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Federal Reserve Bank of Chicago

Tax Credits and the Debt Position
of US Households
Leslie McGranahan

July 2016
WP 2016-12

Preliminary and Incomplete

Tax Credits and the Debt Position of US Households

Leslie McGranahan
Federal Reserve Bank of Chicago

July 2016

This paper investigates the effect of tax credit receipt on the outstanding indebtedness of households.
In particular, we use data on zip code level indebtedness to explore whether debt levels and past due
amounts change more dramatically during tax refund season in those zip codes where households
receive greater Earned Income Tax Credit (EITC) and Additional Child Tax Credit (ACTC) refunds. We see
a substantial decline in debt past due in high tax credit zip codes during tax refund season indicating that
some recipient households use tax refunds to repair their balance sheets. At the same time, we see
increases in both auto and credit card debt during tax refund season showing a link between tax refunds
and asset accumulation and consumption.

I thank Jacob Berman and Kevin Roberts for excellent research assistance and colleagues at the Federal
Reserve Bank of Chicago for helpful comments. The views expressed in this paper are those of the
author and do not reflect the opinions of the Federal Reserve Bank of Chicago or of the Federal Reserve
System.

1. Introduction
The refundable portion of the Earned Income Tax Credit (EITC), combined with the
similarly targeted Additional Child Tax Credit (ACTC), transferred over $85 Billion to
households in 2014 (IRS 2015a). These programs, which we label “tax credits”, provide cash
benefits primarily to low income working families with children. Funds are delivered through
the tax code in the form of a single annual payment as part as the household’s tax refund. Due
to these tax credits, for many low income households with children, tax refunds represent the
single largest incoming transaction each year. Among households receiving the EITC, the EITC
refund alone represents over six weeks of Adjusted Gross Income (AGI) on average (IRS
2015a). The large single payment allows households an opportunity to alleviate financial stress,
fund pressing consumption needs, and improve their overall financial position. The goal of this
paper is to see how the financial position of low income households changes once they receive
their EITC and ACTC. In particular, we explore how household indebtedness responds to this
single large payment using zip code level data on refunds and debt and by exploiting the unique
timing of tax refunds.
Previewing our results, we find that household indebtedness increases in high tax credit
zip codes in the period surrounding tax refund receipt. In particular, we find substantial jumps in
both credit card and auto debt in high tax credit zip codes after refunds have been received. At
the same time, we observe substantial declines in delinquent debt indicating that households also
use tax credit funds to repair their balance sheets.
The paper proceeds as follows. In section 2, we provide background information on the
EITC and ACTC programs. Section 3 discusses previous investigations into household
responses to tax refunds in general and to the EITC and ACTC in particular. Section 4 follows
with an extended introduction to the numerous different data sources we use and presentation of
how we combine them to measure tax credit and financial status variables. We then introduce
the methodology we use and report results in section 5. The final section offers conclusions.
2. Background information on tax refunds and credits
2

As mentioned above, federal income tax refunds represent the single largest incoming
transaction for many households each year. In 2014 (for tax year 2013), 73% of returns received
a refund averaging $2762 (IRS 2016). Households receive a single refund each year shortly after
filing taxes that derives from a few different sources. In aggregate, the single largest source of
tax refunds is overpayment refunds. These most commonly arise when households overwithhold payroll taxes usually by claiming too few allowances on their W-4 forms.
The second largest source of refunds is the EITC. The EITC is an income support
program targeted towards low income working households with children and is the second
largest income support program in the United States, following the Supplemental Nutritional
Assistance Program (SNAP). The EITC matches a percent of earnings up to a maximum
amount, levels out at a “plateau” amount for a range of incomes, and then phases out at higher
earnings levels. It gives more generous benefits to households with more children – up to three.
The EITC has grown in generosity since it was first introduced in 1975. In 2014 (for tax year
2013), the maximum credit of $6,044 was received by households with three or more children
earning between $13,430 and $17,530. (Tax Policy Center 2016). The EITC is claimed via a
schedule attached to the household’s tax return. The EITC is refundable so that funds remaining
after household tax liability is reduced to zero are refunded to the household as part of the annual
tax refund. The great majority of EITC dollars are refunded. For tax year 2013, $59 Billion of
total EITC spending of $68 Billion was refunded. (IRS 2015a)
The third largest source of refunds is the Additional Child Tax Credit (ACTC). The
ACTC is given to households that do not receive their entire non-refundable Child Tax Credit
(CTC). The CTC gives households a per child tax credit, set at $1000 per child since 2010, that
phases out at higher income levels. The CTC is non-refundable so that credit amounts remaining
after tax liability is reduced to zero are not refunded. However, if a household’s tax liability is
less than the value of their CTC benefit, they may be eligible for the ACTC. The ACTC is a
refundable credit. A household is eligible for an ACTC credit equal to 15% of their earned
income above $3,000 up to the value of their unused CTC. The CTC did not exist prior to 1997
and broad refundability via the ACTC began for tax year 2001. (See Crandall-Hollick 2014 and

CPBB 2015). For tax year 2013, the CTC reduced tax liability by $27B, while the ACTC
refunded $28B. 1
Other short term policies, such as First-Time Homebuyer Tax Credit (2008-2010) also
contribute to tax refunds, but are smaller in size and shorter in duration. Due to the targeted
nature of the EITC and ACTC, tax refunds represent a particularly large share of income for low
income households with children.
3. Household Responses to Income Shocks
Tax refunds could be thought of as sizeable, but largely expected, income shock. A
broad literature investigates economic responses to income shocks. The majority of this research
focuses on the consumption response to these income shocks. Much of it has the goal of
investigating the implications of the Life Cycle/Permanent Income Hypothesis (PIH). The PIH
predicts that in the absence of credit constraints, households should not increase their
consumption in response to an anticipated increase in income. The theory also predicts that the
consumption response to an unexpected increase in income should be sustained, reflecting a
transition to a higher consumption path. In contrast to these predictions, much of the existing
empirical research finds short terms bursts in consumption following both anticipated and
unanticipated increases in income. Researchers have attributed this pattern of consumption
behavior to liquidity constraints, myopia and mental accounting.
Tax refunds and rebates are one of the primary sources of income shocks investigated in
the literature.

This probably derives from the fact that these refunds are more measurable,

identifiable and widespread than many other short term changes in income. In addition, refunds
and rebates are a popular type of fiscal policy and the efficacy of the policies hinges on their
ability to induce consumption.

In his 1999 paper investigating the consumption responses to

tax refunds reported in the Consumer Expenditure Survey, Souleles finds excess sensitivity to
refunds especially among liquidity constrained households. Subsequent papers using micro-data
have varied in either the particular refund or rebate that was studied or in the data set used to
measure consumption responses. Across most data sets and refunds, researchers confirm that

The interplay among the EITC and CTC is such that the CTC reduces tax liability to zero before the EITC is applied.
As a result, among households receiving the ACTC, all EITC dollars are refunded.

household consumption responds to refunds. (For example Johnson, Parker and Souleles 2006
and Parker et al 2013). In a recent paper, Baugh, Ben-David and Park (2014) evaluate the
consumption response to the revelation of tax refund amounts and to the receipt of refunds.
They find that households respond to the receipt of the refund rather than to the revelation of its
magnitude and that responses are largest among lower income households. Their results support
a portrayal of households as myopic and responsive to cash on hand rather than to knowledge of
their overall financial situation. In a largely descriptive paper Cole, Thompson and Tufano
(2008) document how H&R Block clients who received their refunds via a stored value card
spend down their refunds and document the speed with which refunds are spent. They find that
households spent their refunds relatively quickly with nearly half of the accounts empty after a
month. They find that 56% of the funds are withdrawn as cash, with most of the remainder used
to purchase items from merchants.
There is less of a literature documenting the response of aspects of household financial
well being other than consumption to tax refunds. This may partly be due to data limitations,
partly due to the fiscal policy relevance of consumption, and partly due to the fact that the PIH
has clear and testable implications for consumption given assumptions about when households
know the magnitude of their payment. Theoretical predictions concerning the response of other
measuring of financial status, such as debt, net worth and savings, to refunds are less clear cut.
As Browing and Lusardi (1996) point out, savings is a residual defined as the difference between
income and consumption. As a result, predictions about changes in savings in response to an
income shock would hinge on the difference between the income shock and the consumption
response. Predictions about debt levels (the financial measure we are investigating here) are
even more difficult to formulate because savings are the combination of increases in assets and
declines in liabilities, and by looking only at changes in liabilities, we have an incomplete picture
of savings.

In this paper, we only have data on debt which is just one part of net savings. In

light of this we treat the effect of refunds on household indebtedness as an empirical question
and discuss suggestive theoretical implications in light of the findings.
There is limited empirical research on the effect of tax refunds or other income shocks on
debt. Agarwal, Liu and Souleles (2007) investigate credit card debt and other responses to the
2001 tax rebates. They find an initial decline in credit card debt following rebate receipt, but a
5

subsequent increase as higher spending offsets the initial decline in debt. By nine months after
the rebate, debt increases are insignificantly positive. They find larger initial debt declines for
better off individuals. Agarwal and Qian (2013) perform a similar exercise for stimulus
payments to Singaporeans which yields a broadly consistent set of results. In particular, they
find a modest decrease in credit card debt in the months after the receipt of a stimulus payment.
However debt subsequently increased and returned to pre-treatment levels.

Aaronson, Agarwal

and French (2012) find that debt increases following minimum wage hikes which they attribute
to increases in collateralized debt particularly for autos. This debt increase persists for a number
of quarters following the minimum wage increase. Combined these papers find short lived
decreases in credit card debt and longer lived increases in auto debt following positive income
shocks.
While the debt response to tax refunds and other income shocks is investigated in a fairly
limited empirical literature, there is a larger descriptive literature that asks individuals how they
spend tax refunds which touches on the subject of debt. Particularly relevant for this paper, a
number of studies use questionnaires to ask EITC recipient families what they do with their tax
refunds. These studies find that reducing household debt burdens is one of the primary uses of
refunds. For example, in their survey of 237 rural working mothers, Mammen and Lawrence
(2006) found that 44% of interviewees say they use their refund to pay current or past due bills.
This was the single greatest reported use of EITC dollars. Halpern-Meeken et al (2015) find that
about a quarter of refunded dollars went to pay debts and bills; about the same amount that went
for day to day expenses among the Boston area families they surveyed. They further find that
among households using refunds to pay off debts, households reduced their debt burden by about
fifty percent. Relatedly, when Tach and Green (2014) asked 194 EITC recipient families how
they manage their household debt, they found that the single most popular answer was through
the use of their EITC refunds. Across these studies using questionnaires, researcher consistently
find high reported debt repayments following EITC receipt. We supplement this research and ask
whether this pattern of debt payoffs is present in a large national data set.
4. Data
We estimate the relationship between debt and tax credits using quarterly zip code level data
on refunds and debt. For this paper, we use four data sources – quarterly data on consumer credit
6

by zip code, annual data on the distribution of refunds across zip codes, monthly national data on
the timing of refund disbursements, and annual data on zip code demographics. In this section,
we present each of these data sources individually and then report how we combine them to
create the data set we use for our analysis.
4.1 Debt Data
To measure quarterly debt at the zip code level, we use the RADAR Consumer Credit Panel
(CCP) from the Federal Reserve Bank of New York. These data are derived from a five percent
random sample of the Equifax credit reports of individuals who have both credit reports and
social security numbers. The data set provides quarterly credit variables for sampled individuals
that measure credit aggregates as of the end of the quarter. For each sampled individual we
know their zip code of residence and numerous credit measures including the number of different
types of credit accounts, the status of those accounts, such as whether they are current or past
due, as well as the balances in those accounts. For our analysis, we measure the total debt
amounts of the sampled households from a zip code. These totals can be multiplied by twenty to
approximate total indebtedness in the zip code. We use quarterly data from 1999 to 2014 and
calculate total past due amounts and auto, credit card, other personal debt outstanding. An
observation in the data is a quarter-zip code combination.
We define past due as at least 30 days late. We investigate auto, credit card and other
personal debt because these types of debt can be adjusted within a quarter. We do not investigate
housing or student loan debt. Mortgage and home equity debt is fairly uncommon among low
income households and is slower to adjust than other debt. 2 We do not look at student loan debt
because our identification comes from quarterly debt patterns and the quarterly pattern of student
loan growth is driven by the academic calendar.

Other personal debt includes sales financing

and personal loans and retail loans from clothing, department stores and gas retailers. 3
Variable means, presented as the average across zip codes of indebtedness per sampled credit
record are presented in Table 1. In the table, the means are weighted by the number of credit
2

According to Tach and Greene, 13% of the EITC recipients they surveyed had mortgage debt. 60% had credit card
debt and 42% had car loans.
3
Medical and utility debt are excluded from most of the variables in the underlying data set unless such payments
go to collections.

records in the data set per zip code so they represent the average per sampled individual. Across
the quarters of the sample, the average sampled individual had $2,623 in debt past due, $3,320
in auto debt, $3,355 credit card debt and $1,904 in other personal debt. These debt measures
capture a snapshot of household indebtedness at a point in time. For credit card debt, which we
also label bank card debt, this combines revolving balances and transactional balance. In other
words, a household that pays off balances in full every month will be listed as having a debt
equal to the value of its unpaid transactions. The average sampled individual lives in a zip code
where there are 1072 credit reports while the average zip code in the sample (based on an
unweighted mean) has 260 credit records.
4.2 The Distribution of Refunds
Our second data set measures the amount of total refunds, EITC refunds and ACTC refunds
received by taxpayers in each zip code. These amounts are available annually, by tax year, from
two sources: the IRS Statistics of Income Tax Stats – Individual Income Tax Statistics – ZIP
Code Data (SOI) and the Brookings Institution’s EITC interactive (Brookings). These data
report aggregate tax related measures by the zip code reported on tax returns. The available
variables differ by year and across the two sources, but in all years a large selection of tax related
measures is available. We use data for tax years 1998 to 2013 which reflect refunds paid out in
calendar years 1999 to 2014. 4
As explained earlier, for the EITC, the refunded amounts are only part of total programmatic
spending because some of the credit serves to reduce tax liability while the remainder of the
credit is refunded to the tax unit. Over the years covered in our sample, approximately 90% of
the value of the credit has been refunded because most EITC households have limited tax
liability. This percent has been fairly stable over the years covered in this paper.
The ACTC operates somewhat differently because the non-refundable CTC and the
refundable ACTC are separate programs. Due to changing program parameters the percent of
spending on the combined CTC and ACTC that is the refundable ACTC has been increasing
over time from less than 5% prior to tax year 2001 to approximately 50% since tax year 2009.

Refunds are paid after taxes are filed which is in the calendar year following the tax year.

In Table 2, panel a, we show variable means by year, calculated as the amount per return,
averaged across zip codes, for the refund and tax credit related variables available in the SOI
data. In particular, we show average total refunds, EITC refunds and ACTC refunds. 5 We also
show total EITC per return, CTC per return, and total taxes due. Total taxes due is a measure of
the amount due from households in the zip code at filing. While some households receive
refunds, other households have remaining tax liability and owe the Federal Government money
when they file their taxes. The SOI data are administrative totals generated from the IRS’s
Individual Master File. The zip code is based on the zip code reported on the tax return. Some
zip codes are omitted, including those with less than 100 returns. (For details see IRS 2015c).
We note that not all variables are available in all years, none of our desired variables is available
in some years, and there are no data files in other years.
In Table 2, panel b, we show relevant available tax refund and tax credit averages by tax year
from the Brookings data. The Brookings data are tabulated by the Brookings Institution based
on data provided by the IRS. (See Brookings 2014). Starting in tax year 2009, the Brookings
data are part year and only cover returns filed between January and June. While this is not a
problem for the total EITC and ACTC variables because about 99% of benefits are paid out in
the first half of the year, it is a problem for the data on the number of tax returns, total refunds
received and taxes due at filing because sizeable payments occur throughout the year. As a
result, we set the total refund and taxes due variables equal to missing for tax years 2009 and
after. 6
The values for the EITC and CTC variables are nearly identical when they are reported in
both data sets. Insofar as sample means differ for the EITC and CTC variables, it is largely
because a slightly wider set of zip codes is covered in the Brookings data because Brookings
imputes suppressed values. The data on taxes due is also comparable across the data sets. The
total refund amounts differ more substantially across zip codes probably due to the deletion of
some returns from the SOI data and definitional differences between variables. Each of these
sources has advantages. The principal advantage of the Brookings data set is that it covers a
5

According to the SOI documentation, their measure of the refundable EITC for a household is (EITC – total income
tax) if the EITC is greater than total income tax and zero otherwise. They note that no other refundable credits are
taken into account for this calculation. They are assuming that the EITC goes first in reducing tax liability to zero
after the non-refundable credits have been applied (including the CTC).
6
We use the total number of tax returns reported in the SOI data for 2009-2012 analysis.

consistent time series with EITC measures for every year between 1998 and 2013. The main
advantage of the SOI dataset is that it has two separate EITC variables in most years it covers. In
particular it has data on both the refundable portion of the EITC and on the total value of the
EITC, while the Brookings data set only has information on the total value of the EITC. Both
data sets have information on the non-refundable CTC and the ACTC for some years.
We combine the data from these two sources to generate estimates of most of the desired tax
related variables for most tax years between 1998 and 2013. We use the SOI data when it is
available and supplement it with values from the Brookings data when a value is not available in
the SOI data set. For any zip code – tax year observation, if data on total EITC spending is
available, but data on the refundable EITC is not available, we estimate refundable EITC
spending by multiplying the national percent of the EITC that is refunded by the zip code total
EITC. Similarly, when ACTC data is unavailable, but CTC data is available we estimate the
ACTC by multiplying the zip code level information on the non-refundable CTC by the ratio of
the refundable ACTC to non-refundable CTC at the national level. 7 For tax years 1998-2000, we
assume that the ACTC is zero for all zip codes because the credit was non-refundable for the vast
majority of households. 8 (National data on the refundable total EITC and total CTC and ACTC
is available from the IRS for all years (IRS 2015a)). Finally we estimate overpayment refunds as
equal to total refunds minus our estimates of the refundable EITC and ACTC. Due to this
calculation, our measure of overpayments includes other small refunds such as the first-time
homebuyer credit and the refundable education credit, but these are tiny relative to overpayment
refunds. Table 2c displays zip code means by tax year for variables measuring refunds and
taxes due at filing from the combined data set.
From this table, we see that on average, across zip codes, overpayments are larger than EITC
and ACTC refunds. We also see the growth over time in the refundable EITC and the ACTC,
especially around the passage of the American Reinvestment and Recovery Act in 2009 which
increased the generosity of both programs beginning in tax year 2009. We also note that while
7

We can evaluate this estimation using the years with data on the refundable EITC and ACTC. In those years, the
correlation between the actual and estimated refundable EITC is 0.99. The correlation between the actual and
estimated ACTC is 0.75. While the ACTC correlation is lower, we only estimate the ACTC for one year while we rely
on this estimate for 9 years for the EITC.
8
In TY2000, the CTC was $20 Billion while the ACTC was just under $1 Billion. In TY2001, the CTC was $22 Billion
while the ACTC was $5 Billion.

the average ACTC refund is less than half the size of the average EITC refund, it is a substantial
fraction of combined tax credits. Due to the similarity in the eligibility for the two programs,
per-return zip code EITC and ACTC refunds are highly correlated (about 0.8) across zip codes
particularly in the more recent tax years. Average tax payments due at filing are smaller than
average total refunds consistent with the fact that that during tax season, the IRS pays out more
than it takes in.
These means mask substantial heterogeneity in refund related variables across zip codes. In
Figure 1, we show a zip code heat map for tax year 2013 of average combined EITC refund and
ACTC per return. Average tax credit amounts vary substantial across space. The highest tax
credit amounts are in Appalachia and the deep south – areas characterized by above average
poverty levels.
The pattern in the map combined with the targeted nature of the tax credit programs suggests
that the heterogeneity in refunds across zip codes partly derives from differences in income
across the zip codes. We further display this by showing refunds per return and refunds as a
share of AGI by zip code-AGI category for tax year 2013 in Figure 2a and 2b. Zip code AGI is
measured based on AGI per return in the SOI data. While refunds levels are largest in the
wealthiest zip codes (2a), refunds are a far larger share of AGI in the poorest zip codes (2b).
This latter difference becomes even more pronounced if we take into account taxes due at filing
and measure net refunds in a zip code (see Figure 2c). At tax filing time, some households in a
zip code receive refunds, while other households owe money. In Figure 2c, we show average
total refunds, taxes due at filing and net tax refunds (defined as refunds minus taxes due for all
returns in the zip code) as a share of AGI. We observe that in the wealthier zip codes, taxes due
among some households nearly or completely offset the refunds received by other households.
In the poorer zip codes, taxes due are small and as a result net refunds are a large share of AGI.
These graphs show that tax time has a very different meaning in low income zip codes than it
does in high income zip codes.
4.3 Refund Timing
The third data source we use is information from the Monthly Treasury Statement (MTS) on
the timing within a year of tax refunds and final tax payments. We need this information as we
11

will use the timing of refunds within the year as a source of identification. In Table 3 we display
average total monthly refunds and refunds broken down into four sources: overpayments, EITC,
ACTC and miscellaneous other refunds paid on average for 1999-2014. Miscellaneous refunds
represent payments from an assortment of programs that used the tax code to spur demand
during the Great Recession – such as Making Work Pay. As discussed earlier, the largest source
of refunds is overpayment refunds, followed by the EITC and the ACTC. Miscellaneous refunds
are small on average, but were relatively large in some years of the sample due to policies
designed to alleviate the effects of the Great Recession on households. We also show total taxes
paid, taxes paid broken into amounts withheld from wages and other income sources, amounts
not withheld, and net taxes. Most individual income taxes are withheld. Within non-withheld
taxes, the MTS does not provide a breakdown between final payments (those due at filing) and
estimated payments (those paid prior to filing but not via withholding). This is in contrast to the
zip code data that captured final payments but did not have a measure of zip code level
withholding or estimated payments. Taxes paid are much higher than refunds, but non-withheld
individual income taxes are of a similar magnitude to refunds.
There are vast differences in the magnitude of refunds and payments made across the months
of the year. In Figure 3a, we graph the annual share of total, overpayment, EITC and ACTC
refunds paid out by the Treasury in each month of the year in 2014 (reflecting tax year 2013).
The vast majority of EITC refunds and ACTC refunds were paid out in February (73%), over
payments also peaked in February (29%) with a substantial amount in March (24%) and April
(25%) as well. This pattern derives from the fact that the lower income families that are eligible
for the EITC and ACTC file their taxes soon after the tax window opens (on January 31 in 2014)
while many of the middle income families who receive overpayment refunds wait until the tax
filing deadline on April 15. The patterns in other years closely parallel those in 2014, although
overpayment refunds peaked in April rather than February prior to 2012. Combining these, we
see that total refunds are highest in February, followed by March and April. In Figure 3b, we
display the fraction of gross taxes paid and withheld and non-withheld taxes paid in each month
of 2014. Tax payments peak in April due to final payments, but are also high in the months
when estimated payments are due (January, April, June, and September) and in December when
holiday bonuses increase withholding. In Figure 3c, we graph the dollar amount of total
refunds, total taxes paid and taxes paid net of refunds by month of 2014. From this graph, we
12

see that February is the only month in which the government refunds more than it receives. The
government has high net receipts in January, April, June, September, and December.
We can combine the data on the timing of different types of refunds from the MTS and the
data on the distribution of refunds across zip codes from the merged SOI and Brookings data to
create estimates of the monthly and quarterly amount of refunds for each zip code. In particular,
we allocate annual refunds in the zip code for each tax year for each type of refund
(overpayment, EITC, ACTC) across the months of the following calendar year according to the
pattern for that refund type in the calendar year’s MTS. We then sum across the months of the
quarter to create quarterly estimates. This combination leads to the finding that individuals in
low income zip codes receive large tax refunds at the start of the year – particularly in February
while higher income tax payers receive more modest refunds, especially relative to income, later
in the year. We cannot perform a similar exercise for tax payments because the timing data
covers the sum of estimated and final payments while the zip code data covers only final
payments.
4.4 Census Demographics
Our fourth data set contains Census zip code tabulation area (ZCTA) demographic
information from the US Census Bureau from the decennial Census and the American
Community Survey. These data are available at most annually. We interpolate and extrapolate
for years where the data is unavailable. In Table 4, we show average measures across zip codes
and years for these Census demographics.
4.5 Merging Data
We merge these four data sets together to create a quarterly data set that includes debt
variables, estimated quarterly refunds received, and demographics at the zip code level. We
delete a number of observations from this merged data set. First off, we only include zip codes
that are present in all three zip code level data sets. Most zip codes lost due to this requirement
are absent from the Census data set. 9 These tend to be places with few tax returns and credit
9

Some postal zip codes do not have corresponding Census Zip Code Tabulation Areas (ZCTAs). Census blocks are
assigned to ZCTAs based on the most prevalent postal zip Code in the block. Some small zip codes do not dominate
any census block. See U.S. Census Bureau 2015 for more details.

records. To insure that the zip codes are defined in a comparable manner across sources and
over time, we also delete zip codes that fail a series of comparability tests. In particular, we
delete the zip codes in the bottom percentile of the distributions of the number of returns, census
population, and number of credit reports. We do this because we would like the census, tax
return and credit report data to cover the same population and we believe this will be least true in
the least populated zip codes. We also delete zip codes with big changes in the number of credit
reports from quarter to quarter because we are concerned that the coverage of the CCP data is
changing. We further restrict the sample to zip codes where the number of tax returns reported
in the zip is less than twice the represented CCP population and the reported Census population.
In this case, we are concerned about zip codes that are used as mailing addresses for tax returns,
but where fewer people live. Because the filing of tax returns is not mandatory, we do not delete
the bottom of the tax return to population distribution. Additionally, we delete zip codes where
the population represented by the CCP and the Census total population diverge dramatically.
We also delete zip codes that had dramatic changes in their geography or changed drastically in
how much per capita EITC and ACTC residents received relative to other zip codes across the
years of the sample. Finally, we use a balanced panel of zip codes – zip codes must meet our
requirements every quarter of the sample period.

Through these processes, we keep 70% of the

zip codes in the Census sample representing 90% of the Census population. 10

The first columns

of Table 5 present variable means (across zip code-quarters) for the merged quarterly sample.
We definite the debt and tax variables in per capita terms using Census population rather in per
tax return or per debt record terms to maximize the comparability across the data sources. In the
average zip code-quarter, households receive $50 per capita from tax credits, with a standard
deviation of $88.
The later columns of Table 5 display variable means separately for zip codes in the
highest tax credit per capita quintile in 2007 and in all other zip codes. Tax credit amounts in
high tax credit zip codes are 2.5 times as high as in other zip codes. High tax credit zip codes
have higher debt past due amounts per capita, but lower levels of other types of debt, particularly
credit card debt.
4. Empirical Approach
10

These tabulations are based on 2005:Q1.

We use this combined data set to investigate whether household indebtedness adjusts in
response to refundable tax credits. In particular, we ask whether there are differential debt
changes in high refundable tax credit zip codes during the period when EITC and ACTC refunds
are received as compared to low refundable tax credit zip codes. Initially, we concentrate on the
targeted credits and will expand our analysis to overpayment refunds later. We begin with a
simple diff-in-diff approach that asks whether the quarterly pattern of indebtedness in high tax
credit zip codes is different from the pattern in other zip codes. We define a high tax credit zip
code as one in the top quintile of (population weighted) combined refundable EITC and ACTC
per capita in tax year 2007. In 2007, about 40 percent of EITC/ACTC payments were made to
households in the top tax credit quintile. We choose 2007 because it is close to the midpoint of
our sample and is a year in which we have actual refundable EITC data (from SOI) and actual
ACTC data (from Brookings). We fix the zip codes we define as high tax credit to be constant
over time to downplay changes in tax credit amounts within a zip code. The zip codes receiving
the highest average tax credit amounts are fairly consistent over time. We are interested in
seeing whether the change in indebtedness that occurs during the first quarter of the calendar
year (when nearly all tax credits are received) is different in high tax credit zip codes.
In our initial specification we estimate:

Debtit − Debtit −1 = α + γ q HighTCi × Quartert + time + time 2 + time3 + qtrt + λi + ε it

(1.1)

We are asking about changes in debt controlling for a cubic time trend in debt growth, for zip
code specific fixed effects, the quarter of the observation (1 through 4), and the quarter interacted
with a dummy for being a high tax credit zip code in 2007. Debt is defined as per capita debt in
the zip code. The coefficients of interest are the γ q , which indicate whether debt growth is
higher or lower in different quarters of the year in high tax credit zip codes. Recall that debt is
measured as of the end of each quarter. Differential patterns of debt growth in the first quarter
of the year in high tax credit zip codes would be indicative of tax credits influencing debt
patterns. We are investigating the effects of the tax credit on debt by exploiting the unique
timing of tax credit refunds.
We estimate this equation for total debt past due, auto debt, bank card debt, and other
personal debt. Recall that other personal debt includes store and gas credit cards, sales financing
15

and personal loans. Results for changes in debt in quarters relative to the third quarter (the γ q )
are presented in Table 6 and displayed graphically in Figure 4. The finally row of the table
displays the first quarter coefficient estimate divided by the standard deviation of the dependent
variable in the high tax credit zip codes to provide a sense of the magnitudes.
We find that debt past due declines substantially in the first quarter in high tax credit zip
codes relative to other zip codes. Debt declines by an additionally $71 this represents a 0.1
standard deviation drop in debt growth. Households appear to be paying off their delinquent
debt in the wake of receiving their tax credits. Debt past due also declines more in the second
and fourth quarters in high tax credit zip codes relative to the omitted third quarter, but these
declines are more modest than the decline in the first quarter.
The results for changes in outstanding auto, bank card and other personal debt are shown
in columns (2)-(4). For all three debt types, outstanding debt grows more dramatically in the
first quarter in high tax credit zip codes. In the first quarter of the year, per capita auto debt grew
by $32 more in high tax credit zip codes than in other zip codes relative to the omitted third
quarter, controlling for average zip code level debt growth and average first quarter debt growth.
Bank card debt grew by $47 per capita and personal debt grew by $17. We note that there are
also statistically significant differences in the other quarters of the year. In particular, there are
also larger auto debt increases in high tax credit zip codes in the second quarter (relative to the
omitted third quarter).
These results show that after tax credits have been received, we see a relative increase in
household auto, credit card and personal debt in high tax credit zip codes. This could be due to
increases in debt issuance or decreases in the rate of paying off loans. For auto loans, while we
do not distinguish between debt increases due to new car loan issuance in the zip code and those
due to a decline in the rate of paying off preexisting car loans, the auto finding is consistent with
other research that shows that households respond to income windfalls by purchasing cars. 11
For credit card loans, as noted earlier, our measure of indebtedness includes both revolving and
transactional balances. In light of this, increases in credit card debt could occur because
households are purchasing more items with their credit card and so have higher transactional
11

We are working on ways to distinguish between new and preexisting loans in the CCP/Equifax micodata. Parker
et al 2013 and Aaronson, Agarwal and French 2012 both find this pattern.

balances or because they are paying off prior purchases more slowly so that revolving balances
are increasing. While we cannot distinguish between these two options, the finding that
outstanding debt increases is consistent with higher credit card sponsored consumption during
refund season. Our pattern of results is consistent with higher levels of debt spending for both
cars and other goods during refund season. This is in sharp contrast to the result that past due
debt declines.
One concern with these results is that they could be driven by different quarterly
borrowing and repayment patterns among the households targeted by the credits rather than by
the credits themselves. For instance households with kids may have different quarterly spending
patterns than households without kids due to factors like school or holiday shopping. We
address this possibility by allowing different types of households to have different quarterly debt
patterns by adding a series of quarter - demographic interactions into the regression. In
particular, we interact the quarter dummies with the percent of households with kids in the zip
code in 2007 and with the percent of the over 25 population with a high school degree in 2007
(A similar adjustment is made in Barrow and McGranahan 2000). Households with kids are
explicitly targeted by the tax credit programs while households with more education are less
likely to be lower income and lower income households are also prime beneficiaries. As with the
tax credits, we fix the demographics in 2007 because they do not vary dramatically over time and
we want to pick up the effect of the different quarters rather than evolving within zip code
demographics.

The model we estimate is:

α + γ q HighTCi × Quarter + γ qd Demogi × Quarter +
Debtit − Debtit −1 =
time + time 2 + time3 + qtrt + λi + ε it

(1.2)

Results for the γ q are presented in Table 7 and Figure 5. The results for past due debt, auto debt
and other personal debt are substantively unchanged. There is a more pronounced change for
bank cards. The first quarter – tax credit interaction falls and second quarter estimate increases
so the debt changes in high tax credit zip codes in these two quarters become similar. Upon
further investigation, we find this alteration in the estimates to be driven by higher first quarter
debt growth in less educated zip codes, not just those receiving high amounts of tax credits.

In the remainder of this section, we perform a series of regressions where we create a
number of different versions of the variables measuring the interaction between the quarter and
the tax credit amount.
For our first additional specification, we replace the high tax credit-quarter terms in
equation 1.2 with a variable that is equal to the percent of annual combined EITC/ACTC benefits
received that quarter interacted with the high tax credit dummy. Instead of four quarter-high tax
credit interactions, we have one variable measuring the percent of annual tax credits in each
quarter interacted with the high tax credit indicator. We estimate equation (1.3). We maintain
the demographic quarter interactions. 12

Debtit − Debtit −1 =
α + γ HighTCi × TC _ Sharet + γ qd Demogi × Quarter +
time + time 2 + time3 + qtrt + λi + ε it

(1.3)

The results are presented in Table 8. The coefficient estimate for the parameter γ tells
us how much extra debt growth we would expect to see in high tax credit zip codes, relative to
other zip codes, in a quarter when 100% of the year’s tax credits were paid out. The final
column of the table shows the coefficient γ divided by the standard deviation of the dependent
variable in high tax credit zip codes. These results confirm the earlier finding that there are
larger increases in indebtedness in high tax credit zip codes in those quarters when more tax
credits are received and larger declines in past due debt.
As a robustness check on the results in Table 8, we add interactions between the quarterly
tax credit share and each of the five (2007) tax credit quintile indicators. We are investigating
whether the response to tax credit shares is unique to the highest tax credit zip codes or is broad
based. We present the results graphically in Figure 6. 13 The quarterly share of tax credits has the
most negative effect on the change in debt past due for the highest tax credit quintile zip codes.
The coefficient increases (becomes less negative) as the tax credit quintile falls. We observe the
opposite pattern with the measures of outstanding debt. For auto debt, bank card debt and other
12

We do not add a variable measuring the quarterly share of annual tax credit share because it is close to co-linear
with the quarter dummies. Adding such a variable does not substantively alter the results.
13
We drop the quarter dummies from this regression because the tax credit shares are fairly consistent
across time so including quarter dummies would be identifying results off of the differences over time in the
distribution of tax credits across quarters. Regressions with quarter dummies yield consistent results.

person debt, the largest increases in response to quarterly tax credit shares are in the highest tax
credit zip codes. These graphs show that the largest responses to tax credit timing are in those
places receiving the largest tax credit amounts.
We next replace the high tax credit dummy in equation 1.2 with the amount of tax credits
per capita in 2007. We are exploiting the fact that we have information on tax credit amount,
not just on whether a zip code is in the top tax credit quintile. The equation we estimate is

Debtit − Debtit −1 =
α + γ q Credits 2007i × Quarter + γ qd Demogi × Quarter +
time + time 2 + time3 + qtrt + λi + ε it

(1.4)

We are allowing the annual tax credit amount from 2007 to have a different effect on
changes in indebtedness each quarter of the year. We would anticipate that the tax credit amount
would have the largest impact in the first quarter when nearly all credits are paid out. We
present the coefficient of interest, the γ q , in Table 9. The coefficient on the tax credit x quarter
variable indicates how much debt changes each quarter of the year in response to a $1 annual
increase in tax credits relative to the omitted third quarter. In all four regressions, the first
quarter of the year is the outlier. Auto indebtedness increases by $0.11 more in the first quarter
than in the omitted third quarter in zip codes where household receive an extra $1 per capita in
average tax credits. Zip codes where households receive more tax credits also increase credit
card and other personal debt more in the first quarter. There are statistically significant increases
in other quarters of the year but they are far more modest. We continue to see the opposite
pattern for debt past due. Debt past due falls more dramatically in the first quarter of the year in
response to tax credit amounts than in the omitted third quarter.
We next use our estimate of quarterly tax credits paid out to households in the zip code.
As noted earlier, we estimate quarterly tax credits by allocating the annual tax credits paid to
households in the zip code across the quarters of the year based on the timing data in the MTS.
In doing so, we are assuming that the quarterly pattern of payouts is identical across all zip
codes. Here, the estimated amount of tax credits paid out in each quarter is as a continuous
variable.

We no longer use the 2007 tax credit amounts, but now use the annual tax credits

from every year of the sample. The equation we estimate is;

Debtit − Debtit −1 =
α + γ QuarterlyCredits + γ qd Demogi × Quarter +
time + time 2 + time3 + qtrt + λi + ε it

(1.5)

Results are presented in Table 10. The coefficient γ tell us what happens to debt growth
in response to an extra dollar of per capita tax credits. We find that in a quarter when households
receive an extra dollar of tax credits, auto debt increases by $0.30, bank card debt by $0.16 and
personal debt by $0.14. In keeping with the prior pattern of results, past due debt falls by $0.40.
Across all of these specifications our findings point to a pattern of increasing auto, credit
card, and personal indebtedness and declining debt past due in response to tax credit receipt.
Households appear to use their tax credits to pay off delinquent debt to repair their balance sheet
while at the same time increasing the levels of non-past due auto and credit card debt.
Until this point, we have only looked at debt responses to tax credits. For most of the
years in our sample, we also have information on overpayment refunds for each zip code. We
create a measure of quarterly overpayments received by households in each zip code in the same
way that we generated the quarterly tax credit measure. In particular, we divide annual
overpayments paid to a zip code across quarters of the year according to the pattern in which
annual overpayments are paid out according to the MTS. As is the case with tax credits, we are
assuming overpayment refunds are paid out in the same way across all zip codes. We add this
estimate of quarterly overpayment refunds to our debt regressions by estimating equation (1.6):

Debtit − Debtit −1 =
α + γ TCTaxCreditsit + γ OP OverPaymentsit + γ qd Demogi × Quarter +
time + time 2 + time3 + qtrt + λi + ε it

(1.6)

We present estimates of the coefficients on tax credit and overpayment refunds in Table
11.

The coefficients on the overpayment variable are far smaller than on the tax credit

variables. Household overpayment refunds do not seem to influence indebtedness to nearly as
great an extent as tax credits. A one dollar increase in tax credit refund amounts leads to a $0.35
reduction in debt past due, while a one dollar increase in overpayment refunds leads to a $0.02
reduction in debt past due. For auto debt, we find large increases in debt in response to tax
credits and modest increases in response to overpayments. The pattern of results for credit cards
is different. In contrast to the increases in credit card debt we find in response to tax credits, we
20

estimate that there are modest declines in debt in response to overpayment refunds. This is
consistent with prior research (in particular Agarwal, Liu and Souleles (2007)) that finds a drop
credit card debt following the receipt of refunds. This is suggestive of different responses by the
lower income households that receive tax credits and the higher income households that receive
overpayment refunds. In the next two rows of the table, we adjust for the fact that overpayments
are larger on average than tax credits. In particular, we rescale the coefficients by multiplying
them by the standard deviation of the relevant refund amount. Even with this adjustment, we
continue to see far larger responses to tax credits than to overpayments.
4. Interpretation
The results are consistent across a range of specifications. We find that auto, credit card,
and personal debt increase in high tax credit quarters in those zip codes that receive large EITC
and ACTC payments. At the same time, we find that debt past due declines by a substantial
amount during tax refund season in high tax credit zip codes.
The decline in debt past due is consistent with a depiction of households using the large
tax credit payment to put their financial house back in order.
The result that credit card and auto debt increase during tax refund season runs counter to
the notion that the EITC and ACTC would serve to reduce household indebtedness because they
have more funds to pay off debt. We evaluate the implications of this finding concerning credit
card and auto debt in the context of the overall household balance sheet and in light of the nature
of our data. We know that the increase in savings in a quarter, defined as the change in assets
minus the change in liabilities, is equal to income minus consumption. In the quarter in which
tax credits are received, after tax income goes up quite substantially among recipient households.
Previous research has shown that consumption increases in response to tax credits, but by an
amount less than the increase in income. (Barrow and McGranahan 2000; Goodman-Bacon and
McGranahan 2008). This implies that there is some additional savings in tax credit quarters. In
other words, the increase in assets must be greater than the increase in liabilities. If we assume
that our finding that liabilities increase in response to tax credits is correct, household assets must
increase more than household liabilities. Households must then use their tax refunds to increase
their asset position. The most commonly held assets among low income households are
21

transaction accounts, autos, and homes. (Federal Reserve Board, 2014) If the tax credit
alleviated liquidity constraints, low income households may choose the first quarter to invest in
durable goods. The tax credit may be sufficient for a down payment on either a home or auto.
Other facts concerning auto debt are particularly consistent with this story. Refinancing
of auto loans is rare due to the rapid depreciation of autos. As a result, increases in auto debt are
likely associated with car purchases. In addition, most auto lenders require substantial down
payments from low income borrowers. If a household is using its tax credits to cover the down
payment for a car, at tax time, the household’s debt balance would increase by the amount of the
car purchase that was financed. The household’s asset position would increase by the total
value of the car. However, our data do not incorporate the corresponding increase in assets.
Assuming that the car is worth something close to what the household paid for it, after the tax
credit induced car purchase, the household balance sheet is stronger. As further support for this
narrative, Figure 7 shows new and used car retail sales by month for 2013. While new car sales
peak in the summer months, sales of used cars peak in February—a pattern which is suggestive
of a tax credit effect. Future research could investigate how assets change in the period
surrounding refund receipt.
Potential explanations for the increase in refund season for credit card and other personal
debt hinges on the nature of the information in the data set. As mentioned earlier, our measure of
credit card balances does not distinguish between transactional and revolving balances. In light
of this, credit card balances could increase because either of these balances increases. Increasing
revolving balances would indicate that households are paying off less of their preexisting
balances. This seems unlikely given that household income increases so dramatically when
refunds are received. Conversely, increases in transactional balances would indicate that
households are spending more on their credit cards. This could arise if households are spending
more in general in response to their increase in income and choosing to use their credit cards to
fund these transactions. In short, it is possible that the increase in our measure of credit card debt
is a sign of increased consumption rather than a sign of an increase in debt.
Combing our results, we conclude that households use their tax credit refunds to pay past
due debts, purchase cars and fund consumption on their credit cards.

Table 1: Zip code means of quarterly credit aggregates, 1999-2014
Obs.

Mean

Average Debt Past Due

2930813

2622.72

Average Auto Debt
Average Bank Card Debt
Other Personal Debt

2930813
2930813
2930813

3319.96
3355.18
1903.79

Year
Quarter

2930813
2930813

2006.63
2.50

Observations Per Zip Code
Obs. Per Zip Code (unweighted)

2930813
2930813

1071.54
259.64

Std. Dev
Description
2087.97 Payments that are late by 30 days or more
1268.33 Auto loans from banks or dealers
1300.40 Revolving Accounts/Credit Cards
1305.34 Retail store and gas cards, sales financing, personal loans
4.58
1.12
699.5 Number of records per zip
459.1 Number of records per zip

Source: Author’s tabulations from Equifax/FRBNY CCP and Haver Analytics.
Notes: Each observation is a zip code-quarter combination and is the average debt per sampled
household. Amounts are in 2013 Dollars. Data are weighted by the number of records per zip code.

Table 2a: Average refund and tax amounts per tax return by zip code, IRS-SOI zip code data

Refundable CTC (NonACTC
Taxes Due at
EITC per
Refundable) (Refundable) Filing Per
Total Refund EITC Per
Return
per Return
per Return Return
Per Return
Return
Tax Year
1998
NA
0.394
NA
NA
NA
NA
1999
---------------------------------------------------No Data File-------------------------------------2000
---------------------------------------------------No Data File-------------------------------------2001
NA
NA
NA
NA
NA
NA
2002
NA
NA
NA
NA
NA
NA
2003
---------------------------------------------------No Data File-------------------------------------2004
NA
0.388
NA
NA
NA
NA
2005
NA
0.381
NA
NA
NA
NA
2006
NA
0.372
NA
NA
NA
NA
2007
1.634
0.347
0.307
0.212
NA
0.750
2008
2.297
0.399
0.349
0.233
NA
0.658
2009
2.442
0.466
0.406
0.223
0.197
0.689
2010
2.565
0.439
0.380
0.217
0.184
0.782
2011
2.164
0.434
0.380
0.207
0.179
0.828
2012
2.052
0.436
0.382
0.197
0.171
0.863
2013
2.080
0.441
0.375
0.189
0.163
0.879

Source: Author’s calculations from IRS 2015b and Haver Analytics.
Notes: Amounts are in thousands of 2013 dollars and deflated by the deflator for year following the tax
year. (For example tax year 2000 data is assumed to be in 2001 nominal dollars). Data are not weighted
by number of returns per zip code. “NA” indicates that the listed variable is not available in the SOI data
set in that tax year. There are no data files for 1999, 2000 and 2003.

Table 2b: Average refund and tax amounts per tax return by zip code, Brookings Zip Code data
Total Refund EITC Per
Per Return
Return
Tax Year
1998
NA
1999
NA
2000
1.980
2001
2.415
2002
2.346
2003
2.427
2004
2.367
2005
2.334
2006
2.529
2007
2.268
2008
2.927
2009
PY
2010
PY
2011
PY
2012
PY
2013
PY

ACTC
Refundable CTC (NonTaxes Due at
EITC per
Refundable) (Refundable) Filing Per
per Return Return
Return
per Return
0.363
NA
NA
NA
NA
0.363
NA
NA
NA
NA
0.356
NA
NA
NA
1.357
0.387
NA
NA
NA
0.957
0.403
NA
NA
NA
0.794
0.400
NA
0.206
NA
0.741
0.394
NA
0.280
0.130
0.915
0.392
NA
0.264
0.127
1.070
0.381
NA
0.248
0.122
1.060
0.342
NA
0.212
0.104
0.951
0.395
NA
0.226
0.141
0.872
0.456
NA
0.215
0.189
PY
0.432
NA
0.210
0.179
PY
0.426
NA
0.200
0.176
PY
0.428
NA
0.194
0.170
PY
0.435
NA
0.187
0.168
PY

Source: Author’s Calculations from Brookings, 2015 and Haver Analytics.
Notes: Amounts are in thousands of 2013 Dollars and deflated by the deflator for year following the tax
year. (Tax year 2000 data assumed to be in 2001 nominal dollars). “ NA” indicates that the listed
variable is not available in the Brookings data set in that tax year. Brookings data become part year in
2009 and only cover returns filed January-June. The great majority of returns (over 90%) are filed in the
first half of the year, but many high income filers with large balances and overpayments file late. As a
result we treat the 2009-2012 data on total refunds and taxes due as missing and label these variables
“PY”. The per return variables for 2009-2012 are divided by the number of returns filed January-June.
Data are not weighted by number of returns per zip code.

Table 2c: Combined and estimated data on refunds and taxes due per tax return

Overpayment Refundable Additional
EITC per
CTC per
Total Refund Refund Per
Taxes Due
Return
Return
Return
Tax Year
Per Return
Per Return
NA
0.000
NA
0.310
1998
NA
0.000
NA
0.314
NA
NA
1999
1.357
0.306
0.000
1.980
1.674
2000
0.957
NA
NA
0.337
2001
2.415
0.794
0.356
NA
NA
2002
2.346
0.082
0.741
1.993
0.352
2.427
2003
0.915
0.332
0.130
2.367
1.905
2004
1.070
0.127
1.876
0.331
2005
2.334
0.122
1.060
0.321
2006
2.529
2.086
0.294
0.104
0.762
2007
1.807
1.366
0.141
0.763
0.346
2.548
2.061
2008
0.689
0.406
0.197
2.442
1.838
2009
0.782
2.000
0.380
0.184
2010
2.565
0.380
0.179
0.828
2.164
1.605
2011
0.171
0.863
1.498
0.382
2012
2.052
0.879
0.375
0.163
2013
2.080
1.542
Source: Author’s Calculations from IRS 2015b, Brookings, 2015 and Haver Analytics.
Notes: Amounts are in thousands of 2013 dollars and deflated by the deflator for year following the tax
year. (Tax year 2000 data assumed to be in 2001 nominal dollars). We assume the ACTC was zero prior
to Tax year 2001 because the CTC was not initially broadly refundable. We calculate overpayments as
total refunds minus the refundable EITC and ACTC.

Table 3: Average Monthly Refund Amounts, 1999-2014

Total Refunds
Overpayment Refunds
Refundable Earned Income Credit
Additional (Refundable) Child Tax Credit
Misc. Refunds (e.g. Making Work Pay)

Average 1999-2014
$
$
$
$
$

Gross Ind. Income Taxes Paid (Final, Withholding,
Estimated)

25,309.54
19,858.11
3,813.57
1,350.09
287.77

118,617.20

Withheld

84,613.82

Nonwithheld (Final and Estimated)

33,998.39

93,314.15

Taxes Paid Net of Refunds
Source: U.S. Department of the Treasury and Haver Analytics.

Note: Amounts in millions of 2013 dollars. Deflated by CPI for month in which refunds were paid.

Table 4: Zip code demographics, 1999-2014

Variable
Population
Percent High School Grad
Percent of Households Containing Kids
Year

Standard
Deviation Minimum Maximum
Observations Mean
496880 9472.63
13610.87
0
118917.6
496880
82.93
11.38
0
100
496880
32.89
11.32
0
100
496880 2006.50
4.61
1999
2014

Source: U.S. Census Bureau
Note: Data are annual and are based on Census zip code tabulation areas (ZCTAs). Values are
interpolated between available years and extrapolated out of sample. Zip codes are only included if
they are available in the 2000 Census and in the 2011, 2012, 2013 and 2014 5-year ACS. These 31,067
zip codes represent about 97% of the US population.

Table 5: Variable Means from the Merged Sample
Total Population
Standard
Deviation
Observations Mean

Top Tax Credit Quintile
Standard
Deviation
Mean

Not Top Tax Credit
Quintile
Standard
Deviation
Mean

Debt Variables
Past Due Debt Per Capita
Auto Debt Per Capita
Bank Card Debt Per Capita
Other Personal Debt Per Capita

1383168
1383168
1383168
1383168

1674.67
2590.37
2479.88
1778.50

1435.22
1136.06
1211.57
1495.50

1918.13
2418.34
1718.82
1773.59

1485.89
1175.07
948.25
1583.98

1630.12
2621.85
2619.14
1779.40

1421.25
1125.93
1202.74
1478.73

Tax Variables
Refundable EITC Per Capita
Refundable ACTC Per Capita
Refundable EITC+ACTC Per Capita
Overpayment Refund Per Capita
Total Refund Per Capita
2007 Tax Credits Per Capita Quintile

1383168
1210272
1210272
1037376
1210272
1383168

36.60
12.78
49.86
191.87
243.97
2.94

67.10
24.48
88.20
235.66
282.03
1.31

77.23
22.23
100.41
149.27
250.22
5.00

119.43
40.84
153.35
148.62
241.10
0.00

29.17
11.05
40.61
199.67
242.83
2.57

48.57
19.61
65.92
247.51
288.88
1.06

Census Variables
Population
Percent High School Grad
Percent of Households Containing Kids

1383168
1383168
1383168

12288.03
83.37
33.45

14426.12
10.09
8.74

16041.02
71.60
38.09

18143.63
10.77
9.82

11601.28
85.52
32.60

13524.36
8.31
8.25

Year
Quarter

1383168
1383168

2006.50
2.50

4.61
1.12

2006.50
2.50

4.61
1.12

2006.50
2.50

4.61
1.12

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
Note: Data are quarterly. Dollar amounts are in 2013 dollars.

Table 6: Quarterly patterns of indebtedness in high tax credit zip codes compared to other zip codes
(1)

(2)

(3)

VARIABLES

past due

auto

bank card

(4)
other
personal

Q1 x High Tax Credit Zip

-70.56***
(5.080)
-15.76***
(4.998)
-21.49***
(4.998)

32.28***
(2.484)
14.21***
(2.443)
-0.702
(2.443)

46.54***
(2.319)
-4.924**
(2.281)
-6.841***
(2.281)

16.54***
(4.107)
-4.836
(4.041)
0.450
(4.041)

1,361,556
0.005
-0.105

1,361,556
0.019
0.0907

1,361,556
0.022
0.144

1,361,556
0.003
0.0274

Q2 x High Tax Credit Zip
Q4 x High Tax Credit Zip

Observations
R-squared
Q1 Effect/ St Dev of Dep. Var. in High TC Zip
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
Note: Each column represents a separate regression where the dependent variable is the quarterly
change in per capita debt of the type listed in the column heading. Third quarter is omitted.
Table 7: Quarterly patterns of indebtedness in high tax credit zip codes compared to other zip codes,
including demographic x quarter controls
(1)

(2)

(3)

VARIABLES

past due

auto

bank card

(4)
other
personal

Q1 x High Tax Credit Zip

-62.76***
(6.021)
-27.45***
(5.923)
-28.30***
(5.923)

19.88***
(2.943)
5.435*
(2.895)
-0.489
(2.895)

13.15***
(2.746)
13.76***
(2.702)
3.642
(2.702)

12.63***
(4.868)
-0.569
(4.789)
-6.203
(4.789)

1,361,556
0.006
-0.0933

1,361,556
0.020
0.0558

1,361,556
0.024
0.0408

1,361,556
0.004
0.0209

Q2 x High Tax Credit Zip
Q4 x High Tax Credit Zip

Observations
R-squared
Q1 Effect/ St Dev of Dep. Var. in High TC Zip
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 8: Quarterly share of tax credits in high tax credit zip codes compared to other zip codes, including
demographic x quarter controls
(1)

(2)

(3)

VARIABLES

past due

auto

bank card

(4)
other
personal

Quarterly Share of Tax Credits x High Tax Credit Zip

-60.80***
(6.362)

21.45***
(3.109)

11.04***
(2.902)

17.47***
(5.144)

Observations
1,361,556
R-squared
0.006
Coefficient on Quarterly Share x High Tax Credit /St. Dev.
of Dep. Var. in High TC Zip
-0.0904
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

1,361,556
0.020

1,361,556
0.024

1,361,556
0.004

0.0603

0.0343

0.0289

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
Note: Each column represents a separate regression where the dependent variable is the quarterly
change in per capita debt of the type listed in the column heading. Third quarter is omitted
Table 9: Quarterly effects of annual tax credits per capita, including demographic x quarter controls
(1)

(2)

(3)

VARIABLES

past due

auto

bankcard

(4)
other
personal

Q1 x Tax Credits Per Capita

-0.353***
(0.0292)
-0.0551**
(0.0267)
-0.137***
(0.0305)

0.105***
(0.0167)
0.0543***
(0.0152)
-0.0254*
(0.0137)

0.150***
(0.0186)
0.0139
(0.0126)
-0.0237
(0.0196)

0.114**
(0.0441)
-0.0276
(0.0355)
-0.0221
(0.0453)

1,361,556
0.006

1,361,556
0.020

1,361,556
0.024

1,361,556
0.004

Q2 x Tax Credits Per Capita
Q4 x Tax Credits Per Capita

Observations
R-squared
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 10: Quarterly tax credits per capita and zip code debt growth

(1)

(2)

(3)

(4)
other
bankcard personal

VARIABLES

past due

auto

Quarterly Tax Credits Per Capita

-0.404*** 0.299*** 0.164*** 0.140***
(0.0193) (0.00910) (0.00851) (0.0145)

Observations
R-squared
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

1,188,660 1,188,660 1,188,660 1,188,660
0.007
0.023
0.027
0.006

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
Note: Each column represents a separate regression where the dependent variable is the quarterly
change in per capita debt of the type listed in the column heading.
Table 11: Quarterly tax credits and overpayment refunds and zip code debt growth, including
demographic-quarter controls

VARIABLES
Quarterly Tax Credits Per Capita
Quarterly Overpayments Per Capita

Effect of Standard Deviation Increase in Tax Credits
Effect of Standard Deviation Increase in Overpayments
Observations
R-squared
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

(1)

(2)

(3)

past due

auto

bankcard

-0.348*** 0.360***
0.229***
(0.0212)
(0.00942) (0.00890)
-0.0191*** 0.0151*** -0.0361***
(0.00543) (0.00241) (0.00228)

(4)
other
personal
0.155***
(0.0142)
0.00171
(0.00363)

-30.68
-4.494

31.79
3.559

20.23
-8.501

13.70
0.403

1,037,376
0.008

1,037,376
0.026

1,037,376
0.028

1,037,376
0.008

Figure 1: Average Combined EITC and ACTC Refund per Return, Tax Year 2013

Source: IRS 2015c, Brookings 2015, U.S. Census Bureau, and Haver Analytics.

Note: There are blank spaces on the map because unpopulated areas and large water bodies are not included in the Census zip
code shapefile and because tax credit data is missing for some zip codes.

Figure 2: Refunds by AGI Category

Panel a: Average Refund per Return by Zip Code, 2013

Average Across Zip Codes
1
2
3

Refunds Per Return, by Zip Code AGI
Tax Year 2013 (Thousands of 2013 Dollars)

<$35k

$35k-$45k $45k-$55k $55k-$75k $75k-$100k

$100k+

Overpayment Refund
ACTC

Total Refund
Refundable EITC

Panel b: Average Refund as a Share of AGI, 2013.

Average Across Zip Codes
.02
.04
.06

.08

Refunds as a Share of AGI, by Zip Code AGI
Tax Year 2013 (Thousands of 2013 Dollars)

<$35k

$35k-$45k $45k-$55k $55k-$75k $75k-$100k
Total Refund
Refundable EITC

$100k+

Overpayment Refund
ACTC

Panel C: Average Refunds, Taxes Due and Net Refunds, 2013

Average Across Zip Codes
.02
.04
.06

.08

Refunds as a Share of AGI, by Zip Code AGI
Tax Year 2013

<$35k

$35k-$45k $45k-$55k $55k-$75k $75k-$100k
Sum of Refunds
Net Refund

$100k+

Tax Due

Source: Author’s tabulations based on data IRS 2015c, Brookings 2015, and Haver Analytics.
Note: Zip code AGI is average real AGI per return in the zip code. The percent of zip codes in each
category are 11% (under $35k), 31% ($35k-$45k), 27% (45k-55k), 20% (55k-75k), 7% (75k-100k) and 6%
($100k+). All dollar amounts are in 2013 dollars.

Figure 3: Refund and Tax Payments by Month, 2014

Panel a: Share of Annual Refunds paid Each Month, By Refund Type

6
7
month

Total Refunds
EITC

Overpayment
ACTC

Panel b: Share of Annual Taxes Paid Each Month By Tax Payment Type

6
7
month

Gross Taxes Paid
Non-withheld Taxes

Withheld Taxes

Mil $
100000
200000

300000

Panel c: Monthly Timing of Gross and Net Taxes

7
6
month

Total Refunds
Tax Net of Refunds

Gross Taxes Paid

Source: Author’s Tabulations from US Department of the Treasury, Monthly Treasury Statement and
Haver Analytics.

Figure 4: Coefficient Estimates of changes in indebtedness by quarter, Baseline
auto

past_due
Q1 x High Tax Credit

Q1 x High Tax Credit

Q2 x High Tax Credit

Q3 x High Tax Credit

Q3 x High Tax Credit
Q4 x High Tax Credit

Q4 x High Tax Credit

40
30
20
10
0
-10
Change in Debt Relative to Q3

0
-20
-60
-40
-80
Change in Debt Relative to Q3
99

Q1 x High Tax Credit

Q2 x High Tax Credit

Q3 x High Tax Credit

Q4 x High Tax Credit

Q4 x High Tax Credit
60
40
0
20
-20
Change in Debt Relative to Q3
95

other_all

bankcard

20
30
10
0
-20
-10
Change in Debt Relative to Q3
99

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
Figure 5: Coefficient Estimates of changes in indebtedness by quarter, with demographics x quarter
controls
past_due

auto

Q1 x High Tax Credit

Q2 x High Tax Credit

Q3 x High Tax Credit

Q4 x High Tax Credit

Q4 x High Tax Credit
-80
-60
-40
-20
0
Change in Debt Relative to Q3
99

-10
0
10
20
30
Change in Debt Relative to Q3

bankcard

other_all

Q1 x High Tax Credit

Q2 x High Tax Credit

Q3 x High Tax Credit

Q4 x High Tax Credit

Q4 x High Tax Credit
-5

0
5
10
15
20
Change in Debt Relative to Q3
99

-20
-10
0
10
20
30
Change in Debt Relative to Q3
99

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
38

Figure 6: Effect of quarterly share of tax credits, by tax credit quintile

Effect of Quarterly Share of Annual Tax Credits
past_due

auto
Top Tax Credit Quintile

Top Tax Credit Quintile
4th Quintile

4th Quintile

3rd Quintile

2nd Quintile

Lowest Quintile

Lowest Quintile
-150

-100
-50
0
50
Quarterly Change in Debt
99

-50

0
50
100
Quarterly Change in Debt
99

other_all

bankcard
Top Tax Credit Quintile

Top Tax Credit Quintile
4th Quintile

4th Quintile

3rd Quintile

2nd Quintile

Lowest Quintile

Lowest Quintile
-50

-100

0
50
100
150
Quarterly Change in Debt
99

-50
0
50
100
Quarterly Change in Debt
99

Source: Author’s tabulations based on data from Equifax/FRBNY CCP, IRS 2015c, Brookings 2015, U.S.
Census Bureau, U.S. Department of the Treasury and Haver Analytics.
Figure 7: Retail Sales of Used and New Car Sales by Month, 2013
9000

80000

8000

70000

7000

60000

6000

50000

5000

40000

4000

30000

3000
2000

20000

1000

10000

0
Jan

Feb Mar Apr May Jun

Jul

Used Car Sales (Left Access, Mil $)

Aug

Sep

Oct

Nov Dec

New Car Sales (Right Axis, Mil $)

Source: U.S. Census Bureau and Haver Analytics
39

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Stefania D’Amico and Thomas B. King

WP-15-10

Firm Entry and Macroeconomic Dynamics: A State-level Analysis
François Gourio, Todd Messer, and Michael Siemer

WP-16-01

Measuring Interest Rate Risk in the Life Insurance Sector: the U.S. and the U.K.
Daniel Hartley, Anna Paulson, and Richard J. Rosen

WP-16-02

Allocating Effort and Talent in Professional Labor Markets
Gadi Barlevy and Derek Neal

WP-16-03

The Life Insurance Industry and Systemic Risk: A Bond Market Perspective
Anna Paulson and Richard Rosen

WP-16-04

Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Scott A. Brave, R. Andrew Butters, and Alejandro Justiniano

WP-16-05

Optimal Monetary Policy in an Open Emerging Market Economy
Tara Iyer

WP-16-06

Forward Guidance and Macroeconomic Outcomes Since the Financial Crisis
Jeffrey R. Campbell, Jonas D. M. Fisher, Alejandro Justiniano, and Leonardo Melosi

WP-16-07

Working Paper Series (continued)
Insurance in Human Capital Models with Limited Enforcement
Tom Krebs, Moritz Kuhn, and Mark Wright

WP-16-08

Accounting for Central Neighborhood Change, 1980-2010
Nathaniel Baum-Snow and Daniel Hartley

WP-16-09

The Effect of the Patient Protection and Affordable Care Act Medicaid Expansions
on Financial Wellbeing
Luojia Hu, Robert Kaestner, Bhashkar Mazumder, Sarah Miller, and Ashley Wong

WP-16-10

The Interplay Between Financial Conditions and Monetary Policy Shock
Marco Bassetto, Luca Benzoni, and Trevor Serrao

WP-16-11

Tax Credits and the Debt Position of US Households
Leslie McGranahan

WP-16-12

Full text of Working Papers (Federal Reserve Bank of Chicago) : Tax Credits and the Debt Position of U.S. Households, Working Paper 2016-12

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