This paper employs a synthetic cohort technique and Consumer Expenditure Survey data to construct average age-profiles of consumption and income over the working lives of typical households across different education and occupation groups. Even after controlling for family and cohort effects, typical consumption profiles are not flat, and seem to track income at young ages. Using these profiles, we estimate a structural model of optimal life-cycle consumption expenditures in the presence of realistic income uncertainty. The model fits the profiles quite well. In addition to providing tight estimates of the discount rate and risk aversion, we find that consumer behavior changes strikingly over the life-cycle. Young consumers behave as "buffer-stock" agents. Around age 43, the typical household starts accumulating liquid assets for retirement and its behavior mimics more closely that of a certainty equivalent consumer. This change in behavior is mostly driven by the life-cycle profile of expected income. Our methodology provides a natural decomposition of saving into its precautionary and retirement components.

¤ This paper was prepared for inclusion in a symposium on saving and consumption in the Journal of Economic Perspectives. The authors thank, without implication, Timothy

This paper shows that standard empirical methods for estimating log-linearized consumption Euler equations cannot successfully uncover structural parameters like the coe#cient of relative risk aversion from a dataset of simulated consumers behaving exactly according to the standard model. Furthermore, consumption growth for the simulated consumers is very highly statistically related to predictable income growth -- and thus standard `excess sensitivity' tests would reject the hypothesis that consumers are behaving according to the standard model. Results are not much better for the second-order approximation to the Euler equation. The paper concludes that empirical estimation of consumption Euler equations should be abandoned, and discusses some alternative empirical strategies that are not subject to the problems of Euler equation estimation. Keywords: Euler equation, uncertainty, consumption, excess sensitivity JEL Classification Codes: C6, D91, E21 + Department of Economics, John...

Abstract: We estimate how the marginal utility of consumption varies with health. To do so, we develop a simple model in which the impact of health on the marginal utility of consumption can be estimated from data on permanent income, health, and utility proxies. We estimate the model using the Health and Retirement Study’s panel data on the elderly and near-elderly, and proxy for utility with measures of subjective well-being. Across a wide range of alternative specifications and assumptions, we find that the marginal utility of consumption declines as health deteriorates, and we are able to clearly reject the null of no state dependence. Our point estimates indicate that a one-standard-deviation increase in the number of chronic diseases is associated with a 10 to 25 percent decline in the marginal utility of consumption relative to this marginal utility when the individual has no chronic diseases. We present some simple, illustrative calibration results that suggest that state dependence of the magnitude we estimate can have a substantial effect on important economic problems such as the optimal level of health insurance benefits and the optimal level of life-cycle savings.

Micro data over the life cycle shows different patterns of consumption for housing and non-housing goods: the consumption profile of non-housing goods is hump-shaped while the consumption profile for housing first increases monotonically and then flattens out. These patterns hold true at each consumption quartile. This paper develops a quantitative, dynamic general equilibrium model of life-cycle behavior, which generates consumption profiles consistent with the observed data. Borrowing constraints are essential in explaining the accumulation of housing assets early in life, while transaction costs are crucial in generating the slow downsizing of the housing assets later in life.

Using data from the Health and Retirement Survey and the Assets and Health Dynamics of the Oldest Old survey, we estimate the stochastic process that determines both the distribution and dynamics of health care costs. We find that the data generating process for log health costs is well represented as the sum of a white noise process and a highly persistent AR(1) process. We also find that the innovations to this process can be modelled with a normal distribution that has been adjusted to capture the risk of catastrophic health care costs. Simulating this model, we find that in any given year 0.1 % of households receive a health cost shock with a present value of at least $125,000.

Abstract: We show that the provision of even incomplete public insurance can substantially crowd out private insurance demand. We examine the interaction of the public Medicaid program with the private market for long-term care insurance and estimate that Medicaid can explain the lack of private insurance purchases for at least two-thirds and as much as 90 percent of the wealth distribution, even if comprehensive, actuarially fair private policies were available. Medicaid’s large crowd out effect stems from the very large implicit tax (on the order of 60 to 75 percent for a median wealth individual) that Medicaid imposes on the benefits paid from private insurance policies. Importantly, Medicaid itself provides an inadequate mechanism for smoothing consumption for most individuals, so that its crowd out effect has important implications for overall risk exposure. An implication of our findings is that public policies designed to stimulate private insurance demand will be of limited efficacy as long as Medicaid continues to impose this large implicit tax.

Precautionary saving reflects the consequences of uncertainty for the rate of change of wealth. In the standard model, an increase in uncertainty will increase the level of saving, but will reduce the marginal propensity to save. Empirical evidence suggests that precautionary effects on saving are substantial, but no consensus has emerged on how to characterize the magnitude of precautionary wealth, either for individuals or in the aggregate. This partly reflects an epistemolgical problem: While theoretical models can contemplate the complete elimination of uncertainty, empirical data are not likely to shed much light on the consequences of such an extreme out-of-sample experiment.

Ten million American households borrowed on payday loans in 2002. Typically, to receive two weeks of liquidity from these loans households paid annualized (compounded) interest rates over 7000%. Using an administrative dataset from a payday lender, we seek to explain demand-side behavior in the payday loan market. We estimate a structural dynamic programming model that includes standard features like liquidity constraints and stochastic income, and we also incorporate institutionally realistic payday loans, default opportunities, and generalizations of the discount function. Method of Simulated Moments estimates of the key parameters are identi…ed by two novel pieces of evidence. First, over half of payday borrowers default on a payday loan within one year of their …rst loans. Second, defaulting borrowers have on average already repaid or serviced …ve payday loans, making interest payments of 90 % of their original loan’s principal. Such costly delay of default, we …nd, is most consistent with partially naive quasihyperbolic discounting, and we statistically reject nested benchmark alternatives.

We consider a life-cycle model with idiosyncratic risk in labor earnings, out-of-pocket medical and nursing home expenses, and survival. Partial insurance is available through welfare, Medicaid, and social security. Calibrating the model to the U.S., we find that nursing home expenses play an important role in the savings of the wealthy. In our policy analysis, we find that elimination of out-of-pocket expenses through public health care would reduce the capital stock by 12 percent, Medicaid and old-age welfare programs crowd out 44 percent of savings and greatly increase wealth inequality, and social security effects are influenced by out-of-pocket health expenses.