How Did COVID-19 and Stabilization Policies Affect Spending and Employment? — NBER Working Paper 27431

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NBER Working Paper 27431, dated June 2020, is by Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner and The Opportunity Insights Team. It describes a publicly available platform that tracks economic activity at a granular level in real time using anonymized data from private companies. The authors show that high-income individuals reduced spending sharply in mid-March 2020, and that businesses serving them in person laid off most of their low-income employees. They estimate that state-ordered reopenings of economies have little impact on local employment and that Paycheck Protection Program loans have had little impact on employment at small businesses. Stimulus payments to low-income households increased consumer spending sharply but had modest impacts on employment in the short run.

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                             NBER WORKING PAPER SERIES

             HOW DID COVID-19 AND STABILIZATION POLICIES
                  AFFECT SPENDING AND EMPLOYMENT?
   A NEW REAL-TIME ECONOMIC TRACKER BASED ON PRIVATE SECTOR DATA

                                       Raj Chetty
                                    John N. Friedman
                                    Nathaniel Hendren
                                     Michael Stepner
                               The Opportunity Insights Team
                                    Working Paper 27431
                            http://www.nber.org/papers/w27431

                    NATIONAL BUREAU OF ECONOMIC RESEARCH
                             1050 Massachusetts Avenue
                               Cambridge, MA 02138
                                     June 2020

A preliminary draft of this paper was previously circulated as “Real-Time Economics: A New
Platform to Track the Impacts of COVID-19 on People, Businesses, and Communities Using
Private Sector Data.” We thank Gabriel Chodorow-Reich, Jason Furman, Xavier Jaravel,
Lawrence Katz, Emmanuel Saez, Ludwig Straub, and Danny Yagan for helpful comments. We
also thank the corporate partners who provided the underlying data used in the Economic
Tracker, who as of this version include: Affinity Solutions (especially Atul Chadha and Arun
Rajagopal), Burning Glass (especially Anton Libsch and Bledi Taska), Earnin (especially Arun
Natesan and Ram Palaniappan), Homebase (especially Ray Sandza and Andrew Vogeley), Intuit
(especially Christina Foo and Krithika Swaminathan), Womply (especially Toby Scammell and
Ryan Thorpe), and Zearn (especially Billy McRae and Shalinee Sharma). We are very grateful to
Ryan Rippel of the Gates Foundation for his support in launching this project and to Gregory
Bruich for early conversations that helped spark this work. The work was funded by the Chan-
Zuckerberg Initiative, Bill & Melinda Gates Foundation, Overdeck Family Foundation, and
Andrew and Melora Balson. The project was approved under Harvard University IRB 20-0586.
†The Opportunity Insights Economic Tracker Team consists of Matthew Bell, Gregory Bruich,
Tina Chelidze, Lucas Chu, Westley Cineus, Sebi Devlin-Foltz, Michael Droste, Shannon Felton
Spence, Dhruv Gaur, Federico Gonzalez, Rayshauna Gray, Abby Hiller, Matthew Jacob, Tyler
Jacobson, Margaret Kallus, Laura Kincaide, Cailtin Kupsc, Sarah LaBauve, Maddie Marino, Kai
Matheson, Kate Musen, Danny Onorato, Sarah Oppenheimer, Trina Ott, Lynn Overmann, Max
Pienkny, Jeremiah Prince, Daniel Reuter, Peter Ruhm, Emanuel Schertz, Kamelia Stavreva,
James Stratton, Elizabeth Thach, Nicolaj Thor, Amanda Wahlers, Kristen Watkins, Alanna
Williams, David Williams, Chase Williamson, Shady Yassin, and Ruby Zhang.

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 Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner, and The
Opportunity Insights Team. 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.
How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New
Real-Time Economic Tracker Based on Private Sector Data
Raj Chetty, John N. Friedman, Nathaniel Hendren, Michael Stepner, and The Opportunity
Insights Team
NBER Working Paper No. 27431
June 2020
JEL No. E0,H0,J0
                                         ABSTRACT

We build a publicly available platform that tracks economic activity at a granular level in real
time using anonymized data from private companies. We report daily statistics on consumer
spending, business revenues, employment rates, and other key indicators disaggregated by
county, industry, and income group. Using these data, we study the mechanisms through which
COVID-19 affected the economy by analyzing heterogeneity in its impacts across geographic
areas and income groups. We first show that high-income individuals reduced spending sharply
in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors
that require physical interaction. This reduction in spending greatly reduced the revenues of
businesses that cater to high-income households in person, notably small businesses in affluent
ZIP codes. These businesses laid off most of their low-income employees, leading to a surge in
unemployment claims in affluent areas. Building on this diagnostic analysis, we use event study
designs to estimate the causal effects of policies aimed at mitigating the adverse impacts of
COVID. State-ordered reopenings of economies have little impact on local employment. Stimulus
payments to low-income households increased consumer spending sharply, but had modest
impacts on employment in the short run, perhaps because very little of the increased spending
flowed to businesses most affected by the COVID-19 shock. Paycheck Protection Program loans
have also had little impact on employment at small businesses. These results suggest that
traditional macroeconomic tools – stimulating aggregate demand or providing liquidity to
businesses – may have diminished capacity to restore employment when consumer spending is
constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic
hardship through social insurance. More broadly, this analysis illustrates how real-time economic
tracking using private sector data can help rapidly identify the origins of economic crises and
facilitate ongoing evaluation of policy impacts.
Raj Chetty                                      Nathaniel Hendren
Department of Economics                         Harvard University
Harvard University                              Department of Economics
Littauer 321                                    Littauer Center Room 235
Cambridge, MA 02138                             Cambridge, MA 02138
and NBER
chetty@fas.harvard.edu                          Michael Stepner
                                                Harvard University
John N. Friedman                                1280 Massachusetts Avenue
Department of Economics                         Cambridge, MA 02138
Robinson Hall                                   stepner@mit.edu
Brown University
Providence, RI 02912                            The Opportunity Insights Team
and NBER                                        1280 Massachusetts Avenue
john_friedman@brown.edu                         Box #201
                                                Cambridge, MA 02138
                                                info@opportunityinsights.org

Economic Tracker and Dowloadable Data is available at www.tracktherecovery.org
I    Introduction

Since the pioneering work of Kuznets (1941), macroeconomic policy decisions have been made on
the basis of data collected from recurring surveys of households and businesses conducted by the
federal government. Although such statistics have great value for understanding the economy,
they have two limitations that have become apparent during the COVID-19 pandemic. First, such
data are typically available only at a low frequency with a significant time lag. For example,
disaggregated quarterly data on consumer expenditures are typically available with a one year lag
in the Consumer Expenditure Survey (CEX). Second, such statistics typically cannot be used to
assess granular variation across geographies or subgroups; due to limitations in sample sizes, most
statistics are typically reported only at the national or state level and breakdowns by subgroups or
sectors are often unavailable.
    In this paper, we address these challenges by building a new, freely accessible platform that
tracks economic activity at a high-frequency, granular level using anonymized and aggregated data
from private companies. Combining data from credit card processors, payroll firms, and financial
services firms, we construct statistics on consumer spending, employment rates, business revenues,
job postings, and other key indicators described in detail in Section II below. We report these
statistics in real time using an automated pipeline that ingests data from businesses and reports
statistics publicly on the data visualization platform, typically less than seven days after the relevant
transactions occur. We present fine disaggregations of the data, reporting each statistic by county
and by industry and, where feasible, by initial (pre-crisis) income level and business size.
    Many firms already analyze their own data internally to inform business decisions and some firms
have begun sharing aggregated data with policymakers and researchers during the current crisis.
Our contribution is to (1) combine these disparate data sources into a single, publicly accessible
platform that eliminates the need to write contracts with specific companies to access relevant data;
(2) systematize these data sources by documenting the samples they cover and benchmarking them
to existing public series; and (3) provide the combined series in an interactive data visualization
tool that facilitates comparisons across outcomes, areas, and subgroups.
    Unlike official government statistics, which are based on sampling frames designed to provide
representative information, our statistics reflect the behavior of the clients of the firms from which
we obtain data. To mitigate selection biases that can arise from this approach, we use data from
companies that have large samples (e.g., at least one million individuals), span well-defined sectors



                                                   1
or subgroups (e.g., small businesses, bottom-income-quintile workers), and track publicly available
benchmarks in historical data. Although there is no guarantee that the statistics from such data
sources capture total economic activity accurately, we believe they contain useful information be-
cause the shocks induced by major crises such as COVID-19 are large relative to plausible biases
due to non-representative sampling, as shown e.g., by Aladangady et al. (2019) and Dunn, Hood,
and Driessen (2020).
    We use these new data to analyze the economic impacts of the coronavirus pandemic (COVID-
19). Government statistics show that COVID led to a very sharp reduction in GDP and an
unprecedented surge in unemployment. Our goal is to demonstrate how the publicly available
data we have constructed can shed light on the sources of these macroeconomic changes in near-
real-time, in particular by disaggregating these changes across subgroups and areas. We therefore
base all of our analysis purely on the statistics that we release publicly rather than the underlying
(confidential) microdata that we obtain from data partners.
    National accounts data reveal that most of the reduction in GDP came from a reduction in con-
sumer spending (rather than business investment, government purchases, or exports). We therefore
begin our analysis by examining the drivers of changes in consumer spending, focusing in particular
on credit and debit card spending. We first establish that card spending closely tracks historical
benchmarks on retail spending and services, which together constitute a large fraction of the re-
duction in total spending in the national accounts. We then show that the vast majority of the
reduction in consumer spending in the U.S. came from reduced spending by high-income house-
holds. As of June 10, more than half of the total reduction in card spending since January had
come from households in the top quartile of the income distribution; only 5% had come from house-
holds in the bottom income quartile.1 This is both because the rich account for a larger share of
total spending to begin with and because high-income households reduced their spending by 17%,
whereas low-income households reduced their spending by only 4% as of June 10.
    Most of the reduction in spending is accounted for by reduced spending on goods or services
that require in-person physical interaction and thereby carry a risk of COVID infection, such as
hotels, transportation, and food services, consistent with the findings of Alexander and Karger


   1. We impute income as the median household income (based on Census data) in the cardholder’s ZIP code. We
verify the quality of this imputation procedure by showing that our estimates of the gap in spending reductions by
income group are aligned with those of Cox et al. (2020), who observe income directly for JPMorgan Chase clients,
as of mid-April 2020, the last date available in their series. We find that spending levels of low-income households
increased much more sharply than those of high-income households since mid-April largely as a result of stimulus
payments.


                                                         2
(2020). The composition of spending cuts – with a large reduction in services – di↵ers sharply
from that in prior recessions, where service spending was essentially unchanged and durable goods
spending fell sharply. Zooming into specific subcategories, we find that spending on luxury goods
that do not require physical contact – such as landscaping services or home swimming pools – did
not fall, while spending at salons and restaurants plummeted. Businesses that o↵er fewer in person
services, such as financial and professional services firms, also experienced much smaller losses.
The fact that spending fell in proportion to the degree of physical exposure required across sectors
suggests that the reduction in spending by the rich was driven primarily by health concerns rather
than a reduction in income or wealth. Indeed, the incomes of the rich have fallen relatively little
in this recession (Cajner et al. 2020). Consistent with the centrality of health concerns, we find
that the reductions in spending and time spent outside home were larger in high-income, high-
density areas with higher rates of COVID infection, perhaps because high-income individuals can
self-isolate more easily (e.g., by substituting to remote work). Together, these results suggest that
consumer spending in the pandemic fell because of changes in firms’ ability to supply certain goods
(e.g., restaurant meals that carry no health risk) rather than because of a reduction in purchasing
power.2
    Next, we turn to the impacts of the consumer spending shock on businesses. To do so, we exploit
the fact that many of the sectors in which spending fell most are non-tradable goods produced by
small local businesses (e.g., restaurants) who serve customers in their local area. Building on the
results on the heterogeneity of the spending shock, we use di↵erences in average incomes and rents
across ZIP codes as a source of variation in the spending shock that businesses face. This geographic
analysis is useful both from the perspective of understanding mechanisms and because prior work
shows that geography plays a central role in the impacts of economic shocks due to low rates of
migration that can lead to hysteresis in local labor markets (Austin, Glaeser, and Summers 2018,
Yagan 2019).
    Small business revenues in the most a✏uent ZIP codes in large cities fell by more than 70%
between March and late April, as compared with 30% in the least a✏uent ZIP codes. These
reductions in revenue resulted in a much higher rate of small business closure in high-rent, high-
income areas within a given county than in less a✏uent areas. This is particularly the case for


    2. This explanation may appear to be inconsistent with the fact that the Consumer Price Index (CPI) shows
little increase in inflation, given that one would expect a supply shock to increase prices. However, the CPI likely
understates inflation in the current crisis because it does not capture the extreme shifts in the consumption bundle
that have occurred as a result of the COVID crisis (Cavallo 2020).


                                                         3
non-tradable goods that require physical interaction – e.g., restaurants and accommodation services
– where revenues fell by more than 80% in the most a✏uent neighborhoods in the country, such as
the Upper East Side of Manhattan or Palo Alto, California. Small businesses that provide fewer
in-person services – such as financial or professional services firms – experience much smaller losses
in revenue even in a✏uent areas.
    As businesses lost revenue, they passed the incidence of the shock on to their employees. Low-
wage hourly workers in small businesses in a✏uent areas are especially likely to have lost their jobs.
In the highest-rent ZIP codes, more than 65% of workers at small businesses were laid o↵ within
two weeks after the COVID crisis began; by contrast, in the lowest-rent ZIP codes, fewer than 30%
lost their jobs. Workers at larger firms and in tradable sectors (e.g., manufacturing) were much
less likely to lose their jobs than those working in small businesses producing non-tradable goods,
irrespective of their geographic location. Job postings also fell much more sharply in more a✏uent
areas, particularly for lower-skilled positions. As a result of these changes in the labor market,
unemployment claims surged even in a✏uent counties, which have generally had relatively low
unemployment rates in prior recessions. For example, more than 15% of residents of Santa Clara
county – the richest county in the United States, located in Silicon Valley – filed for unemployment
benefits by May 2. Perhaps because they face higher rates of job loss and worse future employment
prospects, low-income individuals working in more a✏uent areas cut their own spending much more
than low-income individuals working in less a✏uent areas.
    In summary, the initial impacts of COVID-19 on economic activity appear to be largely driven
by a reduction in spending by higher-income individuals due to health concerns, which in turn
a↵ected businesses that cater to the rich – e.g., small businesses in a✏uent areas – and ultimately
reduced the incomes and expenditure levels of low-wage employees of those businesses. In the final
part of the paper, we analyze the impacts of three major policy e↵orts that were enacted in an
e↵ort to break this chain of events and mitigate the economic impacts of the crisis: state-ordered
reopenings, stimulus payments to households, and loans to small businesses.3
    Reopenings of economies had modest impacts on economic activity. Spending and employment
remained well below baseline levels even after reopenings, and in particular did not rise more
rapidly in states that reopened earlier relative to comparable states that reopened later. Spending


   3. Of course, this set of policies is by no means exhaustive: a vast set of other policy e↵orts ranging from changes
in monetary policy to various state-level programs were also undertaken in response to the crisis. We focus on these
three policies because they illustrate the ways in which the new high-frequency data we have assembled can be used
for real-time policy analysis, and we hope that future work will use these data to analyze other policies.


                                                          4
and employment also fell well before state-level shutdowns were implemented, consistent with other
recent work examining data on hours of work and movement patterns (Bartik et al. 2020, Villas-
Boas et al. 2020).
    Stimulus payments provided through the CARES Act increased spending among low-income
households sharply, nearly restoring their spending to pre-COVID levels as of May 10, consistent
with evidence from Baker et al. (2020). Most of this increase in spending was in sectors that require
limited physical interaction. Purchases of durable goods surged, while consumption of in-person
services (e.g., restaurants) increased very little. As a result, very little of the increased spending
flowed to businesses most a↵ected by the COVID-19 shock, such as small businesses in a✏uent areas
– potentially limiting the capacity of the stimulus to increase economic activity and employment
in the communities where job losses were largest.
    Loans to small businesses as part of the Paycheck Protection Program (PPP) also have had
little impact on employment rates at small businesses to date. Employment rates at small firms
in the hardest-hit sectors trended similarly to those at larger firms that were likely to be ineligible
for PPP loans, and remained far below baseline levels as of May 30. These results suggest that
providing liquidity itself may be inadequate to restore employment at small businesses, at least in
the short run.4
    In sum, our analysis suggests that the primary barrier to economic activity is depressed con-
sumer spending due to the threat of COVID-19 itself as opposed to government restrictions on
economic activity, inadequate income among consumers, or a lack of liquidity for firms. Hence,
the only path to full economic recovery in the long run may be to restore consumer confidence
by addressing the virus itself (e.g., Allen et al. 2020, Romer 2020). Traditional macroeconomic
tools – stimulating aggregate demand or providing liquidity to businesses – may have diminished
short-run impacts in an environment where consumer spending is fundamentally constrained by
health concerns.
    In the meantime, it may be more fruitful to approach this economic crisis from the lens of
providing social insurance to reduce hardship rather than stimulus to increase economic activity.
Rather than attempt to put workers back to work in sectors where spending is temporarily depressed
because of health concerns, it may be best to focus on mitigating income losses for those who have

   4. The PPP also includes price incentives to rehire workers in the form of loan forgiveness for firms that employ
the same number of workers as of June 30 as they did in February. Firms may rehire workers in light of this incentive
in the coming month, a possibility that can be evaluated in real time using the data in the tracker. What is clear at
this stage is that liquidity itself – absent this price incentive or fundamental changes in the public health situation –
appears to be insufficient to restore employment to pre-recession levels.


                                                           5
lost their jobs, consistent with the normative predictions of the theoretical framework developed by
Guerrieri et al. (2020). For instance, providing support to workers who have lost their jobs (e.g.,
via the unemployment benefit system) may be preferable to stimulus payments to all households,
irrespective of their employment situation. Our findings also suggest that may be useful to consider
additional place-based assistance targeted at low-income individuals in areas that have su↵ered the
largest losses – such as a✏uent, urban areas – since historical experience suggests that relatively
few people move to other labor markets to find new jobs after recessions (Yagan 2019).
   Of course, all of these results could change over time: the recession may turn into a more
traditional economic shock with Keynesian spillovers across a wider set of sectors and areas as
time passes, in which case tools such as stimulus and liquidity could become much more impactful
(Guerrieri et al. 2020). The tracker constructed here can be used to monitor the changing dynamics
of the crisis and evaluate policy impacts on an ongoing basis.
   Our work builds on and contributes to a rapidly evolving literature on the economic impacts of
COVID-19 as well as a long literature in macroeconomics on the measurement of economic activity
at business cycle frequencies. Several recent papers have used private sector data analogous to
what we assemble here to analyze consumer spending (e.g., Baker et al. 2020, Chen, Qian, and
Wen 2020, Cox et al. 2020), business revenues (e.g., Alexander and Karger 2020), labor market
trends (e.g., Bartik et al. 2020, Cajner et al. 2020, Kurmann, Lalé, and Ta 2020, Kahn, Lange, and
Wiczer 2020), and social distancing (e.g., Allcott et al. 2020, Chiou and Tucker 2020, Goldfarb and
Tucker 2020, Mongey, Pilossoph, and Weinberg 2020). These papers have identified a number of
important results consistent with our findings, such as concentrated impacts on spending in certain
industries such as food and accommodation; social distancing that is a result of voluntary choices
rather than legislation; and large employment losses for low-income workers. Each of these papers
analyzes a subset of data sources, obtained through a data use agreement with the relevant firm.
By combining these and other datasets and benchmarking them to national aggregates, we are able
to trace the macroeconomic impacts of the COVID shock from consumer spending to businesses
to labor markets. More generally, by integrating these datasets into a unified, freely accessible
platform, we eliminate the need for researchers or local policymakers to obtain specific permissions
to use confidential data from companies. We demonstrate that it is feasible to construct aggregates
from these data that protect privacy while providing sufficient granularity for economic analysis in
real time, thereby providing a new tool for economic policy analysis in this crisis and beyond.
   The paper is organized as follows. The next section describes the data we use to construct


                                                 6
the economic tracker. In Section 3, we analyze the e↵ects of COVID-19 on spending, revenue,
and employment. Section 4 analyzes the impacts of policies enacted to mitigate COVID’s impacts.
Section 5 concludes. Technical details on data, methods, and supplementary analyses are available
in an online appendix.




II     Data and Methods

     We use anonymized data from several private companies to construct indices of spending, em-
ployment, and other metrics. In this section, we describe how we construct each series. To facilitate
comparisons between series, we adopt the following set of principles when constructing each series
(wherever feasible given data availability constraints).
     First, the central challenge in using private sector data to measure economic activity is that
they capture information exclusively about the customers each company serves, and thus are not
necessarily representative of the full population. Instead of attempting to adjust for this non-
representative sampling, we characterize the portion of the economy that each series captures by
comparing the characteristics of each sample we use to national benchmarks.5
     Second, we clean each series to remove artifacts that arise from changes in the data providers’
coverage or systems. For instance, firms’ clients often change discretely, sometimes leading to
discontinuous jumps in series, particularly in small cells. We systematically search for large jumps
in series (e.g., >80%), seek to understand their root causes, and address such discontinuities by
imposing continuity as described below.
     Third, many series exhibit substantial periodic fluctuations across days. We address such fluc-
tuations through aggregation, e.g. reporting 7-day moving averages to smooth daily fluctuations.
Certain series – most notably consumer spending and business revenue – exhibit strong weekly fluc-
tuations that are autocorrelated across years (e.g., a surge in spending around the holiday season).
We de-seasonalize such series by normalizing each week’s value in 2020 relative to corresponding
values for the same week in 2019 in our baseline analysis, but also report raw values for 2020 for
researchers who prefer to make alternative seasonal adjustments.
     Fourth, to protect confidentiality of business market shares, we do not report levels of the series.
  5. An alternative approach is to reweight samples based on observable characteristics – e.g., industry – to match
national benchmarks. We do not pursue such an approach here because the samples we work with track relevant
national benchmarks – at least for the scale of shocks induced by the COVID crisis – without such reweighting.
However, the disaggregated data we report by industry and county can be easily reweighted as desired in future
applications.


                                                        7
Instead, we report indexed values that show percentage changes relative to mean values in January
2020.6 We also suppress small cells and exclude outliers to protect the privacy of individuals and
businesses, with thresholds that vary across datasets as described below.
    Finally, we seek to release data series at the highest possible frequency. To limit revisions, we
permit a sufficient lag to adjust for reporting delays (typically one week). We disaggregate each
series by two-digit NAICS industry code; by county, metro area, and state; and by income quartile
where feasible.7
    We now describe each of the series in turn, discussing the raw data sources, construction of key
variables, and cross-sectional comparisons to publicly available benchmarks.8 All of the data series
described below can be freely downloaded from the Economic Tracker website: www.tracktherecovery.org.


II.A     Consumer Spending: Affinity Solutions

We measure consumer spending using aggregated and anonymized consumer purchase data collected
by Affinity Solutions Inc, a company that aggregates consumer credit and debit card spending
information to support a variety of financial service products.
    We obtain raw data from Affinity Solutions at the county-by-ZIP code income quartile-by-
industry-by-day level starting from January 1, 2019. Industries are defined by grouping together
similar merchant category codes. ZIP code income quartiles are constructed at the national level
using Census data on population and median household income by ZIP. Cells with fewer than five
unique card transactions are masked.
    The raw data include several discontinuous breaks caused by entry or exit of credit card providers
from the sample. We identify these breaks using data on the total number of active cards in the
cell. We then estimate the discontinuous level shift in spending resulting from the break (using
a standard regression discontinuity estimator). At the state level (including Washington, DC),
we adjust the series within each cell by adding the RD estimate back to the raw data to obtain
a smooth series. At the county-level, there is too much noise to implement a reliable correction,
so we exclude counties that exhibit such breaks from the sample. After cleaning the raw data in

   6. We always norm after summing to a given cell (e.g. geographic unit, industry, etc.) rather than at the firm or
individual level. This dollar-weighted approach overweights bigger firms and higher-income individuals, but leads to
smoother series and is arguably more relevant for certain macroeconomic policy questions (e.g., changes in aggregate
spending).
   7. We construct metro area values for large metro areas using a county to metro area crosswalk described in the
Appendix.
   8. We benchmark trends in each series over time to publicly-available data in the context of our analysis in the
next section.


                                                         8
this manner, we construct daily values of the consumer spending series using a seven-day moving
average of the current day and previous six days of spending. We then seasonally adjust the series
by dividing each calendar date’s 2020 value by its corresponding value from 2019.9 Finally, we index
the seasonally-adjusted series relative to pre-COVID-19 spending by dividing each day’s value by
the mean of the seasonally-adjusted seven-day moving average from January 8-28.
    Comparison to QSS and MRTS. Total debit and credit card spending in the U.S. was $7.08
trillion in 2018 (Board of Governors of the Federal Reserve System 2019), approximately 50% of
total personal consumption expenditures recorded in national accounts. Affinity Solutions captures
nearly 10% of debit and credit card spending in the U.S. To assess which categories of spending
are covered by the Affinity data, Appendix Figure 1 compares the spending distributions across
sectors to spending captured in the nationally representative Quarterly Services Survey (QSS) and
Monthly Retail Trade Survey (MRTS). Affinity has broad coverage across industries. However,
as expected, it over-represents categories where credit and debit cards are used for purchases. In
particular, accommodation and food services and clothing are a greater share of the Affinity data
than financial services and motor vehicles. We therefore view Affinity as providing statistics that
are representative of total card spending (but not total consumer spending). We assess whether
Affinity captures changes in card spending around the crisis in Section 3.1 below.

II.B     Small Business Revenue: Womply

We obtain data on small business transactions and revenues from Womply, a company that aggre-
gates data from several credit card processors to provide analytical insights to small businesses and
other clients. In contrast to the Affinity series on consumer spending, which is a cardholder-based
panel covering total spending, Womply is a firm-based panel covering total revenues of small busi-
nesses. The key distinction is that location in Womply refers to the location where the business
transaction occurred as opposed to the location where the cardholder lives.
    We obtain raw data on small business transactions and revenues from Womply at the ZIP-
industry-day level starting from January 1, 2019.10 Small businesses are defined as businesses
with annual revenue below Small Business Administration thresholds. To reduce the influence of
outliers, firms outside twice the interquartile range of firm annual revenue within this sample are
excluded and the sample is further limited to firms with 30 or more transactions in a quarter and
   9. We divide the daily value for February 29, 2020 by the average value between the February 28, 2019 and March
1, 2019.
  10. We crosswalk Womply’s transaction categories to two-digit NAICS codes using an internally generated Womply
category-NAICS crosswalk, and then aggregate to NAICS supersectors.


                                                        9
more than one transaction in 2 out of the 3 months.
   We aggregate these raw data to form two publicly available series at the county by industry level:
one measuring total small business revenue and another measuring the number of small businesses
open. We measure small business revenue as the sum of all credits (generally purchases) minus
debits (generally returns). We define small businesses as being open if they have a transaction in
the last three days. We exclude counties with a total average revenue of less than $250,000 during
the pre-COVID-19 period (January 4-31).
   For each series, we construct daily values in exactly the same way that we constructed the
consumer spending series. We first take a seven-day moving average, then seasonally adjust by
dividing each calendar date’s 2020 value by its corresponding value from 2019. Finally, we index
relative to pre-COVID-19 by dividing the series by its average value over January 4-31.
   Comparison to QSS and MRTS. Appendix Figure 1 shows the distribution of revenues observed
in Womply across industries in comparison to national benchmarks. Womply revenues are again
broadly distributed across sectors, particularly those where card use is common. A larger share of
the Womply revenue data come from industries that have a larger share of small businesses, such as
food services, professional services, and other services, as one would expect given that the Womply
data only cover small businesses.

II.C    Employment and Earnings: Earnin and Homebase

We use two data sources to obtain information on employment and earnings for low-income workers:
Earnin and Homebase.
   Earnin is a financial management application that provides its members with access to their
income as they earn it. Workers sign up for Earnin individually using a cell phone app, which records
payroll information from bank accounts. Many lower-income workers across a wide spectrum of
firms – ranging from the largest firms and government employers in the U.S. to small businesses –
use Earnin; we discuss the characteristics of these workers further below. We obtain raw data from
Earnin at the paycheck level with information on home ZIP, workplace ZIP, industry and firm size
decile from January 2020 to present.11 We restrict this sample to workers who are paid on a weekly
or bi-weekly paycycle. We then use these data to measure employment and earnings for low-income
employees. We assign workers to locations using their workplace ZIP codes. We suppress estimates
for ZIP codes with fewer than 50 worker-days observed in Earnin over the period January 4-31.
 11. We map each firm to a NAICS code using firm names and a custom-built crosswalk constructed by Digital
Divide Data. We obtain data on firm sizes from Reference USA.


                                                   10
   Homebase provides scheduling tools for small businesses (on average, 8.4 employees) such as
restaurants (64% of employees for whom sectoral data are available) and retail stores (15% of
employees for whom sectoral data are available). Unlike Earnin, Homebase provides a complete
roster of workers at a given firm, but only covers workers at small businesses. We obtain de-
identified individual-level data on hours and total pay for employees at firms that contract with
Homebase at the establishment-worker-day level, starting on January 1, 2018. We restrict this
sample to non-salaried employees. We then form each aggregate series at the county and industry
level, assigning location based on the ZIP code of establishment. To protect confidentiality, we
suppress estimates for cells with fewer than 10 Homebase clients in January 2020.
   In both datasets, we measure total employment as a seven-day moving average of total number
of active employees, expressed as a percentage change relative to January 4-31, and total earnings
using a seven-day moving average of earnings divided by the average daily total earnings between
January 4-31. In the Homebase data, employment and earnings are observed on a daily basis. In
the Earnin data, where we observe paychecks, we distribute each worker’s earnings at the end of
their pay period over each day in their pay period, and assume that workers are employed over
their full pay period.
   We also observe wages in both datasets. In the Homebase data, we measure hourly wage rates
using the change in the first reported hourly wage rate in the current week and the average reported
wage between January 4-31, 2020, divided by that average. In the Earnin data, where we do not
observe individual identifiers, we measure wages as the seven-day moving average of daily mean
wages, expressed as a percentage change from daily mean wages between January 4-31.
   Comparisons to OES and QCEW. Appendix Figure 2 compares the industry composition of the
Earnin and Homebase samples to nationally representative statistics from the Quarterly Census
of Employment and Wages (QCEW). The Earnin sample is fairly representative of the broader
industry mix in the U.S., although high-skilled sectors (such as professional services) are under-
represented. Homebase has a much larger share of workers in food services, even relative to small
establishments (those with fewer than 50 employees) in the QCEW, as expected given its client
base.
   Overall, annualizing January earnings would imply median earnings of roughly $23K per year
($11-12 per hour). In Appendix Table 1, we compare the median wage rates of workers in Earnin
and Homebase to nationally representative statistics from the BLS’s Occupational Employment
Statistics. Workers enrolled in Earnin have median wages that are at roughly the 10th percentile of


                                                11
the wage distribution within each NAICS code. The one exception is the food and drink industry,
where the median wages are close to the population median wages in that industry (reflecting that
most workers in food services earn relatively low wages). Homebase exhibits a similar pattern, with
lower wage rates compared to industry averages, except in sectors that have low wages, such as
food services and retail.
   We conclude based on these comparisons that Earnin and Homebase provide statistics that
may be representative of low-wage (bottom-quintile) workers. Earnin provides data covering such
workers in all industries, whereas Homebase is best interpreted as a series that reflects workers in
the restaurant and retail sector.

II.D    Job Postings: Burning Glass

We obtain data on job postings from 2007 to present from Burning Glass Technologies. Burning
Glass aggregates nearly all jobs posted online from approximately 40,000 online job boards in the
United States. Burning Glass then removes duplicate postings across sites and assigns attributes
including geographic locations, required job qualifications, and industry.
   We obtain raw data on job postings at the industry-week-job qualification-county level from
Burning Glass. Industry is defined using select NAICS supersectors, aggregated from 2-digit NAICS
classification codes assigned by a Burning Glass algorithm. Job qualifications are defined us-
ing ONET Job Zones. These job zones are mutually exclusive categories that classify jobs into
five groups: needing little or no preparation, some preparation, medium preparation, consider-
able preparation, or extensive preparation. We also obtain analogous data broken by educational
requirements (e.g., high school degree, college, etc.).
   Comparison to JOLTS. Burning Glass data have been used extensively in prior research in
economics; for instance, see Hershbein and Kahn (2018) and Deming and Kahn (2018). Carnevale,
Jayasundera, and Repnikov (2014) compare the Burning Glass data to government statistics on job
openings and characterize the sample in detail. In Appendix Figure 3, we compare the distribution
of industries in the Burning Glass data to nationally representative statistics from the Bureau of
Labor Statistics’ Job Openings and Labor Market Turnover Survey (JOLTS) in January 2020. In
general, Burning Glass is well aligned across industries with JOLTS, with the one exception that
it under-covers government jobs. We therefore view Burning Glass as a sample representative of
private sector jobs in the U.S.




                                                  12
II.E    Education: Zearn

Zearn is an education nonprofit that partners with schools to provide a math program, typically used
in classrooms, that combines in-person instruction with digital lessons. Many schools continued
to use Zearn as part of their math curriculum after COVID-19 induced schools to shift to remote
learning.
   We obtain data on the number of students using Zearn Math and the number of lessons they
completed at the school-grade-week level. The data we obtain are masked such that any county
with fewer than two districts, fewer than three schools, or fewer than 50 students on average using
Zearn Math during the pre-period is excluded. We fill in these masked county statistics with the
commuting zone mean whenever possible. We winsorize values reflecting an increase of greater than
300% at the school level. We exclude schools who did not use Zearn Math for at least one week
from January 6 to February 7 and schools that never have more than five students using Zearn
Math during our analysis period. To reduce the e↵ects of school breaks, we replace the value of
any week for a given school that reflects a 50% decrease (increase) greater than the week before or
after it with the mean value for the three relevant weeks.
   We measure online math participation as the number of students using Zearn Math in a given
week. We measure student progress in math using the number of lessons completed by students
each week. We aggregate to the county, state, and national level, in each case weighting by the
average number of students using the platform at each school during the base period of January
6-February 7, and we normalize relative to this base period to construct the indices we report.
   Comparison to American Community Survey. In Appendix Table 2, we assess the representa-
tiveness of the Zearn data by comparing the demographic characteristics of the schools for which we
Zearn data (based on the ZIP codes in which they are located) to the demographic characteristics
of K-12 students in the U.S. as a whole. In general, the distribution of income, education, and race
and ethnicity of the schools in the Zearn sample is similar to that in the U.S. as a whole suggesting
that Zearn likely provides a fairly representative picture of online learning for public school students
in the U.S.

II.F    Public Data Sources: UI Records, COVID-19 Incidence, and Google
        Mobility Reports

Unemployment Benefit Claims. We collect county-level data by week on unemployment insurance
claims starting in January 2020 from state government agencies since no weekly, county-level na-


                                                  13
tional data exist. Location is defined as the county where the filer resides. We use the initial claims
reported by states, which sometimes vary in their exact definitions (e.g., including or excluding
certain federal programs). In some cases, states only publish monthly data. For these cases, we
impute the weekly values from the monthly values using the distribution of the weekly state claims
data from the Department of Labor (described below). We construct an unemployment claims
rate by dividing the total number of claims filed by the 2019 Bureau of Labor Statistics labor
force estimates. Note that county-level data are available for 22 states, including the District of
Columbia.
    We also report weekly unemployment insurance claims at the state level from the Office of
Unemployment Insurance at the Department of Labor. Here, location is defined as the state liable
for the benefits payment, regardless of the filer’s residence. We report both new unemployment
claims and total employment claims. Total claims are the count of new claims plus the count of
people receiving unemployment insurance benefits in the same period of eligibility as when they
last received the benefits.
    COVID-19 Data. We report the number of new COVID-19 cases and deaths each day using
publicly available data from the New York Times available at the county, state and national level.12
We also report daily state-level data on the number of tests performed per day per 100,000 people
from the COVID Tracking Project.13 For each measure - cases, deaths, and tests – we report two
daily series per 100,000 people: a seven-day moving average of new daily totals and a cumulative
total through the given date.
    Google Mobility Reports. We use data from Google’s COVID-19 Community Mobility Reports to
construct measures of daily time spent at parks, retail and recreation, grocery, transit locations, and
workplaces.14 We report these values as changes relative to the median value for the corresponding
day of the week during the five-week period from January 3rd - February 6, 2020. Details on place
types and additional information about data collection is available from Google. We use these raw
series to form a measure of time spent outside home as follows. We first use the American Time
Use survey to measure the mean time spent inside home (excluding time asleep) and outside home
in January 2018 for each day of the week. We then multiply time spent inside home in January

  12. See the New York Times data description for a complete discussion of methodology and definitions. Because
the New York Times groups all New York City counties as one entity, we instead use case and death data from New
York City Department of Health data for counties in New York City.
  13. We use the Census Bureau’s 2019 population estimates to define population when normalizing by 100,000 people.
We suppress data where new counts are negative due to adjustments in official statistics.
  14. Google Mobility trends may not precisely reflect time spent at locations, but rather “show how visits and length
of stay at di↵erent places change compared to a baseline.” We call this “time spent at a location” for brevity.


                                                         14
with Google’s percent change in time spent at residential locations to get an estimate of time spent
inside the home for each date. The remainder of waking hours in the day provides an estimate
for time spent outside the home, which we report as changes relative to the mean values for the
corresponding day of the week in January 2018.




III     Economic Impacts of COVID-19

    In this section, we analyze the economic impacts of COVID-19, both to shed light on the
COVID crisis itself and to demonstrate the utility of private sector data sources assembled above
as a complement to national accounts data in tracking economic activity.
    To structure our analysis, we begin from national accounts data released by the Bureau of
Economic Analysis (2020). GDP fell by $247 billion (an annualized rate of 5%) from the fourth
quarter of 2019 to the first quarter of 2020, shown by the first bar in Figure 1a. GDP fell primarily
because of a reduction in personal consumption expenditures (consumer spending), which fell by
$230 billion.15 Government purchases did not change significantly, while net exports increased
by $65 billion and private investment fell by $90 billion.16 We therefore begin our analysis by
studying the determinants of this sharp reduction in consumer spending. We then turn to examine
downstream impacts of the reduction in consumer spending on business activity and the labor
market.

III.A     Consumer Spending

We analyze consumer spending using data on aggregate credit and debit card spending. National
accounts data show that spending that is well captured on credit and debit cards – essentially all
spending excluding housing, healthcare, and motor vehicles – fell by approximately $138 billion,
comprising roughly 60% of the total reduction in personal consumption expenditures.17

  15. GDP is released at a quarterly level in the U.S. The reduction in consumer spending occurred in the last two
weeks of March (Figure 2 below); hence the first quarter GDP estimates capture about one-sixth of the reduction in
spending due to the COVID shock.
  16. Most of the reduction in private investment was driven by a reduction in inventories and equipment investment in
the transportation sector, both of which are plausibly a response to reductions in current and anticipated consumer
spending. The increase in net exports was driven primarily by a reduction in imports, with a large reduction in
imports of travel and transporation services in particular, again reflecting a change in domestic consumer spending
behavior.
  17. The rest of the reduction is largely accounted for by healthcare and motor vehicle expenditures; housing expen-
ditures did not change significantly. We view the incorporation of data sources to study these other major components
of spending as an important direction for future work; however, we believe that the mechanisms discussed below may
apply at least qualitatively to those sectors as well.


                                                         15
    Benchmarking. We begin by assessing whether the credit card data track patterns in corre-
sponding spending categories in the national accounts. Figure 1b plots spending on retail services
(excluding auto-related expenses) in the Affinity Solutions credit card data alongside the Monthly
Retail Trade Survey (MRTS), one of the main inputs used to construct the national accounts. Both
series are indexed to have a value of 1 in January 2020; each point shows the level of spending in
a given month divided by spending in January 2020. Figure 1c replicates Figure 1b for spending
on food services. In both cases, the credit/debit card spending series closely tracks the inputs
that make up the national accounts. In particular, both series show a rapid drop in food services
spending in March and April 2020 and a smaller drop in retail spending, along with an increase
in May. Given that credit card spending data closely tracks the MRTS at the national level, we
proceed to use it to disaggregate the national series in several ways to understand why consumer
spending fell so sharply.
    Heterogeneity by Income. We begin by examining spending changes by household income. We
do not directly observe cardholders’ incomes in our data; instead, we proxy for cardholders’ incomes
using the median household income in the ZIP code in which they live (based on data from the
2014-18 American Community Survey). ZIP-codes are strong predictors of income because of the
degree of segregation in most American cities; however, they are not a perfect proxy for income
and can be prone to bias in certain applications, particularly when studying tail outcomes (Chetty
et al. 2020). To evaluate the accuracy of our ZIP code imputation procedure, we compare our
estimates to those of Cox et al. (2020), who observe cardholder income directly based on checking
account data for clients of JPMorgan Chase. Our estimates are closely aligned with those estimates,
suggesting that the ZIP code proxy is reasonably accurate in this application.18
    Figure 2a plots a seven-day moving average of total daily card spending for households in the
bottom vs. top quartile of ZIP codes based on median household income.19 The solid line shows
data from January to May 2020, while the dashed line shows data for the same days in 2019 as a
reference. Spending fell sharply on March 15, when the National Emergency was declared and the
threat of COVID became widely discussed in the United States. Spending fell from $7.9 billion

  18. Cox et al. (2020) report an eight percentage point (pp) larger decline in spending for the highest income quartile
relative to the lowest income quartile in the second week of April. Our estimate of the gap is also eight pp at that
point, although the levels of the declines in our data are slightly smaller in magnitude for both groups. The JPMorgan
Chase data cannot themselves be used for the analysis that follows because there are no publicly available aggregated
series based on those data at present.
  19. We estimate total card spending by multiplying the raw totals in the Affinity Solutions data by the ratio of
total spending on the categories shown in the last bar of Figure 1a in PCE to total spending in the Affinity data in
January 2020.


                                                          16
per day in February to $5.4 billion per day by the end of March (a 31% reduction) for high-income
households; the corresponding change for low-income households was $3.5 billion to $2.7 billion
(a 23% reduction). Because high-income households both cut spending more in percentage terms
and accounted for a larger share of aggregate spending to begin with, they account for a much
larger share of the decline in total spending in the U.S. than low-income households. We estimate
that as of mid-April, top-quartile households accounted for 39% of the aggregate spending decline
after the COVID shock, while bottom-quartile households accounted for only 13% of the decline.
This gap grew even larger after stimulus payments began in mid-April. By mid June, top-quartile
households accounted for over half of the total spending decline in the U.S. and were still spending
15% less than their January levels, whereas bottom-quartile households were spending almost the
same amount they were in 2019. This heterogeneity in spending changes by income is much larger
than that observed in previous recessions (Petev, Pistaferri, and Eksten 2011, Figure 6) and plays
a central role in understanding the downstream impacts of COVID on businesses and the labor
market, as we show below.
    Heterogeneity Across Sectors. Next, we disaggregate the change in total spending across cate-
gories to understand why households cut spending so rapidly. In particular, we seek to distinguish
two channels: reductions in spending due to loss of income vs. fears of contracting COVID.
    The left bar in Figure 2b plots the share of the total decline in spending from the pre-COVID
period to mid-April accounted for by various categories. Nearly three-fourths of the reduction in
spending comes from reduced spending on goods or services that require in-person contact (and
thereby carry a risk of COVID infection), such as hotels, transportation, and food services.20 This
is particularly striking given that these goods accounted for only one-third of total spending in
January, as shown by the right bar in Figure 2b.
    Next, we zoom in to specific subcategories of spending that di↵er sharply in the degree to which
they require physical interaction in Figure 2c. Spending on luxury goods such as installation of home
pools and landscaping services – which do not require in-person contact – increased slightly after
the COVID shock; by contrast, spending on restaurants, beauty shops, and airlines all plummeted
sharply. Consistent with these substitution patterns, spending at online retailers increase sharply:
online purchases comprised 11% of retail sales in 2019 vs. 22% in April and May of 2020 (Mastercard
2020).21 A conventional reduction in income or wealth would typically reduce spending on all goods

 20. The relative shares of spending reductions across categories are similar for low- and high-income households
(Appendix Figure 4); what di↵ers is the level of spending reduction, as discussed above.
 21. We are unable to distinguish online and in-store transactions in the Affinity Solutions data.


                                                       17
as predicted by their Engel curves (income elasticities); the fact that the spending reductions vary so
sharply across goods that di↵er in terms of their health risks lends further support to the hypothesis
that it is health concerns rather than a lack of purchasing power that drove spending reductions.
    These patterns of spending reductions are particularly remarkable when contrasted with those
observed in prior recessions. Figure 2d compares the change in spending across categories in
national accounts data in the COVID recession and the Great Recession in 2009-10. In the Great
Recession, nearly all of the reduction in consumer spending came from a reduction in spending on
goods; spending on services was almost unchanged. In the COVID recession, 67% of the reduction
in total spending came from a reduction in spending on services, as anticipated by Mathy (2020).
    Heterogeneity by COVID Incidence. To further evaluate the role of health concerns, we next
turn to directly examine the association between incidence of COVID across areas and changes in
spending. Figure 3a presents a binned scatterplot of changes in spending from January to April
vs. the rate of detected COVID cases by county. To construct this figure, we divide the x variable
(COVID cases) into 20 bins, each of which contain 5% of the population, and plot the mean value
of the x and y variables within each bin. Areas with higher rates of COVID infection experience
significantly larger declines in spending, a relationship that holds conditional on controls for median
household income and state fixed e↵ects (Appendix Figure 5).22
    To examine the mechanism driving these spending reductions more directly, in Figure 3b, we
present a binned scatterplot of the amount of time spent outside home (using anonymized cell
phone data from Google) vs. COVID case rates, separately for low- and high-income counties
(median household income in the bottom vs. top income quartile). In both sets of areas, there
is a strong negative relationship: people spend considerably less time outside home in areas with
higher rates of COVID infection. The reduction in spending on services that require physical, in-
person interaction (e.g., restaurants) is mechanically related to this simple but important change
in behavior.
    At all levels of COVID infection, higher-income households spend less time outside. Figure 3c
establishes this point more directly by showing that time spent outside home falls monotonically
with household income across the distribution. These results help explain why the rich reduce
spending more, especially on goods that require in-person interaction: high-income people appar-
ently self-isolate more, perhaps by working remotely or because they have larger living spaces.

  22. Note that there is a substantial reduction in spending even in areas without high rates of realized COVID
infection, which is consistent with widespread concern about the disease even in areas where outbreaks did not
actually occur at high rates.


                                                      18
    In sum, disaggregated data on consumer spending reveals that spending in the initial stages of
the pandemic fell primarily because of health concerns rather than a loss of current or expected
income. Indeed, income losses were relatively modest because relatively few high-income individuals
lost their jobs (Cajner et al. 2020) and lower-income households who experienced job loss had their
incomes more than replaced by unemployment benefits (Ganong, Noel, and Vavra 2020). As a
result, national accounts data actually show an increase in total income of 13% from March to
April 2020. This result implies that the central channel emphasized in Keynesian models that
have guided policy responses to prior recessions – a fall in aggregate demand due to a lack of
purchasing power – has been less important in the early stages of the pandemic, partly as a result
of policies such as increases in unemployment benefits that o↵set lost earnings. Rather, the key
driver of residual changes in aggregate spending is a contraction in firms’ ability to supply certain
goods, namely services that carry no health risks. We now show that this novel source of spending
reductions leads to a distinct pattern of downstream impacts on businesses and the labor market,
potentially calling for di↵erent policy responses than in prior recessions.

III.B     Business Revenues

We now turn to examine how reductions in consumer spending a↵ect business activity. Conceptu-
ally, we seek to understand how a change in revenue for a given firm a↵ects its decisions: whether to
remain open, how many employees to retain, what wage rates to pay them, how many new people
to hire. Ideally, one would analyze these impacts at the firm level, examining how the customer
base of a given firm a↵ected its revenues and employment decisions. Lacking firm-level data, we use
geographic variation as an instrument for the spending shocks that firms face. The motivation for
this geographical approach is that spending fell primarily among high-income households in sectors
that require in-person interaction, such as restaurants. Most of these goods are non-tradable prod-
ucts produced by small local businesses who serve customers in their local area.23 We therefore use
di↵erences in average incomes and rents across ZIP codes as a source of variation in the magnitude
of the spending shock that small businesses face.24


  23. 56% of workers in food and accommodation services and retail (two major non-tradeable sectors) work in
establishments with fewer than 50 employees.
  24. We focus on small businesses because their customers are typically located near the business itself; larger
businesses’ customers (e.g., large retail chains) are more dispersed, making the geographic location of the business
less relevant. One could also in principle use other groups (e.g., sectors) instead of geography as instruments. We
focus primarily on geographic variation because the granularity of the data by ZIP code yields much sharper variation
than what is available across sectors and arguably yields comparisons across more similar firms (e.g., restaurants in
di↵erent neighborhoods rather than airlines vs. manufacturing).


                                                         19
    Benchmarking. We measure small business revenues using data from Womply, which records
revenues from credit card transactions for small businesses (as defined by the Small Business Ad-
ministration). Business revenues in Womply closely track patterns in the Affinity total spending
data, especially in sectors with a large share of small businesses, such as food and accommodation
services (Appendix Figure 6).25
    Heterogeneity Across Areas. We begin our analysis of the Womply data by examining how
small business revenues changed in low- vs. high-income ZIP codes from a baseline period prior
the COVID shock (January 5 to March 7, 2020) to the weeks immediately after the COVID shock
before the stimulus program began (March 22 to April 20, 2020). Figure 4 maps the change in
small business revenue by ZIP code in three large metro areas: New York City, San Francisco,
and Chicago (analogous ZIP-level maps for other cities are available here). There is substantial
heterogeneity in revenue declines across areas. For example, average revenue declines range from -
87% (or below) in the lowest-income-decile of ZIP codes to -12% (or above) in the top-income-decile
in New York City.26
    In all three cities, revenue losses are largest in the most a✏uent parts of the city. For example,
small business lost 73% of their revenue in the Upper East Side in New York, compared with 14%
in the East Bronx; 67% in Lincoln Park vs. 38% in Bronzeville on the South Side of Chicago; and
88% in Nob Hill vs. 37% in Bayview in San Francisco. Revenue losses are also large in the central
business districts in each city (lower Manhattan, the Loop in Chicago, the Financial District in
San Francisco), likely a direct consequence of the fact that many workers who used to work in
these areas are now working remotely. But even within predominantly residential areas, businesses
located in more a✏uent neighborhoods su↵ered much larger revenue losses, consistent with the
heterogeneity in spending reductions observed in the Affinity data.27 More broadly, cities that
have experienced the largest declines in small business revenue on average tend to be a✏uent cities
– such as New York, San Francisco, and Boston (Table 1, Appendix Figure 8).
    Figure 5a generalizes these examples by presenting a binned scatter plot of percent changes in
small business revenue vs. median household incomes, by ZIP code across the entire country. We
observe much larger reductions in revenue at local small businesses in a✏uent ZIP codes. In the

  25. In sectors that have a bigger share of large businesses – such as retail – the Womply small business series exhibits
a larger decline during the COVID crisis than Affinity (or MRTS). This pattern is precisely as expected given other
evidence that consumers shifted spending toward large online retailers such as Amazon (Alexander and Karger 2020).
  26. Very little of this variation is due to sampling error: the reliability of these estimates across ZIP codes within
counties exceeds 0.8, i.e., more than 80% of the variance within each of these maps is due to signal rather than noise.
  27. We find a similar pattern when controlling for di↵erences in industry mix across areas; for instance, the maps
look very similar when we focus solely on small businesses in food and accommodation services (Appendix Figure 7).


                                                           20
richest 5% of ZIP codes, small business revenues fell by 60%, as compared with 40% in the poorest
5% of ZIP codes.28
    As discussed above, spending fell most sharply not just in high-income areas, but particularly
in high-income areas with a high rate of COVID infection. Data on COVID case rates are not
available at the ZIP code level; however, one well established predictor of the rate of spread of
COVID is population density: the infection spreads more rapidly in dense areas. Figure 5b shows
that small business revenues fell more heavily in more densely populated ZIP codes.29
    Figure 5c combines the income and population density mechanisms by plotting revenue changes
vs. median rents (for a two bedroom apartment) by ZIP code. Rents are a simple measure of the
a✏uence of an area that combine income and population density: the highest rent ZIP codes tend
to be high-income, dense areas such as Manhattan. Figure 5c shows a particularly steep gradient
of revenue changes with respect to rents: revenues fell by less than 30% in the lowest-rent ZIP
codes, compared with more than 60% in the highest-rent ZIP codes. This relationship is essentially
unchanged when controlling for worker density in the ZIP code and county fixed e↵ects (Appendix
Table 3).
    In Figure 5d, we examine heterogeneity in this relationship across sectors that require di↵erent
levels of physical interaction: food and accommodation services and retail trade (which largely
require in-person interaction) vs. finance and professional services (which largely can be conducted
remotely). Revenues fall much more sharply for food and retail in higher-rent areas; in contrast,
there is essentially no relationship between rents and revenue changes for finance and professional
services. These findings show that businesses that cater in person to the rich are those that lost
the most businesses. Naturally, many of those businesses are located in high-income areas given
people’s preference for geographic proximity in consuming services.
    As a result of this sharp loss in revenues, small businesses in high-rent areas are much more
likely to close entirely. We measure closure in the Womply data as reporting zero credit card
revenue for three days in a row. Appendix Figure 10 shows that 55% of small businesses in the
highest-rent ZIP codes closed, compared with 40% in the lowest rent ZIP codes. The extensive
  28. Of course, households do not restrict their spending solely to businesses in their own ZIP code. An alternative
way to establish this result at a broader geography is to relate small business revenue changes to the degree of income
inequality across counties. Counties with higher Gini coefficients experienced large losses of small business revenue
(Appendix Figure 9a). This is particularly the case among counties with a large top 1% income share (Appendix
Figure 9b). Poverty rates are not strongly associated with revenue losses at the county level (Appendix Figure 9c),
showing that it is the presence of the rich in particular (as opposed to the middle class) that is most predictive of
economic impacts on local businesses.
  29. Consistent with this pattern, total spending levels and time spent outside also fell much more in high population
density areas.


                                                          21
margin of business closure accounts for most of the decline in total revenues.
   Because businesses located in high-rent areas lose more revenue in percentage terms and tend
to account for a greater share of total revenue to begin with, they account for a very large share of
the total loss in small business revenue. More than half of the total loss in small business revenues
comes from business located in the top-quartile of ZIP codes by rent; only 8% of the revenue loss
comes from businesses located in the bottom quartile. We now examine how the incidence of this
shock is passed on to their employees.

III.C    Impacts on Employment Rates and Low-Income Workers

We analyze the impacts of the COVID shock on employment using data from two sources: Earnin,
which provides data on hours, wages, and employment rates for low-wage (bottom quintile) workers
across a broad range of industries and Homebase, which provides analogous data for hourly workers
in small businesses, especially restaurants and retail shops.
   Benchmarking. As with the other series analyzed above, we begin by benchmarking changes in
these series to nationally representative benchmarks. Figure 6a plots employment rates from the
nationally representative Current Employment Statistics for all workers alongside the overall Earnin
series and Homebase series. We also include the National Employment Report from ADP, a large
payroll processor that covers nearly 20% of employment in the U.S. The ADP data are reweighted
to provide estimates that are intended to represent all workers in the U.S. Cajner et al. (2020)
use ADP data to report estimates of the decline in employment by worker wage quintile, showing
that employment rates fell much more sharply for lower-wage workers. We plot the estimate they
report for workers in the bottom quintile as of April 11 in Figure 6a. Consistent with the findings of
Cajner et al. (2020), the CES and ADP series for all workers exhibit smaller declines in employment
rates than the series that focuses on low-wage (bottom quintile) workers. The ADP estimate for
low-wage workers is roughly aligned with decline observed in Earnin. However, Homebase exhibits
a much larger decline than Earnin.
   The di↵erences between trends in the Homebase data and other series is largely explained by
di↵erences in industry and size composition. Figure 6b establishes this result by replicating Figure
6a for workers in Accommodation and Food Services.30 The Earnin series and overall ADP series
are very closely aligned here, consistent with the fact that workers in the food services sector tend
to have low wage rates (Appendix Table 1). When we further restrict Earnin to small firms – with
 30. Since estimates for Accommodation and Food Services are unavailable in ADP’s National Employment Report,
we use their Leisure and Hospitality Series.


                                                     22
less than 50 employees, comparable to the typical sizes of firms in the Homebase data – we find
closer alignment between the Earnin and Homebase data in terms of the magnitude of decline in
employment.31
    Based on this benchmarking exercise, we conclude that Earnin provides a good representation
of employment rates for low-wage workers across sectors, while Homebase provides estimates that
are representative of workers at small businesses, particularly in restaurants (who comprise 64% of
workers in the Homebase data for whom sectoral data are available). We therefore use Earnin as
our primary dataset for analyzing labor market outcomes for low-income workers, and supplement
it with Homebase to look more closely at workers in restaurants.
    Consistent with the results of Bartik et al. (2020), we find that wage rates have remained
unchanged through the COVID shock for workers who retained their jobs. Additionally, changes in
employment rates are virtually identical to changes in hours because the extensive margin accounts
for the vast majority of hours reductions. As a result, the employment changes in Figure 6 are
almost identical to observed changes in workers’ hours and earnings (Appendix Figure 11).
    Heterogeneity Across Areas. We now use the Earnin and Homebase data to examine the drivers
of employment losses for low-wage workers. Building on the approach developed above, we focus on
geographic heterogeneity in spending reductions and the resulting revenue losses faced by business.
Figure 7 presents maps of changes in hours of work for small- and mid-size businesses (fewer than
500 employees) in the Earnin data by ZIP code in New York, San Francisco, and Chicago (analogous
ZIP-level maps for other cities are available here).32 The patterns closely mirror those observed
for business revenues above, with a wide range of variation across ZIP codes. Hours of work fell
by more than 80% in the most a✏uent areas of these cities, as compared with 30% in the least
a✏uent areas. We observe very similar spatial patterns when we focus solely on workers in food
and accommodation services in the Earnin and Homebase data (Appendix Figure 14) and when
examining variation across counties at the national level (Appendix Figure 12).
    Figure 8a presents a binned scatter plot of changes in hours of work vs. median rents by
employer ZIP code in the Homebase data. Consistent with the results for revenues, we see much
larger reductions in hours of work for workers who work in high-rent areas than low-rent areas.


  31. One area of discrepancy between the datasets is that Homebase data exhibits a larger increase in employment
starting in mid-April than any of the other series. This may be because employment in small restaurants recovered
particularly quickly or because of specific trends in Homebase’s clients.
  32. We focus on small and mid-size businesses here because larger firms exhibit significantly smaller declines in
employment (Appendix Figure 13) and because, as noted above, their markets are likely to extend well beyond the
ZIP code in which they are located.


                                                        23
Figure 8b replicates this result in the Earnin data, separating workers who work in firms with fewer
than 60,000 vs. more than 60,000 employees (which include large multi-establishment firms such as
McDonalds, Starbucks, Home Depot, etc.). Hours fell by more than 55% for workers in the smaller
group of firms located in high-rent ZIP codes, as compared with 25% for workers in low-rent ZIP
codes.
    Interestingly, we observe a similar gradient with respect to local rents for workers at very large
firms: from near zero in the lowest-rent ZIPs to 25% in the highest-rent ZIPs. This presumably
reflects the fact that multi-establishment firms such as Starbucks face larger revenue losses at stores
located in more a✏uent neighborhoods for the reasons documented above, which in turns induces
them to reduce employment in those areas more heavily.33 While there is a similar gradient with
respect to rent levels, the overall level of employment losses for workers at large firms is lower
than at smaller firms. This may be because large firms lost less revenue as a result of the COVID
shock given their line of business (e.g., fast food vs. sit-down restaurants), have a greater ability
to substitute to other modes of business (delivery, online retail), or have more liquidity.
    Because businesses located in high-rent areas lay o↵ more workers and account for a greater
share of employment to begin with, they account for a large share of the total loss in employment
among low-income workers. 36% of the total loss in employment observed in the Earnin data comes
from business located in the top-quartile of ZIP codes by rent; 11% comes from businesses located
in the bottom quartile.
    Job Postings. Prior work suggests that the labor market impacts of the recession may depend
as much upon job postings as they do on the rate of initial layo↵s (e.g., Diamond and Blanchard
1989, Elsby, Michaels, and Ratner 2015). We therefore now turn to examine how the spending
shocks and revenue losses have a↵ected job postings. We measure job postings at the county level
using data from Burning Glass, which prior work has shown is fairly well aligned with government
statistics based on the Job Openings and Labor Turnover Survey (Carnevale, Jayasundera, and
Repnikov 2014, Kahn, Lange, and Wiczer 2020).34 We conduct this analysis at the county level,
pooling firms of all sizes and sectors because workers can substitute across firms and areas when
searching for a new job, making it less relevant which exact firm or ZIP code they work in.
    Figure 8c presents a binned scatter plot of the change in job postings pre- vs. post-COVID vs.

  33. We cannot measure changes in revenue by establishment for large firms because the Womply data on revenues
only cover small businesses. Moreover, one would need data on revenues by establishment within large companies to
conduct such an analysis.
  34. Burning Glass measures the sum of job postings, whereas JOLTS measures job openings at a given point in
time. Hence, jobs that are posted and quickly filled will be included in Burning Glass but not in JOLTS.


                                                       24
median rents by county for jobs that require minimal education. We find a pattern similar to what
we find with current employment: job postings for lower-skilled workers in high-rent areas have
fallen much more sharply (by approximately 30%) than for workers in lower-rent areas. Hence,
low-wage workers in such areas are not only more likely to have lost their jobs to begin with, they
also have poorer prospects of finding a new job. Figure 8d replicates Figure 8c for job postings
that require higher levels of education. For this group, which is much more likely to be employed in
tradable sectors that are less influenced by local conditions (e.g., finance or professional services),
there is no relationship between local rents and the change in job postings, consistent with our
findings above in Figure 5d.35
    Unemployment Rates. The low rates of job postings combined with high rates of job loss in
a✏uent areas combined to create very tight labor markets that produce unemployment in such
areas that are unprecedented in recent history. To illustrate this, we contrast rates of employment
losses by county in the COVID recession (from Feb-April 2020) with the Great Recession (from
2007-2010) using statistics on employment from the Bureau of Labor Statistics.36
    Figure 9 shows that in the Great Recession, counties with lower median incomes tended to
account for a greater share of job losses. In particular, the first set of bars in Figure 9 show
that counties in the bottom quartile (25%) of household median income distribution comprised a
disproportionate (30%) share of job losses. In contrast, in the recent recession they account for
actually less than 25% of the job losses, consistent with the evidence above that employment losses
from the COVID shock have been concentrated among low-income employees in a✏uent areas. In
the final set of bars, we show that in the recent recession this has led to the surprising pattern that
UI claims are almost equally likely to come from high versus low-income counties.37
    Santa Clara CA is the highest income county on the West Coast, yet 16% of its labor force
claimed UI between March 15th to May 2nd. This claim rate is identical to the share of the


  35. The magnitude of the reduction in job postings for highly educated workers is substantial, at approximately
27%. This contrasts with evidence that higher-skilled workers have experienced much lower rates of job loss to date,
and suggests that unemployment rates could begin to rise even for higher-skilled workers going forward.
  36. One notable feature of the current COVID-induced recession is that the increase in unemployment rates between
February and April 2020 (11%) is only two-thirds as large as the decrease in employment (16%). The di↵erence is
due to a 5% decline in the labor force: many people have lost their jobs but are not actively searching for a new job
in the midst of the pandemic. In the three prior recessionary periods, the labor force continued to grow by 0.3% to
0.8% annually.
   We therefore focus on the decline in employment rates.
  37. Unlike our analyses of private data, the publicly released unemployment claims data do not allow us disaggre-
gagate changes in employment by individuals’ income or ZIP code. Given the evidence above that job losses are
concentrated among low-wage workers in high-income areas, there is strong reason to believe that the unemployment
claims in high-income counties are coming from lower-income individuals living in those counties.


                                                         25
labor force that claimed UI in Fresno CA, a low-income county in California’s Central Valley.
Unemployment rates above 10% have happened regularly in Fresno during prior recessions, but
are unprecedented in Santa Clara. In Montgomery County, MD, long one of the richest counties
in the U.S., workers have historically been quite insulated from prior recessions. During the 1991
and 2001 recessions the unemployment rate in Montgomery remained 3%. In 2010 it only hit 6%,
one of the lowest in the country. In May 2020 employment losses and unemployment claims in
Montgomery exceeded 12% of the labor force, resembling many counties with much lower average
incomes.
   In the Great Recession, the areas of the country that experienced the largest increases in
unemployment took many years to recover because workers did not move to find new jobs and
job vacancies remained depressed in hard-hit areas well after the national recession ended (Yagan
2019). Appendix Figure 15 shows early signs of a similar pattern in this recession: job postings
went up significantly in late May in the U.S., but remained significantly lower in high-rent counties
than in low-rent counties (where postings recovered nearly to pre-COVID levels by the end of May).
If this pattern persists going forward, the recovery for low-income workers may take the longest in
the richest parts of the U.S.

III.D      Spending by Low-Income Workers

We close our analysis by showing job loss induced by working for firms in a✏uent areas a↵ected the
consumption of low-income workers themselves. To do so, we return to the credit card spending
data from Affinity Solutions and ask whether low-income individuals working in high-rent ZIP
codes reduce spending more than those working in low-rent ZIP codes.
   Because we cannot measure workplace location in the credit card data itself, we use data from
the Census LEHD Origin-Destination Employment Statistics (LODES) database, which provides
information on the matrix of residential ZIP by work ZIP for all workers in the U.S. in 2017. Using
this matrix, we compute the average workplace median rent level for each residential ZIP. Figure
10a presents a binned scatter plot of changes in hours of work by home (residential) ZIP code and
average workplace rent, restricting the sample to low-income (bottom income quartile) ZIP codes.
This figure confirms that low-income individuals who work in high-rent areas are more likely to
lose their jobs, verifying that the LODES data linked to residential ZIPs produce the same result
as directly using workplace ZIP codes in the Earnin data.
   Figure 10b replicates Figure 10a using spending changes on the y axis. Low-income individuals


                                                 26
who work in high-rent ZIP codes cut spending by 35% on average from the baseline period to
mid-April 2020, compared with 15% for those working in low-rent ZIPs. In Appendix Table 4, we
present a set of regression specifications showing that the relationship remains similar when we
compare ZIP codes within the same county by including county fixed e↵ects, control for rents in
the home (residential) ZIP code, and include other controls. Intuitively, these results show that
among two equally low-income ZIP codes in Queens, those who live in a ZIP code where many
work in an a✏uent area (perhaps because of a proximate subway line into Manhattan) are more
likely to lose their jobs and, as a result, cut their own spending more following the COVID shock.


IV     Evaluation of Policy Responses to COVID-19

We have seen that a chain of events led to substantial employment losses following the COVID-19
shock: (1) reductions in spending by high-income individuals due to health concerns, (2) revenue
losses for businesses catering to those customers, and (3) job losses for low-income workers working
at those businesses. We now turn to study what type of policies can mitigate the economic impacts
of the pandemic, focusing in particular on increasing employment among low-income workers. We
study three sets of policies that target di↵erent points of the economic chain: (1) state-ordered
business reopenings that remove barriers to economic activity; (2) stimulus payments to households,
which aim to spur consumer spending and thereby increase employment; and (3) loans to small
businesses, which provide liquidity to keep workers on payroll.

IV.A     State-Ordered Reopenings

One direct approach to changing consumer spending and employment is via executive orders. Many
states enacted stay-at-home orders and shutdowns of businesses in an e↵ort to limit the spread of
COVID infection and later reopened their economies by removing these restrictions. We begin by
examining how such executive orders a↵ect economic activity, exploiting variation across states in
the timing of shutdowns and reopenings. Throughout this section, we define the reopening date
to be the day that a state began the reopening process. In most states, reopening was a gradual
process in which certain industries and types of businesses opened before others, but there was
a lot of heterogeneity across states in the precise form that the reopening took. Our estimates
should therefore be viewed as an assessment of the average impact of typical re-opening e↵orts on
aggregate economic activity; we defer a more detailed analysis of how di↵erent types of re-openings
a↵ect di↵erent sectors (which can be undertaken with the data we have made publicly available)


                                                27
to future work.
    We begin with a case study comparing Colorado and New Mexico that is representative of our
broader findings. These two states both issued stay-at-home orders during the final week of March
(New Mexico on March 24, Colorado on March 26). Colorado then partially reopened its economy,
permitting a larger group of businesses to operate, on May 1, while New Mexico did not re-open
until two weeks later, on May 16.38
    Figure 11a plots consumer spending (using the Affinity Solutions data) in Colorado and New
Mexico. Spending evolved on a nearly identical path in these two states: in particular, there is
no evidence that the earlier reopening in Colorado did anything to boost spending during the two
intervening weeks before New Mexico reopened.
    Figure 11b generalizes the case study in Figure 11a by studying partial reopenings in the 20
states that issued such orders on or before May 4. For each reopening date (of which there are
five: April 20, 24th, and 27, as well as May 1 and 4), we compare the trajectory of spending in
treated states to a group of control states selected from the group of 13 states that did not issue
reopening orders until after May 18. We select the control states for each of the five reopening
dates by choosing nearest-neighbor matches on pre-period levels of spending (relative to January)
during the weeks ending March 31, April 7, and April 19. Appendix Table 5 lists the control states
we use for each date. We then calculate unweighted means of the outcome variables in the control
and treatment states to construct the two series for each reopening date. Finally, we pool these five
event studies together (redefining calendar time as time relative to the reopening date) to create
Figures 11b.
    Just as in the case study of Colorado vs. New Mexico, the trajectories of spending in the treated
states almost exactly mirror that in the control states. Figure 11c shows that the same is true for
low-wage workers’ employment rates (using Earnin data). Given that earlier reopenings had no
impact on consumer behavior, it is not surprising that it also had little or no downstream impact
on employment.39 These results are consistent with the findings of Lin and Meissner (2020), who
use a state-border discontinuity design and find no impact of stay-at-home orders on job losses.
    Why did these reopenings have so little immediate impact on economic activity?40 The evidence

  38. Specifically, on 1 May Colorado allowed retail businesses to open to the public beyond curbside pick-up and
delivery, and permitted personal services businesses to re-open.
  39. We emphasize that these results apply to average employment rates for low-income workers and are thus not
inconsistent with evidence of modest impacts in specific subsectors, particularly at higher wage levels, as identified
e.g., by Cajner et al. (2020).
  40. Reopenings could have a lagged e↵ect on spending, particularly if they serve as a signal of changes in health
risks; going forward, the real-time data in the tracker can be used to assess such lagged impacts.


                                                         28
in Section 3 suggests that health concerns among consumers were the primary driver of the sharp
decline in economic activity in March and April. Consistent with that evidence, spending fell
sharply in most states before formal state closures (Appendix Figure 16). If health concerns are the
core driver of reductions in spending rather than government-imposed restrictions, governments
may have limited capacity to restore economic activity through reopenings, especially if those
reopenings are not interpreted by consumers as a clear signal of reduced health risks.

IV.B      Stimulus Payments to Households

The Coronavirus Aid, Relief, and Economic Security (CARES) Act made direct payments to nearly
160 million people, totaling $267 billion as of May 31, 2020. Individuals earning less than $75,000
received a stimulus payment of $1,200; married couples earning less than $150,000 received a
payment of $2,400; and households received an additional $500 for each dependent they claimed.
These payments were reduced at higher levels of income and phased out entirely for households
with incomes above $99,000 (for single filers without children) or $198,000 (for married couples
without children). The vast majority of these stimulus payments were deposited on exactly April
15, 2020, while some households received payments on April 14 (Appendix Figure 17).
    The goal of these stimulus payments was to increase consumer spending and restore employ-
ment.41 Was the stimulus e↵ective in achieving these goals? In this section, we analyze this
question using high-frequency event studies examining spending and employment changes in the
days surrounding April 15, comparing outcomes for lower-income and higher-income households.
    Impacts on Consumer Spending. We begin in Figure 12a by plotting a weekly moving average
of spending changes relative to mean levels in January for low-income (bottom income quartile) vs.
high-income (top income quartile ZIP codes) households. As noted above, high-income households
decreased spending by more than low-income households in the immediate aftermath of the COVID
shock; in the week ending April 13th, spending in top-income-quartile households was down by
36% relative to pre-COVID levels, as compared with 28% for bottom-income-quartile households.
Starting on April 15, spending rose very sharply for those in the bottom income quartile, increasing
by nearly 20 percentage points within a week. Spending among top-income-quartile households
increased as well, but by only about 9 percentage points. This simple analysis suggests that the
stimulus payments had a large positive e↵ect on spending, especially for low-income families.42

  41. The Congressional Budget Office (2020) estimates that these payments will cost $293 billion, a considerably
larger sum than similar direct stimulus in 2001 and 2008.
  42. We expect the stimulus program to have a smaller impact on high-income households for three reasons. First,


                                                       29
    To estimate the causal e↵ect of the stimulus payments more precisely, we use a regression
discontinuity estimator with the daily spending data.43 Figures 12b and 12c plot daily spending
levels relative to baseline for low- and high-income households, respectively, for the month of April.
Spending levels jumped sharply from April 13th to 15th. Fitting a linear approximation to the
points on either side of the stimulus, we estimate that spending levels rose discontinuously on April
15 by 26pp in low-income households and 9pp in high-income households.44 Both e↵ects are statis-
tically significantly di↵erent from 0, as well as from each other. These findings are consistent with
Baker et al. (2020) and Karger and Rajan (2020), who use individual transaction data on incomes
and spending patterns of approximately 15,000 primarily low-income individuals to estimate a large
and immediate e↵ect of receiving the stimulus check on spending, especially among the very poorest
households.
    In Figures 12d and 12e, we investigate the composition of goods on which households spent their
stimulus checks. We pool all households in these figures to maximize precision. Figure 12d shows
that spending on durable goods rose by 21 pp following the arrival of the stimulus payments and
further increased thereafter, rising well above pre-crisis levels. But Figure 12e shows that spending
on in-person services rose by only 7 pp, remaining more than 50% below pre-crisis levels. Durable
goods accounted for 44% of the recovery in spending levels from the beginning to the end of April,
despite accounting for just 23% of pre-crisis spending. In-person services accounted for just 18% of
the recovery, despite making up 32% of pre-crisis spending (Appendix Figure 18).45 These results
show that the stimulus increased the overall level of spending, but did not increase spending in the
sectors where spending fell most following the COVID shock (Figure 2b). As a result, the stimulus
did not channel money back to the businesses that lost the most revenue as a result of the COVID
shock.
    Impacts on Business Revenue Across Areas. Next, we investigate how the stimulus program
a↵ected business revenues across areas. In particular, did the businesses that lost the most revenue –
those in high-rent areas – gain business as as result of the stimulus? Figures 13a and 13b replicate
the analysis above using Womply data on small business revenues as the outcome, separately
lower-income households simply received more money than high-income households. Second, low-income households
spend half as much as high-income households prior to the COVID shock (Figure 2a), and hence one would expect a
larger impact on their spending levels as a percentage of baseline spending. Finally, many studies have found higher
marginal propensities to consume (MPCs) among lower-income households, who are often more liquidity constrained.
  43. We use the raw daily data, not the 7-day moving average.
  44. We omit the partially treated date of April 14 (denoted by a hollow dot) since a small fraction of stimulus
payments arrived on that day when estimating this RD specification.
  45. The other major spending categories (non-durable goods and remote services) each accounted for 19% of the
recovery and 23% and 21% of pre-crisis spending, respectively.


                                                        30
for lowest-rent-quartile and highest-rent-quartile ZIP codes. We see a sharp increase of 21 pp
in revenues in small businesses in low-rent neighborhoods exactly at the time when households
received stimulus payments. In contrast, Panel B shows a small, statistically insignificant increase
in revenues of 4 pp for small businesses in high-rent areas.
   This geographic heterogeneity illustrates another important dimension in which the stimulus
did not channel money back to the business that lost the most revenue from the COVID shock.
In fact, the stimulus actually amplified the di↵erence in small business revenue losses rather than
narrowing it across areas. Those in low-rent areas have nearly returned to pre-crisis levels following
the stimulus payments, while those in high-rent areas remained nearly 40% down relative to January
levels in the second half of April (Figure 13c, solid lines).
   Impacts on Low-Income Employment. Finally, we investigate whether the increase in spending
induced by the stimulus increased employment rates, as one would expect in a traditional Keyne-
sian stimulus. Here, we do not use the RD design as we do not expect employment to respond
immediately to increased spending. Instead, we analyze the evolution of employment of low-income
workers in the Earnin data in low vs. high-rent ZIP codes over time in Figure 13c (dashed lines). In
high-rent areas, low-wage employment remains 45% below pre-COVID levels – perhaps not surpris-
ingly, since revenues have not recovered significantly there. But even in low rent areas, payroll has
recovered only slightly, which is a surprising contrast with the sharp recovery of small business rev-
enues. It is unclear why revenues and employment both fell in tandem at very similar rates when
the COVID shock hit, but revenues recovered much more quickly than employment in low-rent
areas. One possibility is that businesses have reopened temporarily with a minimal sta↵ (Lazear,
Shaw, and Stanton 2016) and are planning to recall or hire new workers going forward. A more
worrisome possibility is a “jobless” recovery, in which economic activity shifts away from in-person
labor intensive production, reducing employment opportunities in the longer term (Berger 2012).
   In summary, our analysis suggests that stimulus substantially increased total consumer spending
but did not directly undo the initial spending reductions by returning money back to the businesses
that lost the most revenue. In a frictionless model where businesses and workers could costlessly
reallocate their capital and labor to other sectors, this reallocation of spending might have no
consequence for employment levels. But if workers’ ability to switch jobs is constrained – e.g.,
because of job-specific skills that limit switching across industries or costs that limit moving across
geographic areas, as suggested by Yagan (2019) – the ability of the stimulus to foster a uniform
recovery in employment to pre-COVID levels is likely to be hampered.


                                                   31
IV.C       Loans to Small Businesses

We now turn to evaluate the Paycheck Protection Program (PPP), a policy that sought to reduce
employment losses by providing direct support to small businesses. Congress appropriated nearly
$350 billion for loans to small businesses in an initial tranche that was paid beginning on April 3,
followed by another $175 billion in a second round beginning on April 27. The program o↵ered
loan forgiveness for businesses that maintained sufficiently high employment levels through June
30 (relative to pre-crisis levels), providing an incentive for small businesses to keep employees on
payroll.
    How e↵ective was the PPP program in increasing employment, particularly among low-income
workers? We study this question by exploiting the fact that eligibility for the PPP depended on
business size. Firms with fewer than 500 employees before the COVID crisis qualified for PPP
loans, while those with more than 500 employees generally did not.46 One important exception
to this rule is the food service industry, which was treated di↵erently because of the prevalence of
franchises. We therefore omit the food services sector from the analysis that follows.47
    We estimate the causal e↵ects of the PPP using a di↵erence-in-di↵erences research design,
comparing trends in employment for firms below the 500 employee cuto↵ (the treated group) vs.
those above the 500 employee cuto↵ (the control group) before vs. after April 3, when the PPP
program began. Figure 14a plots the average change in employment rates (inferred from payroll
deposits) relative to January by decile of business size in the Earnin data. To adjust for the
fact that industry composition varies across firms of di↵erent sizes, we reweight firms within each
decile to match the average (2 digit NAICS) industry composition in the sample as a whole when
computing mean employment rates by decile. Recognizing that our size measures in the Earnin
data do not correspond exactly to those used to determine PPP eligibility by the Small Business
Administration, we plot trends in employment for firms of various sizes, not just those just above
vs. below the 500 employee cuto↵. We focus in particular on firms in the 3rd-6th deciles of firm size
in Figure 14a. The 3rd and 4th deciles have an average of about 45 and 130 employees, respectively,

  46. The eligibility rules vary across industries, with some exceptions that allow larger firms to obtain loans. Appendix
Figure 19 plots a histogram of the exact size cuto↵s weighting by employees in the national sample in Reference USA
data (Panel A) and employees in Earnin data (Panel B), in both cases restricting to workers in companies with
300-700 employees. More than 90% of employees work at firms that face the 500 employee threshold. In addition to
employment thresholds, firms may also qualify based on revenue thresholds set by the Small Business Administration;
however, using the distribution of firm size and revenue from Reference USA, we estimate that in practice the size
threshold is the binding constraint for the vast majority of firms. Given these results, we use a pre-COVID employee
size cuto↵ of 500 to define treatment and control groups.
  47. We find no di↵erences in employment trends below vs. above the 500 employee threshold in the food services
sector as well (Appendix Figure 20), consistent with our results below.


                                                           32
and therefore consist of firms that would almost certainly be eligible for the PPP. Most businesses
in the 5th size decile (with an average of 413 employees) were also likely to be eligible. Firms
in the 6th decile are largely above the 500 employee threshold (with an average of roughly 1,500
employees).
    Before April 3, trends in employment are extremely similar across the four groups, showing
that larger businesses (in the 6th decile) are likely to provide a good counterfactual for employment
trends one would have observed in smaller firms absent the PPP program. After April 3, the
trends remain extremely similar across firms of all sizes: in particular, there is no evidence that
employment went up in firms in the smaller deciles relative to larger firms after April 3, as one
would expect if PPP had a substantial treatment e↵ect on employment rates.
    Figure 14b plots the change in employment vs. average firm size, by decile. Again, we see that
the decline in payroll is stable across firm size, varying between -36% and -39% between firms with
an average size ranging from 5 to 30,000 employees.
    Figure 14c replicates Figure 14a, splitting businesses into those located in the highest-rent (top
quartile) ZIP codes and lowest-rent (bottom quartile) ZIP codes. To simplify the plot, we combine
the 3rd and 4th deciles into a single “PPP eligible” group and omit the partially treated 5th size
decile. As noted in Section 3, the decline in hours worked is about 35% larger in high-rent areas
than in low-rent areas. But there is no evidence that the PPP had any significant impact on
employment rates in either of these groups. As in Panels A and B, there is little or no di↵erence in
hours worked across businesses by size. In particular, employment fell about as much as business
revenue did in these areas (Figure 5c). We therefore conclude that the PPP had little material
impact on employment at small businesses: we cannot rule out a small positive employment e↵ect
of the program (of e.g., 3-4 pp on employment rates), but it is clear that the program did not
restore the vast majority of jobs that were lost following the COVID shock.48
    Why did the PPP have small e↵ects on employment rates? One potential explanation is that
the loans were taken by firms that intended not to layo↵ many employees to begin with, i.e. firms
that were inframarginal recipients of loans. Consistent with this, Granja et al. (2020) show that
states and congressional districts that experienced more job losses prior to April 3 actually received
fewer PPP loans. Moreover, PPP loans also were not distributed to the industries most likely


  48. We do not directly observe the loans provided to each firm, and as a result we cannot estimate the “first stage”
of the program on receipt of loans. From a reduced form perspective, we can conclude that the e↵ect of the policy
on aggregate employment was not large, but we cannot directly estimate the causal e↵ect of receiving a PPP loan on
employment rates.


                                                         33
to experience job losses from the COVID crisis. For example, firms in the professional, scientific,
and technical services industry received a greater share of the PPP loans than accommodation and
food services (SBA 2020). Yet accommodation and food services accounted for half of the total
decline in employment between February and March (prior to PPP enactment) in BLS statistics,
while employment in professional, scientific and technical services accounted for less than 5% of the
decline.


V     Conclusion

Data held by private companies provide an unprecedented capacity to measure economic activity at
a granular level very rapidly. These data have become integral to corporations in business decisions.
In this paper, we have constructed a freely available platform that harnesses the same data with
the aim of supporting public policy.
    We use these new data to analyze the initial impacts of COVID-19 on people, businesses,
and communities. We find that COVID-19 induced high-income households to self-isolate and
sharply reduce spending in sectors that require physical interaction. This spending shock in turn
led to losses in business revenue and layo↵s of low-income workers at firms that cater to high-
income consumers, ultimately reducing their own consumption levels. Because the root cause of
the shock appears to be self-isolation driven by health concerns, there is limited capacity to restore
economic activity without addressing the virus itself. In particular, we find that state-ordered
reopenings of economies have only modest impacts on economic activity; stimulus checks increase
spending particularly among low-income households, but very little of the additional spending
flows to the businesses most a↵ected by the COVID shock; and loans to small businesses have
little impact on employment rates. Our analysis therefore suggests that the only e↵ective approach
to mitigating economic hardship in the short run may be to provide benefits to those who have
lost their incomes to mitigate consumption losses while public health measures restore consumer
confidence and ultimately increase spending.
    We focused here on the short-run economic consequences of the COVID-19 crisis. However,
this economic shock could also have long-lasting scarring e↵ects that warrant attention. As an
illustration of how private sector data can be useful in tracking these impacts as well, Figure 15
plots weekly student progress (lessons completed) on Zearn, an online math platform used by many
elementary school students as part of their regular school curriculum. Children in high-income
areas experience a temporary reduction in learning on this platform when the COVID crisis hit,

                                                 34
but soon recover to baseline levels; by contrast, children in lower-income areas remain 50% below
baseline levels persistently. Although this platform captures only one aspect of education, these
findings raise the concern that COVID-19 may reduce social mobility and ultimately further amplify
inequality by having particularly negative e↵ects on human capital development for lower-income
children.
   Going forward, our analysis illustrates two roles for real-time tracking using private sector data
to support economic policy in this crisis and beyond. First, the data can be used to learn rapidly
from heterogeneity across areas, as di↵erent places are often hit by di↵erential shocks and pursue
di↵erent local policy responses. This approach can permit rapid diagnosis of the root factors
underlying an economic crisis. Second, the data can permit rapid evaluation of ongoing policies,
potentially helping to fine-tune policy responses.
   More broadly, the platform built here can be viewed as a preliminary prototype for a system
of “real time national accounts” using administrative data from the private sector, much as the
Bureau of Economic Analysis, building on a prototype developed by Kuznets (1941), instituted a
set of systematic, recurring surveys of businesses and households that are the basis for the National
Income accounts of the United States. The analysis in this paper demonstrates that even this
prototype can yield timely insights that are not apparent in existing data, suggesting that a more
systematic platform that aggregates data from several private companies has great potential for
improving our understanding of economic activity and policymaking going forward.




                                                 35
References
Administration, Small Business. 2020. Paycheck Protection Program (PPP) Report. Technical re-
    port. May.

Aladangady, Aditya, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and
    Claudia Sahm. 2019. “From Transactions Data to Economic Statistics: Constructing Real-
    time, High-frequency, Geographic Measures of Consumer Spending.” NBER Working Paper
    No. 26253.

Alexander, Diane, and Ezra Karger. 2020. “Do stay-at-home orders cause people to stay at home?
    E↵ects of stay-at-home orders on consumer behavior.” Federal Reserve Bank of Chicago Work-
    ing Paper No. 2020-12.

Allcott, Hunt, Levi Boxell, Jacob Conway, Billy Ferguson, Matthew Gentzkow, and Benjamin Gold-
    man. 2020. “Economic and Health Impacts of Social Distancing Policies during the Coronavirus
    Pandemic.” Available at SSRN 53 (3): 571–630.

Allen, Danielle, Sharon Block, Joshua Cohen, Peter Eckersley, and Meredith Rosenthal. 2020.
    “Roadmap to Pandemic Resilience: Massive Scale Testing, Tracing, and Supported Isolation
    (TTSI) as the Path to Pandemic Resilience for a Free Society.” Edmond J. Safra Center For
    Ethics At Harvard University.

Austin, Benjamin A, Edward L Glaeser, and Lawrence H Summers. 2018. “Jobs for the Heartland:
    Place-based policies in 21st century America.” NBER Working Paper No. 24548.

Baker, Scott R, R. A Farrokhnia, Ste↵en Meyer, Michaela Pagel, and Constantine” Yannelis. 2020.
    “Income, Liquidity, and the Consumption Response to the 2020 Economic Stimulus Payments.”
    NBER Working Paper No. 27097.

Bartik, Alexander W., Marianne Bertrand, Feng Lin, Jesse Rothstein, and Matt Unrath. 2020.
    “Measuring the labor market at the onset of the COVID-19 crisis.” Brookings Papers on
    Economic Activity.

Berger, David. 2012. “Countercyclical restructuring and jobless recoveries.”

Board of Governors of the Federal Reserve System. 2019. The 2019 Federal Reserve Payments
    Study.

Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik
    Hurst, Christopher Kurz, and Ahu Yildirmaz. 2020. “The U.S. Labor Market during the Be-
    ginning of the Pandemic Recession.” Working Paper.

Carnevale, Anthony P, Tamara Jayasundera, and Dmitri Repnikov. 2014. “Understanding online
    job ads data.” Georgetown University, Center on Education and the Workforce, Technical
    Report (April).


                                                36
Cavallo, Alberto. 2020. “Inflation with Covid Consumption Baskets.”

Chen, Haiqiang, Wenlan Qian, and Qiang Wen. 2020. “The Impact of the COVID-19 Pandemic on
    Consumption: Learning from High Frequency Transaction Data.” Working Paper.

Chetty, Raj, John N Friedman, Emmanuel Saez, Nicholas Turner, and Danny Yagan. 2020. “In-
    come Segregation and Intergenerational Mobility Across Colleges in the United States.” The
    Quarterly Journal of Economics.

Chiou, Lesley, and Catherine Tucker. 2020. “Social Distancing, Internet Access and Inequality.”
    NBER Working Paper No. 26982.

Congressional Budget Office. 2020. Preliminary Estimate of the E↵ects of H.R. 748, the CARES
    Act.

Cox, Natalie, Peter Ganong, Pascal Noel, Joseph Vavra, Arlene Wong, Diana Farrell, and Fiona
    Greig. 2020. “Initial impacts of the pandemic on consumer behavior: Evidence from linked
    income, spending, and savings data.” Brookings Papers on Economic Activity.

Deming, David, and Lisa B. Kahn. 2018. “Skill Requirements across Firms and Labor Markets:
    Evidence from Job Postings for Professionals.” Journal of Labor Economics 36 (S1): S337–
    S369.

Diamond, Peter, and OJ Blanchard. 1989. “The beveridge curve.” Brookings Papers on Economic
    Activity 1:1–76.

Dunn, Abe, Kyle Hood, and Alexander Driessen. 2020. “Measuring the E↵ects of the COVID-19
    Pandemic on Consumer Spending Using Card Transaction Data.” National Bureau of Economic
    Research.

Elsby, Michael WL, Ryan Michaels, and David Ratner. 2015. “The Beveridge curve: A survey.”
    Journal of Economic Literature 53 (3): 571–630.

Ganong, Peter, Pascal J Noel, and Joseph S Vavra. 2020. US Unemployment Insurance Replacement
    Rates During the Pandemic. Technical report. National Bureau of Economic Research.

Goldfarb, Avi, and Catherine Tucker. 2020. “Which Retail Outlets Generate the Most Physical
    Interactions?” NBER Working Paper No. 27042.

Granja, João, Christos Makridis, Constantine Yannelis, and Eric Zwick. 2020. “Did the Paycheck
    Protection Program Hit the Target?” NBER Working Paper No. 27095.

Guerrieri, Veronica, Guido Lorenzoni, Ludwig Straub, and Iván Werning. 2020. Macroeconomic
    Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages? Working
    Paper, Working Paper Series 26918. National Bureau of Economic Research, April.




                                              37
Hershbein, Brad, and Lisa B. Kahn. 2018. “Do Recessions Accelerate Routine-Biased Technological
    Change? Evidence from Vacancy Postings.” American Economic Review 108 (7): 1737–72.

Kahn, Lisa B, Fabian Lange, and David G Wiczer. 2020. “Labor Demand in the Time of COVID-19:
    Evidence from Vacancy Postings and UI Claims.” NBER Working Paper No. 27061.

Karger, Ezra, and Aastha Rajan. 2020. “Heterogeneity in the Marginal Propensity to Consume:
    Evidence from Covid-19 Stimulus Payments.” FRB of Chicago Working Paper.

Kurmann, André, Etienne Lalé, and Lien Ta. 2020. “The Impact of COVID-19 on U.S. Employment
    and Hours: Real-Time Estimates with Homebase Data.”

Kuznets, Simon. 1941. National Income and Its Composition, 1919-1938. New York: National Bu-
    reau of Economic Research.

Lazear, Edward P., Kathryn L. Shaw, and Christopher Stanton. 2016. “Making Do with Less:
    Working Harder during Recessions.” Journal of Labor Economics 34 (S1): S333–S360.

Lin, Zhixian, and Christopher M Meissner. 2020. “Health vs. Wealth? Public Health Policies and
    the Economy During Covid-19.” NBER Working Paper No. 27099.

Mastercard. 2020. Mastercard Recovery Insights: The Shift to Digital.

Mathy, Gabriel. 2020. The COVID-19 Epidemic will be the First Services Recession and it Could
    be a Bad One.

Mongey, Simon, Laura Pilossoph, and Alex Weinberg. 2020. “Which Workers Bear the Burden of
    Social Distancing Policies?” NBER Working Paper No. 27085.

Petev, Ivaylo, Luigi Pistaferri, and Itay Saporta Eksten. 2011. Consumption and the Great Reces-
    sion: An analysis of trends, perceptions, and distributional e↵ects.

Romer, Paul. 2020. “Roadmap to Responsibly Reopen America.” roadmap.paulromer.net.

U.S. Bureau of Economic Analysis. 2020. National Income and Product Accounts. Data retrieved
    from U.S. Bureau of Economic Analysis, National Income and Product Accounts.

Villas-Boas, Sofia B, James Sears, Miguel Villas-Boas, and Vasco Villas-Boas. 2020. “Are We #Stay-
    ingHome to Flatten the Curve?” UC Berkeley: Department of Agricultural and Resource Eco-
    nomics CUDARE Working Papers.

Yagan, Danny. 2019. “Employment Hysteresis from the Great Recession.” Journal of Political
    Economy 127 (5): 2505–2558.




                                                38
   Supplementary Appendix

   In this appendix, we describe additional details about the key dates in the COVID-19 crisis as
well as geographic definitions used in our analysis.
   Key Dates for COVID-19 Crisis. The Economic Tracker includes information about key dates
relevant for understanding the impacts of the COVID-19 crisis. At the national level, we focus on
three key dates:

   • First U.S. COVID-19 Case: 1/20/2020

   • National Emergency Declared: 3/13/2020

   • CARES Act Signed in to Law: 3/27/2020

At the state level we collect information on the following events:

   • Schools closed statewide: Sourced from COVID-19 Impact: School Status Updates by MCH
     Strategic Data, available here. Compiled from public federal, state and local school informa-
     tion and media updates.

   • Nonessential businesses closed: Sourced from the Institute for Health Metrics and Evalua-
     tion state-level data (available here), who define a non-essential business closure order as:
       Only locally defined ’essential services’ are in operation. Typically, this results in closure
     of public spaces such as stadiums, cinemas, shopping malls, museums, and playgrounds. It
     also includes restrictions on bars and restaurants (they may provide take-away and delivery
     services only), closure of general retail stores, and services (like nail salons, hair salons, and
     barber shops) where appropriate social distancing measures are not practical. There is an
     enforceable consequence for non-compliance such as fines or prosecution.

   • Stay-at-home order goes into e↵ect: Sourced from the New York Times stay at home order
     data, available here.

   • Stay-at-home order ends: Sourced from the New York Times reopening data, available here.
     Defined as the date at which the state government lifted or eased the executive action telling
     residents to stay home.

   • Partial business reopening: Sourced from the New York Times reopening data, available here.
     Defined as the date at which the state government allowed the first set of businesses to reopen.

                                                 39
Geographic Definitions. For many of the series we convert from counties to metros and ZIP codes
to counties. We use the HUD-USPS ZIP Code Crosswalk Files to convert from ZIP code to county.
When a ZIP code corresponds to multiple counties, we assign the entity to the county with the
highest business ratio, as defined by HUD-USPS ZIP Crosswalk. We generate metro values for a
selection of large cities using a custom metro-county crosswalk, available in Appendix Table 6. We
assigned metros to counties and ensured that a significant portion of the county population was
in the metro of interest. Some large metros share a county, in this case the smaller metro was
subsumed into the larger metro.




                                               40
                                                                      Table 1
                                    Cities with Largest Small Business Revenue Losses Following COVID Shock
                                                               % Change in Small             % Change in Low-Wage Worker                   % Change in Low-
                                                                 Bus. Revenue                Hours, Small Restaurants/Retail              Wage Worker Hours
          City                         State                       (Womply)                          (HomeBase)                                (Earnin)

          (1)                            (2)                            (3)                                    (4)                                   (5)


New Orleans                  Louisiana                                -80.8%                                -76.6%                                 -60.9%
Washington                   District of Columbia                     -72.9%                                -73.2%                                 -60.2%
Honolulu                     Hawaii                                   -62.7%                                -75.8%                                 -25.3%
Miami                        Florida                                  -62.2%                                -68.7%                                 -51.1%
Boston                       Massachusetts                            -60.6%                                -79.5%                                 -60.9%
Philadelphia                 Pennsylvania                             -58.7%                                -66.6%                                 -51.8%
Fresno                       California                               -58.7%                                -60.7%                                 -36.6%
San Jose                     California                               -58.6%                                -61.5%                                 -51.9%
New York City                New York                                 -57.0%                                -78.7%                                 -63.4%
Las Vegas                    Nevada                                   -56.1%                                -66.4%                                 -53.0%


Notes : This table shows the ten cities with the largest small business revenue declines as measured in the Womply data (among the fifty largest cities in the U.S.).
The decline is defined as net revenue normalized by revenue in 2019 from March 25th 2020 to April 14th 2020 over the normalized net revenue from Jan 8th to
March 10th 2020. The changes in low-wage worker hours (both for small restaurants/retail - HomeBase and in general - Earnin) are defined as the change in hours
from March 25th 2020 to April 14th 2020 relative to total hours from Jan 8th to March 10th 2020.
                                                                                            Appendix Table 1
                                                                                     Hourly Wage Rates By Industry


                                                                                                                  2019 BLS Wages                                        Median in Private Datasets
                                                                                         10th Percentile           25th Percentile             Median                  Earnin             Homebase
                                                                                            (Pre Tax)                 (Pre Tax)               (Pre Tax)              (Post Tax)           (Pre Tax)
NAICS Code                             NAICS Description                                        (1)                       (2)                    (3)                     (4)                 (5)
    22                                       Utilities                                        18.56                     26.82                   38.06                  15.00
    55                   Management of Companies and Enterprises                              16.09                     22.42                   34.74                  12.34
    54                  Professional, Scientific, and Technical Services                      14.85                     21.62                   34.00                  12.63                13.00
    51                                     Information                                        12.90                     19.56                   32.13                  12.49
    52                               Finance and Insurance                                    14.25                     18.40                   27.42                  12.77
    21                  Mining, Quarrying, and Oil and Gas Extraction                         15.36                     19.11                   25.82                  15.69
    61                                Educational Services                                    11.54                     16.18                   24.47                  13.25                11.50
    23                                    Construction                                        13.78                     17.51                   23.92                  13.94
    42                                  Wholesale Trade                                       12.30                     15.73                   22.05                  11.79
   48-49                       Transportation and Warehousing                                 12.07                     15.49                   20.89                  13.20                15.00
   31-33                                 Manufacturing                                        12.36                     15.35                   20.77                  12.66
    53                       Real Estate and Rental and Leasing                               11.31                     14.14                   19.31                  12.64
    62                        Health Care and Social Assistance                               11.18                     13.59                   19.27                  11.68                14.00
    81                   Other Services (except Public Administration)                         9.73                     12.02                   16.57                  10.97                14.00
    56                               Administrative Support                                   10.33                     12.26                   15.71                  11.82
    71                       Arts, Entertainment, and Recreation                               9.21                     11.17                   14.09                  10.38                12.00
    11                     Agriculture, Forestry, Fishing and Hunting                         11.28                     11.89                   13.38                  11.56
   44-45                                  Retail Trade                                         9.49                     11.18                   13.36                   9.76                12.00
    72                       Accommodation and Food Services                                   8.68                      9.61                   11.81                   9.26                11.00


Notes : This table reports wages at various percentiles for two-digit NAICS sectors. 2019 BLS Wages (1-3) come from the May 2019 Occupational Employment Statistics and are inflated to 2020 dollars using
the Consumer Price Index. Columns (4) and (5) report median wages in two private employment datasets, Earnin and Homebase. In Earnin and Homebase, the median wage is the 50th percentile of hourly
wages for workers of the given industry during the pre-COVID period (January 8th - March 10th). In Earnin (4), wages are calculated by dividing the payment deposited in the individual's bank account by hours
worked and are thus post-tax. Homebase wages are pre-tax. Industries missing from the Homebase data are left blank.
                                 Appendix Table 2
                      Demographic Characteristics of Zearn Users


                                                   Zearn Users              US Population
                                                       (1)                      (2)

Panel A: Income
ZIP Median Household Income
    25th Percentile                                    43,766                   45,655
    Median                                             54,516                   57,869
    75th Percentile                                    70,198                   77,014

Number of ZIP codes                                    5,148                   33,253
Number of People                                      803,794                322,586,624


                                                   Zearn Users            US K-12 Students
Panel B: School Demographics
Share of Black Students
    25th Percentile                                     1.4%                      1.5%
    Median                                              5.6%                      5.8%
    75th Percentile                                    21.3%                     19.1%

Share of Hispanic Students
    25th Percentile                                     4.3%                      5.6%
    Median                                             10.9%                     15.0%
    75th Percentile                                    35.7%                     40.6%

Share of Students Receiving FRPL
    25th Percentile                                    33.8%                     28.2%
    Median                                             55.5%                     50.1%
    75th Percentile                                    78.5%                     74.8%

Number of Schools                                      8,801                    88,459
Number of Students                                    767,310                 49,038,524

Notes : This table reports demographic characteristics for US schools. Household income
percentiles are calculated using the 2017 median household income in each school's ZIP code.
The share of students who are Black, Hispanic, or receive Free or Reduced Price Lunch (FRPL) in
a given school are calculated using school demographic data from the Common Core data set
from MDR Education, a private education data firm. Percentile distributions for each demographic
variable are calculated separately and weighted by the number of students in each school. Column
(1) reports school characteristics for students using Zearn, while Column (2) reports income data
for the entire US population and shares of students who are Black, Hispanic, or receive FRPL for
all US elementary school students.
                                                           Appendix Table 3
                                    Association Between Changes in Business Revenue and Area Rents


                                                                                                                        % Change in Small Business
Outcome:                                                    % Change in Small Business Revenues                          Revenue in Food Services
                                                                                                                           and Accommodation

                                                      (1)             (2)               (3)            (4)                     (5)               (6)


Median 2BR Rent                                    -0.0110        -0.0199            -0.0110        -0.0173                -0.0244            -0.0212
                                                   (0.0006)       (0.0011)           (0.0007)       (0.0011)               (0.0025)           (0.0025)


Controls:
County Fixed Effects                                                   X                                X                       X                 X

Worker Density (Log)                                                                     X              X                                         X


Observations                                        16,477         16,475             16,469         16,467                  9,913             9,910

Notes : This table shows OLS regressions of average percentage changes in business revenue by ZCTA code (using Womply data) on average ZCTA
code median two-bedroom rent and median household income. Standard errors are reported in parentheses. The dependent variable is scaled from 0 to
100, such that, for example, the coefficient of -0.011 in Column (1) implies that a $100 increase in monthly workplace rent is associated with a 1.1% larger
drop in total revenue. Columns (1)-(4) use the percent change in all small business revenue while Columns (5) and (6) use the percent change in food
services and accommodation small business revenue as the outcome. Column (1) shows the baseline regression without any controls while the rest of the
columns add county fixed effects and the log of worker density.
                                       Appendix Table 4
     Association Between Changes in Consumer Spending Home Area and Workplace Area Rents


Dep. Var.: % Change in Total Credit Card Spending

                                                     (1)                      (2)                      (3)


Median Workplace 2BR Rent                          -0.0129                  -0.0089                  -0.0121
                                                  (0.0006)                 (0.0012)                 (0.0039)

Median Home 2BR Rent                                                        -0.0065
                                                                           (0.0017)

Controls:
County Fixed Effects                                                                                    X


Observations                                        8,934                   6,682                    8,934

Notes : This table shows OLS regressions of average percentage changes in consumer spending by ZCTA code (using
data from Affinity Solutions) on average workplace ZCTA code median two-bedroom rent. Standard errors are reported in
parentheses. Workplace ZCTA code rent is computed by using data from the Census LEHD Origin-Destination
Employment Statistics (LODES) database as described in the text. The dependent variable is scaled from 0 to 100 such
that, for example, the coefficient of -0.0129 in Column (1) implies that a $100 increase in monthly workplace rent is
associated with a 1.2% larger drop in total spending. Column (1) shows the baseline regression without any controls,
Column (2) adds median home two bedroom rent and Column (3) adds county level fixed effects.
                                                                  Appendix Table 5
                                           List of Partial Re-Openings and Control States for Event Study

           Date                     States that Re-Opened                         Affinity Controls                           Earnin Controls
                                                                                                                Illinois, Kentucky, Maryland, New Hampshire,
      April 20th, 2020                  South Carolina                      Kentucky, New Hampshire             New Mexico, Oregon, South Dakota, Virginia,
                                                                                                                            Washington, Wisconsin

                                                                     Illinois, Kentucky, Louisiana, Maryland,
                                                                                                                    New Mexico, Oregon, South Dakota,
      April 24th, 2020           Alaska, Georgia, Oklahoma           Michigan, New Hampshire, New Jersey,
                                                                                                                           Virginia, Wisconsin
                                                                    New York, South Dakota, Vermont, Virginia

                                                                                                                   Illinois, New Hampshire, New Mexico,
                                   Minnesota, Mississippi,            Kentucky, Louisiana, New Hampshire,
      April 27th, 2020                                                                                          Oregon, South Dakota, Virginia, Washington,
                                    Montana Tennesseee                              Vermont
                                                                                                                                  Wisconsin
                                  Alabama, Colorado, Iowa,
                                                                      Kentucky, Louisiana, New Hampshire,
       May 1st, 2020             Maine, North Dakota, Texas,                                                     Oregon, South Dakota, Virginia, Wisconsin
                                                                                    Vermont
                                       Utah, Wyoming

                                                                                                                 Connecticut, Delaware, Illinois, Kentucky,
                                  Florida, Indiana, Kansas,                    Kentucky, Louisiana,
                                                                                                                 Maryland, New Hampshire, New Mexico,
      May 4th, 2020               Missouri, Nebraska, Ohio,                 Michigan, New Hampshire,
                                                                                                                Oregon, South Dakota, Virginia, Washington,
                                        West Virginia                               Vermont
                                                                                                                               Wisconsin

Notes : This table lists the treatment and control states for each opening date in Figures 11b-11c.
                                   Appendix Table 6
                               City to County Crosswalk

    City Name           State Name                  County          County Fips Code

Los Angeles        California               Los Angeles                          6037
New York City      New York                 Richmond                            36085
New York City      New York                 Kings                               36047
New York City      New York                 Queens                              36081
New York City      New York                 New York                            36061
New York City      New York                 Bronx                               36005
Chicago            Illinois                 Cook                                17031
Houston            Texas                    Harris                              48201
Phoenix            Arizona                  Maricopa                             4013
San Diego          California               San Diego                            6073
Dallas             Texas                    Dallas                              48113
Las Vegas          Nevada                   Clark                               32003
Seattle            Washington               King                                53033
Fort Worth         Texas                    Tarrant                             48439
San Antonio        Texas                    Bexar                               48029
San Jose           California               Santa Clara                          6085
Detroit            Michigan                 Wayne                               26163
Philadelphia       Pennsylvania             Philadelphia                        42101
Columbus           Ohio                     Franklin                            39049
Austin             Texas                    Travis                              48453
Charlotte          North Carolina           Mecklenburg                         37119
Indianapolis       Indiana                  Marion                              18097
Jacksonville       Florida                  Duval                               12031
Memphis            Tennessee                Shelby                              47157
San Francisco      California               San Francisco                        6075
El Paso            Texas                    El Paso                             48141
Baltimore          Maryland                 Baltimore                           24005
Portland           Oregon                   Multnomah                           41051
Boston             Massachusetts            Suffolk                             25025
Oklahoma City      Oklahoma                 Oklahoma                            40109
Louisville         Kentucky                 Jefferson                           21111
Denver             Colorado                 Denver                               8031
Washington         District of Columbia     District Of Columbia                11001
Nashville          Tennessee                Davidson                            47037
Milwaukee          Wisconsin                Milwaukee                           55079
Albuquerque        New Mexico               Bernalillo                          35001
Tucson             Arizona                  Pima                                 4019
Fresno             California               Fresno                               6019
Sacramento         California               Sacramento                           6067
Atlanta            Georgia                  Fulton                              13121
Kansas City        Missouri                 Jackson                             29095
Miami              Florida                  Dade                                12086
Raleigh            North Carolina           Wake                                37183
Omaha              Nebraska                 Douglas                             31055
Oakland            California               Alameda                              6001
Minneapolis        Minnesota                Hennepin                            27053
Tampa              Florida                  Hillsborough                        12057
New Orleans        Louisiana                Orleans                             22071
Wichita            Kansas                   Sedgwick                            20173
Cleveland          Ohio                     Cuyahoga                            39035
Bakersfield        California               Kern                                 6029
Honolulu           Hawaii                   Honolulu                            15003
Boise              Idaho                    Ada                                 16001
Salt Lake City     Utah                     Salt Lake                           49035
Virginia Beach     Virginia                 Virginia Beach City                 51810
Colorado Springs   Colorado                 El Paso                              8041
Tulsa              Oklahoma                 Tulsa                               40143
Notes : This table shows our metro area (city) to county crosswalk. We assigned metros
to counties and ensured that a significant portion of the county population was in the
metro of interest. Some large metros share a county, in this case the smaller metro was
subsumed into the larger metro.
FIGURE 1: Changes in Consumer Spending: National Accounts vs. Credit Card Data

A. National Accounts: Changes in GDP and its Components

100 4
$64.6B

$7.3B

fo}
1

-100+ -$89.6B
-$138.2B
-2004

-$229.7B
-$247.3B
(-5%)

Contribution to the change in real GDP in
chained (2012) dollars from Q4 2019 to Q1 2020

&

5S

5
L

Gross Private Govt. Net Personal Credit Card
Domestic Domestic Expend. Exports Consumption Spending
Product Investment Expend. (PCE) in PCE

B. Retail Services (Excluding Auto and Gas) in Affinity Solutions C. Food Services in Affinity Solutions Purchase Data vs. Monthly
Purchase Data vs. Monthly Retail Trade Survey Retail Trade Survey

—e— Affinity Solutions Purchase Data
—e— Monthly Retail Trade Survey

—e— Affinity Solutions Purchase Data
—e*— Monthly Retail Trade Survey

Total Revenue (Indexed to 1 in January 2020)
Total Revenue (Indexed to 1 in January 2020)

44

T T T T T T T T T T T T T T T T T T

Jan 2019 Mar 2019 = May 2019 Jul 2019 Sep 2019 Nov2019 Jan 2020 Mar 2020 May 2020 Jan 2019 Mar 2019 = May 2019 Jul 2019 Sep 2019 Nov2019 Jan 2020 Mar 2020 May 2020
Date Date

Notes: This figure relates official measurement sources of spending changes to measures of consumer spending from Affinity
Solutions. Panel A summarizes NIPA data (Tables 1.1.2, 1.1.6 and 2.3.2) comparing Q4 2019 and Q1 2020. The first bar
shows the seasonally adjusted change in real GDP in chained (2012) dollars (-$247.3B). In parentheses under the first bar
is the compound annual growth rate corresponding to this change in real GDP (-5.0%). Bars two through five show the
contribution to the change in real GDP of its components. These contributions are estimated by multiplying the change in
real GDP (-$247.3B) by the contributions to the percent change in real GDP given in Table NIPA 1.1.2. The final bar shows
the contribution of components of Personal Consumption Expenditures (PCE) that are likely to be captured in credit card
spending (-$138.2B). This includes all components of PCE except for motor vehicles and parts, housing and utilities, health
care and the final consumption expenditures of nonprofit institutions serving households. This bar is computed by multiplying
the change in PCE (-$229.7B) by the contributions to the percent change in PCE given in NIPA Table 2.3.2 (excluding the
aforementioned subcategories). Panels B and C report monthly spending from Affinity Solutions compared with that of the
Monthly Retail Trade Survey (MRTS), a Census survey providing current estimates of sales at retail and food services stores
across the United States. Panel B restricts to specifically retail trade sectors (NAICS code 44-45) excluding motor vehicles
(NAICS code 441) and gas (NAICS code 447). Panel C restricts to food services (NAICS code 722) in the MRTS and food
services (NAICS code 722) as well as accommodations (NAICS code 721) in Affinity Solutions. Both series are normalized
relative to January 2020 spending (Jan 1 - Jan 31). Data source: Affinity Solutions
FIGURE 2: Changes in Consumer Spending by Sector

A. Spending Changes by Income Quartile: 2019 vs 2020 B. Spending Changes by Sector

107 100% 4

Durable Goods

Durable Goods

75% +

50% +

In-person

~--- services (67%)

25% 4

Consumer Spending Per Day ($ Billions)

In-person
services (33%)

24
T T T T T T T T T T T T
Jan7 Jan21. Feb4 Feb18 Mar3  Mar17 Mar31 Apr14 Apr28 May12 May26  Jun9

0% +

——-—- 2019 Bottom Income Quartile ZCTAs: 2020 Bottom Income Quartile ZCTAs

Share of Decline Share of Pre-COVID Spending
——-- 2019 Top Income Quartile ZCTAs 2020 Top Income Quartile ZCTAs (Jan to Mar 25-Apr 14)
C. Changes in Spending by Category D. Spending Changes by Sector: COVID vs Great Recession

0.754

<
= 8
& 3
£ —E
& 254 2
Cy c3
oa se
¢ 3° 5
Sof S= 0.504
g 28
> ae
& -254 £9
Cc or
Oo co
om £0 4
a 38 0.25
os 7 OS
oc
5 £5
a sk
5 -754 So
ro) Airlines Q
< S 0.004
® Beauly Shops Oo
@ -1007_ : : : : : : Durables Non-Durables Services
gs Feb 4 Feb 18 Mar 3 Mar 17 Mar 31 Apr 14 Apr 28
Oo Date HE Great Recession [EJ COVID-19

Notes: This figure disaggregates spending changes by income and sector. Panel A plots the 7-day moving average of consumer spending
for the lowest and highest ZCTA median household income quartiles in 2020 and 2019. We scale the 2020 (2019) series by multiplying
by the ratio of January 2020 total spending for components of PCE that are likely captured in credit card spending to the January 2020
(2019) total spending in the Affinity data. The ZCTA median household income quartiles are constructed using population-weighted
2014-2018 ACS median household income. We impute February 29, 2019 with the average of February 22, 2019 and March 7, 2019. Panel
B disaggregates spending changes into Merchant Category Codes (MCCs). The first bar for Panel B shows the share of the decline in
spending which can be attributed to the different sectors. The total decline is defined as ((Spending in March 25 through April 14 2020) -
(Spending in March 26 through April 15 2019)) - ((Spending in January 8 through January 28 2020) - (Spending in January 8 - January
28 2019)). The second bar shows the share of spending in January 8-28 of 2020 for each sector. Merchant category codes (MCCs) which
we were unable to identify are excluded from this figure. We define durable goods as the following MCC groups: motor vehicles, sporting
goods and hobby, home improvement centers, consumer electronics, and telecommunications equipment. Non-durable goods include
wholesale trade, agriculture, forestry and hunting, general merchandise, apparel and accessories, health and personal care stores, and
grocery stores. Remote services include utilities, professional/scientific services, public administration, administration and waste services,
information, construction, education, and finance and insurance. In-person services include real estate and leasing, recreation, health
care services, transportation and warehousing services, and accommodation and food, as well as barber shops, spas, and assorted other
services. Non-durables consist of 5.2% of the decline as show in the left-hand side bar and 23.0% of January spending. Excluding grocery
stores from non-durable spending, non-durables constitute 11.6% of the decline and 10.5% of January spending. Panel C compares trends
in consumer spending in the Affinity data for six categories of goods and services: at-home swimming pools; landscaping and horticultural
services; restaurants and eating places; airlines; barbers and beauty shops; and pooled consumer spending across all categories. Panel D
decomposes the change in personal consumption expenditures (PCE) for the COVID-19 shock and the Great Recession using NIPA data
(Table 2.3.6U). PCE is defined here as the sum of services, durables and non-durables in seasonally adjusted, chained (2012) dollars. For
COVID-19 (Great Recession) the peak is defined as January 2020 (December 2007) and the trough is April 2020 (June 2009). Data
source: Affinity Solutions
FIGURE 3: Association Between COVID-19 Incidence, Spending, and Time Outside Home

A. Spending Changes vs. COVID Cases B. Time Spent Away From Home vs. COVID Cases, by Income
20-4
204

-244

Change in Mobility (%)
Relative to Jan

-267

Relative to Pre-COVID 2020

Change in Consumer Spending (%)

@ Low Income Counties (Q1)
_| @ High Income Counties (Q4)

-284

T T T T T T
5 20 150 1100 5 20 150

T
1100
County-level COVID-19 Cases Per 100,000 People (Log Scale) County-level COVID-19 Cases Per 100,000 People (Log Scale)

C. Time Spent Away From Home vs. Area Income

-20-4

-25-4

Change in Mobility (%)
Relative to Jan.

-30-4

-35-4

40000 60000 80000 100000
County Median Household Income (2018)

Notes: This figure plots three binned scatter plots showing the relationship between changes in spending or time spent away
from home with median income and COVID case rates at the county level. To construct each binned scatter plot, we divide
the x-axis variable into twenty equal-sized bins weighting by the county’s population, and plot the (population-weighted)
means of the y-axis and x-axis variables within each bin. Panel A presents a binned scatter plot of the change in average
weekly consumer spending (using data from Affinity Solutions) in a county from the base period (January 8 - January 28) to
the two-week period from April 1 - April 14 vs. the county’s COVID case rate over the two week period from April 1 - April
14. Panel B presents a second binned scatter plot of the change in time spent outside the home in a county between January
and the three-week period from March 25 - April 14 vs. the county’s COVID case rate separately for low and high-income
counties over the three week period from March 25 - April 14. Low-income and high-income counties have median household
income in the bottom 25% and top 25% of all counties respectively, weighted by county population. Panel C presents a binned
scatter plot of the change in time spent outside home in each county between January and the three-week period from March

25 - April 14 vs. the county’s median household income as measured in the 2012-2016 ACS. Data sources: Affinity Solutions,
Google Mobility
                      FIGURE 4: Changes in Small Business Revenues by ZIP Code


                           A. New York                                                     B. Chicago




                                                       C. San Francisco




Notes: This figure shows ZCTA-level maps of the MSAs corresponding to New York City, San Francisco, and Chicago, colored
by their respective deciles of normalized changes in small businesses revenue within each MSA using data from Womply. The
change in revenue is defined as net revenue normalized by revenue in 2019 from March 22th 2020 to May 4th 2020 over the
normalized net revenue from Jan 5th to March 7th 2020. Panel A is of the New York-Newark-Jersey City, NY-NJ-PA MSA.
Panel B is of the San Francisco-Oakland-Hayward, CA MSA. Panel C is of the Chicago-Naperville-Elgin, IL-IN-WI MSA.
For all panels, please note that although the entire MSA may not be shown in the view of the map, all of the ZCTA-level
data within the MSA is being used to calculate the deciles in the legend. Additionally, each ZCTA can represent a different
number of people, as ZCTAs are drawn according to ZIP codes, thus perceptions of smaller, denser ZCTAs do not necessarily
indicate denser populations. Dark gray areas represent missing data, while lighter gray areas that are not covered by a ZCTA
(as ZCTAs are based on ZIP codes and do not cover all of the nation’s land area). Data source: Womply
FIGURE 5: Changes in Small Business Revenues vs. ZIP Code Characteristics

A. Median Income B. Population Density
-205 -204
-3074

-404

Relative to Jan.

-504

-60+ -604

Change in Small Business Revenue (%)
Change in Small Business Revenue (%)
Relative to Jan

25,000 50,000 75,000 100,000 128,000 20 55 148 403 1097 2981 8103 22026
Median Income in 2018 ($) Population Density - Inhabitants per Square Mile (Log Scale)
C. Median Two Bedroom Rent D. Median Two Bedroom Rent: Non-Tradable vs. Teleworkable

-20-4 -20+
© ° _
3
5 -307 e  -304
2 5
35 eg

5

2 407 3 3-404
3 9 52
az F Q
=8 S
oO =

-50-4 &
oe Bg 50}
c )
® <
e -60- ©
s 2 60,
o 5 Food and Accomodation Services and Retail Trade

T T T T @ Finance and Professional Services
500 1,000 1,500 2,000 ; , ; , ;
Median Two Bedroom Monthly Rent in 2018 ($) 500 1,000 1,500 2,000 2,500

Median Two Bedroom Monthly Rent in 2018 ($)

Notes: This figure plots three binned scatter plots showing the relationship between changes in small business revenue using
data from Womply and different measures of economic activity at the ZCTA level. Binned scatter plots are constructed as
indicated in Figure 3. The changes in business revenue are estimated by comparing the post-COVID period (March 22th
2020 to April 20nd 2020) against the base period (Jan 5th to March 7th 2020). We exclude from the sample ZCTA where the
average total revenue in the base period was less than 1.000 USD and where the changes where larger than 200%. This does
not affect results in any significant way. Panel A plots the declines in revenue against median household income at the ZCTA
level taken from the 2014-2018 ACS. Panel B plots the declines in revenue against to the log number of inhabitants per square
mile. Panel C plots the declines in revenue against median 2BR rent from the 2014-2018 ACS. Finally, Panel D replicates
Panel C for two sectors of the economy: non-tradable business sectors, defined as Food and Accommodation (NAICS 72) and
Retail Trade (NAICS 44 and 45), vs. sectors in which workers are more likely to be able to telework, defined as Finance and
Professional Services (NAICS 52 and NAICS 54). Data source: Womply
FIGURE 6: Changes in Employment Rates Over Time

A. All Industries

10% 4

0% 4

-10% 4

-20% 4

-30% 4

-40% 4

-50% 4 ‘ oe
——O—_ CES - All Workers er ae

-60% | ——@— ADP-All Workers

——@ —- ADP -Low-Wage Workers

o%
-70% 4 __@ amin - Low-Wage Workers, All Firms

Change in Employment, Relative to Feb 2020

secee eee HomeBase - Low-Wage Workers, Small Firms

T T T T T T T T
Feb-12 Feb-26 Mar-11 Mar-25 Apr-8 Apr-22 May-6 May-20 Jun-3

B. Accommodations and Food Services

10%

0% 4
-10% 4
-20% 4
-30% 4

-40% 4

-50% 4

—O— CES - All Workers ‘ -
orkel ., \ -
. ?
-60% 4 = = @ == ADP-All Workers (71-72) ‘ NN ues
Steen e ee ees a eae °
——®— _ Eamin - Low-Wage Workers, All Firms 2S @. —@— Oe —o

-70% 4

— '@— = Earmin - Low-Wage Workers, Small Firms

Change in Employment, Relative to Feb 2020

ween eee HomeBase - Low-Wage Workers, Small Firms

T T T T T T T T
Feb-12 Feb-26 Mar-11 Mar-25 Apr-8 Apr-22 May-6 May-20 Jun-3

Notes: This figure compares employment changes relative to February 2020 within various datasets. In Panel A, we construct
a daily employment series for Homebase for all industries by first summing the total number of employees in each day. We
then construct an employment index by averaging employment over the prior seven days and then norming to the average
value of the seven day moving average over the period, February 8 - February 29, 2020. In Earnin, we plot a weekly series of
employment by summing total employment over each week and dividing by the average value for the three week period starting
on February 13th. The Current Employment Statistics (CES) data are available monthly, so we plot changes in each month
relative to February 2020 using the establishment-level data. The CES reports employment for the pay period including the
12th of each month, so we plot the monthly series on the 12th of the month. The ADP series is the ADP National Employment
Report, put out from the ADP Research Institute. The dashed ADP series is the decline in employment in ADP for the bottom
quintile of workers from the week of February15th to the week of April 11th taken from figure 12 of Cajner et al. 2020. Panel
B replicates the Earnin, Homebase, and CES series from figure A but instead restricts to employment in the two-digit NAICS
sector 72, Accommodations and Food Services. In addition, we plot a series for small NAICS 72 firms in the Earnin data,
defining small as the third decile of Earnin employees, which corresponds to employers of mean size around 45 employees. The
ADP series is also from the National Employment Report, put out from the ADP Research Institute restricting to firms in
NAICS 71 and 72. Data sources: Earnin, HomeBase
                         FIGURE 7: Changes in Employment Rates by ZIP Code
                          A. New York                                                   B. Chicago




                                                     C. San Francisco




Notes: This figure replicates Figure 4 using changes in employment at small businesses based on data from Earnin. The
change in employment is defined as the average decrease inemployment at the ZCTA level from the period of January 8th to
March 10th, 2020 to the period of April 8th to April 28th, 2020. Data sources: Earnin, HomeBase
FIGURE 8: Changes in Employment and Job Postings vs. Rent

A. Hours Worked at Small Businesses and ZIP Median Rent B. Employment at Small Businesses and ZIP Median Rent (Earnin)
(Homebase)

-20+ ° °

-454
-254

304

-354

-404

Percent Decline in Employment at Local Bus.

454
500 1000 1500 2000 2500
Two-Bedroom Rent 2018

Percent Decline in Hours Worked at Local Bus.

T T T 1 T @ Me a arge Businesses a sinesses
500 1000 4500 2000 2500 Medium and Large Businesses e Small Businesses

Two-Bedroom Rent 2018

C. Job Postings for Low-Education Workers and County Median D. Job Postings for High-Education Workers and County Median
Rent (Burning Glass) Rent (Burning Glass)
205 2074
“ © 401
500 1000 1800 2000 500 1000 1800 2000

Two-Bedroom Rent 2018 Two-Bedroom Rent 2018

Notes: This figure shows binned scatterplots of the relationship between median rent and both employment and job
postings. Binned scatter plots are constructed as indicated in Figure 3 by binning areas based on their median rent into 20
equally sized bins and computing the mean change in the outcome variable within each bin. Panels A presents the binned
scatter plots of the relationship between the average change in hours worked at businesses in the Homebase data between
January and April and median 2 bedroom rent at the ZCTA level using data from the. Panel B presents a similar binned
scatter plot showing the relationship between employment changes in the Earnin data and median 2 bedroom rent at the
ZCTA level. Both panels measure the percentage change from January 8-28th, 2020 to April 8-28th, 2020. The change in
hours worked in Panel A is constructed using Data from Homebase, which is comprised of small businesses. The change in
hours worked in Panel B is constructed using data from Earnin, and is shown separately for businesses above vs. below the
8th decile of firm size in the Earnin data. Panel C presents a binned scatterplot of the relationship between the percentage
change in job postings for workers with minimal or some education and median 2 bedroom rent (from the 2014-2018 ACS)
at the county level. Panel D presents a binned scatterplot of the relationship between percentage change in job postings for
workers with moderate, considerable or extensive education and median 2 bedroom rent, with a lowess fit. Data sources:
Burning Glass, Earnin, Homebase
FIGURE 9: Geography of Unemployment in the Great Recession vs. COVID Recession

30% 4
25% 4
20% +
15% 4

10% +

Share of Employment Changes

5% 4

0% =
° 2007 to 2010 Feb to Apr 2020 Week 11 to Week 17 2020
Employment Loss Employment Loss UI Claims

Quartile of County Median Income

HE ottom « E Second EE third EE Top

Notes: This figure displays the share of job losses occurring in counties with differing median incomes, for both the Great
Recession and the COVID recession. To construct the first set of four bars, we first calculate national employment loss between
2007 and 2010 using data from the BLS. We then group counties by median income, and compute the share of employment
loss that occurred in counties in each quartile of the distribution of county median income. The second set of bars replicates
the first set of bars using total job losses that occurred between February 2020 and April 2020. The third set of bars reports
the allocation of county-level UI claims summed between March 15 and May 2 across counties in different income quartiles.
In the first set of bars, county median income is calculated using the 2006 ACS; in the second and third sets of bars, county
median income is calculated using the 2014-2018 ACS.
FIGURE 10: Changes in Consumer Spending vs. Workplace Rent for Low-Income Households

A. Change in Hours Worked vs Workplace Rent among Low-Income Households

-20- e
e
e

Se -254
=
Pu
fe
55 20
NS
o Q
EE
2 -35-

-40-

T T T T T T T
600 800 1,000 1,200 1,400 1,600 1,800

Median Two Bedroom Monthly Rent in 2018 at the Workplace ($)

B. Change in Spending vs Workplace Rent among Low-Income Households

-20-
e

< e
Sy -254
ORS
5a
=5 -304
Ze
2a
58
es -357
oO
0p)

-404 .

600 800 4,000 4,200 1,400 4,600 1,800

Median Two Bedroom Monthly Rent in 2018 at the Workplace ($)

Notes: This figure plots changes in hours worked (Panel A) or in consumer spending (Panel B) by ZCTA vs. the average
median 2 bedroom rent in the workplace ZIPs of individuals who live in a given ZCTA, restricting to ZCTAs in the bottom
quartile of the household income distribution. We construct the average median 2 bedroom rent variable by combining data
on the matrix of home residence by workplace ZCTAs taken from Census’ LEHD Origin-Destination Employment Statistics
(LODES) with data on median rents from the 2014-2018 ACS. In particular, we assign median rents from the ACS to each
ZCTA of workplace in the LODES data and then collapse workplace rents to each home ZCTA, weighting by the number of
jobs in each workplace ZCTA. In Panel A, the change in employment variable is based on data from Earnin. The change is
computed from Jan 5th to March 7th 2020 to the period of April 8th 2020 - April 28th 2020. In Panel B, the spending change
variable is based on data from Affinity Solutions on total card spending, and the change is computed from the period of Jan
5th to March 7th 2020 to the period of March 22th 2020 - April 20nd 2020. Data sources: Affinity Solutions, Earnin.
FIGURE 11: Causal Effects of Re-Openings on Economic Activity: Event Studies

A. Case Study on Business Re-Openings: Colorado vs New Mexico B. Re-Opened States vs. Control States: Consumer Spending

Opening

-20-4

Change in Consumer Spending
Relative to January 2020 (%)
Change in Consumer Spending
relative to January 2020

1
1
1
1
1
1
1
1
1
D
1
1
1
1
1
1
1
1
|
0

304
1 T 7 i

-100 -80 -60 -40 -20
Days Relative to Re-opening

20

—+*— Colorado —e*— New Mexico

—+*— Control States —-e*— Opening States
Diff-in-diff Estimate: +.313p.p. (s.e. = 2.928)

C. Re-Opened States vs. Control States: Employment

Opening

-204

-404

Workers relative to January 2020

-604
1 T 7 i

-100 -80 -60 -40 -20
Days Relative to Re-opening

Change in Employment among Low-Wage

ot------~

20

—+*— Control States —-®— Opening States

Diff-in-diff Estimate: +.432p.p. (s.e. = 2.092)

Notes: Panels A and B show seasonally-adjusted percent change in consumer spending in the Affinity Solutions data (see
Section 2.1 for more details about the seasonal adjustment). Panel A shows the series for both New Mexico and Colorado;
Colorado partially reopened non-essential businesses on May 1, while New Mexico did not do so until May 16. Panel B
presents an event study of states that partially reopened non-essential businesses between April 20th and May 4th, compared
to a matched control group. We construct the control group separately for states on each opening day and then stack the
resulting event studies to align the events. Panel C replicates Panel B but instead plotting the percent change in employment
of workers using Earnin data. In Panels B-C, we provide the coefficient from a difference-in-difference comparing treated vs.
untreated states in the two weeks following and the two weeks prior to the partial re-opening. Data sources: Affinity Solutions,
Earnin
                                                                                                                 FIGURE 12: Impact of Stimulus Payments on Consumer Spending


                                                      A. Seasonally Adjusted Spending Changes by Income Quartile                                                                                                                                                       B. Regression Discontinuity Plot for Lowest Income Quartile ZCTAs


                                                                                                                                                                                                                                                                                                                                                20%




                                                                                                                                                                                                                                                                         Pct. Change in Spending Relative to Jan.
                                                                                           0%




                                            Seasonally Adj. Pct. Change in Spending
                                                                                                                                                                                                                                                                                                                                                10%


                                                                                                                                                                                                                                                                                                                                                         0%
                                                                                      -10%
                                                                                                                                                                                                                                                                                                                                                                        RD Estimate: 0.26 (0.07)
                                                                                                                                                                                                                                                                                                                                       -10%
                                                                                                                                                                                                                   Q1 Apr 15-21: -10.3%



                                                                                      -20%                                                                                                                                                                                                                                             -20%


                                                                                                                                                                                                                                                                                                                                       -30%

                                                                                      -30%                                                                                                   Q1 Apr 7-13: -28.1%
                                                                                                                                                                                                                   Q4 Apr 15-21: -29.8%                                                                                                -40%

                                                                                                                 Q1 ZCTA Income
                                                                                                                                                                               Q4 Apr 7-13: -36.3%
                                                                                                                 Q4 ZCTA Income                                                                                                                                                                                                        -50%
                                                                                      -40%
                                                                                                Jan 7   Jan 21     Feb 4      Feb 18   Mar 3   Mar 17                                          Mar 31     Apr 14   Apr 28     May 12      May 26   Jun 9
                                                                                                                                                                                                                                                                                                                                                              Apr 1              Apr 8                   Apr 15       Apr 22     Apr 29




     C. Regression Discontinuity Plot for Highest Income Quartile ZCTAs                                                                                                                                                                                                                                                                                          D. Regression Discontinuity Plot for Durable Goods


                                                                               20%                                                                                                                                                                                                                                                                        30%




       Pct. Change in Spending Relative to Jan.                                                                                                                                                                                                                                                               Pct. Change in Spending Relative to Jan.
                                                                               10%
                                                                                                                                                                                                                                                                                                                                                          20%


                                                                                      0%
                                                                                                                                                                                                                                                                                                                                                          10%

                                                                      -10%                                                                                                                                                                                                                                                                                                        RD Estimate: 0.21 (0.06)

                                                                                                                                                                                                                                                                                                                                                          0%
                                                                                                                      RD Estimate: 0.09 (0.04)
                                                                      -20%

                                                                                                                                                                                                                                                                                                                                                         -10%
                                                                      -30%


                                                                                                                                                                                                                                                                                                                                                         -20%
                                                                      -40%


                                                                      -50%                                                                                                                                                                                                                                                                               -30%

                                                                                           Apr 1                      Apr 8                      Apr 15                                                             Apr 22                         Apr 29                                                                                                       Apr 1              Apr 8                     Apr 15     Apr 22     Apr 29




                                                                                                                                                                                                 E. Regression Discontinuity Plot for In-Person Services


                                                                                                                                                                                             -30%




                                                                                                                                                  Pct. Change in Spending Relative to Jan.
                                                                                                                                                                                             -40%



                                                                                                                                                                                             -50%
                                                                                                                                                                                                                                   RD Estimate: 0.07 (0.04)


                                                                                                                                                                                             -60%



                                                                                                                                                                                             -70%



                                                                                                                                                                                             -80%



                                                                                                                                                                                             -90%

                                                                                                                                                                                                     Apr 1                         Apr 8                      Apr 15                                                          Apr 22                                    Apr 29




Notes: This figure studies the effect of the stimulus payments on spending in the Affinity Solutions data. Panel A plots the
percent change in seasonally-adjusted consumer spending for both the lowest and highest population-weighted ZCTA median
household income quartiles. We use the ZCTA population and median household income estimates in the 2014-2018 ACS.
For panels B-D, each point is the national level of spending on that day divided by the average level of spending in January.
The points are residualised by day of week and first of the month fixed effects. We estimate the fixed effects using data from
January 1, 2019, to May 10, 2019. The hollow-point and dashed line correspond to April 14th, which is excluded from the
regression. Panel B restricts to ZCTAs in the lowest income quartile. Panel C restricts to ZCTAs in the highest income
quartile. Panel D restricts to spending on durable goods as defined in the notes for Figure 2. Panel E restricts to spending on
in-person services as defined in the notes for Figure 2. Data source: Affinity Solutions
FIGURE 13: Impact of Stimulus Payments on Business Revenue and Employment

A. Regression Discontinuity Plot for Lowest Rent Quartile ZCTAs B. Regression Discontinuity Plot for Highest Rent Quartile ZCTAs
1 1
10% 7] \ . 10% 7] \
é | e e Ss \
ze) I ze) I
Q 0% | ° Q 0% |
a I e e a I
S | e 2 \
B -10%4 RD Estimate: 0.18 (0.10) _® * e B -10%4 !
Oo} I Oo} I
a e! ° bd a I
o e 1 e o 1
3 -20%7° i S 20% |
5 ° i ° e 5 |
a —+—___*.* ! 3 !
& -30%-4 | ° © -30%-4 e RD Estimate: 0.01 (0.06) | ee bd ;
© e | s | e
5 40% -4 ° e ! 5 40% -4 ee! e e . e
o ° \ 6 |e e é ° e
= = e
S) ! ro) en I e e
50% \ 50% e ee ° \
a | a ° I ° e
-60% 4 I -60% 4 ° I
T T 1 1 1 T T 1 1 1
Apr 1 Apr 8 Apr 15 Apr 22 Apr 29 Apr 1 Apr 8 Apr 15 Apr 22 Apr 29

C. Revenue and Worker Earnings Changes Among Small
Businesses, by ZCTA Rent Quartile

20% 4

-20% 4

Percent Decline (%)

-40% |

-60% +

1 1 1 1 1 T 1 1
Feb 22 Mar 7 Mar 21 Apr 4 Apr 18 May 2 May 16 May 30

Employment at Small Bus. - Rent Q1 Small Bus. Revenue - Rent Q1

---C-- Employment at Small Bus. - Rent Q4 —-—-—-—- Small Bus. Revenue - Rent Q4

Notes: Panels A and B of this figure study the effect of the stimulus payments on small business revenue in the Womply data.
In these panels, each point is the level of spending (in that ZCTA median 2-bedroom rent quartile) on that day divided by
the average level of spending in January. The points are residualised by day of week and first of the month fixed effects. We
estimate the fixed effects using data from January 1, 2019, to May 10, 2019. The hollow-point and dashed line correspond
to April 14th, which is excluded from the regression. Panel C plots the percent change in the seven-day moving average of
small-business revenue using the Womply data and change in employment among Earnin users by ZCTA rent-quartile and
restricts to small businesses in the Earnin sample, as defined by being in the bottom seven deciles of employer size. The
revenue series is seasonally-adjusted and the employment change series is relative to January 2020. Data sources: Earnin,
Womply
FIGURE 14: Impact of Paycheck Protection Program on Hours Worked

A. Change in Total Earnings by Decile of Firm Size, All Industries
Excl. NAICS 72

B. Change in Total Earnings vs Decile of Firm Size, All Industries
Excl. NAICS 72

1
-204

0%4 April 3 <500 Employees: |
A | Eligible for PPP |
3x8 1 1
~~ I I
8B -10%4 | Ss |
(© -10% =
g | @ -3074 |
7) ! & I
2 -20%-4 \
= | Ww \
o e e

1 & i)

Ww \ ® ! ° °
© 309 I D e ° I
o 7” | s -407 I
r 1 Fa }
& I S) I
o ——+—— 3rd Decile: ~30 Employees | i]
2 -40%- I I
o ——+—— 4th Decile: ~40 Employees I I
re ——+— 5th Decile: ~100 Employees |

0 —~+—_ 6th Decile: ~1,300 Employees i -50 4 it

-50% 4 T T T T T
1 1 1 1 1, 1 1 + + 5 50 500 5000 50000
Feb 11 Feb 25 Mar 10 Mar 24 Apr 7 Apr 21 May 5 May 19 Jun 2 . . . .
Date Median Firm Size in Decile

C. Change in Total Earnings by Firm Size and Employer ZCTA Rent
Quartile

April 3

-20% 4

——*+——._ 3rd and 4th Decile (Rent Quartile 1)
-40% 7 ——+* —- 6th Decile (Rent Quartile 1)

==t==t——+

Change in Total Earnings vs. Feb. (%)

——+— 3rd and 4th Decile (Rent Quartile 4)

~~ -_--*
a —-

——*-—- 6th Decile (Rent Quartile 4)
-50%

T
Feb 11

T
Feb 25

T
Mar 10

T
Mar 24

1
May 19

T
Jun 2

Notes: Panels A-C show the change in total earnings in a repeated cross-section of Earnin users, by decile of employer size.
Each panel excludes workers in the Accommodation and Food Services sector (NAICS 72). The percent change for each week
is computed with respect to the average earnings between January 29th and February 25th. We estimate the size of firm
deciles 3-8 by matching Earnin employer names and locations to employer names and locations in ReferenceUSA data. We
estimate the size of firm deciles 1-2 by rescaling the number of Earnin users to total number of employees to match the national
distribution of firm sizes using data from the Statistics of U.S. Business (SUSB). The grey dashed line corresponds to April
3, 2020, the first day for enrollment in the Paycheck Protection Program (PPP). Panels A and BC are both reweighted so
that industry composition is constant across firm size deciles. The change in earnings is first calculated within each two-digit
NAICS code, and then reweighted so that the composition of industries within each decile of firm size matches the composition
of industries within all deciles plotted. Panel B plots the average percent change in earnings between April 8th and May 5th
against the median firm size in each decile. As NAICS code is not observed for firms in deciles 1-2 of Earnin data, the change
in earnings for deciles 1-2 reflects the change in earnings in all industries pooled, whereas the change in earnings for deciles
3-8 reflects the change in earnings in all industries other than Accommodation and Food Services. Panel C restricts to firms
that are eligible for the PPP (the 3rd and 4th deciles of employer size) and those that are ineligible (the 6th decile of employer
size) for the PPP, separately by rent quartile of work ZCTA. The population-weighted ZCTA rent income quartiles were
constructed using 2014-2018 ACS estimates of population and median-household income. Data source: Earnin
FIGURE 15: Effects of COVID on Educational Progress by Income Group

E +20%-

oa

&

oa

5B (0%

oO (}

(ob)

N

Cc

fo)

B -20% 4

i)

a

SJ

fe}

O -40% 4

on

Cc

fe}

2 —e— Top Income Quartile
— -60%- 4 —*— Middle Income
= —e— Bottom Income Quartile
=}

T T T T T T T T T T
January 8 January 22 February 5 February 19 March 4 March 18 April 1 April 15 April 29 May 13

Notes: We construct this series using data from Zearn Inc. at the class-week level, which we aggregate to the national-week-
income level according to the median household income of the Zip codes of Zearn schools (weighting by the average number
of students using the platform at each school during the base period). The key outcome is student progress, defined as the
number of accomplishment badges earned in Zearn in each week, relative to the base period of January 6th-February 7th. Our
sample includes all classes with more than 10 students using Zearn during the base period, excluding those with fewer than
five users in all weeks. We index student progress to pre-COVID student progress by dividing weekly progress at the school
level by average weekly progress during the base period and then subtracting 1 to center the data around 0% change. Data
source: Zearn Inc.
 APPENDIX FIGURE 1: Industry Shares of Consumer Spending and Business Revenues Across
                                     Datasets


                                                                                                         A. Compared to QSS                                                                                                                                                                                                                          B. Compared to MRTS



                                                                                                                                                                                                                         QSS                                                       40                                                                                                                                                                                            MRTS




                                                                                                                                                                                                                                    Percent of Total Retail and Food Service (%)
                                            30                                                                                                                                                                           Affinity                                                                                                                                                                                                                                                Affinity




     Percent of Total Service Revenue (%)
                                                                                                                                                                                                                         Womply                                                                                                                                                                                                                                                  Womply
                                                                                                                                                                                                                                                                                   30

                                            20
                                                                                                                                                                                                                                                                                   20


                                            10
                                                                                                                                                                                                                                                                                   10



                                             0                                                                                                                                                                                                                                      0
                                                                                                                                                          s              es
                                                                                                                                                                                                                                                                                                  s                 rs              ge             e                ise           s
                                                                                                                                                                                                                                                                                                                                                                                                  Ca               ria
                                                                                                                                                                                                                                                                                                                                                                                                                        l         g            us        ure         ics           by
                                                       ce          Ca
                                                                                             s           on         ort            on          te                                            me
                                                                                                                                                                                               t          on               es                                                                   icle               ile                        rvic                              on                                           thin
                                                                                            ice                                            sta       litie                                                                                                                                                                     era                                                                   re                                       eo                     on          &H
                                                  an                  re                            rm              pp            ati                               rvic                        n        ati             rvic                                                                                  Re
                                                                                                                                                                                                                                                                                                                                                                    nd         tati                             Ma                                  rnit
                                                 Fin                                   erv            ati          Su         ort         lE        Uti                                 ain          mo                                                                                    rV                                  ev             Se               ha                                 al               te       Clo           an                    ctr                ob
                                                              lth                                                                                                   Se                                  d             Se                                                                     eh                   ta                                                       sS                 on                                         ell        Fu
                                                            He                         lS         Info                       sp         Re                                            tert                                                                                                                                    &B         od                 Me                                             ing                                                 Ele          ing
                                                               a                  na                          min                          a                   er                                   co              al                                                                  Mo                    re                         Fo                    rc         Ga                ers                                       Mis
                                                                                                                          Tra                                                                                       on                                                                                   sto                                                                                              ild                             c
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                                                                                                                                                                                                                ati                                                                                    No                Fo                                                                               Bu                                                               Sp
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                                                                                                                                                                              sa                                                                                                                                                                                                      lth
                                                                           Pro                                                                                                                                 Ed                                                                                                                                    Ge                           He
                                                                                                                                                                           Art                                                                                                                                                                                                       a




Notes: Panel A shows the NAICS two-digit industry mix for two private business credit card transaction datasets compared
with the Quarterly Services Survey (QSS), a survey dataset providing timely estimates of revenue and expenses for selected
service industries. Subsetting to the industries in the QSS, each bar represents the share of revenue in the specified sector
during Q1 2020. We construct spending and revenue shares for the private datasets, Affinity and Womply, by aggregating
firm revenue (from card transactions) in January through March of 2020. Panel B shows the NAICS three-digit industry mix
for the same two private datasets compared with the Monthly Retail Trade Survey (MRTS), another survey dataset which
provides current estimates of sales at retail and food services stores across the United States. Subsetting to the industries in
the MRTS, each bar represents the share of revenue in the specified sector during January 2020. We construct revenue shares
for the private datasets, Affinity and Womply, by aggregating firm revenue (from card transactions) in January 2020. Data
sources: Affinity Solutions, Womply
APPENDIX FIGURE 2: Industry Shares of Employment Across Datasets

505 HEE @CEW All Establishments
HE cEW Small Establishments
40 - fe Earnin

f Homebase

Percent of Total Employment

Notes: This figure shows the NAICS two-digit industry mix for two private employment-based datasets compared with the
Quarterly Census of Employment and Wages (QCEW), an administrative dataset covering the near-universe of firms in the
United States. Each bar represents the share of employees in the given dataset who work in the specified sector. We construct
data for all establishments and small establishments using employment data from the Q1 2019 QCEW. Small establishments are
defined as having fewer than 50 employees. We construct employment shares for the private datasets, Earnin and Homebase,
using January 2020 employment. We define employment in Earnin as the total number of worker-days in the month. We
define employment in Homebase as the number of unique individuals working a positive number of hours in the month. Data
sources: Earnin, HomeBase
APPENDIX FIGURE 3: Industry Shares of Job Postings in Burning Glass and Job Openings in
JOLTS

25 4 Me 8S 06M LoLTs

Hhtiteel...

ane .
= o 1)
\ i |

Share in industry

lo)
oa
1

LJ q q q LJ q q qT q qT q q LJ
> 7) i) @
Z s x ¢ é < g ec § § eC g Ss § xs
< & & s ¢ = S 3 & = e s s gS  #
& s g > & g Sf & iS Y $ eS a }
s & ? RY § RG é & Fo 2 2 é ~ x
% & S § < G S SS > é S = 8
res és < x£ § SS iS RS < & &
x e ES iS Y Ss [e) w <
& ey ® od < <0 xS - =
¢ : S ® Ss i
o < o s o Ss 3
RS § & “ & a
Ry

Notes: This Figure displays the NAICS two-digit industry mix of job postings in Burning Glass and job openings in JOLTS,
the Job Openings and Labor Turnover Survey data provided by the U.S. Bureau of Labor Statistics, in January 2020. Data
source: Burning Glass
APPENDIX FIGURE 4: Spending Changes by Sector and Income Quartile

T
ay J 4 2
Appar otels * Foo" ats * eon aero Groce peat oY i ‘ors of 0” “eos ities

20% 4

0% 4

-20% 4

-40% 4

-60% 7

-80% 4

Pct. Change in Spending Relative to Jan (%)

HES ZCTA Income Q1 =). ZCTIA Income Q2
HEE) ZCTA Income Q3_~—s II). ZCTA Income Q4

Notes: This figure displays the change in spending by sector for the four quartiles of ZCTA median household income
(constructed using 2014-2018 ACS population and income estimates). These sectors were constructed by grouping together
similar merchant category codes, not all merchant category codes were used in this plot. The change in spending displayed
is (the log difference-in-difference of spending -1)*100, where the pre-period used is January 8th-28th and the post-period is
March 25th-April 14th. Data source: Affinity Solutions
               APPENDIX FIGURE 5: Spending Changes vs COVID Cases, by County




                                                             -20




                           Change in Consumer Spending (%)
                                                             -22




                              Relative to Pre-COVID 2020
                                                             -24



                                                             -26



                                                             -28
                                                                   5                  20                      150                   1100
                                                                       County-level COVID-19 Cases Per 100,000 People (Log Scale)




Notes: To construct this figure, we divide the log COVID cases into 20 bins, each of which contain 5% of the population, and
plot the mean value of the log of COVID cases and change of spending variables within each bin, controlling for state fixed
effects and median-household income. COVID cases and decline in spending are both measured during the two week period of
April 1st to April14th, and is benchmarked to the pre-period of January 8th to January 28th. Data source: Affinity Solutions
APPENDIX FIGURE 6: Small Business Revenue Changes vs. Local Income Distribution

A. Retail Services (Excluding Auto and Gas) B. Food Services and Accommodations

—e— Affinity - Total Consumer Spending
—e*— Womply - Small Business Revenues

—e— Affinity - Total Consumer Spending
44 —e— Womply - Small Business Revenues

Total Revenue (Indexed to 1 in January - March)

Total Revenue (Indexed to 1 in January - March)

T T T T T T T T T
Jan 2019 Mar2019 May2019 Jul2019 Sep 2019 Nov2019 Jan 2020 Mar 2020 May 2020 Jan 2019 Mar 2019 May 2019 = Jul2019 Sep 2019 Nov2019 Jan 2020 Mar 2020 May 2020
Date Date

Notes: This figure compares weekly total consumer spending (from Affinity Solutions purchase data) and small business
revenue (from Womply) normalized to the average pre-COVID levels of each year. The pre-COVID period is defined as
January 8 - March 10 and we normalize within each calendar year to account for year fixed effects. Following the sectors
defined in the Monthly Retail Trade Survey (MRTS), Panel A restricts to specifically retail trade sectors (NAICS code 44-45)
excluding motor vehicles (NAICS code 441) and gas (NAICS code 447), and Panel B restricts specifically to food services and
accommodations (NAICS code 72). Data sources: Affinity Solutions, Womply
     APPENDIX FIGURE 7: Changes in Small Business Revenues by ZIP Code for Food and
                         Accommodation Service Businesses

                     A. New York City                                                 B. Chicago




                                                       C. San Francisco




Notes: This Figure displays ZCTA-level maps of the MSAs corresponding to New York City, San Francisco, and Chicago,
colored by their respective deciles of Womply change in revenue for small businesses classified as NAICS 72 within each MSA.
This figure corresponds to the process described in the notes for Figure 4. Data source: Womply
               APPENDIX FIGURE 8: Changes in Small Business Revenues by County




Notes: This figure replicates Figure 4 but for the entire United States instead of a single city and its surrounding area and
graphing counties instead of ZCTAs. See notes to Figure 4 for details. Data source: Womply
APPENDIX FIGURE 9: Womply Business Revenue vs. Poverty Share, Top 1% Share, and Gini
by County

A. Gini Index B. Share of Population in Top 1% of Income Distribution

e
g rt
2 2 -304
3 g
2 @
of a
Ba 2§& -357
o>? ns
£2 29
3 £2
as as 404
= =o
co as
ex
a §* 451
& c
> -504 ° E
5 7 2 -504
6 6

-554 -55-

y y T T 88 T T T T T T
0.40 0.45 0.50 0.55 5 10 15 20 25 30

Gini Index 2018 Share of the Population at the top 1% of the Income Distribution (%)

C. Share of Population below Poverty Line

e
S
ar -304
2 . e e
e
S e
S35 ee
a § e e °
as e ° e
£2 404 ° ° bed
Zo
as
a2
EY -454
Oo
£
> -604
5
5571, T T T T
5 10 15 20 25

Share of the Population Below the Poverty Line 2018 (%)

Notes: This Figure replicates Figure 5 but compares the declines with different measures of inequality. Panel A compares the
within county Gini index against the declines. Panel B uses the share of the county with incomes at the top 1% of the income
distribution. Panel C compares the declines with the share of the county population with incomes below the poverty line in
the 2010 decennial census. See notes to Figure 5 for details. Data source: Womply
APPENDIX FIGURE 10: Womply Business Closures vs. Rent by ZIP

-30-
3S e
8
S 354
17)
®o
&
Qe
aS 404
ao
Eo
oO =
5S 454
VoD
Qa
ox
&
® -504
Cc
5

-55-

500 1,000 1,500 2,000

Median Two Bedroom Monthly Rent in 2018 ($)

Notes: This figure replicates Panel C of Figure 5 but shows average changes in small businesses that remain open
instead of changes in revenue. See notes to Figure 5 for details. Data source: Womply
APPENDIX FIGURE 11: Changes in Wages, Hours Worked and Earnings Over Time

A. Earnin

-40 4

Wage Rates
Employment

Change in Wages, Relative to Jan. 2020 (%)

T T T T T T T T
Feb-12 Feb-26 Mar-11 Mar-25 Apr-8 Apr-22 May-6 May-20

B. HomeBase

—~ 2054

BS

oO 104

N

fo)

N o4 — ———

Cc

o

a

o 710-4

£

o

Z -2044

&

o

Y 204

no

O)

Dm -404

=

& 07

g Wage Rates

6 Employment

5 Earnings

CO 9g
T T T T T T T T
Feb-12 Feb-26 Mar-11 Mar-25 Apr-8 Apr-22 May-6 May-20

Notes: This figure compares changes in mean wages and employment relative to January 2020 within the Earnin (Panel A)
and HomeBase (Panel B) datasets.We construct daily wages for both Earnin and HomeBase by calculating the mean wage on
each day. In the HomeBase dataset, we condition on workers being employed by restricting the sample to workers who are
observed working in every week of the series. We construct employment in the Earnin and Homebase data and earnings in the
Homebase data by summing the total number of hours worked in each day and the total wages earned in each day, respectively.
We then take the mean value of each series over the prior seven days and norm to the average value of the seven-day moving
average over the period January 4 - January 31, 2020. Data sources: Earnin, HomeBase
                  APPENDIX FIGURE 12: Changes in Employment Rates by County




Notes: This figure replicates Figure 7 but for the entire United States instead of a single city and its surrounding area. See
notes to Figure 7 for details. Data sources: Earnin
APPENDIX FIGURE 13: Changes in Total Employment by Firm Size

-15-
c
o
=
>
io)
roe
€
uu
&
o -25-
&
Oo
®
a
c
®
2
o
al
-35-

1 2 3 4 5 6 7 8 9 10
Employer Size Decile

Notes: This figure displays the average declines in employment among workers in the Earnin data, separately for each firm size
decile. The decline is calculated by taking total employment at the firm decile level in a pre-period that spans from January
8th, 2020 to January 28th, 2020, and comparing to employment in a post-period that spans from April 1, 2020 to April 21,
2020. Firms are classified into firm size deciles based on total number of Earnin users at the firm. Data source: Earnin
        APPENDIX FIGURE 14: Changes in Employment Rates by ZIP Code for Food and
                          Accommodation Service Businesses

                    A. New York City                                               B. Chicago




                                                    C. San Francisco




Notes: This Figure displays ZCTA-level maps of the MSAs corresponding to New York City, San Francisco, and Chicago,
coloured by their respective deciles of change in hours worked in businesses classified as NAICS 72 within each MSA. We
calculate total hours worked in each ZCTA by summing total hours worked in Earnin data with total hours worked in
Homebase data, restricting to NAICS 72 employers in both datasets. We then calculate changes in hours worked in each
ZCTA as described in the notes to Figure 7. Data sources: Earnin, HomeBase
Percent Decline in Job Postings

-20-4

-304

-404

-504

APPENDIX FIGURE 15: Changes in Job Postings vs. Rent Over Time

A. Job Postings for Low-Education Workers and County Median
Rent, Over Time

Change from Jan/Feb to May 30

Change from Jan/Feb to Mar 25-April 14

1000 1500
Two-Bedroom Rent in County

500

Percent Decline in Job Postings

-20-4

-304

-404

-504

B. Job Postings for High-Education Workers and County Median

Rent, Over Time

Change from Jan/Feb to May 30

Change from Jan/Feb to Mar 25-April 14

500

1000 1500 2000

Two-Bedroom Rent in County

Notes: This figure shows binned scatterplots of the relationship between median rent and changes in job postings between a
pre-period of January 8 - March 10 and the periods March 25 - April 14 or the period May 30-June 5. The change in job
postings is computed using Burning Glass data. Median two-bedroom rent is computed using the 2014-2018 ACS at the county
level. Panel C presents a binned scatterplot of the relationship between the percentage change in job postings for workers with
minimal or some education and median 2 bedroom rent. Panel D presents a binned scatterplot of the relationship between
the percentage change in job postings for workers with moderate, considerable or extensive education and median 2 bedroom
rent. Data source: Burning Glass
APPENDIX FIGURE 16: Legislated Stay-at-Home Orders and Non-Essential Business Closures

20-

Late
Closings

-20-

Change in
relative to January 2020

Early

404 Closings

-60-

T T T T T T
February 1 February 15 February 29 March 14 March 28 April 11

—e— Early Closers ——e®— Late Closers  ——®— Never Closers

Notes: This figure shows percent change in seasonally-adjusted consumer spending in the Affinity Solutions data, pooling
together states that closed non-essential business early (between March 19th and March 24th), states that closed non-essential
businesses late (between March 30th and April 6th), and those that never closed. Data source: Affinity Solutions
APPENDIX FIGURE 17: IRS Transactions Among Earnin Users

1,500 5

1,000 -

500 5

= _ A mi

Total Amount of IRS Transactions Among Earnin Users
($, Millions)

T T T T T T
Apr 1 Apr 8 Apr 15 Apr 22 Apr 29 May 6

Notes: This figure displays the total dollar amount of IRS transactions for Earnin users. Data source: Earnin
       APPENDIX FIGURE 18: Impact of Stimulus on the Composition of Consumer Spending

100%
             Durable Goods
                 23%                        Durable Goods                   Durable Goods
                                                29%                             30%                              Durable Goods
                                                                                                                     44%
75%
           Non-Durable Goods
                 23%                      Non-Durable Goods               Non-Durable Goods
                                                29%                             27%


50%                                                                                                            Non-Durable Goods
            Remote Services                                                                                          19%
                21%                        Remote Services                 Remote Services
                                               24%                             23%                              Remote Services
                                                                                                                    19%

25%
            In-person Services
                   32%                     In-person Services             In-person Services                    In-person Services
                                                  18%                            20%                                   18%

 0%
              January                     Pre-Stimulus                   Post-Stimulus

                                 Composition of Spending                                            Composition of Recovery




  Notes: See notes of Figure 2 Panel B. The pre-stimulus, post-COVID period is defined as March 25th-April 14th. The
  post-stimulus period is defined as April 29th to May 5th. The total recovery is computed use the post-stimulus period
  and the average weekly spending in the pre-stimulus period. This figure disaggregates spending by Merchant Category
  Codes (MCCs), grouping together similar MCCs.We define durable goods as the following MCC groups: motor vehicles,
  sporting goods and hobby, home improvement centers, consumer electronics, and telecommunications equipment. Non-durable
  goods include wholesale trade, agriculture, forestry and hunting, general merchandise, apparel and accessories, health and
  personal care stores, and grocery stores. Remote services include utilities, professional/scientific services, public administration,
  administration and waste services, information, construction, education, and finance and insurance. In-person services include
  real estate and leasing, recreation, health care services, transportation and warehousing services, and accommodation and
  food, as well as barber shops, spas, and assorted other services. Data source: Affinity Solutions
APPENDIX FIGURE 19: Histograms of PPP Eligibility Firm Size Cutoffs for Firms with 300 to
700 Employees

A. Eligibility Cutoffs in Reference USA Data B. Eligibility Cutoffs in Earnin Data
14 14
85 85
c 67 c 67
Be} Ke}
6 6
s s
Le Le
44 44
25 25
0 T y T 0 7 y
500 750 1000 1250 150 500 750 1000 1250 1500
Employee Cutoff Employee Cutoff

C. Share of Earnin Firms With Over 500 Employees, By Earnin Decile

Fraction of Firms Above 500 Employees
[o>]
1
e

3 4 5 6 7 8 9 10
Earnin Employer Decile

Notes: This figure plots a histogram of the firm size cutoffs for PPP eligibility in the set of firms in Reference USA and the
set of firms in the Earnin sample. In the reference USA data, we take the establishment-size-weighted distribution of PPP
employee-based eligibility thresholds, which are based on parent company size (except in the case of NAICS 72, which is not
included here). In the Earnin sample, we assign a firm size threshold for which the individual’s firm would be eligible for PPP
loans. Panel C shows the proportion of firms in the Earnin data whose parent company has more than 500 employees, split
by firm size deciles based on number of Earnin users.
APPENDIX FIGURE 20: Impact of Paycheck Protection Program on NAICS 72

0% 5

(%)

-20% 4

-40% 4

+ 3rd Decile

Change in Earnings vs. Feb.

——+— 4th Decile:

-60% 4

——+— 5th Decile:

——+— 6th Decile:

April 3
I

‘

\

~45 Employees
~130 Employees
~415 Employees

I
I
|
|
I
I
|
|
I
I
~1500 Employees I + _——
! oe

Feb 11 Feb 25

Mar10 Mar24 Apr? Apr21. May5S May19 Jun2

Date

Notes: This figure replicates Figure 14a for NAICS 72. See notes for Figure 14. Data source: Earnin

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