A late-in-life nursing home stay is one of the biggest risks faced by Americans over the lifecycle. The annual cost of a nursing home
stay in 2015 was $80,300 for a semi-private room and, at age 65, the probability of having a stay of at least 100 days is 0.375 for females and 0.211 for males. It is thus surprising that only about 10 percent of U.S. retirees purchase private
long-term-care (LTC) insurance. However,
we document high rejection rates by US LTC insurers, estimating that nearly 40 percent
of the potential pool of purchasers would be rejected if they applied
for private LTC insurance using
current screening guidelines. This paper explores the extent to which two features of this market can explain the low take-up rates and high
rejection rates: Medicaid and adverse selection. To this end, we develop a model in
which agents have private information about their risk of a nursing home stay and model
both the private and public (Medicaid) provision of LTC insurance. Medicaid crowds-out demand for private LTC insurance. Adverse selection together with marginal cost loads leads the private insurer to reject riskier individuals. Taking these two features together, our model can account for the low coverage rates and high rejections in the
private LTC insurance market. We then use the calibrated model to assess the welfare implications of a number of
previously The Joint Effects of Social Security and Medicaid on Incentives and Welfare
Joint with R. Anton Braun and Tatyana Koreshkova.
Updated: December 2015.
All individuals face some risk of ending up old, sick, alone and poor. Is there a role for social insurance for these risks and, if so, what is a good program? A large literature has analyzed the costs and benefits of pay-as-you-go public pensions and found that the costs exceed the benefits. This paper, instead, considers means-tested social insurance programs for retirees such as Medicaid and Supplemental Security Income. We find that the welfare gains from these programs are large. Moreover, the current scale of means-tested social insurance in the U.S. is too small in the following sense. If we condition on the current Social Security program, increasing the scale of means-tested social insurance by 1/3 benefits both the poor and the affluent when a payroll tax is used to fund the increase.
previously The Impact of Medical and Nursing Home Expenses and Social Insurance Policies on Savings and Welfare.
Joint with Tatyana Koreshkova.
Updated: December 2013.
We consider a life-cycle model with idiosyncratic risk in 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 US we show that (1) savings for old-age, out-of-pocket expenses account for 13.5 percent of aggregate wealth, half of which is due to nursing home expenses; (2) cross-sectional out-of-pocket nursing home risk accounts for 3 percent of aggregate wealth and substantially slows down wealth decumulation at older ages; (3) the impact of medical and nursing home expenses on private savings varies significantly across the lifetime earnings distribution; and (4) all newborns would benefit if social insurance for nursing home stays was made more generous.
The Rouwenhorst method of approximating stationary AR(1) processes has been overlooked by much of the literature despite having many desirable properties unmatched by other methods. In particular, we prove that it can match the conditional and unconditional mean and variance, and the first-order autocorrelation of any stationary AR(1) process. These properties makes the Rouwenhorst method more reliable than others in approximating highly persistent processes and generating accurate model solutions. To illustrate this, we compare the performances of the Rouwenhorst method and four others in solving the stochastic growth model and an income fluctuation problem. We find that (i) the choice of approximation method can have a large impact on the computed model solutions, and (ii) the Rouwenhorst method is more robust than others with respect to variation in the persistence of the process, the number of points used in the discrete approximation and the procedure used to generate model statistics.
The welfare gain to consumers from the introduction of personal computers is estimated here. A simple model of consumer demand is formulated that uses a slightly modified version of standard preferences. The modification permits marginal utility, and hence total utility, to be finite when the consumption of computers is zero. This implies that the good won't be consumed at a high enough price. It also bounds the consumer surplus derived from the product. The model is calibrated/estimated using standard national income and product account data. The welfare gain from the introduction of personal computers is in the range of 2 to 3 percent of consumption expenditure.
A model with leisure production and endogenous retirement is used to explain the declining labor force participation rates of elderly males. The model is calibrated to cross-sectional data on the labor force participation rates of elderly US males by age, their median drop in market consumption and leisure good expenditure share in the year 2000. Running the calibrated model for the period 1850 to 2000, a prediction of the evolution of the cross-section is obtained. The model is able to predict more than 87 percent of the increase in retirement of men over 65. The increase in retirement is driven by rising real wages and a falling price of leisure goods over time.
Suburbanization in the U.S. between 1910 and 1970 was concurrent with the rapid diffusion of the automobile. A circular city model is developed in order to access quantitatively the contribution of automobiles and rising incomes to suburbanization. The model incorporates a number of driving forces of suburbanization and car adoption, including falling automobile prices, rising real incomes, changing costs of traveling by car and with public transportation, and urban population growth. According to the model, 60 percent of postwar (1940-1970) suburbanization can be explained by these factors. Rising real incomes and falling automobile prices are shown to be the key drivers of suburbanization.