The wealthy hand-to-mouth.
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.
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.
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.
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Comments and discussion
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]
[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.
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.
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.
GIOVANNI L. VIOLANTE
New York 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.
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|Title Annotation:||p. 121-153|
|Author:||Kaplan, Greg; Violante, Giovanni L.; Weidner, Justin|
|Publication:||Brookings Papers on Economic Activity|
|Date:||Mar 22, 2014|
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