National Poverty Center Gerald R. Ford School of Public Policy, University of Michigan www.npc.umich.edu
“Consumption, Income, and the Well‐Being of Families and Children” This paper was delivered at a National Poverty Center conference. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the National Poverty Center or any sponsoring agency. Very Preliminary Draft: Do Not Quote or Reference without Permission.
Overspending – Who, Why, and How?
Kerwin Kofi Charles*, Geng Li**, Robert Schoeni***
May 2006
* University of Chicago. ** Federal Reserve Board. *** University of Michigan.
Financial support was received from a grant to the National Poverty Center at the
University of Michigan from the Assistant Secretary for Planning and Evaluation, U. S.
Department of Health and Human Services. Any views expressed in this paper are those
of the authors and not necessarily those of the Federal Reserve Board or other members
of its staff
1.
Introduction
In this paper, we study “overspending” – by which we mean the fact that, in particular
periods, households’ total expenditures exceed their after tax incomes. This topic has
not been the subject of previous systematic formal analysis, despite the fact that the
extent of overspending in the data has important implications for the measurement of
variables like poverty or inequality, and for our understanding of other patterns in the
data. With respect to measurement, recent criticisms about the validity of using incomebased poverty to measure material wellbeing have centered on apparent overspending
among single mothers, welfare recipients and other low income households.1 Since
these families’ meager financial resources make it unlikely that their outlays could
actually exceed their incomes, it is natural to conclude that some overspending is not real,
and is instead the measurement error in the reporting of income. On the other hand,
there has been an explosion in personal indebtedness over the past few decades – a
phenomenon presumably due in part to the very real tendency of at least some households
to truly spend more than their means.
Is overspending real, or is it merely an artifact of measurement error in the data?
If overspending is real, how widespread a phenomenon is it, and are certain types of
households disproportionately likely to engage in it? Do particular events occasion a
family’s propensity to overspend? And, perhaps most importantly, how do families
finance overspending? This paper attempts to fill a void in the literature by answering
these and other questions.
1
A very similar point has been made by others, who have found using ethnographic evidence that there is a
relatively high ownership rate of appliances among households whose low income should leave little slack
available for such purchases.
1
One reason that overspending per se has not been the focus of previous research is
that it is, arithmetically, just the obverse of saving – something to which economists have
devoted considerable attention over the years. However, savings only partly explains
when, why or, most importantly, how households spend more than their means in any
period. Savings and overspending are not perfectly symmetric. To see this, note that
even if capital markets are perfect as the standard model assumes, households can always
save by choosing to consume less than their income in any period. However, with
imperfect capital markets, it is not obvious how the decision to spend more than income
could be financed, especially for those of limited assets. For these and other reasons, we
regard overspending as qualitatively distinct from savings, and thus of independent
research interest.
Another reason for the absence of previous research on overspending is probably
that it has not heretofore been possible to study overspending in any available single data
source. Studying overspending requires good information on both expenditure outlays
and income flows. Unfortunately, the survey which best measures consumption, the
Consumer Expenditure Survey (CE), may not measure income flows as well as do other
data sources. Nor does the CE elicit information about a broad range of demographic or
life course events that would help explain why overspending occurs when it does. On
the other hand, surveys such as the Panel Study of Income Dynamics (PSID) which better
measure income, and demographics and life events through its panel design, have
historically only had limited and incomplete data on expenditures.
In this paper, we exploit an expanded set of questions about expenditures recently
added to the PSID. These new data provide direct information about households’ outlays
2
on a vastly expanded range of consumption items. Although the PSID still does not
cover all of the consumption items inquired about in the CE, we show that about threequarters of spending in the CE is now explicitly asked about in the PSID. We impute
total PSID expenditure using these expanded questions, and find imputed values for total
PSID expenditures that are remarkably close to the total expenditure as measured in the
CE. We use these imputed PSID numbers, in combination with all of the other
information available on the PSID, to answer the questions posed above.
In the first part of our analysis, we offer a comprehensive descriptive overview of
overspending. We show that, in any year, and across nationally representative surveys,
between fifteen and thirty percent of American households report spending that exceeds
their total after tax income. The phenomenon is especially pronounced among
households that are non-white, low income, not highly educated and unmarried.
Overspending also exhibits a distinct U-shaped pattern in age – high during 20s and 30s,
and appreciably smaller during the 40s and 50s. Overspending households report
expenditures that exceed incomes by about 40%, on average, although the disparity is
substantially larger for a small set of families. Finally, expenditures shares information
indicates that, relative to other households, overspending households devote a larger
share of their total expenditure outlays to education, car down payments and health care
expenditures.
We turn next to the important question of whether observed overspending is a real
phenomenon, or whether it is instead an artifact of measurement error in reported income
or expenditures. It is not possible to prove definitively that respondents on surveys do
not mis-report one or the other measure, but we conduct a series of robustness tests which
3
strongly suggest that overspending is real. Our estimates suggest that measurement error
accounts for, at most, only ten percent of observed overspending, and that the essential
character of overspending is unaffected by ignoring those portions of the sample for
which measurement error is of greatest concern.
We turn next to the question of why families overspend. We ask two separate
questions here. The first is whether variation in overspending is accounted for principally
by variation in income or variation in expenditure. Consistent with the life cycle model,
we find that income varies considerably more than expenditures, and is much more
important for explaining variation in observed overspending. We then measure the
importance of various life events in explaining the variation in income and consumption.
Interestingly, we find that although income variation is much more important than
consumption in accounting for overspending, we are much better able to explain
consumption variation using explicit indicators of life events.
Finally, we study how overspending is financed. In a series of regressions we
show that there is a strong cross sectional association between overspending and having
lower asset levels or being in more debt.
We also examine how overspending is
associated with a change in assets and debt over time, and generally find results which
suggest that overspending is financed by the running down of accumulated wealth, or
increases in overall debt.
The remainder of the paper is as follows. In the next section we describe the data.
In section 3, we summarize overspending across surveys and assess the importance of
measurement error. Section 4 studies why overspending occurs. Section 5 discusses how
it is financed. Section 6 concludes.
4
2.
Data
This paper uses data from the Panel Study of Income Dynamics (PSID), the Consumer
Expenditure Survey (CE), and the Survey of Consumer Finances (SCF). We describe
each of these nationally representative data sources in turn, and highlight their similarities
and differences.
Panel of Income Dynamics
The PSID is a nationally representative longitudinal survey that began in 1968 and
continues to interview these families and their descendants today. In addition to
information on an exhaustive set of demographics, the PSID also collects detailed
information on life events such as changes in family composition, employment, and
levels and changes in self reported health. The survey’s measures of income and
consumption expenditures are of principal importance for our purposes. Since its initial
wave, PSID respondents have reported their income from all sources. On the whole,
these income reports are generally considered quite reliable and are very widely used in
the empirical literature. In our analysis, the main income measure is a household’s aftertax income. The PSID does not have direct information on taxes paid, but it is
straightforward to compute a household’s total taxes paid with the TAXIM program from
the National Bureau of Economic Research (NBER).2
Expenditure information in the PSID has historically been limited primarily to
food and mortgages. Beginning with the 1999 wave, however, the PSID dramatically
expanded the expenditure information to nearly three-quarters of total spending as
measured in the CE – the benchmark against which all consumption information must be
2
We use the tax estimation in the Cross-National Equivalent Files from Cornell University.
5
assessed. Using an imputation method similar to Skinner (1987), we impute total
spending in the PSID using these expanded measures.3 The measure of PSID total
expenditures used in the paper is this imputed total value.
Table 1 summarizes the quality of this imputed data. The first two columns show
that the shares of total expenditure constituted by each of the six largest expenditure
items are very similar in the PSID and the CE. The next three columns show that within
each category, the level of imputed PSID consumption is within a few percent of the
measured levels in the CE. The only items for which there are large levels differences
are those like child care and education spending, which constitute a very small fraction of
overall expenditures.
Finally, the PSID has collected information on wealth, checking account balances,
credit card balances and total assets beginning in 1984. This information was previously
collected every five years, but has been collected in every wave beginning with the 1999
survey. Since the expenditure data used in the paper is only available from 1999 on, we
focus on the 1999 and 2001 PSID waves in our analysis.
Consumer Expenditures Survey
The CE is administered by the Bureau of Labor Statistics. It consists of two parts, a diary
survey and an interview survey. We use the interview survey in this paper. The CE
interviews a Consumer Unit (CU) four times, once each quarter. The CU is then retired
from the sample, and new CUs added for sample replenishment. Unlike the PSID, the
CE is a panel with a revolving sample; it interviews at higher frequency and over a
3
A more detailed discussion of the results of this method can be found in Charles et al. (2005).
6
relatively short time horizon (one year). In each wave, the average sample size is around
7000 to 8000 CUs.
As noted above, the CE collects very detailed and comprehensive household
expenditure information. These data are collected at quarterly frequency, so we sum the
quarterly expenditure to obtain annual total expenditure. The CE collects income, debt,
and wealth data annually.4 An advantage of the CE is that expenditure and income
information are collected over the same reference period. Also, because the CE surveys
the CU directly about their after tax income, there was no need to compute total taxes
paid as with the PSID.
The CE collects demographic information, but does not have the comprehensive
data on life events found in the PSID. Finally, because CE is a very short panel, it is
difficult to use it to study the dynamics of overspending and its relationship to outcomes
like wealth accumulation over anything but the very shortest window.
Survey of Consumer Finances
The SCF is conducted by the Federal Reserve Board every three years. The current
version of the survey started in 1989. Our study focuses on the data for 1992, 1995, 1998
and 2001. Unlike the PSID and CE, the SCF is a purely cross sectional survey that
interviewed more than 4000 households in 2001. Just as the CE specializes in measuring
expenditures, the SCF is designed to collect information on wealth and asset holdings.
These variables are right skewed, so the SCF over-samples rich households. The rich
financial data in the SCF covers details of asset holdings, household debts, financial
4
Income data are collected twice, the first interview and the last interview. The debt and wealth data are
collected only at the last interview.
7
attitudes and actions. Unfortunately, the SCF only has limited income information and
essentially no detailed expenditure data.5 Overspending in the SCF is determined from
the answer to a specific question about overspending which we discuss below.
3.
Summary of Overspending
Prevalence of Overspending Across the Population
We begin our analysis with a summary of the prevalence of overspending across multiple
surveys. We define a PSID family as overspending in a given year if its imputed total
expenditures is greater than its after tax income. In the CE, consumer units report annual
total expenditures and annual income (both before and after tax). Overspending is thus
simply the difference between these two measures.
The first two rows of Table 2 show the rate of overspending in the 1999 and 2001
waves of the PSID and the CE. In the PSID, we estimate overspending rates of 33% and
34% in the two years. The corresponding CE estimates of 38% and 39% are quite
similar, especially given the very different approaches of measuring expenditures and
income in these two surveys.
These rates of overspending in the two main national surveys with data on income
and expenditure are high. For comparison purposes, we also show overspending rates in
the SCF. Although the SCF does not collection expenditure information, in various
years the survey asked respondents whether their family spent more than their income.6
5
Beginning in 2004, SCF added questionnaires that surveys food expenditure information. These questions
are almost the same as the questions used in PSID.
6
The specific SCF question was: “Over the past year, would you say that your (your family) spending
exceeded your (your family) income, that it was about the same as your (your family) income, or that you
(your family) spent less than your (your family) income?” Respondents chose from among three variables
indicating that spending exceeded, was equal to, or was less than income.
8
Respondents choose from among three variables indicating that their spending:
“exceeded” income, was “equal” to income, or was “less than” income.
The last row of the table shows that 14% of households chose the response that
spending “exceeded income”. This number is substantial, but much lower than the
overspending rates in the other two surveys. In our view, one reason for the discrepancy
might be the decision rule families use for reporting that separating their spending into
“exceed” or “equal to” category. In particular, if people regarded spending that was very
close to their income as “equal to” then some households that were, strictly speaking,
overspenders may have responded “equal to”. Indeed, if we count half of those who
reported spending equaled income as overspending, then the SCF overspending rate is
close to that in PSID and CEX.
Table 3 shows the overspending rate among various demographic groups, across
several years and in the three different surveys. The estimated frequency of
overspending for any particular demographic group differs across the surveys, as is
consistent with the numbers presented in Table 2. Note, however, that estimated
differences in rates of overspending across groups are similar across the surveys. For
example, all three surveys reveal higher rates of overspending among racial minorities as
opposed to whites, even though the difference in the CE is smaller than the 10 point
difference in the SCF and PSID. Similarly, all three surveys show higher rates of
overspending among unmarried households than among married households, but the 4-5
percentage point difference in the SCF is smaller than the 10 point difference in the other
two surveys.
9
On the whole, these numbers show that overspending is higher among racial
minorities, single households, the less educated, and those at the bottom end of the
income distribution. For this last group in particular, the rate of overspending is quite
striking in the PSID and CE. Across the years studied, these numbers suggest that
almost three-fifths of households in the lowest quartile of the income distribution spent
more than they received in after tax income.
Do particular expenditure categories constitute disproportionate share of the
expenditure outlays of those who overspend relative to the rest of the population? We
might expect that spending on durable goods, such as a down payment for a house or car
or human capital investments, might be especially high for overspenders. Indeed, Table 4
shows that, in the PSID, overspending households devoted larger fractions of their overall
expenditures to car down payments and education expenses. Health care spending is also
disproportionately high for these families. Of course, these larger expenditure shares
must have come at the expense of some items. Our results show that the difference in this
other direction tended to be in rent and mortgage payments.
Finally, we show how overspending varies by age. The bottom panel of the
Figure shows the rate of overspending by age in the PSID, CE, and SCF. The figure has
been smoothed using a three-year moving average. The dotted lines in the figure
represent fitted relationships from regressions which relate overspending to a cubic in
age.7 The figure shows that overspending follows a distinct U-shaped pattern in the two
main data sources: it is high through the mid-30s, low until the early 40s, then grows with
age.
7
These regression results are presented in Appendix Table 1.
10
Levels of Overspending and Possible Role of Measurement Error
By how much does spending exceed people’s budgets when overspending occurs?
Figure 2 presents the cumulative distribution function (CDF) of after-tax income and
expenditures in the PSID and CE. The first noteworthy thing about the figure is that,
apart from slightly higher levels of overspending in the CE sample for reasons discussed
earlier, the patterns are strikingly similar across the two surveys. As shown in Table 2,
about 35% of both samples overspend in a particular year.
In 1999, the average overspending household exceeded its income by about
13,000 in both the PSID and CE.8 On average, these over-spenders exceed their income
budgets by around sixty percent. These levels of overspending, relative to income, are
quite substantial. Indeed, the figure shows that those exhibiting the most dramatic
overspending in the sample have reported expenditures that are more than double their
incomes! The asymmetry between levels of savings and overspending is also
noteworthy. It is striking that average levels of savings in the portion of the sample that
saves are substantially lower than levels of overspending among the rest of the sample.
These results show that if savings is measured as the difference between households’
reported expenditures and their reported after tax incomes, savers save on average
slightly less than one quarter of their income in a given year.
One question raised by the results we have thus far presented is whether
overspending is a “real” phenomenon, or whether it merely reflects the fact that
households report their incomes and expenditures with error. That is, is it possible that
some portion of the 35% of households who we measure as overspending in a particular
8
In the PSID in 1999, the average level of overspending among overspending families was 12,444. The
25th, 50th and 75th percentile values were $2831, $7131, and $148600. In the CE the mean was $13377.
The 25th, 50th and 75th percentile values were $3070, $7470, and $17031.
11
year do not actually exceed their budgets in those years, but rather report levels of income
that are lower than truth or inflated levels of expenditures? For a variety of reasons, we
think that measurement error plays a small role in accounting for the qualitative picture
about overspending we have presented.
Part of the reason for our confidence is the distribution of overspending among
different demographic groups. For example, that overspending is much more prevalent
among groups whose incomes we know are actually low - such as racial minorities, or the
young and the elderly – is very reassuring. Also reassuring, is the incidence of
overspending over time for particular households. Given the panel structure of the PSID,
we can estimate the likelihood that a household is measured as spending more than its
income in 1999 and 2001. If overspending were purely the result of random reporting
error that was serially uncorrelated across reporting years, then the fraction of households
that would be observed to be overspending in both years observed would be around 10%
(0.35 * 0.34). Instead, when we calculate what portion of the sample overspends over
two time periods, we find that these persistent overspenders constitute 16% of the overall
sample. This incidence is about fifty percent higher than would be expected from a pure
measurement error explanation for the phenomenon.
Another way to think about the role of measurement error is to presume that some
error is inevitable in the reporting of income and consumption, and to ask what the
incidence of measurement error would be if we allowed for that reasonable possibility.
For example, suppose we defined a household to be overspending only if its expenditures
exceeded income by at least 10%. The patterns in the CDF indicate that we would still
estimate a rate of overspending of around 25% in both the PSID and CE.
12
In Figure 3, we formally assess how estimated overspending is affected after
deleting from the sample those households for which we have the strongest a priori
reason to suppose that one of the variables determining overspending is measured with
error. Two of the lines depict the CDF of overspending when we delete, in turn,
households whose income reports are in the bottom 5% of the overall distribution of
income reports, then households whose expenditure reports are in the top 5% of the
expenditure distribution. These deleted households are those for which measurement
error is potentially most important for explaining overspending.
The third line in the figure shows the effect of restricting the sample to only those
households who receive at least 80% of their total reported income from labor earnings.
Our reasoning here is that the complicated nature of income receipts for those who
receive much of their income from transfers probably dramatically raises the odds of
honest errors in estimating income. Indeed, income reporting errors for these groups
might not be random at all, as the programs from which transfers are received typically
have rules governing the maximum income the household can receive and remain eligible
for program benefits. Thus, there may be an incentive to deliberately underreport income
for some households.
Figure 3 shows that the main qualitative picture of overspending is only modestly
affected by these restrictions. Dropping these possibly problematic households lowers
the overall estimated rate of overspending in the PSID by about 5 percentage points from
around 35% to 30%. Otherwise, the basic pattern of overspending is quite similar to that
in the full sample. Although we do not present these them in the figure, we find that the
patterns shown in Figure 4 are essentially unchanged when we try other robustness tests,
13
such as simultaneously deleting both arbitrarily low income and arbitrarily high
expenditure reporters, or when we restrict the sample to those for whom labor earnings
constitute at least ninety percent of overall income. We believe that, on the whole, these
results suggest that the overspending estimates presented here represent a phenomenon
that is real and not the artifact of mis-reporting in one or the other of the factors that
determine overspending.
4.
What Explains Overspending Across Households?
Why do households overspend? The standard model of lifecycle consumption offers
some guidance. The key point of the lifecycle/permanent income model is that consumers
try to equalize the marginal utility of consumption (or expenditure in our context) across
their lifetimes, in the face of changing income. In the case where these income changes
are perfectly predictable, the implications can be seen in a very simplified model. It
should be stressed before proceeding that the economic construct of “consumption”
differs from the “expenditure” measure studied in this paper. For example, households
that have paid off their mortgages are consuming housing, although their mortgage
related mortgage expenditures are zero. This problem notwithstanding, we believe that
considering the implications of the lifecycle model are useful for understanding
expenditures.
Suppose that households live for three periods – youth, middle age, and the
mature years – denoted periods 1, 2, and 3 respectively. Consumption in each period is
chosen to maximize lifetime utility. Ignoring discounting and assuming an interest rate of
14
0, the person’s problem in the case in which the path of income is known to be I1 > 0 ,
I 2 > I1 and I 3 = 0 is to choose consumption to solve
max U (C1 ) + U (C2 ) + U (C3 )
s.t
(1)
C1 + C2 + C3 = I1 + I 2
The optimal solution to this problem sets the marginal utility of consumption equal across
periods, leading to the optimal consumption stream of
C * = C1* = C2* = C3* =
I1 + I 2
3
(2)
Thus, to the extent that income follows a predictable pattern of being low in youth, higher
in middle age, and low again during the mature years, consumption smoothing would
thus generate an inverted U-shaped pattern in what we have called overspending.
Not all variation in income is of the predictable lifecycle sort; income may depart
from the expected lifecycle pattern because of idiosyncratic shocks. If income in a given
period is idiosyncratically lower than expected for some reason, the rational consumer
will either run down savings or borrow against future income more than suggested by the
optimal path C * . Unanticipated negative income shocks should thus increase the
likelihood of overspending in any period. Indeed, the event in question could also
simultaneously increase the household’s marginal utility of consumption, which could
also lead to an increase in overspending.
What is the relative importance of predictable income changes and unexpected
life events in observed patterns of overspending? To the extent that the latter set of
factors is important, is their effect felt principally through changes in income or changes
in consumption? The three panels of Figure 4 suggest an important role for all of the
15
factors described thus far. The top panel shows that, on average, income indeed exhibits
the well-established pronounced inverted-U pattern over the lifecycle. To the extent that
consumers are principally responding to this predictable source of income variation, we
would expect to observe the incidence of over-spending follow precisely the U shaped
pattern we have shown. The middle panel shows the standard deviation in income across
ages. This figure shows that income is subject to tremendous short term variation from
one period to the next. Consumers responding to these fluctuations might be led to
overspend.
We use the rich data from the PSID to construct a set of life events that might
affect income and consumption, above and beyond any variation they exhibit for standard
lifecycle reasons. The particular events/conditions on which we focus are poor health,
fertility, employment, and marital status. In the analysis to follow we measure these
events using both a “state” measure (denoting that the household is in the state in
question at the time of the survey), and a “shock” measure indicates that the household
moved into the state since the last survey. The bottom panel of Figure 4 shows the
incidence of these “shock” measures over time.
The unemployment shock variable identifies families in which the head or the
wife changed from employed to unemployed for at least 6 weeks in the preceding year.
The health shock identifies families in which the health status of the head or the wife falls
from excellent, very good, or good to fair or poor. The figure shows that at virtually
every age, households experience at least one of these shocks. While it is true that some
shocks vary systematically with age (like childbearing), others exhibit no systematic age
variation. As discussed above, we wish to assess the importance of these shocks for
16
overspending, above and beyond anything to do with lifecycle consumption having to do
with predictable variation.
Table 5 presents the results of three regressions. The first simply estimates the
likelihood of overspending as a function a cubic in age, and the fixed demographic traits
of race and education. This R-squared on this regression shows that although the
estimated age effects are quite strong, only about 2.5% of the variation in overspending
can be accounted for by purely predictable lifecycle considerations.
The second regression adds controls for the life events, measured as being in a
particular state at the time of the survey. Thus, the fertility variable denotes whether the
household contains a child aged five or less; the health variable measures whether the
household head is experiencing fair or poor health; and the marital status variable
measures whether the household head is divorced or widowed. The portion of the
variation in overspending we are able to explain rises dramatically with the addition of
these variables. Each of the life events is estimated to have a very strongly positive
effect on the likelihood of overspending.
When we measure life events as “shocks” in the third regression, we again find
that the explanatory power of the model is increased, although not be nearly as much as
with the “state” variables. In this specification, we now find that moving into poor health
and having recently experienced a bout of unemployment both increase the likelihood of
overspending. Becoming divorced or widowed raise the likelihood of overspending,
although the estimate is not as strongly significant as when it is measured with the “state”
measure. Finally, we find somewhat surprisingly that the birth of a baby has no
statistically significant effect on overspending probability.
17
Do life events affect overspending mainly through their effect on income, or
through their effect on consumption? Table 6 presents the results for six different
regressions. In the regression labeled (I), we regress the log of after-tax income in a given
year on controls for race, age, education and a cubic in age.
These variables explain
around fourteen percent of the variation in income. It bears emphasizing that the age
controls in the regression capture that portion of income variation that is purely
predictable in a lifecycle sense.
In regress (II), we add to the regression in (I) controls for whether a household is
experiencing a particular life event “state” at the time of the survey. With the exception
of having a young child in the household, each of these variables is associated with
statistically significant large negative effects on after tax income. The addition of these
variables dramatically increases the variation in income that we are able to explain, and
the very large F-statistic presented in the last row of the table shows that we can easily
reject that the effect of these variables if jointly 0.
In regression (III), we measure life events as “shocks”. Each of the shocks, except
for the birth of a child, is associated with reduced after tax income, although the
estimated effects are considerably smaller than the corresponding estimate from
regression (II). The addition of the shock measures for lifetime events also appreciably
raises the explanatory power of the simple regression. The F statistic is much lower than
that from regression (II), but it is again strongly the case that we can reject the notion that
these variables have no effect on income.
Columns (III)-(VI) repeat the exercise described above for total expenditure rather
than income. For fertility, being divorced or widowed, and being in poor health, we find
18
that these life events lower total expenditure. As with income, the estimated effects are
smaller when we use the “shock” measure of the events in question. Having a new baby
within the past year, or being in a home with young child, are both associated with large
increases in total expenditure. As with income, we find that variables are jointly very
strongly significant predictors of expenditure and that their inclusion dramatically
improves the explanatory power of the model.
Taken together, the results paint a very convincing picture. First, they show that
predictable lifecycle considerations represent a large part of the variation in overspending
that can be explained. The role of idiosyncratic life shocks is of smaller importance in
explaining observed patterns, but these factors are quite important.
The two important events of negative health shocks and bouts of unemployment
appear to increase the likelihood of overspending in the period. Overspending increases
with these shocks because they generate reductions in income that are smaller than the
amounts by which they lower consumption. Becoming divorced or widowed is
associated with reduced consumption for both men and women. However, since women
experience larger income changes than do men following these shocks, they only
(marginally) increase the likelihood of overspending for women. Finally, we find no
evidence that having a baby affects overspending in a statistically significant way. This
may be surprising at first blush, since we show that the birth of a baby raises
consumption, while there is no logical reason that it should raise incomes for most
families. However, we find a strong positive association between the birth of a child and
household income. This suggests that childbirth is not a shock at all; rather households
19
may simply time this expenditure intensive event to coincide with periods of anticipated
higher incomes.
5.
How Do Households Pay For Overspending?
The standard model of consumption behavior assumes perfectly functioning capital
markets. This assumption is almost surely invalid for large portions of the population -especially among persons of low income. How, then, do people spend more than they
have?
We present two sets of results in this section.
The results in Table 7 show the estimated association between overspending in a
given year and debt and asset holdings in that year.
The three panels in the table show
estimates from the three surveys. We present results for two measures of assets:
households’ liquid assets which measure (with some slight differences across surveys),
the sum of savings and checking account balances, and total financial assets which is the
sum of saving and checking accounts, plus stocks and bonds. The two debt measures
(again with slight differences across surveys) are households’ total outstanding debt, and
the level of credit card balances.
Across the three surveys, we find a strongly negative association between
overspending and the level of assets. The results are consistent across both asset
measures, so we only discuss the estimates for the liquid asset measure. Overspending
households have liquid assets that are lower by about $2000, on average. The results
from the second specification show, reassuringly, that households in the lowest income
quartile have less liquid assets than the rest of the sample. For these lowest income
20
households, the association between overspending and lower liquid asset holdings is
substantially weaker than the rest of the sample.
How one interprets the magnitude of the effects in this table depends on whether
the regression estimate is compared to the mean or median of the particular outcome. In
general, the means are several times larger than median, because of skewness in the
underlying variable. Thus, the estimated association between overspending and lower
liquid asset holdings is modest when compared to the mean level of asset holding, but is
very large when compared to the median value for that variable of between $1700 and
$3000 across the surveys.
The total debt measure is only available in the CE and the SCF. Further, total debt
in these surveys includes such things as the amount of home mortgages outstanding. The
results show that overspending is strongly associated with higher levels of debt. We find
that households at the bottom of the income distribution have much less debt than the rest
of the sample. Perhaps predictably, we find that overspending households in the bottom
of the income distribution have less total debt than their overspending counterparts in the
rest of the sample. Again, the results are relatively large when measured against the
median, rather than the mean for the debt variable.
Credit card debt outstanding is measured in the CE and SCF directly. In the PSID,
the credit card debt measure combines any outstanding student loans, and loans from
family members.
The column is labeled “credit card” for all surveys, but this difference
should be kept in mind when reading the results. In both the SCF and PSID we find that
overspending households have higher levels of credit card debt, on average, with the
effect least pronounced for lowest income households. The CE results for credit card
21
debt are not consistent with these other two estimates. We find no statistically significant
relationship between overspending and the level of credit card debt in the CE sample as a
whole, and no difference in this pattern for lowest income households.
A limitation of the results in Table 7 is that it is not possible to draw causal
inferences from them. In particular, because these estimates are not for changes over
time, we cannot conclude from them that overspending leads to asset rundown and
increases in debt, although the patterns are strongly suggestive that this is so.
Table 8 provides further suggestive evidence that overspending is financed out of
accumulated assets and increases in indebtedness. These regressions relate the amount by
which households overspend (or save) in a given year to their asset and debt holdings two
years later. These results are restricted to the PSID – the only one of the surveys whose
panel aspect allows for this sort of analysis. These estimates are superior to the results in
Table 7, in the sense that they make use of information about overspending behavior in
one time period to asset and debt outcomes in the future.
These results paint a very convincing picture. We find that the level of
overspending in one year is associated with sharply lower asset holding in the future, and
with higher credit card debt. For example, for each dollar by which expenditure
exceeded income in 2001, non-housing assets were 64 cents lower in 2003, while total
credit card debt was 5 cents higher. Whether evaluated at the mean level of overspending
of in the PSID of 12,000 or the median of 7,000 these point estimates imply very
substantial effects of overspending on reduced debt and overspending.
We have also estimated version of Table 8 in which we relate overspending to the
change in assets and debt. While the signs of most of the estimated effects are consistent
22
with the notion that assets are reduced and debt increased because of overspending, the
very large standard error estimates prevent us from concluding that any of the estimated
effects is statistically different from zero, but for the noteworthy exception of credit card
debt.
6.
Conclusion
Using data from three nationally representative surveys, we have shown in this paper
that, in a given year, between 15% and 35% of American households report expenditures
greater than their income. This phenomenon – which we call “overspending” --- is
especially pronounced among low income families, minorities, those with fewer years of
education, the very young and very old. The estimated amounts by which households
overspend are not small: we find that, on average, overspending household exceed their
incomes by about 40% in the relevant year, with the average level of overspending being
around $12,500 in 1999. Using a variety of tests, we show that in the overwhelming
majority of cases, these numbers indicate genuine overspending, as opposed to
measurement error in income or consumption.
The paper also studies why households overspend.
We argue that a utility
maximizing household might be induced to spend more than income in a given period for
two distinct sets of reasons. One factor is the predictable pattern of income over the
lifecycle. This pattern should lead households to overspend in periods when income is
relatively low (youth and old age) in order to smooth consumption. Unexpected events
which cause both short term deviation of income from its predictable lifetime path, and
short-term changes in the marginal utility of consumption, is the other reason why
23
rational households might be induced to overspend in a given year. We use the rich data
available in the PSID to identity specific life events likely to generate this type of
idiosyncratic variation.
We show that predictable income variation over the life cycle is by far the more
important of these two considerations in explaining overspending. Health disruptions
and unemployment raise the likelihood of overspending appreciably because the income
reductions associated with them are not met with similarly large reductions in
consumption. Marital disruptions have a powerful positive effect on overspending on
women but not for men – mostly because men do not see their incomes fall by nearly the
same amount as women following divorce or widowhood. Interestingly, we find that
having a new baby has no effect on the likelihood that a household overspends, despite
the obvious increases in consumption associated with it. Our results suggest that one
explanation for this might be that households time childbirth to coincide with periods of
higher than usual income.
Despite limitations in the data, we offer some evidence about how households are
able to finance overspending. The results suggest that overspending household run down
previously accumulated assets, and are less likely to accumulate it in the future. We also
find significantly higher levels of credit card indebtedness, both currently and in the
future, among these households.
The results presented here are of independent interest, as they enrich our
understanding of households’ consumption and saving behavior. The paper may also
have important implications for the ongoing policy debate about if poverty should be
measured with income or consumption based indices. To the extent that concerns about
24
the validity of income-based measures derives from the view that measured overspending
among low income populations necessarily indicates cases of income being mismeasured as too low, our findings of apparently genuine levels of overspending among
these low income populations are relatively high call this reasoning into question. Also,
the findings about the apparently important role played by credit cards usage in financing
overspending has important implications for our understanding of the recent dramatic
increase in indebtedness in the population as a whole.
25
Bibliography
To be added.
Table 1. Assessment of Quality of PSID Imputation Using New Expanded PSID Consumption Questions
Share of Total Expenditure
Accounted for by Catergory in 2001
Ratio of Average Expenditure (PSID/CE) in Alternative Years
Category
Total Food
PSID
22.7%
CE
21.6%
2001
1.08
1999
1.03
2003
1.10
Total Housing
40.3%
41.3%
1.00
0.94
0.97
Total transportation
22.8%
24.8%
0.94
0.86
0.93
Total Education
4.6%
3.6%
1.31
1.16
1.13
Total Child Care
1.3%
1.1%
1.25
1.21
1.26
Total health care
8.2%
8.6%
1.10
1.04
1.14
0.96
1.01
Total Expenditures
1.02
PSID Totals are from Imputation Method Described in Charles, Danziger, Li and Schoeni (2005).
Weights are used to calculate all estimates.
Table 2. Percent of Families "Overspending:" PSID, CE, and SCF
Survey
Percent of Families
Overspending
1999
2001
How is it measured?
PSID
33.3
34.3
Computed from Imputed Total Expenditure
and Reported After-Tax Income
CE
39.8
37.8
Computed from Reported Income and
Reported Expenditures
SCF
14.2
14.5
Response to question about whether
typically spend more than their income
Table 3. Percent of Families "Overspending" Among Socio-Economic Groups: PSID, CE, SCF
Survey
PSID
Year
1999
2001
All years
CE
1992
1995
1998
1999
2001
All years
SCF
Race
Education
Marital status
White Nonwhite
31.0
41.2
31.8
42.7
31.4
42.0
Dropout High School Some College College +
43.3
33.2
32.0
26.7
44.0
33.7
34.1
28.6
43.6
33.4
33.1
27.7
Married Unmarried
27.6
39.6
29.8
39.0
28.7
39.3
39.4
39.7
39.6
39.2
36.6
38.8
40.3
44.3
43.7
42.4
40.5
41.7
47.4
50.7
51.4
46.7
44.8
46.1
39.9
38.5
40.3
40.4
37.3
39.8
38.6
41.9
41.8
40.5
37.8
40.0
33.0
34.4
31.1
33.8
32.5
32.9
34.0
36.9
35.9
35.2
33.1
35.0
47.0
45.0
46.0
45.8
42.8
44.9
Income quartile
Bottom 2nd
63.5
38.3
65.1
39.7
64.3
39.0
71.5
72.4
72.8
69.9
71.3
71.0
46.3
46.3
46.3
45.7
40.1
44.4
3rd
23.6
24.3
23.9
Highest
11.0
14.0
12.6
30.3
27.9
29.4
28.7
28.2
28.9
15.2
18.6
13.1
16.6
13.6
15.5
1992
12.3
22.5
21.0
13.1
14.3
12.8
13.1
17.2
21.9
16.5 12.6
8.2
1995
13.9
21.8
16.4
17.2
16.5
13.3
12.2
20.7
22.7
19.1 11.2
9.2
1998
13.0
18.4
16.8
14.4
15.1
12.3
12.3
16.9
19.0
17.2 12.0
8.4
2001
13.1
18.8
19.5
15.9
14.4
10.8
12.9
16.9
23.1
15.6 10.8
8.7
All years
13.1
20.3
18.5
15.2
15.1
12.2
12.6
17.9
21.6
17.1 11.6
8.6
Means in "All years" row for the CE are taken across all years from 1992 to 2002 for which we have data, rather than for the smaller set of years shown.
Table 4. Share of Total Spending Devoted to Various Consumption Categories Among
Overspenders and Savers in PSID
1999
2001
Savers
Over spenders
Savers
Over spenders
Spending component
Food at home
17.6
16.19
16.61
15.38
Food away from home
7.04
6.71
6.7
6.09
Health care
7.23
7.79
6.86
8.06
7.94
10.42
7.36
8.66
Rent
Mortgage
18.98
15.62
17.78
15.87
Car loan payments
5.22
5.19
5.2
5
3.32
6.24
3.6
5.64
Car downpayment
Gasoline
4.58
4.19
5.78
5.36
4.08
5.66
3.69
5.33
Education
Child care
1.62
1.82
1.55
1.42
Table 5. Linear Probability Estimates of Effect of Age, and Various Life Events on Likelihood
of Spending More than Income in Given Year.
Model 1
Age
Age squared*100
Age cubed*10000
-0.0299 **
(0.0080)
0.0487 **
(0.0158)
-0.0239 **
(0.0100)
"State" : In State at Time of Survey
Has child < 5 years old
Model 2
-0.022 **
(0.008)
0.038 **
(0.016)
-0.021 *
(0.010)
0.0664
(0.0123)
0.0827
(0.0129)
0.0367
(0.0138)
0.0882
(0.0125)
Divorced or Widowed
Divorced or Widowed* Female
Fair/poor health
"Shock": Event Occurred in Past Year
Had a baby
-0.0288 **
(0.0080)
0.0469 **
(0.0161)
-0.0231 *
(0.0097)
**
**
**
**
0.0076
(0.0181)
-0.0257
(0.0325)
0.0842 *
(0.0407)
0.0865 **
(0.0215)
0.0779 **
(0.0175)
Became divorced
Became divorced*female
Became unemployed
Moved into fair/poor health
Number of observations
R-squared
Model 3
11264
0.0254
11264
0.0429
11264
0.0289
Regressions control for race and education of head of household. Data from 1999 and 2001 PSID.
*(**) indicates significance at the 0.10 (0.01) level.
Table 6. OLS Estimates on Life Events on Level of After-Tax Income and Level of Total Expenditure
(Standard Errors in Parentheses).
(I)
Indicators for Specific Life Events
"Stock" : In State at Time of Survey
Have Young Kids
Income
(II)
Being Divorced or Widowed
* Female
In Sub-Optimal Health
"Shock": Event Occurred in Past Year
Had a baby
Became divorced*female
Became unemployed
Moved into fair/poor health
Yes
Yes
Yes
F-Statistics for Indicators of Life Events
0.1389
11264
(VI)
0.104 **
(0.015)
-0.519 **
(0.016)
-0.053 **
(0.017)
-0.147 **
(0.016)
0.146 **
(0.045)
-0.012
(0.078)
-0.206 **
(0.098)
-0.131 **
(0.052)
-0.177 **
(0.039)
Became divorced
R-squared
Number of observations
(IV)
0.020
(0.028)
-0.653 **
(0.03)
-0.098 **
(0.032)
-0.266 **
(0.029)
Being Divorced or Widowed
Demographic Controls
Age Cubic
Dummy for Race=Non-White
Dummy for Level of Schooling
(III)
Expenditure
(V)
Yes
Yes
Yes
Yes
Yes
Yes
F = 321.85
F = 10.24
0.2270
11264
0.1424
11264
0.181 **
(0.026)
-0.074 *
(0.045)
-0.050
(0.056)
-0.031
(0.030)
-0.069 **
(0.023)
Yes
Yes
Yes
0.2239
11264
Yes
Yes
Yes
Yes
Yes
Yes
F = 658.59
F = 15.55
0.371
11264
11264
0.229
Table 7. OLS Estimates of Association Between Overspending and Various Measures of Asset Holdings: PSID, CE, and SCF
Liquid Assets
PSID: 1999 & 2001
Mean (median) of dep. var.
Variable
Overspending=1
20933(3000)
-2500
(672)
Financial Assets
Total Debt
Credit Card Debt
74479(6000)
-2250
(810)
-5191
(2335)
10000(5000)
-4041
(2813)
1011
(443)
1662
(500)
Low Income Quartile
-8460
(1024)
-26645
(3558)
-3060
(772)
Low Income Quartile * Overspending
1922
(1457)
5038
(5061)
-1526
(1092)
CE: 1992-2002
Mean (median) of dep. var.
Variable
Overspending=1
12122(1063)
-1815
(275)
30517 (1407)
-496
(340)
-2879
(558)
44050 (11043)
266
(691)
3545
(606)
4630
(745)
3846 (1859)
-10
(66)
158
(74)
Low Income Quartile
-7000
(559)
-15550
(1137)
-15182
(1226)
-821
(194)
Low Income Quartile * Overspending
1386
(664)
2575
(1350)
-9330
(1457)
3.96
(219)
SCF: 1992, 1995, 1998, 2001
(in 2001 Dollar)
Mean (Median)
Variable
Overspending=1
7401(1760)
-1978
(219)
-2365
(272)
113379(14910)
-7095
(850)
-9372
(1039)
46324(11211)
3658
(1441)
6921
(1777)
4346(1800)
1647
(174)
2032
(198)
Low Income Quartile
-3084
(227)
-17023
(874)
-19248
(1496)
-861
(225)
Low Income Quartile * Overspending
2226
(455)
9712
(1746)
-4941
(2984)
-1188
(408)
Liquid Assets: for PSID = Checking Account Balance; CE=Checking Account Balance + Savings Balance; SCF = Checking + Savings + Money Market + Call
Financial Assets: for PSID = Checking Account Balance + Bonds + Stocks; SCF = Checking + Savings + Money Market + Call + All Others
In the PSID, the credit card debt measure combines student loan debt and loans from family members.
Standard errors reported below coefficient estimates.
All models include controls for a cubic in age, and indicators for nonwhite, education (<12; 12; >12), and marital staus.
Standard errors reported below coefficient estimates.
*(**) indicates significance at the 0.10 (0.01) level.
Table 8. OLS Estimates of Effect of Level of Overspending in One Year on Asset and Debt Holding in a Future Year: PSID
Survey year & variable
2001
Amount of overspending in 1999
Number of observations
2003
Amount of overspending in 2001
Liquid Assets
Dependent variable: Amount of…
Non-Housing Assets
Total Wealth
Credit Card Debt
-0.09792 **
(0.0127)
-0.7746 **
(0.1051)
-0.9605 **
(0.1227)
0.02911 *
(0.0138)
5566
5566
5566
5566
-0.1207 **
(0.0167)
-0.6425 **
(0.1143)
-0.5633 **
(0.1408)
0.0406 **
(0.0077)
5550
5550
5550
Number of observations
5550
Standard errors reported below coefficient estimates.
*(**) indicates significance at the 0.10 (0.01) level.
Figure 1. Age Profile of Over Spending: PSID, CE, & SCF
60%
CE
50%
40%
% of Families
30%
Over Spending
PSID
20%
10%
SCF
0%
25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
Age
Figure 2. Distribution of After Tax Income ((Y) Minus Expenditures (E):
PSID & CE in 2001
100%
34% (38%) spend more than their after tax
income in the PSID (CE)
50%
0%
5
[(Y-E)/Y) -50%
10
15
20
25
30
35
40
45
50
55
PSID
CE
-100%
-150%
-200%
Percentile
60
65
70
75
80
85
90
95
99
Figure 3. Distribution of After Tax Income ((Y) Minus Expenditures (E) in 2001 PSID:
Sensitivity Analysis Dropping Observations Most at Risk to Report with Error
100%
50%
0%
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
[(Y-E)/Y)
-50%
Trim bottom 5% of income
Trim top 5% of expenditures
-100%
Families with labor income>80% of total income
-150%
Percentile
90
95
99
Figure 4a: Average After-Tax Income by Age:(PSID: 1999, 2001)
80000
60000
40000
20000
76
73
70
67
64
61
58
55
52
49
46
43
40
37
34
31
28
22
25
0
Age
Figure 4b. Standard Deviation of After-Tax Income, by Age. (PSID: 1999, 2001)
9
8
7
6
5
4
3
2
1
80
79
78
77
76
75
74
73
72
71
70
69
68
67
66
65
64
63
62
61
60
59
58
57
56
55
54
53
52
51
50
49
48
47
46
45
44
43
42
41
40
39
38
37
36
35
34
33
32
31
30
29
28
27
26
25
0
Age
After-Tax Income
Figure 4c: Change in Life Event Since Previous Year
0.40
Proportion of individuals
0.35
0.30
0.25
Any of these
shocks
0.20
0.15
0.10
Had a baby
Health
worsened
Became
unemployed
0.05
Became
divorced/widowed
0.00
22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86
Age