphical representation to put our economic
magnitudes into perspective.
Graph B of Figure 5 plots the predicted log of FOOT_TRAFFICit over time
by small-business type, after removing firm fixed effects. Note that for the
precrisis period, the lines pick up a single time-varying pattern because LOG_
WORKERSPre i is static by firm (and thus absorbed by the fixed effect) until
Mar. 30, when the interaction with POSTt estimates elasticities by firm size. As
the picture illustrates, all types of businesses incur a tremendous reduction in foot
traffic after the shelter-in-place order; the average decline by Apr. 30, 2020, is
77.1%. However, whereas enterprises and nonemployers face 78.7% and 78.3%
declines in their foot traffic relative to Jan. 13, 2020, microbusinesses have some-
what higher revenue resiliency, facing only a 74% decline. This is not to say that a
74% decline in revenues is benign, but microbusinesses seem to be able to ward off
6% of the shock relative to the other types of business, on the order (14% better)
of our finding in the Oakland survey. Although the difference-in-differences spec-
ification has desirable econometric properties, we draw our inference with caution,
being subject to parallel-trend concerns even within industry.

B. Labor-Flexibility Results

In the forthcoming estimations of labor flexibility and committed costs, we
need to control for revenue losses to avoid double-counting any effects we
estimate in Table 4 by firm size. Thus, we create a revenue-loss index variable
(REV_LOSS_INDEX), defined as the average of a standardized version of the
percentage decline in revenue for March from the Oakland survey and a
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Bartlett and Morse 2523

standardized version of the percentage change in foot traffic after the shelter-in-
place order (defined to be April) relative to the preperiod foot traffic of Jan. 13–Feb.
18, 2020. Because some observations lack one or the other variable, we allow solo
contributions of these standardized variables, thus expanding the sample size
relative to using only a respondent’s self-reported revenue decline or using only
observed foot traffic.

1. Oakland Survey Labor-Flexibility Results
We now test whether the labor-flexibility facet of small-business survival
varies by firm size in its reaction to the economic crisis caused by the pandemic.
Table 5 presents estimates of labor flexibility in the Oakland survey in the following
specification:

(6) Fractional logitð%△DECLINE_WORKERSi Þ ¼ β1 LOG_WORKERSPre i
þβ2 REV_LOSS_INDEXi þ β3 LOG_WORKERSPre i REV_LOSS_INDEX i
þμMAIN_STREET þ μESSENTIAL þ μINDUSTRY þ εi :

The dependent variable is a percentage-change decline of workers (full-time
in columns 1–3 and part-time in columns 4–6). The main independent variable is
LOG_WORKERSPre i . Because the distribution of percentage-change decline ranges
from 0 to 1, we estimate a fractional logit for efficiency. As before, we control for the
Main Street and essential business effects and absorb industry effects. We include
the revenue-loss index to control for the effects documented in Table 4. We also
interact the revenue-loss index with LOG_WORKERSPre i in some specifications to
test whether firm size alters the relationship between labor flexibility and revenues.
We focus our analysis on microbusinesses and small enterprises because
nonemployers have no employees.
As Table 5 reports, we find that layoffs of full-time workers (columns 1–3)
exhibit an elasticity to firm size of 0.127, controlling for industry effects (column 2),
and layoffs of part-time workers (columns 4–6) exhibit an elasticity of 0.172
(column 5) to firm size with these controls. A second result in Table 5 is that
although the relationship between revenue losses and labor layoffs is high, as
one would expect, the interaction of revenue loss and precrisis level of workers
does not add explanatory power. Thus, labor flexibility does not appear to be
mediated through differentials in revenue losses among enterprises relative to
microbusinesses.
As before, we turn to a figure to depict the economic meaning of our results.
Graph A of Figure 6 plots the relationship between the marginal effect of
LOG_WORKERSPre i on layoffs, taking all other variables at the mean value. In
contrast to our revenue-resiliency results, the magnitude differences for our
labor-flexibility results reflect a large difference of outcomes. We again interpret
over the entire range of microbusinesses and enterprises to understand the
importance of labor flexibility by stable Main Street firms versus growth-
oriented firms. For full-time workers, enterprises (the triangles in the scatter
plots) on average laid off 38.1% of workers, whereas microbusinesses (the
diamonds) laid off only 17.7%. For part-time workers, enterprises laid off
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2524 Journal of Financial and Quantitative Analysis

TABLE 5
Labor Flexibility: Oakland Survey

The estimates in Table 5 are from a fractional logit specification of the decline in workers, reporting marginal effects. The
sample is small businesses in the City of Oakland COVID-19 small-business survey. The main independent variable is the
precrisis log of workers (employees + 1), defined in the survey as before Mar. 2020. (Nonemployer data are not included
because of the lack of employees.) The dependent variables are the percentage change in workers, defined as workers lost or
laid off relative to the precrisis base, for part- and full-time workers. Included in all columns is an indicator for the business
being street-facing (“Main Street”) and one for essential businesses, both from manual coding. Also included is the
REV_LOSS_INDEX variable, the combination of standardized firm revenue losses from the gross-receipts decline and
standardized percentage change in foot traffic by firm. Columns 3 and 6 include this variable interacted with precrisis log
workers. Columns 2, 3, 5, and 6 add industry fixed effects. Robust standard errors are in parentheses. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1% levels.
Model Fractional Logit: Reporting Marginal Effect

Percentage-Change Decline in Percentage-Change Decline in
Dependent Variable Full-Time Workers Part-Time Workers

1 2 3 4 5 6

LOG_WORKERS (precrisis) 0.117*** 0.127*** 0.128*** 0.165*** 0.172*** 0.173***
(0.0150) (0.0168) (0.0167) (0.0207) (0.0238) (0.0237)
REV_LOSS_INDEX 0.0769*** 0.0692*** 0.0844* 0.0680*** 0.0696*** 0.117*
(0.0200) (0.0193) (0.0452) (0.0239) (0.0243) (0.0685)
LOG_WORKERS (precrisis) 0.00749 0.0214
REV_LOSS_INDEX (0.0196) (0.0306)
MAIN_STREET 0.0718** 0.0238 0.0275 0.0435 0.0893 0.104
(0.0360) (0.174) (0.175) (0.0484) (0.244) (0.243)
ESSENTIAL 0.0842* 0.0636 0.0651 0.137** 0.184** 0.189***
(0.0443) (0.0604) (0.0613) (0.0581) (0.0714) (0.0727)
BUSINESS_SERVICES 0.0105 0.0141 0.159 0.176
(0.174) (0.174) (0.254) (0.252)
CONSTRUCTION/ 0.017 0.0165 0.00468 0.00233
MANUFACTURING /VENUE (0.0611) (0.0609) (0.0841) (0.0828)
FITNESS/GYM/WELLNESS 0.0791 0.0819 0.0436 0.0554
(0.182) (0.181) (0.240) (0.237)
MEDICAL_OFFICES 0.0236 0.0288 0.165 0.183
(0.211) (0.213) (0.299) (0.297)
PERSONAL_SERVICES_HOME 0.0374 0.0375 0.0837 0.0844
(0.112) (0.112) (0.129) (0.127)
PERSONAL_SERVICES_SHOP 0.0859 0.0904 0.176 0.194
(0.186) (0.187) (0.254) (0.252)
RESTAURANT 0.053 0.0567 0.0898 0.105
(0.172) (0.172) (0.240) (0.236)
RETAIL 0.0783 0.0827 0.191 0.209
(0.168) (0.169) (0.233) (0.230)
SALON 0.286 0.288 0.102 0.118
(0.194) (0.192) (0.246) (0.243)
No. of obs. 556 556 556 442 442 442
R2 0.099 0.117 0.118 0.096 0.103 0.104

49.6% of workers and microbusinesses only 23.8%. Recalling that the ability to
lay off employees to downsize costs can represent a positive aspect for survival
(while acknowledging that such language is not indicative of worker welfare),
this result implies that microbusinesses face a much larger risk of not surviving
on this metric. In particular, microbusinesses exhibit roughly half the labor
flexibility of enterprises based on these averages.
As before, we cannot prove mechanisms for these results. However, here,
the intuition is fairly straightforward. In our story frame, the taqueria has core
employees who are necessary to keep the business open. The pizzeria, on the other
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Bartlett and Morse 2525

FIGURE 6
Labor Flexibility by Small-Business Type
The sample in Graph A is the small businesses in Oakland covered by the City of Oakland COVID-19 small-business survey.
Plotted is the predicted decline in employees by small-business type resulting from columns 2 and 5 of Table 5. The sample for
Graphs B, C, and D is from HomeBase. Graph B plots the predicted change in workers for the Oakland area (zip code 94XXX)
small businesses covered by HomeBase using the model from column 3 of Table 6. Graph C plots the predicted change in
workers for all U.S. small businesses covered by HomeBase using the model from column 6 of Table 6, and Graph D plots the
predicted change in wages for all U.S. small businesses covered by HomeBase using the model from column 9 of Table 6. In
Graphs B, C, and D, the y-scale has been normalized to reflect changes in employment and wages for microbusinesses and
enterprises relative to Jan. 5, 2020, and predicted log-differences have been converted to percentage changes in workers/
wages.

Graph B. Small Business Workers – HomeBase
Graph A. Decline in Workers – Oakland Survey Data: Oakland Area
0.2
0.8

Percentage Change in Workers

Predicted Percent Decline in Employees
0.6

–0.4 –0.2 0
0.4

0.2 –0.6
Jan 5, 2020
Jan 12, 2020
Jan 19, 2020
Jan 26, 2020
Feb 2, 2020
Feb 9, 2020
Feb 16, 2020
Feb 23, 2020
0 Mar 1, 2020
Mar 8, 2020
Mar 15, 2020
Mar 22, 2020
0 1–5 6–10 11–15 16–20 21–25 26–30 Mar 29, 2020
Apr 5, 2020
Apr 12, 2020
Pre-Crisis Employee Count
Apr 19, 2020

Microbusiness (FT) Microbusiness (PT) Microbusiness Enterprise
Enterprise (FT) Enterprise (PT) 95% CI 95% CI
95% CI (FT) 95% CI (PT) Microbusiness Enterprise

Graph C. Small Business Workers – Homebase National Graph D: Small Business Payroll – HomeBase National

0.2
0

Percentage Change in Workers Percentage Change in Wages

–0.4 –0.3 –0.2 –0.1 –0.4 –0.2 0

–0.5 –0.6
Jan 5, 2020
Jan 12, 2020 Jan 5, 2020
Jan 12, 2020
Jan 19, 2020
Jan 26, 2020 Jan 19, 2020
Jan 26, 2020
Feb 2, 2020
Feb 9, 2020 Feb 2, 2020
Feb 9, 2020
Feb 16, 2020
Feb 23, 2020 Feb 16, 2020
Feb 23, 2020
Mar 1, 2020
Mar 8, 2020 Mar 1, 2020
Mar 8, 2020
Mar 15, 2020
Mar 22, 2020 Mar 15, 2020
Mar 22, 2020
Mar 29, 2020
Apr 5, 2020 Mar 29, 2020
Apr 5, 2020
Apr 12, 2020
Apr 19, 2020 Apr 12, 2020
Apr 19, 2020

Microbusiness Enterprise Microbusiness Enterprise
95% CI 95% CI 95% CI 95% CI
Microbusiness Enterprise Microbusiness Enterprise
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2526 Journal of Financial and Quantitative Analysis

hand, has the flexibility to downsize noncore employees when demand collapses for
its goods and services.

2. HomeBase Labor-Flexibility Results
Parallel with the Oakland survey data, we now look at the employment and
payroll data from HomeBase. As before, we combine with a revenue-loss data set,
in this case, with foot-traffic data from SafeGraph. Establishments in the Home-
Base data are anonymized; therefore, we cannot match firm to firm. Instead, we
match each HomeBase establishment based on its industry and zip code to Safe-
Graph foot-traffic data and use the mean weekly foot traffic for SafeGraph POIs
within the same industry and zip code as a proxy for revenues. For our measure of
firm size, we use a HomeBase establishment’s average headcount data between
Jan. 1, 2020, and Feb. 15, 2020. We focus our labor-flexibility estimations by
excluding nonemployers.
Denoting index j to indicate the industry and zip code of firm i, we estimate
parameters from the following:
(7) LOG_LABORΔit ¼ β1 LOG_FOOT_TRAFICjt þ β2 LOG_FOOT_TRAFFICjt
POSTt þ β3 LOG_WORKERSPre
i POSTt þ β4 LOG_WORKERSi
Pre

LOG_FOOT_TRAFFICjt þ β5 LOG_WORKERSi Pre

LOG_FOOT_TRAFICjt POSTt þ γi þ δt þ εit ,

where LOG_LABORΔit is either i) the log difference in weekly workers relative to
the week of Jan. 5, 2020 (LOG_WORKERSΔit Þ,or ii) the log difference in weekly
wages relative to the week of Jan. 5, 2020 (LOG_PAYROLLΔit Þ. The variables γi
and δt denote firm and week fixed effects, respectively. POSTt represents the weeks
commencing Mar. 15–Apr. 12, 2020. Results are presented in Table 6, first for the
area surrounding Oakland (the 94XXX zip codes, including the East Bay, North
Bay, and San Francisco) in columns 1–3 and then nationally (in columns 4–9). The
results are not sensitive to defining the Oakland area more narrowly, and we cluster
standard errors at the firm level to balance the panel’s influence. Columns 4–9
widen the sample nationally, and columns 7–9 consider payroll rather than worker
counts, but we are more cautious in magnitude interpretation with regard to payroll
because the sample size declines materially after the crisis relative to the employ-
ment numbers, suggesting a selection problem.
The specification is, as in the foot-traffic estimation, akin to a difference-in-
differences approach, except that we are interested in the POST effect surrounding
the continuous variable LOG_WORKERSPre i . Our main variable of interest,
LOG_WORKERSPre i POSTt , estimates how a firm’s reliance on labor flexibility
during the crisis varies by firm size. We find that the small businesses with
more precrisis workers experience larger decreases in workers and payroll, with
a postperiod shock to the elasticity of labor to the firm size of approximately –0.22
to –0.28 (columns 2, 5, and 8). (We obtain almost identical estimates if we use the
industry and zip code as our unit of observation.)
In columns 3, 6, and 9 of Table 6, we add the 3-way interaction of
LOG_WORKERSPre i LOG_FOOT_TRAFIC jt POSTt to examine whether
TABLE 6
Labor Flexibility: HomeBase Payroll Data

The estimates in Table 6 are from a difference-in-differences specification. The sample is the set of firms in the national HomeBase data, matched to the industry and zip-code data in SafeGraph. In columns 1–3, the
sample is restricted to the firms in the Oakland area (zip codes 94XXX). Observations are a weekly panel from the week commencing Jan. 5, 2020, through the week commencing Apr. 12, 2020. The dependent variable
in columns 1–6 is the log difference between the weekly workers in a week and workers for the week of Jan. 5 (LOG_WORKERSΔ), and in columns 7–9, it is the log difference between the payroll in a week and weekly
payroll for the week of Jan. 5 (LOG_PAYROLLΔ). The main independent variable is the precrisis log of workers (defined in HomeBase as the average from Jan. 1, 2020–Feb. 15, 2020) interacted with POST. Firm and
week fixed effects are included. POST indicates weeks after Mar. 15, 2020. LOG_FOOT_TRAFFIC is from SafeGraph and is matched to HomeBase firms at the industry–zip code level. Nonemployer data are not
included because of the lack of employees. Firm-clustered standard errors are in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Dependent Variable LOG_WORKERSΔ, Oakland Area Sample LOG_WORKERSΔ, National Sample LOG_PAYROLLΔ, National Sample

1 2 3 4 5 6 7 8 9

POST LOG_WORKERS (precrisis) 0.277*** 0.276*** 0.262*** 0.278*** 0.278*** 0.264*** 0.220*** 0.220*** 0.198***
(0.0295) (0.0296) (0.0836) (0.00397) (0.00399) (0.0110) (0.00903) (0.00907) (0.0232)
LOG_FOOT_TRAFFIC 0.0547*** 0.0605*** 0.00517 0.0439*** 0.0461*** 0.0354*** 0.0605*** 0.0601*** 0.0179
(0.0140) (0.0143) (0.0330) (0.00188) (0.00191) (0.00486) (0.00421) (0.00432) (0.0116)
POST LOG_FOOT_TRAFFIC 0.0114 0.0364 0.00483** 0.0258*** 0.000732 0.0124
(0.0183) (0.0490) (0.00196) (0.00527) (0.00458) (0.0135)
LOG_WORKERS (precrisis) LOG_FOOT_TRAFFIC 0.0331* 0.0434*** 0.0389***
(0.0188) (0.00296) (0.00575)
POST LOG_WORKERS (precrisis) LOG_FOOT_TRAFFIC

Bartlett and Morse 2527
0.0122 0.0108*** 0.0063
(0.0268) (0.00318) (0.00674)
Firm fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Day fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
No. of obs. 15,276 15,276 15,276 646,494 646,494 646,494 420,508 420,508 420,508
No. of businesses 1,266 1,266 1,266 52,307 52,307 52,307 34,447 34,447 34,447
R2 0.622 0.622 0.623 0.581 0.581 0.582 0.594 0.594 0.594

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2528 Journal of Financial and Quantitative Analysis

these effects are coming from shocks to the elasticity of labor to revenues varying
by firm size or if the effect is just from labor-utilization adjustments by firm size
that are independent of revenue resiliency. To make such an assessment, we first
look at how the precrisis elasticity of labor costs to revenue vary by firm size.
We find that the revenue-to-cost relationship is highly dependent on firm
size, noting that the inclusion in columns 3, 6, and 9 of LOG_WORKERSPre i
LOG_FOOT_TRAFFIC jt erodes the relationship between LOG_LABORΔit and
LOG_FOOT_TRAFFICjt alone. The interpretation, consistent with the role of
a core employee in our example of a taqueria versus pizza restaurant, is that larger
firms generally exhibit more labor scaling with revenue. Notably, however, the
pandemic shock does not alter the relationship very much: The triple interaction of
POST with firm size and revenues (foot traffic) is not significant in columns 3 and
9. It is positive and significant in column 6, the national sample of workers,
suggesting that, if anything, the pandemic shock makes the elasticity of labor to
the revenue macro shock even stronger for larger enterprises. This result, however,
does not materially affect the statistical or economic significance of the coefficient
on LOG_WORKERSPre i POSTt : This leads to the conclusion that the firm-size
effect on employment is primarily the direct effect of the shock on labor, not one
working through a change in the elasticity of labor cost to revenues that varies
by size.
We turn to graphs to depict the economic magnitude. In particular, we use
the estimations in columns 3, 6, and 9 of Table 6 to depict changes in worker
counts across microbusinesses and enterprises relative to the first week of our panel.
Graph B of Figure 6 presents the column 3 marginal effects for the Oakland region.
The parallel implications from Graph B, next to Graph A, are evident. The fall
in employment is sharp and drastic. Yet microbusinesses experience a noticeably
lower decline in workers relative to precrisis levels, indicating a lower flexibility in
adjustments. When we translate these predictions to business type, we find that by
the week of Apr. 12, 2002, workers decline by 49.6% for enterprises but only by
30.0% for microbusinesses in Oakland. These numbers are very close to our survey
estimates in Graph A of Figure 6 (among part-time employees, 49.6% decline for
enterprises and 23.8% decline for microbusinesses).
Graph C of Figure 6 depicts the national results, with a very similar relation-
ship as Graph B. Relative to the Oakland results, the decline in workers is slightly
less on average; however, the difference between microbusinesses and enterprises
remains evident. Whereas microbusinesses respond to the pandemic with a reduc-
tion in workers of 24.7% by the week of Apr. 12, enterprises on average reduced
their workforce by 45.7%.
Finally, Graph D of Figure 6 plots the predicted time pattern of payrolls for
national establishments from the estimation in column 9 of Table 6, again removing
firm effects. The payroll data are, as mentioned, less reliable in that there appears to
be selection in reporting in the later month. Nevertheless, we find that the percent-
age decline is a large 46.5% on average, but the differential by firm size is a bit
tighter. Whereas enterprises are able to cut back payrolls by 51.7%, microbusi-
nesses only are able to trim these costs by roughly two-thirds as much, 37.8%. Thus,
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Bartlett and Morse 2529

our punchline labor-flexibility result is that when facing a large macro shock,
microbusinesses have only one-half to two-thirds as much labor flexibility as enter-
prises, again with the caveat that our estimation, even in this stringent difference-in-
differences specification, could be subject to other time-series differences correlated
with employment size within industry. Nevertheless, this finding has a direct
implication for the PPP design, offering nuance to the assessment of Chetty et al.
(2020), who find that the PPP failed to spur employment across all small businesses.
We return to this topic in Section V.

C. Committed Costs: Closure Risk
Finally, we turn to committed costs. We cannot observe committed costs
directly. Instead, we take guidance from our framework, which indicates that once
we have removed the heterogeneities of revenue resiliency and labor flexibility, the
residual must contain the role of committed costs in survival. We therefore use the
variable CLOSURE_RISK as a proxy for committed costs, understanding that any
inference we would find is the least well identified of our estimations.
In particular, within the Oakland survey, we estimate the following:

(8) Ordered logitðCLOSURE_RISKi Þ ¼ β1 LOG_WORKERSPre
i
þ β2 REV_LOSS_INDEXi þ β3 %△DECLINE_WORKERSi
P
þ β4 DECLININGi þ K k¼1 ξk INTERIM_OUTCOMEik
þ μMAIN_STREET þ μINDUSTRY þ εi :

CLOSURE_RISKi is the ordered answer to the Oakland survey question of
how concerned a business owner is about closure. We include the revenue-loss
index and percentage-change decline in total workers to absorb those firm-level
determinants of closure. We also include whether the firm was declining in the
February YoY gross receipts. Finally,
P and perhaps most importantly, we include
our interim outcome measures K k¼1 ξk INTERIM_OUTCOMEik that we hand-
collected. The idea is that we want to fully absorb business-level heterogeneities
unrelated to the longer-term effects of committed (fixed) costs. Thus, our col-
lection of the late-April (interim) outcome of the firm allows us to remove any
additional variation related to variable costs that we do not observe perfectly in
the survey data. We also include the MAIN_STREET and INDUSTRY variables
for this purpose.
Once we have removed all of these causes of closure risk, we argue that any
residual variation picked up by β1 LOG_WORKERSPre i reflects the fact that com-
mitted costs vary (if any) by the size of the small business.
The results are presented in Table 7. We find that committed costs (the residual
component of closure risk unaccounted for by the covariates) is increasing in
LOG_WORKERSPre i , across the columns. The exception is column 2, where the
inclusion of the nonemployer dummy causes a horse race for the upward trend. The
covariates of REV_LOSS_INDEXi and %△DECLINE_WORKERSi also strongly
predict closure risk. The other variables included to control for unobservable revenue
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TABLE 7
Committed Costs Results: Residual of Closure Risk

The estimates in Table 7 are from an ordered logit estimation. The sample is small-businesses in the City of Oakland COVID-19
small-business survey. The dependent variable is the ordinal response of how concerned the business owner is about closure
risk: not concerned, somewhat concerned, or very concerned. We present the log odds ratios (the coefficients) of moving from
one category up to the next. For the main independent variable of LOG_WORKERS, defined in the precrisis period in the
survey before Mar. 2020, we also report the odds ratio, displayed beneath the robust standard errors in parentheses. Included
in all columns is an indicator for the business being street-facing (“Main Street”) and one for the percentage change in jobs
reported (%△DECLINE_WORKERS), defined as the total full- and part-time jobs that were lost relative to the number of full-
and part-time jobs before Mar. 2020. Also included as covariates are the REV_LOSS_INDEX variable, whether the business
was declining precrisis (using the gross receipts year over year (YoY) for February), and the interim outcome of the business
(OPEN, TRYING, TEMPORARILY_CLOSED, PERMANENTLY_CLOSED). Columns 3 and 4 are industry random and fixed
effects, respectively. Fixed effects for ordered logit are of questionable consistency; thus, we present the random effects as a
more reliable estimator. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Model Ordered Logit

Dependent Variable CLOSURE_RISK: Not Concerned < Somewhat Concerned < Very Concerned

1 2 3 4

LOG_WORKERS (precrisis) 0.251*** 0.195 0.212** 0.165*
(0.0910) (0.128) (0.105) (0.0975)
Odds ratio: 1.285 1.215 1.236 1.179
NONEMPLOYER 0.192
(0.305)
REV_LOSS_INDEX 0.323*** 0.326*** 0.322*** 0.318***
(0.0947) (0.0952) (0.117) (0.0949)
%△DECLINE_WORKERS 0.774* 0.765 0.735** 0.668
(0.462) (0.469) (0.307) (0.467)
DECLINING 0.879*** 0.884*** 0.851*** 0.807***
(0.187) (0.187) (0.195) (0.190)
STATUS:
TRYING 0.519** 0.530** 0.364 0.125
(0.261) (0.263) (0.400) (0.288)
TEMPORARILY_CLOSED 0.987*** 0.986*** 0.878* 0.679**
(0.275) (0.275) (0.469) (0.312)
PERMANENTLY_CLOSED 0.561** 0.557** 0.442* 0.248
(0.245) (0.245) (0.251) (0.263)
MAIN_STREET 0.447** 0.447** 0.452** 0.0565
(0.218) (0.218) (0.214) (1.240)
Industry effects Random Fixed
No. of obs. 736 736 736 736
R2 0.086 0.087 NA 0.107

changes or variable costs (DECLINING and INTERIM_OUTCOME) are also
important, in signs expected, in explaining expected closure. We include an
industry-fixed-effects model (column 4) as well as a random-effects model
(column 3). Fixed effects for ordered logit are of questionable consistency; thus,
the random effects may be preferred as a more reliable estimator.
In an ordered logit, the coefficients are log odds ratios. We therefore expo-
nentiate these coefficients in the line beneath the standard error to allow for an odds-
ratio interpretation of the effect of LOG_WORKERSPre i . Across respondents, a
10% increase in workers is associated with a 2% increase in the odds of being at a
level higher in closure risk.
As previously, we present these estimated effects graphically to highlight the
marginal effect by the small-business type. Figure 7 plots the marginal effect of worker
size from column 4 of Table 7, taking all the other variables at the mean level. The
picture focuses on the prediction of being in the highest-risk category, by the buckets of
employees, as before. We find that closure risk overall is incredibly high, as we showed
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Bartlett and Morse 2531

FIGURE 7
Residual Closure Risk (Committed Costs) by Small-Business Type
The sample in Figure 7 is small businesses in the City of Oakland COVID-19 small-business survey. Plotted is the predicted
effect of firm size on self-reported closure risk, from the ordered logit estimation of column 4 of Table 7.

Probability of Reporting “Very Concerned”
0.9

0.8

0.7

0.6
0 1–5 6–10 11–15 16–20 21–25 26–30
Pre-Crisis Employee Count

Nonemployer Microbusiness
Enterprise 95% CI

in the summary statistics, and that a clear relationship exists between firm size and
closure risk beyond the effect of the covariates. In particular, in explaining residual
closure risk, enterprises have an overall 11% greater outlook of “very concerned”
compared with microbusinesses and a roughly 20% greater outlook of “very
concerned” relative to nonemployers. We interpret these results as indicating that,
relative to microbusinesses and nonemployers, enterprises face a respective 11% and
20% higher closure risk as a result of committed costs. We, of course, need the caveat
that in drawing this inference, we are relying on a proxy for fixed costs, as guided by
our framework in Section II. Nonetheless, the result is quite intuitive; a larger estab-
lishment faces a higher role of capital (and thus debt) and a higher role of property costs
in its design. What is important is that the finding controls for the importance of
revenue and labor, as well as interim variable costs we can measure with interim
outcomes. Thus, we think our interpretation of committed costs is quite plausible.

V. Policy Program Features
With survival capabilities results in hand, we turn to an examination of how
these survival capabilities are, or are not, compatible with small-business assistance
programs across the classification of businesses we study. We use the following
table to direct the discussion, where the top 3 rows summarize our findings regard-
ing the primary survival capabilities, and the bottom rows examine how these
capabilities relate to policy options.
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Nonemployer Microbusiness Enterprise

Survival Capability Feasibility of Strategy

Exhibit revenue resiliency Moderate High Moderate
Exercise labor costs flexibility Low Low High
Rely on low/flexible committed costs High High Low
Small-Business Assistance Program Compatibility of Program
Subsidized working capital loans X-to-✓ ✓ X-to-✓
Labor-cost grants and subsidies ✓ ✓ X
Lease or debt payment-restructuring subsidies X X ✓

We start with microbusinesses. Microbusinesses have low labor flexibility
because their employees must be jacks-of-all-trades. We find that their survival
depends on maintaining revenues to cover these inflexible labor costs, as well as
having relatively lower residual committed costs. Thus, working capital loan pro-
grams that focus on supporting revenue resiliency through financing activities such
as restocking inventories and conducting repairs are highly compatible with micro-
businesses’ survival capabilities that depend on maintaining precrisis revenues.
Recall, for instance, our hypothetical taqueria faced with a local economic crisis.
Lacking the ability to lay off staff, the business owner was faced with the stark
choice of demonstrating revenue resiliency or shutting down. Assistance to support
these revenue strategies following an adverse economic shock is reflected in
conventional Economic Injury Disaster Loans (EIDLs) offered through the SBA.
According to the SBA, “[t]he sole purpose of an [EIDL] is to help a small business
meet its working capital requirements during the disaster-affected period until
normal operations resume.” To this end, loan proceeds are calculated as a function
of precrisis gross margins, and recipients are prohibited from using proceeds to
refinance long-term debt or expand operations. Instead, loan proceeds are intended
to aid small businesses in rebuilding revenues to precrisis levels. Programs such as
the New York Forward Fund as well as the Main Street Lending Program are
similarly designed to provide working capital to businesses seeking to rebuild
revenues in connection with the COVID-19 pandemic.
Likewise, PPP-like programs are also well suited for microbusinesses. The
taqueria cannot lay off the few jack-of-all-trades employees and still remain open,
thus making microbusinesses an ideal target for the PPP, as well as for several other
programs created by the CARES Act. For instance, the CARES Act provides for
a refundable payroll tax credit for employers to offset the cost of maintaining
employees. The role of the PPP (and similar programs) for microbusinesses
contrasts our findings with those of Chetty et al. (2020), who evaluate the efficacy
of the PPP in stimulating employment. Using a national sample of small busi-
nesses, these authors find that the PPP had no meaningful impact on employment
rates, leading these authors to conclude that “providing liquidity itself may be
inadequate to restore employment at small businesses.” Critically, however, their
sample of firms focused on enterprises; the smallest strata of firms they consid-
ered had an average of 45 employees. Yet, as we have shown, it is precisely these
larger employer firms that are the most likely to rely on their labor flexibility to
weather the COVID-19 pandemic, a survival tactic that is at odds with the PPP’s
labor subsidy.
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Turning to nonemployers, we find that these businesses exhibit neither
revenue resiliency nor labor-cost flexibility. Instead, their survival relies on low
committed costs (20% lower than enterprises). These results are complemented by
prior research showing nonemployers’ personal flexibility in accepting nonpecuni-
ary utility rather than full income in downturns (e.g., Moskowitz and Vissing-
Jørgensen (2002)). Because this personal utility nevertheless consumes personal
wealth (which has limits), policies aimed at preserving incomes for self-employed
individuals are well suited to support nonemployer owners through an economic
downturn. Thus, labor-cost-supporting programs aimed at these individuals, such
as the creation of PUA under the CARES Act, can likewise be viewed as compatible
with nonemployer survival capabilities. In contrast to microbusinesses, working
capital loans may be only somewhat compatible with nonemployers’ survival
capabilities in the short term because revenues are not resilient. This contrasts with
the medium term, where working capital loan programs can support nonemployers’
reduced revenue models as the economy recovers, especially because these busi-
nesses have lower committed costs.
Finally, for enterprises, we find that these businesses exhibit slightly less
revenue resiliency than microbusinesses, but their 50% greater labor flexibility,
compared with microbusinesses, allows enterprises to decrease costs immediately
for survival. However, enterprises also possess the greatest residual exposure to
committed costs, which jeopardizes their short-term survival despite their greater
labor flexibility. For businesses that reduce employee headcount as a means to
survive a macro shock, labor cost grants and subsidies are not likely to be the most
effective use of government support, consistent with Granja et al. (2020) and Chetty
et al.’s (2020) findings regarding the low employment rate by firms (all enterprises)
receiving a PPP loan. Likewise, similar to our assessment of nonemployers’ sur-
vival capabilities, working capital loans may be less effective in supporting enter-
prises’ survival capabilities in the short term because revenues are not as resilient,
but they might support their reduced revenue models as the economy recovers. For
enterprises, however, this support is overshadowed by the risk of failure caused by
committed costs.
Short-term survival for enterprises requires support for their committed costs,
such as those offered by commercial loans or debt-restructuring plans, enabling
these businesses to manage larger fixed costs until they can restore revenues.
Examples of these programs include state and local programs, such as Delaware
County’s Strong Small Business Support Program, which provides grants specif-
ically for payments toward commercial lease obligations. More generally, these
programs also include the newly enacted Small Business Reorganization Act
of 2019 (SBRA). The SBRA created a new Subchapter V of Chapter 11 of the
Bankruptcy Code, which greatly facilitates the use of a Chapter 11 reorganization
for small businesses. Under Subchapter V, a small-business debtor can confirm a
plan of reorganization without the consent of its long-term creditors while allowing
the debtor to maintain its ownership interest. As such, it provides small businesses
that are struggling under the weight of their long-term commitments with valuable
leverage to renegotiate a commercial lease and other committed costs. Our findings
indicate that these costs are most problematic for the survival of enterprises, making
these programs especially relevant for these firms.
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VI. Testing Policies for Survival
On June 3, 2020, the City of Oakland launched a follow-up survey, the
Re-Opening and Recovery Survey, that asked approximately 300 businesses about
the aid (if any) that they had pursued and received, as well as their short- and
medium-term projections for survival. This survey provides a novel evaluation of
the impact of policy programs and an opportunity to test the heterogeneous survival
challenges faced by different-sized firms. In assessing this survey, we are cognizant
of selection into applying to participate in a policy program as well as in survey
participation. We address the issue of selection and discuss any limitations to the
interpretation of our findings accordingly.

A. Data and Statistics

Our primary interest is in 2 dependent variables relating to the risk of short-
term closing and the ability to survive in the medium to long term. Both of these
variables build off the following survey question: “If business disruption con-
tinues at the current rate, how soon will you be at risk of permanently closing your
business?” The choices for answering this question are presented in Table 8,
where we present summary statistics for the follow-up survey. We construct the
SHORT_TERM_CLOSING variable as an indicator equal to 1 if a respondent
either answered this question using the selection “0 to 1 month” or indicated that
the business was already closed in an open-ended question of actions taken, and
0 otherwise. We construct the variable MEDIUM_RUN_SURVIVING as an
indicator equal to 1 if a respondent answered the aforementioned question by
indicating that the business could sustain the present conditions for more than
6 months, and 0 otherwise. Overall, short-term closing represented 10% of the
sample, whereas medium-run surviving businesses represented 35%. This implies
that without policy programs or improvements in the economy, the majority of
respondents faced medium-run closure.
Our primary independent variables of interest are whether the business
successfully applied for a PPP loan and whether the business owner successfully
applied for PUA. Under the terms of the PPP, all respondents should have been
eligible to apply for a PPP loan, given that the program was open to employer and
nonemployer businesses having fewer than 500 employees, and all respondents
reported having employee headcounts that would meet this requirement.10 Eligi-
bility for PUA was limited to individuals who were not eligible for traditional
unemployment insurance; therefore, it was available to respondents who were
either nonemployer business owners or employer business owners who had laid
off all employees and were seeking unemployment insurance for themselves per-
sonally. A large 59% of the survey respondents successfully applied for PPP loans,

10
In addition to this size-based requirement, the PPP was also unavailable to businesses operating in
select industries (e.g., a business primarily engaged in political or lobbying activities, businesses that
derive more than a third of their revenue from gambling, etc.) and to applicants whose owners are
disqualified because they are presently involved in a bankruptcy proceeding or have been convicted of
committing certain felony offenses. Based on a review of the business names in this sample, we assume
that none of the respondent businesses were ineligible for these reasons.
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TABLE 8
City of Oakland Re-Opening and Recovery Survey: Summary Statistics

Table 8 presents tabulations of the 278 responses from the June 2020 City of Oakland Re-Opening and Recovery
Survey.
Cumulative
Count Percentage Percentage

Total Oakland Survey Responses 278
“If business disruption continues at the current rate, how soon will you be at risk of permanently closing your business?”
0–1 month 26 9.4 9.4
1–3 months 85 30.6 39.9
3–6 months 71 25.5 65.5
6–12 months 55 19.8 85.3
Never 41 14.8 100.0
SHORT_TERM_CLOSING
Ongoing concern 250 89.9
Closed now or projected survival of 0–1 month 28 10.1
MEDIUM_RUN_SURVIVING
Surviving 96 34.5
Closing 182 65.5
Application status of Paycheck Protection Program (PPP)
Successfully applied 148 59.4 59.4
Unsuccessfully applied 45 18.1 77.5
Not applied 56 22.5 100.0
Application Success
Rate 77%
Application Status of Pandemic Unemployment Assistance
(PUA)
Successfully applied 82 32.9 32.9
Unsuccessfully applied 39 15.7 48.6
Not applied 128 51.4 100.0
Application Success
Rate 68%
Status as of June 2020
Fully open 20 7.2
Reduced 120 43.2
Closed, at least temporarily 138 49.6
Industry
Business services 7 2.5
Construction/industrial/venue 18 6.5
Fitness/salon/wellness 59 21.2
Health care 17 6.1
Nonprofit 15 5.4
Personal services 14 5.0
Professional services 55 19.8
Restaurant 61 21.9
Retail 32 11.5
Gender Identity
Female 171 61.5
Male 74 26.6
Other/undisclosed 33 11.9
Race/ethnicity
Asian ethnicity 48 17.3
Hispanic ethnicity 21 7.6
White race 120 43.2
Black race 31 11.2
Other/undisclosed/mixed race 58 20.9

with an application success rate of 77%. In addition, 32.9% of survey respondents
successfully applied for PUA funds, with an application success rate of 68%.
Qualifying for PUA implies furloughing or laying off all employees, a seemingly
optimal strategy for many survey respondents.
Finally, we have statistics regarding current operating status, industry, and
owner demographics. The sample of firms represents a cross-section of industries,
and half are coded as temporarily closed at the time of the survey. Sixty-two percent
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of the businesses are female-owned. The racial-ethnic breakdown of the sample is
as follows: White (43%), other/undisclosed/mixed race (20.9%), Asian (17.3%),
Black (11.2%), and Hispanic (7.6%). Given our small sample, we do not try to do
analysis within these categories.

B. Methodology and Selection in Applying for PPP Loans and PUA

A central concern in estimating any effect of a policy program on survival is
selection with regard to survey completion and, especially, with regard to partici-
pating in the PPP or PUA programs. Small businesses may be experiencing differ-
ences in setting, in particular, differences in financial or economic distress, that
would lead to filling out the survey or participating in the PPP or PUA programs.
The concern about selecting into the survey raises the question of general-
izability but should not materially affect the analysis within that selection. The
concern about selection in the applying for PPP or PUA assistance is fundamental
to inference, however.
Our identification takes advantage of i) the existence of an “applied for”
variable in the survey (APPLIED_PPP/APPLIED_PUA) that is specific to each
program (with answer choices of “no,” “yes, successfully,” or “yes,
unsuccessfully”) combined with ii) the unique setting that neither policy required
applicants to demonstrate financial need or lack of access to other finance. Finally,
we also have iii) interim outcome variables of the status and actions taken by
businesses to provide selection tests and conditioning variables.
Our identification relies on the idea that application success rates were likely to
vary across applicants in ways that were largely orthogonal to unobservable factors
affecting medium-term survival. The viability of this assertion is stronger for the
PPP than the PUA. Early reports indicate that PPP applicants were often unable to
acquire a PPP loan because of technological problems incurred by the applicant’s
bank or because its lender was otherwise unable to process the loan as a result of
confusion over the application of bank-secrecy protocols to PPP loans.11 This
variation accordingly allows us to estimate the effect of the PPP à la the idea of
the instrument used in Granja et al. (2020). Said more directly, in our sample, of the
Oakland businesses applying for a PPP loan, a quarter were unsuccessful in their
application attempt. The lack of success of these businesses is likely to be largely
noise, given the power in the first stage of Granja et al. (2020). In this regard, it is
also worth reiterating that the survey did not ask business owners who applied for
the PPP or PUA if they accepted or rejected funding once approved but, rather,
if the owner successfully or unsuccessfully applied. Overall, we believe that this
approach to identification makes it plausible to isolate a causal effect of the PPP on
business medium-run outcomes. That said, in the discussion that follows, we refrain
11
For instance, lack of guidance from the SBA caused banks to vary in the stringency with which they
applied the Bank Secrecy Act to loan applicants, which could result in a PPP denial. See Jensen, “Payroll
Protection Program and the Bank Secrecy Act: Balancing Aid to Small Businesses with Financial
Crime Risks,” White & Case Memo, Apr. 17, 2020. Banks also experienced difficulties in processing
PPP loan applications as a result of problems in accessing the SBA’s overwhelmed E-Tran portal. See
“Banks Report E-Tran Difficulties; SBA Lowers PPP Bulk Submission Threshold,” ABA Banking
Journal, Apr. 27, 2020.
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Bartlett and Morse 2537

from using this language because our sample is small, and we cannot prove the
randomness of the PPP application success or unsuccess of the Oakland businesses.
Our first use of the APPLIED_PPP/APPLIED_PUA variable is through a simple
selection test that examines whether businesses that were successful in applying for a
PPP loan or PUA differed in setting. We use the APPLIED_PPP/APPLIED_PUA
variable in combination with information on the businesses’ current operating
status and an action-taken variable to test for residual selection in successfully
applying for a PPP or PUA beyond the decision to apply. The OPERATING_
STATUS variable is the answer to the question of whether the business (in June
2020) is open, reduced, or closed in its business operations. The ACTION_
TAKEN variables provide information on whether and how a business has adjusted
to the crisis, with answers as follows: “furloughing employees” (20.1%), “having
employees work remotely” (18.0%), “no action” (13.3%), “reduced employees’
hours” (10.4%), and “laid off employees” (7.9%). We note that some of the
differences in these OPERATING_STATUS and ACTION_TAKEN variables
may be an outcome of the PPP because early June is after the PPP’s first wave
and some of the second wave. Yet we prefer to err on overcontrolling for this
possibility rather than overinterpreting our medium-run survival results.
Table 9 presents selection tests. The dependent variable in columns 1–2 is an
ordered logit specification of OPERATING_STATUS: closed < reduced < open.
The dependent variables in columns 3–10 are the ACTION_TAKEN options listed
previously, cast as indicator variables for each answer. In the odd columns of the
table, we include only indicators for whether a business successfully applied for the
PPP or PUA (SUCCESS_PPP/SUCCESS_PUA), plus industry fixed effects. In the
even columns, we include variables for applying for each program, allowing us to
gauge the selection of applying versus application success. At the bottom of the
table, we do the addition of the two coefficients (because success is the union of
applying for the program and succeeding) and show the significance test.
We find that successfully applying for the PUA is highly endogenous to
intermediate status. This is not terribly surprising because the businesses with
employees generally must eliminate all employees in order to qualify as a none-
mployer. We see this result in the ordered logit of OPERATING_STATUS, as well as
on the indicator for the ACTION_TAKEN of “having employees work remotely.”
However, the PPP results are quite different. Focusing on the even-numbered
columns, we find that although OPERATING_STATUS and ACTION_TAKEN are
associated with success in a PPP application in some columns, these effects are
generally offsetting to the coefficient for applying for a PPP loan. Granted, such an
offsetting pattern is associated with collinearity concerns, but we also show the same
pattern without the APPLIED_PPP variable. The only interim variable that suggests a
concern is the “furloughing employees” variable. In the survival analysis that follows,
we therefore present results with and without businesses that express this interim action.

C. PPP and PUA Results
Table 10 presents estimates for our policy tests. Columns 1–3 examine the
association between policy program application success and short-term permanent
closure; columns 4–7 examine the association between application success and
2538 Journal of Financial and Quantitative Analysis
TABLE 9
Selection Tests on Intermediate Outcomes and Actions

The sample in Table 9 is the data set of 278 survey responses from the City of Oakland Re-Opening and Recovery Survey. The observation-count differences across columns come from fully determined observations in
the estimation, not missing information. Columns 1 and 2 report an ordered logit estimation of the OPERATING_STATUS of the firm in June 2020 (closed < reduced operations < open fully). Coefficients are in log odds
ratios of moving from any level of openness to the next. Columns 3–10 present logit estimations of the indicator variables listed in the columns, which are answers to the ACTION_TAKEN question, with marginal effects
shown. Industry effects are included and not shown. The displayed independent variables for each of the Paycheck Protection Program (PPP) and Pandemic Unemployment Assistance (PUA) policies are whether the
owner applies for the policy, and if so, whether the application is successful. Because the full marginal effect of success is applying and succeeding, the sum of the marginal effects and its standard error are reported in
the lower rows. Robust standard errors are in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Model Ordered Logit Logit: Marginal Effects Reported

Business Status ACTION_TAKEN

Dependent Variable OPERATING_STATUS (Closed < Reduced < Open) Furloughed Employees Having Employees Work Remotely Laid Off Employees Reduced Employee Hours

1 2 3 4 5 6 7 8 9 10

APPLIED_PPP 0.738 0.0043 0.206** 0.0359 0.114
(0.460) (0.0831) (0.102) (0.0736) (0.106)
SUCCESS_PPP 0.373 0.791* 0.104** 0.111 0.0605 0.208** 0.0178 0.00721 0.0526 0.131
(0.282) (0.413) (0.0494) (0.0789) (0.0519) (0.101) (0.0439) (0.0655) (0.0540) (0.0990)
APPLIED_PUA 0.749** 0.0681 0.0424 0.0441 0.0615
(0.368) (0.0705) (0.0697) (0.0764) (0.0931)
SUCCESS_PUA 1.316*** 0.670 0.0398 0.0938 0.218*** 0.176* 0.0362 0.00391 0.042 0.110
(0.324) (0.427) (0.0544) (0.0770) (0.0705) (0.0932) (0.0560) (0.0814) (0.0540) (0.102)
Estimate: APPLIED_PPP + SUCCESS_PPP 0.053 0.107* 0.002 0.029 0.016
(0.333) (0.055) (0.070) (0.057) (0.058)
Estimate: APPLIED_PUA + SUCCESS_PUA 1.419*** 0.026 0.218*** 0.048 0.049
(0.338) (0.056) (0.054) (0.050) (0.057)
Odds ratio 0.244
Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
No. of obs. 278 278 278 278 217 217 212 212 194 194
Pseudo-R2 0.170 0.186 0.097 0.100 0.163 0.189 0.060 0.063 0.106 0.119

https://doi.org/10.1017/S0022109021000478
TABLE 10
Did the PPP and PUA Programs Save Businesses?

The sample is small businesses in the City of Oakland Re-Opening and Recovery Survey. All estimations are via logit specifications, presenting marginal effects. Both dependent variables build off the following survey
question: “If business disruption continues at the current rate, how soon will you be at risk of permanently closing your business?” The dependent variable in columns 1 and 2, SHORT_TERM_CLOSING, is an indicator
equal to 1 if the answer to this question is 0–1 month or if the respondent indicates elsewhere that the business is permanently closed already, and 0 otherwise. The dependent variable in columns 3 and 4,
MEDIUM_RUN_SURVIVING, is equal to 1 if the answer to the question is greater than 6 months, and 0 otherwise. The main independent variables are LOG_WORKERS, equal to the natural logarithm of employees plus 1
(the owner) in the precrisis period; whether the owner successfully applied for a Paycheck Protection Program (PPP) loan; whether the owner successfully applied for Pandemic Unemployment Assistance (PUA); and
the interactions of these program variables with LOG_WORKERS (precrisis). We control for race/ethnicity, gender identity, industry, and reported company status (closed, trying but reduced, or open). Robust standard
errors are in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Dependent Variable

SHORT_TERM_CLOSING MEDIUM_RUN_SURVIVING

Model Logit Marginal Effects Linear Probability Logit Marginal Effects

1 2 3 4 5 6 7

APPLIED_PPP 0.0163 0.0161 0.268*** 0.294*** 0.192*
(0.0582) (0.0629) (0.104) (0.110) (0.0989)
SUCCESS_PPP 0.0573 0.0606 0.053 0.268** 0.473*** 0.533*** 0.387***
(0.0717) (0.0825) (0.0797) (0.105) (0.142) (0.150) (0.144)
SUCCESS_PPP LOG_WORKERS (precrisis) 0.0206 0.0232 0.0166 0.133** 0.139** 0.185*** 0.129**
(0.0431) (0.0435) (0.0206) (0.0540) (0.0561) (0.0586) (0.0569)
APPLIED_PUA 0.0641 0.0495 0.0344 0.131 0.0119
(0.0571) (0.0619) (0.0779) (0.0833) (0.0789)
SUCCESS_PUA 0.168** 0.205** 0.142* 0.176* 0.155 0.104 0.154
(0.0756) (0.0827) (0.0819) (0.105) (0.123) (0.136) (0.123)
SUCCESS_PUA LOG_WORKERS (precrisis) 0.164*** 0.159*** 0.129** 0.107 0.0861 0.0139 0.0788
(0.0445) (0.0436) (0.0505) (0.0817) (0.0793) (0.0813) (0.0682)

Bartlett and Morse 2539
LOG_WORKERS (precrisis) 0.0162 0.0135 0.00343 0.144*** 0.158*** 0.169*** 0.136***
(0.0444) (0.0452) (0.0139) (0.0488) (0.0506) (0.0523) (0.0508)
NONEMPLOYER 0.0473 0.0501 0.0369 0.185* 0.224** 0.247** 0.183*
(0.0731) (0.0711) (0.0626) (0.106) (0.101) (0.101) (0.105)
Fixed effects included:
Industry Yes Yes Yes Yes Yes Yes Yes
Gender identity Yes Yes Yes Yes Yes Yes Yes
Race/ethnicity Yes Yes Yes Yes Yes Yes Yes
Status — — — — — — Yes
Action steps — — — — — — Yes
Drops furloughing firms — — — — — Yes —
No. of obs. 238 238 278 278 278 222 278
Pseudo-R2 0.277 0.288 0.208 0.210 0.237 0.279 0.268

https://doi.org/10.1017/S0022109021000478
https://doi.org/10.1017/S0022109021000478
2540 Journal of Financial and Quantitative Analysis

medium-term survival. All columns include industry, gender identity, and race/
ethnicity fixed effects. We discuss each policy in turn.
We find that application success for the PPP has no association with
SHORT_TERM_CLOSING, consistent with the results of Chetty et al. (2020)
and Granja et al. (2020). Column 1 of Table 10 presents the results excluding the
indicators for whether a business applied for either program, to be able to compare
magnitudes when absorbing any selection in applying, which we add in column
2. Column 3 differs from column 2 in that we use a linear probability model to show
that the selection of the perfectly determined variables in the logit (which drop from
the estimation) is not influential (note the difference in observations). We find no
result relating the PPP to short-term closure with or without the application variable.
However, the results are quite different with respect to MEDIUM_RUN_
SURVIVING. Success in a PPP application increases the medium-run survival prob-
ability by 27% in column 4 of Table 10. In column 5, the result holds when we add the
application indicators to address selection. The result continues to hold when we drop
furloughing firms (column 6) and when we add in controls for interim status and
actions taken (column 7). The sum of the coefficients for APPLIED_PPP and SUC-
CESS_PPP in column 5 is 0.205, suggesting that our efforts to address selection
diminishes the effect of the PPP on increasing medium-run survival by 27% to 20.5%.
The finding that PPP application success is strongly associated with increasing
firm survival in the medium run by 20.5% is in stark contrast to the implications that
the PPP was ineffective, as in the employment results in Chetty et al. (2020) and
Granja et al. (2020). The difference is surely due to our being able to study medium-
run effects (and even control for short-term status) and to the fact that prior work has
focused on larger small businesses. Consistent with our results, Granja et al. (2020)
foreshadows our finding insofar that businesses taking PPP loans in their sample
note that they are either making productive use of the capital or saving for survival
purposes. We confirm their intuition.
Our second main result on the PPP effect concerns heterogeneous effects
by firm size. Looking at the interaction of LOG_WORKERS (precrisis) and the
SUCCESS_PPP indicator, we find that as businesses increase in precrisis employee
count, the PPP is no longer associated with staving off closure. Again, we are
cognizant that selection could differentially affect firms of different sizes, but we
note that the selection story on the interaction is one of differential status by firm
size, whose sign is likely to suggest our interaction results are conservative.12
The Oakland data thus reveal heterogeneous effects of the PPP on medium-
term closure risk by firm size. Using the estimates from column 5, we plot this

12
This interaction finding gives direction to the rival selection story and affords us a partial test. In
particular, for this interaction effect to be the product of selection, it must be the case that among PPP
recipients, revenues for larger enterprises are sufficiently distressed that these enterprises are likely to
close in the medium term. Conversely, revenues for microbusinesses that receive PPP loans must be
sufficiently strong that they are likely to remain open. To examine whether this is the case, we test the
association between OPERATING_STATUS (open > reduced > closed) and LOG_WORKERS (pre-
crisis) and its interaction with whether a respondent successfully applied for a PPP loan, holding industry
effects constant. The results (unreported) reveal a significant interaction between LOG_WORKERS and
PPP success, indicating that (if anything) enterprises were experiencing stronger revenues than micro-
businesses, the opposite of what would be required for selection to drive this result.
https://doi.org/10.1017/S0022109021000478
Bartlett and Morse 2541

FIGURE 8
Effect of the PPP on Medium-Run Survival
The sample in Figure 8 is small businesses in the City of Oakland Re-Opening and Recovery Survey. Plotted is the predicted
effect of firm size on medium-run survival, measured as the response to the question asking, “If business disruption continues
at the current rate, how soon will you be at risk of permanently closing your business?” from column 5 of Table 10, taking into
account the coefficients on APPLIED_PPP, SUCCESS_PPP, and SUCCESS_PPP LOG_WORKERS.

0.6

Increase in Pr(Surviving Beyond Six Months)
0.4

0.2

–0.2

0 2 4 6 8 10 12 14 16 18 20
Pre-Crisis Employee Count
95% CI Marginal Effect

pattern in Figure 8, noting that the effect becomes economically immaterial after
approximately 20 employees. Note that in the 2015 census data, 92.9% of busi-
nesses and 17% of employment are in businesses with under 20 employees.
In addition to this result being consistent with Granja et al. (2020) (who find no
overall effect on closure rates among larger small businesses receiving PPP loans),
this result is a test of our compatibility findings. We suggested earlier in this
section that labor-cost-supporting programs would be less effective for enterprises
because these organizations use labor flexibility as their survival strategy. To be
sure, a larger business could use up to 40% of the PPP loan toward nonpayroll costs
if it desired loan forgiveness or even more than this amount if it chose to use the PPP
loan for nonpayroll costs and then repay the loan in 2 years.13 Our data do not permit
us to examine the extent to which larger firms deployed their PPP funds; therefore,
we cannot distinguish whether the reason for its ineffectiveness on medium-term
survival for larger firms was due to the fact that the amount was insufficient (e.g.,
because the borrower used only 40% toward nonpayroll costs or because PPP loan
amounts were based solely on past payroll expenses) or simply because the program
was cast as a payroll subsidy. That it was most effective for microbusinesses and not
enterprises is nevertheless consistent with our compatibility findings.
Turning to the PUA results, we find an opposite pattern. PUA success is
negatively associated with short-term closure but has no association with medium-
term survival. In the short term, business owners who were successful in their
PUA application shuttered their operations with a 9.4%–19.1% lower probability,

13
In the event a PPP borrower failed to qualify for loan forgiveness, PPP loans issued during the
sample period would have a 1% interest rate and a 2-year maturity.
https://doi.org/10.1017/S0022109021000478
2542 Journal of Financial and Quantitative Analysis

suggesting that owners were relying on the PUA to replace their lost business
income to remain in operation. However, this result does not hold for the larger
enterprises; we see that the coefficient on LOG_WORKERS (precrisis) interacted
with the PUA indicator is positive. This latter result should be expected: Recall that
for larger firms, taking PUA implies that the business had to first lay off all workers.
Thus, this unwinding of the effectiveness of the PUA for these businesses is a
mingling of the selection of already-struggling businesses to apply for PUAwith the
inability of the PUA’s income substitution (roughly $600 per week) to support the
owner of a business that has lost so much revenue that it has presumably let go of all
of its employees.

VII. Conclusion
The bundling of small businesses into one category is too coarse for designing
small-business assistance programs during periods of macro distress. Using a unique
set of data, we show that in Oakland, small businesses, all facing distress during the
COVID-19 pandemic, have notably different survival capabilities concerning strat-
egies focused on revenue resilience, labor flexibility, and committed costs. Moreover,
these survival capabilities differ systematically by the size of a firm.
Our punchline, however, is not simply that these differences exist but also that
they have significant policy relevance with regard to the design of small-business
assistance programs. The PPP provides an especially costly example of this basic
point. Originally designed as a $610-billion program to assist U.S. small busi-
nesses, the PPP loan-forgiveness requirements effectively made the program a labor
subsidy. Our unique survey data permit us to show that such a subsidy was
especially effective for the medium-term survival of microbusinesses, but we also
show that the labor-focused strategy of the PPP runs counter to the survival
capabilities of larger enterprises that focus on scaling back labor costs.14
Our framework also provides an important tool for researchers in evaluating
the effectiveness of small-business assistance programs. Again, the PPP and early
studies of its effect on small-business payrolls provide an example. As noted,
Chetty et al. (2020) find that the PPP had no meaningful impact on employment
rates, leading these authors to conclude that liquidity is insufficient to restore
employment at small businesses. By focusing on enterprises having an average
of 45 employees or more, however, the study’s data would not have been able to
identify heterogeneous treatment effects for the PPP within the universe of small
businesses. As we show, expressly grappling with the different survival capabilities
of microbusinesses highlights how labor subsidies are likely to be especially helpful
to these firms while being less helpful to the enterprises studied by Chetty et al.
Finally, our findings speak to the broader policy question of how to optimally
support small businesses through a crisis, given existing evidence that different-
sized small businesses have differential welfare effects. In particular, researchers
examining growth patterns among small businesses have highlighted considerable
14
In this regard, our article complements several recent articles that show the effectiveness of the PPP
on firm survival when the sample of small businesses includes smaller establishments (see, e.g., Hubbard
and Strain (2020), Faulkender, Jackman, and Miran (2021)).
https://doi.org/10.1017/S0022109021000478
Bartlett and Morse 2543

heterogeneity regarding the growth prospects of small businesses, particularly
between employer and nonemployer businesses (Decker et al. (2014), Hurst and
Pugsley (2011)). Differences likewise exist in the extent to which small businesses
contribute to community vibrancy. In combination, these findings provide good
reason for policy makers concerned with promoting job growth to focus on assisting
those small businesses we classify as enterprises, whereas those concerned with
community vibrancy and local tax bases would be well advised to focus on assisting
nonemployers and microbusinesses.
Although it is beyond the scope of this article to address how to prioritize
these competing considerations, we view our findings as providing two pieces of
information that are critical to its resolution. The first concerns the importance of
mapping specific programs to the desired beneficiaries of small-business support
programs. As we show, differentials in survival capabilities across firms have
implications for how to best support firms of different sizes; that is, although labor
subsidies may be effective for supporting microbusinesses, facilitating lease
forgiveness may be more effective for larger enterprises.
The second relates to the temptation to focus exclusively on job growth in the
design of small-business survival, particularly given the fiscal costs these programs
can entail. Within our data, we find that the survival capabilities of nonemployers
and microbusinesses provide welfare effects that would be imprudent to dismiss. It
is the microbusinesses that are the most revenue resilient, despite their need to cover
a less flexible labor-cost structure. During the COVID-19 crisis, these businesses
pulled their weight in supporting the local economy insofar that they displayed
revenue resilience. This revenue resilience translates to income to support the local
economy through preserved returns to labor and sales receipts, as well as to their
property bases. Similarly, nonemployer proprietors, either because of their greater
economic flexibility or the potential to realize nonmonetary utility from operations,
may be more likely to choose to continue operations regardless of the level of
demand for their services. These businesses may not support the labor force or sales/
business taxes with their low-return businesses, but to the extent that they are central
to establishment proliferations (Hurst and Pugsley (2011)) and to the extent that
establishments lead to community vibrancy, these businesses’ self-effort and some-
times self-funded resiliency are valuable to the community.
In short, the framework we provide should enable policy makers to design
small-business support programs that are both better informed and more effective in
achieving the welfare objectives that justify their creation.

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