WORKING PAPER · NO. 2020-82

Initial Impacts of the Pandemic on
Consumer Behavior: Evidence from Linked
Income, Spending, and Savings Data
Natalie Cox, Peter Ganong, Pascal Noel, Joseph Vavra,
Arlene Wong, Diana Farrell, and Fiona GreigJULY 2020

5757 S. University Ave.
Chicago, IL 60637
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I NITIAL I MPACTS OF THE PANDEMIC ON
C ONSUMER B EHAVIOR : E VIDENCE FROML INKED I NCOME , S PENDING , AND S AVINGSD ATA*
Natalie Cox, Princeton University
Peter Ganong, University of Chicago and NBER
Pascal Noel, University of Chicago
Joseph Vavra, University of Chicago and NBER
Arlene Wong, Princeton University and NBER
Diana Farrell, JPMorgan Chase Institute
Fiona Greig, JPMorgan Chase Institute

Abstract

We use U.S. household-level bank account data to investigate the heterogeneous effects ofthe pandemic on spending and savings. Households across the income distribution all cut
spending from March to early April. Since mid April, spending has rebounded most rapidly
for low-income households. We find large increases in liquid asset balances for households
throughout the income distribution. However, lower-income households contribute disproportionately to the aggregate increase in balances, relative to their pre-pandemic shares. Taken
together, our results suggest that spending declines in the initial months of the recession wereprimarily caused by direct effects of the pandemic, rather than resulting from labor market disruptions. The sizable growth in liquid assets we observe for low-income households suggeststhat stimulus and insurance programs during this period likely played an important role inlimiting the effects of labor market disruptions on spending.
* This version: July 21, 2020. This paper was prepared for the Brookings Papers on Economic Activity Conference
on June 25, 2020. We thank Therese Bonomo, Peter Robertson, and Tanya Sonthalia for their outstanding analyticalcontributions to the report. We thank Jonathan Parker, Jan Eberly, and Erik Hurst for helpful discussions. We are
additionally grateful to Samantha Anderson, Maxwell Liebeskind, Robert McDowall, Shantanu Banerjee, Melissa
Obrien, Erica Deadman, Sruthi Rao, Anna Garnitz, Jesse Edgerton, Michael Feroli, Daniel Silver, Joseph Lupton,
Chris Knouss, Preeti Vaidya, and other members of the JP Morgan Chase Institute for their support, contributions,and insights.

Introduction
The Covid-19 pandemic led to a large and immediate decline in U.S. aggregate spending and anincrease in aggregate private savings. In this paper, we use anonymized bank account information
on millions of Chase customers to measure the microeconomic dynamics underlying these aggregate patterns. Specifically, we use our household level account data to explore how spending
and savings over the initial months of the pandemic vary with household-specific demographic
characteristics, such as pre-pandemic income and industry of employment.
Figure 1: Aggregate Consumption and Savings

20

Percent

10

0

−10

−20
2019m42019m72019m102020m1

2020m4

Personal Consumption Expenditures Y−o−Y Growth
Personal Savings Rate Y−o−Y Change

Figure shows the year-over-year growth of Personal Consumption Expenditures and change in the Personal Savings
rate, calculated from monthly Bureau of Economic Analysis data.

Measuring and understanding the link between income, spending, and savings is useful forunderstanding the causes and dynamics of this recession. For instance, the relationship between
individual income, spending and savings can shed light on the role of supply factors (such as shutdowns and reducing activities with high infection risk) versus demand factors (such as Keynesian
spill-overs across sectors as unemployed workers reduce spending). Understanding these factors
can be informative about the effectiveness of different stimulus policies for targeting different
households and businesses. Many data sets have already been used to study the dynamics ofgeographic level spending during the pandemic, but aggregated relationships may or may not
be identical to those at the individual household level at which economic behavior is ultimately
determined.1 Our paper provides an initial step in analyzing these household-level dynamics.2
Focusing first on aggregate results, we find that overall spending fell over 35% in the second
1 See e.g. Chetty et al. (2020).
2 To be clear, our current analysis does not run regressions at the household level, but it does crucially rely on

individual household data to define groups and outcomes of interest. We also focus for now on sorting households
by pre-pandemic characteristics like income level, rather than by changes during the pandemic.

1half of March. In April, spending began to increase from its nadir, but it remained substantially depressed through the end of our sample on May 30. Declines in non-essential spending accounted
for most of the declines in spending.3 Amongst non-essential categories, declines were particularly large for restaurants, hotel accommodations, and clothing and department stores. Amongst
essential categories, declines were most dramatic for healthcare, ground transportation and fuel.
Reassuringly, these patterns are similar to those found using other aggregate sources of spending
data.4 , EarnestResearch (2020), Baker et al. (2020), Chetty et al. (accessed on 06/15/2020), Facteus(accessed on 06/15/2020) and Karger and Rajan (2020). However, this also implies that these results do not rely on the unique features of our micro data, so they are not the main contributionof our paper.
We next turn to results which do rely on our micro data linking household-level observables
on income, spending and savings. First, we find that during the initial stages of the pandemic in
March, there are extremely large declines in spending for all quartiles of the pre-pandemic income
distribution.5 Spending by the top quartile of the income distribution falls by modestly more thanany other quartile (in percentage terms). However, this difference is small relative to the broad
decline in spending by all income groups. Beginning in mid April when aggregate spending begins to recover, substantial differences by income emerge: spending recovers much more rapidly
for low-income households than for high-income households so that large differences arise by theend of May. We show that these relationships between income and spending over the pandemic
hold both in general, as well as within narrow geographic areas like zip codes.6
Second, we explore differences in spending by individual’s industry of employment. Thisvariation is interesting because industries vary substantially in both their exposure to labor market
disruptions and in average income levels. Exploiting joint variation in industry of employment
and household income is thus helpful for better understanding the source of heterogeneity inspending patterns. We find that spending cuts are pervasive, with declines for workers in allindustries of employment. Consistent with the patterns we find by income, workers in industries
with low average pay initially cut spending slightly less and then have spending which recover
more rapidly. For example, grocery store workers have the smallest declines in spending andthe most rapid rebound, while white collar professional workers’ spending is recovering more
3 We define these categories precisely later, but loosely speaking non-essential stores are those which are subject togovernment restrictions as a result of the pandemic.
4 See for example
5 As we discuss more in Section I, since our data arises from bank account information, we under sample the very
lowest income households, but the sample is otherwise broadly representative.
6 High and low income people live in different locations, which might have different exposure to the pandemic.
Using within zip code variation shows that income-spend relationships are not driven by confounding effects ofphysical location.

2

slowly. We then further split workers within given industries of employment by their individual
pre-pandemic income levels. We find that income appears to matter more for spending thanindustry of employment. For example, low-income workers in all industries have rapid increases
in spending in mid April, while these increases are muted for high-income workers.
Finally, we turn to evidence on the distribution of household savings over the pandemic toprovide further insight into the effects of changing income and spending on household liquidity. To the best of our knowledge, we are the first paper to explore these distributional effects.
Aggregate savings have increased substantially over the last two months. Information on the underlying distribution of increases is useful for understanding the sources and consequences of thisincrease. There are several forces during the pandemic that likely affected aggregate savings rates:
1) As discussed above, spending has fallen. This decline is most dramatic at the top of the income
distribution, which will tend to boost savings for these households. 2) Massive increases in unemployment have reduced labor income, and these effects are especially concentrated on low-income
workers. This will tend to reduce savings for low-income households. 3) Stimulus and social insurance programs like Economic Impact Payments (EIP) and expanded unemployment insurance(UI) provide transfers which represent a larger share of income for low than high-income households. This will tend to increase liquidity and savings by low-income households. 4) Delayed tax
filing dates may increase short-term savings if those who owe money delay filing more often thanthose who are owed refunds.
Consistent with aggregate savings data, we find a large initial increase in savings during the
pandemic. By the end of May 2020, average liquid balances are 36% higher than at the same pointin 2019. While increases in liquid balances are pervasive throughout the income distribution,
we find that lower-income households contribute disproportionately to the aggregate increasein balances, relative to their initial pre-pandemic shares. That is, liquid balances at the end of
May are slightly more equally distributed over the income distribution than liquid balances in
February. However, in dollar terms, high-income households contribute most to the aggregate
increase in savings.
Taken together, our results suggest several conclusions. First, labor market disruptions wereunlikely to be a primary factor driving initial spending declines during the recession. Overall
declines in spending were much larger than what could be explained by the rise in unemployment
in this recession, given historical relationships. Furthermore, spending actually declines by lessfor households with greater exposure to labor market disruptions. This does not mean that labormarket disruptions have no effects on spending or that demand spillovers are unimportant, but it
does suggest that at least in these initial months of the recession, the direct effects of the pandemic
are the primary factor driving spending.

3

Second, the composition of typical spending is important for understanding spending declines. Aggregate spending declines by more in non-essential sectors which are more exposed to
shutdowns and health risk. Furthermore, spending declines more for high-income households,
who tend to consume more of these non-essential goods in normal times.
Third, various stimulus and social insurance programs like EIP and expanded UI likely playeda sizable role in helping to stabilize spending and liquid balances, especially for low-income
households. Since fiscal stimulus was ramped up at the same time that many states began toreopen, it is difficult to disentangle general "re-opening" effects from effects of this fiscal stimulus
by looking just at aggregate spending. However, stimulus checks and expanded UI benefits represent a larger share of monthly income for low-income workers than for high-income workers,
and would thus naturally explain the more rapid recovery in spending we observe for low-income
workers. Finally, expanded transfers could also explain the disproportionate increase in savings
that we observe for lower-income households. It is important to note that many of these transfer
programs are likely temporary – the EIP payments are a one-off stimulus, while the expanded
component of UI benefits is slated to end in late July 2020. Households may be less likely to
immediately consume, and more likely to save, these payments because they are non-permanent.
It is important to emphasize that our evidence for now focuses on time-series patterns for
relatively aggregated household groups, and so we do not provide any causal evidence on thestrength of any particular channels driving spending decisions. Thus, our evidence is suggestive
rather than conclusive on this front. The early patterns we find in this paper may also change as
the pandemic progresses and new policy decisions are made. Future work exploring even moredetailed household level results as this recession progresses will hopefully shed further light on
the economic consequences of this pandemic and associated policy responses.

I

Data Description

Our analysis of spending and checking account balances is based on the universe of transactions
from Chase checking accounts, debit cards, and credit cards through May 30, 2020. Our main measure of total spending includes all debit and credit card purchases as well as cash withdrawals.
In robustness checks in the Appendix we show that our conclusions are similar if we add paper
checks to our measure of total spending.7 While we observe credit, debit, cash, and check transactions, we are still working to process electronic checking account transactions such as ACH
payments and so this type of spending is not included in our analysis. For all checking accounts,
7 We do not include paper checks in our main analysis for two reasons. First, we do not know whether the checks

reflect spending, debt payments, or transfers. Second, due to delays in depositing and processing checks, there is alag between when the check was used and when it appears as a withdrawal in the bank account. Hence, it is hard tointerpret the patterns of paper check outflows at the high frequency we use in this analysis.

4Table 1: Income Distribution and Credit Cards Spending

Quartile cut-offs
Quartile 1
Quartile 2
Quartile 3
Quartile 4N$12,000-$27,707$27,707-$41,255$41.255-$63,462$63,462 +5,014,672

Income Quartiles% Sample with Credit
Mean income
Card$20,948
30%$34,18536%$50,927
46%$108,914
57%

Avg. Weekly Credit Card
Spend
$205
$228
$329
$639

∗ The last two columns provide the percentage and average spending of any individuals with a Chase credit card.

we also observe checking account balances.
We impose income and activity screens in order to focus on a sample of individuals who
primarily use their Chase account to manage their finances. Specifically, we filter on those whohave a non-business account, had at least five checking account transactions and at least threecard transactions in every month between January 2018 and March 2020, and had at least $12,000
in labor income in both 2018 and 2019.8 This leaves us with a sample of just over five million
individuals.
We measure labor income using information on payroll direct deposits. We further measureindustry of employment based on the payer associated with direct deposits in February 2020.
However, there is an important caveat that we can match the payer associated with payroll income
to an identified payer for only 24% of households, and most of these payers tend to be large
employers. Finally, it is important to note that while we observe labor income through February,
2020, we are still working to process and interpret data on labor income and government transfers
during the pandemic. As a result, data on income changes during the pandemic are not available
for our current analysis. For this reason, we report various results based on pre-pandemic income,but do not yet have results on how spending has responded to individual income changes.
Given that our sample is drawn from account holders at a single financial institution, we use
income data from the CPS to measure how representative it is of the US population. Table 1
reports quartiles of the labor income distribution for our sample. Figure A.12 plots the averagelabor income by quartile for the Chase sample compared to average labor income for the CPS
population (adjusted for income and payroll taxes since the Chase measure is post-tax). This
figure suggests that our sample is broadly representative, although it somewhat overstates income
at the lowest end of the distribution and slightly understates income at the highest end of the
distribution. The overstatement at the lowest end of the distribution is due to two factors. First,
8 We have explored different thresholds on transactions and results are similar.

5

Initial Balanreliable measurement requires us to impose a minimum threshold of $12,000 in labor income.9
In the CPS, 7.7% of households have labor income below this cutoff. They would be excluded
from our analysis.10 Second, every household in our data set has a bank account. Therefore,
we do not include unbanked households, who are disproportionately low-income. The FDIC
reports that 6.5% of U.S. households did not have a bank account in 2017.11 A final caveat for ouranalysis is that we report average outcomes in terms of spending and liquid balances by income
quartile. However, there may be heterogeneity within quartiles. For example, not all households
were eligible for EIP payments and some unemployed households faced long delays in receiving
UI payments. For all these reasons, our findings that average spending and average balances
are relatively higher for low-income households should not be interpreted to mean that all lowincome households are doing relatively well during the pandemic. There is compelling evidence
that this is not the case (Bitler, Hoynes, and Schanzenbach, (2020).
Our data are unique in their size, sample coverage, and in their individual-level view of income, spending, and balances. Other data sources used to research the consumer response to
COVID-19 tend to be aggregated over region, store, or time (e.g. Earnest, Womply, or Affinity),
which limits the analysis of household balance sheet dynamics. By observing covariates at theindividual level, like geography and industry of employment, we can also directly control for
confounding factors that might be correlated with income and changes in spending. For example, our data can be used to look at how spending varies with income within narrow geographic
areas like zip codes, and thus help control for the fact that high-income locations differ from lowincome locations along a number of dimensions. Our data also allows us to look at how spending
changes by income groups within industry of employment.
Our sample, which captures households across the income distribution, complements thework done using Facteus data (e.g. Karger and Rajan (2020), Alexander and Karger (2020)) andproprietary Fintech data (Baker et al. (2020)) which is primarily focused on low-income households. Finally, the size of the Chase customer base allows for additional precision when calculating statistics of interest as well as for substantially more disaggregated data cuts, relative to data
sets with smaller sample sizes. Our data is closest in structure to that in Andersen et al. (2020),
which uses similar bank account data from a Scandinavian bank. The most important distinctionis that our data covers U.S. households, and thus a dramatically different institutional environment with different social safety nets and government responses to the pandemic.
9 We require at least $12,000 in labor income since it is difficult to distinguish truly low income households frommis-measured higher income households without reliably captured direct deposits.
10 After conditioning on households with labor income above $12,000 in the CPS, mean and median income in thebottom quartile of our sample is very similar to mean and median income in the bottom quartile of the CPS.
11 See 2017 FDIC National Survey of Unbanked and Underbanked Households, Executive Summary.

6IIHousehold Spending

II.A.

Overall Change In Spending

We begin by measuring the change in total spending. Appendix A.1 provides changes in spendingfor each of the components of total spending (credit card, debit card, and cash), as well as paper
checks. Panel (a) of Figure 2 plots the 2020 to 2019 year-over-year percentage change in weekly
spending. Panel (b) shows the average dollar amount of spending in 2020 and 2019. Changes inspending follow a distinctive pattern: spending is stable through the beginning of March, thendeclines precipitously by over 35 percent relative to 2019 from the second through fourth week ofMarch. The size of the spending drop is largely consistent with other estimates from similar data
sources during the same time frame.12 These declines are somewhat larger than the aggregate
spending declines in Figure 1, but this is not particularly surprising. Personal consumption expenditures includes substantial spending on components like housing services, which likely had
little to no decline. Spending showed signs of recovery in May, but remains roughly 15 percent
below pre-pandemic levels as at the end of May.
Figure 2: Average Spending Changes(a) Percent Change(b) Levels

The left panel plots the year-over-year percentage change in weekly spending. The right panel plots the dollar change
in weekly spending. Vertical lines show the date when the national emergency was declared and the date on whicha majority of EIP payments arrive.

The timing of the initial spending drop mirrors the spread of the virus and staggered national
12 See for example, Baker et al. (2020), Chetty et al. (accessed on 06/15/2020), and Facteus (accessed on 06/15/2020).

7

implementation of government social distancing orders. A national emergency was declared on
March 13, 2020. Over the following three weeks, the number of states with stay-at-home orders
increased from zero to forty-five. The prevalence of COVID-19 also increased dramatically overthe course of March.
At the same time, the drop in spending also closely tracks the pattern of initial job losses.
Unemployment Insurance (UI) claims began spiking in the third week of March, with more than
20 million UI claims filed by April 11. Conversely, spending begins to recover in the weeks after
April 15 when a majority of EIP payments arrive and as many of the unemployed workers who
file claims in March and early April begin to receive benefits. This raises a question of how muchof the drop in spending is due to the pandemic itself, the social distancing policies, or income
losses.
It is useful to calibrate the size of the spending drop relative to what we have observed among
those who lose a job involuntarily during normal times. Ganong and Noel (2019) measure thespending drop around job loss among UI recipients, and observe an initial spending drop ofroughly 6%. In other words, the spending drop in March 2020 is roughly six times larger than
the average household spending drop in the first month of unemployment for UI recipients innormal times. This puts into perspective how dramatic the spending drop is and suggests thatthe pandemic and policies aimed at preventing its spread are contributing substantially to thedrop in spending.

II.B.

Change In Household Spending By Categories

While Figure 2 shows a sharp drop in aggregate spending over March and April, there is reason
to think that specific spending categories would be differentially impacted. Many non-essential
businesses, like bars and salons, were closed by state and local governments. Similarly, stay-athome orders limited the ability of individuals to travel. Beyond the mechanical effect of social
distancing regulations, individuals may also have independently curtailed spend in certain categories to avoid risk of infection or as a response to income loss.
While we do not have information on debit card or cash spending by categories, we do havedetailed category splits for credit card spending. We begin by disaggregating total credit card
spending into essential and non-essential categories, as commonly defined in state "stay-at-home"orders. Figure 3, panels a and b, show a dramatic difference in the path of essential and nonessential spending. Essential spending spiked in early March as households stockpiled goods
like groceries. It then fell substantially before eventually stabilizing at a year-over-year declineof around 15 percent.13 In contrast, spending on non-essential categories fell sharply throughout
13 The downward spike in year-over-year essential spending in the week ending April 18, 2020 likely arises because8March, bottoming at a decline of just over 50 percent, and then began to slowly recover throughlate April and May.
Figure 3: Credit Card Spending on "Essential" and "Non-Essential" Categories(a) Percent Change(b) Levels

We use state social distancing orders that restricted non-essential goods and services to categorize spend. "Essential"
categories include fuel, transit, cash, drug stores, discount stores, auto repair, groceries, telecom, utilities, insurance,and healthcare. "Non-essential" includes department stores, other retail, restaurants, entertainment, retail durables,home improvement, professional and personal services, and miscellaneous. Although flights, hotels, and rental cars
are sometimes categorized as "essential" and not technically closed, we include them in the "non-essential" group
because they are affected by stay-at-home restrictions on non-essential travel.

Given the fact that households were ordered to stay at home except to make essential trips
in most states, one might ask why households were still spending roughly $50 a week on nonessential categories in April. First, there was variation in both the degree of closures and inwhat was deemed non-essential across locations. Second, our spending categories do not map
perfectly to each specific non-essential category. Third, households may be able to switch some
non-essential services from in-person to remote; for example from movie theater entertainment toonline streaming or from in-restaurant dining to take-out.
We also quantify how much each category contributed to the aggregate drop in credit card
spend. Table 2 shows what share of aggregate spending went towards essential and non-essential
categories before and during the pandemic. Multiplying the pre-pandemic shares by their relative
percentage drops, we find that non-essential spending accounted for 84% of the aggregate decline,
and essential spending accounted for 16%.
of the timing of Easter, which occurred during this week in 2020 but during the previous week in 2019. Many grocery
stores are closed on Easter, which may explain a dip during this week in 2020 relative to the same week in 2019, which

9Table 2: Credit Card Spending Changes for "Essential" and "Non-Essential" Categories

Apr-19Apr-20Contribution toAggregate Drop in
Spend*

Essential
Share ofYear-over-year
spending
percent change
35%
46%
-18%
16%

Non-Essential
Share ofYear-over-year
spending
percent change
65%
54%
-49%
84%

* Percent contribution to aggregate drop in spend is calculate as: (% Drop in Category A)*(Baseline Share of CategoryA)/(% Drop in Aggregate).

To further illustrate the divergence in spending patterns across categories, we split essential
and non-essential spending into more disaggregated categories in Figure 4. Total essential spending spiked by roughly 20% in early March before dropping by 20% by end-March. However,there are a wide range of spending responses among goods and services deemed essential. In thefirst few weeks of March there was a temporary surge in spending on groceries, discount stores,
and pharmacies. Spending at grocery stores, which contributes the largest share of total essential
spending, remained elevated through the end of our sample, aside from a brief decline in theweek including Easter, when many grocery stores are closed. In contrast, spending fell in several
other essential categories like "hospital", "other healthcare", "transit and ground transportation",and "fuel". Total dollar declines in these categories exceed the dollar increases in grocery spending, so that overall essential spending declines. Focusing on non-essential spending, declines arestrongest in restaurants, hotel accommodations, and clothing and department stores. Overall,these results largely mirror those computed in other aggregate data sets and provide reassurance
that our data is consistent with external evidence.

II.C.

Heterogeneity In Spending Changes By Income

SPENDING CHANGES OVER THE INCOME DISTRIBUTION
We next explore whether spending reductions (both in aggregate and by category) vary with prepandemic income. We stratify our sample into income quartiles based on total labor inflows in
2019.14 For context, those in the bottom quartile make less than $28,000 in take-home labor income
per year, while those in the top quartile earn more than $63,000. As discussed in Section I, ourbottom quartile misses unbanked households and the 8% of U.S. households with labor income
did not include the Easter closures.
14 In future work, we plan to explore also the relationship to income changes during the pandemic.

10

Figure 4: Credit card spending growth across spending categories

below $12,000, since we cannot reliably measure their income.
Figure 5 plots the year-over-year change in spending for each quartile, both in percentage anddollar terms. The top income quartile reduces spending by about 39 percent, or $400, by the fourthweek of March, while the bottom quartile reduces spend by 32 percent, or $100. The differencein the spending drop between income quartiles is starker in dollar terms than percentages, sincehigh-income households have a higher baseline level of spending. However, divergence in spending over the income distribution starting in the second half of April is more striking. By the endof April, the decline in spending partially recovers, with the recovery most pronounced for thelowest income quartiles. The recovery in spending for the lowest income quartiles occurs in thesame week when many stimulus payments are made in mid April. The timing of the divergencein spending by income suggests that stimulus payments may have played an important role inrestoring the ability of low-income households to maintain spending during the pandemic.
Appendix A.2 provides the spending changes over the income distribution by form of payment (debit, credit, cash and check). We observe similar patterns of spend changes across all
forms of payments.
Table A.1 reports the cumulative change in spending by income quartile in 2020 relative to2019 for the 11 "pandemic" weeks in our dataset between March 15 and May 30. The highest
income quartile contributes disproportionately to the change in spending, accounting for 37% of
initial spending and 50% of the spending decline. As a result, the share of spending for the highest
income quartile declined.
While the results so far show that households with higher income cut spending by more and11Figure 5: Spending by income quartiles(a) Percent Change(b) Levels

have slower recoveries in spending than low income households, it is important to note that income is correlated with many other factors which might also affect spending responses, so these
are not necessarily causal relationships. One particular concern in the context of the pandemic
is that income is correlated with physical location, and locations vary in the strength of the pandemic. In particular, high income individuals tend to live in cities, which have greater disease
burden and more restrictive shutdowns. This means that the relationship between income andspending dynamics could reflect features of where high income households live, rather than effects of income itself.15
To differentiate the role of income from the role of physical location, we look at the relationshipbetween income and spending over the pandemic within narrow geographic areas. In particular,
we compute the following regression
c2020,z,q − c2019,z,q= Quartileq + ZIPz + ε z,q¯c2019,q
where ct,z,q is average spending per customer with t as the year (for the time period April 15May 28), z is zip code and q is the income quartile. We take two steps to minimize the influence
15 Note that measuring spending at the geography rather than household-level introduces additional concerns on
this front: high income households are more likely to leave cities than low income households in response to thepandemic, which might induce spurious declines in spending in locations with many high income households prior
to the pandemic.

12¯ which uses everyone in the income quartile.
of outliers. First, note that the denominator is c2019,qThis prevents having one very large or very small ratio from skewing our results. Second, ct,z,q
is winsorized at the 1st and 99th percentile. We focus primarily on specifications with geographyz equal to 5-digit zip codes but also explore more aggregated 3-digit zip codes to again limit the
influence of measurement error.
Comparing odd columns without geographic fixed effects to even columns with fixed effects
shows that relationships between income and spending over the pandemic within zip codes arevery similar to unconditional relationships. That is, high income households cut spending moreduring the pandemic relative to low income households living in the same zip code.16 The similarity of results with and without fixed effects shows that these relationships are not driven by
any observed or unobserved differences across locations where high and low income household
live.
Table 3: Income-Spend Relationships within Geography
Dependent variable:
Spending Growth
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

−0.032∗∗∗−0.028∗∗∗−0.048∗∗∗−0.039∗∗∗−0.035∗∗∗−0.034∗∗∗−0.032∗∗∗(0.004)(0.004)(0.002)(0.001)(0.003)(0.002)(0.009)−0.033∗∗∗(0.008)

Income Q3−0.080∗∗∗(0.004)−0.079∗∗∗(0.004)−0.118∗∗∗(0.002)−0.092∗∗∗(0.001)−0.094∗∗∗(0.003)−0.085∗∗∗(0.002)−0.082∗∗∗(0.009)−0.081∗∗∗(0.008)

Income Q4−0.154∗∗∗(0.004)−0.142∗∗∗(0.004)−0.217∗∗∗(0.002)−0.161∗∗∗(0.001)−0.179∗∗∗(0.003)−0.159∗∗∗(0.002)−0.149∗∗∗(0.009)−0.147∗∗∗(0.008)Constant−0.043∗∗∗(0.003)

Income Q2−0.047∗∗∗(0.001)−0.049∗∗∗(0.002)−0.061∗∗∗(0.006)

Geography FE

NO

YES

NO

YES

NO

YES

NO

YES

Observations
Adjusted R2

70,1890.024

70,1890.235

70,1890.238

70,1890.557

25,4320.137

25,432
0.506

3,608
0.082

3,608
0.272

Table regresses year over year spending growth for the period April 15-May 28, 2020 for individual geography × income
quartiles on income quartile dummies with and without geography fixed effects. Columns (1) and (2) define geography as
5-digit zip codes and equally weight. Columns (3) and (4) use 5-digit zip codes and weight by number of customers in each
zip code × income quartile. Columns (5) and (6) use equal weights and 5-digit zip codes but restrict to zip code × income
quartiles with at least 20 customers. Columns (7) and (8) use equal weights and define geographies as 3-digit zip codes.
Significance: * p < 0.1, ** p < 0.05, *** p < 0.01.

Finally, we further decompose the decline in credit card spending by income quartiles into
16 Note that our information on address is as-of early 2020.

13

essential and non-essential categories.17 Figures 6 and 7 show that the spending declines foressential categories are indistinguishable across income groups, while non-essential credit card
spending diverges more across income groups.
Figure 6: Share of credit card spending decline accounted for by essential and non-essential credit
card spending by income quartiles(a) Essential(b) Non-essential

Figure 7: Reduction in essential vs. non-essential spending by income quartiles

Although all households cut spending dramatically, the fact that high-income households cut
spending by somewhat more may be surprising. Recent research suggests that lower-income
17 Unfortunately, as mentioned above, we do not have the spending split by categories for the other forms of pay-

ment (debit, cash and check).

14

households work in jobs that are harder to perform at home, require higher physical proximity,
and therefore may be more impacted by distancing restrictions (Mongey, Pilossoph, and Weinberg(2020)). Perhaps as a result, recent evidence from administrative ADP data shows that job losses
were four times larger for workers in the bottom income quintile than those in the top income
quintile, with a staggering 35 percent employment decline for the lowest-income workers (Cajneret al. (2020)). In response to greater income losses, we might have expected lower-income workers
to have cut their spending by more. In fact, we find the reverse: higher-income households cut
their spending by slightly more and their spending recovers more slowly.
Differences between high and low-income households in the composition of spending may
be one reason why spending falls by more for high-income households. Non-essential categories
represent a larger share of spending for high-income households – 67 percent of spending in
April 2019 for households in the top income quartile compared to 59 percent for those in the bottom income quartile. In addition, higher-income households have slightly larger drops in theiressential spending. Together, these facts imply that reductions in non-essential spending accountfor a somewhat larger share of total spending declines for high- versus low-income households(85 percent compared to 79 percent, Figure 7). Since these non-essential categories are most affected by the pandemic shut-downs, overall spending of higher-income households may be more
affected by supply-side restrictions. In other words, the effective price of consumption rises more
for higher-income households relative to lower-income households. Thus, the composition of
spending of higher-income households likely contributed to the larger decline in their spending.
As discussed above, the widening of these initial spending declines during the recovery phase
may reflect an important role for economic stimulus and transfer programs. The tax rebate checks
that began to arrive in April amount to a larger share of total income for a low-income household
than for a high-income household. Ganong, Noel, and Vavra (2020) also show that the $600 expansion in UI benefits enacted through Federal Pandemic Unemployment Compensation (FPUC)
boosted wage replacement rates to well over 100 percent for many low-income unemployed workers, providing a substantial income boost once they began receiving benefits.
Finally, higher-income households may be more exposed to negative wealth effects. Higherincome households hold more financial assets, and therefore are exposed to declines in asset prices
during the initial stages of the pandemic. However, wealth effects are unlikely to be a key driverof the heterogeneous spending responses by income, given previous estimates on the strength ofwealth effects together with the fact that the stock market had recovered most of its pandemic
related losses by the end of May.

15

CHANGE IN SPENDING BY INDUSTRY OF EMPLOYMENTWe next examine whether workers in sectors most affected by employment disruptions adjust
spending in ways that differ from workers in less affected sectors.
Figure 8 plots spending changes by industry of employment, for each industry where we havesignificant sample size. We aggregate to industries at the two-digit NAICS code. The one exception is retail, which we break out into grocery stores, drug stores, and discount stores–generally
considered essential businesses and kept open under social distancing policies–and clothing anddepartment stores, which were generally deemed non-essential businesses and where layoffs havebeen greater (Cajner et al. (2020)).
Overall, it is hard to discern systemic patterns between spending declines and the distributionof employment losses by industries. It is true that essential workers like those in grocery storesexhibit smaller spending declines. At the same time, professionals exhibit the largest spending
declines, even though many jobs in this category can more easily be performed remotely.
While industry of employment is closely related to job losses, it is important to note that it isalso highly correlated with income levels and that this may be explaining some of these differences.18 For example, grocery store workers are typically low income, while professional workers
are typically high income. In this sense, patterns when splitting by industry of employment inmany ways mirror those when splitting by income: the largest drops and spending and slowest
recoveries occur in higher-income industries of employment.
To provide a further sense of the separate role of income and industry, in Figure A.11, we
compute spending by industry of employment separately for workers in the highest and lowest
quartile of pre-pandemic income. Comparing variation across industries within income quartilein Figure A.11 to variation across industries without conditioning on income in Figure 8 showsthat controlling for income substantially reduces the role of industry of employment. Similarly,
comparing the same colored line between panels (a) and (b) in Figure A.11 vs. comparing different
colored lines in Figure 8 also shows that income generally has a greater correlation with spending
dynamics than does industry of employment.
One potential interpretation is that the income channel accounts for only a small share ofspending changes through the end of May. This may not be surprising given the magnitude of
the spending decline. As mentioned previously, we document that average household spending
fell over 35 percent, while the typical unemployed worker receiving UI only cuts spending around
6 percent in normal times (Ganong and Noel (2019)).
However, there are several reasons for caution in concluding that income losses play a smallrole in spending effects. First, industry of employment may not fully proxy for job loss in our
18 See Appendix Table A.2.

16

Figure 8: Spending Changes Split by Industry of Employment

sample. To the extent that we can ascertain industry of employment primarily for employees oflarge firms, we may not be capturing the income losses for employees of small businesses.
Second, current conditions of the pandemic make comparing the magnitude of the spending
response in April 2020 to that of UI recipients during normal times highly uncertain. On the one
hand, the economic situation is highly uncertain, and labor markets weakened at an unprecedented pace. This might cause the unemployed to cut spending by more than during normal
unemployment spells. On the other hand, as a result of the CARES Act, UI benefits are much
more generous in level and duration, and available to many more workers. Furthermore, sizablestimulus checks were also sent out in April. These income supports might buffer against labor
income-related spending declines if this stimulus continues. The more rapid recovery of spending for low-income households suggest this channel is at work. The rest of the paper looks at the
behavior of household savings to provide additional evidence on these channels.

III

Household Liquid Balances

Given the unprecedented reduction in spending across income and industries documented above,
we next explore whether there were changes in the distribution of household liquid balances. Figure 1 shows that aggregate private savings increased substantially over the pandemic, reflecting
the combination of large declines in spending and large increases in government transfers fromstimulus programs. However, there are reasons to think that the pandemic could have hetero-17

geneous impacts on household savings and substantial resulting effects on the distribution of
wealth: households experiencing job loss may draw down on savings (or further draw on sourcesof borrowing), while those with job security may be essentially forced to save more, as consumption of many non-essential goods and services is more restricted. In addition, stimulus payments
and other income support programs represent a larger share of pre-pandemic income for highthan for low-income households.
To explore these effects, we calculate how the distribution of end-of-week balances in household checking accounts evolved during the pandemic. Specifically, we explore how various unconditional moments of checking account balances evolved as well as how balances changed
across the income distribution and by industry of employment. While checking account balances
are only a subset of total savings and wealth, they represent some of the most liquid and easily
accessible "cash-on-hand" available for households to smooth consumption and self-insure. A
large literature has shown that liquid assets of this form play a crucial role in consumption. Furthermore, checking account balances have the practical advantage of being precisely and easily
measured since checking accounts are one of our primary data sources.
We begin by plotting the average level of liquid balances, and the percentage year-over-yearchange from January through the end of May 2020. Figure 9 shows that by the last week of May,average balances increased by 33 percent year-over-year, or about $1500 dollars relative to earlier
in the year. This increase is consistent with the large increase in the personal savings rate shownin Figure 1 and with the growth in the stock of commercial bank deposits shown in Appendix
Figure A.13.19 Much of the year-over-year growth in checking balances occurred during andafter the week when most EIP stimulus checks were deposited, which suggests that the increase
was driven by these income inflows, in addition to the reduced spending we documented in theprevious section.20
Figure 10 plots additional moments of the distribution of liquid balances over time. Panel(a) shows that increases in liquid balances are pervasive, with increases observed at various percentiles of the distribution. The dollar increase in balances is greater for households with larger
initial pre-pandemic balances. However, it is important to note that scale effects would be expected to drive that type of pattern: for example, if all balances double, the accounts with thelargest initial balances will have the largest absolute increases. Panel (b) shows that the lower endof the distribution is growing more than the top end of the distribution. Interestingly, the yearover-year growth for lower percentiles shoots up around the time of stimulus payments and then
19 Note that Figure 9 should not be compared directly to the personal savings rate in Figure 1, since aggregate

personal savings is a flow variable while checking account balances are a stock variable. Appendix Figure A.13
provides evidence on the growth in a more comparable aggregate stock variable to Figure 9.
20 We further decompose this trend into checking account inflows and outflows in the Appendix.

18

Figure 9: Level and Year-on-Year Change in Average Checking Account Balances.
(a) Levels(b) Percent Change

trends down. This suggests that households with low initial liquidity received a large increase inliquidity from stimulus payments, but they may be fairly rapidly using up this additional cash.
Figure 10: Change in Distribution of Checking Account Balances.
(a) Levels(b) Percent Change

While the results in Figure 10 show that increases in liquid balances are pervasive, it is interesting to explore the relationship with pre-pandemic income. In particular, it is useful to know
19

whether the increase in aggregate liquid balances was primarily driven by gains at the top of
the income distribution (e.g. by individuals who cut spending most dramatically while generally
maintaining labor income), or by gains at the bottom of the income distribution (e.g. individuals
who cut spending somewhat less and faced larger declines in labor market income – but also hadlarger government transfers). Figure 11 plots checking account balances (in levels and growth
rates) by income quartiles. Similar to the unconditional distribution of balances, we see pervasive
increases in balances with increases observed for all groups. Also similar to the unconditional distribution, there are clear scale effects: the highest income quartile posted the largest dollar gains
of around $2,000. The lowest income quartile increased balances by more than $1,000, which was
the largest increase in year-on-year percentage terms.
Figure 11: Change in Average Checking Account Balances by Income Quartile.
(a) Levels(b) Percent ChangeThis figure plots both average dollar balances and year-on-year percentage change in checking account balances byincome quartile. Balance increases are larger in dollar terms for high-income households (who have higher preCOVID balances), and in percent terms for low-income households (who have lower pre-COVID balances).

Given these scale effects, what should we conclude about the relative role of high- vs. lowincome households in driving the increase in liquid wealth? One way to answer this question is tocompare each group’s contribution to the aggregate increase, relative to that group’s initial shareof savings. If all groups savings grow by the same amount, then each group’s contribution to theaggregate increase is equal to its initial share and the wealth distribution is unchanged. If lowincome households have higher savings growth, then they will contribute more to the aggregate
increase than their initial share and wealth inequality will decline.
20

Table 4: Decomposition of Total Liquid Balances Changes by Income Quartile
InitialBalances

Share of
InitialBalances

Increase inBalances

Share of
Increase inBalances

Final
Balances

Share of
Final
Balances

Quartile 1
Quartile 2
Quartile 3
Quartile 4
Total$2.67B$3.44B$5.22B$12.02B$23.35B

11.4%
14.7%
22.3%
51.5%
100.0%$1.28B$1.39B$1.60B$2.45B$6.72B

19.0%
20.7%
23.8%
36.4%
100.0%$3.95B$4.83B$6.82B$14.47B$30.07B

13.1%
16.1%
22.7%
48.1%
100.0%Top Decile
Top One Percent$7.19B$1.84B

30.8%
7.9%$1305M
$294M

19.4%
4.4%

$8.49B$2.13B

28.2%
7.1%

Initial balances are computed in February 2020 and Final Balances are calculated in May 2020.

To explore this more formally, Table 4 reports the initial balances in February 2020, the increasein balances from February to May, and the final balances in May for each income quartile. Unsurprisingly, higher income quartiles contribute more to the level and change in total liquid balances,since these households have much more liquid wealth. For example, 51.5% (12.02/23.35) of totalliquid balances come from the top income quartile.
It is also true that the top income quartiles drive the majority of the increase in liquid balances
over the pandemic (36%), but importantly, this increase is less than proportional to the initialshare of liquid wealth held by the top quartile. Table 4 shows that lower-income quartiles are actually driving more of the aggregate increase in balances than would be expected from their initialbalance shares, so liquid wealth inequality decreases between February and May. This provides
a concrete sense in which the poor are disproportionately increasing savings relative to the richduring this pandemic. While this shift in the wealth distribution towards low-income households
many not seem huge, it implies a more than three percentage point decline in the share of liquid
wealth held by the richest quartile occurring over a matter of weeks. One important caveat is that
we only measure checking account balances. If higher-income households transferred more assets
out of the checking account, it is possible that we understate the increase in their total assets.21
This increase in savings for the poor very likely reflects the fact that stimulus checks andexpanded UI benefits provide a disproportionate increase in income for these households. This
also means that this shift may reverse in the near future if stimulus is reduced. For example, theexpanded federal supplement to UI insurance which has led to replacement rates above 100% formany families, is set to expire at the end of July 2020. The magnitude of the additional spending
drop induced by initial disease avoidance and social distancing restrictions may also dominate
the consumption response caused solely by income loss. This could lead to an increase in savings,
even for those experiencing job loss, but it might not continue as social distancing is relaxed.
21 On the other hand, if delayed tax payments contributed to the growth in cash balances among high-income

families, liquid asset growth could be short-lived.

21

Figure 12: Growth of Balances by Industry of Employment

Finally, Figure 12 shows liquid balance growth by industry of employment. While increases
are again pervasive, we find that grocery store and department store workers have the largest
growth in checking account balances. This is directly in line with checking account growth byincome, since these are also the lowest income industries in our split.

IV

Conclusion

We find that all individuals across the income distribution cut spending at the start of the pandemic. These declines are massive relative to typical responses to spending responses to unemployment. While high-income households cut spending more than low-income households, thesedifferences are small relative to the huge common declines in spending. However, beginningin mid-April, substantial differences by income emerge: while spending begins to recover for
all groups, it does so much more rapidly for the lowest income quartile. Similar patterns emerge
when cutting by industry of employment, with workers in all industries initially cutting spending
dramatically and then workers in low wage industries seeing spending recover more quickly.
One limitation of this paper is that Chase micro data on income during the pandemic period
is still being processed at the time of writing and not yet available for analysis. We therefore turnto public use data to explore how the income distribution has changed in recent months. Specifically, we simulate how income has likely changed in the first few months of the pandemic using
statutory provisions of the CARES Act, information from the Current Population Survey, and theunemployment insurance calculator in Ganong, Noel, and Vavra (2020). Although labor income
fell the most for lower-income households, we estimate that total income including transfers actu22

ally increased the most for those at the bottom of the income distribution for two reasons. First,
the EIPs were a flat payment and therefore constituted a larger share of income for low-income
households. Second, because the temporary $600 supplement to UI benefits under the CARES Act
is the same for all unemployed workers, it drives up the replacement rate and resulting income
disproportionately for low income workers. In fact, UI benefits now replace more than 100 percent of lost earnings for low-income households (Ganong, Noel, and Vavra (2020)). The details of
this simulation are described in the Appendix.
Figure 13: Estimated changes in income and spending by income quartiles

This figure shows the change in income and spending by income quartile. The change in income compares March, April, and May 2020 toaverage quarterly income in the prior year. The change in income reflects the decline in labor income, the EIPs, and unemployment benefits. Thechange in spending compares April 15-May 30 to spending at the same time in the prior year. Our estimate focuses on this narrower time horizon
after the most immediate impacts of the lockdown—which depressed spending across the income distribution—had subsided.

Figure 13 juxtaposes simulation-based estimates of the change in income alongside the change
in spending from Figure 2. There is a suggestive correlation between the pattern of incomechanges and the relative pattern of spending changes. Spending falls the least for the group
receiving the most income support, and decreases the most for the group with the least income
support. In future work, when Chase micro data on income during the pandemic becomes available, we plan to explore the joint dynamics of income and spending at the household-level tobetter understand these patterns.
Two other pieces of evidence in our paper suggest that government income support could be
driving spending during this period. First, the timing of the more rapid rebound in spending
for low-income households coincides closely with the timing of EIP stimulus and expanded UI
benefits, suggesting an important role for government support in stabilizing spending during
the pandemic, especially for low-income workers. Second, while increases in liquid balances arewidespread during the pandemic and driven in large part by general declines in spending, we
23see that households at the bottom end of the income distribution –who see the largest stimulus
relative to pre-pandemic income – have the largest growth in liquid savings during this period.
As a result, liquid wealth inequality falls between February and May.
Taken together, our results suggest that labor market disruptions were unlikely to be a primary factor driving spending declines in these initial months of the recession. Many of the effectsof labor market disruptions on spending were likely offset by sizable fiscal stimulus and insurance programs. Instead, direct effects of the pandemic were likely the primary factor driving
overall declines in spending. Our analysis does not claim to disentangle the effect of pandemicrelated channels — i.e regulatory shut-downs vs. disease prevalence and fear of infection — on
the spending decline. It instead focuses on the impact of income changes brought about by job
loss and government transfers.
There are some important cautionary implications for future policy. First, it is important tonote that even though aggregate spending has recovered substantially from its nadir, it remains
well below normal. Spending on May 31st, when our sample currently ends, remains very low
in absolute terms, even when compared to spending declines in other severe episodes like theGreat Recession. Spending has partially recovered, but still remains severely depressed relative
to pre-pandemic levels. Policy makers should thus not be too quick to conclude that the economy
has rapidly recovered to normal. Even more importantly, our results suggest that an important
share of this spending recovery has in fact been driven by aggressive fiscal stimulus and insurance
payments. While we see a large spike in savings for low-income households immediately after
EIP, these increases may erode as the EIP gets used and if UI benefits get scaled back. This suggeststhat new support may be needed to maintain spending for low-income, vulnerable households inthe near future. Phasing out broad stimulus too quickly could potentially transform a supply-side
recession driven by direct effects of the pandemic into a broader and more persistent recession
caused by declines in income and aggregate demand.

References
Alexander, Diane and Ezra Karger. 2020. “Do Stay-at-Home Orders Cause People to Stay at
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Andersen, Asger, Emil Hansen, Niels Johannesen, and Adam Sheridan. 2020. “Pandemic, Shutdown and Consumer Spending: Lessons from Scandinavian Policy Responses to COVID-19.”
Baker, Scott R, R.A. Farrokhnia, Steffen Meyer, Michaela Pagel, and Constantine Yannelis. 2020.
“How Does Household Spending Respond to an Epidemic? Consumption During the 2020
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Bitler, Marianne, Hilary Hoynes, and Diane Schanzenbach. (2020). “The social safety net in thewake of COVID-19. BPEA Conference Drafts.” Tech. rep.
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Hurst, Christopher Johann Kurz, and Ahu Yildirmaz. 2020. “The U.S. Labor Market During the
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Friedman Institute for Economics.
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Institute for Economics.
25

A PPENDIX
Natalie Cox

Peter Ganong

Pascal Noel

Diana Farrell

A.1

Joseph Vavra

Arlene Wong

Fiona Greig

Change in Spend by Payment Type
Figure A.1: Average Spending Changes on Credit Cards(a) Percent Change(b) Levels

The left panel plots the year-over-year percentage change in weekly credit card spending. The right panel plots thedollar change in weekly credit card spending.

A.1

Figure A.2: Average Spending Changes on Debit Cards(a) Percent Change(b) Levels

The left panel plots the year-over-year percentage change in weekly debit card spending. The right panel plots
the dollar change in weekly debit card spending. Here we demarcate the date of the first EIP payments fromTreasury. Over 50% total EIP payments were distributed by April 17 (see https://home.treasury.gov/news/pressreleases/sm1025). Therefore, while the delivery of EIP payments was somewhat "staggered" over time, the majority
were received close to this point in time.

Figure A.3: Average Changes in Cash Spending(a) Percent Change(b) Levels

The left panel plots the year-over-year percentage change in weekly cash withdrawals. The right panel plots thedollar change in weekly cash withdrawals. Cash withdrawals at ATMs using debit cards are reflected in the debitcard series, so this series primarily reflects cash withdrawals with bank tellers.

A.2

Figure A.4: Average Spending Changes in Checks(a) Percent Change(b) Levels

The left panel plots the year-over-year percentage change in weekly spending via checks. The right panel plots thedollar change in weekly spending via checks.

Figure A.5: Average Total Spending Changes (Including Check Spending)(a) Percent Change(b) Levels

The left panel plots the year-over-year percentage change in weekly total spending (inclusive of spending via checks).
The right panel plots the dollar change in weekly total spending (including spending via checks).

A.3

A.2

Spending by Payment Type, Split by Income Quartiles
Figure A.6: Credit card spending by income quartiles(b) Levels(a) Percent Change

Figure A.7: Debit card spending by income quartiles(b) Levels(a) Percent Change

A.4

Figure A.8: Cash spending by income quartiles(a) Percent Change(b) Levels

Cash withdrawals at ATMs using debit cards are reflected in the debit card series, so this series primarily reflects cash
withdrawals with bank tellers.

Figure A.9: Check spending by income quartiles(a) Percent Change(b) Levels

A.5

Figure A.10: Total spending by income quartiles (Inclusive of Checks)(a) Percent Change(b) Levels

Table A.1: Decomposition of Total Spending Changes by Income Quartile
InitialSpending

Share of
InitialSpending

Share of
Decrease in Decrease in
Spending
Spending

Quartile 1
Quartile 2
Quartile 3
Quartile 4
Total$7.66B$8.93B$10.71B$15.73B$43.04B

17.8%
20.8%
24.9%
36.5%
100.0%-$0.89B-$1.30B-$2.14B-$4.37B-$8.70B

Top Decile
Top One Percent$7.58B$1.04B

17.6%
2.4%-$2394M-$409M

Final
Spending

Share of
Final
Spending

10.2%
15.0%
24.6%
50.2%
100.0%$6.78B$7.63B$8.57B$11.36B$34.34B

19.7%
22.2%
25.0%
33.1%
100.0%

27.5%
4.7%$5.19B$0.63B

15.1%
1.8%

Initial spending is computed as total spending in 2019 for the 11 weeks between March 15 and May 30. Final spending
is computed as total spending over the same 11 "pandemic" weeks in 2020.

A.6

A.3

Spending by Industry of Employment
Figure A.11: Spending Changes Split by Industry of Employment(b) Spend by Income Quartile 4(a) Spend by Income Quartile 1

A.4

CPS and Chase Sample Income Comparison

A.7

Figure A.12: Comparison of income quartiles for Chase sample and CPS population

This figure compares average labor income by quartile in our analysis sample to those in the CPS. Since we only
observe post-tax income in the Chase sample, we adjust the CPS income measure downwards to account for income
and payroll taxes.

Table A.2: Average Income by Industry of Employment

Industry

Total Monthly Pay

Average Monthly Pay

Clothing/Dept Store

$76,279,559$2,266Grocery/Drug/Discount Store$309,150,823$3,932

Education$169,907,642$3,918Government$1,215,885,445$3,762

Health Care

$114,163,903$5,704

Manufacturing$651,859,889$5,772Finance$344,412,048$5,423

Professional$251,134,078$2,366This table lists the average monthly income for individuals in the debit card sample (column 2) and credit card sample(column 3) that are employed in each industry.

A.8

A.5

Additional Results
Figure A.13: Year-over-year growth in aggregate bank deposits

20

Percent

15

10

52020m1

2020m2

2020m32020m42020m5This figure plots aggregate year over year growth in all commercial bank deposits, using data from the Fed Board ofGovernors Release H.8

A.6

Simulation of distribution of income changes

We simulate the changes in income for households using the March 2019 Current Population Survey Annual Social and Economic Supplement (CPS ASEC). We require average hourly earnings
of at least the federal minimum wage and to avoid issues with eligibility for transfers we also
require that earners are citizens. We cut the sample into weekly earnings quintiles and allow theunemployment rates to vary between quintiles and month. We calculate a separate unemployment rate for each quintile and year using the CPS merged to the Earner Study in the CPS. We
merge to the earner study taken at least 8 month prior to the observation period where we measure unemployment. For 2019, we take the unemployment rates pooling across April, Augustand December. For 2019, we match to the April CPS only. To produce variation by month, were-scale these unemployment rates so as to match the national unemployment rate in aggregate
in each month, including among the unemployed those who were misclassified as absent fromwork for other reasons. We randomly assign individuals in the ASEC to unemployment at therates described above.
If an unemployed worker receives unemployment benefits, we calculate their regular Unemployment Compensation (UC) according to the calculator used in Ganong, Noel, and Vavra (2020).
In our simulation, a worker cannot receive regular UC if they have insufficient earnings history
A.9

according to the rules of their state. Additionally we calculate a recipiency rate among monetarily eligibles, using the ratio of actual benefits paid out in DOL ETA Form 5159 to the impliedvalue of benefits if every eligible person in our simulation was receiving benefits. For example
in April 2020, we calculate that 55% of eligible benefits were paid out and assume that 55% ofeligibles receive benefits. In 2020, we include the possibility of additional unemployment benefits
from the CARES Act: both the $600 weekly supplement (Federal Pandemic Unemployment Compensation) and the insurance for those with insufficient wage earnings to qualify for regular UC(Pandemic Unemployment Assistance). For both of these policies we use data on the rollout ofthese policies from the Hamilton Project (Nunn, Parsons, and Shambaugh 2020). An unemployedworker receives an additional $600 in their UC at random, with the probability corresponding to
the share of workers who live in a state that had rolled out FPUC at that time. An unemployedworker who is monetarily ineligible for benefits receives a total compensation of $600 at random,with the probability corresponding to the share of workers who live in a state that had rolled out
FPUC at that time and adjusting for the recipiency rate described above.
We additionally model Economic Impact Payments (EIPs) assuming that Adjusted Gross Income (which is used to determine EIP eligibility) is equal to household income in the CPS. We
allow each CPS household at most one EIP, which is $1200 plus $500 per child in the household.
We allow a household an economic impact payment if they have an income which is sufficiently
low to receive the full payment: we do not model the phase out, instead we give a household no
EIP if they would have received a reduced payment.
To calculate changes, we find the percentage change in expected income for each householdwith and without the transfers. We then average the expected changes over households within theincome quartiles. The income quartile cutoffs are taken from the Chase sample (so exclude anyone
with income below 12,000), and are calculated on household labor income, which we adjust fortax.

A.10