NBER WORKING PAPER SERIES
HAS THE PAYCHECK PROTECTION PROGRAM SUCCEEDED?
R. Glenn Hubbard
Michael R. Strain
Working Paper 28032
http://www.nber.org/papers/w28032
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
October 2020
The views expressed herein are those of the authors and do not necessarily reflect the views of the
National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
© 2020 by R. Glenn Hubbard and Michael R. Strain. All rights reserved. Short sections of text,
not to exceed two paragraphs, may be quoted without explicit permission provided that full
credit, including © notice, is given to the source.
Has the Paycheck Protection Program Succeeded?
R. Glenn Hubbard and Michael R. Strain
NBER Working Paper No. 28032
October 2020
JEL No. E24,E62,H25,H3,H32
ABSTRACT
Enacted March 27, 2020, the Paycheck Protection Program (PPP) was the most ambitious and
creative fiscal policy response to the Pandemic Recession in the United States. PPP offers
forgivable loans — essentially grants — to businesses with 500 or fewer employees that meet
certain requirements. In this paper, we present evidence that PPP has substantially increased the
employment, financial health, and survival of small businesses, using data from the Dun &
Bradstreet Corporation. We use event studies and standard difference-in-difference models to
estimate the effect of a small business applying for larger PPP loans and of a small business being
eligible for PPP based on size. While our findings are informative, we believe it is too early to
issue conclusive judgment on PPP’s success. We offer lessons for the future from the PPP
experience thus far.
R. Glenn Hubbard
Graduate School of Business
Columbia University, 607 Uris Hall
3022 Broadway
New York, NY 10027
and NBER
Michael R. Strain
American Enterprise Institute
1789 Massachusetts Avenue, NW
Washington, DC 20036
and IZA
1. Introduction
The Paycheck Protection Program was the most ambitious and creative — and,
potentially, the most important — fiscal policy response to the Pandemic Recession in the United
States. With a $669 billion budget, the program is the largest single component of the nation’s
fiscal policy response to the crisis, and by itself approaches the total amount spent by Congress
on the 2009 Recovery Act response to the Great Recession.
It was enacted on March 27, 2020, as part of the Coronavirus Aid, Relief, and Economic
Security (CARES) Act, the $1.8-trillion “Phase 3” response to the pandemic crisis. An entirely
new program, it began issuing loans seven days later, on April 3. It offers forgivable loans —
essentially, grants — to businesses with 500 or fewer employees that meet certain requirements,
including maintaining employment at pre-pandemic levels.
Has it succeeded? In this paper, we present evidence that PPP has substantially increased
the employment, financial health, and survival of small businesses. In addition, we find that the
effect of PPP on small business outcomes is increasing over time, with larger effects in August
than in April or May. We also find some evidence to suggest that PPP was most effective for
relatively smaller firms. We use data from the Dun & Bradstreet Corporation for our analysis,
employing standard difference-in-difference models to estimate the effects of a small business
applying for a PPP loan of greater than $150,000 (we only observe PPP applications for loans of
that size) and of a small business being eligible for PPP based on size, and using event studies to
trace the dynamic effects of PPP.
Despite this finding, our ultimate conclusion is that it is too early to issue any definitive
judgment on PPP’s success. The program had important short-run goals, to be sure. These
include supporting employment and replacing worker wages, maintaining worker-firm
attachments, boosting consumer spending, and ensuring small business continuity during the
shutdown. But the program had important medium-run goals, as well, including preventing a
wave of bankruptcies once the economy partially reopened, increasing productivity by
preserving firm-specific human capital, worker-firm matches, and networks, and helping the
economy recover faster by keeping workers off the unemployment rolls. Our data run through
August, and we cannot adequately investigate any of these outcomes. The effects of PPP are
unfolding, and it will be particularly important to see what happens to businesses that received
PPP and the workers they employ once they have exhausted their forgivable loan.
PPP is a novel program, and many standard intuitions about fiscal policy do not apply to
it. It was not a stimulus program in the sense that its purpose was not to ‘stimulate’ the economy;
that is, it is not a program calling for a measure of the multiplier. Instead, its purpose was to
preserve the productive capacity of the small-business sector and to shorten the transition to a
new, post-virus equilibrium by supporting labor demand over the medium term, allowing for a
more rapid economic recovery. It was not a jobs program in the sense that its goal was not
exclusively to preserve employment. Instead, its goals were to maintain worker-firm
attachments, particularly during the shutdown, and to ensure small business continuity. It
intentionally did not attempt to exclude inframarginal recipients because the unique
circumstances under which it was enacted made this impractical. In the early days of the
shutdown, how could the government have known which firms were inframarginal? And given
the numerous goals of the program, it’s not clear how ‘marginal’ would be defined in this
context. These design features affect intuitive measures of ‘cost per job saved,’ as we describe
later.
In this paper, we discuss the need for, goals of, and key design features in a small
business revenue replacement program (Section 3). We then describe PPP, and contrast select
features of the program to what we view as the best design (Section 4). We discuss the program’s
implementation challenges — extensively covered in the press — and offer qualitative analysis
of PPP (Section 5). In Section 6, we present our empirical analysis of PPP. In Section 7, we offer
a retrospective and discuss lessons for the future.
2. The Pandemic Recession and Potential Policy Responses
The Pandemic Recession is remarkable in both its suddenness and depth. In the week
ending March 14, 2020, there were 282,000 initial claims for unemployment insurance benefits,
about one-third higher than the average number of new claims over the preceding three months.
The next week, there were 3.3 million initial claims, shattering the previous record of 695,000
new claims, set in October 1982. The week after that, ending on March 28, there were 6.9
million initial claims. At the time of this writing, there are still well over 800,000 new claims
each week.
The unemployment rate in February 2020 was 3.5 percent. In March, the first month of
the Pandemic Recession, it stood at 4.4 percent. In April, it hit its peak of 14.7 percent, the
highest rate since the Great Depression.
1
In two months, the official unemployment rate
increased by a factor of four. For comparison, during the Great Recession it took nearly two
years for the unemployment rate to double, from five percent when the recession began in
December 2007 to its peak of 10 percent in October 2009.
2
The pandemic’s economic devastation extended beyond the labor market. Real GDP
contracted at a 31.4 percent annual rate in the second quarter of 2020. Using the same measure,
the worst quarter in the Great Recession saw an 8.4 percent decline, and the only quarter since
the Great Depression to register a double-digit contraction was 1958 Q1, at 10 percent. Relative
to the same quarter one year prior, 2020 Q1 real GDP contracted by nine percent. The peak
contraction using this metric in the Great Recession was 2008 Q3’s 3.9 percent.
In a situation like this, there are standard roles for monetary policy and social insurance
and safety net programs. It is even straightforward to execute lending programs to large
businesses.
But policy to support small and mid-size businesses is less straightforward to formulate.
The need for a prolonged shutdown made interruption loans for such businesses inadequate to
policymakers’ goals of supporting employment and ensuring small business continuity. And
even with a more conventional loan many businesses would likely not be able to survive. Firms
needed more equity to shore up weakening balance sheets and replace lost cash flows and many
businesses would not be interested in adding to debt burdens in any case. Equity injections were
not implementable for many firms of this size, and operationalizing a program based on them
would be extremely difficult to do in the time needed. But a revenue-replacement program for
small business would achieve the policy goal.
3. A Small Business Revenue-Replacement Program
3
1
The official unemployment rate reported by the Bureau of Labor Statistics for April 2020 was 14.7 percent. The
household survey on which the unemployment rate is calculated showed a large increase in the number of
respondents who were classified as employed but absent from work. Most of these responses should have been
classified as unemployed on temporary layoff. Incorporating this change, the actual unemployment rate for April
was likely 19.5 percent.
2
For research on the labor market effects of the pandemic, see Bartik, et al. (2020), Coibion, et al. (2020), Goolsbee
and Syverson (2020), and Kahn, et al. (2020).
3
This section draws on Hubbard and Strain (2020) and Strain (2020).
The goals of a small business revenue replacement program are twofold: to ensure small
business continuity and prevent a cascade of small business failures, and to preserve existing
employment relationships while shelter-in-place orders are in effect. We offer our analysis of
some key program design features to achieve these goals. We also address moral hazard
concerns, and briefly review programs enacted by other major economies.
3.1. The need to replace small business revenue
The pandemic itself can be thought of as a large shock to aggregate supply: Businesses
could no longer produce goods and services because workers could not safely go to work. The
inability of workers to work caused downstream supply chain disruptions, as well.
Shelter-in-place orders ameliorated the supply shock by reducing the spread of the
coronavirus. The catch is that these policies led to a precipitous drop in aggregate demand,
including labor demand (Kahn, et al., 2020) as businesses were temporarily closed and workers
lost jobs, faced hours reductions, and experienced nominal wage cuts (Cajner, et al., 2020). In
the private economy, workers faced a large reduction in earned income and businesses lost
revenue.
The sharp and sudden nature of the Pandemic Recession left smaller services-sector firms
particularly at risk. Unlike larger businesses, these firms could not readily access capital markets
to shore up their balance sheets. Capital-market imperfections link equity contractions to
business fluctuations, and these firms were particularly vulnerable to a lack of collateralizable
net worth (e.g., Gertler and Hubbard, 1989). Small and mid-size businesses generally do not
have diversified revenue streams, as well. And they have limited cash holdings. Only half of
small businesses hold cash reserves sufficient to cover 15 days, and only four in 10 have a three-
week cash buffer (JP Morgan Chase Institute, 2019).
And unlike manufacturing firms, services businesses would not return to partial
operations following the lockdowns with a backlog of orders. Nearly all of the revenue they lost
during the lockdowns was lost forever — for example, diners did not eat twice as many meals in
May and June because restaurants were shut in March and April.
To summarize, the economy was at risk of a cascade of small business bankruptcies.
Small businesses play a critical role in the economy. Firms with fewer than 500 employees
account for 47 percent of private sector employees and 41 percent of private sector payroll.
There are 30.7 million such businesses, 19 percent of which have paid employees (Small
Business Administration, 2019). A wave of small business failures could have created an
aggregate demand doom loop, in which declining incomes and employment opportunities
reinforced each other.
One way to address this concern would have been to lift lockdown orders. But the public
health effects of the virus and concern workers had about getting sick make this option
unattractive to many policymakers. For a short, temporary shutdown, replacing a large fraction
of the revenue businesses would have generated in normal times would go a long way toward
preventing a cascade of bankruptcies and a reinforcing cycle of declining incomes and job
opportunities.
3.2. Goals, cost, and key design features
The specific goals of such a program are to ensure small business continuity and prevent
a wave of bankruptcies and, during the period of the shutdown, to preserve employment
relationships. The overarching objective is to preserve as much of the productive capacity of the
economy as possible while short-term shelter-in-place orders are in place, and to help the
economy transition quickly to a new, post-shutdown equilibrium by supporting labor demand
over the medium term.
For firms, preventing wasteful liquidations allows the black box of productive
technologies and business relationships to remain intact. Professional networks are preserved,
relationships with suppliers and customers are maintained, and knowledge of local conditions
and preferences can continue to be put to productive use. For workers, the value of firm-specific
human capital is maintained, and maintaining employment relationships means they continue to
be paid by their employer, and they are in a position to return to work immediately once shelter-
in-place orders are lifted. No separation takes place, even a temporary furlough of workers. For
both workers and firms, productivity enhancing worker-firm matches are maintained. And the
economy is in a position to snap back quickly because labor demand has been supported.
4
4
Papers that discuss the role of worker-firm matches include Mortensen and Pissarides (1999) and Davis and von
Wachter (2011). Jackson (2013) measures match quality directly in the context of schools, estimating teacher,
school, and match productivity on student outcomes. He finds that teacher-school (worker-firm) match effects are
important, estimating that a one standard deviation increase in match quality increases math scores by an amount
roughly equal to two-thirds of the effect of a one standard deviation increase in teacher quality. Using linked
worker-firm data, Farooq, Kugler, and Muratori (2020) document an important role for match quality, and find that
more generous unemployment insurance benefits leads to higher quality matches. In our context, match quality
likely matters the most for larger PPP-eligible firms.
This observation is especially true in a lockdown because the risk of mass closures is so
real. Without a program to support small business continuity, a wave of closures would be
followed by a period in which new businesses started. Eventually, the economy would reach a
new equilibrium. But during the transition, labor demand would be depressed because there
would be fewer businesses looking for workers, which would lead to lengthy spells of
unemployment for millions of workers and a slower and more sluggish recovery.
If the (aggregate, present discounted value of) social benefits of these businesses exceeds
their (aggregate, present discounted value of) costs, then a subsidy is justified under standard
economic logic. The possibility of an aggregate demand doom loop and the lengthy period of
high-unemployment it would cause would increase the size of the optimal subsidy.
Once lockdown orders are lifted, partial revenue replacement may still be needed to
achieve the policy goals of the program. But it is no longer necessary to compel firms to
maintain pre-lockdown employment relationships or employment levels. After the economy has
partially reopened, this requirement would introduce frictions into the process of reallocating
labor (and capital) to its post-lockdown most productive use, and would slow the process of
firms reorganizing their post-lockdown production functions, working against the key overall
goal of a revenue-replacement program: ensuring small business continuity.
There is an inherent tension between a revenue-replacement program’s goal of
maintaining employment relationships and keeping firms in business and the goal of efficiently
reallocating factor inputs and swiftly transitioning to a new, post-lockdown equilibrium. But for
the reason we discussed earlier, there is less to this tension than meets the eye in this case. A
revenue-replacement program allows that transition to happen faster by preserving many
otherwise-viable firms during the shutdown. Once the economy has partially reopened, severing
the link between program participation and maintain pre-virus employment levels is critical to
minimizing this tension. A revenue-replacement program may also keep some business afloat
that would have shut down in the absence of the pandemic. Presumably most businesses that
were not viable prior to the pandemic will remain unviable once the revenue-replacement
program has ended.
These considerations suggest that a focus on revenue, not simply on payroll costs, would
be most effective in achieving the policy goals of the program. A separate reason to focus on
revenue rather than narrowly focusing on payroll costs is that non-payroll expenses, like rent in
many cities, are significant. A program replacing payroll costs, but not overall revenue, may not
be sufficient to keep many businesses in high-rent cities from closing.
Replacing small business revenue is an expensive proposition. Hubbard and Strain (2020)
estimate that replacing 80 percent of revenue for 12 weeks for service-sector businesses — that
is, for businesses in industries other than manufacturing, finance and insurance, health care, and
educational services — with fewer than 500 employees would cost $1.2 trillion.
Expensive as such an intervention is, the counterfactual would be even costlier, with
cascading business failures, wasteful liquidations, plunging incomes, soaring unemployment, and
little prospect for a rapid recovery because of the devastating effects on the small business
ecosystem. Another budgetary consideration is the offsetting effects of less use of social
insurance programs, like Unemployment Insurance, and safety net programs, like food stamps.
So far, our discussion of a small business revenue-replacement program has been general,
and could be applied to any situation in which small, services-sector businesses needed to shut
down for a period of several weeks. A key feature of the Pandemic Recession is that such a
program did not exist, and Congress wanted to stand one up quickly. Given this context,
Congress chose to rely on the existing relationships many small businesses have (via checking
accounts or loans) with commercial banks rather than to have had the government attempt to
stand up an entirely new direct transfer program.
The way to get money into business accounts as quickly as possible was for the
government to have treated the banks essentially as conduits. Of course, such an approach
requires convincing banks that they will be held harmless in the event of borrower
misrepresentation, both by the current administration and by future administrations. Strong
assurances are necessary.
5
By structuring the revenue-replacement grants as loans that are forgivable if certain
conditions are met, the program would align with an equity infusion. By backing the loans and
allowing banks to charge fees, the government would encourage banks to participate.
5
Prior to the 2008 financial crisis, large U.S. banks routinely made Federal Housing Administration (FHA) loans
designed to help first-time home buyers and buyers with relatively poor credit purchase houses. To reach these
borrowers, the government encouraged lax lending standards. This policy shift contributed to the housing bubble,
and FHA’s solvency was in question following the crash. The government imposed fines on banks, arguing they did
not adhere to FHA underwriting standards. The revenues from the fines helped to shore up FHA. This episode has
left many large banks skittish about using anything but strict underwriting standards as part of government lending
programs.
The pandemic shutdown’s adverse consequences for firms’ collateralizable net worth and
cash flows require equity contributions to keep them afloat. A lending program would result in
more layoffs by firms that would be averse to taking on more debt. This would be particularly
common in the services sector because revenue losses would often be permanent. Even if debt
service could be deferred for a period of one or two years, many would be reluctant to take out a
loan.
6
These businesses often have low profit margins, and a loan program would likely have had
an insufficient take-up rate to meet policymakers’ objectives.
7
It would be difficult for this program to be targeted based on need. In the fog-of-war
atmosphere of the pandemic, policymakers have limited knowledge of the virus’s spread, and
crafting an effective triggering mechanism based on public-health metrics is difficult.
At the beginning of a sudden and unexpected lockdown, demonstrations of hardship or
need would significantly slow down the process of getting funds to businesses, putting the
effectiveness of the program in jeopardy. Once the economy partially reopens, it can be argued
that revenue tests target assistance on firms that need it most, as measured by revenue loss
relative to normal circumstances. On the other hand, forward-looking revenue tests serve as a
disincentive to earn revenue by imposing implicit marginal tax rates on revenue. Backward-
looking revenue tests avoid this disincentive, but are less generous to otherwise identical firms
that are doing better adjusting to the post-lockdown economic circumstances.
The main appeal of revenue tests and hardship demonstrations are lower program costs
and targeting aid based on “need.” The problem is that need is an amorphous concept in a
partially reopened economy, and revenue tests bring their own problems. A targeting strategy
that is broad-based, focusing on a large class of firms defined by size and industry type, avoids
these issues.
3.3. Addressing moral hazard concerns
A program that replaces revenue for small businesses for a period of time is an
extraordinary government intervention in the private economy. It is reasonable to be concerned
6
For a proposal that argues in favor of lending programs, see Ozimek and Lettieri (2020). Hanson, et al. (2020a)
argue for equity-like arrangements and grants to support small business. Hanson, et al. (2020b) argues for payment
assistance to impacted businesses to meet recurring fixed obligations (e.g., interest, rent, and utilities) during the
health emergency.
7
At the time of this writing, the Federal Reserve’s Main Street Lending Facility has very few loans, suggesting that
even among mid-size business taking out debt under non-borrower-friendly terms is not an attractive prospect.
that such a program would lead to excessive risk taking or other imprudent behavior on the part
of firms by potentially creating the perception of a government “business revenue safety net.”
In normal public programs under normal circumstances, this concern is certainly real. But
in this instance, we are much less concerned about moral hazard. The need to shut down large
segments of the economy will occur infrequently, and without advance notice. Businesses cannot
purchase shutdown insurance from private firms in the way they can insure against risks from
fires and floods. If the government communicates the extraordinary nature of the assistance is
driven by the extraordinary nature of the threat, then that would mitigate moral hazard concerns.
3.4. Policy response in other OECD nations
Before turning to the Paycheck Protection Program, we briefly discuss programs enacted
by member countries in the Organization for Economic Cooperation and Development (OECD)
during the Pandemic Recession. See Table A1 for specific program descriptions and parameters
for OECD countries.
Many European nations relied on a version of a wage subsidy scheme in which workers
saw their hours and pay reduced and their government picked up a large part of the cost of
employing them.
8
This type of program was used by Germany (Kuzarbeit, or short-term work)
during the Great Recession, and is widely credited with keeping the German unemployment rate
down during that period. The way it often worked was that firms paid the benefit to their
workers, which was typically somewhat lower than wages, and the government reimbursed the
firm (Blanchard, et al., 2020). Austria implemented a similar program during the pandemic,
replacing up to 90 percent of covered wages.
A few examples: In the United Kingdom, the government reimbursed firms for 80
percent of the wages of furloughed workers. Germany covered 60 percent of wages for childless
workers on furlough and 67 percent for furloughed workers with children. Depending on the
month, the government of France covered 84 percent, or 71 percent (as of June) of wages for
workers on temporary layoff. Notably, these countries did not condition eligibility based on firm
size, in contrast to the U.S. emphasis on small and mid-size firms. Some European economies
conditioned subsidies on a demonstration of a significant decline in revenue (e.g., the
8
Hamilton and Veuger (2020) argue that large expenditures to address the pandemic will heighten concern about the
public finances of some European Union member states, implying that a broader, European approach to fiscal policy
is necessary. They suggest that the eurozone issue Eurobonds to placate markets and to avoid issues associated with
sovereign debt overhang.
Netherlands, Estonia, and the Slovak Republic). Slovenia emphasized state-funded bonuses for
‘hazard pay’ in certain sectors.
These programs are similar to what we describe above. They maintain the worker-firm
relationship during the shutdown period, making it easier for workers, firms, and the economy to
recovery quickly once economic activity partially resumes. Keeping workers paid by the firms
also allows government assistance to reach workers quickly. They are similar to standard
unemployment insurance in that the government is helping support the incomes of workers who
are underemployed, but unlike standard unemployment insurance, they allow for part-time work.
At the same time, European programs have been more focused on supporting workers in
their current employment matches, rather than smoothing a transition toward different
employment matches. Programs generally permitted workers receiving nonwork or part-time
work benefits to remain attached to the firm. As with the U.S. Paycheck Protection Program, the
state effectively assumed a portion of payroll costs for covered workers, albeit through payments
made to firms.
9
The U.S. program formally worked as a combination of loans and outright grants
to firms and wage subsidies. As we describe later, a number of administrative challenges were
‘unforced errors’ in its implementation.
While some European pandemic unemployment or wage subsidy schemes have faced
fewer administrative challenges than in the United States, they still raise concerns (to which we
return later). Importantly, they were and are designed to maintain employment relationships in a
temporary cyclical downturn (e.g., a moderate and short recession or a short pandemic
shutdown). In a ‘reopening’ of the economy, policy shifts would be needed to focus on rehiring
workers and worker transitions by gradually reducing wage subsidies and the generosity of
unemployment benefits.
Employment policy responses in OECD countries outside Europe during the pandemic
have been varied. Canada, for example, focused on rehiring workers previous laid off due to the
COVID-19 experience, with subsidies of up to 75 percent of all covered wages. Israel relied on
relaxing requirements for unemployment benefits, direct and government-guaranteed loans to
business of all size, special support for high-risk businesses, grants for small businesses, and a
9
Norway relied on layoffs, making it easier for firms to use temporary layoffs and increasing the generosity of
unemployment benefits for workers. Norway also instituted a new compensation scheme for businesses that
subsidized fixed costs. Alstadsæter, et al. (2020) find that this program reduced firms’ economic distress by a
similar magnitude to PPP by reducing the negative effects of the crisis on profitability, liquidity, debt, and solvency.
variety of measures to reduce the short-term burden of business taxes. Australia, like large
European economies, implemented a wage subsidy for firms’ retention of employees. Japan
financed wage subsidies for retained workers, but only for small and mid-sized firms. South
Korea increased worker retention subsidies to up to 90 percent of covered wages for three
months for all employers. A less generous subsidy to wages was provided in South Africa for
firms whose operations were at least partially curtailed as a consequence of the COVID-19
pandemic. In Latin America, Chile provided partial support for wage declines, and Colombia
assisted workers in firms with significant revenue declines with support of 40 percent of the
minimum wage.
4. The Paycheck Protection Program
The Paycheck Protection Program (PPP) was created by the Coronavirus Aid, Relief, and
Economic Security Act (CARES Act), the $1.8-trillion “Phase 3” economic recovery package
passed by Congress and signed into law on March 27, 2020. In this section, we outline the
statutory design of PPP, the program’s implementation by the Department of the Treasury and
Small Business Administration, and differences between PPP and the features of a small business
revenue-replacement program we discussed in the previous section.
4.1. PPP’s design
PPP is a forgivable loan program. Businesses or nonprofits with 500 or fewer employees;
sole proprietors, independent contractors, or self-employed individuals; and small businesses,
501(c)(19) veterans organizations, or Tribal business concerns that otherwise meet SBA’s size
standards are eligible. Businesses in the accommodation and food services sector (NAICS code
72) may apply the 500-employee rule to each physical location, not to the corporation as a
whole. Congress appropriated $349 billion for PPP in the CARES Act.
Under the program, businesses can borrow up to 2.5 times their average monthly payroll
costs, capped at $10 million. Loans are issued by banks and are guaranteed by the government.
10
The amount of the loan spent on payroll costs (including benefits), rent, utilities, and mortgage
interest during the 24-week period (originally eight-week period) after the loan is originated is
10
FinTech played an important role, as well. Erel and Liebersohn (2020) study the response of FinTech to demand
for financial services created by PPP. They find that FinTech was disproportionately used in ZIP codes with fewer
bank branches, lower incomes, larger minority share of the population, in industries with less ex ante small business
lending, and in counties where the economic effect of the pandemic were more severe.
forgiven — i.e., it is converted to a grant — provided that 60 percent (originally 75 percent) of
the amount forgiven is spent on payroll (a Treasury/SBA regulation not found in the CARES
Act) and the business does not reduce headcount relative to pre-crisis levels and does not reduce
any employee’s compensation by more than 25 percent of his or her pre-crisis level. If headcount
or compensation are reduced beyond those parameters, the amount of the loan forgiven may be
reduced proportionately under some (but not all) circumstances. PPP encouraged businesses that
had already laid off workers due to the pandemic to rehire them quickly without penalty.
11
Borrowers do not need to demonstrate hardship in order to qualify for a forgivable loan,
which streamlines the process and allows banks to get money to businesses quickly. Instead, they
need to offer a series of good-faith certifications, including: “Current economic uncertainty
makes this loan request necessary to support the ongoing operations of the Applicant.”
12
Borrowers must also certify that the business intends to use the funds received for payroll and
other operating expenses and that they are not applying for a duplicative loan. For a loan to be
forgiven, in some cases, businesses may need to present documentation to lenders demonstrating
that they complied with the terms of the loan. In other cases, businesses simply need to attest to
this.
To get funds to businesses quickly, PPP delegates authority to lenders to determine
borrower eligibility. By the PPP’s structure, lenders do not need to assess the ability of the
borrower to repay the loan. No collateral or personal guarantees from borrowers are required,
11
Rules for loan forgiveness and for loan forgiveness reduction have been evolving. We describe guidance at the
time of this writing in more detail here. Loans can be fully forgiven if loan proceeds are spent and qualifying costs
are incurred during the covered period of the loan, which begins when the loan is disbursed (or during an alternative
covered period, depending on how the borrower manages payroll); at least 60 percent of the loan amount (originally
75 percent) was used on payroll costs; and staffing and compensation levels are maintained in the covered period
relative to the reference period. The covered period is 24 weeks for loans made after June 5, 2020. For loans made
before June 5, 2020, borrowers can choose between a 24-week or eight-week covered period. Borrowers can choose
one of two reference periods: February 15, 2019 to June 30, 2019, or January 1, 2020 to February 29, 2020.
(Seasonal employers have different rules.) PPP also includes a safe harbor provision that allows borrowers to avoid
loan forgiveness reductions due to decreases in headcount or compensation that occurred between February 15, 2020
and April 26, 2020, provided that headcount and compensation are restored by December 31, 2020 (originally June
30, 2020). Loan forgiveness will also not be reduced if borrowers issue written offers to rehire workers who were
employed on February 15, 2020, and those offers are not accepted, or if borrowers document an inability to rehire
similarly qualified workers for vacancies as of December 31, 2020. Loan forgiveness will not be reduced if
borrowers cannot maintain employment levels due to an inability to return to the same level of business as of
February 15, 2020 because they are complying with coronavirus-related guidance for social distancing, sanitation, or
worker or customer safety requirements from various federal agencies and departments between March 1, 2020 and
December 31, 2020. On October 8, Treasury/SBA issued additional guidance that exempted borrowers with loans
under $50,001 from any loan-forgiveness reductions based on failing to maintain headcount or wages.
12
Paycheck Protection Program Borrower Application Form, revised June 24, 2020.
and no credit-elsewhere tests are applied. Lenders simply need to establish that a business was
operational on February 15, 2020 and verify its payroll.
To entice banks to participate, the program allowed them to charge generous fees — five
percent of principal on loans up to $350,000, three percent on loans between $350,000 and $2
million, and one percent on loans above $2 million up to $10 million. Lenders can charge an
interest rate of one percent on the portion of the loan that is not eligible for forgiveness, and
loans have zero weight in banks’ capital requirements. In the statute, lenders are “held harmless”
in the event of borrower misrepresentation, but Treasury/SBA did not waive requirements under
the Bank Secrecy Act and required anti-money-laundering compliance programs.
The Paycheck Protection Program and Health Care Enhancement Act was signed into law
on April 24, 2020, and increased PPP funding by $320 billion. The Paycheck Protection Program
Flexibility Act (PPPFA) was signed into law on June 5, 2020. The covered period of the
forgivable loan was extended from eight weeks to 24 weeks (or until December 31, 2020).
PPPFA also allowed businesses to spend 40 percent of forgivable funds on non-payroll expenses,
rather than the 25 percent previously established by Treasury/SBA regulation. The maturity of
the loans was increased from two years to five years for loans issued after June 5.
4.2. Design concerns
On the whole, PPP’s design corresponds to the objectives for financing during a short-
term shutdown we described earlier. It was able to get an astonishing amount of money to
millions of small businesses very quickly. It relied on (what are essentially) grants and not loans.
It took measures to encourage banks to participate. It avoided revenue tests and it did not target
select industries. Its goals were ensuring small business continuity and preserving employment
relationships.
But four design elements deviated from what we described previously. First, PPP was
heavily focused on payroll expenses. Second, the program was designed with a short lockdown
period in mind. This approach was driven by the widely held expectations about the course of the
pandemic in early March, and to some extent was addressed by PPPFA modifications to the
program. Even still, the program was relatively inflexible post-lockdown with respect to
allowing labor to be reallocated across firms and industries, a problem given a longer period of
partial shutdown. PPP contains incentives that work against this needed reallocation.
Third, PPP’s original CARES Act appropriation of $349 billion quickly proved
inadequate to the program’s demand, and Treasury’s inability to adequately convince banks that
they would be held harmless in the event of borrower misrepresentation hampered its execution.
These led to the reality and public perception that PPP funds were flowing to relatively better-
resourced and less-vulnerable small and mid-size businesses.
Finally, Hubbard and Strain (2020) estimated that the PPP’s original goals would require
around $1 trillion. With only $349 billion originally appropriated for PPP — and the intense
demand for PPP loans in the early days of the program — a perception developed that only
businesses with preexisting relationships with participating lenders would be able to access the
program. Lenders, in a rush to process applications and out of concern that they would not be
held harmless in all circumstances, focused lending on existing bank customers.
5. Evaluating PPP: Program Statistics, Implementation Challenges, and
Existing Evidence
In this section, we present basic statistics about PPP loans, and discuss implementation
challenges. We also review current empirical evidence on the effectiveness of PPP.
5.1. PPP program statistics
Table 1 presents PPP program statistics. As of August 8, PPP had approved 5,212,128
loans representing a total of $525 billion provided by 5,460 lenders. The average loan size is
$101,000. The solid majority of program dollars were included in loans of less than $2 million,
and the overwhelming majority of loans were for less than that amount. Loans of over $2 million
represent 0.6 percent of all loans and 20 percent of all dollars loaned. In contrast, around 87
percent of all PPP loans were made for less than $150,000, and 28 percent of all funds loaned
were part of loans of less than that amount. Figure 1 shows loan counts and loan amounts over
time.
Figures 1A and 1B show loan totals and loan amounts by state, respectively. Granja, et
al. (2020) study the targeting of these loans across geography, and do not find evidence that the
first round of PPP funds went to parts of the country that saw the largest declines in hours
worked or business shutdowns. Further research is needed to study the targeting of the full
program. We also note that the entire country was affected by shutdowns, and the degree to
which different states were affected by the pandemic varied at different times, particularly as the
nation entered the summer months. Figure 2A shows PPP loans by industry and employment
losses by industry.
5.2. Implementation challenges
Table 2 presents a timeline of select PPP events, and includes some implementation
challenges. Before the program officially launched on April 3, banks and other industry
associations were warning of a chaotic beginning to the program, arguing that borrower
verification would be onerous and would hamper the government’s objective of getting money
into the economy quickly, and due to confusion about basic program requirements like how
lenders should calculate payroll costs. Due to confusion about the program, on the day it
launched only eight of the 25 largest SBA 7(a) lenders were taking applications.
The early stage of PPP was also characterized by intense demand. By the end of its
second week, all $349 billion of CARES Act PPP appropriations had been exhausted. Thousands
of submitted applications remained unapproved. There were accusations that large banks violated
the first-come, first-served structure of the program to favor large borrowers.
Articles in the press reported that some publicly traded companies or their subsidiaries
had received PPP loans. On April 23, SBA released guidance that publicly traded companies
would likely find it difficult to certify in good faith that they needed PPP loans.
13
Treasury/SBA
gave businesses until May 7 (later extended to May 14
14
and then May 18
15
) to return PPP funds
without facing a penalty. On April 28, Treasury Secretary Mnuchin announced that a review of
PPP loans in excess of $2 million would take place. The Secretary warned of potential criminal
penalties for borrowers found to have misrepresented themselves or not to have complied with
the terms of the loan.
16
On May 13, SBA attempted to reassure borrowers and indicated that
13
See question 31 in “Paycheck Protection Program Loans: Frequently Asked Questions,” last revised June 25,
2020: “[I]t is unlikely that a public company with substantial market value and access to capital markets will be able
to make the required certification [of economic need] in good faith, and such a company should be prepared to
demonstrate to SBA, upon request, the basis for its certification.”
14
See question 43 in “Paycheck Protection Program Loans: Frequently Asked Questions,” last revised June 25,
2020.
15
See question 47 in “Paycheck Protection Program Loans: Frequently Asked Questions,” last revised June 25,
2020.
16
For example, Secretary Mnuchin made this statement on April 28 on CNBC: “I really fault the borrowers who
made these certifications. Now, there were some banks early on who put things up on their website and prioritized
their customers. We immediately told them that was wrong. They took it down. So, you know, I want to be very
clear: it’s the borrowers who have criminal liability if they made this certification and it’s not true. And as I said,
we’re going to do a full audit of every loan over $2 million. This was a program designed for small businesses, it
loans of less than $2 million would be assumed to have made certifications of need in good
faith.
17
Confusion over eligibility for PPP loans, which borrowers would be audited, and under
what terms those audits would take place had a profound effect on the program.
Figure 1 shows PPP loan counts and dollars loaned over time. During the period of
uncertainty discussed above, shown in the light- and dark-grey bars in Figure 1, the slope of both
lines flattened. Dollars loaned have increased more slowly since this period of Treasury-sown
confusion ended on May 18. New PPP loans continued to be made in the second half of May and
into June and July, but at a much slower rate than in April.
Of course, implementation shortcomings were inevitable to some degree in standing up a
program as ambitious as PPP in a short period of time in the midst of a pandemic. But Treasury’s
muddled management of PPP’s implementation is noteworthy because of its failure to take
seriously the advice it was given by a range of private-sector participants and policy experts,
leading it to make mistakes that were both forecastable and forecasted.
5.3. Brief review of existing economic research on the PPP
Study of the PPP by academic researchers is still in the working-paper stage, but some
notable findings exist that shed light on the early effects of the program. We briefly survey that
research below.
Bartik, et al. (2020) study the original $349 billion of PPP funds. Using a survey of small
businesses, they find that PPP approval increased self-reported firm survival probability by 14 to
30 percentage points. They also find that banks allocated PPP funds to firms with higher PPP
treatment effects. But these firms were also more likely to have stronger connections to banks,
while firms with less cash-on-hand were less likely to have their applications approved. They
find that PPP had a positive but statistically insignificant impact on employment.
was not a program that was designed for public companies that had liquidity. Again, the certification was very clear
in saying that if people had other sources of liquidity, they could not take this loan.”
17
See question 46 in “Paycheck Protection Program Loans: Frequently Asked Questions,” last revised June 25,
2020: “Any borrower that, together with its affiliates, received PPP loans with an original principal amount of less
than $2 million will be deemed to have made the required certification concerning the necessity of the loan request
in good faith.”
Quite modest employment effects are also found by Chetty, et al. (2020), which analyzed
data from Earnin, a financial management application.
18
Granja, et al. (2020) also do not find
evidence that the first round of PPP had a substantial effect on employment, or on other local
economic outcomes. Bartik, et al. (2020) find that states that received more PPP loans and those
with more generous unemployment benefits had labor markets whose declines were relatively
less deep and whose recoveries were relatively more rapid. Chodorow-Reich, et al. (2020) find
that PPP relaxed liquidity constraints facing firms, allowing some firms to pay down existing
credit line balances.
Autor, et al. (2020) use weekly data from Automatic Data Processing (ADP) payroll
records to study PPP’s effect on employment. Using a difference-in-differences event study
framework, they compare employment at firms above and below the 500-employee PPP
eligibility threshold. Through the first week of June, they find that PPP increased employment by
between two percent and 4.5 percent. After scaling by the take-up rate, they estimate PPP
increased aggregate payroll employment by 2.3 million workers, again through the first week of
June.
Autor, et al. divide total program expenditures by their estimate of PPP’s effect on
aggregate employment and report a cost-per-job-supported estimate of around $224,000. The
paper notes that “while this is a substantial cost per job supported, it would be premature to offer
a cost-benefit analysis of the PPP at this time,” and points to the need to take a longer-term view
of PPP’s effects. We agree, and would add that a short-term cost-benefit analysis should include
other factors. For example, many workers who were kept on employer payrolls this spring would
likely have been receiving unemployment insurance benefits in the absence of PPP. A short-term
cost-benefit analysis should include cost savings from reducing the demand for social insurance
and safety net benefits.
More fundamentally, we disagree with Autor, et al. in that we do not find cost per job
supported to be a sufficient statistic to assess PPP’s success. PPP is not exclusively a jobs
program, and any evaluation of its effectiveness per dollar of program expense — even a short-
run estimate — must include the benefit of preserving small businesses and employment
18
Autor, et al. (2020) discuss limitations in the Chetty, et al. (2020) study, including that Earnin data are focused on
very low-wage workers, with median wages equal to roughly the 10
th
percentile of wages in their industry, and that
the absence of reported standard errors makes the Chetty results hard to interpret.
relationships holistically, including social benefits in excess of private benefits and the benefits
from hastening the economic recovery by supporting labor demand over the medium term.
6. Evaluating PPP: Empirical Analysis
We evaluate the effects of PPP on the employment, financial health, and continuity of
small businesses. To do this, we use data from the Dun & Bradstreet Corporation, a company
that provides commercial data and analytics to businesses. We are able to identify businesses in
the D&B data that applied for PPP loans of $150,000 or more. We do not observe if those
companies received a loan, or the exact amount (above $150,000 or more) of any loan received.
We are not able to observe if a business applied for a PPP loan of less than $150,000.
Information on loan applications comes from SBA and is merged into the D&B data.
We estimate standard difference-in-difference models of the effect of PPP application
and of PPP eligibility based on size. We use several treatment-control groups in our analysis. We
also estimate the dynamic effect of PPP application and eligibility using event studies. We find
evidence that PPP increased employment, financial health, and continuity. We also find that the
effect of PPP is unfolding, with effects on employment and financial health growing over time
and reaching their peak in August, the last month for which we have data. In this section, we
discuss the data, our methods, and these results in further detail.
6.1. Dun & Bradstreet
D&B is a global data and analytics company whose clients are businesses. The company
was founded in 1841 as The Mercantile Agency, and became Dun & Bradstreet in 1933. It has
extensive coverage, with over 355 million business records and data curated from tens of
thousands of sources, including public registries, newspapers and websites, its own
investigations and telephone interviews, courts and legal filings, financial statements, insolvency
records, and its own network, making use of proprietary and publicly available information. It is
the world’s largest commercial database, and counts 90 percent of the Fortune 500 companies as
clients, along with every cabinet agency in the U.S. government.
D&B is able to track whether businesses pay their bills on time through its relationships
with landlords, mortgage companies, credit card companies, office suppliers, and the like. Their
clients make use of D&B’s ability to predict whether a particular establishment might be
delinquent in order to help clients manage financial risk. D&B has significant reach. For
example, the U.S. government has historically required companies that want to receive federal
contracts to register with D&B, as does Apple for companies that want to distribute applications
through its App Store. The Food and Drug Administration uses a company’s D&B registration
number as a way to verify that importers of pharmaceutical products are legitimate businesses,
and to confirm that applicant contact information is accurate and complete.
6.2. Sample, variables, and descriptive statistics
Our sample includes all establishments in the D&B database active as of October 2019
with one to 1,000 employees. We do not include sole proprietorships, establishments with zero
reported employees, establishments with missing state and industry codes, and establishments
with modeled employee counts. We assign each establishment to a business-size category (e.g.,
one to 500 employees, 501 to 1,000 employees) based on employment in February 2020. We
also stratify establishments based on whether they applied for a PPP loan worth $150,000 or
more. (We are only able to observe whether businesses applied for PPP loans of at least
$150,000.)
Table 3 presents summary means and standard deviations for key variables and the
distribution of establishments over industry. Businesses that applied for a PPP loan of $150,000
or more are nearly three times as large as those that did not. This difference is likely due to the
relatively large size of the loan we are able to observe. Each group of businesses have
comparable Paydex scores (discussed below), and over the entire sample period establishments
with 501 to 1,000 employees are more likely to go out of business. The group least likely to go
out of business during the sample period are establishments that we observe have applied for
large PPP loans, and by a wide margin.
Key variables for our analysis include PPP application (for loans of at least $150,000),
establishment employment, state, and industry. We use Dun & Bradstreet’s Paydex variable as
our measure of a business’s financial health. Paydex is an indicator based on whether and how a
business is paying its bills. Paydex ranges from zero to 100. A Paydex score of 80 denotes that
payments made to D&B have generally been made within the terms of the covered agreement. A
Paydex score over 80 indicates that payments reported to D&B have been made earlier than their
terms required. Paydex scores of 70, 60, 50, 40, 30, 20, and below 20 indicate that businesses are
15, 22, 30, 60, 90, 120, and over 120 days late, respectively, in paying their financial obligations.
Paydex scores evolve slowly, and for each business a given month’s Paydex score reflects
transactions that have taken place over the previous several months.
Examples of recent papers that have used D&B data to examine changes in the financial
health of small businesses include Barrot and Nanda (forthcoming), which studies the impact of
the 2011 federal Quickpay reform using establishment-level employment data and Paydex scores
from D&B. Chava, Oettl, and Singh (2019) examine the effects of state minimum wage increases
on the financial health of small businesses. The authors use the D&B Paydex score as their
primary measure of financial health for 15.2 million establishments from 1989-2013.
D&B’s out-of-business indicator is our measure of business continuity. It is a zero-one
variable. D&B determines a business is out of business if it is no longer engaging in transactions,
through direct investigations, and in other ways. Two separate authorities — e.g., management or
owners of the company itself, if a business isn’t listed with a landlord at its address, if a business
is no longer licensed, etc. — must confirm a business has closed for it to be recorded as out of
business.
Panels (A) and (B) of Figure 2 plot average establishment employment per month for
establishments with one to 500 employees in our analysis sample (Panel (A)), and establishments
with 501 to 1,000 employees (Panel (B)). These plots indicate that employment among the D&B
sample is very stable. Among businesses with 1-500 employees (Panel (A)), employment
decreased by 1.42 percent in August relative to November. Panel (B) shows employment
declines of 1.83 percent among establishments with 501 to 1,000 employees. In contrast,
employment reported in official statistics shows much larger loses. The summary statistics we
present suggest that employment evolves slowly among firms of all sizes, and our analysis does
not indicate any relationship between the pace of evolution and PPP application. The relative
stability of employment in the D&B data biases against finding a PPP employment effect, in both
our treatment-on-the-treated and intent-to-treat models. We interpret all our estimates of PPP’s
effects relative to trends in the D&B data.
We present the average Paydex score per month in Panels (C) and (D) of Figure 2. These
figures indicate that business’ financial health in our sample is relatively stable, as well, falling
in both panels by less than one point. This apparent stability is most likely due to the relatively
lengthy look-back period for Paydex. As with the stability of employment, this biases against
finding an effect of PPP on financial health.
The share of establishments that went out of business is shown in Panels (E) and (F) of
Figure 2. The share of businesses with less than 500 employees that went out of business
increased by a factor of 15 between November and August (Panel (E)). Businesses with 501 to
1,000 employees saw closure rates increase by a factor of 13 (Panel (F)).
6.2. Estimation strategy
To identify the effect of PPP on business outcomes, we estimate the following equation:
= + (
) +
+
+
+
,
(1)
where
is an outcome experienced by business i in month m. Our analysis sample covers ten
months, November through August, with five months of pre-PPP period (the CARES Act was
signed on March 27) and five months of post period (PPP launched on April 3).
is an
indicator as to whether business i applied for a PPP loan of at least $150,000. This variable is our
measure of PPP — we do not observe whether businesses actually received PPP loans, or if they
did receive loans, the size of the loan.
is a state-by-month effect, and
is an industry-by-
month effect.
equals 1 if business i applied for a PPP loan and the month is
April, May, June, July or August. Standard errors are clustered by state.
The coefficient of interest is , which captures the effect of applying for a PPP loan of
$150,000 or greater on the outcome variable. The industry-month effects capture time varying
shocks to businesses in a given industry, and the state-month effects capture time varying shocks
to businesses in a given state. The effects of the pandemic and the lockdowns varied substantially
across industries and states. Using within-state-by-month and within-industry-by-month
variation to estimate the effect of PPP application helps ensure that our results are not driven by
time varying public health or social-distancing policy differences between states and industries.
To trace the dynamics of PPP over the months since the CARES Act, we estimate a
difference-in-difference event study of the following form:
= + �
(
)
5
=−4
+
+
+
+
,
(2)
where
is a vector of nine parameters estimating the dynamic effect of PPP,
is a month
dummy, and everything else is the same as in equation (1). The dynamics of the effect are
interesting because of lags in receipt time, the time it may take employers to bring workers back
onto payroll, and treatment-control differences driven by the economic outcomes of control
businesses worsening over time because they do not have access to PPP funds. The trend in the
pre-period coefficient vector is a partial check against differential employment trends among
businesses that applied for a PPP loan and those that did not.
We observe whether a business applied for a PPP loan of $150,000 or more. If some
businesses that applied were turned down, then our estimates of PPP’s effect are biased
downward, because the treatment group would be contaminated by control observations. Another
important source of downward bias in our estimates of PPP’s effect is that many businesses in
our control group applied for and received PPP loans of less than $150,000. As presented in
Table 1, around 87 percent of all PPP loans were made for $150,000 or less, and these loans
accounted for 28 percent of all funds disbursed. These are treatment-on-the-treated estimates,
and do not control for selection into applying for PPP. Firms that did not apply could be very
different from those that did, perhaps thinking that they did not need the funds to continue
operating, or, alternatively, perhaps thinking that the situation was hopeless. They might have
also been less financially savvy, which could be correlated with other outcomes and
characteristics.
Knowing how PPP affected firms that selected into participating is interesting and
important, but it confounds demand for PPP with PPP itself. To address this distinction, we
estimate intent-to-treat models. In these models, we do not use information on whether a
business actually applied for a PPP loan. Instead, we compare outcomes for establishments that
were eligible for PPP based on their size to establishments that were ineligible in a difference-in-
differences framework. Specifically, we estimate the following equation:
= +
(
)
+
+
+
+
.
(3)
All variables in equation (3) are the same as in equation (1) except
, which equals 1
if a business is eligible for PPP based on its size, and equals 0 otherwise. We also estimate
intent-to-treat event studies analogous to equation (2).
6.3. Results
Results for employment. Table 4 presents estimates of equations (1) and (3) for (the log
of) employment. The specification in the first column compares establishments with one to 500
employees that applied for a PPP loan of $150,000 or more to establishments in the same size
class but did not apply. PPP application is associated with a 0.90 percent increase in
employment. Columns (2) and (3) present the same specification, but on smaller samples of
establishments. Column (2) looks at establishments between one and 250 employees, and
similarly finds a 0.94 percent increase in employment from PPP. Column (3) analyzes a sample
of establishments of between 251 and 500 employees. Here, the effect on employment is
negative, -3.2 percent. This result might be driven by greater demand for larger PPP loans within
that size class among the treatment group, confounded by many control firms taking out PPP
loans that we do not observe. But in evaluating the program as a whole, it is worth noting that
there are approximately 82 million establishment-months with one to 500 employees in our
sample, and around 360,000 of those are establishment-months with 251 to 500 employees.
These estimates are valuable in part because they implicitly control for establishment-size
category. But they are likely biased downward because the treatment effect is defined as a
business applying for a PPP loan of $150,000 or greater, while most PPP loans were for less than
this amount, so PPP-treated establishments are in the control group. The specification in Column
(4) attempts to address this by defining the treatment group as establishments will less than 500
employees who applied for a PPP loan of at least $150,000 and the control group as
establishments with between 501 and 1,000 employees. Here, we estimate a PPP employment
effect of 1.78 percent, substantially larger in magnitude than the coefficients discussed
previously.
The estimates reported in Columns (1) through (4) are treatment-on-the-treated estimates.
In the context of evaluating PPP, this is interesting because estimating program outcomes
conditional on selection is important and relevant (program participation is voluntary) and survey
evidence finds that over 70 percent of small businesses participated in PPP.
19
But the estimates
do confound the effect of demand for PPP with the effect of PPP, in addition to the limitation
that we only observe PPP loans of at least $150,000.
19
The Small Business Pulse Survey of the U.S. Census Bureau finds that 72.7 percent of small businesses received
financial assistance from PPP since March 13, 2020 as of August 22, 2020.
To address these limitations, Column (6) reports intent-to-treat estimates in which we
define the treatment group purely based on size eligibility — i.e., we do not use information on
whether a business applied for a PPP loan — and the control group is establishments with 501 to
1,000 employees. We estimate that PPP size eligibility increased employment by 1.38 percent.
This result might suggest an important role for smaller PPP loans in supporting employment.
Column (5) also reports intent-to-treat effects but for firms close to the 500-employee
cutoff (eliminating firms near the cutoff). The advantage of this specification is that it directly
controls for firm size. Comparing firms in the 400-600 employee window, we do not find a PPP
employment effect. This result, along with the estimates reported in Column (6), might suggest
that PPP was most effective in supporting employment among smaller firms, at least through
August.
The specification that estimates the effect of PPP within the 400-600 employee window
arguably offer the strongest basis for causal inference assuming that the effect of PPP loans on
employment is similar for firms of different sizes. But this assumption is very strong, and it is
quite likely that PPP loans have effects that vary by firm size. The estimates reported in Table 4
suggest this is the case, and the $10 million maximum for PPP loans also suggests that PPP
would offer relatively more assistance to smaller firms. In the D&B data, 2019 annual sales for
firms with 1-500 employees were $2.4 million, while those for firms with 400-475 employees
were $46.4 million. These consideration suggests that a holistic evaluation of PPP should include
estimating its effects on firms of all eligible sizes. Therefore, our preferred specifications are
presented in Columns (4) and (6).
Our results contrast with Autor, et al., who find employment effects for larger firms using
ADP data. It is interesting to note that Autor, et al.’s estimates become less precise as the
window around the 500-employee eligibility cutoff shrinks. This finding may be due to sample
size, or it could indicate that PPP is relatively less effective at supporting employment for larger
firms in the ADP data.
We present event study graphs using our two preferred treatment and control groups.
Figure 3 presents results from equation (2). Panel (A) shows the dynamic effect of PPP on
employment when the treatment group is establishments with between one and 500 employees
who applied for a PPP loan of at least $150,000 and the control group is establishments with 501
to 1,000 employees. There is no trend in the pre-period coefficients, although the confidence
interval on the negative coefficient in February does not include zero. The absence of a pre-
period trend supports a causal interpretation of the estimates. In the post-period coefficients, the
effect of PPP increases over time, rising to 3.13 percent in August.
Panel (B) shows a similar effect of PPP on employment. Here, the dynamic effect
captures intent to treat, comparing establishments with 500 or fewer employees to those with
between 501 and 1,000, regardless of whether the firms applied for a PPP loan. Like Panel (A),
there is no noticeable trend in the pre-period, and the strength of the effect increases in the post
period with each month. In August PPP eligibility is found to increase employment by 3.83
percent.
To interpret the magnitude of these effects, consider that average establishment
employment fell by 1.6 percent in the D&B data for establishments with one to 1,000 employees
over the sample period, between November and August. In light of this change, the 1.78 percent
increase in employment reported in Column (4) of Table 4 and the 1.38 percent increase reported
in Column (6) of Table 4 are both substantial increases. The effects for the month of August —
3.13 and 3.83 percent, respectively — specifically are even more substantial.
Results for financial health. Table 5 reports results for which the outcome variable is
financial health, as captured by Dun & Bradstreet’s Paydex score. Table 5 is the same as Table 4,
except for the outcome variable. The first three columns of Table 5 report results from
specifications where the treatment and control groups are the same firm employee-size class.
Taken together, they suggest that financial health worsened for firms with between 1 and 250
employees that applied for PPP loans of at least $150,000. We think this puzzling finding is most
likely the result of PPP-treated observations (i.e., establishments with less than 250 employees
that applied for loans of less than $150,000) contaminating the control group.
For reasons discussed previously, our preferred specifications are reported in Columns
(4) and (6). The specification in Column (4) compares firms with 500 or fewer employees that
applied for PPP loans of at least $150,000 with firms with 501 to 1,000 employees that were not
eligible for PPP. PPP predicts a Paydex increase of about 0.31 points. Column (6) presents
results from an intent-to-treat specification. Here, PPP eligibility boosts Paydex by about 0.35
points. Similar to our results for employment, PPP seems to have had a larger impact on firms
with fewer than 400 employees, as suggested by comparing the results in Column (5) with
Column (4).
Figure 3, Panels (C) and (D) present event study graphs that trace out the dynamic effect
of PPP for our two preferred specifications As with employment, the effect of PPP on financial
health (as measured by Paydex) grows over time. Both figures show a flat trend centered on zero
for the pre-period coefficients estimating the effect of PPP in November through February
relative to March. As with employment, this supports a causal interpretation of our estimates.
The effect of PPP application on financial health was estimated imprecisely in April, and
precisely every month after. The magnitude of the effect increased considerably over time, more
than doubling between June and August.
The dynamic intent-to-treat estimate are shown in Panel (D). As with the results in Panel
(C), PPP’s effect on financial health is estimated imprecisely in April but precisely for the
following four months. The magnitude of the effect in August is more than double the effect in
May. PPP eligibility is estimated to have increased Paydex in August by 0.52 points.
The magnitude of the effect is substantial. For all firms with one to 1,000 employees,
average monthly Paydex fell by 0.28 points from November to August. A PPP Paydex effect of
0.31 (Column (4)) and 0.35 (Column (5)) represents a significant increase relative to the change
in financial health of all firms during our sample period. As with employment, the effect of PPP
on Paydex in June is substantially larger than the post-period average, suggesting that the effects
of PPP on financial health may be increasing over time.
Results for business continuity. Table 6 reports results for D&B’s out-of-business
variable. Everything in Table 6 is the same as in Tables 4 and 5, except the outcome variable.
PPP eligibility or application is estimated to have reduced business closure in every specification
at conventional levels of statistical significance, except for Column (5). Column (4) presents
results from the specification that compares firms that applied for a PPP loan of at least $150,000
to firms with between 501 and 1,000 employees, which were ineligible for PPP. PPP application
is estimated to have reduced the odds of business closure by 0.47 percentage points. Column (6)
presents results from our intent-to-treat model. Here, PPP eligibility is estimated to reduce
business closure odds by 0.22 percentage points. Column (5) reports intent-to-treat results for a
smaller window around the 500-employee cutoff. As with employment and financial health, we
do not find a significant effect of PPP on business closure among firms with 400—475
employees.
Panels (E) and (F) of Figure 3 present event studies for those two models. The pre-period
coefficients show a trend, and these results should be interpreted cautiously. The confidence
interval on pre-period coefficients includes zero in several cases. In the post-period, the
magnitude of the effect is larger in June than in April or May. This pattern is similar to our
employment and Paydex results. The magnitude of these effects is large.
To place the difference-in-difference estimates and June event study coefficient estimates
in context, the average establishment out of business indicator in August was 0.42 percentage
points higher than in November for firms with one to 1,000 employees.
6.4. Discussion and conclusions
Our results point to PPP playing a significant role in the health and viability of small
businesses. Applying for a PPP loan of $150,000 or more and PPP eligibility as determined by
firm size both increase employment, financial health, and business continuity. In addition, we
find that it may have taken a month or two for PPP to kick in. An alternative interpretation is that
PPP was more effective in a partially reopened economy (i.e., June—August) than during the
lockdowns.
Several caveats are in order. We avoid making strong statements about the success or
failure of PPP because the program is so young, and we are only analyzing the first five months
of the program. PPP did have important short-run goals, which included maintaining
employment relationships during the lockdowns and supporting consumer spending by allowing
workers to continue to be paid. But PPP has important medium-run goals as well, and it is too
early to say anything definitive about its success or failure. Those goals include mitigating
business closures after the economy had partially reopened (which we observe for about one
month), supporting employment and reducing unemployment, and increasing productivity by
preserving firm-specific human capital, worker-firm matches, and networks. Crucially, by
preserving the productivity capacity of the small business sector, PPP stands to quicken the
recovery by supporting labor demand over the medium run. In addition, the firms in the D&B
data are not nationally representative, and they exhibit employment and financial health
indicators that are likely more stable than typical firms. We also want to stress the tentative
nature of our conclusions. As shown in the dynamics of the effect (in Figure (3)), the effect of
PPP on employment, financial health, and business continuity is evolving, and is much stronger
in July and August than in April and May. The effects of PPP are unfolding, and it will be
particularly important to see what happens to businesses that received PPP and the workers they
employ once they have exhausted their forgivable loan.
7. Retrospective and Lessons for the Future
Many of the common criticisms of the PPP as failed by design and effect were too strong.
Banks were skittish about participating, particularly in the early days of the program. But
program demand by lenders was sufficient to allow the government to transfer funds in an
amount roughly equal to 10 percent of an typical quarter’s GDP to small businesses. With the
vast majority of loans and the sizeable majority of program dollars going to loans of less than $2
million, media coverage suggesting that PPP was in the main offering grants to large and well-
connected firms was overblown. Many of the anecdotes in the media implying fraudulent
participation in the program actually pointed to firms that were eligible for PPP loans under the
statute. The criticism that the original CARES Act appropriation of $349 billion was too small
was quickly proven valid by events, but Congress rectified that swiftly.
Could policymakers have designed a more effective and cost-effective intervention than a
small business revenue replacement program? In theory, one could argue that relying on the
Unemployment Insurance (UI) system to replace workers’ income and using a PPP-like program
to help small businesses with non-payroll cost has appeal to some economists and analysts. But
that plan would require worker-firm separations, albeit temporary, to take place. It would change
the default for small businesses from keeping workers employed (as under a revenue
replacement program) to recalling workers following a separation. The UI system in many states
was simply unable to handle the demands placed on it during the shutdown — increasing those
demands would not likely lead to the most successful outcomes. Finally, having both UI and a
small business revenue replacement allows for redundancy, with multiple programs operating to
replace workers’ incomes.
For the reasons we discussed previously, we do not view a loan program as an adequate
substitute for a small business revenue replacement program. Many businesses would not want to
add to their debt burdens, even under very favorable lending conditions. Many would resort to
layoffs, which would disrupt other businesses, deepen the recession, and hurt workers’
employment and earnings opportunities.
Even though a small business revenue replacement program may have been the best
available option, the PPP would have been more effective at achieving the goals established for it
by Congress in ways we previously discussed: If it focused less on payroll expenses; if banks had
been given stronger assurances that they would be held harmless; and if its initial appropriation
were larger. Much of the confusion about the program was driven by chaotic Treasury/SBA
management which weakened the program’s effectiveness, limited its reach, and ultimately led
to a falloff in demand for PPP funds.
PPP was designed for a short shutdown that would be followed by a strong and rapid
recovery. But the shutdown was longer than anticipated and the recovery decelerated after a
burst of improvement in May and June. In addition, partial shutdowns may remain in some
regions for an extended period of time. Subsequent changes to PPP addressed these concerns, but
changes could have gone further. The economy overall, including workers, will benefit from a
fast transition from the pre- to post-lockdown equilibrium. PPP could facilitate this transition by
eliminating any link between PPP loan forgiveness and pre-crisis employment levels.
A lending program could exist alongside a revenue replacement program, particularly for
a partially reopened economy. One way to structure such a lending program could be in two
stages, following a venture capital model preceded by a broadly available loan. In the first stage,
the Treasury Department could issue a small loan to firms using limited underwriting standards,
knowing that the loan will have a high default rate. In the second stage, surviving firms could
have access to additional funding. This financing would help to give many firms a lifeline for
survival, while still well-stewarding taxpayer funds.
20
An alternative approach would be a federal business interruption insurance program for
small and mid-size firms (analogous to the federal terrorism risk insurance program) layered on
top of private business interruption insurance. Linking a trigger to a pandemic shutdown could
require a shutdown order by a public official (e.g., the governor of the state).
Looking forward, there are broader lessons as well. For a situation in which the
government is shutting down large sections of the economy, if Congress and the White House
are willing to tolerate stories of “undeserving” beneficiaries of economic recovery programs,
20
The Federal Reserve’s Main Street Lending Facility offers another lending vehicle for small and mid-size firms.
While the facility’s design remains in flux, its structure could also mimic better patient equity financing. Terms
could include much longer maturity and very low interest rates, for example.
then the government can get money to borrowers much faster. PPP stands a chance at succeeding
in the goals Congress set for it because its relief was broad based. The Treasury Department was
much more conservative with putting taxpayer dollars at risk when approving the terms of the
PPP, limiting early take-up. The Treasury’s conservative approach has extended to the Federal
Reserve’s Main Street Lending Facility, which received capital funds (along with other Fed
facilities under the CARES Act). As a consequence of Treasury’s aversion to putting that capital
at risk, potentially driven in part by concern about stories of “undeserving borrowers,” the
facility is not supporting the economic recovery yet because it, essentially, is not making loans.
Another broader lesson is that state and federal IT systems are not up to the task of
pursuing policy goals as ambitious as PPP’s. Banks were needed as intermediaries in part
because the government’s IT constraint would not have allowed for it to lend directly to banks in
a timely fashion. Finally, the government’s attempt to support small and mid-size businesses in
the Pandemic Recession calls into question the nature of the division between the Fed and the
Treasury. Following the Dodd Frank Act, Treasury is required to approve the terms of 13(3)
lending programs, including the Main Street programs. But these are labeled as Fed programs,
creating confusion about which agency is ultimately responsible for their success or failure.
Furthermore, Congress appropriated $454 billion in the CARES Act to Treasury to support Fed
lending programs. At the time of this writing, little of those funds have been put to use to support
the recovery, despite congressional intent. If Treasury is unwilling to risk capital losses as part of
Fed lending programs, then Congress might consider whether an alternative structure to support
small and mid-size businesses is advisable.
References
Alstadsæter, Annette, Julie Brun Bjørkheim, Wojciech Kopczuk, and Andreas Økland,
“Norwegian and US Policies Alleviate Business Vulnerability Due to the Covid-19
Shock Equally Well,” NBER Working Paper Series, no. 27637, August 2020.
Autor, David, David Cho, Leland D. Crane, Mita Goldar, Byron Lutz, Joshua Montes, William
B. Peterman, David Ratner, Daniel Villar, and Ahu Yildirmaz, “An Evaluation of the
Paycheck Protection Program Using Administrative Payroll Microdata,” MIT Working
Paper, July 22, 2020.
Barrot, Jean-Noël, and Ramana Nanda, “The Employment Effects of Faster Payment: Evidence
from the Federal Quickpay Reform,” Journal of Finance, forthcoming.
Bartik, Alexander W., Marianne Bertrand, Feng Lin, Jesse Rothstein, and Matthew Unrath,
“Measuring the Labor Market at the Onset of the COVID-19 Crisis,” Brookings Papers
on Economic Activity, BPEA Conference Drafts, June 2020.
Bartik, Alexander W., Zoe B. Cullen, Edward L. Glaeser, Michael Luca, Christopher T. Stanton,
and Adi Sunderam, “The Targeting and Impact of Paycheck Protection Program Loans to
Small Businesses,” NBER Working Paper Series, no. 27623, July 2020.
Blanchard, Olivier, Thomas Philippon, and Jean Pisani-Ferry, “A New Policy Toolkit Is Needed
as Countries Exit COVID-19 Lockdowns,” Policy Brief 20-8, Peterson Institute for
International Economics, June 2020.
Coibion, Olivier, Yuriy Gorodnichenko, and Michael Weber, “Labor Markets During
the COVID-19 Crisis: A Preliminary View,” NBER Working Paper Series, no. 27017,
April 2020.
Chetty, Raj, John N. Friedman, Nathaniel Hendren, Michael Stepner, and The Opportunity
Insights Team, “How Did COVID-19 and Stabilization Policies Affect Spending and
Employment? A New Real-Time Economic Tracker Based on Private Sector Data, NBER
Working Paper Series, no. 27431, June 2020.
Chodorow Reich, Gabriel, Olivier Darmouni, Stephan Luck, and Matthew Plosser, “Bank
Liquidity Provision Across the Firm Size Distribution,” Mimeo, September 2020.
Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik
Hurst, Christopher Kurz, and Ahu Yildirmaz, “The U.S. Labor Market during the
Beginning of the Pandemic Recession,” NBER Working Paper Series, no. 27159, June
2020.
Davis, Steven J., and Till von Wachter, “Recessions and the Cost of Job Loss,” Brookings
Papers on Economic Activity, vol. 2, 2011.
Erel, Isil, and Jack Liebersohn, “Does FinTech Substitute for Banks? Evidence from the
Paycheck Protection Program,” NBER Working Paper Series, no. 27659, August 2020.
Farooq, Ammar, Adriana D. Kugler, and Umberto Muratori, “Do Unemployment Insurance
Benefits Improve Match Quality? Evidence from Recent U.S. Recessions,” NBER
Working Paper Series, no. 27574, July 2020.
Gertler, Mark, and R. Glenn Hubbard, “Financial Factors in Business Fluctuations,” in Financial
Market Volatility: Causes. Consequences and Policy Responses, Federal Reserve Bank of
Kansas City Symposium, 1989.
Goolsbee, Austan, and Chad Syverson, “Fear, Lockdown, and Diversion: Comparing Drivers of
Pandemic Economic Decline 2020,” NBER Working Paper Series, no. 27432, June 2020.
Granja, João, Christos Makridis, Constantine Yannelis, and Eric Zwick, “Did the Paycheck
Protection Program Hit the Target?” NBER Working Paper Series, no. 27095, July 2020.
Hamilton, Steven, and Stan Veuger, “A recession is a public health necessity – let’s keep it
short,” Vox EU, March 21, 2020.
Hanson, Samuel G., Jeremy C. Stein, Adi Sunderam, and Eric Zwick, “Business Continuity
Loans: Keeping America’s Lights on During the Pandemic,” Mimeo, April 7, 2020.
Hanson, Samuel G., Jeremy C. Stein, Adi Sunderam, and Eric Zwick, “Business Continuity
Insurance: Keeping America’s Lights on During the Pandemic,” Mimeo, April 8, 2020.
Hubbard, R. Glenn, and Michael R. Strain, “A Business Fiscal Response to a Covid-19
Recession,” American Enterprise Institute, March 2020.
Jackson, C. Kirabo, “Match Quality, Worker Productivity, and Worker Mobility: Direct
Evidence from Teachers,” Review of Economics and Statistics, vol. 95, no. 4, October
2013.
J. P. Morgan Chase Institute, “Place Matters: Small Business Financial Health in Urban
Communities,” September 2019.
Kahn, Lisa B., Fabian Lange, and David G. Wiczer, “Labor Demand in the Time of COVID-19:
Evidence from Vacancy Postings and UI Claims,” Journal of Public Economics, 2020,
189, 104238..
Mortensen, Dale T., and Christopher A. Pissarides, “New Developments in Models of Search in
the Labor Market,” in Orley C. Ashenfelter and David Card, eds., Handbook of Labor
Economics, vol. 3, Amsterdam: North-Holland, 1999.
Ozimek, Adam, and John Lettieri, “How to Rescue Main Street from Coronavirus before It’s
Too Late,” Economic Innovation Group, March 18, 2020.
Strain, Michael R., “The Paycheck Protection Program: An Introduction,” American Enterprise
Institute, April 2020.
Chava, Sudheer, Alexander Oettl, and Manpreet Singh, “Does a One-Size-Fits-All Minimum
Wage Cause Financial Stress for Small Businesses?” NBER Working Paper Series, no.
26523, December 2019.
U.S. Small Business Administration, Office of Advocacy, “What’s New With Small Business?”
September 2019.
Table 1: Summary of PPP Lending: April 3 - August 8
(1)
(2)
(3)
Cumulative Lending
Loan Count
Net Loans
Number of Lenders
5,212,128
525,012,201,124
5,460
Distribution by Loan Size
Loan Count Net Loans
% of
Count
% of
Amount
150,000 and under
4,552,452
147,477,537,518
87.3%
28.1%
150,000 - 2 million
630,694
272,228,531,130
12.1%
51.9%
Over 2 million
28,982
105,306,132,476
0.6% 20.1%
Notes: Authors’ calculations using SBA Paycheck Protection Program Report for August 8.
Table 2: Timeline of Major Events in the PPP Program
Date
Description
March 27, 2020 CARES Act signed appropriating $349 billion for PPP.
April 2, 2020
Treasury/SBA releases first interim final rule; 75 percent payroll requirement; 2-year repayment period;
0.5 percent interest rate; 8 weeks of covered expenses; application period to June 30th.
April 2, 2020
Faced with complaints from small banks, Treasury raises the interest rate on PPP loans from 0.5 to 1
percent hours before the program launch.
April 2, 2020
Bank associations, J.P. Morgan Chase Bank and industry associations warn of chaotic PPP launch;
borrower verification requirements and payroll cost calculations are unclear.
April 3, 2020
First round of PPP officially launches; only 8 of 25 largest SBA 7(a) lenders are taking applications.
Bank of America and J.P. Morgan Chase begin accepting applications but only for existing customers
April 16, 2020
First round of Paycheck Protection Program ends; original $349 billion appropriation exhausted.
Thousands of submitted applications remain unapproved.
April 20, 2020
Small businesses sue large banks over allocation of loans. They claim that banks violated first-come,
first-serve rules and gave priority to larger applications that would generate more fees.
April 23, 2020
Treasury/SBA warns publicly traded companies and their subsidiaries against seeking loans; May 7
deadline to return funds.
April 23, 2020
Treasury/SBA requires applicants to certify that the funds are necessary due to the current economic
uncertainty, as well as a lack of other sources of funds to support their operations.
April 24, 2020
Paycheck Protection Program and Health Care Enhancement Act signed into law authorizing an
additional $320 billion for PPP.
April 27, 2020 Second round of PPP begins with $320 billion in new funding.
April 27, 2020 Treasury/SBA caps the dollar amount of loans that individual banks can originate at $60 billion.
April 28, 2020
Secretary Mnuchin announces full audits for loans > $2 million; warns of criminal penalties for
noncompliers.
April 29, 2020 SBA temporarily blocks large banks from submitting loans.
April 30, 2020 Justice Department launches probe of PPP.
April 30, 2020
IRS confirms that PPP loans are excluded from gross income, but expenses paid for using PPP loans are
not tax deductible.
May 5, 2020
Senate introduces Small Business Expense Protection Act to treat expenses paid using PPP loans as
ordinary deductible business expenses.
May 5, 2020
Deadline for companies to return funds without penalty under safe harbor provisions extended from
May 7th to May 14th.
May 8, 2020
SBA IG warns requirement of 75 percent payroll costs and 2-year repayment burdens borrowers and
may not reflect statutory intent.
May 13, 2020
SBA announced that loans below $2 million would be assumed to have satisfied good-faith certification
requirements; opportunity for larger loans to be retuned without penalty.
May 13, 2020
Deadline for companies to return funds without penalty under safe harbor provisions extended from
May 14th to May 18th.
May 14, 2020
Treasury says companies must use the total number of employees to determine eligibility for PPP loans
rather than FTE as indicated previously.
May 22, 2020
Treasury/SBA warn that it "may review PPP loans "of any size at any time in SBA’s discretion”;
borrowers required to retain documentation for 6 years.
June 5, 2020
PPP Flexibility Act passed; covered period extended from 8 weeks to 24 weeks; repayment extended
from 2 years to 5 years; payroll costs allowed to be 60 percent of total loan forgiveness amount, down
from 75 percent.
June 12, 2020
For determining PPP eligibility, the look-back period for criminal histories for non-financial felonies
reduced from 5 years to 1 year.
June 30, 2020
Hours before program expiration and with $130 billion left, Congress extends the PPP application
period to August 8.
July 6, 2020
Under pressure from Congress, SBA releases the names of borrowers and lenders and date of approval
for loans more than $150,000, representing 15% of all approved loans and 75% of dollars lent. Exact
loan amounts are not disclosed.
July 7, 2020
Using data released by the SBA, researchers estimate that banks will earn $24 billion in fees from PPP
loans.
July 12, 2020 New York City Comptroller report alleges that the city did not receive its fair share of PPP loans.
July 17, 2020
Secretary Mnuchin asks Congress to consider automatically forgiving all loans for less than $150,000,
extend PPP, and suggest terms for PPP in a Phase 4 economic recovery package.
August 4, 2020
Businesses, lobbyists, and professional organizations ask Congress to exempt PPP income from tax
reporting.
August 6, 2020
SBA releases guidelines on PPP loan forgiveness ahead of August 10 launch of forgiveness application
platform. Many financial institutions delay submitting applications until regulatory and legislative
uncertainty is resolved.
August 8, 2020
PPP application period closes with nearly $140 billion in reserve as Congress debates "Phase 4"
economic recovery package.
Sources: Authors’ summary of various news sources and official documents.
Table 3: Summary Statistics in November 2019-March 2020
(1)
(2)
(3)
(4)
Group
1-500 employees
and applied for a
PPP loan ≥
$150,000
1-500
employees and
did not apply for
a PPP loan ≥
$150,000
All
establishments 1-
500 employees
All
establishments
501-1,000
employees
Mean Number of Employees per
Establishment
33.8 11.5 12.5 722.0
(47.6) (35.2) (36.1) (167.0)
Mean Paydex Score
73.9 72.6 72.7 70.0
(9.57) (14.1) (13.1) (10.7)
Out of Business (%)
0.010 0.157 0.150 0.325
(0.985) (3.96) (3.86) (5.69)
Annual Sales in 2019 ($) 5,603,688 2,168,570 2,338,007 66,242,380
(24,365,380) (70,166,425) (68,691,245) (313,247,030)
Sectors (% share of employment)
Agriculture
2.7
3.4
3.4
0.5
Construction
14.2
8.0
8.2
1.7
Finance, insurance, real estate
3.9
10.3
10.0
5.8
Manufacturing
12.4
4.8
5.2
20.9
Mining
0.5
0.3
0.3
0.7
Public administration
0.1
3.1
2.9
16.5
Retail trade
11.5
13.6
13.5
5.7
Services
41.6
46.6
46.4
40.3
Transportation, communications
4.6
4.9
4.9
5.1
Wholesale trade
8.4
5.0
5.2
2.8
Notes: Authors’ calculations using Dun & Bradstreet data. This table displays means and standard deviations (in parentheses) in
our pre-treatment period, November-March, for the main establishment employee-size groups used in our analyses. We also
calculate the distribution of employment across industries at the 2-digit SIC level. The sample consists of all establishments
operating as of October 2019 that meet our sample selection criteria.
Table 4: Estimating the Effect of PPP Loans on Establishment-Level Employment (D-in-D Estimates)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Employment
Treated x Post x 100
0.902***
0.936***
-3.20***
1.78***
0.0772
1.38***
(0.0656)
(0.0655)
(0.470)
(0.234)
(0.366)
(0.258)
Treatment
1-500; loan
1-250; loan
251-500; loan
1-500; loan
400-475; all estabs
1-500; all estabs
Control
1-500; no loan
1-250; no loan
251-500; no loan
501-1,000; no loan
525-600; all estabs
501-1,000; all estabs
Observations
81,404,032
81,043,431
360,601
3,980,677
110,712
81,523,211
R-squared
0.1390
0.1373
0.0432
0.2343
0.3783
0.0966
Notes: This table reports difference-in-difference estimates for the impact of PPP on establishment level employment. Data on establishment employment
and PPP loan applications are from Dun & Bradstreet. The sample consists of establishments operational as of October 2019 that meet our sample
selection criteria. For all regressions, the pre-treatment period is November-March and the post-treatment period is April-August. Each column uses a
different treatment and control group, where “X-Y” indicates the size of the establishment by employment in February, “loan” indicates that we observe
that the establishment applied for a PPP loan of at least $150,000, “no loan” indicates the opposite, and “all estabs” indicates that we include all
establishments in the analysis sample regardless of whether they applied for a loan. All regressions include state, month, and 2-digit SIC industry code
fixed effects as well as state-by-month and industry-by-month fixed effects. Standard errors are clustered at the state level. Coefficients and standard
errors are multiplied by 100 for ease of interpretation. *** p<0.01, ** p<0.05, * p<0.1
Table 5: Estimating the Effect of PPP Loans on Establishment-Level Credit Scores (D-in-D Estimates)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Paydex Score
Treated x Post
-0.0392*
-0.0379*
-0.0349
0.305***
0.01
0.349***
(0.0220)
(0.0219)
(0.0969)
(0.0686)
(0.154)
(0.0616)
Treatment
1-500; loan
1-250; loan
251-500; loan
1-500; loan
400-475; all estabs
1-500; all estabs
Control
1-500; no loan
1-250; no loan
251-500; no loan
501-1,000; no loan
525-600; all estabs
501-1,000; all estabs
Observations
32,139,590
31,889,423
250,167
3,731,639
81,644
32,225,515
R-squared
0.013
0.013
0.021
0.024
0.027
0.012
Notes: This table reports difference-in-difference estimates for the impact of PPP on establishment level employment. Data on establishment
employment and PPP loan applications are from Dun & Bradstreet. The sample consists of establishments operational as of October 2019 that meet
our sample selection criteria. For all regressions, the pre-treatment period is November-March and the post-treatment period is April-August. Each
column uses a different treatment and control group, where “X-Y” indicates the size of the establishment by employment in February, “loan” indicates
that we observe that the establishment applied for a PPP loan of at least $150,000, “no loan” indicates the opposite, and “all estabs” indicates that we
include all establishments in the analysis sample regardless of whether they applied for a loan. All regressions include state, month, and 2-digit SIC
industry code fixed effects as well as state-by-month and industry-by-month fixed effects. Standard errors are clustered at the state level. Coefficients
and standard errors are multiplied by 100 for ease of interpretation. *** p<0.01, ** p<0.05, * p<0.1
Table 6: Estimating the Effect of PPP Loans on the Probability an Establishment Goes Out of Business (D-in-D Estimates)
(1)
(2)
(3)
(4)
(5)
(6)
Dependent variable: Out of Business = 1
Treated x Post x 100
-0.237***
-0.236***
-0.616***
-0.471***
0.0562
-0.219**
(0.0222)
(0.0224)
(0.0836)
(0.0853)
(0.124)
(0.0683)
Treatment
1-500; loan
1-250; loan
251-500; loan
1-500; loan
400-475; all estabs
1-500; all estabs
Control
1-500; no loan
1-250; no loan
251-500; no loan
501-1,000; no loan
525-600; all estabs
501-1,000; all estabs
Observations
81,625,920
81,262,585
363,335
3,982,131
111,512
81,745,730
R-squared
0.00805
0.00804
0.02154
0.00344
0.0166
0.00789
Notes: This table reports difference-in-difference estimates for the impact of PPP on establishment level employment. Data on establishment employment and
PPP loan applications are from Dun & Bradstreet. The sample consists of establishments operational as of October 2019 that meet our sample selection
criteria. For all regressions, the pre-treatment period is November-March and the post-treatment period is April-August. Each column uses a different
treatment and control group, where “X-Y” indicates the size of the establishment by employment in February 2020, “loan” indicates that we observe that the
establishment applied for a PPP loan of at least $150,000, “no loan” indicates the opposite, and “all estabs” indicates that we include all establishments in the
analysis sample regardless of whether they applied for a loan. All regressions include state, month, and 2-digit SIC industry code fixed effects as well as state-
by-month and industry-by-month fixed effects. Standard errors are clustered at the state level. Coefficients and standard errors are multiplied by 100 for ease
of interpretation. *** p<0.01, ** p<0.05, * p<0.1
Figure 1: Cumulative number of PPP loans and dollars approved, April 3-August 8. This figure displays
cumulative loans and dollars lent during the operation of the PPP program calculated from the microdata provided
by the SBA and Treasury Department as of August 20, 2020. Cumulative dollars lent are overstated in the microdata
due to using the midpoints of loan ranges provided for loans greater than $150,000. The shaded areas represent a
period of uncertainty over audits and the safe harbor deadline. The lightly shaded area covers the total period of
uncertainty over audits from April 28 (audits announced) to May 18 (final deadline to return funds under safe harbor
provision). The darker area covers the period of uncertainty over the safe harbor deadline from May 7 (the original
deadline) to May 18 (the final deadline).
Figure 2: Authors’ calculations of average establishment employment, Paydex scores, and out of business
rates by month. This graph shows average employment, Paydex score, and out of business rates from November
2019 to August 2020 for establishments with 1-500 employees and 501-1,000 employees. Establishments are
assigned to an employment-size group using February 2020 employment. Panels A, C, and E include establishments
with 1-500 employees. Panel B includes all establishments with 501-1,000 employees.
Figure 3: Graphs from Event Study Regressions. This graph shows the results from event study regressions in
equation (2) examining the impact of the Paycheck Protection Program on establishment employment, financial
health, and survival. Panels A, C, and E examine PPP’s effect on employment, credit scores, and survival rates for
establishments with 1-500 employees that applied for a PPP loan of $150,000 or more compared to establishments
with 501-1,000 employees. Panels B, D, and F examine the effect of PPP eligibility on the same outcomes,
comparing all establishments with 1-500 employees to all establishments with 501-1,000 employees (i.e., dynamic
intent-to-treat effects). Establishments are assigned to an employment-size group using February employment.
Coefficients and standard errors for Panels A, B, E, and F are multiplied by 100 to ease interpretation. Error bars
represent 95 percent confidence intervals.
APPENDIX TO: Has the Paycheck Protection Program Succeeded?
Appendix: Additional Tables and Figures
Table A1: Programs Supporting Employment in Response to COVID-19 in OECD Member States
Country
Name
Type
Eligibility
Program Description
Duration of
Subsidy
Australia
Job Keeper
Employers
Wage
Subsidy
Aggregated turnover of less than A$1
billion (for income tax purposes) and
estimate turnover likely to be reduced by
30 percent or more compared to previous
year OR Aggregated turnover of more
than A$1billion and estimated turnover
likely to be reduced by 50 percent
compared to previous year. From
September 28, businesses will need to
demonstrate that they have met the
relevant decline in turnover test for the
preceding quarter. They will have to do
the same on November 3, 2021.
Employers must retain workers.
Eligible employers will be
paid A$1,500 (US$1,076)
per fortnight per eligible
employee. Eligible
employees will receive, at
a minimum, A$1,500 per
fortnight, before tax, and
employers are able to top-
up the payment. Restricted
to workers employed in
March 2020. From
September 28, the payment
rate will be A$1,200
(US$860) per fortnight for
employees working for 20
hours or more a week and
$750 (US$538) per
fortnight for employees
working less than 20 hours
a week. From November 4,
2021, the payment rate for
the two groups reduces to
A$1,000 (US$717) and
A$650 (US$466) per
fortnight respectively. The
program began on March
30, 2020 and is scheduled
to end on March 28, 2021.
The subsidies are
scheduled to last 12
months.
12 months
Austria
Corona-
Kurzarbeit
(Corona short-
time work)
Wage
Subsidy
Short-time work is independent of the size
of the company and possible regardless of
the branch. Public organizations, Bund
and Länder, political parties and the local
community institutions are excluded from
this subsidy. Employers must retain
workers.
The employee receives
90% of wages if the gross
wages received previously
were up to EU1700
(US$2005) per month,
85% if the gross wages
received previously were
between EU1700 and
EU2685 (US$3167) per
month, and 80% for if
gross wages were
previously greater than
EU2685 per month.
Working time reduced by
up to 10%. Phase 3 begins
October 1. After this date,
3 months with
further 3 month
extension if
specific
requirements
are met
working time must have
reduced between 30% and
80%. This program began
on June 1, 2020, and is
currently scheduled to end
on March 31, 2021.
Belgium
Temporary
Unemployment
Scheme
Wage
Subsidy
Workers and employees, temporary
workers, contractual staff and apprentice.
Employers must retain workers.
70 percent (up from the
usual 65 percent) of their
average capped wages
(capped at EUR,754.76
(US$3,249) per month)
plus a supplement of 5.63
euros per day. This
program began on
February 1, 2020, and is
currently scheduled to end
on August 31, 2020.
6 months
Canada
Emergency
Wage Subsidy
Wage
Subsidy
Employers with a CRA payroll account,
that have experienced a reduction in
revenue (15% or more in March, 30% in
April/May, or any level of decline after
June). Employers must retain workers.
Wage subsidy to rehire
workers previously laid off
due to COVID, prevent
further job losses. The
subsidy is 75 percent of
employee wages up to
CA$847 (US$639) per
week per employee. Since
June, subsidies are now
proportional to the
experienced revenue
decline. A "base subsidy"
will be paid to employees
of employers with any
level of revenue decline
while employers that have
experienced revenue
decline greater than 50%
are entitled to a "top-up
subsidy". "Base subsidy"
rate is defined in table in
the link (reducing each
month). This program
began on March 15, 2020,
and is currently scheduled
to end on November 21,
2020.
8 months
Chile
Ley de Ingreso
Mínimo
Garantizado
(Guaranteed
Minimum
Income)
Wage
Subsidy
Dependent workers subject to working
hours of 30-45 hours per week who
receive a gross salary less than
CH$384,363 (US$486) and who belong to
the most vulnerable 90% of the population
according to the Social Registry of
Households. Employers must retain
workers.
Anyone earning below
CH$301,000 (full-time)
receives the maximum
subsidy. The subsidy
amount decreases as gross
salary increases up to
CH$384,363. The monthly
amount of the subsidy will
be calculated
proportionally for part-
time workers. Maximum
subsidy of CH$59,200
(US$75). This program
began on April 1, 2020,
and is currently scheduled
to end on December 31,
2023.
44 months
Colombia
Programa de
Apoyo al
Empleo Formal
(Formal
Employment
Support
Program)
Wage
Subsidy
Any business that has had a 20% reduction
in turnover or sales, when compared with
April 2019 and as long as the business has
not received benefits from the Formal
Employment Support Program (PAEF) of
this decree on four or more occasions.
Employers must have been incorporated
before November 1, 2020, and have an
inscription in the commercial register.
Employers must retain workers.
The national government
will grant monthly a
contribution per employee
corresponding to 40% of
the minimum wage. This
corresponds to
CO$351,000 pesos (US$
93.50). This program
began on May 8, 2020, and
is currently scheduled to
end on September 8, 2020.
4 months
Czech
Republic
Wage Subsidy
Antivirus
employment
protection
program
Wage
Subsidy
Companies must continue to pay all wages
and benefits and need to prove their
problems are due to COVID-19.
Employers must retain workers.
Support is 80% of wages
up to a maximum of CZK
39,000 (US$1,757) per
month for employees who
cannot work because of a
quarantine or a
closure/restriction ordered
by authorities. Support is
60% capped at CZK
29,000 (US$1307) per
month when an employer's
business is affected in a
different way by the
coronavirus outbreak
(reduced demand,
unavailability of supply).
This program began on
March 12, 2020, and is
currently scheduled to end
on August 31, 2020.
5 months
Denmark
Wage Subsidy
L141
Wage
Subsidy
Companies where at least 50 employees or
30 percent of the total workforce had their
employment terminated due to COVID-
19. Employers must retain workers.
State pays up to 75% of
employees’ salaries for
full-time salaried
employees and up to 90%
of salaries for hourly
workers at a maximum of
DKK 30,000 (US$4743)
per month. Companies are
required to pay the rest of
an employee's salary in
full. The company may be
covered by the scheme for
up to 3 months at most.
This program began on
March 9, 2020, and is
currently scheduled to end
on August 29, 2020.
5 months
Estonia
The Estonian
Unemployment
Insurance Fund
Wage
Subsidy
Must satisfy 2 of the following 3
conditions: Employer must have suffered
at least a 30% decline in turnover or
revenue for the month they wish to be
compensated for in comparison to the
same month the previous year. OR the
employer has cut over 30% of employees'
wages by at least 30%. OR the employer is
not able to provide 30% of their
employees with the agreed workload.
(More stringent requirements added in
June 2020: turnover must have decreased
by 50% in June, tax debt must have been
paid by the employer, and the previous
conditions must now apply to 50% of the
employer's workforce compared to 30%).
Employers must retain workers.
The Estonian
Unemployment Insurance
Fund will compensate 70%
of the average wage from
the last 12 months but no
more than EU1,000
(US$1,176). Total cost of
the decreased wages
compensation measure is
EU250 million. Employers
must pay at least EU150
($US176) to each
employee. (Subsidy
reduced to 50% up to
EU800 (US$941 starting
June 2020). This program
began on March 1, 2020,
and ended on June 30,
2020.
3 months
Finland
Business Cost
Support
Forgivable
Loans
Support will be paid to those sectors of
industry where turnover in April 2020 has
decreased by at least 10% compared to
March-June 2019. If a company belongs to
such a sector of industry, a further
precondition is that the company's
turnover in April-May 2020 has decreased
by over 30% when compared to its
turnover in March-June 2019. Employers
must retain workers.
The business cost support
would be at maximum
EUR500,000
(US$589,713) for two
months. Business cost
support less than EUR
2,000 (US$2,359) would
not be paid, as such a low
sum would not be relevant
in preventing bankruptcies.
The amount of business
cost support granted
depends on the magnitude
of the applicant company’s
fixed costs and labor costs.
Fixed costs entitling to
compensation could
amount to no more than
50% of the particular
company’s average
2 months
turnover during the
comparison period. This
program began on July 1,
2020, and is currently
scheduled to end on
August 31, 2020.
France
Chômage Partiel
(Partial
Unemployment)
Wage
Subsidy
Businesses must have reduced hours or
have closed part or all of their operations.
Employers must retain workers.
The employer must pay the
employee compensation
corresponding to 70% of
his gross salary per hour
worked, i.e. approximately
84% of the hourly net
salary. This compensation
cannot be less than €8.03
per hour off work. If the
employee is on minimum
wage, they will be
reimbursed 100%. The
company will be fully
reimbursed by the State,
for salaries up to EU6,927
(US$8149) gross monthly
(4.5 times minimum
wage). This was a pre-
existing program before
the COVID-19 pandemic
and thus has no scheduled
end date.
Maximum
period of 12
months,
renewable
(maximum was
6 months pre-
covid-19)
France
Activité
Partielle de
Longue Durèe
(APLD) (Long
Term Partial
Activity)
Wage
Subsidy
Businesses that have reduced hours or
closed part or all of their operations.
Employees cannot be furloughed more
than 40 percent of their total work time
and there must be an agreement with
workers unions.
Businesses that register for
this scheme will pay their
employees 70% of their
wages within the 4.5 times
the minimum wage limit.
The employer will be
reimbursed 60% by the
government for
agreements concluded
before October 1, 2020.
Reimbursement rates will
be 56% for agreements
after the October 1
deadline. This program
began on July 1, 2020, and
is currently scheduled to
end on June 30, 2022.
24 months
France
Activité
Partielle de droit
commun (Partial
Activity under
Common Law)
Wage
subsidy
Businesses must have reduced hours or
have closed part or all of their operations.
Employees can be furloughed for more
than 40 percent of their total work time.
Employers must retain workers.
Businesses that register for
this scheme will see the
state reimburse 72 percent
of a furloughed employee's
net salary (unless they are
on minimum wage of
which they get 100 percent
reimbursed) but the state
will not cover more than
70 percent of the current
4.5 times the minimum
wage (SMIC). NOTE:
From July, this pre-
existing system will
coexist with long-term
partial activity, as that
system is less restrictive.
This program began on
June 1, 2020, and is
currently scheduled to end
on June 30, 2022.
6 months (can
be renewed up
to 4 times for
max 2 years
(APLD
adaption July
20))
Germany
Expanded
Kurzarbeitergeld
(Expanded
Short-Time
Work
Allowance)
Short-Time
Work
Subsidy
At least 10 percent of workers have hours
cut by more than 10 percent (pre-covid-19,
to qualify for Kurzarbeitergeld, 30 percent
of the workforce had to be affected).
Employers must retain workers.
Government subsidizes 60
percent of lost wages for
workers on short-time
work allowance (67
percent for workers with
children). After 4 months,
this increases to 70 percent
(77 percent for workers
with children). After 7
months, this increases to
80 percent (87 percent for
workers with children)
Months are counted from
March 1st 2020. This
program began on March
1, 2020, and is currently
scheduled to end on
December 31, 2020.
12 months
Greece
SYN-ERGASIA
Short-Time
Work
Subsidy
Businesses will be able to participate
regardless of size or activity, as long as
they can show a loss of 20% turnover in
the month that they join the program.
Employers may only reduce the hours of
full-time salaried employees who were
active May 30, 2020. Employers must
retain workers.
Employers may reduce
unilaterally all or part of
their employees' weekly
work hours by up to 50%.
The state will cover 60%
of the employee's net
salary for the time during
which the employees do
not work. If, after this
wage subsidy, the wage
does not reach minimum
wage, the deficit will be
further subsidized by the
government. This program
began on June 15, 2020,
and is currently scheduled
4 months
to end on October 15,
2020.
Hungary
Short-time work
subsidy
Short-Time
Work
subsidy
Employer and the employee can agree on
reduced working time (minimum 25 % but
maximum 85 % of original working time).
Employer must have evidence that (i) the
difficulties in the business are directly
related to the COVID-19 pandemic and
the state of emergency; (ii) retention of the
employees is in the interest of the national
economy. Employers must retain workers
for the duration of the subsidy plus at least
one month after the subsidy ends.
70 percent of lost salary up
to HUF 214,130 (US$730)
per month (twice the
minimum wage). This
program began on April
16, 2020, and ended on
July 16, 2020.
3 months
Iceland
Wage
Subsidy
Those who are under threat of losing their
jobs will become eligible for
unemployment benefits, which allow them
to move to part time hours for their
employer and claim additional support
from the Government. The benefit
package is open for those who cut back to
as low as 25% of their previous
employment hours or salary. Self-
employed and freelancers are also eligible
for the benefit. Employers must retain
workers.
The Government of
Iceland has committed to
allowing part-time workers
to claim up to 75 percent
of unemployment benefits
up to a combined amount
of ISK 700,000 (US$5109)
per month. Government
will cover 50% of benefits
after June. Companies
experiencing a 75% or
greater decline in revenue
are able to access more
government assistance to
cover up to 85% of wages.
This program began on
March 21, 2020, and is
currently scheduled to end
on August 31, 2020.
5 months
Ireland
Temporary
Wage Subsidy
Scheme (TWSS)
Wage
Subsidy
Introduced for employers in all sectors
who retain staff on payroll; some of the
staff may be temporarily not working or
some may be on reduced hours or reduced
pay. Employers must be able to
demonstrate a 25 percent reduction in
turnover and employers must retain
workers.
(System preceding May 4
2020) €410 per employee
(US $462). (System from
May 4 2020 onwards) The
maximum subsidy payable
is calculated by reference
to the employee’s net
weekly pay for November
and February 2020. The
subsidy is tapered to
ensure that the net weekly
pay (employer’s
contribution and wage
subsidy) of the employee
does not exceed €960 net
per week. This program
Initially 12
weeks, starting
from 26 March
2020. Extended
to 12 months
began on March 26, 2020,
and is currently scheduled
to end on August 31, 2020.
Ireland
Employment
Wage Subsidy
Scheme (EWSS)
Wage
Subsidy
Employers and new firms in sectors
impacted by COVID-19 whose turnover
has fallen 30%. If a worker is already on
TWSS, they must stay on that until it ends
August 31 before applying for EWSS.
Employers must retain workers.
Flat rate subsidy: Rate of
EU203 (US$239) per week
for employees earning
between EU203 and
EU1,462 (US$1719) per
week. Rate of EU151.50
(US$178) for employees
earning between EU151.50
and EU202.99 per week.
No subsidy is paid for
employees paid less than
EU151.50 or more than
EU1,462 per week. This
program began on July 1,
2020, and is currently
scheduled to end on March
31, 2021.
8 months
Israel
The Economic
Assistance
Program
Wage
Subsidy
1) Any self-employed individuals with
taxable income in 2018 between 24,000
(US$7,041) to 240,000 NIS (US$70,411),
and with a 25% decrease in turnover
during March-April compared to the same
period in 2019. 2) Any workers on unpaid
leave.
1) receive a grant up to
6,000 NIS (US$1,760) 2)
Workers on unpaid leave
from their employer are
eligible to claim up to 80%
of their last salary from the
Israeli Employment
Service. This program
began on May 8, 2020, and
is currently scheduled to
end on June 30, 2021.
14 months
Italy
Wage
Supplementary
Fund
Wage
Subsidy
Employers who suspend or reduce their
business activities in 2020 as a result of
the COVID-19 pandemic. Employers must
retain workers.
80 percent of employees’
wages up to a maximum of
EU1,300 (US$1529). This
program began on
February 23, 2020, and is
currently scheduled to end
on August 31, 2020.
14 weeks but
can be
extended to 12
months
Japan
Expanded
Employment
Adjustment
Subsidies
Wage
Subsidy
Any business that has seen a decrease in
production or sales of more than 5% and
has been affected by COVID-19. The
business must submit a closure plan by
June 30 2020. Businesses must still pay
compensation for absence from work of no
less than 60% of normal wages and
employers must retain workers.
For small and medium
sized employers, the
government will cover
80% of the compensation
for absence from work up
to JPY 15,000 (US$141)
per day. Government will
cover 90% if the employer
does not lay off any
employees. For large
businesses, the
government will pay
employers 66 percent of
the compensation up to the
same limit with the
covered percentage rising
to 75% if they do not lay
off any employees. (In the
typical system pre-
COVID-19, the ratios were
66 percent and 50 percent
respectively). This
program began on April 1,
2020, and is currently
scheduled to end on
September 30, 2020.
5 months
(further
extensions
being debated)
Japan
Safety Net for
Financing
Guarantee
Forgivable
Loans
Monthly revenue has decreased by 20%
Loan guarantee for up to
280 million yen ($2.62
million)
Latvia
Downtime
Subsidy
Wage
Subsidy
Employers in 40 industries including
sports, travel, transit, tourism and culture.
Employers must retain workers.
75 percent of their salaries
but not more than EUR
700 (US$821) a month
(minimum wage). The
program is expected to
cost about €102m and
cover 73,000 employees
according to Economics
Ministry estimates. This
program began on May 1,
2020, and ended on June
30, 2020.
2 months
Latvia
Special Wage
Subsidy
Wage
Subsidy
Employers in 40 industries including
sports, travel, transit, tourism and culture.
Employers must retain workers.
Employers can apply for
wage subsidies of 50% up
to a maximum of EUR 430
(US$504). Each wages
subsidy period lasts 4
months. Employers are
only permitted to apply for
wage subsidies for up to
50% of its employees but
no more than 20
employees. Employment
for each person receiving a
wage subsidy must be
guaranteed for 3 months
4 months
following the end of the
subsidy. This program
began on July 1, 2020, and
is currently scheduled to
end on December 31,
2021.
Lithuania
The Economic
and Financial
Action Plan
Wage
Subsidy
All employers can apply for the subsidy
but they cannot require employees to
perform work functions during the
downtime. All employers that apply for
wage subsidies must maintain no less than
50 percent of jobs for 3 months or 6
months following the end of payment of
wage subsidies depending on which
subsidy the employer applies for (see
Program Description).
The government will pay
employers 90 percent of an
employee's wage up to
EUR 607 pre-tax (1x
minimum wage). There is
an obligation to maintain
the employment status of
the employee for 6 months
with this subsidy. Or the
government will pay
employers 70 percent of an
employee's wage up to
EUR 910.5 pre-tax (1.5x
minimum wage). There is
an obligation to maintain
the employment status of
the employee for 3 months
with this subsidy. Self-
employed workers can
apply for a flat rate subsidy
of EUR 257 per month
regardless of the number
of self-employed activities
they carry out. This
program began on April 8,
2020, and will remain in
place until the state of
emergency and quarantine
is ended by the Lithuanian
Government.
1 month (must
renew each
month but
unlimited
renewals)
Luxembourg
Chômage Partiel
(Partial
Unemployment)
Wage
Subsidy
Companies and Organizations based in
Luxembourg with an establishment
authorization and affected by force
majeure, COVID-19. Employers must
retain workers.
80% of workers’ wages –
up to 250 percent social
minimum wage. Workers
cannot be laid off. This
program began on March
18, 2020, and is currently
scheduled to end on
December 31, 2020.
9 months
Mexico
Employment
Guarantee
No state workers will be
fired.
Netherlands
Temporary
Emergency
Bridging
Measure NOW
Wage
Subsidy
Companies facing at least 20 percent
turnover loss over a 3 month stretch
between March 1, 2020, and July 31,
2020. This was extended to a period of
four months under version 2.0 running
from June 6, 2020. Those four months can
be between March 1 and November 30,
2020. Employers must retain workers.
If the turnover loss is 100
percent, the compensation
will amount to 90 percent
of wages. If loss is 50
percent, compensation will
be 45 percent. If loss is 25
percent, the compensation
will amount to 22.5
percent of wages. No
layoffs allowed.
Compensation is capped at
EU 9,538 (US$11,188) per
month. This program
began on March 1, 2020,
and is currently scheduled
to end on September 30,
2020.
Original 3
months
extended to 6
months
New
Zealand
COVID-19
Wage Subsidy
Wage
Subsidy
(REGULAR SUBSIDY): Employers with
a 30 percent or more decline in actual or
predicted revenue during the month due to
COVID-19. Then updated by removing
the 30 percent requirement. Instead
became any employers with a predicted or
actual decline in revenue due to COVID-
19. The regular subsidy ended June 10,
2020. The extension until September 1,
2020, requires demonstration of at least a
40% drop in revenue. Employers must
retain workers.
Flat rate: NZ $585.80
(US$385) for employees
working 20 hours or more
per week before the crisis
(full-time); NZ $350
(US$230) for employees
working less than 20 hours
per week (part-time).
Maximum of NZ$150,000
(US$98,655) per firm.
This program began on
March 18, 2020, and is
currently scheduled to end
on September 1, 2020.
12 weeks.
Additional 8
weeks if
employers can
demonstrate a
40% drop in
revenue.
Norway
Employee
Retention Credit
Wage
Subsidy
Companies that have more than a 10
percent drop in turnover and non-profit
organizations, associations and
foundations for the purpose of taking back
their own lay-offs can apply for support.
The scheme covers all employees,
including apprentices. Employees must
have been laid off or partially laid off as of
May 28 2020 but then taken back from
redundancy at the beginning of July.
Employers must retain workers.
For companies with more
than a 30 percent revenue
drop, they receive
NOK15,000 per person
who has been taken back
from redundancy. For
companies with a revenue
drop between 10 percent
and 30 percent, the aid
amount per person taken
back is (fall in turnover in
percent - 10 percentage
points) * 75,000. This
program began on July 1,
2020, and is currently
scheduled to end on
August 31, 2020.
2 months
(potential to
extend beyond
August)
Poland
Anti-Crisis
Shield-Wage
Subsidy
Wage
Subsidy
The employee must have been fully or
partially laid off as of 28 May 2020.
Business must have experienced more
than 15% decline in turnover compared to
previous year. Employers must retain
workers.
For economic downtime,
subsidy is 50 percent of
minimum wage, EU 290
(US$340). For reductions
of working time at least 20
percent but less than part
time, up to 50 percent of
employee’s salary, but no
more than 40 percent of
the average monthly salary
compared to the previous
quarter. Workers cannot be
laid off. For micro, small,
and medium sized
businesses, a subsidy of
either 50, 70, or 90 percent
of minimum wage per
employee can be given by
the government if total
sales revenue declined by
30, 50, or 80 percent
respectively compared to
the two corresponding
months in 2019. This
program began on March
31, 2020, and ended on
June 30, 2020.
3 months
Portugal
Simplified
Layoff
Wage
Subsidy
Companies in temporary economic
difficulties (i.e. that cease their activity
due to a break in the supply chain as well
as those whose business records a 40
percent drop in turnover compared to the
same period in 2019). Employers must
retain workers for at least 60 days after the
subsidy ends.
Where normal working
hours are reduced, the
employee’s salary is
proportionally
reduced. However, the
employee will be entitled
to a minimum amount
equal to 2/3 of their normal
gross remuneration, or the
value of the national
minimum wage, EUR 635
(US$748) per month,
whichever is higher, up to
three times the NMW
(EUR 1,905.00,
(US$2,245)). This
compensation is supported
by Social Security (70%)
and the employer (30%).
This program began on
March 9, 2020, and is
currently scheduled to end
on September 30, 2020.
3 months
(renewed
monthly) (may
apply for a 4th
month with
"exceptional
circumstances")
Slovak
Republic
None
Wage
Subsidy
Employers who closed or restricted their
business operations due to the decision of
the public health authority or any
employers who had sales reduce by more
than 20%. Employers must retain workers.
80% of average monthly
salary up to EUR 1100 per
employee per month for
employees who are unable
to work. Subsidies for self-
employed people whose
sales declined during the
state of emergency is 540
EUR per month. For
employers with sales
reductions greater than
20% but who do not close
down, compensation for
lost income due to reduced
sales is as follows: >20% -
EU180, >40% - EU300,
>60% - EU420, >80% -
EU540 per month. This
program began on March
13, 2020, and is currently
scheduled to end on March
31, 2021.
12 months
Slovenia
Wage Co-
financing
Regime
Wage
Subsidy
Workers who are temporarily laid off and
workers unable to come to work because
of the COVID-19 pandemic. Employers
must retain workers.
To employers who cannot
provide work to more than
30% of their employees
and send them home to
wait for work. In this case,
the state will reimburse
40% of the salary costs to
the employer, while the
employer bears 60% of the
cost. The maximum
amount of reimbursement
is limited to the maximum
amount of compensation
for unemployment
(currently EUR 892.50
gross). If a healthy
employee is ordered to
stay in quarantine and
cannot work from home. In
this case, the state will
reimburse to the entire cost
of the employee’s salary
compensation, i.e. 80% of
the employee’s average
salary in the last three
months. This program
began on April 2, 2020,
and ended on June 15,
2020.
2 months
South Korea
Employment
Maintenance
Subsidies
Wage
Subsidy
Qualifications for the subsidy include the
following: maintaining the current
employees while exercising “rescue”
measures for at least one month, such as,
(a) a temporary suspension of business
while granting paid leave to the
employees; or (b) reduced employee work
hours which are in excess of 20% of the
total working hours. Employers must
retain workers.
Increases employment
retention subsidies from
66% of wages to 90% for 3
months, April to June
(while maintaining the cap
of
KRW66,000/employee/day
(US$56)). Large firms are
subject to the 66%
threshold. Employment
promotion subsidy for
small and medium sized
enterprises introduced
from July 27 until
December 31 for up to 1
million KRW (US$845)
per hired person. This
program began on April 1,
2020, and ended on June
30, 2020.
3 months
Spain
Expansion of
ERTE Program
to businesses
affected by
Coronavirus
Wage
Subsidy
All workers affected by a reduction in
working hours or temporary suspension of
working contract. Company must prove
reduction in workload due to force
majeure or economical, technical,
organizational or productive causes.
Employers must retain workers for at least
6 months after the program ends.
In the case of total ERTEs
for causes of force
majeure, where all
employees have been sent
home, companies with
fewer than 50 workers will
receive tax exemptions of
70% up to July, 60% in
August and 35% in
September. If a company
has more than 50 workers,
it will be relieved of
paying 50% of employer
contributions up to July,
40% in August and 25% in
September. In the case of
partial ERTEs, where
some workers have
returned, exemptions also
apply. In businesses with
fewer than 50 workers,
companies will receive
exemptions of 60% for
employees who have
returned to work and 35%
for those who remain
suspended. In businesses
with more than 50
workers, the rate is 40%
and 25%, respectively.
This program began on
March 17, 2020, and is
currently scheduled to end
on September 30, 2020.
6 months
Sweden
Short-Time
Work
Allowance
Short-Time
Work
Subsidy
Companies that can show temporary and
serious financial difficulties in coping with
the COVID-19 pandemic. Newly hired
employee (less than 3 months) are not
encompassed in the support. Employers
must retain workers.
Subsidy of 15% of
employee pay with a 20%
reduction of working time,
30% with a 40% reduction
of working time, 45% with
a 60% working time
reduction, and 60% with
an 80% working time
reduction (this most
serious case can only be
applied for May, June and
July). Maximum support
SEK 44,000 (US$5,066)
per person/per month. This
program began on March
16, 2020, and is currently
scheduled to end on
December 31, 2020.
6 months with
extension of 3
months until
end of
December 2020
possible
Switzerland
Expansion of
Chômage Partiel
Wage
subsidy
Employers affected by COVID-19 send
request to local canton for STW benefits.
Apprentices and temporary workers are
included. Employers must retain workers.
Subsidy covers 80 percent
of workers lost earnings
capped at CHF12,350
(US$13,556) per month.
Workers cannot be laid off.
For example, if an
employer has to reduce the
working time to 50%, the
employer continues to pay
the full salary for the 50%
of the time worked, but
only 80% of the salary for
the 50% of the time not
worked. This part is
reimbursed by the
unemployment fund. This
program began on March
20, 2020, and is currently
scheduled to end on March
1, 2021.
12 months
Turkey
Short Labor Pay
Wage
Subsidy
Firms that reduced working hours or
halted operations because of the outbreak.
Employers must retain workers.
Firms can force workers to
take unpaid leave and the
worker will receive 1,170
TL ($180) per month. For
firms that reduced working
hours, a Short-term Work
Allowance provides 1,752
TL/month (around $271)
for those that receive
minimum wage. Beyond
that the government will
pay 60 percent of staff
salaries for 3 months
within the range of 1752
TL and 4381 ($640) TL
(1.5x minimum wage) per
month. This program
began on March 15, 2020,
4 months
and ended on July 31,
2020.
United
Kingdom
Coronavirus Job
Retention
Scheme
Wage
Subsidy
All UK employers with Pay As You Earn
(“PAYE”) payroll schemes that were
opened and in use on or before February
28, 2020. Employers must retain workers.
From March 1, 2020 to
July 31, 2020, the CJRS
subsidizes up to 80% of
employees’ “regular wage”
or up to £2,500.00,
whichever is lower, as well
as all employer National
Insurance Contributions
(“NICs”) and pension
contributions for the hours
that employees are
furloughed. For August
2020, the UK Government
still will pay 80% of wages
up to a cap of £2,500.00,
but employers will be
responsible for the NICs
and pension contributions.
In September 2020, the
UK Government will pay
70% of wages up to a cap
of £2,187.50 for the hours
that employees are
furloughed, and employers
will pay NICs and pension
contributions and will be
required to make up the
difference in employees’
wages. Finally, in October
2020, the CJRS grant will
provide 60% of
employees’ wages up to a
cap of £1,875.00 for the
hours that employees are
furloughed, and employers
will pay NICs and pension
contributions and will be
required to make up the
difference in employees’
wages. This program
began on March 1, 2020,
and is currently scheduled
to end on October 31,
2020.
8 months
United
States
Paycheck
Protection
Program
Forgivable
Loans
Small businesses according to guidelines
from the Small Business Administration.
Generally businesses with 500 employees
or fewer. Employers must retain workers.
Small businesses can apply
for a bank loan covering
24 weeks of expenses up to
$10 million with a 1
percent interest rate and 5-
year repayment period, 60
percent of which must be
spent on payroll. The loan
is forgiven provided no
layoffs occur or workers
that were laid off prior to
obtaining the loan are
rehired. This program
began on April 3, 2020,
and ended on August 8,
2020.
8 or 24 weeks
Notes: The program information in this table is current as of August 12, 2020 and is the authors’ summary of information from various sources,
including: International Labor Organization Appendix on Temporary Wage Subsidies; Lipson, Northend, and Alberzeh; Monitoring the Covid-
19 Employment Response: Policy Approaches Across Countries; Harvard Kennedy School Malcom Weiner Center for Social Policy; Social
Protection and Jobs Responses to COVID-19: A Real-Time Review of Country Measures.
Figure A1: PPP Loans and Total Lent by State. This figure shows cumulative PPP loans and dollars lent by state
from April 3 to August 8. Panel A displays the number of loans and Panel B displays the dollars lent. Data come
from the SBA PPP Report for August 8.
Figure A2: Total PPP Loans by Industry and Share of February 2020 Jobs Lost by April 2020. This figure
displays PPP lending and job losses by industry. The left panel displays PPP loans in billions from April 3 to August
8 according to the SBA Paycheck Protection Program Report for August 8. The right panel displays job losses from
February to April as a share of jobs in February. Employment data come from the BLS as of October 19, 2020.
