How do EITC recipients spend their refunds?
Goodman-Bacon, Andrew ; McGranahan, Leslie
Introduction and summary
The earned income tax credit (EITC) is one of the largest sources
of public support for lower-income working families in the U.S. The EITC
operates as a tax credit that serves to offset the payroll taxes and
supplement the wages of tow-income workers. For tax year 2004, the EITC
transferred over $40 billion to 22 million recipient families (U.S.
Internal Revenue Service, 2006b). Nearly 90 percent of program
expenditures come in the form of tax refunds; the remaining 10 percent
serve to reduce tax liability. While other income support programs
distribute benefits fairly evenly across the calendar year, EITC
payments are concentrated in February and March when tax refunds are
received. Because the EITC makes one relatively large payment per year,
it may provide low-income, credit-constrained households with a rare
opportunity to make important big-ticket purchases.
Research on the EITC has tended to focus on the important labor
supply effects of the program (Eissa and Liebman, 1996; Meyer and
Rosenbaum, 2001; and Grogger, 2003). Relatively little is known about
how recipient households actually use EITC refunds. In this article, we
use data from the U.S. Bureau of Labor Statistics' Consumer
Expenditure Survey (CES) over the period 1997-2006 to investigate how
households spend EITC refunds. (1) Following the methodology of Barrow
and McGranahan (2000), we rely on the particular timing of EITC payouts
to identify the effects of the credit on expenditures. Barrow and
McGranahan found that the EITC has a larger effect on spending on
durable goods than on nondurable goods. In this article, we are
particularly interested in determining what items within the durables
and nondurables categories are purchased using the credit and whether
these expenditures reinforce the EITC's prowork and prochild goals.
Our primary finding is that recipient household spending in response to
EITC payments is concentrated in vehicle purchases and transportation
spending. Given the crucial link between transportation and access to
jobs, we believe this finding is consistent with the EITC's goals.
In the next section, we present a brief history of the EITC and the key
features of the program. We then review prior research on the uses of
the EITC by recipient families. Next, we introduce the CES data and the
methodology we use to investigate the data. Finally, we present our
results and discuss their implications.
History and structure of the EITC
Congress created the EITC in 1975 to offset payroll taxes paid by
tow-income workers with children. The credit is structured as a
supplement to earned income equaling a percentage of earnings up to a
specific threshold (the "phase-in" range), at which point the
credit amount stays constant for an additional amount of earnings (the
"plateau" range). Then this maximum credit is reduced by a
given percentage of earnings until it equals zero (the
"phase-out" range). Income thresholds, the phase-in and
phase-out rates, and, therefore, the credit amount also vary by the
number of qualified children in a household and by marital status; and
all these factors have varied over time. (2) Figure 1 graphs the EITC
program parameters for selected years. The program is implemented as a
part of the tax code, and recipients must file taxes in order to apply
for the program. For tax year 2006, a single mother with two children
earning between $11,340 and $14,810 would have received the maximum
credit of $4,536.
The EITC began as a small program, but its generosity and coverage
have expanded frequently in its 30-year history as is shown in figures 1
and 2. Particularly large expansions enacted in 1986 and 1993 led to
rapid program growth. In 1994, childless families started to receive a
small credit. In 1975, the EITC represented 3.1 percent of federal
means-tested transfers and 9.7 percent of federal means-tested cash
transfers; by 2002, these proportions had increased by three times and
four and a half times, respectively, and the EITC was the second largest
means-tested cash transfer program behind Supplemental Security Income
(SSI). In figure 2, we graph the average credit and number of recipient
families by year. As the figure shows, the size of the EITC was
relatively constant in its first decade, but between 1986 and 2005, both
the number of recipient families and the real average credit amount grew
by more than three times, increasing real federal expenditures on the
program by almost 12 times. In 1986, just over 7 million families
received earned income tax credits averaging $501 in 2005 dollars. By
2002, over 20 million families received credits averaging $1,911 in 2005
dollars (U.S. House of Representatives, Committee on Ways and Means,
2004).
[FIGURE 1 OMITTED]
[FIGURE 2 OMITTED]
Unlike other transfer programs that have monthly benefits, the EITC
pays out a lump sum once per year. The EITC does permit recipients to
receive some portion of payments monthly prior to tax filing in the form
of the advance earned income tax credit, but in 2004 only 0.6 percent of
recipient households received any credit in this manner, representing
just 0.2 percent of payments (U.S. Internal Revenue Service, 2006b).
Figure 3 shows the distribution of refundable EITC payments from
the U.S. Internal Revenue Service by month for 2005--a year with a
payment pattern typical of recent years. As the figure shows, nearly all
EITC payments are made in February and March, and most of these come in
February. The modal month of EITC payments has changed over time, but
since 1997 more payments have been made in February than in any other
month. This pattern is a result of the timing of tax filing. Taxes can
be filed anytime after W-2s (employee wage report forms) are received
(by January 31 ), and refunds are received within six weeks. (3)
[FIGURE 3 OMITTED]
The lump sum payment structure also means that EITC refunds
represent a relatively large share of recipients' income in the
month when they are received. For tax year 2004, the average EITC refund
for recipient families with children was $2,113, or 12 percent of their
annual average adjusted gross income (AGI) of $16,981. Assuming income
was earned evenly across the calendar year, the average recipient
household's income would be approximately two and a half times its
usual monthly value in the month when the EITC payment was received. (4)
For comparison, the mean overpayment refund for non-EITC recipients
in tax year 2004 was $1,692, or 2.9 percent of annual average AGI among
nonrecipients. (5) Overpayment refunds are less concentrated in the
first quarter of the year than EITC refunds. While 87 percent of EITC
refunded dollars for 2004 were distributed in the first quarter, 47
percent of non-EITC refunded dollars were distributed in the first
quarter, and an additional 42 percent were distributed in the second
quarter (U.S. Internal Revenue Service, 2006c). It is worth noting that
the Consumer Expenditure Survey, the data set used for our analysis,
provides additional evidence to show that EITC refunds are concentrated
earlier in the year than other tax refunds. Among families who made an
expenditure on "accounting services," including tax
preparation, 43 percent of EITC eligible families did so in January or
February, versus 29 percent of noneligible families.
The one-time EITC average refund of $2,113 among families with
children in 2004 is also large when compared with the average monthly
payments to recipient families in other transfer programs in 2004, such
as SSI ($429); Temporary Assistance to Needy Families, or TANF ($397);
The Food Stamp Program ($200); and unemployment insurance ($1,141). (6)
Use of EiTC refunds
The majority of research on the EITC and expenditure patterns has
relied on surveys of EITC recipients about how they spent or planned to
spend refunds. The consensus from these surveys is that the primary use
of EITC refunds is to pay bills. Sixty-three percent of respondents in a
survey of participants in the University of Georgia's Consumer
Financial Literacy Program reported that they planned to use most of
their refund to pay or catch up on bills or debts (Linnenbrink et al.,
2006). Similarly, 44 percent of mothers in a study tracking the
well-being of rural families indicated that they used their refund to
pay bills (Mammen and Lawrence, 2006). Using surveys of free tax
preparation clients in Chicago, Smeeding, Phillips, and O'Connor
(2000) report that tax filers who anticipate an EITC refund most often
plan to use it to pay bills. These studies also find that recipients
used their refunds to purchase or repair cars and buy other durables,
such as home furnishings. Some families also report buying
children's clothing and going on vacation. Very few families
planned to save their refund for a rainy day or for retirement.
In contrast to these studies, Barrow and McGranahan (2000) use the
nationally representative Consumer Expenditure Survey to investigate
expenditure uses of EITC refunds. They rely on the unique seasonal
pattern of EITC refunds to determine whether EITC eligible households
have expenditure patterns that differ from those of noneligible
households. They find that EITC eligible households have higher
expenditures on durable goods in February, the modal month of EITC
receipt, relative to noneligible households. They attribute this
increased spending on durables to the EITC. Barrow and McGranahan do not
measure health care, housing, or utility expenditures, so they do not
measure much of what other studies categorize as "bills."
Here we use CES data over the period 1997-2006 to build upon the
work of Barrow and McGranahan (2000). We investigate on which goods,
particularly within the durable goods category, the EITC recipient
households spend more. We also look at both the extensive and intensive
margin of expenditure. In other words, we ask both whether households
are more likely to make any expenditure and whether they make larger
expenditures, given that they make a purchase.
We focus on those goods that have been identified in the literature
as either those that recipients report that they plan to purchase or
those that further the EITC program's goals of
"strengthen[ing] the incentive to work," "help[ing]
low-wage working families make ends meet," and promoting the
well-being of children (Frost, 1993). Vehicle expenditures fall into
both of these categories. They have been mentioned by recipients as an
intended use of the EITC credit and are particularly supportive to work.
According to a Brookings institute report, 88 percent of low-income
Americans commute in a personal vehicle (Blumenberg and Waller, 2003).
in fact, other antipoverty and income support programs explicitly
recognize the link between car ownership and employment through more
lenient limits on cars than on other forms of assets. For example, the
federal SSI program exempts one vehicle from its resource limit.
Similarly, most states exclude the value of one or more vehicles from
resource limits used to determine eligibility for the Food Stamp Program
and TANF Program. In addition to vehicles, we focus on expenditures on
household furnishings and home electronics, as well as on
children's clothing. We do not look at bill paying because the
nature of the CES data precludes such an analysis.
Our primary contribution is to provide evidence on detailed actual
expenditures, using nationally representative survey data. Time-series
variation in EITC payments over the year and cross-sectional variation
in imputed eligibility allow us to identify the EITC's impact.
Similar to Barrow and McGranahan (2000), we find that receiving EITC
refunds increases household expenditures on both durable and nondurable
goods, but more so for durables. Eligible households are more likely
both to purchase big-ticket items in February and to spend more on them,
given that they make any expenditure. Within durables, the strongest
patterns are found for vehicles, confirming the responses given in
surveys. Eligible households also spend slightly more on all other major
subcategories of durables--household goods, appliances, and home
electronics. Within nondurables, the strongest patterns are found for
transportation expenses, such as car repairs. (7)
Data
We create a monthly household-level data set of expenditure,
income, and family structure, using the CES's interview survey data
covering the period 1997-2006. Households, which are called consumer
units (CUs) in the data, are interviewed five times for the survey. (8)
The first interview provides baseline asset information. The second
through fifth interviews cover detailed expenditure information for the
three months prior to the interview date. These interviews occur three
months apart. As a result, in the absence of attrition, a full year of
expenditure data is collected for each household. Households enter and
exit the survey each month, information on income in the 12 months
leading up to the survey date is collected in the second and fifth
interviews. Demographic information is updated every interview.
We begin with the 1997 data because February has been the modal
month of EITC payouts since 1997. This consistency in payments across
time allows us to focus on the February expenditures of recipient
households. In most years prior to 1997, March was the modal month of
EITC payments. (9)
We consider a CU to belong to the calendar year in which we observe
February expenditure (or would have observed it if the household had
responded). Since 1997, this is when the CU is most likely to have
received the previous tax year's EITC refund payment. Therefore,
data over the period 1997-2006 allow us to consider EITC policies in
place during tax years 1996-2005. The average number of observations in
our 120 month-year cells is 4,888, and in total we have 589,568
observations.
Information on EITC receipt is not provided in the CES, so we use
the income and family structure variables to impute EITC eligibility and
the magnitude of EITC payments. Because of our reliance on the income
data, we delete those with incomplete income reports from the analysis.
We assume all households without children are not eligible for the EITC
despite the small credit for childless families that has been available
since 1994. (10) The CUs may contain more than one tax filing unit (TU).
We impute EITC payments and eligibility for each TU within the CU and
combine these to determine CU eligibility and EITC amount. Ideally, we
would observe the income and family structure of each TU for the year
preceding their February interview. However, we lack information on TU
composition and on tax year income. To generate our best guess of income
for the year preceding the February interview, we use the income
information in the second and fifth interviews. For some individuals,
our best guess of tax year income is the reported income from the second
interview; for others, we compute a weighted average of the two income
reports where the weights depend on the number of months for which the
year covered by the income report and tax year overlap.
To assign adults to TUs and generate TU income, we use sex, marital
status, relationship to reference person, and individual income
information. To assign children to TUs for the purpose of the EITC
computation, we use the EITC eligibility rules in place during the year
before their February interview. Before 2001, EITC rules assigned all
qualifying children in a family to the TU with the highest income, but
since 2001, families have been free to choose which TU claimed
qualifying children. Thus, before 2001 we give all children to the
highest-income TU, and after 2001 we give all qualifying children to the
TU for which they generate the largest EITC refund. (11)
Because of this imputation, we are measuring EITC eligibility
rather than EITC receipt. Two issues may affect the accuracy of these
imputations. First, some households that are eligible for the EITC may
not take it up. According to a study by the U.S. Government Accounting
Office (2001), approximately 85 percent of eligible households with
children participate in the EITC program. Second, we may be incorrectly
imputing that eligible households are ineligible or that ineligible
households are eligible because either child or income information is
incorrect in the CES. There is some underreporting of income in the CES,
so we may be assigning eligibility to some households that are in fact
beyond the maximum income for EITC receipt. We also may be assigning
some children to an incorrect TU. These issues make it harder for us to
find an effect of the EITC on consumption. As a result, our estimates
represent a lower bound on the effect of the EITC on recipient
consumption patterns.
Table 1 gives variable means for the demographic, income, and EITC
variables for all families and by imputed EITC eligibility. In the full
sample, 13 percent of household-months (shown as 0.13 in the first
column, fourth row of table l) were eligible for an average credit
of$2,116 in the February in which we observed them. These percentages
and values change over time in keeping with the changes in eligibility
and refund amounts presented in figure 2 (p. 18). When we compare the
EITC eligible and noneligible populations, we find differences that are
consistent with the program rules. For example, EITC eligible households
earn approximately 60 percent of what noneligible households earn on
average, and have more children. In addition, EITC eligible households
are also less likely to have a white household head, are more likely to
be headed by a single parent, and are less educated than noneligible
households. These additional findings are not related explicitly to the
program rules, but result from patterns of earnings in the U.S., and are
consistent with the attributes of participants in other income support
programs.
Our next goal is to generate monthly expenditure data. We combine
all available interviews for each CU. Sixty-three percent of CUs have 12
months of data, and the average CU has 9.9 months of data. The CES
contains very detailed information on expenditures, which we distill
into durable goods and nondurable goods, as well as subcategories of
those groups. Durable goods includes household goods (such as furniture,
linens, and carpets); appliances (such as dishwashers, silverware, and
kitchen electronics); electronics (such as televisions and computers);
and new and used vehicle purchases. Nondurables includes food, alcohol,
and tobacco; apparel; trips (out-of-town travel and expenditure while
traveling); transportation expenses (except vehicle purchases);
entertainment; child support, alimony, and charity; and pensions,
insurance, and social security payments. We do not measure expenditure
on items that we do not consider to be durable or nondurable goods. In
particular, we exclude utilities, rent, education expenses, and health
care. These obligations may be difficult for households to alter on a
month-to-month basis. In addition, the rent and utility variables
reported on the survey capture the amount owed in a given month rather
than the amount paid, making it impossible to assess whether households
are spending money to catch up on overdue payments or prepay
obligations. (12)
Table 2 provides summary statistics on expenditures in all of our
categories as calculated from the CES. It provides three different
measures of expenditure for each category. The first set of three
columns presents expenditure that occurs on the goods category in the
average month as a percent of total annual expenditures on durable and
nondurable goods. The entry for durable goods in the first column
indicates that in the average month, a household spends 1.5 percent of
its total annual durable and nondurable goods expenditures on durable
goods. The second set of three columns reports the probability that a
household makes any expenditure in a category in an average month. In
the average month, 84.5 percent of households purchase a durable good.
The third set of three columns reports the proportion of total annual
expenditure for durable and nondurable goods in that category in a
month, given that some expenditure was made. Among households purchasing
durables in a given month, the average household spends 1.8 percent of
total annual durable and nondurable goods expenditures on durables.
Table 3 reports the average dollar amount (in 2004 dollars based on the
Personal Consumption Expenditures deflator) spent per month conditional
on expenditure.
As seen in table 2, average monthly expenditure shares are fairly
consistent for EITC and non-EITC families with a few exceptions. The
EITC families spend a high share on food and on children's
clothing. The higher expenditure share on food is consistent with the
general finding that food expenditure shares are higher for lower-income
households in the U.S. The higher expenditure share on children's
clothing arises from our restriction that all EITC eligible households
have children, while many noneligible households do not. From the second
group of columns in table 2, we observe that EITC families are generally
less likely than non-EITC families to make expenditures in almost every
category in an average month. As shown in table 3, in dollar terms,
conditional on nonzero expenditure, EITC families spend less on
everything except for tobacco, food, and gasoline. Our analysis
continues by examining the effect of EITC eligibility on spending in the
nondurables category and the nondurable goods subcategories of
children's clothing and transportation, and then we focus our
analysis on durable goods expenditures and specifically on expenditures
for vehicles and consumer electronics.
Methodology
We measure expenditure by household i in month t on category j in
three ways: the proportion of annual
expenditure in each month ([X.sup.j.sub.it]/[X.sub.i,Annual]), the
probability of making any expenditure (P([X.sup.j.sub.it] > 0)), and
the proportion of annual expenditure conditional on making an
expenditure ([X.sup.j.sub.it]/[X.sub.i, Annual]|[X.sup.j.sub.it] > 0)
. (13)
We estimate clustered probit models for the discrete measure of
expenditure and generalized least squares (GLS) regression models for
the expenditure proportion variables. Letting X be one of the three
dependent variables, we estimate the following equation:
1) [X.sup.j.sub.it] = [alpha] + [[gamma].sub.t][M.sub.t] +
[phi][EITC.sub.i] + [[lambda].sub.t]([EITC.sub.i] x [M.sub.t]) +
[beta][C.sub.i] + [[epsilon].sub.it],
where M is a vector of month dummies, EITC is a dummy variable
equal to 1 if the household is imputed to be EITC eligible, and C is a
vector of household-level controls--year of first quarter interview;
income, race, sex, and education of household head; family size; number
of children; family type; and region (all rural households are the
omitted "region"). We allow for correlation among errors
([epsilon]) within a consumer unit over time.
The coefficients in the vector [[gamma].sub.t], measure the common
seasonal pattern of expenditure for all households relative to September
(the omitted month). For the equation measuring the percentage of total
expenditure, [[gamma].sub.t], indicates the fraction of total
expenditure on good j in month t relative to the fraction of total
expenditure in September. The coefficient [phi] measures the constant
difference in the fraction of expenditures between EITC eligible and
noneligible households. Our coefficients of interest are the elements of
the vector [[lambda].sub.t]. which measure the monthly differences in
expenditure (the different seasonality) between eligible and noneligible
households. If all households perfectly smoothed their consumption
across months, [[gamma].sub.t] would be 0 and the difference in
expenditures between EITC eligible and noneligible households would be
constant and entirely captured by [phi]. We interpret the coefficient on
the EITC x February interaction ([[lambda].sub.Feb]) as an indicator of
whether the EITC changes the expenditure patterns of recipients and
report p values for a test of the hypothesis that [[lambda].sub.Feb] =
0.
Our identification strategy relies on two sources of variation:
cross-sectional differences in eligibility and the particular timing of
EITC refunds. We have no reason to believe, a priori, that unobserved
factors such as prices or preferences influence February expenditure
among low-income, working families with children differently than other
families. (14) Thus, we feel confident interpreting our
[[lambda].sub.Feb] as the impact of the EITC.
Results
Figure 4 shows overall expenditure seasonality relative to
September. There are a number of notable patterns in the data. High
expenditure in December due to the holiday season dominates expenditure
patterns. We also observe high durable goods expenditures in the summer
months when many individuals buy cars and household items. There is also
an increase in nondurable goods expenditures in August in part because
of back-to-school shopping. Finally, expenditure is low in February, the
shortest month of the year.
Table 4 presents estimates of [[lambda].sub.Feb] and the associated
p value for the two continuous specifications of equation 1 and marginal
effects based on [[lambda].sub.Feb] and the associated p value for the
probit model. We present these results for total durable and nondurable
expenditure and for numerous subcategories of expenditure. Figures 5-10
graph the coefficients [[gamma].sub.t], [[lambda].sub.t], and
([[gamma].sub.t], + [lambda].sub.t])--labeled "Non-EITC
families," "Marginal EITC effect," and "EITC
families," respectively, in the legend--for the three different
specifications of equation 1 and for selected expenditure categories.
Since we omit September and do not graph [phi], the "Non-EITC
families" and "EITC families" lines represent deviations
from their respective September expenditure measures. "Marginal
EITC effect" is the difference between these two lines. In order to
facilitate comparison between goods, for the continuous variables, the
figures scale the estimated coefficients by the dependent variable mean
(the average monthly expenditure on that good). For the probit model, we
divide the coefficient by the estimated probability of expenditure. The
denominators are listed in each figure panel, along with the p value for
a test of the hypothesis that [[lambda].sub.Feb] = 0. If
[[lambda].sub.Feb] = 0, then we cannot reject the hypothesis that the
EITC does not affect expenditure on that good.
Nondurable goods
Figure 5 depicts seasonal expenditure patterns for nondurable goods
expenditures by EITC eligibility status.
As shown in the figure, we find a small, but statistically
significant and positive, February effect on unconditional expenditures
for EITC families (p value = 0.000). While noneligible families spend
about 4 percent less on nondurables in February than in September, EITC
families spend about the same in February and September. We do not
investigate conditional or discrete expenditure because the probability
of making nondurable goods expenditure is nearly I in a given month.
In figure 6, we present results for a subset of nondurables that is
particularly relevant to the EITC's goals: expenditures on
children's clothes. We estimate these models only for families with
children so that the non-EITC control group is not dominated by
childless families. Overall seasonal patterns between EITC families with
children and non-EITC families with children are very similar,
exhibiting a large increase in expenditures before school starts in
September and during the holiday season (panel A). The EITC families are
more likely to buy children's clothes in February than non-EITC
families (panel B), but since they spend a slightly lower proportion of
their total annual expenditure conditional on buying children's
clothes (panel C), we do not find a statistically significant
unconditional effect.
In figure 7, we present results for the nondurables portion of
transportation. This includes gasoline, local public transportation, and
car expenses outside of vehicle purchases. We find that EITC eligible
households spend about 4 percent more in February than September, while
noneligible households spend about 3 percent less (panel A). Most of
this difference arises from higher spending conditional on positive
expenditure (panel C). If we look at the first column of table 4, we
find that transportation spending increases in February are the largest
single contributor to the overall nondurabies increase. From table 4, we
also observe that EITC households spend relatively more on food and on
trips than noneligible households in February.
Durable goods
Figure 8 presents results for all durable goods. The difference in
expenditure patterns between EITC and non-EITC families in February is
much more pronounced than for nondurable goods. While non-EITC families
spend about 8 percent less on durables in February than in September,
EITC families spend about 18 percent more (panel A). The EITC families
are significantly more likely both to make a durable goods purchase in
February and to spend more conditional on making a purchase (panels B
and C, respectively).
We now examine the subcategories of durable goods that drive the
patterns depicted in figure 8. Figure 9 presents results for new and
used vehicle purchases. (15) While non-EITC families spend about 17
percent less on vehicles in February than in September, EITC families
spend 18 percent more (panel A), for a statistically significant
difference of 35 percent (p value = 0.0332). This difference is entirely
attributable to the fact that relative to September, EITC families are
more than 600 percent more likely than non-EITC families to buy a car in
February (panel B). This difference is about twice as large in February
as in either January or March. These findings are also consistent with
the research of Adams, Einav, and Levin (2007), which shows high demand
for subprime auto loans in tax rebate season among households likely to
be receiving an EITC refund. Interestingly, though, among families
making a vehicle purchase in February (panel C), all families spend the
same proportion of their annual expenditure on these goods (p value =
0.9844). Recall that in dollars, this amount is considerably smaller for
EITC families.
[FIGURE 6 OMITTED]
[FIGURE 7 OMITTED]
[FIGURE 8 OMITTED]
[FIGURE 9 OMITTED]
[FIGURE 10 OMITTED]
Figure 10 graphs the coefficients from models of spending on
consumer electronics, which include television sets, computers, and
video and music players. Considering all observations, non-EITC
households spend about 5 percent more on consumer electronics in
February than in September, and EITC households spend about 15 percent
more (panel A). However, the February effect is relatively small
compared with the overall February effect for durable goods and
substantially smaller than the effect for vehicles.
Results for other subcategories of durable goods are similar to the
findings for electronics. In February, EITC eligible households spend
more than noneligible households on both household goods and appliances,
but the magnitude of these effects is smaller than the magnitude of the
effect for vehicles.
Conclusion
The results presented here indicate that EITC families spend at
least a portion of their refund immediately upon receipt. Consistent
with Barrow and McGranahan (2000), we find that recipients spend more on
durables than on nondurables in response to the EITC. In particular,
recipients are far more likely to purchase vehicles after receiving EITC
refunds. The EITC increases relative average monthly spending on
vehicles in February by about 35 percent for EITC families compared with
their non-EITC counterparts. Within nondurables, expenditure increases
are concentrated in transportation. Given the crucial role of access to
transportation in promoting work, this leads to the conclusion that
recipient spending patterns support the program's prowork goals.
The EITC recipients are also more likely to spend money within the other
durable goods categories, as well as on trips and food.
In future work, we hope to further analyze the consumption effects
of the EITC by taking advantage of differences in state EITCs and by
exploiting expansions in the EITC since its inception, as well as the
changes in the timing of EITC payments.
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NOTES
(1) The 2005 CES contains data for the first quarter of 2006
(2) A qualifying child must meet three requirements. First, this
individual must be a child, stepchild, foster child, sibling, half
sibling. stepsibling, or a descendent of a sibling of the tax filer.
Second, the qualifying child must be younger than 19 at the end of the
year, younger than 24 and a full-time student, or permanently disabled
Third. the qualifying child must live with the tax filer in the US for
at least six months out of the year. If two tax fliers can claim one
qualifying child, they can choose which one claims the child, but they
both cannot claim the same child (U.S. Internal Revenue Service, 2006a).
Starting m 2002, some married taxpayers filing jointly had higher
benefits than singles with the same income and number of children.
(3) For e-filing, the e-file window needs to be open. This occurs
in early January and happened on January 12, 2007.
(4) This was determined from authors calculations based on data
from the US Internal Revenue Service (2006b).
(5) These figures for tax year 2004 are based on calculations using
U.S. Internal Revenue Service (2006b) data. We assume that all
overpayment refunds not due to the EITC are given to non-EITC
recipients. The 26 percent of non-EITC taxpayers who did not receive a
refund are included as zeros in this calculation.
(6) U.S. Social Security Administration (2006); and U.S. Department
of Agriculture. Food and Nutrition Service (2008).
(7) The nondurables portion of transportation consists of gasoline
and motor oil (42 percent), other vehicle expenses (49 percent), and
public transportation (9 percent), according to the U.S. Bureau of Labor
Statistics (2007).
(8) A consumer unit is defined to be an individual or a group of
individuals who are either related or use their income to make joint
expenditures on two of three categories--housing, food, or other living
expenses.
(9) In future work, we plan to take advantage of changes in the
timing of EITC payments and of expansions in the EITC to further
investigate consumption responses to the program.
(10) In 2004, the credit for childless families accounted for only
3 percent of EITC payments despite representing 21 percent of returns
receiving the EITC (US Internal Revenue Service. 2006b).
(11) Our method of dealing with qualifying children could falsely
impute EITC eligibility or inflate refund amounts for CUs with children
and multiple, unrelated TUs. This is only a potential problem tbr the 4
percent of CUs that contain multiple TUs, have any qualifying children,
and were assigned the EITC Furthermore, if EITC eligibility
"truly" has an impact on expenditure, then misallocating
households into the EITC group should bias our results away from finding
a difference in expenditure seasonality between eligible and noneligible
CUs.
(12) Throughout the analysis, we rely on the monthly data in the
CES. In some cases the monthly reformation is unreliable because of the
random attribution of some expenditure to months in the survey. This
attribution would likely operate in the same manner for EITC recipient
and nonrecipient households
(13) For households with 12 observations [X.sub.i, Annual] =
[12.summation over (t=1)] [X.sup.Total.sub.ij]. In order to adjust
monthly expenditure proportions for households with ewer than 12
observations, we regress [X.sup.Total.sub.it] on household
characteristics for 12-month households only and then generate predicted
expenditure proportions tbr all households. The sum of these predicted
monthly proportions gives the expected proportion of annual expenditures
that we actually observe for households with fewer than 12 observations.
Thus, we estimate true annual expenditures by dividing the sum of m (m
< 12) observed expenditures by the sum of m expected monthly
proportions: [m.summation over (t=1)] [X.sup.Total.sub.it]/ [m.summation
over (t=1)] E([X.sup.Total.sub.it]/[X.sub.i, Annual]). We use this
expression as the denominator of monthly expenditure proportions for
households with fewer than 12 observations. It is because of this
adjustment that average monthly expenditures are not equal to 1/12, or
8.33 percent, in table 2. We do not adjust the estimated standard errors
in our regressions for this imputation.
(14) In their study of retail markdowns in Ann Arbor, Michigan,
Warner and Barsky (1995) note that "prices are indeed lowest in
January, but tend to return in February to December's level."
We do not correct tbr the fact that February has fewer day's than
other months, which should, all else being equal, reduce February
expenditures tbr both EITC recipient and nonrecipient households
(15) According to the CES documentation, vehicle expenditures are
defined as the purchase price minus the trade-in value on new and used
domestic and imported cars and trucks and other vehicles, including
motorcycles and private planes
Andrew Goodman-Bacon is currently a graduate student in economics
at the University of Michigan and a former associate economist at the
Federal Reserve Bank of Chicago. Leslie McGranahan is an economist in
the Economic Research Department at the Federal Reserve Bank of Chicago.
The authors thank Lisa Barrow, Eric French, and Anna Paulson for helpful
comments.
TABLE 1
Summary statistics
All Non-EITC EITC
Median real income (2004 dollars) 32,346 36,590 22,548
Mean real income (2004 dollars) 44,130 46,468 28,599
EITC amount (2004 dollars) 277 -- 2,116
EITC eligible 0.13 0.00 1.00
Number of children 0.71 0.52 1.97
White household head 0.84 0.85 0.75
Household head's highest
educational attainment:
Some high school 0.13 0.12 0.22
High school diploma 0.25 0.24 0.34
Some college 0.20 0.20 0.23
College degree 0.42 0.45 0.22
Family type:
Husband, wife, and own kids 0.27 0.25 0.37
Single parent 0.06 0.03 0.25
Single person 0.28 0.32 0.00
Other family type 0.39 0.40 0.39
Observations (family months) 589,568 512,405 77,163
Observations (distinct families) 59,595 51,824 7,771
Note: EITC means earned income tax credit.
Source: Authors' calculations based on data from the U.S.
Bureau of Labor Statistics, Consumer Expenditure Survey.
TABLE 2
Expenditures patterns, by expenditure category and EITC eligibility
Monthly expenditure/
annual expenditure
All Non-EITC EITC
Total 0.084 0.084 0.083
Durable goods 0.015 0.015 0.015
Household goods 0.003 0.003 0.002
Furniture 0.001 0.001 0.001
Drapes, linens, and floor coverings 0.000 0.001 0.000
Miscellaneous household equipment 0.001 0.001 0.001
Appliances 0.001 0.001 0.001
Major appliances 0.001 0.001 0.001
Minor appliances 0.000 0.000 0.000
Electronics 0.004 0.004 0.004
Vehicle purchases 0.007 0.007 0.008
Nondurables 0.068 0.068 0.068
Food, alcohol, and tobacco 0.030 0.030 0.034
Food 0.023 0.022 0.028
Alcohol 0.001 0.001 0.001
Tobacco 0.002 0.002 0.002
Food away from home 0.005 0.005 0.004
Apparel 0.006 0.005 0.007
Trips 0.003 0.003 0.002
Transportation 0.017 0.016 0.017
Gasoline 0.006 0.006 0.007
Other vehicle expenses 0.010 0.010 0.009
Public transportation 0.001 0.001 0.001
Entertainment 0.006 0.006 0.005
Fees, admissions, toys, and sports 0.004 0.004 0.003
Personal care services 0.001 0.001 0.001
Reading 0.001 0.001 0.000
Other nondurables 0.006 0.007 0.004
Child support, alimony, and charity 0.005 0.005 0.002
Pensions, insurance, and social security 0.002 0.002 0.001
Children's clothing 0.001 0.001 0.003
Children's clothing only among
families with children 0.003 0.002 0.003
Probability of
expenditure
All Non-EITC EITC
Total 1.000 1.000 1.000
Durable goods 0.845 0.848 0.822
Household goods 0.285 0.291 0.248
Furniture 0.048 0.048 0.048
Drapes, linens, and floor coverings 0.098 0.098 0.094
Miscellaneous household equipment 0.210 0.216 0.169
Appliances 0.101 0.102 0.100
Major appliances 0.031 0.031 0.032
Minor appliances 0.076 0.076 0.074
Electronics 0.809 0.813 0.783
Vehicle purchases 0.024 0.022 0.034
Nondurables 0.999 0.999 1.000
Food, alcohol, and tobacco 0.997 0.997 0.998
Food 0.992 0.991 0.994
Alcohol 0.350 0.363 0.265
Tobacco 0.257 0.244 0.341
Food away from home 0.808 0.813 0.772
Apparel 0.647 0.643 0.674
Trips 0.186 0.196 0.119
Transportation 0.939 0.938 0.949
Gasoline 0.893 0.894 0.891
Other vehicle expenses 0.671 0.673 0.660
Public transportation 0.134 0.133 0.135
Entertainment 0.901 0.908 0.858
Fees, admissions, toys, and sports 0.670 0.675 0.635
Personal care services 0.734 0.749 0.635
Reading 0.587 0.611 0.428
Other nondurables 0.534 0.554 0.404
Child support, alimony, and charity 0.456 0.475 0.330
Pensions, insurance, and social security 0.189 0.196 0.142
Children's clothing 0.199 0.171 0.386
Children's clothing only among
families with children 0.411 0.425 0.386
Monthly expenditure/
annual expenditure,
conditional
on nonzero expenditure
All Non-EITC EITC
Total 0.084 0.084 0.083
Durable goods 0.018 0.018 0.019
Household goods 0.010 0.010 0.009
Furniture 0.027 0.028 0.024
Drapes, linens, and floor coverings 0.005 0.005 0.004
Miscellaneous household equipment 0.005 0.005 0.004
Appliances 0.009 0.009 0.008
Major appliances 0.022 0.023 0.019
Minor appliances 0.003 0.003 0.003
Electronics 0.005 0.005 0.005
Vehicle purchases 0.302 0.316 0.244
Nondurables 0.068 0.068 0.068
Food, alcohol, and tobacco 0.030 0.030 0.035
Food 0.023 0.022 0.028
Alcohol 0.002 0.002 0.002
Tobacco 0.007 0.007 0.007
Food away from home 0.006 0.007 0.005
Apparel 0.009 0.009 0.010
Trips 0.017 0.017 0.014
Transportation 0.018 0.018 0.018
Gasoline 0.007 0.007 0.008
Other vehicle expenses 0.014 0.015 0.014
Public transportation 0.005 0.005 0.005
Entertainment 0.007 0.007 0.005
Fees, admissions, toys, and sports 0.006 0.006 0.005
Personal care services 0.002 0.002 0.002
Reading 0.001 0.001 0.001
Other nondurables 0.012 0.012 0.009
Child support, alimony, and charity 0.010 0.011 0.007
Pensions, insurance, and social security 0.009 0.009 0.008
Children's clothing 0.006 0.005 0.008
Children's clothing only among
families with children 0.006 0.006 0.008
Notes: EITC means earned income tax credit. For each
column, the subcategories may not total because of rounding.
Children's clothing is a portion of the apparel subcategory.
Source: Authors' calculations based on data from the U.S.
Bureau of Labor Statistics. Consumer Expenditure Survey.
TABLE 3
Expenditure amotutits, by EITC eligibility,
conditional on expenditure
All Non-EITC EITC
(2004 dollars)
Total 1,788.78 1,822.02 1,568.02
Durable goods 475.38 484.59 414.21
Household goods 73.36 77.29 47.30
Furniture 34.27 35.81 24.02
Drapes, linens, and floor coverings 12.24 12.94 7.60
Miscellaneous household equipment 26.85 28.54 15.68
Appliances 20.24 20.97 15.38
Major appliances 14.97 15.52 11.32
Minor appliances 5.27 5.45 4.07
Electronics 79.38 80.87 69.47
Vehicle purchases 302.40 305.47 282.05
Nondurables 1,313.40 1,337.43 1,153.81
Food, alcohol, and tobacco 491.73 487.31 521.08
Food 349.55 341.46 403.29
Alcohol 15.00 15.66 10.60
Tobacco 26.19 24.80 35.42
Food away from home 101.00 105.40 71.77
Apparel 119.46 119.98 115.99
Trips 77.85 83.91 37.57
Transportation 323.12 326.16 302.99
Gasoline 109.67 108.25 119.06
Other vehicle expenses 201.27 205.32 174.40
Public transportation 12.19 12.59 9.53
Entertainment 144.07 151.83 92.53
Fees, admissions, toys, and sports 104.97 110.84 65.97
Personal care services 25.22 26.08 19.52
Reading 13.88 14.91 7.04
Other nondurables 157.15 168.23 83.63
Child support, alimony, and charity 119.03 127.82 60.63
Pensions, insurance, and social secur 38.13 40.40 23.00
Children's clothing 25.20 22.12 45.68
Children's clothing only among
families with children 55.31 60.62 45.68
Notes: EITC means earned income tax credit. For each column, the
subcategories may not total because of rounding. Children's
clothing is a portion of the apparel subcategory.
Source: Authors' calculations based on data from the U.S. Bureau
of Labor Statistics, Consumer Expenditure Survey.
TABLE 4
Effects of FITC eligibility on fetrruarv expenditures
Unconditional expenditure
Feb.
coefficient p value
Total 0.0067 0.0000
Durable goods 0.0039 0.0004
Household goods 0.0009 0.0001
Furniture 0.0008 0.0000
Drapes, linens, and floor coverings 0.0000 0.7757
Miscellaneous household equipment 0.0001 0.2578
Appliances 0.0003 0.0125
Major appliances 0.0001 0.1710
Minor appliances 0.0001 0.0020
Electronics 0.0005 0.0067
Vehicle purchases 0.0023 0.0332
Nondurables 0.0027 0.0000
Food, alcohol, and tobacco 0.0009 0.0007
Food 0.0007 0.0035
Alcohol 0.0000 0.5658
Tobacco 0.0001 0.1492
Food away from home 0.0001 0.1018
Apparel -0.0002 0.3891
Trips 0.0008 0.0000
Transportation 0.0011 0.0003
Gasoline 0.0002 0.0427
Other vehicle expenses 0.0008 0.0047
Public transportation 0.0001 0.0177
Entertainment 0.0000 0.8229
Fees, admissions, toys, and sports 0.0000 0.9810
Personal care services 0.0000 0.1728
Reading 0.0000 0.9818
Other nondurables 0.0001 0.6995
Child support, alimony, and charity 0.0002 0.1614
Pensions, insurance, and social security -0.0001 0.1145
Children's clothing only among
families with children 0.0002 0.1490
Conditional expenditure
Feb.
coefficient p value
Total 0.0067 0.0000
Durable goods 0.0043 0.0012
Household goods 0.0024 0.0087
Furniture 0.0050 0.1133
Drapes, linens, and floor coverings -0.0008 0.2527
Miscellaneous household equipment 0.0001 0.9176
Appliances 0.0003 0.8035
Major appliances -0.0021 0.4006
Minor appliances 0.0013 0.0336
Electronics 0.0005 0.0176
Vehicle purchases 0.0004 0.9844
Nondurables
Food, alcohol, and tobacco 0.0009 0.0008
Food 0.0007 0.0063
Alcohol 0.0000 0.8426
Tobacco 0.0000 0.7804
Food away from home 0.0001 0.5328
Apparel -0.0004 0.2278
Trips 0.0020 0.1402
Transportation 0.0011 0.0006
Gasoline 0.0002 0.0358
Other vehicle expenses 0.0009 0.0187
Public transportation 0.0004 0.2704
Entertainment 0.0000 0.8136
Fees, admissions, toys, and sports -0.0001 0.7525
Personal care services 0.0001 0.1741
Reading 0.0000 0.5927
Other nondurables -0.0001 0.7713
Child support, alimony, and charity 0.0002 0.6355
Pensions, insurance, and social security -0.0004 0.4439
Children's clothing only among
families with children -0.0007 0.0197
Discrete expenditure
Feb.
coefficient p value
Total
Durable goods 0.0144 0.0023
Household goods 0.0312 0.0002
Furniture 0.0195 0.0000
Drapes, linens, and floor coverings 0.0205 0.0004
Miscellaneous household equipment 0.0238 0.0020
Appliances 0.0304 0.0000
Major appliances 0.0094 0.0069
Minor appliances 0.0212 0.0001
Electronics 0.0091 0.0854
Vehicle purchases 0.0092 0.0008
Nondurables
Food, alcohol, and tobacco 0.0001 0.5506
Food 0.0011 0.1427
Alcohol -0.0052 0.4628
Tobacco 0.0083 0.0847
Food away from home 0.0138 0.0136
Apparel 0.0140 0.0763
Trips 0.0243 0.0024
Transportation 0.0022 0.2173
Gasoline 0.0007 0.7920
Other vehicle expenses 0.0122 0.0918
Public transportation 0.0135 0.0061
Entertainment -0.0024 0.5228
Fees, admissions, toys, and sports 0.0057 0.4336
Personal care services 0.0052 0.4177
Reading 0.0063 0.4132
Other nondurables 0.0068 0.3439
Child support, alimony, and charity 0.0143 0.0436
Pensions, insurance, and social security -0.0154 0.0120
Children's clothing only among
families with children 0.0415 0.0000
Notes: EITC means earned income tax credit. Children's clothing
is a portion of the apparel subcategory.
Source: Authors' calculations based on data from the U.S. Bureau
of Labor Statistics, Consumer Expenditure Survey.
