The wealthy hand-to-mouth.
Kaplan, Greg ; Violante, Giovanni L. ; Weidner, Justin 等
VI. Consumption Response of the Wealthy HtM to Transitory Income
Shocks
In the previous sections we documented a sizable presence of
wealthy HtM households across a number of countries, but our survey data
did not allow us to investigate the consumption behavior of this group
of households. In this section we show evidence that, as predicted by
the theory presented in section I, these households have a large
marginal propensity to consume out of transitory income shocks. We use
data from the Panel Study of Income Dynamics (PSID) to estimate the
consumption response to transitory changes in income, using the
methodology proposed by Blundell, Pistaferri, and Preston (2008) and
further examined in Kaplan and Violante (2010). The novelty of our
empirical analysis, relative to that of Blundell and colleagues, is that
we use a more recent sample period with enriched data and, most
importantly, estimate the transmission coefficients of income shocks to
consumption separately for different types of HtM households.
VI.A. Data Source and Sample Selection
Estimating the consumption response to income shocks for households
with different types of HtM status requires a longitudinal data set with
information on income, consumption, and wealth at the household level.
Starting from the 1999 wave, the PSID contains the necessary data. The
PSID started collecting information on a sample of roughly 5,000
households in 1968. Thereafter, both the original families and their
split-offs (children of the original household forming households of
their own) have been followed. The survey was annual until 1996 and
became biennial starting in 1997. In 1999 the survey augmented the
consumption information available to researchers, so that it now covers
more than 70 percent of all consumption items available in the Consumer
Expenditure Survey (CEX), and since 1999 it has included additional
questions on the household balance sheet in every wave. (30)
We start with the PSID Core Sample and drop households with missing
information on race, education, or state of residence, and those whose
income grows more than 500 percent, falls by more than 80 percent, or is
below $100. We drop households that have top-coded income or
consumption. We also drop households that appear in the sample fewer
than three consecutive times, because identification of the coefficients
of interest requires a minimum of three periods. In our baseline
calculations, we keep households where the head is 25 to 55 years old.
Our final sample has 39,772 observations over the pooled years 1999-2011
(seven sample years).
VI.B. Definitions and Methodology
The construction of our consumption measure follows Blundell,
Pistaferri, and Saporta-Eksten (2014). We include food at home and food
away from home, utilities, gasoline, car maintenance, public
transportation, childcare, health expenditures, and education. Our
definition of household income is the labor earnings of a household plus
government transfers. Liquid assets in the PSID include the value of
checking and savings accounts, money market funds, certificates of
deposit, savings bonds, and Treasury bills, together with directly held
shares of stock in publicly held corporations, mutual funds, or
investment trusts. Before 2011, liquid debt is the value of debts other
than mortgages, such as credit cards, student loans, medical or legal
bills, and personal loans. For 2011, liquid debt includes only credit
card debt. Net liquid wealth is liquid assets minus liquid debt.
Net illiquid wealth is the value of home equity plus the net value
of other real estate plus the value of private annuities or IRAs; it
also includes the value of other investments in trusts or estates, bond
funds, and life insurance policies. (31) Net worth is the sum of net
illiquid and net liquid wealth. Given these definitions of income and
wealth, the HtM status indicators are constructed exactly as outlined in
section II, where the pay period is assumed to be two weeks and the
credit limit is one month of income. In our PSID sample, 25 percent of
households are wealthy HtM, roughly in line with the U.S. SCF estimates,
but the share of the poor HtM is 21 percent, which is almost twice as
large as its counterpart in the U.S. SCF.
METHODOLOGY We refer the reader to Blundell, Pistaferri, and
Preston (2008) and to Kaplan and Violante (2010) for a thorough
description of the methodology. Here, we only sketch the key steps. As
in the work of Blundell and colleagues, we first regress log income and
log consumption expenditures on year and cohort dummies, education,
race, family structure, employment, geographic variables, and
interactions of year dummies with education, race, employment, and
region. We then construct the first-differenced residuals of log
consumption [DELTA][c.sub.it] and log income [DELTA][y.sub.it]. Recall
that, since the survey is biannual, a period is two years. The income
process [y.sub.it] is represented as an error component model which
comprises orthogonal permanent and i.i.d. components. Hence, income
growth is given by
13) [DELTA][y.sub.it] = [[eta].sub.it] + [DELTA][[epsilon].sub.it]
where [[eta].sub.it] is the permanent shock and [[epsilon].sub.it]
is the transitory shock. This is a common income process in the
empirical labor literature, at least since Thomas MaCurdy (1982) and
John Abowd and David Card (1989), who showed that this specification is
parsimonious and fits income data well. The Blundell, Pistaferri, and
Preston (2008) estimator of the transmission coefficient of transitory
income shocks to consumption, the marginal propensity to consume (MPC),
is given by
(14) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The true marginal propensity to consume out of a transitory shock
is defined as
(15) MP[C.sub.t] =
cov([DELTA][C.sub.it],[[epsilon].sub.it])/var([[epsilon].sub.it])
The estimator in equation 14 is a consistent estimator of equation
15 if the household has no foresight, or no advance information, about
future shocks, that is:
(16) cov([DELTA][C.sub.it],[[eta].sub.i,t+1]) =
cov([DELTA][C.sub.it],[[epsilon].sub.i,t+1]) = 0.
The estimator is implemented by an IV regression of
[DELTA][c.sub.it] on [DELTA][y.sub.it] instrumented by
[DELTA][y.sub.i,t+1]. Note that [DELTA][y.sub.i,t+1] is correlated with
the transitory shock at t, but not with the permanent one. Kaplan and
Violante (2010) show that the presence of tight borrowing constraints
does not bias the estimate of the transmission coefficient for
transitory shocks--an important finding since we are interested in the
differential response of HtM households, which may be close to a
constraint, and non-HtM households.
VI.C. Results
Table 6 summarizes our results. In our baseline specification, the
marginal propensity to consume of the wealthy HtM group is the highest,
around 30 percent. In other words, in the first two years, the wealthy
HtM households consume 30 percent of an unexpected change in income
whose effect entirely dissipates within the period. The point estimate
of the marginal propensity to consume for the poor HtM is 24 percent,
and for the non-HtM it is less than 13 percent. Given the well known
measurement error present in survey data, especially for consumption
expenditures, and given the small sample size, it is not surprising that
these estimates are somewhat imprecise. However, the difference between
the wealthy HtM and the non-HtM in the marginal propensity to consume is
statistically significant.
When the sample is split between HtM and non-HtM households based
on net worth, the estimated transmission coefficients are very similar
across the two groups. The group of net-worth-defined HtM is essentially
the same as the poor HtM, and in fact their estimated marginal
propensity to consume is similar. However, among the net-worth-defined
non-HtM there are also many wealthy HtM households that artificially
inflate the estimate of the marginal propensity to consume. Based on
this household classification, there is no evidence that the response of
consumption to income shocks differs among households with different HtM
status. By contrast, a classification based on liquid and illiquid
wealth finds economically significant differences.
The remaining rows in table 6 offer a robustness analysis with
respect to the definition of income and consumption, household
composition, and the assumed pay period. The ranking of marginal
propensity to consume among wealthy HtM, poor HtM, and non-HtM is always
as in the baseline specification; moreover, as predicted by the theory,
the gap between HtM households based on the net worth criterion is
always very small or is not statistically significant.
Our key finding that the consumption of the wealthy HtM displays
excess sensitivity to transitory income shocks is in line with some
recent findings. Kanishka Misra and Paolo Surico (2013) expand on the
research of Johnson, Parker, and Souleles (2006) and Parker and others
(2013) on the 2001 and 2008 fiscal stimulus payment episodes in the
United States. They conclude that, for both stimulus programs, the
largest propensity to consume out of the tax rebate is found among
households that own real estate but have high levels of mortgage debt.
James Cloyne and Surico (2013) exploit a long span of expenditure survey
data for the United Kingdom and a narrative measure of exogenous income
tax changes, and they also find that homeowners with high leverage
ratios exhibit large and persistent consumption responses to tax shocks.
Scott Baker (2013) combines several novel sources of household data on
consumption expenditures, income, and household balance sheets to
investigate the co-movement of income and consumption at the micro level
around the Great Recession. He finds that expenditures of highly
indebted households with illiquid assets are especially sensitive to
income fluctuations. Overall, this body of work confirms our finding in
figure 4 that highly leveraged homeowners are likely to be wealthy HtM
and, hence, to have a large marginal propensity to consume out of income
shocks.
VII. Implications for Fiscal Policy
What does the existence of wealthy HtM households, together with
their large propensity to consume out of transitory income shocks, imply
for how one should think about fiscal policy? In this section we use a
series of policy simulations from three alternative models to argue that
wealthy HtM households should be modeled as a separate group: ignoring
them leads to a distorted view of the effects of fiscal stimulus
policies on aggregate consumption.
The first model that we use is the two-asset incomplete-markets
model from Kaplan and Violante (2014a, 2014b). We label this model
SIM-2, since it extends the standard incomplete-markets (SIM) life-cycle
economy by adding a second illiquid asset that pays a higher
return--through both a financial component and a housing services
component--but is subject to a transaction cost. For the reasons
explained in section I, the illiquidity due to the transaction cost
means that the model generates households of all three HtM types. The
version of the model we use here does not allow borrowing and has a
transaction cost of $ 1,000. (32)
The second model, which we label SIM-1, is a standard one-asset
incomplete-markets life-cycle model. The version that we adopt is the
same as in Kaplan and Violante (2014a, 2014b), but with the transaction
cost set to zero and recalibrated to data on net worth alone, rather
than data on illiquid and liquid assets separately. Since this is a
one-asset model, it generates only poor HtM and non-HtM households and
has no wealthy HtM households.
The third model, which we label SP-S, is a spender-saver model in
the spirit of Campbell and Mankiw (1989) and, more recently, Gali,
Lopez-Salido, and Valles (2007), Eggertsson and Krugman (2012), and
Justiniano, Primiceri, and Tambalotti (2013). In the SP-S model, some
households (the savers) act as forward-looking optimizing consumers who
can save in a single risk-free asset. The remaining households (the
spenders) follow the rule-of-thumb consumption policy of consuming all
their income in every period. This class of models is typically
calibrated so that the distinction between the spenders and savers is
based on their holdings of liquid wealth rather than net worth. Thus, in
the SP-S model, the wealthy HtM and the poor HtM households are lumped
together and considered to be the spenders, while the non-HtM households
are considered to be the savers.
To summarize, SIM-2 is a two-asset economy, in which the wealthy
HtM households are explicitly modeled as a distinct group. SIM-1 is a
net-worth economy, in which the wealthy HtM households are treated as if
they were non-HtM households. Compared to SIM-2, SIM-1 greatly
understates the fraction of HtM households. SP-S is a liquid-wealth
economy, in which both the wealthy HtM and the poor HtM are treated
identically as HtM households that have a marginal propensity to consume
always equal to one. Thus, compared to SIM2, SP-S has the correct number
of HtM households, but it greatly overstates their marginal propensity
to consume.
From each of these three models, we simulate a cohort of
households. For each household, we compute the quarterly consumption
response to a one-time unexpected cash windfall, or cash loss, of
different amounts ($50, $500, $2,000). We then divide the simulated
cohort into 27 bins, based on three income terciles, three age classes
(ages 22 to 40, 41 to 60, and over 60) and the three HtM groups. For
each of these bins we compute the average consumption response from the
model. To obtain an aggregate response of the economy as a whole, we
need to know the shares of the population in each of these 27 groups.
For this last step, we use our cross-sectional survey data discussed in
sections IV and V.
Table 7 reports the quarterly average marginal propensity to
consume out of a $500 windfall in the three models, both for the HtM
groups and for some subgroups defined by income and age, using group
shares from the 2010 U.S. SCF. In the SIM-2 model, marginal propensity
to consume is very small for all non-HtM households, except for those
who are income-poor or old. For high-income households that are non-HtM,
the average marginal propensity to consume is slightly negative. The
intuition for this finding is discussed in detail in Kaplan and Violante
(2014a, 2014b). It arises because for a household that has already
accumulated substantial liquid wealth and is close to its planned date
of deposit, the receipt of a $500 windfall may trigger a decision to pay
the transaction cost and make an earlier deposit into the illiquid
account. Since such a household can effectively save at the rate of
return on the illiquid asset, it chooses to consume less and save more
than it would have in the absence of the income windfall. This example
illustrates how explicitly modeling wealthy HtM behavior through
transaction costs can alter the marginal propensity to consume even for
non-HtM households. The marginal propensity to consume for both wealthy
HtM and poor HtM households in the SIM-2 economy is substantial, though
it is slightly larger for the wealthy HtM than the poor HtM,
particularly for households with a high level of income. As explained in
section I, since wealthy HtM households have higher lifetime incomes
than poor HtM households, they have higher target consumption and hence
spend more out of an unexpected moderately sized payment.
In the SIM-1 model, the marginal propensity to consume for HtM
households is almost identical to that for poor HtM households in the
SIM-2 model, and the marginal propensity to consume for non-HtM
households is, in general, even smaller than that for non-HtM households
in the SIM-2 model. In the SP-S model, by construction, the marginal
propensity to consume for the non-HtM households is the same as in the
SIM-1 model and is equal to one for HtM households.
VII.A. Policy Simulations for the United States
We now show that the three models yield very different predictions
for the aggregate marginal propensity to consume out of unexpected,
one-time, lump-sum transfers or taxes of different amounts. Table 8
reports the policy-experiments results (that is, the aggregate quarterly
consumption responses) for the United States using the SCF data from
2010 to estimate the group shares.
We begin by analyzing a policy experiment where every household
receives a $500 transfer, for example a stimulus payment. The aggregate
marginal propensity to consume according to the SIM-2 model is 0.18.
This value is substantially larger than it is according to the SIM-1
model (0.04), because the SIM-1 economy, by treating the wealthy HtM
households as non-HtM, misses a large fraction of the population that
has a high marginal propensity to consume. The aggregate marginal
propensity to consume is highest according to the SP-S model (0.35),
because this model implicitly assumes that all poor HtM and wealthy HtM
households spend the entire $500. However, our earlier discussion of
table 7 suggests that this assumption is extreme: in the SIM-2 economy,
HtM households spend on average only 35 to 45 percent of their payments
during the quarter when they are received.
Table 8 also shows that the degree of size asymmetry in the
aggregate marginal propensity to consume differs remarkably across the
three models. In the SIM-2 model, the consumption response to a $50
windfall is 0.29, while the response to a $2,000 windfall is only 0.05.
The reason for this large asymmetry is the availability of an illiquid
savings instrument subject to a transaction cost. For large enough
windfalls, many HtM households in a SIM-2 economy may find it optimal to
pay the transaction cost and make a deposit into the illiquid asset.
However, for small windfalls, it is never optimal to adjust the illiquid
asset: households thus face an inter-temporal trade-off governed by the
(low) return on the liquid asset, and thus have a large incentive to
consume. This size asymmetry is absent from both the SP-S and SIM-1
models. In the SP-S model it is absent because of the assumed
rule-of-thumb behavior: the HtM households in the SP-S model always
consume their entire transfer, regardless of its size. In the SIM-1
model there is only a modest decline in the marginal propensity to
consume as the size of the payment increases, because households always
face the same intertemporal trade-off when making their consumption
decisions.
The degree of sign asymmetry also differs across the three models.
In the SIM-1 and SIM-2 models, the response to a lump-sum tax of $500 is
substantially larger than the response to a $500 transfer. Even HtM
households, which are at a kink in their budget constraints, desire to
save some part of a positive windfall if it is large enough to push them
off the kink. Negative income changes, however, cannot be smoothed for
households at the constraint, and withdrawing from the illiquid account
is too expensive to be optimal--recall that in the calibrated SIM-2
model, the transaction cost is $1,000. In the SP-S model, the responses
to positive and negative income shocks are essentially the same, since
the HtM households have a marginal propensity to consume of one
regardless of the sign of the shock.
Table 8 reveals that the models have different implications for the
optimal degree of income targeting in the use of fiscal stimulus
transfers to maximize the aggregate consumption response. A widely held
view is that the aggregate consumption response to a fiscal stimulus
policy, per dollar paid out, is strongest when the transfers are
targeted to households with the lowest income, that is, stimulus
payments should be phased out for middle- and high-income households for
maximum effect. This view, which is based on the conjecture that HtM
households are income-poor, ignores the wealthy HtM, a group with
significantly higher income, as we showed in sections IV.B and V. In
line with this observation, the SIM-2 model generates only a very modest
decline (0.26 to 0.20) in the marginal propensity to consume out of a
$500 transfer between households in the lowest income tercile and those
in the middle-income tercile. The corresponding relative declines across
income terciles are much larger under the SIM-1 and SP-S models. In the
SIM-1 model, the only high-marginal propensity to consume households are
the low-income poor HtM; in the SP-S model, all HtM households are
assumed to have the same marginal propensity to consume, while under the
SIM-2 model, as we saw in table 7, among wealthy HtM households the
marginal propensity to consume increases with income.
VI.B. Implied Cross-Country Variation in Effects of Policy
We now explore what the three models predict for the aggregate
response to a $500 fiscal stimulus check (or its equivalent as a
fraction of average income) in each of the eight countries in our
sample. To do this, we use our survey data to estimate the fraction of
households in each country that fall into each of the 27 bins, and then
apply these country-specific group weightings to the model-generated
marginal propensity to consume. To illustrate the differences in model
predictions, figure 11 plots the estimated aggregate marginal propensity
to consume under the SIM-2 model against the corresponding marginal
propensity to consume under the SIM-1 model (triangles) and the SP-S
model (circles).
The figure shows striking differences in the amount of
cross-country dispersion in the aggregate marginal propensity to consume
predicted by the three models. There is much less dispersion in the
SIM-1 model compared to the SIM-2 model because, by treating the wealthy
HtM as non-HtM, the SIM-1 model misses most of the cross-country
variation in HtM behavior. In contrast, there is more dispersion in the
SP-S model than in the SIM-2 model. This is because, by assigning a
marginal propensity to consume of 1.0 to all the wealthy HtM households,
compared to a marginal propensity to consume of 0.44 in the SIM-2 model,
the SP-S model exaggerates existing cross-country heterogeneity in the
fraction of HtM households.
[FIGURE 11 OMITTED]
These experiments clearly illustrate why it is important to think
deeply about the behavior of wealthy HtM households when considering the
design of fiscal policies. In contrast to the traditional views based on
SIM-1 or SP-S models, our model leads to three lessons: (i) there is
limited scope for stimulating aggregate consumption by increasing the
transfer size; (ii) the aggregate consumption response to a lump-sum tax
is much stronger, in absolute value, than the response to an equal-size
transfer; and (iii) targeting stimulus payments exclusively toward
low-income families will miss a substantial fraction of
liquidity-constrained households.
VIII. Concluding Remarks
We set out to investigate, theoretically and empirically, the
behavior of wealthy hand-to-mouth households--an often overlooked but
highly relevant part of the population--and to reflect on its
implications for macroeconomic modeling and fiscal policy design. We
conclude by taking stock of what we have learnt.
Theoretically, we show that wealthy hand-to-mouth behavior can
occur when households face a trade-off between the long-run gain from
investing in illiquid assets (assets that require the payment of a
transaction cost for making unplanned deposits or withdrawals) and the
short-run cost of having fewer liquid assets available to smooth
consumption.
Empirically, we document that 30 percent of households in the
United States are living hand-to-mouth, and that this fraction has been
relatively constant over the past two decades. The share of
hand-to-mouth households varies somewhat across the eight countries in
our study, from less than 20 percent in Australia and Spain to over 30
percent in the United Kingdom and Germany. Given our identification
strategy, these estimates are likely to be a lower bound. The key
finding is that in all countries, the vast majority of hand-to-mouth
households--at least two-thirds of them--are wealthy hand-to-mouth, not
poor hand-to-mouth.
Who are the wealthy hand-to-mouth? We highlight three features.
First, unlike poor hand-to-mouth households, the wealthy hand-to-mouth
are not predominantly young households with low incomes. Rather, the
frequency of wealthy hand-to-mouth status has a hump-shaped age profile
that peaks in the early 40s and an income profile that strongly mirrors
that of the non-hand-to-mouth. Second, the wealthy hand-to-mouth are not
simply poor hand-to-mouth households with very small holdings of
illiquid assets. Rather, they hold substantial wealth in housing and
retirement accounts, in the same proportions as non-hand-to-mouth
households. Finally, their hand-to-mouth status is somewhat more
transient than that of the poor hand-to-mouth.
Why does this group of households deserve the attention of
economists and policymakers? Wealthy hand-to-mouth households are
important because they have large consumption responses to transitory
income shocks--a crucial determinant of the efficacy of many types of
fiscal interventions, such as the fiscal stimulus payments that were
implemented in the last two recessions. To demonstrate this, we use PSID
data to show that the transmission coefficient of transitory income
shocks into consumption is significantly larger for wealthy (and poor)
hand-to-mouth households than for non-hand-to-mouth households.
The wealthy hand-to-mouth thus have consumption responses that, in
many ways, are similar to those of the poor hand-to-mouth, yet they have
demographic characteristics and portfolio compositions that resemble
those of the non-hand-to-mouth. This suggests that for these three types
of hand-to-mouth households, each needs to have its own unique place in
frameworks that are to be used for analyzing and forecasting the effects
of fiscal policy. Macroeconomists need to move beyond one-asset models,
such as those in the spirit of Aiyagari (1994), Huggett (1996), and
Rios-Rull (1995), since these models assume that wealthy hand-to-mouth
households are as unconstrained as non-hand-to-mouth ones. They also
need to move beyond spender-saver models, such as those in the spirit of
Campbell and Mankiw (1989), and Eggertsson and Krugman (2012), since
these models treat all hand-to-mouth households identically and thus
assume that wealthy hand-to-mouth households are as constrained as the
poor hand-to-mouth. In particular, by ignoring the fact that the wealthy
hand-to-mouth can use illiquid assets to buffer large negative shocks,
the latter models exaggerate the financial fragility of this group. We
run several fiscal policy experiments to illustrate where misleading
inferences would be obtained by using either of these two simpler models
of hand-to-mouth behavior.
ACKNOWLEDGMENTS We thank Yu Zhang for outstanding research
assistance, and Mark Aguiar, Karen Pence, Rob Shimer, David Weil, and
the editors for comments. This research is supported by grant no.
1127632 from the National Science Foundation. Greg Kaplan, on leave from
Princeton University, is currently a research advisor at the Reserve
Bank of Australia. He has received grant support from the Reserve Bank
of Australia and the National Science Foundation. Giovanni Violante and
Justin Weidner have no relevant material or financial interests to
declare regarding the content of this paper.
References
Abowd, John M., and David Card. 1989. "On the Covariance
Structure of Earnings and Hours Changes." Econometrica 57, no. 2:
411-45.
Aiyagari, S. Rao. 1994. "Uninsured Idiosyncratic Risk and
Aggregate Saving." Quarterly Journal of Economics 109, no. 3:
659-84.
Alvarez, Fernando, Luigi Guiso, and Francesco Lippi. 2012.
"Durable Consumption and Asset Management with Transaction and
Observation Costs." American Economic Review 102, no. 5: 2272-300.
Angeletos, George-Marios, D. Laibson, A. Repetto, J. Tobacman, and
S. Weinberg. 2001. "The Hyperbolic Consumption Model: Calibration,
Simulation, and Empirical Evaluation." Journal of Economic
Perspectives 15, no. 3: 47-68.
Attanasio, Orazio R, and Guglielmo Weber. 1993. "Consumption
Growth, the Interest Rate and Aggregation." Review of Economic
Studies 60, no. 3: 631-49.
Baker, Scott R. 2013. "Debt and the Consumption Response to
Household Income Shocks." Mimeo. Economics Department, Stanford
University.
Banks, James, and Sarah Tanner. 2002. "Household Portfolios in
the United Kingdom." In Household Portfolios, ed. by Luigi Guiso,
Michael Haliassos, and Tullio Jappelli. MIT Press.
Blundell, Richard, Luigi Pistaferri, and Ian Preston. 2008.
"Consumption Inequality and Partial Insurance." American
Economic Review 98, no. 5: 1887-1921.
Blundell, Richard, Luigi Pistaferri, and Itay Saporta-Eksten. 2014.
"Consumption Inequality and Family Labor Supply." Working
Paper no. 1656, European Central Bank, Frankfurt am Main.
Broda, Christian, and Jonathan Parker. 2014. "The Economic
Stimulus Payments of 2008 and the Aggregate Demand for
Consumption." Mimeo, Massachusetts Institute of Technology,
Cambridge, Mass.
Browning, Martin, and Thomas F. Crossley. 2001. "The
Life-Cycle Model of Consumption and Saving." Journal of Economic
Perspectives 15, no. 3: 3-22.
Campbell, John Y., and N. Gregory Mankiw. 1989. "Consumption,
Income and Interest Rates: Reinterpreting the Time Series
Evidence." In NBER Macroeconomics Annual 1989, volume 4, pp.
185-246. Cambridge, Mass.: National Bureau of Economic Research.
--. 1990. "Permanent Income, Current Income, and
Consumption." Journal
of Business & Economic Statistics 8, no. 3: 265-79.
--. 1991. "The Response of Consumption to Income: A
Cross-Country Investigation." European Economic Review 35, no. 4:
723-56.
Carroll, Christopher D. 1997. "Buffer-Stock Saving and the
Life Cycle/Permanent Income Hypothesis." Quarterly Journal of
Economics 112, no. 1: 1-55.
Carroll, Christopher D., Jiri Slacalek, and Kiichi Tokuoka. 2014a.
"Digestible Microfoundations: Buffer Stock Saving in a
Krusell-Smith World." Mimeo, Johns Hopkins University, Baltimore,
Md.
--. 2014b. "The Distribution of Wealth and the Marginal
Propensity to Consume." Mimeo, Johns Hopkins University, Baltimore,
Md.
Chetty, Raj, and Adam Szeidl. 2007. "Consumption Commitments
and Risk Preferences." Quarterly Journal of Economics 122, no. 2:
831-77.
Cloyne, James, and Paolo Surico. 2013. "Household Debt and the
Dynamic Effects of Income Tax Changes." Discussion Paper no. 9649,
Centre for Economic Policy Research, London.
Cochrane, John H. 1989. "The Sensitivity of Tests of the
Intertemporal Allocation of Consumption to Near-Rational
Alternatives." American Economic Review 79, no. 3:319-37.
Deaton, Angus. 1991. "Saving and Liquidity Constraints."
Econometrica 59, no. 5: 1221-48.
Eggertsson, Gauti B., and Paul Krugman. 2012. "Debt,
Deleveraging, and the Liquidity Trap: A Fisher-Minsky-Koo
Approach." Quarterly Journal of Economics 127, no. 3: 1469-1513.
Eymann, Angelika, and Axel Borsch-Supan. 2002. "Household
Portfolios in Germany." In Household Portfolios, ed. by Luigi
Guiso, Michael Haliassos, and Tullio Jappelli. MIT Press.
Foster, Kevin, Scott Schuh, and Hanbing Zhang. 2013. "The 2010
Survey of Consumer Payment Choice." Research Data Report no. 13-2,
Federal Reserve Bank of Boston.
Friedman, Milton. 1957. A Theory of the Consumption Function.
Oxford and 1BH Publishing Company.
Gali, Jordi, J. David Lopez-Salido, and Javier Valles. 2007.
"Understanding the Effects of Government Spending on
Consumption." Journal of the European Economic Association 5, no.
1: 227-70.
Guiso, Luigi, Michael Haliassos, and Tullio Jappelli. 2002.
Household Portfolios. MIT Press.
Hall, Robert E. 1978. "Stochastic Implications of the Life
Cycle-Permanent Income Hypothesis: Theory and Evidence." Journal of
Political Economy 86, no. 6: 971-87.
Huggett, Mark. 1996. "Wealth Distribution in Life-Cycle
Economies." Journal of Monetary Economics 38, no. 3: 469-94.
Huntley, Jonathan, and Valentina Michelangeli. 2014. "Can Tax
Rebates Stimulate Spending in a Life-Cycle Model?" American
Economic Journal: Macroeconomics 6, no. 1: 162-89.
Jappelli, Tullio, and Luigi Pistaferri. 2010. "The Consumption
Response to Income Changes." Working Paper no. 15739, National
Bureau of Economic Research, Cambridge, Mass.
Johnson, David, Jonathan Parker, and Nicholas Souleles. 2006.
"Household Expenditure and the Income Tax Rebates of 2001."
American Economic Review 96, no. 5: 1589-1610.
Justiniano, Alejandro, Giorgio E. Primiceri, and Andrea Tambalotti.
2013. "Household Leveraging and Deleveraging." Working Paper
no. 18941, National Bureau of Economic Research, Cambridge, Mass.
Kaplan, Greg, and Giovanni Violante. 2010. "How Much
Consumption Insurance Beyond Self-Insurance?" American Economic
Journal: Macroeconomics 2, no. 4: 53-87.
--. 2014a. "A Model of the Consumption Response to Fiscal
Stimulus Payments." Econometrica, forthcoming.
--. 2014b. "A Tale of Two Stimulus Payments: 2001 vs.
2008." American Economic Review (Papers and Proceedings) 104, no.
5.
Krusell, Per, and Anthony Smith. 1996. "Rules of Thumb in
Macroeconomic Equilibrium: A Quantitative Analysis." Journal of
Economic Dynamics and Control 20, no. 4: 527-58.
--. 1997. "Income and Wealth Heterogeneity, Portfolio Choice,
and Equilibrium Asset Returns." Macroeconomic Dynamics 1, no. 2:
387-422.
--. 1998. "Income and Wealth Heterogeneity in the
Macroeconomy." Journal of Political Economy 106, no. 5: 867-96.
Laibson, David, Andrea Repetto, and Jeremy Tobacman. 2003. "A
Debt Puzzle." In Knowledge, Information, and Expectations in Modern
Economics: In Honor of EdmundS. Phelps, edited by Philippe Aghion, Roman
Frydman, Joseph Stiglitz, and Michael Woodford, pp. 228-66. Princeton
University Press.
Ludvigson, Sydney C., and Alexander Michaelides. 2001. "Does
Buffer-Stock Saving Explain the Smoothness and Excess Sensitivity of
Consumption?" American Economic Review 91, no. 3: 631-47.
Lusardi, Annamaria, Daniel Schneider, and Peter Tufano. 2011.
"Financially Fragile Households: Evidence and Implications."
Brookings Papers on Economic Activity 42, no. 1: 83-150.
MaCurdy, Thomas E. 1982. "The Use of Time Series Processes to
Model the Error Structure of Earnings in a Longitudinal Data
Analysis." Journal of Econometrics 18, no. 1: 83-114.
Misra, Kanishka, and Paolo Surico. 2013. "Consumption, Income
Changes and Heterogeneity: Evidence from Two Fiscal Stimulus
Programmes." Discussion Paper no. 9530, Centre for Economic Policy
Research, London.
Organization for Economic Cooperation and Development (OECD). 2013.
"Gross Pension Replacement Rates." In Pensions at a Glance
2013: OECD and G20 Indicators. Paris.
Parker, Jonathan A. 1999. "The Reaction of Household
Consumption to Predictable Changes in Social Security Taxes."
American Economic Review 89, no. 4: 959-73.
Parker, Jonathan A., Nicholas S. Souleles, David S. Johnson, and
Robert McClelland. 2013. "Consumer Spending and the Economic
Stimulus Payments of 2008." American Economic Review 103, no. 6:
2530-53.
Pence, Karen. 2011. "Comment on 'The Financially Fragile
Households: Evidence and Implications,'" Brookings Papers on
Economic Activity 42, no. 1: 141-50.
Rios-Rull, Jose-Victor. 1995. "Models with Heterogeneous
Agents." In Frontiers of Business Cycles Research, edited by Thomas
F. Cooley, pp. 98-125. Princeton University Press.
Shapiro, Matthew, and Joel Slemrod. 2003a. "Consumer Response
to Tax Rebates." American Economic Review 93, no. 1: 381-96.
--. 2003b. "Did the 2001 Tax Rebate Stimulate Spending?
Evidence from Taxpayer Surveys." Tax Policy and the Economy 17:
83-109.
--. 2009. "Did the 2008 Tax Rebates Stimulate Spending?"
American Economic Review 99, no. 2: 374-79.
Shore, Stephen H., and Todd Sinai. 2010. "Commitment, Risk,
and Consumption: Do Birds of a Feather Have Bigger Nests?" Review
of Economics and Statistics 92, no. 2: 408-24.
Souleles, Nicholas. 1999. "The Response of Household
Consumption to Income Tax Refunds." American Economic Review 89,
no. 4: 947-58.
Telyukova, Irina A. 2013. "Household Need for Liquidity and
the Credit Card Debt Puzzle." Review of Economic Studies 80, no. 3:
1148-77.
Vandone, Daniela. 2009. Consumer Credit in Europe: Risks and
Opportunities of a Dynamic Industry. Berlin and Heidelberg: Springer
Verlag.
Zhang, C. Yiwei. 2014. "Monthly Budgeting Heuristics: Evidence
from 'Extra' Paychecks." Mimeo, University of
Pennsylvania, Philadelphia.
Comments and discussion
COMMENT BY
MARK AGUIAR
This paper by Greg Kaplan, Giovanni Violante, and Justin Weidner
tackles a classic question: What is the marginal propensity to consume?
At least since Keynes, the marginal propensity to consume has been an
object of interest in macroeconomics. One reason it has remained so
prominent is the important role it plays in stabilization policy.
Policies designed to boost household demand through transfers or tax
cuts are intermediated through households' consumption-savings
decisions. An important consideration in enhancing the
cost-effectiveness of these policies, therefore, is targeting households
with a high marginal propensity to consume. The conventional wisdom is
that relatively wealthy households, as a rule, have a low marginal
propensity. The paper argues that this wisdom is false.
The modern theory of consumption builds on the permanent income
theory of Milton Friedman (and its close cousin, the life-cycle model of
Franco Modigliani). One key implication of the model is that consumers
smooth transitory fluctuations in their income, saving part of windfalls
and then borrowing in times of scarcity. This theory works well for
large fluctuations in income. For example, Chang-Tai Hsieh (2003)
documents that consumers in Alaska smooth the large, anticipated
payments from Alaska's Permanent Fund. Similarly, Spanish workers
who receive anticipated, periodic bonuses also smooth these large income
fluctuations (Browning and Collado 2001).
The relevant question for realistic policy, however, is how
consumers respond to small fluctuations, such as the one-off tax rebates
of $500 to $ 1,000 that were paid out in the last two recessions. The
permanent income theory suggests that these should have a minimal impact
on aggregate demand, since recipients will save a large fraction of the
checks, but household surveys (Kaplan and Violante, forthcoming) suggest
that consumers spend a fairly large fraction of these transfers
(spending roughly 25 percent of them on nondurables in the quarter of
receipt). A common critique of the permanent income model is that
households cannot borrow easily, and those agents that are
credit-constrained have a higher marginal propensity to consume. If
policies are to have a widespread impact on aggregate demand, this
requires that relatively wealthy households behave as if they were
credit-constrained. The paper makes the case that this is true for a
significant fraction of wealthy households.
Why might wealthy households behave as if they were
credit-constrained? The authors argue that much of that wealth may be
held in illiquid assets, primarily housing and tax-protected pension
accounts, which yield a relatively high return. But wealthy households
do hold liquid wealth as well. To sort this out, it is useful to
consider a simple two-period consumption problem. Suppose agents live
for three periods, t = 0, 1, and 2. In period 0, they start with wealth
x and have the option to invest in an illiquid asset a with two-period
return [R.sup.a] > 1 and cash m with gross return 1. They can save
between periods 1 and 2 in the liquid asset only (one could relax this
by allowing deposits into the illiquid asset, as long as the one-period
return is less than the two-period return). In period 1, they can also
borrow at a gross rate [R.sup.b] > [R.sup.a]. Let b denote the amount
borrowed in period 1. Income is [y.sub.1] and [y.sub.2] in periods 1 and
2, respectively, which is known at time 0. The agent's period 0
problem is:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
subject to
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The second-to-last line represents the fact that agents can only
borrow using b, and cannot save at [R.sup.b]. The last line is the
borrowing constraint.
[FIGURE 1 OMITTED]
This problem is depicted graphically using a Fisher diagram in
figure 1. The vertical axis measures [c.sub.2] and the horizontal axis
[c.sub.1]. The curved line represents an indifference curve between
consumption in period 1 and consumption in period 2. The straight line
is the inter-temporal terms of trade priced by the illiquid asset. The
figure assumes that a and m are interior. The relevant terms-of-trade at
time zero is then [R.sup.a], which is the slope of the straight line
tangent to the indifference curve. The agent plans to consume at the
point of tangency, using cash and period 1 income for [c.sup.1], and
period 2 income and [R.sup.a]a for [c.sub.2]. The optimality conditions
imply that m' = 0 in this case. There is no reason to shift
consumption from period 1 to 2 at a gross return of 1 when the illiquid
asset pays more. The inferiority of m and a also implies b = 0. There is
no reason to borrow at [R.sup.b] > [R.sup.a] in period 1. This is
dominated by investing less in the illiquid asset and holding cash.
The piecewise linear line is the budget set from the perspective of
period 1. Of course, the period 0 plan is feasible in period 1, and so
the budget set includes the planned [c.sub.1] and [c.sub.2].
However, if the consumer altered the plan in period 1 and were to
reduce [c.sub.1] and save, he does so at the gross return 1 <
[R.sup.a]. This is the shallow line extending to the left of the optimal
allocation. If the consumer were to increase [c.sub.1], he must do so by
borrowing at [R.sup.b]. This is the steeper line extending to the right.
There is a limit to borrowing, which is the vertical segment of the
period 1 budget set.
The important point is that the agent is at a kink in his budget
set in period 1. A small (unexpected) transfer in period 1 will be
consumed. Conversely, a small, unexpected decrease in [y.sub.1] will be
taken entirely out of period 1 consumption. Even though this agent may
have a fair amount of assets in period 1 (both cash and illiquid
assets), he will nevertheless have a large marginal propensity to
consume. Specifically, [c.sub.1] = [y.sub.1] + m in period 1, which is
what the measure used by Greg Kaplan, Giovanni Violante, and Justin
Weidner approximates.
In this simple example, the consumer optimally places himself at a
point of (ex post) high marginal propensity to consume, and will operate
in a hand-to-mouth manner for subsequent small changes in disposable
income. While this is intuitively correct, it is also a highly stylized
environment. A large omission in the example is risk; perhaps agents
will hold excess liquidity and therefore will not find themselves forced
to operate hand-to-mouth for small changes in income. The relevant
empirical question is whether agents do find themselves constrained in
this way. The authors answer this question quite convincingly. They find
that the majority (roughly two-thirds) of hand-to-mouth consumers are
relatively wealthy.
If a policymaker wished to target transfers to households with a
relatively high marginal propensity to consume, the data offer only
limited guidance. The poor are clearly prone to be hand-to-mouth, which
accords with the traditional view. On the other hand, the wealthy
hand-to-mouth look similar to agents with low marginal propensity to
consume along many dimensions, such as number of children, presence of
unemployed within the household, median income, marital status, and the
fraction of income from government benefits. An interesting fact
revealed by the paper is that older consumers are not disproportionately
hand-to-mouth. This suggests that at least a significant fraction of
households save enough that they are not forced into living
hand-to-mouth at the time of retirement.
One distinguishing characteristic that does jump out is the
loan-to-value ratio in housing. Households with a loan-to-value ratio
above one are disproportionately hand-to-mouth. This is intuitive in the
sense that households that are committing a large fraction of disposable
income to servicing a mortgage will likely be hand-to-mouth. In terms of
policy, this suggests that a temporary suspension or reduction in
mortgage payments may have a relatively large impact on expenditures,
although the political and legal feasibility of such a policy is
questionable.
Another policy suggested by the analysis is to let households tap
into illiquid wealth during recessions. For example, reducing or
removing penalties for early withdrawal from tax-sheltered retirement
accounts may allow the hand-to-mouth to increase spending. In this case,
the constrained agents would self-identify, so there need be no concern
over accidentally targeting agents with low marginal propensity to
consume. However, such a policy must be placed in the context of why
retirement accounts are tax preferred in the first place. One motivation
frequently put forth is that households have self-control problems and
desire a commitment mechanism to force savings. Allowing early
withdrawals could then raise the temptation to overspend, leaving
retirees without sufficient resources.
In fact, the kink in the budget set in figure 1 can also be
motivated by a kink in inter-temporal preferences, as in the
quasi-hyperbolic consumers described by David Laibson (1997). For
example, suppose at period 0 consumers discount between period 1 and 2
at the rate 1/[R.sup.a]. However, the period 1 consumer discounts
between t = 1 and 2 at the rate 1 < 1/[R.sup.a]. This leads to a
desire to invest in the illiquid asset at time 0 not because of the
higher return, but to prevent the period 1 "self" from
overconsuming. Both models lead to hand-to-mouth behavior in period 1,
but with different welfare implications for policy. Given that the
self-control paradigm plausibly suggests markedly different consumption
and savings patterns around retirement, I am inclined to agree with the
authors that illiquidity is attractive due to the high returns (whether
the enjoyment of housing services from home ownership or the reduction
in tax burden from tax-deferred accounts) rather than primarily as a
commitment device. Nevertheless, even if we subscribe to the
self-control view, there seems to be a case for a cyclical adjustment to
the liquid-illiquid portfolio mix that favors allowing some early
withdrawals in a downturn.
To sum up, this paper argues convincingly that a large fraction of
households both are wealthy and have a high marginal propensity to
consume. This is a striking fact, and one that is important to guide
policies that expand beyond traditional insurance payments. It also
opens the door for creative policies that allow the hand-to-mouth
consumers to self-select into higher consumption by temporarily opening
access to illiquid assets.
REFERENCES FOR THE AGUIAR COMMENT
Browning, Martin, and M. Dolores Collado. 2001. "The Response
of Expenditures to Anticipated Income Changes: Panel Data
Estimates." American Economic Review 91, no. 3: 681-92.
Hsieh, Chang-Tai. 2003. "Do Consumers React to Anticipated
Income Changes? Evidence from the Alaska Permanent Fund." American
Economic Review 93, no. 1: 397-405.
Kaplin, Greg, and Giovanni Violante. (Forthcoming). "A Model
of the Consumption Response to the Fiscal Stimulus Payments."
Econometrica.
Laibson, David. 1997. "Golden Eggs and Hyperbolic
Discounting." Quarterly Journal of Economics 112, no. 2: 443-77.
COMMENT BY
KAREN PENCE (1)
An extraordinary share of households in the United States and,
indeed, in many other advanced economies have very little liquid wealth
beyond that necessary to cover day-to-day expenses. The authors of this
paper, Greg Kaplan, Giovanni Violante, and Justin Weidner, document this
fact by calculating the share of households whose liquid wealth--defined
as checking accounts, savings accounts, money market accounts, mutual
funds, stocks, and bonds, minus credit card debt--is quite low relative
to their monthly incomes. About 30 percent of households in the United
States, Canada, the United Kingdom, and Germany, and around 20 percent
in Australia, France, Italy, and Spain, have low liquid wealth by this
measure.
Perhaps even more surprisingly, around two-thirds of these
liquidity-poor or "hand-to-mouth" households have assets. The
authors term such households "wealthy hand-to-mouth" (or
wealthy HtM). About half of these wealthy HtM households have both home
equity and retirement accounts; the other half generally have one or the
other. The high prevalence of wealthy HtM households is quite consistent
across countries, although differences in public pension systems across
countries affect the share with retirement accounts.
The authors suggest that characterizing households by both their
wealth and their liquidity might yield a richer and more complete
understanding of consumption dynamics. Using data from the Panel Study
of Income Dynamics, they show that wealthy HtM households have a high
marginal propensity to consume from transitory income shocks. This
marginal propensity, in fact, is somewhat higher than that of
hand-to-mouth households that are without wealth (labeled "poor
HtM"), and it is considerably higher than that of households with
both liquidity and wealth (labeled "not hand-to-mouth," or
non-HtM).
The authors also compare the predicted consumption response to an
unexpected, one-time lump-sum transfer from three different models:
their preferred model, which characterizes households by both liquidity
and wealth; a model that characterizes households based only on their
wealth; and a model that considers only liquidity. Relative to a model
that considers only wealth, their preferred model suggests a much larger
consumption response to such transfers, as many households with wealth
have little liquidity. Relative to a model that considers only
liquidity, their preferred model suggests a smaller consumption
response, at least in the case when the transfer payment is large. In
such a situation, the wealthy HtM households will prefer to invest some
of the payments in their illiquid assets rather than consume them.
To my mind, this paper convincingly demonstrates the existence and
empirical importance of the wealthy HtM households. I might quibble with
a couple of details regarding the authors' definition of wealthy
HtM--for example, I would subtract required minimum credit card payments
rather than credit card debt from the liquid asset measure, and I would
count savings bonds as a liquid rather than an illiquid asset. I also
wonder how the authors' findings might change if they included
self-employed households in the sample. However, the authors subject
their results to an exhaustive battery of robustness tests, and I have
no doubt that their conclusions and their data work are correct.
The more interesting questions center on why these wealthy HtM
households exist in the first place. The authors sketch out a model in
which households have the option to invest in either liquid or illiquid
assets. Liquid assets are available for consumption in all periods.
Illiquid assets pay higher returns, but cannot be tapped for consumption
purposes in some periods (or, if so, only at a high fixed cost).
Households that are willing to tolerate larger fluctuations in their
consumption are more likely to invest in illiquid assets. Households
with a flatter income path are also more interested in illiquid assets,
since they place greater value on higher consumption in the future.
Broadly speaking, I can think of three sets of reasons why a strong
correlation might exist between wealthy HtM status and high marginal
propensities to consume. First, along the lines of the authors'
model, households might have chosen this portfolio and consumption
bundle. An easy case to understand is that of a first-time home
purchase. A first-home purchase may require a significant upfront down
payment or other expense; a household might prefer to make that
investment and curtail its consumption in the short run in order to
access the housing consumption services that come with homeownership.
Starting a small business might be another such example.
Second, a household might have experienced a large shock, such as
job loss or a health emergency, and spent down its liquid financial
resources in order to address the shock. In such a case, the
relationship between wealthy HtM status and high marginal propensity to
consume might stem from the underlying shock rather than the
household's portfolio. A third explanation might be that both the
wealthy HtM status and the high marginal propensity to consume stem from
an underlying characteristic of the household, such as impatience, a
lack of financial literacy, or an inability to plan.
All three cases can likely be encompassed in the authors'
model with some extensions and a rich-enough parameterization; indeed, a
more detailed model presented by Greg Kaplan and Giovanni Violante
(2014) allows for shocks. However, assuming that volatile consumption is
considered undesirable, the policy implications are different. In the
first case, the household is on its optimal consumption path, and there
is no rationale for policy intervention, except perhaps to reduce the
fixed upfront costs of some types of investments. In the second case,
policy attention should focus on ameliorating the underlying shocks
rather than addressing the household's portfolio. The third case
suggests drawing on some of the lessons of behavioral economics to
encourage households to make better saving and spending decisions.
The authors present some characteristics of poor HtM, wealthy HtM,
and non-HtM households that provide some clues as to which explanation
best describes wealthy HtM status. Not surprisingly, the biggest
correlates of wealth--income, education, and age--increase monotonically
as one moves from the poor HtM to the wealthy HtM to non-HtM groups,
which suggests that the authors have identified three distinct groups.
The age-income profile is about the same for both wealthy HtM and
non-HtM households, and it is substantially steeper than the profile for
poor HtM households. This fact is a bit of a challenge for the
"optimal portfolio" explanation, since the authors' model
predicts that wealthy HtM households will have flatter income paths. The
fraction of households with at least one unemployed member is elevated
for wealthy HtM households, with heads older than 45, which I take as
evidence of the "shocks" explanation.
To enrich this picture further, I used the 2007 and 2010 Survey of
Consumer Finances data to relate each household's hand-to-mouth
status to some additional variables: whether the household purchased its
first home in the previous 2 or 3 years; has ever declared bankruptcy;
has a short-term horizon (several months or less) for financial
planning; is unwilling to take any risk with investments; and considers
itself unlucky in financial affairs. (2) I estimate these relationships
with regressions in which non-HtM household is the omitted category for
the variable that describes the household's HtM status. The
regressions include controls for age, education, and income. The
standard errors take into account the five replicates provided for each
Survey of Consumer Finances observation in order to measure the
uncertainty associated with the imputations.
My table 1 shows the results. Wealthy HtM households are 3
percentage points more likely than non-HtM households to have become
first-time homebuyers in the previous few years; in contrast, poor HtM
households are quite unlikely to be first-time homebuyers (since, by
definition, they do not have positive home equity). This finding is
consistent with the "optimal portfolio" explanation for
wealthy HtM households. However, since first-time homebuyers represent
only 6.5 percent of wealthy HtM households, other factors must also be
at play for this group.
Wealthy HtM households appear to have experienced more negative
financial shocks than non-HtM households. Both wealthy and poor HtM
households are around 7 percentage points more likely than non-HtM
households to have ever declared bankruptcy. Both types of households
are also more likely than non-HtM households to describe themselves as
"unlucky" in their financial affairs, although poor HtM
households perceive themselves to be particularly unlucky. These
findings are consistent with the "shocks" explanation.
Finally, both wealthy and poor HtM households are around 13
percentage points more likely than non-HtM households to consider the
"next few months" as the most salient time period for planning
their household saving and spending decisions. This finding seems
inconsistent with the "optimal portfolio" explanation, for
under that model wealthy HtM households are willing to forgo consumption
in the short run in order to access higher returns in the longer run.
However, based on another possible gauge of time horizon--whether
households are willing to take any risks with their investments--the
groups line up a bit better. Although both wealthy and poor HtM
households are less willing than non-HtM households to take any risk
with their financial investments, wealthy HtM households appear a bit
more willing than poor HtM households to take on some risk.
On net, these findings and the authors' results suggest to me
that negative shocks or the underlying characteristics of the wealthy
HtM households are a more likely explanation for their high marginal
propensities to consume, rather than these propensities being the
outgrowth of a deliberate portfolio choice. However, these are clearly
broad-brush findings that raise as many questions as they answer.
In some ways, that is one of the most important contributions of
this paper, which thereby furthers a dialogue between two literatures
that do not often speak much to each other. Many papers in
macroeconomics have established the fact that household consumption
responds more strongly to transitory income shocks than the canonical
life-cycle model would suggest. Many papers in the household finance
literature have established that households appear to make suboptimal
decisions with their personal finances. This paper raises the prospect
of bridging these two literatures in a way that may lead to a richer
understanding of household behavior.
REFERENCES FOR THE PENCE COMMENT
Kaplan, Greg, and Giovanni Violante. 2014. "A Model of the
Consumption Response to Fiscal Stimulus Payments." Econometrica
(forthcoming).
(1.) I am grateful to my Federal Reserve colleagues Wendy Dunn,
Laura Feiveson, and Claudia Sahm for helpful conversations that shaped
my thinking about this paper.
(2.) I thank the authors for providing me with their hand-to-mouth
variables.
Table 1. Selected Characteristics of Wealthy and Poor Hand-to-Mouth
Households Relative to Non-Hand-to-Mouth Households (a)
Dependent variable Wealthy Poor
hand-to-mouth hand-to-mouth
Purchased a first home 0.03 -0.07
in the previous 2 or 3 years (0.01) (0.01)
Has ever declared bankruptcy 0.08 0.06
(0.012) (0.015)
Considers self "unlucky" 0.10 0.20
in financial affairs (0.014) (0.02)
Has a short time horizon 0.12 0.15
for financial planning (0.015) (0.019)
Is unwilling to take any 0.08 0.20
risks with investments (0.015) (0.018)
(a.) Each row represents a separate regression.
The second and third columns show the coefficients on the
wealthy HtM and poor HtM dummy variables. The regression
also includes the log of income, dummy variables for 6 age
groups, and dummy variables for four levels of educational
attainment. The regressions are estimated on pooled data
from the 2007 and 2010 Surveys of Consumer Finances.
GENERAL DISCUSSION Benjamin Friedman mildly objected to the
paper's title, specifically to the term "wealthy" as
applied to many of the households in the analysis. He pointed to the
example, from the paper, of a family with $50,000 of illiquid wealth, of
which $30,000 was their housing equity and another $15,000 was in
retirement accounts, leaving just $5,000 for all their other illiquid
wealth. Although such households are not living right on the line, it
seemed a stretch to speak of them as wealthy.
Justin Wolfers said that he was fine with the authors calling
wealthy people wealthy, but not so happy about calling them
hand-to-mouth. He noted that they estimated the marginal propensity to
consume for the wealthy hand-to-mouth at about 0.3 and for the
non-hand-to-mouth at about 0.12, and wondered why the first number was
not actually 1.0 if their situation was truly one of living
hand-to-mouth.
Gregory Mankiw thought one of the asset categories that the authors
describe as liquid might better be described as illiquid, namely direct
ownership of stock. Since selling stock that has significant unrealized
capital gains involves sizable tax costs, it is like taking money out of
a 401 (k) and paying the penalties. He also noted that when the authors
recalculated to include direct stock as illiquid assets it raised the
number of hand-to-mouth households by about 10 percent, which is
substantial.
Susan Collins suggested another way to think about wealthy
hand-to-mouth households. When people anticipate that their income is
likely to grow over time, many will act on the advice that it is best to
invest in as much house as they can "now." Initially, they
will be overinvested in the house and therefore acting hand-to-mouth,
spending all their income, but over time they will transition out of
that stage, even though they remain living in the same house. And that
might explain the observed pattern of people transitioning in and out of
hand-to-mouth status. But other people will not be so lucky--perhaps
their income does not grow--and so they become stuck.
Christopher Carroll said he was impressed by the paper. Commenting
on the modeling it employed, he argued that it is nearly impossible to
construct a quantitative model that uniquely maps from observable
variables to how people are distributed across categories of wealth,
income, and liquid and illiquid assets, because there are very important
kinds of heterogeneity involved that are unmeasured in current data
sources. This heterogeneity can be in expected growth rates in income,
for example, or in beliefs about future rates of return. People who
believe they are going to get a high rate of return on their house are
the ones who theory says should behave in a hand-to-mouth way with
respect to nonhousing assets. Once one permits heterogeneity--even
limiting it to the simplest kind, which is heterogeneity in time
preference rates--standard models already generate a substantial amount
of heterogeneity in marginal propensities to consume. So working out how
to get the distributions right seems to be the next thing that ought to
be tackled. Carroll added that the best measure of a household's
position might be a ratio of liquid assets to permanent income (as
defined by Friedman in 1957).
Katharine Abraham was puzzled by the apparently very high rates of
wealthy households transitioning out of hand-to-mouth status. While the
discussion about these groups often focuses on their investments in
illiquid assets, she noted that they might also have a lot of
flexibility to adjust on the margin of what they are spending. They
might decide to adjust by cutting back on discretionary consumption
expenditures, which allows them to move back out of the hand-to-mouth
state.
William Brainard too remarked on what seemed like a serious problem
with the authors' transition matrix. He added that the rate of
those living non-hand-to-mouth--measured by the authors as 70
percent--seemed much too high. He had done his own tabulation of the SCF
data and found that the fraction of people with no liquid assets and no
debt is roughly half the survey sample, so the authors' criteria,
which make it 70 percent, are somehow putting way too many people into
the "non" category.
He found discussant Mark Aguiar's comment more persuasive,
specifically Aguiar's fissure diagram, which showed a cutoff point
at no borrowing and no lending. This means that choices depend on
whether returns for illiquid assets are high or low. What is key is that
the cost of borrowing is higher than the returns from any investment
people are making, so a large number of people will be piled up at the
zero borrowing/zero lending point, despite heterogeneous preferences,
and they will no longer be on an Euler equation.
It also struck Brainard that the authors' transition rates for
wealthy people moving out of hand-to-mouth status were implausible. In
his own work he has found many people earning close to $200,000 a year
who seemed to have always been living hand-to-mouth.
Ethan Kaplan was concerned that outliers among households were
driving up the average income, skewing the distribution to the right in
the data but not in marginal propensities to consume, which are bound on
the lower end by zero. Such high-income households would have very low
marginal propensities to consume, but that would not be reflected in the
results because the distribution does not skew to the left. He thought
it would therefore be interesting to see semi-parametric plots broken
down by marginal propensity to consume, by income, for both wealthy and
non-wealthy households. He also wondered if there might have been some
differential measurement error in the two groups, potentially due to
differences in education between them, which could have attenuated the
estimated MPCs for the low wealth group.
Michael Klein raised a political issue. He observed that stimulus
efforts targeted at the wealthy hand-to-mouth might come up against the
same public resentment one sees in discussions of mortgage relief, based
on the notion that individuals who purchased homes too big for their
incomes got themselves in trouble. Whether those problems were actually
homebuyers' own fault or not, the government response will stir up
a lot of political resentment, even though the wealthy hand-to-mouth
population the authors examine is not "wealthy" in the
vernacular sense. He added that an important policy answer in stimulus
efforts is the extension of unemployment insurance, which he felt is
much more effective in achieving a high marginal propensity to consume
than trying to identify and then targeting the wealthy hand-to-mouth.
Alan Blinder was skeptical about the empirical basis of the
authors' argument, which he felt rested too heavily on the
assumption that the rate of return on illiquid assets was substantially
higher than on liquid assets, especially once they are risk-adjusted.
Noting that most of the illiquid investment in the authors' data is
in housing, he reminded everyone of Robert Shiller's view that
housing is not a particularly stellar long-term investment. He also had
a question for the authors: What was their rationale for excluding
capital income, such as interest in dividends, given that such an
approach selects for people who have previously saved versus those who
have not.
Responding to Blinder's remarks, Kaplan pointed out that they
did try to measure all the returns on housing, and found that the vast
majority of them came from an imputed service flow. Violante added that
the calibrated financial return on housing was about 2 percent per year.
Blinder then raised an additional point that Mark Aguilar's
comment had led him to consider, namely that hyperbolic discounting
might provide an alternative hypothesis that would lead to the same
outcomes. He mentioned a recent lecture by David Laibson summarizing
experiments in which actual money was at stake and which demonstrated
that people are willing to pay gigantic amounts to constrain themselves,
to create illiquidity in their portfolios rather than liquidity, because
they are dealing with the challenge of self-control.
William Brainard spoke up a second time to point out that the
definition of when one is liquidity-constrained comes from the
Baumol-Tobin within-payment-period calculation, and that is not going to
be an iron rule. In fact, it is easier to have a model with a small
buffer stop, even for people in this general category. Although the
authors report that most of their results do not depend on moving that
boundary, he said he is not convinced by the number they arrive at and
would like to know whether the persistence is very sensitive to size. He
also noted that it takes time and cost to convert illiquid assets into
liquid assets, so restoring one's buffer stock with assets that one
originally expected to hold for the future is a consideration.
David Romer noted first that he found the paper very impressive,
echoing discussant Karen Pence's comment that the amount of data
and work invested in it were remarkable. Nevertheless, he found himself
less than fully convinced, for two reasons. First, he felt it is much
too difficult to identify what margins people have simply by looking at
their portfolios. He offered some introspective examples of how people
judge margins in ways that might not have been picked up by the
authors' model: people will hold a bill in their desk drawer for a
month if it does not have a penalty on it, or they will borrow from a
supply of cash in their child's piggy bank or from their parents,
or they will run up their credit card debt and pay it off at the end of
the month.
The second reason he was not completely convinced, he said, was
that the paper viewed everything through the lens of beautiful
intra-temporal optimization--but that is not how people behave in the
real world. People follow rules of thumb. As Pence put it in her
comment, people do very stupid things. In short, while Romer believed
there were wealthy hand-to-mouth people, he was not sure this model was
the right one, and his suggestion for the authors' next paper on
the subject was for them to talk to regular people, something that
Annamaria Lusardi and her coauthors have done. Specifically, one could
find people whose profiles matched the model and ask them something
like, "If you got hit with an extra expense of $500, what would you
actually do?" And then one could follow up with the question,
"What if you got a windfall of $500--what would you do?" He
suspected there are many people who would find a way to deal with a $500
cost without too much trouble but would still spend all of the $500 on
something fun because they follow the rules of thumb.
Giovanni Violante responded first to discussant Mark Aguiar's
point about the hyperbolic discount. He noted that the paper employs a
model based on rational and consistent behavior, and observed portfolios
do bear this out. But he and his coauthors did not exclude the
possibility of other reasons leading to the same portfolio
configuration. Considering hyperbolic discounting would actually make it
easier to obtain wealthy hand-to-mouth agents in the model, because
illiquidity clearly protects hyperbolic agents from indulging in
consumption splurges and offers an additional reason why households may
want to hold wealth in illiquid form. In response to Romer's
suggestion about exploring what people might do with an unexpected $500,
he mentioned the survey work of Matthew Shapiro and Joel Slemrod, who
already found that the fraction of people who spent their tax rebates
and fiscal stimulus payments lined up well with the estimates of David
Johnson, Jonathan Parker, and Nicholas Souleles in their studies of the
2001 tax rebate and the 2008 fiscal stimulus payment.
Turning to discussant Karen Pence's question whether the
portfolio configurations were due to choice or "luck," he
observed that the model is deterministic, and so the portfolios are
determined by choice. However, he added that the more general model that
he and Greg Kaplan developed is a stochastic life-cycle model with
income shocks, so it therefore models a combination of optimal choices
and luck. He added that if households were facing very frequent
transitory shocks, they would probably hold a lot of liquid wealth. A
more likely scenario, instead, is that they may be more worried about
rare unemployment shocks, which tend to have long-term, persistent
implications for earnings, and elect to use illiquid assets as a way to
smooth them, basically making them liquid by paying a transaction cost
when hit by the shock. Concerning a question about excluding directly
held stock from liquid wealth, he said the reason the number of poor
hand-to-mouth falls so quickly is that they get switched into the
wealthy hand-to-mouth category when stock dividends begin to rise again,
even though they remain hand-to-mouth.
Violante agreed strongly with Carroll's point that a
stochastic life-cycle model, to be accurate, requires good matching of
the joint distribution of liquid wealth, illiquid wealth and income. The
key challenge is replicating the upper tail of the wealth distribution,
and in that respect he agreed that one needs heterogeneity, such as
heterogeneity in impatience. But he also felt that the upper tail is not
as crucial to model well as the lower tail, given the issues they are
seeking to understand, and in that area he remains satisfied with the
paper's success.
Regarding the transition matrix, Violante admitted that the implied
recorded distribution from the transition did not match what he and the
coauthors had estimated, and they were still exploring the reasons. He
found Abraham's suggestion that the wealthy hand-to-mouth might
transition quickly by forgoing some of their discretionary spending to
be a worthwhile hypothesis.
Referring to Brainard's doubts that the paper's estimate
of the portion of the population living hand-to-mouth as 30 percent was
high enough, Violante clarified that this was only a lower bound. In his
view, even if 50 percent of households are at a kink in the budget
constraint, the vast majority of them could still smooth their
consumption in the face of a large shock by liquidating their illiquid
wealth in some manner or another.
GREG KAPLAN
Princeton University
GIOVANNI L. VIOLANTE
New York University
JUSTIN WEIDNER
Princeton University
(1.) Some notable examples of micro-level evidence on excess
sensitivity are Parker (1999), Souleles (1999), Shapiro and Slemrod
(2003a, 2003b, 2009), Johnson, Parker, and Souleles (2006), Parker and
others (2013), and Broda and Parker (2014). See Jappelli and Pistaferri
(2010) for a recent survey. Campbell and Mankiw (1989, 1990, 1991)
provide evidence based on macroeconomic time-series.
(2.) See, again, Campbell and Mankiw (1989, 1990, 1991), but also
Attanasio and Weber (1993), and Ludvigson and Michaelides (2001).
(3.) Equation 7 reveals that the model is homothetic in [y.sub.1],
[y.sub.2], and [omega]). In this sense, a high-income household is as
likely to be a wealthy HtM as a low-income one, as long as the
life-cycle slope of their income profiles is the same.
(4.) In fact, Kaplan and Violante (2014a) show that, in a richer
life-cycle version of this two-asset model with uninsurable income risk,
the average marginal propensity to consume out of transitory income
shocks is larger among wealthy HtM households than among poor HtM
households. We return to this point in section VII.
(5.) The unsecured credit limit is always a hard constraint. The
zero liquid asset position is a hard constraint for the subset of
households that do not have access to credit, and a kink for virtually
all others, since the interest rates on credit cards and other
noncollateralized loans are typically much larger than the return on
liquid assets.
(6.) The only exception to our age range was for the U.K. WAS;
since it provides ages in 5-year age bins, we include households with
heads between 20 and 79 years of age.
(7.) The reference period for the income questions differs between
surveys. For income variables in the SCF, the survey asks for annual
income in the previous year. For example, the 2010 SCF uses 2009 as its
reference period for income. The income reference period differs by
country in the HFCS; France and Germany both use 2009 as a reference
period, Spain uses 2007, and Italy uses 2010. Wave Two of the WAS
(2008-10) asks questions regarding the "usual" amounts for
monthly income and benefits. The 2005 SFS uses 2004 as its reference
period and gives its respondents the option of skipping the income
questions and using linked data from the 2004 tax return. Wave Ten of
the HILDA uses the 2009-10 financial year, which runs from July 1, 2009,
to June 30, 2010, for its reference period for income.
(8.) ISAs are accounts designed for the purpose of saving with a
favorable tax status. A broad range of asset categories, including cash,
can be held in ISAs. There are no restrictions on how much and when
funds can be withdrawn.
(9.) Average cash holdings, excluding large-value holdings in 2010,
was $138. Median checking, saving, money market, and call accounts in
the 2010 SCF was $2,500, making the ratio about 5.5 percent. In the
HFCS, information on cash holdings is available for Spain from a noncore
module. We check the median ratio of cash to sight accounts and find it
to be about 5 percent in Spain.
(10.) Superannuation has some features of private retirement
accounts, such as 401 (k) accounts in the United States, which we
include in illiquid wealth, and some features of public pensions (the
compulsory nature of a minimum contribution), which we exclude from
illiquid wealth. Because of this ambiguity, we also offer a sensitivity
analysis in which we exclude superannuation wealth from illiquid assets.
(11.) In our robustness checks with respect to business equity, we
include all households whose income is entirely from self-employment as
long as they had non-negative income from their business.
(12.) In the survey years, the compulsory minimum employer
contribution rate was 9 percent of the employee salary.
(13.) We thank Yiwei Zhang for providing us with these tabulations
based on Zhang (2014).
(14.) The choice of one month of income for the benchmark is
consistent with the SCF self-reported limits. When we set the limit for
households without credit cards to zero, the median self-reported limit
to income ratio is 0.54 in 1989. It grows steadily to 1.7 in 2007 and
then drops to 1.2 in 2010. This evolution of credit limits is even more
remarkable when conditioning only on credit card holders (around 70
percent of the population): the median limit to income ratio rises from
1.2 in 1989 to 3.4 in 2007, and then drops to 2.8 in 2010.
(15.) Net-worth HtM are always more numerous than the poor HtM
because there are some households with liquid wealth above the
threshold, who are therefore not HtM, but with enough negative illiquid
wealth (that is, negative home equity) to push their net worth below the
threshold.
(16.) These questions (numbered X7510, X7509, and X7508) were
included in the SCF survey starting from 1992.
(17.) Pence (2011) makes a similar point in her discussion of
Lusardi, Schneider, and Tufano (2011).
(18.) When we include business equity, we also include in our
sample all those households whose labor income comes entirely from
self-employment. These households are excluded from the baseline sample.
(19.) In the household finance literature, this observation is
called the credit card puzzle (Telyukova 2013).
(20.) The fraction of homeowners with HELOCs was 7.1 percent in
2001, 12.9 percent in 2007, and 10.7 percent in 2010. The average HELOC
limit in 2001 was $11,087, in 2007 it was $18,984, and in 2010 it was
$19,070. The average percent of the HELOC used was 27.5 percent in 2001,
31.0 percent in 2007, and 31.6 percent in 2010.
(21.) The variables we used in TAXSIM are year, marital status, the
number of children, and the breakdown of income into its parts (wages,
UI benefits, and so on). We deducted federal taxes from gross income. We
assumed each household files its actual marital status and claims all
children living in the household as dependents. As an upper bound, we
have also computed the case where they all file as single without
dependents.
(22.) These plots are based on pooled data from all surveys and do
not control for time or cohort effects. We verified that age profiles
are similar in both cases, but they become more noisy, so we present the
raw data.
(23.) To reduce the sensitivity to outliers, means are computed
after trimming the overall top and bottom 0.1 percent of that
statistic's distribution.
(24.) Recall, though, that the overall median net liquid wealth
across the whole population is less than $2,000 (table 2), so even among
the non-HtM there are households with small amounts of liquid wealth.
(25.) Figure D1 in the online appendix shows age-income profiles
for each country by HtM status and confirms our findings from section
IV.B. The age-income profile for wealthy HtM households is much more
similar to the profile of the non-HtM than to the profile of the poor
HtM. The only two exceptions are Italy and Spain, where the age-income
paths for all three groups are very similar.
(26.) Recall that, based on the definitions in section II, changing
the credit limit affects HtM status only for households with negative
liquid debt.
(27.) That is, for example, US$2,000 for the United States, 2,000
euros for the euro area countries, and so forth.
(28.) There are differences in this question across surveys. The
U.S. SCF and the euro area HFCS ask about the single most valuable asset
not previously mentioned. In the Australian FULDA, they ask about
collectibles. In the Canadian SFS, valuables are meant to include also
the content of the principal residence. In light of this, the result for
Canada is not surprising.
(29.) For the United States, we resort to an imputation based on
TAXSIM as explained in section 5.1.1. The U.K. and Italian surveys ask
households about their tax liabilities.
(30.) Until 1999, the Wealth Files supplemented the annual survey
every five years. Starting in 1999, these files became biannual, like
the survey itself. In 2009 and 2011, the wealth questions were enriched
further with the Housing, Mortgage Distress, and Wealth Data
Supplements.
(31.) The two main discrepancies with the SCF definitions are that
we do not attempt a cash imputation, and both CDs and saving bonds are
in liquid, instead of illiquid, wealth. Since these two saving
instruments are not common, we do not expect this discrepancy to affect
our results. For example, if we classify CDs and saving bonds as liquid
wealth in the 2010 SCF, the fraction of HtM drops by only 1 percentage
point.
(32.) We refer the reader to Kaplan and Violante (2014a, 2014b) for
a full description of the model, its calibration, and a comparison of
the predictions of the model with life-cycle data, and with the
aggregate consumption response to the 2001 and 2008 fiscal stimulus
payments as estimated by Johnson, Parker, and Souleles (2006), and
Parker and others (2013), respectively.
Table 1. Summary Information on the Survey Data Used, Sample Countries
United States Canada (a) Australia
Survey SCF SFS HILDA
years 1989-2010 2005 2010
Initial sample size 35,513 5,267 7,317
Exclusions
Not age 22-79 2,098 373 782
Negative income 9 10 0
All income from 4,334 -- 202
self-employment
Final sample size 29,072 4,884 6,333
United Germany France Italy Spain
Kingdom
Survey WAS HFCS HFCS HFCS HFCS
years 2008-10 2008-10 2008-10 2008-10 2008-10
Initial sample size 18,510 3,565 15,006 7,951 6,197
Exclusions
Not age 22-79 1,655 246 1,428 846 559
Negative income 0 0 0 0 0
All income from 334 228 890 721 658
self-employment
Final sample size 18,176 3,091 12,688 6,384 4,980
Source: Data from national and euro area survey series.
See text for full description.
(a.) Self-employment income is not provided in the SFS for Canada.
Table 2. Household Income, Liquid and Illiquid Wealth Holdings,
and Portfolio Composition, Sample Countries (a)
United States (b) Canada (c)
Fraction Fraction
Median positive Median positive
Income (age 22-59) 47,040 0.984 49,905 1.000
Net worth 56,721 0.883 112,418 0.877
Net liquid wealth 1,714 0.750 2,643 0.716
Cash, checking, saving, 2,640 0.923 2,873 0.864
MM accounts
Directly held stocks 0 0.142 0 0.109
Directly held bonds 0 0.014 0 0.106
Revolving credit card debt 0 0.382 0 0.412
Net illiquid wealth 52,000 0.761 100,713 0.752
Housing net of mortgages 29,000 0.629 64,238 0.648
Retirement accounts 1,508 0.526 871 0.518
Life insurance 0 0.186 0 0.033
Australia United Kingdom
Fraction Fraction
Median positive Median positive
Income (age 22-59) 79,555 0.993 29,340 0.979
Net worth 380,889 0.984 187,157 0.880
Net liquid wealth 12,139 0.880 2,111 0.632
Cash, checking, saving, 8,709 0.978 2,639 0.766
MM accounts
Directly held stocks 0 0.351 0 0.160
Directly held bonds 0 0.015 0 0.154
Revolving credit card debt 0 0.296 0 0.405
Net illiquid wealth 347,500 0.939 17,4999 0.843
Housing net of mortgages 250,000 0.714 81.400 0.677
Retirement accounts 61,000 0.863 58,560 0.766
Life insurance 0 0.064 0 0.110
Germany France
Fraction Fraction
Median positive Median positive
Income (age 22-59) 35,444 0.994 31,518 0.999
Net worth 46,798 0.949 108,976 0.966
Net liquid wealth 1,319 0.853 1,453 0.925
Cash, checking, saving, 1,154 0.876 1,255 0.953
MM accounts
Directly held stocks 0 0.110 0 0.151
Directly held bonds 0 0.050 0 0.015
Revolving credit card debt 0 0.225 0 0.076
Net illiquid wealth 39,306 0.876 104,214 0.922
Housing net of mortgages 0 0.476 86,372 0.607
Retirement accounts 0 0.245 0 0.039
Life insurance 0 0.493 0 0.378
Italy Spain
Fraction Fraction
Median positive Median positive
Income (age 22-59) 26,116 0.987 26,961 0.991
Net worth 165,420 0.919 178,925 0.967
Net liquid wealth 5,226 0.769 2,685 0.890
Cash, checking, saving, 4,181 0.769 2,261 0.908
MM accounts
Directly held stocks 0 0.043 0 0.106
Directly held bonds 0 0.146 0 0.014
Revolving credit card debt 0 0.049 0 0.086
Net illiquid wealth 148,524 0.803 171,161 0.885
Housing net of mortgages 148,524 0.716 162,491 0.847
Retirement accounts 0 0.088 0 0.037
Life insurance 0 0.193 0 0.245
Source: Data from national and euro area survey series.
See text for full description.
(a.) All figures are in local currency units. From the Federal
Reserve Board's G.5 release, the average exchange rates in the
survey years are 1.2 CA$, 1.1 AU$, 0.6 British pounds, and
0.7 euros per U.S. dollar.
(b.) Data for the United States are from the 2010 survey only.
(c.) Data for Canada are adjusted to 2010 Canadian dollars
using the Canadian CPI.
Table 3. Robustness Results for Fraction HtM in Each HtM Category,
United States, SCF, Pooled 1989-2010
P-HtM (i) W-HtM (i) N-HtM (i)
Baseline 0.121 0.192 0.688
In past year, c > y 0.130 0.309 0.561
Usually, c > y 0.089 0.156 0.756
Financially fragile households (a) 0.173 0.331 0.497
Reported credit limit 0.114 0.147 0.738
1-year income credit limit 0.102 0.118 0.780
Weekly pay period 0.106 0.150 0.744
Monthly pay period 0.141 0.261 0.598
Higher illiquid wealth cutoff (b) 0.131 0.181 0.688
Retirement account as liquid 0.121 0.183 0.696
for 60+ (c)
Businesses as illiquid assets (d) 0.114 0.193 0.693
Direct as illiquid assets (c) 0.120 0.217 0.663
Other valuables as illiquid assets 0.117 0.196 0.688
Excludes cc puzzle households 0.163 0.183 0.654
HELOCs as liquid debt 0.120 0.181 0.699
Usual income 0.119 0.198 0.683
Disposable income, reported (f) 0.121 0.188 0.691
Disposable income, single (f) 0.120 0.187 0.693
Committed consumption. 0.102 0.166 0.732
beginning of period (g)
Committed consumption, 0.149 0.272 0.579
end of period (h)
HtM (i) HtM-NW (i)
Baseline 0.312 0.137
In past year, c>y 0.439 --
Usually, c > y 0.244 --
Financially fragile households (a) 0.503 0.209
Reported credit limit 0.262 0.126
1-year income credit limit 0.220 0.108
Weekly pay period 0.256 0.119
Monthly pay period 0.402 0.164
Higher illiquid wealth cutoff (b) 0.312 0.137
Retirement account as liquid 0.304 0.137
for 60+ (c)
Businesses as illiquid assets (d) 0.307 0.129
Direct as illiquid assets (c) 0.337 0.137
Other valuables as illiquid assets 0.312 0.132
Excludes cc puzzle households 0.346 0.177
HELOCs as liquid debt 0.301 0.135
Usual income 0.317 0.137
Disposable income, reported (f) 0.309 0.137
Disposable income, single (f) 0.307 0.136
Committed consumption. 0.268 0.116
beginning of period (g)
Committed consumption, 0.421 0.174
end of period (h)
Source: Authors' calculations, based on U.S. SCF. See text for full
description.
(a.) Includes those households within $2,000 in liquid assets of
their income threshold as HtM.
(b.) Requires households to have above $ 1,000 in illiquid assets
to be considered W-HtM.
(c.) Puts retirement accounts into liquid wealth for households
above age 60.
(d.) Drops the self-employment income sample selection and adds
business assets to illiquid wealth and self-employment income to
income.
(e.) Classifies directly held mutual funds, stocks, corporate and
government bonds as illiquid assets.
(f.) Subtracts federal income taxes estimated from NBER's TAXSIM
from income. Disposable income (reported) assumes that each
household files its actual marital status and number of children as
dependents; disposable income (single) assumes that every household
files as single with no dependents.
(g.) Assumes the household's committed consumption is incurred at
the beginning of the period.
(h.) Assumes the household's committed consumption is incurred at
the end of the period.
(i) P-HtM = poor HtM; W-HtM = wealthy HtM; N-HtM = non-HtM;
HtM-NW = HtM based on net worth.
Table 4. Transition Matrix for the 2007-09 Panel
of the SCF (United States)
07 [right arrow] 09 P W N
P 0.548 0.127 0.326
W 0.101 0.455 0.444
N 0.055 0.129 0.816
Ergodic 0.126 0.191 0.683
Source: Authors' calculations, based on U.S. SCF.
See text for full description.
Note: Fraction of households with the row HtM status
in 2007 and the column HtM status in 2009.
The last row reports the implied ergodic distribution.
Table 5. Robustness Results for Fraction Poor HtM and Wealthy HtM,
Sample Countries
P-HtM
United
States Canada Australia
Baseline 0.138 0.121 0.027
In past year, c > y 0.157 0.181 0.020
Financially fragile households (a) 0.198 0.190 0.042
1-year income credit limit 0.116 0.090 0.024
Weekly pay period 0.119 0.105 0.022
Monthly pay period 0.165 0.149 0.033
Vehicles as illiquid assets (c) 0.060 0.081 0.012
Retirement account as liquid 0.138 0.122 0.027
for 60+ (b)
Businesses as illiquid assets (d) 0.132 0.115 0.027
Direct as illiquid assets (c) 0.137 0.120 0.027
Other valuables as illiquid assets 0.134 0.008 0.025
Excludes cc puzzle households 0.174 0.146 0.034
HELOCs as liquid debt 0.135 0.127 --
Disposable income (f) 0.137 -- --
Committed consumption, beginning 0.116 -- --
of period (g)
Committed consumption, end 0.175 -- --
of period (g)
P-HtM
United
Kingdom Germany France
Baseline 0.103 0.074 0.032
In past year, c > y 0.092 0.090 --
Financially fragile households (a) 0.139 0.110 0.070
1-year income credit limit 0.078 0.070 0.030
Weekly pay period 0.098 0.058 0.021
Monthly pay period 0.111 0.086 0.048
Vehicles as illiquid assets (c) 0.065 0.052 0.002
Retirement account as liquid 0.103 0.074 0.032
for 60+ (b)
Businesses as illiquid assets (d) 0.102 0.071 0.031
Direct as illiquid assets (c) 0.102 0.074 0.032
Other valuables as illiquid assets 0.099 0.071 --
Excludes cc puzzle households 0.124 0.078 0.032
HELOCs as liquid debt 0.103 0.074 0.032
Disposable income (f) 0.103 -- --
Committed consumption, beginning -- 0.066 0.025
of period (g)
Committed consumption, end -- 0.092 0.050
of period (g)
P-HtM
Italy Spain
Baseline 0.083 0.044
In past year, c > y 0.156 0.091
Financially fragile households (a) 0.117 0.092
1-year income credit limit 0.083 0.040
Weekly pay period 0.080 0.036
Monthly pay period 0.091 0.061
Vehicles as illiquid assets (c) 0.028 0.024
Retirement account as liquid 0.083 0.044
for 60+ (b)
Businesses as illiquid assets (d) 0.076 0.043
Direct as illiquid assets (c) 0.083 0.045
Other valuables as illiquid assets 0.034 0.044
Excludes cc puzzle households 0.086 0.046
HELOCs as liquid debt 0.083 0.044
Disposable income (f) 0.080 --
Committed consumption, beginning 0.076 0.036
of period (g)
Committed consumption, end 0.090 0.064
of period (g)
W-HtM
United
States Canada Australia
Baseline 0.202 0.182 0.165
In past year, c > y 0.327 0.409 0.189
Financially fragile households (a) 0.337 0.305 0.261
1-year income credit limit 0.130 0.098 0.117
Weekly pay period 0.155 0.147 0.116
Monthly pay period 0.273 0.247 0.231
Vehicles as illiquid assets (c) 0.281 0.223 0.180
Retirement account as liquid 0.187 0.161 0.153
for 60+ (b)
Businesses as illiquid assets (d) 0.206 0.188 0.166
Direct as illiquid assets (c) 0.220 0.215 0.195
Other valuables as illiquid assets 0.207 0.295 0.167
Excludes cc puzzle households 0.192 0.179 0.151
HELOCs as liquid debt 0.192 0.107 --
Disposable income (f) 0.200 -- --
Committed consumption, beginning 0.173 -- --
of period (g)
Committed consumption, end 0.284 -- --
of period (g)
W-HtM
United
Kingdom Germany France
Baseline 0.232 0.248 0.173
In past year, c > y 0.250 0.392 --
Financially fragile households (a) 0.363 0.523 0.585
1-year income credit limit 0.135 0.229 0.157
Weekly pay period 0.211 0.161 0.087
Monthly pay period 0.276 0.370 0.354
Vehicles as illiquid assets (c) 0.269 0.270 0.204
Retirement account as liquid 0.196 0.245 0.173
for 60+ (b)
Businesses as illiquid assets (d) 0.232 0.251 0.173
Direct as illiquid assets (c) 0.246 0.303 0.198
Other valuables as illiquid assets 0.235 0.252 --
Excludes cc puzzle households 0.247 0.236 0.166
HELOCs as liquid debt 0.154 0.238 0.166
Disposable income (f) 0.237 -- --
Committed consumption, beginning -- 0.219 0.127
of period (g)
Committed consumption, end -- 0.344 0.336
of period (g)
W-HtM
Italy Spain
Baseline 0.155 0.152
In past year, c > y 0.474 0.596
Financially fragile households (a) 0.257 0.404
1-year income credit limit 0.147 0.141
Weekly pay period 0.142 0.119
Monthly pay period 0.188 0.220
Vehicles as illiquid assets (c) 0.211 0.173
Retirement account as liquid 0.154 0.152
for 60+ (b)
Businesses as illiquid assets (d) 0.158 0.154
Direct as illiquid assets (c) 0.165 0.162
Other valuables as illiquid assets 0.204 0.153
Excludes cc puzzle households 0.157 0.148
HELOCs as liquid debt 0.147 0.140
Disposable income (f) 0.149 --
Committed consumption, beginning 0.148 0.138
of period (g)
Committed consumption, end 0.173 0.199
of period (g)
Source: Authors' calculations based on data from national and euro
area survey series. See text for full description.
(a.) Includes those households within 2,000 local currency units in
liquid assets of their income threshold as HtM.
(b.) Puts retirement accounts into liquid wealth for households
above age 60.
(c.) Vehicles as illiquid assets includes the value of other
valuables for France as the survey question combines the value of
vehicles with other valuables.
(d.) Drops the self-employment income sample selection and adds
business assets to illiquid wealth and self-employment income to
labor income.
(e.) Classifies directly held mutual funds, stocks, corporate and
government bonds as illiquid assets.
(f.) Removes taxes from gross income. Taxes for the U.S. are
estimated from NBER's TAXSIM assuming all households file as single
with no dependents.
(g.) Committed consumption, beginning (end) of period assumes
households incur consumption commitments at the beginning (end) of
the pay period.
Table 6. Marginal Propensity to Consume out of Transitory
Income Shocks for Different Types of HtM Households,
United States (a)
P-HtM W-HtM N-HtM
Baseline 0.243 *** 0.301 *** 0.127 ***
(0.065) (0.048) (0.036)
Pre-tax earnings (b) 0.131 *** 0.223 *** 0.122 ***
(0.043) (0.035) (0.027)
Include food 0.217 *** 0.264 *** 0.105 ***
stamps (c) (0.059) (0.045) (0.035)
Continuously manned 0.095 0.193 ** 0.079 *
households (d) (0.194) (0.079) (0.043)
Stable marital 0.239 *** 0.282 *** 0.110 ***
status (e) (0.085) (0.054) (0.038)
Households with 0.186 ** 0 193 *** 0.073 *
male heads (f) (0.080) (0.058) (0.040)
Monthly income (g) 0.229 *** 0.288 *** 0.159 ***
(0.068) (0.053) (0.034)
HtM-NW N-HtM-NW
Baseline 0 229 *** 0.201 ***
(0.054) (0.030)
Pre-tax earnings (b) 0.143 *** 0.164 ***
(0.036) (0.023)
Include food 0.203 *** 0 171 ***
stamps (c) (0.050) (0.029)
Continuously manned -0.048 0.157 ***
households (d) (0.129) (0.042)
Stable marital 0.190 *** 0.195 ***
status (e) (0.070) (0.033)
Households with 0.150 ** 0.129 ***
male heads (f) (0.064) (0.035)
Monthly income (g) 0.236 *** 0 199 ***
(0.057) (0.030)
Source: Authors' calculations, based on United States PSID.
See text for full description.
(a.) Boot-strapped standard errors based on 250 replications
in parentheses. Statistical significance indicated
at the *** 1 percent; ** 5 percent; and
* 10 percent levels.
(b.) Transfers are excluded.
(c.) Food stamps are included among transfers.
(d.) Restricted to continuously married households.
(e) Restricted to households with no change in marital status.
(f.) Households with female heads (mostly single) are excluded.
(g.) Pay period is set to one month instead of two weeks.
Table 7. Quarterly Marginal Propensity to Consume out of an
Unexpected Transfer for the Aggregate Economy, Following
Three Models, United States (a)
SIM-2 (b)
P-HtM W-HtM N-HtM
Average 0.35 0.44 0.06
Low income 0.34 0.37 0.16
Middle income 0.38 0.44 0.09
High income 0.31 0.52 -0.02
Age [less than 0.38 0.42 0.08
or equal to] 40
Age 40-60 0.30 0.42 0.01
Age >60 0.39 0.51 0.13
SIM-I (c) SP-S (a)
HtM N-HtM HtM N-HtM
Average 0.14 0.02 1.00 0.02
Low income 0.15 0.04 1.00 0.04
Middle income 0.11 0.02 1.00 0.02
High income 0.12 0.01 1.00 0.01
Age [less than 0.16 0.02 1.00 0.02
or equal to] 40
Age 40-60 0.11 0.01 1.00 0.01
Age >60 0.04 0.04 1.00 0.04
Source: Authors' calculations. Population shares from national
and euro area survey series. See text for full description.
(a.) Quarterly marginal propensity to consume out
of an unexpected $500 transfer for the aggregate
economy, and for various subgroups of the population,
using group composition from the 2010 SCF.
(b.) SIM-2 = Two-asset, life-cycle, incomplete-market model.
(c.) SIM-1 = One-asset, life-cycle, incomplete-market model.
(d.) SP-S = Spender-saver model.
Table 8. Quarterly Aggregate Consumption Responses under
Three Models, United States (a)
Model (b)
SIM-2 SIM-1 SP-S
$500 transfer 0.18 0.04 0.35
Size asymmetry
$50 transfer 0.29 0.05 0.35
$2,000 transfer 0.05 0.03 0.35
Sign asymmetry
$500 tax 0.42 0.14 0.36
Income targeting
$500 transfer, bottom tercile 0.26 0.07 0.50
$500 transfer, top tercile 0.20 0.03 0.34
Source: Authors' calculations. Population shares from
United States 2010 SCF. See text for full description.
(a.) Quarterly aggregate consumption responses for the
United States using group composition from the
2010 SCF. All taxes and transfers are lump-sum,
one-time, and unexpected.
(b.) See notes to table 7 for model definitions.