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Finance and Economics Discussion Series
Divisions of Research & Statistics and Monetary Affairs
Federal Reserve Board, Washington, D.C.
Ten Days Late and Billions of Dollars Short: The Employment
Effects of Delays in Paycheck Protection Program Financing
Cynthia L. Doniger and Benjamin Kay
2021-003
Please cite this paper as:
Doniger, Cynthia L., and Benjamin Kay (2021). “Ten Days Late and Billions of Dollars
Short: The Employment Effects of Delays in Paycheck Protection Program Financing,”
Finance and Economics Discussion Series 2021-003. Washington: Board of Governors of the
Federal Reserve System, https://doi.org/10.17016/FEDS.2021.003.
NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary
materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth
are those of the authors and do not indicate concurrence by other members of the research staff or the
Board of Governors. References in publications to the Finance and Economics Discussion Series (other than
acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.
Ten Days Late and Billions of Dollars Short:
The Employment Effects of Delays in
Paycheck Protection Program Financing
Cynthia L. Doniger Benjamin Kay
Federal Reserve Board Federal Reserve Board
January 15, 2021—Latest draft on SSRN
Abstract
Delay in the provision of Paycheck Protection Program (PPP) loans due to in-
sufficient initial funding under the CARES Act substantially and persistently reduced
employment. Delayed loans increased job losses in May and persistently reduced recalls
throughout the summer. The magnitude and heterogeneity of effects suggest signifi-
cant barriers to obtaining external financing, particularly among small firms. Effects
are inequitably distributed: larger among the self-employed, less well paid, less well
educated and—importantly for the design of future programs—in very small firms.
Our estimates imply the PPP saved millions of jobs but larger initial funding could
have saved millions more, particularly if it had been directed toward the smallest firms.
About half of the jobs lost to insufficient PPP funding are lost in firms with fewer than
10 employees, despite such firms accounting for less than 20 percent of employment.
JEL Classification:
E24: Employment • Unemployment • Wages • Intergenerational Income Distri-
bution • Aggregate Human Capital • Aggregate Labor Productivity
H81: Governmental Loans • Loan Guarantees • Credits • Grants • Bailouts
J21: Labor Force and Employment, Size, and Structure
G32: Financing Policy • Financial Risk and Risk Management • Capital and
Ownership Structure • Value of Firms • Goodwill
Keywords:
Paycheck Protection Program, CARES Act, Countercyclical Fiscal Policy,
Covid-19, Kurzarbeit, Income Support, Small Business Lending, Small and
Medium Enterprises (SMEs), Financial Frictions
The views expressed here solely reflect those of the authors and not necessarily those of the Federal Reserve
Board, the Federal Reserve System as a whole, nor of anyone else associated with the Federal Reserve
System. We thank Aditya Aladangady, Byron Lutz, Brendan Price, David Ratner, Michael Strain, and
Missaka Warusawitharana for valuable comments, Jonathan Kay for his assistance with address mapping
with ArcGIS, and Arazi Lubis, Mathew Jacob, John Kadlick, and Elizabeth Duncan for their excellent
research assistance. We also thank participants in seminars at the Federal Reserve for helpful comments.
1 Introduction
In the United States, the economic devastation of caused by COVID-19 has hit smaller firms
particularly hard. Unlike larger firms with their better access to credit and substantial cash
reserves (Hankins and Petersen (2020)), smaller firms typically have less than a month of cash
reserves to insulate them (Farrell and Wheat (2016)). The shock of COVID-19, combined
with these limited financial reserves, imperils the jobs of the 60 million workers or 48 percent
of the workforce (SBA (2018)) employed at smaller firms. The CARES act1 provided fund-
ing for loans to small businesses under the Paycheck Protection Program (PPP) in response
to the COVID-19 pandemic and the economic havoc wreaked by non-pharmaceutical inter-
ventions to fight the pandemic. Traditional de facto and de jure American income support
programs like unemployment insurance, stimulus grants, disability insurance, and the Sup-
plemental Nutrition Assistance Program do not promote or preserve the employee-employer
relationship. In contrast, the PPP set aside hundreds of billions of dollars to keep employees
attached to their existing small business employers.
This paper asks three main questions. First, how effective was the PPP in preventing
job losses? Second, were jobs saved by the PPP long lasting or did they disappear when
the funding was exhausted? Third, were the effects equally distributed? These research
questions fit within a broad literature evaluating the efficacy of counter-cyclical fiscal policy.
In addition, the unique design, and design failures, of the PPP program allow our results to
speak to a broader literature on the employment effects of firm liquidity, particularly during
times of economic crisis and within very small businesses.
We make three contributions to answering these questions. First, we find that expedient
PPP loan issuance causes an economically large and a statistically robust improvement in
labor market outcomes, with particularly large effects for employees of small businesses.
These employment benefits extend through September, which is after the three months of
payroll support provided by the PPP loan are exhausted, suggesting the PPP promoted
lasting firm health. The effects of delayed PPP loans are inequitably distributed, with self-
employed workers and workers at the smallest firms particularly hard hit.
Second, the natural experiment we study illustrates the importance of cash on hand for
small businesses’ employment decisions. It is well documented that small businesses use
limited external financing (Vos (1992), Bitler et al. (2001), Mach et al. (2006)). However,
there is also evidence from surveys of small businesses (Vos et al. (2007)) that, under normal
conditions, 90 percent or more of such firms have sufficient external financing. Of course, the
period of the US COVID-19 epidemic is not normal conditions, and this paper contributes
1
The CARES act is also known as H. R. 748 and the Coronavirus Aid, Relief, and Economic Security Act
2
to a growing literature establishing that limited cash-on-hand and working capital lowers
firm labor demand and increases the sensitivity of labor demand to shocks (Vallée et al.
(mimeo), Barrot and Nanda (2020), Bacchetta et al. (2019), Mehrotra and Sergeyev (2020),
and Ghaly et al. (2017)).2
Third, we use these estimates to model the aggregate jobs lost by the ten day delay in
the PPP, the number of jobs saved by the PPP, and the cost per job saved by the PPP.
Our calculations imply that the PPP saved millions of jobs, and did so in a cost effective
way, particularly because of the temporarily elevated replacement rate of unemployment
insurance during the crisis. Further, our results suggest that a program focused on getting
funding quickly to the smallest firms would have saved jobs more cost effectively.
The PPP met unexpectedly high demand and exhausted the initial congressionally al-
located $349 billion in funding on April 16th , less than two weeks after opening. Congress
subsequently allocated an additional $321 billion in PPP funds, and the program resumed
making loans on April 27th . To measure the effects of the PPP on employment, we exploit
the ten-day delay between April 16th and 27th in the approval of PPP loans. We identify
the effect of delayed financing by examining the impact of loan timing in the window of
time from April 14th to 28th . In particular, we examine the labor market consequences of
delaying a PPP loan by ten days using a difference-in-difference with a dynamic treatment
effects estimation strategy. Our key identifying assumption is that borrowers approved on
the 16th and the 27th are identical (conditional on controls) and that delay does not alter loan
demand. To validate our assumptions, we document that while the characteristics of PPP
loan recipients vary dramatically over the course of the program these trends halt during the
ten days of loan delay around which we construct our event window.
Our data and identification strategy enable us to document heterogeneity along key
dimensions. In particular, we investigate the differential effects of the PPP by employer size
and self-employment as well as by education and earning ability.
A ten-day delay in funding PPP loans had substantial negative labor market conse-
quences. We find strong and persistent effects of loan delay from May through September.
For a core-based statistical area (CBSA)3 with 1 percentage point fewer delayed loans within
our event window, the unemployment rate is 10 basis points lower, the labor force partici-
pation rate is 5 basis points higher, and a measure of the nonemployment rate4 is 14 basis
points higher in May with effects remaining above 10 basis points through the summer.
2
Important additional papers in this literature include Chodorow-Reich (2014), Benmelech et al. (2011),
and Greenstone et al. (2020). Key theory papers include Wasmer and Weil (2004), Eckstein et al. (2019),
Gertler and Karadi (2015), and Petrosky-Nadeau and Wasmer (2013)
3
One or more adjacent counties anchored by a city-like area.
4
We construct a broader notion of labor force participation that includes nonparticipants who were
recently participating. In our view, this broader notion of nonemployment more accurately reflects changing
3
These point estimates imply that if the PPP funding under the CARES Act had been 10
percent larger, enabling the share of delayed loans in our event window to be 20 percent
lower, the unemployment rate would have been about 2 percent lower, non-participation
about 1 percent lower, and our measure of non-employment about 2.8 percent lower.
Delayed financing drives labor market divergence through a combination of differential
job-loss and job-finding rates. In the spring, job losses are relatively more important (like
Bacchetta et al. (2019)); however, over the summer persistent differences in job-finding,
primarily driven by the absence of recall from layoff, accumulate to become the dominant
driver of differentials (like Mehrotra and Sergeyev (2020)). These results are consistent
with conceiving of business owners as risk averse entrepreneurs with risky income deriving
from their small business and facing a cash-in-advance constraint that must be internally
financed. Delay in receiving PPP loans forced business owners to run down their buffer stock
of liquidity, driving them closer to the internally financed cash-on-hand constraint and thus
convexifying their value function (Carroll and Kimball (2001)). As a result, recipients of
delayed loans recover employment less quickly as they seek to rebuild their buffer-stock.
There are important heterogeneities in these effects. Employment effects for employees at
smaller firms and among the self-employed are larger, potentially because they have smaller
cash reserves (Farrell and Wheat (2016)) and poorer access to credit (Berger and Udell
(2002). These differences, which our identification strategy and data uniquely allow us to
observe, reconcile our estimated effects, which are large, with those of prominent competing
studies, which are small (Autor et al., 2020; Chetty et al., 2020). Indeed, our point estimates
suggest that nearly half of the jobs lost were lost due to insufficient funding under the CARES
Act and were in firms with fewer than 10 employees while such firms typically account for
less than 20 percent of employment.
Our analysis also reveals greater job loss among the self-employed than the employees of
private firms. We also find greater losses among those predicted to have low earnings and
those with less education. While these individuals’ unemployment insurance replacement
rates were most greatly increased by the CARES Act and subsequent federal interventions
in the unemployment insurance system, our findings regarding differential job loss for these
groups are robust to detailed controls for these variations.5
Our results are robust to alternative definitions of the event window. A narrower window
produces job losses over time that are more persistent and more back-loaded, while a slightly
labor market conditions during the COVID-19 shock because the nature of the shock induces larger than
typical flows out of the labor force.
5
Supplemental regressions, available upon request. show no causal evidence that PPP loan delay and
variation in the unemployment insurance system had any reinforcing or offsetting effects on the baseline
PPP effect.
4
wider event window produces nearly identical results as our preferred specification. We
also estimate an alternative, counter-factual event windows away from the 10-day delay.
Loan delay outside the window is likely correlated with unobserved attributes that predict
employment differentials before treatment. In particular, for alternative event windows we
observe non-trivial pre-trends, indicative of non-random selection.
In addition, our results are robust to a broad set of controls for industry, occupation,
demographics, non-pharmaceutical interventions (NPIs), other fiscal stimulus (unemploy-
ment insurance in particular), survey design, and the ongoing evolution of the COVID-19
pandemic itself.
Section 2 overviews the features of the PPP essential for our analysis. Section 3 discusses
the sources of our data and how we aggregate it. Section 4 details our identification strategy,
econometric specifications, and describes our model. Section 5 presents the results of our
empirical and modeling analysis. Section 6 shows the robustness of ours results to narrower
and wider windows around the ten day delay and shows the results in other problematic
windows. Section 7 explains how our paper fits in with the related PPP and Kurzarbeit
literature and lays out our thought experiment which estimates the total jobs saved and
costs per job saved of the PPP. We conclude with Section 8.
2 The Paycheck Protection Program
The PPP, created by the CARES Act, provided small businesses, small nonprofit and re-
ligious organizations, and sole-proprietors and independent contractors with loans to help
their employees during the extreme COVID-19 related economic difficulties they faced dur-
ing the spring and summer of 2020. Here we provide a brief summary of the parameters of
the program as they applied to the loans in our studied time-frame.6 As our study focuses
on the consequence of exhaustion of the PPP funding supplied by the CARES Act, we then
provide detail of the timing exhaustion and related events.
2.1 PPP Design
The PPP was run by the SBA in consultation with the US Department of the Treasury.
Borrowers had to meet a multi-part test to be eligible for a PPP loan. However, unlike for
virtually all other forms of credit, they were not tested for collateral or ability to repay and
the paperwork requirements were modest (estimated by the SBA at about two hours). Once
a borrower had their PPP application approved, their lender was supposed to send the funds
6
In Appendix A we provide significantly more detail about program as a whole.
5
to the borrower within 10 calendar days.7 The qualifying loan amount per employee was
2.5 times the average total monthly per-employee payments for payroll costs for the year
before the loan date (or, at the option of the borrower, for 2019) including up to $100,000 in
annualized cash compensation (wages, salaries, and cash tips) plus group insurance premiums
(including health care benefits). The PPP loans studied in this paper had a term of two
years at a cost of 1.0 percent per year interest, but were forgivable if they were used for
qualified expenses during a specified period. For the loans we focus on, borrowers were told
they had 8 weeks after fund distribution to spend the proceeds on qualified expenses and at
least 75 percent of funds had to be spent on qualified payroll expenses.
2.2 Exhaustion of the CARES Act Funding Allocation
Table 1 presents a timeline of the major events in the first few weeks of the PPP. The CARES
Act was signed into law on March 27, 2020, and appropriated $349 billion in PPP loans.
The first PPP loans were approved on April 3rd , 2020, and it was almost immediately clear
that these funds were insufficient to meet PPP loan demand, with major political figures
discussing in the business press that additional appropriations were needed. Press accounts
of the early days of the PPP highlight inequities and start up pains as banks introduce PPP
offerings: some banks are slow participate, with many prioritizing their existing customers
and larger firms. PPP loan volumes were high but could have been much higher. Capacity
constraints seemed binding and many ready, qualified, and willing borrowers could not get
a loan. These early approved loans were unusually large, to unusually large firms, from
unusually small banks. Figure 4, Panel E shows that the average loan in the first week was
for about three times the amount and saved three times the jobs of typical loans throughout
the program. The ten largest US commercial banks serviced one-fourth of the PPP loans
over the life of the program but only 8 percent in the first week.
By the second week in April, it was clear that the program was overwhelmed. PPP loan
applications poured into the banks, and banks responded by continuing to prioritize larger
and, especially, existing customers. Banks were also directed to do so by guidance from the
Treasury and the SBA. Funding was being depleted rapidly (see Figure 1). Congress was
working on a second round of PPP appropriations, and it looked likely that it would happen
but it was not clear when the additional appropriations would become available.
On April 16th , the CARES Act PPP funding was depleted. Congressional discussions of
7
This timing changed somewhat over the program. Initial program documentation said that lenders had
ten days to make their distributions, but was unclear on what this timing directive actually required and
when the clock started ticking. Eventually the Treasury and the SBA issued a rule that for loans approved
on or before April 28th , 2020, lenders had 10 calendar days from April 28th , 2020 to fund the loan. For loans
after that point, the loan had to fund in 10 calendar days.
6
a second PPP appropriation were ongoing, resulting in a Senate bill on April 21st , a House
bill on April 23rd , and the President signing the PPP and Health Care Enhancement Act
of 2020 (PPP Act) into law on April 24th . The PPP Act appropriated an additional $321
billion (for a total of $670 billion) for PPP loans, and banks began issuing additional PPP
loans on April 27th . Loan demand was strong in the initial two weeks of the PPP Act funding
before becoming subdued through the remainder of the summer, as can be seen in Figure 1.
Importantly, strong trends in borrowers’ observables abated in the period around the ten
days where there were no PPP loans. These trends are illustrated in Figure 4 and discussed
in greater detail in Section 4. In addition, while there clearly is discernible geographic
variation in areas that received relatively more loans early on from the CARES Act funding
and that received funds later from the PPP Act funding (Figure 6), the variation in earlier
and later loans within the narrow window surrounding the exhaustion of CARES Act funding
and resumption of lending under PPP Act funding has no clear geographic concentrations
(Figure 3).
3 Data
This section describes our main data sources: the SBA’s PPP Loan Level Data and the Bu-
reau of Labor Statistics’ (BLS) Current Population Survey (CPS). In Appendix B we provide
information about auxiliary data sources utilized as controls and in robustness checks.
3.1 SBA PPP Loan Level Data
The U.S. Department of the Treasury and the Small Business Administration (SBA) pro-
vide comprehensive loan-level data on PPP loans in all states, major territories (American
Samoa, Virgin Islands, Guam, and Puerto Rico), and the District of Columbia (U.S. Trea-
sury (2020)). In the August release of PPP data we use, for loans for amounts less than
$150,000, the data include the loan amount, city, state, ZIP Code, NAICS code (industry),
business type (incorporation type or non-profit status), number of jobs retained, approval
data, and lender (bank).8 For the larger loans ($150,000 to $10 million), the exact loan
amounts are replaced with loan ranges (specifically $5-10 million, $2-5 million, $1-2 million,
$350,000-1 million, $150,000-350,000), which we map to the middle of their ranges (e.g., we
treat a loan coded as $1-2 million we treat as a loan for $1.5 million).
We aggregate loan activity by date and the county or CBSA (in general, county and
8
The loan data have additional fields devoted to borrower race, gender, and veteran status, but these
fields are overwhelmingly unanswered, missing for more than 90 percent of loans.
7
CBSA results are very similar but a CBSA better corresponds to a labor market and is our
preferred level of aggregation). Neither county nor CBSA are fields in the PPP data. We
use the HUD ZIP to FIPS and HUD ZIP to CBSA cross-walks respectively, to map each
loan to a county and a CBSA (HUD (2020)). In some cases, the geographic areas of a ZIP
Code is not contained entirely within a county (about 28 percent of ZIP Codes are in more
than one county). More rarely, the geographic area of a ZIP Code is not contained entirely
within a CBSA (about 17 percent of ZIP Codes are in more than one CBSA). In these cases,
we map the ZIP Code to the county and CBSA that contains the plurality of the businesses
of that ZIP Code.
We want to minimize the measurement error introduced by these cross-walks. For the
majority of the loans, we cannot check the resulting mapping. However, the larger loans
(≥ $150, 000) also report a street address in addition to State, City, and ZIP Code. Using
ARC GIS (computerized mapping software), we map each one of these complete addresses
(street information, state, city, and ZIP Code) to a county. In a comparison of the county
mapped by our HUD cross-walk with the plurality of businesses approach to the ARC GIS
approach, the results agree for more than 98 percent of these large loans, which gives us
confidence that we are correctly attributing PPP loans to the county (and therefore CBSA)
in which the borrowing firm operates.
Table 2 reports basic summary statistics about the number of PPP loans, broken down
by loan size. The PPP funded almost 4.9 million loans, of which 4.2 million were for less
than $150,000. However, the roughly 650,000 (14 percent) PPP loans for $150,000 or more
were awarded 74 percent of the funds. The distribution of PPP loans was very broad. Using
the 2018 Census County Business Patterns to estimate the number if establishments, we find
that in a typical county about 60 percent of small businesses received a PPP loan (Bureau
(2020a)). To avoid selection issues induced by firm and managerial quality (much more on
this in Section 4), we focus on the PPP loans issued from April 14th and 28th 2020 (the
event window), about a third of the total. About 44 percent of the loans in this event
window were late (approved on April 27th or 28th with PPP Act funding). Table 3 shows
similar statistics of overall and late loan activity for the 20 most active CBSAs in the U.S.
Treasury (2020) data. Even among large metropolitan areas there is considerable variation,
with Miami having 48 percent of loans late in the window but Philadelphia having only 35
percent of loans late in the window. Across all CBSAs the variation is even larger, with a
standard deviation of late loan share of about 10 percent.
8
3.2 Current Population Survey
At the core of our analysis are the labor market outcomes for American workers, which we
measure using microdata from the BLS’s CPS data from 2019 to 2020. As of the time of
writing the most recent available microdata cover through September 2020. The CPS is a
monthly rotating panel of approximately 65,000 U.S. households. Perhaps the most notable
use of CPS micro-data is that the BLS use it calculate key official statistics, including the
unemployment rate.
The CPS is designed as short rotating panel with a core monthly survey administered to
each household each wave. Each household is surveyed monthly for four consecutive months
and then re-surveyed in the same four months one year later. Each monthly survey contains
a core set of detailed questions about labor market activities during the week containing the
12th day of the month. These questions are used to classify each adult non-institutionalized,
civilian into employed, unemployed, or not participating in the labor force. Linking the
survey across waves enables us to observe whether an individual transits between these
states.
In addition to the variables needed to construct the national unemployment rate and other
aggregates, the core survey questions contain demographic and job characteristics such as
industry, occupation, and class of worker (e.g. self-employed, private sector employee, public
sector employee). In addition, each March, respondents report the size of their employer,
inclusive of all of its establishments. Using the panel structure, we can link each response in
December through June to the respondents employer size in the nearest March.
Our empirical strategy, coupled with these data, allows us to identify the effects of the
PPP on key sub-populations, namely employees of small firms and the self-employed that
are either unmeasurable or unobserved or both in key complementary studies (Autor et al.,
2020; Chetty et al., 2020).
Additional details of the data, which we utilize in robustness checks, are discussed in
Appendix B and include features related to unemployment insurance and the survey instru-
ment’s sensitivity to the Covid-19 outbreak.
3.3 Geographic Merge
Due to insufficient sample size, the CPS cannot be used to construct aggregate statistics
at geographies smaller than a state. In addition, geographic information is suppressed in
the micro-data when geographic identification would violate confidentiality requirements. In
practice, this means that we can identify survey respondents’ county of residence for 281
counties. Just over 40 percent of the sampled population live in these counties. Grouping
9
counties into CBSAs improves geographic coverage since reidentification risk is reduced by
pooling counties within a CBSA and thus a larger fraction of the sample resides in an
identified CBSA. We can identify 258 CBSA in which 75 percent of the sampled population
live. Our primary analysis matches loans in the SBA, NPI, and case-count data to individuals
in the CPS according to CBSA.9 We match SBA loan data, to individuals residing outside
of identified CBSAs according to their state excluding the identified areas, with a distinction
between the rural and urban regions of the state.10
4 Identifying the Effects of Loan Delay
We identify the effects of a delay in PPP funding on employment of current and recent
employees of small firms. To measure the effects of PPP on employment, we exploit a ten
day delay between April 16th and 27th in the approval of PPP loans caused by the exhaustion
and replenishment of PPP funds. See Table 1 for an overview of the key events of the PPP
as reported in the Wall Street Journal. The program met unexpectedly high demand and
exhausted the initial appropriation of $349 billion on April 16th , less than two weeks after
opening, but Congress subsequently appropriated an additional $320 billion in PPP funds,
and the program resumed making loans on April 27th . In our view, due to bank processing
constraints and other complications of participating in a novel program, firms did not have
knowledge of the precise timing of the approval of their PPP loans. Whether a firm received
an early or late PPP loan mostly reflected chance rather than selection (conditional on
appropriate controls), allowing us to interpret the situation as a natural experiment on the
effects of loan delay during an economic calamity.
Identification requires two assumptions:
Assumption 1. Delay in loan timing from the 16th to the 27th is independent of pre-existing
firm attributes.
9
Our general results are very similar when we repeat our analysis based on a county-level match. The
county-level match affords greater geographic precision at the expense of sample size and a greater skew
toward counties with greater population density. In addition, in the context of our paper, greater geographic
precision is not necessarily an advantage. Even if the county of residence of a respondent is disclosed in
the CPS, the respondents relevant labor market is likely comprised of their county of residence and counties
adjacent to or near where they reside. We view the CBSA as more appropriately capturing the relevant
labor market.
10
For example, Montana contains seven CBSAs but only a single identified CBSA in the CPS: Billings.
First, we match outcomes for Billings CBSA residents in the CPS to PPP loans issued to firms in the
Billings CBSA. Next, we match agglomerated outcomes for residents of the other six CBSAs in the CPS to
agglomerated PPP loans across those six CBSAs. Finally, we match agglomerated outcomes for all other
Montana residents (the rural ones) in the CPS to agglomerated loans to firms outside of the CBSAs.
10
More viable, productive, or better-run firms must not be more likely to get loans just
before the April 16th exhaustion of initial PPP funds than other firms. If this assumption
were badly violated, it would compromise our identification by conflating these selection
effects with the effects of delay.
Figure 4 plots the characteristics of the average CBSA receiving a PPP loan (CBSA
characteristics weighted by share of loan dollars in that CBSA) by day from the advent of
the PPP program until several weeks after our event window. These plots show clear trends
in the characteristics of CBSA’ receiving loans over time. In particular, in the early days of
the PPP, the places that received more loans had higher banking density and concentration.
These places contained relatively more White people and were more likely to be rural.
While there is a distinct time trend in these observables, the trend temporarily stabilizes
across the 10-day gap of interest in our event window. This finding suggests that, while
time trends exist over a longer horizon, our results are robust to spurious covariation with
these observables. We conduct additional robustness checks of this assumption in Section 6.
Meanwhile, as we discuss in Section 7, these trends cast doubt on the identification strategy
of Granja et al. (2020).
Assumption 2. Loan demand is not a function of loan timing.
We investigate robustness to this second assumption in Section 6.
Whether a firm gets early or late PPP funding (or none at all) is a firm-level characteris-
tic. Ideally, we would have firm-level labor market outcomes for PPP-eligible firms matched
to firm-level loan information (particularly if they received early or late PPP loans). Un-
fortunately, this is not possible in our data. The CPS does not capture whether individual
firms utilized PPP loans, nor their timing if they did. To work around this data limita-
tion, our analysis is at the CBSA level instead of firm level. We explore how the share of
early loans across CBSAs drives CBSA labor market outcomes. To work at the aggregated
level, we must further assume that loan delay is independently assigned after controlling
for CBSA characteristics, In various specifications, we control for state-month fixed effects
(capturing a variety of state level policies including most of the variation in NPIs), indus-
trial mix, occupation mix, COVID-19 cases and deaths, county level NPIs, unemployment
insurance replacement rates, and the CPS interview type. Our results are robust to all of
these controls.
The last day of loan-making under the initial appropriation was April 16th and the first
day of loan issuance under the second appropriation was April 27th . Our preferred specifica-
tion uses a measure of loan delay, share delayed, as the share of PPP loans issued to a CBSA
in the window of April 14th to 28th on April 27th or 28th , which includes the last three days
11
of the original appropriation and the first two days of the second appropriation. Figure 1
depicts the cumulative loan issuance over the program’s life and the uptake during our event
window. Our window is not symmetric because the data suggest that funding was exhausted
mid-day on April 16th , and thus we use the last two full days under the first appropriation
and the first two full days under the second appropriation. In a classic bias-variance trade-
off, the narrower the window, the more plausible that our results are uncontaminated by
selection effects. However, the wider the window, the more precise our estimates are and the
less measurement error we have in our proxy for financing delay at the county and CBSA
level. Our preferred specification matches nicely with the information readily available to
small business owners at the time, and largely reflects loans applied for before it became
obvious that loan funds would be quickly exhausted. Table 1 shows an overview of the key
PPP events and what potential PPP borrowers would have known about the program from
reading the Wall Street Journal at the time. For those interested in who knew precisely
what and when they knew it, Appendix D has a detailed timeline of news related to PPP
and associated sources.
In actuality, loan issuance spiked in the three days concluding with April 16th and in the
two days beginning with April 27th . Figure 2 also shows that there was a large spike in loan
demand around the April 17th -26th dates with no loans, and that loan issuance spike lasted
longer than just one day on each side. This figure also shows that loan issuance on April
16th fell short of that on the 14th and 15th , indicating that funds were exhausted mid-day. In
sensitivity analysis in Table 11, we find similar results for alternative event windows.
Equation 1 shows our general linear probability model specification:
1
X
I[employed]i,t = α + 0[βm I[month = m, year = 2020]{share delayedc }+
m=1
I[month = m, year = 2020] × controlsi,c,t × Γm ]+
12
(1)
X
[I[month = µ]controlsi,c,t × Ψµ ]+
µ=1
F Ei,t + εi,c,t
where controls contains an indicator for census region; urban, suburban, rural; interview
type; and a month fixed effect. Note that because the CPS is a location-based survey, c and
i are co-linear and, as such, controlling for the individual fixed effect de facto controls for the
CBSA fixed effect. We choose a linear probability model because our specification contains a
12
large number of fixed effects.11 Results are robust to adding additional fixed effects for two-
digit NAICS industry and broad occupations, and state-by-time fixed effects; see Appendix
C. We view state-by-time fixed effects as a flexible functional form that captures the effects
of NPIs, public policy, and to some extent the effects of density, weather and climate, and
use of public transportation. Our preferred specification controls for direct measures of these
covariates, which, being more parsimonious, exhausts fewer degrees of freedom.
The βm (monthly sensitivity of the probability of employment to share delayed) are the
coefficients of interest. January 2020 is m = 0 and October 2020 is m = 9. Given the timing
of the CPS reference week, April 2020 (m = 4) is a pre-event window observation. The
coefficients β5 − β9 trace out the evolving effect of loan delay through the summer and fall
of 2020 while β1 − β3 test for the presence of a pre-trend. We view β4 as comprising part of
the pre-trend, but we refrain from interpreting this coefficient because of the coincidence of
the CPS reference week and the exhaustion of the initial PPP funding allocation.
This flexible specification allows us to test for heterogeneous effects by interacting
share delayed or (sharedelayed)×(month F E) with firm, worker, and neighborhood covari-
ates (e.g., race). In addition to using Equation 1 to measure employment sensitivities to loan
delay, we can use the same specification to estimate the effect on labor force participation
and flows into and out employment by replacing the left hand side variable with an indicator
for participation, employment to non-employment transition, and so on.
5 Results
5.1 Employment
We find that a 1 percentage point increase in loan delay increased unemployment by approx-
imately 10 basis points in May (Table 5). The effect gradually fades through the summer.
These effects are illustrated in Figure 5, Panel A.I, which plots coefficients β0 − β9 and their
90, 95, and 99 percent confidence intervals from a model in which the only control is an
indicator for the month interacted with the year 2020. Panel B.I plots the coefficients on
the month × 2020 fixed effects along with a 95 percent confidence interval, tracing out the
baseline COVID-19 shock. These coefficients are the model implied monthly unemployment
11
Non-linear models such as probit and logit models may produce spurious results in a model with a large
number of fixed effects, in particular, when the number of observations identifying a particular configuration
of fixed effects is small.
13
rates for a CBSA with the average percentage of delayed loans of 42. The baseline COVID-19
shock hits in April and the delayed PPP shock hits in May, as expected.12
Changes in the unemployment rate do not capture the full employment effects of the
COVID-19 shock because that shock also triggered flows from employment to out of the
labor force at a rate unprecedented in past recessions. To present a fuller picture of the
true employment effects, we expand our sample to include those workers recently observed
in employment or unemployment.13 Panels A.II and B.II trace out the effect of delayed PPP
financing and the COVID-19 shock on the labor force participation rate of the expanded
sample. Both shocks increase non-participation with the COVID-19 shock’s effect leading
the PPP delay effect by about one month. Panels A.III and B.III trace out the effect of
delayed PPP financing and the COVID-19 shock on the nonemployment rate of the expanded
sample of potential workers. As anticipated given heavy flows from employment to out of
the labor force, the effects on nonemployment in May 2020 are about a third larger than the
effects solely on unemployment.
A thought experiment translates these regression coefficients into macroeconomic aggre-
gates. Increasing the funding allocation under the CARES Act by 10 percent—an increase in
the funding allocation in the CARES Act of about $35 billion—would have enabled reducing
the delay in our event window by roughly 20 percent in all locations. This change would
have boosted employment by nearly 2.8 million jobs. Further, this money would then not
have needed to be allocated in the PPP Act.14 Our results imply that these effects persisted
long after the PPP funds were spent and through the end of our observation in the fall. As
noted in Section 6, these figures are robust to the width of the event window.
These effects are enormous. To understand these estimates, it is helpful to relate them to
the recent and closely related contribution of Barrot and Nanda (2020). Barrot and Nanda
(2020) find a 5.7 percent increase in employment for a payment of 100 percent of payroll 15
days earlier. This figure rises to roughly 7.2 percent in a “slack” labor market.15 We find
approximately a 14 percentage point drop in nonemployment that, given the nonemployment
12
Recall that the CPS survey is on or near the 12th of the month, so the workers at firms getting PPP
loans in our window (both early and late) did not have their funds when their employees were surveyed
in April. Similarly, most firms did not experience the serious March COVID-19 shock before March 12th.
Thus, in our data, the COVID-19 shock hits in April 2020 and the PPP loan delay shock hits in May.
13
Specifically, we include all respondents who reported employment or unemployment in any of the po-
tential seven CPS interviews before the month in question. Hall and Kudlyak (2019) find that past labor
market experience is a better predictor of job finding than any standard BLS notion of marginally attached,
and therefore we use this more accurate measure.
14
For additional context, the average CBSA has a 44 percent share delayed with a standard deviation of
8 percent.
15
Barrot and Nanda (2020) provide no information about the degree of slackness that they call “slack” or
the semi-elasticity of their estimate to slackness, but it is safe to assume that May through October of 2020
is “slack” by any metric.
14
rate of 21 percent in April of 2020, implies approximately a 17 percent increase in employment
for a payment of 250 percent of payroll ten days earlier. Meanwhile, assuming, valiantly,
that everything is linear the Barrot and Nanda (2020) numbers suggest a 12.16 percentage
point increase in employment for a payment of a payment of 250 percent ten days earlier in a
“slack” labor market. This result places our estimated employment effect only slightly above
theirs and, referring to Barrot and Nanda (2020) Table V, suggests an external financing cost
of approximately 50 percent in spring 2020. This amount is not above the external financing
costs implied by the estimates of Barrot and Nanda (2020)—which they argue pertain in
non-crisis times—in an economically significant way.
Our estimates reflect the effect of earlier spending but not greater spending, which com-
plicates the comparison with other papers that focus on the aggregate effects of spending
regardless of timing. For example, most assessments of the American Recovery and Reinvest-
ment Act (ARRA) also find large employment effects, but for much greater funding outlays.
Wilson (2012) finds that the entire the $262 billion in ARRA government spending created
2.1 million jobs, or a reduction in the unemployment rate of 1.4 percentage points. Feyrer
and Sacerdote (2011) find that the $85 billion in local ARRA spending reduced unemploy-
ment by 0.4 percentage points. Chodorow-Reich et al. (2012) found that the $88 billion
Medicaid grant as part of the ARRA lowered the national unemployment rate by about 2.1
percentage points.
Tables 5 shows that the unemployment and nonemployment effect estimates with controls
for industry fixed effects; occupational exposure to the COVID-19 shock; new COVID-19
cases and deaths in each CBSA each month; controls for variation in survey non-response due
to inability to conduct in-person interviews in spring and early summer; the Oxford index
for the severity of COVID-19 state level non-pharmaceutical interventions; and controls for
the state-level timing of federally mandated changes to unemployment insurance interacted
with workers’ eligibility for the programs. The effects depicted in A.I and A.III are robust.
Columns (1) and (3) present the coefficients plotted in Figure 5, while columns (2) and
(4) present the coefficients in the model with controls. Appendix C shows that the results
are robust to inclusion of various subsets of these controls; controlling for occupation fixed
effects rather than occupational exposure to COVID-19; and controlling for state-by-month
fixed effects in lieu of measures of state-level NPIs, local outbreak severity, and state-level
variation in implementation of federal changes to unemployment insurance.
These jobs gains, which stem solely from the timing of PPP loan allocation, are large
relative to the largest alternative estimate of the efficacy of the entire PPP program (Autor
et al. (2020)). At first glance this result seems shocking; however, it is easily reconciled by the
heterogeneity in effects. Estimated marginal effects for small firms and the self-employed far
15
exceed those for firms near the 500 employee cutoff: the variation that underlies the Autor
et al. (2020) identification strategy. Indeed, our point estimates suggest that more than half
of the jobs lost were lost due to insufficient funding under the CARES Act were in firms
with fewer than 10 employees. In normal times, such firms account for less than 20 percent
of employment.
5.2 Job Flows
The nonemployment effects of the PPP loan delay are driven by excess layoffs and labor
market exit in May, combined with depressed recalls through the summer and early fall. Ta-
ble 6 quantifies these results. Column (1) and (5) record the effects of loan delay each month
on outflows to unemployment and inflows to employment. Columns (2)-(4) dis-aggregate
the outflows into outflows to job seeking (excluding layoff), labeled “Separation”; to non-
participation, labeled “Exits”; and to layoff. Layoffs are the primary driver of outflows,
with exit a close second. Columns (6)-(8) dis-aggregate the inflows into inflows from job
seeking (excluding layoff), labeled ”Accessions”; from non-participation, labeled ”Reentry”;
and from layoff, labeled ”Recalls.” The absence of recalls is the primary driver of depressed
inflows. Note, due to the identification of flows based on information collected from non-
employed individuals, outflows are roughly the sum of constituent parts while inflows are
the weighted average.
On average, outflows from employment are 4 percent and inflows to employment are 40
percent (Shimer, 2012). Volatility around these figures is 0.4 and 5 percent, respectively.
Outflows and inflows reached their pre-COVID-19 peak and trough of 5.8 and 22 percent
during the Great Recession.16 Compared with these historical figures, our marginal effects,
which imply a 0.95 percent increase in job loss and a 2.15 percent decrease in job finding for
a 4 percent increase in the CARES Act funding allocation, are very large.
The shock to outflows from employment, while dramatic, is temporary. In contrast, the
shock to inflows to employment is sustained (the coefficients through September are jointly
statistically significant at the 1 percent level). Further, the May outflow from unemployment
coefficient—coupled with the fraction of individuals in employment, unemployment, and
nonemployemnt at the onset of the pandemic—implies that the May non-employment effects
documented in the preceding subsection are accounted for by excess job-losses. However,
as documented in Shimer (2012) and elsewhere, job finding in the United States is rapid
and, as a result, spikes in job loss do not result in persistently elevated unemployment.
Historically, elevated unemployment stems in large part from depressed rates of job finding.
16
Note, all numbers quoted here are based on the quarterly averaged series to avoid excessive volatility.
16
We find a similar pattern in the response to the PPP funding delay shock. By August the
nonemployment effects we observe are nearly fully accounted for by the accumulated effects
of depressed job finding rates over the summer months.
5.3 Firm Heterogeneity
Effects of PPP loan delay are heterogeneous with respect to employer type and size. Table 7
reports effects by the self-employed and the employees of private companies.17 As a robust-
ness check, it also reports the effects on employees of local, state, or federal governments,
which are ineligible for PPP loans. The self-employed are highly sensitive to the timing
of financing. These workers experience the most dramatic non-employment immediately
following PPP loan delay, recover during the eight-week term of employment specified for
loan forgiveness, and then fall into non-employment again. By comparison, employees of
private firms experience smaller but more persistent increases in non-employment. Statisti-
cally and economically insignificant coefficients for government employees serve to validate
our empirical strategy, as these employees are ineligible for the program.
Table 8 reports effects by size for the months in which employer size can be identified
using the CPS.18 This analysis reveals that the aggregate effect is driven almost exclusively
by the effect of PPP loan delay on the smallest firms. Of the 2.8 million jobs we estimate
could have been saved by increasing the funding allocation in the CARES Act by 10 percent,
1.6 million—more than half—would have been in firms with fewer than 10 employees. For
context, firms with fewer than 10 employees account for roughly 20 percent of employment
in normal times.
5.4 Worker Heterogeneity
The rich worker characteristic data in the CPS enables us to explore the effect of PPP loan
delay on different kinds of workers. Table 9, Columns I - III show that workers in the lowest
two terciles of predicted wages suffered the most from PPP loan delay, even after controlling
17
Note, we classify incorporated self-employed as self-employed rather than employees of private companies,
due to their treatment as self-employed under the PPP forgiveness criteria. This is contrary to the usual
BLS convention.
18
As noted in Section 3.2 employer size is reported by CPS respondents who were surveyed in March. This
rotating design of the CPS panel means that we are able to identify employer size as of the most recent March
for nearly all respondents in March, at most 3/4ths of respondents in April and February; 1/2 of the sample
in May and January; and 1/4th of the sample in December and June. In practice the month-to-month record
linkage is closer to 70 percent (Nekarda, 2009). Thus, the precision of our coefficients is not equal across
months, and June sample sizes in particular were the smallest and therefore the estimates are weakest.
17
for occupational exposure to the COVID-19 shock.19 Controls for occupational exposure are
important because essential workers are paid less than non-essential workers (McNicholas
and Poydock (2020)) and because essential workers were less likely to work for firms that
ceased operating because of the COVID-19 shock. Columns IV - VI show the employment
effects by worker education. The effects of loan delay also hit less-educated workers harder,
again controlling for occupational exposure to the COVID-19 shock. These controls are
important since less-educated workers make up nearly 70 percent of the essential workforce
(McNicholas and Poydock (2020)).20
This finding provides a counterpoint to our results on employment flows. While the
employment flows point to longer-term scarring effects of the delay in PPP funding as en-
trepreneurs rebuild buffers stocks, these results show that the burden of displacement has
fallen on workers for whom the long term costs of displacement are smallest.21
6 Robustness
6.1 Event Window
Figure 3 shows that the window around April 14th to 28th helps minimize the confounding
spatial variation in our data.22 Figure 3 shows there is a high degree of variation in the
timing of PPP loans within this window between adjacent counties such that there are very
few clusters or other obvious geographic patterns. Other periods of PPP loan issuance
suffer from pronounced geographic clustering. Figure 6 shows that there is a strong spatial
correlation to the share of loans made to a county from the second pool of PPP funding
(Panel A) and the second half of an arbitrary window in late April and early May (Panel
B). These spatial correlations are part of a general pattern (though not during our window)
where obtaining earlier loans is correlated with urbanity and geographic region (along with
firm size and sophistication) in likely spurious ways.
Table 10 contrasts our baseline unemployment results with the results of estimating them
on the problematic alternative windows shown in Figures 3. In contrast with our instrument,
19
We predict wages based on an augmented Mincer (1974) regression augmented to include industry,
occupation, race/ethnicity, and sex fixed effects. We prefer terciles of imputed wages because they are
available for all respondents.
20
In supplemental regressions, not reported but available upon request, we find economically important but
statistically insignificant evidence that ability to work from home and employment in an essential occupation
insulated workers from the effects of PPP delay, at least in the short run.
21
Doniger (2019) finds that more highly educated workers experience more persistent negative wage effects
from displacement during a recession.
22
Figure 4 shows that it also helps control for a number of loan, borrower, public policy, and firm charac-
teristics that vary over the life of the program.
18
these correlations present spurious pre-trends (particularly for the case of the late April and
early May window) that likely reflect differences in the quality of loan applicants.
However, our results are robust to making the event window narrower or wider (subject to
the usual unknown bias-variance trade-off of varying the window width). Table 11 contrasts
our baseline unemployment results with the results of estimating them on these wider and
narrower windows around April 17th to 26th . The baseline specification coefficients are
economically similar and statistically indistinguishable from those estimated on the two
alternative windows. Unsurprisingly, estimates of the jobs that would have been saved via
a 10 percent increase in the initial allocation of PPP funding under the CARES Act are
very similar across the three windows. Our preferred specification suggests jobs savings on
the order of 3.5 million persisting through September. Alternative windows suggest nearly
identical values. While the more broadly defined window suggests mildly lesser employment
effects through the summer than our baseline the more narrowly defined window suggests
employment effects more than 1.5 times as large.
6.2 Adjusting for Firm Failure
Our baseline results assume that loan demand is invariant to PPP loan delay. However, firms
that missed the first tranche of PPP funding may have failed or found alternative financing
on less favorable terms. If so, the delay from the exhaustion of PPP funds would reduce
PPP loan demand in the second round and bias our estimate of the share of loans delayed.
Specifically, we measure:
L
Share Delayed = (2)
E+L
where L is the volume of late window PPP loans on April 27th -28th , E is the volume of early
window PPP loans on April 14th -16th , and E + L is the volume of PPP loans in the April
14th -28th window. However, allowing for failure and alternative financing, the share of loan
demand that existed during April 14th -16th and was not met is
L+M
U nmet Demand = (3)
E+L+M
where M is the missing loan demand from firms that either fail or find alternative financing
by April 27th and consequently never seek a PPP loan. Next we make assumptions to
adjust for this unobserved shifting PPP loan demand to recover the true effect of insufficient
funding under the CARES Act on employment and isolate the true effect of a 10-day delay
in financing on survivors.
19
Assumption 3. Only a fraction S of loan demand not met by April 16th eventually seeks a
PPP loan.
With this assumption we can write
L = (1 − E)S and M = (1 − E)(1 − S). (4)
So we have:
(1 − E)S
Share Delayed =
E + (1 − E)S
and solving for (1 − E) gives
Share Delayed
U nmet Demand = 1 − E = .
Share Delayed(1 − S) + S
Now, the linear probability model
I[not employed]i,c,t =α+
8
X
[βm I[month = m, year = 2020]{U nmet Demandc } +
m=0
I[month = m, year = 2020] × controlsi,c,t × Γm ] + (5)
12
X
[I[month = µ]controlsi,c,t XΨµ ] +
µ=0
F E + εi,c,t ,
recovers the effect of loan delay on the nonemployment rate, accounting for firm failure.
In simulations (not shown), we found that if enough firms fail as a result of the delay
of PPP loans (and never get PPP loans or get them outside of our event window), this
outcome can meaningfully bias our estimates even if all other identification assumptions are
met. However, empirically, not enough US firms have failed to make this bias a problem.
Bartik et al. (2020a) estimate that 100,000 US firms failed during this period.23 Bialik and
Gole (2020) estimate essentially the same figure, with 97,966 permanently closed during this
period. This number might not even be unusually high: the SBA estimates that about 45,000
small firms close monthly under normal conditions (Marks (2020)). But, even assuming these
23
They find that 100 of their surveyed 4,969 firms is permanently closed, which is equivalent to about
100,000 of the 4.6 million small businesses they sample from.
20
failures are entirely attributable to the COVID-19 shock, the effects on our estimates are very
small. Circa 2017, the Census estimates that there are six million US firms (Bureau (2020b)).
If all firm failures and permanent closures were caused by the PPP running out of funding,
these figures imply that 1.7 percent of firms permanently closed as a result of the delay
and never received PPP funding.24 A 2 percent bias is well below the threshold at which
our estimates are biased in any meaningful way. Further, using county bankruptcy data
obtained from RAND State Statistics (2020), we test if county-level loan delay is associated
with changes in the fraction of businesses filings for bankruptcy in federal courts. We find
no statistically or economically significant relationship between share delayed and increased
incidence of any chapter of business bankruptcy.
7 Discussion
Here we provide additional context for the results in Section 5. First, we compare our
employment effects of PPP loan delay to other papers studying the efficacy of the PPP
using other metrics and identification strategies. Second, we compare our findings with
estimates of the benefits of the long established Kurzarbeit job programs in Germany. From
all counter-cyclical jobs support programs in developed countries, the Kurzarbeit is the most
similar in design to the PPP. Third, we use our estimates of the effect of a ten day delay
to roughly estimate of the aggregate job effects of the PPP program. Fourth, we use our
aggregate PPP job savings to estimate the cost per job saved of the PPP program and
compare it with the stimulus literature.
7.1 Comparison with Other Studies
When studying the labor market consequences of the PPP, other authors have found a variety
of effects and effect sizes, which vary considerably in their magnitudes and timing. On the
lower end, Chetty et al. (2020) find almost no employment effect for the program (albeit with
large standard errors). Granja et al. (2020) find no employment effects in April and very
small effects in May and June, on the order of 0.8 percent to 1.3 percent of hours worked,
24
We estimate the bias in our coefficients resulting from unobserved firm failure. We find that bias
becomes a serious problem (coefficients off by more than 10 percent) only when the probability of firm
failure conditional on loan delay is about 50 percent. Even if 100 percent of the firm failures were caused by
loan delay (that is all failed firms were PPP eligible and would have been in our event window without failure),
and these failures were entirely in the smallest firms where we find the largest effects, these circumstances
would only allow for a 13 percent failure rate. In our $150,000 or smaller loan category, there are 1.4 million
in the event window, with 46 percent late or 644,000 late loans. If 100,000 firms fail and thus have no loan
demand, the conditional failure probability is 100 / (100 + 644) or 13 percent. In our simulations, this
calculation biases coefficients by less than 2 percent.
21
which is roughly equivalent to one to two million full time jobs. On the higher end, Autor
et al. (2020) find the PPP saved 1.4 million to 3.2 million jobs—using data through June—by
which point 98 percent of PPP loans had been issued. To put these figures into context,
PPP loan participants said that the loans would support about 51 million jobs in aggregate
and SBA (2018) estimates that about 59 million workers work at at firms with 500 or fewer
employees. These papers use different data and vary in their econometric specifications and
controls, and this contributes to differences between their estimates and ours.
Granja et al. (2020), similarly to our paper, uses geographic variation in loan level timing
to measure the effects of the PPP. They use the Homebase and Earning databases for their
primary labor force analysis, which are opportunity samples of predominantly lower income
workers who sign up using a cell phone app. The extent to which the geographic, education,
race, and industry mixture of the workers in these databases matches the broader population
is unknown. Selection into the use of these apps may have further unknown effects on
the external validity of their results. In contrast, we use the CPS, is a large stratified
random sample run by the Bureau of Labor Statistics and its methodology has been vetted
over decades. In addition, as we see in Figure 6, there is a strong geographic component
to the timing of loan funding, with Southwest, upper Midwest, and urban East Coast all
substantially more delayed than the country as a whole. Larger firms were substantially less
delayed. For example, 55 percent of PPP loans over $5 million were in the first funding round,
but only 17 percent of loans under $150,000 were. Furthermore, as we showed in Section
3, the demographic, economic, educational, and geographic composition of the areas where
PPP loans occur shifts considerably over the PPP sample. By comparing the counties by
their exposure to the first and second PPP waves, Granja et al. (2020) assume that the ZIP
Codes they study are otherwise comparable, conditional on their fixed effects specification.
However, we show in Section 3 that the areas receiving early and late loans differ in several
important areas that influence the changes (and not just the levels, which would be absorbed
by fixed effects), and these differences may instead be driving their effects. Table 10, Column
(2) shows estimates using our preferred CPS derived outcome variable with a first wave share
instrument, similar to what is done in Granja et al. (2020). We obtain results similar to
our baseline. However, in Table 10, Column (3) we show that loans outside our narrowly
defined event window co-vary with unobserved covariates and deliver spurious pre-trends.
This outcome illustrates why our event-window design is more robust.
Instead of looking at cross county differences, Chetty et al. (2020) and Autor et al.
(2020) use a difference-in-difference estimation strategy to compare the abnormal employ-
ment effects in earnings data of firms employing 100-500 employees (most of whom were
PPP eligible) with firms employing 501-800 employees (generally PPP ineligible). Similar to
22
Granja et al. (2020), Chetty et al. (2020) use an opportunity sample of lower-income wage
earners that makes it difficult to extrapolate to broader effects. Because larger firms secured
PPP loans relatively early in the sample and the PPP likely saturated eligible demand (be-
cause it ultimately expired in August with excess funds), their estimates are contaminated
by time-varying treatment in the two firm groups they study. Figure 4, Panel E shows that
early in the program the average loan size was for about $500,000 and saved 35 jobs. In
our study window, thse numbers are stable at about $200,000 and 10 jobs per loan, and in
the later part of the sample they fall to $30,000 and three jobs per loan. At no point were
the sorts of larger loans (to firms with 100 employees or more) considered by Chetty et al.
(2020) a “typical” loan, and, indeed, there is evidence (along with the results in Figure 4)
of big shifts in loan composition through the PPP.
Autor et al. (2020) measure the effects of the PPP using payroll data from ADP, a
payroll processing firm, and an intent-to-treat approach, comparing PPP-eligible and PPP-
ineligible firms using industry-level size thresholds. This is also an opportunity sample and
not designed to be statistically representative. For example, Cajner et al. (2018) note that
the ADP data are relatively skewed toward the Northeast and toward larger firms. According
to Grigsby et al. (2019), only about half of firms use payroll processing services, and the
ADP sample skews toward large (but not very large) firms, and pays hourly workers about 6
percent more than comparable workers in the CPS. We are particularly concerned about the
use of ADP by very small firms. In a survey of firms with 20 or fewer employees, Surepayroll
(2020) found only a third use payroll software (like ADP).
The most significant drawback the approach of Chetty et al. (2020) and Autor et al.
(2020), however, is that the exogenous variation—ineligibility of firms with more than 500
employees—that each exploits does not allow them to measure how PPP loans might vary in
job-creating effectiveness by firm size, particularly for the smaller firms that received most of
the loan dollars and indicated they would save most of the jobs. Our identification strategy
and econometric specification allows us to test whether the employment effects on larger
PPP-eligible firms are representative of overall PPP loan effects on smaller firms.25 Table 8
shows the effects on private sector employment by number of firm employees in May, where
the large sample size of CPS workers observable pre- and post-PPP and identify firm size and
thus allows the most precisely estimated comparison. The substantially larger coefficients
for smaller firms show that the PPP had the biggest employment effects on the smallest
firms, the very same ones for which Chetty et al. (2020) cannot estimate a causal effect. In
addition, we report no effect–as expected–for firms larger than 500 employees.26
25
Because we do not observe loan receipt, our estimates constitute intent-to-treat effects.
26
The CPS and the PPP define firm size differently. CPS records total employees across all establishments
23
Hubbard et al. (2020) also compare firms above and below the 500 employee PPP loan
cutoff and find large employment effects, about 1.8 percentage points, on employment. Their
results, using Dun & Bradstreet data on firms with PPP loans of $150,000 or greater, do
not use a narrow size window around the 500 cutoff, and therefore may include some of the
smaller (and more employment sensitive) firms that drive our results. We view our paper as
providing strong and precisely identified evidence that the employment effects they find are
indeed large and robust.
Like in our paper, Humphries et al. (2020) finds that early loans went to disproportion-
ately larger firms and that early loan sizes were disproportionately large. Their paper uses
an opportunity sample internet survey to study the effects of the PPP. Like in our admin-
istrative data, they also find an employment effect of PPP funding. Their results inform
our thinking regarding the aggregate effects implied by our estimates. Our estimates show
that small firms benefit the most from expediently delivered loans. Unfortunately, the roll
out of the PPP favored expedient delivery of funds to larger firms. In our opinion, this
substantively blunted the efficacy of the program. Similarly, Li and Strahan (2020) finds
that proxies for relationship lending at banks (using a smaller banks, being in a bank’s core
market, proximity to older branches, using a bank with higher levels of small business loans)
predict which areas get relatively more early PPP loans.
7.2 Comparison with Kurzarbeit Programs
By sharing the burden of keeping temporarily less-productive workers employed between
employers and the state, the PPP has more in common with German Kurzarbeit programs
than traditional US income support programs. Under the Kurzarbeit program, (literally
German for “short work”), the German government compensates employers for most of the
wage, pension, and insurance costs of keeping them employed during recessions. After the
relative out-performance of the German labor market relative to peers in the Global Financial
Crisis, Contessi et al. (2013) and Felter (2012) argued that a Kurzarbeit-style program could
be cost-effective tool for fighting unemployment in recessions. In a calibrated model of the
labor market effects of fiscal policy in Europe, Faia et al. (2013) find that hiring subsidies
(like Kurzarbeit and the PPP) of 0.5 percent of GDP, about the same size as the PPP
loans in our windows, could reduce unemployment by 1.8 percentage points. The United
States had similar programs with limited availability in the great recession (17 states and
about 1 percent of hours of unemployment claimed), called “short-time compensation”(STC)
programs (Abraham and Houseman (2014)). Unlike the PPP, the Kurzarbeit program is
while PPP size restrictions are applied at the establishment level in key industries. However, this discrepancy
would bias us against finding stronger effects at smaller establishments and, thus, reinforces our findings.
24
open to firms of any size, provides funding for much longer than three months, and does
not pay the full wage and benefits costs of workers (Felter (2012)). Nevertheless, the PPP
does represent the United States’ first national and widely available attempt to implement
a Kurzarbeit-style program, and the results appear to be very successful and in line with
theoretical predictions.
7.3 Estimates of Aggregate Jobs Saved by the PPP
Where possible, the natural inclination in program evaluation is to attempt to find whether
the benefits exceed the costs. Cost-benefit analysis of programs intended to save jobs, as
in the case of the PPP, is difficult since jobs fit neatly neither in the costs nor the benefits
column. Indeed, while the more salient features of jobs (e.g. salaries and benefits) are
generally classified as costs, measures of job creation and preservation also proxy for less
salient features that represent benefits (e.g. match quality). Thus, while PPP supported
jobs are associated with benefits like increased productivity all we see clearly are the costs.
27
Still, creating and preserving jobs was the purpose of the PPP, so instead we consider a
cost-effectiveness analysis: how much the PPP cost per job it saved.28 To estimate the cost
per job saved necessitates estimating the jobs saved by the program as a whole. This section
lays out the (perhaps valiant) assumptions necessary to translate our results in Section 5
into an estimate of jobs saved by the PPP and then calculates that estimate. Section 7.4
translates this estimate of the number of jobs saved into an estimate of costs per job saved
(our measure of cost effectiveness) and compares that result with the cost-effectiveness of
job-saving programs in the US financial crisis.
First, the calculation of jobs saved requires assuming that our estimate of the cost in jobs
of loan delay is invariant across the entire horizon of the PPP program. For several reasons,
this external validity assumption is questionable. For one, the observed and unobserved
attributes of PPP loan recipients are not time invariant. For another, the economic envi-
ronment, and in particular the availability and interest rates of alternative loans or grants
varied over time. We partially address this problem by allowing for different parameters for
small and large firms in our aggregation. While imperfect, this solution partly addresses
both concerns, as the average size of PPP recipients fell dramatically over time and access
27
Labor not used in one endeavor can be used in another or consumed as leisure. Therefore, in a typical
cost-benefit analysis, jobs and associated labor costs are in the cost part of the analysis. As we understand
it, the jobs saved by the PPP are proxies for benefits we actually care about but cannot measure—stable
productive matches between employees that produce significant output that can then be consumed.
28
Cost effectiveness analyses are a common alternative to cost-benefit analysis (CBA). It is common to
use cost effectiveness analysis when a full CBA is impossible, such as when there are measures of policy
effectiveness (here the number of jobs saved) but the measures capture only part of the social benefits of a
policy (Boardman et al. (2001)).
25
to alternative funding sources likely covaries with size. To implement this in our jobs saved
figures, we construct our job-saving estimates by binned firm size, taking seriously the coef-
ficients on delay obtained by firm size reported in Table 8, and then aggregate over all the
firm size bins.
Second, converting our point estimates, which measure jobs lost due to the delay in
funding, into estimates of jobs saved due to the existence of PPP loans requires us to make
an assumption about the amount of time required for a business to obtain funding from its
next-best funding source relative to the time required to receive funding from the PPP. We
know of no estimates of this time to alternative funding (alone or by firm size). Instead,
we posit a difference in the timing of the receipt of funding from a PPP and the next best
alternative of 15 days and assume that firms could have started either application on the
opening date of the PPP program. Using these assumptions, we weight PPP loan amounts
by hypothesized difference in the number of days between PPP loan receipt and the next
best alternative. Using these weights, we cumulate delay weighted loans within firm size
categories.29
This calculation suggests that the PPP program was equivalent to accelerating the deliv-
ery of funding to businesses with less than 10, 10 to 99, and 100-499 employees respectively
by an equivalent of 8, 15 and 20 days respectively. Applying these factors to the coefficients
on effect on nonemployment of a 10-day delay in funding implies that the PPP program
preserved nearly 13 million jobs.3031 Of these 13 million nearly 9 million, or two thirds,
were in firms with fewer than 10 employees. For context, firms of this size account for
roughly 20 percent of employment in normal times. Meanwhile, these assumptions suggest
approximately 1.3 million were in firms with 100-499 employees, which is comparable to the
estimates of Autor et al. (2020) for PPP job savings for firms of this size.
7.4 Costs per Job Saved by the PPP
Although the PPP was appropriated a total of $670 billion that ($349 billion through the
CARES Act and $321 billion under the PPP Act), not all funds were ultimately dispersed.
29
Larger firms may need less time than smaller firms to line up alternative funding. Though larger firms
have better average credit market access, they also need much larger loans, whereas smaller firms may be able
to rely on faster retail credit products like home equity lines of credit and credit card loans. For simplicity,
our primary estimate holds this delay to alternative funding sources fixed by firm size.
30
When we instead assume that it takes 100-499 employee firms 5 days, 10-99 employee firms 10 days, and
less than 10 employee firms 15 days to secure alternative funding, the results are similar (because the largest
effects are in the smallest firms), with the program estimated to save 10 million instead of 12.7 million jobs.
31
This calculation assumes that individuals who were nonemployed at the onset of the pandemic and
subsequently found jobs as a result of the PPP program found them in proportion to historical levels of
employment in firms of these sizes.
26
We calculate (because of binning) that the PPP funded $555 billion in loans. Because we
estimate the PPP saved 13 million jobs (Section 7.3), we calculate a cost per job saved
of about $43,000. Our PPP job-saving estimates is substantially larger than that of other
papers, and so our estimate of the cost per job saved is also much lower. Chetty et al. (2020)
estimates a cost per job saved of $289,000, and Autor et al. (2020) imply a cost per job
saved of $173,000 to $396,000. However, our cost per job saved figure is very much in line
with estimates of the job creation costs of the US stimulus programs of the financial crisis.
Chodorow-Reich (2019) reviews seven studies of the American Recovery and Reinvestment
Act (ARRA) and finds the ARRA programs save jobs at a cost of about $50,000 each, very
much in line with our estimates. If anything, PPP loans might be expected to be even
cheaper than these ARRA programs. The PPP is explicitly a job saving program, and it
seems reasonable that such a program should save jobs at a lower cost (of course, other
programs have other benefits). Given the estimates of the cost per job saved under the
ARRA, the smaller employment results of other PPP papers are surprising. Estimates from
the ARRA imply a typical government stimulus program of $555 billion should save about
11 million jobs. We estimate that the PPP saved 13 million jobs or about 15 percent more
than expected from ARRA estimates, which seems reasonable, as, again, the PPP had a
primary objective to save jobs.
However, while asking “what is the gross-cost per job saved of the PPP?” is a well posed
question, it not the right question, even when we are unable to measure the benefits of
jobs saved by the PPP. Rather, the right question is, “what is the opportunity cost to the
government of saving these jobs?” The CARES Act, like the Emergency Unemployment
Compensation Act of 2008 (EUC 2008) passed by the United States during the US during
the financial crisis, extended unemployment insurance by 13 weeks for a total in most states
of 39 weeks. The federal government further enhanced the unemployment insurance program
during the COVID-19 recession (also in the CARES Act) in two important ways above and
beyond the changes in the EUC 2008. First, it expanded the set of workers who are eligible for
unemployment insurance to include self-employed and contract workers. Second, it increased
all weekly benefits by $600, which alone is worth as much as $10,000 to a worker collecting it
for the maximum of 3/27/20 to 7/25/20. Combined, this payout was a substantial increase
in the generosity of the unemployment insurance system that gave three-fourths of workers
eligible for unemployment insurance an income replacement rate above 100 percent (Ganong
et al. (2020)).
To the extent that the alternative to PPP funding is workers at small firms collecting
unemployment insurance, these costs should be subtracted from the cost of the PPP to
estimate a more accurate net-cost per job saved that better captures the opportunity cost
27
of the federal government of implementing the PPP. Within this institutional setting of a
greater than 100 percent unemployment insurance replacement rate, paying workers their full
salaries may, even if they are completely unproductive for the spell of employment funded by
the PPP loan, save the government money if the alternative is unemployment. In actuality,
such workers are not completely unproductive, and a PPP-style program preserves quality
employee-employer matches, both of which add to the benefits of the PPP.32
The quantitative effects are substantial. A typical job funded by a PPP loan, as measured
by the PPP data, pays $43,000 per year. The 3 month wage bill (the PPP loan size) for this
worker is $11,000. Such a worker is likely eligible for a 145 percent unemployment replace-
ment rate for about three months and regular unemployment benefits after that through the
39th week (Ganong et al. (2020)). If we assume that workers retained due to PPP loans are
50 percent as productive as during normal times (and their normal output is at least their
compensation) and it otherwise takes the workers 6 months to find a new job with the same
productivity as the job they would have kept with a PPP loan, we can estimate the net-
opportunity cost of saving that job. The $43,000 cost per job saved, less the $5,000 cost of six
months of unemployment insurance (3 months at a replacement rate of 145 percent (Ganong
et al. (2020)) and 3 months under the normal average rate of 45 percent (Evermore (2019)))
and $11,000 in additional output for the retained workers gives an opportunity cost per job
saved of about $27,000.33 Given that, like in Kurzarbeit programs, these funds preserve
workers’ idiosyncratic human capital and other valuable aspects of the employer-employee
match, this solution is an attractive alternative to paying workers unemployment insurance,
which effectively pays them to look for work, instead of the PPP, which pays them to do
that work or at minimum remain productively matched in the face of transient shocks.
8 Conclusion
Our estimates indicate a very substantial effect of PPP loan delay with the majority of the
effect being concentrated in the smallest firms and in self-employed, lower-income, and less-
educated workers. In particular, we conclude that a more targeted program that got more
32
This is true for any period where the unemployment insurance system is unusually comprehensive or
generous. The gross-costs per job saved in ARRA estimates are probably also an overestimate of the true
opportunity cost per job saved, because of these supplements to unemployment insurance, but the COVID-19
era changes were larger than those in the EUC 2008.
33
If job losses would be for longer or the PPP retained workers were more productive, the opportunity
cost per job saved would be even lower. If workers would otherwise remain unemployed for nine months and
the workers are fully productive when funded under the PPP, the social cost of the program per job saved is
about $4,000, even without including any multiplier effects. By a year to find a new job and full productivity
for PPP funded workers, the opportunity cost per job saved is negative (meaning the the program pays for
itself).
28
money to the smallest firms faster could have delivered most of the job saving of the PPP
as a whole for a fraction of the price.
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Table 1: Timeline of Major PPP Events
Period Major Events
PPP Passed Into Law as part of the CARES ACT (3/27).
March 27th to April 6th Senator Rubio announces PPP expected to run out in late May and will
require more appropriations.
Congress expresses intent to appropriate $250 billion in additional PPP
funding by end of week.
April 7th to 13th SBA loan infrastructure cannot process volume, experiencing notable crashes.
Banks limit loans primarily to firms with existing credit relationships due to
availability of funding and program interest.
Businesses still report significant delays in receiving PPP funds.
Some banks stop taking new applications in anticipation of funding exhaustion.
April 14th to 20th
Heavy PPP issuance until program exhausts initial appropriation (4/16).
Congressional negotiations over second PPP appropriations resume.
Political discussions over second PPP appropriations continue.
Bill in the house and senate, then signed into law (4/24).
April 21th to 30th
PPP resumes making loans and at high volumes (4/27).
SBA encourages small businesses to apply for PPP funds as soon as possible.
Major slowdown in PPP loans.
Congress explores changing PPP loan requirements to give more time and
May
flexibility to spend funds.
Senate adjourns for recess without extending term for PPP loans.
June Initial end of PPP (6/30).
PPP terms adjusted in law to extend loan forgiveness timeline from eight to
twenty four weeks.
July Reduces payroll requirements to allow 60% of the loan to go towards payroll,
compared to the previous requirement of 75%.
Extension of PPP loan deadline (8/8).
August PPP loan program expires (8/8).
Sources: Various WSJ articles in 2020.
35
Table 2: Summary Statistics of PPP Loans by Headcount and Loan Size.
Loan Loan Loan Jobs Window† Window† Late‡ PPP Act§
Size (#) ($) Retained Loans ($) Loans (#) Loans (%) Loans (%)
A) [$0, $150k] 4,224,172 141,804,112,463 19,669,493 42,537,113,452 1,124,214 56 27
B) ($150k-$350k] 379,062 75,812,379,056 8,727,023 24,420,472,101 122,102 44 32
C) ($350k-$1M] 199,456 134,632,999,456 10,015,580 42,439,337,873 62,873 40 32
D) ($1M-$2M] 53,030 79,545,053,030 5,906,794 24,027,016,018 16,018 39 30
E) ($2M-$5M] 24,838 86,933,024,838 5,167,844 25,935,007,410 7,410 38 30
F) ($5M-$10M] 4,840 36,300,004,840 1,639,326 11,797,501,573 1,573 41 32
All Loan Sizes 4,885,398 555,027,573,683 51,126,060 171,156,448,427 1,334,190 54 27
Loan Loan Jobs Window† Window† Late‡ PPP Act§
Headcount* (#) ($) Retained* Loans ($) Loans (#) Loans (%) Loans (%)
1) [0, 9] 3,630,053 96,520,713,219 11,768,615 28,125,628,870 939,940 57 26
2) [10, 99] 1,172,183 286,449,242,433 29,412,071 90,865,561,688 370,358 46 32
3) [100, 499] 79,380 158,655,830,222 15,001,340 47,529,721,213 22,897 40 29
4) [500] 3,782 13,401,787,809 1,891,000 4,635,536,656 995 47 26
All Headcount 4,885,398 555,027,573,683 58,073,026 171,156,448,427 1,334,190 54 27
Source: SBA / Treasury (2020)
* - Actual headcount (“jobs retained” in the SBA / Treasury data) if headcount > 0 or loansize/headcount <
40, 000, which is 81 percent of the observations (reasonable jobs saved sample). Where headcount is missing,
reported as zero, or the average loan size per employee is well outside the PPP loan parameters, we impute the
headcount with a linear model calibrated on the reasonable jobs saved sample using an OLS regression of the
of logjobsretained on logloansize and dummy variables for major industries. Within this sample, the model
predicts the reported employee size groups in Table 2 with about 90 percent accuracy.
† - Loans issued on or between 4/14/20 and 4/28/20.
‡ - The share of loans on or between 4/14/20 and 4/28/20 issued on 4/27/20 or 4/28/20.
§ - The share of loans issued on or after 4/17/20.
36
Table 3: Top 20 CBSA Loan Statistics.
Loan Count Loan Dollars Avg. Loan Size Jobs Retained CBSA Window Loans† % Late Loans‡
1 154,319 13,047,214,281 84,547 1,167,888 Miami-Fort Lauderdale-West Palm Beach, FL 29,473 47.97
2 111,196 13,533,912,137 121,712 1,244,557 Dallas-Fort Worth-Arlington, TX 33678 47.63
3 352,739 44,520,958,165 126,215 3,294,144 New York-Newark-Jersey City, NY-NJ-PA 90,856 47.36
4 102,586 10,469,857,213 102,059 909,686 Atlanta-Sandy Springs-Roswell, GA 25,293 47.03
5 59,544 7,045,425,371 118,323 696,042 Phoenix-Mesa-Scottsdale, AZ 14937 46.35
6 240,666 28,327,940,293 117,706 2,560,080 Los Angeles-Long Beach-Anaheim, CA 53,210 45.70
7 56,706 8,184,737,556 144,336 705,578 Detroit-Warren-Dearborn, MI 19677 45.04
8 100,788 11,994,684,185 119,009 1,309,297 Houston-The Woodlands-Sugar Land, TX 28,862 44.75
9 52,223 6,255,607,650 119,786 515,238 Denver-Aurora-Lakewood, CO 14100 44.34
10 56,672 8,039,701,898 141,863 511,740 Seattle-Tacoma-Bellevue, WA 16590 44.19
37 11 51,472 6,265,959,580 121,735 640,596 San Diego-Carlsbad, CA 11918 43.74
12 42,432 5,460,393,911 128,685 479,621 St. Louis, MO-IL 14,587 43.63
13 57,036 8,543,943,197 149,799 715,974 Minneapolis-St. Paul-Bloomington, MN-WI 18,641 43.44
14 82,149 12,202,968,669 148,546 870,623 San Francisco-Oakland-Hayward, CA 21,579 43.39
15 38,765 5,012,347,193 129,300 373,960 Portland-Vancouver-Hillsboro, OR-WA 11,749 41.89
16 90,426 13,196,823,594 145,940 996,487 Washington-Arlington-Alexandria, DC-VA-MD-WV 28,201 41.45
17 152,617 19,967,777,000 130,835 1,709,888 Chicago-Naperville-Elgin, IL-IN-WI 43,360 38.74
18 84,633 11,919,347,729 140,835 825,082 Boston-Cambridge-Newton, MA-NH 33,181 37.56
19 86,792 11,964,920,975 137,857 862,339 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 32,615 35.10
20 39,433 53,95,347,424 136,823 460,481 Baltimore-Columbia-Towson, MD 13,867 34.74
(Source: U.S. Treasury (2020))
† - Loans issued on or between 4/14/20 and 4/28/20.
‡ - The share of loans on or between 4/14/20 and 4/28/20 issued on 4/27/20 or 4/28/20.
Table 4: Comparison of Counties by Share of Delayed PPP Loans.
Characteristics of Counties by Share of PPP Loans Delayed.
Below Median Above Median Above - Below
Mean Std. Dev. Mean Std. Dev. Difference t-stat
Demographics
Rural County 0.48 0.50 0.69 0.46 0.22 10.70
Fraction Black Residents 0.10 0.14 0.08 0.15 -0.01 -2.01
Fraction White Residents 0.75 0.19 0.75 0.21 0.01 0.75
Fraction Hispanic Residents 0.09 0.13 0.10 0.14 0.01 1.97
Republican 2016 Pres. Vote Share 0.65 0.16 0.68 0.17 0.03 4.81
Per Capita Income 26,888 6,923 25,851 6,248 -1,037 -3.75
Population (Non-institutional) 140,982 335,878 98,344 417,790 -42,638 -2.68
Log Per Capita Income 10.17 0.24 10.13 0.24 -0.04 -3.63
Log Population (Non-institutional) 10.53 1.63 9.78 1.54 -0.75 -11.30
Fraction Residents < H.S. Edu. 0.14 0.06 0.14 0.07 0.00 1.37
Fraction Residents H.S. or Some College 0.55 0.08 0.57 0.07 0.02 5.56
Fraction Residents College Deg. or More 0.31 0.11 0.29 0.09 -0.02 -4.89
Frac. Workers with Employer Insurance 0.31 0.07 0.30 0.06 -0.02 -5.89
Fraction Self-employed Workers 0.11 0.04 0.13 0.06 0.02 7.88
Fraction Private Workers 0.65 0.08 0.62 0.09 -0.03 -8.00
Fraction Public Sector Workers 0.16 0.06 0.18 0.07 0.01 5.21
Banking
Branches per 100k Population 7.65 15.60 12.57 19.55 4.92 6.60
Deposits per 100k Population 304,949 696,544 464,737 634,684 159,788 5.69
County HHI (Branch Share) 2,667 2,171 3,188 2,307 521 5.52
County HHI (Deposit Share) 3,234 2,191 3,746 2,296 512 5.42
COVID-19
Cases per 100k Population (4/11/20) 62 118 63 176 1 0.18
Deaths per 100k Population (4/11/20) 2 5 2 10 0 1.32
NPI: School Closure (4/11/20) 1.00 0.00 1.00 0.00 0.00 .
NPI: Any Serious (4/11/20) 1.00 0.00 1.00 0.00 0.00 .
Cases per 100k Population (9/11/20) 1,593 1,330 1,511 1,369 -82 -1.45
Deaths per 100k Population (9/11/20) 38 44 37 54 -1 -0.34
Business Bankruptcies
Chapter 7 per 100k Population (12/31/19) 4.01 8.02 3.65 8.21 -0.36 -1.05
Chapter 11 per 100k Population (12/31/19) 1.19 8.59 0.92 4.19 -0.27 -0.94
Chapter 13 per 100k Population (12/31/19) 0.63 2.36 0.64 3.08 0.01 0.07
Chapter 7 per 100k Population (6/30/20) 3.40 6.29 3.55 8.35 0.15 0.48
Chapter 11 per 100k Population (6/30/20) 0.95 3.66 0.73 2.53 -0.22 -1.65
Chapter 13 per 100k Population (6/30/20) 0.62 3.63 0.60 3.03 -0.02 -0.14
Observations 1139 1138 2277
Sources: Census Bureau (2020), Keystone Strategy (2020), JHU CSSE (2020)
RAND State Statistics (2020), MIT Election Data and Science Lab (2018) and SBA (2020).
38
Table 5: Employment Effects of Delay
Panel A: Panel B:
(p.p, Unemployment (p.p, Nonemployment
per p.p Delay) per p.p Delay)
Share delayed × Month in 2020 ×
January -0.00912 -0.0108 -0.0138 -0.0150
(0.0154) (0.0154) (0.0217) (0.0215)
February -0.0193 -0.0224 -0.0155 -0.0179
(0.0191) (0.0189) (0.0251) (0.0245)
March 0.00459 -0.00432 0.0227 0.0121
(0.0231) (0.0226) (0.0286) (0.0276)
April -0.00208 -0.0399 0.0112 -0.0453
(0.0541) (0.0429) (0.0604) (0.0461)
May 0.116** 0.105*** 0.157*** 0.141***
(0.0503) (0.0387) (0.0553) (0.0421)
June 0.115** 0.0932** 0.123** 0.108***
(0.0499) (0.0372) (0.0545) (0.0405)
July 0.113*** 0.0827** 0.115** 0.0827**
(0.0439) (0.0347) (0.0481) (0.0389)
August 0.109*** 0.0842*** 0.140*** 0.106***
(0.0379) (0.0307) (0.0428) (0.0359)
September 0.126*** 0.103*** 0.134*** 0.103***
(0.0363) (0.0305) (0.0435) (0.0361)
October 0.0799** 0.0662** 0.0850** 0.0541
(0.0316) (0.0273) (0.0411) (0.0348)
r2 0.665 0.675 0.639 0.649
N 792,197 792,197 831,737 831,737
Fixed Effects
Individual X X X X
Month X X X X
Month-in-2020 X X X X
Controls
Industry and Occupationa X X
Cases and Deathsb X X
Non-pharmaceutical Interventionsc X X
Unemployment Insuranced X X
Covid Induced Measurement Changese X X
Note: Standard errors, clustered at the CBSA X Industry, in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
Source: Current Population Survey, SBA data, and authors’ calculations. Additional data noted below.
a Fixed effects for 2-Digit NAICS and controls for occupational exposure to the Covid shock fully
interacted with month-in-2020 fixed effects. Occupational exposure is measured by ability to work
from home, required proximity to others, and essentialness. Coding follows Leibovici et al. (2020)
and Jackson (2020).
b : New cases and new deaths in the CBSA. New York City is an outlier, as a result we allow for
the coefficient on cases and deaths to differ for this CBSA. Data:JHU CSSE (2020).
c : Stringency Index: Oxford COVID-19 Government Response Tracker. Hale et al. (2020).
d : Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the
reference week interacted with class of worker. Data: Nunn et al. (2020) and Singh (2020).
e : Fixed effects for month in which the respondent was first interviewed by the CPS and for
interview type fully interacted with month-in-2020 fixed effects.
39
Table 6: Employment Flows (p.p, Flow per p.p Delay)..
Panel A: Panel B:
Outflow from Employment Inflow to Employment
All Separation Exit Layoff All Accession Reentry Recall
Share delayed ×
January -0.0154 -0.00307 -0.00910 -0.00447 0.108 0.201 0.0204 -0.781
(0.0200) (0.00957) (0.0173) (0.00603) (0.165) (0.258) (0.235) (0.625)
February -0.0241 -0.00122 -0.0168 -0.00720 -0.262* -0.265 -0.257 -0.235
(0.0183) (0.00782) (0.0157) (0.00623) (0.157) (0.236) (0.251) (0.498)
March 0.00719 -0.00217 -0.00177 0.0115 -0.0498 0.0114 -0.0937 -0.422
(0.0219) (0.00779) (0.0183) (0.00984) (0.160) (0.237) (0.225) (0.599)
April -0.0290 -0.00751 0.0317 -0.0581* 0.0749 0.137 0.135 -0.618
(0.0392) (0.0129) (0.0281) (0.0325) (0.134) (0.217) (0.188) (0.451)
May 0.116*** 0.00598 0.0479** 0.0753*** -0.263** -0.220 -0.175 -0.500**
(0.0273) (0.00814) (0.0201) (0.0186) (0.117) (0.254) (0.205) (0.238)
June -0.0229 -0.00644 0.00554 -0.0232 -0.194 -0.502* 0.179 -0.548**
(0.0294) (0.00994) (0.0209) (0.0195) (0.134) (0.276) (0.206) (0.249)
July -0.0182 -0.000635 -0.0172 -0.00134 0.0549 0.175 0.219 -0.330
(0.0284) (0.00818) (0.0211) (0.0182) (0.141) (0.220) (0.223) (0.277)
August -0.00546 0.00361 -0.00854 -0.0000866 -0.0934 0.0628 -0.604** -0.351
(0.0270) (0.00915) (0.0227) (0.0122) (0.133) (0.219) (0.266) (0.273)
September 0.0176 -0.000408 0.000595 0.0194** -0.0969 -0.125 0.497** -0.740***
(0.0244) (0.0104) (0.0208) (0.00867) (0.135) (0.199) (0.232) (0.282)
October -0.0166 -0.00819 -0.0169 0.00846 0.0386 -0.0742 -0.00285 -0.246
(0.0194) (0.00928) (0.0164) (0.00653) (0.134) (0.205) (0.214) (0.347)
R2 0.043 0.005 0.019 0.079 0.042 0.079 0.074 0.150
N 600,468 575,129 591,162 577,377 50,541 17,955 20,788 11,798
Note: Standard errors, clustered at the CBSA X Industry, in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
Source: Current Population Survey, SBA data, and authors’ calculations. Additional data noted below.
Controls: Individual, month, and month-in-2020 fixed effects. Fixed effects for 2-Digit NAICS and controls for occupational exposure to the
Covid shock fully interacted with month-in-2020 fixed effects. Occupational exposure is measured by ability to work from home, required
proximity to others, and essentialness. Coding follows Leibovici et al. (2020) and Jackson (2020). New cases and new deaths in the CBSA.
New York City is an outlier, as a result we allow for the coefficient on cases and deaths to differ for this CBSA. Source: JHU CSSE (2020).
Stringency Index of non-pharmesutical interventions. Source: Oxford COVID-19 Government Response Tracker. Hale et al. (2020). Indicator
for whether PUC, PUA, and/or LWA were made in the respondent’s state in the reference week interacted with class of worker. Data: Nunn
et al. (2020) and Singh (2020). Fixed effects for month in which the respondent was first interviewed by the CPS and for interview type fully
interacted with month-in-2020 fixed effects.
40
Table 7: Unemployment by Class of Worker (p.p, Nonemployment per p.p
Delay).
Share delayed × ... Self-employed × ... Private Employee × ... Public Employee × ...
January 0.00787 -0.0161 0.0107
(0.0692) (0.0222) (0.0448)
February 0.0243 -0.0221 0.00788
(0.0734) (0.0259) (0.0451)
March 0.0402 0.0133 -0.0322
(0.0774) (0.0291) (0.0547)
April 0.0164 -0.0412 -0.0820
(0.115) (0.0474) (0.0768)
May 0.233** 0.133*** 0.00123
(0.0968) (0.0440) (0.0766)
June 0.159 0.105** -0.0320
(0.104) (0.0436) (0.0704)
July 0.130 0.0738* -0.0417
(0.0934) (0.0423) (0.0796)
August 0.122 0.104*** -0.0378
(0.0899) (0.0385) (0.0781)
September 0.210** 0.0925** 0.0998
(0.0889) (0.0390) (0.0655)
October 0.0109 0.0606* 0.0694
(0.0855) (0.0367) (0.0593)
Observations 915488
R-squared 0.643
Note: Standard errors, clustered at the individual X Industry, in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
Source: Current Population Survey, SBA DATA, and authors’ calculations. Additional datasets noted below.
Controls: Individual, month, and month-in-2020 fixed effects. Fixed effects for 2-Digit NAICS and controls for
occupational exposure to the Covid shock fully interacted with month-in-2020 fixed effects. Occupational exposure
is measured by ability to work from home, required proximity to others, and essentialness. Coding follows Leibovici
et al. (2020) and Jackson (2020). New cases and new deaths in the CBSA. New York City is an outlier, as a result
we allow for the coefficient on cases and deaths to differ for this CBSA. Source: JHU CSSE (2020). Stringency
Index of non-pharmesutical interventions. Source: Oxford COVID-19 Government Response Tracker. Hale et al.
(2020). Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the reference week
interacted with class of worker. Data: Nunn et al. (2020) and Singh (2020). Fixed effects for month in which
the respondent was first interviewed by the CPS and for interview type fully interacted with month-in-2020 fixed
effects.
41
Table 8: Nonemployment by Firm Size (p.p, Nonemployment per p.p Delay).
Eligiblea Ineligiblea
Unemployed Number of Employees
Share delayed × less than 10 × 10 to 99 × 100 to 499 × 500 to 999 × 1,000 or more
January -0.611 0.0106 -0.0722 -0.0632 0.151 -0.0147
(0.426) (0.0997) (0.0849) (0.118) (0.207) (0.0656)
February -0.383 0.0582 -0.0973 0.000185 0.145 -0.0375
(0.367) (0.0937) (0.0784) (0.112) (0.204) (0.0604)
March -0.0989 0.0666 -0.0761 -0.0217 0.0100 0.0195
(0.361) (0.0896) (0.0785) (0.120) (0.207) (0.0577)
April -0.261 0.0302 -0.139 -0.0584 0.173 0.0333
(0.380) (0.145) (0.133) (0.162) (0.264) (0.0931)
May 0.144 0.454*** 0.0282 0.0468 0.113 0.0788
(0.376) (0.168) (0.144) (0.167) (0.287) (0.0975)
June 0.0552 0.218 -0.111 0.217 -0.0729 0.0705
(0.476) (0.225) (0.190) (0.224) (0.377) (0.119)
Observations 0.624
R-squared 263,330
Note: Standard errors, clustered at the CBSA X Industry, in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
Source: Current Population Survey, SBA Data, and authors’ calculations. Additional data sets noted below.
Controls: Individual, month, and month-in-2020 fixed effects. Fixed effects for 2-Digit NAICS and controls for occupational exposure
to the Covid shock fully interacted with month-in-2020 fixed effects. Occupational exposure is measured by ability to work from
home, required proximity to others, and essentialness. Coding follows Leibovici et al. (2020) and Jackson (2020). New cases and
new deaths in the CBSA. New York City is an outlier, as a result we allow for the coefficient on cases and deaths to differ for this
CBSA. Source: JHU CSSE (2020). Stringency Index of non-pharmesutical interventions. Source: Oxford COVID-19 Government
Response Tracker. Hale et al. (2020). Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the
reference week interacted with class of worker. Data: Nunn et al. (2020) and Singh (2020). Fixed effects for month in which the
respondent was first interviewed by the CPS and for interview type fully interacted with month-in-2020 fixed effects.
42
Table 9: Nonemployment by Worker Characteristics (p.p, Nonenemploy-
ment per p.p Delay).
Tercile of Predicted Wagesa Educational Attainment
Share delayed × I II III Less than High School Some College
January 0.00980 -0.0168 -0.0402 0.135 -0.0210 -0.0344
(0.0450) (0.0340) (0.0271) (0.102) (0.0296) (0.0276)
February -0.0275 -0.0103 -0.0162 0.0221 -0.0420 0.0122
(0.0528) (0.0403) (0.0299) (0.121) (0.0340) (0.0328)
March 0.0276 0.00817 -0.00203 0.127 0.0158 -0.0154
(0.0611) (0.0465) (0.0317) (0.124) (0.0406) (0.0330)
April -0.154 0.0338 -0.0123 0.0709 -0.0983 0.00509
(0.0939) (0.0760) (0.0508) (0.175) (0.0661) (0.0511)
May 0.177** 0.204*** 0.0407 0.318* 0.149** 0.0960*
(0.0872) (0.0646) (0.0452) (0.166) (0.0598) (0.0511)
June 0.123 0.156** 0.0474 0.313* 0.0938 0.0894*
(0.0867) (0.0623) (0.0477) (0.162) (0.0598) (0.0474)
July 0.126 0.122** 0.00379 0.430** 0.0702 0.0457
(0.0872) (0.0623) (0.0400) (0.173) (0.0577) (0.0446)
August 0.211*** 0.0482 0.0517 0.255 0.102* 0.0943**
(0.0777) (0.0614) (0.0393) (0.159) (0.0544) (0.0455)
September 0.139* 0.0915* 0.0709* 0.0889 0.0995* 0.119***
(0.0771) (0.0553) (0.0408) (0.134) (0.0518) (0.0431)
October 0.0435 0.0506 0.0661 -0.00548 0.0527 0.0719
(0.0734) (0.0577) (0.0411) (0.142) (0.0485) (0.0449)
Observations 830,401 830,401
R-squared 0.649 0.649
Note: Standard errors, clustered at the CBSA X Industry, in parenthesise. * p<0.10, ** p<0.05, *** p<0.01.
Source: Current Population Survey, SBA DATA, and authors’ calculations. Additional datasets noted below.
a
Predicted wages conditional on education, potential experience and it’s square, sex, race, ethnicity, broad
industries occupations, and CBSA.
Controls: Individual, month, and month-in-2020 fixed effects. Fixed effects for 2-Digit NAICS and controls for
occupational exposure to the Covid shock fully interacted with month-in-2020 fixed effects. Occupational exposure
is measured by ability to work from home, required proximity to others, and essentialness. Coding follows Leibovici
et al. (2020) and Jackson (2020). New cases and new deaths in the CBSA. New York City is an outlier, as a result
we allow for the coefficient on cases and deaths to differ for this CBSA. Source: JHU CSSE (2020). Stringency
Index of non-pharmesutical interventions. Source: Oxford COVID-19 Government Response Tracker. Hale et al.
(2020). Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the reference week
interacted with class of worker. Data: Nunn et al. (2020) and Singh (2020). Fixed effects for month in which
the respondent was first interviewed by the CPS and for interview type fully interacted with month-in-2020 fixed
effects.
43
Table 10: Falsification Tests (p.p, Unemployment per p.p Delay).
Second Round May 2-3
Baseline Specification Total Loans April 30-May 3
Share Delayed X blank space
January -0.0150 -0.0101 -0.00754
(0.0215) (0.00996) (0.00968)
February -0.0179 -0.00926 -0.0183
(0.0245) (0.0111) (0.0112)
March 0.0121 0.00127 -0.00233
(0.0276) (0.0128) (0.0120)
April -0.0453 0.0285 0.0644***
(0.0461) (0.0206) (0.0211)
May 0.141*** 0.118*** 0.0606***
(0.0421) (0.0208) (0.0203)
June 0.108*** 0.0936*** 0.0561***
(0.0405) (0.0176) (0.0190)
July 0.0827** 0.0733*** 0.0274
(0.0389) (0.0184) (0.0169)
August 0.106*** 0.0711*** 0.0297*
(0.0359) (0.0167) (0.0169)
September 0.103*** 0.0972*** 0.0532***
(0.0361) (0.0166) (0.0168)
October 0.0541 0.0609*** 0.0305*
(0.0348) (0.0164) (0.0164)
Observations 0.649 0.649 0.649
R-squared 831,737 831,737 831,737
Note: Standard errors, clustered at the CBSA X Industry, in parentheses. * p<0.10, ** p<0.05, ***
p<0.01.
Source: Current Population Survey, SBA DATA, and authors’ calculations. Additional data sets
noted below.
Controls: Individual, month, and month-in-2020 fixed effects. Fixed effects for 2-Digit NAICS and
controls for occupational exposure to the Covid shock fully interacted with month-in-2020 fixed
effects. Occupational exposure is measured by ability to work from home, required proximity to
others, and essentialness. Coding follows Leibovici et al. (2020) and Jackson (2020). New cases
and new deaths in the CBSA. New York City is an outlier, as a result we allow for the coefficient
on cases and deaths to differ for this CBSA. Source: JHU CSSE (2020). Stringency Index of non-
pharmesutical interventions. Source: Oxford COVID-19 Government Response Tracker. Hale et al.
(2020). Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the
reference week interacted with class of worker. Data: Nunn et al. (2020) and Singh (2020). Fixed
effects for month in which the respondent was first interviewed by the CPS and for interview type
fully interacted with month-in-2020 fixed effects.
44
Table 11: Narrower and Wider Windows. (p.p, Nonemployment
per p.p Delay).
27 27-30
Baseline Specification 15-27 13-30
January -0.0150 -0.0125 -0.0193
(0.0215) (0.0190) (0.0214)
February -0.0179 -0.00442 -0.0207
(0.0245) (0.0219) (0.0245)
March 0.0121 0.0205 0.0167
(0.0276) (0.0247) (0.0273)
April -0.0453 -0.0549 -0.0148
(0.0461) (0.0394) (0.0465)
May 0.141*** 0.0764** 0.189***
(0.0421) (0.0358) (0.0435)
June 0.108*** 0.0696** 0.140***
(0.0405) (0.0345) (0.0404)
July 0.0827** 0.0770** 0.101**
(0.0389) (0.0338) (0.0395)
August 0.106*** 0.0895*** 0.121***
(0.0359) (0.0331) (0.0356)
September 0.103*** 0.0735** 0.119***
(0.0361) (0.0317) (0.0366)
October 0.0541 0.0417 0.0689*
(0.0348) (0.0308) (0.0355)
Observations 0.649 0.649 0.649
R-squared 831,737 831,737 831,737
Note: Standard errors, clustered at the CBSA X Industry, in parentheses.
* p<0.10, ** p<0.05, *** p<0.01.
Source: Current Population Survey, SBA DATA, and authors’ calculations.
Additional data sets noted below.
Controls: Individual, month, and month-in-2020 fixed effects. Fixed effects for 2-Digit NAICS and
controls for occupational exposure to the Covid shock fully interacted with month-in-2020 fixed
effects. Occupational exposure is measured by ability to work from home, required proximity to
others, and essentialness. Coding follows Leibovici et al. (2020) and Jackson (2020). New cases
and new deaths in the CBSA. New York City is an outlier, as a result we allow for the coefficient
on cases and deaths to differ for this CBSA. Source: JHU CSSE (2020). Stringency Index of non-
pharmesutical interventions. Source: Oxford COVID-19 Government Response Tracker. Hale et al.
(2020). Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the
reference week interacted with class of worker. Data: Nunn et al. (2020) and Singh (2020). Fixed
effects for month in which the respondent was first interviewed by the CPS and for interview type
fully interacted with month-in-2020 fixed effects.
45
Figure 1: Evolution of PPP Loan Issuance
PPP Funds PPP PPP Application
Opens Depleted Reopens Deadline
$700B
PPP funding under the CARES and PPP Acts
$600B
$500B
$400B
PPP funding under the CARES Act
$300B
$200B
$100B
Event Window
$0B
Mar−30 Apr−13 Apr−27 May−11 May−25 Jun−08 Jun−22 Jul−06 Jul−20 Aug−03
Source: U.S. Treasury (2020) and authors’ Calculations
Figure 2: PPP Loan Timing.
Panel A: Loan Count Panel B: Loan Dollars
50000
.5
.4 40000
Millions Millions
.3
.2 20000 30000
.1 10000
0 0
01apr2020 01may2020 01jun2020 01jul2020 01apr2020 01may2020 01jun2020 01jul2020
Source: U.S. Treasury (2020) and authors’ Calculations
46
Figure 3: Geographic Distribution of Loan Timing.
Panel A: Window Share Late (All Counties)
(0.59,1.00]
(0.45,0.59]
(0.35,0.45]
[0.00,0.35]
Panel B: Window Share Late (Observable in CPS)
(0.60,1.00]
(0.46,0.60]
(0.35,0.46]
[0.00,0.35]
Source: U.S. Treasury (2020) and authors’ Calculations
47
Figure 4: Time Variation in Characteristics of CBSAs Receiving Loans.
Panel A: Banking Panel B: Demographics
2000 1.5 .6
Left Axis Right Axis
5 Left Axis Right Axis
Branches Branches Per Capita Income (10K) Rural
Deposits 100k Deposits Population (100K) Republican
Herfindahl-Hirschman Indices
.5
4
.5 1 Income and Population .2 .3 .4
1200 1400 1600 1800 Density per 100k Population Share of CBSA's Population
2 3
1
.1
1000
0 0 0
April 3 April 16 April 27 May 10 April 3 April 16 April 27 May 10
Panel C: Race/Ethnicity Panel D: NPI, School, UI
May 3
.8 Left Axis Right Axis
White Black
PUC Schools
Hispanic
PUA NPI
Racial/Ethnic Mackeup of CBSA
April 30 March 16
.2 .4 .6
...
April 13
March 13
0 April 11
April 3 April 16 April 27 May 10 April 3 April 16 April 27 May 10
Panel E: Loan Size and Jobs Retained Panel F: Cases and Deaths
600000 Left Axis Right Axis 40 400 Cases Deaths
20
Loan Size Jobs Per Loan
Cases / Deaths per 100K population
30 15
400000
20 10
200 300
200000
10 5
0 0 100 0
April 3 April 16 April 27 May 10 April 3 April 16 April 27 May 10
Source: U.S. Treasury (2020), FDIC Summary of Deposits (2019), Keystone Strategy (2020), Nunn et al. (2020), Singh (2020). Census
Demographic Data (2018), JHU CSSE (2020) and authors’ Calculations. Note: hashed lines plot the average of each series in the interval April
14-28.
48
Figure 5: Employment Effects (Coefficient Plots in p.p, Unemployment per p.p Delay).
Panel A: marginal effect of delayed PPP financing
I: unemployment II: labor force participation III: nonemployment
.25 .25 .25
Percentage Points Percentage Points Percentage Points
.05 .15 .05 .15 .05 .15
-.05 -.05 -.05
-.15 -.15 -.15
Jan. Feb. MarchApril May June July Aug. Sept. Oct. Jan. Feb. MarchApril May June July Aug. Sept. Oct. Jan. Feb. MarchApril May June July Aug. Sept. Oct.
Panel B: baseline COVID-19 shocka
I: unemployment II: labor force participation III: nonemployment
20 20 20
15 15 15
Percentage Points Percentage Points Percentage Points
5 10 5 10 5 10
0 0 0
-5 -5 -5
Jan. Feb. MarchApril May June July Aug. Sept. Oct. Jan. Feb. MarchApril May June July Aug. Sept. Oct. Jan. Feb. MarchApril May June July Aug. Sept. Oct.
Note: The average potential worker lives in a CBSA where the share of delayed loans is 42%.
The standard deviation is 7.5%.
a
controls contains month fixed effects.
The baseline COVID-19 shock traces out the interaction between month fixed effects and year 2020.
49
Figure 6: Geographic Distribution of Loan Timing within other Windows.
Panel A: Relative Share of Second Round
(0.47,1.00]
(0.34,0.47]
(0.24,0.34]
[0.05,0.24]
Panel B: Relative Lateness within Second Round
(0.57,1.00]
(0.38,0.57]
(0.20,0.38]
[0.00,0.20]
Source: U.S. Treasury (2020) and authors’ Calculations
50
A Additional Details about the Design and Implemen-
tation of the PPP
This section explains on more detail how the PPP operated. These details are not directly
pertinent to the design of our study, but may help the reader understand more precisely the
terms of the PPP and more generally be valuable to other researchers.
The PPP program was run by the SBA in consultation with the US Department of the
Treasury. Borrowers had to meet a multi-part test to be eligible for a PPP loan. First,
borrowers had to employ 500 or fewer employees in most industries.34 Second, firms are
subject to net worth and income limitations. Firms had to have tangible net worth of less
than $15 million and an average net income after federal taxes of less than $5 million. Third,
borrowers had to be independently owned and operated and located within the United States.
Fourth, firms have to be engaged in a legal activity and not be a household employer nor a
hedge or private equity fund. Fifth, they could not be delinquent or in default on an existing
SBA loan, nor could they be bankrupt.
However, unlike virtually all other forms of credit, these were not tested for collateral
or ability to repay and the paper work requirements were modest (estimated by the SBA
at about two hours).35 Once a borrower had their PPP application approved, their lender
was supposed to send the funds to the borrower within 10 calendar days. This changed
somewhat over the program, but not in our event window. Initial program documentation
said that lenders had 10 days to make their distributions, but was unclear on what this
actually required and when the clock started ticking. Eventually the Treasury and the SBA
issued a rule that for loans approved on or before April 28, 2020, lenders had 10 calendar
days from April 28, 2020 to fund the loan. For loans after this, the loan had to fund in 10
calendar days.
The qualifying loan amount per employee was 2.5 times the average total monthly per-
employee payments for payroll costs for the year prior to the loan date (or, at the option of
the borrower, for 2019) including up to $100,000 in annualized cash compensation (wages,
34
The SBA publishes North American Industry Classification System Codes (NAICS codes) based defini-
tions for small businesses based on their headcounts or annual revenues (SBA (2020)). For about 310 NAICS
code these limits are larger 500 employees and in these cases, the PPP is available for firms with more than
500 employees. However, there are no instances in the PPP data of any firms getting loans to support more
than 500 employees, suggesting that perhaps firms meeting the less restrictive headcount requirements in
these industries were unable to satisfy the other requirements or got multi-establishment loans like those
available to hospitality firms.
35
Despite the lack of traditional loan underwriting, Bartik et al. (2020b) find that 12-25 percent of small
businesses had their PPP denied or wanted to apply for PPP loans but either did not qualify or were told
that they did not.
51
salaries, and cash tips) plus group insurance premiums (including health care benefits).36
The qualifying loan amount is the total over all employees for covered firms, up to a limit
of $10 million.37 PPP loans were forgivable if they were used for qualified expenses during
a specified period.
For the loans we focus on, borrowers were told they had 8 weeks after fund distribution
to spend the proceeds on qualified expenses and at least 75% of funds had to be spent on
qualified payroll expenses. The vast majority (92%) of PPP loans are made before June
5, 2020. Before that, the US Treasury and the SBA guidance was firms had 8 weeks to
spend their funds to qualify for forgiveness and 75% of expenses had to be for payroll. On
June 5, 2020, the Paycheck Protection Program Flexibility Act of 2020 passed and the US
Treasury and the SBA created rules under that act that relaxed the requirement to allow 24
weeks to spend the PPP funds and 60% of expenses had to be for payroll to qualify for loan
forgiveness. The proceeds of any PPP loans that did not qualify for forgiveness are due in
two years from distribution at the cost of one percent per year interest.
A.1 Detailed Timeline of the PPP in the Popular Press
36
Therefore, a PPP loan for an employee with no benefits and an annual salary of $48,000 would be $10,000
12 ), but for an no-benefits employee making $100,000 or more, the loan would be for $20,834.
($48, 000 × 2.5
37
The maximum loan amount from compensation expenses for employees was $46,154 per employee plus
health and retirement benefits. For sole proprietors, independent contractor, or the self-employed, the
maximum of compensation component of the loan was $20,833 and health care and retirement benefits are
not covered. That said, most PPP loans were much smaller: the average loan was for $9,600 per employee
and the average loan size was for $114,000, while the median loan was only $25,000.
52
Tabular Summary of PPP Timeline:
Week of March 29th to April 4th
Date Topic Reporting Summary
3/27/2020 PPP Opening PPP Passed Into Law. Coronavirus relief bill appropriates $350
Billion for PPP. Loan applications open Friday, April 3rd.
Davidson [2020]
3/28/2020 Loan Private Directives from Treasury. Treasury Department
Allocation privately encourages lenders to prioritize their existing customer
base over new applicants in call with American Bankers
Association. Omeokwe [2020a]
4/2/2020 Program Ad- Sec. Mnuchin Press Conference Error. Treasury Secretary
ministration Mnuchin gives wrong address for SBA application portal in a
Coronavirus Task Force briefing, instructing applicants to go to
SBA.com instead of SBA.gov, the application portal. Larson [2020]
Loan Applications handled through SBA approved lenders. Lenders
Applications were not ready to take loan applications. Treasury was late to
issue program rules. Rudegeair et al. [2020a]
Loan Loans made for 8 weeks at 1% interest to help businesses make
Requirements payroll and rent. The expectation of loan forgiveness was widely
reported. Nilsen [2020]
Loan Banks often prioritize larger businesses with established banking
Allocation relationships. Rudegeair and Simon [2020]
4/4/2020 Funding Congress Announces Expectations about Funding. Senator
Expectations Marco Rubio tweets funding for PPP expected to run out in late
May and will require more appropriations. Rubio [2020a]
Week of April 5th to 11th
Date Topic Reporting Summary
4/6/2020 PPP Fed and Treasury Announce Program to Back PPP
Financing Loans. The Federal Reserve Board, in cooperation with Treasury,
announces a new 13(3) facility to offer term financing backed by
PPP loans. Davidson and Timiraos [2020]
Loan Large restaurants, franchises, and hotel chains, notably the New
Recipients York based Shake Shack, gain exemption from employment limits
and announce their intent to seek funding. There is a growing
concern over these businesses competing with smaller firms for
funds. Davis and Haddon [2020]
Week of April 5th to 11th (continued)
Date Topic Reporting Summary
4/7/2020 Funding Congress Announces Intentions about Funding. Congress
Expectations expresses intent to appropriate $250 Billion in additional PPP
funding by end of week. Congress also expresses the intent to
approve new lenders. Rudegeair et al. [2020b]
Loan Processing applications costly for banks and borrowers. Banks
Application take steps to limit applications to firms with existing accounts.
Banks frequently change application rules responding to Treasury
guidance, public criticism, and program capacity. Borrowers
uncertain about application rules and loan availability. SBA loan
infrastruture cannot process volume, experiencing notable crashes.
Zhou [2020]
Loan Banks limit loans primarily to firms with existing credit
Allocation relationships due to availability of funding and program interest.
Unbanked and smaller firms at a significant disadvantage when
seeking loans, as banks have a large incentive to only make loans
to firms with existing credit lines whose ability to repay is better
known. Zhou [2020]
Loan Receipt Small businesses report significant delays receiving funds. Firms
attempt to substitute towards other lending out of desperation for
funds. Hansen [2020], Hayashi [2020a]
Week of April 12th to 18th
Date Topic Reporting Summary
Loan Wells Fargo reports reaching its capacity and stops making PPP
Availability loans. However, the bank still accepts applications expecting fund
replenishment. Niquette and Jacobs [2020], Warmbrodt [2020]
Loan Receipt Businesses still report significant delays in receiving PPP funds.
Senate Democratic Caucus [2020], The Editorial Board [2020]
4/14/2020 Program Ad- Cease and Desist Issued to SBA.com. Cease and Desist
ministration issued to SBA.com by New York Attorney General Letitia James.
Larson [2020]
4/15/2020 Funding Congressional Negotiations Resume. With the expected
Expectations depletion of program funds, Congressional negotiations over PPP
appropriations resume. Dem. Caucus wants appropriations for
hospitals and state and local governments, with the GOP against.
Werner et al. [2020]
Week of April 12th to 18th (continued)
Date Topic Reporting Summary
4/16/2020 Loan PPP Funding Officially Depleted. Some banks stop taking
Availability new applications in anticipation of funding depletion with as many
as 700,000 businesses awaiting funding. Others continue receiving
applications but stop making new loans. Peterson [2020], Rubio
[2020b]
Loan National Federation of Independent Businesses (NFIB) reports
Availability only 4% of surveyed members have been approved for a loan, while
none of those approved have yet to receive funding. Niquette and
Jacobs [2020]
Loan SBA excludes tiered franchises from receiving loans. Bykowicz
Availability [2020]
Program Ad- There is a growing concern that banks will “authorization hoard,”
ministration collecting applications that they do not intend to fulfill which they
can chose among when issued more funds. Warmbrodt [2020]
4/18/2020 Funding Pres. Trump Signals Support for Hospital Funding.
Expectations President Trump signals support for appropriating funds for
hospitals, with the GOP Caucus still unsupportive. Wasson
[2020], House et al. [2020]
Week of April 19th to 25th
Date Topic Reporting Summary
4/20/2020 Funding Democrats Offer More Appropriations for Swing States.
Expectations Democratic Caucus outlines compromise offer appropriating funds
to local governments, with favorable apportionments to swing
states. Flatley et al. [2020a]
4/20/2020 Funding White House Outlines Negotiating Position. White House
Expectations and Sec. Mnuchin report a willingness to appropriate $300 Billion
for the PPP. Flatley et al. [2020b]
4/20/2020 Loan SEC 8-K Filings Released. SEC 8-K filings reveal 70 publically
Recipients traded companies received PPP loans. The White House and the
GOP Caucus face sustained public criticism over recipients. Nix
et al. [2020]
Loan Shake Shack announces they will return their $10 Million PPP
Recepients loan to the SBA. O’Connell [2020a]
Loan Receipt NFIB reports only about 20% of their surveyed members who
applied for a PPP loan had received money by April 17. Further,
about 75% had applied with 26% in the process of applying when
funding ran out. Niquette [2020]
Week of April 19th to 25th (continued)
Date Topic Reporting Summary
4/21/2020 Funding $484 Billion in relief funding passes Senate, with $310 Billion for
Expectations PPP loans and $11 Billion for related fees. Werner and Kim [2020]
Loan Regional and community lenders are more successful than large
Applications banks at making modest loans to a large number of small
businesses. Loan seekers report subsituting towards applying for
PPP loans at smaller banks. Levitt [2020]
Loan JP Morgan Chase faces criticism after disclosing data that reveals
Allocation high rejection rates among small business clients and high
acceptance rates among large clients. Only 6% of JP Morgan’s
300,000 small business customers received loans. McLaughlin and
Davis [2020]
4/23/2020 Funding Congress Passes New PPP Funding. $321 Billion for PPP
Expectations funding and related fees
4/23/2020 Loan SBA Bars Firms with Other Cash Sources. The SBA issues
Recipients new guidance suggesting companies with other sources of cash
would not qualify for PPP loans, and that the firms who have
already borrowed under this standard should return their loan by
May 7th. O’Connell and Gregg [2020]
Loan Kura Sushi and Sweetgreen agree to return millions in PPP loans.
Recipients O’Connell [2020b]
Program Ad- Small business associations express dismay over new funding
ministration numbers. Each expects that replenishment will be exhausted
quickly, and criticizes the program’s rollout as insufficient. Brody
et al. [2020]
Loan Autonation, a national network of auto dealerships, returns more
Recipients than $77 Million in PPP funds after two employees whistleblow to
the Washington Post. O’Connell [2020c]
Loan There is a growing concern that minority owned businesses will be
Allocation shut out of PPP funds, as funding sources more readily available
to them, like Community Development Financial Institutions, are
not PPP approved lenders. Knowles [2020]
Program Ad- Small business owners who were denied PPP loans are becoming
ministration increasingly dissatisfied with the program. There is significant
confusion among applicants and potential applicants over
applications, approvals, where to get funds, and if they were
accepted, whether they would get funds. There is a growing
impression that large businesses had sizable advantages in securing
loans combined with a waning belief in the good faith of the
program. The self employed also feel shut out of the progam’s
rollout. Basak et al. [2020]
Week of April 19th to 25th (continued)
Date Topic Reporting Summary
4/24/2020 Funding President Signs New PPP Funding into Law. $321 Billion
Expectations for PPP funding and related fees
Week of April 26th to May 2nd
Date Topic Reporting Summary
4/26/2020 Program Ad- SBA Institutes Cap on Per-Bank Applications. The SBA
ministration sets a cap on the number of relief loans a single bank can process.
Banks can issue at maximum 10% of the funding authority of
PPP. Niquette and Levitt [2020]
4/27/2020 Loan Applications for New PPP Funds Open. Small Businesses
Availability encouraged to apply as soon as possible to access funds. Davis
et al. [2020]
Loan PPP loans mandate that 75% of the funds go towards salaries and
Requirements 25% go towards rents to receive forgiveness. Businesses in high
rent metros feel the squeeze, and either shut down or worry their
loans will not be forgiven for spending more on rent than the loans
require. Hayashi [2020b]
Week of May 3rd to 9th
Date Topic Reporting Summary
Program Ad- Demand for PPP loans falls, but not demand for loans in general.
ministration Borrowers look elsewhere for funds, as they worry they will not get
PPP loans forgiven and are unconvinced they can apply for and
get funds quickly. Of $310 Billion appropriated, 40% remains
available as of May 8th. Banks finding about 10% of their loan
applications are duplicates. An illustrative quote from a business
owner: “It’s basically a large loan that we are going to be stuck
with.” Hayashi [2020c]
5/8/2020 Program Ad- Inspector General Statement. The SBA inspector general
ministration finds that the SBA did not issue guidance to prioritize rural and
underserved communities following congressional guidance on PPP
administration. Omeokwe [2020b]
Week of May 10th to 16th
Date Topic Reporting Summary
5/14/2020 Program Ad- Census Bureau Small Business Survey. Census Bureau
ministration survey finds that nationally, about 75% of small businesses sought
some sort of Federal aid, with 38% of respondents reporting they
had gotten the loan money. Of those surveyed, 30% sought EIDL
loans, with 10% having received payment. The respondents believe
it will take a significant period of time to return to pre-COVID-19
levels of demand. Omeokwe [2020c]
Week of May 17th to 23rd
Date Topic Reporting Summary
5/17/2020 Loan Congress Plans Changes to PPP Loan Requirements.
Requirements Congress expresses intent to change loan requirements to give
businesses both more time and more flexibility as to how to
disburse PPP funds. Hayashi [2020d]
5/21/2020 Loan Senate Adjourns Without PPP Deal. Senate adjourns for
Requirements recess, failing to pass legislation to extend the term of PPP loans.
Adjournment delays resolution of the problem until at least next
month. Hayashi and Andrews [2020]
Program Ad- SBA’s administration of the program increasingly perceived as
ministration opaque and ignoring legislative priorities. Omeokwe [2020d]
5/23/2020 Program Ad- Treasury Rule Change. Treasury Department revises PPP loan
ministration rules allowing the SBA to review program loans “of any size at
any time of SBA’s discretion” signaling intent to monitor how
businesses spend funds. Borrowers must retain documentation for
6 years after initial loan and alert state unemployment offices if
workers refuse requests to return to work. Hayashi [2020e]
Program Ad- The Treasury guidance is seen as partly insufficient, as it leaves
ministration business’s uncertaint about eligibility for forgiveness and loan
terms untouched. However, the guidance does provide
clairification on how to calculate payroll and non-payroll expenses.
Hayashi [2020e]
Week of May 31st to June 6th
Date Topic Reporting Summary
6/3/2020 Loan Senate Passes PPP Extension. Senate passes bill extending
Requirements loan forgiveness timeline from eight to twenty four weeks. The bill
also reduces payroll requirements to allow 60% of the loan to go
towards payroll, compared to the previous requirement of 75%.
Andrews [2020]
6/5/2020 Loan Senate bill is signed into law, largely unchanged. Deadline to
Requirements apply for new PPP loans Aug. 8th. Hayashi [2020f]
Week of June 7th to 13th
Date Topic Reporting Summary
Loan Changes to program complicate the process of obtaining loan
Forgiveness forgiveness. Uncertainty over the loan spending requirements and
the prospect of increased fraud enforcement scares some businesses
from applying for forgiveness. Lenders determine whether a loan
satisfies forgiveness guidelines and fear being stuck with
unprofitable loans, but the SBA has not yet said how lenders
should submit forgiveness applications. The NFIB reports nearly
one-third of businesses who received PPP loans passed the original
eight week deadline by June 14. Multiple interviewees express that
the loan could end their business if not forgiven instead of saving
it, believing they would be unable to service a debt even if
economic conditions returned to normal. Simon and Rudegeair
[2020a]
Week of June 14th to 20th
Date Topic Reporting Summary
Program Ad- The PPP widely seen as not living up to expectations. Only 4.6
ministration million loans were allocated to 31.7 million US small businesses.
The application process is widely seen as confusing and onerous in
retrospect, with many small businesses report either returning
money or not applying because they did not think they met the
requirements. Simon et al. [2020]
Week of June 21st to 27th
Date Topic Reporting Summary
Loan Firms who received PPP loans early quickly spent the money to
Forgiveness comply with the original rules, but now, cannot reapply to receive
more PPP funding under the PPP’s “one business, one loan”
policy. Simon and Rudegeair [2020b]
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8286. URL https://www.washingtonpost.com/business/on-small-business/
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Zachary Warmbrodt. Small business loan program stumbles as sba system
crashes. Politico, April 2020. URL https://www.politico.com/news/2020/04/06/
small-business-loan-program-system-crashes-169614.
Senate Democratic Caucus. President trump and his administration must fix
these problems immediately: Small business owners across the country say thay
aren’t getting needed funds from the paycheck protection program. Press Re-
lease, April 2020. URL https://www.democrats.senate.gov/imo/media/doc/
SmallBusinessOwnersAreNotReceivingPromisedFundsfromPPPCoronavirusProgram.
pdf.
The Editorial Board. Small Business Loan Snafus. Wall Street Jour-
nal, April 2020. ISSN 0099-9660. URL https://www.wsj.com/articles/
small-business-loan-hold-up-11586907564.
Erica Werner, Aaron Gregg, and Renae Merle. Trump says negotiations underway to revive
small business loan program, as firms impacted by virus grasp for new loans. Washington
Post, April 2020. ISSN 0190-8286. URL https://www.washingtonpost.com/us-policy/
2020/04/16/congress-coronavirus-small-business-trump/.
Kristina Peterson. Small-Business Aid Program Set to Run Out of Money. Wall
Street Journal, April 2020. ISSN 0099-9660. URL https://www.wsj.com/articles/
small-business-aid-program-set-to-run-out-of-money-later-today-11586971294.
Marco Rubio. Senator marco rubio announces ppp program to run out of funding, with
700,000 applications in limbo. twitter.com, April 2020b. URL https://twitter.com/
marcorubio/status/1250516086136754176.
Julie Bykowicz. Small-Business Aid Package Excludes Many Franchises in Coronavirus Crisis.
Wall Street Journal, April 2020. ISSN 0099-9660. URL https://www.wsj.com/articles/
small-business-aid-package-excludes-many-franchises-in-coronavirus-crisis-11587029401.
Erik Wasson. Trump Suggests He Could Back Deal to Fund Hospi-
tals in SBA Bill. Washington Post, April 2020. ISSN 0190-8286.
URL https://www.washingtonpost.com/business/on-small-business/
trump-suggests-he-could-back-deal-to-fund-hospitals-in-sba-bill/2020/
04/17/167f12d2-8109-11ea-84c2-0792d8591911_story.html.
Billy House, Erik Wasson, and Steven T. Dennis. Mnuchin, Democrats Remain Far
Apart on Bolstering Virus Stimulus. Washington Post, April 2020. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
mnuchin-democrats-remain-far-apart-on-bolstering-virus-stimulus/2020/04/
16/f15bcbde-7fe7-11ea-84c2-0792d8591911_story.html.
Daniel Flatley, Hailey Waller, and Billy House. Mnuchin, Democrats Say Aid
Package May Be Passed in Days. Washington Post, April 2020a. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
mnuchin-democrats-say-aid-package-may-be-passed-in-days/2020/04/19/
0765d366-823b-11ea-81a3-9690c9881111_story.html.
Daniel Flatley, Hailey Waller, and Billy House. Trump Joins Chorus Saying Agree-
ment on New Virus Aid is Close. Washington Post, April 2020b. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
trump-joins-chorus-saying-agreement-on-new-virus-aid-is-close/2020/04/19/
9f05b31c-829f-11ea-81a3-9690c9881111_story.html.
Naomi Nix, Mark Niquette, and David McLaughlin. Shake Shack, Potbelly Among
Chains Tapping Small-Biz Funds. Washington Post, April 2020. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
shake-shack-potbelly-among-chains-tapping-small-biz-funds/2020/04/19/
005514d4-8249-11ea-81a3-9690c9881111_story.html.
Jonathan O’Connell. White House, GOP face heat after hotel and restaurant
chains helped run small business program dry. Washington Post, April 2020a.
ISSN 0190-8286. URL https://www.washingtonpost.com/business/2020/04/20/
white-house-gop-face-heat-after-hotel-restaurant-chains-helped-run-small-business-pro
Mark Niquette. Most Small Firms Report Not Yet Getting Money
From Virus Funds. Washington Post, April 2020. ISSN 0190-8286.
URL https://www.washingtonpost.com/business/on-small-business/
most-small-firms-report-not-yet-getting-money-from-virus-funds/2020/
04/20/7419af4e-834b-11ea-81a3-9690c9881111_story.html.
Erica Werner and Seung Min Kim. Senate passes $484 billion bill that would expand
small-business aid, boost money for hospitals and testing. Washington Post, April
2020. ISSN 0190-8286. URL https://www.washingtonpost.com/us-policy/2020/04/
21/congress-coronavirus-small-business/.
Hannah Levitt. Giant U.S. Lenders Outpaced by Rivals in Small-
Business Rescue. Washington Post, April 2020. ISSN 0190-8286.
URL https://www.washingtonpost.com/business/on-small-business/
giant-us-lenders-outpaced-by-rivals-in-small-business-rescue/2020/04/
17/74004e76-80fa-11ea-84c2-0792d8591911_story.html.
David McLaughlin and Michelle F. Davis. JPMorgans Small Business Loans In-
stead Went to Its Biggest Customers. Washington Post, April 2020. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
jpmorgans-small-business-loans-instead-went-to-its-biggest-customers/
2020/04/22/d440d5a0-8477-11ea-81a3-9690c9881111_story.html.
Jonathan O’Connell and Aaron Gregg. New SBA guidelines ask big companies
to consider returning coronavirus loans. Washington Post, April 2020. ISSN
0190-8286. URL https://www.washingtonpost.com/business/2020/04/23/
new-sba-guidelines-say-big-companies-that-got-coronavirus-loans-should-return-money/.
Jonathan O’Connell. Two more restaurant chains are giving back small-business funds.
Washington Post, April 2020b. ISSN 0190-8286. URL https://www.washingtonpost.
com/business/2020/04/23/kura-sushi-sba/.
Ben Brody, Naomi Nix, and Mark Niquette. Small Businesses Fear Fresh Loan
Funding Wont Be Nearly Enough. Washington Post, April 2020. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
small-businesses-fear-fresh-loan-funding-wont-be-nearly-enough/2020/04/
23/47ed6fc2-859f-11ea-81a3-9690c9881111_story.html.
Jonathan O’Connell. AutoNation, a retailer worth billions, says it received
nearly $80 million in SBA funds. Washington Post, April 2020c. ISSN
0190-8286. URL https://www.washingtonpost.com/business/2020/04/24/
autonation-an-auto-retailer-worth-billions-received-nearly-95-million-sba-funds/.
Hannah Knowles. Covid-19 could devastate minority-owned businesses, amid
fears theyll also miss out on aid. Washington Post, April 2020. ISSN
0190-8286. URL https://www.washingtonpost.com/business/2020/04/24/
covid-19-could-devastate-minority-owned-businesses-amid-fears-theyll-also-miss-out-ai
Sonali Basak, Emma Kinery, and Bob Ivry. Resentment Grows on Main Street
Over Bailout Winners and Losers. Washington Post, April 2020. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
resentment-grows-on-main-street-over-bailout-winners-and-losers/2020/04/
24/21f93dbc-860a-11ea-81a3-9690c9881111_story.html.
Mark Niquette and Hannah Levitt. SBA Sets Cap on Amount of Relief
Loans That Banks Can Process. Washington Post, April 2020. ISSN 0190-
8286. URL https://www.washingtonpost.com/business/on-small-business/
sba-sets-cap-on-amount-of-relief-loans-that-banks-can-process/2020/04/26/
55632164-8807-11ea-80df-d24b35a568ae_story.html.
Susan Davis, Claudia Grisales, Kelsey Snell, and Alana Wise. Trump signs lat-
est coronavirus economic relief package, aimed at small businesses. National
Public Radio, April 2020. URL https://www.npr.org/2020/04/23/842706630/
congress-passes-latest-economic-relief-package-to-confront-coronavirus.
Yuka Hayashi. For Small Businesses With High Rents, Coronavirus Aid Falls Short. Wall
Street Journal, May 2020b. ISSN 0099-9660. URL https://www.wsj.com/articles/
for-many-small-businesses-u-s-coronavirus-aid-falls-short-11588325404.
Yuka Hayashi. Demand for Small-Business Loans Cools. Wall Street Jour-
nal, May 2020c. ISSN 0099-9660. URL https://www.wsj.com/articles/
demand-for-small-business-loans-cools-11588930201.
Amara Omeokwe. SBA Veered From Guidelines on Small-Business Loans, Report Says. Wall
Street Journal, May 2020b. ISSN 0099-9660. URL https://www.wsj.com/articles/
sba-veered-from-guidelines-on-small-business-loans-report-says-11588971101.
Amara Omeokwe. Coronavirus Drives 75% of Small Businesses to Seek Federal Aid. Wall
Street Journal, May 2020c. ISSN 0099-9660. URL https://www.wsj.com/articles/
coronavirus-drives-75-of-small-businesses-to-seek-federal-aid-11589476452.
Yuka Hayashi. U.S. Expected to Revise Small-Business Aid Program. Wall Street
Journal, May 2020d. ISSN 0099-9660. URL https://www.wsj.com/articles/
u-s-expected-to-revise-small-business-aid-program-11589707801.
Yuka Hayashi and Natalie Andrews. Senate Adjourns Without Approv-
ing Deal to Extend PPP Spending Window. Wall Street Journal,
May 2020. ISSN 0099-9660. URL https://www.wsj.com/articles/
lawmakers-seek-to-double-duration-of-small-business-loans-11590058811.
Amara Omeokwe. SBA Under Spotlight as Frustrations With
Small-Business Aid Programs Grow. Wall Street Journal, May
2020d. ISSN 0099-9660. URL https://www.wsj.com/articles/
sba-under-spotlight-as-frustrations-with-aid-programs-grow-11590053400.
Yuka Hayashi. Questions Persist After Release of New Small-Business Loan Rules. Wall
Street Journal, May 2020e. ISSN 0099-9660. URL https://www.wsj.com/articles/
questions-persist-after-release-of-new-small-business-loan-rules-11590269875.
Natalie Andrews. Senate Approves Bill Extending Paycheck Protection Program. Wall
Street Journal, June 2020. ISSN 0099-9660. URL https://www.wsj.com/articles/
senate-approves-bill-extending-paycheck-protection-program-11591226261.
Yuka Hayashi. Major Fixes Made to Small-Business Loan Program. Wall Street
Journal, June 2020f. ISSN 0099-9660. URL https://www.wsj.com/articles/
major-fixes-made-to-small-business-loan-program-11591390344.
Ruth Simon and Peter Rudegeair. Small Businesses Tackle New PPP Puzzle: Forgive-
ness. Wall Street Journal, June 2020a. ISSN 0099-9660. URL https://www.wsj.com/
articles/small-businesses-tackle-new-ppp-puzzle-forgiveness-11592136025.
Ruth Simon, Peter Rudegeair, and Yuka Hayashi. PPP Small-Business
Loans Left Behind Many of Americas Neediest Firms. Wall Street Jour-
nal, June 2020. ISSN 0099-9660. URL https://www.wsj.com/articles/
ppp-small-business-loans-left-behind-many-of-americas-neediest-firms-11592407677.
Ruth Simon and Peter Rudegeair. Small Businesses Raced to Spend
PPP Funds but Covid-19 Pandemic Drags On. Wall Street Journal,
June 2020b. ISSN 0099-9660. URL https://www.wsj.com/articles/
small-businesses-raced-to-spend-ppp-funds-but-covid-19-pandemic-drags-on-11593163801.
B Data Appendix
B.1 Outbreak Severity
Because local economic conditions and the resulting employment effects of the PPP are
affected by the severity of the local Covid-19 outbreak and the non-pharmaceutical interven-
tions (NPIs) used to fight it, we use Covid-19 cases, deaths, and NPIs as controls in some
of our specifications. The Covid-19 confirmed cases and deaths are county level data from
JHU CSSE (2020), which are the most prominent and frequently used source of case and
death data. We use only their US data, which are cleaned and organized data provided by
the US Centers for Disease Control and Prevention (CDC).
B.2 Non-pharmaceutical Interventions
NPI data are from the Oxford COVID-19 Government Response Tracker Hale et al. (2020).
Their repository contains NPIs at the State level for all 50 states and the District of Columbia.
We assess the county NPIs during the week that contains the 12th of the month to align
with the monthly timing of our labor market data.38
B.3 Occupational Exposure to the Covid-19 Shock
We rank occupations according to their exposure to the Covid-19 shock on three dimensions,
need for proximity to others, essentialness, and ability to work from home. To do so we follow
the metrics generated by Leibovici et al. (2020) and Jackson (2020).
B.4 Social Safety Net
The CARES Act and subsequent executive action significantly increased eligibility for and
assistance from the social safety net, in particular unemployment insurance. The CARES Act
mandated an expansion of unemployment insurance eligibility–Pandemic Unemployment As-
sistance (PUA)–and an additional $600 per week, through the end of July, in unemployment
insurance for those whose claims were approved–Pandemic Unemployment Compensation
(PUC). States took variable amounts of time to implement the new policies resulting in
one to two month differences in the start date of payments under the two programs (Nunn
et al. (2020)). After these PUC lapsed, President Trump signed an executive order mandat-
ing $300 to $400 extra per week in unemployment insurance under a program called “Lost
38
As noted in section 3.2 the Current Population Survey queries about labor market activities during the
week of the month that contains the 12th .
66
Wages Assistance” (LWA). These funds were supported by grants from the Federal Emer-
gency Management Agency (FEMA). Due to variable time to approval for FEMA funds
states began paying LWA and exhausted Federal funding or the program at different times
(Singh (2020)).
B.5 Additional details about the Current Population Survey
In the fourth and the eighth monthly surveys, respondents report their weekly earnings if
they are public or private employees. In addition, each March respondents also report their
wage and salary income in the past year regardless of their current employment status or class
of worker. Thus, for each respondent, we can calculate one to two proxies for the unemploy-
ment insurance that she would receive were she to claim and be eligible for unemployment
insurance, given state level unemployment insurance laws, state level variation in the PUC,
PUA, and LWA, and her past reported labor income.of Labor (2020) In our baseline specifi-
cation we control for the state-level timing of PUC, PUA, and LWA participation interacted
with class of worker (self-employed, private, public). Results are robust to controlling for
unemployment insurance replacement rates under these programs inferred from the Earner
Study or March Supplement income data and to replacement rates estimated using imputed
earnings based on either of these sources of earnings data.
The COVID-19 outbreak complicated the collection of CPS data in 2020, as it com-
plicated virtually all other government, business, and household activities. The CPS is
typically collected via a mixture of telephone and in-person interviews with in-person inter-
views primarily being used for the first and fifth wave of data collection. In March 2020,
CPS suspended in-person interviews to protect its employees. In person interviews resumed
in some parts of the country in July. These changes in operating protocol resulted in large
increases in non-response and there is evidence that non-response is non-random. There is
some evidence that the COVID-19 pandemic and related policy interventions had a non-
neutral impact on the CPS’ representation of demographic subgroups (Ward and Edwards,
2020). To ameliorate the effects of these survey challenges, we control for interview type in
all of our specifications.
67
C Online Appendix Tables
68
Table 12: Unemployment Effects.
Share Delayed X
January -0.00912 -0.0105 -0.0109 -0.00906 -0.00917 -0.00877 -0.00930 -0.0221 -0.0108
(0.0154) (0.0153) (0.0154) (0.0154) (0.0154) (0.0155) (0.0154) (0.0219) (0.0154)
February -0.0193 -0.0225 -0.0224 -0.0192 -0.0195 -0.0180 -0.0200 -0.0367 -0.0224
(0.0191) (0.0188) (0.0189) (0.0191) (0.0191) (0.0192) (0.0191) (0.0254) (0.0189)
March 0.00459 0.000297 -0.0000610 0.00499 -0.000699 0.00450 0.00407 -0.0434 -0.00432
(0.0231) (0.0225) (0.0225) (0.0231) (0.0231) (0.0231) (0.0231) (0.0313) (0.0226)
April -0.00208 -0.0129 -0.0126 -0.0206 -0.00165 -0.00136 -0.00383 -0.0456 -0.0399
(0.0541) (0.0433) (0.0432) (0.0502) (0.0541) (0.0540) (0.0540) (0.0572) (0.0429)
May 0.116** 0.106*** 0.108*** 0.100** 0.120** 0.117** 0.131** 0.0883* 0.105***
(0.0503) (0.0373) (0.0376) (0.0471) (0.0506) (0.0503) (0.0511) (0.0513) (0.0387)
June 0.115** 0.102*** 0.102*** 0.114** 0.112** 0.117** 0.110** 0.0622 0.0932**
(0.0499) (0.0387) (0.0389) (0.0454) (0.0493) (0.0499) (0.0496) (0.0535) (0.0372)
July 0.113*** 0.0983*** 0.0973*** 0.111*** 0.103** 0.114*** 0.107** 0.0670 0.0827**
(0.0439) (0.0353) (0.0354) (0.0423) (0.0429) (0.0437) (0.0437) (0.0468) (0.0347)
August 0.109*** 0.0953*** 0.0953*** 0.105*** 0.102*** 0.110*** 0.106*** 0.0698* 0.0842***
(0.0379) (0.0307) (0.0305) (0.0380) (0.0372) (0.0378) (0.0378) (0.0420) (0.0307)
September 0.126*** 0.117*** 0.117*** 0.120*** 0.119*** 0.127*** 0.118*** 0.0925** 0.103***
(0.0363) (0.0305) (0.0305) (0.0362) (0.0357) (0.0363) (0.0362) (0.0406) (0.0305)
October 0.0799** 0.0705** 0.0721*** 0.0839*** 0.0720** 0.0800** 0.0775** 0.0517 0.0662**
69 (0.0316) (0.0275) (0.0274) (0.0314) (0.0308) (0.0315) (0.0315) (0.0373) (0.0273)
R2 0.665 0.675 0.674 0.666 0.666 0.666 0.666 0.675 0.675
N 792,197 792,197 792,197 792,197 792,197 792,197 792,197 792,197 792,197
Individual YES YES YES YES YES YES YES YES YES
Month YES YES YES YES YES YES YES YES YES
Month-in-2020 YES YES YES YES YES YES YES YES YES
Controls
Industry and Occupation A B B B
Cases and Deaths C C
Non-pharmaceutical Interventions D D
Covid Induced Measurement Changes E E E
Unemployment Insurance F F
State × Month FE H
Standard errors, clustered at the CBSA X Industry, in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
A: Fixed effects for 2-Digit NAICS and 22 Broad Industries fully interacted with Month-in-2020 fixed effects.
B: Fixed effects for 2-Digit NAICS and controls for occupational exposure to the Covid shock fully interacted with Month-in-2020 fixed effects. Occupational exposure is
measured by ability to work from home, required proximity to others, and essentialness. Coding follows CITE, CITE, and CITE, respectively.
C: New cases and new deaths in the CBSA. New York City is an outlier, as a result we allow for the coefficient on cases and deaths to differ for this CBSA. Data come from
the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, 2020, https://github.com/CSSEGISandData/COVID-19.
D: State level stringency Index: Oxford COVID-19 Government Response Tracker. Hale et al. (2020).
E: Fixed effects for month in which the respondent was first interviewed by the CPS and for interview type fully interacted with Month-in-2020 fixed effects.
F: Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the reference week interacted with class of worker.
Data come from https://www.brookings.edu/blog/up-front/2020/05/13/incomes-have-crashed-how-much-has-unemployment-insurance-helped/ and https://www.savingtoinvest.com/
when-will-300-lwa-unemployment-start-being-paid-in-my-state/.\
Table 13: Nonemployment Effects.
Share Delayed X
January -0.0138 -0.0166 -0.0169 -0.0136 -0.0138 -0.0120 -0.0135 -0.00901 -0.0150
(0.0217) (0.0217) (0.0217) (0.0217) (0.0217) (0.0215) (0.0217) (0.0304) (0.0215)
February -0.0155 -0.0186 -0.0188 -0.0153 -0.0158 -0.0139 -0.0158 -0.0474 -0.0179
(0.0251) (0.0245) (0.0246) (0.0251) (0.0251) (0.0250) (0.0251) (0.0344) (0.0245)
March 0.0227 0.0180 0.0171 0.0234 0.0152 0.0233 0.0227 -0.0358 0.0121
(0.0286) (0.0279) (0.0277) (0.0286) (0.0283) (0.0286) (0.0287) (0.0392) (0.0276)
April 0.0112 0.00244 -0.000630 -0.0245 0.0117 0.0115 0.00791 -0.0261 -0.0453
(0.0604) (0.0479) (0.0480) (0.0545) (0.0604) (0.0602) (0.0600) (0.0629) (0.0461)
May 0.157*** 0.147*** 0.148*** 0.135*** 0.162*** 0.158*** 0.171*** 0.125** 0.141***
(0.0553) (0.0412) (0.0412) (0.0514) (0.0555) (0.0552) (0.0560) (0.0570) (0.0421)
June 0.123** 0.112*** 0.112*** 0.126** 0.118** 0.124** 0.118** 0.0541 0.108***
(0.0545) (0.0423) (0.0425) (0.0497) (0.0537) (0.0544) (0.0542) (0.0581) (0.0405)
July 0.115** 0.102*** 0.102*** 0.112** 0.101** 0.114** 0.110** 0.0487 0.0827**
(0.0481) (0.0395) (0.0396) (0.0465) (0.0470) (0.0481) (0.0480) (0.0543) (0.0389)
August 0.140*** 0.127*** 0.128*** 0.132*** 0.130*** 0.136*** 0.137*** 0.109** 0.106***
(0.0428) (0.0358) (0.0356) (0.0430) (0.0421) (0.0429) (0.0428) (0.0511) (0.0359)
September 0.134*** 0.118*** 0.122*** 0.125*** 0.124*** 0.133*** 0.130*** 0.0962* 0.103***
(0.0435) (0.0357) (0.0357) (0.0433) (0.0426) (0.0433) (0.0443) (0.0501) (0.0361)
October 0.0850** 0.0708** 0.0745** 0.0851** 0.0742* 0.0782* 0.0810** 0.0363 0.0541
(0.0411) (0.0351) (0.0352) (0.0408) (0.0400) (0.0407) (0.0409) (0.0490) (0.0348)
70 R2 0.639 0.645 0.645 0.639 0.639 0.642 0.639 0.649 0.649
N 831,737 831,737 831,737 831,737 831,737 831,737 831,737 831,737 831,737
Individual YES YES YES YES YES YES YES YES YES
Month YES YES YES YES YES YES YES YES YES
Month-in-2020 YES YES YES YES YES YES YES YES YES
Controls
Industry and Occupation A B B B
Cases and Deaths C C
Non-pharmaceutical Interventions D D
Covid Induced Measurement Changes E E E
nonemployment Insurance F F
State × Month FE H
Standard errors, clustered at the CBSA X Industry, in parentheses. * p<0.10, ** p<0.05, *** p<0.01.
A: Fixed effects for 2-Digit NAICS and 22 Broad Industries fully interacted with Month-in-2020 fixed effects.
B: Fixed effects for 2-Digit NAICS and controls for occupational exposure to the Covid shock fully interacted with Month-in-2020 fixed effects. Occupational exposure is
measured by ability to work from home, required proximity to others, and essentialness. Coding follows CITE, CITE, and CITE, respectively.
C: New cases and new deaths in the CBSA. New York City is an outlier, as a result we allow for the coefficient on cases and deaths to differ for this CBSA. Data come from
the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, 2020, https://github.com/CSSEGISandData/COVID-19.
D: State level stringency Index: Oxford COVID-19 Government Response Tracker. Hale et al. (2020).
E: Fixed effects for month in which the respondent was first interviewed by the CPS and for interview type fully interacted with Month-in-2020 fixed effects.
F: Indicator for whether PUC, PUA, and/or LWA were made in the respondent’s state in the reference week interacted with class of worker.
Data come from https://www.brookings.edu/blog/up-front/2020/05/13/incomes-have-crashed-how-much-has-unemployment-insurance-helped/ and https://www.savingtoinvest.com/
when-will-300-lwa-unemployment-start-being-paid-in-my-state/.