The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data (Chetty et al., Version of July 2023)

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A research paper by Raj Chetty, John N. Friedman, Michael Stepner and the Opportunity Insights Team, titled "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," first version May 2020 and this version July 2023. The abstract says the authors build a publicly available database that tracks U.S. economic activity at a granular level in real time using anonymized data from private companies, with weekly statistics on consumer spending, business revenues, job postings and employment rates. It states that high-income individuals reduced spending sharply in March 2020, and that cash stimulus payments led to sharp increases in spending early in the pandemic but much smaller responses later. The authors acknowledge corporate partners that provided the underlying data and name the funders of the work.

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                              The Economic Impacts of COVID-19:                                                           ∗
       Evidence from a New Public Database Built Using Private Sector Data

                                Raj Chetty, John N. Friedman, Michael Stepner,
                                     and the Opportunity Insights Team†

                                                First Version: May 2020
                                                This Version: July 2023



                                                           Abstract
       We build a publicly available database that tracks economic activity in the U.S. at a granular level
       in real time using anonymized data from private companies. We report weekly statistics on consumer
       spending, business revenues, job postings, and employment rates disaggregated by county, sector, and
       income group. Using the publicly available data, we show how the COVID-19 pandemic affected the
       economy by analyzing heterogeneity in its impacts across subgroups. High-income individuals reduced
       spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction
       in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses
       laid off many of their employees, leading to widespread job losses, especially among low-wage workers
       in such areas. High-wage workers experienced a “V-shaped” recession that lasted a few weeks, whereas
       low-wage workers experienced much larger, more persistent job losses. Even though consumer spending
       and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained
       depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led
       to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the impacts
       of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus
       payments led to sharp increases in spending early in the pandemic, but much smaller responses later
       in the pandemic, especially for high-income households. Real-time estimates of marginal propensities
       to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than
       historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in
       consumer spending and job losses, but cannot restore full employment when the initial shock to consumer
       spending arises from health concerns. More broadly, our analysis demonstrates how public statistics
       constructed from private sector data can support many research and real-time policy analyses, providing
       a new tool for empirical macroeconomics. JEL: E01, E32.




   ∗
     We thank the corporate partners who provided the underlying data used to construct the public database built in this paper:
Affinity Solutions (especially Atul Chadha and Arun Rajagopal), Lightcast (Anton Libsch and Bledi Taska), CoinOut (Jeff
Witten), Earnin (Arun Natesan and Ram Palaniappan), Homebase (Ray Sandza and Andrew Vogeley), Intuit (Christina Foo
and Krithika Swaminathan), Kronos (David Gilbertson), Paychex (Mike Nichols and Shadi Sifain), Womply (Derek Doel and
Ryan Thorpe), and Zearn (Billy McRae and Shalinee Sharma). We are very grateful to Nathaniel Hendren, who collaborated
with us to launch the initial version of the database and helped conduct preliminary analyses for the first draft of this paper in
Spring 2020. We are also 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. We thank David Autor, Gabriel Chodorow-Reich, Haley
O’Donnell, Emmanuel Farhi, Jason Furman, Steven Hamilton, Erik Hurst, Xavier Jaravel, Lawrence Katz, Fabian Lange,
Emmanuel Saez, Ludwig Straub, Danny Yagan, and numerous seminar participants for helpful comments. 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 as of July 2023 has consisted of Hamidah Alatas, Camille Baker, Harvey
Barnhard, Matt Bell, Gregory Bruich, Tina Chelidze, Lucas Chu, Westley Cineus, Sebi Devlin-Foltz, Michael Droste, Dhruv
Gaur, Federico Gonzalez, Rayshauna Gray, Abigail Hiller, Matthew Jacob, Tyler Jacobson, Margaret Kallus, Fiona Kastel,
Laura Kincaide, Caitlin Kupsc, Sarah LaBauve, Lucía Lamas, Maddie Marino, Kai Matheson, Jared Miller, Christian Mott,
Kate Musen, Danny Onorato, Sarah Oppenheimer, Trina Ott, Lynn Overmann, Max Pienkny, Jeremiah Prince, Sebastian
Puerta, Daniel Reuter, Peter Ruhm, Tom Rutter, Emanuel Schertz, Shannon Felton Spence, Krista Stapleford, Kamelia
Stavreva, Ceci Steyn, James Stratton, Clare Suter, Elizabeth Thach, Nicolaj Thor, Amanda Wahlers, Kristen Watkins, Alanna
Williams, David Williams, Chase Williamson, Shady Yassin, Ruby Zhang, and Austin Zheng.
I       Introduction

Since Kuznets (1941), macroeconomic policy decisions have been made on the basis of publicly
available statistics constructed from recurring surveys of households and businesses conducted by
the federal government. Although such statistics have great value for understanding total economic
activity, they have two limitations. First, survey-based data typically cannot be used to assess
variation across geographies or subgroups; due to relatively small sample sizes, most statistics are
typically reported only at the national or state level and breakdowns for demographic subgroups or
sectors are unavailable. Second, such statistics are often available only at low frequencies, often
with a significant time lag.1
    In this paper, we address these challenges by (1) building a public database that measures
spending, employment, and other outcomes at a high-frequency, granular level using anonymized
transaction data collected by companies in the private sector and (2) demonstrating how this new
database can be used to obtain insights into the effects of the coronavirus pandemic (COVID-19)
and policy responses in near real-time – within three weeks of the shock or policy change of interest.
    We organize the paper in three parts. First, we construct statistics on consumer spending,
business revenues, employment rates, job postings, and other key indicators – disaggregated by
geographic area (county or ZIP code), industry, and income level – by combining data from credit
card processors, payroll firms, and financial services firms. The main challenge in using transactional
data collected by private companies (which we refer to as “private sector data” in what follows) to
measure economic activity is a tension between research value and privacy protection. For research,
it is beneficial to use raw, disaggregated data – ideally down to the individual consumer or business
level – to maximize precision and flexibility of research designs. But from a privacy perspective, it
is preferable to aggregate and mask data to reduce the risk of disclosure of private information. To
balance these conflicting interests, one must construct statistics that are sufficiently aggregated and
masked to mitigate privacy concerns yet sufficiently granular to support research. Our goal is to
demonstrate how one can produce public statistics that deliver insights analogous to those obtained
from the underlying confidential microdata, thereby improving the transparency, timeliness, and
reproducibility of empirical macroeconomic research (Miguel et al. 2014).
    We construct publicly available series suitable for research from raw transactional data in a
series of steps. We first develop algorithms to clean the raw data by removing data artifacts and

    1
     For example, data on consumer spending disaggregated by geography are only available for selected large metro
areas at a bi-annual level in the Consumer Expenditure Survey (CEX).


                                                        1
smoothing seasonal fluctuations. Raw transactional data can exhibit sharp fluctuations and noise
driven by changes in clientele, platform design, or exogenous events such as holidays (Leamer 2011;
McElroy, Monsell, and Hutchinson 2018). We systematically examine each series for such artifacts
and develop methods to address them. Next, we take steps to limit privacy loss by reporting only
changes since January 2020 (rather than raw levels), masking small cells, and pooling data from
multiple companies to comply with regulations governing the disclosure of material non-public
information. After establishing these protocols, we report the final statistics using an automated
pipeline that ingests data from businesses and publishes processed data, typically within a week
after the relevant transactions occur.
    The new data series we construct are a complement to, rather than a replacement for, existing
public statistics obtained from representative surveys. The benefits of our data are their granularity
and frequency – providing daily or weekly data for sectors and subgroups down to the county level.
The drawback is that there are no ex-ante guarantees that the data provide a representative picture
of economic activity because any one company’s clients are not necessarily a representative sample of
U.S. households or firms. We discuss these tradeoffs in greater detail in Section II.C. To address these
challenges, we benchmark each series to publicly available statistics from representative surveys and
create series that track the survey-based measures closely, making ongoing adjustments to series that
diverge from national statistics (e.g., because of changes in a data provider’s clients). Ultimately,
the statistics we construct from transactional data provide an additional set of (imperfect) signals
on economic activity that can in principle yield better statistical inferences when combined with
existing survey-based statistics.2 Whether these data yield valuable new insights in practice is an
empirical question.
    In the second part of the paper, we evaluate the empirical value of the new data by using them to
analyze the economic impacts of COVID-19, focusing on the period from March 2020 to December
2021 – covering both the decline in economic activity and the recovery to baseline spending levels.
To evaluate how far one can get solely with public statistics rather than confidential data, we
deliberately conduct our empirical analysis using the aggregate statistics we release publicly.3
    National accounts reveal that GDP fell in the second quarter of 2020 following the COVID-19

   2
      Survey-based statistics themselves do not necessarily provide “ground truth” because of sampling error, recall
error, and growing non-response bias (Meyer and Mittag 2021; Dutz et al. 2021). Thus, even for longer-term inferences
at the national level, combining information from transactional data with information from surveys can be valuable.
    3
      We provide a replication kit that generates all of the results in the paper from publicly available data. We use
non-public data for certain robustness checks and validation analyses reported in the Appendix (as documented in the
replication kit).


                                                          2
shock primarily because of a reduction in consumer spending. We find that spending fell primarily
because high-income households started spending much less, using the median household income
in the ZIP code where the cardholder lives as a proxy for household income.4 As of April 2020,
41% of the reduction in total spending since January 2020 came from households who lived in ZIP
codes with median income in the top quartile, while 12% came from households who lived in ZIP
codes with median income in the bottom quartile. This is both because the rich account for a larger
share of spending to begin with and because they cut spending more in percentage terms. Spending
reductions were concentrated in services that require in-person physical interaction, such as hotels
and restaurants, consistent with contemporaneous work by Alexander and Karger (2023) and Cox et
al. (2020). These findings suggest that high-income households reduced spending primarily because
of health concerns rather than a reduction in income or wealth.
    Next, we leverage geographic variation in the demand shocks businesses face to identify the
impacts of the consumer spending shock on businesses. In-person services are typically produced
by small businesses (such as restaurants) that serve customers in their local area. The revenues
of those small businesses in high-income, dense areas (high-rent ZIP codes) fell by 61% between
January and mid-April 2020, compared with 31% in the lowest-rent ZIP codes.
    As businesses lost revenue, they passed the shock on to their employees, particularly low-wage
workers. Postings for jobs with low skill requirements fell sharply in April 2020, with a much larger
reduction in high-rent areas than low-rent areas. Postings for jobs with high skill requirements fell
much less, and exhibit no cross-sectional gradient with respect to rent. As a result of the labor
demand shock, employment rates fell by 39% for workers with wage rates in the bottom quartile
of the pre-COVID wage distribution as of April 15, 2020 (the trough of the COVID recession),
consistent with results first established using other confidential payroll data sources by Cajner et al.
(2020). For those in the top wage quartile, employment rates fell by 14%. Low-wage individuals
working at small businesses in affluent areas were especially likely to lose their jobs. At small
businesses located in the highest-rent ZIP codes, 30% of workers were laid off within two weeks after
the COVID crisis began; in the lowest-rent ZIP codes, 15% lost their jobs.
    Employment levels for workers in the top wage quartile rebounded quickly, returning to pre-
COVID levels by the end of June 2020. In contrast, employment recovered much more slowly for
low-wage workers. The total number of jobs in the bottom quartile of the pre-pandemic wage

   4
     We verify the quality of our publicly available ZIP-code level proxies for income by showing that our estimates
of spending by income group during the pandemic are closely aligned with those of Cox et al. (2020), who observe
household income directly for JPMorgan Chase clients in confidential microdata.


                                                         3
distribution remained 12% below baseline even as of December 2021 (adjusting for wage growth).
Why did employment rates for low-wage workers remain persistently lower? Unlike at the start
of the pandemic, the source of lower employment rates at the end of 2021 was not a lack of labor
demand: total consumer spending and low-skilled job postings were well above pre-COVID baseline
levels throughout 2021. Furthermore, job postings for low-skilled workers were just as high in
high-rent areas as they were in low-rent areas by December 2021. However, employment rates for
low-wage workers continue to exhibit a sharp gradient with respect to rent, with employment levels
(adjusted for wage growth) returning to pre-COVID baseline rates in the lowest-rent areas but
remaining 23pp below pre-COVID levels in the highest-rent areas. Employment rates in December
2021 were much more strongly related to the size of the initial shock to economic activity – e.g.,
the change in employment rates as of April 2020 – than contemporaneous factors such as COVID
case rates or unemployment benefit levels. In short, the initial labor demand shock induced by the
reduction in aggregate demand in March 2020 led to a persistent reduction in labor supply among
low-wage workers in the hardest-hit areas. As a result, business cycle dynamics during the COVID
crisis were not symmetric: on the way down, spending and employment fell in lockstep, but on the
way back, they did not rise together, echoing patterns documented in the Great Recession (Yagan
2019).
   In the third part of the paper, we examine the scope for stabilization policies to break the chain
of events documented above. We focus on the impacts of stimulus payments, whose goal was to
mitigate reductions in economic activity by boosting aggregate spending. The federal government
sent households stimulus checks at three points during the crisis: April 15, 2020, January 4, 2021,
and March 17, 2021. Using an event study design, we find that the stimulus payments made in
April 2020 increased spending uniformly across the household income distribution (again proxying
for income based on ZIP code), with both low- and high-income households increasing spending
substantially in the days after they received checks, consistent with evidence from Baker et al.
(2023) and Cox et al. (2020) using individual-level administrative data.
   In contrast, the January 2021 payments had highly heterogeneous impacts across the income
distribution: low-income households continued to spend a substantial fraction of their stimulus
checks, but high-income households (those living in the top quartile of ZIP codes by median income)
spent virtually none of the money they received. The impacts of the stimulus changed sharply over
the course of the recession because of the heterogeneous spending dynamics documented above:
high-income households cut spending sharply but did not lose much income, and as a result had built


                                                 4
up considerable savings by January 2021, sharply reducing their marginal propensity to consume.
Because our spending data are available with a short lag, we were able to establish this result three
weeks after the second stimulus payment. These results were cited in policy debates regarding who
should receive the March 2021 stimulus payments, which ultimately concluded with policymakers
lowering the income threshold for eligibility relative to initial proposals.
   Finally, as predicted based on impacts of the January 2021 stimulus, we find that the March 2021
stimulus payments increased spending for low-income households, but had little impact on spending
for high-income households who remained eligible. Hence, estimates of marginal propensities to
consume in January 2021 provided much better forecasts of the impacts of the March 2021 stimulus
payments than historical estimates from prior recessions, which suggested there would be little
heterogeneity in MPCs by income level (Sahm, Shapiro, and Slemrod 2012; Broda and Parker
2014), or even estimates from just months earlier in the same recession (April 2020). This example
demonstrates how public statistics constructed from private sector data can support a “real time”
approach to macroeconomic policy, where policies are adjusted based on current evidence on their
impacts rather than relying solely on historical predictions from other economic environments.
   The data can analogously be used to analyze the impacts of other policies beyond stimulus
payments that were implemented during the COVID-19 crisis. We find that results from our publicly
available data match those from studies that use confidential data sources closely. For instance, we
find that state-ordered shutdowns and reopenings of economies had modest impacts on economic
activity (as in Goolsbee and Syverson 2021) and that loans to small businesses as part of the
Paycheck Protection Program (PPP) had small impacts on employment rates (as in Autor et al.
2022a; Granja et al. 2022; and Hubbard and Strain 2020). Additionally, several studies use the
new data constructed here to evaluate the impacts of many other policies, from the impacts of
unemployment benefit changes (as in Casado et al. 2020) to eviction moratoria (as in An, Gabriel,
and Tzur-Ilan 2022).
   We conclude by analyzing whether the combination of government policies was adequate to stem
the decline in economic activity set off by the reduction in consumer spending documented above.
Consumer spending fell sharply in April 2020 in the dense, affluent areas where many low-wage
workers lost their jobs, portending the start of a downward spiral of secondary effects stemming
from the initial aggregate demand shock. However, the relationship between consumer spending
and the rate of local job loss flattened sharply by July 2020. Spending recovered to baseline levels
or above baseline, even in places where many low-wage workers lost their jobs—presumably because


                                                   5
of the substantial infusion of income to such areas in the form of fiscal stimulus, unemployment
benefits, and other programs that led to an increase in disposable income at the bottom of the
distribution (Blanchet, Saez, and Zucman 2022).
   Overall, our findings suggest that fiscal policies can be very valuable for limiting secondary
declines in consumer spending arising from a loss of income as workers lose their jobs. However,
fiscal policy itself does not have the capacity to restore full employment when the initial shock to
consumer spending arises from health concerns (Guerrieri et al. 2022). Furthermore, even after
health concerns have abated, changes in labor supply among those who lost their jobs may lead to
persistent reductions in employment. It may therefore be useful to target re-employment policies to
individuals who held low-wage jobs in places that suffered the largest job losses (Austin, Glaeser,
and Summers 2018). Our data provide a way to monitor the areas and sectors in which job losses
persist, information that can be used to target and evaluate such programs going forward.
   Beyond showing how the COVID-19 pandemic affected economic activity, the broader con-
tribution of this study is the construction of a new public database of granular macroeconomic
statistics that opens new avenues for empirical macroeconomics, from finer analysis of heterogeneous
impacts across subgroups and areas to real-time policy fine tuning. Importantly, such analyses
can be conducted by many researchers and policy analysts, not just those who can secure access
to confidential data and devote resources to cleaning and harmonizing it. In this sense, the data
assembled here provide a prototype for a new system of real-time, granular national accounts that
can be refined in future work, much as Kuznets (1941) and Summers and Heston (1984, 1991)
developed prototypes for national accounts that were refined in subsequent work (e.g., Feenstra,
Inklaar, and Timmer 2015). Going forward, our intention is to continue to maintain and refine this
database in collaboration with researchers at government statistical agencies, with the ultimate aim
of creating a complement to survey-based statistics that yield further detail on economic activity.
   Our work builds on two literatures: a longstanding literature on macroeconomic measurement
and a recent literature on the economics of pandemics. In the macroeconomic measurement literature,
our work is most closely related to studies showing that private sector data sources can be used
to forecast government statistics; see Abraham et al. (2019) for an overview of this work. In the
COVID-19 pandemic literature, numerous papers have used confidential private sector data to
analyze consumer spending; see Vavra (2021) and Brodeur et al. (2021) for surveys. The contribution
of the present study is to present a comprehensive characterization of how COVID-19 and subsequent
stabilization policies affected economic activity by disaggregating data across geographic areas and


                                                 6
subgroups at a high frequency. We discuss specific connections with prior work in the context of
presenting our results.
     The rest of this paper is organized as follows. The next section describes how we construct the
data series we make public. In Section III, we analyze the effects of COVID-19 on spending, revenue,
and employment. Section IV analyzes the impacts of stimulus and other government policies enacted
to mitigate COVID’s impacts. Section V concludes. Technical details are available in an online
appendix, and the data used to produce the results can be downloaded online.


II     Construction of the Public Database

We use anonymized data from several private companies to construct public indices of consumer
spending, small business revenue, job postings, and employment rates. All of the data series
described below can be freely downloaded from the Economic Tracker website.
     We release each data series at the highest available frequency using an automated pipeline that
ingests data from data providers, constructs the relevant statistics, conducts quality control tests,
and outputs the series publicly. Appendix A details the engineering of this pipeline.


II.A     Methods

We disaggregate each series by industrial sector, county, and income quartile wherever feasible. To
systematize our approach and facilitate comparisons between series, we adopt the following four
principles when constructing each series.
     First, we remove artifacts in raw data that arise from changes in 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, study
their root causes by consulting with the data provider, and address such discontinuities by imposing
continuity using series-specific methods described below.
     Second, we smooth low- and high-frequency fluctuations in the data. We address high-frequency
fluctuations through aggregation, e.g. by reporting 7-day moving averages to smooth fluctuations
across days of the week. Certain series – most notably consumer spending and business revenue –
exhibit strong lower-frequency seasonal fluctuations that are autocorrelated across years (e.g., a
surge in spending around the holiday season). We de-seasonalize such series by indexing each week’s
value in 2020 relative to corresponding values for the same week in 2019.


                                                  7
    Third, we take a series of steps to protect the confidentiality of businesses and their clients.
Instead of reporting levels of each series, we report indexed values that show percentage changes
relative to mean values in January 2020.5 We suppress small cells and exclude outliers to meet
privacy and data protection requirements, with thresholds that vary across datasets as described
below. For data obtained from publicly traded firms – whose ability to disclose data is restricted by
Securities and Exchange Commission regulations governing the disclosure of material non-public
information – we combine data from multiple firms so that the statistics we report do not reveal
information about any single company’s activities.
    Finally, we address the challenge that our data sources capture information about the customers
each company serves rather than the general population. Instead of attempting to adjust for this
non-representative sampling, we characterize the portion of the economy that each series represents
by comparing each sample we use to national benchmarks and label the sector and population
subgroup that each series represents.
    We follow these four broad principles to construct every public data series that we release, while
adapting the specific data processing methodology to the specific characteristics of each data source.


II.B     Data Series

This section provides an overview of how we produce each data series. We summarize the data
sources and give an overview of our key processing steps in Appendix Table 1, and provide summary
statistics on sample sizes for each series in Appendix Table 2.


II.B.1    Consumer Spending

We measure consumer spending using aggregated and anonymized data on credit and debit card
spending collected by Affinity Solutions Inc, a company that aggregates consumer credit and debit
card spending information to support a variety of financial service products, such as loyalty programs
for banks. Affinity Solutions captures nearly 10% of debit and credit card spending in the U.S.
We obtain raw data from Affinity Solutions disaggregated by county, quartile of ZIP code median
income, industry and day starting from January 1, 2019.
    We process the raw Affinity data into an analytical series following the four steps above—removing

   5
     We always index to January 2020 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 more relevant for certain macroeconomic policy questions (e.g., changes in aggregate
spending).


                                                           8
artifacts and outliers, de-seasonalizing, indexing, and benchmarking—and describe each step in detail
in Appendix B. As an example of our data processing methods, we detect and remove discontinuous
breaks caused by entry or exit of card providers from the sample. Because these card providers
have geographically concentrated customer bases, the number of active cards in a county exhibits
a sharp upward or downward spike when the sample of local card providers changes (Appendix
Figure 1A). We identify these sudden changes by analyzing the number of unique cards from each
county with at least one transaction in each week, using a Supremum Wald test for a structural
break at an unknown break point. If we identify a structural break in week 𝑡, we impute spending
changes in weeks {𝑡 − 1, 𝑡, 𝑡 + 1} using the mean week-to-week percent change in spending excluding
all counties with a structural break in the same state.
   The Affinity series has broad coverage across industries, but over-represents categories in which
credit and debit cards are used for purchases (see Appendix Figure 2 discussed in Appendix B).
We therefore view the Affinity series as providing statistics that are representative of total card
spending, but not total consumer spending.


II.B.2   Small Business Revenue

We obtain data on small business transactions and revenues from Womply, a company that aggregates
data from several credit card processors to provide analytical insights to small businesses and other
clients. Womply receives data from approximately 500,000 small businesses, which corresponds to
more than 5% of small businesses with 1-499 employees in the U.S. in 2020 (U.S. S.B.A Office of
Advocacy 2020). 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
businesses disaggregated by county, sector and week. Another key distinction is that Womply data
measures the location of the business as opposed to where the cardholder lives.
   We process this small business data following each of the same four broad steps as with the
consumer spending data from Affinity, but we tailor the methodology to the structure of the Womply
data, as detailed in Appendix C. To take one example, there are again discontinuous breaks in the
number of observed small businesses due to churn in the observed sample of payment processors,
analogous to the entry and exit of card providers in consumer spending data. However, unlike the
repeated cross-sections of consumer spending data, we can address such sample churn more directly
using the panel data on small businesses. In each calendar year, we follow the sample of businesses
operating during the first week of the year: no new businesses enter the panel mid-year. We must


                                                 9
still detect cases where a payment processor exits the sample, and we adopt a similar approach to
detecting discontinuous breaks as we applied to consumer spending data. We look for sharp drops
in businesses operating at the state and national levels (Appendix Figure 1B).6 After adjusting
for these discontinuous exits, we proceed with the rest of the steps described in Appendix C to
construct a seasonally-adjusted series for total small business revenue.
    Womply revenues are broadly distributed across sectors. 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 (Appendix Figure 2).


II.B.3     Job Postings

We obtain data on job postings from 2007 to present from Lightcast (formerly known as Burning
Glass Technologies). Lightcast aggregates nearly all jobs posted online from approximately 40,000
online job boards in the United States. Lightcast then removes duplicate postings across sites and
assigns attributes including geographic locations, required job qualifications, and industry.
    We receive raw data from Lightcast on job postings disaggregated by industry, week, job
qualifications and county.7 We report job postings at the weekly level, expressed as changes in
percentage terms relative to the first four complete weeks of 2020.
    Lightcast provides a sample that is representative of private sector job postings in the U.S.
Appendix Figure 3 shows that the distribution of industries in the Lightcast data is well-aligned
with the Bureau of Labor Statistics’ Job Openings and Labor Market Turnover Survey (JOLTS),
consistent with Carnevale, Jayasundera, and Repnikov (2014).


II.B.4     Employment

We use three data sources to obtain information on employment rates: payroll data from Paychex
and Intuit and worker-level data from Earnin. We describe each of these data sources in turn and
then discuss how we construct a weekly series that is broadly representative of private non-farm
employment rates in the U.S (see Appendix Tables 3 and 4).

   6
      We use a higher level of geographic aggregation to detect breaks here than the county-level aggregation used
for consumer spending because the number of small businesses is an order of magnitude smaller than the number of
active credit and debit cards, and so tests for structural breaks have less power.
    7
      Industry is defined using select NAICS supersectors, aggregated from 2-digit NAICS classification codes. Job
qualifications are defined using ONET job zones, which classify jobs into five groups based on the amount of preparation
they require. We also obtain analogous data broken down by educational requirements.


                                                          10
    Paychex and Intuit. Paychex provides payroll services to approximately 670,000 small- and
medium-sized businesses across the United States and pays 8% of U.S. private-sector workers
(Paychex 2020). To track how employment changes vary across the wage distribution, we separate
employees into four groups based on their hourly wage rates. We split the sample into the four
groups whose wages (if they work full time for the full year) would be above/below 100%, 150% and
250% of the federal poverty line (FPL). For convenience, we refer to these groups as “wage quartiles”
because these thresholds group workers approximately into quartiles before the pandemic.8 This
approach allows us to track the total number of jobs in different parts of the wage distribution,
adjusting for inflation over time. We obtain aggregate weekly data on total employment for each
hourly wage group by county, industry (two-digit NAICS), firm size bin, and pay frequency.
    Intuit offers payroll services to businesses as part of its Quickbooks program, covering approxi-
mately one million businesses as of January 2020. Businesses that use Quickbooks tend to be very
small (fewer than 20 employees). We obtain anonymized, aggregated data on month-on-month and
year-on-year changes in total employment (the number of workers paid in the prior month) based
on repeated cross-sections. We construct a national series from population-weighted averages of
state changes in each month.
    To protect business privacy and maximize precision, we combine Paychex and Intuit data to
construct our primary employment series. We clean this series for analysis following the general
principles described above (see Appendix E).9 We do not seasonally adjust our employment series
because we have incomplete data in 2019; fortunately, seasonal fluctuations in employment are an
order of magnitude smaller than those in spending (Appendix Figure 4) and hence are unlikely to
affect our results.
    Earnin. Earnin is a financial management application that provides its members with access to
their income as they earn it, in advance of their paychecks. Because its users tend to have lower
income levels, Earnin primarily provides information on employment for low-wage workers. We

    8
      In January 2020, the thresholds were $13.10, $19.65 and $32.75 and the four bins in ascending order by wage
contained 23.4%, 27.4%, 25.7%, and 23.5% of CPS respondents. The federal poverty line (FPL) is updated annually
at the beginning of each year. We use the annual FPL to set the thresholds each January and smoothly adjust the
thresholds within the year using CPI inflation, as described in Appendix E.
    9
      As an example of the specific data processing challenges that we address in constructing the employment series,
bunching at integer values in the wage distribution generates discontinuities in the number of workers assigned to each
wage group as the thresholds for the groups are updated due to inflation. For example, when the threshold for the
lowest wage group crosses $14/hour, a discrete mass of workers who were previously a part of the second quartile are
now defined as being in the bottom quartile, causing a discontinuity in both series. To address this issue, we spread
workers out from the whole number wages by adding a random number between -0.5 and 0.5 to their hourly wage,
transforming the point mass at the integer wage into a uniform distribution between [𝑤𝑎𝑔𝑒 − 0.5, 𝑤𝑎𝑔𝑒 + 0.5] (see
Appendix E.2 for details).


                                                         11
obtain anonymized data from Earnin from January 2020 onward, describing the date a paycheck
is received, workplace ZIP code, firm size, industry and amount of pay. Earnin complements the
firm-based payroll datasets discussed above by providing a worker-level sample with more granular
ZIP-level geographic identifiers. However, because workers self-select into the sample when they
enter or exit the Earnin customer base, the labor market disruptions of the pandemic generate
substantial sample selection over time. We therefore use the Earnin sample only to study the first
six months of the COVID pandemic, from March to September 2020, when the sample is relatively
stable. We convert the Earnin data into an employment series using an approach similar to that
used to construct the combined Paychex and Intuit employment series (detailed in Appendix E).


II.B.5    Public Data Sources: UI Records, COVID-19 Incidence, and Google Mobility
          Reports

In addition to the new private sector data sources described above, we also collect and use three
sets of data from public sources to supplement our analysis: data on unemployment benefit claims
obtained from the Department of Labor and state government agencies; data on COVID-19 cases
and deaths obtained from the New York Times, Johns Hopkins, the CDC and the U.S. Department
of Health and Human Services; and data on the amount of time people spend at home vs. other
locations obtained from Google’s COVID-19 Community Mobility Reports. Further details on these
data sources are provided in Appendices F to H.


II.C     Limitations

The rest of this paper demonstrates how the database assembled here is valuable for uncovering the
economic impacts of COVID-19. However, these new data also have three important limitations
that users should weigh, especially in future applications.
   First, each data series we construct necessarily reflects the clientele of the data provider, and
thus does not provide guarantees of population representativeness. We take several steps to verify
that each data series is nationally representative: we compare the cross-sectional composition of each
series against nationally representative statistics in this section, and compare trends in each series
during the COVID-19 pandemic to data from publicly available benchmarks in the next section.
But it is impossible to verify the representativeness of each level of disaggregation (e.g. county-level
consumer spending), precisely because no existing public datasets provide similarly granular and
high-frequency data – hence the value of these novel data sources. Given this limitation, it is

                                                  12
valuable to verify empirical results using multiple different data series and triangulate findings
against whatever data are available from representative surveys at coarser levels of aggregation, as
we do in our analysis below.
   Second, the series we construct have sampling error from both idiosyncratic variation across
firms and households as well as from changing client bases and business closures. The economic
shocks associated with COVID-19 were especially large, making their impacts easy to detect even
in the presence of such errors. In Section III.C, we show that the data series are sufficiently
reliable to detect moderate-sized changes in economic activity at local levels (e.g., employment rate
changes of 4pp at the commuting zone level for the 50 largest CZs). Smaller fluctuations – e.g.,
monthly innovations in employment rates during periods of normal economic growth – will not be
distinguishable from sampling noise in these datasets.
   Third, while our data cover certain sectors well – such as spending on items covered by credit
and debit cards – they entirely exclude other sectors, such as spending on housing and durable
goods such as vehicles. In the context of the COVID-19 pandemic, the data series we construct
overlap with the sectors that exhibit the largest changes in economic activity (see Section III), but
in other applications that may not be the case.
   In light of these limitations, the data constructed here should be used to complement represen-
tative survey-based statistics, not as a substitute. Furthermore, the present version of the database
is a prototype that can be improved over time. For example, noise in estimates of employment
rates from changes in payroll firms’ clientele can be mitigated by chaining together estimates of
employment changes from rotating panels of firms instead of relying on repeated cross-sections.
Adding additional data partners can address gaps in coverage, such as spending on housing. Such
refinements could mitigate the limitations described above, though statistics from representative
surveys will remain essential as benchmarks.


III    Economic Impacts of COVID-19

According to the Bureau of Economic Analysis (2020), GDP fell by $1.61 trillion (an annualized
rate of 29.9%) from the first quarter of 2020 to the second quarter of 2020, shown by the first
bar in Appendix Figure 5A. GDP fell primarily because of a reduction in personal consumption
expenditures (consumer spending), which fell by $1.20 trillion. Government purchases and net




                                                  13
exports did not change significantly, while private investment fell by $0.53 trillion.10 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 $0.90 trillion
between the first quarter of 2020 and the second quarter of 2020, comprising 75% of the total
reduction in personal consumption expenditures.11
    Benchmarking. Our card spending series is well aligned with the Advance Monthly Retail Trade
Survey (MARTS), one of the main inputs used to construct the national accounts.12 Appendix
Figure 5B plots the month-on-month changes in spending on retail services (excluding auto-related
expenses) and food services: both series track each other before the pandemic, then food services
spending drops rapidly in March and April 2020, while total retail spending fluctuates much less
during the pandemic. The root mean square error of the Affinity series relative to the MARTS
is 3 to 5 pp, which is small relative to the fluctuations induced by COVID, but calls for caution
in evaluating smaller shocks. Appendix Figure 6A expands this analysis to other categories by
plotting the change in spending from January to April 2020 in the Affinity spending series against
the decline in consumer spending as measured in the MARTS. Despite the fact that the MARTS
category definitions are not perfectly aligned with those in the card spending data, the relative
declines are generally well aligned across sectors, with a correlation of 0.92.13
   10
      Most of the reduction in private investment was driven by a reduction in inventories and equipment investment in
the transportation and retail sectors, both of which are plausibly a response to reductions in current and anticipated
consumer spending. In the first quarter of 2020, consumer spending accounted for an even larger share of the reduction
in GDP, further supporting the view that the initial shock to the economy came from a reduction in consumer spending
(U.S. Bureau of Economic Analysis 2020).
   11
      The rest of the reduction is largely accounted for by healthcare expenditures; housing and motor vehicle
expenditures did not change significantly.
   12
      The series are not perfectly comparable because the category definitions differ slightly across the datasets. For
example, we observe food and accommodation services combined together in the card data but only food services
in the MARTS. In addition, the MARTS includes corporate card transactions, whereas we exclude them in order
to isolate consumer spending. Hence, we would not expect the series to track each other perfectly even if the card
spending data provided a perfect representation of national spending patterns.
   13
      One specific source of potential bias in our spending series is that it does not include cash transactions and thus
could be biased by potential substitution from cash to credit card purchases. We evaluate this concern using receipts
data from CoinOut, which allows us to measure cash spending on groceries (see Appendix B.3). In practice, trends in
card and cash spending track each other closely (Appendix Figure 6B). These results – together with the fact that our


                                                           14
    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 income 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 in contemporaneous work by 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.14
    Figure 1A 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. Spending fell sharply
on March 15, 2020, when the National Emergency was declared and the threat of COVID became
widely discussed in the United States. Spending fell from $8.3 billion per day in February 2020
to $5.5 billion per day between March 25 and April 14, 2020 (a 34% 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 cut spending more in percentage terms and accounted for a
larger share of aggregate spending to begin with, they accounted for a much larger share of the
decline in total spending in the U.S. than low-income households. In Column 2 of Appendix Table
5, Panel A, we estimate that as of mid-April 2020, top-quartile households accounted for 41% of the
aggregate spending decline after the COVID shock, while bottom-quartile households accounted for
only 12% of the decline.
    This gap in spending patterns by income grew even larger over time. By August 2020, spending
had returned to 2019 levels among households in the bottom quartile, whereas spending among high-
income households remained 8% below baseline levels. Spending then continued to rise gradually
in subsequent months and began to exceed pre-COVID levels starting in 2021 for both low- and
high-income groups. The degree of heterogeneity in spending changes by income is larger than that

card spending series closely track estimates from the MARTS – indicate that aggregate fluctuations in card spending
do not appear to have been offset by opposite-signed changes in cash spending.
   14
      Cox et al. (2020) report an eight percentage point 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 at that time is also eight
percentage points, although the levels of the declines in our data are slightly smaller in magnitude for both groups.


                                                         15
observed in previous recessions (see Figure 6 in Petev, Pistaferri, and Saporta-Eksten 2011) and
played a central role in 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 card spending across
categories 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 or spreading
COVID.
   The left bar in Figure 1B plots the share of the total decline in spending from the pre-COVID
period to mid-April 2020 accounted for by various categories. 57% of the reduction in spending came
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. This is particularly striking
given that these goods accounted for less than one-third of total spending in January, as shown
by the right bar in Figure 1B. These gaps grew larger as the pandemic progressed, as consumer
spending increased above pre-pandemic levels for goods and remote services by mid-August 2020,
but remained sharply depressed for in-person services (Appendix Table 5, Panel B). The fact that
the spending reductions vary so sharply across goods in line with their health risks indicates that
health concerns (either one’s own health or altruistic concerns about others’ health) rather than a
lack of purchasing power drove spending reductions.
   These patterns of spending reductions differ sharply from those observed in prior recessions.
Figure 1C 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, 71% of the reduction in total spending came from
a reduction in spending on services.
   Heterogeneity by COVID Incidence. To further evaluate the role of health concerns, we examine
the association between COVID case rates across areas and changes in spending. Figure 2 shows that
spending fell more in counties with higher rates of COVID infection, in both low- and high-income
areas, during the trough in consumer spending from March 25 to April 14, 2020. However, there was
a substantial reduction in spending even in areas without high rates of realized COVID infection,
consistent with widespread concern about the disease even in areas where outbreaks were less
prevalent. To examine the mechanism driving these spending reductions, Appendix Figure 7 uses
anonymized cell phone data from Google to present a binned scatter plot of the amount of time


                                                 16
spent outside home vs. COVID case rates, again separately for low- and high-income counties. As in
Figure 2, there is a strong negative relationship between time spent outside and COVID case rates,
with a steeper slope in low-income counties. The reduction in spending on services that require
physical, in-person interaction (e.g., restaurants) follows directly from this reduction in time spent
outside.
    In sum, disaggregated data on consumer spending reveal that spending in the initial stages of
the pandemic fell primarily because of health concerns rather than a loss of current or expected
income – consistent with the mechanisms emphasized by Eichenbaum, Rebelo, and Trabandt (2021).
Disposable income ultimately fell relatively little because few high-income individuals lost their jobs
(as we show in Section III.C below) and because the income losses of lower-income households who
lost their jobs were more than offset by supplemental unemployment benefits, stimulus payments,
and other transfers (Ganong, Noel, and Vavra 2020; Blanchet, Saez, and Zucman 2022). Next, we
turn to the impacts of the spending reductions induced by these health concerns on businesses and
the labor market.


III.B      Business Revenues

Services that are consumed in person (e.g., restaurants) are typically produced by small businesses
who serve customers in their local area.15 The reduced in-person spending by high-income households
documented above thus has heterogeneous impacts across areas, with businesses located in more
affluent areas facing larger spending shocks. We exploit this geographic heterogeneity to identify
the impacts of the reduction in consumer spending on businesses and their employees, starting by
examining impacts on small business revenues.16
    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
Administration) at the location where the sale occurs. Because there is no publicly available series
on small business revenues, we compare trends in the Womply data to the Affinity consumer
spending data. These series are generally well aligned, especially in sectors with a large share of
small businesses, such as food and accommodation services, where the RMSE of the Womply series
relative to Affinity is 2.68pp (Appendix Figure 8). For retail, where large businesses have a larger
   15
      For example, over 50% of workers in food and accommodation services (a major non-tradeable sector) work in
establishments with fewer than 50 employees (U.S. Census Bureau 2017).
   16
      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.


                                                           17
market share, the RMSE is 6.72pp.
    National Trends. In the aggregate time series (plotted in Appendix Figure 9A), small business
revenues fell by 48% when the pandemic began and then recovered to 11% below pre-COVID levels by
July 2020. Small business revenues then remained at that level until late 2020, reaching pre-COVID
levels only in September 2021. The larger fall and slower recovery of small business revenues relative
to total consumer spending is consistent with evidence that consumer spending shifted toward large
online retailers during the pandemic (Alexander and Karger 2023). Unfortunately, we lack data on
revenues at large businesses, so we cannot examine these impacts directly.
    Heterogeneity Across Areas. To illustrate the data underlying our geographic analysis, we
map the change in small business revenues from January 2020 to the period immediately after
the COVID shock (March 23 to April 12, 2020) by ZIP code in New York City, Chicago and San
Francisco (Appendix Figure 10).17 In all three cities, revenue losses were largest in the most affluent
neighborhoods (e.g. Manhattan in New York and Lincoln Park in Chicago) and in the central
business districts in each city. But even within predominantly residential areas, businesses located
in more affluent neighborhoods suffered much larger revenue losses.
    Figure 3 generalizes these examples by presenting a binned scatter plot of percent changes in
small business revenue vs. median rents (for a two bedroom apartment) by ZIP code.18 In the top
ventile of ZIP codes by rent, small business revenues fell by 61%, as compared to 31% in the bottom
ventile of ZIP codes by rent, consistent with the differences observed in the Affinity consumer
spending data across areas.19
    The business revenue loss vs. rent gradient is similar when we compare ZIP codes within the
same county by regressing revenue changes on rent with county fixed effects (Table 1 Panel A,
Column 2), or when comparing businesses within the same industry across ZIP codes using sector
fixed effects (Appendix Figure 12A). It also remains similar when controlling for the (pre-COVID)
density of high-wage workers in a ZIP code to account for differences that may arise from shifts to

    17
       We use 2010 Census ZIP Code Tabulation Areas (ZCTAs) to perform all geographic analyses of ZIP-level data.
Throughout the text, we refer to these areas simply as “ZIP codes”.
    18
       Rents are a simple measure of the affluence of an area that combine income and population density: the highest
rent ZIP codes tend to be high-income, dense areas such as Manhattan. Plotting small business revenue against
median incomes or population density produces analogous results (Appendix Figure 11).
    19
       Part of the reason that revenues fell so sharply in high-rent ZIP codes is that affluent families moved elsewhere
during the pandemic. To quantify the relative contribution of such “extensive margin” mechanisms vs. intensive-margin
reductions in spending by high-income households who did not leave, we use aggregated mobile phone data from
SafeGraph (e.g., Allcott et al. 2020) to estimate changes in local population at high frequencies. Although population
fell more in high-rent areas, changes in small business revenues as of April 2020 still exhibit a sharp gradient with
respect to local rents even conditional on SafeGraph-based estimates of population counts (12.3% per $1000 rent, s.e.
0.95).


                                                          18
remote work in business districts (Table 1 Panel A, Column 3).20
    In sum, businesses located in dense, affluent areas lost the most revenue – consistent with the
sharp reduction in spending on in-person goods and services by high-income households. Next, we
examine how businesses reacted to this loss of revenue, focusing on the incidence of the shock on
their employees.


III.C     Labor Market Impacts

We begin by analyzing how the loss of revenues affected labor demand using data on job postings
from Lightcast. Figure 4A presents a binned scatter plot of the change in job postings that require
minimal education between January 2020 and the April 2020 trough vs. median rents by county.
Job postings with minimal educational requirements fell much more sharply in high-rent areas than
for workers in lower-rent areas (difference = 6.9pp, or 22.8%), consistent with the larger shocks to
revenue faced by firms located in high-rent areas. By contrast, postings for jobs that require higher
levels of education — which are much more likely to be in tradeable sectors that are less influenced
by local conditions (e.g., finance or professional services) — exhibit no relationship with local rents
(Figure 4B).
    Having established that the pandemic reduced labor demand especially for lower-skilled workers
working in affluent areas, we next turn to examine its impacts on employment rates using data from
payroll companies.
    Benchmarking. Our payroll-based employment series is broadly aligned with measures from
nationally representative statistics. Appendix Figure 13A shows that month-on-month changes in
employment rates for all workers estimated from combined Paychex and Intuit payroll data generally
fall between estimates obtained from the Current Employment Statistics (a survey of businesses)
and Current Population Survey (a survey of households). Turning to specific sectors, Appendix
Figure 13B focuses on month-on-month employment changes in two sectors that experienced very
different trajectories: food services, where employment fell heavily, and professional services, where
it did not. In both cases, our Paychex-Intuit series closely tracks data from the CES. Appendix
Figure 14A shows more generally that changes in employment rates across private non-farm sectors
(two-digit NAICS) are very closely aligned in our series and the CES, with a correlation of 0.97
   20
      Of course, households do not restrict their spending solely to businesses in their own ZIP code. We find similar
patterns when zooming out to the county level. Counties with larger top 1% income shares experienced larger losses
of small business revenue (Appendix Figure 12B). Poverty rates are not strongly associated with revenue losses at the
county level (Appendix Figure 12C), indicating 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.


                                                         19
when looking at changes from January to July 2020.
   Unlike with spending and business revenues, there are publicly available sources of data on
employment rates that can be disaggregated geographically and used to evaluate the representa-
tiveness of our data across areas. Our employment series closely matches state-level variation in
employment changes during the pandemic in the CES, with a population-weighted correlation of
0.73 when looking at changes from January to July 2020 (Appendix Figure 14B). Our estimates are
also well aligned with commuting-zone-level estimates from the Quarterly Census of Employment
and Wages (QCEW) (Appendix Figure 14C). Similarly, disaggregating the national data by wage
rate, we find that our estimates are closely aligned with estimates based on the Current Population
Survey and estimates in Cajner et al. (2020) (Appendix Figure 15).
   These comparisons indicate that our combined employment series provides representative
estimates of changes in employment rates across wage groups and geographic areas during the
COVID pandemic. A natural question going forward is how accurate our local area estimates will
be in more typical periods, where the shocks of interest are likely to be far smaller than during the
pandemic. To evaluate the accuracy of our data from this broader perspective, we calculate the
population-weighted root-mean-squared-error (RMSE) between our estimates of CZ-level changes in
quarterly employment in January to September 2021 and corresponding statistics from the QCEW.
We find an RMSE of 1.69pp for the 50 largest CZs and 3.64pp when including all CZs. Since the
QCEW statistics are based on unemployment insurance records covering the entire population, the
RMSE can be loosely interpreted as the average standard error of our estimate, accounting for
noise arising from both sampling error and changes in non-representative sampling. The relatively
small MSEs indicate that our data can identify employment shocks considerably smaller than those
induced by the pandemic. For instance, the worst-hit quartile of CZs in the U.S. during the Great
Recession had mean employment losses of 8.73pp from 2007 to 2010, while the least-hit quartile of
CZs had mean employment gains of 2.59pp; our data would have been sufficiently precise to reliably
differentiate those CZs. As another example, Aldy (2014) estimates that the 2010 Gulf Oil spill
decreased employment in non-panhandle Gulf-coast Florida counties by 2.7pp; since the population
of this region is equivalent to the 4th largest CZ (with population of 7 million), our payroll-based
series would have been sufficiently precise to detect and monitor this effect in near-real-time as well.
   The key limitation of publicly available employment data is that existing data sources can only
be disaggregated either by county or wage level. Our payroll-based data sources allow us to measure
changes in employment by county and wage level, which we show next proves to be valuable in


                                                  20
understanding the impacts of the COVID shock.21
    Heterogeneity by Wage Rates. Figure 5A plots employment rates by real pre-pandemic wage
quartile. Each series shows the change in the total number of workers employed in jobs with hourly
wage rates that fall in the relevant quartile of the pre-COVID wage distribution (with thresholds
adjusted over time for inflation as described in Section II.B.4) relative to the baseline level in
January 2020.
    We find substantial heterogeneity in job losses by wage rate, consistent with the findings of
Cajner et al. (2020) in prior work using ADP data. Employment rates fell by 39% around the
trough of the recession (April 15, 2020) for workers in the bottom wage quartile (i.e., the total
number of jobs paying <$13.10/hour in January 2020 was 39% lower as of April 15). By contrast,
employment rates fell by only 14% for those in the top wage quartile (those jobs paying more than
$32.75/hour in January 2020) as of April 15.
    High-wage workers not only were less likely to lose their jobs to begin with, but also recovered
their jobs much more quickly. By June 2020 – just three months after the recession began –
employment for high-wage workers had nearly returned to the pre-COVID baseline. Employment
rates in low-wage jobs recovered rapidly to 20% below baseline levels by summer 2020, but then
stalled from that point onward.
    Heterogeneity Across Areas. To identify the mechanisms driving these employment impacts,
we again exploit geographic variation, studying whether employment fell most in the high-rent
areas that faced the largest demand shocks. Figure 4C plots changes in bottom-wage-quartile
employment rates from January to July 2020 vs. median rents, by county. Consistent with the larger
shocks in high-rent areas to business revenue and labor demand for low-skilled workers, low-wage
employment rates fell much more in more affluent counties. Low-wage employment fell by 21.7%
in the highest-rent counties, compared with 16.8% in the lowest-rent counties. We find a similar
pattern at the ZIP code level using employment data from Earnin (Appendix Figure 16A). Table 1,
Panel B presents a set of regression estimates quantifying these impacts. Low wage employment
rates fell more in higher-rent areas (Column 1), even when controlling for the density of high-wage
workers (Column 2) and comparing ZIP codes within the same county (Column 3).
    The concentration of employment losses in more affluent areas is a consequence of the specific
pattern of demand shocks induced by COVID rather than a general feature of recessions. Appendix

  21
     Another benefit of our payroll-based employment series is the timeliness of its local-area estimates: it matches
the county-level granularity of the QCEW (which is released with a lag of 6 months), but with the timeliness of the
monthly employment statistics in the CES that are released at the national level and for 450 MSAs.


                                                         21
Figure 17 shows that in the Great Recession (from 2007-2010), counties in the bottom quartile of
the household median income distribution accounted for 29% of job losses, while those in the top
quartile accounted for 21% of job losses. By contrast, in the COVID recession (from January to
April 2020), counties in the top quartile accounted for a larger share of job losses than counties in
the bottom quartile.
   In summary, the pandemic led to a short “V-shaped” recession for high-wage workers, but a
prolonged reduction in employment for lower-wage workers that persisted until at least December
2021, the end of our analysis period. Geographic disaggregation reveals that the drop in low-wage
employment at the start of the pandemic was driven primarily by a contraction in spending among
high-income individuals – which then reduced labor demand for low-skilled workers – rather than
voluntary reductions in labor supply (that might have been induced, for example, by health concerns
or unemployment benefits). In the next section, we examine why employment rates remained low
even as the economy began to recover.


III.D    Recovery

By the middle of 2021, aggregate consumer spending (Figure 1A) and small business revenues
(Appendix Figure 9A) had met pre-COVID baseline levels and continued to climb upward. Yet
employment rates in jobs that paid wages in the bottom quartile of the pre-pandemic distribution
remained 20% lower even as of December 2021 (Figure 5A). What explains this “jobless recovery”
at the bottom of the wage distribution?
   Wage Growth. Part of the explanation is real wage growth: wage rates rose faster than the
poverty line during the pandemic, leading some workers to move up out of the bottom wage bin
(rather than into non-employment). To quantify the impact of wage growth, we seek to measure how
much wage rates changed in a given job. Lacking panel data at the job level, we measure changes in
wage rates within detailed industry, occupation, and demographic cells using data from the CPS
(see Appendix E.4 for details). Using the estimated wage growth distribution, we estimate that 7.7
percentage points of the reduction in the total number of workers in the lowest wage group as of
December 2021 is due to wage growth, leaving 12pp due to changes in employment patterns – either
exits into non-employment or switches to higher-paying jobs.
   We assess the contribution of switching to higher-paying jobs using two methods: assessing
whether the cross-sectional composition of employment has shifted toward higher-paying jobs and
measuring employment rates by pre-pandemic wage quartile in panel data. To implement the first


                                                 22
test, we measure the wage distribution based on pre-COVID (2019) wage rates in each industry
x occupation x race x gender x region cell of the Current Population Survey. We find that shifts
in the job distribution across these cells actually led to an increase in the share of workers in the
lowest wage group as of December 2021. To implement the second test, we use the CPS Outgoing
Rotation Group panel, consisting of individuals who responded to CPS survey interviews spaced
twelve months apart. In this panel, non-employment rates as of July 2020 to February 2021 are
7.7pp higher for those who started out in the bottom wage quartile pre-COVID (between July
2019 to February 2020) than for those who started out in the top wage quartile.22 These findings
indicate that exits to non-employment explain most of the remaining reduction in bottom-quartile
employment in the cross-sectional data after accounting for wage growth.
    In the rest of this section, we analyze why low-wage workers remained out of work at higher
rates as of December 2021, distinguishing between labor demand and supply channels.
    Labor Demand. Although aggregate demand recovered, consumer demand may have shifted
across sectors and technologies in ways that reduced labor demand for lower-skilled workers in the
U.S. For example, consumer demand shifted persistently over the course of the pandemic toward
larger companies, online vendors, and certain sectors such as retail trade (Carman and Nataraj
2020; Dunn, Hood, and Driessen 2020; Alexander and Karger 2023). Such companies might have
more capital-intensive production functions or outsource more of their production, leading to a
persistent downward shift in the demand for low-skilled labor in the U.S. Furthermore, firms may
have sought efficiencies in their production processes and economic activity may have shifted to
more efficient firms during the recession, potentially further reducing labor demand (Berger 2012;
Lazear, Shaw, and Stanton 2016; Jaimovich and Siu 2020).
    To evaluate this demand-side explanation, we first examine the evolution of aggregate job
postings over time in Appendix Figure 9B. Postings for jobs that required minimal or no skills had
returned to pre-COVID levels by mid-2020 and were well above pre-COVID levels in most of 2021
as businesses sought to restaff after reducing their payrolls earlier in the pandemic, consistent with
the findings of Forsythe et al. (2022).23
    Furthermore, there is no evidence of mismatch in labor demand relative to the supply of low-wage
workers across sectors or places. Figure 5B plots employment for workers in the bottom wage
   22
      We cannot use this panel approach to examine employment beyond February 2021 conditional on pre-COVID
wage rates because households responding to the CPS answer the Outgoing Rotation Group panel questions exactly
twice, twelve months apart.
   23
      The high level of job postings in the second half of 2021 may also reflect a labor supply shortage, as companies
had to post more jobs to fill a given set of positions.


                                                         23
quartile, reweighting the series to match baseline employment shares by county and industry (2
digit NAICS) in the top wage quartile. This reweighting closes very little of the gap between
the two series, showing that differences in industry and location do not explain the differences in
employment trajectories between low- and high-wage workers.24 Similarly, we find no evidence of a
spatial mismatch between job posts and workers: reweighting job postings across counties by the
number of bottom-wage-quartile workers in January 2020 has little impact on the time series of job
postings (Appendix Figure 9B).
    We conclude that low-wage workers appear to have had considerable demand for their skills in
their own counties, yet chose not to take jobs that were available.
    Labor Supply. Given these findings, we next examine mechanisms that may have led to a
reduction in labor supply among low-wage workers. We begin by analyzing how the labor market
recovery differed in high- vs. low-rent counties, building on the analysis in the previous sections.25
    Figure 6A shows that job postings were approximately 20% above pre-COVID baseline levels
in both high-rent and low-rent counties in December 2021. The gradient in job postings with
respect to rent that emerged when the pandemic hit (Figure 4A) disappeared entirely by December
2021. Yet low-wage employment rates remained substantially lower in high-rent areas than low-rent
areas (Figure 6B). In the lowest-rent counties – where the initial reduction in aggregate demand
was smallest (as measured by small business revenues and job postings) – the total number of
workers with jobs in the bottom wage quartile as of December 2021 was 9% lower than it was
pre-COVID. This 9% reduction is roughly consistent with what we would expect based on wage
growth (as discussed above), indicating that employment had roughly fully recovered in places
where the pandemic had minimal effects on aggregate demand initially. In contrast, in the highest-
rent counties, bottom-wage-quartile employment was 23% lower in December 2021 than it was
pre-COVID.
    Panels C and D of Figure 6 characterize the evolution of the job postings and employment
gradients by county-level rents by month from April 2020 to December 2021. They plot slopes
from regressions of job postings and low-wage employment rates on rent across counties (weighted
by population) by month. The job postings gradient begins to flatten starting in January 2021
   24
      Appendix Figure 18 presents a specific example of this result by plotting trends in employment and spending in
the retail trade sector. Total retail spending was 25% higher as of December 2021 relative to the pre-COVID baseline.
Employment of high-wage workers was 5% above baseline levels, yet employment of low-wage workers was still down
by 19% in this sector – as in the economy as a whole.
   25
      We omit California, Massachusetts, and New York in this cross-sectional analysis because they each raised their
minimum wages during our sample, leading to a discrete mechanical reduction in the number of bottom-wage-quartile
workers over the course of the pandemic (see Appendix E.2).


                                                        24
and disappears completely by the last quarter of 2021. In stark contrast, the employment gradient
steepens over time and never recovers during the period we study.26
    The results in Figure 6 suggest that the places that experienced larger demand shocks initially
(namely more affluent, high-rent areas) exhibit persistent declines in employment even as of
December 2021, despite the fact that labor demand had recovered fully in those areas by that point.
One potential explanation for this hysteresis in employment rates is a change in preferences or
commitments that workers made when the pandemic hit that induced persistent changes in labor
supply. For example, low-wage workers may have moved to smaller apartments or changed their
living arrangements such that they could afford to work less when the pandemic hit, and may have
decided that they preferred to retain these arrangements going forward even when labor demand
recovered. Another possibility is that low-wage workers’ human capital decayed and made it more
difficult for them to obtain available jobs.
    In Table 2, we contextualize the magnitude of the cross-sectional variation in low-wage employ-
ment rates by regressing changes in bottom-wage-quartile employment rates from January 2020
to December 2021 on median rents by county and other covariates that reflect contemporaneous
economic conditions. Column 1 replicates the specification in Figure 6B, showing that employment
remains sharply depressed in higher-rent areas (where the initial aggregate demand shock was more
severe) relative to lower-rent areas in December 2021. In Column 2, we include two variables that
measure contemporaneous health and economic conditions – the average COVID case rate from
October to December 2021 (a measure of the risk of COVID exposure) and the number of weeks of
unemployment insurance benefits that individuals were eligible for in their state as of December
2021 – as well as a set of demographic controls. The inclusion of these variables does not affect the
relationship between median rents and employment rates significantly.
    These estimates imply that the ongoing risk of COVID can explain approximately 3.0 percentage
points of the 12pp reduction in bottom-wage-quartile employment that is not due to wage growth.
Similarly, multiplying the coefficient on the UI benefits variable by the mean number of the weeks of
additional UI benefits for which individuals were eligible in December 2021 implies that UI benefit
extensions account for less than 1pp of the reduction in bottom-wage-quartile employment. This

  26
      The differential changes in employment rates in low-wage jobs across low vs. high-rent areas are not driven by
differential changes in wage growth rates or occupational switching. Using the approaches described above at a national
level (see Appendix E.4 for details), we find that wage growth rates are, if anything, lower in high-rent states than
low-rent states and that rates of switching to higher-paying jobs are uncorrelated with state-level rents. Furthermore,
the CPS panel shows that employment for workers who started in the bottom wage quartile pre-pandemic remained
lower in high-rent states in February 2021 (Appendix Figure 19).


                                                         25
cross-sectional estimate based on changes in UI policies across states over time is consistent with the
quasi-experimental elasticities of employment rates with respect to UI benefit length estimated by
Coombs et al. (2022), which also finds that UI benefits appear to have small effects on employment
rates during the pandemic.
   Column 3 of Table 2 presents a variant of the specification in Column 2, replacing median
rent with the change in bottom-wage-quartile employment rates from January to July 2020 – the
immediate loss in low-wage employment after the shock – as the key independent variable. We find
a positive relationship, showing that areas where employment fell more in the immediate aftermath
of the pandemic exhibited persistent declines in employment nearly two years later.
   Column 4 replicates Column 2, replacing the dependent variable with the change in employment
rate for jobs that paid wages in the top quartile of the pre-pandemic wage distribution. We find no
relationship between top-quartile employment rates and rents, consistent with the rapid recovery of
labor demand and employment for high-skilled workers.
   Finally, we evaluate whether changes in the total number of available workers (i.e., the total
population of lower-skilled workers in high rent areas) – rather than changes in labor supply for a
given worker – can explain a significant portion of the shortfall in employment rates. Although the
number of immigrants to the United States fell during the pandemic, CPS data show that trends in
total low-wage employment rates for immigrants and US citizens aged 16 or older were virtually
identical (Appendix Figure 20A). Internal migration from high-rent to lower-rent areas within the
United States also does not explain a significant share of the larger reduction in employment rates in
high-rent areas (Appendix Figure 20B). Demographic trends in aging over this short period are also
too small to explain the shortfall in employment: the working age population (aged 15-64) grew from
205.7 to 207.1 million between January 2020 and 2022 (Organization for Economic Co-operation
and Development 2023). Finally, the share of individuals who moved to self-employment (and hence
were not available to be low-wage employees) is also too small to explain the aggregate shortfall
in bottom-wage-quartile employment: the self-employed share of individuals aged 16 or older rose
from 6.05% in January 2020 to 6.15% in December 2021 (U.S. Bureau of Labor Statistics 2023a,
2023b, 2023c).
   In sum, the persistent reduction in low-wage employment is not readily explained by changes in
labor demand, changes in contemporaneous incentives to work such UI benefits or ongoing health
risks, or changes in the total number of workers. Rather, the strongest predictor of the cross-sectional
variation in employment rates in December 2021 are variables that predict the size of the initial


                                                  26
shock to aggregate demand – echoing the findings of Yagan (2019), who documents hysteresis in the
labor markets that were hit hardest in the Great Recession.


IV      Evaluation of Policy Responses to COVID-19

In this section, we examine the scope for stabilization policies to break the chain of events documented
above: reductions in spending, especially by high-income households, were associated with losses
in business revenue and low-wage employment. We begin by evaluating the stimulus payments
made to households during the pandemic, illustrating how the public data we construct are useful
for real-time policy evaluation. We then briefly discuss other examples of policy evaluations and
conclude by assessing whether the combination of policy responses was sufficient to stabilize economic
activity.


IV.A        Stimulus Payments to Households

The federal government sent a total of $814.4 billion in stimulus checks to households at three points
during the pandemic: April 2020, January 2021, and March 2021 (Internal Revenue Service 2022).
Were these stimulus payments successful in boosting consumer spending?
    We estimate the causal effect of each of the three stimulus payments on spending in the first
month after receipt, focusing in particular on heterogeneity across the income distribution. We focus
on a one month horizon because prior work shows that most of the impact of stimulus payments and
tax refunds is concentrated within three months of receipt (e.g., Sahm, Shapiro, and Slemrod 2010;
Broda and Parker 2014). Moreover, spending impacts in the first month are highly predictive of
spending impacts in the first three months across subgroups (Parker and Souleles 2019, Table 3).27
    April 2020. The Coronavirus Aid, Relief, and Economic Security (CARES) Act made direct
payments to nearly 160 million people starting in mid-April 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.28
IRS statistics show that 69% of stimulus payments made in April were direct-deposited on exactly
April 15, 2020, while some households received payments on April 14 (Bureau of the Fiscal Service

   27
      Prior studies benefited from substantial variation in the timing of payments, permitting identification over a
longer period of time. In contrast, the stimulus payments we study each largely arrived on a single day, making it
challenging to estimate impacts over longer horizons without strong assumptions about counterfactual trends.
   28
      The 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).


                                                        27
2020).
    We evaluate the impacts of these stimulus payments on consumer spending using a high-frequency
difference-in-differences research design applied to our card spending data, comparing daily spending
before vs. after April 15 in 2020 vs. spending on the same calendar date in 2019. To reduce
cyclical fluctuations, we residualize daily spending (indexed to average levels in January 2019) with
respect to day-of-week fixed effects, which we estimate using data for 2019. We then adjust for
a linear pre-trend in spending (which we assume to be common across all income quartiles) in
order to capture aggregate shocks in spending during the pandemic in 2020.29 . In order to capture
high-frequency changes in spending, we do not smooth the daily spending using a 7-day moving
average, unlike in preceding sections.
    Figure 7A plots the difference in daily spending in 2020 vs. 2019 for households who live in ZIP
codes with median household income in the bottom quartile of the national distribution (which we
term “low-income” households for convenience). Spending increases markedly following the arrival
of payments, with particularly high spending in the days when stimulus checks first arrived. To
quantify the magnitude of the (short-run) impact of the stimulus on spending, in Appendix Table
6 we estimate difference-in-differences models using OLS regressions of daily spending by income
quartile (residualized against a common linear pre-trend) in the 25 days before and after April 15 on
an indicator for being pre vs. post-stimulus interacted with calendar year. To capture the non-linear
dynamics evident in the non-parametric event study plots, we estimate separate treatment effects
for the first five days starting on April 15 and from the 6th day onwards; see Appendix K for a
more detailed description of our methodology.
    Using this approach, we estimate that spending increased by 21pp (s.e. = 3.13) for bottom-
income-quartile households in the first month following the stimulus payments. Accounting for the
fraction of households who actually received stimulus checks in this group, this estimate translates
to an increase in spending of $442 during the first month after receiving a $1,200 stimulus check
(see Appendix K for more details). The estimates remain stable when varying the window used to
estimate the treatment effect, with point estimates ranging from $320 to $452, as shown in Appendix
Figure 22.

  29
     We permit pre-trends because spending fell rapidly for all income groups in the days immediately preceding
the April 15 stimulus payments, as shown in Figure 1A. We assume common trends to maximize precision, as we
find no significant differences in pre-trends in spending across income quartiles in the 25 days preceding the stimulus
payments. The differential changes in spending by income quartile discussed in Section III emerged before that period,
immediately after the pandemic began. We also show that not adjusting for pre-trends at all yields qualitatively
similar conclusions in Appendix Figure 21.


                                                         28
    Figure 7B repeats this analysis for high-income households – those who live in ZIP codes
with median household income in the top quartile of the distribution. Once again, we see a clear
increase in spending in the month after the stimulus payments were made relative to the month
before, although there is no immediate spike in spending the day the checks were received, as one
might expect given that higher-income households are less likely to be liquidity constrained at high
frequencies. We estimate that spending for top-income-quartile households increased by 11pp. This
smaller percentage point impact is to be expected because higher income households received smaller
stimulus payments both in absolute terms and as a percentage of their total expenditure. Rescaling
this effect, we estimate that high-income households spent $732 per $1,200 of stimulus payments
received in the first month.
    The first bar in each set of bars plotted in Figure 8 presents estimates of the impact of the April
2020 stimulus payments on spending over a one month horizon for each of the four ZIP-income
quartiles. Across the income distribution, households spent a large fraction of their April 2020
stimulus checks in the month immediately after receipt, consistent with evidence from confidential
data from JPMorgan Chase account holders subsequently reported by Cox et al. (2020).30
    January 2021. The COVID-Related Tax Relief Act made payments of $600 per person to most
Americans available beginning on January 4, 2021. Eligibility criteria largely followed those for the
earlier round of stimulus, with single households eligible for the full stimulus amount up to $75,000
in income ($150,000 for married households). The stimulus amount fell at higher income levels, with
childless households with incomes up to $87,000 (or $174,000 if married filing jointly) receiving a
payment.
    To evaluate whether our data could shed light on this policy’s impact sufficiently rapidly to
inform the design of future stimulus payments, we analyzed the effects of the stimulus payments on
spending from January 4 to 19, 2021 and released results publicly on January 26, 2021 (Chetty,
Friedman, and Stepner 2021). We use the same difference-in-differences design we used to study
the first stimulus, except that we use December 4 to 14, 2020 as the pre-period rather than the
days immediately preceding the stimulus payments because those days coincide with the Christmas
holiday period, when daily spending exhibits 10 times higher variance across days (even when

  30
    Disaggregating the spending data by sector, we find that most of the additional spending from the April 2020
stimulus went to durable goods rather than in-person services. The stimulus thus increased the overall level of
spending, but did not channel money back to the businesses that lost the most revenue due to the COVID shock.
These findings provide evidence for the “broken Keynesian cross” mechanism established in Guerrieri et al. (2022)’s
model, where funds are not recirculated back to the sectors shut down by the pandemic, potentially diminishing
multiplier effects.


                                                       29
looking at changes across years) than during the first half of December (Appendix Figure 23).31 We
also omit pre-trends here because of the gap created by omission of the holiday period.
    Figure 7C replicates the series in Figure 7A and Figure 7B for the January 2021 stimulus, plotting
indexed daily changes in spending in 2021 vs. 2020 for bottom- and top-income-quartile households.
Low-income households increase spending significantly after the arrival of the January stimulus
payments. In contrast, high-income households do not change their spending levels significantly
after January 4, 2021 relative to December 2020. Using difference-in-differences models analogous to
those above, we estimate that low-income households increased spending over the first month after
receiving their stimulus checks by 6pp, while high-income households increased spending by 0.4pp,
an estimate that is not significantly different from zero. The middle bars shown in Figure 8 rescale
these estimated impacts into dollars per $1,200 to facilitate comparisons across stimulus rounds.
While the marginal propensity to consume (MPC) out of these stimulus payments in the first month
fell significantly for all income groups in January 2021 relative to April 2020, the drop in the MPC
for high-income households was especially large. We estimate that low-income households spent $187
per $1,200 of stimulus received, 58% smaller than the $442 estimated in April 2020. High-income
households spent much less of their second stimulus checks – from $732 per $1,200 received in April
2020 to just $35 per $1,200 in January 2021, a reduction of 95%.32 These heterogeneous impacts
on spending across income groups are aligned with results subsequently reported in May 2021 by
Greig, Deadman, and Noel (2021, Box 1, page 20) using confidential data from JPMorgan Chase.
    In short, this analysis demonstrates that one can gauge the (short-term) effects of stimulus
payments with just two weeks of data after the payments are made using what are now publicly
available data – enabling a rapid feedback loop for subsequent policy changes. Indeed, based on
these estimates, we predicted that making further stimulus payments to high-income households
would have modest impacts on their spending, suggesting that targeting the next round of stimulus
towards lower-income households would save substantial resources that could be used to support
other programs, with minimal impact on economic activity.
    March 2021. After extensive debate about whether higher-income households should continue
to receive stimulus payments – including discussion of the evidence described above (Lambert
and Sraders 2021) – Congress passed the American Rescue Plan on March 11, 2021. The final
   31
      Using the same 25-day pre-period window as was used for the first stimulus yields point estimates that are
statistically indistinguishable from those we present, but with much wider confidence intervals due to the greater noise
in the pre-period.
   32
      Both of these estimates are significantly lower (with 𝑝 < 0.005) than those from April 2020 based on a permutation
test; see Appendix Figures 24 and 25 for the full distribution of placebo estimates.


                                                          30
plan continued to pay the full stimulus amount of $1,400 to households earning up to $150,000,
but phased the payments out more rapidly beyond that threshold than initially proposed, so that
households with incomes above $80,000 (for single filers without children) or $160,000 (for married
couples without children) received no stimulus. These revisions reduced the total amount of stimulus
payments made to high-income households by approximately $17 billion relative to the original
proposal in January 2020 (Watson 2021).
   Did the March 2021 stimulus payments in fact have lower impacts on spending of higher-income
households, as predicted based on the January 2021 evidence? Figure 7D replicates the preceding
figures for the 25 days before and after the March 2021 checks were sent out; here, we use exactly the
same estimator as in the first stimulus, as there are no holiday-induced fluctuations in the pre-period.
Bottom-income-quartile households increased spending considerably in the days following the March
payments, while spending for high-income households did not change significantly. The third set
of bars in Figure 8 rescale these effects into dollar impacts per $1,200 of stimulus payment. The
estimated impacts are much more similar to those observed in January 2021 than in April 2020,
with positive impacts on spending for lower-income households but near-zero impacts on spending
for top-quartile households.
   Discussion. Why did the marginal propensity to consume out of cash windfalls fall sharply over
the course of the pandemic, especially for higher-income households? Studies of stimulus payments
in prior recessions find little heterogeneity in MPCs by income, but show that households with
higher liquid wealth balances exhibit lower MPCs (Johnson, Parker, and Souleles 2006; Broda
and Parker 2014; Jappelli and Pistaferri 2014). In normal times, most households even in the top
income quartile tend to have relatively little liquid wealth (Kaplan and Violante 2014), explaining
why they exhibit high MPCs out of windfalls in previous recessions. But during the pandemic,
households started to accumulate substantial liquid wealth because their incomes remained relatively
stable while their spending fell sharply, as discussed above in Section III. The national savings rate
(measured in NIPA Table 2.6) rose from 7.6% in 2019 to 18.5% on average in Q2-Q4 of 2020. Using
confidential data from the JPMorgan Chase, Greig, Deadman, and Noel (2021) further show that
cash balances in checking accounts rose substantially from January to December 2020, with the
largest increases (in dollars) among high-income households. Given this rapid growth in liquid
wealth, it is not surprising that high-income households started to spend less of their stimulus
payments over time.33

  33
       Summarizing the literature on impacts of stimulus payments, Sahm (2019) observes that “households with low


                                                        31
    This analysis illustrates the value of real-time estimation of policy impacts rather than predictions
based on historical estimates. Despite being based on a consensus across a large set of studies,
historical predictions about the lack of heterogeneity in MPCs by income proved to be inaccurate
given the unusual impacts of the pandemic on spending behavior across the income distribution.34
The core challenge is that parameters such as MPCs are not invariant to the economic and policy
environment. By directly estimating such parameters in real time using newly available data, one
can make policy decisions that respond transparently – based on publicly available information – to
current economic conditions.


IV.B      Impacts of Other Policies

The data we make publicly available can also be used to study a range of other policies beyond stim-
ulus payments. For illustration, we briefly discuss four examples of policies that were implemented
during the COVID-19 crisis. The first two are based on analyses we conduct ourselves (detailed
in Appendix L) and the latter two are analyses conducted by other researchers using our data in
combination with other data sources.
    State-Ordered Shutdowns and Reopenings. Many states enacted stay-at-home orders and shut-
downs of businesses in an effort to limit the spread of COVID infection and later reopened their
economies by removing these restrictions. Using our card spending and payroll data, we evaluate
the impacts of these policies using event study designs that compare trends in states that shut
down and re-opened at different dates. We find that state-ordered shutdowns and reopenings had
modest impacts on economic activity. Spending and employment remained well below baseline levels
even after reopenings, and trended similarly in states that reopened earlier relative to comparable
states that reopened later (Appendix Figures 26-27). Spending and employment also fell well before
state-level shutdowns were implemented. These findings are consistent with work by Goolsbee and
Syverson (2021) and Sears et al. (2023) using cell phone location data as well as Bartik et al. (2020)
using timesheet data on hours of work.
    Paycheck Protection Program. The Paycheck Protection Program (PPP) sought to reduce

liquid assets relative to their income tend to spend more (and more quickly) out of additional income than those
households with ample liquidity.” In normal times, Sahm observes that “targeting current low-income or low-wealth
households may not identify the households most likely to spend the stimulus, which could include some wealthy
households.” The link between income and liquid wealth changed during the pandemic, making such targeting more
feasible.
   34
      With the benefit of hindsight, one may have been able to predict that MPCs would begin to fall for high-income
households as their liquid savings rose, but it is difficult to gauge ex-ante which of the many potential dimensions of
heterogeneity and structural change warrant attention.


                                                          32
employment losses by providing forgivable loans worth more than $800 billion in total to small
businesses that maintained sufficiently high employment (relative to pre-crisis levels). Using our
payroll data disaggregated by firm size, we evaluate the impacts of the PPP on employment by
comparing employment trends at firms with fewer than 500 employees (which were eligible for PPP
assistance) with firms in the same sector that had more than 500 employees (who were ineligible).
We find that employment increased by only 2.48 percentage points after the PPP was enacted in
April 2020 relative to larger firms that were ineligible for PPP (Appendix Figure 28). Our point
estimates imply that the cost per job saved by the PPP was $301,863 ($86,201 at the upper bound
of the 95% confidence interval); netting out potential UI payments to these potentially unemployed
workers reduces this number only slightly to $283,513 per job saved (see Appendix L.2 for details).
Autor et al. (2022a) and Autor et al. (2022b) reach similar conclusions using the same research
design with microdata from ADP, another large payroll processor. Granja et al. (2022) use a
different design, exploiting cross-sectional variation in PPP takeup driven by bank composition, and
reach similar conclusions, partly drawing upon the data we make publicly available. Together, all of
these studies suggest the PPP had modest marginal impacts on employment in the short run, likely
because the vast majority of PPP loans went to inframarginal firms that were not planning to lay
off many workers.35
    Unemployment Benefit Increases. The Federal Pandemic Unemployment Compensation (FPUC)
program paid supplemental unemployment benefits of up to $600 per week from March to September
2020. Casado et al. (2020) use county-level variation in wage replacement rates resulting from
differences in industrial composition to estimate the effect of FPUC payments on aggregate spending.
Using our publicly available spending data combined with UI claims data from Illinois, they estimate
that a 1% increase in the replacement rate increased county-level spending by 0.167%, which
implies that each $1 of UI benefits increased aggregate spending at the county level by $1.23.
For comparison, our estimates above imply that $1 of spending in the form of stimulus checks
increased household-level spending by an MPC = 0.256 on average. In a standard Keynesian model,
an MPC of 0.256 would imply an impact on aggregate spending of 1−0.256
                                                                0.256
                                                                       = 0.344, an order of
magnitude smaller than the estimated impact of UI benefits. In the pandemic, where some sectors
were effectively shut down, theory suggests that the multipliers would be even smaller than the
standard Keynesian benchmark (Guerrieri et al. 2022). This comparison suggests that UI benefits


  35
    This analysis focuses solely on short-run employment effects; it remains possible that the PPP may have long-term
benefits by reducing permanent business closures, as emphasized by Hubbard and Strain (2020).


                                                         33
targeted to unemployed individuals were a more potent tool to stimulate aggregate spending than
stimulus payments to all individuals, especially later in the pandemic as employed households built
up a large stock of savings.
   Eviction Moratoria. Many state and local governments enacted moratoria on tenant eviction
during the pandemic to provide stable housing for those who might have lost their jobs. These
moratoria were implemented at different times in different states and counties. An, Gabriel, and
Tzur-Ilan (2022) exploit variation in the timing of such moratoria to estimate their impacts on
spending. Using our publicly available data on consumer spending by category coupled with other
sources, they estimate that a one-week eviction moratorium is associated with a 1% increase in
spending on necessities such as food and groceries. They conclude that eviction moratoria not only
reduced housing instability but also boosted spending on other goods and potentially provided an
aggregate stimulus as a result.
   Methodologically, these examples illustrate that data from private sector sources can be used to
evaluate a wide variety of policies rapidly because many policies have heterogeneous impacts across
geographic areas or other dimensions, such as firm size. Reassuringly, the findings obtained from
our public statistics match those obtained from studies with access to the underlying microdata,
demonstrating that public statistics constructed from private sector data sources can support many
policy analyses. Taken together, these studies suggest that policies targeted directly at households
that suffered the largest income losses – such as those who became unemployed or faced eviction –
had the largest impacts on spending and downstream economic activity in the pandemic.


IV.C    Secondary Impacts on Spending

We conclude by stepping back from the effects of specific policies and analyzing whether the
combination of government policies – those analyzed above as well as other macroeconomic responses
and changes in the economy – was adequate to stem the downward spiral in economic activity set
off by the initial reduction in consumer spending documented in Section III. Did the loss of jobs
among low-wage workers trigger a secondary reduction in their own spending levels due to a lack of
disposable income (rather than health concerns) — potentially setting off further business revenue
losses and employment losses? Or was government intervention adequate to prevent such secondary
responses? We investigate secondary spending responses among low-income individuals by returning
to the geographic heterogeneity in the size of initial consumer demand shocks by local rent levels,
as in Section III. In particular, we compare how spending evolved in low-income ZIP codes whose


                                                34
residents worked predominantly in high-rent areas vs. those whose residents worked predominantly
in low-rent areas.
    Figure 9A presents a binned scatter plot of changes in low-wage employment from January to
April 2020 by home (residential) ZIP code vs. average workplace rent. We construct this figure
by combining ZIP-code-level data on employment rates of low-wage workers from Earnin that we
make publicly available with public data from the Census LEHD Origin-Destination Employment
Statistics (LODES) database, which provides information on the matrix of residential ZIP by work
ZIP for low-income workers in the U.S. in 2017, to compute the average workplace median rent
level for each residential ZIP. Figure 9A shows that low-income individuals who were working in
high-rent areas pre-COVID were much less likely to be employed after the shock hit in April 2020 –
consistent with our findings above.36
    Next, we analyze how these differential shocks to employment affected spending patterns, taking
a step toward mapping the flow of shocks in the economy (Andersen et al. 2022). Figure 9B replicates
Figure 9A using spending changes on the y-axis, restricting to households living in low-income ZIP
codes.37 Low-income individuals living in areas where people tend to work in high-rent ZIP codes
cut spending by 33% on average from January to April 2020, compared with 23% for those living in
areas where people tend to work in low-rent ZIPs (Figure 9B). The relationship remains similar
in magnitude but is less precisely estimated when we compare ZIP codes within the same county
(Appendix Table 7).
    Figure 9B implies that low-income households who lost their jobs at the start of the pandemic
reduced their own spending more at the start of the pandemic—portending the start of the downward
spiral described above. However, while employment losses for low-wage workers persisted over
time, the reductions in spending did not. Figure 9C shows that by October 2020, spending in
low-income ZIP codes was slightly higher than it was pre-COVID on average, and there was no
longer any relationship between workplace rents and spending levels among low-income households
despite the persistence of employment losses in higher-rent areas (Figure 6D). Figure 9D plots the
evolution of spending in bottom-income-quartile ZIP codes that rank in the top quartile of median

   36
      These results are driven by work location rather than sectoral differences in employment across areas: in the
Earnin microdata, we find similar results even when comparing workers employed at the same firm (e.g., a chain
restaurant). People working in high-rent ZIP codes in January 2020 remained less likely to have a job (anywhere) in
April 2020 than their co-workers working in a different establishment of the same firm in lower-rent ZIP codes.
   37
      We restrict this figure to households living in low-income ZIPs because we cannot disaggregate the Affinity data
by individual-level income. Since the employment data already represent only low-income workers, we do not restrict
to low-income ZIPs in the employment analysis above; however, the patterns are very similar when restricting to
low-income ZIPs in the Earnin data.


                                                         35
workplace rent. Despite the fact that these areas faced the largest and most persistent employment
losses, consumer spending recovered very rapidly after falling sharply at the onset of the pandemic,
exceeding pre-COVID levels starting in July 2020.
    In sum, although a sharp gradient of spending reduction with respect to employment losses
emerged early in the pandemic, it vanished within a few months. Total spending in areas where
many workers had lost their jobs and remained out of work remained on par with areas where
workers had lost less income – indicating that the secondary spending response that could have
produced a further downward spiral was effectively shut down shortly after the crisis began. Losses
in earned income likely did not translate to further spending reductions because the fiscal response
to the crisis (e.g., via extended unemployment benefits and stimulus payments) actually increased
the total disposable income of low-income households (Blanchet, Saez, and Zucman 2022; Ganong
et al. 2022). These results suggest that as a whole, macroeconomic policy responses appear to
have been effective in limiting secondary declines in consumer spending as workers lost their jobs
– perhaps even going beyond what was necessary – even if they could not address the losses in
employment that arose from the initial shock to consumer spending driven by health concerns.


V    Conclusion

Transactional data held by private companies have great potential for measuring economic activity,
but to date have been accessible only through contracts to work with confidential microdata. In
this paper, we have constructed a public database to measure economic activity at a high-frequency,
granular level using data from private companies. By systematically cleaning, aggregating, and
benchmarking the underlying micro data, we construct series that can be released publicly without
disclosing sensitive information.
    We use this new public database to analyze the economic impacts of COVID-19, demonstrating
two ways in which the data provide a new tool for empirical macroeconomics. First, the data can be
used to rapidly diagnose the root factors driving an economic crisis by learning from cross-sectional
heterogeneity, since different places and subgroups often face different shocks. In the case of
COVID-19, we find that a sharp reduction in spending by high-income individuals due to health
concerns led to losses of business revenues and persistent reductions in low-wage employment in
affluent areas. Second, the data permit rapid, real-time policy evaluation – as demonstrated by our
analyses showing the changing impacts of fiscal stimulus payments over the course of the pandemic



                                                 36
– opening a path to fine-tuning policy responses based on their observed impacts rather relying
solely on historical estimates.
   The benefit of constructing a public database to conduct such analyses rather than working
directly with private firms’ confidential data is that we centralize the fixed costs of cleaning the data
for research purposes. This facilitates transparency and reproducibility, and enables researchers
to readily access this data to conduct a much broader set of analyses. For example, the data have
been used by local policymakers to inform local policy responses and forecast tax revenue impacts
(e.g., Maine, Missouri, Kansas, and Texas). They have also been used by Congressional staff to
design federal policies, e.g. predicting the impacts and costs of policies targeted based on business
revenue losses (RESTART Act 2020). And they have been used by other researchers to analyze
a broad range of issues, from constructing price indices that account for changes in consumption
bundles (Cavallo 2020) to analyzing the effects of political views on economic outcomes (Makridis
and Hartley 2020).
   While we have focused here on the short-run impacts of COVID-19, private sector data can be
useful in monitoring impacts of economic shocks on long-term outcomes as well. As an illustration,
Figure 10 plots weekly student engagement on Zearn, an online math platform used by nearly
one million elementary school students as part of their regular school curriculum (see Appendix
I). Children in high-income areas learned less when the COVID crisis hit and schools shifted to
remote instruction, but soon recovered to baseline levels. By contrast, children in lower-income
areas completed 41% fewer lessons than they did pre-pandemic through the end of the school year.
These findings – first established in May 2020, and confirmed by subsequent work such as Goldhaber
et al. (2022) and Jack et al. (2023) – raise the concern that the pandemic may have long-lasting
impacts on low-income families not just through persistent reductions in employment documented
above but also through impacts on the next generation.
   Over the 20th century, the Bureau of Economic Analysis built on a prototype developed by
Kuznets (1941) to institute surveys of businesses and households that form the basis for today’s
National Income and Product Accounts. The database built here provides a prototype for a system
of more granular, real time national accounts built using transactional private sector data. The fact
that even this first prototype yields insights that cannot be obtained from existing data suggests
that aggregating data from private companies to construct public statistics has great potential for
improving our understanding of economic activity and policymaking.




                                                   37
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                                               44
Online Appendix

A     Automated Data Processing Pipeline

This appendix describes the automated pipeline we built to ingest raw data, process it to construct
aggregate statistics, and release those statistics publicly. This automated pipeline typically allows
us to post updated statistics within one business day of receiving the raw data. By automating
the data processing to the extent possible, we aim to post data as close to real-time as possible,
while maintaining the quality of the data and minimizing the manual upkeep required. The primary
source of lags in the posted data is therefore driven by lags in the underlying data generating
processes: for example, card transactions can take up to a week to settle and employment income is
typically paid in bi-weekly or monthly payrolls. We summarize our data engineering methods here
for those who may be interested in setting up similar infrastructure in other contexts.
    Step 1: Data Ingestion. To flexibly accommodate diverse data sources with varying secure file
transfer methods and update frequencies, we operate a cloud server that regularly pulls updated
data from each source. We receive data updates from private companies on a daily, weekly, or
monthly cadence. Many companies have unique policies and requirements for securing data transfers,
so we write scripts to intake this data using a variety of secure file transfer services (e.g. Amazon S3
buckets and SFTP servers). We also download or scrape a variety of publicly available statistics
from the web, such as unemployment insurance claims and COVID-19 case counts.
    Three main challenges arise when handling this large volume of frequently updated data: storing,
syncing, and version controlling the data we receive. We store all the raw data we receive as flat
files in a data lake (an Amazon S3 bucket). We use object storage rather than a database or a
more customized storage service (such as Git LFS) to minimize storage costs while maximizing our
flexibility to ingest incoming data which arrives in numerous formats that may change over time.
We version control each snapshot of the data we download within the same Git repository that
stores our code using a tool called DVC (“Data Version Control”). DVC creates a pointer to a hash
of the raw data for each data file or folder (in other words, a shortcut to the files in the data lake),
which we version control in Git and update every time new data is downloaded. This associates
each snapshot of data with the code that existed at the time it was processed, and allows us to
easily roll back our code and data simultaneously to any prior state. DVC also facilitates syncing
the raw data from the data lake by efficiently downloading the data that is associated with each



                                                  45
pointer in the Git repository.
   Step 2: Data Processing. For each dataset, we have an automated pipeline of programs that
process and transform the raw data into the public datasets that we post online. We use an
automated build tool to organize and execute this collection of programs. We mostly process the
data using Stata and execute our automated builds within Stata using the -project- command
developed by Robert Picard.
   This data processing step generates two outputs: (1) a set of CSV files that contain all the data
to be posted publicly and (2) a quality control report. The quality control report is a document
that allows analysts to quickly assess any notable deviations in the data and determine whether
the updated data require further review before being publicly released. Each report flags three
types of changes that would require manual review: revisions made to previously posted data, large
deviations in newly reported data, or newly missing data. The report also contains a series of tables
and figures that preview the data and highlight any changes in the newly processed data.
   Each time new data is ingested, the data processing step is run automatically. If it runs to
completion, a Git pull request is generated with DVC pointers to the newly updated raw data
alongside a link to the quality control report. If the data processing fails (for example, because the
structure of the raw data has changed), an error report is generated. At this point, we pause and
perform a manual review before posting the new data online. If the data processing failed or if
any changes were detected in the quality control report that require further review, we manually
investigate and write new code as needed, then re-process the data and inspect the updated quality
control report before proceeding.
   After reviewing and approving the quality control report, we merge the Git pull request containing
the new data, which automatically triggers the final Data Release step. This manual review and
approval is therefore the only manual step in the data processing pipeline.
   Step 3: Data Release. Once the processed data is ready for release, our scripts automatically
post the updated data to two public destinations. First, we sync the updated data into the database
powering our online data visualization website built by DarkHorse Analytics (tracker.opportun
ityinsights.org). While doing so, we also update the “last updated” and “next expected update”
dates on the website. Second, we upload the CSV files containing all the updated data to our
data downloads page. The updated visualizations and data downloads are then both immediately
available for public use.




                                                 46
B     Consumer Spending Series Construction

B.1    Structure of Initial Data

We receive data from Affinity Solutions in cells corresponding to the intersection of (i) county,
(ii) income quartile, (iii) industry, (iv) day and week. Cells where fewer than five unique cards
transacted are masked. Income quartile is assigned based on ZIP code of residence using 2014-2018
ACS estimates of median household income. We use population weights when defining quartile
thresholds so that each income quartile includes the same number of individuals nationally. County
and ZIP code income quartile are both determined by the cardholder’s residence.


B.2    Internal Data Processing

Adjusting for sharp changes in the client base

The raw Affinity data have discontinuous breaks caused by entry or exit of card providers from
the sample. We identify these sudden changes systematically by regressing the number of weekly
county-level transacting cards on the date, then implementing a Supremum Wald test for a structural
break at an unknown break point. This test is implemented separately for the pre-pandemic period
before March 11, 2020 and the post-pandemic period after that date. We apply a correction to
structural breaks where the p-value of the test is less than 5 × 10−10 .
    For counties with a break below this threshold in only one of the two periods, we correct our
estimates as follows. We first compute the state-level week-to-week percent change, excluding all
counties with a structural break (using the national series for DC and states for which all counties
have a structural break). If we identify a structural break in week 𝑡, we impute the county-level
percent change with the state-level percent change in weeks 𝑡 − 1, 𝑡, and 𝑡 + 1, as we cannot ascertain
the precise date when the structural break occurred (e.g., it may have occurred on the 2nd day of
week 𝑡 − 1 or the 6th day of week 𝑡).
    For example, suppose a county has 𝑛 active cards up until week 𝑡, when the number of cards in
the county increases to 3𝑛. In week 𝑡 − 2, the county would have a level of 𝑛 cards, its reported
value. In week 𝑡 − 1, if counties in the rest of the state had a 5% increase in the number of cards,
we would impute the county with a break to have a level of 1.05𝑛 cards. In week 𝑡, if counties in
the rest of the state had a 10% increase in the number of cards, we would impute 𝑡 to have a level
of (1.10) × (1.05𝑛) = 1.155𝑛. Likewise, if counties in the rest of the state had an 8% decrease in the
number of cards in week 𝑡 + 1, we would impute 𝑡 + 1 to have a level of (0.92) × (1.155𝑛) = 1.0626𝑛.

                                                  47
Finally, if the county with a break had an increase of 3% in week 𝑡 + 2, this would be multiplied by
the imputed level for week 𝑡 + 1 to produce a level of (1.03) × (1.0626𝑛) = 1.0945𝑛 in week 𝑡 + 2.
This final step would be repeated to update levels for all periods after week 𝑡 + 2.
   When there is a change in coverage, we adjust the series to be in line with the lower level of
coverage. In cases where the lower level of coverage was after the break, the above example would
implemented in reverse to impute levels prior to the break.
   We omit counties with multiple structural breaks (identified either by the Supremum Wald test
in both periods or manually) from our series. We also add manually identified structural breaks
which are not identified by the test, for example because the break phases in over more than one
week. We do not remove any counties where the structural break occurred between March 10 and
31, 2020 because the consumer spending response to the COVID-19 was so strong that in many
places it could be classified as a structural break. Additionally, since holiday spending spikes are
also sometimes classified as a structural break, we manually verify any breaks detected in the week
of Thanksgiving or the weeks immediately before, during or after Christmas.


Adjusting for gradual changes in the client base

In our processing, we aim to isolate and remove variation in spending driven by the number of
debit/credit cards in our sample changing due to steady growth or shrinkage in a payment provider’s
customer base. However, we also aim to incorporate extensive margin variation in spending driven
by the number of debit/credit cards in our sample changing due to changes in the utilization of
cards, which can reflect underlying economic conditions. To account for both, we first estimate
a two-state switching model of the relationship between Personal Consumption Expenditure as
measured by the U.S. Bureau of Economic Analysis and the level of credit and debit spending
and number of cards in usage we received from Affinity Solutions. Our estimates imply a state
change between February and August 2020: the number of cards in use is predictive of Personal
Consumption Expenditures during this period, but statistically insignificant outside this period.
This is consistent with a pattern where people exhibited extensive margin changes in card spending
during the initial waves of COVID-19, but outside of that period changes in the number of cards
being used reflect changes in the number of people in-sample as opposed to changes in the propensity
to consume of a given set of people.
   We therefore construct an estimate of spending that reflects total spending between February
and August 2020, and spending per card outside this period. For each location (𝑙), industry (𝑖) and


                                                 48
time (𝑡) we compute:



                         ⎧ 𝑠𝑝𝑒𝑛𝑑𝑖𝑛𝑔𝑙,𝑖,𝑡
                         { 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡 × 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ∈Jan 2020 if 𝑡 ≤ January 2020
                         {
                         {
                         {
                         {
                         { 𝑠𝑝𝑒𝑛𝑑𝑖𝑛𝑔𝑙,𝑖,𝑡                             𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ≥𝑡∣𝑡′ ∈Feb 2020
                         { 𝑐𝑎𝑟𝑑𝑠           × 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ∈Jan 2020 × 𝑐𝑎𝑟𝑑𝑠                       if 𝑡 ∈ February 2020
                         {        𝑙,⋅,𝑡                                       𝑙,⋅,𝑡′ ∈Feb 2020
                         {
                         {
                         {
                         {
adjusted_spending𝑙,𝑖,𝑡 = ⎨ 𝑠𝑝𝑒𝑛𝑑𝑖𝑛𝑔  𝑙,𝑖,𝑡
                             𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡
                                                                         𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡
                                           × 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ∈Jan 2020 × 𝑐𝑎𝑟𝑑𝑠                   if 𝑡 ∈ [March 2020, July 2020]
                                                                           𝑙,⋅,𝑡′ ∈Feb 2020
                         {
                         {
                         {
                         {
                         { 𝑠𝑝𝑒𝑛𝑑𝑖𝑛𝑔𝑙,𝑖,𝑡                             𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ≤𝑡∣𝑡′ ∈Aug 2020
                         { 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡 × 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ∈Jan 2020 × 𝑐𝑎𝑟𝑑𝑠 ′                           if 𝑡 ∈ August 2020
                         {                                                     𝑙,⋅,𝑡 ∈Feb 2020
                         {
                         {
                         {
                         { 𝑠𝑝𝑒𝑛𝑑𝑖𝑛𝑔𝑙,𝑖,𝑡                             𝑐𝑎𝑟𝑑𝑠 ′
                         {                 × 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡′ ∈Jan 2020 × 𝑐𝑎𝑟𝑑𝑠 𝑙,𝑡 ∈Aug 2020 if 𝑡 ≥ September 2020
                         ⎩ 𝑐𝑎𝑟𝑑𝑠𝑙,⋅,𝑡                                      𝑙,⋅,𝑡′ ∈Feb 2020



    We use this adjusted spending series throughout our analysis, except in Section IV.A, where we
always use total spending (thereby including both intensive and extensive margin changes) in order
to ensure consistency in comparisons of our estimates across different stimulus rounds.


Addressing spurious changes

There is an large spike in consumer spending between January 15 and 17, 2019 that is not found
in other data series. This spike in national consumer spending is likely driven by the early release
of February 2019 SNAP benefits due to an impending government shutdown.38 In order to avoid
contaminating our seasonal adjustment with this one-time shock, we replace each impacted day
with the average spending on 𝑡 − 7, 𝑡 + 7, and 𝑡 + 14, where 𝑡 is the impacted day.


Control for seasonal fluctuations in spending

We seasonally adjust the data by calculating, for each week and day, the year-on-year change relative
to the 2019 value. We norm February 29, 2020 (a Saturday) relative to the average of February 23
and March 2, 2019 (both Saturdays). Labor Day in 2019 fell one week earlier than in 2020, so we
adjust the week of Labor Day, as well as the two weeks before, based on the same week in 2019

  38
     Described in “Billions in food stamp payments to come early because of shutdown”, Politico article on January
11, 2019: https://www.politico.com/story/2019/01/11/shutdown-food-stamp-scramble-benifits-1081210


                                                       49
relative to Labor Day rather than the week number in the year. We then calculate the change
relative to the January index period: 2019 data is indexed relative to January 7 to February 3,
2019, data in 2020 onward is indexed relative to January 6 to February 2, 2020. We then seasonally
adjust by dividing by the indexed 2019 value, which represents the difference between the change
since January 2020 compared to the change since January 2019.


B.3    Masking and Publication

Cells with fewer than 5 card transactions are masked. Additionally, because the Supremum Wald
test cannot identify breaks in the most recent few weeks of data, we mask particularly large changes.
If a week-on-week change is > 4x the median change of that series, and < 1/3 of all series have
a large change defined in this way, the value of the change is replaced with the preceding week
multiplied by the national average change. We place this 1/3 restriction to avoid overmasking
holiday spending, when almost all series experience large changes.


Definition of Categories of Goods

In parts of our analysis, we distinguish between four categories of goods and services: durable goods;
non-durable goods; remote services; and in-person services.

  • We define durable goods as the following MCC groups: Building materials, garden equipment,
      and supplies; electronics and appliances; furniture and home furnishings; motor vehicles and
      parts; sporting goods, hobbies, musical instruments, and book stores; and telecommunications.

  • We define nondurable goods as the following MCC groups: General merchandise; wholesale
      trade; clothing and clothing accessories; health and personal care stores; food and beverage
      stores; misc store retailers; and gas stations.

  • We define remote services as the following MCC groups: Utilities, construction, and manufac-
      turing; professional, scientific, and technical services; public administration; administrative
      and support and waste management and remediation services; information; education; finance;
      and nonstore retailers.

  • We define in-person services as the following MCC groups: Rental and leasing; repair and
      maintenance; and personal and laundry services.



                                                  50
Substitution from Card to Cash Spending

A potential concern with our card-based estimates of spending changes is bias from substitution
out of cash purchases, which account for 6.3% of consumer spending in the United States (Greene
and Stavins 2020). For instance, if individuals sought to use more contactless methods to pay or
began placing more orders online, trends in card spending might exhibit excess volatility relative to
overall spending. To assess the importance of such substitution, we examine cash purchases using
receipts data from CoinOut, a company that allows individuals to receive rewards by uploading
photos of their receipts to a mobile app. We focus on grocery spending in the card data because
cash spending in CoinOut is concentrated in certain sectors such as groceries; unfortunately, we
are unable to disaggregate the CoinOut data by sector or align sectoral definitions more precisely
across the datasets.
   Appendix Figure 6B plots week on week changes in aggregate cash purchases in the CoinOut
data vs. aggregate card spending at grocery stores over time. The time trends are very similar
between the two series (with a correlation of 0.67 at the weekly level), showing a sharp spike in
spending in late March 2020 (as households stocked up on groceries), followed by a more sustained
increase in spending from the latter half of April 2020. These results suggest that households shifted
spending similarly across both modes of payment.


B.4    Benchmarking

Comparison to QSS and MARTS. 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. Appendix Figure 2
compares the spending distributions across sectors in the Affinity data to spending captured in
the nationally representative Quarterly Services Survey (QSS) and Advance Monthly Retail Trade
Survey (MARTS), which together cover 92% of the expenditure-weighted categories in the Affinity
data. The Affinity series has broad coverage across industries, but over-represents categories in
which credit and debit cards are used for purchases. In particular, accommodation and food services
and clothing constitute a greater share of the card spending data than financial services and motor
vehicles. We therefore view the Affinity series as providing statistics that are representative of total
card spending, but not total consumer spending. We assess whether the Affinity series accurately
captures changes in total card spending in the COVID recession in Section III.A.



                                                  51
C     Small Business Revenue and Openings Series Construction

C.1    Structure of Initial Data

We receive total small business debit and credit revenue data from Womply, where the total
revenue data is aggregated from settled credit card transactions that Womply receives from payment
processing partners. Additionally, we receive the number of businesses open data from Womply
where the number of businesses open is defined as the number of businesses making at least one
transaction within a three day window. The primary raw data we receive and process is a 52-week
“no-entry” panel of firms at the county by sector by week level, which is a repeated panel following
the set of firms operating in each week 𝑡 over the subsequent 52 weeks (with attrition from the
sample but no entry during the panel). To construct ZIP-level estimates, we additionally use a
cross-sectional sample of firms at the ZIP code by ZIP income quartile by sector by day level.
We measure small business revenue as the sum of all credits (generally purchases) minus debits
(generally returns). All transactions and derived data we receive are tied to the ZIP code or county
containing the business. The sample is limited to small businesses as defined by the Small Business
Administration.


C.2    Internal Data Processing

Adjusting for the evolving client base

In each calendar year, we follow the sample of businesses operating during the first week of the year
(i.e. we start following a new panel each calendar year). No new businesses enter our panel during
the calendar year. Businesses may exit because they stop operating or because the underlying
payment processors ceased providing data.
    We detect cases where a payment processor disappears by detecting sharp drops in businesses
operating, at the national and the state level. We then adjust the series to identify and correct
breaks in the data introduced by these merchant exits from the sample. To do so, we identify breaks
in series from July 2020 to present as downward discontinuities of at least 2.5 percentage points
from week 𝑡 − 1 that persist for at least two weeks—i.e. remain at least 2.5 percentage points below
week 𝑡 − 1 in weeks 𝑡, 𝑡 + 1 and 𝑡 + 2. For weeks prior to July 2020 we manually identify breaks in
weeks 21, 22, 27, 31 nationally and at week 25 for a subset of states (where week numbers refer to
weeks of 2020).
    For breaks identified prior to July 2020, business revenue was in a period of strong recovery so we

                                                  52
assume “momentum” during the weeks with missing data. We calculate the average rate of change
for the 4 preceding weeks to the discontinuity and use that value to impute the rate of change for
the identified week of the discontinuity. For breaks identified from July 2020 to present we adjust a
given merchant or sales series as follows. For a merchant series, for the week of a discontinuity we
impute the value of the number of merchants as the value from week 𝑡 − 1 and adjust the following
weeks accordingly resetting the number of merchant to the level prior to the identified exit from the
sample. For a sales series, if week 𝑡 − 1 has a discontinuity we compute the change in sales between
week 𝑡 − 1 and week 𝑡 − 2 to be zero and adjust the following weeks accordingly. If additionally
week 𝑡 has a discontinuity we correct week 𝑡 following the same procedure and adjust the following
weeks accordingly.


Produce the ZIP code series

As described in Appendix C.1, we do not receive any panel data disaggregated to the ZIP code level:
we only observe cross-sectional ZIP level data. We therefore perform an additive adjustment on
the cross-sectional ZIP level series so that the weighted sum of the processed ZIP series aligns with
the county-level “same store” panel data. The resulting data obtains the levels from the county
“no-entry” panel, and the within-county across-ZIP variation from the ZIP level cross-section.


Reducing the influence of outliers

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 more than one transaction in 2 out of the 3 months.
    We also manually exclude some state x industry breakdowns that present extreme variation from
our state and national level calculations, as well as a small number of counties that demonstrate
extreme variation.


Controlling for seasonality

We seasonally adjust reported revenue by calculating the year on year change relative to the
corresponding value in 2019. We calculate the change relative to the January index period: 2019
data is indexed relative to January 2019, data in 2020 onward is indexed relative to January 2020.
We then seasonally adjust by dividing by the indexed 2019 value.



                                                 53
C.3   Masking and Publication

To preserve the privacy of firms in the data and to avoid displaying noisy estimates for small cells,
we mask cells with less than $250,000 in total revenue during the base period of January 4 to 31,
2020. The ZIP-level data we receive from Womply adds merchants and an imputed revenue quantity
such that every cell with 1 or 2 merchants has no fewer than 3 merchants. This imputation has the
result of dampening the effect of any declines that would otherwise place the number of merchants
in a cell at 1 or 2, lowering the effect of any increase from 1 or 2 merchants to 3 merchants, and
enhancing the effect of any increase from 0 merchants to 1 or 2 merchants. We address the effects
of this imputation by dropping any ZIP code that has imputed values for more than 25% of the
weeks in our period of study.


C.4   Benchmarking

Comparison to QSS and MARTS. Appendix Figure 2 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.


D     Job Postings Series Construction

D.1    Structure of Initial Data

We receive a dataset containing the weekly number of unique new job postings, defined as those that
have not already been posted within a 60 day window. This data is disaggregated by geography
(county, state, national), by 2-digit NAICS code, and by ONET code. Jobs postings are sourced by
Lightcast (formerly known as Burning Glass Technologies) from over 40,000 jobs boards worldwide.


D.2    Internal Data Processing

Reducing the influence of outliers

In order to avoid extreme outliers, we calculate a cutoff of one standard deviation above the 97th
percentile of the state-level data for each variable and mask values that exceed this threshold.


                                                 54
D.3    Masking and Publication

We perform some imputations of the county by subgroup-level data to ensure the privacy of the
underlying job postings dataset, as required by our data use agreement. In the 200 most-populated
counties, total job posts by subgroup are reported directly without imputation. For total job posts
by subgroup in smaller counties, we impute the number of job postings: we report the total county
level postings by subgroup multiplied by the share of total state level postings of the corresponding
subgroup. All state-level data and national data are reported without imputations.


D.4    Benchmarking

Comparison to JOLTS. Lightcast 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) show that the Lightcast data are reasonably well-aligned with government
survey-based statistics on job openings and characterize the sample in detail. In Appendix Figure
3, we compare the distribution of industries in the Lightcast data to nationally representative
statistics from the Bureau of Labor Statistics’ Job Openings and Labor Market Turnover Survey
(JOLTS) in January 2020. In general, Lightcast is well aligned across industries with JOLTS, with
the one exception that it under-covers government jobs. We therefore view Lightcast as a sample
representative of private sector jobs in the U.S.


E     Employment Series Construction

E.1   Structure of Initial Data

The employment series is constructed with data from three data providers: Paychex, Intuit, and
Earnin.


Paychex

We obtain aggregated weekly data on total employment for each county by industry (two-digit
NAICS), hourly wage quartile (defined below), firm size bin and pay frequency. Salaried employees’
wages are translated to hourly wages by dividing weekly pay by 40 hours. To measure private sector
employment, we exclude workers employed in public administration and those with an unclassified
industry (which each represent 0.8% of workers as of January 2020). We restrict the sample to



                                                    55
workers with weekly, bi-weekly, semi-monthly or monthly pay frequencies; these workers represent
over 99.8% of employees in the Paychex data.
   To classify workers by wage quartile while adjusting for wage growth, we construct moving wage
quartile thresholds based on 100%, 150% and 250% of the federal poverty line (FPL). The FPL is
defined as an annual income, which we convert into a full-time-equivalent hourly wage by diving by
2000 hours (50 weeks of work at 40 hours per week). In our benchmark period of January 2020,
the thresholds are $13.10, $19.65 and $32.75. These thresholds group workers approximately into
quartiles: in January 2020, the four bins in ascending order by wage contain 23.4%, 27.4%, 25.7%,
and 23.5% of CPS respondents. Since the FPL is set annually at the beginning of each year, we
estimate monthly thresholds within each year by multiplying the FPL by the growth in the monthly
Consumer Price Index (CPI) since January. When the official FPL estimate is published at the
beginning of the subsequent year, its growth does not exactly match the growth in CPI. To maintain
consistency with the levels of the FPL, we revise the thresholds from the prior year when the FPL
is released. We compute the difference between our CPI-based thresholds and the official FPL
thresholds during the relevant year, divide this residual by 12, and add the rescaled residual to the
projected FPL in each month.
   We provide these moving wage quartile thresholds to Paychex, which are then used internally by
Paychex to assign workers to an hourly wage quartile in the aggregated weekly dataset we receive.
In January of each year, we provide revised quartile thresholds to Paychex and revise our estimates
for the prior year—the revised estimates hold aggregate employment constant but reallocate some
workers across quartiles.
   Using moving thresholds to assign workers to wage quartiles generates occasional discontinuities
in each quartile due to bunching in the wage distribution at integers, as discussed in Section II.B.4
and detailed in Appendix E.2. To correct these discontinuities, we receive weekly counts of the
number of employees earning an integer hourly wage for each integer value between $13 and $25.
These counts are received at the same level of disaggregation as the weekly data on employment:
county by industry (two-digit NAICS), firm size bin and pay frequency.


Intuit

We obtain anonymized, aggregated data on month-on-month and year-on-year changes in total
employment (the number of workers paid in the prior month) and average earnings at the state
and county level by month, based on repeated cross-sections. To develop a national series, we take


                                                 56
population-weighted averages of state changes in each month.


Earnin

We obtain anonymized data from Earnin at the paycheck level with information on industry (2-digit
NAICS), firm size, ZIP code, unemployment status, wages, and earnings. The median worker in the
Earnin sample in January 2020 has an hourly wage rate of $14.98, which falls at the 30th percentile
of the national distribution of private sector non-farm workers in January 2020 CPS data. The
interquartile range of wages in the Earnin sample is $9.36-$22.10 (corresponding to the 6th and
59th percentiles of the national distribution).
   Industry and firm size are not directly measured in Earnin’s administrative databases, so these
variables were attached to the anonymized paycheck-level data by linking a list of employers with
more than ten Earnin users to external databases. To obtain information on industry, we use
a custom-built crosswalk created by Digital Divide Data which contains NAICS codes for each
employer in the Earnin data with more than ten Earnin users. To obtain information on firm
size, we crosswalk Earnin employers to ReferenceUSA data at the firm location level by spatially
matching Earnin employers to ReferenceUSA firms. We begin by geocoding Earnin addresses to
obtain latitudes and longitudes for each Earnin employer. We then remove common prefixes and
suffixes of firm names, such as “inc” and “associated”. Next, we compute the trigram similarities
between firm names for all Earnin and ReferenceUSA firms within twenty-five miles of another. We
then select one “match” for each Earnin firm within the ReferenceUSA data, among the subset of
firms within one mile. We first match Earnin employers to ReferenceUSA firms if the firms are
within one mile of one another, and share the same firm name. Second, where no such match is
available, we choose the geographically closest firm (up to a distance of one mile) among all firms
with string similarities of over 0.6. Third, where no such match is available, we match an Earnin
employer to the ReferenceUSA employer within twenty-five miles with the highest trigram string
similarity, provided that the match has a trigram string similarity of 0.9. We then compute the
modal parent-firm match in the ReferenceUSA data for each parent-firm grouping in Earnin. Where
at least 80% of locations within a parent-firm grouping in Earnin are matched to a single parent-firm
grouping in the ReferenceUSA data, we impute that parent-firm to every Earnin location. In total,
we match around 70% of Earnin employers to ReferenceUSA firms.




                                                  57
E.2   Internal Data Processing

Data is processed in the following ways for data received from the three data providers: Paychex,
Intuit, and Earnin.


Paychex

Addressing anomalous spikes. We manually identify large anomalous spikes in the data and smooth
these by interpolating values from adjacent weeks at the county x 2-digit NAICS code x hourly
wage quartile x 2019 firm size bin x pay frequency level.
   Adjusting for discontinuities when quartile thresholds cross integer wages. We adjust for occasional
discontinuities in employment in each wage quartile due to bunching in the wage distribution. Our
adjustment is mathematically equivalent to adding a uniform random variable distributed between
[−0.5, 0.5] to whole number wages, transforming the point mass of employees at the integer wage
into a uniform distribution between [𝑤𝑎𝑔𝑒 − 0.5, 𝑤𝑎𝑔𝑒 + 0.5]. Rather than adding the random
variable ex ante using employee microdata before aggregating, which we did not access, we applied
an equivalent procedure that is feasible ex post after receiving the aggregated data described above
containing employee counts. As an example, suppose the threshold separating the first wage quartile
(Q1) and the second wage quartile (Q2) is $13.75 in a given month. Then adding the uniform
random variable to the wages of employees earning $14 will move 25% of the point mass below the
threshold. Since we observe the counts on each side of the $13.75 threshold and the count at the $14
point mass, we add 0.25 × (number of employees at $14) to the Q1 count and subtract the same
number from the Q2 count. We perform the analogous process for each threshold in each month,
calculating the share of the point mass at the integer value within $0.50 of the threshold that should
be moved across the threshold, and adding and subtracting that value from the quartiles below and
above the threshold accordingly.
   Mapping paychecks to employment periods. To construct a series of employment as of each
date, we construct a series of pay periods ending as of each date. We take a separate approach for
paychecks following regular weekly cycles (i.e. weekly and bi-weekly paychecks) and for paychecks
following a cycle based on fixed calendar dates (i.e. semi-monthly and monthly paychecks). For
weekly and bi-weekly pay frequencies, we use data provided by Paychex on the distribution of the
number of days between a worker’s pay date and the last date in the worker’s pay period (i.e., date
at which payroll is processed – last date in pay period), for weekly and bi-weekly pay frequencies,



                                                 58
to distribute paychecks to the last date of the corresponding pay period. We treat the distribution
of (date at which payroll is processed – last date in pay period) as constant across geographies and
NAICS codes. For monthly and semi-monthly pay frequencies, where cycles regularly occur on fixed
calendar dates (e.g. the 15th and 30th of each month for semi-monthly paycycles), we assume that
the last date within each pay period is the closest preceding calendar date that is the 15th or the
30th day of the month (semi-monthly paycycles) or the 30th day of the month (monthly paycycles).
We then record a worker as being employed for the full duration of the pay cycle up until the last
date in their pay period, under the assumption that workers are employed for each day during their
pay period.
   Adjusting for an evolving client base. We take steps to adjust for the entry and exit of Paychex
clients from the sample. In each county x industry (two-digit NAICS code) x firm size x wage
quartile cell, we compute the change in employment relative to January 4 to 31, 2020, and the
change in employment relative to July 2020. Let Min Normed July𝑐,𝑖,𝑠,𝑞 be the smallest value
of indexed employment we observe at each date relative to its mean level over July 2020 and
Max Normed January𝑐,𝑖,𝑠,𝑞 be the largest value of indexed employment we observe at each date
relative to its mean level over January 4 to 31, 2020.
   For county x industry x firm size x wage quartile cells with at most 50 employees at all points
between January 2020 and the end of the series, we reduce the weight we place on the series if we
observe large changes in employment that indicate firm entry or exit. In particular, we compute the
weight on the cell for county 𝑐, industry 𝑖, firm size 𝑠, and wage quartile 𝑞 as:




Weight𝑐,𝑖,𝑠,𝑞 =max{1 − 1{Min Normed July𝑐,𝑖,𝑠,𝑞 ≤ 50} × (50 − Min Normed July𝑐,𝑖,𝑠,𝑞 ) × 0.02

               − 1{Max Normed January𝑐,𝑖,𝑠,𝑞 ≥ 4000} × (Max Normed January𝑐,𝑖,𝑠,𝑞 − 4000) × 0.001,

               0}


   That is, we reduce the weight we place on the cell by two percentage points for each percentage
point of decline we observe below 50 percentage points relative to July 2020. We further reduce
the weight by 0.1 percentage points for each percentage point of growth we observe above 4000
percentage points relative to January 2020.
   For county x industry x firm size x wage quartile cells with over 50 employees at any point
between January 2020 and the end of the series, we reduce the weight we place on the series using


                                                  59
more stringent restrictions, reflecting the fact that extreme growth rates are more anomalous in
larger cells. In particular, we compute the weight on the cell for county 𝑐, industry 𝑖, firm size 𝑠,
and wage quartile 𝑞 as:




Weight𝑐,𝑖,𝑠,𝑞 =max{1 − 1{Min Normed July𝑐,𝑖,𝑠,𝑞 ≤ 50} × (50 − Min Normed July𝑐,𝑖,𝑠,𝑞 ) × 0.02

               − 1{Max Normed January𝑐,𝑖,𝑠,𝑞 ≥ 600} × (Max Normed January𝑐,𝑖,𝑠,𝑞 − 600) × 0.005,

               0}


   That is, we reduce the weight we place on the cell by two percentage points for each percentage
point of decline we observe below 50 percentage points relative to July 2020. We further reduce
the weight by 0.5 percentage points for each percentage point of growth we observe above 600
percentage points relative to January 2020.
   In addition, we address the especially concentrated entry and exit of firms from the sample
at the end of each calendar year, where there is significant churn as firms renew their payroll
processing contracts. Due to this seasonal pattern, the raw Paychex data display a downwards
trend in employment at the end of each calendar year as some clients leave Paychex, followed by
an upward trend in employment at the very beginning of each calendar year as new clients join.
To avoid this source of error, we adjust for the end-of-year pattern in the Paychex data using data
from the end of 2019. For each date between November 15, 2020 and January 10, 2021, using
Paychex data on employment at the national level, we compute the change in employment relative
to November 15, 2020 at the two-digit NAICS code x wage quartile level. We also compute the
change in employment between the corresponding day in the previous year and November 15, 2019.
We divide the change in employment relative to November 15, 2020 by the corresponding change
in employment the previous year relative to November 15, 2019. To avoid a break in the series at
January 10, 2021, we adjust the series from January 10 onwards using the adjusted level on January
10 and the unadjusted trend from January 10. We repeat this end-of-year adjustment during each
subsequent year. We then apply the same adjustment to each two-digit NAICS code x wage quartile
cell at the state, county and city levels.
   Adjusting for minimum wage changes. Finally, we address mismeasurement of the change in
employment in the first and second wage quartiles due to minimum wage increases in California,
Massachusetts, and New York during our period of study. By increasing the minimum wage above


                                                 60
the threshold between the first and second wage quartiles at the end of 2020 or start of 2021,
these states mechanically shift workers out of the first wage quartile into the second wage quartile,
leading to spurious changes in the quartile-specific series.39 To address this issue, we make the
following adjustments. We begin by calculating the percentage change in the number of employees
in each state x industry (2-digit NAICS) cell from December 4, 2020 onwards for the first two wage
quartiles combined. From December 4, 2020 onwards, for the first two wage quartiles in minimum
wage change states, we impute the trend in each county x industry x wage quartile cell using the
below-median income employment trend in their own state x industry cell. Since employment in
the first wage quartile recovered less than employment in the second wage quartile in 2021, an
imputation that aggregates the trends in the first and second wage quartiles tends to overstate the
recovery in the first quartile and understate the recovery in the second quartile. As such, we further
rescale the state x industry below-median income trend separately for each wage quartile, using the
coefficient from the regression of that quartile’s employment change on the below-median income
employment change in non-minimum wage-change states without a constant between December 4,
2020 and December 3, 2021. We then aggregate the adjusted employment counts to the relevant
geographies (e.g. state; national) before calculating the change in employment since January 2020.


Paychex and Intuit: Combined Employment Series

We combine Paychex and Intuit data to construct our primary employment series. Our data sharing
agreements requires us to produce combined estimates in our public releases of employment data.
    Imputing local composition of Intuit data. Because Paychex is disaggregated by sector and
covers all sectors and wage levels fairly comprehensively, we use it as the base for the combined
employment series. We then use Intuit to refine the series in cells represented by those datasets.
Intuit provides us with overall national industry shares as of 2019, but does not disaggregate the
monthly employment data we receive by wage level or industry. We therefore impute the Intuit data
to wage-industry cells before combining it with the Paychex data. To do so, we assume that any
differences in employment between Intuit and Paychex are constant (in percentage terms, relative to
the January baseline) by industry and wage quartiles within a given geography and month. For each
date, we impute the number of Intuit employees in a given geography x industry x wage quartile
cell as the number of Paychex employees in that cell reweighted by the national Intuit industry
  39
     Arizona also increased its minimum wage during this period, but its minimum wage ($12.15 in 2021; $12.80 in
2022) remained well below the threshold between the first and second wage quartiles ($13.25 in January 2021; $13.88
in January 2022), so we do not adjust the employment series for this state.


                                                        61
distribution. We then weight the geography-level changes in employment in the Intuit data by the
imputed number of employees in its geography x industry x wage quartile cell.
   Combining Paychex and Intuit data. We take a weighted average of the Paychex data and the
imputed Intuit data to compute the final combined series. We place the majority of the weight on
Paychex, with greater weight on Intuit in sectors where it has greater coverage; the exact weights
are undisclosed to protect privacy. We report seven-day moving averages of these series, expressed
as a percentage change relative to January 4 to 31, 2020.


Earnin

Sample Restrictions. We restrict the sample to workers who are active Earnin users, with non-missing
earnings and hours worked over the last 28 days. Next, we exclude workers whose reported income
over the prior 28 days is greater than $50,000/13 (corresponding to an income of greater than
$50,000 annually). We then restrict the sample to workers who are in paid employment. Users may
continue to use Earnin after they have been laid off; we exclude payments which Earnin classifies as
unemployment payments, either based on the user’s registration with Earnin as being unemployed,
or based on the string description of the transaction.
   Finally, we omit the first and last 32 weeks of data for each user from our analysis to mitigate
non-random entry and exit from the customer base. We observe that a person’s probability of
finding or losing a job is elevated near the time they entered or exited the sample—changes in
employment likely induce people to sign up for the service or reconsider their need for the service.
However, after omitting the first and last 32 weeks, the probability of a user finding or losing a job
is no longer correlated with the number of weeks that elapse after entry into the sample or before
exiting the sample.
   Mapping paychecks to chained employment. We construct an employment series in the Earnin
data from our analysis sample as follows. In the paycheck-level data, we observe the worker’s
paycycle frequency. As in the Paychex data, we use paycycle frequency to construct an employment
series by assuming that workers are employed throughout the full duration of their paycycle. That
is, we assume that a worker paid every two weeks has been fully employed for the two weeks prior
to receiving their paycheck. We exclude workers with pay periods greater than 3 weeks, omitting
approximately 1% of the sample. To account for the delay in receipt of paychecks, we shift the Earnin
series back by one week. We then take the count of employed individuals across the Earnin sample
as our measure of employment. We use that to calculate an indexed measure of week-over-week


                                                 62
employment percentage change, for each geographic unit, indexed to their first week in the sample.
Then, the average percentage change in the four weeks from January 4 to 31, 2020 is set as the
reference value and indexed to zero. The employment series is then expressed as a change relative to
January 4 to 31, 2020. We suppress estimates for ZIP codes with fewer than 10 paychecks observed
over this period.


E.3   Masking and Publication

In the Paychex and Intuit combined series, we suppress cells in a few cases where Intuit data do
not provide coverage for a given geographical region or industry. We also suppress cells in which
Paychex records fewer than 150 monthly employees in January 4 to 31, 2020 at the geography x
wage quartile or geography x industry (2-digit NAICS) x wage quartile level, depending on the series.
When aggregating employment series to the geographical level without breakdowns by industry or
wage quartile, however, we use data from all cells, without masking.


E.4   Accounting for Low-Wage Employment Changes due to Wage Growth

To calculate the share of low-wage employment changes due to wage growth, we decompose the
level of the bottom-quartile-wage series from Figure 5A at the end of December 2021 into the part
explained by real wage growth and not. We perform this decomposition by estimating ventile-specific
wage growth rates in the CPS, calculated as the month-to-month change in ventiles, reweighting
each monthly CPS sample to hold constant the distribution of industry, occupation, race, gender,
age, education, region, and citizenship. This follows the procedure in Gould and Kandra (2022).
Under the assumption that all remaining wage changes within these buckets reflect wage growth
rather than changes in the nature of the jobs themselves, this procedure will estimate wage growth at
each ventile of the wage distribution. There were extreme movements into and out of the labor force
between January and July 2020, for which the reweighting may not adjust correctly. We thus employ
our estimation procedure to estimate wage growth only between July 2020 and December 2021. We
impute wage growth rates between January and July 2020 using a 2.9% annualized wage growth
rate (which prevailed in 2019). Grigsby et al. (2021) suggest that this is likely an upper bound
on the wage growth actually experienced during the early months of the pandemic, so that our
overall wage growth adjustment will also be an upper bound on the changes in bottom-wage-quartile
employment explained by wage growth.
   With ventile-specific wage growth rates in hand, we then adjust the distribution of wages in the

                                                 63
January 2020 CPS and recalculate total employment below the bottom-quartile-wage threshold for
December 2021 ($13.79). We additionally add uniform noise between [-$0.50, $0.50] to whole number
wages to smooth out spikes in the wage distribution at whole numbers, to mirror our treatment of
these integer-wage spikes in the Paychex data (see Section II.B.4 for more details on this issue).
Bottom-quartile-wage employment falls in this counterfactual by 12pp, reflecting the decline not
explained by real wage growth; the remaining 7.7pp deficit in December 2021 is the share explained
by real wage growth.


E.5   Benchmarking

Comparisons to QCEW and OES. Appendix Table 3 compares industry shares in each of the data
sources above to nationally representative statistics from the Quarterly Census of Employment
and Wages (QCEW). The Paychex-Intuit combined sample and the Earnin sample are broadly
representative of the U.S. industry mix. Appendix Table 4 shows that wage rates in our data
sources are similar to nationally representative statistics from the BLS’s Occupational Employment
Statistics. Overall, our combined datasets appear to provide a representative portrait of private
non-farm employment in the United States.


F     Unemployment Claims Series Construction

F.1   Structure of Initial Data

We collect state-level unemployment initial claims data from the Department of Labor, Employment
and Training Administration public data. We additionally collect data on state-level PUA, PUEC
and continued claims from the Bureau of Labor Statistics public data. Finally, we gather county-level
unemployment claims data from 22 state agencies who publish their state’s data in various online
formats, which we scrape and transform into a standard format.


F.2   Internal Data Processing

Harmonizing weekly and monthly estimates

In some cases, states only publish county-level data at the monthly frequency. To align with the
weekly series of most UI data we collect, we impute weekly values for counties with monthly reports
using the county-level monthly totals and the state-level distribution of weekly claims in that month,



                                                 64
as published by the Department of Labor. This imputation implicitly assumes that each county
within the state had the same relative distribution of UI claims within the month.


F.3   Masking and Publication

We apply no further masking beyond any masking applied by state and national agencies.


G     COVID-19 Infections and Vaccinations Series Construction

G.1    Structure of Initial Data

We collect publicly available data on cases and deaths reported by the New York Times and
the Centers for Disease Control and Prevention, publicly available data on hospitalizations from
COVID-19 from the U.S. Department of Health and Human Services, publicly available data on the
number of vaccine doses administered from the Centers for Disease Control and Prevention, and
publicly available data on the number of COVID-19 tests from Johns Hopkins University.


G.2    Internal Data Processing

Reducing the influence of outliers

We manually review any spikes in cases, tests, or deaths that are larger than 25%. If news reports
suggest that the spike is a reporting artifact, we smooth the data by imputing a value for the day of
the spike using the growth rate in the outcome on the prior day.


G.3    Masking and Publication

We apply no further masking beyond any masking applied by state and national agencies.


H     Time Outside Home Series Construction

H.1    Structure of Initial Data

We collect publicly available data on changes in time spent at various locations 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. Additionally we collect publicly available
time use data from the American Time Use Survey.


                                                 65
H.2    Internal Data Processing

Generating measures of time at home and away from home

We use the American Time Use Survey to measure the mean time spent inside the home (excluding
time asleep) and outside the home in January 2018 for each day of the week. We combine the data
on time spent in and outside the home with Google’s data on changes in time spent at and away
from home. To do so we multiply the time spent inside the home in January 2018 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.


H.3    Masking and Publication

Google does not release data for geographies where their internal quality and privacy thresholds are
not met.


I     Educational Progress Series Construction

Zearn is a non-profit math curriculum publisher that combines in-person instruction with digital
lessons. Zearn was used by approximately 970,000 students in the U.S. in Spring 2020. Many schools
continued to use Zearn as part of their math curriculum after COVID-19 induced schools to shift to
remote learning. We use data from Zearn to measure educational progress during the pandemic.


I.1   Structure of Initial Data

We receive data from Zearn on the number of students using Zearn Math and student progress in
Zearn Math as measured by the number of lessons on the platform completed by students in a given
week for a given school.


I.2   Internal Data Processing

Reducing the effects of transitory outliers on the series

To reduce the effects of transitory outliers, 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.


                                                 66
Accounting for missing values

If a school is actively using Zearn at any point in a given semester, then for any week where we
observe no reported values for both the number of students using Zearn Math and for student
progress in Zearn Math, we impute these missing values as zeros.


I.3     Masking and Publication

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 preperiod of January
6 to February 7, 2020 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 which did not have at least 5 students using Zearn Math for
at least one week during January 6 to February 7, 2020. After taking these steps, 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 to February 7, 2020, and we
normalize relative to this base period to construct the indices we report.


I.4     Representativeness of Zearn Data

We assess the representativeness of the Zearn data in Appendix Table 8 by comparing the demographic
characteristics of the schools for which we obtain 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, as measured
in the American Community Survey. 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
provides a representative picture of online learning for students in the U.S.


J      Dates and Geographic Definitions

In this appendix, we provide additional details about how we define key dates and geographic units
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:



                                                 67
• First U.S. COVID-19 Case: 2020-01-20
• National Emergency Declared: 2020-03-13
• CARES Act Signed in to Law: 2020-03-27

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 information
  and media updates.

• Nonessential businesses closed: Sourced from the Institute for Health Metrics and Evaluation
  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 effect: Sourced and verified from the New York Times reopening
  data, available here, and hand-collection from local news and government sources where
  needed.

• Stay-at-home order ends: Sourced and verified from the New York Times reopening data,
  available here, and hand-collected from local news and government sources where needed.
  Defined as the date at which the state government lifted or eased executive action or other
  policies instructing residents to stay home. We code “regional” and “statewide” expiry of
  stay-at-home orders separately. A “regional” expiration of a stay-at-home orders occurs when
  a stay-at-home order expires in one region within a state, but not everywhere within the
  state. A “statewide” expiration of a stay-at-home order occurs when a stay-at-home order
  first expired throughout a whole state, either due to a statewide change in policy, or due to
  the stay-at-home order in each county having expired.

• Partial business reopening: Sourced and verified from the New York Times reopening data,
  available here, and hand-collection from local news and government sources where needed.


                                               68
      Defined as the date at which the state government allowed the first set of major industries to
      reopen (non-essential retail or manufacturing in nearly every case). Deviations from the New
      York Times reopening data are deliberate and usually involve our regional classification or
      our inclusion of manufacturing. A “regional” reopening occurs when businesses are allowed
      to reopen in one region within a state, but not everywhere within the state. A “statewide”
      reopening occurs when businesses are allowed to reopen throughout a whole state, either due
      to a statewide change in policy, or due to restrictions being eased in each individual county.

    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 9.
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. We use the Uniform Data Systems (UDS) Mapper to crosswalk
from ZIP codes to ZCTAs.


K     Analysis of Economic Impact Payments

This appendix presents technical details for our analysis of the effects of the three rounds of economic
impact (i.e., “stimulus”) payments on consumer spending.


K.1    Construction of the Data

In order to prepare the spending data for analysis of the stimulus, we first aggregate the raw data
to calculate total card spending on each date, in each ZIP code income quartile. See Section II.B.1
and Appendix B for more details on the source of the consumer spending series. Note that unlike in
our baseline spending series, we use total spending (thereby including both intensive margin changes
in spending per card and extensive margin changes in the number of cards) to ensure consistency in
comparisons of our estimates across different stimulus rounds.
    We then index spending in each ZIP code income quartile relative to spending between January
4 to 31, 2019. We include data from January 2020 in the “control” series, normed to January 2019,
for the purpose of estimating the effects of the 2nd stimulus in January 2021.

                                                  69
   We residualize indexed spending in each ZIP code income quartile with respect to day-of-week
fixed effects calculated using 2019 data. For the April 2020 and March 2021 stimulus payments,
we further residualize spending with respect to a linear pre-trend estimated in the pre-period data
pooled across income quartiles; this procedure adjusts for economic trends related to the pandemic
that differ from the prior year (and thus remain in the indexed data). We do not adjust for a
linear pre-trend for the January 2021 stimulus payment due to the omission of the holiday period
immediately before the stimulus payments.
   Figure 7 plots these data, differenced treatment minus control for each day relative to the
stimulus events. Appendix Figure 21 depicts analogous plots without adjusting for pre-trends in
any of the three stimulus payment periods. As discussed in Section IV.A, our analysis yields similar
conclusions regardless of the linear adjustment for pre-trends.


K.2    Calculation of Effects of Stimulus Payments on Consumer Spending

We estimate the effects of each stimulus payment on indexed consumer spending using a difference-
in-differences approach (Appendix Table 6). To capture the non-linear dynamics evident in the
non-parametric figure, we estimate separate treatment effects for the first five days (Column 1) and
from the 6th day onwards (Column 2). This window runs through the 25th day for the stimulus
payments in April 2020 and March 2021, and through the 16th day for the stimulus payment in
January 2021 (reflecting the data available at the time of our “real-time estimate”). Finally, we
sum these effects to estimate the causal effect of the stimulus on spending the month after checks
were sent out, under the identification assumption that daily trends in spending in 2020 would
have matched those in 2019 (up to a linear difference in pre-trends for the first and third stimulus).
We convert these estimates into a projected “first month” effect, measured in percentage points
of baseline spending levels, by assuming the daily estimate from the second treatment coefficient
within each stimulus continues through the 31st day (Column 3).


K.3    Calculating Spending Changes per Check Recipient

We map percentage point increases in spending into dollars per $1,200 check in three steps.
   First, we translate the percentage point impact estimated from our difference-in-differences
model (𝛽) for each ZIP-income quartile into total additional dollars of spending. The rescaled total
dollar effect 𝛽 ̃ is 𝛽 × that ZIP-income quartile’s share of total spending in the Affinity data in
January 2019 × total card spending in NIPA Table 2.3.5 in January 2019 (following the method

                                                 70
described in Appendix Figure 5).
   Second, we calculate the total amount of stimulus dollars for which residents of each ZIP-income
quartile were eligible (𝑆), which is the number of households in each set of ZIP codes times the
eligibility rate. We calculate the number of households by household-type x income bin x ZIP
code income quartile from the ACS 2014-2018, where household-type is the combination of single
vs. married and number of children. We use total reported income in the ACS as a proxy for
adjusted gross income (AGI), which determines eligibility for stimulus payments. We assume that
mean household size is constant across income bins within each ZIP code for each household type
(i.e. we assume the mean household size of married and unmarried households does not vary by
income). This assumption permits us to combine these datasets to calculate the number of people
in each income bin x household-type x ZIP code income quartile.
   We then calculate the eligibility rate at the ZIP code level in three steps: (A) computing the
share of people in single households who are eligible for stimulus payments; (B) computing the
share of people in joint-filing households who are eligible for stimulus payments; and (C) combining
these estimates using data on marriage rates to create overall ZIP code-level eligibility rates. We
assign fractional eligiblity rates to reflect partial payments; for instance, a single household with
no children eligible for a $600 check during the first stimulus (which paid $1,200 checks) would be
assigned an eligibility rate of 50%.
   (A) Eligibility among single households. Single households with incomes below $75,000 were
eligible to receive the full stimulus amount. The precise structure of payments depends on the
number of children within the household; for simplicity, we use the structure of payments for families
without children for all families. In practice, since high-income households with children faced
more lenient eligibility requirements than we assume, our estimates represent an upper bound for
consumption per stimulus recipient.
   For the April 2020 stimulus, single households earning $75,000-$99,000 were eligible for partial
stimulus payments. For the January 2021 and March 2021 stimulus payments, the partial eligibility
income ranges were $75,000-$87,000 and $75,000-$80,000 respectively. However, the most granular
income bins available in the ACS 2014-2018 data at a ZIP code level do not disaggregate between
households earning $75,000-$99,999. As such, to assign eligibility rates for single households in this
income bin, we assume that incomes are uniformly distributed within each ZIP code x income bin.
We then assign these households an eligibility rate equal to 0.5 × 99,000−75,000
                                                                   99,999−75,000 for the April 2020

stimulus; 0.5 × 87,000−75,000
                99,999−75,000 for the January 2021 stimulus; and 0.5 × 99,999−75,000 for the March
                                                                       80,000−75,000




                                                 71
2021 stimulus.
   We assign an eligibility rate of zero to single households earning above the partial eligibility
income range.
   (B) Eligibility among married households. We treat married households as joint-filers. Married
households with incomes below $150,000 were eligible to receive the full stimulus amount. As above,
we do not account for the effects of number of children within the household on phase-out.
   For the April 2020 stimulus, joint-filer households earning $150,000-$198,000 were eligible for
partial stimulus payments. For the January 2021 and March 2021 stimulus payments, the partial
eligibility income ranges were $150,000-$174,000 and $150,000-$160,000 respectively. The most
granular income bins available in the ACS 2014-2018 data at a ZIP code level do not disaggregate
between households earning $150,000-$199,999. As before, to assign eligibility rates for married
households in this income bin, we assume that incomes are uniformly distributed within each ZIP
code x income bin. We then assign these households an eligibility rate equal to 0.5× 198,000−150,000
                                                                                     199,999−150,000 for

the April 2020 stimulus; 0.5 × 174,000−150,000
                               199,999−150,000 for the January 2021 stimulus; and 0.5 × 199,999−150,000
                                                                                        160,000−150,000


for the March 2021 stimulus.
   We assign an eligibility rate of zero to married households earning above the partial eligibility
income range.
   (C) ZIP Code-level eligibility rates. Steps (A) and (B) allow us to assign eligibility rates to
each income bin x ZIP code x household type (i.e. single vs. married). We then turn to calculating
the composition of household types within each income bin. To do so, we first regress marriage
rates on median household income at the ZIP code level, weighting by population; we assume
the ZIP code-level relationship between marriage rates and household income approximates the
individual-level relationship between marriage rates and household income. We then assign a
marriage rate equal to the fitted value of marriage rates at the midpoint of each household income
bin. This allows us to estimate eligibility rates within each ZIP code x income bin. Finally, we
calculate a population-weighted mean eligibility rate within each ZIP code income quartile. After
that, we multiply this rate by $1,200 and by the population within each ZIP code income quartile
to calculate total expected stimulus spending in each ZIP code income quartile.
   Third, we adjust for the actual fraction of stimulus checks paid during the treatment period
(𝑓). To do so, we calculate the share of the stimulus payments distributed by the first week of the
treatment period using Daily Treasury Statements. For instance, the total amount disbursed for
stimulus payments under the CARES Act of April 2020 is $271.4 billion (Internal Revenue Service


                                                  72
2022). We use Daily Treasury Statements to calculate that total spending on stimulus payments
prior to April 21, 2020 was roughly $174 billion. We therefore find that roughly 64% of payments
had been distributed by April 21. A similar calculation for payments made under the COVID-related
Tax Relief Act of 2020 yields estimates that roughly 91% of payments were distributed by January
10, 2021, and that 59% of payments made under the American Rescue Plan had occurred by March
23, 2021. We assume that these rates are constant across ZIP code income quartiles; it is possible
that this fraction is higher for higher income households, due to the larger number of low-income
households without bank information on file at the IRS, in which case we would overestimate the
total stimulus payments in the lowest ZIP code income quartile, leading to an underestimate of the
amount spent per recipient for these households.
                                                                                                  ̃
    Combining these three steps, our estimate of the dollars spent per stimulus payment is 𝑆⋅𝑓
                                                                                            𝛽
                                                                                               ,
reported in Appendix Table 6, Column 4. Our final estimate of the dollars spent per $1,200 received
           ̃
is $1,200
    $𝑋 ⋅ 𝑆⋅𝑓 , where $X is the full stimulus payment amount for each stimulus ($1,200 for the April
          𝛽


2020 stimulus; $600 for the January 2021 stimulus; and $1,400 for the March 2021 stimulus). These
estimates are reported in Figure 8 and Appendix Table 6, Column 5.


L     Supplemental Policy Analyses

L.1     State-Ordered Reopenings

Many states enacted stay-at-home orders and shutdowns of businesses in an effort to limit the
spread of COVID infection and later reopened their economies by removing these restrictions. We
examine how these executive orders affected economic activity by 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 (see Appendix J for details). 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 reopening efforts on aggregate
economic activity; we defer a more detailed analysis of how different types of reopenings affected
different sectors (which can be undertaken with the data we have made publicly available) to future
work.
    We begin with a case study comparing Colorado and New Mexico that is representative of our


                                                73
broader findings. These two states both issued stay-at-home orders during the final week of March
2020 (New Mexico on March 24, Colorado on March 26). Colorado then partially reopened its
economy, permitting retail and personal service businesses to open to the public, on May 1, 2020,
while New Mexico did not reopen until two weeks later, on May 16. Appendix Figure 26A plots
consumer spending (using the Affinity Solutions data) in Colorado and New Mexico. Spending
evolved nearly identically in these two states: in particular, there is no evidence that the earlier
reopening in Colorado boosted spending during the two intervening weeks before New Mexico
reopened.
   Appendix Figure 26B generalizes the case study in Appendix Figure 26A by studying partial
reopenings in the five states that issued such orders on or before April 27, 2020. For each reopening
date (April 20, 24 and 27), we compare the trajectory of spending in treated states to a group of
control states that had not reopened as of three weeks after the treated state reopened. We select
multiple control states (listed in Appendix Table 10) for each of the reopening dates by matching
on pre-period indexed spending (relative to January) during the three weeks prior to reopening.
Specifically, for each reopening date 𝑡, we estimate each state’s rank (ranging from 0 to 1) in the
distribution of mean indexed spending pooling weeks 𝑡 − 1, 𝑡 − 2, and 𝑡 − 3. We then select control
states with rank within 0.2 of the treated states’ mean rank, separately for each reopening 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 three event studies together
(redefining calendar time as time relative to the reopening date) to create Appendix Figure 26B.
   As in the case study of Colorado vs. New Mexico, the trajectories of spending in the treated
states almost exactly mirror those in the control states. We formalize the estimate from this design
using a difference-in-differences (DD) design that compares the two weeks before the reopening in
the treated states and two weeks after. We estimate that reopenings led to a 1.27 percentage point
increase in spending. This DD estimate also appears in Appendix Table 11, Column 1. Column
2 replicates that specification with a three-week analysis window; the DD estimate is virtually
unchanged at 1.25 percentage points. Appendix Figure 26B shows that we also find little impact
of reopenings on employment (using the Paychex-Intuit data). Finally, Appendix Figure 26B also
shows (using data from Womply) that there was a 3.74 percentage point increase in the fraction of
small businesses open after states allowed businesses to reopen – confirming that state orders did
have some mechanical impact on the fraction of businesses that were open. However, this mechanical
effect does not appear to translate to noticeable impacts on total employment or spending.


                                                 74
    In line with these small treatment effect estimates, reopenings accounted for a relatively small
share of the overall variation in economic conditions across states. To demonstrate this, we
first calculate the actual variance in spending levels and other outcomes across states. We then
counterfactually add our estimated effect of reopening to all states that were not yet open as of
May 18, 2020, and recalculate the variance. Appendix Figure 26C then plots 1 minus the ratio of
the counterfactual variance to the actual variance, which is a measure of the importance of early
reopenings in explaining the variation in economic activity observed on May 18. These ratios are
very low, showing that early reopenings did not play an important role in explaining why some states
had stronger employment trajectories than others.40 These results are consistent with the findings
of other contemporaneous studies showing that little of the state-level variation in employment,
job vacancies, or time spent outside home is related to state-level stay-at-home orders or business
closures (Bartik et al. 2020; Forsythe et al. 2020; Lin and Meissner 2020; Goolsbee and Syverson
2021; Sears et al. 2023).
    Why did these reopenings have so little immediate impact on economic activity? The evidence
in Section III suggests that health concerns among consumers were the primary driver of the sharp
decline in economic activity in March and April 2020. Consistent with that evidence, spending fell
sharply in most states before formal state closures (Appendix Figure 27). If individuals’ own health
concerns are the core driver of reductions in spending during pandemics, governments may have
limited capacity to mechanically restore economic activity through reopenings if those reopenings
are not interpreted by consumers as a signal of reduced health risks.41


L.2     Paycheck Protection Program: Loans to Small Businesses

The Paycheck Protection Program (PPP) sought to reduce employment losses by providing financial
support to small businesses. Congress appropriated nearly $350 billion for loans to small businesses
in an initial tranche paid beginning on April 3, 2020, followed by another $175 billion in a second
round beginning on April 27, 2020. The program offered loan forgiveness for businesses that
maintained sufficiently high employment (relative to pre-crisis levels).
    According to the House Committee on Small Business (2020), the stated primary purpose of
   40
      We emphasize that these results apply to average employment rates 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).
   41
      In this vein, we stress that our research design only identifies the impacts of individual states opening earlier
vs. later; if one state’s actions impact behavior in other states (e.g., by shaping perceptions about health risks), the
total impacts of shutdowns or reopenings at a national level could be larger. Moreover, these conclusions only apply
to the initial stages of the pandemic that we study here. If health concerns diminish over time (e.g., due to quarantine
fatigue), government restrictions could have larger effects on economic activity.


                                                           75
the PPP was to encourage businesses to maintain employment even as they lost revenue. The
Small Business Administration (2020) emphasized the employment impacts of the PPP as a key
measure of the program’s success, noting that the PPP “ensure[d] that over approximately 50 million
hardworking Americans stay[ed] connected to their jobs” based on self-reports of the number of
jobs retained by firms that received PPP assistance.
    Here, we study the marginal impacts of the PPP on employment directly using payroll data
from Paychex and Earnin, 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. One important exception to this rule was the food
services industry, which was treated differently because of the prevalence of franchises. We therefore
omit the food services sector from the analysis that follows.42
    We estimate the causal effect of the PPP on employment rates at small businesses using a
difference-in-differences research design, comparing trends in employment for firms below the 500
employee cutoff (the treated group) vs. those above the 500 employee cutoff (the control group)
before vs. after April 3, 2020, when the PPP program began.43 We do not condition on firm survival
and simply count the total number of employees still working in each week at firms that initially had
more than 500 vs. less than 500 employees. Our estimates thus take both the intensive (reductions
in employees for surviving firms) and extensive (firm closure) margins into account.
    Appendix Figure 28A plots the average change in employment rates (inferred from payroll
deposits) relative to January 2020 for firms employing 100-499 employees, which were eligible for
PPP loans, vs. firms employing 500-799 employees, which were generally ineligible for PPP loans,
combining data from Paychex and Earnin.44 To adjust for the fact that industry composition varies
across firms of different sizes, we reweight by two-digit NAICS code so that the distribution of
employees across industries in the below-500 and above-500 employee groups matches the overall
distribution of employees across industries in January 2020. We further control for county x wage
quartile x week fixed effects to account for the differential time patterns of employment rates by
   42
       According to SBA data on PPP receipt throughout the life of the program, 10.5% of total PPP loan volume
(7.1% of the total number of loans) was disbursed to firms in the food services sector (NAICS 72). The remaining
exceptions to this rule affect relatively few workers: omitting food services, more than 90% of employees work at firms
that face the 500 employee threshold for eligibility.
    43
       Firms with more than 500 employees were still eligible for the Employee Retention Credit (ERC), which gave all
firms that lost more than 50% of their revenue a tax credit worth up to $5,000 per employee if they did not take up
the PPP. While data on ERC takeup are unavailable, fewer than 10% of CFOs of large firms report revenue losses
larger than 25% (PwC 2020), suggesting that the vast majority of firms with more than 500 employees were not
eligible for the ERC and hence serve as a valid counterfactual for employment in the absence of government assistance.
    44
       We report estimates pooling Paychex and Earnin because our data use agreements do not permit us to report
results based solely on Paychex data, and Intuit does not have coverage around the 500 employee cutoff.


                                                          76
county and wage quartile shown in Section III.C.
    Before April 3, 2020, trends in employment were similar among eligible vs. ineligible firms,
showing that larger businesses provide a good counterfactual for employment trends one would have
observed in smaller firms absent the PPP program (conditional on the reweighting and controls
described above). After April 3, employment in the treated (< 500 employees) and control (≥ 500
employees) groups diverge and follow slightly different trajectories until August 2020, after which
employment rates in the two groups are essentially identical again. These findings imply that the
PPP program had little marginal impact on employment at small businesses under the identification
assumption that employment trends in the two groups would have remained similar absent the PPP.
    Appendix Figure 28B plots the change in employment from January 4 to 31, 2020 to June 2020
by firm size bin. The decline in employment is quite similar across firm sizes, and is not markedly
smaller for firms below the 500 employee eligibility threshold.45
    In Appendix Table 12, we quantify the impacts of the PPP using OLS regressions of the form:


                    Emp𝑠𝑐𝑞𝑖𝑡 = 𝛼𝑐𝑞𝑡 + 𝛿Eligible𝑠 + 𝛽𝐷𝐷 Eligible𝑠 ⋅ Post-PPP𝑡 + 𝜀𝑠𝑐𝑞𝑖𝑡 ,                               (1)

    where Emp𝑠𝑐𝑞𝑖𝑡 is the change in employment within each eligibility group 𝑠 × county 𝑐 × wage
quartile 𝑞 × 2-digit NAICS industry 𝑖 cell on week 𝑡, relative to January 4 to 31, 2020; Eligible𝑠
is an indicator variable for whether firm had fewer than 500 employees in the pre-COVID period;
Post-PPP𝑡 is an indicator variable for the date being on or after April 3, 2020; and 𝛼𝑐𝑞𝑡 represents
a county-wage quartile-week fixed effect. We estimate this regression on the sample of firms with
100-799 employees using data from March 11 to August 15, 2020. We focus on employment impacts
up to August 15 because Appendix Figure 28A suggests that employment rates in the two groups
converged after early August (extending the estimation window would only further reduce the
estimated impacts of the PPP). We reweight by two-digit NAICS code so that the distribution of
employees across industries in the below-500 and above-500 employee groups matches the overall
distribution of employees across industries in January 2020. We cluster standard errors at the
county-industry-eligibility group level to permit correlation in errors across firms and over time
within counties and estimate the regression using OLS, weighting by the total number of employees
in the cell from January 4 to 31, 2020.

   45
      Because of differences in the measurement of firm sizes in our data and the SBA data used to determine PPP
eligibility (see below), there is no sharp discontinuity in eligibility at the 500 cutoff. Hence, we do not interpret this
plot using an RD design, but rather view it as showing that our estimates are insensitive to the bandwidth used to
define the treatment and control groups in the DD analysis.


                                                           77
   Column 1 of Appendix Table 12 presents the baseline estimate obtained from regression equation
(1) of 𝛽𝐷𝐷 = 2.48 percentage points (s.e. = 2.71), an estimate that matches Appendix Figure 28A
and is similar to that obtained in confidential ADP data in contemporaneous work by Autor et
al. (2022a). The mean decline in employment among firms in the control group up to August
15, 2020 was 14.2 percentage points, implying that the PPP saved 17.5% of the jobs that would
otherwise have been lost between April and August 2020. In Column 2, we reduce the bandwidth
to focus more narrowly around the 500-employee size threshold; the estimate is attenuated but not
statistically distinguishable from that in Column 1.
   Our difference-in-differences research design identifies the causal effect of the PPP on eligible
firms under the assumption that the PPP did not have a causal effect on employment at PPP-
ineligible firms. It is possible that the PPP reduced employment at ineligible firms (relative to
the no-PPP counterfactual) through an employment substitution channel: ineligible firms might
have hired workers laid off from eligible firms in the absence of the PPP. In the presence of such
substitution, our DD estimate would overstate the causal effect of the PPP on employment at
small businesses, providing an upper bound for its partial equilibrium impact (ignoring general
equilibrium effects that may have influenced consumer demand and employment at all firms).
   Measurement Error in Firm Sizes. Our measures of firm size – which are based on employment
levels in 2019 from Dun & Bradstreet data for the Paychex sample and ReferenceUSA in the Earnin
sample – do not correspond perfectly to the measures used by the Small Business Administration to
determine PPP eligibility. Such measurement error in firm size attenuates the estimates of 𝛽𝐷𝐷
obtained from (1) relative to the true causal effect of PPP eligibility because some of the firms
classified as having more than 500 employees may have actually received PPP (and vice versa).
   We estimate the degree of this attenuation bias by matching our data on firm sizes to data
publicly released by the Small Business Administration (SBA) on a selected set of PPP recipients
and assessing the extent to which firms are misclassified around the threshold. We restrict attention
to firms receiving loans of at least $150,000, as the names and addresses of these firms are publicly
available from the SBA. We first geocode addresses recorded in SBA and ReferenceUSA-Dun &
Bradstreet data to obtain a latitude and longitude for each firm. We then compute the trigram
similarities between firm names for all SBA and Dun & Bradstreet firms within twenty-five miles
of another. We then select one “match” for each PPP recipient from the Dun & Bradstreet data
for Paychex sample and ReferenceUSA for the Earnin sample, among the subset of firms within
twenty-five miles. For firms with loans of above $150,000, exact loan size is not observed; we impute


                                                 78
loan size as the midpoint of loan range. The SBA released firm names and ZIP codes of PPP
recipients receiving over $150,000 in loans, which represent 72.8% of total PPP expenditure. Of
the roughly 660,000 PPP recipients of these loans, we merge around 60% of firms and 62% of
total expenditure to firm size data. In this matched subset, we find that mean PPP expenditure
per worker is $2,303 for firms we classify as having 100-499 employees and $586 per worker for
firms with 500-799 employees (excluding firms in the food services industry). Given that we match
only 62% of the publicly available PPP expenditure to our data and the publicly available data
covers only 73% of total PPP expenditure, this implies that firms measured as having 100-499
employees in our sample received 0.62×0.73
                                   $2,303
                                           = $5,090 of PPP assistance per worker, while firms
with 500-799 employees received 0.62×0.73
                                   $586
                                          = $1,290 in PPP assistance per worker.46 We calculate
that PPP assistance to eligible firms with between 100 and 799 employees (excluding NAICS 72)
is $5,092 per worker on average.47 Hence, firms with 500-799 workers in the ReferenceUSA-Dun
& Bradstreet data (the control group) were effectively treated at an intensity of $1,290
                                                                                  $5,092 = 25.3%,

whereas firms with 100-499 workers in the ReferenceUSA-Dun & Bradstreet data (the treatment
group) were treated at an intensity of $5,090
                                       $5,092 = 100%. Inflating our baseline reduced-form estimates

by (1−0.253)
       1
             = 1.35 yields estimates of the treatment effect of PPP eligibility adjusted for attenuation
bias due to mismeasurement of firm size.
    Under standard assumptions required to obtain a local average treatment effect in the presence of
non-compliance – no direct effect of being classified as having more than 500 workers independent of
the PPP and a monotonic treatment effect – we can estimate the LATE of the PPP on employment
rates by multiplying the raw estimates reported in Appendix Table 12, Column 1 by 1.35 (Angrist,
Imbens, and Rubin 1996). This gives us a final preferred point estimate for the effect of PPP
eligibility on employment of 3.3 percentage points.
    Costs Per Job Saved. Using Statistics of U.S. Businesses (SUSB) data, we calculate that
approximately 62.4 million workers work at firms eligible for PPP assistance (53.7 million workers
excluding those in the food services industry, NAICS 72). Thus 86.1% of total PPP expenditure was
   46
      This calculation assumes that the degree of misclassification of eligibility among identifiable PPP recipients
matches the degree of misclassification of eligibility in the broader ReferenceUSA sample.
   47
      To compute this statistic, we first calculate the share of total loan amounts received by non-NAICS 72 firms
in the publicly released SBA data. We begin by imputing precise loan amount as the midpoint of minimum and
maximum of loan range, where precise loan amount is not released. We then calculate the share of loans in firms
with firm size between 100 and 499, in NAICS codes other than NAICS 72, under the assumption that our merge
rate is constant by firm size. Using this approach, we calculate that 13.1% of PPP loan spending was allocated to
non-NAICS 72 firms with 100-499 employees. We then rescale the total PPP expenditure to the end of June 2020,
$521 billion, by 0.131 to arrive at an estimate of $68.25 billion in PPP loan spending to non-NAICS 72 firms with
100-499 employees. Finally, we divide $68.25 billion by the number of workers at non-NAICS 72 firms with 100-499
employees to arrive at an estimate of loan spending per worker.


                                                        79
received by non-NAICS 72 firms. We then multiply this share by total PPP expenditure as of August
8, 2020 to reach an estimate of $486 billion in non-NAICS 72 firms. Under the assumption that the
PPP’s effects on firms with between 100 and 499 employees were the same in percentage point terms
as the PPP’s effects on all eligible firms, our baseline estimates in the combined Paychex-Earnin
data (Appendix Table 12, Column 1), adjusted for attenuation bias, imply that the PPP saved 0.03
× 53.6M = 1.61 million jobs from April through August 15, 2020.48 Given a total expenditure on
the PPP program of $486 billion through August 8 (excluding firms in food services), this translates
to an average cost per job saved (over the five months between April and August 2020) of $301,863.
Even at the upper bound of the 95% confidence interval for employment impact, we estimate a cost
per job saved of $86,201.
    In order to compute net costs to government per job saved, we account for the fact that a
reduction in job losses decreases UI spending. We evaluate replacement rates at the mean level
of earnings for workers employed at PPP-eligible firms using the statutory rates in Ganong, Noel,
and Vavra (2020, fig. 3a), which estimates that displaced workers received roughly 120% of weekly
earnings for the seventeen weeks between the beginning of our treatment period (April 3, 2020)
and the end of July 2020, and received roughly 40% of weekly earnings for the following two weeks
until the end of our analysis window (August 15, 2020). Computing expenditure on UI given these
replacement rates and mean earnings, we find that the effect of each job saved by the PPP on UI
payments was $18,350 over our analysis period. Netting these savings out of the gross cost, we
estimate a net cost to the government of $283,513 per job saved (and $67,851 at the upper bound of
the 95% confidence interval for employment impact). For comparison, mean annual earnings for
workers at PPP-eligible firms are only $45,000.




   48
     If the treatment effect of the PPP program on food services were the same in percentage terms as in other sectors,
we estimate the PPP saved a total of 1.87 million jobs.


                                                         80
 Table 1: Association Between Rent and Changes in Business Revenue and Employment
Panel A: Business Revenue
                                        Dep. Var.:     Change in Small Business Revenue (percentage points)
                                                                   from January to April 2020
                                                           (1)                 (2)                 (3)

       Median Two-Bedroom Rent                            -16.71                 -15.95                 -14.33
       (per thousand dollars)                             (0.90)                 (2.20)                 (2.21)
       Log Density of                                                                                    -2.38
       High Wage Workers                                                                                (0.34)
       County FEs                                                                   X                     X
       Observations                                        9,917                  9,917                  9,917
       Geographic Unit                                  ZIP Code               ZIP Code               ZIP Code

Panel B: Low-Wage Employment
                                        Dep. Var.:     Change in Low-Wage Employment (percentage points)
                                                                   from January to July 2020
                                                           (1)                (2)               (3)

       Median Two-Bedroom Rent                             -6.82                  -4.33                  -5.63
       (per thousand dollars)                             (1.17)                 (1.54)                 (0.89)
       Log Density of                                                             -0.86                  -0.74
       High Wage Workers                                                         (0.36)                 (0.28)
       County FEs                                                                                         X
       Observations                                         949                    949                  11,223
       Geographic Unit                                    County                 County               ZIP Code
       Data Source                                   Paychex & Intuit      Paychex & Intuit             Earnin


     Notes: This table presents estimates from population-weighted OLS regressions at the county and ZIP-level.
We regress percentage changes in small business revenue (using Womply data) and low-wage employment (using
Paychex-Intuit and Earnin data) on median two-bedroom rent (as measured in the 2014-2018 ACS). Standard errors
are reported in parentheses; county-level regressions use robust standard errors and ZIP-level regressions use standard
errors clustered by county. The dependent variable is in percentage point units. The dependent variable in Panel A is
the average change in small business revenue measured from March 23 to April 12, 2020, relative to January 4 to
31, 2020. All regressions in Panel A are at the ZIP-code level. The dependent variable in Panel B is the change in
low-wage employment measured from June 27 to July 31, 2020, relative to January 4 to 31, 2020. In Panel B, Column
(1) and (2) are at the county level using combined Paychex and Intuit data, while Column (3) is at the ZIP code level
using Earnin data. In both panels, Column (1) shows the baseline regression without any controls: this specification
corresponds to the estimated slope coefficient and standard error reported in Figure 3 (small business revenue) and
Figure 4C (low-wage employment). In Panel A, Column (2) adds county fixed effects and Column (3) further adds the
log of the density of high wage workers as a control (which is observed using the Census LODES for 92% of ZIP codes
representing 99% of the U.S. population). In Panel B, Column (2) adds the log of the density of high wage workers as
a control to the baseline county level regression, while Column (3) switches to ZIP code level data for a specification
analogous to the one in Column (3) of Panel A. Data sources: Womply, Paychex, Intuit, Earnin, ACS, Census LODES.




                                                          81
    Table 2: Mechanisms Underlying the Persistent Reduction in Low-Wage Employment:
                          Hysteresis vs. Current Conditions
                                                               Change from January 2020 to December 2021 in
                                          Dep. Var.:
                                                       Low-Wage (Q1) Employment         High-Wage (Q4) Employment
                                                          (1)     (2)      (3)                     (4)

Median Two-Bedroom Rent                                  -0.17      -0.19                           -0.04
(per thousand dollars)                                  (0.04)     (0.04)                          (0.04)

Change in Low-Wage (Q1) Employment                                            0.67
from January 2020 to July 2020                                               (0.11)

Average Daily COVID Cases (thousands)                               -0.10     -0.07                 -0.07
in October 2021 to December 2021                                   (0.02)    (0.02)                (0.01)

Maximum Weeks of State UI Benefits                                  0.000     -0.002               -0.003
in December 2021                                                   (0.001)   (0.001)              (0.001)

Demographic Controls                                     No         Yes        Yes                  Yes

N                                                        841        841        841                  626

Change in Employment Explained by COVID Cases                     3.0 p.p.   2.1 p.p.             2.1 p.p.
Change in Employment Explained by UI Extensions                   0.1 p.p.   0.4 p.p.             0.7 p.p.


     Notes: This table presents estimates for a set of population-weighted regressions examining the determinants of
employment patterns in December 2021 at the county level. Robust standard errors are reported in parentheses. The
sample omits California, Massachusetts, and New York due to mismeasurement of low-wage employment changes
as a result of minimum wage increases; see Appendix E.2 for more information. Column (1) reports the results of
regressing the change in low-wage (i.e. bottom quartile) employment from January 2020 to December 2021 against
the average median two-bedroom rent (as measured in the 2014-2018 ACS) at the county level. Column (2) adds the
average COVID-19 case rate in October to December 2021 (a measure of the risk of COVID exposure), the maximum
number of weeks of unemployment insurance eligibility in each state, and a set of demographic controls: foreign-born
population share, non-white population share, share of the population who are working age (25-54), and female
population share. Column (3) repeats the specification in Column (2), replacing median two-bedroom rent with the
size of the initial low-wage employment shock to each county, measured as the change in low-wage employment from
January 2020 to July 2020. Column (4) repeats the specification in Column (2) using the change in high-wage (i.e. top
quartile) employment from January 2020 to December 2021 as the dependent variable. The bottom two rows of the
table report the change in the dependent variable explained by COVID risk exposure and UI extensions, calculated by
multiplying the coefficient by the population-weighted mean of the respective variable. Data sources: Paychex, Intuit.




                                                          82
                     Appendix Table 1: Data Sources and Processing Steps

     Data Series                    Sources              Description of          Overview of Processing Steps
                                                          Processing

                                                                          Adjust for sharp changes in the client base.
                                                                          Adjust for gradual changes in the client
  Consumer Spending            Affinity Solutions         Appendix B      base.
                                                                          Address spurious changes.
                                                                          Control for seasonal fluctuations in
                                                                          spending.

                                                                          Adjust for the evolving client base.
   Small Business                   Womply                Appendix C      Produce the ZIP code series.
Revenue and Openings                                                      Reduce the influence of outliers.
                                                                          Control for seasonality.

     Job Postings                  Lightcast              Appendix D      Reduce the influence of outliers.

                                                                          Paychex
                                                                          Address anomalous spikes.
                                                                          Adjust when quartile thresholds cross
                                                                          integer wages.
                                                                          Map paychecks to employment periods.
                                    Paychex                               Adjust for an evolving client base.
                                                                          Adjust for minimum wage changes.
     Employment                      Intuit               Appendix E
                                                                          Paychex and Intuit Combined
                                     Earnin                               Impute local composition of Intuit data.
                                                                          Combine Paychex and Intuit data.
                                                                          Earnin
                                                                          Sample restrictions.
                                                                          Map paychecks to chained employment.
                            Department of Labor,
                           Employment and Training
                               Administration
Unemployment Claims                                       Appendix F      Harmonize weekly and monthly estimates.
                           Bureau of Labor Statistics
                          State Departments of Labor
                          Centers for Disease Control
                                and Prevention
                                New York Times
 COVID-19 Infections                                      Appendix G      Reduce the influence of outliers.
  and Vaccinations        Johns Hopkins Coronavirus
                               Resource Center
                          U.S. Department of Health
                             and Human Services
                            Google COVID-19
 Time Outside Home       Community Mobility Reports       Appendix H      Generate measures of time at home and
                                                                          away from home.
                          American Time Use Survey

 Educational Progress                Zearn                 Appendix I     Reduce the effects of transitory outliers.
                                                                          Account for missing values.

    Notes: This table provides an overview of the data series constructed in this paper and published at tracker.oppo
rtunityinsights.org. Further details are provided in Section II.B and in the appendices referenced in the table.




                                                         83
                          Appendix Table 2: Distributions of Sample Sizes
Panel A: Spending, Small Business Revenue, Job Posts, Employment
                                                             Distribution of Sample Across Local Areas
                                            10th Percentile 25th Percentile 50th Percentile 75th Percentile 90th Percentile

Consumer Spending: Affinity
  January 2019 Spending (County level)            $ 19,657        $ 111,788     $ 454,612     $ 1,409,311     $ 3,750,812

Small Business Revenue: Womply
  January 2020 Revenue
     County level                                $ 774,550        $ 1,994,485   $ 5,543,055   $ 12,800,000   $ 36,000,000
     ZCTA level                                  $ 10,703          $ 18,577      $ 35,511       $ 65,762      $ 113,147

Job Postings: Lightcast
   January 2020 Job Posts (County level)             34              171          1,076          4,066          11,895

Employment: Paychex-Intuit
  January 2020 Jobs (County level)                  549             3,146         28,390        111,533         219,660

Employment: Earnin
  January 2020 Jobs
    County level                                     92              615          3,373          12,154         35,570
    ZCTA level                                       18               61           171            371            667

Panel B: Education
                                                      Distribution of Sample Across the Most Populous Counties
                                                   Top 5         Top 10        Top 20        Top 50       Top 100
Education: Zearn (County level)
  Average number of Zearn students                 14,820           10,190        7,410          6,790           3,240
  Share of Zearn students                           8%               11 %         16 %           22 %            35 %

Share of US population                              8%               13 %          19 %           30 %           42 %

     Notes: This table presents the distribution of cell sizes for data series we construct from private sector sources.
For Affinity Solutions, we report the population-weighted percentiles of the county-level distribution of the average
daily total spending during January 4 to 31, 2019. For Womply, we report population-weighted percentiles of the
county-level and ZIP-level distributions of weekly revenue in January 4 to 31, 2020. So, for example, the $774,550
under the column “10th Percentile” means that the top 90% of the population lives in counties with at least $774,550
in average weekly small business revenue in the base period. For Lightcast, we report population-weighted percentiles
of the county-level distribution of average weekly job posts in January 4 to 31, 2020. For Paychex-Intuit, we report
population-weighted percentiles of the county-level distribution of employment in January 4 to 31, 2020. For Earnin,
we report population-weighted percentiles of the county-level and ZIP-level distributions of employment in January 4
to 31, 2020. For Zearn, we report the average number of Zearn students and the share of Zearn students in the top 5;
top 10; top 20; top 50; and top 100 counties by county population. We also report the share of the U.S. population in
these counties for reference. Data sources: Affinity Solutions, Womply, Lightcast, Paychex, Intuit, Earnin, Zearn.




                                                             84
              Appendix Table 3: Industry Employment Shares Across Datasets
                                                                                        Industry Shares (%)

 NAICS                                                                QCEW All     QCEW Small       Paychex + Intuit   Earnin
  Code                     NAICS Description                        Establishments Establishments
                                                                          (1)            (2)                (3)         (4)
  11        Agriculture, Forestry, Fishing and Hunting                   0.84           1.04               0.53        0.18
  21        Mining, Quarrying, and Oil and Gas Extraction                0.55           0.43               0.15        0.21
  22        Utilities                                                    0.44           0.29               0.13        0.48
  23        Construction                                                 5.72           7.62               7.39        1.32
 31-33      Manufacturing                                               10.27           5.16               8.05        6.33
  42        Wholesale Trade                                              4.72           5.99               5.73        4.45
 44-45      Retail Trade                                                12.48          14.06               7.81        23.98
 48-49      Transportation and Warehousing                                4.3           2.82               2.04        5.57
  51        Information                                                  2.29           1.64               1.45        4.13
  52        Finance and Insurance                                        4.83            4.6               3.42        7.53
  53        Real Estate and Rental and Leasing                           1.71            2.9                3.2        1.94
  54        Professional, Scientific, and Technical Services             7.63           8.97              14.16        4.91
  55        Management of Companies and Enterprises                      1.93           0.79               0.25        0.37
  56        Administrative Support                                       7.25            5.3               7.07        4.91
  61        Educational Services                                         2.39           1.53               2.36        3.85
  62        Health Care and Social Assistance                           16.16          13.16              15.41        16.72
  71        Arts, Entertainment, and Recreation                          1.78           1.64               1.87        1.57
  72        Accommodation and Food Services                             11.04           15.6               9.89         9.2
  81        Other Services (except Public Administration)                3.57           6.21               9.09        2.34
  99        Unclassified                                                 0.11           0.24

    Notes: This table compares the industry composition (2-digit NAICS) of two payroll-based employment datasets
to the Quarterly Census of Employment and Wages (QCEW), an administrative dataset covering the near-universe of
firms in the United States. Each column displays the share of employees (in percentage terms) in the given dataset
who work in the specified sector. Column (1) displays the industry composition of the QCEW in the first quarter of
2020. Column (2) replicates column (1) restricting to small establishments, defined as establishments with fewer than
50 employees. Column (3) shows the industry composition of Paychex-Intuit data in January 2020. To construct
Column (3), we first separately calculate the number of employees in each 2-digit NAICS code in Paychex and Intuit
as the number of worker-days in each 2-digit NAICS code. We then calculate combined Paychex-Intuit employment in
each 2-digit NAICS code as a weighted sum of Paychex and Intuit, where the weights are undisclosed to meet privacy
protection requirements. Column (4) displays the industry composition in the Earnin data in January 2020. Data
sources: Paychex, Intuit, Earnin, QCEW.




                                                               85
            Appendix Table 4: Hourly Wage Rates By Industry Across Datasets
                                                                     Occupational Employment
                                                                                                 Paychex + Earnin
                                                                         Statistics
                                                                            May 2019(OES)        Paychex - Earnin
NAICS Code                       NAICS Description                              (1)                    (2)
    11            Agriculture, Forestry, Fishing and Hunting                 $16.35                    $20.63
    21            Mining, Quarrying, and Oil and Gas Extraction              $32.15                    $32.89
    22            Utilities                                                  $39.80                    $33.18
    23            Construction                                               $27.87                    $28.74
   31-33          Manufacturing                                              $26.48                    $25.44
    42            Wholesale Trade                                            $28.85                    $27.34
   44-45          Retail Trade                                               $17.02                    $21.07
   48-49          Transportation and Warehousing                             $24.33                    $24.62
    51            Information                                                $39.07                    $32.78
    52            Finance and Insurance                                      $36.73                    $32.82
    53            Real Estate and Rental and Leasing                         $24.98                    $25.66
    54            Professional, Scientific, and Technical Services           $41.83                    $34.37
    55            Management of Companies and Enterprises                    $42.59                    $24.06
    56            Administrative Support                                     $20.50                    $23.51
    61            Educational Services                                       $28.34                    $24.78
    62            Health Care and Social Assistance                          $26.98                    $25.47
    71            Arts, Entertainment, and Recreation                        $19.18                    $22.47
    72            Accommodation and Food Services                            $13.65                    $16.62
    81            Other Services (except Public Administration)              $21.58                    $22.50

                  All                                                        $25.72                    $25.34
                  Industry-Weighted Average of BLS Mean Wages                                          $26.00


    Notes: This table compares mean wages in private sector datasets to mean wages in Occupational Employment
Statistics (OES) data, within each two-digit NAICS code. Column (1) reports mean wages in each NAICS code
in May 2019 OES data. We inflate these wages to 2020 dollars using the BLS Consumer Price Index. Column (2)
reports mean wages in combined Paychex-Earnin data in January 2020. We first compute mean wages separately in
Paychex and Earnin data as mean wages in January 2020, weighting by number of worker-days. In Paychex, wages are
measured as pre-tax wages recorded by the employer. In Earnin, wages are post-tax wages recorded in payroll deposits.
We then take a weighted mean of Paychex and Earnin wages within each industry, where the weights are not disclosed
to meet business privacy requirements. The last row of Column (2) displays BLS mean wages, reweighted to match
the 2-digit NAICS composition within the combined private sector dataset. Data sources: Paychex, Earnin, OES.




                                                          86
Appendix Table 5: Consumer Spending on Debit and Credit Cards, by Income Quartile
                                 and Sector
                                                        Mean Level of Card        Change Relative to Dec 30 2019-Jan 26 2020
                                                        Spending Per Day                          ($ Billions)
                                                            ($ Billions)
                                                            Level as of         Change as of     Change as of    Change as of
                                                      Dec 30 2019-Jan 26 2020   April 14 2020   August 14 2020 December 31 2021
                                                                (1)                  (2)             (3)             (4)
Pooled Card Spending: All Income Quartiles, All Sectors       $23.6                 -$7.3           -$0.9             +$3.6
Panel A: Card Spending, by ZIP Income Quartile
Bottom Quartile                                                $3.5                 -$0.9            +$0.1            +$0.8
                                                             (15.0%)              (11.8%)          (-10.3%)          (21.7%)
Second Quartile                                                $5.3                 -$1.5            -$0.1            +$1.0
                                                             (22.3%)              (20.2%)          (11.3%)           (26.5%)
Third Quartile                                                 $6.5                 -$2.0            -$0.2            +$1.0
                                                             (27.4%)              (27.3%)          (27.2%)           (28.3%)
Top Quartile                                                   $8.3                 -$3.0            -$0.6            +$0.8
                                                             (35.3%)              (40.7%)          (71.8%)           (23.5%)
Panel B: Card Spending, by Sector
Durable Goods                                                  $3.7                 -$0.5           +$0.6             +$0.8
                                                             (15.6%)               (7.1%)         (-55.9%)          (21.1%)
Non-Durable Goods                                              $8.0                 -$1.5           -$0.0             +$2.1
                                                             (33.8%)              (20.5%)          (2.3%)           (55.2%)
Remote Services                                                $5.4                 -$1.2           +$0.3             +$1.3
                                                             (22.9%)              (15.7%)         (-29.9%)          (34.2%)
In-Person Services                                             $6.5                 -$4.2           -$1.8             -$0.4
                                                             (27.7%)              (56.7%)         (183.6%)          (-10.4%)
Panel C: In-Person Services Spending, by Sub-Sector
Hotels & Food                                                  $2.9                 -$1.8            -$0.7             -$0.0
                                                             (44.1%)              (43.1%)          (38.6%)            (5.0%)
Transportation                                                 $1.0                 -$0.8            -$0.6             -$0.5
                                                             (15.9%)              (19.3%)          (34.6%)          (119.2%)
Health Care                                                    $1.0                 -$0.6            -$0.1            +$0.2
                                                             (15.3%)              (13.8%)           (6.1%)          (-37.5%)
Recreation                                                     $0.6                 -$0.4            -$0.2             -$0.0
                                                              (8.9%)               (9.8%)          (12.7%)           (12.4%)
Other In-Person Services                                       $1.0                 -$0.6            -$0.1             -$0.0
                                                             (15.8%)              (13.9%)           (8.0%)            (0.9%)

     Notes: This table presents estimates of the changes in daily national consumer spending from the pre-pandemic
baseline (measured as a daily average between January 6 to February 2, 2020) to April 14, 2020 (in Column 2), August
14, 2020 (in Column 3) and December 31, 2021 (in Column 4). We construct these estimates by combining statistics on
total daily card spending in January 2020 (categories “Furnishings and durable household equipment”, “Recreational
goods and vehicles”, “Other durable goods”, “Food and beverages purchased for off-premises consumption”, “Clothing
and footwear”, “Gasoline and other energy goods”, “Other nondurable goods”, “Transportation services”, “Recreation
services”, “Food services and accommodations”, “Financial services and insurance”, and “Other services” from NIPA
Table 2.3.5) with our consumer spending series from Affinity Solutions. See Section II.B.1 and Appendix B for more
details on the construction of the consumer spending series. We estimate total daily spending (top row in Column 1) by
dividing the NIPA monthly estimate by 31. We estimate the levels in the remaining rows of Column 1 by multiplying
the spending share estimated in the Affinity data in January 2020, by total daily spending. We estimate the changes
in Columns 2-4 in each row by multiplying the level in Column 1 by the change in the relevant Affinity series. The
parentheses in each row report the share of the national spending decline (in Columns 2-4) or of the national spending
total (in Column 1) accounted for by each subset. Panel A disaggregates spending by income quartile, measured
at the ZIP code level using median household income from the 2014-2018 ACS. Panel B disaggregates spending by
sector; Panel C disaggregates in-person service spending by sub-sector (see Appendix B.3 for sector and sub-sector
definitions). Data sources: Affinity Solutions, NIPA.



                                                                87
          Appendix Table 6: Effects of Stimulus Payments on Consumer Spending
                                            Dependent Variable: Change in Consumer Spending
                     First 5 Days       After Day 5     Combined: 31 Days    Combined: 31 Days        Combined: 31 Days
                     Post-Stimulus     Post-Stimulus      Post-Stimulus        Post-Stimulus             Post-Stimulus
                     (p.p. change)     (p.p. change)      (p.p. change)         ($ change)       ($ change per $1200 stimulus)
                        (1)            (2)                     (3)                  (4)                      (5)
Panel A: April 2020 Stimulus Payments (Stimulus 1)
                       19.16          21.81                   21.38               442.28                   442.28
  Income Q1:
                       (6.67)         (3.32)                  (3.13)              (64.75)                  (64.75)
                        12.69              18.54              17.59               564.66                   564.66
   Income Q2:
                        (5.07)             (2.90)             (2.71)              (86.82)                  (86.82)
                         7.45              16.63              15.15                640.19                   640.19
   Income Q3:
                        (4.77)             (2.87)             (2.67)              (112.90)                 (112.90)
                         1.94              12.78              11.03                731.52                   731.52
   Income Q4:
                        (4.14)             (2.98)             (2.75)              (182.41)                 (182.41)
Panel B: January 2021 Stimulus Payments (Stimulus 2)
                      14.56           4.83                     6.40                93.56                   187.12
  Income Q1:
                      (5.56)         (2.42)                   (2.39)              (34.94)                  (69.87)
                         9.46               3.69               4.62               105.41                   210.83
   Income Q2:
                        (4.28)             (2.20)             (2.13)              (48.58)                  (97.15)
                         6.36               2.13               2.81                85.28                    170.56
   Income Q3:
                        (3.85)             (1.95)             (1.91)              (57.89)                  (115.78)
                         3.39               -0.22              0.37                17.73                    35.46
   Income Q4:
                        (3.39)             (1.89)             (1.82)              (88.30)                  (176.60)
Panel C: March 2021 Stimulus Payments (Stimulus 3)
                      26.90          11.54                    14.02               317.29                   271.97
  Income Q1:
                      (6.66)         (3.87)                   (3.78)              (85.47)                  (73.26)
                        15.98               5.64               7.31                259.24                  222.21
   Income Q2:
                        (4.36)             (3.20)             (3.05)              (108.12)                 (92.67)
                         8.59               2.76               3.70                175.11                   150.09
   Income Q3:
                        (3.41)             (3.12)             (2.93)              (138.89)                 (119.05)
                         2.89               0.57               0.95                72.65                    62.27
   Income Q4:
                        (2.67)             (3.33)             (3.08)              (236.80)                 (202.98)

     Notes: This table reports difference-in-differences estimates of the impacts of the three rounds of economic impact
payments on consumer spending, separately for each ZIP income quartile. Panels A, B, and C respectively show
estimates for the payments primarily made on April 15, 2020, January 4, 2021, and March 17, 2021. In Panels A and
C, we use a window of 25 days before and after the stimulus payment date as the estimation sample. In Panel A, we
exclude the partially treated date of April 14, 2020 and in Panel C, we exclude the partially treated dates of March
13 to 16, 2021. In Panel B, we use December 4 to 14, 2020 and January 4 to 19, 2021 as the estimation window;
we exclude the intervening holiday period because of the high degree of volatility of spending during that period
(Appendix Figure 23). We then regress daily consumer spending within the relevant window and the corresponding
period starting in 2019 (after residualizing it on day-of-week fixed effects) on an indicator variable for the first five days
of the post-period, an indicator variable for the rest of the post-period, and their interactions with an indicator for
being in the treated group. In Panels A and C, we also adjust for a linear pre-trend in both the treatment and control
series; we do not adjust for a linear pre-trend in Panel B due to the omission of the holiday period. The coefficients
reported in columns 1 and 2 are those on the interaction of the two post-period indicators with the indicator for the
treated year. Column 3 combines these two spending estimates to project the total percentage effect on spending for
the first 31 days after the reform. Column 4 converts this combined percentage estimate into a total dollar estimate
using base period daily average spending (see Appendix K for details) and column 5 rescales the dollar estimates to
be per $1,200 of stimulus. Robust standard errors are reported in parentheses. Data source: Affinity Solutions.




                                                             88
Appendix Table 7: Association Between Changes in Consumer Spending and Workplace
                               Rent by ZIP Code
                                             Dep. Var.:        Change in Low-Income Consumer Spending (%)
                                                                     (1)                       (2)

Mean Workplace Two-Bedroom Rent                                      -12.82                         -13.55
(per thousand dollars)                                               (1.85)                         (7.05)
County Fixed Effects                                                                                  X
N                                                                    8,974                          8,974


     Notes: This table presents results from regressions of changes in low-income consumer spending in the first month
of the pandemic on the median rent of the workplaces of those low wage workers. We measure the dependent variable
as the average value of our consumer spending index between March 25 and April 14, 2020 (see Section II.B.1 and
Appendix B for details on the construction of this series). Unlike our baseline spending series, we compute these data
for this table at the ZIP code level. We then construct the average workplace rent using the Census’ LODES data (to
measure the workplace ZIP codes for low-wage workers residing in each ZIP code) and the median rent data for each
ZIP code from the 2014-2018 ACS. Our independent variable is the average of median workplace ZIP rents using
the LODES workplace distribution as the weights for each residential ZIP code. We then restrict the sample for the
regression to the residential ZIP codes in the (population-weighted) bottom quartile of median income. We scale the
dependent and independent variables such that the coefficients represent the predicted change in percentage points
for each $1000 increase in rent; for instance, the coefficient of -12.82 in Column (1) means that a $1000 increase in
average workplace median rent for low-wage workers residing a given low-income ZIP code is associated with a 12.82
percentage point reduction in consumer spending in that ZIP code. Column (2) replicates the specification in Column
(1) including county fixed effects. Standard errors are clustered at the county level and reported in parentheses. Data
sources: Affinity Solutions, Census LODES, ACS.




                                                          89
               Appendix Table 8: Demographic Characteristics of Zearn Users
                                                              Zearn Users                 U.S. Population
                                                                  (1)                           (2)
Panel A: Income
ZIP Median Household Income
      25th Percentile                                            43,750                        45,191
      Median                                                     55,429                        57,371
      75th Percentile                                            71,967                        76,080
Number of ZIP codes                                               6,749                        33,229
Sample Population                                               971,843                      326,274,368

Panel B: School Demographics
Share of Black Students
      25th Percentile                                            1.7%                            1.9%
      Median                                                     6.4%                            6.8%
      75th Percentile                                           22.1%                           23.1%
Share of Hispanic Students
      25th Percentile                                            4.7%                            5.2%
      Median                                                    11.9%                           13.7%
      75th Percentile                                           36.4%                           37.0%
Share of Students Receiving FRPL
      25th Percentile                                            34.9%                         31.5%
      Median                                                     56.6%                         54.3%
      75th Percentile                                            79.7%                         79.0%
Number of Schools                                                18,437                        88,459
Total Student Population of Schools                            9,690,036                     49,038,524
Panel C: Region
Share of Students
      Midwest                                                   23.9%                           20.5%
      Northeast                                                 13.7%                           15.6%
      South                                                     32.5%                           39.6%
      West                                                      29.6%                           23.7%

    Notes: This table reports demographic characteristics for Zearn schools vs. the U.S. population. Panel A compares
income characteristics of ZIP codes with Zearn coverage vs. all ZIP codes. We define Zearn to have coverage in a ZIP
code if at least five students at schools in that ZIP code used Zearn between January 6 to February 7, 2020. Column 1
shows income characteristics of Zearn-covered ZIP codes. The first three rows in Panel A display the 25th, 50th, and
75th percentiles of ZIP-level median household income in Zearn-covered ZIP codes, as measured in the 2014-2018 ACS.
The fourth and fifth rows of Panel A display the number of Zearn-covered ZIP codes, and the number of students
using Zearn in those ZIP codes. Column 2 replicates Column 1 using all ZIP codes in the U.S. The fourth and fifth
rows of Column 2 replicates Column 1 using all ZIP codes in the U.S. and counting the total population, respectively.
Panel B presents the demographic composition of schools in the Zearn data (Column 1) and of all U.S. K-12 schools
(Column 2), calculated using school-level data from the Common Core dataset as constructed by MDR Education,
a private education data firm. The first three rows of Panel B show the 25th, 50th, and 75th percentiles of share
of Black students in Zearn schools (Column 1) and in all US K-12 schools (Column 2). Rows 4-6 and 7-9 of Panel
B replicate Rows 1-3 using the share of Hispanic students and the share of students receiving free or reduced-price
lunch meals. Rows 10 and 11 of Panel B display the number of Zearn schools matched to the Common Core data
and the number of students in those schools. Panel C compares the share of students by region in Zearn vs. the US
population. Data sources: Zearn, ACS, Common Core.




                                                         90
                           Appendix Table 9: City to County Crosswalk
                    City                  State                      County            County FIPS Code

                Albuquerque            New Mexico                    Bernalillo             35001
                   Atlanta                Georgia                      Fulton               13121
                   Austin                  Texas                       Travis               48453
                 Bakersfield             California                     Kern                 6029
                 Baltimore               Maryland                    Baltimore              24005
                    Boise                   Idaho                       Ada                 16001
                   Boston             Massachusetts                    Suffolk              25025
                 Charlotte           North Carolina                Mecklenburg              37119
                  Chicago                  Illinois                     Cook                17031
                 Cleveland                  Ohio                     Cuyahoga               39035
              Colorado Springs           Colorado                     El Paso                8041
                 Columbus                   Ohio                      Franklin              39049
                   Dallas                  Texas                       Dallas               48113
                   Denver                Colorado                      Denver                8031
                   Detroit               Michigan                      Wayne                26163
                   El Paso                 Texas                      El Paso               48141
                Fort Worth                 Texas                      Tarrant               48439
                   Fresno                California                    Fresno                6019
                  Honolulu                Hawaii                     Honolulu               15003
                  Houston                  Texas                       Harris               48201
                Indianapolis              Indiana                      Marion               18097
                Jacksonville              Florida                      Duval                12031
                Kansas City              Missouri                     Jackson               29095
                 Las Vegas                Nevada                        Clark               32003
                Los Angeles              California                 Los Angeles              6037
                 Louisville              Kentucky                     Jefferson             21111
                  Memphis               Tennessee                      Shelby               47157
                   Miami                  Florida                       Dade                12086
                 Milwaukee              Wisconsin                    Milwaukee              55079
                Minneapolis             Minnesota                    Hennepin               27053
                  Nashville             Tennessee                    Davidson               47037
                New Orleans              Louisiana                    Orleans               22071
               New York City            New York                       Bronx                36005
               New York City            New York                       Kings                36047
               New York City            New York                     New York               36061
               New York City            New York                       Queens               36081
               New York City            New York                     Richmond               36085
                  Oakland                California                   Alameda                6001
               Oklahoma City            Oklahoma                     Oklahoma               40109
                   Omaha                 Nebraska                     Douglas               31055
                Philadelphia          Pennsylvania                 Philadelphia             42101
                  Phoenix                 Arizona                    Maricopa                4013
                  Portland                Oregon                    Multnomah               41051
                   Raleigh           North Carolina                    Wake                 37183
                Sacramento               California                 Sacramento               6067
               Salt Lake City               Utah                     Salt Lake              49035
                San Antonio                Texas                       Bexar                48029
                 San Diego               California                  San Diego               6073
               San Francisco             California               San Francisco              6075
                  San Jose               California                 Santa Clara              6085
                   Seattle             Washington                       King                53033
                   Tampa                  Florida                  Hillsborough             12057
                   Tucson                 Arizona                       Pima                 4019
                    Tulsa               Oklahoma                        Tulsa               40143
               Virginia Beach             Virginia             Virginia Beach City          51810
                Washington         District of Columbia        District Of Columbia         11001
                  Wichita                 Kansas                     Sedgwick               20173

     Notes: This table shows our metro area (city) to county crosswalk. We assigned metros to counties and verified
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.



                                                          91
  Appendix Table 10: List of Re-Opening States and Control States for Event Studies
  Date          States that Re-Opened    Consumer Spending Controls          Employment Controls        Small Businesses Open Controls
                                                    (1)                             (2)                               (3)



                                                                           California, Connecticut,
                                                                             Delaware, District Of
           th
April 20           South Carolina        Florida, Hawaii, Louisiana       Columbia, Florida, Indiana,     Nebraska, South Dakota,
  2020                                                                     Louisiana, New Mexico,                Virginia
                                                                             Oregon, Washington,
                                                                                  Wisconsin




                                             Delaware, Illinois,           California, Connecticut,       California, Connecticut,
                                          Massachusetts, Missouri,           Delaware, Indiana,         Delaware, Illinois, Louisiana,
April 24th         Alaska, Georgia         New Jersey, New York,          Louisiana, Massachusetts,      Maryland, Massachusetts,
  2020                                      Pennsylvania, South           New Jersey, Pennsylvania,        Missouri, New Jersey,
                                         Dakota, Virginia, Wisconsin             Washington             Pennsylvania, Washington,
                                                                                                                 Wisconsin




                                           Illinois, Nebraska, New          Delaware, District Of
April 27th      Minnesota, Mississippi        Jersey, New York,              Columbia, Illinois,        New Mexico, South Dakota,
  2020                                     Pennsylvania, Virginia,         Maryland, New Mexico,                Virginia
                                                   Wisconsin                 Virginia, Wisconsin



     Notes: This table lists the treatment and control states for the analysis of state reopenings in Appendix Figure 26
and Appendix Table 11. Column (1) displays the control states in the event study of consumer spending (as measured
in the Affinity data) described in Appendix Figure 26 and Appendix Table 11. Column (2) replicates Column (1) for
employment (as measured in the Paychex-Intuit data). Column (3) replicates Column (1) for the number of small
businesses open (as measured in the Womply data).




                                                                     92
         Appendix Table 11: Causal Effects of Re-Openings on Economic Activity
                                          Spending (%)               Employment (%)               Small Businesses
                      Dep. Var.:                                                                     Open (%)
                                          (1)             (2)          (3)             (4)         (5)        (6)
                 DD Estimate of          1.27            1.25         1.01            1.04        3.74       3.79
            Effect of Reopening:        (0.48)          (0.60)       (0.42)          (0.47)      (1.29)     (1.63)

      State-Week Observations:           100             150          128             192          92          138

    Analysis Window (weeks on              2              3            2               3            2           3
      either side of reopening):


    Mean Decline in Outcome                      -30%                         -22%                      -29%
      (January to April 2020):


     Notes: This table estimates the effects of state reopenings on various outcomes using an event study design based
on states that reopened non-essential businesses between April 20 to 27, 2020. Each state that reopens is matched to
multiple control states (listed in Appendix Table 10) that did not reopen within the subsequent 3 weeks but had
similar trends of the outcome variable during the weeks preceding the reopening. We construct the control group
separately for each re-opening day and then stack the resulting event studies to align the events. All estimates are
from OLS regressions at the state x week level on an indicator variable for the state being a state that reopened, an
indicator variable for the date being after the reopening date, and the interaction between these two variables. We
report the coefficient and standard error on the interaction term, which we refer to as the difference-in-differences
(DD) estimate of the effect of reopening. Standard errors are clustered at the state level and reported in parentheses.
The dependent variable is rescaled to be in percentage terms such that, for example, the first row of Column (1)
indicates that the difference-in-differences estimate for the effect of reopening on consumer spending over a two-week
horizon is a 1.27 percentage point increase in consumer spending. The third row indicates the “Analysis Window”
used in the regression: for example, the sample in column (1) is restricted to the two weeks before and after the date
of reopening, whereas the sample in column (2) is restricted to the three weeks before and after the date of reopening.
The last row shows the mean decline in the outcome variable across states from the period January 4 to 31, 2020
to the period March 25 to April 14, 2020 - except in Columns (1) and (2) where the reference period is January 6
to February 2, 2020. Columns (1) and (2) show the estimated effect of reopening on consumer spending using data
from Affinity Solutions. Consumer spending is expressed as a percentage change relative to January 6 to February
2, 2020, and seasonally adjusted using 2019 data. Columns (3) and (4) replicate columns (1) and (2) using changes
in employment as the dependent variable. Employment is calculated using Paychex-Intuit data and expressed as a
percentage change relative to January 4 to 31, 2020. Columns (5) and (6) replicate columns (1) and (2) respectively
using the number of small businesses open as the dependent variable, calculated using Womply data and expressed as
a percentage change relative to January 4 to 31, 2020. Columns (1), (3), and (5) correspond to the specifications
displayed in Appendix Figure 26B. Data sources: Affinity Solutions, Paychex, Intuit, Womply.




                                                               93
Appendix Table 12: Causal Effect of the Paycheck Protection Program on Employment

             Dep. Var.:                                   Change in Employment (%)

                                             (1)                                                (2)
                                     Baseline Estimate                                  Smaller Bandwidth
                                    (100-799 Employees)                                (300-699 Employees)

          DD Estimate                       2.48                                               0.08
                                           (2.71)                                             (4.30)


     Notes: This table reports difference-in-differences (DD) estimates of the effect of PPP eligibility (defined as the
parent firm having fewer than 500 employees) on employment. The outcome variable is employment at the county x
2-digit NAICS x wage quartile x PPP eligibility x week level, excluding the Accommodations and Food Services Sector
(NAICS 72), expressed as a percentage change relative to January 4 to 31, 2020. Both columns present regressions in
combined Paychex-Earnin data. In the baseline estimate in column (1), we begin by restricting the sample to firms
with 100-799 employees. We then reweight these cells so that employment shares by industry within each eligibility
group match the overall employment shares by industry in January 4 to 31, 2020. Finally, we report estimates from
an OLS regression of changes in employment on county x wage quartile x week fixed effects, an indicator for PPP
eligibility (firm size < 500 employees), and an interaction term between PPP eligibility and an indicator for the date
being after April 3, 2020 (the DD estimate). The sample for this regression is limited to weeks ending between March
11 and August 15, 2020. The DD estimate is the coefficient on the interaction term for PPP eligibility and the date
being after April 3. We cluster standard errors (reported in parentheses) at the county x industry x eligibility level,
and winsorize the dependent variable at the 99th percentile. We use reweighted employment in January 4 to 31, 2020,
as regression weights. Column (2) replicates Column (1), restricting to firms with between 300 and 699 employees.
Data sources: Paychex, Earnin.




                                                          94
            Figure 1: Changes in Consumer Spending During the COVID Pandemic
                                                                                                     A. Spending Changes by Income Quartile

                                                                           April 14 2020             August 14 2020                                                                                                            December 31 2021
                                                 $10B
                                                                                                                                                                                        Top Income Quartile                         +$0.8 Billion




                Credit and Debit Card Spending
                                                 $8B

                                                                                                                 –$0.6 Billion



                                                 $6B



                       Per Day ($ Billions)
                                                                                   –$3.0 Billion
                                                                                 (41% of Agg. Decline)                                                                                                                              +$0.8 Billion

                                                 $4B                                                           +$0.1 Billion


                                                                                                                                                                                      Bottom Income Quartile
                                                                                   –$0.9 Billion
                                                                                 (12% of Agg. Decline)
                                                 $2B
                                                        Jan 1       Mar 1        May 1           Jul 1         Sep 1           Nov 1                               Jan 1             Mar 1    May 1      Jul 1    Sep 1        Nov 1
                                                        2020                                                                                                       2021




                                                 B. Spending Changes by Sector                                                                                                      C. Spending Changes by Sector: COVID vs Great Recession

  100%                                                                                                                                                                        75%                                                                           71.0%




                                                                                                                                         Share of Peak to Trough Decline in
              Durable Goods
                                                                                             Durable Goods
                                                                                                                                                                                     61.0%
            Non-Durable Goods
   75%
                                                                                                                                                                              50%                                46.0%
                                                                                           Non-Durable Goods
             Remote Services



                                                     ⎫
          Other In-Person Services




                                                                                                                                       Personal Consumption Expenditure (%)
   50%          Recreation
                Health Care
                                                     ⎪                                      Remote Services
                                                                                                                                                                              25%
                                                                                                                                                                                             17.5%
               Transportation
                                                     ⎬    In-Person
                                                          Services (57%)
                                                                                                                                                                                                                            11.6%
                                                                                                                 ⎫
   25%                                                                                Other In-Person Services

                                                     ⎪
                                                                                             Recreation
                                                                                             Health Care                                                                                                                                            -7.0%
               Hotels & Food                                                               Transportation
                                                                                                                 ⎬In-Person
                                                                                                                  Services (28%)                                              0%

   0%
                                                     ⎭                                       Hotels & Food
                                                                                                                 ⎭
                                                                                                                                                                                        Durables                 Non-Durables                         Services
            Share of Decline                                                   Share of Pre-COVID Spending
         (Jan to Mar 25-Apr 14)                                                                                                                                                                           Great Recession                COVID-19




     Notes: This figure disaggregates spending changes by income and sector using debit and credit card data from
Affinity Solutions and national accounts (NIPA) data. Panel A plots daily spending levels for consumers in the highest
and lowest quartiles of household income by combining total card spending in January 2020 (from NIPA Table 2.3.5)
with our Affinity Solutions spending series. See the notes to Appendix Table 5 for details on this method. Panel B
disaggregates the sectoral shares of seasonally-adjusted spending changes (left bar) and pre-COVID spending levels
(right bar). See Appendix B.3 for the definitions of the sectors plotted in Panel B. Panel C decomposes the change in
personal consumption expenditures (PCE) in the Great Recession and the COVID-19 Recession using NIPA Table
2.3.6. PCE is defined here as the sum of durable goods, non-durable goods and services in seasonally adjusted, chained
(2012) dollars. The peak to trough declines are calculated from December 2007 to June 2009 for the Great Recession
and from January to April 2020 for the COVID-19 Recession. Data sources: Affinity Solutions, NIPA.




                                                                                                                                 95
Figure 2: Association Between COVID-19 Incidence and Changes in Consumer Spending



                                         -25%                                            Low-Income Counties (Q1)




       Change in Consumer Spending (%)
                                                                                         Slope = -2.67 (s.e. = 0.69)




                                         -30%




          from January to April 2020
                                         -35%

                                                High-Income Counties (Q4)
                                                Slope = -0.94 (s.e. = 0.66)

                                         -40%
                                                  5                 20                      150                        1100
                                                      County-level COVID-19 Cases Per 100,000 People (Log Scale)


     Notes: This figure presents a county-level binned scatter plot. To construct it, we divide the data into twenty
equal-sized bins, ranking by the x-axis variable and weighting by the county’s population, and plot the (population-
weighted) means of the y-axis and x-axis variables within each bin. The y-axis presents the change in seasonally
adjusted consumer spending from the base period (January 6 to February 2, 2020) to the three-week period of March
25 to April 14, 2020 (see Section II.B.1 and Appendix B for details on the construction of our consumer spending
series). The x-axis variable is the logarithm of the county’s cumulative COVID case rate per capita as of April 14,
2020; with axis labels showing the levels on a logarithmic scale. We plot values separately for counties in the top
and bottom quartiles of median household income (measured using population-weighted 2014-2018 ACS data). Data
sources: Affinity Solutions, New York Times.




                                                                              96
 Figure 3: Changes in Small Business Revenues vs. Median Two Bedroom Rent, by ZIP




       Change in Small Business Revenue (%)
                                              -30%




                                              -40%




            from January to April 2020
                                              -50%




                                              -60%
                                                      Slope = -16.71%/$1000 (s.e. = 0.90)

                                                     $400                $1,000                $1,600               $2,200
                                                                 Median Two Bedroom Monthly Rent in 2014-2018 ($)


    Notes: This figure presents a binned scatter plot showing the relationship between changes in seasonally-adjusted
small business revenue in Womply data vs. rent at the ZIP code level. The binned scatter plot is constructed as
described in Figure 2. We measure changes in small business revenue as the average value of our index at the ZIP
code level between March 23 and April 12, 2020 (see Section II.B.2 and Appendix C for details on the construction of
our small business revenue series). The x-axis variable is the ZIP code median rent for a two-bedroom apartment in
the 2014-2018 ACS. Data sources: Womply, ACS.




                                                                                  97
                                            Figure 4: Changes in Job Postings and Employment Rates vs. Rent
                              A. Job Postings for Low-Education Workers vs. Median Rent, by County                                                                      B. Job Postings for High-Education Workers vs. Median Rent, by County

                                                             April 2020                                                                                                                                 April 2020


                              -25%                                                                                                                                       -25%




 Change in Job Postings (%)                                                                                                                Change in Job Postings (%)
                              -30%                                                                                                                                       -30%


                              -35%                                                                                                                                       -35%




 from January to April 2020                                                                                                                from January to April 2020
                              -40%                                                                                                                                       -40%


                              -45%                                                                                                                                       -45%


                              -50%
                                      Slope = -10.24%/$1000 (s.e. = 2.36)                                                                                                -50%
                                                                                                                                                                                 Slope = -0.12%/$1000 (s.e. = 1.61)
                                     $500                $1,000                $1,500                                        $2,000                                             $500                $1,000                $1,500               $2,000
                                                 Median Two Bedroom Monthly Rent in 2014-2018 ($)                                                                                           Median Two Bedroom Monthly Rent in 2014-2018 ($)




                                                                                                          C. Low-Wage Employment vs. Median Rent, by County

                                                                                                                                July 2020


                                                                                                 -15%




                                                                     Change in Employment (%)
                                                                                                 -20%



                                                                     from January to July 2020



                                                                                                 -25%    Slope = -6.82%/$1000 (s.e. = 1.17)
                                                                                                        $500                $1,000                $1,500                                             $2,000
                                                                                                                    Median Two Bedroom Monthly Rent in 2014-2018 ($)




     Notes: This figure shows binned scatter plots of the relationship between median rents and changes in job postings
(Panels A and B) or changes in employment rates (Panel C). The binned scatter plots are constructed as described in
Figure 2. Solid lines are best-fit lines estimated using OLS. Each panel also displays the slope coefficient and standard
error of the corresponding linear OLS regression. In each panel, the x-axis variable is the median rent within a county
for a two-bedroom apartment in the 2014-2018 ACS. In Panel A, the y-axis variable is the average value of our job
postings series for jobs requiring minimal or some education between March 25 and April 14, 2020 (see Section II.B.3
and Appendix D for more detail on our job postings series). Panel B replicates Panel A with job postings for workers
with moderate, considerable, or extensive education. In both Panels A and B, we winsorize our job postings series at
the 99th percentile of the (population-weighted) county level distribution within each level of required education. In
Panel C, the y-axis variable is the average value of our bottom wage quartile employment series during the month of
July 2020 (see Section II.B.4 and Appendix E for more detail on the construction of our employment series). Data
sources: Paychex, Intuit, Lightcast, ACS.




                                                                                                                                      98
                                 Figure 5: Changes in Employment by Wage Quartile
                                                                             A. Changes in Employment by Wage Quartile

                                                                    April 15 2020                                                                     December 31 2021
                                                  +10%
                                                                                                                                                                   +3%
                                                                                                                                                                  (1.0m)

                                                                                                                                                                    -1%




                      Change in Employment (%)
                                                   0%                                                                                                             (-0.3m)
                                                                                                                                                                    -1%
                                                                                                                                                                  (-0.3m)



                                                  -10%
                                                                 -14%
                                                                (-4.3m
                                                                 jobs)




                       Relative to January 2020
                                                                 -19%
                                                                                                                                                                   -20%
                                                  -20%          (-6.2m)
                                                                                                                                                                  (-6.3m)




                                                                 -29%                                    Top Wage Quartile
                                                  -30%          (-9.3m)
                                                                                                         Third Quartile

                                                                                                         Second Quartile
                                                                  -39%                                   Bottom Wage Quartile
                                                  -40%          (-12.3m)

                                                          Jan    Mar       May       Jul     Sep     Nov       Jan        Mar    May    Jul    Sep     Nov
                                                         2020                                                 2021




                                                   B. Changes in Employment by Wage Quartile, Reweighting Across Industries and Areas
                                                                                                                                                     December 31 2021

                                                                                            Top Wage Quartile                                                   +2.9%
                                                   0%




                      Change in Employment (%)
                                                  -10%
                                                                                 Bottom Wage Quartile, Reweighted
                                                                                 to Match Top Quartile on County x Industry
                                                                                                                                                                -16.9%




                       Relative to January 2020
                                                  -20%                                                                                                          -20.4%
                                                                                           Bottom Wage Quartile

                                                  -30%



                                                  -40%
                                                          Jan   Mar        May      Jul     Sep    Nov       Jan      Mar       May    Jul    Sep    Nov
                                                         2020                                               2021




     Notes: Panel A plots our combined Paychex-Intuit employment series from January 2020 through December 2021
for each wage quartile. We define moving wage quartiles thresholds in each month based on 100%, 150% and 250% of
the federal poverty line (FPL) for a family of four, adjusted for inflation, then converted into a full-time-equivalent
hourly wage by dividing by 2,000 hours (50 weeks of work at 40 hours per week). In January 2020, the thresholds were
$13.10, $19.65 and $32.75 and the four bins in ascending order by wage contained 23.4%, 27.4%, 25.7%, and 23.5%
of CPS respondents. See Section II.B.4 and Appendix E for details on the construction of this series. In Panel B,
we reweight the county-by-industry (2-digit NAICS) distribution of bottom wage quartile employment to match the
distribution for top wage quartile employment in January 2020. For each series in Panel B, we restrict the sample to
county-by-industry cells with non-zero employment in all four wage quartiles in January 2020; this sample restriction
excludes 2.5% of worker-days from the sample. Data sources: Paychex, Intuit.




                                                                                                      99
                                             Figure 6: Evolution of the Association between Low-Education Job Postings and
                                                                   Low-Wage Employment with Rent
                                            A. Job Postings for Low-Education Workers vs. Median Rent, by County                                                                                          B. Low-Wage Employment vs. Median Rent, by County

                                                                               December 2021                                                                                                                                     December 2021




Change in Low-Education Job Postings (%)
                                           +30%                                                                                                                                             -10%




                                                                                                                                                       Change in Low-Wage Employment (%)
                                           +20%


                                                                                                                                                                                            -20%




                                                                                                                                                       from January 2020 to December 2021
  from January 2020 to December 2021
                                           +10%




                                             0%
                                                                                                                                                                                            -30%    Slope = -16.51%/$1000 (s.e. = 3.58)
                                                   Slope = -0.46%/$1000 (s.e. = 3.14)
                                                  $500                      $1,000                        $1,500                            $2,000                                                 $500                     $1,000                         $1,500                            $2,000
                                                                        Median Two Bedroom Monthly Rent in 2014-2018 ($)                                                                                                 Median Two Bedroom Monthly Rent in 2014-2018 ($)




                                           C. Relationship Between Low-Education Job Postings and Rent, by County                                                                            D. Relationship Between Low-Wage Employment and Rent, by County

                                                                                                                                                                                               0%




   Change in Low-Education Job Postings                                                                                                                  Change in Low-Wage Employment
                                              0%
                                                                                                                                                                                              -5%


                                            -10%
                                                                                                                                                                                             -10%




                                                                                                                                                             vs Rent Slope (%/$1000)
        vs Rent Slope (%/$1000)
                                            -20%
                                                                                                                                                                                             -15%


                                            -30%                                                                                                                                             -20%


                                                                                                                           95% Confidence Interval                                                                                                                          95% Confidence Interval
                                            -40%                                                                                                                                             -25%
                                                         May     Jul     Sep     Nov       Jan     Mar      May       Jul        Sep       Nov                                                            May     Jul     Sep      Nov      Jan     Mar      May       Jul        Sep       Nov
                                                         2020                             2021                                                                                                            2020                             2021




    Notes: This figure presents a summary of the results of a set of regressions documenting the relationship between
job postings and employment with rent over time. Panel A replicates Figure 4A, but using the average value of the
low-education job postings series in December 2021 instead of April 2020. Panel B replicates Figure 4C, but using the
average value of the Paychex-Intuit employment series in December 2021 instead of July 2020. The binned scatter
plots are constructed as described in Figure 2. Panel C plots the slope of the best-fit line from a population-weighted
regression of low-education job postings on median county rent (as in Panel A) for each month from April 2020
through December 2021. The slopes estimated in Figures 4A and 6A are the first and last estimates in this series,
respectively. Panel D replicates Panel C for the slope of the bottom wage quartile employment vs. median rent (as in
Panel B). In both Panels C and D, the dashed lines above and below the solid series represent the upper and lower
boundaries of the 95% confidence interval for the slope estimated in each month. Panels B and D omit counties from
CA, MA, and NY, since these three states raised the minimum wage at some point after July 2020 above our upper
threshold for the bottom wage quartile of employment. Data sources: Lightcast, Paychex, Intuit, ACS.




                                                                                                                                                     100
                                                    Figure 7: Effects of Stimulus Payments on Spending: Event Studies
                                                      A. Stimulus 1: Bottom Income Quartile                                                                                                  B. Stimulus 1: Top Income Quartile
                                                                                      Apr Apr                                                                                                                        Apr Apr
                                                                                      14 15                                                                                                                          14 15




 First-Differenced Change in Indexed                                                                                                  First-Differenced Change in Indexed
                                       +40%                                                                                                                                 +40%




                                       +20%                                                                                                                                 +20%




 Spending Relative to DoW FEs (%)                                                                                                     Spending Relative to DoW FEs (%)
                                        0%                                                                                                                                   0%




                                       -20%                                                                                                                                 -20%
                                          Mar 18    Mar 25    Apr 1       Apr 8        Apr 15     Apr 22      Apr 29     May 6                                                 Mar 18    Mar 25   Apr 1    Apr 8         Apr 15   Apr 22      Apr 29     May 6
                                           2020                                                                                                                                 2020




                                                   C. Stimulus 2: Bottom vs. Top Income Quartile                                                                                        D. Stimulus 3: Bottom vs. Top Income Quartile
                                                                              Jan                                                                                                                                  Mar    Mar
                                                                               4                                                                                                                                   13     17

                                                                                                           Bottom Income Quartile                                                                                                          Bottom Income Quartile




 First-Differenced Change in Indexed                                                                                                  First-Differenced Change in Indexed
                                                                                                           Top Income Quartile                                                                                                             Top Income Quartile
                                       +40%                                                                                                                                 +40%




                                       +20%                                                                                                                                 +20%




 Spending Relative to DoW FEs (%)                                                                                                     Spending Relative to DoW FEs (%)
                                        0%                                                                                                                                   0%




                                       -20%                                                                                                                                 -20%
                                                                         //
                                          Dec 4       Dec 8     Dec 12        Jan 4       Jan 8       Jan 12        Jan 16                                                     Feb 17    Feb 24   Mar 3   Mar 10         Mar 17   Mar 24      Mar 31      Apr 7
                                          2020                                2021                                                                                              2021




     Notes: This figure shows event studies of the effect of stimulus payments on consumer spending. We measure
consumer spending using data from Affinity Solutions. To construct each consumer spending time series, we express
consumer spending on each day as a percentage change relative to mean daily consumer spending over January 2019,
residualize these daily percentage changes with respect to day-of-week fixed effects (estimated out-of-sample using
data in 2019), calculate the first difference with respect to values from the corresponding period starting in 2019,
and adjust the estimates for a linear pre-trend in first differences. Panel A depicts this spending time series for 25
days before and after April 15, 2020 (the modal date for deposits of the CARES Act economic impact payments)
for cardholders with residential addresses in the bottom income quartile of ZIP codes. We exclude April 14, 2020
from the pre-period as some households received stimulus payments on this date. Panel B repeats this figure for the
top income quartile of ZIP codes. Panel C repeats Panels A and B for the days around January 4, 2021 (the modal
date for deposits of the COVID-Related Tax Relief Act economic impact payments), plotting outcomes for both the
bottom and top income quartiles. The pre-period in Panel C runs from December 4 to 14, 2020, with the holiday
period (December 15, 2020 to January 3, 2021) removed due to high daily volatility in spending levels (see Section
IV.A and Appendix Figure 23 for more details). The post-period runs from January 4 to 19, 2021, reflecting the data
available when this analysis was originally published on January 26, 2021. Due to the omission of the holiday period,
we do not remove a linear pre-trend as in Panels A and B. Panel D repeats Panel C for the days around March 17,
2021 (the modal date for deposits of the American Rescue Plan Act economic impact payments). We exclude March
13 to 16, 2021 from the pre-period as payments were made starting March 13. In Panels A, B, and D, we interpolate
the value for Easter Sunday using the average of adjacent daily values. Data sources: Affinity Solutions, ACS.




                                                                                                                                    101
                       Figure 8: Impacts of Stimulus Payments on Spending, by Income Quartile

                                                                                                                                                    p = 0.002
                                                                                                                                              ⎫ ⎫
                                                                                                                                              ⎬ ⎪
                                                                                                                                              p = 0.002
                                                                                                                                              ⎭ ⎬




       Estimated 1-Month Spending Impact
                                                                                                                                                ⎪
                                                                                                                                                ⎭
                                           $800
                                                                                                                                             $732

                                                                                                            $640
                                                      p = 0.009
                                           $600    ⎫ ⎫
                                                   ⎬ ⎪                       $565
                                                   p < 0.001
                                                   ⎭ ⎬
                                                     ⎪
                                                     ⎭
                                                  $442




              Per $1200 of Stimulus
                                           $400          p = 0.218
                                                         ⎫
                                                         ⎬
                                                         ⎭
                                                                $272
                                                                                     $211 $222
                                                         $187                                                      $171
                                           $200                                                                           $150                       p = 0.621
                                                                                                                                                     ⎫
                                                                                                                                                     ⎬
                                                                                                                                                     ⎭
                                                                                                                                                           $62
                                                                                                                                                     $35
                                            $0
                                                  Bottom Quartile           Second Quartile                  Third Quartile                   Top Quartile

                                                                                                 January 2021                    March 2021
                                                                       April 2020                (COVID-Related                  (American Rescue
                                                                       (CARES Act)
                                                                                                 Tax Relief Act)                 Plan Act)




     Notes: This figure plots estimates of the marginal propensity to spend out of stimulus payments in the first month
after receipt for each of the three rounds of stimulus payments, separately by income quartile (based on median ZIP
code income). The estimates are scaled per $1,200 of stimulus payment and correspond to the “Combined Dollar”
estimates reported in Column 5 of Appendix Table 6. See Section IV.A and Appendix K.3 for details on how these
estimates were calculated. We also report p-values testing the null hypothesis of equal effect sizes between each pair
of stimulus rounds, for the highest- and lowest-quartile of ZIP-level incomes. These p-values are based on permutation
tests reported in Appendix Figures 24 and 25. Data source: Affinity Solutions.




                                                                                           102
Figure 9: Changes in Employment and Consumer Spending for Low-Income Households vs.
                                 Workplace Rent
                                            A. Low-Wage Employment vs. Workplace Rent, by ZIP                                                                 B. Spending Among Low-Income Households vs. Workplace Rent, by ZIP

                                                                       April 2020                                                                                                                 April 2020

                                    -15%                                                                                                                       -20%




 Change in Employment (%)                                                                                                   Change in Consumer Spending (%)
                                                                                                                                                               -25%
                                    -20%


                                                                                                                                                               -30%


 from January to April 2020
                                                                                                                               from January to April 2020
                                    -25%
                                                                                                                                                               -35%



                                    -30%
                                            Slope = -10.04%/$1000 (s.e. = 0.59)                                                                                -40%
                                                                                                                                                                      Slope = -12.82/$1000 (s.e. = 1.85)
                                           $600        $800    $1,000   $1,200      $1,400   $1,600     $1,800     $2,000                                             $600         $800       $1,000     $1,200      $1,400      $1,600      $1,800
                                                  Average Two Bedroom Monthly Rent in 2014-2018 in Workplace ZIP ($)                                                         Average Two Bedroom Monthly Rent in 2014-2018 in Workplace ZIP ($)




                                   C. Spending Among Low-Income Households vs. Workplace Rent, by ZIP                       D. Spending by Low-Income Households Living in ZIP Codes with High Workplace
                                                                                                                                                               Rent
                                                                     October 2020

                                    +15%
                                                                                                                                                               +40%




 Change in Consumer Spending (%)
                                                                                                                            Change in Consumer Spending (%)
                                    +10%
                                                                                                                                                               +20%


                                     +5%




   from January to October 2020
                                                                                                                                                                 0%




                                                                                                                                   from January 2020
                                      0%
                                                                                                                                                               -20%



                                     -5%
                                            Slope = 0.69/$1000 (s.e. = 3.45)
                                                                                                                                                               -40%
                                           $600       $800      $1,000     $1,200      $1,400      $1,600      $1,800                                                   Mar       May    Jul   Sep   Nov    Jan   Mar    May    Jul   Sep   Nov
                                               Average Two Bedroom Monthly Rent in 2014-2018 in Workplace ZIP ($)                                                       2020                               2021




     Notes: This figure examines the relationship between low-wage employment and consumer spending for individuals
living in a home ZIP code 𝑧 with the average rent in the ZIP codes of the workplaces for low-wage workers who live in
home ZIP code 𝑧. Panel A presents a binned scatterplot showing the relationship between low-wage employment for
workers living in a home ZIP code and the average median rent in the workplace ZIP codes for low-wage workers
from that home ZIP code. We measure low-wage employment in each home ZIP code using the Earnin employment
series in April 2020. We then match each home ZIP code to the distribution of workplace ZIP codes using the Census’
LODES data for low-wage workers. We calculate the x-axis variable as the average median rent for a two-bedroom
apartment (measured in the 2014-2018 ACS), averaged across workplace ZIP codes using the distribution from the
LODES data for each home ZIP code. See Section IV.C for a detailed discussion. Panel B replicates Panel A for a
different outcome: average consumer spending between March 25 and April 14, 2020, restricting to ZIP codes in the
bottom quartile of median income, as measured in the 2014-2018 ACS. Panel C replicates Panel B with consumer
spending instead measured during the month of October 2020. The binned scatter plots are constructed as described
in Figure 2. Panel D plots the average level of consumer spending for the top quartile of households appearing in
Panels B and C ranked on average median workplace rent (i.e., the five right-most dots) in each month from February
2020 through December 2021. Data sources: Earnin, Affinity Solutions, Census LODES, ACS.




                                                                                                                       103
                     Figure 10: Effects of COVID-19 on Educational Progress by Income Group


                                           +20%
                                                                                                                Top Income Quartile


                                                                                                                                  -0.0%




       Students Using Zearn Platform (%)
                                            0%



                                           -20%



           Relative to January 2020                                                                                              -41.2%
                                           -40%


                                                                                                           Bottom Income Quartile
                                           -60%
                                                  Jan 12   Jan 26   Feb 9   Feb 23     Mar 8   Mar 22   Apr 5         Apr 19          May 3
                                                   2020




     Notes: This figure plots a time series of student engagement on the Zearn Math online platform, splitting schools
into quartiles based on the share of students in the school eligible for Free or Reduced Price Lunch (FRPL). We
measure student engagement as the average number of students using the Zearn Math application in each week,
relative to the mean value of students using the platform in the same classroom during the reference period of January
6 to February 7, 2020. We restrict the sample to classrooms with at least 10 students using Zearn on average and
at least 5 students doing so in each week during the reference period. We measure the share of students eligible for
Free and Reduced Price Lunch in each school using demographic data from the Common Core dataset from MDR
Education, a private education data firm. Data sources: Zearn, Common Core.




                                                                                 104
                            Appendix Figure 1: Correction of Structural Breaks in Spending, Small Business
                                                   Revenue, and Employment
                                      A. Break Adjustment - Spending (Ulster County, NY)                                                                                                   B. Break Adjustment - Small Business Revenue (Connecticut)

                            +600%                                                                     Break date: Aug 9, 2021                                                            0%




                                                                                                                                                  Change in Small Businesses Open (%)
                                                                                                                                                                                                                                                     Small Businesses Open series
                                                                                                                                                                                                                                                     in Connecticut experiences a
                                                                                                                                                                                                                                                     7pp drop in the week ending
                                                                                                                                                                                                                                                     in Aug 3, 2020
                                                                                                                                                                                        -10%


 Change in Spending (%)
                            +300%
                                                                                                                                                                                        -20%




                                                                                                                                                       Relative to January 2020
 Relative to January 2020
                                                                                                                                                                                        -30%
                            +100%

                                                                                                                                                                                        -40%
                            +30%

                              0%                                                                                                                                                        -50%
                                     Jan      Mar        May                           Jul               Sep              Nov            Jan                                                    Jan             Mar         May              Jul       Sep            Nov            Jan
                                    2021                                                                                                2022                                                   2020                                                                                 2021

                                                           Before Adjustments                                  Adjusted                                                                                                         Before Adjustments         Adjusted




                                                                                                      C. Break Adjustment - Employment (National, Q2 and Q3)

                                                                                               +30%                                                                                            Threshold separating Q2 and Q3
                                                                                                                                                                                               crosses above $20/hour




                                                                   Change in Employment (%)
                                                                                               +20%


                                                                                               +10%



                                                                    Relative to January 2020    0%


                                                                                               -10%
                                                                                                      Aug             Oct            Dec                             Feb                           Apr            Jun           Aug
                                                                                                      2020                                                           2021                                                       2021

                                                                                                                                Q2 Before Adjustments                                             Q3 Before Adjustments
                                                                                                                                Q2 Adjusted                                                       Q3 Adjusted




     Notes: This figure presents examples of the data adjustment procedures described in Section II.B for the consumer
spending series (Panel A), the small business revenue series (Panel B) and the combined Paychex-Intuit employment
series (Panel C). Data sources: Affinity Solutions, Womply, Paychex, Intuit.




                                                                                                                                           105
   Appendix Figure 2: Industry Shares of Consumer Spending and Business Revenues
                                 Across Datasets
                               A. Compared to QSS                                                                          B. Compared to MARTS




      Finance & Insurance                                                                                Motor Vehicles

      Health & Soc. Assist.                                                                           Food & Beverage

            Prof. Services                                                                                Food Service

                                                                                                     Nonstore Retailers
               Information
                                                                                                  General Merchandise
            Admin Support
                                                                                                           Gas Stations
     Trans. & Warehousing
                                                                                                 Health & Personal Care
         Rental & Leasing
                                                                                                       Building Material
                   Utilities
                                                                                                               Clothing
            Other Services
                                                                                                         Miscellaneous
       Arts, Entmt., & Rec                                                                                    Furniture
                                                                                QSS                                                                                              MARTS
   Accom. & Food Services                                                                                   Electronics
                                                                                Affinity                                                                                         Affinity
                 Education                                                      Womply                Sporting & Hobby                                                           Womply


                               0%           10%                20%                30%                                      0%              10%                20%                30%
                                     Percent of Total Service Revenue in Q1 2020 (%)                                       Percent of Total Retail and Food Service Revenue in January 2020 (%)




    Notes: This figure compares the industry composition of spending in private sector datasets to the industry
composition of spending in representative survey datasets. Panel A shows the NAICS 2-digit industry mix for
transactions in the Affinity Solutions and Womply 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 Affinity Solutions and Womply datasets (respectively) by aggregating
card transactions in Q1 2020, using the merchant to classify the purchase by sector. Panel B shows the NAICS 3-digit
industry mix for the same two sector private datasets compared with the Advance Monthly Retail Trade Survey
(MARTS), 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 MARTS, 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, QSS, MARTS.




                                                                                           106
      Appendix Figure 3: Industry Shares of Job Postings in Lightcast and JOLTS


                                                              25%




                               Industry Share of JOLTS
                                                              20%
                                                                                                     54             62


                                                              15%




                           Job Openings in January 2020 (%)
                                                                            92
                                                              10%                         72
                                                                                               44

                                                                                        31
                                                              5%                        52
                                                                           81 48
                                                                          23
                                                                         42
                                                                         71 53
                                                                             51    61
                                                              0%                                    Correlation: 0.92
                                                                    0%      5%      10%     15%      20%   25%
                                                                             Industry Share of Lightcast
                                                                          Job Postings in January 2020 (%)


     Notes: This figure presents a scatter plot showing the industry share of each 2-digit NAICS code of job postings
in the Job Openings and Labor Turnover Survey (JOLTS) data in January 2020 vs. the corresponding industry share
in job postings in Lightcast data in January 2020. The solid line is a 45 degree line. The annotation in the bottom
right corner of the panel displays the correlation between 2-digit NAICS industry shares in the JOLTS vs. Lightcast
data in January 2020, excluding NAICS 92 (Public Administration), and weighting according to total job openings in
each 2-digit NAICS code in JOLTS in January 2020. Data sources: Lightcast, JOLTS.




                                                                                        107
   Appendix Figure 4: Seasonal Fluctuations in Consumer Spending vs. Employment
                                                              A. Seasonal Fluctuations in Consumer Spending in MARTS Data


                                                +40%




                     Change in Spending (%)
                                                +20%




                       Relative to January       0%



                                                                No Seasonal Adjustment
                                                                With Seasonal Adjustment                                RMSE: 13.44 p.p.
                                                -20%
                                                       2010    2011    2012     2013     2014   2015     2016    2017     2018    2019     2020




                                                                   B. Seasonal Fluctuations in Employment in CES Data


                                                +40%




                     Change in Employment (%)
                                                +20%




                        Relative to January
                                                 0%



                                                                No Seasonal Adjustment
                                                                Seasonal Adjustment                                     RMSE: 1.99 p.p.
                                                -20%
                                                       2010    2011    2012     2013     2014   2015     2016    2017     2018    2019     2020




     Notes: This figure compares seasonal fluctuations in Advance Monthly Retail Trade Survey (MARTS) data
on consumer spending on retail sales and food services (excluding motor vehicle and gas spending) vs. Current
Employment Statistics (CES) data on private sector non-farm employment. Panel A shows seasonal fluctuations
in consumer spending in MARTS data. The series marked in triangles shows trends in consumer spending without
seasonal adjustment, expressed as percentage changes in consumer spending in each month relative to January of the
same year. The series marked in circles shows trends in consumer spending, as seasonally adjusted by the U.S. Census
Bureau, expressed as percentage changes in consumer spending in each month relative to January of the same year.
The annotation in the lower right hand corner displays the RMSE for the difference between the two series. Panel B
replicates Panel A using CES data on private sector, non-farm employment. Data sources: MARTS, CES.




                                                                                         108
Appendix Figure 5: Consumer Spending in National Accounts vs. Credit and Debit Card
                                    Data
                                                                                              A. National Accounts: Changes in GDP and its Components

                                                                                                                   +$0.06T                   +$0.06T




  Change in Real GDP from Q1 2020 to Q2 2020
                                                   $0



                                                -$0.5T
                                                                                     -$0.53T


                                                -$1.0T                                                                                                                                       -$0.90T




       (in trillions of chained 2012 dollars)
                                                                                                                                                                       -$1.20T

                                                -$1.5T
                                                                -$1.61T
                                                               (-29.9%)
                                                -$2.0T
                                                                Gross                 Private                 Government                        Net                   Personal            Credit Card
                                                               Domestic              Domestic                 Expenditures                    Exports                Consumption           Spending
                                                               Product              Investment                                                                    Expenditures (PCE)        in PCE




                                                                                       B. Affinity Solutions Data vs. Advance Monthly Retail Trade Survey


                                                                          Food and Accommodation Services                                                             Retail
                                                 +50%




Month-on-Month Change in
                                                 +25%



                                                   0%




 Consumer Spending (%)                           -25%


                                                                                                                  RMSE: 5.03 p.p.                                                        RMSE: 3.19 p.p.
                                                                                                                  Correlation: 0.93                                                      Correlation: 0.92
                                                 -50%
                                                         Jan        Jul      Jan        Jul           Jan          Jul           Jan Jan         Jul        Jan         Jul        Jan    Jul           Jan
                                                        2019                2020                     2021                       2022 2019                  2020                   2021                 2022

                                                                                      Affinity Solutions Series               Advance Monthly Retail Trade Survey (MARTS)




     Notes: This figure examines changes in consumer spending measured in Affinity Solutions credit and debit card
data, National Income and Product Accounts (NIPA) data, and Advance Monthly Retail Trade Survey (MARTS)
data. Panel A shows the change in GDP from Q1 to Q2 2020 using NIPA data (Tables 1.1.1, 1.1.6 and 2.3.2). The
first bar shows the seasonally-adjusted decline in real GDP ($1.61T). In parentheses under the first bar we report the
compound annual growth rate corresponding to this change in real GDP (-29.9%). Bars two through five decompose
the change in real GDP, estimated using NIPA Table 1.1.1. The final bar shows the contribution of components of
Personal Consumption Expenditures (PCE) that are likely to be captured in credit card spending ($0.90T), estimated
using NIPA Table 2.3.2. 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
indicates total spending (including spending in other modes of payment such as cash) in categories of goods and
services which are likely to be well represented in card spending data, rather than total card spending itself. Panel
B reports month-on-month changes in average daily spending for each month in the Affinity Solutions credit and
debit card data and the Advance Monthly Retail Trade Survey (MARTS). The Food and Accommodation Services
series in Panel B restricts to NAICS 72; the Retail series restricts to NAICS 44-45. The MARTS series in Panel B are
constructed by dividing the total spending in each category by the number of days in that month, and then indexing
to the average daily spending of January 2019. The Affinity series are constructed by taking the monthly average of
the seven-day moving average series, indexed to January 2019. We also report the root mean squared error (RMSE)
corresponding to the difference between these two series. Data sources: Affinity Solutions, NIPA.



                                                                                                                           109
Appendix Figure 6: Consumer Spending Benchmarks, Affinity vs. MARTS and CoinOut
                                                                     A. Consumer Spending in Affinity Data vs. MARTS, by Industry

                                                                                              April 2020


                                                       +50%




                     Change in Affinity Spending (%)
                                                                                             Home Improvement Centers
                                                                                                                             Grocery and Food Stores
                                                          0%

                                                                   Sporting Goods and Hobby                         General Merchandise Stores




                       from January to April 2020
                                                        -50%
                                                          Apparel and Accessories




                                                                                                                                 Correlation: 0.92
                                                       -100%
                                                            -100%                      -50%                      0%                               +50%
                                                                         Change in Advance Monthly Retail Trade Survey Revenue (%)
                                                                                         from January to April 2020




                                                                    B. Cash Spending in CoinOut Transactions Data vs. Card Spending



                                                       +40 p.p.




                     Week-to-Week Change (p.p.)
                                                       +20 p.p.




                       in Consumer Spending
                                                         0 p.p.



                                                       -20 p.p.
                                                                                                                                      Correlation: 0.67

                                                                   Jan   Feb    Mar    Apr    May     Jun    Jul    Aug    Sep        Oct   Nov      Dec
                                                                  2020

                                                                                 CoinOut Total Spending               Affinity Grocery Spending




    Notes: This figure benchmarks Affinity Solutions data against MARTS and CoinOut data. Panel A displays a
scatter plot of changes in spending at the three-digit NAICS code level between January and April 2020 in the Affinity
data vs. the MARTS data, restricting to industries where the industry definitions in the Affinity Solutions data align
closely with a three-digit NAICS code surveyed in the MARTS. We report the correlation between changes in the
Affinity and MARTS data, weighted by total MARTS spending in January 2020. Panel B compares week-to-week
changes of national trends in cash transactions in CoinOut data vs. card spending on groceries in Affinity Solutions
data in 2020. See Appendix B.3 for a description of the CoinOut data. Data sources: CoinOut, Affinity Solutions,
MARTS.




                                                                                                110
          Appendix Figure 7: Association Between COVID-19 Incidence and Mobility




       Change in Time Spent Away From Home (%)
                                                 -15%



                                                 -20%                                                    Low-Income Counties (Q1)
                                                                                                         Slope = -2.45 (s.e. = 0.34)


                                                 -25%




               from January to April 2020
                                                 -30%
                                                        High-Income Counties (Q4)
                                                        Slope = -1.65 (s.e. = 0.23)

                                                 -35%
                                                          5                  20                      150                         1100
                                                               County-level COVID-19 Cases Per 100,000 People (Log Scale)


     Notes: This figure presents a county-level binned scatter plot, constructed as described in Figure 2. The y-axis
presents the change in time spent away from home from the base period (January 3 to February 6, 2020) to the
three-week period of March 25 to April 14, 2020 (see Appendix H for details on the time-away-from-home series from
Google Community Mobility Reports). The x-axis variable is the logarithm of the county’s cumulative COVID case
rate per capita as of April 14, 2020; with axis labels showing the levels on a logarithmic scale. We plot values separately
for counties in the top and bottom quartiles of median household income (measured using population-weighted
2014-2018 ACS data). Data sources: Google Community Mobility Reports, New York Times.




                                                                                      111
Appendix Figure 8: Small Business Revenue Changes vs. Consumer Spending Changes
                                                                            A. Food Services and Accommodation


                                                    +20 p.p.
                                                                                                                  Spending (Affinity Solutions)
                                                                                                                  Small Business Revenue (Womply)




                     Month-on-Month Change (p.p.)
                                                      0 p.p.




                                                    -20 p.p.




                                                                                                                                       Correlation: 0.97
                                                                                                                                       RMSE: 2.68 p.p.
                                                    -40 p.p.
                                                                Jan   Apr       Jul        Oct         Jan       Apr          Jul           Oct
                                                               2020                                   2021




                                                                                      B. Retail Services


                                                    +40 p.p.
                                                                                                                  Spending (Affinity Solutions)
                                                                                                                  Small Business Revenue (Womply)




                     Month-on-Month Change (p.p.)
                                                    +20 p.p.



                                                      0 p.p.



                                                    -20 p.p.



                                                                                                                                       Correlation: 0.88
                                                    -40 p.p.                                                                           RMSE: 6.72 p.p.

                                                                Jan   Apr       Jul        Oct         Jan       Apr          Jul           Oct
                                                               2020                                   2021




    Notes: This figure compares month-on-month changes in total consumer spending (from Affinity Solutions data)
and small business revenue (from Womply data) between January 2020 and December 2021. The spending series is
expressed as a percentage change relative to January 6 to February 2, 2020 and the small business revenue series is
expressed as a percentage change relative to January 4 to 31, 2020. We do not seasonally adjust spending or small
business revenue in this figure because seasonal fluctuations provide useful variation to assess whether the consumer
spending series tracks the small business revenue series. Panel A restricts to food services and accommodation (NAICS
code 72), and Panel B restricts to retail trade sectors (NAICS code 44-45). The bottom right corner of each panel
reports the root mean squared error (RMSE) corresponding to the difference between the two lines and the correlation
between the month-on-month changes series. Data sources: Affinity Solutions, Womply.




                                                                                           112
 Appendix Figure 9: National Trends in Small Business Revenue and Job Postings for
                            Low-Education Workers
                                                                                   A. Small Business Revenue, January 2020 to December 2021


                                                            +20%




                     Change in Small Business Revenue (%)
                                                            +10%

                                                             0%

                                                            -10%




                           Relative to January 2020
                                                            -20%

                                                            -30%

                                                            -40%

                                                            -50%
                                                                   Jan 1   Mar 1      May 1      Jul 1   Sep 1   Nov 1    Jan 1    Mar 1   May 1    Jul 1    Sep 1    Nov 1
                                                                   2020                                                   2021




                                                                       B. Job Postings for Low-Education Workers, January 2020 to December 2021


                                                            +60%




                     Change in Job Postings (%)
                                                            +40%


                                                            +20%




                      Relative to January 2020
                                                             0%


                                                            -20%


                                                            -40%

                                                                   Jan 1   Mar 1      May 1     Jul 1    Sep 1   Nov 1    Jan 1   Mar 1    May 1    Jul 1   Sep 1     Nov 1
                                                                   2020                                                   2021


                                                                                              National Series            National Series with Q1 employment weights




    Notes: Panel A shows the changes in small business revenue between January 2020 and December 2021, relative
to January 4 to 31, 2020. Panel B shows the changes in job postings for low-education workers between January 2020
and December 2021, relative to January 4 to 31, 2020. To address potential spatial mismatch in job postings, in the
orange series of panel B, we reweight county-level job postings by the number of bottom-wage-quartile workers in
January 2020 (as derived from the Paychex-Intuit data) and aggregate to the national level. Data sources: Womply,
Lightcast, Paychex, Intuit.




                                                                                                                 113
          Appendix Figure 10: Changes in Small Business Revenues by ZIP Code
                        A. San Francisco                                              B. Chicago

                   March 23 to April 12, 2020                                  March 23 to April 12, 2020




                                                     C. New York City

                                                 March 23 to April 12, 2020




     Notes: This figure plots seasonally-adjusted changes in small business revenue by ZIP code in the MSAs
corresponding to San Francisco-Oakland-Hayward, CA (Panel A), Chicago-Naperville-Elgin, IL-IN-WI (Panel B), and
New York-Newark-Jersey City, NY-NJ-PA (Panel C). The changes are measured during March 23 to April 12, 2020
relative to January 4 to 31, 2020. We seasonally-adjust revenue in each week by dividing the indexed value relative to
January for that week in 2020 by the corresponding indexed value from 2019. These maps must be viewed in color to
be interpretable; dark red colors represent areas with larger revenue declines, while dark blue colors represent areas
with smaller declines. Data source: Womply.




                                                           114
Appendix Figure 11: Changes in Small Business Revenues vs. ZIP Code Characteristics
                                                                                         A. Median Income, by ZIP

                                                                                                 April 2020




                     Change in Small Business Revenue (%)
                                                            -40%




                                                            -45%




                          from January to April 2020
                                                            -50%




                                                            -55%
                                                                   Slope = -0.15%/$1000 (s.e. = 0.01)

                                                              $25,000          $50,000      $75,000       $100,000     $125,000           $150,000
                                                                                     Median Household Income in 2014-2018 ($)




                                                                                        B. Population Density, by ZIP

                                                                                                 April 2020


                                                            -30%




                     Change in Small Business Revenue (%)
                                                            -40%




                          from January to April 2020
                                                            -50%




                                                            -60%
                                                                   Slope = -3.81% (s.e. = 0.31)

                                                                   50      100               400             1,600           6,400          25,600
                                                                        Population Density: Inhabitants per Square Mile in 2014-2018 (Log Scale)



     Notes: This figure presents binned scatter plots showing the relationship between changes in seasonally-adjusted
small business revenue in Womply data vs. various local area characteristics at the ZIP code level. The binned
scatter plots are constructed as described in Figure 2. In each panel, we measure changes in small business revenue as
the average value of our index at the ZIP code level between March 23 and April 12, 2020 (see Section II.B.2 and
Appendix C for details on the construction of our small business revenue series). In Panel A, the x-axis variable
is median household income at the ZIP code level from the 2014-2018 ACS. In Panel B, the x-axis variable is the
logarithm of the number of ZIP code inhabitants per square mile in the 2014-18 ACS; with axis labels showing the
levels on a logarithmic scale. Data sources: Womply, ACS.




                                                                                                   115
  Appendix Figure 12: Changes in Small Business Revenues vs. Local Characteristics
                                                                                                                 A. Median Two Bedroom Rent, by ZIP; Controlling for Sector FEs




                                                    Change in Small Business Revenue (%)
                                                                                           -30%




                                                                                           -40%




                                                         from January to April 2020
                                                                                           -50%




                                                                                           -60%
                                                                                                   Slope = -15.32% (s.e. = 0.61)
                                                                                                  $400                         $1,000                $1,600                                                                                         $2,200
                                                                                                                       Median Two Bedroom Monthly Rent in 2014-2018 ($)


     B. Income Share of the Top 1% of the Local Income Distribution, by County                                                                                                          C. Share of the Population Below the Poverty Line, by County

                                             -40%




      Change in Small Business Revenue (%)                                                                                                       Change in Small Business Revenue (%)
                                                                                                                                                                                        -45%
                                             -45%



                                             -50%




           from January to April 2020                                                                                                                 from January to April 2020
                                                                                                                                                                                        -50%


                                             -55%


                                                         Slope = -0.64% (s.e. = 0.08)                                                                                                           Slope = -0.08% (s.e. = 0.13)
                                             -60%                                                                                                                                       -55%
                                                    5%                                     10%      15%         20%        25%    30%      35%                                                 5%               10%               15%                20%              25%
                                                                                                      Top 1% Income Share (%)                                                                         Share of the Population Below the Poverty Line in 2014-2018 (%)




    Notes: This figure shows the association between ZIP-level or county-level characteristics and changes in small
business revenues between January 4 to 31, 2020 and March 23 to April 12, 2020, as measured in Womply data. Panel
A presents a binned scatter plot of changes in small business revenue at the ZIP x sector (2-digit NAICS) level vs.
median two-bedroom rent at the ZIP level, controlling for sector fixed effects. Panels B and C replicate Figure 3 but
compare the declines in small business revenue with various measures of the distribution of income at the county level.
Panel B presents a binned scatter plot of changes in small business revenue vs. the income share of the top 1% of the
income distribution within each county, constructed using the distribution of parent incomes in Chetty et al. (2014).
The top 1% of the income distribution is defined using the distribution of incomes within each county, rather than the
national income distribution. Panel C presents a binned scatter plot of changes in small business revenue vs. the
share of the county population with incomes below the poverty line in the 2014-2018 ACS. The binned scatter plots
are constructed as described in Figure 2. Data source: Womply.




                                                                                                                                             116
                                                   Appendix Figure 13: Changes in Employment Rates Over Time
                                                                                                            A. Pooling All Industries



                             +10 p.p.                                          April 15 2020




 Month-to-Month Change
                                         0 p.p.




  in Employment (p.p)
                               -10 p.p.



                                                                                                                                                             RMSE CES: 1.89 p.p.           RMSE CPS: 1.33 p.p.
                                                                                                                                                             Corr CES: 0.93                Corr CPS: 0.96
                               -20 p.p.
                                                    Jan            Mar           May           Jul         Sep             Nov       Jan      Mar              May          Jul             Sep             Nov
                                                   2020                                                                             2021



                                                                                                        Tracker Employment Series             CES                    CPS




                                                                                          B. Accommodation and Food Services vs. Professional Services




                                                                          Food and Accommodation Services                                             Professional Services
                                                 +20 p.p.                April 15 2020                                                       April 15 2020




                         Month-to-Month Change
                                                   0 p.p.




                          in Employment (p.p.)
                                                 -20 p.p.



                                                                                                               RMSE: 4.00 p.p.                                                          RMSE: 1.75 p.p.
                                                 -40 p.p.                                                      Correlation: 0.92                                                        Correlation: 0.81
                                                             Jan         May        Sep         Jan      May         Sep             Jan    May          Sep          Jan         May         Sep
                                                            2020                               2021                                 2020                             2021




                                                                                                         Tracker Employment Series            CES




     Notes: This figure compares month-to-month changes in employment, as measured in three different datasets.
The series for the combined Paychex-Intuit dataset plots values of the changes measured as of the week of the 15th
of a given month relative to the same value on the week of the 15th of the prior month. The series for the Current
Employment Statistics (CES) and Current Population Survey (CPS) plot the change to the current monthly value
relative to the prior monthly value. Panel A presents data from these three series pooling employment in all private
non-farm sectors. Panel B repeats Panel A restricting to employment in the Food and Accommodation sector (NAICS
72) and the Professional and Business Services sector (NAICS 61-62). Data sources: Paychex, Intuit, CES, CPS.




                                                                                                                      117
Appendix Figure 14: Changes in Employment as of July 2020 in Paychex-Intuit Data vs.
                               CES and QCEW
                                           A. Change in Employment Rates by Industry: Paychex-Intuit vs. CES                                                                                                      B. Change in Employment Rates by State: Paychex-Intuit vs. CES

                                            0%                                                                                                                                                                    0%




 Paychex-Intuit Change in Employment (%)                                                                                                                               Paychex-Intuit Change in Employment (%)
                                                                                                                                                                  23                                                                                                                 ME
                                                                                                                                                    22                                                                                                                                WY     SD
                                                                                                                                                                                                                                                                                     NE
                                                                                                                                           54            52                                                                                                                                    MT
                                                                                                                                                                                                                                                                      NH  CO
                                                                                                                                                                                                                                                                           KSINMO   DE
                                                                                                                                                                                                                                                                          TNMS
                                                                                                                                                                                                                                                                       IL     OK
                                                                                                                                                                                                                                                                             OH
                                                                                                                                    53                                                                                                                            FL    VA  SCAR WI                    ID
                                                                                                                                 42
                                                                                                                                  31-33                                                                                                                                 MD
                                                                                                                                                                                                                                                                        NC
                                                                                                                                                                                                                                                                       MNKY            UT
                                           -10%                                                                         56                                                                                       -10%                                           CTPAOR GA
                                                                                                                                                                                                                                                               MA
                                                                                                                                                                                                                                                               MI
                                                                                                                                62                                                                                                                           NM RI
                                                                                                                                  44-45                                                                                                                          AZ     WA
                                                                                                                                                                                                                                                                         ND
                                                                                                                                                                                                                                                                        TX                        AK
                                                                                                                   81 51                                                                                                                                    NJ
                                                                                                21                     48-49                                                                                                               NV             CA LA                AL
                                                                                                                                          55                                                                                                    NY                              IA
                                                                                                                                                                                                                                                     VT             DCWV




        from January to July 2020                                                                                                                                             from January to July 2020
                                           -20%                                                                                                                                                                  -20%
                                                                71                              61
                                                     72
                                                                                                                                                Correlation: 0.97                                                       HI
                                                                                                                                                                                                                                                                                      Correlation: 0.73
                                                                                                                                                RMSE: 5.24 p.p.                                                                                                                       RMSE: 3.13 p.p.
                                           -30%                                                                                                                                                                  -30%
                                              -20%                                              -15%           -10%              -5%                 0%                                                                  -20%         -15%            -10%            -5%                              0%
                                                                                                       CES Change in Employment (%)                                                                                                      CES Change in Employment (%)
                                                                                                         from January to July 2020                                                                                                         from January to July 2020




                                                                                                                 C. Change in Employment Rates by Commuting Zone: Paychex-Intuit vs. QCEW


                                                                                                     0%




                                                      Paychex-Intuit Change in Employment (%)
                                                                                                                                                                                                                                   Brownsville

                                                                                                     -5%                                                                                           Kansas City
                                                                                                                                                                                               Denver
                                                                                                                                                                                                  Charlotte
                                                                                                                                                                             Port St. Lucie          St. Louis
                                                                                                                                                                                                            Indianapolis
                                                                                                                                                                                   Pittsburgh
                                                                                                                                                                                     ChicagoCleveland
                                                                                                                                           Orlando                                     Nashville Milwaukee
                                                                                                                                                                             Grand Rapids           Cincinnati
                                                                                                                                                                                                             Salt Lake City
                                                                                                                                                                                                  Jacksonville
                                                                                                                                                                                                 Oklahoma    City
                                                                                                                                                                           Washington DC TampaColumbus       Fresno
                                                                                                 -10%                                                                             Houston
                                                                                                                                                                            Buffalo     Sacramento
                                                                                                                                                                                    Bridgeport
                                                                                                                                                                                      Baltimore
                                                                                                                                                                               Philadelphia




                                                             from January to July 2020
                                                                                                                                                                                Atlanta
                                                                                                                                                                                      MinneapolisRaleigh
                                                                                                                                                                  San DiegoBoston
                                                                                                                                                                  Miami       Detroit
                                                                                                                                                                               Providence
                                                                                                                                                                                      Phoenix
                                                                                                                                                                                     San  Antonio
                                                                                                                                                                                    Portland
                                                                                                                                                                                      Seattle     Dallas

                                                                                                                                   New Orleans            Los Angeles
                                                                                                                                                                 Newark                                                              San
                                                                                                                                                                                                                                Austin
                                                                                                                                                                                                                                Fort     Jose
                                                                                                                                                                                                                                     Worth
                                                                                                 -15%                Las Vegas

                                                                                                                                                              San Francisco
                                                                                                                                                                                                                                                            Correlation: 0.75
                                                                                                                               New York                                                                                                                     RMSE: 3.24 p.p.
                                                                                                 -20%
                                                                                                        -20%                              -15%            -10%              -5%                                                                                             0%
                                                                                                                                              QCEW Change in Employment (%)
                                                                                                                                                 from January to July 2020



    Notes: This figure benchmarks the Paychex-Intuit combined employment series to the Current Employment
Statistics (CES) and the Quarterly Census of Employment and Wages (QCEW). Panel A shows a scatter plot of
changes in employment in Paychex-Intuit combined data between January 4 to 31, 2020 and July 17, 2020 vs. changes
in CES employment between January and July by industry (2-digit NAICS code), for private non-farm industries.
Panel B shows a scatter plot of changes in employment in Paychex-Intuit combined data between January 4 to 31,
2020 and July 17, 2020 vs. changes in CES employment between January and July, by state. Panel C shows a scatter
plot of changes in employment in Paychex-Intuit combined data between January 4 to 31, 2020 and July 17, 2020 vs.
changes in QCEW employment between January and July, by commuting zone for the 50 largest commuting zones by
population. In all panels, the bottom right corner displays the correlation between the data points in each graph,
weighted respectively by CES employment in each NAICS code (Panel A), state population (Panel B), and commuting
zone population (Panel C). Data sources: Paychex, Intuit, CES, QCEW.




                                                                                                                                                                  118
                              Appendix Figure 15: Employment in Paychex-Intuit Data vs. ADP and CPS
                                                                                       A. Trends in Employment Rates by Wage Quartile: Paychex-Intuit vs. ADP

                                                                                                                                                                                                   Apr 17



                                                                    0%




                                       Change in Employment (%)
                                                                   -10%
                                                                                       Paychex-Intuit Q1 (Bottom)
                                                                                       Q2




                                        Relative to January 2020
                                                                   -20%                Q3
                                                                                       Q4 (Top)
                                                                                       ADP Q1 (Bottom)
                                                                   -30%                Q2
                                                                                       Q3
                                                                                       Q4
                                                                                       Q5 (Top)
                                                                   -40%
                                                                          Jan 15                             Feb 15                                        Mar 15                                 Apr 15                    May 15



B. Change in Employment Rates by Wage Quartile: Paychex-Intuit vs. ADP vs. CPS                                                                               C. Change in Employment Rates by Wage Quartile: Paychex-Intuit vs. CPS

                                                                          May 2020                                                                                                                 December 2021


                             0%                                                                                                                                          +20%




                                                                                                                                         Change in Employment (%)
                                                                                                                                                                         +10%



 Change in Employment (%)
                            -10%
                                                                                                                                                                          0%


                            -20%                                                                                                                                         -10%



 from January to May 2020
                                                                                                                                    from January 2020 to December 2021
                                                                                                                                                                         -20%
                            -30%
                                                                                                                 Paychex-Intuit                                          -30%
                                                                                                                 CPS                                                                                                                   Paychex-Intuit
                            -40%                                                                                 ADP                                                     -40%                                                          CPS

                                   Bottom Quartile                   Second Quartile        Third Quartile     Top Quartile                                                     Bottom Quartile   Second Quartile    Third Quartile   Top Quartile
                                                                               Wage Quartile                                                                                                                Wage Quartile




     Notes: This figure benchmarks the Paychex-Intuit combined employment series to the Current Population
Survey (CPS) and estimates based on ADP data in Cajner et al. (2020). Panel A shows employment trends in
the Paychex-Intuit combined data (solid series) and ADP data (dashed series), split by wage quartile (combined
Paychex-Intuit data) or wage quintile (ADP data). The Paychex-Intuit series is expressed as a percentage change
relative to January 4 to 31, 2020. The ADP series (from Cajner et al. 2020) is expressed as a percentage change
relative to February 1, 2020. Panel B shows changes in employment in the Paychex-Intuit, ADP, and CPS datasets
from January to May 2020, split by wage quartile. The CPS series is expressed as a percentage change relative to
January 2020. The ADP series is expressed as a percentage change relative to February 1, 2020. We omit the third
wage quintile of the ADP series and compare the fourth and fifth quintiles of the ADP series to the third and fourth
quartiles of the Paychex-Intuit series. Panel C replicates Panel B in December 2021 for the combined Paychex-Intuit
series and CPS. For Panel C, we drop CA, MA, and NY since these three states raised the minimum wage above our
upper wage threshold for bottom wage quartile employment after July 2020. Data sources: Paychex, Intuit, CPS,
ADP.




                                                                                                                                  119
                                    Appendix Figure 16: Changes in Low-Wage Employment by ZIP Code
                                    A. Low-Wage Employment vs. Median Rent, by ZIP                B. Change in Low-Wage Employment in San Francisco

                                                        July 2020                                                    April 2020




                             -20%




 Change in Employment (%)
                             -25%




 from January to July 2020
                             -30%




                             -35%
                                      Slope = -9.33%/$1000 (s.e. = 0.61)

                                    $500          $1,000          $1,500           $2,000
                                        Median Two Bedroom Monthly Rent in 2014-2018 ($)




                                      C. Change in Low-Wage Employment in Chicago                 D. Change in Low-Wage Employment in New York City

                                                       April 2020                                                    April 2020




    Notes: Panel A shows a binned scatter plot of the relationship between low-wage employment and median rent at
the ZIP code level, constructed as described in Figure 2. The x-axis variable is the median rent within a ZIP code for
a two-bedroom apartment in the 2014-2018 ACS. The y-axis variable is the average value of low wage employment at
the ZIP code level from Earnin during the month of July 2020 (see Section II.B.4 and Appendix E for more detail on
the construction of the Earnin employment series). Panels B, C and D replicate Appendix Figure 10 using Earnin
data on changes in employment among low-wage workers, plotted by employer ZIP code. We measure the change
in employment as total average weekly employment between March 27 and April 24, 2020 divided by total average
weekly employment between January 4 and 31, 2020. These maps must be viewed in color to be interpretable; dark
red colors represent areas with larger employment declines, while dark blue colors represent areas with smaller declines.
Data sources: Earnin, ACS.




                                                                                            120
   Appendix Figure 17: Geography of Employment Losses in the Great Recession vs.
                              COVID Recession

                                         30%




       Share of Employment Changes (%)
                                         20%




                                         10%




                                         0%
                                                2007 to 2010             Jan to Apr 2020     Week 11 to Week 14 2020
                                               Employment Loss          Employment Loss             UI Claims

                                                                 Quartile of County Median Income
                                                          Bottom            Second          Third        Top




     Notes: This figure displays the share of job losses occurring in low vs. high income counties during the Great
Recession and the COVID Recession. We split counties into population-weighted quartiles by median household income
in the 2006 ACS for the Great Recession (left bars) and the 2014-2018 ACS for the COVID Recession (middle and
right bars). To construct the first set of four bars, we use BLS data to measure the share of the national employment
losses from 2007 and 2010 occurring within counties in each quartile of median household income. The second set of
bars replicates the first set of bars using the employment losses from January to April 2020. The third set of bars
reports the share of total initial UI claims within each county income quartile between March 15, 2020 (the first
week of COVID-related UI claims) and April 12, 2020. In this third set of bars, we only include counties within
states that issue weekly reports of county-level UI claims data; these states include 53% of the U.S. population. The
increase in unemployment rates between February and April 2020 (11%) was only two-thirds as large as the decrease
in employment (16%). The difference was due to a 5% decline in the labor force: many people lost their jobs but were
not actively searching for a new job in the midst of the pandemic (Coibion, Gorodnichenko, and Weber 2020). In the
three prior recessions, the labor force continued to grow by 0.3% to 0.8% annually. We therefore focus on the decline
in employment rates to obtain comparable statistics on job loss across recessions. Data source: BLS.




                                                                           121
Appendix Figure 18: Changes in Employment by Wage Quartile and Consumer Spending,
                                 Retail Trade
                                                                                                                   December 31 2021

                                                                          Retail Consumer Spending
                                                                                                                              +25.3%
                                   +20%




       Change in Employment (%)
                                                                                                                              +4.6%
                                    0%
                                                                    Retail Employment: Top Wage Quartile



        Relative to January 2020   -20%                                                                                       -19.4%
                                                                   Retail Employment: Bottom Wage Quartile



                                   -40%
                                           Jan   Mar   May   Jul    Sep    Nov    Jan   Mar   May    Jul     Sep   Nov
                                          2020                                   2021




    Notes: This figure plots our combined Paychex-Intuit employment series for top and bottom wage quartile jobs in
the Retail Trade sector (NAICS 44-45), along with our consumer spending series for this sector (see Section II.B.1 and
Appendix B for details on the construction of the consumer spending series). Data sources: Paychex, Intuit, Affinity
Solutions.




                                                                             122
    Appendix Figure 19: Relationship between Employment and Rent in CPS Panel


                                   0%




        Change in Employment
                                  -5%




       vs. Rent Slope (%/$1000)   -10%




                                  -15%
                                         Bottom Quartile   Second Quartile   Third Quartile   Top Quartile




     Notes: This figure plots regression estimates of the relationship between state-level changes in employment,
measured separately for each wage quartile, and median two-bedroom rent in the CPS panel. We obtain the point
estimates and 95% confidence intervals from population-weighted state-level OLS regressions. We use the change in
employment between each respondent’s initial survey (conducted 12 months before their follow-up survey) and their
follow-up survey in July 2020 to February 2021. To construct the CPS panel, we first restrict the sample to the set
of individuals who were employed during their initial survey and had their follow-up survey between July 2020 and
February 2021. We then classify these individuals by their wage quartile during their initial survey (July 2019 to
February 2020) based on inflation-adjusted values of the Federal Poverty Line, adding uniform noise between [-$0.50,
$0.50] to whole number wages to smooth out spikes in the wage distribution at whole numbers. See Section II.B.4 for
more details on the wage quartile thresholds and smoothing. The sample omits California, Massachusetts, and New
York due to mismeasurement of bottom-quartile employment changes as a result of minimum wage increases; see
Appendix E.2 for more details. Data sources: CPS.




                                                                     123
          Appendix Figure 20: Changes in Labor Supply due to Population Shifts
                                                                  A. Trends in Low-Wage Employment for US-Born vs. Naturalized Citizens vs. Non-Citizens




                      Employment Relative to January 2020 (%)
                                                                +20%


                                                                 0%


                                                                -20%


                                                                -40%


                                                                -60%
                                                                        Jan    Mar     May       July   Sep     Nov       Jan       Mar   May      July     Sep     Nov
                                                                       2020                                              2021

                                                                               Q1 Citizens at Birth       Q1 Naturalized Citizens         Q1 Non-citizens          Q1 Total




                                                                                                B. Net In-Migration vs. Rent in 2021


                                                                 +5%




                      Net In-Migration Share (%)
                                                                  0%


                               in 2021




                                                                  -5%
                                                                         Slope = -1.51%/$1000 (s.e. = 0.36)
                                                                        $600         $800      $1,000    $1,200       $1,400     $1,600                           $1,800
                                                                                  Median Two Bedroom Monthly Rent in 2014-2018 by State ($)



    Notes: Panel A uses CPS data to show the employment change relative to January 2020 for workers in the bottom
wage quartile, comparing those who are U.S. citizens at birth, naturalized U.S. citizens, or non-U.S. citizens. Panel B
presents a binned scatter plot showing the association between net in-migration shares in 2021 and median rent at the
state level in the ACS 2014-2018. The binned scatter plot is constructed as described in Figure 2. For each state
we calculate the net in-migration share as in-migration minus out-migration divided by the state population, using
Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Data sources: CPS, ACS.




                                                                                                               124
Appendix Figure 21: Effects of Stimulus Payments on Spending, without Adjusting for
                                   Pre-Trends
                                                   A. Stimulus 1 Event Study, Without Detrending                                                                                       B. Stimulus 2 Event Study, Without Detrending
                                                                                Apr Apr                                                                                                                                     Jan
                                                                                14 15                                                                                                                                        4

                                                                                                      Bottom Income Quartile                                                                                                                                       Bottom Income Quartile




 First-Differenced Change in Indexed                                                                                             First-Differenced Change in Indexed
                                                                                                      Top Income Quartile                                                                                                                                          Top Income Quartile
                                       +40%                                                                                                                            +40%




                                       +20%                                                                                                                            +20%




 Spending Relative to DoW FEs (%)                                                                                                Spending Relative to DoW FEs (%)
                                        0%                                                                                                                              0%




                                       -20%                                                                                                                            -20%
                                                                                                                                                                                                                      //
                                          Mar 18    Mar 25   Apr 1    Apr 8         Apr 15   Apr 22      Apr 29     May 6                                                     Dec 4           Dec 8       Dec 12           Jan 4           Jan 8              Jan 12            Jan 16
                                           2020                                                                                                                               2020                                         2021




                                                   C. Stimulus 3 Event Study, Without Detrending                                                                         D. Stimulus Effect Sizes by Income Quartile, Without Detrending
                                                                              Mar    Mar
                                                                              13     17                                                                                $800
                                                                                                                                                                                                                                                                                  ⎫ ⎫   p = 0.001




                                                                                                                                 Estimated 1-Month Spending Impact
                                                                                                                                                                                                                                                                                  ⎬ ⎪
                                                                                                                                                                                                                                                                                  ⎭ ⎬
                                                                                                      Bottom Income Quartile                                                                                                                                                      p < 0.001
                                                                                                                                                                                                                                                                                    ⎪
                                                                                                                                                                                                                                                                                    ⎭




 First-Differenced Change in Indexed
                                                                                                      Top Income Quartile
                                       +40%                                                                                                                                                                                                                                      $601
                                                                                                                                                                       $600        p = 0.047                                                  $557
                                                                                                                                                                                ⎫ ⎫
                                                                                                                                                                                ⎬ ⎪
                                                                                                                                                                                ⎭ ⎬
                                                                                                                                                                                p < 0.001
                                                                                                                                                                                  ⎪                         $501
                                                                                                                                                                                  ⎭
                                                                                                                                                                                $401 p = 0.001
                                                                                                                                                                                      ⎫
                                                                                                                                                                                      ⎬
                                       +20%                                                                                                                            $400           ⎭




                                                                                                                                        Per $1200 of Stimulus
                                                                                                                                                                                             $325
                                                                                                                                                                                                                                                                                         p = 0.167
                                                                                                                                                                                                                                                                                         ⎫
                                                                                                                                                                                                                                                                                         ⎬
                                                                                                                                                                                                                           $305




 Spending Relative to DoW FEs (%)
                                                                                                                                                                                                                                                                                         ⎭
                                                                                                                                                                                                                                                            $261
                                                                                                                                                                                                                                                                                               $243
                                                                                                                                                                                                                    $211
                                                                                                                                                                                      $187
                                                                                                                                                                       $200                                                                          $171
                                        0%

                                                                                                                                                                                                                                                                                         $35

                                                                                                                                                                        $0
                                       -20%                                                                                                                                    Bottom Quartile             Second Quartile                     Third Quartile                     Top Quartile

                                          Feb 17    Feb 24   Mar 3   Mar 10         Mar 17   Mar 24      Mar 31      Apr 7                                                                                                         January 2021                      March 2021
                                                                                                                                                                                                      April 2020
                                           2021                                                                                                                                                       (CARES Act)                  (COVID-Related                    (American Rescue
                                                                                                                                                                                                                                   Tax Relief Act)                   Plan Act)




    Notes: This figure shows event studies of spending around stimulus payments without adjusting for linear
pre-trends. Panel A shows the equivalent of Figures 7A and 7B without adjusting for linear pre-trends. Panel B is
identical to Figure 7C, since both figures do not adjust for linear pre-trends. Panel C shows the equivalent of Figure
7D without adjusting for linear pre-trends. Panel D shows the equivalent of Figure 8 without adjusting for linear
pre-trends. Data source: Affinity Solutions.




                                                                                                                               125
Appendix Figure 22: Robustness of Stimulus Effect Size to Post-Stimulus Event Window
                                                                                     A. Bottom Income Quartile
                                                         $800




                     Estimated 1-Month Spending Impact
                                                         $600



                                                         $400




                            Per $1200 of Stimulus
                                                         $200



                                                           $0


                                                                                                                              25 Day Post-Period
                                                         -$200
                                                                 7                  14                        21                       28
                                                                            Number of Days After Stimulus Included in Post-Period

                                                                     April 2020 Stimulus     January 2021 Stimulus    March 2021 Stimulus



                                                                                       B. Top Income Quartile
                                                         $800




                     Estimated 1-Month Spending Impact
                                                         $600



                                                         $400




                            Per $1200 of Stimulus
                                                         $200



                                                           $0


                                                                                                                              25 Day Post-Period
                                                         -$200
                                                                 7                  14                        21                       28
                                                                            Number of Days After Stimulus Included in Post-Period

                                                                     April 2020 Stimulus     January 2021 Stimulus    March 2021 Stimulus


     Notes: This figure displays coefficient estimates from varying post-period windows as a robustness check for the
analysis displayed in Figure 7, Figure 8, and Appendix Table 6. Each dot corresponds to a different estimate for the
“Combined Dollar” effects from Appendix Table 6, Column 5. Panel A plots estimates for cardholders residing in
the bottom income quartile of ZIP codes, varying the post-period from 7 to 30 days, for each of the three rounds of
stimulus. Panel B repeats this analysis for cardholders residing in the top income quartile of ZIP codes. For the April
2020 and March 2021 stimulus rounds, the estimate at 25 days matches the estimate in Appendix Table 6, Column 5;
for the January 2021 stimulus round, the estimate at 16 days matches the estimate in Appendix Table 6, Column 5.
See Section IV.A and the notes to Figure 7 and Appendix Table 6 for details on how these estimates were calculated.
Data source: Affinity Solutions.




                                                                                               126
                                                                         Appendix Figure 23: Daily Variance of Consumer Spending, by Week




       Within-Week Variance of Daily Change in Indexed Spending (p.p.)
                                                                                                  Stimulus 1                                                           Stimulus 2            Stimulus 3
                                                                                                               Memorial Day   July 4    Labor Day         Thanksgiving Christmas   President's Day
                                                                          2000 p.p.

                                                                                 log scale


                                                                           800 p.p.          \\




                                                                           600 p.p.




           First-Difference Relative to 2019, Residualized DoW FEs
                                                                           400 p.p.



                                                                           200 p.p.



                                                                             0 p.p.
                                                                                Mar 2020              May 2020            Jul 2020     Sep 2020      Nov 2020          Jan 2021          Mar 2021


                                                                                                         Stimulus Pre-Period                  Stimulus Post-Period                     Holiday Period




     Notes: This figure depicts the within-week daily variance in the first-differenced change in indexed spending,
which we construct as described in Appendix K.1. We then plot a 7-day trailing average of variance, omitting the
points in the 6 days after holidays. The values in the Thanksgiving period range from 804p.p. to 1834p.p. – values
between 800p.p. and 2000p.p. are plotted on a log scale rather than a linear scale to preserve visual clarity. Three
vertical dashed lines mark the timing of deposits for each of the three rounds of stimulus payments, with light green
shading to mark the pre-periods and dark green shading to mark the post-periods in the respective event studies in
Figure 7 and Appendix Table 6. The red shaded dates denote holiday periods that we drop from the event study
analysis (in Figure 7C) and from the permutation analysis (in Appendix Figures 24 and 25). Data source: Affinity
Solutions.




                                                                                                                                       127
                         Appendix Figure 24: Permutation Test for Differences in Stimulus Effects, Bottom
                                                      Income Quartile
                                A. 1st Stimulus Minus 2nd Stimulus, Bottom Income Quartile                                                                                            B. 1st Stimulus Minus 3rd Stimulus, Bottom Income Quartile

                                                                                                    Stim 1 vs. 2 Actual Effect:                                                                                                 Stim 1 vs. 3 Actual Effect:
                                                                                                              255.2                                                                                                                       170.3
                         0.15         S.D. = 79.8                                                                                                                              0.15         S.D. = 71.0
                                95% CI = [-164.2, 147.5]                                                                                                                              95% CI = [-136.5, 137.7]
                                Two-sided p-val = 0.000                                                                                                                               Two-sided p-val = 0.009




 Fraction of Estimates                                                                                                                                 Fraction of Estimates
                          0.1                                                                                                                                                   0.1




                         0.05                                                                                                                                                  0.05




                           0                                                                                                                                                     0
                            -$500                  -$250                $0              $250               $500                                                                   -$500                  -$250                $0              $250               $500
                                    Triple-Diff Estimate for Effect on Consumer Spending per $1200 of Stimulus                                                                            Triple-Diff Estimate for Effect on Consumer Spending per $1200 of Stimulus




                                                                                                    C. 2nd Stimulus Minus 3rd Stimulus, Bottom Income Quartile

                                                                                                                                  Stim 2 vs. 3 Actual Effect:
                                                                                                                                             -84.8
                                                                                             0.15          S.D. = 77.8
                                                                                                    95% CI = [-143.0, 161.2]
                                                                                                     Two-sided p-val = 0.218




                                                                     Fraction of Estimates
                                                                                              0.1




                                                                                             0.05




                                                                                               0
                                                                                                -$500                  -$250                $0              $250               $500
                                                                                                        Triple-Diff Estimate for Effect on Consumer Spending per $1200 of Stimulus




     Notes: These figures present a permutation test of the null hypothesis that there is no difference in spending
impacts across rounds of stimulus payments for households in the bottom income quartile of ZIP codes. We consider
all placebo dates for stimulus payments from August 1, 2020 to May 1, 2021, dropping dates that fall within 25 days of
the beginning or end of an actual pre- or post-stimulus window or within a high-variance holiday period (see Appendix
Figure 23 for more details). We then calculate the “combined dollar” estimate for each placebo date (as in Appendix
Table 6, Column 5) for each income quartile and stimulus round, following the approach described in Appendix K.3.
We include 25 days pre-event and then either 25 days post-event (1st and 3rd stimulus) or 16 days post-event (2nd
stimulus). We drop days in the pre- or post-period that fall within a holiday period, and we omit the estimate entirely
if a holiday falls in the post-period. We adjust for linear pre-trends in both the treatment and control series whenever
there are more than 20 days in the pre-period. This leads to 89 placebo estimates for the first stimulus and third
stimulus, and 59 placebo estimates for the second stimulus. We take the difference between each possible pair of
placebo estimates (1st minus the 2nd stimulus; 1st minus the 3rd stimulus; and 2nd minus the 3rd stimulus) and plot
the distribution of the calculated difference in “Combined Dollar” effects. Panel A plots the distribution of placebo
differences between the 1st minus the 2nd stimulus. Panel B repeats this for the 1st minus the 3rd stimulus, and Panel
C repeats this for the 2nd minus the 3rd stimulus. On each panel, we mark the actual difference in estimates (taken
from the appropriate difference between estimates in Appendix Table 6, Column 5) with a dashed vertical line. We
also report the standard deviation of the placebo draws, the 95% confidence interval (the smallest interval that covers
95% of placebo differences), and the two-sided p-value (twice the minimum of the fraction of placebo differences to
the right of the actual estimate and the fraction to the left of the actual estimate). Data source: Affinity Solutions.




                                                                                                                                               128
Appendix Figure 25: Permutation Test for Differences in Stimulus Effects, Top Income
                                  Quartile
                                  A. 1st Stimulus Minus 2nd Stimulus, Top Income Quartile                                                                                               B. 1st Stimulus Minus 3rd Stimulus, Top Income Quartile

                                                                                                             Stim 1 vs. 2 Actual Effect:                                                                                                    Stim 1 vs. 3 Actual Effect:
                                                                                                                       696.1                                                                                                                          669.2
                         0.15        S.D. = 272.9                                                                                                                              0.15        S.D. = 237.3
                                95% CI = [-630.1, 419.0]                                                                                                                              95% CI = [-466.4, 467.5]
                                Two-sided p-val = 0.002                                                                                                                               Two-sided p-val = 0.002




 Fraction of Estimates                                                                                                                                 Fraction of Estimates
                          0.1                                                                                                                                                   0.1




                         0.05                                                                                                                                                  0.05




                           0                                                                                                                                                     0
                            -$1000                -$500                $0               $500               $1000                                                                  -$1000                -$500                $0               $500               $1000
                                  Triple-Diff Estimate for Effect on Consumer Spending per $1200 of Stimulus                                                                            Triple-Diff Estimate for Effect on Consumer Spending per $1200 of Stimulus




                                                                                                     C. 2nd Stimulus Minus 3rd Stimulus, Top Income Quartile

                                                                                                                                           Stim 2 vs. 3 Actual Effect:
                                                                                                                                                      -26.8
                                                                                            0.15        S.D. = 272.1
                                                                                                   95% CI = [-417.1, 629.4]
                                                                                                   Two-sided p-val = 0.621




                                                                    Fraction of Estimates
                                                                                             0.1




                                                                                            0.05




                                                                                              0
                                                                                               -$1000                -$500                $0               $500               $1000
                                                                                                     Triple-Diff Estimate for Effect on Consumer Spending per $1200 of Stimulus




    Notes: These figures present a permutation test of the null hypothesis that there is no difference in spending
impacts across rounds of stimulus payments for households in the top income quartile of ZIP codes. Each panel is
constructed analogously to the corresponding panel in Appendix Figure 24. Data source: Affinity Solutions.




                                                                                                                                               129
                Appendix Figure 26: Effects of Reopenings on Economic Activity
                                                                                                                            A. Case Study: Colorado vs New Mexico

                                                                                                                                         New Mexico             Colorado                                         New Mexico Begins
                                                                                               +20%                                         Closing             Closing                                          Re-Opening




                                                             Change in Consumer Spending (%)
                                                                                                                                                                        Colorado Begins
                                                                                                                                                                            Re-Opening


                                                                                                    0%




                                                                 Relative to January 2020
                                                                                                -20%



                                                                                                                      New Mexico
                                                                                                                      Colorado
                                                                                                -40%
                                                                                                         Feb 1    Feb 15        Feb 29       Mar 14        Mar 28        Apr 11      Apr 25      May 9          May 23        Jun 6
                                                                                                         2020




                                                                                                           B. Stacked Event Study: Re-Opened States vs. Control States

              Change in Consumer Spending (%), Relative to January 2020                                                    Change in Employment (%), Relative to January 2020                        Change in Small Businesses Open (%), Relative to January 2020
                 0%                                                                                         Opening           0%                                                      Opening           0%                                              Opening




               -10%                                                                                                         -10%                                                                         -10%




               -20%                                                                                                         -20%                                                                         -20%




               -30%                                                                                                         -30%                                                                         -30%
                                  Control States                                                                                               Control States                                                               Control States
                                  Opening States                                                                                               Opening States                                                               Opening States

                        -80         -60      -40         -20                                               0          20             -80         -60      -40         -20            0          20                -80         -60      -40         -20            0   20
                                      Days Relative to Re-opening                                                                                  Days Relative to Re-opening                                                  Days Relative to Re-opening
                      Diff-in-Diff Estimate: +1.27 p.p. (s.e. = 0.48)                                                              Diff-in-Diff Estimate: +1.01 p.p. (s.e. = 0.42)                              Diff-in-Diff Estimate: +3.74 p.p. (s.e. = 1.29)




                                                                                                                              C. Variance Explained by Reopenings

                                                                                                                                                      May 2020




                                                       Share of Variance Explained (%)
                                                                                         15%



                                                                                         10%



                                                                                               5%



                                                                                               0%
                                                                                                                 Spending                                    Employment                              Businesses Open




      Notes: Panel A plots the change in seasonally-adjusted consumer spending relative to January 6 to February
2, 2020 for New Mexico and Colorado. Colorado partially reopened non-essential businesses on May 1, 2020, while
 New Mexico did not do so until May 16, 2020. Panel B1 plots an event study of the same outcome variable for five
“treated” states (SC, AK, GA, MN and MS) that partially reopened non-essential businesses between April 20 and 27,
2020. For each reopening, the treated states are matched to multiple control states (listed in Appendix Table 10) that
 did not reopen within the subsequent 3 weeks but had similar trends of the outcome variable in the preceding 3 weeks.
We then stack the resulting event studies by time relative to the reopenings. Panels B2 and B3 replicate Panel B1
with, respectively, the change in employment and the seasonally-adjusted change in small businesses open. In Panels
 B1 to B3, we report the coefficient from a difference-in-differences regression comparing treated vs. untreated states in
 the two weeks after vs. the two weeks before the reopening (also reported in Appendix Table 11). Panel C reports the
 share of the variance in outcomes explained by reopenings as of May 18, 2020. To estimate these variance shares, we
 first calculate the variance of each outcome across states on May 18, 2020. Then, we add the difference-in-differences
 estimate for the effect of reopening on a given outcome to all states not open on May 18 (adding only half of the
 effect if the state opened between May 11 and 18, 2020). We then recalculate the variance in this counterfactual in
which all states had reopened. The share of variance explained by reopenings for each outcome is 1 − (counterfactual
variance/actual variance). Data sources: Affinity Solutions, Paychex, Intuit, Womply.




                                                                                                                                                         130
Appendix Figure 27: Effects of State-Ordered Business Closures on Consumer Spending
                                                                                                         Early           Late
                                                                                                        Closings       Closings
                                         +20%




       Change in Consumer Spending (%)    0%




           relative to January 2020
                                         -20%




                                         -40%
                                                Feb 1            Feb 15           Feb 29             Mar 14          Mar 28          Apr 11
                                                2020


                                                        Early (Mar 17 - 24) Closers        Late (Mar 30 - Apr 7) Closers      Non-Closers




     Notes: This figure displays trends in seasonally-adjusted consumer spending in the Affinity Solutions data, pooling
 states by the date on which a state-wide order closed non-essential businesses and weighting by state population.
 States are aggregated into three groups: “Early” (state-wide closure order issued between March 17 and 24, 2020),
“Late” (state-wide closure order issued between March 30 and April 7, 2020), and “Non-Closers” (no state-wide closure
 order issued by April 7, 2020). Dashed lines denote the first date on which state-wide orders closing non-essential
 businesses were issued by “Early Closers” (March 17) and “Late Closers” (March 30). The blue and grey shaded areas
 denote the range of closure dates for “Early Closers” and “Late Closers” respectively. Data source: Affinity Solutions.




                                                                                      131
    Appendix Figure 28: Effects of the Paycheck Protection Program on Employment
                                                     A. Change in Employment by PPP Eligibility, All Industries Excluding Food Services

                                                                                         April 3                                             August 15

                                                   0%




                      Change in Employment (%)
                                                                                               Estimated Effect on Firms with 100-499 Employees:
                                                                                                           2.48 p.p. (s.e. = 2.71 p.p.)
                                                  -10%




                       Relative to January 2020
                                                  -20%




                                                  -30%
                                                          Jan      Feb       Mar         Apr         May          Jun         Jul        Aug         Sep
                                                         2020

                                                                                   100-499 Employees                 500-799 Employees




                                                         B. Change in Employment by Firm Size, All Industries Excluding Food Services

                                                                                           June 2020


                                                  -10%




                      Change in Employment (%)
                                                  -20%




                      from January to June 2020
                                                  -30%




                                                  -40%
                                                         100         200           300        400       500       600                      700           800
                                                                                     Firm Size: Number of Employees




     Notes: This figure analyzes the effects of the Paycheck Protection Program on employment using the threshold in
eligibility at 500 employees. We pool all industries except Accommodation and Food Services (NAICS 72), which was
subject to different eligibility rules (discussed in Appendix L.2). Panel A compares employment trends measured
in Paychex and Earnin data among firms with 100-499 employees (generally eligible for PPP loans) to firms with
500-799 employees (generally ineligible for PPP loans). To construct these employment trends, we begin by calculating
weekly employment changes relative to January 4 to 31, 2020 disaggregated by county, industry (2-digit NAICS),
wage quartile and firm size bin. We reweight these cells so that employment shares by industry within each eligibility
group match the overall employment shares by industry over January 4 to 31, 2020. We plot the “control” series
(firms with 500-799 employees) directly as the mean weekly value of the reweighted employment series. We plot the
“treated” series (firms with 100-499 employees) as the sum of the control series and the coefficients from an event
study specification (controlling for county x wage quartile x week fixed effects and leaving out January 3, 2020 as the
reference dummy). We use reweighted employment over January 4 to 31, 2020 as regression weights. The regression
estimate in Column 1 of Appendix Table 12, which uses an estimation window of March 11 to August 15, 2020, is also
reported in the figure. Panel B presents a binned scatter plot of changes in reweighted employment from January to
June 2020 vs. firm size. Data sources: Paychex, Earnin.




                                                                                               132


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