Were Small Businesses More Likely to Permanently Close in the Pandemic? (IZA Discussion Paper No. 15445, July 2022)

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IZA Discussion Paper No. 15445, dated July 2022, by Robert Fairlie, Frank M. Fossen, Reid Johnsen and Gentian Droboniku, asking whether small businesses were more likely to close permanently during the COVID-19 pandemic. Using confidential firm-level data on all businesses filing sales taxes with the California Department of Tax and Fee Administration, the authors find large increases in closure rates in the first two quarters of 2020 and a strong reversal in the third quarter. The rise in closures early in the pandemic was much larger for small businesses than for large ones, though their rebound was also larger. The paper reports that the disproportionate closing of small businesses concentrated market share among larger firms, with only a partial reversal afterward.

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I Z A Institute

of Labor Economics

Initiated by Deutsche Post Foundation

DISCUSSION PAPER SERIES

IZA DP No. 15445
Were Small Businesses More Likely to
Permanently Close in the Pandemic?

Robert Fairlie
Frank M. Fossen
Reid Johnsen
Gentian Droboniku

JULY 2022
| Z A Institute

of Labor Economics

Initiated by Deutsche Post Foundation

DISCUSSION PAPER SERIES

IZA DP No. 15445
Were Small Businesses More Likely to

Permanently Close in the Pandemic?

Robert Fairlie Reid Johnsen
UC Santa Cruz, Stanford University, NBER California Department of Tax
and IZA and Finance Administration

Frank M. Fossen Gentian Droboniku
University of Nevada, Reno, and IZA California Department of Tax
and Finance Administration

JULY 2022

Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may
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IZA DP No. 15445 JULY 2022

ABSTRACT

Were Small Businesses More Likely to
Permanently Close in the Pandemic?’

Previous estimates indicate that COVID-19 led to a large drop in the number of operating
businesses operating early in the pandemic, but surprisingly little is known on whether
these shutdowns turned into permanent closures and whether small businesses were
disproportionately hit. This paper provides the first analysis of permanent business closures
using confidential administrative firm-level panel data covering the universe of businesses
filing sales taxes from the California Department of Tax and Fee Administration. We find
large increases in closures rates in the first two quarters of 2020, but a strong reversal of
this trend in the third quarter of 2020. The increase in closures rates in the first two quarters
of the pandemic was substantially larger for small businesses than large businesses, but
the rebound in the third quarter was also larger. The disproportionate closing of small
businesses led to a sharp concentration of market share among larger businesses as
indicated by the Herfindahl-Hirschman Index with only a partial reversal after the initial
increase. The findings highlight the fragility of small businesses during a large adverse
shock and the consequences for the competitiveness of markets.

JEL Classification: L26, H25, 118

Keywords: small business, entrepreneurship, survival, closures, self-
employment, COVID-19, coronavirus, pandemic, shelter-in-
place restrictions, social distancing restrictions

Corresponding author:
Frank M. Fossen
Department of Economics
University of Nevada, Reno
1664 N. Virginia Street
Reno, Nevada 89557-0030
USA

E-mail: ffossen@unr.edu

* We would like to thank Isabel Guzman at the California Governor's Office of Business and Development, and Irena
Asmundson at the California Department of Finance for earlier help with the data. Any opinions and conclusions
expressed herein are those of the authors and do not necessarily represent the views of the CDTFA. All results have
been reviewed to ensure that no confidential information is disclosed.
1. Introduction

The coronavirus pandemic led to unprecedented, widespread shutdowns of businesses in
the United States and around the world. Stores, restaurants, factories, professional offices, and
many other businesses shut down in the first few months due to policy mandates, downward
demand shifts, health concerns, or other factors. A small but rapidly growing literature documents
these impacts. For example, Fairlie (2020) finds that the number of active business owners in the
United States plummeted from 15.0 million in February 2020 to 11.7 million in April 2020 and
only partially rebounded by June. Losses to small business revenues and sales were also found to
be large in the early stages of the pandemic with estimates ranging from 30 to 50 percent (Kim et
al. 2020; Bloom et al. 2021; Fairlie and Fossen 2022), and owners’ demand expectations were one
third lower than before the crisis (Balla-Elliott et al. 2022). Examining financial account data,
Farrell et al. (2020) find that as early as by the end of March 2020 cash balances were 12 percent
lower for all firms.

The emerging literature on the effects of COVID-19, however, has three major limitations.
First, it provides limited information on the impacts on small businesses. There is a debate in the
literature on whether small firms have a disadvantage during recessions in comparison to large
firms due to their fragility or an advantage due to their flexibility, and empirical results are mixed.
While Moscarini and Postel-Vinay (2012) find relatively stronger growth performance of small
employers relative to large employers during economic downturns, Fort et al. (2013) report that
small firms — both young and old, and especially small and young firms — are more sensitive to
local cyclical shocks than large firms in the United States. Bartz and Winkler (2016) find that
young firms, not small firms in general, were disproportionately negatively affected by the 2009
financial crisis in Germany.' However, the COVID-induced recession is very different from
previous recessions; for example, no previous recession experienced a drop in GDP of 31 percent
in one quarter and then rebounded the next quarter with an increase of 34 percent (U.S. Bureau of
Economic Analysis 2022). As this recession was triggered by an exogenous health crisis, unlike

other cyclical economic downturns that may be related to endogenous Schumpeterian (1934)

' Similarly, previous research on the basic relationship between unemployment and entrepreneurship provides mixed
results. Parker (2018) reviews the literature and cites many previous studies showing positive relationships, negative
relationships, and zero relationships. See Fairlie (2013), for example, for evidence of a positive relationship between
unemployment in local labor markets and business creation using time series and cross-sectional variation, and
Fossen (2021) for consistent results at the individual level.

1
creative destruction, it provides an opportunity to study the resilience of small versus large
businesses toward a large, aggregate negative shock.

Second, there is very little evidence on permanent closures (Crane et al. 2022). Because of
the difficulty in finding timely panel data and measuring long-term business closures the important
question of whether small businesses permanently closed in disproportionate numbers in the
pandemic remains unanswered in the literature. Federal government sources of data are often not
released quickly enough and the approval processes for gaining access to these confidential
microdata sources are too slow to study permanent business closures in the pandemic.” Third,
following from the aforementioned limitations, the empirical literature is silent on the effects of
permanent small business closures on the competitiveness of the market.

These gaps in the literature are important because small businesses might have been
especially devastated by the shutdowns during the pandemic due to lost revenues, limited cash
reserves, and continuing expenses.’ Many of the early-stage shutdowns of small businesses might
have turned into permanent closures. Small businesses likely had less ability to quickly adjust to
changes in regulations and demand when the pandemic hit, and may have had less ability to obtain
financing needed for adjustments. Customers also might have felt fewer health concerns shopping
in large retailers instead of small shops. If these permanent closures and consumer shifts
disproportionately hit small businesses then market share will be concentrated among fewer
retailers lessening overall competition. Additionally, there is a concern that consumers shifted
away from purchasing goods from small brick-and-mortar businesses to online retailers and large
retailers with an online presence.

In this paper, we use firm-level panel data to provide the first examination of permanent
closures among small businesses in the pandemic using government administrative microdata. We
analyze business closures using administrative microdata from the California Department of Tax
and Fee Administration (CDTFA) that cover the universe of businesses with taxable sales in the
state. Our study is the first to use a similar measure as the Census and Bureau of Labor Statistics
(BLS) definition of permanent business closure that requires a full year of no operations using

administrative records. We address three key questions about the performance of small businesses

* For example, the latest published U.S. Census Bureau’s (2021) data only reports closures between 2018 and 2019.
3 Just prior to the pandemic when small business owners were asked what actions they would take if faced with a two-
month revenue loss, roughly half said they would use their own funds and 17 percent said they would close or sell the
business (Mills et al. 2020).
in the pandemic. First, we explore what percentage of businesses with taxable sales closed in the
first few quarters of the COVID-19 pandemic. Second, we examine whether business closure rates
differ by initial business size. In particular, we test the hypothesis that small businesses
disproportionately closed during the pandemic and identify how much of the differential was due
to COVID-19 by adjusting for trend and seasonality. Third, we examine the resulting effects on
market share concentration.

Using CDTFA administrative microdata, we find large increases in closures rates in the
first two quarters of 2020, but a strong reversal of this trend in the third quarter of 2020. The
increase in closures rates in the pandemic was substantially larger for small businesses than large
businesses, but the rebound was also larger. The disproportionate closing of small businesses led
to a concentration of market share among larger businesses. The Herfindahl-Hirschman Index
(HHI) across all retail businesses in California increased markedly from the third quarter of 2019
to the third quarter of 2020 breaking a slow, steady upward trend over the prior four years. It only
partially rebounded until the third quarter of 2021. These timely findings on permanent business
closures among small businesses are crucial to adjust and calibrate adequately targeted policy
responses supporting small businesses and their owners and employees. More generally, the results
demonstrate the fragility of small businesses during a large adverse shock and the negative

consequences for the competitiveness of markets.

2 Previous Studies of Closures in the Pandemic

Crane et al. (2022) provide an overview of the small number of papers studying business
closures during the early stages of the COVID-19 pandemic and potential data sources. They note
that the statistics on business closures provided by the Bureau of Labor Statistics (BLS) and the
Census Bureau (see below) are released with a time lag that is too long to inform timely policy
responses. Therefore, some researchers conduct their own surveys of firm samples (Bartik et al.
2020a) or analyze the narrower subset of closures that result in bankruptcy filings (Wang et al.
2020). The U.S. Census Bureau’s (2020) Small Business Pulse Survey provides valuable
information about continuing small businesses (Mini 2021) but is less useful to measure business
closures because survey non-response is higher for businesses that have ceased operations
(Buffington et al. 2021). Surveys conducted after the pandemic will likely find it very difficult to
reach businesses that permanently closed and capture them in the data. The World Bank COVID-

3
19 Impact Surveys, for example, measure whether businesses are i) currently open, ii) temporarily
shut down (suspended services or production), or iii) permanently closed using survey data.*
Survey response rates are likely dependent on the outcome of interest.

Substantial efforts have been devoted to tap non-traditional “real-time” data sources
collected by private companies for their business purposes. Bartik et al. (2020b) use data from
Homebase, a provider of clock-in/clock-out tracking software, and measure business shutdowns
by firms stopping to report clock events. Kurmann et al. (2021) also use the Homebase data, but
in order to distinguish between firm closures and sample churn, they additionally check whether
business owners update their status in Google Places (the database behind Google Maps) or stop
posting on Facebook if the authors can find a match. Rigobon et al. (2022) use data from Google
Places in the retail and food service sectors in the downtown core of Ottawa/Gatineau. By
comparing scraped data at two points in time, they identify business exits when a business is
removed from the data. De Vaan et al. (2021) rely on foot traffic data from mobile applications
provided by SafeGraph, focusing on service-oriented businesses. They identify closures by the
change in the number of visitors. Yelp (2020) uses its online platform of business reviews to track
business closures when owners update their Yelp pages. Chetty et al. (2020) rely on Womply,
which aggregates transaction and revenue data from several credit card processors. Firms that
report zero credit card revenue for three days in a row are counted as closures. These papers thereby
focus on sectors in which time clocking or use of social media or credit cards are common. In sum,
while these non-traditional data sources provide fast and important first impressions of
developments, there are limits to their representativeness and ability to track permanent closures.
To our knowledge, our study is the first to address these challenges using administrative panel data

covering the universe of businesses with taxable sales over the first three quarters of the pandemic.

3. Measuring Closures, Data and Methods

3.1 Census and BLS Approach to Measuring Closures
The U.S. Census Bureau (2021) and U.S. Bureau of Labor Statistics (2021) provide
measures of business or establishment closures. In both the Census and BLS published numbers

of closures are measured over a one-year window. The Census Bureau’s Business Dynamics Series

4 https://openknowledge. worldbank. org/handle/10986/34636.
4
(BDS) measures closures by establishments that have positive employment in the first quarter of
the initial year and zero employment in the first quarter of the subsequent year. The first quarter
of each year is used because of the timing of employment information (i.e. March 12 payroll).°
The BLS publishes information from the Business Employer Dynamics (BED) on annual survival
rates for establishments with employees. They also report the total number of closures each
quarter.© The BLS has conducted analyses on different time frames for measuring closures

(Sadeghi 2008).’

3.2 Measuring Closures with Administrative Tax Panel Microdata

The panel microdata used here are restricted-access from the California Department of Tax
and Fee Administration (CDTFA). The microdata consist of sales and use tax (SUT) returns and
capture all taxable sales for the universe of businesses in California. Using these panel data, we
track taxable sales over time in each quarter for the same business. To define business closures we
identify businesses that had taxable sales in one quarter but had no taxable sales in the next four
consecutive quarters. If a business has no taxable sales over the next year then we assume that the
business has permanently closed. If a business has any taxable sales in a quarter then it is not
closed. It is rare for a business to come back after four quarters of no taxable sales and thus we
approximate by assuming that this is a permanent closure. The use of a four-consecutive quarter
(i.e. full year) closure measure allows us to examine closures through the third quarter of the
pandemic. We use quarterly data through 2021Q2 to capture closures for up to 2020Q3 (i.e., the
last quarter a business reported sales was in 2020Q2).

Several issues arise in using the CDTFA administrative data and taking our approach. First,
not all SUT accounts file on a quarterly basis. Some small accounts (with less than $1,200 in
annual tax liability) file annually in either June or December, resulting in skewed closure rates in
Q2 and Q4 of each year. Further, as some of these small taxpayers grow they may be required to
change from annual to quarterly filing. We remove from our study these small accounts that at any

point in time filed on an annual basis. We also aggregate monthly filers up to the quarter. Second,

5 https://www.census.gov/programs-surveys/susb/about/glossary.html.

° E.g., Chart 5 at https://www.bls.gov/bdm/entrepreneurship/entrepreneurship.htm.

7 Three measures are compared: i) four consecutive quarters in which there is zero employment in the third month, ii)
five consecutive quarters in which there is zero employment in the third month, and 111) twelve consecutive months of
zero employment.
SUT accounts do not necessarily correspond 1:1 to businesses. A single business can operate more
than one SUT account. This is unusual, and typically relevant only for large businesses. More
commonly, multiple physical sites can file under a single SUT account. In these cases, the data do
not reflect individual storefront closures, only account closures. Third, in a typical year,
approximately 30% of filers report zero sales tax liability. These accounts are effectively closed,
although their permit remains open. We treat these accounts as closed in quarters in which they

file zero or negative tax liability.

3.3 Robustness to Alternative Future Windows for Measuring Closures

We define a business closure using a four-quarter forward looking window. A closure is
defined as a business that does not show up with taxable sales the next four quarters. We do not
check whether the business returns after these first subsequent quarters. We also examine closures
using a shorter forward looking window, and define a closure with a two-quarter forward looking
window. The patterns over time look similar to those for our four-quarter forward looking
definition, which is reassuring. The shorter forward looking time period increases the likelihood
that we will mistakenly define a business closure when that business comes back in a future
quarter. We follow the BLS and Census definitions that focus on a four-quarter or full-year

window.

3.4 Regression Specifications
To adjust for pre-pandemic time trends, allow for seasonal (quarter) fixed effects, and
provide a direct estimate of the impact of the pandemic, we estimate the following regression

equation for the probability of a business closure:

(3.1) Vig = + VoL Vs COVIDS: + B'Xi, HAC + V2, Ws quarters: + Eit,

where Yj, is closure for business i in quarter t, COVID,, are three dummy variables for the quarters
s of the three post-COVID quarters in our data with information on closure (2020Q1-2020Q3), Xit
includes business characteristics, 2 is the slope of a linear time trend (with ¢ set to zero at 2019Q4
and increasing by 0.25 each quarter), quarter,; is a set of quarter of the year dummy variables to

control for seasonality, and €;, is the error term. The analysis sample period covers 22 quarters,
with nineteen pre-COVID quarters (2015Q2 - 2019Q4) and three post-COVID quarters.
Technically, the first quarter of 2020 includes the months of January and February which were
prior to social distancing restrictions, and March only partly captures those restrictions, but as we
show below large negative effects of the pandemic show up on GDP growth in 2020Q1.

The parameters of interest are the y,, which capture the estimates of COVID-19 effects on
business closures relative to pre-pandemic levels after controlling for a time trend and seasonality.
The equation will be estimated with OLS and robust standard errors. Using a linear probability
model has the advantage over probit or logit of direct interpretability of the estimated coefficients
of the interaction terms we introduce below. Additionally, with more than 7 million observations
in the administrative data any losses in efficiency are not a concern.

We also estimate the effects of COVID-19 on small businesses relative to large businesses.
We define small businesses as those with average quarterly taxable sales annualized to less than
$500,000, and calculate this measure over the previous four quarters. We estimate the following

equation:

s=1

AS™ t x Small; + Y3_, 7, quarters, + Y2_,73™ quarters, X Small; + it,

where Small is a dummy variable for small businesses. In this specification, the parameters of
interest are y;’", which capture the estimates of potential disproportionate effects of COVID-19
on small businesses relative to large businesses in the sense of a difference-in-differences
estimator. Relative effects on business closures are estimated for each post-pandemic quarter. Pre-
pandemic trends in business closures rates as well as seasonality are also allowed to differ by
baseline size of the business. These estimates do not estimate causal effects of a policy change but
instead capture more exploratory estimates of changes in business dynamics during the COVID-

19 pandemic.

4. Results
4.1 Graphical Evidence

Before examining business closure rates using the universe of businesses included in the

CDTFA administrative data we present evidence on the economic disruption caused by the
pandemic. Figure 1 displays quarterly gross domestic product (GDP) growth from 1947Q2 to
2021Q1.8 Over the numerous recessions in the second half of the 20" Century and the first two
decades of the 21*' Century there has never been such a large quarter-to-quarter change in GDP as
in the first full quarter in the pandemic, 2020Q2. GDP fell by 31.2 percent in 2020Q2 (Figure 2
focuses on the period from 2005Q1 to 2021Q1). GDP also fell by 5.1 percent in 2020Q1. The next
largest drops in GDP were by -10.0 percent in 1958Q1, -8.5 percent in 2008Q3 (Great Recession),
and -8.0 percent in 1980Q2. Furthermore, only three additional quarters over this time period
experienced larger drops than in 2020Q1. The pandemic created an extremely severe but short
recession. GDP reversed course quickly and grew by 33.8 percent in 2020Q3. The NBER officially
dates the pandemic-induced recession as occurring from February 2020 (peak) to April 2020
(trough). As shown in Figures 1 and 2 both 2020Q1 and 2020Q2 were affected severely by the
beginning of the pandemic.

Having established overall effects on the U.S. economy, we turn to examining the total
number of business closures before the pandemic and in the pandemic.” Figure 3 displays the total
number of business closures in California by quarter from 2015Q2 to 2020Q3. As noted above,
we check for four consecutive quarters to make sure the business is not active before defining a
closure.'° The number of business closures increased substantially in 2020Q1 and remained high
in 2020Q2, capturing the early effects of COVID-19.'! After those initial increases, however, the
number of business closures dropped back down to below pre-pandemic levels in 2020Q3. There
is a strong seasonality component to business closures that makes it difficult to identify changes
in the pandemic. Focusing on pre-pandemic data (i.e. 2015 to 2019), the average number of
business closures over this time period is 17,200 in Q1, 12,600 in Q2, 13,400 in Q3, and 16,500
in Q4. Given that Q2 is typically a lower quarter for the number of closures, the jump in 2020Q2

8 The Bureau of Economic Analysis first reports quarterly GDP growth rates in 1947Q2.

° Appendix Table 1 shows closure rates by quarter and business size.

'0 For example, to define a business closure in 2020Q2 the business must have taxable sales in 2020Q1, but no taxable
sales in 2020Q2, 2020Q3, 2020Q4, or 2021Q1.

'! There are two reasons why we might observe an increase in closures already in 2019Q4. First, about 10% of
businesses usually file their sales taxes more than 30 days late. 30 days after the 2019Q4 filing deadline was March
1, 2020. Late filers who closed in 2020 due to the pandemic may not have filed their 2019Q4 returns and therefore
show up as closing in 2019Q4 although they really closed in 2020. Second, the Marketplace Facilitator Act (Assembly
Bill No. 147) took effect in 2019Q4. Starting in this quarter, California required online selling platforms to collect use
tax on behalf of third party sellers that sell on those platforms. This eliminated the requirement to file for some online
sellers that sold exclusively through online marketplaces and were correctly collecting use tax prior to AB-147 since
the marketplace facilitator collects the tax now.
is especially large relative to pre-pandemic levels. We return to these issues in the regressions
below where we control directly for season/quarter dummies.

We next focus on business closure rates, which is important for comparing small to large
businesses.'* To start, Figure 4 displays business closure rates by quarter from 2015Q2 to 2020Q3.
The pandemic led to large increases in closure rates. Focusing on the YOY change, the closure
rate was 3.7 percent in 2019Q2 and jumped to 6.7 percent in 2020Q2. From 2019Q1 to 2020Q1
the closure rate increased from 4.8 percent to 6.5 percent. After the initial shakeout of these two
quarters in the pandemic, the closure rate in the third quarter dropped precipitously. Focusing on
the YOY comparison, the closure rate in 2020Q3 was 2.9 percent, which was substantially lower
than the closure rate of 4.1 percent in 2019Q3. The drop in closure rates reflects the exceptionally
strong rebound in the economy in 2020Q3 as shown in GDP trends reported in Figure 2. Closure
rates display strong seasonality patterns but no strong downward or upward trend prior to the
pandemic.

The large increase in the closure rate from 2019Q2 to 2020Q2, which captures the worst
of the pandemic, might have been felt very differently by size of businesses. Figure 5 displays
closure rates for several size categories of businesses based on the level of taxable sales in the
previous quarter. There is a clear pattern of lower closure rates with initial size of the business
which holds for all time periods. Closure rates are much higher for the small size classes than for
the large size classes, and this differential holds prior the pandemic and after the pandemic.
Another clear pattern is that all business sizes experienced an increase in closure rates in the
pandemic. The jumps in closure rates disrupted the seasonality patterns. Another finding is that
there does not appear to be a strong upward or downward trend prior to the pandemic in closure
rates for any size class.

To focus on the experience of small businesses relative to large businesses, we simplify by
collapsing categories. We also revise the definition of categories slightly. Instead of using taxable
sales in the previous quarter to define the size class, we use an average of the prior four quarters
to determine if a business is small or large. By averaging over the previous four quarters we avoid
some of the issues with COVID affecting which size class the business is in for calculating closure

rates. Figure 6 displays closure rates for small businesses and large businesses from 2015Q2 to

2 We do not need to report DHS corrected closure rates that average the previous quarter and current quarter number
of businesses because with microdata we can examine closure business by business.

9
2020Q3. We define small businesses as those with average quarterly taxable sales annualized to
less than $500,000. Large businesses are those with $500,000 or more in annualized taxable sales
over the previous 4 quarters. Closure rates are much higher for small businesses than for large
businesses prior to the pandemic and in the pandemic. On average, 5.4 percent of small businesses
closed each quarter from 2015Q1 to 2019Q4. In comparison, an average of 1.4 percent of large
businesses closed each quarter during the same time period.

For both small and large businesses there is a strong seasonality component with the
highest closure rates happening in the first quarter of each year before the pandemic. COVID
affected both small and large businesses. Closure rates increased from 4.6 percent in 2019Q2 to
8.5 percent in 2020Q2 for small businesses and increased from 1.3 percent in 2019Q2 to 2.7
percent in 2020Q2 for large businesses. The increase in closure rates is much larger for small

businesses than for large businesses.

4.2 Regression Estimates

To adjust for pre-pandemic time trends, allow for seasonal fixed effects, and provide a
direct estimate of the impact of the pandemic, we estimate Equation 3.1 for the probability of a
business closure. Estimates are reported in Table 1. We start with Specification 1, which is the
base model. After controlling for the time trend and seasonal effects, we find that COVID-19 had
a large effect on business closures. In 2020Q2 business closures were 2.7 percentage points higher
due to COVID. The coefficient implies a very large relative effect: the average closure rate in 2019
was 4.6 percent. The early effects of the pandemic were also felt in 2020Q1. Our regression model
indicates an increase of 1.2 percentage points in the closure probability. By the summer/early fall
of 2020 (2020Q3) we find that closure rates are lower than expected (i.e. no COVID disruption).
The coefficient estimate for the 2020Q3 dummy indicator is negative implying that they are 1.4
percentage points lower than where they were expected to be.

As expected from the figures above, we do not find a strong pre-pandemic trend in closure
rates over time. The coefficient estimate indicates an increase of only 0.1 percentage points each
year. The coefficients do show a strong seasonality component with the highest closure rates in
the first and fourth quarters and the lowest closure rates in the second and third quarters. All of the
coefficients in these models are precisely estimated using the California administrative data with

more than 7 million observations.

10
In Specification 1, we compare the post-COVID effects to a pre-pandemic time period that
encompasses the entire time period from 2015Q2 to 2019Q4. Our coefficient estimates of post-
COVID quarters implicitly make comparisons to the average closure rate over this entire time
period after adjusting for the linear time trend and seasonality. In Specification 2 we alter the
model to focus the comparison on 2019, which is the last year prior to the pandemic, by including
a dummy variable that is one in any quarter prior to 2019Q1. We find essentially the same estimates
of the effects of COVID on 2020 closure rates as noted above. We find an increase of 2.6
percentage points in the closure probability in 2020Q2. We also find an increase of 1.2 percentage
points in 2020Q1, and a decrease of 1.4 percentage points in 2020Q3.

Overall, the regression estimates indicate that COVID-19 had a large effect on business
closures in the first two quarters of 2020 and especially the second quarter of 2020. Business
closure rates increased substantially in this quarter regardless of whether they are compared to the
year just prior to the start of the pandemic or to a longer pre-pandemic period. The adverse effects
of COVID already show up in the first quarter of 2020, which is consistent with the drop in GDP
in this quarter shown above. Finally, the effects of COVID on closure rates reversed by the third
quarter of 2020 in the economic rebound.

We turn to exploring how small businesses fared in the pandemic relative to large
businesses by estimating Equation 3.2. The main coefficients of interest are the interactions
between the small business dummy variable and the post-COVID quarter dummy variables.
Specification 3 uses the entire 2015Q2 to 2019Q4 time period as the pre-pandemic comparison
period whereas Specification 4 compares to 2019 by including the pre 2019Q1 dummy. The results
from both specifications are again very similar. Looking at the difference-in-difference (DID)
estimate noted above to estimate COVID-19 effects, we find that small businesses experienced a
2.0 percentage points higher closure rate in 2020Q2 relative to large businesses due to COVID-
19. Small business were also more negatively affected in 2020Q1 with a 1.5 percentage point
higher increase in closure rates due to the pandemic.

The negative effects of the pandemic on closure rates among large businesses were large
in the second quarter of 2020 but small in the first quarter of 2020. The coefficient estimates
indicate that the effect of COVID-19 on large businesses was an increase of 1.3 percentage points
in 2020Q2 and 0.2 percentage points in 2020Q1. The combination of these main effect coefficients

and the DID coefficients indicates that small businesses experienced a total increase of the closure

11
rate by 3.3 percentage points in 2020Q2 from COVID. In 2020Q1 the total effect on small business
closure rates was an increase of 1.7 percentage points.

Although small businesses were hit harder in the first two quarters of the pandemic than
large businesses, they experienced a larger rebound in closure rates. In 2020Q3 small business
closure rates decreased by 1.5 percentage points more than large business closure rates. Combining
main and DID estimates we find that the total effect on small business closure rates was a rebound

of 1.9 percentage points in 2020Q3.

4.3 Market Concentration

Did the disproportionate closing of small businesses in the pandemic lead to an increase in
the concentration of taxable sales at large businesses in California? To explore this question, we
measure changes over time in market concentration using the commonly-used Herfindahl-
Hirschman Index (HHI). The use of administrative data covering the universe of businesses in
California with taxable sales is important for an accurate measure of the HHI. Truncated, censored
or windsorized data would otherwise lead to problems that we do not have with our data. For every
quarter from 2015Q1 to 2021Q3 we calculate

(4.1) HHI = S;7 + S.? +83? +... + Sn?
where N is the total number of businesses and Sj is the market share percentage (of taxable sales)
of firm i.'? We do not limit the HHI to a particular industry or regions within California to explore
this question broadly. By taking this approach we take into account that during the pandemic
consumers shifted away from some industries (e.g. accommodation, food services and drinking
places, and arts, entertainment and recreation) towards other industries (e.g. grocery stores,
building materials, and garden equipment) (Fairlie and Fossen 2022). Note that the broader the
industry and the wider the geographical location included to calculate the HHI, the smaller the
HHI will be due to the inclusion of more businesses with market shares. But, we are interested
here in examining possible changes in the HHI over time using a consistent definition, in particular
comparing periods before and after the start of the COVID-19 pandemic. The /evel of the HHI we
calculate cannot be compared directly to industry-specific HHI measures and does not inform

whether the retail market in California is concentrated or not in absolute terms. The quantitative

'3 As usual for the HHI, the shares are expressed as a percent, not a fraction, for example, 5 ifa firm has 5% of
taxable sales in an industry, not 0.05.

12
interpretation of the HHI is the following: The HHI divided by 100 is the probability (in percent)
that two dollars chosen at random among all dollars spent in California in a given quarter are spent
at the same retailer. An increase in the HHI indicates an increasingly concentrated market.

We adjust for a legislation change in California that would otherwise complicate the
comparison of the HHI over time. Due to California’s Marketplace Facilitators Act (Assembly Bill
No. 147), several large online retailers began collecting use tax on behalf of smaller retailers that
used their sales platforms beginning in 2019Q4. In many cases, these large retailers opened new
SUT accounts for their marketplace sales. These accounts tend to report very high taxable sales.
We manually removed 13 marketplace facilitator accounts before calculating the HHI to avoid any
confounding effects. It is possible that there are more marketplace facilitator accounts, since they
are not required to identify themselves as such.

The quarterly HHI series shows some seasonality with higher concentration in the fourth
quarter of each year, which is presumably related to the holiday shopping period. To deseasonalize
the HHI, we first regress the quarterly HHI values from 2015Q1 to 2019Q3 on dummies for Q2,
Q3 and Q4 and a constant. We do not include the quarters potentially impacted by the pandemic
in this regression to estimate seasonal effects. Then we use the estimated model to predict the
residuals based on all quarters from 2015Q1 to 2021Q3 and add back the estimated constant. This
provides a deseasonalized HHI series which equals the observed HHI in each first quarter from
2015-2019.

Figure 7 plots the development of the deseasonalized HHI in California over time. The
figure shows that the HHI increased only slowly and mostly steadily from 19.8 in 2015Q1 to 21.4
in 2019Q3 before the pandemic. During the pandemic, however, the HHI increased to a maximum
of 38.0 during the strictest lockdowns in 2020Q2 and then decreased to 26.0 in 2021Q3 (the last
measurement we have), but remains far higher than before the pandemic. !* Thus, the results show
a clear increase in market concentration during the pandemic with a partial recovery in the second
and third quarters of 2021. The probability that two random dollars were spent at the same retailer

in California within a quarter increased from 0.214 percent in 2019Q3 to 0.260 percent in 2021Q3,

‘4 The observation that the HHI starts increasing in 2019Q4 already is consistent with the observed increase of closures
attributed to 2019Q4 and is likely to be due to late tax filers who closed in 2020 due to the pandemic (see footnote
11).

13
which corresponds to a relative increase of 21 percent. This increased concentration is consistent

with the evidence we provide on closures of small businesses during the COVID-19 pandemic.

5. Conclusions

Although it is well known that COVID-19 led to a massive shutdown of stores, restaurants
and other businesses in the second quarter of 2020, surprisingly little is known about whether these
shutdowns turned into permanent closures. The difficulty is in having a long enough time window
to look for whether a closure is permanent and having comprehensive firm-level panel microdata.
Using confidential administrative microdata from the California Department of Tax and Fee
Administration, we provide new evidence on permanent closures among the universe of businesses
with taxable sales in California. We find large increases in closures rates in the first two quarters
of 2020, but a strong reversal of this trend in the third quarter of 2020. Regression results that
control for the continuation of pre-pandemic trends indicate that COVID led to an increase of 1.2
percentage points in closure rates in the first quarter of 2020 and an increase of 2.6 percentage
points in closure rates in the second quarter of 2020. Both increases in closure rates were large
compared to the base closure rate of 4.6 percent in 2019. The rebound in the third quarter was also
strong at 1.4 percentage points higher than expectations.

The novel evidence presented here on the impacts of COVID-19 on permanent closures
among small business clearly indicates large disproportionate losses in the pandemic. In the first
quarter of 2020, large businesses experienced only a slight increase in closure rates (0.2 percentage
points) whereas small businesses experienced a substantial increase in closure rates (1.7 percentage
points). In the second quarter of 2020, large businesses experienced an increase in closure rates of
1.3 percentage points whereas small businesses experienced an increase in closure rates of 3.3
percentage points due to COVID-19. The rebound in closure rates in the third quarter of 2020,
however, was stronger for small businesses relative to large businesses (1.9 percentage points
compared with 0.4 percentage points). The disproportionate losses among small businesses led to
a concentration of market share among larger businesses as evidenced by an increase of 21 percent
in the Herfindahl-Hirschman Index across all businesses with taxable sales in California.

The large closure rates in the first and second quarters of 2020 are worrisome for the longer-
term survival of small, local businesses throughout the country. Will additional government

assistance for small businesses be needed to reverse the increased concentration of market power

14
among large businesses and keep the economy competitive? The Paycheck Protection Program
(PPP) targeted more than $800 billion in loans to small businesses, but currently there is no plan
for additional funds. Another problem facing small businesses is that the shift in consumer
behavior towards online purchasing during the mandated social distancing restrictions in the
pandemic is unlikely to fully reverse. Consumers have become more accustomed to purchasing
goods and services online, and small businesses are at a disadvantage in online sales relative to
large retailers and online retailers. Small businesses will need to adjust and this might be an area
in which government aid could be targeted. Additionally, some states and local governments have
promoted shopping small and local (e.g. California’s #ShopSafeShopLocal) but can these
programs counteract the closures of small businesses during the pandemic?

Our results suggest that on average the fragility of small businesses in comparison to large
businesses outweighs their higher flexibility when facing a large aggregate negative shock such as
a health crisis, which is consistent with Fort et al. (2013). More research is needed on why the
negative impacts of COVID-19 fell disproportionately on small businesses. Overall, small
businesses might have had less ability to quickly adjust to changes in regulations and demand
when the pandemic hit. Due to high fixed costs and required knowledge, small businesses may
have faced larger barriers to increasing their web presence, expanding takeout services or adding
delivery services, and coping with uncertainty regarding liability during the health crisis. In
particular, small businesses may have had less ability to obtain financing needed for adjustments,
for example, for investing in online ordering and inventory management, due to lower liquidity
reserves, less collateral, and higher uncertainty from the perspective of lenders and investors
during the emerging economic crisis. Customers in general might have felt fewer health concerns
shopping in large retailers instead of small shops. As more data become available these will be

important questions for future research.

15
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18
Quarterly GDP Growth Rate

-20

-30

-40

Figure 1: Quarterly Gross Domestic Product Growth Rate, 1947Q2 - 2021Q1

2005

So1s
Quarterly GDP Growth Rate

-40

Figure 2: Quarterly Gross Domestic Product Growth Rate, 2015Q1 - 2021Q1

20
25,000

Figure 3: Total Number of Business Closures per Quarter, 2015Q2 to 2020Q3

20,000

15,000

10,000

5,000

2015 2016 2017 2018 2019 2020 2021

21
8%

Figure 4: Total Business Quarterly Closure Rate, 2015Q2 to 2020Q3

0%
2015 2016 2017 2018 2019 2020 2021

22
40

Figure 7: Herfindahl-Hirschman Index for Taxable Sales, 2015Q1 to 2021Q3

2015 2016 2017 2018 2019 2020 2021 2022

Notes: Seasonally adjusted Herfindahl-Hirschman Index for all businesses with taxable sales in California
after marketplace facilitators have been removed.

25
Table 1: Probability of Business Closure — Regression Results

Q) (2) (3) (4)
Small 0.051*** 0.051***
(0.000) (0.001)
2020Q1 0.012*** 0.012*** 0.002** 0.002*
(0.000) (0.000) (0.001) (0.001)
2020Q1 xX small 0.015*** 0.015***
(0.001) (0.001)
2020Q2 0.027*** 0.026*** 0.013*** 0.013***
(0.000) (0.000) (0.001) (0.001)
2020Q2 x small 0.020*** 0.020***
(0.001) (0.001)
2020Q3 -0.014*** = -0.014*** -0.004*** = -0.004***
(0.000) (0.000) (0.001) (0.001)
2020Q3 X small -0.015*** = -0.015***
(0.001) (0.001)
Time trend 0.001*** 0.001*** 0.000*** 0.000
(0.000) (0.000) (0.000) (0.000)
Time trend xX small 0.002*** 0.002***
(0.000) (0.000)
Quarter 1 0.002*** 0.002*** 0.005*** 0.005***
(0.000) (0.000) (0.000) (0.000)
Quarter | x small -0.004*** -0.004***
(0.001) (0.001)
Quarter 2 -0.010*** = -0.011*** -0.000 -0.000
(0.000) (0.000) (0.000) (0.000)
Quarter 2 X small -0.014*** -0.014***
(0.001) (0.001)
Quarter 3 -0.008*** — -0.008*** -0.000 -0.000
(0.000) (0.000) (0.000) (0.000)
Quarter 3 x small -0.011*** -0.011***
(0.001) (0.001)
Pre 2019Q1 -0.001** -0.001*
(0.000) (0.001)
Pre 2019Q1 x small 0.000
(0.001)
Constant 0.051*** 0.051*** 0.014*** 0.014***
(0.000) (0.000) (0.000) (0.000)
Observations 7,441,320 7,441,320 7,441,320 7,441,320
R? 0.002 0.002 0.010 0.010

Notes: The data are the universe of businesses with taxable sales in California in each
quarter from the California Department of Tax and Fee Administration (CDTFA). A
closure is defined as a business that does not show up with taxable sales the next four
quarters. Small (large) businesses are defined as those with average quarterly taxable
sales annualized to less (more) than $500,000, which is calculated over the previous
four quarters. Robust standard errors in parentheses. *p<0.1; **p<0.05; ***p<0.01.

26
Appendix Table 1: Quarterly Business Closure Rates, 2015Q2 to 2020Q3

Taxable Taxable

Taxable _— Sales: Sales: Taxable Taxable Taxable
Year/ Sales: 1- 50k - 100k - Sales: Sales:1M_ Sales:

Quarter _ Total 50k 100k 500k 500k-1M -10M >=10M Large Small
2015Q2 3.5% 6.5% 3.0% 2.3% 1.7% 1.2% 0.7% 1.3% 4.2%
2015Q3 3.9% 7.4% 3.3% 2.5% 1.7% 1.2% 0.8% 1.3% 4.7%
201504 4.5% 7.9% 5.3% 3.1% 1.5% 1.1% 0.5% 1.2% 5.5%
2016Q1 5.0% 9.2% 4.3% 3.5% 2.2% 1.7% 1.2% 1.9% 6.1%
2016Q2 3.5% 6.7% 3.1% 2.4% 1.5% 1.1% 0.6% 1.2% 4.3%
2016Q3 3.8% 7.4% 3.2% 2.5% 1.8% 1.0% 0.4% 1.3% 4.6%
201604 4.5% 8.1% 5.3% 3.1% 1.6% 1.1% 0.5% 1.2% 5.6%
2017Q1 4.8% 9.2% 4.2% 3.3% 2.2% 1.6% 1.0% 1.8% 5.9%
2017Q2 3.5% 6.8% 3.1% 2.4% 1.7% 1.1% 1.0% 1.3% 4.3%
201703 4.0% 8.0% 3.3% 2.6% 1.7% 1.1% 0.9% 1.3% 5.0%
201704 4.7% 8.7% 5.7% 3.2% 1.6% 1.1% 0.8% 1.3% 5.9%
2018Q1 5.4% 10.5% 4.9% 3.6% 2.4% 1.5% 1.1% 1.8% 6.7%
2018Q2 4.2% 8.6% 3.6% 2.7% 1.8% 1.1% 0.7% 1.4% 5.3%
2018Q3 4.0% 7.8% 3.8% 2.7% 1.8% 1.1% 0.6% 1.3% 5.0%
201804 4.5% 8.3% 5.3% 3.3% 1.8% 1.0% 0.5% 1.3% 5.7%
2019Q1 4.8% 9.1% 4.3% 3.4% 2.3% 1.6% 1.0% 1.8% 5.9%
2019Q2 3.7% 7.2% 3.4% 2.6% 1.6% 1.1% 0.8% 1.3% 4.6%
201903 4.1% 8.1% 4.0% 2.9% 1.8% 1.1% 0.6% 1.4% 5.2%
201904 6.0% 11.3% 7.0% 4.6% 2.2% 1.5% 0.8% 1.7% 7.7%
2020Q1 6.5% 13.6% 6.0% 4.3% 2.7% 1.8% 1.1% 2.1% 8.4%
2020Q2 6.7% 13.2% 7.0% 4.9% 3.4% 2.4% 1.0% 2.7% 8.5%
202003 2.9% 6.0% 2.9% 2.0% 1.3% 0.9% 0.6% 1.0% 3.7%

Notes: The data are the universe of businesses with taxable sales in California in each quarter from the
California Department of Tax and Fee Administration (CDTFA). A closure is defined as a business that
does not show up with taxable sales the next four quarters. Small (large) businesses are defined as
those with average quarterly taxable sales annualized to less (more) than $500,000, which is calculated
over the previous four quarters.

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